cola Report for GDS4431
Date: 2019-12-25 21:36:59 CET, cola version: 1.3.2
Document is loading... 
Summary
All available functions which can be applied to this res_list object:
res_list
#> A 'ConsensusPartitionList' object with 24 methods.
#> On a matrix with 51882 rows and 146 columns.
#> Top rows are extracted by 'SD, CV, MAD, ATC' methods.
#> Subgroups are detected by 'hclust, kmeans, skmeans, pam, mclust, NMF' method.
#> Number of partitions are tried for k = 2, 3, 4, 5, 6.
#> Performed in total 30000 partitions by row resampling.
#>
#> Following methods can be applied to this 'ConsensusPartitionList' object:
#> [1] "cola_report" "collect_classes" "collect_plots" "collect_stats"
#> [5] "colnames" "functional_enrichment" "get_anno_col" "get_anno"
#> [9] "get_classes" "get_matrix" "get_membership" "get_stats"
#> [13] "is_best_k" "is_stable_k" "ncol" "nrow"
#> [17] "rownames" "show" "suggest_best_k" "test_to_known_factors"
#> [21] "top_rows_heatmap" "top_rows_overlap"
#>
#> You can get result for a single method by, e.g. object["SD", "hclust"] or object["SD:hclust"]
#> or a subset of methods by object[c("SD", "CV")], c("hclust", "kmeans")]
The call of run_all_consensus_partition_methods() was:
#> run_all_consensus_partition_methods(data = mat, mc.cores = 4, anno = anno)
Dimension of the input matrix:
mat = get_matrix(res_list)
dim(mat)
#> [1] 51882 146
Density distribution
The density distribution for each sample is visualized as in one column in the
following heatmap. The clustering is based on the distance which is the
Kolmogorov-Smirnov statistic between two distributions.
library(ComplexHeatmap)
densityHeatmap(mat, top_annotation = HeatmapAnnotation(df = get_anno(res_list),
col = get_anno_col(res_list)), ylab = "value", cluster_columns = TRUE, show_column_names = FALSE,
mc.cores = 4)
Suggest the best k
Folowing table shows the best k (number of partitions) for each combination
of top-value methods and partition methods. Clicking on the method name in
the table goes to the section for a single combination of methods.
The cola vignette
explains the definition of the metrics used for determining the best
number of partitions.
suggest_best_k(res_list)
**: 1-PAC > 0.95, *: 1-PAC > 0.9
CDF of consensus matrices
Cumulative distribution function curves of consensus matrix for all methods.
collect_plots(res_list, fun = plot_ecdf)
Consensus heatmap
Consensus heatmaps for all methods. (What is a consensus heatmap?)
collect_plots(res_list, k = 2, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 3, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 4, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 5, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 6, fun = consensus_heatmap, mc.cores = 4)
Membership heatmap
Membership heatmaps for all methods. (What is a membership heatmap?)
collect_plots(res_list, k = 2, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 3, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 4, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 5, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 6, fun = membership_heatmap, mc.cores = 4)
Signature heatmap
Signature heatmaps for all methods. (What is a signature heatmap?)
Note in following heatmaps, rows are scaled.
collect_plots(res_list, k = 2, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 3, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 4, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 5, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 6, fun = get_signatures, mc.cores = 4)
Statistics table
The statistics used for measuring the stability of consensus partitioning.
(How are they
defined?)
get_stats(res_list, k = 2)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 2 0.944 0.949 0.978 0.496 0.503 0.503
#> CV:NMF 2 1.000 0.972 0.988 0.499 0.503 0.503
#> MAD:NMF 2 0.985 0.950 0.980 0.502 0.497 0.497
#> ATC:NMF 2 0.736 0.864 0.940 0.335 0.648 0.648
#> SD:skmeans 2 1.000 0.961 0.983 0.503 0.497 0.497
#> CV:skmeans 2 1.000 0.974 0.989 0.502 0.498 0.498
#> MAD:skmeans 2 1.000 0.964 0.986 0.503 0.498 0.498
#> ATC:skmeans 2 1.000 0.962 0.986 0.496 0.504 0.504
#> SD:mclust 2 0.864 0.935 0.971 0.501 0.498 0.498
#> CV:mclust 2 1.000 0.996 0.998 0.504 0.497 0.497
#> MAD:mclust 2 1.000 0.969 0.988 0.503 0.497 0.497
#> ATC:mclust 2 0.513 0.868 0.905 0.470 0.499 0.499
#> SD:kmeans 2 1.000 0.975 0.989 0.499 0.501 0.501
#> CV:kmeans 2 1.000 0.966 0.987 0.499 0.500 0.500
#> MAD:kmeans 2 0.929 0.938 0.973 0.502 0.498 0.498
#> ATC:kmeans 2 0.986 0.942 0.976 0.466 0.531 0.531
#> SD:pam 2 0.957 0.956 0.981 0.502 0.497 0.497
#> CV:pam 2 0.997 0.957 0.975 0.501 0.498 0.498
#> MAD:pam 2 1.000 0.968 0.986 0.504 0.497 0.497
#> ATC:pam 2 0.781 0.922 0.965 0.474 0.524 0.524
#> SD:hclust 2 0.591 0.808 0.908 0.357 0.599 0.599
#> CV:hclust 2 0.456 0.819 0.888 0.352 0.679 0.679
#> MAD:hclust 2 0.375 0.678 0.857 0.397 0.582 0.582
#> ATC:hclust 2 0.525 0.830 0.915 0.425 0.551 0.551
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.656 0.818 0.902 0.267 0.841 0.694
#> CV:NMF 3 0.916 0.912 0.951 0.288 0.824 0.661
#> MAD:NMF 3 0.698 0.830 0.911 0.270 0.842 0.690
#> ATC:NMF 3 0.538 0.650 0.857 0.802 0.665 0.512
#> SD:skmeans 3 0.839 0.879 0.938 0.299 0.805 0.627
#> CV:skmeans 3 0.971 0.949 0.977 0.313 0.792 0.604
#> MAD:skmeans 3 0.733 0.830 0.905 0.292 0.786 0.598
#> ATC:skmeans 3 0.732 0.661 0.841 0.243 0.855 0.722
#> SD:mclust 3 0.822 0.906 0.953 0.238 0.746 0.549
#> CV:mclust 3 0.918 0.924 0.940 0.236 0.833 0.678
#> MAD:mclust 3 0.970 0.938 0.973 0.273 0.760 0.562
#> ATC:mclust 3 0.310 0.344 0.672 0.221 0.620 0.417
#> SD:kmeans 3 0.522 0.623 0.804 0.299 0.736 0.524
#> CV:kmeans 3 0.561 0.465 0.685 0.311 0.850 0.707
#> MAD:kmeans 3 0.567 0.657 0.816 0.299 0.770 0.570
#> ATC:kmeans 3 0.851 0.872 0.945 0.331 0.723 0.529
#> SD:pam 3 0.577 0.578 0.775 0.221 0.815 0.644
#> CV:pam 3 0.850 0.864 0.937 0.275 0.810 0.636
#> MAD:pam 3 0.730 0.782 0.905 0.299 0.789 0.597
#> ATC:pam 3 0.715 0.831 0.897 0.265 0.805 0.656
#> SD:hclust 3 0.409 0.613 0.741 0.741 0.749 0.583
#> CV:hclust 3 0.345 0.684 0.824 0.721 0.690 0.559
#> MAD:hclust 3 0.559 0.749 0.856 0.573 0.687 0.507
#> ATC:hclust 3 0.532 0.606 0.822 0.334 0.787 0.645
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.562 0.688 0.827 0.145 0.729 0.416
#> CV:NMF 4 0.569 0.634 0.818 0.137 0.756 0.438
#> MAD:NMF 4 0.614 0.718 0.853 0.133 0.725 0.400
#> ATC:NMF 4 0.577 0.711 0.823 0.214 0.778 0.490
#> SD:skmeans 4 0.971 0.928 0.970 0.154 0.796 0.493
#> CV:skmeans 4 0.917 0.898 0.957 0.140 0.863 0.623
#> MAD:skmeans 4 0.970 0.924 0.966 0.158 0.793 0.483
#> ATC:skmeans 4 0.784 0.757 0.883 0.122 0.855 0.674
#> SD:mclust 4 0.929 0.873 0.946 0.123 0.862 0.660
#> CV:mclust 4 0.748 0.798 0.901 0.121 0.910 0.771
#> MAD:mclust 4 0.800 0.829 0.917 0.107 0.902 0.740
#> ATC:mclust 4 0.491 0.581 0.762 0.211 0.691 0.390
#> SD:kmeans 4 0.692 0.773 0.876 0.132 0.770 0.451
#> CV:kmeans 4 0.824 0.876 0.928 0.139 0.736 0.408
#> MAD:kmeans 4 0.662 0.752 0.838 0.136 0.771 0.441
#> ATC:kmeans 4 0.639 0.609 0.793 0.156 0.820 0.565
#> SD:pam 4 0.631 0.663 0.837 0.141 0.885 0.699
#> CV:pam 4 0.669 0.738 0.860 0.119 0.904 0.739
#> MAD:pam 4 0.583 0.601 0.747 0.103 0.871 0.648
#> ATC:pam 4 0.678 0.547 0.796 0.204 0.837 0.619
#> SD:hclust 4 0.540 0.643 0.796 0.156 0.893 0.700
#> CV:hclust 4 0.456 0.616 0.722 0.180 0.784 0.516
#> MAD:hclust 4 0.579 0.719 0.820 0.156 0.868 0.654
#> ATC:hclust 4 0.614 0.693 0.806 0.213 0.808 0.586
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.529 0.494 0.717 0.0737 0.831 0.492
#> CV:NMF 5 0.586 0.589 0.774 0.0696 0.846 0.516
#> MAD:NMF 5 0.577 0.538 0.744 0.0741 0.860 0.565
#> ATC:NMF 5 0.570 0.617 0.773 0.0711 0.848 0.503
#> SD:skmeans 5 0.749 0.666 0.831 0.0553 0.881 0.581
#> CV:skmeans 5 0.792 0.734 0.831 0.0602 0.934 0.747
#> MAD:skmeans 5 0.736 0.683 0.806 0.0527 0.915 0.683
#> ATC:skmeans 5 0.725 0.680 0.792 0.0783 0.847 0.579
#> SD:mclust 5 0.787 0.756 0.856 0.1044 0.840 0.524
#> CV:mclust 5 0.726 0.666 0.813 0.1049 0.798 0.453
#> MAD:mclust 5 0.787 0.791 0.884 0.0665 0.902 0.697
#> ATC:mclust 5 0.628 0.699 0.818 0.0889 0.896 0.655
#> SD:kmeans 5 0.654 0.537 0.741 0.0683 0.960 0.857
#> CV:kmeans 5 0.742 0.662 0.801 0.0566 0.963 0.857
#> MAD:kmeans 5 0.655 0.532 0.705 0.0649 0.933 0.757
#> ATC:kmeans 5 0.649 0.591 0.757 0.0831 0.897 0.669
#> SD:pam 5 0.655 0.651 0.810 0.0979 0.880 0.618
#> CV:pam 5 0.816 0.857 0.907 0.0736 0.890 0.646
#> MAD:pam 5 0.671 0.651 0.817 0.0827 0.879 0.595
#> ATC:pam 5 0.679 0.554 0.701 0.0739 0.779 0.373
#> SD:hclust 5 0.604 0.698 0.795 0.0709 0.930 0.739
#> CV:hclust 5 0.575 0.661 0.798 0.0790 0.919 0.716
#> MAD:hclust 5 0.584 0.582 0.754 0.0680 0.990 0.963
#> ATC:hclust 5 0.635 0.611 0.801 0.0681 0.929 0.774
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.587 0.579 0.741 0.0441 0.867 0.507
#> CV:NMF 6 0.619 0.568 0.738 0.0387 0.914 0.649
#> MAD:NMF 6 0.636 0.586 0.762 0.0432 0.895 0.599
#> ATC:NMF 6 0.616 0.559 0.744 0.0302 0.872 0.525
#> SD:skmeans 6 0.730 0.584 0.729 0.0418 0.898 0.567
#> CV:skmeans 6 0.761 0.621 0.749 0.0391 0.920 0.651
#> MAD:skmeans 6 0.731 0.569 0.736 0.0438 0.927 0.680
#> ATC:skmeans 6 0.806 0.804 0.863 0.0488 0.897 0.609
#> SD:mclust 6 0.801 0.817 0.894 0.0526 0.855 0.466
#> CV:mclust 6 0.918 0.873 0.933 0.0500 0.882 0.543
#> MAD:mclust 6 0.786 0.771 0.847 0.0603 0.873 0.554
#> ATC:mclust 6 0.688 0.619 0.811 0.0556 0.963 0.840
#> SD:kmeans 6 0.689 0.637 0.738 0.0486 0.876 0.559
#> CV:kmeans 6 0.716 0.702 0.780 0.0408 0.910 0.644
#> MAD:kmeans 6 0.664 0.478 0.679 0.0429 0.842 0.440
#> ATC:kmeans 6 0.659 0.446 0.659 0.0530 0.822 0.399
#> SD:pam 6 0.726 0.675 0.812 0.0452 0.918 0.662
#> CV:pam 6 0.806 0.771 0.869 0.0399 0.958 0.819
#> MAD:pam 6 0.702 0.635 0.804 0.0374 0.863 0.487
#> ATC:pam 6 0.704 0.562 0.755 0.0527 0.852 0.447
#> SD:hclust 6 0.677 0.654 0.801 0.0467 0.972 0.869
#> CV:hclust 6 0.615 0.625 0.756 0.0389 1.000 1.000
#> MAD:hclust 6 0.624 0.577 0.733 0.0412 0.922 0.707
#> ATC:hclust 6 0.650 0.593 0.769 0.0658 0.915 0.695
Following heatmap plots the partition for each combination of methods and the
lightness correspond to the silhouette scores for samples in each method. On
top the consensus subgroup is inferred from all methods by taking the mean
silhouette scores as weight.
collect_stats(res_list, k = 2)
collect_stats(res_list, k = 3)
collect_stats(res_list, k = 4)
collect_stats(res_list, k = 5)
collect_stats(res_list, k = 6)
Partition from all methods
Collect partitions from all methods:
collect_classes(res_list, k = 2)
collect_classes(res_list, k = 3)
collect_classes(res_list, k = 4)
collect_classes(res_list, k = 5)
collect_classes(res_list, k = 6)
Top rows overlap
Overlap of top rows from different top-row methods:
top_rows_overlap(res_list, top_n = 1000, method = "euler")
top_rows_overlap(res_list, top_n = 2000, method = "euler")
top_rows_overlap(res_list, top_n = 3000, method = "euler")
top_rows_overlap(res_list, top_n = 4000, method = "euler")
top_rows_overlap(res_list, top_n = 5000, method = "euler")
Also visualize the correspondance of rankings between different top-row methods:
top_rows_overlap(res_list, top_n = 1000, method = "correspondance")
top_rows_overlap(res_list, top_n = 2000, method = "correspondance")
top_rows_overlap(res_list, top_n = 3000, method = "correspondance")
top_rows_overlap(res_list, top_n = 4000, method = "correspondance")
top_rows_overlap(res_list, top_n = 5000, method = "correspondance")
Heatmaps of the top rows:
top_rows_heatmap(res_list, top_n = 1000)
top_rows_heatmap(res_list, top_n = 2000)
top_rows_heatmap(res_list, top_n = 3000)
top_rows_heatmap(res_list, top_n = 4000)
top_rows_heatmap(res_list, top_n = 5000)
Test to known annotations
Test correlation between subgroups and known annotations. If the known
annotation is numeric, one-way ANOVA test is applied, and if the known
annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res_list, k = 2)
#> n disease.state(p) age(p) other(p) k
#> SD:NMF 143 0.7104 0.496 0.02024 2
#> CV:NMF 144 0.7870 0.312 0.00958 2
#> MAD:NMF 143 0.5714 0.167 0.00998 2
#> ATC:NMF 137 1.0000 0.394 0.10307 2
#> SD:skmeans 145 0.4662 0.216 0.01062 2
#> CV:skmeans 145 0.9624 0.400 0.00700 2
#> MAD:skmeans 143 0.8175 0.401 0.03848 2
#> ATC:skmeans 142 0.4464 0.130 0.01018 2
#> SD:mclust 142 0.4163 0.360 0.04590 2
#> CV:mclust 146 0.5188 0.481 0.14129 2
#> MAD:mclust 143 0.5788 0.421 0.08287 2
#> ATC:mclust 145 0.7037 0.305 0.01116 2
#> SD:kmeans 145 0.8491 0.316 0.01240 2
#> CV:kmeans 144 0.9055 0.406 0.01484 2
#> MAD:kmeans 144 0.7594 0.345 0.03266 2
#> ATC:kmeans 142 1.0000 0.152 0.03741 2
#> SD:pam 145 0.0867 0.448 0.06100 2
#> CV:pam 146 0.0505 0.543 0.14708 2
#> MAD:pam 144 0.0635 0.391 0.06172 2
#> ATC:pam 143 1.0000 0.106 0.05364 2
#> SD:hclust 135 1.0000 0.542 0.17042 2
#> CV:hclust 139 1.0000 0.806 0.18535 2
#> MAD:hclust 112 0.9633 0.676 0.03373 2
#> ATC:hclust 138 0.9118 0.139 0.01119 2
test_to_known_factors(res_list, k = 3)
#> n disease.state(p) age(p) other(p) k
#> SD:NMF 137 0.5008 0.3431 0.03281 3
#> CV:NMF 143 0.5303 0.4548 0.00536 3
#> MAD:NMF 138 0.1577 0.3695 0.02688 3
#> ATC:NMF 112 0.0266 0.5070 0.78192 3
#> SD:skmeans 139 0.2837 0.4367 0.02722 3
#> CV:skmeans 143 0.0471 0.2817 0.01673 3
#> MAD:skmeans 139 0.2702 0.3966 0.01476 3
#> ATC:skmeans 101 0.2178 0.9117 0.00372 3
#> SD:mclust 144 0.4263 0.6629 0.08381 3
#> CV:mclust 145 0.9506 0.7379 0.10291 3
#> MAD:mclust 143 0.3109 0.6986 0.16297 3
#> ATC:mclust 49 0.2118 0.6934 0.57771 3
#> SD:kmeans 115 0.2975 0.6239 0.19591 3
#> CV:kmeans 64 1.0000 0.7009 0.00937 3
#> MAD:kmeans 117 0.3417 0.7174 0.06738 3
#> ATC:kmeans 135 0.9352 0.3836 0.00619 3
#> SD:pam 62 NA NA NA 3
#> CV:pam 136 0.1404 0.1876 0.00917 3
#> MAD:pam 130 0.3379 0.4556 0.01029 3
#> ATC:pam 138 0.3814 0.4373 0.01324 3
#> SD:hclust 122 0.6198 0.7958 0.12392 3
#> CV:hclust 121 0.0152 0.5613 0.00946 3
#> MAD:hclust 131 0.4944 0.7100 0.10620 3
#> ATC:hclust 99 0.5810 0.0868 0.01649 3
test_to_known_factors(res_list, k = 4)
#> n disease.state(p) age(p) other(p) k
#> SD:NMF 124 0.5674 0.0948 0.067645 4
#> CV:NMF 108 0.7472 0.3728 0.171805 4
#> MAD:NMF 127 0.6634 0.2927 0.042518 4
#> ATC:NMF 131 0.0349 0.4838 0.086102 4
#> SD:skmeans 142 0.2451 0.3404 0.053056 4
#> CV:skmeans 140 0.2862 0.3537 0.076990 4
#> MAD:skmeans 139 0.2484 0.3361 0.083470 4
#> ATC:skmeans 131 0.2186 0.0121 0.007793 4
#> SD:mclust 135 0.1286 0.6205 0.025244 4
#> CV:mclust 132 0.0643 0.3752 0.296382 4
#> MAD:mclust 139 0.1744 0.3180 0.176678 4
#> ATC:mclust 114 0.0281 0.8952 0.051673 4
#> SD:kmeans 135 0.2830 0.6167 0.103383 4
#> CV:kmeans 139 0.2014 0.4354 0.074967 4
#> MAD:kmeans 131 0.1049 0.4722 0.339114 4
#> ATC:kmeans 105 0.9422 0.3328 0.033372 4
#> SD:pam 113 0.1839 0.7159 0.049517 4
#> CV:pam 131 0.0137 0.1610 0.008990 4
#> MAD:pam 116 0.7161 0.2745 0.048467 4
#> ATC:pam 80 0.4626 0.8999 0.164032 4
#> SD:hclust 125 0.0766 0.6812 0.129231 4
#> CV:hclust 113 0.0610 0.6762 0.001588 4
#> MAD:hclust 124 0.0410 0.3150 0.465558 4
#> ATC:hclust 123 0.4957 0.1787 0.000895 4
test_to_known_factors(res_list, k = 5)
#> n disease.state(p) age(p) other(p) k
#> SD:NMF 80 0.6518 0.212 0.06674 5
#> CV:NMF 110 0.1526 0.120 0.01487 5
#> MAD:NMF 100 0.2896 0.311 0.03138 5
#> ATC:NMF 118 0.0565 0.531 0.20280 5
#> SD:skmeans 114 0.2840 0.371 0.02210 5
#> CV:skmeans 130 0.1890 0.177 0.18286 5
#> MAD:skmeans 127 0.3733 0.496 0.15324 5
#> ATC:skmeans 122 0.0667 0.105 0.03099 5
#> SD:mclust 133 0.1046 0.660 0.00902 5
#> CV:mclust 125 0.1294 0.455 0.10489 5
#> MAD:mclust 132 0.2748 0.379 0.24098 5
#> ATC:mclust 126 0.0320 0.395 0.08242 5
#> SD:kmeans 99 0.1087 0.327 0.22363 5
#> CV:kmeans 124 0.3896 0.561 0.16410 5
#> MAD:kmeans 93 0.0682 0.338 0.38991 5
#> ATC:kmeans 111 0.2063 0.178 0.09421 5
#> SD:pam 112 0.3517 0.609 0.22803 5
#> CV:pam 139 0.0198 0.329 0.19112 5
#> MAD:pam 116 0.2936 0.309 0.12255 5
#> ATC:pam 89 0.4717 0.858 0.07735 5
#> SD:hclust 121 0.0998 0.930 0.05669 5
#> CV:hclust 114 0.1196 0.765 0.00721 5
#> MAD:hclust 107 0.2172 0.570 0.32620 5
#> ATC:hclust 99 0.1729 0.525 0.01078 5
test_to_known_factors(res_list, k = 6)
#> n disease.state(p) age(p) other(p) k
#> SD:NMF 112 0.2247 0.468 0.17653 6
#> CV:NMF 104 0.2189 0.594 0.13246 6
#> MAD:NMF 114 0.0202 0.282 0.02213 6
#> ATC:NMF 99 0.1106 0.618 0.33009 6
#> SD:skmeans 101 0.3792 0.474 0.18483 6
#> CV:skmeans 111 0.1513 0.742 0.12373 6
#> MAD:skmeans 103 0.6785 0.696 0.30741 6
#> ATC:skmeans 132 0.3295 0.151 0.07444 6
#> SD:mclust 138 0.2985 0.783 0.31969 6
#> CV:mclust 140 0.4019 0.683 0.06710 6
#> MAD:mclust 131 0.1525 0.503 0.14259 6
#> ATC:mclust 111 0.0164 0.578 0.03620 6
#> SD:kmeans 123 0.4629 0.829 0.10023 6
#> CV:kmeans 129 0.1757 0.597 0.18557 6
#> MAD:kmeans 82 0.8240 0.630 0.11749 6
#> ATC:kmeans 76 0.0549 0.870 0.03904 6
#> SD:pam 113 0.5400 0.998 0.06893 6
#> CV:pam 130 0.2480 0.648 0.15703 6
#> MAD:pam 115 0.2157 0.560 0.16655 6
#> ATC:pam 100 0.0905 0.440 0.08576 6
#> SD:hclust 114 0.1223 0.865 0.04971 6
#> CV:hclust 117 0.1019 0.753 0.01817 6
#> MAD:hclust 104 0.0269 0.461 0.61998 6
#> ATC:hclust 100 0.2227 0.699 0.00706 6
Results for each method
SD:hclust
The object with results only for a single top-value method and a single partition method
can be extracted as:
res = res_list["SD", "hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51882 rows and 146 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
- The first row: a plot of the ECDF (empirical cumulative distribution
function) curves of the consensus matrix for each
k and the heatmap of
predicted classes for each k.
- The second row: heatmaps of the consensus matrix for each
k.
- The third row: heatmaps of the membership matrix for each
k.
- The fouth row: heatmaps of the signatures for each
k.
All the plots in panels can be made by individual functions and they are
plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
- ECDF curves of the consensus matrix for each
k;
- 1-PAC. The PAC
score
measures the proportion of the ambiguous subgrouping.
- Mean silhouette score.
- Concordance. The mean probability of fiting the consensus class ids in all
partitions.
- Area increased. Denote \(A_k\) as the area under the ECDF curve for current
k, the area increased is defined as \(A_k - A_{k-1}\).
- Rand index. The percent of pairs of samples that are both in a same cluster
or both are not in a same cluster in the partition of k and k-1.
- Jaccard index. The ratio of pairs of samples are both in a same cluster in
the partition of k and k-1 and the pairs of samples are both in a same
cluster in the partition k or k-1.
The detailed explanations of these statistics can be found in the cola
vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.591 0.808 0.908 0.3572 0.599 0.599
#> 3 3 0.409 0.613 0.741 0.7413 0.749 0.583
#> 4 4 0.540 0.643 0.796 0.1560 0.893 0.700
#> 5 5 0.604 0.698 0.795 0.0709 0.930 0.739
#> 6 6 0.677 0.654 0.801 0.0467 0.972 0.869
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
- All \(k\) with Jaccard index larger than 0.95 are removed because increasing
\(k\) does not provide enough extra information. If all \(k\) are removed, it is
marked as no subgroup is detected.
- For all \(k\) with 1-PAC score larger than 0.9, the maximal \(k\) is taken as
the best \(k\), and other \(k\) are marked as optional \(k\).
- If it does not fit the second rule. The \(k\) with the maximal vote of the
highest 1-PAC score, highest mean silhouette, and highest concordance is
taken as the best \(k\).
suggest_best_k(res)
#> [1] 5
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
show/hide code output
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM627128 2 0.0000 0.9403 0.000 1.000
#> GSM627110 1 0.9087 0.7112 0.676 0.324
#> GSM627132 1 0.0000 0.7464 1.000 0.000
#> GSM627107 2 0.0000 0.9403 0.000 1.000
#> GSM627103 2 0.0000 0.9403 0.000 1.000
#> GSM627114 1 0.9661 0.6294 0.608 0.392
#> GSM627134 2 0.0000 0.9403 0.000 1.000
#> GSM627137 2 0.0000 0.9403 0.000 1.000
#> GSM627148 2 0.1633 0.9179 0.024 0.976
#> GSM627101 2 0.0000 0.9403 0.000 1.000
#> GSM627130 2 0.0000 0.9403 0.000 1.000
#> GSM627071 2 0.6973 0.6912 0.188 0.812
#> GSM627118 2 0.0000 0.9403 0.000 1.000
#> GSM627094 2 0.0000 0.9403 0.000 1.000
#> GSM627122 2 0.7745 0.6080 0.228 0.772
#> GSM627115 2 0.0000 0.9403 0.000 1.000
#> GSM627125 2 0.0000 0.9403 0.000 1.000
#> GSM627174 2 0.0000 0.9403 0.000 1.000
#> GSM627102 2 0.0000 0.9403 0.000 1.000
#> GSM627073 2 0.3431 0.8742 0.064 0.936
#> GSM627108 2 0.0000 0.9403 0.000 1.000
#> GSM627126 1 0.0000 0.7464 1.000 0.000
#> GSM627078 2 0.0000 0.9403 0.000 1.000
#> GSM627090 2 0.0000 0.9403 0.000 1.000
#> GSM627099 2 0.0000 0.9403 0.000 1.000
#> GSM627105 2 0.0000 0.9403 0.000 1.000
#> GSM627117 1 0.9815 0.5858 0.580 0.420
#> GSM627121 2 0.0000 0.9403 0.000 1.000
#> GSM627127 2 0.0000 0.9403 0.000 1.000
#> GSM627087 2 0.0000 0.9403 0.000 1.000
#> GSM627089 1 0.9795 0.5853 0.584 0.416
#> GSM627092 2 0.0000 0.9403 0.000 1.000
#> GSM627076 2 0.0000 0.9403 0.000 1.000
#> GSM627136 2 0.9427 0.2096 0.360 0.640
#> GSM627081 2 0.0000 0.9403 0.000 1.000
#> GSM627091 2 0.0000 0.9403 0.000 1.000
#> GSM627097 2 0.0000 0.9403 0.000 1.000
#> GSM627072 2 0.7376 0.6535 0.208 0.792
#> GSM627080 1 0.0000 0.7464 1.000 0.000
#> GSM627088 2 0.9970 -0.2630 0.468 0.532
#> GSM627109 1 0.8016 0.7574 0.756 0.244
#> GSM627111 1 0.0000 0.7464 1.000 0.000
#> GSM627113 1 0.8713 0.7336 0.708 0.292
#> GSM627133 2 0.0672 0.9333 0.008 0.992
#> GSM627177 1 0.9988 0.4387 0.520 0.480
#> GSM627086 2 0.0000 0.9403 0.000 1.000
#> GSM627095 1 1.0000 0.1985 0.500 0.500
#> GSM627079 2 0.7139 0.6766 0.196 0.804
#> GSM627082 2 0.0000 0.9403 0.000 1.000
#> GSM627074 1 0.8016 0.7574 0.756 0.244
#> GSM627077 1 0.9209 0.6994 0.664 0.336
#> GSM627093 1 0.8016 0.7574 0.756 0.244
#> GSM627120 2 0.0000 0.9403 0.000 1.000
#> GSM627124 2 0.0000 0.9403 0.000 1.000
#> GSM627075 2 0.0000 0.9403 0.000 1.000
#> GSM627085 2 0.0000 0.9403 0.000 1.000
#> GSM627119 1 0.8016 0.7574 0.756 0.244
#> GSM627116 1 0.9993 0.4283 0.516 0.484
#> GSM627084 2 0.9815 -0.0564 0.420 0.580
#> GSM627096 2 0.0000 0.9403 0.000 1.000
#> GSM627100 2 0.0000 0.9403 0.000 1.000
#> GSM627112 2 0.0000 0.9403 0.000 1.000
#> GSM627083 2 1.0000 -0.2259 0.500 0.500
#> GSM627098 2 0.9815 -0.0564 0.420 0.580
#> GSM627104 1 0.8016 0.7574 0.756 0.244
#> GSM627131 2 0.7139 0.6766 0.196 0.804
#> GSM627106 2 0.0000 0.9403 0.000 1.000
#> GSM627123 1 0.0672 0.7474 0.992 0.008
#> GSM627129 2 0.0000 0.9403 0.000 1.000
#> GSM627216 2 0.0672 0.9333 0.008 0.992
#> GSM627212 2 0.0000 0.9403 0.000 1.000
#> GSM627190 1 0.9815 0.5858 0.580 0.420
#> GSM627169 2 0.0000 0.9403 0.000 1.000
#> GSM627167 2 0.0000 0.9403 0.000 1.000
#> GSM627192 1 0.0000 0.7464 1.000 0.000
#> GSM627203 2 0.0000 0.9403 0.000 1.000
#> GSM627151 2 0.2603 0.8969 0.044 0.956
#> GSM627163 1 0.0000 0.7464 1.000 0.000
#> GSM627211 2 0.0000 0.9403 0.000 1.000
#> GSM627171 2 0.0000 0.9403 0.000 1.000
#> GSM627209 2 0.0000 0.9403 0.000 1.000
#> GSM627135 1 0.3431 0.7533 0.936 0.064
#> GSM627170 2 0.0000 0.9403 0.000 1.000
#> GSM627178 1 0.9993 0.4283 0.516 0.484
#> GSM627199 2 0.0000 0.9403 0.000 1.000
#> GSM627213 2 0.0000 0.9403 0.000 1.000
#> GSM627140 2 0.0000 0.9403 0.000 1.000
#> GSM627149 1 0.0672 0.7474 0.992 0.008
#> GSM627147 2 0.0000 0.9403 0.000 1.000
#> GSM627195 2 0.0000 0.9403 0.000 1.000
#> GSM627204 2 0.0000 0.9403 0.000 1.000
#> GSM627207 2 0.0000 0.9403 0.000 1.000
#> GSM627157 1 0.8955 0.7201 0.688 0.312
#> GSM627201 2 0.0000 0.9403 0.000 1.000
#> GSM627146 2 0.0000 0.9403 0.000 1.000
#> GSM627156 2 0.0000 0.9403 0.000 1.000
#> GSM627188 1 0.0000 0.7464 1.000 0.000
#> GSM627197 2 0.0000 0.9403 0.000 1.000
#> GSM627173 2 0.0000 0.9403 0.000 1.000
#> GSM627179 2 0.0000 0.9403 0.000 1.000
#> GSM627208 2 0.0938 0.9296 0.012 0.988
#> GSM627215 2 0.0000 0.9403 0.000 1.000
#> GSM627153 2 0.0000 0.9403 0.000 1.000
#> GSM627155 1 0.0000 0.7464 1.000 0.000
#> GSM627165 2 0.0000 0.9403 0.000 1.000
#> GSM627168 1 0.9129 0.7074 0.672 0.328
#> GSM627183 1 0.9491 0.6626 0.632 0.368
#> GSM627144 2 0.0000 0.9403 0.000 1.000
#> GSM627158 1 0.0000 0.7464 1.000 0.000
#> GSM627196 2 0.0000 0.9403 0.000 1.000
#> GSM627142 2 0.0000 0.9403 0.000 1.000
#> GSM627182 2 0.0938 0.9296 0.012 0.988
#> GSM627202 2 0.9522 0.1634 0.372 0.628
#> GSM627141 1 0.9522 0.6562 0.628 0.372
#> GSM627143 2 0.0000 0.9403 0.000 1.000
#> GSM627145 2 0.6531 0.7274 0.168 0.832
#> GSM627152 2 0.4939 0.8162 0.108 0.892
#> GSM627200 2 0.6531 0.7263 0.168 0.832
#> GSM627159 2 0.0000 0.9403 0.000 1.000
#> GSM627164 2 0.0000 0.9403 0.000 1.000
#> GSM627138 1 0.0000 0.7464 1.000 0.000
#> GSM627175 2 0.0000 0.9403 0.000 1.000
#> GSM627150 2 0.6973 0.6912 0.188 0.812
#> GSM627166 1 0.8713 0.7353 0.708 0.292
#> GSM627186 2 0.0000 0.9403 0.000 1.000
#> GSM627139 2 0.2603 0.8969 0.044 0.956
#> GSM627181 2 0.0000 0.9403 0.000 1.000
#> GSM627205 2 0.0000 0.9403 0.000 1.000
#> GSM627214 2 0.0000 0.9403 0.000 1.000
#> GSM627180 2 0.0000 0.9403 0.000 1.000
#> GSM627172 2 0.0000 0.9403 0.000 1.000
#> GSM627184 1 0.0000 0.7464 1.000 0.000
#> GSM627193 2 0.0000 0.9403 0.000 1.000
#> GSM627191 2 0.8955 0.3859 0.312 0.688
#> GSM627176 2 0.0000 0.9403 0.000 1.000
#> GSM627194 2 0.0000 0.9403 0.000 1.000
#> GSM627154 2 0.0000 0.9403 0.000 1.000
#> GSM627187 1 0.9815 0.5858 0.580 0.420
#> GSM627198 2 0.0000 0.9403 0.000 1.000
#> GSM627160 2 0.0000 0.9403 0.000 1.000
#> GSM627185 1 0.6887 0.7593 0.816 0.184
#> GSM627206 1 0.9795 0.5853 0.584 0.416
#> GSM627161 1 0.0000 0.7464 1.000 0.000
#> GSM627162 2 0.0376 0.9367 0.004 0.996
#> GSM627210 1 0.8016 0.7574 0.756 0.244
#> GSM627189 2 0.0000 0.9403 0.000 1.000
show/hide code output
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM627128 2 0.4121 0.4841 0.000 0.832 0.168
#> GSM627110 1 0.5785 0.6870 0.668 0.000 0.332
#> GSM627132 1 0.0000 0.7201 1.000 0.000 0.000
#> GSM627107 3 0.3619 0.6598 0.000 0.136 0.864
#> GSM627103 2 0.6140 0.7116 0.000 0.596 0.404
#> GSM627114 1 0.6126 0.6073 0.600 0.000 0.400
#> GSM627134 2 0.2537 0.6608 0.000 0.920 0.080
#> GSM627137 2 0.5397 0.7225 0.000 0.720 0.280
#> GSM627148 3 0.2982 0.7159 0.024 0.056 0.920
#> GSM627101 2 0.2537 0.6135 0.000 0.920 0.080
#> GSM627130 2 0.4121 0.4841 0.000 0.832 0.168
#> GSM627071 3 0.5852 0.5924 0.180 0.044 0.776
#> GSM627118 2 0.2261 0.6561 0.000 0.932 0.068
#> GSM627094 2 0.6154 0.7087 0.000 0.592 0.408
#> GSM627122 3 0.6232 0.5193 0.220 0.040 0.740
#> GSM627115 2 0.6154 0.7086 0.000 0.592 0.408
#> GSM627125 2 0.4178 0.4785 0.000 0.828 0.172
#> GSM627174 2 0.6180 0.7014 0.000 0.584 0.416
#> GSM627102 2 0.6026 0.7153 0.000 0.624 0.376
#> GSM627073 3 0.3993 0.7185 0.064 0.052 0.884
#> GSM627108 2 0.6154 0.7087 0.000 0.592 0.408
#> GSM627126 1 0.0000 0.7201 1.000 0.000 0.000
#> GSM627078 2 0.0237 0.6275 0.000 0.996 0.004
#> GSM627090 3 0.2959 0.7140 0.000 0.100 0.900
#> GSM627099 2 0.6111 0.7150 0.000 0.604 0.396
#> GSM627105 2 0.4178 0.4785 0.000 0.828 0.172
#> GSM627117 1 0.6836 0.5564 0.572 0.016 0.412
#> GSM627121 3 0.3752 0.6436 0.000 0.144 0.856
#> GSM627127 2 0.1031 0.6387 0.000 0.976 0.024
#> GSM627087 2 0.6154 0.7086 0.000 0.592 0.408
#> GSM627089 1 0.6410 0.5727 0.576 0.004 0.420
#> GSM627092 2 0.5988 0.5918 0.000 0.632 0.368
#> GSM627076 3 0.3482 0.7013 0.000 0.128 0.872
#> GSM627136 3 0.6954 0.1521 0.352 0.028 0.620
#> GSM627081 3 0.3551 0.6595 0.000 0.132 0.868
#> GSM627091 2 0.6111 0.7150 0.000 0.604 0.396
#> GSM627097 2 0.3482 0.5468 0.000 0.872 0.128
#> GSM627072 3 0.4963 0.5693 0.200 0.008 0.792
#> GSM627080 1 0.0000 0.7201 1.000 0.000 0.000
#> GSM627088 3 0.7283 -0.2913 0.460 0.028 0.512
#> GSM627109 1 0.5138 0.7304 0.748 0.000 0.252
#> GSM627111 1 0.0000 0.7201 1.000 0.000 0.000
#> GSM627113 1 0.5560 0.7052 0.700 0.000 0.300
#> GSM627133 3 0.5115 0.3967 0.004 0.228 0.768
#> GSM627177 1 0.8165 0.5015 0.512 0.072 0.416
#> GSM627086 2 0.5968 0.7221 0.000 0.636 0.364
#> GSM627095 1 0.8489 0.1868 0.496 0.412 0.092
#> GSM627079 3 0.5850 0.5758 0.188 0.040 0.772
#> GSM627082 2 0.4121 0.4841 0.000 0.832 0.168
#> GSM627074 1 0.5138 0.7304 0.748 0.000 0.252
#> GSM627077 1 0.5859 0.6755 0.656 0.000 0.344
#> GSM627093 1 0.5138 0.7304 0.748 0.000 0.252
#> GSM627120 2 0.5810 0.7198 0.000 0.664 0.336
#> GSM627124 2 0.0237 0.6275 0.000 0.996 0.004
#> GSM627075 2 0.6180 0.7038 0.000 0.584 0.416
#> GSM627085 2 0.0000 0.6247 0.000 1.000 0.000
#> GSM627119 1 0.5138 0.7304 0.748 0.000 0.252
#> GSM627116 1 0.8173 0.4954 0.508 0.072 0.420
#> GSM627084 3 0.7192 -0.0809 0.412 0.028 0.560
#> GSM627096 2 0.2261 0.6561 0.000 0.932 0.068
#> GSM627100 3 0.3482 0.7013 0.000 0.128 0.872
#> GSM627112 2 0.2448 0.5767 0.000 0.924 0.076
#> GSM627083 1 0.8489 0.1868 0.496 0.412 0.092
#> GSM627098 3 0.7192 -0.0809 0.412 0.028 0.560
#> GSM627104 1 0.5138 0.7304 0.748 0.000 0.252
#> GSM627131 3 0.5850 0.5758 0.188 0.040 0.772
#> GSM627106 3 0.3551 0.6595 0.000 0.132 0.868
#> GSM627123 1 0.0424 0.7203 0.992 0.000 0.008
#> GSM627129 2 0.2959 0.6687 0.000 0.900 0.100
#> GSM627216 3 0.5115 0.3967 0.004 0.228 0.768
#> GSM627212 2 0.6111 0.7150 0.000 0.604 0.396
#> GSM627190 1 0.6836 0.5564 0.572 0.016 0.412
#> GSM627169 2 0.6235 0.6881 0.000 0.564 0.436
#> GSM627167 2 0.5465 0.6978 0.000 0.712 0.288
#> GSM627192 1 0.0000 0.7201 1.000 0.000 0.000
#> GSM627203 3 0.0592 0.7080 0.000 0.012 0.988
#> GSM627151 3 0.5454 0.6950 0.044 0.152 0.804
#> GSM627163 1 0.0000 0.7201 1.000 0.000 0.000
#> GSM627211 2 0.6154 0.7087 0.000 0.592 0.408
#> GSM627171 2 0.6180 0.7025 0.000 0.584 0.416
#> GSM627209 2 0.5363 0.7199 0.000 0.724 0.276
#> GSM627135 1 0.2261 0.7284 0.932 0.000 0.068
#> GSM627170 2 0.6062 0.7122 0.000 0.616 0.384
#> GSM627178 1 0.8173 0.4954 0.508 0.072 0.420
#> GSM627199 2 0.0000 0.6247 0.000 1.000 0.000
#> GSM627213 2 0.1643 0.6432 0.000 0.956 0.044
#> GSM627140 2 0.4062 0.6014 0.000 0.836 0.164
#> GSM627149 1 0.0424 0.7203 0.992 0.000 0.008
#> GSM627147 2 0.5397 0.6915 0.000 0.720 0.280
#> GSM627195 3 0.0592 0.7080 0.000 0.012 0.988
#> GSM627204 2 0.6154 0.7087 0.000 0.592 0.408
#> GSM627207 2 0.6154 0.7087 0.000 0.592 0.408
#> GSM627157 1 0.5706 0.6931 0.680 0.000 0.320
#> GSM627201 2 0.6180 0.7014 0.000 0.584 0.416
#> GSM627146 2 0.6154 0.7087 0.000 0.592 0.408
#> GSM627156 2 0.6235 0.6881 0.000 0.564 0.436
#> GSM627188 1 0.0000 0.7201 1.000 0.000 0.000
#> GSM627197 2 0.6154 0.7087 0.000 0.592 0.408
#> GSM627173 2 0.6154 0.7087 0.000 0.592 0.408
#> GSM627179 2 0.6140 0.7103 0.000 0.596 0.404
#> GSM627208 3 0.3349 0.6278 0.004 0.108 0.888
#> GSM627215 3 0.4346 0.4645 0.000 0.184 0.816
#> GSM627153 2 0.5363 0.7199 0.000 0.724 0.276
#> GSM627155 1 0.0000 0.7201 1.000 0.000 0.000
#> GSM627165 2 0.5431 0.7218 0.000 0.716 0.284
#> GSM627168 1 0.5810 0.6808 0.664 0.000 0.336
#> GSM627183 1 0.6228 0.6413 0.624 0.004 0.372
#> GSM627144 3 0.0424 0.7092 0.000 0.008 0.992
#> GSM627158 1 0.0000 0.7201 1.000 0.000 0.000
#> GSM627196 2 0.6154 0.7087 0.000 0.592 0.408
#> GSM627142 3 0.6299 0.1094 0.000 0.476 0.524
#> GSM627182 3 0.3349 0.6278 0.004 0.108 0.888
#> GSM627202 3 0.6899 0.0911 0.364 0.024 0.612
#> GSM627141 1 0.6045 0.6351 0.620 0.000 0.380
#> GSM627143 2 0.5733 0.6902 0.000 0.676 0.324
#> GSM627145 3 0.4413 0.6266 0.160 0.008 0.832
#> GSM627152 3 0.5811 0.6816 0.108 0.092 0.800
#> GSM627200 3 0.5466 0.6089 0.160 0.040 0.800
#> GSM627159 2 0.4121 0.4841 0.000 0.832 0.168
#> GSM627164 2 0.6180 0.7025 0.000 0.584 0.416
#> GSM627138 1 0.0000 0.7201 1.000 0.000 0.000
#> GSM627175 2 0.1860 0.6538 0.000 0.948 0.052
#> GSM627150 3 0.5852 0.5924 0.180 0.044 0.776
#> GSM627166 1 0.7064 0.7076 0.704 0.076 0.220
#> GSM627186 2 0.6244 0.6823 0.000 0.560 0.440
#> GSM627139 3 0.5454 0.6950 0.044 0.152 0.804
#> GSM627181 2 0.6154 0.7087 0.000 0.592 0.408
#> GSM627205 3 0.5529 0.0981 0.000 0.296 0.704
#> GSM627214 2 0.5560 0.7220 0.000 0.700 0.300
#> GSM627180 3 0.4346 0.4645 0.000 0.184 0.816
#> GSM627172 2 0.6026 0.7153 0.000 0.624 0.376
#> GSM627184 1 0.0000 0.7201 1.000 0.000 0.000
#> GSM627193 2 0.6154 0.7087 0.000 0.592 0.408
#> GSM627191 2 0.8742 0.0311 0.308 0.556 0.136
#> GSM627176 3 0.2796 0.7150 0.000 0.092 0.908
#> GSM627194 2 0.5706 0.7239 0.000 0.680 0.320
#> GSM627154 2 0.0000 0.6247 0.000 1.000 0.000
#> GSM627187 1 0.6836 0.5564 0.572 0.016 0.412
#> GSM627198 2 0.0000 0.6247 0.000 1.000 0.000
#> GSM627160 2 0.5291 0.4077 0.000 0.732 0.268
#> GSM627185 1 0.4399 0.7349 0.812 0.000 0.188
#> GSM627206 1 0.6410 0.5727 0.576 0.004 0.420
#> GSM627161 1 0.0000 0.7201 1.000 0.000 0.000
#> GSM627162 2 0.6442 0.4738 0.004 0.564 0.432
#> GSM627210 1 0.5138 0.7304 0.748 0.000 0.252
#> GSM627189 2 0.6154 0.7087 0.000 0.592 0.408
show/hide code output
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM627128 4 0.1940 0.69553 0.000 0.000 0.076 0.924
#> GSM627110 1 0.4855 0.63761 0.644 0.000 0.352 0.004
#> GSM627132 1 0.0188 0.68477 0.996 0.000 0.000 0.004
#> GSM627107 3 0.4401 0.69078 0.000 0.112 0.812 0.076
#> GSM627103 2 0.1388 0.87205 0.000 0.960 0.012 0.028
#> GSM627114 1 0.5365 0.55117 0.576 0.008 0.412 0.004
#> GSM627134 4 0.5112 0.56286 0.000 0.384 0.008 0.608
#> GSM627137 2 0.3668 0.71340 0.000 0.808 0.004 0.188
#> GSM627148 3 0.4131 0.70750 0.016 0.108 0.840 0.036
#> GSM627101 4 0.4839 0.74628 0.000 0.200 0.044 0.756
#> GSM627130 4 0.2011 0.69424 0.000 0.000 0.080 0.920
#> GSM627071 3 0.4693 0.59519 0.160 0.012 0.792 0.036
#> GSM627118 4 0.5055 0.59464 0.000 0.368 0.008 0.624
#> GSM627094 2 0.0000 0.87510 0.000 1.000 0.000 0.000
#> GSM627122 3 0.4446 0.52935 0.196 0.000 0.776 0.028
#> GSM627115 2 0.1174 0.87239 0.000 0.968 0.012 0.020
#> GSM627125 4 0.2081 0.69215 0.000 0.000 0.084 0.916
#> GSM627174 2 0.1520 0.86987 0.000 0.956 0.024 0.020
#> GSM627102 2 0.2805 0.82025 0.000 0.888 0.012 0.100
#> GSM627073 3 0.4728 0.68662 0.048 0.152 0.792 0.008
#> GSM627108 2 0.0000 0.87510 0.000 1.000 0.000 0.000
#> GSM627126 1 0.0188 0.68477 0.996 0.000 0.000 0.004
#> GSM627078 4 0.3837 0.74922 0.000 0.224 0.000 0.776
#> GSM627090 3 0.2813 0.68769 0.000 0.024 0.896 0.080
#> GSM627099 2 0.1610 0.86893 0.000 0.952 0.016 0.032
#> GSM627105 4 0.2081 0.69215 0.000 0.000 0.084 0.916
#> GSM627117 1 0.6161 0.52192 0.552 0.044 0.400 0.004
#> GSM627121 3 0.4966 0.67665 0.000 0.156 0.768 0.076
#> GSM627127 4 0.4222 0.71602 0.000 0.272 0.000 0.728
#> GSM627087 2 0.1174 0.87239 0.000 0.968 0.012 0.020
#> GSM627089 1 0.5290 0.49434 0.552 0.004 0.440 0.004
#> GSM627092 2 0.7058 0.31327 0.000 0.560 0.168 0.272
#> GSM627076 3 0.3278 0.67895 0.000 0.020 0.864 0.116
#> GSM627136 3 0.5389 0.22360 0.328 0.004 0.648 0.020
#> GSM627081 3 0.4458 0.69064 0.000 0.116 0.808 0.076
#> GSM627091 2 0.1610 0.86893 0.000 0.952 0.016 0.032
#> GSM627097 4 0.6110 0.68818 0.000 0.240 0.100 0.660
#> GSM627072 3 0.4897 0.58004 0.176 0.032 0.776 0.016
#> GSM627080 1 0.0188 0.68477 0.996 0.000 0.000 0.004
#> GSM627088 3 0.5636 -0.19048 0.436 0.004 0.544 0.016
#> GSM627109 1 0.4401 0.68862 0.724 0.000 0.272 0.004
#> GSM627111 1 0.0188 0.68477 0.996 0.000 0.000 0.004
#> GSM627113 1 0.4699 0.66128 0.676 0.000 0.320 0.004
#> GSM627133 3 0.5709 0.41900 0.004 0.384 0.588 0.024
#> GSM627177 1 0.6330 0.39989 0.492 0.000 0.448 0.060
#> GSM627086 2 0.1637 0.85285 0.000 0.940 0.000 0.060
#> GSM627095 1 0.6862 -0.03789 0.492 0.020 0.056 0.432
#> GSM627079 3 0.4199 0.57810 0.164 0.000 0.804 0.032
#> GSM627082 4 0.2011 0.69424 0.000 0.000 0.080 0.920
#> GSM627074 1 0.4401 0.68862 0.724 0.000 0.272 0.004
#> GSM627077 1 0.4905 0.62344 0.632 0.000 0.364 0.004
#> GSM627093 1 0.4401 0.68862 0.724 0.000 0.272 0.004
#> GSM627120 2 0.3577 0.75496 0.000 0.832 0.012 0.156
#> GSM627124 4 0.3837 0.74922 0.000 0.224 0.000 0.776
#> GSM627075 2 0.0336 0.87334 0.000 0.992 0.008 0.000
#> GSM627085 4 0.3801 0.75056 0.000 0.220 0.000 0.780
#> GSM627119 1 0.4401 0.68862 0.724 0.000 0.272 0.004
#> GSM627116 1 0.6332 0.39184 0.488 0.000 0.452 0.060
#> GSM627084 3 0.5626 0.00711 0.388 0.004 0.588 0.020
#> GSM627096 4 0.5055 0.59464 0.000 0.368 0.008 0.624
#> GSM627100 3 0.3278 0.67895 0.000 0.020 0.864 0.116
#> GSM627112 4 0.2988 0.74468 0.000 0.112 0.012 0.876
#> GSM627083 1 0.6862 -0.03789 0.492 0.020 0.056 0.432
#> GSM627098 3 0.5626 0.00711 0.388 0.004 0.588 0.020
#> GSM627104 1 0.4401 0.68862 0.724 0.000 0.272 0.004
#> GSM627131 3 0.4199 0.57810 0.164 0.000 0.804 0.032
#> GSM627106 3 0.4458 0.69064 0.000 0.116 0.808 0.076
#> GSM627123 1 0.0524 0.68438 0.988 0.000 0.004 0.008
#> GSM627129 4 0.5440 0.55529 0.000 0.384 0.020 0.596
#> GSM627216 3 0.5709 0.41900 0.004 0.384 0.588 0.024
#> GSM627212 2 0.1610 0.86893 0.000 0.952 0.016 0.032
#> GSM627190 1 0.6161 0.52192 0.552 0.044 0.400 0.004
#> GSM627169 2 0.1109 0.86573 0.000 0.968 0.028 0.004
#> GSM627167 2 0.5623 0.48267 0.000 0.660 0.048 0.292
#> GSM627192 1 0.0188 0.68477 0.996 0.000 0.000 0.004
#> GSM627203 3 0.2675 0.70487 0.000 0.100 0.892 0.008
#> GSM627151 3 0.4811 0.68309 0.032 0.068 0.816 0.084
#> GSM627163 1 0.0188 0.68477 0.996 0.000 0.000 0.004
#> GSM627211 2 0.0000 0.87510 0.000 1.000 0.000 0.000
#> GSM627171 2 0.0779 0.87195 0.000 0.980 0.016 0.004
#> GSM627209 2 0.4535 0.51212 0.000 0.704 0.004 0.292
#> GSM627135 1 0.2101 0.69032 0.928 0.000 0.060 0.012
#> GSM627170 2 0.2675 0.84389 0.000 0.908 0.044 0.048
#> GSM627178 1 0.6332 0.39184 0.488 0.000 0.452 0.060
#> GSM627199 4 0.3801 0.75056 0.000 0.220 0.000 0.780
#> GSM627213 4 0.4608 0.68681 0.000 0.304 0.004 0.692
#> GSM627140 4 0.5827 0.55396 0.000 0.316 0.052 0.632
#> GSM627149 1 0.0524 0.68438 0.988 0.000 0.004 0.008
#> GSM627147 2 0.5786 0.43531 0.000 0.640 0.052 0.308
#> GSM627195 3 0.2675 0.70487 0.000 0.100 0.892 0.008
#> GSM627204 2 0.0000 0.87510 0.000 1.000 0.000 0.000
#> GSM627207 2 0.0000 0.87510 0.000 1.000 0.000 0.000
#> GSM627157 1 0.4800 0.64608 0.656 0.000 0.340 0.004
#> GSM627201 2 0.1520 0.86987 0.000 0.956 0.024 0.020
#> GSM627146 2 0.0188 0.87565 0.000 0.996 0.000 0.004
#> GSM627156 2 0.1109 0.86573 0.000 0.968 0.028 0.004
#> GSM627188 1 0.0188 0.68477 0.996 0.000 0.000 0.004
#> GSM627197 2 0.0188 0.87565 0.000 0.996 0.000 0.004
#> GSM627173 2 0.0000 0.87510 0.000 1.000 0.000 0.000
#> GSM627179 2 0.0927 0.87323 0.000 0.976 0.016 0.008
#> GSM627208 3 0.4391 0.64393 0.000 0.252 0.740 0.008
#> GSM627215 3 0.5038 0.51645 0.000 0.336 0.652 0.012
#> GSM627153 2 0.4535 0.51212 0.000 0.704 0.004 0.292
#> GSM627155 1 0.0188 0.68477 0.996 0.000 0.000 0.004
#> GSM627165 2 0.3893 0.69984 0.000 0.796 0.008 0.196
#> GSM627168 1 0.4872 0.63261 0.640 0.000 0.356 0.004
#> GSM627183 1 0.5178 0.58690 0.600 0.004 0.392 0.004
#> GSM627144 3 0.2611 0.70460 0.000 0.096 0.896 0.008
#> GSM627158 1 0.0188 0.68477 0.996 0.000 0.000 0.004
#> GSM627196 2 0.0000 0.87510 0.000 1.000 0.000 0.000
#> GSM627142 4 0.4989 -0.00812 0.000 0.000 0.472 0.528
#> GSM627182 3 0.4391 0.64393 0.000 0.252 0.740 0.008
#> GSM627202 3 0.5167 0.19168 0.340 0.000 0.644 0.016
#> GSM627141 1 0.5311 0.57786 0.596 0.008 0.392 0.004
#> GSM627143 2 0.5397 0.62208 0.000 0.720 0.068 0.212
#> GSM627145 3 0.4569 0.62473 0.140 0.036 0.808 0.016
#> GSM627152 3 0.5054 0.65605 0.100 0.016 0.792 0.092
#> GSM627200 3 0.4375 0.60872 0.144 0.008 0.812 0.036
#> GSM627159 4 0.2011 0.69424 0.000 0.000 0.080 0.920
#> GSM627164 2 0.0779 0.87195 0.000 0.980 0.016 0.004
#> GSM627138 1 0.0188 0.68477 0.996 0.000 0.000 0.004
#> GSM627175 4 0.4522 0.66658 0.000 0.320 0.000 0.680
#> GSM627150 3 0.4693 0.59519 0.160 0.012 0.792 0.036
#> GSM627166 1 0.5790 0.66817 0.684 0.000 0.236 0.080
#> GSM627186 2 0.1356 0.86457 0.000 0.960 0.032 0.008
#> GSM627139 3 0.4811 0.68309 0.032 0.068 0.816 0.084
#> GSM627181 2 0.0188 0.87565 0.000 0.996 0.000 0.004
#> GSM627205 3 0.5506 0.16120 0.000 0.472 0.512 0.016
#> GSM627214 2 0.4212 0.66034 0.000 0.772 0.012 0.216
#> GSM627180 3 0.5038 0.51645 0.000 0.336 0.652 0.012
#> GSM627172 2 0.2805 0.82025 0.000 0.888 0.012 0.100
#> GSM627184 1 0.0188 0.68477 0.996 0.000 0.000 0.004
#> GSM627193 2 0.0000 0.87510 0.000 1.000 0.000 0.000
#> GSM627191 4 0.7285 0.29958 0.308 0.036 0.084 0.572
#> GSM627176 3 0.2965 0.69512 0.000 0.036 0.892 0.072
#> GSM627194 2 0.3074 0.76207 0.000 0.848 0.000 0.152
#> GSM627154 4 0.3801 0.75056 0.000 0.220 0.000 0.780
#> GSM627187 1 0.6161 0.52192 0.552 0.044 0.400 0.004
#> GSM627198 4 0.3801 0.75056 0.000 0.220 0.000 0.780
#> GSM627160 4 0.5979 0.64189 0.000 0.136 0.172 0.692
#> GSM627185 1 0.3831 0.69574 0.792 0.000 0.204 0.004
#> GSM627206 1 0.5290 0.49434 0.552 0.004 0.440 0.004
#> GSM627161 1 0.0188 0.68477 0.996 0.000 0.000 0.004
#> GSM627162 2 0.7516 0.25169 0.004 0.524 0.244 0.228
#> GSM627210 1 0.4401 0.68862 0.724 0.000 0.272 0.004
#> GSM627189 2 0.0000 0.87510 0.000 1.000 0.000 0.000
show/hide code output
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM627128 4 0.3868 0.6959 0.056 0.000 0.016 0.824 0.104
#> GSM627110 3 0.2017 0.8004 0.008 0.000 0.912 0.000 0.080
#> GSM627132 1 0.2424 0.9165 0.868 0.000 0.132 0.000 0.000
#> GSM627107 5 0.4042 0.6888 0.008 0.068 0.028 0.064 0.832
#> GSM627103 2 0.2374 0.8409 0.016 0.912 0.000 0.052 0.020
#> GSM627114 3 0.3123 0.7714 0.000 0.004 0.812 0.000 0.184
#> GSM627134 4 0.5568 0.6523 0.048 0.264 0.004 0.656 0.028
#> GSM627137 2 0.5051 0.6314 0.048 0.708 0.000 0.220 0.024
#> GSM627148 5 0.4432 0.6953 0.000 0.080 0.092 0.032 0.796
#> GSM627101 4 0.4828 0.7827 0.048 0.092 0.004 0.780 0.076
#> GSM627130 4 0.3919 0.6944 0.056 0.000 0.016 0.820 0.108
#> GSM627071 5 0.5510 0.4127 0.016 0.012 0.356 0.024 0.592
#> GSM627118 4 0.5473 0.6781 0.048 0.248 0.004 0.672 0.028
#> GSM627094 2 0.0000 0.8518 0.000 1.000 0.000 0.000 0.000
#> GSM627122 5 0.5071 0.2992 0.012 0.000 0.392 0.020 0.576
#> GSM627115 2 0.2228 0.8417 0.016 0.920 0.000 0.044 0.020
#> GSM627125 4 0.3968 0.6926 0.056 0.000 0.016 0.816 0.112
#> GSM627174 2 0.1989 0.8487 0.004 0.932 0.004 0.032 0.028
#> GSM627102 2 0.2970 0.8035 0.012 0.872 0.004 0.100 0.012
#> GSM627073 5 0.5407 0.6387 0.000 0.128 0.180 0.008 0.684
#> GSM627108 2 0.0000 0.8518 0.000 1.000 0.000 0.000 0.000
#> GSM627126 1 0.2230 0.9205 0.884 0.000 0.116 0.000 0.000
#> GSM627078 4 0.2561 0.7914 0.000 0.144 0.000 0.856 0.000
#> GSM627090 5 0.3418 0.6658 0.028 0.000 0.068 0.044 0.860
#> GSM627099 2 0.2605 0.8353 0.016 0.900 0.000 0.060 0.024
#> GSM627105 4 0.3968 0.6926 0.056 0.000 0.016 0.816 0.112
#> GSM627117 3 0.4028 0.7488 0.000 0.040 0.768 0.000 0.192
#> GSM627121 5 0.4645 0.6786 0.008 0.112 0.028 0.064 0.788
#> GSM627127 4 0.4459 0.7690 0.048 0.152 0.008 0.780 0.012
#> GSM627087 2 0.2228 0.8417 0.016 0.920 0.000 0.044 0.020
#> GSM627089 3 0.3160 0.7602 0.000 0.000 0.808 0.004 0.188
#> GSM627092 2 0.6965 0.3110 0.016 0.516 0.012 0.284 0.172
#> GSM627076 5 0.3480 0.6643 0.028 0.000 0.044 0.072 0.856
#> GSM627136 3 0.4849 0.2600 0.004 0.000 0.548 0.016 0.432
#> GSM627081 5 0.4103 0.6890 0.008 0.072 0.028 0.064 0.828
#> GSM627091 2 0.2605 0.8353 0.016 0.900 0.000 0.060 0.024
#> GSM627097 4 0.6456 0.7443 0.048 0.140 0.080 0.680 0.052
#> GSM627072 5 0.5262 0.3767 0.008 0.024 0.372 0.008 0.588
#> GSM627080 1 0.2280 0.9205 0.880 0.000 0.120 0.000 0.000
#> GSM627088 3 0.4443 0.5834 0.008 0.000 0.680 0.012 0.300
#> GSM627109 3 0.1197 0.7771 0.048 0.000 0.952 0.000 0.000
#> GSM627111 1 0.2424 0.9165 0.868 0.000 0.132 0.000 0.000
#> GSM627113 3 0.2278 0.7962 0.032 0.000 0.908 0.000 0.060
#> GSM627133 5 0.5805 0.4793 0.004 0.352 0.044 0.024 0.576
#> GSM627177 3 0.5103 0.6556 0.024 0.000 0.688 0.040 0.248
#> GSM627086 2 0.2069 0.8295 0.012 0.912 0.000 0.076 0.000
#> GSM627095 1 0.6677 0.1518 0.488 0.012 0.064 0.396 0.040
#> GSM627079 5 0.5075 0.3876 0.016 0.000 0.360 0.020 0.604
#> GSM627082 4 0.3919 0.6944 0.056 0.000 0.016 0.820 0.108
#> GSM627074 3 0.1197 0.7771 0.048 0.000 0.952 0.000 0.000
#> GSM627077 3 0.2193 0.8017 0.008 0.000 0.900 0.000 0.092
#> GSM627093 3 0.1197 0.7771 0.048 0.000 0.952 0.000 0.000
#> GSM627120 2 0.4858 0.7022 0.048 0.756 0.008 0.164 0.024
#> GSM627124 4 0.2561 0.7914 0.000 0.144 0.000 0.856 0.000
#> GSM627075 2 0.1095 0.8469 0.012 0.968 0.008 0.000 0.012
#> GSM627085 4 0.2516 0.7925 0.000 0.140 0.000 0.860 0.000
#> GSM627119 3 0.1197 0.7771 0.048 0.000 0.952 0.000 0.000
#> GSM627116 3 0.5128 0.6546 0.024 0.000 0.684 0.040 0.252
#> GSM627084 3 0.4734 0.4841 0.008 0.000 0.632 0.016 0.344
#> GSM627096 4 0.5473 0.6781 0.048 0.248 0.004 0.672 0.028
#> GSM627100 5 0.3480 0.6643 0.028 0.000 0.044 0.072 0.856
#> GSM627112 4 0.3708 0.7775 0.032 0.084 0.008 0.848 0.028
#> GSM627083 1 0.6677 0.1518 0.488 0.012 0.064 0.396 0.040
#> GSM627098 3 0.4734 0.4841 0.008 0.000 0.632 0.016 0.344
#> GSM627104 3 0.1197 0.7771 0.048 0.000 0.952 0.000 0.000
#> GSM627131 5 0.5075 0.3876 0.016 0.000 0.360 0.020 0.604
#> GSM627106 5 0.4103 0.6890 0.008 0.072 0.028 0.064 0.828
#> GSM627123 1 0.2488 0.9158 0.872 0.000 0.124 0.004 0.000
#> GSM627129 4 0.5718 0.6508 0.048 0.264 0.004 0.648 0.036
#> GSM627216 5 0.5805 0.4793 0.004 0.352 0.044 0.024 0.576
#> GSM627212 2 0.2605 0.8353 0.016 0.900 0.000 0.060 0.024
#> GSM627190 3 0.4028 0.7488 0.000 0.040 0.768 0.000 0.192
#> GSM627169 2 0.1507 0.8387 0.012 0.952 0.012 0.000 0.024
#> GSM627167 2 0.5563 0.4806 0.016 0.628 0.004 0.300 0.052
#> GSM627192 1 0.2230 0.9205 0.884 0.000 0.116 0.000 0.000
#> GSM627203 5 0.2885 0.6926 0.000 0.064 0.052 0.004 0.880
#> GSM627151 5 0.5633 0.6382 0.020 0.036 0.144 0.076 0.724
#> GSM627163 1 0.2377 0.9173 0.872 0.000 0.128 0.000 0.000
#> GSM627211 2 0.0000 0.8518 0.000 1.000 0.000 0.000 0.000
#> GSM627171 2 0.1235 0.8438 0.012 0.964 0.004 0.004 0.016
#> GSM627209 2 0.4474 0.4706 0.012 0.652 0.000 0.332 0.004
#> GSM627135 1 0.3422 0.8403 0.792 0.000 0.200 0.004 0.004
#> GSM627170 2 0.4207 0.7828 0.048 0.816 0.000 0.072 0.064
#> GSM627178 3 0.5128 0.6546 0.024 0.000 0.684 0.040 0.252
#> GSM627199 4 0.2516 0.7925 0.000 0.140 0.000 0.860 0.000
#> GSM627213 4 0.5036 0.7490 0.048 0.188 0.004 0.732 0.028
#> GSM627140 4 0.6349 0.5271 0.044 0.292 0.008 0.592 0.064
#> GSM627149 1 0.2488 0.9158 0.872 0.000 0.124 0.004 0.000
#> GSM627147 2 0.5693 0.4375 0.016 0.608 0.004 0.316 0.056
#> GSM627195 5 0.2885 0.6926 0.000 0.064 0.052 0.004 0.880
#> GSM627204 2 0.0000 0.8518 0.000 1.000 0.000 0.000 0.000
#> GSM627207 2 0.0162 0.8524 0.000 0.996 0.000 0.004 0.000
#> GSM627157 3 0.2270 0.7999 0.020 0.000 0.904 0.000 0.076
#> GSM627201 2 0.1989 0.8487 0.004 0.932 0.004 0.032 0.028
#> GSM627146 2 0.0510 0.8535 0.000 0.984 0.000 0.016 0.000
#> GSM627156 2 0.1507 0.8387 0.012 0.952 0.012 0.000 0.024
#> GSM627188 1 0.2230 0.9205 0.884 0.000 0.116 0.000 0.000
#> GSM627197 2 0.0609 0.8538 0.000 0.980 0.000 0.020 0.000
#> GSM627173 2 0.0404 0.8530 0.000 0.988 0.000 0.012 0.000
#> GSM627179 2 0.1787 0.8459 0.012 0.940 0.000 0.032 0.016
#> GSM627208 5 0.5038 0.6399 0.000 0.220 0.072 0.008 0.700
#> GSM627215 5 0.4854 0.5872 0.000 0.288 0.024 0.016 0.672
#> GSM627153 2 0.4474 0.4706 0.012 0.652 0.000 0.332 0.004
#> GSM627155 1 0.2230 0.9205 0.884 0.000 0.116 0.000 0.000
#> GSM627165 2 0.5189 0.6153 0.048 0.696 0.000 0.228 0.028
#> GSM627168 3 0.2464 0.8003 0.016 0.000 0.888 0.000 0.096
#> GSM627183 3 0.2660 0.7945 0.008 0.000 0.864 0.000 0.128
#> GSM627144 5 0.2954 0.6924 0.000 0.064 0.056 0.004 0.876
#> GSM627158 1 0.2280 0.9205 0.880 0.000 0.120 0.000 0.000
#> GSM627196 2 0.0000 0.8518 0.000 1.000 0.000 0.000 0.000
#> GSM627142 5 0.5692 0.0249 0.040 0.000 0.020 0.452 0.488
#> GSM627182 5 0.5038 0.6399 0.000 0.220 0.072 0.008 0.700
#> GSM627202 3 0.4871 0.3628 0.012 0.000 0.592 0.012 0.384
#> GSM627141 3 0.2488 0.7954 0.000 0.004 0.872 0.000 0.124
#> GSM627143 2 0.5394 0.6159 0.016 0.688 0.004 0.220 0.072
#> GSM627145 5 0.5093 0.4768 0.008 0.024 0.324 0.008 0.636
#> GSM627152 5 0.4665 0.6147 0.020 0.000 0.168 0.056 0.756
#> GSM627200 5 0.5046 0.4466 0.020 0.000 0.328 0.020 0.632
#> GSM627159 4 0.3919 0.6944 0.056 0.000 0.016 0.820 0.108
#> GSM627164 2 0.1235 0.8438 0.012 0.964 0.004 0.004 0.016
#> GSM627138 1 0.2280 0.9205 0.880 0.000 0.120 0.000 0.000
#> GSM627175 4 0.4731 0.7300 0.048 0.208 0.004 0.732 0.008
#> GSM627150 5 0.5510 0.4127 0.016 0.012 0.356 0.024 0.592
#> GSM627166 3 0.3670 0.7392 0.044 0.000 0.848 0.064 0.044
#> GSM627186 2 0.1757 0.8385 0.012 0.944 0.012 0.004 0.028
#> GSM627139 5 0.5633 0.6382 0.020 0.036 0.144 0.076 0.724
#> GSM627181 2 0.0609 0.8538 0.000 0.980 0.000 0.020 0.000
#> GSM627205 5 0.5599 0.2393 0.016 0.432 0.016 0.016 0.520
#> GSM627214 2 0.4486 0.6102 0.012 0.712 0.000 0.256 0.020
#> GSM627180 5 0.4854 0.5872 0.000 0.288 0.024 0.016 0.672
#> GSM627172 2 0.2970 0.8035 0.012 0.872 0.004 0.100 0.012
#> GSM627184 1 0.2230 0.9205 0.884 0.000 0.116 0.000 0.000
#> GSM627193 2 0.0404 0.8530 0.000 0.988 0.000 0.012 0.000
#> GSM627191 4 0.7348 0.2887 0.300 0.028 0.088 0.524 0.060
#> GSM627176 5 0.3547 0.6749 0.028 0.008 0.068 0.036 0.860
#> GSM627194 2 0.4499 0.7171 0.048 0.776 0.008 0.156 0.012
#> GSM627154 4 0.2516 0.7925 0.000 0.140 0.000 0.860 0.000
#> GSM627187 3 0.4028 0.7488 0.000 0.040 0.768 0.000 0.192
#> GSM627198 4 0.2516 0.7925 0.000 0.140 0.000 0.860 0.000
#> GSM627160 4 0.6671 0.6195 0.044 0.124 0.016 0.624 0.192
#> GSM627185 3 0.2329 0.7080 0.124 0.000 0.876 0.000 0.000
#> GSM627206 3 0.3160 0.7602 0.000 0.000 0.808 0.004 0.188
#> GSM627161 1 0.2280 0.9205 0.880 0.000 0.120 0.000 0.000
#> GSM627162 2 0.7519 0.2612 0.016 0.480 0.032 0.240 0.232
#> GSM627210 3 0.1197 0.7771 0.048 0.000 0.952 0.000 0.000
#> GSM627189 2 0.0404 0.8530 0.000 0.988 0.000 0.012 0.000
show/hide code output
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM627128 6 0.2100 0.7244 0.000 0.000 0.000 0.112 0.004 0.884
#> GSM627110 3 0.2358 0.7959 0.000 0.000 0.876 0.000 0.108 0.016
#> GSM627132 1 0.0713 0.9058 0.972 0.000 0.028 0.000 0.000 0.000
#> GSM627107 5 0.3023 0.6768 0.000 0.000 0.000 0.032 0.828 0.140
#> GSM627103 2 0.3101 0.7958 0.000 0.832 0.000 0.136 0.012 0.020
#> GSM627114 3 0.3183 0.7713 0.000 0.004 0.788 0.000 0.200 0.008
#> GSM627134 4 0.1555 0.6554 0.000 0.040 0.000 0.940 0.008 0.012
#> GSM627137 2 0.4598 0.3319 0.000 0.504 0.000 0.464 0.004 0.028
#> GSM627148 5 0.3318 0.7149 0.000 0.040 0.048 0.004 0.852 0.056
#> GSM627101 4 0.3448 0.4955 0.000 0.000 0.000 0.716 0.004 0.280
#> GSM627130 6 0.2006 0.7308 0.000 0.000 0.000 0.104 0.004 0.892
#> GSM627071 5 0.4718 0.4281 0.000 0.000 0.316 0.000 0.616 0.068
#> GSM627118 4 0.1251 0.6632 0.000 0.024 0.000 0.956 0.008 0.012
#> GSM627094 2 0.0865 0.8235 0.000 0.964 0.000 0.036 0.000 0.000
#> GSM627122 5 0.4881 0.3412 0.000 0.000 0.336 0.000 0.588 0.076
#> GSM627115 2 0.2847 0.8014 0.000 0.852 0.000 0.120 0.012 0.016
#> GSM627125 6 0.1958 0.7323 0.000 0.000 0.000 0.100 0.004 0.896
#> GSM627174 2 0.2367 0.8125 0.000 0.888 0.000 0.088 0.008 0.016
#> GSM627102 2 0.2333 0.7706 0.000 0.884 0.000 0.024 0.000 0.092
#> GSM627073 5 0.4695 0.6560 0.000 0.084 0.144 0.012 0.740 0.020
#> GSM627108 2 0.0865 0.8235 0.000 0.964 0.000 0.036 0.000 0.000
#> GSM627126 1 0.0260 0.9108 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM627078 4 0.3337 0.6277 0.000 0.004 0.000 0.736 0.000 0.260
#> GSM627090 5 0.2805 0.6867 0.000 0.000 0.012 0.000 0.828 0.160
#> GSM627099 2 0.3457 0.7765 0.000 0.800 0.000 0.164 0.020 0.016
#> GSM627105 6 0.1958 0.7323 0.000 0.000 0.000 0.100 0.004 0.896
#> GSM627117 3 0.3956 0.7531 0.000 0.040 0.748 0.000 0.204 0.008
#> GSM627121 5 0.3878 0.6644 0.000 0.040 0.000 0.032 0.792 0.136
#> GSM627127 4 0.1610 0.6723 0.000 0.000 0.000 0.916 0.000 0.084
#> GSM627087 2 0.2847 0.8014 0.000 0.852 0.000 0.120 0.012 0.016
#> GSM627089 3 0.3023 0.7567 0.000 0.000 0.784 0.000 0.212 0.004
#> GSM627092 2 0.7020 0.2383 0.000 0.476 0.004 0.144 0.120 0.256
#> GSM627076 5 0.2730 0.6696 0.000 0.000 0.000 0.000 0.808 0.192
#> GSM627136 3 0.4695 0.2373 0.000 0.000 0.508 0.000 0.448 0.044
#> GSM627081 5 0.2983 0.6769 0.000 0.000 0.000 0.032 0.832 0.136
#> GSM627091 2 0.3457 0.7765 0.000 0.800 0.000 0.164 0.020 0.016
#> GSM627097 4 0.4042 0.5834 0.000 0.000 0.040 0.784 0.044 0.132
#> GSM627072 5 0.4224 0.3845 0.000 0.000 0.340 0.000 0.632 0.028
#> GSM627080 1 0.0260 0.9130 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM627088 3 0.4282 0.5848 0.000 0.000 0.656 0.000 0.304 0.040
#> GSM627109 3 0.0000 0.7763 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627111 1 0.0713 0.9058 0.972 0.000 0.028 0.000 0.000 0.000
#> GSM627113 3 0.1802 0.7938 0.012 0.000 0.916 0.000 0.072 0.000
#> GSM627133 5 0.5849 0.4968 0.000 0.280 0.032 0.044 0.600 0.044
#> GSM627177 3 0.4931 0.6157 0.000 0.000 0.636 0.000 0.248 0.116
#> GSM627086 2 0.2871 0.7561 0.000 0.804 0.000 0.192 0.004 0.000
#> GSM627095 1 0.5733 0.0335 0.480 0.000 0.012 0.120 0.000 0.388
#> GSM627079 5 0.4798 0.4322 0.000 0.000 0.300 0.000 0.620 0.080
#> GSM627082 6 0.1958 0.7319 0.000 0.000 0.000 0.100 0.004 0.896
#> GSM627074 3 0.0000 0.7763 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627077 3 0.2494 0.7970 0.000 0.000 0.864 0.000 0.120 0.016
#> GSM627093 3 0.0000 0.7763 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627120 2 0.4470 0.6107 0.000 0.660 0.000 0.296 0.016 0.028
#> GSM627124 4 0.3337 0.6277 0.000 0.004 0.000 0.736 0.000 0.260
#> GSM627075 2 0.0622 0.8165 0.000 0.980 0.000 0.012 0.000 0.008
#> GSM627085 4 0.3221 0.6252 0.000 0.000 0.000 0.736 0.000 0.264
#> GSM627119 3 0.0000 0.7763 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627116 3 0.4952 0.6145 0.000 0.000 0.632 0.000 0.252 0.116
#> GSM627084 3 0.4506 0.4853 0.000 0.000 0.608 0.000 0.348 0.044
#> GSM627096 4 0.1251 0.6632 0.000 0.024 0.000 0.956 0.008 0.012
#> GSM627100 5 0.2730 0.6696 0.000 0.000 0.000 0.000 0.808 0.192
#> GSM627112 4 0.3868 0.1331 0.000 0.000 0.000 0.508 0.000 0.492
#> GSM627083 1 0.5733 0.0335 0.480 0.000 0.012 0.120 0.000 0.388
#> GSM627098 3 0.4506 0.4853 0.000 0.000 0.608 0.000 0.348 0.044
#> GSM627104 3 0.0000 0.7763 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627131 5 0.4798 0.4322 0.000 0.000 0.300 0.000 0.620 0.080
#> GSM627106 5 0.2983 0.6769 0.000 0.000 0.000 0.032 0.832 0.136
#> GSM627123 1 0.0622 0.9064 0.980 0.000 0.008 0.000 0.000 0.012
#> GSM627129 4 0.3317 0.5979 0.000 0.088 0.000 0.828 0.004 0.080
#> GSM627216 5 0.5849 0.4968 0.000 0.280 0.032 0.044 0.600 0.044
#> GSM627212 2 0.3457 0.7765 0.000 0.800 0.000 0.164 0.020 0.016
#> GSM627190 3 0.3956 0.7531 0.000 0.040 0.748 0.000 0.204 0.008
#> GSM627169 2 0.0924 0.8134 0.000 0.972 0.008 0.004 0.008 0.008
#> GSM627167 2 0.5454 0.4423 0.000 0.600 0.000 0.160 0.008 0.232
#> GSM627192 1 0.0000 0.9124 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627203 5 0.0405 0.7080 0.000 0.000 0.000 0.004 0.988 0.008
#> GSM627151 5 0.5395 0.6619 0.000 0.020 0.088 0.048 0.700 0.144
#> GSM627163 1 0.0632 0.9066 0.976 0.000 0.024 0.000 0.000 0.000
#> GSM627211 2 0.0790 0.8233 0.000 0.968 0.000 0.032 0.000 0.000
#> GSM627171 2 0.0508 0.8150 0.000 0.984 0.000 0.004 0.000 0.012
#> GSM627209 4 0.4723 -0.1602 0.000 0.472 0.000 0.488 0.004 0.036
#> GSM627135 1 0.2263 0.8272 0.884 0.000 0.100 0.000 0.000 0.016
#> GSM627170 2 0.5187 0.5714 0.000 0.604 0.000 0.312 0.056 0.028
#> GSM627178 3 0.4952 0.6145 0.000 0.000 0.632 0.000 0.252 0.116
#> GSM627199 4 0.3244 0.6223 0.000 0.000 0.000 0.732 0.000 0.268
#> GSM627213 4 0.2051 0.6655 0.000 0.004 0.000 0.896 0.004 0.096
#> GSM627140 6 0.5849 0.2304 0.000 0.252 0.000 0.228 0.004 0.516
#> GSM627149 1 0.0622 0.9064 0.980 0.000 0.008 0.000 0.000 0.012
#> GSM627147 2 0.5564 0.4049 0.000 0.580 0.000 0.164 0.008 0.248
#> GSM627195 5 0.0405 0.7080 0.000 0.000 0.000 0.004 0.988 0.008
#> GSM627204 2 0.0790 0.8233 0.000 0.968 0.000 0.032 0.000 0.000
#> GSM627207 2 0.0790 0.8239 0.000 0.968 0.000 0.032 0.000 0.000
#> GSM627157 3 0.1858 0.7966 0.004 0.000 0.904 0.000 0.092 0.000
#> GSM627201 2 0.2367 0.8125 0.000 0.888 0.000 0.088 0.008 0.016
#> GSM627146 2 0.1267 0.8238 0.000 0.940 0.000 0.060 0.000 0.000
#> GSM627156 2 0.0924 0.8134 0.000 0.972 0.008 0.004 0.008 0.008
#> GSM627188 1 0.0000 0.9124 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627197 2 0.1327 0.8237 0.000 0.936 0.000 0.064 0.000 0.000
#> GSM627173 2 0.1204 0.8240 0.000 0.944 0.000 0.056 0.000 0.000
#> GSM627179 2 0.2476 0.8108 0.000 0.880 0.000 0.096 0.012 0.012
#> GSM627208 5 0.4521 0.6557 0.000 0.132 0.036 0.032 0.768 0.032
#> GSM627215 5 0.5086 0.5840 0.000 0.180 0.012 0.068 0.704 0.036
#> GSM627153 4 0.4723 -0.1602 0.000 0.472 0.000 0.488 0.004 0.036
#> GSM627155 1 0.0000 0.9124 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627165 2 0.4602 0.3052 0.000 0.492 0.000 0.476 0.004 0.028
#> GSM627168 3 0.1957 0.7972 0.000 0.000 0.888 0.000 0.112 0.000
#> GSM627183 3 0.2442 0.7918 0.000 0.000 0.852 0.000 0.144 0.004
#> GSM627144 5 0.0363 0.7083 0.000 0.000 0.000 0.000 0.988 0.012
#> GSM627158 1 0.0260 0.9130 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM627196 2 0.0790 0.8233 0.000 0.968 0.000 0.032 0.000 0.000
#> GSM627142 6 0.4634 0.1098 0.000 0.000 0.000 0.044 0.400 0.556
#> GSM627182 5 0.4521 0.6557 0.000 0.132 0.036 0.032 0.768 0.032
#> GSM627202 3 0.4701 0.3640 0.004 0.000 0.560 0.000 0.396 0.040
#> GSM627141 3 0.3010 0.7927 0.000 0.004 0.828 0.000 0.148 0.020
#> GSM627143 2 0.5184 0.5562 0.000 0.660 0.000 0.120 0.020 0.200
#> GSM627145 5 0.3990 0.4963 0.000 0.000 0.284 0.000 0.688 0.028
#> GSM627152 5 0.4267 0.6491 0.000 0.000 0.116 0.000 0.732 0.152
#> GSM627200 5 0.4700 0.4880 0.000 0.000 0.268 0.000 0.648 0.084
#> GSM627159 6 0.1958 0.7319 0.000 0.000 0.000 0.100 0.004 0.896
#> GSM627164 2 0.0508 0.8150 0.000 0.984 0.000 0.004 0.000 0.012
#> GSM627138 1 0.0260 0.9130 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM627175 4 0.1265 0.6729 0.000 0.008 0.000 0.948 0.000 0.044
#> GSM627150 5 0.4718 0.4281 0.000 0.000 0.316 0.000 0.616 0.068
#> GSM627166 3 0.3239 0.7315 0.000 0.000 0.840 0.016 0.044 0.100
#> GSM627186 2 0.1140 0.8131 0.000 0.964 0.008 0.008 0.012 0.008
#> GSM627139 5 0.5395 0.6619 0.000 0.020 0.088 0.048 0.700 0.144
#> GSM627181 2 0.1327 0.8237 0.000 0.936 0.000 0.064 0.000 0.000
#> GSM627205 5 0.5742 0.3127 0.000 0.328 0.000 0.088 0.548 0.036
#> GSM627214 2 0.4386 0.2884 0.000 0.516 0.000 0.464 0.004 0.016
#> GSM627180 5 0.5086 0.5840 0.000 0.180 0.012 0.068 0.704 0.036
#> GSM627172 2 0.2333 0.7706 0.000 0.884 0.000 0.024 0.000 0.092
#> GSM627184 1 0.0000 0.9124 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627193 2 0.1204 0.8240 0.000 0.944 0.000 0.056 0.000 0.000
#> GSM627191 6 0.6304 0.3530 0.292 0.008 0.028 0.156 0.000 0.516
#> GSM627176 5 0.2886 0.6945 0.000 0.004 0.016 0.000 0.836 0.144
#> GSM627194 2 0.3615 0.6475 0.000 0.700 0.000 0.292 0.000 0.008
#> GSM627154 4 0.3221 0.6252 0.000 0.000 0.000 0.736 0.000 0.264
#> GSM627187 3 0.3956 0.7531 0.000 0.040 0.748 0.000 0.204 0.008
#> GSM627198 4 0.3244 0.6223 0.000 0.000 0.000 0.732 0.000 0.268
#> GSM627160 6 0.6258 0.4654 0.000 0.088 0.000 0.208 0.128 0.576
#> GSM627185 3 0.1949 0.7327 0.088 0.000 0.904 0.000 0.004 0.004
#> GSM627206 3 0.3023 0.7567 0.000 0.000 0.784 0.000 0.212 0.004
#> GSM627161 1 0.0260 0.9130 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM627162 2 0.7323 0.1757 0.000 0.456 0.020 0.088 0.196 0.240
#> GSM627210 3 0.0000 0.7763 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627189 2 0.1204 0.8240 0.000 0.944 0.000 0.056 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two
samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures
which are significantly different between classes which can be candidate marks
for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known
annotation is numeric, one-way ANOVA test is applied, and if the known
annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) age(p) other(p) k
#> SD:hclust 135 1.0000 0.542 0.1704 2
#> SD:hclust 122 0.6198 0.796 0.1239 3
#> SD:hclust 125 0.0766 0.681 0.1292 4
#> SD:hclust 121 0.0998 0.930 0.0567 5
#> SD:hclust 114 0.1223 0.865 0.0497 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
SD:kmeans**
The object with results only for a single top-value method and a single partition method
can be extracted as:
res = res_list["SD", "kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51882 rows and 146 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
- The first row: a plot of the ECDF (empirical cumulative distribution
function) curves of the consensus matrix for each
k and the heatmap of
predicted classes for each k.
- The second row: heatmaps of the consensus matrix for each
k.
- The third row: heatmaps of the membership matrix for each
k.
- The fouth row: heatmaps of the signatures for each
k.
All the plots in panels can be made by individual functions and they are
plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
- ECDF curves of the consensus matrix for each
k;
- 1-PAC. The PAC
score
measures the proportion of the ambiguous subgrouping.
- Mean silhouette score.
- Concordance. The mean probability of fiting the consensus class ids in all
partitions.
- Area increased. Denote \(A_k\) as the area under the ECDF curve for current
k, the area increased is defined as \(A_k - A_{k-1}\).
- Rand index. The percent of pairs of samples that are both in a same cluster
or both are not in a same cluster in the partition of k and k-1.
- Jaccard index. The ratio of pairs of samples are both in a same cluster in
the partition of k and k-1 and the pairs of samples are both in a same
cluster in the partition k or k-1.
The detailed explanations of these statistics can be found in the cola
vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.975 0.989 0.4988 0.501 0.501
#> 3 3 0.522 0.623 0.804 0.2990 0.736 0.524
#> 4 4 0.692 0.773 0.876 0.1317 0.770 0.451
#> 5 5 0.654 0.537 0.741 0.0683 0.960 0.857
#> 6 6 0.689 0.637 0.738 0.0486 0.876 0.559
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
- All \(k\) with Jaccard index larger than 0.95 are removed because increasing
\(k\) does not provide enough extra information. If all \(k\) are removed, it is
marked as no subgroup is detected.
- For all \(k\) with 1-PAC score larger than 0.9, the maximal \(k\) is taken as
the best \(k\), and other \(k\) are marked as optional \(k\).
- If it does not fit the second rule. The \(k\) with the maximal vote of the
highest 1-PAC score, highest mean silhouette, and highest concordance is
taken as the best \(k\).
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
show/hide code output
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM627128 2 0.0000 0.992 0.000 1.000
#> GSM627110 1 0.0000 0.986 1.000 0.000
#> GSM627132 1 0.0000 0.986 1.000 0.000
#> GSM627107 2 0.0000 0.992 0.000 1.000
#> GSM627103 2 0.0000 0.992 0.000 1.000
#> GSM627114 1 0.0000 0.986 1.000 0.000
#> GSM627134 2 0.0000 0.992 0.000 1.000
#> GSM627137 2 0.0000 0.992 0.000 1.000
#> GSM627148 1 0.0000 0.986 1.000 0.000
#> GSM627101 2 0.0000 0.992 0.000 1.000
#> GSM627130 2 0.0000 0.992 0.000 1.000
#> GSM627071 1 0.0000 0.986 1.000 0.000
#> GSM627118 2 0.0000 0.992 0.000 1.000
#> GSM627094 2 0.0000 0.992 0.000 1.000
#> GSM627122 1 0.0000 0.986 1.000 0.000
#> GSM627115 2 0.0000 0.992 0.000 1.000
#> GSM627125 2 0.0000 0.992 0.000 1.000
#> GSM627174 2 0.0000 0.992 0.000 1.000
#> GSM627102 2 0.0000 0.992 0.000 1.000
#> GSM627073 2 0.2778 0.948 0.048 0.952
#> GSM627108 2 0.0000 0.992 0.000 1.000
#> GSM627126 1 0.0000 0.986 1.000 0.000
#> GSM627078 2 0.0000 0.992 0.000 1.000
#> GSM627090 1 0.0000 0.986 1.000 0.000
#> GSM627099 2 0.0000 0.992 0.000 1.000
#> GSM627105 2 0.0000 0.992 0.000 1.000
#> GSM627117 1 0.0000 0.986 1.000 0.000
#> GSM627121 2 0.0000 0.992 0.000 1.000
#> GSM627127 2 0.0000 0.992 0.000 1.000
#> GSM627087 2 0.0000 0.992 0.000 1.000
#> GSM627089 1 0.0000 0.986 1.000 0.000
#> GSM627092 2 0.0000 0.992 0.000 1.000
#> GSM627076 1 0.0000 0.986 1.000 0.000
#> GSM627136 1 0.0000 0.986 1.000 0.000
#> GSM627081 2 0.2236 0.959 0.036 0.964
#> GSM627091 2 0.0000 0.992 0.000 1.000
#> GSM627097 2 0.0000 0.992 0.000 1.000
#> GSM627072 1 0.0000 0.986 1.000 0.000
#> GSM627080 1 0.0000 0.986 1.000 0.000
#> GSM627088 1 0.0000 0.986 1.000 0.000
#> GSM627109 1 0.0000 0.986 1.000 0.000
#> GSM627111 1 0.0000 0.986 1.000 0.000
#> GSM627113 1 0.0000 0.986 1.000 0.000
#> GSM627133 2 0.0000 0.992 0.000 1.000
#> GSM627177 1 0.0000 0.986 1.000 0.000
#> GSM627086 2 0.0000 0.992 0.000 1.000
#> GSM627095 1 0.0000 0.986 1.000 0.000
#> GSM627079 1 0.0000 0.986 1.000 0.000
#> GSM627082 2 0.8713 0.595 0.292 0.708
#> GSM627074 1 0.0000 0.986 1.000 0.000
#> GSM627077 1 0.0000 0.986 1.000 0.000
#> GSM627093 1 0.0000 0.986 1.000 0.000
#> GSM627120 2 0.0000 0.992 0.000 1.000
#> GSM627124 2 0.0000 0.992 0.000 1.000
#> GSM627075 2 0.0000 0.992 0.000 1.000
#> GSM627085 2 0.0000 0.992 0.000 1.000
#> GSM627119 1 0.0000 0.986 1.000 0.000
#> GSM627116 2 0.0000 0.992 0.000 1.000
#> GSM627084 1 0.0000 0.986 1.000 0.000
#> GSM627096 2 0.0000 0.992 0.000 1.000
#> GSM627100 1 0.0000 0.986 1.000 0.000
#> GSM627112 2 0.0000 0.992 0.000 1.000
#> GSM627083 1 0.0000 0.986 1.000 0.000
#> GSM627098 1 0.0000 0.986 1.000 0.000
#> GSM627104 1 0.0000 0.986 1.000 0.000
#> GSM627131 1 0.0000 0.986 1.000 0.000
#> GSM627106 2 0.2778 0.948 0.048 0.952
#> GSM627123 1 0.0000 0.986 1.000 0.000
#> GSM627129 2 0.0000 0.992 0.000 1.000
#> GSM627216 2 0.0000 0.992 0.000 1.000
#> GSM627212 2 0.0000 0.992 0.000 1.000
#> GSM627190 1 0.0000 0.986 1.000 0.000
#> GSM627169 2 0.0000 0.992 0.000 1.000
#> GSM627167 2 0.0000 0.992 0.000 1.000
#> GSM627192 1 0.0000 0.986 1.000 0.000
#> GSM627203 1 0.0000 0.986 1.000 0.000
#> GSM627151 2 0.0000 0.992 0.000 1.000
#> GSM627163 1 0.0000 0.986 1.000 0.000
#> GSM627211 2 0.0000 0.992 0.000 1.000
#> GSM627171 2 0.0000 0.992 0.000 1.000
#> GSM627209 2 0.0000 0.992 0.000 1.000
#> GSM627135 1 0.0000 0.986 1.000 0.000
#> GSM627170 2 0.0000 0.992 0.000 1.000
#> GSM627178 1 0.0000 0.986 1.000 0.000
#> GSM627199 2 0.0000 0.992 0.000 1.000
#> GSM627213 2 0.0000 0.992 0.000 1.000
#> GSM627140 2 0.0000 0.992 0.000 1.000
#> GSM627149 1 0.0000 0.986 1.000 0.000
#> GSM627147 2 0.0000 0.992 0.000 1.000
#> GSM627195 1 0.0672 0.979 0.992 0.008
#> GSM627204 2 0.0000 0.992 0.000 1.000
#> GSM627207 2 0.0000 0.992 0.000 1.000
#> GSM627157 1 0.0000 0.986 1.000 0.000
#> GSM627201 2 0.0000 0.992 0.000 1.000
#> GSM627146 2 0.0000 0.992 0.000 1.000
#> GSM627156 2 0.0000 0.992 0.000 1.000
#> GSM627188 1 0.0000 0.986 1.000 0.000
#> GSM627197 2 0.0000 0.992 0.000 1.000
#> GSM627173 2 0.0000 0.992 0.000 1.000
#> GSM627179 2 0.0000 0.992 0.000 1.000
#> GSM627208 2 0.0000 0.992 0.000 1.000
#> GSM627215 2 0.0000 0.992 0.000 1.000
#> GSM627153 2 0.0000 0.992 0.000 1.000
#> GSM627155 1 0.0000 0.986 1.000 0.000
#> GSM627165 2 0.0000 0.992 0.000 1.000
#> GSM627168 1 0.0000 0.986 1.000 0.000
#> GSM627183 1 0.0000 0.986 1.000 0.000
#> GSM627144 1 0.3879 0.911 0.924 0.076
#> GSM627158 1 0.0000 0.986 1.000 0.000
#> GSM627196 2 0.0000 0.992 0.000 1.000
#> GSM627142 1 0.0000 0.986 1.000 0.000
#> GSM627182 1 0.7815 0.702 0.768 0.232
#> GSM627202 1 0.0000 0.986 1.000 0.000
#> GSM627141 1 0.0000 0.986 1.000 0.000
#> GSM627143 2 0.0000 0.992 0.000 1.000
#> GSM627145 1 0.0000 0.986 1.000 0.000
#> GSM627152 1 0.0000 0.986 1.000 0.000
#> GSM627200 1 0.0000 0.986 1.000 0.000
#> GSM627159 2 0.3584 0.926 0.068 0.932
#> GSM627164 2 0.0000 0.992 0.000 1.000
#> GSM627138 1 0.0000 0.986 1.000 0.000
#> GSM627175 2 0.0000 0.992 0.000 1.000
#> GSM627150 1 0.0000 0.986 1.000 0.000
#> GSM627166 1 0.0000 0.986 1.000 0.000
#> GSM627186 2 0.0000 0.992 0.000 1.000
#> GSM627139 2 0.3274 0.936 0.060 0.940
#> GSM627181 2 0.0000 0.992 0.000 1.000
#> GSM627205 2 0.0000 0.992 0.000 1.000
#> GSM627214 2 0.0000 0.992 0.000 1.000
#> GSM627180 2 0.0000 0.992 0.000 1.000
#> GSM627172 2 0.0000 0.992 0.000 1.000
#> GSM627184 1 0.0000 0.986 1.000 0.000
#> GSM627193 2 0.0000 0.992 0.000 1.000
#> GSM627191 2 0.4815 0.885 0.104 0.896
#> GSM627176 1 0.0000 0.986 1.000 0.000
#> GSM627194 2 0.0000 0.992 0.000 1.000
#> GSM627154 2 0.0000 0.992 0.000 1.000
#> GSM627187 1 0.0000 0.986 1.000 0.000
#> GSM627198 2 0.0000 0.992 0.000 1.000
#> GSM627160 1 0.9944 0.173 0.544 0.456
#> GSM627185 1 0.0000 0.986 1.000 0.000
#> GSM627206 1 0.0000 0.986 1.000 0.000
#> GSM627161 1 0.0000 0.986 1.000 0.000
#> GSM627162 1 0.5408 0.855 0.876 0.124
#> GSM627210 1 0.0000 0.986 1.000 0.000
#> GSM627189 2 0.0000 0.992 0.000 1.000
show/hide code output
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM627128 3 0.6625 0.17170 0.024 0.316 0.660
#> GSM627110 3 0.6280 0.07590 0.460 0.000 0.540
#> GSM627132 1 0.0000 0.78108 1.000 0.000 0.000
#> GSM627107 3 0.0424 0.60365 0.000 0.008 0.992
#> GSM627103 2 0.0424 0.86601 0.000 0.992 0.008
#> GSM627114 3 0.6305 -0.00743 0.484 0.000 0.516
#> GSM627134 2 0.6126 0.58752 0.000 0.600 0.400
#> GSM627137 2 0.0000 0.86647 0.000 1.000 0.000
#> GSM627148 3 0.4731 0.61608 0.128 0.032 0.840
#> GSM627101 2 0.6299 0.44556 0.000 0.524 0.476
#> GSM627130 3 0.6969 -0.06107 0.024 0.380 0.596
#> GSM627071 3 0.5115 0.54330 0.228 0.004 0.768
#> GSM627118 2 0.6260 0.50190 0.000 0.552 0.448
#> GSM627094 2 0.0424 0.86601 0.000 0.992 0.008
#> GSM627122 3 0.5905 0.40410 0.352 0.000 0.648
#> GSM627115 2 0.0424 0.86601 0.000 0.992 0.008
#> GSM627125 3 0.6625 0.17170 0.024 0.316 0.660
#> GSM627174 2 0.0424 0.86652 0.000 0.992 0.008
#> GSM627102 2 0.0237 0.86621 0.000 0.996 0.004
#> GSM627073 3 0.2550 0.62082 0.024 0.040 0.936
#> GSM627108 2 0.0424 0.86601 0.000 0.992 0.008
#> GSM627126 1 0.0000 0.78108 1.000 0.000 0.000
#> GSM627078 2 0.4002 0.82508 0.000 0.840 0.160
#> GSM627090 3 0.4121 0.60142 0.168 0.000 0.832
#> GSM627099 2 0.2537 0.85159 0.000 0.920 0.080
#> GSM627105 3 0.6527 0.16410 0.020 0.320 0.660
#> GSM627117 3 0.7337 0.13176 0.428 0.032 0.540
#> GSM627121 3 0.1643 0.61393 0.000 0.044 0.956
#> GSM627127 2 0.4002 0.82508 0.000 0.840 0.160
#> GSM627087 2 0.0424 0.86601 0.000 0.992 0.008
#> GSM627089 3 0.5835 0.36350 0.340 0.000 0.660
#> GSM627092 2 0.2537 0.84663 0.000 0.920 0.080
#> GSM627076 3 0.4399 0.59711 0.188 0.000 0.812
#> GSM627136 3 0.6291 0.05372 0.468 0.000 0.532
#> GSM627081 3 0.1529 0.61623 0.000 0.040 0.960
#> GSM627091 2 0.0237 0.86648 0.000 0.996 0.004
#> GSM627097 2 0.5760 0.68572 0.000 0.672 0.328
#> GSM627072 3 0.4731 0.61608 0.128 0.032 0.840
#> GSM627080 1 0.0000 0.78108 1.000 0.000 0.000
#> GSM627088 3 0.6286 0.06429 0.464 0.000 0.536
#> GSM627109 1 0.3686 0.73588 0.860 0.000 0.140
#> GSM627111 1 0.0000 0.78108 1.000 0.000 0.000
#> GSM627113 1 0.5706 0.54965 0.680 0.000 0.320
#> GSM627133 3 0.5178 0.52339 0.000 0.256 0.744
#> GSM627177 3 0.5115 0.54330 0.228 0.004 0.768
#> GSM627086 2 0.0237 0.86648 0.000 0.996 0.004
#> GSM627095 1 0.0000 0.78108 1.000 0.000 0.000
#> GSM627079 3 0.4121 0.60142 0.168 0.000 0.832
#> GSM627082 3 0.7940 0.26835 0.332 0.076 0.592
#> GSM627074 1 0.5678 0.55587 0.684 0.000 0.316
#> GSM627077 1 0.6062 0.40003 0.616 0.000 0.384
#> GSM627093 1 0.5810 0.52174 0.664 0.000 0.336
#> GSM627120 2 0.5733 0.68357 0.000 0.676 0.324
#> GSM627124 2 0.4002 0.82508 0.000 0.840 0.160
#> GSM627075 2 0.0424 0.86601 0.000 0.992 0.008
#> GSM627085 2 0.4002 0.82508 0.000 0.840 0.160
#> GSM627119 1 0.5810 0.52174 0.664 0.000 0.336
#> GSM627116 3 0.6859 -0.17782 0.016 0.420 0.564
#> GSM627084 1 0.4452 0.69424 0.808 0.000 0.192
#> GSM627096 2 0.6267 0.49407 0.000 0.548 0.452
#> GSM627100 3 0.1267 0.59912 0.024 0.004 0.972
#> GSM627112 2 0.6796 0.64224 0.024 0.632 0.344
#> GSM627083 1 0.0892 0.76060 0.980 0.000 0.020
#> GSM627098 1 0.3879 0.72900 0.848 0.000 0.152
#> GSM627104 1 0.3686 0.73588 0.860 0.000 0.140
#> GSM627131 1 0.5835 0.48679 0.660 0.000 0.340
#> GSM627106 3 0.1289 0.61540 0.000 0.032 0.968
#> GSM627123 1 0.0000 0.78108 1.000 0.000 0.000
#> GSM627129 2 0.5760 0.68540 0.000 0.672 0.328
#> GSM627216 2 0.3340 0.77532 0.000 0.880 0.120
#> GSM627212 2 0.0237 0.86648 0.000 0.996 0.004
#> GSM627190 3 0.7337 0.13176 0.428 0.032 0.540
#> GSM627169 2 0.3686 0.75250 0.000 0.860 0.140
#> GSM627167 2 0.5760 0.68486 0.000 0.672 0.328
#> GSM627192 1 0.0000 0.78108 1.000 0.000 0.000
#> GSM627203 3 0.4002 0.60542 0.160 0.000 0.840
#> GSM627151 3 0.5216 0.35190 0.000 0.260 0.740
#> GSM627163 1 0.0000 0.78108 1.000 0.000 0.000
#> GSM627211 2 0.0000 0.86647 0.000 1.000 0.000
#> GSM627171 2 0.1860 0.84142 0.000 0.948 0.052
#> GSM627209 2 0.4002 0.82508 0.000 0.840 0.160
#> GSM627135 1 0.0000 0.78108 1.000 0.000 0.000
#> GSM627170 2 0.0424 0.86601 0.000 0.992 0.008
#> GSM627178 1 0.5431 0.59069 0.716 0.000 0.284
#> GSM627199 2 0.3941 0.82676 0.000 0.844 0.156
#> GSM627213 2 0.5678 0.69882 0.000 0.684 0.316
#> GSM627140 2 0.6726 0.65965 0.024 0.644 0.332
#> GSM627149 1 0.0000 0.78108 1.000 0.000 0.000
#> GSM627147 2 0.4291 0.81615 0.000 0.820 0.180
#> GSM627195 3 0.4371 0.62144 0.108 0.032 0.860
#> GSM627204 2 0.0237 0.86648 0.000 0.996 0.004
#> GSM627207 2 0.0424 0.86601 0.000 0.992 0.008
#> GSM627157 1 0.4121 0.71811 0.832 0.000 0.168
#> GSM627201 2 0.0237 0.86648 0.000 0.996 0.004
#> GSM627146 2 0.0237 0.86648 0.000 0.996 0.004
#> GSM627156 2 0.3752 0.74736 0.000 0.856 0.144
#> GSM627188 1 0.0000 0.78108 1.000 0.000 0.000
#> GSM627197 2 0.0592 0.86456 0.000 0.988 0.012
#> GSM627173 2 0.0424 0.86601 0.000 0.992 0.008
#> GSM627179 2 0.0424 0.86601 0.000 0.992 0.008
#> GSM627208 3 0.5810 0.44516 0.000 0.336 0.664
#> GSM627215 2 0.4346 0.72920 0.000 0.816 0.184
#> GSM627153 2 0.4002 0.82508 0.000 0.840 0.160
#> GSM627155 1 0.0000 0.78108 1.000 0.000 0.000
#> GSM627165 2 0.5948 0.63800 0.000 0.640 0.360
#> GSM627168 3 0.6286 0.06578 0.464 0.000 0.536
#> GSM627183 3 0.6305 -0.00849 0.484 0.000 0.516
#> GSM627144 3 0.4591 0.61904 0.120 0.032 0.848
#> GSM627158 1 0.0000 0.78108 1.000 0.000 0.000
#> GSM627196 2 0.0237 0.86648 0.000 0.996 0.004
#> GSM627142 3 0.1647 0.59490 0.036 0.004 0.960
#> GSM627182 3 0.4291 0.58656 0.008 0.152 0.840
#> GSM627202 1 0.5882 0.46850 0.652 0.000 0.348
#> GSM627141 3 0.6307 -0.02358 0.488 0.000 0.512
#> GSM627143 2 0.4796 0.77275 0.000 0.780 0.220
#> GSM627145 3 0.4062 0.60362 0.164 0.000 0.836
#> GSM627152 3 0.4121 0.60142 0.168 0.000 0.832
#> GSM627200 1 0.6168 0.33262 0.588 0.000 0.412
#> GSM627159 3 0.7027 0.20844 0.044 0.296 0.660
#> GSM627164 2 0.0892 0.86201 0.000 0.980 0.020
#> GSM627138 1 0.1031 0.77539 0.976 0.000 0.024
#> GSM627175 2 0.4002 0.82508 0.000 0.840 0.160
#> GSM627150 3 0.4469 0.61983 0.120 0.028 0.852
#> GSM627166 1 0.5058 0.64744 0.756 0.000 0.244
#> GSM627186 2 0.3816 0.74208 0.000 0.852 0.148
#> GSM627139 3 0.1491 0.59722 0.016 0.016 0.968
#> GSM627181 2 0.0000 0.86647 0.000 1.000 0.000
#> GSM627205 2 0.0747 0.86365 0.000 0.984 0.016
#> GSM627214 2 0.2711 0.84976 0.000 0.912 0.088
#> GSM627180 3 0.1529 0.61623 0.000 0.040 0.960
#> GSM627172 2 0.0237 0.86621 0.000 0.996 0.004
#> GSM627184 1 0.0000 0.78108 1.000 0.000 0.000
#> GSM627193 2 0.0424 0.86601 0.000 0.992 0.008
#> GSM627191 1 0.9996 -0.24822 0.344 0.320 0.336
#> GSM627176 3 0.4062 0.60362 0.164 0.000 0.836
#> GSM627194 2 0.0424 0.86601 0.000 0.992 0.008
#> GSM627154 2 0.4002 0.82508 0.000 0.840 0.160
#> GSM627187 3 0.7240 0.12493 0.432 0.028 0.540
#> GSM627198 2 0.3941 0.82676 0.000 0.844 0.156
#> GSM627160 3 0.6867 0.23630 0.040 0.288 0.672
#> GSM627185 1 0.1031 0.77539 0.976 0.000 0.024
#> GSM627206 3 0.6280 0.07703 0.460 0.000 0.540
#> GSM627161 1 0.0000 0.78108 1.000 0.000 0.000
#> GSM627162 3 0.5967 0.54882 0.216 0.032 0.752
#> GSM627210 1 0.6215 0.29504 0.572 0.000 0.428
#> GSM627189 2 0.0424 0.86601 0.000 0.992 0.008
show/hide code output
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM627128 4 0.1151 0.751 0.000 0.008 0.024 0.968
#> GSM627110 3 0.0524 0.855 0.008 0.004 0.988 0.000
#> GSM627132 1 0.0000 0.910 1.000 0.000 0.000 0.000
#> GSM627107 4 0.3610 0.591 0.000 0.000 0.200 0.800
#> GSM627103 2 0.0336 0.921 0.000 0.992 0.000 0.008
#> GSM627114 3 0.0927 0.854 0.016 0.008 0.976 0.000
#> GSM627134 4 0.3569 0.781 0.000 0.196 0.000 0.804
#> GSM627137 2 0.0592 0.919 0.000 0.984 0.000 0.016
#> GSM627148 3 0.0469 0.855 0.000 0.000 0.988 0.012
#> GSM627101 4 0.0804 0.757 0.000 0.012 0.008 0.980
#> GSM627130 4 0.0927 0.754 0.000 0.008 0.016 0.976
#> GSM627071 3 0.0927 0.856 0.008 0.000 0.976 0.016
#> GSM627118 4 0.3444 0.786 0.000 0.184 0.000 0.816
#> GSM627094 2 0.0336 0.921 0.000 0.992 0.000 0.008
#> GSM627122 3 0.4869 0.774 0.132 0.000 0.780 0.088
#> GSM627115 2 0.0188 0.921 0.000 0.996 0.000 0.004
#> GSM627125 4 0.1109 0.749 0.000 0.004 0.028 0.968
#> GSM627174 2 0.0707 0.918 0.000 0.980 0.000 0.020
#> GSM627102 2 0.0804 0.917 0.000 0.980 0.012 0.008
#> GSM627073 3 0.1867 0.842 0.000 0.000 0.928 0.072
#> GSM627108 2 0.0188 0.919 0.000 0.996 0.004 0.000
#> GSM627126 1 0.0000 0.910 1.000 0.000 0.000 0.000
#> GSM627078 4 0.4679 0.649 0.000 0.352 0.000 0.648
#> GSM627090 3 0.3356 0.792 0.000 0.000 0.824 0.176
#> GSM627099 2 0.4804 0.122 0.000 0.616 0.000 0.384
#> GSM627105 4 0.1109 0.749 0.000 0.004 0.028 0.968
#> GSM627117 3 0.0707 0.852 0.000 0.020 0.980 0.000
#> GSM627121 3 0.4866 0.440 0.000 0.000 0.596 0.404
#> GSM627127 4 0.3942 0.760 0.000 0.236 0.000 0.764
#> GSM627087 2 0.0188 0.921 0.000 0.996 0.000 0.004
#> GSM627089 3 0.0657 0.856 0.004 0.000 0.984 0.012
#> GSM627092 2 0.1724 0.895 0.000 0.948 0.020 0.032
#> GSM627076 3 0.4624 0.631 0.000 0.000 0.660 0.340
#> GSM627136 3 0.0992 0.854 0.012 0.004 0.976 0.008
#> GSM627081 3 0.3074 0.805 0.000 0.000 0.848 0.152
#> GSM627091 2 0.0707 0.918 0.000 0.980 0.000 0.020
#> GSM627097 4 0.3569 0.781 0.000 0.196 0.000 0.804
#> GSM627072 3 0.0336 0.854 0.000 0.008 0.992 0.000
#> GSM627080 1 0.0000 0.910 1.000 0.000 0.000 0.000
#> GSM627088 3 0.0992 0.854 0.012 0.004 0.976 0.008
#> GSM627109 1 0.4836 0.502 0.672 0.000 0.320 0.008
#> GSM627111 1 0.0000 0.910 1.000 0.000 0.000 0.000
#> GSM627113 3 0.4452 0.647 0.260 0.000 0.732 0.008
#> GSM627133 3 0.2149 0.810 0.000 0.088 0.912 0.000
#> GSM627177 3 0.0927 0.856 0.008 0.000 0.976 0.016
#> GSM627086 2 0.0707 0.918 0.000 0.980 0.000 0.020
#> GSM627095 1 0.0000 0.910 1.000 0.000 0.000 0.000
#> GSM627079 3 0.1867 0.846 0.000 0.000 0.928 0.072
#> GSM627082 4 0.0927 0.749 0.008 0.000 0.016 0.976
#> GSM627074 3 0.4511 0.635 0.268 0.000 0.724 0.008
#> GSM627077 3 0.4599 0.715 0.212 0.000 0.760 0.028
#> GSM627093 3 0.3401 0.768 0.152 0.000 0.840 0.008
#> GSM627120 4 0.6114 0.427 0.000 0.428 0.048 0.524
#> GSM627124 4 0.4679 0.649 0.000 0.352 0.000 0.648
#> GSM627075 2 0.0657 0.915 0.000 0.984 0.012 0.004
#> GSM627085 4 0.3942 0.760 0.000 0.236 0.000 0.764
#> GSM627119 3 0.3498 0.762 0.160 0.000 0.832 0.008
#> GSM627116 4 0.3123 0.790 0.000 0.156 0.000 0.844
#> GSM627084 3 0.5220 0.279 0.424 0.000 0.568 0.008
#> GSM627096 4 0.3400 0.787 0.000 0.180 0.000 0.820
#> GSM627100 4 0.5000 -0.262 0.000 0.000 0.496 0.504
#> GSM627112 4 0.2868 0.788 0.000 0.136 0.000 0.864
#> GSM627083 1 0.0000 0.910 1.000 0.000 0.000 0.000
#> GSM627098 1 0.5250 0.174 0.552 0.000 0.440 0.008
#> GSM627104 1 0.4877 0.490 0.664 0.000 0.328 0.008
#> GSM627131 3 0.5207 0.600 0.292 0.000 0.680 0.028
#> GSM627106 3 0.3074 0.805 0.000 0.000 0.848 0.152
#> GSM627123 1 0.0000 0.910 1.000 0.000 0.000 0.000
#> GSM627129 4 0.3528 0.782 0.000 0.192 0.000 0.808
#> GSM627216 2 0.1940 0.865 0.000 0.924 0.076 0.000
#> GSM627212 2 0.0707 0.918 0.000 0.980 0.000 0.020
#> GSM627190 3 0.0707 0.852 0.000 0.020 0.980 0.000
#> GSM627169 2 0.2888 0.809 0.000 0.872 0.124 0.004
#> GSM627167 4 0.3257 0.787 0.000 0.152 0.004 0.844
#> GSM627192 1 0.0000 0.910 1.000 0.000 0.000 0.000
#> GSM627203 3 0.3074 0.805 0.000 0.000 0.848 0.152
#> GSM627151 4 0.6597 0.474 0.000 0.108 0.304 0.588
#> GSM627163 1 0.0000 0.910 1.000 0.000 0.000 0.000
#> GSM627211 2 0.0707 0.918 0.000 0.980 0.000 0.020
#> GSM627171 2 0.1661 0.886 0.000 0.944 0.052 0.004
#> GSM627209 4 0.4713 0.637 0.000 0.360 0.000 0.640
#> GSM627135 1 0.0000 0.910 1.000 0.000 0.000 0.000
#> GSM627170 2 0.0188 0.919 0.000 0.996 0.004 0.000
#> GSM627178 3 0.5343 0.557 0.316 0.000 0.656 0.028
#> GSM627199 4 0.4713 0.637 0.000 0.360 0.000 0.640
#> GSM627213 4 0.3356 0.786 0.000 0.176 0.000 0.824
#> GSM627140 4 0.2973 0.788 0.000 0.144 0.000 0.856
#> GSM627149 1 0.0000 0.910 1.000 0.000 0.000 0.000
#> GSM627147 4 0.4800 0.634 0.000 0.340 0.004 0.656
#> GSM627195 3 0.3074 0.805 0.000 0.000 0.848 0.152
#> GSM627204 2 0.0707 0.918 0.000 0.980 0.000 0.020
#> GSM627207 2 0.0657 0.915 0.000 0.984 0.012 0.004
#> GSM627157 1 0.5268 0.119 0.540 0.000 0.452 0.008
#> GSM627201 2 0.0707 0.918 0.000 0.980 0.000 0.020
#> GSM627146 2 0.0707 0.918 0.000 0.980 0.000 0.020
#> GSM627156 2 0.2714 0.823 0.000 0.884 0.112 0.004
#> GSM627188 1 0.0000 0.910 1.000 0.000 0.000 0.000
#> GSM627197 2 0.0707 0.918 0.000 0.980 0.000 0.020
#> GSM627173 2 0.0336 0.921 0.000 0.992 0.000 0.008
#> GSM627179 2 0.0188 0.919 0.000 0.996 0.004 0.000
#> GSM627208 3 0.2760 0.769 0.000 0.128 0.872 0.000
#> GSM627215 2 0.4643 0.481 0.000 0.656 0.344 0.000
#> GSM627153 4 0.4679 0.649 0.000 0.352 0.000 0.648
#> GSM627155 1 0.0000 0.910 1.000 0.000 0.000 0.000
#> GSM627165 4 0.4999 0.583 0.000 0.328 0.012 0.660
#> GSM627168 3 0.1059 0.855 0.012 0.000 0.972 0.016
#> GSM627183 3 0.1256 0.851 0.028 0.000 0.964 0.008
#> GSM627144 3 0.2814 0.815 0.000 0.000 0.868 0.132
#> GSM627158 1 0.0000 0.910 1.000 0.000 0.000 0.000
#> GSM627196 2 0.0707 0.918 0.000 0.980 0.000 0.020
#> GSM627142 4 0.2973 0.661 0.000 0.000 0.144 0.856
#> GSM627182 3 0.0707 0.852 0.000 0.020 0.980 0.000
#> GSM627202 3 0.5113 0.603 0.292 0.000 0.684 0.024
#> GSM627141 3 0.0927 0.854 0.016 0.008 0.976 0.000
#> GSM627143 2 0.5055 0.528 0.000 0.712 0.032 0.256
#> GSM627145 3 0.0592 0.855 0.000 0.000 0.984 0.016
#> GSM627152 3 0.3486 0.791 0.000 0.000 0.812 0.188
#> GSM627200 3 0.4086 0.706 0.216 0.000 0.776 0.008
#> GSM627159 4 0.1191 0.749 0.004 0.004 0.024 0.968
#> GSM627164 2 0.1209 0.903 0.000 0.964 0.032 0.004
#> GSM627138 1 0.0000 0.910 1.000 0.000 0.000 0.000
#> GSM627175 4 0.4679 0.649 0.000 0.352 0.000 0.648
#> GSM627150 3 0.1940 0.841 0.000 0.000 0.924 0.076
#> GSM627166 3 0.4897 0.523 0.332 0.000 0.660 0.008
#> GSM627186 2 0.2999 0.799 0.000 0.864 0.132 0.004
#> GSM627139 4 0.2149 0.713 0.000 0.000 0.088 0.912
#> GSM627181 2 0.0707 0.918 0.000 0.980 0.000 0.020
#> GSM627205 2 0.0707 0.913 0.000 0.980 0.020 0.000
#> GSM627214 2 0.4730 0.187 0.000 0.636 0.000 0.364
#> GSM627180 3 0.2345 0.831 0.000 0.000 0.900 0.100
#> GSM627172 2 0.0937 0.915 0.000 0.976 0.012 0.012
#> GSM627184 1 0.0000 0.910 1.000 0.000 0.000 0.000
#> GSM627193 2 0.0188 0.919 0.000 0.996 0.004 0.000
#> GSM627191 4 0.2973 0.705 0.144 0.000 0.000 0.856
#> GSM627176 3 0.3356 0.792 0.000 0.000 0.824 0.176
#> GSM627194 2 0.0336 0.921 0.000 0.992 0.000 0.008
#> GSM627154 4 0.3942 0.760 0.000 0.236 0.000 0.764
#> GSM627187 3 0.0895 0.851 0.000 0.020 0.976 0.004
#> GSM627198 4 0.4713 0.637 0.000 0.360 0.000 0.640
#> GSM627160 4 0.0817 0.746 0.000 0.000 0.024 0.976
#> GSM627185 1 0.1743 0.865 0.940 0.000 0.056 0.004
#> GSM627206 3 0.0657 0.855 0.012 0.004 0.984 0.000
#> GSM627161 1 0.0000 0.910 1.000 0.000 0.000 0.000
#> GSM627162 3 0.0895 0.851 0.000 0.020 0.976 0.004
#> GSM627210 3 0.1890 0.840 0.056 0.000 0.936 0.008
#> GSM627189 2 0.0336 0.921 0.000 0.992 0.000 0.008
show/hide code output
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM627128 4 0.1704 0.49766 0.004 0.000 0.000 0.928 0.068
#> GSM627110 3 0.2230 0.60987 0.000 0.000 0.884 0.000 0.116
#> GSM627132 1 0.0566 0.95554 0.984 0.000 0.004 0.000 0.012
#> GSM627107 4 0.6105 -0.39074 0.000 0.000 0.128 0.480 0.392
#> GSM627103 2 0.0609 0.78207 0.000 0.980 0.000 0.000 0.020
#> GSM627114 3 0.2561 0.59959 0.000 0.000 0.856 0.000 0.144
#> GSM627134 4 0.5664 0.60796 0.000 0.152 0.000 0.628 0.220
#> GSM627137 2 0.2020 0.77204 0.000 0.900 0.000 0.000 0.100
#> GSM627148 3 0.4060 0.36857 0.000 0.000 0.640 0.000 0.360
#> GSM627101 4 0.1041 0.52898 0.000 0.004 0.000 0.964 0.032
#> GSM627130 4 0.1704 0.49766 0.004 0.000 0.000 0.928 0.068
#> GSM627071 3 0.1908 0.61516 0.000 0.000 0.908 0.000 0.092
#> GSM627118 4 0.5602 0.60916 0.000 0.148 0.000 0.636 0.216
#> GSM627094 2 0.0404 0.78180 0.000 0.988 0.000 0.000 0.012
#> GSM627122 3 0.3696 0.60571 0.040 0.000 0.840 0.028 0.092
#> GSM627115 2 0.0609 0.78207 0.000 0.980 0.000 0.000 0.020
#> GSM627125 4 0.2536 0.44142 0.004 0.000 0.000 0.868 0.128
#> GSM627174 2 0.2712 0.71612 0.000 0.880 0.000 0.032 0.088
#> GSM627102 2 0.3689 0.69436 0.000 0.740 0.000 0.004 0.256
#> GSM627073 3 0.4902 0.22157 0.000 0.000 0.564 0.028 0.408
#> GSM627108 2 0.2068 0.76880 0.000 0.904 0.000 0.004 0.092
#> GSM627126 1 0.0000 0.95907 1.000 0.000 0.000 0.000 0.000
#> GSM627078 4 0.6463 0.51380 0.000 0.300 0.000 0.488 0.212
#> GSM627090 3 0.6146 0.04137 0.004 0.000 0.488 0.116 0.392
#> GSM627099 2 0.6507 -0.22655 0.000 0.472 0.000 0.316 0.212
#> GSM627105 4 0.2536 0.44142 0.004 0.000 0.000 0.868 0.128
#> GSM627117 3 0.2852 0.58423 0.000 0.000 0.828 0.000 0.172
#> GSM627121 5 0.6396 0.31193 0.000 0.000 0.188 0.324 0.488
#> GSM627127 4 0.6323 0.55258 0.000 0.252 0.000 0.528 0.220
#> GSM627087 2 0.0609 0.78207 0.000 0.980 0.000 0.000 0.020
#> GSM627089 3 0.2773 0.57730 0.000 0.000 0.836 0.000 0.164
#> GSM627092 2 0.4972 0.58509 0.000 0.620 0.000 0.044 0.336
#> GSM627076 4 0.6626 -0.42373 0.004 0.000 0.200 0.464 0.332
#> GSM627136 3 0.0510 0.63314 0.000 0.000 0.984 0.000 0.016
#> GSM627081 3 0.6036 -0.10658 0.000 0.000 0.452 0.116 0.432
#> GSM627091 2 0.4618 0.50215 0.000 0.724 0.000 0.068 0.208
#> GSM627097 4 0.5462 0.61093 0.000 0.136 0.000 0.652 0.212
#> GSM627072 3 0.3774 0.45951 0.000 0.000 0.704 0.000 0.296
#> GSM627080 1 0.0566 0.95554 0.984 0.000 0.004 0.000 0.012
#> GSM627088 3 0.0703 0.63203 0.000 0.000 0.976 0.000 0.024
#> GSM627109 3 0.4921 0.33713 0.340 0.000 0.620 0.000 0.040
#> GSM627111 1 0.0566 0.95554 0.984 0.000 0.004 0.000 0.012
#> GSM627113 3 0.3134 0.59996 0.120 0.000 0.848 0.000 0.032
#> GSM627133 3 0.6134 -0.00786 0.000 0.144 0.516 0.000 0.340
#> GSM627177 3 0.1851 0.62851 0.000 0.000 0.912 0.000 0.088
#> GSM627086 2 0.0955 0.77185 0.000 0.968 0.000 0.004 0.028
#> GSM627095 1 0.0162 0.95817 0.996 0.000 0.000 0.000 0.004
#> GSM627079 3 0.3671 0.49299 0.000 0.000 0.756 0.008 0.236
#> GSM627082 4 0.2172 0.49237 0.016 0.000 0.000 0.908 0.076
#> GSM627074 3 0.3317 0.59837 0.116 0.000 0.840 0.000 0.044
#> GSM627077 3 0.2595 0.62740 0.080 0.000 0.888 0.000 0.032
#> GSM627093 3 0.2729 0.61819 0.060 0.000 0.884 0.000 0.056
#> GSM627120 4 0.7178 -0.06143 0.000 0.272 0.016 0.368 0.344
#> GSM627124 4 0.6463 0.51380 0.000 0.300 0.000 0.488 0.212
#> GSM627075 2 0.3689 0.69529 0.000 0.740 0.000 0.004 0.256
#> GSM627085 4 0.6394 0.52327 0.000 0.292 0.000 0.504 0.204
#> GSM627119 3 0.2654 0.61921 0.064 0.000 0.888 0.000 0.048
#> GSM627116 4 0.5978 0.60687 0.000 0.132 0.024 0.644 0.200
#> GSM627084 3 0.4305 0.53140 0.200 0.000 0.748 0.000 0.052
#> GSM627096 4 0.5602 0.60916 0.000 0.148 0.000 0.636 0.216
#> GSM627100 4 0.6375 -0.34382 0.004 0.000 0.164 0.512 0.320
#> GSM627112 4 0.4215 0.59379 0.004 0.052 0.000 0.772 0.172
#> GSM627083 1 0.0798 0.94243 0.976 0.000 0.000 0.008 0.016
#> GSM627098 3 0.4398 0.49586 0.240 0.000 0.720 0.000 0.040
#> GSM627104 3 0.4905 0.34562 0.336 0.000 0.624 0.000 0.040
#> GSM627131 3 0.3670 0.61139 0.112 0.000 0.820 0.000 0.068
#> GSM627106 3 0.6036 -0.10658 0.000 0.000 0.452 0.116 0.432
#> GSM627123 1 0.0162 0.95817 0.996 0.000 0.000 0.000 0.004
#> GSM627129 4 0.5008 0.60748 0.000 0.140 0.000 0.708 0.152
#> GSM627216 2 0.4558 0.58586 0.000 0.728 0.064 0.000 0.208
#> GSM627212 2 0.3216 0.68369 0.000 0.848 0.000 0.044 0.108
#> GSM627190 3 0.2813 0.58556 0.000 0.000 0.832 0.000 0.168
#> GSM627169 2 0.4389 0.56630 0.000 0.624 0.004 0.004 0.368
#> GSM627167 4 0.4049 0.48984 0.000 0.056 0.000 0.780 0.164
#> GSM627192 1 0.0000 0.95907 1.000 0.000 0.000 0.000 0.000
#> GSM627203 3 0.5408 0.14126 0.000 0.000 0.532 0.060 0.408
#> GSM627151 4 0.7337 0.00331 0.000 0.056 0.336 0.448 0.160
#> GSM627163 1 0.0566 0.95554 0.984 0.000 0.004 0.000 0.012
#> GSM627211 2 0.1557 0.78180 0.000 0.940 0.000 0.008 0.052
#> GSM627171 2 0.4211 0.58093 0.000 0.636 0.000 0.004 0.360
#> GSM627209 4 0.6569 0.46607 0.000 0.336 0.000 0.448 0.216
#> GSM627135 1 0.0290 0.95632 0.992 0.000 0.000 0.000 0.008
#> GSM627170 2 0.2020 0.77135 0.000 0.900 0.000 0.000 0.100
#> GSM627178 3 0.3980 0.60077 0.128 0.000 0.796 0.000 0.076
#> GSM627199 4 0.6576 0.46081 0.000 0.340 0.000 0.444 0.216
#> GSM627213 4 0.5233 0.61194 0.000 0.128 0.000 0.680 0.192
#> GSM627140 4 0.2685 0.53372 0.000 0.028 0.000 0.880 0.092
#> GSM627149 1 0.0000 0.95907 1.000 0.000 0.000 0.000 0.000
#> GSM627147 4 0.6610 0.21729 0.000 0.280 0.000 0.460 0.260
#> GSM627195 3 0.5408 0.14126 0.000 0.000 0.532 0.060 0.408
#> GSM627204 2 0.0865 0.77329 0.000 0.972 0.000 0.004 0.024
#> GSM627207 2 0.3231 0.72589 0.000 0.800 0.000 0.004 0.196
#> GSM627157 3 0.4297 0.50231 0.236 0.000 0.728 0.000 0.036
#> GSM627201 2 0.2331 0.73170 0.000 0.900 0.000 0.020 0.080
#> GSM627146 2 0.2769 0.70996 0.000 0.876 0.000 0.032 0.092
#> GSM627156 2 0.4389 0.56630 0.000 0.624 0.004 0.004 0.368
#> GSM627188 1 0.0000 0.95907 1.000 0.000 0.000 0.000 0.000
#> GSM627197 2 0.2905 0.70196 0.000 0.868 0.000 0.036 0.096
#> GSM627173 2 0.0794 0.78241 0.000 0.972 0.000 0.000 0.028
#> GSM627179 2 0.1792 0.77393 0.000 0.916 0.000 0.000 0.084
#> GSM627208 5 0.6601 0.25457 0.000 0.248 0.292 0.000 0.460
#> GSM627215 2 0.5950 0.14055 0.000 0.592 0.188 0.000 0.220
#> GSM627153 4 0.6536 0.48769 0.000 0.320 0.000 0.464 0.216
#> GSM627155 1 0.0000 0.95907 1.000 0.000 0.000 0.000 0.000
#> GSM627165 4 0.6764 -0.01366 0.000 0.292 0.000 0.400 0.308
#> GSM627168 3 0.0794 0.63339 0.000 0.000 0.972 0.000 0.028
#> GSM627183 3 0.0703 0.63286 0.000 0.000 0.976 0.000 0.024
#> GSM627144 3 0.5148 0.13955 0.000 0.000 0.528 0.040 0.432
#> GSM627158 1 0.0324 0.95767 0.992 0.000 0.004 0.000 0.004
#> GSM627196 2 0.0865 0.77329 0.000 0.972 0.000 0.004 0.024
#> GSM627142 4 0.5789 -0.17164 0.004 0.000 0.104 0.588 0.304
#> GSM627182 3 0.3999 0.42036 0.000 0.000 0.656 0.000 0.344
#> GSM627202 3 0.3532 0.61077 0.128 0.000 0.824 0.000 0.048
#> GSM627141 3 0.2648 0.59808 0.000 0.000 0.848 0.000 0.152
#> GSM627143 2 0.6202 0.41270 0.000 0.496 0.000 0.148 0.356
#> GSM627145 3 0.3395 0.51384 0.000 0.000 0.764 0.000 0.236
#> GSM627152 3 0.6215 0.08740 0.004 0.000 0.528 0.140 0.328
#> GSM627200 3 0.2879 0.61171 0.100 0.000 0.868 0.000 0.032
#> GSM627159 4 0.1831 0.49726 0.004 0.000 0.000 0.920 0.076
#> GSM627164 2 0.4196 0.58689 0.000 0.640 0.000 0.004 0.356
#> GSM627138 1 0.0566 0.95554 0.984 0.000 0.004 0.000 0.012
#> GSM627175 4 0.6442 0.51472 0.000 0.300 0.000 0.492 0.208
#> GSM627150 3 0.4920 0.25871 0.000 0.000 0.584 0.032 0.384
#> GSM627166 3 0.3846 0.57634 0.144 0.000 0.800 0.000 0.056
#> GSM627186 2 0.4389 0.56630 0.000 0.624 0.004 0.004 0.368
#> GSM627139 4 0.4690 0.17855 0.004 0.000 0.048 0.708 0.240
#> GSM627181 2 0.2653 0.72716 0.000 0.880 0.000 0.024 0.096
#> GSM627205 2 0.3424 0.68811 0.000 0.760 0.000 0.000 0.240
#> GSM627214 2 0.5562 0.43121 0.000 0.644 0.000 0.200 0.156
#> GSM627180 3 0.5100 0.11704 0.000 0.000 0.516 0.036 0.448
#> GSM627172 2 0.3835 0.69030 0.000 0.732 0.000 0.008 0.260
#> GSM627184 1 0.0000 0.95907 1.000 0.000 0.000 0.000 0.000
#> GSM627193 2 0.1410 0.77916 0.000 0.940 0.000 0.000 0.060
#> GSM627191 4 0.2221 0.53156 0.052 0.000 0.000 0.912 0.036
#> GSM627176 3 0.6129 0.01058 0.004 0.000 0.476 0.112 0.408
#> GSM627194 2 0.0794 0.77787 0.000 0.972 0.000 0.000 0.028
#> GSM627154 4 0.6286 0.54768 0.000 0.264 0.000 0.532 0.204
#> GSM627187 3 0.3109 0.56511 0.000 0.000 0.800 0.000 0.200
#> GSM627198 4 0.6593 0.45811 0.000 0.340 0.000 0.440 0.220
#> GSM627160 4 0.1831 0.50168 0.004 0.000 0.000 0.920 0.076
#> GSM627185 1 0.5165 0.15866 0.512 0.000 0.448 0.000 0.040
#> GSM627206 3 0.2230 0.61128 0.000 0.000 0.884 0.000 0.116
#> GSM627161 1 0.0162 0.95867 0.996 0.000 0.000 0.000 0.004
#> GSM627162 3 0.4251 0.35142 0.000 0.000 0.624 0.004 0.372
#> GSM627210 3 0.2228 0.62575 0.040 0.000 0.912 0.000 0.048
#> GSM627189 2 0.0162 0.77968 0.000 0.996 0.000 0.000 0.004
show/hide code output
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM627128 6 0.4837 0.7119 0.000 0.000 0.000 0.288 0.088 0.624
#> GSM627110 3 0.3652 0.6914 0.000 0.000 0.768 0.000 0.188 0.044
#> GSM627132 1 0.0653 0.9722 0.980 0.000 0.012 0.000 0.004 0.004
#> GSM627107 5 0.3394 0.4159 0.000 0.000 0.000 0.012 0.752 0.236
#> GSM627103 2 0.3934 0.6547 0.000 0.764 0.000 0.180 0.044 0.012
#> GSM627114 3 0.3721 0.7110 0.000 0.016 0.784 0.000 0.168 0.032
#> GSM627134 4 0.3408 0.6933 0.000 0.016 0.000 0.832 0.080 0.072
#> GSM627137 2 0.3425 0.6856 0.000 0.836 0.000 0.084 0.048 0.032
#> GSM627148 5 0.3265 0.6334 0.000 0.000 0.248 0.000 0.748 0.004
#> GSM627101 6 0.4695 0.4543 0.000 0.000 0.000 0.448 0.044 0.508
#> GSM627130 6 0.4793 0.7123 0.000 0.000 0.000 0.288 0.084 0.628
#> GSM627071 3 0.3721 0.6371 0.000 0.000 0.728 0.004 0.252 0.016
#> GSM627118 4 0.3097 0.6948 0.000 0.012 0.000 0.852 0.064 0.072
#> GSM627094 2 0.2859 0.6734 0.000 0.828 0.000 0.156 0.016 0.000
#> GSM627122 3 0.4777 0.6313 0.012 0.000 0.704 0.004 0.188 0.092
#> GSM627115 2 0.3772 0.6564 0.000 0.772 0.000 0.180 0.040 0.008
#> GSM627125 6 0.4990 0.7212 0.000 0.000 0.000 0.232 0.132 0.636
#> GSM627174 2 0.4572 0.3336 0.000 0.512 0.000 0.460 0.012 0.016
#> GSM627102 2 0.4646 0.5976 0.000 0.728 0.016 0.024 0.040 0.192
#> GSM627073 5 0.3110 0.6814 0.000 0.000 0.196 0.000 0.792 0.012
#> GSM627108 2 0.1471 0.6867 0.000 0.932 0.000 0.064 0.004 0.000
#> GSM627126 1 0.0865 0.9732 0.964 0.000 0.000 0.000 0.000 0.036
#> GSM627078 4 0.1624 0.7411 0.000 0.040 0.000 0.936 0.004 0.020
#> GSM627090 5 0.5227 0.5326 0.000 0.000 0.144 0.004 0.620 0.232
#> GSM627099 4 0.4077 0.5554 0.000 0.212 0.000 0.736 0.044 0.008
#> GSM627105 6 0.4990 0.7212 0.000 0.000 0.000 0.232 0.132 0.636
#> GSM627117 3 0.4348 0.6454 0.000 0.028 0.732 0.000 0.200 0.040
#> GSM627121 5 0.2851 0.5845 0.000 0.004 0.020 0.000 0.844 0.132
#> GSM627127 4 0.2528 0.7257 0.000 0.028 0.000 0.892 0.024 0.056
#> GSM627087 2 0.3772 0.6564 0.000 0.772 0.000 0.180 0.040 0.008
#> GSM627089 3 0.4045 0.2501 0.000 0.000 0.564 0.000 0.428 0.008
#> GSM627092 2 0.5690 0.5540 0.000 0.628 0.012 0.036 0.088 0.236
#> GSM627076 6 0.4488 0.1671 0.000 0.000 0.016 0.008 0.468 0.508
#> GSM627136 3 0.2062 0.7820 0.000 0.000 0.900 0.004 0.088 0.008
#> GSM627081 5 0.3254 0.6922 0.000 0.000 0.124 0.000 0.820 0.056
#> GSM627091 4 0.4845 0.0877 0.000 0.388 0.000 0.560 0.044 0.008
#> GSM627097 4 0.3000 0.6796 0.000 0.004 0.000 0.840 0.032 0.124
#> GSM627072 5 0.3672 0.5549 0.000 0.000 0.304 0.000 0.688 0.008
#> GSM627080 1 0.0508 0.9735 0.984 0.000 0.012 0.000 0.004 0.000
#> GSM627088 3 0.1858 0.7800 0.000 0.000 0.904 0.000 0.092 0.004
#> GSM627109 3 0.3057 0.7398 0.120 0.000 0.844 0.004 0.008 0.024
#> GSM627111 1 0.0653 0.9722 0.980 0.000 0.012 0.000 0.004 0.004
#> GSM627113 3 0.1649 0.7943 0.040 0.000 0.936 0.000 0.016 0.008
#> GSM627133 5 0.6205 0.5696 0.000 0.124 0.144 0.032 0.636 0.064
#> GSM627177 3 0.3159 0.7347 0.000 0.000 0.820 0.008 0.152 0.020
#> GSM627086 2 0.4177 0.5999 0.000 0.684 0.000 0.280 0.032 0.004
#> GSM627095 1 0.1226 0.9701 0.952 0.000 0.004 0.000 0.004 0.040
#> GSM627079 5 0.4793 0.2939 0.000 0.000 0.428 0.008 0.528 0.036
#> GSM627082 6 0.4716 0.7170 0.008 0.000 0.000 0.252 0.072 0.668
#> GSM627074 3 0.1514 0.7946 0.036 0.000 0.944 0.004 0.004 0.012
#> GSM627077 3 0.3769 0.7715 0.036 0.000 0.816 0.004 0.100 0.044
#> GSM627093 3 0.1065 0.7969 0.020 0.000 0.964 0.000 0.008 0.008
#> GSM627120 5 0.7507 -0.0910 0.000 0.284 0.008 0.108 0.364 0.236
#> GSM627124 4 0.1624 0.7411 0.000 0.040 0.000 0.936 0.004 0.020
#> GSM627075 2 0.3769 0.6200 0.000 0.776 0.012 0.000 0.036 0.176
#> GSM627085 4 0.1257 0.7413 0.000 0.028 0.000 0.952 0.000 0.020
#> GSM627119 3 0.1406 0.7965 0.020 0.000 0.952 0.004 0.008 0.016
#> GSM627116 4 0.3513 0.6627 0.000 0.004 0.020 0.824 0.036 0.116
#> GSM627084 3 0.1988 0.7836 0.072 0.000 0.912 0.004 0.004 0.008
#> GSM627096 4 0.3097 0.6948 0.000 0.012 0.000 0.852 0.064 0.072
#> GSM627100 6 0.4256 0.2959 0.000 0.000 0.012 0.004 0.420 0.564
#> GSM627112 4 0.3647 0.0822 0.000 0.000 0.000 0.640 0.000 0.360
#> GSM627083 1 0.2314 0.9326 0.900 0.000 0.008 0.012 0.008 0.072
#> GSM627098 3 0.1956 0.7751 0.080 0.000 0.908 0.000 0.008 0.004
#> GSM627104 3 0.2969 0.7444 0.112 0.000 0.852 0.004 0.008 0.024
#> GSM627131 3 0.3594 0.7710 0.040 0.000 0.836 0.008 0.072 0.044
#> GSM627106 5 0.3270 0.6892 0.000 0.000 0.120 0.000 0.820 0.060
#> GSM627123 1 0.1338 0.9715 0.952 0.000 0.008 0.004 0.004 0.032
#> GSM627129 4 0.3960 0.6526 0.000 0.016 0.000 0.784 0.072 0.128
#> GSM627216 2 0.6603 0.1750 0.000 0.440 0.020 0.084 0.396 0.060
#> GSM627212 2 0.4984 0.1800 0.000 0.476 0.000 0.468 0.048 0.008
#> GSM627190 3 0.4359 0.6428 0.000 0.024 0.724 0.000 0.212 0.040
#> GSM627169 2 0.4754 0.5869 0.000 0.704 0.028 0.000 0.068 0.200
#> GSM627167 6 0.5661 0.4187 0.000 0.104 0.004 0.236 0.036 0.620
#> GSM627192 1 0.0937 0.9718 0.960 0.000 0.000 0.000 0.000 0.040
#> GSM627203 5 0.3542 0.7052 0.000 0.000 0.160 0.000 0.788 0.052
#> GSM627151 4 0.7076 0.1763 0.000 0.008 0.124 0.472 0.268 0.128
#> GSM627163 1 0.0551 0.9730 0.984 0.000 0.008 0.000 0.004 0.004
#> GSM627211 2 0.3384 0.6675 0.000 0.800 0.000 0.168 0.024 0.008
#> GSM627171 2 0.4996 0.5806 0.000 0.688 0.024 0.004 0.080 0.204
#> GSM627209 4 0.1462 0.7381 0.000 0.056 0.000 0.936 0.008 0.000
#> GSM627135 1 0.1413 0.9701 0.948 0.000 0.008 0.004 0.004 0.036
#> GSM627170 2 0.3799 0.6735 0.000 0.804 0.000 0.080 0.096 0.020
#> GSM627178 3 0.3552 0.7713 0.040 0.000 0.840 0.008 0.060 0.052
#> GSM627199 4 0.3003 0.7079 0.000 0.084 0.000 0.860 0.028 0.028
#> GSM627213 4 0.2911 0.6282 0.000 0.000 0.000 0.832 0.024 0.144
#> GSM627140 6 0.4151 0.5689 0.000 0.024 0.004 0.276 0.004 0.692
#> GSM627149 1 0.0603 0.9766 0.980 0.000 0.004 0.000 0.000 0.016
#> GSM627147 4 0.7148 0.0302 0.000 0.300 0.004 0.320 0.060 0.316
#> GSM627195 5 0.3516 0.7056 0.000 0.000 0.164 0.000 0.788 0.048
#> GSM627204 2 0.3905 0.6151 0.000 0.716 0.000 0.256 0.024 0.004
#> GSM627207 2 0.2728 0.6513 0.000 0.872 0.008 0.000 0.040 0.080
#> GSM627157 3 0.2062 0.7728 0.088 0.000 0.900 0.000 0.004 0.008
#> GSM627201 2 0.4727 0.3912 0.000 0.552 0.000 0.408 0.028 0.012
#> GSM627146 2 0.4589 0.4360 0.000 0.580 0.000 0.384 0.028 0.008
#> GSM627156 2 0.4603 0.5908 0.000 0.712 0.020 0.000 0.068 0.200
#> GSM627188 1 0.0937 0.9718 0.960 0.000 0.000 0.000 0.000 0.040
#> GSM627197 2 0.4637 0.3884 0.000 0.556 0.000 0.408 0.028 0.008
#> GSM627173 2 0.3353 0.6704 0.000 0.804 0.000 0.160 0.032 0.004
#> GSM627179 2 0.2456 0.6869 0.000 0.888 0.000 0.076 0.028 0.008
#> GSM627208 5 0.5923 0.5449 0.000 0.176 0.104 0.004 0.632 0.084
#> GSM627215 5 0.6996 0.1798 0.000 0.284 0.052 0.116 0.500 0.048
#> GSM627153 4 0.1398 0.7396 0.000 0.052 0.000 0.940 0.008 0.000
#> GSM627155 1 0.0146 0.9759 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM627165 5 0.7112 0.0131 0.000 0.212 0.000 0.204 0.456 0.128
#> GSM627168 3 0.2871 0.7151 0.000 0.000 0.804 0.000 0.192 0.004
#> GSM627183 3 0.0937 0.7948 0.000 0.000 0.960 0.000 0.040 0.000
#> GSM627144 5 0.3602 0.7065 0.000 0.000 0.160 0.000 0.784 0.056
#> GSM627158 1 0.0291 0.9754 0.992 0.000 0.004 0.000 0.004 0.000
#> GSM627196 2 0.3905 0.6151 0.000 0.716 0.000 0.256 0.024 0.004
#> GSM627142 6 0.4509 0.5179 0.000 0.000 0.008 0.036 0.316 0.640
#> GSM627182 5 0.4793 0.5484 0.000 0.024 0.276 0.000 0.656 0.044
#> GSM627202 3 0.4193 0.7384 0.044 0.000 0.776 0.004 0.140 0.036
#> GSM627141 3 0.3630 0.7376 0.000 0.016 0.804 0.000 0.136 0.044
#> GSM627143 2 0.6307 0.5076 0.000 0.580 0.020 0.056 0.100 0.244
#> GSM627145 5 0.3804 0.5095 0.000 0.000 0.336 0.000 0.656 0.008
#> GSM627152 5 0.5464 0.5116 0.000 0.000 0.176 0.004 0.588 0.232
#> GSM627200 3 0.2405 0.7930 0.036 0.000 0.904 0.004 0.020 0.036
#> GSM627159 6 0.4692 0.7195 0.000 0.000 0.000 0.276 0.080 0.644
#> GSM627164 2 0.4898 0.5851 0.000 0.696 0.024 0.004 0.072 0.204
#> GSM627138 1 0.0837 0.9678 0.972 0.000 0.020 0.000 0.004 0.004
#> GSM627175 4 0.1693 0.7409 0.000 0.044 0.000 0.932 0.004 0.020
#> GSM627150 5 0.3259 0.6758 0.000 0.000 0.216 0.000 0.772 0.012
#> GSM627166 3 0.2478 0.7851 0.040 0.000 0.900 0.008 0.012 0.040
#> GSM627186 2 0.4856 0.5820 0.000 0.696 0.028 0.000 0.076 0.200
#> GSM627139 6 0.5021 0.5696 0.000 0.000 0.004 0.088 0.300 0.608
#> GSM627181 2 0.4518 0.5073 0.000 0.624 0.000 0.336 0.032 0.008
#> GSM627205 2 0.6088 0.3648 0.000 0.504 0.000 0.064 0.352 0.080
#> GSM627214 4 0.5628 0.3016 0.000 0.272 0.000 0.600 0.080 0.048
#> GSM627180 5 0.2768 0.6948 0.000 0.000 0.156 0.000 0.832 0.012
#> GSM627172 2 0.4922 0.5909 0.000 0.712 0.016 0.036 0.044 0.192
#> GSM627184 1 0.0547 0.9751 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM627193 2 0.2558 0.6841 0.000 0.868 0.000 0.104 0.028 0.000
#> GSM627191 6 0.4284 0.6042 0.016 0.000 0.008 0.292 0.008 0.676
#> GSM627176 5 0.5248 0.5284 0.000 0.000 0.144 0.004 0.616 0.236
#> GSM627194 2 0.4326 0.6573 0.000 0.748 0.000 0.168 0.060 0.024
#> GSM627154 4 0.1168 0.7356 0.000 0.016 0.000 0.956 0.000 0.028
#> GSM627187 3 0.4653 0.6367 0.000 0.044 0.724 0.000 0.180 0.052
#> GSM627198 4 0.3104 0.7052 0.000 0.092 0.000 0.852 0.028 0.028
#> GSM627160 6 0.4586 0.7175 0.000 0.000 0.000 0.264 0.076 0.660
#> GSM627185 3 0.3510 0.6464 0.204 0.000 0.772 0.000 0.008 0.016
#> GSM627206 3 0.3778 0.6046 0.000 0.000 0.708 0.000 0.272 0.020
#> GSM627161 1 0.0291 0.9754 0.992 0.000 0.004 0.000 0.004 0.000
#> GSM627162 3 0.6986 -0.0557 0.000 0.080 0.396 0.000 0.332 0.192
#> GSM627210 3 0.1293 0.7959 0.004 0.000 0.956 0.004 0.020 0.016
#> GSM627189 2 0.3453 0.6613 0.000 0.788 0.000 0.180 0.028 0.004
Heatmaps for the consensus matrix. It visualizes the probability of two
samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures
which are significantly different between classes which can be candidate marks
for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known
annotation is numeric, one-way ANOVA test is applied, and if the known
annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) age(p) other(p) k
#> SD:kmeans 145 0.849 0.316 0.0124 2
#> SD:kmeans 115 0.297 0.624 0.1959 3
#> SD:kmeans 135 0.283 0.617 0.1034 4
#> SD:kmeans 99 0.109 0.327 0.2236 5
#> SD:kmeans 123 0.463 0.829 0.1002 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
SD:skmeans**
The object with results only for a single top-value method and a single partition method
can be extracted as:
res = res_list["SD", "skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51882 rows and 146 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
- The first row: a plot of the ECDF (empirical cumulative distribution
function) curves of the consensus matrix for each
k and the heatmap of
predicted classes for each k.
- The second row: heatmaps of the consensus matrix for each
k.
- The third row: heatmaps of the membership matrix for each
k.
- The fouth row: heatmaps of the signatures for each
k.
All the plots in panels can be made by individual functions and they are
plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
- ECDF curves of the consensus matrix for each
k;
- 1-PAC. The PAC
score
measures the proportion of the ambiguous subgrouping.
- Mean silhouette score.
- Concordance. The mean probability of fiting the consensus class ids in all
partitions.
- Area increased. Denote \(A_k\) as the area under the ECDF curve for current
k, the area increased is defined as \(A_k - A_{k-1}\).
- Rand index. The percent of pairs of samples that are both in a same cluster
or both are not in a same cluster in the partition of k and k-1.
- Jaccard index. The ratio of pairs of samples are both in a same cluster in
the partition of k and k-1 and the pairs of samples are both in a same
cluster in the partition k or k-1.
The detailed explanations of these statistics can be found in the cola
vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.961 0.983 0.5026 0.497 0.497
#> 3 3 0.839 0.879 0.938 0.2992 0.805 0.627
#> 4 4 0.971 0.928 0.970 0.1538 0.796 0.493
#> 5 5 0.749 0.666 0.831 0.0553 0.881 0.581
#> 6 6 0.730 0.584 0.729 0.0418 0.898 0.567
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
- All \(k\) with Jaccard index larger than 0.95 are removed because increasing
\(k\) does not provide enough extra information. If all \(k\) are removed, it is
marked as no subgroup is detected.
- For all \(k\) with 1-PAC score larger than 0.9, the maximal \(k\) is taken as
the best \(k\), and other \(k\) are marked as optional \(k\).
- If it does not fit the second rule. The \(k\) with the maximal vote of the
highest 1-PAC score, highest mean silhouette, and highest concordance is
taken as the best \(k\).
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
show/hide code output
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM627128 2 0.0000 0.993 0.000 1.000
#> GSM627110 1 0.0000 0.972 1.000 0.000
#> GSM627132 1 0.0000 0.972 1.000 0.000
#> GSM627107 2 0.0000 0.993 0.000 1.000
#> GSM627103 2 0.0000 0.993 0.000 1.000
#> GSM627114 1 0.0000 0.972 1.000 0.000
#> GSM627134 2 0.0000 0.993 0.000 1.000
#> GSM627137 2 0.0000 0.993 0.000 1.000
#> GSM627148 1 0.0000 0.972 1.000 0.000
#> GSM627101 2 0.0000 0.993 0.000 1.000
#> GSM627130 2 0.0000 0.993 0.000 1.000
#> GSM627071 1 0.0000 0.972 1.000 0.000
#> GSM627118 2 0.0000 0.993 0.000 1.000
#> GSM627094 2 0.0000 0.993 0.000 1.000
#> GSM627122 1 0.0000 0.972 1.000 0.000
#> GSM627115 2 0.0000 0.993 0.000 1.000
#> GSM627125 2 0.0000 0.993 0.000 1.000
#> GSM627174 2 0.0000 0.993 0.000 1.000
#> GSM627102 2 0.0000 0.993 0.000 1.000
#> GSM627073 1 0.8661 0.615 0.712 0.288
#> GSM627108 2 0.0000 0.993 0.000 1.000
#> GSM627126 1 0.0000 0.972 1.000 0.000
#> GSM627078 2 0.0000 0.993 0.000 1.000
#> GSM627090 1 0.0000 0.972 1.000 0.000
#> GSM627099 2 0.0000 0.993 0.000 1.000
#> GSM627105 2 0.0000 0.993 0.000 1.000
#> GSM627117 1 0.0000 0.972 1.000 0.000
#> GSM627121 2 0.0000 0.993 0.000 1.000
#> GSM627127 2 0.0000 0.993 0.000 1.000
#> GSM627087 2 0.0000 0.993 0.000 1.000
#> GSM627089 1 0.0000 0.972 1.000 0.000
#> GSM627092 2 0.0000 0.993 0.000 1.000
#> GSM627076 1 0.0000 0.972 1.000 0.000
#> GSM627136 1 0.0000 0.972 1.000 0.000
#> GSM627081 1 0.8861 0.586 0.696 0.304
#> GSM627091 2 0.0000 0.993 0.000 1.000
#> GSM627097 2 0.0000 0.993 0.000 1.000
#> GSM627072 1 0.0000 0.972 1.000 0.000
#> GSM627080 1 0.0000 0.972 1.000 0.000
#> GSM627088 1 0.0000 0.972 1.000 0.000
#> GSM627109 1 0.0000 0.972 1.000 0.000
#> GSM627111 1 0.0000 0.972 1.000 0.000
#> GSM627113 1 0.0000 0.972 1.000 0.000
#> GSM627133 2 0.0000 0.993 0.000 1.000
#> GSM627177 1 0.0000 0.972 1.000 0.000
#> GSM627086 2 0.0000 0.993 0.000 1.000
#> GSM627095 1 0.0000 0.972 1.000 0.000
#> GSM627079 1 0.0000 0.972 1.000 0.000
#> GSM627082 1 0.8661 0.610 0.712 0.288
#> GSM627074 1 0.0000 0.972 1.000 0.000
#> GSM627077 1 0.0000 0.972 1.000 0.000
#> GSM627093 1 0.0000 0.972 1.000 0.000
#> GSM627120 2 0.0000 0.993 0.000 1.000
#> GSM627124 2 0.0000 0.993 0.000 1.000
#> GSM627075 2 0.0000 0.993 0.000 1.000
#> GSM627085 2 0.0000 0.993 0.000 1.000
#> GSM627119 1 0.0000 0.972 1.000 0.000
#> GSM627116 2 0.4298 0.898 0.088 0.912
#> GSM627084 1 0.0000 0.972 1.000 0.000
#> GSM627096 2 0.0000 0.993 0.000 1.000
#> GSM627100 1 0.0000 0.972 1.000 0.000
#> GSM627112 2 0.0000 0.993 0.000 1.000
#> GSM627083 1 0.0000 0.972 1.000 0.000
#> GSM627098 1 0.0000 0.972 1.000 0.000
#> GSM627104 1 0.0000 0.972 1.000 0.000
#> GSM627131 1 0.0000 0.972 1.000 0.000
#> GSM627106 1 0.8861 0.586 0.696 0.304
#> GSM627123 1 0.0000 0.972 1.000 0.000
#> GSM627129 2 0.0000 0.993 0.000 1.000
#> GSM627216 2 0.0000 0.993 0.000 1.000
#> GSM627212 2 0.0000 0.993 0.000 1.000
#> GSM627190 1 0.0000 0.972 1.000 0.000
#> GSM627169 2 0.0000 0.993 0.000 1.000
#> GSM627167 2 0.0000 0.993 0.000 1.000
#> GSM627192 1 0.0000 0.972 1.000 0.000
#> GSM627203 1 0.0000 0.972 1.000 0.000
#> GSM627151 2 0.0000 0.993 0.000 1.000
#> GSM627163 1 0.0000 0.972 1.000 0.000
#> GSM627211 2 0.0000 0.993 0.000 1.000
#> GSM627171 2 0.0000 0.993 0.000 1.000
#> GSM627209 2 0.0000 0.993 0.000 1.000
#> GSM627135 1 0.0000 0.972 1.000 0.000
#> GSM627170 2 0.0000 0.993 0.000 1.000
#> GSM627178 1 0.0000 0.972 1.000 0.000
#> GSM627199 2 0.0000 0.993 0.000 1.000
#> GSM627213 2 0.0000 0.993 0.000 1.000
#> GSM627140 2 0.0000 0.993 0.000 1.000
#> GSM627149 1 0.0000 0.972 1.000 0.000
#> GSM627147 2 0.0000 0.993 0.000 1.000
#> GSM627195 1 0.0000 0.972 1.000 0.000
#> GSM627204 2 0.0000 0.993 0.000 1.000
#> GSM627207 2 0.0000 0.993 0.000 1.000
#> GSM627157 1 0.0000 0.972 1.000 0.000
#> GSM627201 2 0.0000 0.993 0.000 1.000
#> GSM627146 2 0.0000 0.993 0.000 1.000
#> GSM627156 2 0.0000 0.993 0.000 1.000
#> GSM627188 1 0.0000 0.972 1.000 0.000
#> GSM627197 2 0.0000 0.993 0.000 1.000
#> GSM627173 2 0.0000 0.993 0.000 1.000
#> GSM627179 2 0.0000 0.993 0.000 1.000
#> GSM627208 2 0.0000 0.993 0.000 1.000
#> GSM627215 2 0.0000 0.993 0.000 1.000
#> GSM627153 2 0.0000 0.993 0.000 1.000
#> GSM627155 1 0.0000 0.972 1.000 0.000
#> GSM627165 2 0.0000 0.993 0.000 1.000
#> GSM627168 1 0.0000 0.972 1.000 0.000
#> GSM627183 1 0.0000 0.972 1.000 0.000
#> GSM627144 1 0.0000 0.972 1.000 0.000
#> GSM627158 1 0.0000 0.972 1.000 0.000
#> GSM627196 2 0.0000 0.993 0.000 1.000
#> GSM627142 1 0.0000 0.972 1.000 0.000
#> GSM627182 1 0.0376 0.969 0.996 0.004
#> GSM627202 1 0.0000 0.972 1.000 0.000
#> GSM627141 1 0.0000 0.972 1.000 0.000
#> GSM627143 2 0.0000 0.993 0.000 1.000
#> GSM627145 1 0.0000 0.972 1.000 0.000
#> GSM627152 1 0.0000 0.972 1.000 0.000
#> GSM627200 1 0.0000 0.972 1.000 0.000
#> GSM627159 2 0.8081 0.664 0.248 0.752
#> GSM627164 2 0.0000 0.993 0.000 1.000
#> GSM627138 1 0.0000 0.972 1.000 0.000
#> GSM627175 2 0.0000 0.993 0.000 1.000
#> GSM627150 1 0.0000 0.972 1.000 0.000
#> GSM627166 1 0.0000 0.972 1.000 0.000
#> GSM627186 2 0.0000 0.993 0.000 1.000
#> GSM627139 1 0.9833 0.299 0.576 0.424
#> GSM627181 2 0.0000 0.993 0.000 1.000
#> GSM627205 2 0.0000 0.993 0.000 1.000
#> GSM627214 2 0.0000 0.993 0.000 1.000
#> GSM627180 2 0.0000 0.993 0.000 1.000
#> GSM627172 2 0.0000 0.993 0.000 1.000
#> GSM627184 1 0.0000 0.972 1.000 0.000
#> GSM627193 2 0.0000 0.993 0.000 1.000
#> GSM627191 2 0.6531 0.793 0.168 0.832
#> GSM627176 1 0.0000 0.972 1.000 0.000
#> GSM627194 2 0.0000 0.993 0.000 1.000
#> GSM627154 2 0.0000 0.993 0.000 1.000
#> GSM627187 1 0.0000 0.972 1.000 0.000
#> GSM627198 2 0.0000 0.993 0.000 1.000
#> GSM627160 1 0.8861 0.570 0.696 0.304
#> GSM627185 1 0.0000 0.972 1.000 0.000
#> GSM627206 1 0.0000 0.972 1.000 0.000
#> GSM627161 1 0.0000 0.972 1.000 0.000
#> GSM627162 1 0.0000 0.972 1.000 0.000
#> GSM627210 1 0.0000 0.972 1.000 0.000
#> GSM627189 2 0.0000 0.993 0.000 1.000
show/hide code output
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM627128 3 0.3038 0.872 0.104 0.000 0.896
#> GSM627110 1 0.3412 0.891 0.876 0.000 0.124
#> GSM627132 1 0.0000 0.940 1.000 0.000 0.000
#> GSM627107 3 0.0237 0.891 0.000 0.004 0.996
#> GSM627103 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627114 1 0.3192 0.898 0.888 0.000 0.112
#> GSM627134 2 0.1860 0.923 0.000 0.948 0.052
#> GSM627137 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627148 3 0.2066 0.870 0.060 0.000 0.940
#> GSM627101 3 0.3340 0.819 0.000 0.120 0.880
#> GSM627130 3 0.3983 0.857 0.048 0.068 0.884
#> GSM627071 1 0.6062 0.470 0.616 0.000 0.384
#> GSM627118 2 0.5560 0.602 0.000 0.700 0.300
#> GSM627094 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627122 1 0.0000 0.940 1.000 0.000 0.000
#> GSM627115 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627125 3 0.3377 0.875 0.092 0.012 0.896
#> GSM627174 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627102 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627073 3 0.0424 0.892 0.008 0.000 0.992
#> GSM627108 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627126 1 0.0000 0.940 1.000 0.000 0.000
#> GSM627078 2 0.0747 0.944 0.000 0.984 0.016
#> GSM627090 3 0.2066 0.893 0.060 0.000 0.940
#> GSM627099 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627105 3 0.3377 0.846 0.012 0.092 0.896
#> GSM627117 1 0.3412 0.891 0.876 0.000 0.124
#> GSM627121 3 0.0000 0.891 0.000 0.000 1.000
#> GSM627127 2 0.0747 0.944 0.000 0.984 0.016
#> GSM627087 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627089 3 0.6308 -0.118 0.492 0.000 0.508
#> GSM627092 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627076 3 0.2959 0.881 0.100 0.000 0.900
#> GSM627136 1 0.1860 0.923 0.948 0.000 0.052
#> GSM627081 3 0.0000 0.891 0.000 0.000 1.000
#> GSM627091 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627097 2 0.1753 0.926 0.000 0.952 0.048
#> GSM627072 3 0.2261 0.864 0.068 0.000 0.932
#> GSM627080 1 0.0000 0.940 1.000 0.000 0.000
#> GSM627088 1 0.3192 0.898 0.888 0.000 0.112
#> GSM627109 1 0.0237 0.939 0.996 0.000 0.004
#> GSM627111 1 0.0000 0.940 1.000 0.000 0.000
#> GSM627113 1 0.3038 0.902 0.896 0.000 0.104
#> GSM627133 2 0.5733 0.542 0.000 0.676 0.324
#> GSM627177 1 0.5529 0.657 0.704 0.000 0.296
#> GSM627086 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627095 1 0.0000 0.940 1.000 0.000 0.000
#> GSM627079 3 0.2165 0.889 0.064 0.000 0.936
#> GSM627082 3 0.3412 0.860 0.124 0.000 0.876
#> GSM627074 1 0.3038 0.902 0.896 0.000 0.104
#> GSM627077 1 0.0000 0.940 1.000 0.000 0.000
#> GSM627093 1 0.3038 0.902 0.896 0.000 0.104
#> GSM627120 2 0.1163 0.938 0.000 0.972 0.028
#> GSM627124 2 0.0747 0.944 0.000 0.984 0.016
#> GSM627075 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627085 2 0.0747 0.944 0.000 0.984 0.016
#> GSM627119 1 0.3038 0.902 0.896 0.000 0.104
#> GSM627116 2 0.7481 0.400 0.048 0.596 0.356
#> GSM627084 1 0.0000 0.940 1.000 0.000 0.000
#> GSM627096 2 0.5591 0.595 0.000 0.696 0.304
#> GSM627100 3 0.2878 0.877 0.096 0.000 0.904
#> GSM627112 2 0.2860 0.893 0.004 0.912 0.084
#> GSM627083 1 0.0000 0.940 1.000 0.000 0.000
#> GSM627098 1 0.0237 0.939 0.996 0.000 0.004
#> GSM627104 1 0.0237 0.939 0.996 0.000 0.004
#> GSM627131 1 0.0000 0.940 1.000 0.000 0.000
#> GSM627106 3 0.0000 0.891 0.000 0.000 1.000
#> GSM627123 1 0.0000 0.940 1.000 0.000 0.000
#> GSM627129 2 0.1860 0.923 0.000 0.948 0.052
#> GSM627216 2 0.0747 0.940 0.000 0.984 0.016
#> GSM627212 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627190 1 0.3412 0.891 0.876 0.000 0.124
#> GSM627169 2 0.2165 0.899 0.000 0.936 0.064
#> GSM627167 2 0.1860 0.923 0.000 0.948 0.052
#> GSM627192 1 0.0000 0.940 1.000 0.000 0.000
#> GSM627203 3 0.0747 0.892 0.016 0.000 0.984
#> GSM627151 2 0.6235 0.280 0.000 0.564 0.436
#> GSM627163 1 0.0000 0.940 1.000 0.000 0.000
#> GSM627211 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627171 2 0.0424 0.945 0.000 0.992 0.008
#> GSM627209 2 0.0747 0.944 0.000 0.984 0.016
#> GSM627135 1 0.0000 0.940 1.000 0.000 0.000
#> GSM627170 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627178 1 0.0000 0.940 1.000 0.000 0.000
#> GSM627199 2 0.0747 0.944 0.000 0.984 0.016
#> GSM627213 2 0.2066 0.916 0.000 0.940 0.060
#> GSM627140 2 0.3589 0.885 0.048 0.900 0.052
#> GSM627149 1 0.0000 0.940 1.000 0.000 0.000
#> GSM627147 2 0.0424 0.947 0.000 0.992 0.008
#> GSM627195 3 0.0747 0.892 0.016 0.000 0.984
#> GSM627204 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627207 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627157 1 0.0237 0.939 0.996 0.000 0.004
#> GSM627201 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627146 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627156 2 0.2261 0.895 0.000 0.932 0.068
#> GSM627188 1 0.0000 0.940 1.000 0.000 0.000
#> GSM627197 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627173 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627179 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627208 3 0.6244 0.161 0.000 0.440 0.560
#> GSM627215 2 0.1031 0.935 0.000 0.976 0.024
#> GSM627153 2 0.0747 0.944 0.000 0.984 0.016
#> GSM627155 1 0.0000 0.940 1.000 0.000 0.000
#> GSM627165 2 0.6291 0.143 0.000 0.532 0.468
#> GSM627168 1 0.3340 0.893 0.880 0.000 0.120
#> GSM627183 1 0.3192 0.898 0.888 0.000 0.112
#> GSM627144 3 0.0747 0.892 0.016 0.000 0.984
#> GSM627158 1 0.0000 0.940 1.000 0.000 0.000
#> GSM627196 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627142 3 0.3038 0.872 0.104 0.000 0.896
#> GSM627182 3 0.2743 0.868 0.052 0.020 0.928
#> GSM627202 1 0.0000 0.940 1.000 0.000 0.000
#> GSM627141 1 0.2711 0.909 0.912 0.000 0.088
#> GSM627143 2 0.0424 0.947 0.000 0.992 0.008
#> GSM627145 3 0.2165 0.867 0.064 0.000 0.936
#> GSM627152 3 0.2959 0.881 0.100 0.000 0.900
#> GSM627200 1 0.0000 0.940 1.000 0.000 0.000
#> GSM627159 3 0.3192 0.868 0.112 0.000 0.888
#> GSM627164 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627138 1 0.0237 0.939 0.996 0.000 0.004
#> GSM627175 2 0.0747 0.944 0.000 0.984 0.016
#> GSM627150 3 0.0747 0.892 0.016 0.000 0.984
#> GSM627166 1 0.0000 0.940 1.000 0.000 0.000
#> GSM627186 2 0.2625 0.879 0.000 0.916 0.084
#> GSM627139 3 0.2878 0.877 0.096 0.000 0.904
#> GSM627181 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627205 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627214 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627180 3 0.0424 0.891 0.000 0.008 0.992
#> GSM627172 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627184 1 0.0000 0.940 1.000 0.000 0.000
#> GSM627193 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627191 1 0.2301 0.886 0.936 0.004 0.060
#> GSM627176 3 0.1031 0.894 0.024 0.000 0.976
#> GSM627194 2 0.0000 0.949 0.000 1.000 0.000
#> GSM627154 2 0.0747 0.944 0.000 0.984 0.016
#> GSM627187 1 0.3192 0.898 0.888 0.000 0.112
#> GSM627198 2 0.0747 0.944 0.000 0.984 0.016
#> GSM627160 3 0.6062 0.472 0.384 0.000 0.616
#> GSM627185 1 0.0237 0.939 0.996 0.000 0.004
#> GSM627206 1 0.3412 0.891 0.876 0.000 0.124
#> GSM627161 1 0.0000 0.940 1.000 0.000 0.000
#> GSM627162 1 0.5529 0.661 0.704 0.000 0.296
#> GSM627210 1 0.3038 0.902 0.896 0.000 0.104
#> GSM627189 2 0.0000 0.949 0.000 1.000 0.000
show/hide code output
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM627128 4 0.0000 0.9677 0.000 0.000 0.000 1.000
#> GSM627110 3 0.0000 0.9676 0.000 0.000 1.000 0.000
#> GSM627132 1 0.0000 0.9633 1.000 0.000 0.000 0.000
#> GSM627107 3 0.0707 0.9588 0.000 0.000 0.980 0.020
#> GSM627103 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627114 3 0.0817 0.9487 0.024 0.000 0.976 0.000
#> GSM627134 4 0.0336 0.9697 0.000 0.008 0.000 0.992
#> GSM627137 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627148 3 0.0000 0.9676 0.000 0.000 1.000 0.000
#> GSM627101 4 0.0000 0.9677 0.000 0.000 0.000 1.000
#> GSM627130 4 0.0000 0.9677 0.000 0.000 0.000 1.000
#> GSM627071 3 0.0000 0.9676 0.000 0.000 1.000 0.000
#> GSM627118 4 0.0336 0.9697 0.000 0.008 0.000 0.992
#> GSM627094 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627122 1 0.0000 0.9633 1.000 0.000 0.000 0.000
#> GSM627115 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627125 4 0.0000 0.9677 0.000 0.000 0.000 1.000
#> GSM627174 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627102 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627073 3 0.0188 0.9676 0.000 0.000 0.996 0.004
#> GSM627108 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627126 1 0.0000 0.9633 1.000 0.000 0.000 0.000
#> GSM627078 4 0.0817 0.9626 0.000 0.024 0.000 0.976
#> GSM627090 3 0.0336 0.9661 0.000 0.000 0.992 0.008
#> GSM627099 2 0.4356 0.5754 0.000 0.708 0.000 0.292
#> GSM627105 4 0.0000 0.9677 0.000 0.000 0.000 1.000
#> GSM627117 3 0.0000 0.9676 0.000 0.000 1.000 0.000
#> GSM627121 3 0.0336 0.9661 0.000 0.000 0.992 0.008
#> GSM627127 4 0.0336 0.9697 0.000 0.008 0.000 0.992
#> GSM627087 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627089 3 0.0000 0.9676 0.000 0.000 1.000 0.000
#> GSM627092 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627076 3 0.0817 0.9559 0.000 0.000 0.976 0.024
#> GSM627136 1 0.0188 0.9608 0.996 0.000 0.004 0.000
#> GSM627081 3 0.0188 0.9676 0.000 0.000 0.996 0.004
#> GSM627091 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627097 4 0.0336 0.9697 0.000 0.008 0.000 0.992
#> GSM627072 3 0.0000 0.9676 0.000 0.000 1.000 0.000
#> GSM627080 1 0.0000 0.9633 1.000 0.000 0.000 0.000
#> GSM627088 3 0.5000 -0.0514 0.496 0.000 0.504 0.000
#> GSM627109 1 0.0000 0.9633 1.000 0.000 0.000 0.000
#> GSM627111 1 0.0000 0.9633 1.000 0.000 0.000 0.000
#> GSM627113 1 0.0921 0.9421 0.972 0.000 0.028 0.000
#> GSM627133 3 0.2011 0.8927 0.000 0.080 0.920 0.000
#> GSM627177 1 0.4522 0.5349 0.680 0.000 0.320 0.000
#> GSM627086 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627095 1 0.0000 0.9633 1.000 0.000 0.000 0.000
#> GSM627079 3 0.0188 0.9676 0.000 0.000 0.996 0.004
#> GSM627082 4 0.0000 0.9677 0.000 0.000 0.000 1.000
#> GSM627074 1 0.0336 0.9583 0.992 0.000 0.008 0.000
#> GSM627077 1 0.0000 0.9633 1.000 0.000 0.000 0.000
#> GSM627093 1 0.0336 0.9583 0.992 0.000 0.008 0.000
#> GSM627120 2 0.4776 0.3872 0.000 0.624 0.000 0.376
#> GSM627124 4 0.0817 0.9626 0.000 0.024 0.000 0.976
#> GSM627075 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627085 4 0.0336 0.9697 0.000 0.008 0.000 0.992
#> GSM627119 1 0.0469 0.9554 0.988 0.000 0.012 0.000
#> GSM627116 4 0.0336 0.9697 0.000 0.008 0.000 0.992
#> GSM627084 1 0.0000 0.9633 1.000 0.000 0.000 0.000
#> GSM627096 4 0.0336 0.9697 0.000 0.008 0.000 0.992
#> GSM627100 3 0.0921 0.9526 0.000 0.000 0.972 0.028
#> GSM627112 4 0.0188 0.9690 0.000 0.004 0.000 0.996
#> GSM627083 1 0.0000 0.9633 1.000 0.000 0.000 0.000
#> GSM627098 1 0.0000 0.9633 1.000 0.000 0.000 0.000
#> GSM627104 1 0.0000 0.9633 1.000 0.000 0.000 0.000
#> GSM627131 1 0.0000 0.9633 1.000 0.000 0.000 0.000
#> GSM627106 3 0.0188 0.9676 0.000 0.000 0.996 0.004
#> GSM627123 1 0.0000 0.9633 1.000 0.000 0.000 0.000
#> GSM627129 4 0.0336 0.9697 0.000 0.008 0.000 0.992
#> GSM627216 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627212 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627190 3 0.0000 0.9676 0.000 0.000 1.000 0.000
#> GSM627169 2 0.0188 0.9701 0.000 0.996 0.004 0.000
#> GSM627167 4 0.0592 0.9652 0.000 0.016 0.000 0.984
#> GSM627192 1 0.0000 0.9633 1.000 0.000 0.000 0.000
#> GSM627203 3 0.0188 0.9676 0.000 0.000 0.996 0.004
#> GSM627151 4 0.0188 0.9690 0.000 0.004 0.000 0.996
#> GSM627163 1 0.0000 0.9633 1.000 0.000 0.000 0.000
#> GSM627211 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627171 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627209 4 0.1302 0.9462 0.000 0.044 0.000 0.956
#> GSM627135 1 0.0000 0.9633 1.000 0.000 0.000 0.000
#> GSM627170 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627178 1 0.0000 0.9633 1.000 0.000 0.000 0.000
#> GSM627199 4 0.0817 0.9626 0.000 0.024 0.000 0.976
#> GSM627213 4 0.0336 0.9697 0.000 0.008 0.000 0.992
#> GSM627140 4 0.0188 0.9690 0.000 0.004 0.000 0.996
#> GSM627149 1 0.0000 0.9633 1.000 0.000 0.000 0.000
#> GSM627147 4 0.4331 0.5989 0.000 0.288 0.000 0.712
#> GSM627195 3 0.0188 0.9676 0.000 0.000 0.996 0.004
#> GSM627204 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627207 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627157 1 0.0000 0.9633 1.000 0.000 0.000 0.000
#> GSM627201 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627146 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627156 2 0.0188 0.9701 0.000 0.996 0.004 0.000
#> GSM627188 1 0.0000 0.9633 1.000 0.000 0.000 0.000
#> GSM627197 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627173 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627179 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627208 3 0.0000 0.9676 0.000 0.000 1.000 0.000
#> GSM627215 2 0.2408 0.8678 0.000 0.896 0.104 0.000
#> GSM627153 4 0.0921 0.9599 0.000 0.028 0.000 0.972
#> GSM627155 1 0.0000 0.9633 1.000 0.000 0.000 0.000
#> GSM627165 4 0.4830 0.3396 0.000 0.392 0.000 0.608
#> GSM627168 3 0.0000 0.9676 0.000 0.000 1.000 0.000
#> GSM627183 1 0.5000 0.0358 0.504 0.000 0.496 0.000
#> GSM627144 3 0.0188 0.9676 0.000 0.000 0.996 0.004
#> GSM627158 1 0.0000 0.9633 1.000 0.000 0.000 0.000
#> GSM627196 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627142 4 0.0779 0.9562 0.016 0.000 0.004 0.980
#> GSM627182 3 0.0000 0.9676 0.000 0.000 1.000 0.000
#> GSM627202 1 0.0000 0.9633 1.000 0.000 0.000 0.000
#> GSM627141 1 0.3975 0.6810 0.760 0.000 0.240 0.000
#> GSM627143 2 0.3311 0.7792 0.000 0.828 0.000 0.172
#> GSM627145 3 0.0000 0.9676 0.000 0.000 1.000 0.000
#> GSM627152 3 0.0524 0.9647 0.004 0.000 0.988 0.008
#> GSM627200 1 0.0000 0.9633 1.000 0.000 0.000 0.000
#> GSM627159 4 0.0000 0.9677 0.000 0.000 0.000 1.000
#> GSM627164 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627138 1 0.0000 0.9633 1.000 0.000 0.000 0.000
#> GSM627175 4 0.0817 0.9626 0.000 0.024 0.000 0.976
#> GSM627150 3 0.0188 0.9676 0.000 0.000 0.996 0.004
#> GSM627166 1 0.0000 0.9633 1.000 0.000 0.000 0.000
#> GSM627186 2 0.0188 0.9701 0.000 0.996 0.004 0.000
#> GSM627139 4 0.0000 0.9677 0.000 0.000 0.000 1.000
#> GSM627181 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627205 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627214 2 0.0817 0.9524 0.000 0.976 0.000 0.024
#> GSM627180 3 0.0188 0.9676 0.000 0.000 0.996 0.004
#> GSM627172 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627184 1 0.0000 0.9633 1.000 0.000 0.000 0.000
#> GSM627193 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627191 4 0.0592 0.9611 0.016 0.000 0.000 0.984
#> GSM627176 3 0.0336 0.9661 0.000 0.000 0.992 0.008
#> GSM627194 2 0.0000 0.9735 0.000 1.000 0.000 0.000
#> GSM627154 4 0.0336 0.9697 0.000 0.008 0.000 0.992
#> GSM627187 3 0.0000 0.9676 0.000 0.000 1.000 0.000
#> GSM627198 4 0.0817 0.9626 0.000 0.024 0.000 0.976
#> GSM627160 4 0.0188 0.9673 0.004 0.000 0.000 0.996
#> GSM627185 1 0.0000 0.9633 1.000 0.000 0.000 0.000
#> GSM627206 3 0.0000 0.9676 0.000 0.000 1.000 0.000
#> GSM627161 1 0.0000 0.9633 1.000 0.000 0.000 0.000
#> GSM627162 3 0.4103 0.6331 0.256 0.000 0.744 0.000
#> GSM627210 1 0.3942 0.6904 0.764 0.000 0.236 0.000
#> GSM627189 2 0.0000 0.9735 0.000 1.000 0.000 0.000
show/hide code output
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM627128 5 0.4227 0.34444 0.000 0.000 0.000 0.420 0.580
#> GSM627110 3 0.0880 0.69981 0.000 0.000 0.968 0.000 0.032
#> GSM627132 1 0.0404 0.95540 0.988 0.000 0.012 0.000 0.000
#> GSM627107 5 0.1197 0.59288 0.000 0.000 0.048 0.000 0.952
#> GSM627103 2 0.2516 0.82314 0.000 0.860 0.000 0.140 0.000
#> GSM627114 3 0.0290 0.69108 0.000 0.008 0.992 0.000 0.000
#> GSM627134 4 0.0290 0.84191 0.000 0.000 0.000 0.992 0.008
#> GSM627137 2 0.1908 0.82852 0.000 0.908 0.000 0.092 0.000
#> GSM627148 3 0.3561 0.62723 0.000 0.000 0.740 0.000 0.260
#> GSM627101 4 0.4235 0.00802 0.000 0.000 0.000 0.576 0.424
#> GSM627130 5 0.4227 0.34444 0.000 0.000 0.000 0.420 0.580
#> GSM627071 3 0.2471 0.69584 0.000 0.000 0.864 0.000 0.136
#> GSM627118 4 0.0404 0.84056 0.000 0.000 0.000 0.988 0.012
#> GSM627094 2 0.2424 0.82541 0.000 0.868 0.000 0.132 0.000
#> GSM627122 1 0.0000 0.95776 1.000 0.000 0.000 0.000 0.000
#> GSM627115 2 0.2516 0.82314 0.000 0.860 0.000 0.140 0.000
#> GSM627125 5 0.3109 0.59924 0.000 0.000 0.000 0.200 0.800
#> GSM627174 2 0.3949 0.63027 0.000 0.668 0.000 0.332 0.000
#> GSM627102 2 0.1485 0.78602 0.000 0.948 0.032 0.000 0.020
#> GSM627073 3 0.4192 0.48550 0.000 0.000 0.596 0.000 0.404
#> GSM627108 2 0.1965 0.82869 0.000 0.904 0.000 0.096 0.000
#> GSM627126 1 0.0000 0.95776 1.000 0.000 0.000 0.000 0.000
#> GSM627078 4 0.0794 0.83939 0.000 0.028 0.000 0.972 0.000
#> GSM627090 5 0.1845 0.59415 0.016 0.000 0.056 0.000 0.928
#> GSM627099 4 0.2929 0.66907 0.000 0.180 0.000 0.820 0.000
#> GSM627105 5 0.3074 0.60171 0.000 0.000 0.000 0.196 0.804
#> GSM627117 3 0.0404 0.68975 0.000 0.012 0.988 0.000 0.000
#> GSM627121 5 0.4166 0.04612 0.000 0.004 0.348 0.000 0.648
#> GSM627127 4 0.0290 0.84191 0.000 0.000 0.000 0.992 0.008
#> GSM627087 2 0.2516 0.82314 0.000 0.860 0.000 0.140 0.000
#> GSM627089 3 0.2605 0.69176 0.000 0.000 0.852 0.000 0.148
#> GSM627092 2 0.1800 0.77890 0.000 0.932 0.048 0.000 0.020
#> GSM627076 5 0.1211 0.61459 0.016 0.000 0.024 0.000 0.960
#> GSM627136 1 0.4219 0.30527 0.584 0.000 0.416 0.000 0.000
#> GSM627081 5 0.4291 -0.26858 0.000 0.000 0.464 0.000 0.536
#> GSM627091 4 0.4227 -0.02118 0.000 0.420 0.000 0.580 0.000
#> GSM627097 4 0.0794 0.83003 0.000 0.000 0.000 0.972 0.028
#> GSM627072 3 0.2561 0.69308 0.000 0.000 0.856 0.000 0.144
#> GSM627080 1 0.0162 0.95716 0.996 0.000 0.004 0.000 0.000
#> GSM627088 3 0.1410 0.68489 0.060 0.000 0.940 0.000 0.000
#> GSM627109 1 0.2179 0.88803 0.888 0.000 0.112 0.000 0.000
#> GSM627111 1 0.0510 0.95382 0.984 0.000 0.016 0.000 0.000
#> GSM627113 3 0.4268 0.13716 0.444 0.000 0.556 0.000 0.000
#> GSM627133 3 0.4827 0.59927 0.000 0.136 0.752 0.016 0.096
#> GSM627177 3 0.3255 0.70159 0.052 0.000 0.848 0.000 0.100
#> GSM627086 2 0.2648 0.81682 0.000 0.848 0.000 0.152 0.000
#> GSM627095 1 0.0000 0.95776 1.000 0.000 0.000 0.000 0.000
#> GSM627079 3 0.4249 0.44710 0.000 0.000 0.568 0.000 0.432
#> GSM627082 5 0.5498 0.40148 0.076 0.000 0.000 0.356 0.568
#> GSM627074 3 0.4307 -0.07228 0.496 0.000 0.504 0.000 0.000
#> GSM627077 1 0.0000 0.95776 1.000 0.000 0.000 0.000 0.000
#> GSM627093 3 0.4227 0.17329 0.420 0.000 0.580 0.000 0.000
#> GSM627120 2 0.6250 0.24287 0.000 0.560 0.056 0.332 0.052
#> GSM627124 4 0.0794 0.83939 0.000 0.028 0.000 0.972 0.000
#> GSM627075 2 0.1485 0.78602 0.000 0.948 0.032 0.000 0.020
#> GSM627085 4 0.0404 0.84264 0.000 0.012 0.000 0.988 0.000
#> GSM627119 3 0.4171 0.26514 0.396 0.000 0.604 0.000 0.000
#> GSM627116 4 0.0963 0.82242 0.000 0.000 0.000 0.964 0.036
#> GSM627084 1 0.0404 0.95540 0.988 0.000 0.012 0.000 0.000
#> GSM627096 4 0.0404 0.84056 0.000 0.000 0.000 0.988 0.012
#> GSM627100 5 0.1074 0.61776 0.012 0.000 0.016 0.004 0.968
#> GSM627112 4 0.2127 0.72518 0.000 0.000 0.000 0.892 0.108
#> GSM627083 1 0.0000 0.95776 1.000 0.000 0.000 0.000 0.000
#> GSM627098 1 0.1851 0.90872 0.912 0.000 0.088 0.000 0.000
#> GSM627104 1 0.2605 0.84665 0.852 0.000 0.148 0.000 0.000
#> GSM627131 1 0.0510 0.95284 0.984 0.000 0.016 0.000 0.000
#> GSM627106 5 0.4283 -0.24905 0.000 0.000 0.456 0.000 0.544
#> GSM627123 1 0.0000 0.95776 1.000 0.000 0.000 0.000 0.000
#> GSM627129 4 0.1117 0.82928 0.000 0.016 0.000 0.964 0.020
#> GSM627216 2 0.3169 0.81049 0.000 0.856 0.060 0.084 0.000
#> GSM627212 2 0.4297 0.33347 0.000 0.528 0.000 0.472 0.000
#> GSM627190 3 0.0290 0.69108 0.000 0.008 0.992 0.000 0.000
#> GSM627169 2 0.2390 0.75999 0.000 0.896 0.084 0.000 0.020
#> GSM627167 5 0.6418 0.15366 0.000 0.172 0.000 0.408 0.420
#> GSM627192 1 0.0000 0.95776 1.000 0.000 0.000 0.000 0.000
#> GSM627203 3 0.4297 0.36974 0.000 0.000 0.528 0.000 0.472
#> GSM627151 4 0.1121 0.81998 0.000 0.000 0.000 0.956 0.044
#> GSM627163 1 0.0404 0.95540 0.988 0.000 0.012 0.000 0.000
#> GSM627211 2 0.2127 0.82903 0.000 0.892 0.000 0.108 0.000
#> GSM627171 2 0.2144 0.76913 0.000 0.912 0.068 0.000 0.020
#> GSM627209 4 0.1544 0.80619 0.000 0.068 0.000 0.932 0.000
#> GSM627135 1 0.0000 0.95776 1.000 0.000 0.000 0.000 0.000
#> GSM627170 2 0.1965 0.82892 0.000 0.904 0.000 0.096 0.000
#> GSM627178 1 0.1121 0.93596 0.956 0.000 0.044 0.000 0.000
#> GSM627199 4 0.0794 0.83939 0.000 0.028 0.000 0.972 0.000
#> GSM627213 4 0.0703 0.83314 0.000 0.000 0.000 0.976 0.024
#> GSM627140 4 0.5891 -0.17557 0.000 0.100 0.000 0.468 0.432
#> GSM627149 1 0.0000 0.95776 1.000 0.000 0.000 0.000 0.000
#> GSM627147 2 0.5504 -0.02564 0.000 0.488 0.000 0.448 0.064
#> GSM627195 3 0.4249 0.44702 0.000 0.000 0.568 0.000 0.432
#> GSM627204 2 0.2471 0.82469 0.000 0.864 0.000 0.136 0.000
#> GSM627207 2 0.0290 0.79900 0.000 0.992 0.000 0.000 0.008
#> GSM627157 1 0.2020 0.89849 0.900 0.000 0.100 0.000 0.000
#> GSM627201 2 0.3913 0.64195 0.000 0.676 0.000 0.324 0.000
#> GSM627146 2 0.3932 0.63625 0.000 0.672 0.000 0.328 0.000
#> GSM627156 2 0.2144 0.76913 0.000 0.912 0.068 0.000 0.020
#> GSM627188 1 0.0000 0.95776 1.000 0.000 0.000 0.000 0.000
#> GSM627197 2 0.4088 0.56922 0.000 0.632 0.000 0.368 0.000
#> GSM627173 2 0.2230 0.82840 0.000 0.884 0.000 0.116 0.000
#> GSM627179 2 0.2020 0.82901 0.000 0.900 0.000 0.100 0.000
#> GSM627208 3 0.3216 0.68287 0.000 0.044 0.848 0.000 0.108
#> GSM627215 2 0.6322 0.30787 0.000 0.516 0.372 0.084 0.028
#> GSM627153 4 0.1341 0.81734 0.000 0.056 0.000 0.944 0.000
#> GSM627155 1 0.0000 0.95776 1.000 0.000 0.000 0.000 0.000
#> GSM627165 5 0.6625 0.21898 0.000 0.276 0.000 0.268 0.456
#> GSM627168 3 0.1851 0.70249 0.000 0.000 0.912 0.000 0.088
#> GSM627183 3 0.2488 0.65983 0.124 0.000 0.872 0.000 0.004
#> GSM627144 3 0.4256 0.43111 0.000 0.000 0.564 0.000 0.436
#> GSM627158 1 0.0000 0.95776 1.000 0.000 0.000 0.000 0.000
#> GSM627196 2 0.2471 0.82469 0.000 0.864 0.000 0.136 0.000
#> GSM627142 5 0.1386 0.62894 0.016 0.000 0.000 0.032 0.952
#> GSM627182 3 0.2127 0.69823 0.000 0.000 0.892 0.000 0.108
#> GSM627202 1 0.0000 0.95776 1.000 0.000 0.000 0.000 0.000
#> GSM627141 3 0.4648 -0.01705 0.464 0.012 0.524 0.000 0.000
#> GSM627143 2 0.4638 0.66142 0.000 0.784 0.068 0.104 0.044
#> GSM627145 3 0.2732 0.68746 0.000 0.000 0.840 0.000 0.160
#> GSM627152 5 0.1818 0.59959 0.024 0.000 0.044 0.000 0.932
#> GSM627200 1 0.0703 0.95038 0.976 0.000 0.024 0.000 0.000
#> GSM627159 5 0.4350 0.36444 0.004 0.000 0.000 0.408 0.588
#> GSM627164 2 0.2079 0.77126 0.000 0.916 0.064 0.000 0.020
#> GSM627138 1 0.1341 0.93227 0.944 0.000 0.056 0.000 0.000
#> GSM627175 4 0.0609 0.84154 0.000 0.020 0.000 0.980 0.000
#> GSM627150 3 0.4192 0.48513 0.000 0.000 0.596 0.000 0.404
#> GSM627166 1 0.1341 0.93389 0.944 0.000 0.056 0.000 0.000
#> GSM627186 2 0.2390 0.75999 0.000 0.896 0.084 0.000 0.020
#> GSM627139 5 0.2813 0.61541 0.000 0.000 0.000 0.168 0.832
#> GSM627181 2 0.3730 0.68667 0.000 0.712 0.000 0.288 0.000
#> GSM627205 2 0.1908 0.82852 0.000 0.908 0.000 0.092 0.000
#> GSM627214 4 0.4138 0.16880 0.000 0.384 0.000 0.616 0.000
#> GSM627180 3 0.4235 0.45897 0.000 0.000 0.576 0.000 0.424
#> GSM627172 2 0.1485 0.78602 0.000 0.948 0.032 0.000 0.020
#> GSM627184 1 0.0000 0.95776 1.000 0.000 0.000 0.000 0.000
#> GSM627193 2 0.2280 0.82786 0.000 0.880 0.000 0.120 0.000
#> GSM627191 5 0.6771 0.24638 0.356 0.000 0.000 0.272 0.372
#> GSM627176 5 0.1914 0.59478 0.016 0.000 0.060 0.000 0.924
#> GSM627194 2 0.2561 0.82134 0.000 0.856 0.000 0.144 0.000
#> GSM627154 4 0.0000 0.84279 0.000 0.000 0.000 1.000 0.000
#> GSM627187 3 0.1914 0.65615 0.000 0.060 0.924 0.000 0.016
#> GSM627198 4 0.0794 0.83939 0.000 0.028 0.000 0.972 0.000
#> GSM627160 5 0.5652 0.40356 0.092 0.000 0.000 0.344 0.564
#> GSM627185 1 0.1544 0.92356 0.932 0.000 0.068 0.000 0.000
#> GSM627206 3 0.1341 0.70248 0.000 0.000 0.944 0.000 0.056
#> GSM627161 1 0.0000 0.95776 1.000 0.000 0.000 0.000 0.000
#> GSM627162 3 0.6065 0.52213 0.076 0.140 0.676 0.000 0.108
#> GSM627210 3 0.3534 0.55031 0.256 0.000 0.744 0.000 0.000
#> GSM627189 2 0.2471 0.82421 0.000 0.864 0.000 0.136 0.000
show/hide code output
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM627128 6 0.3984 0.5318 0.000 0.000 0.000 0.336 0.016 0.648
#> GSM627110 3 0.1807 0.7311 0.000 0.000 0.920 0.000 0.060 0.020
#> GSM627132 1 0.0363 0.8799 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM627107 6 0.4688 0.0921 0.000 0.000 0.016 0.020 0.420 0.544
#> GSM627103 2 0.1267 0.8458 0.000 0.940 0.000 0.060 0.000 0.000
#> GSM627114 3 0.1759 0.7314 0.000 0.004 0.924 0.004 0.064 0.004
#> GSM627134 4 0.1265 0.8564 0.000 0.044 0.000 0.948 0.008 0.000
#> GSM627137 2 0.2066 0.7995 0.000 0.908 0.000 0.040 0.052 0.000
#> GSM627148 5 0.5980 0.0512 0.000 0.000 0.292 0.000 0.444 0.264
#> GSM627101 4 0.3652 0.4243 0.000 0.000 0.000 0.720 0.016 0.264
#> GSM627130 6 0.3984 0.5318 0.000 0.000 0.000 0.336 0.016 0.648
#> GSM627071 3 0.3885 0.5741 0.000 0.000 0.736 0.000 0.220 0.044
#> GSM627118 4 0.1010 0.8541 0.000 0.036 0.000 0.960 0.000 0.004
#> GSM627094 2 0.1285 0.8440 0.000 0.944 0.000 0.052 0.004 0.000
#> GSM627122 1 0.0458 0.8747 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM627115 2 0.1141 0.8445 0.000 0.948 0.000 0.052 0.000 0.000
#> GSM627125 6 0.3171 0.6485 0.000 0.000 0.000 0.204 0.012 0.784
#> GSM627174 2 0.3431 0.7317 0.000 0.756 0.000 0.228 0.016 0.000
#> GSM627102 5 0.4177 0.1560 0.000 0.468 0.000 0.012 0.520 0.000
#> GSM627073 5 0.6188 0.1458 0.000 0.000 0.192 0.016 0.452 0.340
#> GSM627108 2 0.1151 0.8254 0.000 0.956 0.000 0.032 0.012 0.000
#> GSM627126 1 0.0000 0.8813 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627078 4 0.2312 0.8533 0.000 0.112 0.000 0.876 0.012 0.000
#> GSM627090 6 0.2504 0.5544 0.004 0.000 0.012 0.004 0.104 0.876
#> GSM627099 4 0.3578 0.4837 0.000 0.340 0.000 0.660 0.000 0.000
#> GSM627105 6 0.3171 0.6485 0.000 0.000 0.000 0.204 0.012 0.784
#> GSM627117 3 0.2093 0.7230 0.000 0.004 0.900 0.004 0.088 0.004
#> GSM627121 5 0.5715 0.0378 0.000 0.000 0.104 0.016 0.444 0.436
#> GSM627127 4 0.1349 0.8595 0.000 0.056 0.000 0.940 0.000 0.004
#> GSM627087 2 0.1141 0.8445 0.000 0.948 0.000 0.052 0.000 0.000
#> GSM627089 3 0.5065 0.4506 0.000 0.000 0.616 0.000 0.260 0.124
#> GSM627092 5 0.4644 0.2012 0.000 0.440 0.000 0.004 0.524 0.032
#> GSM627076 6 0.2214 0.5729 0.004 0.000 0.000 0.012 0.092 0.892
#> GSM627136 3 0.3528 0.4835 0.296 0.000 0.700 0.000 0.004 0.000
#> GSM627081 5 0.5960 0.0978 0.000 0.000 0.140 0.016 0.448 0.396
#> GSM627091 2 0.3851 0.1586 0.000 0.540 0.000 0.460 0.000 0.000
#> GSM627097 4 0.1218 0.8311 0.000 0.012 0.000 0.956 0.004 0.028
#> GSM627072 3 0.5446 0.3355 0.000 0.000 0.540 0.000 0.316 0.144
#> GSM627080 1 0.0260 0.8803 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM627088 3 0.0551 0.7375 0.004 0.000 0.984 0.000 0.008 0.004
#> GSM627109 1 0.4067 0.3440 0.548 0.000 0.444 0.000 0.008 0.000
#> GSM627111 1 0.0363 0.8799 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM627113 3 0.2092 0.7071 0.124 0.000 0.876 0.000 0.000 0.000
#> GSM627133 5 0.7430 -0.0043 0.000 0.248 0.292 0.024 0.376 0.060
#> GSM627177 3 0.3386 0.6340 0.008 0.000 0.788 0.000 0.188 0.016
#> GSM627086 2 0.1812 0.8433 0.000 0.912 0.000 0.080 0.008 0.000
#> GSM627095 1 0.0000 0.8813 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627079 6 0.6122 -0.1542 0.000 0.000 0.312 0.000 0.328 0.360
#> GSM627082 6 0.4978 0.5663 0.072 0.000 0.000 0.268 0.016 0.644
#> GSM627074 3 0.2631 0.6662 0.152 0.000 0.840 0.000 0.008 0.000
#> GSM627077 1 0.1036 0.8724 0.964 0.000 0.024 0.000 0.004 0.008
#> GSM627093 3 0.2191 0.7048 0.120 0.000 0.876 0.000 0.004 0.000
#> GSM627120 5 0.6613 0.2094 0.000 0.248 0.004 0.168 0.516 0.064
#> GSM627124 4 0.2312 0.8533 0.000 0.112 0.000 0.876 0.012 0.000
#> GSM627075 5 0.3999 0.1403 0.000 0.496 0.000 0.004 0.500 0.000
#> GSM627085 4 0.1643 0.8622 0.000 0.068 0.000 0.924 0.008 0.000
#> GSM627119 3 0.2212 0.7100 0.112 0.000 0.880 0.000 0.008 0.000
#> GSM627116 4 0.2014 0.8309 0.000 0.016 0.024 0.924 0.004 0.032
#> GSM627084 1 0.0363 0.8799 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM627096 4 0.0935 0.8521 0.000 0.032 0.000 0.964 0.000 0.004
#> GSM627100 6 0.1442 0.5955 0.004 0.000 0.000 0.012 0.040 0.944
#> GSM627112 4 0.2003 0.7151 0.000 0.000 0.000 0.884 0.000 0.116
#> GSM627083 1 0.0000 0.8813 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627098 1 0.3464 0.5920 0.688 0.000 0.312 0.000 0.000 0.000
#> GSM627104 3 0.4093 -0.1954 0.476 0.000 0.516 0.000 0.008 0.000
#> GSM627131 1 0.2389 0.8097 0.864 0.000 0.128 0.000 0.000 0.008
#> GSM627106 5 0.5912 0.0874 0.000 0.000 0.132 0.016 0.448 0.404
#> GSM627123 1 0.0000 0.8813 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627129 4 0.2358 0.7879 0.000 0.028 0.000 0.900 0.016 0.056
#> GSM627216 2 0.3339 0.7439 0.000 0.824 0.008 0.048 0.120 0.000
#> GSM627212 2 0.3659 0.4623 0.000 0.636 0.000 0.364 0.000 0.000
#> GSM627190 3 0.1876 0.7284 0.000 0.004 0.916 0.004 0.072 0.004
#> GSM627169 5 0.4225 0.1657 0.000 0.480 0.008 0.004 0.508 0.000
#> GSM627167 5 0.6250 -0.1676 0.000 0.068 0.000 0.088 0.468 0.376
#> GSM627192 1 0.0000 0.8813 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627203 5 0.6073 0.1147 0.000 0.000 0.160 0.016 0.440 0.384
#> GSM627151 4 0.2384 0.8343 0.000 0.056 0.000 0.896 0.008 0.040
#> GSM627163 1 0.0363 0.8799 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM627211 2 0.1719 0.8402 0.000 0.924 0.000 0.060 0.016 0.000
#> GSM627171 5 0.4222 0.1685 0.000 0.472 0.008 0.004 0.516 0.000
#> GSM627209 4 0.2446 0.8437 0.000 0.124 0.000 0.864 0.012 0.000
#> GSM627135 1 0.0000 0.8813 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627170 2 0.1995 0.8128 0.000 0.912 0.000 0.052 0.036 0.000
#> GSM627178 1 0.3381 0.7240 0.772 0.000 0.212 0.000 0.008 0.008
#> GSM627199 4 0.2312 0.8533 0.000 0.112 0.000 0.876 0.012 0.000
#> GSM627213 4 0.0891 0.8343 0.000 0.008 0.000 0.968 0.000 0.024
#> GSM627140 6 0.6639 0.2355 0.024 0.032 0.000 0.124 0.404 0.416
#> GSM627149 1 0.0000 0.8813 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627147 5 0.7002 0.1192 0.000 0.180 0.000 0.228 0.472 0.120
#> GSM627195 5 0.6152 0.1382 0.000 0.000 0.180 0.016 0.448 0.356
#> GSM627204 2 0.2006 0.8413 0.000 0.904 0.000 0.080 0.016 0.000
#> GSM627207 2 0.2562 0.5690 0.000 0.828 0.000 0.000 0.172 0.000
#> GSM627157 1 0.3797 0.3982 0.580 0.000 0.420 0.000 0.000 0.000
#> GSM627201 2 0.3023 0.7480 0.000 0.784 0.000 0.212 0.004 0.000
#> GSM627146 2 0.3136 0.7353 0.000 0.768 0.000 0.228 0.004 0.000
#> GSM627156 5 0.4225 0.1657 0.000 0.480 0.008 0.004 0.508 0.000
#> GSM627188 1 0.0000 0.8813 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627197 2 0.3564 0.6796 0.000 0.724 0.000 0.264 0.012 0.000
#> GSM627173 2 0.1584 0.8440 0.000 0.928 0.000 0.064 0.008 0.000
#> GSM627179 2 0.1196 0.8329 0.000 0.952 0.000 0.040 0.008 0.000
#> GSM627208 5 0.6096 -0.1993 0.000 0.016 0.416 0.016 0.448 0.104
#> GSM627215 2 0.5039 0.3851 0.000 0.604 0.028 0.032 0.332 0.004
#> GSM627153 4 0.2357 0.8507 0.000 0.116 0.000 0.872 0.012 0.000
#> GSM627155 1 0.0000 0.8813 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627165 6 0.7473 0.1896 0.000 0.224 0.000 0.316 0.140 0.320
#> GSM627168 3 0.1863 0.7257 0.000 0.000 0.920 0.000 0.044 0.036
#> GSM627183 3 0.0363 0.7368 0.012 0.000 0.988 0.000 0.000 0.000
#> GSM627144 5 0.5965 0.1129 0.000 0.000 0.224 0.000 0.408 0.368
#> GSM627158 1 0.0000 0.8813 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627196 2 0.2006 0.8413 0.000 0.904 0.000 0.080 0.016 0.000
#> GSM627142 6 0.2265 0.6302 0.024 0.000 0.000 0.076 0.004 0.896
#> GSM627182 3 0.5372 0.2500 0.000 0.000 0.484 0.000 0.404 0.112
#> GSM627202 1 0.1049 0.8698 0.960 0.000 0.032 0.000 0.000 0.008
#> GSM627141 3 0.4224 0.6394 0.192 0.004 0.744 0.004 0.052 0.004
#> GSM627143 5 0.5372 0.2304 0.000 0.388 0.004 0.012 0.528 0.068
#> GSM627145 3 0.5634 0.2644 0.000 0.000 0.492 0.000 0.348 0.160
#> GSM627152 6 0.2504 0.5544 0.004 0.000 0.012 0.004 0.104 0.876
#> GSM627200 1 0.2994 0.7376 0.788 0.000 0.208 0.000 0.000 0.004
#> GSM627159 6 0.4224 0.5561 0.012 0.000 0.000 0.312 0.016 0.660
#> GSM627164 5 0.4222 0.1685 0.000 0.472 0.008 0.004 0.516 0.000
#> GSM627138 1 0.1957 0.8209 0.888 0.000 0.112 0.000 0.000 0.000
#> GSM627175 4 0.2170 0.8570 0.000 0.100 0.000 0.888 0.012 0.000
#> GSM627150 5 0.6212 0.1478 0.000 0.000 0.200 0.016 0.452 0.332
#> GSM627166 1 0.4004 0.5031 0.620 0.000 0.368 0.000 0.012 0.000
#> GSM627186 5 0.4225 0.1657 0.000 0.480 0.008 0.004 0.508 0.000
#> GSM627139 6 0.2794 0.6463 0.004 0.000 0.000 0.144 0.012 0.840
#> GSM627181 2 0.3141 0.7656 0.000 0.788 0.000 0.200 0.012 0.000
#> GSM627205 2 0.3455 0.6823 0.000 0.784 0.000 0.036 0.180 0.000
#> GSM627214 4 0.4269 0.2132 0.000 0.412 0.000 0.568 0.020 0.000
#> GSM627180 5 0.6131 0.1374 0.000 0.000 0.176 0.016 0.452 0.356
#> GSM627172 5 0.4177 0.1560 0.000 0.468 0.000 0.012 0.520 0.000
#> GSM627184 1 0.0000 0.8813 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627193 2 0.1141 0.8445 0.000 0.948 0.000 0.052 0.000 0.000
#> GSM627191 1 0.5801 0.2686 0.572 0.000 0.000 0.120 0.032 0.276
#> GSM627176 6 0.2451 0.5540 0.004 0.000 0.008 0.004 0.108 0.876
#> GSM627194 2 0.1471 0.8460 0.000 0.932 0.000 0.064 0.004 0.000
#> GSM627154 4 0.1398 0.8614 0.000 0.052 0.000 0.940 0.008 0.000
#> GSM627187 3 0.2765 0.6899 0.000 0.004 0.840 0.004 0.148 0.004
#> GSM627198 4 0.2357 0.8507 0.000 0.116 0.000 0.872 0.012 0.000
#> GSM627160 6 0.4901 0.5592 0.060 0.000 0.000 0.284 0.016 0.640
#> GSM627185 1 0.3592 0.5356 0.656 0.000 0.344 0.000 0.000 0.000
#> GSM627206 3 0.1950 0.7251 0.000 0.000 0.912 0.000 0.064 0.024
#> GSM627161 1 0.0000 0.8813 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627162 5 0.5138 0.0332 0.000 0.052 0.316 0.004 0.608 0.020
#> GSM627210 3 0.1812 0.7236 0.080 0.000 0.912 0.000 0.008 0.000
#> GSM627189 2 0.1204 0.8453 0.000 0.944 0.000 0.056 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two
samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures
which are significantly different between classes which can be candidate marks
for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known
annotation is numeric, one-way ANOVA test is applied, and if the known
annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) age(p) other(p) k
#> SD:skmeans 145 0.466 0.216 0.0106 2
#> SD:skmeans 139 0.284 0.437 0.0272 3
#> SD:skmeans 142 0.245 0.340 0.0531 4
#> SD:skmeans 114 0.284 0.371 0.0221 5
#> SD:skmeans 101 0.379 0.474 0.1848 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
SD:pam**
The object with results only for a single top-value method and a single partition method
can be extracted as:
res = res_list["SD", "pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51882 rows and 146 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
- The first row: a plot of the ECDF (empirical cumulative distribution
function) curves of the consensus matrix for each
k and the heatmap of
predicted classes for each k.
- The second row: heatmaps of the consensus matrix for each
k.
- The third row: heatmaps of the membership matrix for each
k.
- The fouth row: heatmaps of the signatures for each
k.
All the plots in panels can be made by individual functions and they are
plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
- ECDF curves of the consensus matrix for each
k;
- 1-PAC. The PAC
score
measures the proportion of the ambiguous subgrouping.
- Mean silhouette score.
- Concordance. The mean probability of fiting the consensus class ids in all
partitions.
- Area increased. Denote \(A_k\) as the area under the ECDF curve for current
k, the area increased is defined as \(A_k - A_{k-1}\).
- Rand index. The percent of pairs of samples that are both in a same cluster
or both are not in a same cluster in the partition of k and k-1.
- Jaccard index. The ratio of pairs of samples are both in a same cluster in
the partition of k and k-1 and the pairs of samples are both in a same
cluster in the partition k or k-1.
The detailed explanations of these statistics can be found in the cola
vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.957 0.956 0.981 0.5024 0.497 0.497
#> 3 3 0.577 0.578 0.775 0.2207 0.815 0.644
#> 4 4 0.631 0.663 0.837 0.1405 0.885 0.699
#> 5 5 0.655 0.651 0.810 0.0979 0.880 0.618
#> 6 6 0.726 0.675 0.812 0.0452 0.918 0.662
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
- All \(k\) with Jaccard index larger than 0.95 are removed because increasing
\(k\) does not provide enough extra information. If all \(k\) are removed, it is
marked as no subgroup is detected.
- For all \(k\) with 1-PAC score larger than 0.9, the maximal \(k\) is taken as
the best \(k\), and other \(k\) are marked as optional \(k\).
- If it does not fit the second rule. The \(k\) with the maximal vote of the
highest 1-PAC score, highest mean silhouette, and highest concordance is
taken as the best \(k\).
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
show/hide code output
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM627128 1 0.0000 0.975 1.000 0.000
#> GSM627110 1 0.0000 0.975 1.000 0.000
#> GSM627132 1 0.0000 0.975 1.000 0.000
#> GSM627107 1 0.0000 0.975 1.000 0.000
#> GSM627103 2 0.0000 0.985 0.000 1.000
#> GSM627114 1 0.0000 0.975 1.000 0.000
#> GSM627134 2 0.0000 0.985 0.000 1.000
#> GSM627137 2 0.0000 0.985 0.000 1.000
#> GSM627148 1 0.0672 0.968 0.992 0.008
#> GSM627101 1 0.8555 0.624 0.720 0.280
#> GSM627130 1 0.0000 0.975 1.000 0.000
#> GSM627071 1 0.2603 0.936 0.956 0.044
#> GSM627118 2 0.0000 0.985 0.000 1.000
#> GSM627094 2 0.0000 0.985 0.000 1.000
#> GSM627122 1 0.0000 0.975 1.000 0.000
#> GSM627115 2 0.0000 0.985 0.000 1.000
#> GSM627125 1 0.0000 0.975 1.000 0.000
#> GSM627174 2 0.0000 0.985 0.000 1.000
#> GSM627102 2 0.0000 0.985 0.000 1.000
#> GSM627073 1 0.0000 0.975 1.000 0.000
#> GSM627108 2 0.0000 0.985 0.000 1.000
#> GSM627126 1 0.0000 0.975 1.000 0.000
#> GSM627078 2 0.0000 0.985 0.000 1.000
#> GSM627090 1 0.0000 0.975 1.000 0.000
#> GSM627099 2 0.0000 0.985 0.000 1.000
#> GSM627105 1 0.0000 0.975 1.000 0.000
#> GSM627117 2 0.0000 0.985 0.000 1.000
#> GSM627121 2 0.6801 0.781 0.180 0.820
#> GSM627127 2 0.0000 0.985 0.000 1.000
#> GSM627087 2 0.0000 0.985 0.000 1.000
#> GSM627089 1 0.0000 0.975 1.000 0.000
#> GSM627092 2 0.0000 0.985 0.000 1.000
#> GSM627076 1 0.0000 0.975 1.000 0.000
#> GSM627136 1 0.0000 0.975 1.000 0.000
#> GSM627081 2 0.2236 0.953 0.036 0.964
#> GSM627091 2 0.0000 0.985 0.000 1.000
#> GSM627097 2 0.0000 0.985 0.000 1.000
#> GSM627072 1 0.0000 0.975 1.000 0.000
#> GSM627080 1 0.0000 0.975 1.000 0.000
#> GSM627088 1 0.9970 0.136 0.532 0.468
#> GSM627109 1 0.0000 0.975 1.000 0.000
#> GSM627111 1 0.0000 0.975 1.000 0.000
#> GSM627113 1 0.0000 0.975 1.000 0.000
#> GSM627133 2 0.0000 0.985 0.000 1.000
#> GSM627177 1 0.8267 0.659 0.740 0.260
#> GSM627086 2 0.0000 0.985 0.000 1.000
#> GSM627095 1 0.0000 0.975 1.000 0.000
#> GSM627079 1 0.0000 0.975 1.000 0.000
#> GSM627082 1 0.0000 0.975 1.000 0.000
#> GSM627074 1 0.6247 0.810 0.844 0.156
#> GSM627077 1 0.0000 0.975 1.000 0.000
#> GSM627093 2 0.5519 0.850 0.128 0.872
#> GSM627120 2 0.0000 0.985 0.000 1.000
#> GSM627124 2 0.0000 0.985 0.000 1.000
#> GSM627075 2 0.0000 0.985 0.000 1.000
#> GSM627085 2 0.0000 0.985 0.000 1.000
#> GSM627119 1 0.0000 0.975 1.000 0.000
#> GSM627116 2 0.2603 0.945 0.044 0.956
#> GSM627084 1 0.0000 0.975 1.000 0.000
#> GSM627096 2 0.0000 0.985 0.000 1.000
#> GSM627100 1 0.0000 0.975 1.000 0.000
#> GSM627112 1 0.8861 0.578 0.696 0.304
#> GSM627083 1 0.0000 0.975 1.000 0.000
#> GSM627098 1 0.0000 0.975 1.000 0.000
#> GSM627104 2 0.0000 0.985 0.000 1.000
#> GSM627131 1 0.0000 0.975 1.000 0.000
#> GSM627106 1 0.0000 0.975 1.000 0.000
#> GSM627123 1 0.0000 0.975 1.000 0.000
#> GSM627129 2 0.0000 0.985 0.000 1.000
#> GSM627216 2 0.0000 0.985 0.000 1.000
#> GSM627212 2 0.0000 0.985 0.000 1.000
#> GSM627190 2 0.0000 0.985 0.000 1.000
#> GSM627169 2 0.0000 0.985 0.000 1.000
#> GSM627167 2 0.0000 0.985 0.000 1.000
#> GSM627192 1 0.0000 0.975 1.000 0.000
#> GSM627203 1 0.0000 0.975 1.000 0.000
#> GSM627151 2 0.0000 0.985 0.000 1.000
#> GSM627163 1 0.0000 0.975 1.000 0.000
#> GSM627211 2 0.0000 0.985 0.000 1.000
#> GSM627171 2 0.0000 0.985 0.000 1.000
#> GSM627209 2 0.0000 0.985 0.000 1.000
#> GSM627135 1 0.0000 0.975 1.000 0.000
#> GSM627170 2 0.0000 0.985 0.000 1.000
#> GSM627178 1 0.0000 0.975 1.000 0.000
#> GSM627199 2 0.0000 0.985 0.000 1.000
#> GSM627213 2 0.0000 0.985 0.000 1.000
#> GSM627140 2 0.8144 0.660 0.252 0.748
#> GSM627149 1 0.0000 0.975 1.000 0.000
#> GSM627147 2 0.0000 0.985 0.000 1.000
#> GSM627195 1 0.0000 0.975 1.000 0.000
#> GSM627204 2 0.0000 0.985 0.000 1.000
#> GSM627207 2 0.0000 0.985 0.000 1.000
#> GSM627157 1 0.0000 0.975 1.000 0.000
#> GSM627201 2 0.0000 0.985 0.000 1.000
#> GSM627146 2 0.0000 0.985 0.000 1.000
#> GSM627156 2 0.0000 0.985 0.000 1.000
#> GSM627188 1 0.0000 0.975 1.000 0.000
#> GSM627197 2 0.0000 0.985 0.000 1.000
#> GSM627173 2 0.0000 0.985 0.000 1.000
#> GSM627179 2 0.0000 0.985 0.000 1.000
#> GSM627208 2 0.0000 0.985 0.000 1.000
#> GSM627215 2 0.0000 0.985 0.000 1.000
#> GSM627153 2 0.0000 0.985 0.000 1.000
#> GSM627155 1 0.0000 0.975 1.000 0.000
#> GSM627165 2 0.0000 0.985 0.000 1.000
#> GSM627168 1 0.0000 0.975 1.000 0.000
#> GSM627183 1 0.0000 0.975 1.000 0.000
#> GSM627144 2 0.8327 0.645 0.264 0.736
#> GSM627158 1 0.0000 0.975 1.000 0.000
#> GSM627196 2 0.0000 0.985 0.000 1.000
#> GSM627142 1 0.0000 0.975 1.000 0.000
#> GSM627182 2 0.0000 0.985 0.000 1.000
#> GSM627202 1 0.0000 0.975 1.000 0.000
#> GSM627141 1 0.0000 0.975 1.000 0.000
#> GSM627143 2 0.0000 0.985 0.000 1.000
#> GSM627145 1 0.0000 0.975 1.000 0.000
#> GSM627152 1 0.0000 0.975 1.000 0.000
#> GSM627200 1 0.0000 0.975 1.000 0.000
#> GSM627159 1 0.0000 0.975 1.000 0.000
#> GSM627164 2 0.0000 0.985 0.000 1.000
#> GSM627138 1 0.0000 0.975 1.000 0.000
#> GSM627175 2 0.0000 0.985 0.000 1.000
#> GSM627150 1 0.0000 0.975 1.000 0.000
#> GSM627166 2 0.0376 0.982 0.004 0.996
#> GSM627186 2 0.0000 0.985 0.000 1.000
#> GSM627139 1 0.0000 0.975 1.000 0.000
#> GSM627181 2 0.0000 0.985 0.000 1.000
#> GSM627205 2 0.0000 0.985 0.000 1.000
#> GSM627214 2 0.0000 0.985 0.000 1.000
#> GSM627180 2 0.0672 0.978 0.008 0.992
#> GSM627172 2 0.0000 0.985 0.000 1.000
#> GSM627184 1 0.0000 0.975 1.000 0.000
#> GSM627193 2 0.0000 0.985 0.000 1.000
#> GSM627191 1 0.0000 0.975 1.000 0.000
#> GSM627176 1 0.0000 0.975 1.000 0.000
#> GSM627194 2 0.0000 0.985 0.000 1.000
#> GSM627154 2 0.0000 0.985 0.000 1.000
#> GSM627187 2 0.0000 0.985 0.000 1.000
#> GSM627198 2 0.0000 0.985 0.000 1.000
#> GSM627160 1 0.0000 0.975 1.000 0.000
#> GSM627185 1 0.0000 0.975 1.000 0.000
#> GSM627206 1 0.0000 0.975 1.000 0.000
#> GSM627161 1 0.0000 0.975 1.000 0.000
#> GSM627162 1 0.6148 0.818 0.848 0.152
#> GSM627210 2 0.6148 0.820 0.152 0.848
#> GSM627189 2 0.0000 0.985 0.000 1.000
show/hide code output
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM627128 3 0.5621 0.3875 0.000 0.308 0.692
#> GSM627110 1 0.6111 0.3764 0.604 0.000 0.396
#> GSM627132 3 0.6267 0.2135 0.452 0.000 0.548
#> GSM627107 3 0.7147 0.4101 0.228 0.076 0.696
#> GSM627103 2 0.1643 0.9063 0.044 0.956 0.000
#> GSM627114 1 0.5760 0.4261 0.672 0.000 0.328
#> GSM627134 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627137 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627148 1 0.6057 0.4258 0.656 0.004 0.340
#> GSM627101 3 0.5882 0.3405 0.000 0.348 0.652
#> GSM627130 3 0.5591 0.3895 0.000 0.304 0.696
#> GSM627071 1 0.6771 0.3034 0.548 0.012 0.440
#> GSM627118 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627094 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627122 3 0.5650 0.3950 0.312 0.000 0.688
#> GSM627115 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627125 3 0.7451 0.4169 0.144 0.156 0.700
#> GSM627174 2 0.2356 0.8735 0.072 0.928 0.000
#> GSM627102 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627073 3 0.5815 0.3903 0.304 0.004 0.692
#> GSM627108 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627126 3 0.5988 0.2701 0.368 0.000 0.632
#> GSM627078 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627090 3 0.5560 0.3976 0.300 0.000 0.700
#> GSM627099 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627105 3 0.7298 0.4141 0.100 0.200 0.700
#> GSM627117 1 0.6204 0.3064 0.576 0.424 0.000
#> GSM627121 1 0.8022 0.3798 0.544 0.388 0.068
#> GSM627127 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627087 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627089 1 0.5926 0.4038 0.644 0.000 0.356
#> GSM627092 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627076 3 0.5560 0.3976 0.300 0.000 0.700
#> GSM627136 3 0.6209 0.2893 0.368 0.004 0.628
#> GSM627081 2 0.6879 0.0807 0.428 0.556 0.016
#> GSM627091 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627097 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627072 1 0.6267 0.2971 0.548 0.000 0.452
#> GSM627080 3 0.6267 0.2135 0.452 0.000 0.548
#> GSM627088 1 0.8094 0.4423 0.636 0.124 0.240
#> GSM627109 1 0.4002 0.2266 0.840 0.000 0.160
#> GSM627111 1 0.5733 0.0890 0.676 0.000 0.324
#> GSM627113 1 0.5760 0.4261 0.672 0.000 0.328
#> GSM627133 2 0.1643 0.9063 0.044 0.956 0.000
#> GSM627177 1 0.7940 0.3164 0.524 0.060 0.416
#> GSM627086 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627095 3 0.1529 0.4215 0.040 0.000 0.960
#> GSM627079 3 0.5560 0.3976 0.300 0.000 0.700
#> GSM627082 3 0.0592 0.4390 0.000 0.012 0.988
#> GSM627074 1 0.7531 0.4521 0.672 0.092 0.236
#> GSM627077 3 0.5560 0.4008 0.300 0.000 0.700
#> GSM627093 1 0.5760 0.4212 0.672 0.328 0.000
#> GSM627120 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627124 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627075 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627085 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627119 1 0.5760 0.4261 0.672 0.000 0.328
#> GSM627116 2 0.2537 0.8639 0.000 0.920 0.080
#> GSM627084 3 0.9684 -0.0997 0.340 0.224 0.436
#> GSM627096 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627100 3 0.5560 0.3976 0.300 0.000 0.700
#> GSM627112 3 0.5988 0.3212 0.000 0.368 0.632
#> GSM627083 3 0.5650 0.3850 0.000 0.312 0.688
#> GSM627098 1 0.5968 0.3956 0.636 0.000 0.364
#> GSM627104 2 0.6215 0.2213 0.428 0.572 0.000
#> GSM627131 3 0.5650 0.3950 0.312 0.000 0.688
#> GSM627106 3 0.5733 0.3649 0.324 0.000 0.676
#> GSM627123 3 0.0592 0.4355 0.012 0.000 0.988
#> GSM627129 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627216 2 0.4452 0.6833 0.192 0.808 0.000
#> GSM627212 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627190 1 0.5859 0.4153 0.656 0.344 0.000
#> GSM627169 2 0.1643 0.9063 0.044 0.956 0.000
#> GSM627167 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627192 3 0.5835 0.2877 0.340 0.000 0.660
#> GSM627203 3 0.5733 0.3649 0.324 0.000 0.676
#> GSM627151 2 0.1643 0.9020 0.000 0.956 0.044
#> GSM627163 1 0.5859 0.0606 0.656 0.000 0.344
#> GSM627211 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627171 2 0.6299 -0.0237 0.476 0.524 0.000
#> GSM627209 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627135 3 0.2448 0.4325 0.076 0.000 0.924
#> GSM627170 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627178 3 0.5650 0.3950 0.312 0.000 0.688
#> GSM627199 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627213 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627140 2 0.5254 0.5742 0.000 0.736 0.264
#> GSM627149 3 0.6204 0.2338 0.424 0.000 0.576
#> GSM627147 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627195 3 0.5760 0.3574 0.328 0.000 0.672
#> GSM627204 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627207 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627157 1 0.4842 0.1640 0.776 0.000 0.224
#> GSM627201 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627146 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627156 2 0.1643 0.9063 0.044 0.956 0.000
#> GSM627188 3 0.5835 0.2877 0.340 0.000 0.660
#> GSM627197 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627173 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627179 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627208 1 0.6779 0.2555 0.544 0.444 0.012
#> GSM627215 2 0.1163 0.9198 0.028 0.972 0.000
#> GSM627153 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627155 3 0.6267 0.2135 0.452 0.000 0.548
#> GSM627165 2 0.0592 0.9300 0.000 0.988 0.012
#> GSM627168 1 0.6045 0.3785 0.620 0.000 0.380
#> GSM627183 1 0.6079 0.3759 0.612 0.000 0.388
#> GSM627144 1 0.7660 0.3503 0.548 0.404 0.048
#> GSM627158 3 0.6267 0.2135 0.452 0.000 0.548
#> GSM627196 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627142 3 0.5560 0.3976 0.300 0.000 0.700
#> GSM627182 1 0.6779 0.2555 0.544 0.444 0.012
#> GSM627202 3 0.3340 0.3821 0.120 0.000 0.880
#> GSM627141 1 0.6421 0.3296 0.572 0.004 0.424
#> GSM627143 2 0.1643 0.9063 0.044 0.956 0.000
#> GSM627145 1 0.6295 0.2484 0.528 0.000 0.472
#> GSM627152 3 0.5560 0.3976 0.300 0.000 0.700
#> GSM627200 3 0.5650 0.3950 0.312 0.000 0.688
#> GSM627159 3 0.5560 0.3912 0.000 0.300 0.700
#> GSM627164 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627138 1 0.5733 0.0890 0.676 0.000 0.324
#> GSM627175 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627150 3 0.5882 0.3139 0.348 0.000 0.652
#> GSM627166 2 0.5760 0.4616 0.328 0.672 0.000
#> GSM627186 2 0.5560 0.5158 0.300 0.700 0.000
#> GSM627139 3 0.6857 0.4041 0.052 0.252 0.696
#> GSM627181 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627205 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627214 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627180 2 0.6647 0.2302 0.396 0.592 0.012
#> GSM627172 2 0.1643 0.9020 0.000 0.956 0.044
#> GSM627184 3 0.5835 0.2877 0.340 0.000 0.660
#> GSM627193 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627191 3 0.5650 0.3850 0.000 0.312 0.688
#> GSM627176 1 0.6280 0.2831 0.540 0.000 0.460
#> GSM627194 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627154 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627187 1 0.5760 0.4212 0.672 0.328 0.000
#> GSM627198 2 0.0000 0.9391 0.000 1.000 0.000
#> GSM627160 3 0.5650 0.3850 0.000 0.312 0.688
#> GSM627185 1 0.5098 0.1488 0.752 0.000 0.248
#> GSM627206 1 0.5760 0.4261 0.672 0.000 0.328
#> GSM627161 3 0.6235 0.2257 0.436 0.000 0.564
#> GSM627162 1 0.9146 0.2852 0.472 0.148 0.380
#> GSM627210 1 0.7418 0.4386 0.672 0.248 0.080
#> GSM627189 2 0.0000 0.9391 0.000 1.000 0.000
show/hide code output
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM627128 4 0.0000 0.6254 0.000 0.000 0.000 1.000
#> GSM627110 3 0.0336 0.6806 0.000 0.000 0.992 0.008
#> GSM627132 1 0.1022 0.8888 0.968 0.000 0.032 0.000
#> GSM627107 4 0.2814 0.5554 0.000 0.000 0.132 0.868
#> GSM627103 2 0.2011 0.9024 0.000 0.920 0.000 0.080
#> GSM627114 3 0.1716 0.6852 0.064 0.000 0.936 0.000
#> GSM627134 2 0.2081 0.9018 0.000 0.916 0.000 0.084
#> GSM627137 2 0.0000 0.9221 0.000 1.000 0.000 0.000
#> GSM627148 3 0.0000 0.6793 0.000 0.000 1.000 0.000
#> GSM627101 4 0.5581 0.4697 0.032 0.144 0.064 0.760
#> GSM627130 4 0.0000 0.6254 0.000 0.000 0.000 1.000
#> GSM627071 3 0.3975 0.5389 0.000 0.000 0.760 0.240
#> GSM627118 2 0.4459 0.8182 0.032 0.780 0.000 0.188
#> GSM627094 2 0.0000 0.9221 0.000 1.000 0.000 0.000
#> GSM627122 4 0.4888 0.2862 0.000 0.000 0.412 0.588
#> GSM627115 2 0.0000 0.9221 0.000 1.000 0.000 0.000
#> GSM627125 4 0.1716 0.6026 0.000 0.000 0.064 0.936
#> GSM627174 2 0.2530 0.8304 0.000 0.888 0.112 0.000
#> GSM627102 2 0.2797 0.9045 0.032 0.900 0.000 0.068
#> GSM627073 4 0.4994 0.1493 0.000 0.000 0.480 0.520
#> GSM627108 2 0.0000 0.9221 0.000 1.000 0.000 0.000
#> GSM627126 1 0.3542 0.8141 0.852 0.000 0.028 0.120
#> GSM627078 2 0.3082 0.8977 0.032 0.884 0.000 0.084
#> GSM627090 4 0.3942 0.5120 0.000 0.000 0.236 0.764
#> GSM627099 2 0.0188 0.9217 0.000 0.996 0.000 0.004
#> GSM627105 4 0.1716 0.6026 0.000 0.000 0.064 0.936
#> GSM627117 3 0.1978 0.6696 0.004 0.068 0.928 0.000
#> GSM627121 3 0.6477 0.1434 0.000 0.072 0.508 0.420
#> GSM627127 2 0.0188 0.9217 0.000 0.996 0.000 0.004
#> GSM627087 2 0.0000 0.9221 0.000 1.000 0.000 0.000
#> GSM627089 3 0.2623 0.6828 0.064 0.000 0.908 0.028
#> GSM627092 2 0.0000 0.9221 0.000 1.000 0.000 0.000
#> GSM627076 4 0.2408 0.5943 0.000 0.000 0.104 0.896
#> GSM627136 4 0.4989 0.1733 0.000 0.000 0.472 0.528
#> GSM627081 3 0.7352 0.1573 0.000 0.176 0.496 0.328
#> GSM627091 2 0.0000 0.9221 0.000 1.000 0.000 0.000
#> GSM627097 2 0.2011 0.9024 0.000 0.920 0.000 0.080
#> GSM627072 3 0.1022 0.6709 0.000 0.000 0.968 0.032
#> GSM627080 1 0.1022 0.8888 0.968 0.000 0.032 0.000
#> GSM627088 3 0.5111 0.6084 0.052 0.020 0.780 0.148
#> GSM627109 3 0.7082 0.2445 0.368 0.000 0.500 0.132
#> GSM627111 1 0.1022 0.8888 0.968 0.000 0.032 0.000
#> GSM627113 3 0.5292 0.5382 0.064 0.000 0.728 0.208
#> GSM627133 2 0.4352 0.8336 0.000 0.816 0.104 0.080
#> GSM627177 3 0.4745 0.5626 0.000 0.036 0.756 0.208
#> GSM627086 2 0.1022 0.9167 0.032 0.968 0.000 0.000
#> GSM627095 4 0.5724 0.1969 0.424 0.000 0.028 0.548
#> GSM627079 4 0.4830 0.3206 0.000 0.000 0.392 0.608
#> GSM627082 4 0.0000 0.6254 0.000 0.000 0.000 1.000
#> GSM627074 3 0.1716 0.6852 0.064 0.000 0.936 0.000
#> GSM627077 4 0.4907 0.2750 0.000 0.000 0.420 0.580
#> GSM627093 3 0.1716 0.6852 0.064 0.000 0.936 0.000
#> GSM627120 2 0.2011 0.9024 0.000 0.920 0.000 0.080
#> GSM627124 2 0.3013 0.8990 0.032 0.888 0.000 0.080
#> GSM627075 2 0.0000 0.9221 0.000 1.000 0.000 0.000
#> GSM627085 2 0.1209 0.9161 0.032 0.964 0.000 0.004
#> GSM627119 3 0.1716 0.6852 0.064 0.000 0.936 0.000
#> GSM627116 2 0.3610 0.7980 0.000 0.800 0.000 0.200
#> GSM627084 4 0.5408 0.0960 0.000 0.012 0.488 0.500
#> GSM627096 2 0.4459 0.8182 0.032 0.780 0.000 0.188
#> GSM627100 4 0.0592 0.6231 0.000 0.000 0.016 0.984
#> GSM627112 4 0.5784 0.0511 0.032 0.412 0.000 0.556
#> GSM627083 4 0.6570 0.3678 0.008 0.340 0.072 0.580
#> GSM627098 3 0.6376 0.0107 0.064 0.000 0.504 0.432
#> GSM627104 3 0.5951 0.4021 0.064 0.300 0.636 0.000
#> GSM627131 4 0.4916 0.2684 0.000 0.000 0.424 0.576
#> GSM627106 4 0.4972 0.0401 0.000 0.000 0.456 0.544
#> GSM627123 4 0.4916 0.2368 0.424 0.000 0.000 0.576
#> GSM627129 2 0.2011 0.9024 0.000 0.920 0.000 0.080
#> GSM627216 2 0.6065 0.5691 0.000 0.644 0.276 0.080
#> GSM627212 2 0.0000 0.9221 0.000 1.000 0.000 0.000
#> GSM627190 3 0.2179 0.6860 0.064 0.012 0.924 0.000
#> GSM627169 2 0.3873 0.7300 0.000 0.772 0.228 0.000
#> GSM627167 2 0.3082 0.8977 0.032 0.884 0.000 0.084
#> GSM627192 1 0.2281 0.8374 0.904 0.000 0.000 0.096
#> GSM627203 4 0.4977 0.0331 0.000 0.000 0.460 0.540
#> GSM627151 2 0.3444 0.8114 0.000 0.816 0.000 0.184
#> GSM627163 1 0.1022 0.8888 0.968 0.000 0.032 0.000
#> GSM627211 2 0.1022 0.9167 0.032 0.968 0.000 0.000
#> GSM627171 3 0.4454 0.4672 0.000 0.308 0.692 0.000
#> GSM627209 2 0.1610 0.9167 0.032 0.952 0.000 0.016
#> GSM627135 4 0.6552 0.3675 0.328 0.000 0.096 0.576
#> GSM627170 2 0.0000 0.9221 0.000 1.000 0.000 0.000
#> GSM627178 4 0.4916 0.2684 0.000 0.000 0.424 0.576
#> GSM627199 2 0.3013 0.8990 0.032 0.888 0.000 0.080
#> GSM627213 2 0.4459 0.8182 0.032 0.780 0.000 0.188
#> GSM627140 2 0.4746 0.5438 0.000 0.632 0.000 0.368
#> GSM627149 1 0.1109 0.8753 0.968 0.000 0.004 0.028
#> GSM627147 2 0.0000 0.9221 0.000 1.000 0.000 0.000
#> GSM627195 3 0.4454 0.3766 0.000 0.000 0.692 0.308
#> GSM627204 2 0.1022 0.9167 0.032 0.968 0.000 0.000
#> GSM627207 2 0.0000 0.9221 0.000 1.000 0.000 0.000
#> GSM627157 1 0.4998 -0.0932 0.512 0.000 0.488 0.000
#> GSM627201 2 0.1022 0.9167 0.032 0.968 0.000 0.000
#> GSM627146 2 0.0000 0.9221 0.000 1.000 0.000 0.000
#> GSM627156 2 0.3837 0.7363 0.000 0.776 0.224 0.000
#> GSM627188 1 0.2281 0.8374 0.904 0.000 0.000 0.096
#> GSM627197 2 0.0000 0.9221 0.000 1.000 0.000 0.000
#> GSM627173 2 0.0000 0.9221 0.000 1.000 0.000 0.000
#> GSM627179 2 0.0000 0.9221 0.000 1.000 0.000 0.000
#> GSM627208 3 0.1022 0.6740 0.000 0.032 0.968 0.000
#> GSM627215 2 0.2197 0.9017 0.000 0.916 0.004 0.080
#> GSM627153 2 0.3013 0.8999 0.032 0.888 0.000 0.080
#> GSM627155 1 0.1022 0.8888 0.968 0.000 0.032 0.000
#> GSM627165 2 0.3948 0.8532 0.000 0.840 0.064 0.096
#> GSM627168 3 0.6389 -0.0141 0.064 0.000 0.488 0.448
#> GSM627183 3 0.4267 0.5845 0.024 0.000 0.788 0.188
#> GSM627144 3 0.3749 0.5865 0.000 0.032 0.840 0.128
#> GSM627158 1 0.1022 0.8888 0.968 0.000 0.032 0.000
#> GSM627196 2 0.1022 0.9167 0.032 0.968 0.000 0.000
#> GSM627142 4 0.0000 0.6254 0.000 0.000 0.000 1.000
#> GSM627182 3 0.1022 0.6740 0.000 0.032 0.968 0.000
#> GSM627202 4 0.5167 0.1359 0.488 0.000 0.004 0.508
#> GSM627141 3 0.5233 0.2868 0.020 0.000 0.648 0.332
#> GSM627143 2 0.4352 0.7796 0.000 0.816 0.080 0.104
#> GSM627145 3 0.3444 0.5636 0.000 0.000 0.816 0.184
#> GSM627152 4 0.2281 0.5947 0.000 0.000 0.096 0.904
#> GSM627200 4 0.4916 0.2684 0.000 0.000 0.424 0.576
#> GSM627159 4 0.0000 0.6254 0.000 0.000 0.000 1.000
#> GSM627164 2 0.0000 0.9221 0.000 1.000 0.000 0.000
#> GSM627138 1 0.1022 0.8888 0.968 0.000 0.032 0.000
#> GSM627175 2 0.1209 0.9161 0.032 0.964 0.000 0.004
#> GSM627150 3 0.3837 0.5096 0.000 0.000 0.776 0.224
#> GSM627166 3 0.6862 0.2283 0.000 0.408 0.488 0.104
#> GSM627186 3 0.4843 0.3310 0.000 0.396 0.604 0.000
#> GSM627139 4 0.0336 0.6246 0.000 0.008 0.000 0.992
#> GSM627181 2 0.1022 0.9167 0.032 0.968 0.000 0.000
#> GSM627205 2 0.0000 0.9221 0.000 1.000 0.000 0.000
#> GSM627214 2 0.3013 0.8990 0.032 0.888 0.000 0.080
#> GSM627180 3 0.5788 0.3825 0.000 0.228 0.688 0.084
#> GSM627172 2 0.4984 0.8207 0.032 0.784 0.028 0.156
#> GSM627184 1 0.2281 0.8374 0.904 0.000 0.000 0.096
#> GSM627193 2 0.0000 0.9221 0.000 1.000 0.000 0.000
#> GSM627191 4 0.3975 0.4699 0.000 0.240 0.000 0.760
#> GSM627176 3 0.4916 0.1946 0.000 0.000 0.576 0.424
#> GSM627194 2 0.0000 0.9221 0.000 1.000 0.000 0.000
#> GSM627154 2 0.3694 0.8544 0.032 0.844 0.000 0.124
#> GSM627187 3 0.1716 0.6852 0.064 0.000 0.936 0.000
#> GSM627198 2 0.1022 0.9167 0.032 0.968 0.000 0.000
#> GSM627160 4 0.2760 0.5662 0.000 0.128 0.000 0.872
#> GSM627185 1 0.5759 0.5749 0.688 0.000 0.232 0.080
#> GSM627206 3 0.1716 0.6852 0.064 0.000 0.936 0.000
#> GSM627161 1 0.1182 0.8829 0.968 0.000 0.016 0.016
#> GSM627162 3 0.5035 0.5143 0.000 0.052 0.744 0.204
#> GSM627210 3 0.1716 0.6852 0.064 0.000 0.936 0.000
#> GSM627189 2 0.0000 0.9221 0.000 1.000 0.000 0.000
show/hide code output
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM627128 5 0.3039 0.5958 0.000 0.000 0.000 0.192 0.808
#> GSM627110 3 0.0000 0.7046 0.000 0.000 1.000 0.000 0.000
#> GSM627132 1 0.0000 0.9114 1.000 0.000 0.000 0.000 0.000
#> GSM627107 5 0.4704 0.4798 0.000 0.000 0.112 0.152 0.736
#> GSM627103 2 0.0000 0.8963 0.000 1.000 0.000 0.000 0.000
#> GSM627114 3 0.1197 0.7082 0.048 0.000 0.952 0.000 0.000
#> GSM627134 2 0.2516 0.7546 0.000 0.860 0.000 0.140 0.000
#> GSM627137 2 0.0000 0.8963 0.000 1.000 0.000 0.000 0.000
#> GSM627148 3 0.2068 0.6673 0.000 0.000 0.904 0.004 0.092
#> GSM627101 4 0.1626 0.5689 0.000 0.000 0.044 0.940 0.016
#> GSM627130 5 0.3837 0.5406 0.000 0.000 0.000 0.308 0.692
#> GSM627071 3 0.3491 0.5383 0.000 0.000 0.768 0.004 0.228
#> GSM627118 4 0.2813 0.8144 0.000 0.168 0.000 0.832 0.000
#> GSM627094 2 0.0000 0.8963 0.000 1.000 0.000 0.000 0.000
#> GSM627122 5 0.4114 0.3996 0.000 0.000 0.376 0.000 0.624
#> GSM627115 2 0.0162 0.8939 0.000 0.996 0.000 0.004 0.000
#> GSM627125 5 0.4902 0.5164 0.000 0.000 0.048 0.304 0.648
#> GSM627174 2 0.3048 0.6554 0.000 0.820 0.176 0.004 0.000
#> GSM627102 4 0.4138 0.7898 0.000 0.384 0.000 0.616 0.000
#> GSM627073 3 0.4306 -0.1333 0.000 0.000 0.508 0.000 0.492
#> GSM627108 2 0.0000 0.8963 0.000 1.000 0.000 0.000 0.000
#> GSM627126 1 0.2424 0.8040 0.868 0.000 0.000 0.000 0.132
#> GSM627078 4 0.3395 0.8428 0.000 0.236 0.000 0.764 0.000
#> GSM627090 5 0.1205 0.5959 0.000 0.000 0.040 0.004 0.956
#> GSM627099 2 0.2561 0.7502 0.000 0.856 0.000 0.144 0.000
#> GSM627105 5 0.4902 0.5164 0.000 0.000 0.048 0.304 0.648
#> GSM627117 3 0.1197 0.6963 0.000 0.048 0.952 0.000 0.000
#> GSM627121 3 0.6924 0.1545 0.000 0.176 0.432 0.020 0.372
#> GSM627127 2 0.2561 0.7502 0.000 0.856 0.000 0.144 0.000
#> GSM627087 2 0.0162 0.8939 0.000 0.996 0.000 0.004 0.000
#> GSM627089 3 0.2464 0.7005 0.048 0.000 0.904 0.004 0.044
#> GSM627092 2 0.0000 0.8963 0.000 1.000 0.000 0.000 0.000
#> GSM627076 5 0.0162 0.6091 0.000 0.000 0.000 0.004 0.996
#> GSM627136 5 0.4201 0.3472 0.000 0.000 0.408 0.000 0.592
#> GSM627081 3 0.6750 0.1358 0.000 0.216 0.412 0.004 0.368
#> GSM627091 2 0.0162 0.8939 0.000 0.996 0.000 0.004 0.000
#> GSM627097 2 0.0162 0.8939 0.000 0.996 0.000 0.004 0.000
#> GSM627072 3 0.0510 0.7025 0.000 0.000 0.984 0.000 0.016
#> GSM627080 1 0.0000 0.9114 1.000 0.000 0.000 0.000 0.000
#> GSM627088 3 0.4427 0.5868 0.040 0.020 0.768 0.000 0.172
#> GSM627109 3 0.6615 0.1200 0.388 0.000 0.424 0.004 0.184
#> GSM627111 1 0.0000 0.9114 1.000 0.000 0.000 0.000 0.000
#> GSM627113 3 0.4607 0.5108 0.048 0.000 0.720 0.004 0.228
#> GSM627133 2 0.0000 0.8963 0.000 1.000 0.000 0.000 0.000
#> GSM627177 3 0.3812 0.5602 0.000 0.024 0.772 0.000 0.204
#> GSM627086 4 0.3876 0.8515 0.000 0.316 0.000 0.684 0.000
#> GSM627095 5 0.4114 0.3543 0.376 0.000 0.000 0.000 0.624
#> GSM627079 5 0.3999 0.4185 0.000 0.000 0.344 0.000 0.656
#> GSM627082 5 0.2813 0.6031 0.000 0.000 0.000 0.168 0.832
#> GSM627074 3 0.1357 0.7079 0.048 0.000 0.948 0.004 0.000
#> GSM627077 5 0.4088 0.4081 0.000 0.000 0.368 0.000 0.632
#> GSM627093 3 0.1357 0.7079 0.048 0.000 0.948 0.004 0.000
#> GSM627120 2 0.0000 0.8963 0.000 1.000 0.000 0.000 0.000
#> GSM627124 4 0.3876 0.8515 0.000 0.316 0.000 0.684 0.000
#> GSM627075 2 0.0000 0.8963 0.000 1.000 0.000 0.000 0.000
#> GSM627085 4 0.2852 0.8167 0.000 0.172 0.000 0.828 0.000
#> GSM627119 3 0.1357 0.7079 0.048 0.000 0.948 0.004 0.000
#> GSM627116 2 0.3283 0.7271 0.000 0.832 0.000 0.140 0.028
#> GSM627084 5 0.4350 0.3387 0.000 0.004 0.408 0.000 0.588
#> GSM627096 4 0.2813 0.8144 0.000 0.168 0.000 0.832 0.000
#> GSM627100 5 0.0324 0.6084 0.000 0.000 0.004 0.004 0.992
#> GSM627112 4 0.0510 0.6233 0.000 0.016 0.000 0.984 0.000
#> GSM627083 5 0.4114 0.3718 0.000 0.376 0.000 0.000 0.624
#> GSM627098 5 0.5383 0.2753 0.048 0.000 0.408 0.004 0.540
#> GSM627104 3 0.5292 0.3371 0.048 0.368 0.580 0.004 0.000
#> GSM627131 5 0.4251 0.4011 0.000 0.000 0.372 0.004 0.624
#> GSM627106 5 0.4359 0.0541 0.000 0.000 0.412 0.004 0.584
#> GSM627123 5 0.4114 0.3543 0.376 0.000 0.000 0.000 0.624
#> GSM627129 2 0.0000 0.8963 0.000 1.000 0.000 0.000 0.000
#> GSM627216 2 0.3366 0.5975 0.000 0.768 0.232 0.000 0.000
#> GSM627212 2 0.0000 0.8963 0.000 1.000 0.000 0.000 0.000
#> GSM627190 3 0.1197 0.7082 0.048 0.000 0.952 0.000 0.000
#> GSM627169 2 0.2929 0.6832 0.000 0.820 0.180 0.000 0.000
#> GSM627167 4 0.3752 0.8505 0.000 0.292 0.000 0.708 0.000
#> GSM627192 1 0.1197 0.8821 0.952 0.000 0.000 0.000 0.048
#> GSM627203 5 0.4464 0.0612 0.000 0.000 0.408 0.008 0.584
#> GSM627151 2 0.0000 0.8963 0.000 1.000 0.000 0.000 0.000
#> GSM627163 1 0.0000 0.9114 1.000 0.000 0.000 0.000 0.000
#> GSM627211 4 0.4030 0.8295 0.000 0.352 0.000 0.648 0.000
#> GSM627171 3 0.3816 0.4839 0.000 0.304 0.696 0.000 0.000
#> GSM627209 4 0.3796 0.8541 0.000 0.300 0.000 0.700 0.000
#> GSM627135 5 0.5139 0.4254 0.316 0.000 0.060 0.000 0.624
#> GSM627170 2 0.0000 0.8963 0.000 1.000 0.000 0.000 0.000
#> GSM627178 5 0.4251 0.4011 0.000 0.000 0.372 0.004 0.624
#> GSM627199 4 0.3876 0.8515 0.000 0.316 0.000 0.684 0.000
#> GSM627213 2 0.4305 -0.3873 0.000 0.512 0.000 0.488 0.000
#> GSM627140 2 0.5236 0.5084 0.000 0.684 0.000 0.164 0.152
#> GSM627149 1 0.0000 0.9114 1.000 0.000 0.000 0.000 0.000
#> GSM627147 2 0.0000 0.8963 0.000 1.000 0.000 0.000 0.000
#> GSM627195 3 0.4211 0.3230 0.000 0.000 0.636 0.004 0.360
#> GSM627204 4 0.4101 0.8096 0.000 0.372 0.000 0.628 0.000
#> GSM627207 2 0.0000 0.8963 0.000 1.000 0.000 0.000 0.000
#> GSM627157 1 0.4192 0.1483 0.596 0.000 0.404 0.000 0.000
#> GSM627201 4 0.4287 0.6478 0.000 0.460 0.000 0.540 0.000
#> GSM627146 2 0.0000 0.8963 0.000 1.000 0.000 0.000 0.000
#> GSM627156 2 0.2929 0.6832 0.000 0.820 0.180 0.000 0.000
#> GSM627188 1 0.1197 0.8821 0.952 0.000 0.000 0.000 0.048
#> GSM627197 2 0.0000 0.8963 0.000 1.000 0.000 0.000 0.000
#> GSM627173 2 0.0000 0.8963 0.000 1.000 0.000 0.000 0.000
#> GSM627179 2 0.0000 0.8963 0.000 1.000 0.000 0.000 0.000
#> GSM627208 3 0.2011 0.6699 0.000 0.000 0.908 0.004 0.088
#> GSM627215 2 0.0162 0.8930 0.000 0.996 0.004 0.000 0.000
#> GSM627153 4 0.3210 0.8349 0.000 0.212 0.000 0.788 0.000
#> GSM627155 1 0.0000 0.9114 1.000 0.000 0.000 0.000 0.000
#> GSM627165 2 0.3625 0.7295 0.000 0.840 0.048 0.016 0.096
#> GSM627168 5 0.5261 0.2462 0.048 0.000 0.424 0.000 0.528
#> GSM627183 3 0.3812 0.5607 0.024 0.000 0.772 0.000 0.204
#> GSM627144 3 0.2179 0.6621 0.000 0.000 0.896 0.004 0.100
#> GSM627158 1 0.0000 0.9114 1.000 0.000 0.000 0.000 0.000
#> GSM627196 4 0.3895 0.8500 0.000 0.320 0.000 0.680 0.000
#> GSM627142 5 0.1851 0.6143 0.000 0.000 0.000 0.088 0.912
#> GSM627182 3 0.0000 0.7046 0.000 0.000 1.000 0.000 0.000
#> GSM627202 5 0.4235 0.3035 0.424 0.000 0.000 0.000 0.576
#> GSM627141 3 0.4527 0.0973 0.012 0.000 0.596 0.000 0.392
#> GSM627143 2 0.0000 0.8963 0.000 1.000 0.000 0.000 0.000
#> GSM627145 3 0.3039 0.5651 0.000 0.000 0.808 0.000 0.192
#> GSM627152 5 0.0162 0.6091 0.000 0.000 0.000 0.004 0.996
#> GSM627200 5 0.4114 0.3996 0.000 0.000 0.376 0.000 0.624
#> GSM627159 5 0.2813 0.6031 0.000 0.000 0.000 0.168 0.832
#> GSM627164 2 0.0000 0.8963 0.000 1.000 0.000 0.000 0.000
#> GSM627138 1 0.0000 0.9114 1.000 0.000 0.000 0.000 0.000
#> GSM627175 4 0.2891 0.8191 0.000 0.176 0.000 0.824 0.000
#> GSM627150 3 0.3990 0.4597 0.000 0.000 0.688 0.004 0.308
#> GSM627166 2 0.4350 0.1744 0.000 0.588 0.408 0.004 0.000
#> GSM627186 3 0.4219 0.2367 0.000 0.416 0.584 0.000 0.000
#> GSM627139 5 0.1443 0.6098 0.000 0.044 0.004 0.004 0.948
#> GSM627181 4 0.4249 0.7132 0.000 0.432 0.000 0.568 0.000
#> GSM627205 2 0.0000 0.8963 0.000 1.000 0.000 0.000 0.000
#> GSM627214 4 0.4088 0.8142 0.000 0.368 0.000 0.632 0.000
#> GSM627180 3 0.3804 0.6216 0.000 0.056 0.832 0.020 0.092
#> GSM627172 4 0.4114 0.8058 0.000 0.376 0.000 0.624 0.000
#> GSM627184 1 0.1197 0.8821 0.952 0.000 0.000 0.000 0.048
#> GSM627193 2 0.0000 0.8963 0.000 1.000 0.000 0.000 0.000
#> GSM627191 5 0.5290 0.3976 0.000 0.076 0.000 0.300 0.624
#> GSM627176 5 0.4437 -0.0181 0.000 0.000 0.464 0.004 0.532
#> GSM627194 2 0.0000 0.8963 0.000 1.000 0.000 0.000 0.000
#> GSM627154 4 0.2852 0.8167 0.000 0.172 0.000 0.828 0.000
#> GSM627187 3 0.1197 0.7082 0.048 0.000 0.952 0.000 0.000
#> GSM627198 4 0.3857 0.8519 0.000 0.312 0.000 0.688 0.000
#> GSM627160 5 0.4779 0.5337 0.000 0.200 0.000 0.084 0.716
#> GSM627185 1 0.5038 0.6057 0.716 0.000 0.152 0.004 0.128
#> GSM627206 3 0.1197 0.7082 0.048 0.000 0.952 0.000 0.000
#> GSM627161 1 0.0000 0.9114 1.000 0.000 0.000 0.000 0.000
#> GSM627162 3 0.4618 0.4705 0.000 0.068 0.724 0.000 0.208
#> GSM627210 3 0.1357 0.7079 0.048 0.000 0.948 0.004 0.000
#> GSM627189 2 0.0000 0.8963 0.000 1.000 0.000 0.000 0.000
show/hide code output
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM627128 6 0.1995 0.48695 0.000 0.000 0.000 0.036 0.052 0.912
#> GSM627110 3 0.0146 0.74537 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM627132 1 0.0000 0.94606 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627107 5 0.4801 0.53670 0.000 0.000 0.040 0.072 0.716 0.172
#> GSM627103 2 0.0000 0.91761 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627114 3 0.0000 0.74510 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627134 2 0.2003 0.82477 0.000 0.884 0.000 0.116 0.000 0.000
#> GSM627137 2 0.0000 0.91761 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627148 5 0.3747 0.76992 0.000 0.000 0.396 0.000 0.604 0.000
#> GSM627101 6 0.5705 -0.05098 0.000 0.000 0.000 0.380 0.164 0.456
#> GSM627130 6 0.3123 0.44619 0.000 0.000 0.000 0.112 0.056 0.832
#> GSM627071 3 0.0972 0.74901 0.000 0.000 0.964 0.000 0.008 0.028
#> GSM627118 4 0.0632 0.74101 0.000 0.024 0.000 0.976 0.000 0.000
#> GSM627094 2 0.0000 0.91761 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627122 6 0.3851 -0.01510 0.000 0.000 0.460 0.000 0.000 0.540
#> GSM627115 2 0.1814 0.84777 0.000 0.900 0.000 0.100 0.000 0.000
#> GSM627125 6 0.5029 0.20691 0.000 0.000 0.000 0.112 0.276 0.612
#> GSM627174 2 0.3309 0.51567 0.000 0.720 0.280 0.000 0.000 0.000
#> GSM627102 4 0.3288 0.80424 0.000 0.276 0.000 0.724 0.000 0.000
#> GSM627073 5 0.5196 0.40379 0.000 0.000 0.144 0.000 0.604 0.252
#> GSM627108 2 0.0000 0.91761 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627126 1 0.1267 0.87802 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM627078 4 0.2340 0.80595 0.000 0.148 0.000 0.852 0.000 0.000
#> GSM627090 6 0.3843 0.19659 0.000 0.000 0.000 0.000 0.452 0.548
#> GSM627099 2 0.2762 0.75161 0.000 0.804 0.000 0.196 0.000 0.000
#> GSM627105 6 0.5029 0.20691 0.000 0.000 0.000 0.112 0.276 0.612
#> GSM627117 3 0.0000 0.74510 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627121 5 0.3534 0.89233 0.000 0.000 0.276 0.000 0.716 0.008
#> GSM627127 2 0.2762 0.75161 0.000 0.804 0.000 0.196 0.000 0.000
#> GSM627087 2 0.1814 0.84777 0.000 0.900 0.000 0.100 0.000 0.000
#> GSM627089 3 0.1957 0.63155 0.000 0.000 0.888 0.000 0.112 0.000
#> GSM627092 2 0.0000 0.91761 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627076 6 0.3854 0.17705 0.000 0.000 0.000 0.000 0.464 0.536
#> GSM627136 3 0.3857 0.15958 0.000 0.000 0.532 0.000 0.000 0.468
#> GSM627081 5 0.3351 0.90193 0.000 0.000 0.288 0.000 0.712 0.000
#> GSM627091 2 0.1814 0.84777 0.000 0.900 0.000 0.100 0.000 0.000
#> GSM627097 2 0.1814 0.84777 0.000 0.900 0.000 0.100 0.000 0.000
#> GSM627072 3 0.1141 0.70883 0.000 0.000 0.948 0.000 0.052 0.000
#> GSM627080 1 0.0000 0.94606 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627088 3 0.0935 0.74707 0.000 0.004 0.964 0.000 0.000 0.032
#> GSM627109 3 0.4695 0.68411 0.032 0.000 0.692 0.000 0.232 0.044
#> GSM627111 1 0.0000 0.94606 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627113 3 0.3619 0.71887 0.000 0.000 0.744 0.000 0.232 0.024
#> GSM627133 2 0.0000 0.91761 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627177 3 0.0777 0.74647 0.000 0.004 0.972 0.000 0.000 0.024
#> GSM627086 4 0.2941 0.82096 0.000 0.220 0.000 0.780 0.000 0.000
#> GSM627095 6 0.3851 0.18753 0.460 0.000 0.000 0.000 0.000 0.540
#> GSM627079 6 0.5197 0.25449 0.000 0.000 0.320 0.000 0.112 0.568
#> GSM627082 6 0.1657 0.49224 0.000 0.000 0.000 0.016 0.056 0.928
#> GSM627074 3 0.3023 0.72235 0.000 0.000 0.768 0.000 0.232 0.000
#> GSM627077 6 0.3843 -0.00054 0.000 0.000 0.452 0.000 0.000 0.548
#> GSM627093 3 0.3023 0.72235 0.000 0.000 0.768 0.000 0.232 0.000
#> GSM627120 2 0.0000 0.91761 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627124 4 0.2883 0.81972 0.000 0.212 0.000 0.788 0.000 0.000
#> GSM627075 2 0.0000 0.91761 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627085 4 0.0458 0.73502 0.000 0.016 0.000 0.984 0.000 0.000
#> GSM627119 3 0.3023 0.72235 0.000 0.000 0.768 0.000 0.232 0.000
#> GSM627116 2 0.2383 0.82202 0.000 0.880 0.000 0.096 0.000 0.024
#> GSM627084 3 0.3725 0.53379 0.000 0.008 0.676 0.000 0.000 0.316
#> GSM627096 4 0.1564 0.72013 0.000 0.024 0.000 0.936 0.000 0.040
#> GSM627100 6 0.3854 0.16762 0.000 0.000 0.000 0.000 0.464 0.536
#> GSM627112 4 0.4925 0.18831 0.000 0.004 0.000 0.504 0.052 0.440
#> GSM627083 6 0.3851 0.16821 0.000 0.460 0.000 0.000 0.000 0.540
#> GSM627098 3 0.3725 0.53583 0.000 0.000 0.676 0.000 0.008 0.316
#> GSM627104 3 0.4142 0.68569 0.000 0.056 0.712 0.000 0.232 0.000
#> GSM627131 6 0.4076 -0.01139 0.000 0.000 0.452 0.000 0.008 0.540
#> GSM627106 5 0.3351 0.90193 0.000 0.000 0.288 0.000 0.712 0.000
#> GSM627123 6 0.3851 0.18753 0.460 0.000 0.000 0.000 0.000 0.540
#> GSM627129 2 0.0000 0.91761 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627216 2 0.2340 0.76471 0.000 0.852 0.148 0.000 0.000 0.000
#> GSM627212 2 0.0458 0.90998 0.000 0.984 0.000 0.016 0.000 0.000
#> GSM627190 3 0.0000 0.74510 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627169 2 0.0713 0.89994 0.000 0.972 0.028 0.000 0.000 0.000
#> GSM627167 4 0.3151 0.82021 0.000 0.252 0.000 0.748 0.000 0.000
#> GSM627192 1 0.0000 0.94606 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627203 5 0.3351 0.90193 0.000 0.000 0.288 0.000 0.712 0.000
#> GSM627151 2 0.0000 0.91761 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627163 1 0.0000 0.94606 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627211 4 0.3309 0.79923 0.000 0.280 0.000 0.720 0.000 0.000
#> GSM627171 3 0.1610 0.71279 0.000 0.084 0.916 0.000 0.000 0.000
#> GSM627209 4 0.2340 0.81229 0.000 0.148 0.000 0.852 0.000 0.000
#> GSM627135 6 0.5071 0.29573 0.376 0.000 0.084 0.000 0.000 0.540
#> GSM627170 2 0.0260 0.91420 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM627178 3 0.5870 0.29251 0.000 0.000 0.460 0.000 0.212 0.328
#> GSM627199 4 0.3151 0.82031 0.000 0.252 0.000 0.748 0.000 0.000
#> GSM627213 2 0.5242 -0.14502 0.000 0.516 0.000 0.384 0.000 0.100
#> GSM627140 6 0.5019 0.18725 0.000 0.344 0.000 0.016 0.052 0.588
#> GSM627149 1 0.0000 0.94606 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627147 2 0.0000 0.91761 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627195 5 0.3351 0.90193 0.000 0.000 0.288 0.000 0.712 0.000
#> GSM627204 4 0.3592 0.74385 0.000 0.344 0.000 0.656 0.000 0.000
#> GSM627207 2 0.0865 0.88823 0.000 0.964 0.000 0.036 0.000 0.000
#> GSM627157 3 0.3547 0.56085 0.332 0.000 0.668 0.000 0.000 0.000
#> GSM627201 4 0.3592 0.63943 0.000 0.344 0.000 0.656 0.000 0.000
#> GSM627146 2 0.0000 0.91761 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627156 2 0.0713 0.89994 0.000 0.972 0.028 0.000 0.000 0.000
#> GSM627188 1 0.0000 0.94606 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627197 2 0.0000 0.91761 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627173 2 0.0000 0.91761 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627179 2 0.0000 0.91761 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627208 5 0.3531 0.87512 0.000 0.000 0.328 0.000 0.672 0.000
#> GSM627215 2 0.0146 0.91557 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM627153 4 0.1863 0.79253 0.000 0.104 0.000 0.896 0.000 0.000
#> GSM627155 1 0.0000 0.94606 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627165 2 0.4344 0.44766 0.000 0.628 0.000 0.036 0.336 0.000
#> GSM627168 3 0.3464 0.54549 0.000 0.000 0.688 0.000 0.000 0.312
#> GSM627183 3 0.1204 0.73931 0.000 0.000 0.944 0.000 0.000 0.056
#> GSM627144 5 0.3464 0.88823 0.000 0.000 0.312 0.000 0.688 0.000
#> GSM627158 1 0.0000 0.94606 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627196 4 0.3244 0.81407 0.000 0.268 0.000 0.732 0.000 0.000
#> GSM627142 6 0.0000 0.50737 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM627182 3 0.0260 0.74198 0.000 0.000 0.992 0.000 0.008 0.000
#> GSM627202 6 0.3851 0.18753 0.460 0.000 0.000 0.000 0.000 0.540
#> GSM627141 3 0.3221 0.60078 0.000 0.000 0.736 0.000 0.000 0.264
#> GSM627143 2 0.0000 0.91761 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627145 3 0.4247 0.26464 0.000 0.000 0.700 0.000 0.240 0.060
#> GSM627152 6 0.5057 0.29010 0.000 0.000 0.088 0.000 0.352 0.560
#> GSM627200 6 0.3851 -0.01510 0.000 0.000 0.460 0.000 0.000 0.540
#> GSM627159 6 0.1657 0.49224 0.000 0.000 0.000 0.016 0.056 0.928
#> GSM627164 2 0.0000 0.91761 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627138 1 0.0000 0.94606 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627175 4 0.0547 0.73896 0.000 0.020 0.000 0.980 0.000 0.000
#> GSM627150 5 0.3351 0.90193 0.000 0.000 0.288 0.000 0.712 0.000
#> GSM627166 3 0.4503 0.66869 0.000 0.084 0.684 0.000 0.232 0.000
#> GSM627186 3 0.3428 0.50028 0.000 0.304 0.696 0.000 0.000 0.000
#> GSM627139 6 0.4872 0.31967 0.000 0.064 0.004 0.000 0.336 0.596
#> GSM627181 4 0.3765 0.64185 0.000 0.404 0.000 0.596 0.000 0.000
#> GSM627205 2 0.0000 0.91761 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627214 4 0.3578 0.74898 0.000 0.340 0.000 0.660 0.000 0.000
#> GSM627180 5 0.3351 0.90193 0.000 0.000 0.288 0.000 0.712 0.000
#> GSM627172 4 0.3499 0.77332 0.000 0.320 0.000 0.680 0.000 0.000
#> GSM627184 1 0.0000 0.94606 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627193 2 0.0000 0.91761 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627191 6 0.4837 0.33009 0.000 0.088 0.000 0.288 0.000 0.624
#> GSM627176 3 0.3602 0.53682 0.000 0.000 0.784 0.000 0.160 0.056
#> GSM627194 2 0.0000 0.91761 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627154 4 0.0458 0.73502 0.000 0.016 0.000 0.984 0.000 0.000
#> GSM627187 3 0.0000 0.74510 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627198 4 0.2178 0.79995 0.000 0.132 0.000 0.868 0.000 0.000
#> GSM627160 6 0.3490 0.43616 0.000 0.268 0.000 0.000 0.008 0.724
#> GSM627185 1 0.6849 0.12142 0.440 0.000 0.268 0.000 0.228 0.064
#> GSM627206 3 0.0260 0.74198 0.000 0.000 0.992 0.000 0.008 0.000
#> GSM627161 1 0.0000 0.94606 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627162 3 0.2988 0.71232 0.000 0.028 0.828 0.000 0.000 0.144
#> GSM627210 3 0.3023 0.72235 0.000 0.000 0.768 0.000 0.232 0.000
#> GSM627189 2 0.0000 0.91761 0.000 1.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two
samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures
which are significantly different between classes which can be candidate marks
for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known
annotation is numeric, one-way ANOVA test is applied, and if the known
annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) age(p) other(p) k
#> SD:pam 145 0.0867 0.448 0.0610 2
#> SD:pam 62 NA NA NA 3
#> SD:pam 113 0.1839 0.716 0.0495 4
#> SD:pam 112 0.3517 0.609 0.2280 5
#> SD:pam 113 0.5400 0.998 0.0689 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
SD:mclust*
The object with results only for a single top-value method and a single partition method
can be extracted as:
res = res_list["SD", "mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51882 rows and 146 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
- The first row: a plot of the ECDF (empirical cumulative distribution
function) curves of the consensus matrix for each
k and the heatmap of
predicted classes for each k.
- The second row: heatmaps of the consensus matrix for each
k.
- The third row: heatmaps of the membership matrix for each
k.
- The fouth row: heatmaps of the signatures for each
k.
All the plots in panels can be made by individual functions and they are
plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
- ECDF curves of the consensus matrix for each
k;
- 1-PAC. The PAC
score
measures the proportion of the ambiguous subgrouping.
- Mean silhouette score.
- Concordance. The mean probability of fiting the consensus class ids in all
partitions.
- Area increased. Denote \(A_k\) as the area under the ECDF curve for current
k, the area increased is defined as \(A_k - A_{k-1}\).
- Rand index. The percent of pairs of samples that are both in a same cluster
or both are not in a same cluster in the partition of k and k-1.
- Jaccard index. The ratio of pairs of samples are both in a same cluster in
the partition of k and k-1 and the pairs of samples are both in a same
cluster in the partition k or k-1.
The detailed explanations of these statistics can be found in the cola
vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.864 0.935 0.971 0.5007 0.498 0.498
#> 3 3 0.822 0.906 0.953 0.2378 0.746 0.549
#> 4 4 0.929 0.873 0.946 0.1234 0.862 0.660
#> 5 5 0.787 0.756 0.856 0.1044 0.840 0.524
#> 6 6 0.801 0.817 0.894 0.0526 0.855 0.466
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
- All \(k\) with Jaccard index larger than 0.95 are removed because increasing
\(k\) does not provide enough extra information. If all \(k\) are removed, it is
marked as no subgroup is detected.
- For all \(k\) with 1-PAC score larger than 0.9, the maximal \(k\) is taken as
the best \(k\), and other \(k\) are marked as optional \(k\).
- If it does not fit the second rule. The \(k\) with the maximal vote of the
highest 1-PAC score, highest mean silhouette, and highest concordance is
taken as the best \(k\).
suggest_best_k(res)
#> [1] 4
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
show/hide code output
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM627128 2 0.6438 0.819 0.164 0.836
#> GSM627110 1 0.0000 0.985 1.000 0.000
#> GSM627132 1 0.0000 0.985 1.000 0.000
#> GSM627107 2 0.9963 0.210 0.464 0.536
#> GSM627103 2 0.0000 0.955 0.000 1.000
#> GSM627114 1 0.0000 0.985 1.000 0.000
#> GSM627134 2 0.0000 0.955 0.000 1.000
#> GSM627137 2 0.0000 0.955 0.000 1.000
#> GSM627148 1 0.0000 0.985 1.000 0.000
#> GSM627101 2 0.6438 0.819 0.164 0.836
#> GSM627130 2 0.6438 0.819 0.164 0.836
#> GSM627071 1 0.0000 0.985 1.000 0.000
#> GSM627118 2 0.0376 0.953 0.004 0.996
#> GSM627094 2 0.0000 0.955 0.000 1.000
#> GSM627122 1 0.0000 0.985 1.000 0.000
#> GSM627115 2 0.0000 0.955 0.000 1.000
#> GSM627125 2 0.6438 0.819 0.164 0.836
#> GSM627174 2 0.0376 0.953 0.004 0.996
#> GSM627102 2 0.0000 0.955 0.000 1.000
#> GSM627073 1 0.4562 0.883 0.904 0.096
#> GSM627108 2 0.0000 0.955 0.000 1.000
#> GSM627126 1 0.0000 0.985 1.000 0.000
#> GSM627078 2 0.0000 0.955 0.000 1.000
#> GSM627090 1 0.0000 0.985 1.000 0.000
#> GSM627099 2 0.0000 0.955 0.000 1.000
#> GSM627105 2 0.6438 0.819 0.164 0.836
#> GSM627117 1 0.0000 0.985 1.000 0.000
#> GSM627121 2 0.9850 0.319 0.428 0.572
#> GSM627127 2 0.0000 0.955 0.000 1.000
#> GSM627087 2 0.0000 0.955 0.000 1.000
#> GSM627089 1 0.0000 0.985 1.000 0.000
#> GSM627092 2 0.0000 0.955 0.000 1.000
#> GSM627076 1 0.0376 0.982 0.996 0.004
#> GSM627136 1 0.0000 0.985 1.000 0.000
#> GSM627081 1 0.0376 0.982 0.996 0.004
#> GSM627091 2 0.0000 0.955 0.000 1.000
#> GSM627097 2 0.0000 0.955 0.000 1.000
#> GSM627072 1 0.0000 0.985 1.000 0.000
#> GSM627080 1 0.0000 0.985 1.000 0.000
#> GSM627088 1 0.0000 0.985 1.000 0.000
#> GSM627109 1 0.0000 0.985 1.000 0.000
#> GSM627111 1 0.0000 0.985 1.000 0.000
#> GSM627113 1 0.0000 0.985 1.000 0.000
#> GSM627133 2 0.0376 0.953 0.004 0.996
#> GSM627177 1 0.4815 0.872 0.896 0.104
#> GSM627086 2 0.0000 0.955 0.000 1.000
#> GSM627095 1 0.0000 0.985 1.000 0.000
#> GSM627079 1 0.0000 0.985 1.000 0.000
#> GSM627082 2 0.6438 0.819 0.164 0.836
#> GSM627074 1 0.0000 0.985 1.000 0.000
#> GSM627077 1 0.0000 0.985 1.000 0.000
#> GSM627093 1 0.0000 0.985 1.000 0.000
#> GSM627120 2 0.0000 0.955 0.000 1.000
#> GSM627124 2 0.0000 0.955 0.000 1.000
#> GSM627075 2 0.0000 0.955 0.000 1.000
#> GSM627085 2 0.0000 0.955 0.000 1.000
#> GSM627119 1 0.0000 0.985 1.000 0.000
#> GSM627116 2 0.0000 0.955 0.000 1.000
#> GSM627084 1 0.0000 0.985 1.000 0.000
#> GSM627096 2 0.0000 0.955 0.000 1.000
#> GSM627100 1 0.2423 0.947 0.960 0.040
#> GSM627112 2 0.0000 0.955 0.000 1.000
#> GSM627083 1 0.8813 0.539 0.700 0.300
#> GSM627098 1 0.0000 0.985 1.000 0.000
#> GSM627104 1 0.0000 0.985 1.000 0.000
#> GSM627131 1 0.0000 0.985 1.000 0.000
#> GSM627106 1 0.0376 0.982 0.996 0.004
#> GSM627123 1 0.0000 0.985 1.000 0.000
#> GSM627129 2 0.0000 0.955 0.000 1.000
#> GSM627216 2 0.0000 0.955 0.000 1.000
#> GSM627212 2 0.0000 0.955 0.000 1.000
#> GSM627190 1 0.0000 0.985 1.000 0.000
#> GSM627169 2 0.0000 0.955 0.000 1.000
#> GSM627167 2 0.0000 0.955 0.000 1.000
#> GSM627192 1 0.0000 0.985 1.000 0.000
#> GSM627203 1 0.0376 0.982 0.996 0.004
#> GSM627151 2 0.1843 0.936 0.028 0.972
#> GSM627163 1 0.0000 0.985 1.000 0.000
#> GSM627211 2 0.0000 0.955 0.000 1.000
#> GSM627171 2 0.0000 0.955 0.000 1.000
#> GSM627209 2 0.0000 0.955 0.000 1.000
#> GSM627135 1 0.0000 0.985 1.000 0.000
#> GSM627170 2 0.0000 0.955 0.000 1.000
#> GSM627178 1 0.0000 0.985 1.000 0.000
#> GSM627199 2 0.0000 0.955 0.000 1.000
#> GSM627213 2 0.0000 0.955 0.000 1.000
#> GSM627140 2 0.2603 0.924 0.044 0.956
#> GSM627149 1 0.0000 0.985 1.000 0.000
#> GSM627147 2 0.0000 0.955 0.000 1.000
#> GSM627195 1 0.0376 0.982 0.996 0.004
#> GSM627204 2 0.0000 0.955 0.000 1.000
#> GSM627207 2 0.0000 0.955 0.000 1.000
#> GSM627157 1 0.0000 0.985 1.000 0.000
#> GSM627201 2 0.0000 0.955 0.000 1.000
#> GSM627146 2 0.0000 0.955 0.000 1.000
#> GSM627156 2 0.0000 0.955 0.000 1.000
#> GSM627188 1 0.0000 0.985 1.000 0.000
#> GSM627197 2 0.0000 0.955 0.000 1.000
#> GSM627173 2 0.0000 0.955 0.000 1.000
#> GSM627179 2 0.0000 0.955 0.000 1.000
#> GSM627208 1 0.9427 0.391 0.640 0.360
#> GSM627215 2 0.0000 0.955 0.000 1.000
#> GSM627153 2 0.0000 0.955 0.000 1.000
#> GSM627155 1 0.0000 0.985 1.000 0.000
#> GSM627165 2 0.6438 0.819 0.164 0.836
#> GSM627168 1 0.0000 0.985 1.000 0.000
#> GSM627183 1 0.0000 0.985 1.000 0.000
#> GSM627144 1 0.0376 0.982 0.996 0.004
#> GSM627158 1 0.0000 0.985 1.000 0.000
#> GSM627196 2 0.0000 0.955 0.000 1.000
#> GSM627142 1 0.1184 0.971 0.984 0.016
#> GSM627182 1 0.0376 0.982 0.996 0.004
#> GSM627202 1 0.0000 0.985 1.000 0.000
#> GSM627141 1 0.0000 0.985 1.000 0.000
#> GSM627143 2 0.0000 0.955 0.000 1.000
#> GSM627145 1 0.0000 0.985 1.000 0.000
#> GSM627152 1 0.0376 0.982 0.996 0.004
#> GSM627200 1 0.0000 0.985 1.000 0.000
#> GSM627159 2 0.6438 0.819 0.164 0.836
#> GSM627164 2 0.0000 0.955 0.000 1.000
#> GSM627138 1 0.0000 0.985 1.000 0.000
#> GSM627175 2 0.0000 0.955 0.000 1.000
#> GSM627150 1 0.0376 0.982 0.996 0.004
#> GSM627166 1 0.0000 0.985 1.000 0.000
#> GSM627186 2 0.0000 0.955 0.000 1.000
#> GSM627139 2 0.6438 0.819 0.164 0.836
#> GSM627181 2 0.0000 0.955 0.000 1.000
#> GSM627205 2 0.0000 0.955 0.000 1.000
#> GSM627214 2 0.0000 0.955 0.000 1.000
#> GSM627180 2 0.9944 0.236 0.456 0.544
#> GSM627172 2 0.0000 0.955 0.000 1.000
#> GSM627184 1 0.0000 0.985 1.000 0.000
#> GSM627193 2 0.0000 0.955 0.000 1.000
#> GSM627191 2 0.6531 0.815 0.168 0.832
#> GSM627176 1 0.0376 0.982 0.996 0.004
#> GSM627194 2 0.0000 0.955 0.000 1.000
#> GSM627154 2 0.0000 0.955 0.000 1.000
#> GSM627187 1 0.0000 0.985 1.000 0.000
#> GSM627198 2 0.0000 0.955 0.000 1.000
#> GSM627160 2 0.7376 0.764 0.208 0.792
#> GSM627185 1 0.0000 0.985 1.000 0.000
#> GSM627206 1 0.0000 0.985 1.000 0.000
#> GSM627161 1 0.0000 0.985 1.000 0.000
#> GSM627162 1 0.0376 0.982 0.996 0.004
#> GSM627210 1 0.0000 0.985 1.000 0.000
#> GSM627189 2 0.0000 0.955 0.000 1.000
show/hide code output
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM627128 3 0.4002 0.778 0.000 0.160 0.840
#> GSM627110 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627132 1 0.2878 0.892 0.904 0.000 0.096
#> GSM627107 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627103 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627114 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627134 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627137 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627148 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627101 2 0.2711 0.895 0.000 0.912 0.088
#> GSM627130 3 0.4235 0.758 0.000 0.176 0.824
#> GSM627071 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627118 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627094 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627122 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627115 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627125 3 0.3879 0.787 0.000 0.152 0.848
#> GSM627174 2 0.1753 0.940 0.000 0.952 0.048
#> GSM627102 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627073 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627108 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627126 1 0.0000 0.874 1.000 0.000 0.000
#> GSM627078 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627090 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627099 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627105 3 0.4002 0.778 0.000 0.160 0.840
#> GSM627117 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627121 3 0.1163 0.905 0.000 0.028 0.972
#> GSM627127 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627087 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627089 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627092 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627076 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627136 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627081 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627091 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627097 2 0.1163 0.960 0.000 0.972 0.028
#> GSM627072 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627080 1 0.2878 0.892 0.904 0.000 0.096
#> GSM627088 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627109 1 0.4121 0.860 0.832 0.000 0.168
#> GSM627111 1 0.3116 0.890 0.892 0.000 0.108
#> GSM627113 1 0.5706 0.694 0.680 0.000 0.320
#> GSM627133 3 0.4887 0.684 0.000 0.228 0.772
#> GSM627177 3 0.0424 0.920 0.000 0.008 0.992
#> GSM627086 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627095 1 0.0000 0.874 1.000 0.000 0.000
#> GSM627079 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627082 3 0.4966 0.808 0.100 0.060 0.840
#> GSM627074 1 0.6045 0.579 0.620 0.000 0.380
#> GSM627077 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627093 3 0.5988 0.249 0.368 0.000 0.632
#> GSM627120 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627124 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627075 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627085 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627119 3 0.5291 0.533 0.268 0.000 0.732
#> GSM627116 3 0.6026 0.445 0.000 0.376 0.624
#> GSM627084 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627096 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627100 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627112 2 0.2261 0.919 0.000 0.932 0.068
#> GSM627083 1 0.5992 0.651 0.716 0.016 0.268
#> GSM627098 1 0.5591 0.718 0.696 0.000 0.304
#> GSM627104 1 0.4842 0.787 0.776 0.000 0.224
#> GSM627131 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627106 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627123 1 0.3686 0.877 0.860 0.000 0.140
#> GSM627129 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627216 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627212 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627190 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627169 2 0.1964 0.933 0.000 0.944 0.056
#> GSM627167 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627192 1 0.0000 0.874 1.000 0.000 0.000
#> GSM627203 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627151 3 0.3941 0.783 0.000 0.156 0.844
#> GSM627163 1 0.0000 0.874 1.000 0.000 0.000
#> GSM627211 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627171 2 0.0747 0.972 0.000 0.984 0.016
#> GSM627209 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627135 1 0.0000 0.874 1.000 0.000 0.000
#> GSM627170 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627178 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627199 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627213 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627140 2 0.3816 0.813 0.000 0.852 0.148
#> GSM627149 1 0.2625 0.891 0.916 0.000 0.084
#> GSM627147 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627195 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627204 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627207 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627157 1 0.4842 0.814 0.776 0.000 0.224
#> GSM627201 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627146 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627156 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627188 1 0.0000 0.874 1.000 0.000 0.000
#> GSM627197 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627173 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627179 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627208 3 0.2165 0.870 0.000 0.064 0.936
#> GSM627215 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627153 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627155 1 0.0237 0.875 0.996 0.000 0.004
#> GSM627165 2 0.2165 0.923 0.000 0.936 0.064
#> GSM627168 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627183 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627144 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627158 1 0.2878 0.892 0.904 0.000 0.096
#> GSM627196 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627142 3 0.1163 0.905 0.000 0.028 0.972
#> GSM627182 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627202 3 0.0424 0.919 0.008 0.000 0.992
#> GSM627141 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627143 2 0.2625 0.899 0.000 0.916 0.084
#> GSM627145 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627152 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627200 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627159 3 0.4475 0.787 0.016 0.144 0.840
#> GSM627164 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627138 1 0.3116 0.890 0.892 0.000 0.108
#> GSM627175 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627150 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627166 3 0.5650 0.533 0.312 0.000 0.688
#> GSM627186 2 0.2959 0.880 0.000 0.900 0.100
#> GSM627139 3 0.3482 0.812 0.000 0.128 0.872
#> GSM627181 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627205 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627214 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627180 3 0.0592 0.917 0.000 0.012 0.988
#> GSM627172 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627184 1 0.0000 0.874 1.000 0.000 0.000
#> GSM627193 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627191 3 0.6634 0.704 0.104 0.144 0.752
#> GSM627176 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627194 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627154 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627187 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627198 2 0.0000 0.986 0.000 1.000 0.000
#> GSM627160 3 0.4443 0.825 0.052 0.084 0.864
#> GSM627185 1 0.3619 0.879 0.864 0.000 0.136
#> GSM627206 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627161 1 0.2878 0.892 0.904 0.000 0.096
#> GSM627162 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627210 3 0.0000 0.925 0.000 0.000 1.000
#> GSM627189 2 0.0000 0.986 0.000 1.000 0.000
show/hide code output
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM627128 4 0.0000 0.8178 0.000 0.000 0.000 1.000
#> GSM627110 3 0.0000 0.9337 0.000 0.000 1.000 0.000
#> GSM627132 1 0.0000 0.9425 1.000 0.000 0.000 0.000
#> GSM627107 4 0.0817 0.8297 0.000 0.000 0.024 0.976
#> GSM627103 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627114 3 0.0000 0.9337 0.000 0.000 1.000 0.000
#> GSM627134 2 0.0188 0.9796 0.000 0.996 0.000 0.004
#> GSM627137 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627148 4 0.3219 0.7695 0.000 0.000 0.164 0.836
#> GSM627101 4 0.4989 0.0720 0.000 0.472 0.000 0.528
#> GSM627130 4 0.0000 0.8178 0.000 0.000 0.000 1.000
#> GSM627071 3 0.0000 0.9337 0.000 0.000 1.000 0.000
#> GSM627118 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627094 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627122 3 0.0000 0.9337 0.000 0.000 1.000 0.000
#> GSM627115 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627125 4 0.0000 0.8178 0.000 0.000 0.000 1.000
#> GSM627174 2 0.0707 0.9710 0.000 0.980 0.000 0.020
#> GSM627102 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627073 4 0.1716 0.8377 0.000 0.000 0.064 0.936
#> GSM627108 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627126 1 0.0000 0.9425 1.000 0.000 0.000 0.000
#> GSM627078 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627090 3 0.4585 0.4703 0.000 0.000 0.668 0.332
#> GSM627099 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627105 4 0.0000 0.8178 0.000 0.000 0.000 1.000
#> GSM627117 3 0.0000 0.9337 0.000 0.000 1.000 0.000
#> GSM627121 4 0.1022 0.8326 0.000 0.000 0.032 0.968
#> GSM627127 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627087 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627089 3 0.0000 0.9337 0.000 0.000 1.000 0.000
#> GSM627092 2 0.0707 0.9710 0.000 0.980 0.000 0.020
#> GSM627076 4 0.4661 0.4812 0.000 0.000 0.348 0.652
#> GSM627136 3 0.0000 0.9337 0.000 0.000 1.000 0.000
#> GSM627081 4 0.1716 0.8377 0.000 0.000 0.064 0.936
#> GSM627091 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627097 2 0.0817 0.9684 0.000 0.976 0.000 0.024
#> GSM627072 4 0.4605 0.5358 0.000 0.000 0.336 0.664
#> GSM627080 1 0.0000 0.9425 1.000 0.000 0.000 0.000
#> GSM627088 3 0.0000 0.9337 0.000 0.000 1.000 0.000
#> GSM627109 3 0.4877 0.2396 0.408 0.000 0.592 0.000
#> GSM627111 1 0.0000 0.9425 1.000 0.000 0.000 0.000
#> GSM627113 3 0.0000 0.9337 0.000 0.000 1.000 0.000
#> GSM627133 4 0.5793 0.3828 0.000 0.384 0.036 0.580
#> GSM627177 3 0.1474 0.8949 0.000 0.000 0.948 0.052
#> GSM627086 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627095 1 0.4925 0.2557 0.572 0.000 0.428 0.000
#> GSM627079 3 0.4888 0.2547 0.000 0.000 0.588 0.412
#> GSM627082 4 0.2704 0.7622 0.000 0.000 0.124 0.876
#> GSM627074 3 0.0000 0.9337 0.000 0.000 1.000 0.000
#> GSM627077 3 0.0000 0.9337 0.000 0.000 1.000 0.000
#> GSM627093 3 0.0000 0.9337 0.000 0.000 1.000 0.000
#> GSM627120 2 0.0817 0.9684 0.000 0.976 0.000 0.024
#> GSM627124 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627075 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627085 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627119 3 0.0000 0.9337 0.000 0.000 1.000 0.000
#> GSM627116 4 0.5126 0.2143 0.000 0.444 0.004 0.552
#> GSM627084 3 0.0000 0.9337 0.000 0.000 1.000 0.000
#> GSM627096 2 0.0336 0.9782 0.000 0.992 0.000 0.008
#> GSM627100 4 0.1637 0.8303 0.000 0.000 0.060 0.940
#> GSM627112 2 0.1867 0.9284 0.000 0.928 0.000 0.072
#> GSM627083 3 0.1118 0.9020 0.036 0.000 0.964 0.000
#> GSM627098 3 0.0000 0.9337 0.000 0.000 1.000 0.000
#> GSM627104 3 0.0336 0.9278 0.008 0.000 0.992 0.000
#> GSM627131 3 0.0000 0.9337 0.000 0.000 1.000 0.000
#> GSM627106 4 0.1474 0.8369 0.000 0.000 0.052 0.948
#> GSM627123 3 0.1716 0.8728 0.064 0.000 0.936 0.000
#> GSM627129 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627216 2 0.0707 0.9710 0.000 0.980 0.000 0.020
#> GSM627212 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627190 3 0.0000 0.9337 0.000 0.000 1.000 0.000
#> GSM627169 2 0.1042 0.9653 0.000 0.972 0.008 0.020
#> GSM627167 2 0.0817 0.9684 0.000 0.976 0.000 0.024
#> GSM627192 1 0.0000 0.9425 1.000 0.000 0.000 0.000
#> GSM627203 4 0.2149 0.8251 0.000 0.000 0.088 0.912
#> GSM627151 4 0.4949 0.6690 0.000 0.180 0.060 0.760
#> GSM627163 1 0.0000 0.9425 1.000 0.000 0.000 0.000
#> GSM627211 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627171 2 0.0817 0.9684 0.000 0.976 0.000 0.024
#> GSM627209 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627135 1 0.1474 0.8989 0.948 0.000 0.052 0.000
#> GSM627170 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627178 3 0.0000 0.9337 0.000 0.000 1.000 0.000
#> GSM627199 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627213 2 0.0188 0.9792 0.000 0.996 0.000 0.004
#> GSM627140 2 0.1716 0.9357 0.000 0.936 0.000 0.064
#> GSM627149 1 0.0000 0.9425 1.000 0.000 0.000 0.000
#> GSM627147 2 0.0469 0.9756 0.000 0.988 0.000 0.012
#> GSM627195 4 0.1716 0.8377 0.000 0.000 0.064 0.936
#> GSM627204 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627207 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627157 3 0.0817 0.9140 0.024 0.000 0.976 0.000
#> GSM627201 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627146 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627156 2 0.0336 0.9776 0.000 0.992 0.000 0.008
#> GSM627188 1 0.0000 0.9425 1.000 0.000 0.000 0.000
#> GSM627197 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627173 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627179 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627208 2 0.5808 0.1089 0.000 0.544 0.032 0.424
#> GSM627215 2 0.0817 0.9684 0.000 0.976 0.000 0.024
#> GSM627153 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627155 1 0.0000 0.9425 1.000 0.000 0.000 0.000
#> GSM627165 2 0.3528 0.7517 0.000 0.808 0.000 0.192
#> GSM627168 3 0.0000 0.9337 0.000 0.000 1.000 0.000
#> GSM627183 3 0.0000 0.9337 0.000 0.000 1.000 0.000
#> GSM627144 4 0.1716 0.8377 0.000 0.000 0.064 0.936
#> GSM627158 1 0.0000 0.9425 1.000 0.000 0.000 0.000
#> GSM627196 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627142 4 0.1716 0.8291 0.000 0.000 0.064 0.936
#> GSM627182 4 0.2081 0.8302 0.000 0.000 0.084 0.916
#> GSM627202 3 0.0000 0.9337 0.000 0.000 1.000 0.000
#> GSM627141 3 0.0000 0.9337 0.000 0.000 1.000 0.000
#> GSM627143 2 0.1118 0.9586 0.000 0.964 0.000 0.036
#> GSM627145 3 0.1792 0.8775 0.000 0.000 0.932 0.068
#> GSM627152 3 0.4382 0.5429 0.000 0.000 0.704 0.296
#> GSM627200 3 0.0000 0.9337 0.000 0.000 1.000 0.000
#> GSM627159 4 0.0000 0.8178 0.000 0.000 0.000 1.000
#> GSM627164 2 0.0707 0.9710 0.000 0.980 0.000 0.020
#> GSM627138 1 0.0000 0.9425 1.000 0.000 0.000 0.000
#> GSM627175 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627150 4 0.1716 0.8377 0.000 0.000 0.064 0.936
#> GSM627166 3 0.0000 0.9337 0.000 0.000 1.000 0.000
#> GSM627186 2 0.0707 0.9710 0.000 0.980 0.000 0.020
#> GSM627139 4 0.1022 0.8328 0.000 0.000 0.032 0.968
#> GSM627181 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627205 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627214 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627180 4 0.1716 0.8377 0.000 0.000 0.064 0.936
#> GSM627172 2 0.0188 0.9795 0.000 0.996 0.000 0.004
#> GSM627184 1 0.0000 0.9425 1.000 0.000 0.000 0.000
#> GSM627193 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627191 3 0.2266 0.8660 0.000 0.004 0.912 0.084
#> GSM627176 3 0.4989 0.0436 0.000 0.000 0.528 0.472
#> GSM627194 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627154 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627187 3 0.0000 0.9337 0.000 0.000 1.000 0.000
#> GSM627198 2 0.0000 0.9812 0.000 1.000 0.000 0.000
#> GSM627160 4 0.4877 0.2738 0.000 0.000 0.408 0.592
#> GSM627185 1 0.4040 0.6658 0.752 0.000 0.248 0.000
#> GSM627206 3 0.0000 0.9337 0.000 0.000 1.000 0.000
#> GSM627161 1 0.0000 0.9425 1.000 0.000 0.000 0.000
#> GSM627162 3 0.0592 0.9209 0.000 0.000 0.984 0.016
#> GSM627210 3 0.0000 0.9337 0.000 0.000 1.000 0.000
#> GSM627189 2 0.0000 0.9812 0.000 1.000 0.000 0.000
show/hide code output
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM627128 5 0.4210 0.557 0.000 0.000 0.000 0.412 0.588
#> GSM627110 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000
#> GSM627132 1 0.0000 0.962 1.000 0.000 0.000 0.000 0.000
#> GSM627107 5 0.0000 0.853 0.000 0.000 0.000 0.000 1.000
#> GSM627103 2 0.4302 -0.461 0.000 0.520 0.000 0.480 0.000
#> GSM627114 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000
#> GSM627134 4 0.4114 0.617 0.000 0.376 0.000 0.624 0.000
#> GSM627137 2 0.0290 0.916 0.000 0.992 0.000 0.008 0.000
#> GSM627148 5 0.0963 0.868 0.000 0.000 0.036 0.000 0.964
#> GSM627101 4 0.1205 0.437 0.000 0.004 0.000 0.956 0.040
#> GSM627130 4 0.4307 -0.470 0.000 0.000 0.000 0.504 0.496
#> GSM627071 3 0.3039 0.772 0.000 0.000 0.808 0.000 0.192
#> GSM627118 4 0.4227 0.480 0.000 0.420 0.000 0.580 0.000
#> GSM627094 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> GSM627122 3 0.2074 0.876 0.000 0.000 0.896 0.000 0.104
#> GSM627115 2 0.0162 0.915 0.000 0.996 0.000 0.004 0.000
#> GSM627125 5 0.4171 0.570 0.000 0.000 0.000 0.396 0.604
#> GSM627174 4 0.4192 0.605 0.000 0.404 0.000 0.596 0.000
#> GSM627102 2 0.1121 0.904 0.000 0.956 0.000 0.044 0.000
#> GSM627073 5 0.0963 0.868 0.000 0.000 0.036 0.000 0.964
#> GSM627108 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> GSM627126 1 0.0000 0.962 1.000 0.000 0.000 0.000 0.000
#> GSM627078 2 0.2605 0.823 0.000 0.852 0.000 0.148 0.000
#> GSM627090 5 0.2127 0.825 0.000 0.000 0.108 0.000 0.892
#> GSM627099 2 0.1671 0.888 0.000 0.924 0.000 0.076 0.000
#> GSM627105 5 0.4182 0.567 0.000 0.000 0.000 0.400 0.600
#> GSM627117 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000
#> GSM627121 5 0.1364 0.865 0.000 0.000 0.036 0.012 0.952
#> GSM627127 2 0.2329 0.842 0.000 0.876 0.000 0.124 0.000
#> GSM627087 2 0.0609 0.909 0.000 0.980 0.000 0.020 0.000
#> GSM627089 3 0.1908 0.885 0.000 0.000 0.908 0.000 0.092
#> GSM627092 4 0.4210 0.606 0.000 0.412 0.000 0.588 0.000
#> GSM627076 5 0.0290 0.857 0.000 0.000 0.008 0.000 0.992
#> GSM627136 3 0.0162 0.934 0.000 0.000 0.996 0.000 0.004
#> GSM627081 5 0.0963 0.868 0.000 0.000 0.036 0.000 0.964
#> GSM627091 2 0.0510 0.915 0.000 0.984 0.000 0.016 0.000
#> GSM627097 4 0.3932 0.619 0.000 0.328 0.000 0.672 0.000
#> GSM627072 5 0.1043 0.867 0.000 0.000 0.040 0.000 0.960
#> GSM627080 1 0.0000 0.962 1.000 0.000 0.000 0.000 0.000
#> GSM627088 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000
#> GSM627109 3 0.1851 0.871 0.088 0.000 0.912 0.000 0.000
#> GSM627111 1 0.0000 0.962 1.000 0.000 0.000 0.000 0.000
#> GSM627113 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000
#> GSM627133 4 0.6046 0.285 0.000 0.056 0.036 0.560 0.348
#> GSM627177 3 0.4046 0.592 0.000 0.000 0.696 0.008 0.296
#> GSM627086 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> GSM627095 3 0.3837 0.598 0.308 0.000 0.692 0.000 0.000
#> GSM627079 5 0.1608 0.851 0.000 0.000 0.072 0.000 0.928
#> GSM627082 4 0.6571 -0.410 0.000 0.000 0.204 0.404 0.392
#> GSM627074 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000
#> GSM627077 3 0.1792 0.892 0.000 0.000 0.916 0.000 0.084
#> GSM627093 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000
#> GSM627120 4 0.4192 0.610 0.000 0.404 0.000 0.596 0.000
#> GSM627124 2 0.2179 0.862 0.000 0.888 0.000 0.112 0.000
#> GSM627075 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> GSM627085 2 0.2179 0.847 0.000 0.888 0.000 0.112 0.000
#> GSM627119 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000
#> GSM627116 4 0.2260 0.520 0.000 0.064 0.000 0.908 0.028
#> GSM627084 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000
#> GSM627096 4 0.3796 0.619 0.000 0.300 0.000 0.700 0.000
#> GSM627100 5 0.0000 0.853 0.000 0.000 0.000 0.000 1.000
#> GSM627112 4 0.0404 0.479 0.000 0.012 0.000 0.988 0.000
#> GSM627083 3 0.3090 0.845 0.104 0.000 0.856 0.040 0.000
#> GSM627098 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000
#> GSM627104 3 0.0963 0.907 0.036 0.000 0.964 0.000 0.000
#> GSM627131 3 0.1965 0.882 0.000 0.000 0.904 0.000 0.096
#> GSM627106 5 0.0963 0.868 0.000 0.000 0.036 0.000 0.964
#> GSM627123 3 0.2605 0.831 0.148 0.000 0.852 0.000 0.000
#> GSM627129 4 0.4030 0.618 0.000 0.352 0.000 0.648 0.000
#> GSM627216 4 0.4262 0.595 0.000 0.440 0.000 0.560 0.000
#> GSM627212 2 0.1608 0.886 0.000 0.928 0.000 0.072 0.000
#> GSM627190 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000
#> GSM627169 4 0.4302 0.539 0.000 0.480 0.000 0.520 0.000
#> GSM627167 4 0.3913 0.619 0.000 0.324 0.000 0.676 0.000
#> GSM627192 1 0.0000 0.962 1.000 0.000 0.000 0.000 0.000
#> GSM627203 5 0.0963 0.868 0.000 0.000 0.036 0.000 0.964
#> GSM627151 4 0.5941 0.224 0.000 0.044 0.036 0.544 0.376
#> GSM627163 1 0.0000 0.962 1.000 0.000 0.000 0.000 0.000
#> GSM627211 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> GSM627171 4 0.4262 0.595 0.000 0.440 0.000 0.560 0.000
#> GSM627209 2 0.1270 0.903 0.000 0.948 0.000 0.052 0.000
#> GSM627135 1 0.3796 0.526 0.700 0.000 0.300 0.000 0.000
#> GSM627170 2 0.1270 0.887 0.000 0.948 0.000 0.052 0.000
#> GSM627178 3 0.1792 0.892 0.000 0.000 0.916 0.000 0.084
#> GSM627199 2 0.1732 0.875 0.000 0.920 0.000 0.080 0.000
#> GSM627213 4 0.3876 0.608 0.000 0.316 0.000 0.684 0.000
#> GSM627140 4 0.2074 0.559 0.000 0.104 0.000 0.896 0.000
#> GSM627149 1 0.0162 0.959 0.996 0.000 0.004 0.000 0.000
#> GSM627147 4 0.4015 0.617 0.000 0.348 0.000 0.652 0.000
#> GSM627195 5 0.0963 0.868 0.000 0.000 0.036 0.000 0.964
#> GSM627204 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> GSM627207 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> GSM627157 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000
#> GSM627201 2 0.0290 0.916 0.000 0.992 0.000 0.008 0.000
#> GSM627146 2 0.0290 0.916 0.000 0.992 0.000 0.008 0.000
#> GSM627156 4 0.4302 0.537 0.000 0.480 0.000 0.520 0.000
#> GSM627188 1 0.0000 0.962 1.000 0.000 0.000 0.000 0.000
#> GSM627197 2 0.0880 0.909 0.000 0.968 0.000 0.032 0.000
#> GSM627173 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> GSM627179 2 0.0162 0.915 0.000 0.996 0.000 0.004 0.000
#> GSM627208 4 0.5900 0.254 0.000 0.044 0.036 0.560 0.360
#> GSM627215 4 0.4403 0.597 0.000 0.436 0.004 0.560 0.000
#> GSM627153 2 0.1851 0.883 0.000 0.912 0.000 0.088 0.000
#> GSM627155 1 0.0000 0.962 1.000 0.000 0.000 0.000 0.000
#> GSM627165 4 0.4171 0.611 0.000 0.396 0.000 0.604 0.000
#> GSM627168 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000
#> GSM627183 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000
#> GSM627144 5 0.0963 0.868 0.000 0.000 0.036 0.000 0.964
#> GSM627158 1 0.0000 0.962 1.000 0.000 0.000 0.000 0.000
#> GSM627196 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> GSM627142 5 0.1043 0.839 0.000 0.000 0.000 0.040 0.960
#> GSM627182 5 0.4270 0.657 0.000 0.000 0.048 0.204 0.748
#> GSM627202 3 0.1478 0.904 0.000 0.000 0.936 0.000 0.064
#> GSM627141 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000
#> GSM627143 4 0.4808 0.612 0.000 0.400 0.024 0.576 0.000
#> GSM627145 5 0.1908 0.838 0.000 0.000 0.092 0.000 0.908
#> GSM627152 5 0.4171 0.354 0.000 0.000 0.396 0.000 0.604
#> GSM627200 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000
#> GSM627159 5 0.4182 0.567 0.000 0.000 0.000 0.400 0.600
#> GSM627164 4 0.4262 0.595 0.000 0.440 0.000 0.560 0.000
#> GSM627138 1 0.2074 0.865 0.896 0.000 0.104 0.000 0.000
#> GSM627175 2 0.2074 0.849 0.000 0.896 0.000 0.104 0.000
#> GSM627150 5 0.0963 0.868 0.000 0.000 0.036 0.000 0.964
#> GSM627166 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000
#> GSM627186 4 0.4287 0.570 0.000 0.460 0.000 0.540 0.000
#> GSM627139 5 0.0510 0.862 0.000 0.000 0.016 0.000 0.984
#> GSM627181 2 0.0880 0.909 0.000 0.968 0.000 0.032 0.000
#> GSM627205 4 0.4262 0.595 0.000 0.440 0.000 0.560 0.000
#> GSM627214 2 0.2424 0.817 0.000 0.868 0.000 0.132 0.000
#> GSM627180 5 0.4479 0.566 0.000 0.000 0.036 0.264 0.700
#> GSM627172 4 0.4297 0.519 0.000 0.472 0.000 0.528 0.000
#> GSM627184 1 0.0000 0.962 1.000 0.000 0.000 0.000 0.000
#> GSM627193 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> GSM627191 4 0.4305 -0.374 0.000 0.000 0.488 0.512 0.000
#> GSM627176 5 0.1851 0.841 0.000 0.000 0.088 0.000 0.912
#> GSM627194 2 0.0162 0.915 0.000 0.996 0.000 0.004 0.000
#> GSM627154 2 0.3983 0.395 0.000 0.660 0.000 0.340 0.000
#> GSM627187 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000
#> GSM627198 2 0.1908 0.863 0.000 0.908 0.000 0.092 0.000
#> GSM627160 4 0.6233 -0.320 0.000 0.000 0.168 0.520 0.312
#> GSM627185 3 0.3561 0.641 0.260 0.000 0.740 0.000 0.000
#> GSM627206 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000
#> GSM627161 1 0.0000 0.962 1.000 0.000 0.000 0.000 0.000
#> GSM627162 3 0.2104 0.884 0.000 0.000 0.916 0.024 0.060
#> GSM627210 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000
#> GSM627189 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
show/hide code output
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM627128 6 0.1387 0.9354 0.000 0.000 0.000 0.000 0.068 0.932
#> GSM627110 3 0.0547 0.8935 0.000 0.000 0.980 0.000 0.020 0.000
#> GSM627132 1 0.0146 0.9449 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM627107 5 0.0603 0.8573 0.000 0.000 0.000 0.016 0.980 0.004
#> GSM627103 2 0.1075 0.8887 0.000 0.952 0.000 0.048 0.000 0.000
#> GSM627114 3 0.0146 0.8955 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM627134 4 0.1719 0.8467 0.000 0.060 0.000 0.924 0.000 0.016
#> GSM627137 2 0.2969 0.6094 0.000 0.776 0.000 0.224 0.000 0.000
#> GSM627148 5 0.0000 0.8597 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM627101 6 0.2199 0.8504 0.000 0.020 0.000 0.088 0.000 0.892
#> GSM627130 6 0.1367 0.9287 0.000 0.000 0.000 0.012 0.044 0.944
#> GSM627071 3 0.3907 0.3500 0.000 0.000 0.588 0.004 0.408 0.000
#> GSM627118 4 0.2358 0.8634 0.000 0.108 0.000 0.876 0.000 0.016
#> GSM627094 2 0.0000 0.9073 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627122 5 0.4461 0.2369 0.000 0.000 0.404 0.032 0.564 0.000
#> GSM627115 2 0.0000 0.9073 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627125 6 0.1387 0.9354 0.000 0.000 0.000 0.000 0.068 0.932
#> GSM627174 4 0.3139 0.7900 0.000 0.160 0.000 0.812 0.000 0.028
#> GSM627102 4 0.3843 0.4452 0.000 0.452 0.000 0.548 0.000 0.000
#> GSM627073 5 0.0603 0.8573 0.000 0.000 0.000 0.016 0.980 0.004
#> GSM627108 2 0.0000 0.9073 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627126 1 0.0000 0.9451 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627078 4 0.2664 0.8664 0.000 0.184 0.000 0.816 0.000 0.000
#> GSM627090 5 0.1633 0.8477 0.000 0.000 0.024 0.044 0.932 0.000
#> GSM627099 4 0.2664 0.8664 0.000 0.184 0.000 0.816 0.000 0.000
#> GSM627105 6 0.1387 0.9354 0.000 0.000 0.000 0.000 0.068 0.932
#> GSM627117 3 0.0458 0.8948 0.000 0.000 0.984 0.000 0.016 0.000
#> GSM627121 5 0.1349 0.8396 0.000 0.000 0.000 0.056 0.940 0.004
#> GSM627127 4 0.2664 0.8664 0.000 0.184 0.000 0.816 0.000 0.000
#> GSM627087 2 0.0000 0.9073 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627089 3 0.4256 0.1293 0.000 0.000 0.520 0.016 0.464 0.000
#> GSM627092 4 0.3175 0.6425 0.000 0.256 0.000 0.744 0.000 0.000
#> GSM627076 5 0.1152 0.8545 0.000 0.000 0.004 0.044 0.952 0.000
#> GSM627136 3 0.0632 0.8924 0.000 0.000 0.976 0.000 0.024 0.000
#> GSM627081 5 0.0508 0.8580 0.000 0.000 0.000 0.012 0.984 0.004
#> GSM627091 2 0.3023 0.5979 0.000 0.768 0.000 0.232 0.000 0.000
#> GSM627097 4 0.1219 0.8369 0.000 0.048 0.000 0.948 0.000 0.004
#> GSM627072 5 0.2092 0.7648 0.000 0.000 0.124 0.000 0.876 0.000
#> GSM627080 1 0.0146 0.9449 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM627088 3 0.0458 0.8948 0.000 0.000 0.984 0.000 0.016 0.000
#> GSM627109 3 0.0713 0.8798 0.028 0.000 0.972 0.000 0.000 0.000
#> GSM627111 1 0.0146 0.9449 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM627113 3 0.0000 0.8949 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627133 5 0.5369 0.5268 0.000 0.220 0.000 0.104 0.644 0.032
#> GSM627177 3 0.4318 0.2136 0.000 0.000 0.532 0.020 0.448 0.000
#> GSM627086 2 0.0000 0.9073 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627095 1 0.2219 0.8261 0.864 0.000 0.136 0.000 0.000 0.000
#> GSM627079 5 0.1152 0.8545 0.000 0.000 0.004 0.044 0.952 0.000
#> GSM627082 6 0.1341 0.9084 0.000 0.000 0.024 0.000 0.028 0.948
#> GSM627074 3 0.0000 0.8949 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627077 3 0.3261 0.7051 0.000 0.000 0.780 0.016 0.204 0.000
#> GSM627093 3 0.0000 0.8949 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627120 4 0.3518 0.7385 0.000 0.092 0.000 0.804 0.104 0.000
#> GSM627124 4 0.2664 0.8664 0.000 0.184 0.000 0.816 0.000 0.000
#> GSM627075 2 0.0000 0.9073 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627085 4 0.2664 0.8664 0.000 0.184 0.000 0.816 0.000 0.000
#> GSM627119 3 0.0000 0.8949 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627116 4 0.1957 0.7936 0.000 0.000 0.000 0.888 0.000 0.112
#> GSM627084 3 0.0260 0.8958 0.000 0.000 0.992 0.000 0.008 0.000
#> GSM627096 4 0.2060 0.8576 0.000 0.084 0.000 0.900 0.000 0.016
#> GSM627100 5 0.2842 0.7860 0.000 0.000 0.000 0.044 0.852 0.104
#> GSM627112 4 0.2135 0.7826 0.000 0.000 0.000 0.872 0.000 0.128
#> GSM627083 1 0.4571 0.7318 0.756 0.000 0.136 0.060 0.008 0.040
#> GSM627098 3 0.0000 0.8949 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627104 3 0.0000 0.8949 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627131 3 0.3688 0.6211 0.000 0.000 0.724 0.020 0.256 0.000
#> GSM627106 5 0.0508 0.8580 0.000 0.000 0.000 0.012 0.984 0.004
#> GSM627123 1 0.2454 0.7963 0.840 0.000 0.160 0.000 0.000 0.000
#> GSM627129 4 0.2163 0.8602 0.000 0.092 0.000 0.892 0.000 0.016
#> GSM627216 2 0.2633 0.8361 0.000 0.864 0.000 0.104 0.000 0.032
#> GSM627212 2 0.3782 0.0244 0.000 0.588 0.000 0.412 0.000 0.000
#> GSM627190 3 0.0458 0.8948 0.000 0.000 0.984 0.000 0.016 0.000
#> GSM627169 2 0.2436 0.8494 0.000 0.880 0.000 0.088 0.000 0.032
#> GSM627167 4 0.1858 0.8548 0.000 0.076 0.000 0.912 0.000 0.012
#> GSM627192 1 0.0000 0.9451 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627203 5 0.0937 0.8554 0.000 0.000 0.000 0.040 0.960 0.000
#> GSM627151 5 0.4101 0.5371 0.000 0.000 0.000 0.308 0.664 0.028
#> GSM627163 1 0.0000 0.9451 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627211 2 0.0146 0.9053 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM627171 2 0.2436 0.8494 0.000 0.880 0.000 0.088 0.000 0.032
#> GSM627209 4 0.2664 0.8664 0.000 0.184 0.000 0.816 0.000 0.000
#> GSM627135 1 0.1610 0.8815 0.916 0.000 0.084 0.000 0.000 0.000
#> GSM627170 2 0.0363 0.9032 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM627178 3 0.2994 0.7164 0.000 0.000 0.788 0.004 0.208 0.000
#> GSM627199 4 0.2664 0.8664 0.000 0.184 0.000 0.816 0.000 0.000
#> GSM627213 4 0.2358 0.8639 0.000 0.108 0.000 0.876 0.000 0.016
#> GSM627140 4 0.2100 0.7951 0.000 0.004 0.000 0.884 0.000 0.112
#> GSM627149 1 0.0458 0.9374 0.984 0.000 0.016 0.000 0.000 0.000
#> GSM627147 4 0.1556 0.8548 0.000 0.080 0.000 0.920 0.000 0.000
#> GSM627195 5 0.0291 0.8592 0.000 0.000 0.000 0.004 0.992 0.004
#> GSM627204 2 0.0000 0.9073 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627207 2 0.0000 0.9073 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627157 3 0.0000 0.8949 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627201 2 0.2048 0.7944 0.000 0.880 0.000 0.120 0.000 0.000
#> GSM627146 2 0.1765 0.8245 0.000 0.904 0.000 0.096 0.000 0.000
#> GSM627156 2 0.2436 0.8494 0.000 0.880 0.000 0.088 0.000 0.032
#> GSM627188 1 0.0000 0.9451 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627197 4 0.3390 0.7590 0.000 0.296 0.000 0.704 0.000 0.000
#> GSM627173 2 0.0000 0.9073 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627179 2 0.0000 0.9073 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627208 5 0.5935 0.2208 0.000 0.376 0.000 0.104 0.488 0.032
#> GSM627215 2 0.2633 0.8361 0.000 0.864 0.000 0.104 0.000 0.032
#> GSM627153 4 0.2664 0.8664 0.000 0.184 0.000 0.816 0.000 0.000
#> GSM627155 1 0.0000 0.9451 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627165 4 0.3919 0.7681 0.000 0.072 0.000 0.788 0.124 0.016
#> GSM627168 3 0.0547 0.8940 0.000 0.000 0.980 0.000 0.020 0.000
#> GSM627183 3 0.0000 0.8949 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627144 5 0.0146 0.8593 0.000 0.000 0.000 0.000 0.996 0.004
#> GSM627158 1 0.0146 0.9449 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM627196 2 0.0000 0.9073 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627142 6 0.3938 0.7218 0.000 0.000 0.000 0.044 0.228 0.728
#> GSM627182 5 0.4121 0.6965 0.000 0.000 0.156 0.048 0.768 0.028
#> GSM627202 3 0.2358 0.8096 0.000 0.000 0.876 0.016 0.108 0.000
#> GSM627141 3 0.0260 0.8958 0.000 0.000 0.992 0.000 0.008 0.000
#> GSM627143 4 0.3933 0.5239 0.000 0.308 0.000 0.676 0.008 0.008
#> GSM627145 5 0.1549 0.8512 0.000 0.000 0.020 0.044 0.936 0.000
#> GSM627152 5 0.1934 0.8375 0.000 0.000 0.040 0.044 0.916 0.000
#> GSM627200 3 0.0000 0.8949 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627159 6 0.1204 0.9336 0.000 0.000 0.000 0.000 0.056 0.944
#> GSM627164 2 0.2436 0.8494 0.000 0.880 0.000 0.088 0.000 0.032
#> GSM627138 3 0.3742 0.4318 0.348 0.000 0.648 0.004 0.000 0.000
#> GSM627175 4 0.2664 0.8664 0.000 0.184 0.000 0.816 0.000 0.000
#> GSM627150 5 0.0000 0.8597 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM627166 3 0.0260 0.8958 0.000 0.000 0.992 0.000 0.008 0.000
#> GSM627186 2 0.2436 0.8494 0.000 0.880 0.000 0.088 0.000 0.032
#> GSM627139 5 0.1141 0.8440 0.000 0.000 0.000 0.000 0.948 0.052
#> GSM627181 4 0.3578 0.6919 0.000 0.340 0.000 0.660 0.000 0.000
#> GSM627205 2 0.2263 0.8484 0.000 0.884 0.000 0.100 0.000 0.016
#> GSM627214 4 0.2527 0.8677 0.000 0.168 0.000 0.832 0.000 0.000
#> GSM627180 5 0.1657 0.8307 0.000 0.000 0.000 0.056 0.928 0.016
#> GSM627172 4 0.2020 0.8477 0.000 0.096 0.000 0.896 0.000 0.008
#> GSM627184 1 0.0000 0.9451 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627193 2 0.0000 0.9073 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627191 4 0.3207 0.7665 0.000 0.000 0.044 0.828 0.004 0.124
#> GSM627176 5 0.1007 0.8542 0.000 0.000 0.000 0.044 0.956 0.000
#> GSM627194 2 0.0000 0.9073 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627154 4 0.2454 0.8680 0.000 0.160 0.000 0.840 0.000 0.000
#> GSM627187 3 0.0458 0.8948 0.000 0.000 0.984 0.000 0.016 0.000
#> GSM627198 4 0.2664 0.8664 0.000 0.184 0.000 0.816 0.000 0.000
#> GSM627160 4 0.5082 0.5348 0.000 0.000 0.004 0.648 0.160 0.188
#> GSM627185 3 0.0865 0.8735 0.036 0.000 0.964 0.000 0.000 0.000
#> GSM627206 3 0.0458 0.8948 0.000 0.000 0.984 0.000 0.016 0.000
#> GSM627161 1 0.0146 0.9449 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM627162 3 0.2011 0.8483 0.000 0.000 0.912 0.004 0.064 0.020
#> GSM627210 3 0.0260 0.8958 0.000 0.000 0.992 0.000 0.008 0.000
#> GSM627189 2 0.0000 0.9073 0.000 1.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two
samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures
which are significantly different between classes which can be candidate marks
for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known
annotation is numeric, one-way ANOVA test is applied, and if the known
annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) age(p) other(p) k
#> SD:mclust 142 0.416 0.360 0.04590 2
#> SD:mclust 144 0.426 0.663 0.08381 3
#> SD:mclust 135 0.129 0.620 0.02524 4
#> SD:mclust 133 0.105 0.660 0.00902 5
#> SD:mclust 138 0.298 0.783 0.31969 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
SD:NMF*
The object with results only for a single top-value method and a single partition method
can be extracted as:
res = res_list["SD", "NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51882 rows and 146 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
- The first row: a plot of the ECDF (empirical cumulative distribution
function) curves of the consensus matrix for each
k and the heatmap of
predicted classes for each k.
- The second row: heatmaps of the consensus matrix for each
k.
- The third row: heatmaps of the membership matrix for each
k.
- The fouth row: heatmaps of the signatures for each
k.
All the plots in panels can be made by individual functions and they are
plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
- ECDF curves of the consensus matrix for each
k;
- 1-PAC. The PAC
score
measures the proportion of the ambiguous subgrouping.
- Mean silhouette score.
- Concordance. The mean probability of fiting the consensus class ids in all
partitions.
- Area increased. Denote \(A_k\) as the area under the ECDF curve for current
k, the area increased is defined as \(A_k - A_{k-1}\).
- Rand index. The percent of pairs of samples that are both in a same cluster
or both are not in a same cluster in the partition of k and k-1.
- Jaccard index. The ratio of pairs of samples are both in a same cluster in
the partition of k and k-1 and the pairs of samples are both in a same
cluster in the partition k or k-1.
The detailed explanations of these statistics can be found in the cola
vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.944 0.949 0.978 0.4959 0.503 0.503
#> 3 3 0.656 0.818 0.902 0.2669 0.841 0.694
#> 4 4 0.562 0.688 0.827 0.1455 0.729 0.416
#> 5 5 0.529 0.494 0.717 0.0737 0.831 0.492
#> 6 6 0.587 0.579 0.741 0.0441 0.867 0.507
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
- All \(k\) with Jaccard index larger than 0.95 are removed because increasing
\(k\) does not provide enough extra information. If all \(k\) are removed, it is
marked as no subgroup is detected.
- For all \(k\) with 1-PAC score larger than 0.9, the maximal \(k\) is taken as
the best \(k\), and other \(k\) are marked as optional \(k\).
- If it does not fit the second rule. The \(k\) with the maximal vote of the
highest 1-PAC score, highest mean silhouette, and highest concordance is
taken as the best \(k\).
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
show/hide code output
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM627128 2 0.2948 0.933 0.052 0.948
#> GSM627110 1 0.0000 0.971 1.000 0.000
#> GSM627132 1 0.0000 0.971 1.000 0.000
#> GSM627107 2 0.0000 0.982 0.000 1.000
#> GSM627103 2 0.0000 0.982 0.000 1.000
#> GSM627114 1 0.0000 0.971 1.000 0.000
#> GSM627134 2 0.0000 0.982 0.000 1.000
#> GSM627137 2 0.0000 0.982 0.000 1.000
#> GSM627148 1 0.3584 0.913 0.932 0.068
#> GSM627101 2 0.0000 0.982 0.000 1.000
#> GSM627130 2 0.0000 0.982 0.000 1.000
#> GSM627071 1 0.2423 0.939 0.960 0.040
#> GSM627118 2 0.0000 0.982 0.000 1.000
#> GSM627094 2 0.0000 0.982 0.000 1.000
#> GSM627122 1 0.0000 0.971 1.000 0.000
#> GSM627115 2 0.0000 0.982 0.000 1.000
#> GSM627125 2 0.4690 0.881 0.100 0.900
#> GSM627174 2 0.0000 0.982 0.000 1.000
#> GSM627102 2 0.0000 0.982 0.000 1.000
#> GSM627073 2 0.0000 0.982 0.000 1.000
#> GSM627108 2 0.0000 0.982 0.000 1.000
#> GSM627126 1 0.0000 0.971 1.000 0.000
#> GSM627078 2 0.0000 0.982 0.000 1.000
#> GSM627090 1 0.0000 0.971 1.000 0.000
#> GSM627099 2 0.0000 0.982 0.000 1.000
#> GSM627105 2 0.0000 0.982 0.000 1.000
#> GSM627117 1 0.6887 0.780 0.816 0.184
#> GSM627121 2 0.0000 0.982 0.000 1.000
#> GSM627127 2 0.0000 0.982 0.000 1.000
#> GSM627087 2 0.0000 0.982 0.000 1.000
#> GSM627089 1 0.0000 0.971 1.000 0.000
#> GSM627092 2 0.0000 0.982 0.000 1.000
#> GSM627076 1 0.0000 0.971 1.000 0.000
#> GSM627136 1 0.0376 0.968 0.996 0.004
#> GSM627081 2 0.0938 0.971 0.012 0.988
#> GSM627091 2 0.0000 0.982 0.000 1.000
#> GSM627097 2 0.0000 0.982 0.000 1.000
#> GSM627072 1 0.8443 0.639 0.728 0.272
#> GSM627080 1 0.0000 0.971 1.000 0.000
#> GSM627088 1 0.0000 0.971 1.000 0.000
#> GSM627109 1 0.0000 0.971 1.000 0.000
#> GSM627111 1 0.0000 0.971 1.000 0.000
#> GSM627113 1 0.0000 0.971 1.000 0.000
#> GSM627133 2 0.0000 0.982 0.000 1.000
#> GSM627177 2 0.9000 0.530 0.316 0.684
#> GSM627086 2 0.0000 0.982 0.000 1.000
#> GSM627095 1 0.0000 0.971 1.000 0.000
#> GSM627079 1 0.0000 0.971 1.000 0.000
#> GSM627082 1 0.0000 0.971 1.000 0.000
#> GSM627074 1 0.0000 0.971 1.000 0.000
#> GSM627077 1 0.0000 0.971 1.000 0.000
#> GSM627093 1 0.0000 0.971 1.000 0.000
#> GSM627120 2 0.0000 0.982 0.000 1.000
#> GSM627124 2 0.0000 0.982 0.000 1.000
#> GSM627075 2 0.0000 0.982 0.000 1.000
#> GSM627085 2 0.0000 0.982 0.000 1.000
#> GSM627119 1 0.0000 0.971 1.000 0.000
#> GSM627116 2 0.0000 0.982 0.000 1.000
#> GSM627084 1 0.0000 0.971 1.000 0.000
#> GSM627096 2 0.0000 0.982 0.000 1.000
#> GSM627100 1 0.0000 0.971 1.000 0.000
#> GSM627112 2 0.0000 0.982 0.000 1.000
#> GSM627083 1 0.0376 0.968 0.996 0.004
#> GSM627098 1 0.0000 0.971 1.000 0.000
#> GSM627104 1 0.0000 0.971 1.000 0.000
#> GSM627131 1 0.0000 0.971 1.000 0.000
#> GSM627106 2 0.3584 0.917 0.068 0.932
#> GSM627123 1 0.0000 0.971 1.000 0.000
#> GSM627129 2 0.0000 0.982 0.000 1.000
#> GSM627216 2 0.0000 0.982 0.000 1.000
#> GSM627212 2 0.0000 0.982 0.000 1.000
#> GSM627190 1 0.5178 0.865 0.884 0.116
#> GSM627169 2 0.0000 0.982 0.000 1.000
#> GSM627167 2 0.0000 0.982 0.000 1.000
#> GSM627192 1 0.0000 0.971 1.000 0.000
#> GSM627203 1 0.0000 0.971 1.000 0.000
#> GSM627151 2 0.0000 0.982 0.000 1.000
#> GSM627163 1 0.0000 0.971 1.000 0.000
#> GSM627211 2 0.0000 0.982 0.000 1.000
#> GSM627171 2 0.0000 0.982 0.000 1.000
#> GSM627209 2 0.0000 0.982 0.000 1.000
#> GSM627135 1 0.0000 0.971 1.000 0.000
#> GSM627170 2 0.0000 0.982 0.000 1.000
#> GSM627178 1 0.0000 0.971 1.000 0.000
#> GSM627199 2 0.0000 0.982 0.000 1.000
#> GSM627213 2 0.0000 0.982 0.000 1.000
#> GSM627140 2 0.0000 0.982 0.000 1.000
#> GSM627149 1 0.0000 0.971 1.000 0.000
#> GSM627147 2 0.0000 0.982 0.000 1.000
#> GSM627195 1 0.6531 0.801 0.832 0.168
#> GSM627204 2 0.0000 0.982 0.000 1.000
#> GSM627207 2 0.0000 0.982 0.000 1.000
#> GSM627157 1 0.0000 0.971 1.000 0.000
#> GSM627201 2 0.0000 0.982 0.000 1.000
#> GSM627146 2 0.0000 0.982 0.000 1.000
#> GSM627156 2 0.0000 0.982 0.000 1.000
#> GSM627188 1 0.0000 0.971 1.000 0.000
#> GSM627197 2 0.0000 0.982 0.000 1.000
#> GSM627173 2 0.0000 0.982 0.000 1.000
#> GSM627179 2 0.0000 0.982 0.000 1.000
#> GSM627208 2 0.0000 0.982 0.000 1.000
#> GSM627215 2 0.0000 0.982 0.000 1.000
#> GSM627153 2 0.0000 0.982 0.000 1.000
#> GSM627155 1 0.0000 0.971 1.000 0.000
#> GSM627165 2 0.0000 0.982 0.000 1.000
#> GSM627168 1 0.0000 0.971 1.000 0.000
#> GSM627183 1 0.0000 0.971 1.000 0.000
#> GSM627144 2 0.9580 0.374 0.380 0.620
#> GSM627158 1 0.0000 0.971 1.000 0.000
#> GSM627196 2 0.0000 0.982 0.000 1.000
#> GSM627142 1 0.0000 0.971 1.000 0.000
#> GSM627182 2 0.0000 0.982 0.000 1.000
#> GSM627202 1 0.0000 0.971 1.000 0.000
#> GSM627141 1 0.0000 0.971 1.000 0.000
#> GSM627143 2 0.0000 0.982 0.000 1.000
#> GSM627145 1 0.0000 0.971 1.000 0.000
#> GSM627152 1 0.0000 0.971 1.000 0.000
#> GSM627200 1 0.0000 0.971 1.000 0.000
#> GSM627159 1 0.0000 0.971 1.000 0.000
#> GSM627164 2 0.0000 0.982 0.000 1.000
#> GSM627138 1 0.0000 0.971 1.000 0.000
#> GSM627175 2 0.0000 0.982 0.000 1.000
#> GSM627150 1 0.4939 0.873 0.892 0.108
#> GSM627166 1 0.0000 0.971 1.000 0.000
#> GSM627186 2 0.0000 0.982 0.000 1.000
#> GSM627139 2 0.6712 0.782 0.176 0.824
#> GSM627181 2 0.0000 0.982 0.000 1.000
#> GSM627205 2 0.0000 0.982 0.000 1.000
#> GSM627214 2 0.0000 0.982 0.000 1.000
#> GSM627180 2 0.0000 0.982 0.000 1.000
#> GSM627172 2 0.0000 0.982 0.000 1.000
#> GSM627184 1 0.0000 0.971 1.000 0.000
#> GSM627193 2 0.0000 0.982 0.000 1.000
#> GSM627191 1 0.9881 0.238 0.564 0.436
#> GSM627176 1 0.0000 0.971 1.000 0.000
#> GSM627194 2 0.0000 0.982 0.000 1.000
#> GSM627154 2 0.0000 0.982 0.000 1.000
#> GSM627187 1 0.1633 0.952 0.976 0.024
#> GSM627198 2 0.0000 0.982 0.000 1.000
#> GSM627160 1 0.9635 0.385 0.612 0.388
#> GSM627185 1 0.0000 0.971 1.000 0.000
#> GSM627206 1 0.0000 0.971 1.000 0.000
#> GSM627161 1 0.0000 0.971 1.000 0.000
#> GSM627162 2 0.8386 0.626 0.268 0.732
#> GSM627210 1 0.0376 0.968 0.996 0.004
#> GSM627189 2 0.0000 0.982 0.000 1.000
show/hide code output
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM627128 3 0.1163 0.8241 0.000 0.028 0.972
#> GSM627110 1 0.0237 0.8927 0.996 0.004 0.000
#> GSM627132 1 0.3879 0.8826 0.848 0.000 0.152
#> GSM627107 2 0.1031 0.8842 0.024 0.976 0.000
#> GSM627103 2 0.0000 0.8891 0.000 1.000 0.000
#> GSM627114 1 0.0237 0.8927 0.996 0.004 0.000
#> GSM627134 2 0.0237 0.8892 0.000 0.996 0.004
#> GSM627137 2 0.0237 0.8892 0.000 0.996 0.004
#> GSM627148 1 0.0747 0.8852 0.984 0.016 0.000
#> GSM627101 3 0.3879 0.8053 0.000 0.152 0.848
#> GSM627130 3 0.3267 0.8193 0.000 0.116 0.884
#> GSM627071 1 0.0892 0.8826 0.980 0.020 0.000
#> GSM627118 2 0.0237 0.8892 0.000 0.996 0.004
#> GSM627094 2 0.0000 0.8891 0.000 1.000 0.000
#> GSM627122 1 0.4002 0.8782 0.840 0.000 0.160
#> GSM627115 2 0.3267 0.8464 0.116 0.884 0.000
#> GSM627125 3 0.1031 0.8233 0.000 0.024 0.976
#> GSM627174 2 0.0237 0.8892 0.000 0.996 0.004
#> GSM627102 2 0.0237 0.8892 0.000 0.996 0.004
#> GSM627073 2 0.3879 0.8246 0.152 0.848 0.000
#> GSM627108 2 0.0237 0.8888 0.004 0.996 0.000
#> GSM627126 1 0.4452 0.8516 0.808 0.000 0.192
#> GSM627078 2 0.6225 0.0804 0.000 0.568 0.432
#> GSM627090 1 0.3686 0.8864 0.860 0.000 0.140
#> GSM627099 2 0.0237 0.8892 0.000 0.996 0.004
#> GSM627105 3 0.3482 0.8159 0.000 0.128 0.872
#> GSM627117 1 0.1860 0.8522 0.948 0.052 0.000
#> GSM627121 2 0.3686 0.8328 0.140 0.860 0.000
#> GSM627127 2 0.3340 0.7903 0.000 0.880 0.120
#> GSM627087 2 0.3267 0.8464 0.116 0.884 0.000
#> GSM627089 1 0.0237 0.8927 0.996 0.004 0.000
#> GSM627092 2 0.0237 0.8892 0.000 0.996 0.004
#> GSM627076 1 0.3941 0.8806 0.844 0.000 0.156
#> GSM627136 1 0.0237 0.8927 0.996 0.004 0.000
#> GSM627081 2 0.4062 0.8142 0.164 0.836 0.000
#> GSM627091 2 0.0237 0.8892 0.000 0.996 0.004
#> GSM627097 2 0.4931 0.6222 0.000 0.768 0.232
#> GSM627072 1 0.4121 0.6897 0.832 0.168 0.000
#> GSM627080 1 0.3879 0.8826 0.848 0.000 0.152
#> GSM627088 1 0.0237 0.8927 0.996 0.004 0.000
#> GSM627109 1 0.3412 0.8901 0.876 0.000 0.124
#> GSM627111 1 0.3879 0.8826 0.848 0.000 0.152
#> GSM627113 1 0.0000 0.8932 1.000 0.000 0.000
#> GSM627133 2 0.3879 0.8246 0.152 0.848 0.000
#> GSM627177 2 0.7283 0.2456 0.460 0.512 0.028
#> GSM627086 2 0.0237 0.8892 0.000 0.996 0.004
#> GSM627095 3 0.5968 0.2463 0.364 0.000 0.636
#> GSM627079 1 0.3482 0.8893 0.872 0.000 0.128
#> GSM627082 3 0.0000 0.8138 0.000 0.000 1.000
#> GSM627074 1 0.0237 0.8927 0.996 0.004 0.000
#> GSM627077 1 0.3816 0.8843 0.852 0.000 0.148
#> GSM627093 1 0.0237 0.8927 0.996 0.004 0.000
#> GSM627120 2 0.0237 0.8888 0.004 0.996 0.000
#> GSM627124 3 0.5882 0.5616 0.000 0.348 0.652
#> GSM627075 2 0.0237 0.8888 0.004 0.996 0.000
#> GSM627085 2 0.6302 -0.1303 0.000 0.520 0.480
#> GSM627119 1 0.0237 0.8927 0.996 0.004 0.000
#> GSM627116 3 0.5968 0.5340 0.000 0.364 0.636
#> GSM627084 1 0.3879 0.8826 0.848 0.000 0.152
#> GSM627096 2 0.0424 0.8871 0.000 0.992 0.008
#> GSM627100 1 0.4235 0.8665 0.824 0.000 0.176
#> GSM627112 3 0.3816 0.8077 0.000 0.148 0.852
#> GSM627083 3 0.0000 0.8138 0.000 0.000 1.000
#> GSM627098 1 0.0892 0.8949 0.980 0.000 0.020
#> GSM627104 1 0.0237 0.8927 0.996 0.004 0.000
#> GSM627131 1 0.3752 0.8857 0.856 0.000 0.144
#> GSM627106 2 0.5216 0.7011 0.260 0.740 0.000
#> GSM627123 1 0.4062 0.8756 0.836 0.000 0.164
#> GSM627129 2 0.0237 0.8892 0.000 0.996 0.004
#> GSM627216 2 0.3816 0.8274 0.148 0.852 0.000
#> GSM627212 2 0.0237 0.8892 0.000 0.996 0.004
#> GSM627190 1 0.1411 0.8689 0.964 0.036 0.000
#> GSM627169 2 0.3879 0.8246 0.152 0.848 0.000
#> GSM627167 2 0.3686 0.7636 0.000 0.860 0.140
#> GSM627192 3 0.1289 0.7949 0.032 0.000 0.968
#> GSM627203 1 0.0237 0.8927 0.996 0.004 0.000
#> GSM627151 2 0.0237 0.8892 0.000 0.996 0.004
#> GSM627163 1 0.3941 0.8806 0.844 0.000 0.156
#> GSM627211 2 0.0237 0.8892 0.000 0.996 0.004
#> GSM627171 2 0.3619 0.8352 0.136 0.864 0.000
#> GSM627209 2 0.0237 0.8892 0.000 0.996 0.004
#> GSM627135 1 0.4121 0.8728 0.832 0.000 0.168
#> GSM627170 2 0.1289 0.8815 0.032 0.968 0.000
#> GSM627178 1 0.3941 0.8806 0.844 0.000 0.156
#> GSM627199 3 0.4002 0.8002 0.000 0.160 0.840
#> GSM627213 3 0.3941 0.8033 0.000 0.156 0.844
#> GSM627140 3 0.3816 0.8077 0.000 0.148 0.852
#> GSM627149 1 0.4002 0.8782 0.840 0.000 0.160
#> GSM627147 2 0.0592 0.8850 0.000 0.988 0.012
#> GSM627195 1 0.1753 0.8570 0.952 0.048 0.000
#> GSM627204 2 0.0237 0.8892 0.000 0.996 0.004
#> GSM627207 2 0.2448 0.8648 0.076 0.924 0.000
#> GSM627157 1 0.2796 0.8945 0.908 0.000 0.092
#> GSM627201 2 0.0237 0.8892 0.000 0.996 0.004
#> GSM627146 2 0.0237 0.8892 0.000 0.996 0.004
#> GSM627156 2 0.3879 0.8246 0.152 0.848 0.000
#> GSM627188 3 0.0892 0.8027 0.020 0.000 0.980
#> GSM627197 2 0.0237 0.8892 0.000 0.996 0.004
#> GSM627173 2 0.0237 0.8892 0.000 0.996 0.004
#> GSM627179 2 0.0237 0.8888 0.004 0.996 0.000
#> GSM627208 2 0.3879 0.8246 0.152 0.848 0.000
#> GSM627215 2 0.3816 0.8274 0.148 0.852 0.000
#> GSM627153 2 0.0424 0.8871 0.000 0.992 0.008
#> GSM627155 1 0.4121 0.8728 0.832 0.000 0.168
#> GSM627165 2 0.0000 0.8891 0.000 1.000 0.000
#> GSM627168 1 0.0237 0.8927 0.996 0.004 0.000
#> GSM627183 1 0.0237 0.8927 0.996 0.004 0.000
#> GSM627144 1 0.5138 0.5443 0.748 0.252 0.000
#> GSM627158 1 0.3879 0.8826 0.848 0.000 0.152
#> GSM627196 2 0.0237 0.8892 0.000 0.996 0.004
#> GSM627142 3 0.5810 0.3272 0.336 0.000 0.664
#> GSM627182 2 0.4062 0.8145 0.164 0.836 0.000
#> GSM627202 1 0.3879 0.8826 0.848 0.000 0.152
#> GSM627141 1 0.0237 0.8927 0.996 0.004 0.000
#> GSM627143 2 0.1163 0.8830 0.028 0.972 0.000
#> GSM627145 1 0.0237 0.8927 0.996 0.004 0.000
#> GSM627152 1 0.3816 0.8843 0.852 0.000 0.148
#> GSM627200 1 0.2625 0.8952 0.916 0.000 0.084
#> GSM627159 3 0.0000 0.8138 0.000 0.000 1.000
#> GSM627164 2 0.2796 0.8581 0.092 0.908 0.000
#> GSM627138 1 0.3816 0.8843 0.852 0.000 0.148
#> GSM627175 2 0.3482 0.7799 0.000 0.872 0.128
#> GSM627150 1 0.1289 0.8727 0.968 0.032 0.000
#> GSM627166 1 0.3816 0.8843 0.852 0.000 0.148
#> GSM627186 2 0.3879 0.8246 0.152 0.848 0.000
#> GSM627139 2 0.8065 0.4176 0.092 0.604 0.304
#> GSM627181 2 0.0237 0.8892 0.000 0.996 0.004
#> GSM627205 2 0.3551 0.8377 0.132 0.868 0.000
#> GSM627214 2 0.0237 0.8892 0.000 0.996 0.004
#> GSM627180 2 0.3879 0.8246 0.152 0.848 0.000
#> GSM627172 2 0.0237 0.8892 0.000 0.996 0.004
#> GSM627184 3 0.5529 0.4309 0.296 0.000 0.704
#> GSM627193 2 0.3686 0.8328 0.140 0.860 0.000
#> GSM627191 3 0.0592 0.8197 0.000 0.012 0.988
#> GSM627176 1 0.0424 0.8942 0.992 0.000 0.008
#> GSM627194 2 0.0237 0.8892 0.000 0.996 0.004
#> GSM627154 3 0.4346 0.7819 0.000 0.184 0.816
#> GSM627187 1 0.0747 0.8853 0.984 0.016 0.000
#> GSM627198 3 0.6260 0.3242 0.000 0.448 0.552
#> GSM627160 3 0.0829 0.8183 0.004 0.012 0.984
#> GSM627185 1 0.2625 0.8952 0.916 0.000 0.084
#> GSM627206 1 0.0237 0.8927 0.996 0.004 0.000
#> GSM627161 1 0.3879 0.8826 0.848 0.000 0.152
#> GSM627162 2 0.6244 0.3907 0.440 0.560 0.000
#> GSM627210 1 0.0237 0.8927 0.996 0.004 0.000
#> GSM627189 2 0.0237 0.8892 0.000 0.996 0.004
show/hide code output
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM627128 4 0.0188 0.8744 0.000 0.000 0.004 0.996
#> GSM627110 1 0.4981 0.1697 0.536 0.000 0.464 0.000
#> GSM627132 1 0.0921 0.8007 0.972 0.000 0.028 0.000
#> GSM627107 3 0.1833 0.7069 0.000 0.032 0.944 0.024
#> GSM627103 2 0.1302 0.8702 0.000 0.956 0.044 0.000
#> GSM627114 3 0.3400 0.6039 0.180 0.000 0.820 0.000
#> GSM627134 2 0.5712 0.2884 0.000 0.584 0.384 0.032
#> GSM627137 2 0.5256 0.2944 0.000 0.596 0.392 0.012
#> GSM627148 3 0.1489 0.6969 0.044 0.004 0.952 0.000
#> GSM627101 4 0.1042 0.8694 0.000 0.008 0.020 0.972
#> GSM627130 4 0.0336 0.8748 0.000 0.000 0.008 0.992
#> GSM627071 1 0.5489 0.5324 0.664 0.296 0.040 0.000
#> GSM627118 2 0.3716 0.8340 0.000 0.852 0.096 0.052
#> GSM627094 2 0.0469 0.8720 0.000 0.988 0.012 0.000
#> GSM627122 1 0.4898 0.7048 0.716 0.000 0.260 0.024
#> GSM627115 2 0.0376 0.8695 0.004 0.992 0.004 0.000
#> GSM627125 4 0.3172 0.7462 0.000 0.000 0.160 0.840
#> GSM627174 2 0.0804 0.8707 0.012 0.980 0.008 0.000
#> GSM627102 3 0.5548 0.3818 0.000 0.388 0.588 0.024
#> GSM627073 3 0.3448 0.7162 0.004 0.168 0.828 0.000
#> GSM627108 2 0.2281 0.8507 0.000 0.904 0.096 0.000
#> GSM627126 1 0.1994 0.7841 0.936 0.004 0.008 0.052
#> GSM627078 2 0.3245 0.8200 0.028 0.872 0.000 0.100
#> GSM627090 3 0.4567 0.5024 0.244 0.000 0.740 0.016
#> GSM627099 2 0.0524 0.8716 0.004 0.988 0.008 0.000
#> GSM627105 4 0.4964 0.3199 0.000 0.004 0.380 0.616
#> GSM627117 3 0.2915 0.6989 0.080 0.028 0.892 0.000
#> GSM627121 3 0.2002 0.7098 0.000 0.044 0.936 0.020
#> GSM627127 2 0.1489 0.8578 0.004 0.952 0.000 0.044
#> GSM627087 2 0.0524 0.8682 0.008 0.988 0.004 0.000
#> GSM627089 3 0.4961 0.0354 0.448 0.000 0.552 0.000
#> GSM627092 3 0.4642 0.6590 0.000 0.240 0.740 0.020
#> GSM627076 3 0.4839 0.5587 0.184 0.000 0.764 0.052
#> GSM627136 3 0.3978 0.6096 0.192 0.012 0.796 0.000
#> GSM627081 3 0.1022 0.7119 0.000 0.032 0.968 0.000
#> GSM627091 2 0.0188 0.8684 0.004 0.996 0.000 0.000
#> GSM627097 2 0.3498 0.7301 0.160 0.832 0.000 0.008
#> GSM627072 3 0.4920 0.6722 0.164 0.068 0.768 0.000
#> GSM627080 1 0.0921 0.8003 0.972 0.000 0.028 0.000
#> GSM627088 1 0.3674 0.7874 0.848 0.036 0.116 0.000
#> GSM627109 1 0.1557 0.7668 0.944 0.056 0.000 0.000
#> GSM627111 1 0.1474 0.8048 0.948 0.000 0.052 0.000
#> GSM627113 1 0.1211 0.8031 0.960 0.000 0.040 0.000
#> GSM627133 2 0.3245 0.8355 0.028 0.872 0.100 0.000
#> GSM627177 2 0.4843 0.3117 0.396 0.604 0.000 0.000
#> GSM627086 2 0.1743 0.8685 0.004 0.940 0.056 0.000
#> GSM627095 1 0.3780 0.7040 0.832 0.016 0.004 0.148
#> GSM627079 1 0.2675 0.7961 0.892 0.008 0.100 0.000
#> GSM627082 4 0.0188 0.8744 0.000 0.000 0.004 0.996
#> GSM627074 1 0.2623 0.7760 0.908 0.064 0.028 0.000
#> GSM627077 1 0.3024 0.7900 0.852 0.000 0.148 0.000
#> GSM627093 1 0.2926 0.7862 0.896 0.056 0.048 0.000
#> GSM627120 3 0.4323 0.6912 0.000 0.204 0.776 0.020
#> GSM627124 2 0.3307 0.8116 0.028 0.868 0.000 0.104
#> GSM627075 2 0.4955 0.1262 0.000 0.556 0.444 0.000
#> GSM627085 2 0.1798 0.8534 0.016 0.944 0.000 0.040
#> GSM627119 1 0.3479 0.7034 0.840 0.148 0.012 0.000
#> GSM627116 2 0.4262 0.6289 0.236 0.756 0.000 0.008
#> GSM627084 1 0.4795 0.6834 0.696 0.000 0.292 0.012
#> GSM627096 2 0.4864 0.7856 0.008 0.788 0.060 0.144
#> GSM627100 3 0.4236 0.6377 0.088 0.000 0.824 0.088
#> GSM627112 4 0.1867 0.8319 0.000 0.072 0.000 0.928
#> GSM627083 4 0.1489 0.8492 0.044 0.004 0.000 0.952
#> GSM627098 1 0.1940 0.8056 0.924 0.000 0.076 0.000
#> GSM627104 1 0.4877 0.2688 0.592 0.408 0.000 0.000
#> GSM627131 1 0.1004 0.8003 0.972 0.004 0.024 0.000
#> GSM627106 3 0.0524 0.7057 0.004 0.008 0.988 0.000
#> GSM627123 1 0.4238 0.7746 0.796 0.000 0.176 0.028
#> GSM627129 3 0.4957 0.6827 0.000 0.204 0.748 0.048
#> GSM627216 2 0.3088 0.8250 0.008 0.864 0.128 0.000
#> GSM627212 2 0.0592 0.8728 0.000 0.984 0.016 0.000
#> GSM627190 3 0.3873 0.7066 0.096 0.060 0.844 0.000
#> GSM627169 3 0.4304 0.6273 0.000 0.284 0.716 0.000
#> GSM627167 3 0.6071 0.6206 0.000 0.144 0.684 0.172
#> GSM627192 1 0.5125 0.3623 0.616 0.004 0.004 0.376
#> GSM627203 3 0.4820 0.4506 0.296 0.012 0.692 0.000
#> GSM627151 2 0.1792 0.8300 0.068 0.932 0.000 0.000
#> GSM627163 1 0.0927 0.7968 0.976 0.000 0.016 0.008
#> GSM627211 2 0.2469 0.8395 0.000 0.892 0.108 0.000
#> GSM627171 3 0.3528 0.7060 0.000 0.192 0.808 0.000
#> GSM627209 2 0.2742 0.8559 0.000 0.900 0.076 0.024
#> GSM627135 1 0.1042 0.7846 0.972 0.020 0.000 0.008
#> GSM627170 3 0.4713 0.4902 0.000 0.360 0.640 0.000
#> GSM627178 1 0.3402 0.6751 0.832 0.164 0.000 0.004
#> GSM627199 2 0.4608 0.5512 0.004 0.692 0.000 0.304
#> GSM627213 4 0.4283 0.5796 0.000 0.256 0.004 0.740
#> GSM627140 4 0.1978 0.8452 0.000 0.004 0.068 0.928
#> GSM627149 1 0.4868 0.7507 0.748 0.000 0.212 0.040
#> GSM627147 3 0.6912 0.5311 0.000 0.192 0.592 0.216
#> GSM627195 3 0.7021 0.3086 0.400 0.120 0.480 0.000
#> GSM627204 2 0.1389 0.8695 0.000 0.952 0.048 0.000
#> GSM627207 3 0.4877 0.3772 0.000 0.408 0.592 0.000
#> GSM627157 1 0.2647 0.8001 0.880 0.000 0.120 0.000
#> GSM627201 2 0.1637 0.8657 0.000 0.940 0.060 0.000
#> GSM627146 2 0.0188 0.8708 0.000 0.996 0.004 0.000
#> GSM627156 3 0.3873 0.6850 0.000 0.228 0.772 0.000
#> GSM627188 4 0.5152 0.4327 0.316 0.000 0.020 0.664
#> GSM627197 2 0.1209 0.8733 0.000 0.964 0.032 0.004
#> GSM627173 2 0.0469 0.8723 0.000 0.988 0.012 0.000
#> GSM627179 2 0.2081 0.8565 0.000 0.916 0.084 0.000
#> GSM627208 3 0.3726 0.6968 0.000 0.212 0.788 0.000
#> GSM627215 2 0.2489 0.8670 0.020 0.912 0.068 0.000
#> GSM627153 2 0.3144 0.8523 0.000 0.884 0.072 0.044
#> GSM627155 1 0.4417 0.7696 0.796 0.000 0.160 0.044
#> GSM627165 3 0.4464 0.6859 0.000 0.208 0.768 0.024
#> GSM627168 1 0.3975 0.7420 0.760 0.000 0.240 0.000
#> GSM627183 1 0.2266 0.8048 0.912 0.004 0.084 0.000
#> GSM627144 3 0.3128 0.7012 0.076 0.040 0.884 0.000
#> GSM627158 1 0.3933 0.7682 0.792 0.000 0.200 0.008
#> GSM627196 2 0.1389 0.8695 0.000 0.952 0.048 0.000
#> GSM627142 3 0.6834 0.0488 0.100 0.000 0.476 0.424
#> GSM627182 3 0.3991 0.7164 0.020 0.172 0.808 0.000
#> GSM627202 1 0.4456 0.6904 0.716 0.000 0.280 0.004
#> GSM627141 3 0.4830 0.2158 0.392 0.000 0.608 0.000
#> GSM627143 3 0.4139 0.7038 0.000 0.176 0.800 0.024
#> GSM627145 3 0.4677 0.4250 0.316 0.004 0.680 0.000
#> GSM627152 3 0.5355 0.2563 0.360 0.000 0.620 0.020
#> GSM627200 1 0.2081 0.8056 0.916 0.000 0.084 0.000
#> GSM627159 4 0.0188 0.8744 0.000 0.000 0.004 0.996
#> GSM627164 3 0.3831 0.6996 0.000 0.204 0.792 0.004
#> GSM627138 1 0.3726 0.7640 0.788 0.000 0.212 0.000
#> GSM627175 2 0.3542 0.8275 0.000 0.852 0.028 0.120
#> GSM627150 3 0.5496 0.5964 0.232 0.064 0.704 0.000
#> GSM627166 1 0.5132 0.1528 0.548 0.448 0.000 0.004
#> GSM627186 3 0.3975 0.6747 0.000 0.240 0.760 0.000
#> GSM627139 3 0.4286 0.6971 0.020 0.056 0.840 0.084
#> GSM627181 2 0.4307 0.7321 0.000 0.784 0.192 0.024
#> GSM627205 3 0.3873 0.6845 0.000 0.228 0.772 0.000
#> GSM627214 3 0.5172 0.6312 0.000 0.260 0.704 0.036
#> GSM627180 3 0.3626 0.7123 0.004 0.184 0.812 0.000
#> GSM627172 3 0.5763 0.6554 0.000 0.204 0.700 0.096
#> GSM627184 1 0.5816 0.3490 0.572 0.000 0.036 0.392
#> GSM627193 2 0.1022 0.8701 0.000 0.968 0.032 0.000
#> GSM627191 4 0.0707 0.8651 0.020 0.000 0.000 0.980
#> GSM627176 3 0.1978 0.6825 0.068 0.000 0.928 0.004
#> GSM627194 2 0.0336 0.8714 0.000 0.992 0.008 0.000
#> GSM627154 2 0.4244 0.6961 0.032 0.800 0.000 0.168
#> GSM627187 3 0.1557 0.6906 0.056 0.000 0.944 0.000
#> GSM627198 2 0.2714 0.8352 0.000 0.884 0.004 0.112
#> GSM627160 4 0.0336 0.8748 0.000 0.000 0.008 0.992
#> GSM627185 1 0.0000 0.7940 1.000 0.000 0.000 0.000
#> GSM627206 3 0.4866 0.1649 0.404 0.000 0.596 0.000
#> GSM627161 1 0.4059 0.7668 0.788 0.000 0.200 0.012
#> GSM627162 3 0.1629 0.7088 0.024 0.024 0.952 0.000
#> GSM627210 1 0.4372 0.5628 0.728 0.268 0.004 0.000
#> GSM627189 2 0.0336 0.8714 0.000 0.992 0.008 0.000
show/hide code output
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM627128 4 0.1956 0.8017 0.000 0.008 0.000 0.916 0.076
#> GSM627110 5 0.5936 0.4298 0.160 0.020 0.172 0.000 0.648
#> GSM627132 1 0.2676 0.7410 0.884 0.000 0.036 0.000 0.080
#> GSM627107 3 0.5439 0.2645 0.000 0.024 0.660 0.056 0.260
#> GSM627103 2 0.0000 0.7692 0.000 1.000 0.000 0.000 0.000
#> GSM627114 3 0.4370 0.2694 0.056 0.000 0.744 0.000 0.200
#> GSM627134 5 0.6952 0.3101 0.000 0.312 0.060 0.112 0.516
#> GSM627137 2 0.4220 0.3701 0.000 0.688 0.300 0.004 0.008
#> GSM627148 5 0.4574 0.3326 0.012 0.000 0.412 0.000 0.576
#> GSM627101 4 0.1618 0.8122 0.000 0.008 0.008 0.944 0.040
#> GSM627130 4 0.1012 0.8102 0.000 0.000 0.020 0.968 0.012
#> GSM627071 1 0.7236 0.3488 0.500 0.268 0.040 0.004 0.188
#> GSM627118 5 0.6799 0.1016 0.000 0.400 0.020 0.152 0.428
#> GSM627094 2 0.0404 0.7685 0.000 0.988 0.012 0.000 0.000
#> GSM627122 5 0.6315 0.2727 0.260 0.000 0.172 0.008 0.560
#> GSM627115 2 0.1211 0.7672 0.000 0.960 0.024 0.000 0.016
#> GSM627125 4 0.4347 0.5914 0.000 0.004 0.024 0.716 0.256
#> GSM627174 2 0.1628 0.7648 0.056 0.936 0.008 0.000 0.000
#> GSM627102 2 0.4826 -0.1729 0.000 0.508 0.472 0.020 0.000
#> GSM627073 5 0.4651 0.4946 0.008 0.028 0.248 0.004 0.712
#> GSM627108 2 0.2127 0.7131 0.000 0.892 0.108 0.000 0.000
#> GSM627126 1 0.1560 0.7322 0.948 0.000 0.004 0.028 0.020
#> GSM627078 2 0.3293 0.7401 0.028 0.860 0.004 0.096 0.012
#> GSM627090 3 0.5390 -0.0254 0.076 0.000 0.600 0.000 0.324
#> GSM627099 2 0.4177 0.6820 0.000 0.804 0.020 0.060 0.116
#> GSM627105 4 0.5760 0.3538 0.000 0.008 0.080 0.572 0.340
#> GSM627117 3 0.5437 0.2429 0.012 0.052 0.608 0.000 0.328
#> GSM627121 3 0.5133 0.3382 0.000 0.048 0.704 0.028 0.220
#> GSM627127 2 0.6511 0.4168 0.004 0.588 0.020 0.180 0.208
#> GSM627087 2 0.1310 0.7655 0.000 0.956 0.020 0.000 0.024
#> GSM627089 5 0.4665 0.4829 0.048 0.000 0.260 0.000 0.692
#> GSM627092 3 0.5434 0.3860 0.000 0.408 0.540 0.008 0.044
#> GSM627076 5 0.5272 0.4221 0.008 0.000 0.328 0.048 0.616
#> GSM627136 5 0.3612 0.5152 0.008 0.000 0.228 0.000 0.764
#> GSM627081 5 0.5003 0.2714 0.000 0.016 0.400 0.012 0.572
#> GSM627091 2 0.3047 0.7404 0.004 0.884 0.020 0.036 0.056
#> GSM627097 2 0.8203 0.3107 0.140 0.500 0.036 0.120 0.204
#> GSM627072 5 0.3239 0.5509 0.012 0.004 0.156 0.000 0.828
#> GSM627080 1 0.2505 0.7370 0.888 0.000 0.020 0.000 0.092
#> GSM627088 1 0.5577 0.6868 0.700 0.040 0.092 0.000 0.168
#> GSM627109 1 0.3905 0.6644 0.752 0.004 0.012 0.000 0.232
#> GSM627111 1 0.2769 0.7421 0.876 0.000 0.092 0.000 0.032
#> GSM627113 1 0.3527 0.7161 0.804 0.000 0.024 0.000 0.172
#> GSM627133 5 0.4922 0.1954 0.004 0.424 0.020 0.000 0.552
#> GSM627177 5 0.7691 0.2138 0.208 0.336 0.024 0.024 0.408
#> GSM627086 2 0.0854 0.7702 0.000 0.976 0.012 0.004 0.008
#> GSM627095 1 0.2700 0.6992 0.884 0.000 0.024 0.088 0.004
#> GSM627079 5 0.1885 0.5549 0.044 0.020 0.000 0.004 0.932
#> GSM627082 4 0.1549 0.7986 0.016 0.000 0.040 0.944 0.000
#> GSM627074 1 0.5131 0.4209 0.532 0.008 0.024 0.000 0.436
#> GSM627077 1 0.5113 0.5458 0.620 0.000 0.056 0.000 0.324
#> GSM627093 1 0.5079 0.6603 0.704 0.024 0.048 0.000 0.224
#> GSM627120 3 0.5559 0.3565 0.008 0.440 0.508 0.004 0.040
#> GSM627124 2 0.3405 0.7391 0.052 0.860 0.004 0.072 0.012
#> GSM627075 2 0.4786 0.1837 0.012 0.620 0.356 0.000 0.012
#> GSM627085 2 0.4382 0.6788 0.008 0.780 0.016 0.164 0.032
#> GSM627119 1 0.5223 0.6084 0.680 0.068 0.012 0.000 0.240
#> GSM627116 5 0.7550 0.0740 0.108 0.404 0.024 0.052 0.412
#> GSM627084 1 0.6105 0.4987 0.480 0.000 0.392 0.000 0.128
#> GSM627096 5 0.6883 0.1460 0.000 0.340 0.012 0.208 0.440
#> GSM627100 3 0.5944 -0.0016 0.012 0.000 0.552 0.084 0.352
#> GSM627112 4 0.2206 0.7816 0.004 0.068 0.000 0.912 0.016
#> GSM627083 1 0.5412 0.1536 0.520 0.000 0.048 0.428 0.004
#> GSM627098 1 0.3810 0.7129 0.788 0.000 0.036 0.000 0.176
#> GSM627104 1 0.3521 0.6603 0.824 0.144 0.008 0.000 0.024
#> GSM627131 5 0.4637 -0.1923 0.452 0.000 0.012 0.000 0.536
#> GSM627106 5 0.5012 0.2767 0.000 0.016 0.404 0.012 0.568
#> GSM627123 1 0.5317 0.7260 0.728 0.000 0.144 0.044 0.084
#> GSM627129 3 0.8540 0.2154 0.000 0.220 0.288 0.204 0.288
#> GSM627216 2 0.1568 0.7615 0.000 0.944 0.036 0.000 0.020
#> GSM627212 2 0.3227 0.7351 0.000 0.868 0.020 0.040 0.072
#> GSM627190 3 0.4982 0.4093 0.016 0.076 0.728 0.000 0.180
#> GSM627169 3 0.4811 0.2602 0.008 0.472 0.512 0.000 0.008
#> GSM627167 3 0.6941 0.4705 0.000 0.224 0.532 0.208 0.036
#> GSM627192 1 0.3760 0.6595 0.784 0.000 0.028 0.188 0.000
#> GSM627203 5 0.2305 0.5639 0.012 0.000 0.092 0.000 0.896
#> GSM627151 2 0.6624 0.1342 0.052 0.504 0.024 0.032 0.388
#> GSM627163 1 0.0671 0.7320 0.980 0.000 0.000 0.004 0.016
#> GSM627211 2 0.2127 0.7172 0.000 0.892 0.108 0.000 0.000
#> GSM627171 3 0.4066 0.4797 0.004 0.324 0.672 0.000 0.000
#> GSM627209 2 0.2859 0.7445 0.000 0.876 0.016 0.096 0.012
#> GSM627135 1 0.1492 0.7294 0.948 0.000 0.008 0.004 0.040
#> GSM627170 2 0.4933 0.4108 0.000 0.692 0.228 0.000 0.080
#> GSM627178 1 0.5120 0.5937 0.680 0.056 0.012 0.000 0.252
#> GSM627199 2 0.4050 0.6892 0.036 0.784 0.000 0.172 0.008
#> GSM627213 4 0.3612 0.6723 0.000 0.172 0.000 0.800 0.028
#> GSM627140 4 0.3388 0.6987 0.008 0.000 0.200 0.792 0.000
#> GSM627149 1 0.6422 0.5896 0.532 0.000 0.352 0.056 0.060
#> GSM627147 3 0.7159 0.3689 0.000 0.272 0.448 0.256 0.024
#> GSM627195 5 0.1808 0.5657 0.020 0.004 0.040 0.000 0.936
#> GSM627204 2 0.0510 0.7680 0.000 0.984 0.016 0.000 0.000
#> GSM627207 2 0.4242 -0.0265 0.000 0.572 0.428 0.000 0.000
#> GSM627157 1 0.4162 0.7090 0.768 0.000 0.056 0.000 0.176
#> GSM627201 2 0.0510 0.7682 0.000 0.984 0.016 0.000 0.000
#> GSM627146 2 0.0486 0.7705 0.004 0.988 0.004 0.004 0.000
#> GSM627156 3 0.4735 0.3046 0.000 0.460 0.524 0.000 0.016
#> GSM627188 1 0.5287 0.5472 0.656 0.000 0.080 0.260 0.004
#> GSM627197 2 0.0771 0.7701 0.000 0.976 0.004 0.020 0.000
#> GSM627173 2 0.1830 0.7412 0.008 0.924 0.068 0.000 0.000
#> GSM627179 2 0.1197 0.7585 0.000 0.952 0.048 0.000 0.000
#> GSM627208 3 0.6398 0.4844 0.000 0.300 0.500 0.000 0.200
#> GSM627215 5 0.5061 0.1489 0.000 0.444 0.008 0.020 0.528
#> GSM627153 2 0.3120 0.7349 0.000 0.856 0.012 0.116 0.016
#> GSM627155 1 0.5138 0.7088 0.732 0.000 0.168 0.056 0.044
#> GSM627165 3 0.6665 0.4329 0.000 0.348 0.504 0.032 0.116
#> GSM627168 1 0.6424 0.4898 0.508 0.000 0.240 0.000 0.252
#> GSM627183 5 0.4106 0.3470 0.256 0.000 0.020 0.000 0.724
#> GSM627144 5 0.2909 0.5208 0.000 0.012 0.140 0.000 0.848
#> GSM627158 1 0.5382 0.6686 0.644 0.000 0.252 0.000 0.104
#> GSM627196 2 0.0510 0.7680 0.000 0.984 0.016 0.000 0.000
#> GSM627142 5 0.6120 0.4349 0.008 0.000 0.172 0.224 0.596
#> GSM627182 5 0.6104 0.0939 0.008 0.096 0.432 0.000 0.464
#> GSM627202 5 0.6806 -0.0767 0.296 0.000 0.348 0.000 0.356
#> GSM627141 3 0.5129 -0.0261 0.328 0.020 0.628 0.000 0.024
#> GSM627143 3 0.5646 0.5381 0.000 0.272 0.640 0.028 0.060
#> GSM627145 5 0.2773 0.5614 0.020 0.000 0.112 0.000 0.868
#> GSM627152 5 0.2673 0.5655 0.028 0.000 0.072 0.008 0.892
#> GSM627200 5 0.4576 -0.0548 0.376 0.000 0.016 0.000 0.608
#> GSM627159 4 0.1525 0.8023 0.012 0.000 0.036 0.948 0.004
#> GSM627164 3 0.4196 0.4519 0.000 0.356 0.640 0.000 0.004
#> GSM627138 1 0.6170 0.5519 0.524 0.000 0.320 0.000 0.156
#> GSM627175 2 0.3167 0.7264 0.000 0.836 0.008 0.148 0.008
#> GSM627150 5 0.4181 0.5107 0.016 0.008 0.240 0.000 0.736
#> GSM627166 1 0.5054 0.5590 0.732 0.168 0.024 0.000 0.076
#> GSM627186 3 0.4892 0.2377 0.004 0.488 0.492 0.000 0.016
#> GSM627139 5 0.5528 0.3974 0.000 0.012 0.204 0.112 0.672
#> GSM627181 2 0.2629 0.6775 0.000 0.860 0.136 0.004 0.000
#> GSM627205 2 0.6628 -0.3589 0.000 0.408 0.372 0.000 0.220
#> GSM627214 2 0.6605 0.0475 0.000 0.524 0.344 0.076 0.056
#> GSM627180 5 0.5081 0.4956 0.000 0.092 0.140 0.028 0.740
#> GSM627172 3 0.5352 0.3383 0.004 0.392 0.556 0.048 0.000
#> GSM627184 1 0.5737 0.5451 0.620 0.000 0.104 0.268 0.008
#> GSM627193 2 0.1197 0.7536 0.000 0.952 0.048 0.000 0.000
#> GSM627191 4 0.4635 0.6446 0.180 0.004 0.064 0.748 0.004
#> GSM627176 3 0.4039 0.2415 0.008 0.000 0.720 0.004 0.268
#> GSM627194 2 0.2381 0.7435 0.052 0.908 0.036 0.000 0.004
#> GSM627154 2 0.5469 0.3678 0.020 0.600 0.008 0.348 0.024
#> GSM627187 3 0.3093 0.4307 0.016 0.032 0.872 0.000 0.080
#> GSM627198 2 0.2850 0.7488 0.036 0.872 0.000 0.092 0.000
#> GSM627160 4 0.3435 0.7601 0.004 0.000 0.020 0.820 0.156
#> GSM627185 1 0.1673 0.7383 0.944 0.008 0.016 0.000 0.032
#> GSM627206 3 0.7027 -0.0547 0.280 0.028 0.488 0.000 0.204
#> GSM627161 1 0.5384 0.6523 0.632 0.000 0.288 0.004 0.076
#> GSM627162 3 0.2928 0.4148 0.012 0.008 0.876 0.008 0.096
#> GSM627210 5 0.6300 0.0161 0.376 0.088 0.024 0.000 0.512
#> GSM627189 2 0.0898 0.7685 0.008 0.972 0.020 0.000 0.000
show/hide code output
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM627128 6 0.2743 0.6822 0.000 0.032 0.000 0.028 0.060 0.880
#> GSM627110 4 0.3730 0.5370 0.008 0.000 0.160 0.784 0.048 0.000
#> GSM627132 1 0.1267 0.7400 0.940 0.000 0.000 0.000 0.060 0.000
#> GSM627107 5 0.5755 0.6373 0.028 0.068 0.192 0.004 0.664 0.044
#> GSM627103 2 0.1405 0.7602 0.000 0.948 0.024 0.024 0.004 0.000
#> GSM627114 5 0.5492 0.5413 0.140 0.008 0.272 0.000 0.580 0.000
#> GSM627134 2 0.6095 0.2839 0.000 0.504 0.008 0.032 0.356 0.100
#> GSM627137 3 0.5667 0.5787 0.000 0.332 0.536 0.120 0.004 0.008
#> GSM627148 5 0.2585 0.7475 0.024 0.012 0.084 0.000 0.880 0.000
#> GSM627101 6 0.3150 0.6446 0.000 0.112 0.000 0.012 0.036 0.840
#> GSM627130 6 0.0748 0.6939 0.004 0.000 0.016 0.000 0.004 0.976
#> GSM627071 2 0.5987 0.0101 0.336 0.424 0.000 0.000 0.240 0.000
#> GSM627118 2 0.6711 0.2602 0.000 0.476 0.008 0.068 0.320 0.128
#> GSM627094 2 0.2001 0.7394 0.008 0.912 0.068 0.012 0.000 0.000
#> GSM627122 5 0.2720 0.7125 0.112 0.000 0.008 0.008 0.864 0.008
#> GSM627115 2 0.3634 0.7122 0.000 0.808 0.064 0.116 0.012 0.000
#> GSM627125 6 0.3452 0.6528 0.000 0.000 0.024 0.036 0.116 0.824
#> GSM627174 2 0.2807 0.7368 0.088 0.868 0.028 0.016 0.000 0.000
#> GSM627102 3 0.4941 0.5742 0.008 0.364 0.588 0.020 0.004 0.016
#> GSM627073 5 0.3200 0.7142 0.000 0.092 0.060 0.008 0.840 0.000
#> GSM627108 2 0.2520 0.6735 0.000 0.844 0.152 0.004 0.000 0.000
#> GSM627126 1 0.2282 0.7138 0.904 0.000 0.004 0.052 0.004 0.036
#> GSM627078 2 0.2472 0.7479 0.024 0.900 0.004 0.012 0.004 0.056
#> GSM627090 5 0.5984 0.4515 0.084 0.000 0.368 0.040 0.504 0.004
#> GSM627099 2 0.4339 0.6827 0.000 0.772 0.000 0.108 0.056 0.064
#> GSM627105 6 0.4826 0.5984 0.000 0.016 0.056 0.048 0.136 0.744
#> GSM627117 3 0.5933 0.4130 0.004 0.016 0.548 0.276 0.156 0.000
#> GSM627121 5 0.5320 0.5728 0.016 0.076 0.260 0.000 0.636 0.012
#> GSM627127 4 0.4792 0.4962 0.004 0.160 0.004 0.732 0.032 0.068
#> GSM627087 2 0.3540 0.7204 0.000 0.812 0.036 0.132 0.020 0.000
#> GSM627089 5 0.2212 0.7156 0.112 0.000 0.008 0.000 0.880 0.000
#> GSM627092 3 0.4269 0.5683 0.000 0.044 0.760 0.168 0.012 0.016
#> GSM627076 5 0.4457 0.7129 0.024 0.000 0.108 0.044 0.780 0.044
#> GSM627136 5 0.3147 0.7298 0.008 0.004 0.108 0.036 0.844 0.000
#> GSM627081 5 0.3205 0.7364 0.008 0.020 0.108 0.008 0.848 0.008
#> GSM627091 2 0.3854 0.6794 0.000 0.780 0.000 0.164 0.028 0.028
#> GSM627097 4 0.2488 0.5735 0.024 0.036 0.036 0.900 0.000 0.004
#> GSM627072 5 0.1225 0.7345 0.000 0.000 0.012 0.036 0.952 0.000
#> GSM627080 1 0.1989 0.7368 0.916 0.000 0.004 0.028 0.052 0.000
#> GSM627088 1 0.4483 0.6330 0.672 0.020 0.020 0.004 0.284 0.000
#> GSM627109 1 0.3905 0.6825 0.776 0.004 0.008 0.048 0.164 0.000
#> GSM627111 1 0.1636 0.7386 0.936 0.000 0.036 0.004 0.024 0.000
#> GSM627113 1 0.2762 0.7169 0.804 0.000 0.000 0.000 0.196 0.000
#> GSM627133 4 0.6019 0.3498 0.000 0.332 0.020 0.496 0.152 0.000
#> GSM627177 2 0.7066 0.0983 0.220 0.456 0.000 0.064 0.248 0.012
#> GSM627086 2 0.0790 0.7532 0.000 0.968 0.032 0.000 0.000 0.000
#> GSM627095 1 0.3666 0.6868 0.820 0.000 0.032 0.064 0.000 0.084
#> GSM627079 5 0.4029 0.5596 0.016 0.016 0.000 0.216 0.744 0.008
#> GSM627082 6 0.1401 0.6869 0.020 0.000 0.028 0.004 0.000 0.948
#> GSM627074 4 0.3983 0.5937 0.104 0.008 0.020 0.800 0.068 0.000
#> GSM627077 4 0.6492 0.0897 0.276 0.000 0.028 0.444 0.252 0.000
#> GSM627093 4 0.4960 0.5516 0.176 0.024 0.064 0.716 0.020 0.000
#> GSM627120 3 0.6079 0.6616 0.004 0.260 0.584 0.040 0.104 0.008
#> GSM627124 2 0.2424 0.7493 0.028 0.904 0.004 0.012 0.004 0.048
#> GSM627075 3 0.5444 0.5799 0.000 0.208 0.576 0.216 0.000 0.000
#> GSM627085 2 0.3192 0.7312 0.008 0.856 0.004 0.044 0.008 0.080
#> GSM627119 1 0.4687 0.6594 0.704 0.028 0.004 0.044 0.220 0.000
#> GSM627116 4 0.7441 0.3551 0.064 0.276 0.004 0.480 0.084 0.092
#> GSM627084 1 0.5657 0.5248 0.580 0.000 0.312 0.008 0.064 0.036
#> GSM627096 2 0.6872 0.1953 0.000 0.440 0.004 0.064 0.312 0.180
#> GSM627100 5 0.4542 0.6932 0.012 0.000 0.148 0.004 0.736 0.100
#> GSM627112 6 0.2063 0.6817 0.000 0.060 0.000 0.020 0.008 0.912
#> GSM627083 6 0.5984 0.3195 0.312 0.000 0.048 0.100 0.000 0.540
#> GSM627098 1 0.2805 0.7232 0.812 0.000 0.000 0.004 0.184 0.000
#> GSM627104 1 0.3252 0.6112 0.816 0.156 0.008 0.016 0.004 0.000
#> GSM627131 1 0.5682 0.3166 0.460 0.000 0.000 0.160 0.380 0.000
#> GSM627106 5 0.2964 0.7318 0.000 0.012 0.116 0.008 0.852 0.012
#> GSM627123 1 0.7200 0.4122 0.508 0.000 0.160 0.212 0.032 0.088
#> GSM627129 6 0.8334 -0.0722 0.000 0.112 0.312 0.144 0.108 0.324
#> GSM627216 2 0.1909 0.7565 0.000 0.920 0.052 0.004 0.024 0.000
#> GSM627212 2 0.2497 0.7489 0.000 0.896 0.000 0.040 0.032 0.032
#> GSM627190 3 0.5294 0.3689 0.004 0.028 0.584 0.048 0.336 0.000
#> GSM627169 3 0.3901 0.6174 0.000 0.096 0.768 0.136 0.000 0.000
#> GSM627167 3 0.6591 0.4498 0.008 0.148 0.512 0.000 0.056 0.276
#> GSM627192 1 0.3479 0.6905 0.812 0.000 0.024 0.024 0.000 0.140
#> GSM627203 5 0.1858 0.7215 0.000 0.000 0.012 0.076 0.912 0.000
#> GSM627151 4 0.3200 0.5789 0.004 0.104 0.000 0.844 0.036 0.012
#> GSM627163 1 0.1340 0.7198 0.948 0.000 0.008 0.040 0.004 0.000
#> GSM627211 2 0.2482 0.6813 0.000 0.848 0.148 0.004 0.000 0.000
#> GSM627171 3 0.4149 0.6715 0.000 0.212 0.728 0.004 0.056 0.000
#> GSM627209 2 0.1974 0.7528 0.000 0.920 0.020 0.000 0.012 0.048
#> GSM627135 1 0.3571 0.6481 0.788 0.000 0.008 0.180 0.016 0.008
#> GSM627170 2 0.4782 0.4412 0.000 0.680 0.216 0.008 0.096 0.000
#> GSM627178 1 0.4365 0.6651 0.744 0.016 0.008 0.048 0.184 0.000
#> GSM627199 2 0.3022 0.7325 0.024 0.852 0.004 0.012 0.000 0.108
#> GSM627213 6 0.3770 0.5861 0.000 0.156 0.000 0.036 0.020 0.788
#> GSM627140 6 0.4289 0.3911 0.024 0.000 0.340 0.004 0.000 0.632
#> GSM627149 1 0.7090 0.4561 0.472 0.000 0.304 0.032 0.072 0.120
#> GSM627147 3 0.6832 0.5585 0.008 0.124 0.560 0.060 0.028 0.220
#> GSM627195 5 0.2438 0.7152 0.020 0.008 0.004 0.076 0.892 0.000
#> GSM627204 2 0.1007 0.7487 0.000 0.956 0.044 0.000 0.000 0.000
#> GSM627207 2 0.4093 -0.0101 0.000 0.584 0.404 0.000 0.012 0.000
#> GSM627157 1 0.2668 0.7254 0.828 0.000 0.000 0.004 0.168 0.000
#> GSM627201 2 0.0858 0.7542 0.000 0.968 0.028 0.000 0.004 0.000
#> GSM627146 2 0.1515 0.7577 0.008 0.944 0.020 0.028 0.000 0.000
#> GSM627156 3 0.4733 0.6108 0.000 0.344 0.608 0.020 0.028 0.000
#> GSM627188 1 0.4761 0.5361 0.648 0.000 0.056 0.012 0.000 0.284
#> GSM627197 2 0.1251 0.7595 0.000 0.956 0.008 0.024 0.000 0.012
#> GSM627173 2 0.2257 0.7411 0.020 0.904 0.060 0.016 0.000 0.000
#> GSM627179 2 0.2070 0.7243 0.000 0.892 0.100 0.000 0.008 0.000
#> GSM627208 5 0.5755 0.2016 0.000 0.296 0.204 0.000 0.500 0.000
#> GSM627215 2 0.5345 0.2226 0.000 0.520 0.012 0.048 0.408 0.012
#> GSM627153 2 0.2230 0.7502 0.000 0.904 0.016 0.000 0.016 0.064
#> GSM627155 1 0.4088 0.7151 0.804 0.000 0.088 0.012 0.040 0.056
#> GSM627165 3 0.6437 0.5719 0.000 0.116 0.572 0.236 0.052 0.024
#> GSM627168 1 0.4905 0.3292 0.524 0.000 0.052 0.004 0.420 0.000
#> GSM627183 5 0.3529 0.5914 0.208 0.000 0.000 0.028 0.764 0.000
#> GSM627144 4 0.4847 0.5062 0.000 0.000 0.124 0.656 0.220 0.000
#> GSM627158 1 0.3707 0.7263 0.808 0.000 0.076 0.008 0.104 0.004
#> GSM627196 2 0.1075 0.7478 0.000 0.952 0.048 0.000 0.000 0.000
#> GSM627142 5 0.3178 0.7040 0.008 0.012 0.004 0.008 0.836 0.132
#> GSM627182 5 0.4624 0.6424 0.020 0.176 0.084 0.000 0.720 0.000
#> GSM627202 5 0.3950 0.5437 0.240 0.000 0.040 0.000 0.720 0.000
#> GSM627141 3 0.5336 0.3058 0.124 0.000 0.664 0.176 0.036 0.000
#> GSM627143 3 0.4783 0.6667 0.004 0.156 0.720 0.000 0.100 0.020
#> GSM627145 5 0.1767 0.7354 0.012 0.000 0.020 0.036 0.932 0.000
#> GSM627152 5 0.4423 0.3875 0.008 0.000 0.024 0.312 0.652 0.004
#> GSM627200 4 0.4330 0.5886 0.056 0.000 0.044 0.764 0.136 0.000
#> GSM627159 6 0.1401 0.6886 0.020 0.000 0.028 0.004 0.000 0.948
#> GSM627164 3 0.4427 0.6572 0.000 0.292 0.660 0.000 0.044 0.004
#> GSM627138 1 0.5357 0.5634 0.588 0.000 0.136 0.004 0.272 0.000
#> GSM627175 2 0.2149 0.7427 0.000 0.888 0.004 0.004 0.000 0.104
#> GSM627150 5 0.1426 0.7442 0.028 0.016 0.008 0.000 0.948 0.000
#> GSM627166 4 0.4533 0.0170 0.468 0.024 0.004 0.504 0.000 0.000
#> GSM627186 3 0.4706 0.6649 0.004 0.280 0.660 0.044 0.012 0.000
#> GSM627139 4 0.7429 0.1845 0.000 0.000 0.168 0.392 0.240 0.200
#> GSM627181 2 0.2020 0.7246 0.000 0.896 0.096 0.000 0.000 0.008
#> GSM627205 2 0.6501 -0.2864 0.000 0.380 0.332 0.020 0.268 0.000
#> GSM627214 2 0.3730 0.6595 0.000 0.812 0.076 0.000 0.088 0.024
#> GSM627180 5 0.4068 0.6719 0.000 0.100 0.036 0.044 0.804 0.016
#> GSM627172 3 0.5308 0.5028 0.008 0.400 0.536 0.004 0.024 0.028
#> GSM627184 1 0.4409 0.6213 0.708 0.000 0.048 0.008 0.004 0.232
#> GSM627193 2 0.2373 0.7309 0.004 0.888 0.084 0.024 0.000 0.000
#> GSM627191 6 0.4647 0.5943 0.124 0.000 0.084 0.048 0.000 0.744
#> GSM627176 3 0.5460 0.3271 0.012 0.000 0.628 0.092 0.252 0.016
#> GSM627194 2 0.5250 0.4955 0.016 0.632 0.108 0.244 0.000 0.000
#> GSM627154 2 0.3966 0.6847 0.016 0.784 0.004 0.036 0.004 0.156
#> GSM627187 3 0.3529 0.5684 0.000 0.036 0.788 0.004 0.172 0.000
#> GSM627198 2 0.2687 0.7502 0.016 0.884 0.008 0.020 0.000 0.072
#> GSM627160 6 0.5889 0.3063 0.008 0.000 0.080 0.308 0.040 0.564
#> GSM627185 1 0.1793 0.7308 0.928 0.000 0.004 0.032 0.036 0.000
#> GSM627206 5 0.6023 0.3513 0.296 0.052 0.104 0.000 0.548 0.000
#> GSM627161 1 0.4557 0.7083 0.756 0.000 0.124 0.016 0.088 0.016
#> GSM627162 3 0.3733 0.5698 0.004 0.024 0.784 0.008 0.176 0.004
#> GSM627210 1 0.6507 0.3168 0.484 0.048 0.004 0.148 0.316 0.000
#> GSM627189 2 0.2826 0.7324 0.000 0.856 0.052 0.092 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two
samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures
which are significantly different between classes which can be candidate marks
for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known
annotation is numeric, one-way ANOVA test is applied, and if the known
annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) age(p) other(p) k
#> SD:NMF 143 0.710 0.4959 0.0202 2
#> SD:NMF 137 0.501 0.3431 0.0328 3
#> SD:NMF 124 0.567 0.0948 0.0676 4
#> SD:NMF 80 0.652 0.2115 0.0667 5
#> SD:NMF 112 0.225 0.4684 0.1765 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
CV:hclust
The object with results only for a single top-value method and a single partition method
can be extracted as:
res = res_list["CV", "hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51882 rows and 146 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
- The first row: a plot of the ECDF (empirical cumulative distribution
function) curves of the consensus matrix for each
k and the heatmap of
predicted classes for each k.
- The second row: heatmaps of the consensus matrix for each
k.
- The third row: heatmaps of the membership matrix for each
k.
- The fouth row: heatmaps of the signatures for each
k.
All the plots in panels can be made by individual functions and they are
plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
- ECDF curves of the consensus matrix for each
k;
- 1-PAC. The PAC
score
measures the proportion of the ambiguous subgrouping.
- Mean silhouette score.
- Concordance. The mean probability of fiting the consensus class ids in all
partitions.
- Area increased. Denote \(A_k\) as the area under the ECDF curve for current
k, the area increased is defined as \(A_k - A_{k-1}\).
- Rand index. The percent of pairs of samples that are both in a same cluster
or both are not in a same cluster in the partition of k and k-1.
- Jaccard index. The ratio of pairs of samples are both in a same cluster in
the partition of k and k-1 and the pairs of samples are both in a same
cluster in the partition k or k-1.
The detailed explanations of these statistics can be found in the cola
vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.456 0.819 0.888 0.3523 0.679 0.679
#> 3 3 0.345 0.684 0.824 0.7209 0.690 0.559
#> 4 4 0.456 0.616 0.722 0.1796 0.784 0.516
#> 5 5 0.575 0.661 0.798 0.0790 0.919 0.716
#> 6 6 0.615 0.625 0.756 0.0389 1.000 1.000
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
- All \(k\) with Jaccard index larger than 0.95 are removed because increasing
\(k\) does not provide enough extra information. If all \(k\) are removed, it is
marked as no subgroup is detected.
- For all \(k\) with 1-PAC score larger than 0.9, the maximal \(k\) is taken as
the best \(k\), and other \(k\) are marked as optional \(k\).
- If it does not fit the second rule. The \(k\) with the maximal vote of the
highest 1-PAC score, highest mean silhouette, and highest concordance is
taken as the best \(k\).
suggest_best_k(res)
#> [1] 3
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
show/hide code output
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM627128 2 0.0376 0.8692 0.004 0.996
#> GSM627110 2 0.9552 0.5883 0.376 0.624
#> GSM627132 1 0.0376 0.8914 0.996 0.004
#> GSM627107 2 0.0376 0.8692 0.004 0.996
#> GSM627103 2 0.4161 0.8891 0.084 0.916
#> GSM627114 2 0.9635 0.5640 0.388 0.612
#> GSM627134 2 0.0000 0.8709 0.000 1.000
#> GSM627137 2 0.0672 0.8745 0.008 0.992
#> GSM627148 2 0.8207 0.7805 0.256 0.744
#> GSM627101 2 0.0376 0.8692 0.004 0.996
#> GSM627130 2 0.0376 0.8692 0.004 0.996
#> GSM627071 2 0.8016 0.7930 0.244 0.756
#> GSM627118 2 0.0376 0.8692 0.004 0.996
#> GSM627094 2 0.5178 0.8829 0.116 0.884
#> GSM627122 2 0.8207 0.7813 0.256 0.744
#> GSM627115 2 0.4161 0.8891 0.084 0.916
#> GSM627125 2 0.0376 0.8692 0.004 0.996
#> GSM627174 2 0.3431 0.8887 0.064 0.936
#> GSM627102 2 0.4022 0.8901 0.080 0.920
#> GSM627073 2 0.7453 0.8234 0.212 0.788
#> GSM627108 2 0.5178 0.8829 0.116 0.884
#> GSM627126 1 0.0938 0.8915 0.988 0.012
#> GSM627078 2 0.0376 0.8692 0.004 0.996
#> GSM627090 2 0.3879 0.8891 0.076 0.924
#> GSM627099 2 0.2236 0.8838 0.036 0.964
#> GSM627105 2 0.0376 0.8692 0.004 0.996
#> GSM627117 2 0.7883 0.8055 0.236 0.764
#> GSM627121 2 0.0376 0.8692 0.004 0.996
#> GSM627127 2 0.0376 0.8692 0.004 0.996
#> GSM627087 2 0.4161 0.8891 0.084 0.916
#> GSM627089 2 0.8608 0.7443 0.284 0.716
#> GSM627092 2 0.4022 0.8899 0.080 0.920
#> GSM627076 2 0.3114 0.8881 0.056 0.944
#> GSM627136 2 0.9248 0.6599 0.340 0.660
#> GSM627081 2 0.0376 0.8692 0.004 0.996
#> GSM627091 2 0.2236 0.8838 0.036 0.964
#> GSM627097 2 0.4161 0.8550 0.084 0.916
#> GSM627072 2 0.8661 0.7411 0.288 0.712
#> GSM627080 1 0.0376 0.8914 0.996 0.004
#> GSM627088 2 0.9248 0.6603 0.340 0.660
#> GSM627109 1 0.0672 0.8926 0.992 0.008
#> GSM627111 1 0.0376 0.8914 0.996 0.004
#> GSM627113 1 0.8016 0.6097 0.756 0.244
#> GSM627133 2 0.4431 0.8881 0.092 0.908
#> GSM627177 2 0.9580 0.4835 0.380 0.620
#> GSM627086 2 0.3584 0.8896 0.068 0.932
#> GSM627095 1 0.0938 0.8915 0.988 0.012
#> GSM627079 2 0.7950 0.7977 0.240 0.760
#> GSM627082 2 0.0376 0.8692 0.004 0.996
#> GSM627074 1 0.1414 0.8863 0.980 0.020
#> GSM627077 2 0.9608 0.5726 0.384 0.616
#> GSM627093 1 0.1414 0.8863 0.980 0.020
#> GSM627120 2 0.2948 0.8858 0.052 0.948
#> GSM627124 2 0.0376 0.8692 0.004 0.996
#> GSM627075 2 0.5178 0.8829 0.116 0.884
#> GSM627085 2 0.0376 0.8692 0.004 0.996
#> GSM627119 1 0.0672 0.8926 0.992 0.008
#> GSM627116 2 0.9580 0.4835 0.380 0.620
#> GSM627084 2 0.9323 0.6461 0.348 0.652
#> GSM627096 2 0.0376 0.8692 0.004 0.996
#> GSM627100 2 0.3114 0.8881 0.056 0.944
#> GSM627112 2 0.0672 0.8681 0.008 0.992
#> GSM627083 1 0.9963 0.0276 0.536 0.464
#> GSM627098 2 0.9323 0.6461 0.348 0.652
#> GSM627104 1 0.0672 0.8926 0.992 0.008
#> GSM627131 2 0.8081 0.7905 0.248 0.752
#> GSM627106 2 0.0376 0.8692 0.004 0.996
#> GSM627123 1 0.0938 0.8914 0.988 0.012
#> GSM627129 2 0.0376 0.8728 0.004 0.996
#> GSM627216 2 0.4431 0.8881 0.092 0.908
#> GSM627212 2 0.4298 0.8888 0.088 0.912
#> GSM627190 2 0.7815 0.8093 0.232 0.768
#> GSM627169 2 0.5842 0.8743 0.140 0.860
#> GSM627167 2 0.2948 0.8877 0.052 0.948
#> GSM627192 1 0.0938 0.8915 0.988 0.012
#> GSM627203 2 0.5946 0.8716 0.144 0.856
#> GSM627151 2 0.4815 0.8856 0.104 0.896
#> GSM627163 1 0.0376 0.8914 0.996 0.004
#> GSM627211 2 0.5178 0.8829 0.116 0.884
#> GSM627171 2 0.5059 0.8841 0.112 0.888
#> GSM627209 2 0.0938 0.8759 0.012 0.988
#> GSM627135 1 0.5519 0.7975 0.872 0.128
#> GSM627170 2 0.2043 0.8828 0.032 0.968
#> GSM627178 2 0.9580 0.4835 0.380 0.620
#> GSM627199 2 0.0376 0.8692 0.004 0.996
#> GSM627213 2 0.0376 0.8692 0.004 0.996
#> GSM627140 2 0.4562 0.8563 0.096 0.904
#> GSM627149 1 0.0938 0.8914 0.988 0.012
#> GSM627147 2 0.3879 0.8898 0.076 0.924
#> GSM627195 2 0.5946 0.8716 0.144 0.856
#> GSM627204 2 0.5178 0.8829 0.116 0.884
#> GSM627207 2 0.5178 0.8829 0.116 0.884
#> GSM627157 1 0.9850 0.0845 0.572 0.428
#> GSM627201 2 0.3431 0.8887 0.064 0.936
#> GSM627146 2 0.2948 0.8881 0.052 0.948
#> GSM627156 2 0.5842 0.8743 0.140 0.860
#> GSM627188 1 0.0938 0.8915 0.988 0.012
#> GSM627197 2 0.2236 0.8836 0.036 0.964
#> GSM627173 2 0.5178 0.8829 0.116 0.884
#> GSM627179 2 0.4298 0.8888 0.088 0.912
#> GSM627208 2 0.5737 0.8755 0.136 0.864
#> GSM627215 2 0.4815 0.8875 0.104 0.896
#> GSM627153 2 0.0938 0.8759 0.012 0.988
#> GSM627155 1 0.0672 0.8924 0.992 0.008
#> GSM627165 2 0.0672 0.8745 0.008 0.992
#> GSM627168 1 0.9954 -0.0554 0.540 0.460
#> GSM627183 2 0.8813 0.7232 0.300 0.700
#> GSM627144 2 0.5946 0.8716 0.144 0.856
#> GSM627158 1 0.0376 0.8914 0.996 0.004
#> GSM627196 2 0.5178 0.8829 0.116 0.884
#> GSM627142 2 0.0376 0.8692 0.004 0.996
#> GSM627182 2 0.5737 0.8755 0.136 0.864
#> GSM627202 1 0.9850 0.0845 0.572 0.428
#> GSM627141 2 0.9522 0.5976 0.372 0.628
#> GSM627143 2 0.3733 0.8900 0.072 0.928
#> GSM627145 2 0.7883 0.8008 0.236 0.764
#> GSM627152 2 0.7528 0.8190 0.216 0.784
#> GSM627200 2 0.8327 0.7741 0.264 0.736
#> GSM627159 2 0.0376 0.8692 0.004 0.996
#> GSM627164 2 0.5059 0.8848 0.112 0.888
#> GSM627138 1 0.0376 0.8914 0.996 0.004
#> GSM627175 2 0.0376 0.8692 0.004 0.996
#> GSM627150 2 0.8016 0.7930 0.244 0.756
#> GSM627166 1 0.8555 0.6107 0.720 0.280
#> GSM627186 2 0.5842 0.8743 0.140 0.860
#> GSM627139 2 0.4815 0.8856 0.104 0.896
#> GSM627181 2 0.2236 0.8836 0.036 0.964
#> GSM627205 2 0.4431 0.8881 0.092 0.908
#> GSM627214 2 0.0672 0.8744 0.008 0.992
#> GSM627180 2 0.4815 0.8875 0.104 0.896
#> GSM627172 2 0.4022 0.8901 0.080 0.920
#> GSM627184 1 0.0672 0.8924 0.992 0.008
#> GSM627193 2 0.5178 0.8829 0.116 0.884
#> GSM627191 2 0.4562 0.8479 0.096 0.904
#> GSM627176 2 0.6343 0.8646 0.160 0.840
#> GSM627194 2 0.4690 0.8875 0.100 0.900
#> GSM627154 2 0.0376 0.8692 0.004 0.996
#> GSM627187 2 0.7815 0.8093 0.232 0.768
#> GSM627198 2 0.0376 0.8692 0.004 0.996
#> GSM627160 2 0.4562 0.8563 0.096 0.904
#> GSM627185 1 0.0672 0.8926 0.992 0.008
#> GSM627206 2 0.8608 0.7443 0.284 0.716
#> GSM627161 1 0.0376 0.8914 0.996 0.004
#> GSM627162 2 0.6148 0.8691 0.152 0.848
#> GSM627210 1 0.0672 0.8926 0.992 0.008
#> GSM627189 2 0.5178 0.8829 0.116 0.884
show/hide code output
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM627128 3 0.2066 0.7923 0.000 0.060 0.940
#> GSM627110 2 0.5158 0.6718 0.232 0.764 0.004
#> GSM627132 1 0.0000 0.9283 1.000 0.000 0.000
#> GSM627107 3 0.4555 0.7448 0.000 0.200 0.800
#> GSM627103 2 0.4887 0.6891 0.000 0.772 0.228
#> GSM627114 2 0.5058 0.6600 0.244 0.756 0.000
#> GSM627134 3 0.2711 0.7802 0.000 0.088 0.912
#> GSM627137 2 0.6235 0.3529 0.000 0.564 0.436
#> GSM627148 2 0.4063 0.7336 0.112 0.868 0.020
#> GSM627101 3 0.1163 0.7970 0.000 0.028 0.972
#> GSM627130 3 0.3192 0.7704 0.000 0.112 0.888
#> GSM627071 2 0.3832 0.7363 0.100 0.880 0.020
#> GSM627118 3 0.1031 0.7977 0.000 0.024 0.976
#> GSM627094 2 0.3686 0.7339 0.000 0.860 0.140
#> GSM627122 2 0.5334 0.7238 0.120 0.820 0.060
#> GSM627115 2 0.4842 0.6925 0.000 0.776 0.224
#> GSM627125 3 0.3192 0.7704 0.000 0.112 0.888
#> GSM627174 2 0.5058 0.6706 0.000 0.756 0.244
#> GSM627102 2 0.5650 0.5889 0.000 0.688 0.312
#> GSM627073 2 0.3530 0.7448 0.068 0.900 0.032
#> GSM627108 2 0.3686 0.7339 0.000 0.860 0.140
#> GSM627126 1 0.0475 0.9295 0.992 0.004 0.004
#> GSM627078 3 0.0592 0.7961 0.000 0.012 0.988
#> GSM627090 2 0.6798 0.1797 0.016 0.584 0.400
#> GSM627099 2 0.6045 0.4823 0.000 0.620 0.380
#> GSM627105 3 0.3192 0.7704 0.000 0.112 0.888
#> GSM627117 2 0.3459 0.7446 0.096 0.892 0.012
#> GSM627121 3 0.4555 0.7448 0.000 0.200 0.800
#> GSM627127 3 0.0424 0.7943 0.000 0.008 0.992
#> GSM627087 2 0.4842 0.6925 0.000 0.776 0.224
#> GSM627089 2 0.4411 0.7223 0.140 0.844 0.016
#> GSM627092 2 0.4346 0.7046 0.000 0.816 0.184
#> GSM627076 2 0.6633 0.0315 0.008 0.548 0.444
#> GSM627136 2 0.5122 0.6906 0.200 0.788 0.012
#> GSM627081 3 0.4555 0.7448 0.000 0.200 0.800
#> GSM627091 2 0.6045 0.4823 0.000 0.620 0.380
#> GSM627097 3 0.6982 0.6280 0.072 0.220 0.708
#> GSM627072 2 0.4164 0.7225 0.144 0.848 0.008
#> GSM627080 1 0.0000 0.9283 1.000 0.000 0.000
#> GSM627088 2 0.5122 0.6905 0.200 0.788 0.012
#> GSM627109 1 0.0592 0.9292 0.988 0.012 0.000
#> GSM627111 1 0.0000 0.9283 1.000 0.000 0.000
#> GSM627113 1 0.5988 0.3298 0.632 0.368 0.000
#> GSM627133 2 0.4062 0.7332 0.000 0.836 0.164
#> GSM627177 2 0.9539 0.2576 0.336 0.460 0.204
#> GSM627086 2 0.5497 0.6213 0.000 0.708 0.292
#> GSM627095 1 0.0475 0.9295 0.992 0.004 0.004
#> GSM627079 2 0.5576 0.7172 0.104 0.812 0.084
#> GSM627082 3 0.3192 0.7704 0.000 0.112 0.888
#> GSM627074 1 0.1753 0.9063 0.952 0.048 0.000
#> GSM627077 2 0.5420 0.6647 0.240 0.752 0.008
#> GSM627093 1 0.1753 0.9063 0.952 0.048 0.000
#> GSM627120 2 0.6274 0.2975 0.000 0.544 0.456
#> GSM627124 3 0.0592 0.7961 0.000 0.012 0.988
#> GSM627075 2 0.3619 0.7352 0.000 0.864 0.136
#> GSM627085 3 0.0592 0.7961 0.000 0.012 0.988
#> GSM627119 1 0.0747 0.9275 0.984 0.016 0.000
#> GSM627116 2 0.9539 0.2576 0.336 0.460 0.204
#> GSM627084 2 0.5220 0.6845 0.208 0.780 0.012
#> GSM627096 3 0.1031 0.7977 0.000 0.024 0.976
#> GSM627100 3 0.6647 0.3119 0.008 0.452 0.540
#> GSM627112 3 0.0829 0.7958 0.004 0.012 0.984
#> GSM627083 1 0.8227 0.1894 0.536 0.080 0.384
#> GSM627098 2 0.5220 0.6845 0.208 0.780 0.012
#> GSM627104 1 0.0592 0.9292 0.988 0.012 0.000
#> GSM627131 2 0.5481 0.7181 0.108 0.816 0.076
#> GSM627106 3 0.4555 0.7448 0.000 0.200 0.800
#> GSM627123 1 0.0892 0.9265 0.980 0.020 0.000
#> GSM627129 3 0.3551 0.7524 0.000 0.132 0.868
#> GSM627216 2 0.4062 0.7332 0.000 0.836 0.164
#> GSM627212 2 0.4750 0.6989 0.000 0.784 0.216
#> GSM627190 2 0.3377 0.7455 0.092 0.896 0.012
#> GSM627169 2 0.1129 0.7512 0.004 0.976 0.020
#> GSM627167 2 0.6026 0.4622 0.000 0.624 0.376
#> GSM627192 1 0.0475 0.9295 0.992 0.004 0.004
#> GSM627203 2 0.1529 0.7462 0.000 0.960 0.040
#> GSM627151 2 0.6651 0.5028 0.024 0.656 0.320
#> GSM627163 1 0.0000 0.9283 1.000 0.000 0.000
#> GSM627211 2 0.3686 0.7339 0.000 0.860 0.140
#> GSM627171 2 0.3482 0.7423 0.000 0.872 0.128
#> GSM627209 3 0.6204 0.1274 0.000 0.424 0.576
#> GSM627135 1 0.4519 0.8026 0.852 0.116 0.032
#> GSM627170 2 0.5859 0.5453 0.000 0.656 0.344
#> GSM627178 2 0.9539 0.2576 0.336 0.460 0.204
#> GSM627199 3 0.2066 0.7873 0.000 0.060 0.940
#> GSM627213 3 0.1163 0.7973 0.000 0.028 0.972
#> GSM627140 3 0.7851 0.4513 0.080 0.304 0.616
#> GSM627149 1 0.0592 0.9295 0.988 0.012 0.000
#> GSM627147 2 0.4555 0.6910 0.000 0.800 0.200
#> GSM627195 2 0.1529 0.7462 0.000 0.960 0.040
#> GSM627204 2 0.3686 0.7339 0.000 0.860 0.140
#> GSM627207 2 0.3686 0.7339 0.000 0.860 0.140
#> GSM627157 2 0.6754 0.3116 0.432 0.556 0.012
#> GSM627201 2 0.5058 0.6706 0.000 0.756 0.244
#> GSM627146 3 0.6111 0.2950 0.000 0.396 0.604
#> GSM627156 2 0.1129 0.7512 0.004 0.976 0.020
#> GSM627188 1 0.0475 0.9295 0.992 0.004 0.004
#> GSM627197 3 0.5785 0.4730 0.000 0.332 0.668
#> GSM627173 2 0.3686 0.7339 0.000 0.860 0.140
#> GSM627179 2 0.4750 0.6989 0.000 0.784 0.216
#> GSM627208 2 0.1753 0.7524 0.000 0.952 0.048
#> GSM627215 2 0.3267 0.7471 0.000 0.884 0.116
#> GSM627153 3 0.6204 0.1274 0.000 0.424 0.576
#> GSM627155 1 0.0237 0.9296 0.996 0.004 0.000
#> GSM627165 2 0.6244 0.3410 0.000 0.560 0.440
#> GSM627168 2 0.6661 0.3905 0.400 0.588 0.012
#> GSM627183 2 0.4353 0.7154 0.156 0.836 0.008
#> GSM627144 2 0.1529 0.7462 0.000 0.960 0.040
#> GSM627158 1 0.0424 0.9293 0.992 0.008 0.000
#> GSM627196 2 0.3686 0.7339 0.000 0.860 0.140
#> GSM627142 3 0.3482 0.7673 0.000 0.128 0.872
#> GSM627182 2 0.1753 0.7524 0.000 0.952 0.048
#> GSM627202 2 0.6754 0.3116 0.432 0.556 0.012
#> GSM627141 2 0.4887 0.6746 0.228 0.772 0.000
#> GSM627143 2 0.5378 0.6688 0.008 0.756 0.236
#> GSM627145 2 0.3933 0.7360 0.092 0.880 0.028
#> GSM627152 2 0.6023 0.7060 0.092 0.788 0.120
#> GSM627200 2 0.5677 0.7130 0.124 0.804 0.072
#> GSM627159 3 0.3192 0.7704 0.000 0.112 0.888
#> GSM627164 2 0.3116 0.7476 0.000 0.892 0.108
#> GSM627138 1 0.1289 0.9184 0.968 0.032 0.000
#> GSM627175 3 0.0424 0.7943 0.000 0.008 0.992
#> GSM627150 2 0.3832 0.7363 0.100 0.880 0.020
#> GSM627166 1 0.7524 0.6019 0.688 0.196 0.116
#> GSM627186 2 0.1129 0.7512 0.004 0.976 0.020
#> GSM627139 2 0.6651 0.5028 0.024 0.656 0.320
#> GSM627181 3 0.5785 0.4730 0.000 0.332 0.668
#> GSM627205 2 0.4399 0.7243 0.000 0.812 0.188
#> GSM627214 3 0.6154 0.2151 0.000 0.408 0.592
#> GSM627180 2 0.3267 0.7471 0.000 0.884 0.116
#> GSM627172 2 0.5650 0.5889 0.000 0.688 0.312
#> GSM627184 1 0.0237 0.9296 0.996 0.004 0.000
#> GSM627193 2 0.3686 0.7339 0.000 0.860 0.140
#> GSM627191 3 0.7031 0.6486 0.088 0.196 0.716
#> GSM627176 2 0.4369 0.7415 0.040 0.864 0.096
#> GSM627194 2 0.4750 0.7013 0.000 0.784 0.216
#> GSM627154 3 0.0592 0.7961 0.000 0.012 0.988
#> GSM627187 2 0.3377 0.7455 0.092 0.896 0.012
#> GSM627198 3 0.2066 0.7873 0.000 0.060 0.940
#> GSM627160 3 0.7851 0.4513 0.080 0.304 0.616
#> GSM627185 1 0.0424 0.9299 0.992 0.008 0.000
#> GSM627206 2 0.4411 0.7223 0.140 0.844 0.016
#> GSM627161 1 0.0424 0.9293 0.992 0.008 0.000
#> GSM627162 2 0.3406 0.7582 0.028 0.904 0.068
#> GSM627210 1 0.0747 0.9275 0.984 0.016 0.000
#> GSM627189 2 0.3686 0.7339 0.000 0.860 0.140
show/hide code output
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM627128 4 0.3557 0.7290 0.000 0.108 0.036 0.856
#> GSM627110 3 0.4799 0.7126 0.224 0.032 0.744 0.000
#> GSM627132 1 0.0000 0.9242 1.000 0.000 0.000 0.000
#> GSM627107 4 0.3710 0.6514 0.000 0.004 0.192 0.804
#> GSM627103 2 0.3606 0.7227 0.000 0.840 0.140 0.020
#> GSM627114 3 0.5596 0.7012 0.236 0.068 0.696 0.000
#> GSM627134 4 0.5112 0.6489 0.000 0.384 0.008 0.608
#> GSM627137 2 0.4875 0.5625 0.000 0.772 0.068 0.160
#> GSM627148 3 0.3674 0.7301 0.104 0.044 0.852 0.000
#> GSM627101 4 0.4609 0.7365 0.000 0.224 0.024 0.752
#> GSM627130 4 0.2593 0.7026 0.000 0.004 0.104 0.892
#> GSM627071 3 0.3399 0.7264 0.092 0.040 0.868 0.000
#> GSM627118 4 0.4836 0.7179 0.000 0.320 0.008 0.672
#> GSM627094 2 0.3610 0.7078 0.000 0.800 0.200 0.000
#> GSM627122 3 0.4011 0.7295 0.112 0.020 0.844 0.024
#> GSM627115 2 0.3658 0.7231 0.000 0.836 0.144 0.020
#> GSM627125 4 0.2654 0.7018 0.000 0.004 0.108 0.888
#> GSM627174 2 0.3948 0.7212 0.000 0.828 0.136 0.036
#> GSM627102 2 0.5855 0.6763 0.000 0.704 0.160 0.136
#> GSM627073 3 0.4301 0.6686 0.064 0.120 0.816 0.000
#> GSM627108 2 0.3610 0.7078 0.000 0.800 0.200 0.000
#> GSM627126 1 0.0524 0.9242 0.988 0.008 0.000 0.004
#> GSM627078 4 0.4331 0.7312 0.000 0.288 0.000 0.712
#> GSM627090 3 0.5004 0.1566 0.000 0.004 0.604 0.392
#> GSM627099 2 0.3919 0.6267 0.000 0.840 0.056 0.104
#> GSM627105 4 0.2654 0.7018 0.000 0.004 0.108 0.888
#> GSM627117 3 0.6750 -0.0204 0.092 0.436 0.472 0.000
#> GSM627121 4 0.3710 0.6514 0.000 0.004 0.192 0.804
#> GSM627127 4 0.4522 0.7212 0.000 0.320 0.000 0.680
#> GSM627087 2 0.3658 0.7231 0.000 0.836 0.144 0.020
#> GSM627089 3 0.3984 0.7324 0.132 0.040 0.828 0.000
#> GSM627092 2 0.6640 0.5178 0.000 0.552 0.352 0.096
#> GSM627076 3 0.5112 0.0402 0.000 0.004 0.560 0.436
#> GSM627136 3 0.5321 0.7198 0.192 0.064 0.740 0.004
#> GSM627081 4 0.3710 0.6514 0.000 0.004 0.192 0.804
#> GSM627091 2 0.3919 0.6267 0.000 0.840 0.056 0.104
#> GSM627097 4 0.8785 0.5227 0.072 0.232 0.224 0.472
#> GSM627072 3 0.4123 0.7323 0.136 0.044 0.820 0.000
#> GSM627080 1 0.0188 0.9244 0.996 0.000 0.000 0.004
#> GSM627088 3 0.5250 0.7218 0.192 0.060 0.744 0.004
#> GSM627109 1 0.0469 0.9239 0.988 0.000 0.012 0.000
#> GSM627111 1 0.0000 0.9242 1.000 0.000 0.000 0.000
#> GSM627113 1 0.4936 0.2444 0.624 0.004 0.372 0.000
#> GSM627133 2 0.5298 0.5510 0.000 0.612 0.372 0.016
#> GSM627177 3 0.8030 0.2954 0.332 0.056 0.504 0.108
#> GSM627086 2 0.4332 0.6987 0.000 0.816 0.112 0.072
#> GSM627095 1 0.0524 0.9242 0.988 0.008 0.000 0.004
#> GSM627079 3 0.3829 0.7207 0.096 0.012 0.856 0.036
#> GSM627082 4 0.2593 0.7026 0.000 0.004 0.104 0.892
#> GSM627074 1 0.1489 0.8983 0.952 0.004 0.044 0.000
#> GSM627077 3 0.4775 0.7092 0.232 0.028 0.740 0.000
#> GSM627093 1 0.1489 0.8983 0.952 0.004 0.044 0.000
#> GSM627120 2 0.6855 0.5157 0.000 0.580 0.144 0.276
#> GSM627124 4 0.4331 0.7312 0.000 0.288 0.000 0.712
#> GSM627075 2 0.3649 0.7053 0.000 0.796 0.204 0.000
#> GSM627085 4 0.4382 0.7294 0.000 0.296 0.000 0.704
#> GSM627119 1 0.0657 0.9221 0.984 0.004 0.012 0.000
#> GSM627116 3 0.8030 0.2954 0.332 0.056 0.504 0.108
#> GSM627084 3 0.5398 0.7176 0.200 0.064 0.732 0.004
#> GSM627096 4 0.4836 0.7179 0.000 0.320 0.008 0.672
#> GSM627100 4 0.5151 0.1927 0.000 0.004 0.464 0.532
#> GSM627112 4 0.4841 0.7331 0.004 0.272 0.012 0.712
#> GSM627083 1 0.8195 0.2189 0.532 0.196 0.048 0.224
#> GSM627098 3 0.5398 0.7176 0.200 0.064 0.732 0.004
#> GSM627104 1 0.0469 0.9239 0.988 0.000 0.012 0.000
#> GSM627131 3 0.3798 0.7236 0.100 0.012 0.856 0.032
#> GSM627106 4 0.3710 0.6514 0.000 0.004 0.192 0.804
#> GSM627123 1 0.1229 0.9183 0.968 0.004 0.020 0.008
#> GSM627129 4 0.5329 0.5782 0.000 0.420 0.012 0.568
#> GSM627216 2 0.5298 0.5510 0.000 0.612 0.372 0.016
#> GSM627212 2 0.4010 0.7238 0.000 0.816 0.156 0.028
#> GSM627190 3 0.6705 -0.0324 0.088 0.440 0.472 0.000
#> GSM627169 2 0.4920 0.4953 0.004 0.628 0.368 0.000
#> GSM627167 2 0.6513 0.6143 0.000 0.640 0.176 0.184
#> GSM627192 1 0.0524 0.9242 0.988 0.008 0.000 0.004
#> GSM627203 3 0.2125 0.6680 0.000 0.076 0.920 0.004
#> GSM627151 3 0.7510 0.3679 0.024 0.180 0.584 0.212
#> GSM627163 1 0.0000 0.9242 1.000 0.000 0.000 0.000
#> GSM627211 2 0.3610 0.7078 0.000 0.800 0.200 0.000
#> GSM627171 2 0.4422 0.6805 0.000 0.736 0.256 0.008
#> GSM627209 2 0.5786 0.3035 0.000 0.640 0.052 0.308
#> GSM627135 1 0.3812 0.7927 0.848 0.008 0.116 0.028
#> GSM627170 2 0.3903 0.6610 0.000 0.844 0.080 0.076
#> GSM627178 3 0.8030 0.2954 0.332 0.056 0.504 0.108
#> GSM627199 4 0.4713 0.6794 0.000 0.360 0.000 0.640
#> GSM627213 4 0.4857 0.7150 0.000 0.324 0.008 0.668
#> GSM627140 2 0.8300 -0.1692 0.080 0.436 0.092 0.392
#> GSM627149 1 0.0859 0.9239 0.980 0.004 0.008 0.008
#> GSM627147 2 0.6819 0.5147 0.000 0.540 0.348 0.112
#> GSM627195 3 0.2125 0.6680 0.000 0.076 0.920 0.004
#> GSM627204 2 0.3610 0.7078 0.000 0.800 0.200 0.000
#> GSM627207 2 0.3610 0.7078 0.000 0.800 0.200 0.000
#> GSM627157 3 0.5220 0.4040 0.424 0.008 0.568 0.000
#> GSM627201 2 0.3948 0.7212 0.000 0.828 0.136 0.036
#> GSM627146 2 0.6263 0.1227 0.000 0.576 0.068 0.356
#> GSM627156 2 0.4920 0.4953 0.004 0.628 0.368 0.000
#> GSM627188 1 0.0524 0.9242 0.988 0.008 0.000 0.004
#> GSM627197 2 0.5980 -0.1037 0.000 0.560 0.044 0.396
#> GSM627173 2 0.3610 0.7078 0.000 0.800 0.200 0.000
#> GSM627179 2 0.4010 0.7238 0.000 0.816 0.156 0.028
#> GSM627208 3 0.4585 0.2728 0.000 0.332 0.668 0.000
#> GSM627215 2 0.5406 0.3060 0.000 0.508 0.480 0.012
#> GSM627153 2 0.5786 0.3035 0.000 0.640 0.052 0.308
#> GSM627155 1 0.0524 0.9245 0.988 0.004 0.000 0.008
#> GSM627165 2 0.4920 0.5566 0.000 0.768 0.068 0.164
#> GSM627168 3 0.5138 0.4607 0.392 0.008 0.600 0.000
#> GSM627183 3 0.4274 0.7312 0.148 0.044 0.808 0.000
#> GSM627144 3 0.2197 0.6662 0.000 0.080 0.916 0.004
#> GSM627158 1 0.0524 0.9241 0.988 0.000 0.008 0.004
#> GSM627196 2 0.3610 0.7078 0.000 0.800 0.200 0.000
#> GSM627142 4 0.2831 0.6976 0.000 0.004 0.120 0.876
#> GSM627182 3 0.4585 0.2728 0.000 0.332 0.668 0.000
#> GSM627202 3 0.5220 0.4040 0.424 0.008 0.568 0.000
#> GSM627141 3 0.5466 0.7095 0.220 0.068 0.712 0.000
#> GSM627143 2 0.6385 0.6402 0.008 0.640 0.268 0.084
#> GSM627145 3 0.3082 0.7248 0.084 0.032 0.884 0.000
#> GSM627152 3 0.4348 0.7037 0.088 0.012 0.832 0.068
#> GSM627200 3 0.4036 0.7261 0.116 0.012 0.840 0.032
#> GSM627159 4 0.2593 0.7026 0.000 0.004 0.104 0.892
#> GSM627164 2 0.5003 0.6214 0.000 0.676 0.308 0.016
#> GSM627138 1 0.1398 0.9044 0.956 0.000 0.040 0.004
#> GSM627175 4 0.4522 0.7212 0.000 0.320 0.000 0.680
#> GSM627150 3 0.3399 0.7264 0.092 0.040 0.868 0.000
#> GSM627166 1 0.6422 0.5779 0.684 0.052 0.216 0.048
#> GSM627186 2 0.4920 0.4953 0.004 0.628 0.368 0.000
#> GSM627139 3 0.7510 0.3679 0.024 0.180 0.584 0.212
#> GSM627181 2 0.5980 -0.1037 0.000 0.560 0.044 0.396
#> GSM627205 2 0.4963 0.6644 0.000 0.696 0.284 0.020
#> GSM627214 2 0.5847 0.2342 0.000 0.628 0.052 0.320
#> GSM627180 2 0.5406 0.3060 0.000 0.508 0.480 0.012
#> GSM627172 2 0.5855 0.6763 0.000 0.704 0.160 0.136
#> GSM627184 1 0.0524 0.9245 0.988 0.004 0.000 0.008
#> GSM627193 2 0.3610 0.7078 0.000 0.800 0.200 0.000
#> GSM627191 4 0.8193 0.4501 0.088 0.352 0.080 0.480
#> GSM627176 3 0.5911 0.5047 0.024 0.208 0.712 0.056
#> GSM627194 2 0.4552 0.7255 0.000 0.784 0.172 0.044
#> GSM627154 4 0.4382 0.7294 0.000 0.296 0.000 0.704
#> GSM627187 3 0.6705 -0.0324 0.088 0.440 0.472 0.000
#> GSM627198 4 0.4713 0.6794 0.000 0.360 0.000 0.640
#> GSM627160 2 0.8344 -0.1759 0.080 0.432 0.096 0.392
#> GSM627185 1 0.0336 0.9247 0.992 0.000 0.008 0.000
#> GSM627206 3 0.3984 0.7324 0.132 0.040 0.828 0.000
#> GSM627161 1 0.0524 0.9241 0.988 0.000 0.008 0.004
#> GSM627162 2 0.6255 0.4469 0.028 0.568 0.384 0.020
#> GSM627210 1 0.0657 0.9221 0.984 0.004 0.012 0.000
#> GSM627189 2 0.3610 0.7078 0.000 0.800 0.200 0.000
show/hide code output
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM627128 5 0.4268 0.203 0.000 0.000 0.000 0.444 0.556
#> GSM627110 3 0.3748 0.750 0.164 0.020 0.804 0.000 0.012
#> GSM627132 1 0.0162 0.893 0.996 0.000 0.000 0.000 0.004
#> GSM627107 5 0.3169 0.789 0.000 0.000 0.060 0.084 0.856
#> GSM627103 2 0.2361 0.734 0.000 0.892 0.012 0.096 0.000
#> GSM627114 3 0.5294 0.721 0.168 0.096 0.716 0.004 0.016
#> GSM627134 4 0.3392 0.740 0.000 0.080 0.008 0.852 0.060
#> GSM627137 2 0.4670 0.512 0.000 0.648 0.016 0.328 0.008
#> GSM627148 3 0.2060 0.771 0.052 0.016 0.924 0.000 0.008
#> GSM627101 4 0.4327 0.339 0.000 0.008 0.000 0.632 0.360
#> GSM627130 5 0.2280 0.780 0.000 0.000 0.000 0.120 0.880
#> GSM627071 3 0.1651 0.765 0.036 0.012 0.944 0.000 0.008
#> GSM627118 4 0.2457 0.746 0.000 0.016 0.008 0.900 0.076
#> GSM627094 2 0.0579 0.745 0.000 0.984 0.008 0.008 0.000
#> GSM627122 3 0.2972 0.757 0.064 0.004 0.880 0.004 0.048
#> GSM627115 2 0.2305 0.735 0.000 0.896 0.012 0.092 0.000
#> GSM627125 5 0.2230 0.781 0.000 0.000 0.000 0.116 0.884
#> GSM627174 2 0.3513 0.725 0.000 0.828 0.036 0.132 0.004
#> GSM627102 2 0.4847 0.615 0.000 0.708 0.040 0.236 0.016
#> GSM627073 3 0.2956 0.731 0.020 0.096 0.872 0.000 0.012
#> GSM627108 2 0.0579 0.745 0.000 0.984 0.008 0.008 0.000
#> GSM627126 1 0.1651 0.889 0.944 0.000 0.012 0.008 0.036
#> GSM627078 4 0.2270 0.746 0.000 0.020 0.000 0.904 0.076
#> GSM627090 5 0.4826 0.187 0.000 0.000 0.472 0.020 0.508
#> GSM627099 2 0.4359 0.569 0.000 0.692 0.016 0.288 0.004
#> GSM627105 5 0.2230 0.781 0.000 0.000 0.000 0.116 0.884
#> GSM627117 2 0.5951 0.380 0.060 0.588 0.324 0.004 0.024
#> GSM627121 5 0.3169 0.789 0.000 0.000 0.060 0.084 0.856
#> GSM627127 4 0.1725 0.753 0.000 0.020 0.000 0.936 0.044
#> GSM627087 2 0.2305 0.735 0.000 0.896 0.012 0.092 0.000
#> GSM627089 3 0.2228 0.772 0.076 0.012 0.908 0.000 0.004
#> GSM627092 2 0.6218 0.615 0.000 0.644 0.200 0.092 0.064
#> GSM627076 5 0.4861 0.305 0.000 0.000 0.428 0.024 0.548
#> GSM627136 3 0.4872 0.751 0.124 0.084 0.764 0.004 0.024
#> GSM627081 5 0.3169 0.789 0.000 0.000 0.060 0.084 0.856
#> GSM627091 2 0.4359 0.569 0.000 0.692 0.016 0.288 0.004
#> GSM627097 4 0.7496 0.342 0.020 0.048 0.216 0.528 0.188
#> GSM627072 3 0.2270 0.773 0.072 0.016 0.908 0.000 0.004
#> GSM627080 1 0.0324 0.893 0.992 0.000 0.000 0.004 0.004
#> GSM627088 3 0.4872 0.752 0.124 0.084 0.764 0.004 0.024
#> GSM627109 1 0.1914 0.875 0.924 0.000 0.060 0.000 0.016
#> GSM627111 1 0.0162 0.893 0.996 0.000 0.000 0.000 0.004
#> GSM627113 1 0.4510 0.149 0.560 0.000 0.432 0.000 0.008
#> GSM627133 2 0.5129 0.630 0.000 0.684 0.248 0.052 0.016
#> GSM627177 3 0.7167 0.435 0.248 0.000 0.536 0.080 0.136
#> GSM627086 2 0.3171 0.672 0.000 0.816 0.008 0.176 0.000
#> GSM627095 1 0.1651 0.889 0.944 0.000 0.012 0.008 0.036
#> GSM627079 3 0.3170 0.745 0.052 0.004 0.868 0.004 0.072
#> GSM627082 5 0.2329 0.778 0.000 0.000 0.000 0.124 0.876
#> GSM627074 1 0.2464 0.847 0.888 0.000 0.096 0.000 0.016
#> GSM627077 3 0.3693 0.751 0.168 0.012 0.804 0.000 0.016
#> GSM627093 1 0.2464 0.847 0.888 0.000 0.096 0.000 0.016
#> GSM627120 2 0.5843 0.503 0.000 0.636 0.008 0.168 0.188
#> GSM627124 4 0.2270 0.746 0.000 0.020 0.000 0.904 0.076
#> GSM627075 2 0.0579 0.745 0.000 0.984 0.008 0.008 0.000
#> GSM627085 4 0.2036 0.752 0.000 0.024 0.000 0.920 0.056
#> GSM627119 1 0.1981 0.873 0.920 0.000 0.064 0.000 0.016
#> GSM627116 3 0.7167 0.435 0.248 0.000 0.536 0.080 0.136
#> GSM627084 3 0.5015 0.747 0.132 0.088 0.752 0.004 0.024
#> GSM627096 4 0.2457 0.746 0.000 0.016 0.008 0.900 0.076
#> GSM627100 5 0.4890 0.494 0.000 0.000 0.332 0.040 0.628
#> GSM627112 4 0.2338 0.718 0.000 0.004 0.000 0.884 0.112
#> GSM627083 1 0.7432 0.201 0.500 0.064 0.024 0.320 0.092
#> GSM627098 3 0.5015 0.747 0.132 0.088 0.752 0.004 0.024
#> GSM627104 1 0.1914 0.875 0.924 0.000 0.060 0.000 0.016
#> GSM627131 3 0.3043 0.749 0.052 0.004 0.876 0.004 0.064
#> GSM627106 5 0.3169 0.789 0.000 0.000 0.060 0.084 0.856
#> GSM627123 1 0.1799 0.891 0.940 0.000 0.028 0.012 0.020
#> GSM627129 4 0.4093 0.717 0.000 0.124 0.008 0.800 0.068
#> GSM627216 2 0.5129 0.630 0.000 0.684 0.248 0.052 0.016
#> GSM627212 2 0.2248 0.736 0.000 0.900 0.012 0.088 0.000
#> GSM627190 2 0.5869 0.387 0.060 0.592 0.324 0.004 0.020
#> GSM627169 2 0.3594 0.686 0.000 0.804 0.172 0.004 0.020
#> GSM627167 2 0.5887 0.560 0.000 0.640 0.048 0.252 0.060
#> GSM627192 1 0.1651 0.889 0.944 0.000 0.012 0.008 0.036
#> GSM627203 3 0.2230 0.720 0.000 0.044 0.912 0.000 0.044
#> GSM627151 3 0.7557 0.363 0.004 0.176 0.536 0.136 0.148
#> GSM627163 1 0.0404 0.893 0.988 0.000 0.000 0.000 0.012
#> GSM627211 2 0.0579 0.745 0.000 0.984 0.008 0.008 0.000
#> GSM627171 2 0.2409 0.744 0.000 0.908 0.060 0.012 0.020
#> GSM627209 2 0.5222 0.180 0.000 0.512 0.008 0.452 0.028
#> GSM627135 1 0.4469 0.755 0.776 0.000 0.148 0.020 0.056
#> GSM627170 2 0.4245 0.627 0.000 0.736 0.020 0.236 0.008
#> GSM627178 3 0.7167 0.435 0.248 0.000 0.536 0.080 0.136
#> GSM627199 4 0.2193 0.748 0.000 0.060 0.000 0.912 0.028
#> GSM627213 4 0.2364 0.750 0.000 0.020 0.008 0.908 0.064
#> GSM627140 4 0.7351 0.420 0.052 0.284 0.024 0.528 0.112
#> GSM627149 1 0.1314 0.895 0.960 0.000 0.016 0.012 0.012
#> GSM627147 2 0.6373 0.603 0.000 0.632 0.196 0.108 0.064
#> GSM627195 3 0.2230 0.720 0.000 0.044 0.912 0.000 0.044
#> GSM627204 2 0.0579 0.745 0.000 0.984 0.008 0.008 0.000
#> GSM627207 2 0.0579 0.745 0.000 0.984 0.008 0.008 0.000
#> GSM627157 3 0.4354 0.473 0.368 0.000 0.624 0.000 0.008
#> GSM627201 2 0.3513 0.725 0.000 0.828 0.036 0.132 0.004
#> GSM627146 4 0.4658 0.242 0.000 0.432 0.004 0.556 0.008
#> GSM627156 2 0.3594 0.686 0.000 0.804 0.172 0.004 0.020
#> GSM627188 1 0.1651 0.889 0.944 0.000 0.012 0.008 0.036
#> GSM627197 4 0.4491 0.421 0.000 0.364 0.004 0.624 0.008
#> GSM627173 2 0.0579 0.745 0.000 0.984 0.008 0.008 0.000
#> GSM627179 2 0.2248 0.736 0.000 0.900 0.012 0.088 0.000
#> GSM627208 3 0.4430 0.329 0.000 0.360 0.628 0.000 0.012
#> GSM627215 2 0.5482 0.418 0.000 0.572 0.372 0.040 0.016
#> GSM627153 2 0.5222 0.180 0.000 0.512 0.008 0.452 0.028
#> GSM627155 1 0.1173 0.891 0.964 0.000 0.004 0.012 0.020
#> GSM627165 2 0.4774 0.507 0.000 0.644 0.016 0.328 0.012
#> GSM627168 3 0.4323 0.530 0.332 0.000 0.656 0.000 0.012
#> GSM627183 3 0.2577 0.772 0.084 0.016 0.892 0.000 0.008
#> GSM627144 3 0.2228 0.720 0.000 0.048 0.912 0.000 0.040
#> GSM627158 1 0.0727 0.895 0.980 0.000 0.012 0.004 0.004
#> GSM627196 2 0.0579 0.745 0.000 0.984 0.008 0.008 0.000
#> GSM627142 5 0.2462 0.786 0.000 0.000 0.008 0.112 0.880
#> GSM627182 3 0.4430 0.329 0.000 0.360 0.628 0.000 0.012
#> GSM627202 3 0.4354 0.473 0.368 0.000 0.624 0.000 0.008
#> GSM627141 3 0.5033 0.734 0.156 0.092 0.736 0.004 0.012
#> GSM627143 2 0.5897 0.667 0.008 0.684 0.140 0.140 0.028
#> GSM627145 3 0.1483 0.760 0.028 0.008 0.952 0.000 0.012
#> GSM627152 3 0.3727 0.721 0.048 0.004 0.832 0.008 0.108
#> GSM627200 3 0.3180 0.752 0.064 0.004 0.868 0.004 0.060
#> GSM627159 5 0.2329 0.778 0.000 0.000 0.000 0.124 0.876
#> GSM627164 2 0.3553 0.721 0.000 0.832 0.128 0.016 0.024
#> GSM627138 1 0.1282 0.883 0.952 0.000 0.044 0.004 0.000
#> GSM627175 4 0.1725 0.753 0.000 0.020 0.000 0.936 0.044
#> GSM627150 3 0.1651 0.765 0.036 0.012 0.944 0.000 0.008
#> GSM627166 1 0.6394 0.485 0.592 0.000 0.272 0.076 0.060
#> GSM627186 2 0.3594 0.686 0.000 0.804 0.172 0.004 0.020
#> GSM627139 3 0.7557 0.363 0.004 0.176 0.536 0.136 0.148
#> GSM627181 4 0.4491 0.421 0.000 0.364 0.004 0.624 0.008
#> GSM627205 2 0.4333 0.705 0.000 0.784 0.144 0.056 0.016
#> GSM627214 2 0.5897 0.110 0.000 0.476 0.008 0.440 0.076
#> GSM627180 2 0.5482 0.418 0.000 0.572 0.372 0.040 0.016
#> GSM627172 2 0.4847 0.615 0.000 0.708 0.040 0.236 0.016
#> GSM627184 1 0.1173 0.891 0.964 0.000 0.004 0.012 0.020
#> GSM627193 2 0.0579 0.745 0.000 0.984 0.008 0.008 0.000
#> GSM627191 4 0.7372 0.543 0.052 0.156 0.044 0.588 0.160
#> GSM627176 3 0.6487 0.446 0.020 0.280 0.580 0.012 0.108
#> GSM627194 2 0.2305 0.741 0.000 0.896 0.012 0.092 0.000
#> GSM627154 4 0.2036 0.752 0.000 0.024 0.000 0.920 0.056
#> GSM627187 2 0.5869 0.387 0.060 0.592 0.324 0.004 0.020
#> GSM627198 4 0.2193 0.748 0.000 0.060 0.000 0.912 0.028
#> GSM627160 4 0.7411 0.425 0.052 0.280 0.028 0.528 0.112
#> GSM627185 1 0.0898 0.894 0.972 0.000 0.020 0.000 0.008
#> GSM627206 3 0.2228 0.772 0.076 0.012 0.908 0.000 0.004
#> GSM627161 1 0.0727 0.895 0.980 0.000 0.012 0.004 0.004
#> GSM627162 2 0.5175 0.621 0.016 0.700 0.236 0.016 0.032
#> GSM627210 1 0.1981 0.873 0.920 0.000 0.064 0.000 0.016
#> GSM627189 2 0.0579 0.745 0.000 0.984 0.008 0.008 0.000
show/hide code output
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM627128 6 0.3945 0.2295 0.000 0.000 NA 0.380 0.000 0.612
#> GSM627110 5 0.3577 0.7390 0.056 0.004 NA 0.000 0.808 0.004
#> GSM627132 1 0.1531 0.7642 0.928 0.000 NA 0.000 0.000 0.004
#> GSM627107 6 0.2434 0.7992 0.000 0.000 NA 0.032 0.056 0.896
#> GSM627103 2 0.2510 0.7188 0.000 0.884 NA 0.080 0.008 0.000
#> GSM627114 5 0.4993 0.6985 0.064 0.060 NA 0.000 0.704 0.000
#> GSM627134 4 0.4047 0.6926 0.000 0.080 NA 0.804 0.008 0.072
#> GSM627137 2 0.4833 0.5275 0.000 0.640 NA 0.288 0.012 0.000
#> GSM627148 5 0.1268 0.7586 0.000 0.008 NA 0.000 0.952 0.004
#> GSM627101 4 0.4395 0.2632 0.000 0.000 NA 0.568 0.000 0.404
#> GSM627130 6 0.1082 0.7950 0.000 0.000 NA 0.040 0.000 0.956
#> GSM627071 5 0.0603 0.7525 0.000 0.000 NA 0.000 0.980 0.004
#> GSM627118 4 0.3216 0.6988 0.000 0.012 NA 0.848 0.008 0.096
#> GSM627094 2 0.0291 0.7305 0.000 0.992 NA 0.000 0.004 0.000
#> GSM627122 5 0.2335 0.7456 0.024 0.000 NA 0.000 0.904 0.028
#> GSM627115 2 0.2456 0.7197 0.000 0.888 NA 0.076 0.008 0.000
#> GSM627125 6 0.1010 0.7959 0.000 0.000 NA 0.036 0.000 0.960
#> GSM627174 2 0.3835 0.7113 0.000 0.796 NA 0.116 0.016 0.000
#> GSM627102 2 0.5339 0.5625 0.000 0.652 NA 0.188 0.016 0.004
#> GSM627073 5 0.2176 0.7235 0.000 0.080 NA 0.000 0.896 0.000
#> GSM627108 2 0.0291 0.7305 0.000 0.992 NA 0.000 0.004 0.000
#> GSM627126 1 0.3632 0.7369 0.752 0.000 NA 0.004 0.008 0.008
#> GSM627078 4 0.3253 0.7097 0.000 0.004 NA 0.832 0.000 0.068
#> GSM627090 6 0.5111 0.2125 0.000 0.000 NA 0.004 0.436 0.492
#> GSM627099 2 0.4495 0.5755 0.000 0.676 NA 0.276 0.012 0.004
#> GSM627105 6 0.1010 0.7959 0.000 0.000 NA 0.036 0.000 0.960
#> GSM627117 2 0.6207 0.4180 0.016 0.508 NA 0.004 0.216 0.000
#> GSM627121 6 0.2434 0.7992 0.000 0.000 NA 0.032 0.056 0.896
#> GSM627127 4 0.1801 0.7122 0.000 0.004 NA 0.924 0.000 0.056
#> GSM627087 2 0.2456 0.7197 0.000 0.888 NA 0.076 0.008 0.000
#> GSM627089 5 0.1327 0.7573 0.000 0.000 NA 0.000 0.936 0.000
#> GSM627092 2 0.6879 0.5638 0.000 0.568 NA 0.080 0.128 0.044
#> GSM627076 6 0.5058 0.3293 0.000 0.000 NA 0.004 0.392 0.536
#> GSM627136 5 0.4254 0.7286 0.032 0.052 NA 0.000 0.760 0.000
#> GSM627081 6 0.2434 0.7992 0.000 0.000 NA 0.032 0.056 0.896
#> GSM627091 2 0.4495 0.5755 0.000 0.676 NA 0.276 0.012 0.004
#> GSM627097 4 0.7773 0.3977 0.000 0.052 NA 0.456 0.144 0.140
#> GSM627072 5 0.1668 0.7597 0.008 0.004 NA 0.000 0.928 0.000
#> GSM627080 1 0.0713 0.7711 0.972 0.000 NA 0.000 0.000 0.000
#> GSM627088 5 0.4274 0.7313 0.036 0.056 NA 0.000 0.764 0.000
#> GSM627109 1 0.4264 0.6979 0.732 0.000 NA 0.000 0.080 0.004
#> GSM627111 1 0.1411 0.7633 0.936 0.000 NA 0.000 0.000 0.004
#> GSM627113 1 0.5296 -0.0488 0.452 0.000 NA 0.000 0.448 0.000
#> GSM627133 2 0.5414 0.5872 0.000 0.640 NA 0.040 0.244 0.004
#> GSM627177 5 0.7119 0.3902 0.116 0.000 NA 0.032 0.464 0.076
#> GSM627086 2 0.3128 0.6679 0.000 0.812 NA 0.168 0.008 0.000
#> GSM627095 1 0.3632 0.7369 0.752 0.000 NA 0.004 0.008 0.008
#> GSM627079 5 0.2532 0.7351 0.020 0.000 NA 0.000 0.892 0.052
#> GSM627082 6 0.1152 0.7935 0.000 0.000 NA 0.044 0.000 0.952
#> GSM627074 1 0.4694 0.6640 0.684 0.000 NA 0.000 0.100 0.004
#> GSM627077 5 0.3368 0.7432 0.060 0.000 NA 0.000 0.820 0.004
#> GSM627093 1 0.4694 0.6640 0.684 0.000 NA 0.000 0.100 0.004
#> GSM627120 2 0.5464 0.5196 0.000 0.636 NA 0.116 0.012 0.224
#> GSM627124 4 0.3253 0.7097 0.000 0.004 NA 0.832 0.000 0.068
#> GSM627075 2 0.0551 0.7302 0.000 0.984 NA 0.004 0.008 0.000
#> GSM627085 4 0.3047 0.7167 0.000 0.008 NA 0.852 0.000 0.060
#> GSM627119 1 0.4313 0.6946 0.728 0.000 NA 0.000 0.084 0.004
#> GSM627116 5 0.7119 0.3902 0.116 0.000 NA 0.032 0.464 0.076
#> GSM627084 5 0.4451 0.7238 0.036 0.056 NA 0.000 0.744 0.000
#> GSM627096 4 0.3216 0.6988 0.000 0.012 NA 0.848 0.008 0.096
#> GSM627100 6 0.4773 0.5107 0.000 0.000 NA 0.004 0.296 0.632
#> GSM627112 4 0.3782 0.6873 0.000 0.000 NA 0.780 0.000 0.096
#> GSM627083 1 0.7961 0.1432 0.416 0.052 NA 0.264 0.016 0.064
#> GSM627098 5 0.4451 0.7238 0.036 0.056 NA 0.000 0.744 0.000
#> GSM627104 1 0.4264 0.6979 0.732 0.000 NA 0.000 0.080 0.004
#> GSM627131 5 0.2401 0.7386 0.020 0.000 NA 0.000 0.900 0.044
#> GSM627106 6 0.2434 0.7992 0.000 0.000 NA 0.032 0.056 0.896
#> GSM627123 1 0.3799 0.7460 0.756 0.000 NA 0.000 0.024 0.012
#> GSM627129 4 0.4771 0.6703 0.000 0.112 NA 0.748 0.008 0.084
#> GSM627216 2 0.5414 0.5872 0.000 0.640 NA 0.040 0.244 0.004
#> GSM627212 2 0.2549 0.7213 0.000 0.884 NA 0.072 0.008 0.000
#> GSM627190 2 0.6191 0.4246 0.016 0.512 NA 0.004 0.216 0.000
#> GSM627169 2 0.4149 0.6466 0.000 0.728 NA 0.004 0.056 0.000
#> GSM627167 2 0.6510 0.5157 0.000 0.572 NA 0.200 0.024 0.048
#> GSM627192 1 0.3632 0.7369 0.752 0.000 NA 0.004 0.008 0.008
#> GSM627203 5 0.2750 0.6951 0.000 0.000 NA 0.000 0.844 0.020
#> GSM627151 5 0.7911 0.3223 0.000 0.128 NA 0.108 0.472 0.128
#> GSM627163 1 0.1970 0.7663 0.900 0.000 NA 0.000 0.000 0.008
#> GSM627211 2 0.0291 0.7305 0.000 0.992 NA 0.000 0.004 0.000
#> GSM627171 2 0.2747 0.7173 0.000 0.860 NA 0.000 0.028 0.004
#> GSM627209 2 0.5136 0.2349 0.000 0.512 NA 0.432 0.008 0.032
#> GSM627135 1 0.5638 0.6619 0.576 0.000 NA 0.008 0.108 0.012
#> GSM627170 2 0.4404 0.6259 0.000 0.724 NA 0.196 0.012 0.000
#> GSM627178 5 0.7119 0.3902 0.116 0.000 NA 0.032 0.464 0.076
#> GSM627199 4 0.3174 0.7118 0.000 0.040 NA 0.840 0.000 0.012
#> GSM627213 4 0.3051 0.7058 0.000 0.016 NA 0.864 0.008 0.076
#> GSM627140 4 0.7744 0.4149 0.036 0.244 NA 0.448 0.020 0.060
#> GSM627149 1 0.3231 0.7582 0.800 0.000 NA 0.000 0.012 0.008
#> GSM627147 2 0.7019 0.5491 0.000 0.556 NA 0.096 0.128 0.044
#> GSM627195 5 0.2750 0.6951 0.000 0.000 NA 0.000 0.844 0.020
#> GSM627204 2 0.0291 0.7305 0.000 0.992 NA 0.000 0.004 0.000
#> GSM627207 2 0.0291 0.7305 0.000 0.992 NA 0.000 0.004 0.000
#> GSM627157 5 0.4764 0.5169 0.272 0.000 NA 0.000 0.640 0.000
#> GSM627201 2 0.3835 0.7113 0.000 0.796 NA 0.116 0.016 0.000
#> GSM627146 4 0.4547 0.2322 0.000 0.420 NA 0.552 0.004 0.004
#> GSM627156 2 0.4149 0.6466 0.000 0.728 NA 0.004 0.056 0.000
#> GSM627188 1 0.3632 0.7369 0.752 0.000 NA 0.004 0.008 0.008
#> GSM627197 4 0.4349 0.4188 0.000 0.340 NA 0.632 0.004 0.004
#> GSM627173 2 0.0291 0.7305 0.000 0.992 NA 0.000 0.004 0.000
#> GSM627179 2 0.2549 0.7213 0.000 0.884 NA 0.072 0.008 0.000
#> GSM627208 5 0.4713 0.3606 0.000 0.320 NA 0.000 0.620 0.004
#> GSM627215 2 0.5774 0.3690 0.000 0.524 NA 0.032 0.364 0.004
#> GSM627153 2 0.5136 0.2349 0.000 0.512 NA 0.432 0.008 0.032
#> GSM627155 1 0.3230 0.7418 0.776 0.000 NA 0.000 0.000 0.012
#> GSM627165 2 0.4969 0.5228 0.000 0.636 NA 0.288 0.012 0.004
#> GSM627168 5 0.4503 0.5665 0.240 0.000 NA 0.000 0.680 0.000
#> GSM627183 5 0.1901 0.7578 0.008 0.004 NA 0.000 0.912 0.000
#> GSM627144 5 0.3037 0.6860 0.000 0.000 NA 0.000 0.808 0.016
#> GSM627158 1 0.2214 0.7700 0.892 0.000 NA 0.000 0.012 0.004
#> GSM627196 2 0.0291 0.7305 0.000 0.992 NA 0.000 0.004 0.000
#> GSM627142 6 0.1049 0.7989 0.000 0.000 NA 0.032 0.008 0.960
#> GSM627182 5 0.4713 0.3606 0.000 0.320 NA 0.000 0.620 0.004
#> GSM627202 5 0.4764 0.5169 0.272 0.000 NA 0.000 0.640 0.000
#> GSM627141 5 0.4760 0.7092 0.052 0.060 NA 0.000 0.724 0.000
#> GSM627143 2 0.6366 0.6259 0.000 0.620 NA 0.108 0.104 0.024
#> GSM627145 5 0.0363 0.7510 0.000 0.000 NA 0.000 0.988 0.000
#> GSM627152 5 0.3091 0.7138 0.020 0.000 NA 0.000 0.852 0.092
#> GSM627200 5 0.2570 0.7411 0.032 0.000 NA 0.000 0.892 0.040
#> GSM627159 6 0.1152 0.7935 0.000 0.000 NA 0.044 0.000 0.952
#> GSM627164 2 0.4101 0.6900 0.000 0.776 NA 0.008 0.072 0.008
#> GSM627138 1 0.2213 0.7637 0.904 0.000 NA 0.000 0.048 0.004
#> GSM627175 4 0.1801 0.7122 0.000 0.004 NA 0.924 0.000 0.056
#> GSM627150 5 0.0603 0.7525 0.000 0.000 NA 0.000 0.980 0.004
#> GSM627166 1 0.6596 0.3770 0.420 0.000 NA 0.028 0.212 0.004
#> GSM627186 2 0.4149 0.6466 0.000 0.728 NA 0.004 0.056 0.000
#> GSM627139 5 0.7911 0.3223 0.000 0.128 NA 0.108 0.472 0.128
#> GSM627181 4 0.4349 0.4188 0.000 0.340 NA 0.632 0.004 0.004
#> GSM627205 2 0.4727 0.6743 0.000 0.740 NA 0.044 0.140 0.004
#> GSM627214 2 0.6005 0.1922 0.000 0.480 NA 0.396 0.008 0.084
#> GSM627180 2 0.5774 0.3690 0.000 0.524 NA 0.032 0.364 0.004
#> GSM627172 2 0.5339 0.5625 0.000 0.652 NA 0.188 0.016 0.004
#> GSM627184 1 0.3230 0.7418 0.776 0.000 NA 0.000 0.000 0.012
#> GSM627193 2 0.0291 0.7305 0.000 0.992 NA 0.000 0.004 0.000
#> GSM627191 4 0.7706 0.5342 0.036 0.132 NA 0.508 0.028 0.100
#> GSM627176 5 0.7064 0.3969 0.008 0.204 NA 0.004 0.480 0.072
#> GSM627194 2 0.2308 0.7283 0.000 0.896 NA 0.076 0.016 0.000
#> GSM627154 4 0.3047 0.7167 0.000 0.008 NA 0.852 0.000 0.060
#> GSM627187 2 0.6191 0.4246 0.016 0.512 NA 0.004 0.216 0.000
#> GSM627198 4 0.3174 0.7118 0.000 0.040 NA 0.840 0.000 0.012
#> GSM627160 4 0.7797 0.4198 0.036 0.240 NA 0.448 0.024 0.060
#> GSM627185 1 0.2476 0.7580 0.880 0.000 NA 0.000 0.024 0.004
#> GSM627206 5 0.1327 0.7573 0.000 0.000 NA 0.000 0.936 0.000
#> GSM627161 1 0.2214 0.7700 0.892 0.000 NA 0.000 0.012 0.004
#> GSM627162 2 0.5569 0.6007 0.004 0.640 NA 0.012 0.156 0.008
#> GSM627210 1 0.4313 0.6946 0.728 0.000 NA 0.000 0.084 0.004
#> GSM627189 2 0.0291 0.7305 0.000 0.992 NA 0.000 0.004 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two
samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures
which are significantly different between classes which can be candidate marks
for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known
annotation is numeric, one-way ANOVA test is applied, and if the known
annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) age(p) other(p) k
#> CV:hclust 139 1.0000 0.806 0.18535 2
#> CV:hclust 121 0.0152 0.561 0.00946 3
#> CV:hclust 113 0.0610 0.676 0.00159 4
#> CV:hclust 114 0.1196 0.765 0.00721 5
#> CV:hclust 117 0.1019 0.753 0.01817 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
CV:kmeans**
The object with results only for a single top-value method and a single partition method
can be extracted as:
res = res_list["CV", "kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51882 rows and 146 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
- The first row: a plot of the ECDF (empirical cumulative distribution
function) curves of the consensus matrix for each
k and the heatmap of
predicted classes for each k.
- The second row: heatmaps of the consensus matrix for each
k.
- The third row: heatmaps of the membership matrix for each
k.
- The fouth row: heatmaps of the signatures for each
k.
All the plots in panels can be made by individual functions and they are
plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
- ECDF curves of the consensus matrix for each
k;
- 1-PAC. The PAC
score
measures the proportion of the ambiguous subgrouping.
- Mean silhouette score.
- Concordance. The mean probability of fiting the consensus class ids in all
partitions.
- Area increased. Denote \(A_k\) as the area under the ECDF curve for current
k, the area increased is defined as \(A_k - A_{k-1}\).
- Rand index. The percent of pairs of samples that are both in a same cluster
or both are not in a same cluster in the partition of k and k-1.
- Jaccard index. The ratio of pairs of samples are both in a same cluster in
the partition of k and k-1 and the pairs of samples are both in a same
cluster in the partition k or k-1.
The detailed explanations of these statistics can be found in the cola
vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.966 0.987 0.4989 0.500 0.500
#> 3 3 0.561 0.465 0.685 0.3114 0.850 0.707
#> 4 4 0.824 0.876 0.928 0.1395 0.736 0.408
#> 5 5 0.742 0.662 0.801 0.0566 0.963 0.857
#> 6 6 0.716 0.702 0.780 0.0408 0.910 0.644
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
- All \(k\) with Jaccard index larger than 0.95 are removed because increasing
\(k\) does not provide enough extra information. If all \(k\) are removed, it is
marked as no subgroup is detected.
- For all \(k\) with 1-PAC score larger than 0.9, the maximal \(k\) is taken as
the best \(k\), and other \(k\) are marked as optional \(k\).
- If it does not fit the second rule. The \(k\) with the maximal vote of the
highest 1-PAC score, highest mean silhouette, and highest concordance is
taken as the best \(k\).
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
show/hide code output
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM627128 2 0.0000 0.995 0.000 1.000
#> GSM627110 1 0.0000 0.976 1.000 0.000
#> GSM627132 1 0.0000 0.976 1.000 0.000
#> GSM627107 2 0.0000 0.995 0.000 1.000
#> GSM627103 2 0.0000 0.995 0.000 1.000
#> GSM627114 1 0.0000 0.976 1.000 0.000
#> GSM627134 2 0.0000 0.995 0.000 1.000
#> GSM627137 2 0.0000 0.995 0.000 1.000
#> GSM627148 1 0.0000 0.976 1.000 0.000
#> GSM627101 2 0.0000 0.995 0.000 1.000
#> GSM627130 2 0.0000 0.995 0.000 1.000
#> GSM627071 1 0.0000 0.976 1.000 0.000
#> GSM627118 2 0.0000 0.995 0.000 1.000
#> GSM627094 2 0.0000 0.995 0.000 1.000
#> GSM627122 1 0.0000 0.976 1.000 0.000
#> GSM627115 2 0.0000 0.995 0.000 1.000
#> GSM627125 2 0.0000 0.995 0.000 1.000
#> GSM627174 2 0.0000 0.995 0.000 1.000
#> GSM627102 2 0.0000 0.995 0.000 1.000
#> GSM627073 2 0.2603 0.950 0.044 0.956
#> GSM627108 2 0.0000 0.995 0.000 1.000
#> GSM627126 1 0.0000 0.976 1.000 0.000
#> GSM627078 2 0.0000 0.995 0.000 1.000
#> GSM627090 1 0.0000 0.976 1.000 0.000
#> GSM627099 2 0.0000 0.995 0.000 1.000
#> GSM627105 2 0.0000 0.995 0.000 1.000
#> GSM627117 1 0.0000 0.976 1.000 0.000
#> GSM627121 2 0.0000 0.995 0.000 1.000
#> GSM627127 2 0.0000 0.995 0.000 1.000
#> GSM627087 2 0.0000 0.995 0.000 1.000
#> GSM627089 1 0.0000 0.976 1.000 0.000
#> GSM627092 2 0.0000 0.995 0.000 1.000
#> GSM627076 1 0.0000 0.976 1.000 0.000
#> GSM627136 1 0.0000 0.976 1.000 0.000
#> GSM627081 2 0.0000 0.995 0.000 1.000
#> GSM627091 2 0.0000 0.995 0.000 1.000
#> GSM627097 2 0.0000 0.995 0.000 1.000
#> GSM627072 1 0.0000 0.976 1.000 0.000
#> GSM627080 1 0.0000 0.976 1.000 0.000
#> GSM627088 1 0.0000 0.976 1.000 0.000
#> GSM627109 1 0.0000 0.976 1.000 0.000
#> GSM627111 1 0.0000 0.976 1.000 0.000
#> GSM627113 1 0.0000 0.976 1.000 0.000
#> GSM627133 2 0.0000 0.995 0.000 1.000
#> GSM627177 1 0.0000 0.976 1.000 0.000
#> GSM627086 2 0.0000 0.995 0.000 1.000
#> GSM627095 1 0.0000 0.976 1.000 0.000
#> GSM627079 1 0.0000 0.976 1.000 0.000
#> GSM627082 2 0.0000 0.995 0.000 1.000
#> GSM627074 1 0.0000 0.976 1.000 0.000
#> GSM627077 1 0.0000 0.976 1.000 0.000
#> GSM627093 1 0.0000 0.976 1.000 0.000
#> GSM627120 2 0.0000 0.995 0.000 1.000
#> GSM627124 2 0.0000 0.995 0.000 1.000
#> GSM627075 2 0.0000 0.995 0.000 1.000
#> GSM627085 2 0.0000 0.995 0.000 1.000
#> GSM627119 1 0.0000 0.976 1.000 0.000
#> GSM627116 2 0.0000 0.995 0.000 1.000
#> GSM627084 1 0.0000 0.976 1.000 0.000
#> GSM627096 2 0.0000 0.995 0.000 1.000
#> GSM627100 1 0.8267 0.654 0.740 0.260
#> GSM627112 2 0.0000 0.995 0.000 1.000
#> GSM627083 1 0.5294 0.854 0.880 0.120
#> GSM627098 1 0.0000 0.976 1.000 0.000
#> GSM627104 1 0.0000 0.976 1.000 0.000
#> GSM627131 1 0.0000 0.976 1.000 0.000
#> GSM627106 2 0.0000 0.995 0.000 1.000
#> GSM627123 1 0.0000 0.976 1.000 0.000
#> GSM627129 2 0.0000 0.995 0.000 1.000
#> GSM627216 2 0.0000 0.995 0.000 1.000
#> GSM627212 2 0.0000 0.995 0.000 1.000
#> GSM627190 1 0.0000 0.976 1.000 0.000
#> GSM627169 1 0.9963 0.152 0.536 0.464
#> GSM627167 2 0.0000 0.995 0.000 1.000
#> GSM627192 1 0.0000 0.976 1.000 0.000
#> GSM627203 1 0.0000 0.976 1.000 0.000
#> GSM627151 2 0.0000 0.995 0.000 1.000
#> GSM627163 1 0.0000 0.976 1.000 0.000
#> GSM627211 2 0.0000 0.995 0.000 1.000
#> GSM627171 2 0.0000 0.995 0.000 1.000
#> GSM627209 2 0.0000 0.995 0.000 1.000
#> GSM627135 1 0.0000 0.976 1.000 0.000
#> GSM627170 2 0.0000 0.995 0.000 1.000
#> GSM627178 1 0.0000 0.976 1.000 0.000
#> GSM627199 2 0.0000 0.995 0.000 1.000
#> GSM627213 2 0.0000 0.995 0.000 1.000
#> GSM627140 2 0.0000 0.995 0.000 1.000
#> GSM627149 1 0.0000 0.976 1.000 0.000
#> GSM627147 2 0.0000 0.995 0.000 1.000
#> GSM627195 1 0.1184 0.962 0.984 0.016
#> GSM627204 2 0.0000 0.995 0.000 1.000
#> GSM627207 2 0.0000 0.995 0.000 1.000
#> GSM627157 1 0.0000 0.976 1.000 0.000
#> GSM627201 2 0.0000 0.995 0.000 1.000
#> GSM627146 2 0.0000 0.995 0.000 1.000
#> GSM627156 2 0.0000 0.995 0.000 1.000
#> GSM627188 1 0.0000 0.976 1.000 0.000
#> GSM627197 2 0.0000 0.995 0.000 1.000
#> GSM627173 2 0.0000 0.995 0.000 1.000
#> GSM627179 2 0.0000 0.995 0.000 1.000
#> GSM627208 2 0.0000 0.995 0.000 1.000
#> GSM627215 2 0.0000 0.995 0.000 1.000
#> GSM627153 2 0.0000 0.995 0.000 1.000
#> GSM627155 1 0.0000 0.976 1.000 0.000
#> GSM627165 2 0.0000 0.995 0.000 1.000
#> GSM627168 1 0.0000 0.976 1.000 0.000
#> GSM627183 1 0.0000 0.976 1.000 0.000
#> GSM627144 1 0.0000 0.976 1.000 0.000
#> GSM627158 1 0.0000 0.976 1.000 0.000
#> GSM627196 2 0.0000 0.995 0.000 1.000
#> GSM627142 1 0.8144 0.667 0.748 0.252
#> GSM627182 1 0.0000 0.976 1.000 0.000
#> GSM627202 1 0.0000 0.976 1.000 0.000
#> GSM627141 1 0.0000 0.976 1.000 0.000
#> GSM627143 2 0.0000 0.995 0.000 1.000
#> GSM627145 1 0.0000 0.976 1.000 0.000
#> GSM627152 1 0.0000 0.976 1.000 0.000
#> GSM627200 1 0.0000 0.976 1.000 0.000
#> GSM627159 2 0.0000 0.995 0.000 1.000
#> GSM627164 2 0.0000 0.995 0.000 1.000
#> GSM627138 1 0.0000 0.976 1.000 0.000
#> GSM627175 2 0.0000 0.995 0.000 1.000
#> GSM627150 1 0.0938 0.965 0.988 0.012
#> GSM627166 1 0.0000 0.976 1.000 0.000
#> GSM627186 1 0.9963 0.152 0.536 0.464
#> GSM627139 2 0.0000 0.995 0.000 1.000
#> GSM627181 2 0.0000 0.995 0.000 1.000
#> GSM627205 2 0.0000 0.995 0.000 1.000
#> GSM627214 2 0.0000 0.995 0.000 1.000
#> GSM627180 2 0.0000 0.995 0.000 1.000
#> GSM627172 2 0.0000 0.995 0.000 1.000
#> GSM627184 1 0.0000 0.976 1.000 0.000
#> GSM627193 2 0.0000 0.995 0.000 1.000
#> GSM627191 2 0.0000 0.995 0.000 1.000
#> GSM627176 1 0.0000 0.976 1.000 0.000
#> GSM627194 2 0.0000 0.995 0.000 1.000
#> GSM627154 2 0.0000 0.995 0.000 1.000
#> GSM627187 1 0.0000 0.976 1.000 0.000
#> GSM627198 2 0.0000 0.995 0.000 1.000
#> GSM627160 2 0.8763 0.562 0.296 0.704
#> GSM627185 1 0.0000 0.976 1.000 0.000
#> GSM627206 1 0.0000 0.976 1.000 0.000
#> GSM627161 1 0.0000 0.976 1.000 0.000
#> GSM627162 1 0.0000 0.976 1.000 0.000
#> GSM627210 1 0.0000 0.976 1.000 0.000
#> GSM627189 2 0.0000 0.995 0.000 1.000
show/hide code output
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM627128 2 0.5760 0.21251 0.000 0.672 0.328
#> GSM627110 1 0.5098 0.71618 0.752 0.000 0.248
#> GSM627132 1 0.0000 0.80486 1.000 0.000 0.000
#> GSM627107 2 0.6291 -0.13687 0.000 0.532 0.468
#> GSM627103 2 0.6280 0.48227 0.000 0.540 0.460
#> GSM627114 1 0.5098 0.71618 0.752 0.000 0.248
#> GSM627134 2 0.0592 0.52032 0.000 0.988 0.012
#> GSM627137 2 0.6244 0.49792 0.000 0.560 0.440
#> GSM627148 1 0.6280 0.41220 0.540 0.000 0.460
#> GSM627101 2 0.5291 0.28110 0.000 0.732 0.268
#> GSM627130 2 0.5650 0.23781 0.000 0.688 0.312
#> GSM627071 1 0.6168 0.50711 0.588 0.000 0.412
#> GSM627118 2 0.4654 0.35098 0.000 0.792 0.208
#> GSM627094 2 0.6295 0.47093 0.000 0.528 0.472
#> GSM627122 3 0.6944 -0.33595 0.468 0.016 0.516
#> GSM627115 2 0.6305 0.45808 0.000 0.516 0.484
#> GSM627125 2 0.5760 0.21251 0.000 0.672 0.328
#> GSM627174 2 0.6244 0.49792 0.000 0.560 0.440
#> GSM627102 2 0.6244 0.49792 0.000 0.560 0.440
#> GSM627073 3 0.9340 0.22315 0.264 0.220 0.516
#> GSM627108 2 0.6305 0.45808 0.000 0.516 0.484
#> GSM627126 1 0.2165 0.78225 0.936 0.000 0.064
#> GSM627078 2 0.0237 0.52743 0.000 0.996 0.004
#> GSM627090 3 0.6897 -0.27943 0.436 0.016 0.548
#> GSM627099 2 0.2261 0.52818 0.000 0.932 0.068
#> GSM627105 2 0.5760 0.21251 0.000 0.672 0.328
#> GSM627117 1 0.5098 0.71618 0.752 0.000 0.248
#> GSM627121 2 0.6308 -0.17965 0.000 0.508 0.492
#> GSM627127 2 0.0000 0.52607 0.000 1.000 0.000
#> GSM627087 2 0.6305 0.45808 0.000 0.516 0.484
#> GSM627089 1 0.6095 0.54085 0.608 0.000 0.392
#> GSM627092 2 0.6305 0.45808 0.000 0.516 0.484
#> GSM627076 3 0.9208 0.18955 0.244 0.220 0.536
#> GSM627136 1 0.5098 0.71618 0.752 0.000 0.248
#> GSM627081 3 0.7578 0.21056 0.040 0.460 0.500
#> GSM627091 2 0.6244 0.49792 0.000 0.560 0.440
#> GSM627097 2 0.1031 0.51378 0.000 0.976 0.024
#> GSM627072 1 0.6215 0.47805 0.572 0.000 0.428
#> GSM627080 1 0.0747 0.80135 0.984 0.000 0.016
#> GSM627088 1 0.5098 0.71618 0.752 0.000 0.248
#> GSM627109 1 0.0000 0.80486 1.000 0.000 0.000
#> GSM627111 1 0.0000 0.80486 1.000 0.000 0.000
#> GSM627113 1 0.1643 0.80404 0.956 0.000 0.044
#> GSM627133 3 0.6512 -0.01612 0.024 0.300 0.676
#> GSM627177 1 0.6180 0.49995 0.584 0.000 0.416
#> GSM627086 2 0.6244 0.49792 0.000 0.560 0.440
#> GSM627095 1 0.2066 0.78365 0.940 0.000 0.060
#> GSM627079 1 0.6955 0.30487 0.496 0.016 0.488
#> GSM627082 2 0.5760 0.21251 0.000 0.672 0.328
#> GSM627074 1 0.0237 0.80568 0.996 0.000 0.004
#> GSM627077 1 0.3686 0.78214 0.860 0.000 0.140
#> GSM627093 1 0.0592 0.80634 0.988 0.000 0.012
#> GSM627120 2 0.3192 0.46518 0.000 0.888 0.112
#> GSM627124 2 0.0747 0.52999 0.000 0.984 0.016
#> GSM627075 2 0.6305 0.45808 0.000 0.516 0.484
#> GSM627085 2 0.0237 0.52743 0.000 0.996 0.004
#> GSM627119 1 0.0592 0.80634 0.988 0.000 0.012
#> GSM627116 2 0.4555 0.36334 0.000 0.800 0.200
#> GSM627084 1 0.0237 0.80568 0.996 0.000 0.004
#> GSM627096 2 0.4654 0.35098 0.000 0.792 0.208
#> GSM627100 3 0.6299 0.15076 0.000 0.476 0.524
#> GSM627112 2 0.3879 0.42413 0.000 0.848 0.152
#> GSM627083 1 0.3406 0.75896 0.904 0.028 0.068
#> GSM627098 1 0.0424 0.80616 0.992 0.000 0.008
#> GSM627104 1 0.0000 0.80486 1.000 0.000 0.000
#> GSM627131 1 0.5291 0.69652 0.732 0.000 0.268
#> GSM627106 3 0.7665 0.21600 0.044 0.456 0.500
#> GSM627123 1 0.2165 0.78225 0.936 0.000 0.064
#> GSM627129 2 0.0000 0.52607 0.000 1.000 0.000
#> GSM627216 3 0.6026 -0.18621 0.000 0.376 0.624
#> GSM627212 2 0.6244 0.49792 0.000 0.560 0.440
#> GSM627190 1 0.5098 0.71618 0.752 0.000 0.248
#> GSM627169 3 0.7218 -0.01113 0.052 0.296 0.652
#> GSM627167 2 0.2537 0.47769 0.000 0.920 0.080
#> GSM627192 1 0.2261 0.77994 0.932 0.000 0.068
#> GSM627203 3 0.8984 0.00681 0.368 0.136 0.496
#> GSM627151 2 0.3686 0.51424 0.000 0.860 0.140
#> GSM627163 1 0.0424 0.80347 0.992 0.000 0.008
#> GSM627211 2 0.6244 0.49792 0.000 0.560 0.440
#> GSM627171 3 0.5948 -0.14729 0.000 0.360 0.640
#> GSM627209 2 0.0747 0.52999 0.000 0.984 0.016
#> GSM627135 1 0.2165 0.78225 0.936 0.000 0.064
#> GSM627170 2 0.6305 0.45808 0.000 0.516 0.484
#> GSM627178 1 0.3686 0.77933 0.860 0.000 0.140
#> GSM627199 2 0.4750 0.51266 0.000 0.784 0.216
#> GSM627213 2 0.1964 0.49459 0.000 0.944 0.056
#> GSM627140 2 0.2356 0.48541 0.000 0.928 0.072
#> GSM627149 1 0.2165 0.78225 0.936 0.000 0.064
#> GSM627147 2 0.6225 0.49907 0.000 0.568 0.432
#> GSM627195 3 0.9229 0.09238 0.336 0.168 0.496
#> GSM627204 2 0.6244 0.49792 0.000 0.560 0.440
#> GSM627207 2 0.6305 0.45808 0.000 0.516 0.484
#> GSM627157 1 0.0424 0.80616 0.992 0.000 0.008
#> GSM627201 2 0.6244 0.49792 0.000 0.560 0.440
#> GSM627146 2 0.6244 0.49792 0.000 0.560 0.440
#> GSM627156 3 0.6769 -0.05999 0.028 0.320 0.652
#> GSM627188 1 0.2261 0.77994 0.932 0.000 0.068
#> GSM627197 2 0.6244 0.49792 0.000 0.560 0.440
#> GSM627173 2 0.6295 0.47093 0.000 0.528 0.472
#> GSM627179 2 0.6305 0.45808 0.000 0.516 0.484
#> GSM627208 3 0.6847 0.08541 0.060 0.232 0.708
#> GSM627215 3 0.6026 -0.18621 0.000 0.376 0.624
#> GSM627153 2 0.0747 0.52999 0.000 0.984 0.016
#> GSM627155 1 0.2261 0.77994 0.932 0.000 0.068
#> GSM627165 2 0.2796 0.46768 0.000 0.908 0.092
#> GSM627168 1 0.5397 0.68798 0.720 0.000 0.280
#> GSM627183 1 0.4654 0.74135 0.792 0.000 0.208
#> GSM627144 1 0.6955 0.30719 0.496 0.016 0.488
#> GSM627158 1 0.0892 0.80012 0.980 0.000 0.020
#> GSM627196 2 0.6244 0.49792 0.000 0.560 0.440
#> GSM627142 3 0.6307 0.13347 0.000 0.488 0.512
#> GSM627182 3 0.6192 0.29864 0.176 0.060 0.764
#> GSM627202 1 0.4399 0.76574 0.812 0.000 0.188
#> GSM627141 1 0.5098 0.71618 0.752 0.000 0.248
#> GSM627143 2 0.5988 0.48657 0.000 0.632 0.368
#> GSM627145 1 0.6215 0.47805 0.572 0.000 0.428
#> GSM627152 3 0.6948 -0.33613 0.472 0.016 0.512
#> GSM627200 1 0.3619 0.77759 0.864 0.000 0.136
#> GSM627159 2 0.5760 0.21251 0.000 0.672 0.328
#> GSM627164 3 0.6215 -0.31557 0.000 0.428 0.572
#> GSM627138 1 0.0000 0.80486 1.000 0.000 0.000
#> GSM627175 2 0.0237 0.52743 0.000 0.996 0.004
#> GSM627150 3 0.9229 0.09238 0.336 0.168 0.496
#> GSM627166 1 0.0000 0.80486 1.000 0.000 0.000
#> GSM627186 3 0.7218 -0.01113 0.052 0.296 0.652
#> GSM627139 2 0.6180 0.02701 0.000 0.584 0.416
#> GSM627181 2 0.6244 0.49792 0.000 0.560 0.440
#> GSM627205 2 0.6307 0.45206 0.000 0.512 0.488
#> GSM627214 2 0.0747 0.52999 0.000 0.984 0.016
#> GSM627180 3 0.6806 0.28811 0.060 0.228 0.712
#> GSM627172 2 0.6244 0.49792 0.000 0.560 0.440
#> GSM627184 1 0.2261 0.77994 0.932 0.000 0.068
#> GSM627193 2 0.6307 0.45172 0.000 0.512 0.488
#> GSM627191 2 0.4002 0.41685 0.000 0.840 0.160
#> GSM627176 3 0.6654 -0.32150 0.456 0.008 0.536
#> GSM627194 2 0.6280 0.48227 0.000 0.540 0.460
#> GSM627154 2 0.0000 0.52607 0.000 1.000 0.000
#> GSM627187 1 0.5058 0.71860 0.756 0.000 0.244
#> GSM627198 2 0.0747 0.52999 0.000 0.984 0.016
#> GSM627160 2 0.5902 0.22725 0.004 0.680 0.316
#> GSM627185 1 0.0000 0.80486 1.000 0.000 0.000
#> GSM627206 1 0.5678 0.64769 0.684 0.000 0.316
#> GSM627161 1 0.1860 0.78841 0.948 0.000 0.052
#> GSM627162 1 0.5058 0.71883 0.756 0.000 0.244
#> GSM627210 1 0.1529 0.80457 0.960 0.000 0.040
#> GSM627189 2 0.6295 0.47093 0.000 0.528 0.472
show/hide code output
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM627128 4 0.0779 0.8799 0.004 0.000 0.016 0.980
#> GSM627110 3 0.1004 0.9022 0.024 0.004 0.972 0.000
#> GSM627132 1 0.0188 0.9320 0.996 0.000 0.004 0.000
#> GSM627107 4 0.1474 0.8678 0.000 0.000 0.052 0.948
#> GSM627103 2 0.0188 0.9705 0.000 0.996 0.000 0.004
#> GSM627114 3 0.1576 0.8939 0.048 0.004 0.948 0.000
#> GSM627134 4 0.3024 0.8964 0.000 0.148 0.000 0.852
#> GSM627137 2 0.0188 0.9705 0.000 0.996 0.000 0.004
#> GSM627148 3 0.0000 0.9061 0.000 0.000 1.000 0.000
#> GSM627101 4 0.0804 0.8874 0.000 0.008 0.012 0.980
#> GSM627130 4 0.0376 0.8804 0.004 0.000 0.004 0.992
#> GSM627071 3 0.0000 0.9061 0.000 0.000 1.000 0.000
#> GSM627118 4 0.1489 0.9021 0.000 0.044 0.004 0.952
#> GSM627094 2 0.0000 0.9705 0.000 1.000 0.000 0.000
#> GSM627122 3 0.2714 0.8598 0.004 0.000 0.884 0.112
#> GSM627115 2 0.0000 0.9705 0.000 1.000 0.000 0.000
#> GSM627125 4 0.0779 0.8799 0.004 0.000 0.016 0.980
#> GSM627174 2 0.0336 0.9694 0.000 0.992 0.000 0.008
#> GSM627102 2 0.0000 0.9705 0.000 1.000 0.000 0.000
#> GSM627073 3 0.0707 0.9033 0.000 0.000 0.980 0.020
#> GSM627108 2 0.0000 0.9705 0.000 1.000 0.000 0.000
#> GSM627126 1 0.0817 0.9290 0.976 0.000 0.000 0.024
#> GSM627078 4 0.3024 0.8964 0.000 0.148 0.000 0.852
#> GSM627090 3 0.2714 0.8574 0.004 0.000 0.884 0.112
#> GSM627099 4 0.3873 0.8143 0.000 0.228 0.000 0.772
#> GSM627105 4 0.0779 0.8799 0.004 0.000 0.016 0.980
#> GSM627117 3 0.1576 0.8939 0.048 0.004 0.948 0.000
#> GSM627121 3 0.4981 0.2003 0.000 0.000 0.536 0.464
#> GSM627127 4 0.3024 0.8964 0.000 0.148 0.000 0.852
#> GSM627087 2 0.0000 0.9705 0.000 1.000 0.000 0.000
#> GSM627089 3 0.0000 0.9061 0.000 0.000 1.000 0.000
#> GSM627092 2 0.0000 0.9705 0.000 1.000 0.000 0.000
#> GSM627076 3 0.2999 0.8441 0.004 0.000 0.864 0.132
#> GSM627136 3 0.1389 0.8951 0.048 0.000 0.952 0.000
#> GSM627081 3 0.2589 0.8558 0.000 0.000 0.884 0.116
#> GSM627091 2 0.0336 0.9694 0.000 0.992 0.000 0.008
#> GSM627097 4 0.2973 0.8978 0.000 0.144 0.000 0.856
#> GSM627072 3 0.0000 0.9061 0.000 0.000 1.000 0.000
#> GSM627080 1 0.0188 0.9320 0.996 0.000 0.004 0.000
#> GSM627088 3 0.0895 0.9030 0.020 0.004 0.976 0.000
#> GSM627109 1 0.0188 0.9320 0.996 0.000 0.004 0.000
#> GSM627111 1 0.0188 0.9320 0.996 0.000 0.004 0.000
#> GSM627113 3 0.4697 0.4243 0.356 0.000 0.644 0.000
#> GSM627133 3 0.3649 0.7200 0.000 0.204 0.796 0.000
#> GSM627177 3 0.0000 0.9061 0.000 0.000 1.000 0.000
#> GSM627086 2 0.0188 0.9705 0.000 0.996 0.000 0.004
#> GSM627095 1 0.0469 0.9309 0.988 0.000 0.000 0.012
#> GSM627079 3 0.1302 0.8959 0.000 0.000 0.956 0.044
#> GSM627082 4 0.0376 0.8804 0.004 0.000 0.004 0.992
#> GSM627074 1 0.2973 0.8265 0.856 0.000 0.144 0.000
#> GSM627077 3 0.2737 0.8595 0.104 0.000 0.888 0.008
#> GSM627093 1 0.4978 0.4047 0.612 0.004 0.384 0.000
#> GSM627120 4 0.2271 0.9075 0.000 0.076 0.008 0.916
#> GSM627124 4 0.3024 0.8964 0.000 0.148 0.000 0.852
#> GSM627075 2 0.0000 0.9705 0.000 1.000 0.000 0.000
#> GSM627085 4 0.3024 0.8964 0.000 0.148 0.000 0.852
#> GSM627119 1 0.4843 0.3759 0.604 0.000 0.396 0.000
#> GSM627116 4 0.2530 0.9068 0.000 0.100 0.004 0.896
#> GSM627084 1 0.1637 0.8990 0.940 0.000 0.060 0.000
#> GSM627096 4 0.1489 0.9021 0.000 0.044 0.004 0.952
#> GSM627100 4 0.5028 0.2217 0.004 0.000 0.400 0.596
#> GSM627112 4 0.1489 0.9006 0.004 0.044 0.000 0.952
#> GSM627083 1 0.1118 0.9219 0.964 0.000 0.000 0.036
#> GSM627098 1 0.3400 0.7787 0.820 0.000 0.180 0.000
#> GSM627104 1 0.0188 0.9320 0.996 0.000 0.004 0.000
#> GSM627131 3 0.2565 0.8787 0.056 0.000 0.912 0.032
#> GSM627106 3 0.2589 0.8558 0.000 0.000 0.884 0.116
#> GSM627123 1 0.0707 0.9304 0.980 0.000 0.000 0.020
#> GSM627129 4 0.2868 0.9002 0.000 0.136 0.000 0.864
#> GSM627216 2 0.1022 0.9478 0.000 0.968 0.032 0.000
#> GSM627212 2 0.0336 0.9694 0.000 0.992 0.000 0.008
#> GSM627190 3 0.1489 0.8958 0.044 0.004 0.952 0.000
#> GSM627169 2 0.3278 0.8432 0.020 0.864 0.116 0.000
#> GSM627167 4 0.2081 0.9077 0.000 0.084 0.000 0.916
#> GSM627192 1 0.0817 0.9290 0.976 0.000 0.000 0.024
#> GSM627203 3 0.1637 0.8889 0.000 0.000 0.940 0.060
#> GSM627151 4 0.6906 0.6043 0.000 0.264 0.156 0.580
#> GSM627163 1 0.0188 0.9320 0.996 0.000 0.004 0.000
#> GSM627211 2 0.0188 0.9705 0.000 0.996 0.000 0.004
#> GSM627171 2 0.2149 0.8958 0.000 0.912 0.088 0.000
#> GSM627209 4 0.3024 0.8964 0.000 0.148 0.000 0.852
#> GSM627135 1 0.0707 0.9304 0.980 0.000 0.000 0.020
#> GSM627170 2 0.0336 0.9694 0.000 0.992 0.000 0.008
#> GSM627178 3 0.5768 0.0818 0.456 0.000 0.516 0.028
#> GSM627199 4 0.3764 0.8298 0.000 0.216 0.000 0.784
#> GSM627213 4 0.2281 0.9074 0.000 0.096 0.000 0.904
#> GSM627140 4 0.1978 0.9052 0.004 0.068 0.000 0.928
#> GSM627149 1 0.0707 0.9304 0.980 0.000 0.000 0.020
#> GSM627147 2 0.0707 0.9595 0.000 0.980 0.000 0.020
#> GSM627195 3 0.1474 0.8925 0.000 0.000 0.948 0.052
#> GSM627204 2 0.0188 0.9705 0.000 0.996 0.000 0.004
#> GSM627207 2 0.0000 0.9705 0.000 1.000 0.000 0.000
#> GSM627157 1 0.3649 0.7465 0.796 0.000 0.204 0.000
#> GSM627201 2 0.0336 0.9694 0.000 0.992 0.000 0.008
#> GSM627146 2 0.0336 0.9694 0.000 0.992 0.000 0.008
#> GSM627156 2 0.2589 0.8649 0.000 0.884 0.116 0.000
#> GSM627188 1 0.0817 0.9290 0.976 0.000 0.000 0.024
#> GSM627197 2 0.0336 0.9694 0.000 0.992 0.000 0.008
#> GSM627173 2 0.0000 0.9705 0.000 1.000 0.000 0.000
#> GSM627179 2 0.0000 0.9705 0.000 1.000 0.000 0.000
#> GSM627208 3 0.0707 0.9019 0.000 0.020 0.980 0.000
#> GSM627215 2 0.1635 0.9406 0.000 0.948 0.044 0.008
#> GSM627153 4 0.3024 0.8964 0.000 0.148 0.000 0.852
#> GSM627155 1 0.0817 0.9290 0.976 0.000 0.000 0.024
#> GSM627165 4 0.2345 0.9023 0.000 0.100 0.000 0.900
#> GSM627168 3 0.0188 0.9059 0.004 0.000 0.996 0.000
#> GSM627183 3 0.1474 0.8930 0.052 0.000 0.948 0.000
#> GSM627144 3 0.0000 0.9061 0.000 0.000 1.000 0.000
#> GSM627158 1 0.0188 0.9320 0.996 0.000 0.004 0.000
#> GSM627196 2 0.0188 0.9705 0.000 0.996 0.000 0.004
#> GSM627142 4 0.1209 0.8727 0.004 0.000 0.032 0.964
#> GSM627182 3 0.0188 0.9056 0.000 0.004 0.996 0.000
#> GSM627202 3 0.2376 0.8777 0.068 0.000 0.916 0.016
#> GSM627141 3 0.1576 0.8939 0.048 0.004 0.948 0.000
#> GSM627143 2 0.3545 0.7646 0.000 0.828 0.008 0.164
#> GSM627145 3 0.0000 0.9061 0.000 0.000 1.000 0.000
#> GSM627152 3 0.2654 0.8618 0.004 0.000 0.888 0.108
#> GSM627200 3 0.1867 0.8828 0.072 0.000 0.928 0.000
#> GSM627159 4 0.0376 0.8804 0.004 0.000 0.004 0.992
#> GSM627164 2 0.2081 0.8998 0.000 0.916 0.084 0.000
#> GSM627138 1 0.0188 0.9320 0.996 0.000 0.004 0.000
#> GSM627175 4 0.3024 0.8964 0.000 0.148 0.000 0.852
#> GSM627150 3 0.1022 0.8998 0.000 0.000 0.968 0.032
#> GSM627166 1 0.1118 0.9159 0.964 0.000 0.036 0.000
#> GSM627186 2 0.3278 0.8432 0.020 0.864 0.116 0.000
#> GSM627139 4 0.1489 0.8670 0.004 0.000 0.044 0.952
#> GSM627181 2 0.0336 0.9694 0.000 0.992 0.000 0.008
#> GSM627205 2 0.0336 0.9694 0.000 0.992 0.000 0.008
#> GSM627214 4 0.3024 0.8964 0.000 0.148 0.000 0.852
#> GSM627180 3 0.0188 0.9055 0.000 0.000 0.996 0.004
#> GSM627172 2 0.0000 0.9705 0.000 1.000 0.000 0.000
#> GSM627184 1 0.0817 0.9290 0.976 0.000 0.000 0.024
#> GSM627193 2 0.0000 0.9705 0.000 1.000 0.000 0.000
#> GSM627191 4 0.1356 0.8968 0.008 0.032 0.000 0.960
#> GSM627176 3 0.0895 0.9042 0.004 0.000 0.976 0.020
#> GSM627194 2 0.0188 0.9705 0.000 0.996 0.000 0.004
#> GSM627154 4 0.2973 0.8978 0.000 0.144 0.000 0.856
#> GSM627187 3 0.1576 0.8939 0.048 0.004 0.948 0.000
#> GSM627198 4 0.3024 0.8964 0.000 0.148 0.000 0.852
#> GSM627160 4 0.1114 0.8890 0.008 0.016 0.004 0.972
#> GSM627185 1 0.0188 0.9320 0.996 0.000 0.004 0.000
#> GSM627206 3 0.0469 0.9049 0.012 0.000 0.988 0.000
#> GSM627161 1 0.0707 0.9304 0.980 0.000 0.000 0.020
#> GSM627162 3 0.1305 0.8989 0.036 0.004 0.960 0.000
#> GSM627210 3 0.4961 0.1404 0.448 0.000 0.552 0.000
#> GSM627189 2 0.0188 0.9705 0.000 0.996 0.000 0.004
show/hide code output
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM627128 4 0.4434 0.2796 0.000 0.000 0.004 0.536 0.460
#> GSM627110 3 0.4887 0.5970 0.284 0.004 0.668 0.000 0.044
#> GSM627132 1 0.3790 0.7224 0.724 0.000 0.004 0.000 0.272
#> GSM627107 5 0.6041 0.8092 0.000 0.000 0.356 0.128 0.516
#> GSM627103 2 0.1430 0.8925 0.000 0.944 0.000 0.052 0.004
#> GSM627114 3 0.4887 0.5962 0.284 0.004 0.668 0.000 0.044
#> GSM627134 4 0.1774 0.7658 0.000 0.052 0.000 0.932 0.016
#> GSM627137 2 0.1597 0.8927 0.000 0.940 0.000 0.048 0.012
#> GSM627148 3 0.0404 0.7074 0.000 0.000 0.988 0.000 0.012
#> GSM627101 4 0.4256 0.3327 0.000 0.000 0.000 0.564 0.436
#> GSM627130 4 0.4287 0.2903 0.000 0.000 0.000 0.540 0.460
#> GSM627071 3 0.0898 0.7123 0.020 0.000 0.972 0.000 0.008
#> GSM627118 4 0.1041 0.7540 0.000 0.004 0.000 0.964 0.032
#> GSM627094 2 0.1281 0.8947 0.000 0.956 0.000 0.032 0.012
#> GSM627122 3 0.2331 0.6593 0.000 0.000 0.900 0.020 0.080
#> GSM627115 2 0.1251 0.8948 0.000 0.956 0.000 0.036 0.008
#> GSM627125 4 0.4446 0.2374 0.000 0.000 0.004 0.520 0.476
#> GSM627174 2 0.3123 0.8329 0.000 0.828 0.000 0.160 0.012
#> GSM627102 2 0.2011 0.8669 0.000 0.908 0.000 0.004 0.088
#> GSM627073 3 0.1638 0.6804 0.000 0.000 0.932 0.004 0.064
#> GSM627108 2 0.0671 0.8887 0.000 0.980 0.000 0.004 0.016
#> GSM627126 1 0.4101 0.7191 0.664 0.000 0.004 0.000 0.332
#> GSM627078 4 0.1522 0.7714 0.000 0.044 0.000 0.944 0.012
#> GSM627090 3 0.4132 0.2251 0.000 0.000 0.720 0.020 0.260
#> GSM627099 4 0.2624 0.7182 0.000 0.116 0.000 0.872 0.012
#> GSM627105 4 0.4446 0.2374 0.000 0.000 0.004 0.520 0.476
#> GSM627117 3 0.5883 0.5517 0.296 0.016 0.600 0.000 0.088
#> GSM627121 3 0.5204 -0.3786 0.000 0.000 0.580 0.052 0.368
#> GSM627127 4 0.0963 0.7733 0.000 0.036 0.000 0.964 0.000
#> GSM627087 2 0.1251 0.8948 0.000 0.956 0.000 0.036 0.008
#> GSM627089 3 0.0000 0.7105 0.000 0.000 1.000 0.000 0.000
#> GSM627092 2 0.2445 0.8524 0.000 0.884 0.004 0.004 0.108
#> GSM627076 5 0.5168 0.6780 0.000 0.000 0.452 0.040 0.508
#> GSM627136 3 0.4672 0.6008 0.284 0.004 0.680 0.000 0.032
#> GSM627081 3 0.4058 0.3018 0.000 0.000 0.740 0.024 0.236
#> GSM627091 2 0.3013 0.8334 0.000 0.832 0.000 0.160 0.008
#> GSM627097 4 0.1168 0.7731 0.000 0.032 0.000 0.960 0.008
#> GSM627072 3 0.0451 0.7124 0.008 0.000 0.988 0.000 0.004
#> GSM627080 1 0.3684 0.7218 0.720 0.000 0.000 0.000 0.280
#> GSM627088 3 0.4725 0.6014 0.280 0.004 0.680 0.000 0.036
#> GSM627109 1 0.0404 0.6604 0.988 0.000 0.012 0.000 0.000
#> GSM627111 1 0.3636 0.7218 0.728 0.000 0.000 0.000 0.272
#> GSM627113 1 0.4560 -0.2656 0.508 0.000 0.484 0.000 0.008
#> GSM627133 2 0.5640 0.1045 0.000 0.496 0.436 0.004 0.064
#> GSM627177 3 0.1059 0.7120 0.020 0.000 0.968 0.004 0.008
#> GSM627086 2 0.2411 0.8694 0.000 0.884 0.000 0.108 0.008
#> GSM627095 1 0.3949 0.7187 0.668 0.000 0.000 0.000 0.332
#> GSM627079 3 0.1808 0.6860 0.004 0.000 0.936 0.020 0.040
#> GSM627082 4 0.4306 0.2805 0.000 0.000 0.000 0.508 0.492
#> GSM627074 1 0.3519 0.4634 0.776 0.000 0.216 0.000 0.008
#> GSM627077 3 0.2616 0.6908 0.100 0.000 0.880 0.000 0.020
#> GSM627093 1 0.5107 0.0939 0.596 0.000 0.356 0.000 0.048
#> GSM627120 4 0.3910 0.6998 0.000 0.040 0.012 0.808 0.140
#> GSM627124 4 0.1701 0.7701 0.000 0.048 0.000 0.936 0.016
#> GSM627075 2 0.1608 0.8711 0.000 0.928 0.000 0.000 0.072
#> GSM627085 4 0.1282 0.7717 0.000 0.044 0.000 0.952 0.004
#> GSM627119 1 0.4298 0.1712 0.640 0.000 0.352 0.000 0.008
#> GSM627116 4 0.1211 0.7714 0.000 0.024 0.000 0.960 0.016
#> GSM627084 1 0.2909 0.5664 0.848 0.000 0.140 0.000 0.012
#> GSM627096 4 0.1041 0.7540 0.000 0.004 0.000 0.964 0.032
#> GSM627100 5 0.5685 0.7794 0.000 0.000 0.396 0.084 0.520
#> GSM627112 4 0.2719 0.7063 0.000 0.004 0.000 0.852 0.144
#> GSM627083 1 0.4735 0.6934 0.624 0.000 0.004 0.020 0.352
#> GSM627098 1 0.3957 0.3574 0.712 0.000 0.280 0.000 0.008
#> GSM627104 1 0.0404 0.6604 0.988 0.000 0.012 0.000 0.000
#> GSM627131 3 0.2144 0.6932 0.068 0.000 0.912 0.000 0.020
#> GSM627106 3 0.4114 0.2753 0.000 0.000 0.732 0.024 0.244
#> GSM627123 1 0.4084 0.7204 0.668 0.000 0.004 0.000 0.328
#> GSM627129 4 0.1597 0.7709 0.000 0.048 0.000 0.940 0.012
#> GSM627216 2 0.2761 0.8740 0.000 0.896 0.048 0.028 0.028
#> GSM627212 2 0.3013 0.8334 0.000 0.832 0.000 0.160 0.008
#> GSM627190 3 0.5883 0.5517 0.296 0.016 0.600 0.000 0.088
#> GSM627169 2 0.2984 0.8354 0.000 0.860 0.032 0.000 0.108
#> GSM627167 4 0.4067 0.5793 0.000 0.008 0.000 0.692 0.300
#> GSM627192 1 0.4101 0.7191 0.664 0.000 0.004 0.000 0.332
#> GSM627203 3 0.2390 0.6477 0.000 0.000 0.896 0.020 0.084
#> GSM627151 4 0.5252 0.5382 0.000 0.208 0.056 0.704 0.032
#> GSM627163 1 0.3684 0.7218 0.720 0.000 0.000 0.000 0.280
#> GSM627211 2 0.1469 0.8949 0.000 0.948 0.000 0.036 0.016
#> GSM627171 2 0.3002 0.8350 0.000 0.856 0.028 0.000 0.116
#> GSM627209 4 0.1740 0.7654 0.000 0.056 0.000 0.932 0.012
#> GSM627135 1 0.4084 0.7204 0.668 0.000 0.004 0.000 0.328
#> GSM627170 2 0.2260 0.8872 0.000 0.908 0.000 0.064 0.028
#> GSM627178 3 0.4315 0.4361 0.276 0.000 0.700 0.000 0.024
#> GSM627199 4 0.2448 0.7389 0.000 0.088 0.000 0.892 0.020
#> GSM627213 4 0.1403 0.7651 0.000 0.024 0.000 0.952 0.024
#> GSM627140 4 0.4484 0.5881 0.000 0.024 0.000 0.668 0.308
#> GSM627149 1 0.4084 0.7204 0.668 0.000 0.004 0.000 0.328
#> GSM627147 2 0.4088 0.7793 0.000 0.776 0.000 0.168 0.056
#> GSM627195 3 0.2331 0.6526 0.000 0.000 0.900 0.020 0.080
#> GSM627204 2 0.1469 0.8949 0.000 0.948 0.000 0.036 0.016
#> GSM627207 2 0.0703 0.8860 0.000 0.976 0.000 0.000 0.024
#> GSM627157 1 0.3790 0.3783 0.724 0.000 0.272 0.000 0.004
#> GSM627201 2 0.3013 0.8334 0.000 0.832 0.000 0.160 0.008
#> GSM627146 2 0.2624 0.8645 0.000 0.872 0.000 0.116 0.012
#> GSM627156 2 0.2984 0.8354 0.000 0.860 0.032 0.000 0.108
#> GSM627188 1 0.4101 0.7191 0.664 0.000 0.004 0.000 0.332
#> GSM627197 2 0.3318 0.8132 0.000 0.808 0.000 0.180 0.012
#> GSM627173 2 0.0771 0.8899 0.000 0.976 0.000 0.004 0.020
#> GSM627179 2 0.1124 0.8951 0.000 0.960 0.000 0.036 0.004
#> GSM627208 3 0.4737 0.4715 0.000 0.224 0.708 0.000 0.068
#> GSM627215 2 0.4216 0.8247 0.000 0.808 0.100 0.064 0.028
#> GSM627153 4 0.1670 0.7675 0.000 0.052 0.000 0.936 0.012
#> GSM627155 1 0.4101 0.7191 0.664 0.000 0.004 0.000 0.332
#> GSM627165 4 0.5060 0.5797 0.000 0.092 0.000 0.684 0.224
#> GSM627168 3 0.0162 0.7116 0.000 0.000 0.996 0.000 0.004
#> GSM627183 3 0.4127 0.5974 0.312 0.000 0.680 0.000 0.008
#> GSM627144 3 0.1571 0.6915 0.000 0.004 0.936 0.000 0.060
#> GSM627158 1 0.4009 0.7218 0.684 0.000 0.004 0.000 0.312
#> GSM627196 2 0.1469 0.8949 0.000 0.948 0.000 0.036 0.016
#> GSM627142 5 0.6205 0.8042 0.000 0.000 0.332 0.156 0.512
#> GSM627182 3 0.2844 0.6943 0.032 0.016 0.888 0.000 0.064
#> GSM627202 3 0.1800 0.6919 0.048 0.000 0.932 0.000 0.020
#> GSM627141 3 0.5080 0.5896 0.284 0.004 0.656 0.000 0.056
#> GSM627143 2 0.5331 0.7016 0.000 0.712 0.020 0.144 0.124
#> GSM627145 3 0.0162 0.7094 0.000 0.000 0.996 0.000 0.004
#> GSM627152 3 0.3016 0.5824 0.000 0.000 0.848 0.020 0.132
#> GSM627200 3 0.4564 0.5245 0.372 0.000 0.612 0.000 0.016
#> GSM627159 4 0.4297 0.2713 0.000 0.000 0.000 0.528 0.472
#> GSM627164 2 0.3073 0.8345 0.000 0.856 0.024 0.004 0.116
#> GSM627138 1 0.2077 0.6638 0.920 0.000 0.040 0.000 0.040
#> GSM627175 4 0.0963 0.7733 0.000 0.036 0.000 0.964 0.000
#> GSM627150 3 0.2079 0.6694 0.000 0.000 0.916 0.020 0.064
#> GSM627166 1 0.2719 0.5603 0.852 0.000 0.144 0.000 0.004
#> GSM627186 2 0.2984 0.8354 0.000 0.860 0.032 0.000 0.108
#> GSM627139 5 0.6175 0.3815 0.000 0.000 0.152 0.332 0.516
#> GSM627181 2 0.3318 0.8132 0.000 0.808 0.000 0.180 0.012
#> GSM627205 2 0.2260 0.8872 0.000 0.908 0.000 0.064 0.028
#> GSM627214 4 0.1965 0.7622 0.000 0.052 0.000 0.924 0.024
#> GSM627180 3 0.1731 0.6818 0.000 0.004 0.932 0.004 0.060
#> GSM627172 2 0.2011 0.8669 0.000 0.908 0.000 0.004 0.088
#> GSM627184 1 0.4101 0.7191 0.664 0.000 0.004 0.000 0.332
#> GSM627193 2 0.1211 0.8935 0.000 0.960 0.000 0.024 0.016
#> GSM627191 4 0.4047 0.5830 0.004 0.000 0.000 0.676 0.320
#> GSM627176 3 0.2124 0.6751 0.000 0.004 0.900 0.000 0.096
#> GSM627194 2 0.1809 0.8896 0.000 0.928 0.000 0.060 0.012
#> GSM627154 4 0.1124 0.7732 0.000 0.036 0.000 0.960 0.004
#> GSM627187 3 0.6477 0.5160 0.296 0.040 0.564 0.000 0.100
#> GSM627198 4 0.1774 0.7680 0.000 0.052 0.000 0.932 0.016
#> GSM627160 4 0.4253 0.5672 0.000 0.004 0.004 0.660 0.332
#> GSM627185 1 0.0162 0.6626 0.996 0.000 0.004 0.000 0.000
#> GSM627206 3 0.1251 0.7107 0.036 0.000 0.956 0.000 0.008
#> GSM627161 1 0.4047 0.7214 0.676 0.000 0.004 0.000 0.320
#> GSM627162 3 0.6544 0.5128 0.292 0.040 0.560 0.000 0.108
#> GSM627210 1 0.4425 0.0441 0.600 0.000 0.392 0.000 0.008
#> GSM627189 2 0.1568 0.8950 0.000 0.944 0.000 0.036 0.020
show/hide code output
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM627128 6 0.3756 0.6363 0.000 0.000 0.000 0.352 0.004 0.644
#> GSM627110 3 0.4587 0.5331 0.000 0.000 0.596 0.000 0.356 0.048
#> GSM627132 1 0.3231 0.7726 0.784 0.000 0.200 0.000 0.000 0.016
#> GSM627107 6 0.4914 0.3162 0.000 0.000 0.004 0.052 0.428 0.516
#> GSM627103 2 0.1555 0.8352 0.000 0.940 0.012 0.040 0.000 0.008
#> GSM627114 3 0.4466 0.5624 0.000 0.000 0.620 0.000 0.336 0.044
#> GSM627134 4 0.2546 0.7683 0.000 0.040 0.020 0.896 0.004 0.040
#> GSM627137 2 0.1464 0.8398 0.000 0.944 0.004 0.036 0.000 0.016
#> GSM627148 5 0.1151 0.8019 0.000 0.000 0.032 0.000 0.956 0.012
#> GSM627101 6 0.3965 0.5967 0.000 0.000 0.004 0.376 0.004 0.616
#> GSM627130 6 0.3620 0.6336 0.000 0.000 0.000 0.352 0.000 0.648
#> GSM627071 5 0.2266 0.7885 0.000 0.000 0.108 0.000 0.880 0.012
#> GSM627118 4 0.1616 0.7776 0.000 0.000 0.020 0.932 0.000 0.048
#> GSM627094 2 0.1261 0.8393 0.000 0.952 0.000 0.024 0.000 0.024
#> GSM627122 5 0.3555 0.7832 0.012 0.000 0.068 0.004 0.824 0.092
#> GSM627115 2 0.1149 0.8379 0.000 0.960 0.008 0.024 0.000 0.008
#> GSM627125 6 0.3636 0.6617 0.000 0.000 0.000 0.320 0.004 0.676
#> GSM627174 2 0.3743 0.7669 0.000 0.788 0.028 0.160 0.000 0.024
#> GSM627102 2 0.4563 0.7307 0.000 0.712 0.152 0.004 0.000 0.132
#> GSM627073 5 0.1116 0.7958 0.000 0.000 0.008 0.004 0.960 0.028
#> GSM627108 2 0.0632 0.8352 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM627126 1 0.0622 0.9317 0.980 0.000 0.000 0.008 0.000 0.012
#> GSM627078 4 0.0603 0.7990 0.000 0.016 0.000 0.980 0.000 0.004
#> GSM627090 5 0.3572 0.7015 0.000 0.000 0.032 0.000 0.764 0.204
#> GSM627099 4 0.3348 0.6804 0.000 0.152 0.016 0.812 0.000 0.020
#> GSM627105 6 0.3636 0.6617 0.000 0.000 0.000 0.320 0.004 0.676
#> GSM627117 3 0.4781 0.6070 0.004 0.008 0.660 0.000 0.268 0.060
#> GSM627121 5 0.3788 0.4713 0.000 0.000 0.004 0.012 0.704 0.280
#> GSM627127 4 0.0862 0.7992 0.000 0.016 0.004 0.972 0.000 0.008
#> GSM627087 2 0.1251 0.8374 0.000 0.956 0.012 0.024 0.000 0.008
#> GSM627089 5 0.2146 0.7799 0.000 0.000 0.116 0.000 0.880 0.004
#> GSM627092 2 0.4833 0.7188 0.000 0.692 0.168 0.004 0.004 0.132
#> GSM627076 6 0.3955 0.4400 0.004 0.000 0.008 0.000 0.340 0.648
#> GSM627136 3 0.4844 0.3669 0.000 0.000 0.504 0.000 0.440 0.056
#> GSM627081 5 0.2810 0.6924 0.000 0.000 0.004 0.008 0.832 0.156
#> GSM627091 2 0.2944 0.7808 0.000 0.832 0.012 0.148 0.000 0.008
#> GSM627097 4 0.1296 0.7922 0.000 0.004 0.012 0.952 0.000 0.032
#> GSM627072 5 0.2121 0.7855 0.000 0.000 0.096 0.000 0.892 0.012
#> GSM627080 1 0.2070 0.8669 0.892 0.000 0.100 0.000 0.000 0.008
#> GSM627088 3 0.4660 0.4274 0.000 0.000 0.540 0.000 0.416 0.044
#> GSM627109 3 0.3874 0.3686 0.356 0.000 0.636 0.000 0.000 0.008
#> GSM627111 1 0.3201 0.7619 0.780 0.000 0.208 0.000 0.000 0.012
#> GSM627113 3 0.4455 0.6939 0.072 0.000 0.728 0.000 0.184 0.016
#> GSM627133 5 0.6059 0.1168 0.000 0.396 0.048 0.012 0.484 0.060
#> GSM627177 5 0.2651 0.7875 0.000 0.000 0.112 0.000 0.860 0.028
#> GSM627086 2 0.2473 0.8099 0.000 0.876 0.012 0.104 0.000 0.008
#> GSM627095 1 0.0520 0.9316 0.984 0.000 0.000 0.008 0.000 0.008
#> GSM627079 5 0.2471 0.8021 0.000 0.000 0.056 0.004 0.888 0.052
#> GSM627082 6 0.3938 0.6378 0.016 0.000 0.000 0.324 0.000 0.660
#> GSM627074 3 0.3765 0.6733 0.156 0.000 0.780 0.000 0.060 0.004
#> GSM627077 5 0.5222 0.5013 0.020 0.000 0.264 0.004 0.636 0.076
#> GSM627093 3 0.3516 0.7078 0.096 0.000 0.812 0.000 0.088 0.004
#> GSM627120 4 0.5930 0.5482 0.000 0.060 0.032 0.660 0.092 0.156
#> GSM627124 4 0.0692 0.7992 0.000 0.020 0.000 0.976 0.000 0.004
#> GSM627075 2 0.3953 0.7571 0.000 0.764 0.132 0.000 0.000 0.104
#> GSM627085 4 0.0603 0.7990 0.000 0.016 0.000 0.980 0.000 0.004
#> GSM627119 3 0.3927 0.7025 0.120 0.000 0.776 0.000 0.100 0.004
#> GSM627116 4 0.1851 0.7786 0.000 0.004 0.012 0.924 0.004 0.056
#> GSM627084 3 0.4851 0.6535 0.196 0.000 0.708 0.004 0.044 0.048
#> GSM627096 4 0.1616 0.7776 0.000 0.000 0.020 0.932 0.000 0.048
#> GSM627100 6 0.3652 0.5655 0.000 0.000 0.000 0.016 0.264 0.720
#> GSM627112 4 0.2778 0.6378 0.000 0.000 0.008 0.824 0.000 0.168
#> GSM627083 1 0.2303 0.8740 0.904 0.000 0.024 0.020 0.000 0.052
#> GSM627098 3 0.4315 0.6972 0.144 0.000 0.744 0.000 0.104 0.008
#> GSM627104 3 0.3874 0.3686 0.356 0.000 0.636 0.000 0.000 0.008
#> GSM627131 5 0.4046 0.7500 0.016 0.000 0.128 0.004 0.784 0.068
#> GSM627106 5 0.2848 0.6866 0.000 0.000 0.004 0.008 0.828 0.160
#> GSM627123 1 0.0146 0.9325 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM627129 4 0.2262 0.7797 0.000 0.036 0.020 0.908 0.000 0.036
#> GSM627216 2 0.3909 0.7746 0.000 0.812 0.016 0.032 0.104 0.036
#> GSM627212 2 0.2944 0.7808 0.000 0.832 0.012 0.148 0.000 0.008
#> GSM627190 3 0.4806 0.5936 0.004 0.004 0.636 0.000 0.296 0.060
#> GSM627169 2 0.5171 0.6828 0.000 0.652 0.196 0.000 0.012 0.140
#> GSM627167 4 0.4727 0.1669 0.000 0.008 0.036 0.568 0.000 0.388
#> GSM627192 1 0.0622 0.9317 0.980 0.000 0.000 0.008 0.000 0.012
#> GSM627203 5 0.1555 0.7934 0.000 0.000 0.004 0.004 0.932 0.060
#> GSM627151 4 0.6020 0.5061 0.000 0.188 0.028 0.640 0.068 0.076
#> GSM627163 1 0.2912 0.8065 0.816 0.000 0.172 0.000 0.000 0.012
#> GSM627211 2 0.1245 0.8373 0.000 0.952 0.000 0.016 0.000 0.032
#> GSM627171 2 0.5240 0.6964 0.000 0.660 0.168 0.004 0.012 0.156
#> GSM627209 4 0.1218 0.7973 0.000 0.028 0.004 0.956 0.000 0.012
#> GSM627135 1 0.0767 0.9272 0.976 0.000 0.008 0.004 0.000 0.012
#> GSM627170 2 0.3332 0.8068 0.000 0.856 0.012 0.044 0.052 0.036
#> GSM627178 5 0.5338 0.6245 0.076 0.000 0.172 0.004 0.684 0.064
#> GSM627199 4 0.2573 0.7542 0.000 0.064 0.008 0.884 0.000 0.044
#> GSM627213 4 0.1196 0.7804 0.000 0.008 0.000 0.952 0.000 0.040
#> GSM627140 4 0.7011 0.1319 0.072 0.020 0.132 0.460 0.000 0.316
#> GSM627149 1 0.0363 0.9314 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM627147 2 0.6138 0.6584 0.000 0.608 0.140 0.124 0.000 0.128
#> GSM627195 5 0.1219 0.7941 0.000 0.000 0.000 0.004 0.948 0.048
#> GSM627204 2 0.1498 0.8399 0.000 0.940 0.000 0.032 0.000 0.028
#> GSM627207 2 0.0865 0.8335 0.000 0.964 0.000 0.000 0.000 0.036
#> GSM627157 3 0.4526 0.6936 0.152 0.000 0.728 0.000 0.108 0.012
#> GSM627201 2 0.2982 0.7777 0.000 0.828 0.012 0.152 0.000 0.008
#> GSM627146 2 0.2633 0.8099 0.000 0.864 0.004 0.112 0.000 0.020
#> GSM627156 2 0.5144 0.6863 0.000 0.656 0.192 0.000 0.012 0.140
#> GSM627188 1 0.0622 0.9317 0.980 0.000 0.000 0.008 0.000 0.012
#> GSM627197 2 0.3455 0.7367 0.000 0.776 0.004 0.200 0.000 0.020
#> GSM627173 2 0.1152 0.8344 0.000 0.952 0.000 0.004 0.000 0.044
#> GSM627179 2 0.0547 0.8395 0.000 0.980 0.000 0.020 0.000 0.000
#> GSM627208 5 0.5089 0.5979 0.000 0.120 0.068 0.008 0.724 0.080
#> GSM627215 2 0.4599 0.7253 0.000 0.752 0.016 0.044 0.152 0.036
#> GSM627153 4 0.1218 0.7973 0.000 0.028 0.004 0.956 0.000 0.012
#> GSM627155 1 0.0146 0.9325 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM627165 4 0.6456 0.4042 0.000 0.088 0.020 0.584 0.092 0.216
#> GSM627168 5 0.2593 0.7529 0.000 0.000 0.148 0.000 0.844 0.008
#> GSM627183 3 0.4205 0.4618 0.000 0.000 0.564 0.000 0.420 0.016
#> GSM627144 5 0.1995 0.8057 0.000 0.000 0.036 0.000 0.912 0.052
#> GSM627158 1 0.0622 0.9288 0.980 0.000 0.008 0.000 0.000 0.012
#> GSM627196 2 0.1498 0.8399 0.000 0.940 0.000 0.032 0.000 0.028
#> GSM627142 6 0.3938 0.6140 0.000 0.000 0.000 0.044 0.228 0.728
#> GSM627182 5 0.3493 0.7007 0.000 0.000 0.136 0.000 0.800 0.064
#> GSM627202 5 0.3837 0.7590 0.020 0.000 0.124 0.000 0.796 0.060
#> GSM627141 3 0.4585 0.5823 0.000 0.000 0.632 0.000 0.308 0.060
#> GSM627143 2 0.6792 0.5611 0.000 0.532 0.184 0.120 0.004 0.160
#> GSM627145 5 0.1753 0.7955 0.000 0.000 0.084 0.000 0.912 0.004
#> GSM627152 5 0.3185 0.7835 0.004 0.000 0.048 0.000 0.832 0.116
#> GSM627200 3 0.5334 0.4740 0.024 0.000 0.548 0.000 0.368 0.060
#> GSM627159 6 0.3563 0.6489 0.000 0.000 0.000 0.336 0.000 0.664
#> GSM627164 2 0.5071 0.6997 0.000 0.668 0.168 0.000 0.012 0.152
#> GSM627138 3 0.4015 0.3794 0.372 0.000 0.616 0.000 0.000 0.012
#> GSM627175 4 0.0862 0.7992 0.000 0.016 0.004 0.972 0.000 0.008
#> GSM627150 5 0.0508 0.8018 0.000 0.000 0.000 0.004 0.984 0.012
#> GSM627166 3 0.4248 0.6100 0.212 0.000 0.732 0.004 0.040 0.012
#> GSM627186 2 0.5198 0.6802 0.000 0.648 0.200 0.000 0.012 0.140
#> GSM627139 6 0.5443 0.6216 0.000 0.000 0.020 0.120 0.244 0.616
#> GSM627181 2 0.3534 0.7357 0.000 0.772 0.004 0.200 0.000 0.024
#> GSM627205 2 0.3870 0.7890 0.000 0.824 0.020 0.044 0.072 0.040
#> GSM627214 4 0.2879 0.7557 0.000 0.052 0.020 0.876 0.004 0.048
#> GSM627180 5 0.1503 0.7898 0.000 0.000 0.016 0.008 0.944 0.032
#> GSM627172 2 0.4707 0.7270 0.000 0.704 0.152 0.008 0.000 0.136
#> GSM627184 1 0.0260 0.9319 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM627193 2 0.1092 0.8400 0.000 0.960 0.000 0.020 0.000 0.020
#> GSM627191 4 0.5896 0.1552 0.104 0.000 0.036 0.532 0.000 0.328
#> GSM627176 5 0.3456 0.7810 0.004 0.000 0.076 0.000 0.816 0.104
#> GSM627194 2 0.2007 0.8379 0.000 0.920 0.012 0.036 0.000 0.032
#> GSM627154 4 0.0603 0.7990 0.000 0.016 0.000 0.980 0.000 0.004
#> GSM627187 3 0.4359 0.6176 0.004 0.016 0.748 0.000 0.168 0.064
#> GSM627198 4 0.1261 0.7946 0.000 0.024 0.000 0.952 0.000 0.024
#> GSM627160 4 0.6602 0.0992 0.096 0.000 0.092 0.484 0.004 0.324
#> GSM627185 3 0.3967 0.3669 0.356 0.000 0.632 0.000 0.000 0.012
#> GSM627206 5 0.2988 0.7333 0.000 0.000 0.152 0.000 0.824 0.024
#> GSM627161 1 0.0508 0.9300 0.984 0.000 0.004 0.000 0.000 0.012
#> GSM627162 3 0.5232 0.5286 0.004 0.020 0.672 0.000 0.172 0.132
#> GSM627210 3 0.3930 0.7104 0.104 0.000 0.776 0.000 0.116 0.004
#> GSM627189 2 0.1418 0.8396 0.000 0.944 0.000 0.024 0.000 0.032
Heatmaps for the consensus matrix. It visualizes the probability of two
samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures
which are significantly different between classes which can be candidate marks
for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known
annotation is numeric, one-way ANOVA test is applied, and if the known
annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) age(p) other(p) k
#> CV:kmeans 144 0.905 0.406 0.01484 2
#> CV:kmeans 64 1.000 0.701 0.00937 3
#> CV:kmeans 139 0.201 0.435 0.07497 4
#> CV:kmeans 124 0.390 0.561 0.16410 5
#> CV:kmeans 129 0.176 0.597 0.18557 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
CV:skmeans*
The object with results only for a single top-value method and a single partition method
can be extracted as:
res = res_list["CV", "skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51882 rows and 146 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
- The first row: a plot of the ECDF (empirical cumulative distribution
function) curves of the consensus matrix for each
k and the heatmap of
predicted classes for each k.
- The second row: heatmaps of the consensus matrix for each
k.
- The third row: heatmaps of the membership matrix for each
k.
- The fouth row: heatmaps of the signatures for each
k.
All the plots in panels can be made by individual functions and they are
plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
- ECDF curves of the consensus matrix for each
k;
- 1-PAC. The PAC
score
measures the proportion of the ambiguous subgrouping.
- Mean silhouette score.
- Concordance. The mean probability of fiting the consensus class ids in all
partitions.
- Area increased. Denote \(A_k\) as the area under the ECDF curve for current
k, the area increased is defined as \(A_k - A_{k-1}\).
- Rand index. The percent of pairs of samples that are both in a same cluster
or both are not in a same cluster in the partition of k and k-1.
- Jaccard index. The ratio of pairs of samples are both in a same cluster in
the partition of k and k-1 and the pairs of samples are both in a same
cluster in the partition k or k-1.
The detailed explanations of these statistics can be found in the cola
vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.974 0.989 0.5020 0.498 0.498
#> 3 3 0.971 0.949 0.977 0.3134 0.792 0.604
#> 4 4 0.917 0.898 0.957 0.1403 0.863 0.623
#> 5 5 0.792 0.734 0.831 0.0602 0.934 0.747
#> 6 6 0.761 0.621 0.749 0.0391 0.920 0.651
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
- All \(k\) with Jaccard index larger than 0.95 are removed because increasing
\(k\) does not provide enough extra information. If all \(k\) are removed, it is
marked as no subgroup is detected.
- For all \(k\) with 1-PAC score larger than 0.9, the maximal \(k\) is taken as
the best \(k\), and other \(k\) are marked as optional \(k\).
- If it does not fit the second rule. The \(k\) with the maximal vote of the
highest 1-PAC score, highest mean silhouette, and highest concordance is
taken as the best \(k\).
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
show/hide code output
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM627128 2 0.0000 0.992 0.000 1.000
#> GSM627110 1 0.0000 0.985 1.000 0.000
#> GSM627132 1 0.0000 0.985 1.000 0.000
#> GSM627107 2 0.0000 0.992 0.000 1.000
#> GSM627103 2 0.0000 0.992 0.000 1.000
#> GSM627114 1 0.0000 0.985 1.000 0.000
#> GSM627134 2 0.0000 0.992 0.000 1.000
#> GSM627137 2 0.0000 0.992 0.000 1.000
#> GSM627148 1 0.0000 0.985 1.000 0.000
#> GSM627101 2 0.0000 0.992 0.000 1.000
#> GSM627130 2 0.0000 0.992 0.000 1.000
#> GSM627071 1 0.0000 0.985 1.000 0.000
#> GSM627118 2 0.0000 0.992 0.000 1.000
#> GSM627094 2 0.0000 0.992 0.000 1.000
#> GSM627122 1 0.0000 0.985 1.000 0.000
#> GSM627115 2 0.0000 0.992 0.000 1.000
#> GSM627125 2 0.0000 0.992 0.000 1.000
#> GSM627174 2 0.0000 0.992 0.000 1.000
#> GSM627102 2 0.0000 0.992 0.000 1.000
#> GSM627073 1 0.8713 0.584 0.708 0.292
#> GSM627108 2 0.0000 0.992 0.000 1.000
#> GSM627126 1 0.0000 0.985 1.000 0.000
#> GSM627078 2 0.0000 0.992 0.000 1.000
#> GSM627090 1 0.0000 0.985 1.000 0.000
#> GSM627099 2 0.0000 0.992 0.000 1.000
#> GSM627105 2 0.0000 0.992 0.000 1.000
#> GSM627117 1 0.0000 0.985 1.000 0.000
#> GSM627121 2 0.0000 0.992 0.000 1.000
#> GSM627127 2 0.0000 0.992 0.000 1.000
#> GSM627087 2 0.0000 0.992 0.000 1.000
#> GSM627089 1 0.0000 0.985 1.000 0.000
#> GSM627092 2 0.0000 0.992 0.000 1.000
#> GSM627076 1 0.0000 0.985 1.000 0.000
#> GSM627136 1 0.0000 0.985 1.000 0.000
#> GSM627081 2 0.5294 0.866 0.120 0.880
#> GSM627091 2 0.0000 0.992 0.000 1.000
#> GSM627097 2 0.0000 0.992 0.000 1.000
#> GSM627072 1 0.0000 0.985 1.000 0.000
#> GSM627080 1 0.0000 0.985 1.000 0.000
#> GSM627088 1 0.0000 0.985 1.000 0.000
#> GSM627109 1 0.0000 0.985 1.000 0.000
#> GSM627111 1 0.0000 0.985 1.000 0.000
#> GSM627113 1 0.0000 0.985 1.000 0.000
#> GSM627133 2 0.0376 0.988 0.004 0.996
#> GSM627177 1 0.0000 0.985 1.000 0.000
#> GSM627086 2 0.0000 0.992 0.000 1.000
#> GSM627095 1 0.0000 0.985 1.000 0.000
#> GSM627079 1 0.0000 0.985 1.000 0.000
#> GSM627082 2 0.0000 0.992 0.000 1.000
#> GSM627074 1 0.0000 0.985 1.000 0.000
#> GSM627077 1 0.0000 0.985 1.000 0.000
#> GSM627093 1 0.0000 0.985 1.000 0.000
#> GSM627120 2 0.0000 0.992 0.000 1.000
#> GSM627124 2 0.0000 0.992 0.000 1.000
#> GSM627075 2 0.0000 0.992 0.000 1.000
#> GSM627085 2 0.0000 0.992 0.000 1.000
#> GSM627119 1 0.0000 0.985 1.000 0.000
#> GSM627116 2 0.0000 0.992 0.000 1.000
#> GSM627084 1 0.0000 0.985 1.000 0.000
#> GSM627096 2 0.0000 0.992 0.000 1.000
#> GSM627100 1 0.0000 0.985 1.000 0.000
#> GSM627112 2 0.0000 0.992 0.000 1.000
#> GSM627083 1 0.3733 0.915 0.928 0.072
#> GSM627098 1 0.0000 0.985 1.000 0.000
#> GSM627104 1 0.0000 0.985 1.000 0.000
#> GSM627131 1 0.0000 0.985 1.000 0.000
#> GSM627106 2 0.7745 0.710 0.228 0.772
#> GSM627123 1 0.0000 0.985 1.000 0.000
#> GSM627129 2 0.0000 0.992 0.000 1.000
#> GSM627216 2 0.0000 0.992 0.000 1.000
#> GSM627212 2 0.0000 0.992 0.000 1.000
#> GSM627190 1 0.0000 0.985 1.000 0.000
#> GSM627169 1 0.5629 0.847 0.868 0.132
#> GSM627167 2 0.0000 0.992 0.000 1.000
#> GSM627192 1 0.0000 0.985 1.000 0.000
#> GSM627203 1 0.0000 0.985 1.000 0.000
#> GSM627151 2 0.0000 0.992 0.000 1.000
#> GSM627163 1 0.0000 0.985 1.000 0.000
#> GSM627211 2 0.0000 0.992 0.000 1.000
#> GSM627171 2 0.0000 0.992 0.000 1.000
#> GSM627209 2 0.0000 0.992 0.000 1.000
#> GSM627135 1 0.0000 0.985 1.000 0.000
#> GSM627170 2 0.0000 0.992 0.000 1.000
#> GSM627178 1 0.0000 0.985 1.000 0.000
#> GSM627199 2 0.0000 0.992 0.000 1.000
#> GSM627213 2 0.0000 0.992 0.000 1.000
#> GSM627140 2 0.0000 0.992 0.000 1.000
#> GSM627149 1 0.0000 0.985 1.000 0.000
#> GSM627147 2 0.0000 0.992 0.000 1.000
#> GSM627195 1 0.0000 0.985 1.000 0.000
#> GSM627204 2 0.0000 0.992 0.000 1.000
#> GSM627207 2 0.0000 0.992 0.000 1.000
#> GSM627157 1 0.0000 0.985 1.000 0.000
#> GSM627201 2 0.0000 0.992 0.000 1.000
#> GSM627146 2 0.0000 0.992 0.000 1.000
#> GSM627156 2 0.0000 0.992 0.000 1.000
#> GSM627188 1 0.0000 0.985 1.000 0.000
#> GSM627197 2 0.0000 0.992 0.000 1.000
#> GSM627173 2 0.0000 0.992 0.000 1.000
#> GSM627179 2 0.0000 0.992 0.000 1.000
#> GSM627208 2 0.6148 0.823 0.152 0.848
#> GSM627215 2 0.0000 0.992 0.000 1.000
#> GSM627153 2 0.0000 0.992 0.000 1.000
#> GSM627155 1 0.0000 0.985 1.000 0.000
#> GSM627165 2 0.0000 0.992 0.000 1.000
#> GSM627168 1 0.0000 0.985 1.000 0.000
#> GSM627183 1 0.0000 0.985 1.000 0.000
#> GSM627144 1 0.0000 0.985 1.000 0.000
#> GSM627158 1 0.0000 0.985 1.000 0.000
#> GSM627196 2 0.0000 0.992 0.000 1.000
#> GSM627142 1 0.0000 0.985 1.000 0.000
#> GSM627182 1 0.0000 0.985 1.000 0.000
#> GSM627202 1 0.0000 0.985 1.000 0.000
#> GSM627141 1 0.0000 0.985 1.000 0.000
#> GSM627143 2 0.0000 0.992 0.000 1.000
#> GSM627145 1 0.0000 0.985 1.000 0.000
#> GSM627152 1 0.0000 0.985 1.000 0.000
#> GSM627200 1 0.0000 0.985 1.000 0.000
#> GSM627159 2 0.0000 0.992 0.000 1.000
#> GSM627164 2 0.0000 0.992 0.000 1.000
#> GSM627138 1 0.0000 0.985 1.000 0.000
#> GSM627175 2 0.0000 0.992 0.000 1.000
#> GSM627150 1 0.0000 0.985 1.000 0.000
#> GSM627166 1 0.0000 0.985 1.000 0.000
#> GSM627186 1 0.4690 0.885 0.900 0.100
#> GSM627139 2 0.1184 0.977 0.016 0.984
#> GSM627181 2 0.0000 0.992 0.000 1.000
#> GSM627205 2 0.0000 0.992 0.000 1.000
#> GSM627214 2 0.0000 0.992 0.000 1.000
#> GSM627180 2 0.4298 0.903 0.088 0.912
#> GSM627172 2 0.0000 0.992 0.000 1.000
#> GSM627184 1 0.0000 0.985 1.000 0.000
#> GSM627193 2 0.0000 0.992 0.000 1.000
#> GSM627191 2 0.0000 0.992 0.000 1.000
#> GSM627176 1 0.0000 0.985 1.000 0.000
#> GSM627194 2 0.0000 0.992 0.000 1.000
#> GSM627154 2 0.0000 0.992 0.000 1.000
#> GSM627187 1 0.0000 0.985 1.000 0.000
#> GSM627198 2 0.0000 0.992 0.000 1.000
#> GSM627160 1 0.9909 0.212 0.556 0.444
#> GSM627185 1 0.0000 0.985 1.000 0.000
#> GSM627206 1 0.0000 0.985 1.000 0.000
#> GSM627161 1 0.0000 0.985 1.000 0.000
#> GSM627162 1 0.0000 0.985 1.000 0.000
#> GSM627210 1 0.0000 0.985 1.000 0.000
#> GSM627189 2 0.0000 0.992 0.000 1.000
show/hide code output
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM627128 3 0.0000 0.975 0.000 0.000 1.000
#> GSM627110 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627132 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627107 3 0.0000 0.975 0.000 0.000 1.000
#> GSM627103 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627114 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627134 3 0.0592 0.974 0.000 0.012 0.988
#> GSM627137 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627148 1 0.0237 0.979 0.996 0.000 0.004
#> GSM627101 3 0.0000 0.975 0.000 0.000 1.000
#> GSM627130 3 0.0000 0.975 0.000 0.000 1.000
#> GSM627071 1 0.0237 0.979 0.996 0.000 0.004
#> GSM627118 3 0.0000 0.975 0.000 0.000 1.000
#> GSM627094 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627122 1 0.0747 0.972 0.984 0.000 0.016
#> GSM627115 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627125 3 0.0000 0.975 0.000 0.000 1.000
#> GSM627174 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627102 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627073 1 0.7069 0.089 0.508 0.020 0.472
#> GSM627108 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627126 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627078 3 0.0747 0.972 0.000 0.016 0.984
#> GSM627090 1 0.0892 0.969 0.980 0.000 0.020
#> GSM627099 3 0.2625 0.909 0.000 0.084 0.916
#> GSM627105 3 0.0000 0.975 0.000 0.000 1.000
#> GSM627117 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627121 3 0.0000 0.975 0.000 0.000 1.000
#> GSM627127 3 0.0592 0.974 0.000 0.012 0.988
#> GSM627087 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627089 1 0.0237 0.979 0.996 0.000 0.004
#> GSM627092 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627076 1 0.4842 0.726 0.776 0.000 0.224
#> GSM627136 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627081 3 0.0000 0.975 0.000 0.000 1.000
#> GSM627091 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627097 3 0.0424 0.975 0.000 0.008 0.992
#> GSM627072 1 0.0237 0.979 0.996 0.000 0.004
#> GSM627080 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627088 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627109 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627111 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627113 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627133 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627177 1 0.0237 0.979 0.996 0.000 0.004
#> GSM627086 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627095 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627079 1 0.0747 0.972 0.984 0.000 0.016
#> GSM627082 3 0.0000 0.975 0.000 0.000 1.000
#> GSM627074 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627077 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627093 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627120 3 0.0424 0.975 0.000 0.008 0.992
#> GSM627124 3 0.0747 0.972 0.000 0.016 0.984
#> GSM627075 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627085 3 0.0747 0.972 0.000 0.016 0.984
#> GSM627119 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627116 3 0.0237 0.975 0.000 0.004 0.996
#> GSM627084 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627096 3 0.0000 0.975 0.000 0.000 1.000
#> GSM627100 3 0.0000 0.975 0.000 0.000 1.000
#> GSM627112 3 0.0237 0.975 0.000 0.004 0.996
#> GSM627083 3 0.5650 0.554 0.312 0.000 0.688
#> GSM627098 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627104 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627131 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627106 3 0.0000 0.975 0.000 0.000 1.000
#> GSM627123 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627129 3 0.0592 0.974 0.000 0.012 0.988
#> GSM627216 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627212 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627190 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627169 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627167 3 0.0237 0.975 0.000 0.004 0.996
#> GSM627192 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627203 1 0.1529 0.951 0.960 0.000 0.040
#> GSM627151 3 0.3116 0.879 0.000 0.108 0.892
#> GSM627163 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627211 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627171 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627209 3 0.0747 0.972 0.000 0.016 0.984
#> GSM627135 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627170 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627178 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627199 3 0.4887 0.705 0.000 0.228 0.772
#> GSM627213 3 0.0424 0.975 0.000 0.008 0.992
#> GSM627140 3 0.0592 0.974 0.000 0.012 0.988
#> GSM627149 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627147 2 0.3941 0.797 0.000 0.844 0.156
#> GSM627195 1 0.3879 0.827 0.848 0.000 0.152
#> GSM627204 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627207 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627157 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627201 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627146 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627156 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627188 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627197 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627173 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627179 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627208 2 0.0237 0.966 0.000 0.996 0.004
#> GSM627215 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627153 3 0.0747 0.972 0.000 0.016 0.984
#> GSM627155 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627165 3 0.0592 0.974 0.000 0.012 0.988
#> GSM627168 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627183 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627144 1 0.0747 0.972 0.984 0.000 0.016
#> GSM627158 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627196 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627142 3 0.0000 0.975 0.000 0.000 1.000
#> GSM627182 2 0.6247 0.385 0.376 0.620 0.004
#> GSM627202 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627141 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627143 2 0.5835 0.480 0.000 0.660 0.340
#> GSM627145 1 0.0237 0.979 0.996 0.000 0.004
#> GSM627152 1 0.0747 0.972 0.984 0.000 0.016
#> GSM627200 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627159 3 0.0000 0.975 0.000 0.000 1.000
#> GSM627164 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627138 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627175 3 0.0747 0.972 0.000 0.016 0.984
#> GSM627150 1 0.3686 0.842 0.860 0.000 0.140
#> GSM627166 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627186 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627139 3 0.0000 0.975 0.000 0.000 1.000
#> GSM627181 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627205 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627214 3 0.0892 0.969 0.000 0.020 0.980
#> GSM627180 2 0.5618 0.644 0.008 0.732 0.260
#> GSM627172 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627184 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627193 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627191 3 0.0237 0.974 0.004 0.000 0.996
#> GSM627176 1 0.0592 0.974 0.988 0.000 0.012
#> GSM627194 2 0.0000 0.970 0.000 1.000 0.000
#> GSM627154 3 0.0592 0.974 0.000 0.012 0.988
#> GSM627187 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627198 3 0.0747 0.972 0.000 0.016 0.984
#> GSM627160 3 0.0237 0.974 0.004 0.000 0.996
#> GSM627185 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627206 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627161 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627162 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627210 1 0.0000 0.981 1.000 0.000 0.000
#> GSM627189 2 0.0000 0.970 0.000 1.000 0.000
show/hide code output
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM627128 4 0.0000 0.9815 0.000 0.000 0.000 1.000
#> GSM627110 3 0.4992 -0.0328 0.476 0.000 0.524 0.000
#> GSM627132 1 0.0000 0.9371 1.000 0.000 0.000 0.000
#> GSM627107 3 0.3726 0.6877 0.000 0.000 0.788 0.212
#> GSM627103 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627114 1 0.4661 0.5184 0.652 0.000 0.348 0.000
#> GSM627134 4 0.0000 0.9815 0.000 0.000 0.000 1.000
#> GSM627137 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627148 3 0.0000 0.9052 0.000 0.000 1.000 0.000
#> GSM627101 4 0.0000 0.9815 0.000 0.000 0.000 1.000
#> GSM627130 4 0.0000 0.9815 0.000 0.000 0.000 1.000
#> GSM627071 3 0.0000 0.9052 0.000 0.000 1.000 0.000
#> GSM627118 4 0.0000 0.9815 0.000 0.000 0.000 1.000
#> GSM627094 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627122 3 0.4746 0.4648 0.368 0.000 0.632 0.000
#> GSM627115 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627125 4 0.0000 0.9815 0.000 0.000 0.000 1.000
#> GSM627174 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627102 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627073 3 0.0000 0.9052 0.000 0.000 1.000 0.000
#> GSM627108 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627126 1 0.0000 0.9371 1.000 0.000 0.000 0.000
#> GSM627078 4 0.0000 0.9815 0.000 0.000 0.000 1.000
#> GSM627090 3 0.0000 0.9052 0.000 0.000 1.000 0.000
#> GSM627099 4 0.0469 0.9706 0.000 0.012 0.000 0.988
#> GSM627105 4 0.0000 0.9815 0.000 0.000 0.000 1.000
#> GSM627117 1 0.3726 0.7376 0.788 0.000 0.212 0.000
#> GSM627121 3 0.1022 0.8854 0.000 0.000 0.968 0.032
#> GSM627127 4 0.0000 0.9815 0.000 0.000 0.000 1.000
#> GSM627087 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627089 3 0.0000 0.9052 0.000 0.000 1.000 0.000
#> GSM627092 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627076 3 0.0000 0.9052 0.000 0.000 1.000 0.000
#> GSM627136 1 0.2216 0.8679 0.908 0.000 0.092 0.000
#> GSM627081 3 0.0000 0.9052 0.000 0.000 1.000 0.000
#> GSM627091 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627097 4 0.0000 0.9815 0.000 0.000 0.000 1.000
#> GSM627072 3 0.0000 0.9052 0.000 0.000 1.000 0.000
#> GSM627080 1 0.0000 0.9371 1.000 0.000 0.000 0.000
#> GSM627088 1 0.4643 0.5266 0.656 0.000 0.344 0.000
#> GSM627109 1 0.0000 0.9371 1.000 0.000 0.000 0.000
#> GSM627111 1 0.0000 0.9371 1.000 0.000 0.000 0.000
#> GSM627113 1 0.0921 0.9191 0.972 0.000 0.028 0.000
#> GSM627133 2 0.3356 0.7658 0.000 0.824 0.176 0.000
#> GSM627177 3 0.3444 0.7367 0.184 0.000 0.816 0.000
#> GSM627086 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627095 1 0.0000 0.9371 1.000 0.000 0.000 0.000
#> GSM627079 3 0.0000 0.9052 0.000 0.000 1.000 0.000
#> GSM627082 4 0.0000 0.9815 0.000 0.000 0.000 1.000
#> GSM627074 1 0.0000 0.9371 1.000 0.000 0.000 0.000
#> GSM627077 1 0.0188 0.9347 0.996 0.000 0.004 0.000
#> GSM627093 1 0.0000 0.9371 1.000 0.000 0.000 0.000
#> GSM627120 4 0.0000 0.9815 0.000 0.000 0.000 1.000
#> GSM627124 4 0.0000 0.9815 0.000 0.000 0.000 1.000
#> GSM627075 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627085 4 0.0000 0.9815 0.000 0.000 0.000 1.000
#> GSM627119 1 0.0000 0.9371 1.000 0.000 0.000 0.000
#> GSM627116 4 0.0000 0.9815 0.000 0.000 0.000 1.000
#> GSM627084 1 0.0000 0.9371 1.000 0.000 0.000 0.000
#> GSM627096 4 0.0000 0.9815 0.000 0.000 0.000 1.000
#> GSM627100 3 0.2814 0.7944 0.000 0.000 0.868 0.132
#> GSM627112 4 0.0000 0.9815 0.000 0.000 0.000 1.000
#> GSM627083 1 0.0000 0.9371 1.000 0.000 0.000 0.000
#> GSM627098 1 0.0000 0.9371 1.000 0.000 0.000 0.000
#> GSM627104 1 0.0000 0.9371 1.000 0.000 0.000 0.000
#> GSM627131 3 0.4916 0.3348 0.424 0.000 0.576 0.000
#> GSM627106 3 0.0000 0.9052 0.000 0.000 1.000 0.000
#> GSM627123 1 0.0000 0.9371 1.000 0.000 0.000 0.000
#> GSM627129 4 0.0000 0.9815 0.000 0.000 0.000 1.000
#> GSM627216 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627212 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627190 1 0.4564 0.5578 0.672 0.000 0.328 0.000
#> GSM627169 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627167 4 0.0000 0.9815 0.000 0.000 0.000 1.000
#> GSM627192 1 0.0000 0.9371 1.000 0.000 0.000 0.000
#> GSM627203 3 0.0000 0.9052 0.000 0.000 1.000 0.000
#> GSM627151 4 0.0707 0.9628 0.000 0.020 0.000 0.980
#> GSM627163 1 0.0000 0.9371 1.000 0.000 0.000 0.000
#> GSM627211 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627171 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627209 4 0.0000 0.9815 0.000 0.000 0.000 1.000
#> GSM627135 1 0.0000 0.9371 1.000 0.000 0.000 0.000
#> GSM627170 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627178 1 0.2589 0.8242 0.884 0.000 0.116 0.000
#> GSM627199 4 0.0188 0.9782 0.000 0.004 0.000 0.996
#> GSM627213 4 0.0000 0.9815 0.000 0.000 0.000 1.000
#> GSM627140 4 0.0000 0.9815 0.000 0.000 0.000 1.000
#> GSM627149 1 0.0000 0.9371 1.000 0.000 0.000 0.000
#> GSM627147 2 0.4746 0.4222 0.000 0.632 0.000 0.368
#> GSM627195 3 0.0000 0.9052 0.000 0.000 1.000 0.000
#> GSM627204 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627207 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627157 1 0.0000 0.9371 1.000 0.000 0.000 0.000
#> GSM627201 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627146 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627156 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627188 1 0.0000 0.9371 1.000 0.000 0.000 0.000
#> GSM627197 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627173 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627179 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627208 3 0.2530 0.8137 0.000 0.112 0.888 0.000
#> GSM627215 2 0.0188 0.9717 0.000 0.996 0.004 0.000
#> GSM627153 4 0.0000 0.9815 0.000 0.000 0.000 1.000
#> GSM627155 1 0.0000 0.9371 1.000 0.000 0.000 0.000
#> GSM627165 4 0.0921 0.9545 0.000 0.028 0.000 0.972
#> GSM627168 3 0.0000 0.9052 0.000 0.000 1.000 0.000
#> GSM627183 1 0.4624 0.5350 0.660 0.000 0.340 0.000
#> GSM627144 3 0.0000 0.9052 0.000 0.000 1.000 0.000
#> GSM627158 1 0.0000 0.9371 1.000 0.000 0.000 0.000
#> GSM627196 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627142 4 0.4967 0.1386 0.000 0.000 0.452 0.548
#> GSM627182 3 0.0000 0.9052 0.000 0.000 1.000 0.000
#> GSM627202 3 0.4941 0.3057 0.436 0.000 0.564 0.000
#> GSM627141 1 0.3726 0.7376 0.788 0.000 0.212 0.000
#> GSM627143 2 0.4454 0.5545 0.000 0.692 0.000 0.308
#> GSM627145 3 0.0000 0.9052 0.000 0.000 1.000 0.000
#> GSM627152 3 0.1118 0.8832 0.036 0.000 0.964 0.000
#> GSM627200 1 0.0000 0.9371 1.000 0.000 0.000 0.000
#> GSM627159 4 0.0000 0.9815 0.000 0.000 0.000 1.000
#> GSM627164 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627138 1 0.0000 0.9371 1.000 0.000 0.000 0.000
#> GSM627175 4 0.0000 0.9815 0.000 0.000 0.000 1.000
#> GSM627150 3 0.0000 0.9052 0.000 0.000 1.000 0.000
#> GSM627166 1 0.0000 0.9371 1.000 0.000 0.000 0.000
#> GSM627186 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627139 4 0.0000 0.9815 0.000 0.000 0.000 1.000
#> GSM627181 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627205 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627214 4 0.0000 0.9815 0.000 0.000 0.000 1.000
#> GSM627180 3 0.0000 0.9052 0.000 0.000 1.000 0.000
#> GSM627172 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627184 1 0.0000 0.9371 1.000 0.000 0.000 0.000
#> GSM627193 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627191 4 0.0000 0.9815 0.000 0.000 0.000 1.000
#> GSM627176 3 0.2345 0.8349 0.100 0.000 0.900 0.000
#> GSM627194 2 0.0000 0.9754 0.000 1.000 0.000 0.000
#> GSM627154 4 0.0000 0.9815 0.000 0.000 0.000 1.000
#> GSM627187 1 0.3726 0.7376 0.788 0.000 0.212 0.000
#> GSM627198 4 0.0000 0.9815 0.000 0.000 0.000 1.000
#> GSM627160 4 0.2081 0.8906 0.084 0.000 0.000 0.916
#> GSM627185 1 0.0000 0.9371 1.000 0.000 0.000 0.000
#> GSM627206 3 0.0000 0.9052 0.000 0.000 1.000 0.000
#> GSM627161 1 0.0000 0.9371 1.000 0.000 0.000 0.000
#> GSM627162 1 0.1022 0.9161 0.968 0.000 0.032 0.000
#> GSM627210 1 0.0000 0.9371 1.000 0.000 0.000 0.000
#> GSM627189 2 0.0000 0.9754 0.000 1.000 0.000 0.000
show/hide code output
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM627128 4 0.1908 0.88101 0.000 0.000 0.000 0.908 0.092
#> GSM627110 3 0.1341 0.70451 0.000 0.000 0.944 0.000 0.056
#> GSM627132 1 0.3774 0.62461 0.704 0.000 0.296 0.000 0.000
#> GSM627107 5 0.0290 0.76806 0.000 0.000 0.000 0.008 0.992
#> GSM627103 2 0.0162 0.90625 0.000 0.996 0.000 0.004 0.000
#> GSM627114 3 0.1502 0.70650 0.004 0.000 0.940 0.000 0.056
#> GSM627134 4 0.0162 0.91319 0.000 0.004 0.000 0.996 0.000
#> GSM627137 2 0.0290 0.90581 0.000 0.992 0.000 0.008 0.000
#> GSM627148 5 0.3816 0.64454 0.000 0.000 0.304 0.000 0.696
#> GSM627101 4 0.1341 0.89584 0.000 0.000 0.000 0.944 0.056
#> GSM627130 4 0.1908 0.88101 0.000 0.000 0.000 0.908 0.092
#> GSM627071 5 0.4118 0.60195 0.004 0.000 0.336 0.000 0.660
#> GSM627118 4 0.0000 0.91283 0.000 0.000 0.000 1.000 0.000
#> GSM627094 2 0.0000 0.90617 0.000 1.000 0.000 0.000 0.000
#> GSM627122 5 0.4350 0.32610 0.408 0.000 0.000 0.004 0.588
#> GSM627115 2 0.0000 0.90617 0.000 1.000 0.000 0.000 0.000
#> GSM627125 4 0.3684 0.71525 0.000 0.000 0.000 0.720 0.280
#> GSM627174 2 0.1121 0.89491 0.000 0.956 0.000 0.044 0.000
#> GSM627102 2 0.2020 0.86473 0.000 0.900 0.100 0.000 0.000
#> GSM627073 5 0.2424 0.78300 0.000 0.000 0.132 0.000 0.868
#> GSM627108 2 0.0000 0.90617 0.000 1.000 0.000 0.000 0.000
#> GSM627126 1 0.0000 0.77313 1.000 0.000 0.000 0.000 0.000
#> GSM627078 4 0.0162 0.91319 0.000 0.004 0.000 0.996 0.000
#> GSM627090 5 0.0609 0.76636 0.020 0.000 0.000 0.000 0.980
#> GSM627099 4 0.2516 0.79763 0.000 0.140 0.000 0.860 0.000
#> GSM627105 4 0.3684 0.71525 0.000 0.000 0.000 0.720 0.280
#> GSM627117 3 0.0162 0.71163 0.000 0.004 0.996 0.000 0.000
#> GSM627121 5 0.1410 0.79443 0.000 0.000 0.060 0.000 0.940
#> GSM627127 4 0.0162 0.91319 0.000 0.004 0.000 0.996 0.000
#> GSM627087 2 0.0162 0.90625 0.000 0.996 0.000 0.004 0.000
#> GSM627089 5 0.3857 0.63529 0.000 0.000 0.312 0.000 0.688
#> GSM627092 2 0.3143 0.79318 0.000 0.796 0.204 0.000 0.000
#> GSM627076 5 0.2179 0.71720 0.100 0.000 0.000 0.004 0.896
#> GSM627136 3 0.3462 0.62760 0.196 0.000 0.792 0.000 0.012
#> GSM627081 5 0.1608 0.79678 0.000 0.000 0.072 0.000 0.928
#> GSM627091 2 0.1121 0.89491 0.000 0.956 0.000 0.044 0.000
#> GSM627097 4 0.0000 0.91283 0.000 0.000 0.000 1.000 0.000
#> GSM627072 5 0.4030 0.58171 0.000 0.000 0.352 0.000 0.648
#> GSM627080 1 0.1671 0.75462 0.924 0.000 0.076 0.000 0.000
#> GSM627088 3 0.2079 0.70666 0.020 0.000 0.916 0.000 0.064
#> GSM627109 1 0.3999 0.57402 0.656 0.000 0.344 0.000 0.000
#> GSM627111 1 0.3966 0.58352 0.664 0.000 0.336 0.000 0.000
#> GSM627113 3 0.3857 0.45621 0.312 0.000 0.688 0.000 0.000
#> GSM627133 2 0.5488 0.49552 0.000 0.608 0.300 0.000 0.092
#> GSM627177 5 0.4497 0.56457 0.016 0.000 0.352 0.000 0.632
#> GSM627086 2 0.1043 0.89635 0.000 0.960 0.000 0.040 0.000
#> GSM627095 1 0.0000 0.77313 1.000 0.000 0.000 0.000 0.000
#> GSM627079 5 0.1965 0.79667 0.000 0.000 0.096 0.000 0.904
#> GSM627082 4 0.3759 0.82398 0.092 0.000 0.000 0.816 0.092
#> GSM627074 3 0.4088 0.30288 0.368 0.000 0.632 0.000 0.000
#> GSM627077 1 0.1205 0.76667 0.956 0.000 0.040 0.000 0.004
#> GSM627093 3 0.2813 0.64185 0.168 0.000 0.832 0.000 0.000
#> GSM627120 4 0.0798 0.91018 0.000 0.008 0.000 0.976 0.016
#> GSM627124 4 0.0162 0.91319 0.000 0.004 0.000 0.996 0.000
#> GSM627075 2 0.1965 0.86688 0.000 0.904 0.096 0.000 0.000
#> GSM627085 4 0.0162 0.91319 0.000 0.004 0.000 0.996 0.000
#> GSM627119 3 0.3895 0.43870 0.320 0.000 0.680 0.000 0.000
#> GSM627116 4 0.0000 0.91283 0.000 0.000 0.000 1.000 0.000
#> GSM627084 1 0.3752 0.62816 0.708 0.000 0.292 0.000 0.000
#> GSM627096 4 0.0000 0.91283 0.000 0.000 0.000 1.000 0.000
#> GSM627100 5 0.0771 0.76415 0.020 0.000 0.000 0.004 0.976
#> GSM627112 4 0.0880 0.90337 0.000 0.000 0.000 0.968 0.032
#> GSM627083 1 0.0000 0.77313 1.000 0.000 0.000 0.000 0.000
#> GSM627098 1 0.4126 0.51530 0.620 0.000 0.380 0.000 0.000
#> GSM627104 1 0.3999 0.57402 0.656 0.000 0.344 0.000 0.000
#> GSM627131 1 0.4735 -0.02012 0.524 0.000 0.016 0.000 0.460
#> GSM627106 5 0.1608 0.79678 0.000 0.000 0.072 0.000 0.928
#> GSM627123 1 0.0000 0.77313 1.000 0.000 0.000 0.000 0.000
#> GSM627129 4 0.0162 0.91319 0.000 0.004 0.000 0.996 0.000
#> GSM627216 2 0.0324 0.90453 0.000 0.992 0.004 0.000 0.004
#> GSM627212 2 0.1121 0.89491 0.000 0.956 0.000 0.044 0.000
#> GSM627190 3 0.0290 0.71296 0.000 0.000 0.992 0.000 0.008
#> GSM627169 2 0.3774 0.70178 0.000 0.704 0.296 0.000 0.000
#> GSM627167 4 0.1792 0.88418 0.000 0.000 0.000 0.916 0.084
#> GSM627192 1 0.0000 0.77313 1.000 0.000 0.000 0.000 0.000
#> GSM627203 5 0.1792 0.79741 0.000 0.000 0.084 0.000 0.916
#> GSM627151 4 0.0703 0.90271 0.000 0.024 0.000 0.976 0.000
#> GSM627163 1 0.2329 0.73342 0.876 0.000 0.124 0.000 0.000
#> GSM627211 2 0.0000 0.90617 0.000 1.000 0.000 0.000 0.000
#> GSM627171 2 0.3480 0.75420 0.000 0.752 0.248 0.000 0.000
#> GSM627209 4 0.0162 0.91319 0.000 0.004 0.000 0.996 0.000
#> GSM627135 1 0.0000 0.77313 1.000 0.000 0.000 0.000 0.000
#> GSM627170 2 0.0290 0.90581 0.000 0.992 0.000 0.008 0.000
#> GSM627178 1 0.0898 0.76533 0.972 0.000 0.008 0.000 0.020
#> GSM627199 4 0.0404 0.90989 0.000 0.012 0.000 0.988 0.000
#> GSM627213 4 0.0000 0.91283 0.000 0.000 0.000 1.000 0.000
#> GSM627140 4 0.5440 0.67760 0.236 0.000 0.020 0.672 0.072
#> GSM627149 1 0.0000 0.77313 1.000 0.000 0.000 0.000 0.000
#> GSM627147 2 0.4425 0.58424 0.000 0.680 0.024 0.296 0.000
#> GSM627195 5 0.1851 0.79725 0.000 0.000 0.088 0.000 0.912
#> GSM627204 2 0.0000 0.90617 0.000 1.000 0.000 0.000 0.000
#> GSM627207 2 0.0162 0.90493 0.000 0.996 0.004 0.000 0.000
#> GSM627157 1 0.4227 0.42245 0.580 0.000 0.420 0.000 0.000
#> GSM627201 2 0.1121 0.89491 0.000 0.956 0.000 0.044 0.000
#> GSM627146 2 0.1121 0.89491 0.000 0.956 0.000 0.044 0.000
#> GSM627156 2 0.3684 0.72069 0.000 0.720 0.280 0.000 0.000
#> GSM627188 1 0.0000 0.77313 1.000 0.000 0.000 0.000 0.000
#> GSM627197 2 0.1121 0.89491 0.000 0.956 0.000 0.044 0.000
#> GSM627173 2 0.0000 0.90617 0.000 1.000 0.000 0.000 0.000
#> GSM627179 2 0.0000 0.90617 0.000 1.000 0.000 0.000 0.000
#> GSM627208 3 0.5255 -0.03711 0.000 0.052 0.560 0.000 0.388
#> GSM627215 2 0.2859 0.81919 0.000 0.876 0.056 0.000 0.068
#> GSM627153 4 0.0162 0.91319 0.000 0.004 0.000 0.996 0.000
#> GSM627155 1 0.0000 0.77313 1.000 0.000 0.000 0.000 0.000
#> GSM627165 4 0.3954 0.75499 0.000 0.036 0.000 0.772 0.192
#> GSM627168 5 0.4227 0.45542 0.000 0.000 0.420 0.000 0.580
#> GSM627183 3 0.4548 0.64117 0.128 0.000 0.752 0.000 0.120
#> GSM627144 5 0.2605 0.77166 0.000 0.000 0.148 0.000 0.852
#> GSM627158 1 0.0000 0.77313 1.000 0.000 0.000 0.000 0.000
#> GSM627196 2 0.0162 0.90625 0.000 0.996 0.000 0.004 0.000
#> GSM627142 5 0.4577 0.57480 0.108 0.000 0.000 0.144 0.748
#> GSM627182 3 0.4182 -0.01053 0.000 0.000 0.600 0.000 0.400
#> GSM627202 1 0.4542 0.00208 0.536 0.000 0.008 0.000 0.456
#> GSM627141 3 0.0865 0.72240 0.024 0.000 0.972 0.000 0.004
#> GSM627143 2 0.6666 0.37021 0.000 0.476 0.232 0.288 0.004
#> GSM627145 5 0.3857 0.63544 0.000 0.000 0.312 0.000 0.688
#> GSM627152 5 0.2179 0.71315 0.112 0.000 0.000 0.000 0.888
#> GSM627200 1 0.4150 0.46723 0.612 0.000 0.388 0.000 0.000
#> GSM627159 4 0.2193 0.87769 0.008 0.000 0.000 0.900 0.092
#> GSM627164 2 0.3424 0.76193 0.000 0.760 0.240 0.000 0.000
#> GSM627138 1 0.4101 0.53029 0.628 0.000 0.372 0.000 0.000
#> GSM627175 4 0.0162 0.91319 0.000 0.004 0.000 0.996 0.000
#> GSM627150 5 0.1965 0.79667 0.000 0.000 0.096 0.000 0.904
#> GSM627166 1 0.2813 0.71283 0.832 0.000 0.168 0.000 0.000
#> GSM627186 2 0.3796 0.69671 0.000 0.700 0.300 0.000 0.000
#> GSM627139 4 0.3949 0.68549 0.004 0.000 0.000 0.696 0.300
#> GSM627181 2 0.1121 0.89491 0.000 0.956 0.000 0.044 0.000
#> GSM627205 2 0.0404 0.90498 0.000 0.988 0.000 0.012 0.000
#> GSM627214 4 0.0290 0.91173 0.000 0.008 0.000 0.992 0.000
#> GSM627180 5 0.2020 0.79577 0.000 0.000 0.100 0.000 0.900
#> GSM627172 2 0.2074 0.86242 0.000 0.896 0.104 0.000 0.000
#> GSM627184 1 0.0000 0.77313 1.000 0.000 0.000 0.000 0.000
#> GSM627193 2 0.0000 0.90617 0.000 1.000 0.000 0.000 0.000
#> GSM627191 4 0.5308 0.59238 0.304 0.000 0.000 0.620 0.076
#> GSM627176 5 0.4410 0.62020 0.112 0.000 0.124 0.000 0.764
#> GSM627194 2 0.0162 0.90625 0.000 0.996 0.000 0.004 0.000
#> GSM627154 4 0.0162 0.91319 0.000 0.004 0.000 0.996 0.000
#> GSM627187 3 0.0324 0.71358 0.004 0.004 0.992 0.000 0.000
#> GSM627198 4 0.0162 0.91319 0.000 0.004 0.000 0.996 0.000
#> GSM627160 4 0.5723 0.42712 0.388 0.000 0.004 0.532 0.076
#> GSM627185 1 0.3999 0.57402 0.656 0.000 0.344 0.000 0.000
#> GSM627206 3 0.4306 -0.25399 0.000 0.000 0.508 0.000 0.492
#> GSM627161 1 0.0000 0.77313 1.000 0.000 0.000 0.000 0.000
#> GSM627162 3 0.2179 0.66062 0.100 0.004 0.896 0.000 0.000
#> GSM627210 3 0.3876 0.44802 0.316 0.000 0.684 0.000 0.000
#> GSM627189 2 0.0000 0.90617 0.000 1.000 0.000 0.000 0.000
show/hide code output
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM627128 4 0.6607 0.3677 0.000 0.000 0.160 0.436 0.056 0.348
#> GSM627110 3 0.3187 0.6417 0.004 0.000 0.796 0.000 0.188 0.012
#> GSM627132 1 0.3101 0.5512 0.756 0.000 0.244 0.000 0.000 0.000
#> GSM627107 5 0.4965 0.4999 0.000 0.000 0.156 0.004 0.664 0.176
#> GSM627103 2 0.0260 0.9103 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM627114 3 0.3187 0.6433 0.004 0.000 0.796 0.000 0.188 0.012
#> GSM627134 4 0.0405 0.7963 0.000 0.000 0.000 0.988 0.004 0.008
#> GSM627137 2 0.0520 0.9089 0.000 0.984 0.000 0.008 0.000 0.008
#> GSM627148 5 0.2668 0.6801 0.000 0.000 0.168 0.000 0.828 0.004
#> GSM627101 4 0.4239 0.6561 0.000 0.000 0.016 0.740 0.052 0.192
#> GSM627130 4 0.6612 0.3636 0.000 0.000 0.160 0.432 0.056 0.352
#> GSM627071 5 0.3394 0.6369 0.000 0.000 0.236 0.000 0.752 0.012
#> GSM627118 4 0.0405 0.7963 0.000 0.000 0.000 0.988 0.004 0.008
#> GSM627094 2 0.0146 0.9096 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM627122 1 0.7514 0.0155 0.400 0.000 0.172 0.004 0.236 0.188
#> GSM627115 2 0.0291 0.9097 0.000 0.992 0.000 0.004 0.000 0.004
#> GSM627125 4 0.7232 0.2813 0.000 0.000 0.160 0.356 0.132 0.352
#> GSM627174 2 0.2053 0.8572 0.000 0.888 0.000 0.108 0.000 0.004
#> GSM627102 6 0.4364 0.6044 0.000 0.364 0.024 0.004 0.000 0.608
#> GSM627073 5 0.1700 0.7129 0.000 0.000 0.080 0.000 0.916 0.004
#> GSM627108 2 0.0146 0.9061 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM627126 1 0.0146 0.7507 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM627078 4 0.0291 0.7967 0.000 0.004 0.000 0.992 0.000 0.004
#> GSM627090 5 0.5650 0.4262 0.004 0.000 0.172 0.004 0.572 0.248
#> GSM627099 4 0.3298 0.5309 0.000 0.236 0.000 0.756 0.000 0.008
#> GSM627105 4 0.7232 0.2813 0.000 0.000 0.160 0.356 0.132 0.352
#> GSM627117 3 0.3517 0.6589 0.004 0.004 0.812 0.000 0.052 0.128
#> GSM627121 5 0.2679 0.6562 0.000 0.000 0.040 0.000 0.864 0.096
#> GSM627127 4 0.0146 0.7969 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM627087 2 0.0405 0.9103 0.000 0.988 0.000 0.008 0.000 0.004
#> GSM627089 5 0.2941 0.6534 0.000 0.000 0.220 0.000 0.780 0.000
#> GSM627092 6 0.4538 0.6289 0.000 0.340 0.048 0.000 0.000 0.612
#> GSM627076 5 0.6616 0.3041 0.044 0.000 0.168 0.004 0.448 0.336
#> GSM627136 3 0.3857 0.7180 0.152 0.000 0.768 0.000 0.080 0.000
#> GSM627081 5 0.0508 0.7052 0.000 0.000 0.004 0.000 0.984 0.012
#> GSM627091 2 0.2100 0.8480 0.000 0.884 0.000 0.112 0.000 0.004
#> GSM627097 4 0.0000 0.7969 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM627072 5 0.3151 0.6257 0.000 0.000 0.252 0.000 0.748 0.000
#> GSM627080 1 0.1007 0.7402 0.956 0.000 0.044 0.000 0.000 0.000
#> GSM627088 3 0.3152 0.6397 0.004 0.000 0.792 0.000 0.196 0.008
#> GSM627109 1 0.3810 0.1585 0.572 0.000 0.428 0.000 0.000 0.000
#> GSM627111 1 0.3747 0.2469 0.604 0.000 0.396 0.000 0.000 0.000
#> GSM627113 3 0.3714 0.7005 0.196 0.000 0.760 0.000 0.044 0.000
#> GSM627133 2 0.5627 0.2918 0.000 0.540 0.084 0.000 0.348 0.028
#> GSM627177 5 0.3790 0.6063 0.004 0.000 0.264 0.000 0.716 0.016
#> GSM627086 2 0.1644 0.8804 0.000 0.920 0.000 0.076 0.000 0.004
#> GSM627095 1 0.0146 0.7507 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM627079 5 0.1701 0.7158 0.000 0.000 0.072 0.000 0.920 0.008
#> GSM627082 4 0.7411 0.2993 0.044 0.000 0.160 0.372 0.056 0.368
#> GSM627074 3 0.3499 0.5466 0.320 0.000 0.680 0.000 0.000 0.000
#> GSM627077 1 0.1788 0.7275 0.916 0.000 0.076 0.000 0.004 0.004
#> GSM627093 3 0.2883 0.6834 0.212 0.000 0.788 0.000 0.000 0.000
#> GSM627120 4 0.3305 0.7270 0.000 0.000 0.012 0.832 0.048 0.108
#> GSM627124 4 0.0291 0.7967 0.000 0.004 0.000 0.992 0.000 0.004
#> GSM627075 6 0.4254 0.5490 0.000 0.404 0.020 0.000 0.000 0.576
#> GSM627085 4 0.0146 0.7969 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM627119 3 0.3101 0.6640 0.244 0.000 0.756 0.000 0.000 0.000
#> GSM627116 4 0.0363 0.7943 0.000 0.000 0.000 0.988 0.000 0.012
#> GSM627084 1 0.3464 0.4368 0.688 0.000 0.312 0.000 0.000 0.000
#> GSM627096 4 0.0405 0.7963 0.000 0.000 0.000 0.988 0.004 0.008
#> GSM627100 5 0.5954 0.3231 0.004 0.000 0.168 0.004 0.468 0.356
#> GSM627112 4 0.1814 0.7515 0.000 0.000 0.000 0.900 0.000 0.100
#> GSM627083 1 0.0748 0.7402 0.976 0.000 0.004 0.004 0.000 0.016
#> GSM627098 3 0.3851 0.1947 0.460 0.000 0.540 0.000 0.000 0.000
#> GSM627104 1 0.3833 0.1044 0.556 0.000 0.444 0.000 0.000 0.000
#> GSM627131 1 0.5128 0.4386 0.636 0.000 0.116 0.000 0.240 0.008
#> GSM627106 5 0.0508 0.7052 0.000 0.000 0.004 0.000 0.984 0.012
#> GSM627123 1 0.0000 0.7509 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627129 4 0.0405 0.7963 0.000 0.000 0.000 0.988 0.004 0.008
#> GSM627216 2 0.1168 0.8932 0.000 0.956 0.000 0.000 0.028 0.016
#> GSM627212 2 0.2006 0.8570 0.000 0.892 0.000 0.104 0.000 0.004
#> GSM627190 3 0.3383 0.6615 0.004 0.004 0.812 0.000 0.148 0.032
#> GSM627169 6 0.4887 0.6349 0.000 0.324 0.080 0.000 0.000 0.596
#> GSM627167 6 0.5998 -0.3353 0.000 0.000 0.148 0.380 0.016 0.456
#> GSM627192 1 0.0146 0.7507 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM627203 5 0.0858 0.7138 0.000 0.000 0.028 0.000 0.968 0.004
#> GSM627151 4 0.2170 0.7087 0.000 0.100 0.000 0.888 0.000 0.012
#> GSM627163 1 0.1501 0.7210 0.924 0.000 0.076 0.000 0.000 0.000
#> GSM627211 2 0.0405 0.9069 0.000 0.988 0.000 0.004 0.000 0.008
#> GSM627171 6 0.4700 0.6290 0.000 0.340 0.060 0.000 0.000 0.600
#> GSM627209 4 0.0291 0.7967 0.000 0.004 0.000 0.992 0.000 0.004
#> GSM627135 1 0.0146 0.7510 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM627170 2 0.1053 0.9039 0.000 0.964 0.000 0.012 0.020 0.004
#> GSM627178 1 0.1922 0.7331 0.924 0.000 0.040 0.000 0.024 0.012
#> GSM627199 4 0.1092 0.7809 0.000 0.020 0.000 0.960 0.000 0.020
#> GSM627213 4 0.0000 0.7969 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM627140 6 0.4184 0.3418 0.120 0.000 0.004 0.124 0.000 0.752
#> GSM627149 1 0.0000 0.7509 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627147 6 0.4480 0.6048 0.000 0.304 0.004 0.044 0.000 0.648
#> GSM627195 5 0.1082 0.7152 0.000 0.000 0.040 0.000 0.956 0.004
#> GSM627204 2 0.0291 0.9087 0.000 0.992 0.000 0.004 0.000 0.004
#> GSM627207 2 0.1327 0.8460 0.000 0.936 0.000 0.000 0.000 0.064
#> GSM627157 3 0.3823 0.2738 0.436 0.000 0.564 0.000 0.000 0.000
#> GSM627201 2 0.1814 0.8625 0.000 0.900 0.000 0.100 0.000 0.000
#> GSM627146 2 0.1327 0.8893 0.000 0.936 0.000 0.064 0.000 0.000
#> GSM627156 6 0.4887 0.6349 0.000 0.324 0.080 0.000 0.000 0.596
#> GSM627188 1 0.0146 0.7507 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM627197 2 0.2118 0.8592 0.000 0.888 0.000 0.104 0.000 0.008
#> GSM627173 2 0.0260 0.9039 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM627179 2 0.0146 0.9096 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM627208 5 0.4478 0.5392 0.000 0.024 0.296 0.000 0.660 0.020
#> GSM627215 2 0.3141 0.6878 0.000 0.788 0.000 0.000 0.200 0.012
#> GSM627153 4 0.0291 0.7967 0.000 0.004 0.000 0.992 0.000 0.004
#> GSM627155 1 0.0000 0.7509 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627165 4 0.5044 0.6308 0.000 0.020 0.012 0.708 0.148 0.112
#> GSM627168 5 0.3789 0.3673 0.000 0.000 0.416 0.000 0.584 0.000
#> GSM627183 3 0.3511 0.6354 0.024 0.000 0.760 0.000 0.216 0.000
#> GSM627144 5 0.1908 0.7126 0.000 0.000 0.096 0.000 0.900 0.004
#> GSM627158 1 0.0458 0.7491 0.984 0.000 0.016 0.000 0.000 0.000
#> GSM627196 2 0.0291 0.9087 0.000 0.992 0.000 0.004 0.000 0.004
#> GSM627142 6 0.7791 -0.2394 0.052 0.000 0.168 0.084 0.340 0.356
#> GSM627182 5 0.4026 0.4952 0.000 0.000 0.348 0.000 0.636 0.016
#> GSM627202 1 0.4710 0.4712 0.668 0.000 0.084 0.000 0.244 0.004
#> GSM627141 3 0.3684 0.7034 0.048 0.000 0.812 0.000 0.112 0.028
#> GSM627143 6 0.5204 0.6187 0.000 0.236 0.052 0.056 0.000 0.656
#> GSM627145 5 0.2793 0.6648 0.000 0.000 0.200 0.000 0.800 0.000
#> GSM627152 5 0.6812 0.3664 0.084 0.000 0.168 0.004 0.500 0.244
#> GSM627200 1 0.3833 0.0650 0.556 0.000 0.444 0.000 0.000 0.000
#> GSM627159 4 0.6617 0.3598 0.000 0.000 0.160 0.428 0.056 0.356
#> GSM627164 6 0.4687 0.6319 0.000 0.336 0.060 0.000 0.000 0.604
#> GSM627138 3 0.3867 0.0954 0.488 0.000 0.512 0.000 0.000 0.000
#> GSM627175 4 0.0291 0.7967 0.000 0.004 0.000 0.992 0.000 0.004
#> GSM627150 5 0.1204 0.7158 0.000 0.000 0.056 0.000 0.944 0.000
#> GSM627166 1 0.2706 0.6546 0.832 0.000 0.160 0.000 0.000 0.008
#> GSM627186 6 0.4887 0.6349 0.000 0.324 0.080 0.000 0.000 0.596
#> GSM627139 6 0.7553 -0.2766 0.004 0.000 0.168 0.300 0.172 0.356
#> GSM627181 2 0.2218 0.8567 0.000 0.884 0.000 0.104 0.000 0.012
#> GSM627205 2 0.1511 0.8934 0.000 0.944 0.000 0.012 0.032 0.012
#> GSM627214 4 0.0603 0.7948 0.000 0.000 0.000 0.980 0.004 0.016
#> GSM627180 5 0.1584 0.7151 0.000 0.000 0.064 0.000 0.928 0.008
#> GSM627172 6 0.4475 0.6112 0.004 0.356 0.024 0.004 0.000 0.612
#> GSM627184 1 0.0146 0.7493 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM627193 2 0.0000 0.9074 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627191 4 0.7779 0.1527 0.280 0.000 0.152 0.292 0.008 0.268
#> GSM627176 5 0.6933 0.3257 0.076 0.000 0.180 0.004 0.456 0.284
#> GSM627194 2 0.0547 0.9088 0.000 0.980 0.000 0.020 0.000 0.000
#> GSM627154 4 0.0146 0.7969 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM627187 3 0.3043 0.6162 0.000 0.004 0.796 0.000 0.004 0.196
#> GSM627198 4 0.0291 0.7967 0.000 0.004 0.000 0.992 0.000 0.004
#> GSM627160 1 0.8130 -0.0913 0.340 0.000 0.152 0.200 0.040 0.268
#> GSM627185 1 0.3828 0.1121 0.560 0.000 0.440 0.000 0.000 0.000
#> GSM627206 5 0.4067 0.2836 0.000 0.000 0.444 0.000 0.548 0.008
#> GSM627161 1 0.0260 0.7507 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM627162 6 0.4360 0.1102 0.012 0.004 0.404 0.000 0.004 0.576
#> GSM627210 3 0.3076 0.6686 0.240 0.000 0.760 0.000 0.000 0.000
#> GSM627189 2 0.0146 0.9096 0.000 0.996 0.000 0.004 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two
samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures
which are significantly different between classes which can be candidate marks
for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known
annotation is numeric, one-way ANOVA test is applied, and if the known
annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) age(p) other(p) k
#> CV:skmeans 145 0.9624 0.400 0.0070 2
#> CV:skmeans 143 0.0471 0.282 0.0167 3
#> CV:skmeans 140 0.2862 0.354 0.0770 4
#> CV:skmeans 130 0.1890 0.177 0.1829 5
#> CV:skmeans 111 0.1513 0.742 0.1237 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
CV:pam**
The object with results only for a single top-value method and a single partition method
can be extracted as:
res = res_list["CV", "pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51882 rows and 146 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
- The first row: a plot of the ECDF (empirical cumulative distribution
function) curves of the consensus matrix for each
k and the heatmap of
predicted classes for each k.
- The second row: heatmaps of the consensus matrix for each
k.
- The third row: heatmaps of the membership matrix for each
k.
- The fouth row: heatmaps of the signatures for each
k.
All the plots in panels can be made by individual functions and they are
plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
- ECDF curves of the consensus matrix for each
k;
- 1-PAC. The PAC
score
measures the proportion of the ambiguous subgrouping.
- Mean silhouette score.
- Concordance. The mean probability of fiting the consensus class ids in all
partitions.
- Area increased. Denote \(A_k\) as the area under the ECDF curve for current
k, the area increased is defined as \(A_k - A_{k-1}\).
- Rand index. The percent of pairs of samples that are both in a same cluster
or both are not in a same cluster in the partition of k and k-1.
- Jaccard index. The ratio of pairs of samples are both in a same cluster in
the partition of k and k-1 and the pairs of samples are both in a same
cluster in the partition k or k-1.
The detailed explanations of these statistics can be found in the cola
vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.997 0.957 0.975 0.5008 0.498 0.498
#> 3 3 0.850 0.864 0.937 0.2747 0.810 0.636
#> 4 4 0.669 0.738 0.860 0.1189 0.904 0.739
#> 5 5 0.816 0.857 0.907 0.0736 0.890 0.646
#> 6 6 0.806 0.771 0.869 0.0399 0.958 0.819
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
- All \(k\) with Jaccard index larger than 0.95 are removed because increasing
\(k\) does not provide enough extra information. If all \(k\) are removed, it is
marked as no subgroup is detected.
- For all \(k\) with 1-PAC score larger than 0.9, the maximal \(k\) is taken as
the best \(k\), and other \(k\) are marked as optional \(k\).
- If it does not fit the second rule. The \(k\) with the maximal vote of the
highest 1-PAC score, highest mean silhouette, and highest concordance is
taken as the best \(k\).
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
show/hide code output
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM627128 1 0.3733 0.943 0.928 0.072
#> GSM627110 1 0.0000 0.970 1.000 0.000
#> GSM627132 1 0.0000 0.970 1.000 0.000
#> GSM627107 1 0.3733 0.943 0.928 0.072
#> GSM627103 2 0.0000 0.979 0.000 1.000
#> GSM627114 1 0.0000 0.970 1.000 0.000
#> GSM627134 2 0.0000 0.979 0.000 1.000
#> GSM627137 2 0.0000 0.979 0.000 1.000
#> GSM627148 1 0.4161 0.908 0.916 0.084
#> GSM627101 1 0.3733 0.943 0.928 0.072
#> GSM627130 1 0.3733 0.943 0.928 0.072
#> GSM627071 1 0.2603 0.957 0.956 0.044
#> GSM627118 2 0.0000 0.979 0.000 1.000
#> GSM627094 2 0.0000 0.979 0.000 1.000
#> GSM627122 1 0.0938 0.967 0.988 0.012
#> GSM627115 2 0.0000 0.979 0.000 1.000
#> GSM627125 1 0.3733 0.943 0.928 0.072
#> GSM627174 2 0.0000 0.979 0.000 1.000
#> GSM627102 2 0.0000 0.979 0.000 1.000
#> GSM627073 1 0.3431 0.947 0.936 0.064
#> GSM627108 2 0.0000 0.979 0.000 1.000
#> GSM627126 1 0.0000 0.970 1.000 0.000
#> GSM627078 2 0.0000 0.979 0.000 1.000
#> GSM627090 1 0.0000 0.970 1.000 0.000
#> GSM627099 2 0.0000 0.979 0.000 1.000
#> GSM627105 1 0.3733 0.943 0.928 0.072
#> GSM627117 2 0.3733 0.924 0.072 0.928
#> GSM627121 1 0.3431 0.947 0.936 0.064
#> GSM627127 2 0.0000 0.979 0.000 1.000
#> GSM627087 2 0.0000 0.979 0.000 1.000
#> GSM627089 1 0.0000 0.970 1.000 0.000
#> GSM627092 2 0.0000 0.979 0.000 1.000
#> GSM627076 1 0.0000 0.970 1.000 0.000
#> GSM627136 1 0.0000 0.970 1.000 0.000
#> GSM627081 1 0.5842 0.874 0.860 0.140
#> GSM627091 2 0.0000 0.979 0.000 1.000
#> GSM627097 2 0.0000 0.979 0.000 1.000
#> GSM627072 1 0.1184 0.966 0.984 0.016
#> GSM627080 1 0.0000 0.970 1.000 0.000
#> GSM627088 2 0.8499 0.660 0.276 0.724
#> GSM627109 1 0.0000 0.970 1.000 0.000
#> GSM627111 1 0.0000 0.970 1.000 0.000
#> GSM627113 1 0.0000 0.970 1.000 0.000
#> GSM627133 2 0.0000 0.979 0.000 1.000
#> GSM627177 1 0.3431 0.948 0.936 0.064
#> GSM627086 2 0.0000 0.979 0.000 1.000
#> GSM627095 1 0.0000 0.970 1.000 0.000
#> GSM627079 1 0.0376 0.969 0.996 0.004
#> GSM627082 1 0.3733 0.943 0.928 0.072
#> GSM627074 2 0.6048 0.851 0.148 0.852
#> GSM627077 1 0.0000 0.970 1.000 0.000
#> GSM627093 2 0.3733 0.924 0.072 0.928
#> GSM627120 2 0.0000 0.979 0.000 1.000
#> GSM627124 2 0.0000 0.979 0.000 1.000
#> GSM627075 2 0.0000 0.979 0.000 1.000
#> GSM627085 2 0.0000 0.979 0.000 1.000
#> GSM627119 1 0.5519 0.856 0.872 0.128
#> GSM627116 2 0.8386 0.621 0.268 0.732
#> GSM627084 1 0.0376 0.969 0.996 0.004
#> GSM627096 2 0.0938 0.971 0.012 0.988
#> GSM627100 1 0.3114 0.951 0.944 0.056
#> GSM627112 1 0.7528 0.772 0.784 0.216
#> GSM627083 1 0.1633 0.964 0.976 0.024
#> GSM627098 1 0.0000 0.970 1.000 0.000
#> GSM627104 2 0.3733 0.924 0.072 0.928
#> GSM627131 1 0.0000 0.970 1.000 0.000
#> GSM627106 1 0.3431 0.947 0.936 0.064
#> GSM627123 1 0.0000 0.970 1.000 0.000
#> GSM627129 2 0.0000 0.979 0.000 1.000
#> GSM627216 2 0.0000 0.979 0.000 1.000
#> GSM627212 2 0.0000 0.979 0.000 1.000
#> GSM627190 2 0.3733 0.924 0.072 0.928
#> GSM627169 2 0.0672 0.974 0.008 0.992
#> GSM627167 2 0.0000 0.979 0.000 1.000
#> GSM627192 1 0.0000 0.970 1.000 0.000
#> GSM627203 1 0.2778 0.954 0.952 0.048
#> GSM627151 2 0.0000 0.979 0.000 1.000
#> GSM627163 1 0.0000 0.970 1.000 0.000
#> GSM627211 2 0.0000 0.979 0.000 1.000
#> GSM627171 2 0.0672 0.974 0.008 0.992
#> GSM627209 2 0.0000 0.979 0.000 1.000
#> GSM627135 1 0.0000 0.970 1.000 0.000
#> GSM627170 2 0.0000 0.979 0.000 1.000
#> GSM627178 1 0.0000 0.970 1.000 0.000
#> GSM627199 2 0.0000 0.979 0.000 1.000
#> GSM627213 2 0.0000 0.979 0.000 1.000
#> GSM627140 2 0.0000 0.979 0.000 1.000
#> GSM627149 1 0.0000 0.970 1.000 0.000
#> GSM627147 2 0.0000 0.979 0.000 1.000
#> GSM627195 1 0.2778 0.954 0.952 0.048
#> GSM627204 2 0.0000 0.979 0.000 1.000
#> GSM627207 2 0.0000 0.979 0.000 1.000
#> GSM627157 1 0.0000 0.970 1.000 0.000
#> GSM627201 2 0.0000 0.979 0.000 1.000
#> GSM627146 2 0.0000 0.979 0.000 1.000
#> GSM627156 2 0.0000 0.979 0.000 1.000
#> GSM627188 1 0.0000 0.970 1.000 0.000
#> GSM627197 2 0.0000 0.979 0.000 1.000
#> GSM627173 2 0.0000 0.979 0.000 1.000
#> GSM627179 2 0.0000 0.979 0.000 1.000
#> GSM627208 2 0.0672 0.974 0.008 0.992
#> GSM627215 2 0.0000 0.979 0.000 1.000
#> GSM627153 2 0.0000 0.979 0.000 1.000
#> GSM627155 1 0.0000 0.970 1.000 0.000
#> GSM627165 2 0.0000 0.979 0.000 1.000
#> GSM627168 1 0.0000 0.970 1.000 0.000
#> GSM627183 1 0.0000 0.970 1.000 0.000
#> GSM627144 2 0.7056 0.770 0.192 0.808
#> GSM627158 1 0.0000 0.970 1.000 0.000
#> GSM627196 2 0.0000 0.979 0.000 1.000
#> GSM627142 1 0.3431 0.947 0.936 0.064
#> GSM627182 2 0.0672 0.974 0.008 0.992
#> GSM627202 1 0.0000 0.970 1.000 0.000
#> GSM627141 1 0.0000 0.970 1.000 0.000
#> GSM627143 2 0.0000 0.979 0.000 1.000
#> GSM627145 1 0.0000 0.970 1.000 0.000
#> GSM627152 1 0.0000 0.970 1.000 0.000
#> GSM627200 1 0.0000 0.970 1.000 0.000
#> GSM627159 1 0.3733 0.943 0.928 0.072
#> GSM627164 2 0.0000 0.979 0.000 1.000
#> GSM627138 1 0.0000 0.970 1.000 0.000
#> GSM627175 2 0.0000 0.979 0.000 1.000
#> GSM627150 1 0.3431 0.947 0.936 0.064
#> GSM627166 2 0.2236 0.955 0.036 0.964
#> GSM627186 2 0.0672 0.974 0.008 0.992
#> GSM627139 1 0.3733 0.943 0.928 0.072
#> GSM627181 2 0.0000 0.979 0.000 1.000
#> GSM627205 2 0.0000 0.979 0.000 1.000
#> GSM627214 2 0.0000 0.979 0.000 1.000
#> GSM627180 2 0.1184 0.968 0.016 0.984
#> GSM627172 2 0.0000 0.979 0.000 1.000
#> GSM627184 1 0.0000 0.970 1.000 0.000
#> GSM627193 2 0.0000 0.979 0.000 1.000
#> GSM627191 1 0.3733 0.943 0.928 0.072
#> GSM627176 1 0.0376 0.969 0.996 0.004
#> GSM627194 2 0.0000 0.979 0.000 1.000
#> GSM627154 2 0.0000 0.979 0.000 1.000
#> GSM627187 2 0.3733 0.924 0.072 0.928
#> GSM627198 2 0.0000 0.979 0.000 1.000
#> GSM627160 1 0.4298 0.930 0.912 0.088
#> GSM627185 1 0.0000 0.970 1.000 0.000
#> GSM627206 1 0.0000 0.970 1.000 0.000
#> GSM627161 1 0.0000 0.970 1.000 0.000
#> GSM627162 2 0.5946 0.852 0.144 0.856
#> GSM627210 2 0.3879 0.921 0.076 0.924
#> GSM627189 2 0.0000 0.979 0.000 1.000
show/hide code output
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM627128 3 0.1860 0.887952 0.000 0.052 0.948
#> GSM627110 1 0.2066 0.845860 0.940 0.000 0.060
#> GSM627132 1 0.0000 0.849084 1.000 0.000 0.000
#> GSM627107 3 0.0237 0.904979 0.000 0.004 0.996
#> GSM627103 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627114 1 0.1753 0.849527 0.952 0.000 0.048
#> GSM627134 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627137 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627148 1 0.1964 0.846963 0.944 0.000 0.056
#> GSM627101 3 0.1753 0.889624 0.000 0.048 0.952
#> GSM627130 3 0.1753 0.889624 0.000 0.048 0.952
#> GSM627071 3 0.2810 0.896817 0.036 0.036 0.928
#> GSM627118 2 0.0237 0.978620 0.000 0.996 0.004
#> GSM627094 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627122 3 0.2356 0.885802 0.072 0.000 0.928
#> GSM627115 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627125 3 0.0424 0.905074 0.000 0.008 0.992
#> GSM627174 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627102 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627073 3 0.2165 0.886993 0.000 0.064 0.936
#> GSM627108 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627126 1 0.6308 0.000962 0.508 0.000 0.492
#> GSM627078 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627090 3 0.0000 0.904887 0.000 0.000 1.000
#> GSM627099 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627105 3 0.0592 0.904707 0.000 0.012 0.988
#> GSM627117 1 0.4339 0.792281 0.868 0.084 0.048
#> GSM627121 3 0.0000 0.904887 0.000 0.000 1.000
#> GSM627127 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627087 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627089 1 0.6252 0.258587 0.556 0.000 0.444
#> GSM627092 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627076 3 0.0000 0.904887 0.000 0.000 1.000
#> GSM627136 3 0.6280 0.010073 0.460 0.000 0.540
#> GSM627081 3 0.2537 0.856198 0.000 0.080 0.920
#> GSM627091 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627097 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627072 3 0.5760 0.444948 0.328 0.000 0.672
#> GSM627080 1 0.0000 0.849084 1.000 0.000 0.000
#> GSM627088 1 0.7391 0.633968 0.696 0.196 0.108
#> GSM627109 1 0.0000 0.849084 1.000 0.000 0.000
#> GSM627111 1 0.0000 0.849084 1.000 0.000 0.000
#> GSM627113 1 0.1643 0.849941 0.956 0.000 0.044
#> GSM627133 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627177 3 0.2774 0.881411 0.008 0.072 0.920
#> GSM627086 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627095 1 0.6308 0.000962 0.508 0.000 0.492
#> GSM627079 3 0.2356 0.885802 0.072 0.000 0.928
#> GSM627082 3 0.2116 0.891902 0.040 0.012 0.948
#> GSM627074 1 0.0424 0.849968 0.992 0.000 0.008
#> GSM627077 3 0.2448 0.885731 0.076 0.000 0.924
#> GSM627093 1 0.1753 0.849527 0.952 0.000 0.048
#> GSM627120 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627124 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627075 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627085 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627119 1 0.1753 0.849527 0.952 0.000 0.048
#> GSM627116 2 0.5291 0.611063 0.000 0.732 0.268
#> GSM627084 1 0.6935 0.369352 0.604 0.024 0.372
#> GSM627096 2 0.0592 0.971428 0.000 0.988 0.012
#> GSM627100 3 0.0000 0.904887 0.000 0.000 1.000
#> GSM627112 3 0.4931 0.685028 0.000 0.232 0.768
#> GSM627083 3 0.4075 0.882134 0.072 0.048 0.880
#> GSM627098 1 0.1753 0.849527 0.952 0.000 0.048
#> GSM627104 1 0.0237 0.849533 0.996 0.000 0.004
#> GSM627131 3 0.2356 0.885802 0.072 0.000 0.928
#> GSM627106 3 0.0000 0.904887 0.000 0.000 1.000
#> GSM627123 1 0.6267 0.145801 0.548 0.000 0.452
#> GSM627129 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627216 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627212 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627190 1 0.1753 0.849527 0.952 0.000 0.048
#> GSM627169 2 0.0237 0.978161 0.000 0.996 0.004
#> GSM627167 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627192 3 0.3412 0.867862 0.124 0.000 0.876
#> GSM627203 3 0.0747 0.901333 0.016 0.000 0.984
#> GSM627151 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627163 1 0.0000 0.849084 1.000 0.000 0.000
#> GSM627211 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627171 2 0.1529 0.943415 0.000 0.960 0.040
#> GSM627209 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627135 3 0.3267 0.872225 0.116 0.000 0.884
#> GSM627170 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627178 3 0.2356 0.885802 0.072 0.000 0.928
#> GSM627199 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627213 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627140 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627149 1 0.6079 0.307619 0.612 0.000 0.388
#> GSM627147 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627195 3 0.0000 0.904887 0.000 0.000 1.000
#> GSM627204 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627207 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627157 1 0.0000 0.849084 1.000 0.000 0.000
#> GSM627201 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627146 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627156 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627188 3 0.3340 0.870423 0.120 0.000 0.880
#> GSM627197 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627173 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627179 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627208 2 0.1964 0.929148 0.000 0.944 0.056
#> GSM627215 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627153 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627155 1 0.0000 0.849084 1.000 0.000 0.000
#> GSM627165 2 0.1411 0.951583 0.000 0.964 0.036
#> GSM627168 1 0.3482 0.799717 0.872 0.000 0.128
#> GSM627183 1 0.4178 0.765053 0.828 0.000 0.172
#> GSM627144 2 0.6054 0.730468 0.052 0.768 0.180
#> GSM627158 1 0.0000 0.849084 1.000 0.000 0.000
#> GSM627196 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627142 3 0.0000 0.904887 0.000 0.000 1.000
#> GSM627182 2 0.1860 0.932208 0.000 0.948 0.052
#> GSM627202 1 0.6079 0.307619 0.612 0.000 0.388
#> GSM627141 1 0.4291 0.756892 0.820 0.000 0.180
#> GSM627143 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627145 3 0.2537 0.881333 0.080 0.000 0.920
#> GSM627152 3 0.0000 0.904887 0.000 0.000 1.000
#> GSM627200 3 0.2537 0.881528 0.080 0.000 0.920
#> GSM627159 3 0.1753 0.889624 0.000 0.048 0.952
#> GSM627164 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627138 1 0.0000 0.849084 1.000 0.000 0.000
#> GSM627175 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627150 3 0.2066 0.889658 0.000 0.060 0.940
#> GSM627166 2 0.6451 0.327856 0.384 0.608 0.008
#> GSM627186 2 0.0237 0.978161 0.000 0.996 0.004
#> GSM627139 3 0.2711 0.871359 0.000 0.088 0.912
#> GSM627181 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627205 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627214 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627180 2 0.2261 0.919534 0.000 0.932 0.068
#> GSM627172 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627184 3 0.3482 0.854884 0.128 0.000 0.872
#> GSM627193 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627191 3 0.3340 0.839685 0.000 0.120 0.880
#> GSM627176 3 0.0000 0.904887 0.000 0.000 1.000
#> GSM627194 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627154 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627187 1 0.1753 0.849527 0.952 0.000 0.048
#> GSM627198 2 0.0000 0.981616 0.000 1.000 0.000
#> GSM627160 3 0.3551 0.826164 0.000 0.132 0.868
#> GSM627185 1 0.0000 0.849084 1.000 0.000 0.000
#> GSM627206 1 0.1753 0.849527 0.952 0.000 0.048
#> GSM627161 1 0.0237 0.848567 0.996 0.000 0.004
#> GSM627162 1 0.7872 0.577547 0.652 0.236 0.112
#> GSM627210 1 0.1753 0.849527 0.952 0.000 0.048
#> GSM627189 2 0.0000 0.981616 0.000 1.000 0.000
show/hide code output
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM627128 4 0.2281 0.7073 0.000 0.000 0.096 0.904
#> GSM627110 1 0.3946 0.7734 0.812 0.000 0.168 0.020
#> GSM627132 1 0.0000 0.7768 1.000 0.000 0.000 0.000
#> GSM627107 4 0.3688 0.5327 0.000 0.000 0.208 0.792
#> GSM627103 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627114 1 0.3806 0.7766 0.824 0.000 0.156 0.020
#> GSM627134 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627137 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627148 1 0.6391 0.5428 0.588 0.000 0.328 0.084
#> GSM627101 4 0.0707 0.7281 0.000 0.000 0.020 0.980
#> GSM627130 4 0.2281 0.7073 0.000 0.000 0.096 0.904
#> GSM627071 3 0.0376 0.7440 0.004 0.004 0.992 0.000
#> GSM627118 4 0.3074 0.8345 0.000 0.152 0.000 0.848
#> GSM627094 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627122 3 0.3266 0.6437 0.168 0.000 0.832 0.000
#> GSM627115 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627125 4 0.1022 0.7222 0.000 0.000 0.032 0.968
#> GSM627174 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627102 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627073 3 0.0336 0.7441 0.000 0.000 0.992 0.008
#> GSM627108 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627126 1 0.4907 0.0770 0.580 0.000 0.420 0.000
#> GSM627078 4 0.3726 0.8439 0.000 0.212 0.000 0.788
#> GSM627090 3 0.2589 0.7432 0.000 0.000 0.884 0.116
#> GSM627099 4 0.4040 0.8156 0.000 0.248 0.000 0.752
#> GSM627105 4 0.1022 0.7222 0.000 0.000 0.032 0.968
#> GSM627117 1 0.5763 0.7243 0.740 0.084 0.156 0.020
#> GSM627121 3 0.4933 0.3784 0.000 0.000 0.568 0.432
#> GSM627127 4 0.3688 0.8477 0.000 0.208 0.000 0.792
#> GSM627087 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627089 3 0.5183 -0.0729 0.408 0.000 0.584 0.008
#> GSM627092 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627076 3 0.3311 0.7261 0.000 0.000 0.828 0.172
#> GSM627136 3 0.4898 0.0382 0.416 0.000 0.584 0.000
#> GSM627081 3 0.5074 0.6569 0.000 0.040 0.724 0.236
#> GSM627091 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627097 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627072 3 0.4121 0.5270 0.184 0.000 0.796 0.020
#> GSM627080 1 0.0000 0.7768 1.000 0.000 0.000 0.000
#> GSM627088 1 0.6984 0.6212 0.636 0.148 0.196 0.020
#> GSM627109 1 0.0000 0.7768 1.000 0.000 0.000 0.000
#> GSM627111 1 0.0000 0.7768 1.000 0.000 0.000 0.000
#> GSM627113 1 0.3554 0.7815 0.844 0.000 0.136 0.020
#> GSM627133 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627177 3 0.0524 0.7442 0.004 0.008 0.988 0.000
#> GSM627086 2 0.0336 0.9273 0.000 0.992 0.000 0.008
#> GSM627095 1 0.4916 0.0680 0.576 0.000 0.424 0.000
#> GSM627079 3 0.0336 0.7428 0.008 0.000 0.992 0.000
#> GSM627082 3 0.4543 0.5964 0.000 0.000 0.676 0.324
#> GSM627074 1 0.3037 0.7842 0.880 0.000 0.100 0.020
#> GSM627077 3 0.3266 0.6471 0.168 0.000 0.832 0.000
#> GSM627093 1 0.3806 0.7766 0.824 0.000 0.156 0.020
#> GSM627120 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627124 2 0.0921 0.9129 0.000 0.972 0.000 0.028
#> GSM627075 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627085 4 0.3649 0.8484 0.000 0.204 0.000 0.796
#> GSM627119 1 0.3806 0.7766 0.824 0.000 0.156 0.020
#> GSM627116 4 0.5894 0.5647 0.000 0.392 0.040 0.568
#> GSM627084 1 0.5992 0.2631 0.516 0.040 0.444 0.000
#> GSM627096 4 0.3751 0.8499 0.000 0.196 0.004 0.800
#> GSM627100 3 0.3400 0.7221 0.000 0.000 0.820 0.180
#> GSM627112 4 0.4199 0.8369 0.000 0.164 0.032 0.804
#> GSM627083 3 0.6149 0.5937 0.144 0.180 0.676 0.000
#> GSM627098 1 0.3356 0.7728 0.824 0.000 0.176 0.000
#> GSM627104 1 0.1109 0.7817 0.968 0.000 0.028 0.004
#> GSM627131 3 0.3266 0.6437 0.168 0.000 0.832 0.000
#> GSM627106 3 0.3311 0.7259 0.000 0.000 0.828 0.172
#> GSM627123 1 0.4776 0.2062 0.624 0.000 0.376 0.000
#> GSM627129 2 0.0469 0.9242 0.000 0.988 0.000 0.012
#> GSM627216 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627212 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627190 1 0.3806 0.7766 0.824 0.000 0.156 0.020
#> GSM627169 2 0.1520 0.8924 0.000 0.956 0.024 0.020
#> GSM627167 2 0.4948 -0.0838 0.000 0.560 0.000 0.440
#> GSM627192 3 0.4643 0.5464 0.344 0.000 0.656 0.000
#> GSM627203 3 0.3266 0.7277 0.000 0.000 0.832 0.168
#> GSM627151 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627163 1 0.0000 0.7768 1.000 0.000 0.000 0.000
#> GSM627211 2 0.0188 0.9296 0.000 0.996 0.000 0.004
#> GSM627171 2 0.3074 0.7591 0.000 0.848 0.152 0.000
#> GSM627209 2 0.0336 0.9273 0.000 0.992 0.000 0.008
#> GSM627135 3 0.4040 0.6108 0.248 0.000 0.752 0.000
#> GSM627170 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627178 3 0.0336 0.7428 0.008 0.000 0.992 0.000
#> GSM627199 2 0.1389 0.8940 0.000 0.952 0.000 0.048
#> GSM627213 4 0.3610 0.8498 0.000 0.200 0.000 0.800
#> GSM627140 2 0.0469 0.9240 0.000 0.988 0.000 0.012
#> GSM627149 1 0.4040 0.4805 0.752 0.000 0.248 0.000
#> GSM627147 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627195 3 0.2149 0.7244 0.000 0.000 0.912 0.088
#> GSM627204 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627207 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627157 1 0.0000 0.7768 1.000 0.000 0.000 0.000
#> GSM627201 2 0.0336 0.9273 0.000 0.992 0.000 0.008
#> GSM627146 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627156 2 0.0707 0.9161 0.000 0.980 0.000 0.020
#> GSM627188 3 0.4643 0.5464 0.344 0.000 0.656 0.000
#> GSM627197 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627173 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627179 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627208 2 0.6374 0.4068 0.000 0.592 0.324 0.084
#> GSM627215 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627153 4 0.4250 0.7887 0.000 0.276 0.000 0.724
#> GSM627155 1 0.0000 0.7768 1.000 0.000 0.000 0.000
#> GSM627165 2 0.3498 0.7505 0.000 0.832 0.008 0.160
#> GSM627168 1 0.4522 0.6428 0.680 0.000 0.320 0.000
#> GSM627183 1 0.4642 0.7303 0.740 0.000 0.240 0.020
#> GSM627144 2 0.5970 0.5344 0.000 0.668 0.244 0.088
#> GSM627158 1 0.0000 0.7768 1.000 0.000 0.000 0.000
#> GSM627196 2 0.0336 0.9273 0.000 0.992 0.000 0.008
#> GSM627142 3 0.3172 0.7173 0.000 0.000 0.840 0.160
#> GSM627182 2 0.5773 0.4676 0.000 0.632 0.320 0.048
#> GSM627202 1 0.4040 0.4805 0.752 0.000 0.248 0.000
#> GSM627141 1 0.4767 0.7145 0.724 0.000 0.256 0.020
#> GSM627143 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627145 3 0.1059 0.7402 0.012 0.000 0.972 0.016
#> GSM627152 3 0.2469 0.7436 0.000 0.000 0.892 0.108
#> GSM627200 3 0.3448 0.6412 0.168 0.000 0.828 0.004
#> GSM627159 3 0.4543 0.5964 0.000 0.000 0.676 0.324
#> GSM627164 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627138 1 0.0000 0.7768 1.000 0.000 0.000 0.000
#> GSM627175 4 0.3610 0.8499 0.000 0.200 0.000 0.800
#> GSM627150 3 0.1792 0.7322 0.000 0.000 0.932 0.068
#> GSM627166 2 0.6859 0.0580 0.380 0.512 0.108 0.000
#> GSM627186 2 0.3099 0.7955 0.000 0.876 0.104 0.020
#> GSM627139 3 0.5875 0.5419 0.000 0.104 0.692 0.204
#> GSM627181 2 0.0336 0.9273 0.000 0.992 0.000 0.008
#> GSM627205 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627214 2 0.0707 0.9188 0.000 0.980 0.000 0.020
#> GSM627180 2 0.5466 0.5788 0.000 0.712 0.220 0.068
#> GSM627172 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627184 3 0.4776 0.5120 0.376 0.000 0.624 0.000
#> GSM627193 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627191 3 0.4699 0.4621 0.000 0.320 0.676 0.004
#> GSM627176 3 0.1474 0.7375 0.000 0.000 0.948 0.052
#> GSM627194 2 0.0000 0.9316 0.000 1.000 0.000 0.000
#> GSM627154 4 0.3569 0.8502 0.000 0.196 0.000 0.804
#> GSM627187 1 0.3806 0.7766 0.824 0.000 0.156 0.020
#> GSM627198 2 0.1474 0.8901 0.000 0.948 0.000 0.052
#> GSM627160 3 0.4741 0.4500 0.000 0.328 0.668 0.004
#> GSM627185 1 0.0000 0.7768 1.000 0.000 0.000 0.000
#> GSM627206 1 0.5152 0.6244 0.664 0.000 0.316 0.020
#> GSM627161 1 0.0188 0.7755 0.996 0.000 0.004 0.000
#> GSM627162 1 0.7264 0.5461 0.604 0.216 0.160 0.020
#> GSM627210 1 0.3806 0.7766 0.824 0.000 0.156 0.020
#> GSM627189 2 0.0000 0.9316 0.000 1.000 0.000 0.000
show/hide code output
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM627128 4 0.2891 0.8011 0.000 0.000 0.000 0.824 0.176
#> GSM627110 3 0.0992 0.8840 0.024 0.000 0.968 0.000 0.008
#> GSM627132 1 0.0000 0.9419 1.000 0.000 0.000 0.000 0.000
#> GSM627107 4 0.3577 0.7781 0.000 0.000 0.032 0.808 0.160
#> GSM627103 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627114 3 0.0880 0.8848 0.032 0.000 0.968 0.000 0.000
#> GSM627134 2 0.0794 0.9420 0.000 0.972 0.000 0.028 0.000
#> GSM627137 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627148 3 0.2859 0.8022 0.000 0.000 0.876 0.056 0.068
#> GSM627101 4 0.2595 0.8210 0.000 0.000 0.032 0.888 0.080
#> GSM627130 4 0.2561 0.8227 0.000 0.000 0.000 0.856 0.144
#> GSM627071 5 0.2230 0.8768 0.000 0.000 0.116 0.000 0.884
#> GSM627118 4 0.1444 0.8658 0.000 0.040 0.000 0.948 0.012
#> GSM627094 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627122 5 0.2230 0.8768 0.000 0.000 0.116 0.000 0.884
#> GSM627115 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627125 4 0.2879 0.8129 0.000 0.000 0.032 0.868 0.100
#> GSM627174 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627102 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627073 5 0.2574 0.8786 0.000 0.000 0.112 0.012 0.876
#> GSM627108 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627126 1 0.4030 0.4501 0.648 0.000 0.000 0.000 0.352
#> GSM627078 4 0.1851 0.8716 0.000 0.088 0.000 0.912 0.000
#> GSM627090 5 0.0798 0.8606 0.000 0.000 0.008 0.016 0.976
#> GSM627099 4 0.3366 0.7633 0.000 0.232 0.000 0.768 0.000
#> GSM627105 4 0.2824 0.8148 0.000 0.000 0.032 0.872 0.096
#> GSM627117 3 0.0880 0.8848 0.032 0.000 0.968 0.000 0.000
#> GSM627121 5 0.5052 0.1784 0.000 0.000 0.036 0.412 0.552
#> GSM627127 4 0.2329 0.8587 0.000 0.124 0.000 0.876 0.000
#> GSM627087 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627089 3 0.5652 0.4443 0.036 0.000 0.616 0.040 0.308
#> GSM627092 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627076 5 0.2209 0.8321 0.000 0.000 0.032 0.056 0.912
#> GSM627136 5 0.2763 0.8626 0.000 0.004 0.148 0.000 0.848
#> GSM627081 5 0.4514 0.7225 0.000 0.040 0.040 0.140 0.780
#> GSM627091 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627097 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627072 3 0.0880 0.8747 0.000 0.000 0.968 0.000 0.032
#> GSM627080 1 0.0000 0.9419 1.000 0.000 0.000 0.000 0.000
#> GSM627088 3 0.5458 0.2419 0.020 0.420 0.532 0.000 0.028
#> GSM627109 1 0.0000 0.9419 1.000 0.000 0.000 0.000 0.000
#> GSM627111 1 0.0000 0.9419 1.000 0.000 0.000 0.000 0.000
#> GSM627113 3 0.1732 0.8579 0.080 0.000 0.920 0.000 0.000
#> GSM627133 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627177 5 0.2389 0.8774 0.000 0.004 0.116 0.000 0.880
#> GSM627086 2 0.0510 0.9509 0.000 0.984 0.000 0.016 0.000
#> GSM627095 1 0.1965 0.8711 0.904 0.000 0.000 0.000 0.096
#> GSM627079 5 0.2230 0.8768 0.000 0.000 0.116 0.000 0.884
#> GSM627082 5 0.1608 0.8431 0.000 0.000 0.000 0.072 0.928
#> GSM627074 3 0.1410 0.8698 0.060 0.000 0.940 0.000 0.000
#> GSM627077 5 0.2864 0.8739 0.024 0.000 0.112 0.000 0.864
#> GSM627093 3 0.0963 0.8837 0.036 0.000 0.964 0.000 0.000
#> GSM627120 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627124 2 0.2561 0.8346 0.000 0.856 0.000 0.144 0.000
#> GSM627075 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627085 4 0.1732 0.8738 0.000 0.080 0.000 0.920 0.000
#> GSM627119 3 0.0963 0.8837 0.036 0.000 0.964 0.000 0.000
#> GSM627116 4 0.5102 0.5096 0.000 0.376 0.000 0.580 0.044
#> GSM627084 5 0.3640 0.8661 0.024 0.036 0.100 0.000 0.840
#> GSM627096 4 0.1732 0.8738 0.000 0.080 0.000 0.920 0.000
#> GSM627100 5 0.2278 0.8296 0.000 0.000 0.032 0.060 0.908
#> GSM627112 4 0.1648 0.8647 0.000 0.040 0.000 0.940 0.020
#> GSM627083 5 0.2605 0.7852 0.000 0.148 0.000 0.000 0.852
#> GSM627098 5 0.3719 0.8556 0.068 0.000 0.116 0.000 0.816
#> GSM627104 1 0.4161 0.3072 0.608 0.000 0.392 0.000 0.000
#> GSM627131 5 0.2338 0.8770 0.004 0.000 0.112 0.000 0.884
#> GSM627106 5 0.2520 0.8372 0.000 0.000 0.048 0.056 0.896
#> GSM627123 1 0.0794 0.9255 0.972 0.000 0.000 0.000 0.028
#> GSM627129 2 0.0609 0.9469 0.000 0.980 0.000 0.020 0.000
#> GSM627216 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627212 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627190 3 0.0880 0.8848 0.032 0.000 0.968 0.000 0.000
#> GSM627169 3 0.2280 0.7792 0.000 0.120 0.880 0.000 0.000
#> GSM627167 2 0.4291 -0.0399 0.000 0.536 0.000 0.464 0.000
#> GSM627192 1 0.0963 0.9209 0.964 0.000 0.000 0.000 0.036
#> GSM627203 5 0.2520 0.8372 0.000 0.000 0.048 0.056 0.896
#> GSM627151 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627163 1 0.0162 0.9404 0.996 0.000 0.000 0.000 0.004
#> GSM627211 2 0.0162 0.9577 0.000 0.996 0.000 0.004 0.000
#> GSM627171 2 0.2424 0.8193 0.000 0.868 0.132 0.000 0.000
#> GSM627209 2 0.2280 0.8596 0.000 0.880 0.000 0.120 0.000
#> GSM627135 5 0.3234 0.8617 0.064 0.000 0.084 0.000 0.852
#> GSM627170 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627178 5 0.2230 0.8768 0.000 0.000 0.116 0.000 0.884
#> GSM627199 2 0.2179 0.8662 0.000 0.888 0.000 0.112 0.000
#> GSM627213 4 0.2074 0.8675 0.000 0.104 0.000 0.896 0.000
#> GSM627140 2 0.0404 0.9524 0.000 0.988 0.000 0.012 0.000
#> GSM627149 1 0.0000 0.9419 1.000 0.000 0.000 0.000 0.000
#> GSM627147 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627195 3 0.5284 0.2410 0.000 0.000 0.568 0.056 0.376
#> GSM627204 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627207 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627157 1 0.0000 0.9419 1.000 0.000 0.000 0.000 0.000
#> GSM627201 2 0.0290 0.9556 0.000 0.992 0.000 0.008 0.000
#> GSM627146 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627156 3 0.2605 0.7386 0.000 0.148 0.852 0.000 0.000
#> GSM627188 1 0.0963 0.9209 0.964 0.000 0.000 0.000 0.036
#> GSM627197 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627173 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627179 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627208 3 0.0000 0.8758 0.000 0.000 1.000 0.000 0.000
#> GSM627215 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627153 4 0.2690 0.8252 0.000 0.156 0.000 0.844 0.000
#> GSM627155 1 0.0162 0.9404 0.996 0.000 0.000 0.000 0.004
#> GSM627165 2 0.4062 0.7779 0.000 0.820 0.032 0.056 0.092
#> GSM627168 5 0.3115 0.8676 0.036 0.000 0.112 0.000 0.852
#> GSM627183 3 0.3521 0.6465 0.004 0.000 0.764 0.000 0.232
#> GSM627144 3 0.1992 0.8344 0.000 0.000 0.924 0.044 0.032
#> GSM627158 1 0.0000 0.9419 1.000 0.000 0.000 0.000 0.000
#> GSM627196 2 0.0290 0.9556 0.000 0.992 0.000 0.008 0.000
#> GSM627142 5 0.0703 0.8594 0.000 0.000 0.000 0.024 0.976
#> GSM627182 3 0.1568 0.8656 0.000 0.020 0.944 0.000 0.036
#> GSM627202 1 0.0000 0.9419 1.000 0.000 0.000 0.000 0.000
#> GSM627141 3 0.1106 0.8796 0.012 0.000 0.964 0.000 0.024
#> GSM627143 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627145 5 0.2890 0.8590 0.000 0.000 0.160 0.004 0.836
#> GSM627152 5 0.0324 0.8627 0.000 0.000 0.004 0.004 0.992
#> GSM627200 5 0.3123 0.8375 0.004 0.000 0.184 0.000 0.812
#> GSM627159 5 0.1671 0.8411 0.000 0.000 0.000 0.076 0.924
#> GSM627164 2 0.0162 0.9570 0.000 0.996 0.004 0.000 0.000
#> GSM627138 1 0.0000 0.9419 1.000 0.000 0.000 0.000 0.000
#> GSM627175 4 0.1478 0.8726 0.000 0.064 0.000 0.936 0.000
#> GSM627150 5 0.3527 0.8506 0.000 0.000 0.116 0.056 0.828
#> GSM627166 2 0.2361 0.8490 0.000 0.892 0.096 0.000 0.012
#> GSM627186 3 0.1197 0.8594 0.000 0.048 0.952 0.000 0.000
#> GSM627139 5 0.3243 0.8287 0.000 0.092 0.012 0.036 0.860
#> GSM627181 2 0.0290 0.9556 0.000 0.992 0.000 0.008 0.000
#> GSM627205 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627214 2 0.0880 0.9400 0.000 0.968 0.000 0.032 0.000
#> GSM627180 2 0.5141 0.6862 0.000 0.748 0.120 0.052 0.080
#> GSM627172 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627184 1 0.0609 0.9324 0.980 0.000 0.000 0.000 0.020
#> GSM627193 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627191 5 0.3099 0.7990 0.000 0.124 0.000 0.028 0.848
#> GSM627176 5 0.3595 0.8631 0.000 0.000 0.140 0.044 0.816
#> GSM627194 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
#> GSM627154 4 0.1671 0.8741 0.000 0.076 0.000 0.924 0.000
#> GSM627187 3 0.0880 0.8848 0.032 0.000 0.968 0.000 0.000
#> GSM627198 2 0.2773 0.8105 0.000 0.836 0.000 0.164 0.000
#> GSM627160 5 0.2773 0.7672 0.000 0.164 0.000 0.000 0.836
#> GSM627185 1 0.0000 0.9419 1.000 0.000 0.000 0.000 0.000
#> GSM627206 3 0.0963 0.8837 0.036 0.000 0.964 0.000 0.000
#> GSM627161 1 0.0000 0.9419 1.000 0.000 0.000 0.000 0.000
#> GSM627162 3 0.1012 0.8778 0.000 0.012 0.968 0.000 0.020
#> GSM627210 3 0.0880 0.8848 0.032 0.000 0.968 0.000 0.000
#> GSM627189 2 0.0000 0.9597 0.000 1.000 0.000 0.000 0.000
show/hide code output
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM627128 4 0.5169 0.45577 0.000 0.000 0.000 0.588 0.120 0.292
#> GSM627110 3 0.0000 0.82336 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627132 1 0.0000 0.91963 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627107 5 0.2176 0.63033 0.000 0.000 0.000 0.080 0.896 0.024
#> GSM627103 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627114 3 0.0146 0.82421 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM627134 2 0.1327 0.89246 0.000 0.936 0.000 0.064 0.000 0.000
#> GSM627137 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627148 5 0.3823 0.42321 0.000 0.000 0.436 0.000 0.564 0.000
#> GSM627101 4 0.4134 0.54315 0.000 0.000 0.000 0.656 0.316 0.028
#> GSM627130 4 0.4045 0.63268 0.000 0.000 0.000 0.756 0.120 0.124
#> GSM627071 6 0.3253 0.77468 0.000 0.000 0.192 0.000 0.020 0.788
#> GSM627118 4 0.0000 0.74415 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM627094 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627122 6 0.2257 0.82962 0.000 0.000 0.116 0.000 0.008 0.876
#> GSM627115 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627125 4 0.4664 0.46210 0.000 0.000 0.000 0.584 0.364 0.052
#> GSM627174 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627102 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627073 6 0.5386 0.29158 0.000 0.000 0.120 0.000 0.368 0.512
#> GSM627108 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627126 1 0.3737 0.37601 0.608 0.000 0.000 0.000 0.000 0.392
#> GSM627078 4 0.0146 0.74374 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM627090 6 0.2805 0.78271 0.000 0.000 0.004 0.000 0.184 0.812
#> GSM627099 4 0.3446 0.53158 0.000 0.308 0.000 0.692 0.000 0.000
#> GSM627105 4 0.4362 0.45140 0.000 0.000 0.000 0.584 0.388 0.028
#> GSM627117 3 0.0000 0.82336 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627121 5 0.2398 0.72198 0.000 0.000 0.080 0.004 0.888 0.028
#> GSM627127 4 0.3101 0.59269 0.000 0.244 0.000 0.756 0.000 0.000
#> GSM627087 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627089 5 0.5739 0.52754 0.004 0.000 0.284 0.000 0.528 0.184
#> GSM627092 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627076 6 0.3136 0.72168 0.000 0.000 0.004 0.000 0.228 0.768
#> GSM627136 6 0.2697 0.79361 0.000 0.000 0.188 0.000 0.000 0.812
#> GSM627081 5 0.3058 0.76103 0.000 0.008 0.136 0.004 0.836 0.016
#> GSM627091 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627097 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627072 3 0.0000 0.82336 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627080 1 0.0000 0.91963 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627088 3 0.4334 0.19488 0.000 0.408 0.568 0.000 0.000 0.024
#> GSM627109 1 0.2910 0.82055 0.852 0.000 0.000 0.000 0.080 0.068
#> GSM627111 1 0.0000 0.91963 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627113 3 0.4002 0.76027 0.052 0.000 0.800 0.000 0.080 0.068
#> GSM627133 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627177 6 0.3296 0.78093 0.000 0.008 0.188 0.000 0.012 0.792
#> GSM627086 2 0.2416 0.80235 0.000 0.844 0.000 0.156 0.000 0.000
#> GSM627095 1 0.2454 0.79318 0.840 0.000 0.000 0.000 0.000 0.160
#> GSM627079 6 0.2257 0.82962 0.000 0.000 0.116 0.000 0.008 0.876
#> GSM627082 6 0.2302 0.79619 0.000 0.000 0.000 0.008 0.120 0.872
#> GSM627074 3 0.3742 0.76975 0.036 0.000 0.816 0.000 0.080 0.068
#> GSM627077 6 0.2542 0.83409 0.044 0.000 0.080 0.000 0.000 0.876
#> GSM627093 3 0.3597 0.77472 0.028 0.000 0.824 0.000 0.080 0.068
#> GSM627120 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627124 2 0.3804 0.35791 0.000 0.576 0.000 0.424 0.000 0.000
#> GSM627075 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627085 4 0.0000 0.74415 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM627119 3 0.3597 0.77472 0.028 0.000 0.824 0.000 0.080 0.068
#> GSM627116 4 0.5339 0.37088 0.000 0.404 0.000 0.488 0.000 0.108
#> GSM627084 6 0.2917 0.82649 0.048 0.040 0.040 0.000 0.000 0.872
#> GSM627096 4 0.0000 0.74415 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM627100 5 0.3847 -0.02383 0.000 0.000 0.000 0.000 0.544 0.456
#> GSM627112 4 0.0972 0.73800 0.000 0.000 0.000 0.964 0.028 0.008
#> GSM627083 6 0.1765 0.78303 0.000 0.096 0.000 0.000 0.000 0.904
#> GSM627098 6 0.1780 0.82722 0.028 0.000 0.048 0.000 0.000 0.924
#> GSM627104 1 0.5994 0.33883 0.552 0.000 0.300 0.000 0.080 0.068
#> GSM627131 6 0.2588 0.83405 0.024 0.000 0.092 0.000 0.008 0.876
#> GSM627106 5 0.3065 0.76695 0.000 0.000 0.152 0.000 0.820 0.028
#> GSM627123 1 0.0000 0.91963 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627129 2 0.0363 0.93081 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM627216 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627212 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627190 3 0.0000 0.82336 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627169 3 0.2527 0.70807 0.000 0.168 0.832 0.000 0.000 0.000
#> GSM627167 4 0.3828 0.21410 0.000 0.440 0.000 0.560 0.000 0.000
#> GSM627192 1 0.0713 0.91032 0.972 0.000 0.000 0.000 0.000 0.028
#> GSM627203 5 0.3065 0.76695 0.000 0.000 0.152 0.000 0.820 0.028
#> GSM627151 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627163 1 0.0713 0.91032 0.972 0.000 0.000 0.000 0.000 0.028
#> GSM627211 2 0.0632 0.92338 0.000 0.976 0.000 0.024 0.000 0.000
#> GSM627171 2 0.2883 0.68848 0.000 0.788 0.212 0.000 0.000 0.000
#> GSM627209 2 0.3782 0.38478 0.000 0.588 0.000 0.412 0.000 0.000
#> GSM627135 6 0.2106 0.81767 0.064 0.000 0.032 0.000 0.000 0.904
#> GSM627170 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627178 6 0.1599 0.82390 0.024 0.000 0.028 0.000 0.008 0.940
#> GSM627199 2 0.3126 0.68605 0.000 0.752 0.000 0.248 0.000 0.000
#> GSM627213 4 0.1141 0.73219 0.000 0.052 0.000 0.948 0.000 0.000
#> GSM627140 2 0.0146 0.93614 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM627149 1 0.0000 0.91963 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627147 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627195 5 0.2902 0.75165 0.000 0.000 0.196 0.000 0.800 0.004
#> GSM627204 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627207 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627157 1 0.0000 0.91963 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627201 2 0.1610 0.87426 0.000 0.916 0.000 0.084 0.000 0.000
#> GSM627146 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627156 3 0.2730 0.67293 0.000 0.192 0.808 0.000 0.000 0.000
#> GSM627188 1 0.0713 0.91032 0.972 0.000 0.000 0.000 0.000 0.028
#> GSM627197 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627173 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627179 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627208 3 0.0260 0.81971 0.000 0.000 0.992 0.000 0.008 0.000
#> GSM627215 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627153 4 0.0865 0.73296 0.000 0.036 0.000 0.964 0.000 0.000
#> GSM627155 1 0.0713 0.91032 0.972 0.000 0.000 0.000 0.000 0.028
#> GSM627165 5 0.3993 0.05332 0.000 0.476 0.000 0.000 0.520 0.004
#> GSM627168 6 0.3475 0.81026 0.028 0.000 0.140 0.000 0.020 0.812
#> GSM627183 3 0.2793 0.57953 0.000 0.000 0.800 0.000 0.000 0.200
#> GSM627144 3 0.2219 0.68326 0.000 0.000 0.864 0.000 0.136 0.000
#> GSM627158 1 0.0000 0.91963 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627196 2 0.1863 0.85518 0.000 0.896 0.000 0.104 0.000 0.000
#> GSM627142 6 0.1765 0.80903 0.000 0.000 0.000 0.000 0.096 0.904
#> GSM627182 3 0.0363 0.81682 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM627202 1 0.0000 0.91963 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627141 3 0.0909 0.82251 0.020 0.000 0.968 0.000 0.000 0.012
#> GSM627143 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627145 6 0.6076 -0.00109 0.000 0.000 0.272 0.000 0.344 0.384
#> GSM627152 6 0.2146 0.81304 0.000 0.000 0.004 0.000 0.116 0.880
#> GSM627200 6 0.3279 0.78654 0.028 0.000 0.176 0.000 0.000 0.796
#> GSM627159 6 0.2494 0.79188 0.000 0.000 0.000 0.016 0.120 0.864
#> GSM627164 2 0.0146 0.93596 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM627138 1 0.0000 0.91963 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627175 4 0.0000 0.74415 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM627150 5 0.3284 0.76141 0.000 0.000 0.168 0.000 0.800 0.032
#> GSM627166 2 0.3652 0.76020 0.000 0.816 0.020 0.000 0.080 0.084
#> GSM627186 3 0.2378 0.72741 0.000 0.152 0.848 0.000 0.000 0.000
#> GSM627139 6 0.2663 0.80216 0.000 0.084 0.028 0.000 0.012 0.876
#> GSM627181 2 0.1075 0.90546 0.000 0.952 0.000 0.048 0.000 0.000
#> GSM627205 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627214 2 0.2854 0.74004 0.000 0.792 0.000 0.208 0.000 0.000
#> GSM627180 5 0.3481 0.74196 0.000 0.048 0.160 0.000 0.792 0.000
#> GSM627172 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627184 1 0.0713 0.91032 0.972 0.000 0.000 0.000 0.000 0.028
#> GSM627193 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627191 6 0.2542 0.80049 0.000 0.044 0.000 0.080 0.000 0.876
#> GSM627176 6 0.4466 0.69911 0.000 0.000 0.176 0.000 0.116 0.708
#> GSM627194 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627154 4 0.0000 0.74415 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM627187 3 0.0146 0.82424 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM627198 2 0.3810 0.34875 0.000 0.572 0.000 0.428 0.000 0.000
#> GSM627160 6 0.2178 0.74797 0.000 0.132 0.000 0.000 0.000 0.868
#> GSM627185 1 0.2856 0.82380 0.856 0.000 0.000 0.000 0.076 0.068
#> GSM627206 3 0.0858 0.82142 0.028 0.000 0.968 0.000 0.004 0.000
#> GSM627161 1 0.0000 0.91963 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627162 3 0.1958 0.77785 0.004 0.100 0.896 0.000 0.000 0.000
#> GSM627210 3 0.3520 0.77642 0.024 0.000 0.828 0.000 0.080 0.068
#> GSM627189 2 0.0000 0.93872 0.000 1.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two
samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures
which are significantly different between classes which can be candidate marks
for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known
annotation is numeric, one-way ANOVA test is applied, and if the known
annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) age(p) other(p) k
#> CV:pam 146 0.0505 0.543 0.14708 2
#> CV:pam 136 0.1404 0.188 0.00917 3
#> CV:pam 131 0.0137 0.161 0.00899 4
#> CV:pam 139 0.0198 0.329 0.19112 5
#> CV:pam 130 0.2480 0.648 0.15703 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
CV:mclust*
The object with results only for a single top-value method and a single partition method
can be extracted as:
res = res_list["CV", "mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51882 rows and 146 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
- The first row: a plot of the ECDF (empirical cumulative distribution
function) curves of the consensus matrix for each
k and the heatmap of
predicted classes for each k.
- The second row: heatmaps of the consensus matrix for each
k.
- The third row: heatmaps of the membership matrix for each
k.
- The fouth row: heatmaps of the signatures for each
k.
All the plots in panels can be made by individual functions and they are
plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
- ECDF curves of the consensus matrix for each
k;
- 1-PAC. The PAC
score
measures the proportion of the ambiguous subgrouping.
- Mean silhouette score.
- Concordance. The mean probability of fiting the consensus class ids in all
partitions.
- Area increased. Denote \(A_k\) as the area under the ECDF curve for current
k, the area increased is defined as \(A_k - A_{k-1}\).
- Rand index. The percent of pairs of samples that are both in a same cluster
or both are not in a same cluster in the partition of k and k-1.
- Jaccard index. The ratio of pairs of samples are both in a same cluster in
the partition of k and k-1 and the pairs of samples are both in a same
cluster in the partition k or k-1.
The detailed explanations of these statistics can be found in the cola
vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.996 0.998 0.504 0.497 0.497
#> 3 3 0.918 0.924 0.940 0.236 0.833 0.678
#> 4 4 0.748 0.798 0.901 0.121 0.910 0.771
#> 5 5 0.726 0.666 0.813 0.105 0.798 0.453
#> 6 6 0.918 0.873 0.933 0.050 0.882 0.543
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
- All \(k\) with Jaccard index larger than 0.95 are removed because increasing
\(k\) does not provide enough extra information. If all \(k\) are removed, it is
marked as no subgroup is detected.
- For all \(k\) with 1-PAC score larger than 0.9, the maximal \(k\) is taken as
the best \(k\), and other \(k\) are marked as optional \(k\).
- If it does not fit the second rule. The \(k\) with the maximal vote of the
highest 1-PAC score, highest mean silhouette, and highest concordance is
taken as the best \(k\).
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
show/hide code output
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM627128 2 0.000 0.998 0.000 1.000
#> GSM627110 1 0.000 0.998 1.000 0.000
#> GSM627132 1 0.000 0.998 1.000 0.000
#> GSM627107 1 0.000 0.998 1.000 0.000
#> GSM627103 2 0.000 0.998 0.000 1.000
#> GSM627114 1 0.000 0.998 1.000 0.000
#> GSM627134 2 0.000 0.998 0.000 1.000
#> GSM627137 2 0.000 0.998 0.000 1.000
#> GSM627148 1 0.000 0.998 1.000 0.000
#> GSM627101 2 0.000 0.998 0.000 1.000
#> GSM627130 2 0.000 0.998 0.000 1.000
#> GSM627071 1 0.000 0.998 1.000 0.000
#> GSM627118 2 0.000 0.998 0.000 1.000
#> GSM627094 2 0.000 0.998 0.000 1.000
#> GSM627122 1 0.000 0.998 1.000 0.000
#> GSM627115 2 0.000 0.998 0.000 1.000
#> GSM627125 2 0.000 0.998 0.000 1.000
#> GSM627174 2 0.000 0.998 0.000 1.000
#> GSM627102 2 0.000 0.998 0.000 1.000
#> GSM627073 1 0.000 0.998 1.000 0.000
#> GSM627108 2 0.000 0.998 0.000 1.000
#> GSM627126 1 0.000 0.998 1.000 0.000
#> GSM627078 2 0.000 0.998 0.000 1.000
#> GSM627090 1 0.000 0.998 1.000 0.000
#> GSM627099 2 0.000 0.998 0.000 1.000
#> GSM627105 2 0.000 0.998 0.000 1.000
#> GSM627117 1 0.000 0.998 1.000 0.000
#> GSM627121 1 0.118 0.983 0.984 0.016
#> GSM627127 2 0.000 0.998 0.000 1.000
#> GSM627087 2 0.000 0.998 0.000 1.000
#> GSM627089 1 0.000 0.998 1.000 0.000
#> GSM627092 2 0.000 0.998 0.000 1.000
#> GSM627076 1 0.000 0.998 1.000 0.000
#> GSM627136 1 0.000 0.998 1.000 0.000
#> GSM627081 1 0.000 0.998 1.000 0.000
#> GSM627091 2 0.000 0.998 0.000 1.000
#> GSM627097 2 0.000 0.998 0.000 1.000
#> GSM627072 1 0.000 0.998 1.000 0.000
#> GSM627080 1 0.000 0.998 1.000 0.000
#> GSM627088 1 0.000 0.998 1.000 0.000
#> GSM627109 1 0.000 0.998 1.000 0.000
#> GSM627111 1 0.000 0.998 1.000 0.000
#> GSM627113 1 0.000 0.998 1.000 0.000
#> GSM627133 2 0.000 0.998 0.000 1.000
#> GSM627177 1 0.295 0.947 0.948 0.052
#> GSM627086 2 0.000 0.998 0.000 1.000
#> GSM627095 1 0.000 0.998 1.000 0.000
#> GSM627079 1 0.000 0.998 1.000 0.000
#> GSM627082 2 0.000 0.998 0.000 1.000
#> GSM627074 1 0.000 0.998 1.000 0.000
#> GSM627077 1 0.000 0.998 1.000 0.000
#> GSM627093 1 0.000 0.998 1.000 0.000
#> GSM627120 2 0.000 0.998 0.000 1.000
#> GSM627124 2 0.000 0.998 0.000 1.000
#> GSM627075 2 0.000 0.998 0.000 1.000
#> GSM627085 2 0.000 0.998 0.000 1.000
#> GSM627119 1 0.000 0.998 1.000 0.000
#> GSM627116 2 0.000 0.998 0.000 1.000
#> GSM627084 1 0.000 0.998 1.000 0.000
#> GSM627096 2 0.000 0.998 0.000 1.000
#> GSM627100 1 0.000 0.998 1.000 0.000
#> GSM627112 2 0.000 0.998 0.000 1.000
#> GSM627083 2 0.625 0.816 0.156 0.844
#> GSM627098 1 0.000 0.998 1.000 0.000
#> GSM627104 1 0.000 0.998 1.000 0.000
#> GSM627131 1 0.000 0.998 1.000 0.000
#> GSM627106 1 0.000 0.998 1.000 0.000
#> GSM627123 1 0.000 0.998 1.000 0.000
#> GSM627129 2 0.000 0.998 0.000 1.000
#> GSM627216 2 0.000 0.998 0.000 1.000
#> GSM627212 2 0.000 0.998 0.000 1.000
#> GSM627190 1 0.000 0.998 1.000 0.000
#> GSM627169 2 0.000 0.998 0.000 1.000
#> GSM627167 2 0.000 0.998 0.000 1.000
#> GSM627192 1 0.000 0.998 1.000 0.000
#> GSM627203 1 0.000 0.998 1.000 0.000
#> GSM627151 2 0.000 0.998 0.000 1.000
#> GSM627163 1 0.000 0.998 1.000 0.000
#> GSM627211 2 0.000 0.998 0.000 1.000
#> GSM627171 2 0.000 0.998 0.000 1.000
#> GSM627209 2 0.000 0.998 0.000 1.000
#> GSM627135 1 0.000 0.998 1.000 0.000
#> GSM627170 2 0.000 0.998 0.000 1.000
#> GSM627178 1 0.000 0.998 1.000 0.000
#> GSM627199 2 0.000 0.998 0.000 1.000
#> GSM627213 2 0.000 0.998 0.000 1.000
#> GSM627140 2 0.000 0.998 0.000 1.000
#> GSM627149 1 0.000 0.998 1.000 0.000
#> GSM627147 2 0.000 0.998 0.000 1.000
#> GSM627195 1 0.000 0.998 1.000 0.000
#> GSM627204 2 0.000 0.998 0.000 1.000
#> GSM627207 2 0.000 0.998 0.000 1.000
#> GSM627157 1 0.000 0.998 1.000 0.000
#> GSM627201 2 0.000 0.998 0.000 1.000
#> GSM627146 2 0.000 0.998 0.000 1.000
#> GSM627156 2 0.000 0.998 0.000 1.000
#> GSM627188 1 0.000 0.998 1.000 0.000
#> GSM627197 2 0.000 0.998 0.000 1.000
#> GSM627173 2 0.000 0.998 0.000 1.000
#> GSM627179 2 0.000 0.998 0.000 1.000
#> GSM627208 1 0.000 0.998 1.000 0.000
#> GSM627215 2 0.000 0.998 0.000 1.000
#> GSM627153 2 0.000 0.998 0.000 1.000
#> GSM627155 1 0.000 0.998 1.000 0.000
#> GSM627165 2 0.000 0.998 0.000 1.000
#> GSM627168 1 0.000 0.998 1.000 0.000
#> GSM627183 1 0.000 0.998 1.000 0.000
#> GSM627144 1 0.000 0.998 1.000 0.000
#> GSM627158 1 0.000 0.998 1.000 0.000
#> GSM627196 2 0.000 0.998 0.000 1.000
#> GSM627142 1 0.224 0.964 0.964 0.036
#> GSM627182 1 0.000 0.998 1.000 0.000
#> GSM627202 1 0.000 0.998 1.000 0.000
#> GSM627141 1 0.000 0.998 1.000 0.000
#> GSM627143 2 0.000 0.998 0.000 1.000
#> GSM627145 1 0.000 0.998 1.000 0.000
#> GSM627152 1 0.000 0.998 1.000 0.000
#> GSM627200 1 0.000 0.998 1.000 0.000
#> GSM627159 2 0.000 0.998 0.000 1.000
#> GSM627164 2 0.000 0.998 0.000 1.000
#> GSM627138 1 0.000 0.998 1.000 0.000
#> GSM627175 2 0.000 0.998 0.000 1.000
#> GSM627150 1 0.000 0.998 1.000 0.000
#> GSM627166 1 0.000 0.998 1.000 0.000
#> GSM627186 2 0.000 0.998 0.000 1.000
#> GSM627139 2 0.000 0.998 0.000 1.000
#> GSM627181 2 0.000 0.998 0.000 1.000
#> GSM627205 2 0.000 0.998 0.000 1.000
#> GSM627214 2 0.000 0.998 0.000 1.000
#> GSM627180 1 0.242 0.960 0.960 0.040
#> GSM627172 2 0.000 0.998 0.000 1.000
#> GSM627184 1 0.000 0.998 1.000 0.000
#> GSM627193 2 0.000 0.998 0.000 1.000
#> GSM627191 2 0.000 0.998 0.000 1.000
#> GSM627176 1 0.000 0.998 1.000 0.000
#> GSM627194 2 0.000 0.998 0.000 1.000
#> GSM627154 2 0.000 0.998 0.000 1.000
#> GSM627187 1 0.000 0.998 1.000 0.000
#> GSM627198 2 0.000 0.998 0.000 1.000
#> GSM627160 2 0.000 0.998 0.000 1.000
#> GSM627185 1 0.000 0.998 1.000 0.000
#> GSM627206 1 0.000 0.998 1.000 0.000
#> GSM627161 1 0.000 0.998 1.000 0.000
#> GSM627162 1 0.000 0.998 1.000 0.000
#> GSM627210 1 0.000 0.998 1.000 0.000
#> GSM627189 2 0.000 0.998 0.000 1.000
show/hide code output
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM627128 2 0.2261 0.902 0.000 0.932 0.068
#> GSM627110 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627132 1 0.2537 0.965 0.920 0.000 0.080
#> GSM627107 2 0.4974 0.697 0.000 0.764 0.236
#> GSM627103 2 0.2448 0.938 0.076 0.924 0.000
#> GSM627114 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627134 2 0.0237 0.939 0.004 0.996 0.000
#> GSM627137 2 0.2448 0.938 0.076 0.924 0.000
#> GSM627148 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627101 2 0.0237 0.939 0.000 0.996 0.004
#> GSM627130 2 0.1860 0.914 0.000 0.948 0.052
#> GSM627071 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627118 2 0.0237 0.939 0.004 0.996 0.000
#> GSM627094 2 0.2448 0.938 0.076 0.924 0.000
#> GSM627122 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627115 2 0.2448 0.938 0.076 0.924 0.000
#> GSM627125 2 0.2261 0.902 0.000 0.932 0.068
#> GSM627174 2 0.2537 0.903 0.000 0.920 0.080
#> GSM627102 2 0.2682 0.938 0.076 0.920 0.004
#> GSM627073 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627108 2 0.2448 0.938 0.076 0.924 0.000
#> GSM627126 1 0.2537 0.965 0.920 0.000 0.080
#> GSM627078 2 0.0237 0.939 0.004 0.996 0.000
#> GSM627090 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627099 2 0.0237 0.939 0.004 0.996 0.000
#> GSM627105 2 0.2261 0.902 0.000 0.932 0.068
#> GSM627117 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627121 3 0.3879 0.789 0.000 0.152 0.848
#> GSM627127 2 0.0237 0.939 0.004 0.996 0.000
#> GSM627087 2 0.2448 0.938 0.076 0.924 0.000
#> GSM627089 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627092 2 0.2845 0.937 0.068 0.920 0.012
#> GSM627076 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627136 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627081 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627091 2 0.2448 0.938 0.076 0.924 0.000
#> GSM627097 2 0.0237 0.939 0.000 0.996 0.004
#> GSM627072 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627080 1 0.2537 0.965 0.920 0.000 0.080
#> GSM627088 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627109 1 0.2537 0.965 0.920 0.000 0.080
#> GSM627111 1 0.2537 0.965 0.920 0.000 0.080
#> GSM627113 1 0.6008 0.571 0.628 0.000 0.372
#> GSM627133 3 0.5111 0.732 0.024 0.168 0.808
#> GSM627177 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627086 2 0.2448 0.938 0.076 0.924 0.000
#> GSM627095 1 0.2537 0.965 0.920 0.000 0.080
#> GSM627079 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627082 2 0.2261 0.902 0.000 0.932 0.068
#> GSM627074 1 0.2537 0.965 0.920 0.000 0.080
#> GSM627077 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627093 1 0.3551 0.927 0.868 0.000 0.132
#> GSM627120 2 0.0237 0.939 0.004 0.996 0.000
#> GSM627124 2 0.0237 0.939 0.004 0.996 0.000
#> GSM627075 2 0.2448 0.938 0.076 0.924 0.000
#> GSM627085 2 0.0237 0.939 0.004 0.996 0.000
#> GSM627119 1 0.3482 0.931 0.872 0.000 0.128
#> GSM627116 2 0.0237 0.939 0.000 0.996 0.004
#> GSM627084 1 0.4121 0.894 0.832 0.000 0.168
#> GSM627096 2 0.0237 0.939 0.004 0.996 0.000
#> GSM627100 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627112 2 0.0237 0.939 0.000 0.996 0.004
#> GSM627083 2 0.7523 0.560 0.260 0.660 0.080
#> GSM627098 1 0.3267 0.940 0.884 0.000 0.116
#> GSM627104 1 0.2537 0.965 0.920 0.000 0.080
#> GSM627131 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627106 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627123 1 0.2537 0.965 0.920 0.000 0.080
#> GSM627129 2 0.0237 0.939 0.004 0.996 0.000
#> GSM627216 2 0.2682 0.937 0.076 0.920 0.004
#> GSM627212 2 0.2448 0.938 0.076 0.924 0.000
#> GSM627190 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627169 3 0.5216 0.612 0.000 0.260 0.740
#> GSM627167 2 0.0237 0.939 0.004 0.996 0.000
#> GSM627192 1 0.2537 0.965 0.920 0.000 0.080
#> GSM627203 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627151 2 0.2537 0.903 0.000 0.920 0.080
#> GSM627163 1 0.2537 0.965 0.920 0.000 0.080
#> GSM627211 2 0.2448 0.938 0.076 0.924 0.000
#> GSM627171 2 0.6209 0.460 0.004 0.628 0.368
#> GSM627209 2 0.0237 0.939 0.004 0.996 0.000
#> GSM627135 1 0.2537 0.965 0.920 0.000 0.080
#> GSM627170 2 0.2448 0.938 0.076 0.924 0.000
#> GSM627178 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627199 2 0.0237 0.939 0.000 0.996 0.004
#> GSM627213 2 0.0237 0.939 0.004 0.996 0.000
#> GSM627140 2 0.0592 0.938 0.000 0.988 0.012
#> GSM627149 1 0.2537 0.965 0.920 0.000 0.080
#> GSM627147 2 0.0237 0.939 0.000 0.996 0.004
#> GSM627195 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627204 2 0.2448 0.938 0.076 0.924 0.000
#> GSM627207 2 0.2448 0.938 0.076 0.924 0.000
#> GSM627157 1 0.2537 0.965 0.920 0.000 0.080
#> GSM627201 2 0.2448 0.938 0.076 0.924 0.000
#> GSM627146 2 0.2448 0.938 0.076 0.924 0.000
#> GSM627156 3 0.6284 0.528 0.016 0.304 0.680
#> GSM627188 1 0.2537 0.965 0.920 0.000 0.080
#> GSM627197 2 0.2448 0.938 0.076 0.924 0.000
#> GSM627173 2 0.2682 0.938 0.076 0.920 0.004
#> GSM627179 2 0.2448 0.938 0.076 0.924 0.000
#> GSM627208 3 0.0237 0.965 0.000 0.004 0.996
#> GSM627215 2 0.2682 0.937 0.076 0.920 0.004
#> GSM627153 2 0.0237 0.939 0.004 0.996 0.000
#> GSM627155 1 0.2537 0.965 0.920 0.000 0.080
#> GSM627165 2 0.0237 0.939 0.000 0.996 0.004
#> GSM627168 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627183 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627144 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627158 1 0.2537 0.965 0.920 0.000 0.080
#> GSM627196 2 0.2448 0.938 0.076 0.924 0.000
#> GSM627142 3 0.0237 0.965 0.000 0.004 0.996
#> GSM627182 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627202 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627141 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627143 2 0.2066 0.918 0.000 0.940 0.060
#> GSM627145 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627152 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627200 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627159 2 0.2261 0.902 0.000 0.932 0.068
#> GSM627164 2 0.4095 0.914 0.064 0.880 0.056
#> GSM627138 1 0.2537 0.965 0.920 0.000 0.080
#> GSM627175 2 0.0237 0.939 0.004 0.996 0.000
#> GSM627150 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627166 1 0.3941 0.907 0.844 0.000 0.156
#> GSM627186 3 0.2537 0.871 0.000 0.080 0.920
#> GSM627139 2 0.5785 0.563 0.000 0.668 0.332
#> GSM627181 2 0.2537 0.938 0.080 0.920 0.000
#> GSM627205 2 0.2804 0.938 0.060 0.924 0.016
#> GSM627214 2 0.0237 0.939 0.004 0.996 0.000
#> GSM627180 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627172 2 0.2682 0.906 0.004 0.920 0.076
#> GSM627184 1 0.2537 0.965 0.920 0.000 0.080
#> GSM627193 2 0.2448 0.938 0.076 0.924 0.000
#> GSM627191 2 0.2261 0.907 0.000 0.932 0.068
#> GSM627176 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627194 2 0.2448 0.938 0.076 0.924 0.000
#> GSM627154 2 0.0237 0.939 0.004 0.996 0.000
#> GSM627187 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627198 2 0.0237 0.939 0.004 0.996 0.000
#> GSM627160 2 0.3686 0.853 0.000 0.860 0.140
#> GSM627185 1 0.2537 0.965 0.920 0.000 0.080
#> GSM627206 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627161 1 0.2537 0.965 0.920 0.000 0.080
#> GSM627162 3 0.0000 0.969 0.000 0.000 1.000
#> GSM627210 1 0.5835 0.638 0.660 0.000 0.340
#> GSM627189 2 0.2448 0.938 0.076 0.924 0.000
show/hide code output
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM627128 4 0.2345 0.8991 0.000 0.100 0.000 0.900
#> GSM627110 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> GSM627132 1 0.0000 0.9102 1.000 0.000 0.000 0.000
#> GSM627107 4 0.0188 0.8117 0.000 0.000 0.004 0.996
#> GSM627103 2 0.0000 0.8434 0.000 1.000 0.000 0.000
#> GSM627114 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> GSM627134 2 0.3311 0.7517 0.000 0.828 0.000 0.172
#> GSM627137 2 0.0000 0.8434 0.000 1.000 0.000 0.000
#> GSM627148 3 0.1389 0.9180 0.000 0.000 0.952 0.048
#> GSM627101 4 0.2216 0.9009 0.000 0.092 0.000 0.908
#> GSM627130 4 0.2345 0.8991 0.000 0.100 0.000 0.900
#> GSM627071 3 0.0707 0.9218 0.000 0.000 0.980 0.020
#> GSM627118 4 0.4804 0.3072 0.000 0.384 0.000 0.616
#> GSM627094 2 0.0000 0.8434 0.000 1.000 0.000 0.000
#> GSM627122 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> GSM627115 2 0.0000 0.8434 0.000 1.000 0.000 0.000
#> GSM627125 4 0.1211 0.8703 0.000 0.040 0.000 0.960
#> GSM627174 2 0.0336 0.8421 0.000 0.992 0.000 0.008
#> GSM627102 2 0.0000 0.8434 0.000 1.000 0.000 0.000
#> GSM627073 3 0.2760 0.8890 0.000 0.000 0.872 0.128
#> GSM627108 2 0.0000 0.8434 0.000 1.000 0.000 0.000
#> GSM627126 1 0.0000 0.9102 1.000 0.000 0.000 0.000
#> GSM627078 2 0.4406 0.6019 0.000 0.700 0.000 0.300
#> GSM627090 3 0.2081 0.9067 0.000 0.000 0.916 0.084
#> GSM627099 2 0.2011 0.8133 0.000 0.920 0.000 0.080
#> GSM627105 4 0.1389 0.8779 0.000 0.048 0.000 0.952
#> GSM627117 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> GSM627121 3 0.3873 0.7971 0.000 0.000 0.772 0.228
#> GSM627127 2 0.4925 0.3372 0.000 0.572 0.000 0.428
#> GSM627087 2 0.0000 0.8434 0.000 1.000 0.000 0.000
#> GSM627089 3 0.0336 0.9225 0.000 0.000 0.992 0.008
#> GSM627092 2 0.0469 0.8409 0.000 0.988 0.000 0.012
#> GSM627076 3 0.2814 0.8871 0.000 0.000 0.868 0.132
#> GSM627136 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> GSM627081 3 0.2973 0.8796 0.000 0.000 0.856 0.144
#> GSM627091 2 0.0000 0.8434 0.000 1.000 0.000 0.000
#> GSM627097 2 0.2760 0.7850 0.000 0.872 0.000 0.128
#> GSM627072 3 0.1302 0.9188 0.000 0.000 0.956 0.044
#> GSM627080 1 0.0000 0.9102 1.000 0.000 0.000 0.000
#> GSM627088 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> GSM627109 1 0.0000 0.9102 1.000 0.000 0.000 0.000
#> GSM627111 1 0.0000 0.9102 1.000 0.000 0.000 0.000
#> GSM627113 3 0.4907 0.0936 0.420 0.000 0.580 0.000
#> GSM627133 3 0.4881 0.7094 0.000 0.196 0.756 0.048
#> GSM627177 3 0.0921 0.9210 0.000 0.000 0.972 0.028
#> GSM627086 2 0.0000 0.8434 0.000 1.000 0.000 0.000
#> GSM627095 1 0.0000 0.9102 1.000 0.000 0.000 0.000
#> GSM627079 3 0.1302 0.9193 0.000 0.000 0.956 0.044
#> GSM627082 4 0.3435 0.8761 0.000 0.100 0.036 0.864
#> GSM627074 1 0.3123 0.8246 0.844 0.000 0.156 0.000
#> GSM627077 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> GSM627093 1 0.3764 0.7808 0.784 0.000 0.216 0.000
#> GSM627120 2 0.3172 0.7620 0.000 0.840 0.000 0.160
#> GSM627124 2 0.2216 0.8055 0.000 0.908 0.000 0.092
#> GSM627075 2 0.0000 0.8434 0.000 1.000 0.000 0.000
#> GSM627085 2 0.4761 0.4718 0.000 0.628 0.000 0.372
#> GSM627119 1 0.3801 0.7765 0.780 0.000 0.220 0.000
#> GSM627116 2 0.1716 0.8199 0.000 0.936 0.000 0.064
#> GSM627084 1 0.3942 0.7575 0.764 0.000 0.236 0.000
#> GSM627096 2 0.4989 0.1932 0.000 0.528 0.000 0.472
#> GSM627100 3 0.3688 0.8186 0.000 0.000 0.792 0.208
#> GSM627112 2 0.4933 0.3271 0.000 0.568 0.000 0.432
#> GSM627083 2 0.6104 0.1113 0.472 0.488 0.036 0.004
#> GSM627098 1 0.3726 0.7845 0.788 0.000 0.212 0.000
#> GSM627104 1 0.0000 0.9102 1.000 0.000 0.000 0.000
#> GSM627131 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> GSM627106 3 0.2973 0.8796 0.000 0.000 0.856 0.144
#> GSM627123 1 0.0000 0.9102 1.000 0.000 0.000 0.000
#> GSM627129 2 0.4477 0.5835 0.000 0.688 0.000 0.312
#> GSM627216 2 0.0000 0.8434 0.000 1.000 0.000 0.000
#> GSM627212 2 0.0000 0.8434 0.000 1.000 0.000 0.000
#> GSM627190 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> GSM627169 2 0.4999 0.0328 0.000 0.508 0.492 0.000
#> GSM627167 2 0.4830 0.4261 0.000 0.608 0.000 0.392
#> GSM627192 1 0.0000 0.9102 1.000 0.000 0.000 0.000
#> GSM627203 3 0.2760 0.8890 0.000 0.000 0.872 0.128
#> GSM627151 2 0.0524 0.8412 0.000 0.988 0.004 0.008
#> GSM627163 1 0.0000 0.9102 1.000 0.000 0.000 0.000
#> GSM627211 2 0.0000 0.8434 0.000 1.000 0.000 0.000
#> GSM627171 2 0.3486 0.6539 0.000 0.812 0.188 0.000
#> GSM627209 2 0.3024 0.7716 0.000 0.852 0.000 0.148
#> GSM627135 1 0.0000 0.9102 1.000 0.000 0.000 0.000
#> GSM627170 2 0.0000 0.8434 0.000 1.000 0.000 0.000
#> GSM627178 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> GSM627199 2 0.1637 0.8219 0.000 0.940 0.000 0.060
#> GSM627213 2 0.4925 0.3372 0.000 0.572 0.000 0.428
#> GSM627140 2 0.3279 0.7875 0.000 0.872 0.032 0.096
#> GSM627149 1 0.0000 0.9102 1.000 0.000 0.000 0.000
#> GSM627147 2 0.0469 0.8409 0.000 0.988 0.000 0.012
#> GSM627195 3 0.2868 0.8846 0.000 0.000 0.864 0.136
#> GSM627204 2 0.0000 0.8434 0.000 1.000 0.000 0.000
#> GSM627207 2 0.0000 0.8434 0.000 1.000 0.000 0.000
#> GSM627157 1 0.3688 0.7881 0.792 0.000 0.208 0.000
#> GSM627201 2 0.0000 0.8434 0.000 1.000 0.000 0.000
#> GSM627146 2 0.0000 0.8434 0.000 1.000 0.000 0.000
#> GSM627156 2 0.4193 0.4905 0.000 0.732 0.268 0.000
#> GSM627188 1 0.0000 0.9102 1.000 0.000 0.000 0.000
#> GSM627197 2 0.0000 0.8434 0.000 1.000 0.000 0.000
#> GSM627173 2 0.0000 0.8434 0.000 1.000 0.000 0.000
#> GSM627179 2 0.0000 0.8434 0.000 1.000 0.000 0.000
#> GSM627208 3 0.3308 0.8841 0.000 0.036 0.872 0.092
#> GSM627215 2 0.0000 0.8434 0.000 1.000 0.000 0.000
#> GSM627153 2 0.3801 0.7050 0.000 0.780 0.000 0.220
#> GSM627155 1 0.0000 0.9102 1.000 0.000 0.000 0.000
#> GSM627165 2 0.4543 0.5704 0.000 0.676 0.000 0.324
#> GSM627168 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> GSM627183 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> GSM627144 3 0.2704 0.8908 0.000 0.000 0.876 0.124
#> GSM627158 1 0.0000 0.9102 1.000 0.000 0.000 0.000
#> GSM627196 2 0.0000 0.8434 0.000 1.000 0.000 0.000
#> GSM627142 3 0.3486 0.8335 0.000 0.000 0.812 0.188
#> GSM627182 3 0.1722 0.9166 0.000 0.008 0.944 0.048
#> GSM627202 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> GSM627141 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> GSM627143 2 0.1118 0.8324 0.000 0.964 0.000 0.036
#> GSM627145 3 0.1389 0.9180 0.000 0.000 0.952 0.048
#> GSM627152 3 0.0817 0.9217 0.000 0.000 0.976 0.024
#> GSM627200 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> GSM627159 4 0.2281 0.9008 0.000 0.096 0.000 0.904
#> GSM627164 2 0.0188 0.8410 0.000 0.996 0.004 0.000
#> GSM627138 1 0.2149 0.8674 0.912 0.000 0.088 0.000
#> GSM627175 2 0.4925 0.3372 0.000 0.572 0.000 0.428
#> GSM627150 3 0.2760 0.8890 0.000 0.000 0.872 0.128
#> GSM627166 1 0.2868 0.8384 0.864 0.000 0.136 0.000
#> GSM627186 3 0.4250 0.5694 0.000 0.276 0.724 0.000
#> GSM627139 3 0.4163 0.8022 0.000 0.076 0.828 0.096
#> GSM627181 2 0.0000 0.8434 0.000 1.000 0.000 0.000
#> GSM627205 2 0.0188 0.8428 0.000 0.996 0.000 0.004
#> GSM627214 2 0.3688 0.7174 0.000 0.792 0.000 0.208
#> GSM627180 3 0.2760 0.8890 0.000 0.000 0.872 0.128
#> GSM627172 2 0.0336 0.8420 0.000 0.992 0.000 0.008
#> GSM627184 1 0.0000 0.9102 1.000 0.000 0.000 0.000
#> GSM627193 2 0.0000 0.8434 0.000 1.000 0.000 0.000
#> GSM627191 2 0.4149 0.7447 0.000 0.812 0.036 0.152
#> GSM627176 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> GSM627194 2 0.0000 0.8434 0.000 1.000 0.000 0.000
#> GSM627154 2 0.4925 0.3372 0.000 0.572 0.000 0.428
#> GSM627187 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> GSM627198 2 0.4304 0.6255 0.000 0.716 0.000 0.284
#> GSM627160 2 0.5217 0.6498 0.000 0.756 0.136 0.108
#> GSM627185 1 0.0000 0.9102 1.000 0.000 0.000 0.000
#> GSM627206 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> GSM627161 1 0.0000 0.9102 1.000 0.000 0.000 0.000
#> GSM627162 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> GSM627210 1 0.4955 0.3624 0.556 0.000 0.444 0.000
#> GSM627189 2 0.0000 0.8434 0.000 1.000 0.000 0.000
show/hide code output
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM627128 4 0.0000 0.569585 0.000 0.000 0.000 1.000 0.000
#> GSM627110 3 0.0000 0.743789 0.000 0.000 1.000 0.000 0.000
#> GSM627132 1 0.0404 0.915926 0.988 0.000 0.012 0.000 0.000
#> GSM627107 5 0.3837 0.547004 0.000 0.000 0.000 0.308 0.692
#> GSM627103 2 0.0000 0.847614 0.000 1.000 0.000 0.000 0.000
#> GSM627114 3 0.0000 0.743789 0.000 0.000 1.000 0.000 0.000
#> GSM627134 4 0.6817 0.507683 0.000 0.344 0.000 0.348 0.308
#> GSM627137 2 0.1608 0.814806 0.000 0.928 0.000 0.000 0.072
#> GSM627148 5 0.4101 0.734530 0.000 0.000 0.372 0.000 0.628
#> GSM627101 4 0.0000 0.569585 0.000 0.000 0.000 1.000 0.000
#> GSM627130 4 0.0000 0.569585 0.000 0.000 0.000 1.000 0.000
#> GSM627071 3 0.4294 -0.381574 0.000 0.000 0.532 0.000 0.468
#> GSM627118 4 0.5568 0.800827 0.000 0.096 0.000 0.596 0.308
#> GSM627094 2 0.0000 0.847614 0.000 1.000 0.000 0.000 0.000
#> GSM627122 5 0.4150 0.710494 0.000 0.000 0.388 0.000 0.612
#> GSM627115 2 0.0000 0.847614 0.000 1.000 0.000 0.000 0.000
#> GSM627125 4 0.0162 0.566439 0.000 0.000 0.000 0.996 0.004
#> GSM627174 2 0.3274 0.673214 0.000 0.780 0.000 0.000 0.220
#> GSM627102 2 0.1270 0.823305 0.000 0.948 0.000 0.000 0.052
#> GSM627073 5 0.3876 0.788165 0.000 0.000 0.316 0.000 0.684
#> GSM627108 2 0.0000 0.847614 0.000 1.000 0.000 0.000 0.000
#> GSM627126 1 0.0000 0.922300 1.000 0.000 0.000 0.000 0.000
#> GSM627078 4 0.5896 0.808579 0.000 0.128 0.000 0.564 0.308
#> GSM627090 5 0.3837 0.791280 0.000 0.000 0.308 0.000 0.692
#> GSM627099 2 0.3990 0.541765 0.000 0.688 0.000 0.004 0.308
#> GSM627105 4 0.0162 0.566439 0.000 0.000 0.000 0.996 0.004
#> GSM627117 3 0.0000 0.743789 0.000 0.000 1.000 0.000 0.000
#> GSM627121 5 0.4847 0.745817 0.000 0.000 0.240 0.068 0.692
#> GSM627127 4 0.5820 0.810692 0.000 0.120 0.000 0.572 0.308
#> GSM627087 2 0.0000 0.847614 0.000 1.000 0.000 0.000 0.000
#> GSM627089 5 0.4249 0.633522 0.000 0.000 0.432 0.000 0.568
#> GSM627092 2 0.1608 0.814418 0.000 0.928 0.000 0.000 0.072
#> GSM627076 5 0.4297 0.563582 0.000 0.000 0.020 0.288 0.692
#> GSM627136 3 0.2377 0.619331 0.000 0.000 0.872 0.000 0.128
#> GSM627081 5 0.3837 0.791280 0.000 0.000 0.308 0.000 0.692
#> GSM627091 2 0.0162 0.846443 0.000 0.996 0.000 0.000 0.004
#> GSM627097 4 0.6352 0.773174 0.000 0.188 0.000 0.504 0.308
#> GSM627072 5 0.4227 0.651419 0.000 0.000 0.420 0.000 0.580
#> GSM627080 1 0.0000 0.922300 1.000 0.000 0.000 0.000 0.000
#> GSM627088 3 0.0963 0.717805 0.000 0.000 0.964 0.000 0.036
#> GSM627109 1 0.3752 0.644557 0.708 0.000 0.292 0.000 0.000
#> GSM627111 1 0.1908 0.860453 0.908 0.000 0.092 0.000 0.000
#> GSM627113 3 0.0000 0.743789 0.000 0.000 1.000 0.000 0.000
#> GSM627133 2 0.6093 0.185899 0.000 0.568 0.240 0.000 0.192
#> GSM627177 3 0.4300 -0.407911 0.000 0.000 0.524 0.000 0.476
#> GSM627086 2 0.0000 0.847614 0.000 1.000 0.000 0.000 0.000
#> GSM627095 1 0.0000 0.922300 1.000 0.000 0.000 0.000 0.000
#> GSM627079 5 0.3857 0.790734 0.000 0.000 0.312 0.000 0.688
#> GSM627082 4 0.0000 0.569585 0.000 0.000 0.000 1.000 0.000
#> GSM627074 3 0.3395 0.484280 0.236 0.000 0.764 0.000 0.000
#> GSM627077 3 0.3730 0.311729 0.000 0.000 0.712 0.000 0.288
#> GSM627093 3 0.0000 0.743789 0.000 0.000 1.000 0.000 0.000
#> GSM627120 2 0.6778 -0.409836 0.000 0.392 0.000 0.296 0.312
#> GSM627124 4 0.6670 0.688183 0.000 0.256 0.000 0.436 0.308
#> GSM627075 2 0.0000 0.847614 0.000 1.000 0.000 0.000 0.000
#> GSM627085 4 0.5820 0.810692 0.000 0.120 0.000 0.572 0.308
#> GSM627119 3 0.0703 0.729216 0.024 0.000 0.976 0.000 0.000
#> GSM627116 4 0.6562 0.731680 0.000 0.228 0.000 0.464 0.308
#> GSM627084 3 0.2648 0.614203 0.152 0.000 0.848 0.000 0.000
#> GSM627096 4 0.5740 0.808277 0.000 0.112 0.000 0.580 0.308
#> GSM627100 5 0.3837 0.547004 0.000 0.000 0.000 0.308 0.692
#> GSM627112 4 0.5781 0.809786 0.000 0.116 0.000 0.576 0.308
#> GSM627083 1 0.2127 0.786286 0.892 0.108 0.000 0.000 0.000
#> GSM627098 3 0.0000 0.743789 0.000 0.000 1.000 0.000 0.000
#> GSM627104 1 0.4192 0.457705 0.596 0.000 0.404 0.000 0.000
#> GSM627131 3 0.3949 0.170587 0.000 0.000 0.668 0.000 0.332
#> GSM627106 5 0.3837 0.791280 0.000 0.000 0.308 0.000 0.692
#> GSM627123 1 0.0000 0.922300 1.000 0.000 0.000 0.000 0.000
#> GSM627129 4 0.6275 0.782083 0.000 0.176 0.000 0.516 0.308
#> GSM627216 2 0.0000 0.847614 0.000 1.000 0.000 0.000 0.000
#> GSM627212 2 0.1965 0.796461 0.000 0.904 0.000 0.000 0.096
#> GSM627190 3 0.0000 0.743789 0.000 0.000 1.000 0.000 0.000
#> GSM627169 2 0.1908 0.765308 0.000 0.908 0.092 0.000 0.000
#> GSM627167 4 0.5820 0.810692 0.000 0.120 0.000 0.572 0.308
#> GSM627192 1 0.0000 0.922300 1.000 0.000 0.000 0.000 0.000
#> GSM627203 5 0.3837 0.791280 0.000 0.000 0.308 0.000 0.692
#> GSM627151 2 0.4398 0.614031 0.000 0.720 0.040 0.000 0.240
#> GSM627163 1 0.0000 0.922300 1.000 0.000 0.000 0.000 0.000
#> GSM627211 2 0.0000 0.847614 0.000 1.000 0.000 0.000 0.000
#> GSM627171 2 0.1544 0.798341 0.000 0.932 0.068 0.000 0.000
#> GSM627209 2 0.6796 -0.443813 0.000 0.380 0.000 0.312 0.308
#> GSM627135 1 0.0000 0.922300 1.000 0.000 0.000 0.000 0.000
#> GSM627170 2 0.0000 0.847614 0.000 1.000 0.000 0.000 0.000
#> GSM627178 3 0.4576 -0.000698 0.016 0.000 0.608 0.000 0.376
#> GSM627199 2 0.6817 -0.528367 0.000 0.348 0.000 0.344 0.308
#> GSM627213 4 0.5820 0.810692 0.000 0.120 0.000 0.572 0.308
#> GSM627140 4 0.6525 0.740920 0.000 0.220 0.000 0.472 0.308
#> GSM627149 1 0.0000 0.922300 1.000 0.000 0.000 0.000 0.000
#> GSM627147 2 0.3837 0.547965 0.000 0.692 0.000 0.000 0.308
#> GSM627195 5 0.3837 0.791280 0.000 0.000 0.308 0.000 0.692
#> GSM627204 2 0.0000 0.847614 0.000 1.000 0.000 0.000 0.000
#> GSM627207 2 0.0000 0.847614 0.000 1.000 0.000 0.000 0.000
#> GSM627157 3 0.1478 0.696630 0.064 0.000 0.936 0.000 0.000
#> GSM627201 2 0.2377 0.770206 0.000 0.872 0.000 0.000 0.128
#> GSM627146 2 0.0162 0.846443 0.000 0.996 0.000 0.000 0.004
#> GSM627156 2 0.0000 0.847614 0.000 1.000 0.000 0.000 0.000
#> GSM627188 1 0.0000 0.922300 1.000 0.000 0.000 0.000 0.000
#> GSM627197 2 0.3424 0.647828 0.000 0.760 0.000 0.000 0.240
#> GSM627173 2 0.0000 0.847614 0.000 1.000 0.000 0.000 0.000
#> GSM627179 2 0.0000 0.847614 0.000 1.000 0.000 0.000 0.000
#> GSM627208 5 0.6745 0.376979 0.000 0.280 0.312 0.000 0.408
#> GSM627215 2 0.0000 0.847614 0.000 1.000 0.000 0.000 0.000
#> GSM627153 4 0.6399 0.766047 0.000 0.196 0.000 0.496 0.308
#> GSM627155 1 0.0000 0.922300 1.000 0.000 0.000 0.000 0.000
#> GSM627165 4 0.6820 0.504756 0.000 0.344 0.000 0.344 0.312
#> GSM627168 3 0.3612 0.364874 0.000 0.000 0.732 0.000 0.268
#> GSM627183 3 0.0000 0.743789 0.000 0.000 1.000 0.000 0.000
#> GSM627144 5 0.3857 0.790734 0.000 0.000 0.312 0.000 0.688
#> GSM627158 1 0.0000 0.922300 1.000 0.000 0.000 0.000 0.000
#> GSM627196 2 0.0000 0.847614 0.000 1.000 0.000 0.000 0.000
#> GSM627142 5 0.4088 0.500429 0.000 0.000 0.000 0.368 0.632
#> GSM627182 3 0.4242 -0.236951 0.000 0.000 0.572 0.000 0.428
#> GSM627202 3 0.3913 0.197893 0.000 0.000 0.676 0.000 0.324
#> GSM627141 3 0.0000 0.743789 0.000 0.000 1.000 0.000 0.000
#> GSM627143 2 0.3864 0.686014 0.000 0.784 0.008 0.020 0.188
#> GSM627145 5 0.4101 0.733225 0.000 0.000 0.372 0.000 0.628
#> GSM627152 5 0.4171 0.689582 0.000 0.000 0.396 0.000 0.604
#> GSM627200 3 0.0000 0.743789 0.000 0.000 1.000 0.000 0.000
#> GSM627159 4 0.0000 0.569585 0.000 0.000 0.000 1.000 0.000
#> GSM627164 2 0.0000 0.847614 0.000 1.000 0.000 0.000 0.000
#> GSM627138 3 0.3837 0.295189 0.308 0.000 0.692 0.000 0.000
#> GSM627175 4 0.5820 0.810692 0.000 0.120 0.000 0.572 0.308
#> GSM627150 5 0.3857 0.790734 0.000 0.000 0.312 0.000 0.688
#> GSM627166 3 0.4101 0.149240 0.372 0.000 0.628 0.000 0.000
#> GSM627186 2 0.1732 0.773122 0.000 0.920 0.080 0.000 0.000
#> GSM627139 5 0.4314 0.630243 0.000 0.016 0.196 0.028 0.760
#> GSM627181 2 0.3837 0.547965 0.000 0.692 0.000 0.000 0.308
#> GSM627205 2 0.0000 0.847614 0.000 1.000 0.000 0.000 0.000
#> GSM627214 2 0.6683 -0.289660 0.000 0.432 0.000 0.260 0.308
#> GSM627180 5 0.3857 0.790734 0.000 0.000 0.312 0.000 0.688
#> GSM627172 2 0.1043 0.832611 0.000 0.960 0.000 0.000 0.040
#> GSM627184 1 0.0000 0.922300 1.000 0.000 0.000 0.000 0.000
#> GSM627193 2 0.0000 0.847614 0.000 1.000 0.000 0.000 0.000
#> GSM627191 4 0.6002 0.804022 0.000 0.140 0.000 0.552 0.308
#> GSM627176 5 0.4304 0.490879 0.000 0.000 0.484 0.000 0.516
#> GSM627194 2 0.0000 0.847614 0.000 1.000 0.000 0.000 0.000
#> GSM627154 4 0.5820 0.810692 0.000 0.120 0.000 0.572 0.308
#> GSM627187 3 0.0000 0.743789 0.000 0.000 1.000 0.000 0.000
#> GSM627198 4 0.5967 0.805750 0.000 0.136 0.000 0.556 0.308
#> GSM627160 4 0.6486 0.750273 0.000 0.212 0.000 0.480 0.308
#> GSM627185 1 0.4192 0.457666 0.596 0.000 0.404 0.000 0.000
#> GSM627206 3 0.3210 0.492956 0.000 0.000 0.788 0.000 0.212
#> GSM627161 1 0.0000 0.922300 1.000 0.000 0.000 0.000 0.000
#> GSM627162 3 0.0000 0.743789 0.000 0.000 1.000 0.000 0.000
#> GSM627210 3 0.0404 0.737300 0.012 0.000 0.988 0.000 0.000
#> GSM627189 2 0.0000 0.847614 0.000 1.000 0.000 0.000 0.000
show/hide code output
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM627128 6 0.1204 0.9188 0.000 0.000 0.000 0.056 0.000 0.944
#> GSM627110 3 0.0937 0.8892 0.000 0.000 0.960 0.000 0.040 0.000
#> GSM627132 1 0.3240 0.6534 0.752 0.000 0.244 0.000 0.000 0.004
#> GSM627107 5 0.1092 0.8809 0.000 0.000 0.000 0.020 0.960 0.020
#> GSM627103 2 0.0291 0.9427 0.000 0.992 0.000 0.004 0.004 0.000
#> GSM627114 3 0.0458 0.9031 0.000 0.000 0.984 0.000 0.016 0.000
#> GSM627134 4 0.1226 0.9468 0.000 0.040 0.004 0.952 0.004 0.000
#> GSM627137 2 0.0865 0.9264 0.000 0.964 0.000 0.036 0.000 0.000
#> GSM627148 5 0.0865 0.8978 0.000 0.000 0.036 0.000 0.964 0.000
#> GSM627101 6 0.1444 0.9094 0.000 0.000 0.000 0.072 0.000 0.928
#> GSM627130 6 0.1204 0.9188 0.000 0.000 0.000 0.056 0.000 0.944
#> GSM627071 5 0.2562 0.8305 0.000 0.000 0.172 0.000 0.828 0.000
#> GSM627118 4 0.0717 0.9406 0.000 0.008 0.000 0.976 0.000 0.016
#> GSM627094 2 0.0291 0.9434 0.000 0.992 0.004 0.004 0.000 0.000
#> GSM627122 5 0.1700 0.8893 0.000 0.000 0.024 0.000 0.928 0.048
#> GSM627115 2 0.0291 0.9434 0.000 0.992 0.004 0.004 0.000 0.000
#> GSM627125 6 0.1349 0.9190 0.000 0.000 0.000 0.056 0.004 0.940
#> GSM627174 2 0.3997 -0.0712 0.000 0.508 0.000 0.488 0.004 0.000
#> GSM627102 2 0.2482 0.7903 0.000 0.848 0.000 0.148 0.004 0.000
#> GSM627073 5 0.0547 0.8927 0.000 0.000 0.000 0.020 0.980 0.000
#> GSM627108 2 0.0405 0.9430 0.000 0.988 0.008 0.004 0.000 0.000
#> GSM627126 1 0.0000 0.9676 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627078 4 0.0547 0.9528 0.000 0.020 0.000 0.980 0.000 0.000
#> GSM627090 5 0.1528 0.8888 0.000 0.000 0.016 0.000 0.936 0.048
#> GSM627099 4 0.1700 0.9166 0.000 0.080 0.004 0.916 0.000 0.000
#> GSM627105 6 0.1349 0.9190 0.000 0.000 0.000 0.056 0.004 0.940
#> GSM627117 3 0.0363 0.9037 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM627121 5 0.0806 0.8878 0.000 0.000 0.000 0.020 0.972 0.008
#> GSM627127 4 0.0547 0.9528 0.000 0.020 0.000 0.980 0.000 0.000
#> GSM627087 2 0.0146 0.9433 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM627089 5 0.1204 0.8939 0.000 0.000 0.056 0.000 0.944 0.000
#> GSM627092 2 0.0405 0.9400 0.000 0.988 0.000 0.008 0.004 0.000
#> GSM627076 5 0.1625 0.8819 0.000 0.000 0.012 0.000 0.928 0.060
#> GSM627136 3 0.3868 -0.1451 0.000 0.000 0.504 0.000 0.496 0.000
#> GSM627081 5 0.0547 0.8927 0.000 0.000 0.000 0.020 0.980 0.000
#> GSM627091 2 0.0146 0.9433 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM627097 4 0.1003 0.9509 0.000 0.028 0.000 0.964 0.004 0.004
#> GSM627072 5 0.1411 0.8923 0.000 0.000 0.060 0.004 0.936 0.000
#> GSM627080 1 0.0146 0.9668 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM627088 3 0.3797 0.1595 0.000 0.000 0.580 0.000 0.420 0.000
#> GSM627109 3 0.2320 0.7982 0.132 0.000 0.864 0.000 0.000 0.004
#> GSM627111 3 0.3769 0.4080 0.356 0.000 0.640 0.000 0.000 0.004
#> GSM627113 3 0.0508 0.9037 0.004 0.000 0.984 0.000 0.012 0.000
#> GSM627133 2 0.3074 0.6947 0.000 0.792 0.000 0.004 0.200 0.004
#> GSM627177 5 0.2562 0.8265 0.000 0.000 0.172 0.000 0.828 0.000
#> GSM627086 2 0.0260 0.9422 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM627095 1 0.0000 0.9676 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627079 5 0.0717 0.8970 0.000 0.000 0.016 0.000 0.976 0.008
#> GSM627082 6 0.1349 0.9182 0.000 0.000 0.000 0.056 0.004 0.940
#> GSM627074 3 0.0717 0.8982 0.016 0.000 0.976 0.000 0.008 0.000
#> GSM627077 5 0.3714 0.7964 0.000 0.000 0.196 0.000 0.760 0.044
#> GSM627093 3 0.0508 0.9037 0.004 0.000 0.984 0.000 0.012 0.000
#> GSM627120 4 0.2814 0.8806 0.000 0.080 0.004 0.864 0.052 0.000
#> GSM627124 4 0.0858 0.9517 0.000 0.028 0.000 0.968 0.004 0.000
#> GSM627075 2 0.0291 0.9434 0.000 0.992 0.004 0.004 0.000 0.000
#> GSM627085 4 0.0547 0.9528 0.000 0.020 0.000 0.980 0.000 0.000
#> GSM627119 3 0.0508 0.9037 0.004 0.000 0.984 0.000 0.012 0.000
#> GSM627116 4 0.1116 0.9509 0.000 0.028 0.000 0.960 0.004 0.008
#> GSM627084 3 0.0405 0.9032 0.004 0.000 0.988 0.000 0.008 0.000
#> GSM627096 4 0.0603 0.9499 0.000 0.016 0.000 0.980 0.000 0.004
#> GSM627100 6 0.4167 0.2864 0.000 0.000 0.000 0.020 0.368 0.612
#> GSM627112 4 0.1053 0.9489 0.000 0.020 0.000 0.964 0.004 0.012
#> GSM627083 1 0.2145 0.8612 0.912 0.020 0.000 0.056 0.004 0.008
#> GSM627098 3 0.0363 0.9037 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM627104 3 0.2006 0.8273 0.104 0.000 0.892 0.000 0.000 0.004
#> GSM627131 5 0.3618 0.8128 0.000 0.000 0.176 0.000 0.776 0.048
#> GSM627106 5 0.0547 0.8927 0.000 0.000 0.000 0.020 0.980 0.000
#> GSM627123 1 0.0146 0.9668 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM627129 4 0.0547 0.9528 0.000 0.020 0.000 0.980 0.000 0.000
#> GSM627216 2 0.0146 0.9417 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM627212 2 0.0713 0.9285 0.000 0.972 0.000 0.028 0.000 0.000
#> GSM627190 3 0.0458 0.9031 0.000 0.000 0.984 0.000 0.016 0.000
#> GSM627169 2 0.0653 0.9346 0.000 0.980 0.012 0.000 0.004 0.004
#> GSM627167 4 0.0692 0.9525 0.000 0.020 0.000 0.976 0.004 0.000
#> GSM627192 1 0.0000 0.9676 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627203 5 0.0458 0.8937 0.000 0.000 0.000 0.016 0.984 0.000
#> GSM627151 4 0.3606 0.6867 0.000 0.256 0.000 0.728 0.016 0.000
#> GSM627163 1 0.0146 0.9668 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM627211 2 0.0146 0.9433 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM627171 2 0.0508 0.9371 0.000 0.984 0.000 0.000 0.012 0.004
#> GSM627209 4 0.1267 0.9352 0.000 0.060 0.000 0.940 0.000 0.000
#> GSM627135 1 0.0000 0.9676 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627170 2 0.0653 0.9390 0.000 0.980 0.004 0.012 0.004 0.000
#> GSM627178 5 0.3272 0.8533 0.004 0.000 0.124 0.000 0.824 0.048
#> GSM627199 4 0.1285 0.9422 0.000 0.052 0.000 0.944 0.004 0.000
#> GSM627213 4 0.0547 0.9528 0.000 0.020 0.000 0.980 0.000 0.000
#> GSM627140 4 0.1003 0.9509 0.000 0.028 0.000 0.964 0.004 0.004
#> GSM627149 1 0.0000 0.9676 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627147 4 0.1858 0.9078 0.000 0.092 0.000 0.904 0.004 0.000
#> GSM627195 5 0.0547 0.8927 0.000 0.000 0.000 0.020 0.980 0.000
#> GSM627204 2 0.0291 0.9434 0.000 0.992 0.004 0.004 0.000 0.000
#> GSM627207 2 0.0291 0.9434 0.000 0.992 0.004 0.004 0.000 0.000
#> GSM627157 3 0.0520 0.9020 0.008 0.000 0.984 0.000 0.008 0.000
#> GSM627201 2 0.1219 0.9117 0.000 0.948 0.004 0.048 0.000 0.000
#> GSM627146 2 0.0146 0.9433 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM627156 2 0.0436 0.9407 0.000 0.988 0.004 0.000 0.004 0.004
#> GSM627188 1 0.0000 0.9676 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627197 4 0.3151 0.6940 0.000 0.252 0.000 0.748 0.000 0.000
#> GSM627173 2 0.0551 0.9428 0.000 0.984 0.008 0.004 0.004 0.000
#> GSM627179 2 0.0146 0.9433 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM627208 5 0.4194 0.5106 0.000 0.308 0.008 0.020 0.664 0.000
#> GSM627215 2 0.0146 0.9417 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM627153 4 0.0547 0.9528 0.000 0.020 0.000 0.980 0.000 0.000
#> GSM627155 1 0.0000 0.9676 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627165 4 0.1493 0.9370 0.000 0.056 0.004 0.936 0.004 0.000
#> GSM627168 5 0.2491 0.8392 0.000 0.000 0.164 0.000 0.836 0.000
#> GSM627183 3 0.1556 0.8486 0.000 0.000 0.920 0.000 0.080 0.000
#> GSM627144 5 0.0405 0.8951 0.000 0.000 0.004 0.008 0.988 0.000
#> GSM627158 1 0.0146 0.9668 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM627196 2 0.0291 0.9434 0.000 0.992 0.004 0.004 0.000 0.000
#> GSM627142 6 0.1010 0.8601 0.000 0.000 0.004 0.000 0.036 0.960
#> GSM627182 5 0.2624 0.8416 0.000 0.004 0.148 0.004 0.844 0.000
#> GSM627202 5 0.3651 0.8126 0.000 0.000 0.180 0.000 0.772 0.048
#> GSM627141 3 0.0458 0.9031 0.000 0.000 0.984 0.000 0.016 0.000
#> GSM627143 2 0.4086 0.0581 0.000 0.528 0.000 0.464 0.008 0.000
#> GSM627145 5 0.0937 0.8973 0.000 0.000 0.040 0.000 0.960 0.000
#> GSM627152 5 0.1528 0.8888 0.000 0.000 0.016 0.000 0.936 0.048
#> GSM627200 3 0.1075 0.8808 0.000 0.000 0.952 0.000 0.048 0.000
#> GSM627159 6 0.1349 0.9182 0.000 0.000 0.000 0.056 0.004 0.940
#> GSM627164 2 0.0260 0.9405 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM627138 3 0.0858 0.8868 0.028 0.000 0.968 0.000 0.000 0.004
#> GSM627175 4 0.0547 0.9528 0.000 0.020 0.000 0.980 0.000 0.000
#> GSM627150 5 0.0547 0.8927 0.000 0.000 0.000 0.020 0.980 0.000
#> GSM627166 3 0.0777 0.8956 0.024 0.000 0.972 0.000 0.004 0.000
#> GSM627186 2 0.0551 0.9382 0.000 0.984 0.008 0.000 0.004 0.004
#> GSM627139 5 0.1760 0.8544 0.000 0.020 0.000 0.048 0.928 0.004
#> GSM627181 4 0.1556 0.9179 0.000 0.080 0.000 0.920 0.000 0.000
#> GSM627205 2 0.0436 0.9425 0.000 0.988 0.004 0.004 0.004 0.000
#> GSM627214 4 0.1219 0.9433 0.000 0.048 0.004 0.948 0.000 0.000
#> GSM627180 5 0.0458 0.8937 0.000 0.000 0.000 0.016 0.984 0.000
#> GSM627172 2 0.1531 0.8843 0.000 0.928 0.000 0.068 0.004 0.000
#> GSM627184 1 0.0000 0.9676 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627193 2 0.0405 0.9430 0.000 0.988 0.008 0.004 0.000 0.000
#> GSM627191 4 0.1053 0.9489 0.000 0.020 0.000 0.964 0.004 0.012
#> GSM627176 5 0.0891 0.8983 0.000 0.000 0.024 0.000 0.968 0.008
#> GSM627194 2 0.0146 0.9433 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM627154 4 0.0547 0.9528 0.000 0.020 0.000 0.980 0.000 0.000
#> GSM627187 3 0.0363 0.9037 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM627198 4 0.0547 0.9528 0.000 0.020 0.000 0.980 0.000 0.000
#> GSM627160 4 0.1096 0.9489 0.000 0.020 0.004 0.964 0.004 0.008
#> GSM627185 3 0.1806 0.8413 0.088 0.000 0.908 0.000 0.000 0.004
#> GSM627206 5 0.3175 0.7287 0.000 0.000 0.256 0.000 0.744 0.000
#> GSM627161 1 0.0146 0.9668 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM627162 3 0.0458 0.9028 0.000 0.000 0.984 0.000 0.016 0.000
#> GSM627210 3 0.0363 0.9037 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM627189 2 0.0291 0.9434 0.000 0.992 0.004 0.004 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two
samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures
which are significantly different between classes which can be candidate marks
for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known
annotation is numeric, one-way ANOVA test is applied, and if the known
annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) age(p) other(p) k
#> CV:mclust 146 0.5188 0.481 0.1413 2
#> CV:mclust 145 0.9506 0.738 0.1029 3
#> CV:mclust 132 0.0643 0.375 0.2964 4
#> CV:mclust 125 0.1294 0.455 0.1049 5
#> CV:mclust 140 0.4019 0.683 0.0671 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
CV:NMF*
The object with results only for a single top-value method and a single partition method
can be extracted as:
res = res_list["CV", "NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51882 rows and 146 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
- The first row: a plot of the ECDF (empirical cumulative distribution
function) curves of the consensus matrix for each
k and the heatmap of
predicted classes for each k.
- The second row: heatmaps of the consensus matrix for each
k.
- The third row: heatmaps of the membership matrix for each
k.
- The fouth row: heatmaps of the signatures for each
k.
All the plots in panels can be made by individual functions and they are
plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
- ECDF curves of the consensus matrix for each
k;
- 1-PAC. The PAC
score
measures the proportion of the ambiguous subgrouping.
- Mean silhouette score.
- Concordance. The mean probability of fiting the consensus class ids in all
partitions.
- Area increased. Denote \(A_k\) as the area under the ECDF curve for current
k, the area increased is defined as \(A_k - A_{k-1}\).
- Rand index. The percent of pairs of samples that are both in a same cluster
or both are not in a same cluster in the partition of k and k-1.
- Jaccard index. The ratio of pairs of samples are both in a same cluster in
the partition of k and k-1 and the pairs of samples are both in a same
cluster in the partition k or k-1.
The detailed explanations of these statistics can be found in the cola
vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.972 0.988 0.4990 0.503 0.503
#> 3 3 0.916 0.912 0.951 0.2881 0.824 0.661
#> 4 4 0.569 0.634 0.818 0.1365 0.756 0.438
#> 5 5 0.586 0.589 0.774 0.0696 0.846 0.516
#> 6 6 0.619 0.568 0.738 0.0387 0.914 0.649
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
- All \(k\) with Jaccard index larger than 0.95 are removed because increasing
\(k\) does not provide enough extra information. If all \(k\) are removed, it is
marked as no subgroup is detected.
- For all \(k\) with 1-PAC score larger than 0.9, the maximal \(k\) is taken as
the best \(k\), and other \(k\) are marked as optional \(k\).
- If it does not fit the second rule. The \(k\) with the maximal vote of the
highest 1-PAC score, highest mean silhouette, and highest concordance is
taken as the best \(k\).
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
show/hide code output
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM627128 2 0.0000 0.983 0.000 1.000
#> GSM627110 1 0.0000 0.994 1.000 0.000
#> GSM627132 1 0.0000 0.994 1.000 0.000
#> GSM627107 2 0.0000 0.983 0.000 1.000
#> GSM627103 2 0.0000 0.983 0.000 1.000
#> GSM627114 1 0.0000 0.994 1.000 0.000
#> GSM627134 2 0.0000 0.983 0.000 1.000
#> GSM627137 2 0.0000 0.983 0.000 1.000
#> GSM627148 1 0.0000 0.994 1.000 0.000
#> GSM627101 2 0.0000 0.983 0.000 1.000
#> GSM627130 2 0.0000 0.983 0.000 1.000
#> GSM627071 1 0.0000 0.994 1.000 0.000
#> GSM627118 2 0.0000 0.983 0.000 1.000
#> GSM627094 2 0.0000 0.983 0.000 1.000
#> GSM627122 1 0.0000 0.994 1.000 0.000
#> GSM627115 2 0.0000 0.983 0.000 1.000
#> GSM627125 2 0.0672 0.976 0.008 0.992
#> GSM627174 2 0.0000 0.983 0.000 1.000
#> GSM627102 2 0.0000 0.983 0.000 1.000
#> GSM627073 2 0.2778 0.940 0.048 0.952
#> GSM627108 2 0.0000 0.983 0.000 1.000
#> GSM627126 1 0.0000 0.994 1.000 0.000
#> GSM627078 2 0.0000 0.983 0.000 1.000
#> GSM627090 1 0.0000 0.994 1.000 0.000
#> GSM627099 2 0.0000 0.983 0.000 1.000
#> GSM627105 2 0.0000 0.983 0.000 1.000
#> GSM627117 1 0.0000 0.994 1.000 0.000
#> GSM627121 2 0.0000 0.983 0.000 1.000
#> GSM627127 2 0.0000 0.983 0.000 1.000
#> GSM627087 2 0.0000 0.983 0.000 1.000
#> GSM627089 1 0.0000 0.994 1.000 0.000
#> GSM627092 2 0.0000 0.983 0.000 1.000
#> GSM627076 1 0.0000 0.994 1.000 0.000
#> GSM627136 1 0.0000 0.994 1.000 0.000
#> GSM627081 2 0.1184 0.970 0.016 0.984
#> GSM627091 2 0.0000 0.983 0.000 1.000
#> GSM627097 2 0.0000 0.983 0.000 1.000
#> GSM627072 1 0.0376 0.991 0.996 0.004
#> GSM627080 1 0.0000 0.994 1.000 0.000
#> GSM627088 1 0.0000 0.994 1.000 0.000
#> GSM627109 1 0.0000 0.994 1.000 0.000
#> GSM627111 1 0.0000 0.994 1.000 0.000
#> GSM627113 1 0.0000 0.994 1.000 0.000
#> GSM627133 2 0.0000 0.983 0.000 1.000
#> GSM627177 2 0.9866 0.253 0.432 0.568
#> GSM627086 2 0.0000 0.983 0.000 1.000
#> GSM627095 1 0.0000 0.994 1.000 0.000
#> GSM627079 1 0.0000 0.994 1.000 0.000
#> GSM627082 1 0.0376 0.991 0.996 0.004
#> GSM627074 1 0.0000 0.994 1.000 0.000
#> GSM627077 1 0.0000 0.994 1.000 0.000
#> GSM627093 1 0.0000 0.994 1.000 0.000
#> GSM627120 2 0.0000 0.983 0.000 1.000
#> GSM627124 2 0.0000 0.983 0.000 1.000
#> GSM627075 2 0.0000 0.983 0.000 1.000
#> GSM627085 2 0.0000 0.983 0.000 1.000
#> GSM627119 1 0.0000 0.994 1.000 0.000
#> GSM627116 2 0.0000 0.983 0.000 1.000
#> GSM627084 1 0.0000 0.994 1.000 0.000
#> GSM627096 2 0.0000 0.983 0.000 1.000
#> GSM627100 1 0.0000 0.994 1.000 0.000
#> GSM627112 2 0.0000 0.983 0.000 1.000
#> GSM627083 1 0.4298 0.903 0.912 0.088
#> GSM627098 1 0.0000 0.994 1.000 0.000
#> GSM627104 1 0.0000 0.994 1.000 0.000
#> GSM627131 1 0.0000 0.994 1.000 0.000
#> GSM627106 2 0.8016 0.682 0.244 0.756
#> GSM627123 1 0.0000 0.994 1.000 0.000
#> GSM627129 2 0.0000 0.983 0.000 1.000
#> GSM627216 2 0.0000 0.983 0.000 1.000
#> GSM627212 2 0.0000 0.983 0.000 1.000
#> GSM627190 1 0.0000 0.994 1.000 0.000
#> GSM627169 2 0.0000 0.983 0.000 1.000
#> GSM627167 2 0.0000 0.983 0.000 1.000
#> GSM627192 1 0.0000 0.994 1.000 0.000
#> GSM627203 1 0.0000 0.994 1.000 0.000
#> GSM627151 2 0.0000 0.983 0.000 1.000
#> GSM627163 1 0.0000 0.994 1.000 0.000
#> GSM627211 2 0.0000 0.983 0.000 1.000
#> GSM627171 2 0.0000 0.983 0.000 1.000
#> GSM627209 2 0.0000 0.983 0.000 1.000
#> GSM627135 1 0.0000 0.994 1.000 0.000
#> GSM627170 2 0.0000 0.983 0.000 1.000
#> GSM627178 1 0.0000 0.994 1.000 0.000
#> GSM627199 2 0.0000 0.983 0.000 1.000
#> GSM627213 2 0.0000 0.983 0.000 1.000
#> GSM627140 2 0.0000 0.983 0.000 1.000
#> GSM627149 1 0.0000 0.994 1.000 0.000
#> GSM627147 2 0.0000 0.983 0.000 1.000
#> GSM627195 1 0.0000 0.994 1.000 0.000
#> GSM627204 2 0.0000 0.983 0.000 1.000
#> GSM627207 2 0.0000 0.983 0.000 1.000
#> GSM627157 1 0.0000 0.994 1.000 0.000
#> GSM627201 2 0.0000 0.983 0.000 1.000
#> GSM627146 2 0.0000 0.983 0.000 1.000
#> GSM627156 2 0.0000 0.983 0.000 1.000
#> GSM627188 1 0.0000 0.994 1.000 0.000
#> GSM627197 2 0.0000 0.983 0.000 1.000
#> GSM627173 2 0.0000 0.983 0.000 1.000
#> GSM627179 2 0.0000 0.983 0.000 1.000
#> GSM627208 2 0.0000 0.983 0.000 1.000
#> GSM627215 2 0.0000 0.983 0.000 1.000
#> GSM627153 2 0.0000 0.983 0.000 1.000
#> GSM627155 1 0.0000 0.994 1.000 0.000
#> GSM627165 2 0.0000 0.983 0.000 1.000
#> GSM627168 1 0.0000 0.994 1.000 0.000
#> GSM627183 1 0.0000 0.994 1.000 0.000
#> GSM627144 1 0.0938 0.984 0.988 0.012
#> GSM627158 1 0.0000 0.994 1.000 0.000
#> GSM627196 2 0.0000 0.983 0.000 1.000
#> GSM627142 1 0.0000 0.994 1.000 0.000
#> GSM627182 2 0.2423 0.948 0.040 0.960
#> GSM627202 1 0.0000 0.994 1.000 0.000
#> GSM627141 1 0.0000 0.994 1.000 0.000
#> GSM627143 2 0.0000 0.983 0.000 1.000
#> GSM627145 1 0.0000 0.994 1.000 0.000
#> GSM627152 1 0.0000 0.994 1.000 0.000
#> GSM627200 1 0.0000 0.994 1.000 0.000
#> GSM627159 1 0.5842 0.837 0.860 0.140
#> GSM627164 2 0.0000 0.983 0.000 1.000
#> GSM627138 1 0.0000 0.994 1.000 0.000
#> GSM627175 2 0.0000 0.983 0.000 1.000
#> GSM627150 1 0.1184 0.980 0.984 0.016
#> GSM627166 1 0.0000 0.994 1.000 0.000
#> GSM627186 2 0.0000 0.983 0.000 1.000
#> GSM627139 2 0.4298 0.897 0.088 0.912
#> GSM627181 2 0.0000 0.983 0.000 1.000
#> GSM627205 2 0.0000 0.983 0.000 1.000
#> GSM627214 2 0.0000 0.983 0.000 1.000
#> GSM627180 2 0.0000 0.983 0.000 1.000
#> GSM627172 2 0.0000 0.983 0.000 1.000
#> GSM627184 1 0.0000 0.994 1.000 0.000
#> GSM627193 2 0.0000 0.983 0.000 1.000
#> GSM627191 2 0.2603 0.944 0.044 0.956
#> GSM627176 1 0.0000 0.994 1.000 0.000
#> GSM627194 2 0.0000 0.983 0.000 1.000
#> GSM627154 2 0.0000 0.983 0.000 1.000
#> GSM627187 1 0.0000 0.994 1.000 0.000
#> GSM627198 2 0.0000 0.983 0.000 1.000
#> GSM627160 2 0.9850 0.263 0.428 0.572
#> GSM627185 1 0.0000 0.994 1.000 0.000
#> GSM627206 1 0.0000 0.994 1.000 0.000
#> GSM627161 1 0.0000 0.994 1.000 0.000
#> GSM627162 1 0.4431 0.899 0.908 0.092
#> GSM627210 1 0.0000 0.994 1.000 0.000
#> GSM627189 2 0.0000 0.983 0.000 1.000
show/hide code output
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM627128 3 0.0000 0.917 0.000 0.000 1.000
#> GSM627110 1 0.1163 0.952 0.972 0.028 0.000
#> GSM627132 1 0.0424 0.955 0.992 0.000 0.008
#> GSM627107 2 0.1411 0.954 0.000 0.964 0.036
#> GSM627103 2 0.0892 0.958 0.000 0.980 0.020
#> GSM627114 1 0.1289 0.950 0.968 0.032 0.000
#> GSM627134 2 0.1529 0.952 0.000 0.960 0.040
#> GSM627137 2 0.1163 0.957 0.000 0.972 0.028
#> GSM627148 1 0.1529 0.946 0.960 0.040 0.000
#> GSM627101 3 0.0592 0.917 0.000 0.012 0.988
#> GSM627130 3 0.0000 0.917 0.000 0.000 1.000
#> GSM627071 1 0.1031 0.953 0.976 0.024 0.000
#> GSM627118 2 0.1860 0.943 0.000 0.948 0.052
#> GSM627094 2 0.0892 0.958 0.000 0.980 0.020
#> GSM627122 1 0.1753 0.936 0.952 0.000 0.048
#> GSM627115 2 0.0000 0.954 0.000 1.000 0.000
#> GSM627125 3 0.0000 0.917 0.000 0.000 1.000
#> GSM627174 2 0.1289 0.956 0.000 0.968 0.032
#> GSM627102 2 0.1529 0.952 0.000 0.960 0.040
#> GSM627073 2 0.1753 0.919 0.048 0.952 0.000
#> GSM627108 2 0.0237 0.955 0.000 0.996 0.004
#> GSM627126 1 0.4504 0.772 0.804 0.000 0.196
#> GSM627078 3 0.2356 0.886 0.000 0.072 0.928
#> GSM627090 1 0.0424 0.955 0.992 0.000 0.008
#> GSM627099 2 0.1163 0.957 0.000 0.972 0.028
#> GSM627105 3 0.0237 0.918 0.000 0.004 0.996
#> GSM627117 1 0.2066 0.932 0.940 0.060 0.000
#> GSM627121 2 0.0424 0.950 0.008 0.992 0.000
#> GSM627127 3 0.6204 0.295 0.000 0.424 0.576
#> GSM627087 2 0.0000 0.954 0.000 1.000 0.000
#> GSM627089 1 0.1031 0.953 0.976 0.024 0.000
#> GSM627092 2 0.1031 0.958 0.000 0.976 0.024
#> GSM627076 1 0.1753 0.936 0.952 0.000 0.048
#> GSM627136 1 0.0747 0.955 0.984 0.016 0.000
#> GSM627081 2 0.1643 0.923 0.044 0.956 0.000
#> GSM627091 2 0.1163 0.957 0.000 0.972 0.028
#> GSM627097 3 0.4974 0.702 0.000 0.236 0.764
#> GSM627072 1 0.3879 0.831 0.848 0.152 0.000
#> GSM627080 1 0.0892 0.952 0.980 0.000 0.020
#> GSM627088 1 0.1411 0.948 0.964 0.036 0.000
#> GSM627109 1 0.0424 0.955 0.992 0.000 0.008
#> GSM627111 1 0.0424 0.955 0.992 0.000 0.008
#> GSM627113 1 0.0592 0.955 0.988 0.012 0.000
#> GSM627133 2 0.0892 0.943 0.020 0.980 0.000
#> GSM627177 2 0.6204 0.290 0.424 0.576 0.000
#> GSM627086 2 0.1163 0.957 0.000 0.972 0.028
#> GSM627095 1 0.5650 0.572 0.688 0.000 0.312
#> GSM627079 1 0.0424 0.955 0.992 0.000 0.008
#> GSM627082 3 0.0892 0.909 0.020 0.000 0.980
#> GSM627074 1 0.1031 0.953 0.976 0.024 0.000
#> GSM627077 1 0.0424 0.955 0.992 0.000 0.008
#> GSM627093 1 0.1529 0.946 0.960 0.040 0.000
#> GSM627120 2 0.1289 0.956 0.000 0.968 0.032
#> GSM627124 3 0.1031 0.914 0.000 0.024 0.976
#> GSM627075 2 0.0237 0.955 0.000 0.996 0.004
#> GSM627085 3 0.1411 0.909 0.000 0.036 0.964
#> GSM627119 1 0.1163 0.952 0.972 0.028 0.000
#> GSM627116 3 0.0747 0.917 0.000 0.016 0.984
#> GSM627084 1 0.0592 0.955 0.988 0.000 0.012
#> GSM627096 2 0.5098 0.679 0.000 0.752 0.248
#> GSM627100 1 0.4702 0.752 0.788 0.000 0.212
#> GSM627112 3 0.0424 0.918 0.000 0.008 0.992
#> GSM627083 3 0.0892 0.909 0.020 0.000 0.980
#> GSM627098 1 0.0000 0.956 1.000 0.000 0.000
#> GSM627104 1 0.0592 0.955 0.988 0.012 0.000
#> GSM627131 1 0.0747 0.953 0.984 0.000 0.016
#> GSM627106 2 0.4452 0.730 0.192 0.808 0.000
#> GSM627123 1 0.1289 0.946 0.968 0.000 0.032
#> GSM627129 2 0.1643 0.949 0.000 0.956 0.044
#> GSM627216 2 0.0592 0.948 0.012 0.988 0.000
#> GSM627212 2 0.1163 0.957 0.000 0.972 0.028
#> GSM627190 1 0.2448 0.919 0.924 0.076 0.000
#> GSM627169 2 0.0892 0.943 0.020 0.980 0.000
#> GSM627167 3 0.6260 0.212 0.000 0.448 0.552
#> GSM627192 3 0.2165 0.880 0.064 0.000 0.936
#> GSM627203 1 0.0747 0.955 0.984 0.016 0.000
#> GSM627151 2 0.1163 0.957 0.000 0.972 0.028
#> GSM627163 1 0.1031 0.950 0.976 0.000 0.024
#> GSM627211 2 0.1289 0.956 0.000 0.968 0.032
#> GSM627171 2 0.0424 0.950 0.008 0.992 0.000
#> GSM627209 2 0.1753 0.947 0.000 0.952 0.048
#> GSM627135 1 0.1860 0.934 0.948 0.000 0.052
#> GSM627170 2 0.0000 0.954 0.000 1.000 0.000
#> GSM627178 1 0.1031 0.950 0.976 0.000 0.024
#> GSM627199 3 0.1031 0.914 0.000 0.024 0.976
#> GSM627213 3 0.0892 0.915 0.000 0.020 0.980
#> GSM627140 3 0.0424 0.918 0.000 0.008 0.992
#> GSM627149 1 0.1529 0.942 0.960 0.000 0.040
#> GSM627147 2 0.3619 0.855 0.000 0.864 0.136
#> GSM627195 1 0.2448 0.919 0.924 0.076 0.000
#> GSM627204 2 0.1163 0.957 0.000 0.972 0.028
#> GSM627207 2 0.0000 0.954 0.000 1.000 0.000
#> GSM627157 1 0.0000 0.956 1.000 0.000 0.000
#> GSM627201 2 0.1163 0.957 0.000 0.972 0.028
#> GSM627146 2 0.1163 0.957 0.000 0.972 0.028
#> GSM627156 2 0.0747 0.946 0.016 0.984 0.000
#> GSM627188 3 0.2165 0.880 0.064 0.000 0.936
#> GSM627197 2 0.1529 0.952 0.000 0.960 0.040
#> GSM627173 2 0.0892 0.958 0.000 0.980 0.020
#> GSM627179 2 0.0424 0.956 0.000 0.992 0.008
#> GSM627208 2 0.1031 0.940 0.024 0.976 0.000
#> GSM627215 2 0.0424 0.950 0.008 0.992 0.000
#> GSM627153 2 0.2356 0.927 0.000 0.928 0.072
#> GSM627155 1 0.1860 0.934 0.948 0.000 0.052
#> GSM627165 2 0.1163 0.957 0.000 0.972 0.028
#> GSM627168 1 0.0747 0.955 0.984 0.016 0.000
#> GSM627183 1 0.0747 0.955 0.984 0.016 0.000
#> GSM627144 1 0.2959 0.894 0.900 0.100 0.000
#> GSM627158 1 0.0892 0.952 0.980 0.000 0.020
#> GSM627196 2 0.1163 0.957 0.000 0.972 0.028
#> GSM627142 3 0.1163 0.903 0.028 0.000 0.972
#> GSM627182 2 0.1964 0.911 0.056 0.944 0.000
#> GSM627202 1 0.0592 0.955 0.988 0.000 0.012
#> GSM627141 1 0.1163 0.952 0.972 0.028 0.000
#> GSM627143 2 0.1163 0.958 0.000 0.972 0.028
#> GSM627145 1 0.1163 0.952 0.972 0.028 0.000
#> GSM627152 1 0.0892 0.952 0.980 0.000 0.020
#> GSM627200 1 0.0424 0.955 0.992 0.000 0.008
#> GSM627159 3 0.0892 0.909 0.020 0.000 0.980
#> GSM627164 2 0.0000 0.954 0.000 1.000 0.000
#> GSM627138 1 0.0424 0.955 0.992 0.000 0.008
#> GSM627175 3 0.4842 0.719 0.000 0.224 0.776
#> GSM627150 1 0.2261 0.926 0.932 0.068 0.000
#> GSM627166 1 0.0592 0.955 0.988 0.000 0.012
#> GSM627186 2 0.0892 0.943 0.020 0.980 0.000
#> GSM627139 3 0.5408 0.807 0.052 0.136 0.812
#> GSM627181 2 0.1289 0.956 0.000 0.968 0.032
#> GSM627205 2 0.0000 0.954 0.000 1.000 0.000
#> GSM627214 2 0.1289 0.956 0.000 0.968 0.032
#> GSM627180 2 0.0747 0.946 0.016 0.984 0.000
#> GSM627172 2 0.1860 0.943 0.000 0.948 0.052
#> GSM627184 3 0.4555 0.723 0.200 0.000 0.800
#> GSM627193 2 0.0424 0.950 0.008 0.992 0.000
#> GSM627191 3 0.0237 0.916 0.004 0.000 0.996
#> GSM627176 1 0.0424 0.955 0.992 0.000 0.008
#> GSM627194 2 0.0892 0.958 0.000 0.980 0.020
#> GSM627154 3 0.0892 0.915 0.000 0.020 0.980
#> GSM627187 1 0.1860 0.938 0.948 0.052 0.000
#> GSM627198 3 0.2165 0.891 0.000 0.064 0.936
#> GSM627160 3 0.0424 0.915 0.008 0.000 0.992
#> GSM627185 1 0.0000 0.956 1.000 0.000 0.000
#> GSM627206 1 0.1163 0.952 0.972 0.028 0.000
#> GSM627161 1 0.0892 0.952 0.980 0.000 0.020
#> GSM627162 1 0.4235 0.798 0.824 0.176 0.000
#> GSM627210 1 0.1163 0.952 0.972 0.028 0.000
#> GSM627189 2 0.0892 0.958 0.000 0.980 0.020
show/hide code output
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM627128 4 0.0376 0.8458 0.000 0.004 0.004 0.992
#> GSM627110 1 0.4967 0.2297 0.548 0.000 0.452 0.000
#> GSM627132 1 0.0336 0.8228 0.992 0.000 0.008 0.000
#> GSM627107 3 0.1629 0.6822 0.000 0.024 0.952 0.024
#> GSM627103 2 0.0469 0.8287 0.000 0.988 0.012 0.000
#> GSM627114 3 0.3688 0.5503 0.208 0.000 0.792 0.000
#> GSM627134 2 0.2676 0.7845 0.000 0.896 0.092 0.012
#> GSM627137 2 0.4500 0.4618 0.000 0.684 0.316 0.000
#> GSM627148 3 0.1489 0.6693 0.044 0.004 0.952 0.000
#> GSM627101 4 0.0592 0.8422 0.000 0.016 0.000 0.984
#> GSM627130 4 0.0000 0.8468 0.000 0.000 0.000 1.000
#> GSM627071 1 0.7050 0.4151 0.568 0.252 0.180 0.000
#> GSM627118 2 0.5188 0.6890 0.000 0.756 0.096 0.148
#> GSM627094 2 0.0188 0.8289 0.000 0.996 0.004 0.000
#> GSM627122 1 0.6491 0.2271 0.496 0.000 0.432 0.072
#> GSM627115 2 0.0188 0.8289 0.000 0.996 0.004 0.000
#> GSM627125 4 0.1211 0.8279 0.000 0.000 0.040 0.960
#> GSM627174 2 0.0336 0.8292 0.000 0.992 0.008 0.000
#> GSM627102 2 0.5158 -0.0118 0.000 0.524 0.472 0.004
#> GSM627073 3 0.6581 0.6090 0.144 0.232 0.624 0.000
#> GSM627108 2 0.1474 0.8118 0.000 0.948 0.052 0.000
#> GSM627126 1 0.1940 0.7854 0.924 0.000 0.000 0.076
#> GSM627078 2 0.3975 0.6363 0.000 0.760 0.000 0.240
#> GSM627090 3 0.3597 0.6006 0.148 0.000 0.836 0.016
#> GSM627099 2 0.1151 0.8260 0.000 0.968 0.024 0.008
#> GSM627105 4 0.1824 0.8126 0.000 0.004 0.060 0.936
#> GSM627117 3 0.4940 0.6635 0.128 0.096 0.776 0.000
#> GSM627121 3 0.0657 0.6798 0.000 0.012 0.984 0.004
#> GSM627127 2 0.2530 0.7764 0.000 0.888 0.000 0.112
#> GSM627087 2 0.0188 0.8289 0.000 0.996 0.004 0.000
#> GSM627089 3 0.4761 0.2176 0.372 0.000 0.628 0.000
#> GSM627092 3 0.4790 0.4161 0.000 0.380 0.620 0.000
#> GSM627076 3 0.4956 0.5764 0.108 0.000 0.776 0.116
#> GSM627136 3 0.4621 0.4706 0.284 0.008 0.708 0.000
#> GSM627081 3 0.1109 0.6841 0.004 0.028 0.968 0.000
#> GSM627091 2 0.0376 0.8287 0.000 0.992 0.004 0.004
#> GSM627097 2 0.3501 0.7459 0.020 0.848 0.000 0.132
#> GSM627072 3 0.6134 0.5501 0.236 0.104 0.660 0.000
#> GSM627080 1 0.0469 0.8231 0.988 0.000 0.012 0.000
#> GSM627088 1 0.4163 0.7113 0.792 0.020 0.188 0.000
#> GSM627109 1 0.0524 0.8202 0.988 0.008 0.004 0.000
#> GSM627111 1 0.0469 0.8231 0.988 0.000 0.012 0.000
#> GSM627113 1 0.0469 0.8225 0.988 0.000 0.012 0.000
#> GSM627133 2 0.1209 0.8257 0.004 0.964 0.032 0.000
#> GSM627177 2 0.7003 0.0596 0.424 0.460 0.116 0.000
#> GSM627086 2 0.0336 0.8292 0.000 0.992 0.008 0.000
#> GSM627095 1 0.3764 0.6206 0.784 0.000 0.000 0.216
#> GSM627079 1 0.3942 0.6672 0.764 0.000 0.236 0.000
#> GSM627082 4 0.0336 0.8475 0.008 0.000 0.000 0.992
#> GSM627074 1 0.0804 0.8220 0.980 0.008 0.012 0.000
#> GSM627077 1 0.2011 0.8144 0.920 0.000 0.080 0.000
#> GSM627093 1 0.1256 0.8235 0.964 0.008 0.028 0.000
#> GSM627120 3 0.4746 0.5373 0.000 0.304 0.688 0.008
#> GSM627124 2 0.4053 0.6422 0.004 0.768 0.000 0.228
#> GSM627075 2 0.4713 0.3576 0.000 0.640 0.360 0.000
#> GSM627085 2 0.3583 0.7082 0.000 0.816 0.004 0.180
#> GSM627119 1 0.1174 0.8205 0.968 0.012 0.020 0.000
#> GSM627116 2 0.7072 0.4629 0.212 0.624 0.020 0.144
#> GSM627084 1 0.5143 0.5237 0.628 0.000 0.360 0.012
#> GSM627096 2 0.6324 0.4166 0.000 0.584 0.076 0.340
#> GSM627100 3 0.3853 0.5975 0.020 0.000 0.820 0.160
#> GSM627112 4 0.0592 0.8416 0.000 0.016 0.000 0.984
#> GSM627083 4 0.0707 0.8434 0.020 0.000 0.000 0.980
#> GSM627098 1 0.1211 0.8225 0.960 0.000 0.040 0.000
#> GSM627104 1 0.1022 0.8087 0.968 0.032 0.000 0.000
#> GSM627131 1 0.1792 0.8145 0.932 0.000 0.068 0.000
#> GSM627106 3 0.1042 0.6761 0.020 0.008 0.972 0.000
#> GSM627123 1 0.3991 0.7834 0.832 0.000 0.120 0.048
#> GSM627129 2 0.7402 0.1678 0.000 0.500 0.308 0.192
#> GSM627216 2 0.0469 0.8287 0.000 0.988 0.012 0.000
#> GSM627212 2 0.0779 0.8285 0.000 0.980 0.016 0.004
#> GSM627190 3 0.4718 0.6787 0.092 0.116 0.792 0.000
#> GSM627169 3 0.4804 0.4044 0.000 0.384 0.616 0.000
#> GSM627167 3 0.6396 0.3454 0.000 0.076 0.564 0.360
#> GSM627192 4 0.4977 0.2202 0.460 0.000 0.000 0.540
#> GSM627203 3 0.4643 0.2869 0.344 0.000 0.656 0.000
#> GSM627151 2 0.0844 0.8267 0.004 0.980 0.012 0.004
#> GSM627163 1 0.0000 0.8212 1.000 0.000 0.000 0.000
#> GSM627211 2 0.0707 0.8275 0.000 0.980 0.020 0.000
#> GSM627171 3 0.2589 0.6794 0.000 0.116 0.884 0.000
#> GSM627209 2 0.1042 0.8274 0.000 0.972 0.008 0.020
#> GSM627135 1 0.0524 0.8180 0.988 0.008 0.000 0.004
#> GSM627170 2 0.4888 0.2329 0.000 0.588 0.412 0.000
#> GSM627178 1 0.1610 0.8100 0.952 0.032 0.016 0.000
#> GSM627199 4 0.4564 0.4097 0.000 0.328 0.000 0.672
#> GSM627213 4 0.3486 0.6671 0.000 0.188 0.000 0.812
#> GSM627140 4 0.0336 0.8475 0.008 0.000 0.000 0.992
#> GSM627149 1 0.5067 0.7109 0.736 0.000 0.216 0.048
#> GSM627147 3 0.7806 0.2307 0.004 0.208 0.408 0.380
#> GSM627195 1 0.7253 -0.1570 0.432 0.144 0.424 0.000
#> GSM627204 2 0.0188 0.8295 0.000 0.996 0.004 0.000
#> GSM627207 2 0.4916 0.1720 0.000 0.576 0.424 0.000
#> GSM627157 1 0.1792 0.8194 0.932 0.000 0.068 0.000
#> GSM627201 2 0.0336 0.8292 0.000 0.992 0.008 0.000
#> GSM627146 2 0.0000 0.8291 0.000 1.000 0.000 0.000
#> GSM627156 3 0.4624 0.4898 0.000 0.340 0.660 0.000
#> GSM627188 4 0.4722 0.5564 0.300 0.000 0.008 0.692
#> GSM627197 2 0.0895 0.8282 0.000 0.976 0.020 0.004
#> GSM627173 2 0.1022 0.8212 0.000 0.968 0.032 0.000
#> GSM627179 2 0.0707 0.8275 0.000 0.980 0.020 0.000
#> GSM627208 3 0.3907 0.6080 0.000 0.232 0.768 0.000
#> GSM627215 2 0.2334 0.7894 0.004 0.908 0.088 0.000
#> GSM627153 2 0.1722 0.8203 0.000 0.944 0.008 0.048
#> GSM627155 1 0.3972 0.7758 0.840 0.000 0.080 0.080
#> GSM627165 3 0.4933 0.2932 0.000 0.432 0.568 0.000
#> GSM627168 1 0.4855 0.4684 0.600 0.000 0.400 0.000
#> GSM627183 1 0.2704 0.7854 0.876 0.000 0.124 0.000
#> GSM627144 3 0.0707 0.6716 0.020 0.000 0.980 0.000
#> GSM627158 1 0.3355 0.7721 0.836 0.000 0.160 0.004
#> GSM627196 2 0.0336 0.8292 0.000 0.992 0.008 0.000
#> GSM627142 4 0.4741 0.4303 0.000 0.004 0.328 0.668
#> GSM627182 3 0.5532 0.6295 0.068 0.228 0.704 0.000
#> GSM627202 1 0.4781 0.5846 0.660 0.000 0.336 0.004
#> GSM627141 3 0.4584 0.4100 0.300 0.004 0.696 0.000
#> GSM627143 3 0.4353 0.6125 0.000 0.232 0.756 0.012
#> GSM627145 3 0.4972 0.0759 0.456 0.000 0.544 0.000
#> GSM627152 3 0.5256 0.1551 0.392 0.000 0.596 0.012
#> GSM627200 1 0.1940 0.8152 0.924 0.000 0.076 0.000
#> GSM627159 4 0.0188 0.8476 0.004 0.000 0.000 0.996
#> GSM627164 3 0.3123 0.6678 0.000 0.156 0.844 0.000
#> GSM627138 1 0.3801 0.7328 0.780 0.000 0.220 0.000
#> GSM627175 2 0.3764 0.6784 0.000 0.784 0.000 0.216
#> GSM627150 3 0.6603 0.3851 0.328 0.100 0.572 0.000
#> GSM627166 1 0.3942 0.5670 0.764 0.236 0.000 0.000
#> GSM627186 3 0.4843 0.3861 0.000 0.396 0.604 0.000
#> GSM627139 3 0.5408 0.0419 0.000 0.012 0.500 0.488
#> GSM627181 2 0.3142 0.7445 0.000 0.860 0.132 0.008
#> GSM627205 3 0.4985 0.1875 0.000 0.468 0.532 0.000
#> GSM627214 2 0.4212 0.6310 0.000 0.772 0.216 0.012
#> GSM627180 3 0.4877 0.3689 0.000 0.408 0.592 0.000
#> GSM627172 3 0.7122 0.4307 0.004 0.304 0.552 0.140
#> GSM627184 4 0.5189 0.4046 0.372 0.000 0.012 0.616
#> GSM627193 2 0.1557 0.8037 0.000 0.944 0.056 0.000
#> GSM627191 4 0.0336 0.8475 0.008 0.000 0.000 0.992
#> GSM627176 3 0.2179 0.6596 0.064 0.000 0.924 0.012
#> GSM627194 2 0.0592 0.8274 0.000 0.984 0.016 0.000
#> GSM627154 2 0.4889 0.4334 0.000 0.636 0.004 0.360
#> GSM627187 3 0.1824 0.6619 0.060 0.004 0.936 0.000
#> GSM627198 2 0.4222 0.6016 0.000 0.728 0.000 0.272
#> GSM627160 4 0.0188 0.8476 0.004 0.000 0.000 0.996
#> GSM627185 1 0.0000 0.8212 1.000 0.000 0.000 0.000
#> GSM627206 3 0.4304 0.4265 0.284 0.000 0.716 0.000
#> GSM627161 1 0.3810 0.7506 0.804 0.000 0.188 0.008
#> GSM627162 3 0.2714 0.6343 0.112 0.004 0.884 0.000
#> GSM627210 1 0.2413 0.7842 0.916 0.064 0.020 0.000
#> GSM627189 2 0.0592 0.8273 0.000 0.984 0.016 0.000
show/hide code output
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM627128 4 0.2504 0.7648 0.000 0.040 0.000 0.896 0.064
#> GSM627110 3 0.6934 0.0825 0.352 0.012 0.420 0.000 0.216
#> GSM627132 1 0.0771 0.8582 0.976 0.000 0.004 0.000 0.020
#> GSM627107 5 0.2617 0.7355 0.000 0.036 0.032 0.028 0.904
#> GSM627103 2 0.1251 0.7131 0.000 0.956 0.036 0.000 0.008
#> GSM627114 5 0.5027 0.5378 0.056 0.000 0.304 0.000 0.640
#> GSM627134 2 0.5454 0.0718 0.000 0.488 0.000 0.060 0.452
#> GSM627137 2 0.4410 -0.1855 0.000 0.556 0.440 0.000 0.004
#> GSM627148 5 0.2046 0.7374 0.016 0.000 0.068 0.000 0.916
#> GSM627101 4 0.2914 0.7461 0.000 0.076 0.000 0.872 0.052
#> GSM627130 4 0.0898 0.7828 0.000 0.000 0.020 0.972 0.008
#> GSM627071 5 0.6267 0.4961 0.224 0.236 0.000 0.000 0.540
#> GSM627118 5 0.5534 0.0849 0.000 0.424 0.000 0.068 0.508
#> GSM627094 2 0.2130 0.6839 0.012 0.908 0.080 0.000 0.000
#> GSM627122 5 0.2273 0.7500 0.048 0.008 0.008 0.016 0.920
#> GSM627115 2 0.2102 0.6938 0.012 0.916 0.068 0.000 0.004
#> GSM627125 4 0.2069 0.7697 0.000 0.012 0.000 0.912 0.076
#> GSM627174 2 0.1605 0.7124 0.004 0.944 0.040 0.012 0.000
#> GSM627102 3 0.5336 0.4289 0.000 0.428 0.528 0.036 0.008
#> GSM627073 5 0.2389 0.7093 0.000 0.116 0.004 0.000 0.880
#> GSM627108 2 0.2930 0.6047 0.000 0.832 0.164 0.000 0.004
#> GSM627126 1 0.2575 0.7998 0.884 0.004 0.012 0.100 0.000
#> GSM627078 2 0.4374 0.5655 0.000 0.700 0.000 0.272 0.028
#> GSM627090 3 0.5121 -0.3076 0.028 0.000 0.500 0.004 0.468
#> GSM627099 2 0.3771 0.6754 0.000 0.804 0.004 0.036 0.156
#> GSM627105 4 0.3409 0.7077 0.000 0.024 0.000 0.816 0.160
#> GSM627117 3 0.5550 0.6040 0.092 0.120 0.720 0.000 0.068
#> GSM627121 5 0.3478 0.7149 0.004 0.016 0.124 0.016 0.840
#> GSM627127 2 0.4750 0.5969 0.000 0.712 0.012 0.236 0.040
#> GSM627087 2 0.1605 0.7122 0.004 0.944 0.040 0.000 0.012
#> GSM627089 5 0.2570 0.7302 0.084 0.000 0.028 0.000 0.888
#> GSM627092 3 0.4470 0.5031 0.000 0.372 0.616 0.000 0.012
#> GSM627076 5 0.4272 0.6546 0.020 0.000 0.212 0.016 0.752
#> GSM627136 5 0.3159 0.7314 0.088 0.000 0.056 0.000 0.856
#> GSM627081 5 0.2045 0.7430 0.004 0.020 0.044 0.004 0.928
#> GSM627091 2 0.2429 0.7143 0.000 0.904 0.008 0.020 0.068
#> GSM627097 2 0.5724 0.5666 0.120 0.692 0.028 0.156 0.004
#> GSM627072 5 0.1596 0.7504 0.012 0.028 0.012 0.000 0.948
#> GSM627080 1 0.0510 0.8576 0.984 0.000 0.000 0.000 0.016
#> GSM627088 1 0.4200 0.5431 0.672 0.004 0.004 0.000 0.320
#> GSM627109 1 0.1331 0.8563 0.952 0.000 0.008 0.000 0.040
#> GSM627111 1 0.1117 0.8577 0.964 0.000 0.020 0.000 0.016
#> GSM627113 1 0.1792 0.8518 0.916 0.000 0.000 0.000 0.084
#> GSM627133 2 0.4069 0.6628 0.012 0.796 0.044 0.000 0.148
#> GSM627177 5 0.6404 0.2850 0.092 0.360 0.008 0.016 0.524
#> GSM627086 2 0.1697 0.7182 0.000 0.932 0.000 0.008 0.060
#> GSM627095 1 0.3819 0.6910 0.772 0.004 0.016 0.208 0.000
#> GSM627079 5 0.3563 0.7354 0.092 0.060 0.000 0.008 0.840
#> GSM627082 4 0.0609 0.7819 0.000 0.000 0.020 0.980 0.000
#> GSM627074 1 0.1710 0.8535 0.940 0.004 0.016 0.000 0.040
#> GSM627077 1 0.2753 0.8194 0.856 0.000 0.008 0.000 0.136
#> GSM627093 1 0.3726 0.7936 0.840 0.036 0.088 0.000 0.036
#> GSM627120 3 0.6072 0.4359 0.000 0.396 0.512 0.020 0.072
#> GSM627124 2 0.4202 0.6292 0.012 0.744 0.000 0.228 0.016
#> GSM627075 3 0.4306 0.3382 0.000 0.492 0.508 0.000 0.000
#> GSM627085 2 0.5573 0.4423 0.000 0.612 0.008 0.304 0.076
#> GSM627119 1 0.1740 0.8541 0.932 0.000 0.012 0.000 0.056
#> GSM627116 2 0.7549 0.2362 0.056 0.484 0.012 0.292 0.156
#> GSM627084 3 0.6192 -0.2659 0.428 0.000 0.480 0.056 0.036
#> GSM627096 5 0.5861 0.1205 0.000 0.400 0.000 0.100 0.500
#> GSM627100 5 0.4758 0.6714 0.008 0.000 0.160 0.088 0.744
#> GSM627112 4 0.1331 0.7751 0.000 0.040 0.000 0.952 0.008
#> GSM627083 4 0.2367 0.7540 0.072 0.004 0.020 0.904 0.000
#> GSM627098 1 0.2583 0.8261 0.864 0.000 0.004 0.000 0.132
#> GSM627104 1 0.1405 0.8485 0.956 0.020 0.008 0.000 0.016
#> GSM627131 5 0.4288 0.3394 0.384 0.000 0.004 0.000 0.612
#> GSM627106 5 0.1843 0.7436 0.004 0.012 0.044 0.004 0.936
#> GSM627123 1 0.4860 0.7495 0.756 0.000 0.088 0.132 0.024
#> GSM627129 2 0.6858 0.3808 0.000 0.516 0.028 0.280 0.176
#> GSM627216 2 0.2278 0.7165 0.000 0.908 0.032 0.000 0.060
#> GSM627212 2 0.2812 0.7072 0.000 0.876 0.004 0.024 0.096
#> GSM627190 3 0.6662 0.5016 0.064 0.128 0.600 0.000 0.208
#> GSM627169 3 0.4299 0.4941 0.000 0.388 0.608 0.000 0.004
#> GSM627167 4 0.5868 0.4784 0.000 0.016 0.248 0.628 0.108
#> GSM627192 1 0.4571 0.5222 0.664 0.004 0.020 0.312 0.000
#> GSM627203 5 0.1483 0.7509 0.028 0.012 0.008 0.000 0.952
#> GSM627151 2 0.3767 0.6994 0.048 0.848 0.012 0.020 0.072
#> GSM627163 1 0.0566 0.8566 0.984 0.000 0.004 0.000 0.012
#> GSM627211 2 0.2798 0.6385 0.000 0.852 0.140 0.000 0.008
#> GSM627171 3 0.2915 0.6035 0.000 0.116 0.860 0.000 0.024
#> GSM627209 2 0.3532 0.6966 0.000 0.832 0.000 0.076 0.092
#> GSM627135 1 0.0902 0.8532 0.976 0.008 0.008 0.004 0.004
#> GSM627170 2 0.4649 0.5869 0.000 0.716 0.064 0.000 0.220
#> GSM627178 1 0.1757 0.8553 0.936 0.004 0.012 0.000 0.048
#> GSM627199 4 0.4283 0.3382 0.008 0.348 0.000 0.644 0.000
#> GSM627213 4 0.4777 0.4368 0.000 0.292 0.000 0.664 0.044
#> GSM627140 4 0.1851 0.7512 0.000 0.000 0.088 0.912 0.000
#> GSM627149 3 0.7430 -0.3497 0.392 0.000 0.396 0.144 0.068
#> GSM627147 3 0.7288 0.4816 0.000 0.240 0.496 0.212 0.052
#> GSM627195 5 0.2673 0.7478 0.044 0.060 0.004 0.000 0.892
#> GSM627204 2 0.1124 0.7112 0.004 0.960 0.036 0.000 0.000
#> GSM627207 2 0.4798 -0.2200 0.000 0.540 0.440 0.000 0.020
#> GSM627157 1 0.2966 0.8187 0.848 0.000 0.016 0.000 0.136
#> GSM627201 2 0.1442 0.7199 0.000 0.952 0.012 0.004 0.032
#> GSM627146 2 0.0955 0.7140 0.004 0.968 0.028 0.000 0.000
#> GSM627156 3 0.4848 0.4496 0.000 0.420 0.556 0.000 0.024
#> GSM627188 4 0.4686 0.2612 0.384 0.000 0.020 0.596 0.000
#> GSM627197 2 0.1809 0.7047 0.000 0.928 0.060 0.012 0.000
#> GSM627173 2 0.3318 0.5684 0.012 0.808 0.180 0.000 0.000
#> GSM627179 2 0.2409 0.7053 0.000 0.900 0.068 0.000 0.032
#> GSM627208 5 0.4114 0.6613 0.000 0.164 0.060 0.000 0.776
#> GSM627215 5 0.5072 0.0375 0.000 0.456 0.008 0.020 0.516
#> GSM627153 2 0.4322 0.6666 0.000 0.768 0.000 0.144 0.088
#> GSM627155 1 0.5244 0.6869 0.708 0.000 0.116 0.164 0.012
#> GSM627165 3 0.4881 0.3765 0.000 0.460 0.520 0.004 0.016
#> GSM627168 5 0.5987 0.2982 0.324 0.000 0.132 0.000 0.544
#> GSM627183 5 0.3305 0.6352 0.224 0.000 0.000 0.000 0.776
#> GSM627144 5 0.4047 0.4759 0.004 0.000 0.320 0.000 0.676
#> GSM627158 1 0.4498 0.7789 0.772 0.000 0.108 0.008 0.112
#> GSM627196 2 0.1216 0.7171 0.000 0.960 0.020 0.000 0.020
#> GSM627142 5 0.3559 0.6603 0.000 0.012 0.008 0.176 0.804
#> GSM627182 5 0.3450 0.7255 0.012 0.096 0.044 0.000 0.848
#> GSM627202 5 0.5816 0.4112 0.280 0.000 0.132 0.000 0.588
#> GSM627141 3 0.4557 0.5138 0.160 0.044 0.768 0.000 0.028
#> GSM627143 3 0.5414 0.5840 0.000 0.228 0.684 0.048 0.040
#> GSM627145 5 0.2027 0.7505 0.040 0.024 0.008 0.000 0.928
#> GSM627152 5 0.3021 0.7255 0.060 0.000 0.064 0.004 0.872
#> GSM627200 1 0.2848 0.8111 0.840 0.000 0.004 0.000 0.156
#> GSM627159 4 0.0609 0.7819 0.000 0.000 0.020 0.980 0.000
#> GSM627164 3 0.4179 0.6058 0.000 0.152 0.776 0.000 0.072
#> GSM627138 1 0.6245 0.5123 0.544 0.000 0.236 0.000 0.220
#> GSM627175 2 0.4350 0.5980 0.000 0.704 0.000 0.268 0.028
#> GSM627150 5 0.1704 0.7364 0.004 0.068 0.000 0.000 0.928
#> GSM627166 1 0.1525 0.8361 0.948 0.036 0.012 0.000 0.004
#> GSM627186 3 0.4434 0.3975 0.000 0.460 0.536 0.000 0.004
#> GSM627139 4 0.5225 0.2997 0.000 0.024 0.016 0.576 0.384
#> GSM627181 2 0.2723 0.6509 0.000 0.864 0.124 0.000 0.012
#> GSM627205 2 0.5393 0.1675 0.000 0.504 0.056 0.000 0.440
#> GSM627214 2 0.4714 0.6010 0.000 0.712 0.008 0.044 0.236
#> GSM627180 5 0.2806 0.6778 0.000 0.152 0.000 0.004 0.844
#> GSM627172 3 0.5988 0.5303 0.000 0.300 0.584 0.104 0.012
#> GSM627184 4 0.4709 0.3063 0.364 0.000 0.024 0.612 0.000
#> GSM627193 2 0.2886 0.6208 0.008 0.844 0.148 0.000 0.000
#> GSM627191 4 0.1399 0.7728 0.028 0.000 0.020 0.952 0.000
#> GSM627176 3 0.4025 0.3902 0.012 0.000 0.780 0.024 0.184
#> GSM627194 2 0.3242 0.5807 0.012 0.816 0.172 0.000 0.000
#> GSM627154 2 0.5687 0.1315 0.000 0.496 0.004 0.432 0.068
#> GSM627187 3 0.3405 0.5361 0.012 0.036 0.848 0.000 0.104
#> GSM627198 2 0.4025 0.6006 0.008 0.748 0.012 0.232 0.000
#> GSM627160 4 0.0609 0.7819 0.000 0.000 0.020 0.980 0.000
#> GSM627185 1 0.0727 0.8564 0.980 0.004 0.004 0.000 0.012
#> GSM627206 5 0.5339 0.5953 0.116 0.000 0.224 0.000 0.660
#> GSM627161 1 0.6201 0.6098 0.596 0.000 0.272 0.028 0.104
#> GSM627162 3 0.2963 0.5362 0.016 0.012 0.876 0.004 0.092
#> GSM627210 1 0.2900 0.8282 0.876 0.020 0.012 0.000 0.092
#> GSM627189 2 0.2674 0.6501 0.012 0.868 0.120 0.000 0.000
show/hide code output
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM627128 6 0.2380 0.7020 0.000 0.016 0.000 0.048 0.036 0.900
#> GSM627110 4 0.6046 0.2097 0.108 0.000 0.352 0.500 0.040 0.000
#> GSM627132 1 0.1010 0.7688 0.960 0.000 0.000 0.004 0.036 0.000
#> GSM627107 5 0.2649 0.7457 0.000 0.048 0.032 0.020 0.892 0.008
#> GSM627103 2 0.1049 0.7327 0.000 0.960 0.032 0.008 0.000 0.000
#> GSM627114 5 0.4498 0.6584 0.080 0.000 0.188 0.012 0.720 0.000
#> GSM627134 2 0.6246 0.3280 0.000 0.544 0.008 0.044 0.284 0.120
#> GSM627137 2 0.4857 -0.2518 0.000 0.524 0.424 0.048 0.000 0.004
#> GSM627148 5 0.1410 0.7504 0.004 0.000 0.044 0.008 0.944 0.000
#> GSM627101 6 0.2859 0.6928 0.000 0.060 0.000 0.020 0.048 0.872
#> GSM627130 6 0.0436 0.7062 0.000 0.000 0.004 0.004 0.004 0.988
#> GSM627071 5 0.5928 0.4535 0.184 0.216 0.000 0.028 0.572 0.000
#> GSM627118 2 0.7271 0.0612 0.000 0.388 0.012 0.080 0.332 0.188
#> GSM627094 2 0.1333 0.7216 0.000 0.944 0.048 0.008 0.000 0.000
#> GSM627122 5 0.3551 0.7492 0.056 0.008 0.032 0.024 0.852 0.028
#> GSM627115 2 0.1863 0.7243 0.000 0.920 0.044 0.036 0.000 0.000
#> GSM627125 6 0.3400 0.6732 0.000 0.004 0.008 0.064 0.092 0.832
#> GSM627174 2 0.2208 0.7300 0.016 0.912 0.052 0.008 0.000 0.012
#> GSM627102 3 0.4828 0.5280 0.000 0.384 0.568 0.016 0.000 0.032
#> GSM627073 5 0.3546 0.6887 0.000 0.128 0.056 0.008 0.808 0.000
#> GSM627108 2 0.2520 0.6607 0.000 0.844 0.152 0.000 0.004 0.000
#> GSM627126 1 0.1500 0.7590 0.936 0.000 0.000 0.012 0.000 0.052
#> GSM627078 2 0.3423 0.6667 0.008 0.812 0.000 0.016 0.012 0.152
#> GSM627090 5 0.5961 0.2654 0.004 0.000 0.388 0.188 0.420 0.000
#> GSM627099 2 0.4451 0.6323 0.000 0.760 0.008 0.136 0.072 0.024
#> GSM627105 6 0.3764 0.6578 0.000 0.004 0.008 0.084 0.100 0.804
#> GSM627117 3 0.5594 0.3071 0.072 0.036 0.612 0.272 0.008 0.000
#> GSM627121 5 0.3097 0.7360 0.000 0.020 0.112 0.012 0.848 0.008
#> GSM627127 4 0.6303 0.1647 0.000 0.348 0.004 0.384 0.004 0.260
#> GSM627087 2 0.1562 0.7331 0.000 0.940 0.024 0.032 0.004 0.000
#> GSM627089 5 0.1630 0.7454 0.024 0.000 0.016 0.020 0.940 0.000
#> GSM627092 3 0.3689 0.5188 0.000 0.072 0.800 0.120 0.008 0.000
#> GSM627076 5 0.5765 0.5584 0.000 0.000 0.196 0.136 0.620 0.048
#> GSM627136 5 0.4189 0.7035 0.048 0.000 0.148 0.028 0.772 0.004
#> GSM627081 5 0.1579 0.7513 0.000 0.020 0.024 0.008 0.944 0.004
#> GSM627091 2 0.3855 0.5969 0.000 0.760 0.008 0.204 0.016 0.012
#> GSM627097 4 0.5706 0.5624 0.036 0.164 0.036 0.672 0.000 0.092
#> GSM627072 5 0.1606 0.7462 0.008 0.000 0.004 0.056 0.932 0.000
#> GSM627080 1 0.0914 0.7675 0.968 0.000 0.000 0.016 0.016 0.000
#> GSM627088 1 0.4661 0.4267 0.628 0.008 0.028 0.008 0.328 0.000
#> GSM627109 1 0.2509 0.7451 0.876 0.000 0.000 0.088 0.036 0.000
#> GSM627111 1 0.0870 0.7676 0.972 0.000 0.004 0.012 0.012 0.000
#> GSM627113 1 0.2301 0.7571 0.884 0.000 0.000 0.020 0.096 0.000
#> GSM627133 4 0.5224 0.5152 0.000 0.300 0.024 0.608 0.068 0.000
#> GSM627177 5 0.6277 0.3213 0.040 0.284 0.000 0.144 0.528 0.004
#> GSM627086 2 0.0862 0.7337 0.000 0.972 0.008 0.004 0.016 0.000
#> GSM627095 1 0.2540 0.7362 0.872 0.000 0.004 0.020 0.000 0.104
#> GSM627079 5 0.4231 0.6156 0.020 0.024 0.000 0.248 0.708 0.000
#> GSM627082 6 0.0862 0.7002 0.016 0.000 0.004 0.008 0.000 0.972
#> GSM627074 4 0.4590 0.3136 0.308 0.004 0.020 0.648 0.020 0.000
#> GSM627077 1 0.4459 0.6400 0.708 0.000 0.004 0.084 0.204 0.000
#> GSM627093 1 0.6392 -0.0295 0.428 0.028 0.188 0.356 0.000 0.000
#> GSM627120 2 0.6504 -0.2422 0.004 0.456 0.396 0.028 0.084 0.032
#> GSM627124 2 0.3312 0.6798 0.020 0.828 0.000 0.012 0.008 0.132
#> GSM627075 3 0.5223 0.4798 0.000 0.396 0.508 0.096 0.000 0.000
#> GSM627085 2 0.5078 0.3414 0.000 0.608 0.000 0.052 0.024 0.316
#> GSM627119 1 0.3107 0.7277 0.832 0.000 0.000 0.116 0.052 0.000
#> GSM627116 4 0.6470 0.4323 0.016 0.168 0.000 0.584 0.068 0.164
#> GSM627084 1 0.4818 0.4927 0.600 0.000 0.348 0.004 0.008 0.040
#> GSM627096 2 0.7441 -0.0171 0.000 0.352 0.012 0.088 0.328 0.220
#> GSM627100 5 0.3789 0.7300 0.000 0.000 0.060 0.056 0.816 0.068
#> GSM627112 6 0.1168 0.7081 0.000 0.016 0.000 0.028 0.000 0.956
#> GSM627083 6 0.3264 0.5738 0.184 0.000 0.008 0.012 0.000 0.796
#> GSM627098 1 0.2520 0.7556 0.872 0.000 0.012 0.008 0.108 0.000
#> GSM627104 1 0.1453 0.7629 0.944 0.008 0.000 0.040 0.008 0.000
#> GSM627131 5 0.6163 0.1598 0.316 0.004 0.000 0.268 0.412 0.000
#> GSM627106 5 0.1414 0.7515 0.000 0.012 0.020 0.012 0.952 0.004
#> GSM627123 1 0.6618 0.4797 0.560 0.000 0.224 0.108 0.016 0.092
#> GSM627129 6 0.7186 0.2679 0.000 0.304 0.128 0.060 0.044 0.464
#> GSM627216 2 0.1592 0.7344 0.000 0.940 0.020 0.008 0.032 0.000
#> GSM627212 2 0.2942 0.7009 0.000 0.856 0.004 0.100 0.036 0.004
#> GSM627190 3 0.5978 0.3815 0.060 0.044 0.604 0.032 0.260 0.000
#> GSM627169 3 0.3782 0.5629 0.004 0.140 0.784 0.072 0.000 0.000
#> GSM627167 6 0.5716 0.4775 0.000 0.064 0.244 0.016 0.048 0.628
#> GSM627192 1 0.2884 0.7114 0.824 0.000 0.004 0.008 0.000 0.164
#> GSM627203 5 0.2773 0.7174 0.008 0.004 0.000 0.152 0.836 0.000
#> GSM627151 4 0.4763 0.5611 0.016 0.256 0.020 0.684 0.020 0.004
#> GSM627163 1 0.0405 0.7648 0.988 0.000 0.000 0.008 0.000 0.004
#> GSM627211 2 0.1910 0.6990 0.000 0.892 0.108 0.000 0.000 0.000
#> GSM627171 3 0.5554 0.5922 0.004 0.268 0.616 0.048 0.064 0.000
#> GSM627209 2 0.2965 0.7118 0.000 0.864 0.008 0.008 0.036 0.084
#> GSM627135 1 0.1528 0.7621 0.936 0.000 0.000 0.048 0.000 0.016
#> GSM627170 2 0.4060 0.6201 0.000 0.764 0.116 0.004 0.116 0.000
#> GSM627178 1 0.3411 0.7173 0.816 0.004 0.000 0.120 0.060 0.000
#> GSM627199 6 0.4654 0.2461 0.020 0.400 0.000 0.016 0.000 0.564
#> GSM627213 6 0.3561 0.6402 0.000 0.120 0.000 0.056 0.012 0.812
#> GSM627140 6 0.2655 0.6474 0.004 0.000 0.140 0.008 0.000 0.848
#> GSM627149 1 0.7637 0.2657 0.400 0.000 0.292 0.156 0.024 0.128
#> GSM627147 3 0.5732 0.3857 0.000 0.072 0.592 0.020 0.024 0.292
#> GSM627195 5 0.3938 0.6847 0.020 0.032 0.000 0.184 0.764 0.000
#> GSM627204 2 0.0858 0.7310 0.000 0.968 0.028 0.004 0.000 0.000
#> GSM627207 2 0.3702 0.4782 0.000 0.720 0.264 0.004 0.012 0.000
#> GSM627157 1 0.2581 0.7485 0.856 0.000 0.000 0.016 0.128 0.000
#> GSM627201 2 0.0777 0.7332 0.000 0.972 0.024 0.000 0.004 0.000
#> GSM627146 2 0.0458 0.7315 0.000 0.984 0.000 0.016 0.000 0.000
#> GSM627156 3 0.3975 0.5100 0.000 0.392 0.600 0.000 0.008 0.000
#> GSM627188 1 0.3791 0.5862 0.688 0.000 0.004 0.008 0.000 0.300
#> GSM627197 2 0.1498 0.7268 0.000 0.940 0.028 0.032 0.000 0.000
#> GSM627173 2 0.2699 0.6674 0.008 0.864 0.108 0.020 0.000 0.000
#> GSM627179 2 0.1787 0.7230 0.000 0.920 0.068 0.008 0.004 0.000
#> GSM627208 5 0.5025 0.5736 0.000 0.204 0.128 0.008 0.660 0.000
#> GSM627215 2 0.5539 -0.0190 0.000 0.456 0.020 0.064 0.456 0.004
#> GSM627153 2 0.3275 0.6852 0.000 0.820 0.000 0.008 0.032 0.140
#> GSM627155 1 0.3884 0.7282 0.812 0.000 0.020 0.064 0.012 0.092
#> GSM627165 3 0.5865 0.4465 0.000 0.400 0.464 0.116 0.000 0.020
#> GSM627168 5 0.5003 0.5210 0.252 0.000 0.044 0.044 0.660 0.000
#> GSM627183 5 0.3612 0.6713 0.168 0.000 0.000 0.052 0.780 0.000
#> GSM627144 4 0.4877 0.4440 0.000 0.008 0.188 0.680 0.124 0.000
#> GSM627158 1 0.2246 0.7670 0.908 0.000 0.012 0.020 0.056 0.004
#> GSM627196 2 0.0777 0.7313 0.000 0.972 0.024 0.004 0.000 0.000
#> GSM627142 5 0.3390 0.6679 0.000 0.008 0.000 0.012 0.780 0.200
#> GSM627182 5 0.3992 0.6941 0.008 0.136 0.072 0.004 0.780 0.000
#> GSM627202 5 0.3910 0.6857 0.140 0.000 0.028 0.044 0.788 0.000
#> GSM627141 3 0.4371 0.3486 0.208 0.008 0.732 0.028 0.024 0.000
#> GSM627143 3 0.5180 0.6031 0.004 0.300 0.628 0.012 0.024 0.032
#> GSM627145 5 0.1483 0.7509 0.008 0.000 0.012 0.036 0.944 0.000
#> GSM627152 5 0.5330 0.1445 0.012 0.000 0.044 0.456 0.476 0.012
#> GSM627200 4 0.5062 0.4366 0.224 0.000 0.040 0.672 0.064 0.000
#> GSM627159 6 0.1554 0.7021 0.008 0.000 0.004 0.044 0.004 0.940
#> GSM627164 3 0.4436 0.6227 0.000 0.272 0.676 0.008 0.044 0.000
#> GSM627138 1 0.4976 0.5960 0.656 0.000 0.072 0.020 0.252 0.000
#> GSM627175 2 0.3463 0.6212 0.000 0.748 0.000 0.008 0.004 0.240
#> GSM627150 5 0.1768 0.7497 0.004 0.044 0.008 0.012 0.932 0.000
#> GSM627166 1 0.4199 0.4037 0.620 0.016 0.000 0.360 0.004 0.000
#> GSM627186 3 0.4453 0.5794 0.004 0.340 0.628 0.020 0.008 0.000
#> GSM627139 6 0.6495 0.3955 0.000 0.012 0.052 0.180 0.200 0.556
#> GSM627181 2 0.1429 0.7240 0.000 0.940 0.052 0.004 0.000 0.004
#> GSM627205 2 0.5492 0.4960 0.000 0.640 0.136 0.032 0.192 0.000
#> GSM627214 2 0.4037 0.6584 0.000 0.792 0.084 0.004 0.100 0.020
#> GSM627180 5 0.4360 0.6671 0.000 0.112 0.024 0.084 0.772 0.008
#> GSM627172 3 0.5699 0.4768 0.004 0.372 0.520 0.008 0.008 0.088
#> GSM627184 1 0.4555 0.5179 0.616 0.000 0.004 0.040 0.000 0.340
#> GSM627193 2 0.2358 0.6731 0.000 0.876 0.108 0.016 0.000 0.000
#> GSM627191 6 0.1624 0.6914 0.044 0.000 0.008 0.012 0.000 0.936
#> GSM627176 3 0.4857 0.2789 0.000 0.000 0.676 0.208 0.108 0.008
#> GSM627194 2 0.4352 0.5276 0.000 0.724 0.148 0.128 0.000 0.000
#> GSM627154 6 0.5219 0.1974 0.000 0.416 0.000 0.056 0.016 0.512
#> GSM627187 3 0.2663 0.5091 0.012 0.012 0.884 0.016 0.076 0.000
#> GSM627198 2 0.4625 0.4822 0.008 0.656 0.012 0.028 0.000 0.296
#> GSM627160 6 0.4846 0.4243 0.004 0.000 0.084 0.244 0.004 0.664
#> GSM627185 1 0.0508 0.7662 0.984 0.000 0.000 0.012 0.004 0.000
#> GSM627206 5 0.5142 0.6665 0.112 0.008 0.064 0.096 0.720 0.000
#> GSM627161 1 0.4223 0.7177 0.780 0.000 0.060 0.108 0.052 0.000
#> GSM627162 3 0.2044 0.5064 0.004 0.004 0.908 0.008 0.076 0.000
#> GSM627210 1 0.5292 0.3782 0.560 0.004 0.000 0.332 0.104 0.000
#> GSM627189 2 0.3607 0.6252 0.000 0.796 0.092 0.112 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two
samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures
which are significantly different between classes which can be candidate marks
for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known
annotation is numeric, one-way ANOVA test is applied, and if the known
annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) age(p) other(p) k
#> CV:NMF 144 0.787 0.312 0.00958 2
#> CV:NMF 143 0.530 0.455 0.00536 3
#> CV:NMF 108 0.747 0.373 0.17180 4
#> CV:NMF 110 0.153 0.120 0.01487 5
#> CV:NMF 104 0.219 0.594 0.13246 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
MAD:hclust
The object with results only for a single top-value method and a single partition method
can be extracted as:
res = res_list["MAD", "hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51882 rows and 146 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
- The first row: a plot of the ECDF (empirical cumulative distribution
function) curves of the consensus matrix for each
k and the heatmap of
predicted classes for each k.
- The second row: heatmaps of the consensus matrix for each
k.
- The third row: heatmaps of the membership matrix for each
k.
- The fouth row: heatmaps of the signatures for each
k.
All the plots in panels can be made by individual functions and they are
plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
- ECDF curves of the consensus matrix for each
k;
- 1-PAC. The PAC
score
measures the proportion of the ambiguous subgrouping.
- Mean silhouette score.
- Concordance. The mean probability of fiting the consensus class ids in all
partitions.
- Area increased. Denote \(A_k\) as the area under the ECDF curve for current
k, the area increased is defined as \(A_k - A_{k-1}\).
- Rand index. The percent of pairs of samples that are both in a same cluster
or both are not in a same cluster in the partition of k and k-1.
- Jaccard index. The ratio of pairs of samples are both in a same cluster in
the partition of k and k-1 and the pairs of samples are both in a same
cluster in the partition k or k-1.
The detailed explanations of these statistics can be found in the cola
vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.375 0.678 0.857 0.3969 0.582 0.582
#> 3 3 0.559 0.749 0.856 0.5730 0.687 0.507
#> 4 4 0.579 0.719 0.820 0.1558 0.868 0.654
#> 5 5 0.584 0.582 0.754 0.0680 0.990 0.963
#> 6 6 0.624 0.577 0.733 0.0412 0.922 0.707
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
- All \(k\) with Jaccard index larger than 0.95 are removed because increasing
\(k\) does not provide enough extra information. If all \(k\) are removed, it is
marked as no subgroup is detected.
- For all \(k\) with 1-PAC score larger than 0.9, the maximal \(k\) is taken as
the best \(k\), and other \(k\) are marked as optional \(k\).
- If it does not fit the second rule. The \(k\) with the maximal vote of the
highest 1-PAC score, highest mean silhouette, and highest concordance is
taken as the best \(k\).
suggest_best_k(res)
#> [1] 3
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
show/hide code output
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM627128 2 0.5408 0.774 0.124 0.876
#> GSM627110 1 0.9044 0.571 0.680 0.320
#> GSM627132 1 0.0000 0.751 1.000 0.000
#> GSM627107 2 0.9000 0.538 0.316 0.684
#> GSM627103 2 0.0000 0.826 0.000 1.000
#> GSM627114 1 0.9710 0.436 0.600 0.400
#> GSM627134 2 0.1184 0.825 0.016 0.984
#> GSM627137 2 0.0000 0.826 0.000 1.000
#> GSM627148 2 0.9129 0.517 0.328 0.672
#> GSM627101 2 0.3733 0.804 0.072 0.928
#> GSM627130 2 0.5519 0.772 0.128 0.872
#> GSM627071 2 0.9732 0.324 0.404 0.596
#> GSM627118 2 0.1184 0.825 0.016 0.984
#> GSM627094 2 0.0000 0.826 0.000 1.000
#> GSM627122 2 0.9710 0.341 0.400 0.600
#> GSM627115 2 0.0000 0.826 0.000 1.000
#> GSM627125 2 0.5408 0.774 0.124 0.876
#> GSM627174 2 0.0000 0.826 0.000 1.000
#> GSM627102 2 0.0376 0.826 0.004 0.996
#> GSM627073 2 0.9323 0.474 0.348 0.652
#> GSM627108 2 0.0000 0.826 0.000 1.000
#> GSM627126 1 0.0000 0.751 1.000 0.000
#> GSM627078 2 0.0000 0.826 0.000 1.000
#> GSM627090 2 0.9522 0.422 0.372 0.628
#> GSM627099 2 0.0000 0.826 0.000 1.000
#> GSM627105 2 0.5408 0.774 0.124 0.876
#> GSM627117 1 0.9775 0.411 0.588 0.412
#> GSM627121 2 0.8955 0.546 0.312 0.688
#> GSM627127 2 0.0000 0.826 0.000 1.000
#> GSM627087 2 0.0000 0.826 0.000 1.000
#> GSM627089 1 0.9833 0.374 0.576 0.424
#> GSM627092 2 0.2778 0.816 0.048 0.952
#> GSM627076 2 0.9460 0.441 0.364 0.636
#> GSM627136 1 0.9896 0.318 0.560 0.440
#> GSM627081 2 0.9000 0.538 0.316 0.684
#> GSM627091 2 0.0000 0.826 0.000 1.000
#> GSM627097 2 0.4298 0.797 0.088 0.912
#> GSM627072 2 0.9896 0.196 0.440 0.560
#> GSM627080 1 0.0000 0.751 1.000 0.000
#> GSM627088 1 0.9732 0.426 0.596 0.404
#> GSM627109 1 0.0672 0.753 0.992 0.008
#> GSM627111 1 0.0000 0.751 1.000 0.000
#> GSM627113 1 0.8909 0.584 0.692 0.308
#> GSM627133 2 0.6531 0.731 0.168 0.832
#> GSM627177 1 0.8016 0.640 0.756 0.244
#> GSM627086 2 0.0000 0.826 0.000 1.000
#> GSM627095 2 0.9460 0.415 0.364 0.636
#> GSM627079 2 0.9732 0.329 0.404 0.596
#> GSM627082 2 0.5519 0.772 0.128 0.872
#> GSM627074 1 0.6048 0.713 0.852 0.148
#> GSM627077 1 1.0000 0.086 0.504 0.496
#> GSM627093 1 0.6048 0.713 0.852 0.148
#> GSM627120 2 0.1414 0.825 0.020 0.980
#> GSM627124 2 0.0000 0.826 0.000 1.000
#> GSM627075 2 0.0000 0.826 0.000 1.000
#> GSM627085 2 0.0000 0.826 0.000 1.000
#> GSM627119 1 0.0938 0.754 0.988 0.012
#> GSM627116 1 0.8016 0.640 0.756 0.244
#> GSM627084 1 0.9732 0.426 0.596 0.404
#> GSM627096 2 0.1184 0.825 0.016 0.984
#> GSM627100 2 0.9460 0.441 0.364 0.636
#> GSM627112 2 0.1414 0.825 0.020 0.980
#> GSM627083 2 0.9460 0.415 0.364 0.636
#> GSM627098 1 0.9732 0.426 0.596 0.404
#> GSM627104 1 0.0672 0.753 0.992 0.008
#> GSM627131 2 0.9732 0.329 0.404 0.596
#> GSM627106 2 0.9000 0.538 0.316 0.684
#> GSM627123 1 0.0938 0.753 0.988 0.012
#> GSM627129 2 0.1414 0.824 0.020 0.980
#> GSM627216 2 0.6531 0.731 0.168 0.832
#> GSM627212 2 0.0000 0.826 0.000 1.000
#> GSM627190 1 0.9775 0.411 0.588 0.412
#> GSM627169 2 0.0000 0.826 0.000 1.000
#> GSM627167 2 0.1633 0.823 0.024 0.976
#> GSM627192 1 0.0000 0.751 1.000 0.000
#> GSM627203 2 0.9358 0.467 0.352 0.648
#> GSM627151 2 0.5059 0.784 0.112 0.888
#> GSM627163 1 0.0000 0.751 1.000 0.000
#> GSM627211 2 0.0000 0.826 0.000 1.000
#> GSM627171 2 0.0000 0.826 0.000 1.000
#> GSM627209 2 0.0000 0.826 0.000 1.000
#> GSM627135 1 0.0938 0.753 0.988 0.012
#> GSM627170 2 0.0938 0.826 0.012 0.988
#> GSM627178 1 0.8016 0.640 0.756 0.244
#> GSM627199 2 0.0000 0.826 0.000 1.000
#> GSM627213 2 0.1184 0.825 0.016 0.984
#> GSM627140 2 0.2603 0.818 0.044 0.956
#> GSM627149 1 0.0938 0.753 0.988 0.012
#> GSM627147 2 0.1633 0.823 0.024 0.976
#> GSM627195 2 0.9358 0.467 0.352 0.648
#> GSM627204 2 0.0000 0.826 0.000 1.000
#> GSM627207 2 0.0000 0.826 0.000 1.000
#> GSM627157 1 0.9044 0.569 0.680 0.320
#> GSM627201 2 0.0000 0.826 0.000 1.000
#> GSM627146 2 0.0000 0.826 0.000 1.000
#> GSM627156 2 0.0000 0.826 0.000 1.000
#> GSM627188 1 0.0000 0.751 1.000 0.000
#> GSM627197 2 0.0000 0.826 0.000 1.000
#> GSM627173 2 0.0000 0.826 0.000 1.000
#> GSM627179 2 0.0000 0.826 0.000 1.000
#> GSM627208 2 0.8713 0.581 0.292 0.708
#> GSM627215 2 0.8016 0.646 0.244 0.756
#> GSM627153 2 0.0000 0.826 0.000 1.000
#> GSM627155 1 0.0000 0.751 1.000 0.000
#> GSM627165 2 0.0000 0.826 0.000 1.000
#> GSM627168 1 0.9044 0.569 0.680 0.320
#> GSM627183 1 0.9491 0.495 0.632 0.368
#> GSM627144 2 0.9460 0.439 0.364 0.636
#> GSM627158 1 0.0000 0.751 1.000 0.000
#> GSM627196 2 0.0000 0.826 0.000 1.000
#> GSM627142 2 0.6973 0.720 0.188 0.812
#> GSM627182 2 0.8713 0.581 0.292 0.708
#> GSM627202 2 0.9815 0.270 0.420 0.580
#> GSM627141 1 0.9732 0.428 0.596 0.404
#> GSM627143 2 0.1633 0.824 0.024 0.976
#> GSM627145 2 0.9732 0.329 0.404 0.596
#> GSM627152 2 0.9552 0.411 0.376 0.624
#> GSM627200 2 0.9686 0.353 0.396 0.604
#> GSM627159 2 0.5519 0.772 0.128 0.872
#> GSM627164 2 0.0000 0.826 0.000 1.000
#> GSM627138 1 0.0000 0.751 1.000 0.000
#> GSM627175 2 0.0000 0.826 0.000 1.000
#> GSM627150 2 0.9732 0.324 0.404 0.596
#> GSM627166 1 0.3584 0.741 0.932 0.068
#> GSM627186 2 0.0000 0.826 0.000 1.000
#> GSM627139 2 0.5059 0.784 0.112 0.888
#> GSM627181 2 0.0000 0.826 0.000 1.000
#> GSM627205 2 0.7299 0.694 0.204 0.796
#> GSM627214 2 0.0000 0.826 0.000 1.000
#> GSM627180 2 0.8016 0.646 0.244 0.756
#> GSM627172 2 0.0376 0.826 0.004 0.996
#> GSM627184 1 0.0000 0.751 1.000 0.000
#> GSM627193 2 0.0000 0.826 0.000 1.000
#> GSM627191 2 0.9323 0.450 0.348 0.652
#> GSM627176 2 0.9491 0.433 0.368 0.632
#> GSM627194 2 0.0000 0.826 0.000 1.000
#> GSM627154 2 0.0000 0.826 0.000 1.000
#> GSM627187 1 0.9775 0.411 0.588 0.412
#> GSM627198 2 0.0000 0.826 0.000 1.000
#> GSM627160 2 0.5059 0.785 0.112 0.888
#> GSM627185 1 0.0938 0.753 0.988 0.012
#> GSM627206 1 0.9833 0.374 0.576 0.424
#> GSM627161 1 0.0000 0.751 1.000 0.000
#> GSM627162 2 0.2778 0.816 0.048 0.952
#> GSM627210 1 0.0938 0.754 0.988 0.012
#> GSM627189 2 0.0000 0.826 0.000 1.000
show/hide code output
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM627128 2 0.6260 0.328 0.000 0.552 0.448
#> GSM627110 3 0.6008 0.509 0.372 0.000 0.628
#> GSM627132 1 0.0000 0.914 1.000 0.000 0.000
#> GSM627107 3 0.3532 0.751 0.008 0.108 0.884
#> GSM627103 2 0.1289 0.871 0.000 0.968 0.032
#> GSM627114 3 0.5553 0.669 0.272 0.004 0.724
#> GSM627134 2 0.4346 0.762 0.000 0.816 0.184
#> GSM627137 2 0.1031 0.870 0.000 0.976 0.024
#> GSM627148 3 0.3120 0.766 0.012 0.080 0.908
#> GSM627101 2 0.5678 0.608 0.000 0.684 0.316
#> GSM627130 2 0.6468 0.332 0.004 0.552 0.444
#> GSM627071 3 0.4217 0.778 0.100 0.032 0.868
#> GSM627118 2 0.4555 0.751 0.000 0.800 0.200
#> GSM627094 2 0.1289 0.871 0.000 0.968 0.032
#> GSM627122 3 0.3502 0.778 0.084 0.020 0.896
#> GSM627115 2 0.1289 0.871 0.000 0.968 0.032
#> GSM627125 2 0.6260 0.328 0.000 0.552 0.448
#> GSM627174 2 0.1289 0.871 0.000 0.968 0.032
#> GSM627102 2 0.1411 0.871 0.000 0.964 0.036
#> GSM627073 3 0.3499 0.774 0.028 0.072 0.900
#> GSM627108 2 0.1289 0.871 0.000 0.968 0.032
#> GSM627126 1 0.0592 0.919 0.988 0.000 0.012
#> GSM627078 2 0.1289 0.854 0.000 0.968 0.032
#> GSM627090 3 0.2269 0.775 0.040 0.016 0.944
#> GSM627099 2 0.1289 0.871 0.000 0.968 0.032
#> GSM627105 2 0.6260 0.328 0.000 0.552 0.448
#> GSM627117 3 0.5656 0.678 0.264 0.008 0.728
#> GSM627121 3 0.3965 0.738 0.008 0.132 0.860
#> GSM627127 2 0.1289 0.854 0.000 0.968 0.032
#> GSM627087 2 0.1289 0.871 0.000 0.968 0.032
#> GSM627089 3 0.5404 0.685 0.256 0.004 0.740
#> GSM627092 2 0.5835 0.571 0.000 0.660 0.340
#> GSM627076 3 0.2050 0.775 0.028 0.020 0.952
#> GSM627136 3 0.5158 0.706 0.232 0.004 0.764
#> GSM627081 3 0.3532 0.751 0.008 0.108 0.884
#> GSM627091 2 0.1289 0.871 0.000 0.968 0.032
#> GSM627097 2 0.4609 0.800 0.052 0.856 0.092
#> GSM627072 3 0.3607 0.768 0.112 0.008 0.880
#> GSM627080 1 0.0424 0.916 0.992 0.000 0.008
#> GSM627088 3 0.6033 0.598 0.336 0.004 0.660
#> GSM627109 1 0.0892 0.918 0.980 0.000 0.020
#> GSM627111 1 0.0000 0.914 1.000 0.000 0.000
#> GSM627113 3 0.6298 0.495 0.388 0.004 0.608
#> GSM627133 3 0.5621 0.523 0.000 0.308 0.692
#> GSM627177 1 0.5845 0.587 0.688 0.004 0.308
#> GSM627086 2 0.0747 0.868 0.000 0.984 0.016
#> GSM627095 2 0.9299 0.309 0.324 0.496 0.180
#> GSM627079 3 0.2866 0.774 0.076 0.008 0.916
#> GSM627082 2 0.6451 0.353 0.004 0.560 0.436
#> GSM627074 1 0.4555 0.750 0.800 0.000 0.200
#> GSM627077 3 0.5220 0.727 0.208 0.012 0.780
#> GSM627093 1 0.4555 0.750 0.800 0.000 0.200
#> GSM627120 2 0.3879 0.801 0.000 0.848 0.152
#> GSM627124 2 0.1289 0.854 0.000 0.968 0.032
#> GSM627075 2 0.1289 0.871 0.000 0.968 0.032
#> GSM627085 2 0.1289 0.854 0.000 0.968 0.032
#> GSM627119 1 0.1031 0.918 0.976 0.000 0.024
#> GSM627116 1 0.5845 0.587 0.688 0.004 0.308
#> GSM627084 3 0.6033 0.598 0.336 0.004 0.660
#> GSM627096 2 0.4555 0.751 0.000 0.800 0.200
#> GSM627100 3 0.2050 0.775 0.028 0.020 0.952
#> GSM627112 2 0.5070 0.721 0.004 0.772 0.224
#> GSM627083 2 0.9299 0.309 0.324 0.496 0.180
#> GSM627098 3 0.6033 0.598 0.336 0.004 0.660
#> GSM627104 1 0.0892 0.918 0.980 0.000 0.020
#> GSM627131 3 0.2866 0.774 0.076 0.008 0.916
#> GSM627106 3 0.3532 0.751 0.008 0.108 0.884
#> GSM627123 1 0.1860 0.905 0.948 0.000 0.052
#> GSM627129 2 0.4452 0.758 0.000 0.808 0.192
#> GSM627216 3 0.5621 0.523 0.000 0.308 0.692
#> GSM627212 2 0.1289 0.871 0.000 0.968 0.032
#> GSM627190 3 0.5656 0.678 0.264 0.008 0.728
#> GSM627169 2 0.1411 0.870 0.000 0.964 0.036
#> GSM627167 2 0.2165 0.863 0.000 0.936 0.064
#> GSM627192 1 0.0592 0.919 0.988 0.000 0.012
#> GSM627203 3 0.1877 0.773 0.012 0.032 0.956
#> GSM627151 3 0.6984 0.202 0.020 0.420 0.560
#> GSM627163 1 0.0000 0.914 1.000 0.000 0.000
#> GSM627211 2 0.1289 0.871 0.000 0.968 0.032
#> GSM627171 2 0.1529 0.869 0.000 0.960 0.040
#> GSM627209 2 0.0892 0.866 0.000 0.980 0.020
#> GSM627135 1 0.1647 0.912 0.960 0.004 0.036
#> GSM627170 2 0.1753 0.866 0.000 0.952 0.048
#> GSM627178 1 0.5845 0.587 0.688 0.004 0.308
#> GSM627199 2 0.1031 0.855 0.000 0.976 0.024
#> GSM627213 2 0.4399 0.761 0.000 0.812 0.188
#> GSM627140 2 0.5461 0.701 0.008 0.748 0.244
#> GSM627149 1 0.1860 0.905 0.948 0.000 0.052
#> GSM627147 2 0.2165 0.863 0.000 0.936 0.064
#> GSM627195 3 0.1877 0.773 0.012 0.032 0.956
#> GSM627204 2 0.1289 0.871 0.000 0.968 0.032
#> GSM627207 2 0.1289 0.871 0.000 0.968 0.032
#> GSM627157 3 0.6247 0.521 0.376 0.004 0.620
#> GSM627201 2 0.1289 0.871 0.000 0.968 0.032
#> GSM627146 2 0.1031 0.869 0.000 0.976 0.024
#> GSM627156 2 0.1411 0.870 0.000 0.964 0.036
#> GSM627188 1 0.0592 0.919 0.988 0.000 0.012
#> GSM627197 2 0.1031 0.869 0.000 0.976 0.024
#> GSM627173 2 0.1163 0.871 0.000 0.972 0.028
#> GSM627179 2 0.1289 0.871 0.000 0.968 0.032
#> GSM627208 3 0.4475 0.737 0.016 0.144 0.840
#> GSM627215 3 0.4452 0.696 0.000 0.192 0.808
#> GSM627153 2 0.0892 0.866 0.000 0.980 0.020
#> GSM627155 1 0.0592 0.919 0.988 0.000 0.012
#> GSM627165 2 0.1163 0.871 0.000 0.972 0.028
#> GSM627168 3 0.6247 0.521 0.376 0.004 0.620
#> GSM627183 3 0.5956 0.609 0.324 0.004 0.672
#> GSM627144 3 0.1182 0.771 0.012 0.012 0.976
#> GSM627158 1 0.0592 0.917 0.988 0.000 0.012
#> GSM627196 2 0.1289 0.871 0.000 0.968 0.032
#> GSM627142 3 0.6651 0.353 0.020 0.340 0.640
#> GSM627182 3 0.4475 0.737 0.016 0.144 0.840
#> GSM627202 3 0.4209 0.772 0.120 0.020 0.860
#> GSM627141 3 0.5553 0.670 0.272 0.004 0.724
#> GSM627143 2 0.4351 0.795 0.004 0.828 0.168
#> GSM627145 3 0.2774 0.774 0.072 0.008 0.920
#> GSM627152 3 0.2152 0.777 0.036 0.016 0.948
#> GSM627200 3 0.3272 0.776 0.080 0.016 0.904
#> GSM627159 2 0.6451 0.353 0.004 0.560 0.436
#> GSM627164 2 0.1529 0.869 0.000 0.960 0.040
#> GSM627138 1 0.2165 0.893 0.936 0.000 0.064
#> GSM627175 2 0.1163 0.856 0.000 0.972 0.028
#> GSM627150 3 0.4217 0.778 0.100 0.032 0.868
#> GSM627166 1 0.3425 0.863 0.884 0.004 0.112
#> GSM627186 2 0.1529 0.869 0.000 0.960 0.040
#> GSM627139 3 0.6984 0.202 0.020 0.420 0.560
#> GSM627181 2 0.1031 0.869 0.000 0.976 0.024
#> GSM627205 3 0.6079 0.348 0.000 0.388 0.612
#> GSM627214 2 0.1163 0.869 0.000 0.972 0.028
#> GSM627180 3 0.4452 0.696 0.000 0.192 0.808
#> GSM627172 2 0.1411 0.871 0.000 0.964 0.036
#> GSM627184 1 0.0592 0.919 0.988 0.000 0.012
#> GSM627193 2 0.1289 0.871 0.000 0.968 0.032
#> GSM627191 2 0.9287 0.336 0.304 0.508 0.188
#> GSM627176 3 0.2176 0.776 0.032 0.020 0.948
#> GSM627194 2 0.1289 0.871 0.000 0.968 0.032
#> GSM627154 2 0.1289 0.854 0.000 0.968 0.032
#> GSM627187 3 0.5656 0.678 0.264 0.008 0.728
#> GSM627198 2 0.1289 0.854 0.000 0.968 0.032
#> GSM627160 2 0.6345 0.458 0.004 0.596 0.400
#> GSM627185 1 0.1163 0.917 0.972 0.000 0.028
#> GSM627206 3 0.5404 0.685 0.256 0.004 0.740
#> GSM627161 1 0.0592 0.917 0.988 0.000 0.012
#> GSM627162 2 0.5859 0.564 0.000 0.656 0.344
#> GSM627210 1 0.1031 0.918 0.976 0.000 0.024
#> GSM627189 2 0.1289 0.871 0.000 0.968 0.032
show/hide code output
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM627128 4 0.2611 0.619 0.000 0.008 0.096 0.896
#> GSM627110 3 0.6505 0.489 0.360 0.012 0.572 0.056
#> GSM627132 1 0.0000 0.923 1.000 0.000 0.000 0.000
#> GSM627107 3 0.4322 0.697 0.000 0.044 0.804 0.152
#> GSM627103 2 0.0657 0.890 0.000 0.984 0.004 0.012
#> GSM627114 3 0.4134 0.707 0.260 0.000 0.740 0.000
#> GSM627134 4 0.5786 0.599 0.000 0.308 0.052 0.640
#> GSM627137 2 0.3024 0.798 0.000 0.852 0.000 0.148
#> GSM627148 3 0.3555 0.747 0.004 0.048 0.868 0.080
#> GSM627101 4 0.5309 0.656 0.000 0.164 0.092 0.744
#> GSM627130 4 0.2401 0.617 0.000 0.004 0.092 0.904
#> GSM627071 3 0.4108 0.792 0.092 0.012 0.844 0.052
#> GSM627118 4 0.5815 0.612 0.000 0.288 0.060 0.652
#> GSM627094 2 0.0000 0.890 0.000 1.000 0.000 0.000
#> GSM627122 3 0.3392 0.788 0.072 0.000 0.872 0.056
#> GSM627115 2 0.0657 0.890 0.000 0.984 0.004 0.012
#> GSM627125 4 0.2466 0.615 0.000 0.004 0.096 0.900
#> GSM627174 2 0.1743 0.881 0.000 0.940 0.004 0.056
#> GSM627102 2 0.1557 0.878 0.000 0.944 0.000 0.056
#> GSM627073 3 0.3166 0.774 0.024 0.056 0.896 0.024
#> GSM627108 2 0.0000 0.890 0.000 1.000 0.000 0.000
#> GSM627126 1 0.0592 0.924 0.984 0.000 0.000 0.016
#> GSM627078 4 0.4961 0.358 0.000 0.448 0.000 0.552
#> GSM627090 3 0.3598 0.757 0.028 0.000 0.848 0.124
#> GSM627099 2 0.2197 0.866 0.000 0.916 0.004 0.080
#> GSM627105 4 0.2466 0.615 0.000 0.004 0.096 0.900
#> GSM627117 3 0.4252 0.713 0.252 0.004 0.744 0.000
#> GSM627121 3 0.4534 0.698 0.000 0.068 0.800 0.132
#> GSM627127 4 0.4981 0.313 0.000 0.464 0.000 0.536
#> GSM627087 2 0.0657 0.890 0.000 0.984 0.004 0.012
#> GSM627089 3 0.4008 0.720 0.244 0.000 0.756 0.000
#> GSM627092 4 0.7325 0.427 0.000 0.368 0.160 0.472
#> GSM627076 3 0.3647 0.740 0.016 0.000 0.832 0.152
#> GSM627136 3 0.4284 0.736 0.224 0.000 0.764 0.012
#> GSM627081 3 0.4322 0.697 0.000 0.044 0.804 0.152
#> GSM627091 2 0.2197 0.866 0.000 0.916 0.004 0.080
#> GSM627097 4 0.6703 0.462 0.052 0.380 0.020 0.548
#> GSM627072 3 0.3143 0.786 0.100 0.000 0.876 0.024
#> GSM627080 1 0.0469 0.923 0.988 0.000 0.012 0.000
#> GSM627088 3 0.5110 0.642 0.328 0.000 0.656 0.016
#> GSM627109 1 0.0707 0.924 0.980 0.000 0.020 0.000
#> GSM627111 1 0.0000 0.923 1.000 0.000 0.000 0.000
#> GSM627113 3 0.4776 0.559 0.376 0.000 0.624 0.000
#> GSM627133 3 0.5522 0.488 0.000 0.288 0.668 0.044
#> GSM627177 1 0.5816 0.646 0.688 0.000 0.224 0.088
#> GSM627086 2 0.3610 0.720 0.000 0.800 0.000 0.200
#> GSM627095 4 0.7256 0.405 0.320 0.084 0.032 0.564
#> GSM627079 3 0.2908 0.788 0.064 0.000 0.896 0.040
#> GSM627082 4 0.2412 0.622 0.000 0.008 0.084 0.908
#> GSM627074 1 0.4763 0.791 0.800 0.012 0.132 0.056
#> GSM627077 3 0.4446 0.753 0.196 0.000 0.776 0.028
#> GSM627093 1 0.4763 0.791 0.800 0.012 0.132 0.056
#> GSM627120 2 0.5280 0.627 0.000 0.752 0.128 0.120
#> GSM627124 4 0.4961 0.358 0.000 0.448 0.000 0.552
#> GSM627075 2 0.0000 0.890 0.000 1.000 0.000 0.000
#> GSM627085 4 0.4961 0.358 0.000 0.448 0.000 0.552
#> GSM627119 1 0.0817 0.923 0.976 0.000 0.024 0.000
#> GSM627116 1 0.5816 0.646 0.688 0.000 0.224 0.088
#> GSM627084 3 0.5110 0.642 0.328 0.000 0.656 0.016
#> GSM627096 4 0.5815 0.612 0.000 0.288 0.060 0.652
#> GSM627100 3 0.3647 0.740 0.016 0.000 0.832 0.152
#> GSM627112 4 0.4831 0.645 0.000 0.208 0.040 0.752
#> GSM627083 4 0.7256 0.405 0.320 0.084 0.032 0.564
#> GSM627098 3 0.5110 0.642 0.328 0.000 0.656 0.016
#> GSM627104 1 0.0707 0.924 0.980 0.000 0.020 0.000
#> GSM627131 3 0.2908 0.788 0.064 0.000 0.896 0.040
#> GSM627106 3 0.4322 0.697 0.000 0.044 0.804 0.152
#> GSM627123 1 0.1833 0.915 0.944 0.000 0.024 0.032
#> GSM627129 4 0.5836 0.604 0.000 0.304 0.056 0.640
#> GSM627216 3 0.5522 0.488 0.000 0.288 0.668 0.044
#> GSM627212 2 0.2197 0.866 0.000 0.916 0.004 0.080
#> GSM627190 3 0.4252 0.713 0.252 0.004 0.744 0.000
#> GSM627169 2 0.0188 0.890 0.000 0.996 0.004 0.000
#> GSM627167 2 0.3157 0.795 0.000 0.852 0.004 0.144
#> GSM627192 1 0.0592 0.924 0.984 0.000 0.000 0.016
#> GSM627203 3 0.1004 0.770 0.000 0.004 0.972 0.024
#> GSM627151 4 0.7611 0.215 0.016 0.128 0.412 0.444
#> GSM627163 1 0.0000 0.923 1.000 0.000 0.000 0.000
#> GSM627211 2 0.0000 0.890 0.000 1.000 0.000 0.000
#> GSM627171 2 0.0336 0.888 0.000 0.992 0.008 0.000
#> GSM627209 2 0.3975 0.654 0.000 0.760 0.000 0.240
#> GSM627135 1 0.1452 0.916 0.956 0.000 0.008 0.036
#> GSM627170 2 0.3117 0.842 0.000 0.880 0.028 0.092
#> GSM627178 1 0.5816 0.646 0.688 0.000 0.224 0.088
#> GSM627199 4 0.4981 0.319 0.000 0.464 0.000 0.536
#> GSM627213 4 0.5742 0.604 0.000 0.300 0.052 0.648
#> GSM627140 4 0.5834 0.598 0.008 0.288 0.044 0.660
#> GSM627149 1 0.1833 0.915 0.944 0.000 0.024 0.032
#> GSM627147 2 0.3208 0.792 0.000 0.848 0.004 0.148
#> GSM627195 3 0.1004 0.770 0.000 0.004 0.972 0.024
#> GSM627204 2 0.0000 0.890 0.000 1.000 0.000 0.000
#> GSM627207 2 0.0000 0.890 0.000 1.000 0.000 0.000
#> GSM627157 3 0.4730 0.581 0.364 0.000 0.636 0.000
#> GSM627201 2 0.1743 0.881 0.000 0.940 0.004 0.056
#> GSM627146 2 0.1118 0.886 0.000 0.964 0.000 0.036
#> GSM627156 2 0.0188 0.890 0.000 0.996 0.004 0.000
#> GSM627188 1 0.0592 0.924 0.984 0.000 0.000 0.016
#> GSM627197 2 0.1118 0.886 0.000 0.964 0.000 0.036
#> GSM627173 2 0.0336 0.891 0.000 0.992 0.000 0.008
#> GSM627179 2 0.0336 0.890 0.000 0.992 0.000 0.008
#> GSM627208 3 0.3908 0.719 0.008 0.116 0.844 0.032
#> GSM627215 3 0.4544 0.655 0.000 0.164 0.788 0.048
#> GSM627153 2 0.3975 0.654 0.000 0.760 0.000 0.240
#> GSM627155 1 0.0592 0.924 0.984 0.000 0.000 0.016
#> GSM627165 2 0.3208 0.796 0.000 0.848 0.004 0.148
#> GSM627168 3 0.4730 0.581 0.364 0.000 0.636 0.000
#> GSM627183 3 0.4477 0.658 0.312 0.000 0.688 0.000
#> GSM627144 3 0.2021 0.767 0.000 0.012 0.932 0.056
#> GSM627158 1 0.0592 0.923 0.984 0.000 0.016 0.000
#> GSM627196 2 0.0000 0.890 0.000 1.000 0.000 0.000
#> GSM627142 4 0.5464 0.246 0.020 0.004 0.344 0.632
#> GSM627182 3 0.3908 0.719 0.008 0.116 0.844 0.032
#> GSM627202 3 0.3978 0.786 0.108 0.000 0.836 0.056
#> GSM627141 3 0.4134 0.706 0.260 0.000 0.740 0.000
#> GSM627143 2 0.5896 0.361 0.004 0.648 0.052 0.296
#> GSM627145 3 0.2443 0.789 0.060 0.000 0.916 0.024
#> GSM627152 3 0.3763 0.749 0.024 0.000 0.832 0.144
#> GSM627200 3 0.3239 0.789 0.068 0.000 0.880 0.052
#> GSM627159 4 0.2412 0.622 0.000 0.008 0.084 0.908
#> GSM627164 2 0.0336 0.888 0.000 0.992 0.008 0.000
#> GSM627138 1 0.1792 0.891 0.932 0.000 0.068 0.000
#> GSM627175 2 0.4866 0.178 0.000 0.596 0.000 0.404
#> GSM627150 3 0.4108 0.792 0.092 0.012 0.844 0.052
#> GSM627166 1 0.3156 0.880 0.884 0.000 0.068 0.048
#> GSM627186 2 0.0469 0.886 0.000 0.988 0.012 0.000
#> GSM627139 4 0.7611 0.215 0.016 0.128 0.412 0.444
#> GSM627181 2 0.1118 0.886 0.000 0.964 0.000 0.036
#> GSM627205 3 0.5613 0.300 0.000 0.380 0.592 0.028
#> GSM627214 2 0.3725 0.745 0.000 0.812 0.008 0.180
#> GSM627180 3 0.4544 0.655 0.000 0.164 0.788 0.048
#> GSM627172 2 0.1557 0.878 0.000 0.944 0.000 0.056
#> GSM627184 1 0.0592 0.924 0.984 0.000 0.000 0.016
#> GSM627193 2 0.0000 0.890 0.000 1.000 0.000 0.000
#> GSM627191 4 0.7460 0.440 0.300 0.100 0.036 0.564
#> GSM627176 3 0.4082 0.746 0.020 0.008 0.820 0.152
#> GSM627194 2 0.2081 0.861 0.000 0.916 0.000 0.084
#> GSM627154 4 0.4961 0.358 0.000 0.448 0.000 0.552
#> GSM627187 3 0.4252 0.713 0.252 0.004 0.744 0.000
#> GSM627198 4 0.4955 0.366 0.000 0.444 0.000 0.556
#> GSM627160 4 0.6725 0.619 0.004 0.180 0.184 0.632
#> GSM627185 1 0.0921 0.922 0.972 0.000 0.028 0.000
#> GSM627206 3 0.4008 0.720 0.244 0.000 0.756 0.000
#> GSM627161 1 0.0592 0.923 0.984 0.000 0.016 0.000
#> GSM627162 4 0.7375 0.458 0.000 0.348 0.172 0.480
#> GSM627210 1 0.0817 0.923 0.976 0.000 0.024 0.000
#> GSM627189 2 0.0000 0.890 0.000 1.000 0.000 0.000
show/hide code output
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM627128 4 0.5249 -0.1217 0.000 0.004 0.036 0.508 0.452
#> GSM627110 3 0.6163 0.4301 0.164 0.000 0.536 0.000 0.300
#> GSM627132 1 0.0963 0.8444 0.964 0.000 0.000 0.000 0.036
#> GSM627107 3 0.4660 0.5167 0.000 0.016 0.728 0.036 0.220
#> GSM627103 2 0.3141 0.8027 0.000 0.852 0.000 0.108 0.040
#> GSM627114 3 0.4365 0.6721 0.116 0.000 0.768 0.000 0.116
#> GSM627134 4 0.3617 0.4535 0.000 0.060 0.012 0.840 0.088
#> GSM627137 2 0.5180 0.6168 0.000 0.624 0.000 0.312 0.064
#> GSM627148 3 0.3769 0.6173 0.004 0.028 0.796 0.000 0.172
#> GSM627101 4 0.4482 0.2648 0.000 0.004 0.032 0.712 0.252
#> GSM627130 4 0.5109 -0.1342 0.000 0.000 0.036 0.504 0.460
#> GSM627071 3 0.3053 0.7035 0.044 0.000 0.872 0.008 0.076
#> GSM627118 4 0.3855 0.4374 0.000 0.044 0.020 0.824 0.112
#> GSM627094 2 0.0865 0.8151 0.000 0.972 0.000 0.024 0.004
#> GSM627122 3 0.3151 0.6831 0.036 0.000 0.876 0.032 0.056
#> GSM627115 2 0.3141 0.8027 0.000 0.852 0.000 0.108 0.040
#> GSM627125 4 0.5106 -0.1353 0.000 0.000 0.036 0.508 0.456
#> GSM627174 2 0.3868 0.7886 0.000 0.800 0.000 0.140 0.060
#> GSM627102 2 0.2580 0.7966 0.000 0.892 0.000 0.064 0.044
#> GSM627073 3 0.3359 0.6804 0.012 0.040 0.868 0.012 0.068
#> GSM627108 2 0.0865 0.8151 0.000 0.972 0.000 0.024 0.004
#> GSM627126 1 0.1121 0.8396 0.956 0.000 0.000 0.000 0.044
#> GSM627078 4 0.3123 0.4904 0.000 0.184 0.000 0.812 0.004
#> GSM627090 3 0.3962 0.5940 0.012 0.000 0.800 0.036 0.152
#> GSM627099 2 0.4325 0.7380 0.000 0.736 0.000 0.220 0.044
#> GSM627105 4 0.5106 -0.1353 0.000 0.000 0.036 0.508 0.456
#> GSM627117 3 0.4425 0.6756 0.112 0.004 0.772 0.000 0.112
#> GSM627121 3 0.4862 0.5326 0.000 0.036 0.724 0.028 0.212
#> GSM627127 4 0.3231 0.4811 0.000 0.196 0.000 0.800 0.004
#> GSM627087 2 0.3141 0.8027 0.000 0.852 0.000 0.108 0.040
#> GSM627089 3 0.4171 0.6790 0.104 0.000 0.784 0.000 0.112
#> GSM627092 4 0.8185 0.1415 0.000 0.260 0.112 0.356 0.272
#> GSM627076 3 0.3883 0.5559 0.000 0.000 0.780 0.036 0.184
#> GSM627136 3 0.4144 0.6872 0.100 0.000 0.800 0.008 0.092
#> GSM627081 3 0.4660 0.5167 0.000 0.016 0.728 0.036 0.220
#> GSM627091 2 0.4325 0.7380 0.000 0.736 0.000 0.220 0.044
#> GSM627097 4 0.6959 0.4400 0.040 0.196 0.012 0.580 0.172
#> GSM627072 3 0.2390 0.7046 0.032 0.000 0.912 0.012 0.044
#> GSM627080 1 0.1195 0.8425 0.960 0.000 0.012 0.000 0.028
#> GSM627088 3 0.5376 0.6227 0.196 0.000 0.688 0.012 0.104
#> GSM627109 1 0.3844 0.8040 0.792 0.000 0.044 0.000 0.164
#> GSM627111 1 0.0963 0.8444 0.964 0.000 0.000 0.000 0.036
#> GSM627113 3 0.5408 0.5686 0.228 0.000 0.652 0.000 0.120
#> GSM627133 3 0.6133 0.3717 0.000 0.236 0.620 0.028 0.116
#> GSM627177 1 0.6816 0.5561 0.552 0.000 0.188 0.036 0.224
#> GSM627086 2 0.5160 0.5801 0.000 0.608 0.000 0.336 0.056
#> GSM627095 4 0.7516 0.0570 0.300 0.016 0.020 0.428 0.236
#> GSM627079 3 0.2696 0.6884 0.032 0.000 0.900 0.028 0.040
#> GSM627082 4 0.5039 -0.1168 0.000 0.000 0.032 0.512 0.456
#> GSM627074 1 0.5644 0.6700 0.584 0.000 0.100 0.000 0.316
#> GSM627077 3 0.4567 0.6868 0.100 0.000 0.784 0.028 0.088
#> GSM627093 1 0.5644 0.6700 0.584 0.000 0.100 0.000 0.316
#> GSM627120 2 0.7062 0.5274 0.000 0.556 0.116 0.236 0.092
#> GSM627124 4 0.3123 0.4904 0.000 0.184 0.000 0.812 0.004
#> GSM627075 2 0.1124 0.8021 0.000 0.960 0.000 0.004 0.036
#> GSM627085 4 0.3123 0.4904 0.000 0.184 0.000 0.812 0.004
#> GSM627119 1 0.3914 0.8023 0.788 0.000 0.048 0.000 0.164
#> GSM627116 1 0.6816 0.5561 0.552 0.000 0.188 0.036 0.224
#> GSM627084 3 0.5376 0.6227 0.196 0.000 0.688 0.012 0.104
#> GSM627096 4 0.3855 0.4374 0.000 0.044 0.020 0.824 0.112
#> GSM627100 3 0.3883 0.5559 0.000 0.000 0.780 0.036 0.184
#> GSM627112 4 0.3004 0.4121 0.000 0.020 0.008 0.864 0.108
#> GSM627083 4 0.7516 0.0570 0.300 0.016 0.020 0.428 0.236
#> GSM627098 3 0.5376 0.6227 0.196 0.000 0.688 0.012 0.104
#> GSM627104 1 0.3844 0.8040 0.792 0.000 0.044 0.000 0.164
#> GSM627131 3 0.2696 0.6884 0.032 0.000 0.900 0.028 0.040
#> GSM627106 3 0.4660 0.5167 0.000 0.016 0.728 0.036 0.220
#> GSM627123 1 0.2060 0.8356 0.924 0.000 0.016 0.008 0.052
#> GSM627129 4 0.3738 0.4519 0.000 0.064 0.012 0.832 0.092
#> GSM627216 3 0.6133 0.3717 0.000 0.236 0.620 0.028 0.116
#> GSM627212 2 0.4325 0.7380 0.000 0.736 0.000 0.220 0.044
#> GSM627190 3 0.4425 0.6756 0.112 0.004 0.772 0.000 0.112
#> GSM627169 2 0.1285 0.8019 0.000 0.956 0.004 0.004 0.036
#> GSM627167 2 0.4364 0.7078 0.000 0.768 0.000 0.120 0.112
#> GSM627192 1 0.1121 0.8396 0.956 0.000 0.000 0.000 0.044
#> GSM627203 3 0.2052 0.6713 0.000 0.004 0.912 0.004 0.080
#> GSM627151 4 0.7603 -0.5054 0.008 0.028 0.340 0.364 0.260
#> GSM627163 1 0.0794 0.8419 0.972 0.000 0.000 0.000 0.028
#> GSM627211 2 0.0898 0.8099 0.000 0.972 0.000 0.008 0.020
#> GSM627171 2 0.1443 0.8027 0.000 0.948 0.004 0.004 0.044
#> GSM627209 2 0.5330 0.4799 0.000 0.548 0.000 0.396 0.056
#> GSM627135 1 0.2116 0.8379 0.912 0.000 0.004 0.008 0.076
#> GSM627170 2 0.5559 0.6946 0.000 0.664 0.016 0.228 0.092
#> GSM627178 1 0.6816 0.5561 0.552 0.000 0.188 0.036 0.224
#> GSM627199 4 0.3388 0.4836 0.000 0.200 0.000 0.792 0.008
#> GSM627213 4 0.3426 0.4531 0.000 0.052 0.012 0.852 0.084
#> GSM627140 4 0.6813 0.3093 0.008 0.172 0.020 0.548 0.252
#> GSM627149 1 0.2060 0.8356 0.924 0.000 0.016 0.008 0.052
#> GSM627147 2 0.4454 0.7029 0.000 0.760 0.000 0.128 0.112
#> GSM627195 3 0.2052 0.6713 0.000 0.004 0.912 0.004 0.080
#> GSM627204 2 0.0898 0.8099 0.000 0.972 0.000 0.008 0.020
#> GSM627207 2 0.0771 0.8088 0.000 0.976 0.000 0.004 0.020
#> GSM627157 3 0.5341 0.5842 0.212 0.000 0.664 0.000 0.124
#> GSM627201 2 0.3868 0.7886 0.000 0.800 0.000 0.140 0.060
#> GSM627146 2 0.2389 0.8009 0.000 0.880 0.000 0.116 0.004
#> GSM627156 2 0.1285 0.8019 0.000 0.956 0.004 0.004 0.036
#> GSM627188 1 0.1121 0.8396 0.956 0.000 0.000 0.000 0.044
#> GSM627197 2 0.2389 0.8009 0.000 0.880 0.000 0.116 0.004
#> GSM627173 2 0.1831 0.8121 0.000 0.920 0.000 0.076 0.004
#> GSM627179 2 0.2236 0.8142 0.000 0.908 0.000 0.068 0.024
#> GSM627208 3 0.3955 0.6084 0.000 0.084 0.800 0.000 0.116
#> GSM627215 3 0.5077 0.5364 0.000 0.120 0.736 0.020 0.124
#> GSM627153 2 0.5330 0.4799 0.000 0.548 0.000 0.396 0.056
#> GSM627155 1 0.1121 0.8396 0.956 0.000 0.000 0.000 0.044
#> GSM627165 2 0.5330 0.6134 0.000 0.620 0.004 0.312 0.064
#> GSM627168 3 0.5341 0.5842 0.212 0.000 0.664 0.000 0.124
#> GSM627183 3 0.4855 0.6397 0.168 0.000 0.720 0.000 0.112
#> GSM627144 3 0.3123 0.6273 0.000 0.000 0.812 0.004 0.184
#> GSM627158 1 0.1117 0.8430 0.964 0.000 0.016 0.000 0.020
#> GSM627196 2 0.0898 0.8099 0.000 0.972 0.000 0.008 0.020
#> GSM627142 5 0.6910 0.0000 0.004 0.000 0.292 0.312 0.392
#> GSM627182 3 0.3955 0.6084 0.000 0.084 0.800 0.000 0.116
#> GSM627202 3 0.3649 0.6920 0.056 0.000 0.848 0.032 0.064
#> GSM627141 3 0.4365 0.6718 0.116 0.000 0.768 0.000 0.116
#> GSM627143 2 0.6742 0.3323 0.000 0.552 0.032 0.244 0.172
#> GSM627145 3 0.2082 0.6985 0.024 0.000 0.928 0.016 0.032
#> GSM627152 3 0.3848 0.5689 0.000 0.000 0.788 0.040 0.172
#> GSM627200 3 0.3170 0.6827 0.036 0.000 0.876 0.040 0.048
#> GSM627159 4 0.5039 -0.1168 0.000 0.000 0.032 0.512 0.456
#> GSM627164 2 0.1443 0.8027 0.000 0.948 0.004 0.004 0.044
#> GSM627138 1 0.2782 0.8176 0.880 0.000 0.072 0.000 0.048
#> GSM627175 4 0.4907 0.2137 0.000 0.292 0.000 0.656 0.052
#> GSM627150 3 0.3053 0.7035 0.044 0.000 0.872 0.008 0.076
#> GSM627166 1 0.5065 0.7579 0.692 0.000 0.068 0.008 0.232
#> GSM627186 2 0.1492 0.8018 0.000 0.948 0.008 0.004 0.040
#> GSM627139 4 0.7603 -0.5054 0.008 0.028 0.340 0.364 0.260
#> GSM627181 2 0.2389 0.8009 0.000 0.880 0.000 0.116 0.004
#> GSM627205 3 0.6469 0.2152 0.000 0.300 0.564 0.044 0.092
#> GSM627214 2 0.5516 0.5881 0.000 0.608 0.000 0.296 0.096
#> GSM627180 3 0.5077 0.5364 0.000 0.120 0.736 0.020 0.124
#> GSM627172 2 0.2580 0.7966 0.000 0.892 0.000 0.064 0.044
#> GSM627184 1 0.1121 0.8396 0.956 0.000 0.000 0.000 0.044
#> GSM627193 2 0.1041 0.8153 0.000 0.964 0.000 0.032 0.004
#> GSM627191 4 0.7630 0.0711 0.284 0.024 0.020 0.436 0.236
#> GSM627176 3 0.4360 0.5277 0.008 0.000 0.728 0.024 0.240
#> GSM627194 2 0.4558 0.7321 0.000 0.724 0.000 0.216 0.060
#> GSM627154 4 0.3123 0.4904 0.000 0.184 0.000 0.812 0.004
#> GSM627187 3 0.4425 0.6756 0.112 0.004 0.772 0.000 0.112
#> GSM627198 4 0.3123 0.4898 0.000 0.184 0.000 0.812 0.004
#> GSM627160 4 0.7619 -0.0486 0.004 0.088 0.136 0.464 0.308
#> GSM627185 1 0.3365 0.8221 0.836 0.000 0.044 0.000 0.120
#> GSM627206 3 0.4171 0.6790 0.104 0.000 0.784 0.000 0.112
#> GSM627161 1 0.1117 0.8430 0.964 0.000 0.016 0.000 0.020
#> GSM627162 4 0.8166 0.1170 0.000 0.232 0.116 0.368 0.284
#> GSM627210 1 0.3914 0.8023 0.788 0.000 0.048 0.000 0.164
#> GSM627189 2 0.1041 0.8153 0.000 0.964 0.000 0.032 0.004
show/hide code output
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM627128 6 0.1065 0.6032 0.000 0.000 0.008 0.020 0.008 0.964
#> GSM627110 5 0.4517 0.3458 0.004 0.000 0.464 0.016 0.512 0.004
#> GSM627132 1 0.2562 0.8067 0.828 0.000 0.172 0.000 0.000 0.000
#> GSM627107 5 0.4766 0.6574 0.000 0.000 0.072 0.044 0.724 0.160
#> GSM627103 2 0.3847 0.5759 0.000 0.644 0.008 0.348 0.000 0.000
#> GSM627114 5 0.3604 0.6841 0.012 0.000 0.216 0.012 0.760 0.000
#> GSM627134 4 0.5287 0.0898 0.000 0.028 0.028 0.500 0.008 0.436
#> GSM627137 4 0.4411 0.1322 0.000 0.356 0.028 0.612 0.000 0.004
#> GSM627148 5 0.4195 0.7126 0.000 0.016 0.060 0.032 0.796 0.096
#> GSM627101 6 0.5060 0.2899 0.000 0.000 0.060 0.324 0.016 0.600
#> GSM627130 6 0.1109 0.6050 0.004 0.000 0.012 0.016 0.004 0.964
#> GSM627071 5 0.3151 0.7521 0.004 0.000 0.072 0.016 0.856 0.052
#> GSM627118 4 0.5537 0.0795 0.000 0.016 0.056 0.500 0.012 0.416
#> GSM627094 2 0.2135 0.7479 0.000 0.872 0.000 0.128 0.000 0.000
#> GSM627122 5 0.2981 0.7351 0.008 0.000 0.040 0.000 0.852 0.100
#> GSM627115 2 0.3847 0.5759 0.000 0.644 0.008 0.348 0.000 0.000
#> GSM627125 6 0.0976 0.6043 0.000 0.000 0.008 0.016 0.008 0.968
#> GSM627174 2 0.3804 0.5523 0.000 0.656 0.008 0.336 0.000 0.000
#> GSM627102 2 0.2249 0.7266 0.000 0.900 0.004 0.064 0.000 0.032
#> GSM627073 5 0.3228 0.7406 0.000 0.024 0.068 0.032 0.860 0.016
#> GSM627108 2 0.2135 0.7479 0.000 0.872 0.000 0.128 0.000 0.000
#> GSM627126 1 0.0146 0.8369 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM627078 4 0.3570 0.4603 0.000 0.004 0.016 0.752 0.000 0.228
#> GSM627090 5 0.3418 0.6947 0.008 0.000 0.016 0.000 0.784 0.192
#> GSM627099 2 0.4634 0.2959 0.000 0.496 0.008 0.472 0.000 0.024
#> GSM627105 6 0.0976 0.6043 0.000 0.000 0.008 0.016 0.008 0.968
#> GSM627117 5 0.3679 0.6888 0.008 0.004 0.208 0.016 0.764 0.000
#> GSM627121 5 0.5104 0.6616 0.000 0.016 0.072 0.052 0.724 0.136
#> GSM627127 4 0.3590 0.4660 0.000 0.004 0.032 0.776 0.000 0.188
#> GSM627087 2 0.3847 0.5759 0.000 0.644 0.008 0.348 0.000 0.000
#> GSM627089 5 0.3341 0.6928 0.004 0.000 0.208 0.012 0.776 0.000
#> GSM627092 6 0.8002 0.2587 0.000 0.252 0.076 0.156 0.104 0.412
#> GSM627076 5 0.3454 0.6739 0.004 0.000 0.012 0.000 0.760 0.224
#> GSM627136 5 0.3321 0.7103 0.008 0.000 0.180 0.000 0.796 0.016
#> GSM627081 5 0.4766 0.6574 0.000 0.000 0.072 0.044 0.724 0.160
#> GSM627091 2 0.4634 0.2959 0.000 0.496 0.008 0.472 0.000 0.024
#> GSM627097 4 0.5580 0.2065 0.052 0.008 0.024 0.552 0.004 0.360
#> GSM627072 5 0.2451 0.7480 0.004 0.000 0.068 0.000 0.888 0.040
#> GSM627080 1 0.2982 0.8112 0.828 0.000 0.152 0.008 0.012 0.000
#> GSM627088 5 0.4762 0.6386 0.060 0.000 0.232 0.004 0.688 0.016
#> GSM627109 3 0.4378 0.6845 0.328 0.000 0.632 0.000 0.040 0.000
#> GSM627111 1 0.2562 0.8067 0.828 0.000 0.172 0.000 0.000 0.000
#> GSM627113 5 0.4867 0.5587 0.076 0.000 0.272 0.008 0.644 0.000
#> GSM627133 5 0.5787 0.5098 0.000 0.208 0.080 0.088 0.624 0.000
#> GSM627177 3 0.6921 0.6464 0.244 0.000 0.484 0.004 0.180 0.088
#> GSM627086 4 0.3852 0.2063 0.000 0.324 0.012 0.664 0.000 0.000
#> GSM627095 6 0.6103 0.4356 0.328 0.008 0.012 0.136 0.004 0.512
#> GSM627079 5 0.2476 0.7407 0.008 0.000 0.032 0.000 0.888 0.072
#> GSM627082 6 0.1109 0.6048 0.004 0.000 0.012 0.016 0.004 0.964
#> GSM627074 3 0.3782 0.6875 0.116 0.000 0.796 0.004 0.080 0.004
#> GSM627077 5 0.4324 0.7129 0.012 0.000 0.168 0.004 0.748 0.068
#> GSM627093 3 0.3782 0.6875 0.116 0.000 0.796 0.004 0.080 0.004
#> GSM627120 4 0.6456 0.0270 0.000 0.372 0.040 0.460 0.116 0.012
#> GSM627124 4 0.3570 0.4603 0.000 0.004 0.016 0.752 0.000 0.228
#> GSM627075 2 0.0000 0.7514 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627085 4 0.3653 0.4589 0.000 0.004 0.020 0.748 0.000 0.228
#> GSM627119 3 0.4424 0.6893 0.324 0.000 0.632 0.000 0.044 0.000
#> GSM627116 3 0.6921 0.6464 0.244 0.000 0.484 0.004 0.180 0.088
#> GSM627084 5 0.4762 0.6386 0.060 0.000 0.232 0.004 0.688 0.016
#> GSM627096 4 0.5537 0.0795 0.000 0.016 0.056 0.500 0.012 0.416
#> GSM627100 5 0.3454 0.6739 0.004 0.000 0.012 0.000 0.760 0.224
#> GSM627112 6 0.4787 0.0714 0.004 0.000 0.032 0.456 0.004 0.504
#> GSM627083 6 0.6103 0.4356 0.328 0.008 0.012 0.136 0.004 0.512
#> GSM627098 5 0.4762 0.6386 0.060 0.000 0.232 0.004 0.688 0.016
#> GSM627104 3 0.4392 0.6798 0.332 0.000 0.628 0.000 0.040 0.000
#> GSM627131 5 0.2476 0.7407 0.008 0.000 0.032 0.000 0.888 0.072
#> GSM627106 5 0.4766 0.6574 0.000 0.000 0.072 0.044 0.724 0.160
#> GSM627123 1 0.2265 0.8277 0.912 0.000 0.032 0.004 0.024 0.028
#> GSM627129 4 0.5292 0.0734 0.000 0.032 0.024 0.488 0.008 0.448
#> GSM627216 5 0.5787 0.5098 0.000 0.208 0.080 0.088 0.624 0.000
#> GSM627212 2 0.4634 0.2959 0.000 0.496 0.008 0.472 0.000 0.024
#> GSM627190 5 0.3679 0.6888 0.008 0.004 0.208 0.016 0.764 0.000
#> GSM627169 2 0.0146 0.7507 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM627167 2 0.4178 0.6025 0.000 0.764 0.012 0.104 0.000 0.120
#> GSM627192 1 0.0146 0.8369 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM627203 5 0.1949 0.7349 0.000 0.000 0.088 0.004 0.904 0.004
#> GSM627151 6 0.7405 0.3723 0.008 0.016 0.064 0.192 0.312 0.408
#> GSM627163 1 0.2092 0.8195 0.876 0.000 0.124 0.000 0.000 0.000
#> GSM627211 2 0.0790 0.7588 0.000 0.968 0.000 0.032 0.000 0.000
#> GSM627171 2 0.0551 0.7503 0.000 0.984 0.008 0.004 0.004 0.000
#> GSM627209 4 0.3809 0.3248 0.000 0.264 0.012 0.716 0.000 0.008
#> GSM627135 1 0.2657 0.7648 0.880 0.000 0.076 0.000 0.024 0.020
#> GSM627170 4 0.5065 -0.0714 0.000 0.400 0.052 0.536 0.012 0.000
#> GSM627178 3 0.6921 0.6464 0.244 0.000 0.484 0.004 0.180 0.088
#> GSM627199 4 0.3976 0.4661 0.000 0.020 0.020 0.740 0.000 0.220
#> GSM627213 4 0.5154 0.0884 0.000 0.020 0.028 0.504 0.008 0.440
#> GSM627140 6 0.5946 0.3541 0.008 0.160 0.024 0.196 0.004 0.608
#> GSM627149 1 0.2265 0.8277 0.912 0.000 0.032 0.004 0.024 0.028
#> GSM627147 2 0.4263 0.5942 0.000 0.756 0.012 0.108 0.000 0.124
#> GSM627195 5 0.1949 0.7349 0.000 0.000 0.088 0.004 0.904 0.004
#> GSM627204 2 0.0790 0.7588 0.000 0.968 0.000 0.032 0.000 0.000
#> GSM627207 2 0.0713 0.7580 0.000 0.972 0.000 0.028 0.000 0.000
#> GSM627157 5 0.4717 0.5792 0.064 0.000 0.272 0.008 0.656 0.000
#> GSM627201 2 0.3804 0.5523 0.000 0.656 0.008 0.336 0.000 0.000
#> GSM627146 2 0.3290 0.6860 0.000 0.744 0.000 0.252 0.000 0.004
#> GSM627156 2 0.0146 0.7507 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM627188 1 0.0146 0.8369 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM627197 2 0.3290 0.6860 0.000 0.744 0.000 0.252 0.000 0.004
#> GSM627173 2 0.2912 0.7158 0.000 0.784 0.000 0.216 0.000 0.000
#> GSM627179 2 0.3371 0.6431 0.000 0.708 0.000 0.292 0.000 0.000
#> GSM627208 5 0.4165 0.6895 0.000 0.056 0.108 0.052 0.784 0.000
#> GSM627215 5 0.5106 0.6425 0.000 0.088 0.108 0.080 0.720 0.004
#> GSM627153 4 0.3809 0.3248 0.000 0.264 0.012 0.716 0.000 0.008
#> GSM627155 1 0.0146 0.8369 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM627165 4 0.4534 0.1397 0.000 0.352 0.028 0.612 0.004 0.004
#> GSM627168 5 0.4717 0.5792 0.064 0.000 0.272 0.008 0.656 0.000
#> GSM627183 5 0.4130 0.6461 0.036 0.000 0.240 0.008 0.716 0.000
#> GSM627144 5 0.3087 0.6967 0.000 0.000 0.176 0.012 0.808 0.004
#> GSM627158 1 0.2834 0.8232 0.848 0.000 0.128 0.008 0.016 0.000
#> GSM627196 2 0.0790 0.7588 0.000 0.968 0.000 0.032 0.000 0.000
#> GSM627142 6 0.3691 0.4517 0.008 0.000 0.008 0.000 0.260 0.724
#> GSM627182 5 0.4165 0.6895 0.000 0.056 0.108 0.052 0.784 0.000
#> GSM627202 5 0.3400 0.7383 0.004 0.000 0.064 0.008 0.832 0.092
#> GSM627141 5 0.3596 0.6840 0.008 0.000 0.216 0.016 0.760 0.000
#> GSM627143 2 0.6358 0.2456 0.000 0.552 0.028 0.136 0.024 0.260
#> GSM627145 5 0.1794 0.7484 0.000 0.000 0.036 0.000 0.924 0.040
#> GSM627152 5 0.3488 0.6847 0.004 0.000 0.016 0.000 0.764 0.216
#> GSM627200 5 0.2841 0.7374 0.012 0.000 0.032 0.000 0.864 0.092
#> GSM627159 6 0.1109 0.6048 0.004 0.000 0.012 0.016 0.004 0.964
#> GSM627164 2 0.0551 0.7503 0.000 0.984 0.008 0.004 0.004 0.000
#> GSM627138 1 0.4044 0.7047 0.756 0.000 0.176 0.008 0.060 0.000
#> GSM627175 4 0.1991 0.4839 0.000 0.012 0.024 0.920 0.000 0.044
#> GSM627150 5 0.3151 0.7521 0.004 0.000 0.072 0.016 0.856 0.052
#> GSM627166 3 0.5289 0.7011 0.300 0.000 0.612 0.004 0.052 0.032
#> GSM627186 2 0.0405 0.7500 0.000 0.988 0.000 0.008 0.004 0.000
#> GSM627139 6 0.7405 0.3723 0.008 0.016 0.064 0.192 0.312 0.408
#> GSM627181 2 0.3290 0.6860 0.000 0.744 0.000 0.252 0.000 0.004
#> GSM627205 5 0.6477 0.3825 0.000 0.180 0.064 0.204 0.548 0.004
#> GSM627214 4 0.4871 0.1974 0.000 0.324 0.024 0.616 0.000 0.036
#> GSM627180 5 0.5106 0.6425 0.000 0.088 0.108 0.080 0.720 0.004
#> GSM627172 2 0.2249 0.7266 0.000 0.900 0.004 0.064 0.000 0.032
#> GSM627184 1 0.0146 0.8369 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM627193 2 0.2300 0.7424 0.000 0.856 0.000 0.144 0.000 0.000
#> GSM627191 6 0.6276 0.4372 0.308 0.016 0.012 0.144 0.004 0.516
#> GSM627176 5 0.4649 0.6521 0.004 0.000 0.100 0.008 0.716 0.172
#> GSM627194 4 0.4407 -0.2336 0.000 0.480 0.024 0.496 0.000 0.000
#> GSM627154 4 0.3653 0.4589 0.000 0.004 0.020 0.748 0.000 0.228
#> GSM627187 5 0.3679 0.6888 0.008 0.004 0.208 0.016 0.764 0.000
#> GSM627198 4 0.3761 0.4618 0.000 0.008 0.020 0.744 0.000 0.228
#> GSM627160 6 0.6546 0.4708 0.004 0.064 0.032 0.176 0.120 0.604
#> GSM627185 1 0.4523 0.1891 0.592 0.000 0.372 0.004 0.032 0.000
#> GSM627206 5 0.3341 0.6928 0.004 0.000 0.208 0.012 0.776 0.000
#> GSM627161 1 0.2834 0.8232 0.848 0.000 0.128 0.008 0.016 0.000
#> GSM627162 6 0.8025 0.2906 0.000 0.224 0.080 0.164 0.108 0.424
#> GSM627210 3 0.4424 0.6893 0.324 0.000 0.632 0.000 0.044 0.000
#> GSM627189 2 0.2300 0.7424 0.000 0.856 0.000 0.144 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two
samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures
which are significantly different between classes which can be candidate marks
for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known
annotation is numeric, one-way ANOVA test is applied, and if the known
annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) age(p) other(p) k
#> MAD:hclust 112 0.9633 0.676 0.0337 2
#> MAD:hclust 131 0.4944 0.710 0.1062 3
#> MAD:hclust 124 0.0410 0.315 0.4656 4
#> MAD:hclust 107 0.2172 0.570 0.3262 5
#> MAD:hclust 104 0.0269 0.461 0.6200 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
MAD:kmeans*
The object with results only for a single top-value method and a single partition method
can be extracted as:
res = res_list["MAD", "kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51882 rows and 146 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
- The first row: a plot of the ECDF (empirical cumulative distribution
function) curves of the consensus matrix for each
k and the heatmap of
predicted classes for each k.
- The second row: heatmaps of the consensus matrix for each
k.
- The third row: heatmaps of the membership matrix for each
k.
- The fouth row: heatmaps of the signatures for each
k.
All the plots in panels can be made by individual functions and they are
plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
- ECDF curves of the consensus matrix for each
k;
- 1-PAC. The PAC
score
measures the proportion of the ambiguous subgrouping.
- Mean silhouette score.
- Concordance. The mean probability of fiting the consensus class ids in all
partitions.
- Area increased. Denote \(A_k\) as the area under the ECDF curve for current
k, the area increased is defined as \(A_k - A_{k-1}\).
- Rand index. The percent of pairs of samples that are both in a same cluster
or both are not in a same cluster in the partition of k and k-1.
- Jaccard index. The ratio of pairs of samples are both in a same cluster in
the partition of k and k-1 and the pairs of samples are both in a same
cluster in the partition k or k-1.
The detailed explanations of these statistics can be found in the cola
vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.929 0.938 0.973 0.5019 0.498 0.498
#> 3 3 0.567 0.657 0.816 0.2988 0.770 0.570
#> 4 4 0.662 0.752 0.838 0.1363 0.771 0.441
#> 5 5 0.655 0.532 0.705 0.0649 0.933 0.757
#> 6 6 0.664 0.478 0.679 0.0429 0.842 0.440
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
- All \(k\) with Jaccard index larger than 0.95 are removed because increasing
\(k\) does not provide enough extra information. If all \(k\) are removed, it is
marked as no subgroup is detected.
- For all \(k\) with 1-PAC score larger than 0.9, the maximal \(k\) is taken as
the best \(k\), and other \(k\) are marked as optional \(k\).
- If it does not fit the second rule. The \(k\) with the maximal vote of the
highest 1-PAC score, highest mean silhouette, and highest concordance is
taken as the best \(k\).
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
show/hide code output
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM627128 2 0.8081 0.694 0.248 0.752
#> GSM627110 1 0.0000 0.987 1.000 0.000
#> GSM627132 1 0.0000 0.987 1.000 0.000
#> GSM627107 2 0.8909 0.594 0.308 0.692
#> GSM627103 2 0.0000 0.957 0.000 1.000
#> GSM627114 1 0.0000 0.987 1.000 0.000
#> GSM627134 2 0.0000 0.957 0.000 1.000
#> GSM627137 2 0.0000 0.957 0.000 1.000
#> GSM627148 1 0.0000 0.987 1.000 0.000
#> GSM627101 2 0.0000 0.957 0.000 1.000
#> GSM627130 2 0.0000 0.957 0.000 1.000
#> GSM627071 1 0.0000 0.987 1.000 0.000
#> GSM627118 2 0.0000 0.957 0.000 1.000
#> GSM627094 2 0.0000 0.957 0.000 1.000
#> GSM627122 1 0.0000 0.987 1.000 0.000
#> GSM627115 2 0.0000 0.957 0.000 1.000
#> GSM627125 2 0.7883 0.712 0.236 0.764
#> GSM627174 2 0.0000 0.957 0.000 1.000
#> GSM627102 2 0.0000 0.957 0.000 1.000
#> GSM627073 1 0.3584 0.917 0.932 0.068
#> GSM627108 2 0.0000 0.957 0.000 1.000
#> GSM627126 1 0.0000 0.987 1.000 0.000
#> GSM627078 2 0.0000 0.957 0.000 1.000
#> GSM627090 1 0.0000 0.987 1.000 0.000
#> GSM627099 2 0.0000 0.957 0.000 1.000
#> GSM627105 2 0.6148 0.817 0.152 0.848
#> GSM627117 1 0.0000 0.987 1.000 0.000
#> GSM627121 2 0.8909 0.594 0.308 0.692
#> GSM627127 2 0.0000 0.957 0.000 1.000
#> GSM627087 2 0.0000 0.957 0.000 1.000
#> GSM627089 1 0.0000 0.987 1.000 0.000
#> GSM627092 2 0.0000 0.957 0.000 1.000
#> GSM627076 1 0.0000 0.987 1.000 0.000
#> GSM627136 1 0.0000 0.987 1.000 0.000
#> GSM627081 1 0.3584 0.917 0.932 0.068
#> GSM627091 2 0.0000 0.957 0.000 1.000
#> GSM627097 2 0.0000 0.957 0.000 1.000
#> GSM627072 1 0.0000 0.987 1.000 0.000
#> GSM627080 1 0.0000 0.987 1.000 0.000
#> GSM627088 1 0.0000 0.987 1.000 0.000
#> GSM627109 1 0.0000 0.987 1.000 0.000
#> GSM627111 1 0.0000 0.987 1.000 0.000
#> GSM627113 1 0.0000 0.987 1.000 0.000
#> GSM627133 2 0.0376 0.954 0.004 0.996
#> GSM627177 1 0.0000 0.987 1.000 0.000
#> GSM627086 2 0.0000 0.957 0.000 1.000
#> GSM627095 1 0.0000 0.987 1.000 0.000
#> GSM627079 1 0.0000 0.987 1.000 0.000
#> GSM627082 2 0.9209 0.539 0.336 0.664
#> GSM627074 1 0.0000 0.987 1.000 0.000
#> GSM627077 1 0.0000 0.987 1.000 0.000
#> GSM627093 1 0.0000 0.987 1.000 0.000
#> GSM627120 2 0.0000 0.957 0.000 1.000
#> GSM627124 2 0.0000 0.957 0.000 1.000
#> GSM627075 2 0.0000 0.957 0.000 1.000
#> GSM627085 2 0.0000 0.957 0.000 1.000
#> GSM627119 1 0.0000 0.987 1.000 0.000
#> GSM627116 2 0.8267 0.674 0.260 0.740
#> GSM627084 1 0.0000 0.987 1.000 0.000
#> GSM627096 2 0.0000 0.957 0.000 1.000
#> GSM627100 1 0.0000 0.987 1.000 0.000
#> GSM627112 2 0.0000 0.957 0.000 1.000
#> GSM627083 2 0.8267 0.675 0.260 0.740
#> GSM627098 1 0.0000 0.987 1.000 0.000
#> GSM627104 1 0.0000 0.987 1.000 0.000
#> GSM627131 1 0.0000 0.987 1.000 0.000
#> GSM627106 1 0.3584 0.917 0.932 0.068
#> GSM627123 1 0.0000 0.987 1.000 0.000
#> GSM627129 2 0.0000 0.957 0.000 1.000
#> GSM627216 2 0.0000 0.957 0.000 1.000
#> GSM627212 2 0.0000 0.957 0.000 1.000
#> GSM627190 1 0.0000 0.987 1.000 0.000
#> GSM627169 2 0.0000 0.957 0.000 1.000
#> GSM627167 2 0.0000 0.957 0.000 1.000
#> GSM627192 1 0.0000 0.987 1.000 0.000
#> GSM627203 1 0.0000 0.987 1.000 0.000
#> GSM627151 2 0.0938 0.948 0.012 0.988
#> GSM627163 1 0.0000 0.987 1.000 0.000
#> GSM627211 2 0.0000 0.957 0.000 1.000
#> GSM627171 2 0.0000 0.957 0.000 1.000
#> GSM627209 2 0.0000 0.957 0.000 1.000
#> GSM627135 1 0.0000 0.987 1.000 0.000
#> GSM627170 2 0.0000 0.957 0.000 1.000
#> GSM627178 1 0.0000 0.987 1.000 0.000
#> GSM627199 2 0.0000 0.957 0.000 1.000
#> GSM627213 2 0.0000 0.957 0.000 1.000
#> GSM627140 2 0.0000 0.957 0.000 1.000
#> GSM627149 1 0.0000 0.987 1.000 0.000
#> GSM627147 2 0.0000 0.957 0.000 1.000
#> GSM627195 1 0.0000 0.987 1.000 0.000
#> GSM627204 2 0.0000 0.957 0.000 1.000
#> GSM627207 2 0.0000 0.957 0.000 1.000
#> GSM627157 1 0.0000 0.987 1.000 0.000
#> GSM627201 2 0.0000 0.957 0.000 1.000
#> GSM627146 2 0.0000 0.957 0.000 1.000
#> GSM627156 2 0.0000 0.957 0.000 1.000
#> GSM627188 1 0.0000 0.987 1.000 0.000
#> GSM627197 2 0.0000 0.957 0.000 1.000
#> GSM627173 2 0.0000 0.957 0.000 1.000
#> GSM627179 2 0.0000 0.957 0.000 1.000
#> GSM627208 2 0.7528 0.735 0.216 0.784
#> GSM627215 2 0.0000 0.957 0.000 1.000
#> GSM627153 2 0.0000 0.957 0.000 1.000
#> GSM627155 1 0.0000 0.987 1.000 0.000
#> GSM627165 2 0.0000 0.957 0.000 1.000
#> GSM627168 1 0.0000 0.987 1.000 0.000
#> GSM627183 1 0.0000 0.987 1.000 0.000
#> GSM627144 1 0.0000 0.987 1.000 0.000
#> GSM627158 1 0.0000 0.987 1.000 0.000
#> GSM627196 2 0.0000 0.957 0.000 1.000
#> GSM627142 1 0.0000 0.987 1.000 0.000
#> GSM627182 1 0.0000 0.987 1.000 0.000
#> GSM627202 1 0.0000 0.987 1.000 0.000
#> GSM627141 1 0.0000 0.987 1.000 0.000
#> GSM627143 2 0.0000 0.957 0.000 1.000
#> GSM627145 1 0.0000 0.987 1.000 0.000
#> GSM627152 1 0.0000 0.987 1.000 0.000
#> GSM627200 1 0.0000 0.987 1.000 0.000
#> GSM627159 2 0.9209 0.539 0.336 0.664
#> GSM627164 2 0.0000 0.957 0.000 1.000
#> GSM627138 1 0.0000 0.987 1.000 0.000
#> GSM627175 2 0.0000 0.957 0.000 1.000
#> GSM627150 1 0.0000 0.987 1.000 0.000
#> GSM627166 1 0.0000 0.987 1.000 0.000
#> GSM627186 2 0.0000 0.957 0.000 1.000
#> GSM627139 2 0.9922 0.256 0.448 0.552
#> GSM627181 2 0.0000 0.957 0.000 1.000
#> GSM627205 2 0.0000 0.957 0.000 1.000
#> GSM627214 2 0.0000 0.957 0.000 1.000
#> GSM627180 1 0.9580 0.339 0.620 0.380
#> GSM627172 2 0.0000 0.957 0.000 1.000
#> GSM627184 1 0.0000 0.987 1.000 0.000
#> GSM627193 2 0.0000 0.957 0.000 1.000
#> GSM627191 2 0.2236 0.929 0.036 0.964
#> GSM627176 1 0.0000 0.987 1.000 0.000
#> GSM627194 2 0.0000 0.957 0.000 1.000
#> GSM627154 2 0.0000 0.957 0.000 1.000
#> GSM627187 1 0.0000 0.987 1.000 0.000
#> GSM627198 2 0.0000 0.957 0.000 1.000
#> GSM627160 1 0.8016 0.657 0.756 0.244
#> GSM627185 1 0.0000 0.987 1.000 0.000
#> GSM627206 1 0.0000 0.987 1.000 0.000
#> GSM627161 1 0.0000 0.987 1.000 0.000
#> GSM627162 1 0.0000 0.987 1.000 0.000
#> GSM627210 1 0.0000 0.987 1.000 0.000
#> GSM627189 2 0.0000 0.957 0.000 1.000
show/hide code output
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM627128 3 0.3572 0.5818 0.060 0.040 0.900
#> GSM627110 1 0.6154 0.3016 0.592 0.000 0.408
#> GSM627132 1 0.0000 0.7525 1.000 0.000 0.000
#> GSM627107 3 0.1964 0.6125 0.056 0.000 0.944
#> GSM627103 2 0.0237 0.8675 0.000 0.996 0.004
#> GSM627114 1 0.6126 0.3215 0.600 0.000 0.400
#> GSM627134 2 0.6225 0.5836 0.000 0.568 0.432
#> GSM627137 2 0.0000 0.8680 0.000 1.000 0.000
#> GSM627148 3 0.5785 0.5351 0.332 0.000 0.668
#> GSM627101 3 0.6062 -0.2154 0.000 0.384 0.616
#> GSM627130 3 0.4725 0.5471 0.060 0.088 0.852
#> GSM627071 3 0.6295 0.1911 0.472 0.000 0.528
#> GSM627118 2 0.6235 0.5768 0.000 0.564 0.436
#> GSM627094 2 0.0237 0.8675 0.000 0.996 0.004
#> GSM627122 3 0.6295 0.3037 0.472 0.000 0.528
#> GSM627115 2 0.0237 0.8675 0.000 0.996 0.004
#> GSM627125 3 0.3337 0.5857 0.060 0.032 0.908
#> GSM627174 2 0.0000 0.8680 0.000 1.000 0.000
#> GSM627102 2 0.0000 0.8680 0.000 1.000 0.000
#> GSM627073 3 0.5016 0.6172 0.240 0.000 0.760
#> GSM627108 2 0.0237 0.8675 0.000 0.996 0.004
#> GSM627126 1 0.2261 0.7187 0.932 0.000 0.068
#> GSM627078 2 0.4654 0.8061 0.000 0.792 0.208
#> GSM627090 3 0.5835 0.5699 0.340 0.000 0.660
#> GSM627099 2 0.4121 0.8246 0.000 0.832 0.168
#> GSM627105 3 0.3456 0.5840 0.060 0.036 0.904
#> GSM627117 1 0.6168 0.2942 0.588 0.000 0.412
#> GSM627121 3 0.3112 0.6158 0.056 0.028 0.916
#> GSM627127 2 0.4750 0.8016 0.000 0.784 0.216
#> GSM627087 2 0.0237 0.8675 0.000 0.996 0.004
#> GSM627089 3 0.6308 0.0995 0.492 0.000 0.508
#> GSM627092 2 0.2356 0.8452 0.000 0.928 0.072
#> GSM627076 3 0.5291 0.5836 0.268 0.000 0.732
#> GSM627136 1 0.6140 0.3129 0.596 0.000 0.404
#> GSM627081 3 0.4887 0.6222 0.228 0.000 0.772
#> GSM627091 2 0.0000 0.8680 0.000 1.000 0.000
#> GSM627097 2 0.6026 0.6729 0.000 0.624 0.376
#> GSM627072 3 0.6154 0.3825 0.408 0.000 0.592
#> GSM627080 1 0.0592 0.7497 0.988 0.000 0.012
#> GSM627088 1 0.6140 0.3129 0.596 0.000 0.404
#> GSM627109 1 0.1411 0.7527 0.964 0.000 0.036
#> GSM627111 1 0.0000 0.7525 1.000 0.000 0.000
#> GSM627113 1 0.3116 0.7331 0.892 0.000 0.108
#> GSM627133 3 0.7262 0.4660 0.044 0.332 0.624
#> GSM627177 3 0.6295 0.1911 0.472 0.000 0.528
#> GSM627086 2 0.0000 0.8680 0.000 1.000 0.000
#> GSM627095 1 0.2356 0.7152 0.928 0.000 0.072
#> GSM627079 3 0.5988 0.4726 0.368 0.000 0.632
#> GSM627082 3 0.3690 0.5698 0.100 0.016 0.884
#> GSM627074 1 0.2878 0.7384 0.904 0.000 0.096
#> GSM627077 1 0.5882 0.3503 0.652 0.000 0.348
#> GSM627093 1 0.2878 0.7384 0.904 0.000 0.096
#> GSM627120 2 0.5859 0.7004 0.000 0.656 0.344
#> GSM627124 2 0.4654 0.8061 0.000 0.792 0.208
#> GSM627075 2 0.0237 0.8675 0.000 0.996 0.004
#> GSM627085 2 0.4654 0.8061 0.000 0.792 0.208
#> GSM627119 1 0.3038 0.7350 0.896 0.000 0.104
#> GSM627116 3 0.6297 0.4472 0.060 0.184 0.756
#> GSM627084 1 0.0424 0.7508 0.992 0.000 0.008
#> GSM627096 2 0.6235 0.5768 0.000 0.564 0.436
#> GSM627100 3 0.4842 0.6045 0.224 0.000 0.776
#> GSM627112 2 0.6962 0.6072 0.020 0.568 0.412
#> GSM627083 1 0.7065 0.2782 0.644 0.040 0.316
#> GSM627098 1 0.2165 0.7468 0.936 0.000 0.064
#> GSM627104 1 0.1964 0.7481 0.944 0.000 0.056
#> GSM627131 1 0.3340 0.7116 0.880 0.000 0.120
#> GSM627106 3 0.4750 0.6260 0.216 0.000 0.784
#> GSM627123 1 0.2261 0.7187 0.932 0.000 0.068
#> GSM627129 2 0.5835 0.7051 0.000 0.660 0.340
#> GSM627216 2 0.0237 0.8675 0.000 0.996 0.004
#> GSM627212 2 0.0000 0.8680 0.000 1.000 0.000
#> GSM627190 1 0.6168 0.2942 0.588 0.000 0.412
#> GSM627169 2 0.0424 0.8663 0.000 0.992 0.008
#> GSM627167 2 0.5882 0.6990 0.000 0.652 0.348
#> GSM627192 1 0.2356 0.7152 0.928 0.000 0.072
#> GSM627203 3 0.5650 0.5645 0.312 0.000 0.688
#> GSM627151 3 0.5138 0.3514 0.000 0.252 0.748
#> GSM627163 1 0.0592 0.7497 0.988 0.000 0.012
#> GSM627211 2 0.0000 0.8680 0.000 1.000 0.000
#> GSM627171 2 0.0424 0.8663 0.000 0.992 0.008
#> GSM627209 2 0.4291 0.8194 0.000 0.820 0.180
#> GSM627135 1 0.2261 0.7187 0.932 0.000 0.068
#> GSM627170 2 0.0237 0.8675 0.000 0.996 0.004
#> GSM627178 1 0.1753 0.7508 0.952 0.000 0.048
#> GSM627199 2 0.4121 0.8242 0.000 0.832 0.168
#> GSM627213 2 0.5810 0.7100 0.000 0.664 0.336
#> GSM627140 2 0.6962 0.6072 0.020 0.568 0.412
#> GSM627149 1 0.2261 0.7187 0.932 0.000 0.068
#> GSM627147 2 0.5291 0.7687 0.000 0.732 0.268
#> GSM627195 3 0.5591 0.5722 0.304 0.000 0.696
#> GSM627204 2 0.0000 0.8680 0.000 1.000 0.000
#> GSM627207 2 0.0237 0.8675 0.000 0.996 0.004
#> GSM627157 1 0.2356 0.7457 0.928 0.000 0.072
#> GSM627201 2 0.0000 0.8680 0.000 1.000 0.000
#> GSM627146 2 0.0000 0.8680 0.000 1.000 0.000
#> GSM627156 2 0.0424 0.8663 0.000 0.992 0.008
#> GSM627188 1 0.2356 0.7152 0.928 0.000 0.072
#> GSM627197 2 0.0000 0.8680 0.000 1.000 0.000
#> GSM627173 2 0.0237 0.8675 0.000 0.996 0.004
#> GSM627179 2 0.0237 0.8675 0.000 0.996 0.004
#> GSM627208 3 0.7528 0.4923 0.072 0.280 0.648
#> GSM627215 2 0.6111 0.2891 0.000 0.604 0.396
#> GSM627153 2 0.4654 0.8061 0.000 0.792 0.208
#> GSM627155 1 0.2261 0.7187 0.932 0.000 0.068
#> GSM627165 2 0.6079 0.6467 0.000 0.612 0.388
#> GSM627168 1 0.6140 0.3129 0.596 0.000 0.404
#> GSM627183 1 0.6062 0.3591 0.616 0.000 0.384
#> GSM627144 3 0.5591 0.5720 0.304 0.000 0.696
#> GSM627158 1 0.0592 0.7497 0.988 0.000 0.012
#> GSM627196 2 0.0000 0.8680 0.000 1.000 0.000
#> GSM627142 3 0.4504 0.6070 0.196 0.000 0.804
#> GSM627182 3 0.5968 0.4837 0.364 0.000 0.636
#> GSM627202 1 0.5529 0.4624 0.704 0.000 0.296
#> GSM627141 1 0.6126 0.3215 0.600 0.000 0.400
#> GSM627143 2 0.4235 0.8107 0.000 0.824 0.176
#> GSM627145 3 0.6126 0.4025 0.400 0.000 0.600
#> GSM627152 3 0.5859 0.5686 0.344 0.000 0.656
#> GSM627200 1 0.3412 0.7095 0.876 0.000 0.124
#> GSM627159 3 0.3678 0.5776 0.080 0.028 0.892
#> GSM627164 2 0.0424 0.8663 0.000 0.992 0.008
#> GSM627138 1 0.1753 0.7520 0.952 0.000 0.048
#> GSM627175 2 0.4654 0.8061 0.000 0.792 0.208
#> GSM627150 3 0.5621 0.5697 0.308 0.000 0.692
#> GSM627166 1 0.0592 0.7533 0.988 0.000 0.012
#> GSM627186 2 0.0747 0.8620 0.000 0.984 0.016
#> GSM627139 3 0.2486 0.5918 0.060 0.008 0.932
#> GSM627181 2 0.0000 0.8680 0.000 1.000 0.000
#> GSM627205 2 0.2261 0.8441 0.000 0.932 0.068
#> GSM627214 2 0.3752 0.8332 0.000 0.856 0.144
#> GSM627180 3 0.4654 0.6274 0.208 0.000 0.792
#> GSM627172 2 0.0000 0.8680 0.000 1.000 0.000
#> GSM627184 1 0.2356 0.7152 0.928 0.000 0.072
#> GSM627193 2 0.0237 0.8675 0.000 0.996 0.004
#> GSM627191 2 0.8489 0.5000 0.092 0.496 0.412
#> GSM627176 3 0.5785 0.5779 0.332 0.000 0.668
#> GSM627194 2 0.0237 0.8675 0.000 0.996 0.004
#> GSM627154 2 0.4750 0.8016 0.000 0.784 0.216
#> GSM627187 1 0.6168 0.2942 0.588 0.000 0.412
#> GSM627198 2 0.4121 0.8242 0.000 0.832 0.168
#> GSM627160 3 0.3856 0.5757 0.072 0.040 0.888
#> GSM627185 1 0.2066 0.7472 0.940 0.000 0.060
#> GSM627206 1 0.6154 0.3016 0.592 0.000 0.408
#> GSM627161 1 0.0892 0.7463 0.980 0.000 0.020
#> GSM627162 3 0.6215 0.3176 0.428 0.000 0.572
#> GSM627210 1 0.3192 0.7311 0.888 0.000 0.112
#> GSM627189 2 0.0237 0.8675 0.000 0.996 0.004
show/hide code output
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM627128 4 0.3464 0.6681 0.056 0.000 0.076 0.868
#> GSM627110 3 0.2149 0.7896 0.088 0.000 0.912 0.000
#> GSM627132 1 0.1867 0.8521 0.928 0.000 0.072 0.000
#> GSM627107 3 0.5405 0.6296 0.004 0.024 0.660 0.312
#> GSM627103 2 0.1022 0.9128 0.000 0.968 0.000 0.032
#> GSM627114 3 0.2593 0.7793 0.104 0.000 0.892 0.004
#> GSM627134 4 0.4549 0.7259 0.000 0.188 0.036 0.776
#> GSM627137 2 0.1635 0.9095 0.008 0.948 0.000 0.044
#> GSM627148 3 0.0336 0.8159 0.000 0.000 0.992 0.008
#> GSM627101 4 0.2730 0.6827 0.016 0.000 0.088 0.896
#> GSM627130 4 0.3245 0.6715 0.056 0.000 0.064 0.880
#> GSM627071 3 0.2060 0.8102 0.052 0.000 0.932 0.016
#> GSM627118 4 0.3958 0.7354 0.000 0.144 0.032 0.824
#> GSM627094 2 0.1398 0.9114 0.004 0.956 0.000 0.040
#> GSM627122 3 0.5266 0.7693 0.108 0.000 0.752 0.140
#> GSM627115 2 0.1022 0.9128 0.000 0.968 0.000 0.032
#> GSM627125 4 0.3464 0.6681 0.056 0.000 0.076 0.868
#> GSM627174 2 0.2053 0.9025 0.004 0.924 0.000 0.072
#> GSM627102 2 0.1732 0.9054 0.008 0.948 0.004 0.040
#> GSM627073 3 0.3088 0.8033 0.000 0.008 0.864 0.128
#> GSM627108 2 0.0188 0.9094 0.004 0.996 0.000 0.000
#> GSM627126 1 0.1854 0.8294 0.940 0.000 0.012 0.048
#> GSM627078 4 0.5080 0.4984 0.004 0.420 0.000 0.576
#> GSM627090 3 0.4104 0.7794 0.028 0.000 0.808 0.164
#> GSM627099 4 0.5132 0.4406 0.004 0.448 0.000 0.548
#> GSM627105 4 0.3464 0.6681 0.056 0.000 0.076 0.868
#> GSM627117 3 0.3030 0.7890 0.076 0.028 0.892 0.004
#> GSM627121 3 0.4579 0.7552 0.000 0.032 0.768 0.200
#> GSM627127 4 0.4372 0.6845 0.004 0.268 0.000 0.728
#> GSM627087 2 0.1022 0.9128 0.000 0.968 0.000 0.032
#> GSM627089 3 0.1824 0.8047 0.060 0.000 0.936 0.004
#> GSM627092 2 0.2186 0.8816 0.008 0.932 0.012 0.048
#> GSM627076 3 0.5386 0.7072 0.056 0.000 0.708 0.236
#> GSM627136 3 0.2466 0.7886 0.096 0.000 0.900 0.004
#> GSM627081 3 0.3636 0.7847 0.000 0.008 0.820 0.172
#> GSM627091 2 0.2466 0.8824 0.004 0.900 0.000 0.096
#> GSM627097 4 0.3649 0.7214 0.000 0.204 0.000 0.796
#> GSM627072 3 0.1209 0.8125 0.032 0.000 0.964 0.004
#> GSM627080 1 0.1716 0.8520 0.936 0.000 0.064 0.000
#> GSM627088 3 0.2408 0.7807 0.104 0.000 0.896 0.000
#> GSM627109 1 0.3074 0.8436 0.848 0.000 0.152 0.000
#> GSM627111 1 0.1867 0.8521 0.928 0.000 0.072 0.000
#> GSM627113 1 0.4761 0.6045 0.628 0.000 0.372 0.000
#> GSM627133 3 0.2892 0.7909 0.000 0.068 0.896 0.036
#> GSM627177 3 0.2142 0.8096 0.056 0.000 0.928 0.016
#> GSM627086 2 0.1978 0.9046 0.004 0.928 0.000 0.068
#> GSM627095 1 0.1854 0.8294 0.940 0.000 0.012 0.048
#> GSM627079 3 0.2805 0.8104 0.012 0.000 0.888 0.100
#> GSM627082 4 0.3323 0.6721 0.060 0.000 0.064 0.876
#> GSM627074 1 0.4304 0.7433 0.716 0.000 0.284 0.000
#> GSM627077 3 0.5010 0.5595 0.276 0.000 0.700 0.024
#> GSM627093 1 0.4643 0.6616 0.656 0.000 0.344 0.000
#> GSM627120 2 0.6991 0.0195 0.000 0.540 0.136 0.324
#> GSM627124 4 0.5080 0.4984 0.004 0.420 0.000 0.576
#> GSM627075 2 0.1229 0.8992 0.008 0.968 0.004 0.020
#> GSM627085 4 0.4584 0.6570 0.004 0.300 0.000 0.696
#> GSM627119 1 0.4830 0.5699 0.608 0.000 0.392 0.000
#> GSM627116 4 0.3360 0.7325 0.004 0.084 0.036 0.876
#> GSM627084 1 0.3172 0.8417 0.840 0.000 0.160 0.000
#> GSM627096 4 0.3907 0.7357 0.000 0.140 0.032 0.828
#> GSM627100 3 0.5508 0.6885 0.056 0.000 0.692 0.252
#> GSM627112 4 0.3143 0.7365 0.024 0.100 0.000 0.876
#> GSM627083 4 0.4977 0.2322 0.460 0.000 0.000 0.540
#> GSM627098 1 0.3356 0.8341 0.824 0.000 0.176 0.000
#> GSM627104 1 0.3266 0.8381 0.832 0.000 0.168 0.000
#> GSM627131 1 0.5311 0.6382 0.648 0.000 0.328 0.024
#> GSM627106 3 0.3636 0.7847 0.000 0.008 0.820 0.172
#> GSM627123 1 0.1854 0.8294 0.940 0.000 0.012 0.048
#> GSM627129 4 0.4420 0.7086 0.000 0.240 0.012 0.748
#> GSM627216 2 0.2578 0.8780 0.000 0.912 0.052 0.036
#> GSM627212 2 0.2266 0.8941 0.004 0.912 0.000 0.084
#> GSM627190 3 0.3030 0.7890 0.076 0.028 0.892 0.004
#> GSM627169 2 0.2927 0.8427 0.008 0.900 0.068 0.024
#> GSM627167 4 0.4540 0.7043 0.004 0.248 0.008 0.740
#> GSM627192 1 0.1854 0.8294 0.940 0.000 0.012 0.048
#> GSM627203 3 0.2973 0.7968 0.000 0.000 0.856 0.144
#> GSM627151 4 0.3970 0.7227 0.000 0.084 0.076 0.840
#> GSM627163 1 0.1716 0.8520 0.936 0.000 0.064 0.000
#> GSM627211 2 0.1902 0.9062 0.004 0.932 0.000 0.064
#> GSM627171 2 0.1739 0.8910 0.008 0.952 0.016 0.024
#> GSM627209 4 0.5132 0.4406 0.004 0.448 0.000 0.548
#> GSM627135 1 0.1677 0.8318 0.948 0.000 0.012 0.040
#> GSM627170 2 0.1617 0.8989 0.008 0.956 0.012 0.024
#> GSM627178 1 0.4898 0.7428 0.716 0.000 0.260 0.024
#> GSM627199 4 0.5143 0.4218 0.004 0.456 0.000 0.540
#> GSM627213 4 0.3873 0.7106 0.000 0.228 0.000 0.772
#> GSM627140 4 0.4680 0.7263 0.048 0.160 0.004 0.788
#> GSM627149 1 0.1854 0.8294 0.940 0.000 0.012 0.048
#> GSM627147 4 0.5210 0.6360 0.008 0.332 0.008 0.652
#> GSM627195 3 0.2868 0.7999 0.000 0.000 0.864 0.136
#> GSM627204 2 0.1978 0.9046 0.004 0.928 0.000 0.068
#> GSM627207 2 0.1339 0.8973 0.008 0.964 0.004 0.024
#> GSM627157 1 0.3649 0.8144 0.796 0.000 0.204 0.000
#> GSM627201 2 0.2053 0.9025 0.004 0.924 0.000 0.072
#> GSM627146 2 0.2053 0.9025 0.004 0.924 0.000 0.072
#> GSM627156 2 0.2927 0.8427 0.008 0.900 0.068 0.024
#> GSM627188 1 0.1854 0.8294 0.940 0.000 0.012 0.048
#> GSM627197 2 0.2125 0.8997 0.004 0.920 0.000 0.076
#> GSM627173 2 0.1661 0.9103 0.004 0.944 0.000 0.052
#> GSM627179 2 0.0376 0.9103 0.004 0.992 0.000 0.004
#> GSM627208 3 0.3344 0.7782 0.008 0.092 0.876 0.024
#> GSM627215 3 0.5865 0.4283 0.000 0.340 0.612 0.048
#> GSM627153 4 0.5112 0.4669 0.004 0.436 0.000 0.560
#> GSM627155 1 0.1854 0.8294 0.940 0.000 0.012 0.048
#> GSM627165 4 0.6148 0.2728 0.000 0.468 0.048 0.484
#> GSM627168 3 0.2334 0.7906 0.088 0.000 0.908 0.004
#> GSM627183 3 0.2589 0.7723 0.116 0.000 0.884 0.000
#> GSM627144 3 0.2408 0.8091 0.000 0.000 0.896 0.104
#> GSM627158 1 0.1109 0.8458 0.968 0.000 0.028 0.004
#> GSM627196 2 0.1978 0.9046 0.004 0.928 0.000 0.068
#> GSM627142 3 0.6242 0.4117 0.056 0.000 0.520 0.424
#> GSM627182 3 0.2673 0.8014 0.020 0.048 0.916 0.016
#> GSM627202 3 0.5560 0.2224 0.392 0.000 0.584 0.024
#> GSM627141 3 0.2593 0.7793 0.104 0.000 0.892 0.004
#> GSM627143 2 0.3172 0.8421 0.008 0.884 0.020 0.088
#> GSM627145 3 0.1398 0.8107 0.040 0.000 0.956 0.004
#> GSM627152 3 0.3900 0.7841 0.020 0.000 0.816 0.164
#> GSM627200 1 0.4661 0.6413 0.652 0.000 0.348 0.000
#> GSM627159 4 0.3323 0.6721 0.060 0.000 0.064 0.876
#> GSM627164 2 0.1739 0.8910 0.008 0.952 0.016 0.024
#> GSM627138 1 0.2921 0.8462 0.860 0.000 0.140 0.000
#> GSM627175 4 0.5080 0.4984 0.004 0.420 0.000 0.576
#> GSM627150 3 0.2469 0.8088 0.000 0.000 0.892 0.108
#> GSM627166 1 0.3219 0.8395 0.836 0.000 0.164 0.000
#> GSM627186 2 0.3279 0.8165 0.008 0.880 0.088 0.024
#> GSM627139 4 0.3533 0.6645 0.056 0.000 0.080 0.864
#> GSM627181 2 0.2053 0.9025 0.004 0.924 0.000 0.072
#> GSM627205 2 0.1958 0.8875 0.008 0.944 0.028 0.020
#> GSM627214 2 0.3626 0.7389 0.000 0.812 0.004 0.184
#> GSM627180 3 0.3401 0.7929 0.000 0.008 0.840 0.152
#> GSM627172 2 0.1822 0.9040 0.008 0.944 0.004 0.044
#> GSM627184 1 0.1854 0.8294 0.940 0.000 0.012 0.048
#> GSM627193 2 0.0707 0.9130 0.000 0.980 0.000 0.020
#> GSM627191 4 0.4655 0.6803 0.160 0.040 0.008 0.792
#> GSM627176 3 0.4057 0.7808 0.028 0.000 0.812 0.160
#> GSM627194 2 0.1489 0.9110 0.004 0.952 0.000 0.044
#> GSM627154 4 0.4372 0.6845 0.004 0.268 0.000 0.728
#> GSM627187 3 0.3030 0.7890 0.076 0.028 0.892 0.004
#> GSM627198 4 0.5143 0.4218 0.004 0.456 0.000 0.540
#> GSM627160 4 0.3247 0.6741 0.060 0.000 0.060 0.880
#> GSM627185 1 0.3074 0.8436 0.848 0.000 0.152 0.000
#> GSM627206 3 0.2281 0.7875 0.096 0.000 0.904 0.000
#> GSM627161 1 0.1510 0.8433 0.956 0.000 0.028 0.016
#> GSM627162 3 0.3187 0.7967 0.028 0.052 0.896 0.024
#> GSM627210 3 0.4933 -0.0185 0.432 0.000 0.568 0.000
#> GSM627189 2 0.1824 0.9078 0.004 0.936 0.000 0.060
show/hide code output
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM627128 4 0.4858 0.1525 0.008 0.000 0.012 0.556 0.424
#> GSM627110 3 0.1877 0.6288 0.012 0.000 0.924 0.000 0.064
#> GSM627132 1 0.1012 0.8299 0.968 0.000 0.020 0.000 0.012
#> GSM627107 5 0.5738 0.6026 0.004 0.000 0.292 0.104 0.600
#> GSM627103 2 0.1571 0.7463 0.000 0.936 0.000 0.060 0.004
#> GSM627114 3 0.2813 0.6161 0.024 0.000 0.868 0.000 0.108
#> GSM627134 4 0.3485 0.5660 0.000 0.048 0.000 0.828 0.124
#> GSM627137 2 0.1981 0.7434 0.000 0.920 0.000 0.016 0.064
#> GSM627148 3 0.3461 0.5528 0.000 0.004 0.772 0.000 0.224
#> GSM627101 4 0.4517 0.2328 0.000 0.000 0.012 0.600 0.388
#> GSM627130 4 0.4844 0.1742 0.012 0.000 0.008 0.564 0.416
#> GSM627071 3 0.2953 0.6214 0.028 0.000 0.868 0.004 0.100
#> GSM627118 4 0.2871 0.5771 0.000 0.040 0.000 0.872 0.088
#> GSM627094 2 0.1638 0.7452 0.000 0.932 0.000 0.064 0.004
#> GSM627122 3 0.5295 0.4855 0.096 0.000 0.684 0.008 0.212
#> GSM627115 2 0.1341 0.7469 0.000 0.944 0.000 0.056 0.000
#> GSM627125 4 0.4891 0.0843 0.008 0.000 0.012 0.532 0.448
#> GSM627174 2 0.3730 0.5648 0.000 0.712 0.000 0.288 0.000
#> GSM627102 2 0.3476 0.7060 0.000 0.804 0.000 0.020 0.176
#> GSM627073 3 0.4260 0.4463 0.004 0.000 0.680 0.008 0.308
#> GSM627108 2 0.1444 0.7463 0.000 0.948 0.000 0.012 0.040
#> GSM627126 1 0.0912 0.8268 0.972 0.000 0.000 0.016 0.012
#> GSM627078 4 0.3895 0.4113 0.000 0.320 0.000 0.680 0.000
#> GSM627090 3 0.4852 0.2873 0.012 0.000 0.624 0.016 0.348
#> GSM627099 4 0.4138 0.2878 0.000 0.384 0.000 0.616 0.000
#> GSM627105 4 0.4891 0.0843 0.008 0.000 0.012 0.532 0.448
#> GSM627117 3 0.3127 0.6087 0.020 0.004 0.848 0.000 0.128
#> GSM627121 5 0.5227 0.0361 0.000 0.008 0.460 0.028 0.504
#> GSM627127 4 0.3143 0.5385 0.000 0.204 0.000 0.796 0.000
#> GSM627087 2 0.1341 0.7469 0.000 0.944 0.000 0.056 0.000
#> GSM627089 3 0.2777 0.6082 0.016 0.000 0.864 0.000 0.120
#> GSM627092 2 0.4520 0.6375 0.000 0.684 0.000 0.032 0.284
#> GSM627076 5 0.6261 0.6088 0.012 0.000 0.320 0.124 0.544
#> GSM627136 3 0.1300 0.6257 0.028 0.000 0.956 0.000 0.016
#> GSM627081 3 0.4517 0.3226 0.004 0.000 0.616 0.008 0.372
#> GSM627091 2 0.4219 0.3288 0.000 0.584 0.000 0.416 0.000
#> GSM627097 4 0.2514 0.5804 0.000 0.044 0.000 0.896 0.060
#> GSM627072 3 0.2605 0.5950 0.000 0.000 0.852 0.000 0.148
#> GSM627080 1 0.0807 0.8313 0.976 0.000 0.012 0.000 0.012
#> GSM627088 3 0.2423 0.6188 0.024 0.000 0.896 0.000 0.080
#> GSM627109 1 0.4975 0.6510 0.668 0.000 0.276 0.004 0.052
#> GSM627111 1 0.1012 0.8299 0.968 0.000 0.020 0.000 0.012
#> GSM627113 3 0.4965 0.2994 0.304 0.000 0.644 0.000 0.052
#> GSM627133 3 0.4886 0.5053 0.000 0.036 0.648 0.004 0.312
#> GSM627177 3 0.3304 0.6195 0.028 0.000 0.840 0.004 0.128
#> GSM627086 2 0.2561 0.7109 0.000 0.856 0.000 0.144 0.000
#> GSM627095 1 0.0912 0.8268 0.972 0.000 0.000 0.016 0.012
#> GSM627079 3 0.3282 0.5608 0.008 0.000 0.804 0.000 0.188
#> GSM627082 4 0.5163 0.1600 0.028 0.000 0.008 0.556 0.408
#> GSM627074 3 0.5473 -0.0962 0.416 0.000 0.520 0.000 0.064
#> GSM627077 3 0.4298 0.5543 0.184 0.000 0.756 0.000 0.060
#> GSM627093 3 0.5312 0.3792 0.248 0.000 0.652 0.000 0.100
#> GSM627120 2 0.7370 0.2712 0.000 0.416 0.052 0.168 0.364
#> GSM627124 4 0.3895 0.4113 0.000 0.320 0.000 0.680 0.000
#> GSM627075 2 0.3132 0.7067 0.000 0.820 0.000 0.008 0.172
#> GSM627085 4 0.3684 0.4628 0.000 0.280 0.000 0.720 0.000
#> GSM627119 3 0.5100 0.3893 0.256 0.000 0.672 0.004 0.068
#> GSM627116 4 0.2666 0.5638 0.004 0.016 0.012 0.896 0.072
#> GSM627084 1 0.5080 0.5989 0.628 0.000 0.316 0.000 0.056
#> GSM627096 4 0.2793 0.5760 0.000 0.036 0.000 0.876 0.088
#> GSM627100 5 0.6268 0.6481 0.012 0.000 0.276 0.144 0.568
#> GSM627112 4 0.2753 0.5439 0.008 0.012 0.000 0.876 0.104
#> GSM627083 1 0.4528 0.5292 0.728 0.000 0.000 0.212 0.060
#> GSM627098 1 0.5142 0.5519 0.600 0.000 0.348 0.000 0.052
#> GSM627104 1 0.4975 0.6510 0.668 0.000 0.276 0.004 0.052
#> GSM627131 3 0.5672 0.3320 0.312 0.000 0.584 0.000 0.104
#> GSM627106 3 0.4530 0.3140 0.004 0.000 0.612 0.008 0.376
#> GSM627123 1 0.0912 0.8274 0.972 0.000 0.000 0.016 0.012
#> GSM627129 4 0.3861 0.5598 0.000 0.068 0.000 0.804 0.128
#> GSM627216 2 0.6098 0.6055 0.000 0.648 0.100 0.048 0.204
#> GSM627212 2 0.4211 0.4428 0.000 0.636 0.000 0.360 0.004
#> GSM627190 3 0.3031 0.6120 0.020 0.004 0.856 0.000 0.120
#> GSM627169 2 0.4695 0.6284 0.000 0.672 0.024 0.008 0.296
#> GSM627167 4 0.5744 0.3607 0.000 0.092 0.000 0.528 0.380
#> GSM627192 1 0.0912 0.8268 0.972 0.000 0.000 0.016 0.012
#> GSM627203 3 0.4299 0.4002 0.004 0.000 0.672 0.008 0.316
#> GSM627151 4 0.3969 0.5166 0.004 0.016 0.032 0.812 0.136
#> GSM627163 1 0.0807 0.8313 0.976 0.000 0.012 0.000 0.012
#> GSM627211 2 0.2249 0.7381 0.000 0.896 0.000 0.096 0.008
#> GSM627171 2 0.4582 0.6369 0.000 0.684 0.016 0.012 0.288
#> GSM627209 4 0.4045 0.3521 0.000 0.356 0.000 0.644 0.000
#> GSM627135 1 0.0807 0.8285 0.976 0.000 0.000 0.012 0.012
#> GSM627170 2 0.2824 0.7300 0.000 0.864 0.000 0.020 0.116
#> GSM627178 3 0.5960 -0.1526 0.444 0.000 0.460 0.004 0.092
#> GSM627199 4 0.4015 0.3690 0.000 0.348 0.000 0.652 0.000
#> GSM627213 4 0.2446 0.5808 0.000 0.044 0.000 0.900 0.056
#> GSM627140 4 0.5188 0.3717 0.000 0.056 0.000 0.600 0.344
#> GSM627149 1 0.1018 0.8253 0.968 0.000 0.000 0.016 0.016
#> GSM627147 4 0.6030 0.4357 0.000 0.224 0.000 0.580 0.196
#> GSM627195 3 0.4299 0.4083 0.004 0.000 0.672 0.008 0.316
#> GSM627204 2 0.2674 0.7143 0.000 0.856 0.000 0.140 0.004
#> GSM627207 2 0.3013 0.7123 0.000 0.832 0.000 0.008 0.160
#> GSM627157 1 0.5308 0.3971 0.532 0.000 0.416 0.000 0.052
#> GSM627201 2 0.3661 0.5789 0.000 0.724 0.000 0.276 0.000
#> GSM627146 2 0.3636 0.5838 0.000 0.728 0.000 0.272 0.000
#> GSM627156 2 0.4695 0.6287 0.000 0.672 0.024 0.008 0.296
#> GSM627188 1 0.0912 0.8268 0.972 0.000 0.000 0.016 0.012
#> GSM627197 2 0.3661 0.5789 0.000 0.724 0.000 0.276 0.000
#> GSM627173 2 0.2124 0.7367 0.000 0.900 0.000 0.096 0.004
#> GSM627179 2 0.1469 0.7466 0.000 0.948 0.000 0.016 0.036
#> GSM627208 3 0.5375 0.4339 0.000 0.076 0.604 0.000 0.320
#> GSM627215 3 0.7300 0.2103 0.000 0.196 0.476 0.048 0.280
#> GSM627153 4 0.4015 0.3689 0.000 0.348 0.000 0.652 0.000
#> GSM627155 1 0.0912 0.8268 0.972 0.000 0.000 0.016 0.012
#> GSM627165 4 0.7147 0.1592 0.000 0.332 0.012 0.336 0.320
#> GSM627168 3 0.1403 0.6283 0.024 0.000 0.952 0.000 0.024
#> GSM627183 3 0.2228 0.6105 0.040 0.000 0.912 0.000 0.048
#> GSM627144 3 0.3906 0.4688 0.004 0.000 0.704 0.000 0.292
#> GSM627158 1 0.0404 0.8318 0.988 0.000 0.012 0.000 0.000
#> GSM627196 2 0.2674 0.7143 0.000 0.856 0.000 0.140 0.004
#> GSM627142 5 0.6103 0.5251 0.012 0.000 0.132 0.264 0.592
#> GSM627182 3 0.3969 0.5398 0.000 0.004 0.692 0.000 0.304
#> GSM627202 3 0.5275 0.4807 0.276 0.000 0.640 0.000 0.084
#> GSM627141 3 0.2964 0.6119 0.024 0.000 0.856 0.000 0.120
#> GSM627143 2 0.5107 0.5976 0.000 0.632 0.008 0.040 0.320
#> GSM627145 3 0.2648 0.5870 0.000 0.000 0.848 0.000 0.152
#> GSM627152 3 0.4822 0.2986 0.012 0.000 0.632 0.016 0.340
#> GSM627200 3 0.4990 0.2753 0.324 0.000 0.628 0.000 0.048
#> GSM627159 4 0.5014 0.1628 0.020 0.000 0.008 0.560 0.412
#> GSM627164 2 0.4561 0.6391 0.000 0.688 0.016 0.012 0.284
#> GSM627138 1 0.3750 0.7110 0.756 0.000 0.232 0.000 0.012
#> GSM627175 4 0.3983 0.3816 0.000 0.340 0.000 0.660 0.000
#> GSM627150 3 0.4111 0.4705 0.004 0.000 0.708 0.008 0.280
#> GSM627166 1 0.5409 0.5429 0.588 0.000 0.348 0.004 0.060
#> GSM627186 2 0.4715 0.6258 0.000 0.668 0.024 0.008 0.300
#> GSM627139 5 0.5002 -0.1123 0.008 0.000 0.016 0.484 0.492
#> GSM627181 2 0.3274 0.6438 0.000 0.780 0.000 0.220 0.000
#> GSM627205 2 0.4573 0.6721 0.000 0.728 0.020 0.024 0.228
#> GSM627214 2 0.5593 0.4058 0.000 0.572 0.000 0.340 0.088
#> GSM627180 3 0.4484 0.3873 0.004 0.004 0.636 0.004 0.352
#> GSM627172 2 0.3550 0.7029 0.000 0.796 0.000 0.020 0.184
#> GSM627184 1 0.0912 0.8268 0.972 0.000 0.000 0.016 0.012
#> GSM627193 2 0.1043 0.7480 0.000 0.960 0.000 0.040 0.000
#> GSM627191 4 0.5669 0.3077 0.088 0.008 0.000 0.612 0.292
#> GSM627176 3 0.4841 0.2611 0.008 0.000 0.600 0.016 0.376
#> GSM627194 2 0.2020 0.7360 0.000 0.900 0.000 0.100 0.000
#> GSM627154 4 0.3177 0.5354 0.000 0.208 0.000 0.792 0.000
#> GSM627187 3 0.3594 0.5847 0.020 0.004 0.804 0.000 0.172
#> GSM627198 4 0.4015 0.3690 0.000 0.348 0.000 0.652 0.000
#> GSM627160 4 0.5006 0.1692 0.020 0.000 0.008 0.564 0.408
#> GSM627185 1 0.4777 0.6619 0.680 0.000 0.268 0.000 0.052
#> GSM627206 3 0.2331 0.6311 0.020 0.000 0.900 0.000 0.080
#> GSM627161 1 0.0324 0.8313 0.992 0.000 0.004 0.000 0.004
#> GSM627162 3 0.5031 0.4691 0.000 0.036 0.656 0.012 0.296
#> GSM627210 3 0.4741 0.4851 0.204 0.000 0.724 0.004 0.068
#> GSM627189 2 0.1965 0.7354 0.000 0.904 0.000 0.096 0.000
show/hide code output
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM627128 6 0.2620 0.72717 0.024 0.000 0.000 0.040 0.048 0.888
#> GSM627110 3 0.4310 0.28024 0.000 0.012 0.512 0.004 0.472 0.000
#> GSM627132 1 0.2473 0.84435 0.856 0.008 0.136 0.000 0.000 0.000
#> GSM627107 5 0.3695 0.46333 0.000 0.000 0.000 0.016 0.712 0.272
#> GSM627103 2 0.4819 0.48761 0.000 0.596 0.032 0.356 0.008 0.008
#> GSM627114 3 0.5025 0.37577 0.000 0.064 0.532 0.004 0.400 0.000
#> GSM627134 4 0.5516 0.06764 0.000 0.004 0.028 0.528 0.056 0.384
#> GSM627137 2 0.3670 0.58974 0.000 0.736 0.024 0.240 0.000 0.000
#> GSM627148 5 0.2019 0.61693 0.000 0.000 0.088 0.012 0.900 0.000
#> GSM627101 6 0.3172 0.65556 0.000 0.000 0.000 0.148 0.036 0.816
#> GSM627130 6 0.2685 0.72680 0.024 0.000 0.000 0.052 0.040 0.884
#> GSM627071 5 0.4502 -0.09515 0.008 0.004 0.428 0.000 0.548 0.012
#> GSM627118 4 0.5225 0.06359 0.000 0.004 0.024 0.520 0.036 0.416
#> GSM627094 2 0.4057 0.46622 0.000 0.600 0.012 0.388 0.000 0.000
#> GSM627122 5 0.6027 0.22652 0.072 0.000 0.296 0.000 0.552 0.080
#> GSM627115 2 0.4808 0.49278 0.000 0.600 0.032 0.352 0.008 0.008
#> GSM627125 6 0.3051 0.72056 0.024 0.000 0.000 0.032 0.088 0.856
#> GSM627174 4 0.4346 0.20888 0.000 0.288 0.020 0.676 0.008 0.008
#> GSM627102 2 0.4406 0.60090 0.004 0.772 0.088 0.096 0.000 0.040
#> GSM627073 5 0.1536 0.63935 0.000 0.000 0.040 0.016 0.940 0.004
#> GSM627108 2 0.3634 0.55910 0.000 0.696 0.008 0.296 0.000 0.000
#> GSM627126 1 0.1168 0.90394 0.956 0.000 0.016 0.000 0.000 0.028
#> GSM627078 4 0.1858 0.58120 0.000 0.004 0.000 0.904 0.000 0.092
#> GSM627090 5 0.4739 0.56822 0.024 0.004 0.060 0.000 0.708 0.204
#> GSM627099 4 0.2998 0.56518 0.000 0.028 0.032 0.872 0.008 0.060
#> GSM627105 6 0.3051 0.72056 0.024 0.000 0.000 0.032 0.088 0.856
#> GSM627117 3 0.5035 0.38890 0.000 0.068 0.548 0.004 0.380 0.000
#> GSM627121 5 0.3370 0.60765 0.000 0.012 0.012 0.020 0.828 0.128
#> GSM627127 4 0.3817 0.44335 0.000 0.008 0.016 0.744 0.004 0.228
#> GSM627087 2 0.4808 0.49278 0.000 0.600 0.032 0.352 0.008 0.008
#> GSM627089 5 0.3323 0.43303 0.000 0.000 0.240 0.000 0.752 0.008
#> GSM627092 2 0.4568 0.56109 0.004 0.768 0.104 0.020 0.016 0.088
#> GSM627076 5 0.5055 0.21138 0.024 0.004 0.024 0.000 0.512 0.436
#> GSM627136 3 0.3830 0.48371 0.000 0.000 0.620 0.004 0.376 0.000
#> GSM627081 5 0.1594 0.64774 0.000 0.000 0.000 0.016 0.932 0.052
#> GSM627091 4 0.3720 0.46465 0.000 0.132 0.032 0.808 0.008 0.020
#> GSM627097 4 0.4660 -0.01527 0.004 0.000 0.024 0.508 0.004 0.460
#> GSM627072 5 0.3265 0.43048 0.000 0.000 0.248 0.004 0.748 0.000
#> GSM627080 1 0.1958 0.86848 0.896 0.004 0.100 0.000 0.000 0.000
#> GSM627088 3 0.4274 0.50730 0.000 0.024 0.636 0.004 0.336 0.000
#> GSM627109 3 0.3647 0.53970 0.232 0.004 0.748 0.000 0.004 0.012
#> GSM627111 1 0.2473 0.84435 0.856 0.008 0.136 0.000 0.000 0.000
#> GSM627113 3 0.4086 0.65105 0.088 0.004 0.768 0.000 0.136 0.004
#> GSM627133 5 0.5711 0.45200 0.004 0.128 0.148 0.036 0.668 0.016
#> GSM627177 3 0.4531 0.29398 0.008 0.004 0.520 0.000 0.456 0.012
#> GSM627086 4 0.4091 -0.28048 0.000 0.472 0.008 0.520 0.000 0.000
#> GSM627095 1 0.1245 0.90318 0.952 0.000 0.016 0.000 0.000 0.032
#> GSM627079 5 0.3869 0.46432 0.004 0.004 0.240 0.000 0.732 0.020
#> GSM627082 6 0.2755 0.72352 0.056 0.000 0.000 0.036 0.028 0.880
#> GSM627074 3 0.3708 0.64660 0.124 0.004 0.800 0.000 0.068 0.004
#> GSM627077 3 0.5690 0.22818 0.072 0.004 0.460 0.000 0.440 0.024
#> GSM627093 3 0.3174 0.64980 0.056 0.000 0.836 0.000 0.104 0.004
#> GSM627120 2 0.8026 0.15967 0.004 0.432 0.108 0.072 0.236 0.148
#> GSM627124 4 0.1858 0.58120 0.000 0.004 0.000 0.904 0.000 0.092
#> GSM627075 2 0.3873 0.61020 0.004 0.808 0.084 0.080 0.000 0.024
#> GSM627085 4 0.2100 0.56925 0.000 0.000 0.004 0.884 0.000 0.112
#> GSM627119 3 0.3873 0.64921 0.068 0.004 0.796 0.000 0.120 0.012
#> GSM627116 4 0.5386 -0.06787 0.004 0.004 0.060 0.468 0.008 0.456
#> GSM627084 3 0.4127 0.54889 0.252 0.004 0.712 0.000 0.024 0.008
#> GSM627096 4 0.5230 0.06058 0.000 0.004 0.024 0.516 0.036 0.420
#> GSM627100 5 0.4925 0.17228 0.016 0.004 0.024 0.000 0.500 0.456
#> GSM627112 6 0.4127 0.29784 0.008 0.000 0.004 0.400 0.000 0.588
#> GSM627083 1 0.3191 0.74316 0.812 0.000 0.012 0.012 0.000 0.164
#> GSM627098 3 0.3652 0.61640 0.196 0.004 0.768 0.000 0.032 0.000
#> GSM627104 3 0.3728 0.54346 0.228 0.008 0.748 0.000 0.004 0.012
#> GSM627131 3 0.5586 0.46589 0.084 0.004 0.576 0.000 0.312 0.024
#> GSM627106 5 0.1657 0.64684 0.000 0.000 0.000 0.016 0.928 0.056
#> GSM627123 1 0.1074 0.90261 0.960 0.000 0.012 0.000 0.000 0.028
#> GSM627129 6 0.5478 0.00782 0.000 0.008 0.032 0.448 0.036 0.476
#> GSM627216 2 0.7177 0.32807 0.004 0.440 0.060 0.176 0.304 0.016
#> GSM627212 4 0.4194 0.38549 0.000 0.196 0.032 0.748 0.008 0.016
#> GSM627190 3 0.5051 0.37786 0.000 0.068 0.540 0.004 0.388 0.000
#> GSM627169 2 0.3464 0.59571 0.004 0.832 0.116 0.012 0.012 0.024
#> GSM627167 6 0.6470 0.51672 0.004 0.220 0.068 0.128 0.008 0.572
#> GSM627192 1 0.1168 0.90394 0.956 0.000 0.016 0.000 0.000 0.028
#> GSM627203 5 0.1988 0.65043 0.004 0.004 0.024 0.000 0.920 0.048
#> GSM627151 4 0.6039 -0.06985 0.004 0.004 0.036 0.448 0.076 0.432
#> GSM627163 1 0.2445 0.85578 0.868 0.008 0.120 0.000 0.000 0.004
#> GSM627211 2 0.3975 0.38123 0.000 0.544 0.004 0.452 0.000 0.000
#> GSM627171 2 0.3679 0.59293 0.004 0.824 0.112 0.016 0.016 0.028
#> GSM627209 4 0.1956 0.58587 0.000 0.008 0.004 0.908 0.000 0.080
#> GSM627135 1 0.1176 0.90492 0.956 0.000 0.020 0.000 0.000 0.024
#> GSM627170 2 0.5493 0.54062 0.000 0.636 0.028 0.252 0.068 0.016
#> GSM627178 3 0.5375 0.57778 0.120 0.004 0.672 0.000 0.168 0.036
#> GSM627199 4 0.2056 0.58432 0.000 0.012 0.004 0.904 0.000 0.080
#> GSM627213 4 0.4374 0.02107 0.000 0.000 0.016 0.532 0.004 0.448
#> GSM627140 6 0.6003 0.59802 0.032 0.128 0.068 0.112 0.000 0.660
#> GSM627149 1 0.0993 0.90232 0.964 0.000 0.012 0.000 0.000 0.024
#> GSM627147 6 0.7390 0.24892 0.004 0.308 0.076 0.228 0.008 0.376
#> GSM627195 5 0.1596 0.65220 0.004 0.004 0.008 0.008 0.944 0.032
#> GSM627204 4 0.3999 -0.31698 0.000 0.496 0.004 0.500 0.000 0.000
#> GSM627207 2 0.2740 0.61613 0.000 0.852 0.028 0.120 0.000 0.000
#> GSM627157 3 0.3760 0.63348 0.184 0.004 0.768 0.000 0.044 0.000
#> GSM627201 4 0.4541 0.08244 0.000 0.344 0.024 0.620 0.008 0.004
#> GSM627146 4 0.3967 0.07613 0.000 0.356 0.012 0.632 0.000 0.000
#> GSM627156 2 0.3555 0.59443 0.004 0.828 0.116 0.012 0.016 0.024
#> GSM627188 1 0.1074 0.90401 0.960 0.000 0.012 0.000 0.000 0.028
#> GSM627197 4 0.3601 0.17126 0.000 0.312 0.004 0.684 0.000 0.000
#> GSM627173 2 0.4051 0.40978 0.000 0.560 0.008 0.432 0.000 0.000
#> GSM627179 2 0.4078 0.56543 0.000 0.700 0.016 0.272 0.004 0.008
#> GSM627208 5 0.5241 0.46344 0.000 0.160 0.116 0.028 0.688 0.008
#> GSM627215 5 0.6583 0.35357 0.000 0.212 0.068 0.124 0.572 0.024
#> GSM627153 4 0.1897 0.58430 0.000 0.004 0.004 0.908 0.000 0.084
#> GSM627155 1 0.0951 0.90433 0.968 0.004 0.008 0.000 0.000 0.020
#> GSM627165 6 0.8095 0.25870 0.000 0.248 0.036 0.184 0.176 0.356
#> GSM627168 3 0.4091 0.31672 0.000 0.000 0.520 0.000 0.472 0.008
#> GSM627183 3 0.3266 0.59270 0.000 0.000 0.728 0.000 0.272 0.000
#> GSM627144 5 0.2272 0.64845 0.000 0.004 0.056 0.000 0.900 0.040
#> GSM627158 1 0.1219 0.89353 0.948 0.004 0.048 0.000 0.000 0.000
#> GSM627196 4 0.3999 -0.31698 0.000 0.496 0.004 0.500 0.000 0.000
#> GSM627142 6 0.4886 0.22610 0.032 0.000 0.024 0.000 0.348 0.596
#> GSM627182 5 0.4575 0.46020 0.000 0.060 0.192 0.020 0.724 0.004
#> GSM627202 5 0.5813 -0.23247 0.092 0.004 0.436 0.000 0.448 0.020
#> GSM627141 3 0.5043 0.38620 0.000 0.068 0.544 0.004 0.384 0.000
#> GSM627143 2 0.5269 0.50014 0.004 0.720 0.116 0.028 0.028 0.104
#> GSM627145 5 0.3141 0.49762 0.000 0.000 0.200 0.000 0.788 0.012
#> GSM627152 5 0.5082 0.55643 0.024 0.004 0.100 0.000 0.688 0.184
#> GSM627200 3 0.4938 0.59961 0.080 0.000 0.680 0.000 0.216 0.024
#> GSM627159 6 0.2777 0.72904 0.036 0.000 0.000 0.036 0.048 0.880
#> GSM627164 2 0.3588 0.59468 0.004 0.828 0.112 0.012 0.016 0.028
#> GSM627138 1 0.4214 0.04006 0.528 0.008 0.460 0.000 0.004 0.000
#> GSM627175 4 0.1806 0.58320 0.000 0.004 0.000 0.908 0.000 0.088
#> GSM627150 5 0.1667 0.64101 0.004 0.000 0.044 0.008 0.936 0.008
#> GSM627166 3 0.3798 0.60635 0.188 0.004 0.772 0.000 0.020 0.016
#> GSM627186 2 0.3690 0.59015 0.004 0.820 0.120 0.016 0.016 0.024
#> GSM627139 6 0.3114 0.70314 0.024 0.000 0.004 0.016 0.108 0.848
#> GSM627181 4 0.3899 -0.08484 0.000 0.404 0.004 0.592 0.000 0.000
#> GSM627205 2 0.6540 0.49050 0.000 0.584 0.056 0.164 0.168 0.028
#> GSM627214 4 0.5702 0.38683 0.000 0.208 0.032 0.652 0.032 0.076
#> GSM627180 5 0.1959 0.63870 0.000 0.000 0.032 0.020 0.924 0.024
#> GSM627172 2 0.4592 0.59856 0.004 0.764 0.088 0.100 0.004 0.040
#> GSM627184 1 0.0806 0.90407 0.972 0.000 0.008 0.000 0.000 0.020
#> GSM627193 2 0.3927 0.52072 0.000 0.644 0.012 0.344 0.000 0.000
#> GSM627191 6 0.4197 0.67788 0.092 0.004 0.028 0.092 0.000 0.784
#> GSM627176 5 0.4711 0.57053 0.024 0.004 0.060 0.000 0.712 0.200
#> GSM627194 2 0.4442 0.37545 0.000 0.536 0.020 0.440 0.000 0.004
#> GSM627154 4 0.2772 0.49733 0.000 0.000 0.004 0.816 0.000 0.180
#> GSM627187 3 0.5260 0.40124 0.000 0.096 0.552 0.004 0.348 0.000
#> GSM627198 4 0.2002 0.58510 0.000 0.012 0.004 0.908 0.000 0.076
#> GSM627160 6 0.2427 0.72607 0.040 0.000 0.008 0.032 0.016 0.904
#> GSM627185 3 0.3560 0.50356 0.256 0.008 0.732 0.000 0.000 0.004
#> GSM627206 5 0.4467 -0.27126 0.000 0.020 0.480 0.004 0.496 0.000
#> GSM627161 1 0.1010 0.89937 0.960 0.004 0.036 0.000 0.000 0.000
#> GSM627162 3 0.6781 0.02923 0.004 0.208 0.400 0.004 0.352 0.032
#> GSM627210 3 0.3845 0.64495 0.052 0.004 0.792 0.000 0.140 0.012
#> GSM627189 2 0.4152 0.38969 0.000 0.548 0.012 0.440 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two
samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures
which are significantly different between classes which can be candidate marks
for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known
annotation is numeric, one-way ANOVA test is applied, and if the known
annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) age(p) other(p) k
#> MAD:kmeans 144 0.7594 0.345 0.0327 2
#> MAD:kmeans 117 0.3417 0.717 0.0674 3
#> MAD:kmeans 131 0.1049 0.472 0.3391 4
#> MAD:kmeans 93 0.0682 0.338 0.3899 5
#> MAD:kmeans 82 0.8240 0.630 0.1175 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
MAD:skmeans**
The object with results only for a single top-value method and a single partition method
can be extracted as:
res = res_list["MAD", "skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51882 rows and 146 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
- The first row: a plot of the ECDF (empirical cumulative distribution
function) curves of the consensus matrix for each
k and the heatmap of
predicted classes for each k.
- The second row: heatmaps of the consensus matrix for each
k.
- The third row: heatmaps of the membership matrix for each
k.
- The fouth row: heatmaps of the signatures for each
k.
All the plots in panels can be made by individual functions and they are
plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
- ECDF curves of the consensus matrix for each
k;
- 1-PAC. The PAC
score
measures the proportion of the ambiguous subgrouping.
- Mean silhouette score.
- Concordance. The mean probability of fiting the consensus class ids in all
partitions.
- Area increased. Denote \(A_k\) as the area under the ECDF curve for current
k, the area increased is defined as \(A_k - A_{k-1}\).
- Rand index. The percent of pairs of samples that are both in a same cluster
or both are not in a same cluster in the partition of k and k-1.
- Jaccard index. The ratio of pairs of samples are both in a same cluster in
the partition of k and k-1 and the pairs of samples are both in a same
cluster in the partition k or k-1.
The detailed explanations of these statistics can be found in the cola
vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.964 0.986 0.5032 0.498 0.498
#> 3 3 0.733 0.830 0.905 0.2923 0.786 0.598
#> 4 4 0.970 0.924 0.966 0.1581 0.793 0.483
#> 5 5 0.736 0.683 0.806 0.0527 0.915 0.683
#> 6 6 0.731 0.569 0.736 0.0438 0.927 0.680
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
- All \(k\) with Jaccard index larger than 0.95 are removed because increasing
\(k\) does not provide enough extra information. If all \(k\) are removed, it is
marked as no subgroup is detected.
- For all \(k\) with 1-PAC score larger than 0.9, the maximal \(k\) is taken as
the best \(k\), and other \(k\) are marked as optional \(k\).
- If it does not fit the second rule. The \(k\) with the maximal vote of the
highest 1-PAC score, highest mean silhouette, and highest concordance is
taken as the best \(k\).
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
show/hide code output
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM627128 2 0.0376 0.973 0.004 0.996
#> GSM627110 1 0.0000 0.995 1.000 0.000
#> GSM627132 1 0.0000 0.995 1.000 0.000
#> GSM627107 2 0.8861 0.577 0.304 0.696
#> GSM627103 2 0.0000 0.976 0.000 1.000
#> GSM627114 1 0.0000 0.995 1.000 0.000
#> GSM627134 2 0.0000 0.976 0.000 1.000
#> GSM627137 2 0.0000 0.976 0.000 1.000
#> GSM627148 1 0.0000 0.995 1.000 0.000
#> GSM627101 2 0.0000 0.976 0.000 1.000
#> GSM627130 2 0.0000 0.976 0.000 1.000
#> GSM627071 1 0.0000 0.995 1.000 0.000
#> GSM627118 2 0.0000 0.976 0.000 1.000
#> GSM627094 2 0.0000 0.976 0.000 1.000
#> GSM627122 1 0.0000 0.995 1.000 0.000
#> GSM627115 2 0.0000 0.976 0.000 1.000
#> GSM627125 2 0.0376 0.973 0.004 0.996
#> GSM627174 2 0.0000 0.976 0.000 1.000
#> GSM627102 2 0.0000 0.976 0.000 1.000
#> GSM627073 1 0.0000 0.995 1.000 0.000
#> GSM627108 2 0.0000 0.976 0.000 1.000
#> GSM627126 1 0.0000 0.995 1.000 0.000
#> GSM627078 2 0.0000 0.976 0.000 1.000
#> GSM627090 1 0.0000 0.995 1.000 0.000
#> GSM627099 2 0.0000 0.976 0.000 1.000
#> GSM627105 2 0.0000 0.976 0.000 1.000
#> GSM627117 1 0.0000 0.995 1.000 0.000
#> GSM627121 2 0.8955 0.562 0.312 0.688
#> GSM627127 2 0.0000 0.976 0.000 1.000
#> GSM627087 2 0.0000 0.976 0.000 1.000
#> GSM627089 1 0.0000 0.995 1.000 0.000
#> GSM627092 2 0.0000 0.976 0.000 1.000
#> GSM627076 1 0.0000 0.995 1.000 0.000
#> GSM627136 1 0.0000 0.995 1.000 0.000
#> GSM627081 1 0.0000 0.995 1.000 0.000
#> GSM627091 2 0.0000 0.976 0.000 1.000
#> GSM627097 2 0.0000 0.976 0.000 1.000
#> GSM627072 1 0.0000 0.995 1.000 0.000
#> GSM627080 1 0.0000 0.995 1.000 0.000
#> GSM627088 1 0.0000 0.995 1.000 0.000
#> GSM627109 1 0.0000 0.995 1.000 0.000
#> GSM627111 1 0.0000 0.995 1.000 0.000
#> GSM627113 1 0.0000 0.995 1.000 0.000
#> GSM627133 2 0.1633 0.955 0.024 0.976
#> GSM627177 1 0.0000 0.995 1.000 0.000
#> GSM627086 2 0.0000 0.976 0.000 1.000
#> GSM627095 1 0.0000 0.995 1.000 0.000
#> GSM627079 1 0.0000 0.995 1.000 0.000
#> GSM627082 2 0.0376 0.973 0.004 0.996
#> GSM627074 1 0.0000 0.995 1.000 0.000
#> GSM627077 1 0.0000 0.995 1.000 0.000
#> GSM627093 1 0.0000 0.995 1.000 0.000
#> GSM627120 2 0.0000 0.976 0.000 1.000
#> GSM627124 2 0.0000 0.976 0.000 1.000
#> GSM627075 2 0.0000 0.976 0.000 1.000
#> GSM627085 2 0.0000 0.976 0.000 1.000
#> GSM627119 1 0.0000 0.995 1.000 0.000
#> GSM627116 2 0.9635 0.373 0.388 0.612
#> GSM627084 1 0.0000 0.995 1.000 0.000
#> GSM627096 2 0.0000 0.976 0.000 1.000
#> GSM627100 1 0.0000 0.995 1.000 0.000
#> GSM627112 2 0.0000 0.976 0.000 1.000
#> GSM627083 2 0.0938 0.966 0.012 0.988
#> GSM627098 1 0.0000 0.995 1.000 0.000
#> GSM627104 1 0.0000 0.995 1.000 0.000
#> GSM627131 1 0.0000 0.995 1.000 0.000
#> GSM627106 1 0.0000 0.995 1.000 0.000
#> GSM627123 1 0.0000 0.995 1.000 0.000
#> GSM627129 2 0.0000 0.976 0.000 1.000
#> GSM627216 2 0.0000 0.976 0.000 1.000
#> GSM627212 2 0.0000 0.976 0.000 1.000
#> GSM627190 1 0.0000 0.995 1.000 0.000
#> GSM627169 2 0.0000 0.976 0.000 1.000
#> GSM627167 2 0.0000 0.976 0.000 1.000
#> GSM627192 1 0.0000 0.995 1.000 0.000
#> GSM627203 1 0.0000 0.995 1.000 0.000
#> GSM627151 2 0.0000 0.976 0.000 1.000
#> GSM627163 1 0.0000 0.995 1.000 0.000
#> GSM627211 2 0.0000 0.976 0.000 1.000
#> GSM627171 2 0.0000 0.976 0.000 1.000
#> GSM627209 2 0.0000 0.976 0.000 1.000
#> GSM627135 1 0.0000 0.995 1.000 0.000
#> GSM627170 2 0.0000 0.976 0.000 1.000
#> GSM627178 1 0.0000 0.995 1.000 0.000
#> GSM627199 2 0.0000 0.976 0.000 1.000
#> GSM627213 2 0.0000 0.976 0.000 1.000
#> GSM627140 2 0.0000 0.976 0.000 1.000
#> GSM627149 1 0.0000 0.995 1.000 0.000
#> GSM627147 2 0.0000 0.976 0.000 1.000
#> GSM627195 1 0.0000 0.995 1.000 0.000
#> GSM627204 2 0.0000 0.976 0.000 1.000
#> GSM627207 2 0.0000 0.976 0.000 1.000
#> GSM627157 1 0.0000 0.995 1.000 0.000
#> GSM627201 2 0.0000 0.976 0.000 1.000
#> GSM627146 2 0.0000 0.976 0.000 1.000
#> GSM627156 2 0.0000 0.976 0.000 1.000
#> GSM627188 1 0.0000 0.995 1.000 0.000
#> GSM627197 2 0.0000 0.976 0.000 1.000
#> GSM627173 2 0.0000 0.976 0.000 1.000
#> GSM627179 2 0.0000 0.976 0.000 1.000
#> GSM627208 2 0.9323 0.487 0.348 0.652
#> GSM627215 2 0.0000 0.976 0.000 1.000
#> GSM627153 2 0.0000 0.976 0.000 1.000
#> GSM627155 1 0.0000 0.995 1.000 0.000
#> GSM627165 2 0.0000 0.976 0.000 1.000
#> GSM627168 1 0.0000 0.995 1.000 0.000
#> GSM627183 1 0.0000 0.995 1.000 0.000
#> GSM627144 1 0.0000 0.995 1.000 0.000
#> GSM627158 1 0.0000 0.995 1.000 0.000
#> GSM627196 2 0.0000 0.976 0.000 1.000
#> GSM627142 1 0.0000 0.995 1.000 0.000
#> GSM627182 1 0.0000 0.995 1.000 0.000
#> GSM627202 1 0.0000 0.995 1.000 0.000
#> GSM627141 1 0.0000 0.995 1.000 0.000
#> GSM627143 2 0.0000 0.976 0.000 1.000
#> GSM627145 1 0.0000 0.995 1.000 0.000
#> GSM627152 1 0.0000 0.995 1.000 0.000
#> GSM627200 1 0.0000 0.995 1.000 0.000
#> GSM627159 2 0.0376 0.973 0.004 0.996
#> GSM627164 2 0.0000 0.976 0.000 1.000
#> GSM627138 1 0.0000 0.995 1.000 0.000
#> GSM627175 2 0.0000 0.976 0.000 1.000
#> GSM627150 1 0.0000 0.995 1.000 0.000
#> GSM627166 1 0.0000 0.995 1.000 0.000
#> GSM627186 2 0.0000 0.976 0.000 1.000
#> GSM627139 2 0.9580 0.413 0.380 0.620
#> GSM627181 2 0.0000 0.976 0.000 1.000
#> GSM627205 2 0.0000 0.976 0.000 1.000
#> GSM627214 2 0.0000 0.976 0.000 1.000
#> GSM627180 1 0.0376 0.991 0.996 0.004
#> GSM627172 2 0.0000 0.976 0.000 1.000
#> GSM627184 1 0.0000 0.995 1.000 0.000
#> GSM627193 2 0.0000 0.976 0.000 1.000
#> GSM627191 2 0.0000 0.976 0.000 1.000
#> GSM627176 1 0.0000 0.995 1.000 0.000
#> GSM627194 2 0.0000 0.976 0.000 1.000
#> GSM627154 2 0.0000 0.976 0.000 1.000
#> GSM627187 1 0.0000 0.995 1.000 0.000
#> GSM627198 2 0.0000 0.976 0.000 1.000
#> GSM627160 1 0.9087 0.504 0.676 0.324
#> GSM627185 1 0.0000 0.995 1.000 0.000
#> GSM627206 1 0.0000 0.995 1.000 0.000
#> GSM627161 1 0.0000 0.995 1.000 0.000
#> GSM627162 1 0.0000 0.995 1.000 0.000
#> GSM627210 1 0.0000 0.995 1.000 0.000
#> GSM627189 2 0.0000 0.976 0.000 1.000
show/hide code output
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM627128 3 0.2959 0.764 0.100 0.000 0.900
#> GSM627110 1 0.3551 0.823 0.868 0.000 0.132
#> GSM627132 1 0.0000 0.856 1.000 0.000 0.000
#> GSM627107 3 0.1031 0.780 0.024 0.000 0.976
#> GSM627103 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627114 1 0.3551 0.823 0.868 0.000 0.132
#> GSM627134 2 0.0892 0.961 0.000 0.980 0.020
#> GSM627137 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627148 3 0.4399 0.752 0.188 0.000 0.812
#> GSM627101 3 0.3816 0.689 0.000 0.148 0.852
#> GSM627130 3 0.4256 0.745 0.096 0.036 0.868
#> GSM627071 3 0.6299 0.210 0.476 0.000 0.524
#> GSM627118 2 0.3412 0.868 0.000 0.876 0.124
#> GSM627094 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627122 1 0.5216 0.619 0.740 0.000 0.260
#> GSM627115 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627125 3 0.3112 0.763 0.096 0.004 0.900
#> GSM627174 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627102 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627073 3 0.3752 0.782 0.144 0.000 0.856
#> GSM627108 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627126 1 0.3412 0.787 0.876 0.000 0.124
#> GSM627078 2 0.0747 0.963 0.000 0.984 0.016
#> GSM627090 3 0.4555 0.785 0.200 0.000 0.800
#> GSM627099 2 0.0592 0.964 0.000 0.988 0.012
#> GSM627105 3 0.3112 0.763 0.096 0.004 0.900
#> GSM627117 1 0.3551 0.823 0.868 0.000 0.132
#> GSM627121 3 0.3116 0.786 0.108 0.000 0.892
#> GSM627127 2 0.0892 0.961 0.000 0.980 0.020
#> GSM627087 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627089 3 0.6267 0.290 0.452 0.000 0.548
#> GSM627092 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627076 3 0.3267 0.767 0.116 0.000 0.884
#> GSM627136 1 0.2625 0.848 0.916 0.000 0.084
#> GSM627081 3 0.3686 0.783 0.140 0.000 0.860
#> GSM627091 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627097 2 0.2959 0.892 0.000 0.900 0.100
#> GSM627072 3 0.6126 0.434 0.400 0.000 0.600
#> GSM627080 1 0.0747 0.853 0.984 0.000 0.016
#> GSM627088 1 0.3412 0.828 0.876 0.000 0.124
#> GSM627109 1 0.1289 0.857 0.968 0.000 0.032
#> GSM627111 1 0.0000 0.856 1.000 0.000 0.000
#> GSM627113 1 0.2878 0.842 0.904 0.000 0.096
#> GSM627133 2 0.7451 0.221 0.040 0.564 0.396
#> GSM627177 1 0.5560 0.550 0.700 0.000 0.300
#> GSM627086 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627095 1 0.3412 0.787 0.876 0.000 0.124
#> GSM627079 3 0.4178 0.769 0.172 0.000 0.828
#> GSM627082 3 0.3551 0.746 0.132 0.000 0.868
#> GSM627074 1 0.2625 0.848 0.916 0.000 0.084
#> GSM627077 1 0.0237 0.855 0.996 0.000 0.004
#> GSM627093 1 0.2878 0.842 0.904 0.000 0.096
#> GSM627120 2 0.1031 0.960 0.000 0.976 0.024
#> GSM627124 2 0.0747 0.963 0.000 0.984 0.016
#> GSM627075 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627085 2 0.0747 0.963 0.000 0.984 0.016
#> GSM627119 1 0.2878 0.842 0.904 0.000 0.096
#> GSM627116 3 0.9645 0.142 0.380 0.208 0.412
#> GSM627084 1 0.1289 0.846 0.968 0.000 0.032
#> GSM627096 2 0.3412 0.868 0.000 0.876 0.124
#> GSM627100 3 0.3192 0.768 0.112 0.000 0.888
#> GSM627112 2 0.6001 0.741 0.052 0.772 0.176
#> GSM627083 1 0.4326 0.756 0.844 0.012 0.144
#> GSM627098 1 0.2261 0.853 0.932 0.000 0.068
#> GSM627104 1 0.2261 0.853 0.932 0.000 0.068
#> GSM627131 1 0.0000 0.856 1.000 0.000 0.000
#> GSM627106 3 0.3686 0.783 0.140 0.000 0.860
#> GSM627123 1 0.3412 0.787 0.876 0.000 0.124
#> GSM627129 2 0.0892 0.961 0.000 0.980 0.020
#> GSM627216 2 0.0237 0.965 0.000 0.996 0.004
#> GSM627212 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627190 1 0.3551 0.823 0.868 0.000 0.132
#> GSM627169 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627167 2 0.0892 0.961 0.000 0.980 0.020
#> GSM627192 1 0.3412 0.787 0.876 0.000 0.124
#> GSM627203 3 0.3752 0.782 0.144 0.000 0.856
#> GSM627151 2 0.3816 0.841 0.000 0.852 0.148
#> GSM627163 1 0.0892 0.851 0.980 0.000 0.020
#> GSM627211 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627171 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627209 2 0.0747 0.963 0.000 0.984 0.016
#> GSM627135 1 0.3412 0.787 0.876 0.000 0.124
#> GSM627170 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627178 1 0.0747 0.853 0.984 0.000 0.016
#> GSM627199 2 0.0747 0.963 0.000 0.984 0.016
#> GSM627213 2 0.0892 0.961 0.000 0.980 0.020
#> GSM627140 2 0.5435 0.785 0.048 0.808 0.144
#> GSM627149 1 0.3412 0.787 0.876 0.000 0.124
#> GSM627147 2 0.0424 0.965 0.000 0.992 0.008
#> GSM627195 3 0.3752 0.782 0.144 0.000 0.856
#> GSM627204 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627207 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627157 1 0.2261 0.853 0.932 0.000 0.068
#> GSM627201 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627146 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627156 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627188 1 0.3412 0.787 0.876 0.000 0.124
#> GSM627197 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627173 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627179 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627208 3 0.7327 0.671 0.132 0.160 0.708
#> GSM627215 2 0.2878 0.879 0.000 0.904 0.096
#> GSM627153 2 0.0747 0.963 0.000 0.984 0.016
#> GSM627155 1 0.3412 0.787 0.876 0.000 0.124
#> GSM627165 2 0.5254 0.663 0.000 0.736 0.264
#> GSM627168 1 0.3551 0.823 0.868 0.000 0.132
#> GSM627183 1 0.3340 0.831 0.880 0.000 0.120
#> GSM627144 3 0.3752 0.782 0.144 0.000 0.856
#> GSM627158 1 0.0892 0.851 0.980 0.000 0.020
#> GSM627196 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627142 3 0.3192 0.767 0.112 0.000 0.888
#> GSM627182 3 0.5988 0.504 0.368 0.000 0.632
#> GSM627202 1 0.0000 0.856 1.000 0.000 0.000
#> GSM627141 1 0.3267 0.833 0.884 0.000 0.116
#> GSM627143 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627145 3 0.5948 0.520 0.360 0.000 0.640
#> GSM627152 3 0.4605 0.784 0.204 0.000 0.796
#> GSM627200 1 0.0000 0.856 1.000 0.000 0.000
#> GSM627159 3 0.3551 0.746 0.132 0.000 0.868
#> GSM627164 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627138 1 0.2261 0.853 0.932 0.000 0.068
#> GSM627175 2 0.0747 0.963 0.000 0.984 0.016
#> GSM627150 3 0.3752 0.782 0.144 0.000 0.856
#> GSM627166 1 0.0747 0.853 0.984 0.000 0.016
#> GSM627186 2 0.0237 0.965 0.000 0.996 0.004
#> GSM627139 3 0.2959 0.764 0.100 0.000 0.900
#> GSM627181 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627205 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627214 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627180 3 0.3619 0.783 0.136 0.000 0.864
#> GSM627172 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627184 1 0.3412 0.787 0.876 0.000 0.124
#> GSM627193 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627191 1 0.9108 0.234 0.520 0.316 0.164
#> GSM627176 3 0.4555 0.785 0.200 0.000 0.800
#> GSM627194 2 0.0000 0.967 0.000 1.000 0.000
#> GSM627154 2 0.0892 0.961 0.000 0.980 0.020
#> GSM627187 1 0.3551 0.823 0.868 0.000 0.132
#> GSM627198 2 0.0747 0.963 0.000 0.984 0.016
#> GSM627160 1 0.6280 0.126 0.540 0.000 0.460
#> GSM627185 1 0.2261 0.853 0.932 0.000 0.068
#> GSM627206 1 0.3551 0.823 0.868 0.000 0.132
#> GSM627161 1 0.1753 0.837 0.952 0.000 0.048
#> GSM627162 1 0.4931 0.705 0.768 0.000 0.232
#> GSM627210 1 0.2878 0.842 0.904 0.000 0.096
#> GSM627189 2 0.0000 0.967 0.000 1.000 0.000
show/hide code output
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM627128 4 0.0469 0.951 0.000 0.000 0.012 0.988
#> GSM627110 3 0.0592 0.945 0.016 0.000 0.984 0.000
#> GSM627132 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM627107 3 0.0188 0.947 0.000 0.000 0.996 0.004
#> GSM627103 2 0.0336 0.981 0.000 0.992 0.000 0.008
#> GSM627114 3 0.1118 0.933 0.036 0.000 0.964 0.000
#> GSM627134 4 0.0336 0.955 0.000 0.008 0.000 0.992
#> GSM627137 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> GSM627148 3 0.0000 0.947 0.000 0.000 1.000 0.000
#> GSM627101 4 0.0469 0.951 0.000 0.000 0.012 0.988
#> GSM627130 4 0.0469 0.951 0.000 0.000 0.012 0.988
#> GSM627071 3 0.1118 0.933 0.036 0.000 0.964 0.000
#> GSM627118 4 0.0188 0.955 0.000 0.004 0.000 0.996
#> GSM627094 2 0.0336 0.981 0.000 0.992 0.000 0.008
#> GSM627122 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM627115 2 0.0336 0.981 0.000 0.992 0.000 0.008
#> GSM627125 4 0.0592 0.949 0.000 0.000 0.016 0.984
#> GSM627174 2 0.0336 0.981 0.000 0.992 0.000 0.008
#> GSM627102 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> GSM627073 3 0.0188 0.947 0.000 0.000 0.996 0.004
#> GSM627108 2 0.0188 0.980 0.000 0.996 0.000 0.004
#> GSM627126 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM627078 4 0.1389 0.938 0.000 0.048 0.000 0.952
#> GSM627090 3 0.0188 0.947 0.000 0.000 0.996 0.004
#> GSM627099 4 0.2814 0.864 0.000 0.132 0.000 0.868
#> GSM627105 4 0.0592 0.949 0.000 0.000 0.016 0.984
#> GSM627117 3 0.1118 0.933 0.036 0.000 0.964 0.000
#> GSM627121 3 0.0188 0.947 0.000 0.000 0.996 0.004
#> GSM627127 4 0.0336 0.955 0.000 0.008 0.000 0.992
#> GSM627087 2 0.0336 0.981 0.000 0.992 0.000 0.008
#> GSM627089 3 0.0592 0.945 0.016 0.000 0.984 0.000
#> GSM627092 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> GSM627076 3 0.0188 0.947 0.000 0.000 0.996 0.004
#> GSM627136 1 0.0188 0.970 0.996 0.000 0.004 0.000
#> GSM627081 3 0.0188 0.947 0.000 0.000 0.996 0.004
#> GSM627091 2 0.0592 0.976 0.000 0.984 0.000 0.016
#> GSM627097 4 0.0188 0.955 0.000 0.004 0.000 0.996
#> GSM627072 3 0.0469 0.946 0.012 0.000 0.988 0.000
#> GSM627080 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM627088 3 0.4790 0.397 0.380 0.000 0.620 0.000
#> GSM627109 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM627111 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM627113 1 0.0707 0.957 0.980 0.000 0.020 0.000
#> GSM627133 3 0.1059 0.934 0.000 0.016 0.972 0.012
#> GSM627177 1 0.4661 0.428 0.652 0.000 0.348 0.000
#> GSM627086 2 0.0469 0.979 0.000 0.988 0.000 0.012
#> GSM627095 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM627079 3 0.0188 0.947 0.000 0.000 0.996 0.004
#> GSM627082 4 0.0469 0.951 0.000 0.000 0.012 0.988
#> GSM627074 1 0.0188 0.970 0.996 0.000 0.004 0.000
#> GSM627077 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM627093 1 0.0188 0.970 0.996 0.000 0.004 0.000
#> GSM627120 2 0.3123 0.797 0.000 0.844 0.000 0.156
#> GSM627124 4 0.1389 0.938 0.000 0.048 0.000 0.952
#> GSM627075 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> GSM627085 4 0.0336 0.955 0.000 0.008 0.000 0.992
#> GSM627119 1 0.0188 0.970 0.996 0.000 0.004 0.000
#> GSM627116 4 0.0188 0.953 0.004 0.000 0.000 0.996
#> GSM627084 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM627096 4 0.0188 0.955 0.000 0.004 0.000 0.996
#> GSM627100 3 0.0188 0.947 0.000 0.000 0.996 0.004
#> GSM627112 4 0.0188 0.955 0.000 0.004 0.000 0.996
#> GSM627083 1 0.1398 0.933 0.956 0.004 0.000 0.040
#> GSM627098 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM627104 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM627131 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM627106 3 0.0188 0.947 0.000 0.000 0.996 0.004
#> GSM627123 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM627129 4 0.0469 0.955 0.000 0.012 0.000 0.988
#> GSM627216 2 0.0336 0.981 0.000 0.992 0.000 0.008
#> GSM627212 2 0.0469 0.979 0.000 0.988 0.000 0.012
#> GSM627190 3 0.0592 0.945 0.016 0.000 0.984 0.000
#> GSM627169 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> GSM627167 4 0.0592 0.953 0.000 0.016 0.000 0.984
#> GSM627192 1 0.0188 0.970 0.996 0.000 0.000 0.004
#> GSM627203 3 0.0188 0.947 0.000 0.000 0.996 0.004
#> GSM627151 4 0.0188 0.955 0.000 0.004 0.000 0.996
#> GSM627163 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM627211 2 0.0336 0.981 0.000 0.992 0.000 0.008
#> GSM627171 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> GSM627209 4 0.2469 0.889 0.000 0.108 0.000 0.892
#> GSM627135 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM627170 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> GSM627178 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM627199 4 0.1716 0.928 0.000 0.064 0.000 0.936
#> GSM627213 4 0.0188 0.955 0.000 0.004 0.000 0.996
#> GSM627140 4 0.0469 0.953 0.000 0.012 0.000 0.988
#> GSM627149 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM627147 4 0.2868 0.859 0.000 0.136 0.000 0.864
#> GSM627195 3 0.0188 0.947 0.000 0.000 0.996 0.004
#> GSM627204 2 0.0336 0.981 0.000 0.992 0.000 0.008
#> GSM627207 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> GSM627157 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM627201 2 0.0336 0.981 0.000 0.992 0.000 0.008
#> GSM627146 2 0.0336 0.981 0.000 0.992 0.000 0.008
#> GSM627156 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> GSM627188 1 0.0188 0.970 0.996 0.000 0.000 0.004
#> GSM627197 2 0.0336 0.981 0.000 0.992 0.000 0.008
#> GSM627173 2 0.0336 0.981 0.000 0.992 0.000 0.008
#> GSM627179 2 0.0336 0.981 0.000 0.992 0.000 0.008
#> GSM627208 3 0.0592 0.942 0.000 0.016 0.984 0.000
#> GSM627215 2 0.5204 0.374 0.000 0.612 0.376 0.012
#> GSM627153 4 0.2081 0.912 0.000 0.084 0.000 0.916
#> GSM627155 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM627165 4 0.5488 0.201 0.000 0.452 0.016 0.532
#> GSM627168 3 0.0592 0.945 0.016 0.000 0.984 0.000
#> GSM627183 1 0.4998 -0.013 0.512 0.000 0.488 0.000
#> GSM627144 3 0.0000 0.947 0.000 0.000 1.000 0.000
#> GSM627158 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM627196 2 0.0336 0.981 0.000 0.992 0.000 0.008
#> GSM627142 3 0.3224 0.833 0.016 0.000 0.864 0.120
#> GSM627182 3 0.0469 0.946 0.012 0.000 0.988 0.000
#> GSM627202 1 0.0188 0.970 0.996 0.000 0.004 0.000
#> GSM627141 3 0.4992 0.124 0.476 0.000 0.524 0.000
#> GSM627143 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> GSM627145 3 0.0469 0.946 0.012 0.000 0.988 0.000
#> GSM627152 3 0.0188 0.947 0.000 0.000 0.996 0.004
#> GSM627200 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM627159 4 0.0469 0.951 0.000 0.000 0.012 0.988
#> GSM627164 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> GSM627138 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM627175 4 0.1389 0.938 0.000 0.048 0.000 0.952
#> GSM627150 3 0.0188 0.947 0.000 0.000 0.996 0.004
#> GSM627166 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM627186 2 0.0188 0.977 0.000 0.996 0.004 0.000
#> GSM627139 4 0.0592 0.949 0.000 0.000 0.016 0.984
#> GSM627181 2 0.0336 0.981 0.000 0.992 0.000 0.008
#> GSM627205 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> GSM627214 2 0.0707 0.973 0.000 0.980 0.000 0.020
#> GSM627180 3 0.0188 0.947 0.000 0.000 0.996 0.004
#> GSM627172 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> GSM627184 1 0.0188 0.970 0.996 0.000 0.000 0.004
#> GSM627193 2 0.0336 0.981 0.000 0.992 0.000 0.008
#> GSM627191 4 0.0524 0.952 0.004 0.008 0.000 0.988
#> GSM627176 3 0.0188 0.947 0.000 0.000 0.996 0.004
#> GSM627194 2 0.0336 0.981 0.000 0.992 0.000 0.008
#> GSM627154 4 0.0336 0.955 0.000 0.008 0.000 0.992
#> GSM627187 3 0.1118 0.933 0.036 0.000 0.964 0.000
#> GSM627198 4 0.1792 0.925 0.000 0.068 0.000 0.932
#> GSM627160 4 0.2741 0.875 0.096 0.000 0.012 0.892
#> GSM627185 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM627206 3 0.0592 0.945 0.016 0.000 0.984 0.000
#> GSM627161 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM627162 3 0.5112 0.363 0.384 0.008 0.608 0.000
#> GSM627210 1 0.0921 0.949 0.972 0.000 0.028 0.000
#> GSM627189 2 0.0336 0.981 0.000 0.992 0.000 0.008
show/hide code output
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM627128 5 0.2605 0.7003 0.000 0.000 0.000 0.148 0.852
#> GSM627110 3 0.2611 0.7888 0.072 0.000 0.896 0.016 0.016
#> GSM627132 1 0.0290 0.8904 0.992 0.000 0.008 0.000 0.000
#> GSM627107 5 0.4074 0.4201 0.000 0.000 0.364 0.000 0.636
#> GSM627103 2 0.0703 0.7193 0.000 0.976 0.000 0.024 0.000
#> GSM627114 3 0.3399 0.7737 0.080 0.000 0.856 0.048 0.016
#> GSM627134 4 0.5519 0.7627 0.000 0.204 0.000 0.648 0.148
#> GSM627137 2 0.1197 0.7241 0.000 0.952 0.000 0.048 0.000
#> GSM627148 3 0.1270 0.7975 0.000 0.000 0.948 0.000 0.052
#> GSM627101 5 0.3715 0.5524 0.000 0.004 0.000 0.260 0.736
#> GSM627130 5 0.2648 0.6968 0.000 0.000 0.000 0.152 0.848
#> GSM627071 3 0.1041 0.8066 0.032 0.000 0.964 0.000 0.004
#> GSM627118 4 0.5546 0.7446 0.000 0.172 0.000 0.648 0.180
#> GSM627094 2 0.0609 0.7210 0.000 0.980 0.000 0.020 0.000
#> GSM627122 1 0.2233 0.8628 0.892 0.000 0.004 0.000 0.104
#> GSM627115 2 0.0609 0.7206 0.000 0.980 0.000 0.020 0.000
#> GSM627125 5 0.1544 0.7381 0.000 0.000 0.000 0.068 0.932
#> GSM627174 2 0.3612 0.3479 0.000 0.732 0.000 0.268 0.000
#> GSM627102 2 0.3966 0.6342 0.000 0.664 0.000 0.336 0.000
#> GSM627073 3 0.2377 0.7687 0.000 0.000 0.872 0.000 0.128
#> GSM627108 2 0.0609 0.7257 0.000 0.980 0.000 0.020 0.000
#> GSM627126 1 0.1792 0.8741 0.916 0.000 0.000 0.000 0.084
#> GSM627078 4 0.4620 0.7566 0.000 0.320 0.000 0.652 0.028
#> GSM627090 5 0.4030 0.4114 0.000 0.000 0.352 0.000 0.648
#> GSM627099 4 0.4030 0.7220 0.000 0.352 0.000 0.648 0.000
#> GSM627105 5 0.1544 0.7381 0.000 0.000 0.000 0.068 0.932
#> GSM627117 3 0.4344 0.7413 0.080 0.004 0.800 0.100 0.016
#> GSM627121 3 0.3857 0.5059 0.000 0.000 0.688 0.000 0.312
#> GSM627127 4 0.5450 0.7687 0.000 0.216 0.000 0.652 0.132
#> GSM627087 2 0.0609 0.7206 0.000 0.980 0.000 0.020 0.000
#> GSM627089 3 0.0566 0.8078 0.012 0.000 0.984 0.000 0.004
#> GSM627092 2 0.4235 0.6279 0.000 0.656 0.000 0.336 0.008
#> GSM627076 5 0.3109 0.6381 0.000 0.000 0.200 0.000 0.800
#> GSM627136 1 0.3343 0.7693 0.812 0.000 0.172 0.000 0.016
#> GSM627081 3 0.3242 0.6801 0.000 0.000 0.784 0.000 0.216
#> GSM627091 4 0.4305 0.4292 0.000 0.488 0.000 0.512 0.000
#> GSM627097 4 0.5398 0.6812 0.000 0.112 0.000 0.648 0.240
#> GSM627072 3 0.0162 0.8075 0.004 0.000 0.996 0.000 0.000
#> GSM627080 1 0.0451 0.8915 0.988 0.000 0.004 0.000 0.008
#> GSM627088 3 0.3943 0.7061 0.184 0.000 0.784 0.016 0.016
#> GSM627109 1 0.1281 0.8822 0.956 0.000 0.032 0.000 0.012
#> GSM627111 1 0.0451 0.8899 0.988 0.000 0.008 0.000 0.004
#> GSM627113 1 0.4482 0.4860 0.636 0.000 0.348 0.000 0.016
#> GSM627133 3 0.1739 0.7978 0.000 0.032 0.940 0.024 0.004
#> GSM627177 3 0.3689 0.5959 0.256 0.000 0.740 0.000 0.004
#> GSM627086 2 0.1270 0.6983 0.000 0.948 0.000 0.052 0.000
#> GSM627095 1 0.1792 0.8741 0.916 0.000 0.000 0.000 0.084
#> GSM627079 3 0.2674 0.7633 0.004 0.000 0.856 0.000 0.140
#> GSM627082 5 0.2824 0.7098 0.020 0.000 0.000 0.116 0.864
#> GSM627074 1 0.3562 0.7418 0.788 0.000 0.196 0.000 0.016
#> GSM627077 1 0.1364 0.8907 0.952 0.000 0.012 0.000 0.036
#> GSM627093 1 0.4288 0.6491 0.720 0.000 0.256 0.008 0.016
#> GSM627120 2 0.5258 0.5547 0.000 0.564 0.020 0.396 0.020
#> GSM627124 4 0.4620 0.7566 0.000 0.320 0.000 0.652 0.028
#> GSM627075 2 0.3949 0.6333 0.000 0.668 0.000 0.332 0.000
#> GSM627085 4 0.4620 0.7566 0.000 0.320 0.000 0.652 0.028
#> GSM627119 1 0.4435 0.5122 0.648 0.000 0.336 0.000 0.016
#> GSM627116 4 0.5332 0.6298 0.004 0.080 0.000 0.648 0.268
#> GSM627084 1 0.0290 0.8911 0.992 0.000 0.000 0.000 0.008
#> GSM627096 4 0.5546 0.7446 0.000 0.172 0.000 0.648 0.180
#> GSM627100 5 0.3109 0.6381 0.000 0.000 0.200 0.000 0.800
#> GSM627112 4 0.5084 0.5142 0.000 0.052 0.000 0.616 0.332
#> GSM627083 1 0.2424 0.8401 0.868 0.000 0.000 0.000 0.132
#> GSM627098 1 0.1701 0.8741 0.936 0.000 0.048 0.000 0.016
#> GSM627104 1 0.1549 0.8777 0.944 0.000 0.040 0.000 0.016
#> GSM627131 1 0.1836 0.8909 0.932 0.000 0.032 0.000 0.036
#> GSM627106 3 0.3242 0.6801 0.000 0.000 0.784 0.000 0.216
#> GSM627123 1 0.1965 0.8681 0.904 0.000 0.000 0.000 0.096
#> GSM627129 4 0.5478 0.7300 0.000 0.164 0.000 0.656 0.180
#> GSM627216 2 0.1579 0.7145 0.000 0.944 0.024 0.032 0.000
#> GSM627212 2 0.4304 -0.3969 0.000 0.516 0.000 0.484 0.000
#> GSM627190 3 0.3605 0.7680 0.080 0.000 0.844 0.060 0.016
#> GSM627169 2 0.4015 0.6258 0.000 0.652 0.000 0.348 0.000
#> GSM627167 5 0.5295 0.4306 0.000 0.048 0.000 0.464 0.488
#> GSM627192 1 0.1851 0.8724 0.912 0.000 0.000 0.000 0.088
#> GSM627203 3 0.3039 0.7103 0.000 0.000 0.808 0.000 0.192
#> GSM627151 4 0.5450 0.6977 0.000 0.124 0.000 0.648 0.228
#> GSM627163 1 0.0162 0.8910 0.996 0.000 0.000 0.000 0.004
#> GSM627211 2 0.0290 0.7244 0.000 0.992 0.000 0.008 0.000
#> GSM627171 2 0.4015 0.6258 0.000 0.652 0.000 0.348 0.000
#> GSM627209 4 0.4166 0.7274 0.000 0.348 0.000 0.648 0.004
#> GSM627135 1 0.1792 0.8741 0.916 0.000 0.000 0.000 0.084
#> GSM627170 2 0.1043 0.7242 0.000 0.960 0.000 0.040 0.000
#> GSM627178 1 0.1364 0.8911 0.952 0.000 0.012 0.000 0.036
#> GSM627199 4 0.4491 0.7502 0.000 0.328 0.000 0.652 0.020
#> GSM627213 4 0.5464 0.7025 0.000 0.128 0.000 0.648 0.224
#> GSM627140 5 0.4684 0.4899 0.004 0.008 0.000 0.452 0.536
#> GSM627149 1 0.1965 0.8681 0.904 0.000 0.000 0.000 0.096
#> GSM627147 4 0.5589 0.1259 0.000 0.244 0.000 0.628 0.128
#> GSM627195 3 0.2561 0.7579 0.000 0.000 0.856 0.000 0.144
#> GSM627204 2 0.0794 0.7171 0.000 0.972 0.000 0.028 0.000
#> GSM627207 2 0.3508 0.6596 0.000 0.748 0.000 0.252 0.000
#> GSM627157 1 0.1845 0.8701 0.928 0.000 0.056 0.000 0.016
#> GSM627201 2 0.3508 0.3863 0.000 0.748 0.000 0.252 0.000
#> GSM627146 2 0.3534 0.3772 0.000 0.744 0.000 0.256 0.000
#> GSM627156 2 0.4015 0.6258 0.000 0.652 0.000 0.348 0.000
#> GSM627188 1 0.1965 0.8681 0.904 0.000 0.000 0.000 0.096
#> GSM627197 2 0.3752 0.2813 0.000 0.708 0.000 0.292 0.000
#> GSM627173 2 0.0510 0.7225 0.000 0.984 0.000 0.016 0.000
#> GSM627179 2 0.0290 0.7257 0.000 0.992 0.000 0.008 0.000
#> GSM627208 3 0.1018 0.8054 0.000 0.016 0.968 0.016 0.000
#> GSM627215 3 0.5099 0.1550 0.000 0.440 0.528 0.028 0.004
#> GSM627153 4 0.4151 0.7326 0.000 0.344 0.000 0.652 0.004
#> GSM627155 1 0.1851 0.8724 0.912 0.000 0.000 0.000 0.088
#> GSM627165 2 0.6437 -0.0041 0.000 0.464 0.004 0.156 0.376
#> GSM627168 3 0.2270 0.7909 0.076 0.000 0.904 0.000 0.020
#> GSM627183 3 0.3942 0.6604 0.232 0.000 0.748 0.000 0.020
#> GSM627144 3 0.2516 0.7597 0.000 0.000 0.860 0.000 0.140
#> GSM627158 1 0.0963 0.8888 0.964 0.000 0.000 0.000 0.036
#> GSM627196 2 0.0794 0.7171 0.000 0.972 0.000 0.028 0.000
#> GSM627142 5 0.1410 0.7311 0.000 0.000 0.060 0.000 0.940
#> GSM627182 3 0.0510 0.8070 0.000 0.000 0.984 0.016 0.000
#> GSM627202 1 0.1836 0.8909 0.932 0.000 0.032 0.000 0.036
#> GSM627141 3 0.5706 0.5886 0.236 0.000 0.648 0.100 0.016
#> GSM627143 2 0.4166 0.6235 0.000 0.648 0.000 0.348 0.004
#> GSM627145 3 0.0451 0.8074 0.004 0.000 0.988 0.000 0.008
#> GSM627152 5 0.4763 0.4159 0.032 0.000 0.336 0.000 0.632
#> GSM627200 1 0.1914 0.8816 0.924 0.000 0.060 0.000 0.016
#> GSM627159 5 0.2439 0.7149 0.004 0.000 0.000 0.120 0.876
#> GSM627164 2 0.4015 0.6258 0.000 0.652 0.000 0.348 0.000
#> GSM627138 1 0.1774 0.8722 0.932 0.000 0.052 0.000 0.016
#> GSM627175 4 0.4620 0.7566 0.000 0.320 0.000 0.652 0.028
#> GSM627150 3 0.2377 0.7687 0.000 0.000 0.872 0.000 0.128
#> GSM627166 1 0.0290 0.8904 0.992 0.000 0.008 0.000 0.000
#> GSM627186 2 0.4166 0.6233 0.000 0.648 0.004 0.348 0.000
#> GSM627139 5 0.1502 0.7389 0.000 0.000 0.004 0.056 0.940
#> GSM627181 2 0.2891 0.5320 0.000 0.824 0.000 0.176 0.000
#> GSM627205 2 0.1557 0.7212 0.000 0.940 0.008 0.052 0.000
#> GSM627214 2 0.4060 0.0350 0.000 0.640 0.000 0.360 0.000
#> GSM627180 3 0.2690 0.7455 0.000 0.000 0.844 0.000 0.156
#> GSM627172 2 0.3966 0.6342 0.000 0.664 0.000 0.336 0.000
#> GSM627184 1 0.1965 0.8681 0.904 0.000 0.000 0.000 0.096
#> GSM627193 2 0.0404 0.7232 0.000 0.988 0.000 0.012 0.000
#> GSM627191 5 0.4630 0.6278 0.116 0.000 0.000 0.140 0.744
#> GSM627176 5 0.3990 0.4932 0.000 0.000 0.308 0.004 0.688
#> GSM627194 2 0.0703 0.7193 0.000 0.976 0.000 0.024 0.000
#> GSM627154 4 0.5181 0.7696 0.000 0.268 0.000 0.652 0.080
#> GSM627187 3 0.4892 0.7031 0.080 0.004 0.752 0.148 0.016
#> GSM627198 4 0.4491 0.7502 0.000 0.328 0.000 0.652 0.020
#> GSM627160 5 0.3176 0.7002 0.064 0.000 0.000 0.080 0.856
#> GSM627185 1 0.1300 0.8820 0.956 0.000 0.028 0.000 0.016
#> GSM627206 3 0.2378 0.7933 0.064 0.000 0.908 0.012 0.016
#> GSM627161 1 0.1544 0.8801 0.932 0.000 0.000 0.000 0.068
#> GSM627162 4 0.8248 -0.4180 0.216 0.016 0.344 0.348 0.076
#> GSM627210 1 0.4640 0.3518 0.584 0.000 0.400 0.000 0.016
#> GSM627189 2 0.0703 0.7193 0.000 0.976 0.000 0.024 0.000
show/hide code output
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM627128 6 0.4171 0.67715 0.008 0.000 0.000 0.236 0.040 0.716
#> GSM627110 3 0.1663 0.63490 0.000 0.000 0.912 0.000 0.088 0.000
#> GSM627132 1 0.1814 0.83914 0.900 0.000 0.100 0.000 0.000 0.000
#> GSM627107 5 0.3533 0.43210 0.000 0.000 0.012 0.004 0.748 0.236
#> GSM627103 2 0.0632 0.65737 0.000 0.976 0.000 0.024 0.000 0.000
#> GSM627114 3 0.1471 0.65201 0.000 0.000 0.932 0.000 0.064 0.004
#> GSM627134 4 0.2719 0.79637 0.000 0.072 0.000 0.876 0.040 0.012
#> GSM627137 2 0.1390 0.65403 0.000 0.948 0.000 0.004 0.016 0.032
#> GSM627148 5 0.3499 0.62640 0.000 0.000 0.320 0.000 0.680 0.000
#> GSM627101 4 0.4561 -0.14185 0.000 0.000 0.000 0.536 0.036 0.428
#> GSM627130 6 0.4039 0.66610 0.008 0.000 0.000 0.248 0.028 0.716
#> GSM627071 3 0.3969 0.12158 0.008 0.000 0.644 0.004 0.344 0.000
#> GSM627118 4 0.2247 0.79392 0.000 0.060 0.000 0.904 0.024 0.012
#> GSM627094 2 0.0547 0.65702 0.000 0.980 0.000 0.020 0.000 0.000
#> GSM627122 1 0.1563 0.82904 0.932 0.000 0.012 0.000 0.000 0.056
#> GSM627115 2 0.0632 0.65737 0.000 0.976 0.000 0.024 0.000 0.000
#> GSM627125 6 0.4559 0.68810 0.008 0.000 0.000 0.184 0.096 0.712
#> GSM627174 2 0.3351 0.34072 0.000 0.712 0.000 0.288 0.000 0.000
#> GSM627102 2 0.7393 0.37941 0.000 0.412 0.028 0.060 0.224 0.276
#> GSM627073 5 0.2996 0.69270 0.000 0.000 0.228 0.000 0.772 0.000
#> GSM627108 2 0.0000 0.65923 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627126 1 0.0260 0.85602 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM627078 4 0.2996 0.78669 0.000 0.228 0.000 0.772 0.000 0.000
#> GSM627090 6 0.5661 0.41184 0.040 0.000 0.056 0.004 0.364 0.536
#> GSM627099 4 0.3330 0.72156 0.000 0.284 0.000 0.716 0.000 0.000
#> GSM627105 6 0.4559 0.68810 0.008 0.000 0.000 0.184 0.096 0.712
#> GSM627117 3 0.1155 0.65537 0.000 0.000 0.956 0.004 0.036 0.004
#> GSM627121 5 0.3828 0.63753 0.000 0.000 0.100 0.000 0.776 0.124
#> GSM627127 4 0.2266 0.80944 0.000 0.108 0.000 0.880 0.000 0.012
#> GSM627087 2 0.0632 0.65737 0.000 0.976 0.000 0.024 0.000 0.000
#> GSM627089 3 0.3782 -0.09274 0.000 0.000 0.588 0.000 0.412 0.000
#> GSM627092 2 0.7374 0.34647 0.000 0.392 0.036 0.044 0.224 0.304
#> GSM627076 6 0.5117 0.56342 0.036 0.000 0.004 0.036 0.300 0.624
#> GSM627136 3 0.3198 0.51247 0.260 0.000 0.740 0.000 0.000 0.000
#> GSM627081 5 0.3822 0.67286 0.000 0.000 0.128 0.000 0.776 0.096
#> GSM627091 4 0.3862 0.35032 0.000 0.476 0.000 0.524 0.000 0.000
#> GSM627097 4 0.1921 0.75594 0.000 0.032 0.000 0.916 0.000 0.052
#> GSM627072 3 0.3854 -0.26775 0.000 0.000 0.536 0.000 0.464 0.000
#> GSM627080 1 0.1204 0.85367 0.944 0.000 0.056 0.000 0.000 0.000
#> GSM627088 3 0.1261 0.67380 0.024 0.000 0.952 0.000 0.024 0.000
#> GSM627109 1 0.3489 0.66249 0.708 0.000 0.288 0.004 0.000 0.000
#> GSM627111 1 0.1910 0.83540 0.892 0.000 0.108 0.000 0.000 0.000
#> GSM627113 3 0.2558 0.66188 0.156 0.000 0.840 0.000 0.004 0.000
#> GSM627133 5 0.6153 0.47524 0.004 0.132 0.328 0.016 0.512 0.008
#> GSM627177 3 0.4197 0.31981 0.032 0.000 0.680 0.004 0.284 0.000
#> GSM627086 2 0.1387 0.63659 0.000 0.932 0.000 0.068 0.000 0.000
#> GSM627095 1 0.0260 0.85602 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM627079 5 0.3862 0.54764 0.000 0.000 0.388 0.000 0.608 0.004
#> GSM627082 6 0.4461 0.67999 0.068 0.000 0.000 0.204 0.012 0.716
#> GSM627074 3 0.3109 0.55592 0.224 0.000 0.772 0.004 0.000 0.000
#> GSM627077 1 0.2593 0.80495 0.844 0.000 0.148 0.000 0.000 0.008
#> GSM627093 3 0.2491 0.64738 0.164 0.000 0.836 0.000 0.000 0.000
#> GSM627120 5 0.7415 -0.32920 0.000 0.316 0.028 0.048 0.344 0.264
#> GSM627124 4 0.3023 0.78412 0.000 0.232 0.000 0.768 0.000 0.000
#> GSM627075 2 0.7206 0.38149 0.000 0.428 0.028 0.044 0.224 0.276
#> GSM627085 4 0.2527 0.80817 0.000 0.168 0.000 0.832 0.000 0.000
#> GSM627119 3 0.2402 0.66576 0.140 0.000 0.856 0.004 0.000 0.000
#> GSM627116 4 0.2103 0.71619 0.012 0.000 0.020 0.912 0.000 0.056
#> GSM627084 1 0.1141 0.85354 0.948 0.000 0.052 0.000 0.000 0.000
#> GSM627096 4 0.2186 0.79164 0.000 0.056 0.000 0.908 0.024 0.012
#> GSM627100 6 0.5013 0.57442 0.028 0.000 0.004 0.040 0.292 0.636
#> GSM627112 4 0.2668 0.59556 0.000 0.004 0.000 0.828 0.000 0.168
#> GSM627083 1 0.0993 0.83936 0.964 0.000 0.000 0.012 0.000 0.024
#> GSM627098 3 0.3868 -0.19799 0.492 0.000 0.508 0.000 0.000 0.000
#> GSM627104 1 0.3961 0.34457 0.556 0.000 0.440 0.004 0.000 0.000
#> GSM627131 1 0.3161 0.74475 0.776 0.000 0.216 0.000 0.000 0.008
#> GSM627106 5 0.3832 0.66419 0.000 0.000 0.120 0.000 0.776 0.104
#> GSM627123 1 0.0260 0.85602 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM627129 4 0.3125 0.76961 0.000 0.064 0.000 0.856 0.024 0.056
#> GSM627216 2 0.3445 0.59193 0.004 0.816 0.004 0.024 0.144 0.008
#> GSM627212 2 0.3854 -0.22024 0.000 0.536 0.000 0.464 0.000 0.000
#> GSM627190 3 0.1471 0.65201 0.000 0.000 0.932 0.000 0.064 0.004
#> GSM627169 2 0.7484 0.36640 0.000 0.404 0.048 0.044 0.224 0.280
#> GSM627167 6 0.5696 0.29286 0.000 0.028 0.000 0.148 0.220 0.604
#> GSM627192 1 0.0260 0.85602 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM627203 5 0.4014 0.68011 0.000 0.000 0.148 0.000 0.756 0.096
#> GSM627151 4 0.2340 0.75767 0.004 0.056 0.000 0.896 0.000 0.044
#> GSM627163 1 0.1327 0.85109 0.936 0.000 0.064 0.000 0.000 0.000
#> GSM627211 2 0.0935 0.65679 0.000 0.964 0.000 0.032 0.004 0.000
#> GSM627171 2 0.7484 0.36895 0.000 0.404 0.044 0.048 0.224 0.280
#> GSM627209 4 0.3023 0.78412 0.000 0.232 0.000 0.768 0.000 0.000
#> GSM627135 1 0.0000 0.85640 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627170 2 0.1686 0.64073 0.000 0.924 0.000 0.012 0.064 0.000
#> GSM627178 1 0.2544 0.81327 0.852 0.000 0.140 0.004 0.000 0.004
#> GSM627199 4 0.3050 0.77970 0.000 0.236 0.000 0.764 0.000 0.000
#> GSM627213 4 0.1700 0.77636 0.000 0.048 0.000 0.928 0.000 0.024
#> GSM627140 6 0.5704 0.34544 0.032 0.004 0.004 0.108 0.224 0.628
#> GSM627149 1 0.0260 0.85602 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM627147 6 0.7107 -0.00938 0.000 0.084 0.000 0.336 0.216 0.364
#> GSM627195 5 0.3543 0.70161 0.000 0.000 0.200 0.000 0.768 0.032
#> GSM627204 2 0.1267 0.64545 0.000 0.940 0.000 0.060 0.000 0.000
#> GSM627207 2 0.3830 0.59473 0.000 0.788 0.000 0.044 0.020 0.148
#> GSM627157 3 0.3857 -0.11326 0.468 0.000 0.532 0.000 0.000 0.000
#> GSM627201 2 0.3175 0.39224 0.000 0.744 0.000 0.256 0.000 0.000
#> GSM627146 2 0.3221 0.38669 0.000 0.736 0.000 0.264 0.000 0.000
#> GSM627156 2 0.7435 0.36933 0.000 0.408 0.044 0.044 0.224 0.280
#> GSM627188 1 0.0260 0.85602 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM627197 2 0.3531 0.24199 0.000 0.672 0.000 0.328 0.000 0.000
#> GSM627173 2 0.1082 0.65490 0.000 0.956 0.000 0.040 0.004 0.000
#> GSM627179 2 0.0000 0.65923 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627208 5 0.4537 0.51150 0.000 0.020 0.384 0.000 0.584 0.012
#> GSM627215 5 0.5317 0.04699 0.004 0.464 0.020 0.036 0.472 0.004
#> GSM627153 4 0.3023 0.78412 0.000 0.232 0.000 0.768 0.000 0.000
#> GSM627155 1 0.0260 0.85602 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM627165 2 0.7474 -0.12931 0.000 0.328 0.000 0.312 0.144 0.216
#> GSM627168 3 0.1918 0.64647 0.008 0.000 0.904 0.000 0.088 0.000
#> GSM627183 3 0.1713 0.67626 0.044 0.000 0.928 0.000 0.028 0.000
#> GSM627144 5 0.4392 0.61363 0.000 0.000 0.332 0.000 0.628 0.040
#> GSM627158 1 0.0458 0.85753 0.984 0.000 0.016 0.000 0.000 0.000
#> GSM627196 2 0.1267 0.64545 0.000 0.940 0.000 0.060 0.000 0.000
#> GSM627142 6 0.5076 0.64193 0.032 0.000 0.000 0.084 0.208 0.676
#> GSM627182 5 0.4057 0.44268 0.000 0.000 0.436 0.000 0.556 0.008
#> GSM627202 1 0.3424 0.74564 0.780 0.000 0.196 0.000 0.020 0.004
#> GSM627141 3 0.1786 0.67093 0.032 0.000 0.932 0.004 0.028 0.004
#> GSM627143 2 0.7528 0.33007 0.000 0.376 0.048 0.044 0.224 0.308
#> GSM627145 5 0.3867 0.32840 0.000 0.000 0.488 0.000 0.512 0.000
#> GSM627152 6 0.5722 0.39787 0.044 0.000 0.068 0.000 0.360 0.528
#> GSM627200 1 0.3244 0.67939 0.732 0.000 0.268 0.000 0.000 0.000
#> GSM627159 6 0.4465 0.68673 0.032 0.000 0.000 0.216 0.036 0.716
#> GSM627164 2 0.7433 0.37166 0.000 0.408 0.040 0.048 0.224 0.280
#> GSM627138 1 0.3866 0.19619 0.516 0.000 0.484 0.000 0.000 0.000
#> GSM627175 4 0.2996 0.78669 0.000 0.228 0.000 0.772 0.000 0.000
#> GSM627150 5 0.3023 0.69164 0.000 0.000 0.232 0.000 0.768 0.000
#> GSM627166 1 0.3189 0.73298 0.760 0.000 0.236 0.004 0.000 0.000
#> GSM627186 2 0.7575 0.35987 0.000 0.396 0.056 0.044 0.224 0.280
#> GSM627139 6 0.4681 0.68695 0.012 0.000 0.000 0.176 0.104 0.708
#> GSM627181 2 0.2854 0.48155 0.000 0.792 0.000 0.208 0.000 0.000
#> GSM627205 2 0.3357 0.58704 0.000 0.816 0.000 0.020 0.144 0.020
#> GSM627214 2 0.4555 -0.04094 0.000 0.548 0.000 0.420 0.028 0.004
#> GSM627180 5 0.3620 0.70276 0.000 0.000 0.184 0.000 0.772 0.044
#> GSM627172 2 0.7393 0.37941 0.000 0.412 0.028 0.060 0.224 0.276
#> GSM627184 1 0.0260 0.85602 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM627193 2 0.0458 0.65809 0.000 0.984 0.000 0.016 0.000 0.000
#> GSM627191 6 0.6600 0.41804 0.336 0.000 0.000 0.140 0.068 0.456
#> GSM627176 6 0.5720 0.47315 0.040 0.000 0.060 0.008 0.328 0.564
#> GSM627194 2 0.0937 0.64880 0.000 0.960 0.000 0.040 0.000 0.000
#> GSM627154 4 0.2260 0.81354 0.000 0.140 0.000 0.860 0.000 0.000
#> GSM627187 3 0.1707 0.64942 0.000 0.000 0.928 0.004 0.056 0.012
#> GSM627198 4 0.3023 0.78412 0.000 0.232 0.000 0.768 0.000 0.000
#> GSM627160 6 0.4736 0.65644 0.140 0.000 0.000 0.164 0.004 0.692
#> GSM627185 1 0.3747 0.44818 0.604 0.000 0.396 0.000 0.000 0.000
#> GSM627206 3 0.2300 0.59260 0.000 0.000 0.856 0.000 0.144 0.000
#> GSM627161 1 0.0458 0.85753 0.984 0.000 0.016 0.000 0.000 0.000
#> GSM627162 3 0.7254 0.04639 0.004 0.012 0.348 0.044 0.312 0.280
#> GSM627210 3 0.2278 0.66947 0.128 0.000 0.868 0.004 0.000 0.000
#> GSM627189 2 0.0632 0.65624 0.000 0.976 0.000 0.024 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two
samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures
which are significantly different between classes which can be candidate marks
for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known
annotation is numeric, one-way ANOVA test is applied, and if the known
annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) age(p) other(p) k
#> MAD:skmeans 143 0.817 0.401 0.0385 2
#> MAD:skmeans 139 0.270 0.397 0.0148 3
#> MAD:skmeans 139 0.248 0.336 0.0835 4
#> MAD:skmeans 127 0.373 0.496 0.1532 5
#> MAD:skmeans 103 0.679 0.696 0.3074 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
MAD:pam**
The object with results only for a single top-value method and a single partition method
can be extracted as:
res = res_list["MAD", "pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51882 rows and 146 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
- The first row: a plot of the ECDF (empirical cumulative distribution
function) curves of the consensus matrix for each
k and the heatmap of
predicted classes for each k.
- The second row: heatmaps of the consensus matrix for each
k.
- The third row: heatmaps of the membership matrix for each
k.
- The fouth row: heatmaps of the signatures for each
k.
All the plots in panels can be made by individual functions and they are
plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
- ECDF curves of the consensus matrix for each
k;
- 1-PAC. The PAC
score
measures the proportion of the ambiguous subgrouping.
- Mean silhouette score.
- Concordance. The mean probability of fiting the consensus class ids in all
partitions.
- Area increased. Denote \(A_k\) as the area under the ECDF curve for current
k, the area increased is defined as \(A_k - A_{k-1}\).
- Rand index. The percent of pairs of samples that are both in a same cluster
or both are not in a same cluster in the partition of k and k-1.
- Jaccard index. The ratio of pairs of samples are both in a same cluster in
the partition of k and k-1 and the pairs of samples are both in a same
cluster in the partition k or k-1.
The detailed explanations of these statistics can be found in the cola
vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.968 0.986 0.5036 0.497 0.497
#> 3 3 0.730 0.782 0.905 0.2988 0.789 0.597
#> 4 4 0.583 0.601 0.747 0.1026 0.871 0.648
#> 5 5 0.671 0.651 0.817 0.0827 0.879 0.595
#> 6 6 0.702 0.635 0.804 0.0374 0.863 0.487
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
- All \(k\) with Jaccard index larger than 0.95 are removed because increasing
\(k\) does not provide enough extra information. If all \(k\) are removed, it is
marked as no subgroup is detected.
- For all \(k\) with 1-PAC score larger than 0.9, the maximal \(k\) is taken as
the best \(k\), and other \(k\) are marked as optional \(k\).
- If it does not fit the second rule. The \(k\) with the maximal vote of the
highest 1-PAC score, highest mean silhouette, and highest concordance is
taken as the best \(k\).
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
show/hide code output
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM627128 1 0.0000 0.981 1.000 0.000
#> GSM627110 1 0.0000 0.981 1.000 0.000
#> GSM627132 1 0.0000 0.981 1.000 0.000
#> GSM627107 1 0.0000 0.981 1.000 0.000
#> GSM627103 2 0.0000 0.992 0.000 1.000
#> GSM627114 1 0.0000 0.981 1.000 0.000
#> GSM627134 2 0.0000 0.992 0.000 1.000
#> GSM627137 2 0.0000 0.992 0.000 1.000
#> GSM627148 1 0.0000 0.981 1.000 0.000
#> GSM627101 1 0.0000 0.981 1.000 0.000
#> GSM627130 1 0.0000 0.981 1.000 0.000
#> GSM627071 1 0.0000 0.981 1.000 0.000
#> GSM627118 2 0.0000 0.992 0.000 1.000
#> GSM627094 2 0.0000 0.992 0.000 1.000
#> GSM627122 1 0.0000 0.981 1.000 0.000
#> GSM627115 2 0.0000 0.992 0.000 1.000
#> GSM627125 1 0.0000 0.981 1.000 0.000
#> GSM627174 2 0.0000 0.992 0.000 1.000
#> GSM627102 2 0.0000 0.992 0.000 1.000
#> GSM627073 1 0.0000 0.981 1.000 0.000
#> GSM627108 2 0.0000 0.992 0.000 1.000
#> GSM627126 1 0.0000 0.981 1.000 0.000
#> GSM627078 2 0.0000 0.992 0.000 1.000
#> GSM627090 1 0.0000 0.981 1.000 0.000
#> GSM627099 2 0.0000 0.992 0.000 1.000
#> GSM627105 1 0.0000 0.981 1.000 0.000
#> GSM627117 2 0.0000 0.992 0.000 1.000
#> GSM627121 1 0.7299 0.747 0.796 0.204
#> GSM627127 2 0.0000 0.992 0.000 1.000
#> GSM627087 2 0.0000 0.992 0.000 1.000
#> GSM627089 1 0.0000 0.981 1.000 0.000
#> GSM627092 2 0.0000 0.992 0.000 1.000
#> GSM627076 1 0.0000 0.981 1.000 0.000
#> GSM627136 1 0.0000 0.981 1.000 0.000
#> GSM627081 1 0.8608 0.613 0.716 0.284
#> GSM627091 2 0.0000 0.992 0.000 1.000
#> GSM627097 2 0.0000 0.992 0.000 1.000
#> GSM627072 1 0.0000 0.981 1.000 0.000
#> GSM627080 1 0.0000 0.981 1.000 0.000
#> GSM627088 1 0.3114 0.929 0.944 0.056
#> GSM627109 1 0.0000 0.981 1.000 0.000
#> GSM627111 1 0.0000 0.981 1.000 0.000
#> GSM627113 1 0.0000 0.981 1.000 0.000
#> GSM627133 2 0.0000 0.992 0.000 1.000
#> GSM627177 1 0.0000 0.981 1.000 0.000
#> GSM627086 2 0.0000 0.992 0.000 1.000
#> GSM627095 1 0.0000 0.981 1.000 0.000
#> GSM627079 1 0.0000 0.981 1.000 0.000
#> GSM627082 1 0.0000 0.981 1.000 0.000
#> GSM627074 1 0.5059 0.869 0.888 0.112
#> GSM627077 1 0.0000 0.981 1.000 0.000
#> GSM627093 2 0.4690 0.885 0.100 0.900
#> GSM627120 2 0.0000 0.992 0.000 1.000
#> GSM627124 2 0.0000 0.992 0.000 1.000
#> GSM627075 2 0.0000 0.992 0.000 1.000
#> GSM627085 2 0.0000 0.992 0.000 1.000
#> GSM627119 1 0.0000 0.981 1.000 0.000
#> GSM627116 2 0.9323 0.456 0.348 0.652
#> GSM627084 1 0.0000 0.981 1.000 0.000
#> GSM627096 2 0.0000 0.992 0.000 1.000
#> GSM627100 1 0.0000 0.981 1.000 0.000
#> GSM627112 1 0.8327 0.647 0.736 0.264
#> GSM627083 1 0.0000 0.981 1.000 0.000
#> GSM627098 1 0.0000 0.981 1.000 0.000
#> GSM627104 2 0.0000 0.992 0.000 1.000
#> GSM627131 1 0.0000 0.981 1.000 0.000
#> GSM627106 1 0.0000 0.981 1.000 0.000
#> GSM627123 1 0.0000 0.981 1.000 0.000
#> GSM627129 2 0.0000 0.992 0.000 1.000
#> GSM627216 2 0.0000 0.992 0.000 1.000
#> GSM627212 2 0.0000 0.992 0.000 1.000
#> GSM627190 2 0.4022 0.909 0.080 0.920
#> GSM627169 2 0.0000 0.992 0.000 1.000
#> GSM627167 2 0.0000 0.992 0.000 1.000
#> GSM627192 1 0.0000 0.981 1.000 0.000
#> GSM627203 1 0.0000 0.981 1.000 0.000
#> GSM627151 2 0.0000 0.992 0.000 1.000
#> GSM627163 1 0.0000 0.981 1.000 0.000
#> GSM627211 2 0.0000 0.992 0.000 1.000
#> GSM627171 2 0.0000 0.992 0.000 1.000
#> GSM627209 2 0.0000 0.992 0.000 1.000
#> GSM627135 1 0.0000 0.981 1.000 0.000
#> GSM627170 2 0.0000 0.992 0.000 1.000
#> GSM627178 1 0.0000 0.981 1.000 0.000
#> GSM627199 2 0.0000 0.992 0.000 1.000
#> GSM627213 2 0.0000 0.992 0.000 1.000
#> GSM627140 2 0.0000 0.992 0.000 1.000
#> GSM627149 1 0.0000 0.981 1.000 0.000
#> GSM627147 2 0.0000 0.992 0.000 1.000
#> GSM627195 1 0.0000 0.981 1.000 0.000
#> GSM627204 2 0.0000 0.992 0.000 1.000
#> GSM627207 2 0.0000 0.992 0.000 1.000
#> GSM627157 1 0.0000 0.981 1.000 0.000
#> GSM627201 2 0.0000 0.992 0.000 1.000
#> GSM627146 2 0.0000 0.992 0.000 1.000
#> GSM627156 2 0.0000 0.992 0.000 1.000
#> GSM627188 1 0.0000 0.981 1.000 0.000
#> GSM627197 2 0.0000 0.992 0.000 1.000
#> GSM627173 2 0.0000 0.992 0.000 1.000
#> GSM627179 2 0.0000 0.992 0.000 1.000
#> GSM627208 2 0.0000 0.992 0.000 1.000
#> GSM627215 2 0.0000 0.992 0.000 1.000
#> GSM627153 2 0.0000 0.992 0.000 1.000
#> GSM627155 1 0.0000 0.981 1.000 0.000
#> GSM627165 2 0.0000 0.992 0.000 1.000
#> GSM627168 1 0.0000 0.981 1.000 0.000
#> GSM627183 1 0.0000 0.981 1.000 0.000
#> GSM627144 1 0.9815 0.291 0.580 0.420
#> GSM627158 1 0.0000 0.981 1.000 0.000
#> GSM627196 2 0.0000 0.992 0.000 1.000
#> GSM627142 1 0.0000 0.981 1.000 0.000
#> GSM627182 2 0.0000 0.992 0.000 1.000
#> GSM627202 1 0.0000 0.981 1.000 0.000
#> GSM627141 1 0.0000 0.981 1.000 0.000
#> GSM627143 2 0.0000 0.992 0.000 1.000
#> GSM627145 1 0.0000 0.981 1.000 0.000
#> GSM627152 1 0.0000 0.981 1.000 0.000
#> GSM627200 1 0.0000 0.981 1.000 0.000
#> GSM627159 1 0.0000 0.981 1.000 0.000
#> GSM627164 2 0.0000 0.992 0.000 1.000
#> GSM627138 1 0.0000 0.981 1.000 0.000
#> GSM627175 2 0.0000 0.992 0.000 1.000
#> GSM627150 1 0.0000 0.981 1.000 0.000
#> GSM627166 2 0.0672 0.984 0.008 0.992
#> GSM627186 2 0.0000 0.992 0.000 1.000
#> GSM627139 1 0.0000 0.981 1.000 0.000
#> GSM627181 2 0.0000 0.992 0.000 1.000
#> GSM627205 2 0.0000 0.992 0.000 1.000
#> GSM627214 2 0.0000 0.992 0.000 1.000
#> GSM627180 1 0.3114 0.930 0.944 0.056
#> GSM627172 2 0.0000 0.992 0.000 1.000
#> GSM627184 1 0.0000 0.981 1.000 0.000
#> GSM627193 2 0.0000 0.992 0.000 1.000
#> GSM627191 1 0.0000 0.981 1.000 0.000
#> GSM627176 1 0.0000 0.981 1.000 0.000
#> GSM627194 2 0.0000 0.992 0.000 1.000
#> GSM627154 2 0.0000 0.992 0.000 1.000
#> GSM627187 2 0.2423 0.953 0.040 0.960
#> GSM627198 2 0.0000 0.992 0.000 1.000
#> GSM627160 1 0.0000 0.981 1.000 0.000
#> GSM627185 1 0.0000 0.981 1.000 0.000
#> GSM627206 1 0.0000 0.981 1.000 0.000
#> GSM627161 1 0.0000 0.981 1.000 0.000
#> GSM627162 1 0.0376 0.977 0.996 0.004
#> GSM627210 1 0.0938 0.971 0.988 0.012
#> GSM627189 2 0.0000 0.992 0.000 1.000
show/hide code output
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM627128 3 0.0000 0.892 0.000 0.000 1.000
#> GSM627110 1 0.0892 0.789 0.980 0.000 0.020
#> GSM627132 1 0.6291 -0.130 0.532 0.000 0.468
#> GSM627107 3 0.0000 0.892 0.000 0.000 1.000
#> GSM627103 2 0.5058 0.661 0.244 0.756 0.000
#> GSM627114 1 0.0000 0.790 1.000 0.000 0.000
#> GSM627134 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627137 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627148 1 0.0592 0.788 0.988 0.000 0.012
#> GSM627101 3 0.0237 0.891 0.000 0.004 0.996
#> GSM627130 3 0.0000 0.892 0.000 0.000 1.000
#> GSM627071 1 0.6260 0.332 0.552 0.000 0.448
#> GSM627118 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627094 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627122 3 0.0424 0.891 0.008 0.000 0.992
#> GSM627115 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627125 3 0.0000 0.892 0.000 0.000 1.000
#> GSM627174 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627102 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627073 3 0.0000 0.892 0.000 0.000 1.000
#> GSM627108 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627126 3 0.3941 0.771 0.156 0.000 0.844
#> GSM627078 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627090 3 0.0000 0.892 0.000 0.000 1.000
#> GSM627099 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627105 3 0.0000 0.892 0.000 0.000 1.000
#> GSM627117 1 0.3038 0.746 0.896 0.104 0.000
#> GSM627121 1 0.8425 0.446 0.540 0.096 0.364
#> GSM627127 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627087 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627089 1 0.3816 0.707 0.852 0.000 0.148
#> GSM627092 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627076 3 0.0000 0.892 0.000 0.000 1.000
#> GSM627136 3 0.3941 0.771 0.156 0.000 0.844
#> GSM627081 1 0.8975 0.369 0.484 0.132 0.384
#> GSM627091 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627097 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627072 1 0.6244 0.351 0.560 0.000 0.440
#> GSM627080 3 0.6244 0.348 0.440 0.000 0.560
#> GSM627088 1 0.1643 0.783 0.956 0.000 0.044
#> GSM627109 1 0.0892 0.788 0.980 0.000 0.020
#> GSM627111 1 0.0892 0.788 0.980 0.000 0.020
#> GSM627113 1 0.0747 0.789 0.984 0.000 0.016
#> GSM627133 2 0.5327 0.617 0.272 0.728 0.000
#> GSM627177 1 0.6786 0.330 0.540 0.012 0.448
#> GSM627086 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627095 3 0.3816 0.780 0.148 0.000 0.852
#> GSM627079 3 0.0000 0.892 0.000 0.000 1.000
#> GSM627082 3 0.0592 0.887 0.000 0.012 0.988
#> GSM627074 1 0.0000 0.790 1.000 0.000 0.000
#> GSM627077 3 0.0592 0.891 0.012 0.000 0.988
#> GSM627093 1 0.0000 0.790 1.000 0.000 0.000
#> GSM627120 2 0.2537 0.871 0.080 0.920 0.000
#> GSM627124 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627075 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627085 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627119 1 0.0000 0.790 1.000 0.000 0.000
#> GSM627116 2 0.6244 0.254 0.000 0.560 0.440
#> GSM627084 1 0.5591 0.557 0.696 0.000 0.304
#> GSM627096 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627100 3 0.0000 0.892 0.000 0.000 1.000
#> GSM627112 3 0.5327 0.547 0.000 0.272 0.728
#> GSM627083 3 0.0592 0.887 0.000 0.012 0.988
#> GSM627098 1 0.1289 0.786 0.968 0.000 0.032
#> GSM627104 1 0.0237 0.790 0.996 0.004 0.000
#> GSM627131 3 0.0592 0.891 0.012 0.000 0.988
#> GSM627106 3 0.0000 0.892 0.000 0.000 1.000
#> GSM627123 3 0.1643 0.876 0.044 0.000 0.956
#> GSM627129 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627216 2 0.4235 0.764 0.176 0.824 0.000
#> GSM627212 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627190 1 0.0000 0.790 1.000 0.000 0.000
#> GSM627169 2 0.5327 0.619 0.272 0.728 0.000
#> GSM627167 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627192 3 0.0592 0.891 0.012 0.000 0.988
#> GSM627203 3 0.1163 0.882 0.028 0.000 0.972
#> GSM627151 2 0.5291 0.638 0.000 0.732 0.268
#> GSM627163 1 0.1529 0.778 0.960 0.000 0.040
#> GSM627211 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627171 1 0.6305 0.109 0.516 0.484 0.000
#> GSM627209 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627135 3 0.0592 0.891 0.012 0.000 0.988
#> GSM627170 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627178 3 0.1860 0.870 0.052 0.000 0.948
#> GSM627199 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627213 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627140 2 0.4974 0.683 0.000 0.764 0.236
#> GSM627149 3 0.4796 0.695 0.220 0.000 0.780
#> GSM627147 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627195 3 0.1163 0.879 0.028 0.000 0.972
#> GSM627204 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627207 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627157 1 0.0892 0.788 0.980 0.000 0.020
#> GSM627201 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627146 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627156 2 0.5327 0.619 0.272 0.728 0.000
#> GSM627188 3 0.0592 0.891 0.012 0.000 0.988
#> GSM627197 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627173 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627179 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627208 1 0.6745 0.254 0.560 0.428 0.012
#> GSM627215 2 0.5016 0.667 0.240 0.760 0.000
#> GSM627153 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627155 3 0.6225 0.364 0.432 0.000 0.568
#> GSM627165 2 0.0592 0.932 0.000 0.988 0.012
#> GSM627168 1 0.3551 0.727 0.868 0.000 0.132
#> GSM627183 1 0.4702 0.657 0.788 0.000 0.212
#> GSM627144 1 0.8685 0.535 0.584 0.156 0.260
#> GSM627158 3 0.6244 0.348 0.440 0.000 0.560
#> GSM627196 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627142 3 0.0000 0.892 0.000 0.000 1.000
#> GSM627182 1 0.6745 0.254 0.560 0.428 0.012
#> GSM627202 3 0.5465 0.599 0.288 0.000 0.712
#> GSM627141 1 0.1753 0.779 0.952 0.000 0.048
#> GSM627143 2 0.5843 0.626 0.252 0.732 0.016
#> GSM627145 3 0.2066 0.852 0.060 0.000 0.940
#> GSM627152 3 0.0000 0.892 0.000 0.000 1.000
#> GSM627200 3 0.0592 0.891 0.012 0.000 0.988
#> GSM627159 3 0.0000 0.892 0.000 0.000 1.000
#> GSM627164 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627138 1 0.0892 0.788 0.980 0.000 0.020
#> GSM627175 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627150 3 0.0892 0.883 0.020 0.000 0.980
#> GSM627166 1 0.6161 0.576 0.708 0.272 0.020
#> GSM627186 1 0.6295 0.127 0.528 0.472 0.000
#> GSM627139 3 0.0000 0.892 0.000 0.000 1.000
#> GSM627181 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627205 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627214 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627180 3 0.6441 0.455 0.276 0.028 0.696
#> GSM627172 2 0.4521 0.754 0.004 0.816 0.180
#> GSM627184 3 0.0747 0.890 0.016 0.000 0.984
#> GSM627193 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627191 3 0.0592 0.887 0.000 0.012 0.988
#> GSM627176 3 0.6299 -0.167 0.476 0.000 0.524
#> GSM627194 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627154 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627187 1 0.0000 0.790 1.000 0.000 0.000
#> GSM627198 2 0.0000 0.943 0.000 1.000 0.000
#> GSM627160 3 0.0592 0.887 0.000 0.012 0.988
#> GSM627185 1 0.0000 0.790 1.000 0.000 0.000
#> GSM627206 1 0.0000 0.790 1.000 0.000 0.000
#> GSM627161 3 0.5733 0.549 0.324 0.000 0.676
#> GSM627162 1 0.6113 0.557 0.688 0.012 0.300
#> GSM627210 1 0.0000 0.790 1.000 0.000 0.000
#> GSM627189 2 0.0000 0.943 0.000 1.000 0.000
show/hide code output
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM627128 3 0.0000 0.7547 0.000 0.000 1.000 0.000
#> GSM627110 1 0.1284 0.6661 0.964 0.000 0.012 0.024
#> GSM627132 1 0.7768 0.1584 0.428 0.000 0.260 0.312
#> GSM627107 3 0.3583 0.6919 0.004 0.000 0.816 0.180
#> GSM627103 2 0.0657 0.8123 0.004 0.984 0.012 0.000
#> GSM627114 1 0.0000 0.6698 1.000 0.000 0.000 0.000
#> GSM627134 2 0.0469 0.8133 0.000 0.988 0.012 0.000
#> GSM627137 2 0.0000 0.8194 0.000 1.000 0.000 0.000
#> GSM627148 1 0.3400 0.6017 0.820 0.000 0.000 0.180
#> GSM627101 4 0.4477 0.0941 0.000 0.000 0.312 0.688
#> GSM627130 3 0.0000 0.7547 0.000 0.000 1.000 0.000
#> GSM627071 1 0.5000 0.0123 0.504 0.000 0.496 0.000
#> GSM627118 4 0.6425 0.8063 0.000 0.424 0.068 0.508
#> GSM627094 2 0.0000 0.8194 0.000 1.000 0.000 0.000
#> GSM627122 3 0.2647 0.7301 0.120 0.000 0.880 0.000
#> GSM627115 2 0.0000 0.8194 0.000 1.000 0.000 0.000
#> GSM627125 3 0.3356 0.6945 0.000 0.000 0.824 0.176
#> GSM627174 2 0.0469 0.8103 0.012 0.988 0.000 0.000
#> GSM627102 4 0.5168 0.8811 0.000 0.492 0.004 0.504
#> GSM627073 3 0.4731 0.7153 0.160 0.000 0.780 0.060
#> GSM627108 2 0.0000 0.8194 0.000 1.000 0.000 0.000
#> GSM627126 3 0.6461 0.5388 0.144 0.000 0.640 0.216
#> GSM627078 4 0.5406 0.8799 0.000 0.480 0.012 0.508
#> GSM627090 3 0.2450 0.7536 0.072 0.000 0.912 0.016
#> GSM627099 2 0.2469 0.6441 0.000 0.892 0.000 0.108
#> GSM627105 3 0.3400 0.6916 0.000 0.000 0.820 0.180
#> GSM627117 1 0.2408 0.6387 0.896 0.104 0.000 0.000
#> GSM627121 1 0.9173 0.1740 0.392 0.100 0.328 0.180
#> GSM627127 2 0.0000 0.8194 0.000 1.000 0.000 0.000
#> GSM627087 2 0.0000 0.8194 0.000 1.000 0.000 0.000
#> GSM627089 1 0.2973 0.5896 0.856 0.000 0.144 0.000
#> GSM627092 2 0.0000 0.8194 0.000 1.000 0.000 0.000
#> GSM627076 3 0.3758 0.7399 0.048 0.000 0.848 0.104
#> GSM627136 3 0.4643 0.5086 0.344 0.000 0.656 0.000
#> GSM627081 1 0.9416 0.2244 0.396 0.136 0.288 0.180
#> GSM627091 2 0.0000 0.8194 0.000 1.000 0.000 0.000
#> GSM627097 2 0.0469 0.8133 0.000 0.988 0.012 0.000
#> GSM627072 1 0.6568 0.2487 0.572 0.000 0.332 0.096
#> GSM627080 1 0.7768 0.1584 0.428 0.000 0.260 0.312
#> GSM627088 1 0.2216 0.6439 0.908 0.000 0.092 0.000
#> GSM627109 1 0.3311 0.6302 0.828 0.000 0.000 0.172
#> GSM627111 1 0.4477 0.5584 0.688 0.000 0.000 0.312
#> GSM627113 1 0.2976 0.6465 0.872 0.000 0.008 0.120
#> GSM627133 2 0.1059 0.8038 0.016 0.972 0.012 0.000
#> GSM627177 1 0.5000 0.0123 0.504 0.000 0.496 0.000
#> GSM627086 4 0.4999 0.8836 0.000 0.492 0.000 0.508
#> GSM627095 3 0.5979 0.5963 0.136 0.000 0.692 0.172
#> GSM627079 3 0.3123 0.7186 0.156 0.000 0.844 0.000
#> GSM627082 3 0.0000 0.7547 0.000 0.000 1.000 0.000
#> GSM627074 1 0.0000 0.6698 1.000 0.000 0.000 0.000
#> GSM627077 3 0.2741 0.7491 0.096 0.000 0.892 0.012
#> GSM627093 1 0.0000 0.6698 1.000 0.000 0.000 0.000
#> GSM627120 2 0.0469 0.8133 0.000 0.988 0.012 0.000
#> GSM627124 4 0.5406 0.8799 0.000 0.480 0.012 0.508
#> GSM627075 2 0.0000 0.8194 0.000 1.000 0.000 0.000
#> GSM627085 4 0.4999 0.8836 0.000 0.492 0.000 0.508
#> GSM627119 1 0.0000 0.6698 1.000 0.000 0.000 0.000
#> GSM627116 2 0.5007 0.1445 0.008 0.636 0.356 0.000
#> GSM627084 1 0.6007 0.2729 0.548 0.044 0.408 0.000
#> GSM627096 4 0.6425 0.8063 0.000 0.424 0.068 0.508
#> GSM627100 3 0.3257 0.7090 0.004 0.000 0.844 0.152
#> GSM627112 4 0.7407 0.5880 0.000 0.288 0.204 0.508
#> GSM627083 3 0.3577 0.6578 0.012 0.156 0.832 0.000
#> GSM627098 1 0.2868 0.6242 0.864 0.000 0.136 0.000
#> GSM627104 1 0.3024 0.6038 0.852 0.148 0.000 0.000
#> GSM627131 3 0.3311 0.7080 0.172 0.000 0.828 0.000
#> GSM627106 3 0.5811 0.6428 0.116 0.000 0.704 0.180
#> GSM627123 3 0.4459 0.6979 0.032 0.000 0.780 0.188
#> GSM627129 2 0.0469 0.8133 0.000 0.988 0.012 0.000
#> GSM627216 2 0.0937 0.8070 0.012 0.976 0.012 0.000
#> GSM627212 2 0.0000 0.8194 0.000 1.000 0.000 0.000
#> GSM627190 1 0.0000 0.6698 1.000 0.000 0.000 0.000
#> GSM627169 2 0.1716 0.7497 0.064 0.936 0.000 0.000
#> GSM627167 4 0.5508 0.8777 0.000 0.476 0.016 0.508
#> GSM627192 3 0.4477 0.6240 0.000 0.000 0.688 0.312
#> GSM627203 3 0.5811 0.6428 0.116 0.000 0.704 0.180
#> GSM627151 2 0.0817 0.8035 0.000 0.976 0.024 0.000
#> GSM627163 1 0.5322 0.5361 0.660 0.000 0.028 0.312
#> GSM627211 4 0.4999 0.8836 0.000 0.492 0.000 0.508
#> GSM627171 2 0.4855 0.1958 0.400 0.600 0.000 0.000
#> GSM627209 4 0.4999 0.8836 0.000 0.492 0.000 0.508
#> GSM627135 3 0.4046 0.7303 0.048 0.000 0.828 0.124
#> GSM627170 2 0.0000 0.8194 0.000 1.000 0.000 0.000
#> GSM627178 3 0.3764 0.6908 0.216 0.000 0.784 0.000
#> GSM627199 4 0.5167 0.8842 0.000 0.488 0.004 0.508
#> GSM627213 2 0.5851 -0.1237 0.000 0.660 0.068 0.272
#> GSM627140 2 0.2011 0.7262 0.000 0.920 0.080 0.000
#> GSM627149 3 0.7519 0.3439 0.208 0.000 0.480 0.312
#> GSM627147 2 0.4543 -0.2932 0.000 0.676 0.000 0.324
#> GSM627195 3 0.6550 0.5762 0.184 0.000 0.636 0.180
#> GSM627204 4 0.5000 0.8781 0.000 0.496 0.000 0.504
#> GSM627207 2 0.3569 0.3675 0.000 0.804 0.000 0.196
#> GSM627157 1 0.4406 0.5659 0.700 0.000 0.000 0.300
#> GSM627201 2 0.4999 -0.8625 0.000 0.508 0.000 0.492
#> GSM627146 2 0.0000 0.8194 0.000 1.000 0.000 0.000
#> GSM627156 2 0.2081 0.7194 0.084 0.916 0.000 0.000
#> GSM627188 3 0.4477 0.6240 0.000 0.000 0.688 0.312
#> GSM627197 2 0.0188 0.8163 0.000 0.996 0.000 0.004
#> GSM627173 2 0.0000 0.8194 0.000 1.000 0.000 0.000
#> GSM627179 2 0.0000 0.8194 0.000 1.000 0.000 0.000
#> GSM627208 1 0.7026 0.4346 0.572 0.248 0.000 0.180
#> GSM627215 2 0.0657 0.8123 0.004 0.984 0.012 0.000
#> GSM627153 4 0.5294 0.8831 0.000 0.484 0.008 0.508
#> GSM627155 1 0.7795 0.1415 0.420 0.000 0.268 0.312
#> GSM627165 2 0.4079 0.5234 0.000 0.800 0.020 0.180
#> GSM627168 1 0.3942 0.5370 0.764 0.000 0.236 0.000
#> GSM627183 1 0.3486 0.5783 0.812 0.000 0.188 0.000
#> GSM627144 1 0.9077 0.3449 0.484 0.156 0.180 0.180
#> GSM627158 1 0.7768 0.1584 0.428 0.000 0.260 0.312
#> GSM627196 4 0.4999 0.8836 0.000 0.492 0.000 0.508
#> GSM627142 3 0.0188 0.7550 0.004 0.000 0.996 0.000
#> GSM627182 1 0.7026 0.4346 0.572 0.248 0.000 0.180
#> GSM627202 3 0.7503 0.3149 0.276 0.000 0.496 0.228
#> GSM627141 1 0.1637 0.6583 0.940 0.000 0.060 0.000
#> GSM627143 2 0.0927 0.8014 0.016 0.976 0.008 0.000
#> GSM627145 3 0.5312 0.6550 0.236 0.000 0.712 0.052
#> GSM627152 3 0.1743 0.7559 0.056 0.000 0.940 0.004
#> GSM627200 3 0.3311 0.7080 0.172 0.000 0.828 0.000
#> GSM627159 3 0.0000 0.7547 0.000 0.000 1.000 0.000
#> GSM627164 2 0.0000 0.8194 0.000 1.000 0.000 0.000
#> GSM627138 1 0.4477 0.5584 0.688 0.000 0.000 0.312
#> GSM627175 4 0.4999 0.8836 0.000 0.492 0.000 0.508
#> GSM627150 3 0.6750 0.6095 0.208 0.000 0.612 0.180
#> GSM627166 1 0.5320 0.1769 0.572 0.416 0.012 0.000
#> GSM627186 2 0.4564 0.3356 0.328 0.672 0.000 0.000
#> GSM627139 3 0.0000 0.7547 0.000 0.000 1.000 0.000
#> GSM627181 2 0.4992 -0.8245 0.000 0.524 0.000 0.476
#> GSM627205 2 0.0000 0.8194 0.000 1.000 0.000 0.000
#> GSM627214 4 0.5409 0.8654 0.000 0.492 0.012 0.496
#> GSM627180 1 0.8608 0.0186 0.424 0.052 0.344 0.180
#> GSM627172 2 0.5427 -0.6654 0.000 0.568 0.016 0.416
#> GSM627184 3 0.4655 0.6214 0.004 0.000 0.684 0.312
#> GSM627193 2 0.0000 0.8194 0.000 1.000 0.000 0.000
#> GSM627191 3 0.2345 0.7018 0.000 0.100 0.900 0.000
#> GSM627176 3 0.7338 0.0409 0.376 0.000 0.464 0.160
#> GSM627194 2 0.0000 0.8194 0.000 1.000 0.000 0.000
#> GSM627154 4 0.5294 0.8830 0.000 0.484 0.008 0.508
#> GSM627187 1 0.0000 0.6698 1.000 0.000 0.000 0.000
#> GSM627198 4 0.4999 0.8836 0.000 0.492 0.000 0.508
#> GSM627160 3 0.1118 0.7458 0.000 0.036 0.964 0.000
#> GSM627185 1 0.4477 0.5584 0.688 0.000 0.000 0.312
#> GSM627206 1 0.0000 0.6698 1.000 0.000 0.000 0.000
#> GSM627161 3 0.7896 0.0970 0.312 0.000 0.376 0.312
#> GSM627162 1 0.4697 0.3580 0.644 0.000 0.356 0.000
#> GSM627210 1 0.0000 0.6698 1.000 0.000 0.000 0.000
#> GSM627189 2 0.0000 0.8194 0.000 1.000 0.000 0.000
show/hide code output
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM627128 5 0.0510 0.74013 0.000 0.000 0.000 0.016 0.984
#> GSM627110 3 0.3421 0.61797 0.204 0.000 0.788 0.000 0.008
#> GSM627132 1 0.0000 0.73306 1.000 0.000 0.000 0.000 0.000
#> GSM627107 5 0.4252 0.48593 0.000 0.000 0.340 0.008 0.652
#> GSM627103 2 0.0000 0.88424 0.000 1.000 0.000 0.000 0.000
#> GSM627114 3 0.3983 0.59886 0.340 0.000 0.660 0.000 0.000
#> GSM627134 2 0.0290 0.88124 0.000 0.992 0.000 0.008 0.000
#> GSM627137 2 0.0000 0.88424 0.000 1.000 0.000 0.000 0.000
#> GSM627148 3 0.0404 0.58561 0.000 0.000 0.988 0.000 0.012
#> GSM627101 4 0.3970 0.67808 0.000 0.000 0.224 0.752 0.024
#> GSM627130 5 0.0703 0.73932 0.000 0.000 0.000 0.024 0.976
#> GSM627071 5 0.4138 0.43411 0.000 0.000 0.384 0.000 0.616
#> GSM627118 4 0.1851 0.86002 0.000 0.088 0.000 0.912 0.000
#> GSM627094 2 0.0000 0.88424 0.000 1.000 0.000 0.000 0.000
#> GSM627122 5 0.2690 0.70461 0.000 0.000 0.156 0.000 0.844
#> GSM627115 2 0.0963 0.86585 0.000 0.964 0.000 0.036 0.000
#> GSM627125 5 0.3877 0.62437 0.000 0.000 0.212 0.024 0.764
#> GSM627174 2 0.0162 0.88254 0.000 0.996 0.004 0.000 0.000
#> GSM627102 4 0.3143 0.83651 0.000 0.204 0.000 0.796 0.000
#> GSM627073 5 0.3534 0.68426 0.000 0.000 0.256 0.000 0.744
#> GSM627108 2 0.0000 0.88424 0.000 1.000 0.000 0.000 0.000
#> GSM627126 1 0.4305 0.04791 0.512 0.000 0.000 0.000 0.488
#> GSM627078 4 0.0510 0.84704 0.000 0.016 0.000 0.984 0.000
#> GSM627090 5 0.0510 0.74059 0.000 0.000 0.016 0.000 0.984
#> GSM627099 2 0.3480 0.66169 0.000 0.752 0.000 0.248 0.000
#> GSM627105 5 0.4338 0.55916 0.000 0.000 0.280 0.024 0.696
#> GSM627117 3 0.4928 0.59025 0.284 0.056 0.660 0.000 0.000
#> GSM627121 3 0.5702 0.11849 0.000 0.104 0.576 0.000 0.320
#> GSM627127 2 0.2179 0.81125 0.000 0.888 0.000 0.112 0.000
#> GSM627087 2 0.1197 0.85844 0.000 0.952 0.000 0.048 0.000
#> GSM627089 3 0.4424 0.60942 0.224 0.000 0.728 0.000 0.048
#> GSM627092 2 0.0000 0.88424 0.000 1.000 0.000 0.000 0.000
#> GSM627076 5 0.1965 0.71149 0.000 0.000 0.096 0.000 0.904
#> GSM627136 5 0.5182 0.56629 0.112 0.000 0.208 0.000 0.680
#> GSM627081 3 0.5954 0.17940 0.000 0.152 0.576 0.000 0.272
#> GSM627091 2 0.2074 0.81667 0.000 0.896 0.000 0.104 0.000
#> GSM627097 2 0.2127 0.81403 0.000 0.892 0.000 0.108 0.000
#> GSM627072 3 0.2561 0.55728 0.000 0.000 0.856 0.000 0.144
#> GSM627080 1 0.0000 0.73306 1.000 0.000 0.000 0.000 0.000
#> GSM627088 3 0.6758 0.33854 0.336 0.000 0.392 0.000 0.272
#> GSM627109 1 0.3366 0.35920 0.768 0.000 0.232 0.000 0.000
#> GSM627111 1 0.0000 0.73306 1.000 0.000 0.000 0.000 0.000
#> GSM627113 1 0.4938 0.06853 0.640 0.000 0.312 0.000 0.048
#> GSM627133 2 0.0162 0.88247 0.000 0.996 0.004 0.000 0.000
#> GSM627177 5 0.4161 0.41745 0.000 0.000 0.392 0.000 0.608
#> GSM627086 4 0.3109 0.83831 0.000 0.200 0.000 0.800 0.000
#> GSM627095 5 0.3561 0.58458 0.260 0.000 0.000 0.000 0.740
#> GSM627079 5 0.2605 0.70833 0.000 0.000 0.148 0.000 0.852
#> GSM627082 5 0.0510 0.74013 0.000 0.000 0.000 0.016 0.984
#> GSM627074 3 0.3983 0.59886 0.340 0.000 0.660 0.000 0.000
#> GSM627077 5 0.1121 0.74438 0.000 0.000 0.044 0.000 0.956
#> GSM627093 3 0.3983 0.59886 0.340 0.000 0.660 0.000 0.000
#> GSM627120 2 0.0000 0.88424 0.000 1.000 0.000 0.000 0.000
#> GSM627124 4 0.1197 0.85586 0.000 0.048 0.000 0.952 0.000
#> GSM627075 2 0.0000 0.88424 0.000 1.000 0.000 0.000 0.000
#> GSM627085 4 0.0510 0.84704 0.000 0.016 0.000 0.984 0.000
#> GSM627119 3 0.3983 0.59886 0.340 0.000 0.660 0.000 0.000
#> GSM627116 2 0.4370 0.45186 0.000 0.656 0.004 0.008 0.332
#> GSM627084 5 0.5423 0.48710 0.112 0.000 0.244 0.000 0.644
#> GSM627096 4 0.1851 0.86002 0.000 0.088 0.000 0.912 0.000
#> GSM627100 5 0.2732 0.67019 0.000 0.000 0.160 0.000 0.840
#> GSM627112 4 0.0290 0.83740 0.000 0.008 0.000 0.992 0.000
#> GSM627083 5 0.2690 0.65316 0.000 0.156 0.000 0.000 0.844
#> GSM627098 5 0.6667 0.00347 0.328 0.000 0.244 0.000 0.428
#> GSM627104 3 0.6576 0.35097 0.340 0.216 0.444 0.000 0.000
#> GSM627131 5 0.2690 0.70461 0.000 0.000 0.156 0.000 0.844
#> GSM627106 5 0.4305 0.25057 0.000 0.000 0.488 0.000 0.512
#> GSM627123 5 0.3242 0.61382 0.216 0.000 0.000 0.000 0.784
#> GSM627129 2 0.0000 0.88424 0.000 1.000 0.000 0.000 0.000
#> GSM627216 2 0.0000 0.88424 0.000 1.000 0.000 0.000 0.000
#> GSM627212 2 0.2020 0.81986 0.000 0.900 0.000 0.100 0.000
#> GSM627190 3 0.3949 0.60136 0.332 0.000 0.668 0.000 0.000
#> GSM627169 2 0.3395 0.66856 0.000 0.764 0.236 0.000 0.000
#> GSM627167 4 0.3039 0.83730 0.000 0.192 0.000 0.808 0.000
#> GSM627192 1 0.3983 0.47945 0.660 0.000 0.000 0.000 0.340
#> GSM627203 5 0.4305 0.25057 0.000 0.000 0.488 0.000 0.512
#> GSM627151 2 0.0000 0.88424 0.000 1.000 0.000 0.000 0.000
#> GSM627163 1 0.0000 0.73306 1.000 0.000 0.000 0.000 0.000
#> GSM627211 4 0.2605 0.85848 0.000 0.148 0.000 0.852 0.000
#> GSM627171 2 0.3480 0.61649 0.000 0.752 0.248 0.000 0.000
#> GSM627209 4 0.0703 0.85029 0.000 0.024 0.000 0.976 0.000
#> GSM627135 5 0.3289 0.69550 0.108 0.000 0.048 0.000 0.844
#> GSM627170 2 0.0000 0.88424 0.000 1.000 0.000 0.000 0.000
#> GSM627178 5 0.3691 0.68918 0.040 0.000 0.156 0.000 0.804
#> GSM627199 4 0.2471 0.84907 0.000 0.136 0.000 0.864 0.000
#> GSM627213 2 0.4307 -0.05931 0.000 0.504 0.000 0.496 0.000
#> GSM627140 2 0.0510 0.87503 0.000 0.984 0.000 0.016 0.000
#> GSM627149 1 0.2690 0.66560 0.844 0.000 0.000 0.000 0.156
#> GSM627147 2 0.3966 0.36372 0.000 0.664 0.000 0.336 0.000
#> GSM627195 3 0.3452 0.35038 0.000 0.000 0.756 0.000 0.244
#> GSM627204 4 0.3336 0.81604 0.000 0.228 0.000 0.772 0.000
#> GSM627207 2 0.3074 0.66986 0.000 0.804 0.000 0.196 0.000
#> GSM627157 1 0.1043 0.69614 0.960 0.000 0.040 0.000 0.000
#> GSM627201 4 0.2891 0.82185 0.000 0.176 0.000 0.824 0.000
#> GSM627146 2 0.0000 0.88424 0.000 1.000 0.000 0.000 0.000
#> GSM627156 2 0.3612 0.62946 0.000 0.732 0.268 0.000 0.000
#> GSM627188 1 0.3983 0.47945 0.660 0.000 0.000 0.000 0.340
#> GSM627197 2 0.0162 0.88229 0.000 0.996 0.000 0.004 0.000
#> GSM627173 2 0.0000 0.88424 0.000 1.000 0.000 0.000 0.000
#> GSM627179 2 0.0000 0.88424 0.000 1.000 0.000 0.000 0.000
#> GSM627208 3 0.0000 0.59071 0.000 0.000 1.000 0.000 0.000
#> GSM627215 2 0.0000 0.88424 0.000 1.000 0.000 0.000 0.000
#> GSM627153 4 0.0510 0.84704 0.000 0.016 0.000 0.984 0.000
#> GSM627155 1 0.0162 0.73252 0.996 0.000 0.000 0.000 0.004
#> GSM627165 2 0.4173 0.65847 0.000 0.748 0.224 0.016 0.012
#> GSM627168 5 0.6443 0.20523 0.248 0.000 0.248 0.000 0.504
#> GSM627183 3 0.6758 0.22094 0.272 0.000 0.392 0.000 0.336
#> GSM627144 3 0.1965 0.53224 0.000 0.000 0.904 0.000 0.096
#> GSM627158 1 0.0000 0.73306 1.000 0.000 0.000 0.000 0.000
#> GSM627196 4 0.3177 0.83286 0.000 0.208 0.000 0.792 0.000
#> GSM627142 5 0.0404 0.73992 0.000 0.000 0.000 0.012 0.988
#> GSM627182 3 0.0000 0.59071 0.000 0.000 1.000 0.000 0.000
#> GSM627202 1 0.4305 -0.03658 0.512 0.000 0.000 0.000 0.488
#> GSM627141 3 0.4973 0.58341 0.320 0.000 0.632 0.000 0.048
#> GSM627143 2 0.0000 0.88424 0.000 1.000 0.000 0.000 0.000
#> GSM627145 5 0.4088 0.52932 0.000 0.000 0.368 0.000 0.632
#> GSM627152 5 0.0162 0.74092 0.000 0.000 0.004 0.000 0.996
#> GSM627200 5 0.2690 0.70461 0.000 0.000 0.156 0.000 0.844
#> GSM627159 5 0.0510 0.74013 0.000 0.000 0.000 0.016 0.984
#> GSM627164 2 0.0000 0.88424 0.000 1.000 0.000 0.000 0.000
#> GSM627138 1 0.0000 0.73306 1.000 0.000 0.000 0.000 0.000
#> GSM627175 4 0.0510 0.84704 0.000 0.016 0.000 0.984 0.000
#> GSM627150 3 0.4242 -0.27665 0.000 0.000 0.572 0.000 0.428
#> GSM627166 2 0.5739 0.38626 0.128 0.624 0.244 0.000 0.004
#> GSM627186 2 0.4297 0.17313 0.000 0.528 0.472 0.000 0.000
#> GSM627139 5 0.0162 0.74040 0.000 0.000 0.000 0.004 0.996
#> GSM627181 4 0.3774 0.72788 0.000 0.296 0.000 0.704 0.000
#> GSM627205 2 0.0000 0.88424 0.000 1.000 0.000 0.000 0.000
#> GSM627214 4 0.3508 0.79016 0.000 0.252 0.000 0.748 0.000
#> GSM627180 3 0.2604 0.53750 0.000 0.020 0.896 0.012 0.072
#> GSM627172 4 0.4291 0.33574 0.000 0.464 0.000 0.536 0.000
#> GSM627184 1 0.3966 0.48570 0.664 0.000 0.000 0.000 0.336
#> GSM627193 2 0.0000 0.88424 0.000 1.000 0.000 0.000 0.000
#> GSM627191 5 0.3182 0.68059 0.000 0.032 0.000 0.124 0.844
#> GSM627176 5 0.4015 0.49921 0.000 0.000 0.348 0.000 0.652
#> GSM627194 2 0.0000 0.88424 0.000 1.000 0.000 0.000 0.000
#> GSM627154 4 0.0510 0.84704 0.000 0.016 0.000 0.984 0.000
#> GSM627187 3 0.3983 0.59886 0.340 0.000 0.660 0.000 0.000
#> GSM627198 4 0.0703 0.85029 0.000 0.024 0.000 0.976 0.000
#> GSM627160 5 0.1914 0.72341 0.000 0.060 0.000 0.016 0.924
#> GSM627185 1 0.0000 0.73306 1.000 0.000 0.000 0.000 0.000
#> GSM627206 3 0.3983 0.59886 0.340 0.000 0.660 0.000 0.000
#> GSM627161 1 0.1851 0.69827 0.912 0.000 0.000 0.000 0.088
#> GSM627162 3 0.5779 0.17984 0.092 0.000 0.508 0.000 0.400
#> GSM627210 3 0.3983 0.59886 0.340 0.000 0.660 0.000 0.000
#> GSM627189 2 0.0000 0.88424 0.000 1.000 0.000 0.000 0.000
show/hide code output
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM627128 6 0.0603 0.5932 0.016 0.000 0.000 0.000 0.004 0.980
#> GSM627110 3 0.2473 0.6909 0.008 0.000 0.856 0.000 0.136 0.000
#> GSM627132 1 0.2562 0.7890 0.828 0.000 0.172 0.000 0.000 0.000
#> GSM627107 5 0.2048 0.6260 0.000 0.000 0.000 0.000 0.880 0.120
#> GSM627103 2 0.0000 0.8868 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627114 3 0.2048 0.6936 0.000 0.000 0.880 0.000 0.120 0.000
#> GSM627134 2 0.0363 0.8824 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM627137 2 0.0260 0.8854 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM627148 5 0.1610 0.6973 0.000 0.000 0.084 0.000 0.916 0.000
#> GSM627101 6 0.5065 0.3458 0.000 0.000 0.000 0.172 0.192 0.636
#> GSM627130 6 0.0865 0.5922 0.000 0.000 0.000 0.000 0.036 0.964
#> GSM627071 3 0.6081 0.5554 0.156 0.000 0.584 0.000 0.056 0.204
#> GSM627118 4 0.1168 0.7851 0.000 0.028 0.000 0.956 0.016 0.000
#> GSM627094 2 0.0000 0.8868 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627122 6 0.6258 -0.2458 0.156 0.000 0.400 0.000 0.028 0.416
#> GSM627115 2 0.1204 0.8553 0.000 0.944 0.000 0.056 0.000 0.000
#> GSM627125 6 0.2300 0.5189 0.000 0.000 0.000 0.000 0.144 0.856
#> GSM627174 2 0.0508 0.8809 0.000 0.984 0.012 0.004 0.000 0.000
#> GSM627102 4 0.3302 0.7563 0.004 0.232 0.000 0.760 0.000 0.004
#> GSM627073 5 0.4700 0.5186 0.128 0.000 0.008 0.000 0.704 0.160
#> GSM627108 2 0.0146 0.8863 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM627126 1 0.3017 0.5384 0.816 0.000 0.000 0.000 0.020 0.164
#> GSM627078 4 0.0000 0.7834 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM627090 5 0.5565 0.2415 0.152 0.000 0.000 0.000 0.508 0.340
#> GSM627099 2 0.3998 0.5058 0.000 0.644 0.000 0.340 0.016 0.000
#> GSM627105 6 0.3126 0.3968 0.000 0.000 0.000 0.000 0.248 0.752
#> GSM627117 3 0.2494 0.6934 0.000 0.016 0.864 0.000 0.120 0.000
#> GSM627121 5 0.0748 0.7142 0.000 0.004 0.016 0.000 0.976 0.004
#> GSM627127 2 0.3290 0.7008 0.000 0.776 0.000 0.208 0.016 0.000
#> GSM627087 2 0.1444 0.8435 0.000 0.928 0.000 0.072 0.000 0.000
#> GSM627089 3 0.2623 0.6938 0.000 0.000 0.852 0.000 0.132 0.016
#> GSM627092 2 0.0436 0.8844 0.004 0.988 0.000 0.004 0.000 0.004
#> GSM627076 5 0.5016 0.2758 0.076 0.000 0.000 0.000 0.532 0.392
#> GSM627136 3 0.6936 0.4266 0.156 0.000 0.460 0.000 0.108 0.276
#> GSM627081 5 0.0603 0.7119 0.000 0.004 0.016 0.000 0.980 0.000
#> GSM627091 2 0.2883 0.7105 0.000 0.788 0.000 0.212 0.000 0.000
#> GSM627097 2 0.2883 0.7086 0.000 0.788 0.000 0.212 0.000 0.000
#> GSM627072 3 0.4296 0.5967 0.052 0.000 0.700 0.000 0.244 0.004
#> GSM627080 1 0.2562 0.7890 0.828 0.000 0.172 0.000 0.000 0.000
#> GSM627088 3 0.4403 0.6836 0.004 0.004 0.740 0.000 0.124 0.128
#> GSM627109 3 0.2697 0.5291 0.188 0.000 0.812 0.000 0.000 0.000
#> GSM627111 1 0.2562 0.7890 0.828 0.000 0.172 0.000 0.000 0.000
#> GSM627113 3 0.2383 0.6349 0.096 0.000 0.880 0.000 0.000 0.024
#> GSM627133 2 0.0146 0.8858 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM627177 3 0.6656 0.5945 0.156 0.012 0.572 0.000 0.124 0.136
#> GSM627086 4 0.3076 0.7556 0.000 0.240 0.000 0.760 0.000 0.000
#> GSM627095 1 0.4144 0.2107 0.620 0.000 0.000 0.000 0.020 0.360
#> GSM627079 6 0.7337 0.0911 0.156 0.000 0.212 0.000 0.216 0.416
#> GSM627082 6 0.0547 0.5949 0.000 0.000 0.000 0.000 0.020 0.980
#> GSM627074 3 0.0000 0.6866 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627077 3 0.6187 0.3107 0.160 0.000 0.456 0.000 0.024 0.360
#> GSM627093 3 0.0000 0.6866 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627120 2 0.0000 0.8868 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627124 4 0.1387 0.7946 0.000 0.068 0.000 0.932 0.000 0.000
#> GSM627075 2 0.0291 0.8844 0.004 0.992 0.000 0.000 0.000 0.004
#> GSM627085 4 0.0000 0.7834 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM627119 3 0.0000 0.6866 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627116 2 0.3309 0.6649 0.000 0.788 0.004 0.000 0.016 0.192
#> GSM627084 3 0.6101 0.4848 0.156 0.012 0.560 0.000 0.020 0.252
#> GSM627096 4 0.2756 0.7275 0.000 0.028 0.000 0.872 0.016 0.084
#> GSM627100 5 0.4228 0.3475 0.020 0.000 0.000 0.000 0.588 0.392
#> GSM627112 6 0.4076 0.1404 0.000 0.000 0.000 0.364 0.016 0.620
#> GSM627083 2 0.6188 -0.2045 0.168 0.428 0.000 0.000 0.020 0.384
#> GSM627098 3 0.3983 0.6098 0.012 0.000 0.720 0.000 0.020 0.248
#> GSM627104 3 0.1444 0.6652 0.000 0.072 0.928 0.000 0.000 0.000
#> GSM627131 3 0.6163 0.3162 0.156 0.000 0.460 0.000 0.024 0.360
#> GSM627106 5 0.0914 0.7177 0.000 0.000 0.016 0.000 0.968 0.016
#> GSM627123 1 0.4533 0.2872 0.632 0.000 0.020 0.000 0.020 0.328
#> GSM627129 2 0.0000 0.8868 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627216 2 0.0000 0.8868 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627212 2 0.2823 0.7196 0.000 0.796 0.000 0.204 0.000 0.000
#> GSM627190 3 0.2048 0.6936 0.000 0.000 0.880 0.000 0.120 0.000
#> GSM627169 2 0.2333 0.7879 0.004 0.872 0.120 0.000 0.000 0.004
#> GSM627167 4 0.3848 0.6775 0.004 0.040 0.000 0.752 0.000 0.204
#> GSM627192 1 0.0146 0.7040 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM627203 5 0.0914 0.7177 0.000 0.000 0.016 0.000 0.968 0.016
#> GSM627151 2 0.0000 0.8868 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627163 1 0.2454 0.7862 0.840 0.000 0.160 0.000 0.000 0.000
#> GSM627211 4 0.2300 0.7888 0.000 0.144 0.000 0.856 0.000 0.000
#> GSM627171 3 0.4230 0.2923 0.004 0.444 0.544 0.004 0.000 0.004
#> GSM627209 4 0.0000 0.7834 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM627135 1 0.5086 0.1362 0.572 0.000 0.048 0.000 0.020 0.360
#> GSM627170 2 0.0146 0.8863 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM627178 3 0.5515 0.4668 0.128 0.000 0.608 0.000 0.020 0.244
#> GSM627199 4 0.2883 0.7602 0.000 0.212 0.000 0.788 0.000 0.000
#> GSM627213 4 0.6351 0.1821 0.000 0.344 0.000 0.408 0.016 0.232
#> GSM627140 6 0.3782 0.2354 0.004 0.360 0.000 0.000 0.000 0.636
#> GSM627149 1 0.2454 0.7881 0.840 0.000 0.160 0.000 0.000 0.000
#> GSM627147 2 0.4100 0.1840 0.004 0.612 0.000 0.376 0.004 0.004
#> GSM627195 5 0.1225 0.7127 0.000 0.000 0.036 0.000 0.952 0.012
#> GSM627204 4 0.3126 0.7505 0.000 0.248 0.000 0.752 0.000 0.000
#> GSM627207 2 0.3756 0.3873 0.004 0.676 0.000 0.316 0.000 0.004
#> GSM627157 1 0.3309 0.6878 0.720 0.000 0.280 0.000 0.000 0.000
#> GSM627201 4 0.1501 0.7797 0.000 0.076 0.000 0.924 0.000 0.000
#> GSM627146 2 0.0146 0.8863 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM627156 2 0.2504 0.7710 0.004 0.856 0.136 0.000 0.000 0.004
#> GSM627188 1 0.0146 0.7040 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM627197 2 0.0363 0.8840 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM627173 2 0.0000 0.8868 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627179 2 0.0146 0.8863 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM627208 5 0.2454 0.6539 0.000 0.000 0.160 0.000 0.840 0.000
#> GSM627215 2 0.0000 0.8868 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627153 4 0.0000 0.7834 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM627155 1 0.2454 0.7862 0.840 0.000 0.160 0.000 0.000 0.000
#> GSM627165 2 0.4326 0.5201 0.000 0.656 0.000 0.044 0.300 0.000
#> GSM627168 3 0.3770 0.6186 0.000 0.000 0.728 0.000 0.028 0.244
#> GSM627183 3 0.5307 0.6625 0.044 0.000 0.676 0.000 0.124 0.156
#> GSM627144 5 0.2219 0.6673 0.000 0.000 0.136 0.000 0.864 0.000
#> GSM627158 1 0.2562 0.7890 0.828 0.000 0.172 0.000 0.000 0.000
#> GSM627196 4 0.3023 0.7591 0.000 0.232 0.000 0.768 0.000 0.000
#> GSM627142 6 0.3247 0.5205 0.156 0.000 0.000 0.000 0.036 0.808
#> GSM627182 3 0.3371 0.5611 0.000 0.000 0.708 0.000 0.292 0.000
#> GSM627202 1 0.5503 0.5517 0.552 0.000 0.172 0.000 0.000 0.276
#> GSM627141 3 0.2488 0.7012 0.004 0.000 0.864 0.000 0.124 0.008
#> GSM627143 2 0.0000 0.8868 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627145 5 0.7318 0.0547 0.128 0.000 0.260 0.000 0.396 0.216
#> GSM627152 5 0.6508 0.1241 0.156 0.000 0.048 0.000 0.432 0.364
#> GSM627200 3 0.6163 0.3162 0.156 0.000 0.460 0.000 0.024 0.360
#> GSM627159 6 0.0547 0.5949 0.000 0.000 0.000 0.000 0.020 0.980
#> GSM627164 2 0.0436 0.8844 0.004 0.988 0.000 0.004 0.000 0.004
#> GSM627138 1 0.2562 0.7890 0.828 0.000 0.172 0.000 0.000 0.000
#> GSM627175 4 0.0000 0.7834 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM627150 5 0.2066 0.6982 0.000 0.000 0.024 0.000 0.904 0.072
#> GSM627166 3 0.3601 0.4586 0.004 0.312 0.684 0.000 0.000 0.000
#> GSM627186 3 0.4103 0.1893 0.004 0.448 0.544 0.000 0.000 0.004
#> GSM627139 6 0.4671 0.3908 0.156 0.000 0.000 0.000 0.156 0.688
#> GSM627181 4 0.3371 0.7142 0.000 0.292 0.000 0.708 0.000 0.000
#> GSM627205 2 0.0146 0.8863 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM627214 4 0.3351 0.7214 0.000 0.288 0.000 0.712 0.000 0.000
#> GSM627180 5 0.1003 0.7139 0.000 0.000 0.020 0.000 0.964 0.016
#> GSM627172 4 0.4093 0.4096 0.004 0.440 0.000 0.552 0.000 0.004
#> GSM627184 1 0.0291 0.7073 0.992 0.000 0.004 0.000 0.000 0.004
#> GSM627193 2 0.0000 0.8868 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627191 6 0.5346 0.4747 0.164 0.020 0.000 0.112 0.020 0.684
#> GSM627176 3 0.6071 0.5143 0.024 0.000 0.516 0.000 0.296 0.164
#> GSM627194 2 0.0000 0.8868 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627154 4 0.0146 0.7821 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM627187 3 0.2048 0.6936 0.000 0.000 0.880 0.000 0.120 0.000
#> GSM627198 4 0.0000 0.7834 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM627160 6 0.4734 0.4199 0.060 0.224 0.000 0.000 0.024 0.692
#> GSM627185 1 0.3371 0.7264 0.708 0.000 0.292 0.000 0.000 0.000
#> GSM627206 3 0.2048 0.6936 0.000 0.000 0.880 0.000 0.120 0.000
#> GSM627161 1 0.2562 0.7890 0.828 0.000 0.172 0.000 0.000 0.000
#> GSM627162 3 0.5263 0.5977 0.160 0.012 0.688 0.000 0.024 0.116
#> GSM627210 3 0.0000 0.6866 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627189 2 0.0000 0.8868 0.000 1.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two
samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures
which are significantly different between classes which can be candidate marks
for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known
annotation is numeric, one-way ANOVA test is applied, and if the known
annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) age(p) other(p) k
#> MAD:pam 144 0.0635 0.391 0.0617 2
#> MAD:pam 130 0.3379 0.456 0.0103 3
#> MAD:pam 116 0.7161 0.275 0.0485 4
#> MAD:pam 116 0.2936 0.309 0.1225 5
#> MAD:pam 115 0.2157 0.560 0.1665 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
MAD:mclust**
The object with results only for a single top-value method and a single partition method
can be extracted as:
res = res_list["MAD", "mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51882 rows and 146 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
- The first row: a plot of the ECDF (empirical cumulative distribution
function) curves of the consensus matrix for each
k and the heatmap of
predicted classes for each k.
- The second row: heatmaps of the consensus matrix for each
k.
- The third row: heatmaps of the membership matrix for each
k.
- The fouth row: heatmaps of the signatures for each
k.
All the plots in panels can be made by individual functions and they are
plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
- ECDF curves of the consensus matrix for each
k;
- 1-PAC. The PAC
score
measures the proportion of the ambiguous subgrouping.
- Mean silhouette score.
- Concordance. The mean probability of fiting the consensus class ids in all
partitions.
- Area increased. Denote \(A_k\) as the area under the ECDF curve for current
k, the area increased is defined as \(A_k - A_{k-1}\).
- Rand index. The percent of pairs of samples that are both in a same cluster
or both are not in a same cluster in the partition of k and k-1.
- Jaccard index. The ratio of pairs of samples are both in a same cluster in
the partition of k and k-1 and the pairs of samples are both in a same
cluster in the partition k or k-1.
The detailed explanations of these statistics can be found in the cola
vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.969 0.988 0.5031 0.497 0.497
#> 3 3 0.970 0.938 0.973 0.2733 0.760 0.562
#> 4 4 0.800 0.829 0.917 0.1069 0.902 0.740
#> 5 5 0.787 0.791 0.884 0.0665 0.902 0.697
#> 6 6 0.786 0.771 0.847 0.0603 0.873 0.554
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
- All \(k\) with Jaccard index larger than 0.95 are removed because increasing
\(k\) does not provide enough extra information. If all \(k\) are removed, it is
marked as no subgroup is detected.
- For all \(k\) with 1-PAC score larger than 0.9, the maximal \(k\) is taken as
the best \(k\), and other \(k\) are marked as optional \(k\).
- If it does not fit the second rule. The \(k\) with the maximal vote of the
highest 1-PAC score, highest mean silhouette, and highest concordance is
taken as the best \(k\).
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
show/hide code output
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM627128 2 0.000 0.986 0.000 1.000
#> GSM627110 1 0.000 0.989 1.000 0.000
#> GSM627132 1 0.000 0.989 1.000 0.000
#> GSM627107 2 0.980 0.290 0.416 0.584
#> GSM627103 2 0.000 0.986 0.000 1.000
#> GSM627114 1 0.000 0.989 1.000 0.000
#> GSM627134 2 0.000 0.986 0.000 1.000
#> GSM627137 2 0.000 0.986 0.000 1.000
#> GSM627148 1 0.000 0.989 1.000 0.000
#> GSM627101 2 0.000 0.986 0.000 1.000
#> GSM627130 2 0.000 0.986 0.000 1.000
#> GSM627071 1 0.000 0.989 1.000 0.000
#> GSM627118 2 0.000 0.986 0.000 1.000
#> GSM627094 2 0.000 0.986 0.000 1.000
#> GSM627122 1 0.000 0.989 1.000 0.000
#> GSM627115 2 0.000 0.986 0.000 1.000
#> GSM627125 2 0.000 0.986 0.000 1.000
#> GSM627174 2 0.000 0.986 0.000 1.000
#> GSM627102 2 0.000 0.986 0.000 1.000
#> GSM627073 1 0.000 0.989 1.000 0.000
#> GSM627108 2 0.000 0.986 0.000 1.000
#> GSM627126 1 0.000 0.989 1.000 0.000
#> GSM627078 2 0.000 0.986 0.000 1.000
#> GSM627090 1 0.000 0.989 1.000 0.000
#> GSM627099 2 0.000 0.986 0.000 1.000
#> GSM627105 2 0.000 0.986 0.000 1.000
#> GSM627117 1 0.000 0.989 1.000 0.000
#> GSM627121 2 0.961 0.377 0.384 0.616
#> GSM627127 2 0.000 0.986 0.000 1.000
#> GSM627087 2 0.000 0.986 0.000 1.000
#> GSM627089 1 0.000 0.989 1.000 0.000
#> GSM627092 2 0.000 0.986 0.000 1.000
#> GSM627076 1 0.000 0.989 1.000 0.000
#> GSM627136 1 0.000 0.989 1.000 0.000
#> GSM627081 1 0.000 0.989 1.000 0.000
#> GSM627091 2 0.000 0.986 0.000 1.000
#> GSM627097 2 0.000 0.986 0.000 1.000
#> GSM627072 1 0.000 0.989 1.000 0.000
#> GSM627080 1 0.000 0.989 1.000 0.000
#> GSM627088 1 0.000 0.989 1.000 0.000
#> GSM627109 1 0.000 0.989 1.000 0.000
#> GSM627111 1 0.000 0.989 1.000 0.000
#> GSM627113 1 0.000 0.989 1.000 0.000
#> GSM627133 2 0.242 0.946 0.040 0.960
#> GSM627177 1 0.000 0.989 1.000 0.000
#> GSM627086 2 0.000 0.986 0.000 1.000
#> GSM627095 1 0.000 0.989 1.000 0.000
#> GSM627079 1 0.000 0.989 1.000 0.000
#> GSM627082 2 0.000 0.986 0.000 1.000
#> GSM627074 1 0.000 0.989 1.000 0.000
#> GSM627077 1 0.000 0.989 1.000 0.000
#> GSM627093 1 0.000 0.989 1.000 0.000
#> GSM627120 2 0.000 0.986 0.000 1.000
#> GSM627124 2 0.000 0.986 0.000 1.000
#> GSM627075 2 0.000 0.986 0.000 1.000
#> GSM627085 2 0.000 0.986 0.000 1.000
#> GSM627119 1 0.000 0.989 1.000 0.000
#> GSM627116 2 0.000 0.986 0.000 1.000
#> GSM627084 1 0.000 0.989 1.000 0.000
#> GSM627096 2 0.000 0.986 0.000 1.000
#> GSM627100 1 0.000 0.989 1.000 0.000
#> GSM627112 2 0.000 0.986 0.000 1.000
#> GSM627083 1 0.952 0.402 0.628 0.372
#> GSM627098 1 0.000 0.989 1.000 0.000
#> GSM627104 1 0.000 0.989 1.000 0.000
#> GSM627131 1 0.000 0.989 1.000 0.000
#> GSM627106 1 0.000 0.989 1.000 0.000
#> GSM627123 1 0.000 0.989 1.000 0.000
#> GSM627129 2 0.000 0.986 0.000 1.000
#> GSM627216 2 0.000 0.986 0.000 1.000
#> GSM627212 2 0.000 0.986 0.000 1.000
#> GSM627190 1 0.000 0.989 1.000 0.000
#> GSM627169 2 0.000 0.986 0.000 1.000
#> GSM627167 2 0.000 0.986 0.000 1.000
#> GSM627192 1 0.000 0.989 1.000 0.000
#> GSM627203 1 0.000 0.989 1.000 0.000
#> GSM627151 2 0.000 0.986 0.000 1.000
#> GSM627163 1 0.000 0.989 1.000 0.000
#> GSM627211 2 0.000 0.986 0.000 1.000
#> GSM627171 2 0.000 0.986 0.000 1.000
#> GSM627209 2 0.000 0.986 0.000 1.000
#> GSM627135 1 0.000 0.989 1.000 0.000
#> GSM627170 2 0.000 0.986 0.000 1.000
#> GSM627178 1 0.000 0.989 1.000 0.000
#> GSM627199 2 0.000 0.986 0.000 1.000
#> GSM627213 2 0.000 0.986 0.000 1.000
#> GSM627140 2 0.000 0.986 0.000 1.000
#> GSM627149 1 0.000 0.989 1.000 0.000
#> GSM627147 2 0.000 0.986 0.000 1.000
#> GSM627195 1 0.000 0.989 1.000 0.000
#> GSM627204 2 0.000 0.986 0.000 1.000
#> GSM627207 2 0.000 0.986 0.000 1.000
#> GSM627157 1 0.000 0.989 1.000 0.000
#> GSM627201 2 0.000 0.986 0.000 1.000
#> GSM627146 2 0.000 0.986 0.000 1.000
#> GSM627156 2 0.000 0.986 0.000 1.000
#> GSM627188 1 0.000 0.989 1.000 0.000
#> GSM627197 2 0.000 0.986 0.000 1.000
#> GSM627173 2 0.000 0.986 0.000 1.000
#> GSM627179 2 0.000 0.986 0.000 1.000
#> GSM627208 1 0.653 0.794 0.832 0.168
#> GSM627215 2 0.000 0.986 0.000 1.000
#> GSM627153 2 0.000 0.986 0.000 1.000
#> GSM627155 1 0.000 0.989 1.000 0.000
#> GSM627165 2 0.000 0.986 0.000 1.000
#> GSM627168 1 0.000 0.989 1.000 0.000
#> GSM627183 1 0.000 0.989 1.000 0.000
#> GSM627144 1 0.000 0.989 1.000 0.000
#> GSM627158 1 0.000 0.989 1.000 0.000
#> GSM627196 2 0.000 0.986 0.000 1.000
#> GSM627142 1 0.000 0.989 1.000 0.000
#> GSM627182 1 0.000 0.989 1.000 0.000
#> GSM627202 1 0.000 0.989 1.000 0.000
#> GSM627141 1 0.000 0.989 1.000 0.000
#> GSM627143 2 0.000 0.986 0.000 1.000
#> GSM627145 1 0.000 0.989 1.000 0.000
#> GSM627152 1 0.000 0.989 1.000 0.000
#> GSM627200 1 0.000 0.989 1.000 0.000
#> GSM627159 2 0.000 0.986 0.000 1.000
#> GSM627164 2 0.000 0.986 0.000 1.000
#> GSM627138 1 0.000 0.989 1.000 0.000
#> GSM627175 2 0.000 0.986 0.000 1.000
#> GSM627150 1 0.000 0.989 1.000 0.000
#> GSM627166 1 0.000 0.989 1.000 0.000
#> GSM627186 2 0.000 0.986 0.000 1.000
#> GSM627139 2 0.000 0.986 0.000 1.000
#> GSM627181 2 0.000 0.986 0.000 1.000
#> GSM627205 2 0.000 0.986 0.000 1.000
#> GSM627214 2 0.000 0.986 0.000 1.000
#> GSM627180 1 0.781 0.696 0.768 0.232
#> GSM627172 2 0.000 0.986 0.000 1.000
#> GSM627184 1 0.000 0.989 1.000 0.000
#> GSM627193 2 0.000 0.986 0.000 1.000
#> GSM627191 2 0.000 0.986 0.000 1.000
#> GSM627176 1 0.000 0.989 1.000 0.000
#> GSM627194 2 0.000 0.986 0.000 1.000
#> GSM627154 2 0.000 0.986 0.000 1.000
#> GSM627187 1 0.000 0.989 1.000 0.000
#> GSM627198 2 0.000 0.986 0.000 1.000
#> GSM627160 2 0.738 0.732 0.208 0.792
#> GSM627185 1 0.000 0.989 1.000 0.000
#> GSM627206 1 0.000 0.989 1.000 0.000
#> GSM627161 1 0.000 0.989 1.000 0.000
#> GSM627162 1 0.000 0.989 1.000 0.000
#> GSM627210 1 0.000 0.989 1.000 0.000
#> GSM627189 2 0.000 0.986 0.000 1.000
show/hide code output
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM627128 3 0.1774 0.9122 0.016 0.024 0.960
#> GSM627110 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627132 1 0.0747 0.9705 0.984 0.000 0.016
#> GSM627107 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627103 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627114 3 0.0592 0.9306 0.012 0.000 0.988
#> GSM627134 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627137 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627148 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627101 2 0.1585 0.9632 0.008 0.964 0.028
#> GSM627130 3 0.6823 0.0912 0.012 0.484 0.504
#> GSM627071 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627118 2 0.0237 0.9936 0.004 0.996 0.000
#> GSM627094 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627122 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627115 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627125 3 0.0747 0.9280 0.016 0.000 0.984
#> GSM627174 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627102 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627073 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627108 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627126 1 0.0000 0.9661 1.000 0.000 0.000
#> GSM627078 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627090 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627099 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627105 3 0.1170 0.9240 0.016 0.008 0.976
#> GSM627117 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627121 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627127 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627087 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627089 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627092 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627076 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627136 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627081 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627091 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627097 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627072 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627080 1 0.0747 0.9705 0.984 0.000 0.016
#> GSM627088 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627109 1 0.0747 0.9705 0.984 0.000 0.016
#> GSM627111 1 0.0747 0.9705 0.984 0.000 0.016
#> GSM627113 1 0.2796 0.9115 0.908 0.000 0.092
#> GSM627133 3 0.0747 0.9263 0.000 0.016 0.984
#> GSM627177 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627086 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627095 1 0.0000 0.9661 1.000 0.000 0.000
#> GSM627079 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627082 3 0.4862 0.7623 0.020 0.160 0.820
#> GSM627074 1 0.1163 0.9655 0.972 0.000 0.028
#> GSM627077 3 0.0592 0.9312 0.012 0.000 0.988
#> GSM627093 1 0.1529 0.9582 0.960 0.000 0.040
#> GSM627120 2 0.0424 0.9891 0.000 0.992 0.008
#> GSM627124 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627075 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627085 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627119 1 0.1753 0.9522 0.952 0.000 0.048
#> GSM627116 3 0.6869 0.2831 0.016 0.424 0.560
#> GSM627084 1 0.2537 0.9189 0.920 0.000 0.080
#> GSM627096 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627100 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627112 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627083 1 0.5393 0.7878 0.808 0.148 0.044
#> GSM627098 1 0.1411 0.9608 0.964 0.000 0.036
#> GSM627104 1 0.0000 0.9661 1.000 0.000 0.000
#> GSM627131 3 0.4062 0.7783 0.164 0.000 0.836
#> GSM627106 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627123 1 0.0747 0.9705 0.984 0.000 0.016
#> GSM627129 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627216 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627212 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627190 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627169 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627167 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627192 1 0.0000 0.9661 1.000 0.000 0.000
#> GSM627203 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627151 3 0.5431 0.6150 0.000 0.284 0.716
#> GSM627163 1 0.0000 0.9661 1.000 0.000 0.000
#> GSM627211 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627171 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627209 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627135 1 0.0000 0.9661 1.000 0.000 0.000
#> GSM627170 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627178 1 0.4931 0.7158 0.768 0.000 0.232
#> GSM627199 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627213 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627140 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627149 1 0.0747 0.9705 0.984 0.000 0.016
#> GSM627147 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627195 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627204 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627207 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627157 1 0.1031 0.9674 0.976 0.000 0.024
#> GSM627201 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627146 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627156 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627188 1 0.0000 0.9661 1.000 0.000 0.000
#> GSM627197 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627173 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627179 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627208 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627215 2 0.1529 0.9558 0.000 0.960 0.040
#> GSM627153 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627155 1 0.0747 0.9705 0.984 0.000 0.016
#> GSM627165 2 0.0237 0.9936 0.004 0.996 0.000
#> GSM627168 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627183 3 0.0892 0.9249 0.020 0.000 0.980
#> GSM627144 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627158 1 0.0747 0.9705 0.984 0.000 0.016
#> GSM627196 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627142 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627182 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627202 3 0.4931 0.6705 0.232 0.000 0.768
#> GSM627141 3 0.0237 0.9356 0.004 0.000 0.996
#> GSM627143 2 0.1031 0.9727 0.000 0.976 0.024
#> GSM627145 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627152 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627200 3 0.4399 0.7379 0.188 0.000 0.812
#> GSM627159 3 0.1774 0.9119 0.016 0.024 0.960
#> GSM627164 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627138 1 0.0747 0.9705 0.984 0.000 0.016
#> GSM627175 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627150 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627166 1 0.0237 0.9667 0.996 0.000 0.004
#> GSM627186 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627139 3 0.0747 0.9263 0.000 0.016 0.984
#> GSM627181 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627205 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627214 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627180 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627172 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627184 1 0.0000 0.9661 1.000 0.000 0.000
#> GSM627193 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627191 2 0.2599 0.9297 0.016 0.932 0.052
#> GSM627176 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627194 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627154 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627187 3 0.0424 0.9332 0.008 0.000 0.992
#> GSM627198 2 0.0000 0.9969 0.000 1.000 0.000
#> GSM627160 3 0.4782 0.7599 0.016 0.164 0.820
#> GSM627185 1 0.0747 0.9705 0.984 0.000 0.016
#> GSM627206 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627161 1 0.0747 0.9705 0.984 0.000 0.016
#> GSM627162 3 0.0000 0.9378 0.000 0.000 1.000
#> GSM627210 3 0.6295 0.0582 0.472 0.000 0.528
#> GSM627189 2 0.0000 0.9969 0.000 1.000 0.000
show/hide code output
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM627128 4 0.0000 0.7897 0.000 0.000 0.000 1.000
#> GSM627110 3 0.0000 0.9072 0.000 0.000 1.000 0.000
#> GSM627132 1 0.0000 0.9044 1.000 0.000 0.000 0.000
#> GSM627107 4 0.3569 0.6536 0.000 0.000 0.196 0.804
#> GSM627103 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627114 3 0.0000 0.9072 0.000 0.000 1.000 0.000
#> GSM627134 2 0.4564 0.5606 0.000 0.672 0.000 0.328
#> GSM627137 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627148 3 0.1940 0.8906 0.000 0.000 0.924 0.076
#> GSM627101 4 0.0469 0.7938 0.000 0.012 0.000 0.988
#> GSM627130 4 0.1118 0.7936 0.000 0.036 0.000 0.964
#> GSM627071 3 0.0000 0.9072 0.000 0.000 1.000 0.000
#> GSM627118 2 0.4382 0.6191 0.000 0.704 0.000 0.296
#> GSM627094 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627122 3 0.0000 0.9072 0.000 0.000 1.000 0.000
#> GSM627115 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627125 4 0.0000 0.7897 0.000 0.000 0.000 1.000
#> GSM627174 2 0.1474 0.8985 0.000 0.948 0.000 0.052
#> GSM627102 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627073 3 0.3123 0.8447 0.000 0.000 0.844 0.156
#> GSM627108 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627126 1 0.0000 0.9044 1.000 0.000 0.000 0.000
#> GSM627078 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627090 3 0.1211 0.9030 0.000 0.000 0.960 0.040
#> GSM627099 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627105 4 0.0000 0.7897 0.000 0.000 0.000 1.000
#> GSM627117 3 0.0000 0.9072 0.000 0.000 1.000 0.000
#> GSM627121 4 0.4955 0.0604 0.000 0.000 0.444 0.556
#> GSM627127 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627087 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627089 3 0.0000 0.9072 0.000 0.000 1.000 0.000
#> GSM627092 2 0.3024 0.8184 0.000 0.852 0.000 0.148
#> GSM627076 3 0.2408 0.8794 0.000 0.000 0.896 0.104
#> GSM627136 3 0.0000 0.9072 0.000 0.000 1.000 0.000
#> GSM627081 3 0.3123 0.8447 0.000 0.000 0.844 0.156
#> GSM627091 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627097 2 0.4898 0.3414 0.000 0.584 0.000 0.416
#> GSM627072 3 0.1022 0.9047 0.000 0.000 0.968 0.032
#> GSM627080 1 0.0000 0.9044 1.000 0.000 0.000 0.000
#> GSM627088 3 0.0000 0.9072 0.000 0.000 1.000 0.000
#> GSM627109 1 0.0000 0.9044 1.000 0.000 0.000 0.000
#> GSM627111 1 0.0000 0.9044 1.000 0.000 0.000 0.000
#> GSM627113 1 0.4585 0.6062 0.668 0.000 0.332 0.000
#> GSM627133 3 0.4655 0.6100 0.000 0.004 0.684 0.312
#> GSM627177 3 0.0000 0.9072 0.000 0.000 1.000 0.000
#> GSM627086 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627095 1 0.0000 0.9044 1.000 0.000 0.000 0.000
#> GSM627079 3 0.1211 0.9030 0.000 0.000 0.960 0.040
#> GSM627082 4 0.1398 0.7916 0.004 0.040 0.000 0.956
#> GSM627074 1 0.3907 0.7374 0.768 0.000 0.232 0.000
#> GSM627077 3 0.0000 0.9072 0.000 0.000 1.000 0.000
#> GSM627093 1 0.5000 0.1679 0.504 0.000 0.496 0.000
#> GSM627120 2 0.4522 0.5763 0.000 0.680 0.000 0.320
#> GSM627124 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627075 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627085 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627119 3 0.4761 0.2861 0.372 0.000 0.628 0.000
#> GSM627116 4 0.3790 0.7526 0.000 0.164 0.016 0.820
#> GSM627084 3 0.3123 0.7546 0.156 0.000 0.844 0.000
#> GSM627096 4 0.4522 0.5009 0.000 0.320 0.000 0.680
#> GSM627100 3 0.3172 0.8434 0.000 0.000 0.840 0.160
#> GSM627112 4 0.4331 0.5845 0.000 0.288 0.000 0.712
#> GSM627083 4 0.7464 0.3373 0.344 0.028 0.100 0.528
#> GSM627098 1 0.3907 0.7378 0.768 0.000 0.232 0.000
#> GSM627104 1 0.1557 0.8747 0.944 0.000 0.056 0.000
#> GSM627131 3 0.1211 0.8797 0.040 0.000 0.960 0.000
#> GSM627106 3 0.3266 0.8359 0.000 0.000 0.832 0.168
#> GSM627123 1 0.0921 0.8927 0.972 0.000 0.028 0.000
#> GSM627129 2 0.3024 0.8177 0.000 0.852 0.000 0.148
#> GSM627216 2 0.2868 0.8295 0.000 0.864 0.000 0.136
#> GSM627212 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627190 3 0.0000 0.9072 0.000 0.000 1.000 0.000
#> GSM627169 2 0.1022 0.9102 0.000 0.968 0.000 0.032
#> GSM627167 2 0.3764 0.7404 0.000 0.784 0.000 0.216
#> GSM627192 1 0.0000 0.9044 1.000 0.000 0.000 0.000
#> GSM627203 3 0.2921 0.8567 0.000 0.000 0.860 0.140
#> GSM627151 4 0.4677 0.7431 0.000 0.176 0.048 0.776
#> GSM627163 1 0.0000 0.9044 1.000 0.000 0.000 0.000
#> GSM627211 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627171 2 0.1557 0.8961 0.000 0.944 0.000 0.056
#> GSM627209 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627135 1 0.0000 0.9044 1.000 0.000 0.000 0.000
#> GSM627170 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627178 3 0.4761 0.3086 0.372 0.000 0.628 0.000
#> GSM627199 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627213 2 0.1118 0.9059 0.000 0.964 0.000 0.036
#> GSM627140 2 0.4624 0.5356 0.000 0.660 0.000 0.340
#> GSM627149 1 0.0000 0.9044 1.000 0.000 0.000 0.000
#> GSM627147 2 0.1716 0.8906 0.000 0.936 0.000 0.064
#> GSM627195 3 0.3074 0.8477 0.000 0.000 0.848 0.152
#> GSM627204 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627207 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627157 1 0.3688 0.7621 0.792 0.000 0.208 0.000
#> GSM627201 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627146 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627156 2 0.1022 0.9102 0.000 0.968 0.000 0.032
#> GSM627188 1 0.0000 0.9044 1.000 0.000 0.000 0.000
#> GSM627197 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627173 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627179 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627208 3 0.3925 0.8134 0.000 0.016 0.808 0.176
#> GSM627215 2 0.4277 0.6473 0.000 0.720 0.000 0.280
#> GSM627153 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627155 1 0.0000 0.9044 1.000 0.000 0.000 0.000
#> GSM627165 2 0.4877 0.3991 0.000 0.592 0.000 0.408
#> GSM627168 3 0.0000 0.9072 0.000 0.000 1.000 0.000
#> GSM627183 3 0.0000 0.9072 0.000 0.000 1.000 0.000
#> GSM627144 3 0.2973 0.8538 0.000 0.000 0.856 0.144
#> GSM627158 1 0.0000 0.9044 1.000 0.000 0.000 0.000
#> GSM627196 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627142 3 0.3873 0.7720 0.000 0.000 0.772 0.228
#> GSM627182 3 0.2647 0.8687 0.000 0.000 0.880 0.120
#> GSM627202 3 0.0000 0.9072 0.000 0.000 1.000 0.000
#> GSM627141 3 0.0000 0.9072 0.000 0.000 1.000 0.000
#> GSM627143 2 0.4222 0.6586 0.000 0.728 0.000 0.272
#> GSM627145 3 0.1022 0.9043 0.000 0.000 0.968 0.032
#> GSM627152 3 0.1302 0.9019 0.000 0.000 0.956 0.044
#> GSM627200 3 0.0000 0.9072 0.000 0.000 1.000 0.000
#> GSM627159 4 0.0336 0.7930 0.000 0.008 0.000 0.992
#> GSM627164 2 0.1637 0.8937 0.000 0.940 0.000 0.060
#> GSM627138 1 0.1940 0.8630 0.924 0.000 0.076 0.000
#> GSM627175 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627150 3 0.3024 0.8508 0.000 0.000 0.852 0.148
#> GSM627166 1 0.3610 0.7501 0.800 0.000 0.200 0.000
#> GSM627186 2 0.1474 0.8985 0.000 0.948 0.000 0.052
#> GSM627139 4 0.1867 0.7682 0.000 0.000 0.072 0.928
#> GSM627181 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627205 2 0.3074 0.8165 0.000 0.848 0.000 0.152
#> GSM627214 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627180 3 0.3528 0.8119 0.000 0.000 0.808 0.192
#> GSM627172 2 0.0188 0.9240 0.000 0.996 0.000 0.004
#> GSM627184 1 0.0000 0.9044 1.000 0.000 0.000 0.000
#> GSM627193 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627191 4 0.3123 0.7478 0.000 0.156 0.000 0.844
#> GSM627176 3 0.1557 0.8981 0.000 0.000 0.944 0.056
#> GSM627194 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627154 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627187 3 0.0000 0.9072 0.000 0.000 1.000 0.000
#> GSM627198 2 0.0000 0.9258 0.000 1.000 0.000 0.000
#> GSM627160 4 0.5573 0.5866 0.000 0.052 0.272 0.676
#> GSM627185 1 0.0188 0.9029 0.996 0.000 0.004 0.000
#> GSM627206 3 0.0000 0.9072 0.000 0.000 1.000 0.000
#> GSM627161 1 0.0000 0.9044 1.000 0.000 0.000 0.000
#> GSM627162 3 0.0817 0.9054 0.000 0.000 0.976 0.024
#> GSM627210 3 0.0000 0.9072 0.000 0.000 1.000 0.000
#> GSM627189 2 0.0000 0.9258 0.000 1.000 0.000 0.000
show/hide code output
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM627128 4 0.0794 0.7906 0.000 0.000 0.000 0.972 0.028
#> GSM627110 3 0.0290 0.8295 0.000 0.000 0.992 0.000 0.008
#> GSM627132 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.000
#> GSM627107 5 0.1668 0.8948 0.000 0.000 0.028 0.032 0.940
#> GSM627103 2 0.1894 0.8757 0.000 0.920 0.000 0.008 0.072
#> GSM627114 3 0.0000 0.8316 0.000 0.000 1.000 0.000 0.000
#> GSM627134 2 0.4398 0.6804 0.000 0.720 0.000 0.240 0.040
#> GSM627137 2 0.0162 0.8873 0.000 0.996 0.000 0.004 0.000
#> GSM627148 5 0.2074 0.9529 0.000 0.000 0.104 0.000 0.896
#> GSM627101 4 0.0880 0.7903 0.000 0.000 0.000 0.968 0.032
#> GSM627130 4 0.0794 0.7906 0.000 0.000 0.000 0.972 0.028
#> GSM627071 3 0.4126 0.3282 0.000 0.000 0.620 0.000 0.380
#> GSM627118 2 0.5049 0.0449 0.000 0.488 0.000 0.480 0.032
#> GSM627094 2 0.0963 0.8824 0.000 0.964 0.000 0.000 0.036
#> GSM627122 3 0.3816 0.5053 0.000 0.000 0.696 0.000 0.304
#> GSM627115 2 0.0963 0.8824 0.000 0.964 0.000 0.000 0.036
#> GSM627125 4 0.1197 0.7780 0.000 0.000 0.000 0.952 0.048
#> GSM627174 2 0.2077 0.8747 0.000 0.920 0.000 0.040 0.040
#> GSM627102 2 0.0451 0.8878 0.000 0.988 0.000 0.008 0.004
#> GSM627073 5 0.2020 0.9525 0.000 0.000 0.100 0.000 0.900
#> GSM627108 2 0.0963 0.8824 0.000 0.964 0.000 0.000 0.036
#> GSM627126 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.000
#> GSM627078 2 0.1121 0.8799 0.000 0.956 0.000 0.044 0.000
#> GSM627090 5 0.2424 0.9370 0.000 0.000 0.132 0.000 0.868
#> GSM627099 2 0.0794 0.8845 0.000 0.972 0.000 0.028 0.000
#> GSM627105 4 0.0880 0.7889 0.000 0.000 0.000 0.968 0.032
#> GSM627117 3 0.0000 0.8316 0.000 0.000 1.000 0.000 0.000
#> GSM627121 5 0.1732 0.9401 0.000 0.000 0.080 0.000 0.920
#> GSM627127 2 0.1478 0.8703 0.000 0.936 0.000 0.064 0.000
#> GSM627087 2 0.0963 0.8824 0.000 0.964 0.000 0.000 0.036
#> GSM627089 3 0.3661 0.5547 0.000 0.000 0.724 0.000 0.276
#> GSM627092 2 0.3477 0.8011 0.000 0.824 0.000 0.136 0.040
#> GSM627076 5 0.2074 0.9505 0.000 0.000 0.104 0.000 0.896
#> GSM627136 3 0.0963 0.8163 0.000 0.000 0.964 0.000 0.036
#> GSM627081 5 0.2020 0.9525 0.000 0.000 0.100 0.000 0.900
#> GSM627091 2 0.0290 0.8872 0.000 0.992 0.000 0.008 0.000
#> GSM627097 2 0.5100 0.1650 0.000 0.516 0.000 0.448 0.036
#> GSM627072 5 0.2074 0.9529 0.000 0.000 0.104 0.000 0.896
#> GSM627080 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.000
#> GSM627088 3 0.0000 0.8316 0.000 0.000 1.000 0.000 0.000
#> GSM627109 1 0.3534 0.6194 0.744 0.000 0.256 0.000 0.000
#> GSM627111 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.000
#> GSM627113 3 0.1043 0.8179 0.040 0.000 0.960 0.000 0.000
#> GSM627133 5 0.4409 0.7695 0.000 0.060 0.064 0.072 0.804
#> GSM627177 3 0.4138 0.3173 0.000 0.000 0.616 0.000 0.384
#> GSM627086 2 0.0963 0.8824 0.000 0.964 0.000 0.000 0.036
#> GSM627095 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.000
#> GSM627079 5 0.3210 0.8454 0.000 0.000 0.212 0.000 0.788
#> GSM627082 4 0.0794 0.7906 0.000 0.000 0.000 0.972 0.028
#> GSM627074 3 0.1410 0.8012 0.060 0.000 0.940 0.000 0.000
#> GSM627077 3 0.3661 0.5547 0.000 0.000 0.724 0.000 0.276
#> GSM627093 3 0.0794 0.8244 0.028 0.000 0.972 0.000 0.000
#> GSM627120 2 0.4284 0.7176 0.000 0.752 0.004 0.204 0.040
#> GSM627124 2 0.0880 0.8838 0.000 0.968 0.000 0.032 0.000
#> GSM627075 2 0.0963 0.8824 0.000 0.964 0.000 0.000 0.036
#> GSM627085 2 0.1270 0.8763 0.000 0.948 0.000 0.052 0.000
#> GSM627119 3 0.0794 0.8244 0.028 0.000 0.972 0.000 0.000
#> GSM627116 4 0.2813 0.7373 0.000 0.168 0.000 0.832 0.000
#> GSM627084 3 0.0404 0.8300 0.012 0.000 0.988 0.000 0.000
#> GSM627096 4 0.4908 0.3822 0.000 0.356 0.000 0.608 0.036
#> GSM627100 5 0.1732 0.9397 0.000 0.000 0.080 0.000 0.920
#> GSM627112 4 0.3177 0.7153 0.000 0.208 0.000 0.792 0.000
#> GSM627083 1 0.4210 0.3288 0.588 0.000 0.000 0.412 0.000
#> GSM627098 3 0.1121 0.8146 0.044 0.000 0.956 0.000 0.000
#> GSM627104 3 0.4294 0.0855 0.468 0.000 0.532 0.000 0.000
#> GSM627131 3 0.4475 0.5765 0.032 0.000 0.692 0.000 0.276
#> GSM627106 5 0.2020 0.9525 0.000 0.000 0.100 0.000 0.900
#> GSM627123 1 0.2929 0.7282 0.820 0.000 0.180 0.000 0.000
#> GSM627129 2 0.4054 0.7343 0.000 0.760 0.000 0.204 0.036
#> GSM627216 2 0.3420 0.8330 0.000 0.840 0.000 0.084 0.076
#> GSM627212 2 0.0703 0.8857 0.000 0.976 0.000 0.024 0.000
#> GSM627190 3 0.0000 0.8316 0.000 0.000 1.000 0.000 0.000
#> GSM627169 2 0.2535 0.8652 0.000 0.892 0.000 0.032 0.076
#> GSM627167 2 0.4946 0.4240 0.000 0.596 0.000 0.368 0.036
#> GSM627192 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.000
#> GSM627203 5 0.2074 0.9529 0.000 0.000 0.104 0.000 0.896
#> GSM627151 4 0.7446 0.3260 0.000 0.324 0.052 0.432 0.192
#> GSM627163 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.000
#> GSM627211 2 0.0000 0.8872 0.000 1.000 0.000 0.000 0.000
#> GSM627171 2 0.2694 0.8613 0.000 0.884 0.000 0.040 0.076
#> GSM627209 2 0.0794 0.8845 0.000 0.972 0.000 0.028 0.000
#> GSM627135 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.000
#> GSM627170 2 0.2491 0.8674 0.000 0.896 0.000 0.036 0.068
#> GSM627178 3 0.4425 0.4009 0.392 0.000 0.600 0.000 0.008
#> GSM627199 2 0.0880 0.8838 0.000 0.968 0.000 0.032 0.000
#> GSM627213 2 0.4410 0.1820 0.000 0.556 0.000 0.440 0.004
#> GSM627140 4 0.4101 0.3974 0.000 0.372 0.000 0.628 0.000
#> GSM627149 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.000
#> GSM627147 2 0.3115 0.8348 0.000 0.852 0.000 0.112 0.036
#> GSM627195 5 0.2074 0.9529 0.000 0.000 0.104 0.000 0.896
#> GSM627204 2 0.0963 0.8824 0.000 0.964 0.000 0.000 0.036
#> GSM627207 2 0.0963 0.8824 0.000 0.964 0.000 0.000 0.036
#> GSM627157 3 0.1792 0.7779 0.084 0.000 0.916 0.000 0.000
#> GSM627201 2 0.0000 0.8872 0.000 1.000 0.000 0.000 0.000
#> GSM627146 2 0.0000 0.8872 0.000 1.000 0.000 0.000 0.000
#> GSM627156 2 0.2694 0.8613 0.000 0.884 0.000 0.040 0.076
#> GSM627188 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.000
#> GSM627197 2 0.0703 0.8854 0.000 0.976 0.000 0.024 0.000
#> GSM627173 2 0.0963 0.8824 0.000 0.964 0.000 0.000 0.036
#> GSM627179 2 0.0963 0.8824 0.000 0.964 0.000 0.000 0.036
#> GSM627208 5 0.1544 0.9295 0.000 0.000 0.068 0.000 0.932
#> GSM627215 2 0.4874 0.7352 0.000 0.756 0.056 0.148 0.040
#> GSM627153 2 0.0794 0.8845 0.000 0.972 0.000 0.028 0.000
#> GSM627155 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.000
#> GSM627165 2 0.5323 0.5128 0.000 0.624 0.000 0.296 0.080
#> GSM627168 3 0.1270 0.8062 0.000 0.000 0.948 0.000 0.052
#> GSM627183 3 0.0000 0.8316 0.000 0.000 1.000 0.000 0.000
#> GSM627144 5 0.2074 0.9529 0.000 0.000 0.104 0.000 0.896
#> GSM627158 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.000
#> GSM627196 2 0.0963 0.8824 0.000 0.964 0.000 0.000 0.036
#> GSM627142 5 0.2830 0.8710 0.000 0.000 0.044 0.080 0.876
#> GSM627182 5 0.2329 0.9424 0.000 0.000 0.124 0.000 0.876
#> GSM627202 3 0.4374 0.5795 0.028 0.000 0.700 0.000 0.272
#> GSM627141 3 0.0000 0.8316 0.000 0.000 1.000 0.000 0.000
#> GSM627143 2 0.4562 0.7492 0.000 0.764 0.028 0.168 0.040
#> GSM627145 5 0.2732 0.9101 0.000 0.000 0.160 0.000 0.840
#> GSM627152 5 0.3366 0.8120 0.000 0.000 0.232 0.000 0.768
#> GSM627200 3 0.0000 0.8316 0.000 0.000 1.000 0.000 0.000
#> GSM627159 4 0.0794 0.7906 0.000 0.000 0.000 0.972 0.028
#> GSM627164 2 0.2708 0.8605 0.000 0.884 0.000 0.044 0.072
#> GSM627138 3 0.3366 0.6332 0.232 0.000 0.768 0.000 0.000
#> GSM627175 2 0.1270 0.8763 0.000 0.948 0.000 0.052 0.000
#> GSM627150 5 0.2074 0.9529 0.000 0.000 0.104 0.000 0.896
#> GSM627166 3 0.4045 0.3958 0.356 0.000 0.644 0.000 0.000
#> GSM627186 2 0.2694 0.8613 0.000 0.884 0.000 0.040 0.076
#> GSM627139 4 0.3048 0.6938 0.000 0.000 0.004 0.820 0.176
#> GSM627181 2 0.0609 0.8861 0.000 0.980 0.000 0.020 0.000
#> GSM627205 2 0.3803 0.7834 0.000 0.804 0.000 0.140 0.056
#> GSM627214 2 0.0880 0.8851 0.000 0.968 0.000 0.032 0.000
#> GSM627180 5 0.1965 0.9507 0.000 0.000 0.096 0.000 0.904
#> GSM627172 2 0.1117 0.8870 0.000 0.964 0.000 0.020 0.016
#> GSM627184 1 0.0000 0.9402 1.000 0.000 0.000 0.000 0.000
#> GSM627193 2 0.0963 0.8824 0.000 0.964 0.000 0.000 0.036
#> GSM627191 4 0.2561 0.7505 0.000 0.144 0.000 0.856 0.000
#> GSM627176 5 0.2280 0.9458 0.000 0.000 0.120 0.000 0.880
#> GSM627194 2 0.0963 0.8824 0.000 0.964 0.000 0.000 0.036
#> GSM627154 2 0.2424 0.8116 0.000 0.868 0.000 0.132 0.000
#> GSM627187 3 0.0000 0.8316 0.000 0.000 1.000 0.000 0.000
#> GSM627198 2 0.0880 0.8838 0.000 0.968 0.000 0.032 0.000
#> GSM627160 4 0.1331 0.7741 0.000 0.000 0.040 0.952 0.008
#> GSM627185 3 0.4300 0.0574 0.476 0.000 0.524 0.000 0.000
#> GSM627206 3 0.0290 0.8295 0.000 0.000 0.992 0.000 0.008
#> GSM627161 1 0.0162 0.9367 0.996 0.000 0.004 0.000 0.000
#> GSM627162 3 0.1661 0.8043 0.000 0.000 0.940 0.024 0.036
#> GSM627210 3 0.0000 0.8316 0.000 0.000 1.000 0.000 0.000
#> GSM627189 2 0.0963 0.8824 0.000 0.964 0.000 0.000 0.036
show/hide code output
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM627128 6 0.0291 0.9860 0.000 0.000 0.000 0.004 0.004 0.992
#> GSM627110 3 0.0547 0.8470 0.000 0.020 0.980 0.000 0.000 0.000
#> GSM627132 1 0.0363 0.9202 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM627107 5 0.0146 0.8157 0.000 0.000 0.000 0.004 0.996 0.000
#> GSM627103 2 0.3151 0.9417 0.000 0.748 0.000 0.252 0.000 0.000
#> GSM627114 3 0.0146 0.8509 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM627134 4 0.0603 0.8343 0.000 0.016 0.000 0.980 0.000 0.004
#> GSM627137 4 0.3330 0.3898 0.000 0.284 0.000 0.716 0.000 0.000
#> GSM627148 5 0.0717 0.8185 0.000 0.016 0.008 0.000 0.976 0.000
#> GSM627101 6 0.0820 0.9700 0.000 0.016 0.000 0.012 0.000 0.972
#> GSM627130 6 0.0260 0.9853 0.000 0.000 0.000 0.008 0.000 0.992
#> GSM627071 3 0.5104 0.1552 0.000 0.088 0.540 0.000 0.372 0.000
#> GSM627118 4 0.0717 0.8335 0.000 0.016 0.000 0.976 0.000 0.008
#> GSM627094 2 0.3198 0.9458 0.000 0.740 0.000 0.260 0.000 0.000
#> GSM627122 5 0.3938 0.7853 0.000 0.228 0.044 0.000 0.728 0.000
#> GSM627115 2 0.3198 0.9458 0.000 0.740 0.000 0.260 0.000 0.000
#> GSM627125 6 0.0692 0.9794 0.000 0.000 0.000 0.004 0.020 0.976
#> GSM627174 4 0.2165 0.7566 0.000 0.108 0.000 0.884 0.000 0.008
#> GSM627102 4 0.2969 0.5519 0.000 0.224 0.000 0.776 0.000 0.000
#> GSM627073 5 0.0000 0.8169 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM627108 2 0.3198 0.9458 0.000 0.740 0.000 0.260 0.000 0.000
#> GSM627126 1 0.0000 0.9294 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627078 4 0.0260 0.8371 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM627090 5 0.3463 0.7961 0.000 0.240 0.008 0.000 0.748 0.004
#> GSM627099 4 0.0260 0.8371 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM627105 6 0.0692 0.9794 0.000 0.000 0.000 0.004 0.020 0.976
#> GSM627117 3 0.0146 0.8509 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM627121 5 0.0000 0.8169 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM627127 4 0.0260 0.8371 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM627087 2 0.3151 0.9441 0.000 0.748 0.000 0.252 0.000 0.000
#> GSM627089 5 0.5944 0.2012 0.000 0.216 0.384 0.000 0.400 0.000
#> GSM627092 4 0.1556 0.7938 0.000 0.080 0.000 0.920 0.000 0.000
#> GSM627076 5 0.3354 0.7964 0.000 0.240 0.004 0.000 0.752 0.004
#> GSM627136 3 0.0692 0.8435 0.000 0.004 0.976 0.000 0.020 0.000
#> GSM627081 5 0.0000 0.8169 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM627091 4 0.2664 0.6306 0.000 0.184 0.000 0.816 0.000 0.000
#> GSM627097 4 0.2631 0.6870 0.000 0.000 0.000 0.820 0.000 0.180
#> GSM627072 5 0.2981 0.7218 0.000 0.020 0.160 0.000 0.820 0.000
#> GSM627080 1 0.0000 0.9294 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627088 3 0.0146 0.8509 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM627109 3 0.3634 0.4287 0.356 0.000 0.644 0.000 0.000 0.000
#> GSM627111 1 0.2664 0.7014 0.816 0.000 0.184 0.000 0.000 0.000
#> GSM627113 3 0.0000 0.8507 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627133 5 0.2980 0.6440 0.000 0.192 0.000 0.008 0.800 0.000
#> GSM627177 3 0.4709 0.1899 0.000 0.040 0.556 0.004 0.400 0.000
#> GSM627086 2 0.3198 0.9458 0.000 0.740 0.000 0.260 0.000 0.000
#> GSM627095 1 0.0146 0.9281 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM627079 5 0.3731 0.7919 0.000 0.240 0.020 0.000 0.736 0.004
#> GSM627082 6 0.0146 0.9854 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM627074 3 0.0146 0.8501 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM627077 3 0.5917 -0.2051 0.000 0.208 0.404 0.000 0.388 0.000
#> GSM627093 3 0.0146 0.8501 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM627120 4 0.3158 0.6800 0.000 0.020 0.000 0.812 0.164 0.004
#> GSM627124 4 0.0260 0.8371 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM627075 2 0.3198 0.9458 0.000 0.740 0.000 0.260 0.000 0.000
#> GSM627085 4 0.0260 0.8371 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM627119 3 0.0146 0.8501 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM627116 4 0.3221 0.5595 0.000 0.000 0.000 0.736 0.000 0.264
#> GSM627084 3 0.0146 0.8501 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM627096 4 0.1320 0.8195 0.000 0.016 0.000 0.948 0.000 0.036
#> GSM627100 5 0.3354 0.7964 0.000 0.240 0.004 0.000 0.752 0.004
#> GSM627112 4 0.3515 0.4722 0.000 0.000 0.000 0.676 0.000 0.324
#> GSM627083 1 0.5464 0.2656 0.564 0.000 0.000 0.176 0.000 0.260
#> GSM627098 3 0.0146 0.8501 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM627104 3 0.0858 0.8356 0.028 0.004 0.968 0.000 0.000 0.000
#> GSM627131 3 0.5911 -0.1092 0.000 0.212 0.432 0.000 0.356 0.000
#> GSM627106 5 0.0000 0.8169 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM627123 1 0.0458 0.9187 0.984 0.000 0.016 0.000 0.000 0.000
#> GSM627129 4 0.0458 0.8346 0.000 0.016 0.000 0.984 0.000 0.000
#> GSM627216 2 0.3151 0.9371 0.000 0.748 0.000 0.252 0.000 0.000
#> GSM627212 4 0.2730 0.6007 0.000 0.192 0.000 0.808 0.000 0.000
#> GSM627190 3 0.0146 0.8509 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM627169 2 0.3126 0.9402 0.000 0.752 0.000 0.248 0.000 0.000
#> GSM627167 4 0.0717 0.8335 0.000 0.016 0.000 0.976 0.000 0.008
#> GSM627192 1 0.0000 0.9294 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627203 5 0.3023 0.7997 0.000 0.232 0.000 0.000 0.768 0.000
#> GSM627151 4 0.5615 0.3946 0.000 0.016 0.000 0.600 0.184 0.200
#> GSM627163 1 0.0000 0.9294 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627211 2 0.3684 0.7829 0.000 0.628 0.000 0.372 0.000 0.000
#> GSM627171 2 0.3151 0.9371 0.000 0.748 0.000 0.252 0.000 0.000
#> GSM627209 4 0.0260 0.8371 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM627135 1 0.0146 0.9281 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM627170 2 0.3151 0.9371 0.000 0.748 0.000 0.252 0.000 0.000
#> GSM627178 1 0.4737 0.6306 0.712 0.152 0.120 0.000 0.016 0.000
#> GSM627199 4 0.0260 0.8371 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM627213 4 0.0405 0.8365 0.000 0.008 0.000 0.988 0.000 0.004
#> GSM627140 4 0.3221 0.5595 0.000 0.000 0.000 0.736 0.000 0.264
#> GSM627149 1 0.0146 0.9281 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM627147 4 0.0458 0.8346 0.000 0.016 0.000 0.984 0.000 0.000
#> GSM627195 5 0.0000 0.8169 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM627204 2 0.3198 0.9458 0.000 0.740 0.000 0.260 0.000 0.000
#> GSM627207 2 0.3198 0.9458 0.000 0.740 0.000 0.260 0.000 0.000
#> GSM627157 3 0.0291 0.8487 0.004 0.004 0.992 0.000 0.000 0.000
#> GSM627201 4 0.3854 -0.3959 0.000 0.464 0.000 0.536 0.000 0.000
#> GSM627146 2 0.3869 0.4780 0.000 0.500 0.000 0.500 0.000 0.000
#> GSM627156 2 0.3126 0.9402 0.000 0.752 0.000 0.248 0.000 0.000
#> GSM627188 1 0.0000 0.9294 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627197 4 0.1075 0.8111 0.000 0.048 0.000 0.952 0.000 0.000
#> GSM627173 2 0.3198 0.9458 0.000 0.740 0.000 0.260 0.000 0.000
#> GSM627179 2 0.3198 0.9458 0.000 0.740 0.000 0.260 0.000 0.000
#> GSM627208 5 0.2978 0.7440 0.000 0.052 0.084 0.008 0.856 0.000
#> GSM627215 2 0.5134 0.3060 0.000 0.524 0.000 0.088 0.388 0.000
#> GSM627153 4 0.0260 0.8371 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM627155 1 0.0000 0.9294 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627165 4 0.0603 0.8343 0.000 0.016 0.000 0.980 0.000 0.004
#> GSM627168 3 0.1341 0.8289 0.000 0.024 0.948 0.000 0.028 0.000
#> GSM627183 3 0.0363 0.8496 0.000 0.012 0.988 0.000 0.000 0.000
#> GSM627144 5 0.0000 0.8169 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM627158 1 0.0000 0.9294 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627196 2 0.3198 0.9458 0.000 0.740 0.000 0.260 0.000 0.000
#> GSM627142 5 0.5690 0.6267 0.000 0.240 0.004 0.004 0.568 0.184
#> GSM627182 5 0.3309 0.5191 0.000 0.000 0.280 0.000 0.720 0.000
#> GSM627202 3 0.5844 0.0596 0.000 0.216 0.476 0.000 0.308 0.000
#> GSM627141 3 0.0146 0.8509 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM627143 4 0.3023 0.6064 0.000 0.212 0.000 0.784 0.000 0.004
#> GSM627145 5 0.3694 0.7937 0.000 0.232 0.028 0.000 0.740 0.000
#> GSM627152 5 0.3559 0.7950 0.000 0.240 0.012 0.000 0.744 0.004
#> GSM627200 3 0.0458 0.8476 0.000 0.016 0.984 0.000 0.000 0.000
#> GSM627159 6 0.0146 0.9854 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM627164 2 0.3126 0.9402 0.000 0.752 0.000 0.248 0.000 0.000
#> GSM627138 3 0.3023 0.6442 0.232 0.000 0.768 0.000 0.000 0.000
#> GSM627175 4 0.0260 0.8371 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM627150 5 0.0146 0.8173 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM627166 3 0.3163 0.6127 0.232 0.004 0.764 0.000 0.000 0.000
#> GSM627186 2 0.3126 0.9402 0.000 0.752 0.000 0.248 0.000 0.000
#> GSM627139 5 0.3240 0.6526 0.000 0.000 0.000 0.004 0.752 0.244
#> GSM627181 4 0.1714 0.7680 0.000 0.092 0.000 0.908 0.000 0.000
#> GSM627205 2 0.3151 0.9371 0.000 0.748 0.000 0.252 0.000 0.000
#> GSM627214 4 0.0547 0.8354 0.000 0.020 0.000 0.980 0.000 0.000
#> GSM627180 5 0.0000 0.8169 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM627172 4 0.0363 0.8351 0.000 0.012 0.000 0.988 0.000 0.000
#> GSM627184 1 0.0000 0.9294 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627193 2 0.3198 0.9458 0.000 0.740 0.000 0.260 0.000 0.000
#> GSM627191 4 0.3221 0.5595 0.000 0.000 0.000 0.736 0.000 0.264
#> GSM627176 5 0.3323 0.7971 0.000 0.240 0.008 0.000 0.752 0.000
#> GSM627194 2 0.3198 0.9458 0.000 0.740 0.000 0.260 0.000 0.000
#> GSM627154 4 0.0260 0.8371 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM627187 3 0.0260 0.8506 0.000 0.008 0.992 0.000 0.000 0.000
#> GSM627198 4 0.0260 0.8371 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM627160 4 0.5185 0.2912 0.000 0.000 0.000 0.564 0.108 0.328
#> GSM627185 3 0.0858 0.8356 0.028 0.004 0.968 0.000 0.000 0.000
#> GSM627206 3 0.0508 0.8477 0.000 0.004 0.984 0.000 0.012 0.000
#> GSM627161 1 0.0000 0.9294 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627162 3 0.2879 0.6977 0.000 0.004 0.816 0.004 0.176 0.000
#> GSM627210 3 0.0260 0.8506 0.000 0.008 0.992 0.000 0.000 0.000
#> GSM627189 2 0.3198 0.9458 0.000 0.740 0.000 0.260 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two
samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures
which are significantly different between classes which can be candidate marks
for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known
annotation is numeric, one-way ANOVA test is applied, and if the known
annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) age(p) other(p) k
#> MAD:mclust 143 0.579 0.421 0.0829 2
#> MAD:mclust 143 0.311 0.699 0.1630 3
#> MAD:mclust 139 0.174 0.318 0.1767 4
#> MAD:mclust 132 0.275 0.379 0.2410 5
#> MAD:mclust 131 0.152 0.503 0.1426 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
MAD:NMF**
The object with results only for a single top-value method and a single partition method
can be extracted as:
res = res_list["MAD", "NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51882 rows and 146 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
- The first row: a plot of the ECDF (empirical cumulative distribution
function) curves of the consensus matrix for each
k and the heatmap of
predicted classes for each k.
- The second row: heatmaps of the consensus matrix for each
k.
- The third row: heatmaps of the membership matrix for each
k.
- The fouth row: heatmaps of the signatures for each
k.
All the plots in panels can be made by individual functions and they are
plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
- ECDF curves of the consensus matrix for each
k;
- 1-PAC. The PAC
score
measures the proportion of the ambiguous subgrouping.
- Mean silhouette score.
- Concordance. The mean probability of fiting the consensus class ids in all
partitions.
- Area increased. Denote \(A_k\) as the area under the ECDF curve for current
k, the area increased is defined as \(A_k - A_{k-1}\).
- Rand index. The percent of pairs of samples that are both in a same cluster
or both are not in a same cluster in the partition of k and k-1.
- Jaccard index. The ratio of pairs of samples are both in a same cluster in
the partition of k and k-1 and the pairs of samples are both in a same
cluster in the partition k or k-1.
The detailed explanations of these statistics can be found in the cola
vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.985 0.950 0.980 0.5025 0.497 0.497
#> 3 3 0.698 0.830 0.911 0.2700 0.842 0.690
#> 4 4 0.614 0.718 0.853 0.1329 0.725 0.400
#> 5 5 0.577 0.538 0.744 0.0741 0.860 0.565
#> 6 6 0.636 0.586 0.762 0.0432 0.895 0.599
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
- All \(k\) with Jaccard index larger than 0.95 are removed because increasing
\(k\) does not provide enough extra information. If all \(k\) are removed, it is
marked as no subgroup is detected.
- For all \(k\) with 1-PAC score larger than 0.9, the maximal \(k\) is taken as
the best \(k\), and other \(k\) are marked as optional \(k\).
- If it does not fit the second rule. The \(k\) with the maximal vote of the
highest 1-PAC score, highest mean silhouette, and highest concordance is
taken as the best \(k\).
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
show/hide code output
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM627128 2 0.8016 0.6708 0.244 0.756
#> GSM627110 1 0.0000 0.9739 1.000 0.000
#> GSM627132 1 0.0000 0.9739 1.000 0.000
#> GSM627107 2 0.3431 0.9238 0.064 0.936
#> GSM627103 2 0.0000 0.9834 0.000 1.000
#> GSM627114 1 0.0000 0.9739 1.000 0.000
#> GSM627134 2 0.0000 0.9834 0.000 1.000
#> GSM627137 2 0.0000 0.9834 0.000 1.000
#> GSM627148 1 0.0000 0.9739 1.000 0.000
#> GSM627101 2 0.0000 0.9834 0.000 1.000
#> GSM627130 2 0.0000 0.9834 0.000 1.000
#> GSM627071 1 0.0000 0.9739 1.000 0.000
#> GSM627118 2 0.0000 0.9834 0.000 1.000
#> GSM627094 2 0.0000 0.9834 0.000 1.000
#> GSM627122 1 0.0000 0.9739 1.000 0.000
#> GSM627115 2 0.0000 0.9834 0.000 1.000
#> GSM627125 2 0.5946 0.8280 0.144 0.856
#> GSM627174 2 0.0000 0.9834 0.000 1.000
#> GSM627102 2 0.0000 0.9834 0.000 1.000
#> GSM627073 1 0.8713 0.5900 0.708 0.292
#> GSM627108 2 0.0000 0.9834 0.000 1.000
#> GSM627126 1 0.0000 0.9739 1.000 0.000
#> GSM627078 2 0.0000 0.9834 0.000 1.000
#> GSM627090 1 0.0000 0.9739 1.000 0.000
#> GSM627099 2 0.0000 0.9834 0.000 1.000
#> GSM627105 2 0.0000 0.9834 0.000 1.000
#> GSM627117 1 0.0000 0.9739 1.000 0.000
#> GSM627121 2 0.3274 0.9279 0.060 0.940
#> GSM627127 2 0.0000 0.9834 0.000 1.000
#> GSM627087 2 0.0000 0.9834 0.000 1.000
#> GSM627089 1 0.0000 0.9739 1.000 0.000
#> GSM627092 2 0.0000 0.9834 0.000 1.000
#> GSM627076 1 0.0000 0.9739 1.000 0.000
#> GSM627136 1 0.0000 0.9739 1.000 0.000
#> GSM627081 1 0.9963 0.1376 0.536 0.464
#> GSM627091 2 0.0000 0.9834 0.000 1.000
#> GSM627097 2 0.0000 0.9834 0.000 1.000
#> GSM627072 1 0.0000 0.9739 1.000 0.000
#> GSM627080 1 0.0000 0.9739 1.000 0.000
#> GSM627088 1 0.0000 0.9739 1.000 0.000
#> GSM627109 1 0.0000 0.9739 1.000 0.000
#> GSM627111 1 0.0000 0.9739 1.000 0.000
#> GSM627113 1 0.0000 0.9739 1.000 0.000
#> GSM627133 2 0.0000 0.9834 0.000 1.000
#> GSM627177 1 0.0000 0.9739 1.000 0.000
#> GSM627086 2 0.0000 0.9834 0.000 1.000
#> GSM627095 1 0.0000 0.9739 1.000 0.000
#> GSM627079 1 0.0000 0.9739 1.000 0.000
#> GSM627082 1 0.0376 0.9705 0.996 0.004
#> GSM627074 1 0.0000 0.9739 1.000 0.000
#> GSM627077 1 0.0000 0.9739 1.000 0.000
#> GSM627093 1 0.0000 0.9739 1.000 0.000
#> GSM627120 2 0.0000 0.9834 0.000 1.000
#> GSM627124 2 0.0000 0.9834 0.000 1.000
#> GSM627075 2 0.0000 0.9834 0.000 1.000
#> GSM627085 2 0.0000 0.9834 0.000 1.000
#> GSM627119 1 0.0000 0.9739 1.000 0.000
#> GSM627116 2 0.1414 0.9659 0.020 0.980
#> GSM627084 1 0.0000 0.9739 1.000 0.000
#> GSM627096 2 0.0000 0.9834 0.000 1.000
#> GSM627100 1 0.0000 0.9739 1.000 0.000
#> GSM627112 2 0.0000 0.9834 0.000 1.000
#> GSM627083 1 0.5842 0.8304 0.860 0.140
#> GSM627098 1 0.0000 0.9739 1.000 0.000
#> GSM627104 1 0.0000 0.9739 1.000 0.000
#> GSM627131 1 0.0000 0.9739 1.000 0.000
#> GSM627106 1 0.8661 0.5975 0.712 0.288
#> GSM627123 1 0.0000 0.9739 1.000 0.000
#> GSM627129 2 0.0000 0.9834 0.000 1.000
#> GSM627216 2 0.0000 0.9834 0.000 1.000
#> GSM627212 2 0.0000 0.9834 0.000 1.000
#> GSM627190 1 0.0000 0.9739 1.000 0.000
#> GSM627169 2 0.0000 0.9834 0.000 1.000
#> GSM627167 2 0.0000 0.9834 0.000 1.000
#> GSM627192 1 0.0000 0.9739 1.000 0.000
#> GSM627203 1 0.0000 0.9739 1.000 0.000
#> GSM627151 2 0.0000 0.9834 0.000 1.000
#> GSM627163 1 0.0000 0.9739 1.000 0.000
#> GSM627211 2 0.0000 0.9834 0.000 1.000
#> GSM627171 2 0.0000 0.9834 0.000 1.000
#> GSM627209 2 0.0000 0.9834 0.000 1.000
#> GSM627135 1 0.0000 0.9739 1.000 0.000
#> GSM627170 2 0.0000 0.9834 0.000 1.000
#> GSM627178 1 0.0000 0.9739 1.000 0.000
#> GSM627199 2 0.0000 0.9834 0.000 1.000
#> GSM627213 2 0.0000 0.9834 0.000 1.000
#> GSM627140 2 0.0000 0.9834 0.000 1.000
#> GSM627149 1 0.0000 0.9739 1.000 0.000
#> GSM627147 2 0.0000 0.9834 0.000 1.000
#> GSM627195 1 0.0000 0.9739 1.000 0.000
#> GSM627204 2 0.0000 0.9834 0.000 1.000
#> GSM627207 2 0.0000 0.9834 0.000 1.000
#> GSM627157 1 0.0000 0.9739 1.000 0.000
#> GSM627201 2 0.0000 0.9834 0.000 1.000
#> GSM627146 2 0.0000 0.9834 0.000 1.000
#> GSM627156 2 0.0000 0.9834 0.000 1.000
#> GSM627188 1 0.0000 0.9739 1.000 0.000
#> GSM627197 2 0.0000 0.9834 0.000 1.000
#> GSM627173 2 0.0000 0.9834 0.000 1.000
#> GSM627179 2 0.0000 0.9834 0.000 1.000
#> GSM627208 2 0.0000 0.9834 0.000 1.000
#> GSM627215 2 0.0000 0.9834 0.000 1.000
#> GSM627153 2 0.0000 0.9834 0.000 1.000
#> GSM627155 1 0.0000 0.9739 1.000 0.000
#> GSM627165 2 0.0000 0.9834 0.000 1.000
#> GSM627168 1 0.0000 0.9739 1.000 0.000
#> GSM627183 1 0.0000 0.9739 1.000 0.000
#> GSM627144 1 0.0000 0.9739 1.000 0.000
#> GSM627158 1 0.0000 0.9739 1.000 0.000
#> GSM627196 2 0.0000 0.9834 0.000 1.000
#> GSM627142 1 0.0000 0.9739 1.000 0.000
#> GSM627182 1 0.9323 0.4709 0.652 0.348
#> GSM627202 1 0.0000 0.9739 1.000 0.000
#> GSM627141 1 0.0000 0.9739 1.000 0.000
#> GSM627143 2 0.0000 0.9834 0.000 1.000
#> GSM627145 1 0.0000 0.9739 1.000 0.000
#> GSM627152 1 0.0000 0.9739 1.000 0.000
#> GSM627200 1 0.0000 0.9739 1.000 0.000
#> GSM627159 1 0.3274 0.9180 0.940 0.060
#> GSM627164 2 0.0000 0.9834 0.000 1.000
#> GSM627138 1 0.0000 0.9739 1.000 0.000
#> GSM627175 2 0.0000 0.9834 0.000 1.000
#> GSM627150 1 0.0000 0.9739 1.000 0.000
#> GSM627166 1 0.0000 0.9739 1.000 0.000
#> GSM627186 2 0.0000 0.9834 0.000 1.000
#> GSM627139 2 1.0000 -0.0118 0.496 0.504
#> GSM627181 2 0.0000 0.9834 0.000 1.000
#> GSM627205 2 0.0000 0.9834 0.000 1.000
#> GSM627214 2 0.0000 0.9834 0.000 1.000
#> GSM627180 2 0.3733 0.9154 0.072 0.928
#> GSM627172 2 0.0000 0.9834 0.000 1.000
#> GSM627184 1 0.0000 0.9739 1.000 0.000
#> GSM627193 2 0.0000 0.9834 0.000 1.000
#> GSM627191 2 0.4161 0.9018 0.084 0.916
#> GSM627176 1 0.0000 0.9739 1.000 0.000
#> GSM627194 2 0.0000 0.9834 0.000 1.000
#> GSM627154 2 0.0000 0.9834 0.000 1.000
#> GSM627187 1 0.0000 0.9739 1.000 0.000
#> GSM627198 2 0.0000 0.9834 0.000 1.000
#> GSM627160 1 0.6531 0.7929 0.832 0.168
#> GSM627185 1 0.0000 0.9739 1.000 0.000
#> GSM627206 1 0.0000 0.9739 1.000 0.000
#> GSM627161 1 0.0000 0.9739 1.000 0.000
#> GSM627162 1 0.0000 0.9739 1.000 0.000
#> GSM627210 1 0.0000 0.9739 1.000 0.000
#> GSM627189 2 0.0000 0.9834 0.000 1.000
show/hide code output
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM627128 3 0.0237 0.8385 0.000 0.004 0.996
#> GSM627110 1 0.0237 0.8851 0.996 0.004 0.000
#> GSM627132 1 0.4235 0.8447 0.824 0.000 0.176
#> GSM627107 2 0.4702 0.7206 0.212 0.788 0.000
#> GSM627103 2 0.0000 0.9248 0.000 1.000 0.000
#> GSM627114 1 0.0424 0.8834 0.992 0.008 0.000
#> GSM627134 2 0.0424 0.9241 0.000 0.992 0.008
#> GSM627137 2 0.0237 0.9248 0.000 0.996 0.004
#> GSM627148 1 0.0237 0.8851 0.996 0.004 0.000
#> GSM627101 3 0.4062 0.7957 0.000 0.164 0.836
#> GSM627130 3 0.1289 0.8427 0.000 0.032 0.968
#> GSM627071 1 0.0237 0.8867 0.996 0.000 0.004
#> GSM627118 2 0.0424 0.9241 0.000 0.992 0.008
#> GSM627094 2 0.0000 0.9248 0.000 1.000 0.000
#> GSM627122 1 0.4346 0.8397 0.816 0.000 0.184
#> GSM627115 2 0.1643 0.9044 0.044 0.956 0.000
#> GSM627125 3 0.0237 0.8385 0.000 0.004 0.996
#> GSM627174 2 0.0424 0.9241 0.000 0.992 0.008
#> GSM627102 2 0.0424 0.9241 0.000 0.992 0.008
#> GSM627073 1 0.4842 0.6227 0.776 0.224 0.000
#> GSM627108 2 0.0237 0.9239 0.004 0.996 0.000
#> GSM627126 1 0.4796 0.8078 0.780 0.000 0.220
#> GSM627078 3 0.5678 0.6083 0.000 0.316 0.684
#> GSM627090 1 0.3267 0.8707 0.884 0.000 0.116
#> GSM627099 2 0.0424 0.9241 0.000 0.992 0.008
#> GSM627105 3 0.1964 0.8402 0.000 0.056 0.944
#> GSM627117 1 0.0424 0.8834 0.992 0.008 0.000
#> GSM627121 2 0.5254 0.6822 0.264 0.736 0.000
#> GSM627127 2 0.6008 0.3197 0.000 0.628 0.372
#> GSM627087 2 0.1964 0.8968 0.056 0.944 0.000
#> GSM627089 1 0.0000 0.8862 1.000 0.000 0.000
#> GSM627092 2 0.0000 0.9248 0.000 1.000 0.000
#> GSM627076 1 0.4399 0.8367 0.812 0.000 0.188
#> GSM627136 1 0.0237 0.8867 0.996 0.000 0.004
#> GSM627081 1 0.5882 0.3810 0.652 0.348 0.000
#> GSM627091 2 0.0424 0.9241 0.000 0.992 0.008
#> GSM627097 3 0.4399 0.7799 0.000 0.188 0.812
#> GSM627072 1 0.0237 0.8851 0.996 0.004 0.000
#> GSM627080 1 0.4235 0.8447 0.824 0.000 0.176
#> GSM627088 1 0.0424 0.8834 0.992 0.008 0.000
#> GSM627109 1 0.3340 0.8694 0.880 0.000 0.120
#> GSM627111 1 0.3816 0.8590 0.852 0.000 0.148
#> GSM627113 1 0.0237 0.8867 0.996 0.000 0.004
#> GSM627133 2 0.4291 0.7907 0.180 0.820 0.000
#> GSM627177 1 0.1753 0.8845 0.952 0.000 0.048
#> GSM627086 2 0.0000 0.9248 0.000 1.000 0.000
#> GSM627095 3 0.5760 0.3460 0.328 0.000 0.672
#> GSM627079 1 0.2537 0.8793 0.920 0.000 0.080
#> GSM627082 3 0.0000 0.8366 0.000 0.000 1.000
#> GSM627074 1 0.0000 0.8862 1.000 0.000 0.000
#> GSM627077 1 0.3551 0.8651 0.868 0.000 0.132
#> GSM627093 1 0.0424 0.8834 0.992 0.008 0.000
#> GSM627120 2 0.0892 0.9174 0.020 0.980 0.000
#> GSM627124 3 0.4399 0.7793 0.000 0.188 0.812
#> GSM627075 2 0.0000 0.9248 0.000 1.000 0.000
#> GSM627085 3 0.4974 0.7305 0.000 0.236 0.764
#> GSM627119 1 0.0000 0.8862 1.000 0.000 0.000
#> GSM627116 3 0.0424 0.8395 0.000 0.008 0.992
#> GSM627084 1 0.4235 0.8447 0.824 0.000 0.176
#> GSM627096 2 0.1529 0.9022 0.000 0.960 0.040
#> GSM627100 1 0.4504 0.8303 0.804 0.000 0.196
#> GSM627112 3 0.3116 0.8243 0.000 0.108 0.892
#> GSM627083 3 0.0000 0.8366 0.000 0.000 1.000
#> GSM627098 1 0.0592 0.8870 0.988 0.000 0.012
#> GSM627104 1 0.0000 0.8862 1.000 0.000 0.000
#> GSM627131 1 0.4235 0.8447 0.824 0.000 0.176
#> GSM627106 1 0.4346 0.6807 0.816 0.184 0.000
#> GSM627123 1 0.4399 0.8367 0.812 0.000 0.188
#> GSM627129 2 0.0424 0.9241 0.000 0.992 0.008
#> GSM627216 2 0.3941 0.8150 0.156 0.844 0.000
#> GSM627212 2 0.0237 0.9248 0.000 0.996 0.004
#> GSM627190 1 0.0424 0.8834 0.992 0.008 0.000
#> GSM627169 2 0.4235 0.7950 0.176 0.824 0.000
#> GSM627167 2 0.6180 0.1333 0.000 0.584 0.416
#> GSM627192 3 0.2261 0.7869 0.068 0.000 0.932
#> GSM627203 1 0.0237 0.8867 0.996 0.000 0.004
#> GSM627151 2 0.0424 0.9241 0.000 0.992 0.008
#> GSM627163 1 0.4291 0.8423 0.820 0.000 0.180
#> GSM627211 2 0.0424 0.9241 0.000 0.992 0.008
#> GSM627171 2 0.2878 0.8676 0.096 0.904 0.000
#> GSM627209 2 0.0424 0.9241 0.000 0.992 0.008
#> GSM627135 1 0.4750 0.8117 0.784 0.000 0.216
#> GSM627170 2 0.0747 0.9193 0.016 0.984 0.000
#> GSM627178 1 0.4346 0.8397 0.816 0.000 0.184
#> GSM627199 3 0.4399 0.7785 0.000 0.188 0.812
#> GSM627213 3 0.4235 0.7876 0.000 0.176 0.824
#> GSM627140 3 0.2878 0.8289 0.000 0.096 0.904
#> GSM627149 1 0.4452 0.8337 0.808 0.000 0.192
#> GSM627147 2 0.1163 0.9108 0.000 0.972 0.028
#> GSM627195 1 0.0237 0.8851 0.996 0.004 0.000
#> GSM627204 2 0.0237 0.9248 0.000 0.996 0.004
#> GSM627207 2 0.0237 0.9239 0.004 0.996 0.000
#> GSM627157 1 0.1860 0.8841 0.948 0.000 0.052
#> GSM627201 2 0.0237 0.9248 0.000 0.996 0.004
#> GSM627146 2 0.0237 0.9248 0.000 0.996 0.004
#> GSM627156 2 0.4178 0.7991 0.172 0.828 0.000
#> GSM627188 3 0.1529 0.8108 0.040 0.000 0.960
#> GSM627197 2 0.0424 0.9241 0.000 0.992 0.008
#> GSM627173 2 0.0000 0.9248 0.000 1.000 0.000
#> GSM627179 2 0.0237 0.9239 0.004 0.996 0.000
#> GSM627208 2 0.4399 0.7820 0.188 0.812 0.000
#> GSM627215 2 0.4002 0.8111 0.160 0.840 0.000
#> GSM627153 2 0.0424 0.9241 0.000 0.992 0.008
#> GSM627155 1 0.4702 0.8156 0.788 0.000 0.212
#> GSM627165 2 0.0237 0.9248 0.000 0.996 0.004
#> GSM627168 1 0.0000 0.8862 1.000 0.000 0.000
#> GSM627183 1 0.0237 0.8867 0.996 0.000 0.004
#> GSM627144 1 0.0592 0.8812 0.988 0.012 0.000
#> GSM627158 1 0.4291 0.8423 0.820 0.000 0.180
#> GSM627196 2 0.0424 0.9241 0.000 0.992 0.008
#> GSM627142 1 0.5810 0.6457 0.664 0.000 0.336
#> GSM627182 1 0.6252 0.0642 0.556 0.444 0.000
#> GSM627202 1 0.4121 0.8492 0.832 0.000 0.168
#> GSM627141 1 0.0237 0.8851 0.996 0.004 0.000
#> GSM627143 2 0.1643 0.9045 0.044 0.956 0.000
#> GSM627145 1 0.0000 0.8862 1.000 0.000 0.000
#> GSM627152 1 0.3879 0.8569 0.848 0.000 0.152
#> GSM627200 1 0.3116 0.8727 0.892 0.000 0.108
#> GSM627159 3 0.0000 0.8366 0.000 0.000 1.000
#> GSM627164 2 0.0237 0.9239 0.004 0.996 0.000
#> GSM627138 1 0.3038 0.8737 0.896 0.000 0.104
#> GSM627175 2 0.1964 0.8866 0.000 0.944 0.056
#> GSM627150 1 0.0000 0.8862 1.000 0.000 0.000
#> GSM627166 1 0.4002 0.8533 0.840 0.000 0.160
#> GSM627186 2 0.4235 0.7950 0.176 0.824 0.000
#> GSM627139 3 0.5928 0.4190 0.296 0.008 0.696
#> GSM627181 2 0.0424 0.9241 0.000 0.992 0.008
#> GSM627205 2 0.2165 0.8914 0.064 0.936 0.000
#> GSM627214 2 0.0424 0.9241 0.000 0.992 0.008
#> GSM627180 2 0.5016 0.7173 0.240 0.760 0.000
#> GSM627172 2 0.0592 0.9225 0.000 0.988 0.012
#> GSM627184 3 0.5835 0.3100 0.340 0.000 0.660
#> GSM627193 2 0.2878 0.8675 0.096 0.904 0.000
#> GSM627191 3 0.0237 0.8385 0.000 0.004 0.996
#> GSM627176 1 0.1031 0.8869 0.976 0.000 0.024
#> GSM627194 2 0.0000 0.9248 0.000 1.000 0.000
#> GSM627154 3 0.4291 0.7847 0.000 0.180 0.820
#> GSM627187 1 0.0424 0.8834 0.992 0.008 0.000
#> GSM627198 3 0.6274 0.2978 0.000 0.456 0.544
#> GSM627160 3 0.0000 0.8366 0.000 0.000 1.000
#> GSM627185 1 0.1643 0.8850 0.956 0.000 0.044
#> GSM627206 1 0.0237 0.8851 0.996 0.004 0.000
#> GSM627161 1 0.4291 0.8423 0.820 0.000 0.180
#> GSM627162 1 0.0592 0.8812 0.988 0.012 0.000
#> GSM627210 1 0.0237 0.8851 0.996 0.004 0.000
#> GSM627189 2 0.0000 0.9248 0.000 1.000 0.000
show/hide code output
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM627128 4 0.0592 0.8737 0.000 0.000 0.016 0.984
#> GSM627110 3 0.4250 0.5635 0.276 0.000 0.724 0.000
#> GSM627132 1 0.0707 0.8529 0.980 0.000 0.020 0.000
#> GSM627107 3 0.0707 0.7107 0.000 0.020 0.980 0.000
#> GSM627103 2 0.0469 0.8897 0.000 0.988 0.012 0.000
#> GSM627114 3 0.3400 0.6662 0.180 0.000 0.820 0.000
#> GSM627134 2 0.5070 0.3015 0.000 0.620 0.372 0.008
#> GSM627137 2 0.3528 0.7295 0.000 0.808 0.192 0.000
#> GSM627148 3 0.1637 0.7218 0.060 0.000 0.940 0.000
#> GSM627101 4 0.1302 0.8625 0.000 0.000 0.044 0.956
#> GSM627130 4 0.0469 0.8740 0.000 0.000 0.012 0.988
#> GSM627071 1 0.3935 0.8152 0.840 0.060 0.100 0.000
#> GSM627118 2 0.2489 0.8580 0.000 0.912 0.068 0.020
#> GSM627094 2 0.0000 0.8905 0.000 1.000 0.000 0.000
#> GSM627122 1 0.5093 0.5463 0.640 0.000 0.348 0.012
#> GSM627115 2 0.0336 0.8898 0.008 0.992 0.000 0.000
#> GSM627125 4 0.2408 0.8094 0.000 0.000 0.104 0.896
#> GSM627174 2 0.0804 0.8884 0.012 0.980 0.000 0.008
#> GSM627102 2 0.4781 0.4745 0.000 0.660 0.336 0.004
#> GSM627073 3 0.3919 0.7220 0.104 0.056 0.840 0.000
#> GSM627108 2 0.0817 0.8872 0.000 0.976 0.024 0.000
#> GSM627126 1 0.1042 0.8469 0.972 0.000 0.008 0.020
#> GSM627078 2 0.2760 0.8144 0.000 0.872 0.000 0.128
#> GSM627090 3 0.3831 0.6349 0.204 0.000 0.792 0.004
#> GSM627099 2 0.0524 0.8896 0.008 0.988 0.000 0.004
#> GSM627105 4 0.3764 0.6727 0.000 0.000 0.216 0.784
#> GSM627117 3 0.1716 0.7213 0.064 0.000 0.936 0.000
#> GSM627121 3 0.0707 0.7107 0.000 0.020 0.980 0.000
#> GSM627127 2 0.1510 0.8778 0.016 0.956 0.000 0.028
#> GSM627087 2 0.0336 0.8898 0.008 0.992 0.000 0.000
#> GSM627089 3 0.4697 0.4065 0.356 0.000 0.644 0.000
#> GSM627092 3 0.4888 0.2914 0.000 0.412 0.588 0.000
#> GSM627076 3 0.4986 0.5921 0.216 0.000 0.740 0.044
#> GSM627136 3 0.4164 0.5890 0.264 0.000 0.736 0.000
#> GSM627081 3 0.0895 0.7134 0.004 0.020 0.976 0.000
#> GSM627091 2 0.0927 0.8855 0.016 0.976 0.000 0.008
#> GSM627097 2 0.5382 0.6579 0.132 0.744 0.000 0.124
#> GSM627072 3 0.3726 0.6489 0.212 0.000 0.788 0.000
#> GSM627080 1 0.0707 0.8529 0.980 0.000 0.020 0.000
#> GSM627088 1 0.2973 0.8188 0.856 0.000 0.144 0.000
#> GSM627109 1 0.0657 0.8446 0.984 0.012 0.004 0.000
#> GSM627111 1 0.0707 0.8529 0.980 0.000 0.020 0.000
#> GSM627113 1 0.0921 0.8536 0.972 0.000 0.028 0.000
#> GSM627133 2 0.1356 0.8865 0.008 0.960 0.032 0.000
#> GSM627177 1 0.4955 0.5479 0.728 0.244 0.024 0.004
#> GSM627086 2 0.0592 0.8889 0.000 0.984 0.016 0.000
#> GSM627095 1 0.2589 0.7860 0.884 0.000 0.000 0.116
#> GSM627079 1 0.3764 0.7297 0.784 0.000 0.216 0.000
#> GSM627082 4 0.0524 0.8738 0.004 0.000 0.008 0.988
#> GSM627074 1 0.0895 0.8404 0.976 0.020 0.004 0.000
#> GSM627077 1 0.4477 0.6210 0.688 0.000 0.312 0.000
#> GSM627093 1 0.1629 0.8475 0.952 0.024 0.024 0.000
#> GSM627120 3 0.3837 0.6306 0.000 0.224 0.776 0.000
#> GSM627124 2 0.3208 0.7898 0.004 0.848 0.000 0.148
#> GSM627075 2 0.4222 0.6011 0.000 0.728 0.272 0.000
#> GSM627085 2 0.1624 0.8771 0.020 0.952 0.000 0.028
#> GSM627119 1 0.0927 0.8448 0.976 0.016 0.008 0.000
#> GSM627116 2 0.5760 0.2025 0.448 0.524 0.000 0.028
#> GSM627084 1 0.3975 0.7531 0.760 0.000 0.240 0.000
#> GSM627096 2 0.4238 0.7479 0.000 0.796 0.028 0.176
#> GSM627100 3 0.3764 0.6914 0.072 0.000 0.852 0.076
#> GSM627112 4 0.0469 0.8664 0.000 0.012 0.000 0.988
#> GSM627083 4 0.0921 0.8626 0.028 0.000 0.000 0.972
#> GSM627098 1 0.1211 0.8538 0.960 0.000 0.040 0.000
#> GSM627104 1 0.1557 0.8118 0.944 0.056 0.000 0.000
#> GSM627131 1 0.0921 0.8536 0.972 0.000 0.028 0.000
#> GSM627106 3 0.0524 0.7170 0.008 0.004 0.988 0.000
#> GSM627123 1 0.4538 0.7646 0.760 0.000 0.216 0.024
#> GSM627129 3 0.5672 0.5605 0.000 0.276 0.668 0.056
#> GSM627216 2 0.1389 0.8766 0.000 0.952 0.048 0.000
#> GSM627212 2 0.0524 0.8906 0.000 0.988 0.008 0.004
#> GSM627190 3 0.2149 0.7194 0.088 0.000 0.912 0.000
#> GSM627169 3 0.4998 0.0416 0.000 0.488 0.512 0.000
#> GSM627167 3 0.6112 0.0891 0.004 0.040 0.544 0.412
#> GSM627192 1 0.3266 0.7273 0.832 0.000 0.000 0.168
#> GSM627203 3 0.4008 0.5999 0.244 0.000 0.756 0.000
#> GSM627151 2 0.3161 0.7911 0.124 0.864 0.000 0.012
#> GSM627163 1 0.0657 0.8505 0.984 0.000 0.012 0.004
#> GSM627211 2 0.1209 0.8837 0.000 0.964 0.032 0.004
#> GSM627171 3 0.3172 0.6627 0.000 0.160 0.840 0.000
#> GSM627209 2 0.1174 0.8886 0.000 0.968 0.020 0.012
#> GSM627135 1 0.0376 0.8453 0.992 0.004 0.000 0.004
#> GSM627170 3 0.4977 0.1642 0.000 0.460 0.540 0.000
#> GSM627178 1 0.0967 0.8409 0.976 0.016 0.004 0.004
#> GSM627199 2 0.4584 0.5575 0.004 0.696 0.000 0.300
#> GSM627213 4 0.3356 0.6982 0.000 0.176 0.000 0.824
#> GSM627140 4 0.0895 0.8729 0.004 0.000 0.020 0.976
#> GSM627149 1 0.5055 0.7251 0.712 0.000 0.256 0.032
#> GSM627147 4 0.7545 0.1057 0.000 0.192 0.368 0.440
#> GSM627195 3 0.4697 0.4271 0.356 0.000 0.644 0.000
#> GSM627204 2 0.0524 0.8909 0.000 0.988 0.008 0.004
#> GSM627207 2 0.4761 0.3829 0.000 0.628 0.372 0.000
#> GSM627157 1 0.1716 0.8520 0.936 0.000 0.064 0.000
#> GSM627201 2 0.0707 0.8878 0.000 0.980 0.020 0.000
#> GSM627146 2 0.0524 0.8894 0.008 0.988 0.000 0.004
#> GSM627156 3 0.3975 0.6128 0.000 0.240 0.760 0.000
#> GSM627188 4 0.5229 0.1479 0.428 0.000 0.008 0.564
#> GSM627197 2 0.0937 0.8911 0.000 0.976 0.012 0.012
#> GSM627173 2 0.0469 0.8894 0.012 0.988 0.000 0.000
#> GSM627179 2 0.0707 0.8878 0.000 0.980 0.020 0.000
#> GSM627208 3 0.3649 0.6469 0.000 0.204 0.796 0.000
#> GSM627215 2 0.1940 0.8601 0.000 0.924 0.076 0.000
#> GSM627153 2 0.1174 0.8886 0.000 0.968 0.020 0.012
#> GSM627155 1 0.3863 0.8172 0.828 0.000 0.144 0.028
#> GSM627165 3 0.4720 0.5035 0.000 0.324 0.672 0.004
#> GSM627168 1 0.4193 0.7200 0.732 0.000 0.268 0.000
#> GSM627183 1 0.1867 0.8509 0.928 0.000 0.072 0.000
#> GSM627144 3 0.1118 0.7189 0.036 0.000 0.964 0.000
#> GSM627158 1 0.3688 0.7789 0.792 0.000 0.208 0.000
#> GSM627196 2 0.0336 0.8902 0.000 0.992 0.008 0.000
#> GSM627142 3 0.6248 0.4993 0.100 0.000 0.640 0.260
#> GSM627182 3 0.3948 0.7179 0.064 0.096 0.840 0.000
#> GSM627202 1 0.4500 0.6526 0.684 0.000 0.316 0.000
#> GSM627141 3 0.3400 0.6667 0.180 0.000 0.820 0.000
#> GSM627143 3 0.2647 0.6840 0.000 0.120 0.880 0.000
#> GSM627145 3 0.4605 0.4620 0.336 0.000 0.664 0.000
#> GSM627152 3 0.4936 0.4509 0.316 0.000 0.672 0.012
#> GSM627200 1 0.2868 0.8240 0.864 0.000 0.136 0.000
#> GSM627159 4 0.0524 0.8738 0.004 0.000 0.008 0.988
#> GSM627164 3 0.3400 0.6506 0.000 0.180 0.820 0.000
#> GSM627138 1 0.4072 0.7391 0.748 0.000 0.252 0.000
#> GSM627175 2 0.1356 0.8879 0.000 0.960 0.008 0.032
#> GSM627150 3 0.4103 0.6067 0.256 0.000 0.744 0.000
#> GSM627166 1 0.3016 0.7376 0.872 0.120 0.004 0.004
#> GSM627186 3 0.4277 0.5694 0.000 0.280 0.720 0.000
#> GSM627139 3 0.4079 0.6245 0.020 0.000 0.800 0.180
#> GSM627181 2 0.1489 0.8775 0.000 0.952 0.044 0.004
#> GSM627205 3 0.4164 0.5923 0.000 0.264 0.736 0.000
#> GSM627214 3 0.4936 0.4507 0.000 0.372 0.624 0.004
#> GSM627180 3 0.4286 0.7031 0.052 0.136 0.812 0.000
#> GSM627172 3 0.6761 0.4592 0.004 0.252 0.612 0.132
#> GSM627184 1 0.5436 0.4535 0.620 0.000 0.024 0.356
#> GSM627193 2 0.0188 0.8902 0.004 0.996 0.000 0.000
#> GSM627191 4 0.0376 0.8727 0.004 0.000 0.004 0.992
#> GSM627176 3 0.1022 0.7185 0.032 0.000 0.968 0.000
#> GSM627194 2 0.0592 0.8875 0.016 0.984 0.000 0.000
#> GSM627154 2 0.4599 0.6656 0.028 0.760 0.000 0.212
#> GSM627187 3 0.1389 0.7203 0.048 0.000 0.952 0.000
#> GSM627198 2 0.1637 0.8732 0.000 0.940 0.000 0.060
#> GSM627160 4 0.0779 0.8738 0.004 0.000 0.016 0.980
#> GSM627185 1 0.0707 0.8529 0.980 0.000 0.020 0.000
#> GSM627206 3 0.4500 0.4750 0.316 0.000 0.684 0.000
#> GSM627161 1 0.3764 0.7721 0.784 0.000 0.216 0.000
#> GSM627162 3 0.1118 0.7180 0.036 0.000 0.964 0.000
#> GSM627210 1 0.1209 0.8331 0.964 0.032 0.004 0.000
#> GSM627189 2 0.0592 0.8875 0.016 0.984 0.000 0.000
show/hide code output
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM627128 4 0.1798 0.7435 0.000 0.004 0.004 0.928 0.064
#> GSM627110 5 0.4850 0.5578 0.076 0.000 0.224 0.000 0.700
#> GSM627132 1 0.1648 0.7791 0.940 0.000 0.020 0.000 0.040
#> GSM627107 5 0.5506 0.3858 0.000 0.032 0.292 0.040 0.636
#> GSM627103 2 0.0960 0.7642 0.004 0.972 0.016 0.000 0.008
#> GSM627114 3 0.5509 -0.1004 0.064 0.000 0.472 0.000 0.464
#> GSM627134 5 0.6192 0.3393 0.000 0.240 0.020 0.136 0.604
#> GSM627137 2 0.3039 0.5716 0.000 0.808 0.192 0.000 0.000
#> GSM627148 5 0.3242 0.5880 0.012 0.000 0.172 0.000 0.816
#> GSM627101 4 0.2142 0.7403 0.000 0.028 0.004 0.920 0.048
#> GSM627130 4 0.1628 0.7531 0.000 0.000 0.056 0.936 0.008
#> GSM627071 1 0.5566 0.6035 0.644 0.060 0.024 0.000 0.272
#> GSM627118 5 0.7458 0.0884 0.000 0.352 0.060 0.168 0.420
#> GSM627094 2 0.0693 0.7602 0.012 0.980 0.008 0.000 0.000
#> GSM627122 5 0.4967 0.5035 0.220 0.000 0.060 0.012 0.708
#> GSM627115 2 0.1857 0.7596 0.004 0.928 0.060 0.000 0.008
#> GSM627125 4 0.3662 0.5635 0.000 0.000 0.004 0.744 0.252
#> GSM627174 2 0.2331 0.7489 0.068 0.908 0.016 0.008 0.000
#> GSM627102 2 0.4451 -0.1827 0.000 0.504 0.492 0.004 0.000
#> GSM627073 5 0.1644 0.6499 0.000 0.004 0.048 0.008 0.940
#> GSM627108 2 0.1478 0.7326 0.000 0.936 0.064 0.000 0.000
#> GSM627126 1 0.1493 0.7508 0.948 0.000 0.024 0.028 0.000
#> GSM627078 2 0.3427 0.7256 0.028 0.836 0.008 0.128 0.000
#> GSM627090 5 0.5140 0.4062 0.040 0.000 0.328 0.008 0.624
#> GSM627099 2 0.4928 0.6728 0.000 0.768 0.072 0.096 0.064
#> GSM627105 4 0.4607 0.4330 0.000 0.004 0.020 0.656 0.320
#> GSM627117 5 0.5944 -0.0397 0.052 0.024 0.456 0.000 0.468
#> GSM627121 5 0.5149 0.3193 0.000 0.020 0.356 0.020 0.604
#> GSM627127 2 0.6955 0.4315 0.000 0.556 0.164 0.224 0.056
#> GSM627087 2 0.1830 0.7567 0.004 0.932 0.052 0.000 0.012
#> GSM627089 5 0.3532 0.6148 0.092 0.000 0.076 0.000 0.832
#> GSM627092 3 0.4961 0.3038 0.000 0.448 0.524 0.000 0.028
#> GSM627076 5 0.4025 0.6068 0.016 0.000 0.156 0.032 0.796
#> GSM627136 5 0.1195 0.6536 0.012 0.000 0.028 0.000 0.960
#> GSM627081 5 0.2237 0.6369 0.000 0.008 0.084 0.004 0.904
#> GSM627091 2 0.3678 0.7296 0.004 0.848 0.064 0.064 0.020
#> GSM627097 2 0.8399 0.2462 0.096 0.452 0.176 0.240 0.036
#> GSM627072 5 0.0162 0.6530 0.000 0.000 0.004 0.000 0.996
#> GSM627080 1 0.1549 0.7781 0.944 0.000 0.016 0.000 0.040
#> GSM627088 1 0.4295 0.6990 0.740 0.000 0.044 0.000 0.216
#> GSM627109 1 0.2069 0.7753 0.912 0.000 0.012 0.000 0.076
#> GSM627111 1 0.1741 0.7773 0.936 0.000 0.040 0.000 0.024
#> GSM627113 1 0.2824 0.7711 0.864 0.000 0.020 0.000 0.116
#> GSM627133 2 0.6396 0.0746 0.004 0.460 0.148 0.000 0.388
#> GSM627177 1 0.7363 0.3153 0.484 0.176 0.024 0.020 0.296
#> GSM627086 2 0.0566 0.7612 0.004 0.984 0.012 0.000 0.000
#> GSM627095 1 0.2928 0.7272 0.872 0.000 0.064 0.064 0.000
#> GSM627079 5 0.3766 0.6059 0.036 0.004 0.112 0.016 0.832
#> GSM627082 4 0.2464 0.7400 0.016 0.000 0.096 0.888 0.000
#> GSM627074 1 0.4833 0.7051 0.736 0.004 0.136 0.000 0.124
#> GSM627077 5 0.5115 -0.2356 0.480 0.000 0.036 0.000 0.484
#> GSM627093 1 0.3260 0.7684 0.856 0.004 0.056 0.000 0.084
#> GSM627120 3 0.6767 0.4874 0.000 0.368 0.428 0.008 0.196
#> GSM627124 2 0.3456 0.7311 0.036 0.844 0.012 0.108 0.000
#> GSM627075 2 0.4135 0.2374 0.000 0.656 0.340 0.000 0.004
#> GSM627085 2 0.4322 0.6933 0.016 0.780 0.048 0.156 0.000
#> GSM627119 1 0.2784 0.7718 0.872 0.004 0.016 0.000 0.108
#> GSM627116 1 0.9526 -0.0117 0.320 0.256 0.156 0.100 0.168
#> GSM627084 1 0.4622 0.7201 0.712 0.000 0.240 0.004 0.044
#> GSM627096 5 0.7439 0.1302 0.000 0.276 0.056 0.204 0.464
#> GSM627100 5 0.4305 0.5582 0.000 0.000 0.200 0.052 0.748
#> GSM627112 4 0.1235 0.7456 0.004 0.016 0.012 0.964 0.004
#> GSM627083 4 0.5717 0.3311 0.324 0.000 0.104 0.572 0.000
#> GSM627098 1 0.2707 0.7753 0.876 0.000 0.024 0.000 0.100
#> GSM627104 1 0.1074 0.7658 0.968 0.016 0.004 0.000 0.012
#> GSM627131 1 0.4297 0.6431 0.692 0.000 0.020 0.000 0.288
#> GSM627106 5 0.2116 0.6404 0.000 0.004 0.076 0.008 0.912
#> GSM627123 1 0.4746 0.7422 0.756 0.000 0.164 0.032 0.048
#> GSM627129 4 0.8376 -0.0179 0.000 0.236 0.148 0.324 0.292
#> GSM627216 2 0.1310 0.7580 0.000 0.956 0.020 0.000 0.024
#> GSM627212 2 0.3143 0.7404 0.000 0.872 0.044 0.068 0.016
#> GSM627190 3 0.5526 -0.0372 0.040 0.012 0.484 0.000 0.464
#> GSM627169 3 0.4906 0.2501 0.000 0.480 0.496 0.000 0.024
#> GSM627167 3 0.7697 0.2762 0.000 0.140 0.444 0.308 0.108
#> GSM627192 1 0.3812 0.7013 0.812 0.000 0.096 0.092 0.000
#> GSM627203 5 0.1124 0.6487 0.004 0.000 0.036 0.000 0.960
#> GSM627151 2 0.8363 0.3518 0.072 0.500 0.164 0.080 0.184
#> GSM627163 1 0.0324 0.7653 0.992 0.000 0.004 0.000 0.004
#> GSM627211 2 0.1043 0.7491 0.000 0.960 0.040 0.000 0.000
#> GSM627171 3 0.5358 0.5919 0.000 0.248 0.648 0.000 0.104
#> GSM627209 2 0.2124 0.7485 0.000 0.900 0.004 0.096 0.000
#> GSM627135 1 0.1306 0.7649 0.960 0.000 0.008 0.016 0.016
#> GSM627170 2 0.4660 0.4761 0.000 0.728 0.080 0.000 0.192
#> GSM627178 1 0.2597 0.7729 0.884 0.000 0.024 0.000 0.092
#> GSM627199 2 0.4494 0.6432 0.028 0.728 0.012 0.232 0.000
#> GSM627213 4 0.3190 0.6513 0.000 0.140 0.008 0.840 0.012
#> GSM627140 4 0.3798 0.7017 0.024 0.012 0.160 0.804 0.000
#> GSM627149 1 0.6061 0.5562 0.540 0.000 0.372 0.044 0.044
#> GSM627147 3 0.7532 0.2238 0.000 0.236 0.384 0.336 0.044
#> GSM627195 5 0.2172 0.6381 0.016 0.000 0.076 0.000 0.908
#> GSM627204 2 0.0566 0.7589 0.004 0.984 0.012 0.000 0.000
#> GSM627207 2 0.4288 0.2354 0.000 0.664 0.324 0.000 0.012
#> GSM627157 1 0.2795 0.7748 0.872 0.000 0.028 0.000 0.100
#> GSM627201 2 0.0290 0.7593 0.000 0.992 0.008 0.000 0.000
#> GSM627146 2 0.1186 0.7650 0.008 0.964 0.020 0.008 0.000
#> GSM627156 2 0.5509 -0.3497 0.000 0.472 0.464 0.000 0.064
#> GSM627188 1 0.6022 0.4013 0.564 0.000 0.156 0.280 0.000
#> GSM627197 2 0.1444 0.7638 0.000 0.948 0.012 0.040 0.000
#> GSM627173 2 0.1582 0.7500 0.028 0.944 0.028 0.000 0.000
#> GSM627179 2 0.0703 0.7551 0.000 0.976 0.024 0.000 0.000
#> GSM627208 5 0.6100 0.0882 0.000 0.184 0.252 0.000 0.564
#> GSM627215 5 0.5239 0.3805 0.000 0.284 0.052 0.012 0.652
#> GSM627153 2 0.2439 0.7402 0.000 0.876 0.004 0.120 0.000
#> GSM627155 1 0.4542 0.6917 0.724 0.000 0.232 0.036 0.008
#> GSM627165 2 0.6551 -0.3448 0.000 0.440 0.428 0.024 0.108
#> GSM627168 1 0.5423 0.6193 0.644 0.000 0.112 0.000 0.244
#> GSM627183 5 0.4510 -0.0505 0.432 0.000 0.008 0.000 0.560
#> GSM627144 5 0.2929 0.6001 0.000 0.000 0.180 0.000 0.820
#> GSM627158 1 0.5013 0.7032 0.680 0.000 0.240 0.000 0.080
#> GSM627196 2 0.0566 0.7589 0.004 0.984 0.012 0.000 0.000
#> GSM627142 5 0.3922 0.5990 0.000 0.000 0.040 0.180 0.780
#> GSM627182 5 0.4472 0.5458 0.024 0.032 0.184 0.000 0.760
#> GSM627202 1 0.6478 0.1622 0.420 0.000 0.184 0.000 0.396
#> GSM627141 3 0.5752 0.3139 0.164 0.004 0.636 0.000 0.196
#> GSM627143 3 0.5973 0.5711 0.000 0.216 0.616 0.008 0.160
#> GSM627145 5 0.0579 0.6544 0.008 0.000 0.008 0.000 0.984
#> GSM627152 5 0.3077 0.6405 0.028 0.000 0.100 0.008 0.864
#> GSM627200 1 0.5873 0.4975 0.564 0.000 0.124 0.000 0.312
#> GSM627159 4 0.2305 0.7421 0.012 0.000 0.092 0.896 0.000
#> GSM627164 3 0.5702 0.5391 0.000 0.320 0.576 0.000 0.104
#> GSM627138 1 0.6109 0.5547 0.556 0.000 0.272 0.000 0.172
#> GSM627175 2 0.2230 0.7438 0.000 0.884 0.000 0.116 0.000
#> GSM627150 5 0.1901 0.6493 0.012 0.000 0.056 0.004 0.928
#> GSM627166 1 0.3272 0.7169 0.856 0.032 0.100 0.000 0.012
#> GSM627186 3 0.5236 0.3062 0.000 0.464 0.492 0.000 0.044
#> GSM627139 5 0.5064 0.4545 0.000 0.000 0.088 0.232 0.680
#> GSM627181 2 0.1484 0.7435 0.000 0.944 0.048 0.008 0.000
#> GSM627205 5 0.6552 -0.1802 0.000 0.348 0.208 0.000 0.444
#> GSM627214 2 0.5963 0.2935 0.000 0.656 0.188 0.032 0.124
#> GSM627180 5 0.2075 0.6361 0.000 0.040 0.032 0.004 0.924
#> GSM627172 3 0.4946 0.4326 0.000 0.348 0.620 0.016 0.016
#> GSM627184 1 0.5500 0.5550 0.648 0.000 0.140 0.212 0.000
#> GSM627193 2 0.1251 0.7555 0.008 0.956 0.036 0.000 0.000
#> GSM627191 4 0.3390 0.7204 0.060 0.000 0.100 0.840 0.000
#> GSM627176 3 0.4786 0.2127 0.012 0.000 0.620 0.012 0.356
#> GSM627194 2 0.1893 0.7603 0.024 0.928 0.048 0.000 0.000
#> GSM627154 2 0.5646 0.5165 0.036 0.628 0.044 0.292 0.000
#> GSM627187 3 0.4540 0.3723 0.024 0.008 0.700 0.000 0.268
#> GSM627198 2 0.3368 0.7384 0.016 0.844 0.020 0.120 0.000
#> GSM627160 4 0.3790 0.7284 0.012 0.000 0.136 0.816 0.036
#> GSM627185 1 0.1281 0.7768 0.956 0.000 0.012 0.000 0.032
#> GSM627206 5 0.6788 0.0431 0.320 0.000 0.296 0.000 0.384
#> GSM627161 1 0.5219 0.6709 0.644 0.000 0.288 0.004 0.064
#> GSM627162 3 0.5156 0.3406 0.044 0.008 0.660 0.004 0.284
#> GSM627210 1 0.4781 0.6887 0.724 0.012 0.052 0.000 0.212
#> GSM627189 2 0.1725 0.7615 0.020 0.936 0.044 0.000 0.000
show/hide code output
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM627128 6 0.2696 0.6899 0.004 0.012 0.000 0.044 0.056 0.884
#> GSM627110 4 0.4183 0.5536 0.020 0.000 0.148 0.764 0.068 0.000
#> GSM627132 1 0.1700 0.7770 0.916 0.000 0.000 0.004 0.080 0.000
#> GSM627107 5 0.3096 0.6910 0.008 0.040 0.040 0.012 0.876 0.024
#> GSM627103 2 0.0837 0.8142 0.000 0.972 0.004 0.020 0.000 0.004
#> GSM627114 5 0.3857 0.6445 0.084 0.000 0.112 0.012 0.792 0.000
#> GSM627134 5 0.6474 0.1265 0.000 0.384 0.008 0.052 0.448 0.108
#> GSM627137 2 0.5502 -0.2694 0.000 0.484 0.408 0.100 0.000 0.008
#> GSM627148 5 0.1321 0.7035 0.024 0.000 0.020 0.004 0.952 0.000
#> GSM627101 6 0.5086 0.4630 0.000 0.084 0.004 0.016 0.236 0.660
#> GSM627130 6 0.2002 0.7197 0.008 0.000 0.056 0.000 0.020 0.916
#> GSM627071 5 0.4916 -0.0119 0.436 0.044 0.000 0.008 0.512 0.000
#> GSM627118 5 0.7047 0.0891 0.000 0.364 0.004 0.100 0.392 0.140
#> GSM627094 2 0.1629 0.8052 0.004 0.940 0.024 0.028 0.000 0.004
#> GSM627122 5 0.1866 0.6898 0.084 0.000 0.000 0.008 0.908 0.000
#> GSM627115 2 0.2662 0.7825 0.000 0.856 0.024 0.120 0.000 0.000
#> GSM627125 6 0.3687 0.6966 0.004 0.000 0.044 0.064 0.060 0.828
#> GSM627174 2 0.2662 0.7719 0.108 0.868 0.012 0.004 0.000 0.008
#> GSM627102 3 0.5047 0.3968 0.000 0.428 0.516 0.032 0.000 0.024
#> GSM627073 5 0.1148 0.7068 0.000 0.016 0.020 0.004 0.960 0.000
#> GSM627108 2 0.1812 0.7770 0.000 0.912 0.080 0.008 0.000 0.000
#> GSM627126 1 0.1850 0.7486 0.924 0.000 0.008 0.052 0.000 0.016
#> GSM627078 2 0.2824 0.7930 0.020 0.872 0.004 0.020 0.000 0.084
#> GSM627090 5 0.5484 0.3629 0.024 0.000 0.372 0.072 0.532 0.000
#> GSM627099 2 0.5314 0.6045 0.000 0.652 0.004 0.232 0.032 0.080
#> GSM627105 6 0.3682 0.6732 0.004 0.000 0.020 0.088 0.068 0.820
#> GSM627117 3 0.5561 0.2435 0.012 0.004 0.500 0.400 0.084 0.000
#> GSM627121 5 0.3202 0.6852 0.008 0.056 0.060 0.004 0.860 0.012
#> GSM627127 4 0.5713 0.3857 0.000 0.204 0.004 0.608 0.020 0.164
#> GSM627087 2 0.2538 0.7816 0.000 0.860 0.016 0.124 0.000 0.000
#> GSM627089 5 0.1462 0.6951 0.056 0.000 0.000 0.008 0.936 0.000
#> GSM627092 3 0.4030 0.4883 0.000 0.024 0.728 0.236 0.004 0.008
#> GSM627076 5 0.5142 0.6050 0.024 0.000 0.100 0.092 0.732 0.052
#> GSM627136 5 0.2866 0.6835 0.024 0.000 0.020 0.092 0.864 0.000
#> GSM627081 5 0.0767 0.7056 0.000 0.000 0.012 0.008 0.976 0.004
#> GSM627091 2 0.4305 0.6574 0.000 0.704 0.004 0.236 0.000 0.056
#> GSM627097 4 0.2910 0.6323 0.028 0.048 0.004 0.876 0.000 0.044
#> GSM627072 5 0.1313 0.7044 0.016 0.000 0.004 0.028 0.952 0.000
#> GSM627080 1 0.1500 0.7763 0.936 0.000 0.000 0.012 0.052 0.000
#> GSM627088 1 0.3903 0.6200 0.680 0.000 0.012 0.004 0.304 0.000
#> GSM627109 1 0.2263 0.7658 0.896 0.000 0.000 0.056 0.048 0.000
#> GSM627111 1 0.1942 0.7784 0.916 0.000 0.008 0.012 0.064 0.000
#> GSM627113 1 0.2669 0.7574 0.836 0.000 0.000 0.008 0.156 0.000
#> GSM627133 4 0.4681 0.5307 0.000 0.188 0.016 0.708 0.088 0.000
#> GSM627177 1 0.7631 0.1671 0.416 0.232 0.004 0.064 0.248 0.036
#> GSM627086 2 0.0508 0.8087 0.000 0.984 0.012 0.004 0.000 0.000
#> GSM627095 1 0.3142 0.7255 0.848 0.000 0.044 0.016 0.000 0.092
#> GSM627079 5 0.4117 0.5101 0.020 0.000 0.004 0.264 0.704 0.008
#> GSM627082 6 0.2126 0.7107 0.020 0.000 0.072 0.004 0.000 0.904
#> GSM627074 4 0.3698 0.5517 0.240 0.004 0.004 0.740 0.012 0.000
#> GSM627077 5 0.5885 0.1923 0.332 0.000 0.024 0.124 0.520 0.000
#> GSM627093 4 0.4679 0.2129 0.396 0.008 0.024 0.568 0.004 0.000
#> GSM627120 5 0.6897 -0.2475 0.008 0.308 0.296 0.012 0.364 0.012
#> GSM627124 2 0.2932 0.7930 0.028 0.868 0.004 0.020 0.000 0.080
#> GSM627075 3 0.5290 0.4448 0.000 0.392 0.504 0.104 0.000 0.000
#> GSM627085 2 0.3839 0.7440 0.012 0.792 0.008 0.040 0.000 0.148
#> GSM627119 1 0.3159 0.7588 0.832 0.000 0.000 0.068 0.100 0.000
#> GSM627116 4 0.5897 0.5503 0.148 0.076 0.004 0.668 0.020 0.084
#> GSM627084 1 0.4353 0.6973 0.720 0.000 0.228 0.012 0.028 0.012
#> GSM627096 5 0.7276 0.0923 0.000 0.320 0.004 0.096 0.376 0.204
#> GSM627100 5 0.2722 0.6958 0.012 0.000 0.024 0.020 0.888 0.056
#> GSM627112 6 0.1551 0.7027 0.008 0.020 0.004 0.016 0.004 0.948
#> GSM627083 6 0.4357 0.5347 0.224 0.000 0.076 0.000 0.000 0.700
#> GSM627098 1 0.2624 0.7619 0.844 0.000 0.004 0.004 0.148 0.000
#> GSM627104 1 0.1592 0.7576 0.944 0.016 0.000 0.024 0.012 0.004
#> GSM627131 1 0.4634 0.6510 0.692 0.000 0.000 0.164 0.144 0.000
#> GSM627106 5 0.0912 0.7057 0.000 0.004 0.012 0.008 0.972 0.004
#> GSM627123 1 0.6628 0.3274 0.484 0.000 0.260 0.208 0.004 0.044
#> GSM627129 6 0.8426 0.1233 0.004 0.104 0.204 0.224 0.096 0.368
#> GSM627216 2 0.2094 0.8116 0.000 0.920 0.016 0.032 0.028 0.004
#> GSM627212 2 0.2714 0.7905 0.000 0.872 0.004 0.060 0.000 0.064
#> GSM627190 5 0.5265 0.0966 0.028 0.000 0.408 0.044 0.520 0.000
#> GSM627169 3 0.3946 0.5826 0.000 0.088 0.772 0.136 0.004 0.000
#> GSM627167 6 0.6624 0.0609 0.000 0.100 0.376 0.012 0.064 0.448
#> GSM627192 1 0.3075 0.7312 0.848 0.000 0.032 0.008 0.004 0.108
#> GSM627203 5 0.2655 0.6554 0.008 0.000 0.000 0.140 0.848 0.004
#> GSM627151 4 0.2607 0.6456 0.036 0.052 0.000 0.892 0.012 0.008
#> GSM627163 1 0.1003 0.7590 0.964 0.000 0.004 0.028 0.000 0.004
#> GSM627211 2 0.1444 0.7857 0.000 0.928 0.072 0.000 0.000 0.000
#> GSM627171 3 0.4883 0.6197 0.012 0.240 0.676 0.008 0.064 0.000
#> GSM627209 2 0.2114 0.8012 0.000 0.904 0.012 0.008 0.000 0.076
#> GSM627135 1 0.2326 0.7432 0.888 0.000 0.008 0.092 0.000 0.012
#> GSM627170 2 0.4403 0.6144 0.000 0.744 0.044 0.040 0.172 0.000
#> GSM627178 1 0.2630 0.7600 0.872 0.000 0.000 0.064 0.064 0.000
#> GSM627199 2 0.4688 0.6777 0.064 0.712 0.004 0.020 0.000 0.200
#> GSM627213 6 0.3130 0.6476 0.000 0.080 0.004 0.044 0.016 0.856
#> GSM627140 6 0.3954 0.5380 0.016 0.004 0.296 0.000 0.000 0.684
#> GSM627149 1 0.6418 0.3786 0.448 0.000 0.404 0.028 0.032 0.088
#> GSM627147 3 0.6360 0.2959 0.000 0.072 0.548 0.076 0.016 0.288
#> GSM627195 5 0.3362 0.6393 0.016 0.008 0.008 0.156 0.812 0.000
#> GSM627204 2 0.0622 0.8090 0.000 0.980 0.012 0.008 0.000 0.000
#> GSM627207 2 0.3673 0.5010 0.000 0.736 0.244 0.004 0.016 0.000
#> GSM627157 1 0.2553 0.7609 0.848 0.000 0.000 0.008 0.144 0.000
#> GSM627201 2 0.0603 0.8080 0.000 0.980 0.016 0.004 0.000 0.000
#> GSM627146 2 0.1218 0.8130 0.000 0.956 0.004 0.028 0.000 0.012
#> GSM627156 3 0.4927 0.3201 0.000 0.468 0.484 0.016 0.032 0.000
#> GSM627188 1 0.5171 0.5155 0.616 0.000 0.104 0.008 0.000 0.272
#> GSM627197 2 0.2074 0.8090 0.000 0.912 0.004 0.036 0.000 0.048
#> GSM627173 2 0.2402 0.7969 0.040 0.904 0.020 0.032 0.000 0.004
#> GSM627179 2 0.2164 0.7891 0.000 0.908 0.060 0.020 0.012 0.000
#> GSM627208 5 0.4247 0.5791 0.008 0.184 0.060 0.004 0.744 0.000
#> GSM627215 5 0.5680 0.2324 0.000 0.384 0.008 0.056 0.520 0.032
#> GSM627153 2 0.2367 0.7961 0.000 0.888 0.016 0.008 0.000 0.088
#> GSM627155 1 0.4946 0.7179 0.736 0.000 0.140 0.020 0.056 0.048
#> GSM627165 3 0.6743 0.4216 0.000 0.140 0.464 0.336 0.028 0.032
#> GSM627168 1 0.4754 0.4326 0.568 0.000 0.032 0.012 0.388 0.000
#> GSM627183 5 0.3956 0.4139 0.292 0.000 0.000 0.024 0.684 0.000
#> GSM627144 4 0.3565 0.5956 0.004 0.000 0.096 0.808 0.092 0.000
#> GSM627158 1 0.3794 0.7526 0.792 0.000 0.080 0.008 0.120 0.000
#> GSM627196 2 0.0508 0.8087 0.000 0.984 0.012 0.004 0.000 0.000
#> GSM627142 5 0.2182 0.6940 0.016 0.004 0.000 0.004 0.904 0.072
#> GSM627182 5 0.3051 0.6926 0.024 0.064 0.036 0.008 0.868 0.000
#> GSM627202 5 0.4144 0.5393 0.224 0.000 0.032 0.016 0.728 0.000
#> GSM627141 3 0.3526 0.5064 0.088 0.000 0.820 0.012 0.080 0.000
#> GSM627143 3 0.5065 0.6228 0.008 0.196 0.688 0.000 0.088 0.020
#> GSM627145 5 0.1672 0.7009 0.016 0.000 0.004 0.048 0.932 0.000
#> GSM627152 4 0.4912 0.2899 0.020 0.000 0.024 0.564 0.388 0.004
#> GSM627200 4 0.4011 0.6243 0.144 0.000 0.028 0.780 0.048 0.000
#> GSM627159 6 0.1845 0.7144 0.004 0.000 0.072 0.008 0.000 0.916
#> GSM627164 3 0.5114 0.5695 0.004 0.328 0.596 0.004 0.064 0.004
#> GSM627138 1 0.5409 0.5650 0.584 0.000 0.124 0.008 0.284 0.000
#> GSM627175 2 0.2053 0.7952 0.004 0.888 0.000 0.000 0.000 0.108
#> GSM627150 5 0.0922 0.7054 0.024 0.000 0.004 0.004 0.968 0.000
#> GSM627166 1 0.3788 0.5397 0.704 0.012 0.004 0.280 0.000 0.000
#> GSM627186 3 0.4372 0.6252 0.004 0.292 0.668 0.032 0.004 0.000
#> GSM627139 4 0.6171 0.4613 0.004 0.000 0.072 0.600 0.144 0.180
#> GSM627181 2 0.1080 0.8057 0.000 0.960 0.032 0.004 0.000 0.004
#> GSM627205 2 0.6658 0.0247 0.000 0.436 0.128 0.056 0.372 0.008
#> GSM627214 2 0.4265 0.6755 0.000 0.776 0.040 0.008 0.136 0.040
#> GSM627180 5 0.3020 0.6672 0.000 0.060 0.008 0.060 0.864 0.008
#> GSM627172 2 0.5494 -0.2765 0.008 0.476 0.452 0.008 0.016 0.040
#> GSM627184 1 0.5368 0.6235 0.668 0.000 0.100 0.012 0.024 0.196
#> GSM627193 2 0.1649 0.8037 0.000 0.932 0.032 0.036 0.000 0.000
#> GSM627191 6 0.3006 0.6855 0.064 0.000 0.092 0.000 0.000 0.844
#> GSM627176 3 0.3495 0.5028 0.004 0.000 0.808 0.128 0.060 0.000
#> GSM627194 2 0.3230 0.7775 0.016 0.844 0.056 0.084 0.000 0.000
#> GSM627154 2 0.4962 0.5974 0.028 0.656 0.008 0.036 0.000 0.272
#> GSM627187 3 0.2955 0.5233 0.004 0.008 0.816 0.000 0.172 0.000
#> GSM627198 2 0.4056 0.7645 0.028 0.788 0.004 0.052 0.000 0.128
#> GSM627160 4 0.6104 0.0981 0.008 0.004 0.112 0.472 0.016 0.388
#> GSM627185 1 0.1625 0.7771 0.928 0.000 0.000 0.012 0.060 0.000
#> GSM627206 5 0.4644 0.5520 0.196 0.000 0.080 0.016 0.708 0.000
#> GSM627161 1 0.4685 0.7278 0.728 0.000 0.152 0.012 0.100 0.008
#> GSM627162 3 0.3704 0.5326 0.008 0.008 0.796 0.024 0.160 0.004
#> GSM627210 1 0.4201 0.7110 0.756 0.008 0.000 0.104 0.132 0.000
#> GSM627189 2 0.2756 0.7950 0.016 0.872 0.028 0.084 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two
samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures
which are significantly different between classes which can be candidate marks
for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known
annotation is numeric, one-way ANOVA test is applied, and if the known
annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) age(p) other(p) k
#> MAD:NMF 143 0.5714 0.167 0.00998 2
#> MAD:NMF 138 0.1577 0.369 0.02688 3
#> MAD:NMF 127 0.6634 0.293 0.04252 4
#> MAD:NMF 100 0.2896 0.311 0.03138 5
#> MAD:NMF 114 0.0202 0.282 0.02213 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
ATC:hclust
The object with results only for a single top-value method and a single partition method
can be extracted as:
res = res_list["ATC", "hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51882 rows and 146 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
- The first row: a plot of the ECDF (empirical cumulative distribution
function) curves of the consensus matrix for each
k and the heatmap of
predicted classes for each k.
- The second row: heatmaps of the consensus matrix for each
k.
- The third row: heatmaps of the membership matrix for each
k.
- The fouth row: heatmaps of the signatures for each
k.
All the plots in panels can be made by individual functions and they are
plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
- ECDF curves of the consensus matrix for each
k;
- 1-PAC. The PAC
score
measures the proportion of the ambiguous subgrouping.
- Mean silhouette score.
- Concordance. The mean probability of fiting the consensus class ids in all
partitions.
- Area increased. Denote \(A_k\) as the area under the ECDF curve for current
k, the area increased is defined as \(A_k - A_{k-1}\).
- Rand index. The percent of pairs of samples that are both in a same cluster
or both are not in a same cluster in the partition of k and k-1.
- Jaccard index. The ratio of pairs of samples are both in a same cluster in
the partition of k and k-1 and the pairs of samples are both in a same
cluster in the partition k or k-1.
The detailed explanations of these statistics can be found in the cola
vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.525 0.830 0.915 0.4255 0.551 0.551
#> 3 3 0.532 0.606 0.822 0.3342 0.787 0.645
#> 4 4 0.614 0.693 0.806 0.2133 0.808 0.586
#> 5 5 0.635 0.611 0.801 0.0681 0.929 0.774
#> 6 6 0.650 0.593 0.769 0.0658 0.915 0.695
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
- All \(k\) with Jaccard index larger than 0.95 are removed because increasing
\(k\) does not provide enough extra information. If all \(k\) are removed, it is
marked as no subgroup is detected.
- For all \(k\) with 1-PAC score larger than 0.9, the maximal \(k\) is taken as
the best \(k\), and other \(k\) are marked as optional \(k\).
- If it does not fit the second rule. The \(k\) with the maximal vote of the
highest 1-PAC score, highest mean silhouette, and highest concordance is
taken as the best \(k\).
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
show/hide code output
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM627128 2 0.2778 0.900 0.048 0.952
#> GSM627110 1 0.9129 0.604 0.672 0.328
#> GSM627132 1 0.0000 0.848 1.000 0.000
#> GSM627107 2 0.0000 0.924 0.000 1.000
#> GSM627103 2 0.0000 0.924 0.000 1.000
#> GSM627114 1 0.9323 0.573 0.652 0.348
#> GSM627134 2 0.0000 0.924 0.000 1.000
#> GSM627137 2 0.0000 0.924 0.000 1.000
#> GSM627148 2 0.2423 0.905 0.040 0.960
#> GSM627101 2 0.0000 0.924 0.000 1.000
#> GSM627130 2 0.2778 0.900 0.048 0.952
#> GSM627071 2 0.7139 0.768 0.196 0.804
#> GSM627118 2 0.0000 0.924 0.000 1.000
#> GSM627094 2 0.0000 0.924 0.000 1.000
#> GSM627122 1 0.8909 0.642 0.692 0.308
#> GSM627115 2 0.0000 0.924 0.000 1.000
#> GSM627125 2 0.0000 0.924 0.000 1.000
#> GSM627174 1 0.9833 0.398 0.576 0.424
#> GSM627102 2 0.6048 0.826 0.148 0.852
#> GSM627073 2 0.0000 0.924 0.000 1.000
#> GSM627108 2 0.0000 0.924 0.000 1.000
#> GSM627126 1 0.0000 0.848 1.000 0.000
#> GSM627078 2 0.0000 0.924 0.000 1.000
#> GSM627090 2 0.9248 0.481 0.340 0.660
#> GSM627099 2 0.0000 0.924 0.000 1.000
#> GSM627105 2 0.0000 0.924 0.000 1.000
#> GSM627117 2 0.6623 0.801 0.172 0.828
#> GSM627121 2 0.0000 0.924 0.000 1.000
#> GSM627127 2 0.0000 0.924 0.000 1.000
#> GSM627087 2 0.0000 0.924 0.000 1.000
#> GSM627089 2 0.7674 0.726 0.224 0.776
#> GSM627092 2 0.6048 0.826 0.148 0.852
#> GSM627076 2 0.9248 0.481 0.340 0.660
#> GSM627136 1 0.9608 0.499 0.616 0.384
#> GSM627081 2 0.0000 0.924 0.000 1.000
#> GSM627091 2 0.0000 0.924 0.000 1.000
#> GSM627097 2 0.0376 0.923 0.004 0.996
#> GSM627072 2 0.5737 0.834 0.136 0.864
#> GSM627080 1 0.0000 0.848 1.000 0.000
#> GSM627088 1 0.9580 0.507 0.620 0.380
#> GSM627109 1 0.0000 0.848 1.000 0.000
#> GSM627111 1 0.0000 0.848 1.000 0.000
#> GSM627113 1 0.1414 0.850 0.980 0.020
#> GSM627133 2 0.0000 0.924 0.000 1.000
#> GSM627177 2 0.7139 0.768 0.196 0.804
#> GSM627086 2 0.0000 0.924 0.000 1.000
#> GSM627095 1 0.0000 0.848 1.000 0.000
#> GSM627079 2 0.8267 0.660 0.260 0.740
#> GSM627082 1 0.8327 0.698 0.736 0.264
#> GSM627074 1 0.1414 0.850 0.980 0.020
#> GSM627077 1 0.3431 0.840 0.936 0.064
#> GSM627093 1 0.1414 0.850 0.980 0.020
#> GSM627120 2 0.0000 0.924 0.000 1.000
#> GSM627124 2 0.0000 0.924 0.000 1.000
#> GSM627075 2 0.0000 0.924 0.000 1.000
#> GSM627085 2 0.0000 0.924 0.000 1.000
#> GSM627119 1 0.1414 0.850 0.980 0.020
#> GSM627116 2 0.5737 0.837 0.136 0.864
#> GSM627084 1 0.6801 0.777 0.820 0.180
#> GSM627096 2 0.0000 0.924 0.000 1.000
#> GSM627100 2 0.7674 0.726 0.224 0.776
#> GSM627112 2 0.6712 0.798 0.176 0.824
#> GSM627083 1 0.7139 0.765 0.804 0.196
#> GSM627098 1 0.4298 0.830 0.912 0.088
#> GSM627104 1 0.0000 0.848 1.000 0.000
#> GSM627131 1 0.3431 0.840 0.936 0.064
#> GSM627106 2 0.0000 0.924 0.000 1.000
#> GSM627123 1 0.1184 0.850 0.984 0.016
#> GSM627129 2 0.0000 0.924 0.000 1.000
#> GSM627216 2 0.0000 0.924 0.000 1.000
#> GSM627212 2 0.0000 0.924 0.000 1.000
#> GSM627190 2 0.6623 0.802 0.172 0.828
#> GSM627169 2 0.6048 0.826 0.148 0.852
#> GSM627167 2 0.0000 0.924 0.000 1.000
#> GSM627192 1 0.0000 0.848 1.000 0.000
#> GSM627203 2 0.4815 0.864 0.104 0.896
#> GSM627151 2 0.3733 0.886 0.072 0.928
#> GSM627163 1 0.0000 0.848 1.000 0.000
#> GSM627211 2 0.0000 0.924 0.000 1.000
#> GSM627171 2 0.0376 0.923 0.004 0.996
#> GSM627209 2 0.0000 0.924 0.000 1.000
#> GSM627135 1 0.1184 0.850 0.984 0.016
#> GSM627170 2 0.0000 0.924 0.000 1.000
#> GSM627178 1 0.1414 0.850 0.980 0.020
#> GSM627199 2 0.6048 0.826 0.148 0.852
#> GSM627213 2 0.0000 0.924 0.000 1.000
#> GSM627140 2 0.7139 0.773 0.196 0.804
#> GSM627149 1 0.1184 0.850 0.984 0.016
#> GSM627147 2 0.6048 0.826 0.148 0.852
#> GSM627195 2 0.0000 0.924 0.000 1.000
#> GSM627204 2 0.0000 0.924 0.000 1.000
#> GSM627207 2 0.0000 0.924 0.000 1.000
#> GSM627157 1 0.1414 0.850 0.980 0.020
#> GSM627201 2 0.0000 0.924 0.000 1.000
#> GSM627146 2 0.0000 0.924 0.000 1.000
#> GSM627156 2 0.0000 0.924 0.000 1.000
#> GSM627188 1 0.0000 0.848 1.000 0.000
#> GSM627197 2 0.0000 0.924 0.000 1.000
#> GSM627173 2 0.6048 0.826 0.148 0.852
#> GSM627179 2 0.0000 0.924 0.000 1.000
#> GSM627208 2 0.0000 0.924 0.000 1.000
#> GSM627215 2 0.0000 0.924 0.000 1.000
#> GSM627153 2 0.0000 0.924 0.000 1.000
#> GSM627155 1 0.0000 0.848 1.000 0.000
#> GSM627165 2 0.0000 0.924 0.000 1.000
#> GSM627168 1 0.9608 0.497 0.616 0.384
#> GSM627183 1 0.9286 0.582 0.656 0.344
#> GSM627144 2 0.0000 0.924 0.000 1.000
#> GSM627158 1 0.0000 0.848 1.000 0.000
#> GSM627196 2 0.0000 0.924 0.000 1.000
#> GSM627142 2 0.9248 0.481 0.340 0.660
#> GSM627182 2 0.0000 0.924 0.000 1.000
#> GSM627202 1 0.3431 0.840 0.936 0.064
#> GSM627141 1 0.9323 0.573 0.652 0.348
#> GSM627143 2 0.6623 0.801 0.172 0.828
#> GSM627145 2 0.6712 0.795 0.176 0.824
#> GSM627152 1 0.8713 0.659 0.708 0.292
#> GSM627200 1 0.6343 0.790 0.840 0.160
#> GSM627159 1 0.9970 0.248 0.532 0.468
#> GSM627164 2 0.6048 0.826 0.148 0.852
#> GSM627138 1 0.0000 0.848 1.000 0.000
#> GSM627175 2 0.0000 0.924 0.000 1.000
#> GSM627150 2 0.0000 0.924 0.000 1.000
#> GSM627166 1 0.1414 0.850 0.980 0.020
#> GSM627186 2 0.6247 0.818 0.156 0.844
#> GSM627139 2 0.2778 0.901 0.048 0.952
#> GSM627181 2 0.0000 0.924 0.000 1.000
#> GSM627205 2 0.0000 0.924 0.000 1.000
#> GSM627214 2 0.0000 0.924 0.000 1.000
#> GSM627180 2 0.0000 0.924 0.000 1.000
#> GSM627172 2 0.6048 0.826 0.148 0.852
#> GSM627184 1 0.0000 0.848 1.000 0.000
#> GSM627193 2 0.0000 0.924 0.000 1.000
#> GSM627191 1 0.7815 0.731 0.768 0.232
#> GSM627176 1 0.9129 0.604 0.672 0.328
#> GSM627194 2 0.0000 0.924 0.000 1.000
#> GSM627154 2 0.0000 0.924 0.000 1.000
#> GSM627187 2 0.6801 0.793 0.180 0.820
#> GSM627198 2 0.0000 0.924 0.000 1.000
#> GSM627160 1 0.9248 0.589 0.660 0.340
#> GSM627185 1 0.0000 0.848 1.000 0.000
#> GSM627206 2 0.9710 0.283 0.400 0.600
#> GSM627161 1 0.0000 0.848 1.000 0.000
#> GSM627162 2 0.7528 0.742 0.216 0.784
#> GSM627210 1 0.7883 0.715 0.764 0.236
#> GSM627189 2 0.0000 0.924 0.000 1.000
show/hide code output
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM627128 2 0.5216 0.68641 0.000 0.740 0.260
#> GSM627110 3 0.5608 0.52992 0.120 0.072 0.808
#> GSM627132 1 0.0000 0.93999 1.000 0.000 0.000
#> GSM627107 2 0.0000 0.85323 0.000 1.000 0.000
#> GSM627103 2 0.2356 0.84343 0.000 0.928 0.072
#> GSM627114 3 0.4165 0.54138 0.076 0.048 0.876
#> GSM627134 2 0.1163 0.85306 0.000 0.972 0.028
#> GSM627137 2 0.0000 0.85323 0.000 1.000 0.000
#> GSM627148 2 0.4346 0.76673 0.000 0.816 0.184
#> GSM627101 2 0.0000 0.85323 0.000 1.000 0.000
#> GSM627130 2 0.5254 0.68227 0.000 0.736 0.264
#> GSM627071 3 0.6295 -0.12339 0.000 0.472 0.528
#> GSM627118 2 0.0000 0.85323 0.000 1.000 0.000
#> GSM627094 2 0.0592 0.85535 0.000 0.988 0.012
#> GSM627122 3 0.3805 0.53580 0.092 0.024 0.884
#> GSM627115 2 0.0424 0.85473 0.000 0.992 0.008
#> GSM627125 2 0.3482 0.81496 0.000 0.872 0.128
#> GSM627174 3 0.1753 0.54891 0.000 0.048 0.952
#> GSM627102 2 0.6307 0.24096 0.000 0.512 0.488
#> GSM627073 2 0.2356 0.84403 0.000 0.928 0.072
#> GSM627108 2 0.0424 0.85473 0.000 0.992 0.008
#> GSM627126 1 0.0000 0.93999 1.000 0.000 0.000
#> GSM627078 2 0.3879 0.80080 0.000 0.848 0.152
#> GSM627090 3 0.5529 0.36406 0.000 0.296 0.704
#> GSM627099 2 0.0000 0.85323 0.000 1.000 0.000
#> GSM627105 2 0.3482 0.81496 0.000 0.872 0.128
#> GSM627117 3 0.6307 -0.18544 0.000 0.488 0.512
#> GSM627121 2 0.0000 0.85323 0.000 1.000 0.000
#> GSM627127 2 0.0237 0.85432 0.000 0.996 0.004
#> GSM627087 2 0.0424 0.85473 0.000 0.992 0.008
#> GSM627089 3 0.6215 0.02325 0.000 0.428 0.572
#> GSM627092 2 0.6307 0.24096 0.000 0.512 0.488
#> GSM627076 3 0.5529 0.36406 0.000 0.296 0.704
#> GSM627136 3 0.2313 0.54972 0.024 0.032 0.944
#> GSM627081 2 0.0000 0.85323 0.000 1.000 0.000
#> GSM627091 2 0.0424 0.85473 0.000 0.992 0.008
#> GSM627097 2 0.3752 0.80306 0.000 0.856 0.144
#> GSM627072 2 0.6154 0.42321 0.000 0.592 0.408
#> GSM627080 1 0.0000 0.93999 1.000 0.000 0.000
#> GSM627088 3 0.2689 0.55054 0.032 0.036 0.932
#> GSM627109 3 0.6215 0.15040 0.428 0.000 0.572
#> GSM627111 1 0.0000 0.93999 1.000 0.000 0.000
#> GSM627113 3 0.6062 0.23241 0.384 0.000 0.616
#> GSM627133 2 0.1163 0.85306 0.000 0.972 0.028
#> GSM627177 3 0.6295 -0.12339 0.000 0.472 0.528
#> GSM627086 2 0.0000 0.85323 0.000 1.000 0.000
#> GSM627095 3 0.6305 0.00878 0.484 0.000 0.516
#> GSM627079 3 0.6140 0.11217 0.000 0.404 0.596
#> GSM627082 3 0.3989 0.50753 0.124 0.012 0.864
#> GSM627074 3 0.6095 0.22722 0.392 0.000 0.608
#> GSM627077 3 0.5650 0.34276 0.312 0.000 0.688
#> GSM627093 3 0.6095 0.22722 0.392 0.000 0.608
#> GSM627120 2 0.0592 0.85521 0.000 0.988 0.012
#> GSM627124 2 0.3879 0.80080 0.000 0.848 0.152
#> GSM627075 2 0.0424 0.85473 0.000 0.992 0.008
#> GSM627085 2 0.3619 0.81220 0.000 0.864 0.136
#> GSM627119 3 0.6095 0.22722 0.392 0.000 0.608
#> GSM627116 2 0.5810 0.55978 0.000 0.664 0.336
#> GSM627084 3 0.4504 0.45671 0.196 0.000 0.804
#> GSM627096 2 0.0000 0.85323 0.000 1.000 0.000
#> GSM627100 3 0.6192 0.04658 0.000 0.420 0.580
#> GSM627112 3 0.6274 -0.09225 0.000 0.456 0.544
#> GSM627083 3 0.4291 0.46790 0.180 0.000 0.820
#> GSM627098 3 0.5465 0.37088 0.288 0.000 0.712
#> GSM627104 3 0.6215 0.15040 0.428 0.000 0.572
#> GSM627131 3 0.5650 0.34276 0.312 0.000 0.688
#> GSM627106 2 0.0000 0.85323 0.000 1.000 0.000
#> GSM627123 1 0.5138 0.65676 0.748 0.000 0.252
#> GSM627129 2 0.2356 0.84343 0.000 0.928 0.072
#> GSM627216 2 0.1163 0.85306 0.000 0.972 0.028
#> GSM627212 2 0.0424 0.85473 0.000 0.992 0.008
#> GSM627190 3 0.6307 -0.18626 0.000 0.488 0.512
#> GSM627169 2 0.6307 0.24096 0.000 0.512 0.488
#> GSM627167 2 0.2711 0.83766 0.000 0.912 0.088
#> GSM627192 1 0.0000 0.93999 1.000 0.000 0.000
#> GSM627203 2 0.5431 0.64099 0.000 0.716 0.284
#> GSM627151 2 0.5098 0.69819 0.000 0.752 0.248
#> GSM627163 1 0.0000 0.93999 1.000 0.000 0.000
#> GSM627211 2 0.0424 0.85473 0.000 0.992 0.008
#> GSM627171 2 0.4399 0.77100 0.000 0.812 0.188
#> GSM627209 2 0.1163 0.85389 0.000 0.972 0.028
#> GSM627135 1 0.5138 0.65676 0.748 0.000 0.252
#> GSM627170 2 0.0000 0.85323 0.000 1.000 0.000
#> GSM627178 3 0.6008 0.25283 0.372 0.000 0.628
#> GSM627199 2 0.6307 0.24096 0.000 0.512 0.488
#> GSM627213 2 0.2356 0.84343 0.000 0.928 0.072
#> GSM627140 3 0.6225 -0.01034 0.000 0.432 0.568
#> GSM627149 1 0.4452 0.74830 0.808 0.000 0.192
#> GSM627147 2 0.6307 0.24096 0.000 0.512 0.488
#> GSM627195 2 0.1289 0.85215 0.000 0.968 0.032
#> GSM627204 2 0.0424 0.85473 0.000 0.992 0.008
#> GSM627207 2 0.0424 0.85473 0.000 0.992 0.008
#> GSM627157 3 0.6062 0.23241 0.384 0.000 0.616
#> GSM627201 2 0.0000 0.85323 0.000 1.000 0.000
#> GSM627146 2 0.2711 0.83837 0.000 0.912 0.088
#> GSM627156 2 0.3551 0.79363 0.000 0.868 0.132
#> GSM627188 1 0.0000 0.93999 1.000 0.000 0.000
#> GSM627197 2 0.2066 0.84868 0.000 0.940 0.060
#> GSM627173 2 0.6307 0.24096 0.000 0.512 0.488
#> GSM627179 2 0.0424 0.85473 0.000 0.992 0.008
#> GSM627208 2 0.0000 0.85323 0.000 1.000 0.000
#> GSM627215 2 0.1163 0.85306 0.000 0.972 0.028
#> GSM627153 2 0.1163 0.85389 0.000 0.972 0.028
#> GSM627155 1 0.0000 0.93999 1.000 0.000 0.000
#> GSM627165 2 0.0000 0.85323 0.000 1.000 0.000
#> GSM627168 3 0.3692 0.55247 0.048 0.056 0.896
#> GSM627183 3 0.3213 0.54591 0.060 0.028 0.912
#> GSM627144 2 0.2261 0.84431 0.000 0.932 0.068
#> GSM627158 1 0.0000 0.93999 1.000 0.000 0.000
#> GSM627196 2 0.0424 0.85473 0.000 0.992 0.008
#> GSM627142 3 0.5497 0.37056 0.000 0.292 0.708
#> GSM627182 2 0.1964 0.84912 0.000 0.944 0.056
#> GSM627202 3 0.5650 0.34276 0.312 0.000 0.688
#> GSM627141 3 0.4165 0.54138 0.076 0.048 0.876
#> GSM627143 2 0.6309 0.20338 0.000 0.504 0.496
#> GSM627145 2 0.6305 0.23327 0.000 0.516 0.484
#> GSM627152 3 0.5069 0.52117 0.128 0.044 0.828
#> GSM627200 3 0.4750 0.44319 0.216 0.000 0.784
#> GSM627159 3 0.2796 0.54520 0.000 0.092 0.908
#> GSM627164 2 0.6307 0.24096 0.000 0.512 0.488
#> GSM627138 1 0.0000 0.93999 1.000 0.000 0.000
#> GSM627175 2 0.0000 0.85323 0.000 1.000 0.000
#> GSM627150 2 0.3267 0.82298 0.000 0.884 0.116
#> GSM627166 3 0.6095 0.22722 0.392 0.000 0.608
#> GSM627186 2 0.6308 0.22533 0.000 0.508 0.492
#> GSM627139 2 0.4842 0.72550 0.000 0.776 0.224
#> GSM627181 2 0.0000 0.85323 0.000 1.000 0.000
#> GSM627205 2 0.0000 0.85323 0.000 1.000 0.000
#> GSM627214 2 0.0000 0.85323 0.000 1.000 0.000
#> GSM627180 2 0.2165 0.84682 0.000 0.936 0.064
#> GSM627172 2 0.6307 0.24096 0.000 0.512 0.488
#> GSM627184 1 0.0000 0.93999 1.000 0.000 0.000
#> GSM627193 2 0.0424 0.85473 0.000 0.992 0.008
#> GSM627191 3 0.3752 0.48703 0.144 0.000 0.856
#> GSM627176 3 0.5608 0.52992 0.120 0.072 0.808
#> GSM627194 2 0.0747 0.85514 0.000 0.984 0.016
#> GSM627154 2 0.3619 0.81220 0.000 0.864 0.136
#> GSM627187 3 0.6267 -0.07801 0.000 0.452 0.548
#> GSM627198 2 0.2711 0.83837 0.000 0.912 0.088
#> GSM627160 3 0.4146 0.54422 0.080 0.044 0.876
#> GSM627185 3 0.6308 -0.02650 0.492 0.000 0.508
#> GSM627206 3 0.5016 0.46432 0.000 0.240 0.760
#> GSM627161 1 0.0000 0.93999 1.000 0.000 0.000
#> GSM627162 3 0.6180 0.05294 0.000 0.416 0.584
#> GSM627210 3 0.6756 0.44030 0.232 0.056 0.712
#> GSM627189 2 0.0424 0.85473 0.000 0.992 0.008
show/hide code output
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM627128 4 0.4855 0.283 0.000 0.400 0.000 0.600
#> GSM627110 3 0.4661 0.556 0.000 0.000 0.652 0.348
#> GSM627132 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> GSM627107 2 0.0336 0.858 0.000 0.992 0.000 0.008
#> GSM627103 2 0.3688 0.737 0.000 0.792 0.000 0.208
#> GSM627114 3 0.4830 0.561 0.000 0.000 0.608 0.392
#> GSM627134 2 0.2530 0.812 0.000 0.888 0.000 0.112
#> GSM627137 2 0.0000 0.858 0.000 1.000 0.000 0.000
#> GSM627148 4 0.5229 0.207 0.000 0.428 0.008 0.564
#> GSM627101 2 0.0336 0.858 0.000 0.992 0.000 0.008
#> GSM627130 4 0.4830 0.306 0.000 0.392 0.000 0.608
#> GSM627071 4 0.4071 0.724 0.000 0.064 0.104 0.832
#> GSM627118 2 0.0000 0.858 0.000 1.000 0.000 0.000
#> GSM627094 2 0.1302 0.857 0.000 0.956 0.000 0.044
#> GSM627122 3 0.4643 0.616 0.000 0.000 0.656 0.344
#> GSM627115 2 0.1211 0.857 0.000 0.960 0.000 0.040
#> GSM627125 2 0.4961 0.258 0.000 0.552 0.000 0.448
#> GSM627174 3 0.4972 0.446 0.000 0.000 0.544 0.456
#> GSM627102 4 0.1584 0.754 0.000 0.036 0.012 0.952
#> GSM627073 2 0.3400 0.763 0.000 0.820 0.000 0.180
#> GSM627108 2 0.1211 0.857 0.000 0.960 0.000 0.040
#> GSM627126 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> GSM627078 2 0.4830 0.469 0.000 0.608 0.000 0.392
#> GSM627090 4 0.4630 0.492 0.000 0.016 0.252 0.732
#> GSM627099 2 0.0000 0.858 0.000 1.000 0.000 0.000
#> GSM627105 2 0.4961 0.258 0.000 0.552 0.000 0.448
#> GSM627117 4 0.2586 0.752 0.000 0.040 0.048 0.912
#> GSM627121 2 0.0336 0.858 0.000 0.992 0.000 0.008
#> GSM627127 2 0.0188 0.859 0.000 0.996 0.000 0.004
#> GSM627087 2 0.1211 0.857 0.000 0.960 0.000 0.040
#> GSM627089 4 0.3694 0.703 0.000 0.032 0.124 0.844
#> GSM627092 4 0.1677 0.754 0.000 0.040 0.012 0.948
#> GSM627076 4 0.4630 0.492 0.000 0.016 0.252 0.732
#> GSM627136 3 0.4941 0.482 0.000 0.000 0.564 0.436
#> GSM627081 2 0.0336 0.858 0.000 0.992 0.000 0.008
#> GSM627091 2 0.1211 0.857 0.000 0.960 0.000 0.040
#> GSM627097 2 0.4981 0.200 0.000 0.536 0.000 0.464
#> GSM627072 4 0.5594 0.661 0.000 0.192 0.092 0.716
#> GSM627080 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> GSM627088 3 0.4941 0.473 0.000 0.000 0.564 0.436
#> GSM627109 3 0.2589 0.640 0.116 0.000 0.884 0.000
#> GSM627111 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> GSM627113 3 0.1867 0.668 0.072 0.000 0.928 0.000
#> GSM627133 2 0.2530 0.812 0.000 0.888 0.000 0.112
#> GSM627177 4 0.4071 0.724 0.000 0.064 0.104 0.832
#> GSM627086 2 0.0000 0.858 0.000 1.000 0.000 0.000
#> GSM627095 3 0.3801 0.526 0.220 0.000 0.780 0.000
#> GSM627079 4 0.4589 0.668 0.000 0.048 0.168 0.784
#> GSM627082 3 0.4277 0.667 0.000 0.000 0.720 0.280
#> GSM627074 3 0.1940 0.673 0.076 0.000 0.924 0.000
#> GSM627077 3 0.1938 0.718 0.012 0.000 0.936 0.052
#> GSM627093 3 0.1940 0.673 0.076 0.000 0.924 0.000
#> GSM627120 2 0.1302 0.854 0.000 0.956 0.000 0.044
#> GSM627124 2 0.4830 0.469 0.000 0.608 0.000 0.392
#> GSM627075 2 0.1211 0.857 0.000 0.960 0.000 0.040
#> GSM627085 2 0.4776 0.508 0.000 0.624 0.000 0.376
#> GSM627119 3 0.2125 0.674 0.076 0.000 0.920 0.004
#> GSM627116 4 0.5966 0.518 0.000 0.316 0.060 0.624
#> GSM627084 3 0.3649 0.720 0.000 0.000 0.796 0.204
#> GSM627096 2 0.0000 0.858 0.000 1.000 0.000 0.000
#> GSM627100 4 0.3497 0.695 0.000 0.024 0.124 0.852
#> GSM627112 4 0.1305 0.725 0.000 0.004 0.036 0.960
#> GSM627083 3 0.3726 0.711 0.000 0.000 0.788 0.212
#> GSM627098 3 0.2402 0.724 0.012 0.000 0.912 0.076
#> GSM627104 3 0.2589 0.640 0.116 0.000 0.884 0.000
#> GSM627131 3 0.1938 0.718 0.012 0.000 0.936 0.052
#> GSM627106 2 0.0336 0.858 0.000 0.992 0.000 0.008
#> GSM627123 1 0.4989 0.306 0.528 0.000 0.472 0.000
#> GSM627129 2 0.3726 0.732 0.000 0.788 0.000 0.212
#> GSM627216 2 0.2530 0.812 0.000 0.888 0.000 0.112
#> GSM627212 2 0.1211 0.857 0.000 0.960 0.000 0.040
#> GSM627190 4 0.2408 0.750 0.000 0.036 0.044 0.920
#> GSM627169 4 0.1584 0.754 0.000 0.036 0.012 0.952
#> GSM627167 2 0.3942 0.708 0.000 0.764 0.000 0.236
#> GSM627192 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> GSM627203 4 0.5475 0.529 0.000 0.308 0.036 0.656
#> GSM627151 4 0.5244 0.317 0.000 0.388 0.012 0.600
#> GSM627163 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> GSM627211 2 0.1211 0.857 0.000 0.960 0.000 0.040
#> GSM627171 2 0.4898 0.391 0.000 0.584 0.000 0.416
#> GSM627209 2 0.1302 0.851 0.000 0.956 0.000 0.044
#> GSM627135 1 0.4989 0.306 0.528 0.000 0.472 0.000
#> GSM627170 2 0.0000 0.858 0.000 1.000 0.000 0.000
#> GSM627178 3 0.1637 0.675 0.060 0.000 0.940 0.000
#> GSM627199 4 0.1584 0.754 0.000 0.036 0.012 0.952
#> GSM627213 2 0.3726 0.732 0.000 0.788 0.000 0.212
#> GSM627140 4 0.1557 0.712 0.000 0.000 0.056 0.944
#> GSM627149 1 0.4746 0.509 0.632 0.000 0.368 0.000
#> GSM627147 4 0.1677 0.754 0.000 0.040 0.012 0.948
#> GSM627195 2 0.2704 0.803 0.000 0.876 0.000 0.124
#> GSM627204 2 0.1211 0.857 0.000 0.960 0.000 0.040
#> GSM627207 2 0.1211 0.857 0.000 0.960 0.000 0.040
#> GSM627157 3 0.1867 0.668 0.072 0.000 0.928 0.000
#> GSM627201 2 0.0000 0.858 0.000 1.000 0.000 0.000
#> GSM627146 2 0.4008 0.710 0.000 0.756 0.000 0.244
#> GSM627156 2 0.4008 0.678 0.000 0.756 0.000 0.244
#> GSM627188 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> GSM627197 2 0.3649 0.756 0.000 0.796 0.000 0.204
#> GSM627173 4 0.1584 0.754 0.000 0.036 0.012 0.952
#> GSM627179 2 0.1211 0.857 0.000 0.960 0.000 0.040
#> GSM627208 2 0.0188 0.858 0.000 0.996 0.000 0.004
#> GSM627215 2 0.2530 0.812 0.000 0.888 0.000 0.112
#> GSM627153 2 0.1302 0.851 0.000 0.956 0.000 0.044
#> GSM627155 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> GSM627165 2 0.0000 0.858 0.000 1.000 0.000 0.000
#> GSM627168 3 0.5132 0.459 0.004 0.000 0.548 0.448
#> GSM627183 3 0.4843 0.548 0.000 0.000 0.604 0.396
#> GSM627144 2 0.4746 0.458 0.000 0.632 0.000 0.368
#> GSM627158 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> GSM627196 2 0.1211 0.857 0.000 0.960 0.000 0.040
#> GSM627142 4 0.4576 0.482 0.000 0.012 0.260 0.728
#> GSM627182 2 0.3024 0.791 0.000 0.852 0.000 0.148
#> GSM627202 3 0.1938 0.718 0.012 0.000 0.936 0.052
#> GSM627141 3 0.4830 0.561 0.000 0.000 0.608 0.392
#> GSM627143 4 0.2926 0.753 0.000 0.056 0.048 0.896
#> GSM627145 4 0.4487 0.723 0.000 0.100 0.092 0.808
#> GSM627152 3 0.4608 0.618 0.004 0.000 0.692 0.304
#> GSM627200 3 0.3539 0.730 0.004 0.000 0.820 0.176
#> GSM627159 4 0.5163 -0.272 0.000 0.004 0.480 0.516
#> GSM627164 4 0.1584 0.754 0.000 0.036 0.012 0.952
#> GSM627138 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> GSM627175 2 0.0000 0.858 0.000 1.000 0.000 0.000
#> GSM627150 2 0.4454 0.584 0.000 0.692 0.000 0.308
#> GSM627166 3 0.1940 0.673 0.076 0.000 0.924 0.000
#> GSM627186 4 0.2124 0.755 0.000 0.040 0.028 0.932
#> GSM627139 4 0.5353 0.196 0.000 0.432 0.012 0.556
#> GSM627181 2 0.0000 0.858 0.000 1.000 0.000 0.000
#> GSM627205 2 0.0000 0.858 0.000 1.000 0.000 0.000
#> GSM627214 2 0.0000 0.858 0.000 1.000 0.000 0.000
#> GSM627180 2 0.3219 0.779 0.000 0.836 0.000 0.164
#> GSM627172 4 0.1584 0.754 0.000 0.036 0.012 0.952
#> GSM627184 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> GSM627193 2 0.1211 0.857 0.000 0.960 0.000 0.040
#> GSM627191 3 0.4040 0.690 0.000 0.000 0.752 0.248
#> GSM627176 3 0.4661 0.556 0.000 0.000 0.652 0.348
#> GSM627194 2 0.1389 0.857 0.000 0.952 0.000 0.048
#> GSM627154 2 0.4776 0.508 0.000 0.624 0.000 0.376
#> GSM627187 4 0.1661 0.722 0.000 0.004 0.052 0.944
#> GSM627198 2 0.4008 0.710 0.000 0.756 0.000 0.244
#> GSM627160 3 0.4950 0.565 0.004 0.000 0.620 0.376
#> GSM627185 3 0.3975 0.507 0.240 0.000 0.760 0.000
#> GSM627206 4 0.4980 0.375 0.000 0.016 0.304 0.680
#> GSM627161 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> GSM627162 4 0.2530 0.676 0.000 0.004 0.100 0.896
#> GSM627210 3 0.5631 0.648 0.072 0.000 0.696 0.232
#> GSM627189 2 0.1211 0.857 0.000 0.960 0.000 0.040
show/hide code output
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM627128 4 0.6208 0.36777 0.000 0.376 0.000 0.480 0.144
#> GSM627110 3 0.6238 0.10878 0.000 0.000 0.476 0.148 0.376
#> GSM627132 1 0.0000 0.89205 1.000 0.000 0.000 0.000 0.000
#> GSM627107 2 0.0510 0.85014 0.000 0.984 0.000 0.016 0.000
#> GSM627103 2 0.3715 0.67092 0.000 0.736 0.000 0.260 0.004
#> GSM627114 5 0.3697 0.66722 0.000 0.000 0.100 0.080 0.820
#> GSM627134 2 0.2583 0.78465 0.000 0.864 0.000 0.132 0.004
#> GSM627137 2 0.0000 0.85201 0.000 1.000 0.000 0.000 0.000
#> GSM627148 4 0.4585 0.37166 0.000 0.352 0.000 0.628 0.020
#> GSM627101 2 0.0510 0.85014 0.000 0.984 0.000 0.016 0.000
#> GSM627130 4 0.6282 0.38472 0.000 0.368 0.000 0.476 0.156
#> GSM627071 4 0.4384 0.38070 0.000 0.016 0.000 0.660 0.324
#> GSM627118 2 0.0000 0.85201 0.000 1.000 0.000 0.000 0.000
#> GSM627094 2 0.1341 0.84875 0.000 0.944 0.000 0.056 0.000
#> GSM627122 5 0.3555 0.65053 0.000 0.000 0.124 0.052 0.824
#> GSM627115 2 0.1197 0.84896 0.000 0.952 0.000 0.048 0.000
#> GSM627125 4 0.4747 -0.07212 0.000 0.484 0.000 0.500 0.016
#> GSM627174 5 0.2864 0.65130 0.000 0.000 0.024 0.112 0.864
#> GSM627102 4 0.2338 0.57804 0.000 0.000 0.004 0.884 0.112
#> GSM627073 2 0.3720 0.70634 0.000 0.760 0.000 0.228 0.012
#> GSM627108 2 0.1121 0.84867 0.000 0.956 0.000 0.044 0.000
#> GSM627126 1 0.0000 0.89205 1.000 0.000 0.000 0.000 0.000
#> GSM627078 2 0.4533 0.35640 0.000 0.544 0.000 0.448 0.008
#> GSM627090 5 0.4613 0.22895 0.000 0.004 0.008 0.408 0.580
#> GSM627099 2 0.0000 0.85201 0.000 1.000 0.000 0.000 0.000
#> GSM627105 4 0.4747 -0.07212 0.000 0.484 0.000 0.500 0.016
#> GSM627117 4 0.3920 0.49750 0.000 0.004 0.004 0.724 0.268
#> GSM627121 2 0.0510 0.85014 0.000 0.984 0.000 0.016 0.000
#> GSM627127 2 0.0162 0.85251 0.000 0.996 0.000 0.004 0.000
#> GSM627087 2 0.1197 0.84896 0.000 0.952 0.000 0.048 0.000
#> GSM627089 4 0.4299 0.28957 0.000 0.004 0.000 0.608 0.388
#> GSM627092 4 0.2497 0.57941 0.000 0.004 0.004 0.880 0.112
#> GSM627076 5 0.4613 0.22895 0.000 0.004 0.008 0.408 0.580
#> GSM627136 5 0.3479 0.66575 0.000 0.000 0.084 0.080 0.836
#> GSM627081 2 0.0510 0.85014 0.000 0.984 0.000 0.016 0.000
#> GSM627091 2 0.1270 0.84906 0.000 0.948 0.000 0.052 0.000
#> GSM627097 4 0.4740 -0.00835 0.000 0.468 0.000 0.516 0.016
#> GSM627072 4 0.5531 0.43031 0.000 0.120 0.000 0.632 0.248
#> GSM627080 1 0.0000 0.89205 1.000 0.000 0.000 0.000 0.000
#> GSM627088 5 0.3291 0.67352 0.000 0.000 0.064 0.088 0.848
#> GSM627109 3 0.1282 0.68900 0.044 0.000 0.952 0.000 0.004
#> GSM627111 1 0.0000 0.89205 1.000 0.000 0.000 0.000 0.000
#> GSM627113 3 0.3888 0.66417 0.056 0.000 0.796 0.000 0.148
#> GSM627133 2 0.2629 0.78315 0.000 0.860 0.000 0.136 0.004
#> GSM627177 4 0.4384 0.38070 0.000 0.016 0.000 0.660 0.324
#> GSM627086 2 0.0000 0.85201 0.000 1.000 0.000 0.000 0.000
#> GSM627095 3 0.3141 0.61471 0.152 0.000 0.832 0.000 0.016
#> GSM627079 4 0.4288 0.23321 0.000 0.004 0.000 0.612 0.384
#> GSM627082 5 0.2629 0.57606 0.000 0.000 0.136 0.004 0.860
#> GSM627074 3 0.0324 0.68862 0.004 0.000 0.992 0.000 0.004
#> GSM627077 3 0.4283 0.32089 0.000 0.000 0.544 0.000 0.456
#> GSM627093 3 0.0324 0.68862 0.004 0.000 0.992 0.000 0.004
#> GSM627120 2 0.1831 0.83690 0.000 0.920 0.000 0.076 0.004
#> GSM627124 2 0.4533 0.35640 0.000 0.544 0.000 0.448 0.008
#> GSM627075 2 0.1121 0.84867 0.000 0.956 0.000 0.044 0.000
#> GSM627085 2 0.4510 0.39829 0.000 0.560 0.000 0.432 0.008
#> GSM627119 3 0.1116 0.68622 0.004 0.000 0.964 0.004 0.028
#> GSM627116 4 0.5515 0.49162 0.000 0.260 0.000 0.628 0.112
#> GSM627084 5 0.4161 0.39318 0.000 0.000 0.280 0.016 0.704
#> GSM627096 2 0.0000 0.85201 0.000 1.000 0.000 0.000 0.000
#> GSM627100 4 0.4397 0.20429 0.000 0.004 0.000 0.564 0.432
#> GSM627112 4 0.3491 0.48280 0.000 0.000 0.004 0.768 0.228
#> GSM627083 5 0.3177 0.48379 0.000 0.000 0.208 0.000 0.792
#> GSM627098 3 0.4304 0.23903 0.000 0.000 0.516 0.000 0.484
#> GSM627104 3 0.1282 0.68900 0.044 0.000 0.952 0.000 0.004
#> GSM627131 3 0.4278 0.32758 0.000 0.000 0.548 0.000 0.452
#> GSM627106 2 0.0510 0.85014 0.000 0.984 0.000 0.016 0.000
#> GSM627123 1 0.5524 0.17050 0.516 0.000 0.416 0.000 0.068
#> GSM627129 2 0.3741 0.66517 0.000 0.732 0.000 0.264 0.004
#> GSM627216 2 0.2583 0.78465 0.000 0.864 0.000 0.132 0.004
#> GSM627212 2 0.1121 0.84867 0.000 0.956 0.000 0.044 0.000
#> GSM627190 4 0.3010 0.55484 0.000 0.000 0.004 0.824 0.172
#> GSM627169 4 0.2338 0.57804 0.000 0.000 0.004 0.884 0.112
#> GSM627167 2 0.3884 0.63947 0.000 0.708 0.000 0.288 0.004
#> GSM627192 1 0.0000 0.89205 1.000 0.000 0.000 0.000 0.000
#> GSM627203 4 0.5091 0.51153 0.000 0.236 0.000 0.676 0.088
#> GSM627151 4 0.4905 0.40995 0.000 0.336 0.000 0.624 0.040
#> GSM627163 1 0.0000 0.89205 1.000 0.000 0.000 0.000 0.000
#> GSM627211 2 0.1121 0.84867 0.000 0.956 0.000 0.044 0.000
#> GSM627171 2 0.5019 0.29775 0.000 0.532 0.000 0.436 0.032
#> GSM627209 2 0.1892 0.82642 0.000 0.916 0.000 0.080 0.004
#> GSM627135 1 0.5524 0.17050 0.516 0.000 0.416 0.000 0.068
#> GSM627170 2 0.0000 0.85201 0.000 1.000 0.000 0.000 0.000
#> GSM627178 3 0.3608 0.66491 0.040 0.000 0.812 0.000 0.148
#> GSM627199 4 0.2439 0.57600 0.000 0.000 0.004 0.876 0.120
#> GSM627213 2 0.3741 0.66517 0.000 0.732 0.000 0.264 0.004
#> GSM627140 4 0.3790 0.44583 0.000 0.000 0.004 0.724 0.272
#> GSM627149 1 0.5139 0.41686 0.624 0.000 0.316 0.000 0.060
#> GSM627147 4 0.2548 0.57860 0.000 0.004 0.004 0.876 0.116
#> GSM627195 2 0.2843 0.77175 0.000 0.848 0.000 0.144 0.008
#> GSM627204 2 0.1121 0.84867 0.000 0.956 0.000 0.044 0.000
#> GSM627207 2 0.1121 0.84867 0.000 0.956 0.000 0.044 0.000
#> GSM627157 3 0.3888 0.66417 0.056 0.000 0.796 0.000 0.148
#> GSM627201 2 0.0162 0.85219 0.000 0.996 0.000 0.004 0.000
#> GSM627146 2 0.3949 0.63999 0.000 0.696 0.000 0.300 0.004
#> GSM627156 2 0.3807 0.65732 0.000 0.748 0.000 0.240 0.012
#> GSM627188 1 0.0000 0.89205 1.000 0.000 0.000 0.000 0.000
#> GSM627197 2 0.3662 0.70046 0.000 0.744 0.000 0.252 0.004
#> GSM627173 4 0.2439 0.57600 0.000 0.000 0.004 0.876 0.120
#> GSM627179 2 0.1121 0.84867 0.000 0.956 0.000 0.044 0.000
#> GSM627208 2 0.0404 0.85072 0.000 0.988 0.000 0.012 0.000
#> GSM627215 2 0.2583 0.78465 0.000 0.864 0.000 0.132 0.004
#> GSM627153 2 0.1892 0.82642 0.000 0.916 0.000 0.080 0.004
#> GSM627155 1 0.0000 0.89205 1.000 0.000 0.000 0.000 0.000
#> GSM627165 2 0.0000 0.85201 0.000 1.000 0.000 0.000 0.000
#> GSM627168 5 0.4457 0.66001 0.000 0.000 0.124 0.116 0.760
#> GSM627183 5 0.3639 0.67013 0.000 0.000 0.100 0.076 0.824
#> GSM627144 2 0.4321 0.33576 0.000 0.600 0.000 0.396 0.004
#> GSM627158 1 0.0000 0.89205 1.000 0.000 0.000 0.000 0.000
#> GSM627196 2 0.1121 0.84867 0.000 0.956 0.000 0.044 0.000
#> GSM627142 5 0.4553 0.25759 0.000 0.004 0.008 0.384 0.604
#> GSM627182 2 0.3282 0.74921 0.000 0.804 0.000 0.188 0.008
#> GSM627202 3 0.4283 0.32089 0.000 0.000 0.544 0.000 0.456
#> GSM627141 5 0.3697 0.66722 0.000 0.000 0.100 0.080 0.820
#> GSM627143 4 0.3720 0.51112 0.000 0.012 0.000 0.760 0.228
#> GSM627145 4 0.4380 0.42866 0.000 0.032 0.000 0.708 0.260
#> GSM627152 3 0.5901 0.16349 0.000 0.000 0.496 0.104 0.400
#> GSM627200 5 0.4909 0.12257 0.000 0.000 0.412 0.028 0.560
#> GSM627159 5 0.2886 0.64935 0.000 0.000 0.008 0.148 0.844
#> GSM627164 4 0.2439 0.57600 0.000 0.000 0.004 0.876 0.120
#> GSM627138 1 0.0000 0.89205 1.000 0.000 0.000 0.000 0.000
#> GSM627175 2 0.0000 0.85201 0.000 1.000 0.000 0.000 0.000
#> GSM627150 2 0.4470 0.46160 0.000 0.616 0.000 0.372 0.012
#> GSM627166 3 0.0324 0.68862 0.004 0.000 0.992 0.000 0.004
#> GSM627186 4 0.2674 0.57265 0.000 0.000 0.004 0.856 0.140
#> GSM627139 4 0.5080 0.33466 0.000 0.368 0.000 0.588 0.044
#> GSM627181 2 0.0000 0.85201 0.000 1.000 0.000 0.000 0.000
#> GSM627205 2 0.0290 0.85229 0.000 0.992 0.000 0.008 0.000
#> GSM627214 2 0.0000 0.85201 0.000 1.000 0.000 0.000 0.000
#> GSM627180 2 0.3519 0.72542 0.000 0.776 0.000 0.216 0.008
#> GSM627172 4 0.2439 0.57600 0.000 0.000 0.004 0.876 0.120
#> GSM627184 1 0.0000 0.89205 1.000 0.000 0.000 0.000 0.000
#> GSM627193 2 0.1197 0.84896 0.000 0.952 0.000 0.048 0.000
#> GSM627191 5 0.2813 0.54523 0.000 0.000 0.168 0.000 0.832
#> GSM627176 3 0.6238 0.10878 0.000 0.000 0.476 0.148 0.376
#> GSM627194 2 0.1341 0.84964 0.000 0.944 0.000 0.056 0.000
#> GSM627154 2 0.4510 0.39829 0.000 0.560 0.000 0.432 0.008
#> GSM627187 4 0.3835 0.46150 0.000 0.000 0.008 0.732 0.260
#> GSM627198 2 0.3949 0.63999 0.000 0.696 0.000 0.300 0.004
#> GSM627160 5 0.5379 0.40443 0.000 0.000 0.268 0.096 0.636
#> GSM627185 3 0.3318 0.58749 0.180 0.000 0.808 0.000 0.012
#> GSM627206 5 0.4547 0.26504 0.000 0.000 0.012 0.400 0.588
#> GSM627161 1 0.0000 0.89205 1.000 0.000 0.000 0.000 0.000
#> GSM627162 4 0.4506 0.37255 0.000 0.000 0.028 0.676 0.296
#> GSM627210 3 0.4901 0.47049 0.004 0.000 0.716 0.084 0.196
#> GSM627189 2 0.1197 0.84896 0.000 0.952 0.000 0.048 0.000
show/hide code output
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM627128 5 0.5375 0.4765 0.000 0.128 0.000 0.268 0.596 0.008
#> GSM627110 3 0.6514 0.1960 0.000 0.000 0.464 0.272 0.036 0.228
#> GSM627132 1 0.0000 0.8932 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627107 2 0.1957 0.7723 0.000 0.888 0.000 0.000 0.112 0.000
#> GSM627103 2 0.4818 0.3253 0.000 0.572 0.000 0.064 0.364 0.000
#> GSM627114 6 0.3258 0.6461 0.000 0.000 0.016 0.120 0.032 0.832
#> GSM627134 2 0.3578 0.5065 0.000 0.660 0.000 0.000 0.340 0.000
#> GSM627137 2 0.0260 0.8056 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM627148 5 0.4818 0.3799 0.000 0.100 0.000 0.232 0.664 0.004
#> GSM627101 2 0.1957 0.7723 0.000 0.888 0.000 0.000 0.112 0.000
#> GSM627130 5 0.5533 0.4677 0.000 0.124 0.000 0.272 0.588 0.016
#> GSM627071 5 0.5481 0.2146 0.000 0.000 0.000 0.176 0.560 0.264
#> GSM627118 2 0.0260 0.8056 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM627094 2 0.2003 0.7993 0.000 0.912 0.000 0.044 0.044 0.000
#> GSM627122 6 0.3965 0.6495 0.000 0.000 0.060 0.108 0.036 0.796
#> GSM627115 2 0.1863 0.8006 0.000 0.920 0.000 0.044 0.036 0.000
#> GSM627125 5 0.3896 0.5448 0.000 0.204 0.000 0.052 0.744 0.000
#> GSM627174 6 0.3627 0.6147 0.000 0.000 0.004 0.224 0.020 0.752
#> GSM627102 4 0.1765 0.8692 0.000 0.000 0.000 0.904 0.096 0.000
#> GSM627073 5 0.3868 -0.2035 0.000 0.496 0.000 0.000 0.504 0.000
#> GSM627108 2 0.1007 0.8031 0.000 0.956 0.000 0.044 0.000 0.000
#> GSM627126 1 0.0000 0.8932 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627078 5 0.5629 0.1254 0.000 0.404 0.000 0.148 0.448 0.000
#> GSM627090 6 0.5969 0.3096 0.000 0.000 0.000 0.240 0.324 0.436
#> GSM627099 2 0.0260 0.8056 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM627105 5 0.3896 0.5448 0.000 0.204 0.000 0.052 0.744 0.000
#> GSM627117 4 0.4890 0.6763 0.000 0.000 0.000 0.660 0.160 0.180
#> GSM627121 2 0.1957 0.7723 0.000 0.888 0.000 0.000 0.112 0.000
#> GSM627127 2 0.0458 0.8068 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM627087 2 0.1863 0.8006 0.000 0.920 0.000 0.044 0.036 0.000
#> GSM627089 5 0.5786 0.0879 0.000 0.000 0.000 0.208 0.492 0.300
#> GSM627092 4 0.1908 0.8671 0.000 0.004 0.000 0.900 0.096 0.000
#> GSM627076 6 0.5969 0.3096 0.000 0.000 0.000 0.240 0.324 0.436
#> GSM627136 6 0.4844 0.6342 0.000 0.000 0.068 0.176 0.044 0.712
#> GSM627081 2 0.2003 0.7711 0.000 0.884 0.000 0.000 0.116 0.000
#> GSM627091 2 0.1934 0.8002 0.000 0.916 0.000 0.044 0.040 0.000
#> GSM627097 5 0.3892 0.5427 0.000 0.188 0.000 0.060 0.752 0.000
#> GSM627072 5 0.5598 0.3439 0.000 0.044 0.000 0.108 0.628 0.220
#> GSM627080 1 0.0000 0.8932 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627088 6 0.4432 0.6402 0.000 0.000 0.044 0.184 0.036 0.736
#> GSM627109 3 0.1082 0.7199 0.040 0.000 0.956 0.000 0.004 0.000
#> GSM627111 1 0.0000 0.8932 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627113 3 0.5072 0.5880 0.056 0.000 0.652 0.000 0.036 0.256
#> GSM627133 2 0.3592 0.5014 0.000 0.656 0.000 0.000 0.344 0.000
#> GSM627177 5 0.5481 0.2146 0.000 0.000 0.000 0.176 0.560 0.264
#> GSM627086 2 0.0458 0.8070 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM627095 3 0.4514 0.6539 0.148 0.000 0.744 0.000 0.032 0.076
#> GSM627079 5 0.5583 0.0840 0.000 0.000 0.000 0.156 0.508 0.336
#> GSM627082 6 0.0858 0.6154 0.000 0.000 0.000 0.004 0.028 0.968
#> GSM627074 3 0.0000 0.7203 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627077 6 0.4389 0.1333 0.000 0.000 0.372 0.000 0.032 0.596
#> GSM627093 3 0.0000 0.7203 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627120 2 0.2730 0.7256 0.000 0.808 0.000 0.000 0.192 0.000
#> GSM627124 5 0.5629 0.1254 0.000 0.404 0.000 0.148 0.448 0.000
#> GSM627075 2 0.1007 0.8031 0.000 0.956 0.000 0.044 0.000 0.000
#> GSM627085 5 0.5635 0.0738 0.000 0.420 0.000 0.148 0.432 0.000
#> GSM627119 3 0.0777 0.7153 0.000 0.000 0.972 0.004 0.000 0.024
#> GSM627116 5 0.4832 0.4457 0.000 0.044 0.000 0.156 0.720 0.080
#> GSM627084 6 0.3649 0.5560 0.000 0.000 0.136 0.020 0.040 0.804
#> GSM627096 2 0.0260 0.8056 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM627100 5 0.5998 -0.0408 0.000 0.000 0.000 0.264 0.436 0.300
#> GSM627112 4 0.1461 0.8054 0.000 0.000 0.000 0.940 0.016 0.044
#> GSM627083 6 0.1625 0.5814 0.000 0.000 0.060 0.000 0.012 0.928
#> GSM627098 6 0.4306 0.2095 0.000 0.000 0.344 0.000 0.032 0.624
#> GSM627104 3 0.1082 0.7199 0.040 0.000 0.956 0.000 0.004 0.000
#> GSM627131 6 0.4400 0.1232 0.000 0.000 0.376 0.000 0.032 0.592
#> GSM627106 2 0.2003 0.7711 0.000 0.884 0.000 0.000 0.116 0.000
#> GSM627123 1 0.6085 0.2194 0.516 0.000 0.312 0.000 0.032 0.140
#> GSM627129 2 0.4828 0.3149 0.000 0.568 0.000 0.064 0.368 0.000
#> GSM627216 2 0.3578 0.5065 0.000 0.660 0.000 0.000 0.340 0.000
#> GSM627212 2 0.1007 0.8031 0.000 0.956 0.000 0.044 0.000 0.000
#> GSM627190 4 0.3384 0.8403 0.000 0.000 0.000 0.812 0.120 0.068
#> GSM627169 4 0.1714 0.8701 0.000 0.000 0.000 0.908 0.092 0.000
#> GSM627167 2 0.4638 0.4159 0.000 0.636 0.000 0.068 0.296 0.000
#> GSM627192 1 0.0000 0.8932 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627203 5 0.3791 0.4569 0.000 0.032 0.000 0.104 0.808 0.056
#> GSM627151 5 0.5406 0.4468 0.000 0.132 0.000 0.248 0.608 0.012
#> GSM627163 1 0.0000 0.8932 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627211 2 0.1007 0.8031 0.000 0.956 0.000 0.044 0.000 0.000
#> GSM627171 2 0.6188 0.0522 0.000 0.476 0.000 0.212 0.296 0.016
#> GSM627209 2 0.1863 0.7616 0.000 0.896 0.000 0.000 0.104 0.000
#> GSM627135 1 0.6085 0.2194 0.516 0.000 0.312 0.000 0.032 0.140
#> GSM627170 2 0.0458 0.8060 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM627178 3 0.4377 0.6292 0.040 0.000 0.728 0.000 0.028 0.204
#> GSM627199 4 0.1663 0.8733 0.000 0.000 0.000 0.912 0.088 0.000
#> GSM627213 2 0.4828 0.3149 0.000 0.568 0.000 0.064 0.368 0.000
#> GSM627140 4 0.2060 0.7719 0.000 0.000 0.000 0.900 0.016 0.084
#> GSM627149 1 0.5561 0.4430 0.624 0.000 0.220 0.000 0.032 0.124
#> GSM627147 4 0.1908 0.8685 0.000 0.004 0.000 0.900 0.096 0.000
#> GSM627195 2 0.3782 0.3760 0.000 0.588 0.000 0.000 0.412 0.000
#> GSM627204 2 0.1007 0.8031 0.000 0.956 0.000 0.044 0.000 0.000
#> GSM627207 2 0.1007 0.8031 0.000 0.956 0.000 0.044 0.000 0.000
#> GSM627157 3 0.5072 0.5880 0.056 0.000 0.652 0.000 0.036 0.256
#> GSM627201 2 0.0291 0.8069 0.000 0.992 0.000 0.004 0.004 0.000
#> GSM627146 2 0.4887 0.4283 0.000 0.624 0.000 0.096 0.280 0.000
#> GSM627156 2 0.3349 0.6185 0.000 0.748 0.000 0.244 0.008 0.000
#> GSM627188 1 0.0000 0.8932 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627197 2 0.4614 0.5293 0.000 0.676 0.000 0.096 0.228 0.000
#> GSM627173 4 0.1663 0.8733 0.000 0.000 0.000 0.912 0.088 0.000
#> GSM627179 2 0.1007 0.8031 0.000 0.956 0.000 0.044 0.000 0.000
#> GSM627208 2 0.1957 0.7738 0.000 0.888 0.000 0.000 0.112 0.000
#> GSM627215 2 0.3578 0.5065 0.000 0.660 0.000 0.000 0.340 0.000
#> GSM627153 2 0.1863 0.7616 0.000 0.896 0.000 0.000 0.104 0.000
#> GSM627155 1 0.0000 0.8932 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627165 2 0.0260 0.8056 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM627168 6 0.5234 0.6245 0.000 0.000 0.088 0.196 0.044 0.672
#> GSM627183 6 0.4440 0.6487 0.000 0.000 0.060 0.156 0.036 0.748
#> GSM627144 5 0.4392 0.3100 0.000 0.332 0.000 0.040 0.628 0.000
#> GSM627158 1 0.0000 0.8932 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627196 2 0.1007 0.8031 0.000 0.956 0.000 0.044 0.000 0.000
#> GSM627142 6 0.5918 0.3363 0.000 0.000 0.000 0.232 0.312 0.456
#> GSM627182 2 0.3789 0.3829 0.000 0.584 0.000 0.000 0.416 0.000
#> GSM627202 6 0.4389 0.1333 0.000 0.000 0.372 0.000 0.032 0.596
#> GSM627141 6 0.3258 0.6461 0.000 0.000 0.016 0.120 0.032 0.832
#> GSM627143 4 0.5511 0.5792 0.000 0.004 0.000 0.580 0.236 0.180
#> GSM627145 5 0.5036 0.2887 0.000 0.000 0.000 0.140 0.632 0.228
#> GSM627152 3 0.6358 0.2045 0.000 0.000 0.484 0.232 0.028 0.256
#> GSM627200 6 0.4833 0.4110 0.000 0.000 0.268 0.032 0.040 0.660
#> GSM627159 6 0.4223 0.6083 0.000 0.000 0.000 0.236 0.060 0.704
#> GSM627164 4 0.1663 0.8733 0.000 0.000 0.000 0.912 0.088 0.000
#> GSM627138 1 0.0000 0.8932 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627175 2 0.0458 0.8070 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM627150 5 0.3847 0.2689 0.000 0.348 0.000 0.008 0.644 0.000
#> GSM627166 3 0.0000 0.7203 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM627186 4 0.2972 0.8477 0.000 0.000 0.000 0.836 0.128 0.036
#> GSM627139 5 0.4348 0.5220 0.000 0.116 0.000 0.124 0.748 0.012
#> GSM627181 2 0.0363 0.8070 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM627205 2 0.0603 0.8084 0.000 0.980 0.000 0.004 0.016 0.000
#> GSM627214 2 0.0458 0.8070 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM627180 2 0.3833 0.3204 0.000 0.556 0.000 0.000 0.444 0.000
#> GSM627172 4 0.1663 0.8733 0.000 0.000 0.000 0.912 0.088 0.000
#> GSM627184 1 0.0000 0.8932 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627193 2 0.1863 0.8006 0.000 0.920 0.000 0.044 0.036 0.000
#> GSM627191 6 0.1448 0.6085 0.000 0.000 0.024 0.012 0.016 0.948
#> GSM627176 3 0.6514 0.1960 0.000 0.000 0.464 0.272 0.036 0.228
#> GSM627194 2 0.2258 0.7987 0.000 0.896 0.000 0.044 0.060 0.000
#> GSM627154 5 0.5635 0.0738 0.000 0.420 0.000 0.148 0.432 0.000
#> GSM627187 4 0.2420 0.7737 0.000 0.000 0.004 0.888 0.032 0.076
#> GSM627198 2 0.4887 0.4283 0.000 0.624 0.000 0.096 0.280 0.000
#> GSM627160 6 0.6275 0.3965 0.000 0.000 0.256 0.228 0.024 0.492
#> GSM627185 3 0.4699 0.6246 0.176 0.000 0.720 0.000 0.032 0.072
#> GSM627206 6 0.5866 0.3632 0.000 0.000 0.004 0.252 0.232 0.512
#> GSM627161 1 0.0000 0.8932 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627162 4 0.3185 0.7124 0.000 0.000 0.024 0.840 0.024 0.112
#> GSM627210 3 0.4176 0.5393 0.000 0.000 0.716 0.220 0.000 0.064
#> GSM627189 2 0.1863 0.8006 0.000 0.920 0.000 0.044 0.036 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two
samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures
which are significantly different between classes which can be candidate marks
for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known
annotation is numeric, one-way ANOVA test is applied, and if the known
annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) age(p) other(p) k
#> ATC:hclust 138 0.912 0.1393 0.011188 2
#> ATC:hclust 99 0.581 0.0868 0.016489 3
#> ATC:hclust 123 0.496 0.1787 0.000895 4
#> ATC:hclust 99 0.173 0.5247 0.010781 5
#> ATC:hclust 100 0.223 0.6985 0.007062 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
ATC:kmeans**
The object with results only for a single top-value method and a single partition method
can be extracted as:
res = res_list["ATC", "kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51882 rows and 146 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
- The first row: a plot of the ECDF (empirical cumulative distribution
function) curves of the consensus matrix for each
k and the heatmap of
predicted classes for each k.
- The second row: heatmaps of the consensus matrix for each
k.
- The third row: heatmaps of the membership matrix for each
k.
- The fouth row: heatmaps of the signatures for each
k.
All the plots in panels can be made by individual functions and they are
plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
- ECDF curves of the consensus matrix for each
k;
- 1-PAC. The PAC
score
measures the proportion of the ambiguous subgrouping.
- Mean silhouette score.
- Concordance. The mean probability of fiting the consensus class ids in all
partitions.
- Area increased. Denote \(A_k\) as the area under the ECDF curve for current
k, the area increased is defined as \(A_k - A_{k-1}\).
- Rand index. The percent of pairs of samples that are both in a same cluster
or both are not in a same cluster in the partition of k and k-1.
- Jaccard index. The ratio of pairs of samples are both in a same cluster in
the partition of k and k-1 and the pairs of samples are both in a same
cluster in the partition k or k-1.
The detailed explanations of these statistics can be found in the cola
vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.986 0.942 0.976 0.4661 0.531 0.531
#> 3 3 0.851 0.872 0.945 0.3313 0.723 0.529
#> 4 4 0.639 0.609 0.793 0.1557 0.820 0.565
#> 5 5 0.649 0.591 0.757 0.0831 0.897 0.669
#> 6 6 0.659 0.446 0.659 0.0530 0.822 0.399
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
- All \(k\) with Jaccard index larger than 0.95 are removed because increasing
\(k\) does not provide enough extra information. If all \(k\) are removed, it is
marked as no subgroup is detected.
- For all \(k\) with 1-PAC score larger than 0.9, the maximal \(k\) is taken as
the best \(k\), and other \(k\) are marked as optional \(k\).
- If it does not fit the second rule. The \(k\) with the maximal vote of the
highest 1-PAC score, highest mean silhouette, and highest concordance is
taken as the best \(k\).
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
show/hide code output
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM627128 2 0.000 0.9834 0.000 1.000
#> GSM627110 1 0.430 0.8982 0.912 0.088
#> GSM627132 1 0.000 0.9603 1.000 0.000
#> GSM627107 2 0.000 0.9834 0.000 1.000
#> GSM627103 2 0.000 0.9834 0.000 1.000
#> GSM627114 1 0.000 0.9603 1.000 0.000
#> GSM627134 2 0.000 0.9834 0.000 1.000
#> GSM627137 2 0.000 0.9834 0.000 1.000
#> GSM627148 2 0.000 0.9834 0.000 1.000
#> GSM627101 2 0.000 0.9834 0.000 1.000
#> GSM627130 2 0.000 0.9834 0.000 1.000
#> GSM627071 2 0.000 0.9834 0.000 1.000
#> GSM627118 2 0.000 0.9834 0.000 1.000
#> GSM627094 2 0.000 0.9834 0.000 1.000
#> GSM627122 1 0.000 0.9603 1.000 0.000
#> GSM627115 2 0.000 0.9834 0.000 1.000
#> GSM627125 2 0.000 0.9834 0.000 1.000
#> GSM627174 2 0.814 0.6416 0.252 0.748
#> GSM627102 2 0.000 0.9834 0.000 1.000
#> GSM627073 2 0.000 0.9834 0.000 1.000
#> GSM627108 2 0.000 0.9834 0.000 1.000
#> GSM627126 1 0.000 0.9603 1.000 0.000
#> GSM627078 2 0.000 0.9834 0.000 1.000
#> GSM627090 1 0.615 0.8293 0.848 0.152
#> GSM627099 2 0.000 0.9834 0.000 1.000
#> GSM627105 2 0.000 0.9834 0.000 1.000
#> GSM627117 2 0.000 0.9834 0.000 1.000
#> GSM627121 2 0.000 0.9834 0.000 1.000
#> GSM627127 2 0.000 0.9834 0.000 1.000
#> GSM627087 2 0.000 0.9834 0.000 1.000
#> GSM627089 2 0.990 0.1608 0.440 0.560
#> GSM627092 2 0.000 0.9834 0.000 1.000
#> GSM627076 1 0.430 0.8982 0.912 0.088
#> GSM627136 1 0.430 0.8982 0.912 0.088
#> GSM627081 2 0.000 0.9834 0.000 1.000
#> GSM627091 2 0.000 0.9834 0.000 1.000
#> GSM627097 2 0.000 0.9834 0.000 1.000
#> GSM627072 2 0.000 0.9834 0.000 1.000
#> GSM627080 1 0.000 0.9603 1.000 0.000
#> GSM627088 1 0.430 0.8982 0.912 0.088
#> GSM627109 1 0.000 0.9603 1.000 0.000
#> GSM627111 1 0.000 0.9603 1.000 0.000
#> GSM627113 1 0.000 0.9603 1.000 0.000
#> GSM627133 2 0.000 0.9834 0.000 1.000
#> GSM627177 2 0.000 0.9834 0.000 1.000
#> GSM627086 2 0.000 0.9834 0.000 1.000
#> GSM627095 1 0.000 0.9603 1.000 0.000
#> GSM627079 2 0.844 0.6004 0.272 0.728
#> GSM627082 1 0.000 0.9603 1.000 0.000
#> GSM627074 1 0.000 0.9603 1.000 0.000
#> GSM627077 1 0.000 0.9603 1.000 0.000
#> GSM627093 1 0.000 0.9603 1.000 0.000
#> GSM627120 2 0.000 0.9834 0.000 1.000
#> GSM627124 2 0.000 0.9834 0.000 1.000
#> GSM627075 2 0.000 0.9834 0.000 1.000
#> GSM627085 2 0.000 0.9834 0.000 1.000
#> GSM627119 1 0.000 0.9603 1.000 0.000
#> GSM627116 2 0.000 0.9834 0.000 1.000
#> GSM627084 1 0.000 0.9603 1.000 0.000
#> GSM627096 2 0.000 0.9834 0.000 1.000
#> GSM627100 2 0.000 0.9834 0.000 1.000
#> GSM627112 2 0.000 0.9834 0.000 1.000
#> GSM627083 1 0.000 0.9603 1.000 0.000
#> GSM627098 1 0.000 0.9603 1.000 0.000
#> GSM627104 1 0.000 0.9603 1.000 0.000
#> GSM627131 1 0.000 0.9603 1.000 0.000
#> GSM627106 2 0.000 0.9834 0.000 1.000
#> GSM627123 1 0.000 0.9603 1.000 0.000
#> GSM627129 2 0.000 0.9834 0.000 1.000
#> GSM627216 2 0.000 0.9834 0.000 1.000
#> GSM627212 2 0.000 0.9834 0.000 1.000
#> GSM627190 2 0.000 0.9834 0.000 1.000
#> GSM627169 2 0.000 0.9834 0.000 1.000
#> GSM627167 2 0.000 0.9834 0.000 1.000
#> GSM627192 1 0.000 0.9603 1.000 0.000
#> GSM627203 2 0.000 0.9834 0.000 1.000
#> GSM627151 2 0.000 0.9834 0.000 1.000
#> GSM627163 1 0.000 0.9603 1.000 0.000
#> GSM627211 2 0.000 0.9834 0.000 1.000
#> GSM627171 2 0.000 0.9834 0.000 1.000
#> GSM627209 2 0.000 0.9834 0.000 1.000
#> GSM627135 1 0.000 0.9603 1.000 0.000
#> GSM627170 2 0.000 0.9834 0.000 1.000
#> GSM627178 1 0.000 0.9603 1.000 0.000
#> GSM627199 2 0.000 0.9834 0.000 1.000
#> GSM627213 2 0.000 0.9834 0.000 1.000
#> GSM627140 1 0.430 0.8982 0.912 0.088
#> GSM627149 1 0.000 0.9603 1.000 0.000
#> GSM627147 2 0.000 0.9834 0.000 1.000
#> GSM627195 2 0.000 0.9834 0.000 1.000
#> GSM627204 2 0.000 0.9834 0.000 1.000
#> GSM627207 2 0.000 0.9834 0.000 1.000
#> GSM627157 1 0.000 0.9603 1.000 0.000
#> GSM627201 2 0.000 0.9834 0.000 1.000
#> GSM627146 2 0.000 0.9834 0.000 1.000
#> GSM627156 2 0.000 0.9834 0.000 1.000
#> GSM627188 1 0.000 0.9603 1.000 0.000
#> GSM627197 2 0.000 0.9834 0.000 1.000
#> GSM627173 2 0.000 0.9834 0.000 1.000
#> GSM627179 2 0.000 0.9834 0.000 1.000
#> GSM627208 2 0.000 0.9834 0.000 1.000
#> GSM627215 2 0.000 0.9834 0.000 1.000
#> GSM627153 2 0.000 0.9834 0.000 1.000
#> GSM627155 1 0.000 0.9603 1.000 0.000
#> GSM627165 2 0.000 0.9834 0.000 1.000
#> GSM627168 1 0.000 0.9603 1.000 0.000
#> GSM627183 1 0.000 0.9603 1.000 0.000
#> GSM627144 2 0.000 0.9834 0.000 1.000
#> GSM627158 1 0.000 0.9603 1.000 0.000
#> GSM627196 2 0.000 0.9834 0.000 1.000
#> GSM627142 1 0.963 0.4029 0.612 0.388
#> GSM627182 2 0.000 0.9834 0.000 1.000
#> GSM627202 1 0.000 0.9603 1.000 0.000
#> GSM627141 1 0.000 0.9603 1.000 0.000
#> GSM627143 2 0.000 0.9834 0.000 1.000
#> GSM627145 2 0.000 0.9834 0.000 1.000
#> GSM627152 1 0.000 0.9603 1.000 0.000
#> GSM627200 1 0.000 0.9603 1.000 0.000
#> GSM627159 1 0.994 0.2041 0.544 0.456
#> GSM627164 2 0.000 0.9834 0.000 1.000
#> GSM627138 1 0.000 0.9603 1.000 0.000
#> GSM627175 2 0.000 0.9834 0.000 1.000
#> GSM627150 2 0.000 0.9834 0.000 1.000
#> GSM627166 1 0.000 0.9603 1.000 0.000
#> GSM627186 2 0.000 0.9834 0.000 1.000
#> GSM627139 2 0.000 0.9834 0.000 1.000
#> GSM627181 2 0.000 0.9834 0.000 1.000
#> GSM627205 2 0.000 0.9834 0.000 1.000
#> GSM627214 2 0.000 0.9834 0.000 1.000
#> GSM627180 2 0.000 0.9834 0.000 1.000
#> GSM627172 2 0.000 0.9834 0.000 1.000
#> GSM627184 1 0.000 0.9603 1.000 0.000
#> GSM627193 2 0.000 0.9834 0.000 1.000
#> GSM627191 1 0.000 0.9603 1.000 0.000
#> GSM627176 1 0.430 0.8982 0.912 0.088
#> GSM627194 2 0.000 0.9834 0.000 1.000
#> GSM627154 2 0.000 0.9834 0.000 1.000
#> GSM627187 1 0.730 0.7621 0.796 0.204
#> GSM627198 2 0.000 0.9834 0.000 1.000
#> GSM627160 1 0.000 0.9603 1.000 0.000
#> GSM627185 1 0.000 0.9603 1.000 0.000
#> GSM627206 2 0.995 0.0882 0.460 0.540
#> GSM627161 1 0.000 0.9603 1.000 0.000
#> GSM627162 1 0.833 0.6683 0.736 0.264
#> GSM627210 1 0.327 0.9198 0.940 0.060
#> GSM627189 2 0.000 0.9834 0.000 1.000
show/hide code output
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM627128 3 0.0892 0.898 0.000 0.020 0.980
#> GSM627110 3 0.0592 0.903 0.012 0.000 0.988
#> GSM627132 1 0.0000 0.877 1.000 0.000 0.000
#> GSM627107 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627103 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627114 3 0.0747 0.903 0.016 0.000 0.984
#> GSM627134 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627137 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627148 3 0.6126 0.346 0.000 0.400 0.600
#> GSM627101 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627130 3 0.6079 0.397 0.000 0.388 0.612
#> GSM627071 3 0.0747 0.901 0.000 0.016 0.984
#> GSM627118 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627094 2 0.0747 0.964 0.000 0.984 0.016
#> GSM627122 3 0.0747 0.903 0.016 0.000 0.984
#> GSM627115 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627125 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627174 3 0.0000 0.901 0.000 0.000 1.000
#> GSM627102 2 0.4178 0.799 0.000 0.828 0.172
#> GSM627073 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627108 2 0.0592 0.967 0.000 0.988 0.012
#> GSM627126 1 0.0000 0.877 1.000 0.000 0.000
#> GSM627078 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627090 3 0.0747 0.903 0.016 0.000 0.984
#> GSM627099 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627105 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627117 3 0.0000 0.901 0.000 0.000 1.000
#> GSM627121 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627127 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627087 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627089 3 0.0829 0.902 0.004 0.012 0.984
#> GSM627092 2 0.2959 0.887 0.000 0.900 0.100
#> GSM627076 3 0.0747 0.903 0.016 0.000 0.984
#> GSM627136 3 0.0747 0.903 0.016 0.000 0.984
#> GSM627081 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627091 2 0.0424 0.969 0.000 0.992 0.008
#> GSM627097 2 0.4796 0.713 0.000 0.780 0.220
#> GSM627072 3 0.5905 0.466 0.000 0.352 0.648
#> GSM627080 1 0.0000 0.877 1.000 0.000 0.000
#> GSM627088 3 0.0747 0.903 0.016 0.000 0.984
#> GSM627109 1 0.0000 0.877 1.000 0.000 0.000
#> GSM627111 1 0.0000 0.877 1.000 0.000 0.000
#> GSM627113 1 0.4555 0.733 0.800 0.000 0.200
#> GSM627133 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627177 3 0.0747 0.901 0.000 0.016 0.984
#> GSM627086 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627095 1 0.0000 0.877 1.000 0.000 0.000
#> GSM627079 3 0.0747 0.901 0.000 0.016 0.984
#> GSM627082 3 0.0747 0.903 0.016 0.000 0.984
#> GSM627074 1 0.5733 0.596 0.676 0.000 0.324
#> GSM627077 3 0.0747 0.903 0.016 0.000 0.984
#> GSM627093 1 0.6140 0.467 0.596 0.000 0.404
#> GSM627120 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627124 2 0.0747 0.964 0.000 0.984 0.016
#> GSM627075 2 0.0747 0.964 0.000 0.984 0.016
#> GSM627085 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627119 1 0.6307 0.245 0.512 0.000 0.488
#> GSM627116 3 0.0747 0.901 0.000 0.016 0.984
#> GSM627084 3 0.0747 0.903 0.016 0.000 0.984
#> GSM627096 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627100 3 0.0747 0.901 0.000 0.016 0.984
#> GSM627112 3 0.0000 0.901 0.000 0.000 1.000
#> GSM627083 3 0.4399 0.682 0.188 0.000 0.812
#> GSM627098 1 0.6154 0.459 0.592 0.000 0.408
#> GSM627104 1 0.5760 0.591 0.672 0.000 0.328
#> GSM627131 3 0.2878 0.817 0.096 0.000 0.904
#> GSM627106 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627123 1 0.0000 0.877 1.000 0.000 0.000
#> GSM627129 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627216 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627212 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627190 3 0.0000 0.901 0.000 0.000 1.000
#> GSM627169 3 0.4931 0.617 0.000 0.232 0.768
#> GSM627167 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627192 1 0.0000 0.877 1.000 0.000 0.000
#> GSM627203 3 0.5905 0.466 0.000 0.352 0.648
#> GSM627151 3 0.0592 0.902 0.000 0.012 0.988
#> GSM627163 1 0.0000 0.877 1.000 0.000 0.000
#> GSM627211 2 0.0592 0.967 0.000 0.988 0.012
#> GSM627171 2 0.0237 0.972 0.000 0.996 0.004
#> GSM627209 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627135 1 0.0000 0.877 1.000 0.000 0.000
#> GSM627170 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627178 1 0.6154 0.459 0.592 0.000 0.408
#> GSM627199 3 0.6260 0.195 0.000 0.448 0.552
#> GSM627213 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627140 3 0.0000 0.901 0.000 0.000 1.000
#> GSM627149 1 0.0000 0.877 1.000 0.000 0.000
#> GSM627147 2 0.2878 0.891 0.000 0.904 0.096
#> GSM627195 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627204 2 0.0592 0.967 0.000 0.988 0.012
#> GSM627207 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627157 1 0.0000 0.877 1.000 0.000 0.000
#> GSM627201 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627146 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627156 2 0.0747 0.964 0.000 0.984 0.016
#> GSM627188 1 0.0000 0.877 1.000 0.000 0.000
#> GSM627197 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627173 2 0.2625 0.904 0.000 0.916 0.084
#> GSM627179 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627208 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627215 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627153 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627155 1 0.0000 0.877 1.000 0.000 0.000
#> GSM627165 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627168 3 0.0747 0.903 0.016 0.000 0.984
#> GSM627183 3 0.0747 0.903 0.016 0.000 0.984
#> GSM627144 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627158 1 0.0000 0.877 1.000 0.000 0.000
#> GSM627196 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627142 3 0.0829 0.902 0.004 0.012 0.984
#> GSM627182 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627202 3 0.4974 0.592 0.236 0.000 0.764
#> GSM627141 3 0.0747 0.903 0.016 0.000 0.984
#> GSM627143 2 0.5810 0.487 0.000 0.664 0.336
#> GSM627145 3 0.0892 0.898 0.000 0.020 0.980
#> GSM627152 3 0.0747 0.903 0.016 0.000 0.984
#> GSM627200 3 0.0747 0.903 0.016 0.000 0.984
#> GSM627159 3 0.0829 0.902 0.004 0.012 0.984
#> GSM627164 2 0.4931 0.708 0.000 0.768 0.232
#> GSM627138 1 0.0000 0.877 1.000 0.000 0.000
#> GSM627175 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627150 2 0.5016 0.663 0.000 0.760 0.240
#> GSM627166 1 0.6154 0.459 0.592 0.000 0.408
#> GSM627186 3 0.4654 0.655 0.000 0.208 0.792
#> GSM627139 3 0.1753 0.871 0.000 0.048 0.952
#> GSM627181 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627205 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627214 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627180 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627172 3 0.0237 0.900 0.000 0.004 0.996
#> GSM627184 1 0.0000 0.877 1.000 0.000 0.000
#> GSM627193 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627191 3 0.0747 0.903 0.016 0.000 0.984
#> GSM627176 3 0.0000 0.901 0.000 0.000 1.000
#> GSM627194 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627154 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627187 3 0.0000 0.901 0.000 0.000 1.000
#> GSM627198 2 0.0000 0.974 0.000 1.000 0.000
#> GSM627160 3 0.0592 0.903 0.012 0.000 0.988
#> GSM627185 1 0.0000 0.877 1.000 0.000 0.000
#> GSM627206 3 0.0747 0.903 0.016 0.000 0.984
#> GSM627161 1 0.0000 0.877 1.000 0.000 0.000
#> GSM627162 3 0.0000 0.901 0.000 0.000 1.000
#> GSM627210 3 0.0592 0.903 0.012 0.000 0.988
#> GSM627189 2 0.0000 0.974 0.000 1.000 0.000
show/hide code output
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM627128 4 0.6785 0.43510 0.000 0.108 0.352 0.540
#> GSM627110 3 0.3975 0.41124 0.000 0.000 0.760 0.240
#> GSM627132 1 0.0000 0.85633 1.000 0.000 0.000 0.000
#> GSM627107 2 0.0592 0.87385 0.000 0.984 0.000 0.016
#> GSM627103 2 0.0336 0.87818 0.000 0.992 0.000 0.008
#> GSM627114 3 0.0817 0.62528 0.000 0.000 0.976 0.024
#> GSM627134 2 0.0188 0.87845 0.000 0.996 0.000 0.004
#> GSM627137 2 0.1792 0.87446 0.000 0.932 0.000 0.068
#> GSM627148 4 0.6974 0.49260 0.000 0.152 0.284 0.564
#> GSM627101 2 0.0592 0.87385 0.000 0.984 0.000 0.016
#> GSM627130 4 0.6805 0.52073 0.000 0.176 0.220 0.604
#> GSM627071 4 0.4989 0.27837 0.000 0.000 0.472 0.528
#> GSM627118 2 0.0000 0.87889 0.000 1.000 0.000 0.000
#> GSM627094 2 0.4382 0.75907 0.000 0.704 0.000 0.296
#> GSM627122 3 0.1716 0.60253 0.000 0.000 0.936 0.064
#> GSM627115 2 0.2408 0.86387 0.000 0.896 0.000 0.104
#> GSM627125 4 0.6229 0.42571 0.000 0.416 0.056 0.528
#> GSM627174 3 0.4925 -0.00339 0.000 0.000 0.572 0.428
#> GSM627102 4 0.1867 0.56210 0.000 0.072 0.000 0.928
#> GSM627073 2 0.1474 0.85497 0.000 0.948 0.000 0.052
#> GSM627108 2 0.4331 0.76538 0.000 0.712 0.000 0.288
#> GSM627126 1 0.0000 0.85633 1.000 0.000 0.000 0.000
#> GSM627078 2 0.4304 0.77242 0.000 0.716 0.000 0.284
#> GSM627090 3 0.4888 0.05425 0.000 0.000 0.588 0.412
#> GSM627099 2 0.0000 0.87889 0.000 1.000 0.000 0.000
#> GSM627105 2 0.3311 0.72789 0.000 0.828 0.000 0.172
#> GSM627117 4 0.4981 0.28447 0.000 0.000 0.464 0.536
#> GSM627121 2 0.0592 0.87385 0.000 0.984 0.000 0.016
#> GSM627127 2 0.0000 0.87889 0.000 1.000 0.000 0.000
#> GSM627087 2 0.1211 0.87789 0.000 0.960 0.000 0.040
#> GSM627089 3 0.4907 0.03343 0.000 0.000 0.580 0.420
#> GSM627092 4 0.2011 0.56037 0.000 0.080 0.000 0.920
#> GSM627076 3 0.4866 0.07029 0.000 0.000 0.596 0.404
#> GSM627136 3 0.4790 0.09472 0.000 0.000 0.620 0.380
#> GSM627081 2 0.0592 0.87385 0.000 0.984 0.000 0.016
#> GSM627091 2 0.4250 0.77580 0.000 0.724 0.000 0.276
#> GSM627097 4 0.6630 0.52478 0.000 0.252 0.136 0.612
#> GSM627072 4 0.6904 0.47588 0.000 0.132 0.312 0.556
#> GSM627080 1 0.0000 0.85633 1.000 0.000 0.000 0.000
#> GSM627088 3 0.4877 0.06089 0.000 0.000 0.592 0.408
#> GSM627109 1 0.4804 0.58082 0.616 0.000 0.384 0.000
#> GSM627111 1 0.0000 0.85633 1.000 0.000 0.000 0.000
#> GSM627113 1 0.4998 0.38230 0.512 0.000 0.488 0.000
#> GSM627133 2 0.0707 0.87419 0.000 0.980 0.000 0.020
#> GSM627177 4 0.4992 0.27034 0.000 0.000 0.476 0.524
#> GSM627086 2 0.1792 0.87446 0.000 0.932 0.000 0.068
#> GSM627095 1 0.4776 0.59213 0.624 0.000 0.376 0.000
#> GSM627079 3 0.4955 -0.05501 0.000 0.000 0.556 0.444
#> GSM627082 3 0.2469 0.56759 0.000 0.000 0.892 0.108
#> GSM627074 3 0.5821 -0.26373 0.432 0.000 0.536 0.032
#> GSM627077 3 0.0592 0.62787 0.000 0.000 0.984 0.016
#> GSM627093 3 0.5247 0.22066 0.284 0.000 0.684 0.032
#> GSM627120 2 0.0188 0.87845 0.000 0.996 0.000 0.004
#> GSM627124 4 0.2814 0.54268 0.000 0.132 0.000 0.868
#> GSM627075 2 0.4356 0.76156 0.000 0.708 0.000 0.292
#> GSM627085 2 0.4304 0.77034 0.000 0.716 0.000 0.284
#> GSM627119 3 0.5113 0.27330 0.264 0.000 0.704 0.032
#> GSM627116 4 0.4992 0.27034 0.000 0.000 0.476 0.524
#> GSM627084 3 0.0707 0.62301 0.000 0.000 0.980 0.020
#> GSM627096 2 0.0469 0.87573 0.000 0.988 0.000 0.012
#> GSM627100 4 0.4992 0.27034 0.000 0.000 0.476 0.524
#> GSM627112 4 0.2281 0.55156 0.000 0.000 0.096 0.904
#> GSM627083 3 0.0376 0.62780 0.004 0.000 0.992 0.004
#> GSM627098 3 0.4222 0.28010 0.272 0.000 0.728 0.000
#> GSM627104 3 0.5815 -0.25183 0.428 0.000 0.540 0.032
#> GSM627131 3 0.0817 0.62506 0.000 0.000 0.976 0.024
#> GSM627106 2 0.0592 0.87385 0.000 0.984 0.000 0.016
#> GSM627123 1 0.4776 0.59213 0.624 0.000 0.376 0.000
#> GSM627129 2 0.0188 0.87845 0.000 0.996 0.000 0.004
#> GSM627216 2 0.0188 0.87845 0.000 0.996 0.000 0.004
#> GSM627212 2 0.3649 0.82236 0.000 0.796 0.000 0.204
#> GSM627190 4 0.4643 0.37811 0.000 0.000 0.344 0.656
#> GSM627169 4 0.2124 0.55120 0.000 0.028 0.040 0.932
#> GSM627167 2 0.4855 0.62984 0.000 0.600 0.000 0.400
#> GSM627192 1 0.0000 0.85633 1.000 0.000 0.000 0.000
#> GSM627203 4 0.6973 0.48199 0.000 0.144 0.300 0.556
#> GSM627151 4 0.5951 0.49970 0.000 0.064 0.300 0.636
#> GSM627163 1 0.0000 0.85633 1.000 0.000 0.000 0.000
#> GSM627211 2 0.4356 0.76156 0.000 0.708 0.000 0.292
#> GSM627171 4 0.4040 0.44279 0.000 0.248 0.000 0.752
#> GSM627209 2 0.1867 0.87351 0.000 0.928 0.000 0.072
#> GSM627135 1 0.1474 0.83175 0.948 0.000 0.052 0.000
#> GSM627170 2 0.0000 0.87889 0.000 1.000 0.000 0.000
#> GSM627178 3 0.4882 0.26210 0.272 0.000 0.708 0.020
#> GSM627199 4 0.1792 0.56294 0.000 0.068 0.000 0.932
#> GSM627213 2 0.2011 0.83002 0.000 0.920 0.000 0.080
#> GSM627140 4 0.4855 0.22112 0.000 0.000 0.400 0.600
#> GSM627149 1 0.0000 0.85633 1.000 0.000 0.000 0.000
#> GSM627147 4 0.2281 0.55585 0.000 0.096 0.000 0.904
#> GSM627195 2 0.0592 0.87385 0.000 0.984 0.000 0.016
#> GSM627204 2 0.4356 0.76156 0.000 0.708 0.000 0.292
#> GSM627207 2 0.3610 0.82441 0.000 0.800 0.000 0.200
#> GSM627157 1 0.4985 0.42782 0.532 0.000 0.468 0.000
#> GSM627201 2 0.1792 0.87446 0.000 0.932 0.000 0.068
#> GSM627146 2 0.4040 0.79541 0.000 0.752 0.000 0.248
#> GSM627156 2 0.4585 0.72268 0.000 0.668 0.000 0.332
#> GSM627188 1 0.0000 0.85633 1.000 0.000 0.000 0.000
#> GSM627197 2 0.4040 0.79541 0.000 0.752 0.000 0.248
#> GSM627173 4 0.2081 0.55830 0.000 0.084 0.000 0.916
#> GSM627179 2 0.3444 0.83219 0.000 0.816 0.000 0.184
#> GSM627208 2 0.0000 0.87889 0.000 1.000 0.000 0.000
#> GSM627215 2 0.0469 0.87573 0.000 0.988 0.000 0.012
#> GSM627153 2 0.1792 0.87446 0.000 0.932 0.000 0.068
#> GSM627155 1 0.0000 0.85633 1.000 0.000 0.000 0.000
#> GSM627165 2 0.0000 0.87889 0.000 1.000 0.000 0.000
#> GSM627168 3 0.0592 0.62785 0.000 0.000 0.984 0.016
#> GSM627183 3 0.0592 0.62787 0.000 0.000 0.984 0.016
#> GSM627144 2 0.3311 0.72850 0.000 0.828 0.000 0.172
#> GSM627158 1 0.0000 0.85633 1.000 0.000 0.000 0.000
#> GSM627196 2 0.3837 0.81073 0.000 0.776 0.000 0.224
#> GSM627142 3 0.4907 0.03035 0.000 0.000 0.580 0.420
#> GSM627182 2 0.0817 0.87181 0.000 0.976 0.000 0.024
#> GSM627202 3 0.0524 0.62791 0.004 0.000 0.988 0.008
#> GSM627141 3 0.0469 0.62830 0.000 0.000 0.988 0.012
#> GSM627143 4 0.6504 0.54048 0.000 0.148 0.216 0.636
#> GSM627145 4 0.5155 0.28503 0.000 0.004 0.468 0.528
#> GSM627152 3 0.1022 0.62510 0.000 0.000 0.968 0.032
#> GSM627200 3 0.0707 0.62301 0.000 0.000 0.980 0.020
#> GSM627159 3 0.4907 0.03035 0.000 0.000 0.580 0.420
#> GSM627164 4 0.1867 0.56210 0.000 0.072 0.000 0.928
#> GSM627138 1 0.0000 0.85633 1.000 0.000 0.000 0.000
#> GSM627175 2 0.0000 0.87889 0.000 1.000 0.000 0.000
#> GSM627150 4 0.7020 0.45146 0.000 0.332 0.136 0.532
#> GSM627166 3 0.5247 0.22066 0.284 0.000 0.684 0.032
#> GSM627186 4 0.1635 0.54960 0.000 0.008 0.044 0.948
#> GSM627139 4 0.6522 0.50855 0.000 0.112 0.280 0.608
#> GSM627181 2 0.2345 0.86507 0.000 0.900 0.000 0.100
#> GSM627205 2 0.0000 0.87889 0.000 1.000 0.000 0.000
#> GSM627214 2 0.0000 0.87889 0.000 1.000 0.000 0.000
#> GSM627180 2 0.0817 0.87181 0.000 0.976 0.000 0.024
#> GSM627172 4 0.1576 0.54726 0.000 0.004 0.048 0.948
#> GSM627184 1 0.0000 0.85633 1.000 0.000 0.000 0.000
#> GSM627193 2 0.3444 0.83219 0.000 0.816 0.000 0.184
#> GSM627191 3 0.0707 0.62794 0.000 0.000 0.980 0.020
#> GSM627176 4 0.4981 0.11858 0.000 0.000 0.464 0.536
#> GSM627194 2 0.1867 0.87412 0.000 0.928 0.000 0.072
#> GSM627154 4 0.3975 0.45601 0.000 0.240 0.000 0.760
#> GSM627187 4 0.4981 0.11858 0.000 0.000 0.464 0.536
#> GSM627198 2 0.4250 0.77421 0.000 0.724 0.000 0.276
#> GSM627160 3 0.3172 0.54210 0.000 0.000 0.840 0.160
#> GSM627185 1 0.4776 0.59213 0.624 0.000 0.376 0.000
#> GSM627206 3 0.4888 0.04966 0.000 0.000 0.588 0.412
#> GSM627161 1 0.0000 0.85633 1.000 0.000 0.000 0.000
#> GSM627162 4 0.4972 0.13960 0.000 0.000 0.456 0.544
#> GSM627210 3 0.4746 0.28189 0.000 0.000 0.632 0.368
#> GSM627189 2 0.3610 0.82441 0.000 0.800 0.000 0.200
show/hide code output
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM627128 5 0.3399 0.6260 0.000 0.004 0.012 0.172 0.812
#> GSM627110 3 0.5358 0.4041 0.000 0.000 0.648 0.248 0.104
#> GSM627132 1 0.0000 0.9679 1.000 0.000 0.000 0.000 0.000
#> GSM627107 2 0.3274 0.7109 0.000 0.780 0.000 0.000 0.220
#> GSM627103 2 0.4707 0.5687 0.000 0.588 0.000 0.020 0.392
#> GSM627114 3 0.6219 0.1160 0.000 0.000 0.548 0.212 0.240
#> GSM627134 2 0.3242 0.7129 0.000 0.784 0.000 0.000 0.216
#> GSM627137 2 0.0000 0.7512 0.000 1.000 0.000 0.000 0.000
#> GSM627148 5 0.1756 0.5245 0.000 0.008 0.016 0.036 0.940
#> GSM627101 2 0.3274 0.7109 0.000 0.780 0.000 0.000 0.220
#> GSM627130 5 0.2835 0.5932 0.000 0.016 0.004 0.112 0.868
#> GSM627071 5 0.5820 0.6701 0.000 0.000 0.196 0.192 0.612
#> GSM627118 2 0.2230 0.7417 0.000 0.884 0.000 0.000 0.116
#> GSM627094 2 0.6368 0.1567 0.000 0.472 0.000 0.356 0.172
#> GSM627122 3 0.6085 -0.1989 0.000 0.000 0.472 0.124 0.404
#> GSM627115 2 0.0794 0.7459 0.000 0.972 0.000 0.028 0.000
#> GSM627125 5 0.2068 0.4308 0.000 0.092 0.000 0.004 0.904
#> GSM627174 5 0.6289 0.6344 0.000 0.000 0.232 0.232 0.536
#> GSM627102 4 0.3550 0.6535 0.000 0.004 0.000 0.760 0.236
#> GSM627073 2 0.4504 0.5321 0.000 0.564 0.000 0.008 0.428
#> GSM627108 2 0.3816 0.5515 0.000 0.696 0.000 0.304 0.000
#> GSM627126 1 0.0000 0.9679 1.000 0.000 0.000 0.000 0.000
#> GSM627078 2 0.6339 0.2504 0.000 0.508 0.000 0.304 0.188
#> GSM627090 5 0.6268 0.6456 0.000 0.000 0.228 0.232 0.540
#> GSM627099 2 0.0404 0.7524 0.000 0.988 0.000 0.000 0.012
#> GSM627105 5 0.3934 0.1232 0.000 0.276 0.000 0.008 0.716
#> GSM627117 4 0.6583 -0.1184 0.000 0.000 0.256 0.468 0.276
#> GSM627121 2 0.3274 0.7109 0.000 0.780 0.000 0.000 0.220
#> GSM627127 2 0.1341 0.7527 0.000 0.944 0.000 0.000 0.056
#> GSM627087 2 0.0404 0.7506 0.000 0.988 0.000 0.012 0.000
#> GSM627089 5 0.6246 0.6487 0.000 0.000 0.224 0.232 0.544
#> GSM627092 4 0.3550 0.6535 0.000 0.004 0.000 0.760 0.236
#> GSM627076 5 0.6268 0.6456 0.000 0.000 0.228 0.232 0.540
#> GSM627136 5 0.6661 0.4269 0.000 0.000 0.356 0.232 0.412
#> GSM627081 2 0.3424 0.6995 0.000 0.760 0.000 0.000 0.240
#> GSM627091 2 0.5470 0.4604 0.000 0.612 0.000 0.296 0.092
#> GSM627097 5 0.1836 0.5258 0.000 0.016 0.008 0.040 0.936
#> GSM627072 5 0.1310 0.5494 0.000 0.000 0.020 0.024 0.956
#> GSM627080 1 0.0000 0.9679 1.000 0.000 0.000 0.000 0.000
#> GSM627088 5 0.6349 0.6271 0.000 0.000 0.244 0.232 0.524
#> GSM627109 3 0.4367 0.3446 0.372 0.000 0.620 0.008 0.000
#> GSM627111 1 0.0000 0.9679 1.000 0.000 0.000 0.000 0.000
#> GSM627113 3 0.4225 0.3644 0.364 0.000 0.632 0.004 0.000
#> GSM627133 2 0.4456 0.6486 0.000 0.660 0.000 0.020 0.320
#> GSM627177 5 0.5902 0.6659 0.000 0.000 0.208 0.192 0.600
#> GSM627086 2 0.0000 0.7512 0.000 1.000 0.000 0.000 0.000
#> GSM627095 3 0.4264 0.3393 0.376 0.000 0.620 0.004 0.000
#> GSM627079 5 0.6201 0.6545 0.000 0.000 0.216 0.232 0.552
#> GSM627082 3 0.6092 -0.2015 0.000 0.000 0.464 0.124 0.412
#> GSM627074 3 0.3630 0.5680 0.204 0.000 0.780 0.016 0.000
#> GSM627077 3 0.2130 0.6592 0.000 0.000 0.908 0.012 0.080
#> GSM627093 3 0.2624 0.6535 0.116 0.000 0.872 0.012 0.000
#> GSM627120 2 0.3242 0.7129 0.000 0.784 0.000 0.000 0.216
#> GSM627124 4 0.5398 0.5929 0.000 0.112 0.000 0.648 0.240
#> GSM627075 2 0.3966 0.5093 0.000 0.664 0.000 0.336 0.000
#> GSM627085 2 0.3949 0.5520 0.000 0.696 0.000 0.300 0.004
#> GSM627119 3 0.2624 0.6535 0.116 0.000 0.872 0.012 0.000
#> GSM627116 5 0.6084 0.6632 0.000 0.000 0.208 0.220 0.572
#> GSM627084 3 0.0693 0.6868 0.000 0.000 0.980 0.012 0.008
#> GSM627096 2 0.3242 0.7129 0.000 0.784 0.000 0.000 0.216
#> GSM627100 5 0.6049 0.6655 0.000 0.000 0.192 0.232 0.576
#> GSM627112 4 0.2694 0.5348 0.000 0.004 0.004 0.864 0.128
#> GSM627083 3 0.1364 0.6829 0.000 0.000 0.952 0.012 0.036
#> GSM627098 3 0.0898 0.6910 0.020 0.000 0.972 0.000 0.008
#> GSM627104 3 0.3630 0.5680 0.204 0.000 0.780 0.016 0.000
#> GSM627131 3 0.1942 0.6658 0.000 0.000 0.920 0.012 0.068
#> GSM627106 2 0.3424 0.6995 0.000 0.760 0.000 0.000 0.240
#> GSM627123 3 0.4264 0.3393 0.376 0.000 0.620 0.004 0.000
#> GSM627129 2 0.3242 0.7129 0.000 0.784 0.000 0.000 0.216
#> GSM627216 2 0.3596 0.7164 0.000 0.776 0.000 0.012 0.212
#> GSM627212 2 0.3274 0.6370 0.000 0.780 0.000 0.220 0.000
#> GSM627190 4 0.6203 0.2842 0.000 0.000 0.188 0.544 0.268
#> GSM627169 4 0.3579 0.6520 0.000 0.000 0.004 0.756 0.240
#> GSM627167 2 0.6629 0.0605 0.000 0.436 0.000 0.332 0.232
#> GSM627192 1 0.0000 0.9679 1.000 0.000 0.000 0.000 0.000
#> GSM627203 5 0.1059 0.5423 0.000 0.008 0.020 0.004 0.968
#> GSM627151 5 0.3996 0.5214 0.000 0.008 0.012 0.228 0.752
#> GSM627163 1 0.0000 0.9679 1.000 0.000 0.000 0.000 0.000
#> GSM627211 2 0.3837 0.5463 0.000 0.692 0.000 0.308 0.000
#> GSM627171 4 0.6778 0.2987 0.000 0.276 0.000 0.368 0.356
#> GSM627209 2 0.0510 0.7492 0.000 0.984 0.000 0.016 0.000
#> GSM627135 1 0.4166 0.3983 0.648 0.000 0.348 0.004 0.000
#> GSM627170 2 0.1410 0.7518 0.000 0.940 0.000 0.000 0.060
#> GSM627178 3 0.2416 0.6625 0.100 0.000 0.888 0.012 0.000
#> GSM627199 4 0.2719 0.6233 0.000 0.004 0.000 0.852 0.144
#> GSM627213 2 0.4504 0.5233 0.000 0.564 0.000 0.008 0.428
#> GSM627140 4 0.4678 0.3047 0.000 0.000 0.224 0.712 0.064
#> GSM627149 1 0.0162 0.9648 0.996 0.000 0.000 0.004 0.000
#> GSM627147 4 0.3550 0.6535 0.000 0.004 0.000 0.760 0.236
#> GSM627195 2 0.3424 0.6995 0.000 0.760 0.000 0.000 0.240
#> GSM627204 2 0.3876 0.5358 0.000 0.684 0.000 0.316 0.000
#> GSM627207 2 0.3242 0.6390 0.000 0.784 0.000 0.216 0.000
#> GSM627157 3 0.4238 0.3565 0.368 0.000 0.628 0.004 0.000
#> GSM627201 2 0.0000 0.7512 0.000 1.000 0.000 0.000 0.000
#> GSM627146 2 0.5116 0.5433 0.000 0.668 0.000 0.248 0.084
#> GSM627156 4 0.6440 -0.0470 0.000 0.412 0.000 0.412 0.176
#> GSM627188 1 0.0000 0.9679 1.000 0.000 0.000 0.000 0.000
#> GSM627197 2 0.3424 0.6169 0.000 0.760 0.000 0.240 0.000
#> GSM627173 4 0.3550 0.6535 0.000 0.004 0.000 0.760 0.236
#> GSM627179 2 0.2280 0.7052 0.000 0.880 0.000 0.120 0.000
#> GSM627208 2 0.2424 0.7384 0.000 0.868 0.000 0.000 0.132
#> GSM627215 2 0.3242 0.7129 0.000 0.784 0.000 0.000 0.216
#> GSM627153 2 0.0290 0.7509 0.000 0.992 0.000 0.008 0.000
#> GSM627155 1 0.0000 0.9679 1.000 0.000 0.000 0.000 0.000
#> GSM627165 2 0.0404 0.7524 0.000 0.988 0.000 0.000 0.012
#> GSM627168 3 0.5082 0.4430 0.000 0.000 0.684 0.220 0.096
#> GSM627183 3 0.5240 0.4330 0.000 0.000 0.676 0.204 0.120
#> GSM627144 2 0.5103 0.4824 0.000 0.524 0.004 0.028 0.444
#> GSM627158 1 0.0000 0.9679 1.000 0.000 0.000 0.000 0.000
#> GSM627196 2 0.3395 0.6208 0.000 0.764 0.000 0.236 0.000
#> GSM627142 5 0.6201 0.6507 0.000 0.000 0.216 0.232 0.552
#> GSM627182 2 0.4555 0.6289 0.000 0.636 0.000 0.020 0.344
#> GSM627202 3 0.1195 0.6824 0.000 0.000 0.960 0.012 0.028
#> GSM627141 3 0.2236 0.6606 0.000 0.000 0.908 0.024 0.068
#> GSM627143 5 0.1731 0.5292 0.000 0.012 0.008 0.040 0.940
#> GSM627145 5 0.3780 0.6218 0.000 0.000 0.116 0.072 0.812
#> GSM627152 3 0.4617 0.4912 0.000 0.000 0.716 0.224 0.060
#> GSM627200 3 0.0579 0.6873 0.000 0.000 0.984 0.008 0.008
#> GSM627159 5 0.6201 0.6507 0.000 0.000 0.216 0.232 0.552
#> GSM627164 4 0.3579 0.6533 0.000 0.004 0.000 0.756 0.240
#> GSM627138 1 0.0000 0.9679 1.000 0.000 0.000 0.000 0.000
#> GSM627175 2 0.0162 0.7518 0.000 0.996 0.000 0.000 0.004
#> GSM627150 5 0.1892 0.4495 0.000 0.080 0.000 0.004 0.916
#> GSM627166 3 0.2624 0.6535 0.116 0.000 0.872 0.012 0.000
#> GSM627186 4 0.3579 0.6520 0.000 0.000 0.004 0.756 0.240
#> GSM627139 5 0.3005 0.5916 0.000 0.008 0.012 0.124 0.856
#> GSM627181 2 0.0510 0.7477 0.000 0.984 0.000 0.016 0.000
#> GSM627205 2 0.0963 0.7530 0.000 0.964 0.000 0.000 0.036
#> GSM627214 2 0.1410 0.7518 0.000 0.940 0.000 0.000 0.060
#> GSM627180 2 0.4626 0.6083 0.000 0.616 0.000 0.020 0.364
#> GSM627172 4 0.2763 0.6206 0.000 0.000 0.004 0.848 0.148
#> GSM627184 1 0.0000 0.9679 1.000 0.000 0.000 0.000 0.000
#> GSM627193 2 0.1851 0.7218 0.000 0.912 0.000 0.088 0.000
#> GSM627191 3 0.3421 0.6227 0.000 0.000 0.840 0.080 0.080
#> GSM627176 4 0.5304 0.1522 0.000 0.000 0.292 0.628 0.080
#> GSM627194 2 0.1117 0.7526 0.000 0.964 0.000 0.020 0.016
#> GSM627154 4 0.6805 0.2459 0.000 0.312 0.000 0.372 0.316
#> GSM627187 4 0.5245 0.1788 0.000 0.000 0.280 0.640 0.080
#> GSM627198 2 0.5163 0.4915 0.000 0.636 0.000 0.296 0.068
#> GSM627160 3 0.5188 0.4876 0.000 0.000 0.612 0.328 0.060
#> GSM627185 3 0.4264 0.3393 0.376 0.000 0.620 0.004 0.000
#> GSM627206 5 0.6329 0.6314 0.000 0.000 0.240 0.232 0.528
#> GSM627161 1 0.0000 0.9679 1.000 0.000 0.000 0.000 0.000
#> GSM627162 4 0.5164 0.2378 0.000 0.000 0.256 0.660 0.084
#> GSM627210 3 0.4557 0.2379 0.000 0.000 0.516 0.476 0.008
#> GSM627189 2 0.3210 0.6423 0.000 0.788 0.000 0.212 0.000
show/hide code output
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM627128 6 0.5196 0.25656 0.000 0.000 0.000 0.252 0.144 0.604
#> GSM627110 5 0.4253 0.23752 0.000 0.000 0.196 0.004 0.728 0.072
#> GSM627132 1 0.0000 0.94479 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627107 4 0.3314 0.63346 0.000 0.256 0.004 0.740 0.000 0.000
#> GSM627103 4 0.3617 0.58635 0.000 0.244 0.000 0.736 0.000 0.020
#> GSM627114 5 0.2664 0.52478 0.000 0.000 0.000 0.000 0.816 0.184
#> GSM627134 4 0.3636 0.64772 0.000 0.320 0.000 0.676 0.000 0.004
#> GSM627137 2 0.3508 0.51652 0.000 0.704 0.004 0.292 0.000 0.000
#> GSM627148 6 0.4444 0.32626 0.000 0.000 0.000 0.436 0.028 0.536
#> GSM627101 4 0.3583 0.62991 0.000 0.260 0.004 0.728 0.000 0.008
#> GSM627130 6 0.5107 0.30764 0.000 0.004 0.004 0.288 0.088 0.616
#> GSM627071 6 0.5052 -0.16232 0.000 0.000 0.000 0.080 0.388 0.532
#> GSM627118 4 0.3940 0.53781 0.000 0.336 0.004 0.652 0.000 0.008
#> GSM627094 2 0.3757 0.60300 0.000 0.808 0.024 0.104 0.000 0.064
#> GSM627122 5 0.3534 0.51147 0.000 0.000 0.016 0.000 0.740 0.244
#> GSM627115 2 0.2584 0.63473 0.000 0.848 0.004 0.144 0.000 0.004
#> GSM627125 4 0.3838 -0.12237 0.000 0.000 0.000 0.552 0.000 0.448
#> GSM627174 5 0.4212 0.43642 0.000 0.000 0.008 0.008 0.592 0.392
#> GSM627102 6 0.6438 0.37227 0.000 0.140 0.348 0.052 0.000 0.460
#> GSM627073 4 0.3655 0.55799 0.000 0.136 0.000 0.788 0.000 0.076
#> GSM627108 2 0.0692 0.68933 0.000 0.976 0.020 0.000 0.000 0.004
#> GSM627126 1 0.0000 0.94479 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627078 2 0.3043 0.61697 0.000 0.836 0.008 0.132 0.000 0.024
#> GSM627090 5 0.4205 0.41071 0.000 0.000 0.000 0.016 0.564 0.420
#> GSM627099 2 0.3998 -0.11579 0.000 0.504 0.004 0.492 0.000 0.000
#> GSM627105 4 0.4431 0.36212 0.000 0.080 0.000 0.692 0.000 0.228
#> GSM627117 6 0.5978 0.10892 0.000 0.000 0.200 0.016 0.252 0.532
#> GSM627121 4 0.3314 0.63346 0.000 0.256 0.004 0.740 0.000 0.000
#> GSM627127 4 0.4293 0.33092 0.000 0.448 0.004 0.536 0.000 0.012
#> GSM627087 2 0.2703 0.60152 0.000 0.824 0.000 0.172 0.000 0.004
#> GSM627089 5 0.4366 0.39140 0.000 0.000 0.000 0.024 0.548 0.428
#> GSM627092 6 0.6438 0.37227 0.000 0.140 0.348 0.052 0.000 0.460
#> GSM627076 5 0.3930 0.42175 0.000 0.000 0.000 0.004 0.576 0.420
#> GSM627136 5 0.3563 0.47065 0.000 0.000 0.000 0.000 0.664 0.336
#> GSM627081 4 0.3566 0.65344 0.000 0.224 0.000 0.752 0.000 0.024
#> GSM627091 2 0.2325 0.65181 0.000 0.884 0.008 0.100 0.000 0.008
#> GSM627097 6 0.4635 0.33390 0.000 0.000 0.000 0.336 0.056 0.608
#> GSM627072 6 0.4838 0.32870 0.000 0.000 0.000 0.396 0.060 0.544
#> GSM627080 1 0.0000 0.94479 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627088 5 0.3819 0.45327 0.000 0.000 0.000 0.004 0.624 0.372
#> GSM627109 3 0.5476 0.57960 0.132 0.000 0.576 0.008 0.284 0.000
#> GSM627111 1 0.0000 0.94479 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627113 3 0.5824 0.56568 0.188 0.000 0.504 0.004 0.304 0.000
#> GSM627133 4 0.4428 0.58912 0.000 0.244 0.000 0.684 0.000 0.072
#> GSM627177 6 0.4985 -0.18367 0.000 0.000 0.000 0.072 0.400 0.528
#> GSM627086 2 0.3508 0.51968 0.000 0.704 0.004 0.292 0.000 0.000
#> GSM627095 3 0.5835 0.56560 0.192 0.000 0.504 0.004 0.300 0.000
#> GSM627079 5 0.4695 0.33526 0.000 0.000 0.000 0.044 0.508 0.448
#> GSM627082 5 0.3999 0.50312 0.000 0.000 0.032 0.000 0.696 0.272
#> GSM627074 3 0.4898 0.57650 0.060 0.000 0.604 0.008 0.328 0.000
#> GSM627077 5 0.2912 0.16740 0.000 0.000 0.216 0.000 0.784 0.000
#> GSM627093 3 0.4549 0.55351 0.028 0.000 0.596 0.008 0.368 0.000
#> GSM627120 4 0.3428 0.64960 0.000 0.304 0.000 0.696 0.000 0.000
#> GSM627124 2 0.6693 0.10990 0.000 0.460 0.080 0.140 0.000 0.320
#> GSM627075 2 0.1675 0.67855 0.000 0.936 0.024 0.008 0.000 0.032
#> GSM627085 2 0.2632 0.66017 0.000 0.880 0.012 0.076 0.000 0.032
#> GSM627119 3 0.4560 0.55267 0.028 0.000 0.592 0.008 0.372 0.000
#> GSM627116 6 0.4901 -0.30195 0.000 0.000 0.000 0.060 0.456 0.484
#> GSM627084 5 0.3659 -0.17747 0.000 0.000 0.364 0.000 0.636 0.000
#> GSM627096 4 0.3650 0.63769 0.000 0.272 0.004 0.716 0.000 0.008
#> GSM627100 5 0.4837 0.34283 0.000 0.000 0.000 0.056 0.512 0.432
#> GSM627112 6 0.6436 0.33637 0.000 0.108 0.364 0.012 0.044 0.472
#> GSM627083 5 0.3428 0.00223 0.000 0.000 0.304 0.000 0.696 0.000
#> GSM627098 5 0.3695 -0.19810 0.000 0.000 0.376 0.000 0.624 0.000
#> GSM627104 3 0.4898 0.57650 0.060 0.000 0.604 0.008 0.328 0.000
#> GSM627131 5 0.3126 0.10865 0.000 0.000 0.248 0.000 0.752 0.000
#> GSM627106 4 0.3705 0.65233 0.000 0.224 0.004 0.748 0.000 0.024
#> GSM627123 3 0.5835 0.56560 0.192 0.000 0.504 0.004 0.300 0.000
#> GSM627129 4 0.3804 0.63257 0.000 0.336 0.000 0.656 0.000 0.008
#> GSM627216 4 0.3954 0.61504 0.000 0.352 0.000 0.636 0.000 0.012
#> GSM627212 2 0.1196 0.68887 0.000 0.952 0.008 0.040 0.000 0.000
#> GSM627190 6 0.6104 0.15756 0.000 0.000 0.408 0.024 0.140 0.428
#> GSM627169 6 0.6187 0.36405 0.000 0.116 0.364 0.044 0.000 0.476
#> GSM627167 2 0.5335 0.46221 0.000 0.640 0.016 0.188 0.000 0.156
#> GSM627192 1 0.0000 0.94479 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627203 6 0.4620 0.32924 0.000 0.000 0.000 0.428 0.040 0.532
#> GSM627151 6 0.4788 0.33099 0.000 0.000 0.024 0.224 0.064 0.688
#> GSM627163 1 0.0000 0.94479 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627211 2 0.2515 0.67716 0.000 0.888 0.024 0.072 0.000 0.016
#> GSM627171 2 0.5836 0.37424 0.000 0.572 0.020 0.192 0.000 0.216
#> GSM627209 2 0.3109 0.60360 0.000 0.772 0.004 0.224 0.000 0.000
#> GSM627135 1 0.6022 -0.25178 0.432 0.000 0.356 0.004 0.208 0.000
#> GSM627170 4 0.3899 0.40506 0.000 0.404 0.004 0.592 0.000 0.000
#> GSM627178 3 0.4353 0.53898 0.020 0.000 0.588 0.004 0.388 0.000
#> GSM627199 6 0.6219 0.36189 0.000 0.144 0.344 0.020 0.008 0.484
#> GSM627213 4 0.3945 0.56521 0.000 0.200 0.004 0.748 0.000 0.048
#> GSM627140 3 0.6191 -0.18039 0.000 0.004 0.444 0.012 0.176 0.364
#> GSM627149 1 0.1237 0.90502 0.956 0.000 0.020 0.004 0.020 0.000
#> GSM627147 6 0.6438 0.37227 0.000 0.140 0.348 0.052 0.000 0.460
#> GSM627195 4 0.4443 0.65233 0.000 0.276 0.000 0.664 0.000 0.060
#> GSM627204 2 0.1232 0.68489 0.000 0.956 0.024 0.004 0.000 0.016
#> GSM627207 2 0.2362 0.66990 0.000 0.860 0.004 0.136 0.000 0.000
#> GSM627157 3 0.5824 0.56568 0.188 0.000 0.504 0.004 0.304 0.000
#> GSM627201 2 0.3508 0.51968 0.000 0.704 0.004 0.292 0.000 0.000
#> GSM627146 2 0.2643 0.65333 0.000 0.856 0.008 0.128 0.000 0.008
#> GSM627156 2 0.4797 0.53723 0.000 0.728 0.040 0.112 0.000 0.120
#> GSM627188 1 0.0000 0.94479 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627197 2 0.1340 0.68990 0.000 0.948 0.008 0.040 0.000 0.004
#> GSM627173 6 0.6464 0.36939 0.000 0.144 0.348 0.052 0.000 0.456
#> GSM627179 2 0.2442 0.66614 0.000 0.852 0.004 0.144 0.000 0.000
#> GSM627208 4 0.3584 0.57909 0.000 0.308 0.004 0.688 0.000 0.000
#> GSM627215 4 0.4011 0.65728 0.000 0.304 0.000 0.672 0.000 0.024
#> GSM627153 2 0.3248 0.60754 0.000 0.768 0.004 0.224 0.000 0.004
#> GSM627155 1 0.0000 0.94479 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627165 2 0.3996 -0.08695 0.000 0.512 0.004 0.484 0.000 0.000
#> GSM627168 5 0.1934 0.42785 0.000 0.000 0.040 0.000 0.916 0.044
#> GSM627183 5 0.1141 0.46369 0.000 0.000 0.000 0.000 0.948 0.052
#> GSM627144 4 0.4666 0.50317 0.000 0.168 0.000 0.688 0.000 0.144
#> GSM627158 1 0.0000 0.94479 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627196 2 0.2165 0.67919 0.000 0.884 0.008 0.108 0.000 0.000
#> GSM627142 5 0.4276 0.40795 0.000 0.000 0.000 0.020 0.564 0.416
#> GSM627182 4 0.4405 0.58829 0.000 0.240 0.000 0.688 0.000 0.072
#> GSM627202 5 0.3409 -0.01200 0.000 0.000 0.300 0.000 0.700 0.000
#> GSM627141 5 0.2883 0.17327 0.000 0.000 0.212 0.000 0.788 0.000
#> GSM627143 6 0.4408 0.35097 0.000 0.000 0.000 0.356 0.036 0.608
#> GSM627145 6 0.5480 0.08872 0.000 0.000 0.000 0.184 0.252 0.564
#> GSM627152 5 0.3549 0.19130 0.000 0.000 0.192 0.004 0.776 0.028
#> GSM627200 5 0.3862 -0.37957 0.000 0.000 0.476 0.000 0.524 0.000
#> GSM627159 5 0.4462 0.39565 0.000 0.000 0.008 0.016 0.540 0.436
#> GSM627164 6 0.6417 0.37201 0.000 0.136 0.352 0.052 0.000 0.460
#> GSM627138 1 0.0000 0.94479 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627175 2 0.3878 0.46668 0.000 0.668 0.008 0.320 0.000 0.004
#> GSM627150 4 0.4169 -0.20002 0.000 0.000 0.000 0.532 0.012 0.456
#> GSM627166 3 0.4549 0.55351 0.028 0.000 0.596 0.008 0.368 0.000
#> GSM627186 6 0.6118 0.36057 0.000 0.100 0.372 0.048 0.000 0.480
#> GSM627139 6 0.4911 0.30427 0.000 0.000 0.000 0.276 0.100 0.624
#> GSM627181 2 0.3512 0.54870 0.000 0.720 0.008 0.272 0.000 0.000
#> GSM627205 4 0.3944 0.33398 0.000 0.428 0.004 0.568 0.000 0.000
#> GSM627214 4 0.3965 0.43452 0.000 0.388 0.008 0.604 0.000 0.000
#> GSM627180 4 0.4328 0.57244 0.000 0.212 0.000 0.708 0.000 0.080
#> GSM627172 6 0.6048 0.35463 0.000 0.116 0.364 0.020 0.008 0.492
#> GSM627184 1 0.0000 0.94479 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627193 2 0.1753 0.67337 0.000 0.912 0.004 0.084 0.000 0.000
#> GSM627191 5 0.1814 0.33059 0.000 0.000 0.100 0.000 0.900 0.000
#> GSM627176 3 0.6090 -0.02706 0.000 0.000 0.448 0.004 0.268 0.280
#> GSM627194 2 0.3103 0.55855 0.000 0.784 0.000 0.208 0.000 0.008
#> GSM627154 2 0.5891 0.33215 0.000 0.552 0.016 0.228 0.000 0.204
#> GSM627187 3 0.6185 -0.03102 0.000 0.000 0.444 0.008 0.264 0.284
#> GSM627198 2 0.2425 0.65023 0.000 0.880 0.008 0.100 0.000 0.012
#> GSM627160 5 0.4794 0.01924 0.000 0.000 0.228 0.004 0.668 0.100
#> GSM627185 3 0.5835 0.56560 0.192 0.000 0.504 0.004 0.300 0.000
#> GSM627206 5 0.4110 0.44430 0.000 0.000 0.000 0.016 0.608 0.376
#> GSM627161 1 0.0000 0.94479 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627162 3 0.6151 -0.06115 0.000 0.000 0.444 0.008 0.232 0.316
#> GSM627210 3 0.5771 0.28626 0.000 0.000 0.480 0.008 0.372 0.140
#> GSM627189 2 0.1285 0.68697 0.000 0.944 0.004 0.052 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two
samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures
which are significantly different between classes which can be candidate marks
for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known
annotation is numeric, one-way ANOVA test is applied, and if the known
annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) age(p) other(p) k
#> ATC:kmeans 142 1.0000 0.152 0.03741 2
#> ATC:kmeans 135 0.9352 0.384 0.00619 3
#> ATC:kmeans 105 0.9422 0.333 0.03337 4
#> ATC:kmeans 111 0.2063 0.178 0.09421 5
#> ATC:kmeans 76 0.0549 0.870 0.03904 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
ATC:skmeans**
The object with results only for a single top-value method and a single partition method
can be extracted as:
res = res_list["ATC", "skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51882 rows and 146 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
- The first row: a plot of the ECDF (empirical cumulative distribution
function) curves of the consensus matrix for each
k and the heatmap of
predicted classes for each k.
- The second row: heatmaps of the consensus matrix for each
k.
- The third row: heatmaps of the membership matrix for each
k.
- The fouth row: heatmaps of the signatures for each
k.
All the plots in panels can be made by individual functions and they are
plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
- ECDF curves of the consensus matrix for each
k;
- 1-PAC. The PAC
score
measures the proportion of the ambiguous subgrouping.
- Mean silhouette score.
- Concordance. The mean probability of fiting the consensus class ids in all
partitions.
- Area increased. Denote \(A_k\) as the area under the ECDF curve for current
k, the area increased is defined as \(A_k - A_{k-1}\).
- Rand index. The percent of pairs of samples that are both in a same cluster
or both are not in a same cluster in the partition of k and k-1.
- Jaccard index. The ratio of pairs of samples are both in a same cluster in
the partition of k and k-1 and the pairs of samples are both in a same
cluster in the partition k or k-1.
The detailed explanations of these statistics can be found in the cola
vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.962 0.986 0.4963 0.504 0.504
#> 3 3 0.732 0.661 0.841 0.2430 0.855 0.722
#> 4 4 0.784 0.757 0.883 0.1219 0.855 0.674
#> 5 5 0.725 0.680 0.792 0.0783 0.847 0.579
#> 6 6 0.806 0.804 0.863 0.0488 0.897 0.609
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
- All \(k\) with Jaccard index larger than 0.95 are removed because increasing
\(k\) does not provide enough extra information. If all \(k\) are removed, it is
marked as no subgroup is detected.
- For all \(k\) with 1-PAC score larger than 0.9, the maximal \(k\) is taken as
the best \(k\), and other \(k\) are marked as optional \(k\).
- If it does not fit the second rule. The \(k\) with the maximal vote of the
highest 1-PAC score, highest mean silhouette, and highest concordance is
taken as the best \(k\).
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
show/hide code output
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM627128 2 0.000 0.986 0.000 1.000
#> GSM627110 1 0.000 0.984 1.000 0.000
#> GSM627132 1 0.000 0.984 1.000 0.000
#> GSM627107 2 0.000 0.986 0.000 1.000
#> GSM627103 2 0.000 0.986 0.000 1.000
#> GSM627114 1 0.000 0.984 1.000 0.000
#> GSM627134 2 0.000 0.986 0.000 1.000
#> GSM627137 2 0.000 0.986 0.000 1.000
#> GSM627148 2 0.000 0.986 0.000 1.000
#> GSM627101 2 0.000 0.986 0.000 1.000
#> GSM627130 2 0.000 0.986 0.000 1.000
#> GSM627071 1 0.966 0.357 0.608 0.392
#> GSM627118 2 0.000 0.986 0.000 1.000
#> GSM627094 2 0.000 0.986 0.000 1.000
#> GSM627122 1 0.000 0.984 1.000 0.000
#> GSM627115 2 0.000 0.986 0.000 1.000
#> GSM627125 2 0.000 0.986 0.000 1.000
#> GSM627174 1 0.000 0.984 1.000 0.000
#> GSM627102 2 0.000 0.986 0.000 1.000
#> GSM627073 2 0.000 0.986 0.000 1.000
#> GSM627108 2 0.000 0.986 0.000 1.000
#> GSM627126 1 0.000 0.984 1.000 0.000
#> GSM627078 2 0.000 0.986 0.000 1.000
#> GSM627090 1 0.000 0.984 1.000 0.000
#> GSM627099 2 0.000 0.986 0.000 1.000
#> GSM627105 2 0.000 0.986 0.000 1.000
#> GSM627117 1 0.000 0.984 1.000 0.000
#> GSM627121 2 0.000 0.986 0.000 1.000
#> GSM627127 2 0.000 0.986 0.000 1.000
#> GSM627087 2 0.000 0.986 0.000 1.000
#> GSM627089 1 0.000 0.984 1.000 0.000
#> GSM627092 2 0.000 0.986 0.000 1.000
#> GSM627076 1 0.000 0.984 1.000 0.000
#> GSM627136 1 0.000 0.984 1.000 0.000
#> GSM627081 2 0.000 0.986 0.000 1.000
#> GSM627091 2 0.000 0.986 0.000 1.000
#> GSM627097 2 0.000 0.986 0.000 1.000
#> GSM627072 2 0.000 0.986 0.000 1.000
#> GSM627080 1 0.000 0.984 1.000 0.000
#> GSM627088 1 0.000 0.984 1.000 0.000
#> GSM627109 1 0.000 0.984 1.000 0.000
#> GSM627111 1 0.000 0.984 1.000 0.000
#> GSM627113 1 0.000 0.984 1.000 0.000
#> GSM627133 2 0.000 0.986 0.000 1.000
#> GSM627177 1 0.722 0.742 0.800 0.200
#> GSM627086 2 0.000 0.986 0.000 1.000
#> GSM627095 1 0.000 0.984 1.000 0.000
#> GSM627079 1 0.000 0.984 1.000 0.000
#> GSM627082 1 0.000 0.984 1.000 0.000
#> GSM627074 1 0.000 0.984 1.000 0.000
#> GSM627077 1 0.000 0.984 1.000 0.000
#> GSM627093 1 0.000 0.984 1.000 0.000
#> GSM627120 2 0.000 0.986 0.000 1.000
#> GSM627124 2 0.000 0.986 0.000 1.000
#> GSM627075 2 0.000 0.986 0.000 1.000
#> GSM627085 2 0.000 0.986 0.000 1.000
#> GSM627119 1 0.000 0.984 1.000 0.000
#> GSM627116 1 0.000 0.984 1.000 0.000
#> GSM627084 1 0.000 0.984 1.000 0.000
#> GSM627096 2 0.000 0.986 0.000 1.000
#> GSM627100 1 0.000 0.984 1.000 0.000
#> GSM627112 2 0.993 0.172 0.452 0.548
#> GSM627083 1 0.000 0.984 1.000 0.000
#> GSM627098 1 0.000 0.984 1.000 0.000
#> GSM627104 1 0.000 0.984 1.000 0.000
#> GSM627131 1 0.000 0.984 1.000 0.000
#> GSM627106 2 0.000 0.986 0.000 1.000
#> GSM627123 1 0.000 0.984 1.000 0.000
#> GSM627129 2 0.000 0.986 0.000 1.000
#> GSM627216 2 0.000 0.986 0.000 1.000
#> GSM627212 2 0.000 0.986 0.000 1.000
#> GSM627190 1 0.971 0.336 0.600 0.400
#> GSM627169 2 0.000 0.986 0.000 1.000
#> GSM627167 2 0.000 0.986 0.000 1.000
#> GSM627192 1 0.000 0.984 1.000 0.000
#> GSM627203 2 0.000 0.986 0.000 1.000
#> GSM627151 2 0.000 0.986 0.000 1.000
#> GSM627163 1 0.000 0.984 1.000 0.000
#> GSM627211 2 0.000 0.986 0.000 1.000
#> GSM627171 2 0.000 0.986 0.000 1.000
#> GSM627209 2 0.000 0.986 0.000 1.000
#> GSM627135 1 0.000 0.984 1.000 0.000
#> GSM627170 2 0.000 0.986 0.000 1.000
#> GSM627178 1 0.000 0.984 1.000 0.000
#> GSM627199 2 0.000 0.986 0.000 1.000
#> GSM627213 2 0.000 0.986 0.000 1.000
#> GSM627140 1 0.000 0.984 1.000 0.000
#> GSM627149 1 0.000 0.984 1.000 0.000
#> GSM627147 2 0.000 0.986 0.000 1.000
#> GSM627195 2 0.000 0.986 0.000 1.000
#> GSM627204 2 0.000 0.986 0.000 1.000
#> GSM627207 2 0.000 0.986 0.000 1.000
#> GSM627157 1 0.000 0.984 1.000 0.000
#> GSM627201 2 0.000 0.986 0.000 1.000
#> GSM627146 2 0.000 0.986 0.000 1.000
#> GSM627156 2 0.000 0.986 0.000 1.000
#> GSM627188 1 0.000 0.984 1.000 0.000
#> GSM627197 2 0.000 0.986 0.000 1.000
#> GSM627173 2 0.000 0.986 0.000 1.000
#> GSM627179 2 0.000 0.986 0.000 1.000
#> GSM627208 2 0.000 0.986 0.000 1.000
#> GSM627215 2 0.000 0.986 0.000 1.000
#> GSM627153 2 0.000 0.986 0.000 1.000
#> GSM627155 1 0.000 0.984 1.000 0.000
#> GSM627165 2 0.000 0.986 0.000 1.000
#> GSM627168 1 0.000 0.984 1.000 0.000
#> GSM627183 1 0.000 0.984 1.000 0.000
#> GSM627144 2 0.000 0.986 0.000 1.000
#> GSM627158 1 0.000 0.984 1.000 0.000
#> GSM627196 2 0.000 0.986 0.000 1.000
#> GSM627142 1 0.000 0.984 1.000 0.000
#> GSM627182 2 0.000 0.986 0.000 1.000
#> GSM627202 1 0.000 0.984 1.000 0.000
#> GSM627141 1 0.000 0.984 1.000 0.000
#> GSM627143 2 0.000 0.986 0.000 1.000
#> GSM627145 2 0.983 0.243 0.424 0.576
#> GSM627152 1 0.000 0.984 1.000 0.000
#> GSM627200 1 0.000 0.984 1.000 0.000
#> GSM627159 1 0.000 0.984 1.000 0.000
#> GSM627164 2 0.000 0.986 0.000 1.000
#> GSM627138 1 0.000 0.984 1.000 0.000
#> GSM627175 2 0.000 0.986 0.000 1.000
#> GSM627150 2 0.000 0.986 0.000 1.000
#> GSM627166 1 0.000 0.984 1.000 0.000
#> GSM627186 2 0.000 0.986 0.000 1.000
#> GSM627139 2 0.000 0.986 0.000 1.000
#> GSM627181 2 0.000 0.986 0.000 1.000
#> GSM627205 2 0.000 0.986 0.000 1.000
#> GSM627214 2 0.000 0.986 0.000 1.000
#> GSM627180 2 0.000 0.986 0.000 1.000
#> GSM627172 2 0.753 0.717 0.216 0.784
#> GSM627184 1 0.000 0.984 1.000 0.000
#> GSM627193 2 0.000 0.986 0.000 1.000
#> GSM627191 1 0.000 0.984 1.000 0.000
#> GSM627176 1 0.000 0.984 1.000 0.000
#> GSM627194 2 0.000 0.986 0.000 1.000
#> GSM627154 2 0.000 0.986 0.000 1.000
#> GSM627187 1 0.000 0.984 1.000 0.000
#> GSM627198 2 0.000 0.986 0.000 1.000
#> GSM627160 1 0.000 0.984 1.000 0.000
#> GSM627185 1 0.000 0.984 1.000 0.000
#> GSM627206 1 0.000 0.984 1.000 0.000
#> GSM627161 1 0.000 0.984 1.000 0.000
#> GSM627162 1 0.000 0.984 1.000 0.000
#> GSM627210 1 0.000 0.984 1.000 0.000
#> GSM627189 2 0.000 0.986 0.000 1.000
show/hide code output
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM627128 3 0.6260 0.3656 0.000 0.448 0.552
#> GSM627110 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627132 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627107 2 0.6126 0.1399 0.000 0.600 0.400
#> GSM627103 2 0.1860 0.7987 0.000 0.948 0.052
#> GSM627114 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627134 2 0.2165 0.7938 0.000 0.936 0.064
#> GSM627137 2 0.0000 0.8124 0.000 1.000 0.000
#> GSM627148 2 0.6126 0.1399 0.000 0.600 0.400
#> GSM627101 2 0.6126 0.1399 0.000 0.600 0.400
#> GSM627130 3 0.6260 0.3656 0.000 0.448 0.552
#> GSM627071 3 0.6062 0.4238 0.000 0.384 0.616
#> GSM627118 2 0.2165 0.7938 0.000 0.936 0.064
#> GSM627094 2 0.0000 0.8124 0.000 1.000 0.000
#> GSM627122 1 0.6274 0.8795 0.544 0.000 0.456
#> GSM627115 2 0.0000 0.8124 0.000 1.000 0.000
#> GSM627125 3 0.6267 0.3597 0.000 0.452 0.548
#> GSM627174 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627102 2 0.6260 0.3467 0.448 0.552 0.000
#> GSM627073 2 0.6126 0.1399 0.000 0.600 0.400
#> GSM627108 2 0.0000 0.8124 0.000 1.000 0.000
#> GSM627126 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627078 2 0.0000 0.8124 0.000 1.000 0.000
#> GSM627090 3 0.2165 0.2366 0.064 0.000 0.936
#> GSM627099 2 0.2165 0.7938 0.000 0.936 0.064
#> GSM627105 3 0.6286 0.3301 0.000 0.464 0.536
#> GSM627117 1 0.0000 0.4290 1.000 0.000 0.000
#> GSM627121 2 0.6126 0.1399 0.000 0.600 0.400
#> GSM627127 2 0.2165 0.7938 0.000 0.936 0.064
#> GSM627087 2 0.0000 0.8124 0.000 1.000 0.000
#> GSM627089 3 0.2165 0.2366 0.064 0.000 0.936
#> GSM627092 2 0.6260 0.3467 0.448 0.552 0.000
#> GSM627076 3 0.3116 0.0983 0.108 0.000 0.892
#> GSM627136 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627081 2 0.6126 0.1399 0.000 0.600 0.400
#> GSM627091 2 0.0000 0.8124 0.000 1.000 0.000
#> GSM627097 2 0.6244 -0.0282 0.000 0.560 0.440
#> GSM627072 3 0.6267 0.3597 0.000 0.452 0.548
#> GSM627080 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627088 1 0.6274 0.8795 0.544 0.000 0.456
#> GSM627109 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627111 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627113 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627133 2 0.2165 0.7938 0.000 0.936 0.064
#> GSM627177 3 0.5660 0.5428 0.028 0.200 0.772
#> GSM627086 2 0.0000 0.8124 0.000 1.000 0.000
#> GSM627095 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627079 3 0.1163 0.3098 0.028 0.000 0.972
#> GSM627082 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627074 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627077 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627093 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627120 2 0.2165 0.7938 0.000 0.936 0.064
#> GSM627124 2 0.0000 0.8124 0.000 1.000 0.000
#> GSM627075 2 0.0000 0.8124 0.000 1.000 0.000
#> GSM627085 2 0.0000 0.8124 0.000 1.000 0.000
#> GSM627119 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627116 3 0.0747 0.3296 0.016 0.000 0.984
#> GSM627084 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627096 2 0.4750 0.5930 0.000 0.784 0.216
#> GSM627100 3 0.0237 0.3574 0.000 0.004 0.996
#> GSM627112 1 0.6809 -0.3870 0.524 0.464 0.012
#> GSM627083 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627098 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627104 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627131 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627106 2 0.6126 0.1399 0.000 0.600 0.400
#> GSM627123 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627129 2 0.2165 0.7938 0.000 0.936 0.064
#> GSM627216 2 0.1964 0.7972 0.000 0.944 0.056
#> GSM627212 2 0.0000 0.8124 0.000 1.000 0.000
#> GSM627190 1 0.0747 0.4052 0.984 0.016 0.000
#> GSM627169 2 0.6260 0.3467 0.448 0.552 0.000
#> GSM627167 2 0.0000 0.8124 0.000 1.000 0.000
#> GSM627192 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627203 3 0.6267 0.3597 0.000 0.452 0.548
#> GSM627151 2 0.6488 0.6234 0.192 0.744 0.064
#> GSM627163 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627211 2 0.0000 0.8124 0.000 1.000 0.000
#> GSM627171 2 0.0000 0.8124 0.000 1.000 0.000
#> GSM627209 2 0.0000 0.8124 0.000 1.000 0.000
#> GSM627135 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627170 2 0.2165 0.7938 0.000 0.936 0.064
#> GSM627178 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627199 2 0.6260 0.3467 0.448 0.552 0.000
#> GSM627213 2 0.2165 0.7938 0.000 0.936 0.064
#> GSM627140 1 0.0000 0.4290 1.000 0.000 0.000
#> GSM627149 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627147 2 0.6260 0.3467 0.448 0.552 0.000
#> GSM627195 2 0.6126 0.1399 0.000 0.600 0.400
#> GSM627204 2 0.0000 0.8124 0.000 1.000 0.000
#> GSM627207 2 0.0000 0.8124 0.000 1.000 0.000
#> GSM627157 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627201 2 0.0000 0.8124 0.000 1.000 0.000
#> GSM627146 2 0.0000 0.8124 0.000 1.000 0.000
#> GSM627156 2 0.0000 0.8124 0.000 1.000 0.000
#> GSM627188 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627197 2 0.0000 0.8124 0.000 1.000 0.000
#> GSM627173 2 0.6260 0.3467 0.448 0.552 0.000
#> GSM627179 2 0.0000 0.8124 0.000 1.000 0.000
#> GSM627208 2 0.2165 0.7938 0.000 0.936 0.064
#> GSM627215 2 0.2165 0.7938 0.000 0.936 0.064
#> GSM627153 2 0.0000 0.8124 0.000 1.000 0.000
#> GSM627155 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627165 2 0.1860 0.7987 0.000 0.948 0.052
#> GSM627168 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627183 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627144 2 0.6045 0.2031 0.000 0.620 0.380
#> GSM627158 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627196 2 0.0000 0.8124 0.000 1.000 0.000
#> GSM627142 3 0.2165 0.2366 0.064 0.000 0.936
#> GSM627182 2 0.2261 0.7908 0.000 0.932 0.068
#> GSM627202 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627141 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627143 2 0.1753 0.8001 0.000 0.952 0.048
#> GSM627145 3 0.6260 0.3656 0.000 0.448 0.552
#> GSM627152 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627200 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627159 3 0.2165 0.2366 0.064 0.000 0.936
#> GSM627164 2 0.6260 0.3467 0.448 0.552 0.000
#> GSM627138 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627175 2 0.1643 0.8013 0.000 0.956 0.044
#> GSM627150 3 0.6286 0.3302 0.000 0.464 0.536
#> GSM627166 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627186 2 0.6260 0.3467 0.448 0.552 0.000
#> GSM627139 3 0.6280 0.3408 0.000 0.460 0.540
#> GSM627181 2 0.0000 0.8124 0.000 1.000 0.000
#> GSM627205 2 0.2165 0.7938 0.000 0.936 0.064
#> GSM627214 2 0.2165 0.7938 0.000 0.936 0.064
#> GSM627180 2 0.2261 0.7908 0.000 0.932 0.068
#> GSM627172 1 0.6204 -0.2972 0.576 0.424 0.000
#> GSM627184 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627193 2 0.0000 0.8124 0.000 1.000 0.000
#> GSM627191 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627176 1 0.1031 0.4527 0.976 0.000 0.024
#> GSM627194 2 0.0000 0.8124 0.000 1.000 0.000
#> GSM627154 2 0.0000 0.8124 0.000 1.000 0.000
#> GSM627187 1 0.0000 0.4290 1.000 0.000 0.000
#> GSM627198 2 0.0000 0.8124 0.000 1.000 0.000
#> GSM627160 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627185 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627206 1 0.6305 0.8488 0.516 0.000 0.484
#> GSM627161 1 0.6260 0.8873 0.552 0.000 0.448
#> GSM627162 1 0.0000 0.4290 1.000 0.000 0.000
#> GSM627210 1 0.0000 0.4290 1.000 0.000 0.000
#> GSM627189 2 0.0000 0.8124 0.000 1.000 0.000
show/hide code output
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM627128 3 0.4790 0.94517 0.000 0.000 0.620 0.380
#> GSM627110 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627132 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627107 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627103 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627114 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627134 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627137 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627148 2 0.0336 0.81102 0.000 0.992 0.008 0.000
#> GSM627101 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627130 2 0.5288 -0.00372 0.000 0.520 0.472 0.008
#> GSM627071 3 0.4790 0.94517 0.000 0.000 0.620 0.380
#> GSM627118 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627094 2 0.5099 0.61706 0.000 0.612 0.380 0.008
#> GSM627122 1 0.5099 0.35673 0.612 0.000 0.008 0.380
#> GSM627115 2 0.2973 0.75953 0.000 0.856 0.144 0.000
#> GSM627125 2 0.4194 0.59178 0.000 0.764 0.228 0.008
#> GSM627174 1 0.0336 0.91666 0.992 0.000 0.000 0.008
#> GSM627102 4 0.4790 0.73944 0.000 0.000 0.380 0.620
#> GSM627073 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627108 2 0.5099 0.61706 0.000 0.612 0.380 0.008
#> GSM627126 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627078 2 0.4790 0.62566 0.000 0.620 0.380 0.000
#> GSM627090 3 0.5099 0.94173 0.008 0.000 0.612 0.380
#> GSM627099 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627105 2 0.3610 0.63643 0.000 0.800 0.200 0.000
#> GSM627117 4 0.4888 0.45520 0.412 0.000 0.000 0.588
#> GSM627121 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627127 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627087 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627089 3 0.4790 0.94517 0.000 0.000 0.620 0.380
#> GSM627092 4 0.4790 0.73944 0.000 0.000 0.380 0.620
#> GSM627076 3 0.6718 0.87430 0.096 0.000 0.524 0.380
#> GSM627136 1 0.5070 0.37698 0.620 0.000 0.008 0.372
#> GSM627081 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627091 2 0.4790 0.62566 0.000 0.620 0.380 0.000
#> GSM627097 2 0.1867 0.76963 0.000 0.928 0.072 0.000
#> GSM627072 2 0.7729 -0.16289 0.000 0.400 0.228 0.372
#> GSM627080 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627088 1 0.5099 0.35673 0.612 0.000 0.008 0.380
#> GSM627109 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627111 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627113 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627133 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627177 3 0.4790 0.94517 0.000 0.000 0.620 0.380
#> GSM627086 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627095 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627079 3 0.4790 0.94517 0.000 0.000 0.620 0.380
#> GSM627082 1 0.4964 0.36679 0.616 0.000 0.004 0.380
#> GSM627074 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627077 1 0.0336 0.91668 0.992 0.000 0.000 0.008
#> GSM627093 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627120 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627124 2 0.5099 0.61706 0.000 0.612 0.380 0.008
#> GSM627075 2 0.5099 0.61706 0.000 0.612 0.380 0.008
#> GSM627085 2 0.4790 0.62566 0.000 0.620 0.380 0.000
#> GSM627119 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627116 3 0.4790 0.94517 0.000 0.000 0.620 0.380
#> GSM627084 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627096 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627100 3 0.4790 0.94517 0.000 0.000 0.620 0.380
#> GSM627112 4 0.4790 0.73944 0.000 0.000 0.380 0.620
#> GSM627083 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627098 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627104 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627131 1 0.4950 0.37671 0.620 0.000 0.004 0.376
#> GSM627106 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627123 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627129 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627216 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627212 2 0.4790 0.62566 0.000 0.620 0.380 0.000
#> GSM627190 4 0.4790 0.50771 0.380 0.000 0.000 0.620
#> GSM627169 4 0.4790 0.73944 0.000 0.000 0.380 0.620
#> GSM627167 2 0.4790 0.62566 0.000 0.620 0.380 0.000
#> GSM627192 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627203 2 0.6296 0.45487 0.000 0.652 0.224 0.124
#> GSM627151 2 0.3219 0.67370 0.000 0.836 0.000 0.164
#> GSM627163 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627211 2 0.5099 0.61706 0.000 0.612 0.380 0.008
#> GSM627171 2 0.4746 0.63392 0.000 0.632 0.368 0.000
#> GSM627209 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627135 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627170 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627178 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627199 4 0.4790 0.73944 0.000 0.000 0.380 0.620
#> GSM627213 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627140 4 0.4817 0.49823 0.388 0.000 0.000 0.612
#> GSM627149 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627147 4 0.4790 0.73944 0.000 0.000 0.380 0.620
#> GSM627195 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627204 2 0.5099 0.61706 0.000 0.612 0.380 0.008
#> GSM627207 2 0.4790 0.62566 0.000 0.620 0.380 0.000
#> GSM627157 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627201 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627146 2 0.4790 0.62566 0.000 0.620 0.380 0.000
#> GSM627156 2 0.5099 0.61706 0.000 0.612 0.380 0.008
#> GSM627188 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627197 2 0.4790 0.62566 0.000 0.620 0.380 0.000
#> GSM627173 4 0.4790 0.73944 0.000 0.000 0.380 0.620
#> GSM627179 2 0.4790 0.62566 0.000 0.620 0.380 0.000
#> GSM627208 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627215 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627153 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627155 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627165 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627168 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627183 1 0.4936 0.38602 0.624 0.000 0.004 0.372
#> GSM627144 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627158 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627196 2 0.4790 0.62566 0.000 0.620 0.380 0.000
#> GSM627142 3 0.6396 0.90094 0.072 0.000 0.548 0.380
#> GSM627182 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627202 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627141 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627143 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627145 3 0.4790 0.94517 0.000 0.000 0.620 0.380
#> GSM627152 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627200 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627159 3 0.6396 0.90094 0.072 0.000 0.548 0.380
#> GSM627164 4 0.4790 0.73944 0.000 0.000 0.380 0.620
#> GSM627138 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627175 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627150 2 0.4049 0.61351 0.000 0.780 0.212 0.008
#> GSM627166 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627186 4 0.4790 0.73944 0.000 0.000 0.380 0.620
#> GSM627139 2 0.4053 0.59636 0.000 0.768 0.228 0.004
#> GSM627181 2 0.4431 0.67364 0.000 0.696 0.304 0.000
#> GSM627205 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627214 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627180 2 0.0000 0.81530 0.000 1.000 0.000 0.000
#> GSM627172 4 0.4790 0.73944 0.000 0.000 0.380 0.620
#> GSM627184 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627193 2 0.4746 0.63404 0.000 0.632 0.368 0.000
#> GSM627191 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627176 1 0.4981 -0.16453 0.536 0.000 0.000 0.464
#> GSM627194 2 0.0188 0.81419 0.000 0.996 0.004 0.000
#> GSM627154 2 0.4790 0.62566 0.000 0.620 0.380 0.000
#> GSM627187 4 0.4830 0.49209 0.392 0.000 0.000 0.608
#> GSM627198 2 0.4790 0.62566 0.000 0.620 0.380 0.000
#> GSM627160 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627185 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627206 3 0.7663 0.68665 0.212 0.000 0.408 0.380
#> GSM627161 1 0.0000 0.92376 1.000 0.000 0.000 0.000
#> GSM627162 4 0.4817 0.49807 0.388 0.000 0.000 0.612
#> GSM627210 4 0.4981 0.33200 0.464 0.000 0.000 0.536
#> GSM627189 2 0.4790 0.62566 0.000 0.620 0.380 0.000
show/hide code output
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM627128 3 0.4262 0.6415 0.000 0.000 0.560 0.000 0.440
#> GSM627110 1 0.1410 0.4507 0.940 0.000 0.000 0.060 0.000
#> GSM627132 1 0.4242 0.8075 0.572 0.000 0.428 0.000 0.000
#> GSM627107 5 0.4262 0.8769 0.000 0.440 0.000 0.000 0.560
#> GSM627103 2 0.2852 0.5490 0.000 0.828 0.000 0.000 0.172
#> GSM627114 1 0.4242 0.8075 0.572 0.000 0.428 0.000 0.000
#> GSM627134 5 0.4268 0.8747 0.000 0.444 0.000 0.000 0.556
#> GSM627137 2 0.1043 0.7801 0.000 0.960 0.000 0.000 0.040
#> GSM627148 5 0.4256 0.8764 0.000 0.436 0.000 0.000 0.564
#> GSM627101 5 0.4262 0.8769 0.000 0.440 0.000 0.000 0.560
#> GSM627130 5 0.3888 0.5013 0.000 0.148 0.056 0.000 0.796
#> GSM627071 3 0.4242 0.6439 0.000 0.000 0.572 0.000 0.428
#> GSM627118 5 0.4262 0.8769 0.000 0.440 0.000 0.000 0.560
#> GSM627094 2 0.1851 0.7969 0.000 0.912 0.000 0.088 0.000
#> GSM627122 3 0.1671 0.3493 0.076 0.000 0.924 0.000 0.000
#> GSM627115 2 0.0703 0.7920 0.000 0.976 0.000 0.000 0.024
#> GSM627125 5 0.3783 0.6918 0.000 0.252 0.008 0.000 0.740
#> GSM627174 1 0.4242 0.8075 0.572 0.000 0.428 0.000 0.000
#> GSM627102 4 0.1410 0.8550 0.000 0.060 0.000 0.940 0.000
#> GSM627073 5 0.4256 0.8764 0.000 0.436 0.000 0.000 0.564
#> GSM627108 2 0.1851 0.7969 0.000 0.912 0.000 0.088 0.000
#> GSM627126 1 0.4242 0.8075 0.572 0.000 0.428 0.000 0.000
#> GSM627078 2 0.1341 0.8204 0.000 0.944 0.000 0.056 0.000
#> GSM627090 3 0.4138 0.6524 0.000 0.000 0.616 0.000 0.384
#> GSM627099 2 0.2127 0.6863 0.000 0.892 0.000 0.000 0.108
#> GSM627105 5 0.3837 0.7605 0.000 0.308 0.000 0.000 0.692
#> GSM627117 1 0.3424 0.0819 0.760 0.000 0.000 0.240 0.000
#> GSM627121 5 0.4262 0.8769 0.000 0.440 0.000 0.000 0.560
#> GSM627127 2 0.3143 0.4596 0.000 0.796 0.000 0.000 0.204
#> GSM627087 2 0.1270 0.7675 0.000 0.948 0.000 0.000 0.052
#> GSM627089 3 0.4227 0.6467 0.000 0.000 0.580 0.000 0.420
#> GSM627092 4 0.1608 0.8508 0.000 0.072 0.000 0.928 0.000
#> GSM627076 3 0.3885 0.6533 0.008 0.000 0.724 0.000 0.268
#> GSM627136 3 0.3210 -0.0108 0.212 0.000 0.788 0.000 0.000
#> GSM627081 5 0.4256 0.8764 0.000 0.436 0.000 0.000 0.564
#> GSM627091 2 0.1341 0.8204 0.000 0.944 0.000 0.056 0.000
#> GSM627097 2 0.3684 0.4498 0.000 0.720 0.000 0.000 0.280
#> GSM627072 5 0.6586 0.4403 0.000 0.304 0.236 0.000 0.460
#> GSM627080 1 0.4242 0.8075 0.572 0.000 0.428 0.000 0.000
#> GSM627088 3 0.1608 0.3566 0.072 0.000 0.928 0.000 0.000
#> GSM627109 1 0.1211 0.5011 0.960 0.000 0.016 0.024 0.000
#> GSM627111 1 0.4242 0.8075 0.572 0.000 0.428 0.000 0.000
#> GSM627113 1 0.4242 0.8075 0.572 0.000 0.428 0.000 0.000
#> GSM627133 5 0.4268 0.8747 0.000 0.444 0.000 0.000 0.556
#> GSM627177 3 0.4242 0.6439 0.000 0.000 0.572 0.000 0.428
#> GSM627086 2 0.1197 0.7717 0.000 0.952 0.000 0.000 0.048
#> GSM627095 1 0.4242 0.8075 0.572 0.000 0.428 0.000 0.000
#> GSM627079 3 0.4242 0.6439 0.000 0.000 0.572 0.000 0.428
#> GSM627082 3 0.3534 -0.1457 0.256 0.000 0.744 0.000 0.000
#> GSM627074 1 0.0880 0.4818 0.968 0.000 0.000 0.032 0.000
#> GSM627077 1 0.4242 0.8075 0.572 0.000 0.428 0.000 0.000
#> GSM627093 1 0.1106 0.4986 0.964 0.000 0.012 0.024 0.000
#> GSM627120 5 0.4273 0.8688 0.000 0.448 0.000 0.000 0.552
#> GSM627124 2 0.2280 0.7587 0.000 0.880 0.000 0.120 0.000
#> GSM627075 2 0.1851 0.7969 0.000 0.912 0.000 0.088 0.000
#> GSM627085 2 0.1410 0.8184 0.000 0.940 0.000 0.060 0.000
#> GSM627119 1 0.0865 0.4923 0.972 0.000 0.004 0.024 0.000
#> GSM627116 3 0.4262 0.6415 0.000 0.000 0.560 0.000 0.440
#> GSM627084 1 0.4242 0.8075 0.572 0.000 0.428 0.000 0.000
#> GSM627096 5 0.4262 0.8769 0.000 0.440 0.000 0.000 0.560
#> GSM627100 3 0.4242 0.6439 0.000 0.000 0.572 0.000 0.428
#> GSM627112 4 0.2032 0.8550 0.020 0.052 0.000 0.924 0.004
#> GSM627083 1 0.4242 0.8075 0.572 0.000 0.428 0.000 0.000
#> GSM627098 1 0.4242 0.8075 0.572 0.000 0.428 0.000 0.000
#> GSM627104 1 0.1106 0.4986 0.964 0.000 0.012 0.024 0.000
#> GSM627131 3 0.3508 -0.1424 0.252 0.000 0.748 0.000 0.000
#> GSM627106 5 0.4256 0.8764 0.000 0.436 0.000 0.000 0.564
#> GSM627123 1 0.4242 0.8075 0.572 0.000 0.428 0.000 0.000
#> GSM627129 5 0.4268 0.8747 0.000 0.444 0.000 0.000 0.556
#> GSM627216 2 0.4304 -0.7381 0.000 0.516 0.000 0.000 0.484
#> GSM627212 2 0.1341 0.8204 0.000 0.944 0.000 0.056 0.000
#> GSM627190 4 0.3508 0.7306 0.252 0.000 0.000 0.748 0.000
#> GSM627169 4 0.0609 0.8508 0.000 0.020 0.000 0.980 0.000
#> GSM627167 2 0.1341 0.8204 0.000 0.944 0.000 0.056 0.000
#> GSM627192 1 0.4242 0.8075 0.572 0.000 0.428 0.000 0.000
#> GSM627203 5 0.4249 0.7283 0.000 0.296 0.016 0.000 0.688
#> GSM627151 5 0.6221 0.6148 0.000 0.300 0.000 0.172 0.528
#> GSM627163 1 0.4242 0.8075 0.572 0.000 0.428 0.000 0.000
#> GSM627211 2 0.1851 0.7969 0.000 0.912 0.000 0.088 0.000
#> GSM627171 2 0.1197 0.8199 0.000 0.952 0.000 0.048 0.000
#> GSM627209 2 0.0880 0.7865 0.000 0.968 0.000 0.000 0.032
#> GSM627135 1 0.4242 0.8075 0.572 0.000 0.428 0.000 0.000
#> GSM627170 5 0.4268 0.8747 0.000 0.444 0.000 0.000 0.556
#> GSM627178 1 0.4201 0.7941 0.592 0.000 0.408 0.000 0.000
#> GSM627199 4 0.1544 0.8527 0.000 0.068 0.000 0.932 0.000
#> GSM627213 2 0.4242 -0.5734 0.000 0.572 0.000 0.000 0.428
#> GSM627140 4 0.3561 0.7263 0.260 0.000 0.000 0.740 0.000
#> GSM627149 1 0.4242 0.8075 0.572 0.000 0.428 0.000 0.000
#> GSM627147 4 0.1732 0.8455 0.000 0.080 0.000 0.920 0.000
#> GSM627195 5 0.4256 0.8764 0.000 0.436 0.000 0.000 0.564
#> GSM627204 2 0.1851 0.7969 0.000 0.912 0.000 0.088 0.000
#> GSM627207 2 0.1341 0.8204 0.000 0.944 0.000 0.056 0.000
#> GSM627157 1 0.4242 0.8075 0.572 0.000 0.428 0.000 0.000
#> GSM627201 2 0.0963 0.7832 0.000 0.964 0.000 0.000 0.036
#> GSM627146 2 0.1341 0.8204 0.000 0.944 0.000 0.056 0.000
#> GSM627156 2 0.3177 0.6333 0.000 0.792 0.000 0.208 0.000
#> GSM627188 1 0.4242 0.8075 0.572 0.000 0.428 0.000 0.000
#> GSM627197 2 0.1341 0.8204 0.000 0.944 0.000 0.056 0.000
#> GSM627173 4 0.1792 0.8421 0.000 0.084 0.000 0.916 0.000
#> GSM627179 2 0.1502 0.8205 0.000 0.940 0.000 0.056 0.004
#> GSM627208 5 0.4268 0.8747 0.000 0.444 0.000 0.000 0.556
#> GSM627215 5 0.4268 0.8747 0.000 0.444 0.000 0.000 0.556
#> GSM627153 2 0.0880 0.7865 0.000 0.968 0.000 0.000 0.032
#> GSM627155 1 0.4242 0.8075 0.572 0.000 0.428 0.000 0.000
#> GSM627165 2 0.2179 0.6799 0.000 0.888 0.000 0.000 0.112
#> GSM627168 1 0.4235 0.8049 0.576 0.000 0.424 0.000 0.000
#> GSM627183 3 0.3684 -0.2347 0.280 0.000 0.720 0.000 0.000
#> GSM627144 5 0.4262 0.8769 0.000 0.440 0.000 0.000 0.560
#> GSM627158 1 0.4242 0.8075 0.572 0.000 0.428 0.000 0.000
#> GSM627196 2 0.1341 0.8204 0.000 0.944 0.000 0.056 0.000
#> GSM627142 3 0.3642 0.6471 0.008 0.000 0.760 0.000 0.232
#> GSM627182 5 0.4268 0.8747 0.000 0.444 0.000 0.000 0.556
#> GSM627202 1 0.4242 0.8075 0.572 0.000 0.428 0.000 0.000
#> GSM627141 1 0.4242 0.8075 0.572 0.000 0.428 0.000 0.000
#> GSM627143 2 0.3210 0.4210 0.000 0.788 0.000 0.000 0.212
#> GSM627145 3 0.4242 0.6439 0.000 0.000 0.572 0.000 0.428
#> GSM627152 1 0.0703 0.4890 0.976 0.000 0.000 0.024 0.000
#> GSM627200 1 0.4242 0.8075 0.572 0.000 0.428 0.000 0.000
#> GSM627159 3 0.3728 0.6500 0.008 0.000 0.748 0.000 0.244
#> GSM627164 4 0.1410 0.8550 0.000 0.060 0.000 0.940 0.000
#> GSM627138 1 0.4242 0.8075 0.572 0.000 0.428 0.000 0.000
#> GSM627175 2 0.1544 0.7468 0.000 0.932 0.000 0.000 0.068
#> GSM627150 5 0.3983 0.7880 0.000 0.340 0.000 0.000 0.660
#> GSM627166 1 0.0992 0.4956 0.968 0.000 0.008 0.024 0.000
#> GSM627186 4 0.1341 0.8142 0.056 0.000 0.000 0.944 0.000
#> GSM627139 5 0.4017 0.6817 0.000 0.248 0.004 0.012 0.736
#> GSM627181 2 0.0579 0.8049 0.000 0.984 0.000 0.008 0.008
#> GSM627205 2 0.4300 -0.7203 0.000 0.524 0.000 0.000 0.476
#> GSM627214 5 0.4300 0.8175 0.000 0.476 0.000 0.000 0.524
#> GSM627180 5 0.4262 0.8769 0.000 0.440 0.000 0.000 0.560
#> GSM627172 4 0.0609 0.8508 0.000 0.020 0.000 0.980 0.000
#> GSM627184 1 0.4242 0.8075 0.572 0.000 0.428 0.000 0.000
#> GSM627193 2 0.0579 0.8049 0.000 0.984 0.000 0.008 0.008
#> GSM627191 1 0.4242 0.8075 0.572 0.000 0.428 0.000 0.000
#> GSM627176 1 0.3983 -0.2649 0.660 0.000 0.000 0.340 0.000
#> GSM627194 2 0.1410 0.7604 0.000 0.940 0.000 0.000 0.060
#> GSM627154 2 0.1410 0.8184 0.000 0.940 0.000 0.060 0.000
#> GSM627187 4 0.4242 0.6148 0.428 0.000 0.000 0.572 0.000
#> GSM627198 2 0.1410 0.8184 0.000 0.940 0.000 0.060 0.000
#> GSM627160 1 0.0963 0.4780 0.964 0.000 0.000 0.036 0.000
#> GSM627185 1 0.4242 0.8075 0.572 0.000 0.428 0.000 0.000
#> GSM627206 3 0.3336 0.6281 0.000 0.000 0.772 0.000 0.228
#> GSM627161 1 0.4242 0.8075 0.572 0.000 0.428 0.000 0.000
#> GSM627162 4 0.4242 0.6148 0.428 0.000 0.000 0.572 0.000
#> GSM627210 1 0.3039 0.1722 0.808 0.000 0.000 0.192 0.000
#> GSM627189 2 0.1502 0.8205 0.000 0.940 0.000 0.056 0.004
show/hide code output
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM627128 6 0.5482 0.66930 0.000 0.232 0.072 0.036 0.012 0.648
#> GSM627110 3 0.1610 0.89390 0.084 0.000 0.916 0.000 0.000 0.000
#> GSM627132 1 0.0000 0.93872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627107 5 0.0146 0.80524 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM627103 5 0.3851 -0.41997 0.000 0.460 0.000 0.000 0.540 0.000
#> GSM627114 1 0.0291 0.93452 0.992 0.000 0.004 0.004 0.000 0.000
#> GSM627134 5 0.0547 0.80149 0.000 0.020 0.000 0.000 0.980 0.000
#> GSM627137 2 0.3428 0.91760 0.000 0.696 0.000 0.000 0.304 0.000
#> GSM627148 5 0.0000 0.80518 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM627101 5 0.0405 0.80458 0.000 0.008 0.004 0.000 0.988 0.000
#> GSM627130 5 0.7566 0.07415 0.000 0.280 0.072 0.036 0.408 0.204
#> GSM627071 6 0.0146 0.87904 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM627118 5 0.0777 0.79977 0.000 0.024 0.004 0.000 0.972 0.000
#> GSM627094 2 0.4002 0.90803 0.000 0.704 0.000 0.036 0.260 0.000
#> GSM627122 1 0.4049 0.26683 0.580 0.000 0.004 0.004 0.000 0.412
#> GSM627115 2 0.3584 0.91888 0.000 0.688 0.000 0.004 0.308 0.000
#> GSM627125 5 0.6591 0.40660 0.000 0.240 0.072 0.036 0.568 0.084
#> GSM627174 1 0.0806 0.91849 0.972 0.000 0.020 0.008 0.000 0.000
#> GSM627102 4 0.0937 0.94535 0.000 0.040 0.000 0.960 0.000 0.000
#> GSM627073 5 0.0146 0.80524 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM627108 2 0.3834 0.91762 0.000 0.708 0.000 0.024 0.268 0.000
#> GSM627126 1 0.0000 0.93872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627078 2 0.3383 0.92836 0.000 0.728 0.000 0.004 0.268 0.000
#> GSM627090 6 0.0713 0.87682 0.028 0.000 0.000 0.000 0.000 0.972
#> GSM627099 2 0.3547 0.88600 0.000 0.668 0.000 0.000 0.332 0.000
#> GSM627105 5 0.6410 0.42801 0.000 0.240 0.072 0.036 0.584 0.068
#> GSM627117 3 0.2776 0.85395 0.052 0.000 0.860 0.088 0.000 0.000
#> GSM627121 5 0.0146 0.80524 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM627127 5 0.4097 -0.52058 0.000 0.492 0.008 0.000 0.500 0.000
#> GSM627087 2 0.3464 0.91370 0.000 0.688 0.000 0.000 0.312 0.000
#> GSM627089 6 0.0146 0.87904 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM627092 4 0.1007 0.94414 0.000 0.044 0.000 0.956 0.000 0.000
#> GSM627076 6 0.2416 0.79713 0.156 0.000 0.000 0.000 0.000 0.844
#> GSM627136 1 0.4015 0.31904 0.596 0.000 0.004 0.004 0.000 0.396
#> GSM627081 5 0.0000 0.80518 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM627091 2 0.3426 0.92936 0.000 0.720 0.000 0.004 0.276 0.000
#> GSM627097 2 0.4978 0.26769 0.000 0.720 0.072 0.036 0.160 0.012
#> GSM627072 5 0.4300 0.22140 0.000 0.020 0.000 0.000 0.548 0.432
#> GSM627080 1 0.0000 0.93872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627088 1 0.4128 -0.00245 0.504 0.000 0.004 0.004 0.000 0.488
#> GSM627109 3 0.2378 0.89435 0.152 0.000 0.848 0.000 0.000 0.000
#> GSM627111 1 0.0000 0.93872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627113 1 0.0000 0.93872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627133 5 0.0363 0.80255 0.000 0.012 0.000 0.000 0.988 0.000
#> GSM627177 6 0.0603 0.87814 0.000 0.016 0.000 0.004 0.000 0.980
#> GSM627086 2 0.3428 0.91760 0.000 0.696 0.000 0.000 0.304 0.000
#> GSM627095 1 0.0000 0.93872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627079 6 0.0000 0.87916 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM627082 1 0.1719 0.88494 0.932 0.000 0.032 0.004 0.000 0.032
#> GSM627074 3 0.2178 0.90401 0.132 0.000 0.868 0.000 0.000 0.000
#> GSM627077 1 0.0291 0.93452 0.992 0.000 0.004 0.004 0.000 0.000
#> GSM627093 3 0.2454 0.88557 0.160 0.000 0.840 0.000 0.000 0.000
#> GSM627120 5 0.0713 0.79442 0.000 0.028 0.000 0.000 0.972 0.000
#> GSM627124 2 0.4158 0.89105 0.000 0.704 0.000 0.052 0.244 0.000
#> GSM627075 2 0.4002 0.90803 0.000 0.704 0.000 0.036 0.260 0.000
#> GSM627085 2 0.3586 0.92519 0.000 0.720 0.000 0.012 0.268 0.000
#> GSM627119 3 0.2340 0.89804 0.148 0.000 0.852 0.000 0.000 0.000
#> GSM627116 6 0.2662 0.82096 0.000 0.152 0.004 0.004 0.000 0.840
#> GSM627084 1 0.0000 0.93872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627096 5 0.0891 0.79918 0.000 0.024 0.008 0.000 0.968 0.000
#> GSM627100 6 0.0000 0.87916 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM627112 4 0.2282 0.91754 0.000 0.088 0.024 0.888 0.000 0.000
#> GSM627083 1 0.0000 0.93872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627098 1 0.0000 0.93872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627104 3 0.2340 0.89801 0.148 0.000 0.852 0.000 0.000 0.000
#> GSM627131 1 0.3302 0.66525 0.760 0.000 0.004 0.004 0.000 0.232
#> GSM627106 5 0.0146 0.80524 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM627123 1 0.0000 0.93872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627129 5 0.1219 0.78437 0.000 0.048 0.004 0.000 0.948 0.000
#> GSM627216 5 0.1663 0.73565 0.000 0.088 0.000 0.000 0.912 0.000
#> GSM627212 2 0.3426 0.92936 0.000 0.720 0.000 0.004 0.276 0.000
#> GSM627190 3 0.3221 0.62463 0.000 0.000 0.736 0.264 0.000 0.000
#> GSM627169 4 0.1151 0.93542 0.000 0.012 0.032 0.956 0.000 0.000
#> GSM627167 2 0.3244 0.92877 0.000 0.732 0.000 0.000 0.268 0.000
#> GSM627192 1 0.0000 0.93872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627203 5 0.2982 0.72792 0.000 0.068 0.000 0.012 0.860 0.060
#> GSM627151 5 0.5617 0.53234 0.000 0.208 0.040 0.124 0.628 0.000
#> GSM627163 1 0.0000 0.93872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627211 2 0.3934 0.91222 0.000 0.708 0.000 0.032 0.260 0.000
#> GSM627171 2 0.3330 0.92698 0.000 0.716 0.000 0.000 0.284 0.000
#> GSM627209 2 0.3409 0.92031 0.000 0.700 0.000 0.000 0.300 0.000
#> GSM627135 1 0.0000 0.93872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627170 5 0.1007 0.78869 0.000 0.044 0.000 0.000 0.956 0.000
#> GSM627178 1 0.0865 0.90695 0.964 0.000 0.036 0.000 0.000 0.000
#> GSM627199 4 0.1866 0.93567 0.000 0.084 0.008 0.908 0.000 0.000
#> GSM627213 5 0.4424 0.37095 0.000 0.276 0.036 0.012 0.676 0.000
#> GSM627140 4 0.3584 0.70130 0.004 0.012 0.244 0.740 0.000 0.000
#> GSM627149 1 0.0000 0.93872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627147 4 0.1444 0.94073 0.000 0.072 0.000 0.928 0.000 0.000
#> GSM627195 5 0.0000 0.80518 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM627204 2 0.3934 0.91222 0.000 0.708 0.000 0.032 0.260 0.000
#> GSM627207 2 0.3426 0.92936 0.000 0.720 0.000 0.004 0.276 0.000
#> GSM627157 1 0.0000 0.93872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627201 2 0.3428 0.91760 0.000 0.696 0.000 0.000 0.304 0.000
#> GSM627146 2 0.3405 0.92935 0.000 0.724 0.000 0.004 0.272 0.000
#> GSM627156 2 0.5538 0.45725 0.000 0.512 0.000 0.340 0.148 0.000
#> GSM627188 1 0.0000 0.93872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627197 2 0.3405 0.92935 0.000 0.724 0.000 0.004 0.272 0.000
#> GSM627173 4 0.1007 0.94414 0.000 0.044 0.000 0.956 0.000 0.000
#> GSM627179 2 0.3448 0.92940 0.000 0.716 0.000 0.004 0.280 0.000
#> GSM627208 5 0.0547 0.79802 0.000 0.020 0.000 0.000 0.980 0.000
#> GSM627215 5 0.0000 0.80518 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM627153 2 0.3428 0.91760 0.000 0.696 0.000 0.000 0.304 0.000
#> GSM627155 1 0.0000 0.93872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627165 2 0.3531 0.89199 0.000 0.672 0.000 0.000 0.328 0.000
#> GSM627168 1 0.1082 0.90236 0.956 0.000 0.040 0.004 0.000 0.000
#> GSM627183 1 0.3302 0.66487 0.760 0.000 0.004 0.004 0.000 0.232
#> GSM627144 5 0.0291 0.80274 0.000 0.004 0.000 0.004 0.992 0.000
#> GSM627158 1 0.0000 0.93872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627196 2 0.3383 0.92836 0.000 0.728 0.000 0.004 0.268 0.000
#> GSM627142 6 0.2772 0.77084 0.180 0.000 0.004 0.000 0.000 0.816
#> GSM627182 5 0.0146 0.80470 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM627202 1 0.0146 0.93639 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM627141 1 0.0000 0.93872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627143 5 0.4045 -0.29810 0.000 0.428 0.000 0.008 0.564 0.000
#> GSM627145 6 0.0713 0.87461 0.000 0.028 0.000 0.000 0.000 0.972
#> GSM627152 3 0.2219 0.90316 0.136 0.000 0.864 0.000 0.000 0.000
#> GSM627200 1 0.0000 0.93872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627159 6 0.4506 0.75786 0.172 0.020 0.036 0.024 0.000 0.748
#> GSM627164 4 0.0937 0.94535 0.000 0.040 0.000 0.960 0.000 0.000
#> GSM627138 1 0.0000 0.93872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627175 2 0.3446 0.91382 0.000 0.692 0.000 0.000 0.308 0.000
#> GSM627150 5 0.0935 0.79401 0.000 0.004 0.000 0.000 0.964 0.032
#> GSM627166 3 0.2300 0.90026 0.144 0.000 0.856 0.000 0.000 0.000
#> GSM627186 4 0.1204 0.91996 0.000 0.000 0.056 0.944 0.000 0.000
#> GSM627139 5 0.6914 0.32132 0.000 0.292 0.072 0.036 0.504 0.096
#> GSM627181 2 0.3390 0.92255 0.000 0.704 0.000 0.000 0.296 0.000
#> GSM627205 5 0.2300 0.66218 0.000 0.144 0.000 0.000 0.856 0.000
#> GSM627214 5 0.1814 0.73196 0.000 0.100 0.000 0.000 0.900 0.000
#> GSM627180 5 0.0000 0.80518 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM627172 4 0.1480 0.93532 0.000 0.020 0.040 0.940 0.000 0.000
#> GSM627184 1 0.0000 0.93872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627193 2 0.3565 0.92147 0.000 0.692 0.000 0.004 0.304 0.000
#> GSM627191 1 0.0000 0.93872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627176 3 0.1794 0.86689 0.036 0.000 0.924 0.040 0.000 0.000
#> GSM627194 2 0.3782 0.85029 0.000 0.636 0.000 0.004 0.360 0.000
#> GSM627154 2 0.3287 0.87657 0.000 0.768 0.000 0.012 0.220 0.000
#> GSM627187 3 0.1501 0.82601 0.000 0.000 0.924 0.076 0.000 0.000
#> GSM627198 2 0.3383 0.92836 0.000 0.728 0.000 0.004 0.268 0.000
#> GSM627160 3 0.2135 0.90406 0.128 0.000 0.872 0.000 0.000 0.000
#> GSM627185 1 0.0000 0.93872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627206 6 0.2051 0.82260 0.096 0.000 0.004 0.004 0.000 0.896
#> GSM627161 1 0.0000 0.93872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627162 3 0.1501 0.82601 0.000 0.000 0.924 0.076 0.000 0.000
#> GSM627210 3 0.1765 0.87892 0.052 0.000 0.924 0.024 0.000 0.000
#> GSM627189 2 0.3426 0.92936 0.000 0.720 0.000 0.004 0.276 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two
samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures
which are significantly different between classes which can be candidate marks
for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known
annotation is numeric, one-way ANOVA test is applied, and if the known
annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) age(p) other(p) k
#> ATC:skmeans 142 0.4464 0.1300 0.01018 2
#> ATC:skmeans 101 0.2178 0.9117 0.00372 3
#> ATC:skmeans 131 0.2186 0.0121 0.00779 4
#> ATC:skmeans 122 0.0667 0.1048 0.03099 5
#> ATC:skmeans 132 0.3295 0.1507 0.07444 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
ATC:pam
The object with results only for a single top-value method and a single partition method
can be extracted as:
res = res_list["ATC", "pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51882 rows and 146 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
- The first row: a plot of the ECDF (empirical cumulative distribution
function) curves of the consensus matrix for each
k and the heatmap of
predicted classes for each k.
- The second row: heatmaps of the consensus matrix for each
k.
- The third row: heatmaps of the membership matrix for each
k.
- The fouth row: heatmaps of the signatures for each
k.
All the plots in panels can be made by individual functions and they are
plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
- ECDF curves of the consensus matrix for each
k;
- 1-PAC. The PAC
score
measures the proportion of the ambiguous subgrouping.
- Mean silhouette score.
- Concordance. The mean probability of fiting the consensus class ids in all
partitions.
- Area increased. Denote \(A_k\) as the area under the ECDF curve for current
k, the area increased is defined as \(A_k - A_{k-1}\).
- Rand index. The percent of pairs of samples that are both in a same cluster
or both are not in a same cluster in the partition of k and k-1.
- Jaccard index. The ratio of pairs of samples are both in a same cluster in
the partition of k and k-1 and the pairs of samples are both in a same
cluster in the partition k or k-1.
The detailed explanations of these statistics can be found in the cola
vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.781 0.922 0.965 0.4741 0.524 0.524
#> 3 3 0.715 0.831 0.897 0.2645 0.805 0.656
#> 4 4 0.678 0.547 0.796 0.2037 0.837 0.619
#> 5 5 0.679 0.554 0.701 0.0739 0.779 0.373
#> 6 6 0.704 0.562 0.755 0.0527 0.852 0.447
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
- All \(k\) with Jaccard index larger than 0.95 are removed because increasing
\(k\) does not provide enough extra information. If all \(k\) are removed, it is
marked as no subgroup is detected.
- For all \(k\) with 1-PAC score larger than 0.9, the maximal \(k\) is taken as
the best \(k\), and other \(k\) are marked as optional \(k\).
- If it does not fit the second rule. The \(k\) with the maximal vote of the
highest 1-PAC score, highest mean silhouette, and highest concordance is
taken as the best \(k\).
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
show/hide code output
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM627128 2 0.6343 0.8301 0.160 0.840
#> GSM627110 1 0.5842 0.8213 0.860 0.140
#> GSM627132 1 0.0000 0.9576 1.000 0.000
#> GSM627107 2 0.0000 0.9635 0.000 1.000
#> GSM627103 2 0.0000 0.9635 0.000 1.000
#> GSM627114 1 0.0000 0.9576 1.000 0.000
#> GSM627134 2 0.0000 0.9635 0.000 1.000
#> GSM627137 2 0.0000 0.9635 0.000 1.000
#> GSM627148 2 0.0000 0.9635 0.000 1.000
#> GSM627101 2 0.0000 0.9635 0.000 1.000
#> GSM627130 2 0.2603 0.9320 0.044 0.956
#> GSM627071 2 0.6343 0.8301 0.160 0.840
#> GSM627118 2 0.0000 0.9635 0.000 1.000
#> GSM627094 2 0.0000 0.9635 0.000 1.000
#> GSM627122 1 0.0000 0.9576 1.000 0.000
#> GSM627115 2 0.0000 0.9635 0.000 1.000
#> GSM627125 2 0.0000 0.9635 0.000 1.000
#> GSM627174 1 0.9393 0.4307 0.644 0.356
#> GSM627102 2 0.0000 0.9635 0.000 1.000
#> GSM627073 2 0.0000 0.9635 0.000 1.000
#> GSM627108 2 0.0000 0.9635 0.000 1.000
#> GSM627126 1 0.0000 0.9576 1.000 0.000
#> GSM627078 2 0.0000 0.9635 0.000 1.000
#> GSM627090 1 0.1184 0.9458 0.984 0.016
#> GSM627099 2 0.0000 0.9635 0.000 1.000
#> GSM627105 2 0.0000 0.9635 0.000 1.000
#> GSM627117 2 0.6343 0.8301 0.160 0.840
#> GSM627121 2 0.0000 0.9635 0.000 1.000
#> GSM627127 2 0.0000 0.9635 0.000 1.000
#> GSM627087 2 0.0000 0.9635 0.000 1.000
#> GSM627089 1 0.8909 0.5531 0.692 0.308
#> GSM627092 2 0.0000 0.9635 0.000 1.000
#> GSM627076 1 0.0376 0.9550 0.996 0.004
#> GSM627136 1 0.0376 0.9550 0.996 0.004
#> GSM627081 2 0.0000 0.9635 0.000 1.000
#> GSM627091 2 0.0000 0.9635 0.000 1.000
#> GSM627097 2 0.1633 0.9468 0.024 0.976
#> GSM627072 2 0.0000 0.9635 0.000 1.000
#> GSM627080 1 0.0000 0.9576 1.000 0.000
#> GSM627088 1 0.0376 0.9550 0.996 0.004
#> GSM627109 1 0.0000 0.9576 1.000 0.000
#> GSM627111 1 0.0000 0.9576 1.000 0.000
#> GSM627113 1 0.0000 0.9576 1.000 0.000
#> GSM627133 2 0.0000 0.9635 0.000 1.000
#> GSM627177 2 0.6343 0.8301 0.160 0.840
#> GSM627086 2 0.0000 0.9635 0.000 1.000
#> GSM627095 1 0.0000 0.9576 1.000 0.000
#> GSM627079 2 0.6343 0.8301 0.160 0.840
#> GSM627082 1 0.0000 0.9576 1.000 0.000
#> GSM627074 1 0.0000 0.9576 1.000 0.000
#> GSM627077 1 0.0000 0.9576 1.000 0.000
#> GSM627093 1 0.0000 0.9576 1.000 0.000
#> GSM627120 2 0.0000 0.9635 0.000 1.000
#> GSM627124 2 0.0000 0.9635 0.000 1.000
#> GSM627075 2 0.0000 0.9635 0.000 1.000
#> GSM627085 2 0.0000 0.9635 0.000 1.000
#> GSM627119 1 0.0000 0.9576 1.000 0.000
#> GSM627116 2 0.6343 0.8301 0.160 0.840
#> GSM627084 1 0.0000 0.9576 1.000 0.000
#> GSM627096 2 0.0000 0.9635 0.000 1.000
#> GSM627100 2 0.6343 0.8301 0.160 0.840
#> GSM627112 2 0.6343 0.8301 0.160 0.840
#> GSM627083 1 0.0000 0.9576 1.000 0.000
#> GSM627098 1 0.0000 0.9576 1.000 0.000
#> GSM627104 1 0.0000 0.9576 1.000 0.000
#> GSM627131 1 0.0000 0.9576 1.000 0.000
#> GSM627106 2 0.0000 0.9635 0.000 1.000
#> GSM627123 1 0.0000 0.9576 1.000 0.000
#> GSM627129 2 0.0000 0.9635 0.000 1.000
#> GSM627216 2 0.0000 0.9635 0.000 1.000
#> GSM627212 2 0.0000 0.9635 0.000 1.000
#> GSM627190 2 0.6343 0.8301 0.160 0.840
#> GSM627169 2 0.6343 0.8301 0.160 0.840
#> GSM627167 2 0.0000 0.9635 0.000 1.000
#> GSM627192 1 0.0000 0.9576 1.000 0.000
#> GSM627203 2 0.0000 0.9635 0.000 1.000
#> GSM627151 2 0.6343 0.8301 0.160 0.840
#> GSM627163 1 0.0000 0.9576 1.000 0.000
#> GSM627211 2 0.0000 0.9635 0.000 1.000
#> GSM627171 2 0.0000 0.9635 0.000 1.000
#> GSM627209 2 0.0000 0.9635 0.000 1.000
#> GSM627135 1 0.0000 0.9576 1.000 0.000
#> GSM627170 2 0.0000 0.9635 0.000 1.000
#> GSM627178 1 0.0000 0.9576 1.000 0.000
#> GSM627199 2 0.6148 0.8382 0.152 0.848
#> GSM627213 2 0.0000 0.9635 0.000 1.000
#> GSM627140 1 0.7674 0.7038 0.776 0.224
#> GSM627149 1 0.0000 0.9576 1.000 0.000
#> GSM627147 2 0.0000 0.9635 0.000 1.000
#> GSM627195 2 0.0000 0.9635 0.000 1.000
#> GSM627204 2 0.0000 0.9635 0.000 1.000
#> GSM627207 2 0.0000 0.9635 0.000 1.000
#> GSM627157 1 0.0000 0.9576 1.000 0.000
#> GSM627201 2 0.0000 0.9635 0.000 1.000
#> GSM627146 2 0.0000 0.9635 0.000 1.000
#> GSM627156 2 0.0000 0.9635 0.000 1.000
#> GSM627188 1 0.0000 0.9576 1.000 0.000
#> GSM627197 2 0.0000 0.9635 0.000 1.000
#> GSM627173 2 0.0000 0.9635 0.000 1.000
#> GSM627179 2 0.0000 0.9635 0.000 1.000
#> GSM627208 2 0.0000 0.9635 0.000 1.000
#> GSM627215 2 0.0000 0.9635 0.000 1.000
#> GSM627153 2 0.0000 0.9635 0.000 1.000
#> GSM627155 1 0.0000 0.9576 1.000 0.000
#> GSM627165 2 0.0000 0.9635 0.000 1.000
#> GSM627168 1 0.0000 0.9576 1.000 0.000
#> GSM627183 1 0.0000 0.9576 1.000 0.000
#> GSM627144 2 0.0000 0.9635 0.000 1.000
#> GSM627158 1 0.0000 0.9576 1.000 0.000
#> GSM627196 2 0.0000 0.9635 0.000 1.000
#> GSM627142 1 0.1633 0.9394 0.976 0.024
#> GSM627182 2 0.0000 0.9635 0.000 1.000
#> GSM627202 1 0.0000 0.9576 1.000 0.000
#> GSM627141 1 0.0000 0.9576 1.000 0.000
#> GSM627143 2 0.0000 0.9635 0.000 1.000
#> GSM627145 2 0.6343 0.8301 0.160 0.840
#> GSM627152 1 0.0000 0.9576 1.000 0.000
#> GSM627200 1 0.0000 0.9576 1.000 0.000
#> GSM627159 1 0.5519 0.8346 0.872 0.128
#> GSM627164 2 0.0000 0.9635 0.000 1.000
#> GSM627138 1 0.0000 0.9576 1.000 0.000
#> GSM627175 2 0.0000 0.9635 0.000 1.000
#> GSM627150 2 0.0000 0.9635 0.000 1.000
#> GSM627166 1 0.0000 0.9576 1.000 0.000
#> GSM627186 2 0.5519 0.8606 0.128 0.872
#> GSM627139 2 0.6343 0.8301 0.160 0.840
#> GSM627181 2 0.0000 0.9635 0.000 1.000
#> GSM627205 2 0.0000 0.9635 0.000 1.000
#> GSM627214 2 0.0000 0.9635 0.000 1.000
#> GSM627180 2 0.0000 0.9635 0.000 1.000
#> GSM627172 2 0.6343 0.8301 0.160 0.840
#> GSM627184 1 0.0000 0.9576 1.000 0.000
#> GSM627193 2 0.0000 0.9635 0.000 1.000
#> GSM627191 1 0.0000 0.9576 1.000 0.000
#> GSM627176 2 0.9732 0.3566 0.404 0.596
#> GSM627194 2 0.0000 0.9635 0.000 1.000
#> GSM627154 2 0.0000 0.9635 0.000 1.000
#> GSM627187 1 0.7883 0.6882 0.764 0.236
#> GSM627198 2 0.0000 0.9635 0.000 1.000
#> GSM627160 1 0.0000 0.9576 1.000 0.000
#> GSM627185 1 0.0000 0.9576 1.000 0.000
#> GSM627206 1 0.8207 0.6538 0.744 0.256
#> GSM627161 1 0.0000 0.9576 1.000 0.000
#> GSM627162 1 0.9977 0.0746 0.528 0.472
#> GSM627210 1 0.0000 0.9576 1.000 0.000
#> GSM627189 2 0.0000 0.9635 0.000 1.000
show/hide code output
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM627128 3 0.6299 -0.2802 0.000 0.476 0.524
#> GSM627110 3 0.4555 0.8507 0.200 0.000 0.800
#> GSM627132 1 0.0000 0.9685 1.000 0.000 0.000
#> GSM627107 2 0.4555 0.8199 0.000 0.800 0.200
#> GSM627103 2 0.0237 0.9130 0.000 0.996 0.004
#> GSM627114 3 0.4605 0.8512 0.204 0.000 0.796
#> GSM627134 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627137 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627148 2 0.4605 0.8184 0.000 0.796 0.204
#> GSM627101 2 0.4555 0.8199 0.000 0.800 0.200
#> GSM627130 2 0.6140 0.5549 0.000 0.596 0.404
#> GSM627071 3 0.0000 0.7646 0.000 0.000 1.000
#> GSM627118 2 0.1163 0.9049 0.000 0.972 0.028
#> GSM627094 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627122 3 0.4605 0.8512 0.204 0.000 0.796
#> GSM627115 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627125 2 0.4605 0.8184 0.000 0.796 0.204
#> GSM627174 3 0.1905 0.7807 0.028 0.016 0.956
#> GSM627102 2 0.0237 0.9130 0.000 0.996 0.004
#> GSM627073 2 0.4605 0.8184 0.000 0.796 0.204
#> GSM627108 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627126 1 0.0000 0.9685 1.000 0.000 0.000
#> GSM627078 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627090 3 0.0000 0.7646 0.000 0.000 1.000
#> GSM627099 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627105 2 0.4605 0.8184 0.000 0.796 0.204
#> GSM627117 3 0.1163 0.7638 0.000 0.028 0.972
#> GSM627121 2 0.4555 0.8199 0.000 0.800 0.200
#> GSM627127 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627087 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627089 3 0.0237 0.7657 0.004 0.000 0.996
#> GSM627092 2 0.0237 0.9130 0.000 0.996 0.004
#> GSM627076 3 0.0000 0.7646 0.000 0.000 1.000
#> GSM627136 3 0.4605 0.8512 0.204 0.000 0.796
#> GSM627081 2 0.4555 0.8199 0.000 0.800 0.200
#> GSM627091 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627097 2 0.5431 0.7234 0.000 0.716 0.284
#> GSM627072 2 0.4605 0.8184 0.000 0.796 0.204
#> GSM627080 1 0.0000 0.9685 1.000 0.000 0.000
#> GSM627088 3 0.4605 0.8512 0.204 0.000 0.796
#> GSM627109 3 0.6225 0.4934 0.432 0.000 0.568
#> GSM627111 1 0.0000 0.9685 1.000 0.000 0.000
#> GSM627113 3 0.4605 0.8512 0.204 0.000 0.796
#> GSM627133 2 0.0592 0.9113 0.000 0.988 0.012
#> GSM627177 3 0.0000 0.7646 0.000 0.000 1.000
#> GSM627086 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627095 1 0.0237 0.9641 0.996 0.000 0.004
#> GSM627079 3 0.0000 0.7646 0.000 0.000 1.000
#> GSM627082 3 0.4605 0.8512 0.204 0.000 0.796
#> GSM627074 3 0.4605 0.8512 0.204 0.000 0.796
#> GSM627077 3 0.4605 0.8512 0.204 0.000 0.796
#> GSM627093 3 0.4605 0.8512 0.204 0.000 0.796
#> GSM627120 2 0.1411 0.9023 0.000 0.964 0.036
#> GSM627124 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627075 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627085 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627119 3 0.4605 0.8512 0.204 0.000 0.796
#> GSM627116 3 0.0000 0.7646 0.000 0.000 1.000
#> GSM627084 3 0.4605 0.8512 0.204 0.000 0.796
#> GSM627096 2 0.1163 0.9049 0.000 0.972 0.028
#> GSM627100 3 0.0000 0.7646 0.000 0.000 1.000
#> GSM627112 3 0.5733 0.4904 0.000 0.324 0.676
#> GSM627083 3 0.4605 0.8512 0.204 0.000 0.796
#> GSM627098 3 0.4605 0.8512 0.204 0.000 0.796
#> GSM627104 3 0.4605 0.8512 0.204 0.000 0.796
#> GSM627131 3 0.4605 0.8512 0.204 0.000 0.796
#> GSM627106 2 0.4555 0.8199 0.000 0.800 0.200
#> GSM627123 1 0.0000 0.9685 1.000 0.000 0.000
#> GSM627129 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627216 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627212 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627190 3 0.1163 0.7638 0.000 0.028 0.972
#> GSM627169 2 0.5733 0.4892 0.000 0.676 0.324
#> GSM627167 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627192 1 0.0000 0.9685 1.000 0.000 0.000
#> GSM627203 2 0.4605 0.8184 0.000 0.796 0.204
#> GSM627151 2 0.6309 0.2856 0.000 0.504 0.496
#> GSM627163 1 0.0000 0.9685 1.000 0.000 0.000
#> GSM627211 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627171 2 0.1163 0.9049 0.000 0.972 0.028
#> GSM627209 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627135 1 0.0000 0.9685 1.000 0.000 0.000
#> GSM627170 2 0.1163 0.9049 0.000 0.972 0.028
#> GSM627178 3 0.4605 0.8512 0.204 0.000 0.796
#> GSM627199 2 0.5678 0.5072 0.000 0.684 0.316
#> GSM627213 2 0.0237 0.9130 0.000 0.996 0.004
#> GSM627140 3 0.5292 0.8348 0.172 0.028 0.800
#> GSM627149 1 0.0000 0.9685 1.000 0.000 0.000
#> GSM627147 2 0.0237 0.9130 0.000 0.996 0.004
#> GSM627195 2 0.4605 0.8184 0.000 0.796 0.204
#> GSM627204 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627207 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627157 3 0.4605 0.8512 0.204 0.000 0.796
#> GSM627201 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627146 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627156 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627188 1 0.0000 0.9685 1.000 0.000 0.000
#> GSM627197 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627173 2 0.0237 0.9130 0.000 0.996 0.004
#> GSM627179 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627208 2 0.1163 0.9049 0.000 0.972 0.028
#> GSM627215 2 0.4555 0.8199 0.000 0.800 0.200
#> GSM627153 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627155 1 0.0000 0.9685 1.000 0.000 0.000
#> GSM627165 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627168 3 0.4605 0.8512 0.204 0.000 0.796
#> GSM627183 3 0.4605 0.8512 0.204 0.000 0.796
#> GSM627144 2 0.4235 0.8301 0.000 0.824 0.176
#> GSM627158 1 0.0000 0.9685 1.000 0.000 0.000
#> GSM627196 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627142 3 0.0000 0.7646 0.000 0.000 1.000
#> GSM627182 2 0.4605 0.8184 0.000 0.796 0.204
#> GSM627202 3 0.4605 0.8512 0.204 0.000 0.796
#> GSM627141 3 0.4605 0.8512 0.204 0.000 0.796
#> GSM627143 2 0.4796 0.8062 0.000 0.780 0.220
#> GSM627145 3 0.0000 0.7646 0.000 0.000 1.000
#> GSM627152 3 0.4555 0.8507 0.200 0.000 0.800
#> GSM627200 3 0.4605 0.8512 0.204 0.000 0.796
#> GSM627159 3 0.0000 0.7646 0.000 0.000 1.000
#> GSM627164 2 0.0237 0.9130 0.000 0.996 0.004
#> GSM627138 1 0.0000 0.9685 1.000 0.000 0.000
#> GSM627175 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627150 2 0.4605 0.8184 0.000 0.796 0.204
#> GSM627166 3 0.4605 0.8512 0.204 0.000 0.796
#> GSM627186 2 0.4399 0.7363 0.000 0.812 0.188
#> GSM627139 3 0.6302 -0.2917 0.000 0.480 0.520
#> GSM627181 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627205 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627214 2 0.1163 0.9049 0.000 0.972 0.028
#> GSM627180 2 0.4605 0.8184 0.000 0.796 0.204
#> GSM627172 2 0.6225 0.1860 0.000 0.568 0.432
#> GSM627184 1 0.0000 0.9685 1.000 0.000 0.000
#> GSM627193 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627191 3 0.4605 0.8512 0.204 0.000 0.796
#> GSM627176 3 0.5292 0.8348 0.172 0.028 0.800
#> GSM627194 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627154 2 0.0237 0.9130 0.000 0.996 0.004
#> GSM627187 3 0.4555 0.8507 0.200 0.000 0.800
#> GSM627198 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM627160 3 0.4555 0.8507 0.200 0.000 0.800
#> GSM627185 1 0.6062 0.0842 0.616 0.000 0.384
#> GSM627206 3 0.0237 0.7657 0.004 0.000 0.996
#> GSM627161 1 0.0000 0.9685 1.000 0.000 0.000
#> GSM627162 3 0.4733 0.8492 0.196 0.004 0.800
#> GSM627210 3 0.4555 0.8507 0.200 0.000 0.800
#> GSM627189 2 0.0000 0.9139 0.000 1.000 0.000
show/hide code output
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM627128 4 0.4914 0.28913 0.000 0.312 0.012 0.676
#> GSM627110 3 0.0188 0.89726 0.000 0.004 0.996 0.000
#> GSM627132 1 0.0000 0.97296 1.000 0.000 0.000 0.000
#> GSM627107 4 0.4981 0.01050 0.000 0.464 0.000 0.536
#> GSM627103 4 0.0336 0.55567 0.000 0.008 0.000 0.992
#> GSM627114 3 0.0000 0.89729 0.000 0.000 1.000 0.000
#> GSM627134 4 0.0188 0.55667 0.000 0.004 0.000 0.996
#> GSM627137 2 0.4477 0.42413 0.000 0.688 0.000 0.312
#> GSM627148 4 0.4103 0.34483 0.000 0.256 0.000 0.744
#> GSM627101 4 0.4981 0.01050 0.000 0.464 0.000 0.536
#> GSM627130 4 0.4699 0.29185 0.000 0.320 0.004 0.676
#> GSM627071 3 0.4655 0.71143 0.000 0.312 0.684 0.004
#> GSM627118 4 0.4985 0.00848 0.000 0.468 0.000 0.532
#> GSM627094 4 0.3907 0.28769 0.000 0.232 0.000 0.768
#> GSM627122 3 0.0000 0.89729 0.000 0.000 1.000 0.000
#> GSM627115 4 0.4746 0.11512 0.000 0.368 0.000 0.632
#> GSM627125 4 0.4522 0.29541 0.000 0.320 0.000 0.680
#> GSM627174 3 0.2469 0.85183 0.000 0.108 0.892 0.000
#> GSM627102 2 0.4977 0.02350 0.000 0.540 0.000 0.460
#> GSM627073 4 0.0000 0.55663 0.000 0.000 0.000 1.000
#> GSM627108 2 0.4477 0.42413 0.000 0.688 0.000 0.312
#> GSM627126 1 0.0000 0.97296 1.000 0.000 0.000 0.000
#> GSM627078 2 0.4843 0.33147 0.000 0.604 0.000 0.396
#> GSM627090 3 0.1557 0.87595 0.000 0.056 0.944 0.000
#> GSM627099 2 0.4477 0.42413 0.000 0.688 0.000 0.312
#> GSM627105 4 0.0469 0.55111 0.000 0.012 0.000 0.988
#> GSM627117 3 0.4655 0.71107 0.000 0.312 0.684 0.004
#> GSM627121 4 0.0336 0.55415 0.000 0.008 0.000 0.992
#> GSM627127 2 0.4843 0.33324 0.000 0.604 0.000 0.396
#> GSM627087 4 0.3837 0.29059 0.000 0.224 0.000 0.776
#> GSM627089 3 0.4535 0.72985 0.000 0.292 0.704 0.004
#> GSM627092 2 0.4989 0.01253 0.000 0.528 0.000 0.472
#> GSM627076 3 0.0188 0.89726 0.000 0.004 0.996 0.000
#> GSM627136 3 0.3123 0.82473 0.000 0.156 0.844 0.000
#> GSM627081 4 0.0000 0.55663 0.000 0.000 0.000 1.000
#> GSM627091 4 0.4454 0.20297 0.000 0.308 0.000 0.692
#> GSM627097 2 0.4985 0.01772 0.000 0.532 0.000 0.468
#> GSM627072 4 0.4477 0.30023 0.000 0.312 0.000 0.688
#> GSM627080 1 0.0000 0.97296 1.000 0.000 0.000 0.000
#> GSM627088 3 0.3024 0.82966 0.000 0.148 0.852 0.000
#> GSM627109 3 0.4925 0.13634 0.428 0.000 0.572 0.000
#> GSM627111 1 0.0000 0.97296 1.000 0.000 0.000 0.000
#> GSM627113 3 0.0000 0.89729 0.000 0.000 1.000 0.000
#> GSM627133 4 0.0188 0.55667 0.000 0.004 0.000 0.996
#> GSM627177 3 0.4477 0.71483 0.000 0.312 0.688 0.000
#> GSM627086 4 0.4985 0.00848 0.000 0.468 0.000 0.532
#> GSM627095 1 0.0336 0.96801 0.992 0.000 0.008 0.000
#> GSM627079 3 0.4456 0.73896 0.000 0.280 0.716 0.004
#> GSM627082 3 0.0000 0.89729 0.000 0.000 1.000 0.000
#> GSM627074 3 0.0000 0.89729 0.000 0.000 1.000 0.000
#> GSM627077 3 0.0000 0.89729 0.000 0.000 1.000 0.000
#> GSM627093 3 0.0000 0.89729 0.000 0.000 1.000 0.000
#> GSM627120 4 0.0188 0.55667 0.000 0.004 0.000 0.996
#> GSM627124 2 0.4790 0.05727 0.000 0.620 0.000 0.380
#> GSM627075 2 0.4477 0.42413 0.000 0.688 0.000 0.312
#> GSM627085 2 0.4477 0.42413 0.000 0.688 0.000 0.312
#> GSM627119 3 0.0000 0.89729 0.000 0.000 1.000 0.000
#> GSM627116 3 0.4331 0.73548 0.000 0.288 0.712 0.000
#> GSM627084 3 0.0000 0.89729 0.000 0.000 1.000 0.000
#> GSM627096 4 0.3219 0.42267 0.000 0.164 0.000 0.836
#> GSM627100 3 0.4584 0.72296 0.000 0.300 0.696 0.004
#> GSM627112 2 0.6937 -0.14131 0.000 0.508 0.376 0.116
#> GSM627083 3 0.0000 0.89729 0.000 0.000 1.000 0.000
#> GSM627098 3 0.0000 0.89729 0.000 0.000 1.000 0.000
#> GSM627104 3 0.0000 0.89729 0.000 0.000 1.000 0.000
#> GSM627131 3 0.0000 0.89729 0.000 0.000 1.000 0.000
#> GSM627106 4 0.3024 0.43808 0.000 0.148 0.000 0.852
#> GSM627123 1 0.0188 0.97120 0.996 0.000 0.004 0.000
#> GSM627129 4 0.0188 0.55667 0.000 0.004 0.000 0.996
#> GSM627216 4 0.0188 0.55667 0.000 0.004 0.000 0.996
#> GSM627212 2 0.4477 0.42413 0.000 0.688 0.000 0.312
#> GSM627190 3 0.4477 0.71483 0.000 0.312 0.688 0.000
#> GSM627169 2 0.4981 0.02012 0.000 0.536 0.000 0.464
#> GSM627167 4 0.2408 0.49261 0.000 0.104 0.000 0.896
#> GSM627192 1 0.0000 0.97296 1.000 0.000 0.000 0.000
#> GSM627203 4 0.4454 0.30341 0.000 0.308 0.000 0.692
#> GSM627151 2 0.5399 0.01240 0.000 0.520 0.012 0.468
#> GSM627163 1 0.0000 0.97296 1.000 0.000 0.000 0.000
#> GSM627211 2 0.4477 0.42413 0.000 0.688 0.000 0.312
#> GSM627171 4 0.4500 0.30061 0.000 0.316 0.000 0.684
#> GSM627209 2 0.4477 0.42413 0.000 0.688 0.000 0.312
#> GSM627135 1 0.0188 0.97120 0.996 0.000 0.004 0.000
#> GSM627170 4 0.4985 0.00848 0.000 0.468 0.000 0.532
#> GSM627178 3 0.0000 0.89729 0.000 0.000 1.000 0.000
#> GSM627199 2 0.4977 0.02350 0.000 0.540 0.000 0.460
#> GSM627213 4 0.0188 0.55667 0.000 0.004 0.000 0.996
#> GSM627140 3 0.0188 0.89726 0.000 0.004 0.996 0.000
#> GSM627149 1 0.0188 0.97120 0.996 0.000 0.004 0.000
#> GSM627147 2 0.4817 0.05467 0.000 0.612 0.000 0.388
#> GSM627195 4 0.0000 0.55663 0.000 0.000 0.000 1.000
#> GSM627204 2 0.4477 0.42413 0.000 0.688 0.000 0.312
#> GSM627207 2 0.4477 0.42413 0.000 0.688 0.000 0.312
#> GSM627157 3 0.0000 0.89729 0.000 0.000 1.000 0.000
#> GSM627201 2 0.4855 0.27066 0.000 0.600 0.000 0.400
#> GSM627146 4 0.0188 0.55667 0.000 0.004 0.000 0.996
#> GSM627156 4 0.3942 0.28609 0.000 0.236 0.000 0.764
#> GSM627188 1 0.0000 0.97296 1.000 0.000 0.000 0.000
#> GSM627197 2 0.4543 0.41386 0.000 0.676 0.000 0.324
#> GSM627173 4 0.4933 0.07563 0.000 0.432 0.000 0.568
#> GSM627179 2 0.4477 0.42413 0.000 0.688 0.000 0.312
#> GSM627208 4 0.4985 0.00848 0.000 0.468 0.000 0.532
#> GSM627215 4 0.0000 0.55663 0.000 0.000 0.000 1.000
#> GSM627153 2 0.4477 0.42413 0.000 0.688 0.000 0.312
#> GSM627155 1 0.0000 0.97296 1.000 0.000 0.000 0.000
#> GSM627165 4 0.4994 -0.01482 0.000 0.480 0.000 0.520
#> GSM627168 3 0.0188 0.89726 0.000 0.004 0.996 0.000
#> GSM627183 3 0.0000 0.89729 0.000 0.000 1.000 0.000
#> GSM627144 4 0.1940 0.49520 0.000 0.076 0.000 0.924
#> GSM627158 1 0.0000 0.97296 1.000 0.000 0.000 0.000
#> GSM627196 2 0.4477 0.42413 0.000 0.688 0.000 0.312
#> GSM627142 3 0.4535 0.72985 0.000 0.292 0.704 0.004
#> GSM627182 4 0.0000 0.55663 0.000 0.000 0.000 1.000
#> GSM627202 3 0.0000 0.89729 0.000 0.000 1.000 0.000
#> GSM627141 3 0.0000 0.89729 0.000 0.000 1.000 0.000
#> GSM627143 4 0.4543 0.29229 0.000 0.324 0.000 0.676
#> GSM627145 3 0.4608 0.71931 0.000 0.304 0.692 0.004
#> GSM627152 3 0.0188 0.89726 0.000 0.004 0.996 0.000
#> GSM627200 3 0.0000 0.89729 0.000 0.000 1.000 0.000
#> GSM627159 3 0.2469 0.85174 0.000 0.108 0.892 0.000
#> GSM627164 2 0.4981 0.02012 0.000 0.536 0.000 0.464
#> GSM627138 1 0.0000 0.97296 1.000 0.000 0.000 0.000
#> GSM627175 4 0.4985 0.00848 0.000 0.468 0.000 0.532
#> GSM627150 4 0.4477 0.30023 0.000 0.312 0.000 0.688
#> GSM627166 3 0.0000 0.89729 0.000 0.000 1.000 0.000
#> GSM627186 2 0.5151 0.02015 0.000 0.532 0.004 0.464
#> GSM627139 4 0.4643 0.26960 0.000 0.344 0.000 0.656
#> GSM627181 4 0.4985 0.00848 0.000 0.468 0.000 0.532
#> GSM627205 4 0.4985 0.00848 0.000 0.468 0.000 0.532
#> GSM627214 4 0.4985 0.00848 0.000 0.468 0.000 0.532
#> GSM627180 4 0.0000 0.55663 0.000 0.000 0.000 1.000
#> GSM627172 2 0.6898 0.04662 0.000 0.524 0.116 0.360
#> GSM627184 1 0.0000 0.97296 1.000 0.000 0.000 0.000
#> GSM627193 4 0.3610 0.32539 0.000 0.200 0.000 0.800
#> GSM627191 3 0.0000 0.89729 0.000 0.000 1.000 0.000
#> GSM627176 3 0.0188 0.89726 0.000 0.004 0.996 0.000
#> GSM627194 4 0.1022 0.54105 0.000 0.032 0.000 0.968
#> GSM627154 2 0.4804 0.05628 0.000 0.616 0.000 0.384
#> GSM627187 3 0.0188 0.89726 0.000 0.004 0.996 0.000
#> GSM627198 2 0.4585 0.40053 0.000 0.668 0.000 0.332
#> GSM627160 3 0.0188 0.89726 0.000 0.004 0.996 0.000
#> GSM627185 1 0.4804 0.42619 0.616 0.000 0.384 0.000
#> GSM627206 3 0.4356 0.73234 0.000 0.292 0.708 0.000
#> GSM627161 1 0.0000 0.97296 1.000 0.000 0.000 0.000
#> GSM627162 3 0.0188 0.89726 0.000 0.004 0.996 0.000
#> GSM627210 3 0.0188 0.89726 0.000 0.004 0.996 0.000
#> GSM627189 2 0.4843 0.33324 0.000 0.604 0.000 0.396
show/hide code output
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM627128 3 0.1908 0.4226 0.000 0.000 0.908 0.000 0.092
#> GSM627110 4 0.1908 0.5900 0.000 0.000 0.092 0.908 0.000
#> GSM627132 1 0.0000 0.8108 1.000 0.000 0.000 0.000 0.000
#> GSM627107 5 0.1626 0.7518 0.000 0.044 0.016 0.000 0.940
#> GSM627103 5 0.0451 0.7677 0.000 0.008 0.004 0.000 0.988
#> GSM627114 3 0.6207 0.3572 0.000 0.140 0.460 0.400 0.000
#> GSM627134 5 0.0162 0.7685 0.000 0.004 0.000 0.000 0.996
#> GSM627137 2 0.4278 0.7861 0.000 0.548 0.000 0.000 0.452
#> GSM627148 5 0.4192 0.1317 0.000 0.000 0.404 0.000 0.596
#> GSM627101 5 0.1522 0.7509 0.000 0.044 0.012 0.000 0.944
#> GSM627130 3 0.2694 0.3978 0.000 0.076 0.884 0.000 0.040
#> GSM627071 3 0.0162 0.4679 0.000 0.000 0.996 0.000 0.004
#> GSM627118 5 0.4307 -0.7163 0.000 0.500 0.000 0.000 0.500
#> GSM627094 2 0.4161 0.7645 0.000 0.608 0.000 0.000 0.392
#> GSM627122 3 0.4726 0.4028 0.000 0.020 0.580 0.400 0.000
#> GSM627115 2 0.4045 0.7915 0.000 0.644 0.000 0.000 0.356
#> GSM627125 3 0.4294 0.1048 0.000 0.000 0.532 0.000 0.468
#> GSM627174 3 0.3177 0.4709 0.000 0.000 0.792 0.208 0.000
#> GSM627102 4 0.6351 0.5820 0.000 0.112 0.256 0.596 0.036
#> GSM627073 5 0.1544 0.7179 0.000 0.000 0.068 0.000 0.932
#> GSM627108 2 0.3983 0.7928 0.000 0.660 0.000 0.000 0.340
#> GSM627126 1 0.0000 0.8108 1.000 0.000 0.000 0.000 0.000
#> GSM627078 2 0.4114 0.7757 0.000 0.624 0.000 0.000 0.376
#> GSM627090 3 0.3752 0.4481 0.000 0.000 0.708 0.292 0.000
#> GSM627099 2 0.4227 0.8080 0.000 0.580 0.000 0.000 0.420
#> GSM627105 5 0.2605 0.6265 0.000 0.000 0.148 0.000 0.852
#> GSM627117 4 0.4268 0.5097 0.000 0.000 0.444 0.556 0.000
#> GSM627121 5 0.0798 0.7697 0.000 0.008 0.016 0.000 0.976
#> GSM627127 5 0.4291 -0.6143 0.000 0.464 0.000 0.000 0.536
#> GSM627087 2 0.4210 0.7543 0.000 0.588 0.000 0.000 0.412
#> GSM627089 3 0.0566 0.4736 0.000 0.000 0.984 0.012 0.004
#> GSM627092 4 0.6546 0.5785 0.000 0.112 0.244 0.592 0.052
#> GSM627076 3 0.4182 0.4072 0.000 0.000 0.600 0.400 0.000
#> GSM627136 3 0.3081 0.4775 0.000 0.012 0.832 0.156 0.000
#> GSM627081 5 0.0510 0.7693 0.000 0.000 0.016 0.000 0.984
#> GSM627091 2 0.4161 0.7647 0.000 0.608 0.000 0.000 0.392
#> GSM627097 3 0.4434 0.1215 0.000 0.004 0.536 0.000 0.460
#> GSM627072 3 0.4287 0.1231 0.000 0.000 0.540 0.000 0.460
#> GSM627080 1 0.0000 0.8108 1.000 0.000 0.000 0.000 0.000
#> GSM627088 3 0.3123 0.4775 0.000 0.012 0.828 0.160 0.000
#> GSM627109 1 0.7557 0.4271 0.404 0.340 0.056 0.200 0.000
#> GSM627111 1 0.0000 0.8108 1.000 0.000 0.000 0.000 0.000
#> GSM627113 1 0.7909 0.3701 0.368 0.340 0.088 0.204 0.000
#> GSM627133 5 0.0290 0.7678 0.000 0.008 0.000 0.000 0.992
#> GSM627177 3 0.0162 0.4690 0.000 0.000 0.996 0.004 0.000
#> GSM627086 2 0.4278 0.7861 0.000 0.548 0.000 0.000 0.452
#> GSM627095 1 0.4135 0.7018 0.656 0.340 0.000 0.004 0.000
#> GSM627079 3 0.0771 0.4759 0.000 0.000 0.976 0.020 0.004
#> GSM627082 3 0.6485 0.3652 0.000 0.196 0.460 0.344 0.000
#> GSM627074 4 0.4863 0.5230 0.000 0.204 0.088 0.708 0.000
#> GSM627077 3 0.6485 0.3652 0.000 0.196 0.460 0.344 0.000
#> GSM627093 4 0.4280 0.4526 0.000 0.140 0.088 0.772 0.000
#> GSM627120 5 0.0324 0.7691 0.000 0.004 0.004 0.000 0.992
#> GSM627124 2 0.6105 0.4679 0.000 0.600 0.212 0.008 0.180
#> GSM627075 2 0.3983 0.7928 0.000 0.660 0.000 0.000 0.340
#> GSM627085 2 0.4210 0.8097 0.000 0.588 0.000 0.000 0.412
#> GSM627119 4 0.4280 0.4526 0.000 0.140 0.088 0.772 0.000
#> GSM627116 3 0.0609 0.4744 0.000 0.000 0.980 0.020 0.000
#> GSM627084 3 0.6470 0.3648 0.000 0.192 0.460 0.348 0.000
#> GSM627096 5 0.4045 -0.2710 0.000 0.356 0.000 0.000 0.644
#> GSM627100 3 0.0324 0.4701 0.000 0.000 0.992 0.004 0.004
#> GSM627112 4 0.4310 0.5225 0.000 0.004 0.392 0.604 0.000
#> GSM627083 3 0.6498 0.3256 0.000 0.340 0.460 0.200 0.000
#> GSM627098 3 0.6498 0.3256 0.000 0.340 0.460 0.200 0.000
#> GSM627104 4 0.3551 0.5960 0.000 0.136 0.044 0.820 0.000
#> GSM627131 3 0.6511 0.3641 0.000 0.204 0.460 0.336 0.000
#> GSM627106 5 0.1386 0.7605 0.000 0.032 0.016 0.000 0.952
#> GSM627123 1 0.4135 0.7018 0.656 0.340 0.000 0.004 0.000
#> GSM627129 5 0.0162 0.7685 0.000 0.004 0.000 0.000 0.996
#> GSM627216 5 0.0290 0.7678 0.000 0.008 0.000 0.000 0.992
#> GSM627212 2 0.4227 0.8072 0.000 0.580 0.000 0.000 0.420
#> GSM627190 4 0.4268 0.5097 0.000 0.000 0.444 0.556 0.000
#> GSM627169 4 0.6351 0.5820 0.000 0.112 0.256 0.596 0.036
#> GSM627167 2 0.4451 0.7484 0.000 0.504 0.004 0.000 0.492
#> GSM627192 1 0.0000 0.8108 1.000 0.000 0.000 0.000 0.000
#> GSM627203 3 0.4287 0.1231 0.000 0.000 0.540 0.000 0.460
#> GSM627151 3 0.5278 0.1588 0.000 0.004 0.536 0.040 0.420
#> GSM627163 1 0.0000 0.8108 1.000 0.000 0.000 0.000 0.000
#> GSM627211 2 0.4201 0.8101 0.000 0.592 0.000 0.000 0.408
#> GSM627171 2 0.6897 0.3989 0.000 0.532 0.256 0.036 0.176
#> GSM627209 2 0.4219 0.8084 0.000 0.584 0.000 0.000 0.416
#> GSM627135 1 0.3983 0.7035 0.660 0.340 0.000 0.000 0.000
#> GSM627170 5 0.1478 0.7277 0.000 0.064 0.000 0.000 0.936
#> GSM627178 3 0.6210 0.3554 0.000 0.140 0.456 0.404 0.000
#> GSM627199 4 0.6002 0.5657 0.000 0.064 0.304 0.596 0.036
#> GSM627213 5 0.3336 0.2616 0.000 0.228 0.000 0.000 0.772
#> GSM627140 4 0.0162 0.6234 0.000 0.000 0.004 0.996 0.000
#> GSM627149 1 0.3983 0.7035 0.660 0.340 0.000 0.000 0.000
#> GSM627147 2 0.6915 0.4215 0.000 0.564 0.212 0.056 0.168
#> GSM627195 5 0.0510 0.7693 0.000 0.000 0.016 0.000 0.984
#> GSM627204 2 0.3983 0.7928 0.000 0.660 0.000 0.000 0.340
#> GSM627207 2 0.3983 0.7928 0.000 0.660 0.000 0.000 0.340
#> GSM627157 1 0.7909 0.3701 0.368 0.340 0.088 0.204 0.000
#> GSM627201 2 0.4278 0.7861 0.000 0.548 0.000 0.000 0.452
#> GSM627146 5 0.4256 -0.6250 0.000 0.436 0.000 0.000 0.564
#> GSM627156 2 0.5206 0.7248 0.000 0.572 0.004 0.040 0.384
#> GSM627188 1 0.0000 0.8108 1.000 0.000 0.000 0.000 0.000
#> GSM627197 2 0.4273 0.7910 0.000 0.552 0.000 0.000 0.448
#> GSM627173 4 0.6867 0.5629 0.000 0.112 0.196 0.592 0.100
#> GSM627179 2 0.4088 0.8045 0.000 0.632 0.000 0.000 0.368
#> GSM627208 5 0.1357 0.7443 0.000 0.048 0.004 0.000 0.948
#> GSM627215 5 0.0510 0.7693 0.000 0.000 0.016 0.000 0.984
#> GSM627153 2 0.4227 0.8080 0.000 0.580 0.000 0.000 0.420
#> GSM627155 1 0.0000 0.8108 1.000 0.000 0.000 0.000 0.000
#> GSM627165 2 0.4278 0.7861 0.000 0.548 0.000 0.000 0.452
#> GSM627168 3 0.4547 0.4050 0.000 0.012 0.588 0.400 0.000
#> GSM627183 3 0.6207 0.3572 0.000 0.140 0.460 0.400 0.000
#> GSM627144 5 0.3432 0.6216 0.000 0.040 0.132 0.000 0.828
#> GSM627158 1 0.0000 0.8108 1.000 0.000 0.000 0.000 0.000
#> GSM627196 2 0.4249 0.8015 0.000 0.568 0.000 0.000 0.432
#> GSM627142 3 0.0566 0.4736 0.000 0.000 0.984 0.012 0.004
#> GSM627182 5 0.0510 0.7693 0.000 0.000 0.016 0.000 0.984
#> GSM627202 3 0.6523 0.3276 0.000 0.332 0.460 0.208 0.000
#> GSM627141 3 0.6207 0.3572 0.000 0.140 0.460 0.400 0.000
#> GSM627143 3 0.4306 0.0746 0.000 0.000 0.508 0.000 0.492
#> GSM627145 3 0.0162 0.4679 0.000 0.000 0.996 0.000 0.004
#> GSM627152 4 0.1908 0.5900 0.000 0.000 0.092 0.908 0.000
#> GSM627200 3 0.6207 0.3572 0.000 0.140 0.460 0.400 0.000
#> GSM627159 3 0.3143 0.4712 0.000 0.000 0.796 0.204 0.000
#> GSM627164 4 0.6351 0.5820 0.000 0.112 0.256 0.596 0.036
#> GSM627138 1 0.0000 0.8108 1.000 0.000 0.000 0.000 0.000
#> GSM627175 2 0.4278 0.7861 0.000 0.548 0.000 0.000 0.452
#> GSM627150 3 0.4306 0.0733 0.000 0.000 0.508 0.000 0.492
#> GSM627166 4 0.4280 0.4526 0.000 0.140 0.088 0.772 0.000
#> GSM627186 4 0.6351 0.5820 0.000 0.112 0.256 0.596 0.036
#> GSM627139 3 0.4283 0.1298 0.000 0.000 0.544 0.000 0.456
#> GSM627181 2 0.4278 0.7861 0.000 0.548 0.000 0.000 0.452
#> GSM627205 5 0.3661 0.1909 0.000 0.276 0.000 0.000 0.724
#> GSM627214 2 0.4300 0.7480 0.000 0.524 0.000 0.000 0.476
#> GSM627180 5 0.0510 0.7693 0.000 0.000 0.016 0.000 0.984
#> GSM627172 4 0.4436 0.5205 0.000 0.008 0.396 0.596 0.000
#> GSM627184 1 0.0000 0.8108 1.000 0.000 0.000 0.000 0.000
#> GSM627193 5 0.2020 0.6453 0.000 0.100 0.000 0.000 0.900
#> GSM627191 3 0.6233 0.3583 0.000 0.144 0.460 0.396 0.000
#> GSM627176 4 0.1197 0.6206 0.000 0.000 0.048 0.952 0.000
#> GSM627194 5 0.0703 0.7636 0.000 0.024 0.000 0.000 0.976
#> GSM627154 2 0.5765 0.2700 0.000 0.488 0.424 0.000 0.088
#> GSM627187 4 0.1197 0.6206 0.000 0.000 0.048 0.952 0.000
#> GSM627198 2 0.4278 0.7867 0.000 0.548 0.000 0.000 0.452
#> GSM627160 4 0.1851 0.5935 0.000 0.000 0.088 0.912 0.000
#> GSM627185 1 0.6576 0.5117 0.468 0.340 0.004 0.188 0.000
#> GSM627206 3 0.0510 0.4735 0.000 0.000 0.984 0.016 0.000
#> GSM627161 1 0.0000 0.8108 1.000 0.000 0.000 0.000 0.000
#> GSM627162 4 0.1197 0.6206 0.000 0.000 0.048 0.952 0.000
#> GSM627210 4 0.1121 0.6209 0.000 0.000 0.044 0.956 0.000
#> GSM627189 2 0.4273 0.7772 0.000 0.552 0.000 0.000 0.448
show/hide code output
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM627128 6 0.4201 0.6803 0.000 0.000 0.036 0.000 0.300 0.664
#> GSM627110 3 0.3607 0.4774 0.000 0.000 0.652 0.348 0.000 0.000
#> GSM627132 1 0.0000 0.8591 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627107 5 0.3076 0.6132 0.000 0.240 0.000 0.000 0.760 0.000
#> GSM627103 5 0.5760 0.6685 0.000 0.128 0.000 0.016 0.528 0.328
#> GSM627114 3 0.1141 0.6963 0.000 0.000 0.948 0.000 0.000 0.052
#> GSM627134 5 0.5480 0.6731 0.000 0.144 0.000 0.000 0.528 0.328
#> GSM627137 2 0.0000 0.8106 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627148 5 0.3144 0.4770 0.000 0.016 0.000 0.004 0.808 0.172
#> GSM627101 5 0.3351 0.5782 0.000 0.288 0.000 0.000 0.712 0.000
#> GSM627130 6 0.4320 0.6804 0.000 0.004 0.036 0.000 0.296 0.664
#> GSM627071 6 0.4368 0.6811 0.000 0.000 0.048 0.000 0.296 0.656
#> GSM627118 2 0.0937 0.7901 0.000 0.960 0.000 0.000 0.040 0.000
#> GSM627094 2 0.6562 0.0809 0.000 0.436 0.000 0.040 0.196 0.328
#> GSM627122 3 0.3076 0.5413 0.000 0.000 0.760 0.000 0.000 0.240
#> GSM627115 2 0.4732 0.4469 0.000 0.620 0.000 0.016 0.036 0.328
#> GSM627125 6 0.3706 0.6114 0.000 0.000 0.000 0.000 0.380 0.620
#> GSM627174 3 0.4097 -0.0410 0.000 0.000 0.500 0.000 0.008 0.492
#> GSM627102 4 0.1267 0.7267 0.000 0.000 0.000 0.940 0.000 0.060
#> GSM627073 5 0.2260 0.6785 0.000 0.140 0.000 0.000 0.860 0.000
#> GSM627108 2 0.0458 0.8036 0.000 0.984 0.000 0.016 0.000 0.000
#> GSM627126 1 0.0000 0.8591 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627078 2 0.1625 0.7874 0.000 0.928 0.000 0.012 0.060 0.000
#> GSM627090 3 0.4717 0.1919 0.000 0.000 0.580 0.000 0.056 0.364
#> GSM627099 2 0.0000 0.8106 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627105 5 0.3746 0.6039 0.000 0.140 0.000 0.000 0.780 0.080
#> GSM627117 6 0.5171 -0.1395 0.000 0.000 0.088 0.416 0.000 0.496
#> GSM627121 5 0.2631 0.6569 0.000 0.180 0.000 0.000 0.820 0.000
#> GSM627127 2 0.4905 0.4700 0.000 0.672 0.000 0.004 0.164 0.160
#> GSM627087 2 0.6358 -0.0444 0.000 0.416 0.000 0.016 0.240 0.328
#> GSM627089 6 0.4606 0.6784 0.000 0.000 0.076 0.000 0.268 0.656
#> GSM627092 4 0.3647 0.5202 0.000 0.000 0.000 0.640 0.000 0.360
#> GSM627076 3 0.4284 0.4456 0.000 0.000 0.688 0.000 0.056 0.256
#> GSM627136 6 0.3868 0.0565 0.000 0.000 0.496 0.000 0.000 0.504
#> GSM627081 5 0.2260 0.6785 0.000 0.140 0.000 0.000 0.860 0.000
#> GSM627091 2 0.5090 0.5134 0.000 0.672 0.000 0.016 0.144 0.168
#> GSM627097 6 0.0777 0.4648 0.000 0.000 0.000 0.004 0.024 0.972
#> GSM627072 6 0.3531 0.6452 0.000 0.000 0.000 0.000 0.328 0.672
#> GSM627080 1 0.0000 0.8591 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627088 6 0.3868 0.0590 0.000 0.000 0.496 0.000 0.000 0.504
#> GSM627109 3 0.6315 0.2641 0.216 0.000 0.576 0.060 0.140 0.008
#> GSM627111 1 0.0000 0.8591 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627113 3 0.4542 0.5896 0.036 0.000 0.756 0.060 0.140 0.008
#> GSM627133 5 0.5760 0.6685 0.000 0.128 0.000 0.016 0.528 0.328
#> GSM627177 6 0.4915 0.6235 0.000 0.000 0.188 0.000 0.156 0.656
#> GSM627086 2 0.0000 0.8106 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627095 1 0.6688 0.4354 0.480 0.000 0.312 0.060 0.140 0.008
#> GSM627079 6 0.4918 0.6297 0.000 0.000 0.184 0.000 0.160 0.656
#> GSM627082 3 0.2782 0.6897 0.000 0.000 0.876 0.024 0.032 0.068
#> GSM627074 3 0.5095 0.5442 0.000 0.000 0.632 0.256 0.104 0.008
#> GSM627077 3 0.2411 0.6944 0.000 0.000 0.900 0.024 0.032 0.044
#> GSM627093 3 0.1124 0.6955 0.000 0.000 0.956 0.036 0.000 0.008
#> GSM627120 5 0.5480 0.6731 0.000 0.144 0.000 0.000 0.528 0.328
#> GSM627124 2 0.5946 0.0235 0.000 0.436 0.000 0.336 0.000 0.228
#> GSM627075 2 0.1391 0.7962 0.000 0.944 0.000 0.016 0.040 0.000
#> GSM627085 2 0.1010 0.7995 0.000 0.960 0.000 0.004 0.036 0.000
#> GSM627119 3 0.1265 0.6939 0.000 0.000 0.948 0.044 0.000 0.008
#> GSM627116 6 0.3309 0.4856 0.000 0.000 0.280 0.000 0.000 0.720
#> GSM627084 3 0.2328 0.6954 0.000 0.000 0.904 0.020 0.032 0.044
#> GSM627096 2 0.5934 -0.0621 0.000 0.444 0.000 0.000 0.328 0.228
#> GSM627100 6 0.4247 0.6816 0.000 0.000 0.040 0.000 0.296 0.664
#> GSM627112 4 0.3747 0.2936 0.000 0.000 0.000 0.604 0.000 0.396
#> GSM627083 3 0.4426 0.6392 0.000 0.000 0.756 0.060 0.140 0.044
#> GSM627098 3 0.4426 0.6392 0.000 0.000 0.756 0.060 0.140 0.044
#> GSM627104 3 0.5520 0.2613 0.000 0.000 0.448 0.444 0.100 0.008
#> GSM627131 3 0.2565 0.6933 0.000 0.000 0.892 0.032 0.032 0.044
#> GSM627106 5 0.2260 0.6785 0.000 0.140 0.000 0.000 0.860 0.000
#> GSM627123 1 0.6688 0.4354 0.480 0.000 0.312 0.060 0.140 0.008
#> GSM627129 5 0.5480 0.6731 0.000 0.144 0.000 0.000 0.528 0.328
#> GSM627216 5 0.5480 0.6731 0.000 0.144 0.000 0.000 0.528 0.328
#> GSM627212 2 0.1082 0.7982 0.000 0.956 0.000 0.004 0.040 0.000
#> GSM627190 4 0.5666 0.1120 0.000 0.000 0.156 0.456 0.000 0.388
#> GSM627169 4 0.1267 0.7267 0.000 0.000 0.000 0.940 0.000 0.060
#> GSM627167 2 0.6218 0.4231 0.000 0.568 0.000 0.204 0.060 0.168
#> GSM627192 1 0.0000 0.8591 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627203 5 0.3756 -0.2327 0.000 0.000 0.000 0.000 0.600 0.400
#> GSM627151 6 0.0603 0.4719 0.000 0.000 0.000 0.004 0.016 0.980
#> GSM627163 1 0.0000 0.8591 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627211 2 0.0000 0.8106 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627171 6 0.6023 -0.3230 0.000 0.260 0.000 0.320 0.000 0.420
#> GSM627209 2 0.0000 0.8106 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627135 1 0.6322 0.4924 0.532 0.000 0.268 0.060 0.140 0.000
#> GSM627170 2 0.3464 0.3461 0.000 0.688 0.000 0.000 0.312 0.000
#> GSM627178 3 0.0260 0.7037 0.000 0.000 0.992 0.000 0.000 0.008
#> GSM627199 4 0.1814 0.7071 0.000 0.000 0.000 0.900 0.000 0.100
#> GSM627213 5 0.5665 0.6391 0.000 0.172 0.000 0.000 0.500 0.328
#> GSM627140 4 0.2912 0.4773 0.000 0.000 0.216 0.784 0.000 0.000
#> GSM627149 1 0.6322 0.4924 0.532 0.000 0.268 0.060 0.140 0.000
#> GSM627147 4 0.5347 0.1640 0.000 0.384 0.000 0.504 0.000 0.112
#> GSM627195 5 0.2260 0.6785 0.000 0.140 0.000 0.000 0.860 0.000
#> GSM627204 2 0.0405 0.8076 0.000 0.988 0.000 0.008 0.004 0.000
#> GSM627207 2 0.0000 0.8106 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627157 3 0.4542 0.5896 0.036 0.000 0.756 0.060 0.140 0.008
#> GSM627201 2 0.0000 0.8106 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627146 2 0.6131 -0.3193 0.000 0.340 0.000 0.000 0.332 0.328
#> GSM627156 4 0.6982 0.0572 0.000 0.256 0.000 0.356 0.060 0.328
#> GSM627188 1 0.0000 0.8591 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627197 2 0.0937 0.7986 0.000 0.960 0.000 0.000 0.040 0.000
#> GSM627173 4 0.2553 0.6817 0.000 0.000 0.000 0.848 0.008 0.144
#> GSM627179 2 0.0000 0.8106 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627208 5 0.3868 0.1999 0.000 0.496 0.000 0.000 0.504 0.000
#> GSM627215 5 0.5451 0.6748 0.000 0.140 0.000 0.000 0.532 0.328
#> GSM627153 2 0.0000 0.8106 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627155 1 0.0000 0.8591 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627165 2 0.0000 0.8106 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627168 3 0.3175 0.5198 0.000 0.000 0.744 0.000 0.000 0.256
#> GSM627183 3 0.1007 0.6985 0.000 0.000 0.956 0.000 0.000 0.044
#> GSM627144 5 0.5760 0.6685 0.000 0.128 0.000 0.016 0.528 0.328
#> GSM627158 1 0.0000 0.8591 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627196 2 0.0000 0.8106 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627142 6 0.4247 0.6816 0.000 0.000 0.040 0.000 0.296 0.664
#> GSM627182 5 0.2872 0.6823 0.000 0.140 0.000 0.000 0.836 0.024
#> GSM627202 3 0.4349 0.6438 0.000 0.000 0.764 0.060 0.132 0.044
#> GSM627141 3 0.1007 0.6985 0.000 0.000 0.956 0.000 0.000 0.044
#> GSM627143 6 0.0632 0.4747 0.000 0.000 0.000 0.000 0.024 0.976
#> GSM627145 6 0.4247 0.6816 0.000 0.000 0.040 0.000 0.296 0.664
#> GSM627152 3 0.3607 0.4774 0.000 0.000 0.652 0.348 0.000 0.000
#> GSM627200 3 0.0260 0.7041 0.000 0.000 0.992 0.000 0.000 0.008
#> GSM627159 3 0.4856 -0.1355 0.000 0.000 0.480 0.000 0.056 0.464
#> GSM627164 4 0.1267 0.7267 0.000 0.000 0.000 0.940 0.000 0.060
#> GSM627138 1 0.0000 0.8591 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627175 2 0.0000 0.8106 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627150 5 0.3862 -0.4132 0.000 0.000 0.000 0.000 0.524 0.476
#> GSM627166 3 0.1124 0.6955 0.000 0.000 0.956 0.036 0.000 0.008
#> GSM627186 4 0.1267 0.7267 0.000 0.000 0.000 0.940 0.000 0.060
#> GSM627139 6 0.3446 0.6601 0.000 0.000 0.000 0.000 0.308 0.692
#> GSM627181 2 0.0000 0.8106 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627205 2 0.2092 0.7216 0.000 0.876 0.000 0.000 0.124 0.000
#> GSM627214 2 0.0000 0.8106 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM627180 5 0.2790 0.6870 0.000 0.140 0.000 0.000 0.840 0.020
#> GSM627172 4 0.1444 0.7218 0.000 0.000 0.000 0.928 0.000 0.072
#> GSM627184 1 0.0000 0.8591 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627193 5 0.5480 0.6731 0.000 0.144 0.000 0.000 0.528 0.328
#> GSM627191 3 0.1152 0.6989 0.000 0.000 0.952 0.004 0.000 0.044
#> GSM627176 3 0.3843 0.3249 0.000 0.000 0.548 0.452 0.000 0.000
#> GSM627194 5 0.5582 0.6726 0.000 0.140 0.000 0.004 0.528 0.328
#> GSM627154 6 0.3742 0.1921 0.000 0.348 0.000 0.004 0.000 0.648
#> GSM627187 3 0.3843 0.3249 0.000 0.000 0.548 0.452 0.000 0.000
#> GSM627198 2 0.1267 0.7884 0.000 0.940 0.000 0.000 0.060 0.000
#> GSM627160 3 0.3810 0.3662 0.000 0.000 0.572 0.428 0.000 0.000
#> GSM627185 3 0.6657 0.0126 0.300 0.000 0.492 0.060 0.140 0.008
#> GSM627206 6 0.4481 0.4869 0.000 0.000 0.296 0.000 0.056 0.648
#> GSM627161 1 0.0000 0.8591 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627162 3 0.3843 0.3249 0.000 0.000 0.548 0.452 0.000 0.000
#> GSM627210 3 0.4010 0.3627 0.000 0.000 0.584 0.408 0.000 0.008
#> GSM627189 2 0.3621 0.6524 0.000 0.788 0.000 0.004 0.048 0.160
Heatmaps for the consensus matrix. It visualizes the probability of two
samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures
which are significantly different between classes which can be candidate marks
for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known
annotation is numeric, one-way ANOVA test is applied, and if the known
annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) age(p) other(p) k
#> ATC:pam 143 1.0000 0.106 0.0536 2
#> ATC:pam 138 0.3814 0.437 0.0132 3
#> ATC:pam 80 0.4626 0.900 0.1640 4
#> ATC:pam 89 0.4717 0.858 0.0774 5
#> ATC:pam 100 0.0905 0.440 0.0858 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
ATC:mclust
The object with results only for a single top-value method and a single partition method
can be extracted as:
res = res_list["ATC", "mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51882 rows and 146 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
- The first row: a plot of the ECDF (empirical cumulative distribution
function) curves of the consensus matrix for each
k and the heatmap of
predicted classes for each k.
- The second row: heatmaps of the consensus matrix for each
k.
- The third row: heatmaps of the membership matrix for each
k.
- The fouth row: heatmaps of the signatures for each
k.
All the plots in panels can be made by individual functions and they are
plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
- ECDF curves of the consensus matrix for each
k;
- 1-PAC. The PAC
score
measures the proportion of the ambiguous subgrouping.
- Mean silhouette score.
- Concordance. The mean probability of fiting the consensus class ids in all
partitions.
- Area increased. Denote \(A_k\) as the area under the ECDF curve for current
k, the area increased is defined as \(A_k - A_{k-1}\).
- Rand index. The percent of pairs of samples that are both in a same cluster
or both are not in a same cluster in the partition of k and k-1.
- Jaccard index. The ratio of pairs of samples are both in a same cluster in
the partition of k and k-1 and the pairs of samples are both in a same
cluster in the partition k or k-1.
The detailed explanations of these statistics can be found in the cola
vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.513 0.868 0.905 0.4697 0.499 0.499
#> 3 3 0.310 0.344 0.672 0.2207 0.620 0.417
#> 4 4 0.491 0.581 0.762 0.2112 0.691 0.390
#> 5 5 0.628 0.699 0.818 0.0889 0.896 0.655
#> 6 6 0.688 0.619 0.811 0.0556 0.963 0.840
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
- All \(k\) with Jaccard index larger than 0.95 are removed because increasing
\(k\) does not provide enough extra information. If all \(k\) are removed, it is
marked as no subgroup is detected.
- For all \(k\) with 1-PAC score larger than 0.9, the maximal \(k\) is taken as
the best \(k\), and other \(k\) are marked as optional \(k\).
- If it does not fit the second rule. The \(k\) with the maximal vote of the
highest 1-PAC score, highest mean silhouette, and highest concordance is
taken as the best \(k\).
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
show/hide code output
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM627128 2 0.9358 0.548 0.352 0.648
#> GSM627110 1 0.6048 0.904 0.852 0.148
#> GSM627132 1 0.0000 0.886 1.000 0.000
#> GSM627107 2 0.0672 0.908 0.008 0.992
#> GSM627103 2 0.0000 0.911 0.000 1.000
#> GSM627114 1 0.3879 0.930 0.924 0.076
#> GSM627134 2 0.0000 0.911 0.000 1.000
#> GSM627137 2 0.0000 0.911 0.000 1.000
#> GSM627148 1 0.7299 0.848 0.796 0.204
#> GSM627101 2 0.0672 0.908 0.008 0.992
#> GSM627130 2 0.9248 0.572 0.340 0.660
#> GSM627071 1 0.5408 0.904 0.876 0.124
#> GSM627118 2 0.0000 0.911 0.000 1.000
#> GSM627094 2 0.4562 0.870 0.096 0.904
#> GSM627122 1 0.3879 0.930 0.924 0.076
#> GSM627115 2 0.0000 0.911 0.000 1.000
#> GSM627125 2 0.9286 0.564 0.344 0.656
#> GSM627174 2 0.9087 0.600 0.324 0.676
#> GSM627102 2 0.4690 0.868 0.100 0.900
#> GSM627073 2 0.0672 0.908 0.008 0.992
#> GSM627108 2 0.0000 0.911 0.000 1.000
#> GSM627126 1 0.0000 0.886 1.000 0.000
#> GSM627078 2 0.0000 0.911 0.000 1.000
#> GSM627090 1 0.3879 0.930 0.924 0.076
#> GSM627099 2 0.0000 0.911 0.000 1.000
#> GSM627105 2 0.6531 0.784 0.168 0.832
#> GSM627117 1 0.6623 0.883 0.828 0.172
#> GSM627121 2 0.0672 0.908 0.008 0.992
#> GSM627127 2 0.0000 0.911 0.000 1.000
#> GSM627087 2 0.0000 0.911 0.000 1.000
#> GSM627089 1 0.3879 0.930 0.924 0.076
#> GSM627092 2 0.4690 0.868 0.100 0.900
#> GSM627076 1 0.3879 0.930 0.924 0.076
#> GSM627136 1 0.3879 0.930 0.924 0.076
#> GSM627081 1 0.9491 0.605 0.632 0.368
#> GSM627091 2 0.0000 0.911 0.000 1.000
#> GSM627097 2 0.8661 0.650 0.288 0.712
#> GSM627072 1 0.6247 0.898 0.844 0.156
#> GSM627080 1 0.0000 0.886 1.000 0.000
#> GSM627088 1 0.3879 0.930 0.924 0.076
#> GSM627109 1 0.6048 0.904 0.852 0.148
#> GSM627111 1 0.0000 0.886 1.000 0.000
#> GSM627113 1 0.3879 0.930 0.924 0.076
#> GSM627133 1 0.8763 0.738 0.704 0.296
#> GSM627177 1 0.3879 0.930 0.924 0.076
#> GSM627086 2 0.0000 0.911 0.000 1.000
#> GSM627095 1 0.3879 0.930 0.924 0.076
#> GSM627079 1 0.3879 0.930 0.924 0.076
#> GSM627082 2 0.9358 0.548 0.352 0.648
#> GSM627074 1 0.6048 0.904 0.852 0.148
#> GSM627077 1 0.3879 0.930 0.924 0.076
#> GSM627093 1 0.6048 0.904 0.852 0.148
#> GSM627120 2 0.0000 0.911 0.000 1.000
#> GSM627124 2 0.4690 0.868 0.100 0.900
#> GSM627075 2 0.0000 0.911 0.000 1.000
#> GSM627085 2 0.0000 0.911 0.000 1.000
#> GSM627119 1 0.6048 0.904 0.852 0.148
#> GSM627116 1 0.5059 0.920 0.888 0.112
#> GSM627084 1 0.3879 0.930 0.924 0.076
#> GSM627096 2 0.0376 0.909 0.004 0.996
#> GSM627100 1 0.4298 0.926 0.912 0.088
#> GSM627112 2 0.4690 0.868 0.100 0.900
#> GSM627083 2 0.9358 0.548 0.352 0.648
#> GSM627098 1 0.3879 0.930 0.924 0.076
#> GSM627104 1 0.6247 0.898 0.844 0.156
#> GSM627131 1 0.3879 0.930 0.924 0.076
#> GSM627106 2 0.5737 0.796 0.136 0.864
#> GSM627123 1 0.3879 0.930 0.924 0.076
#> GSM627129 2 0.0000 0.911 0.000 1.000
#> GSM627216 2 0.0000 0.911 0.000 1.000
#> GSM627212 2 0.0000 0.911 0.000 1.000
#> GSM627190 1 0.6623 0.883 0.828 0.172
#> GSM627169 2 0.4690 0.868 0.100 0.900
#> GSM627167 2 0.0000 0.911 0.000 1.000
#> GSM627192 1 0.0000 0.886 1.000 0.000
#> GSM627203 1 0.6148 0.901 0.848 0.152
#> GSM627151 2 0.8144 0.687 0.252 0.748
#> GSM627163 1 0.0000 0.886 1.000 0.000
#> GSM627211 2 0.0000 0.911 0.000 1.000
#> GSM627171 2 0.4690 0.868 0.100 0.900
#> GSM627209 2 0.0000 0.911 0.000 1.000
#> GSM627135 1 0.3274 0.923 0.940 0.060
#> GSM627170 2 0.0000 0.911 0.000 1.000
#> GSM627178 1 0.4431 0.927 0.908 0.092
#> GSM627199 2 0.4690 0.868 0.100 0.900
#> GSM627213 2 0.0000 0.911 0.000 1.000
#> GSM627140 2 0.4690 0.868 0.100 0.900
#> GSM627149 1 0.2043 0.907 0.968 0.032
#> GSM627147 2 0.4690 0.868 0.100 0.900
#> GSM627195 1 0.8443 0.769 0.728 0.272
#> GSM627204 2 0.0000 0.911 0.000 1.000
#> GSM627207 2 0.0000 0.911 0.000 1.000
#> GSM627157 1 0.3879 0.930 0.924 0.076
#> GSM627201 2 0.0000 0.911 0.000 1.000
#> GSM627146 2 0.0000 0.911 0.000 1.000
#> GSM627156 2 0.4690 0.868 0.100 0.900
#> GSM627188 1 0.0000 0.886 1.000 0.000
#> GSM627197 2 0.0000 0.911 0.000 1.000
#> GSM627173 2 0.4690 0.868 0.100 0.900
#> GSM627179 2 0.0000 0.911 0.000 1.000
#> GSM627208 2 0.0000 0.911 0.000 1.000
#> GSM627215 2 0.0672 0.907 0.008 0.992
#> GSM627153 2 0.0000 0.911 0.000 1.000
#> GSM627155 1 0.0000 0.886 1.000 0.000
#> GSM627165 2 0.0000 0.911 0.000 1.000
#> GSM627168 1 0.3879 0.930 0.924 0.076
#> GSM627183 1 0.3879 0.930 0.924 0.076
#> GSM627144 1 0.6531 0.887 0.832 0.168
#> GSM627158 1 0.0000 0.886 1.000 0.000
#> GSM627196 2 0.0000 0.911 0.000 1.000
#> GSM627142 1 0.4431 0.924 0.908 0.092
#> GSM627182 1 0.9635 0.565 0.612 0.388
#> GSM627202 1 0.3879 0.930 0.924 0.076
#> GSM627141 1 0.3879 0.930 0.924 0.076
#> GSM627143 2 0.4690 0.868 0.100 0.900
#> GSM627145 1 0.3879 0.930 0.924 0.076
#> GSM627152 1 0.5946 0.906 0.856 0.144
#> GSM627200 1 0.3879 0.930 0.924 0.076
#> GSM627159 2 0.9358 0.548 0.352 0.648
#> GSM627164 2 0.4690 0.868 0.100 0.900
#> GSM627138 1 0.0000 0.886 1.000 0.000
#> GSM627175 2 0.0000 0.911 0.000 1.000
#> GSM627150 1 0.6623 0.869 0.828 0.172
#> GSM627166 1 0.6048 0.904 0.852 0.148
#> GSM627186 2 0.4690 0.868 0.100 0.900
#> GSM627139 2 0.8661 0.654 0.288 0.712
#> GSM627181 2 0.0000 0.911 0.000 1.000
#> GSM627205 2 0.0000 0.911 0.000 1.000
#> GSM627214 2 0.0000 0.911 0.000 1.000
#> GSM627180 2 0.9248 0.396 0.340 0.660
#> GSM627172 2 0.4690 0.868 0.100 0.900
#> GSM627184 1 0.0000 0.886 1.000 0.000
#> GSM627193 2 0.0000 0.911 0.000 1.000
#> GSM627191 2 0.9358 0.548 0.352 0.648
#> GSM627176 1 0.6438 0.891 0.836 0.164
#> GSM627194 2 0.0000 0.911 0.000 1.000
#> GSM627154 2 0.5294 0.855 0.120 0.880
#> GSM627187 1 0.6623 0.883 0.828 0.172
#> GSM627198 2 0.0000 0.911 0.000 1.000
#> GSM627160 1 0.6048 0.904 0.852 0.148
#> GSM627185 1 0.3879 0.930 0.924 0.076
#> GSM627206 1 0.3879 0.930 0.924 0.076
#> GSM627161 1 0.0000 0.886 1.000 0.000
#> GSM627162 1 0.8144 0.781 0.748 0.252
#> GSM627210 1 0.6048 0.904 0.852 0.148
#> GSM627189 2 0.0000 0.911 0.000 1.000
show/hide code output
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM627128 2 0.5756 0.12071 0.208 0.764 0.028
#> GSM627110 1 0.8857 0.55098 0.524 0.344 0.132
#> GSM627132 3 0.0000 0.93060 0.000 0.000 1.000
#> GSM627107 2 0.0000 0.48175 0.000 1.000 0.000
#> GSM627103 2 0.1753 0.50027 0.048 0.952 0.000
#> GSM627114 1 0.9296 0.52208 0.436 0.404 0.160
#> GSM627134 2 0.0892 0.49246 0.020 0.980 0.000
#> GSM627137 2 0.6267 0.46435 0.452 0.548 0.000
#> GSM627148 2 0.4891 0.33864 0.040 0.836 0.124
#> GSM627101 2 0.0000 0.48175 0.000 1.000 0.000
#> GSM627130 2 0.5690 0.24117 0.288 0.708 0.004
#> GSM627071 2 0.8775 -0.42804 0.384 0.500 0.116
#> GSM627118 2 0.0892 0.49246 0.020 0.980 0.000
#> GSM627094 1 0.6267 -0.39148 0.548 0.452 0.000
#> GSM627122 1 0.9189 0.51444 0.436 0.416 0.148
#> GSM627115 2 0.5706 0.48328 0.320 0.680 0.000
#> GSM627125 2 0.3349 0.35253 0.108 0.888 0.004
#> GSM627174 1 0.7397 0.29550 0.484 0.484 0.032
#> GSM627102 1 0.5760 -0.26481 0.672 0.328 0.000
#> GSM627073 2 0.0747 0.47160 0.016 0.984 0.000
#> GSM627108 1 0.6305 -0.41558 0.516 0.484 0.000
#> GSM627126 3 0.0000 0.93060 0.000 0.000 1.000
#> GSM627078 2 0.6267 0.46435 0.452 0.548 0.000
#> GSM627090 1 0.9111 0.50672 0.436 0.424 0.140
#> GSM627099 2 0.5098 0.49570 0.248 0.752 0.000
#> GSM627105 2 0.0000 0.48175 0.000 1.000 0.000
#> GSM627117 1 0.8790 0.55279 0.540 0.328 0.132
#> GSM627121 2 0.0000 0.48175 0.000 1.000 0.000
#> GSM627127 2 0.3192 0.50702 0.112 0.888 0.000
#> GSM627087 2 0.4062 0.50408 0.164 0.836 0.000
#> GSM627089 1 0.9028 0.49856 0.436 0.432 0.132
#> GSM627092 1 0.5810 -0.26917 0.664 0.336 0.000
#> GSM627076 1 0.9111 0.50672 0.436 0.424 0.140
#> GSM627136 1 0.9151 0.50956 0.436 0.420 0.144
#> GSM627081 2 0.4063 0.37621 0.020 0.868 0.112
#> GSM627091 2 0.6267 0.46435 0.452 0.548 0.000
#> GSM627097 2 0.2796 0.38516 0.092 0.908 0.000
#> GSM627072 2 0.7495 0.00960 0.188 0.692 0.120
#> GSM627080 3 0.0000 0.93060 0.000 0.000 1.000
#> GSM627088 1 0.9111 0.50672 0.436 0.424 0.140
#> GSM627109 1 0.9089 0.55615 0.524 0.312 0.164
#> GSM627111 3 0.0000 0.93060 0.000 0.000 1.000
#> GSM627113 1 0.9457 0.54676 0.484 0.312 0.204
#> GSM627133 2 0.4731 0.35051 0.032 0.840 0.128
#> GSM627177 2 0.8892 -0.50118 0.436 0.444 0.120
#> GSM627086 2 0.6267 0.46435 0.452 0.548 0.000
#> GSM627095 1 0.9736 0.36522 0.416 0.228 0.356
#> GSM627079 2 0.8984 -0.50926 0.436 0.436 0.128
#> GSM627082 2 0.9433 -0.41415 0.404 0.420 0.176
#> GSM627074 1 0.9089 0.55615 0.524 0.312 0.164
#> GSM627077 1 0.9329 0.52434 0.436 0.400 0.164
#> GSM627093 1 0.9089 0.55615 0.524 0.312 0.164
#> GSM627120 2 0.0592 0.48869 0.012 0.988 0.000
#> GSM627124 2 0.6308 0.43679 0.492 0.508 0.000
#> GSM627075 1 0.6295 -0.40273 0.528 0.472 0.000
#> GSM627085 2 0.6267 0.46435 0.452 0.548 0.000
#> GSM627119 1 0.9089 0.55615 0.524 0.312 0.164
#> GSM627116 2 0.8892 -0.50118 0.436 0.444 0.120
#> GSM627084 1 0.9411 0.53446 0.444 0.380 0.176
#> GSM627096 2 0.0000 0.48175 0.000 1.000 0.000
#> GSM627100 2 0.8887 -0.48695 0.424 0.456 0.120
#> GSM627112 1 0.5760 -0.25956 0.672 0.328 0.000
#> GSM627083 2 0.9550 -0.41880 0.404 0.404 0.192
#> GSM627098 1 0.9762 0.53291 0.408 0.360 0.232
#> GSM627104 1 0.8853 0.51746 0.572 0.252 0.176
#> GSM627131 1 0.9361 0.52631 0.436 0.396 0.168
#> GSM627106 2 0.2383 0.44578 0.016 0.940 0.044
#> GSM627123 1 0.9688 0.39897 0.440 0.228 0.332
#> GSM627129 2 0.1411 0.49771 0.036 0.964 0.000
#> GSM627216 2 0.1529 0.49734 0.040 0.960 0.000
#> GSM627212 2 0.6267 0.46435 0.452 0.548 0.000
#> GSM627190 1 0.8991 0.48896 0.476 0.392 0.132
#> GSM627169 1 0.4346 -0.07369 0.816 0.184 0.000
#> GSM627167 2 0.6267 0.46435 0.452 0.548 0.000
#> GSM627192 3 0.0000 0.93060 0.000 0.000 1.000
#> GSM627203 2 0.8759 -0.39015 0.360 0.520 0.120
#> GSM627151 2 0.3752 0.31003 0.144 0.856 0.000
#> GSM627163 3 0.0000 0.93060 0.000 0.000 1.000
#> GSM627211 2 0.6299 0.44202 0.476 0.524 0.000
#> GSM627171 1 0.6286 -0.40661 0.536 0.464 0.000
#> GSM627209 2 0.6267 0.46435 0.452 0.548 0.000
#> GSM627135 3 0.7997 0.18431 0.360 0.072 0.568
#> GSM627170 2 0.2356 0.50411 0.072 0.928 0.000
#> GSM627178 1 0.9199 0.55716 0.504 0.328 0.168
#> GSM627199 1 0.5760 -0.26258 0.672 0.328 0.000
#> GSM627213 2 0.3267 0.50459 0.116 0.884 0.000
#> GSM627140 1 0.3340 0.00933 0.880 0.120 0.000
#> GSM627149 3 0.5728 0.53099 0.272 0.008 0.720
#> GSM627147 1 0.5905 -0.28960 0.648 0.352 0.000
#> GSM627195 2 0.4209 0.36713 0.020 0.860 0.120
#> GSM627204 2 0.6286 0.45364 0.464 0.536 0.000
#> GSM627207 2 0.6274 0.46093 0.456 0.544 0.000
#> GSM627157 1 0.9654 0.41313 0.452 0.228 0.320
#> GSM627201 2 0.6267 0.46435 0.452 0.548 0.000
#> GSM627146 2 0.6267 0.46435 0.452 0.548 0.000
#> GSM627156 1 0.6045 -0.31963 0.620 0.380 0.000
#> GSM627188 3 0.0000 0.93060 0.000 0.000 1.000
#> GSM627197 2 0.6267 0.46435 0.452 0.548 0.000
#> GSM627173 1 0.5926 -0.29194 0.644 0.356 0.000
#> GSM627179 2 0.6267 0.46435 0.452 0.548 0.000
#> GSM627208 2 0.0747 0.49069 0.016 0.984 0.000
#> GSM627215 2 0.0892 0.46830 0.020 0.980 0.000
#> GSM627153 2 0.6267 0.46435 0.452 0.548 0.000
#> GSM627155 3 0.0000 0.93060 0.000 0.000 1.000
#> GSM627165 2 0.5678 0.48153 0.316 0.684 0.000
#> GSM627168 1 0.9151 0.50956 0.436 0.420 0.144
#> GSM627183 1 0.9262 0.51940 0.436 0.408 0.156
#> GSM627144 2 0.8520 -0.17509 0.280 0.588 0.132
#> GSM627158 3 0.0000 0.93060 0.000 0.000 1.000
#> GSM627196 2 0.6267 0.46435 0.452 0.548 0.000
#> GSM627142 2 0.9027 -0.50688 0.428 0.440 0.132
#> GSM627182 2 0.3832 0.38964 0.020 0.880 0.100
#> GSM627202 1 0.9746 0.52956 0.408 0.364 0.228
#> GSM627141 1 0.9296 0.52208 0.436 0.404 0.160
#> GSM627143 2 0.0892 0.46811 0.020 0.980 0.000
#> GSM627145 2 0.8880 -0.47682 0.416 0.464 0.120
#> GSM627152 1 0.8906 0.55128 0.520 0.344 0.136
#> GSM627200 1 0.9457 0.55309 0.468 0.340 0.192
#> GSM627159 2 0.9009 -0.39108 0.404 0.464 0.132
#> GSM627164 1 0.5785 -0.26919 0.668 0.332 0.000
#> GSM627138 3 0.0000 0.93060 0.000 0.000 1.000
#> GSM627175 2 0.6235 0.46722 0.436 0.564 0.000
#> GSM627150 2 0.6529 0.18586 0.124 0.760 0.116
#> GSM627166 1 0.9108 0.55680 0.520 0.316 0.164
#> GSM627186 1 0.5363 -0.09231 0.724 0.276 0.000
#> GSM627139 2 0.2796 0.38516 0.092 0.908 0.000
#> GSM627181 2 0.6267 0.46435 0.452 0.548 0.000
#> GSM627205 2 0.3619 0.50625 0.136 0.864 0.000
#> GSM627214 2 0.5178 0.49537 0.256 0.744 0.000
#> GSM627180 2 0.2527 0.44158 0.020 0.936 0.044
#> GSM627172 1 0.4346 -0.08062 0.816 0.184 0.000
#> GSM627184 3 0.0000 0.93060 0.000 0.000 1.000
#> GSM627193 2 0.6244 0.46622 0.440 0.560 0.000
#> GSM627191 2 0.9494 -0.41593 0.404 0.412 0.184
#> GSM627176 1 0.8712 0.55345 0.556 0.312 0.132
#> GSM627194 2 0.4974 0.49723 0.236 0.764 0.000
#> GSM627154 2 0.6295 0.45245 0.472 0.528 0.000
#> GSM627187 1 0.8712 0.55345 0.556 0.312 0.132
#> GSM627198 2 0.6267 0.46435 0.452 0.548 0.000
#> GSM627160 1 0.8910 0.55576 0.540 0.312 0.148
#> GSM627185 1 0.9623 0.43157 0.464 0.232 0.304
#> GSM627206 1 0.9028 0.49914 0.436 0.432 0.132
#> GSM627161 3 0.0000 0.93060 0.000 0.000 1.000
#> GSM627162 1 0.8859 0.48737 0.500 0.376 0.124
#> GSM627210 1 0.9046 0.55609 0.528 0.312 0.160
#> GSM627189 2 0.6267 0.46435 0.452 0.548 0.000
show/hide code output
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM627128 4 0.6869 -0.0951 0.000 0.132 0.304 0.564
#> GSM627110 3 0.4685 0.6268 0.000 0.156 0.784 0.060
#> GSM627132 1 0.0000 0.9311 1.000 0.000 0.000 0.000
#> GSM627107 4 0.0000 0.7139 0.000 0.000 0.000 1.000
#> GSM627103 4 0.0000 0.7139 0.000 0.000 0.000 1.000
#> GSM627114 3 0.6549 0.5819 0.000 0.120 0.612 0.268
#> GSM627134 4 0.0000 0.7139 0.000 0.000 0.000 1.000
#> GSM627137 2 0.4543 0.7869 0.000 0.676 0.000 0.324
#> GSM627148 4 0.6920 0.0527 0.000 0.132 0.316 0.552
#> GSM627101 4 0.0000 0.7139 0.000 0.000 0.000 1.000
#> GSM627130 4 0.7247 0.0857 0.000 0.216 0.240 0.544
#> GSM627071 3 0.7231 0.4312 0.000 0.144 0.464 0.392
#> GSM627118 4 0.0000 0.7139 0.000 0.000 0.000 1.000
#> GSM627094 2 0.4277 0.7959 0.000 0.720 0.000 0.280
#> GSM627122 3 0.6133 0.5874 0.000 0.088 0.644 0.268
#> GSM627115 4 0.4999 -0.5158 0.000 0.492 0.000 0.508
#> GSM627125 4 0.5849 0.3697 0.000 0.132 0.164 0.704
#> GSM627174 2 0.7493 -0.2099 0.000 0.480 0.320 0.200
#> GSM627102 2 0.0336 0.6881 0.000 0.992 0.000 0.008
#> GSM627073 4 0.0592 0.7129 0.000 0.000 0.016 0.984
#> GSM627108 2 0.4277 0.7951 0.000 0.720 0.000 0.280
#> GSM627126 1 0.0000 0.9311 1.000 0.000 0.000 0.000
#> GSM627078 2 0.4356 0.7939 0.000 0.708 0.000 0.292
#> GSM627090 3 0.6770 0.5756 0.000 0.140 0.592 0.268
#> GSM627099 4 0.2530 0.5994 0.000 0.112 0.000 0.888
#> GSM627105 4 0.0469 0.7129 0.000 0.000 0.012 0.988
#> GSM627117 3 0.5280 0.6122 0.000 0.156 0.748 0.096
#> GSM627121 4 0.0000 0.7139 0.000 0.000 0.000 1.000
#> GSM627127 4 0.2081 0.6320 0.000 0.084 0.000 0.916
#> GSM627087 4 0.4605 -0.0948 0.000 0.336 0.000 0.664
#> GSM627089 3 0.6770 0.5756 0.000 0.140 0.592 0.268
#> GSM627092 2 0.1970 0.6615 0.000 0.932 0.060 0.008
#> GSM627076 3 0.6770 0.5756 0.000 0.140 0.592 0.268
#> GSM627136 3 0.6770 0.5756 0.000 0.140 0.592 0.268
#> GSM627081 4 0.3569 0.5733 0.000 0.000 0.196 0.804
#> GSM627091 2 0.4522 0.7888 0.000 0.680 0.000 0.320
#> GSM627097 4 0.5972 0.3005 0.000 0.132 0.176 0.692
#> GSM627072 4 0.7073 -0.1230 0.000 0.132 0.364 0.504
#> GSM627080 1 0.0000 0.9311 1.000 0.000 0.000 0.000
#> GSM627088 3 0.6770 0.5756 0.000 0.140 0.592 0.268
#> GSM627109 3 0.4191 0.5742 0.068 0.024 0.848 0.060
#> GSM627111 1 0.0000 0.9311 1.000 0.000 0.000 0.000
#> GSM627113 3 0.4431 0.4264 0.304 0.000 0.696 0.000
#> GSM627133 4 0.3528 0.5791 0.000 0.000 0.192 0.808
#> GSM627177 3 0.7096 0.5104 0.000 0.140 0.516 0.344
#> GSM627086 2 0.4543 0.7869 0.000 0.676 0.000 0.324
#> GSM627095 3 0.6585 0.2225 0.412 0.008 0.520 0.060
#> GSM627079 3 0.6770 0.5756 0.000 0.140 0.592 0.268
#> GSM627082 3 0.9819 0.4029 0.220 0.192 0.344 0.244
#> GSM627074 3 0.3629 0.5958 0.040 0.024 0.876 0.060
#> GSM627077 3 0.4193 0.5796 0.000 0.000 0.732 0.268
#> GSM627093 3 0.3538 0.5983 0.036 0.024 0.880 0.060
#> GSM627120 4 0.0000 0.7139 0.000 0.000 0.000 1.000
#> GSM627124 2 0.3080 0.7142 0.000 0.880 0.024 0.096
#> GSM627075 2 0.4072 0.7925 0.000 0.748 0.000 0.252
#> GSM627085 2 0.4522 0.7888 0.000 0.680 0.000 0.320
#> GSM627119 3 0.3538 0.5983 0.036 0.024 0.880 0.060
#> GSM627116 3 0.7119 0.5026 0.000 0.140 0.508 0.352
#> GSM627084 3 0.5951 0.4638 0.300 0.000 0.636 0.064
#> GSM627096 4 0.0000 0.7139 0.000 0.000 0.000 1.000
#> GSM627100 3 0.6770 0.5756 0.000 0.140 0.592 0.268
#> GSM627112 2 0.2450 0.6541 0.000 0.912 0.072 0.016
#> GSM627083 3 0.8979 0.2876 0.264 0.100 0.460 0.176
#> GSM627098 3 0.4584 0.4349 0.300 0.000 0.696 0.004
#> GSM627104 3 0.4468 0.5589 0.084 0.024 0.832 0.060
#> GSM627131 3 0.4193 0.5796 0.000 0.000 0.732 0.268
#> GSM627106 4 0.1940 0.6851 0.000 0.000 0.076 0.924
#> GSM627123 3 0.4776 0.2852 0.376 0.000 0.624 0.000
#> GSM627129 4 0.0000 0.7139 0.000 0.000 0.000 1.000
#> GSM627216 4 0.0000 0.7139 0.000 0.000 0.000 1.000
#> GSM627212 2 0.4543 0.7869 0.000 0.676 0.000 0.324
#> GSM627190 3 0.5369 0.6083 0.000 0.164 0.740 0.096
#> GSM627169 2 0.2342 0.6458 0.000 0.912 0.080 0.008
#> GSM627167 2 0.4134 0.7935 0.000 0.740 0.000 0.260
#> GSM627192 1 0.0000 0.9311 1.000 0.000 0.000 0.000
#> GSM627203 3 0.7143 0.3719 0.000 0.132 0.460 0.408
#> GSM627151 4 0.7050 -0.1184 0.000 0.156 0.292 0.552
#> GSM627163 1 0.0000 0.9311 1.000 0.000 0.000 0.000
#> GSM627211 2 0.4072 0.7923 0.000 0.748 0.000 0.252
#> GSM627171 2 0.3208 0.7156 0.000 0.848 0.004 0.148
#> GSM627209 2 0.4543 0.7869 0.000 0.676 0.000 0.324
#> GSM627135 1 0.5119 0.1470 0.556 0.000 0.440 0.004
#> GSM627170 4 0.1716 0.6594 0.000 0.064 0.000 0.936
#> GSM627178 3 0.2563 0.5732 0.072 0.020 0.908 0.000
#> GSM627199 2 0.0336 0.6881 0.000 0.992 0.000 0.008
#> GSM627213 4 0.2197 0.6631 0.000 0.080 0.004 0.916
#> GSM627140 2 0.1059 0.6709 0.000 0.972 0.012 0.016
#> GSM627149 1 0.4500 0.5265 0.684 0.000 0.316 0.000
#> GSM627147 2 0.0336 0.6881 0.000 0.992 0.000 0.008
#> GSM627195 4 0.4356 0.4223 0.000 0.000 0.292 0.708
#> GSM627204 2 0.4103 0.7931 0.000 0.744 0.000 0.256
#> GSM627207 2 0.4193 0.7944 0.000 0.732 0.000 0.268
#> GSM627157 3 0.4936 0.2985 0.372 0.000 0.624 0.004
#> GSM627201 2 0.4543 0.7869 0.000 0.676 0.000 0.324
#> GSM627146 2 0.4543 0.7869 0.000 0.676 0.000 0.324
#> GSM627156 2 0.3208 0.7484 0.000 0.848 0.004 0.148
#> GSM627188 1 0.0000 0.9311 1.000 0.000 0.000 0.000
#> GSM627197 2 0.4543 0.7869 0.000 0.676 0.000 0.324
#> GSM627173 2 0.0524 0.6877 0.000 0.988 0.004 0.008
#> GSM627179 2 0.4543 0.7869 0.000 0.676 0.000 0.324
#> GSM627208 4 0.0000 0.7139 0.000 0.000 0.000 1.000
#> GSM627215 4 0.0336 0.7144 0.000 0.000 0.008 0.992
#> GSM627153 2 0.4543 0.7869 0.000 0.676 0.000 0.324
#> GSM627155 1 0.0000 0.9311 1.000 0.000 0.000 0.000
#> GSM627165 4 0.3942 0.3961 0.000 0.236 0.000 0.764
#> GSM627168 3 0.6685 0.5783 0.000 0.132 0.600 0.268
#> GSM627183 3 0.5619 0.5879 0.000 0.056 0.676 0.268
#> GSM627144 3 0.6187 0.5572 0.000 0.144 0.672 0.184
#> GSM627158 1 0.0000 0.9311 1.000 0.000 0.000 0.000
#> GSM627196 2 0.4522 0.7888 0.000 0.680 0.000 0.320
#> GSM627142 3 0.6770 0.5756 0.000 0.140 0.592 0.268
#> GSM627182 4 0.3356 0.5952 0.000 0.000 0.176 0.824
#> GSM627202 3 0.4193 0.5796 0.000 0.000 0.732 0.268
#> GSM627141 3 0.4193 0.5796 0.000 0.000 0.732 0.268
#> GSM627143 4 0.4257 0.5294 0.000 0.140 0.048 0.812
#> GSM627145 3 0.7108 0.4407 0.000 0.140 0.512 0.348
#> GSM627152 3 0.1978 0.6283 0.000 0.068 0.928 0.004
#> GSM627200 3 0.2840 0.6129 0.056 0.000 0.900 0.044
#> GSM627159 4 0.7505 -0.2136 0.000 0.200 0.324 0.476
#> GSM627164 2 0.0336 0.6881 0.000 0.992 0.000 0.008
#> GSM627138 1 0.0188 0.9270 0.996 0.000 0.004 0.000
#> GSM627175 2 0.4817 0.6923 0.000 0.612 0.000 0.388
#> GSM627150 4 0.6936 0.0606 0.000 0.132 0.320 0.548
#> GSM627166 3 0.3538 0.5983 0.036 0.024 0.880 0.060
#> GSM627186 2 0.6731 0.2480 0.000 0.604 0.248 0.148
#> GSM627139 4 0.6523 0.1401 0.000 0.136 0.236 0.628
#> GSM627181 2 0.4543 0.7869 0.000 0.676 0.000 0.324
#> GSM627205 4 0.2281 0.6173 0.000 0.096 0.000 0.904
#> GSM627214 4 0.4356 0.2978 0.000 0.292 0.000 0.708
#> GSM627180 4 0.2921 0.6346 0.000 0.000 0.140 0.860
#> GSM627172 2 0.0336 0.6881 0.000 0.992 0.000 0.008
#> GSM627184 1 0.0000 0.9311 1.000 0.000 0.000 0.000
#> GSM627193 2 0.4804 0.7163 0.000 0.616 0.000 0.384
#> GSM627191 3 0.9050 0.2782 0.264 0.112 0.456 0.168
#> GSM627176 3 0.6321 0.6129 0.036 0.156 0.712 0.096
#> GSM627194 4 0.4804 -0.2545 0.000 0.384 0.000 0.616
#> GSM627154 2 0.3626 0.7319 0.000 0.812 0.004 0.184
#> GSM627187 3 0.6407 0.6091 0.036 0.164 0.704 0.096
#> GSM627198 2 0.4522 0.7888 0.000 0.680 0.000 0.320
#> GSM627160 3 0.5363 0.6271 0.036 0.096 0.784 0.084
#> GSM627185 3 0.5400 0.2942 0.372 0.020 0.608 0.000
#> GSM627206 3 0.6814 0.5715 0.000 0.140 0.584 0.276
#> GSM627161 1 0.0000 0.9311 1.000 0.000 0.000 0.000
#> GSM627162 3 0.6842 0.5824 0.036 0.204 0.660 0.100
#> GSM627210 3 0.5880 0.6248 0.036 0.156 0.740 0.068
#> GSM627189 2 0.4522 0.7888 0.000 0.680 0.000 0.320
show/hide code output
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM627128 3 0.4367 0.4066 0.000 0.000 0.620 0.372 0.008
#> GSM627110 5 0.2329 0.8773 0.000 0.000 0.124 0.000 0.876
#> GSM627132 1 0.0000 0.8027 1.000 0.000 0.000 0.000 0.000
#> GSM627107 4 0.1331 0.8316 0.000 0.000 0.040 0.952 0.008
#> GSM627103 4 0.0510 0.8194 0.000 0.000 0.000 0.984 0.016
#> GSM627114 3 0.0794 0.7704 0.000 0.000 0.972 0.000 0.028
#> GSM627134 4 0.0671 0.8253 0.000 0.000 0.016 0.980 0.004
#> GSM627137 2 0.4632 0.6362 0.000 0.540 0.000 0.448 0.012
#> GSM627148 4 0.4867 0.3653 0.000 0.000 0.432 0.544 0.024
#> GSM627101 4 0.1444 0.8310 0.000 0.000 0.040 0.948 0.012
#> GSM627130 4 0.4633 0.3994 0.000 0.004 0.348 0.632 0.016
#> GSM627071 3 0.2462 0.6889 0.000 0.000 0.880 0.112 0.008
#> GSM627118 4 0.1549 0.8303 0.000 0.000 0.040 0.944 0.016
#> GSM627094 2 0.3242 0.7839 0.000 0.784 0.000 0.216 0.000
#> GSM627122 3 0.0703 0.7707 0.000 0.000 0.976 0.000 0.024
#> GSM627115 2 0.4624 0.7745 0.000 0.636 0.000 0.340 0.024
#> GSM627125 4 0.3197 0.7745 0.000 0.000 0.140 0.836 0.024
#> GSM627174 3 0.4702 0.3201 0.000 0.000 0.552 0.432 0.016
#> GSM627102 2 0.0510 0.6788 0.000 0.984 0.000 0.000 0.016
#> GSM627073 4 0.1205 0.8315 0.000 0.000 0.040 0.956 0.004
#> GSM627108 2 0.3143 0.7836 0.000 0.796 0.000 0.204 0.000
#> GSM627126 1 0.0000 0.8027 1.000 0.000 0.000 0.000 0.000
#> GSM627078 2 0.4288 0.7839 0.000 0.664 0.000 0.324 0.012
#> GSM627090 3 0.0000 0.7733 0.000 0.000 1.000 0.000 0.000
#> GSM627099 4 0.0404 0.8190 0.000 0.000 0.000 0.988 0.012
#> GSM627105 4 0.1725 0.8295 0.000 0.000 0.044 0.936 0.020
#> GSM627117 5 0.2909 0.8619 0.000 0.012 0.140 0.000 0.848
#> GSM627121 4 0.1205 0.8315 0.000 0.000 0.040 0.956 0.004
#> GSM627127 4 0.0798 0.8243 0.000 0.000 0.008 0.976 0.016
#> GSM627087 4 0.2408 0.7209 0.000 0.092 0.000 0.892 0.016
#> GSM627089 3 0.0000 0.7733 0.000 0.000 1.000 0.000 0.000
#> GSM627092 2 0.0898 0.6738 0.000 0.972 0.020 0.000 0.008
#> GSM627076 3 0.0162 0.7725 0.000 0.000 0.996 0.000 0.004
#> GSM627136 3 0.0290 0.7725 0.000 0.000 0.992 0.000 0.008
#> GSM627081 4 0.4613 0.5049 0.000 0.000 0.360 0.620 0.020
#> GSM627091 2 0.4620 0.7844 0.000 0.652 0.000 0.320 0.028
#> GSM627097 4 0.3326 0.7455 0.000 0.000 0.152 0.824 0.024
#> GSM627072 3 0.4900 -0.1915 0.000 0.000 0.512 0.464 0.024
#> GSM627080 1 0.0000 0.8027 1.000 0.000 0.000 0.000 0.000
#> GSM627088 3 0.0000 0.7733 0.000 0.000 1.000 0.000 0.000
#> GSM627109 5 0.1608 0.8869 0.000 0.000 0.072 0.000 0.928
#> GSM627111 1 0.0000 0.8027 1.000 0.000 0.000 0.000 0.000
#> GSM627113 1 0.6416 0.3084 0.452 0.000 0.372 0.000 0.176
#> GSM627133 4 0.4054 0.6860 0.000 0.000 0.224 0.748 0.028
#> GSM627177 3 0.2077 0.7152 0.000 0.000 0.908 0.084 0.008
#> GSM627086 2 0.4648 0.5970 0.000 0.524 0.000 0.464 0.012
#> GSM627095 1 0.5825 0.4172 0.536 0.000 0.360 0.000 0.104
#> GSM627079 3 0.0000 0.7733 0.000 0.000 1.000 0.000 0.000
#> GSM627082 3 0.4956 0.5134 0.004 0.000 0.644 0.312 0.040
#> GSM627074 5 0.1608 0.8869 0.000 0.000 0.072 0.000 0.928
#> GSM627077 3 0.1792 0.7452 0.000 0.000 0.916 0.000 0.084
#> GSM627093 5 0.1608 0.8869 0.000 0.000 0.072 0.000 0.928
#> GSM627120 4 0.1124 0.8312 0.000 0.000 0.036 0.960 0.004
#> GSM627124 2 0.3274 0.7854 0.000 0.780 0.000 0.220 0.000
#> GSM627075 2 0.2516 0.7626 0.000 0.860 0.000 0.140 0.000
#> GSM627085 2 0.4387 0.7751 0.000 0.640 0.000 0.348 0.012
#> GSM627119 5 0.1608 0.8869 0.000 0.000 0.072 0.000 0.928
#> GSM627116 3 0.0290 0.7725 0.000 0.000 0.992 0.000 0.008
#> GSM627084 3 0.5176 0.0302 0.040 0.000 0.492 0.000 0.468
#> GSM627096 4 0.1549 0.8303 0.000 0.000 0.040 0.944 0.016
#> GSM627100 3 0.0162 0.7729 0.000 0.000 0.996 0.000 0.004
#> GSM627112 2 0.2722 0.6877 0.000 0.892 0.060 0.040 0.008
#> GSM627083 3 0.7294 0.3788 0.096 0.000 0.488 0.312 0.104
#> GSM627098 3 0.4496 0.5702 0.156 0.000 0.752 0.000 0.092
#> GSM627104 5 0.1608 0.8869 0.000 0.000 0.072 0.000 0.928
#> GSM627131 3 0.1851 0.7428 0.000 0.000 0.912 0.000 0.088
#> GSM627106 4 0.4138 0.6388 0.000 0.000 0.276 0.708 0.016
#> GSM627123 1 0.5751 0.4215 0.540 0.000 0.364 0.000 0.096
#> GSM627129 4 0.0324 0.8182 0.000 0.000 0.004 0.992 0.004
#> GSM627216 4 0.0290 0.8152 0.000 0.000 0.000 0.992 0.008
#> GSM627212 2 0.4451 0.7793 0.000 0.644 0.000 0.340 0.016
#> GSM627190 5 0.3937 0.8474 0.000 0.060 0.132 0.004 0.804
#> GSM627169 2 0.1281 0.6716 0.000 0.956 0.012 0.000 0.032
#> GSM627167 2 0.4127 0.7869 0.000 0.680 0.000 0.312 0.008
#> GSM627192 1 0.0000 0.8027 1.000 0.000 0.000 0.000 0.000
#> GSM627203 3 0.3845 0.5514 0.000 0.000 0.768 0.208 0.024
#> GSM627151 4 0.3081 0.7286 0.000 0.000 0.156 0.832 0.012
#> GSM627163 1 0.0000 0.8027 1.000 0.000 0.000 0.000 0.000
#> GSM627211 2 0.3109 0.7827 0.000 0.800 0.000 0.200 0.000
#> GSM627171 2 0.4464 0.6823 0.000 0.584 0.000 0.408 0.008
#> GSM627209 2 0.4430 0.7594 0.000 0.628 0.000 0.360 0.012
#> GSM627135 1 0.5088 0.6140 0.680 0.000 0.228 0.000 0.092
#> GSM627170 4 0.1041 0.8307 0.000 0.000 0.032 0.964 0.004
#> GSM627178 3 0.4817 0.2753 0.024 0.000 0.572 0.000 0.404
#> GSM627199 2 0.0510 0.6788 0.000 0.984 0.000 0.000 0.016
#> GSM627213 4 0.0807 0.8262 0.000 0.000 0.012 0.976 0.012
#> GSM627140 2 0.1386 0.6757 0.000 0.952 0.032 0.000 0.016
#> GSM627149 1 0.3736 0.7019 0.808 0.000 0.140 0.000 0.052
#> GSM627147 2 0.0510 0.6788 0.000 0.984 0.000 0.000 0.016
#> GSM627195 4 0.4746 0.4750 0.000 0.000 0.376 0.600 0.024
#> GSM627204 2 0.3210 0.7851 0.000 0.788 0.000 0.212 0.000
#> GSM627207 2 0.3885 0.7909 0.000 0.724 0.000 0.268 0.008
#> GSM627157 1 0.5794 0.3810 0.520 0.000 0.384 0.000 0.096
#> GSM627201 2 0.4648 0.6046 0.000 0.524 0.000 0.464 0.012
#> GSM627146 2 0.4387 0.7751 0.000 0.640 0.000 0.348 0.012
#> GSM627156 2 0.1768 0.7337 0.000 0.924 0.000 0.072 0.004
#> GSM627188 1 0.0000 0.8027 1.000 0.000 0.000 0.000 0.000
#> GSM627197 2 0.4339 0.7799 0.000 0.652 0.000 0.336 0.012
#> GSM627173 2 0.0693 0.6774 0.000 0.980 0.008 0.000 0.012
#> GSM627179 2 0.4339 0.7810 0.000 0.652 0.000 0.336 0.012
#> GSM627208 4 0.0324 0.8182 0.000 0.000 0.004 0.992 0.004
#> GSM627215 4 0.1251 0.8322 0.000 0.000 0.036 0.956 0.008
#> GSM627153 2 0.4622 0.6413 0.000 0.548 0.000 0.440 0.012
#> GSM627155 1 0.0000 0.8027 1.000 0.000 0.000 0.000 0.000
#> GSM627165 4 0.3530 0.4789 0.000 0.204 0.000 0.784 0.012
#> GSM627168 3 0.0609 0.7710 0.000 0.000 0.980 0.000 0.020
#> GSM627183 3 0.0794 0.7698 0.000 0.000 0.972 0.000 0.028
#> GSM627144 5 0.6338 0.0642 0.000 0.000 0.160 0.392 0.448
#> GSM627158 1 0.0000 0.8027 1.000 0.000 0.000 0.000 0.000
#> GSM627196 2 0.4251 0.7868 0.000 0.672 0.000 0.316 0.012
#> GSM627142 3 0.0290 0.7725 0.000 0.000 0.992 0.000 0.008
#> GSM627182 4 0.4380 0.5863 0.000 0.000 0.304 0.676 0.020
#> GSM627202 3 0.1908 0.7401 0.000 0.000 0.908 0.000 0.092
#> GSM627141 3 0.1792 0.7462 0.000 0.000 0.916 0.000 0.084
#> GSM627143 4 0.2284 0.7857 0.000 0.004 0.096 0.896 0.004
#> GSM627145 3 0.3284 0.6423 0.000 0.000 0.828 0.148 0.024
#> GSM627152 3 0.4161 0.2917 0.000 0.000 0.608 0.000 0.392
#> GSM627200 3 0.3141 0.6840 0.016 0.000 0.832 0.000 0.152
#> GSM627159 3 0.4419 0.5110 0.000 0.000 0.668 0.312 0.020
#> GSM627164 2 0.0510 0.6788 0.000 0.984 0.000 0.000 0.016
#> GSM627138 1 0.0000 0.8027 1.000 0.000 0.000 0.000 0.000
#> GSM627175 2 0.4655 0.5781 0.000 0.512 0.000 0.476 0.012
#> GSM627150 4 0.4872 0.3492 0.000 0.000 0.436 0.540 0.024
#> GSM627166 5 0.1671 0.8839 0.000 0.000 0.076 0.000 0.924
#> GSM627186 2 0.0693 0.6774 0.000 0.980 0.008 0.000 0.012
#> GSM627139 4 0.3282 0.7132 0.000 0.000 0.188 0.804 0.008
#> GSM627181 2 0.4387 0.7716 0.000 0.640 0.000 0.348 0.012
#> GSM627205 4 0.0162 0.8149 0.000 0.000 0.000 0.996 0.004
#> GSM627214 4 0.2878 0.7559 0.000 0.084 0.024 0.880 0.012
#> GSM627180 4 0.1818 0.8286 0.000 0.000 0.044 0.932 0.024
#> GSM627172 2 0.0510 0.6788 0.000 0.984 0.000 0.000 0.016
#> GSM627184 1 0.0000 0.8027 1.000 0.000 0.000 0.000 0.000
#> GSM627193 2 0.4371 0.7779 0.000 0.644 0.000 0.344 0.012
#> GSM627191 3 0.6578 0.4553 0.040 0.000 0.544 0.312 0.104
#> GSM627176 5 0.2280 0.8780 0.000 0.000 0.120 0.000 0.880
#> GSM627194 4 0.4080 0.3772 0.000 0.252 0.000 0.728 0.020
#> GSM627154 2 0.4608 0.7723 0.000 0.640 0.000 0.336 0.024
#> GSM627187 5 0.3862 0.8381 0.000 0.088 0.104 0.000 0.808
#> GSM627198 2 0.4323 0.7820 0.000 0.656 0.000 0.332 0.012
#> GSM627160 5 0.2852 0.8054 0.000 0.000 0.172 0.000 0.828
#> GSM627185 1 0.5770 0.4073 0.532 0.000 0.372 0.000 0.096
#> GSM627206 3 0.0290 0.7725 0.000 0.000 0.992 0.000 0.008
#> GSM627161 1 0.0000 0.8027 1.000 0.000 0.000 0.000 0.000
#> GSM627162 5 0.4647 0.7562 0.000 0.184 0.084 0.000 0.732
#> GSM627210 5 0.1732 0.8874 0.000 0.000 0.080 0.000 0.920
#> GSM627189 2 0.4419 0.7858 0.000 0.668 0.000 0.312 0.020
show/hide code output
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM627128 5 0.4622 0.6826 0.000 0.000 0.024 0.080 0.724 0.172
#> GSM627110 3 0.1267 0.8968 0.000 0.000 0.940 0.000 0.060 0.000
#> GSM627132 1 0.0000 0.8702 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627107 4 0.1082 0.8674 0.000 0.040 0.000 0.956 0.000 0.004
#> GSM627103 4 0.1643 0.8608 0.000 0.068 0.008 0.924 0.000 0.000
#> GSM627114 5 0.0508 0.8052 0.000 0.000 0.004 0.000 0.984 0.012
#> GSM627134 4 0.1219 0.8674 0.000 0.048 0.004 0.948 0.000 0.000
#> GSM627137 2 0.3133 0.5880 0.000 0.780 0.000 0.212 0.000 0.008
#> GSM627148 4 0.2796 0.8316 0.000 0.008 0.032 0.884 0.024 0.052
#> GSM627101 4 0.1082 0.8674 0.000 0.040 0.000 0.956 0.000 0.004
#> GSM627130 6 0.6435 -0.2749 0.000 0.024 0.000 0.324 0.224 0.428
#> GSM627071 5 0.3593 0.6231 0.000 0.000 0.024 0.228 0.748 0.000
#> GSM627118 4 0.0865 0.8675 0.000 0.036 0.000 0.964 0.000 0.000
#> GSM627094 2 0.4766 0.2929 0.000 0.612 0.000 0.072 0.000 0.316
#> GSM627122 5 0.0622 0.8045 0.000 0.000 0.008 0.000 0.980 0.012
#> GSM627115 2 0.3682 0.5740 0.000 0.792 0.008 0.148 0.000 0.052
#> GSM627125 4 0.1858 0.8579 0.000 0.024 0.016 0.932 0.024 0.004
#> GSM627174 5 0.5534 0.3751 0.000 0.000 0.000 0.132 0.444 0.424
#> GSM627102 6 0.3869 0.1785 0.000 0.500 0.000 0.000 0.000 0.500
#> GSM627073 4 0.2749 0.8612 0.000 0.048 0.020 0.884 0.004 0.044
#> GSM627108 2 0.3979 0.3974 0.000 0.708 0.000 0.036 0.000 0.256
#> GSM627126 1 0.0000 0.8702 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627078 2 0.1757 0.6167 0.000 0.916 0.000 0.076 0.000 0.008
#> GSM627090 5 0.0458 0.8049 0.000 0.000 0.000 0.016 0.984 0.000
#> GSM627099 4 0.2340 0.8108 0.000 0.148 0.000 0.852 0.000 0.000
#> GSM627105 4 0.0922 0.8668 0.000 0.024 0.004 0.968 0.000 0.004
#> GSM627117 3 0.2565 0.8284 0.000 0.000 0.872 0.016 0.104 0.008
#> GSM627121 4 0.1152 0.8673 0.000 0.044 0.000 0.952 0.000 0.004
#> GSM627127 4 0.0632 0.8671 0.000 0.024 0.000 0.976 0.000 0.000
#> GSM627087 4 0.3547 0.6007 0.000 0.300 0.000 0.696 0.000 0.004
#> GSM627089 5 0.0547 0.8042 0.000 0.000 0.000 0.020 0.980 0.000
#> GSM627092 2 0.4499 -0.3539 0.000 0.500 0.012 0.000 0.012 0.476
#> GSM627076 5 0.0000 0.8064 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM627136 5 0.0000 0.8064 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM627081 4 0.2697 0.8588 0.000 0.040 0.024 0.888 0.004 0.044
#> GSM627091 2 0.3400 0.5899 0.000 0.816 0.008 0.132 0.000 0.044
#> GSM627097 4 0.0858 0.8490 0.000 0.000 0.004 0.968 0.028 0.000
#> GSM627072 4 0.5241 0.0713 0.000 0.000 0.032 0.500 0.432 0.036
#> GSM627080 1 0.0000 0.8702 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627088 5 0.0000 0.8064 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM627109 3 0.0632 0.9028 0.000 0.000 0.976 0.000 0.024 0.000
#> GSM627111 1 0.0000 0.8702 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627113 5 0.5848 0.3495 0.236 0.000 0.204 0.000 0.548 0.012
#> GSM627133 4 0.3476 0.8184 0.000 0.068 0.032 0.840 0.004 0.056
#> GSM627177 5 0.2006 0.7640 0.000 0.000 0.016 0.080 0.904 0.000
#> GSM627086 2 0.2632 0.6073 0.000 0.832 0.000 0.164 0.000 0.004
#> GSM627095 1 0.5919 0.3640 0.512 0.000 0.172 0.000 0.304 0.012
#> GSM627079 5 0.0692 0.8041 0.000 0.000 0.004 0.020 0.976 0.000
#> GSM627082 5 0.4542 0.4889 0.000 0.000 0.008 0.020 0.532 0.440
#> GSM627074 3 0.0632 0.9028 0.000 0.000 0.976 0.000 0.024 0.000
#> GSM627077 5 0.0622 0.8045 0.000 0.000 0.008 0.000 0.980 0.012
#> GSM627093 3 0.0632 0.9028 0.000 0.000 0.976 0.000 0.024 0.000
#> GSM627120 4 0.1141 0.8672 0.000 0.052 0.000 0.948 0.000 0.000
#> GSM627124 2 0.2052 0.5942 0.000 0.912 0.000 0.056 0.004 0.028
#> GSM627075 2 0.4004 0.1441 0.000 0.620 0.000 0.012 0.000 0.368
#> GSM627085 2 0.2738 0.6059 0.000 0.820 0.000 0.176 0.000 0.004
#> GSM627119 3 0.0632 0.9028 0.000 0.000 0.976 0.000 0.024 0.000
#> GSM627116 5 0.1480 0.7943 0.000 0.000 0.020 0.040 0.940 0.000
#> GSM627084 5 0.3110 0.6875 0.000 0.000 0.196 0.000 0.792 0.012
#> GSM627096 4 0.0937 0.8675 0.000 0.040 0.000 0.960 0.000 0.000
#> GSM627100 5 0.0458 0.8049 0.000 0.000 0.000 0.016 0.984 0.000
#> GSM627112 2 0.4307 0.1469 0.000 0.652 0.004 0.012 0.012 0.320
#> GSM627083 5 0.5779 0.4073 0.056 0.000 0.020 0.020 0.464 0.440
#> GSM627098 5 0.2312 0.7527 0.000 0.000 0.112 0.000 0.876 0.012
#> GSM627104 3 0.0858 0.9018 0.000 0.000 0.968 0.000 0.028 0.004
#> GSM627131 5 0.0622 0.8045 0.000 0.000 0.008 0.000 0.980 0.012
#> GSM627106 4 0.2457 0.8635 0.000 0.044 0.016 0.900 0.004 0.036
#> GSM627123 1 0.5655 0.3355 0.524 0.000 0.120 0.000 0.344 0.012
#> GSM627129 4 0.1219 0.8674 0.000 0.048 0.004 0.948 0.000 0.000
#> GSM627216 4 0.3438 0.8001 0.000 0.184 0.008 0.788 0.000 0.020
#> GSM627212 2 0.3794 0.5816 0.000 0.792 0.008 0.120 0.000 0.080
#> GSM627190 3 0.3591 0.8096 0.000 0.016 0.816 0.000 0.104 0.064
#> GSM627169 2 0.5027 -0.2970 0.000 0.496 0.052 0.000 0.008 0.444
#> GSM627167 2 0.1866 0.6133 0.000 0.908 0.000 0.084 0.000 0.008
#> GSM627192 1 0.0000 0.8702 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627203 5 0.4760 0.4829 0.000 0.000 0.024 0.296 0.644 0.036
#> GSM627151 4 0.4547 0.7047 0.000 0.028 0.020 0.752 0.160 0.040
#> GSM627163 1 0.0000 0.8702 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627211 2 0.3440 0.4596 0.000 0.776 0.000 0.028 0.000 0.196
#> GSM627171 2 0.2101 0.6111 0.000 0.892 0.000 0.100 0.004 0.004
#> GSM627209 2 0.2053 0.6132 0.000 0.888 0.000 0.108 0.000 0.004
#> GSM627135 1 0.3578 0.7412 0.804 0.000 0.044 0.000 0.140 0.012
#> GSM627170 4 0.2340 0.8126 0.000 0.148 0.000 0.852 0.000 0.000
#> GSM627178 5 0.3804 0.3337 0.000 0.000 0.424 0.000 0.576 0.000
#> GSM627199 2 0.3999 -0.3895 0.000 0.500 0.000 0.000 0.004 0.496
#> GSM627213 4 0.1007 0.8671 0.000 0.044 0.000 0.956 0.000 0.000
#> GSM627140 2 0.3996 -0.3609 0.000 0.512 0.000 0.000 0.004 0.484
#> GSM627149 1 0.2699 0.7740 0.856 0.000 0.008 0.000 0.124 0.012
#> GSM627147 6 0.3869 0.1785 0.000 0.500 0.000 0.000 0.000 0.500
#> GSM627195 4 0.2638 0.8468 0.000 0.020 0.028 0.892 0.008 0.052
#> GSM627204 2 0.3706 0.5002 0.000 0.772 0.000 0.056 0.000 0.172
#> GSM627207 2 0.3227 0.5628 0.000 0.824 0.000 0.060 0.000 0.116
#> GSM627157 5 0.5723 -0.0528 0.412 0.000 0.116 0.000 0.460 0.012
#> GSM627201 2 0.2994 0.5867 0.000 0.788 0.000 0.208 0.000 0.004
#> GSM627146 2 0.2632 0.6112 0.000 0.832 0.000 0.164 0.000 0.004
#> GSM627156 2 0.3706 0.0649 0.000 0.620 0.000 0.000 0.000 0.380
#> GSM627188 1 0.0000 0.8702 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627197 2 0.1958 0.6245 0.000 0.896 0.000 0.100 0.000 0.004
#> GSM627173 2 0.4227 -0.3739 0.000 0.500 0.004 0.000 0.008 0.488
#> GSM627179 2 0.4111 0.5902 0.000 0.748 0.000 0.144 0.000 0.108
#> GSM627208 4 0.2581 0.8272 0.000 0.128 0.000 0.856 0.000 0.016
#> GSM627215 4 0.2752 0.8587 0.000 0.044 0.024 0.880 0.000 0.052
#> GSM627153 2 0.2100 0.6113 0.000 0.884 0.000 0.112 0.000 0.004
#> GSM627155 1 0.0000 0.8702 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627165 4 0.3864 0.0739 0.000 0.480 0.000 0.520 0.000 0.000
#> GSM627168 5 0.0260 0.8066 0.000 0.000 0.008 0.000 0.992 0.000
#> GSM627183 5 0.0260 0.8062 0.000 0.000 0.008 0.000 0.992 0.000
#> GSM627144 3 0.5405 0.3136 0.000 0.024 0.572 0.344 0.008 0.052
#> GSM627158 1 0.0000 0.8702 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627196 2 0.3297 0.5697 0.000 0.820 0.000 0.068 0.000 0.112
#> GSM627142 5 0.0363 0.8056 0.000 0.000 0.000 0.012 0.988 0.000
#> GSM627182 4 0.3516 0.8218 0.000 0.076 0.036 0.832 0.000 0.056
#> GSM627202 5 0.0909 0.8007 0.000 0.000 0.020 0.000 0.968 0.012
#> GSM627141 5 0.0622 0.8045 0.000 0.000 0.008 0.000 0.980 0.012
#> GSM627143 4 0.2237 0.8511 0.000 0.064 0.004 0.904 0.024 0.004
#> GSM627145 5 0.2838 0.6726 0.000 0.000 0.000 0.188 0.808 0.004
#> GSM627152 5 0.3620 0.4450 0.000 0.000 0.352 0.000 0.648 0.000
#> GSM627200 5 0.2980 0.6953 0.000 0.000 0.180 0.000 0.808 0.012
#> GSM627159 5 0.4566 0.4925 0.000 0.000 0.004 0.028 0.540 0.428
#> GSM627164 6 0.3869 0.1785 0.000 0.500 0.000 0.000 0.000 0.500
#> GSM627138 1 0.0000 0.8702 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627175 2 0.3050 0.5675 0.000 0.764 0.000 0.236 0.000 0.000
#> GSM627150 4 0.5413 0.3210 0.000 0.024 0.024 0.564 0.360 0.028
#> GSM627166 3 0.0632 0.9028 0.000 0.000 0.976 0.000 0.024 0.000
#> GSM627186 2 0.4413 -0.3687 0.000 0.492 0.012 0.000 0.008 0.488
#> GSM627139 4 0.2805 0.7449 0.000 0.000 0.012 0.828 0.160 0.000
#> GSM627181 2 0.2053 0.6071 0.000 0.888 0.000 0.108 0.000 0.004
#> GSM627205 4 0.2378 0.8089 0.000 0.152 0.000 0.848 0.000 0.000
#> GSM627214 4 0.3371 0.6486 0.000 0.292 0.000 0.708 0.000 0.000
#> GSM627180 4 0.3316 0.8186 0.000 0.072 0.028 0.844 0.000 0.056
#> GSM627172 2 0.3999 -0.3895 0.000 0.500 0.000 0.000 0.004 0.496
#> GSM627184 1 0.0000 0.8702 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627193 2 0.3453 0.6094 0.000 0.808 0.008 0.144 0.000 0.040
#> GSM627191 5 0.4542 0.4889 0.000 0.000 0.008 0.020 0.532 0.440
#> GSM627176 3 0.0937 0.8959 0.000 0.000 0.960 0.000 0.040 0.000
#> GSM627194 2 0.4300 -0.0277 0.000 0.528 0.004 0.456 0.000 0.012
#> GSM627154 2 0.2730 0.6016 0.000 0.808 0.000 0.192 0.000 0.000
#> GSM627187 3 0.2265 0.8681 0.000 0.008 0.900 0.000 0.024 0.068
#> GSM627198 2 0.1531 0.6149 0.000 0.928 0.000 0.068 0.000 0.004
#> GSM627160 3 0.1714 0.8688 0.000 0.000 0.908 0.000 0.092 0.000
#> GSM627185 1 0.5924 0.1863 0.456 0.000 0.148 0.000 0.384 0.012
#> GSM627206 5 0.0458 0.8049 0.000 0.000 0.000 0.016 0.984 0.000
#> GSM627161 1 0.0000 0.8702 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627162 3 0.3295 0.7821 0.000 0.012 0.800 0.000 0.012 0.176
#> GSM627210 3 0.1010 0.9018 0.000 0.000 0.960 0.000 0.036 0.004
#> GSM627189 2 0.4210 0.5540 0.000 0.756 0.008 0.120 0.000 0.116
Heatmaps for the consensus matrix. It visualizes the probability of two
samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures
which are significantly different between classes which can be candidate marks
for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known
annotation is numeric, one-way ANOVA test is applied, and if the known
annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) age(p) other(p) k
#> ATC:mclust 145 0.7037 0.305 0.0112 2
#> ATC:mclust 49 0.2118 0.693 0.5777 3
#> ATC:mclust 114 0.0281 0.895 0.0517 4
#> ATC:mclust 126 0.0320 0.395 0.0824 5
#> ATC:mclust 111 0.0164 0.578 0.0362 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
ATC:NMF
The object with results only for a single top-value method and a single partition method
can be extracted as:
res = res_list["ATC", "NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51882 rows and 146 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
- The first row: a plot of the ECDF (empirical cumulative distribution
function) curves of the consensus matrix for each
k and the heatmap of
predicted classes for each k.
- The second row: heatmaps of the consensus matrix for each
k.
- The third row: heatmaps of the membership matrix for each
k.
- The fouth row: heatmaps of the signatures for each
k.
All the plots in panels can be made by individual functions and they are
plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
- ECDF curves of the consensus matrix for each
k;
- 1-PAC. The PAC
score
measures the proportion of the ambiguous subgrouping.
- Mean silhouette score.
- Concordance. The mean probability of fiting the consensus class ids in all
partitions.
- Area increased. Denote \(A_k\) as the area under the ECDF curve for current
k, the area increased is defined as \(A_k - A_{k-1}\).
- Rand index. The percent of pairs of samples that are both in a same cluster
or both are not in a same cluster in the partition of k and k-1.
- Jaccard index. The ratio of pairs of samples are both in a same cluster in
the partition of k and k-1 and the pairs of samples are both in a same
cluster in the partition k or k-1.
The detailed explanations of these statistics can be found in the cola
vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.736 0.864 0.940 0.3345 0.648 0.648
#> 3 3 0.538 0.650 0.857 0.8022 0.665 0.512
#> 4 4 0.577 0.711 0.823 0.2143 0.778 0.490
#> 5 5 0.570 0.617 0.773 0.0711 0.848 0.503
#> 6 6 0.616 0.559 0.744 0.0302 0.872 0.525
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
- All \(k\) with Jaccard index larger than 0.95 are removed because increasing
\(k\) does not provide enough extra information. If all \(k\) are removed, it is
marked as no subgroup is detected.
- For all \(k\) with 1-PAC score larger than 0.9, the maximal \(k\) is taken as
the best \(k\), and other \(k\) are marked as optional \(k\).
- If it does not fit the second rule. The \(k\) with the maximal vote of the
highest 1-PAC score, highest mean silhouette, and highest concordance is
taken as the best \(k\).
suggest_best_k(res)
#> [1] 4
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
show/hide code output
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM627128 2 0.0000 0.957 0.000 1.000
#> GSM627110 2 0.7815 0.627 0.232 0.768
#> GSM627132 1 0.0000 0.831 1.000 0.000
#> GSM627107 2 0.0000 0.957 0.000 1.000
#> GSM627103 2 0.0000 0.957 0.000 1.000
#> GSM627114 2 0.8813 0.476 0.300 0.700
#> GSM627134 2 0.0000 0.957 0.000 1.000
#> GSM627137 2 0.0000 0.957 0.000 1.000
#> GSM627148 2 0.0000 0.957 0.000 1.000
#> GSM627101 2 0.0000 0.957 0.000 1.000
#> GSM627130 2 0.0000 0.957 0.000 1.000
#> GSM627071 2 0.0000 0.957 0.000 1.000
#> GSM627118 2 0.0000 0.957 0.000 1.000
#> GSM627094 2 0.0000 0.957 0.000 1.000
#> GSM627122 2 0.8661 0.505 0.288 0.712
#> GSM627115 2 0.0000 0.957 0.000 1.000
#> GSM627125 2 0.0000 0.957 0.000 1.000
#> GSM627174 2 0.0000 0.957 0.000 1.000
#> GSM627102 2 0.0000 0.957 0.000 1.000
#> GSM627073 2 0.0000 0.957 0.000 1.000
#> GSM627108 2 0.0000 0.957 0.000 1.000
#> GSM627126 1 0.0000 0.831 1.000 0.000
#> GSM627078 2 0.0000 0.957 0.000 1.000
#> GSM627090 2 0.0000 0.957 0.000 1.000
#> GSM627099 2 0.0000 0.957 0.000 1.000
#> GSM627105 2 0.0000 0.957 0.000 1.000
#> GSM627117 2 0.0000 0.957 0.000 1.000
#> GSM627121 2 0.0000 0.957 0.000 1.000
#> GSM627127 2 0.0000 0.957 0.000 1.000
#> GSM627087 2 0.0000 0.957 0.000 1.000
#> GSM627089 2 0.0000 0.957 0.000 1.000
#> GSM627092 2 0.0000 0.957 0.000 1.000
#> GSM627076 2 0.3114 0.897 0.056 0.944
#> GSM627136 2 0.8016 0.602 0.244 0.756
#> GSM627081 2 0.0000 0.957 0.000 1.000
#> GSM627091 2 0.0000 0.957 0.000 1.000
#> GSM627097 2 0.0000 0.957 0.000 1.000
#> GSM627072 2 0.0000 0.957 0.000 1.000
#> GSM627080 1 0.0000 0.831 1.000 0.000
#> GSM627088 2 0.0000 0.957 0.000 1.000
#> GSM627109 1 0.5842 0.812 0.860 0.140
#> GSM627111 1 0.0000 0.831 1.000 0.000
#> GSM627113 1 0.7219 0.786 0.800 0.200
#> GSM627133 2 0.0000 0.957 0.000 1.000
#> GSM627177 2 0.0000 0.957 0.000 1.000
#> GSM627086 2 0.0000 0.957 0.000 1.000
#> GSM627095 1 0.3733 0.827 0.928 0.072
#> GSM627079 2 0.0000 0.957 0.000 1.000
#> GSM627082 2 0.0000 0.957 0.000 1.000
#> GSM627074 1 0.7219 0.786 0.800 0.200
#> GSM627077 1 0.9710 0.535 0.600 0.400
#> GSM627093 1 0.7950 0.757 0.760 0.240
#> GSM627120 2 0.0000 0.957 0.000 1.000
#> GSM627124 2 0.0000 0.957 0.000 1.000
#> GSM627075 2 0.0000 0.957 0.000 1.000
#> GSM627085 2 0.0000 0.957 0.000 1.000
#> GSM627119 1 0.9686 0.544 0.604 0.396
#> GSM627116 2 0.0000 0.957 0.000 1.000
#> GSM627084 1 0.9866 0.453 0.568 0.432
#> GSM627096 2 0.0000 0.957 0.000 1.000
#> GSM627100 2 0.0000 0.957 0.000 1.000
#> GSM627112 2 0.0000 0.957 0.000 1.000
#> GSM627083 1 0.9754 0.516 0.592 0.408
#> GSM627098 1 0.7528 0.776 0.784 0.216
#> GSM627104 1 0.7950 0.757 0.760 0.240
#> GSM627131 1 0.9661 0.552 0.608 0.392
#> GSM627106 2 0.0000 0.957 0.000 1.000
#> GSM627123 1 0.1843 0.831 0.972 0.028
#> GSM627129 2 0.0000 0.957 0.000 1.000
#> GSM627216 2 0.0000 0.957 0.000 1.000
#> GSM627212 2 0.0000 0.957 0.000 1.000
#> GSM627190 2 0.0000 0.957 0.000 1.000
#> GSM627169 2 0.0000 0.957 0.000 1.000
#> GSM627167 2 0.0000 0.957 0.000 1.000
#> GSM627192 1 0.0000 0.831 1.000 0.000
#> GSM627203 2 0.0000 0.957 0.000 1.000
#> GSM627151 2 0.0000 0.957 0.000 1.000
#> GSM627163 1 0.0000 0.831 1.000 0.000
#> GSM627211 2 0.0000 0.957 0.000 1.000
#> GSM627171 2 0.0000 0.957 0.000 1.000
#> GSM627209 2 0.0000 0.957 0.000 1.000
#> GSM627135 1 0.0000 0.831 1.000 0.000
#> GSM627170 2 0.0000 0.957 0.000 1.000
#> GSM627178 1 0.8861 0.688 0.696 0.304
#> GSM627199 2 0.0000 0.957 0.000 1.000
#> GSM627213 2 0.0000 0.957 0.000 1.000
#> GSM627140 2 0.0672 0.949 0.008 0.992
#> GSM627149 1 0.0000 0.831 1.000 0.000
#> GSM627147 2 0.0000 0.957 0.000 1.000
#> GSM627195 2 0.0000 0.957 0.000 1.000
#> GSM627204 2 0.0000 0.957 0.000 1.000
#> GSM627207 2 0.0000 0.957 0.000 1.000
#> GSM627157 1 0.4939 0.821 0.892 0.108
#> GSM627201 2 0.0000 0.957 0.000 1.000
#> GSM627146 2 0.0000 0.957 0.000 1.000
#> GSM627156 2 0.0000 0.957 0.000 1.000
#> GSM627188 1 0.0000 0.831 1.000 0.000
#> GSM627197 2 0.0000 0.957 0.000 1.000
#> GSM627173 2 0.0000 0.957 0.000 1.000
#> GSM627179 2 0.0000 0.957 0.000 1.000
#> GSM627208 2 0.0000 0.957 0.000 1.000
#> GSM627215 2 0.0000 0.957 0.000 1.000
#> GSM627153 2 0.0000 0.957 0.000 1.000
#> GSM627155 1 0.0000 0.831 1.000 0.000
#> GSM627165 2 0.0000 0.957 0.000 1.000
#> GSM627168 2 0.9710 0.163 0.400 0.600
#> GSM627183 2 0.9710 0.163 0.400 0.600
#> GSM627144 2 0.0000 0.957 0.000 1.000
#> GSM627158 1 0.0000 0.831 1.000 0.000
#> GSM627196 2 0.0000 0.957 0.000 1.000
#> GSM627142 2 0.0000 0.957 0.000 1.000
#> GSM627182 2 0.0000 0.957 0.000 1.000
#> GSM627202 1 0.8499 0.722 0.724 0.276
#> GSM627141 2 0.8955 0.445 0.312 0.688
#> GSM627143 2 0.0000 0.957 0.000 1.000
#> GSM627145 2 0.0000 0.957 0.000 1.000
#> GSM627152 2 0.9732 0.147 0.404 0.596
#> GSM627200 1 0.9686 0.544 0.604 0.396
#> GSM627159 2 0.0000 0.957 0.000 1.000
#> GSM627164 2 0.0000 0.957 0.000 1.000
#> GSM627138 1 0.0000 0.831 1.000 0.000
#> GSM627175 2 0.0000 0.957 0.000 1.000
#> GSM627150 2 0.0000 0.957 0.000 1.000
#> GSM627166 1 0.9460 0.601 0.636 0.364
#> GSM627186 2 0.0000 0.957 0.000 1.000
#> GSM627139 2 0.0000 0.957 0.000 1.000
#> GSM627181 2 0.0000 0.957 0.000 1.000
#> GSM627205 2 0.0000 0.957 0.000 1.000
#> GSM627214 2 0.0000 0.957 0.000 1.000
#> GSM627180 2 0.0000 0.957 0.000 1.000
#> GSM627172 2 0.0000 0.957 0.000 1.000
#> GSM627184 1 0.0000 0.831 1.000 0.000
#> GSM627193 2 0.0000 0.957 0.000 1.000
#> GSM627191 2 0.9491 0.278 0.368 0.632
#> GSM627176 2 0.0938 0.945 0.012 0.988
#> GSM627194 2 0.0000 0.957 0.000 1.000
#> GSM627154 2 0.0000 0.957 0.000 1.000
#> GSM627187 2 0.4161 0.863 0.084 0.916
#> GSM627198 2 0.0000 0.957 0.000 1.000
#> GSM627160 2 0.8909 0.455 0.308 0.692
#> GSM627185 1 0.5408 0.818 0.876 0.124
#> GSM627206 2 0.0000 0.957 0.000 1.000
#> GSM627161 1 0.0000 0.831 1.000 0.000
#> GSM627162 2 0.2603 0.911 0.044 0.956
#> GSM627210 2 0.9732 0.147 0.404 0.596
#> GSM627189 2 0.0000 0.957 0.000 1.000
show/hide code output
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM627128 3 0.0000 0.84692 0.000 0.000 1.000
#> GSM627110 2 0.6079 0.05605 0.388 0.612 0.000
#> GSM627132 1 0.0000 0.81906 1.000 0.000 0.000
#> GSM627107 3 0.0000 0.84692 0.000 0.000 1.000
#> GSM627103 3 0.4002 0.77438 0.000 0.160 0.840
#> GSM627114 3 0.2063 0.83097 0.044 0.008 0.948
#> GSM627134 3 0.0747 0.84786 0.000 0.016 0.984
#> GSM627137 3 0.5810 0.50983 0.000 0.336 0.664
#> GSM627148 3 0.2537 0.82031 0.000 0.080 0.920
#> GSM627101 3 0.0000 0.84692 0.000 0.000 1.000
#> GSM627130 3 0.0000 0.84692 0.000 0.000 1.000
#> GSM627071 3 0.1411 0.84299 0.000 0.036 0.964
#> GSM627118 3 0.0237 0.84736 0.000 0.004 0.996
#> GSM627094 2 0.0000 0.72898 0.000 1.000 0.000
#> GSM627122 3 0.0000 0.84692 0.000 0.000 1.000
#> GSM627115 2 0.5138 0.55686 0.000 0.748 0.252
#> GSM627125 3 0.0000 0.84692 0.000 0.000 1.000
#> GSM627174 3 0.3340 0.79045 0.000 0.120 0.880
#> GSM627102 2 0.0000 0.72898 0.000 1.000 0.000
#> GSM627073 3 0.1411 0.84299 0.000 0.036 0.964
#> GSM627108 2 0.1753 0.71255 0.000 0.952 0.048
#> GSM627126 1 0.0000 0.81906 1.000 0.000 0.000
#> GSM627078 3 0.6215 0.29560 0.000 0.428 0.572
#> GSM627090 3 0.0237 0.84755 0.000 0.004 0.996
#> GSM627099 3 0.1753 0.83668 0.000 0.048 0.952
#> GSM627105 3 0.0237 0.84755 0.000 0.004 0.996
#> GSM627117 2 0.0000 0.72898 0.000 1.000 0.000
#> GSM627121 3 0.0000 0.84692 0.000 0.000 1.000
#> GSM627127 3 0.1529 0.84161 0.000 0.040 0.960
#> GSM627087 3 0.6192 0.33934 0.000 0.420 0.580
#> GSM627089 3 0.0000 0.84692 0.000 0.000 1.000
#> GSM627092 2 0.0000 0.72898 0.000 1.000 0.000
#> GSM627076 3 0.2448 0.82056 0.000 0.076 0.924
#> GSM627136 3 0.2918 0.82783 0.032 0.044 0.924
#> GSM627081 3 0.0000 0.84692 0.000 0.000 1.000
#> GSM627091 2 0.5098 0.56175 0.000 0.752 0.248
#> GSM627097 3 0.1860 0.83943 0.000 0.052 0.948
#> GSM627072 3 0.1643 0.83970 0.000 0.044 0.956
#> GSM627080 1 0.0000 0.81906 1.000 0.000 0.000
#> GSM627088 3 0.0237 0.84755 0.000 0.004 0.996
#> GSM627109 2 0.6309 -0.28104 0.496 0.504 0.000
#> GSM627111 1 0.0000 0.81906 1.000 0.000 0.000
#> GSM627113 1 0.4555 0.70787 0.800 0.200 0.000
#> GSM627133 3 0.5254 0.65356 0.000 0.264 0.736
#> GSM627177 3 0.0237 0.84755 0.000 0.004 0.996
#> GSM627086 3 0.4842 0.68369 0.000 0.224 0.776
#> GSM627095 1 0.6168 0.45221 0.588 0.412 0.000
#> GSM627079 3 0.1860 0.83586 0.000 0.052 0.948
#> GSM627082 3 0.0000 0.84692 0.000 0.000 1.000
#> GSM627074 2 0.6045 0.07214 0.380 0.620 0.000
#> GSM627077 3 0.4293 0.72540 0.164 0.004 0.832
#> GSM627093 2 0.6126 0.01471 0.400 0.600 0.000
#> GSM627120 3 0.0237 0.84736 0.000 0.004 0.996
#> GSM627124 2 0.5785 0.40658 0.000 0.668 0.332
#> GSM627075 2 0.0000 0.72898 0.000 1.000 0.000
#> GSM627085 3 0.6299 0.13724 0.000 0.476 0.524
#> GSM627119 2 0.6026 0.09114 0.376 0.624 0.000
#> GSM627116 3 0.3482 0.77438 0.000 0.128 0.872
#> GSM627084 1 0.6264 0.50258 0.616 0.380 0.004
#> GSM627096 3 0.0237 0.84736 0.000 0.004 0.996
#> GSM627100 3 0.0000 0.84692 0.000 0.000 1.000
#> GSM627112 2 0.4235 0.64903 0.000 0.824 0.176
#> GSM627083 1 0.6140 0.31342 0.596 0.000 0.404
#> GSM627098 1 0.4682 0.65968 0.804 0.004 0.192
#> GSM627104 2 0.5098 0.39034 0.248 0.752 0.000
#> GSM627131 3 0.5760 0.43799 0.328 0.000 0.672
#> GSM627106 3 0.0000 0.84692 0.000 0.000 1.000
#> GSM627123 1 0.3038 0.77454 0.896 0.104 0.000
#> GSM627129 3 0.1163 0.84716 0.000 0.028 0.972
#> GSM627216 3 0.4931 0.70661 0.000 0.232 0.768
#> GSM627212 2 0.5760 0.40240 0.000 0.672 0.328
#> GSM627190 2 0.0000 0.72898 0.000 1.000 0.000
#> GSM627169 2 0.0000 0.72898 0.000 1.000 0.000
#> GSM627167 3 0.5431 0.60237 0.000 0.284 0.716
#> GSM627192 1 0.0000 0.81906 1.000 0.000 0.000
#> GSM627203 3 0.1289 0.84425 0.000 0.032 0.968
#> GSM627151 3 0.6026 0.47870 0.000 0.376 0.624
#> GSM627163 1 0.0000 0.81906 1.000 0.000 0.000
#> GSM627211 2 0.4121 0.65918 0.000 0.832 0.168
#> GSM627171 3 0.4399 0.72535 0.000 0.188 0.812
#> GSM627209 3 0.5859 0.48843 0.000 0.344 0.656
#> GSM627135 1 0.0000 0.81906 1.000 0.000 0.000
#> GSM627170 3 0.0237 0.84736 0.000 0.004 0.996
#> GSM627178 1 0.5138 0.66032 0.748 0.252 0.000
#> GSM627199 2 0.0000 0.72898 0.000 1.000 0.000
#> GSM627213 3 0.0237 0.84736 0.000 0.004 0.996
#> GSM627140 2 0.0000 0.72898 0.000 1.000 0.000
#> GSM627149 1 0.0000 0.81906 1.000 0.000 0.000
#> GSM627147 2 0.0000 0.72898 0.000 1.000 0.000
#> GSM627195 3 0.1411 0.84299 0.000 0.036 0.964
#> GSM627204 2 0.3619 0.67198 0.000 0.864 0.136
#> GSM627207 2 0.6244 0.11665 0.000 0.560 0.440
#> GSM627157 1 0.1529 0.80457 0.960 0.040 0.000
#> GSM627201 3 0.5138 0.65059 0.000 0.252 0.748
#> GSM627146 3 0.6302 0.16917 0.000 0.480 0.520
#> GSM627156 2 0.0000 0.72898 0.000 1.000 0.000
#> GSM627188 1 0.0000 0.81906 1.000 0.000 0.000
#> GSM627197 3 0.6299 0.13724 0.000 0.476 0.524
#> GSM627173 2 0.0000 0.72898 0.000 1.000 0.000
#> GSM627179 2 0.6252 0.08800 0.000 0.556 0.444
#> GSM627208 3 0.1031 0.84774 0.000 0.024 0.976
#> GSM627215 3 0.1529 0.84305 0.000 0.040 0.960
#> GSM627153 3 0.5178 0.64093 0.000 0.256 0.744
#> GSM627155 1 0.0000 0.81906 1.000 0.000 0.000
#> GSM627165 3 0.2261 0.83066 0.000 0.068 0.932
#> GSM627168 3 0.9520 0.02257 0.352 0.196 0.452
#> GSM627183 3 0.5793 0.72513 0.116 0.084 0.800
#> GSM627144 3 0.6299 0.13378 0.000 0.476 0.524
#> GSM627158 1 0.0000 0.81906 1.000 0.000 0.000
#> GSM627196 2 0.6026 0.32012 0.000 0.624 0.376
#> GSM627142 3 0.0000 0.84692 0.000 0.000 1.000
#> GSM627182 3 0.3686 0.76858 0.000 0.140 0.860
#> GSM627202 1 0.6235 0.24888 0.564 0.000 0.436
#> GSM627141 3 0.7885 0.38870 0.336 0.072 0.592
#> GSM627143 3 0.2878 0.82348 0.000 0.096 0.904
#> GSM627145 3 0.1289 0.84429 0.000 0.032 0.968
#> GSM627152 1 0.6345 0.47420 0.596 0.400 0.004
#> GSM627200 1 0.4555 0.70787 0.800 0.200 0.000
#> GSM627159 3 0.0000 0.84692 0.000 0.000 1.000
#> GSM627164 2 0.0000 0.72898 0.000 1.000 0.000
#> GSM627138 1 0.0000 0.81906 1.000 0.000 0.000
#> GSM627175 3 0.2448 0.82195 0.000 0.076 0.924
#> GSM627150 3 0.0000 0.84692 0.000 0.000 1.000
#> GSM627166 2 0.6008 0.10311 0.372 0.628 0.000
#> GSM627186 2 0.0000 0.72898 0.000 1.000 0.000
#> GSM627139 3 0.1643 0.84189 0.000 0.044 0.956
#> GSM627181 3 0.6008 0.42882 0.000 0.372 0.628
#> GSM627205 3 0.1753 0.84020 0.000 0.048 0.952
#> GSM627214 3 0.0237 0.84736 0.000 0.004 0.996
#> GSM627180 3 0.3941 0.74996 0.000 0.156 0.844
#> GSM627172 2 0.0000 0.72898 0.000 1.000 0.000
#> GSM627184 1 0.0000 0.81906 1.000 0.000 0.000
#> GSM627193 2 0.6235 0.10661 0.000 0.564 0.436
#> GSM627191 1 0.7069 0.24439 0.568 0.024 0.408
#> GSM627176 2 0.0000 0.72898 0.000 1.000 0.000
#> GSM627194 2 0.6280 -0.00197 0.000 0.540 0.460
#> GSM627154 3 0.6095 0.38157 0.000 0.392 0.608
#> GSM627187 2 0.0237 0.72591 0.004 0.996 0.000
#> GSM627198 2 0.6302 -0.01295 0.000 0.520 0.480
#> GSM627160 2 0.0592 0.71937 0.012 0.988 0.000
#> GSM627185 1 0.6079 0.49460 0.612 0.388 0.000
#> GSM627206 3 0.0237 0.84755 0.000 0.004 0.996
#> GSM627161 1 0.0000 0.81906 1.000 0.000 0.000
#> GSM627162 2 0.0237 0.72591 0.004 0.996 0.000
#> GSM627210 2 0.0424 0.72279 0.008 0.992 0.000
#> GSM627189 2 0.5098 0.56008 0.000 0.752 0.248
show/hide code output
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM627128 4 0.4624 0.5470 0.000 0.000 0.340 0.660
#> GSM627110 3 0.2944 0.7352 0.004 0.128 0.868 0.000
#> GSM627132 1 0.0000 0.8729 1.000 0.000 0.000 0.000
#> GSM627107 4 0.4522 0.5861 0.000 0.000 0.320 0.680
#> GSM627103 4 0.3958 0.7857 0.000 0.032 0.144 0.824
#> GSM627114 3 0.7394 0.5057 0.076 0.052 0.580 0.292
#> GSM627134 4 0.4252 0.6807 0.000 0.004 0.252 0.744
#> GSM627137 4 0.2256 0.8058 0.000 0.020 0.056 0.924
#> GSM627148 3 0.1488 0.8429 0.000 0.012 0.956 0.032
#> GSM627101 4 0.3610 0.7418 0.000 0.000 0.200 0.800
#> GSM627130 4 0.1637 0.8043 0.000 0.000 0.060 0.940
#> GSM627071 3 0.4018 0.6962 0.000 0.004 0.772 0.224
#> GSM627118 4 0.3266 0.7685 0.000 0.000 0.168 0.832
#> GSM627094 2 0.1474 0.8194 0.000 0.948 0.000 0.052
#> GSM627122 3 0.6897 0.5296 0.180 0.000 0.592 0.228
#> GSM627115 2 0.4203 0.7574 0.000 0.824 0.108 0.068
#> GSM627125 4 0.4697 0.5176 0.000 0.000 0.356 0.644
#> GSM627174 4 0.1452 0.8032 0.000 0.008 0.036 0.956
#> GSM627102 2 0.1867 0.8175 0.000 0.928 0.000 0.072
#> GSM627073 3 0.2704 0.8024 0.000 0.000 0.876 0.124
#> GSM627108 2 0.2589 0.8045 0.000 0.884 0.000 0.116
#> GSM627126 1 0.0000 0.8729 1.000 0.000 0.000 0.000
#> GSM627078 4 0.2149 0.7556 0.000 0.088 0.000 0.912
#> GSM627090 3 0.1118 0.8402 0.000 0.000 0.964 0.036
#> GSM627099 4 0.3088 0.7909 0.000 0.008 0.128 0.864
#> GSM627105 4 0.4898 0.3950 0.000 0.000 0.416 0.584
#> GSM627117 2 0.4955 0.1935 0.000 0.556 0.444 0.000
#> GSM627121 4 0.4277 0.6336 0.000 0.000 0.280 0.720
#> GSM627127 4 0.3681 0.7729 0.000 0.008 0.176 0.816
#> GSM627087 4 0.6706 0.5270 0.000 0.288 0.124 0.588
#> GSM627089 3 0.1302 0.8372 0.000 0.000 0.956 0.044
#> GSM627092 2 0.0469 0.8017 0.000 0.988 0.012 0.000
#> GSM627076 3 0.0188 0.8418 0.000 0.004 0.996 0.000
#> GSM627136 3 0.5062 0.5007 0.284 0.000 0.692 0.024
#> GSM627081 3 0.3074 0.7749 0.000 0.000 0.848 0.152
#> GSM627091 2 0.4764 0.7430 0.000 0.788 0.088 0.124
#> GSM627097 4 0.4535 0.6730 0.000 0.004 0.292 0.704
#> GSM627072 3 0.0376 0.8432 0.000 0.004 0.992 0.004
#> GSM627080 1 0.0000 0.8729 1.000 0.000 0.000 0.000
#> GSM627088 3 0.1389 0.8364 0.000 0.000 0.952 0.048
#> GSM627109 1 0.5537 0.6806 0.688 0.256 0.056 0.000
#> GSM627111 1 0.0000 0.8729 1.000 0.000 0.000 0.000
#> GSM627113 1 0.4534 0.8013 0.800 0.132 0.068 0.000
#> GSM627133 3 0.1211 0.8254 0.000 0.040 0.960 0.000
#> GSM627177 3 0.3172 0.7703 0.000 0.000 0.840 0.160
#> GSM627086 4 0.1635 0.7872 0.000 0.044 0.008 0.948
#> GSM627095 1 0.3300 0.8174 0.848 0.144 0.008 0.000
#> GSM627079 3 0.0188 0.8418 0.000 0.004 0.996 0.000
#> GSM627082 4 0.0469 0.7995 0.000 0.000 0.012 0.988
#> GSM627074 2 0.7143 -0.1882 0.408 0.460 0.132 0.000
#> GSM627077 3 0.0779 0.8452 0.004 0.000 0.980 0.016
#> GSM627093 1 0.6323 0.6516 0.640 0.248 0.112 0.000
#> GSM627120 4 0.2611 0.8016 0.000 0.008 0.096 0.896
#> GSM627124 4 0.4164 0.5400 0.000 0.264 0.000 0.736
#> GSM627075 2 0.1792 0.8180 0.000 0.932 0.000 0.068
#> GSM627085 4 0.3761 0.7989 0.000 0.068 0.080 0.852
#> GSM627119 2 0.6398 0.1655 0.344 0.576 0.080 0.000
#> GSM627116 3 0.0376 0.8434 0.000 0.004 0.992 0.004
#> GSM627084 1 0.4514 0.8003 0.800 0.136 0.064 0.000
#> GSM627096 4 0.3311 0.7652 0.000 0.000 0.172 0.828
#> GSM627100 3 0.3444 0.7479 0.000 0.000 0.816 0.184
#> GSM627112 4 0.4585 0.4323 0.000 0.332 0.000 0.668
#> GSM627083 4 0.6498 0.0422 0.440 0.000 0.072 0.488
#> GSM627098 1 0.4423 0.7297 0.792 0.000 0.168 0.040
#> GSM627104 2 0.2466 0.7391 0.096 0.900 0.004 0.000
#> GSM627131 3 0.0469 0.8447 0.000 0.000 0.988 0.012
#> GSM627106 3 0.4522 0.5142 0.000 0.000 0.680 0.320
#> GSM627123 1 0.3474 0.8364 0.868 0.068 0.064 0.000
#> GSM627129 4 0.2888 0.7942 0.000 0.004 0.124 0.872
#> GSM627216 4 0.7289 0.3948 0.000 0.268 0.200 0.532
#> GSM627212 2 0.2342 0.8179 0.000 0.912 0.008 0.080
#> GSM627190 2 0.2081 0.7707 0.000 0.916 0.084 0.000
#> GSM627169 2 0.0000 0.8054 0.000 1.000 0.000 0.000
#> GSM627167 4 0.1867 0.7657 0.000 0.072 0.000 0.928
#> GSM627192 1 0.0000 0.8729 1.000 0.000 0.000 0.000
#> GSM627203 3 0.0188 0.8441 0.000 0.000 0.996 0.004
#> GSM627151 2 0.7674 0.1071 0.000 0.436 0.340 0.224
#> GSM627163 1 0.0000 0.8729 1.000 0.000 0.000 0.000
#> GSM627211 2 0.4072 0.6938 0.000 0.748 0.000 0.252
#> GSM627171 4 0.1716 0.7708 0.000 0.064 0.000 0.936
#> GSM627209 4 0.1557 0.7768 0.000 0.056 0.000 0.944
#> GSM627135 1 0.2814 0.8089 0.868 0.000 0.132 0.000
#> GSM627170 4 0.2814 0.7896 0.000 0.000 0.132 0.868
#> GSM627178 1 0.6258 0.5551 0.600 0.076 0.324 0.000
#> GSM627199 2 0.2216 0.8138 0.000 0.908 0.000 0.092
#> GSM627213 4 0.2216 0.7989 0.000 0.000 0.092 0.908
#> GSM627140 2 0.1867 0.8175 0.000 0.928 0.000 0.072
#> GSM627149 1 0.0921 0.8646 0.972 0.000 0.028 0.000
#> GSM627147 2 0.1474 0.8183 0.000 0.948 0.000 0.052
#> GSM627195 3 0.0188 0.8441 0.000 0.000 0.996 0.004
#> GSM627204 2 0.4543 0.5922 0.000 0.676 0.000 0.324
#> GSM627207 2 0.4998 0.1698 0.000 0.512 0.000 0.488
#> GSM627157 1 0.0927 0.8694 0.976 0.008 0.016 0.000
#> GSM627201 4 0.1624 0.8008 0.000 0.028 0.020 0.952
#> GSM627146 4 0.2704 0.7316 0.000 0.124 0.000 0.876
#> GSM627156 2 0.1557 0.8182 0.000 0.944 0.000 0.056
#> GSM627188 1 0.0000 0.8729 1.000 0.000 0.000 0.000
#> GSM627197 4 0.2149 0.7577 0.000 0.088 0.000 0.912
#> GSM627173 2 0.1118 0.8159 0.000 0.964 0.000 0.036
#> GSM627179 2 0.4382 0.6280 0.000 0.704 0.000 0.296
#> GSM627208 3 0.6082 0.0311 0.000 0.044 0.480 0.476
#> GSM627215 3 0.3610 0.7267 0.000 0.000 0.800 0.200
#> GSM627153 4 0.1389 0.7803 0.000 0.048 0.000 0.952
#> GSM627155 1 0.0000 0.8729 1.000 0.000 0.000 0.000
#> GSM627165 4 0.2924 0.8026 0.000 0.016 0.100 0.884
#> GSM627168 3 0.0592 0.8361 0.000 0.016 0.984 0.000
#> GSM627183 3 0.0188 0.8418 0.000 0.004 0.996 0.000
#> GSM627144 3 0.2281 0.7731 0.000 0.096 0.904 0.000
#> GSM627158 1 0.0000 0.8729 1.000 0.000 0.000 0.000
#> GSM627196 4 0.3942 0.5845 0.000 0.236 0.000 0.764
#> GSM627142 4 0.4830 0.4335 0.000 0.000 0.392 0.608
#> GSM627182 3 0.3818 0.7607 0.000 0.108 0.844 0.048
#> GSM627202 1 0.6136 0.4710 0.632 0.000 0.288 0.080
#> GSM627141 3 0.2040 0.8275 0.048 0.004 0.936 0.012
#> GSM627143 4 0.2965 0.7822 0.000 0.072 0.036 0.892
#> GSM627145 3 0.0188 0.8441 0.000 0.000 0.996 0.004
#> GSM627152 3 0.2408 0.7620 0.000 0.104 0.896 0.000
#> GSM627200 3 0.6276 0.0760 0.380 0.064 0.556 0.000
#> GSM627159 4 0.2868 0.7863 0.000 0.000 0.136 0.864
#> GSM627164 2 0.1940 0.8171 0.000 0.924 0.000 0.076
#> GSM627138 1 0.0000 0.8729 1.000 0.000 0.000 0.000
#> GSM627175 4 0.1902 0.8030 0.000 0.004 0.064 0.932
#> GSM627150 3 0.3444 0.7466 0.000 0.000 0.816 0.184
#> GSM627166 1 0.7597 0.4213 0.468 0.308 0.224 0.000
#> GSM627186 2 0.0000 0.8054 0.000 1.000 0.000 0.000
#> GSM627139 4 0.4053 0.7127 0.000 0.004 0.228 0.768
#> GSM627181 4 0.1474 0.7780 0.000 0.052 0.000 0.948
#> GSM627205 4 0.3324 0.7910 0.000 0.012 0.136 0.852
#> GSM627214 4 0.0469 0.7997 0.000 0.000 0.012 0.988
#> GSM627180 3 0.1854 0.8308 0.000 0.048 0.940 0.012
#> GSM627172 2 0.1867 0.8175 0.000 0.928 0.000 0.072
#> GSM627184 1 0.0000 0.8729 1.000 0.000 0.000 0.000
#> GSM627193 2 0.3933 0.7384 0.000 0.792 0.008 0.200
#> GSM627191 4 0.6658 0.1384 0.388 0.068 0.008 0.536
#> GSM627176 2 0.1637 0.7748 0.000 0.940 0.060 0.000
#> GSM627194 2 0.5836 0.6549 0.000 0.700 0.188 0.112
#> GSM627154 4 0.2546 0.8008 0.000 0.008 0.092 0.900
#> GSM627187 2 0.0592 0.7998 0.000 0.984 0.016 0.000
#> GSM627198 4 0.2704 0.7254 0.000 0.124 0.000 0.876
#> GSM627160 2 0.6142 0.5063 0.140 0.676 0.184 0.000
#> GSM627185 1 0.3610 0.7780 0.800 0.200 0.000 0.000
#> GSM627206 3 0.4635 0.6299 0.012 0.000 0.720 0.268
#> GSM627161 1 0.0000 0.8729 1.000 0.000 0.000 0.000
#> GSM627162 2 0.0188 0.8044 0.000 0.996 0.004 0.000
#> GSM627210 2 0.1557 0.7778 0.000 0.944 0.056 0.000
#> GSM627189 2 0.3390 0.7980 0.000 0.852 0.016 0.132
show/hide code output
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM627128 4 0.3160 0.6514 0.000 0.004 0.000 0.808 0.188
#> GSM627110 5 0.3123 0.6815 0.000 0.012 0.160 0.000 0.828
#> GSM627132 1 0.0162 0.8574 0.996 0.000 0.000 0.000 0.004
#> GSM627107 2 0.5967 0.3416 0.000 0.556 0.000 0.136 0.308
#> GSM627103 2 0.4458 0.6949 0.000 0.800 0.072 0.052 0.076
#> GSM627114 2 0.2925 0.6618 0.024 0.884 0.024 0.000 0.068
#> GSM627134 2 0.6286 0.5239 0.000 0.584 0.012 0.220 0.184
#> GSM627137 2 0.5310 0.6402 0.000 0.704 0.044 0.204 0.048
#> GSM627148 5 0.4326 0.6925 0.000 0.264 0.028 0.000 0.708
#> GSM627101 4 0.4203 0.6683 0.000 0.092 0.000 0.780 0.128
#> GSM627130 4 0.1408 0.6965 0.000 0.000 0.008 0.948 0.044
#> GSM627071 5 0.4949 0.4615 0.000 0.396 0.000 0.032 0.572
#> GSM627118 2 0.6401 0.1814 0.000 0.448 0.000 0.380 0.172
#> GSM627094 3 0.4074 0.4826 0.000 0.364 0.636 0.000 0.000
#> GSM627122 5 0.6133 0.6033 0.108 0.056 0.000 0.180 0.656
#> GSM627115 2 0.4024 0.6138 0.000 0.752 0.220 0.000 0.028
#> GSM627125 4 0.3491 0.6251 0.000 0.004 0.000 0.768 0.228
#> GSM627174 2 0.4763 0.6242 0.000 0.716 0.020 0.232 0.032
#> GSM627102 3 0.2362 0.7728 0.000 0.076 0.900 0.024 0.000
#> GSM627073 5 0.4049 0.7317 0.000 0.084 0.000 0.124 0.792
#> GSM627108 2 0.4450 -0.0746 0.000 0.508 0.488 0.004 0.000
#> GSM627126 1 0.0162 0.8574 0.996 0.000 0.000 0.000 0.004
#> GSM627078 2 0.5738 0.5229 0.000 0.604 0.132 0.264 0.000
#> GSM627090 5 0.2370 0.7766 0.000 0.056 0.000 0.040 0.904
#> GSM627099 2 0.5195 0.6107 0.000 0.692 0.008 0.212 0.088
#> GSM627105 4 0.4403 0.4981 0.000 0.008 0.004 0.648 0.340
#> GSM627117 2 0.6206 0.3606 0.000 0.528 0.304 0.000 0.168
#> GSM627121 2 0.2966 0.6322 0.000 0.848 0.000 0.016 0.136
#> GSM627127 4 0.5508 0.5817 0.000 0.120 0.000 0.636 0.244
#> GSM627087 2 0.3835 0.6691 0.000 0.796 0.156 0.000 0.048
#> GSM627089 5 0.3160 0.7485 0.000 0.188 0.000 0.004 0.808
#> GSM627092 3 0.0671 0.7664 0.000 0.016 0.980 0.000 0.004
#> GSM627076 5 0.2069 0.7429 0.000 0.000 0.012 0.076 0.912
#> GSM627136 5 0.6062 -0.0441 0.452 0.028 0.012 0.032 0.476
#> GSM627081 2 0.4641 -0.0647 0.000 0.532 0.000 0.012 0.456
#> GSM627091 3 0.4161 0.6832 0.000 0.208 0.752 0.000 0.040
#> GSM627097 4 0.4491 0.4860 0.000 0.004 0.008 0.624 0.364
#> GSM627072 5 0.2763 0.7703 0.000 0.148 0.004 0.000 0.848
#> GSM627080 1 0.0162 0.8574 0.996 0.000 0.000 0.000 0.004
#> GSM627088 2 0.4596 -0.2089 0.004 0.496 0.000 0.004 0.496
#> GSM627109 1 0.5084 0.5942 0.616 0.000 0.332 0.000 0.052
#> GSM627111 1 0.0000 0.8566 1.000 0.000 0.000 0.000 0.000
#> GSM627113 1 0.3734 0.8069 0.812 0.000 0.128 0.000 0.060
#> GSM627133 5 0.4780 0.6208 0.000 0.248 0.060 0.000 0.692
#> GSM627177 5 0.3442 0.7580 0.000 0.104 0.000 0.060 0.836
#> GSM627086 2 0.3442 0.6843 0.000 0.836 0.060 0.104 0.000
#> GSM627095 1 0.4155 0.7476 0.744 0.000 0.228 0.004 0.024
#> GSM627079 5 0.1200 0.7719 0.000 0.008 0.012 0.016 0.964
#> GSM627082 4 0.1095 0.6827 0.000 0.012 0.008 0.968 0.012
#> GSM627074 3 0.5882 0.4185 0.184 0.012 0.640 0.000 0.164
#> GSM627077 5 0.4328 0.7526 0.076 0.116 0.000 0.016 0.792
#> GSM627093 1 0.4902 0.7543 0.724 0.004 0.172 0.000 0.100
#> GSM627120 2 0.1638 0.6730 0.000 0.932 0.004 0.000 0.064
#> GSM627124 3 0.5466 0.5710 0.000 0.244 0.640 0.116 0.000
#> GSM627075 3 0.2970 0.7496 0.000 0.168 0.828 0.004 0.000
#> GSM627085 4 0.5995 0.5273 0.000 0.060 0.260 0.628 0.052
#> GSM627119 1 0.6136 0.5005 0.548 0.016 0.340 0.000 0.096
#> GSM627116 5 0.2585 0.7290 0.000 0.008 0.024 0.072 0.896
#> GSM627084 1 0.3888 0.8007 0.796 0.000 0.148 0.000 0.056
#> GSM627096 4 0.6351 0.3187 0.000 0.280 0.000 0.516 0.204
#> GSM627100 5 0.4766 0.7052 0.000 0.136 0.000 0.132 0.732
#> GSM627112 4 0.3128 0.6269 0.000 0.004 0.168 0.824 0.004
#> GSM627083 1 0.5157 0.5334 0.628 0.012 0.000 0.324 0.036
#> GSM627098 1 0.3983 0.7498 0.796 0.028 0.000 0.016 0.160
#> GSM627104 3 0.3992 0.4637 0.268 0.000 0.720 0.000 0.012
#> GSM627131 5 0.2312 0.7560 0.032 0.004 0.004 0.044 0.916
#> GSM627106 5 0.4958 0.4137 0.000 0.400 0.000 0.032 0.568
#> GSM627123 1 0.3180 0.8256 0.856 0.000 0.076 0.000 0.068
#> GSM627129 2 0.6000 0.4939 0.000 0.584 0.008 0.288 0.120
#> GSM627216 2 0.2388 0.6867 0.000 0.900 0.072 0.000 0.028
#> GSM627212 3 0.2424 0.7692 0.000 0.132 0.868 0.000 0.000
#> GSM627190 3 0.4904 0.5820 0.000 0.240 0.688 0.000 0.072
#> GSM627169 3 0.1043 0.7739 0.000 0.040 0.960 0.000 0.000
#> GSM627167 4 0.3051 0.6361 0.000 0.076 0.060 0.864 0.000
#> GSM627192 1 0.0000 0.8566 1.000 0.000 0.000 0.000 0.000
#> GSM627203 5 0.0963 0.7822 0.000 0.036 0.000 0.000 0.964
#> GSM627151 3 0.6319 0.0463 0.000 0.020 0.472 0.092 0.416
#> GSM627163 1 0.0000 0.8566 1.000 0.000 0.000 0.000 0.000
#> GSM627211 3 0.3921 0.7397 0.000 0.128 0.800 0.072 0.000
#> GSM627171 2 0.2139 0.6861 0.000 0.916 0.052 0.032 0.000
#> GSM627209 2 0.4577 0.6418 0.000 0.740 0.084 0.176 0.000
#> GSM627135 1 0.2583 0.8073 0.864 0.000 0.004 0.000 0.132
#> GSM627170 2 0.4564 0.6439 0.000 0.748 0.004 0.176 0.072
#> GSM627178 1 0.6037 0.3226 0.496 0.004 0.088 0.004 0.408
#> GSM627199 3 0.2848 0.7426 0.000 0.028 0.868 0.104 0.000
#> GSM627213 4 0.2592 0.6930 0.000 0.052 0.000 0.892 0.056
#> GSM627140 3 0.2708 0.7581 0.000 0.044 0.884 0.072 0.000
#> GSM627149 1 0.1444 0.8440 0.948 0.000 0.000 0.012 0.040
#> GSM627147 3 0.1836 0.7688 0.000 0.032 0.932 0.036 0.000
#> GSM627195 5 0.1571 0.7814 0.000 0.060 0.000 0.004 0.936
#> GSM627204 3 0.4503 0.6266 0.000 0.268 0.696 0.036 0.000
#> GSM627207 2 0.3551 0.5871 0.000 0.772 0.220 0.008 0.000
#> GSM627157 1 0.1300 0.8534 0.956 0.000 0.016 0.000 0.028
#> GSM627201 2 0.4234 0.6601 0.000 0.776 0.040 0.172 0.012
#> GSM627146 4 0.6312 0.0576 0.000 0.156 0.392 0.452 0.000
#> GSM627156 3 0.3074 0.7303 0.000 0.196 0.804 0.000 0.000
#> GSM627188 1 0.0000 0.8566 1.000 0.000 0.000 0.000 0.000
#> GSM627197 4 0.6202 0.3063 0.000 0.228 0.220 0.552 0.000
#> GSM627173 3 0.1410 0.7775 0.000 0.060 0.940 0.000 0.000
#> GSM627179 2 0.3838 0.5115 0.000 0.716 0.280 0.004 0.000
#> GSM627208 2 0.1914 0.6753 0.000 0.924 0.016 0.000 0.060
#> GSM627215 2 0.4181 0.3857 0.000 0.676 0.004 0.004 0.316
#> GSM627153 2 0.4587 0.6334 0.000 0.728 0.068 0.204 0.000
#> GSM627155 1 0.0000 0.8566 1.000 0.000 0.000 0.000 0.000
#> GSM627165 2 0.4914 0.6527 0.000 0.736 0.028 0.184 0.052
#> GSM627168 5 0.3138 0.7594 0.048 0.024 0.052 0.000 0.876
#> GSM627183 5 0.2249 0.7846 0.000 0.096 0.008 0.000 0.896
#> GSM627144 5 0.3111 0.6889 0.000 0.012 0.144 0.004 0.840
#> GSM627158 1 0.0162 0.8574 0.996 0.000 0.000 0.000 0.004
#> GSM627196 2 0.5213 0.4722 0.000 0.640 0.284 0.076 0.000
#> GSM627142 4 0.5232 0.0667 0.000 0.044 0.000 0.500 0.456
#> GSM627182 2 0.4840 0.3028 0.000 0.640 0.040 0.000 0.320
#> GSM627202 1 0.5520 0.6491 0.692 0.020 0.000 0.124 0.164
#> GSM627141 2 0.4630 0.5463 0.116 0.744 0.000 0.000 0.140
#> GSM627143 2 0.1798 0.6859 0.000 0.928 0.064 0.004 0.004
#> GSM627145 5 0.2228 0.7850 0.000 0.068 0.008 0.012 0.912
#> GSM627152 5 0.3163 0.6912 0.000 0.012 0.128 0.012 0.848
#> GSM627200 5 0.4866 0.6264 0.144 0.012 0.072 0.012 0.760
#> GSM627159 4 0.1638 0.6972 0.000 0.000 0.004 0.932 0.064
#> GSM627164 3 0.2771 0.7694 0.000 0.128 0.860 0.012 0.000
#> GSM627138 1 0.0162 0.8574 0.996 0.000 0.000 0.000 0.004
#> GSM627175 2 0.5435 0.2954 0.000 0.512 0.000 0.428 0.060
#> GSM627150 5 0.4382 0.6554 0.000 0.288 0.000 0.024 0.688
#> GSM627166 3 0.6773 0.1793 0.232 0.012 0.496 0.000 0.260
#> GSM627186 3 0.1851 0.7787 0.000 0.088 0.912 0.000 0.000
#> GSM627139 4 0.4442 0.5549 0.000 0.016 0.004 0.676 0.304
#> GSM627181 2 0.5359 0.5644 0.000 0.644 0.100 0.256 0.000
#> GSM627205 2 0.4507 0.6741 0.000 0.776 0.028 0.148 0.048
#> GSM627214 2 0.3250 0.6858 0.000 0.844 0.020 0.128 0.008
#> GSM627180 5 0.3226 0.7524 0.000 0.088 0.060 0.000 0.852
#> GSM627172 3 0.2491 0.7694 0.000 0.068 0.896 0.036 0.000
#> GSM627184 1 0.0162 0.8574 0.996 0.000 0.000 0.000 0.004
#> GSM627193 2 0.3534 0.5531 0.000 0.744 0.256 0.000 0.000
#> GSM627191 4 0.5498 -0.1553 0.444 0.008 0.036 0.508 0.004
#> GSM627176 3 0.2416 0.6969 0.000 0.012 0.888 0.000 0.100
#> GSM627194 3 0.6240 0.2024 0.000 0.364 0.524 0.020 0.092
#> GSM627154 4 0.2104 0.6989 0.000 0.024 0.000 0.916 0.060
#> GSM627187 3 0.1251 0.7703 0.000 0.036 0.956 0.000 0.008
#> GSM627198 4 0.4645 0.5420 0.000 0.072 0.204 0.724 0.000
#> GSM627160 3 0.5373 0.5447 0.084 0.012 0.712 0.012 0.180
#> GSM627185 1 0.3365 0.7912 0.808 0.008 0.180 0.000 0.004
#> GSM627206 2 0.2921 0.6256 0.004 0.844 0.000 0.004 0.148
#> GSM627161 1 0.0162 0.8574 0.996 0.000 0.000 0.000 0.004
#> GSM627162 3 0.0451 0.7628 0.000 0.008 0.988 0.000 0.004
#> GSM627210 3 0.1942 0.7204 0.000 0.012 0.920 0.000 0.068
#> GSM627189 3 0.4109 0.6143 0.000 0.288 0.700 0.000 0.012
show/hide code output
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM627128 6 0.3840 0.5751 0.000 0.012 0.000 0.064 0.136 0.788
#> GSM627110 5 0.5114 -0.1218 0.000 0.000 0.068 0.440 0.488 0.004
#> GSM627132 1 0.0146 0.8504 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM627107 2 0.5915 0.0553 0.000 0.444 0.000 0.104 0.424 0.028
#> GSM627103 2 0.2138 0.7003 0.000 0.912 0.008 0.060 0.012 0.008
#> GSM627114 5 0.7814 0.2197 0.068 0.144 0.048 0.304 0.420 0.016
#> GSM627134 2 0.4539 0.6073 0.000 0.744 0.020 0.036 0.176 0.024
#> GSM627137 2 0.1708 0.6956 0.000 0.932 0.004 0.040 0.000 0.024
#> GSM627148 5 0.2711 0.6154 0.000 0.008 0.028 0.076 0.880 0.008
#> GSM627101 6 0.5902 0.2405 0.000 0.364 0.000 0.044 0.084 0.508
#> GSM627130 6 0.1769 0.6049 0.000 0.012 0.000 0.004 0.060 0.924
#> GSM627071 5 0.3404 0.5696 0.000 0.004 0.012 0.184 0.792 0.008
#> GSM627118 2 0.3000 0.6655 0.000 0.852 0.000 0.096 0.008 0.044
#> GSM627094 2 0.4582 0.6037 0.000 0.676 0.256 0.060 0.000 0.008
#> GSM627122 5 0.2911 0.6145 0.024 0.000 0.000 0.036 0.868 0.072
#> GSM627115 2 0.1590 0.7052 0.000 0.936 0.008 0.048 0.000 0.008
#> GSM627125 6 0.4715 0.5533 0.000 0.040 0.000 0.112 0.112 0.736
#> GSM627174 2 0.1706 0.7029 0.004 0.936 0.004 0.032 0.000 0.024
#> GSM627102 3 0.1777 0.7903 0.000 0.032 0.932 0.012 0.000 0.024
#> GSM627073 5 0.1679 0.6279 0.000 0.000 0.012 0.016 0.936 0.036
#> GSM627108 2 0.4808 0.5294 0.000 0.628 0.304 0.060 0.000 0.008
#> GSM627126 1 0.0363 0.8515 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM627078 2 0.4697 0.6509 0.000 0.732 0.152 0.044 0.000 0.072
#> GSM627090 5 0.2196 0.6060 0.000 0.004 0.000 0.108 0.884 0.004
#> GSM627099 2 0.2095 0.6858 0.000 0.904 0.000 0.076 0.004 0.016
#> GSM627105 6 0.6878 0.2797 0.000 0.100 0.000 0.288 0.152 0.460
#> GSM627117 4 0.7846 0.1587 0.004 0.260 0.212 0.328 0.192 0.004
#> GSM627121 5 0.6329 0.2568 0.000 0.256 0.004 0.268 0.460 0.012
#> GSM627127 2 0.4520 0.5508 0.000 0.676 0.004 0.276 0.020 0.024
#> GSM627087 2 0.1196 0.7009 0.000 0.952 0.000 0.040 0.000 0.008
#> GSM627089 5 0.0937 0.6308 0.000 0.000 0.000 0.040 0.960 0.000
#> GSM627092 3 0.2651 0.7569 0.000 0.028 0.860 0.112 0.000 0.000
#> GSM627076 5 0.3756 0.3566 0.000 0.000 0.004 0.316 0.676 0.004
#> GSM627136 5 0.4874 0.2414 0.300 0.004 0.008 0.056 0.632 0.000
#> GSM627081 2 0.5314 0.3785 0.000 0.572 0.000 0.088 0.328 0.012
#> GSM627091 2 0.4053 0.6566 0.000 0.772 0.080 0.136 0.000 0.012
#> GSM627097 2 0.6015 0.2718 0.000 0.480 0.004 0.396 0.068 0.052
#> GSM627072 5 0.1967 0.6227 0.000 0.012 0.000 0.084 0.904 0.000
#> GSM627080 1 0.0363 0.8515 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM627088 5 0.4259 0.5463 0.000 0.076 0.000 0.176 0.740 0.008
#> GSM627109 1 0.4851 0.6334 0.680 0.000 0.212 0.096 0.012 0.000
#> GSM627111 1 0.0146 0.8517 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM627113 1 0.4318 0.7377 0.760 0.000 0.064 0.032 0.144 0.000
#> GSM627133 2 0.5670 0.5388 0.000 0.628 0.032 0.204 0.132 0.004
#> GSM627177 5 0.2095 0.6219 0.000 0.016 0.000 0.076 0.904 0.004
#> GSM627086 2 0.2948 0.6889 0.000 0.860 0.044 0.084 0.000 0.012
#> GSM627095 1 0.4320 0.7175 0.740 0.000 0.184 0.056 0.020 0.000
#> GSM627079 5 0.3565 0.4272 0.000 0.000 0.004 0.276 0.716 0.004
#> GSM627082 6 0.2820 0.5975 0.008 0.012 0.004 0.024 0.072 0.880
#> GSM627074 4 0.6664 0.3910 0.232 0.000 0.228 0.488 0.048 0.004
#> GSM627077 5 0.1890 0.6137 0.060 0.000 0.000 0.024 0.916 0.000
#> GSM627093 1 0.5365 0.6512 0.680 0.000 0.108 0.148 0.064 0.000
#> GSM627120 2 0.5880 0.4529 0.000 0.568 0.008 0.252 0.160 0.012
#> GSM627124 3 0.5305 0.5062 0.000 0.240 0.644 0.040 0.000 0.076
#> GSM627075 3 0.4283 0.5671 0.000 0.244 0.704 0.044 0.000 0.008
#> GSM627085 2 0.5871 0.2767 0.000 0.532 0.032 0.108 0.000 0.328
#> GSM627119 1 0.6110 0.4278 0.568 0.000 0.236 0.144 0.052 0.000
#> GSM627116 4 0.4969 0.1473 0.000 0.024 0.000 0.532 0.416 0.028
#> GSM627084 1 0.4580 0.7622 0.752 0.000 0.064 0.064 0.120 0.000
#> GSM627096 2 0.4114 0.6287 0.000 0.784 0.000 0.108 0.032 0.076
#> GSM627100 5 0.2599 0.6216 0.008 0.004 0.000 0.048 0.888 0.052
#> GSM627112 6 0.4022 0.3652 0.000 0.004 0.300 0.004 0.012 0.680
#> GSM627083 1 0.4531 0.6604 0.692 0.000 0.000 0.012 0.056 0.240
#> GSM627098 1 0.3593 0.6999 0.748 0.000 0.000 0.024 0.228 0.000
#> GSM627104 3 0.3316 0.6229 0.164 0.000 0.804 0.028 0.004 0.000
#> GSM627131 5 0.4105 0.3066 0.004 0.004 0.000 0.344 0.640 0.008
#> GSM627106 5 0.5613 0.1286 0.000 0.392 0.000 0.088 0.500 0.020
#> GSM627123 1 0.3256 0.7931 0.836 0.000 0.020 0.112 0.032 0.000
#> GSM627129 2 0.2720 0.6910 0.000 0.884 0.016 0.056 0.004 0.040
#> GSM627216 2 0.5955 0.5623 0.000 0.624 0.084 0.212 0.068 0.012
#> GSM627212 2 0.5958 0.1699 0.000 0.452 0.392 0.140 0.000 0.016
#> GSM627190 3 0.6123 0.3753 0.000 0.040 0.588 0.160 0.204 0.008
#> GSM627169 3 0.1745 0.7829 0.000 0.020 0.924 0.056 0.000 0.000
#> GSM627167 6 0.4153 0.4952 0.000 0.020 0.208 0.016 0.012 0.744
#> GSM627192 1 0.0363 0.8515 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM627203 5 0.3819 0.4232 0.000 0.020 0.000 0.280 0.700 0.000
#> GSM627151 4 0.6682 0.2273 0.000 0.236 0.160 0.532 0.056 0.016
#> GSM627163 1 0.0363 0.8515 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM627211 3 0.3401 0.7503 0.000 0.072 0.840 0.036 0.000 0.052
#> GSM627171 2 0.6981 0.3491 0.000 0.480 0.072 0.292 0.136 0.020
#> GSM627209 2 0.2979 0.6966 0.000 0.868 0.052 0.044 0.000 0.036
#> GSM627135 1 0.2941 0.8033 0.856 0.004 0.000 0.076 0.064 0.000
#> GSM627170 2 0.1297 0.6981 0.000 0.948 0.000 0.040 0.000 0.012
#> GSM627178 4 0.6607 0.4260 0.284 0.000 0.048 0.464 0.204 0.000
#> GSM627199 3 0.2313 0.7715 0.000 0.004 0.884 0.012 0.000 0.100
#> GSM627213 6 0.5419 0.0219 0.000 0.444 0.000 0.044 0.036 0.476
#> GSM627140 3 0.2114 0.7770 0.000 0.000 0.904 0.012 0.008 0.076
#> GSM627149 1 0.1610 0.8281 0.916 0.000 0.000 0.000 0.084 0.000
#> GSM627147 3 0.2137 0.7857 0.000 0.012 0.912 0.048 0.000 0.028
#> GSM627195 5 0.4970 0.2631 0.000 0.084 0.000 0.336 0.580 0.000
#> GSM627204 3 0.4732 0.4783 0.000 0.276 0.660 0.040 0.000 0.024
#> GSM627207 2 0.4871 0.6233 0.000 0.692 0.184 0.108 0.000 0.016
#> GSM627157 1 0.3395 0.7922 0.820 0.000 0.020 0.028 0.132 0.000
#> GSM627201 2 0.1138 0.7010 0.000 0.960 0.004 0.024 0.000 0.012
#> GSM627146 2 0.6281 0.3983 0.000 0.512 0.284 0.040 0.000 0.164
#> GSM627156 3 0.2740 0.7664 0.000 0.076 0.864 0.060 0.000 0.000
#> GSM627188 1 0.0146 0.8519 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM627197 2 0.5431 0.5510 0.000 0.652 0.088 0.052 0.000 0.208
#> GSM627173 3 0.0909 0.7909 0.000 0.012 0.968 0.020 0.000 0.000
#> GSM627179 2 0.3076 0.6920 0.000 0.840 0.112 0.044 0.000 0.004
#> GSM627208 2 0.6728 0.3870 0.000 0.492 0.048 0.224 0.228 0.008
#> GSM627215 2 0.5011 0.4471 0.000 0.616 0.004 0.064 0.308 0.008
#> GSM627153 2 0.2917 0.6967 0.000 0.872 0.048 0.040 0.000 0.040
#> GSM627155 1 0.0000 0.8515 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627165 2 0.1982 0.6920 0.000 0.912 0.004 0.068 0.000 0.016
#> GSM627168 5 0.4169 0.5118 0.048 0.000 0.020 0.180 0.752 0.000
#> GSM627183 5 0.1910 0.6080 0.000 0.000 0.000 0.108 0.892 0.000
#> GSM627144 4 0.4956 0.3500 0.000 0.004 0.072 0.592 0.332 0.000
#> GSM627158 1 0.0000 0.8515 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627196 2 0.4575 0.6367 0.000 0.720 0.196 0.052 0.000 0.032
#> GSM627142 5 0.3229 0.5920 0.004 0.000 0.000 0.048 0.828 0.120
#> GSM627182 5 0.5813 0.4508 0.000 0.068 0.076 0.204 0.640 0.012
#> GSM627202 5 0.5079 0.3236 0.280 0.000 0.004 0.048 0.640 0.028
#> GSM627141 2 0.7304 0.0686 0.324 0.416 0.000 0.132 0.112 0.016
#> GSM627143 2 0.6833 0.3942 0.000 0.496 0.096 0.296 0.096 0.016
#> GSM627145 5 0.1285 0.6227 0.000 0.000 0.004 0.052 0.944 0.000
#> GSM627152 5 0.4921 -0.0476 0.000 0.000 0.064 0.420 0.516 0.000
#> GSM627200 4 0.5845 0.1986 0.112 0.000 0.020 0.452 0.416 0.000
#> GSM627159 6 0.2356 0.5976 0.000 0.004 0.008 0.004 0.100 0.884
#> GSM627164 3 0.2344 0.7804 0.000 0.028 0.896 0.068 0.000 0.008
#> GSM627138 1 0.1624 0.8384 0.936 0.004 0.000 0.020 0.040 0.000
#> GSM627175 2 0.2052 0.6918 0.000 0.912 0.000 0.028 0.004 0.056
#> GSM627150 5 0.2959 0.6029 0.000 0.024 0.000 0.124 0.844 0.008
#> GSM627166 4 0.6496 0.4599 0.220 0.004 0.144 0.560 0.068 0.004
#> GSM627186 3 0.1832 0.7926 0.000 0.032 0.928 0.032 0.000 0.008
#> GSM627139 6 0.6187 0.3751 0.000 0.032 0.012 0.164 0.216 0.576
#> GSM627181 2 0.4370 0.6765 0.000 0.772 0.096 0.060 0.000 0.072
#> GSM627205 2 0.0935 0.7012 0.000 0.964 0.004 0.032 0.000 0.000
#> GSM627214 2 0.4580 0.6318 0.000 0.740 0.032 0.180 0.024 0.024
#> GSM627180 5 0.4683 0.5095 0.000 0.052 0.096 0.108 0.744 0.000
#> GSM627172 3 0.2384 0.7800 0.000 0.004 0.900 0.056 0.008 0.032
#> GSM627184 1 0.0000 0.8515 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627193 2 0.4261 0.6626 0.000 0.748 0.148 0.096 0.000 0.008
#> GSM627191 6 0.6012 0.4324 0.164 0.000 0.084 0.024 0.080 0.648
#> GSM627176 3 0.3509 0.5971 0.000 0.000 0.744 0.240 0.016 0.000
#> GSM627194 2 0.3770 0.6392 0.000 0.760 0.024 0.204 0.000 0.012
#> GSM627154 2 0.5708 0.1697 0.000 0.496 0.016 0.092 0.004 0.392
#> GSM627187 3 0.1938 0.7757 0.000 0.008 0.920 0.052 0.020 0.000
#> GSM627198 6 0.5958 -0.0288 0.000 0.392 0.140 0.016 0.000 0.452
#> GSM627160 3 0.5185 0.2198 0.008 0.000 0.568 0.344 0.080 0.000
#> GSM627185 1 0.3701 0.7621 0.792 0.000 0.160 0.032 0.012 0.004
#> GSM627206 5 0.5777 0.4180 0.008 0.120 0.012 0.248 0.604 0.008
#> GSM627161 1 0.0000 0.8515 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM627162 3 0.1082 0.7775 0.000 0.000 0.956 0.040 0.004 0.000
#> GSM627210 3 0.3214 0.6614 0.000 0.004 0.788 0.200 0.004 0.004
#> GSM627189 2 0.3663 0.6723 0.000 0.796 0.128 0.072 0.000 0.004
Heatmaps for the consensus matrix. It visualizes the probability of two
samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures
which are significantly different between classes which can be candidate marks
for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known
annotation is numeric, one-way ANOVA test is applied, and if the known
annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) age(p) other(p) k
#> ATC:NMF 137 1.0000 0.394 0.1031 2
#> ATC:NMF 112 0.0266 0.507 0.7819 3
#> ATC:NMF 131 0.0349 0.484 0.0861 4
#> ATC:NMF 118 0.0565 0.531 0.2028 5
#> ATC:NMF 99 0.1106 0.618 0.3301 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Session info
sessionInfo()
#> R version 3.6.0 (2019-04-26)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: CentOS Linux 7 (Core)
#>
#> Matrix products: default
#> BLAS: /usr/lib64/libblas.so.3.4.2
#> LAPACK: /usr/lib64/liblapack.so.3.4.2
#>
#> locale:
#> [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C LC_TIME=en_GB.UTF-8
#> [4] LC_COLLATE=en_GB.UTF-8 LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
#> [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] grid stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] genefilter_1.66.0 ComplexHeatmap_2.3.1 markdown_1.1 knitr_1.26
#> [5] GetoptLong_0.1.7 cola_1.3.2
#>
#> loaded via a namespace (and not attached):
#> [1] circlize_0.4.8 shape_1.4.4 xfun_0.11 slam_0.1-46
#> [5] lattice_0.20-38 splines_3.6.0 colorspace_1.4-1 vctrs_0.2.0
#> [9] stats4_3.6.0 blob_1.2.0 XML_3.98-1.20 survival_2.44-1.1
#> [13] rlang_0.4.2 pillar_1.4.2 DBI_1.0.0 BiocGenerics_0.30.0
#> [17] bit64_0.9-7 RColorBrewer_1.1-2 matrixStats_0.55.0 stringr_1.4.0
#> [21] GlobalOptions_0.1.1 evaluate_0.14 memoise_1.1.0 Biobase_2.44.0
#> [25] IRanges_2.18.3 parallel_3.6.0 AnnotationDbi_1.46.1 highr_0.8
#> [29] Rcpp_1.0.3 xtable_1.8-4 backports_1.1.5 S4Vectors_0.22.1
#> [33] annotate_1.62.0 skmeans_0.2-11 bit_1.1-14 microbenchmark_1.4-7
#> [37] brew_1.0-6 impute_1.58.0 rjson_0.2.20 png_0.1-7
#> [41] digest_0.6.23 stringi_1.4.3 polyclip_1.10-0 clue_0.3-57
#> [45] tools_3.6.0 bitops_1.0-6 magrittr_1.5 eulerr_6.0.0
#> [49] RCurl_1.95-4.12 RSQLite_2.1.4 tibble_2.1.3 cluster_2.1.0
#> [53] crayon_1.3.4 pkgconfig_2.0.3 zeallot_0.1.0 Matrix_1.2-17
#> [57] xml2_1.2.2 httr_1.4.1 R6_2.4.1 mclust_5.4.5
#> [61] compiler_3.6.0