Date: 2019-12-25 21:00:09 CET, cola version: 1.3.2
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All available functions which can be applied to this res_list object:
res_list
#> A 'ConsensusPartitionList' object with 24 methods.
#> On a matrix with 51941 rows and 120 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] 51941 120
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)
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)
| The best k | 1-PAC | Mean silhouette | Concordance | Optional k | ||
|---|---|---|---|---|---|---|
| SD:NMF | 3 | 0.998 | 0.949 | 0.974 | ** | |
| MAD:skmeans | 6 | 0.982 | 0.944 | 0.964 | ** | 2,4,5 |
| SD:pam | 2 | 0.966 | 0.969 | 0.987 | ** | |
| CV:mclust | 2 | 0.965 | 0.941 | 0.972 | ** | |
| CV:skmeans | 6 | 0.954 | 0.887 | 0.944 | ** | 2,4,5 |
| MAD:NMF | 3 | 0.953 | 0.944 | 0.977 | ** | |
| ATC:skmeans | 4 | 0.953 | 0.909 | 0.962 | ** | 2,3 |
| SD:skmeans | 6 | 0.940 | 0.870 | 0.918 | * | 2,4,5 |
| ATC:pam | 6 | 0.935 | 0.896 | 0.957 | * | 2 |
| CV:NMF | 3 | 0.931 | 0.929 | 0.970 | * | |
| ATC:hclust | 2 | 0.915 | 0.947 | 0.976 | * | |
| SD:mclust | 6 | 0.915 | 0.885 | 0.949 | * | 3,4 |
| MAD:mclust | 6 | 0.906 | 0.884 | 0.945 | * | 3 |
| MAD:pam | 6 | 0.903 | 0.854 | 0.912 | * | 2 |
| ATC:NMF | 2 | 0.898 | 0.937 | 0.973 | ||
| CV:kmeans | 4 | 0.781 | 0.854 | 0.885 | ||
| MAD:kmeans | 4 | 0.758 | 0.892 | 0.890 | ||
| ATC:kmeans | 4 | 0.697 | 0.782 | 0.874 | ||
| CV:pam | 2 | 0.686 | 0.840 | 0.930 | ||
| SD:hclust | 5 | 0.656 | 0.697 | 0.782 | ||
| MAD:hclust | 4 | 0.629 | 0.819 | 0.847 | ||
| SD:kmeans | 2 | 0.483 | 0.807 | 0.872 | ||
| ATC:mclust | 2 | 0.473 | 0.875 | 0.904 | ||
| CV:hclust | 2 | 0.256 | 0.802 | 0.876 |
**: 1-PAC > 0.95, *: 1-PAC > 0.9
Cumulative distribution function curves of consensus matrix for all methods.
collect_plots(res_list, fun = plot_ecdf)
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 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 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)
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 0.520 0.736 0.893 0.493 0.519 0.519
#> CV:NMF 2 0.629 0.859 0.931 0.501 0.499 0.499
#> MAD:NMF 2 0.707 0.870 0.943 0.492 0.503 0.503
#> ATC:NMF 2 0.898 0.937 0.973 0.477 0.516 0.516
#> SD:skmeans 2 1.000 0.987 0.994 0.505 0.496 0.496
#> CV:skmeans 2 1.000 0.980 0.986 0.504 0.496 0.496
#> MAD:skmeans 2 1.000 0.991 0.994 0.504 0.496 0.496
#> ATC:skmeans 2 1.000 0.958 0.983 0.479 0.519 0.519
#> SD:mclust 2 0.368 0.826 0.860 0.385 0.658 0.658
#> CV:mclust 2 0.965 0.941 0.972 0.344 0.667 0.667
#> MAD:mclust 2 0.499 0.846 0.891 0.364 0.688 0.688
#> ATC:mclust 2 0.473 0.875 0.904 0.460 0.497 0.497
#> SD:kmeans 2 0.483 0.807 0.872 0.503 0.496 0.496
#> CV:kmeans 2 0.487 0.636 0.783 0.501 0.496 0.496
#> MAD:kmeans 2 0.495 0.768 0.854 0.502 0.496 0.496
#> ATC:kmeans 2 0.858 0.964 0.977 0.342 0.630 0.630
#> SD:pam 2 0.966 0.969 0.987 0.504 0.496 0.496
#> CV:pam 2 0.686 0.840 0.930 0.502 0.496 0.496
#> MAD:pam 2 1.000 0.972 0.990 0.504 0.496 0.496
#> ATC:pam 2 1.000 0.988 0.996 0.176 0.832 0.832
#> SD:hclust 2 0.154 0.483 0.729 0.422 0.564 0.564
#> CV:hclust 2 0.256 0.802 0.876 0.475 0.497 0.497
#> MAD:hclust 2 0.322 0.703 0.813 0.484 0.498 0.498
#> ATC:hclust 2 0.915 0.947 0.976 0.217 0.792 0.792
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.998 0.949 0.974 0.315 0.743 0.544
#> CV:NMF 3 0.931 0.929 0.970 0.326 0.681 0.447
#> MAD:NMF 3 0.953 0.944 0.977 0.324 0.636 0.401
#> ATC:NMF 3 0.649 0.841 0.895 0.367 0.687 0.461
#> SD:skmeans 3 0.707 0.852 0.877 0.302 0.741 0.526
#> CV:skmeans 3 0.731 0.830 0.897 0.326 0.720 0.494
#> MAD:skmeans 3 0.706 0.878 0.832 0.301 0.752 0.542
#> ATC:skmeans 3 0.944 0.946 0.975 0.403 0.769 0.571
#> SD:mclust 3 0.926 0.917 0.963 0.674 0.663 0.501
#> CV:mclust 3 0.687 0.807 0.894 0.858 0.676 0.519
#> MAD:mclust 3 0.909 0.896 0.944 0.768 0.661 0.511
#> ATC:mclust 3 0.813 0.725 0.883 0.340 0.861 0.731
#> SD:kmeans 3 0.567 0.599 0.744 0.298 0.824 0.656
#> CV:kmeans 3 0.503 0.660 0.771 0.316 0.841 0.685
#> MAD:kmeans 3 0.596 0.512 0.731 0.300 0.725 0.500
#> ATC:kmeans 3 0.543 0.730 0.857 0.686 0.725 0.594
#> SD:pam 3 0.866 0.867 0.945 0.300 0.782 0.588
#> CV:pam 3 0.758 0.718 0.874 0.321 0.824 0.656
#> MAD:pam 3 0.853 0.837 0.939 0.287 0.774 0.575
#> ATC:pam 3 0.783 0.842 0.939 2.110 0.649 0.578
#> SD:hclust 3 0.361 0.622 0.759 0.398 0.689 0.502
#> CV:hclust 3 0.436 0.701 0.836 0.282 0.852 0.707
#> MAD:hclust 3 0.521 0.735 0.814 0.273 0.798 0.616
#> ATC:hclust 3 0.439 0.760 0.857 1.086 0.690 0.613
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.849 0.861 0.937 0.1280 0.792 0.494
#> CV:NMF 4 0.875 0.882 0.945 0.1264 0.754 0.406
#> MAD:NMF 4 0.695 0.748 0.862 0.1165 0.867 0.651
#> ATC:NMF 4 0.655 0.542 0.761 0.1016 0.754 0.424
#> SD:skmeans 4 0.966 0.959 0.981 0.1505 0.827 0.541
#> CV:skmeans 4 1.000 0.994 0.997 0.1308 0.815 0.512
#> MAD:skmeans 4 1.000 0.983 0.993 0.1502 0.828 0.545
#> ATC:skmeans 4 0.953 0.909 0.962 0.1073 0.896 0.697
#> SD:mclust 4 0.927 0.930 0.951 0.1197 0.898 0.720
#> CV:mclust 4 0.883 0.906 0.955 0.1365 0.867 0.651
#> MAD:mclust 4 0.736 0.870 0.896 0.1310 0.883 0.688
#> ATC:mclust 4 0.627 0.801 0.871 0.0841 0.858 0.662
#> SD:kmeans 4 0.753 0.860 0.861 0.1291 0.816 0.528
#> CV:kmeans 4 0.781 0.854 0.885 0.1270 0.866 0.634
#> MAD:kmeans 4 0.758 0.892 0.890 0.1361 0.834 0.551
#> ATC:kmeans 4 0.697 0.782 0.874 0.2327 0.770 0.520
#> SD:pam 4 0.768 0.858 0.901 0.0871 0.876 0.678
#> CV:pam 4 0.736 0.825 0.855 0.1087 0.857 0.617
#> MAD:pam 4 0.760 0.780 0.859 0.0998 0.922 0.782
#> ATC:pam 4 0.800 0.835 0.935 0.2775 0.815 0.615
#> SD:hclust 4 0.587 0.567 0.780 0.1580 0.840 0.621
#> CV:hclust 4 0.545 0.604 0.711 0.1500 0.824 0.582
#> MAD:hclust 4 0.629 0.819 0.847 0.1833 0.870 0.641
#> ATC:hclust 4 0.574 0.759 0.881 0.2304 0.918 0.842
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.656 0.626 0.799 0.0405 0.830 0.492
#> CV:NMF 5 0.648 0.630 0.806 0.0541 0.808 0.405
#> MAD:NMF 5 0.648 0.668 0.817 0.0698 0.838 0.514
#> ATC:NMF 5 0.631 0.587 0.783 0.0706 0.834 0.512
#> SD:skmeans 5 0.957 0.952 0.964 0.0487 0.956 0.823
#> CV:skmeans 5 0.989 0.961 0.976 0.0509 0.955 0.817
#> MAD:skmeans 5 0.932 0.871 0.886 0.0500 0.949 0.799
#> ATC:skmeans 5 0.864 0.819 0.912 0.0642 0.925 0.717
#> SD:mclust 5 0.730 0.761 0.858 0.0806 0.845 0.516
#> CV:mclust 5 0.723 0.772 0.857 0.0763 0.905 0.665
#> MAD:mclust 5 0.851 0.754 0.893 0.0807 0.876 0.586
#> ATC:mclust 5 0.798 0.812 0.907 0.1697 0.881 0.622
#> SD:kmeans 5 0.814 0.705 0.819 0.0703 0.942 0.773
#> CV:kmeans 5 0.837 0.818 0.875 0.0655 0.951 0.805
#> MAD:kmeans 5 0.841 0.695 0.833 0.0643 0.963 0.856
#> ATC:kmeans 5 0.695 0.678 0.814 0.0897 0.912 0.700
#> SD:pam 5 0.803 0.771 0.877 0.0738 0.941 0.804
#> CV:pam 5 0.760 0.784 0.852 0.0648 0.881 0.591
#> MAD:pam 5 0.864 0.802 0.879 0.0815 0.854 0.557
#> ATC:pam 5 0.890 0.871 0.945 0.1081 0.910 0.706
#> SD:hclust 5 0.656 0.697 0.782 0.1066 0.849 0.554
#> CV:hclust 5 0.700 0.747 0.836 0.0880 0.849 0.550
#> MAD:hclust 5 0.772 0.818 0.874 0.0725 0.955 0.823
#> ATC:hclust 5 0.602 0.730 0.863 0.0606 0.953 0.898
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.713 0.693 0.837 0.0501 0.868 0.535
#> CV:NMF 6 0.733 0.684 0.825 0.0368 0.883 0.538
#> MAD:NMF 6 0.663 0.625 0.786 0.0302 0.913 0.658
#> ATC:NMF 6 0.608 0.498 0.705 0.0579 0.871 0.522
#> SD:skmeans 6 0.940 0.870 0.918 0.0394 0.960 0.813
#> CV:skmeans 6 0.954 0.887 0.944 0.0394 0.967 0.842
#> MAD:skmeans 6 0.982 0.944 0.964 0.0373 0.954 0.788
#> ATC:skmeans 6 0.794 0.705 0.809 0.0381 0.949 0.766
#> SD:mclust 6 0.915 0.885 0.949 0.0428 0.893 0.578
#> CV:mclust 6 0.871 0.860 0.933 0.0247 0.883 0.546
#> MAD:mclust 6 0.906 0.884 0.945 0.0412 0.886 0.541
#> ATC:mclust 6 0.844 0.817 0.899 0.0255 0.929 0.705
#> SD:kmeans 6 0.798 0.691 0.801 0.0396 0.928 0.684
#> CV:kmeans 6 0.835 0.717 0.801 0.0387 0.936 0.709
#> MAD:kmeans 6 0.796 0.723 0.810 0.0407 0.914 0.652
#> ATC:kmeans 6 0.733 0.563 0.752 0.0539 0.909 0.629
#> SD:pam 6 0.847 0.837 0.911 0.0690 0.883 0.568
#> CV:pam 6 0.834 0.825 0.907 0.0478 0.936 0.719
#> MAD:pam 6 0.903 0.854 0.912 0.0566 0.908 0.628
#> ATC:pam 6 0.935 0.896 0.957 0.0456 0.957 0.811
#> SD:hclust 6 0.681 0.640 0.755 0.0333 0.955 0.803
#> CV:hclust 6 0.718 0.615 0.779 0.0565 0.968 0.856
#> MAD:hclust 6 0.808 0.740 0.803 0.0344 0.956 0.806
#> ATC:hclust 6 0.527 0.501 0.710 0.2188 0.822 0.583
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)
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)
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 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) individual(p) k
#> SD:NMF 96 2.48e-13 0.941536 2.34e-09 9.48e-01 2
#> CV:NMF 115 3.85e-03 0.079188 3.26e-02 2.19e-02 2
#> MAD:NMF 115 1.15e-02 0.046946 9.39e-02 1.13e-02 2
#> ATC:NMF 117 4.64e-05 0.754632 2.76e-03 1.72e-01 2
#> SD:skmeans 120 6.85e-20 0.999779 2.52e-15 1.00e+00 2
#> CV:skmeans 119 1.96e-20 0.999969 1.17e-15 1.00e+00 2
#> MAD:skmeans 120 6.85e-20 0.999779 2.52e-15 1.00e+00 2
#> ATC:skmeans 116 7.11e-06 0.551181 1.57e-03 2.08e-01 2
#> SD:mclust 119 9.62e-01 0.007797 9.42e-01 1.37e-05 2
#> CV:mclust 118 9.24e-01 0.007416 9.41e-01 1.77e-05 2
#> MAD:mclust 117 8.91e-01 0.004934 9.09e-01 2.40e-05 2
#> ATC:mclust 113 4.02e-19 0.999606 1.78e-14 1.00e+00 2
#> SD:kmeans 120 6.85e-20 0.999779 2.52e-15 1.00e+00 2
#> CV:kmeans 119 1.96e-20 0.999969 1.17e-15 1.00e+00 2
#> MAD:kmeans 119 1.96e-20 0.999896 1.13e-15 1.00e+00 2
#> ATC:kmeans 120 1.05e-02 0.080773 4.26e-02 1.63e-02 2
#> SD:pam 118 5.93e-21 0.999847 2.36e-16 1.00e+00 2
#> CV:pam 114 2.49e-19 0.999598 7.73e-15 1.00e+00 2
#> MAD:pam 118 5.93e-21 0.999967 3.67e-16 1.00e+00 2
#> ATC:pam 119 5.08e-01 0.000108 1.29e-01 5.32e-04 2
#> SD:hclust 72 4.35e-04 0.113142 6.64e-03 4.51e-02 2
#> CV:hclust 118 2.73e-01 0.000385 4.78e-02 6.94e-04 2
#> MAD:hclust 114 1.00e+00 0.001879 2.47e-01 6.98e-04 2
#> ATC:hclust 119 7.50e-01 0.000109 2.26e-01 7.43e-05 2
test_to_known_factors(res_list, k = 3)
#> n disease.state(p) age(p) other(p) individual(p) k
#> SD:NMF 117 8.48e-20 0.96440 3.24e-17 0.98166 3
#> CV:NMF 117 1.01e-17 0.82156 5.65e-15 0.82568 3
#> MAD:NMF 116 2.34e-18 0.82674 2.68e-14 0.97068 3
#> ATC:NMF 114 9.01e-04 0.04506 7.06e-04 0.01087 3
#> SD:skmeans 116 7.06e-17 0.32247 4.98e-11 0.82550 3
#> CV:skmeans 118 1.33e-16 0.64299 1.23e-14 0.73618 3
#> MAD:skmeans 120 1.72e-16 0.27929 9.68e-11 0.78513 3
#> ATC:skmeans 118 5.54e-04 0.03684 4.60e-02 0.00177 3
#> SD:mclust 115 2.31e-15 0.21937 1.31e-08 0.09662 3
#> CV:mclust 118 8.52e-16 0.33030 6.72e-09 0.13595 3
#> MAD:mclust 117 2.76e-14 0.13997 1.74e-08 0.05949 3
#> ATC:mclust 97 1.08e-15 0.28974 1.69e-10 0.59637 3
#> SD:kmeans 110 2.59e-19 0.87885 2.43e-15 0.98811 3
#> CV:kmeans 113 4.86e-20 0.92557 1.13e-17 0.98689 3
#> MAD:kmeans 69 2.83e-11 0.82833 2.71e-07 0.93753 3
#> ATC:kmeans 107 1.63e-07 0.02230 2.43e-05 0.04458 3
#> SD:pam 107 8.29e-16 0.23120 1.01e-09 0.72816 3
#> CV:pam 93 9.01e-14 0.24581 1.16e-07 0.66794 3
#> MAD:pam 106 2.21e-15 0.29615 3.48e-09 0.81641 3
#> ATC:pam 107 3.13e-03 0.00651 8.34e-02 0.00214 3
#> SD:hclust 96 2.45e-15 0.49189 1.99e-12 0.69062 3
#> CV:hclust 101 2.19e-07 0.01564 4.83e-05 0.12862 3
#> MAD:hclust 112 3.44e-08 0.02495 1.13e-04 0.06181 3
#> ATC:hclust 109 9.34e-06 0.00656 9.59e-04 0.01149 3
test_to_known_factors(res_list, k = 4)
#> n disease.state(p) age(p) other(p) individual(p) k
#> SD:NMF 114 4.48e-14 0.443315 3.97e-11 0.22495 4
#> CV:NMF 114 6.18e-12 0.267553 2.12e-09 0.18720 4
#> MAD:NMF 107 7.27e-16 0.656635 4.01e-12 0.48113 4
#> ATC:NMF 81 1.98e-13 0.483791 1.39e-11 0.54995 4
#> SD:skmeans 119 7.86e-19 0.430340 1.03e-12 0.85827 4
#> CV:skmeans 120 8.49e-20 0.516577 9.69e-14 0.91540 4
#> MAD:skmeans 119 9.13e-19 0.396978 1.08e-12 0.87957 4
#> ATC:skmeans 113 3.40e-07 0.033298 3.62e-05 0.01305 4
#> SD:mclust 118 9.27e-16 0.089804 3.60e-12 0.03421 4
#> CV:mclust 116 1.14e-15 0.112949 1.24e-13 0.05760 4
#> MAD:mclust 118 1.13e-15 0.077163 1.22e-12 0.03983 4
#> ATC:mclust 114 4.40e-17 0.258187 2.18e-09 0.55786 4
#> SD:kmeans 116 4.96e-19 0.335604 8.57e-13 0.85473 4
#> CV:kmeans 112 3.02e-18 0.299403 1.82e-12 0.78068 4
#> MAD:kmeans 117 2.40e-18 0.366054 6.29e-12 0.85119 4
#> ATC:kmeans 110 1.38e-04 0.000444 2.47e-02 0.00217 4
#> SD:pam 118 5.04e-14 0.118754 1.33e-07 0.25619 4
#> CV:pam 117 2.33e-18 0.532704 9.89e-14 0.85218 4
#> MAD:pam 115 1.50e-14 0.087089 2.89e-07 0.27636 4
#> ATC:pam 113 3.72e-06 0.027122 1.23e-03 0.01188 4
#> SD:hclust 87 3.19e-13 0.161896 3.08e-10 0.35175 4
#> CV:hclust 91 2.20e-06 0.029381 3.22e-05 0.06148 4
#> MAD:hclust 113 3.34e-18 0.525985 7.62e-12 0.85983 4
#> ATC:hclust 108 1.59e-06 0.076707 1.77e-03 0.01581 4
test_to_known_factors(res_list, k = 5)
#> n disease.state(p) age(p) other(p) individual(p) k
#> SD:NMF 87 1.54e-11 0.681752 7.13e-09 0.662625 5
#> CV:NMF 92 8.22e-09 0.153688 1.49e-05 0.049332 5
#> MAD:NMF 101 2.43e-09 0.238651 4.23e-06 0.095413 5
#> ATC:NMF 94 1.56e-11 0.073832 3.60e-08 0.012861 5
#> SD:skmeans 120 1.26e-18 0.360595 1.23e-13 0.663885 5
#> CV:skmeans 120 2.43e-19 0.470799 2.03e-14 0.750314 5
#> MAD:skmeans 113 5.48e-19 0.499380 1.06e-13 0.800393 5
#> ATC:skmeans 113 1.02e-12 0.304692 1.99e-08 0.277791 5
#> SD:mclust 110 2.13e-16 0.345940 2.04e-09 0.413340 5
#> CV:mclust 115 4.42e-15 0.115212 2.24e-11 0.072011 5
#> MAD:mclust 106 6.80e-14 0.177956 8.41e-10 0.102737 5
#> ATC:mclust 113 1.94e-15 0.238354 4.91e-09 0.269105 5
#> SD:kmeans 93 9.15e-15 0.041751 2.54e-11 0.381143 5
#> CV:kmeans 113 3.93e-17 0.358563 1.21e-12 0.656204 5
#> MAD:kmeans 100 3.84e-16 0.297335 7.29e-13 0.643774 5
#> ATC:kmeans 102 3.61e-07 0.007388 6.24e-06 0.009919 5
#> SD:pam 113 1.38e-14 0.002421 1.82e-08 0.078827 5
#> CV:pam 106 1.20e-14 0.217817 3.98e-08 0.323999 5
#> MAD:pam 112 1.32e-15 0.151879 9.87e-09 0.379272 5
#> ATC:pam 112 1.51e-05 0.000879 3.70e-03 0.000176 5
#> SD:hclust 103 6.73e-16 0.089787 2.08e-11 0.394215 5
#> CV:hclust 102 4.92e-15 0.103337 1.16e-11 0.555458 5
#> MAD:hclust 113 1.79e-16 0.067424 1.27e-11 0.556265 5
#> ATC:hclust 99 9.45e-06 0.193496 5.88e-04 0.008916 5
test_to_known_factors(res_list, k = 6)
#> n disease.state(p) age(p) other(p) individual(p) k
#> SD:NMF 98 4.81e-12 0.26112 6.65e-08 0.215615 6
#> CV:NMF 100 3.81e-11 0.13537 2.25e-07 0.088745 6
#> MAD:NMF 92 2.71e-09 0.40360 6.05e-06 0.127400 6
#> ATC:NMF 74 1.36e-06 0.00700 1.94e-05 0.002183 6
#> SD:skmeans 113 1.21e-16 0.20065 5.16e-10 0.383068 6
#> CV:skmeans 113 2.21e-17 0.26004 8.09e-11 0.394416 6
#> MAD:skmeans 119 8.23e-17 0.04182 1.16e-10 0.266173 6
#> ATC:skmeans 101 4.89e-12 0.18787 5.82e-09 0.208864 6
#> SD:mclust 115 1.90e-18 0.54474 2.10e-11 0.727994 6
#> CV:mclust 115 1.52e-17 0.53558 9.96e-12 0.717142 6
#> MAD:mclust 115 8.46e-17 0.24931 1.53e-11 0.533989 6
#> ATC:mclust 112 1.80e-15 0.04079 2.07e-08 0.546154 6
#> SD:kmeans 103 1.30e-15 0.21014 4.09e-09 0.399855 6
#> CV:kmeans 104 6.65e-16 0.39102 2.38e-09 0.413781 6
#> MAD:kmeans 103 4.77e-15 0.16788 2.02e-09 0.340241 6
#> ATC:kmeans 72 2.39e-07 0.08283 4.48e-06 0.074066 6
#> SD:pam 113 8.12e-16 0.06685 1.00e-10 0.267430 6
#> CV:pam 110 3.48e-15 0.12676 2.71e-11 0.257196 6
#> MAD:pam 116 1.37e-14 0.00521 3.52e-09 0.108328 6
#> ATC:pam 115 8.39e-07 0.00311 1.29e-04 0.000371 6
#> SD:hclust 90 6.11e-13 0.14151 4.47e-11 0.558449 6
#> CV:hclust 88 1.09e-12 0.13380 1.29e-11 0.493639 6
#> MAD:hclust 114 3.82e-16 0.11265 2.06e-12 0.503893 6
#> ATC:hclust 74 2.90e-06 0.03415 3.43e-04 0.077764 6
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"]
# you can also extract it by
# 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 51941 rows and 120 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:
k and the heatmap of
predicted classes for each k.k.k.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:
k;k, the area increased is defined as \(A_k - A_{k-1}\).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 0.154 0.483 0.729 0.4219 0.564 0.564
#> 3 3 0.361 0.622 0.759 0.3982 0.689 0.502
#> 4 4 0.587 0.567 0.780 0.1580 0.840 0.621
#> 5 5 0.656 0.697 0.782 0.1066 0.849 0.554
#> 6 6 0.681 0.640 0.755 0.0333 0.955 0.803
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
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.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM494565 2 0.9988 0.0486 0.480 0.520
#> GSM494594 2 0.0000 0.7090 0.000 1.000
#> GSM494604 1 0.5178 0.5707 0.884 0.116
#> GSM494564 2 0.6973 0.7241 0.188 0.812
#> GSM494591 2 0.0000 0.7090 0.000 1.000
#> GSM494567 2 0.6247 0.7355 0.156 0.844
#> GSM494602 1 0.5294 0.5636 0.880 0.120
#> GSM494613 2 0.5519 0.7457 0.128 0.872
#> GSM494589 2 0.6973 0.7241 0.188 0.812
#> GSM494598 1 0.5408 0.5646 0.876 0.124
#> GSM494593 1 0.5178 0.5707 0.884 0.116
#> GSM494583 1 0.9944 0.2051 0.544 0.456
#> GSM494612 1 0.5059 0.5605 0.888 0.112
#> GSM494558 2 0.9552 0.1056 0.376 0.624
#> GSM494556 2 0.5519 0.7457 0.128 0.872
#> GSM494559 2 0.6148 0.7452 0.152 0.848
#> GSM494571 2 0.0000 0.7090 0.000 1.000
#> GSM494614 2 0.5519 0.7457 0.128 0.872
#> GSM494603 2 0.9977 -0.1885 0.472 0.528
#> GSM494568 2 0.9977 -0.1885 0.472 0.528
#> GSM494572 2 0.0000 0.7090 0.000 1.000
#> GSM494600 2 0.6973 0.7241 0.188 0.812
#> GSM494562 1 0.5408 0.5646 0.876 0.124
#> GSM494615 2 0.5519 0.7457 0.128 0.872
#> GSM494582 1 0.5059 0.5605 0.888 0.112
#> GSM494599 1 0.5178 0.5707 0.884 0.116
#> GSM494610 1 0.5408 0.5646 0.876 0.124
#> GSM494587 1 0.9427 0.4045 0.640 0.360
#> GSM494581 1 0.9795 0.2997 0.584 0.416
#> GSM494580 2 0.6247 0.7355 0.156 0.844
#> GSM494563 2 0.9358 0.4672 0.352 0.648
#> GSM494576 1 0.8909 0.4666 0.692 0.308
#> GSM494605 1 0.7950 0.5542 0.760 0.240
#> GSM494584 2 0.7056 0.7108 0.192 0.808
#> GSM494586 1 0.6048 0.5586 0.852 0.148
#> GSM494578 2 0.6247 0.7355 0.156 0.844
#> GSM494585 1 0.9209 0.4357 0.664 0.336
#> GSM494611 1 0.5059 0.5605 0.888 0.112
#> GSM494560 2 0.6973 0.7241 0.188 0.812
#> GSM494595 1 0.7745 0.5322 0.772 0.228
#> GSM494570 2 0.7056 0.7192 0.192 0.808
#> GSM494597 2 0.5737 0.6931 0.136 0.864
#> GSM494607 1 0.5178 0.5707 0.884 0.116
#> GSM494561 2 0.6247 0.7443 0.156 0.844
#> GSM494569 1 0.9988 0.2834 0.520 0.480
#> GSM494592 1 0.5178 0.5707 0.884 0.116
#> GSM494577 1 0.9323 0.4150 0.652 0.348
#> GSM494588 2 0.6973 0.7241 0.188 0.812
#> GSM494590 2 0.0000 0.7090 0.000 1.000
#> GSM494609 1 0.9795 0.2997 0.584 0.416
#> GSM494608 1 1.0000 0.0250 0.504 0.496
#> GSM494606 1 0.5178 0.5707 0.884 0.116
#> GSM494574 1 0.5408 0.5646 0.876 0.124
#> GSM494573 2 0.6973 0.7241 0.188 0.812
#> GSM494566 1 0.9686 0.3591 0.604 0.396
#> GSM494601 1 0.8555 0.4744 0.720 0.280
#> GSM494557 2 0.5519 0.7457 0.128 0.872
#> GSM494579 1 0.9248 0.4384 0.660 0.340
#> GSM494596 2 0.0000 0.7090 0.000 1.000
#> GSM494575 1 0.5059 0.5605 0.888 0.112
#> GSM494625 1 0.9983 0.2946 0.524 0.476
#> GSM494654 2 0.0376 0.7075 0.004 0.996
#> GSM494664 1 0.7950 0.5542 0.760 0.240
#> GSM494624 1 0.9983 0.2946 0.524 0.476
#> GSM494651 1 0.9983 0.2946 0.524 0.476
#> GSM494662 1 0.9970 0.3072 0.532 0.468
#> GSM494627 2 0.9998 -0.2422 0.492 0.508
#> GSM494673 1 0.0000 0.6022 1.000 0.000
#> GSM494649 1 0.9983 0.2946 0.524 0.476
#> GSM494658 1 0.0000 0.6022 1.000 0.000
#> GSM494653 1 0.0000 0.6022 1.000 0.000
#> GSM494643 1 0.9983 0.2946 0.524 0.476
#> GSM494672 1 0.0000 0.6022 1.000 0.000
#> GSM494618 1 0.9983 0.2946 0.524 0.476
#> GSM494631 2 0.9087 0.4588 0.324 0.676
#> GSM494619 1 0.9983 0.2946 0.524 0.476
#> GSM494674 1 0.0000 0.6022 1.000 0.000
#> GSM494616 1 0.9983 0.2946 0.524 0.476
#> GSM494663 2 0.9998 -0.2422 0.492 0.508
#> GSM494628 1 1.0000 0.2430 0.504 0.496
#> GSM494632 1 0.9427 0.4446 0.640 0.360
#> GSM494660 1 0.9983 0.2946 0.524 0.476
#> GSM494622 2 1.0000 -0.2548 0.496 0.504
#> GSM494642 1 0.0000 0.6022 1.000 0.000
#> GSM494647 1 0.0000 0.6022 1.000 0.000
#> GSM494659 1 0.0000 0.6022 1.000 0.000
#> GSM494670 1 0.0000 0.6022 1.000 0.000
#> GSM494675 2 0.5737 0.6931 0.136 0.864
#> GSM494641 1 0.0000 0.6022 1.000 0.000
#> GSM494636 1 0.9881 0.3527 0.564 0.436
#> GSM494640 1 0.9983 0.2946 0.524 0.476
#> GSM494623 1 0.9983 0.2946 0.524 0.476
#> GSM494644 1 0.7950 0.5542 0.760 0.240
#> GSM494646 1 0.7950 0.5542 0.760 0.240
#> GSM494665 1 0.7950 0.5542 0.760 0.240
#> GSM494638 1 0.9933 0.3302 0.548 0.452
#> GSM494645 1 0.7950 0.5542 0.760 0.240
#> GSM494671 1 0.0000 0.6022 1.000 0.000
#> GSM494655 1 0.0000 0.6022 1.000 0.000
#> GSM494620 1 0.9983 0.2946 0.524 0.476
#> GSM494630 1 0.9983 0.2946 0.524 0.476
#> GSM494657 2 0.0000 0.7090 0.000 1.000
#> GSM494667 1 0.0000 0.6022 1.000 0.000
#> GSM494621 1 0.9983 0.2946 0.524 0.476
#> GSM494629 1 0.9988 0.2834 0.520 0.480
#> GSM494637 1 0.9983 0.2946 0.524 0.476
#> GSM494652 1 0.0000 0.6022 1.000 0.000
#> GSM494648 1 0.9983 0.2946 0.524 0.476
#> GSM494650 1 0.9998 0.2601 0.508 0.492
#> GSM494669 1 0.0000 0.6022 1.000 0.000
#> GSM494666 1 0.7950 0.5542 0.760 0.240
#> GSM494668 1 0.0000 0.6022 1.000 0.000
#> GSM494633 1 0.9983 0.2946 0.524 0.476
#> GSM494634 1 0.0000 0.6022 1.000 0.000
#> GSM494639 1 0.9881 0.3527 0.564 0.436
#> GSM494661 1 0.7950 0.5542 0.760 0.240
#> GSM494617 1 0.9983 0.2946 0.524 0.476
#> GSM494626 1 0.9983 0.2946 0.524 0.476
#> GSM494656 2 0.0376 0.7075 0.004 0.996
#> GSM494635 1 0.7950 0.5542 0.760 0.240
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM494565 2 0.9136 0.2229 0.264 0.540 0.196
#> GSM494594 3 0.1529 0.5704 0.040 0.000 0.960
#> GSM494604 2 0.3500 0.7100 0.116 0.880 0.004
#> GSM494564 3 0.9719 0.6458 0.360 0.224 0.416
#> GSM494591 3 0.1529 0.5704 0.040 0.000 0.960
#> GSM494567 3 0.9090 0.6984 0.332 0.156 0.512
#> GSM494602 2 0.1411 0.7603 0.036 0.964 0.000
#> GSM494613 3 0.8798 0.7095 0.356 0.124 0.520
#> GSM494589 3 0.9719 0.6458 0.360 0.224 0.416
#> GSM494598 2 0.1411 0.7567 0.000 0.964 0.036
#> GSM494593 2 0.1989 0.7595 0.048 0.948 0.004
#> GSM494583 2 0.8300 0.4334 0.244 0.620 0.136
#> GSM494612 2 0.0424 0.7557 0.000 0.992 0.008
#> GSM494558 1 0.4235 0.4840 0.824 0.000 0.176
#> GSM494556 3 0.8798 0.7095 0.356 0.124 0.520
#> GSM494559 3 0.8957 0.6967 0.376 0.132 0.492
#> GSM494571 3 0.1529 0.5704 0.040 0.000 0.960
#> GSM494614 3 0.8798 0.7095 0.356 0.124 0.520
#> GSM494603 1 0.2448 0.6609 0.924 0.000 0.076
#> GSM494568 1 0.2448 0.6609 0.924 0.000 0.076
#> GSM494572 3 0.1529 0.5704 0.040 0.000 0.960
#> GSM494600 3 0.9719 0.6458 0.360 0.224 0.416
#> GSM494562 2 0.1411 0.7567 0.000 0.964 0.036
#> GSM494615 3 0.8798 0.7095 0.356 0.124 0.520
#> GSM494582 2 0.0424 0.7557 0.000 0.992 0.008
#> GSM494599 2 0.1989 0.7595 0.048 0.948 0.004
#> GSM494610 2 0.1411 0.7567 0.000 0.964 0.036
#> GSM494587 2 0.6962 0.6129 0.184 0.724 0.092
#> GSM494581 2 0.7906 0.5243 0.220 0.656 0.124
#> GSM494580 3 0.9090 0.6984 0.332 0.156 0.512
#> GSM494563 2 0.9924 -0.3168 0.320 0.392 0.288
#> GSM494576 2 0.6007 0.6489 0.184 0.768 0.048
#> GSM494605 1 0.4974 0.6631 0.764 0.236 0.000
#> GSM494584 3 0.9465 0.6631 0.332 0.196 0.472
#> GSM494586 2 0.1919 0.7598 0.020 0.956 0.024
#> GSM494578 3 0.9090 0.6984 0.332 0.156 0.512
#> GSM494585 2 0.6737 0.6406 0.156 0.744 0.100
#> GSM494611 2 0.0424 0.7557 0.000 0.992 0.008
#> GSM494560 3 0.9719 0.6458 0.360 0.224 0.416
#> GSM494595 2 0.3826 0.7281 0.124 0.868 0.008
#> GSM494570 3 0.9724 0.6417 0.364 0.224 0.412
#> GSM494597 3 0.8043 0.6054 0.228 0.128 0.644
#> GSM494607 2 0.3500 0.7100 0.116 0.880 0.004
#> GSM494561 3 0.8967 0.6933 0.380 0.132 0.488
#> GSM494569 1 0.0237 0.7221 0.996 0.000 0.004
#> GSM494592 2 0.1989 0.7595 0.048 0.948 0.004
#> GSM494577 2 0.6541 0.6026 0.212 0.732 0.056
#> GSM494588 3 0.9719 0.6458 0.360 0.224 0.416
#> GSM494590 3 0.1529 0.5704 0.040 0.000 0.960
#> GSM494609 2 0.7906 0.5243 0.220 0.656 0.124
#> GSM494608 2 0.9537 0.2059 0.256 0.488 0.256
#> GSM494606 2 0.1989 0.7595 0.048 0.948 0.004
#> GSM494574 2 0.1411 0.7567 0.000 0.964 0.036
#> GSM494573 3 0.9719 0.6458 0.360 0.224 0.416
#> GSM494566 2 0.8293 0.4963 0.272 0.608 0.120
#> GSM494601 2 0.6012 0.6867 0.088 0.788 0.124
#> GSM494557 3 0.8798 0.7095 0.356 0.124 0.520
#> GSM494579 2 0.7677 0.5837 0.244 0.660 0.096
#> GSM494596 3 0.1529 0.5704 0.040 0.000 0.960
#> GSM494575 2 0.0424 0.7557 0.000 0.992 0.008
#> GSM494625 1 0.0000 0.7253 1.000 0.000 0.000
#> GSM494654 3 0.1753 0.5683 0.048 0.000 0.952
#> GSM494664 1 0.4974 0.6631 0.764 0.236 0.000
#> GSM494624 1 0.0000 0.7253 1.000 0.000 0.000
#> GSM494651 1 0.0000 0.7253 1.000 0.000 0.000
#> GSM494662 1 0.0424 0.7263 0.992 0.008 0.000
#> GSM494627 1 0.1964 0.6858 0.944 0.000 0.056
#> GSM494673 1 0.6669 0.4312 0.524 0.468 0.008
#> GSM494649 1 0.0000 0.7253 1.000 0.000 0.000
#> GSM494658 1 0.6669 0.4312 0.524 0.468 0.008
#> GSM494653 1 0.6669 0.4312 0.524 0.468 0.008
#> GSM494643 1 0.0000 0.7253 1.000 0.000 0.000
#> GSM494672 1 0.6669 0.4312 0.524 0.468 0.008
#> GSM494618 1 0.0000 0.7253 1.000 0.000 0.000
#> GSM494631 1 0.7557 -0.0221 0.656 0.080 0.264
#> GSM494619 1 0.0000 0.7253 1.000 0.000 0.000
#> GSM494674 1 0.6669 0.4312 0.524 0.468 0.008
#> GSM494616 1 0.0000 0.7253 1.000 0.000 0.000
#> GSM494663 1 0.1964 0.6858 0.944 0.000 0.056
#> GSM494628 1 0.1163 0.7056 0.972 0.000 0.028
#> GSM494632 1 0.3267 0.7153 0.884 0.116 0.000
#> GSM494660 1 0.0000 0.7253 1.000 0.000 0.000
#> GSM494622 1 0.1753 0.6928 0.952 0.000 0.048
#> GSM494642 1 0.6669 0.4312 0.524 0.468 0.008
#> GSM494647 1 0.6669 0.4312 0.524 0.468 0.008
#> GSM494659 1 0.6669 0.4312 0.524 0.468 0.008
#> GSM494670 1 0.6669 0.4312 0.524 0.468 0.008
#> GSM494675 3 0.8043 0.6054 0.228 0.128 0.644
#> GSM494641 1 0.6669 0.4312 0.524 0.468 0.008
#> GSM494636 1 0.1529 0.7256 0.960 0.040 0.000
#> GSM494640 1 0.0000 0.7253 1.000 0.000 0.000
#> GSM494623 1 0.0000 0.7253 1.000 0.000 0.000
#> GSM494644 1 0.4974 0.6631 0.764 0.236 0.000
#> GSM494646 1 0.4974 0.6631 0.764 0.236 0.000
#> GSM494665 1 0.4974 0.6631 0.764 0.236 0.000
#> GSM494638 1 0.1031 0.7267 0.976 0.024 0.000
#> GSM494645 1 0.4974 0.6631 0.764 0.236 0.000
#> GSM494671 1 0.6669 0.4312 0.524 0.468 0.008
#> GSM494655 1 0.6669 0.4312 0.524 0.468 0.008
#> GSM494620 1 0.0000 0.7253 1.000 0.000 0.000
#> GSM494630 1 0.0000 0.7253 1.000 0.000 0.000
#> GSM494657 3 0.1529 0.5704 0.040 0.000 0.960
#> GSM494667 1 0.6669 0.4312 0.524 0.468 0.008
#> GSM494621 1 0.0000 0.7253 1.000 0.000 0.000
#> GSM494629 1 0.0237 0.7221 0.996 0.000 0.004
#> GSM494637 1 0.0000 0.7253 1.000 0.000 0.000
#> GSM494652 1 0.6669 0.4312 0.524 0.468 0.008
#> GSM494648 1 0.0000 0.7253 1.000 0.000 0.000
#> GSM494650 1 0.0747 0.7129 0.984 0.000 0.016
#> GSM494669 1 0.6669 0.4312 0.524 0.468 0.008
#> GSM494666 1 0.4974 0.6631 0.764 0.236 0.000
#> GSM494668 1 0.6669 0.4312 0.524 0.468 0.008
#> GSM494633 1 0.0000 0.7253 1.000 0.000 0.000
#> GSM494634 1 0.6669 0.4312 0.524 0.468 0.008
#> GSM494639 1 0.1529 0.7256 0.960 0.040 0.000
#> GSM494661 1 0.4974 0.6631 0.764 0.236 0.000
#> GSM494617 1 0.0000 0.7253 1.000 0.000 0.000
#> GSM494626 1 0.0000 0.7253 1.000 0.000 0.000
#> GSM494656 3 0.1753 0.5683 0.048 0.000 0.952
#> GSM494635 1 0.4974 0.6631 0.764 0.236 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM494565 3 0.7382 0.3697 0.120 0.308 0.552 0.020
#> GSM494594 1 0.3668 0.9785 0.808 0.000 0.188 0.004
#> GSM494604 2 0.3477 0.6055 0.008 0.872 0.032 0.088
#> GSM494564 3 0.0895 0.7541 0.000 0.004 0.976 0.020
#> GSM494591 1 0.3688 0.9793 0.792 0.000 0.208 0.000
#> GSM494567 3 0.3965 0.7412 0.120 0.032 0.840 0.008
#> GSM494602 2 0.1516 0.6167 0.008 0.960 0.016 0.016
#> GSM494613 3 0.3278 0.7441 0.116 0.000 0.864 0.020
#> GSM494589 3 0.0895 0.7541 0.000 0.004 0.976 0.020
#> GSM494598 2 0.4123 0.5856 0.136 0.820 0.044 0.000
#> GSM494593 2 0.1624 0.6173 0.000 0.952 0.028 0.020
#> GSM494583 3 0.6425 0.1632 0.056 0.436 0.504 0.004
#> GSM494612 2 0.2521 0.6045 0.064 0.912 0.024 0.000
#> GSM494558 4 0.4565 0.6336 0.140 0.000 0.064 0.796
#> GSM494556 3 0.3335 0.7414 0.120 0.000 0.860 0.020
#> GSM494559 3 0.3107 0.7544 0.080 0.000 0.884 0.036
#> GSM494571 1 0.3528 0.9807 0.808 0.000 0.192 0.000
#> GSM494614 3 0.3278 0.7441 0.116 0.000 0.864 0.020
#> GSM494603 4 0.2871 0.7481 0.072 0.000 0.032 0.896
#> GSM494568 4 0.2871 0.7481 0.072 0.000 0.032 0.896
#> GSM494572 1 0.3649 0.9839 0.796 0.000 0.204 0.000
#> GSM494600 3 0.0895 0.7541 0.000 0.004 0.976 0.020
#> GSM494562 2 0.4123 0.5856 0.136 0.820 0.044 0.000
#> GSM494615 3 0.3278 0.7441 0.116 0.000 0.864 0.020
#> GSM494582 2 0.2596 0.6029 0.068 0.908 0.024 0.000
#> GSM494599 2 0.1624 0.6173 0.000 0.952 0.028 0.020
#> GSM494610 2 0.4123 0.5856 0.136 0.820 0.044 0.000
#> GSM494587 2 0.4995 0.2759 0.004 0.648 0.344 0.004
#> GSM494581 2 0.5355 0.0991 0.004 0.580 0.408 0.008
#> GSM494580 3 0.3965 0.7412 0.120 0.032 0.840 0.008
#> GSM494563 3 0.5188 0.6089 0.148 0.056 0.776 0.020
#> GSM494576 2 0.7008 0.2583 0.136 0.572 0.288 0.004
#> GSM494605 4 0.4516 0.6171 0.012 0.252 0.000 0.736
#> GSM494584 3 0.4599 0.7263 0.108 0.072 0.812 0.008
#> GSM494586 2 0.4834 0.5578 0.120 0.784 0.096 0.000
#> GSM494578 3 0.3965 0.7412 0.120 0.032 0.840 0.008
#> GSM494585 2 0.5033 0.3180 0.008 0.664 0.324 0.004
#> GSM494611 2 0.2521 0.6045 0.064 0.912 0.024 0.000
#> GSM494560 3 0.0895 0.7541 0.000 0.004 0.976 0.020
#> GSM494595 2 0.5590 0.3995 0.064 0.692 0.244 0.000
#> GSM494570 3 0.1004 0.7525 0.000 0.004 0.972 0.024
#> GSM494597 3 0.6103 0.2139 0.452 0.020 0.512 0.016
#> GSM494607 2 0.3477 0.6055 0.008 0.872 0.032 0.088
#> GSM494561 3 0.3198 0.7527 0.080 0.000 0.880 0.040
#> GSM494569 4 0.0927 0.7988 0.008 0.000 0.016 0.976
#> GSM494592 2 0.1624 0.6173 0.000 0.952 0.028 0.020
#> GSM494577 2 0.7324 0.0502 0.140 0.488 0.368 0.004
#> GSM494588 3 0.0895 0.7541 0.000 0.004 0.976 0.020
#> GSM494590 1 0.3649 0.9839 0.796 0.000 0.204 0.000
#> GSM494609 2 0.5355 0.0991 0.004 0.580 0.408 0.008
#> GSM494608 3 0.7638 0.1937 0.084 0.416 0.460 0.040
#> GSM494606 2 0.1624 0.6173 0.000 0.952 0.028 0.020
#> GSM494574 2 0.4123 0.5856 0.136 0.820 0.044 0.000
#> GSM494573 3 0.0895 0.7541 0.000 0.004 0.976 0.020
#> GSM494566 2 0.6849 0.2100 0.016 0.540 0.376 0.068
#> GSM494601 2 0.4431 0.4503 0.004 0.740 0.252 0.004
#> GSM494557 3 0.3278 0.7441 0.116 0.000 0.864 0.020
#> GSM494579 2 0.8368 0.2692 0.128 0.488 0.316 0.068
#> GSM494596 1 0.3649 0.9839 0.796 0.000 0.204 0.000
#> GSM494575 2 0.2521 0.6045 0.064 0.912 0.024 0.000
#> GSM494625 4 0.0469 0.8020 0.000 0.000 0.012 0.988
#> GSM494654 1 0.3768 0.9716 0.808 0.000 0.184 0.008
#> GSM494664 4 0.4516 0.6171 0.012 0.252 0.000 0.736
#> GSM494624 4 0.0469 0.8020 0.000 0.000 0.012 0.988
#> GSM494651 4 0.0804 0.7998 0.008 0.000 0.012 0.980
#> GSM494662 4 0.0524 0.7983 0.000 0.008 0.004 0.988
#> GSM494627 4 0.2443 0.7665 0.060 0.000 0.024 0.916
#> GSM494673 4 0.5862 0.1082 0.032 0.484 0.000 0.484
#> GSM494649 4 0.0469 0.8020 0.000 0.000 0.012 0.988
#> GSM494658 4 0.5862 0.1082 0.032 0.484 0.000 0.484
#> GSM494653 4 0.5862 0.1082 0.032 0.484 0.000 0.484
#> GSM494643 4 0.0469 0.8020 0.000 0.000 0.012 0.988
#> GSM494672 2 0.5862 -0.1507 0.032 0.484 0.000 0.484
#> GSM494618 4 0.0804 0.7998 0.008 0.000 0.012 0.980
#> GSM494631 3 0.6948 0.1870 0.096 0.004 0.484 0.416
#> GSM494619 4 0.0469 0.8020 0.000 0.000 0.012 0.988
#> GSM494674 4 0.5862 0.1082 0.032 0.484 0.000 0.484
#> GSM494616 4 0.0804 0.7998 0.008 0.000 0.012 0.980
#> GSM494663 4 0.2443 0.7665 0.060 0.000 0.024 0.916
#> GSM494628 4 0.1610 0.7866 0.032 0.000 0.016 0.952
#> GSM494632 4 0.3196 0.7237 0.008 0.136 0.000 0.856
#> GSM494660 4 0.0469 0.8020 0.000 0.000 0.012 0.988
#> GSM494622 4 0.2174 0.7737 0.052 0.000 0.020 0.928
#> GSM494642 4 0.5862 0.1082 0.032 0.484 0.000 0.484
#> GSM494647 4 0.5862 0.1082 0.032 0.484 0.000 0.484
#> GSM494659 2 0.5862 -0.1507 0.032 0.484 0.000 0.484
#> GSM494670 2 0.5862 -0.1507 0.032 0.484 0.000 0.484
#> GSM494675 3 0.6103 0.2139 0.452 0.020 0.512 0.016
#> GSM494641 2 0.5862 -0.1507 0.032 0.484 0.000 0.484
#> GSM494636 4 0.1824 0.7742 0.004 0.060 0.000 0.936
#> GSM494640 4 0.0469 0.8020 0.000 0.000 0.012 0.988
#> GSM494623 4 0.0469 0.8020 0.000 0.000 0.012 0.988
#> GSM494644 4 0.4516 0.6171 0.012 0.252 0.000 0.736
#> GSM494646 4 0.4516 0.6171 0.012 0.252 0.000 0.736
#> GSM494665 4 0.4516 0.6171 0.012 0.252 0.000 0.736
#> GSM494638 4 0.1305 0.7854 0.004 0.036 0.000 0.960
#> GSM494645 4 0.4516 0.6171 0.012 0.252 0.000 0.736
#> GSM494671 4 0.5862 0.1082 0.032 0.484 0.000 0.484
#> GSM494655 4 0.5862 0.1082 0.032 0.484 0.000 0.484
#> GSM494620 4 0.0469 0.8020 0.000 0.000 0.012 0.988
#> GSM494630 4 0.0469 0.8020 0.000 0.000 0.012 0.988
#> GSM494657 1 0.3649 0.9839 0.796 0.000 0.204 0.000
#> GSM494667 2 0.5862 -0.1507 0.032 0.484 0.000 0.484
#> GSM494621 4 0.0469 0.8020 0.000 0.000 0.012 0.988
#> GSM494629 4 0.0779 0.8000 0.004 0.000 0.016 0.980
#> GSM494637 4 0.0469 0.8020 0.000 0.000 0.012 0.988
#> GSM494652 2 0.5862 -0.1507 0.032 0.484 0.000 0.484
#> GSM494648 4 0.0469 0.8020 0.000 0.000 0.012 0.988
#> GSM494650 4 0.1297 0.7919 0.020 0.000 0.016 0.964
#> GSM494669 2 0.5862 -0.1507 0.032 0.484 0.000 0.484
#> GSM494666 4 0.4516 0.6171 0.012 0.252 0.000 0.736
#> GSM494668 2 0.5862 -0.1507 0.032 0.484 0.000 0.484
#> GSM494633 4 0.0469 0.8020 0.000 0.000 0.012 0.988
#> GSM494634 2 0.5862 -0.1507 0.032 0.484 0.000 0.484
#> GSM494639 4 0.1824 0.7742 0.004 0.060 0.000 0.936
#> GSM494661 4 0.4516 0.6171 0.012 0.252 0.000 0.736
#> GSM494617 4 0.0804 0.7998 0.008 0.000 0.012 0.980
#> GSM494626 4 0.0804 0.7998 0.008 0.000 0.012 0.980
#> GSM494656 1 0.3768 0.9716 0.808 0.000 0.184 0.008
#> GSM494635 4 0.4516 0.6171 0.012 0.252 0.000 0.736
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM494565 5 0.5514 0.0719 0.008 0.420 0.048 0.000 0.524
#> GSM494594 3 0.4222 0.9533 0.028 0.016 0.792 0.008 0.156
#> GSM494604 1 0.4974 -0.1731 0.560 0.408 0.000 0.000 0.032
#> GSM494564 5 0.0566 0.7737 0.012 0.004 0.000 0.000 0.984
#> GSM494591 3 0.2732 0.9679 0.000 0.000 0.840 0.000 0.160
#> GSM494567 5 0.3892 0.7635 0.016 0.040 0.116 0.004 0.824
#> GSM494602 2 0.4048 0.7131 0.196 0.772 0.016 0.000 0.016
#> GSM494613 5 0.2972 0.7712 0.004 0.024 0.108 0.000 0.864
#> GSM494589 5 0.0566 0.7737 0.012 0.004 0.000 0.000 0.984
#> GSM494598 2 0.2872 0.7180 0.060 0.884 0.048 0.000 0.008
#> GSM494593 2 0.3929 0.7105 0.208 0.764 0.000 0.000 0.028
#> GSM494583 2 0.5831 0.1931 0.024 0.472 0.044 0.000 0.460
#> GSM494612 2 0.3346 0.7086 0.064 0.844 0.092 0.000 0.000
#> GSM494558 4 0.4274 0.6770 0.024 0.016 0.084 0.820 0.056
#> GSM494556 5 0.2915 0.7670 0.000 0.024 0.116 0.000 0.860
#> GSM494559 5 0.2434 0.7781 0.012 0.004 0.064 0.012 0.908
#> GSM494571 3 0.3170 0.9672 0.008 0.000 0.828 0.004 0.160
#> GSM494614 5 0.2972 0.7712 0.004 0.024 0.108 0.000 0.864
#> GSM494603 4 0.2569 0.8112 0.032 0.016 0.012 0.912 0.028
#> GSM494568 4 0.2569 0.8112 0.032 0.016 0.012 0.912 0.028
#> GSM494572 3 0.2690 0.9712 0.000 0.000 0.844 0.000 0.156
#> GSM494600 5 0.0566 0.7737 0.012 0.004 0.000 0.000 0.984
#> GSM494562 2 0.2872 0.7180 0.060 0.884 0.048 0.000 0.008
#> GSM494615 5 0.2972 0.7712 0.004 0.024 0.108 0.000 0.864
#> GSM494582 2 0.4121 0.6887 0.112 0.788 0.100 0.000 0.000
#> GSM494599 2 0.3929 0.7105 0.208 0.764 0.000 0.000 0.028
#> GSM494610 2 0.2872 0.7180 0.060 0.884 0.048 0.000 0.008
#> GSM494587 2 0.5036 0.5621 0.040 0.648 0.008 0.000 0.304
#> GSM494581 2 0.5632 0.4248 0.080 0.528 0.000 0.000 0.392
#> GSM494580 5 0.3892 0.7635 0.016 0.040 0.116 0.004 0.824
#> GSM494563 5 0.4533 0.5981 0.020 0.148 0.060 0.000 0.772
#> GSM494576 2 0.5544 0.5691 0.040 0.664 0.048 0.000 0.248
#> GSM494605 1 0.4192 0.5583 0.596 0.000 0.000 0.404 0.000
#> GSM494584 5 0.4431 0.7468 0.020 0.076 0.104 0.004 0.796
#> GSM494586 2 0.4280 0.7151 0.056 0.812 0.072 0.000 0.060
#> GSM494578 5 0.3892 0.7635 0.016 0.040 0.116 0.004 0.824
#> GSM494585 2 0.5015 0.5771 0.048 0.652 0.004 0.000 0.296
#> GSM494611 2 0.3346 0.7086 0.064 0.844 0.092 0.000 0.000
#> GSM494560 5 0.0566 0.7737 0.012 0.004 0.000 0.000 0.984
#> GSM494595 2 0.6206 0.6058 0.064 0.648 0.096 0.000 0.192
#> GSM494570 5 0.0727 0.7721 0.012 0.004 0.000 0.004 0.980
#> GSM494597 5 0.6077 0.1281 0.012 0.084 0.432 0.000 0.472
#> GSM494607 1 0.4974 -0.1731 0.560 0.408 0.000 0.000 0.032
#> GSM494561 5 0.2538 0.7774 0.012 0.004 0.064 0.016 0.904
#> GSM494569 4 0.1041 0.8729 0.032 0.000 0.000 0.964 0.004
#> GSM494592 2 0.3929 0.7105 0.208 0.764 0.000 0.000 0.028
#> GSM494577 2 0.5777 0.4385 0.028 0.592 0.052 0.000 0.328
#> GSM494588 5 0.0566 0.7737 0.012 0.004 0.000 0.000 0.984
#> GSM494590 3 0.2690 0.9712 0.000 0.000 0.844 0.000 0.156
#> GSM494609 2 0.5632 0.4248 0.080 0.528 0.000 0.000 0.392
#> GSM494608 5 0.7566 -0.0702 0.084 0.368 0.068 0.028 0.452
#> GSM494606 2 0.3929 0.7105 0.208 0.764 0.000 0.000 0.028
#> GSM494574 2 0.2872 0.7180 0.060 0.884 0.048 0.000 0.008
#> GSM494573 5 0.0566 0.7737 0.012 0.004 0.000 0.000 0.984
#> GSM494566 2 0.7011 0.2705 0.272 0.364 0.008 0.000 0.356
#> GSM494601 2 0.5578 0.6343 0.104 0.648 0.008 0.000 0.240
#> GSM494557 5 0.2972 0.7712 0.004 0.024 0.108 0.000 0.864
#> GSM494579 2 0.7576 0.3267 0.260 0.408 0.048 0.000 0.284
#> GSM494596 3 0.2690 0.9712 0.000 0.000 0.844 0.000 0.156
#> GSM494575 2 0.3346 0.7086 0.064 0.844 0.092 0.000 0.000
#> GSM494625 4 0.1478 0.8790 0.064 0.000 0.000 0.936 0.000
#> GSM494654 3 0.4390 0.9481 0.028 0.016 0.788 0.016 0.152
#> GSM494664 1 0.4192 0.5583 0.596 0.000 0.000 0.404 0.000
#> GSM494624 4 0.1478 0.8790 0.064 0.000 0.000 0.936 0.000
#> GSM494651 4 0.0404 0.8698 0.012 0.000 0.000 0.988 0.000
#> GSM494662 4 0.2773 0.7576 0.164 0.000 0.000 0.836 0.000
#> GSM494627 4 0.2073 0.8316 0.032 0.016 0.008 0.932 0.012
#> GSM494673 1 0.2536 0.8220 0.868 0.004 0.000 0.128 0.000
#> GSM494649 4 0.1478 0.8790 0.064 0.000 0.000 0.936 0.000
#> GSM494658 1 0.2536 0.8220 0.868 0.004 0.000 0.128 0.000
#> GSM494653 1 0.2536 0.8220 0.868 0.004 0.000 0.128 0.000
#> GSM494643 4 0.1478 0.8790 0.064 0.000 0.000 0.936 0.000
#> GSM494672 1 0.2536 0.8220 0.868 0.004 0.000 0.128 0.000
#> GSM494618 4 0.0404 0.8698 0.012 0.000 0.000 0.988 0.000
#> GSM494631 5 0.6492 0.1976 0.016 0.020 0.068 0.420 0.476
#> GSM494619 4 0.1478 0.8790 0.064 0.000 0.000 0.936 0.000
#> GSM494674 1 0.2536 0.8220 0.868 0.004 0.000 0.128 0.000
#> GSM494616 4 0.0404 0.8698 0.012 0.000 0.000 0.988 0.000
#> GSM494663 4 0.2073 0.8316 0.032 0.016 0.008 0.932 0.012
#> GSM494628 4 0.1121 0.8550 0.016 0.008 0.004 0.968 0.004
#> GSM494632 4 0.4287 -0.1360 0.460 0.000 0.000 0.540 0.000
#> GSM494660 4 0.1478 0.8790 0.064 0.000 0.000 0.936 0.000
#> GSM494622 4 0.1770 0.8399 0.028 0.012 0.008 0.944 0.008
#> GSM494642 1 0.2536 0.8220 0.868 0.004 0.000 0.128 0.000
#> GSM494647 1 0.2536 0.8220 0.868 0.004 0.000 0.128 0.000
#> GSM494659 1 0.2536 0.8220 0.868 0.004 0.000 0.128 0.000
#> GSM494670 1 0.2536 0.8220 0.868 0.004 0.000 0.128 0.000
#> GSM494675 5 0.6077 0.1281 0.012 0.084 0.432 0.000 0.472
#> GSM494641 1 0.2536 0.8220 0.868 0.004 0.000 0.128 0.000
#> GSM494636 4 0.4126 0.2331 0.380 0.000 0.000 0.620 0.000
#> GSM494640 4 0.1478 0.8790 0.064 0.000 0.000 0.936 0.000
#> GSM494623 4 0.1478 0.8790 0.064 0.000 0.000 0.936 0.000
#> GSM494644 1 0.4201 0.5502 0.592 0.000 0.000 0.408 0.000
#> GSM494646 1 0.4201 0.5502 0.592 0.000 0.000 0.408 0.000
#> GSM494665 1 0.4192 0.5583 0.596 0.000 0.000 0.404 0.000
#> GSM494638 4 0.3837 0.4572 0.308 0.000 0.000 0.692 0.000
#> GSM494645 1 0.4201 0.5502 0.592 0.000 0.000 0.408 0.000
#> GSM494671 1 0.2536 0.8220 0.868 0.004 0.000 0.128 0.000
#> GSM494655 1 0.2536 0.8220 0.868 0.004 0.000 0.128 0.000
#> GSM494620 4 0.1478 0.8790 0.064 0.000 0.000 0.936 0.000
#> GSM494630 4 0.1478 0.8790 0.064 0.000 0.000 0.936 0.000
#> GSM494657 3 0.2690 0.9712 0.000 0.000 0.844 0.000 0.156
#> GSM494667 1 0.2536 0.8220 0.868 0.004 0.000 0.128 0.000
#> GSM494621 4 0.1478 0.8790 0.064 0.000 0.000 0.936 0.000
#> GSM494629 4 0.1571 0.8777 0.060 0.000 0.000 0.936 0.004
#> GSM494637 4 0.1478 0.8790 0.064 0.000 0.000 0.936 0.000
#> GSM494652 1 0.2536 0.8220 0.868 0.004 0.000 0.128 0.000
#> GSM494648 4 0.1478 0.8790 0.064 0.000 0.000 0.936 0.000
#> GSM494650 4 0.0671 0.8613 0.016 0.000 0.000 0.980 0.004
#> GSM494669 1 0.2536 0.8220 0.868 0.004 0.000 0.128 0.000
#> GSM494666 1 0.4192 0.5583 0.596 0.000 0.000 0.404 0.000
#> GSM494668 1 0.2536 0.8220 0.868 0.004 0.000 0.128 0.000
#> GSM494633 4 0.1478 0.8790 0.064 0.000 0.000 0.936 0.000
#> GSM494634 1 0.2536 0.8220 0.868 0.004 0.000 0.128 0.000
#> GSM494639 4 0.4126 0.2331 0.380 0.000 0.000 0.620 0.000
#> GSM494661 1 0.4192 0.5583 0.596 0.000 0.000 0.404 0.000
#> GSM494617 4 0.0404 0.8698 0.012 0.000 0.000 0.988 0.000
#> GSM494626 4 0.0404 0.8698 0.012 0.000 0.000 0.988 0.000
#> GSM494656 3 0.4390 0.9481 0.028 0.016 0.788 0.016 0.152
#> GSM494635 1 0.4201 0.5502 0.592 0.000 0.000 0.408 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM494565 3 0.6032 -0.04146 0.000 0.288 0.424 0.000 0.288 0.000
#> GSM494594 6 0.2384 0.91521 0.004 0.000 0.032 0.016 0.044 0.904
#> GSM494604 1 0.4913 -0.13747 0.540 0.408 0.040 0.000 0.012 0.000
#> GSM494564 5 0.0458 0.88893 0.000 0.000 0.016 0.000 0.984 0.000
#> GSM494591 6 0.2201 0.94294 0.000 0.000 0.052 0.000 0.048 0.900
#> GSM494567 3 0.5328 0.49062 0.000 0.016 0.564 0.004 0.352 0.064
#> GSM494602 2 0.3461 0.68025 0.152 0.804 0.036 0.000 0.000 0.008
#> GSM494613 3 0.4648 0.45362 0.000 0.000 0.548 0.000 0.408 0.044
#> GSM494589 5 0.0000 0.89188 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494598 2 0.3197 0.66789 0.004 0.800 0.184 0.000 0.008 0.004
#> GSM494593 2 0.3464 0.67425 0.140 0.812 0.032 0.000 0.016 0.000
#> GSM494583 3 0.5784 -0.26111 0.000 0.404 0.420 0.000 0.176 0.000
#> GSM494612 2 0.1861 0.67569 0.036 0.928 0.016 0.000 0.000 0.020
#> GSM494558 4 0.3419 0.68408 0.000 0.000 0.072 0.828 0.012 0.088
#> GSM494556 3 0.4893 0.45740 0.000 0.000 0.536 0.000 0.400 0.064
#> GSM494559 5 0.2687 0.80073 0.000 0.000 0.072 0.008 0.876 0.044
#> GSM494571 6 0.2314 0.93703 0.000 0.000 0.036 0.008 0.056 0.900
#> GSM494614 3 0.4634 0.45648 0.000 0.000 0.556 0.000 0.400 0.044
#> GSM494603 4 0.1973 0.80192 0.004 0.000 0.036 0.924 0.008 0.028
#> GSM494568 4 0.1973 0.80192 0.004 0.000 0.036 0.924 0.008 0.028
#> GSM494572 6 0.2136 0.94617 0.000 0.000 0.048 0.000 0.048 0.904
#> GSM494600 5 0.0000 0.89188 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494562 2 0.3197 0.66789 0.004 0.800 0.184 0.000 0.008 0.004
#> GSM494615 3 0.4648 0.45362 0.000 0.000 0.548 0.000 0.408 0.044
#> GSM494582 2 0.3483 0.62886 0.088 0.832 0.044 0.000 0.000 0.036
#> GSM494599 2 0.3444 0.67419 0.140 0.812 0.036 0.000 0.012 0.000
#> GSM494610 2 0.3197 0.66789 0.004 0.800 0.184 0.000 0.008 0.004
#> GSM494587 2 0.5323 0.48839 0.012 0.632 0.204 0.000 0.152 0.000
#> GSM494581 2 0.6230 0.35862 0.036 0.532 0.232 0.000 0.200 0.000
#> GSM494580 3 0.5328 0.49062 0.000 0.016 0.564 0.004 0.352 0.064
#> GSM494563 5 0.3986 0.43341 0.000 0.020 0.316 0.000 0.664 0.000
#> GSM494576 2 0.5095 0.47841 0.004 0.508 0.428 0.000 0.056 0.004
#> GSM494605 1 0.3717 0.55381 0.616 0.000 0.000 0.384 0.000 0.000
#> GSM494584 3 0.5831 0.47241 0.000 0.056 0.540 0.004 0.344 0.056
#> GSM494586 2 0.3468 0.65712 0.004 0.784 0.192 0.000 0.012 0.008
#> GSM494578 3 0.5328 0.49062 0.000 0.016 0.564 0.004 0.352 0.064
#> GSM494585 2 0.5198 0.51013 0.012 0.652 0.168 0.000 0.168 0.000
#> GSM494611 2 0.1861 0.67569 0.036 0.928 0.016 0.000 0.000 0.020
#> GSM494560 5 0.0000 0.89188 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494595 2 0.6235 0.49832 0.084 0.608 0.220 0.000 0.052 0.036
#> GSM494570 5 0.0692 0.88638 0.000 0.000 0.020 0.004 0.976 0.000
#> GSM494597 3 0.5731 -0.14794 0.000 0.004 0.508 0.004 0.136 0.348
#> GSM494607 1 0.4913 -0.13747 0.540 0.408 0.040 0.000 0.012 0.000
#> GSM494561 5 0.2787 0.79769 0.000 0.000 0.072 0.012 0.872 0.044
#> GSM494569 4 0.1010 0.85635 0.036 0.000 0.000 0.960 0.000 0.004
#> GSM494592 2 0.3444 0.67419 0.140 0.812 0.036 0.000 0.012 0.000
#> GSM494577 3 0.5108 -0.42636 0.000 0.436 0.484 0.000 0.080 0.000
#> GSM494588 5 0.0547 0.88736 0.000 0.000 0.020 0.000 0.980 0.000
#> GSM494590 6 0.2136 0.94617 0.000 0.000 0.048 0.000 0.048 0.904
#> GSM494609 2 0.6230 0.35862 0.036 0.532 0.232 0.000 0.200 0.000
#> GSM494608 2 0.7836 0.00992 0.036 0.368 0.324 0.024 0.204 0.044
#> GSM494606 2 0.3464 0.67425 0.140 0.812 0.032 0.000 0.016 0.000
#> GSM494574 2 0.3197 0.66789 0.004 0.800 0.184 0.000 0.008 0.004
#> GSM494573 5 0.0000 0.89188 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494566 2 0.7586 0.10825 0.248 0.348 0.196 0.000 0.208 0.000
#> GSM494601 2 0.5399 0.56952 0.052 0.672 0.136 0.000 0.140 0.000
#> GSM494557 3 0.4648 0.45362 0.000 0.000 0.548 0.000 0.408 0.044
#> GSM494579 3 0.7508 -0.21121 0.236 0.296 0.324 0.000 0.144 0.000
#> GSM494596 6 0.2136 0.94617 0.000 0.000 0.048 0.000 0.048 0.904
#> GSM494575 2 0.1861 0.67569 0.036 0.928 0.016 0.000 0.000 0.020
#> GSM494625 4 0.1757 0.86089 0.076 0.000 0.008 0.916 0.000 0.000
#> GSM494654 6 0.2554 0.90815 0.004 0.000 0.032 0.024 0.044 0.896
#> GSM494664 1 0.3717 0.55381 0.616 0.000 0.000 0.384 0.000 0.000
#> GSM494624 4 0.1757 0.86089 0.076 0.000 0.008 0.916 0.000 0.000
#> GSM494651 4 0.0632 0.85352 0.024 0.000 0.000 0.976 0.000 0.000
#> GSM494662 4 0.2882 0.74241 0.180 0.000 0.008 0.812 0.000 0.000
#> GSM494627 4 0.1485 0.81894 0.004 0.000 0.028 0.944 0.000 0.024
#> GSM494673 1 0.1765 0.81954 0.904 0.000 0.000 0.096 0.000 0.000
#> GSM494649 4 0.1812 0.85995 0.080 0.000 0.008 0.912 0.000 0.000
#> GSM494658 1 0.1765 0.81954 0.904 0.000 0.000 0.096 0.000 0.000
#> GSM494653 1 0.1765 0.81954 0.904 0.000 0.000 0.096 0.000 0.000
#> GSM494643 4 0.1812 0.85995 0.080 0.000 0.008 0.912 0.000 0.000
#> GSM494672 1 0.1765 0.81954 0.904 0.000 0.000 0.096 0.000 0.000
#> GSM494618 4 0.0632 0.85352 0.024 0.000 0.000 0.976 0.000 0.000
#> GSM494631 4 0.7117 -0.15018 0.004 0.000 0.300 0.424 0.180 0.092
#> GSM494619 4 0.1757 0.86089 0.076 0.000 0.008 0.916 0.000 0.000
#> GSM494674 1 0.1765 0.81954 0.904 0.000 0.000 0.096 0.000 0.000
#> GSM494616 4 0.0632 0.85352 0.024 0.000 0.000 0.976 0.000 0.000
#> GSM494663 4 0.1485 0.81894 0.004 0.000 0.028 0.944 0.000 0.024
#> GSM494628 4 0.1086 0.83953 0.012 0.000 0.012 0.964 0.000 0.012
#> GSM494632 4 0.3862 -0.12843 0.476 0.000 0.000 0.524 0.000 0.000
#> GSM494660 4 0.1812 0.85995 0.080 0.000 0.008 0.912 0.000 0.000
#> GSM494622 4 0.1434 0.82666 0.008 0.000 0.024 0.948 0.000 0.020
#> GSM494642 1 0.1765 0.81954 0.904 0.000 0.000 0.096 0.000 0.000
#> GSM494647 1 0.1765 0.81954 0.904 0.000 0.000 0.096 0.000 0.000
#> GSM494659 1 0.1765 0.81954 0.904 0.000 0.000 0.096 0.000 0.000
#> GSM494670 1 0.1765 0.81954 0.904 0.000 0.000 0.096 0.000 0.000
#> GSM494675 3 0.5731 -0.14794 0.000 0.004 0.508 0.004 0.136 0.348
#> GSM494641 1 0.1765 0.81954 0.904 0.000 0.000 0.096 0.000 0.000
#> GSM494636 4 0.3747 0.23059 0.396 0.000 0.000 0.604 0.000 0.000
#> GSM494640 4 0.1812 0.85995 0.080 0.000 0.008 0.912 0.000 0.000
#> GSM494623 4 0.1757 0.86089 0.076 0.000 0.008 0.916 0.000 0.000
#> GSM494644 1 0.3737 0.53829 0.608 0.000 0.000 0.392 0.000 0.000
#> GSM494646 1 0.3737 0.53829 0.608 0.000 0.000 0.392 0.000 0.000
#> GSM494665 1 0.3717 0.55381 0.616 0.000 0.000 0.384 0.000 0.000
#> GSM494638 4 0.3515 0.44754 0.324 0.000 0.000 0.676 0.000 0.000
#> GSM494645 1 0.3737 0.53829 0.608 0.000 0.000 0.392 0.000 0.000
#> GSM494671 1 0.1765 0.81954 0.904 0.000 0.000 0.096 0.000 0.000
#> GSM494655 1 0.1765 0.81954 0.904 0.000 0.000 0.096 0.000 0.000
#> GSM494620 4 0.1757 0.86089 0.076 0.000 0.008 0.916 0.000 0.000
#> GSM494630 4 0.1812 0.85995 0.080 0.000 0.008 0.912 0.000 0.000
#> GSM494657 6 0.2136 0.94617 0.000 0.000 0.048 0.000 0.048 0.904
#> GSM494667 1 0.1765 0.81954 0.904 0.000 0.000 0.096 0.000 0.000
#> GSM494621 4 0.1757 0.86089 0.076 0.000 0.008 0.916 0.000 0.000
#> GSM494629 4 0.1674 0.86021 0.068 0.000 0.004 0.924 0.000 0.004
#> GSM494637 4 0.1812 0.85995 0.080 0.000 0.008 0.912 0.000 0.000
#> GSM494652 1 0.1765 0.81954 0.904 0.000 0.000 0.096 0.000 0.000
#> GSM494648 4 0.1757 0.86089 0.076 0.000 0.008 0.916 0.000 0.000
#> GSM494650 4 0.0976 0.84602 0.016 0.000 0.008 0.968 0.000 0.008
#> GSM494669 1 0.1765 0.81954 0.904 0.000 0.000 0.096 0.000 0.000
#> GSM494666 1 0.3717 0.55381 0.616 0.000 0.000 0.384 0.000 0.000
#> GSM494668 1 0.1765 0.81954 0.904 0.000 0.000 0.096 0.000 0.000
#> GSM494633 4 0.1812 0.85995 0.080 0.000 0.008 0.912 0.000 0.000
#> GSM494634 1 0.1765 0.81954 0.904 0.000 0.000 0.096 0.000 0.000
#> GSM494639 4 0.3747 0.23059 0.396 0.000 0.000 0.604 0.000 0.000
#> GSM494661 1 0.3717 0.55381 0.616 0.000 0.000 0.384 0.000 0.000
#> GSM494617 4 0.0632 0.85352 0.024 0.000 0.000 0.976 0.000 0.000
#> GSM494626 4 0.0632 0.85352 0.024 0.000 0.000 0.976 0.000 0.000
#> GSM494656 6 0.2554 0.90815 0.004 0.000 0.032 0.024 0.044 0.896
#> GSM494635 1 0.3737 0.53829 0.608 0.000 0.000 0.392 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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 5)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 6)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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.
# code only for demonstration
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) individual(p) k
#> SD:hclust 72 4.35e-04 0.1131 6.64e-03 0.0451 2
#> SD:hclust 96 2.45e-15 0.4919 1.99e-12 0.6906 3
#> SD:hclust 87 3.19e-13 0.1619 3.08e-10 0.3517 4
#> SD:hclust 103 6.73e-16 0.0898 2.08e-11 0.3942 5
#> SD:hclust 90 6.11e-13 0.1415 4.47e-11 0.5584 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.
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"]
# you can also extract it by
# 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 51941 rows and 120 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:
k and the heatmap of
predicted classes for each k.k.k.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:
k;k, the area increased is defined as \(A_k - A_{k-1}\).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 0.483 0.807 0.872 0.5026 0.496 0.496
#> 3 3 0.567 0.599 0.744 0.2976 0.824 0.656
#> 4 4 0.753 0.860 0.861 0.1291 0.816 0.528
#> 5 5 0.814 0.705 0.819 0.0703 0.942 0.773
#> 6 6 0.798 0.691 0.801 0.0396 0.928 0.684
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
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.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM494565 2 0.8386 0.849 0.268 0.732
#> GSM494594 2 0.8386 0.849 0.268 0.732
#> GSM494604 1 0.9323 0.737 0.652 0.348
#> GSM494564 2 0.8386 0.849 0.268 0.732
#> GSM494591 2 0.8386 0.849 0.268 0.732
#> GSM494567 2 0.8386 0.849 0.268 0.732
#> GSM494602 2 0.0672 0.766 0.008 0.992
#> GSM494613 2 0.8386 0.849 0.268 0.732
#> GSM494589 2 0.8386 0.849 0.268 0.732
#> GSM494598 2 0.0672 0.766 0.008 0.992
#> GSM494593 2 0.0672 0.766 0.008 0.992
#> GSM494583 2 0.8386 0.849 0.268 0.732
#> GSM494612 2 0.0672 0.766 0.008 0.992
#> GSM494558 2 0.8386 0.849 0.268 0.732
#> GSM494556 2 0.8386 0.849 0.268 0.732
#> GSM494559 2 0.8386 0.849 0.268 0.732
#> GSM494571 2 0.8386 0.849 0.268 0.732
#> GSM494614 2 0.8386 0.849 0.268 0.732
#> GSM494603 2 0.8386 0.849 0.268 0.732
#> GSM494568 1 0.6531 0.568 0.832 0.168
#> GSM494572 2 0.8386 0.849 0.268 0.732
#> GSM494600 2 0.8386 0.849 0.268 0.732
#> GSM494562 2 0.0000 0.769 0.000 1.000
#> GSM494615 2 0.8386 0.849 0.268 0.732
#> GSM494582 2 0.0672 0.766 0.008 0.992
#> GSM494599 2 0.0672 0.766 0.008 0.992
#> GSM494610 2 0.0672 0.766 0.008 0.992
#> GSM494587 2 0.0672 0.773 0.008 0.992
#> GSM494581 2 0.0672 0.773 0.008 0.992
#> GSM494580 2 0.8386 0.849 0.268 0.732
#> GSM494563 2 0.8386 0.849 0.268 0.732
#> GSM494576 2 0.6531 0.827 0.168 0.832
#> GSM494605 1 0.8386 0.814 0.732 0.268
#> GSM494584 2 0.8386 0.849 0.268 0.732
#> GSM494586 2 0.0000 0.769 0.000 1.000
#> GSM494578 2 0.8386 0.849 0.268 0.732
#> GSM494585 2 0.0672 0.773 0.008 0.992
#> GSM494611 2 0.0672 0.766 0.008 0.992
#> GSM494560 2 0.8386 0.849 0.268 0.732
#> GSM494595 2 0.0672 0.766 0.008 0.992
#> GSM494570 2 0.8386 0.849 0.268 0.732
#> GSM494597 2 0.8386 0.849 0.268 0.732
#> GSM494607 2 0.2948 0.722 0.052 0.948
#> GSM494561 2 0.8386 0.849 0.268 0.732
#> GSM494569 1 0.0672 0.806 0.992 0.008
#> GSM494592 2 0.0672 0.766 0.008 0.992
#> GSM494577 2 0.6531 0.827 0.168 0.832
#> GSM494588 2 0.8386 0.849 0.268 0.732
#> GSM494590 2 0.8386 0.849 0.268 0.732
#> GSM494609 2 0.0672 0.766 0.008 0.992
#> GSM494608 2 0.0672 0.766 0.008 0.992
#> GSM494606 2 0.0672 0.766 0.008 0.992
#> GSM494574 2 0.0672 0.766 0.008 0.992
#> GSM494573 2 0.8386 0.849 0.268 0.732
#> GSM494566 2 0.6801 0.830 0.180 0.820
#> GSM494601 2 0.0672 0.766 0.008 0.992
#> GSM494557 2 0.8386 0.849 0.268 0.732
#> GSM494579 2 0.0672 0.773 0.008 0.992
#> GSM494596 2 0.8386 0.849 0.268 0.732
#> GSM494575 2 0.0672 0.766 0.008 0.992
#> GSM494625 1 0.0672 0.806 0.992 0.008
#> GSM494654 2 0.9896 0.626 0.440 0.560
#> GSM494664 1 0.8386 0.814 0.732 0.268
#> GSM494624 1 0.0000 0.809 1.000 0.000
#> GSM494651 1 0.0672 0.806 0.992 0.008
#> GSM494662 1 0.0000 0.809 1.000 0.000
#> GSM494627 1 0.0672 0.806 0.992 0.008
#> GSM494673 1 0.8386 0.814 0.732 0.268
#> GSM494649 1 0.0672 0.806 0.992 0.008
#> GSM494658 1 0.8386 0.814 0.732 0.268
#> GSM494653 1 0.8386 0.814 0.732 0.268
#> GSM494643 1 0.0000 0.809 1.000 0.000
#> GSM494672 1 0.8386 0.814 0.732 0.268
#> GSM494618 1 0.0672 0.806 0.992 0.008
#> GSM494631 2 0.9896 0.626 0.440 0.560
#> GSM494619 1 0.0000 0.809 1.000 0.000
#> GSM494674 1 0.8386 0.814 0.732 0.268
#> GSM494616 1 0.0672 0.806 0.992 0.008
#> GSM494663 1 0.0672 0.806 0.992 0.008
#> GSM494628 1 0.0672 0.806 0.992 0.008
#> GSM494632 1 0.8386 0.814 0.732 0.268
#> GSM494660 1 0.0672 0.806 0.992 0.008
#> GSM494622 1 0.0672 0.806 0.992 0.008
#> GSM494642 1 0.8386 0.814 0.732 0.268
#> GSM494647 1 0.8386 0.814 0.732 0.268
#> GSM494659 1 0.8386 0.814 0.732 0.268
#> GSM494670 1 0.8386 0.814 0.732 0.268
#> GSM494675 2 0.8386 0.849 0.268 0.732
#> GSM494641 1 0.8386 0.814 0.732 0.268
#> GSM494636 1 0.0000 0.809 1.000 0.000
#> GSM494640 1 0.0672 0.806 0.992 0.008
#> GSM494623 1 0.0000 0.809 1.000 0.000
#> GSM494644 1 0.8386 0.814 0.732 0.268
#> GSM494646 1 0.8386 0.814 0.732 0.268
#> GSM494665 1 0.8386 0.814 0.732 0.268
#> GSM494638 1 0.0000 0.809 1.000 0.000
#> GSM494645 1 0.8386 0.814 0.732 0.268
#> GSM494671 1 0.8386 0.814 0.732 0.268
#> GSM494655 1 0.8386 0.814 0.732 0.268
#> GSM494620 1 0.0000 0.809 1.000 0.000
#> GSM494630 1 0.0000 0.809 1.000 0.000
#> GSM494657 2 0.8386 0.849 0.268 0.732
#> GSM494667 1 0.8386 0.814 0.732 0.268
#> GSM494621 1 0.0000 0.809 1.000 0.000
#> GSM494629 1 0.0672 0.806 0.992 0.008
#> GSM494637 1 0.0672 0.806 0.992 0.008
#> GSM494652 1 0.8386 0.814 0.732 0.268
#> GSM494648 1 0.0000 0.809 1.000 0.000
#> GSM494650 1 0.0672 0.806 0.992 0.008
#> GSM494669 1 0.8386 0.814 0.732 0.268
#> GSM494666 1 0.8386 0.814 0.732 0.268
#> GSM494668 1 0.8386 0.814 0.732 0.268
#> GSM494633 1 0.0672 0.806 0.992 0.008
#> GSM494634 1 0.8386 0.814 0.732 0.268
#> GSM494639 1 0.8386 0.814 0.732 0.268
#> GSM494661 1 0.8386 0.814 0.732 0.268
#> GSM494617 1 0.0000 0.809 1.000 0.000
#> GSM494626 1 0.0000 0.809 1.000 0.000
#> GSM494656 2 0.8386 0.849 0.268 0.732
#> GSM494635 1 0.8386 0.814 0.732 0.268
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM494565 3 0.6192 -0.266 0.000 0.420 0.580
#> GSM494594 3 0.1031 0.734 0.000 0.024 0.976
#> GSM494604 1 0.6244 -0.263 0.560 0.440 0.000
#> GSM494564 3 0.2711 0.655 0.000 0.088 0.912
#> GSM494591 3 0.0000 0.727 0.000 0.000 1.000
#> GSM494567 3 0.1031 0.734 0.000 0.024 0.976
#> GSM494602 2 0.9027 0.694 0.160 0.532 0.308
#> GSM494613 3 0.0424 0.731 0.000 0.008 0.992
#> GSM494589 3 0.1643 0.695 0.000 0.044 0.956
#> GSM494598 2 0.9027 0.694 0.160 0.532 0.308
#> GSM494593 2 0.9027 0.694 0.160 0.532 0.308
#> GSM494583 2 0.6509 0.535 0.004 0.524 0.472
#> GSM494612 2 0.9027 0.694 0.160 0.532 0.308
#> GSM494558 3 0.5591 0.558 0.000 0.304 0.696
#> GSM494556 3 0.0237 0.729 0.000 0.004 0.996
#> GSM494559 3 0.2959 0.639 0.000 0.100 0.900
#> GSM494571 3 0.5016 0.600 0.000 0.240 0.760
#> GSM494614 3 0.2796 0.642 0.000 0.092 0.908
#> GSM494603 3 0.6742 0.532 0.028 0.316 0.656
#> GSM494568 3 0.7484 0.258 0.036 0.460 0.504
#> GSM494572 3 0.1031 0.734 0.000 0.024 0.976
#> GSM494600 3 0.2711 0.655 0.000 0.088 0.912
#> GSM494562 2 0.7256 0.582 0.028 0.532 0.440
#> GSM494615 3 0.1289 0.730 0.000 0.032 0.968
#> GSM494582 2 0.9027 0.694 0.160 0.532 0.308
#> GSM494599 2 0.9111 0.578 0.292 0.532 0.176
#> GSM494610 2 0.9027 0.694 0.160 0.532 0.308
#> GSM494587 2 0.6659 0.557 0.008 0.532 0.460
#> GSM494581 2 0.6659 0.557 0.008 0.532 0.460
#> GSM494580 3 0.1031 0.734 0.000 0.024 0.976
#> GSM494563 3 0.6302 -0.439 0.000 0.480 0.520
#> GSM494576 2 0.6500 0.551 0.004 0.532 0.464
#> GSM494605 1 0.3551 0.732 0.868 0.132 0.000
#> GSM494584 3 0.6154 -0.247 0.000 0.408 0.592
#> GSM494586 2 0.6659 0.557 0.008 0.532 0.460
#> GSM494578 3 0.0892 0.733 0.000 0.020 0.980
#> GSM494585 2 0.6659 0.557 0.008 0.532 0.460
#> GSM494611 2 0.9027 0.694 0.160 0.532 0.308
#> GSM494560 3 0.2959 0.639 0.000 0.100 0.900
#> GSM494595 2 0.8825 0.676 0.132 0.532 0.336
#> GSM494570 3 0.4654 0.643 0.000 0.208 0.792
#> GSM494597 3 0.0237 0.729 0.000 0.004 0.996
#> GSM494607 2 0.8180 0.478 0.392 0.532 0.076
#> GSM494561 3 0.6867 0.520 0.028 0.336 0.636
#> GSM494569 1 0.8210 0.698 0.468 0.460 0.072
#> GSM494592 2 0.8949 0.551 0.320 0.532 0.148
#> GSM494577 2 0.6500 0.551 0.004 0.532 0.464
#> GSM494588 3 0.6204 -0.278 0.000 0.424 0.576
#> GSM494590 3 0.1031 0.734 0.000 0.024 0.976
#> GSM494609 2 0.9027 0.694 0.160 0.532 0.308
#> GSM494608 2 0.9027 0.694 0.160 0.532 0.308
#> GSM494606 2 0.9236 0.645 0.220 0.532 0.248
#> GSM494574 2 0.9027 0.694 0.160 0.532 0.308
#> GSM494573 3 0.2959 0.639 0.000 0.100 0.900
#> GSM494566 2 0.6500 0.551 0.004 0.532 0.464
#> GSM494601 2 0.9027 0.694 0.160 0.532 0.308
#> GSM494557 3 0.0000 0.727 0.000 0.000 1.000
#> GSM494579 2 0.6799 0.563 0.012 0.532 0.456
#> GSM494596 3 0.0237 0.729 0.000 0.004 0.996
#> GSM494575 2 0.9027 0.694 0.160 0.532 0.308
#> GSM494625 2 0.8070 -0.715 0.468 0.468 0.064
#> GSM494654 3 0.5810 0.534 0.000 0.336 0.664
#> GSM494664 1 0.3879 0.736 0.848 0.152 0.000
#> GSM494624 1 0.6291 0.731 0.532 0.468 0.000
#> GSM494651 1 0.8210 0.698 0.468 0.460 0.072
#> GSM494662 1 0.6654 0.732 0.536 0.456 0.008
#> GSM494627 1 0.8210 0.698 0.468 0.460 0.072
#> GSM494673 1 0.0000 0.694 1.000 0.000 0.000
#> GSM494649 2 0.8070 -0.715 0.468 0.468 0.064
#> GSM494658 1 0.0000 0.694 1.000 0.000 0.000
#> GSM494653 1 0.0000 0.694 1.000 0.000 0.000
#> GSM494643 1 0.6286 0.732 0.536 0.464 0.000
#> GSM494672 1 0.0000 0.694 1.000 0.000 0.000
#> GSM494618 1 0.8210 0.698 0.468 0.460 0.072
#> GSM494631 3 0.5810 0.534 0.000 0.336 0.664
#> GSM494619 1 0.6286 0.732 0.536 0.464 0.000
#> GSM494674 1 0.0000 0.694 1.000 0.000 0.000
#> GSM494616 1 0.8210 0.698 0.468 0.460 0.072
#> GSM494663 1 0.8210 0.698 0.468 0.460 0.072
#> GSM494628 1 0.8210 0.698 0.468 0.460 0.072
#> GSM494632 1 0.4235 0.738 0.824 0.176 0.000
#> GSM494660 1 0.8070 0.699 0.468 0.468 0.064
#> GSM494622 1 0.8210 0.698 0.468 0.460 0.072
#> GSM494642 1 0.0000 0.694 1.000 0.000 0.000
#> GSM494647 1 0.0000 0.694 1.000 0.000 0.000
#> GSM494659 1 0.0000 0.694 1.000 0.000 0.000
#> GSM494670 1 0.0000 0.694 1.000 0.000 0.000
#> GSM494675 3 0.0424 0.731 0.000 0.008 0.992
#> GSM494641 1 0.0000 0.694 1.000 0.000 0.000
#> GSM494636 1 0.6654 0.732 0.536 0.456 0.008
#> GSM494640 1 0.8210 0.698 0.468 0.460 0.072
#> GSM494623 1 0.6286 0.732 0.536 0.464 0.000
#> GSM494644 1 0.0000 0.694 1.000 0.000 0.000
#> GSM494646 1 0.3879 0.736 0.848 0.152 0.000
#> GSM494665 1 0.0237 0.696 0.996 0.004 0.000
#> GSM494638 1 0.6654 0.732 0.536 0.456 0.008
#> GSM494645 1 0.3879 0.736 0.848 0.152 0.000
#> GSM494671 1 0.0000 0.694 1.000 0.000 0.000
#> GSM494655 1 0.0000 0.694 1.000 0.000 0.000
#> GSM494620 1 0.6286 0.732 0.536 0.464 0.000
#> GSM494630 1 0.6286 0.732 0.536 0.464 0.000
#> GSM494657 3 0.1031 0.734 0.000 0.024 0.976
#> GSM494667 1 0.0000 0.694 1.000 0.000 0.000
#> GSM494621 1 0.6286 0.732 0.536 0.464 0.000
#> GSM494629 2 0.8581 -0.696 0.444 0.460 0.096
#> GSM494637 1 0.8210 0.698 0.468 0.460 0.072
#> GSM494652 1 0.0000 0.694 1.000 0.000 0.000
#> GSM494648 1 0.6286 0.732 0.536 0.464 0.000
#> GSM494650 1 0.8210 0.698 0.468 0.460 0.072
#> GSM494669 1 0.0000 0.694 1.000 0.000 0.000
#> GSM494666 1 0.3879 0.736 0.848 0.152 0.000
#> GSM494668 1 0.0000 0.694 1.000 0.000 0.000
#> GSM494633 1 0.6944 0.725 0.516 0.468 0.016
#> GSM494634 1 0.0000 0.694 1.000 0.000 0.000
#> GSM494639 1 0.4062 0.737 0.836 0.164 0.000
#> GSM494661 1 0.3686 0.734 0.860 0.140 0.000
#> GSM494617 1 0.6654 0.732 0.536 0.456 0.008
#> GSM494626 1 0.6659 0.731 0.532 0.460 0.008
#> GSM494656 3 0.5621 0.555 0.000 0.308 0.692
#> GSM494635 1 0.3879 0.736 0.848 0.152 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM494565 2 0.6702 0.573 0.000 0.616 0.216 0.168
#> GSM494594 3 0.2089 0.859 0.000 0.020 0.932 0.048
#> GSM494604 1 0.4643 0.465 0.656 0.344 0.000 0.000
#> GSM494564 3 0.6310 0.670 0.000 0.188 0.660 0.152
#> GSM494591 3 0.2494 0.866 0.000 0.036 0.916 0.048
#> GSM494567 3 0.1388 0.870 0.000 0.028 0.960 0.012
#> GSM494602 2 0.0524 0.903 0.008 0.988 0.000 0.004
#> GSM494613 3 0.1452 0.870 0.000 0.036 0.956 0.008
#> GSM494589 3 0.4149 0.818 0.000 0.036 0.812 0.152
#> GSM494598 2 0.1452 0.903 0.008 0.956 0.000 0.036
#> GSM494593 2 0.0336 0.903 0.008 0.992 0.000 0.000
#> GSM494583 2 0.5533 0.734 0.000 0.732 0.136 0.132
#> GSM494612 2 0.0524 0.903 0.008 0.988 0.000 0.004
#> GSM494558 3 0.1356 0.853 0.008 0.000 0.960 0.032
#> GSM494556 3 0.2408 0.864 0.000 0.036 0.920 0.044
#> GSM494559 3 0.6664 0.601 0.000 0.232 0.616 0.152
#> GSM494571 3 0.1637 0.850 0.000 0.000 0.940 0.060
#> GSM494614 3 0.6341 0.651 0.000 0.212 0.652 0.136
#> GSM494603 4 0.4228 0.497 0.008 0.000 0.232 0.760
#> GSM494568 4 0.4635 0.778 0.080 0.000 0.124 0.796
#> GSM494572 3 0.2319 0.865 0.000 0.036 0.924 0.040
#> GSM494600 3 0.4562 0.805 0.000 0.056 0.792 0.152
#> GSM494562 2 0.1820 0.901 0.000 0.944 0.020 0.036
#> GSM494615 3 0.1767 0.865 0.000 0.012 0.944 0.044
#> GSM494582 2 0.1452 0.903 0.008 0.956 0.000 0.036
#> GSM494599 2 0.0817 0.895 0.024 0.976 0.000 0.000
#> GSM494610 2 0.1452 0.903 0.008 0.956 0.000 0.036
#> GSM494587 2 0.1256 0.900 0.000 0.964 0.028 0.008
#> GSM494581 2 0.1706 0.894 0.000 0.948 0.036 0.016
#> GSM494580 3 0.1584 0.870 0.000 0.036 0.952 0.012
#> GSM494563 2 0.6678 0.589 0.000 0.620 0.208 0.172
#> GSM494576 2 0.2300 0.895 0.000 0.924 0.028 0.048
#> GSM494605 1 0.0376 0.947 0.992 0.004 0.004 0.000
#> GSM494584 2 0.6164 0.585 0.000 0.656 0.240 0.104
#> GSM494586 2 0.2032 0.897 0.000 0.936 0.028 0.036
#> GSM494578 3 0.1584 0.870 0.000 0.036 0.952 0.012
#> GSM494585 2 0.1109 0.900 0.000 0.968 0.028 0.004
#> GSM494611 2 0.0524 0.903 0.008 0.988 0.000 0.004
#> GSM494560 3 0.6917 0.490 0.000 0.288 0.568 0.144
#> GSM494595 2 0.1706 0.902 0.000 0.948 0.016 0.036
#> GSM494570 3 0.5452 0.521 0.000 0.016 0.556 0.428
#> GSM494597 3 0.2816 0.867 0.000 0.036 0.900 0.064
#> GSM494607 2 0.3448 0.745 0.168 0.828 0.000 0.004
#> GSM494561 4 0.4899 0.262 0.004 0.008 0.300 0.688
#> GSM494569 4 0.5056 0.926 0.224 0.000 0.044 0.732
#> GSM494592 2 0.0921 0.892 0.028 0.972 0.000 0.000
#> GSM494577 2 0.3542 0.859 0.000 0.852 0.028 0.120
#> GSM494588 2 0.6724 0.541 0.000 0.612 0.224 0.164
#> GSM494590 3 0.2319 0.865 0.000 0.036 0.924 0.040
#> GSM494609 2 0.1262 0.902 0.008 0.968 0.008 0.016
#> GSM494608 2 0.1262 0.902 0.008 0.968 0.008 0.016
#> GSM494606 2 0.0707 0.897 0.020 0.980 0.000 0.000
#> GSM494574 2 0.1452 0.903 0.008 0.956 0.000 0.036
#> GSM494573 3 0.6310 0.670 0.000 0.188 0.660 0.152
#> GSM494566 2 0.3745 0.837 0.000 0.852 0.060 0.088
#> GSM494601 2 0.0336 0.903 0.008 0.992 0.000 0.000
#> GSM494557 3 0.1584 0.870 0.000 0.036 0.952 0.012
#> GSM494579 2 0.3278 0.864 0.000 0.864 0.020 0.116
#> GSM494596 3 0.2494 0.866 0.000 0.036 0.916 0.048
#> GSM494575 2 0.0524 0.903 0.008 0.988 0.000 0.004
#> GSM494625 4 0.4086 0.926 0.216 0.008 0.000 0.776
#> GSM494654 3 0.3649 0.719 0.000 0.000 0.796 0.204
#> GSM494664 1 0.0672 0.934 0.984 0.000 0.008 0.008
#> GSM494624 4 0.4086 0.926 0.216 0.008 0.000 0.776
#> GSM494651 4 0.5056 0.926 0.224 0.000 0.044 0.732
#> GSM494662 4 0.4453 0.923 0.244 0.000 0.012 0.744
#> GSM494627 4 0.4888 0.926 0.224 0.000 0.036 0.740
#> GSM494673 1 0.0707 0.956 0.980 0.020 0.000 0.000
#> GSM494649 4 0.4086 0.926 0.216 0.008 0.000 0.776
#> GSM494658 1 0.0707 0.956 0.980 0.020 0.000 0.000
#> GSM494653 1 0.0707 0.956 0.980 0.020 0.000 0.000
#> GSM494643 4 0.4228 0.927 0.232 0.008 0.000 0.760
#> GSM494672 1 0.0707 0.956 0.980 0.020 0.000 0.000
#> GSM494618 4 0.5056 0.926 0.224 0.000 0.044 0.732
#> GSM494631 3 0.3528 0.716 0.000 0.000 0.808 0.192
#> GSM494619 4 0.4086 0.926 0.216 0.008 0.000 0.776
#> GSM494674 1 0.0707 0.956 0.980 0.020 0.000 0.000
#> GSM494616 4 0.5056 0.926 0.224 0.000 0.044 0.732
#> GSM494663 4 0.4888 0.926 0.224 0.000 0.036 0.740
#> GSM494628 4 0.4974 0.926 0.224 0.000 0.040 0.736
#> GSM494632 1 0.4328 0.512 0.748 0.000 0.008 0.244
#> GSM494660 4 0.4086 0.926 0.216 0.008 0.000 0.776
#> GSM494622 4 0.5056 0.926 0.224 0.000 0.044 0.732
#> GSM494642 1 0.0707 0.956 0.980 0.020 0.000 0.000
#> GSM494647 1 0.0707 0.956 0.980 0.020 0.000 0.000
#> GSM494659 1 0.0707 0.956 0.980 0.020 0.000 0.000
#> GSM494670 1 0.0707 0.956 0.980 0.020 0.000 0.000
#> GSM494675 3 0.2739 0.864 0.000 0.036 0.904 0.060
#> GSM494641 1 0.0707 0.956 0.980 0.020 0.000 0.000
#> GSM494636 4 0.4453 0.923 0.244 0.000 0.012 0.744
#> GSM494640 4 0.4399 0.930 0.224 0.000 0.016 0.760
#> GSM494623 4 0.4086 0.926 0.216 0.008 0.000 0.776
#> GSM494644 1 0.0592 0.954 0.984 0.016 0.000 0.000
#> GSM494646 1 0.0672 0.934 0.984 0.000 0.008 0.008
#> GSM494665 1 0.0657 0.951 0.984 0.012 0.004 0.000
#> GSM494638 4 0.4903 0.921 0.248 0.000 0.028 0.724
#> GSM494645 1 0.0188 0.944 0.996 0.000 0.004 0.000
#> GSM494671 1 0.0707 0.956 0.980 0.020 0.000 0.000
#> GSM494655 1 0.0592 0.954 0.984 0.016 0.000 0.000
#> GSM494620 4 0.4086 0.926 0.216 0.008 0.000 0.776
#> GSM494630 4 0.4086 0.926 0.216 0.008 0.000 0.776
#> GSM494657 3 0.2319 0.865 0.000 0.036 0.924 0.040
#> GSM494667 1 0.0707 0.956 0.980 0.020 0.000 0.000
#> GSM494621 4 0.4086 0.926 0.216 0.008 0.000 0.776
#> GSM494629 4 0.5212 0.904 0.192 0.000 0.068 0.740
#> GSM494637 4 0.4468 0.929 0.232 0.000 0.016 0.752
#> GSM494652 1 0.0707 0.956 0.980 0.020 0.000 0.000
#> GSM494648 4 0.4086 0.926 0.216 0.008 0.000 0.776
#> GSM494650 4 0.5056 0.926 0.224 0.000 0.044 0.732
#> GSM494669 1 0.0707 0.956 0.980 0.020 0.000 0.000
#> GSM494666 1 0.0672 0.934 0.984 0.000 0.008 0.008
#> GSM494668 1 0.0707 0.956 0.980 0.020 0.000 0.000
#> GSM494633 4 0.4086 0.926 0.216 0.008 0.000 0.776
#> GSM494634 1 0.0707 0.956 0.980 0.020 0.000 0.000
#> GSM494639 1 0.3300 0.739 0.848 0.000 0.008 0.144
#> GSM494661 1 0.0376 0.947 0.992 0.004 0.004 0.000
#> GSM494617 4 0.4872 0.921 0.244 0.000 0.028 0.728
#> GSM494626 4 0.4900 0.922 0.236 0.000 0.032 0.732
#> GSM494656 3 0.1792 0.846 0.000 0.000 0.932 0.068
#> GSM494635 1 0.0188 0.944 0.996 0.000 0.004 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM494565 5 0.4434 0.443 0.000 0.208 0.056 0.000 0.736
#> GSM494594 3 0.4192 0.748 0.000 0.000 0.596 0.000 0.404
#> GSM494604 1 0.3612 0.725 0.784 0.204 0.004 0.004 0.004
#> GSM494564 5 0.2446 0.498 0.000 0.044 0.056 0.000 0.900
#> GSM494591 3 0.4219 0.737 0.000 0.000 0.584 0.000 0.416
#> GSM494567 5 0.4718 -0.439 0.000 0.000 0.444 0.016 0.540
#> GSM494602 2 0.0000 0.910 0.000 1.000 0.000 0.000 0.000
#> GSM494613 5 0.4735 -0.481 0.000 0.000 0.460 0.016 0.524
#> GSM494589 5 0.0609 0.471 0.000 0.020 0.000 0.000 0.980
#> GSM494598 2 0.2079 0.898 0.000 0.916 0.064 0.020 0.000
#> GSM494593 2 0.0000 0.910 0.000 1.000 0.000 0.000 0.000
#> GSM494583 2 0.5845 0.455 0.000 0.572 0.052 0.028 0.348
#> GSM494612 2 0.0162 0.910 0.000 0.996 0.000 0.004 0.000
#> GSM494558 3 0.6672 0.372 0.000 0.000 0.440 0.288 0.272
#> GSM494556 5 0.4717 -0.336 0.000 0.000 0.396 0.020 0.584
#> GSM494559 5 0.2520 0.499 0.000 0.048 0.056 0.000 0.896
#> GSM494571 3 0.4192 0.748 0.000 0.000 0.596 0.000 0.404
#> GSM494614 5 0.3454 0.466 0.000 0.076 0.044 0.024 0.856
#> GSM494603 4 0.4508 0.311 0.000 0.000 0.020 0.648 0.332
#> GSM494568 4 0.2297 0.764 0.008 0.000 0.020 0.912 0.060
#> GSM494572 3 0.4192 0.748 0.000 0.000 0.596 0.000 0.404
#> GSM494600 5 0.0703 0.475 0.000 0.024 0.000 0.000 0.976
#> GSM494562 2 0.2144 0.896 0.000 0.912 0.068 0.020 0.000
#> GSM494615 5 0.4726 -0.337 0.000 0.000 0.400 0.020 0.580
#> GSM494582 2 0.2171 0.897 0.000 0.912 0.064 0.024 0.000
#> GSM494599 2 0.0162 0.909 0.000 0.996 0.000 0.000 0.004
#> GSM494610 2 0.2079 0.898 0.000 0.916 0.064 0.020 0.000
#> GSM494587 2 0.0693 0.909 0.000 0.980 0.008 0.012 0.000
#> GSM494581 2 0.1310 0.896 0.000 0.956 0.000 0.020 0.024
#> GSM494580 5 0.4718 -0.439 0.000 0.000 0.444 0.016 0.540
#> GSM494563 5 0.4998 0.431 0.000 0.208 0.068 0.012 0.712
#> GSM494576 2 0.2694 0.891 0.000 0.892 0.068 0.032 0.008
#> GSM494605 1 0.0162 0.960 0.996 0.000 0.000 0.004 0.000
#> GSM494584 5 0.4767 0.157 0.000 0.420 0.000 0.020 0.560
#> GSM494586 2 0.2144 0.896 0.000 0.912 0.068 0.020 0.000
#> GSM494578 5 0.4718 -0.439 0.000 0.000 0.444 0.016 0.540
#> GSM494585 2 0.0404 0.908 0.000 0.988 0.000 0.012 0.000
#> GSM494611 2 0.0162 0.910 0.000 0.996 0.000 0.004 0.000
#> GSM494560 5 0.2563 0.496 0.000 0.120 0.008 0.000 0.872
#> GSM494595 2 0.2012 0.899 0.000 0.920 0.060 0.020 0.000
#> GSM494570 5 0.3596 0.421 0.000 0.000 0.200 0.016 0.784
#> GSM494597 3 0.4533 0.686 0.000 0.000 0.544 0.008 0.448
#> GSM494607 2 0.1526 0.874 0.040 0.948 0.004 0.004 0.004
#> GSM494561 5 0.5737 0.280 0.000 0.000 0.288 0.120 0.592
#> GSM494569 4 0.1197 0.847 0.048 0.000 0.000 0.952 0.000
#> GSM494592 2 0.0162 0.909 0.000 0.996 0.000 0.000 0.004
#> GSM494577 2 0.5575 0.651 0.000 0.664 0.068 0.028 0.240
#> GSM494588 5 0.4219 0.474 0.000 0.156 0.072 0.000 0.772
#> GSM494590 3 0.4201 0.749 0.000 0.000 0.592 0.000 0.408
#> GSM494609 2 0.1117 0.900 0.000 0.964 0.000 0.020 0.016
#> GSM494608 2 0.1117 0.900 0.000 0.964 0.000 0.020 0.016
#> GSM494606 2 0.0000 0.910 0.000 1.000 0.000 0.000 0.000
#> GSM494574 2 0.2079 0.898 0.000 0.916 0.064 0.020 0.000
#> GSM494573 5 0.1121 0.489 0.000 0.044 0.000 0.000 0.956
#> GSM494566 2 0.4804 0.453 0.000 0.624 0.004 0.024 0.348
#> GSM494601 2 0.0000 0.910 0.000 1.000 0.000 0.000 0.000
#> GSM494557 5 0.4735 -0.481 0.000 0.000 0.460 0.016 0.524
#> GSM494579 2 0.5748 0.625 0.000 0.644 0.068 0.032 0.256
#> GSM494596 3 0.4227 0.739 0.000 0.000 0.580 0.000 0.420
#> GSM494575 2 0.0162 0.910 0.000 0.996 0.000 0.004 0.000
#> GSM494625 4 0.5272 0.776 0.048 0.000 0.328 0.616 0.008
#> GSM494654 3 0.5896 0.555 0.000 0.000 0.600 0.216 0.184
#> GSM494664 1 0.0162 0.960 0.996 0.000 0.000 0.004 0.000
#> GSM494624 4 0.5556 0.771 0.048 0.000 0.328 0.604 0.020
#> GSM494651 4 0.1197 0.847 0.048 0.000 0.000 0.952 0.000
#> GSM494662 4 0.1872 0.847 0.052 0.000 0.020 0.928 0.000
#> GSM494627 4 0.1357 0.847 0.048 0.000 0.004 0.948 0.000
#> GSM494673 1 0.0324 0.960 0.992 0.000 0.004 0.000 0.004
#> GSM494649 4 0.5272 0.776 0.048 0.000 0.328 0.616 0.008
#> GSM494658 1 0.0324 0.960 0.992 0.000 0.004 0.000 0.004
#> GSM494653 1 0.0324 0.960 0.992 0.000 0.004 0.000 0.004
#> GSM494643 4 0.4563 0.800 0.048 0.000 0.244 0.708 0.000
#> GSM494672 1 0.0324 0.960 0.992 0.000 0.004 0.000 0.004
#> GSM494618 4 0.1197 0.847 0.048 0.000 0.000 0.952 0.000
#> GSM494631 3 0.6734 0.343 0.000 0.000 0.388 0.356 0.256
#> GSM494619 4 0.5556 0.771 0.048 0.000 0.328 0.604 0.020
#> GSM494674 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000
#> GSM494616 4 0.1197 0.847 0.048 0.000 0.000 0.952 0.000
#> GSM494663 4 0.1357 0.847 0.048 0.000 0.004 0.948 0.000
#> GSM494628 4 0.1197 0.847 0.048 0.000 0.000 0.952 0.000
#> GSM494632 1 0.4060 0.369 0.640 0.000 0.000 0.360 0.000
#> GSM494660 4 0.5272 0.776 0.048 0.000 0.328 0.616 0.008
#> GSM494622 4 0.1357 0.845 0.048 0.000 0.004 0.948 0.000
#> GSM494642 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000
#> GSM494659 1 0.0324 0.960 0.992 0.000 0.004 0.000 0.004
#> GSM494670 1 0.0324 0.960 0.992 0.000 0.004 0.000 0.004
#> GSM494675 5 0.4401 -0.186 0.000 0.000 0.328 0.016 0.656
#> GSM494641 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000
#> GSM494636 4 0.1800 0.847 0.048 0.000 0.020 0.932 0.000
#> GSM494640 4 0.1981 0.847 0.048 0.000 0.028 0.924 0.000
#> GSM494623 4 0.5556 0.771 0.048 0.000 0.328 0.604 0.020
#> GSM494644 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000
#> GSM494646 1 0.0609 0.946 0.980 0.000 0.000 0.020 0.000
#> GSM494665 1 0.0162 0.960 0.996 0.000 0.000 0.004 0.000
#> GSM494638 4 0.1270 0.845 0.052 0.000 0.000 0.948 0.000
#> GSM494645 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000
#> GSM494671 1 0.0324 0.960 0.992 0.000 0.004 0.000 0.004
#> GSM494655 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000
#> GSM494620 4 0.5556 0.771 0.048 0.000 0.328 0.604 0.020
#> GSM494630 4 0.5556 0.771 0.048 0.000 0.328 0.604 0.020
#> GSM494657 3 0.4201 0.749 0.000 0.000 0.592 0.000 0.408
#> GSM494667 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000
#> GSM494621 4 0.5556 0.771 0.048 0.000 0.328 0.604 0.020
#> GSM494629 4 0.1357 0.847 0.048 0.000 0.004 0.948 0.000
#> GSM494637 4 0.1981 0.847 0.048 0.000 0.028 0.924 0.000
#> GSM494652 1 0.0162 0.961 0.996 0.000 0.000 0.000 0.004
#> GSM494648 4 0.5556 0.771 0.048 0.000 0.328 0.604 0.020
#> GSM494650 4 0.1357 0.845 0.048 0.000 0.004 0.948 0.000
#> GSM494669 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000
#> GSM494666 1 0.0162 0.960 0.996 0.000 0.000 0.004 0.000
#> GSM494668 1 0.0324 0.960 0.992 0.000 0.004 0.000 0.004
#> GSM494633 4 0.5556 0.771 0.048 0.000 0.328 0.604 0.020
#> GSM494634 1 0.0324 0.960 0.992 0.000 0.004 0.000 0.004
#> GSM494639 1 0.3752 0.537 0.708 0.000 0.000 0.292 0.000
#> GSM494661 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000
#> GSM494617 4 0.1197 0.847 0.048 0.000 0.000 0.952 0.000
#> GSM494626 4 0.1197 0.847 0.048 0.000 0.000 0.952 0.000
#> GSM494656 3 0.5740 0.646 0.000 0.000 0.600 0.128 0.272
#> GSM494635 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM494565 5 0.3264 0.7533 0.000 0.088 0.076 0.004 0.832 0.000
#> GSM494594 3 0.0000 0.7709 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494604 1 0.4035 0.7182 0.760 0.192 0.000 0.020 0.016 0.012
#> GSM494564 5 0.3569 0.7623 0.000 0.032 0.128 0.004 0.816 0.020
#> GSM494591 3 0.0603 0.7668 0.000 0.000 0.980 0.016 0.004 0.000
#> GSM494567 3 0.4693 0.6709 0.000 0.000 0.684 0.140 0.176 0.000
#> GSM494602 2 0.0000 0.8420 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494613 3 0.4704 0.6810 0.000 0.004 0.696 0.140 0.160 0.000
#> GSM494589 5 0.3109 0.7359 0.000 0.016 0.168 0.004 0.812 0.000
#> GSM494598 2 0.3491 0.8019 0.000 0.804 0.000 0.148 0.040 0.008
#> GSM494593 2 0.0458 0.8414 0.000 0.984 0.000 0.016 0.000 0.000
#> GSM494583 5 0.6066 0.1474 0.000 0.388 0.040 0.104 0.468 0.000
#> GSM494612 2 0.0000 0.8420 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494558 4 0.5163 0.1449 0.000 0.000 0.268 0.628 0.088 0.016
#> GSM494556 3 0.5598 0.4828 0.000 0.004 0.552 0.164 0.280 0.000
#> GSM494559 5 0.4031 0.7564 0.000 0.044 0.124 0.020 0.796 0.016
#> GSM494571 3 0.0000 0.7709 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494614 5 0.5962 0.5285 0.000 0.040 0.212 0.164 0.584 0.000
#> GSM494603 4 0.5051 0.4043 0.000 0.000 0.004 0.648 0.208 0.140
#> GSM494568 4 0.4403 0.5310 0.000 0.000 0.000 0.708 0.096 0.196
#> GSM494572 3 0.0146 0.7708 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM494600 5 0.3158 0.7414 0.000 0.020 0.164 0.004 0.812 0.000
#> GSM494562 2 0.3551 0.7930 0.000 0.784 0.000 0.168 0.048 0.000
#> GSM494615 3 0.5974 0.4321 0.000 0.004 0.484 0.244 0.268 0.000
#> GSM494582 2 0.3172 0.8063 0.000 0.816 0.000 0.148 0.036 0.000
#> GSM494599 2 0.0436 0.8402 0.004 0.988 0.000 0.004 0.004 0.000
#> GSM494610 2 0.3621 0.7981 0.000 0.796 0.000 0.148 0.048 0.008
#> GSM494587 2 0.1584 0.8369 0.000 0.928 0.000 0.064 0.008 0.000
#> GSM494581 2 0.2905 0.7682 0.000 0.852 0.000 0.084 0.064 0.000
#> GSM494580 3 0.4662 0.6720 0.000 0.000 0.688 0.140 0.172 0.000
#> GSM494563 5 0.4431 0.7038 0.000 0.092 0.064 0.076 0.768 0.000
#> GSM494576 2 0.4503 0.7431 0.000 0.696 0.000 0.204 0.100 0.000
#> GSM494605 1 0.2122 0.8954 0.900 0.000 0.000 0.024 0.076 0.000
#> GSM494584 5 0.6923 0.2493 0.000 0.372 0.080 0.172 0.376 0.000
#> GSM494586 2 0.3516 0.7948 0.000 0.788 0.000 0.164 0.048 0.000
#> GSM494578 3 0.4830 0.6677 0.000 0.004 0.680 0.140 0.176 0.000
#> GSM494585 2 0.1333 0.8335 0.000 0.944 0.000 0.048 0.008 0.000
#> GSM494611 2 0.0260 0.8428 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM494560 5 0.3254 0.7652 0.000 0.056 0.124 0.000 0.820 0.000
#> GSM494595 2 0.2726 0.8197 0.000 0.856 0.000 0.112 0.032 0.000
#> GSM494570 5 0.3762 0.7086 0.000 0.000 0.080 0.012 0.800 0.108
#> GSM494597 3 0.2074 0.7481 0.000 0.000 0.912 0.048 0.036 0.004
#> GSM494607 2 0.2781 0.7999 0.048 0.884 0.000 0.044 0.012 0.012
#> GSM494561 5 0.5400 0.2893 0.000 0.000 0.020 0.064 0.488 0.428
#> GSM494569 4 0.4651 0.7036 0.012 0.000 0.000 0.588 0.028 0.372
#> GSM494592 2 0.0436 0.8402 0.004 0.988 0.000 0.004 0.004 0.000
#> GSM494577 2 0.5834 0.3308 0.000 0.468 0.000 0.204 0.328 0.000
#> GSM494588 5 0.3687 0.7624 0.000 0.072 0.072 0.004 0.824 0.028
#> GSM494590 3 0.0363 0.7693 0.000 0.000 0.988 0.012 0.000 0.000
#> GSM494609 2 0.2331 0.7965 0.000 0.888 0.000 0.080 0.032 0.000
#> GSM494608 2 0.2331 0.7965 0.000 0.888 0.000 0.080 0.032 0.000
#> GSM494606 2 0.0806 0.8393 0.000 0.972 0.000 0.020 0.008 0.000
#> GSM494574 2 0.3621 0.7981 0.000 0.796 0.000 0.148 0.048 0.008
#> GSM494573 5 0.3280 0.7531 0.000 0.032 0.152 0.004 0.812 0.000
#> GSM494566 2 0.5974 -0.0906 0.000 0.428 0.000 0.236 0.336 0.000
#> GSM494601 2 0.0458 0.8414 0.000 0.984 0.000 0.016 0.000 0.000
#> GSM494557 3 0.4768 0.6751 0.000 0.004 0.688 0.140 0.168 0.000
#> GSM494579 2 0.5870 0.3016 0.000 0.460 0.000 0.212 0.328 0.000
#> GSM494596 3 0.0508 0.7682 0.000 0.000 0.984 0.012 0.004 0.000
#> GSM494575 2 0.0000 0.8420 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494625 6 0.0653 0.7638 0.012 0.000 0.000 0.004 0.004 0.980
#> GSM494654 3 0.2376 0.6937 0.000 0.000 0.884 0.096 0.012 0.008
#> GSM494664 1 0.2122 0.8954 0.900 0.000 0.000 0.024 0.076 0.000
#> GSM494624 6 0.0622 0.7649 0.012 0.000 0.000 0.000 0.008 0.980
#> GSM494651 4 0.4651 0.7043 0.012 0.000 0.000 0.588 0.028 0.372
#> GSM494662 6 0.5877 -0.4731 0.020 0.000 0.000 0.420 0.116 0.444
#> GSM494627 4 0.4697 0.6766 0.012 0.000 0.000 0.568 0.028 0.392
#> GSM494673 1 0.0520 0.9275 0.984 0.000 0.000 0.008 0.008 0.000
#> GSM494649 6 0.0767 0.7618 0.012 0.000 0.000 0.008 0.004 0.976
#> GSM494658 1 0.1275 0.9166 0.956 0.000 0.000 0.016 0.016 0.012
#> GSM494653 1 0.0520 0.9275 0.984 0.000 0.000 0.008 0.008 0.000
#> GSM494643 6 0.3299 0.6073 0.012 0.000 0.000 0.092 0.060 0.836
#> GSM494672 1 0.0862 0.9242 0.972 0.000 0.000 0.016 0.008 0.004
#> GSM494618 4 0.4651 0.7043 0.012 0.000 0.000 0.588 0.028 0.372
#> GSM494631 4 0.5546 0.0795 0.000 0.000 0.300 0.588 0.068 0.044
#> GSM494619 6 0.0622 0.7649 0.012 0.000 0.000 0.000 0.008 0.980
#> GSM494674 1 0.0000 0.9287 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494616 4 0.4717 0.7024 0.012 0.000 0.000 0.584 0.032 0.372
#> GSM494663 4 0.4763 0.6717 0.012 0.000 0.000 0.564 0.032 0.392
#> GSM494628 4 0.4323 0.7052 0.012 0.000 0.000 0.612 0.012 0.364
#> GSM494632 1 0.6424 0.3255 0.548 0.000 0.000 0.108 0.108 0.236
#> GSM494660 6 0.0767 0.7618 0.012 0.000 0.000 0.008 0.004 0.976
#> GSM494622 4 0.4345 0.6987 0.012 0.000 0.000 0.628 0.016 0.344
#> GSM494642 1 0.0000 0.9287 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0146 0.9287 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM494659 1 0.0520 0.9275 0.984 0.000 0.000 0.008 0.008 0.000
#> GSM494670 1 0.0964 0.9237 0.968 0.000 0.000 0.012 0.016 0.004
#> GSM494675 3 0.5623 0.4113 0.000 0.000 0.532 0.152 0.312 0.004
#> GSM494641 1 0.0000 0.9287 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494636 6 0.5731 -0.4962 0.012 0.000 0.000 0.432 0.116 0.440
#> GSM494640 6 0.5148 -0.3983 0.012 0.000 0.000 0.424 0.056 0.508
#> GSM494623 6 0.0622 0.7649 0.012 0.000 0.000 0.000 0.008 0.980
#> GSM494644 1 0.0260 0.9276 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM494646 1 0.3416 0.8432 0.832 0.000 0.000 0.040 0.100 0.028
#> GSM494665 1 0.2122 0.8954 0.900 0.000 0.000 0.024 0.076 0.000
#> GSM494638 4 0.5875 0.5872 0.024 0.000 0.000 0.488 0.112 0.376
#> GSM494645 1 0.1075 0.9160 0.952 0.000 0.000 0.000 0.048 0.000
#> GSM494671 1 0.0862 0.9242 0.972 0.000 0.000 0.016 0.008 0.004
#> GSM494655 1 0.0000 0.9287 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494620 6 0.0622 0.7649 0.012 0.000 0.000 0.000 0.008 0.980
#> GSM494630 6 0.0767 0.7631 0.012 0.000 0.000 0.004 0.008 0.976
#> GSM494657 3 0.0146 0.7708 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM494667 1 0.0146 0.9287 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM494621 6 0.0622 0.7649 0.012 0.000 0.000 0.000 0.008 0.980
#> GSM494629 4 0.4713 0.6655 0.012 0.000 0.000 0.560 0.028 0.400
#> GSM494637 6 0.5276 -0.3891 0.012 0.000 0.000 0.412 0.068 0.508
#> GSM494652 1 0.0520 0.9275 0.984 0.000 0.000 0.008 0.008 0.000
#> GSM494648 6 0.0622 0.7649 0.012 0.000 0.000 0.000 0.008 0.980
#> GSM494650 4 0.4323 0.7052 0.012 0.000 0.000 0.612 0.012 0.364
#> GSM494669 1 0.0000 0.9287 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494666 1 0.2122 0.8954 0.900 0.000 0.000 0.024 0.076 0.000
#> GSM494668 1 0.0436 0.9283 0.988 0.000 0.000 0.004 0.004 0.004
#> GSM494633 6 0.0767 0.7631 0.012 0.000 0.000 0.004 0.008 0.976
#> GSM494634 1 0.0622 0.9266 0.980 0.000 0.000 0.012 0.008 0.000
#> GSM494639 1 0.5609 0.5145 0.632 0.000 0.000 0.052 0.100 0.216
#> GSM494661 1 0.1387 0.9094 0.932 0.000 0.000 0.000 0.068 0.000
#> GSM494617 4 0.5062 0.6770 0.012 0.000 0.000 0.560 0.056 0.372
#> GSM494626 4 0.4780 0.6984 0.012 0.000 0.000 0.580 0.036 0.372
#> GSM494656 3 0.1644 0.7212 0.000 0.000 0.920 0.076 0.004 0.000
#> GSM494635 1 0.2060 0.8958 0.900 0.000 0.000 0.016 0.084 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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 5)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 6)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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.
# code only for demonstration
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) individual(p) k
#> SD:kmeans 120 6.85e-20 0.9998 2.52e-15 1.000 2
#> SD:kmeans 110 2.59e-19 0.8788 2.43e-15 0.988 3
#> SD:kmeans 116 4.96e-19 0.3356 8.57e-13 0.855 4
#> SD:kmeans 93 9.15e-15 0.0418 2.54e-11 0.381 5
#> SD:kmeans 103 1.30e-15 0.2101 4.09e-09 0.400 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.
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"]
# you can also extract it by
# 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 51941 rows and 120 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 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:
k and the heatmap of
predicted classes for each k.k.k.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:
k;k, the area increased is defined as \(A_k - A_{k-1}\).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 0.987 0.994 0.5046 0.496 0.496
#> 3 3 0.707 0.852 0.877 0.3024 0.741 0.526
#> 4 4 0.966 0.959 0.981 0.1505 0.827 0.541
#> 5 5 0.957 0.952 0.964 0.0487 0.956 0.823
#> 6 6 0.940 0.870 0.918 0.0394 0.960 0.813
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 4 5
There is also optional best \(k\) = 2 4 5 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.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM494565 2 0.000 0.991 0.000 1.000
#> GSM494594 2 0.000 0.991 0.000 1.000
#> GSM494604 1 0.416 0.908 0.916 0.084
#> GSM494564 2 0.000 0.991 0.000 1.000
#> GSM494591 2 0.000 0.991 0.000 1.000
#> GSM494567 2 0.000 0.991 0.000 1.000
#> GSM494602 2 0.000 0.991 0.000 1.000
#> GSM494613 2 0.000 0.991 0.000 1.000
#> GSM494589 2 0.000 0.991 0.000 1.000
#> GSM494598 2 0.000 0.991 0.000 1.000
#> GSM494593 2 0.000 0.991 0.000 1.000
#> GSM494583 2 0.000 0.991 0.000 1.000
#> GSM494612 2 0.000 0.991 0.000 1.000
#> GSM494558 2 0.000 0.991 0.000 1.000
#> GSM494556 2 0.000 0.991 0.000 1.000
#> GSM494559 2 0.000 0.991 0.000 1.000
#> GSM494571 2 0.000 0.991 0.000 1.000
#> GSM494614 2 0.000 0.991 0.000 1.000
#> GSM494603 2 0.000 0.991 0.000 1.000
#> GSM494568 1 0.402 0.913 0.920 0.080
#> GSM494572 2 0.000 0.991 0.000 1.000
#> GSM494600 2 0.000 0.991 0.000 1.000
#> GSM494562 2 0.000 0.991 0.000 1.000
#> GSM494615 2 0.000 0.991 0.000 1.000
#> GSM494582 2 0.000 0.991 0.000 1.000
#> GSM494599 2 0.000 0.991 0.000 1.000
#> GSM494610 2 0.000 0.991 0.000 1.000
#> GSM494587 2 0.000 0.991 0.000 1.000
#> GSM494581 2 0.000 0.991 0.000 1.000
#> GSM494580 2 0.000 0.991 0.000 1.000
#> GSM494563 2 0.000 0.991 0.000 1.000
#> GSM494576 2 0.000 0.991 0.000 1.000
#> GSM494605 1 0.000 0.997 1.000 0.000
#> GSM494584 2 0.000 0.991 0.000 1.000
#> GSM494586 2 0.000 0.991 0.000 1.000
#> GSM494578 2 0.000 0.991 0.000 1.000
#> GSM494585 2 0.000 0.991 0.000 1.000
#> GSM494611 2 0.000 0.991 0.000 1.000
#> GSM494560 2 0.000 0.991 0.000 1.000
#> GSM494595 2 0.000 0.991 0.000 1.000
#> GSM494570 2 0.000 0.991 0.000 1.000
#> GSM494597 2 0.000 0.991 0.000 1.000
#> GSM494607 2 0.358 0.923 0.068 0.932
#> GSM494561 2 0.000 0.991 0.000 1.000
#> GSM494569 1 0.000 0.997 1.000 0.000
#> GSM494592 2 0.000 0.991 0.000 1.000
#> GSM494577 2 0.000 0.991 0.000 1.000
#> GSM494588 2 0.000 0.991 0.000 1.000
#> GSM494590 2 0.000 0.991 0.000 1.000
#> GSM494609 2 0.000 0.991 0.000 1.000
#> GSM494608 2 0.000 0.991 0.000 1.000
#> GSM494606 2 0.000 0.991 0.000 1.000
#> GSM494574 2 0.000 0.991 0.000 1.000
#> GSM494573 2 0.000 0.991 0.000 1.000
#> GSM494566 2 0.000 0.991 0.000 1.000
#> GSM494601 2 0.000 0.991 0.000 1.000
#> GSM494557 2 0.000 0.991 0.000 1.000
#> GSM494579 2 0.000 0.991 0.000 1.000
#> GSM494596 2 0.000 0.991 0.000 1.000
#> GSM494575 2 0.000 0.991 0.000 1.000
#> GSM494625 1 0.000 0.997 1.000 0.000
#> GSM494654 2 0.855 0.617 0.280 0.720
#> GSM494664 1 0.000 0.997 1.000 0.000
#> GSM494624 1 0.000 0.997 1.000 0.000
#> GSM494651 1 0.000 0.997 1.000 0.000
#> GSM494662 1 0.000 0.997 1.000 0.000
#> GSM494627 1 0.000 0.997 1.000 0.000
#> GSM494673 1 0.000 0.997 1.000 0.000
#> GSM494649 1 0.000 0.997 1.000 0.000
#> GSM494658 1 0.000 0.997 1.000 0.000
#> GSM494653 1 0.000 0.997 1.000 0.000
#> GSM494643 1 0.000 0.997 1.000 0.000
#> GSM494672 1 0.000 0.997 1.000 0.000
#> GSM494618 1 0.000 0.997 1.000 0.000
#> GSM494631 2 0.689 0.777 0.184 0.816
#> GSM494619 1 0.000 0.997 1.000 0.000
#> GSM494674 1 0.000 0.997 1.000 0.000
#> GSM494616 1 0.000 0.997 1.000 0.000
#> GSM494663 1 0.000 0.997 1.000 0.000
#> GSM494628 1 0.000 0.997 1.000 0.000
#> GSM494632 1 0.000 0.997 1.000 0.000
#> GSM494660 1 0.000 0.997 1.000 0.000
#> GSM494622 1 0.000 0.997 1.000 0.000
#> GSM494642 1 0.000 0.997 1.000 0.000
#> GSM494647 1 0.000 0.997 1.000 0.000
#> GSM494659 1 0.000 0.997 1.000 0.000
#> GSM494670 1 0.000 0.997 1.000 0.000
#> GSM494675 2 0.000 0.991 0.000 1.000
#> GSM494641 1 0.000 0.997 1.000 0.000
#> GSM494636 1 0.000 0.997 1.000 0.000
#> GSM494640 1 0.000 0.997 1.000 0.000
#> GSM494623 1 0.000 0.997 1.000 0.000
#> GSM494644 1 0.000 0.997 1.000 0.000
#> GSM494646 1 0.000 0.997 1.000 0.000
#> GSM494665 1 0.000 0.997 1.000 0.000
#> GSM494638 1 0.000 0.997 1.000 0.000
#> GSM494645 1 0.000 0.997 1.000 0.000
#> GSM494671 1 0.000 0.997 1.000 0.000
#> GSM494655 1 0.000 0.997 1.000 0.000
#> GSM494620 1 0.000 0.997 1.000 0.000
#> GSM494630 1 0.000 0.997 1.000 0.000
#> GSM494657 2 0.000 0.991 0.000 1.000
#> GSM494667 1 0.000 0.997 1.000 0.000
#> GSM494621 1 0.000 0.997 1.000 0.000
#> GSM494629 1 0.000 0.997 1.000 0.000
#> GSM494637 1 0.000 0.997 1.000 0.000
#> GSM494652 1 0.000 0.997 1.000 0.000
#> GSM494648 1 0.000 0.997 1.000 0.000
#> GSM494650 1 0.000 0.997 1.000 0.000
#> GSM494669 1 0.000 0.997 1.000 0.000
#> GSM494666 1 0.000 0.997 1.000 0.000
#> GSM494668 1 0.000 0.997 1.000 0.000
#> GSM494633 1 0.000 0.997 1.000 0.000
#> GSM494634 1 0.000 0.997 1.000 0.000
#> GSM494639 1 0.000 0.997 1.000 0.000
#> GSM494661 1 0.000 0.997 1.000 0.000
#> GSM494617 1 0.000 0.997 1.000 0.000
#> GSM494626 1 0.000 0.997 1.000 0.000
#> GSM494656 2 0.000 0.991 0.000 1.000
#> GSM494635 1 0.000 0.997 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM494565 2 0.4178 0.890 0.172 0.828 0.000
#> GSM494594 2 0.5921 0.875 0.212 0.756 0.032
#> GSM494604 1 0.4702 0.718 0.788 0.212 0.000
#> GSM494564 2 0.4702 0.885 0.212 0.788 0.000
#> GSM494591 2 0.5921 0.875 0.212 0.756 0.032
#> GSM494567 2 0.5921 0.875 0.212 0.756 0.032
#> GSM494602 2 0.0000 0.886 0.000 1.000 0.000
#> GSM494613 2 0.5921 0.875 0.212 0.756 0.032
#> GSM494589 2 0.4702 0.885 0.212 0.788 0.000
#> GSM494598 2 0.0000 0.886 0.000 1.000 0.000
#> GSM494593 2 0.0000 0.886 0.000 1.000 0.000
#> GSM494583 2 0.0237 0.887 0.004 0.996 0.000
#> GSM494612 2 0.0000 0.886 0.000 1.000 0.000
#> GSM494558 3 0.4702 0.740 0.212 0.000 0.788
#> GSM494556 2 0.5921 0.875 0.212 0.756 0.032
#> GSM494559 2 0.4702 0.885 0.212 0.788 0.000
#> GSM494571 3 0.6541 0.689 0.212 0.056 0.732
#> GSM494614 2 0.4702 0.885 0.212 0.788 0.000
#> GSM494603 3 0.4702 0.740 0.212 0.000 0.788
#> GSM494568 3 0.4702 0.740 0.212 0.000 0.788
#> GSM494572 2 0.5921 0.875 0.212 0.756 0.032
#> GSM494600 2 0.4702 0.885 0.212 0.788 0.000
#> GSM494562 2 0.0000 0.886 0.000 1.000 0.000
#> GSM494615 2 0.5921 0.875 0.212 0.756 0.032
#> GSM494582 2 0.0000 0.886 0.000 1.000 0.000
#> GSM494599 1 0.6079 0.487 0.612 0.388 0.000
#> GSM494610 2 0.0000 0.886 0.000 1.000 0.000
#> GSM494587 2 0.0000 0.886 0.000 1.000 0.000
#> GSM494581 2 0.0000 0.886 0.000 1.000 0.000
#> GSM494580 2 0.5921 0.875 0.212 0.756 0.032
#> GSM494563 2 0.3816 0.892 0.148 0.852 0.000
#> GSM494576 2 0.0000 0.886 0.000 1.000 0.000
#> GSM494605 1 0.4702 0.930 0.788 0.000 0.212
#> GSM494584 2 0.3192 0.892 0.112 0.888 0.000
#> GSM494586 2 0.0000 0.886 0.000 1.000 0.000
#> GSM494578 2 0.5921 0.875 0.212 0.756 0.032
#> GSM494585 2 0.0000 0.886 0.000 1.000 0.000
#> GSM494611 2 0.0000 0.886 0.000 1.000 0.000
#> GSM494560 2 0.4702 0.885 0.212 0.788 0.000
#> GSM494595 2 0.0000 0.886 0.000 1.000 0.000
#> GSM494570 3 0.9653 -0.099 0.212 0.364 0.424
#> GSM494597 2 0.5921 0.875 0.212 0.756 0.032
#> GSM494607 1 0.5058 0.690 0.756 0.244 0.000
#> GSM494561 3 0.4702 0.740 0.212 0.000 0.788
#> GSM494569 3 0.0000 0.881 0.000 0.000 1.000
#> GSM494592 1 0.5988 0.525 0.632 0.368 0.000
#> GSM494577 2 0.0000 0.886 0.000 1.000 0.000
#> GSM494588 2 0.4121 0.891 0.168 0.832 0.000
#> GSM494590 2 0.5921 0.875 0.212 0.756 0.032
#> GSM494609 2 0.0000 0.886 0.000 1.000 0.000
#> GSM494608 2 0.0000 0.886 0.000 1.000 0.000
#> GSM494606 1 0.6274 0.329 0.544 0.456 0.000
#> GSM494574 2 0.0000 0.886 0.000 1.000 0.000
#> GSM494573 2 0.4702 0.885 0.212 0.788 0.000
#> GSM494566 2 0.0000 0.886 0.000 1.000 0.000
#> GSM494601 2 0.0000 0.886 0.000 1.000 0.000
#> GSM494557 2 0.5551 0.879 0.212 0.768 0.020
#> GSM494579 2 0.0000 0.886 0.000 1.000 0.000
#> GSM494596 2 0.5921 0.875 0.212 0.756 0.032
#> GSM494575 2 0.0000 0.886 0.000 1.000 0.000
#> GSM494625 3 0.0000 0.881 0.000 0.000 1.000
#> GSM494654 3 0.4702 0.740 0.212 0.000 0.788
#> GSM494664 1 0.4702 0.930 0.788 0.000 0.212
#> GSM494624 3 0.1289 0.869 0.032 0.000 0.968
#> GSM494651 3 0.0000 0.881 0.000 0.000 1.000
#> GSM494662 3 0.1289 0.869 0.032 0.000 0.968
#> GSM494627 3 0.0000 0.881 0.000 0.000 1.000
#> GSM494673 1 0.4702 0.930 0.788 0.000 0.212
#> GSM494649 3 0.0000 0.881 0.000 0.000 1.000
#> GSM494658 1 0.4702 0.930 0.788 0.000 0.212
#> GSM494653 1 0.4702 0.930 0.788 0.000 0.212
#> GSM494643 3 0.1289 0.869 0.032 0.000 0.968
#> GSM494672 1 0.4702 0.930 0.788 0.000 0.212
#> GSM494618 3 0.0000 0.881 0.000 0.000 1.000
#> GSM494631 3 0.4702 0.740 0.212 0.000 0.788
#> GSM494619 3 0.1289 0.869 0.032 0.000 0.968
#> GSM494674 1 0.4702 0.930 0.788 0.000 0.212
#> GSM494616 3 0.0000 0.881 0.000 0.000 1.000
#> GSM494663 3 0.0000 0.881 0.000 0.000 1.000
#> GSM494628 3 0.0000 0.881 0.000 0.000 1.000
#> GSM494632 1 0.4702 0.930 0.788 0.000 0.212
#> GSM494660 3 0.0000 0.881 0.000 0.000 1.000
#> GSM494622 3 0.0000 0.881 0.000 0.000 1.000
#> GSM494642 1 0.4702 0.930 0.788 0.000 0.212
#> GSM494647 1 0.4702 0.930 0.788 0.000 0.212
#> GSM494659 1 0.4702 0.930 0.788 0.000 0.212
#> GSM494670 1 0.4702 0.930 0.788 0.000 0.212
#> GSM494675 2 0.5921 0.875 0.212 0.756 0.032
#> GSM494641 1 0.4702 0.930 0.788 0.000 0.212
#> GSM494636 3 0.1289 0.869 0.032 0.000 0.968
#> GSM494640 3 0.0000 0.881 0.000 0.000 1.000
#> GSM494623 3 0.1289 0.869 0.032 0.000 0.968
#> GSM494644 1 0.4702 0.930 0.788 0.000 0.212
#> GSM494646 1 0.4702 0.930 0.788 0.000 0.212
#> GSM494665 1 0.4702 0.930 0.788 0.000 0.212
#> GSM494638 3 0.6244 -0.176 0.440 0.000 0.560
#> GSM494645 1 0.4702 0.930 0.788 0.000 0.212
#> GSM494671 1 0.4702 0.930 0.788 0.000 0.212
#> GSM494655 1 0.4702 0.930 0.788 0.000 0.212
#> GSM494620 3 0.1289 0.869 0.032 0.000 0.968
#> GSM494630 3 0.1289 0.869 0.032 0.000 0.968
#> GSM494657 2 0.5921 0.875 0.212 0.756 0.032
#> GSM494667 1 0.4702 0.930 0.788 0.000 0.212
#> GSM494621 3 0.1289 0.869 0.032 0.000 0.968
#> GSM494629 3 0.0747 0.872 0.016 0.000 0.984
#> GSM494637 3 0.0000 0.881 0.000 0.000 1.000
#> GSM494652 1 0.4702 0.930 0.788 0.000 0.212
#> GSM494648 3 0.1289 0.869 0.032 0.000 0.968
#> GSM494650 3 0.0000 0.881 0.000 0.000 1.000
#> GSM494669 1 0.4702 0.930 0.788 0.000 0.212
#> GSM494666 1 0.4702 0.930 0.788 0.000 0.212
#> GSM494668 1 0.4702 0.930 0.788 0.000 0.212
#> GSM494633 3 0.1163 0.870 0.028 0.000 0.972
#> GSM494634 1 0.4702 0.930 0.788 0.000 0.212
#> GSM494639 1 0.4702 0.930 0.788 0.000 0.212
#> GSM494661 1 0.4702 0.930 0.788 0.000 0.212
#> GSM494617 3 0.1289 0.869 0.032 0.000 0.968
#> GSM494626 3 0.0747 0.876 0.016 0.000 0.984
#> GSM494656 3 0.4702 0.740 0.212 0.000 0.788
#> GSM494635 1 0.4702 0.930 0.788 0.000 0.212
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM494565 2 0.480 0.444 0.000 0.616 0.384 0.000
#> GSM494594 3 0.000 0.988 0.000 0.000 1.000 0.000
#> GSM494604 1 0.349 0.772 0.812 0.188 0.000 0.000
#> GSM494564 3 0.000 0.988 0.000 0.000 1.000 0.000
#> GSM494591 3 0.000 0.988 0.000 0.000 1.000 0.000
#> GSM494567 3 0.000 0.988 0.000 0.000 1.000 0.000
#> GSM494602 2 0.000 0.956 0.000 1.000 0.000 0.000
#> GSM494613 3 0.000 0.988 0.000 0.000 1.000 0.000
#> GSM494589 3 0.000 0.988 0.000 0.000 1.000 0.000
#> GSM494598 2 0.000 0.956 0.000 1.000 0.000 0.000
#> GSM494593 2 0.000 0.956 0.000 1.000 0.000 0.000
#> GSM494583 2 0.259 0.857 0.000 0.884 0.116 0.000
#> GSM494612 2 0.000 0.956 0.000 1.000 0.000 0.000
#> GSM494558 3 0.000 0.988 0.000 0.000 1.000 0.000
#> GSM494556 3 0.000 0.988 0.000 0.000 1.000 0.000
#> GSM494559 3 0.000 0.988 0.000 0.000 1.000 0.000
#> GSM494571 3 0.000 0.988 0.000 0.000 1.000 0.000
#> GSM494614 3 0.000 0.988 0.000 0.000 1.000 0.000
#> GSM494603 3 0.000 0.988 0.000 0.000 1.000 0.000
#> GSM494568 4 0.380 0.712 0.000 0.000 0.220 0.780
#> GSM494572 3 0.000 0.988 0.000 0.000 1.000 0.000
#> GSM494600 3 0.000 0.988 0.000 0.000 1.000 0.000
#> GSM494562 2 0.000 0.956 0.000 1.000 0.000 0.000
#> GSM494615 3 0.000 0.988 0.000 0.000 1.000 0.000
#> GSM494582 2 0.000 0.956 0.000 1.000 0.000 0.000
#> GSM494599 2 0.000 0.956 0.000 1.000 0.000 0.000
#> GSM494610 2 0.000 0.956 0.000 1.000 0.000 0.000
#> GSM494587 2 0.000 0.956 0.000 1.000 0.000 0.000
#> GSM494581 2 0.000 0.956 0.000 1.000 0.000 0.000
#> GSM494580 3 0.000 0.988 0.000 0.000 1.000 0.000
#> GSM494563 2 0.443 0.610 0.000 0.696 0.304 0.000
#> GSM494576 2 0.000 0.956 0.000 1.000 0.000 0.000
#> GSM494605 1 0.000 0.993 1.000 0.000 0.000 0.000
#> GSM494584 2 0.349 0.778 0.000 0.812 0.188 0.000
#> GSM494586 2 0.000 0.956 0.000 1.000 0.000 0.000
#> GSM494578 3 0.000 0.988 0.000 0.000 1.000 0.000
#> GSM494585 2 0.000 0.956 0.000 1.000 0.000 0.000
#> GSM494611 2 0.000 0.956 0.000 1.000 0.000 0.000
#> GSM494560 3 0.000 0.988 0.000 0.000 1.000 0.000
#> GSM494595 2 0.000 0.956 0.000 1.000 0.000 0.000
#> GSM494570 3 0.000 0.988 0.000 0.000 1.000 0.000
#> GSM494597 3 0.000 0.988 0.000 0.000 1.000 0.000
#> GSM494607 2 0.000 0.956 0.000 1.000 0.000 0.000
#> GSM494561 3 0.000 0.988 0.000 0.000 1.000 0.000
#> GSM494569 4 0.000 0.986 0.000 0.000 0.000 1.000
#> GSM494592 2 0.000 0.956 0.000 1.000 0.000 0.000
#> GSM494577 2 0.000 0.956 0.000 1.000 0.000 0.000
#> GSM494588 2 0.450 0.588 0.000 0.684 0.316 0.000
#> GSM494590 3 0.000 0.988 0.000 0.000 1.000 0.000
#> GSM494609 2 0.000 0.956 0.000 1.000 0.000 0.000
#> GSM494608 2 0.000 0.956 0.000 1.000 0.000 0.000
#> GSM494606 2 0.000 0.956 0.000 1.000 0.000 0.000
#> GSM494574 2 0.000 0.956 0.000 1.000 0.000 0.000
#> GSM494573 3 0.000 0.988 0.000 0.000 1.000 0.000
#> GSM494566 2 0.000 0.956 0.000 1.000 0.000 0.000
#> GSM494601 2 0.000 0.956 0.000 1.000 0.000 0.000
#> GSM494557 3 0.000 0.988 0.000 0.000 1.000 0.000
#> GSM494579 2 0.000 0.956 0.000 1.000 0.000 0.000
#> GSM494596 3 0.000 0.988 0.000 0.000 1.000 0.000
#> GSM494575 2 0.000 0.956 0.000 1.000 0.000 0.000
#> GSM494625 4 0.000 0.986 0.000 0.000 0.000 1.000
#> GSM494654 3 0.312 0.812 0.000 0.000 0.844 0.156
#> GSM494664 1 0.000 0.993 1.000 0.000 0.000 0.000
#> GSM494624 4 0.000 0.986 0.000 0.000 0.000 1.000
#> GSM494651 4 0.000 0.986 0.000 0.000 0.000 1.000
#> GSM494662 4 0.000 0.986 0.000 0.000 0.000 1.000
#> GSM494627 4 0.000 0.986 0.000 0.000 0.000 1.000
#> GSM494673 1 0.000 0.993 1.000 0.000 0.000 0.000
#> GSM494649 4 0.000 0.986 0.000 0.000 0.000 1.000
#> GSM494658 1 0.000 0.993 1.000 0.000 0.000 0.000
#> GSM494653 1 0.000 0.993 1.000 0.000 0.000 0.000
#> GSM494643 4 0.000 0.986 0.000 0.000 0.000 1.000
#> GSM494672 1 0.000 0.993 1.000 0.000 0.000 0.000
#> GSM494618 4 0.000 0.986 0.000 0.000 0.000 1.000
#> GSM494631 3 0.331 0.790 0.000 0.000 0.828 0.172
#> GSM494619 4 0.000 0.986 0.000 0.000 0.000 1.000
#> GSM494674 1 0.000 0.993 1.000 0.000 0.000 0.000
#> GSM494616 4 0.000 0.986 0.000 0.000 0.000 1.000
#> GSM494663 4 0.000 0.986 0.000 0.000 0.000 1.000
#> GSM494628 4 0.000 0.986 0.000 0.000 0.000 1.000
#> GSM494632 1 0.000 0.993 1.000 0.000 0.000 0.000
#> GSM494660 4 0.000 0.986 0.000 0.000 0.000 1.000
#> GSM494622 4 0.000 0.986 0.000 0.000 0.000 1.000
#> GSM494642 1 0.000 0.993 1.000 0.000 0.000 0.000
#> GSM494647 1 0.000 0.993 1.000 0.000 0.000 0.000
#> GSM494659 1 0.000 0.993 1.000 0.000 0.000 0.000
#> GSM494670 1 0.000 0.993 1.000 0.000 0.000 0.000
#> GSM494675 3 0.000 0.988 0.000 0.000 1.000 0.000
#> GSM494641 1 0.000 0.993 1.000 0.000 0.000 0.000
#> GSM494636 4 0.000 0.986 0.000 0.000 0.000 1.000
#> GSM494640 4 0.000 0.986 0.000 0.000 0.000 1.000
#> GSM494623 4 0.000 0.986 0.000 0.000 0.000 1.000
#> GSM494644 1 0.000 0.993 1.000 0.000 0.000 0.000
#> GSM494646 1 0.000 0.993 1.000 0.000 0.000 0.000
#> GSM494665 1 0.000 0.993 1.000 0.000 0.000 0.000
#> GSM494638 4 0.340 0.777 0.180 0.000 0.000 0.820
#> GSM494645 1 0.000 0.993 1.000 0.000 0.000 0.000
#> GSM494671 1 0.000 0.993 1.000 0.000 0.000 0.000
#> GSM494655 1 0.000 0.993 1.000 0.000 0.000 0.000
#> GSM494620 4 0.000 0.986 0.000 0.000 0.000 1.000
#> GSM494630 4 0.000 0.986 0.000 0.000 0.000 1.000
#> GSM494657 3 0.000 0.988 0.000 0.000 1.000 0.000
#> GSM494667 1 0.000 0.993 1.000 0.000 0.000 0.000
#> GSM494621 4 0.000 0.986 0.000 0.000 0.000 1.000
#> GSM494629 4 0.000 0.986 0.000 0.000 0.000 1.000
#> GSM494637 4 0.000 0.986 0.000 0.000 0.000 1.000
#> GSM494652 1 0.000 0.993 1.000 0.000 0.000 0.000
#> GSM494648 4 0.000 0.986 0.000 0.000 0.000 1.000
#> GSM494650 4 0.000 0.986 0.000 0.000 0.000 1.000
#> GSM494669 1 0.000 0.993 1.000 0.000 0.000 0.000
#> GSM494666 1 0.000 0.993 1.000 0.000 0.000 0.000
#> GSM494668 1 0.000 0.993 1.000 0.000 0.000 0.000
#> GSM494633 4 0.000 0.986 0.000 0.000 0.000 1.000
#> GSM494634 1 0.000 0.993 1.000 0.000 0.000 0.000
#> GSM494639 1 0.000 0.993 1.000 0.000 0.000 0.000
#> GSM494661 1 0.000 0.993 1.000 0.000 0.000 0.000
#> GSM494617 4 0.000 0.986 0.000 0.000 0.000 1.000
#> GSM494626 4 0.000 0.986 0.000 0.000 0.000 1.000
#> GSM494656 3 0.000 0.988 0.000 0.000 1.000 0.000
#> GSM494635 1 0.000 0.993 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM494565 5 0.2482 0.938 0.000 0.024 0.084 0.000 0.892
#> GSM494594 3 0.0000 0.972 0.000 0.000 1.000 0.000 0.000
#> GSM494604 1 0.3039 0.748 0.808 0.192 0.000 0.000 0.000
#> GSM494564 5 0.1851 0.945 0.000 0.000 0.088 0.000 0.912
#> GSM494591 3 0.0162 0.969 0.000 0.000 0.996 0.000 0.004
#> GSM494567 3 0.0000 0.972 0.000 0.000 1.000 0.000 0.000
#> GSM494602 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> GSM494613 3 0.0000 0.972 0.000 0.000 1.000 0.000 0.000
#> GSM494589 5 0.2127 0.942 0.000 0.000 0.108 0.000 0.892
#> GSM494598 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> GSM494593 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> GSM494583 2 0.3051 0.842 0.000 0.852 0.028 0.000 0.120
#> GSM494612 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> GSM494558 3 0.2074 0.904 0.000 0.000 0.920 0.044 0.036
#> GSM494556 3 0.0000 0.972 0.000 0.000 1.000 0.000 0.000
#> GSM494559 5 0.1851 0.945 0.000 0.000 0.088 0.000 0.912
#> GSM494571 3 0.0000 0.972 0.000 0.000 1.000 0.000 0.000
#> GSM494614 5 0.3932 0.647 0.000 0.000 0.328 0.000 0.672
#> GSM494603 5 0.2378 0.846 0.000 0.000 0.048 0.048 0.904
#> GSM494568 4 0.3620 0.843 0.000 0.000 0.068 0.824 0.108
#> GSM494572 3 0.0000 0.972 0.000 0.000 1.000 0.000 0.000
#> GSM494600 5 0.2127 0.942 0.000 0.000 0.108 0.000 0.892
#> GSM494562 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> GSM494615 3 0.0000 0.972 0.000 0.000 1.000 0.000 0.000
#> GSM494582 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> GSM494599 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> GSM494610 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> GSM494587 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> GSM494581 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> GSM494580 3 0.0000 0.972 0.000 0.000 1.000 0.000 0.000
#> GSM494563 5 0.2535 0.932 0.000 0.032 0.076 0.000 0.892
#> GSM494576 2 0.0404 0.971 0.000 0.988 0.000 0.000 0.012
#> GSM494605 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000
#> GSM494584 2 0.3180 0.842 0.000 0.856 0.076 0.000 0.068
#> GSM494586 2 0.0162 0.976 0.000 0.996 0.000 0.000 0.004
#> GSM494578 3 0.0000 0.972 0.000 0.000 1.000 0.000 0.000
#> GSM494585 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> GSM494611 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> GSM494560 5 0.2304 0.943 0.000 0.008 0.100 0.000 0.892
#> GSM494595 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> GSM494570 5 0.1544 0.936 0.000 0.000 0.068 0.000 0.932
#> GSM494597 3 0.1544 0.907 0.000 0.000 0.932 0.000 0.068
#> GSM494607 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> GSM494561 5 0.1628 0.923 0.000 0.000 0.056 0.008 0.936
#> GSM494569 4 0.1410 0.941 0.000 0.000 0.000 0.940 0.060
#> GSM494592 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> GSM494577 2 0.2020 0.897 0.000 0.900 0.000 0.000 0.100
#> GSM494588 5 0.1671 0.941 0.000 0.000 0.076 0.000 0.924
#> GSM494590 3 0.0000 0.972 0.000 0.000 1.000 0.000 0.000
#> GSM494609 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> GSM494608 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> GSM494606 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> GSM494574 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> GSM494573 5 0.2127 0.942 0.000 0.000 0.108 0.000 0.892
#> GSM494566 2 0.1197 0.944 0.000 0.952 0.000 0.000 0.048
#> GSM494601 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> GSM494557 3 0.0000 0.972 0.000 0.000 1.000 0.000 0.000
#> GSM494579 2 0.1965 0.901 0.000 0.904 0.000 0.000 0.096
#> GSM494596 3 0.0000 0.972 0.000 0.000 1.000 0.000 0.000
#> GSM494575 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> GSM494625 4 0.1197 0.940 0.000 0.000 0.000 0.952 0.048
#> GSM494654 3 0.1914 0.906 0.000 0.000 0.924 0.016 0.060
#> GSM494664 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000
#> GSM494624 4 0.1197 0.940 0.000 0.000 0.000 0.952 0.048
#> GSM494651 4 0.1410 0.941 0.000 0.000 0.000 0.940 0.060
#> GSM494662 4 0.0000 0.945 0.000 0.000 0.000 1.000 0.000
#> GSM494627 4 0.1410 0.941 0.000 0.000 0.000 0.940 0.060
#> GSM494673 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000
#> GSM494649 4 0.1197 0.940 0.000 0.000 0.000 0.952 0.048
#> GSM494658 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000
#> GSM494653 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000
#> GSM494643 4 0.0794 0.942 0.000 0.000 0.000 0.972 0.028
#> GSM494672 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000
#> GSM494618 4 0.1410 0.941 0.000 0.000 0.000 0.940 0.060
#> GSM494631 3 0.1914 0.906 0.000 0.000 0.924 0.016 0.060
#> GSM494619 4 0.1197 0.940 0.000 0.000 0.000 0.952 0.048
#> GSM494674 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000
#> GSM494616 4 0.1410 0.941 0.000 0.000 0.000 0.940 0.060
#> GSM494663 4 0.1410 0.941 0.000 0.000 0.000 0.940 0.060
#> GSM494628 4 0.1410 0.941 0.000 0.000 0.000 0.940 0.060
#> GSM494632 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000
#> GSM494660 4 0.1197 0.940 0.000 0.000 0.000 0.952 0.048
#> GSM494622 4 0.1410 0.941 0.000 0.000 0.000 0.940 0.060
#> GSM494642 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000
#> GSM494670 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000
#> GSM494675 3 0.1544 0.907 0.000 0.000 0.932 0.000 0.068
#> GSM494641 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000
#> GSM494636 4 0.0000 0.945 0.000 0.000 0.000 1.000 0.000
#> GSM494640 4 0.0000 0.945 0.000 0.000 0.000 1.000 0.000
#> GSM494623 4 0.1197 0.940 0.000 0.000 0.000 0.952 0.048
#> GSM494644 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000
#> GSM494646 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000
#> GSM494665 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000
#> GSM494638 4 0.3305 0.708 0.224 0.000 0.000 0.776 0.000
#> GSM494645 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000
#> GSM494671 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000
#> GSM494620 4 0.1197 0.940 0.000 0.000 0.000 0.952 0.048
#> GSM494630 4 0.1197 0.940 0.000 0.000 0.000 0.952 0.048
#> GSM494657 3 0.0000 0.972 0.000 0.000 1.000 0.000 0.000
#> GSM494667 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000
#> GSM494621 4 0.1197 0.940 0.000 0.000 0.000 0.952 0.048
#> GSM494629 4 0.1410 0.941 0.000 0.000 0.000 0.940 0.060
#> GSM494637 4 0.0000 0.945 0.000 0.000 0.000 1.000 0.000
#> GSM494652 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000
#> GSM494648 4 0.1197 0.940 0.000 0.000 0.000 0.952 0.048
#> GSM494650 4 0.1410 0.941 0.000 0.000 0.000 0.940 0.060
#> GSM494669 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000
#> GSM494666 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000
#> GSM494668 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000
#> GSM494633 4 0.1197 0.940 0.000 0.000 0.000 0.952 0.048
#> GSM494634 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000
#> GSM494639 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000
#> GSM494661 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000
#> GSM494617 4 0.1410 0.941 0.000 0.000 0.000 0.940 0.060
#> GSM494626 4 0.1410 0.941 0.000 0.000 0.000 0.940 0.060
#> GSM494656 3 0.1300 0.937 0.000 0.000 0.956 0.016 0.028
#> GSM494635 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM494565 5 0.0405 0.9392 0.000 0.004 0.008 0.000 0.988 0.000
#> GSM494594 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494604 1 0.3168 0.7355 0.792 0.192 0.000 0.016 0.000 0.000
#> GSM494564 5 0.0508 0.9416 0.000 0.000 0.012 0.000 0.984 0.004
#> GSM494591 3 0.0363 0.9736 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM494567 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494602 2 0.0260 0.9182 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM494613 3 0.0146 0.9809 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM494589 5 0.0458 0.9421 0.000 0.000 0.016 0.000 0.984 0.000
#> GSM494598 2 0.1265 0.9120 0.000 0.948 0.000 0.044 0.008 0.000
#> GSM494593 2 0.0260 0.9182 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM494583 2 0.4367 0.5064 0.000 0.604 0.000 0.032 0.364 0.000
#> GSM494612 2 0.0363 0.9177 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM494558 4 0.4183 0.0258 0.000 0.000 0.480 0.508 0.012 0.000
#> GSM494556 3 0.0146 0.9809 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM494559 5 0.0777 0.9359 0.000 0.000 0.024 0.000 0.972 0.004
#> GSM494571 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494614 5 0.3440 0.7346 0.000 0.000 0.196 0.028 0.776 0.000
#> GSM494603 4 0.4440 0.1787 0.000 0.000 0.016 0.556 0.420 0.008
#> GSM494568 4 0.2001 0.8327 0.000 0.000 0.016 0.920 0.020 0.044
#> GSM494572 3 0.0146 0.9809 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM494600 5 0.0458 0.9421 0.000 0.000 0.016 0.000 0.984 0.000
#> GSM494562 2 0.1461 0.9096 0.000 0.940 0.000 0.044 0.016 0.000
#> GSM494615 3 0.0146 0.9809 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM494582 2 0.0972 0.9155 0.000 0.964 0.000 0.028 0.008 0.000
#> GSM494599 2 0.0363 0.9177 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM494610 2 0.1367 0.9111 0.000 0.944 0.000 0.044 0.012 0.000
#> GSM494587 2 0.1168 0.9137 0.000 0.956 0.000 0.028 0.016 0.000
#> GSM494581 2 0.0363 0.9177 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM494580 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494563 5 0.0665 0.9349 0.000 0.004 0.008 0.008 0.980 0.000
#> GSM494576 2 0.1934 0.8971 0.000 0.916 0.000 0.044 0.040 0.000
#> GSM494605 1 0.0000 0.9890 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494584 2 0.4702 0.5926 0.000 0.644 0.020 0.036 0.300 0.000
#> GSM494586 2 0.1461 0.9096 0.000 0.940 0.000 0.044 0.016 0.000
#> GSM494578 3 0.0146 0.9809 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM494585 2 0.0603 0.9186 0.000 0.980 0.000 0.016 0.004 0.000
#> GSM494611 2 0.0000 0.9185 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494560 5 0.0508 0.9413 0.000 0.004 0.012 0.000 0.984 0.000
#> GSM494595 2 0.0891 0.9159 0.000 0.968 0.000 0.024 0.008 0.000
#> GSM494570 5 0.0603 0.9406 0.000 0.000 0.016 0.000 0.980 0.004
#> GSM494597 3 0.2176 0.8890 0.000 0.000 0.896 0.024 0.080 0.000
#> GSM494607 2 0.0692 0.9163 0.004 0.976 0.000 0.020 0.000 0.000
#> GSM494561 5 0.3922 0.5212 0.000 0.000 0.016 0.000 0.664 0.320
#> GSM494569 4 0.1387 0.8580 0.000 0.000 0.000 0.932 0.000 0.068
#> GSM494592 2 0.0363 0.9177 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM494577 2 0.4453 0.5599 0.000 0.624 0.000 0.044 0.332 0.000
#> GSM494588 5 0.0405 0.9403 0.000 0.000 0.008 0.000 0.988 0.004
#> GSM494590 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494609 2 0.0363 0.9177 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM494608 2 0.0363 0.9177 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM494606 2 0.0363 0.9177 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM494574 2 0.1367 0.9111 0.000 0.944 0.000 0.044 0.012 0.000
#> GSM494573 5 0.0458 0.9421 0.000 0.000 0.016 0.000 0.984 0.000
#> GSM494566 2 0.3980 0.7182 0.000 0.732 0.000 0.052 0.216 0.000
#> GSM494601 2 0.0260 0.9182 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM494557 3 0.0146 0.9809 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM494579 2 0.4371 0.6306 0.000 0.664 0.000 0.052 0.284 0.000
#> GSM494596 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494575 2 0.0260 0.9182 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM494625 6 0.0000 0.8555 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494654 3 0.0405 0.9735 0.000 0.000 0.988 0.004 0.008 0.000
#> GSM494664 1 0.0260 0.9834 0.992 0.000 0.000 0.008 0.000 0.000
#> GSM494624 6 0.0000 0.8555 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494651 4 0.1387 0.8580 0.000 0.000 0.000 0.932 0.000 0.068
#> GSM494662 6 0.3955 0.3051 0.000 0.000 0.000 0.436 0.004 0.560
#> GSM494627 4 0.1866 0.8533 0.000 0.000 0.000 0.908 0.008 0.084
#> GSM494673 1 0.0000 0.9890 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494649 6 0.0260 0.8546 0.000 0.000 0.000 0.008 0.000 0.992
#> GSM494658 1 0.0000 0.9890 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494653 1 0.0000 0.9890 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494643 6 0.0405 0.8530 0.000 0.000 0.000 0.008 0.004 0.988
#> GSM494672 1 0.0000 0.9890 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494618 4 0.1387 0.8580 0.000 0.000 0.000 0.932 0.000 0.068
#> GSM494631 3 0.1462 0.9306 0.000 0.000 0.936 0.056 0.008 0.000
#> GSM494619 6 0.0000 0.8555 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494674 1 0.0000 0.9890 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494616 4 0.1387 0.8580 0.000 0.000 0.000 0.932 0.000 0.068
#> GSM494663 4 0.1970 0.8482 0.000 0.000 0.000 0.900 0.008 0.092
#> GSM494628 4 0.1757 0.8560 0.000 0.000 0.000 0.916 0.008 0.076
#> GSM494632 1 0.0858 0.9639 0.968 0.000 0.000 0.028 0.004 0.000
#> GSM494660 6 0.0260 0.8546 0.000 0.000 0.000 0.008 0.000 0.992
#> GSM494622 4 0.1701 0.8549 0.000 0.000 0.000 0.920 0.008 0.072
#> GSM494642 1 0.0000 0.9890 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.9890 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.9890 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494670 1 0.0000 0.9890 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494675 3 0.2176 0.8890 0.000 0.000 0.896 0.024 0.080 0.000
#> GSM494641 1 0.0000 0.9890 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494636 6 0.3996 0.1704 0.000 0.000 0.000 0.484 0.004 0.512
#> GSM494640 6 0.3955 0.3051 0.000 0.000 0.000 0.436 0.004 0.560
#> GSM494623 6 0.0000 0.8555 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494644 1 0.0000 0.9890 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494646 1 0.0508 0.9774 0.984 0.000 0.000 0.012 0.004 0.000
#> GSM494665 1 0.0000 0.9890 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494638 4 0.5493 0.2585 0.396 0.000 0.000 0.488 0.004 0.112
#> GSM494645 1 0.0000 0.9890 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494671 1 0.0000 0.9890 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.9890 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494620 6 0.0000 0.8555 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494630 6 0.0260 0.8546 0.000 0.000 0.000 0.008 0.000 0.992
#> GSM494657 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494667 1 0.0000 0.9890 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494621 6 0.0000 0.8555 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494629 4 0.1814 0.8322 0.000 0.000 0.000 0.900 0.000 0.100
#> GSM494637 6 0.3955 0.3051 0.000 0.000 0.000 0.436 0.004 0.560
#> GSM494652 1 0.0000 0.9890 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494648 6 0.0000 0.8555 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494650 4 0.1757 0.8560 0.000 0.000 0.000 0.916 0.008 0.076
#> GSM494669 1 0.0000 0.9890 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494666 1 0.0000 0.9890 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494668 1 0.0000 0.9890 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494633 6 0.0260 0.8546 0.000 0.000 0.000 0.008 0.000 0.992
#> GSM494634 1 0.0000 0.9890 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494639 1 0.0603 0.9745 0.980 0.000 0.000 0.016 0.004 0.000
#> GSM494661 1 0.0000 0.9890 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494617 4 0.1387 0.8580 0.000 0.000 0.000 0.932 0.000 0.068
#> GSM494626 4 0.1387 0.8580 0.000 0.000 0.000 0.932 0.000 0.068
#> GSM494656 3 0.0260 0.9763 0.000 0.000 0.992 0.000 0.008 0.000
#> GSM494635 1 0.0146 0.9864 0.996 0.000 0.000 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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 4)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 5)
get_signatures(res, k = 6)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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.
# code only for demonstration
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) individual(p) k
#> SD:skmeans 120 6.85e-20 1.000 2.52e-15 1.000 2
#> SD:skmeans 116 7.06e-17 0.322 4.98e-11 0.826 3
#> SD:skmeans 119 7.86e-19 0.430 1.03e-12 0.858 4
#> SD:skmeans 120 1.26e-18 0.361 1.23e-13 0.664 5
#> SD:skmeans 113 1.21e-16 0.201 5.16e-10 0.383 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.
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"]
# you can also extract it by
# 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 51941 rows and 120 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:
k and the heatmap of
predicted classes for each k.k.k.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:
k;k, the area increased is defined as \(A_k - A_{k-1}\).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 0.966 0.969 0.987 0.5043 0.496 0.496
#> 3 3 0.866 0.867 0.945 0.2995 0.782 0.588
#> 4 4 0.768 0.858 0.901 0.0871 0.876 0.678
#> 5 5 0.803 0.771 0.877 0.0738 0.941 0.804
#> 6 6 0.847 0.837 0.911 0.0690 0.883 0.568
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
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.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM494565 2 0.000 0.976 0.000 1.000
#> GSM494594 2 0.000 0.976 0.000 1.000
#> GSM494604 1 0.000 0.996 1.000 0.000
#> GSM494564 2 0.000 0.976 0.000 1.000
#> GSM494591 2 0.000 0.976 0.000 1.000
#> GSM494567 2 0.000 0.976 0.000 1.000
#> GSM494602 2 0.000 0.976 0.000 1.000
#> GSM494613 2 0.000 0.976 0.000 1.000
#> GSM494589 2 0.000 0.976 0.000 1.000
#> GSM494598 2 0.000 0.976 0.000 1.000
#> GSM494593 2 0.000 0.976 0.000 1.000
#> GSM494583 2 0.000 0.976 0.000 1.000
#> GSM494612 2 0.000 0.976 0.000 1.000
#> GSM494558 2 0.615 0.817 0.152 0.848
#> GSM494556 2 0.000 0.976 0.000 1.000
#> GSM494559 2 0.000 0.976 0.000 1.000
#> GSM494571 2 0.000 0.976 0.000 1.000
#> GSM494614 2 0.000 0.976 0.000 1.000
#> GSM494603 2 0.000 0.976 0.000 1.000
#> GSM494568 1 0.506 0.871 0.888 0.112
#> GSM494572 2 0.000 0.976 0.000 1.000
#> GSM494600 2 0.000 0.976 0.000 1.000
#> GSM494562 2 0.000 0.976 0.000 1.000
#> GSM494615 2 0.000 0.976 0.000 1.000
#> GSM494582 2 0.000 0.976 0.000 1.000
#> GSM494599 2 0.662 0.791 0.172 0.828
#> GSM494610 2 0.000 0.976 0.000 1.000
#> GSM494587 2 0.000 0.976 0.000 1.000
#> GSM494581 2 0.000 0.976 0.000 1.000
#> GSM494580 2 0.000 0.976 0.000 1.000
#> GSM494563 2 0.000 0.976 0.000 1.000
#> GSM494576 2 0.000 0.976 0.000 1.000
#> GSM494605 1 0.000 0.996 1.000 0.000
#> GSM494584 2 0.000 0.976 0.000 1.000
#> GSM494586 2 0.000 0.976 0.000 1.000
#> GSM494578 2 0.000 0.976 0.000 1.000
#> GSM494585 2 0.000 0.976 0.000 1.000
#> GSM494611 2 0.000 0.976 0.000 1.000
#> GSM494560 2 0.000 0.976 0.000 1.000
#> GSM494595 2 0.000 0.976 0.000 1.000
#> GSM494570 2 0.000 0.976 0.000 1.000
#> GSM494597 2 0.000 0.976 0.000 1.000
#> GSM494607 2 0.000 0.976 0.000 1.000
#> GSM494561 2 0.949 0.435 0.368 0.632
#> GSM494569 1 0.000 0.996 1.000 0.000
#> GSM494592 2 0.000 0.976 0.000 1.000
#> GSM494577 2 0.000 0.976 0.000 1.000
#> GSM494588 2 0.000 0.976 0.000 1.000
#> GSM494590 2 0.000 0.976 0.000 1.000
#> GSM494609 2 0.000 0.976 0.000 1.000
#> GSM494608 2 0.909 0.533 0.324 0.676
#> GSM494606 2 0.000 0.976 0.000 1.000
#> GSM494574 2 0.000 0.976 0.000 1.000
#> GSM494573 2 0.000 0.976 0.000 1.000
#> GSM494566 2 0.000 0.976 0.000 1.000
#> GSM494601 2 0.000 0.976 0.000 1.000
#> GSM494557 2 0.000 0.976 0.000 1.000
#> GSM494579 2 0.000 0.976 0.000 1.000
#> GSM494596 2 0.000 0.976 0.000 1.000
#> GSM494575 2 0.000 0.976 0.000 1.000
#> GSM494625 1 0.000 0.996 1.000 0.000
#> GSM494654 1 0.506 0.871 0.888 0.112
#> GSM494664 1 0.000 0.996 1.000 0.000
#> GSM494624 1 0.000 0.996 1.000 0.000
#> GSM494651 1 0.000 0.996 1.000 0.000
#> GSM494662 1 0.000 0.996 1.000 0.000
#> GSM494627 1 0.000 0.996 1.000 0.000
#> GSM494673 1 0.000 0.996 1.000 0.000
#> GSM494649 1 0.000 0.996 1.000 0.000
#> GSM494658 1 0.000 0.996 1.000 0.000
#> GSM494653 1 0.000 0.996 1.000 0.000
#> GSM494643 1 0.000 0.996 1.000 0.000
#> GSM494672 1 0.000 0.996 1.000 0.000
#> GSM494618 1 0.000 0.996 1.000 0.000
#> GSM494631 2 0.936 0.470 0.352 0.648
#> GSM494619 1 0.000 0.996 1.000 0.000
#> GSM494674 1 0.000 0.996 1.000 0.000
#> GSM494616 1 0.000 0.996 1.000 0.000
#> GSM494663 1 0.000 0.996 1.000 0.000
#> GSM494628 1 0.000 0.996 1.000 0.000
#> GSM494632 1 0.000 0.996 1.000 0.000
#> GSM494660 1 0.000 0.996 1.000 0.000
#> GSM494622 1 0.000 0.996 1.000 0.000
#> GSM494642 1 0.000 0.996 1.000 0.000
#> GSM494647 1 0.000 0.996 1.000 0.000
#> GSM494659 1 0.000 0.996 1.000 0.000
#> GSM494670 1 0.000 0.996 1.000 0.000
#> GSM494675 2 0.000 0.976 0.000 1.000
#> GSM494641 1 0.000 0.996 1.000 0.000
#> GSM494636 1 0.000 0.996 1.000 0.000
#> GSM494640 1 0.000 0.996 1.000 0.000
#> GSM494623 1 0.000 0.996 1.000 0.000
#> GSM494644 1 0.000 0.996 1.000 0.000
#> GSM494646 1 0.000 0.996 1.000 0.000
#> GSM494665 1 0.000 0.996 1.000 0.000
#> GSM494638 1 0.000 0.996 1.000 0.000
#> GSM494645 1 0.000 0.996 1.000 0.000
#> GSM494671 1 0.000 0.996 1.000 0.000
#> GSM494655 1 0.000 0.996 1.000 0.000
#> GSM494620 1 0.000 0.996 1.000 0.000
#> GSM494630 1 0.000 0.996 1.000 0.000
#> GSM494657 2 0.000 0.976 0.000 1.000
#> GSM494667 1 0.000 0.996 1.000 0.000
#> GSM494621 1 0.000 0.996 1.000 0.000
#> GSM494629 1 0.000 0.996 1.000 0.000
#> GSM494637 1 0.000 0.996 1.000 0.000
#> GSM494652 1 0.000 0.996 1.000 0.000
#> GSM494648 1 0.000 0.996 1.000 0.000
#> GSM494650 1 0.000 0.996 1.000 0.000
#> GSM494669 1 0.000 0.996 1.000 0.000
#> GSM494666 1 0.000 0.996 1.000 0.000
#> GSM494668 1 0.000 0.996 1.000 0.000
#> GSM494633 1 0.000 0.996 1.000 0.000
#> GSM494634 1 0.000 0.996 1.000 0.000
#> GSM494639 1 0.000 0.996 1.000 0.000
#> GSM494661 1 0.000 0.996 1.000 0.000
#> GSM494617 1 0.000 0.996 1.000 0.000
#> GSM494626 1 0.000 0.996 1.000 0.000
#> GSM494656 2 0.000 0.976 0.000 1.000
#> GSM494635 1 0.000 0.996 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM494565 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494594 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494604 1 0.0000 0.9582 1.000 0.000 0.000
#> GSM494564 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494591 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494567 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494602 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494613 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494589 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494598 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494593 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494583 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494612 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494558 3 0.4452 0.6934 0.000 0.192 0.808
#> GSM494556 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494559 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494571 2 0.6079 0.3987 0.000 0.612 0.388
#> GSM494614 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494603 2 0.6095 0.3898 0.000 0.608 0.392
#> GSM494568 3 0.0747 0.8640 0.000 0.016 0.984
#> GSM494572 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494600 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494562 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494615 2 0.1163 0.9447 0.000 0.972 0.028
#> GSM494582 2 0.0424 0.9630 0.008 0.992 0.000
#> GSM494599 1 0.3816 0.7912 0.852 0.148 0.000
#> GSM494610 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494587 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494581 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494580 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494563 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494576 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494605 1 0.0000 0.9582 1.000 0.000 0.000
#> GSM494584 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494586 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494578 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494585 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494611 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494560 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494595 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494570 2 0.5431 0.6090 0.000 0.716 0.284
#> GSM494597 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494607 1 0.4346 0.7445 0.816 0.184 0.000
#> GSM494561 3 0.5926 0.3799 0.000 0.356 0.644
#> GSM494569 3 0.0000 0.8740 0.000 0.000 1.000
#> GSM494592 1 0.0592 0.9466 0.988 0.012 0.000
#> GSM494577 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494588 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494590 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494609 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494608 1 0.6518 0.6992 0.752 0.168 0.080
#> GSM494606 2 0.5948 0.4248 0.360 0.640 0.000
#> GSM494574 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494573 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494566 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494601 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494557 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494579 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494596 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494575 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494625 3 0.0000 0.8740 0.000 0.000 1.000
#> GSM494654 3 0.0424 0.8695 0.000 0.008 0.992
#> GSM494664 1 0.0000 0.9582 1.000 0.000 0.000
#> GSM494624 3 0.0000 0.8740 0.000 0.000 1.000
#> GSM494651 3 0.0000 0.8740 0.000 0.000 1.000
#> GSM494662 3 0.6095 0.4285 0.392 0.000 0.608
#> GSM494627 3 0.0000 0.8740 0.000 0.000 1.000
#> GSM494673 1 0.0000 0.9582 1.000 0.000 0.000
#> GSM494649 3 0.0000 0.8740 0.000 0.000 1.000
#> GSM494658 1 0.0000 0.9582 1.000 0.000 0.000
#> GSM494653 1 0.0000 0.9582 1.000 0.000 0.000
#> GSM494643 3 0.0237 0.8727 0.004 0.000 0.996
#> GSM494672 1 0.0000 0.9582 1.000 0.000 0.000
#> GSM494618 3 0.0000 0.8740 0.000 0.000 1.000
#> GSM494631 3 0.2537 0.8169 0.000 0.080 0.920
#> GSM494619 3 0.0000 0.8740 0.000 0.000 1.000
#> GSM494674 1 0.0000 0.9582 1.000 0.000 0.000
#> GSM494616 3 0.0000 0.8740 0.000 0.000 1.000
#> GSM494663 3 0.0000 0.8740 0.000 0.000 1.000
#> GSM494628 3 0.0000 0.8740 0.000 0.000 1.000
#> GSM494632 3 0.6095 0.4285 0.392 0.000 0.608
#> GSM494660 3 0.0000 0.8740 0.000 0.000 1.000
#> GSM494622 3 0.0000 0.8740 0.000 0.000 1.000
#> GSM494642 1 0.0000 0.9582 1.000 0.000 0.000
#> GSM494647 1 0.0000 0.9582 1.000 0.000 0.000
#> GSM494659 1 0.0000 0.9582 1.000 0.000 0.000
#> GSM494670 1 0.0000 0.9582 1.000 0.000 0.000
#> GSM494675 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494641 1 0.0000 0.9582 1.000 0.000 0.000
#> GSM494636 3 0.0424 0.8710 0.008 0.000 0.992
#> GSM494640 3 0.0000 0.8740 0.000 0.000 1.000
#> GSM494623 3 0.0000 0.8740 0.000 0.000 1.000
#> GSM494644 1 0.0000 0.9582 1.000 0.000 0.000
#> GSM494646 1 0.6204 0.0937 0.576 0.000 0.424
#> GSM494665 1 0.0000 0.9582 1.000 0.000 0.000
#> GSM494638 3 0.6095 0.4285 0.392 0.000 0.608
#> GSM494645 1 0.0000 0.9582 1.000 0.000 0.000
#> GSM494671 1 0.0000 0.9582 1.000 0.000 0.000
#> GSM494655 1 0.0000 0.9582 1.000 0.000 0.000
#> GSM494620 3 0.6095 0.4285 0.392 0.000 0.608
#> GSM494630 3 0.6095 0.4285 0.392 0.000 0.608
#> GSM494657 2 0.0000 0.9703 0.000 1.000 0.000
#> GSM494667 1 0.0000 0.9582 1.000 0.000 0.000
#> GSM494621 3 0.0747 0.8669 0.016 0.000 0.984
#> GSM494629 3 0.0000 0.8740 0.000 0.000 1.000
#> GSM494637 3 0.0000 0.8740 0.000 0.000 1.000
#> GSM494652 1 0.0000 0.9582 1.000 0.000 0.000
#> GSM494648 3 0.6095 0.4285 0.392 0.000 0.608
#> GSM494650 3 0.0000 0.8740 0.000 0.000 1.000
#> GSM494669 1 0.0000 0.9582 1.000 0.000 0.000
#> GSM494666 1 0.0000 0.9582 1.000 0.000 0.000
#> GSM494668 1 0.0000 0.9582 1.000 0.000 0.000
#> GSM494633 3 0.0000 0.8740 0.000 0.000 1.000
#> GSM494634 1 0.0000 0.9582 1.000 0.000 0.000
#> GSM494639 3 0.6095 0.4285 0.392 0.000 0.608
#> GSM494661 1 0.0000 0.9582 1.000 0.000 0.000
#> GSM494617 3 0.0747 0.8669 0.016 0.000 0.984
#> GSM494626 3 0.0237 0.8727 0.004 0.000 0.996
#> GSM494656 3 0.3551 0.7591 0.000 0.132 0.868
#> GSM494635 3 0.6308 0.1523 0.492 0.000 0.508
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM494565 2 0.3494 0.893 0.000 0.824 0.004 0.172
#> GSM494594 2 0.3870 0.881 0.000 0.788 0.004 0.208
#> GSM494604 1 0.3074 0.820 0.848 0.152 0.000 0.000
#> GSM494564 2 0.3494 0.893 0.000 0.824 0.004 0.172
#> GSM494591 2 0.3870 0.881 0.000 0.788 0.004 0.208
#> GSM494567 2 0.3494 0.893 0.000 0.824 0.004 0.172
#> GSM494602 2 0.0000 0.875 0.000 1.000 0.000 0.000
#> GSM494613 2 0.3494 0.893 0.000 0.824 0.004 0.172
#> GSM494589 2 0.3494 0.893 0.000 0.824 0.004 0.172
#> GSM494598 2 0.0000 0.875 0.000 1.000 0.000 0.000
#> GSM494593 2 0.0000 0.875 0.000 1.000 0.000 0.000
#> GSM494583 2 0.3402 0.894 0.000 0.832 0.004 0.164
#> GSM494612 2 0.0000 0.875 0.000 1.000 0.000 0.000
#> GSM494558 3 0.3311 0.737 0.000 0.000 0.828 0.172
#> GSM494556 2 0.3494 0.893 0.000 0.824 0.004 0.172
#> GSM494559 2 0.3494 0.893 0.000 0.824 0.004 0.172
#> GSM494571 2 0.7325 0.526 0.000 0.528 0.264 0.208
#> GSM494614 2 0.3494 0.893 0.000 0.824 0.004 0.172
#> GSM494603 3 0.4379 0.702 0.000 0.036 0.792 0.172
#> GSM494568 3 0.1557 0.842 0.000 0.000 0.944 0.056
#> GSM494572 2 0.3870 0.881 0.000 0.788 0.004 0.208
#> GSM494600 2 0.3494 0.893 0.000 0.824 0.004 0.172
#> GSM494562 2 0.0000 0.875 0.000 1.000 0.000 0.000
#> GSM494615 3 0.6439 0.505 0.000 0.180 0.648 0.172
#> GSM494582 2 0.0000 0.875 0.000 1.000 0.000 0.000
#> GSM494599 1 0.3764 0.758 0.784 0.216 0.000 0.000
#> GSM494610 2 0.0000 0.875 0.000 1.000 0.000 0.000
#> GSM494587 2 0.0000 0.875 0.000 1.000 0.000 0.000
#> GSM494581 2 0.0336 0.877 0.000 0.992 0.000 0.008
#> GSM494580 2 0.3494 0.893 0.000 0.824 0.004 0.172
#> GSM494563 2 0.3448 0.894 0.000 0.828 0.004 0.168
#> GSM494576 2 0.0188 0.876 0.000 0.996 0.000 0.004
#> GSM494605 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> GSM494584 2 0.3355 0.894 0.000 0.836 0.004 0.160
#> GSM494586 2 0.0000 0.875 0.000 1.000 0.000 0.000
#> GSM494578 2 0.3494 0.893 0.000 0.824 0.004 0.172
#> GSM494585 2 0.0000 0.875 0.000 1.000 0.000 0.000
#> GSM494611 2 0.0000 0.875 0.000 1.000 0.000 0.000
#> GSM494560 2 0.3494 0.893 0.000 0.824 0.004 0.172
#> GSM494595 2 0.0000 0.875 0.000 1.000 0.000 0.000
#> GSM494570 4 0.1211 0.663 0.000 0.040 0.000 0.960
#> GSM494597 2 0.3831 0.882 0.000 0.792 0.004 0.204
#> GSM494607 1 0.3764 0.758 0.784 0.216 0.000 0.000
#> GSM494561 4 0.1661 0.700 0.000 0.004 0.052 0.944
#> GSM494569 3 0.0188 0.883 0.004 0.000 0.996 0.000
#> GSM494592 1 0.3356 0.800 0.824 0.176 0.000 0.000
#> GSM494577 2 0.0336 0.877 0.000 0.992 0.000 0.008
#> GSM494588 2 0.5163 0.487 0.000 0.516 0.004 0.480
#> GSM494590 2 0.3870 0.881 0.000 0.788 0.004 0.208
#> GSM494609 2 0.0000 0.875 0.000 1.000 0.000 0.000
#> GSM494608 1 0.6110 0.664 0.720 0.176 0.064 0.040
#> GSM494606 2 0.1940 0.815 0.076 0.924 0.000 0.000
#> GSM494574 2 0.0000 0.875 0.000 1.000 0.000 0.000
#> GSM494573 2 0.3494 0.893 0.000 0.824 0.004 0.172
#> GSM494566 2 0.0895 0.880 0.000 0.976 0.004 0.020
#> GSM494601 2 0.0000 0.875 0.000 1.000 0.000 0.000
#> GSM494557 2 0.3494 0.893 0.000 0.824 0.004 0.172
#> GSM494579 2 0.2831 0.892 0.000 0.876 0.004 0.120
#> GSM494596 2 0.3870 0.881 0.000 0.788 0.004 0.208
#> GSM494575 2 0.0000 0.875 0.000 1.000 0.000 0.000
#> GSM494625 4 0.3688 0.934 0.000 0.000 0.208 0.792
#> GSM494654 3 0.3688 0.712 0.000 0.000 0.792 0.208
#> GSM494664 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> GSM494624 4 0.3688 0.934 0.000 0.000 0.208 0.792
#> GSM494651 3 0.0188 0.883 0.004 0.000 0.996 0.000
#> GSM494662 1 0.3688 0.750 0.792 0.000 0.208 0.000
#> GSM494627 3 0.0188 0.882 0.000 0.000 0.996 0.004
#> GSM494673 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> GSM494649 4 0.3688 0.934 0.000 0.000 0.208 0.792
#> GSM494658 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> GSM494653 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> GSM494643 4 0.4661 0.742 0.000 0.000 0.348 0.652
#> GSM494672 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> GSM494618 3 0.0188 0.883 0.004 0.000 0.996 0.000
#> GSM494631 2 0.5386 0.520 0.000 0.632 0.344 0.024
#> GSM494619 4 0.3688 0.934 0.000 0.000 0.208 0.792
#> GSM494674 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> GSM494616 3 0.0188 0.883 0.004 0.000 0.996 0.000
#> GSM494663 3 0.0188 0.882 0.000 0.000 0.996 0.004
#> GSM494628 3 0.0188 0.882 0.000 0.000 0.996 0.004
#> GSM494632 1 0.3688 0.750 0.792 0.000 0.208 0.000
#> GSM494660 4 0.3688 0.934 0.000 0.000 0.208 0.792
#> GSM494622 3 0.0188 0.883 0.004 0.000 0.996 0.000
#> GSM494642 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> GSM494670 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> GSM494675 2 0.3636 0.892 0.000 0.820 0.008 0.172
#> GSM494641 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> GSM494636 1 0.4776 0.497 0.624 0.000 0.376 0.000
#> GSM494640 3 0.0336 0.879 0.000 0.000 0.992 0.008
#> GSM494623 4 0.3688 0.934 0.000 0.000 0.208 0.792
#> GSM494644 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> GSM494646 1 0.1389 0.892 0.952 0.000 0.048 0.000
#> GSM494665 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> GSM494638 1 0.3688 0.750 0.792 0.000 0.208 0.000
#> GSM494645 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> GSM494671 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> GSM494620 4 0.3688 0.934 0.000 0.000 0.208 0.792
#> GSM494630 4 0.3688 0.934 0.000 0.000 0.208 0.792
#> GSM494657 2 0.3870 0.881 0.000 0.788 0.004 0.208
#> GSM494667 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> GSM494621 4 0.3688 0.934 0.000 0.000 0.208 0.792
#> GSM494629 3 0.0188 0.882 0.000 0.000 0.996 0.004
#> GSM494637 3 0.1489 0.842 0.004 0.000 0.952 0.044
#> GSM494652 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> GSM494648 4 0.3688 0.934 0.000 0.000 0.208 0.792
#> GSM494650 3 0.0188 0.883 0.004 0.000 0.996 0.000
#> GSM494669 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> GSM494666 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> GSM494668 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> GSM494633 4 0.3688 0.934 0.000 0.000 0.208 0.792
#> GSM494634 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> GSM494639 1 0.3688 0.750 0.792 0.000 0.208 0.000
#> GSM494661 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> GSM494617 3 0.0817 0.863 0.024 0.000 0.976 0.000
#> GSM494626 3 0.0188 0.883 0.004 0.000 0.996 0.000
#> GSM494656 3 0.4137 0.702 0.000 0.012 0.780 0.208
#> GSM494635 1 0.3311 0.789 0.828 0.000 0.172 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM494565 2 0.4588 0.60402 0.000 0.604 0.380 0.000 0.016
#> GSM494594 3 0.0000 0.89651 0.000 0.000 1.000 0.000 0.000
#> GSM494604 1 0.2966 0.77300 0.816 0.184 0.000 0.000 0.000
#> GSM494564 2 0.4171 0.59898 0.000 0.604 0.396 0.000 0.000
#> GSM494591 3 0.0510 0.88070 0.000 0.000 0.984 0.000 0.016
#> GSM494567 2 0.4278 0.50155 0.000 0.548 0.452 0.000 0.000
#> GSM494602 2 0.0000 0.72512 0.000 1.000 0.000 0.000 0.000
#> GSM494613 2 0.4171 0.59898 0.000 0.604 0.396 0.000 0.000
#> GSM494589 2 0.4171 0.59898 0.000 0.604 0.396 0.000 0.000
#> GSM494598 2 0.0963 0.72550 0.000 0.964 0.000 0.000 0.036
#> GSM494593 2 0.0000 0.72512 0.000 1.000 0.000 0.000 0.000
#> GSM494583 2 0.4551 0.61079 0.000 0.616 0.368 0.000 0.016
#> GSM494612 2 0.0000 0.72512 0.000 1.000 0.000 0.000 0.000
#> GSM494558 4 0.3242 0.64947 0.000 0.000 0.216 0.784 0.000
#> GSM494556 2 0.4171 0.59898 0.000 0.604 0.396 0.000 0.000
#> GSM494559 2 0.4138 0.60585 0.000 0.616 0.384 0.000 0.000
#> GSM494571 3 0.0000 0.89651 0.000 0.000 1.000 0.000 0.000
#> GSM494614 2 0.4060 0.62209 0.000 0.640 0.360 0.000 0.000
#> GSM494603 4 0.3242 0.64947 0.000 0.000 0.216 0.784 0.000
#> GSM494568 4 0.1341 0.87448 0.000 0.000 0.056 0.944 0.000
#> GSM494572 3 0.0000 0.89651 0.000 0.000 1.000 0.000 0.000
#> GSM494600 2 0.4505 0.60321 0.000 0.604 0.384 0.000 0.012
#> GSM494562 2 0.0963 0.72550 0.000 0.964 0.000 0.000 0.036
#> GSM494615 4 0.4171 0.21585 0.000 0.000 0.396 0.604 0.000
#> GSM494582 2 0.0404 0.72477 0.000 0.988 0.000 0.000 0.012
#> GSM494599 1 0.4291 0.42541 0.536 0.464 0.000 0.000 0.000
#> GSM494610 2 0.0963 0.72550 0.000 0.964 0.000 0.000 0.036
#> GSM494587 2 0.0794 0.72796 0.000 0.972 0.028 0.000 0.000
#> GSM494581 2 0.0703 0.72797 0.000 0.976 0.024 0.000 0.000
#> GSM494580 2 0.4268 0.51814 0.000 0.556 0.444 0.000 0.000
#> GSM494563 2 0.4921 0.60991 0.000 0.604 0.360 0.000 0.036
#> GSM494576 2 0.1106 0.72784 0.000 0.964 0.012 0.000 0.024
#> GSM494605 1 0.0000 0.90132 1.000 0.000 0.000 0.000 0.000
#> GSM494584 2 0.4126 0.61298 0.000 0.620 0.380 0.000 0.000
#> GSM494586 2 0.0963 0.72550 0.000 0.964 0.000 0.000 0.036
#> GSM494578 2 0.4171 0.59898 0.000 0.604 0.396 0.000 0.000
#> GSM494585 2 0.0000 0.72512 0.000 1.000 0.000 0.000 0.000
#> GSM494611 2 0.0404 0.72477 0.000 0.988 0.000 0.000 0.012
#> GSM494560 2 0.4171 0.59898 0.000 0.604 0.396 0.000 0.000
#> GSM494595 2 0.0000 0.72512 0.000 1.000 0.000 0.000 0.000
#> GSM494570 5 0.3305 0.63283 0.000 0.000 0.224 0.000 0.776
#> GSM494597 3 0.3961 0.45900 0.000 0.248 0.736 0.000 0.016
#> GSM494607 1 0.4467 0.58861 0.640 0.344 0.000 0.000 0.016
#> GSM494561 5 0.3318 0.68656 0.000 0.008 0.192 0.000 0.800
#> GSM494569 4 0.0000 0.92478 0.000 0.000 0.000 1.000 0.000
#> GSM494592 1 0.4291 0.42541 0.536 0.464 0.000 0.000 0.000
#> GSM494577 2 0.0963 0.72550 0.000 0.964 0.000 0.000 0.036
#> GSM494588 3 0.6824 -0.00931 0.000 0.332 0.344 0.000 0.324
#> GSM494590 3 0.0000 0.89651 0.000 0.000 1.000 0.000 0.000
#> GSM494609 2 0.0000 0.72512 0.000 1.000 0.000 0.000 0.000
#> GSM494608 1 0.6460 0.34034 0.496 0.388 0.040 0.076 0.000
#> GSM494606 2 0.0963 0.69274 0.036 0.964 0.000 0.000 0.000
#> GSM494574 2 0.0963 0.72550 0.000 0.964 0.000 0.000 0.036
#> GSM494573 2 0.4310 0.60070 0.000 0.604 0.392 0.000 0.004
#> GSM494566 2 0.3336 0.67514 0.000 0.772 0.228 0.000 0.000
#> GSM494601 2 0.0000 0.72512 0.000 1.000 0.000 0.000 0.000
#> GSM494557 2 0.4171 0.59898 0.000 0.604 0.396 0.000 0.000
#> GSM494579 2 0.3655 0.69223 0.000 0.804 0.160 0.000 0.036
#> GSM494596 3 0.0000 0.89651 0.000 0.000 1.000 0.000 0.000
#> GSM494575 2 0.0000 0.72512 0.000 1.000 0.000 0.000 0.000
#> GSM494625 5 0.0963 0.92727 0.000 0.000 0.000 0.036 0.964
#> GSM494654 3 0.0000 0.89651 0.000 0.000 1.000 0.000 0.000
#> GSM494664 1 0.0000 0.90132 1.000 0.000 0.000 0.000 0.000
#> GSM494624 5 0.0963 0.92727 0.000 0.000 0.000 0.036 0.964
#> GSM494651 4 0.0000 0.92478 0.000 0.000 0.000 1.000 0.000
#> GSM494662 1 0.3242 0.74966 0.784 0.000 0.000 0.216 0.000
#> GSM494627 4 0.0000 0.92478 0.000 0.000 0.000 1.000 0.000
#> GSM494673 1 0.0000 0.90132 1.000 0.000 0.000 0.000 0.000
#> GSM494649 5 0.0963 0.92727 0.000 0.000 0.000 0.036 0.964
#> GSM494658 1 0.0000 0.90132 1.000 0.000 0.000 0.000 0.000
#> GSM494653 1 0.0000 0.90132 1.000 0.000 0.000 0.000 0.000
#> GSM494643 5 0.3242 0.76116 0.000 0.000 0.000 0.216 0.784
#> GSM494672 1 0.0000 0.90132 1.000 0.000 0.000 0.000 0.000
#> GSM494618 4 0.0000 0.92478 0.000 0.000 0.000 1.000 0.000
#> GSM494631 2 0.5874 0.52829 0.000 0.604 0.208 0.188 0.000
#> GSM494619 5 0.0963 0.92727 0.000 0.000 0.000 0.036 0.964
#> GSM494674 1 0.0000 0.90132 1.000 0.000 0.000 0.000 0.000
#> GSM494616 4 0.0000 0.92478 0.000 0.000 0.000 1.000 0.000
#> GSM494663 4 0.0000 0.92478 0.000 0.000 0.000 1.000 0.000
#> GSM494628 4 0.0000 0.92478 0.000 0.000 0.000 1.000 0.000
#> GSM494632 1 0.3242 0.74966 0.784 0.000 0.000 0.216 0.000
#> GSM494660 5 0.3003 0.79561 0.000 0.000 0.000 0.188 0.812
#> GSM494622 4 0.0000 0.92478 0.000 0.000 0.000 1.000 0.000
#> GSM494642 1 0.0000 0.90132 1.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.90132 1.000 0.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.90132 1.000 0.000 0.000 0.000 0.000
#> GSM494670 1 0.0000 0.90132 1.000 0.000 0.000 0.000 0.000
#> GSM494675 2 0.5639 0.47290 0.000 0.524 0.396 0.080 0.000
#> GSM494641 1 0.0000 0.90132 1.000 0.000 0.000 0.000 0.000
#> GSM494636 1 0.3242 0.74966 0.784 0.000 0.000 0.216 0.000
#> GSM494640 4 0.0000 0.92478 0.000 0.000 0.000 1.000 0.000
#> GSM494623 5 0.0963 0.92727 0.000 0.000 0.000 0.036 0.964
#> GSM494644 1 0.0000 0.90132 1.000 0.000 0.000 0.000 0.000
#> GSM494646 1 0.0880 0.88468 0.968 0.000 0.000 0.032 0.000
#> GSM494665 1 0.0000 0.90132 1.000 0.000 0.000 0.000 0.000
#> GSM494638 1 0.3242 0.74966 0.784 0.000 0.000 0.216 0.000
#> GSM494645 1 0.0000 0.90132 1.000 0.000 0.000 0.000 0.000
#> GSM494671 1 0.0000 0.90132 1.000 0.000 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.90132 1.000 0.000 0.000 0.000 0.000
#> GSM494620 5 0.0963 0.92727 0.000 0.000 0.000 0.036 0.964
#> GSM494630 5 0.0963 0.92727 0.000 0.000 0.000 0.036 0.964
#> GSM494657 3 0.0000 0.89651 0.000 0.000 1.000 0.000 0.000
#> GSM494667 1 0.0000 0.90132 1.000 0.000 0.000 0.000 0.000
#> GSM494621 5 0.0963 0.92727 0.000 0.000 0.000 0.036 0.964
#> GSM494629 4 0.0000 0.92478 0.000 0.000 0.000 1.000 0.000
#> GSM494637 4 0.0794 0.90143 0.000 0.000 0.000 0.972 0.028
#> GSM494652 1 0.0000 0.90132 1.000 0.000 0.000 0.000 0.000
#> GSM494648 5 0.0963 0.92727 0.000 0.000 0.000 0.036 0.964
#> GSM494650 4 0.0000 0.92478 0.000 0.000 0.000 1.000 0.000
#> GSM494669 1 0.0000 0.90132 1.000 0.000 0.000 0.000 0.000
#> GSM494666 1 0.0000 0.90132 1.000 0.000 0.000 0.000 0.000
#> GSM494668 1 0.0000 0.90132 1.000 0.000 0.000 0.000 0.000
#> GSM494633 5 0.0963 0.92727 0.000 0.000 0.000 0.036 0.964
#> GSM494634 1 0.0000 0.90132 1.000 0.000 0.000 0.000 0.000
#> GSM494639 1 0.3242 0.74966 0.784 0.000 0.000 0.216 0.000
#> GSM494661 1 0.0000 0.90132 1.000 0.000 0.000 0.000 0.000
#> GSM494617 4 0.0000 0.92478 0.000 0.000 0.000 1.000 0.000
#> GSM494626 4 0.0000 0.92478 0.000 0.000 0.000 1.000 0.000
#> GSM494656 3 0.0000 0.89651 0.000 0.000 1.000 0.000 0.000
#> GSM494635 1 0.2929 0.78143 0.820 0.000 0.000 0.180 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM494565 5 0.2048 0.844 0.000 0.000 0.120 0.000 0.880 0.000
#> GSM494594 3 0.0000 0.924 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494604 1 0.2346 0.847 0.868 0.124 0.000 0.000 0.008 0.000
#> GSM494564 5 0.2697 0.849 0.000 0.000 0.188 0.000 0.812 0.000
#> GSM494591 3 0.1267 0.863 0.000 0.000 0.940 0.000 0.060 0.000
#> GSM494567 3 0.0790 0.904 0.000 0.000 0.968 0.000 0.032 0.000
#> GSM494602 2 0.0000 0.848 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494613 2 0.4494 0.261 0.000 0.544 0.424 0.000 0.032 0.000
#> GSM494589 5 0.2697 0.849 0.000 0.000 0.188 0.000 0.812 0.000
#> GSM494598 2 0.2762 0.809 0.000 0.804 0.000 0.000 0.196 0.000
#> GSM494593 2 0.0000 0.848 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494583 5 0.2311 0.792 0.000 0.104 0.016 0.000 0.880 0.000
#> GSM494612 2 0.0000 0.848 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494558 4 0.2941 0.686 0.000 0.000 0.220 0.780 0.000 0.000
#> GSM494556 5 0.3672 0.610 0.000 0.000 0.368 0.000 0.632 0.000
#> GSM494559 5 0.3423 0.820 0.000 0.100 0.088 0.000 0.812 0.000
#> GSM494571 3 0.0000 0.924 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494614 5 0.3672 0.610 0.000 0.000 0.368 0.000 0.632 0.000
#> GSM494603 4 0.2941 0.686 0.000 0.000 0.220 0.780 0.000 0.000
#> GSM494568 4 0.1267 0.861 0.000 0.000 0.060 0.940 0.000 0.000
#> GSM494572 3 0.0000 0.924 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494600 5 0.2092 0.846 0.000 0.000 0.124 0.000 0.876 0.000
#> GSM494562 2 0.2941 0.802 0.000 0.780 0.000 0.000 0.220 0.000
#> GSM494615 4 0.3672 0.448 0.000 0.000 0.368 0.632 0.000 0.000
#> GSM494582 2 0.1814 0.831 0.000 0.900 0.000 0.000 0.100 0.000
#> GSM494599 2 0.0000 0.848 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494610 2 0.2941 0.802 0.000 0.780 0.000 0.000 0.220 0.000
#> GSM494587 2 0.2815 0.777 0.000 0.848 0.120 0.000 0.032 0.000
#> GSM494581 2 0.2384 0.797 0.000 0.884 0.084 0.000 0.032 0.000
#> GSM494580 3 0.0790 0.904 0.000 0.000 0.968 0.000 0.032 0.000
#> GSM494563 5 0.0000 0.771 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494576 2 0.3672 0.788 0.000 0.776 0.056 0.000 0.168 0.000
#> GSM494605 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494584 2 0.4958 0.340 0.000 0.560 0.364 0.000 0.076 0.000
#> GSM494586 2 0.2941 0.802 0.000 0.780 0.000 0.000 0.220 0.000
#> GSM494578 3 0.0790 0.904 0.000 0.000 0.968 0.000 0.032 0.000
#> GSM494585 2 0.0790 0.841 0.000 0.968 0.000 0.000 0.032 0.000
#> GSM494611 2 0.1814 0.831 0.000 0.900 0.000 0.000 0.100 0.000
#> GSM494560 5 0.2697 0.849 0.000 0.000 0.188 0.000 0.812 0.000
#> GSM494595 2 0.0000 0.848 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494570 5 0.3585 0.835 0.000 0.000 0.172 0.000 0.780 0.048
#> GSM494597 3 0.1556 0.851 0.000 0.000 0.920 0.000 0.080 0.000
#> GSM494607 2 0.5374 0.250 0.380 0.504 0.000 0.000 0.116 0.000
#> GSM494561 6 0.3508 0.744 0.000 0.068 0.132 0.000 0.000 0.800
#> GSM494569 4 0.0000 0.900 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494592 2 0.0000 0.848 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494577 5 0.0000 0.771 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494588 5 0.3673 0.818 0.000 0.100 0.088 0.000 0.804 0.008
#> GSM494590 3 0.0000 0.924 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494609 2 0.0000 0.848 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494608 2 0.1367 0.819 0.044 0.944 0.000 0.012 0.000 0.000
#> GSM494606 2 0.0000 0.848 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494574 2 0.2697 0.810 0.000 0.812 0.000 0.000 0.188 0.000
#> GSM494573 5 0.2664 0.851 0.000 0.000 0.184 0.000 0.816 0.000
#> GSM494566 2 0.6358 0.274 0.000 0.496 0.244 0.228 0.032 0.000
#> GSM494601 2 0.0000 0.848 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494557 3 0.4508 0.171 0.000 0.396 0.568 0.000 0.036 0.000
#> GSM494579 2 0.3348 0.798 0.000 0.768 0.016 0.000 0.216 0.000
#> GSM494596 3 0.0000 0.924 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494575 2 0.0000 0.848 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494625 6 0.0000 0.942 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494654 3 0.0000 0.924 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494664 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494624 6 0.0000 0.942 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494651 4 0.0000 0.900 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494662 1 0.2941 0.774 0.780 0.000 0.000 0.220 0.000 0.000
#> GSM494627 4 0.0000 0.900 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494673 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494649 6 0.0000 0.942 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494658 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494653 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494643 6 0.2941 0.729 0.000 0.000 0.000 0.220 0.000 0.780
#> GSM494672 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494618 4 0.0000 0.900 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494631 4 0.3385 0.722 0.000 0.000 0.180 0.788 0.032 0.000
#> GSM494619 6 0.0000 0.942 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494674 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494616 4 0.0000 0.900 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494663 4 0.0000 0.900 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494628 4 0.0000 0.900 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494632 1 0.2941 0.774 0.780 0.000 0.000 0.220 0.000 0.000
#> GSM494660 6 0.2664 0.777 0.000 0.000 0.000 0.184 0.000 0.816
#> GSM494622 4 0.0000 0.900 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494642 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494670 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494675 4 0.5285 0.270 0.000 0.000 0.368 0.524 0.108 0.000
#> GSM494641 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494636 1 0.2941 0.774 0.780 0.000 0.000 0.220 0.000 0.000
#> GSM494640 4 0.0000 0.900 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494623 6 0.0000 0.942 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494644 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494646 1 0.0937 0.925 0.960 0.000 0.000 0.040 0.000 0.000
#> GSM494665 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494638 1 0.2941 0.774 0.780 0.000 0.000 0.220 0.000 0.000
#> GSM494645 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494671 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494620 6 0.0000 0.942 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494630 6 0.0000 0.942 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494657 3 0.0000 0.924 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494667 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494621 6 0.0000 0.942 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494629 4 0.0000 0.900 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494637 4 0.0713 0.881 0.000 0.000 0.000 0.972 0.000 0.028
#> GSM494652 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494648 6 0.0000 0.942 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494650 4 0.0000 0.900 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494669 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494666 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494668 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494633 6 0.0000 0.942 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494634 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494639 1 0.2941 0.774 0.780 0.000 0.000 0.220 0.000 0.000
#> GSM494661 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494617 4 0.0000 0.900 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494626 4 0.0000 0.900 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494656 3 0.0000 0.924 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494635 1 0.2631 0.813 0.820 0.000 0.000 0.180 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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 3)
get_signatures(res, k = 4)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 5)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 6)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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.
# code only for demonstration
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) individual(p) k
#> SD:pam 118 5.93e-21 0.99985 2.36e-16 1.0000 2
#> SD:pam 107 8.29e-16 0.23120 1.01e-09 0.7282 3
#> SD:pam 118 5.04e-14 0.11875 1.33e-07 0.2562 4
#> SD:pam 113 1.38e-14 0.00242 1.82e-08 0.0788 5
#> SD:pam 113 8.12e-16 0.06685 1.00e-10 0.2674 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.
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"]
# you can also extract it by
# 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 51941 rows and 120 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 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:
k and the heatmap of
predicted classes for each k.k.k.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:
k;k, the area increased is defined as \(A_k - A_{k-1}\).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 0.368 0.826 0.860 0.3849 0.658 0.658
#> 3 3 0.926 0.917 0.963 0.6739 0.663 0.501
#> 4 4 0.927 0.930 0.951 0.1197 0.898 0.720
#> 5 5 0.730 0.761 0.858 0.0806 0.845 0.516
#> 6 6 0.915 0.885 0.949 0.0428 0.893 0.578
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 3 4
There is also optional best \(k\) = 3 4 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.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM494565 2 0.6887 0.899 0.184 0.816
#> GSM494594 1 0.7602 0.618 0.780 0.220
#> GSM494604 1 0.2043 0.863 0.968 0.032
#> GSM494564 2 0.6887 0.899 0.184 0.816
#> GSM494591 1 0.7602 0.618 0.780 0.220
#> GSM494567 1 0.0000 0.863 1.000 0.000
#> GSM494602 1 0.0000 0.863 1.000 0.000
#> GSM494613 1 0.0000 0.863 1.000 0.000
#> GSM494589 2 0.6887 0.899 0.184 0.816
#> GSM494598 1 0.0000 0.863 1.000 0.000
#> GSM494593 1 0.0000 0.863 1.000 0.000
#> GSM494583 1 0.0938 0.857 0.988 0.012
#> GSM494612 1 0.0000 0.863 1.000 0.000
#> GSM494558 1 0.7602 0.618 0.780 0.220
#> GSM494556 1 0.0000 0.863 1.000 0.000
#> GSM494559 2 0.6887 0.899 0.184 0.816
#> GSM494571 1 0.7602 0.618 0.780 0.220
#> GSM494614 1 0.0000 0.863 1.000 0.000
#> GSM494603 2 0.7528 0.877 0.216 0.784
#> GSM494568 1 0.6712 0.735 0.824 0.176
#> GSM494572 1 0.7602 0.618 0.780 0.220
#> GSM494600 2 0.6887 0.899 0.184 0.816
#> GSM494562 1 0.0000 0.863 1.000 0.000
#> GSM494615 1 0.0000 0.863 1.000 0.000
#> GSM494582 1 0.0000 0.863 1.000 0.000
#> GSM494599 1 0.0000 0.863 1.000 0.000
#> GSM494610 1 0.0000 0.863 1.000 0.000
#> GSM494587 1 0.0000 0.863 1.000 0.000
#> GSM494581 1 0.0000 0.863 1.000 0.000
#> GSM494580 1 0.0672 0.858 0.992 0.008
#> GSM494563 2 0.6887 0.899 0.184 0.816
#> GSM494576 1 0.0000 0.863 1.000 0.000
#> GSM494605 1 0.6887 0.847 0.816 0.184
#> GSM494584 1 0.0000 0.863 1.000 0.000
#> GSM494586 1 0.0000 0.863 1.000 0.000
#> GSM494578 1 0.0000 0.863 1.000 0.000
#> GSM494585 1 0.0000 0.863 1.000 0.000
#> GSM494611 1 0.0000 0.863 1.000 0.000
#> GSM494560 2 0.6887 0.899 0.184 0.816
#> GSM494595 1 0.0000 0.863 1.000 0.000
#> GSM494570 2 0.6887 0.899 0.184 0.816
#> GSM494597 1 0.7528 0.625 0.784 0.216
#> GSM494607 1 0.0000 0.863 1.000 0.000
#> GSM494561 2 0.6887 0.899 0.184 0.816
#> GSM494569 1 0.6048 0.855 0.852 0.148
#> GSM494592 1 0.0000 0.863 1.000 0.000
#> GSM494577 1 0.3879 0.807 0.924 0.076
#> GSM494588 2 0.6887 0.899 0.184 0.816
#> GSM494590 1 0.7602 0.618 0.780 0.220
#> GSM494609 1 0.0000 0.863 1.000 0.000
#> GSM494608 1 0.0000 0.863 1.000 0.000
#> GSM494606 1 0.0000 0.863 1.000 0.000
#> GSM494574 1 0.0000 0.863 1.000 0.000
#> GSM494573 2 0.6887 0.899 0.184 0.816
#> GSM494566 1 0.0000 0.863 1.000 0.000
#> GSM494601 1 0.0000 0.863 1.000 0.000
#> GSM494557 1 0.0000 0.863 1.000 0.000
#> GSM494579 1 0.0000 0.863 1.000 0.000
#> GSM494596 1 0.7602 0.618 0.780 0.220
#> GSM494575 1 0.0000 0.863 1.000 0.000
#> GSM494625 2 0.4161 0.901 0.084 0.916
#> GSM494654 1 0.7674 0.620 0.776 0.224
#> GSM494664 1 0.6887 0.847 0.816 0.184
#> GSM494624 2 0.4161 0.901 0.084 0.916
#> GSM494651 1 0.6048 0.855 0.852 0.148
#> GSM494662 1 0.6148 0.854 0.848 0.152
#> GSM494627 2 0.9996 -0.282 0.488 0.512
#> GSM494673 1 0.6887 0.847 0.816 0.184
#> GSM494649 2 0.4161 0.901 0.084 0.916
#> GSM494658 1 0.4690 0.857 0.900 0.100
#> GSM494653 1 0.6887 0.847 0.816 0.184
#> GSM494643 2 0.6247 0.842 0.156 0.844
#> GSM494672 1 0.6887 0.847 0.816 0.184
#> GSM494618 1 0.6048 0.855 0.852 0.148
#> GSM494631 1 0.1414 0.863 0.980 0.020
#> GSM494619 2 0.4161 0.901 0.084 0.916
#> GSM494674 1 0.6887 0.847 0.816 0.184
#> GSM494616 1 0.6048 0.855 0.852 0.148
#> GSM494663 2 0.7674 0.751 0.224 0.776
#> GSM494628 1 0.6887 0.844 0.816 0.184
#> GSM494632 1 0.6801 0.848 0.820 0.180
#> GSM494660 2 0.4161 0.901 0.084 0.916
#> GSM494622 1 0.4690 0.857 0.900 0.100
#> GSM494642 1 0.6887 0.847 0.816 0.184
#> GSM494647 1 0.6887 0.847 0.816 0.184
#> GSM494659 1 0.6887 0.847 0.816 0.184
#> GSM494670 1 0.6712 0.849 0.824 0.176
#> GSM494675 1 0.7883 0.576 0.764 0.236
#> GSM494641 1 0.6887 0.847 0.816 0.184
#> GSM494636 1 0.6887 0.847 0.816 0.184
#> GSM494640 1 0.9000 0.712 0.684 0.316
#> GSM494623 2 0.4161 0.901 0.084 0.916
#> GSM494644 1 0.6887 0.847 0.816 0.184
#> GSM494646 1 0.6887 0.847 0.816 0.184
#> GSM494665 1 0.6712 0.849 0.824 0.176
#> GSM494638 1 0.5946 0.856 0.856 0.144
#> GSM494645 1 0.6887 0.847 0.816 0.184
#> GSM494671 1 0.6887 0.847 0.816 0.184
#> GSM494655 1 0.6887 0.847 0.816 0.184
#> GSM494620 2 0.4161 0.901 0.084 0.916
#> GSM494630 2 0.4161 0.901 0.084 0.916
#> GSM494657 1 0.7602 0.618 0.780 0.220
#> GSM494667 1 0.6887 0.847 0.816 0.184
#> GSM494621 2 0.4161 0.901 0.084 0.916
#> GSM494629 1 0.8267 0.784 0.740 0.260
#> GSM494637 1 0.8267 0.781 0.740 0.260
#> GSM494652 1 0.6887 0.847 0.816 0.184
#> GSM494648 2 0.4161 0.901 0.084 0.916
#> GSM494650 1 0.6048 0.855 0.852 0.148
#> GSM494669 1 0.6887 0.847 0.816 0.184
#> GSM494666 1 0.6887 0.847 0.816 0.184
#> GSM494668 1 0.6887 0.847 0.816 0.184
#> GSM494633 2 0.4161 0.901 0.084 0.916
#> GSM494634 1 0.6887 0.847 0.816 0.184
#> GSM494639 1 0.6887 0.847 0.816 0.184
#> GSM494661 1 0.6887 0.847 0.816 0.184
#> GSM494617 1 0.6048 0.855 0.852 0.148
#> GSM494626 1 0.6048 0.855 0.852 0.148
#> GSM494656 1 0.7602 0.618 0.780 0.220
#> GSM494635 1 0.6887 0.847 0.816 0.184
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM494565 3 0.0000 0.9772 0.000 0.000 1.000
#> GSM494594 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494604 1 0.6225 0.2656 0.568 0.432 0.000
#> GSM494564 3 0.0000 0.9772 0.000 0.000 1.000
#> GSM494591 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494567 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494602 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494613 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494589 3 0.0000 0.9772 0.000 0.000 1.000
#> GSM494598 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494593 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494583 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494612 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494558 2 0.7801 0.0922 0.428 0.520 0.052
#> GSM494556 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494559 3 0.0000 0.9772 0.000 0.000 1.000
#> GSM494571 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494614 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494603 3 0.9378 0.1724 0.336 0.184 0.480
#> GSM494568 1 0.9098 0.4123 0.540 0.276 0.184
#> GSM494572 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494600 3 0.0000 0.9772 0.000 0.000 1.000
#> GSM494562 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494615 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494582 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494599 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494610 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494587 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494581 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494580 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494563 3 0.0000 0.9772 0.000 0.000 1.000
#> GSM494576 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494605 1 0.0000 0.9334 1.000 0.000 0.000
#> GSM494584 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494586 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494578 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494585 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494611 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494560 3 0.0000 0.9772 0.000 0.000 1.000
#> GSM494595 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494570 3 0.0000 0.9772 0.000 0.000 1.000
#> GSM494597 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494607 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494561 3 0.0000 0.9772 0.000 0.000 1.000
#> GSM494569 1 0.1643 0.9150 0.956 0.000 0.044
#> GSM494592 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494577 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494588 3 0.0000 0.9772 0.000 0.000 1.000
#> GSM494590 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494609 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494608 2 0.2711 0.8883 0.088 0.912 0.000
#> GSM494606 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494574 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494573 3 0.0000 0.9772 0.000 0.000 1.000
#> GSM494566 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494601 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494557 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494579 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494596 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494575 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494625 3 0.0000 0.9772 0.000 0.000 1.000
#> GSM494654 2 0.4002 0.8019 0.160 0.840 0.000
#> GSM494664 1 0.0000 0.9334 1.000 0.000 0.000
#> GSM494624 3 0.0000 0.9772 0.000 0.000 1.000
#> GSM494651 1 0.1643 0.9150 0.956 0.000 0.044
#> GSM494662 1 0.0000 0.9334 1.000 0.000 0.000
#> GSM494627 1 0.4504 0.7702 0.804 0.000 0.196
#> GSM494673 1 0.0000 0.9334 1.000 0.000 0.000
#> GSM494649 3 0.0237 0.9737 0.004 0.000 0.996
#> GSM494658 1 0.4931 0.6761 0.768 0.232 0.000
#> GSM494653 1 0.0000 0.9334 1.000 0.000 0.000
#> GSM494643 1 0.6045 0.4504 0.620 0.000 0.380
#> GSM494672 1 0.0000 0.9334 1.000 0.000 0.000
#> GSM494618 1 0.1643 0.9150 0.956 0.000 0.044
#> GSM494631 2 0.4602 0.8277 0.108 0.852 0.040
#> GSM494619 3 0.0000 0.9772 0.000 0.000 1.000
#> GSM494674 1 0.0000 0.9334 1.000 0.000 0.000
#> GSM494616 1 0.1643 0.9150 0.956 0.000 0.044
#> GSM494663 1 0.5733 0.5688 0.676 0.000 0.324
#> GSM494628 1 0.2625 0.8865 0.916 0.000 0.084
#> GSM494632 1 0.0000 0.9334 1.000 0.000 0.000
#> GSM494660 3 0.0237 0.9737 0.004 0.000 0.996
#> GSM494622 1 0.3875 0.8635 0.888 0.068 0.044
#> GSM494642 1 0.0000 0.9334 1.000 0.000 0.000
#> GSM494647 1 0.0000 0.9334 1.000 0.000 0.000
#> GSM494659 1 0.0000 0.9334 1.000 0.000 0.000
#> GSM494670 1 0.0000 0.9334 1.000 0.000 0.000
#> GSM494675 2 0.2066 0.9203 0.000 0.940 0.060
#> GSM494641 1 0.0000 0.9334 1.000 0.000 0.000
#> GSM494636 1 0.0000 0.9334 1.000 0.000 0.000
#> GSM494640 1 0.4291 0.7896 0.820 0.000 0.180
#> GSM494623 3 0.0000 0.9772 0.000 0.000 1.000
#> GSM494644 1 0.0000 0.9334 1.000 0.000 0.000
#> GSM494646 1 0.0000 0.9334 1.000 0.000 0.000
#> GSM494665 1 0.0000 0.9334 1.000 0.000 0.000
#> GSM494638 1 0.1529 0.9170 0.960 0.000 0.040
#> GSM494645 1 0.0000 0.9334 1.000 0.000 0.000
#> GSM494671 1 0.0000 0.9334 1.000 0.000 0.000
#> GSM494655 1 0.0000 0.9334 1.000 0.000 0.000
#> GSM494620 3 0.0000 0.9772 0.000 0.000 1.000
#> GSM494630 3 0.0000 0.9772 0.000 0.000 1.000
#> GSM494657 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494667 1 0.0000 0.9334 1.000 0.000 0.000
#> GSM494621 3 0.0000 0.9772 0.000 0.000 1.000
#> GSM494629 1 0.2261 0.8992 0.932 0.000 0.068
#> GSM494637 1 0.4235 0.7944 0.824 0.000 0.176
#> GSM494652 1 0.0000 0.9334 1.000 0.000 0.000
#> GSM494648 3 0.0000 0.9772 0.000 0.000 1.000
#> GSM494650 1 0.1643 0.9150 0.956 0.000 0.044
#> GSM494669 1 0.0000 0.9334 1.000 0.000 0.000
#> GSM494666 1 0.0000 0.9334 1.000 0.000 0.000
#> GSM494668 1 0.0000 0.9334 1.000 0.000 0.000
#> GSM494633 3 0.0000 0.9772 0.000 0.000 1.000
#> GSM494634 1 0.0000 0.9334 1.000 0.000 0.000
#> GSM494639 1 0.0000 0.9334 1.000 0.000 0.000
#> GSM494661 1 0.0000 0.9334 1.000 0.000 0.000
#> GSM494617 1 0.1031 0.9243 0.976 0.000 0.024
#> GSM494626 1 0.1163 0.9226 0.972 0.000 0.028
#> GSM494656 2 0.0000 0.9784 0.000 1.000 0.000
#> GSM494635 1 0.0000 0.9334 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM494565 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> GSM494594 3 0.1022 0.825 0.000 0.032 0.968 0.000
#> GSM494604 2 0.3494 0.690 0.172 0.824 0.004 0.000
#> GSM494564 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> GSM494591 3 0.1118 0.825 0.000 0.036 0.964 0.000
#> GSM494567 3 0.4222 0.817 0.000 0.272 0.728 0.000
#> GSM494602 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> GSM494613 3 0.4193 0.820 0.000 0.268 0.732 0.000
#> GSM494589 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> GSM494598 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> GSM494593 2 0.0188 0.978 0.000 0.996 0.004 0.000
#> GSM494583 2 0.0336 0.975 0.000 0.992 0.008 0.000
#> GSM494612 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> GSM494558 3 0.3873 0.829 0.000 0.228 0.772 0.000
#> GSM494556 3 0.4222 0.817 0.000 0.272 0.728 0.000
#> GSM494559 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> GSM494571 3 0.1022 0.825 0.000 0.032 0.968 0.000
#> GSM494614 3 0.4776 0.669 0.000 0.376 0.624 0.000
#> GSM494603 4 0.8656 0.193 0.172 0.080 0.252 0.496
#> GSM494568 3 0.8396 0.485 0.172 0.076 0.536 0.216
#> GSM494572 3 0.1118 0.825 0.000 0.036 0.964 0.000
#> GSM494600 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> GSM494562 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> GSM494615 3 0.4222 0.817 0.000 0.272 0.728 0.000
#> GSM494582 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> GSM494599 2 0.0188 0.978 0.000 0.996 0.004 0.000
#> GSM494610 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> GSM494587 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> GSM494581 2 0.0469 0.973 0.000 0.988 0.012 0.000
#> GSM494580 3 0.4222 0.817 0.000 0.272 0.728 0.000
#> GSM494563 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> GSM494576 2 0.0336 0.975 0.000 0.992 0.008 0.000
#> GSM494605 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM494584 2 0.2760 0.806 0.000 0.872 0.128 0.000
#> GSM494586 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> GSM494578 3 0.4222 0.817 0.000 0.272 0.728 0.000
#> GSM494585 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> GSM494611 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> GSM494560 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> GSM494595 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> GSM494570 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> GSM494597 3 0.3907 0.830 0.000 0.232 0.768 0.000
#> GSM494607 2 0.0188 0.978 0.000 0.996 0.004 0.000
#> GSM494561 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> GSM494569 1 0.0336 0.981 0.992 0.000 0.008 0.000
#> GSM494592 2 0.0188 0.978 0.000 0.996 0.004 0.000
#> GSM494577 2 0.0336 0.975 0.000 0.992 0.008 0.000
#> GSM494588 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> GSM494590 3 0.1118 0.825 0.000 0.036 0.964 0.000
#> GSM494609 2 0.0336 0.976 0.000 0.992 0.008 0.000
#> GSM494608 2 0.0188 0.978 0.000 0.996 0.004 0.000
#> GSM494606 2 0.0188 0.978 0.000 0.996 0.004 0.000
#> GSM494574 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> GSM494573 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> GSM494566 2 0.1118 0.947 0.000 0.964 0.036 0.000
#> GSM494601 2 0.0188 0.978 0.000 0.996 0.004 0.000
#> GSM494557 3 0.4193 0.820 0.000 0.268 0.732 0.000
#> GSM494579 2 0.0336 0.975 0.000 0.992 0.008 0.000
#> GSM494596 3 0.1118 0.825 0.000 0.036 0.964 0.000
#> GSM494575 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> GSM494625 4 0.0188 0.973 0.000 0.000 0.004 0.996
#> GSM494654 3 0.1022 0.825 0.000 0.032 0.968 0.000
#> GSM494664 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM494624 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> GSM494651 1 0.0817 0.972 0.976 0.000 0.024 0.000
#> GSM494662 1 0.0188 0.981 0.996 0.000 0.004 0.000
#> GSM494627 1 0.0895 0.973 0.976 0.000 0.004 0.020
#> GSM494673 1 0.0817 0.977 0.976 0.000 0.024 0.000
#> GSM494649 4 0.0376 0.970 0.004 0.000 0.004 0.992
#> GSM494658 1 0.0336 0.979 0.992 0.008 0.000 0.000
#> GSM494653 1 0.0707 0.978 0.980 0.000 0.020 0.000
#> GSM494643 1 0.4372 0.641 0.728 0.000 0.004 0.268
#> GSM494672 1 0.0817 0.977 0.976 0.000 0.024 0.000
#> GSM494618 1 0.0188 0.981 0.996 0.000 0.004 0.000
#> GSM494631 3 0.4134 0.822 0.000 0.260 0.740 0.000
#> GSM494619 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> GSM494674 1 0.0817 0.977 0.976 0.000 0.024 0.000
#> GSM494616 1 0.0336 0.981 0.992 0.000 0.008 0.000
#> GSM494663 1 0.2999 0.851 0.864 0.000 0.004 0.132
#> GSM494628 1 0.0524 0.980 0.988 0.000 0.004 0.008
#> GSM494632 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM494660 4 0.0524 0.965 0.008 0.000 0.004 0.988
#> GSM494622 1 0.0376 0.980 0.992 0.004 0.004 0.000
#> GSM494642 1 0.0817 0.977 0.976 0.000 0.024 0.000
#> GSM494647 1 0.0817 0.977 0.976 0.000 0.024 0.000
#> GSM494659 1 0.0817 0.977 0.976 0.000 0.024 0.000
#> GSM494670 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM494675 3 0.5599 0.774 0.000 0.288 0.664 0.048
#> GSM494641 1 0.0469 0.980 0.988 0.000 0.012 0.000
#> GSM494636 1 0.0188 0.981 0.996 0.000 0.004 0.000
#> GSM494640 1 0.0524 0.980 0.988 0.000 0.004 0.008
#> GSM494623 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> GSM494644 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM494646 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM494665 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM494638 1 0.0376 0.980 0.992 0.004 0.004 0.000
#> GSM494645 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM494671 1 0.0817 0.977 0.976 0.000 0.024 0.000
#> GSM494655 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM494620 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> GSM494630 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> GSM494657 3 0.1118 0.825 0.000 0.036 0.964 0.000
#> GSM494667 1 0.0817 0.977 0.976 0.000 0.024 0.000
#> GSM494621 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> GSM494629 1 0.0524 0.980 0.988 0.000 0.004 0.008
#> GSM494637 1 0.0524 0.980 0.988 0.000 0.004 0.008
#> GSM494652 1 0.0817 0.977 0.976 0.000 0.024 0.000
#> GSM494648 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> GSM494650 1 0.0817 0.972 0.976 0.000 0.024 0.000
#> GSM494669 1 0.0817 0.977 0.976 0.000 0.024 0.000
#> GSM494666 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM494668 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM494633 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> GSM494634 1 0.0817 0.977 0.976 0.000 0.024 0.000
#> GSM494639 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM494661 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM494617 1 0.0336 0.981 0.992 0.000 0.008 0.000
#> GSM494626 1 0.0336 0.981 0.992 0.000 0.008 0.000
#> GSM494656 3 0.1022 0.825 0.000 0.032 0.968 0.000
#> GSM494635 1 0.0000 0.982 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM494565 5 0.0290 1.000 0.000 0.000 0.008 0.000 0.992
#> GSM494594 3 0.0794 0.834 0.000 0.000 0.972 0.028 0.000
#> GSM494604 2 0.0963 0.816 0.000 0.964 0.036 0.000 0.000
#> GSM494564 5 0.0290 1.000 0.000 0.000 0.008 0.000 0.992
#> GSM494591 3 0.3146 0.841 0.000 0.128 0.844 0.028 0.000
#> GSM494567 3 0.3151 0.839 0.000 0.144 0.836 0.020 0.000
#> GSM494602 2 0.0000 0.821 0.000 1.000 0.000 0.000 0.000
#> GSM494613 3 0.3016 0.844 0.000 0.132 0.848 0.020 0.000
#> GSM494589 5 0.0290 1.000 0.000 0.000 0.008 0.000 0.992
#> GSM494598 2 0.0000 0.821 0.000 1.000 0.000 0.000 0.000
#> GSM494593 2 0.0000 0.821 0.000 1.000 0.000 0.000 0.000
#> GSM494583 2 0.4318 0.664 0.000 0.688 0.292 0.020 0.000
#> GSM494612 2 0.0000 0.821 0.000 1.000 0.000 0.000 0.000
#> GSM494558 3 0.3106 0.841 0.000 0.140 0.840 0.020 0.000
#> GSM494556 3 0.4132 0.674 0.000 0.260 0.720 0.020 0.000
#> GSM494559 5 0.0290 1.000 0.000 0.000 0.008 0.000 0.992
#> GSM494571 3 0.0794 0.834 0.000 0.000 0.972 0.028 0.000
#> GSM494614 2 0.4651 0.525 0.000 0.608 0.372 0.020 0.000
#> GSM494603 2 0.5460 0.591 0.000 0.620 0.316 0.024 0.040
#> GSM494568 2 0.5367 0.578 0.000 0.616 0.328 0.024 0.032
#> GSM494572 3 0.0794 0.834 0.000 0.000 0.972 0.028 0.000
#> GSM494600 5 0.0290 1.000 0.000 0.000 0.008 0.000 0.992
#> GSM494562 2 0.0000 0.821 0.000 1.000 0.000 0.000 0.000
#> GSM494615 3 0.3690 0.776 0.000 0.200 0.780 0.020 0.000
#> GSM494582 2 0.0000 0.821 0.000 1.000 0.000 0.000 0.000
#> GSM494599 2 0.0000 0.821 0.000 1.000 0.000 0.000 0.000
#> GSM494610 2 0.0000 0.821 0.000 1.000 0.000 0.000 0.000
#> GSM494587 2 0.1851 0.802 0.000 0.912 0.088 0.000 0.000
#> GSM494581 2 0.4318 0.664 0.000 0.688 0.292 0.020 0.000
#> GSM494580 3 0.4054 0.698 0.000 0.248 0.732 0.020 0.000
#> GSM494563 5 0.0290 1.000 0.000 0.000 0.008 0.000 0.992
#> GSM494576 2 0.4297 0.669 0.000 0.692 0.288 0.020 0.000
#> GSM494605 1 0.2773 0.889 0.868 0.020 0.000 0.112 0.000
#> GSM494584 2 0.4318 0.664 0.000 0.688 0.292 0.020 0.000
#> GSM494586 2 0.1410 0.811 0.000 0.940 0.060 0.000 0.000
#> GSM494578 3 0.3151 0.839 0.000 0.144 0.836 0.020 0.000
#> GSM494585 2 0.1410 0.811 0.000 0.940 0.060 0.000 0.000
#> GSM494611 2 0.0000 0.821 0.000 1.000 0.000 0.000 0.000
#> GSM494560 5 0.0290 1.000 0.000 0.000 0.008 0.000 0.992
#> GSM494595 2 0.0000 0.821 0.000 1.000 0.000 0.000 0.000
#> GSM494570 5 0.0290 1.000 0.000 0.000 0.008 0.000 0.992
#> GSM494597 3 0.3399 0.816 0.000 0.168 0.812 0.020 0.000
#> GSM494607 2 0.0000 0.821 0.000 1.000 0.000 0.000 0.000
#> GSM494561 5 0.0290 1.000 0.000 0.000 0.008 0.000 0.992
#> GSM494569 4 0.3058 0.674 0.044 0.000 0.096 0.860 0.000
#> GSM494592 2 0.0000 0.821 0.000 1.000 0.000 0.000 0.000
#> GSM494577 2 0.4297 0.669 0.000 0.692 0.288 0.020 0.000
#> GSM494588 5 0.0290 1.000 0.000 0.000 0.008 0.000 0.992
#> GSM494590 3 0.0794 0.834 0.000 0.000 0.972 0.028 0.000
#> GSM494609 2 0.3707 0.687 0.000 0.716 0.284 0.000 0.000
#> GSM494608 2 0.3177 0.742 0.000 0.792 0.208 0.000 0.000
#> GSM494606 2 0.0000 0.821 0.000 1.000 0.000 0.000 0.000
#> GSM494574 2 0.0000 0.821 0.000 1.000 0.000 0.000 0.000
#> GSM494573 5 0.0290 1.000 0.000 0.000 0.008 0.000 0.992
#> GSM494566 2 0.4297 0.669 0.000 0.692 0.288 0.020 0.000
#> GSM494601 2 0.0000 0.821 0.000 1.000 0.000 0.000 0.000
#> GSM494557 3 0.3016 0.844 0.000 0.132 0.848 0.020 0.000
#> GSM494579 2 0.3707 0.687 0.000 0.716 0.284 0.000 0.000
#> GSM494596 3 0.0794 0.834 0.000 0.000 0.972 0.028 0.000
#> GSM494575 2 0.0000 0.821 0.000 1.000 0.000 0.000 0.000
#> GSM494625 4 0.3707 0.562 0.000 0.000 0.000 0.716 0.284
#> GSM494654 3 0.0794 0.834 0.000 0.000 0.972 0.028 0.000
#> GSM494664 1 0.3882 0.803 0.756 0.020 0.000 0.224 0.000
#> GSM494624 4 0.4302 0.362 0.000 0.000 0.000 0.520 0.480
#> GSM494651 4 0.0963 0.720 0.036 0.000 0.000 0.964 0.000
#> GSM494662 4 0.2852 0.639 0.172 0.000 0.000 0.828 0.000
#> GSM494627 4 0.0794 0.721 0.028 0.000 0.000 0.972 0.000
#> GSM494673 1 0.0000 0.895 1.000 0.000 0.000 0.000 0.000
#> GSM494649 4 0.3636 0.575 0.000 0.000 0.000 0.728 0.272
#> GSM494658 2 0.4735 0.658 0.048 0.680 0.272 0.000 0.000
#> GSM494653 1 0.0000 0.895 1.000 0.000 0.000 0.000 0.000
#> GSM494643 4 0.0794 0.721 0.028 0.000 0.000 0.972 0.000
#> GSM494672 1 0.0000 0.895 1.000 0.000 0.000 0.000 0.000
#> GSM494618 4 0.0963 0.720 0.036 0.000 0.000 0.964 0.000
#> GSM494631 3 0.3106 0.841 0.000 0.140 0.840 0.020 0.000
#> GSM494619 4 0.4302 0.362 0.000 0.000 0.000 0.520 0.480
#> GSM494674 1 0.0000 0.895 1.000 0.000 0.000 0.000 0.000
#> GSM494616 4 0.0963 0.720 0.036 0.000 0.000 0.964 0.000
#> GSM494663 4 0.0794 0.721 0.028 0.000 0.000 0.972 0.000
#> GSM494628 4 0.0794 0.721 0.028 0.000 0.000 0.972 0.000
#> GSM494632 1 0.4639 0.562 0.612 0.020 0.000 0.368 0.000
#> GSM494660 4 0.3636 0.575 0.000 0.000 0.000 0.728 0.272
#> GSM494622 3 0.6497 0.561 0.028 0.136 0.568 0.268 0.000
#> GSM494642 1 0.0000 0.895 1.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.895 1.000 0.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.895 1.000 0.000 0.000 0.000 0.000
#> GSM494670 1 0.2773 0.889 0.868 0.020 0.000 0.112 0.000
#> GSM494675 2 0.5284 0.583 0.000 0.620 0.328 0.020 0.032
#> GSM494641 1 0.0000 0.895 1.000 0.000 0.000 0.000 0.000
#> GSM494636 4 0.2813 0.644 0.168 0.000 0.000 0.832 0.000
#> GSM494640 4 0.0794 0.721 0.028 0.000 0.000 0.972 0.000
#> GSM494623 4 0.4302 0.362 0.000 0.000 0.000 0.520 0.480
#> GSM494644 1 0.2773 0.889 0.868 0.020 0.000 0.112 0.000
#> GSM494646 1 0.3999 0.786 0.740 0.020 0.000 0.240 0.000
#> GSM494665 1 0.2773 0.889 0.868 0.020 0.000 0.112 0.000
#> GSM494638 4 0.6624 0.244 0.164 0.012 0.336 0.488 0.000
#> GSM494645 1 0.2824 0.887 0.864 0.020 0.000 0.116 0.000
#> GSM494671 1 0.0000 0.895 1.000 0.000 0.000 0.000 0.000
#> GSM494655 1 0.2773 0.889 0.868 0.020 0.000 0.112 0.000
#> GSM494620 4 0.4302 0.362 0.000 0.000 0.000 0.520 0.480
#> GSM494630 4 0.4300 0.366 0.000 0.000 0.000 0.524 0.476
#> GSM494657 3 0.0794 0.834 0.000 0.000 0.972 0.028 0.000
#> GSM494667 1 0.0000 0.895 1.000 0.000 0.000 0.000 0.000
#> GSM494621 4 0.4302 0.362 0.000 0.000 0.000 0.520 0.480
#> GSM494629 4 0.4982 0.144 0.032 0.000 0.412 0.556 0.000
#> GSM494637 4 0.0794 0.721 0.028 0.000 0.000 0.972 0.000
#> GSM494652 1 0.0000 0.895 1.000 0.000 0.000 0.000 0.000
#> GSM494648 4 0.4302 0.362 0.000 0.000 0.000 0.520 0.480
#> GSM494650 4 0.3192 0.663 0.040 0.000 0.112 0.848 0.000
#> GSM494669 1 0.0000 0.895 1.000 0.000 0.000 0.000 0.000
#> GSM494666 1 0.3106 0.872 0.840 0.020 0.000 0.140 0.000
#> GSM494668 1 0.2773 0.889 0.868 0.020 0.000 0.112 0.000
#> GSM494633 4 0.4249 0.421 0.000 0.000 0.000 0.568 0.432
#> GSM494634 1 0.0000 0.895 1.000 0.000 0.000 0.000 0.000
#> GSM494639 1 0.3999 0.786 0.740 0.020 0.000 0.240 0.000
#> GSM494661 1 0.2773 0.889 0.868 0.020 0.000 0.112 0.000
#> GSM494617 4 0.3109 0.605 0.200 0.000 0.000 0.800 0.000
#> GSM494626 4 0.2230 0.691 0.116 0.000 0.000 0.884 0.000
#> GSM494656 3 0.0794 0.834 0.000 0.000 0.972 0.028 0.000
#> GSM494635 1 0.3942 0.794 0.748 0.020 0.000 0.232 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM494565 5 0.0000 0.9655 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494594 3 0.0000 0.9278 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494604 2 0.4196 0.6753 0.144 0.740 0.000 0.116 0.000 0.000
#> GSM494564 5 0.0000 0.9655 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494591 3 0.0000 0.9278 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494567 3 0.0508 0.9265 0.000 0.012 0.984 0.004 0.000 0.000
#> GSM494602 2 0.0363 0.9691 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM494613 3 0.0363 0.9265 0.000 0.012 0.988 0.000 0.000 0.000
#> GSM494589 5 0.0000 0.9655 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494598 2 0.0363 0.9691 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM494593 2 0.0000 0.9709 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494583 2 0.0000 0.9709 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494612 2 0.0363 0.9691 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM494558 3 0.0820 0.9210 0.000 0.012 0.972 0.016 0.000 0.000
#> GSM494556 3 0.0725 0.9236 0.000 0.012 0.976 0.012 0.000 0.000
#> GSM494559 5 0.0000 0.9655 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494571 3 0.0000 0.9278 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494614 3 0.3866 0.0773 0.000 0.484 0.516 0.000 0.000 0.000
#> GSM494603 4 0.6154 0.3258 0.000 0.296 0.180 0.504 0.016 0.004
#> GSM494568 4 0.0622 0.8936 0.000 0.008 0.000 0.980 0.012 0.000
#> GSM494572 3 0.0000 0.9278 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494600 5 0.0000 0.9655 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494562 2 0.0000 0.9709 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494615 3 0.1367 0.8969 0.000 0.012 0.944 0.044 0.000 0.000
#> GSM494582 2 0.0363 0.9691 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM494599 2 0.0363 0.9691 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM494610 2 0.0363 0.9691 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM494587 2 0.0000 0.9709 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494581 2 0.0000 0.9709 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494580 3 0.0622 0.9255 0.000 0.012 0.980 0.008 0.000 0.000
#> GSM494563 5 0.0000 0.9655 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494576 2 0.0000 0.9709 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494605 4 0.3695 0.4485 0.376 0.000 0.000 0.624 0.000 0.000
#> GSM494584 2 0.0146 0.9684 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM494586 2 0.0000 0.9709 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494578 3 0.0622 0.9255 0.000 0.012 0.980 0.008 0.000 0.000
#> GSM494585 2 0.0000 0.9709 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494611 2 0.0363 0.9691 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM494560 5 0.0000 0.9655 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494595 2 0.0000 0.9709 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494570 5 0.0146 0.9614 0.000 0.000 0.004 0.000 0.996 0.000
#> GSM494597 3 0.0000 0.9278 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494607 2 0.0363 0.9691 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM494561 5 0.3652 0.5043 0.000 0.000 0.004 0.000 0.672 0.324
#> GSM494569 4 0.0363 0.8987 0.000 0.000 0.000 0.988 0.000 0.012
#> GSM494592 2 0.0363 0.9691 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM494577 2 0.0000 0.9709 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494588 5 0.0000 0.9655 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494590 3 0.0000 0.9278 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494609 2 0.0000 0.9709 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494608 2 0.0146 0.9684 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM494606 2 0.0000 0.9709 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494574 2 0.0363 0.9691 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM494573 5 0.0000 0.9655 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494566 2 0.0000 0.9709 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494601 2 0.0000 0.9709 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494557 3 0.0363 0.9265 0.000 0.012 0.988 0.000 0.000 0.000
#> GSM494579 2 0.0000 0.9709 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494596 3 0.0000 0.9278 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494575 2 0.0363 0.9691 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM494625 6 0.0146 0.9429 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM494654 3 0.0146 0.9281 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM494664 4 0.3126 0.6849 0.248 0.000 0.000 0.752 0.000 0.000
#> GSM494624 6 0.0000 0.9438 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494651 4 0.0363 0.8987 0.000 0.000 0.000 0.988 0.000 0.012
#> GSM494662 4 0.0363 0.9014 0.012 0.000 0.000 0.988 0.000 0.000
#> GSM494627 4 0.0458 0.8973 0.000 0.000 0.000 0.984 0.000 0.016
#> GSM494673 1 0.0000 0.9378 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494649 6 0.2697 0.7477 0.000 0.000 0.000 0.188 0.000 0.812
#> GSM494658 2 0.5375 0.4089 0.208 0.588 0.000 0.204 0.000 0.000
#> GSM494653 1 0.0000 0.9378 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494643 4 0.1714 0.8407 0.000 0.000 0.000 0.908 0.000 0.092
#> GSM494672 1 0.0000 0.9378 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494618 4 0.0363 0.9014 0.012 0.000 0.000 0.988 0.000 0.000
#> GSM494631 3 0.3564 0.6069 0.000 0.012 0.724 0.264 0.000 0.000
#> GSM494619 6 0.0000 0.9438 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494674 1 0.0000 0.9378 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494616 4 0.0363 0.8987 0.000 0.000 0.000 0.988 0.000 0.012
#> GSM494663 4 0.0458 0.8973 0.000 0.000 0.000 0.984 0.000 0.016
#> GSM494628 4 0.0458 0.8973 0.000 0.000 0.000 0.984 0.000 0.016
#> GSM494632 4 0.0363 0.9014 0.012 0.000 0.000 0.988 0.000 0.000
#> GSM494660 6 0.2697 0.7477 0.000 0.000 0.000 0.188 0.000 0.812
#> GSM494622 4 0.0363 0.9014 0.012 0.000 0.000 0.988 0.000 0.000
#> GSM494642 1 0.0000 0.9378 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.9378 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.9378 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494670 1 0.2048 0.8579 0.880 0.000 0.000 0.120 0.000 0.000
#> GSM494675 3 0.3420 0.6980 0.000 0.204 0.776 0.008 0.012 0.000
#> GSM494641 1 0.0146 0.9358 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM494636 4 0.0405 0.9011 0.008 0.000 0.000 0.988 0.000 0.004
#> GSM494640 4 0.0458 0.8973 0.000 0.000 0.000 0.984 0.000 0.016
#> GSM494623 6 0.0000 0.9438 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494644 1 0.2260 0.8357 0.860 0.000 0.000 0.140 0.000 0.000
#> GSM494646 4 0.1501 0.8671 0.076 0.000 0.000 0.924 0.000 0.000
#> GSM494665 4 0.3756 0.3884 0.400 0.000 0.000 0.600 0.000 0.000
#> GSM494638 4 0.0363 0.9014 0.012 0.000 0.000 0.988 0.000 0.000
#> GSM494645 1 0.3499 0.5031 0.680 0.000 0.000 0.320 0.000 0.000
#> GSM494671 1 0.0000 0.9378 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494655 1 0.1957 0.8642 0.888 0.000 0.000 0.112 0.000 0.000
#> GSM494620 6 0.0000 0.9438 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494630 6 0.0146 0.9429 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM494657 3 0.0000 0.9278 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494667 1 0.0000 0.9378 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494621 6 0.0000 0.9438 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494629 4 0.0508 0.9012 0.012 0.000 0.000 0.984 0.000 0.004
#> GSM494637 4 0.0458 0.8973 0.000 0.000 0.000 0.984 0.000 0.016
#> GSM494652 1 0.0000 0.9378 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494648 6 0.0000 0.9438 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494650 4 0.0405 0.9011 0.008 0.000 0.000 0.988 0.000 0.004
#> GSM494669 1 0.0000 0.9378 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494666 4 0.3244 0.6581 0.268 0.000 0.000 0.732 0.000 0.000
#> GSM494668 1 0.2048 0.8579 0.880 0.000 0.000 0.120 0.000 0.000
#> GSM494633 6 0.0146 0.9429 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM494634 1 0.0000 0.9378 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494639 4 0.0790 0.8931 0.032 0.000 0.000 0.968 0.000 0.000
#> GSM494661 4 0.3266 0.6520 0.272 0.000 0.000 0.728 0.000 0.000
#> GSM494617 4 0.0363 0.9014 0.012 0.000 0.000 0.988 0.000 0.000
#> GSM494626 4 0.0363 0.9014 0.012 0.000 0.000 0.988 0.000 0.000
#> GSM494656 3 0.0146 0.9281 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM494635 4 0.1910 0.8422 0.108 0.000 0.000 0.892 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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 3)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 4)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 5)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 6)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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.
# code only for demonstration
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) individual(p) k
#> SD:mclust 119 9.62e-01 0.0078 9.42e-01 1.37e-05 2
#> SD:mclust 115 2.31e-15 0.2194 1.31e-08 9.66e-02 3
#> SD:mclust 118 9.27e-16 0.0898 3.60e-12 3.42e-02 4
#> SD:mclust 110 2.13e-16 0.3459 2.04e-09 4.13e-01 5
#> SD:mclust 115 1.90e-18 0.5447 2.10e-11 7.28e-01 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.
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"]
# you can also extract it by
# 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 51941 rows and 120 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 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:
k and the heatmap of
predicted classes for each k.k.k.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:
k;k, the area increased is defined as \(A_k - A_{k-1}\).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 0.520 0.736 0.893 0.4929 0.519 0.519
#> 3 3 0.998 0.949 0.974 0.3147 0.743 0.544
#> 4 4 0.849 0.861 0.937 0.1280 0.792 0.494
#> 5 5 0.656 0.626 0.799 0.0405 0.830 0.492
#> 6 6 0.713 0.693 0.837 0.0501 0.868 0.535
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
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.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM494565 2 0.0000 0.8927 0.000 1.000
#> GSM494594 2 0.0000 0.8927 0.000 1.000
#> GSM494604 1 0.0000 0.8460 1.000 0.000
#> GSM494564 2 0.0000 0.8927 0.000 1.000
#> GSM494591 2 0.0000 0.8927 0.000 1.000
#> GSM494567 2 0.0000 0.8927 0.000 1.000
#> GSM494602 1 0.9686 0.3644 0.604 0.396
#> GSM494613 2 0.0000 0.8927 0.000 1.000
#> GSM494589 2 0.0000 0.8927 0.000 1.000
#> GSM494598 1 0.9732 0.3452 0.596 0.404
#> GSM494593 1 0.9775 0.3242 0.588 0.412
#> GSM494583 2 0.0000 0.8927 0.000 1.000
#> GSM494612 1 0.9608 0.3896 0.616 0.384
#> GSM494558 2 0.0000 0.8927 0.000 1.000
#> GSM494556 2 0.0000 0.8927 0.000 1.000
#> GSM494559 2 0.0000 0.8927 0.000 1.000
#> GSM494571 2 0.0000 0.8927 0.000 1.000
#> GSM494614 2 0.0000 0.8927 0.000 1.000
#> GSM494603 2 0.0000 0.8927 0.000 1.000
#> GSM494568 2 0.1843 0.8703 0.028 0.972
#> GSM494572 2 0.0000 0.8927 0.000 1.000
#> GSM494600 2 0.0000 0.8927 0.000 1.000
#> GSM494562 2 0.8443 0.5802 0.272 0.728
#> GSM494615 2 0.0000 0.8927 0.000 1.000
#> GSM494582 1 0.9710 0.3553 0.600 0.400
#> GSM494599 1 0.9248 0.4730 0.660 0.340
#> GSM494610 1 0.9710 0.3553 0.600 0.400
#> GSM494587 2 0.3274 0.8445 0.060 0.940
#> GSM494581 2 0.9491 0.3803 0.368 0.632
#> GSM494580 2 0.0000 0.8927 0.000 1.000
#> GSM494563 2 0.0672 0.8873 0.008 0.992
#> GSM494576 2 0.0000 0.8927 0.000 1.000
#> GSM494605 1 0.0000 0.8460 1.000 0.000
#> GSM494584 2 0.0000 0.8927 0.000 1.000
#> GSM494586 2 0.9209 0.4560 0.336 0.664
#> GSM494578 2 0.0000 0.8927 0.000 1.000
#> GSM494585 2 0.7376 0.6779 0.208 0.792
#> GSM494611 1 0.9710 0.3553 0.600 0.400
#> GSM494560 2 0.0000 0.8927 0.000 1.000
#> GSM494595 2 0.9963 0.0802 0.464 0.536
#> GSM494570 2 0.0000 0.8927 0.000 1.000
#> GSM494597 2 0.0000 0.8927 0.000 1.000
#> GSM494607 1 0.7528 0.6574 0.784 0.216
#> GSM494561 2 0.8861 0.4720 0.304 0.696
#> GSM494569 1 0.3274 0.8079 0.940 0.060
#> GSM494592 1 0.8555 0.5710 0.720 0.280
#> GSM494577 2 0.0000 0.8927 0.000 1.000
#> GSM494588 2 0.8207 0.6073 0.256 0.744
#> GSM494590 2 0.0000 0.8927 0.000 1.000
#> GSM494609 1 0.9661 0.3730 0.608 0.392
#> GSM494608 1 0.0000 0.8460 1.000 0.000
#> GSM494606 1 0.7745 0.6427 0.772 0.228
#> GSM494574 1 0.9710 0.3553 0.600 0.400
#> GSM494573 2 0.0000 0.8927 0.000 1.000
#> GSM494566 2 0.7883 0.6385 0.236 0.764
#> GSM494601 1 0.9686 0.3644 0.604 0.396
#> GSM494557 2 0.0000 0.8927 0.000 1.000
#> GSM494579 2 0.9358 0.4196 0.352 0.648
#> GSM494596 2 0.0000 0.8927 0.000 1.000
#> GSM494575 1 0.9710 0.3553 0.600 0.400
#> GSM494625 1 0.8555 0.5735 0.720 0.280
#> GSM494654 2 0.9358 0.3662 0.352 0.648
#> GSM494664 1 0.0000 0.8460 1.000 0.000
#> GSM494624 1 0.0000 0.8460 1.000 0.000
#> GSM494651 1 0.9000 0.5160 0.684 0.316
#> GSM494662 1 0.0000 0.8460 1.000 0.000
#> GSM494627 1 0.9954 0.1797 0.540 0.460
#> GSM494673 1 0.0000 0.8460 1.000 0.000
#> GSM494649 1 0.8267 0.6025 0.740 0.260
#> GSM494658 1 0.0000 0.8460 1.000 0.000
#> GSM494653 1 0.0000 0.8460 1.000 0.000
#> GSM494643 1 0.0000 0.8460 1.000 0.000
#> GSM494672 1 0.0000 0.8460 1.000 0.000
#> GSM494618 1 0.6247 0.7254 0.844 0.156
#> GSM494631 2 0.7528 0.6332 0.216 0.784
#> GSM494619 1 0.0000 0.8460 1.000 0.000
#> GSM494674 1 0.0000 0.8460 1.000 0.000
#> GSM494616 1 0.7745 0.6451 0.772 0.228
#> GSM494663 1 0.2043 0.8271 0.968 0.032
#> GSM494628 1 0.9129 0.4952 0.672 0.328
#> GSM494632 1 0.0000 0.8460 1.000 0.000
#> GSM494660 1 0.9248 0.4729 0.660 0.340
#> GSM494622 1 0.5629 0.7503 0.868 0.132
#> GSM494642 1 0.0000 0.8460 1.000 0.000
#> GSM494647 1 0.0000 0.8460 1.000 0.000
#> GSM494659 1 0.0000 0.8460 1.000 0.000
#> GSM494670 1 0.0000 0.8460 1.000 0.000
#> GSM494675 2 0.0000 0.8927 0.000 1.000
#> GSM494641 1 0.0000 0.8460 1.000 0.000
#> GSM494636 1 0.0000 0.8460 1.000 0.000
#> GSM494640 1 0.9323 0.4573 0.652 0.348
#> GSM494623 1 0.0000 0.8460 1.000 0.000
#> GSM494644 1 0.0000 0.8460 1.000 0.000
#> GSM494646 1 0.0000 0.8460 1.000 0.000
#> GSM494665 1 0.0000 0.8460 1.000 0.000
#> GSM494638 1 0.0000 0.8460 1.000 0.000
#> GSM494645 1 0.0000 0.8460 1.000 0.000
#> GSM494671 1 0.0000 0.8460 1.000 0.000
#> GSM494655 1 0.0000 0.8460 1.000 0.000
#> GSM494620 1 0.0000 0.8460 1.000 0.000
#> GSM494630 1 0.0000 0.8460 1.000 0.000
#> GSM494657 2 0.0000 0.8927 0.000 1.000
#> GSM494667 1 0.0000 0.8460 1.000 0.000
#> GSM494621 1 0.0000 0.8460 1.000 0.000
#> GSM494629 2 0.9686 0.2555 0.396 0.604
#> GSM494637 1 0.7815 0.6396 0.768 0.232
#> GSM494652 1 0.0000 0.8460 1.000 0.000
#> GSM494648 1 0.0000 0.8460 1.000 0.000
#> GSM494650 1 0.9460 0.4247 0.636 0.364
#> GSM494669 1 0.0000 0.8460 1.000 0.000
#> GSM494666 1 0.0000 0.8460 1.000 0.000
#> GSM494668 1 0.0000 0.8460 1.000 0.000
#> GSM494633 1 0.1184 0.8369 0.984 0.016
#> GSM494634 1 0.0000 0.8460 1.000 0.000
#> GSM494639 1 0.0000 0.8460 1.000 0.000
#> GSM494661 1 0.0000 0.8460 1.000 0.000
#> GSM494617 1 0.0000 0.8460 1.000 0.000
#> GSM494626 1 0.0000 0.8460 1.000 0.000
#> GSM494656 2 0.1184 0.8805 0.016 0.984
#> GSM494635 1 0.0000 0.8460 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM494565 2 0.1289 0.962 0.000 0.968 0.032
#> GSM494594 3 0.0000 0.960 0.000 0.000 1.000
#> GSM494604 2 0.2711 0.874 0.088 0.912 0.000
#> GSM494564 3 0.1753 0.923 0.000 0.048 0.952
#> GSM494591 3 0.0000 0.960 0.000 0.000 1.000
#> GSM494567 3 0.0000 0.960 0.000 0.000 1.000
#> GSM494602 2 0.0000 0.967 0.000 1.000 0.000
#> GSM494613 3 0.0000 0.960 0.000 0.000 1.000
#> GSM494589 3 0.0000 0.960 0.000 0.000 1.000
#> GSM494598 2 0.0424 0.968 0.000 0.992 0.008
#> GSM494593 2 0.0237 0.968 0.000 0.996 0.004
#> GSM494583 2 0.1163 0.965 0.000 0.972 0.028
#> GSM494612 2 0.0000 0.967 0.000 1.000 0.000
#> GSM494558 3 0.0747 0.955 0.016 0.000 0.984
#> GSM494556 3 0.0000 0.960 0.000 0.000 1.000
#> GSM494559 3 0.6140 0.299 0.000 0.404 0.596
#> GSM494571 3 0.0237 0.959 0.004 0.000 0.996
#> GSM494614 2 0.2711 0.910 0.000 0.912 0.088
#> GSM494603 3 0.0424 0.958 0.008 0.000 0.992
#> GSM494568 3 0.0892 0.953 0.020 0.000 0.980
#> GSM494572 3 0.0000 0.960 0.000 0.000 1.000
#> GSM494600 3 0.1163 0.941 0.000 0.028 0.972
#> GSM494562 2 0.0892 0.968 0.000 0.980 0.020
#> GSM494615 3 0.0424 0.958 0.008 0.000 0.992
#> GSM494582 2 0.0000 0.967 0.000 1.000 0.000
#> GSM494599 2 0.0000 0.967 0.000 1.000 0.000
#> GSM494610 2 0.0424 0.968 0.000 0.992 0.008
#> GSM494587 2 0.1031 0.967 0.000 0.976 0.024
#> GSM494581 2 0.0892 0.968 0.000 0.980 0.020
#> GSM494580 3 0.0000 0.960 0.000 0.000 1.000
#> GSM494563 2 0.1163 0.965 0.000 0.972 0.028
#> GSM494576 2 0.0892 0.968 0.000 0.980 0.020
#> GSM494605 1 0.0892 0.979 0.980 0.020 0.000
#> GSM494584 2 0.1163 0.965 0.000 0.972 0.028
#> GSM494586 2 0.0892 0.968 0.000 0.980 0.020
#> GSM494578 3 0.0000 0.960 0.000 0.000 1.000
#> GSM494585 2 0.0892 0.968 0.000 0.980 0.020
#> GSM494611 2 0.0000 0.967 0.000 1.000 0.000
#> GSM494560 2 0.2066 0.939 0.000 0.940 0.060
#> GSM494595 2 0.0892 0.968 0.000 0.980 0.020
#> GSM494570 3 0.0892 0.953 0.020 0.000 0.980
#> GSM494597 3 0.0000 0.960 0.000 0.000 1.000
#> GSM494607 2 0.0000 0.967 0.000 1.000 0.000
#> GSM494561 3 0.0892 0.953 0.020 0.000 0.980
#> GSM494569 1 0.1031 0.966 0.976 0.000 0.024
#> GSM494592 2 0.0000 0.967 0.000 1.000 0.000
#> GSM494577 2 0.1031 0.967 0.000 0.976 0.024
#> GSM494588 2 0.1289 0.962 0.000 0.968 0.032
#> GSM494590 3 0.0000 0.960 0.000 0.000 1.000
#> GSM494609 2 0.0000 0.967 0.000 1.000 0.000
#> GSM494608 2 0.5926 0.428 0.356 0.644 0.000
#> GSM494606 2 0.0000 0.967 0.000 1.000 0.000
#> GSM494574 2 0.0237 0.968 0.000 0.996 0.004
#> GSM494573 3 0.3686 0.818 0.000 0.140 0.860
#> GSM494566 2 0.0892 0.968 0.000 0.980 0.020
#> GSM494601 2 0.0000 0.967 0.000 1.000 0.000
#> GSM494557 3 0.0000 0.960 0.000 0.000 1.000
#> GSM494579 2 0.0892 0.968 0.000 0.980 0.020
#> GSM494596 3 0.0000 0.960 0.000 0.000 1.000
#> GSM494575 2 0.0000 0.967 0.000 1.000 0.000
#> GSM494625 1 0.0237 0.980 0.996 0.000 0.004
#> GSM494654 3 0.0892 0.953 0.020 0.000 0.980
#> GSM494664 1 0.0424 0.981 0.992 0.008 0.000
#> GSM494624 1 0.0000 0.981 1.000 0.000 0.000
#> GSM494651 1 0.0000 0.981 1.000 0.000 0.000
#> GSM494662 1 0.0000 0.981 1.000 0.000 0.000
#> GSM494627 3 0.5431 0.609 0.284 0.000 0.716
#> GSM494673 1 0.1031 0.977 0.976 0.024 0.000
#> GSM494649 1 0.0000 0.981 1.000 0.000 0.000
#> GSM494658 1 0.1529 0.966 0.960 0.040 0.000
#> GSM494653 1 0.0892 0.979 0.980 0.020 0.000
#> GSM494643 1 0.0000 0.981 1.000 0.000 0.000
#> GSM494672 1 0.1163 0.975 0.972 0.028 0.000
#> GSM494618 1 0.0000 0.981 1.000 0.000 0.000
#> GSM494631 3 0.0892 0.953 0.020 0.000 0.980
#> GSM494619 1 0.0000 0.981 1.000 0.000 0.000
#> GSM494674 1 0.0892 0.979 0.980 0.020 0.000
#> GSM494616 1 0.0000 0.981 1.000 0.000 0.000
#> GSM494663 1 0.0000 0.981 1.000 0.000 0.000
#> GSM494628 1 0.0592 0.975 0.988 0.000 0.012
#> GSM494632 1 0.0000 0.981 1.000 0.000 0.000
#> GSM494660 1 0.0592 0.975 0.988 0.000 0.012
#> GSM494622 1 0.1411 0.955 0.964 0.000 0.036
#> GSM494642 1 0.0892 0.979 0.980 0.020 0.000
#> GSM494647 1 0.0892 0.979 0.980 0.020 0.000
#> GSM494659 1 0.0892 0.979 0.980 0.020 0.000
#> GSM494670 1 0.0892 0.979 0.980 0.020 0.000
#> GSM494675 3 0.0000 0.960 0.000 0.000 1.000
#> GSM494641 1 0.0892 0.979 0.980 0.020 0.000
#> GSM494636 1 0.0000 0.981 1.000 0.000 0.000
#> GSM494640 1 0.5810 0.481 0.664 0.000 0.336
#> GSM494623 1 0.0000 0.981 1.000 0.000 0.000
#> GSM494644 1 0.0892 0.979 0.980 0.020 0.000
#> GSM494646 1 0.0237 0.981 0.996 0.004 0.000
#> GSM494665 1 0.0892 0.979 0.980 0.020 0.000
#> GSM494638 1 0.0000 0.981 1.000 0.000 0.000
#> GSM494645 1 0.0747 0.980 0.984 0.016 0.000
#> GSM494671 1 0.1163 0.975 0.972 0.028 0.000
#> GSM494655 1 0.0892 0.979 0.980 0.020 0.000
#> GSM494620 1 0.0000 0.981 1.000 0.000 0.000
#> GSM494630 1 0.0000 0.981 1.000 0.000 0.000
#> GSM494657 3 0.0000 0.960 0.000 0.000 1.000
#> GSM494667 1 0.0892 0.979 0.980 0.020 0.000
#> GSM494621 1 0.0000 0.981 1.000 0.000 0.000
#> GSM494629 3 0.1860 0.925 0.052 0.000 0.948
#> GSM494637 1 0.0237 0.980 0.996 0.000 0.004
#> GSM494652 1 0.0892 0.979 0.980 0.020 0.000
#> GSM494648 1 0.0000 0.981 1.000 0.000 0.000
#> GSM494650 1 0.2448 0.912 0.924 0.000 0.076
#> GSM494669 1 0.0892 0.979 0.980 0.020 0.000
#> GSM494666 1 0.0424 0.981 0.992 0.008 0.000
#> GSM494668 1 0.0892 0.979 0.980 0.020 0.000
#> GSM494633 1 0.0237 0.980 0.996 0.000 0.004
#> GSM494634 1 0.1163 0.975 0.972 0.028 0.000
#> GSM494639 1 0.0000 0.981 1.000 0.000 0.000
#> GSM494661 1 0.0747 0.980 0.984 0.016 0.000
#> GSM494617 1 0.0000 0.981 1.000 0.000 0.000
#> GSM494626 1 0.0000 0.981 1.000 0.000 0.000
#> GSM494656 3 0.0892 0.953 0.020 0.000 0.980
#> GSM494635 1 0.0747 0.980 0.984 0.016 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM494565 2 0.0000 0.946 0.000 1.000 0.000 0.000
#> GSM494594 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> GSM494604 1 0.0000 0.861 1.000 0.000 0.000 0.000
#> GSM494564 2 0.0592 0.937 0.000 0.984 0.000 0.016
#> GSM494591 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> GSM494567 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> GSM494602 1 0.0817 0.851 0.976 0.024 0.000 0.000
#> GSM494613 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> GSM494589 2 0.0000 0.946 0.000 1.000 0.000 0.000
#> GSM494598 2 0.0000 0.946 0.000 1.000 0.000 0.000
#> GSM494593 1 0.3942 0.620 0.764 0.236 0.000 0.000
#> GSM494583 2 0.0000 0.946 0.000 1.000 0.000 0.000
#> GSM494612 1 0.0707 0.853 0.980 0.020 0.000 0.000
#> GSM494558 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> GSM494556 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> GSM494559 2 0.0592 0.937 0.000 0.984 0.000 0.016
#> GSM494571 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> GSM494614 2 0.0000 0.946 0.000 1.000 0.000 0.000
#> GSM494603 2 0.1733 0.914 0.000 0.948 0.024 0.028
#> GSM494568 3 0.2741 0.859 0.000 0.012 0.892 0.096
#> GSM494572 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> GSM494600 2 0.0000 0.946 0.000 1.000 0.000 0.000
#> GSM494562 2 0.2647 0.844 0.120 0.880 0.000 0.000
#> GSM494615 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> GSM494582 1 0.1940 0.815 0.924 0.076 0.000 0.000
#> GSM494599 1 0.0188 0.861 0.996 0.004 0.000 0.000
#> GSM494610 2 0.0000 0.946 0.000 1.000 0.000 0.000
#> GSM494587 2 0.4072 0.663 0.252 0.748 0.000 0.000
#> GSM494581 2 0.0000 0.946 0.000 1.000 0.000 0.000
#> GSM494580 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> GSM494563 2 0.0000 0.946 0.000 1.000 0.000 0.000
#> GSM494576 2 0.0000 0.946 0.000 1.000 0.000 0.000
#> GSM494605 4 0.1557 0.917 0.056 0.000 0.000 0.944
#> GSM494584 2 0.0000 0.946 0.000 1.000 0.000 0.000
#> GSM494586 2 0.0000 0.946 0.000 1.000 0.000 0.000
#> GSM494578 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> GSM494585 2 0.1716 0.901 0.064 0.936 0.000 0.000
#> GSM494611 1 0.2011 0.814 0.920 0.080 0.000 0.000
#> GSM494560 2 0.0000 0.946 0.000 1.000 0.000 0.000
#> GSM494595 2 0.0000 0.946 0.000 1.000 0.000 0.000
#> GSM494570 2 0.1118 0.921 0.000 0.964 0.000 0.036
#> GSM494597 3 0.3172 0.789 0.000 0.160 0.840 0.000
#> GSM494607 1 0.0000 0.861 1.000 0.000 0.000 0.000
#> GSM494561 4 0.1211 0.907 0.000 0.040 0.000 0.960
#> GSM494569 3 0.5231 0.333 0.012 0.000 0.604 0.384
#> GSM494592 1 0.0188 0.861 0.996 0.004 0.000 0.000
#> GSM494577 2 0.0000 0.946 0.000 1.000 0.000 0.000
#> GSM494588 2 0.0592 0.937 0.000 0.984 0.000 0.016
#> GSM494590 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> GSM494609 2 0.4804 0.407 0.384 0.616 0.000 0.000
#> GSM494608 1 0.1389 0.847 0.952 0.000 0.000 0.048
#> GSM494606 1 0.0000 0.861 1.000 0.000 0.000 0.000
#> GSM494574 2 0.0817 0.930 0.024 0.976 0.000 0.000
#> GSM494573 2 0.0000 0.946 0.000 1.000 0.000 0.000
#> GSM494566 2 0.5161 0.354 0.400 0.592 0.008 0.000
#> GSM494601 1 0.0592 0.855 0.984 0.016 0.000 0.000
#> GSM494557 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> GSM494579 2 0.0000 0.946 0.000 1.000 0.000 0.000
#> GSM494596 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> GSM494575 1 0.1389 0.835 0.952 0.048 0.000 0.000
#> GSM494625 4 0.0000 0.933 0.000 0.000 0.000 1.000
#> GSM494654 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> GSM494664 4 0.1211 0.926 0.040 0.000 0.000 0.960
#> GSM494624 4 0.0000 0.933 0.000 0.000 0.000 1.000
#> GSM494651 3 0.4155 0.667 0.004 0.000 0.756 0.240
#> GSM494662 4 0.0469 0.934 0.012 0.000 0.000 0.988
#> GSM494627 4 0.0469 0.928 0.000 0.000 0.012 0.988
#> GSM494673 1 0.1211 0.850 0.960 0.000 0.000 0.040
#> GSM494649 4 0.0000 0.933 0.000 0.000 0.000 1.000
#> GSM494658 1 0.4977 0.160 0.540 0.000 0.000 0.460
#> GSM494653 4 0.4103 0.679 0.256 0.000 0.000 0.744
#> GSM494643 4 0.0000 0.933 0.000 0.000 0.000 1.000
#> GSM494672 1 0.0000 0.861 1.000 0.000 0.000 0.000
#> GSM494618 4 0.0657 0.933 0.012 0.000 0.004 0.984
#> GSM494631 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> GSM494619 4 0.0000 0.933 0.000 0.000 0.000 1.000
#> GSM494674 4 0.3764 0.745 0.216 0.000 0.000 0.784
#> GSM494616 4 0.4452 0.648 0.008 0.000 0.260 0.732
#> GSM494663 4 0.0000 0.933 0.000 0.000 0.000 1.000
#> GSM494628 4 0.0000 0.933 0.000 0.000 0.000 1.000
#> GSM494632 4 0.0817 0.932 0.024 0.000 0.000 0.976
#> GSM494660 4 0.0000 0.933 0.000 0.000 0.000 1.000
#> GSM494622 4 0.5125 0.366 0.008 0.000 0.388 0.604
#> GSM494642 4 0.3266 0.811 0.168 0.000 0.000 0.832
#> GSM494647 1 0.3837 0.706 0.776 0.000 0.000 0.224
#> GSM494659 1 0.4331 0.604 0.712 0.000 0.000 0.288
#> GSM494670 4 0.3444 0.790 0.184 0.000 0.000 0.816
#> GSM494675 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> GSM494641 4 0.3172 0.821 0.160 0.000 0.000 0.840
#> GSM494636 4 0.0469 0.934 0.012 0.000 0.000 0.988
#> GSM494640 4 0.0000 0.933 0.000 0.000 0.000 1.000
#> GSM494623 4 0.0000 0.933 0.000 0.000 0.000 1.000
#> GSM494644 4 0.1302 0.924 0.044 0.000 0.000 0.956
#> GSM494646 4 0.0707 0.933 0.020 0.000 0.000 0.980
#> GSM494665 4 0.4477 0.565 0.312 0.000 0.000 0.688
#> GSM494638 4 0.0921 0.931 0.028 0.000 0.000 0.972
#> GSM494645 4 0.1022 0.929 0.032 0.000 0.000 0.968
#> GSM494671 1 0.0000 0.861 1.000 0.000 0.000 0.000
#> GSM494655 4 0.1389 0.922 0.048 0.000 0.000 0.952
#> GSM494620 4 0.0000 0.933 0.000 0.000 0.000 1.000
#> GSM494630 4 0.0000 0.933 0.000 0.000 0.000 1.000
#> GSM494657 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> GSM494667 1 0.3801 0.710 0.780 0.000 0.000 0.220
#> GSM494621 4 0.0000 0.933 0.000 0.000 0.000 1.000
#> GSM494629 3 0.1867 0.885 0.000 0.000 0.928 0.072
#> GSM494637 4 0.0000 0.933 0.000 0.000 0.000 1.000
#> GSM494652 1 0.3764 0.715 0.784 0.000 0.000 0.216
#> GSM494648 4 0.0000 0.933 0.000 0.000 0.000 1.000
#> GSM494650 3 0.2408 0.851 0.000 0.000 0.896 0.104
#> GSM494669 1 0.4961 0.203 0.552 0.000 0.000 0.448
#> GSM494666 4 0.1302 0.924 0.044 0.000 0.000 0.956
#> GSM494668 4 0.2081 0.896 0.084 0.000 0.000 0.916
#> GSM494633 4 0.0000 0.933 0.000 0.000 0.000 1.000
#> GSM494634 1 0.0000 0.861 1.000 0.000 0.000 0.000
#> GSM494639 4 0.0707 0.933 0.020 0.000 0.000 0.980
#> GSM494661 4 0.2081 0.896 0.084 0.000 0.000 0.916
#> GSM494617 4 0.0817 0.932 0.024 0.000 0.000 0.976
#> GSM494626 4 0.0707 0.933 0.020 0.000 0.000 0.980
#> GSM494656 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> GSM494635 4 0.0592 0.933 0.016 0.000 0.000 0.984
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM494565 5 0.3932 0.3919 0.000 0.328 0.000 0.000 0.672
#> GSM494594 3 0.0703 0.7843 0.000 0.024 0.976 0.000 0.000
#> GSM494604 1 0.2046 0.5758 0.916 0.000 0.000 0.016 0.068
#> GSM494564 5 0.1792 0.7198 0.000 0.084 0.000 0.000 0.916
#> GSM494591 3 0.0290 0.7868 0.000 0.008 0.992 0.000 0.000
#> GSM494567 3 0.2329 0.7417 0.000 0.124 0.876 0.000 0.000
#> GSM494602 1 0.3612 0.3983 0.732 0.268 0.000 0.000 0.000
#> GSM494613 3 0.4974 0.3603 0.000 0.408 0.560 0.000 0.032
#> GSM494589 5 0.2280 0.7119 0.000 0.120 0.000 0.000 0.880
#> GSM494598 1 0.5849 0.3515 0.508 0.100 0.000 0.000 0.392
#> GSM494593 2 0.2583 0.6679 0.132 0.864 0.000 0.000 0.004
#> GSM494583 2 0.2813 0.6470 0.000 0.832 0.000 0.000 0.168
#> GSM494612 2 0.4331 0.3255 0.400 0.596 0.000 0.004 0.000
#> GSM494558 3 0.4339 0.4076 0.000 0.012 0.652 0.000 0.336
#> GSM494556 3 0.3531 0.7066 0.000 0.148 0.816 0.000 0.036
#> GSM494559 2 0.2381 0.6806 0.008 0.912 0.008 0.012 0.060
#> GSM494571 3 0.0162 0.7872 0.000 0.004 0.996 0.000 0.000
#> GSM494614 2 0.2920 0.6584 0.000 0.852 0.016 0.000 0.132
#> GSM494603 5 0.1205 0.6950 0.004 0.040 0.000 0.000 0.956
#> GSM494568 5 0.3269 0.6400 0.004 0.012 0.144 0.004 0.836
#> GSM494572 3 0.0880 0.7836 0.000 0.032 0.968 0.000 0.000
#> GSM494600 5 0.2280 0.7127 0.000 0.120 0.000 0.000 0.880
#> GSM494562 1 0.5715 0.4315 0.564 0.100 0.000 0.000 0.336
#> GSM494615 3 0.3226 0.7376 0.000 0.088 0.852 0.000 0.060
#> GSM494582 1 0.4201 0.1822 0.664 0.328 0.000 0.000 0.008
#> GSM494599 1 0.1117 0.5643 0.964 0.020 0.000 0.016 0.000
#> GSM494610 1 0.5844 0.3044 0.484 0.096 0.000 0.000 0.420
#> GSM494587 2 0.2674 0.6894 0.084 0.888 0.008 0.000 0.020
#> GSM494581 2 0.0912 0.6921 0.000 0.972 0.012 0.000 0.016
#> GSM494580 3 0.2280 0.7457 0.000 0.120 0.880 0.000 0.000
#> GSM494563 5 0.1704 0.6967 0.004 0.068 0.000 0.000 0.928
#> GSM494576 2 0.4102 0.4894 0.004 0.692 0.004 0.000 0.300
#> GSM494605 4 0.1043 0.8747 0.040 0.000 0.000 0.960 0.000
#> GSM494584 2 0.1281 0.6982 0.000 0.956 0.012 0.000 0.032
#> GSM494586 2 0.6157 0.2862 0.140 0.496 0.000 0.000 0.364
#> GSM494578 2 0.4538 -0.1222 0.000 0.540 0.452 0.000 0.008
#> GSM494585 2 0.1740 0.6929 0.056 0.932 0.012 0.000 0.000
#> GSM494611 1 0.3934 0.4127 0.740 0.244 0.000 0.000 0.016
#> GSM494560 5 0.4283 0.1792 0.000 0.456 0.000 0.000 0.544
#> GSM494595 2 0.2616 0.6796 0.020 0.880 0.000 0.000 0.100
#> GSM494570 5 0.1894 0.7148 0.008 0.072 0.000 0.000 0.920
#> GSM494597 3 0.4528 0.2482 0.000 0.008 0.548 0.000 0.444
#> GSM494607 1 0.1026 0.5720 0.968 0.004 0.000 0.004 0.024
#> GSM494561 5 0.4790 0.5347 0.008 0.064 0.004 0.184 0.740
#> GSM494569 4 0.3197 0.7931 0.012 0.004 0.152 0.832 0.000
#> GSM494592 1 0.2464 0.5378 0.888 0.096 0.000 0.016 0.000
#> GSM494577 2 0.4702 0.2558 0.016 0.552 0.000 0.000 0.432
#> GSM494588 5 0.3684 0.5725 0.000 0.280 0.000 0.000 0.720
#> GSM494590 3 0.0000 0.7868 0.000 0.000 1.000 0.000 0.000
#> GSM494609 2 0.2403 0.6873 0.072 0.904 0.012 0.012 0.000
#> GSM494608 2 0.6086 0.2226 0.116 0.556 0.008 0.320 0.000
#> GSM494606 2 0.4872 0.2071 0.436 0.540 0.000 0.024 0.000
#> GSM494574 1 0.5729 0.3523 0.516 0.088 0.000 0.000 0.396
#> GSM494573 5 0.2966 0.6798 0.000 0.184 0.000 0.000 0.816
#> GSM494566 1 0.5778 0.4417 0.576 0.096 0.004 0.000 0.324
#> GSM494601 1 0.4306 -0.1071 0.508 0.492 0.000 0.000 0.000
#> GSM494557 3 0.4894 0.2568 0.000 0.456 0.520 0.000 0.024
#> GSM494579 5 0.2233 0.6816 0.016 0.080 0.000 0.000 0.904
#> GSM494596 3 0.0162 0.7872 0.000 0.004 0.996 0.000 0.000
#> GSM494575 2 0.2852 0.6427 0.172 0.828 0.000 0.000 0.000
#> GSM494625 4 0.4235 0.5197 0.008 0.000 0.000 0.656 0.336
#> GSM494654 3 0.0000 0.7868 0.000 0.000 1.000 0.000 0.000
#> GSM494664 4 0.1041 0.8769 0.032 0.000 0.000 0.964 0.004
#> GSM494624 5 0.4533 0.0248 0.008 0.000 0.000 0.448 0.544
#> GSM494651 3 0.4422 0.4749 0.004 0.012 0.664 0.320 0.000
#> GSM494662 4 0.0693 0.8726 0.008 0.000 0.000 0.980 0.012
#> GSM494627 4 0.2710 0.8464 0.008 0.000 0.064 0.892 0.036
#> GSM494673 4 0.3876 0.5864 0.316 0.000 0.000 0.684 0.000
#> GSM494649 4 0.2929 0.8060 0.008 0.000 0.000 0.840 0.152
#> GSM494658 1 0.4558 0.4214 0.652 0.000 0.000 0.324 0.024
#> GSM494653 4 0.1410 0.8703 0.060 0.000 0.000 0.940 0.000
#> GSM494643 4 0.0798 0.8715 0.008 0.000 0.000 0.976 0.016
#> GSM494672 1 0.2970 0.5338 0.828 0.004 0.000 0.168 0.000
#> GSM494618 4 0.2631 0.8588 0.004 0.012 0.044 0.904 0.036
#> GSM494631 3 0.1830 0.7796 0.004 0.052 0.932 0.012 0.000
#> GSM494619 4 0.4425 0.3849 0.008 0.000 0.000 0.600 0.392
#> GSM494674 4 0.1478 0.8680 0.064 0.000 0.000 0.936 0.000
#> GSM494616 4 0.2462 0.8324 0.000 0.008 0.112 0.880 0.000
#> GSM494663 4 0.3768 0.6963 0.008 0.004 0.000 0.760 0.228
#> GSM494628 4 0.5075 0.6804 0.004 0.008 0.084 0.720 0.184
#> GSM494632 4 0.0162 0.8767 0.004 0.000 0.000 0.996 0.000
#> GSM494660 4 0.3583 0.7779 0.008 0.016 0.000 0.808 0.168
#> GSM494622 3 0.7550 0.1298 0.024 0.012 0.416 0.252 0.296
#> GSM494642 4 0.1270 0.8717 0.052 0.000 0.000 0.948 0.000
#> GSM494647 4 0.2471 0.8234 0.136 0.000 0.000 0.864 0.000
#> GSM494659 4 0.3143 0.7568 0.204 0.000 0.000 0.796 0.000
#> GSM494670 1 0.4183 0.4160 0.668 0.000 0.000 0.324 0.008
#> GSM494675 5 0.2859 0.6669 0.016 0.012 0.096 0.000 0.876
#> GSM494641 4 0.1197 0.8727 0.048 0.000 0.000 0.952 0.000
#> GSM494636 4 0.0693 0.8726 0.008 0.000 0.000 0.980 0.012
#> GSM494640 4 0.1087 0.8727 0.008 0.000 0.008 0.968 0.016
#> GSM494623 5 0.4533 0.0429 0.008 0.000 0.000 0.448 0.544
#> GSM494644 4 0.0794 0.8762 0.028 0.000 0.000 0.972 0.000
#> GSM494646 4 0.0162 0.8756 0.000 0.000 0.000 0.996 0.004
#> GSM494665 4 0.2230 0.8453 0.116 0.000 0.000 0.884 0.000
#> GSM494638 4 0.1278 0.8758 0.016 0.004 0.020 0.960 0.000
#> GSM494645 4 0.0794 0.8762 0.028 0.000 0.000 0.972 0.000
#> GSM494671 1 0.2230 0.5638 0.884 0.000 0.000 0.116 0.000
#> GSM494655 4 0.0963 0.8755 0.036 0.000 0.000 0.964 0.000
#> GSM494620 4 0.2612 0.8151 0.008 0.000 0.000 0.868 0.124
#> GSM494630 4 0.1644 0.8644 0.008 0.004 0.000 0.940 0.048
#> GSM494657 3 0.0162 0.7872 0.000 0.004 0.996 0.000 0.000
#> GSM494667 4 0.2891 0.7841 0.176 0.000 0.000 0.824 0.000
#> GSM494621 4 0.4557 0.1346 0.008 0.000 0.000 0.516 0.476
#> GSM494629 3 0.4298 0.4311 0.008 0.000 0.640 0.352 0.000
#> GSM494637 4 0.0960 0.8721 0.008 0.000 0.004 0.972 0.016
#> GSM494652 4 0.2424 0.8233 0.132 0.000 0.000 0.868 0.000
#> GSM494648 4 0.4354 0.4427 0.008 0.000 0.000 0.624 0.368
#> GSM494650 3 0.3348 0.6872 0.004 0.012 0.836 0.140 0.008
#> GSM494669 4 0.1792 0.8595 0.084 0.000 0.000 0.916 0.000
#> GSM494666 4 0.0963 0.8755 0.036 0.000 0.000 0.964 0.000
#> GSM494668 4 0.2612 0.8369 0.124 0.000 0.000 0.868 0.008
#> GSM494633 4 0.2647 0.8454 0.008 0.024 0.000 0.892 0.076
#> GSM494634 1 0.4307 -0.1123 0.504 0.000 0.000 0.496 0.000
#> GSM494639 4 0.0290 0.8767 0.008 0.000 0.000 0.992 0.000
#> GSM494661 4 0.1043 0.8747 0.040 0.000 0.000 0.960 0.000
#> GSM494617 4 0.0955 0.8768 0.028 0.004 0.000 0.968 0.000
#> GSM494626 4 0.1748 0.8771 0.028 0.008 0.004 0.944 0.016
#> GSM494656 3 0.0000 0.7868 0.000 0.000 1.000 0.000 0.000
#> GSM494635 4 0.0451 0.8741 0.008 0.000 0.000 0.988 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM494565 5 0.2015 0.7476 0.000 0.056 0.000 0.012 0.916 0.016
#> GSM494594 3 0.0436 0.8662 0.000 0.000 0.988 0.004 0.004 0.004
#> GSM494604 5 0.5235 0.1576 0.068 0.004 0.000 0.428 0.496 0.004
#> GSM494564 6 0.3632 0.5970 0.000 0.012 0.000 0.012 0.220 0.756
#> GSM494591 3 0.0767 0.8638 0.000 0.000 0.976 0.012 0.008 0.004
#> GSM494567 3 0.1843 0.8419 0.004 0.040 0.932 0.008 0.012 0.004
#> GSM494602 4 0.2838 0.6204 0.000 0.188 0.000 0.808 0.004 0.000
#> GSM494613 2 0.2540 0.7706 0.000 0.892 0.044 0.000 0.020 0.044
#> GSM494589 6 0.3897 0.5955 0.000 0.036 0.000 0.012 0.192 0.760
#> GSM494598 5 0.2121 0.7481 0.000 0.012 0.000 0.096 0.892 0.000
#> GSM494593 2 0.1644 0.7782 0.000 0.920 0.000 0.076 0.004 0.000
#> GSM494583 5 0.3360 0.6060 0.000 0.264 0.000 0.004 0.732 0.000
#> GSM494612 2 0.3288 0.5823 0.000 0.724 0.000 0.276 0.000 0.000
#> GSM494558 3 0.4876 0.3290 0.000 0.020 0.580 0.004 0.024 0.372
#> GSM494556 2 0.6709 0.2849 0.000 0.448 0.168 0.016 0.032 0.336
#> GSM494559 2 0.2585 0.7628 0.000 0.880 0.000 0.012 0.024 0.084
#> GSM494571 3 0.0291 0.8659 0.000 0.000 0.992 0.004 0.004 0.000
#> GSM494614 2 0.3628 0.7504 0.000 0.832 0.016 0.016 0.060 0.076
#> GSM494603 5 0.3608 0.5803 0.004 0.012 0.000 0.000 0.736 0.248
#> GSM494568 6 0.5519 0.4901 0.004 0.020 0.116 0.004 0.216 0.640
#> GSM494572 3 0.0964 0.8604 0.000 0.000 0.968 0.012 0.004 0.016
#> GSM494600 6 0.4303 0.3990 0.000 0.016 0.000 0.012 0.332 0.640
#> GSM494562 5 0.2750 0.7306 0.000 0.020 0.000 0.136 0.844 0.000
#> GSM494615 6 0.3792 0.6853 0.004 0.036 0.096 0.012 0.028 0.824
#> GSM494582 4 0.2165 0.6607 0.000 0.108 0.000 0.884 0.008 0.000
#> GSM494599 4 0.1245 0.6634 0.016 0.032 0.000 0.952 0.000 0.000
#> GSM494610 5 0.1858 0.7525 0.000 0.012 0.000 0.076 0.912 0.000
#> GSM494587 2 0.2231 0.7855 0.000 0.908 0.028 0.048 0.016 0.000
#> GSM494581 2 0.1194 0.7862 0.000 0.956 0.004 0.008 0.032 0.000
#> GSM494580 3 0.1269 0.8557 0.000 0.020 0.956 0.012 0.012 0.000
#> GSM494563 5 0.1606 0.7456 0.000 0.008 0.000 0.004 0.932 0.056
#> GSM494576 5 0.3850 0.5891 0.000 0.260 0.000 0.020 0.716 0.004
#> GSM494605 1 0.0260 0.8808 0.992 0.000 0.000 0.008 0.000 0.000
#> GSM494584 2 0.4427 0.5542 0.000 0.684 0.040 0.012 0.264 0.000
#> GSM494586 5 0.3227 0.7287 0.000 0.084 0.000 0.088 0.828 0.000
#> GSM494578 2 0.2946 0.7094 0.000 0.824 0.160 0.012 0.000 0.004
#> GSM494585 2 0.1218 0.7871 0.000 0.956 0.004 0.028 0.012 0.000
#> GSM494611 4 0.1983 0.6634 0.000 0.072 0.000 0.908 0.020 0.000
#> GSM494560 2 0.6388 0.0246 0.000 0.372 0.000 0.012 0.332 0.284
#> GSM494595 2 0.2487 0.7629 0.000 0.876 0.000 0.032 0.092 0.000
#> GSM494570 6 0.2566 0.7044 0.000 0.012 0.000 0.008 0.112 0.868
#> GSM494597 5 0.3168 0.6373 0.000 0.016 0.192 0.000 0.792 0.000
#> GSM494607 5 0.4514 0.3954 0.040 0.000 0.000 0.372 0.588 0.000
#> GSM494561 6 0.1149 0.7459 0.008 0.008 0.000 0.000 0.024 0.960
#> GSM494569 1 0.2569 0.8284 0.880 0.000 0.092 0.004 0.012 0.012
#> GSM494592 4 0.2948 0.6236 0.008 0.188 0.000 0.804 0.000 0.000
#> GSM494577 5 0.1349 0.7523 0.000 0.056 0.000 0.004 0.940 0.000
#> GSM494588 2 0.5774 0.4078 0.000 0.548 0.000 0.012 0.276 0.164
#> GSM494590 3 0.0291 0.8669 0.000 0.000 0.992 0.004 0.004 0.000
#> GSM494609 2 0.0972 0.7865 0.000 0.964 0.000 0.028 0.008 0.000
#> GSM494608 2 0.2333 0.7599 0.040 0.896 0.004 0.060 0.000 0.000
#> GSM494606 2 0.2400 0.7511 0.008 0.872 0.000 0.116 0.000 0.004
#> GSM494574 5 0.1967 0.7508 0.000 0.012 0.000 0.084 0.904 0.000
#> GSM494573 5 0.4763 0.2431 0.000 0.032 0.000 0.012 0.564 0.392
#> GSM494566 4 0.5976 0.0961 0.020 0.032 0.004 0.524 0.368 0.052
#> GSM494601 4 0.4018 0.2416 0.000 0.412 0.000 0.580 0.008 0.000
#> GSM494557 2 0.2367 0.7608 0.000 0.888 0.088 0.000 0.016 0.008
#> GSM494579 5 0.1452 0.7546 0.000 0.012 0.000 0.020 0.948 0.020
#> GSM494596 3 0.0405 0.8662 0.000 0.000 0.988 0.008 0.004 0.000
#> GSM494575 2 0.1863 0.7670 0.000 0.896 0.000 0.104 0.000 0.000
#> GSM494625 6 0.2278 0.7517 0.128 0.000 0.000 0.004 0.000 0.868
#> GSM494654 3 0.0436 0.8665 0.000 0.000 0.988 0.004 0.004 0.004
#> GSM494664 1 0.2177 0.8619 0.916 0.012 0.000 0.012 0.016 0.044
#> GSM494624 6 0.1759 0.7687 0.064 0.004 0.000 0.004 0.004 0.924
#> GSM494651 3 0.4864 0.4310 0.328 0.020 0.620 0.000 0.020 0.012
#> GSM494662 1 0.1026 0.8776 0.968 0.004 0.008 0.008 0.000 0.012
#> GSM494627 1 0.4787 0.6934 0.720 0.008 0.068 0.004 0.016 0.184
#> GSM494673 1 0.2597 0.7683 0.824 0.000 0.000 0.176 0.000 0.000
#> GSM494649 6 0.2827 0.7534 0.132 0.008 0.000 0.004 0.008 0.848
#> GSM494658 1 0.5551 0.4727 0.620 0.004 0.000 0.248 0.100 0.028
#> GSM494653 1 0.0790 0.8782 0.968 0.000 0.000 0.032 0.000 0.000
#> GSM494643 1 0.2194 0.8397 0.892 0.004 0.004 0.004 0.000 0.096
#> GSM494672 4 0.2838 0.6122 0.188 0.004 0.000 0.808 0.000 0.000
#> GSM494618 1 0.5498 0.4201 0.584 0.020 0.056 0.000 0.016 0.324
#> GSM494631 3 0.0696 0.8652 0.000 0.008 0.980 0.004 0.004 0.004
#> GSM494619 6 0.4158 0.2742 0.400 0.004 0.000 0.004 0.004 0.588
#> GSM494674 1 0.0777 0.8785 0.972 0.004 0.000 0.024 0.000 0.000
#> GSM494616 1 0.2170 0.8572 0.916 0.016 0.044 0.000 0.016 0.008
#> GSM494663 1 0.4968 0.2010 0.524 0.016 0.000 0.004 0.028 0.428
#> GSM494628 6 0.4364 0.7081 0.152 0.024 0.024 0.008 0.020 0.772
#> GSM494632 1 0.0146 0.8803 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM494660 6 0.1957 0.7660 0.072 0.008 0.000 0.000 0.008 0.912
#> GSM494622 6 0.4291 0.7305 0.068 0.024 0.076 0.004 0.028 0.800
#> GSM494642 1 0.0458 0.8791 0.984 0.000 0.000 0.016 0.000 0.000
#> GSM494647 1 0.1007 0.8742 0.956 0.000 0.000 0.044 0.000 0.000
#> GSM494659 1 0.1267 0.8667 0.940 0.000 0.000 0.060 0.000 0.000
#> GSM494670 4 0.5823 0.4139 0.244 0.004 0.000 0.588 0.024 0.140
#> GSM494675 5 0.6024 0.0575 0.000 0.020 0.088 0.016 0.468 0.408
#> GSM494641 1 0.0291 0.8802 0.992 0.004 0.000 0.004 0.000 0.000
#> GSM494636 1 0.0862 0.8784 0.972 0.000 0.004 0.008 0.000 0.016
#> GSM494640 1 0.3045 0.8309 0.864 0.004 0.052 0.008 0.004 0.068
#> GSM494623 6 0.2635 0.7614 0.100 0.004 0.000 0.004 0.020 0.872
#> GSM494644 1 0.0291 0.8804 0.992 0.004 0.000 0.000 0.000 0.004
#> GSM494646 1 0.0508 0.8805 0.984 0.000 0.000 0.004 0.000 0.012
#> GSM494665 1 0.1814 0.8466 0.900 0.000 0.000 0.100 0.000 0.000
#> GSM494638 1 0.1293 0.8753 0.956 0.004 0.020 0.004 0.000 0.016
#> GSM494645 1 0.0146 0.8806 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM494671 4 0.3747 0.3210 0.396 0.000 0.000 0.604 0.000 0.000
#> GSM494655 1 0.0146 0.8803 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM494620 1 0.3878 0.4932 0.644 0.004 0.000 0.004 0.000 0.348
#> GSM494630 1 0.2062 0.8446 0.900 0.004 0.000 0.008 0.000 0.088
#> GSM494657 3 0.0146 0.8667 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM494667 1 0.1141 0.8692 0.948 0.000 0.000 0.052 0.000 0.000
#> GSM494621 6 0.3189 0.6397 0.236 0.000 0.000 0.004 0.000 0.760
#> GSM494629 3 0.4612 0.4750 0.284 0.000 0.656 0.008 0.000 0.052
#> GSM494637 1 0.2086 0.8536 0.912 0.004 0.012 0.008 0.000 0.064
#> GSM494652 1 0.0935 0.8761 0.964 0.004 0.000 0.032 0.000 0.000
#> GSM494648 1 0.3993 0.1406 0.520 0.000 0.000 0.004 0.000 0.476
#> GSM494650 3 0.4545 0.6543 0.156 0.020 0.752 0.000 0.020 0.052
#> GSM494669 1 0.0790 0.8764 0.968 0.000 0.000 0.032 0.000 0.000
#> GSM494666 1 0.0260 0.8808 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM494668 1 0.4937 0.6466 0.684 0.004 0.000 0.116 0.008 0.188
#> GSM494633 6 0.2946 0.7445 0.160 0.012 0.000 0.004 0.000 0.824
#> GSM494634 1 0.1908 0.8353 0.900 0.000 0.000 0.096 0.000 0.004
#> GSM494639 1 0.0291 0.8809 0.992 0.000 0.000 0.004 0.000 0.004
#> GSM494661 1 0.0000 0.8803 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494617 1 0.1173 0.8730 0.960 0.016 0.000 0.000 0.016 0.008
#> GSM494626 1 0.3910 0.7562 0.788 0.020 0.012 0.004 0.016 0.160
#> GSM494656 3 0.0436 0.8662 0.000 0.000 0.988 0.004 0.004 0.004
#> GSM494635 1 0.0405 0.8806 0.988 0.004 0.000 0.000 0.000 0.008
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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 4)
get_signatures(res, k = 5)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 6)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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.
# code only for demonstration
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) individual(p) k
#> SD:NMF 96 2.48e-13 0.942 2.34e-09 0.948 2
#> SD:NMF 117 8.48e-20 0.964 3.24e-17 0.982 3
#> SD:NMF 114 4.48e-14 0.443 3.97e-11 0.225 4
#> SD:NMF 87 1.54e-11 0.682 7.13e-09 0.663 5
#> SD:NMF 98 4.81e-12 0.261 6.65e-08 0.216 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.
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"]
# you can also extract it by
# 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 51941 rows and 120 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 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:
k and the heatmap of
predicted classes for each k.k.k.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:
k;k, the area increased is defined as \(A_k - A_{k-1}\).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 0.256 0.802 0.876 0.4745 0.497 0.497
#> 3 3 0.436 0.701 0.836 0.2822 0.852 0.707
#> 4 4 0.545 0.604 0.711 0.1500 0.824 0.582
#> 5 5 0.700 0.747 0.836 0.0880 0.849 0.550
#> 6 6 0.718 0.615 0.779 0.0565 0.968 0.856
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
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.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM494565 1 0.8081 0.6776 0.752 0.248
#> GSM494594 2 0.0672 0.8413 0.008 0.992
#> GSM494604 1 0.0000 0.8724 1.000 0.000
#> GSM494564 2 0.8327 0.6765 0.264 0.736
#> GSM494591 2 0.0672 0.8413 0.008 0.992
#> GSM494567 2 0.4939 0.8338 0.108 0.892
#> GSM494602 1 0.0376 0.8721 0.996 0.004
#> GSM494613 2 0.4939 0.8338 0.108 0.892
#> GSM494589 2 0.8327 0.6765 0.264 0.736
#> GSM494598 1 0.0376 0.8721 0.996 0.004
#> GSM494593 1 0.0376 0.8721 0.996 0.004
#> GSM494583 1 0.8081 0.6713 0.752 0.248
#> GSM494612 1 0.0376 0.8721 0.996 0.004
#> GSM494558 2 0.2423 0.8554 0.040 0.960
#> GSM494556 2 0.5059 0.8350 0.112 0.888
#> GSM494559 2 0.8207 0.6792 0.256 0.744
#> GSM494571 2 0.0672 0.8413 0.008 0.992
#> GSM494614 1 0.9970 0.0849 0.532 0.468
#> GSM494603 2 0.4022 0.8679 0.080 0.920
#> GSM494568 2 0.4022 0.8679 0.080 0.920
#> GSM494572 2 0.0672 0.8413 0.008 0.992
#> GSM494600 2 0.8327 0.6765 0.264 0.736
#> GSM494562 1 0.0672 0.8717 0.992 0.008
#> GSM494615 2 0.4939 0.8338 0.108 0.892
#> GSM494582 1 0.0376 0.8721 0.996 0.004
#> GSM494599 1 0.0376 0.8721 0.996 0.004
#> GSM494610 1 0.0376 0.8721 0.996 0.004
#> GSM494587 1 0.6801 0.7611 0.820 0.180
#> GSM494581 1 0.5629 0.8094 0.868 0.132
#> GSM494580 2 0.4939 0.8338 0.108 0.892
#> GSM494563 2 0.9866 0.2726 0.432 0.568
#> GSM494576 1 0.5946 0.7978 0.856 0.144
#> GSM494605 1 0.8813 0.5712 0.700 0.300
#> GSM494584 1 0.8267 0.6616 0.740 0.260
#> GSM494586 1 0.4562 0.8317 0.904 0.096
#> GSM494578 2 0.4939 0.8338 0.108 0.892
#> GSM494585 1 0.6801 0.7630 0.820 0.180
#> GSM494611 1 0.0376 0.8721 0.996 0.004
#> GSM494560 2 0.8327 0.6765 0.264 0.736
#> GSM494595 1 0.1414 0.8686 0.980 0.020
#> GSM494570 2 0.8207 0.6792 0.256 0.744
#> GSM494597 2 0.0672 0.8413 0.008 0.992
#> GSM494607 1 0.0000 0.8724 1.000 0.000
#> GSM494561 2 0.8207 0.6792 0.256 0.744
#> GSM494569 2 0.5629 0.8754 0.132 0.868
#> GSM494592 1 0.0376 0.8721 0.996 0.004
#> GSM494577 1 0.7883 0.6960 0.764 0.236
#> GSM494588 2 0.8267 0.6777 0.260 0.740
#> GSM494590 2 0.0672 0.8413 0.008 0.992
#> GSM494609 1 0.5737 0.8073 0.864 0.136
#> GSM494608 1 0.5737 0.8073 0.864 0.136
#> GSM494606 1 0.0376 0.8721 0.996 0.004
#> GSM494574 1 0.0376 0.8721 0.996 0.004
#> GSM494573 2 0.8327 0.6765 0.264 0.736
#> GSM494566 1 0.8267 0.6849 0.740 0.260
#> GSM494601 1 0.3584 0.8454 0.932 0.068
#> GSM494557 2 0.4939 0.8338 0.108 0.892
#> GSM494579 1 0.7674 0.7135 0.776 0.224
#> GSM494596 2 0.0672 0.8413 0.008 0.992
#> GSM494575 1 0.0376 0.8721 0.996 0.004
#> GSM494625 2 0.5629 0.8754 0.132 0.868
#> GSM494654 2 0.0672 0.8413 0.008 0.992
#> GSM494664 1 0.8813 0.5712 0.700 0.300
#> GSM494624 2 0.5629 0.8754 0.132 0.868
#> GSM494651 2 0.5629 0.8754 0.132 0.868
#> GSM494662 2 0.6887 0.8319 0.184 0.816
#> GSM494627 2 0.5408 0.8754 0.124 0.876
#> GSM494673 1 0.0672 0.8724 0.992 0.008
#> GSM494649 2 0.5629 0.8754 0.132 0.868
#> GSM494658 1 0.0672 0.8724 0.992 0.008
#> GSM494653 1 0.0672 0.8724 0.992 0.008
#> GSM494643 2 0.5629 0.8754 0.132 0.868
#> GSM494672 1 0.0672 0.8724 0.992 0.008
#> GSM494618 2 0.5629 0.8754 0.132 0.868
#> GSM494631 2 0.5178 0.8358 0.116 0.884
#> GSM494619 2 0.5629 0.8754 0.132 0.868
#> GSM494674 1 0.0672 0.8724 0.992 0.008
#> GSM494616 2 0.5629 0.8754 0.132 0.868
#> GSM494663 2 0.5408 0.8754 0.124 0.876
#> GSM494628 2 0.5408 0.8754 0.124 0.876
#> GSM494632 2 0.7376 0.8047 0.208 0.792
#> GSM494660 2 0.5629 0.8754 0.132 0.868
#> GSM494622 2 0.5408 0.8754 0.124 0.876
#> GSM494642 1 0.0672 0.8724 0.992 0.008
#> GSM494647 1 0.0672 0.8724 0.992 0.008
#> GSM494659 1 0.0672 0.8724 0.992 0.008
#> GSM494670 1 0.0672 0.8724 0.992 0.008
#> GSM494675 2 0.0672 0.8413 0.008 0.992
#> GSM494641 1 0.0672 0.8724 0.992 0.008
#> GSM494636 2 0.7299 0.8093 0.204 0.796
#> GSM494640 2 0.5629 0.8754 0.132 0.868
#> GSM494623 2 0.5629 0.8754 0.132 0.868
#> GSM494644 1 0.8813 0.5712 0.700 0.300
#> GSM494646 1 0.8861 0.5637 0.696 0.304
#> GSM494665 1 0.8813 0.5712 0.700 0.300
#> GSM494638 2 0.6887 0.8319 0.184 0.816
#> GSM494645 1 0.8813 0.5712 0.700 0.300
#> GSM494671 1 0.0672 0.8724 0.992 0.008
#> GSM494655 1 0.0672 0.8724 0.992 0.008
#> GSM494620 2 0.5629 0.8754 0.132 0.868
#> GSM494630 2 0.5629 0.8754 0.132 0.868
#> GSM494657 2 0.0672 0.8413 0.008 0.992
#> GSM494667 1 0.0672 0.8724 0.992 0.008
#> GSM494621 2 0.5629 0.8754 0.132 0.868
#> GSM494629 2 0.5629 0.8754 0.132 0.868
#> GSM494637 2 0.5629 0.8754 0.132 0.868
#> GSM494652 1 0.0672 0.8724 0.992 0.008
#> GSM494648 2 0.5629 0.8754 0.132 0.868
#> GSM494650 2 0.5629 0.8754 0.132 0.868
#> GSM494669 1 0.0672 0.8724 0.992 0.008
#> GSM494666 1 0.8813 0.5712 0.700 0.300
#> GSM494668 1 0.0672 0.8724 0.992 0.008
#> GSM494633 2 0.5629 0.8754 0.132 0.868
#> GSM494634 1 0.0672 0.8724 0.992 0.008
#> GSM494639 2 0.7883 0.7679 0.236 0.764
#> GSM494661 1 0.8813 0.5712 0.700 0.300
#> GSM494617 2 0.5629 0.8754 0.132 0.868
#> GSM494626 2 0.5629 0.8754 0.132 0.868
#> GSM494656 2 0.0672 0.8413 0.008 0.992
#> GSM494635 1 0.8813 0.5712 0.700 0.300
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM494565 2 0.6159 0.6026 0.048 0.756 0.196
#> GSM494594 3 0.0000 0.6615 0.000 0.000 1.000
#> GSM494604 2 0.0892 0.7828 0.020 0.980 0.000
#> GSM494564 3 0.9665 0.6087 0.276 0.260 0.464
#> GSM494591 3 0.0000 0.6615 0.000 0.000 1.000
#> GSM494567 3 0.8637 0.4216 0.448 0.100 0.452
#> GSM494602 2 0.0000 0.7791 0.000 1.000 0.000
#> GSM494613 3 0.8637 0.4216 0.448 0.100 0.452
#> GSM494589 3 0.9665 0.6087 0.276 0.260 0.464
#> GSM494598 2 0.0000 0.7791 0.000 1.000 0.000
#> GSM494593 2 0.0000 0.7791 0.000 1.000 0.000
#> GSM494583 2 0.6159 0.5971 0.048 0.756 0.196
#> GSM494612 2 0.0000 0.7791 0.000 1.000 0.000
#> GSM494558 1 0.3192 0.7756 0.888 0.000 0.112
#> GSM494556 1 0.8637 -0.4405 0.456 0.100 0.444
#> GSM494559 3 0.9654 0.6059 0.288 0.248 0.464
#> GSM494571 3 0.0000 0.6615 0.000 0.000 1.000
#> GSM494614 2 0.7699 0.0315 0.048 0.532 0.420
#> GSM494603 1 0.2261 0.8470 0.932 0.000 0.068
#> GSM494568 1 0.2261 0.8470 0.932 0.000 0.068
#> GSM494572 3 0.0000 0.6615 0.000 0.000 1.000
#> GSM494600 3 0.9665 0.6087 0.276 0.260 0.464
#> GSM494562 2 0.0237 0.7782 0.000 0.996 0.004
#> GSM494615 3 0.8637 0.4216 0.448 0.100 0.452
#> GSM494582 2 0.0000 0.7791 0.000 1.000 0.000
#> GSM494599 2 0.0000 0.7791 0.000 1.000 0.000
#> GSM494610 2 0.0000 0.7791 0.000 1.000 0.000
#> GSM494587 2 0.4802 0.6743 0.020 0.824 0.156
#> GSM494581 2 0.3995 0.7212 0.016 0.868 0.116
#> GSM494580 3 0.8637 0.4216 0.448 0.100 0.452
#> GSM494563 2 0.9532 -0.2549 0.192 0.432 0.376
#> GSM494576 2 0.4413 0.7185 0.036 0.860 0.104
#> GSM494605 2 0.6225 0.4368 0.432 0.568 0.000
#> GSM494584 2 0.6302 0.5862 0.048 0.744 0.208
#> GSM494586 2 0.3043 0.7424 0.008 0.908 0.084
#> GSM494578 3 0.8637 0.4216 0.448 0.100 0.452
#> GSM494585 2 0.4802 0.6757 0.020 0.824 0.156
#> GSM494611 2 0.0000 0.7791 0.000 1.000 0.000
#> GSM494560 3 0.9665 0.6087 0.276 0.260 0.464
#> GSM494595 2 0.0829 0.7763 0.004 0.984 0.012
#> GSM494570 3 0.9654 0.6059 0.288 0.248 0.464
#> GSM494597 3 0.0237 0.6614 0.004 0.000 0.996
#> GSM494607 2 0.0892 0.7828 0.020 0.980 0.000
#> GSM494561 3 0.9657 0.5954 0.300 0.240 0.460
#> GSM494569 1 0.0237 0.9273 0.996 0.000 0.004
#> GSM494592 2 0.0000 0.7791 0.000 1.000 0.000
#> GSM494577 2 0.6007 0.6208 0.048 0.768 0.184
#> GSM494588 3 0.9676 0.6050 0.288 0.252 0.460
#> GSM494590 3 0.0000 0.6615 0.000 0.000 1.000
#> GSM494609 2 0.4068 0.7193 0.016 0.864 0.120
#> GSM494608 2 0.4068 0.7193 0.016 0.864 0.120
#> GSM494606 2 0.0000 0.7791 0.000 1.000 0.000
#> GSM494574 2 0.0000 0.7791 0.000 1.000 0.000
#> GSM494573 3 0.9665 0.6087 0.276 0.260 0.464
#> GSM494566 2 0.6449 0.6135 0.056 0.740 0.204
#> GSM494601 2 0.2492 0.7578 0.016 0.936 0.048
#> GSM494557 3 0.8637 0.4216 0.448 0.100 0.452
#> GSM494579 2 0.5798 0.6367 0.044 0.780 0.176
#> GSM494596 3 0.0000 0.6615 0.000 0.000 1.000
#> GSM494575 2 0.0000 0.7791 0.000 1.000 0.000
#> GSM494625 1 0.0000 0.9283 1.000 0.000 0.000
#> GSM494654 3 0.0000 0.6615 0.000 0.000 1.000
#> GSM494664 2 0.6225 0.4368 0.432 0.568 0.000
#> GSM494624 1 0.0000 0.9283 1.000 0.000 0.000
#> GSM494651 1 0.0237 0.9273 0.996 0.000 0.004
#> GSM494662 1 0.1860 0.8690 0.948 0.052 0.000
#> GSM494627 1 0.0424 0.9234 0.992 0.000 0.008
#> GSM494673 2 0.3686 0.7813 0.140 0.860 0.000
#> GSM494649 1 0.0000 0.9283 1.000 0.000 0.000
#> GSM494658 2 0.3686 0.7813 0.140 0.860 0.000
#> GSM494653 2 0.3686 0.7813 0.140 0.860 0.000
#> GSM494643 1 0.0000 0.9283 1.000 0.000 0.000
#> GSM494672 2 0.3686 0.7813 0.140 0.860 0.000
#> GSM494618 1 0.0237 0.9273 0.996 0.000 0.004
#> GSM494631 1 0.8587 -0.3307 0.500 0.100 0.400
#> GSM494619 1 0.0000 0.9283 1.000 0.000 0.000
#> GSM494674 2 0.3686 0.7813 0.140 0.860 0.000
#> GSM494616 1 0.0237 0.9273 0.996 0.000 0.004
#> GSM494663 1 0.0424 0.9234 0.992 0.000 0.008
#> GSM494628 1 0.0424 0.9234 0.992 0.000 0.008
#> GSM494632 1 0.2448 0.8392 0.924 0.076 0.000
#> GSM494660 1 0.0000 0.9283 1.000 0.000 0.000
#> GSM494622 1 0.0424 0.9234 0.992 0.000 0.008
#> GSM494642 2 0.3686 0.7813 0.140 0.860 0.000
#> GSM494647 2 0.3686 0.7813 0.140 0.860 0.000
#> GSM494659 2 0.3686 0.7813 0.140 0.860 0.000
#> GSM494670 2 0.3686 0.7813 0.140 0.860 0.000
#> GSM494675 3 0.0237 0.6614 0.004 0.000 0.996
#> GSM494641 2 0.3686 0.7813 0.140 0.860 0.000
#> GSM494636 1 0.2356 0.8443 0.928 0.072 0.000
#> GSM494640 1 0.0000 0.9283 1.000 0.000 0.000
#> GSM494623 1 0.0000 0.9283 1.000 0.000 0.000
#> GSM494644 2 0.6225 0.4368 0.432 0.568 0.000
#> GSM494646 2 0.6235 0.4275 0.436 0.564 0.000
#> GSM494665 2 0.6225 0.4368 0.432 0.568 0.000
#> GSM494638 1 0.1860 0.8690 0.948 0.052 0.000
#> GSM494645 2 0.6225 0.4368 0.432 0.568 0.000
#> GSM494671 2 0.3686 0.7813 0.140 0.860 0.000
#> GSM494655 2 0.3686 0.7813 0.140 0.860 0.000
#> GSM494620 1 0.0000 0.9283 1.000 0.000 0.000
#> GSM494630 1 0.0000 0.9283 1.000 0.000 0.000
#> GSM494657 3 0.0000 0.6615 0.000 0.000 1.000
#> GSM494667 2 0.3686 0.7813 0.140 0.860 0.000
#> GSM494621 1 0.0000 0.9283 1.000 0.000 0.000
#> GSM494629 1 0.0237 0.9273 0.996 0.000 0.004
#> GSM494637 1 0.0000 0.9283 1.000 0.000 0.000
#> GSM494652 2 0.3686 0.7813 0.140 0.860 0.000
#> GSM494648 1 0.0000 0.9283 1.000 0.000 0.000
#> GSM494650 1 0.0237 0.9273 0.996 0.000 0.004
#> GSM494669 2 0.3686 0.7813 0.140 0.860 0.000
#> GSM494666 2 0.6225 0.4368 0.432 0.568 0.000
#> GSM494668 2 0.3686 0.7813 0.140 0.860 0.000
#> GSM494633 1 0.0000 0.9283 1.000 0.000 0.000
#> GSM494634 2 0.3686 0.7813 0.140 0.860 0.000
#> GSM494639 1 0.3038 0.7992 0.896 0.104 0.000
#> GSM494661 2 0.6225 0.4368 0.432 0.568 0.000
#> GSM494617 1 0.0237 0.9273 0.996 0.000 0.004
#> GSM494626 1 0.0237 0.9273 0.996 0.000 0.004
#> GSM494656 3 0.0000 0.6615 0.000 0.000 1.000
#> GSM494635 2 0.6225 0.4368 0.432 0.568 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM494565 3 0.6104 -0.05802 0.036 0.472 0.488 0.004
#> GSM494594 1 0.4382 0.98857 0.704 0.000 0.296 0.000
#> GSM494604 2 0.2353 0.70623 0.056 0.924 0.008 0.012
#> GSM494564 3 0.2124 0.56055 0.000 0.008 0.924 0.068
#> GSM494591 1 0.4382 0.98857 0.704 0.000 0.296 0.000
#> GSM494567 3 0.6102 0.49716 0.048 0.000 0.532 0.420
#> GSM494602 2 0.0524 0.70405 0.004 0.988 0.008 0.000
#> GSM494613 3 0.6102 0.49716 0.048 0.000 0.532 0.420
#> GSM494589 3 0.2124 0.56055 0.000 0.008 0.924 0.068
#> GSM494598 2 0.1488 0.69840 0.032 0.956 0.012 0.000
#> GSM494593 2 0.0469 0.70474 0.000 0.988 0.012 0.000
#> GSM494583 3 0.5775 -0.07613 0.020 0.488 0.488 0.004
#> GSM494612 2 0.1151 0.69975 0.024 0.968 0.008 0.000
#> GSM494558 4 0.3392 0.62864 0.072 0.000 0.056 0.872
#> GSM494556 3 0.6114 0.49207 0.048 0.000 0.524 0.428
#> GSM494559 3 0.2197 0.55760 0.004 0.000 0.916 0.080
#> GSM494571 1 0.4382 0.98857 0.704 0.000 0.296 0.000
#> GSM494614 3 0.5035 0.36840 0.016 0.284 0.696 0.004
#> GSM494603 4 0.2174 0.69990 0.052 0.000 0.020 0.928
#> GSM494568 4 0.2174 0.69990 0.052 0.000 0.020 0.928
#> GSM494572 1 0.4382 0.98857 0.704 0.000 0.296 0.000
#> GSM494600 3 0.2124 0.56055 0.000 0.008 0.924 0.068
#> GSM494562 2 0.1610 0.69654 0.032 0.952 0.016 0.000
#> GSM494615 3 0.6102 0.49716 0.048 0.000 0.532 0.420
#> GSM494582 2 0.1356 0.69696 0.032 0.960 0.008 0.000
#> GSM494599 2 0.0336 0.70464 0.000 0.992 0.008 0.000
#> GSM494610 2 0.1488 0.69840 0.032 0.956 0.012 0.000
#> GSM494587 2 0.4679 0.36744 0.000 0.648 0.352 0.000
#> GSM494581 2 0.3831 0.58864 0.004 0.792 0.204 0.000
#> GSM494580 3 0.6102 0.49716 0.048 0.000 0.532 0.420
#> GSM494563 3 0.4790 0.45503 0.032 0.148 0.796 0.024
#> GSM494576 2 0.6032 0.24339 0.040 0.576 0.380 0.004
#> GSM494605 4 0.7770 -0.00740 0.240 0.364 0.000 0.396
#> GSM494584 2 0.5336 0.03529 0.004 0.496 0.496 0.004
#> GSM494586 2 0.5619 0.35769 0.040 0.640 0.320 0.000
#> GSM494578 3 0.6102 0.49716 0.048 0.000 0.532 0.420
#> GSM494585 2 0.4697 0.36229 0.000 0.644 0.356 0.000
#> GSM494611 2 0.1151 0.69975 0.024 0.968 0.008 0.000
#> GSM494560 3 0.2124 0.56055 0.000 0.008 0.924 0.068
#> GSM494595 2 0.3895 0.62544 0.036 0.832 0.132 0.000
#> GSM494570 3 0.2197 0.55760 0.004 0.000 0.916 0.080
#> GSM494597 1 0.4643 0.94650 0.656 0.000 0.344 0.000
#> GSM494607 2 0.2353 0.70623 0.056 0.924 0.008 0.012
#> GSM494561 3 0.2401 0.55399 0.004 0.000 0.904 0.092
#> GSM494569 4 0.0779 0.76276 0.004 0.000 0.016 0.980
#> GSM494592 2 0.0336 0.70464 0.000 0.992 0.008 0.000
#> GSM494577 2 0.6178 0.01462 0.040 0.480 0.476 0.004
#> GSM494588 3 0.2011 0.55854 0.000 0.000 0.920 0.080
#> GSM494590 1 0.4382 0.98857 0.704 0.000 0.296 0.000
#> GSM494609 2 0.3870 0.58614 0.004 0.788 0.208 0.000
#> GSM494608 2 0.3870 0.58614 0.004 0.788 0.208 0.000
#> GSM494606 2 0.0592 0.70410 0.000 0.984 0.016 0.000
#> GSM494574 2 0.1488 0.69840 0.032 0.956 0.012 0.000
#> GSM494573 3 0.2124 0.56055 0.000 0.008 0.924 0.068
#> GSM494566 2 0.6260 0.08122 0.032 0.500 0.456 0.012
#> GSM494601 2 0.2814 0.63093 0.000 0.868 0.132 0.000
#> GSM494557 3 0.6102 0.49716 0.048 0.000 0.532 0.420
#> GSM494579 2 0.5503 0.10553 0.016 0.516 0.468 0.000
#> GSM494596 1 0.4382 0.98857 0.704 0.000 0.296 0.000
#> GSM494575 2 0.1151 0.69975 0.024 0.968 0.008 0.000
#> GSM494625 4 0.1211 0.77085 0.000 0.000 0.040 0.960
#> GSM494654 1 0.4382 0.98857 0.704 0.000 0.296 0.000
#> GSM494664 4 0.7770 -0.00740 0.240 0.364 0.000 0.396
#> GSM494624 4 0.1211 0.77085 0.000 0.000 0.040 0.960
#> GSM494651 4 0.0779 0.76276 0.004 0.000 0.016 0.980
#> GSM494662 4 0.2831 0.74950 0.044 0.008 0.040 0.908
#> GSM494627 4 0.0469 0.76299 0.012 0.000 0.000 0.988
#> GSM494673 2 0.6220 0.66832 0.248 0.648 0.000 0.104
#> GSM494649 4 0.1211 0.77085 0.000 0.000 0.040 0.960
#> GSM494658 2 0.6220 0.66832 0.248 0.648 0.000 0.104
#> GSM494653 2 0.6220 0.66832 0.248 0.648 0.000 0.104
#> GSM494643 4 0.1211 0.77085 0.000 0.000 0.040 0.960
#> GSM494672 2 0.6220 0.66832 0.248 0.648 0.000 0.104
#> GSM494618 4 0.0779 0.76276 0.004 0.000 0.016 0.980
#> GSM494631 3 0.6010 0.42344 0.040 0.000 0.488 0.472
#> GSM494619 4 0.1211 0.77085 0.000 0.000 0.040 0.960
#> GSM494674 2 0.6220 0.66832 0.248 0.648 0.000 0.104
#> GSM494616 4 0.0779 0.76276 0.004 0.000 0.016 0.980
#> GSM494663 4 0.0469 0.76299 0.012 0.000 0.000 0.988
#> GSM494628 4 0.0469 0.76299 0.012 0.000 0.000 0.988
#> GSM494632 4 0.3515 0.73017 0.072 0.012 0.040 0.876
#> GSM494660 4 0.1211 0.77085 0.000 0.000 0.040 0.960
#> GSM494622 4 0.0469 0.76299 0.012 0.000 0.000 0.988
#> GSM494642 2 0.6220 0.66832 0.248 0.648 0.000 0.104
#> GSM494647 2 0.6220 0.66832 0.248 0.648 0.000 0.104
#> GSM494659 2 0.6220 0.66832 0.248 0.648 0.000 0.104
#> GSM494670 2 0.6220 0.66832 0.248 0.648 0.000 0.104
#> GSM494675 1 0.4643 0.94650 0.656 0.000 0.344 0.000
#> GSM494641 2 0.6220 0.66832 0.248 0.648 0.000 0.104
#> GSM494636 4 0.3442 0.73291 0.068 0.012 0.040 0.880
#> GSM494640 4 0.1211 0.77085 0.000 0.000 0.040 0.960
#> GSM494623 4 0.1211 0.77085 0.000 0.000 0.040 0.960
#> GSM494644 4 0.7770 -0.00740 0.240 0.364 0.000 0.396
#> GSM494646 4 0.7720 0.02572 0.228 0.360 0.000 0.412
#> GSM494665 4 0.7770 -0.00740 0.240 0.364 0.000 0.396
#> GSM494638 4 0.2831 0.74950 0.044 0.008 0.040 0.908
#> GSM494645 4 0.7770 -0.00740 0.240 0.364 0.000 0.396
#> GSM494671 2 0.6220 0.66832 0.248 0.648 0.000 0.104
#> GSM494655 2 0.6220 0.66832 0.248 0.648 0.000 0.104
#> GSM494620 4 0.1211 0.77085 0.000 0.000 0.040 0.960
#> GSM494630 4 0.1211 0.77085 0.000 0.000 0.040 0.960
#> GSM494657 1 0.4382 0.98857 0.704 0.000 0.296 0.000
#> GSM494667 2 0.6220 0.66832 0.248 0.648 0.000 0.104
#> GSM494621 4 0.1211 0.77085 0.000 0.000 0.040 0.960
#> GSM494629 4 0.0779 0.76276 0.004 0.000 0.016 0.980
#> GSM494637 4 0.1211 0.77085 0.000 0.000 0.040 0.960
#> GSM494652 2 0.6220 0.66832 0.248 0.648 0.000 0.104
#> GSM494648 4 0.1211 0.77085 0.000 0.000 0.040 0.960
#> GSM494650 4 0.0779 0.76276 0.004 0.000 0.016 0.980
#> GSM494669 2 0.6220 0.66832 0.248 0.648 0.000 0.104
#> GSM494666 4 0.7770 -0.00740 0.240 0.364 0.000 0.396
#> GSM494668 2 0.6220 0.66832 0.248 0.648 0.000 0.104
#> GSM494633 4 0.1211 0.77085 0.000 0.000 0.040 0.960
#> GSM494634 2 0.6220 0.66832 0.248 0.648 0.000 0.104
#> GSM494639 4 0.4085 0.70836 0.092 0.020 0.040 0.848
#> GSM494661 4 0.7770 -0.00740 0.240 0.364 0.000 0.396
#> GSM494617 4 0.0779 0.76276 0.004 0.000 0.016 0.980
#> GSM494626 4 0.0779 0.76276 0.004 0.000 0.016 0.980
#> GSM494656 1 0.4382 0.98857 0.704 0.000 0.296 0.000
#> GSM494635 4 0.7740 0.00861 0.232 0.364 0.000 0.404
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM494565 2 0.4341 0.435 0.008 0.628 0.000 0.000 0.364
#> GSM494594 3 0.0162 0.985 0.000 0.000 0.996 0.004 0.000
#> GSM494604 2 0.4307 0.369 0.496 0.504 0.000 0.000 0.000
#> GSM494564 5 0.0807 0.668 0.000 0.000 0.012 0.012 0.976
#> GSM494591 3 0.0000 0.986 0.000 0.000 1.000 0.000 0.000
#> GSM494567 5 0.6490 0.488 0.008 0.016 0.092 0.416 0.468
#> GSM494602 2 0.3242 0.759 0.216 0.784 0.000 0.000 0.000
#> GSM494613 5 0.6490 0.488 0.008 0.016 0.092 0.416 0.468
#> GSM494589 5 0.0807 0.668 0.000 0.000 0.012 0.012 0.976
#> GSM494598 2 0.3264 0.766 0.164 0.820 0.000 0.000 0.016
#> GSM494593 2 0.3336 0.757 0.228 0.772 0.000 0.000 0.000
#> GSM494583 2 0.4588 0.444 0.016 0.604 0.000 0.000 0.380
#> GSM494612 2 0.2773 0.765 0.164 0.836 0.000 0.000 0.000
#> GSM494558 4 0.3192 0.791 0.004 0.016 0.076 0.872 0.032
#> GSM494556 5 0.6454 0.472 0.008 0.016 0.088 0.424 0.464
#> GSM494559 5 0.1710 0.672 0.012 0.000 0.020 0.024 0.944
#> GSM494571 3 0.0000 0.986 0.000 0.000 1.000 0.000 0.000
#> GSM494614 5 0.5376 0.107 0.004 0.404 0.048 0.000 0.544
#> GSM494603 4 0.1557 0.857 0.000 0.000 0.052 0.940 0.008
#> GSM494568 4 0.1557 0.857 0.000 0.000 0.052 0.940 0.008
#> GSM494572 3 0.0000 0.986 0.000 0.000 1.000 0.000 0.000
#> GSM494600 5 0.0807 0.668 0.000 0.000 0.012 0.012 0.976
#> GSM494562 2 0.3359 0.767 0.164 0.816 0.000 0.000 0.020
#> GSM494615 5 0.6490 0.488 0.008 0.016 0.092 0.416 0.468
#> GSM494582 2 0.2848 0.764 0.156 0.840 0.000 0.000 0.004
#> GSM494599 2 0.3305 0.757 0.224 0.776 0.000 0.000 0.000
#> GSM494610 2 0.3264 0.766 0.164 0.820 0.000 0.000 0.016
#> GSM494587 2 0.5602 0.623 0.092 0.612 0.004 0.000 0.292
#> GSM494581 2 0.5726 0.712 0.188 0.640 0.000 0.004 0.168
#> GSM494580 5 0.6490 0.488 0.008 0.016 0.092 0.416 0.468
#> GSM494563 5 0.4003 0.360 0.008 0.288 0.000 0.000 0.704
#> GSM494576 2 0.3783 0.574 0.008 0.740 0.000 0.000 0.252
#> GSM494605 1 0.3857 0.653 0.688 0.000 0.000 0.312 0.000
#> GSM494584 2 0.4804 0.447 0.008 0.612 0.016 0.000 0.364
#> GSM494586 2 0.3675 0.627 0.024 0.788 0.000 0.000 0.188
#> GSM494578 5 0.6490 0.488 0.008 0.016 0.092 0.416 0.468
#> GSM494585 2 0.5620 0.617 0.092 0.608 0.004 0.000 0.296
#> GSM494611 2 0.2773 0.765 0.164 0.836 0.000 0.000 0.000
#> GSM494560 5 0.0807 0.668 0.000 0.000 0.012 0.012 0.976
#> GSM494595 2 0.3281 0.741 0.092 0.848 0.000 0.000 0.060
#> GSM494570 5 0.1710 0.672 0.012 0.000 0.020 0.024 0.944
#> GSM494597 3 0.1571 0.940 0.000 0.004 0.936 0.000 0.060
#> GSM494607 2 0.4307 0.369 0.496 0.504 0.000 0.000 0.000
#> GSM494561 5 0.1869 0.670 0.012 0.000 0.016 0.036 0.936
#> GSM494569 4 0.1200 0.889 0.008 0.016 0.000 0.964 0.012
#> GSM494592 2 0.3305 0.757 0.224 0.776 0.000 0.000 0.000
#> GSM494577 2 0.4298 0.451 0.008 0.640 0.000 0.000 0.352
#> GSM494588 5 0.1617 0.672 0.012 0.000 0.020 0.020 0.948
#> GSM494590 3 0.0000 0.986 0.000 0.000 1.000 0.000 0.000
#> GSM494609 2 0.5759 0.709 0.188 0.636 0.000 0.004 0.172
#> GSM494608 2 0.5759 0.709 0.188 0.636 0.000 0.004 0.172
#> GSM494606 2 0.3461 0.758 0.224 0.772 0.000 0.000 0.004
#> GSM494574 2 0.3264 0.766 0.164 0.820 0.000 0.000 0.016
#> GSM494573 5 0.0807 0.668 0.000 0.000 0.012 0.012 0.976
#> GSM494566 2 0.6257 0.446 0.052 0.580 0.036 0.012 0.320
#> GSM494601 2 0.4852 0.747 0.184 0.716 0.000 0.000 0.100
#> GSM494557 5 0.6490 0.488 0.008 0.016 0.092 0.416 0.468
#> GSM494579 2 0.5142 0.478 0.052 0.600 0.000 0.000 0.348
#> GSM494596 3 0.0000 0.986 0.000 0.000 1.000 0.000 0.000
#> GSM494575 2 0.2773 0.765 0.164 0.836 0.000 0.000 0.000
#> GSM494625 4 0.2006 0.904 0.012 0.000 0.000 0.916 0.072
#> GSM494654 3 0.0162 0.985 0.000 0.000 0.996 0.004 0.000
#> GSM494664 1 0.3857 0.653 0.688 0.000 0.000 0.312 0.000
#> GSM494624 4 0.2006 0.904 0.012 0.000 0.000 0.916 0.072
#> GSM494651 4 0.1200 0.889 0.008 0.016 0.000 0.964 0.012
#> GSM494662 4 0.2782 0.870 0.072 0.000 0.000 0.880 0.048
#> GSM494627 4 0.0000 0.895 0.000 0.000 0.000 1.000 0.000
#> GSM494673 1 0.0510 0.830 0.984 0.000 0.000 0.016 0.000
#> GSM494649 4 0.2006 0.904 0.012 0.000 0.000 0.916 0.072
#> GSM494658 1 0.0510 0.830 0.984 0.000 0.000 0.016 0.000
#> GSM494653 1 0.0510 0.830 0.984 0.000 0.000 0.016 0.000
#> GSM494643 4 0.2006 0.904 0.012 0.000 0.000 0.916 0.072
#> GSM494672 1 0.0510 0.830 0.984 0.000 0.000 0.016 0.000
#> GSM494618 4 0.1200 0.889 0.008 0.016 0.000 0.964 0.012
#> GSM494631 4 0.6329 -0.413 0.008 0.016 0.076 0.468 0.432
#> GSM494619 4 0.2006 0.904 0.012 0.000 0.000 0.916 0.072
#> GSM494674 1 0.0510 0.830 0.984 0.000 0.000 0.016 0.000
#> GSM494616 4 0.1200 0.889 0.008 0.016 0.000 0.964 0.012
#> GSM494663 4 0.0000 0.895 0.000 0.000 0.000 1.000 0.000
#> GSM494628 4 0.0000 0.895 0.000 0.000 0.000 1.000 0.000
#> GSM494632 4 0.3389 0.826 0.116 0.000 0.000 0.836 0.048
#> GSM494660 4 0.2006 0.904 0.012 0.000 0.000 0.916 0.072
#> GSM494622 4 0.0000 0.895 0.000 0.000 0.000 1.000 0.000
#> GSM494642 1 0.0510 0.830 0.984 0.000 0.000 0.016 0.000
#> GSM494647 1 0.0510 0.830 0.984 0.000 0.000 0.016 0.000
#> GSM494659 1 0.0510 0.830 0.984 0.000 0.000 0.016 0.000
#> GSM494670 1 0.0510 0.830 0.984 0.000 0.000 0.016 0.000
#> GSM494675 3 0.1571 0.940 0.000 0.004 0.936 0.000 0.060
#> GSM494641 1 0.0510 0.830 0.984 0.000 0.000 0.016 0.000
#> GSM494636 4 0.3339 0.831 0.112 0.000 0.000 0.840 0.048
#> GSM494640 4 0.2006 0.904 0.012 0.000 0.000 0.916 0.072
#> GSM494623 4 0.2006 0.904 0.012 0.000 0.000 0.916 0.072
#> GSM494644 1 0.3857 0.653 0.688 0.000 0.000 0.312 0.000
#> GSM494646 1 0.3932 0.626 0.672 0.000 0.000 0.328 0.000
#> GSM494665 1 0.3857 0.653 0.688 0.000 0.000 0.312 0.000
#> GSM494638 4 0.2782 0.870 0.072 0.000 0.000 0.880 0.048
#> GSM494645 1 0.3857 0.653 0.688 0.000 0.000 0.312 0.000
#> GSM494671 1 0.0510 0.830 0.984 0.000 0.000 0.016 0.000
#> GSM494655 1 0.0510 0.830 0.984 0.000 0.000 0.016 0.000
#> GSM494620 4 0.2006 0.904 0.012 0.000 0.000 0.916 0.072
#> GSM494630 4 0.2006 0.904 0.012 0.000 0.000 0.916 0.072
#> GSM494657 3 0.0000 0.986 0.000 0.000 1.000 0.000 0.000
#> GSM494667 1 0.0510 0.830 0.984 0.000 0.000 0.016 0.000
#> GSM494621 4 0.2006 0.904 0.012 0.000 0.000 0.916 0.072
#> GSM494629 4 0.1200 0.889 0.008 0.016 0.000 0.964 0.012
#> GSM494637 4 0.2006 0.904 0.012 0.000 0.000 0.916 0.072
#> GSM494652 1 0.0510 0.830 0.984 0.000 0.000 0.016 0.000
#> GSM494648 4 0.2006 0.904 0.012 0.000 0.000 0.916 0.072
#> GSM494650 4 0.1200 0.889 0.008 0.016 0.000 0.964 0.012
#> GSM494669 1 0.0510 0.830 0.984 0.000 0.000 0.016 0.000
#> GSM494666 1 0.3857 0.653 0.688 0.000 0.000 0.312 0.000
#> GSM494668 1 0.0510 0.830 0.984 0.000 0.000 0.016 0.000
#> GSM494633 4 0.2006 0.904 0.012 0.000 0.000 0.916 0.072
#> GSM494634 1 0.0510 0.830 0.984 0.000 0.000 0.016 0.000
#> GSM494639 4 0.3794 0.781 0.152 0.000 0.000 0.800 0.048
#> GSM494661 1 0.3857 0.653 0.688 0.000 0.000 0.312 0.000
#> GSM494617 4 0.1200 0.889 0.008 0.016 0.000 0.964 0.012
#> GSM494626 4 0.1200 0.889 0.008 0.016 0.000 0.964 0.012
#> GSM494656 3 0.0162 0.985 0.000 0.000 0.996 0.004 0.000
#> GSM494635 1 0.3895 0.639 0.680 0.000 0.000 0.320 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM494565 5 0.4806 -0.202 0.000 0.460 0.000 0.000 0.488 0.052
#> GSM494594 3 0.0260 0.982 0.000 0.000 0.992 0.000 0.000 0.008
#> GSM494604 2 0.4097 0.347 0.488 0.504 0.000 0.000 0.008 0.000
#> GSM494564 5 0.5392 0.422 0.004 0.004 0.000 0.084 0.492 0.416
#> GSM494591 3 0.0000 0.982 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494567 6 0.2070 0.597 0.000 0.000 0.044 0.048 0.000 0.908
#> GSM494602 2 0.2454 0.715 0.160 0.840 0.000 0.000 0.000 0.000
#> GSM494613 6 0.2070 0.597 0.000 0.000 0.044 0.048 0.000 0.908
#> GSM494589 5 0.5258 0.434 0.000 0.004 0.000 0.084 0.500 0.412
#> GSM494598 2 0.3252 0.712 0.108 0.824 0.000 0.000 0.068 0.000
#> GSM494593 2 0.2668 0.714 0.168 0.828 0.000 0.000 0.000 0.004
#> GSM494583 2 0.5711 0.125 0.000 0.492 0.000 0.000 0.328 0.180
#> GSM494612 2 0.2889 0.710 0.108 0.848 0.000 0.000 0.044 0.000
#> GSM494558 6 0.6390 -0.323 0.000 0.000 0.064 0.388 0.108 0.440
#> GSM494556 6 0.2129 0.592 0.000 0.000 0.040 0.056 0.000 0.904
#> GSM494559 6 0.5497 -0.361 0.004 0.004 0.000 0.096 0.408 0.488
#> GSM494571 3 0.0000 0.982 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494614 6 0.6024 -0.220 0.000 0.284 0.008 0.000 0.220 0.488
#> GSM494603 4 0.5335 0.659 0.000 0.000 0.044 0.668 0.108 0.180
#> GSM494568 4 0.5335 0.659 0.000 0.000 0.044 0.668 0.108 0.180
#> GSM494572 3 0.0000 0.982 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494600 5 0.5258 0.434 0.000 0.004 0.000 0.084 0.500 0.412
#> GSM494562 2 0.3252 0.713 0.108 0.824 0.000 0.000 0.068 0.000
#> GSM494615 6 0.2070 0.597 0.000 0.000 0.044 0.048 0.000 0.908
#> GSM494582 2 0.3138 0.708 0.108 0.832 0.000 0.000 0.060 0.000
#> GSM494599 2 0.2527 0.713 0.168 0.832 0.000 0.000 0.000 0.000
#> GSM494610 2 0.3252 0.712 0.108 0.824 0.000 0.000 0.068 0.000
#> GSM494587 2 0.6111 0.454 0.056 0.576 0.000 0.000 0.148 0.220
#> GSM494581 2 0.5167 0.637 0.140 0.668 0.000 0.000 0.020 0.172
#> GSM494580 6 0.2070 0.597 0.000 0.000 0.044 0.048 0.000 0.908
#> GSM494563 5 0.4204 0.326 0.000 0.132 0.000 0.000 0.740 0.128
#> GSM494576 2 0.4334 0.300 0.000 0.568 0.000 0.000 0.408 0.024
#> GSM494605 1 0.3409 0.695 0.700 0.000 0.000 0.300 0.000 0.000
#> GSM494584 2 0.5869 0.168 0.000 0.504 0.004 0.000 0.208 0.284
#> GSM494586 2 0.4034 0.392 0.004 0.624 0.000 0.000 0.364 0.008
#> GSM494578 6 0.2070 0.597 0.000 0.000 0.044 0.048 0.000 0.908
#> GSM494585 2 0.6001 0.466 0.060 0.584 0.000 0.000 0.116 0.240
#> GSM494611 2 0.2889 0.710 0.108 0.848 0.000 0.000 0.044 0.000
#> GSM494560 5 0.5258 0.434 0.000 0.004 0.000 0.084 0.500 0.412
#> GSM494595 2 0.4108 0.650 0.060 0.748 0.000 0.000 0.184 0.008
#> GSM494570 6 0.5505 -0.371 0.004 0.004 0.000 0.096 0.416 0.480
#> GSM494597 3 0.2074 0.931 0.000 0.004 0.912 0.000 0.036 0.048
#> GSM494607 2 0.4097 0.347 0.488 0.504 0.000 0.000 0.008 0.000
#> GSM494561 6 0.5602 -0.356 0.004 0.004 0.000 0.108 0.408 0.476
#> GSM494569 4 0.5313 0.469 0.000 0.000 0.000 0.508 0.108 0.384
#> GSM494592 2 0.2527 0.713 0.168 0.832 0.000 0.000 0.000 0.000
#> GSM494577 5 0.4651 -0.220 0.000 0.476 0.000 0.000 0.484 0.040
#> GSM494588 6 0.5501 -0.370 0.004 0.004 0.000 0.096 0.412 0.484
#> GSM494590 3 0.0146 0.982 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM494609 2 0.5197 0.634 0.140 0.664 0.000 0.000 0.020 0.176
#> GSM494608 2 0.5197 0.634 0.140 0.664 0.000 0.000 0.020 0.176
#> GSM494606 2 0.2778 0.714 0.168 0.824 0.000 0.000 0.000 0.008
#> GSM494574 2 0.3252 0.712 0.108 0.824 0.000 0.000 0.068 0.000
#> GSM494573 5 0.5258 0.434 0.000 0.004 0.000 0.084 0.500 0.412
#> GSM494566 2 0.7258 0.176 0.044 0.480 0.028 0.012 0.180 0.256
#> GSM494601 2 0.4410 0.678 0.144 0.744 0.000 0.000 0.016 0.096
#> GSM494557 6 0.2070 0.597 0.000 0.000 0.044 0.048 0.000 0.908
#> GSM494579 2 0.6403 0.204 0.036 0.496 0.000 0.000 0.232 0.236
#> GSM494596 3 0.0000 0.982 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494575 2 0.2889 0.710 0.108 0.848 0.000 0.000 0.044 0.000
#> GSM494625 4 0.0000 0.777 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494654 3 0.0260 0.982 0.000 0.000 0.992 0.000 0.000 0.008
#> GSM494664 1 0.3409 0.695 0.700 0.000 0.000 0.300 0.000 0.000
#> GSM494624 4 0.0000 0.777 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494651 4 0.5330 0.452 0.000 0.000 0.000 0.496 0.108 0.396
#> GSM494662 4 0.1267 0.752 0.060 0.000 0.000 0.940 0.000 0.000
#> GSM494627 4 0.4286 0.694 0.000 0.000 0.000 0.728 0.108 0.164
#> GSM494673 1 0.0146 0.850 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM494649 4 0.0000 0.777 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494658 1 0.0146 0.850 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM494653 1 0.0146 0.850 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM494643 4 0.0000 0.777 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494672 1 0.0146 0.850 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM494618 4 0.5330 0.452 0.000 0.000 0.000 0.496 0.108 0.396
#> GSM494631 6 0.2881 0.559 0.000 0.000 0.040 0.084 0.012 0.864
#> GSM494619 4 0.0000 0.777 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494674 1 0.0146 0.850 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM494616 4 0.5330 0.452 0.000 0.000 0.000 0.496 0.108 0.396
#> GSM494663 4 0.4286 0.694 0.000 0.000 0.000 0.728 0.108 0.164
#> GSM494628 4 0.4286 0.694 0.000 0.000 0.000 0.728 0.108 0.164
#> GSM494632 4 0.1910 0.718 0.108 0.000 0.000 0.892 0.000 0.000
#> GSM494660 4 0.0000 0.777 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494622 4 0.4286 0.694 0.000 0.000 0.000 0.728 0.108 0.164
#> GSM494642 1 0.0146 0.850 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM494647 1 0.0146 0.850 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM494659 1 0.0146 0.850 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM494670 1 0.0146 0.850 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM494675 3 0.2074 0.931 0.000 0.004 0.912 0.000 0.036 0.048
#> GSM494641 1 0.0146 0.850 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM494636 4 0.1863 0.722 0.104 0.000 0.000 0.896 0.000 0.000
#> GSM494640 4 0.0000 0.777 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494623 4 0.0000 0.777 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494644 1 0.3409 0.695 0.700 0.000 0.000 0.300 0.000 0.000
#> GSM494646 1 0.3499 0.665 0.680 0.000 0.000 0.320 0.000 0.000
#> GSM494665 1 0.3409 0.695 0.700 0.000 0.000 0.300 0.000 0.000
#> GSM494638 4 0.1267 0.752 0.060 0.000 0.000 0.940 0.000 0.000
#> GSM494645 1 0.3409 0.695 0.700 0.000 0.000 0.300 0.000 0.000
#> GSM494671 1 0.0146 0.850 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM494655 1 0.0146 0.850 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM494620 4 0.0000 0.777 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494630 4 0.0000 0.777 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494657 3 0.0146 0.982 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM494667 1 0.0146 0.850 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM494621 4 0.0000 0.777 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494629 4 0.5313 0.469 0.000 0.000 0.000 0.508 0.108 0.384
#> GSM494637 4 0.0000 0.777 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494652 1 0.0146 0.850 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM494648 4 0.0000 0.777 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494650 4 0.5330 0.452 0.000 0.000 0.000 0.496 0.108 0.396
#> GSM494669 1 0.0146 0.850 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM494666 1 0.3409 0.695 0.700 0.000 0.000 0.300 0.000 0.000
#> GSM494668 1 0.0146 0.850 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM494633 4 0.0000 0.777 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494634 1 0.0146 0.850 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM494639 4 0.2416 0.667 0.156 0.000 0.000 0.844 0.000 0.000
#> GSM494661 1 0.3409 0.695 0.700 0.000 0.000 0.300 0.000 0.000
#> GSM494617 4 0.5330 0.452 0.000 0.000 0.000 0.496 0.108 0.396
#> GSM494626 4 0.5330 0.452 0.000 0.000 0.000 0.496 0.108 0.396
#> GSM494656 3 0.0260 0.982 0.000 0.000 0.992 0.000 0.000 0.008
#> GSM494635 1 0.3446 0.685 0.692 0.000 0.000 0.308 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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 3)
get_signatures(res, k = 4)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 5)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 6)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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.
# code only for demonstration
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) individual(p) k
#> CV:hclust 118 2.73e-01 0.000385 4.78e-02 0.000694 2
#> CV:hclust 101 2.19e-07 0.015644 4.83e-05 0.128619 3
#> CV:hclust 91 2.20e-06 0.029381 3.22e-05 0.061477 4
#> CV:hclust 102 4.92e-15 0.103337 1.16e-11 0.555458 5
#> CV:hclust 88 1.09e-12 0.133799 1.29e-11 0.493639 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.
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"]
# you can also extract it by
# 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 51941 rows and 120 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 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:
k and the heatmap of
predicted classes for each k.k.k.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:
k;k, the area increased is defined as \(A_k - A_{k-1}\).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 0.487 0.636 0.783 0.5011 0.496 0.496
#> 3 3 0.503 0.660 0.771 0.3165 0.841 0.685
#> 4 4 0.781 0.854 0.885 0.1270 0.866 0.634
#> 5 5 0.837 0.818 0.875 0.0655 0.951 0.805
#> 6 6 0.835 0.717 0.801 0.0387 0.936 0.709
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
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.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM494565 2 0.9963 0.724 0.464 0.536
#> GSM494594 2 0.9963 0.724 0.464 0.536
#> GSM494604 1 0.9970 0.653 0.532 0.468
#> GSM494564 2 0.9963 0.724 0.464 0.536
#> GSM494591 2 0.9963 0.724 0.464 0.536
#> GSM494567 2 0.9963 0.724 0.464 0.536
#> GSM494602 2 0.0000 0.520 0.000 1.000
#> GSM494613 2 0.9963 0.724 0.464 0.536
#> GSM494589 2 0.9963 0.724 0.464 0.536
#> GSM494598 2 0.0000 0.520 0.000 1.000
#> GSM494593 2 0.0000 0.520 0.000 1.000
#> GSM494583 2 0.9552 0.687 0.376 0.624
#> GSM494612 2 0.0000 0.520 0.000 1.000
#> GSM494558 2 0.9963 0.724 0.464 0.536
#> GSM494556 2 0.9963 0.724 0.464 0.536
#> GSM494559 2 0.9963 0.724 0.464 0.536
#> GSM494571 2 0.9963 0.724 0.464 0.536
#> GSM494614 2 0.9963 0.724 0.464 0.536
#> GSM494603 2 0.9963 0.724 0.464 0.536
#> GSM494568 2 0.9963 0.724 0.464 0.536
#> GSM494572 2 0.9963 0.724 0.464 0.536
#> GSM494600 2 0.9963 0.724 0.464 0.536
#> GSM494562 2 0.1184 0.529 0.016 0.984
#> GSM494615 2 0.9963 0.724 0.464 0.536
#> GSM494582 2 0.0000 0.520 0.000 1.000
#> GSM494599 2 0.0000 0.520 0.000 1.000
#> GSM494610 2 0.0000 0.520 0.000 1.000
#> GSM494587 2 0.4161 0.565 0.084 0.916
#> GSM494581 2 0.3114 0.551 0.056 0.944
#> GSM494580 2 0.9963 0.724 0.464 0.536
#> GSM494563 2 0.9963 0.724 0.464 0.536
#> GSM494576 2 0.6887 0.606 0.184 0.816
#> GSM494605 1 0.9963 0.657 0.536 0.464
#> GSM494584 2 0.9963 0.724 0.464 0.536
#> GSM494586 2 0.0938 0.527 0.012 0.988
#> GSM494578 2 0.9963 0.724 0.464 0.536
#> GSM494585 2 0.3431 0.556 0.064 0.936
#> GSM494611 2 0.0000 0.520 0.000 1.000
#> GSM494560 2 0.9963 0.724 0.464 0.536
#> GSM494595 2 0.0376 0.522 0.004 0.996
#> GSM494570 2 0.9963 0.724 0.464 0.536
#> GSM494597 2 0.9963 0.724 0.464 0.536
#> GSM494607 2 0.4939 0.327 0.108 0.892
#> GSM494561 2 0.9963 0.724 0.464 0.536
#> GSM494569 1 0.0000 0.595 1.000 0.000
#> GSM494592 2 0.0000 0.520 0.000 1.000
#> GSM494577 2 0.6887 0.606 0.184 0.816
#> GSM494588 2 0.9963 0.724 0.464 0.536
#> GSM494590 2 0.9963 0.724 0.464 0.536
#> GSM494609 2 0.0000 0.520 0.000 1.000
#> GSM494608 2 0.0000 0.520 0.000 1.000
#> GSM494606 2 0.0000 0.520 0.000 1.000
#> GSM494574 2 0.0000 0.520 0.000 1.000
#> GSM494573 2 0.9963 0.724 0.464 0.536
#> GSM494566 2 0.9963 0.724 0.464 0.536
#> GSM494601 2 0.0000 0.520 0.000 1.000
#> GSM494557 2 0.9963 0.724 0.464 0.536
#> GSM494579 2 0.3584 0.558 0.068 0.932
#> GSM494596 2 0.9963 0.724 0.464 0.536
#> GSM494575 2 0.0000 0.520 0.000 1.000
#> GSM494625 1 0.0000 0.595 1.000 0.000
#> GSM494654 2 0.9963 0.724 0.464 0.536
#> GSM494664 1 0.9963 0.657 0.536 0.464
#> GSM494624 1 0.0672 0.601 0.992 0.008
#> GSM494651 1 0.0000 0.595 1.000 0.000
#> GSM494662 1 0.1414 0.607 0.980 0.020
#> GSM494627 1 0.0000 0.595 1.000 0.000
#> GSM494673 1 0.9963 0.657 0.536 0.464
#> GSM494649 1 0.0000 0.595 1.000 0.000
#> GSM494658 1 0.9963 0.657 0.536 0.464
#> GSM494653 1 0.9963 0.657 0.536 0.464
#> GSM494643 1 0.1414 0.607 0.980 0.020
#> GSM494672 1 0.9963 0.657 0.536 0.464
#> GSM494618 1 0.0376 0.598 0.996 0.004
#> GSM494631 2 0.9963 0.724 0.464 0.536
#> GSM494619 1 0.1414 0.607 0.980 0.020
#> GSM494674 1 0.9963 0.657 0.536 0.464
#> GSM494616 1 0.0000 0.595 1.000 0.000
#> GSM494663 1 0.0000 0.595 1.000 0.000
#> GSM494628 1 0.0000 0.595 1.000 0.000
#> GSM494632 1 0.9963 0.657 0.536 0.464
#> GSM494660 1 0.0000 0.595 1.000 0.000
#> GSM494622 1 0.0000 0.595 1.000 0.000
#> GSM494642 1 0.9963 0.657 0.536 0.464
#> GSM494647 1 0.9963 0.657 0.536 0.464
#> GSM494659 1 0.9963 0.657 0.536 0.464
#> GSM494670 1 0.9963 0.657 0.536 0.464
#> GSM494675 2 0.9963 0.724 0.464 0.536
#> GSM494641 1 0.9963 0.657 0.536 0.464
#> GSM494636 1 0.1414 0.607 0.980 0.020
#> GSM494640 1 0.0000 0.595 1.000 0.000
#> GSM494623 1 0.1414 0.607 0.980 0.020
#> GSM494644 1 0.9963 0.657 0.536 0.464
#> GSM494646 1 0.9963 0.657 0.536 0.464
#> GSM494665 1 0.9963 0.657 0.536 0.464
#> GSM494638 1 0.1414 0.607 0.980 0.020
#> GSM494645 1 0.9963 0.657 0.536 0.464
#> GSM494671 1 0.9963 0.657 0.536 0.464
#> GSM494655 1 0.9963 0.657 0.536 0.464
#> GSM494620 1 0.1414 0.607 0.980 0.020
#> GSM494630 1 0.1414 0.607 0.980 0.020
#> GSM494657 2 0.9963 0.724 0.464 0.536
#> GSM494667 1 0.9963 0.657 0.536 0.464
#> GSM494621 1 0.1414 0.607 0.980 0.020
#> GSM494629 1 0.0000 0.595 1.000 0.000
#> GSM494637 1 0.0000 0.595 1.000 0.000
#> GSM494652 1 0.9963 0.657 0.536 0.464
#> GSM494648 1 0.1414 0.607 0.980 0.020
#> GSM494650 1 0.0000 0.595 1.000 0.000
#> GSM494669 1 0.9963 0.657 0.536 0.464
#> GSM494666 1 0.9963 0.657 0.536 0.464
#> GSM494668 1 0.9963 0.657 0.536 0.464
#> GSM494633 1 0.0000 0.595 1.000 0.000
#> GSM494634 1 0.9963 0.657 0.536 0.464
#> GSM494639 1 0.9963 0.657 0.536 0.464
#> GSM494661 1 0.9963 0.657 0.536 0.464
#> GSM494617 1 0.1184 0.605 0.984 0.016
#> GSM494626 1 0.0672 0.601 0.992 0.008
#> GSM494656 2 0.9963 0.724 0.464 0.536
#> GSM494635 1 0.9963 0.657 0.536 0.464
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM494565 2 0.6420 0.548 0.024 0.688 0.288
#> GSM494594 3 0.1878 0.804 0.004 0.044 0.952
#> GSM494604 2 0.5621 0.375 0.308 0.692 0.000
#> GSM494564 3 0.6287 0.644 0.024 0.272 0.704
#> GSM494591 3 0.4233 0.809 0.004 0.160 0.836
#> GSM494567 3 0.3896 0.828 0.008 0.128 0.864
#> GSM494602 2 0.0000 0.774 0.000 1.000 0.000
#> GSM494613 3 0.4033 0.828 0.008 0.136 0.856
#> GSM494589 3 0.5053 0.799 0.024 0.164 0.812
#> GSM494598 2 0.0000 0.774 0.000 1.000 0.000
#> GSM494593 2 0.0000 0.774 0.000 1.000 0.000
#> GSM494583 2 0.5465 0.583 0.000 0.712 0.288
#> GSM494612 2 0.0000 0.774 0.000 1.000 0.000
#> GSM494558 3 0.0424 0.783 0.008 0.000 0.992
#> GSM494556 3 0.4033 0.828 0.008 0.136 0.856
#> GSM494559 3 0.6952 0.414 0.024 0.376 0.600
#> GSM494571 3 0.0237 0.784 0.004 0.000 0.996
#> GSM494614 3 0.6209 0.454 0.004 0.368 0.628
#> GSM494603 3 0.0983 0.785 0.016 0.004 0.980
#> GSM494568 3 0.1031 0.769 0.024 0.000 0.976
#> GSM494572 3 0.3851 0.828 0.004 0.136 0.860
#> GSM494600 3 0.5585 0.752 0.024 0.204 0.772
#> GSM494562 2 0.3267 0.725 0.000 0.884 0.116
#> GSM494615 3 0.0848 0.788 0.008 0.008 0.984
#> GSM494582 2 0.0000 0.774 0.000 1.000 0.000
#> GSM494599 2 0.3686 0.657 0.140 0.860 0.000
#> GSM494610 2 0.0000 0.774 0.000 1.000 0.000
#> GSM494587 2 0.5465 0.583 0.000 0.712 0.288
#> GSM494581 2 0.5465 0.583 0.000 0.712 0.288
#> GSM494580 3 0.4033 0.828 0.008 0.136 0.856
#> GSM494563 2 0.6420 0.548 0.024 0.688 0.288
#> GSM494576 2 0.5465 0.583 0.000 0.712 0.288
#> GSM494605 1 0.1031 0.709 0.976 0.024 0.000
#> GSM494584 2 0.6264 0.372 0.004 0.616 0.380
#> GSM494586 2 0.3267 0.725 0.000 0.884 0.116
#> GSM494578 3 0.4033 0.828 0.008 0.136 0.856
#> GSM494585 2 0.5465 0.583 0.000 0.712 0.288
#> GSM494611 2 0.0000 0.774 0.000 1.000 0.000
#> GSM494560 3 0.7043 0.353 0.024 0.400 0.576
#> GSM494595 2 0.0237 0.773 0.000 0.996 0.004
#> GSM494570 3 0.3637 0.816 0.024 0.084 0.892
#> GSM494597 3 0.4047 0.820 0.004 0.148 0.848
#> GSM494607 2 0.4121 0.623 0.168 0.832 0.000
#> GSM494561 3 0.1031 0.777 0.024 0.000 0.976
#> GSM494569 1 0.6026 0.570 0.624 0.000 0.376
#> GSM494592 2 0.3686 0.657 0.140 0.860 0.000
#> GSM494577 2 0.5465 0.583 0.000 0.712 0.288
#> GSM494588 2 0.6451 0.544 0.024 0.684 0.292
#> GSM494590 3 0.3851 0.828 0.004 0.136 0.860
#> GSM494609 2 0.0000 0.774 0.000 1.000 0.000
#> GSM494608 2 0.0000 0.774 0.000 1.000 0.000
#> GSM494606 2 0.3686 0.657 0.140 0.860 0.000
#> GSM494574 2 0.0000 0.774 0.000 1.000 0.000
#> GSM494573 3 0.6879 0.453 0.024 0.360 0.616
#> GSM494566 2 0.6129 0.501 0.008 0.668 0.324
#> GSM494601 2 0.0000 0.774 0.000 1.000 0.000
#> GSM494557 3 0.4353 0.814 0.008 0.156 0.836
#> GSM494579 2 0.5465 0.583 0.000 0.712 0.288
#> GSM494596 3 0.4047 0.820 0.004 0.148 0.848
#> GSM494575 2 0.0000 0.774 0.000 1.000 0.000
#> GSM494625 1 0.5948 0.575 0.640 0.000 0.360
#> GSM494654 3 0.0424 0.781 0.008 0.000 0.992
#> GSM494664 1 0.0892 0.710 0.980 0.020 0.000
#> GSM494624 1 0.4178 0.705 0.828 0.000 0.172
#> GSM494651 1 0.6062 0.560 0.616 0.000 0.384
#> GSM494662 1 0.4235 0.705 0.824 0.000 0.176
#> GSM494627 1 0.6062 0.560 0.616 0.000 0.384
#> GSM494673 1 0.5650 0.578 0.688 0.312 0.000
#> GSM494649 1 0.5948 0.575 0.640 0.000 0.360
#> GSM494658 1 0.5678 0.574 0.684 0.316 0.000
#> GSM494653 1 0.5650 0.578 0.688 0.312 0.000
#> GSM494643 1 0.4121 0.706 0.832 0.000 0.168
#> GSM494672 1 0.5678 0.574 0.684 0.316 0.000
#> GSM494618 1 0.6026 0.570 0.624 0.000 0.376
#> GSM494631 3 0.0747 0.777 0.016 0.000 0.984
#> GSM494619 1 0.4178 0.705 0.828 0.000 0.172
#> GSM494674 1 0.5621 0.581 0.692 0.308 0.000
#> GSM494616 1 0.6026 0.570 0.624 0.000 0.376
#> GSM494663 1 0.5968 0.574 0.636 0.000 0.364
#> GSM494628 1 0.6062 0.560 0.616 0.000 0.384
#> GSM494632 1 0.1315 0.711 0.972 0.020 0.008
#> GSM494660 1 0.5948 0.575 0.640 0.000 0.360
#> GSM494622 1 0.6062 0.560 0.616 0.000 0.384
#> GSM494642 1 0.5650 0.578 0.688 0.312 0.000
#> GSM494647 1 0.5650 0.578 0.688 0.312 0.000
#> GSM494659 1 0.5650 0.578 0.688 0.312 0.000
#> GSM494670 1 0.5650 0.578 0.688 0.312 0.000
#> GSM494675 3 0.4099 0.826 0.008 0.140 0.852
#> GSM494641 1 0.5650 0.578 0.688 0.312 0.000
#> GSM494636 1 0.4235 0.705 0.824 0.000 0.176
#> GSM494640 1 0.6045 0.562 0.620 0.000 0.380
#> GSM494623 1 0.4178 0.705 0.828 0.000 0.172
#> GSM494644 1 0.5621 0.581 0.692 0.308 0.000
#> GSM494646 1 0.0892 0.710 0.980 0.020 0.000
#> GSM494665 1 0.4974 0.621 0.764 0.236 0.000
#> GSM494638 1 0.4645 0.707 0.816 0.008 0.176
#> GSM494645 1 0.1031 0.709 0.976 0.024 0.000
#> GSM494671 1 0.5678 0.574 0.684 0.316 0.000
#> GSM494655 1 0.5621 0.581 0.692 0.308 0.000
#> GSM494620 1 0.4178 0.705 0.828 0.000 0.172
#> GSM494630 1 0.4178 0.705 0.828 0.000 0.172
#> GSM494657 3 0.3851 0.828 0.004 0.136 0.860
#> GSM494667 1 0.5650 0.578 0.688 0.312 0.000
#> GSM494621 1 0.4178 0.705 0.828 0.000 0.172
#> GSM494629 3 0.6244 -0.195 0.440 0.000 0.560
#> GSM494637 1 0.5968 0.574 0.636 0.000 0.364
#> GSM494652 1 0.5650 0.578 0.688 0.312 0.000
#> GSM494648 1 0.4178 0.705 0.828 0.000 0.172
#> GSM494650 1 0.6062 0.560 0.616 0.000 0.384
#> GSM494669 1 0.5650 0.578 0.688 0.312 0.000
#> GSM494666 1 0.1031 0.709 0.976 0.024 0.000
#> GSM494668 1 0.5621 0.581 0.692 0.308 0.000
#> GSM494633 1 0.4399 0.699 0.812 0.000 0.188
#> GSM494634 1 0.5678 0.574 0.684 0.316 0.000
#> GSM494639 1 0.0892 0.710 0.980 0.020 0.000
#> GSM494661 1 0.4002 0.659 0.840 0.160 0.000
#> GSM494617 1 0.4399 0.703 0.812 0.000 0.188
#> GSM494626 1 0.5859 0.600 0.656 0.000 0.344
#> GSM494656 3 0.0237 0.784 0.004 0.000 0.996
#> GSM494635 1 0.1031 0.709 0.976 0.024 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM494565 2 0.4332 0.824 0.112 0.816 0.072 0.000
#> GSM494594 3 0.1284 0.862 0.024 0.000 0.964 0.012
#> GSM494604 1 0.3726 0.729 0.788 0.212 0.000 0.000
#> GSM494564 3 0.6190 0.675 0.112 0.168 0.704 0.016
#> GSM494591 3 0.1617 0.864 0.024 0.008 0.956 0.012
#> GSM494567 3 0.1174 0.864 0.000 0.020 0.968 0.012
#> GSM494602 2 0.0921 0.927 0.028 0.972 0.000 0.000
#> GSM494613 3 0.1174 0.864 0.000 0.020 0.968 0.012
#> GSM494589 3 0.3736 0.807 0.108 0.020 0.856 0.016
#> GSM494598 2 0.0921 0.927 0.028 0.972 0.000 0.000
#> GSM494593 2 0.0921 0.927 0.028 0.972 0.000 0.000
#> GSM494583 2 0.3383 0.867 0.076 0.872 0.052 0.000
#> GSM494612 2 0.0921 0.927 0.028 0.972 0.000 0.000
#> GSM494558 3 0.1520 0.859 0.024 0.000 0.956 0.020
#> GSM494556 3 0.1174 0.864 0.000 0.020 0.968 0.012
#> GSM494559 3 0.7458 0.160 0.112 0.412 0.460 0.016
#> GSM494571 3 0.1284 0.862 0.024 0.000 0.964 0.012
#> GSM494614 3 0.5988 0.615 0.100 0.224 0.676 0.000
#> GSM494603 3 0.7371 0.367 0.112 0.016 0.512 0.360
#> GSM494568 4 0.5862 -0.121 0.032 0.000 0.484 0.484
#> GSM494572 3 0.1617 0.864 0.024 0.008 0.956 0.012
#> GSM494600 3 0.3736 0.807 0.108 0.020 0.856 0.016
#> GSM494562 2 0.0524 0.923 0.004 0.988 0.008 0.000
#> GSM494615 3 0.1059 0.864 0.000 0.016 0.972 0.012
#> GSM494582 2 0.0921 0.927 0.028 0.972 0.000 0.000
#> GSM494599 2 0.0921 0.927 0.028 0.972 0.000 0.000
#> GSM494610 2 0.0921 0.927 0.028 0.972 0.000 0.000
#> GSM494587 2 0.1118 0.911 0.000 0.964 0.036 0.000
#> GSM494581 2 0.1305 0.913 0.004 0.960 0.036 0.000
#> GSM494580 3 0.1174 0.864 0.000 0.020 0.968 0.012
#> GSM494563 2 0.4571 0.821 0.116 0.808 0.072 0.004
#> GSM494576 2 0.1798 0.904 0.016 0.944 0.040 0.000
#> GSM494605 1 0.3355 0.948 0.836 0.004 0.000 0.160
#> GSM494584 2 0.5759 0.609 0.080 0.688 0.232 0.000
#> GSM494586 2 0.0524 0.923 0.004 0.988 0.008 0.000
#> GSM494578 3 0.1174 0.864 0.000 0.020 0.968 0.012
#> GSM494585 2 0.1109 0.915 0.004 0.968 0.028 0.000
#> GSM494611 2 0.1022 0.927 0.032 0.968 0.000 0.000
#> GSM494560 3 0.7365 0.128 0.112 0.424 0.452 0.012
#> GSM494595 2 0.0336 0.926 0.008 0.992 0.000 0.000
#> GSM494570 3 0.7633 0.373 0.120 0.024 0.500 0.356
#> GSM494597 3 0.1617 0.864 0.024 0.008 0.956 0.012
#> GSM494607 2 0.3801 0.705 0.220 0.780 0.000 0.000
#> GSM494561 3 0.7505 0.315 0.120 0.016 0.480 0.384
#> GSM494569 4 0.1284 0.920 0.012 0.000 0.024 0.964
#> GSM494592 2 0.0921 0.927 0.028 0.972 0.000 0.000
#> GSM494577 2 0.3286 0.873 0.080 0.876 0.044 0.000
#> GSM494588 2 0.4905 0.813 0.120 0.800 0.060 0.020
#> GSM494590 3 0.1617 0.864 0.024 0.008 0.956 0.012
#> GSM494609 2 0.0592 0.927 0.016 0.984 0.000 0.000
#> GSM494608 2 0.0921 0.927 0.028 0.972 0.000 0.000
#> GSM494606 2 0.0921 0.927 0.028 0.972 0.000 0.000
#> GSM494574 2 0.0921 0.927 0.028 0.972 0.000 0.000
#> GSM494573 3 0.6136 0.677 0.108 0.168 0.708 0.016
#> GSM494566 2 0.5056 0.741 0.076 0.760 0.164 0.000
#> GSM494601 2 0.0921 0.927 0.028 0.972 0.000 0.000
#> GSM494557 3 0.1174 0.864 0.000 0.020 0.968 0.012
#> GSM494579 2 0.2699 0.890 0.068 0.904 0.028 0.000
#> GSM494596 3 0.1617 0.864 0.024 0.008 0.956 0.012
#> GSM494575 2 0.0921 0.927 0.028 0.972 0.000 0.000
#> GSM494625 4 0.0657 0.920 0.012 0.004 0.000 0.984
#> GSM494654 3 0.1520 0.859 0.024 0.000 0.956 0.020
#> GSM494664 1 0.3356 0.931 0.824 0.000 0.000 0.176
#> GSM494624 4 0.1209 0.917 0.032 0.004 0.000 0.964
#> GSM494651 4 0.1388 0.918 0.012 0.000 0.028 0.960
#> GSM494662 4 0.1211 0.912 0.040 0.000 0.000 0.960
#> GSM494627 4 0.1256 0.919 0.008 0.000 0.028 0.964
#> GSM494673 1 0.3803 0.971 0.836 0.032 0.000 0.132
#> GSM494649 4 0.0657 0.920 0.012 0.004 0.000 0.984
#> GSM494658 1 0.3934 0.957 0.836 0.048 0.000 0.116
#> GSM494653 1 0.3803 0.971 0.836 0.032 0.000 0.132
#> GSM494643 4 0.0779 0.918 0.016 0.004 0.000 0.980
#> GSM494672 1 0.3934 0.957 0.836 0.048 0.000 0.116
#> GSM494618 4 0.1284 0.920 0.012 0.000 0.024 0.964
#> GSM494631 3 0.1209 0.860 0.000 0.004 0.964 0.032
#> GSM494619 4 0.1305 0.916 0.036 0.004 0.000 0.960
#> GSM494674 1 0.3803 0.971 0.836 0.032 0.000 0.132
#> GSM494616 4 0.1284 0.920 0.012 0.000 0.024 0.964
#> GSM494663 4 0.1042 0.921 0.008 0.000 0.020 0.972
#> GSM494628 4 0.1388 0.918 0.012 0.000 0.028 0.960
#> GSM494632 4 0.4406 0.477 0.300 0.000 0.000 0.700
#> GSM494660 4 0.0657 0.920 0.012 0.004 0.000 0.984
#> GSM494622 4 0.1388 0.918 0.012 0.000 0.028 0.960
#> GSM494642 1 0.3803 0.971 0.836 0.032 0.000 0.132
#> GSM494647 1 0.3803 0.971 0.836 0.032 0.000 0.132
#> GSM494659 1 0.3803 0.971 0.836 0.032 0.000 0.132
#> GSM494670 1 0.3803 0.971 0.836 0.032 0.000 0.132
#> GSM494675 3 0.1404 0.865 0.012 0.012 0.964 0.012
#> GSM494641 1 0.3803 0.971 0.836 0.032 0.000 0.132
#> GSM494636 4 0.1211 0.912 0.040 0.000 0.000 0.960
#> GSM494640 4 0.0779 0.922 0.004 0.000 0.016 0.980
#> GSM494623 4 0.1305 0.916 0.036 0.004 0.000 0.960
#> GSM494644 1 0.3803 0.971 0.836 0.032 0.000 0.132
#> GSM494646 1 0.3444 0.927 0.816 0.000 0.000 0.184
#> GSM494665 1 0.3606 0.967 0.844 0.024 0.000 0.132
#> GSM494638 4 0.1211 0.912 0.040 0.000 0.000 0.960
#> GSM494645 1 0.3356 0.935 0.824 0.000 0.000 0.176
#> GSM494671 1 0.3907 0.961 0.836 0.044 0.000 0.120
#> GSM494655 1 0.3803 0.971 0.836 0.032 0.000 0.132
#> GSM494620 4 0.1305 0.916 0.036 0.004 0.000 0.960
#> GSM494630 4 0.1305 0.916 0.036 0.004 0.000 0.960
#> GSM494657 3 0.1617 0.864 0.024 0.008 0.956 0.012
#> GSM494667 1 0.3803 0.971 0.836 0.032 0.000 0.132
#> GSM494621 4 0.1305 0.916 0.036 0.004 0.000 0.960
#> GSM494629 4 0.2737 0.838 0.008 0.000 0.104 0.888
#> GSM494637 4 0.0779 0.922 0.004 0.000 0.016 0.980
#> GSM494652 1 0.3803 0.971 0.836 0.032 0.000 0.132
#> GSM494648 4 0.1305 0.916 0.036 0.004 0.000 0.960
#> GSM494650 4 0.2101 0.892 0.012 0.000 0.060 0.928
#> GSM494669 1 0.3803 0.971 0.836 0.032 0.000 0.132
#> GSM494666 1 0.3400 0.931 0.820 0.000 0.000 0.180
#> GSM494668 1 0.3803 0.971 0.836 0.032 0.000 0.132
#> GSM494633 4 0.1209 0.917 0.032 0.004 0.000 0.964
#> GSM494634 1 0.3842 0.968 0.836 0.036 0.000 0.128
#> GSM494639 4 0.4804 0.221 0.384 0.000 0.000 0.616
#> GSM494661 1 0.3257 0.954 0.844 0.004 0.000 0.152
#> GSM494617 4 0.1545 0.914 0.040 0.000 0.008 0.952
#> GSM494626 4 0.1284 0.920 0.012 0.000 0.024 0.964
#> GSM494656 3 0.1520 0.859 0.024 0.000 0.956 0.020
#> GSM494635 1 0.3356 0.935 0.824 0.000 0.000 0.176
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM494565 5 0.4758 0.604 0.000 0.276 0.048 0.000 0.676
#> GSM494594 3 0.0000 0.886 0.000 0.000 1.000 0.000 0.000
#> GSM494604 1 0.3320 0.807 0.820 0.164 0.000 0.004 0.012
#> GSM494564 5 0.3835 0.661 0.000 0.008 0.260 0.000 0.732
#> GSM494591 3 0.0162 0.886 0.000 0.000 0.996 0.000 0.004
#> GSM494567 3 0.2864 0.862 0.000 0.000 0.864 0.024 0.112
#> GSM494602 2 0.0000 0.892 0.000 1.000 0.000 0.000 0.000
#> GSM494613 3 0.2813 0.863 0.000 0.000 0.868 0.024 0.108
#> GSM494589 5 0.4135 0.601 0.000 0.004 0.340 0.000 0.656
#> GSM494598 2 0.1041 0.888 0.000 0.964 0.000 0.004 0.032
#> GSM494593 2 0.0162 0.892 0.000 0.996 0.000 0.004 0.000
#> GSM494583 2 0.4658 0.289 0.000 0.576 0.000 0.016 0.408
#> GSM494612 2 0.0000 0.892 0.000 1.000 0.000 0.000 0.000
#> GSM494558 3 0.3731 0.816 0.000 0.000 0.816 0.112 0.072
#> GSM494556 3 0.3060 0.847 0.000 0.000 0.848 0.024 0.128
#> GSM494559 5 0.5035 0.727 0.000 0.144 0.124 0.008 0.724
#> GSM494571 3 0.0000 0.886 0.000 0.000 1.000 0.000 0.000
#> GSM494614 5 0.5363 0.621 0.000 0.056 0.320 0.008 0.616
#> GSM494603 5 0.5773 0.492 0.000 0.000 0.100 0.356 0.544
#> GSM494568 4 0.3506 0.655 0.000 0.000 0.064 0.832 0.104
#> GSM494572 3 0.0000 0.886 0.000 0.000 1.000 0.000 0.000
#> GSM494600 5 0.4135 0.601 0.000 0.004 0.340 0.000 0.656
#> GSM494562 2 0.1282 0.887 0.000 0.952 0.000 0.004 0.044
#> GSM494615 3 0.3134 0.852 0.000 0.000 0.848 0.032 0.120
#> GSM494582 2 0.1041 0.888 0.000 0.964 0.000 0.004 0.032
#> GSM494599 2 0.0000 0.892 0.000 1.000 0.000 0.000 0.000
#> GSM494610 2 0.1041 0.888 0.000 0.964 0.000 0.004 0.032
#> GSM494587 2 0.1485 0.887 0.000 0.948 0.000 0.020 0.032
#> GSM494581 2 0.1310 0.882 0.000 0.956 0.000 0.020 0.024
#> GSM494580 3 0.2864 0.862 0.000 0.000 0.864 0.024 0.112
#> GSM494563 5 0.4842 0.614 0.000 0.264 0.048 0.004 0.684
#> GSM494576 2 0.2909 0.800 0.000 0.848 0.000 0.012 0.140
#> GSM494605 1 0.0807 0.972 0.976 0.000 0.000 0.012 0.012
#> GSM494584 5 0.6491 0.381 0.000 0.396 0.112 0.020 0.472
#> GSM494586 2 0.1282 0.887 0.000 0.952 0.000 0.004 0.044
#> GSM494578 3 0.2864 0.862 0.000 0.000 0.864 0.024 0.112
#> GSM494585 2 0.1012 0.888 0.000 0.968 0.000 0.020 0.012
#> GSM494611 2 0.0000 0.892 0.000 1.000 0.000 0.000 0.000
#> GSM494560 5 0.5177 0.689 0.000 0.220 0.104 0.000 0.676
#> GSM494595 2 0.1205 0.888 0.000 0.956 0.000 0.004 0.040
#> GSM494570 5 0.2516 0.661 0.000 0.000 0.140 0.000 0.860
#> GSM494597 3 0.0162 0.886 0.000 0.000 0.996 0.000 0.004
#> GSM494607 2 0.0963 0.860 0.036 0.964 0.000 0.000 0.000
#> GSM494561 5 0.3401 0.596 0.000 0.000 0.096 0.064 0.840
#> GSM494569 4 0.0865 0.834 0.024 0.000 0.004 0.972 0.000
#> GSM494592 2 0.0000 0.892 0.000 1.000 0.000 0.000 0.000
#> GSM494577 2 0.4473 0.296 0.000 0.580 0.000 0.008 0.412
#> GSM494588 5 0.4170 0.682 0.000 0.192 0.048 0.000 0.760
#> GSM494590 3 0.0162 0.886 0.000 0.000 0.996 0.000 0.004
#> GSM494609 2 0.1117 0.886 0.000 0.964 0.000 0.020 0.016
#> GSM494608 2 0.1117 0.886 0.000 0.964 0.000 0.020 0.016
#> GSM494606 2 0.0162 0.892 0.000 0.996 0.000 0.004 0.000
#> GSM494574 2 0.1041 0.888 0.000 0.964 0.000 0.004 0.032
#> GSM494573 5 0.4235 0.607 0.000 0.008 0.336 0.000 0.656
#> GSM494566 2 0.5971 -0.235 0.000 0.468 0.044 0.032 0.456
#> GSM494601 2 0.0566 0.891 0.000 0.984 0.000 0.004 0.012
#> GSM494557 3 0.2864 0.862 0.000 0.000 0.864 0.024 0.112
#> GSM494579 2 0.4505 0.357 0.000 0.604 0.000 0.012 0.384
#> GSM494596 3 0.0162 0.886 0.000 0.000 0.996 0.000 0.004
#> GSM494575 2 0.0000 0.892 0.000 1.000 0.000 0.000 0.000
#> GSM494625 4 0.4484 0.793 0.024 0.000 0.000 0.668 0.308
#> GSM494654 3 0.1341 0.845 0.000 0.000 0.944 0.056 0.000
#> GSM494664 1 0.0807 0.972 0.976 0.000 0.000 0.012 0.012
#> GSM494624 4 0.4820 0.780 0.036 0.000 0.000 0.632 0.332
#> GSM494651 4 0.0865 0.834 0.024 0.000 0.004 0.972 0.000
#> GSM494662 4 0.2850 0.839 0.036 0.000 0.000 0.872 0.092
#> GSM494627 4 0.1267 0.834 0.024 0.000 0.004 0.960 0.012
#> GSM494673 1 0.0404 0.982 0.988 0.012 0.000 0.000 0.000
#> GSM494649 4 0.4445 0.796 0.024 0.000 0.000 0.676 0.300
#> GSM494658 1 0.0807 0.981 0.976 0.012 0.000 0.000 0.012
#> GSM494653 1 0.0404 0.982 0.988 0.012 0.000 0.000 0.000
#> GSM494643 4 0.4269 0.815 0.036 0.000 0.000 0.732 0.232
#> GSM494672 1 0.0404 0.982 0.988 0.012 0.000 0.000 0.000
#> GSM494618 4 0.0865 0.834 0.024 0.000 0.004 0.972 0.000
#> GSM494631 3 0.5191 0.610 0.000 0.000 0.660 0.252 0.088
#> GSM494619 4 0.4820 0.780 0.036 0.000 0.000 0.632 0.332
#> GSM494674 1 0.0404 0.982 0.988 0.012 0.000 0.000 0.000
#> GSM494616 4 0.0865 0.834 0.024 0.000 0.004 0.972 0.000
#> GSM494663 4 0.1211 0.836 0.024 0.000 0.000 0.960 0.016
#> GSM494628 4 0.0865 0.834 0.024 0.000 0.004 0.972 0.000
#> GSM494632 4 0.4223 0.678 0.248 0.000 0.000 0.724 0.028
#> GSM494660 4 0.4445 0.796 0.024 0.000 0.000 0.676 0.300
#> GSM494622 4 0.0865 0.834 0.024 0.000 0.004 0.972 0.000
#> GSM494642 1 0.0404 0.982 0.988 0.012 0.000 0.000 0.000
#> GSM494647 1 0.0404 0.982 0.988 0.012 0.000 0.000 0.000
#> GSM494659 1 0.0404 0.982 0.988 0.012 0.000 0.000 0.000
#> GSM494670 1 0.0807 0.981 0.976 0.012 0.000 0.000 0.012
#> GSM494675 3 0.2674 0.860 0.000 0.000 0.868 0.012 0.120
#> GSM494641 1 0.0404 0.982 0.988 0.012 0.000 0.000 0.000
#> GSM494636 4 0.2850 0.839 0.036 0.000 0.000 0.872 0.092
#> GSM494640 4 0.2761 0.839 0.024 0.000 0.000 0.872 0.104
#> GSM494623 4 0.4820 0.780 0.036 0.000 0.000 0.632 0.332
#> GSM494644 1 0.0404 0.978 0.988 0.000 0.000 0.000 0.012
#> GSM494646 1 0.1195 0.960 0.960 0.000 0.000 0.012 0.028
#> GSM494665 1 0.0807 0.972 0.976 0.000 0.000 0.012 0.012
#> GSM494638 4 0.2228 0.837 0.040 0.000 0.000 0.912 0.048
#> GSM494645 1 0.0404 0.978 0.988 0.000 0.000 0.000 0.012
#> GSM494671 1 0.0404 0.982 0.988 0.012 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.979 1.000 0.000 0.000 0.000 0.000
#> GSM494620 4 0.4820 0.780 0.036 0.000 0.000 0.632 0.332
#> GSM494630 4 0.4820 0.780 0.036 0.000 0.000 0.632 0.332
#> GSM494657 3 0.0000 0.886 0.000 0.000 1.000 0.000 0.000
#> GSM494667 1 0.0404 0.982 0.988 0.012 0.000 0.000 0.000
#> GSM494621 4 0.4820 0.780 0.036 0.000 0.000 0.632 0.332
#> GSM494629 4 0.1314 0.830 0.016 0.000 0.012 0.960 0.012
#> GSM494637 4 0.2761 0.839 0.024 0.000 0.000 0.872 0.104
#> GSM494652 1 0.0404 0.982 0.988 0.012 0.000 0.000 0.000
#> GSM494648 4 0.4820 0.780 0.036 0.000 0.000 0.632 0.332
#> GSM494650 4 0.0865 0.834 0.024 0.000 0.004 0.972 0.000
#> GSM494669 1 0.0404 0.982 0.988 0.012 0.000 0.000 0.000
#> GSM494666 1 0.0807 0.972 0.976 0.000 0.000 0.012 0.012
#> GSM494668 1 0.0807 0.981 0.976 0.012 0.000 0.000 0.012
#> GSM494633 4 0.4820 0.780 0.036 0.000 0.000 0.632 0.332
#> GSM494634 1 0.0404 0.982 0.988 0.012 0.000 0.000 0.000
#> GSM494639 4 0.4976 0.242 0.468 0.000 0.000 0.504 0.028
#> GSM494661 1 0.0404 0.978 0.988 0.000 0.000 0.000 0.012
#> GSM494617 4 0.0865 0.834 0.024 0.000 0.004 0.972 0.000
#> GSM494626 4 0.0865 0.834 0.024 0.000 0.004 0.972 0.000
#> GSM494656 3 0.1270 0.848 0.000 0.000 0.948 0.052 0.000
#> GSM494635 1 0.0404 0.978 0.988 0.000 0.000 0.000 0.012
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM494565 5 0.2362 0.7719 0.000 0.080 0.016 0.012 0.892 0.000
#> GSM494594 3 0.0000 0.8193 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494604 1 0.3128 0.7722 0.812 0.168 0.000 0.012 0.008 0.000
#> GSM494564 5 0.2650 0.7739 0.000 0.004 0.072 0.004 0.880 0.040
#> GSM494591 3 0.0291 0.8204 0.000 0.000 0.992 0.004 0.004 0.000
#> GSM494567 3 0.4701 0.7322 0.000 0.000 0.684 0.168 0.148 0.000
#> GSM494602 2 0.0000 0.8562 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494613 3 0.4734 0.7320 0.000 0.000 0.680 0.168 0.152 0.000
#> GSM494589 5 0.2146 0.7568 0.000 0.000 0.116 0.004 0.880 0.000
#> GSM494598 2 0.2869 0.8277 0.000 0.832 0.000 0.148 0.020 0.000
#> GSM494593 2 0.0260 0.8564 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM494583 5 0.5806 -0.0478 0.000 0.408 0.004 0.156 0.432 0.000
#> GSM494612 2 0.0000 0.8562 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494558 4 0.4808 -0.3415 0.000 0.000 0.468 0.480 0.052 0.000
#> GSM494556 3 0.4830 0.7202 0.000 0.000 0.668 0.172 0.160 0.000
#> GSM494559 5 0.2699 0.7844 0.000 0.028 0.048 0.000 0.884 0.040
#> GSM494571 3 0.0146 0.8176 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM494614 5 0.4565 0.6636 0.000 0.008 0.108 0.168 0.716 0.000
#> GSM494603 4 0.5463 -0.2715 0.000 0.000 0.016 0.464 0.444 0.076
#> GSM494568 4 0.4279 0.4984 0.000 0.000 0.008 0.732 0.068 0.192
#> GSM494572 3 0.0146 0.8202 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM494600 5 0.2146 0.7568 0.000 0.000 0.116 0.004 0.880 0.000
#> GSM494562 2 0.2830 0.8288 0.000 0.836 0.000 0.144 0.020 0.000
#> GSM494615 3 0.5556 0.5344 0.000 0.000 0.512 0.336 0.152 0.000
#> GSM494582 2 0.2667 0.8339 0.000 0.852 0.000 0.128 0.020 0.000
#> GSM494599 2 0.0291 0.8547 0.004 0.992 0.000 0.000 0.004 0.000
#> GSM494610 2 0.2869 0.8277 0.000 0.832 0.000 0.148 0.020 0.000
#> GSM494587 2 0.2377 0.8305 0.000 0.868 0.004 0.124 0.004 0.000
#> GSM494581 2 0.2218 0.8221 0.000 0.884 0.000 0.104 0.012 0.000
#> GSM494580 3 0.4734 0.7320 0.000 0.000 0.680 0.168 0.152 0.000
#> GSM494563 5 0.2988 0.7560 0.000 0.080 0.016 0.044 0.860 0.000
#> GSM494576 2 0.5741 0.5137 0.000 0.540 0.004 0.236 0.220 0.000
#> GSM494605 1 0.1908 0.9194 0.916 0.000 0.000 0.028 0.056 0.000
#> GSM494584 5 0.6458 0.5073 0.000 0.236 0.048 0.208 0.508 0.000
#> GSM494586 2 0.2981 0.8280 0.000 0.820 0.000 0.160 0.020 0.000
#> GSM494578 3 0.4765 0.7293 0.000 0.000 0.676 0.172 0.152 0.000
#> GSM494585 2 0.1958 0.8295 0.000 0.896 0.000 0.100 0.004 0.000
#> GSM494611 2 0.1007 0.8557 0.000 0.956 0.000 0.044 0.000 0.000
#> GSM494560 5 0.2384 0.7833 0.000 0.064 0.048 0.000 0.888 0.000
#> GSM494595 2 0.2750 0.8362 0.000 0.844 0.000 0.136 0.020 0.000
#> GSM494570 5 0.2461 0.7696 0.000 0.000 0.044 0.004 0.888 0.064
#> GSM494597 3 0.0291 0.8204 0.000 0.000 0.992 0.004 0.004 0.000
#> GSM494607 2 0.2314 0.8180 0.056 0.900 0.000 0.036 0.008 0.000
#> GSM494561 5 0.4922 0.3896 0.000 0.000 0.020 0.032 0.556 0.392
#> GSM494569 4 0.3819 0.6932 0.000 0.000 0.000 0.624 0.004 0.372
#> GSM494592 2 0.0291 0.8547 0.004 0.992 0.000 0.000 0.004 0.000
#> GSM494577 2 0.6092 0.1277 0.000 0.400 0.004 0.224 0.372 0.000
#> GSM494588 5 0.2450 0.7828 0.000 0.048 0.016 0.000 0.896 0.040
#> GSM494590 3 0.0291 0.8204 0.000 0.000 0.992 0.004 0.004 0.000
#> GSM494609 2 0.2006 0.8273 0.000 0.892 0.000 0.104 0.004 0.000
#> GSM494608 2 0.2006 0.8273 0.000 0.892 0.000 0.104 0.004 0.000
#> GSM494606 2 0.0713 0.8551 0.000 0.972 0.000 0.028 0.000 0.000
#> GSM494574 2 0.2869 0.8277 0.000 0.832 0.000 0.148 0.020 0.000
#> GSM494573 5 0.2100 0.7613 0.000 0.004 0.112 0.000 0.884 0.000
#> GSM494566 5 0.6360 0.4337 0.000 0.208 0.020 0.380 0.392 0.000
#> GSM494601 2 0.0865 0.8541 0.000 0.964 0.000 0.036 0.000 0.000
#> GSM494557 3 0.4734 0.7320 0.000 0.000 0.680 0.168 0.152 0.000
#> GSM494579 2 0.5954 0.1413 0.000 0.408 0.000 0.220 0.372 0.000
#> GSM494596 3 0.0291 0.8204 0.000 0.000 0.992 0.004 0.004 0.000
#> GSM494575 2 0.0000 0.8562 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494625 6 0.0405 0.7585 0.000 0.000 0.000 0.004 0.008 0.988
#> GSM494654 3 0.1010 0.7949 0.000 0.000 0.960 0.036 0.000 0.004
#> GSM494664 1 0.1908 0.9194 0.916 0.000 0.000 0.028 0.056 0.000
#> GSM494624 6 0.0260 0.7602 0.000 0.000 0.000 0.000 0.008 0.992
#> GSM494651 4 0.3684 0.6969 0.000 0.000 0.000 0.628 0.000 0.372
#> GSM494662 6 0.4798 0.2671 0.000 0.000 0.000 0.312 0.076 0.612
#> GSM494627 4 0.3986 0.6906 0.000 0.000 0.004 0.608 0.004 0.384
#> GSM494673 1 0.0260 0.9468 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM494649 6 0.0603 0.7553 0.000 0.000 0.000 0.016 0.004 0.980
#> GSM494658 1 0.0622 0.9434 0.980 0.000 0.000 0.012 0.008 0.000
#> GSM494653 1 0.0260 0.9468 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM494643 6 0.1682 0.7232 0.000 0.000 0.000 0.020 0.052 0.928
#> GSM494672 1 0.0260 0.9468 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM494618 4 0.3684 0.6969 0.000 0.000 0.000 0.628 0.000 0.372
#> GSM494631 4 0.4776 -0.1007 0.000 0.000 0.340 0.604 0.048 0.008
#> GSM494619 6 0.0260 0.7602 0.000 0.000 0.000 0.000 0.008 0.992
#> GSM494674 1 0.0000 0.9475 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494616 4 0.3684 0.6969 0.000 0.000 0.000 0.628 0.000 0.372
#> GSM494663 4 0.4090 0.6854 0.000 0.000 0.004 0.604 0.008 0.384
#> GSM494628 4 0.3965 0.6969 0.000 0.000 0.004 0.616 0.004 0.376
#> GSM494632 6 0.6957 0.0623 0.340 0.000 0.000 0.192 0.076 0.392
#> GSM494660 6 0.0603 0.7553 0.000 0.000 0.000 0.016 0.004 0.980
#> GSM494622 4 0.3965 0.6969 0.000 0.000 0.004 0.616 0.004 0.376
#> GSM494642 1 0.0000 0.9475 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.9475 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494659 1 0.0260 0.9468 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM494670 1 0.0520 0.9451 0.984 0.000 0.000 0.008 0.008 0.000
#> GSM494675 3 0.4316 0.7487 0.000 0.000 0.728 0.128 0.144 0.000
#> GSM494641 1 0.0000 0.9475 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494636 6 0.4798 0.2671 0.000 0.000 0.000 0.312 0.076 0.612
#> GSM494640 6 0.4315 0.2388 0.000 0.000 0.000 0.328 0.036 0.636
#> GSM494623 6 0.0260 0.7602 0.000 0.000 0.000 0.000 0.008 0.992
#> GSM494644 1 0.0622 0.9429 0.980 0.000 0.000 0.008 0.012 0.000
#> GSM494646 1 0.3490 0.8439 0.832 0.000 0.000 0.028 0.072 0.068
#> GSM494665 1 0.1908 0.9194 0.916 0.000 0.000 0.028 0.056 0.000
#> GSM494638 6 0.5064 -0.2320 0.000 0.000 0.000 0.432 0.076 0.492
#> GSM494645 1 0.1398 0.9288 0.940 0.000 0.000 0.008 0.052 0.000
#> GSM494671 1 0.0260 0.9468 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM494655 1 0.0000 0.9475 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494620 6 0.0260 0.7602 0.000 0.000 0.000 0.000 0.008 0.992
#> GSM494630 6 0.0520 0.7581 0.000 0.000 0.000 0.008 0.008 0.984
#> GSM494657 3 0.0146 0.8202 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM494667 1 0.0000 0.9475 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494621 6 0.0260 0.7602 0.000 0.000 0.000 0.000 0.008 0.992
#> GSM494629 4 0.4079 0.6892 0.000 0.000 0.004 0.608 0.008 0.380
#> GSM494637 6 0.4408 0.2527 0.000 0.000 0.000 0.320 0.044 0.636
#> GSM494652 1 0.0260 0.9468 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM494648 6 0.0260 0.7602 0.000 0.000 0.000 0.000 0.008 0.992
#> GSM494650 4 0.3954 0.6977 0.000 0.000 0.004 0.620 0.004 0.372
#> GSM494669 1 0.0000 0.9475 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494666 1 0.1908 0.9194 0.916 0.000 0.000 0.028 0.056 0.000
#> GSM494668 1 0.0146 0.9472 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM494633 6 0.0405 0.7594 0.000 0.000 0.000 0.004 0.008 0.988
#> GSM494634 1 0.0260 0.9468 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM494639 1 0.5572 0.2052 0.544 0.000 0.000 0.032 0.072 0.352
#> GSM494661 1 0.1398 0.9288 0.940 0.000 0.000 0.008 0.052 0.000
#> GSM494617 4 0.4088 0.6708 0.000 0.000 0.000 0.616 0.016 0.368
#> GSM494626 4 0.3684 0.6969 0.000 0.000 0.000 0.628 0.000 0.372
#> GSM494656 3 0.0865 0.7974 0.000 0.000 0.964 0.036 0.000 0.000
#> GSM494635 1 0.1895 0.9155 0.912 0.000 0.000 0.016 0.072 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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 6)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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.
# code only for demonstration
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) individual(p) k
#> CV:kmeans 119 1.96e-20 1.000 1.17e-15 1.000 2
#> CV:kmeans 113 4.86e-20 0.926 1.13e-17 0.987 3
#> CV:kmeans 112 3.02e-18 0.299 1.82e-12 0.781 4
#> CV:kmeans 113 3.93e-17 0.359 1.21e-12 0.656 5
#> CV:kmeans 104 6.65e-16 0.391 2.38e-09 0.414 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.
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"]
# you can also extract it by
# 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 51941 rows and 120 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 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:
k and the heatmap of
predicted classes for each k.k.k.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:
k;k, the area increased is defined as \(A_k - A_{k-1}\).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 0.980 0.986 0.5042 0.496 0.496
#> 3 3 0.731 0.830 0.897 0.3262 0.720 0.494
#> 4 4 1.000 0.994 0.997 0.1308 0.815 0.512
#> 5 5 0.989 0.961 0.976 0.0509 0.955 0.817
#> 6 6 0.954 0.887 0.944 0.0394 0.967 0.842
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 4 5
There is also optional best \(k\) = 2 4 5 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.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM494565 2 0.000 0.984 0.000 1.000
#> GSM494594 2 0.000 0.984 0.000 1.000
#> GSM494604 1 0.000 0.987 1.000 0.000
#> GSM494564 2 0.000 0.984 0.000 1.000
#> GSM494591 2 0.000 0.984 0.000 1.000
#> GSM494567 2 0.000 0.984 0.000 1.000
#> GSM494602 2 0.163 0.980 0.024 0.976
#> GSM494613 2 0.000 0.984 0.000 1.000
#> GSM494589 2 0.000 0.984 0.000 1.000
#> GSM494598 2 0.163 0.980 0.024 0.976
#> GSM494593 2 0.163 0.980 0.024 0.976
#> GSM494583 2 0.163 0.980 0.024 0.976
#> GSM494612 2 0.163 0.980 0.024 0.976
#> GSM494558 2 0.000 0.984 0.000 1.000
#> GSM494556 2 0.000 0.984 0.000 1.000
#> GSM494559 2 0.000 0.984 0.000 1.000
#> GSM494571 2 0.000 0.984 0.000 1.000
#> GSM494614 2 0.000 0.984 0.000 1.000
#> GSM494603 2 0.000 0.984 0.000 1.000
#> GSM494568 2 0.000 0.984 0.000 1.000
#> GSM494572 2 0.000 0.984 0.000 1.000
#> GSM494600 2 0.000 0.984 0.000 1.000
#> GSM494562 2 0.163 0.980 0.024 0.976
#> GSM494615 2 0.000 0.984 0.000 1.000
#> GSM494582 2 0.163 0.980 0.024 0.976
#> GSM494599 2 0.163 0.980 0.024 0.976
#> GSM494610 2 0.163 0.980 0.024 0.976
#> GSM494587 2 0.163 0.980 0.024 0.976
#> GSM494581 2 0.163 0.980 0.024 0.976
#> GSM494580 2 0.000 0.984 0.000 1.000
#> GSM494563 2 0.000 0.984 0.000 1.000
#> GSM494576 2 0.163 0.980 0.024 0.976
#> GSM494605 1 0.000 0.987 1.000 0.000
#> GSM494584 2 0.000 0.984 0.000 1.000
#> GSM494586 2 0.163 0.980 0.024 0.976
#> GSM494578 2 0.000 0.984 0.000 1.000
#> GSM494585 2 0.163 0.980 0.024 0.976
#> GSM494611 2 0.163 0.980 0.024 0.976
#> GSM494560 2 0.000 0.984 0.000 1.000
#> GSM494595 2 0.163 0.980 0.024 0.976
#> GSM494570 2 0.000 0.984 0.000 1.000
#> GSM494597 2 0.000 0.984 0.000 1.000
#> GSM494607 2 0.955 0.433 0.376 0.624
#> GSM494561 2 0.000 0.984 0.000 1.000
#> GSM494569 1 0.163 0.987 0.976 0.024
#> GSM494592 2 0.163 0.980 0.024 0.976
#> GSM494577 2 0.163 0.980 0.024 0.976
#> GSM494588 2 0.000 0.984 0.000 1.000
#> GSM494590 2 0.000 0.984 0.000 1.000
#> GSM494609 2 0.163 0.980 0.024 0.976
#> GSM494608 2 0.163 0.980 0.024 0.976
#> GSM494606 2 0.163 0.980 0.024 0.976
#> GSM494574 2 0.163 0.980 0.024 0.976
#> GSM494573 2 0.000 0.984 0.000 1.000
#> GSM494566 2 0.141 0.981 0.020 0.980
#> GSM494601 2 0.163 0.980 0.024 0.976
#> GSM494557 2 0.000 0.984 0.000 1.000
#> GSM494579 2 0.163 0.980 0.024 0.976
#> GSM494596 2 0.000 0.984 0.000 1.000
#> GSM494575 2 0.163 0.980 0.024 0.976
#> GSM494625 1 0.163 0.987 0.976 0.024
#> GSM494654 2 0.000 0.984 0.000 1.000
#> GSM494664 1 0.000 0.987 1.000 0.000
#> GSM494624 1 0.163 0.987 0.976 0.024
#> GSM494651 1 0.163 0.987 0.976 0.024
#> GSM494662 1 0.163 0.987 0.976 0.024
#> GSM494627 1 0.163 0.987 0.976 0.024
#> GSM494673 1 0.000 0.987 1.000 0.000
#> GSM494649 1 0.163 0.987 0.976 0.024
#> GSM494658 1 0.000 0.987 1.000 0.000
#> GSM494653 1 0.000 0.987 1.000 0.000
#> GSM494643 1 0.163 0.987 0.976 0.024
#> GSM494672 1 0.000 0.987 1.000 0.000
#> GSM494618 1 0.163 0.987 0.976 0.024
#> GSM494631 2 0.000 0.984 0.000 1.000
#> GSM494619 1 0.163 0.987 0.976 0.024
#> GSM494674 1 0.000 0.987 1.000 0.000
#> GSM494616 1 0.163 0.987 0.976 0.024
#> GSM494663 1 0.163 0.987 0.976 0.024
#> GSM494628 1 0.163 0.987 0.976 0.024
#> GSM494632 1 0.000 0.987 1.000 0.000
#> GSM494660 1 0.163 0.987 0.976 0.024
#> GSM494622 1 0.163 0.987 0.976 0.024
#> GSM494642 1 0.000 0.987 1.000 0.000
#> GSM494647 1 0.000 0.987 1.000 0.000
#> GSM494659 1 0.000 0.987 1.000 0.000
#> GSM494670 1 0.000 0.987 1.000 0.000
#> GSM494675 2 0.000 0.984 0.000 1.000
#> GSM494641 1 0.000 0.987 1.000 0.000
#> GSM494636 1 0.163 0.987 0.976 0.024
#> GSM494640 1 0.163 0.987 0.976 0.024
#> GSM494623 1 0.163 0.987 0.976 0.024
#> GSM494644 1 0.000 0.987 1.000 0.000
#> GSM494646 1 0.000 0.987 1.000 0.000
#> GSM494665 1 0.000 0.987 1.000 0.000
#> GSM494638 1 0.163 0.987 0.976 0.024
#> GSM494645 1 0.000 0.987 1.000 0.000
#> GSM494671 1 0.000 0.987 1.000 0.000
#> GSM494655 1 0.000 0.987 1.000 0.000
#> GSM494620 1 0.163 0.987 0.976 0.024
#> GSM494630 1 0.163 0.987 0.976 0.024
#> GSM494657 2 0.000 0.984 0.000 1.000
#> GSM494667 1 0.000 0.987 1.000 0.000
#> GSM494621 1 0.163 0.987 0.976 0.024
#> GSM494629 1 0.163 0.987 0.976 0.024
#> GSM494637 1 0.163 0.987 0.976 0.024
#> GSM494652 1 0.000 0.987 1.000 0.000
#> GSM494648 1 0.163 0.987 0.976 0.024
#> GSM494650 1 0.163 0.987 0.976 0.024
#> GSM494669 1 0.000 0.987 1.000 0.000
#> GSM494666 1 0.000 0.987 1.000 0.000
#> GSM494668 1 0.000 0.987 1.000 0.000
#> GSM494633 1 0.163 0.987 0.976 0.024
#> GSM494634 1 0.000 0.987 1.000 0.000
#> GSM494639 1 0.000 0.987 1.000 0.000
#> GSM494661 1 0.000 0.987 1.000 0.000
#> GSM494617 1 0.163 0.987 0.976 0.024
#> GSM494626 1 0.163 0.987 0.976 0.024
#> GSM494656 2 0.000 0.984 0.000 1.000
#> GSM494635 1 0.000 0.987 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM494565 2 0.0000 0.918 0.000 1.000 0.000
#> GSM494594 3 0.5058 0.779 0.000 0.244 0.756
#> GSM494604 1 0.6095 0.189 0.608 0.392 0.000
#> GSM494564 2 0.1411 0.894 0.000 0.964 0.036
#> GSM494591 3 0.5465 0.754 0.000 0.288 0.712
#> GSM494567 3 0.5465 0.754 0.000 0.288 0.712
#> GSM494602 2 0.1860 0.914 0.052 0.948 0.000
#> GSM494613 3 0.5465 0.754 0.000 0.288 0.712
#> GSM494589 3 0.5621 0.727 0.000 0.308 0.692
#> GSM494598 2 0.1860 0.914 0.052 0.948 0.000
#> GSM494593 2 0.1860 0.914 0.052 0.948 0.000
#> GSM494583 2 0.0000 0.918 0.000 1.000 0.000
#> GSM494612 2 0.1860 0.914 0.052 0.948 0.000
#> GSM494558 3 0.1860 0.843 0.000 0.052 0.948
#> GSM494556 3 0.5465 0.754 0.000 0.288 0.712
#> GSM494559 2 0.1031 0.905 0.000 0.976 0.024
#> GSM494571 3 0.1964 0.843 0.000 0.056 0.944
#> GSM494614 2 0.1031 0.905 0.000 0.976 0.024
#> GSM494603 3 0.3879 0.820 0.000 0.152 0.848
#> GSM494568 3 0.1753 0.843 0.000 0.048 0.952
#> GSM494572 3 0.5465 0.754 0.000 0.288 0.712
#> GSM494600 2 0.5968 0.196 0.000 0.636 0.364
#> GSM494562 2 0.0000 0.918 0.000 1.000 0.000
#> GSM494615 3 0.3816 0.821 0.000 0.148 0.852
#> GSM494582 2 0.1860 0.914 0.052 0.948 0.000
#> GSM494599 2 0.5465 0.655 0.288 0.712 0.000
#> GSM494610 2 0.1860 0.914 0.052 0.948 0.000
#> GSM494587 2 0.0000 0.918 0.000 1.000 0.000
#> GSM494581 2 0.0000 0.918 0.000 1.000 0.000
#> GSM494580 3 0.5465 0.754 0.000 0.288 0.712
#> GSM494563 2 0.0000 0.918 0.000 1.000 0.000
#> GSM494576 2 0.0000 0.918 0.000 1.000 0.000
#> GSM494605 1 0.0000 0.886 1.000 0.000 0.000
#> GSM494584 2 0.0892 0.907 0.000 0.980 0.020
#> GSM494586 2 0.0000 0.918 0.000 1.000 0.000
#> GSM494578 3 0.5465 0.754 0.000 0.288 0.712
#> GSM494585 2 0.0000 0.918 0.000 1.000 0.000
#> GSM494611 2 0.1860 0.914 0.052 0.948 0.000
#> GSM494560 2 0.1031 0.905 0.000 0.976 0.024
#> GSM494595 2 0.1860 0.914 0.052 0.948 0.000
#> GSM494570 3 0.5254 0.769 0.000 0.264 0.736
#> GSM494597 3 0.5465 0.754 0.000 0.288 0.712
#> GSM494607 2 0.5465 0.655 0.288 0.712 0.000
#> GSM494561 3 0.1860 0.843 0.000 0.052 0.948
#> GSM494569 3 0.1031 0.828 0.024 0.000 0.976
#> GSM494592 2 0.5465 0.655 0.288 0.712 0.000
#> GSM494577 2 0.0000 0.918 0.000 1.000 0.000
#> GSM494588 2 0.0000 0.918 0.000 1.000 0.000
#> GSM494590 3 0.5465 0.754 0.000 0.288 0.712
#> GSM494609 2 0.1860 0.914 0.052 0.948 0.000
#> GSM494608 2 0.3267 0.862 0.116 0.884 0.000
#> GSM494606 2 0.5465 0.655 0.288 0.712 0.000
#> GSM494574 2 0.1860 0.914 0.052 0.948 0.000
#> GSM494573 2 0.1031 0.905 0.000 0.976 0.024
#> GSM494566 2 0.0000 0.918 0.000 1.000 0.000
#> GSM494601 2 0.1860 0.914 0.052 0.948 0.000
#> GSM494557 3 0.5465 0.754 0.000 0.288 0.712
#> GSM494579 2 0.0000 0.918 0.000 1.000 0.000
#> GSM494596 3 0.5465 0.754 0.000 0.288 0.712
#> GSM494575 2 0.1860 0.914 0.052 0.948 0.000
#> GSM494625 3 0.1031 0.828 0.024 0.000 0.976
#> GSM494654 3 0.1860 0.843 0.000 0.052 0.948
#> GSM494664 1 0.1860 0.877 0.948 0.000 0.052
#> GSM494624 1 0.5465 0.757 0.712 0.000 0.288
#> GSM494651 3 0.1031 0.828 0.024 0.000 0.976
#> GSM494662 1 0.5465 0.757 0.712 0.000 0.288
#> GSM494627 3 0.1031 0.828 0.024 0.000 0.976
#> GSM494673 1 0.0000 0.886 1.000 0.000 0.000
#> GSM494649 3 0.1031 0.828 0.024 0.000 0.976
#> GSM494658 1 0.0000 0.886 1.000 0.000 0.000
#> GSM494653 1 0.0000 0.886 1.000 0.000 0.000
#> GSM494643 1 0.5465 0.757 0.712 0.000 0.288
#> GSM494672 1 0.0000 0.886 1.000 0.000 0.000
#> GSM494618 3 0.1031 0.828 0.024 0.000 0.976
#> GSM494631 3 0.1860 0.843 0.000 0.052 0.948
#> GSM494619 1 0.5465 0.757 0.712 0.000 0.288
#> GSM494674 1 0.0000 0.886 1.000 0.000 0.000
#> GSM494616 3 0.1031 0.828 0.024 0.000 0.976
#> GSM494663 3 0.1031 0.828 0.024 0.000 0.976
#> GSM494628 3 0.1031 0.828 0.024 0.000 0.976
#> GSM494632 1 0.1860 0.877 0.948 0.000 0.052
#> GSM494660 3 0.1031 0.828 0.024 0.000 0.976
#> GSM494622 3 0.1031 0.828 0.024 0.000 0.976
#> GSM494642 1 0.0000 0.886 1.000 0.000 0.000
#> GSM494647 1 0.0000 0.886 1.000 0.000 0.000
#> GSM494659 1 0.0000 0.886 1.000 0.000 0.000
#> GSM494670 1 0.0000 0.886 1.000 0.000 0.000
#> GSM494675 3 0.5465 0.754 0.000 0.288 0.712
#> GSM494641 1 0.0000 0.886 1.000 0.000 0.000
#> GSM494636 1 0.5465 0.757 0.712 0.000 0.288
#> GSM494640 3 0.1031 0.828 0.024 0.000 0.976
#> GSM494623 1 0.5465 0.757 0.712 0.000 0.288
#> GSM494644 1 0.0000 0.886 1.000 0.000 0.000
#> GSM494646 1 0.1860 0.877 0.948 0.000 0.052
#> GSM494665 1 0.0000 0.886 1.000 0.000 0.000
#> GSM494638 1 0.5465 0.757 0.712 0.000 0.288
#> GSM494645 1 0.1529 0.880 0.960 0.000 0.040
#> GSM494671 1 0.0000 0.886 1.000 0.000 0.000
#> GSM494655 1 0.0000 0.886 1.000 0.000 0.000
#> GSM494620 1 0.5465 0.757 0.712 0.000 0.288
#> GSM494630 1 0.5465 0.757 0.712 0.000 0.288
#> GSM494657 3 0.5465 0.754 0.000 0.288 0.712
#> GSM494667 1 0.0000 0.886 1.000 0.000 0.000
#> GSM494621 1 0.5465 0.757 0.712 0.000 0.288
#> GSM494629 3 0.1031 0.828 0.024 0.000 0.976
#> GSM494637 3 0.1031 0.828 0.024 0.000 0.976
#> GSM494652 1 0.0000 0.886 1.000 0.000 0.000
#> GSM494648 1 0.5465 0.757 0.712 0.000 0.288
#> GSM494650 3 0.1031 0.828 0.024 0.000 0.976
#> GSM494669 1 0.0000 0.886 1.000 0.000 0.000
#> GSM494666 1 0.1860 0.877 0.948 0.000 0.052
#> GSM494668 1 0.0000 0.886 1.000 0.000 0.000
#> GSM494633 1 0.5706 0.716 0.680 0.000 0.320
#> GSM494634 1 0.0000 0.886 1.000 0.000 0.000
#> GSM494639 1 0.1860 0.877 0.948 0.000 0.052
#> GSM494661 1 0.0000 0.886 1.000 0.000 0.000
#> GSM494617 1 0.5465 0.757 0.712 0.000 0.288
#> GSM494626 3 0.1031 0.828 0.024 0.000 0.976
#> GSM494656 3 0.1860 0.843 0.000 0.052 0.948
#> GSM494635 1 0.1643 0.879 0.956 0.000 0.044
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM494565 2 0.0000 0.991 0.000 1.000 0.000 0
#> GSM494594 3 0.0000 0.999 0.000 0.000 1.000 0
#> GSM494604 1 0.1474 0.945 0.948 0.052 0.000 0
#> GSM494564 3 0.0000 0.999 0.000 0.000 1.000 0
#> GSM494591 3 0.0000 0.999 0.000 0.000 1.000 0
#> GSM494567 3 0.0000 0.999 0.000 0.000 1.000 0
#> GSM494602 2 0.0000 0.991 0.000 1.000 0.000 0
#> GSM494613 3 0.0000 0.999 0.000 0.000 1.000 0
#> GSM494589 3 0.0000 0.999 0.000 0.000 1.000 0
#> GSM494598 2 0.0000 0.991 0.000 1.000 0.000 0
#> GSM494593 2 0.0000 0.991 0.000 1.000 0.000 0
#> GSM494583 2 0.0000 0.991 0.000 1.000 0.000 0
#> GSM494612 2 0.0000 0.991 0.000 1.000 0.000 0
#> GSM494558 3 0.0000 0.999 0.000 0.000 1.000 0
#> GSM494556 3 0.0000 0.999 0.000 0.000 1.000 0
#> GSM494559 3 0.0592 0.984 0.000 0.016 0.984 0
#> GSM494571 3 0.0000 0.999 0.000 0.000 1.000 0
#> GSM494614 3 0.0000 0.999 0.000 0.000 1.000 0
#> GSM494603 3 0.0000 0.999 0.000 0.000 1.000 0
#> GSM494568 3 0.0000 0.999 0.000 0.000 1.000 0
#> GSM494572 3 0.0000 0.999 0.000 0.000 1.000 0
#> GSM494600 3 0.0000 0.999 0.000 0.000 1.000 0
#> GSM494562 2 0.0000 0.991 0.000 1.000 0.000 0
#> GSM494615 3 0.0000 0.999 0.000 0.000 1.000 0
#> GSM494582 2 0.0000 0.991 0.000 1.000 0.000 0
#> GSM494599 2 0.0000 0.991 0.000 1.000 0.000 0
#> GSM494610 2 0.0000 0.991 0.000 1.000 0.000 0
#> GSM494587 2 0.0000 0.991 0.000 1.000 0.000 0
#> GSM494581 2 0.0000 0.991 0.000 1.000 0.000 0
#> GSM494580 3 0.0000 0.999 0.000 0.000 1.000 0
#> GSM494563 2 0.0000 0.991 0.000 1.000 0.000 0
#> GSM494576 2 0.0000 0.991 0.000 1.000 0.000 0
#> GSM494605 1 0.0000 0.998 1.000 0.000 0.000 0
#> GSM494584 2 0.3074 0.823 0.000 0.848 0.152 0
#> GSM494586 2 0.0000 0.991 0.000 1.000 0.000 0
#> GSM494578 3 0.0000 0.999 0.000 0.000 1.000 0
#> GSM494585 2 0.0000 0.991 0.000 1.000 0.000 0
#> GSM494611 2 0.0000 0.991 0.000 1.000 0.000 0
#> GSM494560 3 0.0592 0.984 0.000 0.016 0.984 0
#> GSM494595 2 0.0000 0.991 0.000 1.000 0.000 0
#> GSM494570 3 0.0000 0.999 0.000 0.000 1.000 0
#> GSM494597 3 0.0000 0.999 0.000 0.000 1.000 0
#> GSM494607 2 0.0000 0.991 0.000 1.000 0.000 0
#> GSM494561 3 0.0000 0.999 0.000 0.000 1.000 0
#> GSM494569 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM494592 2 0.0000 0.991 0.000 1.000 0.000 0
#> GSM494577 2 0.0000 0.991 0.000 1.000 0.000 0
#> GSM494588 2 0.0000 0.991 0.000 1.000 0.000 0
#> GSM494590 3 0.0000 0.999 0.000 0.000 1.000 0
#> GSM494609 2 0.0000 0.991 0.000 1.000 0.000 0
#> GSM494608 2 0.0000 0.991 0.000 1.000 0.000 0
#> GSM494606 2 0.0000 0.991 0.000 1.000 0.000 0
#> GSM494574 2 0.0000 0.991 0.000 1.000 0.000 0
#> GSM494573 3 0.0000 0.999 0.000 0.000 1.000 0
#> GSM494566 2 0.2345 0.890 0.000 0.900 0.100 0
#> GSM494601 2 0.0000 0.991 0.000 1.000 0.000 0
#> GSM494557 3 0.0000 0.999 0.000 0.000 1.000 0
#> GSM494579 2 0.0000 0.991 0.000 1.000 0.000 0
#> GSM494596 3 0.0000 0.999 0.000 0.000 1.000 0
#> GSM494575 2 0.0000 0.991 0.000 1.000 0.000 0
#> GSM494625 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM494654 3 0.0000 0.999 0.000 0.000 1.000 0
#> GSM494664 1 0.0000 0.998 1.000 0.000 0.000 0
#> GSM494624 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM494651 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM494662 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM494627 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM494673 1 0.0000 0.998 1.000 0.000 0.000 0
#> GSM494649 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM494658 1 0.0000 0.998 1.000 0.000 0.000 0
#> GSM494653 1 0.0000 0.998 1.000 0.000 0.000 0
#> GSM494643 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM494672 1 0.0000 0.998 1.000 0.000 0.000 0
#> GSM494618 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM494631 3 0.0000 0.999 0.000 0.000 1.000 0
#> GSM494619 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM494674 1 0.0000 0.998 1.000 0.000 0.000 0
#> GSM494616 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM494663 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM494628 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM494632 1 0.0000 0.998 1.000 0.000 0.000 0
#> GSM494660 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM494622 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM494642 1 0.0000 0.998 1.000 0.000 0.000 0
#> GSM494647 1 0.0000 0.998 1.000 0.000 0.000 0
#> GSM494659 1 0.0000 0.998 1.000 0.000 0.000 0
#> GSM494670 1 0.0000 0.998 1.000 0.000 0.000 0
#> GSM494675 3 0.0000 0.999 0.000 0.000 1.000 0
#> GSM494641 1 0.0000 0.998 1.000 0.000 0.000 0
#> GSM494636 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM494640 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM494623 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM494644 1 0.0000 0.998 1.000 0.000 0.000 0
#> GSM494646 1 0.0000 0.998 1.000 0.000 0.000 0
#> GSM494665 1 0.0000 0.998 1.000 0.000 0.000 0
#> GSM494638 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM494645 1 0.0000 0.998 1.000 0.000 0.000 0
#> GSM494671 1 0.0000 0.998 1.000 0.000 0.000 0
#> GSM494655 1 0.0000 0.998 1.000 0.000 0.000 0
#> GSM494620 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM494630 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM494657 3 0.0000 0.999 0.000 0.000 1.000 0
#> GSM494667 1 0.0000 0.998 1.000 0.000 0.000 0
#> GSM494621 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM494629 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM494637 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM494652 1 0.0000 0.998 1.000 0.000 0.000 0
#> GSM494648 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM494650 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM494669 1 0.0000 0.998 1.000 0.000 0.000 0
#> GSM494666 1 0.0000 0.998 1.000 0.000 0.000 0
#> GSM494668 1 0.0000 0.998 1.000 0.000 0.000 0
#> GSM494633 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM494634 1 0.0000 0.998 1.000 0.000 0.000 0
#> GSM494639 1 0.0000 0.998 1.000 0.000 0.000 0
#> GSM494661 1 0.0000 0.998 1.000 0.000 0.000 0
#> GSM494617 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM494626 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM494656 3 0.0000 0.999 0.000 0.000 1.000 0
#> GSM494635 1 0.0000 0.998 1.000 0.000 0.000 0
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM494565 5 0.1168 0.954 0.000 0.032 0.008 0.000 0.960
#> GSM494594 3 0.0290 0.993 0.000 0.000 0.992 0.000 0.008
#> GSM494604 1 0.3730 0.605 0.712 0.288 0.000 0.000 0.000
#> GSM494564 5 0.0880 0.963 0.000 0.000 0.032 0.000 0.968
#> GSM494591 3 0.0290 0.993 0.000 0.000 0.992 0.000 0.008
#> GSM494567 3 0.0290 0.993 0.000 0.000 0.992 0.000 0.008
#> GSM494602 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> GSM494613 3 0.0290 0.993 0.000 0.000 0.992 0.000 0.008
#> GSM494589 5 0.1043 0.962 0.000 0.000 0.040 0.000 0.960
#> GSM494598 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> GSM494593 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> GSM494583 2 0.3177 0.768 0.000 0.792 0.000 0.000 0.208
#> GSM494612 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> GSM494558 3 0.0566 0.976 0.000 0.000 0.984 0.004 0.012
#> GSM494556 3 0.0290 0.993 0.000 0.000 0.992 0.000 0.008
#> GSM494559 5 0.0912 0.963 0.000 0.012 0.016 0.000 0.972
#> GSM494571 3 0.0290 0.993 0.000 0.000 0.992 0.000 0.008
#> GSM494614 5 0.1732 0.933 0.000 0.000 0.080 0.000 0.920
#> GSM494603 5 0.2929 0.804 0.000 0.000 0.180 0.000 0.820
#> GSM494568 3 0.0912 0.964 0.000 0.000 0.972 0.016 0.012
#> GSM494572 3 0.0290 0.993 0.000 0.000 0.992 0.000 0.008
#> GSM494600 5 0.1043 0.962 0.000 0.000 0.040 0.000 0.960
#> GSM494562 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> GSM494615 3 0.0000 0.989 0.000 0.000 1.000 0.000 0.000
#> GSM494582 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> GSM494599 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> GSM494610 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> GSM494587 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> GSM494581 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> GSM494580 3 0.0290 0.993 0.000 0.000 0.992 0.000 0.008
#> GSM494563 5 0.1168 0.954 0.000 0.032 0.008 0.000 0.960
#> GSM494576 2 0.2230 0.862 0.000 0.884 0.000 0.000 0.116
#> GSM494605 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000
#> GSM494584 2 0.3690 0.732 0.000 0.764 0.012 0.000 0.224
#> GSM494586 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> GSM494578 3 0.0290 0.993 0.000 0.000 0.992 0.000 0.008
#> GSM494585 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> GSM494611 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> GSM494560 5 0.1216 0.962 0.000 0.020 0.020 0.000 0.960
#> GSM494595 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> GSM494570 5 0.0451 0.956 0.000 0.000 0.008 0.004 0.988
#> GSM494597 3 0.0290 0.993 0.000 0.000 0.992 0.000 0.008
#> GSM494607 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> GSM494561 5 0.0566 0.956 0.000 0.000 0.012 0.004 0.984
#> GSM494569 4 0.0693 0.980 0.000 0.000 0.008 0.980 0.012
#> GSM494592 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> GSM494577 2 0.3210 0.763 0.000 0.788 0.000 0.000 0.212
#> GSM494588 5 0.0771 0.958 0.000 0.020 0.004 0.000 0.976
#> GSM494590 3 0.0290 0.993 0.000 0.000 0.992 0.000 0.008
#> GSM494609 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> GSM494608 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> GSM494606 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> GSM494574 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> GSM494573 5 0.1043 0.962 0.000 0.000 0.040 0.000 0.960
#> GSM494566 2 0.4891 0.670 0.000 0.716 0.172 0.000 0.112
#> GSM494601 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> GSM494557 3 0.0290 0.993 0.000 0.000 0.992 0.000 0.008
#> GSM494579 2 0.3003 0.792 0.000 0.812 0.000 0.000 0.188
#> GSM494596 3 0.0290 0.993 0.000 0.000 0.992 0.000 0.008
#> GSM494575 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> GSM494625 4 0.0794 0.979 0.000 0.000 0.000 0.972 0.028
#> GSM494654 3 0.0324 0.983 0.000 0.000 0.992 0.004 0.004
#> GSM494664 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000
#> GSM494624 4 0.0794 0.979 0.000 0.000 0.000 0.972 0.028
#> GSM494651 4 0.0693 0.980 0.000 0.000 0.008 0.980 0.012
#> GSM494662 4 0.0000 0.982 0.000 0.000 0.000 1.000 0.000
#> GSM494627 4 0.0693 0.980 0.000 0.000 0.008 0.980 0.012
#> GSM494673 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000
#> GSM494649 4 0.0794 0.979 0.000 0.000 0.000 0.972 0.028
#> GSM494658 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000
#> GSM494653 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000
#> GSM494643 4 0.0703 0.979 0.000 0.000 0.000 0.976 0.024
#> GSM494672 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000
#> GSM494618 4 0.0693 0.980 0.000 0.000 0.008 0.980 0.012
#> GSM494631 3 0.0162 0.986 0.000 0.000 0.996 0.000 0.004
#> GSM494619 4 0.0794 0.979 0.000 0.000 0.000 0.972 0.028
#> GSM494674 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000
#> GSM494616 4 0.0693 0.980 0.000 0.000 0.008 0.980 0.012
#> GSM494663 4 0.0693 0.980 0.000 0.000 0.008 0.980 0.012
#> GSM494628 4 0.0693 0.980 0.000 0.000 0.008 0.980 0.012
#> GSM494632 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000
#> GSM494660 4 0.0794 0.979 0.000 0.000 0.000 0.972 0.028
#> GSM494622 4 0.0693 0.980 0.000 0.000 0.008 0.980 0.012
#> GSM494642 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000
#> GSM494670 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000
#> GSM494675 3 0.0290 0.993 0.000 0.000 0.992 0.000 0.008
#> GSM494641 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000
#> GSM494636 4 0.0000 0.982 0.000 0.000 0.000 1.000 0.000
#> GSM494640 4 0.0000 0.982 0.000 0.000 0.000 1.000 0.000
#> GSM494623 4 0.0794 0.979 0.000 0.000 0.000 0.972 0.028
#> GSM494644 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000
#> GSM494646 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000
#> GSM494665 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000
#> GSM494638 4 0.0000 0.982 0.000 0.000 0.000 1.000 0.000
#> GSM494645 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000
#> GSM494671 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000
#> GSM494620 4 0.0794 0.979 0.000 0.000 0.000 0.972 0.028
#> GSM494630 4 0.0794 0.979 0.000 0.000 0.000 0.972 0.028
#> GSM494657 3 0.0290 0.993 0.000 0.000 0.992 0.000 0.008
#> GSM494667 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000
#> GSM494621 4 0.0794 0.979 0.000 0.000 0.000 0.972 0.028
#> GSM494629 4 0.0693 0.980 0.000 0.000 0.008 0.980 0.012
#> GSM494637 4 0.0000 0.982 0.000 0.000 0.000 1.000 0.000
#> GSM494652 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000
#> GSM494648 4 0.0794 0.979 0.000 0.000 0.000 0.972 0.028
#> GSM494650 4 0.0693 0.980 0.000 0.000 0.008 0.980 0.012
#> GSM494669 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000
#> GSM494666 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000
#> GSM494668 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000
#> GSM494633 4 0.0794 0.979 0.000 0.000 0.000 0.972 0.028
#> GSM494634 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000
#> GSM494639 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000
#> GSM494661 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000
#> GSM494617 4 0.0693 0.980 0.000 0.000 0.008 0.980 0.012
#> GSM494626 4 0.0693 0.980 0.000 0.000 0.008 0.980 0.012
#> GSM494656 3 0.0324 0.983 0.000 0.000 0.992 0.004 0.004
#> GSM494635 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM494565 5 0.0260 0.924 0.000 0.008 0.000 0.000 0.992 0.000
#> GSM494594 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494604 1 0.3714 0.492 0.656 0.340 0.000 0.004 0.000 0.000
#> GSM494564 5 0.0291 0.925 0.000 0.000 0.004 0.000 0.992 0.004
#> GSM494591 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494567 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494602 2 0.0000 0.904 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494613 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494589 5 0.0260 0.925 0.000 0.000 0.008 0.000 0.992 0.000
#> GSM494598 2 0.0713 0.902 0.000 0.972 0.000 0.028 0.000 0.000
#> GSM494593 2 0.0000 0.904 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494583 2 0.4461 0.405 0.000 0.564 0.000 0.032 0.404 0.000
#> GSM494612 2 0.0000 0.904 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494558 3 0.3789 0.276 0.000 0.000 0.584 0.416 0.000 0.000
#> GSM494556 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494559 5 0.0291 0.925 0.000 0.004 0.000 0.000 0.992 0.004
#> GSM494571 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494614 5 0.1480 0.891 0.000 0.000 0.040 0.020 0.940 0.000
#> GSM494603 5 0.4872 0.115 0.000 0.000 0.040 0.452 0.500 0.008
#> GSM494568 4 0.2431 0.813 0.000 0.000 0.132 0.860 0.000 0.008
#> GSM494572 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494600 5 0.0260 0.925 0.000 0.000 0.008 0.000 0.992 0.000
#> GSM494562 2 0.0713 0.902 0.000 0.972 0.000 0.028 0.000 0.000
#> GSM494615 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494582 2 0.0632 0.902 0.000 0.976 0.000 0.024 0.000 0.000
#> GSM494599 2 0.0000 0.904 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494610 2 0.0713 0.902 0.000 0.972 0.000 0.028 0.000 0.000
#> GSM494587 2 0.0858 0.901 0.000 0.968 0.000 0.028 0.004 0.000
#> GSM494581 2 0.0146 0.904 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM494580 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494563 5 0.0260 0.924 0.000 0.008 0.000 0.000 0.992 0.000
#> GSM494576 2 0.3101 0.780 0.000 0.820 0.000 0.032 0.148 0.000
#> GSM494605 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494584 2 0.4620 0.361 0.000 0.544 0.004 0.032 0.420 0.000
#> GSM494586 2 0.0790 0.901 0.000 0.968 0.000 0.032 0.000 0.000
#> GSM494578 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494585 2 0.0713 0.902 0.000 0.972 0.000 0.028 0.000 0.000
#> GSM494611 2 0.0000 0.904 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494560 5 0.0260 0.924 0.000 0.008 0.000 0.000 0.992 0.000
#> GSM494595 2 0.0713 0.902 0.000 0.972 0.000 0.028 0.000 0.000
#> GSM494570 5 0.0260 0.924 0.000 0.000 0.000 0.000 0.992 0.008
#> GSM494597 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494607 2 0.0146 0.904 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM494561 5 0.3309 0.595 0.000 0.000 0.000 0.000 0.720 0.280
#> GSM494569 4 0.0790 0.977 0.000 0.000 0.000 0.968 0.000 0.032
#> GSM494592 2 0.0000 0.904 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494577 2 0.4475 0.387 0.000 0.556 0.000 0.032 0.412 0.000
#> GSM494588 5 0.0291 0.925 0.000 0.004 0.000 0.000 0.992 0.004
#> GSM494590 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494609 2 0.0146 0.904 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM494608 2 0.0146 0.904 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM494606 2 0.0000 0.904 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494574 2 0.0713 0.902 0.000 0.972 0.000 0.028 0.000 0.000
#> GSM494573 5 0.0260 0.925 0.000 0.000 0.008 0.000 0.992 0.000
#> GSM494566 2 0.6572 0.376 0.000 0.512 0.108 0.108 0.272 0.000
#> GSM494601 2 0.0146 0.904 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM494557 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494579 2 0.4319 0.513 0.000 0.620 0.000 0.032 0.348 0.000
#> GSM494596 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494575 2 0.0000 0.904 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494625 6 0.0000 0.876 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494654 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494664 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494624 6 0.0000 0.876 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494651 4 0.0790 0.977 0.000 0.000 0.000 0.968 0.000 0.032
#> GSM494662 6 0.3804 0.617 0.000 0.000 0.000 0.336 0.008 0.656
#> GSM494627 4 0.0937 0.975 0.000 0.000 0.000 0.960 0.000 0.040
#> GSM494673 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494649 6 0.0790 0.869 0.000 0.000 0.000 0.032 0.000 0.968
#> GSM494658 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494653 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494643 6 0.0520 0.874 0.000 0.000 0.000 0.008 0.008 0.984
#> GSM494672 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494618 4 0.0790 0.977 0.000 0.000 0.000 0.968 0.000 0.032
#> GSM494631 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494619 6 0.0000 0.876 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494674 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494616 4 0.0790 0.977 0.000 0.000 0.000 0.968 0.000 0.032
#> GSM494663 4 0.1501 0.942 0.000 0.000 0.000 0.924 0.000 0.076
#> GSM494628 4 0.0937 0.975 0.000 0.000 0.000 0.960 0.000 0.040
#> GSM494632 1 0.0520 0.972 0.984 0.000 0.000 0.008 0.008 0.000
#> GSM494660 6 0.0790 0.869 0.000 0.000 0.000 0.032 0.000 0.968
#> GSM494622 4 0.0937 0.975 0.000 0.000 0.000 0.960 0.000 0.040
#> GSM494642 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494670 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494675 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494641 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494636 6 0.3804 0.617 0.000 0.000 0.000 0.336 0.008 0.656
#> GSM494640 6 0.3804 0.617 0.000 0.000 0.000 0.336 0.008 0.656
#> GSM494623 6 0.0000 0.876 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494644 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494646 1 0.0260 0.979 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM494665 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494638 6 0.4088 0.588 0.008 0.000 0.000 0.348 0.008 0.636
#> GSM494645 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494671 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494620 6 0.0000 0.876 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494630 6 0.0260 0.875 0.000 0.000 0.000 0.008 0.000 0.992
#> GSM494657 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494667 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494621 6 0.0000 0.876 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494629 4 0.0790 0.977 0.000 0.000 0.000 0.968 0.000 0.032
#> GSM494637 6 0.3804 0.617 0.000 0.000 0.000 0.336 0.008 0.656
#> GSM494652 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494648 6 0.0000 0.876 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494650 4 0.0937 0.975 0.000 0.000 0.000 0.960 0.000 0.040
#> GSM494669 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494666 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494668 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494633 6 0.0260 0.875 0.000 0.000 0.000 0.008 0.000 0.992
#> GSM494634 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494639 1 0.0260 0.979 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM494661 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494617 4 0.0790 0.977 0.000 0.000 0.000 0.968 0.000 0.032
#> GSM494626 4 0.0790 0.977 0.000 0.000 0.000 0.968 0.000 0.032
#> GSM494656 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494635 1 0.0000 0.985 1.000 0.000 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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 3)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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.
# code only for demonstration
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) individual(p) k
#> CV:skmeans 119 1.96e-20 1.000 1.17e-15 1.000 2
#> CV:skmeans 118 1.33e-16 0.643 1.23e-14 0.736 3
#> CV:skmeans 120 8.49e-20 0.517 9.69e-14 0.915 4
#> CV:skmeans 120 2.43e-19 0.471 2.03e-14 0.750 5
#> CV:skmeans 113 2.21e-17 0.260 8.09e-11 0.394 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.
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"]
# you can also extract it by
# 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 51941 rows and 120 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:
k and the heatmap of
predicted classes for each k.k.k.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:
k;k, the area increased is defined as \(A_k - A_{k-1}\).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 0.686 0.840 0.930 0.5025 0.496 0.496
#> 3 3 0.758 0.718 0.874 0.3207 0.824 0.656
#> 4 4 0.736 0.825 0.855 0.1087 0.857 0.617
#> 5 5 0.760 0.784 0.852 0.0648 0.881 0.591
#> 6 6 0.834 0.825 0.907 0.0478 0.936 0.719
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
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.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM494565 2 0.0000 0.896 0.000 1.000
#> GSM494594 2 0.0000 0.896 0.000 1.000
#> GSM494604 1 0.0000 0.939 1.000 0.000
#> GSM494564 2 0.0000 0.896 0.000 1.000
#> GSM494591 2 0.0000 0.896 0.000 1.000
#> GSM494567 2 0.0000 0.896 0.000 1.000
#> GSM494602 2 0.8909 0.623 0.308 0.692
#> GSM494613 2 0.0000 0.896 0.000 1.000
#> GSM494589 2 0.0000 0.896 0.000 1.000
#> GSM494598 2 0.8909 0.623 0.308 0.692
#> GSM494593 2 0.8909 0.623 0.308 0.692
#> GSM494583 2 0.0000 0.896 0.000 1.000
#> GSM494612 2 0.8909 0.623 0.308 0.692
#> GSM494558 2 0.0000 0.896 0.000 1.000
#> GSM494556 2 0.0000 0.896 0.000 1.000
#> GSM494559 2 0.0000 0.896 0.000 1.000
#> GSM494571 2 0.0000 0.896 0.000 1.000
#> GSM494614 2 0.0000 0.896 0.000 1.000
#> GSM494603 2 0.0000 0.896 0.000 1.000
#> GSM494568 2 0.9833 0.187 0.424 0.576
#> GSM494572 2 0.0000 0.896 0.000 1.000
#> GSM494600 2 0.0000 0.896 0.000 1.000
#> GSM494562 2 0.0000 0.896 0.000 1.000
#> GSM494615 2 0.0000 0.896 0.000 1.000
#> GSM494582 2 0.8909 0.623 0.308 0.692
#> GSM494599 1 0.8955 0.447 0.688 0.312
#> GSM494610 2 0.8909 0.623 0.308 0.692
#> GSM494587 2 0.0000 0.896 0.000 1.000
#> GSM494581 2 0.0000 0.896 0.000 1.000
#> GSM494580 2 0.0000 0.896 0.000 1.000
#> GSM494563 2 0.0000 0.896 0.000 1.000
#> GSM494576 2 0.0000 0.896 0.000 1.000
#> GSM494605 1 0.0000 0.939 1.000 0.000
#> GSM494584 2 0.0000 0.896 0.000 1.000
#> GSM494586 2 0.0000 0.896 0.000 1.000
#> GSM494578 2 0.0000 0.896 0.000 1.000
#> GSM494585 2 0.0000 0.896 0.000 1.000
#> GSM494611 2 0.8909 0.623 0.308 0.692
#> GSM494560 2 0.0000 0.896 0.000 1.000
#> GSM494595 2 0.4815 0.826 0.104 0.896
#> GSM494570 2 0.0000 0.896 0.000 1.000
#> GSM494597 2 0.0000 0.896 0.000 1.000
#> GSM494607 2 0.9775 0.427 0.412 0.588
#> GSM494561 2 0.7528 0.671 0.216 0.784
#> GSM494569 1 0.0000 0.939 1.000 0.000
#> GSM494592 2 0.9833 0.398 0.424 0.576
#> GSM494577 2 0.0000 0.896 0.000 1.000
#> GSM494588 2 0.0000 0.896 0.000 1.000
#> GSM494590 2 0.0000 0.896 0.000 1.000
#> GSM494609 2 0.8909 0.623 0.308 0.692
#> GSM494608 1 0.5178 0.813 0.884 0.116
#> GSM494606 2 0.9732 0.444 0.404 0.596
#> GSM494574 2 0.8909 0.623 0.308 0.692
#> GSM494573 2 0.0000 0.896 0.000 1.000
#> GSM494566 2 0.0000 0.896 0.000 1.000
#> GSM494601 2 0.2423 0.872 0.040 0.960
#> GSM494557 2 0.0000 0.896 0.000 1.000
#> GSM494579 2 0.0000 0.896 0.000 1.000
#> GSM494596 2 0.0000 0.896 0.000 1.000
#> GSM494575 2 0.8909 0.623 0.308 0.692
#> GSM494625 1 0.0000 0.939 1.000 0.000
#> GSM494654 1 0.9896 0.267 0.560 0.440
#> GSM494664 1 0.0000 0.939 1.000 0.000
#> GSM494624 1 0.0000 0.939 1.000 0.000
#> GSM494651 1 0.2778 0.897 0.952 0.048
#> GSM494662 1 0.0000 0.939 1.000 0.000
#> GSM494627 1 0.8909 0.567 0.692 0.308
#> GSM494673 1 0.0000 0.939 1.000 0.000
#> GSM494649 1 0.0000 0.939 1.000 0.000
#> GSM494658 1 0.0000 0.939 1.000 0.000
#> GSM494653 1 0.0000 0.939 1.000 0.000
#> GSM494643 1 0.0000 0.939 1.000 0.000
#> GSM494672 1 0.0000 0.939 1.000 0.000
#> GSM494618 1 0.0000 0.939 1.000 0.000
#> GSM494631 2 0.7139 0.701 0.196 0.804
#> GSM494619 1 0.0000 0.939 1.000 0.000
#> GSM494674 1 0.0000 0.939 1.000 0.000
#> GSM494616 1 0.0672 0.933 0.992 0.008
#> GSM494663 1 0.0000 0.939 1.000 0.000
#> GSM494628 1 0.8909 0.567 0.692 0.308
#> GSM494632 1 0.0000 0.939 1.000 0.000
#> GSM494660 1 0.0000 0.939 1.000 0.000
#> GSM494622 1 0.8909 0.567 0.692 0.308
#> GSM494642 1 0.0000 0.939 1.000 0.000
#> GSM494647 1 0.0000 0.939 1.000 0.000
#> GSM494659 1 0.0000 0.939 1.000 0.000
#> GSM494670 1 0.0000 0.939 1.000 0.000
#> GSM494675 2 0.0000 0.896 0.000 1.000
#> GSM494641 1 0.0000 0.939 1.000 0.000
#> GSM494636 1 0.0000 0.939 1.000 0.000
#> GSM494640 1 0.8909 0.567 0.692 0.308
#> GSM494623 1 0.0000 0.939 1.000 0.000
#> GSM494644 1 0.0000 0.939 1.000 0.000
#> GSM494646 1 0.0000 0.939 1.000 0.000
#> GSM494665 1 0.0000 0.939 1.000 0.000
#> GSM494638 1 0.0000 0.939 1.000 0.000
#> GSM494645 1 0.0000 0.939 1.000 0.000
#> GSM494671 1 0.0000 0.939 1.000 0.000
#> GSM494655 1 0.0000 0.939 1.000 0.000
#> GSM494620 1 0.0000 0.939 1.000 0.000
#> GSM494630 1 0.0000 0.939 1.000 0.000
#> GSM494657 2 0.0000 0.896 0.000 1.000
#> GSM494667 1 0.0000 0.939 1.000 0.000
#> GSM494621 1 0.0000 0.939 1.000 0.000
#> GSM494629 1 0.8909 0.567 0.692 0.308
#> GSM494637 1 0.8909 0.567 0.692 0.308
#> GSM494652 1 0.0000 0.939 1.000 0.000
#> GSM494648 1 0.0000 0.939 1.000 0.000
#> GSM494650 1 0.8909 0.567 0.692 0.308
#> GSM494669 1 0.0000 0.939 1.000 0.000
#> GSM494666 1 0.0000 0.939 1.000 0.000
#> GSM494668 1 0.0000 0.939 1.000 0.000
#> GSM494633 1 0.0000 0.939 1.000 0.000
#> GSM494634 1 0.0000 0.939 1.000 0.000
#> GSM494639 1 0.0000 0.939 1.000 0.000
#> GSM494661 1 0.0000 0.939 1.000 0.000
#> GSM494617 1 0.0000 0.939 1.000 0.000
#> GSM494626 1 0.0000 0.939 1.000 0.000
#> GSM494656 2 0.0000 0.896 0.000 1.000
#> GSM494635 1 0.0000 0.939 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM494565 2 0.0000 0.822 0.000 1.000 0.000
#> GSM494594 2 0.3551 0.724 0.000 0.868 0.132
#> GSM494604 1 0.0892 0.980 0.980 0.000 0.020
#> GSM494564 2 0.0000 0.822 0.000 1.000 0.000
#> GSM494591 2 0.0000 0.822 0.000 1.000 0.000
#> GSM494567 2 0.0892 0.814 0.000 0.980 0.020
#> GSM494602 2 0.6308 0.202 0.492 0.508 0.000
#> GSM494613 2 0.0000 0.822 0.000 1.000 0.000
#> GSM494589 2 0.0000 0.822 0.000 1.000 0.000
#> GSM494598 2 0.6154 0.394 0.408 0.592 0.000
#> GSM494593 2 0.6008 0.459 0.372 0.628 0.000
#> GSM494583 2 0.0000 0.822 0.000 1.000 0.000
#> GSM494612 2 0.6308 0.202 0.492 0.508 0.000
#> GSM494558 2 0.6309 0.119 0.000 0.504 0.496
#> GSM494556 2 0.0000 0.822 0.000 1.000 0.000
#> GSM494559 2 0.0000 0.822 0.000 1.000 0.000
#> GSM494571 2 0.6280 0.216 0.000 0.540 0.460
#> GSM494614 2 0.0424 0.821 0.008 0.992 0.000
#> GSM494603 2 0.6280 0.216 0.000 0.540 0.460
#> GSM494568 2 0.6307 0.142 0.000 0.512 0.488
#> GSM494572 2 0.0892 0.814 0.000 0.980 0.020
#> GSM494600 2 0.0000 0.822 0.000 1.000 0.000
#> GSM494562 2 0.1031 0.817 0.024 0.976 0.000
#> GSM494615 2 0.6244 0.255 0.000 0.560 0.440
#> GSM494582 2 0.6308 0.202 0.492 0.508 0.000
#> GSM494599 1 0.0892 0.946 0.980 0.020 0.000
#> GSM494610 2 0.5733 0.535 0.324 0.676 0.000
#> GSM494587 2 0.1031 0.817 0.024 0.976 0.000
#> GSM494581 2 0.1031 0.817 0.024 0.976 0.000
#> GSM494580 2 0.0000 0.822 0.000 1.000 0.000
#> GSM494563 2 0.0000 0.822 0.000 1.000 0.000
#> GSM494576 2 0.1031 0.817 0.024 0.976 0.000
#> GSM494605 1 0.1031 0.983 0.976 0.000 0.024
#> GSM494584 2 0.0000 0.822 0.000 1.000 0.000
#> GSM494586 2 0.1031 0.817 0.024 0.976 0.000
#> GSM494578 2 0.0000 0.822 0.000 1.000 0.000
#> GSM494585 2 0.1031 0.817 0.024 0.976 0.000
#> GSM494611 2 0.6308 0.202 0.492 0.508 0.000
#> GSM494560 2 0.0000 0.822 0.000 1.000 0.000
#> GSM494595 2 0.1031 0.817 0.024 0.976 0.000
#> GSM494570 2 0.2165 0.784 0.000 0.936 0.064
#> GSM494597 2 0.0892 0.814 0.000 0.980 0.020
#> GSM494607 1 0.0000 0.962 1.000 0.000 0.000
#> GSM494561 2 0.6309 0.119 0.000 0.504 0.496
#> GSM494569 3 0.0000 0.819 0.000 0.000 1.000
#> GSM494592 1 0.0892 0.946 0.980 0.020 0.000
#> GSM494577 2 0.1031 0.817 0.024 0.976 0.000
#> GSM494588 2 0.0000 0.822 0.000 1.000 0.000
#> GSM494590 2 0.0892 0.814 0.000 0.980 0.020
#> GSM494609 2 0.6215 0.350 0.428 0.572 0.000
#> GSM494608 1 0.3009 0.905 0.920 0.052 0.028
#> GSM494606 1 0.0892 0.946 0.980 0.020 0.000
#> GSM494574 2 0.6260 0.310 0.448 0.552 0.000
#> GSM494573 2 0.0000 0.822 0.000 1.000 0.000
#> GSM494566 2 0.0000 0.822 0.000 1.000 0.000
#> GSM494601 2 0.5291 0.607 0.268 0.732 0.000
#> GSM494557 2 0.0000 0.822 0.000 1.000 0.000
#> GSM494579 2 0.1031 0.817 0.024 0.976 0.000
#> GSM494596 2 0.0000 0.822 0.000 1.000 0.000
#> GSM494575 2 0.6308 0.202 0.492 0.508 0.000
#> GSM494625 3 0.0000 0.819 0.000 0.000 1.000
#> GSM494654 3 0.6308 -0.124 0.000 0.492 0.508
#> GSM494664 1 0.1860 0.961 0.948 0.000 0.052
#> GSM494624 3 0.0000 0.819 0.000 0.000 1.000
#> GSM494651 3 0.0000 0.819 0.000 0.000 1.000
#> GSM494662 3 0.6280 0.292 0.460 0.000 0.540
#> GSM494627 3 0.0000 0.819 0.000 0.000 1.000
#> GSM494673 1 0.1031 0.983 0.976 0.000 0.024
#> GSM494649 3 0.0000 0.819 0.000 0.000 1.000
#> GSM494658 1 0.1031 0.983 0.976 0.000 0.024
#> GSM494653 1 0.1031 0.983 0.976 0.000 0.024
#> GSM494643 3 0.1529 0.799 0.040 0.000 0.960
#> GSM494672 1 0.1031 0.983 0.976 0.000 0.024
#> GSM494618 3 0.0000 0.819 0.000 0.000 1.000
#> GSM494631 2 0.6309 0.103 0.000 0.500 0.500
#> GSM494619 3 0.0424 0.816 0.008 0.000 0.992
#> GSM494674 1 0.1031 0.983 0.976 0.000 0.024
#> GSM494616 3 0.0000 0.819 0.000 0.000 1.000
#> GSM494663 3 0.0000 0.819 0.000 0.000 1.000
#> GSM494628 3 0.0000 0.819 0.000 0.000 1.000
#> GSM494632 3 0.6280 0.292 0.460 0.000 0.540
#> GSM494660 3 0.0000 0.819 0.000 0.000 1.000
#> GSM494622 3 0.0000 0.819 0.000 0.000 1.000
#> GSM494642 1 0.1031 0.983 0.976 0.000 0.024
#> GSM494647 1 0.1031 0.983 0.976 0.000 0.024
#> GSM494659 1 0.1031 0.983 0.976 0.000 0.024
#> GSM494670 1 0.1031 0.983 0.976 0.000 0.024
#> GSM494675 2 0.0892 0.814 0.000 0.980 0.020
#> GSM494641 1 0.1031 0.983 0.976 0.000 0.024
#> GSM494636 3 0.2796 0.763 0.092 0.000 0.908
#> GSM494640 3 0.0000 0.819 0.000 0.000 1.000
#> GSM494623 3 0.0000 0.819 0.000 0.000 1.000
#> GSM494644 1 0.1753 0.964 0.952 0.000 0.048
#> GSM494646 3 0.6280 0.292 0.460 0.000 0.540
#> GSM494665 1 0.1031 0.983 0.976 0.000 0.024
#> GSM494638 3 0.6280 0.292 0.460 0.000 0.540
#> GSM494645 1 0.1860 0.961 0.948 0.000 0.052
#> GSM494671 1 0.1031 0.983 0.976 0.000 0.024
#> GSM494655 1 0.1031 0.983 0.976 0.000 0.024
#> GSM494620 3 0.6280 0.292 0.460 0.000 0.540
#> GSM494630 3 0.6280 0.292 0.460 0.000 0.540
#> GSM494657 2 0.0892 0.814 0.000 0.980 0.020
#> GSM494667 1 0.1031 0.983 0.976 0.000 0.024
#> GSM494621 3 0.1753 0.794 0.048 0.000 0.952
#> GSM494629 3 0.0000 0.819 0.000 0.000 1.000
#> GSM494637 3 0.0000 0.819 0.000 0.000 1.000
#> GSM494652 1 0.1031 0.983 0.976 0.000 0.024
#> GSM494648 3 0.6280 0.292 0.460 0.000 0.540
#> GSM494650 3 0.0000 0.819 0.000 0.000 1.000
#> GSM494669 1 0.1031 0.983 0.976 0.000 0.024
#> GSM494666 1 0.1860 0.961 0.948 0.000 0.052
#> GSM494668 1 0.1031 0.983 0.976 0.000 0.024
#> GSM494633 3 0.0424 0.816 0.008 0.000 0.992
#> GSM494634 1 0.1031 0.983 0.976 0.000 0.024
#> GSM494639 3 0.6280 0.292 0.460 0.000 0.540
#> GSM494661 1 0.1860 0.961 0.948 0.000 0.052
#> GSM494617 3 0.0000 0.819 0.000 0.000 1.000
#> GSM494626 3 0.0000 0.819 0.000 0.000 1.000
#> GSM494656 3 0.6308 -0.124 0.000 0.492 0.508
#> GSM494635 3 0.6280 0.292 0.460 0.000 0.540
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM494565 3 0.4643 0.817 0.000 0.344 0.656 0.000
#> GSM494594 3 0.3311 0.794 0.000 0.172 0.828 0.000
#> GSM494604 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM494564 3 0.4643 0.817 0.000 0.344 0.656 0.000
#> GSM494591 3 0.3688 0.807 0.000 0.208 0.792 0.000
#> GSM494567 3 0.5877 0.814 0.000 0.276 0.656 0.068
#> GSM494602 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> GSM494613 3 0.4643 0.817 0.000 0.344 0.656 0.000
#> GSM494589 3 0.4643 0.817 0.000 0.344 0.656 0.000
#> GSM494598 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> GSM494593 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> GSM494583 3 0.4790 0.782 0.000 0.380 0.620 0.000
#> GSM494612 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> GSM494558 3 0.2011 0.658 0.000 0.000 0.920 0.080
#> GSM494556 3 0.4643 0.817 0.000 0.344 0.656 0.000
#> GSM494559 3 0.4643 0.817 0.000 0.344 0.656 0.000
#> GSM494571 3 0.0188 0.680 0.000 0.004 0.996 0.000
#> GSM494614 3 0.4679 0.810 0.000 0.352 0.648 0.000
#> GSM494603 3 0.3611 0.685 0.000 0.060 0.860 0.080
#> GSM494568 3 0.3280 0.648 0.000 0.016 0.860 0.124
#> GSM494572 3 0.3688 0.807 0.000 0.208 0.792 0.000
#> GSM494600 3 0.4643 0.817 0.000 0.344 0.656 0.000
#> GSM494562 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> GSM494615 3 0.3521 0.698 0.000 0.084 0.864 0.052
#> GSM494582 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> GSM494599 2 0.4406 0.524 0.300 0.700 0.000 0.000
#> GSM494610 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> GSM494587 2 0.1716 0.819 0.000 0.936 0.064 0.000
#> GSM494581 2 0.1716 0.819 0.000 0.936 0.064 0.000
#> GSM494580 3 0.4222 0.819 0.000 0.272 0.728 0.000
#> GSM494563 3 0.4843 0.770 0.000 0.396 0.604 0.000
#> GSM494576 2 0.3400 0.608 0.000 0.820 0.180 0.000
#> GSM494605 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM494584 3 0.4643 0.817 0.000 0.344 0.656 0.000
#> GSM494586 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> GSM494578 3 0.4643 0.817 0.000 0.344 0.656 0.000
#> GSM494585 2 0.1474 0.832 0.000 0.948 0.052 0.000
#> GSM494611 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> GSM494560 3 0.4643 0.817 0.000 0.344 0.656 0.000
#> GSM494595 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> GSM494570 3 0.5953 0.676 0.000 0.076 0.656 0.268
#> GSM494597 3 0.3688 0.807 0.000 0.208 0.792 0.000
#> GSM494607 1 0.3726 0.728 0.788 0.212 0.000 0.000
#> GSM494561 3 0.5110 0.629 0.000 0.016 0.656 0.328
#> GSM494569 4 0.3688 0.852 0.000 0.000 0.208 0.792
#> GSM494592 2 0.4500 0.503 0.316 0.684 0.000 0.000
#> GSM494577 2 0.1557 0.823 0.000 0.944 0.056 0.000
#> GSM494588 3 0.5384 0.812 0.000 0.324 0.648 0.028
#> GSM494590 3 0.3688 0.807 0.000 0.208 0.792 0.000
#> GSM494609 2 0.1722 0.844 0.048 0.944 0.008 0.000
#> GSM494608 2 0.6168 0.147 0.452 0.504 0.004 0.040
#> GSM494606 2 0.4500 0.503 0.316 0.684 0.000 0.000
#> GSM494574 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> GSM494573 3 0.4643 0.817 0.000 0.344 0.656 0.000
#> GSM494566 3 0.4898 0.731 0.000 0.416 0.584 0.000
#> GSM494601 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> GSM494557 3 0.4643 0.817 0.000 0.344 0.656 0.000
#> GSM494579 2 0.0469 0.868 0.000 0.988 0.012 0.000
#> GSM494596 3 0.3688 0.807 0.000 0.208 0.792 0.000
#> GSM494575 2 0.0000 0.876 0.000 1.000 0.000 0.000
#> GSM494625 4 0.0000 0.843 0.000 0.000 0.000 1.000
#> GSM494654 3 0.0000 0.676 0.000 0.000 1.000 0.000
#> GSM494664 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM494624 4 0.0000 0.843 0.000 0.000 0.000 1.000
#> GSM494651 4 0.3688 0.852 0.000 0.000 0.208 0.792
#> GSM494662 4 0.4331 0.682 0.288 0.000 0.000 0.712
#> GSM494627 4 0.3688 0.852 0.000 0.000 0.208 0.792
#> GSM494673 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM494649 4 0.0000 0.843 0.000 0.000 0.000 1.000
#> GSM494658 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM494653 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM494643 4 0.1284 0.849 0.012 0.000 0.024 0.964
#> GSM494672 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM494618 4 0.3688 0.852 0.000 0.000 0.208 0.792
#> GSM494631 3 0.5630 0.746 0.000 0.140 0.724 0.136
#> GSM494619 4 0.0469 0.843 0.012 0.000 0.000 0.988
#> GSM494674 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM494616 4 0.3688 0.852 0.000 0.000 0.208 0.792
#> GSM494663 4 0.3649 0.852 0.000 0.000 0.204 0.796
#> GSM494628 4 0.3688 0.852 0.000 0.000 0.208 0.792
#> GSM494632 4 0.4331 0.682 0.288 0.000 0.000 0.712
#> GSM494660 4 0.0188 0.844 0.000 0.000 0.004 0.996
#> GSM494622 4 0.3688 0.852 0.000 0.000 0.208 0.792
#> GSM494642 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM494670 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM494675 3 0.5989 0.811 0.000 0.264 0.656 0.080
#> GSM494641 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM494636 4 0.4595 0.788 0.176 0.000 0.044 0.780
#> GSM494640 4 0.3688 0.852 0.000 0.000 0.208 0.792
#> GSM494623 4 0.0000 0.843 0.000 0.000 0.000 1.000
#> GSM494644 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM494646 4 0.4967 0.369 0.452 0.000 0.000 0.548
#> GSM494665 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM494638 4 0.4483 0.686 0.284 0.000 0.004 0.712
#> GSM494645 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM494671 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM494620 4 0.2011 0.816 0.080 0.000 0.000 0.920
#> GSM494630 4 0.2011 0.816 0.080 0.000 0.000 0.920
#> GSM494657 3 0.3688 0.807 0.000 0.208 0.792 0.000
#> GSM494667 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM494621 4 0.0707 0.841 0.020 0.000 0.000 0.980
#> GSM494629 4 0.3688 0.852 0.000 0.000 0.208 0.792
#> GSM494637 4 0.1867 0.852 0.000 0.000 0.072 0.928
#> GSM494652 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM494648 4 0.2011 0.816 0.080 0.000 0.000 0.920
#> GSM494650 4 0.3688 0.852 0.000 0.000 0.208 0.792
#> GSM494669 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM494666 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM494668 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM494633 4 0.0469 0.843 0.012 0.000 0.000 0.988
#> GSM494634 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM494639 4 0.4331 0.682 0.288 0.000 0.000 0.712
#> GSM494661 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM494617 4 0.3688 0.852 0.000 0.000 0.208 0.792
#> GSM494626 4 0.3688 0.852 0.000 0.000 0.208 0.792
#> GSM494656 3 0.0000 0.676 0.000 0.000 1.000 0.000
#> GSM494635 4 0.4967 0.368 0.452 0.000 0.000 0.548
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM494565 3 0.5625 0.767 0.000 0.204 0.636 0.160 0.000
#> GSM494594 3 0.0000 0.799 0.000 0.000 1.000 0.000 0.000
#> GSM494604 1 0.0000 0.894 1.000 0.000 0.000 0.000 0.000
#> GSM494564 3 0.6484 0.763 0.000 0.120 0.636 0.160 0.084
#> GSM494591 3 0.0000 0.799 0.000 0.000 1.000 0.000 0.000
#> GSM494567 3 0.2773 0.827 0.000 0.164 0.836 0.000 0.000
#> GSM494602 2 0.0000 0.888 0.000 1.000 0.000 0.000 0.000
#> GSM494613 3 0.3177 0.816 0.000 0.208 0.792 0.000 0.000
#> GSM494589 3 0.6176 0.772 0.000 0.172 0.636 0.160 0.032
#> GSM494598 2 0.1478 0.889 0.000 0.936 0.000 0.064 0.000
#> GSM494593 2 0.0000 0.888 0.000 1.000 0.000 0.000 0.000
#> GSM494583 3 0.6071 0.689 0.000 0.284 0.556 0.160 0.000
#> GSM494612 2 0.0162 0.888 0.004 0.996 0.000 0.000 0.000
#> GSM494558 4 0.4235 0.317 0.000 0.000 0.424 0.576 0.000
#> GSM494556 3 0.3300 0.818 0.000 0.204 0.792 0.004 0.000
#> GSM494559 3 0.6000 0.709 0.000 0.268 0.572 0.160 0.000
#> GSM494571 3 0.0000 0.799 0.000 0.000 1.000 0.000 0.000
#> GSM494614 3 0.4073 0.809 0.000 0.216 0.752 0.032 0.000
#> GSM494603 4 0.5681 0.268 0.000 0.084 0.336 0.576 0.004
#> GSM494568 4 0.4659 0.420 0.000 0.020 0.332 0.644 0.004
#> GSM494572 3 0.0000 0.799 0.000 0.000 1.000 0.000 0.000
#> GSM494600 3 0.5625 0.767 0.000 0.204 0.636 0.160 0.000
#> GSM494562 2 0.1478 0.889 0.000 0.936 0.000 0.064 0.000
#> GSM494615 3 0.4139 0.784 0.000 0.084 0.784 0.132 0.000
#> GSM494582 2 0.1478 0.889 0.000 0.936 0.000 0.064 0.000
#> GSM494599 2 0.1965 0.824 0.096 0.904 0.000 0.000 0.000
#> GSM494610 2 0.1478 0.889 0.000 0.936 0.000 0.064 0.000
#> GSM494587 2 0.1197 0.862 0.000 0.952 0.048 0.000 0.000
#> GSM494581 2 0.0992 0.879 0.000 0.968 0.008 0.024 0.000
#> GSM494580 3 0.2179 0.829 0.000 0.112 0.888 0.000 0.000
#> GSM494563 3 0.6083 0.712 0.000 0.204 0.572 0.224 0.000
#> GSM494576 2 0.4155 0.692 0.000 0.780 0.144 0.076 0.000
#> GSM494605 1 0.0000 0.894 1.000 0.000 0.000 0.000 0.000
#> GSM494584 3 0.3300 0.818 0.000 0.204 0.792 0.004 0.000
#> GSM494586 2 0.1608 0.887 0.000 0.928 0.000 0.072 0.000
#> GSM494578 3 0.3300 0.818 0.000 0.204 0.792 0.000 0.004
#> GSM494585 2 0.0162 0.887 0.000 0.996 0.004 0.000 0.000
#> GSM494611 2 0.1478 0.889 0.000 0.936 0.000 0.064 0.000
#> GSM494560 3 0.5625 0.767 0.000 0.204 0.636 0.160 0.000
#> GSM494595 2 0.1410 0.889 0.000 0.940 0.000 0.060 0.000
#> GSM494570 5 0.4781 0.619 0.000 0.000 0.112 0.160 0.728
#> GSM494597 3 0.0000 0.799 0.000 0.000 1.000 0.000 0.000
#> GSM494607 2 0.5509 0.163 0.464 0.472 0.000 0.064 0.000
#> GSM494561 5 0.4136 0.625 0.000 0.000 0.188 0.048 0.764
#> GSM494569 4 0.3336 0.854 0.000 0.000 0.000 0.772 0.228
#> GSM494592 2 0.1965 0.824 0.096 0.904 0.000 0.000 0.000
#> GSM494577 2 0.4284 0.727 0.000 0.736 0.040 0.224 0.000
#> GSM494588 5 0.7483 0.290 0.000 0.268 0.084 0.160 0.488
#> GSM494590 3 0.0000 0.799 0.000 0.000 1.000 0.000 0.000
#> GSM494609 2 0.0404 0.886 0.012 0.988 0.000 0.000 0.000
#> GSM494608 2 0.4757 0.341 0.380 0.596 0.000 0.000 0.024
#> GSM494606 2 0.1965 0.824 0.096 0.904 0.000 0.000 0.000
#> GSM494574 2 0.1478 0.889 0.000 0.936 0.000 0.064 0.000
#> GSM494573 3 0.5625 0.767 0.000 0.204 0.636 0.160 0.000
#> GSM494566 3 0.3999 0.703 0.000 0.344 0.656 0.000 0.000
#> GSM494601 2 0.0000 0.888 0.000 1.000 0.000 0.000 0.000
#> GSM494557 3 0.3300 0.818 0.000 0.204 0.792 0.004 0.000
#> GSM494579 2 0.2669 0.859 0.000 0.876 0.020 0.104 0.000
#> GSM494596 3 0.0000 0.799 0.000 0.000 1.000 0.000 0.000
#> GSM494575 2 0.0000 0.888 0.000 1.000 0.000 0.000 0.000
#> GSM494625 5 0.0162 0.870 0.000 0.000 0.000 0.004 0.996
#> GSM494654 3 0.0000 0.799 0.000 0.000 1.000 0.000 0.000
#> GSM494664 1 0.0000 0.894 1.000 0.000 0.000 0.000 0.000
#> GSM494624 5 0.0162 0.870 0.000 0.000 0.000 0.004 0.996
#> GSM494651 4 0.3336 0.854 0.000 0.000 0.000 0.772 0.228
#> GSM494662 1 0.6053 0.408 0.576 0.000 0.000 0.196 0.228
#> GSM494627 4 0.3305 0.854 0.000 0.000 0.000 0.776 0.224
#> GSM494673 1 0.0000 0.894 1.000 0.000 0.000 0.000 0.000
#> GSM494649 5 0.0000 0.869 0.000 0.000 0.000 0.000 1.000
#> GSM494658 1 0.0000 0.894 1.000 0.000 0.000 0.000 0.000
#> GSM494653 1 0.0000 0.894 1.000 0.000 0.000 0.000 0.000
#> GSM494643 5 0.3074 0.551 0.000 0.000 0.000 0.196 0.804
#> GSM494672 1 0.0000 0.894 1.000 0.000 0.000 0.000 0.000
#> GSM494618 4 0.3305 0.854 0.000 0.000 0.000 0.776 0.224
#> GSM494631 3 0.3583 0.711 0.000 0.012 0.792 0.192 0.004
#> GSM494619 5 0.0162 0.870 0.000 0.000 0.000 0.004 0.996
#> GSM494674 1 0.0000 0.894 1.000 0.000 0.000 0.000 0.000
#> GSM494616 4 0.3336 0.854 0.000 0.000 0.000 0.772 0.228
#> GSM494663 4 0.3305 0.854 0.000 0.000 0.000 0.776 0.224
#> GSM494628 4 0.3305 0.854 0.000 0.000 0.000 0.776 0.224
#> GSM494632 1 0.6030 0.415 0.580 0.000 0.000 0.196 0.224
#> GSM494660 5 0.0290 0.862 0.000 0.000 0.000 0.008 0.992
#> GSM494622 4 0.3305 0.854 0.000 0.000 0.000 0.776 0.224
#> GSM494642 1 0.0000 0.894 1.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.894 1.000 0.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.894 1.000 0.000 0.000 0.000 0.000
#> GSM494670 1 0.0000 0.894 1.000 0.000 0.000 0.000 0.000
#> GSM494675 3 0.4064 0.796 0.000 0.092 0.792 0.116 0.000
#> GSM494641 1 0.0000 0.894 1.000 0.000 0.000 0.000 0.000
#> GSM494636 1 0.6120 0.388 0.564 0.000 0.000 0.196 0.240
#> GSM494640 4 0.3819 0.840 0.000 0.000 0.016 0.756 0.228
#> GSM494623 5 0.0162 0.870 0.000 0.000 0.000 0.004 0.996
#> GSM494644 1 0.0000 0.894 1.000 0.000 0.000 0.000 0.000
#> GSM494646 1 0.5791 0.478 0.616 0.000 0.000 0.196 0.188
#> GSM494665 1 0.0000 0.894 1.000 0.000 0.000 0.000 0.000
#> GSM494638 1 0.6053 0.408 0.576 0.000 0.000 0.196 0.228
#> GSM494645 1 0.0000 0.894 1.000 0.000 0.000 0.000 0.000
#> GSM494671 1 0.0000 0.894 1.000 0.000 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.894 1.000 0.000 0.000 0.000 0.000
#> GSM494620 5 0.0000 0.869 0.000 0.000 0.000 0.000 1.000
#> GSM494630 5 0.0000 0.869 0.000 0.000 0.000 0.000 1.000
#> GSM494657 3 0.0000 0.799 0.000 0.000 1.000 0.000 0.000
#> GSM494667 1 0.0000 0.894 1.000 0.000 0.000 0.000 0.000
#> GSM494621 5 0.0162 0.870 0.000 0.000 0.000 0.004 0.996
#> GSM494629 4 0.3336 0.854 0.000 0.000 0.000 0.772 0.228
#> GSM494637 4 0.4273 0.498 0.000 0.000 0.000 0.552 0.448
#> GSM494652 1 0.0000 0.894 1.000 0.000 0.000 0.000 0.000
#> GSM494648 5 0.0162 0.870 0.000 0.000 0.000 0.004 0.996
#> GSM494650 4 0.3305 0.854 0.000 0.000 0.000 0.776 0.224
#> GSM494669 1 0.0000 0.894 1.000 0.000 0.000 0.000 0.000
#> GSM494666 1 0.0000 0.894 1.000 0.000 0.000 0.000 0.000
#> GSM494668 1 0.0000 0.894 1.000 0.000 0.000 0.000 0.000
#> GSM494633 5 0.0000 0.869 0.000 0.000 0.000 0.000 1.000
#> GSM494634 1 0.0000 0.894 1.000 0.000 0.000 0.000 0.000
#> GSM494639 1 0.6030 0.415 0.580 0.000 0.000 0.196 0.224
#> GSM494661 1 0.0000 0.894 1.000 0.000 0.000 0.000 0.000
#> GSM494617 4 0.3336 0.854 0.000 0.000 0.000 0.772 0.228
#> GSM494626 4 0.3305 0.854 0.000 0.000 0.000 0.776 0.224
#> GSM494656 3 0.0000 0.799 0.000 0.000 1.000 0.000 0.000
#> GSM494635 1 0.5877 0.458 0.604 0.000 0.000 0.196 0.200
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM494565 5 0.2420 0.829 0.000 0.040 0.076 0.000 0.884 0.000
#> GSM494594 3 0.0000 0.936 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494604 1 0.0260 0.952 0.992 0.008 0.000 0.000 0.000 0.000
#> GSM494564 5 0.2092 0.829 0.000 0.000 0.124 0.000 0.876 0.000
#> GSM494591 3 0.0000 0.936 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494567 3 0.0146 0.933 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM494602 2 0.0000 0.861 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494613 3 0.2902 0.675 0.000 0.196 0.800 0.000 0.004 0.000
#> GSM494589 5 0.2092 0.829 0.000 0.000 0.124 0.000 0.876 0.000
#> GSM494598 2 0.2346 0.844 0.000 0.868 0.000 0.000 0.124 0.008
#> GSM494593 2 0.0000 0.861 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494583 5 0.2100 0.797 0.000 0.112 0.004 0.000 0.884 0.000
#> GSM494612 2 0.0000 0.861 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494558 4 0.2562 0.746 0.000 0.000 0.172 0.828 0.000 0.000
#> GSM494556 5 0.4844 0.260 0.000 0.000 0.440 0.056 0.504 0.000
#> GSM494559 5 0.2312 0.800 0.000 0.112 0.012 0.000 0.876 0.000
#> GSM494571 3 0.0000 0.936 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494614 5 0.3756 0.439 0.000 0.000 0.400 0.000 0.600 0.000
#> GSM494603 4 0.2340 0.763 0.000 0.000 0.148 0.852 0.000 0.000
#> GSM494568 4 0.2752 0.781 0.000 0.000 0.108 0.856 0.000 0.036
#> GSM494572 3 0.0000 0.936 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494600 5 0.2092 0.829 0.000 0.000 0.124 0.000 0.876 0.000
#> GSM494562 2 0.2389 0.843 0.000 0.864 0.000 0.000 0.128 0.008
#> GSM494615 4 0.3817 0.337 0.000 0.000 0.432 0.568 0.000 0.000
#> GSM494582 2 0.2257 0.845 0.000 0.876 0.000 0.000 0.116 0.008
#> GSM494599 2 0.0000 0.861 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494610 2 0.2389 0.843 0.000 0.864 0.000 0.000 0.128 0.008
#> GSM494587 2 0.2513 0.758 0.000 0.852 0.140 0.000 0.008 0.000
#> GSM494581 2 0.2489 0.775 0.000 0.860 0.012 0.000 0.128 0.000
#> GSM494580 3 0.0146 0.933 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM494563 5 0.0000 0.786 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494576 2 0.4513 0.607 0.000 0.692 0.096 0.000 0.212 0.000
#> GSM494605 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494584 5 0.5255 0.253 0.000 0.096 0.428 0.000 0.476 0.000
#> GSM494586 2 0.2513 0.840 0.000 0.852 0.000 0.000 0.140 0.008
#> GSM494578 3 0.0146 0.933 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM494585 2 0.0291 0.858 0.000 0.992 0.004 0.000 0.004 0.000
#> GSM494611 2 0.2003 0.846 0.000 0.884 0.000 0.000 0.116 0.000
#> GSM494560 5 0.2048 0.830 0.000 0.000 0.120 0.000 0.880 0.000
#> GSM494595 2 0.1918 0.853 0.000 0.904 0.000 0.000 0.088 0.008
#> GSM494570 5 0.2618 0.809 0.000 0.000 0.052 0.000 0.872 0.076
#> GSM494597 3 0.0000 0.936 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494607 2 0.5294 0.402 0.356 0.532 0.000 0.000 0.112 0.000
#> GSM494561 6 0.1498 0.912 0.000 0.000 0.032 0.028 0.000 0.940
#> GSM494569 4 0.0865 0.846 0.000 0.000 0.000 0.964 0.000 0.036
#> GSM494592 2 0.0000 0.861 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494577 5 0.0260 0.783 0.000 0.000 0.000 0.000 0.992 0.008
#> GSM494588 5 0.2455 0.799 0.000 0.112 0.012 0.000 0.872 0.004
#> GSM494590 3 0.0000 0.936 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494609 2 0.0000 0.861 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494608 2 0.4219 0.375 0.360 0.620 0.000 0.012 0.000 0.008
#> GSM494606 2 0.0000 0.861 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494574 2 0.2346 0.844 0.000 0.868 0.000 0.000 0.124 0.008
#> GSM494573 5 0.2048 0.830 0.000 0.000 0.120 0.000 0.880 0.000
#> GSM494566 2 0.5900 0.221 0.000 0.500 0.276 0.220 0.004 0.000
#> GSM494601 2 0.0000 0.861 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494557 3 0.4336 -0.227 0.000 0.020 0.504 0.000 0.476 0.000
#> GSM494579 2 0.3490 0.745 0.000 0.724 0.000 0.000 0.268 0.008
#> GSM494596 3 0.0000 0.936 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494575 2 0.0000 0.861 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494625 6 0.1007 0.932 0.000 0.000 0.000 0.044 0.000 0.956
#> GSM494654 3 0.0000 0.936 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494664 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494624 6 0.0937 0.934 0.000 0.000 0.000 0.040 0.000 0.960
#> GSM494651 4 0.0713 0.848 0.000 0.000 0.000 0.972 0.000 0.028
#> GSM494662 1 0.3134 0.828 0.820 0.000 0.000 0.144 0.000 0.036
#> GSM494627 4 0.0000 0.849 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494673 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494649 6 0.0713 0.936 0.000 0.000 0.000 0.028 0.000 0.972
#> GSM494658 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494653 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494643 6 0.2300 0.855 0.000 0.000 0.000 0.144 0.000 0.856
#> GSM494672 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494618 4 0.0000 0.849 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494631 4 0.3961 0.310 0.000 0.000 0.440 0.556 0.004 0.000
#> GSM494619 6 0.0865 0.935 0.000 0.000 0.000 0.036 0.000 0.964
#> GSM494674 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494616 4 0.0790 0.847 0.000 0.000 0.000 0.968 0.000 0.032
#> GSM494663 4 0.1663 0.798 0.000 0.000 0.000 0.912 0.000 0.088
#> GSM494628 4 0.0000 0.849 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494632 1 0.3062 0.832 0.824 0.000 0.000 0.144 0.000 0.032
#> GSM494660 6 0.2135 0.873 0.000 0.000 0.000 0.128 0.000 0.872
#> GSM494622 4 0.0146 0.848 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM494642 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494670 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494675 4 0.3961 0.312 0.000 0.000 0.440 0.556 0.004 0.000
#> GSM494641 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494636 1 0.3332 0.817 0.808 0.000 0.000 0.144 0.000 0.048
#> GSM494640 4 0.0865 0.846 0.000 0.000 0.000 0.964 0.000 0.036
#> GSM494623 6 0.1007 0.932 0.000 0.000 0.000 0.044 0.000 0.956
#> GSM494644 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494646 1 0.2942 0.842 0.836 0.000 0.000 0.132 0.000 0.032
#> GSM494665 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494638 1 0.3134 0.828 0.820 0.000 0.000 0.144 0.000 0.036
#> GSM494645 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494671 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494620 6 0.0260 0.938 0.000 0.000 0.000 0.008 0.000 0.992
#> GSM494630 6 0.1957 0.887 0.000 0.000 0.000 0.112 0.000 0.888
#> GSM494657 3 0.0000 0.936 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494667 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494621 6 0.0363 0.939 0.000 0.000 0.000 0.012 0.000 0.988
#> GSM494629 4 0.0865 0.846 0.000 0.000 0.000 0.964 0.000 0.036
#> GSM494637 4 0.3409 0.527 0.000 0.000 0.000 0.700 0.000 0.300
#> GSM494652 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494648 6 0.0363 0.939 0.000 0.000 0.000 0.012 0.000 0.988
#> GSM494650 4 0.0000 0.849 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494669 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494666 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494668 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494633 6 0.1501 0.913 0.000 0.000 0.000 0.076 0.000 0.924
#> GSM494634 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494639 1 0.3062 0.832 0.824 0.000 0.000 0.144 0.000 0.032
#> GSM494661 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494617 4 0.0865 0.846 0.000 0.000 0.000 0.964 0.000 0.036
#> GSM494626 4 0.0632 0.849 0.000 0.000 0.000 0.976 0.000 0.024
#> GSM494656 3 0.0000 0.936 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494635 1 0.3062 0.832 0.824 0.000 0.000 0.144 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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 5)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 6)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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.
# code only for demonstration
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) individual(p) k
#> CV:pam 114 2.49e-19 1.000 7.73e-15 1.000 2
#> CV:pam 93 9.01e-14 0.246 1.16e-07 0.668 3
#> CV:pam 117 2.33e-18 0.533 9.89e-14 0.852 4
#> CV:pam 106 1.20e-14 0.218 3.98e-08 0.324 5
#> CV:pam 110 3.48e-15 0.127 2.71e-11 0.257 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.
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"]
# you can also extract it by
# 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 51941 rows and 120 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 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:
k and the heatmap of
predicted classes for each k.k.k.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:
k;k, the area increased is defined as \(A_k - A_{k-1}\).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 0.965 0.941 0.972 0.3440 0.667 0.667
#> 3 3 0.687 0.807 0.894 0.8581 0.676 0.519
#> 4 4 0.883 0.906 0.955 0.1365 0.867 0.651
#> 5 5 0.723 0.772 0.857 0.0763 0.905 0.665
#> 6 6 0.871 0.860 0.933 0.0247 0.883 0.546
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
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.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM494565 2 0.0938 0.96512 0.012 0.988
#> GSM494594 1 0.1633 0.96008 0.976 0.024
#> GSM494604 1 0.0376 0.97357 0.996 0.004
#> GSM494564 2 0.0938 0.96512 0.012 0.988
#> GSM494591 1 0.1633 0.96008 0.976 0.024
#> GSM494567 1 0.0000 0.97383 1.000 0.000
#> GSM494602 1 0.0000 0.97383 1.000 0.000
#> GSM494613 1 0.0000 0.97383 1.000 0.000
#> GSM494589 2 0.0938 0.96512 0.012 0.988
#> GSM494598 1 0.0000 0.97383 1.000 0.000
#> GSM494593 1 0.0000 0.97383 1.000 0.000
#> GSM494583 1 0.7674 0.70151 0.776 0.224
#> GSM494612 1 0.0000 0.97383 1.000 0.000
#> GSM494558 1 0.1633 0.96008 0.976 0.024
#> GSM494556 1 0.0000 0.97383 1.000 0.000
#> GSM494559 2 0.0938 0.96512 0.012 0.988
#> GSM494571 1 0.1633 0.96008 0.976 0.024
#> GSM494614 1 0.0000 0.97383 1.000 0.000
#> GSM494603 2 0.8016 0.69604 0.244 0.756
#> GSM494568 1 0.2603 0.93933 0.956 0.044
#> GSM494572 1 0.1633 0.96008 0.976 0.024
#> GSM494600 2 0.0938 0.96512 0.012 0.988
#> GSM494562 1 0.0000 0.97383 1.000 0.000
#> GSM494615 1 0.0000 0.97383 1.000 0.000
#> GSM494582 1 0.0000 0.97383 1.000 0.000
#> GSM494599 1 0.0000 0.97383 1.000 0.000
#> GSM494610 1 0.0000 0.97383 1.000 0.000
#> GSM494587 1 0.0000 0.97383 1.000 0.000
#> GSM494581 1 0.0376 0.97269 0.996 0.004
#> GSM494580 1 0.0000 0.97383 1.000 0.000
#> GSM494563 2 0.0938 0.96512 0.012 0.988
#> GSM494576 1 0.0000 0.97383 1.000 0.000
#> GSM494605 1 0.0376 0.97357 0.996 0.004
#> GSM494584 1 0.0000 0.97383 1.000 0.000
#> GSM494586 1 0.0000 0.97383 1.000 0.000
#> GSM494578 1 0.0000 0.97383 1.000 0.000
#> GSM494585 1 0.0000 0.97383 1.000 0.000
#> GSM494611 1 0.0000 0.97383 1.000 0.000
#> GSM494560 2 0.0938 0.96512 0.012 0.988
#> GSM494595 1 0.0000 0.97383 1.000 0.000
#> GSM494570 2 0.0938 0.96512 0.012 0.988
#> GSM494597 1 0.1633 0.96008 0.976 0.024
#> GSM494607 1 0.0000 0.97383 1.000 0.000
#> GSM494561 2 0.0938 0.96512 0.012 0.988
#> GSM494569 1 0.0376 0.97357 0.996 0.004
#> GSM494592 1 0.0000 0.97383 1.000 0.000
#> GSM494577 1 0.8443 0.61629 0.728 0.272
#> GSM494588 2 0.0938 0.96512 0.012 0.988
#> GSM494590 1 0.1633 0.96008 0.976 0.024
#> GSM494609 1 0.0000 0.97383 1.000 0.000
#> GSM494608 1 0.0000 0.97383 1.000 0.000
#> GSM494606 1 0.0000 0.97383 1.000 0.000
#> GSM494574 1 0.0000 0.97383 1.000 0.000
#> GSM494573 2 0.0938 0.96512 0.012 0.988
#> GSM494566 1 0.0000 0.97383 1.000 0.000
#> GSM494601 1 0.0000 0.97383 1.000 0.000
#> GSM494557 1 0.0000 0.97383 1.000 0.000
#> GSM494579 1 0.0376 0.97258 0.996 0.004
#> GSM494596 1 0.1633 0.96008 0.976 0.024
#> GSM494575 1 0.0000 0.97383 1.000 0.000
#> GSM494625 2 0.0672 0.96511 0.008 0.992
#> GSM494654 1 0.1633 0.96008 0.976 0.024
#> GSM494664 1 0.0376 0.97357 0.996 0.004
#> GSM494624 2 0.0672 0.96511 0.008 0.992
#> GSM494651 1 0.0376 0.97357 0.996 0.004
#> GSM494662 1 0.2948 0.93506 0.948 0.052
#> GSM494627 1 0.8955 0.55278 0.688 0.312
#> GSM494673 1 0.0938 0.97054 0.988 0.012
#> GSM494649 2 0.0672 0.96511 0.008 0.992
#> GSM494658 1 0.0376 0.97357 0.996 0.004
#> GSM494653 1 0.0938 0.97054 0.988 0.012
#> GSM494643 2 0.7453 0.73993 0.212 0.788
#> GSM494672 1 0.0938 0.97054 0.988 0.012
#> GSM494618 1 0.0376 0.97357 0.996 0.004
#> GSM494631 1 0.0000 0.97383 1.000 0.000
#> GSM494619 2 0.0672 0.96511 0.008 0.992
#> GSM494674 1 0.0938 0.97054 0.988 0.012
#> GSM494616 1 0.0376 0.97357 0.996 0.004
#> GSM494663 2 0.8955 0.55858 0.312 0.688
#> GSM494628 1 0.0938 0.97069 0.988 0.012
#> GSM494632 1 0.0376 0.97357 0.996 0.004
#> GSM494660 2 0.0672 0.96511 0.008 0.992
#> GSM494622 1 0.0376 0.97357 0.996 0.004
#> GSM494642 1 0.0938 0.97054 0.988 0.012
#> GSM494647 1 0.0938 0.97054 0.988 0.012
#> GSM494659 1 0.0938 0.97054 0.988 0.012
#> GSM494670 1 0.0938 0.97054 0.988 0.012
#> GSM494675 1 0.0376 0.97252 0.996 0.004
#> GSM494641 1 0.0938 0.97054 0.988 0.012
#> GSM494636 1 0.2043 0.95461 0.968 0.032
#> GSM494640 1 0.9358 0.45695 0.648 0.352
#> GSM494623 2 0.0672 0.96511 0.008 0.992
#> GSM494644 1 0.0376 0.97357 0.996 0.004
#> GSM494646 1 0.0376 0.97357 0.996 0.004
#> GSM494665 1 0.0376 0.97357 0.996 0.004
#> GSM494638 1 0.0376 0.97357 0.996 0.004
#> GSM494645 1 0.0376 0.97357 0.996 0.004
#> GSM494671 1 0.0938 0.97054 0.988 0.012
#> GSM494655 1 0.0938 0.97054 0.988 0.012
#> GSM494620 2 0.0672 0.96511 0.008 0.992
#> GSM494630 2 0.0672 0.96511 0.008 0.992
#> GSM494657 1 0.1633 0.96008 0.976 0.024
#> GSM494667 1 0.0938 0.97054 0.988 0.012
#> GSM494621 2 0.0672 0.96511 0.008 0.992
#> GSM494629 1 0.1843 0.95999 0.972 0.028
#> GSM494637 1 0.9998 0.00665 0.508 0.492
#> GSM494652 1 0.0938 0.97054 0.988 0.012
#> GSM494648 2 0.0672 0.96511 0.008 0.992
#> GSM494650 1 0.0376 0.97357 0.996 0.004
#> GSM494669 1 0.0938 0.97054 0.988 0.012
#> GSM494666 1 0.0376 0.97357 0.996 0.004
#> GSM494668 1 0.0938 0.97054 0.988 0.012
#> GSM494633 2 0.0672 0.96511 0.008 0.992
#> GSM494634 1 0.0938 0.97054 0.988 0.012
#> GSM494639 1 0.0376 0.97357 0.996 0.004
#> GSM494661 1 0.0672 0.97236 0.992 0.008
#> GSM494617 1 0.0376 0.97357 0.996 0.004
#> GSM494626 1 0.0376 0.97357 0.996 0.004
#> GSM494656 1 0.1633 0.96008 0.976 0.024
#> GSM494635 1 0.0376 0.97357 0.996 0.004
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM494565 3 0.0237 0.925 0.000 0.004 0.996
#> GSM494594 2 0.5905 0.658 0.000 0.648 0.352
#> GSM494604 2 0.4504 0.700 0.196 0.804 0.000
#> GSM494564 3 0.0237 0.925 0.000 0.004 0.996
#> GSM494591 2 0.5905 0.658 0.000 0.648 0.352
#> GSM494567 2 0.5363 0.711 0.000 0.724 0.276
#> GSM494602 2 0.0000 0.835 0.000 1.000 0.000
#> GSM494613 2 0.5363 0.711 0.000 0.724 0.276
#> GSM494589 3 0.0237 0.925 0.000 0.004 0.996
#> GSM494598 2 0.0000 0.835 0.000 1.000 0.000
#> GSM494593 2 0.0000 0.835 0.000 1.000 0.000
#> GSM494583 2 0.0000 0.835 0.000 1.000 0.000
#> GSM494612 2 0.0000 0.835 0.000 1.000 0.000
#> GSM494558 2 0.6081 0.661 0.004 0.652 0.344
#> GSM494556 2 0.5363 0.711 0.000 0.724 0.276
#> GSM494559 3 0.0237 0.925 0.000 0.004 0.996
#> GSM494571 2 0.5905 0.658 0.000 0.648 0.352
#> GSM494614 2 0.0237 0.834 0.000 0.996 0.004
#> GSM494603 3 0.4749 0.836 0.072 0.076 0.852
#> GSM494568 2 0.9613 0.280 0.228 0.464 0.308
#> GSM494572 2 0.5905 0.658 0.000 0.648 0.352
#> GSM494600 3 0.0237 0.925 0.000 0.004 0.996
#> GSM494562 2 0.0000 0.835 0.000 1.000 0.000
#> GSM494615 2 0.5363 0.711 0.000 0.724 0.276
#> GSM494582 2 0.0000 0.835 0.000 1.000 0.000
#> GSM494599 2 0.0000 0.835 0.000 1.000 0.000
#> GSM494610 2 0.0000 0.835 0.000 1.000 0.000
#> GSM494587 2 0.0237 0.834 0.000 0.996 0.004
#> GSM494581 2 0.0000 0.835 0.000 1.000 0.000
#> GSM494580 2 0.5363 0.711 0.000 0.724 0.276
#> GSM494563 3 0.0237 0.925 0.000 0.004 0.996
#> GSM494576 2 0.0000 0.835 0.000 1.000 0.000
#> GSM494605 1 0.0000 0.877 1.000 0.000 0.000
#> GSM494584 2 0.0237 0.834 0.000 0.996 0.004
#> GSM494586 2 0.0000 0.835 0.000 1.000 0.000
#> GSM494578 2 0.5363 0.711 0.000 0.724 0.276
#> GSM494585 2 0.0237 0.834 0.000 0.996 0.004
#> GSM494611 2 0.0000 0.835 0.000 1.000 0.000
#> GSM494560 3 0.0237 0.925 0.000 0.004 0.996
#> GSM494595 2 0.0000 0.835 0.000 1.000 0.000
#> GSM494570 3 0.0237 0.925 0.000 0.004 0.996
#> GSM494597 2 0.5905 0.658 0.000 0.648 0.352
#> GSM494607 2 0.0000 0.835 0.000 1.000 0.000
#> GSM494561 3 0.0237 0.925 0.000 0.004 0.996
#> GSM494569 1 0.5254 0.689 0.736 0.000 0.264
#> GSM494592 2 0.0000 0.835 0.000 1.000 0.000
#> GSM494577 2 0.0000 0.835 0.000 1.000 0.000
#> GSM494588 3 0.0237 0.925 0.000 0.004 0.996
#> GSM494590 2 0.5905 0.658 0.000 0.648 0.352
#> GSM494609 2 0.0000 0.835 0.000 1.000 0.000
#> GSM494608 2 0.0000 0.835 0.000 1.000 0.000
#> GSM494606 2 0.0000 0.835 0.000 1.000 0.000
#> GSM494574 2 0.0000 0.835 0.000 1.000 0.000
#> GSM494573 3 0.0237 0.925 0.000 0.004 0.996
#> GSM494566 2 0.0237 0.834 0.000 0.996 0.004
#> GSM494601 2 0.0000 0.835 0.000 1.000 0.000
#> GSM494557 2 0.5363 0.711 0.000 0.724 0.276
#> GSM494579 2 0.0000 0.835 0.000 1.000 0.000
#> GSM494596 2 0.5905 0.658 0.000 0.648 0.352
#> GSM494575 2 0.0000 0.835 0.000 1.000 0.000
#> GSM494625 3 0.2537 0.925 0.080 0.000 0.920
#> GSM494654 2 0.6587 0.640 0.016 0.632 0.352
#> GSM494664 1 0.0000 0.877 1.000 0.000 0.000
#> GSM494624 3 0.2537 0.925 0.080 0.000 0.920
#> GSM494651 1 0.5327 0.675 0.728 0.000 0.272
#> GSM494662 1 0.1529 0.858 0.960 0.000 0.040
#> GSM494627 1 0.5254 0.688 0.736 0.000 0.264
#> GSM494673 1 0.0000 0.877 1.000 0.000 0.000
#> GSM494649 3 0.2711 0.918 0.088 0.000 0.912
#> GSM494658 1 0.0000 0.877 1.000 0.000 0.000
#> GSM494653 1 0.0000 0.877 1.000 0.000 0.000
#> GSM494643 3 0.5948 0.402 0.360 0.000 0.640
#> GSM494672 1 0.0000 0.877 1.000 0.000 0.000
#> GSM494618 1 0.5216 0.692 0.740 0.000 0.260
#> GSM494631 2 0.5763 0.706 0.008 0.716 0.276
#> GSM494619 3 0.2537 0.925 0.080 0.000 0.920
#> GSM494674 1 0.0000 0.877 1.000 0.000 0.000
#> GSM494616 1 0.5216 0.692 0.740 0.000 0.260
#> GSM494663 1 0.5465 0.655 0.712 0.000 0.288
#> GSM494628 1 0.5216 0.692 0.740 0.000 0.260
#> GSM494632 1 0.0000 0.877 1.000 0.000 0.000
#> GSM494660 3 0.2625 0.921 0.084 0.000 0.916
#> GSM494622 1 0.7728 0.571 0.640 0.084 0.276
#> GSM494642 1 0.0000 0.877 1.000 0.000 0.000
#> GSM494647 1 0.0000 0.877 1.000 0.000 0.000
#> GSM494659 1 0.0000 0.877 1.000 0.000 0.000
#> GSM494670 1 0.0000 0.877 1.000 0.000 0.000
#> GSM494675 2 0.5363 0.711 0.000 0.724 0.276
#> GSM494641 1 0.0000 0.877 1.000 0.000 0.000
#> GSM494636 1 0.2711 0.830 0.912 0.000 0.088
#> GSM494640 1 0.5327 0.679 0.728 0.000 0.272
#> GSM494623 3 0.2537 0.925 0.080 0.000 0.920
#> GSM494644 1 0.0000 0.877 1.000 0.000 0.000
#> GSM494646 1 0.0000 0.877 1.000 0.000 0.000
#> GSM494665 1 0.0000 0.877 1.000 0.000 0.000
#> GSM494638 1 0.4346 0.763 0.816 0.000 0.184
#> GSM494645 1 0.0000 0.877 1.000 0.000 0.000
#> GSM494671 1 0.0000 0.877 1.000 0.000 0.000
#> GSM494655 1 0.0000 0.877 1.000 0.000 0.000
#> GSM494620 3 0.2537 0.925 0.080 0.000 0.920
#> GSM494630 3 0.2537 0.925 0.080 0.000 0.920
#> GSM494657 2 0.5905 0.658 0.000 0.648 0.352
#> GSM494667 1 0.0000 0.877 1.000 0.000 0.000
#> GSM494621 3 0.2537 0.925 0.080 0.000 0.920
#> GSM494629 1 0.5327 0.675 0.728 0.000 0.272
#> GSM494637 1 0.5327 0.679 0.728 0.000 0.272
#> GSM494652 1 0.0000 0.877 1.000 0.000 0.000
#> GSM494648 3 0.2537 0.925 0.080 0.000 0.920
#> GSM494650 1 0.5363 0.671 0.724 0.000 0.276
#> GSM494669 1 0.0000 0.877 1.000 0.000 0.000
#> GSM494666 1 0.0000 0.877 1.000 0.000 0.000
#> GSM494668 1 0.0000 0.877 1.000 0.000 0.000
#> GSM494633 3 0.2537 0.925 0.080 0.000 0.920
#> GSM494634 1 0.0000 0.877 1.000 0.000 0.000
#> GSM494639 1 0.0000 0.877 1.000 0.000 0.000
#> GSM494661 1 0.0000 0.877 1.000 0.000 0.000
#> GSM494617 1 0.5216 0.692 0.740 0.000 0.260
#> GSM494626 1 0.5216 0.692 0.740 0.000 0.260
#> GSM494656 2 0.5905 0.658 0.000 0.648 0.352
#> GSM494635 1 0.0000 0.877 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM494565 4 0.0000 0.9970 0.000 0.000 0.000 1.000
#> GSM494594 3 0.0000 0.8668 0.000 0.000 1.000 0.000
#> GSM494604 2 0.2814 0.7874 0.132 0.868 0.000 0.000
#> GSM494564 4 0.0000 0.9970 0.000 0.000 0.000 1.000
#> GSM494591 3 0.0000 0.8668 0.000 0.000 1.000 0.000
#> GSM494567 3 0.2814 0.8655 0.000 0.132 0.868 0.000
#> GSM494602 2 0.0000 0.9689 0.000 1.000 0.000 0.000
#> GSM494613 3 0.2760 0.8667 0.000 0.128 0.872 0.000
#> GSM494589 4 0.0000 0.9970 0.000 0.000 0.000 1.000
#> GSM494598 2 0.0000 0.9689 0.000 1.000 0.000 0.000
#> GSM494593 2 0.0000 0.9689 0.000 1.000 0.000 0.000
#> GSM494583 2 0.1302 0.9409 0.000 0.956 0.044 0.000
#> GSM494612 2 0.0000 0.9689 0.000 1.000 0.000 0.000
#> GSM494558 3 0.2814 0.8655 0.000 0.132 0.868 0.000
#> GSM494556 3 0.2814 0.8655 0.000 0.132 0.868 0.000
#> GSM494559 4 0.0000 0.9970 0.000 0.000 0.000 1.000
#> GSM494571 3 0.0000 0.8668 0.000 0.000 1.000 0.000
#> GSM494614 3 0.4746 0.5292 0.000 0.368 0.632 0.000
#> GSM494603 3 0.8316 0.5665 0.120 0.176 0.568 0.136
#> GSM494568 3 0.3342 0.8230 0.100 0.032 0.868 0.000
#> GSM494572 3 0.0000 0.8668 0.000 0.000 1.000 0.000
#> GSM494600 4 0.0000 0.9970 0.000 0.000 0.000 1.000
#> GSM494562 2 0.0000 0.9689 0.000 1.000 0.000 0.000
#> GSM494615 3 0.2814 0.8655 0.000 0.132 0.868 0.000
#> GSM494582 2 0.0000 0.9689 0.000 1.000 0.000 0.000
#> GSM494599 2 0.0000 0.9689 0.000 1.000 0.000 0.000
#> GSM494610 2 0.0000 0.9689 0.000 1.000 0.000 0.000
#> GSM494587 2 0.0000 0.9689 0.000 1.000 0.000 0.000
#> GSM494581 2 0.1302 0.9409 0.000 0.956 0.044 0.000
#> GSM494580 3 0.2814 0.8655 0.000 0.132 0.868 0.000
#> GSM494563 4 0.0000 0.9970 0.000 0.000 0.000 1.000
#> GSM494576 2 0.1302 0.9409 0.000 0.956 0.044 0.000
#> GSM494605 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494584 2 0.4331 0.5378 0.000 0.712 0.288 0.000
#> GSM494586 2 0.0000 0.9689 0.000 1.000 0.000 0.000
#> GSM494578 3 0.2814 0.8655 0.000 0.132 0.868 0.000
#> GSM494585 2 0.0000 0.9689 0.000 1.000 0.000 0.000
#> GSM494611 2 0.0000 0.9689 0.000 1.000 0.000 0.000
#> GSM494560 4 0.0000 0.9970 0.000 0.000 0.000 1.000
#> GSM494595 2 0.0000 0.9689 0.000 1.000 0.000 0.000
#> GSM494570 4 0.0000 0.9970 0.000 0.000 0.000 1.000
#> GSM494597 3 0.0000 0.8668 0.000 0.000 1.000 0.000
#> GSM494607 2 0.0000 0.9689 0.000 1.000 0.000 0.000
#> GSM494561 4 0.0000 0.9970 0.000 0.000 0.000 1.000
#> GSM494569 1 0.0469 0.9521 0.988 0.000 0.012 0.000
#> GSM494592 2 0.0000 0.9689 0.000 1.000 0.000 0.000
#> GSM494577 2 0.1302 0.9409 0.000 0.956 0.044 0.000
#> GSM494588 4 0.0000 0.9970 0.000 0.000 0.000 1.000
#> GSM494590 3 0.0000 0.8668 0.000 0.000 1.000 0.000
#> GSM494609 2 0.1302 0.9409 0.000 0.956 0.044 0.000
#> GSM494608 2 0.0000 0.9689 0.000 1.000 0.000 0.000
#> GSM494606 2 0.0000 0.9689 0.000 1.000 0.000 0.000
#> GSM494574 2 0.0000 0.9689 0.000 1.000 0.000 0.000
#> GSM494573 4 0.0000 0.9970 0.000 0.000 0.000 1.000
#> GSM494566 3 0.4222 0.7067 0.000 0.272 0.728 0.000
#> GSM494601 2 0.0000 0.9689 0.000 1.000 0.000 0.000
#> GSM494557 3 0.2760 0.8667 0.000 0.128 0.872 0.000
#> GSM494579 2 0.1302 0.9409 0.000 0.956 0.044 0.000
#> GSM494596 3 0.0000 0.8668 0.000 0.000 1.000 0.000
#> GSM494575 2 0.0000 0.9689 0.000 1.000 0.000 0.000
#> GSM494625 4 0.1302 0.9420 0.044 0.000 0.000 0.956
#> GSM494654 3 0.0000 0.8668 0.000 0.000 1.000 0.000
#> GSM494664 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494624 4 0.0000 0.9970 0.000 0.000 0.000 1.000
#> GSM494651 1 0.2868 0.8352 0.864 0.000 0.136 0.000
#> GSM494662 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494627 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494673 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494649 1 0.4961 0.2074 0.552 0.000 0.000 0.448
#> GSM494658 1 0.4999 0.0311 0.508 0.492 0.000 0.000
#> GSM494653 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494643 1 0.0921 0.9369 0.972 0.000 0.000 0.028
#> GSM494672 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494618 1 0.0336 0.9552 0.992 0.000 0.008 0.000
#> GSM494631 3 0.2704 0.8673 0.000 0.124 0.876 0.000
#> GSM494619 4 0.0000 0.9970 0.000 0.000 0.000 1.000
#> GSM494674 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494616 1 0.0336 0.9552 0.992 0.000 0.008 0.000
#> GSM494663 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494628 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494632 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494660 1 0.4967 0.1949 0.548 0.000 0.000 0.452
#> GSM494622 3 0.4999 0.0747 0.492 0.000 0.508 0.000
#> GSM494642 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494670 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494675 3 0.2814 0.8655 0.000 0.132 0.868 0.000
#> GSM494641 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494636 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494640 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494623 4 0.0000 0.9970 0.000 0.000 0.000 1.000
#> GSM494644 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494646 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494665 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494638 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494645 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494671 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494620 4 0.0000 0.9970 0.000 0.000 0.000 1.000
#> GSM494630 4 0.0000 0.9970 0.000 0.000 0.000 1.000
#> GSM494657 3 0.0000 0.8668 0.000 0.000 1.000 0.000
#> GSM494667 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494621 4 0.0000 0.9970 0.000 0.000 0.000 1.000
#> GSM494629 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494637 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494652 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494648 4 0.0000 0.9970 0.000 0.000 0.000 1.000
#> GSM494650 1 0.2868 0.8352 0.864 0.000 0.136 0.000
#> GSM494669 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494666 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494668 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494633 4 0.0000 0.9970 0.000 0.000 0.000 1.000
#> GSM494634 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494639 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494661 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494617 1 0.0000 0.9606 1.000 0.000 0.000 0.000
#> GSM494626 1 0.0188 0.9579 0.996 0.000 0.004 0.000
#> GSM494656 3 0.0000 0.8668 0.000 0.000 1.000 0.000
#> GSM494635 1 0.0000 0.9606 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM494565 5 0.0000 0.8446 0.000 0.000 0.000 0.000 1.000
#> GSM494594 3 0.0000 0.9015 0.000 0.000 1.000 0.000 0.000
#> GSM494604 2 0.0510 0.8467 0.016 0.984 0.000 0.000 0.000
#> GSM494564 5 0.0000 0.8446 0.000 0.000 0.000 0.000 1.000
#> GSM494591 3 0.0000 0.9015 0.000 0.000 1.000 0.000 0.000
#> GSM494567 3 0.0290 0.9036 0.000 0.008 0.992 0.000 0.000
#> GSM494602 2 0.0162 0.8548 0.004 0.996 0.000 0.000 0.000
#> GSM494613 3 0.0290 0.9036 0.000 0.008 0.992 0.000 0.000
#> GSM494589 5 0.0000 0.8446 0.000 0.000 0.000 0.000 1.000
#> GSM494598 2 0.0000 0.8551 0.000 1.000 0.000 0.000 0.000
#> GSM494593 2 0.0000 0.8551 0.000 1.000 0.000 0.000 0.000
#> GSM494583 2 0.4030 0.5529 0.000 0.648 0.352 0.000 0.000
#> GSM494612 2 0.0162 0.8548 0.004 0.996 0.000 0.000 0.000
#> GSM494558 3 0.0290 0.9036 0.000 0.008 0.992 0.000 0.000
#> GSM494556 3 0.0290 0.9036 0.000 0.008 0.992 0.000 0.000
#> GSM494559 5 0.0000 0.8446 0.000 0.000 0.000 0.000 1.000
#> GSM494571 3 0.0000 0.9015 0.000 0.000 1.000 0.000 0.000
#> GSM494614 3 0.3895 0.4346 0.000 0.320 0.680 0.000 0.000
#> GSM494603 3 0.5008 0.6575 0.000 0.180 0.732 0.028 0.060
#> GSM494568 3 0.2502 0.8394 0.000 0.060 0.904 0.024 0.012
#> GSM494572 3 0.0000 0.9015 0.000 0.000 1.000 0.000 0.000
#> GSM494600 5 0.0000 0.8446 0.000 0.000 0.000 0.000 1.000
#> GSM494562 2 0.0000 0.8551 0.000 1.000 0.000 0.000 0.000
#> GSM494615 3 0.0290 0.9036 0.000 0.008 0.992 0.000 0.000
#> GSM494582 2 0.0000 0.8551 0.000 1.000 0.000 0.000 0.000
#> GSM494599 2 0.0162 0.8548 0.004 0.996 0.000 0.000 0.000
#> GSM494610 2 0.0000 0.8551 0.000 1.000 0.000 0.000 0.000
#> GSM494587 2 0.2648 0.7713 0.000 0.848 0.152 0.000 0.000
#> GSM494581 2 0.4045 0.5467 0.000 0.644 0.356 0.000 0.000
#> GSM494580 3 0.0290 0.9036 0.000 0.008 0.992 0.000 0.000
#> GSM494563 5 0.0000 0.8446 0.000 0.000 0.000 0.000 1.000
#> GSM494576 2 0.4030 0.5529 0.000 0.648 0.352 0.000 0.000
#> GSM494605 1 0.4795 0.7088 0.704 0.000 0.072 0.224 0.000
#> GSM494584 3 0.4262 0.0251 0.000 0.440 0.560 0.000 0.000
#> GSM494586 2 0.1478 0.8276 0.000 0.936 0.064 0.000 0.000
#> GSM494578 3 0.0290 0.9036 0.000 0.008 0.992 0.000 0.000
#> GSM494585 2 0.2471 0.7832 0.000 0.864 0.136 0.000 0.000
#> GSM494611 2 0.0000 0.8551 0.000 1.000 0.000 0.000 0.000
#> GSM494560 5 0.0000 0.8446 0.000 0.000 0.000 0.000 1.000
#> GSM494595 2 0.0290 0.8533 0.000 0.992 0.008 0.000 0.000
#> GSM494570 5 0.0000 0.8446 0.000 0.000 0.000 0.000 1.000
#> GSM494597 3 0.0290 0.9036 0.000 0.008 0.992 0.000 0.000
#> GSM494607 2 0.0162 0.8548 0.004 0.996 0.000 0.000 0.000
#> GSM494561 5 0.0000 0.8446 0.000 0.000 0.000 0.000 1.000
#> GSM494569 4 0.4666 0.8274 0.088 0.000 0.180 0.732 0.000
#> GSM494592 2 0.0162 0.8548 0.004 0.996 0.000 0.000 0.000
#> GSM494577 2 0.4030 0.5529 0.000 0.648 0.352 0.000 0.000
#> GSM494588 5 0.0000 0.8446 0.000 0.000 0.000 0.000 1.000
#> GSM494590 3 0.0000 0.9015 0.000 0.000 1.000 0.000 0.000
#> GSM494609 2 0.4045 0.5467 0.000 0.644 0.356 0.000 0.000
#> GSM494608 2 0.3895 0.5986 0.000 0.680 0.320 0.000 0.000
#> GSM494606 2 0.0162 0.8548 0.004 0.996 0.000 0.000 0.000
#> GSM494574 2 0.0000 0.8551 0.000 1.000 0.000 0.000 0.000
#> GSM494573 5 0.0000 0.8446 0.000 0.000 0.000 0.000 1.000
#> GSM494566 3 0.3983 0.4056 0.000 0.340 0.660 0.000 0.000
#> GSM494601 2 0.0000 0.8551 0.000 1.000 0.000 0.000 0.000
#> GSM494557 3 0.0290 0.9036 0.000 0.008 0.992 0.000 0.000
#> GSM494579 2 0.4030 0.5529 0.000 0.648 0.352 0.000 0.000
#> GSM494596 3 0.0000 0.9015 0.000 0.000 1.000 0.000 0.000
#> GSM494575 2 0.0162 0.8548 0.004 0.996 0.000 0.000 0.000
#> GSM494625 4 0.5369 0.5311 0.000 0.000 0.124 0.660 0.216
#> GSM494654 3 0.0000 0.9015 0.000 0.000 1.000 0.000 0.000
#> GSM494664 1 0.5250 0.6596 0.668 0.000 0.108 0.224 0.000
#> GSM494624 5 0.4101 0.7463 0.000 0.000 0.000 0.372 0.628
#> GSM494651 4 0.4238 0.8445 0.068 0.000 0.164 0.768 0.000
#> GSM494662 4 0.4503 0.7894 0.120 0.000 0.124 0.756 0.000
#> GSM494627 4 0.2690 0.8579 0.000 0.000 0.156 0.844 0.000
#> GSM494673 1 0.0000 0.8213 1.000 0.000 0.000 0.000 0.000
#> GSM494649 4 0.5341 0.5401 0.000 0.000 0.124 0.664 0.212
#> GSM494658 2 0.6739 0.0149 0.348 0.392 0.260 0.000 0.000
#> GSM494653 1 0.0000 0.8213 1.000 0.000 0.000 0.000 0.000
#> GSM494643 4 0.2690 0.8579 0.000 0.000 0.156 0.844 0.000
#> GSM494672 1 0.0000 0.8213 1.000 0.000 0.000 0.000 0.000
#> GSM494618 4 0.4412 0.8390 0.080 0.000 0.164 0.756 0.000
#> GSM494631 3 0.0290 0.9036 0.000 0.008 0.992 0.000 0.000
#> GSM494619 5 0.4101 0.7463 0.000 0.000 0.000 0.372 0.628
#> GSM494674 1 0.0000 0.8213 1.000 0.000 0.000 0.000 0.000
#> GSM494616 4 0.4297 0.8430 0.072 0.000 0.164 0.764 0.000
#> GSM494663 4 0.2690 0.8579 0.000 0.000 0.156 0.844 0.000
#> GSM494628 4 0.2690 0.8579 0.000 0.000 0.156 0.844 0.000
#> GSM494632 1 0.5500 0.6132 0.640 0.000 0.124 0.236 0.000
#> GSM494660 4 0.5341 0.5401 0.000 0.000 0.124 0.664 0.212
#> GSM494622 3 0.4854 0.2200 0.044 0.000 0.648 0.308 0.000
#> GSM494642 1 0.0000 0.8213 1.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.8213 1.000 0.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.8213 1.000 0.000 0.000 0.000 0.000
#> GSM494670 1 0.0510 0.8192 0.984 0.000 0.000 0.016 0.000
#> GSM494675 3 0.0290 0.9036 0.000 0.008 0.992 0.000 0.000
#> GSM494641 1 0.0000 0.8213 1.000 0.000 0.000 0.000 0.000
#> GSM494636 4 0.4964 0.7822 0.132 0.000 0.156 0.712 0.000
#> GSM494640 4 0.2690 0.8579 0.000 0.000 0.156 0.844 0.000
#> GSM494623 5 0.4101 0.7463 0.000 0.000 0.000 0.372 0.628
#> GSM494644 1 0.3305 0.7638 0.776 0.000 0.000 0.224 0.000
#> GSM494646 1 0.5329 0.6436 0.656 0.000 0.108 0.236 0.000
#> GSM494665 1 0.4617 0.7216 0.716 0.000 0.060 0.224 0.000
#> GSM494638 4 0.6326 0.5853 0.208 0.000 0.268 0.524 0.000
#> GSM494645 1 0.3461 0.7620 0.772 0.000 0.004 0.224 0.000
#> GSM494671 1 0.0000 0.8213 1.000 0.000 0.000 0.000 0.000
#> GSM494655 1 0.3210 0.7691 0.788 0.000 0.000 0.212 0.000
#> GSM494620 5 0.4101 0.7463 0.000 0.000 0.000 0.372 0.628
#> GSM494630 5 0.4101 0.7463 0.000 0.000 0.000 0.372 0.628
#> GSM494657 3 0.0000 0.9015 0.000 0.000 1.000 0.000 0.000
#> GSM494667 1 0.0000 0.8213 1.000 0.000 0.000 0.000 0.000
#> GSM494621 5 0.4101 0.7463 0.000 0.000 0.000 0.372 0.628
#> GSM494629 4 0.2732 0.8572 0.000 0.000 0.160 0.840 0.000
#> GSM494637 4 0.2690 0.8579 0.000 0.000 0.156 0.844 0.000
#> GSM494652 1 0.0000 0.8213 1.000 0.000 0.000 0.000 0.000
#> GSM494648 5 0.4101 0.7463 0.000 0.000 0.000 0.372 0.628
#> GSM494650 4 0.5010 0.7739 0.076 0.000 0.248 0.676 0.000
#> GSM494669 1 0.0000 0.8213 1.000 0.000 0.000 0.000 0.000
#> GSM494666 1 0.5060 0.6834 0.684 0.000 0.092 0.224 0.000
#> GSM494668 1 0.3305 0.7638 0.776 0.000 0.000 0.224 0.000
#> GSM494633 5 0.4161 0.7215 0.000 0.000 0.000 0.392 0.608
#> GSM494634 1 0.0000 0.8213 1.000 0.000 0.000 0.000 0.000
#> GSM494639 1 0.5500 0.6132 0.640 0.000 0.124 0.236 0.000
#> GSM494661 1 0.4424 0.7327 0.728 0.000 0.048 0.224 0.000
#> GSM494617 4 0.5510 0.7251 0.184 0.000 0.164 0.652 0.000
#> GSM494626 4 0.4521 0.8345 0.088 0.000 0.164 0.748 0.000
#> GSM494656 3 0.0000 0.9015 0.000 0.000 1.000 0.000 0.000
#> GSM494635 1 0.5500 0.6132 0.640 0.000 0.124 0.236 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM494565 5 0.0000 0.986 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494594 3 0.0000 0.997 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494604 2 0.1075 0.801 0.000 0.952 0.000 0.048 0.000 0.000
#> GSM494564 5 0.0000 0.986 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494591 3 0.0000 0.997 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494567 3 0.0146 0.997 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM494602 2 0.0000 0.829 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494613 3 0.0146 0.997 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM494589 5 0.0000 0.986 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494598 2 0.0000 0.829 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494593 2 0.0000 0.829 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494583 2 0.3309 0.721 0.000 0.720 0.280 0.000 0.000 0.000
#> GSM494612 2 0.0000 0.829 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494558 3 0.0000 0.997 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494556 3 0.0146 0.997 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM494559 5 0.0000 0.986 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494571 3 0.0000 0.997 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494614 2 0.3986 0.373 0.000 0.532 0.464 0.004 0.000 0.000
#> GSM494603 4 0.3848 0.562 0.000 0.004 0.292 0.692 0.012 0.000
#> GSM494568 4 0.3330 0.580 0.000 0.000 0.284 0.716 0.000 0.000
#> GSM494572 3 0.0000 0.997 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494600 5 0.0000 0.986 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494562 2 0.0000 0.829 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494615 3 0.0458 0.982 0.000 0.000 0.984 0.016 0.000 0.000
#> GSM494582 2 0.0000 0.829 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494599 2 0.0000 0.829 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494610 2 0.0000 0.829 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494587 2 0.2969 0.763 0.000 0.776 0.224 0.000 0.000 0.000
#> GSM494581 2 0.3309 0.721 0.000 0.720 0.280 0.000 0.000 0.000
#> GSM494580 3 0.0146 0.997 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM494563 5 0.0000 0.986 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494576 2 0.3309 0.721 0.000 0.720 0.280 0.000 0.000 0.000
#> GSM494605 4 0.0458 0.904 0.016 0.000 0.000 0.984 0.000 0.000
#> GSM494584 2 0.3309 0.721 0.000 0.720 0.280 0.000 0.000 0.000
#> GSM494586 2 0.2597 0.789 0.000 0.824 0.176 0.000 0.000 0.000
#> GSM494578 3 0.0146 0.997 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM494585 2 0.2854 0.773 0.000 0.792 0.208 0.000 0.000 0.000
#> GSM494611 2 0.0000 0.829 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494560 5 0.0000 0.986 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494595 2 0.0632 0.826 0.000 0.976 0.024 0.000 0.000 0.000
#> GSM494570 5 0.0000 0.986 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494597 3 0.0000 0.997 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494607 2 0.0000 0.829 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494561 5 0.2277 0.849 0.000 0.000 0.000 0.076 0.892 0.032
#> GSM494569 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494592 2 0.0000 0.829 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494577 2 0.3309 0.721 0.000 0.720 0.280 0.000 0.000 0.000
#> GSM494588 5 0.0000 0.986 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494590 3 0.0000 0.997 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494609 2 0.3309 0.721 0.000 0.720 0.280 0.000 0.000 0.000
#> GSM494608 2 0.2969 0.763 0.000 0.776 0.224 0.000 0.000 0.000
#> GSM494606 2 0.0000 0.829 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494574 2 0.0000 0.829 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494573 5 0.0000 0.986 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494566 2 0.6032 0.312 0.000 0.424 0.284 0.292 0.000 0.000
#> GSM494601 2 0.0000 0.829 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494557 3 0.0146 0.997 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM494579 2 0.3309 0.721 0.000 0.720 0.280 0.000 0.000 0.000
#> GSM494596 3 0.0000 0.997 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494575 2 0.0000 0.829 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494625 4 0.3531 0.531 0.000 0.000 0.000 0.672 0.000 0.328
#> GSM494654 3 0.0000 0.997 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494664 4 0.0458 0.904 0.016 0.000 0.000 0.984 0.000 0.000
#> GSM494624 6 0.0000 0.963 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494651 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494662 4 0.0146 0.907 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM494627 4 0.0146 0.907 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM494673 1 0.0000 0.928 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494649 4 0.3531 0.531 0.000 0.000 0.000 0.672 0.000 0.328
#> GSM494658 2 0.3766 0.452 0.012 0.684 0.000 0.304 0.000 0.000
#> GSM494653 1 0.0000 0.928 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494643 4 0.0363 0.905 0.000 0.000 0.000 0.988 0.000 0.012
#> GSM494672 1 0.0000 0.928 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494618 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494631 4 0.3647 0.449 0.000 0.000 0.360 0.640 0.000 0.000
#> GSM494619 6 0.0000 0.963 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494674 1 0.0000 0.928 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494616 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494663 4 0.0146 0.907 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM494628 4 0.0146 0.907 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM494632 4 0.0260 0.906 0.008 0.000 0.000 0.992 0.000 0.000
#> GSM494660 4 0.3531 0.531 0.000 0.000 0.000 0.672 0.000 0.328
#> GSM494622 4 0.0363 0.902 0.000 0.000 0.012 0.988 0.000 0.000
#> GSM494642 1 0.0000 0.928 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.928 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.928 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494670 1 0.2854 0.716 0.792 0.000 0.000 0.208 0.000 0.000
#> GSM494675 3 0.0146 0.997 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM494641 1 0.0000 0.928 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494636 4 0.0146 0.907 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM494640 4 0.0146 0.907 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM494623 6 0.0000 0.963 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494644 4 0.3756 0.263 0.400 0.000 0.000 0.600 0.000 0.000
#> GSM494646 4 0.0458 0.904 0.016 0.000 0.000 0.984 0.000 0.000
#> GSM494665 4 0.1075 0.883 0.048 0.000 0.000 0.952 0.000 0.000
#> GSM494638 4 0.0146 0.907 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM494645 4 0.0790 0.894 0.032 0.000 0.000 0.968 0.000 0.000
#> GSM494671 1 0.0000 0.928 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494655 1 0.3330 0.627 0.716 0.000 0.000 0.284 0.000 0.000
#> GSM494620 6 0.0000 0.963 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494630 6 0.0146 0.960 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM494657 3 0.0000 0.997 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494667 1 0.0000 0.928 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494621 6 0.0000 0.963 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494629 4 0.0146 0.907 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM494637 4 0.0146 0.907 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM494652 1 0.0000 0.928 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494648 6 0.0000 0.963 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494650 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494669 1 0.0000 0.928 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494666 4 0.0458 0.904 0.016 0.000 0.000 0.984 0.000 0.000
#> GSM494668 1 0.3330 0.627 0.716 0.000 0.000 0.284 0.000 0.000
#> GSM494633 6 0.2527 0.743 0.000 0.000 0.000 0.168 0.000 0.832
#> GSM494634 1 0.0000 0.928 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494639 4 0.0458 0.904 0.016 0.000 0.000 0.984 0.000 0.000
#> GSM494661 4 0.2300 0.786 0.144 0.000 0.000 0.856 0.000 0.000
#> GSM494617 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494626 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494656 3 0.0000 0.997 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494635 4 0.0458 0.904 0.016 0.000 0.000 0.984 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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 3)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 4)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 5)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 6)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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.
# code only for demonstration
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) individual(p) k
#> CV:mclust 118 9.24e-01 0.00742 9.41e-01 1.77e-05 2
#> CV:mclust 118 8.52e-16 0.33030 6.72e-09 1.36e-01 3
#> CV:mclust 116 1.14e-15 0.11295 1.24e-13 5.76e-02 4
#> CV:mclust 115 4.42e-15 0.11521 2.24e-11 7.20e-02 5
#> CV:mclust 115 1.52e-17 0.53558 9.96e-12 7.17e-01 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.
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"]
# you can also extract it by
# 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 51941 rows and 120 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:
k and the heatmap of
predicted classes for each k.k.k.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:
k;k, the area increased is defined as \(A_k - A_{k-1}\).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 0.629 0.859 0.931 0.5008 0.499 0.499
#> 3 3 0.931 0.929 0.970 0.3264 0.681 0.447
#> 4 4 0.875 0.882 0.945 0.1264 0.754 0.406
#> 5 5 0.648 0.630 0.806 0.0541 0.808 0.405
#> 6 6 0.733 0.684 0.825 0.0368 0.883 0.538
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
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.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM494565 2 0.0000 0.918 0.000 1.000
#> GSM494594 2 0.0000 0.918 0.000 1.000
#> GSM494604 1 0.0000 0.928 1.000 0.000
#> GSM494564 2 0.0000 0.918 0.000 1.000
#> GSM494591 2 0.0000 0.918 0.000 1.000
#> GSM494567 2 0.0000 0.918 0.000 1.000
#> GSM494602 1 0.2603 0.902 0.956 0.044
#> GSM494613 2 0.0000 0.918 0.000 1.000
#> GSM494589 2 0.0000 0.918 0.000 1.000
#> GSM494598 1 0.6973 0.779 0.812 0.188
#> GSM494593 1 0.7139 0.770 0.804 0.196
#> GSM494583 2 0.0000 0.918 0.000 1.000
#> GSM494612 1 0.1633 0.915 0.976 0.024
#> GSM494558 2 0.0000 0.918 0.000 1.000
#> GSM494556 2 0.0000 0.918 0.000 1.000
#> GSM494559 2 0.0000 0.918 0.000 1.000
#> GSM494571 2 0.0000 0.918 0.000 1.000
#> GSM494614 2 0.0000 0.918 0.000 1.000
#> GSM494603 2 0.0000 0.918 0.000 1.000
#> GSM494568 2 0.0000 0.918 0.000 1.000
#> GSM494572 2 0.0000 0.918 0.000 1.000
#> GSM494600 2 0.0000 0.918 0.000 1.000
#> GSM494562 1 0.9460 0.519 0.636 0.364
#> GSM494615 2 0.0000 0.918 0.000 1.000
#> GSM494582 1 0.4022 0.878 0.920 0.080
#> GSM494599 1 0.0376 0.926 0.996 0.004
#> GSM494610 1 0.7139 0.770 0.804 0.196
#> GSM494587 2 0.5408 0.809 0.124 0.876
#> GSM494581 1 0.8144 0.704 0.748 0.252
#> GSM494580 2 0.0000 0.918 0.000 1.000
#> GSM494563 2 0.0000 0.918 0.000 1.000
#> GSM494576 2 0.0000 0.918 0.000 1.000
#> GSM494605 1 0.0000 0.928 1.000 0.000
#> GSM494584 2 0.0000 0.918 0.000 1.000
#> GSM494586 1 0.8207 0.698 0.744 0.256
#> GSM494578 2 0.0000 0.918 0.000 1.000
#> GSM494585 1 0.9988 0.215 0.520 0.480
#> GSM494611 1 0.4690 0.862 0.900 0.100
#> GSM494560 2 0.0000 0.918 0.000 1.000
#> GSM494595 1 0.7299 0.761 0.796 0.204
#> GSM494570 2 0.0000 0.918 0.000 1.000
#> GSM494597 2 0.0000 0.918 0.000 1.000
#> GSM494607 1 0.0000 0.928 1.000 0.000
#> GSM494561 2 0.0000 0.918 0.000 1.000
#> GSM494569 2 0.8813 0.663 0.300 0.700
#> GSM494592 1 0.0000 0.928 1.000 0.000
#> GSM494577 2 0.0376 0.915 0.004 0.996
#> GSM494588 1 0.9608 0.477 0.616 0.384
#> GSM494590 2 0.0000 0.918 0.000 1.000
#> GSM494609 1 0.4431 0.870 0.908 0.092
#> GSM494608 1 0.0000 0.928 1.000 0.000
#> GSM494606 1 0.0000 0.928 1.000 0.000
#> GSM494574 1 0.6148 0.816 0.848 0.152
#> GSM494573 2 0.0000 0.918 0.000 1.000
#> GSM494566 2 0.4298 0.848 0.088 0.912
#> GSM494601 1 0.5842 0.827 0.860 0.140
#> GSM494557 2 0.0000 0.918 0.000 1.000
#> GSM494579 1 0.9661 0.459 0.608 0.392
#> GSM494596 2 0.0000 0.918 0.000 1.000
#> GSM494575 1 0.3733 0.884 0.928 0.072
#> GSM494625 2 0.7745 0.761 0.228 0.772
#> GSM494654 2 0.0376 0.916 0.004 0.996
#> GSM494664 1 0.0000 0.928 1.000 0.000
#> GSM494624 1 0.6247 0.765 0.844 0.156
#> GSM494651 2 0.7139 0.794 0.196 0.804
#> GSM494662 1 0.0000 0.928 1.000 0.000
#> GSM494627 2 0.6887 0.804 0.184 0.816
#> GSM494673 1 0.0000 0.928 1.000 0.000
#> GSM494649 2 0.7453 0.778 0.212 0.788
#> GSM494658 1 0.0000 0.928 1.000 0.000
#> GSM494653 1 0.0000 0.928 1.000 0.000
#> GSM494643 1 0.0000 0.928 1.000 0.000
#> GSM494672 1 0.0000 0.928 1.000 0.000
#> GSM494618 2 0.8386 0.709 0.268 0.732
#> GSM494631 2 0.0000 0.918 0.000 1.000
#> GSM494619 1 0.0000 0.928 1.000 0.000
#> GSM494674 1 0.0000 0.928 1.000 0.000
#> GSM494616 2 0.7602 0.770 0.220 0.780
#> GSM494663 2 0.8861 0.655 0.304 0.696
#> GSM494628 2 0.7139 0.794 0.196 0.804
#> GSM494632 1 0.0000 0.928 1.000 0.000
#> GSM494660 2 0.7376 0.783 0.208 0.792
#> GSM494622 2 0.7056 0.798 0.192 0.808
#> GSM494642 1 0.0000 0.928 1.000 0.000
#> GSM494647 1 0.0000 0.928 1.000 0.000
#> GSM494659 1 0.0000 0.928 1.000 0.000
#> GSM494670 1 0.0000 0.928 1.000 0.000
#> GSM494675 2 0.0000 0.918 0.000 1.000
#> GSM494641 1 0.0000 0.928 1.000 0.000
#> GSM494636 1 0.0000 0.928 1.000 0.000
#> GSM494640 2 0.7376 0.783 0.208 0.792
#> GSM494623 1 0.0000 0.928 1.000 0.000
#> GSM494644 1 0.0000 0.928 1.000 0.000
#> GSM494646 1 0.0000 0.928 1.000 0.000
#> GSM494665 1 0.0000 0.928 1.000 0.000
#> GSM494638 1 0.0000 0.928 1.000 0.000
#> GSM494645 1 0.0000 0.928 1.000 0.000
#> GSM494671 1 0.0000 0.928 1.000 0.000
#> GSM494655 1 0.0000 0.928 1.000 0.000
#> GSM494620 1 0.0000 0.928 1.000 0.000
#> GSM494630 1 0.0000 0.928 1.000 0.000
#> GSM494657 2 0.0000 0.918 0.000 1.000
#> GSM494667 1 0.0000 0.928 1.000 0.000
#> GSM494621 1 0.0000 0.928 1.000 0.000
#> GSM494629 2 0.5178 0.852 0.116 0.884
#> GSM494637 2 0.9044 0.627 0.320 0.680
#> GSM494652 1 0.0000 0.928 1.000 0.000
#> GSM494648 1 0.0000 0.928 1.000 0.000
#> GSM494650 2 0.7139 0.794 0.196 0.804
#> GSM494669 1 0.0000 0.928 1.000 0.000
#> GSM494666 1 0.0000 0.928 1.000 0.000
#> GSM494668 1 0.0000 0.928 1.000 0.000
#> GSM494633 2 0.9963 0.282 0.464 0.536
#> GSM494634 1 0.0000 0.928 1.000 0.000
#> GSM494639 1 0.0000 0.928 1.000 0.000
#> GSM494661 1 0.0000 0.928 1.000 0.000
#> GSM494617 1 0.1633 0.910 0.976 0.024
#> GSM494626 1 0.9635 0.247 0.612 0.388
#> GSM494656 2 0.0000 0.918 0.000 1.000
#> GSM494635 1 0.0000 0.928 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM494565 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494594 3 0.0000 0.9567 0.000 0.000 1.000
#> GSM494604 2 0.4291 0.7764 0.180 0.820 0.000
#> GSM494564 3 0.3941 0.8116 0.000 0.156 0.844
#> GSM494591 3 0.0000 0.9567 0.000 0.000 1.000
#> GSM494567 3 0.0000 0.9567 0.000 0.000 1.000
#> GSM494602 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494613 3 0.0000 0.9567 0.000 0.000 1.000
#> GSM494589 3 0.0000 0.9567 0.000 0.000 1.000
#> GSM494598 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494593 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494583 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494612 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494558 3 0.0000 0.9567 0.000 0.000 1.000
#> GSM494556 3 0.0000 0.9567 0.000 0.000 1.000
#> GSM494559 2 0.3551 0.8310 0.000 0.868 0.132
#> GSM494571 3 0.0000 0.9567 0.000 0.000 1.000
#> GSM494614 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494603 3 0.0000 0.9567 0.000 0.000 1.000
#> GSM494568 3 0.0000 0.9567 0.000 0.000 1.000
#> GSM494572 3 0.0000 0.9567 0.000 0.000 1.000
#> GSM494600 3 0.2537 0.8934 0.000 0.080 0.920
#> GSM494562 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494615 3 0.0000 0.9567 0.000 0.000 1.000
#> GSM494582 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494599 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494610 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494587 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494581 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494580 3 0.0000 0.9567 0.000 0.000 1.000
#> GSM494563 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494576 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494605 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494584 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494586 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494578 3 0.0000 0.9567 0.000 0.000 1.000
#> GSM494585 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494611 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494560 2 0.0237 0.9740 0.000 0.996 0.004
#> GSM494595 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494570 3 0.0000 0.9567 0.000 0.000 1.000
#> GSM494597 3 0.0000 0.9567 0.000 0.000 1.000
#> GSM494607 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494561 3 0.0000 0.9567 0.000 0.000 1.000
#> GSM494569 1 0.5926 0.4386 0.644 0.000 0.356
#> GSM494592 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494577 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494588 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494590 3 0.0000 0.9567 0.000 0.000 1.000
#> GSM494609 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494608 2 0.6045 0.3892 0.380 0.620 0.000
#> GSM494606 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494574 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494573 3 0.5560 0.5842 0.000 0.300 0.700
#> GSM494566 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494601 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494557 3 0.0000 0.9567 0.000 0.000 1.000
#> GSM494579 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494596 3 0.0000 0.9567 0.000 0.000 1.000
#> GSM494575 2 0.0000 0.9775 0.000 1.000 0.000
#> GSM494625 1 0.6302 0.0575 0.520 0.000 0.480
#> GSM494654 3 0.0000 0.9567 0.000 0.000 1.000
#> GSM494664 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494624 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494651 3 0.5178 0.6634 0.256 0.000 0.744
#> GSM494662 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494627 3 0.0237 0.9544 0.004 0.000 0.996
#> GSM494673 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494649 3 0.5760 0.5192 0.328 0.000 0.672
#> GSM494658 1 0.0237 0.9653 0.996 0.004 0.000
#> GSM494653 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494643 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494672 1 0.0237 0.9653 0.996 0.004 0.000
#> GSM494618 1 0.0592 0.9590 0.988 0.000 0.012
#> GSM494631 3 0.0000 0.9567 0.000 0.000 1.000
#> GSM494619 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494674 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494616 1 0.5254 0.6333 0.736 0.000 0.264
#> GSM494663 1 0.2448 0.8986 0.924 0.000 0.076
#> GSM494628 3 0.2711 0.8918 0.088 0.000 0.912
#> GSM494632 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494660 3 0.2959 0.8794 0.100 0.000 0.900
#> GSM494622 3 0.2537 0.8998 0.080 0.000 0.920
#> GSM494642 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494647 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494659 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494670 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494675 3 0.0000 0.9567 0.000 0.000 1.000
#> GSM494641 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494636 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494640 3 0.1860 0.9222 0.052 0.000 0.948
#> GSM494623 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494644 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494646 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494665 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494638 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494645 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494671 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494655 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494620 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494630 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494657 3 0.0000 0.9567 0.000 0.000 1.000
#> GSM494667 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494621 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494629 3 0.0000 0.9567 0.000 0.000 1.000
#> GSM494637 1 0.4178 0.7819 0.828 0.000 0.172
#> GSM494652 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494648 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494650 3 0.1163 0.9397 0.028 0.000 0.972
#> GSM494669 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494666 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494668 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494633 1 0.1289 0.9416 0.968 0.000 0.032
#> GSM494634 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494639 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494661 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494617 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494626 1 0.0000 0.9686 1.000 0.000 0.000
#> GSM494656 3 0.0000 0.9567 0.000 0.000 1.000
#> GSM494635 1 0.0000 0.9686 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM494565 2 0.0000 0.966 0.000 1.000 0.000 0.000
#> GSM494594 3 0.0000 0.993 0.000 0.000 1.000 0.000
#> GSM494604 1 0.0000 0.883 1.000 0.000 0.000 0.000
#> GSM494564 2 0.0469 0.960 0.000 0.988 0.000 0.012
#> GSM494591 3 0.0000 0.993 0.000 0.000 1.000 0.000
#> GSM494567 3 0.0000 0.993 0.000 0.000 1.000 0.000
#> GSM494602 1 0.0469 0.879 0.988 0.012 0.000 0.000
#> GSM494613 3 0.0000 0.993 0.000 0.000 1.000 0.000
#> GSM494589 2 0.0000 0.966 0.000 1.000 0.000 0.000
#> GSM494598 2 0.0336 0.962 0.008 0.992 0.000 0.000
#> GSM494593 1 0.3837 0.676 0.776 0.224 0.000 0.000
#> GSM494583 2 0.0000 0.966 0.000 1.000 0.000 0.000
#> GSM494612 1 0.0469 0.879 0.988 0.012 0.000 0.000
#> GSM494558 3 0.0000 0.993 0.000 0.000 1.000 0.000
#> GSM494556 3 0.0000 0.993 0.000 0.000 1.000 0.000
#> GSM494559 2 0.0469 0.960 0.000 0.988 0.000 0.012
#> GSM494571 3 0.0000 0.993 0.000 0.000 1.000 0.000
#> GSM494614 2 0.0000 0.966 0.000 1.000 0.000 0.000
#> GSM494603 2 0.0779 0.956 0.000 0.980 0.004 0.016
#> GSM494568 3 0.0000 0.993 0.000 0.000 1.000 0.000
#> GSM494572 3 0.0000 0.993 0.000 0.000 1.000 0.000
#> GSM494600 2 0.0000 0.966 0.000 1.000 0.000 0.000
#> GSM494562 2 0.3942 0.695 0.236 0.764 0.000 0.000
#> GSM494615 3 0.0000 0.993 0.000 0.000 1.000 0.000
#> GSM494582 1 0.2081 0.830 0.916 0.084 0.000 0.000
#> GSM494599 1 0.0188 0.882 0.996 0.004 0.000 0.000
#> GSM494610 2 0.0336 0.962 0.008 0.992 0.000 0.000
#> GSM494587 2 0.3266 0.797 0.168 0.832 0.000 0.000
#> GSM494581 2 0.0000 0.966 0.000 1.000 0.000 0.000
#> GSM494580 3 0.0000 0.993 0.000 0.000 1.000 0.000
#> GSM494563 2 0.0000 0.966 0.000 1.000 0.000 0.000
#> GSM494576 2 0.0000 0.966 0.000 1.000 0.000 0.000
#> GSM494605 4 0.2704 0.833 0.124 0.000 0.000 0.876
#> GSM494584 2 0.0000 0.966 0.000 1.000 0.000 0.000
#> GSM494586 2 0.0188 0.964 0.004 0.996 0.000 0.000
#> GSM494578 3 0.0000 0.993 0.000 0.000 1.000 0.000
#> GSM494585 2 0.0188 0.964 0.004 0.996 0.000 0.000
#> GSM494611 1 0.1474 0.855 0.948 0.052 0.000 0.000
#> GSM494560 2 0.0000 0.966 0.000 1.000 0.000 0.000
#> GSM494595 2 0.0188 0.964 0.004 0.996 0.000 0.000
#> GSM494570 2 0.1557 0.922 0.000 0.944 0.000 0.056
#> GSM494597 3 0.0000 0.993 0.000 0.000 1.000 0.000
#> GSM494607 1 0.0000 0.883 1.000 0.000 0.000 0.000
#> GSM494561 4 0.2868 0.803 0.000 0.136 0.000 0.864
#> GSM494569 3 0.1824 0.925 0.004 0.000 0.936 0.060
#> GSM494592 1 0.0000 0.883 1.000 0.000 0.000 0.000
#> GSM494577 2 0.0000 0.966 0.000 1.000 0.000 0.000
#> GSM494588 2 0.0469 0.960 0.000 0.988 0.000 0.012
#> GSM494590 3 0.0000 0.993 0.000 0.000 1.000 0.000
#> GSM494609 2 0.1637 0.920 0.060 0.940 0.000 0.000
#> GSM494608 1 0.0707 0.883 0.980 0.000 0.000 0.020
#> GSM494606 1 0.0000 0.883 1.000 0.000 0.000 0.000
#> GSM494574 2 0.3873 0.703 0.228 0.772 0.000 0.000
#> GSM494573 2 0.0000 0.966 0.000 1.000 0.000 0.000
#> GSM494566 1 0.7251 0.431 0.536 0.192 0.272 0.000
#> GSM494601 1 0.0707 0.877 0.980 0.020 0.000 0.000
#> GSM494557 3 0.0000 0.993 0.000 0.000 1.000 0.000
#> GSM494579 2 0.0000 0.966 0.000 1.000 0.000 0.000
#> GSM494596 3 0.0000 0.993 0.000 0.000 1.000 0.000
#> GSM494575 1 0.1118 0.865 0.964 0.036 0.000 0.000
#> GSM494625 4 0.0000 0.918 0.000 0.000 0.000 1.000
#> GSM494654 3 0.0000 0.993 0.000 0.000 1.000 0.000
#> GSM494664 4 0.1637 0.892 0.060 0.000 0.000 0.940
#> GSM494624 4 0.0000 0.918 0.000 0.000 0.000 1.000
#> GSM494651 3 0.0000 0.993 0.000 0.000 1.000 0.000
#> GSM494662 4 0.0188 0.918 0.004 0.000 0.000 0.996
#> GSM494627 4 0.4222 0.640 0.000 0.000 0.272 0.728
#> GSM494673 1 0.0188 0.884 0.996 0.000 0.000 0.004
#> GSM494649 4 0.0000 0.918 0.000 0.000 0.000 1.000
#> GSM494658 1 0.3123 0.790 0.844 0.000 0.000 0.156
#> GSM494653 1 0.4222 0.643 0.728 0.000 0.000 0.272
#> GSM494643 4 0.0000 0.918 0.000 0.000 0.000 1.000
#> GSM494672 1 0.0000 0.883 1.000 0.000 0.000 0.000
#> GSM494618 4 0.4964 0.444 0.004 0.000 0.380 0.616
#> GSM494631 3 0.0000 0.993 0.000 0.000 1.000 0.000
#> GSM494619 4 0.0000 0.918 0.000 0.000 0.000 1.000
#> GSM494674 1 0.4356 0.609 0.708 0.000 0.000 0.292
#> GSM494616 3 0.2011 0.906 0.000 0.000 0.920 0.080
#> GSM494663 4 0.0000 0.918 0.000 0.000 0.000 1.000
#> GSM494628 4 0.4866 0.367 0.000 0.000 0.404 0.596
#> GSM494632 4 0.0592 0.916 0.016 0.000 0.000 0.984
#> GSM494660 4 0.0000 0.918 0.000 0.000 0.000 1.000
#> GSM494622 3 0.0188 0.989 0.000 0.000 0.996 0.004
#> GSM494642 1 0.4977 0.190 0.540 0.000 0.000 0.460
#> GSM494647 1 0.0817 0.882 0.976 0.000 0.000 0.024
#> GSM494659 1 0.0921 0.881 0.972 0.000 0.000 0.028
#> GSM494670 1 0.3356 0.770 0.824 0.000 0.000 0.176
#> GSM494675 3 0.0000 0.993 0.000 0.000 1.000 0.000
#> GSM494641 4 0.4888 0.251 0.412 0.000 0.000 0.588
#> GSM494636 4 0.0469 0.917 0.012 0.000 0.000 0.988
#> GSM494640 4 0.0336 0.916 0.000 0.000 0.008 0.992
#> GSM494623 4 0.0000 0.918 0.000 0.000 0.000 1.000
#> GSM494644 4 0.1389 0.900 0.048 0.000 0.000 0.952
#> GSM494646 4 0.0469 0.917 0.012 0.000 0.000 0.988
#> GSM494665 1 0.4898 0.328 0.584 0.000 0.000 0.416
#> GSM494638 4 0.0592 0.916 0.016 0.000 0.000 0.984
#> GSM494645 4 0.1022 0.909 0.032 0.000 0.000 0.968
#> GSM494671 1 0.0000 0.883 1.000 0.000 0.000 0.000
#> GSM494655 4 0.1716 0.889 0.064 0.000 0.000 0.936
#> GSM494620 4 0.0000 0.918 0.000 0.000 0.000 1.000
#> GSM494630 4 0.0000 0.918 0.000 0.000 0.000 1.000
#> GSM494657 3 0.0000 0.993 0.000 0.000 1.000 0.000
#> GSM494667 1 0.0921 0.881 0.972 0.000 0.000 0.028
#> GSM494621 4 0.0000 0.918 0.000 0.000 0.000 1.000
#> GSM494629 3 0.0469 0.981 0.000 0.000 0.988 0.012
#> GSM494637 4 0.0000 0.918 0.000 0.000 0.000 1.000
#> GSM494652 1 0.1118 0.878 0.964 0.000 0.000 0.036
#> GSM494648 4 0.0000 0.918 0.000 0.000 0.000 1.000
#> GSM494650 3 0.0000 0.993 0.000 0.000 1.000 0.000
#> GSM494669 1 0.3528 0.752 0.808 0.000 0.000 0.192
#> GSM494666 4 0.1557 0.895 0.056 0.000 0.000 0.944
#> GSM494668 4 0.3873 0.691 0.228 0.000 0.000 0.772
#> GSM494633 4 0.0000 0.918 0.000 0.000 0.000 1.000
#> GSM494634 1 0.0000 0.883 1.000 0.000 0.000 0.000
#> GSM494639 4 0.0469 0.917 0.012 0.000 0.000 0.988
#> GSM494661 4 0.3444 0.759 0.184 0.000 0.000 0.816
#> GSM494617 4 0.0817 0.913 0.024 0.000 0.000 0.976
#> GSM494626 4 0.3166 0.834 0.016 0.000 0.116 0.868
#> GSM494656 3 0.0000 0.993 0.000 0.000 1.000 0.000
#> GSM494635 4 0.0469 0.917 0.012 0.000 0.000 0.988
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM494565 5 0.3336 0.6733 0.000 0.000 0.000 0.228 0.772
#> GSM494594 3 0.0000 0.8415 0.000 0.000 1.000 0.000 0.000
#> GSM494604 2 0.0798 0.7170 0.016 0.976 0.000 0.000 0.008
#> GSM494564 4 0.2127 0.5472 0.000 0.000 0.000 0.892 0.108
#> GSM494591 3 0.0000 0.8415 0.000 0.000 1.000 0.000 0.000
#> GSM494567 3 0.1877 0.8259 0.000 0.000 0.924 0.012 0.064
#> GSM494602 2 0.1197 0.7008 0.000 0.952 0.000 0.000 0.048
#> GSM494613 3 0.5110 0.6995 0.000 0.016 0.728 0.116 0.140
#> GSM494589 4 0.2732 0.4969 0.000 0.000 0.000 0.840 0.160
#> GSM494598 2 0.6133 0.2670 0.000 0.524 0.000 0.148 0.328
#> GSM494593 5 0.2773 0.7276 0.000 0.164 0.000 0.000 0.836
#> GSM494583 5 0.1831 0.7877 0.000 0.004 0.000 0.076 0.920
#> GSM494612 2 0.2230 0.6728 0.000 0.884 0.000 0.000 0.116
#> GSM494558 3 0.1908 0.8032 0.000 0.000 0.908 0.092 0.000
#> GSM494556 3 0.2563 0.7960 0.000 0.000 0.872 0.120 0.008
#> GSM494559 5 0.2648 0.7413 0.000 0.000 0.000 0.152 0.848
#> GSM494571 3 0.0000 0.8415 0.000 0.000 1.000 0.000 0.000
#> GSM494614 5 0.2970 0.7740 0.000 0.004 0.000 0.168 0.828
#> GSM494603 4 0.3039 0.5326 0.000 0.012 0.000 0.836 0.152
#> GSM494568 4 0.4337 0.4536 0.000 0.016 0.284 0.696 0.004
#> GSM494572 3 0.0671 0.8396 0.000 0.000 0.980 0.016 0.004
#> GSM494600 4 0.3039 0.4616 0.000 0.000 0.000 0.808 0.192
#> GSM494562 2 0.5617 0.4246 0.000 0.620 0.000 0.124 0.256
#> GSM494615 3 0.3123 0.7604 0.000 0.000 0.812 0.184 0.004
#> GSM494582 2 0.2189 0.6793 0.000 0.904 0.000 0.012 0.084
#> GSM494599 2 0.1571 0.7204 0.060 0.936 0.000 0.000 0.004
#> GSM494610 2 0.6287 0.2590 0.000 0.512 0.000 0.176 0.312
#> GSM494587 5 0.3336 0.7621 0.000 0.144 0.008 0.016 0.832
#> GSM494581 5 0.1981 0.7899 0.000 0.016 0.000 0.064 0.920
#> GSM494580 3 0.1764 0.8272 0.000 0.000 0.928 0.008 0.064
#> GSM494563 4 0.4138 0.4058 0.000 0.016 0.000 0.708 0.276
#> GSM494576 5 0.3527 0.7631 0.000 0.056 0.000 0.116 0.828
#> GSM494605 1 0.0609 0.8195 0.980 0.020 0.000 0.000 0.000
#> GSM494584 5 0.1074 0.7977 0.000 0.016 0.004 0.012 0.968
#> GSM494586 5 0.4386 0.7297 0.000 0.096 0.000 0.140 0.764
#> GSM494578 3 0.5292 0.5854 0.000 0.016 0.656 0.052 0.276
#> GSM494585 5 0.2378 0.7890 0.000 0.048 0.000 0.048 0.904
#> GSM494611 2 0.2077 0.6834 0.000 0.908 0.000 0.008 0.084
#> GSM494560 5 0.4291 0.3578 0.000 0.000 0.000 0.464 0.536
#> GSM494595 5 0.2983 0.7804 0.000 0.076 0.000 0.056 0.868
#> GSM494570 4 0.1502 0.5733 0.004 0.000 0.000 0.940 0.056
#> GSM494597 3 0.3152 0.7603 0.000 0.000 0.840 0.136 0.024
#> GSM494607 2 0.0510 0.7172 0.016 0.984 0.000 0.000 0.000
#> GSM494561 4 0.2293 0.6259 0.084 0.000 0.000 0.900 0.016
#> GSM494569 3 0.3949 0.5150 0.332 0.000 0.668 0.000 0.000
#> GSM494592 2 0.2104 0.7178 0.060 0.916 0.000 0.000 0.024
#> GSM494577 5 0.4333 0.7124 0.000 0.060 0.000 0.188 0.752
#> GSM494588 4 0.4291 -0.1962 0.000 0.000 0.000 0.536 0.464
#> GSM494590 3 0.0000 0.8415 0.000 0.000 1.000 0.000 0.000
#> GSM494609 5 0.2664 0.7836 0.004 0.064 0.000 0.040 0.892
#> GSM494608 1 0.6696 0.0243 0.432 0.148 0.000 0.016 0.404
#> GSM494606 2 0.4497 0.6040 0.060 0.732 0.000 0.000 0.208
#> GSM494574 2 0.5954 0.3719 0.000 0.576 0.000 0.152 0.272
#> GSM494573 4 0.3366 0.4032 0.000 0.000 0.000 0.768 0.232
#> GSM494566 2 0.6598 0.5323 0.012 0.644 0.128 0.064 0.152
#> GSM494601 5 0.4275 0.5807 0.020 0.284 0.000 0.000 0.696
#> GSM494557 3 0.5115 0.6490 0.000 0.012 0.696 0.068 0.224
#> GSM494579 4 0.5425 -0.0905 0.000 0.060 0.000 0.520 0.420
#> GSM494596 3 0.0000 0.8415 0.000 0.000 1.000 0.000 0.000
#> GSM494575 5 0.3913 0.5569 0.000 0.324 0.000 0.000 0.676
#> GSM494625 4 0.3913 0.5452 0.324 0.000 0.000 0.676 0.000
#> GSM494654 3 0.0000 0.8415 0.000 0.000 1.000 0.000 0.000
#> GSM494664 1 0.1701 0.8098 0.936 0.048 0.000 0.016 0.000
#> GSM494624 4 0.3508 0.6041 0.252 0.000 0.000 0.748 0.000
#> GSM494651 3 0.0451 0.8403 0.004 0.000 0.988 0.008 0.000
#> GSM494662 1 0.1608 0.7833 0.928 0.000 0.000 0.072 0.000
#> GSM494627 3 0.6301 0.2962 0.308 0.000 0.512 0.180 0.000
#> GSM494673 2 0.4101 0.3709 0.372 0.628 0.000 0.000 0.000
#> GSM494649 4 0.3796 0.5587 0.300 0.000 0.000 0.700 0.000
#> GSM494658 2 0.3305 0.6176 0.224 0.776 0.000 0.000 0.000
#> GSM494653 1 0.2732 0.7379 0.840 0.160 0.000 0.000 0.000
#> GSM494643 1 0.1671 0.7802 0.924 0.000 0.000 0.076 0.000
#> GSM494672 2 0.1792 0.7171 0.084 0.916 0.000 0.000 0.000
#> GSM494618 3 0.4455 0.6550 0.188 0.000 0.744 0.068 0.000
#> GSM494631 3 0.0510 0.8402 0.000 0.000 0.984 0.016 0.000
#> GSM494619 4 0.4015 0.5193 0.348 0.000 0.000 0.652 0.000
#> GSM494674 1 0.2074 0.7790 0.896 0.104 0.000 0.000 0.000
#> GSM494616 3 0.3171 0.7193 0.176 0.000 0.816 0.008 0.000
#> GSM494663 4 0.4268 0.3099 0.444 0.000 0.000 0.556 0.000
#> GSM494628 3 0.6381 0.1510 0.172 0.000 0.464 0.364 0.000
#> GSM494632 1 0.0162 0.8197 0.996 0.004 0.000 0.000 0.000
#> GSM494660 4 0.3816 0.5533 0.304 0.000 0.000 0.696 0.000
#> GSM494622 3 0.4402 0.4356 0.012 0.000 0.636 0.352 0.000
#> GSM494642 1 0.1544 0.8037 0.932 0.068 0.000 0.000 0.000
#> GSM494647 1 0.4088 0.4072 0.632 0.368 0.000 0.000 0.000
#> GSM494659 2 0.4201 0.2831 0.408 0.592 0.000 0.000 0.000
#> GSM494670 2 0.4141 0.6005 0.236 0.736 0.000 0.028 0.000
#> GSM494675 4 0.4919 0.3010 0.000 0.012 0.368 0.604 0.016
#> GSM494641 1 0.0794 0.8181 0.972 0.028 0.000 0.000 0.000
#> GSM494636 1 0.1671 0.7802 0.924 0.000 0.000 0.076 0.000
#> GSM494640 1 0.1956 0.7788 0.916 0.000 0.008 0.076 0.000
#> GSM494623 4 0.3752 0.5833 0.292 0.000 0.000 0.708 0.000
#> GSM494644 1 0.0404 0.8201 0.988 0.012 0.000 0.000 0.000
#> GSM494646 1 0.0880 0.8080 0.968 0.000 0.000 0.032 0.000
#> GSM494665 1 0.3999 0.4690 0.656 0.344 0.000 0.000 0.000
#> GSM494638 1 0.0671 0.8149 0.980 0.000 0.004 0.016 0.000
#> GSM494645 1 0.0290 0.8201 0.992 0.008 0.000 0.000 0.000
#> GSM494671 2 0.1671 0.7178 0.076 0.924 0.000 0.000 0.000
#> GSM494655 1 0.0510 0.8201 0.984 0.016 0.000 0.000 0.000
#> GSM494620 1 0.4161 0.2121 0.608 0.000 0.000 0.392 0.000
#> GSM494630 1 0.2377 0.7534 0.872 0.000 0.000 0.128 0.000
#> GSM494657 3 0.0000 0.8415 0.000 0.000 1.000 0.000 0.000
#> GSM494667 1 0.4126 0.3786 0.620 0.380 0.000 0.000 0.000
#> GSM494621 4 0.3796 0.5736 0.300 0.000 0.000 0.700 0.000
#> GSM494629 3 0.2580 0.7974 0.064 0.000 0.892 0.044 0.000
#> GSM494637 1 0.1671 0.7802 0.924 0.000 0.000 0.076 0.000
#> GSM494652 1 0.3424 0.6314 0.760 0.240 0.000 0.000 0.000
#> GSM494648 4 0.4101 0.4789 0.372 0.000 0.000 0.628 0.000
#> GSM494650 3 0.0510 0.8390 0.000 0.000 0.984 0.016 0.000
#> GSM494669 1 0.3274 0.6721 0.780 0.220 0.000 0.000 0.000
#> GSM494666 1 0.0510 0.8201 0.984 0.016 0.000 0.000 0.000
#> GSM494668 1 0.4874 0.3442 0.600 0.368 0.000 0.032 0.000
#> GSM494633 1 0.4278 0.1134 0.548 0.000 0.000 0.452 0.000
#> GSM494634 2 0.4192 0.3002 0.404 0.596 0.000 0.000 0.000
#> GSM494639 1 0.0162 0.8182 0.996 0.000 0.000 0.004 0.000
#> GSM494661 1 0.0510 0.8201 0.984 0.016 0.000 0.000 0.000
#> GSM494617 1 0.0854 0.8192 0.976 0.012 0.004 0.008 0.000
#> GSM494626 1 0.4841 0.5587 0.716 0.012 0.220 0.052 0.000
#> GSM494656 3 0.0000 0.8415 0.000 0.000 1.000 0.000 0.000
#> GSM494635 1 0.0404 0.8161 0.988 0.000 0.000 0.012 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM494565 5 0.2520 0.8704 0.000 0.152 0.000 0.000 0.844 0.004
#> GSM494594 3 0.0260 0.8542 0.000 0.008 0.992 0.000 0.000 0.000
#> GSM494604 4 0.3596 0.7213 0.040 0.004 0.000 0.796 0.156 0.004
#> GSM494564 6 0.3394 0.5890 0.000 0.024 0.000 0.000 0.200 0.776
#> GSM494591 3 0.0146 0.8543 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM494567 3 0.0692 0.8515 0.000 0.020 0.976 0.004 0.000 0.000
#> GSM494602 4 0.1219 0.7675 0.000 0.048 0.000 0.948 0.004 0.000
#> GSM494613 2 0.4267 0.6873 0.000 0.760 0.120 0.000 0.016 0.104
#> GSM494589 6 0.3938 0.5348 0.000 0.044 0.000 0.000 0.228 0.728
#> GSM494598 5 0.3307 0.8677 0.000 0.108 0.000 0.072 0.820 0.000
#> GSM494593 2 0.2402 0.7492 0.000 0.868 0.000 0.120 0.012 0.000
#> GSM494583 5 0.2969 0.8109 0.000 0.224 0.000 0.000 0.776 0.000
#> GSM494612 4 0.3996 -0.1612 0.000 0.484 0.000 0.512 0.004 0.000
#> GSM494558 3 0.2932 0.7900 0.000 0.020 0.868 0.004 0.028 0.080
#> GSM494556 3 0.6379 0.1638 0.000 0.164 0.460 0.004 0.028 0.344
#> GSM494559 2 0.2790 0.7253 0.000 0.844 0.000 0.000 0.024 0.132
#> GSM494571 3 0.0653 0.8527 0.000 0.004 0.980 0.000 0.012 0.004
#> GSM494614 2 0.4059 0.6339 0.000 0.768 0.024 0.004 0.172 0.032
#> GSM494603 5 0.4109 0.1845 0.000 0.024 0.000 0.000 0.648 0.328
#> GSM494568 6 0.6279 0.3315 0.000 0.024 0.316 0.008 0.152 0.500
#> GSM494572 3 0.0870 0.8507 0.000 0.004 0.972 0.000 0.012 0.012
#> GSM494600 6 0.4135 0.4635 0.000 0.032 0.000 0.000 0.300 0.668
#> GSM494562 5 0.3612 0.8488 0.000 0.104 0.000 0.100 0.796 0.000
#> GSM494615 6 0.3201 0.6778 0.000 0.040 0.072 0.004 0.028 0.856
#> GSM494582 4 0.1649 0.7673 0.000 0.032 0.000 0.932 0.036 0.000
#> GSM494599 4 0.0405 0.7781 0.004 0.008 0.000 0.988 0.000 0.000
#> GSM494610 5 0.2747 0.8743 0.000 0.096 0.000 0.044 0.860 0.000
#> GSM494587 2 0.2643 0.7618 0.000 0.888 0.036 0.040 0.036 0.000
#> GSM494581 2 0.1082 0.7590 0.000 0.956 0.000 0.000 0.040 0.004
#> GSM494580 3 0.0405 0.8540 0.000 0.008 0.988 0.004 0.000 0.000
#> GSM494563 5 0.2685 0.8348 0.000 0.060 0.000 0.000 0.868 0.072
#> GSM494576 5 0.2946 0.8593 0.000 0.176 0.000 0.012 0.812 0.000
#> GSM494605 1 0.1003 0.8642 0.964 0.004 0.000 0.028 0.004 0.000
#> GSM494584 2 0.3776 0.6302 0.000 0.756 0.048 0.000 0.196 0.000
#> GSM494586 5 0.3102 0.8696 0.000 0.156 0.000 0.028 0.816 0.000
#> GSM494578 2 0.2969 0.6626 0.000 0.776 0.224 0.000 0.000 0.000
#> GSM494585 2 0.1794 0.7629 0.000 0.924 0.000 0.040 0.036 0.000
#> GSM494611 4 0.0909 0.7770 0.000 0.020 0.000 0.968 0.012 0.000
#> GSM494560 6 0.5650 0.2075 0.000 0.168 0.000 0.000 0.332 0.500
#> GSM494595 2 0.4428 0.1721 0.000 0.580 0.000 0.032 0.388 0.000
#> GSM494570 6 0.1682 0.6924 0.000 0.020 0.000 0.000 0.052 0.928
#> GSM494597 3 0.3000 0.7367 0.000 0.016 0.824 0.000 0.156 0.004
#> GSM494607 4 0.2306 0.7599 0.016 0.004 0.000 0.888 0.092 0.000
#> GSM494561 6 0.0806 0.7010 0.000 0.020 0.000 0.000 0.008 0.972
#> GSM494569 3 0.4432 0.2549 0.444 0.008 0.536 0.004 0.008 0.000
#> GSM494592 4 0.2234 0.7069 0.004 0.124 0.000 0.872 0.000 0.000
#> GSM494577 5 0.2300 0.8747 0.000 0.144 0.000 0.000 0.856 0.000
#> GSM494588 2 0.5149 0.4506 0.000 0.624 0.000 0.000 0.192 0.184
#> GSM494590 3 0.0146 0.8545 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM494609 2 0.1498 0.7646 0.000 0.940 0.000 0.028 0.032 0.000
#> GSM494608 2 0.3405 0.6812 0.112 0.812 0.000 0.076 0.000 0.000
#> GSM494606 2 0.3240 0.6332 0.004 0.752 0.000 0.244 0.000 0.000
#> GSM494574 5 0.3103 0.8687 0.000 0.100 0.000 0.064 0.836 0.000
#> GSM494573 6 0.4666 0.2715 0.000 0.048 0.000 0.000 0.388 0.564
#> GSM494566 4 0.6122 0.5519 0.008 0.028 0.176 0.636 0.120 0.032
#> GSM494601 2 0.3905 0.5086 0.000 0.668 0.000 0.316 0.016 0.000
#> GSM494557 2 0.3473 0.6726 0.000 0.780 0.192 0.000 0.024 0.004
#> GSM494579 5 0.2266 0.8724 0.000 0.108 0.000 0.000 0.880 0.012
#> GSM494596 3 0.0291 0.8543 0.000 0.004 0.992 0.000 0.000 0.004
#> GSM494575 2 0.3240 0.6506 0.000 0.752 0.000 0.244 0.004 0.000
#> GSM494625 6 0.2711 0.6954 0.036 0.008 0.000 0.000 0.084 0.872
#> GSM494654 3 0.0146 0.8544 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM494664 1 0.3931 0.7691 0.812 0.024 0.000 0.028 0.104 0.032
#> GSM494624 6 0.1649 0.7064 0.032 0.000 0.000 0.000 0.036 0.932
#> GSM494651 3 0.2954 0.8116 0.036 0.020 0.884 0.004 0.028 0.028
#> GSM494662 1 0.0291 0.8645 0.992 0.004 0.000 0.004 0.000 0.000
#> GSM494627 1 0.7680 0.0343 0.392 0.020 0.260 0.004 0.092 0.232
#> GSM494673 1 0.3789 0.3830 0.584 0.000 0.000 0.416 0.000 0.000
#> GSM494649 6 0.1364 0.7065 0.020 0.012 0.000 0.000 0.016 0.952
#> GSM494658 4 0.6461 0.0866 0.388 0.016 0.000 0.428 0.148 0.020
#> GSM494653 1 0.2092 0.8216 0.876 0.000 0.000 0.124 0.000 0.000
#> GSM494643 1 0.1296 0.8489 0.948 0.004 0.000 0.000 0.004 0.044
#> GSM494672 4 0.0790 0.7759 0.032 0.000 0.000 0.968 0.000 0.000
#> GSM494618 3 0.6675 0.4523 0.244 0.024 0.560 0.008 0.052 0.112
#> GSM494631 3 0.0767 0.8536 0.000 0.008 0.976 0.004 0.012 0.000
#> GSM494619 6 0.4559 0.6050 0.184 0.008 0.000 0.000 0.096 0.712
#> GSM494674 1 0.1141 0.8568 0.948 0.000 0.000 0.052 0.000 0.000
#> GSM494616 3 0.5626 0.2908 0.404 0.020 0.516 0.004 0.028 0.028
#> GSM494663 6 0.6003 0.0965 0.416 0.020 0.000 0.004 0.116 0.444
#> GSM494628 6 0.5741 0.6250 0.052 0.032 0.092 0.008 0.120 0.696
#> GSM494632 1 0.0146 0.8649 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM494660 6 0.1364 0.7043 0.012 0.020 0.000 0.000 0.016 0.952
#> GSM494622 6 0.5853 0.5047 0.004 0.032 0.224 0.004 0.124 0.612
#> GSM494642 1 0.0790 0.8622 0.968 0.000 0.000 0.032 0.000 0.000
#> GSM494647 1 0.1957 0.8304 0.888 0.000 0.000 0.112 0.000 0.000
#> GSM494659 1 0.2854 0.7489 0.792 0.000 0.000 0.208 0.000 0.000
#> GSM494670 4 0.4662 0.6641 0.048 0.012 0.000 0.760 0.112 0.068
#> GSM494675 6 0.5955 0.2545 0.000 0.020 0.368 0.004 0.116 0.492
#> GSM494641 1 0.0363 0.8651 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM494636 1 0.0405 0.8642 0.988 0.000 0.000 0.004 0.000 0.008
#> GSM494640 1 0.2507 0.8239 0.888 0.004 0.072 0.004 0.000 0.032
#> GSM494623 6 0.3286 0.6858 0.044 0.012 0.000 0.000 0.112 0.832
#> GSM494644 1 0.0146 0.8651 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM494646 1 0.0146 0.8649 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM494665 1 0.3244 0.6770 0.732 0.000 0.000 0.268 0.000 0.000
#> GSM494638 1 0.0951 0.8599 0.968 0.008 0.020 0.004 0.000 0.000
#> GSM494645 1 0.0291 0.8653 0.992 0.000 0.000 0.004 0.004 0.000
#> GSM494671 4 0.1204 0.7662 0.056 0.000 0.000 0.944 0.000 0.000
#> GSM494655 1 0.0146 0.8651 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM494620 1 0.4086 0.1210 0.528 0.008 0.000 0.000 0.000 0.464
#> GSM494630 1 0.1745 0.8384 0.920 0.012 0.000 0.000 0.000 0.068
#> GSM494657 3 0.0146 0.8545 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM494667 1 0.2562 0.7844 0.828 0.000 0.000 0.172 0.000 0.000
#> GSM494621 6 0.2476 0.7019 0.072 0.008 0.000 0.000 0.032 0.888
#> GSM494629 3 0.2499 0.7809 0.096 0.004 0.880 0.004 0.000 0.016
#> GSM494637 1 0.1396 0.8545 0.952 0.008 0.012 0.004 0.000 0.024
#> GSM494652 1 0.1267 0.8535 0.940 0.000 0.000 0.060 0.000 0.000
#> GSM494648 6 0.5048 0.3717 0.344 0.008 0.000 0.000 0.068 0.580
#> GSM494650 3 0.2139 0.8243 0.000 0.020 0.920 0.008 0.024 0.028
#> GSM494669 1 0.1957 0.8296 0.888 0.000 0.000 0.112 0.000 0.000
#> GSM494666 1 0.0748 0.8641 0.976 0.004 0.000 0.004 0.016 0.000
#> GSM494668 1 0.7067 0.3218 0.500 0.012 0.000 0.192 0.100 0.196
#> GSM494633 6 0.3450 0.6187 0.188 0.032 0.000 0.000 0.000 0.780
#> GSM494634 1 0.3050 0.6833 0.764 0.000 0.000 0.236 0.000 0.000
#> GSM494639 1 0.0291 0.8653 0.992 0.000 0.000 0.000 0.004 0.004
#> GSM494661 1 0.0146 0.8651 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM494617 1 0.2170 0.8424 0.920 0.020 0.004 0.004 0.028 0.024
#> GSM494626 1 0.6163 0.5835 0.648 0.028 0.136 0.008 0.048 0.132
#> GSM494656 3 0.0146 0.8544 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM494635 1 0.0146 0.8649 0.996 0.000 0.000 0.000 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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 3)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 4)
get_signatures(res, k = 5)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 6)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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.
# code only for demonstration
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) individual(p) k
#> CV:NMF 115 3.85e-03 0.0792 3.26e-02 0.0219 2
#> CV:NMF 117 1.01e-17 0.8216 5.65e-15 0.8257 3
#> CV:NMF 114 6.18e-12 0.2676 2.12e-09 0.1872 4
#> CV:NMF 92 8.22e-09 0.1537 1.49e-05 0.0493 5
#> CV:NMF 100 3.81e-11 0.1354 2.25e-07 0.0887 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.
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"]
# you can also extract it by
# 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 51941 rows and 120 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 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:
k and the heatmap of
predicted classes for each k.k.k.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:
k;k, the area increased is defined as \(A_k - A_{k-1}\).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 0.322 0.703 0.813 0.4835 0.498 0.498
#> 3 3 0.521 0.735 0.814 0.2733 0.798 0.616
#> 4 4 0.629 0.819 0.847 0.1833 0.870 0.641
#> 5 5 0.772 0.818 0.874 0.0725 0.955 0.823
#> 6 6 0.808 0.740 0.803 0.0344 0.956 0.806
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
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.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM494565 2 0.871 0.3215 0.292 0.708
#> GSM494594 2 0.000 0.7469 0.000 1.000
#> GSM494604 1 0.625 0.7717 0.844 0.156
#> GSM494564 2 0.000 0.7469 0.000 1.000
#> GSM494591 2 0.000 0.7469 0.000 1.000
#> GSM494567 2 0.000 0.7469 0.000 1.000
#> GSM494602 1 0.795 0.7377 0.760 0.240
#> GSM494613 2 0.000 0.7469 0.000 1.000
#> GSM494589 2 0.000 0.7469 0.000 1.000
#> GSM494598 1 0.795 0.7377 0.760 0.240
#> GSM494593 1 0.738 0.7550 0.792 0.208
#> GSM494583 2 0.969 -0.0332 0.396 0.604
#> GSM494612 1 0.795 0.7377 0.760 0.240
#> GSM494558 2 0.913 0.7201 0.328 0.672
#> GSM494556 2 0.000 0.7469 0.000 1.000
#> GSM494559 2 0.260 0.7471 0.044 0.956
#> GSM494571 2 0.000 0.7469 0.000 1.000
#> GSM494614 2 0.000 0.7469 0.000 1.000
#> GSM494603 2 0.917 0.7196 0.332 0.668
#> GSM494568 2 0.917 0.7196 0.332 0.668
#> GSM494572 2 0.000 0.7469 0.000 1.000
#> GSM494600 2 0.000 0.7469 0.000 1.000
#> GSM494562 1 0.795 0.7377 0.760 0.240
#> GSM494615 2 0.000 0.7469 0.000 1.000
#> GSM494582 1 0.795 0.7377 0.760 0.240
#> GSM494599 1 0.738 0.7550 0.792 0.208
#> GSM494610 1 0.795 0.7377 0.760 0.240
#> GSM494587 1 0.952 0.6283 0.628 0.372
#> GSM494581 1 0.932 0.6462 0.652 0.348
#> GSM494580 2 0.000 0.7469 0.000 1.000
#> GSM494563 2 0.662 0.5842 0.172 0.828
#> GSM494576 1 0.921 0.6723 0.664 0.336
#> GSM494605 1 0.443 0.7384 0.908 0.092
#> GSM494584 2 0.706 0.5325 0.192 0.808
#> GSM494586 1 0.861 0.7167 0.716 0.284
#> GSM494578 2 0.000 0.7469 0.000 1.000
#> GSM494585 1 0.952 0.6283 0.628 0.372
#> GSM494611 1 0.795 0.7377 0.760 0.240
#> GSM494560 2 0.000 0.7469 0.000 1.000
#> GSM494595 1 0.808 0.7355 0.752 0.248
#> GSM494570 2 0.260 0.7471 0.044 0.956
#> GSM494597 2 0.000 0.7469 0.000 1.000
#> GSM494607 1 0.625 0.7717 0.844 0.156
#> GSM494561 2 0.260 0.7471 0.044 0.956
#> GSM494569 2 0.921 0.7180 0.336 0.664
#> GSM494592 1 0.738 0.7550 0.792 0.208
#> GSM494577 2 0.990 -0.1836 0.440 0.560
#> GSM494588 2 0.260 0.7471 0.044 0.956
#> GSM494590 2 0.000 0.7469 0.000 1.000
#> GSM494609 1 0.932 0.6462 0.652 0.348
#> GSM494608 1 0.932 0.6462 0.652 0.348
#> GSM494606 1 0.745 0.7531 0.788 0.212
#> GSM494574 1 0.795 0.7377 0.760 0.240
#> GSM494573 2 0.000 0.7469 0.000 1.000
#> GSM494566 1 0.929 0.6441 0.656 0.344
#> GSM494601 1 0.891 0.6916 0.692 0.308
#> GSM494557 2 0.000 0.7469 0.000 1.000
#> GSM494579 1 0.929 0.6441 0.656 0.344
#> GSM494596 2 0.000 0.7469 0.000 1.000
#> GSM494575 1 0.795 0.7377 0.760 0.240
#> GSM494625 2 0.925 0.7169 0.340 0.660
#> GSM494654 2 0.000 0.7469 0.000 1.000
#> GSM494664 1 0.443 0.7384 0.908 0.092
#> GSM494624 2 0.925 0.7169 0.340 0.660
#> GSM494651 2 0.925 0.7169 0.340 0.660
#> GSM494662 2 0.958 0.6606 0.380 0.620
#> GSM494627 2 0.917 0.7196 0.332 0.668
#> GSM494673 1 0.000 0.7830 1.000 0.000
#> GSM494649 2 0.925 0.7169 0.340 0.660
#> GSM494658 1 0.000 0.7830 1.000 0.000
#> GSM494653 1 0.000 0.7830 1.000 0.000
#> GSM494643 2 0.925 0.7169 0.340 0.660
#> GSM494672 1 0.000 0.7830 1.000 0.000
#> GSM494618 2 0.925 0.7169 0.340 0.660
#> GSM494631 2 0.184 0.7484 0.028 0.972
#> GSM494619 2 0.925 0.7169 0.340 0.660
#> GSM494674 1 0.000 0.7830 1.000 0.000
#> GSM494616 2 0.925 0.7169 0.340 0.660
#> GSM494663 2 0.917 0.7196 0.332 0.668
#> GSM494628 2 0.925 0.7169 0.340 0.660
#> GSM494632 1 0.552 0.6977 0.872 0.128
#> GSM494660 2 0.925 0.7169 0.340 0.660
#> GSM494622 2 0.921 0.7180 0.336 0.664
#> GSM494642 1 0.000 0.7830 1.000 0.000
#> GSM494647 1 0.000 0.7830 1.000 0.000
#> GSM494659 1 0.000 0.7830 1.000 0.000
#> GSM494670 1 0.000 0.7830 1.000 0.000
#> GSM494675 2 0.000 0.7469 0.000 1.000
#> GSM494641 1 0.000 0.7830 1.000 0.000
#> GSM494636 1 0.886 0.3183 0.696 0.304
#> GSM494640 2 0.925 0.7169 0.340 0.660
#> GSM494623 2 0.925 0.7169 0.340 0.660
#> GSM494644 1 0.456 0.7346 0.904 0.096
#> GSM494646 1 0.456 0.7346 0.904 0.096
#> GSM494665 1 0.443 0.7384 0.908 0.092
#> GSM494638 1 0.895 0.2934 0.688 0.312
#> GSM494645 1 0.456 0.7346 0.904 0.096
#> GSM494671 1 0.000 0.7830 1.000 0.000
#> GSM494655 1 0.000 0.7830 1.000 0.000
#> GSM494620 2 0.925 0.7169 0.340 0.660
#> GSM494630 2 0.925 0.7169 0.340 0.660
#> GSM494657 2 0.000 0.7469 0.000 1.000
#> GSM494667 1 0.000 0.7830 1.000 0.000
#> GSM494621 2 0.925 0.7169 0.340 0.660
#> GSM494629 2 0.921 0.7180 0.336 0.664
#> GSM494637 2 0.925 0.7169 0.340 0.660
#> GSM494652 1 0.000 0.7830 1.000 0.000
#> GSM494648 2 0.925 0.7169 0.340 0.660
#> GSM494650 2 0.925 0.7169 0.340 0.660
#> GSM494669 1 0.000 0.7830 1.000 0.000
#> GSM494666 1 0.443 0.7384 0.908 0.092
#> GSM494668 1 0.000 0.7830 1.000 0.000
#> GSM494633 2 0.925 0.7169 0.340 0.660
#> GSM494634 1 0.000 0.7830 1.000 0.000
#> GSM494639 1 0.886 0.3183 0.696 0.304
#> GSM494661 1 0.443 0.7384 0.908 0.092
#> GSM494617 2 0.925 0.7169 0.340 0.660
#> GSM494626 2 0.925 0.7169 0.340 0.660
#> GSM494656 2 0.000 0.7469 0.000 1.000
#> GSM494635 1 0.456 0.7346 0.904 0.096
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM494565 3 0.9462 0.3200 0.180 0.400 0.420
#> GSM494594 3 0.0000 0.7310 0.000 0.000 1.000
#> GSM494604 2 0.3192 0.7305 0.112 0.888 0.000
#> GSM494564 3 0.5291 0.8312 0.268 0.000 0.732
#> GSM494591 3 0.0000 0.7310 0.000 0.000 1.000
#> GSM494567 3 0.5291 0.8312 0.268 0.000 0.732
#> GSM494602 2 0.0000 0.7169 0.000 1.000 0.000
#> GSM494613 3 0.5291 0.8312 0.268 0.000 0.732
#> GSM494589 3 0.5291 0.8312 0.268 0.000 0.732
#> GSM494598 2 0.0000 0.7169 0.000 1.000 0.000
#> GSM494593 2 0.1753 0.7287 0.048 0.952 0.000
#> GSM494583 2 0.8571 0.0771 0.112 0.548 0.340
#> GSM494612 2 0.0000 0.7169 0.000 1.000 0.000
#> GSM494558 1 0.0747 0.9279 0.984 0.000 0.016
#> GSM494556 3 0.5291 0.8312 0.268 0.000 0.732
#> GSM494559 3 0.5785 0.7742 0.332 0.000 0.668
#> GSM494571 3 0.0000 0.7310 0.000 0.000 1.000
#> GSM494614 3 0.5291 0.8312 0.268 0.000 0.732
#> GSM494603 1 0.0592 0.9333 0.988 0.000 0.012
#> GSM494568 1 0.0592 0.9333 0.988 0.000 0.012
#> GSM494572 3 0.0000 0.7310 0.000 0.000 1.000
#> GSM494600 3 0.5291 0.8312 0.268 0.000 0.732
#> GSM494562 2 0.0000 0.7169 0.000 1.000 0.000
#> GSM494615 3 0.5291 0.8312 0.268 0.000 0.732
#> GSM494582 2 0.0000 0.7169 0.000 1.000 0.000
#> GSM494599 2 0.1753 0.7287 0.048 0.952 0.000
#> GSM494610 2 0.0000 0.7169 0.000 1.000 0.000
#> GSM494587 2 0.4413 0.6248 0.024 0.852 0.124
#> GSM494581 2 0.5998 0.6486 0.084 0.788 0.128
#> GSM494580 3 0.5291 0.8312 0.268 0.000 0.732
#> GSM494563 3 0.9506 0.6022 0.268 0.240 0.492
#> GSM494576 2 0.3481 0.6757 0.052 0.904 0.044
#> GSM494605 2 0.6225 0.5796 0.432 0.568 0.000
#> GSM494584 3 0.8042 0.6608 0.136 0.216 0.648
#> GSM494586 2 0.1765 0.6988 0.004 0.956 0.040
#> GSM494578 3 0.5291 0.8312 0.268 0.000 0.732
#> GSM494585 2 0.4413 0.6248 0.024 0.852 0.124
#> GSM494611 2 0.0000 0.7169 0.000 1.000 0.000
#> GSM494560 3 0.5291 0.8312 0.268 0.000 0.732
#> GSM494595 2 0.0424 0.7167 0.008 0.992 0.000
#> GSM494570 3 0.5785 0.7742 0.332 0.000 0.668
#> GSM494597 3 0.5254 0.8300 0.264 0.000 0.736
#> GSM494607 2 0.3192 0.7305 0.112 0.888 0.000
#> GSM494561 3 0.5785 0.7742 0.332 0.000 0.668
#> GSM494569 1 0.0237 0.9393 0.996 0.000 0.004
#> GSM494592 2 0.1753 0.7287 0.048 0.952 0.000
#> GSM494577 2 0.8322 0.2214 0.120 0.604 0.276
#> GSM494588 3 0.5785 0.7742 0.332 0.000 0.668
#> GSM494590 3 0.0000 0.7310 0.000 0.000 1.000
#> GSM494609 2 0.5998 0.6486 0.084 0.788 0.128
#> GSM494608 2 0.5998 0.6486 0.084 0.788 0.128
#> GSM494606 2 0.1529 0.7272 0.040 0.960 0.000
#> GSM494574 2 0.0000 0.7169 0.000 1.000 0.000
#> GSM494573 3 0.5291 0.8312 0.268 0.000 0.732
#> GSM494566 2 0.6605 0.6464 0.096 0.752 0.152
#> GSM494601 2 0.4920 0.6796 0.052 0.840 0.108
#> GSM494557 3 0.5291 0.8312 0.268 0.000 0.732
#> GSM494579 2 0.6605 0.6464 0.096 0.752 0.152
#> GSM494596 3 0.0000 0.7310 0.000 0.000 1.000
#> GSM494575 2 0.0000 0.7169 0.000 1.000 0.000
#> GSM494625 1 0.0000 0.9427 1.000 0.000 0.000
#> GSM494654 3 0.5591 0.4776 0.304 0.000 0.696
#> GSM494664 2 0.6225 0.5796 0.432 0.568 0.000
#> GSM494624 1 0.0000 0.9427 1.000 0.000 0.000
#> GSM494651 1 0.0000 0.9427 1.000 0.000 0.000
#> GSM494662 1 0.1529 0.8948 0.960 0.040 0.000
#> GSM494627 1 0.0592 0.9333 0.988 0.000 0.012
#> GSM494673 2 0.5835 0.6872 0.340 0.660 0.000
#> GSM494649 1 0.0000 0.9427 1.000 0.000 0.000
#> GSM494658 2 0.5835 0.6872 0.340 0.660 0.000
#> GSM494653 2 0.5835 0.6872 0.340 0.660 0.000
#> GSM494643 1 0.0000 0.9427 1.000 0.000 0.000
#> GSM494672 2 0.5835 0.6872 0.340 0.660 0.000
#> GSM494618 1 0.0000 0.9427 1.000 0.000 0.000
#> GSM494631 3 0.6267 0.5964 0.452 0.000 0.548
#> GSM494619 1 0.0000 0.9427 1.000 0.000 0.000
#> GSM494674 2 0.5835 0.6872 0.340 0.660 0.000
#> GSM494616 1 0.0000 0.9427 1.000 0.000 0.000
#> GSM494663 1 0.0592 0.9333 0.988 0.000 0.012
#> GSM494628 1 0.0000 0.9427 1.000 0.000 0.000
#> GSM494632 2 0.6291 0.5077 0.468 0.532 0.000
#> GSM494660 1 0.0000 0.9427 1.000 0.000 0.000
#> GSM494622 1 0.0424 0.9369 0.992 0.000 0.008
#> GSM494642 2 0.5835 0.6872 0.340 0.660 0.000
#> GSM494647 2 0.5835 0.6872 0.340 0.660 0.000
#> GSM494659 2 0.5835 0.6872 0.340 0.660 0.000
#> GSM494670 2 0.5835 0.6872 0.340 0.660 0.000
#> GSM494675 3 0.5254 0.8300 0.264 0.000 0.736
#> GSM494641 2 0.5835 0.6872 0.340 0.660 0.000
#> GSM494636 1 0.5926 0.1216 0.644 0.356 0.000
#> GSM494640 1 0.0000 0.9427 1.000 0.000 0.000
#> GSM494623 1 0.0000 0.9427 1.000 0.000 0.000
#> GSM494644 2 0.6235 0.5726 0.436 0.564 0.000
#> GSM494646 2 0.6235 0.5726 0.436 0.564 0.000
#> GSM494665 2 0.6225 0.5796 0.432 0.568 0.000
#> GSM494638 1 0.5882 0.1528 0.652 0.348 0.000
#> GSM494645 2 0.6235 0.5726 0.436 0.564 0.000
#> GSM494671 2 0.5835 0.6872 0.340 0.660 0.000
#> GSM494655 2 0.5835 0.6872 0.340 0.660 0.000
#> GSM494620 1 0.0000 0.9427 1.000 0.000 0.000
#> GSM494630 1 0.0000 0.9427 1.000 0.000 0.000
#> GSM494657 3 0.0000 0.7310 0.000 0.000 1.000
#> GSM494667 2 0.5835 0.6872 0.340 0.660 0.000
#> GSM494621 1 0.0000 0.9427 1.000 0.000 0.000
#> GSM494629 1 0.0237 0.9393 0.996 0.000 0.004
#> GSM494637 1 0.0000 0.9427 1.000 0.000 0.000
#> GSM494652 2 0.5835 0.6872 0.340 0.660 0.000
#> GSM494648 1 0.0000 0.9427 1.000 0.000 0.000
#> GSM494650 1 0.0000 0.9427 1.000 0.000 0.000
#> GSM494669 2 0.5835 0.6872 0.340 0.660 0.000
#> GSM494666 2 0.6225 0.5796 0.432 0.568 0.000
#> GSM494668 2 0.5835 0.6872 0.340 0.660 0.000
#> GSM494633 1 0.0000 0.9427 1.000 0.000 0.000
#> GSM494634 2 0.5835 0.6872 0.340 0.660 0.000
#> GSM494639 1 0.5926 0.1216 0.644 0.356 0.000
#> GSM494661 2 0.6225 0.5796 0.432 0.568 0.000
#> GSM494617 1 0.0000 0.9427 1.000 0.000 0.000
#> GSM494626 1 0.0000 0.9427 1.000 0.000 0.000
#> GSM494656 3 0.5591 0.4776 0.304 0.000 0.696
#> GSM494635 2 0.6235 0.5726 0.436 0.564 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM494565 3 0.7869 0.147 0.056 0.360 0.496 0.088
#> GSM494594 3 0.3681 0.725 0.000 0.176 0.816 0.008
#> GSM494604 1 0.4992 -0.411 0.524 0.476 0.000 0.000
#> GSM494564 3 0.3224 0.842 0.000 0.016 0.864 0.120
#> GSM494591 3 0.3681 0.725 0.000 0.176 0.816 0.008
#> GSM494567 3 0.2589 0.846 0.000 0.000 0.884 0.116
#> GSM494602 2 0.3528 0.862 0.192 0.808 0.000 0.000
#> GSM494613 3 0.2589 0.846 0.000 0.000 0.884 0.116
#> GSM494589 3 0.3224 0.842 0.000 0.016 0.864 0.120
#> GSM494598 2 0.3528 0.862 0.192 0.808 0.000 0.000
#> GSM494593 2 0.4250 0.822 0.276 0.724 0.000 0.000
#> GSM494583 2 0.7841 0.328 0.092 0.472 0.388 0.048
#> GSM494612 2 0.3528 0.862 0.192 0.808 0.000 0.000
#> GSM494558 4 0.0844 0.979 0.004 0.004 0.012 0.980
#> GSM494556 3 0.2589 0.846 0.000 0.000 0.884 0.116
#> GSM494559 3 0.4012 0.814 0.000 0.016 0.800 0.184
#> GSM494571 3 0.3681 0.725 0.000 0.176 0.816 0.008
#> GSM494614 3 0.2589 0.846 0.000 0.000 0.884 0.116
#> GSM494603 4 0.0712 0.982 0.004 0.004 0.008 0.984
#> GSM494568 4 0.0712 0.982 0.004 0.004 0.008 0.984
#> GSM494572 3 0.3681 0.725 0.000 0.176 0.816 0.008
#> GSM494600 3 0.3224 0.842 0.000 0.016 0.864 0.120
#> GSM494562 2 0.3528 0.862 0.192 0.808 0.000 0.000
#> GSM494615 3 0.2589 0.846 0.000 0.000 0.884 0.116
#> GSM494582 2 0.3528 0.862 0.192 0.808 0.000 0.000
#> GSM494599 2 0.4250 0.822 0.276 0.724 0.000 0.000
#> GSM494610 2 0.3528 0.862 0.192 0.808 0.000 0.000
#> GSM494587 2 0.5848 0.825 0.152 0.716 0.128 0.004
#> GSM494581 2 0.6775 0.796 0.228 0.628 0.136 0.008
#> GSM494580 3 0.2589 0.846 0.000 0.000 0.884 0.116
#> GSM494563 3 0.6474 0.564 0.000 0.256 0.624 0.120
#> GSM494576 2 0.5080 0.840 0.136 0.784 0.064 0.016
#> GSM494605 1 0.2216 0.847 0.908 0.000 0.000 0.092
#> GSM494584 3 0.6390 0.629 0.080 0.136 0.720 0.064
#> GSM494586 2 0.4149 0.859 0.168 0.804 0.028 0.000
#> GSM494578 3 0.2589 0.846 0.000 0.000 0.884 0.116
#> GSM494585 2 0.5848 0.825 0.152 0.716 0.128 0.004
#> GSM494611 2 0.3528 0.862 0.192 0.808 0.000 0.000
#> GSM494560 3 0.3224 0.842 0.000 0.016 0.864 0.120
#> GSM494595 2 0.3768 0.862 0.184 0.808 0.008 0.000
#> GSM494570 3 0.4012 0.814 0.000 0.016 0.800 0.184
#> GSM494597 3 0.2773 0.845 0.000 0.004 0.880 0.116
#> GSM494607 1 0.4992 -0.411 0.524 0.476 0.000 0.000
#> GSM494561 3 0.4012 0.814 0.000 0.016 0.800 0.184
#> GSM494569 4 0.0336 0.991 0.008 0.000 0.000 0.992
#> GSM494592 2 0.4250 0.822 0.276 0.724 0.000 0.000
#> GSM494577 2 0.7711 0.437 0.088 0.532 0.328 0.052
#> GSM494588 3 0.4012 0.814 0.000 0.016 0.800 0.184
#> GSM494590 3 0.3681 0.725 0.000 0.176 0.816 0.008
#> GSM494609 2 0.6775 0.796 0.228 0.628 0.136 0.008
#> GSM494608 2 0.6775 0.796 0.228 0.628 0.136 0.008
#> GSM494606 2 0.4193 0.828 0.268 0.732 0.000 0.000
#> GSM494574 2 0.3528 0.862 0.192 0.808 0.000 0.000
#> GSM494573 3 0.3224 0.842 0.000 0.016 0.864 0.120
#> GSM494566 2 0.6906 0.744 0.264 0.580 0.156 0.000
#> GSM494601 2 0.6164 0.821 0.240 0.656 0.104 0.000
#> GSM494557 3 0.2589 0.846 0.000 0.000 0.884 0.116
#> GSM494579 2 0.6906 0.744 0.264 0.580 0.156 0.000
#> GSM494596 3 0.3681 0.725 0.000 0.176 0.816 0.008
#> GSM494575 2 0.3528 0.862 0.192 0.808 0.000 0.000
#> GSM494625 4 0.0469 0.993 0.012 0.000 0.000 0.988
#> GSM494654 3 0.7299 0.388 0.000 0.176 0.512 0.312
#> GSM494664 1 0.2216 0.847 0.908 0.000 0.000 0.092
#> GSM494624 4 0.0469 0.993 0.012 0.000 0.000 0.988
#> GSM494651 4 0.0469 0.993 0.012 0.000 0.000 0.988
#> GSM494662 4 0.1792 0.926 0.068 0.000 0.000 0.932
#> GSM494627 4 0.0712 0.982 0.004 0.004 0.008 0.984
#> GSM494673 1 0.0000 0.867 1.000 0.000 0.000 0.000
#> GSM494649 4 0.0469 0.993 0.012 0.000 0.000 0.988
#> GSM494658 1 0.0469 0.857 0.988 0.012 0.000 0.000
#> GSM494653 1 0.0000 0.867 1.000 0.000 0.000 0.000
#> GSM494643 4 0.0469 0.993 0.012 0.000 0.000 0.988
#> GSM494672 1 0.0000 0.867 1.000 0.000 0.000 0.000
#> GSM494618 4 0.0469 0.993 0.012 0.000 0.000 0.988
#> GSM494631 3 0.4406 0.669 0.000 0.000 0.700 0.300
#> GSM494619 4 0.0469 0.993 0.012 0.000 0.000 0.988
#> GSM494674 1 0.0000 0.867 1.000 0.000 0.000 0.000
#> GSM494616 4 0.0469 0.993 0.012 0.000 0.000 0.988
#> GSM494663 4 0.0712 0.982 0.004 0.004 0.008 0.984
#> GSM494628 4 0.0657 0.990 0.012 0.000 0.004 0.984
#> GSM494632 1 0.2760 0.815 0.872 0.000 0.000 0.128
#> GSM494660 4 0.0469 0.993 0.012 0.000 0.000 0.988
#> GSM494622 4 0.0859 0.985 0.008 0.004 0.008 0.980
#> GSM494642 1 0.0000 0.867 1.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.867 1.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.867 1.000 0.000 0.000 0.000
#> GSM494670 1 0.0000 0.867 1.000 0.000 0.000 0.000
#> GSM494675 3 0.2773 0.845 0.000 0.004 0.880 0.116
#> GSM494641 1 0.0000 0.867 1.000 0.000 0.000 0.000
#> GSM494636 1 0.4431 0.598 0.696 0.000 0.000 0.304
#> GSM494640 4 0.0469 0.993 0.012 0.000 0.000 0.988
#> GSM494623 4 0.0469 0.993 0.012 0.000 0.000 0.988
#> GSM494644 1 0.2281 0.845 0.904 0.000 0.000 0.096
#> GSM494646 1 0.2281 0.845 0.904 0.000 0.000 0.096
#> GSM494665 1 0.2216 0.847 0.908 0.000 0.000 0.092
#> GSM494638 1 0.4477 0.584 0.688 0.000 0.000 0.312
#> GSM494645 1 0.2281 0.845 0.904 0.000 0.000 0.096
#> GSM494671 1 0.0000 0.867 1.000 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.867 1.000 0.000 0.000 0.000
#> GSM494620 4 0.0469 0.993 0.012 0.000 0.000 0.988
#> GSM494630 4 0.0469 0.993 0.012 0.000 0.000 0.988
#> GSM494657 3 0.3681 0.725 0.000 0.176 0.816 0.008
#> GSM494667 1 0.0000 0.867 1.000 0.000 0.000 0.000
#> GSM494621 4 0.0469 0.993 0.012 0.000 0.000 0.988
#> GSM494629 4 0.0336 0.991 0.008 0.000 0.000 0.992
#> GSM494637 4 0.0469 0.993 0.012 0.000 0.000 0.988
#> GSM494652 1 0.0000 0.867 1.000 0.000 0.000 0.000
#> GSM494648 4 0.0469 0.993 0.012 0.000 0.000 0.988
#> GSM494650 4 0.0469 0.993 0.012 0.000 0.000 0.988
#> GSM494669 1 0.0000 0.867 1.000 0.000 0.000 0.000
#> GSM494666 1 0.2216 0.847 0.908 0.000 0.000 0.092
#> GSM494668 1 0.0000 0.867 1.000 0.000 0.000 0.000
#> GSM494633 4 0.0469 0.993 0.012 0.000 0.000 0.988
#> GSM494634 1 0.0000 0.867 1.000 0.000 0.000 0.000
#> GSM494639 1 0.4431 0.598 0.696 0.000 0.000 0.304
#> GSM494661 1 0.2216 0.847 0.908 0.000 0.000 0.092
#> GSM494617 4 0.0469 0.993 0.012 0.000 0.000 0.988
#> GSM494626 4 0.0469 0.993 0.012 0.000 0.000 0.988
#> GSM494656 3 0.7299 0.388 0.000 0.176 0.512 0.312
#> GSM494635 1 0.2281 0.845 0.904 0.000 0.000 0.096
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM494565 5 0.4182 0.0127 0.000 0.400 0.000 0.000 0.600
#> GSM494594 3 0.2798 0.8485 0.000 0.000 0.852 0.008 0.140
#> GSM494604 2 0.4268 0.4142 0.444 0.556 0.000 0.000 0.000
#> GSM494564 5 0.0000 0.7449 0.000 0.000 0.000 0.000 1.000
#> GSM494591 3 0.2561 0.8581 0.000 0.000 0.856 0.000 0.144
#> GSM494567 5 0.3395 0.7327 0.000 0.000 0.236 0.000 0.764
#> GSM494602 2 0.0404 0.8367 0.012 0.988 0.000 0.000 0.000
#> GSM494613 5 0.3395 0.7327 0.000 0.000 0.236 0.000 0.764
#> GSM494589 5 0.0000 0.7449 0.000 0.000 0.000 0.000 1.000
#> GSM494598 2 0.0000 0.8357 0.000 1.000 0.000 0.000 0.000
#> GSM494593 2 0.2074 0.8214 0.104 0.896 0.000 0.000 0.000
#> GSM494583 2 0.4278 0.4022 0.000 0.548 0.000 0.000 0.452
#> GSM494612 2 0.0000 0.8357 0.000 1.000 0.000 0.000 0.000
#> GSM494558 4 0.2536 0.9151 0.000 0.000 0.128 0.868 0.004
#> GSM494556 5 0.3424 0.7287 0.000 0.000 0.240 0.000 0.760
#> GSM494559 5 0.1701 0.7155 0.000 0.000 0.016 0.048 0.936
#> GSM494571 3 0.2561 0.8581 0.000 0.000 0.856 0.000 0.144
#> GSM494614 5 0.3395 0.7327 0.000 0.000 0.236 0.000 0.764
#> GSM494603 4 0.2488 0.9172 0.000 0.000 0.124 0.872 0.004
#> GSM494568 4 0.2488 0.9172 0.000 0.000 0.124 0.872 0.004
#> GSM494572 3 0.2561 0.8581 0.000 0.000 0.856 0.000 0.144
#> GSM494600 5 0.0000 0.7449 0.000 0.000 0.000 0.000 1.000
#> GSM494562 2 0.0000 0.8357 0.000 1.000 0.000 0.000 0.000
#> GSM494615 5 0.3395 0.7327 0.000 0.000 0.236 0.000 0.764
#> GSM494582 2 0.0000 0.8357 0.000 1.000 0.000 0.000 0.000
#> GSM494599 2 0.2074 0.8214 0.104 0.896 0.000 0.000 0.000
#> GSM494610 2 0.0000 0.8357 0.000 1.000 0.000 0.000 0.000
#> GSM494587 2 0.2605 0.8034 0.000 0.852 0.000 0.000 0.148
#> GSM494581 2 0.4686 0.7797 0.104 0.736 0.000 0.000 0.160
#> GSM494580 5 0.3395 0.7327 0.000 0.000 0.236 0.000 0.764
#> GSM494563 5 0.3424 0.4936 0.000 0.240 0.000 0.000 0.760
#> GSM494576 2 0.1965 0.8197 0.000 0.904 0.000 0.000 0.096
#> GSM494605 1 0.1908 0.9025 0.908 0.000 0.000 0.092 0.000
#> GSM494584 5 0.6156 0.4823 0.000 0.216 0.224 0.000 0.560
#> GSM494586 2 0.1121 0.8347 0.000 0.956 0.000 0.000 0.044
#> GSM494578 5 0.3395 0.7327 0.000 0.000 0.236 0.000 0.764
#> GSM494585 2 0.2605 0.8034 0.000 0.852 0.000 0.000 0.148
#> GSM494611 2 0.0000 0.8357 0.000 1.000 0.000 0.000 0.000
#> GSM494560 5 0.0000 0.7449 0.000 0.000 0.000 0.000 1.000
#> GSM494595 2 0.0290 0.8359 0.000 0.992 0.000 0.000 0.008
#> GSM494570 5 0.1701 0.7155 0.000 0.000 0.016 0.048 0.936
#> GSM494597 5 0.3508 0.7156 0.000 0.000 0.252 0.000 0.748
#> GSM494607 2 0.4268 0.4142 0.444 0.556 0.000 0.000 0.000
#> GSM494561 5 0.1701 0.7155 0.000 0.000 0.016 0.048 0.936
#> GSM494569 4 0.2179 0.9274 0.004 0.000 0.100 0.896 0.000
#> GSM494592 2 0.2074 0.8214 0.104 0.896 0.000 0.000 0.000
#> GSM494577 2 0.4171 0.4679 0.000 0.604 0.000 0.000 0.396
#> GSM494588 5 0.1701 0.7155 0.000 0.000 0.016 0.048 0.936
#> GSM494590 3 0.2561 0.8581 0.000 0.000 0.856 0.000 0.144
#> GSM494609 2 0.4686 0.7797 0.104 0.736 0.000 0.000 0.160
#> GSM494608 2 0.4686 0.7797 0.104 0.736 0.000 0.000 0.160
#> GSM494606 2 0.2020 0.8235 0.100 0.900 0.000 0.000 0.000
#> GSM494574 2 0.0000 0.8357 0.000 1.000 0.000 0.000 0.000
#> GSM494573 5 0.0000 0.7449 0.000 0.000 0.000 0.000 1.000
#> GSM494566 2 0.5546 0.7121 0.180 0.648 0.000 0.000 0.172
#> GSM494601 2 0.3906 0.8126 0.084 0.804 0.000 0.000 0.112
#> GSM494557 5 0.3395 0.7327 0.000 0.000 0.236 0.000 0.764
#> GSM494579 2 0.5546 0.7121 0.180 0.648 0.000 0.000 0.172
#> GSM494596 3 0.2561 0.8581 0.000 0.000 0.856 0.000 0.144
#> GSM494575 2 0.0000 0.8357 0.000 1.000 0.000 0.000 0.000
#> GSM494625 4 0.0798 0.9272 0.008 0.000 0.016 0.976 0.000
#> GSM494654 3 0.3496 0.5983 0.000 0.000 0.788 0.200 0.012
#> GSM494664 1 0.1908 0.9025 0.908 0.000 0.000 0.092 0.000
#> GSM494624 4 0.0798 0.9272 0.008 0.000 0.016 0.976 0.000
#> GSM494651 4 0.2563 0.9253 0.008 0.000 0.120 0.872 0.000
#> GSM494662 4 0.1478 0.8850 0.064 0.000 0.000 0.936 0.000
#> GSM494627 4 0.2439 0.9192 0.000 0.000 0.120 0.876 0.004
#> GSM494673 1 0.0000 0.9225 1.000 0.000 0.000 0.000 0.000
#> GSM494649 4 0.0798 0.9272 0.008 0.000 0.016 0.976 0.000
#> GSM494658 1 0.0404 0.9141 0.988 0.012 0.000 0.000 0.000
#> GSM494653 1 0.0000 0.9225 1.000 0.000 0.000 0.000 0.000
#> GSM494643 4 0.0290 0.9289 0.008 0.000 0.000 0.992 0.000
#> GSM494672 1 0.0000 0.9225 1.000 0.000 0.000 0.000 0.000
#> GSM494618 4 0.2563 0.9253 0.008 0.000 0.120 0.872 0.000
#> GSM494631 5 0.5232 0.5777 0.000 0.000 0.228 0.104 0.668
#> GSM494619 4 0.0798 0.9272 0.008 0.000 0.016 0.976 0.000
#> GSM494674 1 0.0000 0.9225 1.000 0.000 0.000 0.000 0.000
#> GSM494616 4 0.2563 0.9253 0.008 0.000 0.120 0.872 0.000
#> GSM494663 4 0.2488 0.9172 0.000 0.000 0.124 0.872 0.004
#> GSM494628 4 0.2722 0.9235 0.008 0.000 0.120 0.868 0.004
#> GSM494632 1 0.2377 0.8732 0.872 0.000 0.000 0.128 0.000
#> GSM494660 4 0.0798 0.9272 0.008 0.000 0.016 0.976 0.000
#> GSM494622 4 0.2646 0.9197 0.004 0.000 0.124 0.868 0.004
#> GSM494642 1 0.0000 0.9225 1.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.9225 1.000 0.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.9225 1.000 0.000 0.000 0.000 0.000
#> GSM494670 1 0.0000 0.9225 1.000 0.000 0.000 0.000 0.000
#> GSM494675 5 0.3508 0.7156 0.000 0.000 0.252 0.000 0.748
#> GSM494641 1 0.0000 0.9225 1.000 0.000 0.000 0.000 0.000
#> GSM494636 1 0.3816 0.6552 0.696 0.000 0.000 0.304 0.000
#> GSM494640 4 0.0290 0.9289 0.008 0.000 0.000 0.992 0.000
#> GSM494623 4 0.0798 0.9272 0.008 0.000 0.016 0.976 0.000
#> GSM494644 1 0.1965 0.9006 0.904 0.000 0.000 0.096 0.000
#> GSM494646 1 0.1965 0.9006 0.904 0.000 0.000 0.096 0.000
#> GSM494665 1 0.1908 0.9025 0.908 0.000 0.000 0.092 0.000
#> GSM494638 1 0.3990 0.6423 0.688 0.000 0.004 0.308 0.000
#> GSM494645 1 0.1965 0.9006 0.904 0.000 0.000 0.096 0.000
#> GSM494671 1 0.0000 0.9225 1.000 0.000 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.9225 1.000 0.000 0.000 0.000 0.000
#> GSM494620 4 0.0798 0.9272 0.008 0.000 0.016 0.976 0.000
#> GSM494630 4 0.0798 0.9272 0.008 0.000 0.016 0.976 0.000
#> GSM494657 3 0.2561 0.8581 0.000 0.000 0.856 0.000 0.144
#> GSM494667 1 0.0000 0.9225 1.000 0.000 0.000 0.000 0.000
#> GSM494621 4 0.0798 0.9272 0.008 0.000 0.016 0.976 0.000
#> GSM494629 4 0.1831 0.9296 0.004 0.000 0.076 0.920 0.000
#> GSM494637 4 0.0290 0.9289 0.008 0.000 0.000 0.992 0.000
#> GSM494652 1 0.0000 0.9225 1.000 0.000 0.000 0.000 0.000
#> GSM494648 4 0.0798 0.9272 0.008 0.000 0.016 0.976 0.000
#> GSM494650 4 0.2563 0.9253 0.008 0.000 0.120 0.872 0.000
#> GSM494669 1 0.0000 0.9225 1.000 0.000 0.000 0.000 0.000
#> GSM494666 1 0.1908 0.9025 0.908 0.000 0.000 0.092 0.000
#> GSM494668 1 0.0000 0.9225 1.000 0.000 0.000 0.000 0.000
#> GSM494633 4 0.0798 0.9272 0.008 0.000 0.016 0.976 0.000
#> GSM494634 1 0.0000 0.9225 1.000 0.000 0.000 0.000 0.000
#> GSM494639 1 0.3816 0.6552 0.696 0.000 0.000 0.304 0.000
#> GSM494661 1 0.1908 0.9025 0.908 0.000 0.000 0.092 0.000
#> GSM494617 4 0.2563 0.9253 0.008 0.000 0.120 0.872 0.000
#> GSM494626 4 0.2563 0.9253 0.008 0.000 0.120 0.872 0.000
#> GSM494656 3 0.3496 0.5983 0.000 0.000 0.788 0.200 0.012
#> GSM494635 1 0.1965 0.9006 0.904 0.000 0.000 0.096 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM494565 5 0.5303 0.274 0.000 0.232 0.172 0.000 0.596 0.000
#> GSM494594 6 0.3741 0.781 0.000 0.000 0.320 0.000 0.008 0.672
#> GSM494604 1 0.6145 -0.323 0.432 0.340 0.000 0.000 0.220 0.008
#> GSM494564 5 0.3717 0.665 0.000 0.000 0.384 0.000 0.616 0.000
#> GSM494591 6 0.3706 0.816 0.000 0.000 0.380 0.000 0.000 0.620
#> GSM494567 3 0.0146 0.911 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM494602 2 0.3219 0.763 0.012 0.792 0.000 0.000 0.192 0.004
#> GSM494613 3 0.0146 0.911 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM494589 5 0.3717 0.665 0.000 0.000 0.384 0.000 0.616 0.000
#> GSM494598 2 0.0806 0.739 0.000 0.972 0.000 0.000 0.020 0.008
#> GSM494593 2 0.4650 0.746 0.096 0.688 0.000 0.000 0.212 0.004
#> GSM494583 5 0.5367 -0.181 0.000 0.344 0.124 0.000 0.532 0.000
#> GSM494612 2 0.0520 0.730 0.000 0.984 0.000 0.000 0.008 0.008
#> GSM494558 4 0.1151 0.777 0.000 0.000 0.000 0.956 0.032 0.012
#> GSM494556 3 0.0291 0.908 0.000 0.000 0.992 0.000 0.004 0.004
#> GSM494559 5 0.3984 0.664 0.000 0.000 0.336 0.000 0.648 0.016
#> GSM494571 6 0.3706 0.816 0.000 0.000 0.380 0.000 0.000 0.620
#> GSM494614 3 0.0713 0.897 0.000 0.000 0.972 0.000 0.028 0.000
#> GSM494603 4 0.0508 0.794 0.000 0.000 0.000 0.984 0.012 0.004
#> GSM494568 4 0.0508 0.794 0.000 0.000 0.000 0.984 0.012 0.004
#> GSM494572 6 0.3706 0.816 0.000 0.000 0.380 0.000 0.000 0.620
#> GSM494600 5 0.3717 0.665 0.000 0.000 0.384 0.000 0.616 0.000
#> GSM494562 2 0.0806 0.739 0.000 0.972 0.000 0.000 0.020 0.008
#> GSM494615 3 0.0146 0.911 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM494582 2 0.0520 0.730 0.000 0.984 0.000 0.000 0.008 0.008
#> GSM494599 2 0.4650 0.746 0.096 0.688 0.000 0.000 0.212 0.004
#> GSM494610 2 0.0806 0.739 0.000 0.972 0.000 0.000 0.020 0.008
#> GSM494587 2 0.4509 0.696 0.000 0.640 0.036 0.000 0.316 0.008
#> GSM494581 2 0.6126 0.641 0.096 0.500 0.036 0.000 0.360 0.008
#> GSM494580 3 0.0146 0.911 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM494563 5 0.4532 0.541 0.000 0.108 0.196 0.000 0.696 0.000
#> GSM494576 2 0.3726 0.741 0.000 0.752 0.028 0.000 0.216 0.004
#> GSM494605 1 0.2129 0.865 0.904 0.000 0.000 0.040 0.000 0.056
#> GSM494584 3 0.4308 0.492 0.000 0.152 0.728 0.000 0.120 0.000
#> GSM494586 2 0.2979 0.757 0.000 0.804 0.004 0.000 0.188 0.004
#> GSM494578 3 0.0146 0.911 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM494585 2 0.4509 0.696 0.000 0.640 0.036 0.000 0.316 0.008
#> GSM494611 2 0.0520 0.730 0.000 0.984 0.000 0.000 0.008 0.008
#> GSM494560 5 0.3717 0.665 0.000 0.000 0.384 0.000 0.616 0.000
#> GSM494595 2 0.1900 0.751 0.000 0.916 0.008 0.000 0.068 0.008
#> GSM494570 5 0.3984 0.664 0.000 0.000 0.336 0.000 0.648 0.016
#> GSM494597 3 0.1461 0.869 0.000 0.000 0.940 0.000 0.044 0.016
#> GSM494607 1 0.6145 -0.323 0.432 0.340 0.000 0.000 0.220 0.008
#> GSM494561 5 0.3984 0.664 0.000 0.000 0.336 0.000 0.648 0.016
#> GSM494569 4 0.1082 0.803 0.000 0.000 0.000 0.956 0.004 0.040
#> GSM494592 2 0.4650 0.746 0.096 0.688 0.000 0.000 0.212 0.004
#> GSM494577 5 0.5368 -0.295 0.000 0.400 0.112 0.000 0.488 0.000
#> GSM494588 5 0.3984 0.664 0.000 0.000 0.336 0.000 0.648 0.016
#> GSM494590 6 0.3706 0.816 0.000 0.000 0.380 0.000 0.000 0.620
#> GSM494609 2 0.6126 0.641 0.096 0.500 0.036 0.000 0.360 0.008
#> GSM494608 2 0.6126 0.641 0.096 0.500 0.036 0.000 0.360 0.008
#> GSM494606 2 0.4606 0.748 0.092 0.692 0.000 0.000 0.212 0.004
#> GSM494574 2 0.0806 0.739 0.000 0.972 0.000 0.000 0.020 0.008
#> GSM494573 5 0.3717 0.665 0.000 0.000 0.384 0.000 0.616 0.000
#> GSM494566 2 0.6665 0.540 0.172 0.412 0.036 0.000 0.372 0.008
#> GSM494601 2 0.5262 0.696 0.076 0.572 0.008 0.000 0.340 0.004
#> GSM494557 3 0.0146 0.911 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM494579 2 0.6665 0.540 0.172 0.412 0.036 0.000 0.372 0.008
#> GSM494596 6 0.3706 0.816 0.000 0.000 0.380 0.000 0.000 0.620
#> GSM494575 2 0.0520 0.730 0.000 0.984 0.000 0.000 0.008 0.008
#> GSM494625 4 0.4671 0.791 0.000 0.000 0.000 0.628 0.068 0.304
#> GSM494654 6 0.4253 0.550 0.000 0.000 0.020 0.300 0.012 0.668
#> GSM494664 1 0.2129 0.865 0.904 0.000 0.000 0.040 0.000 0.056
#> GSM494624 4 0.4671 0.791 0.000 0.000 0.000 0.628 0.068 0.304
#> GSM494651 4 0.0692 0.791 0.000 0.000 0.000 0.976 0.020 0.004
#> GSM494662 4 0.4651 0.781 0.056 0.000 0.000 0.692 0.020 0.232
#> GSM494627 4 0.0622 0.795 0.000 0.000 0.000 0.980 0.012 0.008
#> GSM494673 1 0.0000 0.885 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494649 4 0.4671 0.791 0.000 0.000 0.000 0.628 0.068 0.304
#> GSM494658 1 0.0717 0.872 0.976 0.008 0.000 0.000 0.016 0.000
#> GSM494653 1 0.0000 0.885 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494643 4 0.3711 0.805 0.000 0.000 0.000 0.720 0.020 0.260
#> GSM494672 1 0.0000 0.885 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494618 4 0.0692 0.791 0.000 0.000 0.000 0.976 0.020 0.004
#> GSM494631 3 0.2664 0.637 0.000 0.000 0.816 0.184 0.000 0.000
#> GSM494619 4 0.4671 0.791 0.000 0.000 0.000 0.628 0.068 0.304
#> GSM494674 1 0.0000 0.885 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494616 4 0.0692 0.791 0.000 0.000 0.000 0.976 0.020 0.004
#> GSM494663 4 0.0508 0.794 0.000 0.000 0.000 0.984 0.012 0.004
#> GSM494628 4 0.0632 0.790 0.000 0.000 0.000 0.976 0.024 0.000
#> GSM494632 1 0.2688 0.838 0.868 0.000 0.000 0.068 0.000 0.064
#> GSM494660 4 0.4671 0.791 0.000 0.000 0.000 0.628 0.068 0.304
#> GSM494622 4 0.0508 0.794 0.000 0.000 0.000 0.984 0.012 0.004
#> GSM494642 1 0.0000 0.885 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.885 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.885 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494670 1 0.0000 0.885 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494675 3 0.1461 0.869 0.000 0.000 0.940 0.000 0.044 0.016
#> GSM494641 1 0.0000 0.885 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494636 1 0.4325 0.618 0.692 0.000 0.000 0.244 0.000 0.064
#> GSM494640 4 0.3711 0.805 0.000 0.000 0.000 0.720 0.020 0.260
#> GSM494623 4 0.4671 0.791 0.000 0.000 0.000 0.628 0.068 0.304
#> GSM494644 1 0.2190 0.863 0.900 0.000 0.000 0.040 0.000 0.060
#> GSM494646 1 0.2190 0.863 0.900 0.000 0.000 0.040 0.000 0.060
#> GSM494665 1 0.2129 0.865 0.904 0.000 0.000 0.040 0.000 0.056
#> GSM494638 1 0.4370 0.607 0.684 0.000 0.000 0.252 0.000 0.064
#> GSM494645 1 0.2190 0.863 0.900 0.000 0.000 0.040 0.000 0.060
#> GSM494671 1 0.0000 0.885 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.885 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494620 4 0.4671 0.791 0.000 0.000 0.000 0.628 0.068 0.304
#> GSM494630 4 0.4671 0.791 0.000 0.000 0.000 0.628 0.068 0.304
#> GSM494657 6 0.3706 0.816 0.000 0.000 0.380 0.000 0.000 0.620
#> GSM494667 1 0.0000 0.885 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494621 4 0.4671 0.791 0.000 0.000 0.000 0.628 0.068 0.304
#> GSM494629 4 0.1753 0.807 0.000 0.000 0.000 0.912 0.004 0.084
#> GSM494637 4 0.3711 0.805 0.000 0.000 0.000 0.720 0.020 0.260
#> GSM494652 1 0.0000 0.885 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494648 4 0.4671 0.791 0.000 0.000 0.000 0.628 0.068 0.304
#> GSM494650 4 0.0000 0.798 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494669 1 0.0000 0.885 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494666 1 0.2129 0.865 0.904 0.000 0.000 0.040 0.000 0.056
#> GSM494668 1 0.0000 0.885 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494633 4 0.4671 0.791 0.000 0.000 0.000 0.628 0.068 0.304
#> GSM494634 1 0.0000 0.885 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494639 1 0.4325 0.618 0.692 0.000 0.000 0.244 0.000 0.064
#> GSM494661 1 0.2129 0.865 0.904 0.000 0.000 0.040 0.000 0.056
#> GSM494617 4 0.0692 0.791 0.000 0.000 0.000 0.976 0.020 0.004
#> GSM494626 4 0.0692 0.791 0.000 0.000 0.000 0.976 0.020 0.004
#> GSM494656 6 0.4253 0.550 0.000 0.000 0.020 0.300 0.012 0.668
#> GSM494635 1 0.2190 0.863 0.900 0.000 0.000 0.040 0.000 0.060
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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 6)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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.
# code only for demonstration
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) individual(p) k
#> MAD:hclust 114 1.00e+00 0.00188 2.47e-01 0.000698 2
#> MAD:hclust 112 3.44e-08 0.02495 1.13e-04 0.061806 3
#> MAD:hclust 113 3.34e-18 0.52598 7.62e-12 0.859826 4
#> MAD:hclust 113 1.79e-16 0.06742 1.27e-11 0.556265 5
#> MAD:hclust 114 3.82e-16 0.11265 2.06e-12 0.503893 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.
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"]
# you can also extract it by
# 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 51941 rows and 120 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 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:
k and the heatmap of
predicted classes for each k.k.k.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:
k;k, the area increased is defined as \(A_k - A_{k-1}\).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 0.495 0.768 0.854 0.5015 0.496 0.496
#> 3 3 0.596 0.512 0.731 0.3005 0.725 0.500
#> 4 4 0.758 0.892 0.890 0.1361 0.834 0.551
#> 5 5 0.841 0.695 0.833 0.0643 0.963 0.856
#> 6 6 0.796 0.723 0.810 0.0407 0.914 0.652
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
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.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM494565 2 0.909 0.8281 0.324 0.676
#> GSM494594 2 0.909 0.8281 0.324 0.676
#> GSM494604 1 0.909 0.7727 0.676 0.324
#> GSM494564 2 0.909 0.8281 0.324 0.676
#> GSM494591 2 0.909 0.8281 0.324 0.676
#> GSM494567 2 0.909 0.8281 0.324 0.676
#> GSM494602 2 0.000 0.7062 0.000 1.000
#> GSM494613 2 0.909 0.8281 0.324 0.676
#> GSM494589 2 0.909 0.8281 0.324 0.676
#> GSM494598 2 0.000 0.7062 0.000 1.000
#> GSM494593 2 0.000 0.7062 0.000 1.000
#> GSM494583 2 0.909 0.8281 0.324 0.676
#> GSM494612 2 0.000 0.7062 0.000 1.000
#> GSM494558 2 0.909 0.8281 0.324 0.676
#> GSM494556 2 0.909 0.8281 0.324 0.676
#> GSM494559 2 0.909 0.8281 0.324 0.676
#> GSM494571 2 0.909 0.8281 0.324 0.676
#> GSM494614 2 0.909 0.8281 0.324 0.676
#> GSM494603 2 0.943 0.7906 0.360 0.640
#> GSM494568 1 0.886 0.0419 0.696 0.304
#> GSM494572 2 0.909 0.8281 0.324 0.676
#> GSM494600 2 0.909 0.8281 0.324 0.676
#> GSM494562 2 0.000 0.7062 0.000 1.000
#> GSM494615 2 0.909 0.8281 0.324 0.676
#> GSM494582 2 0.000 0.7062 0.000 1.000
#> GSM494599 2 0.000 0.7062 0.000 1.000
#> GSM494610 2 0.000 0.7062 0.000 1.000
#> GSM494587 2 0.343 0.7358 0.064 0.936
#> GSM494581 2 0.416 0.7439 0.084 0.916
#> GSM494580 2 0.909 0.8281 0.324 0.676
#> GSM494563 2 0.909 0.8281 0.324 0.676
#> GSM494576 2 0.871 0.8185 0.292 0.708
#> GSM494605 1 0.909 0.7727 0.676 0.324
#> GSM494584 2 0.909 0.8281 0.324 0.676
#> GSM494586 2 0.000 0.7062 0.000 1.000
#> GSM494578 2 0.909 0.8281 0.324 0.676
#> GSM494585 2 0.163 0.7181 0.024 0.976
#> GSM494611 2 0.000 0.7062 0.000 1.000
#> GSM494560 2 0.909 0.8281 0.324 0.676
#> GSM494595 2 0.000 0.7062 0.000 1.000
#> GSM494570 2 0.909 0.8281 0.324 0.676
#> GSM494597 2 0.909 0.8281 0.324 0.676
#> GSM494607 2 0.000 0.7062 0.000 1.000
#> GSM494561 2 0.909 0.8281 0.324 0.676
#> GSM494569 1 0.000 0.7592 1.000 0.000
#> GSM494592 2 0.000 0.7062 0.000 1.000
#> GSM494577 2 0.900 0.8260 0.316 0.684
#> GSM494588 2 0.909 0.8281 0.324 0.676
#> GSM494590 2 0.909 0.8281 0.324 0.676
#> GSM494609 2 0.000 0.7062 0.000 1.000
#> GSM494608 2 0.000 0.7062 0.000 1.000
#> GSM494606 2 0.000 0.7062 0.000 1.000
#> GSM494574 2 0.000 0.7062 0.000 1.000
#> GSM494573 2 0.909 0.8281 0.324 0.676
#> GSM494566 2 0.529 0.7572 0.120 0.880
#> GSM494601 2 0.000 0.7062 0.000 1.000
#> GSM494557 2 0.909 0.8281 0.324 0.676
#> GSM494579 2 0.224 0.7237 0.036 0.964
#> GSM494596 2 0.909 0.8281 0.324 0.676
#> GSM494575 2 0.000 0.7062 0.000 1.000
#> GSM494625 1 0.000 0.7592 1.000 0.000
#> GSM494654 2 0.909 0.8281 0.324 0.676
#> GSM494664 1 0.909 0.7727 0.676 0.324
#> GSM494624 1 0.000 0.7592 1.000 0.000
#> GSM494651 1 0.000 0.7592 1.000 0.000
#> GSM494662 1 0.000 0.7592 1.000 0.000
#> GSM494627 1 0.000 0.7592 1.000 0.000
#> GSM494673 1 0.909 0.7727 0.676 0.324
#> GSM494649 1 0.000 0.7592 1.000 0.000
#> GSM494658 1 0.909 0.7727 0.676 0.324
#> GSM494653 1 0.909 0.7727 0.676 0.324
#> GSM494643 1 0.000 0.7592 1.000 0.000
#> GSM494672 1 0.909 0.7727 0.676 0.324
#> GSM494618 1 0.000 0.7592 1.000 0.000
#> GSM494631 2 0.909 0.8281 0.324 0.676
#> GSM494619 1 0.000 0.7592 1.000 0.000
#> GSM494674 1 0.909 0.7727 0.676 0.324
#> GSM494616 1 0.000 0.7592 1.000 0.000
#> GSM494663 1 0.000 0.7592 1.000 0.000
#> GSM494628 1 0.000 0.7592 1.000 0.000
#> GSM494632 1 0.909 0.7727 0.676 0.324
#> GSM494660 1 0.000 0.7592 1.000 0.000
#> GSM494622 1 0.000 0.7592 1.000 0.000
#> GSM494642 1 0.909 0.7727 0.676 0.324
#> GSM494647 1 0.909 0.7727 0.676 0.324
#> GSM494659 1 0.909 0.7727 0.676 0.324
#> GSM494670 1 0.909 0.7727 0.676 0.324
#> GSM494675 2 0.909 0.8281 0.324 0.676
#> GSM494641 1 0.909 0.7727 0.676 0.324
#> GSM494636 1 0.000 0.7592 1.000 0.000
#> GSM494640 1 0.000 0.7592 1.000 0.000
#> GSM494623 1 0.000 0.7592 1.000 0.000
#> GSM494644 1 0.909 0.7727 0.676 0.324
#> GSM494646 1 0.909 0.7727 0.676 0.324
#> GSM494665 1 0.909 0.7727 0.676 0.324
#> GSM494638 1 0.000 0.7592 1.000 0.000
#> GSM494645 1 0.909 0.7727 0.676 0.324
#> GSM494671 1 0.909 0.7727 0.676 0.324
#> GSM494655 1 0.909 0.7727 0.676 0.324
#> GSM494620 1 0.000 0.7592 1.000 0.000
#> GSM494630 1 0.000 0.7592 1.000 0.000
#> GSM494657 2 0.909 0.8281 0.324 0.676
#> GSM494667 1 0.909 0.7727 0.676 0.324
#> GSM494621 1 0.000 0.7592 1.000 0.000
#> GSM494629 1 0.000 0.7592 1.000 0.000
#> GSM494637 1 0.000 0.7592 1.000 0.000
#> GSM494652 1 0.909 0.7727 0.676 0.324
#> GSM494648 1 0.000 0.7592 1.000 0.000
#> GSM494650 1 0.000 0.7592 1.000 0.000
#> GSM494669 1 0.909 0.7727 0.676 0.324
#> GSM494666 1 0.909 0.7727 0.676 0.324
#> GSM494668 1 0.909 0.7727 0.676 0.324
#> GSM494633 1 0.000 0.7592 1.000 0.000
#> GSM494634 1 0.909 0.7727 0.676 0.324
#> GSM494639 1 0.909 0.7727 0.676 0.324
#> GSM494661 1 0.909 0.7727 0.676 0.324
#> GSM494617 1 0.000 0.7592 1.000 0.000
#> GSM494626 1 0.000 0.7592 1.000 0.000
#> GSM494656 2 0.909 0.8281 0.324 0.676
#> GSM494635 1 0.909 0.7727 0.676 0.324
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM494565 3 0.1315 0.7937 0.008 0.020 0.972
#> GSM494594 3 0.0237 0.8020 0.004 0.000 0.996
#> GSM494604 2 0.0747 0.4221 0.016 0.984 0.000
#> GSM494564 3 0.0661 0.8009 0.008 0.004 0.988
#> GSM494591 3 0.0237 0.8018 0.000 0.004 0.996
#> GSM494567 3 0.0237 0.8020 0.004 0.000 0.996
#> GSM494602 2 0.5497 0.2863 0.000 0.708 0.292
#> GSM494613 3 0.0000 0.8026 0.000 0.000 1.000
#> GSM494589 3 0.0661 0.8009 0.008 0.004 0.988
#> GSM494598 2 0.5497 0.2863 0.000 0.708 0.292
#> GSM494593 2 0.5497 0.2863 0.000 0.708 0.292
#> GSM494583 3 0.6307 0.2189 0.000 0.488 0.512
#> GSM494612 2 0.5497 0.2863 0.000 0.708 0.292
#> GSM494558 3 0.5785 0.5071 0.332 0.000 0.668
#> GSM494556 3 0.0000 0.8026 0.000 0.000 1.000
#> GSM494559 3 0.0661 0.8009 0.008 0.004 0.988
#> GSM494571 3 0.4291 0.6668 0.180 0.000 0.820
#> GSM494614 3 0.0747 0.7967 0.000 0.016 0.984
#> GSM494603 3 0.6008 0.4352 0.372 0.000 0.628
#> GSM494568 1 0.6225 0.0555 0.568 0.000 0.432
#> GSM494572 3 0.0237 0.8020 0.004 0.000 0.996
#> GSM494600 3 0.0661 0.8009 0.008 0.004 0.988
#> GSM494562 2 0.6280 -0.1278 0.000 0.540 0.460
#> GSM494615 3 0.0237 0.8020 0.004 0.000 0.996
#> GSM494582 2 0.5497 0.2863 0.000 0.708 0.292
#> GSM494599 2 0.3482 0.3749 0.000 0.872 0.128
#> GSM494610 2 0.5497 0.2863 0.000 0.708 0.292
#> GSM494587 3 0.6309 0.2047 0.000 0.496 0.504
#> GSM494581 3 0.6309 0.2047 0.000 0.496 0.504
#> GSM494580 3 0.0237 0.8020 0.004 0.000 0.996
#> GSM494563 3 0.1315 0.7937 0.008 0.020 0.972
#> GSM494576 3 0.6308 0.2122 0.000 0.492 0.508
#> GSM494605 1 0.6008 0.4059 0.628 0.372 0.000
#> GSM494584 3 0.2448 0.7538 0.000 0.076 0.924
#> GSM494586 3 0.6309 0.2047 0.000 0.496 0.504
#> GSM494578 3 0.0000 0.8026 0.000 0.000 1.000
#> GSM494585 3 0.6309 0.2047 0.000 0.496 0.504
#> GSM494611 2 0.5497 0.2863 0.000 0.708 0.292
#> GSM494560 3 0.1170 0.7954 0.008 0.016 0.976
#> GSM494595 2 0.5733 0.2259 0.000 0.676 0.324
#> GSM494570 3 0.5529 0.5671 0.296 0.000 0.704
#> GSM494597 3 0.0000 0.8026 0.000 0.000 1.000
#> GSM494607 2 0.0000 0.4181 0.000 1.000 0.000
#> GSM494561 3 0.5810 0.5124 0.336 0.000 0.664
#> GSM494569 1 0.1031 0.8576 0.976 0.000 0.024
#> GSM494592 2 0.3482 0.3749 0.000 0.872 0.128
#> GSM494577 3 0.6308 0.2122 0.000 0.492 0.508
#> GSM494588 3 0.1315 0.7937 0.008 0.020 0.972
#> GSM494590 3 0.0237 0.8020 0.004 0.000 0.996
#> GSM494609 2 0.5591 0.2650 0.000 0.696 0.304
#> GSM494608 2 0.5497 0.2863 0.000 0.708 0.292
#> GSM494606 2 0.5497 0.2863 0.000 0.708 0.292
#> GSM494574 2 0.5497 0.2863 0.000 0.708 0.292
#> GSM494573 3 0.0661 0.8009 0.008 0.004 0.988
#> GSM494566 2 0.6286 -0.1373 0.000 0.536 0.464
#> GSM494601 2 0.5497 0.2863 0.000 0.708 0.292
#> GSM494557 3 0.0237 0.8018 0.000 0.004 0.996
#> GSM494579 2 0.6280 -0.1281 0.000 0.540 0.460
#> GSM494596 3 0.0000 0.8026 0.000 0.000 1.000
#> GSM494575 2 0.5497 0.2863 0.000 0.708 0.292
#> GSM494625 1 0.0747 0.8572 0.984 0.000 0.016
#> GSM494654 3 0.5678 0.5298 0.316 0.000 0.684
#> GSM494664 1 0.5560 0.5562 0.700 0.300 0.000
#> GSM494624 1 0.0237 0.8589 0.996 0.000 0.004
#> GSM494651 1 0.1031 0.8576 0.976 0.000 0.024
#> GSM494662 1 0.0424 0.8592 0.992 0.000 0.008
#> GSM494627 1 0.1031 0.8576 0.976 0.000 0.024
#> GSM494673 2 0.6307 -0.0130 0.488 0.512 0.000
#> GSM494649 1 0.0747 0.8572 0.984 0.000 0.016
#> GSM494658 2 0.6307 -0.0130 0.488 0.512 0.000
#> GSM494653 2 0.6307 -0.0130 0.488 0.512 0.000
#> GSM494643 1 0.0237 0.8589 0.996 0.000 0.004
#> GSM494672 2 0.6307 -0.0130 0.488 0.512 0.000
#> GSM494618 1 0.0592 0.8594 0.988 0.000 0.012
#> GSM494631 3 0.5529 0.5618 0.296 0.000 0.704
#> GSM494619 1 0.0000 0.8580 1.000 0.000 0.000
#> GSM494674 2 0.6307 -0.0130 0.488 0.512 0.000
#> GSM494616 1 0.1031 0.8576 0.976 0.000 0.024
#> GSM494663 1 0.1031 0.8576 0.976 0.000 0.024
#> GSM494628 1 0.1031 0.8576 0.976 0.000 0.024
#> GSM494632 1 0.5058 0.6270 0.756 0.244 0.000
#> GSM494660 1 0.0747 0.8572 0.984 0.000 0.016
#> GSM494622 1 0.1031 0.8576 0.976 0.000 0.024
#> GSM494642 2 0.6307 -0.0130 0.488 0.512 0.000
#> GSM494647 2 0.6307 -0.0130 0.488 0.512 0.000
#> GSM494659 2 0.6307 -0.0130 0.488 0.512 0.000
#> GSM494670 2 0.6307 -0.0130 0.488 0.512 0.000
#> GSM494675 3 0.0000 0.8026 0.000 0.000 1.000
#> GSM494641 2 0.6307 -0.0130 0.488 0.512 0.000
#> GSM494636 1 0.0424 0.8592 0.992 0.000 0.008
#> GSM494640 1 0.1031 0.8576 0.976 0.000 0.024
#> GSM494623 1 0.0000 0.8580 1.000 0.000 0.000
#> GSM494644 2 0.6307 -0.0130 0.488 0.512 0.000
#> GSM494646 1 0.5497 0.5676 0.708 0.292 0.000
#> GSM494665 1 0.6299 0.0990 0.524 0.476 0.000
#> GSM494638 1 0.0424 0.8592 0.992 0.000 0.008
#> GSM494645 1 0.5560 0.5562 0.700 0.300 0.000
#> GSM494671 2 0.6307 -0.0130 0.488 0.512 0.000
#> GSM494655 2 0.6307 -0.0130 0.488 0.512 0.000
#> GSM494620 1 0.0000 0.8580 1.000 0.000 0.000
#> GSM494630 1 0.0000 0.8580 1.000 0.000 0.000
#> GSM494657 3 0.0237 0.8020 0.004 0.000 0.996
#> GSM494667 2 0.6307 -0.0130 0.488 0.512 0.000
#> GSM494621 1 0.0000 0.8580 1.000 0.000 0.000
#> GSM494629 1 0.1031 0.8576 0.976 0.000 0.024
#> GSM494637 1 0.1031 0.8576 0.976 0.000 0.024
#> GSM494652 2 0.6307 -0.0130 0.488 0.512 0.000
#> GSM494648 1 0.0000 0.8580 1.000 0.000 0.000
#> GSM494650 1 0.1031 0.8576 0.976 0.000 0.024
#> GSM494669 2 0.6307 -0.0130 0.488 0.512 0.000
#> GSM494666 1 0.5560 0.5562 0.700 0.300 0.000
#> GSM494668 2 0.6307 -0.0130 0.488 0.512 0.000
#> GSM494633 1 0.0747 0.8572 0.984 0.000 0.016
#> GSM494634 2 0.6307 -0.0130 0.488 0.512 0.000
#> GSM494639 1 0.5397 0.5835 0.720 0.280 0.000
#> GSM494661 1 0.5785 0.4950 0.668 0.332 0.000
#> GSM494617 1 0.0424 0.8592 0.992 0.000 0.008
#> GSM494626 1 0.0424 0.8592 0.992 0.000 0.008
#> GSM494656 3 0.5497 0.5618 0.292 0.000 0.708
#> GSM494635 1 0.5560 0.5562 0.700 0.300 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM494565 3 0.5177 0.857 0.104 0.104 0.780 0.012
#> GSM494594 3 0.2196 0.894 0.032 0.016 0.936 0.016
#> GSM494604 1 0.3266 0.774 0.832 0.168 0.000 0.000
#> GSM494564 3 0.4152 0.893 0.100 0.048 0.840 0.012
#> GSM494591 3 0.2313 0.906 0.032 0.044 0.924 0.000
#> GSM494567 3 0.1389 0.910 0.000 0.048 0.952 0.000
#> GSM494602 2 0.1118 0.959 0.036 0.964 0.000 0.000
#> GSM494613 3 0.1576 0.910 0.004 0.048 0.948 0.000
#> GSM494589 3 0.4152 0.893 0.100 0.048 0.840 0.012
#> GSM494598 2 0.1118 0.959 0.036 0.964 0.000 0.000
#> GSM494593 2 0.1118 0.959 0.036 0.964 0.000 0.000
#> GSM494583 2 0.3754 0.870 0.064 0.852 0.084 0.000
#> GSM494612 2 0.1118 0.959 0.036 0.964 0.000 0.000
#> GSM494558 3 0.3906 0.819 0.020 0.020 0.848 0.112
#> GSM494556 3 0.2089 0.909 0.020 0.048 0.932 0.000
#> GSM494559 3 0.4212 0.892 0.104 0.048 0.836 0.012
#> GSM494571 3 0.1796 0.889 0.032 0.004 0.948 0.016
#> GSM494614 3 0.3471 0.900 0.072 0.060 0.868 0.000
#> GSM494603 4 0.7077 0.235 0.072 0.024 0.368 0.536
#> GSM494568 4 0.3740 0.842 0.028 0.020 0.088 0.864
#> GSM494572 3 0.2313 0.906 0.032 0.044 0.924 0.000
#> GSM494600 3 0.4152 0.893 0.100 0.048 0.840 0.012
#> GSM494562 2 0.1211 0.947 0.000 0.960 0.040 0.000
#> GSM494615 3 0.1624 0.907 0.020 0.028 0.952 0.000
#> GSM494582 2 0.1118 0.959 0.036 0.964 0.000 0.000
#> GSM494599 2 0.1637 0.938 0.060 0.940 0.000 0.000
#> GSM494610 2 0.1118 0.959 0.036 0.964 0.000 0.000
#> GSM494587 2 0.1743 0.936 0.004 0.940 0.056 0.000
#> GSM494581 2 0.2521 0.921 0.024 0.912 0.064 0.000
#> GSM494580 3 0.1389 0.910 0.000 0.048 0.952 0.000
#> GSM494563 3 0.5839 0.807 0.104 0.156 0.728 0.012
#> GSM494576 2 0.1824 0.934 0.004 0.936 0.060 0.000
#> GSM494605 1 0.3672 0.949 0.824 0.012 0.000 0.164
#> GSM494584 3 0.6037 0.596 0.068 0.304 0.628 0.000
#> GSM494586 2 0.1557 0.937 0.000 0.944 0.056 0.000
#> GSM494578 3 0.1389 0.910 0.000 0.048 0.952 0.000
#> GSM494585 2 0.1743 0.936 0.004 0.940 0.056 0.000
#> GSM494611 2 0.1118 0.959 0.036 0.964 0.000 0.000
#> GSM494560 3 0.4212 0.892 0.104 0.048 0.836 0.012
#> GSM494595 2 0.1256 0.952 0.008 0.964 0.028 0.000
#> GSM494570 3 0.5336 0.821 0.112 0.020 0.776 0.092
#> GSM494597 3 0.2313 0.906 0.032 0.044 0.924 0.000
#> GSM494607 2 0.2011 0.915 0.080 0.920 0.000 0.000
#> GSM494561 3 0.6180 0.752 0.112 0.024 0.716 0.148
#> GSM494569 4 0.1943 0.901 0.008 0.016 0.032 0.944
#> GSM494592 2 0.1637 0.938 0.060 0.940 0.000 0.000
#> GSM494577 2 0.3323 0.894 0.060 0.876 0.064 0.000
#> GSM494588 3 0.6002 0.802 0.116 0.156 0.716 0.012
#> GSM494590 3 0.2313 0.906 0.032 0.044 0.924 0.000
#> GSM494609 2 0.1388 0.957 0.028 0.960 0.012 0.000
#> GSM494608 2 0.1118 0.959 0.036 0.964 0.000 0.000
#> GSM494606 2 0.1118 0.959 0.036 0.964 0.000 0.000
#> GSM494574 2 0.1118 0.959 0.036 0.964 0.000 0.000
#> GSM494573 3 0.4152 0.893 0.100 0.048 0.840 0.012
#> GSM494566 2 0.2699 0.919 0.028 0.904 0.068 0.000
#> GSM494601 2 0.1022 0.959 0.032 0.968 0.000 0.000
#> GSM494557 3 0.1389 0.910 0.000 0.048 0.952 0.000
#> GSM494579 2 0.1677 0.944 0.012 0.948 0.040 0.000
#> GSM494596 3 0.2313 0.906 0.032 0.044 0.924 0.000
#> GSM494575 2 0.1118 0.959 0.036 0.964 0.000 0.000
#> GSM494625 4 0.1877 0.900 0.020 0.012 0.020 0.948
#> GSM494654 3 0.2920 0.867 0.032 0.020 0.908 0.040
#> GSM494664 1 0.3444 0.931 0.816 0.000 0.000 0.184
#> GSM494624 4 0.1877 0.900 0.020 0.012 0.020 0.948
#> GSM494651 4 0.1943 0.901 0.008 0.016 0.032 0.944
#> GSM494662 4 0.0817 0.902 0.024 0.000 0.000 0.976
#> GSM494627 4 0.1913 0.900 0.000 0.020 0.040 0.940
#> GSM494673 1 0.3659 0.974 0.840 0.024 0.000 0.136
#> GSM494649 4 0.1877 0.900 0.020 0.012 0.020 0.948
#> GSM494658 1 0.3707 0.970 0.840 0.028 0.000 0.132
#> GSM494653 1 0.3659 0.974 0.840 0.024 0.000 0.136
#> GSM494643 4 0.1377 0.902 0.008 0.008 0.020 0.964
#> GSM494672 1 0.3707 0.970 0.840 0.028 0.000 0.132
#> GSM494618 4 0.1943 0.901 0.008 0.016 0.032 0.944
#> GSM494631 3 0.3946 0.747 0.000 0.020 0.812 0.168
#> GSM494619 4 0.1877 0.900 0.020 0.012 0.020 0.948
#> GSM494674 1 0.3659 0.974 0.840 0.024 0.000 0.136
#> GSM494616 4 0.1943 0.901 0.008 0.016 0.032 0.944
#> GSM494663 4 0.1913 0.900 0.000 0.020 0.040 0.940
#> GSM494628 4 0.2007 0.899 0.004 0.020 0.036 0.940
#> GSM494632 4 0.3801 0.663 0.220 0.000 0.000 0.780
#> GSM494660 4 0.1877 0.900 0.020 0.012 0.020 0.948
#> GSM494622 4 0.2007 0.899 0.004 0.020 0.036 0.940
#> GSM494642 1 0.3659 0.974 0.840 0.024 0.000 0.136
#> GSM494647 1 0.3659 0.974 0.840 0.024 0.000 0.136
#> GSM494659 1 0.3659 0.974 0.840 0.024 0.000 0.136
#> GSM494670 1 0.3659 0.974 0.840 0.024 0.000 0.136
#> GSM494675 3 0.2399 0.910 0.032 0.048 0.920 0.000
#> GSM494641 1 0.3659 0.974 0.840 0.024 0.000 0.136
#> GSM494636 4 0.0817 0.902 0.024 0.000 0.000 0.976
#> GSM494640 4 0.0779 0.905 0.004 0.000 0.016 0.980
#> GSM494623 4 0.1877 0.900 0.020 0.012 0.020 0.948
#> GSM494644 1 0.3443 0.970 0.848 0.016 0.000 0.136
#> GSM494646 4 0.4998 -0.195 0.488 0.000 0.000 0.512
#> GSM494665 1 0.3606 0.970 0.840 0.020 0.000 0.140
#> GSM494638 4 0.1629 0.902 0.024 0.000 0.024 0.952
#> GSM494645 1 0.3400 0.933 0.820 0.000 0.000 0.180
#> GSM494671 1 0.3659 0.974 0.840 0.024 0.000 0.136
#> GSM494655 1 0.3554 0.972 0.844 0.020 0.000 0.136
#> GSM494620 4 0.1877 0.900 0.020 0.012 0.020 0.948
#> GSM494630 4 0.1877 0.900 0.020 0.012 0.020 0.948
#> GSM494657 3 0.2313 0.906 0.032 0.044 0.924 0.000
#> GSM494667 1 0.3659 0.974 0.840 0.024 0.000 0.136
#> GSM494621 4 0.1877 0.900 0.020 0.012 0.020 0.948
#> GSM494629 4 0.2706 0.871 0.000 0.020 0.080 0.900
#> GSM494637 4 0.0927 0.905 0.008 0.000 0.016 0.976
#> GSM494652 1 0.3659 0.974 0.840 0.024 0.000 0.136
#> GSM494648 4 0.1877 0.900 0.020 0.012 0.020 0.948
#> GSM494650 4 0.2007 0.899 0.004 0.020 0.036 0.940
#> GSM494669 1 0.3659 0.974 0.840 0.024 0.000 0.136
#> GSM494666 1 0.3444 0.931 0.816 0.000 0.000 0.184
#> GSM494668 1 0.3659 0.974 0.840 0.024 0.000 0.136
#> GSM494633 4 0.1877 0.900 0.020 0.012 0.020 0.948
#> GSM494634 1 0.3659 0.974 0.840 0.024 0.000 0.136
#> GSM494639 4 0.4585 0.405 0.332 0.000 0.000 0.668
#> GSM494661 1 0.3356 0.937 0.824 0.000 0.000 0.176
#> GSM494617 4 0.1993 0.900 0.016 0.016 0.024 0.944
#> GSM494626 4 0.1993 0.900 0.016 0.016 0.024 0.944
#> GSM494656 3 0.2531 0.876 0.032 0.020 0.924 0.024
#> GSM494635 1 0.3444 0.929 0.816 0.000 0.000 0.184
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM494565 3 0.4808 0.2161 0.000 0.024 0.576 0.000 0.400
#> GSM494594 3 0.3106 0.5841 0.000 0.000 0.844 0.024 0.132
#> GSM494604 1 0.3496 0.8118 0.844 0.056 0.000 0.008 0.092
#> GSM494564 3 0.4649 0.2188 0.000 0.000 0.580 0.016 0.404
#> GSM494591 3 0.3106 0.5841 0.000 0.000 0.844 0.024 0.132
#> GSM494567 3 0.0290 0.5881 0.000 0.000 0.992 0.008 0.000
#> GSM494602 2 0.0290 0.9257 0.000 0.992 0.000 0.008 0.000
#> GSM494613 3 0.0290 0.5881 0.000 0.000 0.992 0.008 0.000
#> GSM494589 3 0.4192 0.2553 0.000 0.000 0.596 0.000 0.404
#> GSM494598 2 0.0992 0.9221 0.000 0.968 0.000 0.008 0.024
#> GSM494593 2 0.1408 0.9192 0.000 0.948 0.000 0.008 0.044
#> GSM494583 2 0.5693 0.4849 0.000 0.620 0.144 0.000 0.236
#> GSM494612 2 0.0290 0.9257 0.000 0.992 0.000 0.008 0.000
#> GSM494558 3 0.6519 -0.1216 0.000 0.000 0.456 0.204 0.340
#> GSM494556 3 0.2886 0.5183 0.000 0.000 0.844 0.008 0.148
#> GSM494559 3 0.4640 0.2214 0.000 0.000 0.584 0.016 0.400
#> GSM494571 3 0.3152 0.5821 0.000 0.000 0.840 0.024 0.136
#> GSM494614 3 0.4183 0.3910 0.000 0.008 0.712 0.008 0.272
#> GSM494603 5 0.6466 -0.4024 0.004 0.000 0.164 0.360 0.472
#> GSM494568 4 0.5374 0.7049 0.004 0.000 0.052 0.568 0.376
#> GSM494572 3 0.3106 0.5841 0.000 0.000 0.844 0.024 0.132
#> GSM494600 3 0.4192 0.2553 0.000 0.000 0.596 0.000 0.404
#> GSM494562 2 0.0290 0.9257 0.000 0.992 0.000 0.000 0.008
#> GSM494615 3 0.3053 0.5145 0.000 0.000 0.828 0.008 0.164
#> GSM494582 2 0.0162 0.9258 0.000 0.996 0.000 0.004 0.000
#> GSM494599 2 0.1830 0.9133 0.000 0.924 0.000 0.008 0.068
#> GSM494610 2 0.0992 0.9221 0.000 0.968 0.000 0.008 0.024
#> GSM494587 2 0.0404 0.9235 0.000 0.988 0.012 0.000 0.000
#> GSM494581 2 0.3479 0.8420 0.000 0.836 0.080 0.000 0.084
#> GSM494580 3 0.0290 0.5881 0.000 0.000 0.992 0.008 0.000
#> GSM494563 3 0.6213 -0.0214 0.000 0.140 0.452 0.000 0.408
#> GSM494576 2 0.0510 0.9226 0.000 0.984 0.016 0.000 0.000
#> GSM494605 1 0.0963 0.9164 0.964 0.000 0.000 0.000 0.036
#> GSM494584 3 0.6694 0.0477 0.000 0.260 0.496 0.008 0.236
#> GSM494586 2 0.0000 0.9255 0.000 1.000 0.000 0.000 0.000
#> GSM494578 3 0.0290 0.5881 0.000 0.000 0.992 0.008 0.000
#> GSM494585 2 0.0404 0.9235 0.000 0.988 0.012 0.000 0.000
#> GSM494611 2 0.0290 0.9257 0.000 0.992 0.000 0.008 0.000
#> GSM494560 3 0.4331 0.2511 0.000 0.004 0.596 0.000 0.400
#> GSM494595 2 0.0000 0.9255 0.000 1.000 0.000 0.000 0.000
#> GSM494570 5 0.6551 0.1603 0.000 0.000 0.384 0.200 0.416
#> GSM494597 3 0.3106 0.5841 0.000 0.000 0.844 0.024 0.132
#> GSM494607 2 0.2193 0.9022 0.000 0.900 0.000 0.008 0.092
#> GSM494561 5 0.6596 0.1831 0.000 0.000 0.372 0.212 0.416
#> GSM494569 4 0.5014 0.7867 0.040 0.000 0.000 0.592 0.368
#> GSM494592 2 0.1484 0.9181 0.000 0.944 0.000 0.008 0.048
#> GSM494577 2 0.4815 0.6249 0.000 0.692 0.064 0.000 0.244
#> GSM494588 3 0.6438 -0.2915 0.000 0.004 0.436 0.152 0.408
#> GSM494590 3 0.3106 0.5841 0.000 0.000 0.844 0.024 0.132
#> GSM494609 2 0.1725 0.9152 0.000 0.936 0.020 0.000 0.044
#> GSM494608 2 0.1725 0.9152 0.000 0.936 0.020 0.000 0.044
#> GSM494606 2 0.1408 0.9192 0.000 0.948 0.000 0.008 0.044
#> GSM494574 2 0.0992 0.9221 0.000 0.968 0.000 0.008 0.024
#> GSM494573 3 0.4192 0.2553 0.000 0.000 0.596 0.000 0.404
#> GSM494566 2 0.5956 0.5594 0.000 0.616 0.152 0.008 0.224
#> GSM494601 2 0.1121 0.9195 0.000 0.956 0.000 0.000 0.044
#> GSM494557 3 0.0290 0.5881 0.000 0.000 0.992 0.008 0.000
#> GSM494579 2 0.3574 0.7861 0.000 0.804 0.028 0.000 0.168
#> GSM494596 3 0.3106 0.5841 0.000 0.000 0.844 0.024 0.132
#> GSM494575 2 0.0290 0.9257 0.000 0.992 0.000 0.008 0.000
#> GSM494625 4 0.1043 0.7391 0.040 0.000 0.000 0.960 0.000
#> GSM494654 3 0.4639 0.3524 0.000 0.000 0.632 0.024 0.344
#> GSM494664 1 0.1043 0.9139 0.960 0.000 0.000 0.000 0.040
#> GSM494624 4 0.1043 0.7391 0.040 0.000 0.000 0.960 0.000
#> GSM494651 4 0.4990 0.7917 0.040 0.000 0.000 0.600 0.360
#> GSM494662 4 0.4398 0.8017 0.040 0.000 0.000 0.720 0.240
#> GSM494627 4 0.4840 0.7985 0.040 0.000 0.000 0.640 0.320
#> GSM494673 1 0.0404 0.9318 0.988 0.000 0.000 0.000 0.012
#> GSM494649 4 0.1043 0.7391 0.040 0.000 0.000 0.960 0.000
#> GSM494658 1 0.1618 0.9061 0.944 0.008 0.000 0.008 0.040
#> GSM494653 1 0.0290 0.9328 0.992 0.000 0.000 0.000 0.008
#> GSM494643 4 0.3192 0.7742 0.040 0.000 0.000 0.848 0.112
#> GSM494672 1 0.0992 0.9223 0.968 0.008 0.000 0.000 0.024
#> GSM494618 4 0.4990 0.7917 0.040 0.000 0.000 0.600 0.360
#> GSM494631 3 0.5490 0.1258 0.000 0.000 0.644 0.128 0.228
#> GSM494619 4 0.1043 0.7391 0.040 0.000 0.000 0.960 0.000
#> GSM494674 1 0.0000 0.9339 1.000 0.000 0.000 0.000 0.000
#> GSM494616 4 0.4990 0.7917 0.040 0.000 0.000 0.600 0.360
#> GSM494663 4 0.4840 0.7985 0.040 0.000 0.000 0.640 0.320
#> GSM494628 4 0.4990 0.7917 0.040 0.000 0.000 0.600 0.360
#> GSM494632 1 0.5641 -0.1115 0.488 0.000 0.000 0.436 0.076
#> GSM494660 4 0.1043 0.7391 0.040 0.000 0.000 0.960 0.000
#> GSM494622 4 0.4886 0.7772 0.032 0.000 0.000 0.596 0.372
#> GSM494642 1 0.0000 0.9339 1.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.9339 1.000 0.000 0.000 0.000 0.000
#> GSM494659 1 0.0290 0.9328 0.992 0.000 0.000 0.000 0.008
#> GSM494670 1 0.0703 0.9273 0.976 0.000 0.000 0.000 0.024
#> GSM494675 3 0.3053 0.5140 0.000 0.000 0.828 0.008 0.164
#> GSM494641 1 0.0000 0.9339 1.000 0.000 0.000 0.000 0.000
#> GSM494636 4 0.4552 0.8013 0.040 0.000 0.000 0.696 0.264
#> GSM494640 4 0.4284 0.8002 0.040 0.000 0.000 0.736 0.224
#> GSM494623 4 0.1043 0.7391 0.040 0.000 0.000 0.960 0.000
#> GSM494644 1 0.0000 0.9339 1.000 0.000 0.000 0.000 0.000
#> GSM494646 1 0.4284 0.6186 0.736 0.000 0.000 0.224 0.040
#> GSM494665 1 0.0963 0.9164 0.964 0.000 0.000 0.000 0.036
#> GSM494638 4 0.4990 0.7917 0.040 0.000 0.000 0.600 0.360
#> GSM494645 1 0.0000 0.9339 1.000 0.000 0.000 0.000 0.000
#> GSM494671 1 0.0992 0.9223 0.968 0.008 0.000 0.000 0.024
#> GSM494655 1 0.0000 0.9339 1.000 0.000 0.000 0.000 0.000
#> GSM494620 4 0.1043 0.7391 0.040 0.000 0.000 0.960 0.000
#> GSM494630 4 0.1043 0.7391 0.040 0.000 0.000 0.960 0.000
#> GSM494657 3 0.3106 0.5841 0.000 0.000 0.844 0.024 0.132
#> GSM494667 1 0.0000 0.9339 1.000 0.000 0.000 0.000 0.000
#> GSM494621 4 0.1043 0.7391 0.040 0.000 0.000 0.960 0.000
#> GSM494629 4 0.4697 0.7951 0.032 0.000 0.000 0.648 0.320
#> GSM494637 4 0.4284 0.8002 0.040 0.000 0.000 0.736 0.224
#> GSM494652 1 0.0162 0.9334 0.996 0.000 0.000 0.000 0.004
#> GSM494648 4 0.1043 0.7391 0.040 0.000 0.000 0.960 0.000
#> GSM494650 4 0.5026 0.7839 0.040 0.000 0.000 0.588 0.372
#> GSM494669 1 0.0000 0.9339 1.000 0.000 0.000 0.000 0.000
#> GSM494666 1 0.1043 0.9139 0.960 0.000 0.000 0.000 0.040
#> GSM494668 1 0.0609 0.9287 0.980 0.000 0.000 0.000 0.020
#> GSM494633 4 0.1043 0.7391 0.040 0.000 0.000 0.960 0.000
#> GSM494634 1 0.0290 0.9328 0.992 0.000 0.000 0.000 0.008
#> GSM494639 1 0.5342 0.3422 0.612 0.000 0.000 0.312 0.076
#> GSM494661 1 0.0000 0.9339 1.000 0.000 0.000 0.000 0.000
#> GSM494617 4 0.4990 0.7917 0.040 0.000 0.000 0.600 0.360
#> GSM494626 4 0.4990 0.7917 0.040 0.000 0.000 0.600 0.360
#> GSM494656 3 0.3368 0.5691 0.000 0.000 0.820 0.024 0.156
#> GSM494635 1 0.0000 0.9339 1.000 0.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM494565 5 0.0767 0.6906 0.000 0.012 0.008 0.000 0.976 0.004
#> GSM494594 3 0.2664 0.7996 0.000 0.000 0.816 0.000 0.184 0.000
#> GSM494604 1 0.4052 0.8019 0.812 0.036 0.048 0.000 0.024 0.080
#> GSM494564 5 0.0891 0.6905 0.000 0.000 0.024 0.000 0.968 0.008
#> GSM494591 3 0.2730 0.8018 0.000 0.000 0.808 0.000 0.192 0.000
#> GSM494567 3 0.5771 0.5537 0.000 0.000 0.500 0.004 0.328 0.168
#> GSM494602 2 0.0000 0.8785 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494613 3 0.5809 0.5599 0.000 0.000 0.496 0.004 0.324 0.176
#> GSM494589 5 0.0790 0.6883 0.000 0.000 0.032 0.000 0.968 0.000
#> GSM494598 2 0.1585 0.8696 0.000 0.940 0.012 0.000 0.012 0.036
#> GSM494593 2 0.1616 0.8713 0.000 0.932 0.020 0.000 0.000 0.048
#> GSM494583 5 0.5635 -0.1507 0.000 0.420 0.000 0.000 0.432 0.148
#> GSM494612 2 0.0000 0.8785 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494558 4 0.5230 0.4697 0.000 0.000 0.208 0.648 0.016 0.128
#> GSM494556 5 0.5934 -0.2821 0.000 0.000 0.376 0.004 0.436 0.184
#> GSM494559 5 0.1003 0.6916 0.000 0.000 0.020 0.000 0.964 0.016
#> GSM494571 3 0.2631 0.7977 0.000 0.000 0.820 0.000 0.180 0.000
#> GSM494614 5 0.5439 0.3316 0.000 0.008 0.152 0.004 0.620 0.216
#> GSM494603 4 0.4739 0.5526 0.000 0.000 0.048 0.732 0.076 0.144
#> GSM494568 4 0.3382 0.6382 0.000 0.000 0.048 0.820 0.008 0.124
#> GSM494572 3 0.2730 0.8018 0.000 0.000 0.808 0.000 0.192 0.000
#> GSM494600 5 0.0790 0.6883 0.000 0.000 0.032 0.000 0.968 0.000
#> GSM494562 2 0.1049 0.8765 0.000 0.960 0.000 0.000 0.008 0.032
#> GSM494615 5 0.6027 -0.2760 0.000 0.000 0.372 0.008 0.436 0.184
#> GSM494582 2 0.0777 0.8773 0.000 0.972 0.000 0.000 0.004 0.024
#> GSM494599 2 0.2642 0.8582 0.000 0.884 0.032 0.000 0.020 0.064
#> GSM494610 2 0.1657 0.8687 0.000 0.936 0.012 0.000 0.012 0.040
#> GSM494587 2 0.2431 0.8505 0.000 0.860 0.000 0.000 0.008 0.132
#> GSM494581 2 0.5155 0.7067 0.000 0.668 0.020 0.000 0.132 0.180
#> GSM494580 3 0.5771 0.5537 0.000 0.000 0.500 0.004 0.328 0.168
#> GSM494563 5 0.1082 0.6758 0.000 0.040 0.000 0.000 0.956 0.004
#> GSM494576 2 0.3377 0.8166 0.000 0.808 0.000 0.000 0.056 0.136
#> GSM494605 1 0.2765 0.8722 0.872 0.000 0.064 0.056 0.000 0.008
#> GSM494584 5 0.5762 0.3904 0.000 0.172 0.012 0.000 0.556 0.260
#> GSM494586 2 0.1471 0.8763 0.000 0.932 0.000 0.000 0.004 0.064
#> GSM494578 3 0.5818 0.5440 0.000 0.000 0.492 0.004 0.328 0.176
#> GSM494585 2 0.2278 0.8526 0.000 0.868 0.000 0.000 0.004 0.128
#> GSM494611 2 0.0146 0.8781 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM494560 5 0.0951 0.6916 0.000 0.008 0.020 0.000 0.968 0.004
#> GSM494595 2 0.1010 0.8785 0.000 0.960 0.000 0.000 0.004 0.036
#> GSM494570 5 0.2147 0.6528 0.000 0.000 0.020 0.000 0.896 0.084
#> GSM494597 3 0.2871 0.8004 0.000 0.000 0.804 0.000 0.192 0.004
#> GSM494607 2 0.3401 0.8422 0.004 0.840 0.044 0.000 0.024 0.088
#> GSM494561 5 0.4002 0.4794 0.000 0.000 0.036 0.000 0.704 0.260
#> GSM494569 4 0.0291 0.7520 0.004 0.000 0.000 0.992 0.000 0.004
#> GSM494592 2 0.1974 0.8684 0.000 0.920 0.020 0.000 0.012 0.048
#> GSM494577 2 0.5461 0.3964 0.000 0.528 0.000 0.000 0.332 0.140
#> GSM494588 5 0.1531 0.6704 0.000 0.000 0.004 0.000 0.928 0.068
#> GSM494590 3 0.2730 0.8018 0.000 0.000 0.808 0.000 0.192 0.000
#> GSM494609 2 0.3229 0.8370 0.000 0.804 0.020 0.000 0.004 0.172
#> GSM494608 2 0.3296 0.8352 0.000 0.796 0.020 0.000 0.004 0.180
#> GSM494606 2 0.2039 0.8717 0.000 0.904 0.020 0.000 0.000 0.076
#> GSM494574 2 0.1657 0.8687 0.000 0.936 0.012 0.000 0.012 0.040
#> GSM494573 5 0.0790 0.6883 0.000 0.000 0.032 0.000 0.968 0.000
#> GSM494566 2 0.6936 0.2251 0.000 0.400 0.028 0.016 0.256 0.300
#> GSM494601 2 0.2094 0.8721 0.000 0.900 0.020 0.000 0.000 0.080
#> GSM494557 3 0.5818 0.5538 0.000 0.000 0.492 0.004 0.328 0.176
#> GSM494579 2 0.5341 0.6607 0.000 0.632 0.012 0.000 0.188 0.168
#> GSM494596 3 0.2730 0.8018 0.000 0.000 0.808 0.000 0.192 0.000
#> GSM494575 2 0.0000 0.8785 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494625 6 0.3756 0.9740 0.004 0.000 0.000 0.352 0.000 0.644
#> GSM494654 3 0.2605 0.6251 0.000 0.000 0.864 0.108 0.028 0.000
#> GSM494664 1 0.3055 0.8601 0.852 0.000 0.068 0.072 0.000 0.008
#> GSM494624 6 0.3878 0.9769 0.004 0.000 0.000 0.348 0.004 0.644
#> GSM494651 4 0.0146 0.7518 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM494662 4 0.4482 0.3715 0.004 0.000 0.096 0.712 0.000 0.188
#> GSM494627 4 0.2074 0.7261 0.004 0.000 0.048 0.912 0.000 0.036
#> GSM494673 1 0.0146 0.9131 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM494649 6 0.3756 0.9740 0.004 0.000 0.000 0.352 0.000 0.644
#> GSM494658 1 0.2255 0.8831 0.912 0.004 0.028 0.000 0.020 0.036
#> GSM494653 1 0.0146 0.9131 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM494643 6 0.4767 0.7310 0.004 0.000 0.040 0.444 0.000 0.512
#> GSM494672 1 0.1007 0.9046 0.968 0.004 0.004 0.000 0.016 0.008
#> GSM494618 4 0.0146 0.7518 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM494631 4 0.6778 -0.0703 0.000 0.000 0.332 0.424 0.064 0.180
#> GSM494619 6 0.3878 0.9769 0.004 0.000 0.000 0.348 0.004 0.644
#> GSM494674 1 0.0000 0.9137 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494616 4 0.0146 0.7518 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM494663 4 0.1938 0.7262 0.004 0.000 0.036 0.920 0.000 0.040
#> GSM494628 4 0.0935 0.7489 0.004 0.000 0.032 0.964 0.000 0.000
#> GSM494632 1 0.5701 0.1835 0.480 0.000 0.088 0.408 0.000 0.024
#> GSM494660 6 0.3756 0.9740 0.004 0.000 0.000 0.352 0.000 0.644
#> GSM494622 4 0.1151 0.7459 0.000 0.000 0.032 0.956 0.000 0.012
#> GSM494642 1 0.0000 0.9137 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.9137 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.9137 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494670 1 0.1369 0.9044 0.952 0.000 0.016 0.000 0.016 0.016
#> GSM494675 5 0.5877 -0.3052 0.000 0.000 0.380 0.004 0.444 0.172
#> GSM494641 1 0.0000 0.9137 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494636 4 0.4259 0.4268 0.004 0.000 0.096 0.740 0.000 0.160
#> GSM494640 4 0.4526 0.1324 0.004 0.000 0.052 0.656 0.000 0.288
#> GSM494623 6 0.3878 0.9769 0.004 0.000 0.000 0.348 0.004 0.644
#> GSM494644 1 0.0717 0.9104 0.976 0.000 0.016 0.000 0.000 0.008
#> GSM494646 1 0.5217 0.5831 0.640 0.000 0.088 0.248 0.000 0.024
#> GSM494665 1 0.2703 0.8744 0.876 0.000 0.064 0.052 0.000 0.008
#> GSM494638 4 0.2622 0.6679 0.004 0.000 0.104 0.868 0.000 0.024
#> GSM494645 1 0.1643 0.8952 0.924 0.000 0.068 0.000 0.000 0.008
#> GSM494671 1 0.1007 0.9046 0.968 0.004 0.004 0.000 0.016 0.008
#> GSM494655 1 0.0000 0.9137 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494620 6 0.3878 0.9769 0.004 0.000 0.000 0.348 0.004 0.644
#> GSM494630 6 0.4014 0.9739 0.004 0.000 0.004 0.348 0.004 0.640
#> GSM494657 3 0.2730 0.8018 0.000 0.000 0.808 0.000 0.192 0.000
#> GSM494667 1 0.0000 0.9137 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494621 6 0.3878 0.9769 0.004 0.000 0.000 0.348 0.004 0.644
#> GSM494629 4 0.1930 0.7264 0.000 0.000 0.048 0.916 0.000 0.036
#> GSM494637 4 0.4409 0.1311 0.004 0.000 0.044 0.664 0.000 0.288
#> GSM494652 1 0.0000 0.9137 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494648 6 0.3878 0.9769 0.004 0.000 0.000 0.348 0.004 0.644
#> GSM494650 4 0.1080 0.7489 0.004 0.000 0.032 0.960 0.000 0.004
#> GSM494669 1 0.0000 0.9137 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494666 1 0.2999 0.8628 0.856 0.000 0.068 0.068 0.000 0.008
#> GSM494668 1 0.1275 0.9057 0.956 0.000 0.012 0.000 0.016 0.016
#> GSM494633 6 0.3878 0.9769 0.004 0.000 0.000 0.348 0.004 0.644
#> GSM494634 1 0.0291 0.9115 0.992 0.004 0.004 0.000 0.000 0.000
#> GSM494639 1 0.5546 0.4133 0.560 0.000 0.088 0.328 0.000 0.024
#> GSM494661 1 0.1643 0.8952 0.924 0.000 0.068 0.000 0.000 0.008
#> GSM494617 4 0.1349 0.7167 0.004 0.000 0.056 0.940 0.000 0.000
#> GSM494626 4 0.0405 0.7488 0.004 0.000 0.008 0.988 0.000 0.000
#> GSM494656 3 0.2581 0.7440 0.000 0.000 0.860 0.020 0.120 0.000
#> GSM494635 1 0.2763 0.8716 0.868 0.000 0.088 0.036 0.000 0.008
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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 3)
get_signatures(res, k = 4)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 5)
get_signatures(res, k = 6)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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.
# code only for demonstration
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) individual(p) k
#> MAD:kmeans 119 1.96e-20 1.000 1.13e-15 1.000 2
#> MAD:kmeans 69 2.83e-11 0.828 2.71e-07 0.938 3
#> MAD:kmeans 117 2.40e-18 0.366 6.29e-12 0.851 4
#> MAD:kmeans 100 3.84e-16 0.297 7.29e-13 0.644 5
#> MAD:kmeans 103 4.77e-15 0.168 2.02e-09 0.340 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.
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"]
# you can also extract it by
# 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 51941 rows and 120 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 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:
k and the heatmap of
predicted classes for each k.k.k.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:
k;k, the area increased is defined as \(A_k - A_{k-1}\).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 0.991 0.994 0.5045 0.496 0.496
#> 3 3 0.706 0.878 0.832 0.3014 0.752 0.542
#> 4 4 1.000 0.983 0.993 0.1502 0.828 0.545
#> 5 5 0.932 0.871 0.886 0.0500 0.949 0.799
#> 6 6 0.982 0.944 0.964 0.0373 0.954 0.788
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 4 5
There is also optional best \(k\) = 2 4 5 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.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM494565 2 0.0000 0.995 0.000 1.000
#> GSM494594 2 0.0000 0.995 0.000 1.000
#> GSM494604 1 0.0000 0.993 1.000 0.000
#> GSM494564 2 0.0000 0.995 0.000 1.000
#> GSM494591 2 0.0000 0.995 0.000 1.000
#> GSM494567 2 0.0000 0.995 0.000 1.000
#> GSM494602 2 0.0672 0.994 0.008 0.992
#> GSM494613 2 0.0000 0.995 0.000 1.000
#> GSM494589 2 0.0000 0.995 0.000 1.000
#> GSM494598 2 0.0672 0.994 0.008 0.992
#> GSM494593 2 0.0672 0.994 0.008 0.992
#> GSM494583 2 0.0000 0.995 0.000 1.000
#> GSM494612 2 0.0672 0.994 0.008 0.992
#> GSM494558 2 0.0000 0.995 0.000 1.000
#> GSM494556 2 0.0000 0.995 0.000 1.000
#> GSM494559 2 0.0000 0.995 0.000 1.000
#> GSM494571 2 0.0000 0.995 0.000 1.000
#> GSM494614 2 0.0000 0.995 0.000 1.000
#> GSM494603 2 0.3879 0.917 0.076 0.924
#> GSM494568 1 0.7139 0.765 0.804 0.196
#> GSM494572 2 0.0000 0.995 0.000 1.000
#> GSM494600 2 0.0000 0.995 0.000 1.000
#> GSM494562 2 0.0672 0.994 0.008 0.992
#> GSM494615 2 0.0000 0.995 0.000 1.000
#> GSM494582 2 0.0672 0.994 0.008 0.992
#> GSM494599 2 0.0672 0.994 0.008 0.992
#> GSM494610 2 0.0672 0.994 0.008 0.992
#> GSM494587 2 0.0672 0.994 0.008 0.992
#> GSM494581 2 0.0672 0.994 0.008 0.992
#> GSM494580 2 0.0000 0.995 0.000 1.000
#> GSM494563 2 0.0000 0.995 0.000 1.000
#> GSM494576 2 0.0672 0.994 0.008 0.992
#> GSM494605 1 0.0000 0.993 1.000 0.000
#> GSM494584 2 0.0000 0.995 0.000 1.000
#> GSM494586 2 0.0672 0.994 0.008 0.992
#> GSM494578 2 0.0000 0.995 0.000 1.000
#> GSM494585 2 0.0672 0.994 0.008 0.992
#> GSM494611 2 0.0672 0.994 0.008 0.992
#> GSM494560 2 0.0000 0.995 0.000 1.000
#> GSM494595 2 0.0672 0.994 0.008 0.992
#> GSM494570 2 0.0000 0.995 0.000 1.000
#> GSM494597 2 0.0000 0.995 0.000 1.000
#> GSM494607 2 0.0672 0.994 0.008 0.992
#> GSM494561 2 0.0000 0.995 0.000 1.000
#> GSM494569 1 0.0672 0.993 0.992 0.008
#> GSM494592 2 0.0672 0.994 0.008 0.992
#> GSM494577 2 0.0672 0.994 0.008 0.992
#> GSM494588 2 0.0000 0.995 0.000 1.000
#> GSM494590 2 0.0000 0.995 0.000 1.000
#> GSM494609 2 0.0672 0.994 0.008 0.992
#> GSM494608 2 0.0672 0.994 0.008 0.992
#> GSM494606 2 0.0672 0.994 0.008 0.992
#> GSM494574 2 0.0672 0.994 0.008 0.992
#> GSM494573 2 0.0000 0.995 0.000 1.000
#> GSM494566 2 0.0672 0.994 0.008 0.992
#> GSM494601 2 0.0672 0.994 0.008 0.992
#> GSM494557 2 0.0000 0.995 0.000 1.000
#> GSM494579 2 0.0672 0.994 0.008 0.992
#> GSM494596 2 0.0000 0.995 0.000 1.000
#> GSM494575 2 0.0672 0.994 0.008 0.992
#> GSM494625 1 0.0672 0.993 0.992 0.008
#> GSM494654 2 0.0000 0.995 0.000 1.000
#> GSM494664 1 0.0000 0.993 1.000 0.000
#> GSM494624 1 0.0672 0.993 0.992 0.008
#> GSM494651 1 0.0672 0.993 0.992 0.008
#> GSM494662 1 0.0672 0.993 0.992 0.008
#> GSM494627 1 0.0672 0.993 0.992 0.008
#> GSM494673 1 0.0000 0.993 1.000 0.000
#> GSM494649 1 0.0672 0.993 0.992 0.008
#> GSM494658 1 0.0000 0.993 1.000 0.000
#> GSM494653 1 0.0000 0.993 1.000 0.000
#> GSM494643 1 0.0672 0.993 0.992 0.008
#> GSM494672 1 0.0000 0.993 1.000 0.000
#> GSM494618 1 0.0672 0.993 0.992 0.008
#> GSM494631 2 0.0376 0.993 0.004 0.996
#> GSM494619 1 0.0672 0.993 0.992 0.008
#> GSM494674 1 0.0000 0.993 1.000 0.000
#> GSM494616 1 0.0672 0.993 0.992 0.008
#> GSM494663 1 0.0672 0.993 0.992 0.008
#> GSM494628 1 0.0672 0.993 0.992 0.008
#> GSM494632 1 0.0000 0.993 1.000 0.000
#> GSM494660 1 0.0672 0.993 0.992 0.008
#> GSM494622 1 0.0672 0.993 0.992 0.008
#> GSM494642 1 0.0000 0.993 1.000 0.000
#> GSM494647 1 0.0000 0.993 1.000 0.000
#> GSM494659 1 0.0000 0.993 1.000 0.000
#> GSM494670 1 0.0000 0.993 1.000 0.000
#> GSM494675 2 0.0000 0.995 0.000 1.000
#> GSM494641 1 0.0000 0.993 1.000 0.000
#> GSM494636 1 0.0672 0.993 0.992 0.008
#> GSM494640 1 0.0672 0.993 0.992 0.008
#> GSM494623 1 0.0672 0.993 0.992 0.008
#> GSM494644 1 0.0000 0.993 1.000 0.000
#> GSM494646 1 0.0000 0.993 1.000 0.000
#> GSM494665 1 0.0000 0.993 1.000 0.000
#> GSM494638 1 0.0672 0.993 0.992 0.008
#> GSM494645 1 0.0000 0.993 1.000 0.000
#> GSM494671 1 0.0000 0.993 1.000 0.000
#> GSM494655 1 0.0000 0.993 1.000 0.000
#> GSM494620 1 0.0672 0.993 0.992 0.008
#> GSM494630 1 0.0672 0.993 0.992 0.008
#> GSM494657 2 0.0000 0.995 0.000 1.000
#> GSM494667 1 0.0000 0.993 1.000 0.000
#> GSM494621 1 0.0672 0.993 0.992 0.008
#> GSM494629 1 0.0672 0.993 0.992 0.008
#> GSM494637 1 0.0672 0.993 0.992 0.008
#> GSM494652 1 0.0000 0.993 1.000 0.000
#> GSM494648 1 0.0672 0.993 0.992 0.008
#> GSM494650 1 0.0672 0.993 0.992 0.008
#> GSM494669 1 0.0000 0.993 1.000 0.000
#> GSM494666 1 0.0000 0.993 1.000 0.000
#> GSM494668 1 0.0000 0.993 1.000 0.000
#> GSM494633 1 0.0672 0.993 0.992 0.008
#> GSM494634 1 0.0000 0.993 1.000 0.000
#> GSM494639 1 0.0000 0.993 1.000 0.000
#> GSM494661 1 0.0000 0.993 1.000 0.000
#> GSM494617 1 0.0672 0.993 0.992 0.008
#> GSM494626 1 0.0672 0.993 0.992 0.008
#> GSM494656 2 0.0000 0.995 0.000 1.000
#> GSM494635 1 0.0000 0.993 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM494565 2 0.000 0.886 0.000 1.000 0.000
#> GSM494594 2 0.000 0.886 0.000 1.000 0.000
#> GSM494604 1 0.000 0.721 1.000 0.000 0.000
#> GSM494564 2 0.000 0.886 0.000 1.000 0.000
#> GSM494591 2 0.000 0.886 0.000 1.000 0.000
#> GSM494567 2 0.000 0.886 0.000 1.000 0.000
#> GSM494602 2 0.497 0.873 0.236 0.764 0.000
#> GSM494613 2 0.000 0.886 0.000 1.000 0.000
#> GSM494589 2 0.000 0.886 0.000 1.000 0.000
#> GSM494598 2 0.497 0.873 0.236 0.764 0.000
#> GSM494593 2 0.497 0.873 0.236 0.764 0.000
#> GSM494583 2 0.493 0.875 0.232 0.768 0.000
#> GSM494612 2 0.497 0.873 0.236 0.764 0.000
#> GSM494558 3 0.497 0.738 0.000 0.236 0.764
#> GSM494556 2 0.000 0.886 0.000 1.000 0.000
#> GSM494559 2 0.000 0.886 0.000 1.000 0.000
#> GSM494571 3 0.611 0.508 0.000 0.396 0.604
#> GSM494614 2 0.000 0.886 0.000 1.000 0.000
#> GSM494603 3 0.497 0.738 0.000 0.236 0.764
#> GSM494568 3 0.493 0.741 0.000 0.232 0.768
#> GSM494572 2 0.000 0.886 0.000 1.000 0.000
#> GSM494600 2 0.000 0.886 0.000 1.000 0.000
#> GSM494562 2 0.493 0.875 0.232 0.768 0.000
#> GSM494615 2 0.000 0.886 0.000 1.000 0.000
#> GSM494582 2 0.497 0.873 0.236 0.764 0.000
#> GSM494599 1 0.226 0.646 0.932 0.068 0.000
#> GSM494610 2 0.497 0.873 0.236 0.764 0.000
#> GSM494587 2 0.493 0.875 0.232 0.768 0.000
#> GSM494581 2 0.493 0.875 0.232 0.768 0.000
#> GSM494580 2 0.000 0.886 0.000 1.000 0.000
#> GSM494563 2 0.000 0.886 0.000 1.000 0.000
#> GSM494576 2 0.493 0.875 0.232 0.768 0.000
#> GSM494605 1 0.493 0.944 0.768 0.000 0.232
#> GSM494584 2 0.103 0.886 0.024 0.976 0.000
#> GSM494586 2 0.493 0.875 0.232 0.768 0.000
#> GSM494578 2 0.000 0.886 0.000 1.000 0.000
#> GSM494585 2 0.493 0.875 0.232 0.768 0.000
#> GSM494611 2 0.497 0.873 0.236 0.764 0.000
#> GSM494560 2 0.000 0.886 0.000 1.000 0.000
#> GSM494595 2 0.497 0.873 0.236 0.764 0.000
#> GSM494570 3 0.497 0.738 0.000 0.236 0.764
#> GSM494597 2 0.000 0.886 0.000 1.000 0.000
#> GSM494607 1 0.000 0.721 1.000 0.000 0.000
#> GSM494561 3 0.497 0.738 0.000 0.236 0.764
#> GSM494569 3 0.000 0.906 0.000 0.000 1.000
#> GSM494592 1 0.226 0.646 0.932 0.068 0.000
#> GSM494577 2 0.493 0.875 0.232 0.768 0.000
#> GSM494588 2 0.000 0.886 0.000 1.000 0.000
#> GSM494590 2 0.000 0.886 0.000 1.000 0.000
#> GSM494609 2 0.497 0.873 0.236 0.764 0.000
#> GSM494608 2 0.497 0.873 0.236 0.764 0.000
#> GSM494606 2 0.595 0.748 0.360 0.640 0.000
#> GSM494574 2 0.497 0.873 0.236 0.764 0.000
#> GSM494573 2 0.000 0.886 0.000 1.000 0.000
#> GSM494566 2 0.493 0.875 0.232 0.768 0.000
#> GSM494601 2 0.497 0.873 0.236 0.764 0.000
#> GSM494557 2 0.000 0.886 0.000 1.000 0.000
#> GSM494579 2 0.493 0.875 0.232 0.768 0.000
#> GSM494596 2 0.000 0.886 0.000 1.000 0.000
#> GSM494575 2 0.497 0.873 0.236 0.764 0.000
#> GSM494625 3 0.000 0.906 0.000 0.000 1.000
#> GSM494654 3 0.497 0.738 0.000 0.236 0.764
#> GSM494664 1 0.497 0.941 0.764 0.000 0.236
#> GSM494624 3 0.000 0.906 0.000 0.000 1.000
#> GSM494651 3 0.000 0.906 0.000 0.000 1.000
#> GSM494662 3 0.000 0.906 0.000 0.000 1.000
#> GSM494627 3 0.000 0.906 0.000 0.000 1.000
#> GSM494673 1 0.493 0.944 0.768 0.000 0.232
#> GSM494649 3 0.000 0.906 0.000 0.000 1.000
#> GSM494658 1 0.207 0.779 0.940 0.000 0.060
#> GSM494653 1 0.493 0.944 0.768 0.000 0.232
#> GSM494643 3 0.000 0.906 0.000 0.000 1.000
#> GSM494672 1 0.493 0.944 0.768 0.000 0.232
#> GSM494618 3 0.000 0.906 0.000 0.000 1.000
#> GSM494631 3 0.562 0.663 0.000 0.308 0.692
#> GSM494619 3 0.000 0.906 0.000 0.000 1.000
#> GSM494674 1 0.493 0.944 0.768 0.000 0.232
#> GSM494616 3 0.000 0.906 0.000 0.000 1.000
#> GSM494663 3 0.000 0.906 0.000 0.000 1.000
#> GSM494628 3 0.000 0.906 0.000 0.000 1.000
#> GSM494632 1 0.497 0.941 0.764 0.000 0.236
#> GSM494660 3 0.000 0.906 0.000 0.000 1.000
#> GSM494622 3 0.000 0.906 0.000 0.000 1.000
#> GSM494642 1 0.493 0.944 0.768 0.000 0.232
#> GSM494647 1 0.493 0.944 0.768 0.000 0.232
#> GSM494659 1 0.493 0.944 0.768 0.000 0.232
#> GSM494670 1 0.493 0.944 0.768 0.000 0.232
#> GSM494675 2 0.000 0.886 0.000 1.000 0.000
#> GSM494641 1 0.493 0.944 0.768 0.000 0.232
#> GSM494636 3 0.000 0.906 0.000 0.000 1.000
#> GSM494640 3 0.000 0.906 0.000 0.000 1.000
#> GSM494623 3 0.000 0.906 0.000 0.000 1.000
#> GSM494644 1 0.493 0.944 0.768 0.000 0.232
#> GSM494646 1 0.497 0.941 0.764 0.000 0.236
#> GSM494665 1 0.493 0.944 0.768 0.000 0.232
#> GSM494638 3 0.216 0.830 0.064 0.000 0.936
#> GSM494645 1 0.497 0.941 0.764 0.000 0.236
#> GSM494671 1 0.493 0.944 0.768 0.000 0.232
#> GSM494655 1 0.493 0.944 0.768 0.000 0.232
#> GSM494620 3 0.000 0.906 0.000 0.000 1.000
#> GSM494630 3 0.000 0.906 0.000 0.000 1.000
#> GSM494657 2 0.000 0.886 0.000 1.000 0.000
#> GSM494667 1 0.493 0.944 0.768 0.000 0.232
#> GSM494621 3 0.000 0.906 0.000 0.000 1.000
#> GSM494629 3 0.103 0.889 0.000 0.024 0.976
#> GSM494637 3 0.000 0.906 0.000 0.000 1.000
#> GSM494652 1 0.493 0.944 0.768 0.000 0.232
#> GSM494648 3 0.000 0.906 0.000 0.000 1.000
#> GSM494650 3 0.000 0.906 0.000 0.000 1.000
#> GSM494669 1 0.493 0.944 0.768 0.000 0.232
#> GSM494666 1 0.497 0.941 0.764 0.000 0.236
#> GSM494668 1 0.493 0.944 0.768 0.000 0.232
#> GSM494633 3 0.000 0.906 0.000 0.000 1.000
#> GSM494634 1 0.493 0.944 0.768 0.000 0.232
#> GSM494639 1 0.497 0.941 0.764 0.000 0.236
#> GSM494661 1 0.493 0.944 0.768 0.000 0.232
#> GSM494617 3 0.000 0.906 0.000 0.000 1.000
#> GSM494626 3 0.000 0.906 0.000 0.000 1.000
#> GSM494656 3 0.497 0.738 0.000 0.236 0.764
#> GSM494635 1 0.497 0.941 0.764 0.000 0.236
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM494565 3 0.0188 0.978 0.000 0.004 0.996 0.000
#> GSM494594 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> GSM494604 1 0.0921 0.971 0.972 0.028 0.000 0.000
#> GSM494564 3 0.0188 0.978 0.000 0.004 0.996 0.000
#> GSM494591 3 0.0188 0.978 0.000 0.004 0.996 0.000
#> GSM494567 3 0.0188 0.978 0.000 0.004 0.996 0.000
#> GSM494602 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> GSM494613 3 0.0188 0.978 0.000 0.004 0.996 0.000
#> GSM494589 3 0.0188 0.978 0.000 0.004 0.996 0.000
#> GSM494598 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> GSM494593 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> GSM494583 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> GSM494612 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> GSM494558 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> GSM494556 3 0.0188 0.978 0.000 0.004 0.996 0.000
#> GSM494559 3 0.0188 0.978 0.000 0.004 0.996 0.000
#> GSM494571 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> GSM494614 3 0.0188 0.978 0.000 0.004 0.996 0.000
#> GSM494603 3 0.3569 0.744 0.000 0.000 0.804 0.196
#> GSM494568 4 0.0188 0.997 0.000 0.000 0.004 0.996
#> GSM494572 3 0.0188 0.978 0.000 0.004 0.996 0.000
#> GSM494600 3 0.0188 0.978 0.000 0.004 0.996 0.000
#> GSM494562 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> GSM494615 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> GSM494582 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> GSM494599 2 0.0188 0.996 0.004 0.996 0.000 0.000
#> GSM494610 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> GSM494587 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> GSM494581 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> GSM494580 3 0.0188 0.978 0.000 0.004 0.996 0.000
#> GSM494563 3 0.0188 0.978 0.000 0.004 0.996 0.000
#> GSM494576 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> GSM494605 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> GSM494584 3 0.4994 0.083 0.000 0.480 0.520 0.000
#> GSM494586 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> GSM494578 3 0.0188 0.978 0.000 0.004 0.996 0.000
#> GSM494585 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> GSM494611 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> GSM494560 3 0.0188 0.978 0.000 0.004 0.996 0.000
#> GSM494595 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> GSM494570 3 0.0188 0.976 0.000 0.000 0.996 0.004
#> GSM494597 3 0.0188 0.978 0.000 0.004 0.996 0.000
#> GSM494607 2 0.0188 0.996 0.004 0.996 0.000 0.000
#> GSM494561 3 0.0188 0.976 0.000 0.000 0.996 0.004
#> GSM494569 4 0.0188 0.997 0.000 0.000 0.004 0.996
#> GSM494592 2 0.0188 0.996 0.004 0.996 0.000 0.000
#> GSM494577 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> GSM494588 3 0.0188 0.978 0.000 0.004 0.996 0.000
#> GSM494590 3 0.0188 0.978 0.000 0.004 0.996 0.000
#> GSM494609 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> GSM494608 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> GSM494606 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> GSM494574 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> GSM494573 3 0.0188 0.978 0.000 0.004 0.996 0.000
#> GSM494566 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> GSM494601 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> GSM494557 3 0.0188 0.978 0.000 0.004 0.996 0.000
#> GSM494579 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> GSM494596 3 0.0188 0.978 0.000 0.004 0.996 0.000
#> GSM494575 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> GSM494625 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM494654 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> GSM494664 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> GSM494624 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM494651 4 0.0188 0.997 0.000 0.000 0.004 0.996
#> GSM494662 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM494627 4 0.0188 0.997 0.000 0.000 0.004 0.996
#> GSM494673 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> GSM494649 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM494658 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> GSM494653 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> GSM494643 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM494672 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> GSM494618 4 0.0188 0.997 0.000 0.000 0.004 0.996
#> GSM494631 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> GSM494619 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM494674 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> GSM494616 4 0.0188 0.997 0.000 0.000 0.004 0.996
#> GSM494663 4 0.0188 0.997 0.000 0.000 0.004 0.996
#> GSM494628 4 0.0188 0.997 0.000 0.000 0.004 0.996
#> GSM494632 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> GSM494660 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM494622 4 0.0188 0.997 0.000 0.000 0.004 0.996
#> GSM494642 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> GSM494670 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> GSM494675 3 0.0188 0.978 0.000 0.004 0.996 0.000
#> GSM494641 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> GSM494636 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM494640 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM494623 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM494644 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> GSM494646 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> GSM494665 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> GSM494638 4 0.0921 0.971 0.028 0.000 0.000 0.972
#> GSM494645 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> GSM494671 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> GSM494620 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM494630 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM494657 3 0.0188 0.978 0.000 0.004 0.996 0.000
#> GSM494667 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> GSM494621 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM494629 4 0.0188 0.997 0.000 0.000 0.004 0.996
#> GSM494637 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM494652 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> GSM494648 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM494650 4 0.0188 0.997 0.000 0.000 0.004 0.996
#> GSM494669 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> GSM494666 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> GSM494668 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> GSM494633 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM494634 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> GSM494639 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> GSM494661 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> GSM494617 4 0.0188 0.997 0.000 0.000 0.004 0.996
#> GSM494626 4 0.0188 0.997 0.000 0.000 0.004 0.996
#> GSM494656 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> GSM494635 1 0.0000 0.999 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM494565 5 0.0000 0.922 0.000 0.000 0.000 0.000 1.000
#> GSM494594 3 0.4242 0.800 0.000 0.000 0.572 0.000 0.428
#> GSM494604 1 0.0794 0.968 0.972 0.028 0.000 0.000 0.000
#> GSM494564 5 0.0000 0.922 0.000 0.000 0.000 0.000 1.000
#> GSM494591 3 0.4242 0.800 0.000 0.000 0.572 0.000 0.428
#> GSM494567 3 0.4242 0.800 0.000 0.000 0.572 0.000 0.428
#> GSM494602 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> GSM494613 3 0.4242 0.800 0.000 0.000 0.572 0.000 0.428
#> GSM494589 5 0.0000 0.922 0.000 0.000 0.000 0.000 1.000
#> GSM494598 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> GSM494593 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> GSM494583 2 0.0404 0.970 0.000 0.988 0.000 0.000 0.012
#> GSM494612 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> GSM494558 3 0.0000 0.412 0.000 0.000 1.000 0.000 0.000
#> GSM494556 3 0.4242 0.800 0.000 0.000 0.572 0.000 0.428
#> GSM494559 5 0.0000 0.922 0.000 0.000 0.000 0.000 1.000
#> GSM494571 3 0.4242 0.800 0.000 0.000 0.572 0.000 0.428
#> GSM494614 5 0.3177 0.459 0.000 0.000 0.208 0.000 0.792
#> GSM494603 3 0.1907 0.352 0.000 0.000 0.928 0.028 0.044
#> GSM494568 3 0.1732 0.325 0.000 0.000 0.920 0.080 0.000
#> GSM494572 3 0.4242 0.800 0.000 0.000 0.572 0.000 0.428
#> GSM494600 5 0.0000 0.922 0.000 0.000 0.000 0.000 1.000
#> GSM494562 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> GSM494615 3 0.4242 0.800 0.000 0.000 0.572 0.000 0.428
#> GSM494582 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> GSM494599 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> GSM494610 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> GSM494587 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> GSM494581 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> GSM494580 3 0.4242 0.800 0.000 0.000 0.572 0.000 0.428
#> GSM494563 5 0.0510 0.907 0.000 0.016 0.000 0.000 0.984
#> GSM494576 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> GSM494605 1 0.0000 0.999 1.000 0.000 0.000 0.000 0.000
#> GSM494584 2 0.4825 0.105 0.000 0.568 0.024 0.000 0.408
#> GSM494586 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> GSM494578 3 0.4242 0.800 0.000 0.000 0.572 0.000 0.428
#> GSM494585 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> GSM494611 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> GSM494560 5 0.0000 0.922 0.000 0.000 0.000 0.000 1.000
#> GSM494595 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> GSM494570 5 0.1792 0.854 0.000 0.000 0.000 0.084 0.916
#> GSM494597 3 0.4242 0.800 0.000 0.000 0.572 0.000 0.428
#> GSM494607 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> GSM494561 5 0.1792 0.854 0.000 0.000 0.000 0.084 0.916
#> GSM494569 4 0.4242 0.761 0.000 0.000 0.428 0.572 0.000
#> GSM494592 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> GSM494577 2 0.0290 0.974 0.000 0.992 0.000 0.000 0.008
#> GSM494588 5 0.1792 0.854 0.000 0.000 0.000 0.084 0.916
#> GSM494590 3 0.4242 0.800 0.000 0.000 0.572 0.000 0.428
#> GSM494609 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> GSM494608 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> GSM494606 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> GSM494574 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> GSM494573 5 0.0000 0.922 0.000 0.000 0.000 0.000 1.000
#> GSM494566 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> GSM494601 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> GSM494557 3 0.4242 0.800 0.000 0.000 0.572 0.000 0.428
#> GSM494579 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> GSM494596 3 0.4242 0.800 0.000 0.000 0.572 0.000 0.428
#> GSM494575 2 0.0000 0.981 0.000 1.000 0.000 0.000 0.000
#> GSM494625 4 0.0000 0.811 0.000 0.000 0.000 1.000 0.000
#> GSM494654 3 0.2020 0.491 0.000 0.000 0.900 0.000 0.100
#> GSM494664 1 0.0000 0.999 1.000 0.000 0.000 0.000 0.000
#> GSM494624 4 0.0000 0.811 0.000 0.000 0.000 1.000 0.000
#> GSM494651 4 0.4242 0.761 0.000 0.000 0.428 0.572 0.000
#> GSM494662 4 0.1792 0.818 0.000 0.000 0.084 0.916 0.000
#> GSM494627 4 0.4242 0.761 0.000 0.000 0.428 0.572 0.000
#> GSM494673 1 0.0000 0.999 1.000 0.000 0.000 0.000 0.000
#> GSM494649 4 0.0000 0.811 0.000 0.000 0.000 1.000 0.000
#> GSM494658 1 0.0000 0.999 1.000 0.000 0.000 0.000 0.000
#> GSM494653 1 0.0000 0.999 1.000 0.000 0.000 0.000 0.000
#> GSM494643 4 0.0609 0.814 0.000 0.000 0.020 0.980 0.000
#> GSM494672 1 0.0000 0.999 1.000 0.000 0.000 0.000 0.000
#> GSM494618 4 0.4242 0.761 0.000 0.000 0.428 0.572 0.000
#> GSM494631 3 0.2127 0.497 0.000 0.000 0.892 0.000 0.108
#> GSM494619 4 0.0000 0.811 0.000 0.000 0.000 1.000 0.000
#> GSM494674 1 0.0000 0.999 1.000 0.000 0.000 0.000 0.000
#> GSM494616 4 0.4242 0.761 0.000 0.000 0.428 0.572 0.000
#> GSM494663 4 0.4242 0.761 0.000 0.000 0.428 0.572 0.000
#> GSM494628 4 0.4242 0.761 0.000 0.000 0.428 0.572 0.000
#> GSM494632 1 0.0000 0.999 1.000 0.000 0.000 0.000 0.000
#> GSM494660 4 0.0000 0.811 0.000 0.000 0.000 1.000 0.000
#> GSM494622 4 0.4242 0.761 0.000 0.000 0.428 0.572 0.000
#> GSM494642 1 0.0000 0.999 1.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.999 1.000 0.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.999 1.000 0.000 0.000 0.000 0.000
#> GSM494670 1 0.0000 0.999 1.000 0.000 0.000 0.000 0.000
#> GSM494675 3 0.4242 0.800 0.000 0.000 0.572 0.000 0.428
#> GSM494641 1 0.0000 0.999 1.000 0.000 0.000 0.000 0.000
#> GSM494636 4 0.1792 0.818 0.000 0.000 0.084 0.916 0.000
#> GSM494640 4 0.1792 0.818 0.000 0.000 0.084 0.916 0.000
#> GSM494623 4 0.0000 0.811 0.000 0.000 0.000 1.000 0.000
#> GSM494644 1 0.0000 0.999 1.000 0.000 0.000 0.000 0.000
#> GSM494646 1 0.0000 0.999 1.000 0.000 0.000 0.000 0.000
#> GSM494665 1 0.0000 0.999 1.000 0.000 0.000 0.000 0.000
#> GSM494638 4 0.2654 0.810 0.032 0.000 0.084 0.884 0.000
#> GSM494645 1 0.0000 0.999 1.000 0.000 0.000 0.000 0.000
#> GSM494671 1 0.0000 0.999 1.000 0.000 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.999 1.000 0.000 0.000 0.000 0.000
#> GSM494620 4 0.0000 0.811 0.000 0.000 0.000 1.000 0.000
#> GSM494630 4 0.0000 0.811 0.000 0.000 0.000 1.000 0.000
#> GSM494657 3 0.4242 0.800 0.000 0.000 0.572 0.000 0.428
#> GSM494667 1 0.0000 0.999 1.000 0.000 0.000 0.000 0.000
#> GSM494621 4 0.0000 0.811 0.000 0.000 0.000 1.000 0.000
#> GSM494629 4 0.4150 0.770 0.000 0.000 0.388 0.612 0.000
#> GSM494637 4 0.1792 0.818 0.000 0.000 0.084 0.916 0.000
#> GSM494652 1 0.0000 0.999 1.000 0.000 0.000 0.000 0.000
#> GSM494648 4 0.0000 0.811 0.000 0.000 0.000 1.000 0.000
#> GSM494650 4 0.4242 0.761 0.000 0.000 0.428 0.572 0.000
#> GSM494669 1 0.0000 0.999 1.000 0.000 0.000 0.000 0.000
#> GSM494666 1 0.0000 0.999 1.000 0.000 0.000 0.000 0.000
#> GSM494668 1 0.0000 0.999 1.000 0.000 0.000 0.000 0.000
#> GSM494633 4 0.0000 0.811 0.000 0.000 0.000 1.000 0.000
#> GSM494634 1 0.0000 0.999 1.000 0.000 0.000 0.000 0.000
#> GSM494639 1 0.0000 0.999 1.000 0.000 0.000 0.000 0.000
#> GSM494661 1 0.0000 0.999 1.000 0.000 0.000 0.000 0.000
#> GSM494617 4 0.4242 0.761 0.000 0.000 0.428 0.572 0.000
#> GSM494626 4 0.4242 0.761 0.000 0.000 0.428 0.572 0.000
#> GSM494656 3 0.4242 0.800 0.000 0.000 0.572 0.000 0.428
#> GSM494635 1 0.0000 0.999 1.000 0.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM494565 5 0.1327 0.978 0.000 0.000 0.064 0.000 0.936 0.000
#> GSM494594 3 0.0146 0.974 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM494604 1 0.1267 0.930 0.940 0.060 0.000 0.000 0.000 0.000
#> GSM494564 5 0.1501 0.979 0.000 0.000 0.076 0.000 0.924 0.000
#> GSM494591 3 0.0000 0.976 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494567 3 0.0000 0.976 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494602 2 0.0000 0.973 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494613 3 0.0000 0.976 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494589 5 0.1501 0.979 0.000 0.000 0.076 0.000 0.924 0.000
#> GSM494598 2 0.0146 0.973 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM494593 2 0.0000 0.973 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494583 2 0.2214 0.878 0.000 0.888 0.016 0.000 0.096 0.000
#> GSM494612 2 0.0000 0.973 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494558 4 0.1010 0.943 0.000 0.000 0.036 0.960 0.004 0.000
#> GSM494556 3 0.0000 0.976 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494559 5 0.1444 0.979 0.000 0.000 0.072 0.000 0.928 0.000
#> GSM494571 3 0.0146 0.974 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM494614 3 0.3221 0.608 0.000 0.000 0.736 0.000 0.264 0.000
#> GSM494603 4 0.1088 0.949 0.000 0.000 0.024 0.960 0.016 0.000
#> GSM494568 4 0.0717 0.959 0.000 0.000 0.016 0.976 0.008 0.000
#> GSM494572 3 0.0000 0.976 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494600 5 0.1501 0.979 0.000 0.000 0.076 0.000 0.924 0.000
#> GSM494562 2 0.0146 0.973 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM494615 3 0.0000 0.976 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494582 2 0.0000 0.973 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494599 2 0.0000 0.973 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494610 2 0.0146 0.973 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM494587 2 0.0260 0.972 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM494581 2 0.0458 0.966 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM494580 3 0.0000 0.976 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494563 5 0.1411 0.976 0.000 0.004 0.060 0.000 0.936 0.000
#> GSM494576 2 0.0458 0.968 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM494605 1 0.0260 0.986 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM494584 2 0.4620 0.362 0.000 0.584 0.368 0.000 0.048 0.000
#> GSM494586 2 0.0260 0.972 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM494578 3 0.0000 0.976 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494585 2 0.0260 0.972 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM494611 2 0.0000 0.973 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494560 5 0.1327 0.978 0.000 0.000 0.064 0.000 0.936 0.000
#> GSM494595 2 0.0260 0.972 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM494570 5 0.1789 0.962 0.000 0.000 0.044 0.000 0.924 0.032
#> GSM494597 3 0.0146 0.974 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM494607 2 0.0000 0.973 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494561 5 0.2088 0.924 0.000 0.000 0.028 0.000 0.904 0.068
#> GSM494569 4 0.0806 0.966 0.000 0.000 0.000 0.972 0.020 0.008
#> GSM494592 2 0.0000 0.973 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494577 2 0.1765 0.896 0.000 0.904 0.000 0.000 0.096 0.000
#> GSM494588 5 0.1549 0.967 0.000 0.000 0.044 0.000 0.936 0.020
#> GSM494590 3 0.0000 0.976 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494609 2 0.0260 0.971 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM494608 2 0.0260 0.971 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM494606 2 0.0000 0.973 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494574 2 0.0146 0.973 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM494573 5 0.1501 0.979 0.000 0.000 0.076 0.000 0.924 0.000
#> GSM494566 2 0.0405 0.971 0.000 0.988 0.000 0.004 0.008 0.000
#> GSM494601 2 0.0000 0.973 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494557 3 0.0000 0.976 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494579 2 0.0405 0.971 0.000 0.988 0.000 0.004 0.008 0.000
#> GSM494596 3 0.0000 0.976 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494575 2 0.0000 0.973 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494625 6 0.0000 0.902 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494654 3 0.1245 0.936 0.000 0.000 0.952 0.032 0.016 0.000
#> GSM494664 1 0.0806 0.974 0.972 0.000 0.000 0.008 0.020 0.000
#> GSM494624 6 0.0000 0.902 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494651 4 0.0622 0.968 0.000 0.000 0.000 0.980 0.012 0.008
#> GSM494662 6 0.4167 0.710 0.000 0.000 0.000 0.236 0.056 0.708
#> GSM494627 4 0.0777 0.961 0.000 0.000 0.000 0.972 0.004 0.024
#> GSM494673 1 0.0000 0.989 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494649 6 0.0000 0.902 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494658 1 0.0000 0.989 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494653 1 0.0000 0.989 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494643 6 0.0865 0.888 0.000 0.000 0.000 0.000 0.036 0.964
#> GSM494672 1 0.0000 0.989 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494618 4 0.0622 0.968 0.000 0.000 0.000 0.980 0.012 0.008
#> GSM494631 3 0.1333 0.929 0.000 0.000 0.944 0.048 0.008 0.000
#> GSM494619 6 0.0000 0.902 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494674 1 0.0000 0.989 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494616 4 0.0806 0.966 0.000 0.000 0.000 0.972 0.020 0.008
#> GSM494663 4 0.1010 0.954 0.000 0.000 0.000 0.960 0.004 0.036
#> GSM494628 4 0.0146 0.968 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM494632 1 0.1700 0.941 0.928 0.000 0.000 0.024 0.048 0.000
#> GSM494660 6 0.0000 0.902 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494622 4 0.0000 0.967 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494642 1 0.0000 0.989 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.989 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.989 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494670 1 0.0000 0.989 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494675 3 0.0291 0.972 0.000 0.000 0.992 0.004 0.004 0.000
#> GSM494641 1 0.0000 0.989 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494636 6 0.4239 0.697 0.000 0.000 0.000 0.248 0.056 0.696
#> GSM494640 6 0.3860 0.725 0.000 0.000 0.000 0.236 0.036 0.728
#> GSM494623 6 0.0000 0.902 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494644 1 0.0000 0.989 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494646 1 0.1075 0.961 0.952 0.000 0.000 0.000 0.048 0.000
#> GSM494665 1 0.0146 0.987 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM494638 6 0.5006 0.656 0.032 0.000 0.000 0.256 0.056 0.656
#> GSM494645 1 0.0146 0.987 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM494671 1 0.0000 0.989 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.989 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494620 6 0.0000 0.902 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494630 6 0.0146 0.901 0.000 0.000 0.000 0.000 0.004 0.996
#> GSM494657 3 0.0000 0.976 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494667 1 0.0000 0.989 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494621 6 0.0000 0.902 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494629 4 0.2531 0.832 0.000 0.000 0.000 0.856 0.012 0.132
#> GSM494637 6 0.3860 0.725 0.000 0.000 0.000 0.236 0.036 0.728
#> GSM494652 1 0.0000 0.989 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494648 6 0.0000 0.902 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494650 4 0.0291 0.967 0.000 0.000 0.000 0.992 0.004 0.004
#> GSM494669 1 0.0000 0.989 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494666 1 0.0547 0.979 0.980 0.000 0.000 0.000 0.020 0.000
#> GSM494668 1 0.0000 0.989 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494633 6 0.0000 0.902 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494634 1 0.0000 0.989 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494639 1 0.1434 0.952 0.940 0.000 0.000 0.012 0.048 0.000
#> GSM494661 1 0.0146 0.987 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM494617 4 0.0891 0.964 0.000 0.000 0.000 0.968 0.024 0.008
#> GSM494626 4 0.0622 0.968 0.000 0.000 0.000 0.980 0.012 0.008
#> GSM494656 3 0.0717 0.958 0.000 0.000 0.976 0.016 0.008 0.000
#> GSM494635 1 0.0865 0.969 0.964 0.000 0.000 0.000 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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 5)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 6)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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.
# code only for demonstration
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) individual(p) k
#> MAD:skmeans 120 6.85e-20 0.9998 2.52e-15 1.000 2
#> MAD:skmeans 120 1.72e-16 0.2793 9.68e-11 0.785 3
#> MAD:skmeans 119 9.13e-19 0.3970 1.08e-12 0.880 4
#> MAD:skmeans 113 5.48e-19 0.4994 1.06e-13 0.800 5
#> MAD:skmeans 119 8.23e-17 0.0418 1.16e-10 0.266 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.
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"]
# you can also extract it by
# 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 51941 rows and 120 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 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:
k and the heatmap of
predicted classes for each k.k.k.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:
k;k, the area increased is defined as \(A_k - A_{k-1}\).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 0.990 0.5044 0.496 0.496
#> 3 3 0.853 0.837 0.939 0.2866 0.774 0.575
#> 4 4 0.760 0.780 0.859 0.0998 0.922 0.782
#> 5 5 0.864 0.802 0.879 0.0815 0.854 0.557
#> 6 6 0.903 0.854 0.912 0.0566 0.908 0.628
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> 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.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM494565 2 0.000 0.9892 0.000 1.000
#> GSM494594 2 0.000 0.9892 0.000 1.000
#> GSM494604 1 0.416 0.9003 0.916 0.084
#> GSM494564 2 0.000 0.9892 0.000 1.000
#> GSM494591 2 0.000 0.9892 0.000 1.000
#> GSM494567 2 0.000 0.9892 0.000 1.000
#> GSM494602 2 0.000 0.9892 0.000 1.000
#> GSM494613 2 0.000 0.9892 0.000 1.000
#> GSM494589 2 0.000 0.9892 0.000 1.000
#> GSM494598 2 0.000 0.9892 0.000 1.000
#> GSM494593 2 0.000 0.9892 0.000 1.000
#> GSM494583 2 0.000 0.9892 0.000 1.000
#> GSM494612 2 0.000 0.9892 0.000 1.000
#> GSM494558 2 0.615 0.8140 0.152 0.848
#> GSM494556 2 0.000 0.9892 0.000 1.000
#> GSM494559 2 0.000 0.9892 0.000 1.000
#> GSM494571 2 0.000 0.9892 0.000 1.000
#> GSM494614 2 0.000 0.9892 0.000 1.000
#> GSM494603 2 0.000 0.9892 0.000 1.000
#> GSM494568 1 1.000 0.0300 0.512 0.488
#> GSM494572 2 0.000 0.9892 0.000 1.000
#> GSM494600 2 0.000 0.9892 0.000 1.000
#> GSM494562 2 0.000 0.9892 0.000 1.000
#> GSM494615 2 0.000 0.9892 0.000 1.000
#> GSM494582 2 0.000 0.9892 0.000 1.000
#> GSM494599 2 0.000 0.9892 0.000 1.000
#> GSM494610 2 0.000 0.9892 0.000 1.000
#> GSM494587 2 0.000 0.9892 0.000 1.000
#> GSM494581 2 0.000 0.9892 0.000 1.000
#> GSM494580 2 0.000 0.9892 0.000 1.000
#> GSM494563 2 0.000 0.9892 0.000 1.000
#> GSM494576 2 0.000 0.9892 0.000 1.000
#> GSM494605 1 0.000 0.9900 1.000 0.000
#> GSM494584 2 0.000 0.9892 0.000 1.000
#> GSM494586 2 0.000 0.9892 0.000 1.000
#> GSM494578 2 0.000 0.9892 0.000 1.000
#> GSM494585 2 0.000 0.9892 0.000 1.000
#> GSM494611 2 0.000 0.9892 0.000 1.000
#> GSM494560 2 0.000 0.9892 0.000 1.000
#> GSM494595 2 0.000 0.9892 0.000 1.000
#> GSM494570 2 0.000 0.9892 0.000 1.000
#> GSM494597 2 0.000 0.9892 0.000 1.000
#> GSM494607 2 0.000 0.9892 0.000 1.000
#> GSM494561 2 0.000 0.9892 0.000 1.000
#> GSM494569 1 0.000 0.9900 1.000 0.000
#> GSM494592 2 0.000 0.9892 0.000 1.000
#> GSM494577 2 0.000 0.9892 0.000 1.000
#> GSM494588 2 0.000 0.9892 0.000 1.000
#> GSM494590 2 0.000 0.9892 0.000 1.000
#> GSM494609 2 0.000 0.9892 0.000 1.000
#> GSM494608 2 0.000 0.9892 0.000 1.000
#> GSM494606 2 0.000 0.9892 0.000 1.000
#> GSM494574 2 0.000 0.9892 0.000 1.000
#> GSM494573 2 0.000 0.9892 0.000 1.000
#> GSM494566 2 0.000 0.9892 0.000 1.000
#> GSM494601 2 0.000 0.9892 0.000 1.000
#> GSM494557 2 0.000 0.9892 0.000 1.000
#> GSM494579 2 0.000 0.9892 0.000 1.000
#> GSM494596 2 0.000 0.9892 0.000 1.000
#> GSM494575 2 0.000 0.9892 0.000 1.000
#> GSM494625 1 0.000 0.9900 1.000 0.000
#> GSM494654 2 0.999 0.0503 0.484 0.516
#> GSM494664 1 0.000 0.9900 1.000 0.000
#> GSM494624 1 0.000 0.9900 1.000 0.000
#> GSM494651 1 0.000 0.9900 1.000 0.000
#> GSM494662 1 0.000 0.9900 1.000 0.000
#> GSM494627 1 0.000 0.9900 1.000 0.000
#> GSM494673 1 0.000 0.9900 1.000 0.000
#> GSM494649 1 0.000 0.9900 1.000 0.000
#> GSM494658 1 0.000 0.9900 1.000 0.000
#> GSM494653 1 0.000 0.9900 1.000 0.000
#> GSM494643 1 0.000 0.9900 1.000 0.000
#> GSM494672 1 0.000 0.9900 1.000 0.000
#> GSM494618 1 0.000 0.9900 1.000 0.000
#> GSM494631 2 0.000 0.9892 0.000 1.000
#> GSM494619 1 0.000 0.9900 1.000 0.000
#> GSM494674 1 0.000 0.9900 1.000 0.000
#> GSM494616 1 0.000 0.9900 1.000 0.000
#> GSM494663 1 0.000 0.9900 1.000 0.000
#> GSM494628 1 0.000 0.9900 1.000 0.000
#> GSM494632 1 0.000 0.9900 1.000 0.000
#> GSM494660 1 0.000 0.9900 1.000 0.000
#> GSM494622 1 0.000 0.9900 1.000 0.000
#> GSM494642 1 0.000 0.9900 1.000 0.000
#> GSM494647 1 0.000 0.9900 1.000 0.000
#> GSM494659 1 0.000 0.9900 1.000 0.000
#> GSM494670 1 0.000 0.9900 1.000 0.000
#> GSM494675 2 0.000 0.9892 0.000 1.000
#> GSM494641 1 0.000 0.9900 1.000 0.000
#> GSM494636 1 0.000 0.9900 1.000 0.000
#> GSM494640 1 0.000 0.9900 1.000 0.000
#> GSM494623 1 0.000 0.9900 1.000 0.000
#> GSM494644 1 0.000 0.9900 1.000 0.000
#> GSM494646 1 0.000 0.9900 1.000 0.000
#> GSM494665 1 0.000 0.9900 1.000 0.000
#> GSM494638 1 0.000 0.9900 1.000 0.000
#> GSM494645 1 0.000 0.9900 1.000 0.000
#> GSM494671 1 0.000 0.9900 1.000 0.000
#> GSM494655 1 0.000 0.9900 1.000 0.000
#> GSM494620 1 0.000 0.9900 1.000 0.000
#> GSM494630 1 0.000 0.9900 1.000 0.000
#> GSM494657 2 0.000 0.9892 0.000 1.000
#> GSM494667 1 0.000 0.9900 1.000 0.000
#> GSM494621 1 0.000 0.9900 1.000 0.000
#> GSM494629 1 0.000 0.9900 1.000 0.000
#> GSM494637 1 0.000 0.9900 1.000 0.000
#> GSM494652 1 0.000 0.9900 1.000 0.000
#> GSM494648 1 0.000 0.9900 1.000 0.000
#> GSM494650 1 0.000 0.9900 1.000 0.000
#> GSM494669 1 0.000 0.9900 1.000 0.000
#> GSM494666 1 0.000 0.9900 1.000 0.000
#> GSM494668 1 0.000 0.9900 1.000 0.000
#> GSM494633 1 0.000 0.9900 1.000 0.000
#> GSM494634 1 0.000 0.9900 1.000 0.000
#> GSM494639 1 0.000 0.9900 1.000 0.000
#> GSM494661 1 0.000 0.9900 1.000 0.000
#> GSM494617 1 0.000 0.9900 1.000 0.000
#> GSM494626 1 0.000 0.9900 1.000 0.000
#> GSM494656 2 0.000 0.9892 0.000 1.000
#> GSM494635 1 0.000 0.9900 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM494565 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494594 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494604 1 0.0000 0.8962 1.000 0.000 0.000
#> GSM494564 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494591 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494567 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494602 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494613 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494589 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494598 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494593 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494583 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494612 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494558 3 0.5291 0.6071 0.000 0.268 0.732
#> GSM494556 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494559 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494571 2 0.5988 0.3505 0.000 0.632 0.368
#> GSM494614 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494603 3 0.6235 0.2633 0.000 0.436 0.564
#> GSM494568 3 0.5363 0.5913 0.000 0.276 0.724
#> GSM494572 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494600 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494562 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494615 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494582 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494599 1 0.4654 0.6789 0.792 0.208 0.000
#> GSM494610 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494587 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494581 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494580 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494563 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494576 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494605 1 0.0000 0.8962 1.000 0.000 0.000
#> GSM494584 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494586 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494578 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494585 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494611 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494560 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494595 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494570 3 0.6235 0.2609 0.000 0.436 0.564
#> GSM494597 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494607 1 0.4002 0.7374 0.840 0.160 0.000
#> GSM494561 3 0.6225 0.2714 0.000 0.432 0.568
#> GSM494569 3 0.0424 0.8454 0.008 0.000 0.992
#> GSM494592 1 0.2959 0.8032 0.900 0.100 0.000
#> GSM494577 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494588 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494590 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494609 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494608 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494606 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494574 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494573 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494566 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494601 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494557 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494579 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494596 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494575 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494625 3 0.0000 0.8480 0.000 0.000 1.000
#> GSM494654 3 0.0000 0.8480 0.000 0.000 1.000
#> GSM494664 1 0.0000 0.8962 1.000 0.000 0.000
#> GSM494624 3 0.0000 0.8480 0.000 0.000 1.000
#> GSM494651 3 0.0424 0.8454 0.008 0.000 0.992
#> GSM494662 3 0.2796 0.7655 0.092 0.000 0.908
#> GSM494627 3 0.0000 0.8480 0.000 0.000 1.000
#> GSM494673 1 0.0000 0.8962 1.000 0.000 0.000
#> GSM494649 3 0.0000 0.8480 0.000 0.000 1.000
#> GSM494658 1 0.0000 0.8962 1.000 0.000 0.000
#> GSM494653 1 0.0000 0.8962 1.000 0.000 0.000
#> GSM494643 3 0.0000 0.8480 0.000 0.000 1.000
#> GSM494672 1 0.0000 0.8962 1.000 0.000 0.000
#> GSM494618 3 0.0424 0.8454 0.008 0.000 0.992
#> GSM494631 2 0.3879 0.7969 0.000 0.848 0.152
#> GSM494619 3 0.0000 0.8480 0.000 0.000 1.000
#> GSM494674 1 0.0000 0.8962 1.000 0.000 0.000
#> GSM494616 3 0.0424 0.8454 0.008 0.000 0.992
#> GSM494663 3 0.0000 0.8480 0.000 0.000 1.000
#> GSM494628 3 0.0000 0.8480 0.000 0.000 1.000
#> GSM494632 1 0.6225 0.3408 0.568 0.000 0.432
#> GSM494660 3 0.0000 0.8480 0.000 0.000 1.000
#> GSM494622 3 0.0237 0.8468 0.004 0.000 0.996
#> GSM494642 1 0.0000 0.8962 1.000 0.000 0.000
#> GSM494647 1 0.0000 0.8962 1.000 0.000 0.000
#> GSM494659 1 0.0000 0.8962 1.000 0.000 0.000
#> GSM494670 1 0.0000 0.8962 1.000 0.000 0.000
#> GSM494675 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494641 1 0.0000 0.8962 1.000 0.000 0.000
#> GSM494636 3 0.0424 0.8454 0.008 0.000 0.992
#> GSM494640 3 0.0000 0.8480 0.000 0.000 1.000
#> GSM494623 3 0.0000 0.8480 0.000 0.000 1.000
#> GSM494644 1 0.0000 0.8962 1.000 0.000 0.000
#> GSM494646 1 0.6168 0.3818 0.588 0.000 0.412
#> GSM494665 1 0.0000 0.8962 1.000 0.000 0.000
#> GSM494638 1 0.6225 0.3408 0.568 0.000 0.432
#> GSM494645 1 0.0000 0.8962 1.000 0.000 0.000
#> GSM494671 1 0.0000 0.8962 1.000 0.000 0.000
#> GSM494655 1 0.0000 0.8962 1.000 0.000 0.000
#> GSM494620 3 0.6291 -0.0989 0.468 0.000 0.532
#> GSM494630 1 0.6252 0.3175 0.556 0.000 0.444
#> GSM494657 2 0.0000 0.9887 0.000 1.000 0.000
#> GSM494667 1 0.0000 0.8962 1.000 0.000 0.000
#> GSM494621 3 0.0000 0.8480 0.000 0.000 1.000
#> GSM494629 3 0.0000 0.8480 0.000 0.000 1.000
#> GSM494637 3 0.0000 0.8480 0.000 0.000 1.000
#> GSM494652 1 0.0000 0.8962 1.000 0.000 0.000
#> GSM494648 3 0.6295 -0.1120 0.472 0.000 0.528
#> GSM494650 3 0.0424 0.8454 0.008 0.000 0.992
#> GSM494669 1 0.0000 0.8962 1.000 0.000 0.000
#> GSM494666 1 0.0000 0.8962 1.000 0.000 0.000
#> GSM494668 1 0.0000 0.8962 1.000 0.000 0.000
#> GSM494633 3 0.0000 0.8480 0.000 0.000 1.000
#> GSM494634 1 0.0000 0.8962 1.000 0.000 0.000
#> GSM494639 1 0.6225 0.3408 0.568 0.000 0.432
#> GSM494661 1 0.0000 0.8962 1.000 0.000 0.000
#> GSM494617 3 0.6295 -0.0954 0.472 0.000 0.528
#> GSM494626 3 0.0424 0.8454 0.008 0.000 0.992
#> GSM494656 3 0.6244 0.2520 0.000 0.440 0.560
#> GSM494635 1 0.6062 0.4346 0.616 0.000 0.384
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM494565 2 0.4193 0.849 0.000 0.732 0.268 0.000
#> GSM494594 2 0.4277 0.844 0.000 0.720 0.280 0.000
#> GSM494604 1 0.3837 0.726 0.776 0.224 0.000 0.000
#> GSM494564 2 0.4193 0.849 0.000 0.732 0.268 0.000
#> GSM494591 2 0.4277 0.844 0.000 0.720 0.280 0.000
#> GSM494567 2 0.4193 0.849 0.000 0.732 0.268 0.000
#> GSM494602 2 0.0000 0.828 0.000 1.000 0.000 0.000
#> GSM494613 2 0.4193 0.849 0.000 0.732 0.268 0.000
#> GSM494589 2 0.4193 0.849 0.000 0.732 0.268 0.000
#> GSM494598 2 0.0000 0.828 0.000 1.000 0.000 0.000
#> GSM494593 2 0.0000 0.828 0.000 1.000 0.000 0.000
#> GSM494583 2 0.4193 0.849 0.000 0.732 0.268 0.000
#> GSM494612 2 0.0000 0.828 0.000 1.000 0.000 0.000
#> GSM494558 3 0.0336 0.572 0.000 0.000 0.992 0.008
#> GSM494556 2 0.4193 0.849 0.000 0.732 0.268 0.000
#> GSM494559 2 0.4193 0.849 0.000 0.732 0.268 0.000
#> GSM494571 2 0.4985 0.597 0.000 0.532 0.468 0.000
#> GSM494614 2 0.4193 0.849 0.000 0.732 0.268 0.000
#> GSM494603 3 0.0469 0.565 0.000 0.012 0.988 0.000
#> GSM494568 3 0.4019 0.720 0.000 0.012 0.792 0.196
#> GSM494572 2 0.4277 0.844 0.000 0.720 0.280 0.000
#> GSM494600 2 0.4193 0.849 0.000 0.732 0.268 0.000
#> GSM494562 2 0.0000 0.828 0.000 1.000 0.000 0.000
#> GSM494615 3 0.4776 -0.236 0.000 0.376 0.624 0.000
#> GSM494582 2 0.0000 0.828 0.000 1.000 0.000 0.000
#> GSM494599 1 0.4585 0.599 0.668 0.332 0.000 0.000
#> GSM494610 2 0.0000 0.828 0.000 1.000 0.000 0.000
#> GSM494587 2 0.0000 0.828 0.000 1.000 0.000 0.000
#> GSM494581 2 0.0336 0.829 0.000 0.992 0.008 0.000
#> GSM494580 2 0.4193 0.849 0.000 0.732 0.268 0.000
#> GSM494563 2 0.4193 0.849 0.000 0.732 0.268 0.000
#> GSM494576 2 0.0000 0.828 0.000 1.000 0.000 0.000
#> GSM494605 1 0.0000 0.902 1.000 0.000 0.000 0.000
#> GSM494584 2 0.4193 0.849 0.000 0.732 0.268 0.000
#> GSM494586 2 0.0000 0.828 0.000 1.000 0.000 0.000
#> GSM494578 2 0.4193 0.849 0.000 0.732 0.268 0.000
#> GSM494585 2 0.0000 0.828 0.000 1.000 0.000 0.000
#> GSM494611 2 0.0000 0.828 0.000 1.000 0.000 0.000
#> GSM494560 2 0.4193 0.849 0.000 0.732 0.268 0.000
#> GSM494595 2 0.0000 0.828 0.000 1.000 0.000 0.000
#> GSM494570 4 0.4485 0.539 0.000 0.012 0.248 0.740
#> GSM494597 2 0.4222 0.847 0.000 0.728 0.272 0.000
#> GSM494607 1 0.4222 0.679 0.728 0.272 0.000 0.000
#> GSM494561 4 0.4248 0.571 0.000 0.012 0.220 0.768
#> GSM494569 3 0.5203 0.756 0.048 0.000 0.720 0.232
#> GSM494592 1 0.4222 0.679 0.728 0.272 0.000 0.000
#> GSM494577 2 0.0000 0.828 0.000 1.000 0.000 0.000
#> GSM494588 4 0.7824 -0.152 0.000 0.328 0.268 0.404
#> GSM494590 2 0.4277 0.844 0.000 0.720 0.280 0.000
#> GSM494609 2 0.0000 0.828 0.000 1.000 0.000 0.000
#> GSM494608 2 0.0000 0.828 0.000 1.000 0.000 0.000
#> GSM494606 2 0.0000 0.828 0.000 1.000 0.000 0.000
#> GSM494574 2 0.0000 0.828 0.000 1.000 0.000 0.000
#> GSM494573 2 0.4193 0.849 0.000 0.732 0.268 0.000
#> GSM494566 2 0.0336 0.829 0.000 0.992 0.008 0.000
#> GSM494601 2 0.0000 0.828 0.000 1.000 0.000 0.000
#> GSM494557 2 0.4193 0.849 0.000 0.732 0.268 0.000
#> GSM494579 2 0.0000 0.828 0.000 1.000 0.000 0.000
#> GSM494596 2 0.4277 0.844 0.000 0.720 0.280 0.000
#> GSM494575 2 0.0000 0.828 0.000 1.000 0.000 0.000
#> GSM494625 4 0.0000 0.839 0.000 0.000 0.000 1.000
#> GSM494654 3 0.0000 0.568 0.000 0.000 1.000 0.000
#> GSM494664 1 0.0000 0.902 1.000 0.000 0.000 0.000
#> GSM494624 4 0.0000 0.839 0.000 0.000 0.000 1.000
#> GSM494651 3 0.5203 0.756 0.048 0.000 0.720 0.232
#> GSM494662 1 0.6889 0.398 0.592 0.000 0.176 0.232
#> GSM494627 3 0.4277 0.745 0.000 0.000 0.720 0.280
#> GSM494673 1 0.0000 0.902 1.000 0.000 0.000 0.000
#> GSM494649 4 0.0000 0.839 0.000 0.000 0.000 1.000
#> GSM494658 1 0.0000 0.902 1.000 0.000 0.000 0.000
#> GSM494653 1 0.0000 0.902 1.000 0.000 0.000 0.000
#> GSM494643 4 0.3528 0.521 0.000 0.000 0.192 0.808
#> GSM494672 1 0.0000 0.902 1.000 0.000 0.000 0.000
#> GSM494618 3 0.5203 0.756 0.048 0.000 0.720 0.232
#> GSM494631 2 0.4193 0.849 0.000 0.732 0.268 0.000
#> GSM494619 4 0.0000 0.839 0.000 0.000 0.000 1.000
#> GSM494674 1 0.0000 0.902 1.000 0.000 0.000 0.000
#> GSM494616 3 0.5203 0.756 0.048 0.000 0.720 0.232
#> GSM494663 3 0.4277 0.745 0.000 0.000 0.720 0.280
#> GSM494628 3 0.4277 0.745 0.000 0.000 0.720 0.280
#> GSM494632 1 0.3907 0.699 0.768 0.000 0.000 0.232
#> GSM494660 4 0.0000 0.839 0.000 0.000 0.000 1.000
#> GSM494622 3 0.4539 0.748 0.008 0.000 0.720 0.272
#> GSM494642 1 0.0000 0.902 1.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.902 1.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.902 1.000 0.000 0.000 0.000
#> GSM494670 1 0.0000 0.902 1.000 0.000 0.000 0.000
#> GSM494675 2 0.4193 0.849 0.000 0.732 0.268 0.000
#> GSM494641 1 0.0000 0.902 1.000 0.000 0.000 0.000
#> GSM494636 1 0.7394 0.207 0.520 0.000 0.244 0.236
#> GSM494640 3 0.4866 0.606 0.000 0.000 0.596 0.404
#> GSM494623 4 0.0000 0.839 0.000 0.000 0.000 1.000
#> GSM494644 1 0.0000 0.902 1.000 0.000 0.000 0.000
#> GSM494646 1 0.3444 0.755 0.816 0.000 0.000 0.184
#> GSM494665 1 0.0000 0.902 1.000 0.000 0.000 0.000
#> GSM494638 1 0.3907 0.699 0.768 0.000 0.000 0.232
#> GSM494645 1 0.0000 0.902 1.000 0.000 0.000 0.000
#> GSM494671 1 0.0000 0.902 1.000 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.902 1.000 0.000 0.000 0.000
#> GSM494620 4 0.0000 0.839 0.000 0.000 0.000 1.000
#> GSM494630 4 0.0000 0.839 0.000 0.000 0.000 1.000
#> GSM494657 2 0.4277 0.844 0.000 0.720 0.280 0.000
#> GSM494667 1 0.0000 0.902 1.000 0.000 0.000 0.000
#> GSM494621 4 0.0000 0.839 0.000 0.000 0.000 1.000
#> GSM494629 3 0.4277 0.745 0.000 0.000 0.720 0.280
#> GSM494637 3 0.4925 0.565 0.000 0.000 0.572 0.428
#> GSM494652 1 0.0000 0.902 1.000 0.000 0.000 0.000
#> GSM494648 4 0.0000 0.839 0.000 0.000 0.000 1.000
#> GSM494650 3 0.5203 0.756 0.048 0.000 0.720 0.232
#> GSM494669 1 0.0000 0.902 1.000 0.000 0.000 0.000
#> GSM494666 1 0.0000 0.902 1.000 0.000 0.000 0.000
#> GSM494668 1 0.0000 0.902 1.000 0.000 0.000 0.000
#> GSM494633 4 0.0000 0.839 0.000 0.000 0.000 1.000
#> GSM494634 1 0.0000 0.902 1.000 0.000 0.000 0.000
#> GSM494639 1 0.3907 0.699 0.768 0.000 0.000 0.232
#> GSM494661 1 0.0000 0.902 1.000 0.000 0.000 0.000
#> GSM494617 3 0.7325 0.513 0.236 0.000 0.532 0.232
#> GSM494626 3 0.5203 0.756 0.048 0.000 0.720 0.232
#> GSM494656 3 0.2216 0.472 0.000 0.092 0.908 0.000
#> GSM494635 1 0.2973 0.794 0.856 0.000 0.000 0.144
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM494565 3 0.0000 0.7267 0.000 0.000 1.000 0.000 0.000
#> GSM494594 3 0.4249 0.5932 0.000 0.000 0.568 0.000 0.432
#> GSM494604 1 0.3932 0.5033 0.672 0.328 0.000 0.000 0.000
#> GSM494564 3 0.0000 0.7267 0.000 0.000 1.000 0.000 0.000
#> GSM494591 3 0.4249 0.5932 0.000 0.000 0.568 0.000 0.432
#> GSM494567 3 0.0000 0.7267 0.000 0.000 1.000 0.000 0.000
#> GSM494602 2 0.4192 0.8556 0.000 0.596 0.404 0.000 0.000
#> GSM494613 3 0.0000 0.7267 0.000 0.000 1.000 0.000 0.000
#> GSM494589 3 0.0000 0.7267 0.000 0.000 1.000 0.000 0.000
#> GSM494598 2 0.4192 0.8556 0.000 0.596 0.404 0.000 0.000
#> GSM494593 2 0.4201 0.8553 0.000 0.592 0.408 0.000 0.000
#> GSM494583 3 0.0000 0.7267 0.000 0.000 1.000 0.000 0.000
#> GSM494612 2 0.4192 0.8556 0.000 0.596 0.404 0.000 0.000
#> GSM494558 4 0.1965 0.8799 0.000 0.000 0.052 0.924 0.024
#> GSM494556 3 0.0000 0.7267 0.000 0.000 1.000 0.000 0.000
#> GSM494559 3 0.0000 0.7267 0.000 0.000 1.000 0.000 0.000
#> GSM494571 3 0.4249 0.5932 0.000 0.000 0.568 0.000 0.432
#> GSM494614 3 0.0000 0.7267 0.000 0.000 1.000 0.000 0.000
#> GSM494603 4 0.1732 0.8687 0.000 0.000 0.080 0.920 0.000
#> GSM494568 4 0.1740 0.8860 0.000 0.000 0.056 0.932 0.012
#> GSM494572 3 0.4249 0.5932 0.000 0.000 0.568 0.000 0.432
#> GSM494600 3 0.0000 0.7267 0.000 0.000 1.000 0.000 0.000
#> GSM494562 2 0.4242 0.8462 0.000 0.572 0.428 0.000 0.000
#> GSM494615 3 0.4030 0.3968 0.000 0.000 0.648 0.352 0.000
#> GSM494582 2 0.4192 0.8556 0.000 0.596 0.404 0.000 0.000
#> GSM494599 2 0.4192 0.2111 0.404 0.596 0.000 0.000 0.000
#> GSM494610 2 0.4192 0.8556 0.000 0.596 0.404 0.000 0.000
#> GSM494587 2 0.4249 0.8444 0.000 0.568 0.432 0.000 0.000
#> GSM494581 2 0.4262 0.8363 0.000 0.560 0.440 0.000 0.000
#> GSM494580 3 0.0000 0.7267 0.000 0.000 1.000 0.000 0.000
#> GSM494563 3 0.0000 0.7267 0.000 0.000 1.000 0.000 0.000
#> GSM494576 2 0.4249 0.8444 0.000 0.568 0.432 0.000 0.000
#> GSM494605 1 0.0000 0.9667 1.000 0.000 0.000 0.000 0.000
#> GSM494584 3 0.0000 0.7267 0.000 0.000 1.000 0.000 0.000
#> GSM494586 2 0.4249 0.8444 0.000 0.568 0.432 0.000 0.000
#> GSM494578 3 0.0000 0.7267 0.000 0.000 1.000 0.000 0.000
#> GSM494585 2 0.4249 0.8444 0.000 0.568 0.432 0.000 0.000
#> GSM494611 2 0.4192 0.8556 0.000 0.596 0.404 0.000 0.000
#> GSM494560 3 0.0000 0.7267 0.000 0.000 1.000 0.000 0.000
#> GSM494595 2 0.4192 0.8556 0.000 0.596 0.404 0.000 0.000
#> GSM494570 3 0.6539 -0.0470 0.000 0.404 0.464 0.024 0.108
#> GSM494597 3 0.2516 0.6906 0.000 0.000 0.860 0.000 0.140
#> GSM494607 2 0.4192 0.2111 0.404 0.596 0.000 0.000 0.000
#> GSM494561 2 0.7260 -0.6169 0.000 0.404 0.316 0.024 0.256
#> GSM494569 4 0.0703 0.9242 0.024 0.000 0.000 0.976 0.000
#> GSM494592 2 0.4192 0.2111 0.404 0.596 0.000 0.000 0.000
#> GSM494577 2 0.4249 0.8444 0.000 0.568 0.432 0.000 0.000
#> GSM494588 3 0.5815 0.0911 0.000 0.396 0.508 0.000 0.096
#> GSM494590 3 0.4249 0.5932 0.000 0.000 0.568 0.000 0.432
#> GSM494609 2 0.4201 0.8553 0.000 0.592 0.408 0.000 0.000
#> GSM494608 2 0.4201 0.8553 0.000 0.592 0.408 0.000 0.000
#> GSM494606 2 0.4201 0.8553 0.000 0.592 0.408 0.000 0.000
#> GSM494574 2 0.4192 0.8556 0.000 0.596 0.404 0.000 0.000
#> GSM494573 3 0.0000 0.7267 0.000 0.000 1.000 0.000 0.000
#> GSM494566 2 0.4297 0.7906 0.000 0.528 0.472 0.000 0.000
#> GSM494601 2 0.4192 0.8556 0.000 0.596 0.404 0.000 0.000
#> GSM494557 3 0.0000 0.7267 0.000 0.000 1.000 0.000 0.000
#> GSM494579 2 0.4262 0.8365 0.000 0.560 0.440 0.000 0.000
#> GSM494596 3 0.4249 0.5932 0.000 0.000 0.568 0.000 0.432
#> GSM494575 2 0.4192 0.8556 0.000 0.596 0.404 0.000 0.000
#> GSM494625 5 0.4893 0.9945 0.000 0.404 0.000 0.028 0.568
#> GSM494654 3 0.4249 0.5932 0.000 0.000 0.568 0.000 0.432
#> GSM494664 1 0.0000 0.9667 1.000 0.000 0.000 0.000 0.000
#> GSM494624 5 0.4893 0.9945 0.000 0.404 0.000 0.028 0.568
#> GSM494651 4 0.0703 0.9242 0.024 0.000 0.000 0.976 0.000
#> GSM494662 1 0.1628 0.9271 0.936 0.000 0.000 0.056 0.008
#> GSM494627 4 0.0000 0.9205 0.000 0.000 0.000 1.000 0.000
#> GSM494673 1 0.0000 0.9667 1.000 0.000 0.000 0.000 0.000
#> GSM494649 5 0.4893 0.9945 0.000 0.404 0.000 0.028 0.568
#> GSM494658 1 0.0000 0.9667 1.000 0.000 0.000 0.000 0.000
#> GSM494653 1 0.0000 0.9667 1.000 0.000 0.000 0.000 0.000
#> GSM494643 5 0.5652 0.9372 0.000 0.404 0.000 0.080 0.516
#> GSM494672 1 0.0000 0.9667 1.000 0.000 0.000 0.000 0.000
#> GSM494618 4 0.0703 0.9242 0.024 0.000 0.000 0.976 0.000
#> GSM494631 3 0.0000 0.7267 0.000 0.000 1.000 0.000 0.000
#> GSM494619 5 0.4893 0.9945 0.000 0.404 0.000 0.028 0.568
#> GSM494674 1 0.0000 0.9667 1.000 0.000 0.000 0.000 0.000
#> GSM494616 4 0.0703 0.9242 0.024 0.000 0.000 0.976 0.000
#> GSM494663 4 0.0703 0.9084 0.000 0.000 0.000 0.976 0.024
#> GSM494628 4 0.0000 0.9205 0.000 0.000 0.000 1.000 0.000
#> GSM494632 1 0.1341 0.9321 0.944 0.000 0.000 0.056 0.000
#> GSM494660 5 0.4893 0.9945 0.000 0.404 0.000 0.028 0.568
#> GSM494622 4 0.0162 0.9219 0.004 0.000 0.000 0.996 0.000
#> GSM494642 1 0.0000 0.9667 1.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.9667 1.000 0.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.9667 1.000 0.000 0.000 0.000 0.000
#> GSM494670 1 0.0000 0.9667 1.000 0.000 0.000 0.000 0.000
#> GSM494675 3 0.0000 0.7267 0.000 0.000 1.000 0.000 0.000
#> GSM494641 1 0.0000 0.9667 1.000 0.000 0.000 0.000 0.000
#> GSM494636 1 0.5411 0.5951 0.664 0.000 0.000 0.160 0.176
#> GSM494640 4 0.3913 0.5247 0.000 0.000 0.000 0.676 0.324
#> GSM494623 5 0.4893 0.9945 0.000 0.404 0.000 0.028 0.568
#> GSM494644 1 0.0000 0.9667 1.000 0.000 0.000 0.000 0.000
#> GSM494646 1 0.1341 0.9321 0.944 0.000 0.000 0.056 0.000
#> GSM494665 1 0.0000 0.9667 1.000 0.000 0.000 0.000 0.000
#> GSM494638 1 0.1341 0.9321 0.944 0.000 0.000 0.056 0.000
#> GSM494645 1 0.0000 0.9667 1.000 0.000 0.000 0.000 0.000
#> GSM494671 1 0.0000 0.9667 1.000 0.000 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.9667 1.000 0.000 0.000 0.000 0.000
#> GSM494620 5 0.4893 0.9945 0.000 0.404 0.000 0.028 0.568
#> GSM494630 5 0.4893 0.9945 0.000 0.404 0.000 0.028 0.568
#> GSM494657 3 0.4249 0.5932 0.000 0.000 0.568 0.000 0.432
#> GSM494667 1 0.0000 0.9667 1.000 0.000 0.000 0.000 0.000
#> GSM494621 5 0.4893 0.9945 0.000 0.404 0.000 0.028 0.568
#> GSM494629 4 0.0000 0.9205 0.000 0.000 0.000 1.000 0.000
#> GSM494637 4 0.4015 0.4754 0.000 0.000 0.000 0.652 0.348
#> GSM494652 1 0.0000 0.9667 1.000 0.000 0.000 0.000 0.000
#> GSM494648 5 0.4893 0.9945 0.000 0.404 0.000 0.028 0.568
#> GSM494650 4 0.0703 0.9242 0.024 0.000 0.000 0.976 0.000
#> GSM494669 1 0.0000 0.9667 1.000 0.000 0.000 0.000 0.000
#> GSM494666 1 0.0000 0.9667 1.000 0.000 0.000 0.000 0.000
#> GSM494668 1 0.0000 0.9667 1.000 0.000 0.000 0.000 0.000
#> GSM494633 5 0.4893 0.9945 0.000 0.404 0.000 0.028 0.568
#> GSM494634 1 0.0000 0.9667 1.000 0.000 0.000 0.000 0.000
#> GSM494639 1 0.1341 0.9321 0.944 0.000 0.000 0.056 0.000
#> GSM494661 1 0.0000 0.9667 1.000 0.000 0.000 0.000 0.000
#> GSM494617 4 0.0703 0.9242 0.024 0.000 0.000 0.976 0.000
#> GSM494626 4 0.0703 0.9242 0.024 0.000 0.000 0.976 0.000
#> GSM494656 3 0.4249 0.5932 0.000 0.000 0.568 0.000 0.432
#> GSM494635 1 0.1341 0.9321 0.944 0.000 0.000 0.056 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM494565 5 0.0632 0.7502 0.000 0.000 0.024 0.000 0.976 0.000
#> GSM494594 3 0.0790 0.9944 0.000 0.000 0.968 0.000 0.032 0.000
#> GSM494604 1 0.3812 0.5783 0.712 0.264 0.000 0.000 0.024 0.000
#> GSM494564 5 0.0632 0.7502 0.000 0.000 0.024 0.000 0.976 0.000
#> GSM494591 3 0.0790 0.9944 0.000 0.000 0.968 0.000 0.032 0.000
#> GSM494567 5 0.3330 0.7013 0.000 0.000 0.284 0.000 0.716 0.000
#> GSM494602 2 0.0000 0.9812 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494613 5 0.3448 0.7038 0.000 0.004 0.280 0.000 0.716 0.000
#> GSM494589 5 0.0632 0.7502 0.000 0.000 0.024 0.000 0.976 0.000
#> GSM494598 2 0.0000 0.9812 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494593 2 0.0937 0.9708 0.000 0.960 0.000 0.000 0.040 0.000
#> GSM494583 5 0.0713 0.7503 0.000 0.000 0.028 0.000 0.972 0.000
#> GSM494612 2 0.0000 0.9812 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494558 4 0.1141 0.8723 0.000 0.000 0.052 0.948 0.000 0.000
#> GSM494556 5 0.3126 0.7151 0.000 0.000 0.248 0.000 0.752 0.000
#> GSM494559 5 0.0000 0.7446 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494571 3 0.0790 0.9944 0.000 0.000 0.968 0.000 0.032 0.000
#> GSM494614 5 0.3288 0.7060 0.000 0.000 0.276 0.000 0.724 0.000
#> GSM494603 4 0.1480 0.8615 0.000 0.000 0.020 0.940 0.040 0.000
#> GSM494568 4 0.1515 0.8704 0.000 0.000 0.020 0.944 0.028 0.008
#> GSM494572 3 0.0790 0.9944 0.000 0.000 0.968 0.000 0.032 0.000
#> GSM494600 5 0.0632 0.7502 0.000 0.000 0.024 0.000 0.976 0.000
#> GSM494562 5 0.3563 0.6318 0.000 0.336 0.000 0.000 0.664 0.000
#> GSM494615 4 0.5855 0.0865 0.000 0.000 0.276 0.484 0.240 0.000
#> GSM494582 2 0.0000 0.9812 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494599 2 0.0632 0.9780 0.000 0.976 0.000 0.000 0.024 0.000
#> GSM494610 2 0.0790 0.9576 0.000 0.968 0.000 0.000 0.032 0.000
#> GSM494587 5 0.3464 0.6537 0.000 0.312 0.000 0.000 0.688 0.000
#> GSM494581 5 0.3309 0.6674 0.000 0.280 0.000 0.000 0.720 0.000
#> GSM494580 5 0.3330 0.7013 0.000 0.000 0.284 0.000 0.716 0.000
#> GSM494563 5 0.0632 0.7502 0.000 0.000 0.024 0.000 0.976 0.000
#> GSM494576 5 0.3489 0.6643 0.000 0.288 0.004 0.000 0.708 0.000
#> GSM494605 1 0.0000 0.9650 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494584 5 0.3288 0.7060 0.000 0.000 0.276 0.000 0.724 0.000
#> GSM494586 5 0.3547 0.6363 0.000 0.332 0.000 0.000 0.668 0.000
#> GSM494578 5 0.3330 0.7013 0.000 0.000 0.284 0.000 0.716 0.000
#> GSM494585 5 0.3371 0.6572 0.000 0.292 0.000 0.000 0.708 0.000
#> GSM494611 2 0.0000 0.9812 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494560 5 0.0632 0.7502 0.000 0.000 0.024 0.000 0.976 0.000
#> GSM494595 2 0.0260 0.9782 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM494570 6 0.4065 0.6071 0.000 0.000 0.028 0.000 0.300 0.672
#> GSM494597 5 0.3847 0.4113 0.000 0.000 0.456 0.000 0.544 0.000
#> GSM494607 2 0.0260 0.9811 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM494561 6 0.3766 0.6211 0.000 0.000 0.212 0.000 0.040 0.748
#> GSM494569 4 0.0000 0.9060 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494592 2 0.0632 0.9780 0.000 0.976 0.000 0.000 0.024 0.000
#> GSM494577 5 0.1657 0.7442 0.000 0.056 0.016 0.000 0.928 0.000
#> GSM494588 5 0.3756 0.0126 0.000 0.000 0.000 0.000 0.600 0.400
#> GSM494590 3 0.0790 0.9944 0.000 0.000 0.968 0.000 0.032 0.000
#> GSM494609 2 0.1007 0.9680 0.000 0.956 0.000 0.000 0.044 0.000
#> GSM494608 2 0.1007 0.9680 0.000 0.956 0.000 0.000 0.044 0.000
#> GSM494606 2 0.1007 0.9680 0.000 0.956 0.000 0.000 0.044 0.000
#> GSM494574 2 0.0000 0.9812 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494573 5 0.0632 0.7502 0.000 0.000 0.024 0.000 0.976 0.000
#> GSM494566 5 0.3489 0.6616 0.000 0.288 0.004 0.000 0.708 0.000
#> GSM494601 2 0.0632 0.9780 0.000 0.976 0.000 0.000 0.024 0.000
#> GSM494557 5 0.3309 0.7039 0.000 0.000 0.280 0.000 0.720 0.000
#> GSM494579 5 0.3371 0.6572 0.000 0.292 0.000 0.000 0.708 0.000
#> GSM494596 3 0.0790 0.9944 0.000 0.000 0.968 0.000 0.032 0.000
#> GSM494575 2 0.0000 0.9812 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494625 6 0.0000 0.9401 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494654 3 0.0000 0.9553 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494664 1 0.0000 0.9650 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494624 6 0.0000 0.9401 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494651 4 0.0000 0.9060 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494662 1 0.2302 0.9001 0.900 0.000 0.032 0.060 0.000 0.008
#> GSM494627 4 0.0000 0.9060 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494673 1 0.0000 0.9650 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494649 6 0.0000 0.9401 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494658 1 0.0000 0.9650 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494653 1 0.0000 0.9650 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494643 6 0.2046 0.8726 0.000 0.000 0.032 0.060 0.000 0.908
#> GSM494672 1 0.0000 0.9650 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494618 4 0.0000 0.9060 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494631 5 0.3534 0.7032 0.000 0.000 0.276 0.008 0.716 0.000
#> GSM494619 6 0.0000 0.9401 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494674 1 0.0000 0.9650 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494616 4 0.0000 0.9060 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494663 4 0.0713 0.8915 0.000 0.000 0.000 0.972 0.000 0.028
#> GSM494628 4 0.0000 0.9060 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494632 1 0.1267 0.9272 0.940 0.000 0.000 0.060 0.000 0.000
#> GSM494660 6 0.0000 0.9401 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494622 4 0.0000 0.9060 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494642 1 0.0000 0.9650 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.9650 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.9650 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494670 1 0.0000 0.9650 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494675 5 0.3198 0.7119 0.000 0.000 0.260 0.000 0.740 0.000
#> GSM494641 1 0.0000 0.9650 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494636 1 0.5358 0.6042 0.660 0.000 0.032 0.164 0.000 0.144
#> GSM494640 4 0.4186 0.5231 0.000 0.000 0.032 0.656 0.000 0.312
#> GSM494623 6 0.0000 0.9401 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494644 1 0.0000 0.9650 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494646 1 0.1267 0.9272 0.940 0.000 0.000 0.060 0.000 0.000
#> GSM494665 1 0.0000 0.9650 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494638 1 0.2046 0.9060 0.908 0.000 0.032 0.060 0.000 0.000
#> GSM494645 1 0.0000 0.9650 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494671 1 0.0000 0.9650 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.9650 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494620 6 0.0000 0.9401 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494630 6 0.0790 0.9248 0.000 0.000 0.032 0.000 0.000 0.968
#> GSM494657 3 0.0790 0.9944 0.000 0.000 0.968 0.000 0.032 0.000
#> GSM494667 1 0.0000 0.9650 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494621 6 0.0000 0.9401 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494629 4 0.0790 0.8900 0.000 0.000 0.032 0.968 0.000 0.000
#> GSM494637 4 0.4278 0.4763 0.000 0.000 0.032 0.632 0.000 0.336
#> GSM494652 1 0.0000 0.9650 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494648 6 0.0000 0.9401 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494650 4 0.0000 0.9060 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494669 1 0.0000 0.9650 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494666 1 0.0000 0.9650 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494668 1 0.0000 0.9650 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494633 6 0.0632 0.9291 0.000 0.000 0.024 0.000 0.000 0.976
#> GSM494634 1 0.0000 0.9650 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494639 1 0.1267 0.9272 0.940 0.000 0.000 0.060 0.000 0.000
#> GSM494661 1 0.0000 0.9650 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494617 4 0.0000 0.9060 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494626 4 0.0000 0.9060 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494656 3 0.0790 0.9944 0.000 0.000 0.968 0.000 0.032 0.000
#> GSM494635 1 0.1267 0.9272 0.940 0.000 0.000 0.060 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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 3)
get_signatures(res, k = 4)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 5)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 6)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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.
# code only for demonstration
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) individual(p) k
#> MAD:pam 118 5.93e-21 0.99997 3.67e-16 1.000 2
#> MAD:pam 106 2.21e-15 0.29615 3.48e-09 0.816 3
#> MAD:pam 115 1.50e-14 0.08709 2.89e-07 0.276 4
#> MAD:pam 112 1.32e-15 0.15188 9.87e-09 0.379 5
#> MAD:pam 116 1.37e-14 0.00521 3.52e-09 0.108 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.
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"]
# you can also extract it by
# 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 51941 rows and 120 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 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:
k and the heatmap of
predicted classes for each k.k.k.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:
k;k, the area increased is defined as \(A_k - A_{k-1}\).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 0.499 0.846 0.891 0.3642 0.688 0.688
#> 3 3 0.909 0.896 0.944 0.7682 0.661 0.511
#> 4 4 0.736 0.870 0.896 0.1310 0.883 0.688
#> 5 5 0.851 0.754 0.893 0.0807 0.876 0.586
#> 6 6 0.906 0.884 0.945 0.0412 0.886 0.541
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 3
There is also optional best \(k\) = 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.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM494565 2 0.000 0.862 0.000 1.000
#> GSM494594 1 0.722 0.869 0.800 0.200
#> GSM494604 1 0.518 0.868 0.884 0.116
#> GSM494564 2 0.000 0.862 0.000 1.000
#> GSM494591 1 0.722 0.869 0.800 0.200
#> GSM494567 1 0.722 0.869 0.800 0.200
#> GSM494602 1 0.722 0.869 0.800 0.200
#> GSM494613 1 0.722 0.869 0.800 0.200
#> GSM494589 2 0.000 0.862 0.000 1.000
#> GSM494598 1 0.722 0.869 0.800 0.200
#> GSM494593 1 0.722 0.869 0.800 0.200
#> GSM494583 1 0.850 0.808 0.724 0.276
#> GSM494612 1 0.722 0.869 0.800 0.200
#> GSM494558 1 0.595 0.869 0.856 0.144
#> GSM494556 1 0.722 0.869 0.800 0.200
#> GSM494559 2 0.000 0.862 0.000 1.000
#> GSM494571 1 0.722 0.869 0.800 0.200
#> GSM494614 1 0.722 0.869 0.800 0.200
#> GSM494603 1 0.891 0.692 0.692 0.308
#> GSM494568 1 0.518 0.868 0.884 0.116
#> GSM494572 1 0.722 0.869 0.800 0.200
#> GSM494600 2 0.000 0.862 0.000 1.000
#> GSM494562 1 0.722 0.869 0.800 0.200
#> GSM494615 1 0.722 0.869 0.800 0.200
#> GSM494582 1 0.722 0.869 0.800 0.200
#> GSM494599 1 0.722 0.869 0.800 0.200
#> GSM494610 1 0.722 0.869 0.800 0.200
#> GSM494587 1 0.722 0.869 0.800 0.200
#> GSM494581 1 0.827 0.823 0.740 0.260
#> GSM494580 1 0.722 0.869 0.800 0.200
#> GSM494563 2 0.000 0.862 0.000 1.000
#> GSM494576 1 0.722 0.869 0.800 0.200
#> GSM494605 1 0.000 0.855 1.000 0.000
#> GSM494584 1 0.722 0.869 0.800 0.200
#> GSM494586 1 0.722 0.869 0.800 0.200
#> GSM494578 1 0.722 0.869 0.800 0.200
#> GSM494585 1 0.722 0.869 0.800 0.200
#> GSM494611 1 0.722 0.869 0.800 0.200
#> GSM494560 2 0.000 0.862 0.000 1.000
#> GSM494595 1 0.722 0.869 0.800 0.200
#> GSM494570 2 0.000 0.862 0.000 1.000
#> GSM494597 1 0.722 0.869 0.800 0.200
#> GSM494607 1 0.722 0.869 0.800 0.200
#> GSM494561 2 0.000 0.862 0.000 1.000
#> GSM494569 1 0.000 0.855 1.000 0.000
#> GSM494592 1 0.722 0.869 0.800 0.200
#> GSM494577 1 0.722 0.869 0.800 0.200
#> GSM494588 2 0.000 0.862 0.000 1.000
#> GSM494590 1 0.722 0.869 0.800 0.200
#> GSM494609 1 0.722 0.869 0.800 0.200
#> GSM494608 1 0.722 0.869 0.800 0.200
#> GSM494606 1 0.722 0.869 0.800 0.200
#> GSM494574 1 0.722 0.869 0.800 0.200
#> GSM494573 2 0.000 0.862 0.000 1.000
#> GSM494566 1 0.722 0.869 0.800 0.200
#> GSM494601 1 0.722 0.869 0.800 0.200
#> GSM494557 1 0.722 0.869 0.800 0.200
#> GSM494579 1 0.722 0.869 0.800 0.200
#> GSM494596 1 0.722 0.869 0.800 0.200
#> GSM494575 1 0.722 0.869 0.800 0.200
#> GSM494625 2 0.722 0.872 0.200 0.800
#> GSM494654 1 0.574 0.869 0.864 0.136
#> GSM494664 1 0.000 0.855 1.000 0.000
#> GSM494624 2 0.722 0.872 0.200 0.800
#> GSM494651 1 0.000 0.855 1.000 0.000
#> GSM494662 1 0.373 0.793 0.928 0.072
#> GSM494627 1 0.469 0.760 0.900 0.100
#> GSM494673 1 0.000 0.855 1.000 0.000
#> GSM494649 2 0.722 0.872 0.200 0.800
#> GSM494658 1 0.000 0.855 1.000 0.000
#> GSM494653 1 0.000 0.855 1.000 0.000
#> GSM494643 2 0.833 0.815 0.264 0.736
#> GSM494672 1 0.000 0.855 1.000 0.000
#> GSM494618 1 0.000 0.855 1.000 0.000
#> GSM494631 1 0.574 0.869 0.864 0.136
#> GSM494619 2 0.722 0.872 0.200 0.800
#> GSM494674 1 0.000 0.855 1.000 0.000
#> GSM494616 1 0.000 0.855 1.000 0.000
#> GSM494663 1 0.955 0.109 0.624 0.376
#> GSM494628 1 0.000 0.855 1.000 0.000
#> GSM494632 1 0.000 0.855 1.000 0.000
#> GSM494660 2 0.722 0.872 0.200 0.800
#> GSM494622 1 0.000 0.855 1.000 0.000
#> GSM494642 1 0.000 0.855 1.000 0.000
#> GSM494647 1 0.000 0.855 1.000 0.000
#> GSM494659 1 0.000 0.855 1.000 0.000
#> GSM494670 1 0.000 0.855 1.000 0.000
#> GSM494675 1 0.722 0.869 0.800 0.200
#> GSM494641 1 0.000 0.855 1.000 0.000
#> GSM494636 1 0.000 0.855 1.000 0.000
#> GSM494640 1 0.833 0.461 0.736 0.264
#> GSM494623 2 0.722 0.872 0.200 0.800
#> GSM494644 1 0.000 0.855 1.000 0.000
#> GSM494646 1 0.000 0.855 1.000 0.000
#> GSM494665 1 0.000 0.855 1.000 0.000
#> GSM494638 1 0.000 0.855 1.000 0.000
#> GSM494645 1 0.000 0.855 1.000 0.000
#> GSM494671 1 0.000 0.855 1.000 0.000
#> GSM494655 1 0.000 0.855 1.000 0.000
#> GSM494620 2 0.722 0.872 0.200 0.800
#> GSM494630 2 0.722 0.872 0.200 0.800
#> GSM494657 1 0.722 0.869 0.800 0.200
#> GSM494667 1 0.000 0.855 1.000 0.000
#> GSM494621 2 0.722 0.872 0.200 0.800
#> GSM494629 1 0.000 0.855 1.000 0.000
#> GSM494637 1 0.861 0.409 0.716 0.284
#> GSM494652 1 0.000 0.855 1.000 0.000
#> GSM494648 2 0.722 0.872 0.200 0.800
#> GSM494650 1 0.000 0.855 1.000 0.000
#> GSM494669 1 0.000 0.855 1.000 0.000
#> GSM494666 1 0.000 0.855 1.000 0.000
#> GSM494668 1 0.000 0.855 1.000 0.000
#> GSM494633 2 0.722 0.872 0.200 0.800
#> GSM494634 1 0.000 0.855 1.000 0.000
#> GSM494639 1 0.000 0.855 1.000 0.000
#> GSM494661 1 0.000 0.855 1.000 0.000
#> GSM494617 1 0.000 0.855 1.000 0.000
#> GSM494626 1 0.000 0.855 1.000 0.000
#> GSM494656 1 0.722 0.869 0.800 0.200
#> GSM494635 1 0.000 0.855 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM494565 3 0.0000 0.9637 0.000 0.000 1.000
#> GSM494594 2 0.0592 0.9429 0.000 0.988 0.012
#> GSM494604 1 0.7919 0.0253 0.480 0.464 0.056
#> GSM494564 3 0.0000 0.9637 0.000 0.000 1.000
#> GSM494591 2 0.0592 0.9429 0.000 0.988 0.012
#> GSM494567 2 0.1643 0.9269 0.000 0.956 0.044
#> GSM494602 2 0.1964 0.9536 0.000 0.944 0.056
#> GSM494613 2 0.0592 0.9429 0.000 0.988 0.012
#> GSM494589 3 0.0000 0.9637 0.000 0.000 1.000
#> GSM494598 2 0.1964 0.9536 0.000 0.944 0.056
#> GSM494593 2 0.1964 0.9536 0.000 0.944 0.056
#> GSM494583 2 0.1964 0.9536 0.000 0.944 0.056
#> GSM494612 2 0.1964 0.9536 0.000 0.944 0.056
#> GSM494558 1 0.5986 0.6401 0.704 0.284 0.012
#> GSM494556 2 0.0592 0.9429 0.000 0.988 0.012
#> GSM494559 3 0.0000 0.9637 0.000 0.000 1.000
#> GSM494571 2 0.0592 0.9429 0.000 0.988 0.012
#> GSM494614 2 0.0592 0.9429 0.000 0.988 0.012
#> GSM494603 1 0.5737 0.6812 0.732 0.256 0.012
#> GSM494568 1 0.5737 0.6812 0.732 0.256 0.012
#> GSM494572 2 0.0592 0.9429 0.000 0.988 0.012
#> GSM494600 3 0.0000 0.9637 0.000 0.000 1.000
#> GSM494562 2 0.1964 0.9536 0.000 0.944 0.056
#> GSM494615 2 0.0592 0.9429 0.000 0.988 0.012
#> GSM494582 2 0.1964 0.9536 0.000 0.944 0.056
#> GSM494599 2 0.1964 0.9536 0.000 0.944 0.056
#> GSM494610 2 0.1964 0.9536 0.000 0.944 0.056
#> GSM494587 2 0.1860 0.9531 0.000 0.948 0.052
#> GSM494581 2 0.1964 0.9536 0.000 0.944 0.056
#> GSM494580 2 0.0747 0.9437 0.000 0.984 0.016
#> GSM494563 3 0.0000 0.9637 0.000 0.000 1.000
#> GSM494576 2 0.1964 0.9536 0.000 0.944 0.056
#> GSM494605 1 0.0000 0.9190 1.000 0.000 0.000
#> GSM494584 2 0.1643 0.9521 0.000 0.956 0.044
#> GSM494586 2 0.1964 0.9536 0.000 0.944 0.056
#> GSM494578 2 0.0747 0.9437 0.000 0.984 0.016
#> GSM494585 2 0.1964 0.9536 0.000 0.944 0.056
#> GSM494611 2 0.1964 0.9536 0.000 0.944 0.056
#> GSM494560 3 0.0000 0.9637 0.000 0.000 1.000
#> GSM494595 2 0.1964 0.9536 0.000 0.944 0.056
#> GSM494570 3 0.0000 0.9637 0.000 0.000 1.000
#> GSM494597 2 0.0592 0.9429 0.000 0.988 0.012
#> GSM494607 2 0.1964 0.9536 0.000 0.944 0.056
#> GSM494561 3 0.0000 0.9637 0.000 0.000 1.000
#> GSM494569 1 0.2550 0.8867 0.932 0.056 0.012
#> GSM494592 2 0.1964 0.9536 0.000 0.944 0.056
#> GSM494577 2 0.1964 0.9536 0.000 0.944 0.056
#> GSM494588 3 0.0000 0.9637 0.000 0.000 1.000
#> GSM494590 2 0.0592 0.9429 0.000 0.988 0.012
#> GSM494609 2 0.1964 0.9536 0.000 0.944 0.056
#> GSM494608 2 0.1964 0.9536 0.000 0.944 0.056
#> GSM494606 2 0.1964 0.9536 0.000 0.944 0.056
#> GSM494574 2 0.1964 0.9536 0.000 0.944 0.056
#> GSM494573 3 0.0000 0.9637 0.000 0.000 1.000
#> GSM494566 2 0.0592 0.9429 0.000 0.988 0.012
#> GSM494601 2 0.1964 0.9536 0.000 0.944 0.056
#> GSM494557 2 0.0592 0.9429 0.000 0.988 0.012
#> GSM494579 2 0.1964 0.9536 0.000 0.944 0.056
#> GSM494596 2 0.0592 0.9429 0.000 0.988 0.012
#> GSM494575 2 0.1964 0.9536 0.000 0.944 0.056
#> GSM494625 3 0.1964 0.9625 0.056 0.000 0.944
#> GSM494654 2 0.6735 0.1639 0.424 0.564 0.012
#> GSM494664 1 0.0237 0.9177 0.996 0.000 0.004
#> GSM494624 3 0.1964 0.9625 0.056 0.000 0.944
#> GSM494651 1 0.1620 0.9071 0.964 0.024 0.012
#> GSM494662 1 0.0592 0.9145 0.988 0.000 0.012
#> GSM494627 1 0.4002 0.7847 0.840 0.000 0.160
#> GSM494673 1 0.0000 0.9190 1.000 0.000 0.000
#> GSM494649 3 0.1964 0.9625 0.056 0.000 0.944
#> GSM494658 1 0.4811 0.7726 0.828 0.148 0.024
#> GSM494653 1 0.0000 0.9190 1.000 0.000 0.000
#> GSM494643 1 0.6302 0.0758 0.520 0.000 0.480
#> GSM494672 1 0.0000 0.9190 1.000 0.000 0.000
#> GSM494618 1 0.1751 0.9050 0.960 0.028 0.012
#> GSM494631 2 0.5884 0.5730 0.272 0.716 0.012
#> GSM494619 3 0.1964 0.9625 0.056 0.000 0.944
#> GSM494674 1 0.0000 0.9190 1.000 0.000 0.000
#> GSM494616 1 0.1620 0.9071 0.964 0.024 0.012
#> GSM494663 1 0.4121 0.7751 0.832 0.000 0.168
#> GSM494628 1 0.1877 0.9027 0.956 0.032 0.012
#> GSM494632 1 0.0000 0.9190 1.000 0.000 0.000
#> GSM494660 3 0.1964 0.9625 0.056 0.000 0.944
#> GSM494622 1 0.3539 0.8531 0.888 0.100 0.012
#> GSM494642 1 0.0000 0.9190 1.000 0.000 0.000
#> GSM494647 1 0.0000 0.9190 1.000 0.000 0.000
#> GSM494659 1 0.0000 0.9190 1.000 0.000 0.000
#> GSM494670 1 0.0000 0.9190 1.000 0.000 0.000
#> GSM494675 2 0.0592 0.9429 0.000 0.988 0.012
#> GSM494641 1 0.0000 0.9190 1.000 0.000 0.000
#> GSM494636 1 0.0592 0.9145 0.988 0.000 0.012
#> GSM494640 1 0.5706 0.5298 0.680 0.000 0.320
#> GSM494623 3 0.1964 0.9625 0.056 0.000 0.944
#> GSM494644 1 0.0000 0.9190 1.000 0.000 0.000
#> GSM494646 1 0.0000 0.9190 1.000 0.000 0.000
#> GSM494665 1 0.0000 0.9190 1.000 0.000 0.000
#> GSM494638 1 0.1015 0.9133 0.980 0.008 0.012
#> GSM494645 1 0.0000 0.9190 1.000 0.000 0.000
#> GSM494671 1 0.0000 0.9190 1.000 0.000 0.000
#> GSM494655 1 0.0000 0.9190 1.000 0.000 0.000
#> GSM494620 3 0.1964 0.9625 0.056 0.000 0.944
#> GSM494630 3 0.1964 0.9625 0.056 0.000 0.944
#> GSM494657 2 0.0592 0.9429 0.000 0.988 0.012
#> GSM494667 1 0.0000 0.9190 1.000 0.000 0.000
#> GSM494621 3 0.1964 0.9625 0.056 0.000 0.944
#> GSM494629 1 0.4859 0.8230 0.840 0.044 0.116
#> GSM494637 1 0.5706 0.5298 0.680 0.000 0.320
#> GSM494652 1 0.0000 0.9190 1.000 0.000 0.000
#> GSM494648 3 0.1964 0.9625 0.056 0.000 0.944
#> GSM494650 1 0.2550 0.8867 0.932 0.056 0.012
#> GSM494669 1 0.0000 0.9190 1.000 0.000 0.000
#> GSM494666 1 0.0000 0.9190 1.000 0.000 0.000
#> GSM494668 1 0.0000 0.9190 1.000 0.000 0.000
#> GSM494633 3 0.1964 0.9625 0.056 0.000 0.944
#> GSM494634 1 0.0000 0.9190 1.000 0.000 0.000
#> GSM494639 1 0.0000 0.9190 1.000 0.000 0.000
#> GSM494661 1 0.0000 0.9190 1.000 0.000 0.000
#> GSM494617 1 0.1620 0.9071 0.964 0.024 0.012
#> GSM494626 1 0.1620 0.9071 0.964 0.024 0.012
#> GSM494656 2 0.0592 0.9429 0.000 0.988 0.012
#> GSM494635 1 0.0000 0.9190 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM494565 4 0.2647 0.899 0.000 0.000 0.120 0.880
#> GSM494594 3 0.1118 0.891 0.000 0.036 0.964 0.000
#> GSM494604 2 0.2380 0.854 0.064 0.920 0.008 0.008
#> GSM494564 4 0.2647 0.899 0.000 0.000 0.120 0.880
#> GSM494591 3 0.1118 0.891 0.000 0.036 0.964 0.000
#> GSM494567 3 0.3942 0.788 0.000 0.236 0.764 0.000
#> GSM494602 2 0.0000 0.929 0.000 1.000 0.000 0.000
#> GSM494613 3 0.2760 0.872 0.000 0.128 0.872 0.000
#> GSM494589 4 0.2647 0.899 0.000 0.000 0.120 0.880
#> GSM494598 2 0.0000 0.929 0.000 1.000 0.000 0.000
#> GSM494593 2 0.0336 0.928 0.000 0.992 0.008 0.000
#> GSM494583 2 0.2814 0.840 0.000 0.868 0.132 0.000
#> GSM494612 2 0.0000 0.929 0.000 1.000 0.000 0.000
#> GSM494558 3 0.1661 0.892 0.004 0.052 0.944 0.000
#> GSM494556 3 0.3356 0.846 0.000 0.176 0.824 0.000
#> GSM494559 4 0.2647 0.899 0.000 0.000 0.120 0.880
#> GSM494571 3 0.1118 0.891 0.000 0.036 0.964 0.000
#> GSM494614 3 0.4679 0.580 0.000 0.352 0.648 0.000
#> GSM494603 3 0.6948 0.715 0.044 0.180 0.664 0.112
#> GSM494568 3 0.6413 0.743 0.048 0.124 0.716 0.112
#> GSM494572 3 0.1118 0.891 0.000 0.036 0.964 0.000
#> GSM494600 4 0.2647 0.899 0.000 0.000 0.120 0.880
#> GSM494562 2 0.0000 0.929 0.000 1.000 0.000 0.000
#> GSM494615 3 0.3356 0.846 0.000 0.176 0.824 0.000
#> GSM494582 2 0.0000 0.929 0.000 1.000 0.000 0.000
#> GSM494599 2 0.0336 0.928 0.000 0.992 0.008 0.000
#> GSM494610 2 0.0000 0.929 0.000 1.000 0.000 0.000
#> GSM494587 2 0.2530 0.857 0.000 0.888 0.112 0.000
#> GSM494581 2 0.2760 0.844 0.000 0.872 0.128 0.000
#> GSM494580 3 0.3942 0.788 0.000 0.236 0.764 0.000
#> GSM494563 4 0.2647 0.899 0.000 0.000 0.120 0.880
#> GSM494576 2 0.2760 0.843 0.000 0.872 0.128 0.000
#> GSM494605 1 0.2714 0.909 0.884 0.000 0.004 0.112
#> GSM494584 2 0.4996 -0.120 0.000 0.516 0.484 0.000
#> GSM494586 2 0.0000 0.929 0.000 1.000 0.000 0.000
#> GSM494578 3 0.3873 0.797 0.000 0.228 0.772 0.000
#> GSM494585 2 0.0000 0.929 0.000 1.000 0.000 0.000
#> GSM494611 2 0.0000 0.929 0.000 1.000 0.000 0.000
#> GSM494560 4 0.2647 0.899 0.000 0.000 0.120 0.880
#> GSM494595 2 0.0000 0.929 0.000 1.000 0.000 0.000
#> GSM494570 4 0.2647 0.899 0.000 0.000 0.120 0.880
#> GSM494597 3 0.2081 0.888 0.000 0.084 0.916 0.000
#> GSM494607 2 0.0336 0.928 0.000 0.992 0.008 0.000
#> GSM494561 4 0.2647 0.899 0.000 0.000 0.120 0.880
#> GSM494569 1 0.4662 0.875 0.796 0.000 0.092 0.112
#> GSM494592 2 0.0336 0.928 0.000 0.992 0.008 0.000
#> GSM494577 2 0.2760 0.844 0.000 0.872 0.128 0.000
#> GSM494588 4 0.2647 0.899 0.000 0.000 0.120 0.880
#> GSM494590 3 0.1118 0.891 0.000 0.036 0.964 0.000
#> GSM494609 2 0.1474 0.906 0.000 0.948 0.052 0.000
#> GSM494608 2 0.2281 0.873 0.000 0.904 0.096 0.000
#> GSM494606 2 0.0336 0.928 0.000 0.992 0.008 0.000
#> GSM494574 2 0.0000 0.929 0.000 1.000 0.000 0.000
#> GSM494573 4 0.2647 0.899 0.000 0.000 0.120 0.880
#> GSM494566 2 0.3486 0.773 0.000 0.812 0.188 0.000
#> GSM494601 2 0.0000 0.929 0.000 1.000 0.000 0.000
#> GSM494557 3 0.1792 0.891 0.000 0.068 0.932 0.000
#> GSM494579 2 0.1211 0.914 0.000 0.960 0.040 0.000
#> GSM494596 3 0.1118 0.891 0.000 0.036 0.964 0.000
#> GSM494575 2 0.0000 0.929 0.000 1.000 0.000 0.000
#> GSM494625 4 0.0921 0.889 0.000 0.000 0.028 0.972
#> GSM494654 3 0.1118 0.890 0.000 0.036 0.964 0.000
#> GSM494664 1 0.2714 0.909 0.884 0.000 0.004 0.112
#> GSM494624 4 0.0000 0.902 0.000 0.000 0.000 1.000
#> GSM494651 1 0.5416 0.839 0.740 0.000 0.148 0.112
#> GSM494662 1 0.2530 0.910 0.888 0.000 0.000 0.112
#> GSM494627 1 0.3764 0.899 0.844 0.000 0.040 0.116
#> GSM494673 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> GSM494649 4 0.2224 0.858 0.032 0.000 0.040 0.928
#> GSM494658 1 0.5517 0.225 0.568 0.412 0.000 0.020
#> GSM494653 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> GSM494643 1 0.5681 0.511 0.568 0.000 0.028 0.404
#> GSM494672 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> GSM494618 1 0.4261 0.888 0.820 0.000 0.068 0.112
#> GSM494631 3 0.2739 0.838 0.000 0.036 0.904 0.060
#> GSM494619 4 0.0000 0.902 0.000 0.000 0.000 1.000
#> GSM494674 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> GSM494616 1 0.4724 0.877 0.792 0.000 0.096 0.112
#> GSM494663 1 0.3764 0.899 0.844 0.000 0.040 0.116
#> GSM494628 1 0.4662 0.880 0.796 0.000 0.092 0.112
#> GSM494632 1 0.2714 0.909 0.884 0.000 0.004 0.112
#> GSM494660 4 0.2124 0.862 0.028 0.000 0.040 0.932
#> GSM494622 1 0.5212 0.867 0.788 0.028 0.072 0.112
#> GSM494642 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> GSM494670 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> GSM494675 3 0.3528 0.833 0.000 0.192 0.808 0.000
#> GSM494641 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> GSM494636 1 0.2530 0.910 0.888 0.000 0.000 0.112
#> GSM494640 1 0.4378 0.870 0.796 0.000 0.040 0.164
#> GSM494623 4 0.0000 0.902 0.000 0.000 0.000 1.000
#> GSM494644 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> GSM494646 1 0.2530 0.910 0.888 0.000 0.000 0.112
#> GSM494665 1 0.2714 0.909 0.884 0.000 0.004 0.112
#> GSM494638 1 0.2714 0.909 0.884 0.000 0.004 0.112
#> GSM494645 1 0.1637 0.910 0.940 0.000 0.000 0.060
#> GSM494671 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> GSM494620 4 0.0000 0.902 0.000 0.000 0.000 1.000
#> GSM494630 4 0.0000 0.902 0.000 0.000 0.000 1.000
#> GSM494657 3 0.1118 0.891 0.000 0.036 0.964 0.000
#> GSM494667 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> GSM494621 4 0.0000 0.902 0.000 0.000 0.000 1.000
#> GSM494629 1 0.3231 0.906 0.868 0.004 0.012 0.116
#> GSM494637 1 0.4332 0.873 0.800 0.000 0.040 0.160
#> GSM494652 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> GSM494648 4 0.0000 0.902 0.000 0.000 0.000 1.000
#> GSM494650 1 0.5066 0.855 0.768 0.000 0.120 0.112
#> GSM494669 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> GSM494666 1 0.2714 0.909 0.884 0.000 0.004 0.112
#> GSM494668 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> GSM494633 4 0.0000 0.902 0.000 0.000 0.000 1.000
#> GSM494634 1 0.0000 0.903 1.000 0.000 0.000 0.000
#> GSM494639 1 0.2530 0.910 0.888 0.000 0.000 0.112
#> GSM494661 1 0.1211 0.908 0.960 0.000 0.000 0.040
#> GSM494617 1 0.4261 0.888 0.820 0.000 0.068 0.112
#> GSM494626 1 0.4261 0.888 0.820 0.000 0.068 0.112
#> GSM494656 3 0.1118 0.891 0.000 0.036 0.964 0.000
#> GSM494635 1 0.2530 0.910 0.888 0.000 0.000 0.112
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM494565 5 0.0000 0.7913 0.000 0.000 0.000 0.000 1.000
#> GSM494594 3 0.0000 0.8424 0.000 0.000 1.000 0.000 0.000
#> GSM494604 2 0.4457 0.3639 0.368 0.620 0.012 0.000 0.000
#> GSM494564 5 0.0000 0.7913 0.000 0.000 0.000 0.000 1.000
#> GSM494591 3 0.0880 0.8512 0.000 0.032 0.968 0.000 0.000
#> GSM494567 3 0.1557 0.8502 0.000 0.052 0.940 0.000 0.008
#> GSM494602 2 0.0000 0.9056 0.000 1.000 0.000 0.000 0.000
#> GSM494613 3 0.1121 0.8518 0.000 0.044 0.956 0.000 0.000
#> GSM494589 5 0.0000 0.7913 0.000 0.000 0.000 0.000 1.000
#> GSM494598 2 0.0703 0.8978 0.000 0.976 0.024 0.000 0.000
#> GSM494593 2 0.0000 0.9056 0.000 1.000 0.000 0.000 0.000
#> GSM494583 3 0.6215 0.3164 0.000 0.348 0.500 0.000 0.152
#> GSM494612 2 0.0000 0.9056 0.000 1.000 0.000 0.000 0.000
#> GSM494558 3 0.1579 0.8450 0.000 0.032 0.944 0.024 0.000
#> GSM494556 3 0.1270 0.8508 0.000 0.052 0.948 0.000 0.000
#> GSM494559 5 0.0000 0.7913 0.000 0.000 0.000 0.000 1.000
#> GSM494571 3 0.0000 0.8424 0.000 0.000 1.000 0.000 0.000
#> GSM494614 3 0.1410 0.8479 0.000 0.060 0.940 0.000 0.000
#> GSM494603 3 0.2221 0.8391 0.000 0.052 0.912 0.036 0.000
#> GSM494568 3 0.2221 0.8391 0.000 0.052 0.912 0.036 0.000
#> GSM494572 3 0.0000 0.8424 0.000 0.000 1.000 0.000 0.000
#> GSM494600 5 0.0000 0.7913 0.000 0.000 0.000 0.000 1.000
#> GSM494562 2 0.0000 0.9056 0.000 1.000 0.000 0.000 0.000
#> GSM494615 3 0.1270 0.8508 0.000 0.052 0.948 0.000 0.000
#> GSM494582 2 0.0000 0.9056 0.000 1.000 0.000 0.000 0.000
#> GSM494599 2 0.0000 0.9056 0.000 1.000 0.000 0.000 0.000
#> GSM494610 2 0.0794 0.8953 0.000 0.972 0.028 0.000 0.000
#> GSM494587 2 0.2074 0.8223 0.000 0.896 0.104 0.000 0.000
#> GSM494581 3 0.5447 0.2195 0.000 0.440 0.500 0.000 0.060
#> GSM494580 3 0.1430 0.8508 0.000 0.052 0.944 0.000 0.004
#> GSM494563 5 0.0000 0.7913 0.000 0.000 0.000 0.000 1.000
#> GSM494576 3 0.4307 0.1343 0.000 0.500 0.500 0.000 0.000
#> GSM494605 1 0.0162 0.9360 0.996 0.000 0.000 0.004 0.000
#> GSM494584 3 0.4287 0.2505 0.000 0.460 0.540 0.000 0.000
#> GSM494586 2 0.2329 0.7986 0.000 0.876 0.124 0.000 0.000
#> GSM494578 3 0.1430 0.8508 0.000 0.052 0.944 0.000 0.004
#> GSM494585 2 0.1671 0.8528 0.000 0.924 0.076 0.000 0.000
#> GSM494611 2 0.0000 0.9056 0.000 1.000 0.000 0.000 0.000
#> GSM494560 5 0.0000 0.7913 0.000 0.000 0.000 0.000 1.000
#> GSM494595 2 0.0703 0.8978 0.000 0.976 0.024 0.000 0.000
#> GSM494570 5 0.0000 0.7913 0.000 0.000 0.000 0.000 1.000
#> GSM494597 3 0.1270 0.8508 0.000 0.052 0.948 0.000 0.000
#> GSM494607 2 0.0000 0.9056 0.000 1.000 0.000 0.000 0.000
#> GSM494561 5 0.0000 0.7913 0.000 0.000 0.000 0.000 1.000
#> GSM494569 4 0.4294 0.2433 0.468 0.000 0.000 0.532 0.000
#> GSM494592 2 0.0000 0.9056 0.000 1.000 0.000 0.000 0.000
#> GSM494577 3 0.4307 0.1343 0.000 0.500 0.500 0.000 0.000
#> GSM494588 5 0.0000 0.7913 0.000 0.000 0.000 0.000 1.000
#> GSM494590 3 0.0000 0.8424 0.000 0.000 1.000 0.000 0.000
#> GSM494609 2 0.4305 -0.1596 0.000 0.512 0.488 0.000 0.000
#> GSM494608 2 0.3752 0.4933 0.000 0.708 0.292 0.000 0.000
#> GSM494606 2 0.0000 0.9056 0.000 1.000 0.000 0.000 0.000
#> GSM494574 2 0.0703 0.8978 0.000 0.976 0.024 0.000 0.000
#> GSM494573 5 0.0000 0.7913 0.000 0.000 0.000 0.000 1.000
#> GSM494566 3 0.4256 0.2761 0.000 0.436 0.564 0.000 0.000
#> GSM494601 2 0.0000 0.9056 0.000 1.000 0.000 0.000 0.000
#> GSM494557 3 0.0963 0.8517 0.000 0.036 0.964 0.000 0.000
#> GSM494579 3 0.4307 0.1343 0.000 0.500 0.500 0.000 0.000
#> GSM494596 3 0.0000 0.8424 0.000 0.000 1.000 0.000 0.000
#> GSM494575 2 0.0000 0.9056 0.000 1.000 0.000 0.000 0.000
#> GSM494625 4 0.1792 0.7237 0.000 0.000 0.000 0.916 0.084
#> GSM494654 3 0.0000 0.8424 0.000 0.000 1.000 0.000 0.000
#> GSM494664 1 0.0162 0.9360 0.996 0.000 0.000 0.004 0.000
#> GSM494624 5 0.4114 0.5901 0.000 0.000 0.000 0.376 0.624
#> GSM494651 4 0.0000 0.7947 0.000 0.000 0.000 1.000 0.000
#> GSM494662 4 0.3274 0.6720 0.220 0.000 0.000 0.780 0.000
#> GSM494627 4 0.0000 0.7947 0.000 0.000 0.000 1.000 0.000
#> GSM494673 1 0.0000 0.9361 1.000 0.000 0.000 0.000 0.000
#> GSM494649 4 0.1792 0.7237 0.000 0.000 0.000 0.916 0.084
#> GSM494658 1 0.3772 0.6779 0.792 0.036 0.172 0.000 0.000
#> GSM494653 1 0.0000 0.9361 1.000 0.000 0.000 0.000 0.000
#> GSM494643 4 0.0000 0.7947 0.000 0.000 0.000 1.000 0.000
#> GSM494672 1 0.0000 0.9361 1.000 0.000 0.000 0.000 0.000
#> GSM494618 4 0.2020 0.7552 0.100 0.000 0.000 0.900 0.000
#> GSM494631 3 0.0880 0.8512 0.000 0.032 0.968 0.000 0.000
#> GSM494619 5 0.4114 0.5901 0.000 0.000 0.000 0.376 0.624
#> GSM494674 1 0.0000 0.9361 1.000 0.000 0.000 0.000 0.000
#> GSM494616 4 0.0000 0.7947 0.000 0.000 0.000 1.000 0.000
#> GSM494663 4 0.0000 0.7947 0.000 0.000 0.000 1.000 0.000
#> GSM494628 4 0.0000 0.7947 0.000 0.000 0.000 1.000 0.000
#> GSM494632 1 0.1410 0.8963 0.940 0.000 0.000 0.060 0.000
#> GSM494660 4 0.1792 0.7237 0.000 0.000 0.000 0.916 0.084
#> GSM494622 1 0.6992 -0.0826 0.388 0.008 0.336 0.268 0.000
#> GSM494642 1 0.0000 0.9361 1.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.9361 1.000 0.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.9361 1.000 0.000 0.000 0.000 0.000
#> GSM494670 1 0.0162 0.9360 0.996 0.000 0.000 0.004 0.000
#> GSM494675 3 0.1410 0.8479 0.000 0.060 0.940 0.000 0.000
#> GSM494641 1 0.0000 0.9361 1.000 0.000 0.000 0.000 0.000
#> GSM494636 4 0.2891 0.7073 0.176 0.000 0.000 0.824 0.000
#> GSM494640 4 0.0000 0.7947 0.000 0.000 0.000 1.000 0.000
#> GSM494623 5 0.4114 0.5901 0.000 0.000 0.000 0.376 0.624
#> GSM494644 1 0.0162 0.9360 0.996 0.000 0.000 0.004 0.000
#> GSM494646 1 0.1410 0.8963 0.940 0.000 0.000 0.060 0.000
#> GSM494665 1 0.0162 0.9360 0.996 0.000 0.000 0.004 0.000
#> GSM494638 1 0.2046 0.8783 0.916 0.000 0.016 0.068 0.000
#> GSM494645 1 0.0162 0.9360 0.996 0.000 0.000 0.004 0.000
#> GSM494671 1 0.0000 0.9361 1.000 0.000 0.000 0.000 0.000
#> GSM494655 1 0.0162 0.9360 0.996 0.000 0.000 0.004 0.000
#> GSM494620 5 0.4114 0.5901 0.000 0.000 0.000 0.376 0.624
#> GSM494630 5 0.4114 0.5901 0.000 0.000 0.000 0.376 0.624
#> GSM494657 3 0.0000 0.8424 0.000 0.000 1.000 0.000 0.000
#> GSM494667 1 0.0000 0.9361 1.000 0.000 0.000 0.000 0.000
#> GSM494621 5 0.4114 0.5901 0.000 0.000 0.000 0.376 0.624
#> GSM494629 4 0.4026 0.5525 0.020 0.000 0.244 0.736 0.000
#> GSM494637 4 0.0000 0.7947 0.000 0.000 0.000 1.000 0.000
#> GSM494652 1 0.0000 0.9361 1.000 0.000 0.000 0.000 0.000
#> GSM494648 5 0.4114 0.5901 0.000 0.000 0.000 0.376 0.624
#> GSM494650 4 0.4196 0.5026 0.356 0.000 0.004 0.640 0.000
#> GSM494669 1 0.0000 0.9361 1.000 0.000 0.000 0.000 0.000
#> GSM494666 1 0.0162 0.9360 0.996 0.000 0.000 0.004 0.000
#> GSM494668 1 0.0162 0.9360 0.996 0.000 0.000 0.004 0.000
#> GSM494633 5 0.4210 0.5266 0.000 0.000 0.000 0.412 0.588
#> GSM494634 1 0.0000 0.9361 1.000 0.000 0.000 0.000 0.000
#> GSM494639 1 0.1341 0.8996 0.944 0.000 0.000 0.056 0.000
#> GSM494661 1 0.0162 0.9360 0.996 0.000 0.000 0.004 0.000
#> GSM494617 1 0.4300 -0.1411 0.524 0.000 0.000 0.476 0.000
#> GSM494626 4 0.4306 0.1754 0.492 0.000 0.000 0.508 0.000
#> GSM494656 3 0.0000 0.8424 0.000 0.000 1.000 0.000 0.000
#> GSM494635 1 0.1478 0.8937 0.936 0.000 0.000 0.064 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM494565 5 0.0000 0.9984 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494594 3 0.0000 0.9238 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494604 1 0.4003 0.7577 0.756 0.152 0.000 0.092 0.000 0.000
#> GSM494564 5 0.0000 0.9984 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494591 3 0.0000 0.9238 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494567 3 0.1152 0.9160 0.000 0.000 0.952 0.044 0.004 0.000
#> GSM494602 2 0.0000 0.9607 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494613 3 0.0790 0.9211 0.000 0.000 0.968 0.032 0.000 0.000
#> GSM494589 5 0.0000 0.9984 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494598 2 0.0260 0.9603 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM494593 2 0.0000 0.9607 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494583 2 0.3313 0.8212 0.000 0.816 0.060 0.000 0.124 0.000
#> GSM494612 2 0.0000 0.9607 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494558 3 0.3076 0.6567 0.000 0.000 0.760 0.240 0.000 0.000
#> GSM494556 3 0.0790 0.9211 0.000 0.000 0.968 0.032 0.000 0.000
#> GSM494559 5 0.0000 0.9984 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494571 3 0.0000 0.9238 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494614 3 0.4378 0.3548 0.000 0.368 0.600 0.032 0.000 0.000
#> GSM494603 4 0.1556 0.8418 0.000 0.000 0.080 0.920 0.000 0.000
#> GSM494568 4 0.0458 0.9073 0.000 0.000 0.016 0.984 0.000 0.000
#> GSM494572 3 0.0000 0.9238 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494600 5 0.0000 0.9984 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494562 2 0.0458 0.9553 0.000 0.984 0.016 0.000 0.000 0.000
#> GSM494615 3 0.0790 0.9211 0.000 0.000 0.968 0.032 0.000 0.000
#> GSM494582 2 0.0000 0.9607 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494599 2 0.0000 0.9607 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494610 2 0.0260 0.9603 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM494587 2 0.0458 0.9553 0.000 0.984 0.016 0.000 0.000 0.000
#> GSM494581 2 0.1890 0.9178 0.000 0.916 0.060 0.000 0.024 0.000
#> GSM494580 3 0.1152 0.9160 0.000 0.000 0.952 0.044 0.004 0.000
#> GSM494563 5 0.0000 0.9984 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494576 2 0.0937 0.9441 0.000 0.960 0.040 0.000 0.000 0.000
#> GSM494605 1 0.2135 0.8534 0.872 0.000 0.000 0.128 0.000 0.000
#> GSM494584 2 0.2003 0.8776 0.000 0.884 0.116 0.000 0.000 0.000
#> GSM494586 2 0.0260 0.9603 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM494578 3 0.1152 0.9160 0.000 0.000 0.952 0.044 0.004 0.000
#> GSM494585 2 0.0000 0.9607 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494611 2 0.0000 0.9607 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494560 5 0.0000 0.9984 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494595 2 0.0260 0.9603 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM494570 5 0.0000 0.9984 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494597 3 0.0000 0.9238 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494607 2 0.0000 0.9607 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494561 5 0.0458 0.9836 0.000 0.000 0.000 0.000 0.984 0.016
#> GSM494569 4 0.0000 0.9167 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494592 2 0.0000 0.9607 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494577 2 0.1267 0.9294 0.000 0.940 0.060 0.000 0.000 0.000
#> GSM494588 5 0.0000 0.9984 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494590 3 0.0000 0.9238 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494609 2 0.0713 0.9515 0.000 0.972 0.028 0.000 0.000 0.000
#> GSM494608 2 0.0603 0.9570 0.000 0.980 0.016 0.000 0.004 0.000
#> GSM494606 2 0.0000 0.9607 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494574 2 0.0260 0.9603 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM494573 5 0.0000 0.9984 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494566 2 0.4955 0.4570 0.000 0.608 0.296 0.096 0.000 0.000
#> GSM494601 2 0.0000 0.9607 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494557 3 0.0790 0.9211 0.000 0.000 0.968 0.032 0.000 0.000
#> GSM494579 2 0.1204 0.9326 0.000 0.944 0.056 0.000 0.000 0.000
#> GSM494596 3 0.0000 0.9238 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494575 2 0.0000 0.9607 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494625 6 0.0458 0.9123 0.000 0.000 0.000 0.016 0.000 0.984
#> GSM494654 3 0.0632 0.9172 0.000 0.000 0.976 0.024 0.000 0.000
#> GSM494664 1 0.3101 0.7395 0.756 0.000 0.000 0.244 0.000 0.000
#> GSM494624 6 0.0000 0.9179 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494651 4 0.0000 0.9167 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494662 4 0.2092 0.8483 0.000 0.000 0.000 0.876 0.000 0.124
#> GSM494627 4 0.1075 0.9049 0.000 0.000 0.000 0.952 0.000 0.048
#> GSM494673 1 0.0000 0.9116 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494649 6 0.2092 0.8224 0.000 0.000 0.000 0.124 0.000 0.876
#> GSM494658 1 0.3606 0.8119 0.800 0.052 0.008 0.140 0.000 0.000
#> GSM494653 1 0.0000 0.9116 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494643 6 0.3823 0.1584 0.000 0.000 0.000 0.436 0.000 0.564
#> GSM494672 1 0.0000 0.9116 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494618 4 0.0000 0.9167 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494631 3 0.3706 0.4389 0.000 0.000 0.620 0.380 0.000 0.000
#> GSM494619 6 0.0000 0.9179 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494674 1 0.0000 0.9116 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494616 4 0.0000 0.9167 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494663 4 0.1267 0.8989 0.000 0.000 0.000 0.940 0.000 0.060
#> GSM494628 4 0.0790 0.9102 0.000 0.000 0.000 0.968 0.000 0.032
#> GSM494632 4 0.1556 0.8705 0.080 0.000 0.000 0.920 0.000 0.000
#> GSM494660 6 0.2092 0.8224 0.000 0.000 0.000 0.124 0.000 0.876
#> GSM494622 4 0.0146 0.9149 0.000 0.000 0.004 0.996 0.000 0.000
#> GSM494642 1 0.0000 0.9116 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.9116 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.9116 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494670 1 0.0000 0.9116 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494675 3 0.1575 0.9003 0.000 0.032 0.936 0.032 0.000 0.000
#> GSM494641 1 0.0000 0.9116 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494636 4 0.1219 0.8968 0.048 0.000 0.000 0.948 0.000 0.004
#> GSM494640 4 0.2454 0.8095 0.000 0.000 0.000 0.840 0.000 0.160
#> GSM494623 6 0.0000 0.9179 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494644 1 0.1267 0.8928 0.940 0.000 0.000 0.060 0.000 0.000
#> GSM494646 1 0.3647 0.5213 0.640 0.000 0.000 0.360 0.000 0.000
#> GSM494665 1 0.2135 0.8534 0.872 0.000 0.000 0.128 0.000 0.000
#> GSM494638 4 0.1296 0.8994 0.044 0.000 0.004 0.948 0.000 0.004
#> GSM494645 1 0.1501 0.8861 0.924 0.000 0.000 0.076 0.000 0.000
#> GSM494671 1 0.0000 0.9116 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.9116 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494620 6 0.0000 0.9179 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494630 6 0.0000 0.9179 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494657 3 0.0000 0.9238 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494667 1 0.0000 0.9116 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494621 6 0.0000 0.9179 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494629 4 0.1075 0.9049 0.000 0.000 0.000 0.952 0.000 0.048
#> GSM494637 4 0.2454 0.8095 0.000 0.000 0.000 0.840 0.000 0.160
#> GSM494652 1 0.0000 0.9116 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494648 6 0.0000 0.9179 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494650 4 0.0000 0.9167 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494669 1 0.0000 0.9116 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494666 1 0.2597 0.8139 0.824 0.000 0.000 0.176 0.000 0.000
#> GSM494668 1 0.0000 0.9116 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494633 6 0.0363 0.9144 0.000 0.000 0.000 0.012 0.000 0.988
#> GSM494634 1 0.0000 0.9116 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494639 4 0.3847 0.0378 0.456 0.000 0.000 0.544 0.000 0.000
#> GSM494661 1 0.2597 0.8139 0.824 0.000 0.000 0.176 0.000 0.000
#> GSM494617 4 0.0146 0.9158 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM494626 4 0.0000 0.9167 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494656 3 0.0000 0.9238 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494635 1 0.3428 0.6382 0.696 0.000 0.000 0.304 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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 3)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 4)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 5)
get_signatures(res, k = 6)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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.
# code only for demonstration
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) individual(p) k
#> MAD:mclust 117 8.91e-01 0.00493 9.09e-01 0.000024 2
#> MAD:mclust 117 2.76e-14 0.13997 1.74e-08 0.059486 3
#> MAD:mclust 118 1.13e-15 0.07716 1.22e-12 0.039834 4
#> MAD:mclust 106 6.80e-14 0.17796 8.41e-10 0.102737 5
#> MAD:mclust 115 8.46e-17 0.24931 1.53e-11 0.533989 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.
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"]
# you can also extract it by
# 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 51941 rows and 120 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 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:
k and the heatmap of
predicted classes for each k.k.k.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:
k;k, the area increased is defined as \(A_k - A_{k-1}\).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 0.707 0.870 0.943 0.4923 0.503 0.503
#> 3 3 0.953 0.944 0.977 0.3236 0.636 0.401
#> 4 4 0.695 0.748 0.862 0.1165 0.867 0.651
#> 5 5 0.648 0.668 0.817 0.0698 0.838 0.514
#> 6 6 0.663 0.625 0.786 0.0302 0.913 0.658
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
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.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM494565 2 0.0000 0.9118 0.000 1.000
#> GSM494594 2 0.0000 0.9118 0.000 1.000
#> GSM494604 1 0.0000 0.9544 1.000 0.000
#> GSM494564 2 0.0000 0.9118 0.000 1.000
#> GSM494591 2 0.0000 0.9118 0.000 1.000
#> GSM494567 2 0.0000 0.9118 0.000 1.000
#> GSM494602 1 0.0000 0.9544 1.000 0.000
#> GSM494613 2 0.0000 0.9118 0.000 1.000
#> GSM494589 2 0.0000 0.9118 0.000 1.000
#> GSM494598 1 0.0000 0.9544 1.000 0.000
#> GSM494593 1 0.0672 0.9481 0.992 0.008
#> GSM494583 2 0.0000 0.9118 0.000 1.000
#> GSM494612 1 0.0000 0.9544 1.000 0.000
#> GSM494558 2 0.0000 0.9118 0.000 1.000
#> GSM494556 2 0.0000 0.9118 0.000 1.000
#> GSM494559 2 0.0000 0.9118 0.000 1.000
#> GSM494571 2 0.0000 0.9118 0.000 1.000
#> GSM494614 2 0.0000 0.9118 0.000 1.000
#> GSM494603 2 0.0000 0.9118 0.000 1.000
#> GSM494568 2 0.0000 0.9118 0.000 1.000
#> GSM494572 2 0.0000 0.9118 0.000 1.000
#> GSM494600 2 0.0000 0.9118 0.000 1.000
#> GSM494562 1 0.7299 0.7281 0.796 0.204
#> GSM494615 2 0.0000 0.9118 0.000 1.000
#> GSM494582 1 0.0000 0.9544 1.000 0.000
#> GSM494599 1 0.0000 0.9544 1.000 0.000
#> GSM494610 1 0.0000 0.9544 1.000 0.000
#> GSM494587 2 0.9795 0.2590 0.416 0.584
#> GSM494581 1 0.3584 0.8922 0.932 0.068
#> GSM494580 2 0.0000 0.9118 0.000 1.000
#> GSM494563 2 0.0672 0.9075 0.008 0.992
#> GSM494576 2 0.5178 0.8225 0.116 0.884
#> GSM494605 1 0.0000 0.9544 1.000 0.000
#> GSM494584 2 0.0000 0.9118 0.000 1.000
#> GSM494586 1 0.5629 0.8216 0.868 0.132
#> GSM494578 2 0.0000 0.9118 0.000 1.000
#> GSM494585 1 0.8327 0.6390 0.736 0.264
#> GSM494611 1 0.0000 0.9544 1.000 0.000
#> GSM494560 2 0.0000 0.9118 0.000 1.000
#> GSM494595 1 0.0000 0.9544 1.000 0.000
#> GSM494570 2 0.0000 0.9118 0.000 1.000
#> GSM494597 2 0.0000 0.9118 0.000 1.000
#> GSM494607 1 0.0000 0.9544 1.000 0.000
#> GSM494561 2 0.0000 0.9118 0.000 1.000
#> GSM494569 1 0.9977 -0.0528 0.528 0.472
#> GSM494592 1 0.0000 0.9544 1.000 0.000
#> GSM494577 2 0.2043 0.8923 0.032 0.968
#> GSM494588 1 0.9866 0.2558 0.568 0.432
#> GSM494590 2 0.0000 0.9118 0.000 1.000
#> GSM494609 1 0.0000 0.9544 1.000 0.000
#> GSM494608 1 0.0000 0.9544 1.000 0.000
#> GSM494606 1 0.0000 0.9544 1.000 0.000
#> GSM494574 1 0.0000 0.9544 1.000 0.000
#> GSM494573 2 0.0000 0.9118 0.000 1.000
#> GSM494566 1 0.8499 0.6220 0.724 0.276
#> GSM494601 1 0.0000 0.9544 1.000 0.000
#> GSM494557 2 0.0000 0.9118 0.000 1.000
#> GSM494579 1 0.6973 0.7506 0.812 0.188
#> GSM494596 2 0.0000 0.9118 0.000 1.000
#> GSM494575 1 0.0000 0.9544 1.000 0.000
#> GSM494625 2 0.7453 0.7590 0.212 0.788
#> GSM494654 2 0.0000 0.9118 0.000 1.000
#> GSM494664 1 0.0000 0.9544 1.000 0.000
#> GSM494624 1 0.8861 0.5024 0.696 0.304
#> GSM494651 2 0.9248 0.5726 0.340 0.660
#> GSM494662 1 0.0000 0.9544 1.000 0.000
#> GSM494627 2 0.5408 0.8352 0.124 0.876
#> GSM494673 1 0.0000 0.9544 1.000 0.000
#> GSM494649 2 0.8144 0.7112 0.252 0.748
#> GSM494658 1 0.0000 0.9544 1.000 0.000
#> GSM494653 1 0.0000 0.9544 1.000 0.000
#> GSM494643 1 0.7376 0.6966 0.792 0.208
#> GSM494672 1 0.0000 0.9544 1.000 0.000
#> GSM494618 2 0.9833 0.3786 0.424 0.576
#> GSM494631 2 0.0000 0.9118 0.000 1.000
#> GSM494619 1 0.2423 0.9197 0.960 0.040
#> GSM494674 1 0.0000 0.9544 1.000 0.000
#> GSM494616 2 0.9044 0.6085 0.320 0.680
#> GSM494663 2 0.9248 0.5727 0.340 0.660
#> GSM494628 2 0.6973 0.7831 0.188 0.812
#> GSM494632 1 0.0000 0.9544 1.000 0.000
#> GSM494660 2 0.7299 0.7675 0.204 0.796
#> GSM494622 2 0.9635 0.4702 0.388 0.612
#> GSM494642 1 0.0000 0.9544 1.000 0.000
#> GSM494647 1 0.0000 0.9544 1.000 0.000
#> GSM494659 1 0.0000 0.9544 1.000 0.000
#> GSM494670 1 0.0000 0.9544 1.000 0.000
#> GSM494675 2 0.0000 0.9118 0.000 1.000
#> GSM494641 1 0.0000 0.9544 1.000 0.000
#> GSM494636 1 0.0000 0.9544 1.000 0.000
#> GSM494640 2 0.7219 0.7715 0.200 0.800
#> GSM494623 1 0.0000 0.9544 1.000 0.000
#> GSM494644 1 0.0000 0.9544 1.000 0.000
#> GSM494646 1 0.0000 0.9544 1.000 0.000
#> GSM494665 1 0.0000 0.9544 1.000 0.000
#> GSM494638 1 0.0000 0.9544 1.000 0.000
#> GSM494645 1 0.0000 0.9544 1.000 0.000
#> GSM494671 1 0.0000 0.9544 1.000 0.000
#> GSM494655 1 0.0000 0.9544 1.000 0.000
#> GSM494620 1 0.0000 0.9544 1.000 0.000
#> GSM494630 1 0.2236 0.9235 0.964 0.036
#> GSM494657 2 0.0000 0.9118 0.000 1.000
#> GSM494667 1 0.0000 0.9544 1.000 0.000
#> GSM494621 1 0.0672 0.9481 0.992 0.008
#> GSM494629 2 0.1633 0.8998 0.024 0.976
#> GSM494637 2 0.8207 0.7059 0.256 0.744
#> GSM494652 1 0.0000 0.9544 1.000 0.000
#> GSM494648 1 0.0000 0.9544 1.000 0.000
#> GSM494650 2 0.6712 0.7952 0.176 0.824
#> GSM494669 1 0.0000 0.9544 1.000 0.000
#> GSM494666 1 0.0000 0.9544 1.000 0.000
#> GSM494668 1 0.0000 0.9544 1.000 0.000
#> GSM494633 2 0.7528 0.7552 0.216 0.784
#> GSM494634 1 0.0000 0.9544 1.000 0.000
#> GSM494639 1 0.0000 0.9544 1.000 0.000
#> GSM494661 1 0.0000 0.9544 1.000 0.000
#> GSM494617 1 0.0000 0.9544 1.000 0.000
#> GSM494626 1 0.1633 0.9348 0.976 0.024
#> GSM494656 2 0.0000 0.9118 0.000 1.000
#> GSM494635 1 0.0000 0.9544 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM494565 3 0.2959 0.870 0.000 0.100 0.900
#> GSM494594 3 0.0000 0.970 0.000 0.000 1.000
#> GSM494604 2 0.0000 0.961 0.000 1.000 0.000
#> GSM494564 3 0.0000 0.970 0.000 0.000 1.000
#> GSM494591 3 0.0000 0.970 0.000 0.000 1.000
#> GSM494567 3 0.0000 0.970 0.000 0.000 1.000
#> GSM494602 2 0.0000 0.961 0.000 1.000 0.000
#> GSM494613 3 0.0000 0.970 0.000 0.000 1.000
#> GSM494589 3 0.0000 0.970 0.000 0.000 1.000
#> GSM494598 2 0.0000 0.961 0.000 1.000 0.000
#> GSM494593 2 0.0000 0.961 0.000 1.000 0.000
#> GSM494583 2 0.0592 0.951 0.000 0.988 0.012
#> GSM494612 2 0.0000 0.961 0.000 1.000 0.000
#> GSM494558 3 0.0000 0.970 0.000 0.000 1.000
#> GSM494556 3 0.0000 0.970 0.000 0.000 1.000
#> GSM494559 3 0.0000 0.970 0.000 0.000 1.000
#> GSM494571 3 0.0000 0.970 0.000 0.000 1.000
#> GSM494614 3 0.4750 0.713 0.000 0.216 0.784
#> GSM494603 3 0.0000 0.970 0.000 0.000 1.000
#> GSM494568 3 0.0000 0.970 0.000 0.000 1.000
#> GSM494572 3 0.0000 0.970 0.000 0.000 1.000
#> GSM494600 3 0.0000 0.970 0.000 0.000 1.000
#> GSM494562 2 0.0000 0.961 0.000 1.000 0.000
#> GSM494615 3 0.0000 0.970 0.000 0.000 1.000
#> GSM494582 2 0.0000 0.961 0.000 1.000 0.000
#> GSM494599 2 0.0000 0.961 0.000 1.000 0.000
#> GSM494610 2 0.0000 0.961 0.000 1.000 0.000
#> GSM494587 2 0.0000 0.961 0.000 1.000 0.000
#> GSM494581 2 0.0000 0.961 0.000 1.000 0.000
#> GSM494580 3 0.0000 0.970 0.000 0.000 1.000
#> GSM494563 2 0.5968 0.417 0.000 0.636 0.364
#> GSM494576 2 0.0000 0.961 0.000 1.000 0.000
#> GSM494605 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494584 2 0.5859 0.465 0.000 0.656 0.344
#> GSM494586 2 0.0000 0.961 0.000 1.000 0.000
#> GSM494578 3 0.0000 0.970 0.000 0.000 1.000
#> GSM494585 2 0.0000 0.961 0.000 1.000 0.000
#> GSM494611 2 0.0000 0.961 0.000 1.000 0.000
#> GSM494560 3 0.0000 0.970 0.000 0.000 1.000
#> GSM494595 2 0.0000 0.961 0.000 1.000 0.000
#> GSM494570 3 0.0000 0.970 0.000 0.000 1.000
#> GSM494597 3 0.0000 0.970 0.000 0.000 1.000
#> GSM494607 2 0.0000 0.961 0.000 1.000 0.000
#> GSM494561 3 0.0000 0.970 0.000 0.000 1.000
#> GSM494569 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494592 2 0.0000 0.961 0.000 1.000 0.000
#> GSM494577 2 0.0000 0.961 0.000 1.000 0.000
#> GSM494588 3 0.8426 0.216 0.092 0.384 0.524
#> GSM494590 3 0.0000 0.970 0.000 0.000 1.000
#> GSM494609 2 0.0000 0.961 0.000 1.000 0.000
#> GSM494608 2 0.0592 0.950 0.012 0.988 0.000
#> GSM494606 2 0.0000 0.961 0.000 1.000 0.000
#> GSM494574 2 0.0000 0.961 0.000 1.000 0.000
#> GSM494573 3 0.0000 0.970 0.000 0.000 1.000
#> GSM494566 2 0.0000 0.961 0.000 1.000 0.000
#> GSM494601 2 0.0000 0.961 0.000 1.000 0.000
#> GSM494557 3 0.0000 0.970 0.000 0.000 1.000
#> GSM494579 2 0.0000 0.961 0.000 1.000 0.000
#> GSM494596 3 0.0000 0.970 0.000 0.000 1.000
#> GSM494575 2 0.0000 0.961 0.000 1.000 0.000
#> GSM494625 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494654 3 0.0000 0.970 0.000 0.000 1.000
#> GSM494664 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494624 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494651 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494662 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494627 1 0.3340 0.859 0.880 0.000 0.120
#> GSM494673 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494649 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494658 2 0.2066 0.900 0.060 0.940 0.000
#> GSM494653 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494643 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494672 2 0.5650 0.538 0.312 0.688 0.000
#> GSM494618 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494631 3 0.0000 0.970 0.000 0.000 1.000
#> GSM494619 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494674 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494616 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494663 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494628 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494632 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494660 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494622 1 0.0892 0.968 0.980 0.000 0.020
#> GSM494642 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494647 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494659 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494670 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494675 3 0.0000 0.970 0.000 0.000 1.000
#> GSM494641 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494636 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494640 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494623 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494644 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494646 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494665 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494638 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494645 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494671 1 0.5810 0.492 0.664 0.336 0.000
#> GSM494655 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494620 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494630 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494657 3 0.0000 0.970 0.000 0.000 1.000
#> GSM494667 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494621 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494629 3 0.4178 0.767 0.172 0.000 0.828
#> GSM494637 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494652 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494648 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494650 1 0.1643 0.945 0.956 0.000 0.044
#> GSM494669 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494666 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494668 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494633 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494634 1 0.4235 0.783 0.824 0.176 0.000
#> GSM494639 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494661 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494617 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494626 1 0.0000 0.987 1.000 0.000 0.000
#> GSM494656 3 0.0000 0.970 0.000 0.000 1.000
#> GSM494635 1 0.0000 0.987 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM494565 2 0.2921 0.8172 0.140 0.860 0.000 0.000
#> GSM494594 3 0.0000 0.9672 0.000 0.000 1.000 0.000
#> GSM494604 1 0.3444 0.7464 0.816 0.000 0.000 0.184
#> GSM494564 2 0.0188 0.7494 0.004 0.996 0.000 0.000
#> GSM494591 3 0.0000 0.9672 0.000 0.000 1.000 0.000
#> GSM494567 3 0.0469 0.9591 0.000 0.012 0.988 0.000
#> GSM494602 1 0.0707 0.7530 0.980 0.020 0.000 0.000
#> GSM494613 3 0.0000 0.9672 0.000 0.000 1.000 0.000
#> GSM494589 2 0.3399 0.7741 0.040 0.868 0.092 0.000
#> GSM494598 2 0.4103 0.7778 0.256 0.744 0.000 0.000
#> GSM494593 1 0.2345 0.6956 0.900 0.100 0.000 0.000
#> GSM494583 2 0.3610 0.8159 0.200 0.800 0.000 0.000
#> GSM494612 1 0.0592 0.7541 0.984 0.016 0.000 0.000
#> GSM494558 3 0.0000 0.9672 0.000 0.000 1.000 0.000
#> GSM494556 3 0.0000 0.9672 0.000 0.000 1.000 0.000
#> GSM494559 2 0.0336 0.7407 0.000 0.992 0.000 0.008
#> GSM494571 3 0.0000 0.9672 0.000 0.000 1.000 0.000
#> GSM494614 2 0.6565 0.6516 0.148 0.628 0.224 0.000
#> GSM494603 2 0.6716 -0.0561 0.000 0.504 0.404 0.092
#> GSM494568 3 0.3463 0.8402 0.000 0.096 0.864 0.040
#> GSM494572 3 0.0000 0.9672 0.000 0.000 1.000 0.000
#> GSM494600 2 0.3279 0.8089 0.096 0.872 0.032 0.000
#> GSM494562 1 0.3907 0.5094 0.768 0.232 0.000 0.000
#> GSM494615 3 0.0000 0.9672 0.000 0.000 1.000 0.000
#> GSM494582 1 0.0592 0.7541 0.984 0.016 0.000 0.000
#> GSM494599 1 0.1716 0.7789 0.936 0.000 0.000 0.064
#> GSM494610 2 0.3837 0.8034 0.224 0.776 0.000 0.000
#> GSM494587 1 0.4697 0.2032 0.644 0.356 0.000 0.000
#> GSM494581 2 0.3610 0.8159 0.200 0.800 0.000 0.000
#> GSM494580 3 0.0000 0.9672 0.000 0.000 1.000 0.000
#> GSM494563 2 0.2704 0.8136 0.124 0.876 0.000 0.000
#> GSM494576 2 0.3649 0.8143 0.204 0.796 0.000 0.000
#> GSM494605 4 0.1118 0.8088 0.036 0.000 0.000 0.964
#> GSM494584 2 0.3610 0.8159 0.200 0.800 0.000 0.000
#> GSM494586 2 0.3764 0.8085 0.216 0.784 0.000 0.000
#> GSM494578 3 0.0188 0.9647 0.000 0.004 0.996 0.000
#> GSM494585 1 0.4356 0.3692 0.708 0.292 0.000 0.000
#> GSM494611 1 0.1474 0.7320 0.948 0.052 0.000 0.000
#> GSM494560 2 0.2973 0.8177 0.144 0.856 0.000 0.000
#> GSM494595 2 0.4164 0.7733 0.264 0.736 0.000 0.000
#> GSM494570 2 0.0921 0.7233 0.000 0.972 0.000 0.028
#> GSM494597 3 0.0921 0.9452 0.000 0.028 0.972 0.000
#> GSM494607 1 0.2216 0.7789 0.908 0.000 0.000 0.092
#> GSM494561 2 0.5523 -0.0190 0.000 0.596 0.024 0.380
#> GSM494569 4 0.4877 0.4852 0.008 0.000 0.328 0.664
#> GSM494592 1 0.2011 0.7798 0.920 0.000 0.000 0.080
#> GSM494577 2 0.3610 0.8159 0.200 0.800 0.000 0.000
#> GSM494588 2 0.0336 0.7407 0.000 0.992 0.000 0.008
#> GSM494590 3 0.0000 0.9672 0.000 0.000 1.000 0.000
#> GSM494609 2 0.4624 0.6620 0.340 0.660 0.000 0.000
#> GSM494608 1 0.3726 0.7381 0.788 0.000 0.000 0.212
#> GSM494606 1 0.2589 0.7732 0.884 0.000 0.000 0.116
#> GSM494574 2 0.4746 0.6144 0.368 0.632 0.000 0.000
#> GSM494573 2 0.3505 0.8040 0.088 0.864 0.048 0.000
#> GSM494566 1 0.3669 0.7514 0.876 0.040 0.052 0.032
#> GSM494601 1 0.0188 0.7596 0.996 0.004 0.000 0.000
#> GSM494557 3 0.0000 0.9672 0.000 0.000 1.000 0.000
#> GSM494579 2 0.3610 0.8159 0.200 0.800 0.000 0.000
#> GSM494596 3 0.0000 0.9672 0.000 0.000 1.000 0.000
#> GSM494575 1 0.0707 0.7516 0.980 0.020 0.000 0.000
#> GSM494625 4 0.3486 0.7848 0.000 0.188 0.000 0.812
#> GSM494654 3 0.0000 0.9672 0.000 0.000 1.000 0.000
#> GSM494664 4 0.0592 0.8162 0.016 0.000 0.000 0.984
#> GSM494624 4 0.3610 0.7788 0.000 0.200 0.000 0.800
#> GSM494651 4 0.5310 0.2835 0.012 0.000 0.412 0.576
#> GSM494662 4 0.2345 0.8085 0.000 0.100 0.000 0.900
#> GSM494627 4 0.4079 0.7835 0.000 0.180 0.020 0.800
#> GSM494673 1 0.4817 0.4496 0.612 0.000 0.000 0.388
#> GSM494649 4 0.3444 0.7865 0.000 0.184 0.000 0.816
#> GSM494658 1 0.4989 0.1977 0.528 0.000 0.000 0.472
#> GSM494653 4 0.1792 0.7911 0.068 0.000 0.000 0.932
#> GSM494643 4 0.3400 0.7881 0.000 0.180 0.000 0.820
#> GSM494672 1 0.3726 0.7354 0.788 0.000 0.000 0.212
#> GSM494618 4 0.0336 0.8191 0.000 0.008 0.000 0.992
#> GSM494631 3 0.0000 0.9672 0.000 0.000 1.000 0.000
#> GSM494619 4 0.3610 0.7788 0.000 0.200 0.000 0.800
#> GSM494674 4 0.1792 0.7914 0.068 0.000 0.000 0.932
#> GSM494616 4 0.1545 0.8080 0.008 0.000 0.040 0.952
#> GSM494663 4 0.3400 0.7881 0.000 0.180 0.000 0.820
#> GSM494628 4 0.3224 0.8027 0.000 0.120 0.016 0.864
#> GSM494632 4 0.0469 0.8171 0.012 0.000 0.000 0.988
#> GSM494660 4 0.3569 0.7810 0.000 0.196 0.000 0.804
#> GSM494622 4 0.5189 0.3736 0.012 0.000 0.372 0.616
#> GSM494642 4 0.1389 0.8030 0.048 0.000 0.000 0.952
#> GSM494647 4 0.4730 0.3318 0.364 0.000 0.000 0.636
#> GSM494659 4 0.4585 0.4147 0.332 0.000 0.000 0.668
#> GSM494670 4 0.3801 0.6263 0.220 0.000 0.000 0.780
#> GSM494675 3 0.0469 0.9590 0.000 0.012 0.988 0.000
#> GSM494641 4 0.1637 0.7961 0.060 0.000 0.000 0.940
#> GSM494636 4 0.2011 0.8119 0.000 0.080 0.000 0.920
#> GSM494640 4 0.3725 0.7866 0.000 0.180 0.008 0.812
#> GSM494623 4 0.3610 0.7788 0.000 0.200 0.000 0.800
#> GSM494644 4 0.0469 0.8171 0.012 0.000 0.000 0.988
#> GSM494646 4 0.0336 0.8191 0.000 0.008 0.000 0.992
#> GSM494665 4 0.4331 0.5102 0.288 0.000 0.000 0.712
#> GSM494638 4 0.0524 0.8183 0.008 0.004 0.000 0.988
#> GSM494645 4 0.0469 0.8171 0.012 0.000 0.000 0.988
#> GSM494671 1 0.3764 0.7318 0.784 0.000 0.000 0.216
#> GSM494655 4 0.0592 0.8162 0.016 0.000 0.000 0.984
#> GSM494620 4 0.3486 0.7848 0.000 0.188 0.000 0.812
#> GSM494630 4 0.3610 0.7788 0.000 0.200 0.000 0.800
#> GSM494657 3 0.0000 0.9672 0.000 0.000 1.000 0.000
#> GSM494667 4 0.4972 0.0143 0.456 0.000 0.000 0.544
#> GSM494621 4 0.3610 0.7788 0.000 0.200 0.000 0.800
#> GSM494629 3 0.5314 0.6873 0.000 0.108 0.748 0.144
#> GSM494637 4 0.3400 0.7881 0.000 0.180 0.000 0.820
#> GSM494652 4 0.4382 0.4951 0.296 0.000 0.000 0.704
#> GSM494648 4 0.3610 0.7788 0.000 0.200 0.000 0.800
#> GSM494650 3 0.3356 0.7582 0.000 0.000 0.824 0.176
#> GSM494669 4 0.4222 0.5397 0.272 0.000 0.000 0.728
#> GSM494666 4 0.0707 0.8149 0.020 0.000 0.000 0.980
#> GSM494668 4 0.2281 0.7694 0.096 0.000 0.000 0.904
#> GSM494633 4 0.3610 0.7788 0.000 0.200 0.000 0.800
#> GSM494634 1 0.4103 0.6883 0.744 0.000 0.000 0.256
#> GSM494639 4 0.0336 0.8178 0.008 0.000 0.000 0.992
#> GSM494661 4 0.0921 0.8120 0.028 0.000 0.000 0.972
#> GSM494617 4 0.0592 0.8162 0.016 0.000 0.000 0.984
#> GSM494626 4 0.0592 0.8162 0.016 0.000 0.000 0.984
#> GSM494656 3 0.0000 0.9672 0.000 0.000 1.000 0.000
#> GSM494635 4 0.0336 0.8191 0.000 0.008 0.000 0.992
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM494565 4 0.4060 0.50582 0.000 0.000 0.000 0.640 0.360
#> GSM494594 3 0.0671 0.86699 0.000 0.000 0.980 0.004 0.016
#> GSM494604 2 0.2835 0.71859 0.036 0.880 0.000 0.004 0.080
#> GSM494564 4 0.2127 0.68047 0.000 0.000 0.000 0.892 0.108
#> GSM494591 3 0.0898 0.86897 0.000 0.000 0.972 0.020 0.008
#> GSM494567 3 0.2270 0.85047 0.004 0.000 0.908 0.016 0.072
#> GSM494602 2 0.3579 0.57796 0.000 0.756 0.000 0.004 0.240
#> GSM494613 3 0.4505 0.75560 0.000 0.004 0.760 0.152 0.084
#> GSM494589 4 0.1965 0.67984 0.000 0.000 0.000 0.904 0.096
#> GSM494598 2 0.4661 0.55744 0.000 0.656 0.000 0.032 0.312
#> GSM494593 5 0.4595 0.45788 0.004 0.400 0.000 0.008 0.588
#> GSM494583 5 0.0865 0.74908 0.000 0.004 0.000 0.024 0.972
#> GSM494612 2 0.2966 0.60552 0.000 0.816 0.000 0.000 0.184
#> GSM494558 3 0.3480 0.66653 0.000 0.000 0.752 0.248 0.000
#> GSM494556 3 0.3399 0.79840 0.000 0.000 0.812 0.168 0.020
#> GSM494559 4 0.4074 0.36212 0.000 0.000 0.000 0.636 0.364
#> GSM494571 3 0.0162 0.86788 0.000 0.000 0.996 0.004 0.000
#> GSM494614 5 0.6069 0.54782 0.000 0.012 0.176 0.196 0.616
#> GSM494603 4 0.3925 0.65929 0.028 0.004 0.004 0.792 0.172
#> GSM494568 4 0.4567 0.65388 0.036 0.004 0.152 0.776 0.032
#> GSM494572 3 0.1205 0.86572 0.000 0.000 0.956 0.040 0.004
#> GSM494600 4 0.2377 0.67293 0.000 0.000 0.000 0.872 0.128
#> GSM494562 2 0.4292 0.61982 0.000 0.704 0.000 0.024 0.272
#> GSM494615 3 0.3992 0.71019 0.000 0.000 0.720 0.268 0.012
#> GSM494582 2 0.2179 0.69058 0.000 0.888 0.000 0.000 0.112
#> GSM494599 2 0.1082 0.71557 0.008 0.964 0.000 0.000 0.028
#> GSM494610 2 0.5107 0.47429 0.000 0.596 0.000 0.048 0.356
#> GSM494587 5 0.3106 0.75521 0.000 0.116 0.008 0.020 0.856
#> GSM494581 5 0.2407 0.73351 0.000 0.012 0.004 0.088 0.896
#> GSM494580 3 0.1251 0.86529 0.000 0.000 0.956 0.008 0.036
#> GSM494563 4 0.3395 0.64044 0.000 0.000 0.000 0.764 0.236
#> GSM494576 5 0.2074 0.75718 0.000 0.044 0.000 0.036 0.920
#> GSM494605 1 0.1270 0.82902 0.948 0.052 0.000 0.000 0.000
#> GSM494584 5 0.1299 0.75558 0.000 0.008 0.012 0.020 0.960
#> GSM494586 5 0.4193 0.56333 0.000 0.256 0.000 0.024 0.720
#> GSM494578 3 0.4712 0.72724 0.004 0.000 0.736 0.080 0.180
#> GSM494585 5 0.3368 0.73421 0.000 0.156 0.000 0.024 0.820
#> GSM494611 2 0.2707 0.70052 0.000 0.860 0.000 0.008 0.132
#> GSM494560 4 0.3480 0.58179 0.000 0.000 0.000 0.752 0.248
#> GSM494595 5 0.2813 0.74883 0.000 0.108 0.000 0.024 0.868
#> GSM494570 4 0.1341 0.68907 0.000 0.000 0.000 0.944 0.056
#> GSM494597 3 0.3780 0.77997 0.000 0.000 0.812 0.116 0.072
#> GSM494607 2 0.1788 0.72340 0.008 0.932 0.000 0.004 0.056
#> GSM494561 4 0.1364 0.69490 0.036 0.000 0.000 0.952 0.012
#> GSM494569 1 0.4654 0.62888 0.720 0.008 0.240 0.012 0.020
#> GSM494592 2 0.1522 0.71287 0.012 0.944 0.000 0.000 0.044
#> GSM494577 5 0.2712 0.70812 0.000 0.032 0.000 0.088 0.880
#> GSM494588 4 0.2773 0.66130 0.000 0.000 0.000 0.836 0.164
#> GSM494590 3 0.0162 0.86788 0.000 0.000 0.996 0.004 0.000
#> GSM494609 5 0.3474 0.72663 0.032 0.112 0.004 0.008 0.844
#> GSM494608 5 0.6495 0.19837 0.148 0.380 0.008 0.000 0.464
#> GSM494606 2 0.5390 0.30442 0.048 0.620 0.004 0.008 0.320
#> GSM494574 2 0.4679 0.54995 0.000 0.652 0.000 0.032 0.316
#> GSM494573 4 0.2648 0.66350 0.000 0.000 0.000 0.848 0.152
#> GSM494566 2 0.3600 0.69966 0.016 0.840 0.008 0.020 0.116
#> GSM494601 2 0.3336 0.63513 0.000 0.772 0.000 0.000 0.228
#> GSM494557 3 0.3848 0.78632 0.000 0.004 0.816 0.076 0.104
#> GSM494579 4 0.5224 0.49256 0.000 0.080 0.000 0.644 0.276
#> GSM494596 3 0.0324 0.86897 0.000 0.000 0.992 0.004 0.004
#> GSM494575 5 0.4350 0.46437 0.000 0.408 0.000 0.004 0.588
#> GSM494625 4 0.4325 0.57814 0.300 0.012 0.000 0.684 0.004
#> GSM494654 3 0.0290 0.86872 0.000 0.000 0.992 0.008 0.000
#> GSM494664 1 0.1205 0.83082 0.956 0.040 0.000 0.004 0.000
#> GSM494624 4 0.3618 0.67116 0.196 0.012 0.000 0.788 0.004
#> GSM494651 3 0.4262 0.56491 0.288 0.004 0.696 0.012 0.000
#> GSM494662 1 0.1173 0.82168 0.964 0.012 0.000 0.020 0.004
#> GSM494627 1 0.3606 0.77405 0.848 0.012 0.072 0.064 0.004
#> GSM494673 1 0.4300 0.22803 0.524 0.476 0.000 0.000 0.000
#> GSM494649 4 0.4779 0.18036 0.448 0.012 0.000 0.536 0.004
#> GSM494658 2 0.4724 0.55939 0.248 0.704 0.000 0.008 0.040
#> GSM494653 1 0.1544 0.82403 0.932 0.068 0.000 0.000 0.000
#> GSM494643 1 0.1970 0.79959 0.924 0.012 0.000 0.060 0.004
#> GSM494672 2 0.2852 0.62545 0.172 0.828 0.000 0.000 0.000
#> GSM494618 1 0.2952 0.78670 0.872 0.004 0.036 0.088 0.000
#> GSM494631 3 0.1710 0.86528 0.000 0.004 0.940 0.040 0.016
#> GSM494619 4 0.4474 0.54114 0.332 0.012 0.000 0.652 0.004
#> GSM494674 1 0.1965 0.81368 0.904 0.096 0.000 0.000 0.000
#> GSM494616 1 0.2392 0.79308 0.888 0.004 0.104 0.004 0.000
#> GSM494663 1 0.4283 0.47178 0.692 0.012 0.000 0.292 0.004
#> GSM494628 1 0.5731 0.28542 0.568 0.000 0.104 0.328 0.000
#> GSM494632 1 0.0290 0.83117 0.992 0.008 0.000 0.000 0.000
#> GSM494660 4 0.4567 0.43589 0.356 0.012 0.000 0.628 0.004
#> GSM494622 4 0.6395 0.09972 0.108 0.016 0.424 0.452 0.000
#> GSM494642 1 0.1410 0.82597 0.940 0.060 0.000 0.000 0.000
#> GSM494647 1 0.2891 0.75997 0.824 0.176 0.000 0.000 0.000
#> GSM494659 1 0.3424 0.69268 0.760 0.240 0.000 0.000 0.000
#> GSM494670 2 0.4352 0.55095 0.244 0.720 0.000 0.036 0.000
#> GSM494675 4 0.4637 0.61765 0.000 0.004 0.160 0.748 0.088
#> GSM494641 1 0.0963 0.83050 0.964 0.036 0.000 0.000 0.000
#> GSM494636 1 0.1267 0.81832 0.960 0.012 0.000 0.024 0.004
#> GSM494640 1 0.2299 0.80180 0.916 0.012 0.012 0.056 0.004
#> GSM494623 4 0.3934 0.65461 0.236 0.012 0.000 0.748 0.004
#> GSM494644 1 0.0290 0.83071 0.992 0.008 0.000 0.000 0.000
#> GSM494646 1 0.0613 0.82625 0.984 0.008 0.000 0.004 0.004
#> GSM494665 1 0.4288 0.44739 0.612 0.384 0.000 0.004 0.000
#> GSM494638 1 0.1918 0.81716 0.940 0.012 0.012 0.016 0.020
#> GSM494645 1 0.0451 0.83098 0.988 0.008 0.000 0.004 0.000
#> GSM494671 2 0.2648 0.64451 0.152 0.848 0.000 0.000 0.000
#> GSM494655 1 0.0609 0.83112 0.980 0.020 0.000 0.000 0.000
#> GSM494620 1 0.4745 0.10598 0.560 0.012 0.000 0.424 0.004
#> GSM494630 1 0.2629 0.78158 0.880 0.012 0.000 0.104 0.004
#> GSM494657 3 0.0000 0.86808 0.000 0.000 1.000 0.000 0.000
#> GSM494667 1 0.3561 0.66958 0.740 0.260 0.000 0.000 0.000
#> GSM494621 4 0.4283 0.59643 0.292 0.012 0.000 0.692 0.004
#> GSM494629 3 0.5171 0.57264 0.248 0.012 0.688 0.044 0.008
#> GSM494637 1 0.2037 0.80071 0.920 0.012 0.000 0.064 0.004
#> GSM494652 1 0.2424 0.79277 0.868 0.132 0.000 0.000 0.000
#> GSM494648 4 0.4567 0.49789 0.356 0.012 0.000 0.628 0.004
#> GSM494650 3 0.2354 0.81885 0.076 0.008 0.904 0.012 0.000
#> GSM494669 1 0.2966 0.75087 0.816 0.184 0.000 0.000 0.000
#> GSM494666 1 0.0794 0.83100 0.972 0.028 0.000 0.000 0.000
#> GSM494668 1 0.5083 0.25970 0.532 0.432 0.000 0.036 0.000
#> GSM494633 1 0.4796 -0.00467 0.516 0.012 0.000 0.468 0.004
#> GSM494634 1 0.4630 0.36378 0.572 0.416 0.000 0.008 0.004
#> GSM494639 1 0.0324 0.82966 0.992 0.004 0.000 0.004 0.000
#> GSM494661 1 0.0880 0.83092 0.968 0.032 0.000 0.000 0.000
#> GSM494617 1 0.0671 0.83161 0.980 0.016 0.000 0.004 0.000
#> GSM494626 1 0.1845 0.81997 0.928 0.016 0.000 0.056 0.000
#> GSM494656 3 0.0451 0.86803 0.000 0.000 0.988 0.004 0.008
#> GSM494635 1 0.0727 0.82499 0.980 0.012 0.000 0.004 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM494565 4 0.5370 0.5656 0.000 0.000 0.000 0.588 0.192 0.220
#> GSM494594 3 0.0405 0.8498 0.000 0.004 0.988 0.000 0.008 0.000
#> GSM494604 4 0.4452 0.3367 0.040 0.312 0.000 0.644 0.004 0.000
#> GSM494564 6 0.2538 0.6486 0.000 0.000 0.000 0.124 0.016 0.860
#> GSM494591 3 0.1768 0.8329 0.000 0.012 0.932 0.004 0.008 0.044
#> GSM494567 3 0.1734 0.8299 0.000 0.004 0.932 0.008 0.048 0.008
#> GSM494602 2 0.2593 0.6170 0.000 0.844 0.000 0.008 0.148 0.000
#> GSM494613 5 0.5874 0.5428 0.000 0.012 0.212 0.036 0.620 0.120
#> GSM494589 6 0.2547 0.6586 0.000 0.000 0.004 0.080 0.036 0.880
#> GSM494598 4 0.4597 0.5551 0.000 0.148 0.000 0.716 0.128 0.008
#> GSM494593 5 0.3965 0.3816 0.000 0.376 0.000 0.004 0.616 0.004
#> GSM494583 5 0.3905 0.2272 0.000 0.000 0.004 0.356 0.636 0.004
#> GSM494612 2 0.3563 0.3563 0.000 0.664 0.000 0.000 0.336 0.000
#> GSM494558 3 0.4948 0.5534 0.000 0.012 0.648 0.080 0.000 0.260
#> GSM494556 6 0.6441 0.2340 0.000 0.000 0.276 0.056 0.160 0.508
#> GSM494559 5 0.4264 0.0188 0.000 0.000 0.000 0.016 0.496 0.488
#> GSM494571 3 0.1053 0.8453 0.000 0.012 0.964 0.020 0.000 0.004
#> GSM494614 5 0.5666 0.4353 0.000 0.016 0.056 0.032 0.584 0.312
#> GSM494603 4 0.3012 0.5663 0.008 0.000 0.000 0.796 0.000 0.196
#> GSM494568 4 0.5386 0.4457 0.016 0.004 0.108 0.628 0.000 0.244
#> GSM494572 3 0.2327 0.8214 0.000 0.012 0.908 0.028 0.008 0.044
#> GSM494600 6 0.2527 0.6491 0.000 0.000 0.000 0.108 0.024 0.868
#> GSM494562 2 0.5361 0.0334 0.000 0.452 0.000 0.440 0.108 0.000
#> GSM494615 6 0.4607 0.5782 0.000 0.028 0.104 0.056 0.040 0.772
#> GSM494582 2 0.2431 0.6212 0.000 0.860 0.000 0.008 0.132 0.000
#> GSM494599 2 0.1225 0.6442 0.012 0.952 0.000 0.000 0.036 0.000
#> GSM494610 4 0.4277 0.5893 0.000 0.084 0.000 0.764 0.128 0.024
#> GSM494587 5 0.3478 0.6422 0.000 0.064 0.060 0.040 0.836 0.000
#> GSM494581 5 0.0508 0.6505 0.012 0.000 0.004 0.000 0.984 0.000
#> GSM494580 3 0.1121 0.8453 0.000 0.004 0.964 0.008 0.016 0.008
#> GSM494563 4 0.4517 0.2196 0.000 0.000 0.000 0.524 0.032 0.444
#> GSM494576 5 0.4553 0.5447 0.000 0.040 0.020 0.216 0.716 0.008
#> GSM494605 1 0.0692 0.8744 0.976 0.020 0.000 0.004 0.000 0.000
#> GSM494584 5 0.4624 0.5133 0.000 0.004 0.096 0.208 0.692 0.000
#> GSM494586 2 0.6230 0.2442 0.000 0.452 0.000 0.248 0.288 0.012
#> GSM494578 5 0.4416 0.2251 0.012 0.004 0.440 0.000 0.540 0.004
#> GSM494585 5 0.1655 0.6512 0.000 0.044 0.012 0.004 0.936 0.004
#> GSM494611 2 0.2019 0.6406 0.000 0.900 0.000 0.012 0.088 0.000
#> GSM494560 6 0.4729 0.5209 0.000 0.000 0.000 0.128 0.196 0.676
#> GSM494595 5 0.2262 0.6392 0.000 0.080 0.000 0.016 0.896 0.008
#> GSM494570 6 0.2001 0.6691 0.004 0.000 0.000 0.092 0.004 0.900
#> GSM494597 4 0.4018 0.3078 0.000 0.000 0.412 0.580 0.000 0.008
#> GSM494607 2 0.4442 0.0570 0.020 0.536 0.000 0.440 0.004 0.000
#> GSM494561 6 0.1226 0.6796 0.004 0.000 0.004 0.040 0.000 0.952
#> GSM494569 1 0.2527 0.8513 0.896 0.004 0.056 0.032 0.008 0.004
#> GSM494592 2 0.1812 0.6416 0.008 0.912 0.000 0.000 0.080 0.000
#> GSM494577 4 0.3584 0.5581 0.000 0.004 0.000 0.740 0.244 0.012
#> GSM494588 6 0.5388 0.3933 0.004 0.000 0.000 0.196 0.196 0.604
#> GSM494590 3 0.0260 0.8500 0.000 0.008 0.992 0.000 0.000 0.000
#> GSM494609 5 0.1282 0.6499 0.012 0.024 0.004 0.004 0.956 0.000
#> GSM494608 5 0.4308 0.5734 0.028 0.160 0.004 0.040 0.764 0.004
#> GSM494606 5 0.3871 0.4465 0.016 0.308 0.000 0.000 0.676 0.000
#> GSM494574 4 0.4292 0.5652 0.000 0.120 0.000 0.748 0.124 0.008
#> GSM494573 6 0.3637 0.5941 0.000 0.000 0.000 0.164 0.056 0.780
#> GSM494566 2 0.5587 0.1626 0.032 0.504 0.008 0.420 0.024 0.012
#> GSM494601 2 0.3284 0.5987 0.000 0.784 0.000 0.020 0.196 0.000
#> GSM494557 5 0.5051 0.3259 0.000 0.000 0.396 0.040 0.544 0.020
#> GSM494579 4 0.4805 0.6200 0.000 0.064 0.000 0.732 0.072 0.132
#> GSM494596 3 0.0260 0.8500 0.000 0.008 0.992 0.000 0.000 0.000
#> GSM494575 5 0.3728 0.4319 0.000 0.344 0.000 0.004 0.652 0.000
#> GSM494625 6 0.4286 0.6439 0.132 0.020 0.000 0.088 0.000 0.760
#> GSM494654 3 0.0798 0.8485 0.000 0.004 0.976 0.012 0.004 0.004
#> GSM494664 1 0.2755 0.8191 0.844 0.004 0.000 0.140 0.000 0.012
#> GSM494624 6 0.3092 0.6863 0.088 0.016 0.000 0.044 0.000 0.852
#> GSM494651 3 0.5328 0.1623 0.420 0.000 0.496 0.072 0.000 0.012
#> GSM494662 1 0.1672 0.8645 0.944 0.020 0.012 0.008 0.004 0.012
#> GSM494627 1 0.5558 0.7098 0.692 0.020 0.080 0.136 0.000 0.072
#> GSM494673 1 0.3175 0.6896 0.744 0.256 0.000 0.000 0.000 0.000
#> GSM494649 6 0.3134 0.6709 0.148 0.016 0.000 0.012 0.000 0.824
#> GSM494658 4 0.5598 0.2046 0.208 0.220 0.000 0.568 0.000 0.004
#> GSM494653 1 0.1745 0.8658 0.924 0.056 0.000 0.020 0.000 0.000
#> GSM494643 1 0.2445 0.8440 0.896 0.020 0.000 0.028 0.000 0.056
#> GSM494672 2 0.2697 0.5585 0.188 0.812 0.000 0.000 0.000 0.000
#> GSM494618 1 0.5308 0.6920 0.696 0.008 0.048 0.144 0.000 0.104
#> GSM494631 3 0.1565 0.8372 0.000 0.000 0.940 0.028 0.028 0.004
#> GSM494619 6 0.5799 0.2399 0.372 0.020 0.000 0.112 0.000 0.496
#> GSM494674 1 0.0937 0.8714 0.960 0.040 0.000 0.000 0.000 0.000
#> GSM494616 1 0.2270 0.8522 0.900 0.000 0.020 0.072 0.004 0.004
#> GSM494663 1 0.5590 0.5645 0.612 0.020 0.000 0.180 0.000 0.188
#> GSM494628 1 0.6686 0.0507 0.420 0.012 0.024 0.204 0.000 0.340
#> GSM494632 1 0.0436 0.8724 0.988 0.000 0.000 0.004 0.004 0.004
#> GSM494660 6 0.2757 0.6916 0.104 0.016 0.000 0.016 0.000 0.864
#> GSM494622 6 0.7320 0.3117 0.148 0.000 0.224 0.212 0.000 0.416
#> GSM494642 1 0.0547 0.8731 0.980 0.020 0.000 0.000 0.000 0.000
#> GSM494647 1 0.1327 0.8649 0.936 0.064 0.000 0.000 0.000 0.000
#> GSM494659 1 0.1387 0.8629 0.932 0.068 0.000 0.000 0.000 0.000
#> GSM494670 2 0.6285 0.3006 0.180 0.532 0.000 0.244 0.000 0.044
#> GSM494675 4 0.5742 0.4986 0.000 0.012 0.196 0.568 0.000 0.224
#> GSM494641 1 0.0520 0.8735 0.984 0.008 0.000 0.008 0.000 0.000
#> GSM494636 1 0.1579 0.8625 0.944 0.020 0.000 0.008 0.004 0.024
#> GSM494640 1 0.3539 0.8177 0.840 0.020 0.072 0.016 0.000 0.052
#> GSM494623 6 0.4610 0.6204 0.100 0.020 0.000 0.152 0.000 0.728
#> GSM494644 1 0.0582 0.8721 0.984 0.004 0.000 0.004 0.004 0.004
#> GSM494646 1 0.0653 0.8717 0.980 0.004 0.000 0.004 0.000 0.012
#> GSM494665 1 0.3230 0.7426 0.776 0.212 0.000 0.012 0.000 0.000
#> GSM494638 1 0.2596 0.8502 0.900 0.024 0.044 0.016 0.008 0.008
#> GSM494645 1 0.0458 0.8742 0.984 0.000 0.000 0.016 0.000 0.000
#> GSM494671 2 0.3163 0.5096 0.232 0.764 0.000 0.004 0.000 0.000
#> GSM494655 1 0.0146 0.8726 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM494620 1 0.4859 0.4982 0.628 0.020 0.000 0.044 0.000 0.308
#> GSM494630 1 0.2213 0.8442 0.904 0.020 0.000 0.004 0.004 0.068
#> GSM494657 3 0.0291 0.8502 0.000 0.004 0.992 0.000 0.004 0.000
#> GSM494667 1 0.1814 0.8469 0.900 0.100 0.000 0.000 0.000 0.000
#> GSM494621 6 0.4400 0.6103 0.180 0.020 0.000 0.064 0.000 0.736
#> GSM494629 3 0.5212 0.5011 0.252 0.020 0.660 0.016 0.004 0.048
#> GSM494637 1 0.2568 0.8492 0.900 0.020 0.012 0.016 0.004 0.048
#> GSM494652 1 0.0692 0.8721 0.976 0.020 0.000 0.000 0.004 0.000
#> GSM494648 1 0.5263 0.3244 0.552 0.020 0.000 0.060 0.000 0.368
#> GSM494650 3 0.4519 0.6597 0.096 0.004 0.752 0.124 0.000 0.024
#> GSM494669 1 0.1327 0.8654 0.936 0.064 0.000 0.000 0.000 0.000
#> GSM494666 1 0.0725 0.8743 0.976 0.012 0.000 0.012 0.000 0.000
#> GSM494668 1 0.5858 0.5868 0.620 0.188 0.000 0.128 0.000 0.064
#> GSM494633 6 0.3144 0.6671 0.172 0.016 0.000 0.000 0.004 0.808
#> GSM494634 1 0.1858 0.8444 0.904 0.092 0.000 0.000 0.004 0.000
#> GSM494639 1 0.0291 0.8729 0.992 0.004 0.000 0.000 0.000 0.004
#> GSM494661 1 0.0993 0.8750 0.964 0.012 0.000 0.024 0.000 0.000
#> GSM494617 1 0.2032 0.8592 0.912 0.012 0.000 0.068 0.004 0.004
#> GSM494626 1 0.4017 0.7917 0.788 0.016 0.012 0.140 0.000 0.044
#> GSM494656 3 0.0551 0.8492 0.000 0.004 0.984 0.008 0.000 0.004
#> GSM494635 1 0.1007 0.8695 0.968 0.008 0.000 0.004 0.004 0.016
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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 3)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 4)
get_signatures(res, k = 5)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 6)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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.
# code only for demonstration
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) individual(p) k
#> MAD:NMF 115 1.15e-02 0.0469 9.39e-02 0.0113 2
#> MAD:NMF 116 2.34e-18 0.8267 2.68e-14 0.9707 3
#> MAD:NMF 107 7.27e-16 0.6566 4.01e-12 0.4811 4
#> MAD:NMF 101 2.43e-09 0.2387 4.23e-06 0.0954 5
#> MAD:NMF 92 2.71e-09 0.4036 6.05e-06 0.1274 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.
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"]
# you can also extract it by
# 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 51941 rows and 120 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:
k and the heatmap of
predicted classes for each k.k.k.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:
k;k, the area increased is defined as \(A_k - A_{k-1}\).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 0.915 0.947 0.976 0.2168 0.792 0.792
#> 3 3 0.439 0.760 0.857 1.0861 0.690 0.613
#> 4 4 0.574 0.759 0.881 0.2304 0.918 0.842
#> 5 5 0.602 0.730 0.863 0.0606 0.953 0.898
#> 6 6 0.527 0.501 0.710 0.2188 0.822 0.583
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
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.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM494565 1 0.0000 0.9809 1.000 0.000
#> GSM494594 2 0.0000 0.9202 0.000 1.000
#> GSM494604 1 0.0000 0.9809 1.000 0.000
#> GSM494564 1 0.0000 0.9809 1.000 0.000
#> GSM494591 2 0.0000 0.9202 0.000 1.000
#> GSM494567 1 0.0000 0.9809 1.000 0.000
#> GSM494602 1 0.0000 0.9809 1.000 0.000
#> GSM494613 1 0.5737 0.8429 0.864 0.136
#> GSM494589 1 0.0000 0.9809 1.000 0.000
#> GSM494598 1 0.0000 0.9809 1.000 0.000
#> GSM494593 1 0.0000 0.9809 1.000 0.000
#> GSM494583 1 0.2043 0.9576 0.968 0.032
#> GSM494612 1 0.0672 0.9760 0.992 0.008
#> GSM494558 1 0.9983 0.0189 0.524 0.476
#> GSM494556 1 0.2043 0.9569 0.968 0.032
#> GSM494559 1 0.0000 0.9809 1.000 0.000
#> GSM494571 2 0.0000 0.9202 0.000 1.000
#> GSM494614 1 0.0000 0.9809 1.000 0.000
#> GSM494603 1 0.0000 0.9809 1.000 0.000
#> GSM494568 1 0.0000 0.9809 1.000 0.000
#> GSM494572 2 0.0000 0.9202 0.000 1.000
#> GSM494600 1 0.0000 0.9809 1.000 0.000
#> GSM494562 1 0.2043 0.9576 0.968 0.032
#> GSM494615 1 0.0000 0.9809 1.000 0.000
#> GSM494582 1 0.0672 0.9760 0.992 0.008
#> GSM494599 1 0.0000 0.9809 1.000 0.000
#> GSM494610 1 0.0672 0.9760 0.992 0.008
#> GSM494587 1 0.2043 0.9576 0.968 0.032
#> GSM494581 1 0.0376 0.9786 0.996 0.004
#> GSM494580 1 0.6438 0.8046 0.836 0.164
#> GSM494563 1 0.0000 0.9809 1.000 0.000
#> GSM494576 1 0.2043 0.9576 0.968 0.032
#> GSM494605 1 0.0000 0.9809 1.000 0.000
#> GSM494584 1 0.2043 0.9576 0.968 0.032
#> GSM494586 1 0.2043 0.9576 0.968 0.032
#> GSM494578 1 0.6438 0.8046 0.836 0.164
#> GSM494585 1 0.2043 0.9576 0.968 0.032
#> GSM494611 1 0.0000 0.9809 1.000 0.000
#> GSM494560 1 0.0000 0.9809 1.000 0.000
#> GSM494595 1 0.0376 0.9786 0.996 0.004
#> GSM494570 1 0.0000 0.9809 1.000 0.000
#> GSM494597 2 0.0000 0.9202 0.000 1.000
#> GSM494607 1 0.0000 0.9809 1.000 0.000
#> GSM494561 1 0.0000 0.9809 1.000 0.000
#> GSM494569 1 0.0000 0.9809 1.000 0.000
#> GSM494592 1 0.0000 0.9809 1.000 0.000
#> GSM494577 1 0.2043 0.9576 0.968 0.032
#> GSM494588 1 0.0000 0.9809 1.000 0.000
#> GSM494590 2 0.0000 0.9202 0.000 1.000
#> GSM494609 1 0.0000 0.9809 1.000 0.000
#> GSM494608 1 0.0376 0.9786 0.996 0.004
#> GSM494606 1 0.0000 0.9809 1.000 0.000
#> GSM494574 1 0.0672 0.9760 0.992 0.008
#> GSM494573 1 0.0000 0.9809 1.000 0.000
#> GSM494566 1 0.0000 0.9809 1.000 0.000
#> GSM494601 2 0.9209 0.5494 0.336 0.664
#> GSM494557 1 0.5737 0.8429 0.864 0.136
#> GSM494579 1 0.0000 0.9809 1.000 0.000
#> GSM494596 2 0.0000 0.9202 0.000 1.000
#> GSM494575 1 0.0672 0.9760 0.992 0.008
#> GSM494625 1 0.0000 0.9809 1.000 0.000
#> GSM494654 2 0.0000 0.9202 0.000 1.000
#> GSM494664 1 0.0000 0.9809 1.000 0.000
#> GSM494624 1 0.0000 0.9809 1.000 0.000
#> GSM494651 2 0.6343 0.8116 0.160 0.840
#> GSM494662 1 0.0000 0.9809 1.000 0.000
#> GSM494627 1 0.0000 0.9809 1.000 0.000
#> GSM494673 1 0.0000 0.9809 1.000 0.000
#> GSM494649 1 0.0000 0.9809 1.000 0.000
#> GSM494658 1 0.0000 0.9809 1.000 0.000
#> GSM494653 1 0.0000 0.9809 1.000 0.000
#> GSM494643 1 0.3431 0.9233 0.936 0.064
#> GSM494672 1 0.0000 0.9809 1.000 0.000
#> GSM494618 1 0.0000 0.9809 1.000 0.000
#> GSM494631 1 0.6438 0.8046 0.836 0.164
#> GSM494619 1 0.0000 0.9809 1.000 0.000
#> GSM494674 1 0.0000 0.9809 1.000 0.000
#> GSM494616 1 0.0000 0.9809 1.000 0.000
#> GSM494663 1 0.0000 0.9809 1.000 0.000
#> GSM494628 1 0.0000 0.9809 1.000 0.000
#> GSM494632 1 0.0000 0.9809 1.000 0.000
#> GSM494660 1 0.0000 0.9809 1.000 0.000
#> GSM494622 1 0.2603 0.9440 0.956 0.044
#> GSM494642 1 0.0000 0.9809 1.000 0.000
#> GSM494647 1 0.0000 0.9809 1.000 0.000
#> GSM494659 1 0.0000 0.9809 1.000 0.000
#> GSM494670 1 0.0000 0.9809 1.000 0.000
#> GSM494675 1 0.0938 0.9732 0.988 0.012
#> GSM494641 1 0.0000 0.9809 1.000 0.000
#> GSM494636 1 0.0000 0.9809 1.000 0.000
#> GSM494640 1 0.6247 0.8159 0.844 0.156
#> GSM494623 1 0.0000 0.9809 1.000 0.000
#> GSM494644 1 0.0000 0.9809 1.000 0.000
#> GSM494646 1 0.0000 0.9809 1.000 0.000
#> GSM494665 1 0.0000 0.9809 1.000 0.000
#> GSM494638 1 0.0000 0.9809 1.000 0.000
#> GSM494645 1 0.0000 0.9809 1.000 0.000
#> GSM494671 1 0.0000 0.9809 1.000 0.000
#> GSM494655 1 0.0000 0.9809 1.000 0.000
#> GSM494620 1 0.0000 0.9809 1.000 0.000
#> GSM494630 1 0.0000 0.9809 1.000 0.000
#> GSM494657 2 0.0000 0.9202 0.000 1.000
#> GSM494667 1 0.0000 0.9809 1.000 0.000
#> GSM494621 1 0.0000 0.9809 1.000 0.000
#> GSM494629 1 0.0000 0.9809 1.000 0.000
#> GSM494637 1 0.0938 0.9726 0.988 0.012
#> GSM494652 1 0.0000 0.9809 1.000 0.000
#> GSM494648 1 0.0000 0.9809 1.000 0.000
#> GSM494650 2 0.6343 0.8116 0.160 0.840
#> GSM494669 1 0.0000 0.9809 1.000 0.000
#> GSM494666 1 0.0000 0.9809 1.000 0.000
#> GSM494668 1 0.0000 0.9809 1.000 0.000
#> GSM494633 1 0.0000 0.9809 1.000 0.000
#> GSM494634 1 0.0000 0.9809 1.000 0.000
#> GSM494639 1 0.0000 0.9809 1.000 0.000
#> GSM494661 2 0.9209 0.5494 0.336 0.664
#> GSM494617 1 0.0000 0.9809 1.000 0.000
#> GSM494626 1 0.0000 0.9809 1.000 0.000
#> GSM494656 2 0.0000 0.9202 0.000 1.000
#> GSM494635 1 0.0000 0.9809 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM494565 1 0.5465 0.4899 0.712 0.288 0.000
#> GSM494594 3 0.0000 0.9401 0.000 0.000 1.000
#> GSM494604 1 0.1411 0.8672 0.964 0.036 0.000
#> GSM494564 1 0.0237 0.8817 0.996 0.004 0.000
#> GSM494591 3 0.0000 0.9401 0.000 0.000 1.000
#> GSM494567 1 0.6274 -0.2613 0.544 0.456 0.000
#> GSM494602 1 0.4654 0.6655 0.792 0.208 0.000
#> GSM494613 2 0.4702 0.7645 0.212 0.788 0.000
#> GSM494589 1 0.1860 0.8674 0.948 0.052 0.000
#> GSM494598 2 0.6126 0.6914 0.400 0.600 0.000
#> GSM494593 1 0.4654 0.6655 0.792 0.208 0.000
#> GSM494583 2 0.5497 0.8152 0.292 0.708 0.000
#> GSM494612 2 0.5678 0.8035 0.316 0.684 0.000
#> GSM494558 2 0.8763 0.2913 0.136 0.552 0.312
#> GSM494556 2 0.6168 0.6597 0.412 0.588 0.000
#> GSM494559 1 0.1860 0.8674 0.948 0.052 0.000
#> GSM494571 3 0.0000 0.9401 0.000 0.000 1.000
#> GSM494614 1 0.5431 0.4950 0.716 0.284 0.000
#> GSM494603 1 0.3482 0.8002 0.872 0.128 0.000
#> GSM494568 1 0.3482 0.8002 0.872 0.128 0.000
#> GSM494572 3 0.0000 0.9401 0.000 0.000 1.000
#> GSM494600 1 0.1860 0.8674 0.948 0.052 0.000
#> GSM494562 2 0.5497 0.8152 0.292 0.708 0.000
#> GSM494615 1 0.5431 0.4950 0.716 0.284 0.000
#> GSM494582 2 0.5678 0.8035 0.316 0.684 0.000
#> GSM494599 1 0.3551 0.7840 0.868 0.132 0.000
#> GSM494610 2 0.5678 0.8035 0.316 0.684 0.000
#> GSM494587 2 0.5497 0.8152 0.292 0.708 0.000
#> GSM494581 2 0.6295 0.5109 0.472 0.528 0.000
#> GSM494580 2 0.4346 0.7284 0.184 0.816 0.000
#> GSM494563 1 0.1860 0.8674 0.948 0.052 0.000
#> GSM494576 2 0.5497 0.8152 0.292 0.708 0.000
#> GSM494605 1 0.0000 0.8810 1.000 0.000 0.000
#> GSM494584 2 0.5497 0.8152 0.292 0.708 0.000
#> GSM494586 2 0.5497 0.8152 0.292 0.708 0.000
#> GSM494578 2 0.4346 0.7284 0.184 0.816 0.000
#> GSM494585 2 0.5497 0.8152 0.292 0.708 0.000
#> GSM494611 2 0.6126 0.6914 0.400 0.600 0.000
#> GSM494560 1 0.1860 0.8674 0.948 0.052 0.000
#> GSM494595 2 0.5926 0.7598 0.356 0.644 0.000
#> GSM494570 1 0.0237 0.8817 0.996 0.004 0.000
#> GSM494597 3 0.0000 0.9401 0.000 0.000 1.000
#> GSM494607 1 0.1411 0.8672 0.964 0.036 0.000
#> GSM494561 1 0.1411 0.8751 0.964 0.036 0.000
#> GSM494569 1 0.3267 0.8128 0.884 0.116 0.000
#> GSM494592 1 0.3551 0.7840 0.868 0.132 0.000
#> GSM494577 2 0.5497 0.8152 0.292 0.708 0.000
#> GSM494588 1 0.0237 0.8817 0.996 0.004 0.000
#> GSM494590 3 0.0000 0.9401 0.000 0.000 1.000
#> GSM494609 1 0.4654 0.6655 0.792 0.208 0.000
#> GSM494608 2 0.6295 0.5109 0.472 0.528 0.000
#> GSM494606 1 0.4654 0.6655 0.792 0.208 0.000
#> GSM494574 2 0.5678 0.8035 0.316 0.684 0.000
#> GSM494573 1 0.1860 0.8674 0.948 0.052 0.000
#> GSM494566 1 0.3941 0.7672 0.844 0.156 0.000
#> GSM494601 2 0.5835 -0.3928 0.000 0.660 0.340
#> GSM494557 2 0.4702 0.7645 0.212 0.788 0.000
#> GSM494579 1 0.3941 0.7672 0.844 0.156 0.000
#> GSM494596 3 0.0000 0.9401 0.000 0.000 1.000
#> GSM494575 2 0.5678 0.8035 0.316 0.684 0.000
#> GSM494625 1 0.0000 0.8810 1.000 0.000 0.000
#> GSM494654 3 0.0000 0.9401 0.000 0.000 1.000
#> GSM494664 1 0.0000 0.8810 1.000 0.000 0.000
#> GSM494624 1 0.0000 0.8810 1.000 0.000 0.000
#> GSM494651 3 0.6286 0.6261 0.000 0.464 0.536
#> GSM494662 1 0.0000 0.8810 1.000 0.000 0.000
#> GSM494627 1 0.6111 0.0392 0.604 0.396 0.000
#> GSM494673 1 0.0237 0.8817 0.996 0.004 0.000
#> GSM494649 1 0.0000 0.8810 1.000 0.000 0.000
#> GSM494658 1 0.1411 0.8672 0.964 0.036 0.000
#> GSM494653 1 0.0237 0.8817 0.996 0.004 0.000
#> GSM494643 2 0.6192 0.5989 0.420 0.580 0.000
#> GSM494672 1 0.0237 0.8817 0.996 0.004 0.000
#> GSM494618 1 0.3267 0.8128 0.884 0.116 0.000
#> GSM494631 2 0.4346 0.7284 0.184 0.816 0.000
#> GSM494619 1 0.0000 0.8810 1.000 0.000 0.000
#> GSM494674 1 0.0237 0.8817 0.996 0.004 0.000
#> GSM494616 1 0.3267 0.8128 0.884 0.116 0.000
#> GSM494663 1 0.3482 0.8002 0.872 0.128 0.000
#> GSM494628 1 0.3267 0.8128 0.884 0.116 0.000
#> GSM494632 1 0.1289 0.8760 0.968 0.032 0.000
#> GSM494660 1 0.0000 0.8810 1.000 0.000 0.000
#> GSM494622 1 0.5016 0.6409 0.760 0.240 0.000
#> GSM494642 1 0.0237 0.8817 0.996 0.004 0.000
#> GSM494647 1 0.0237 0.8817 0.996 0.004 0.000
#> GSM494659 1 0.0237 0.8817 0.996 0.004 0.000
#> GSM494670 1 0.1411 0.8672 0.964 0.036 0.000
#> GSM494675 1 0.6008 0.1818 0.628 0.372 0.000
#> GSM494641 1 0.0237 0.8817 0.996 0.004 0.000
#> GSM494636 1 0.1289 0.8760 0.968 0.032 0.000
#> GSM494640 2 0.5621 0.7137 0.308 0.692 0.000
#> GSM494623 1 0.0000 0.8810 1.000 0.000 0.000
#> GSM494644 1 0.0747 0.8798 0.984 0.016 0.000
#> GSM494646 1 0.1163 0.8774 0.972 0.028 0.000
#> GSM494665 1 0.0000 0.8810 1.000 0.000 0.000
#> GSM494638 1 0.1289 0.8760 0.968 0.032 0.000
#> GSM494645 1 0.1163 0.8774 0.972 0.028 0.000
#> GSM494671 1 0.0237 0.8817 0.996 0.004 0.000
#> GSM494655 1 0.0237 0.8817 0.996 0.004 0.000
#> GSM494620 1 0.0000 0.8810 1.000 0.000 0.000
#> GSM494630 1 0.0000 0.8810 1.000 0.000 0.000
#> GSM494657 3 0.0000 0.9401 0.000 0.000 1.000
#> GSM494667 1 0.0237 0.8817 0.996 0.004 0.000
#> GSM494621 1 0.0000 0.8810 1.000 0.000 0.000
#> GSM494629 1 0.6111 0.0392 0.604 0.396 0.000
#> GSM494637 1 0.6192 -0.0785 0.580 0.420 0.000
#> GSM494652 1 0.0237 0.8817 0.996 0.004 0.000
#> GSM494648 1 0.0000 0.8810 1.000 0.000 0.000
#> GSM494650 3 0.6286 0.6261 0.000 0.464 0.536
#> GSM494669 1 0.0237 0.8817 0.996 0.004 0.000
#> GSM494666 1 0.0000 0.8810 1.000 0.000 0.000
#> GSM494668 1 0.0237 0.8817 0.996 0.004 0.000
#> GSM494633 1 0.0000 0.8810 1.000 0.000 0.000
#> GSM494634 1 0.0237 0.8817 0.996 0.004 0.000
#> GSM494639 1 0.1289 0.8760 0.968 0.032 0.000
#> GSM494661 2 0.5835 -0.3928 0.000 0.660 0.340
#> GSM494617 1 0.3267 0.8128 0.884 0.116 0.000
#> GSM494626 1 0.3267 0.8128 0.884 0.116 0.000
#> GSM494656 3 0.0000 0.9401 0.000 0.000 1.000
#> GSM494635 1 0.0747 0.8798 0.984 0.016 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM494565 1 0.5746 0.3278 0.572 0.396 0.000 0.032
#> GSM494594 3 0.0000 0.9653 0.000 0.000 1.000 0.000
#> GSM494604 1 0.1792 0.8496 0.932 0.068 0.000 0.000
#> GSM494564 1 0.0188 0.8802 0.996 0.004 0.000 0.000
#> GSM494591 3 0.0000 0.9653 0.000 0.000 1.000 0.000
#> GSM494567 2 0.5558 0.5134 0.324 0.640 0.000 0.036
#> GSM494602 1 0.4776 0.4478 0.624 0.376 0.000 0.000
#> GSM494613 2 0.3610 0.6540 0.000 0.800 0.000 0.200
#> GSM494589 1 0.2222 0.8596 0.924 0.060 0.000 0.016
#> GSM494598 2 0.3400 0.6591 0.180 0.820 0.000 0.000
#> GSM494593 1 0.4776 0.4478 0.624 0.376 0.000 0.000
#> GSM494583 2 0.0817 0.7202 0.000 0.976 0.000 0.024
#> GSM494612 2 0.0000 0.7180 0.000 1.000 0.000 0.000
#> GSM494558 2 0.7596 0.0760 0.000 0.456 0.212 0.332
#> GSM494556 2 0.5434 0.6421 0.188 0.728 0.000 0.084
#> GSM494559 1 0.2222 0.8596 0.924 0.060 0.000 0.016
#> GSM494571 3 0.2345 0.9155 0.000 0.000 0.900 0.100
#> GSM494614 1 0.5735 0.3331 0.576 0.392 0.000 0.032
#> GSM494603 1 0.3972 0.7495 0.788 0.204 0.000 0.008
#> GSM494568 1 0.3972 0.7495 0.788 0.204 0.000 0.008
#> GSM494572 3 0.2345 0.9155 0.000 0.000 0.900 0.100
#> GSM494600 1 0.2222 0.8596 0.924 0.060 0.000 0.016
#> GSM494562 2 0.0817 0.7202 0.000 0.976 0.000 0.024
#> GSM494615 1 0.5735 0.3331 0.576 0.392 0.000 0.032
#> GSM494582 2 0.0000 0.7180 0.000 1.000 0.000 0.000
#> GSM494599 1 0.4250 0.6459 0.724 0.276 0.000 0.000
#> GSM494610 2 0.0000 0.7180 0.000 1.000 0.000 0.000
#> GSM494587 2 0.0817 0.7202 0.000 0.976 0.000 0.024
#> GSM494581 2 0.4540 0.6384 0.196 0.772 0.000 0.032
#> GSM494580 2 0.3975 0.6269 0.000 0.760 0.000 0.240
#> GSM494563 1 0.2222 0.8596 0.924 0.060 0.000 0.016
#> GSM494576 2 0.0817 0.7202 0.000 0.976 0.000 0.024
#> GSM494605 1 0.0000 0.8799 1.000 0.000 0.000 0.000
#> GSM494584 2 0.0817 0.7202 0.000 0.976 0.000 0.024
#> GSM494586 2 0.0817 0.7202 0.000 0.976 0.000 0.024
#> GSM494578 2 0.3975 0.6269 0.000 0.760 0.000 0.240
#> GSM494585 2 0.0817 0.7202 0.000 0.976 0.000 0.024
#> GSM494611 2 0.3266 0.6670 0.168 0.832 0.000 0.000
#> GSM494560 1 0.2222 0.8596 0.924 0.060 0.000 0.016
#> GSM494595 2 0.2149 0.7035 0.088 0.912 0.000 0.000
#> GSM494570 1 0.0188 0.8802 0.996 0.004 0.000 0.000
#> GSM494597 3 0.2345 0.9155 0.000 0.000 0.900 0.100
#> GSM494607 1 0.1792 0.8496 0.932 0.068 0.000 0.000
#> GSM494561 1 0.2149 0.8540 0.912 0.088 0.000 0.000
#> GSM494569 1 0.3668 0.7696 0.808 0.188 0.000 0.004
#> GSM494592 1 0.4250 0.6459 0.724 0.276 0.000 0.000
#> GSM494577 2 0.0817 0.7202 0.000 0.976 0.000 0.024
#> GSM494588 1 0.0188 0.8802 0.996 0.004 0.000 0.000
#> GSM494590 3 0.0000 0.9653 0.000 0.000 1.000 0.000
#> GSM494609 1 0.4776 0.4478 0.624 0.376 0.000 0.000
#> GSM494608 2 0.4540 0.6384 0.196 0.772 0.000 0.032
#> GSM494606 1 0.4776 0.4478 0.624 0.376 0.000 0.000
#> GSM494574 2 0.0000 0.7180 0.000 1.000 0.000 0.000
#> GSM494573 1 0.2222 0.8596 0.924 0.060 0.000 0.016
#> GSM494566 1 0.4630 0.6794 0.732 0.252 0.000 0.016
#> GSM494601 4 0.3108 0.8370 0.000 0.112 0.016 0.872
#> GSM494557 2 0.3610 0.6540 0.000 0.800 0.000 0.200
#> GSM494579 1 0.4630 0.6794 0.732 0.252 0.000 0.016
#> GSM494596 3 0.0000 0.9653 0.000 0.000 1.000 0.000
#> GSM494575 2 0.0000 0.7180 0.000 1.000 0.000 0.000
#> GSM494625 1 0.0000 0.8799 1.000 0.000 0.000 0.000
#> GSM494654 3 0.0000 0.9653 0.000 0.000 1.000 0.000
#> GSM494664 1 0.0000 0.8799 1.000 0.000 0.000 0.000
#> GSM494624 1 0.0000 0.8799 1.000 0.000 0.000 0.000
#> GSM494651 4 0.3208 0.8099 0.000 0.004 0.148 0.848
#> GSM494662 1 0.0000 0.8799 1.000 0.000 0.000 0.000
#> GSM494627 2 0.5716 0.2783 0.420 0.552 0.000 0.028
#> GSM494673 1 0.0188 0.8804 0.996 0.004 0.000 0.000
#> GSM494649 1 0.0000 0.8799 1.000 0.000 0.000 0.000
#> GSM494658 1 0.1792 0.8496 0.932 0.068 0.000 0.000
#> GSM494653 1 0.0188 0.8804 0.996 0.004 0.000 0.000
#> GSM494643 2 0.6449 0.5845 0.220 0.640 0.000 0.140
#> GSM494672 1 0.0188 0.8804 0.996 0.004 0.000 0.000
#> GSM494618 1 0.3668 0.7696 0.808 0.188 0.000 0.004
#> GSM494631 2 0.3975 0.6269 0.000 0.760 0.000 0.240
#> GSM494619 1 0.0000 0.8799 1.000 0.000 0.000 0.000
#> GSM494674 1 0.0188 0.8804 0.996 0.004 0.000 0.000
#> GSM494616 1 0.3668 0.7696 0.808 0.188 0.000 0.004
#> GSM494663 1 0.3972 0.7495 0.788 0.204 0.000 0.008
#> GSM494628 1 0.3668 0.7696 0.808 0.188 0.000 0.004
#> GSM494632 1 0.2281 0.8512 0.904 0.096 0.000 0.000
#> GSM494660 1 0.0000 0.8799 1.000 0.000 0.000 0.000
#> GSM494622 1 0.5786 0.5260 0.640 0.308 0.000 0.052
#> GSM494642 1 0.0188 0.8804 0.996 0.004 0.000 0.000
#> GSM494647 1 0.0336 0.8800 0.992 0.008 0.000 0.000
#> GSM494659 1 0.0188 0.8804 0.996 0.004 0.000 0.000
#> GSM494670 1 0.1792 0.8496 0.932 0.068 0.000 0.000
#> GSM494675 2 0.6077 0.0875 0.460 0.496 0.000 0.044
#> GSM494641 1 0.0188 0.8804 0.996 0.004 0.000 0.000
#> GSM494636 1 0.2281 0.8512 0.904 0.096 0.000 0.000
#> GSM494640 2 0.6394 0.5918 0.120 0.636 0.000 0.244
#> GSM494623 1 0.0000 0.8799 1.000 0.000 0.000 0.000
#> GSM494644 1 0.0921 0.8765 0.972 0.028 0.000 0.000
#> GSM494646 1 0.2081 0.8561 0.916 0.084 0.000 0.000
#> GSM494665 1 0.0000 0.8799 1.000 0.000 0.000 0.000
#> GSM494638 1 0.2281 0.8512 0.904 0.096 0.000 0.000
#> GSM494645 1 0.2081 0.8561 0.916 0.084 0.000 0.000
#> GSM494671 1 0.0188 0.8804 0.996 0.004 0.000 0.000
#> GSM494655 1 0.0188 0.8804 0.996 0.004 0.000 0.000
#> GSM494620 1 0.0000 0.8799 1.000 0.000 0.000 0.000
#> GSM494630 1 0.0000 0.8799 1.000 0.000 0.000 0.000
#> GSM494657 3 0.0000 0.9653 0.000 0.000 1.000 0.000
#> GSM494667 1 0.0188 0.8804 0.996 0.004 0.000 0.000
#> GSM494621 1 0.0000 0.8799 1.000 0.000 0.000 0.000
#> GSM494629 2 0.5716 0.2783 0.420 0.552 0.000 0.028
#> GSM494637 2 0.6052 0.3433 0.396 0.556 0.000 0.048
#> GSM494652 1 0.0188 0.8804 0.996 0.004 0.000 0.000
#> GSM494648 1 0.0000 0.8799 1.000 0.000 0.000 0.000
#> GSM494650 4 0.3208 0.8099 0.000 0.004 0.148 0.848
#> GSM494669 1 0.0188 0.8804 0.996 0.004 0.000 0.000
#> GSM494666 1 0.0000 0.8799 1.000 0.000 0.000 0.000
#> GSM494668 1 0.0188 0.8804 0.996 0.004 0.000 0.000
#> GSM494633 1 0.0000 0.8799 1.000 0.000 0.000 0.000
#> GSM494634 1 0.0188 0.8804 0.996 0.004 0.000 0.000
#> GSM494639 1 0.2149 0.8540 0.912 0.088 0.000 0.000
#> GSM494661 4 0.3108 0.8370 0.000 0.112 0.016 0.872
#> GSM494617 1 0.3668 0.7696 0.808 0.188 0.000 0.004
#> GSM494626 1 0.3668 0.7696 0.808 0.188 0.000 0.004
#> GSM494656 3 0.0000 0.9653 0.000 0.000 1.000 0.000
#> GSM494635 1 0.0817 0.8771 0.976 0.024 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM494565 1 0.6102 0.2573 0.568 0.200 0.000 0.232 0.000
#> GSM494594 3 0.0000 0.8929 0.000 0.000 1.000 0.000 0.000
#> GSM494604 1 0.1704 0.8242 0.928 0.068 0.000 0.000 0.004
#> GSM494564 1 0.0162 0.8622 0.996 0.000 0.000 0.004 0.000
#> GSM494591 3 0.0000 0.8929 0.000 0.000 1.000 0.000 0.000
#> GSM494567 4 0.6432 0.5217 0.320 0.196 0.000 0.484 0.000
#> GSM494602 1 0.4114 0.3934 0.624 0.376 0.000 0.000 0.000
#> GSM494613 4 0.4321 0.3756 0.000 0.396 0.000 0.600 0.004
#> GSM494589 1 0.2067 0.8364 0.920 0.032 0.000 0.048 0.000
#> GSM494598 2 0.2929 0.5807 0.180 0.820 0.000 0.000 0.000
#> GSM494593 1 0.4114 0.3934 0.624 0.376 0.000 0.000 0.000
#> GSM494583 2 0.0963 0.8575 0.000 0.964 0.000 0.036 0.000
#> GSM494612 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000
#> GSM494558 4 0.0880 0.0368 0.000 0.000 0.000 0.968 0.032
#> GSM494556 4 0.6478 0.3967 0.184 0.396 0.000 0.420 0.000
#> GSM494559 1 0.2067 0.8364 0.920 0.032 0.000 0.048 0.000
#> GSM494571 3 0.4442 0.7086 0.000 0.000 0.688 0.284 0.028
#> GSM494614 1 0.6066 0.2671 0.572 0.188 0.000 0.240 0.000
#> GSM494603 1 0.3530 0.7208 0.784 0.012 0.000 0.204 0.000
#> GSM494568 1 0.3530 0.7208 0.784 0.012 0.000 0.204 0.000
#> GSM494572 3 0.4442 0.7086 0.000 0.000 0.688 0.284 0.028
#> GSM494600 1 0.2067 0.8364 0.920 0.032 0.000 0.048 0.000
#> GSM494562 2 0.0963 0.8575 0.000 0.964 0.000 0.036 0.000
#> GSM494615 1 0.6066 0.2671 0.572 0.188 0.000 0.240 0.000
#> GSM494582 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000
#> GSM494599 1 0.3814 0.5777 0.720 0.276 0.000 0.000 0.004
#> GSM494610 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000
#> GSM494587 2 0.0963 0.8575 0.000 0.964 0.000 0.036 0.000
#> GSM494581 2 0.5148 0.3595 0.192 0.688 0.000 0.120 0.000
#> GSM494580 4 0.4196 0.4170 0.000 0.356 0.000 0.640 0.004
#> GSM494563 1 0.2067 0.8364 0.920 0.032 0.000 0.048 0.000
#> GSM494576 2 0.0963 0.8575 0.000 0.964 0.000 0.036 0.000
#> GSM494605 1 0.0162 0.8620 0.996 0.000 0.000 0.000 0.004
#> GSM494584 2 0.0963 0.8575 0.000 0.964 0.000 0.036 0.000
#> GSM494586 2 0.0963 0.8575 0.000 0.964 0.000 0.036 0.000
#> GSM494578 4 0.4196 0.4170 0.000 0.356 0.000 0.640 0.004
#> GSM494585 2 0.0963 0.8575 0.000 0.964 0.000 0.036 0.000
#> GSM494611 2 0.2813 0.6063 0.168 0.832 0.000 0.000 0.000
#> GSM494560 1 0.2067 0.8364 0.920 0.032 0.000 0.048 0.000
#> GSM494595 2 0.1851 0.7476 0.088 0.912 0.000 0.000 0.000
#> GSM494570 1 0.0162 0.8622 0.996 0.000 0.000 0.004 0.000
#> GSM494597 3 0.4442 0.7086 0.000 0.000 0.688 0.284 0.028
#> GSM494607 1 0.1704 0.8242 0.928 0.068 0.000 0.000 0.004
#> GSM494561 1 0.1908 0.8284 0.908 0.000 0.000 0.092 0.000
#> GSM494569 1 0.3074 0.7421 0.804 0.000 0.000 0.196 0.000
#> GSM494592 1 0.3814 0.5777 0.720 0.276 0.000 0.000 0.004
#> GSM494577 2 0.0963 0.8575 0.000 0.964 0.000 0.036 0.000
#> GSM494588 1 0.0162 0.8622 0.996 0.000 0.000 0.004 0.000
#> GSM494590 3 0.0000 0.8929 0.000 0.000 1.000 0.000 0.000
#> GSM494609 1 0.4114 0.3934 0.624 0.376 0.000 0.000 0.000
#> GSM494608 2 0.5148 0.3595 0.192 0.688 0.000 0.120 0.000
#> GSM494606 1 0.4114 0.3934 0.624 0.376 0.000 0.000 0.000
#> GSM494574 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000
#> GSM494573 1 0.2067 0.8364 0.920 0.032 0.000 0.048 0.000
#> GSM494566 1 0.4718 0.6428 0.728 0.092 0.000 0.180 0.000
#> GSM494601 5 0.1106 0.8700 0.000 0.024 0.000 0.012 0.964
#> GSM494557 4 0.4321 0.3756 0.000 0.396 0.000 0.600 0.004
#> GSM494579 1 0.4718 0.6428 0.728 0.092 0.000 0.180 0.000
#> GSM494596 3 0.0000 0.8929 0.000 0.000 1.000 0.000 0.000
#> GSM494575 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000
#> GSM494625 1 0.0000 0.8625 1.000 0.000 0.000 0.000 0.000
#> GSM494654 3 0.0000 0.8929 0.000 0.000 1.000 0.000 0.000
#> GSM494664 1 0.0162 0.8620 0.996 0.000 0.000 0.000 0.004
#> GSM494624 1 0.0000 0.8625 1.000 0.000 0.000 0.000 0.000
#> GSM494651 5 0.3177 0.8639 0.000 0.000 0.000 0.208 0.792
#> GSM494662 1 0.0000 0.8625 1.000 0.000 0.000 0.000 0.000
#> GSM494627 4 0.6062 0.3519 0.416 0.120 0.000 0.464 0.000
#> GSM494673 1 0.0451 0.8612 0.988 0.004 0.000 0.000 0.008
#> GSM494649 1 0.0000 0.8625 1.000 0.000 0.000 0.000 0.000
#> GSM494658 1 0.1704 0.8242 0.928 0.068 0.000 0.000 0.004
#> GSM494653 1 0.0451 0.8612 0.988 0.004 0.000 0.000 0.008
#> GSM494643 4 0.5954 0.5550 0.216 0.192 0.000 0.592 0.000
#> GSM494672 1 0.0451 0.8612 0.988 0.004 0.000 0.000 0.008
#> GSM494618 1 0.3074 0.7421 0.804 0.000 0.000 0.196 0.000
#> GSM494631 4 0.4196 0.4170 0.000 0.356 0.000 0.640 0.004
#> GSM494619 1 0.0000 0.8625 1.000 0.000 0.000 0.000 0.000
#> GSM494674 1 0.0451 0.8612 0.988 0.004 0.000 0.000 0.008
#> GSM494616 1 0.3074 0.7421 0.804 0.000 0.000 0.196 0.000
#> GSM494663 1 0.3530 0.7208 0.784 0.012 0.000 0.204 0.000
#> GSM494628 1 0.3074 0.7421 0.804 0.000 0.000 0.196 0.000
#> GSM494632 1 0.2407 0.8266 0.896 0.012 0.000 0.088 0.004
#> GSM494660 1 0.0000 0.8625 1.000 0.000 0.000 0.000 0.000
#> GSM494622 1 0.5470 0.4591 0.636 0.112 0.000 0.252 0.000
#> GSM494642 1 0.0451 0.8612 0.988 0.004 0.000 0.000 0.008
#> GSM494647 1 0.0579 0.8609 0.984 0.008 0.000 0.000 0.008
#> GSM494659 1 0.0451 0.8612 0.988 0.004 0.000 0.000 0.008
#> GSM494670 1 0.1704 0.8242 0.928 0.068 0.000 0.000 0.004
#> GSM494675 1 0.6616 -0.2047 0.456 0.252 0.000 0.292 0.000
#> GSM494641 1 0.0451 0.8612 0.988 0.004 0.000 0.000 0.008
#> GSM494636 1 0.2407 0.8266 0.896 0.012 0.000 0.088 0.004
#> GSM494640 4 0.5006 0.5446 0.116 0.180 0.000 0.704 0.000
#> GSM494623 1 0.0000 0.8625 1.000 0.000 0.000 0.000 0.000
#> GSM494644 1 0.1026 0.8580 0.968 0.004 0.000 0.024 0.004
#> GSM494646 1 0.2112 0.8321 0.908 0.004 0.000 0.084 0.004
#> GSM494665 1 0.0162 0.8620 0.996 0.000 0.000 0.000 0.004
#> GSM494638 1 0.2407 0.8266 0.896 0.012 0.000 0.088 0.004
#> GSM494645 1 0.2112 0.8321 0.908 0.004 0.000 0.084 0.004
#> GSM494671 1 0.0451 0.8612 0.988 0.004 0.000 0.000 0.008
#> GSM494655 1 0.0451 0.8612 0.988 0.004 0.000 0.000 0.008
#> GSM494620 1 0.0000 0.8625 1.000 0.000 0.000 0.000 0.000
#> GSM494630 1 0.0000 0.8625 1.000 0.000 0.000 0.000 0.000
#> GSM494657 3 0.0000 0.8929 0.000 0.000 1.000 0.000 0.000
#> GSM494667 1 0.0451 0.8612 0.988 0.004 0.000 0.000 0.008
#> GSM494621 1 0.0000 0.8625 1.000 0.000 0.000 0.000 0.000
#> GSM494629 4 0.6062 0.3519 0.416 0.120 0.000 0.464 0.000
#> GSM494637 4 0.6072 0.4148 0.392 0.124 0.000 0.484 0.000
#> GSM494652 1 0.0451 0.8612 0.988 0.004 0.000 0.000 0.008
#> GSM494648 1 0.0000 0.8625 1.000 0.000 0.000 0.000 0.000
#> GSM494650 5 0.3177 0.8639 0.000 0.000 0.000 0.208 0.792
#> GSM494669 1 0.0451 0.8612 0.988 0.004 0.000 0.000 0.008
#> GSM494666 1 0.0162 0.8620 0.996 0.000 0.000 0.000 0.004
#> GSM494668 1 0.0451 0.8612 0.988 0.004 0.000 0.000 0.008
#> GSM494633 1 0.0000 0.8625 1.000 0.000 0.000 0.000 0.000
#> GSM494634 1 0.0451 0.8612 0.988 0.004 0.000 0.000 0.008
#> GSM494639 1 0.2170 0.8294 0.904 0.004 0.000 0.088 0.004
#> GSM494661 5 0.1106 0.8700 0.000 0.024 0.000 0.012 0.964
#> GSM494617 1 0.3074 0.7421 0.804 0.000 0.000 0.196 0.000
#> GSM494626 1 0.3074 0.7421 0.804 0.000 0.000 0.196 0.000
#> GSM494656 3 0.0000 0.8929 0.000 0.000 1.000 0.000 0.000
#> GSM494635 1 0.0865 0.8585 0.972 0.000 0.000 0.024 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM494565 5 0.6580 0.221765 0.160 0.136 0.000 0.152 0.552 0.000
#> GSM494594 3 0.0000 0.892766 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494604 1 0.1411 0.578762 0.936 0.060 0.000 0.000 0.004 0.000
#> GSM494564 5 0.3868 0.000869 0.496 0.000 0.000 0.000 0.504 0.000
#> GSM494591 3 0.0000 0.892766 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494567 4 0.6406 0.436597 0.088 0.084 0.000 0.456 0.372 0.000
#> GSM494602 1 0.4064 0.308718 0.624 0.360 0.000 0.000 0.016 0.000
#> GSM494613 4 0.4130 0.503599 0.000 0.260 0.000 0.696 0.044 0.000
#> GSM494589 5 0.3266 0.542014 0.272 0.000 0.000 0.000 0.728 0.000
#> GSM494598 2 0.2805 0.671509 0.184 0.812 0.000 0.000 0.004 0.000
#> GSM494593 1 0.4064 0.308718 0.624 0.360 0.000 0.000 0.016 0.000
#> GSM494583 2 0.2006 0.843261 0.000 0.904 0.000 0.080 0.016 0.000
#> GSM494612 2 0.0146 0.846525 0.004 0.996 0.000 0.000 0.000 0.000
#> GSM494558 4 0.2092 0.144419 0.000 0.000 0.000 0.876 0.124 0.000
#> GSM494556 4 0.6641 0.477994 0.064 0.272 0.000 0.484 0.180 0.000
#> GSM494559 5 0.3288 0.533360 0.276 0.000 0.000 0.000 0.724 0.000
#> GSM494571 3 0.4634 0.708383 0.000 0.000 0.688 0.188 0.124 0.000
#> GSM494614 5 0.6598 0.242836 0.168 0.128 0.000 0.156 0.548 0.000
#> GSM494603 5 0.5362 0.579900 0.344 0.004 0.000 0.108 0.544 0.000
#> GSM494568 5 0.5362 0.579900 0.344 0.004 0.000 0.108 0.544 0.000
#> GSM494572 3 0.4634 0.708383 0.000 0.000 0.688 0.188 0.124 0.000
#> GSM494600 5 0.3266 0.542014 0.272 0.000 0.000 0.000 0.728 0.000
#> GSM494562 2 0.1471 0.846801 0.000 0.932 0.000 0.064 0.004 0.000
#> GSM494615 5 0.6598 0.242836 0.168 0.128 0.000 0.156 0.548 0.000
#> GSM494582 2 0.0146 0.846525 0.004 0.996 0.000 0.000 0.000 0.000
#> GSM494599 1 0.3629 0.400565 0.724 0.260 0.000 0.000 0.016 0.000
#> GSM494610 2 0.0146 0.846525 0.004 0.996 0.000 0.000 0.000 0.000
#> GSM494587 2 0.2006 0.843261 0.000 0.904 0.000 0.080 0.016 0.000
#> GSM494581 2 0.6217 0.380440 0.088 0.588 0.000 0.144 0.180 0.000
#> GSM494580 4 0.3900 0.525202 0.000 0.232 0.000 0.728 0.040 0.000
#> GSM494563 5 0.3288 0.533360 0.276 0.000 0.000 0.000 0.724 0.000
#> GSM494576 2 0.2006 0.843261 0.000 0.904 0.000 0.080 0.016 0.000
#> GSM494605 1 0.3309 0.389176 0.720 0.000 0.000 0.000 0.280 0.000
#> GSM494584 2 0.2006 0.843261 0.000 0.904 0.000 0.080 0.016 0.000
#> GSM494586 2 0.1471 0.846801 0.000 0.932 0.000 0.064 0.004 0.000
#> GSM494578 4 0.3900 0.525202 0.000 0.232 0.000 0.728 0.040 0.000
#> GSM494585 2 0.2006 0.843261 0.000 0.904 0.000 0.080 0.016 0.000
#> GSM494611 2 0.2703 0.688359 0.172 0.824 0.000 0.000 0.004 0.000
#> GSM494560 5 0.3288 0.533360 0.276 0.000 0.000 0.000 0.724 0.000
#> GSM494595 2 0.2019 0.776861 0.088 0.900 0.000 0.000 0.012 0.000
#> GSM494570 5 0.3868 0.000869 0.496 0.000 0.000 0.000 0.504 0.000
#> GSM494597 3 0.4634 0.708383 0.000 0.000 0.688 0.188 0.124 0.000
#> GSM494607 1 0.1411 0.578762 0.936 0.060 0.000 0.000 0.004 0.000
#> GSM494561 5 0.3899 0.441900 0.364 0.000 0.000 0.008 0.628 0.000
#> GSM494569 5 0.5241 0.566574 0.364 0.000 0.000 0.104 0.532 0.000
#> GSM494592 1 0.3629 0.400565 0.724 0.260 0.000 0.000 0.016 0.000
#> GSM494577 2 0.2006 0.843261 0.000 0.904 0.000 0.080 0.016 0.000
#> GSM494588 5 0.3867 -0.001180 0.488 0.000 0.000 0.000 0.512 0.000
#> GSM494590 3 0.0000 0.892766 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494609 1 0.4064 0.308718 0.624 0.360 0.000 0.000 0.016 0.000
#> GSM494608 2 0.6217 0.380440 0.088 0.588 0.000 0.144 0.180 0.000
#> GSM494606 1 0.4064 0.308718 0.624 0.360 0.000 0.000 0.016 0.000
#> GSM494574 2 0.0146 0.846525 0.004 0.996 0.000 0.000 0.000 0.000
#> GSM494573 5 0.3288 0.533360 0.276 0.000 0.000 0.000 0.724 0.000
#> GSM494566 5 0.5628 0.513457 0.272 0.048 0.000 0.080 0.600 0.000
#> GSM494601 6 0.0000 0.870143 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494557 4 0.4193 0.486151 0.000 0.272 0.000 0.684 0.044 0.000
#> GSM494579 5 0.5679 0.516765 0.284 0.048 0.000 0.080 0.588 0.000
#> GSM494596 3 0.0000 0.892766 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494575 2 0.0146 0.846525 0.004 0.996 0.000 0.000 0.000 0.000
#> GSM494625 1 0.3866 0.037403 0.516 0.000 0.000 0.000 0.484 0.000
#> GSM494654 3 0.0000 0.892766 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494664 1 0.3309 0.389176 0.720 0.000 0.000 0.000 0.280 0.000
#> GSM494624 1 0.3862 0.063730 0.524 0.000 0.000 0.000 0.476 0.000
#> GSM494651 6 0.3883 0.864007 0.000 0.000 0.000 0.144 0.088 0.768
#> GSM494662 1 0.3774 0.174862 0.592 0.000 0.000 0.000 0.408 0.000
#> GSM494627 4 0.6143 0.218300 0.136 0.028 0.000 0.420 0.416 0.000
#> GSM494673 1 0.0000 0.611364 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494649 1 0.3866 0.037403 0.516 0.000 0.000 0.000 0.484 0.000
#> GSM494658 1 0.1411 0.578762 0.936 0.060 0.000 0.000 0.004 0.000
#> GSM494653 1 0.0000 0.611364 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494643 4 0.5285 0.554990 0.040 0.048 0.000 0.596 0.316 0.000
#> GSM494672 1 0.0000 0.611364 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494618 5 0.5241 0.566574 0.364 0.000 0.000 0.104 0.532 0.000
#> GSM494631 4 0.3900 0.525202 0.000 0.232 0.000 0.728 0.040 0.000
#> GSM494619 1 0.3862 0.063730 0.524 0.000 0.000 0.000 0.476 0.000
#> GSM494674 1 0.0000 0.611364 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494616 5 0.5241 0.566574 0.364 0.000 0.000 0.104 0.532 0.000
#> GSM494663 5 0.5362 0.579900 0.344 0.004 0.000 0.108 0.544 0.000
#> GSM494628 5 0.5241 0.566574 0.364 0.000 0.000 0.104 0.532 0.000
#> GSM494632 1 0.4232 0.092091 0.640 0.012 0.000 0.012 0.336 0.000
#> GSM494660 1 0.3866 0.037403 0.516 0.000 0.000 0.000 0.484 0.000
#> GSM494622 5 0.6533 0.389170 0.296 0.040 0.000 0.204 0.460 0.000
#> GSM494642 1 0.0000 0.611364 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0146 0.610176 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.611364 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494670 1 0.1411 0.578762 0.936 0.060 0.000 0.000 0.004 0.000
#> GSM494675 5 0.7104 -0.122992 0.136 0.172 0.000 0.236 0.456 0.000
#> GSM494641 1 0.0000 0.611364 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494636 1 0.4232 0.092091 0.640 0.012 0.000 0.012 0.336 0.000
#> GSM494640 4 0.3979 0.559648 0.000 0.036 0.000 0.708 0.256 0.000
#> GSM494623 1 0.3862 0.063730 0.524 0.000 0.000 0.000 0.476 0.000
#> GSM494644 1 0.2149 0.538672 0.888 0.004 0.000 0.004 0.104 0.000
#> GSM494646 1 0.3243 0.396155 0.780 0.004 0.000 0.008 0.208 0.000
#> GSM494665 1 0.3309 0.389176 0.720 0.000 0.000 0.000 0.280 0.000
#> GSM494638 1 0.4232 0.092091 0.640 0.012 0.000 0.012 0.336 0.000
#> GSM494645 1 0.3243 0.396155 0.780 0.004 0.000 0.008 0.208 0.000
#> GSM494671 1 0.0000 0.611364 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494655 1 0.0146 0.610479 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM494620 1 0.3862 0.063730 0.524 0.000 0.000 0.000 0.476 0.000
#> GSM494630 1 0.3862 0.063730 0.524 0.000 0.000 0.000 0.476 0.000
#> GSM494657 3 0.0000 0.892766 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494667 1 0.0146 0.610479 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM494621 1 0.3862 0.063730 0.524 0.000 0.000 0.000 0.476 0.000
#> GSM494629 4 0.6143 0.218300 0.136 0.028 0.000 0.420 0.416 0.000
#> GSM494637 4 0.6110 0.272313 0.132 0.028 0.000 0.444 0.396 0.000
#> GSM494652 1 0.0000 0.611364 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494648 1 0.3862 0.063730 0.524 0.000 0.000 0.000 0.476 0.000
#> GSM494650 6 0.3883 0.864007 0.000 0.000 0.000 0.144 0.088 0.768
#> GSM494669 1 0.0146 0.610479 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM494666 1 0.3309 0.389176 0.720 0.000 0.000 0.000 0.280 0.000
#> GSM494668 1 0.0000 0.611364 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494633 1 0.3862 0.063730 0.524 0.000 0.000 0.000 0.476 0.000
#> GSM494634 1 0.0000 0.611364 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494639 1 0.3955 0.168369 0.668 0.004 0.000 0.012 0.316 0.000
#> GSM494661 6 0.0000 0.870143 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494617 5 0.5241 0.566574 0.364 0.000 0.000 0.104 0.532 0.000
#> GSM494626 5 0.5241 0.566574 0.364 0.000 0.000 0.104 0.532 0.000
#> GSM494656 3 0.0000 0.892766 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494635 1 0.2100 0.536957 0.884 0.000 0.000 0.004 0.112 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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 3)
get_signatures(res, k = 4)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 5)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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.
# code only for demonstration
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) individual(p) k
#> ATC:hclust 119 7.50e-01 0.000109 0.225555 7.43e-05 2
#> ATC:hclust 109 9.34e-06 0.006558 0.000959 1.15e-02 3
#> ATC:hclust 108 1.59e-06 0.076707 0.001767 1.58e-02 4
#> ATC:hclust 99 9.45e-06 0.193496 0.000588 8.92e-03 5
#> ATC:hclust 74 2.90e-06 0.034149 0.000343 7.78e-02 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.
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"]
# you can also extract it by
# 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 51941 rows and 120 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 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:
k and the heatmap of
predicted classes for each k.k.k.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:
k;k, the area increased is defined as \(A_k - A_{k-1}\).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 0.858 0.964 0.977 0.3423 0.630 0.630
#> 3 3 0.543 0.730 0.857 0.6864 0.725 0.594
#> 4 4 0.697 0.782 0.874 0.2327 0.770 0.520
#> 5 5 0.695 0.678 0.814 0.0897 0.912 0.700
#> 6 6 0.733 0.563 0.752 0.0539 0.909 0.629
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
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.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM494565 1 0.000 1.000 1.000 0.000
#> GSM494594 2 0.000 0.899 0.000 1.000
#> GSM494604 1 0.000 1.000 1.000 0.000
#> GSM494564 1 0.000 1.000 1.000 0.000
#> GSM494591 2 0.000 0.899 0.000 1.000
#> GSM494567 1 0.000 1.000 1.000 0.000
#> GSM494602 1 0.000 1.000 1.000 0.000
#> GSM494613 2 0.767 0.806 0.224 0.776
#> GSM494589 1 0.000 1.000 1.000 0.000
#> GSM494598 1 0.000 1.000 1.000 0.000
#> GSM494593 1 0.000 1.000 1.000 0.000
#> GSM494583 2 0.767 0.806 0.224 0.776
#> GSM494612 1 0.000 1.000 1.000 0.000
#> GSM494558 2 0.000 0.899 0.000 1.000
#> GSM494556 1 0.000 1.000 1.000 0.000
#> GSM494559 1 0.000 1.000 1.000 0.000
#> GSM494571 2 0.000 0.899 0.000 1.000
#> GSM494614 1 0.000 1.000 1.000 0.000
#> GSM494603 1 0.000 1.000 1.000 0.000
#> GSM494568 1 0.000 1.000 1.000 0.000
#> GSM494572 2 0.000 0.899 0.000 1.000
#> GSM494600 1 0.000 1.000 1.000 0.000
#> GSM494562 2 0.767 0.806 0.224 0.776
#> GSM494615 1 0.000 1.000 1.000 0.000
#> GSM494582 1 0.000 1.000 1.000 0.000
#> GSM494599 1 0.000 1.000 1.000 0.000
#> GSM494610 1 0.000 1.000 1.000 0.000
#> GSM494587 2 0.767 0.806 0.224 0.776
#> GSM494581 1 0.000 1.000 1.000 0.000
#> GSM494580 2 0.000 0.899 0.000 1.000
#> GSM494563 1 0.000 1.000 1.000 0.000
#> GSM494576 2 0.925 0.628 0.340 0.660
#> GSM494605 1 0.000 1.000 1.000 0.000
#> GSM494584 2 0.767 0.806 0.224 0.776
#> GSM494586 2 0.767 0.806 0.224 0.776
#> GSM494578 2 0.767 0.806 0.224 0.776
#> GSM494585 2 0.767 0.806 0.224 0.776
#> GSM494611 1 0.000 1.000 1.000 0.000
#> GSM494560 1 0.000 1.000 1.000 0.000
#> GSM494595 1 0.000 1.000 1.000 0.000
#> GSM494570 1 0.000 1.000 1.000 0.000
#> GSM494597 2 0.000 0.899 0.000 1.000
#> GSM494607 1 0.000 1.000 1.000 0.000
#> GSM494561 1 0.000 1.000 1.000 0.000
#> GSM494569 1 0.000 1.000 1.000 0.000
#> GSM494592 1 0.000 1.000 1.000 0.000
#> GSM494577 2 0.925 0.628 0.340 0.660
#> GSM494588 1 0.000 1.000 1.000 0.000
#> GSM494590 2 0.000 0.899 0.000 1.000
#> GSM494609 1 0.000 1.000 1.000 0.000
#> GSM494608 1 0.000 1.000 1.000 0.000
#> GSM494606 1 0.000 1.000 1.000 0.000
#> GSM494574 1 0.000 1.000 1.000 0.000
#> GSM494573 1 0.000 1.000 1.000 0.000
#> GSM494566 1 0.000 1.000 1.000 0.000
#> GSM494601 2 0.000 0.899 0.000 1.000
#> GSM494557 2 0.000 0.899 0.000 1.000
#> GSM494579 1 0.000 1.000 1.000 0.000
#> GSM494596 2 0.000 0.899 0.000 1.000
#> GSM494575 1 0.000 1.000 1.000 0.000
#> GSM494625 1 0.000 1.000 1.000 0.000
#> GSM494654 2 0.000 0.899 0.000 1.000
#> GSM494664 1 0.000 1.000 1.000 0.000
#> GSM494624 1 0.000 1.000 1.000 0.000
#> GSM494651 2 0.000 0.899 0.000 1.000
#> GSM494662 1 0.000 1.000 1.000 0.000
#> GSM494627 1 0.000 1.000 1.000 0.000
#> GSM494673 1 0.000 1.000 1.000 0.000
#> GSM494649 1 0.000 1.000 1.000 0.000
#> GSM494658 1 0.000 1.000 1.000 0.000
#> GSM494653 1 0.000 1.000 1.000 0.000
#> GSM494643 1 0.000 1.000 1.000 0.000
#> GSM494672 1 0.000 1.000 1.000 0.000
#> GSM494618 1 0.000 1.000 1.000 0.000
#> GSM494631 2 0.767 0.806 0.224 0.776
#> GSM494619 1 0.000 1.000 1.000 0.000
#> GSM494674 1 0.000 1.000 1.000 0.000
#> GSM494616 1 0.000 1.000 1.000 0.000
#> GSM494663 1 0.000 1.000 1.000 0.000
#> GSM494628 1 0.000 1.000 1.000 0.000
#> GSM494632 1 0.000 1.000 1.000 0.000
#> GSM494660 1 0.000 1.000 1.000 0.000
#> GSM494622 1 0.000 1.000 1.000 0.000
#> GSM494642 1 0.000 1.000 1.000 0.000
#> GSM494647 1 0.000 1.000 1.000 0.000
#> GSM494659 1 0.000 1.000 1.000 0.000
#> GSM494670 1 0.000 1.000 1.000 0.000
#> GSM494675 1 0.000 1.000 1.000 0.000
#> GSM494641 1 0.000 1.000 1.000 0.000
#> GSM494636 1 0.000 1.000 1.000 0.000
#> GSM494640 2 0.343 0.880 0.064 0.936
#> GSM494623 1 0.000 1.000 1.000 0.000
#> GSM494644 1 0.000 1.000 1.000 0.000
#> GSM494646 1 0.000 1.000 1.000 0.000
#> GSM494665 1 0.000 1.000 1.000 0.000
#> GSM494638 1 0.000 1.000 1.000 0.000
#> GSM494645 1 0.000 1.000 1.000 0.000
#> GSM494671 1 0.000 1.000 1.000 0.000
#> GSM494655 1 0.000 1.000 1.000 0.000
#> GSM494620 1 0.000 1.000 1.000 0.000
#> GSM494630 1 0.000 1.000 1.000 0.000
#> GSM494657 2 0.000 0.899 0.000 1.000
#> GSM494667 1 0.000 1.000 1.000 0.000
#> GSM494621 1 0.000 1.000 1.000 0.000
#> GSM494629 1 0.000 1.000 1.000 0.000
#> GSM494637 1 0.000 1.000 1.000 0.000
#> GSM494652 1 0.000 1.000 1.000 0.000
#> GSM494648 1 0.000 1.000 1.000 0.000
#> GSM494650 2 0.000 0.899 0.000 1.000
#> GSM494669 1 0.000 1.000 1.000 0.000
#> GSM494666 1 0.000 1.000 1.000 0.000
#> GSM494668 1 0.000 1.000 1.000 0.000
#> GSM494633 1 0.000 1.000 1.000 0.000
#> GSM494634 1 0.000 1.000 1.000 0.000
#> GSM494639 1 0.000 1.000 1.000 0.000
#> GSM494661 2 0.000 0.899 0.000 1.000
#> GSM494617 1 0.000 1.000 1.000 0.000
#> GSM494626 1 0.000 1.000 1.000 0.000
#> GSM494656 2 0.000 0.899 0.000 1.000
#> GSM494635 1 0.000 1.000 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM494565 2 0.5058 0.6669 0.244 0.756 0.000
#> GSM494594 3 0.3879 0.9624 0.000 0.152 0.848
#> GSM494604 1 0.6291 0.7910 0.768 0.080 0.152
#> GSM494564 1 0.0237 0.8373 0.996 0.004 0.000
#> GSM494591 3 0.3879 0.9624 0.000 0.152 0.848
#> GSM494567 2 0.4931 0.6660 0.232 0.768 0.000
#> GSM494602 1 0.8957 0.4839 0.536 0.312 0.152
#> GSM494613 2 0.2165 0.7171 0.064 0.936 0.000
#> GSM494589 1 0.5905 0.3156 0.648 0.352 0.000
#> GSM494598 2 0.6605 0.6474 0.096 0.752 0.152
#> GSM494593 2 0.9118 0.0713 0.352 0.496 0.152
#> GSM494583 2 0.0424 0.7305 0.008 0.992 0.000
#> GSM494612 2 0.4110 0.7161 0.004 0.844 0.152
#> GSM494558 2 0.5012 0.4937 0.008 0.788 0.204
#> GSM494556 2 0.4750 0.6811 0.216 0.784 0.000
#> GSM494559 1 0.4605 0.6433 0.796 0.204 0.000
#> GSM494571 3 0.3879 0.9624 0.000 0.152 0.848
#> GSM494614 2 0.4750 0.6811 0.216 0.784 0.000
#> GSM494603 1 0.2356 0.7980 0.928 0.072 0.000
#> GSM494568 1 0.2356 0.7980 0.928 0.072 0.000
#> GSM494572 3 0.3879 0.9624 0.000 0.152 0.848
#> GSM494600 1 0.5905 0.3156 0.648 0.352 0.000
#> GSM494562 2 0.0000 0.7275 0.000 1.000 0.000
#> GSM494615 2 0.6180 0.4505 0.416 0.584 0.000
#> GSM494582 2 0.4110 0.7161 0.004 0.844 0.152
#> GSM494599 1 0.6291 0.7910 0.768 0.080 0.152
#> GSM494610 2 0.4110 0.7161 0.004 0.844 0.152
#> GSM494587 2 0.0424 0.7305 0.008 0.992 0.000
#> GSM494581 2 0.4291 0.7224 0.008 0.840 0.152
#> GSM494580 2 0.2866 0.6681 0.008 0.916 0.076
#> GSM494563 1 0.3340 0.7562 0.880 0.120 0.000
#> GSM494576 2 0.0424 0.7305 0.008 0.992 0.000
#> GSM494605 1 0.4280 0.8170 0.856 0.020 0.124
#> GSM494584 2 0.0424 0.7305 0.008 0.992 0.000
#> GSM494586 2 0.0000 0.7275 0.000 1.000 0.000
#> GSM494578 2 0.2261 0.7153 0.068 0.932 0.000
#> GSM494585 2 0.0424 0.7305 0.008 0.992 0.000
#> GSM494611 2 0.4873 0.7062 0.024 0.824 0.152
#> GSM494560 1 0.6062 0.2189 0.616 0.384 0.000
#> GSM494595 2 0.3879 0.7180 0.000 0.848 0.152
#> GSM494570 1 0.0237 0.8373 0.996 0.004 0.000
#> GSM494597 3 0.3879 0.9624 0.000 0.152 0.848
#> GSM494607 1 0.6372 0.7886 0.764 0.084 0.152
#> GSM494561 1 0.0237 0.8373 0.996 0.004 0.000
#> GSM494569 1 0.0237 0.8373 0.996 0.004 0.000
#> GSM494592 1 0.6372 0.7886 0.764 0.084 0.152
#> GSM494577 2 0.0424 0.7305 0.008 0.992 0.000
#> GSM494588 1 0.0000 0.8381 1.000 0.000 0.000
#> GSM494590 3 0.3879 0.9624 0.000 0.152 0.848
#> GSM494609 2 0.9229 -0.2061 0.424 0.424 0.152
#> GSM494608 2 0.4291 0.7224 0.008 0.840 0.152
#> GSM494606 1 0.9130 0.3851 0.492 0.356 0.152
#> GSM494574 2 0.4110 0.7161 0.004 0.844 0.152
#> GSM494573 1 0.4605 0.6433 0.796 0.204 0.000
#> GSM494566 1 0.3116 0.7677 0.892 0.108 0.000
#> GSM494601 2 0.6260 -0.2518 0.000 0.552 0.448
#> GSM494557 2 0.2774 0.6719 0.008 0.920 0.072
#> GSM494579 1 0.8984 0.4569 0.524 0.328 0.148
#> GSM494596 3 0.3879 0.9624 0.000 0.152 0.848
#> GSM494575 2 0.4110 0.7161 0.004 0.844 0.152
#> GSM494625 1 0.0237 0.8373 0.996 0.004 0.000
#> GSM494654 3 0.3879 0.9624 0.000 0.152 0.848
#> GSM494664 1 0.0424 0.8376 0.992 0.008 0.000
#> GSM494624 1 0.0000 0.8381 1.000 0.000 0.000
#> GSM494651 3 0.6307 0.3679 0.000 0.488 0.512
#> GSM494662 1 0.0000 0.8381 1.000 0.000 0.000
#> GSM494627 1 0.2711 0.7856 0.912 0.088 0.000
#> GSM494673 1 0.6291 0.7910 0.768 0.080 0.152
#> GSM494649 1 0.0237 0.8373 0.996 0.004 0.000
#> GSM494658 1 0.6291 0.7910 0.768 0.080 0.152
#> GSM494653 1 0.6291 0.7910 0.768 0.080 0.152
#> GSM494643 2 0.5835 0.5749 0.340 0.660 0.000
#> GSM494672 1 0.6291 0.7910 0.768 0.080 0.152
#> GSM494618 1 0.0237 0.8373 0.996 0.004 0.000
#> GSM494631 2 0.2261 0.7153 0.068 0.932 0.000
#> GSM494619 1 0.0000 0.8381 1.000 0.000 0.000
#> GSM494674 1 0.6291 0.7910 0.768 0.080 0.152
#> GSM494616 1 0.0237 0.8373 0.996 0.004 0.000
#> GSM494663 1 0.0237 0.8373 0.996 0.004 0.000
#> GSM494628 1 0.0237 0.8373 0.996 0.004 0.000
#> GSM494632 1 0.0000 0.8381 1.000 0.000 0.000
#> GSM494660 1 0.0237 0.8373 0.996 0.004 0.000
#> GSM494622 2 0.5327 0.6382 0.272 0.728 0.000
#> GSM494642 1 0.6291 0.7910 0.768 0.080 0.152
#> GSM494647 1 0.6291 0.7910 0.768 0.080 0.152
#> GSM494659 1 0.6291 0.7910 0.768 0.080 0.152
#> GSM494670 1 0.6291 0.7910 0.768 0.080 0.152
#> GSM494675 2 0.4750 0.6811 0.216 0.784 0.000
#> GSM494641 1 0.6291 0.7910 0.768 0.080 0.152
#> GSM494636 1 0.0237 0.8373 0.996 0.004 0.000
#> GSM494640 2 0.3045 0.7021 0.064 0.916 0.020
#> GSM494623 1 0.0000 0.8381 1.000 0.000 0.000
#> GSM494644 1 0.6291 0.7910 0.768 0.080 0.152
#> GSM494646 1 0.0661 0.8376 0.988 0.008 0.004
#> GSM494665 1 0.5884 0.7973 0.788 0.064 0.148
#> GSM494638 1 0.0237 0.8373 0.996 0.004 0.000
#> GSM494645 1 0.6291 0.7910 0.768 0.080 0.152
#> GSM494671 1 0.6291 0.7910 0.768 0.080 0.152
#> GSM494655 1 0.5944 0.7958 0.784 0.064 0.152
#> GSM494620 1 0.0000 0.8381 1.000 0.000 0.000
#> GSM494630 1 0.0000 0.8381 1.000 0.000 0.000
#> GSM494657 3 0.3879 0.9624 0.000 0.152 0.848
#> GSM494667 1 0.6291 0.7910 0.768 0.080 0.152
#> GSM494621 1 0.0000 0.8381 1.000 0.000 0.000
#> GSM494629 1 0.2711 0.7856 0.912 0.088 0.000
#> GSM494637 1 0.2711 0.7856 0.912 0.088 0.000
#> GSM494652 1 0.6291 0.7910 0.768 0.080 0.152
#> GSM494648 1 0.0000 0.8381 1.000 0.000 0.000
#> GSM494650 3 0.3879 0.9624 0.000 0.152 0.848
#> GSM494669 1 0.6291 0.7910 0.768 0.080 0.152
#> GSM494666 1 0.3043 0.8277 0.908 0.008 0.084
#> GSM494668 1 0.6291 0.7910 0.768 0.080 0.152
#> GSM494633 1 0.0237 0.8373 0.996 0.004 0.000
#> GSM494634 1 0.6291 0.7910 0.768 0.080 0.152
#> GSM494639 1 0.0000 0.8381 1.000 0.000 0.000
#> GSM494661 2 0.6260 -0.2518 0.000 0.552 0.448
#> GSM494617 1 0.0000 0.8381 1.000 0.000 0.000
#> GSM494626 1 0.0237 0.8373 0.996 0.004 0.000
#> GSM494656 3 0.3879 0.9624 0.000 0.152 0.848
#> GSM494635 1 0.4291 0.8124 0.840 0.008 0.152
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM494565 2 0.5290 0.42638 0.008 0.516 0.000 0.476
#> GSM494594 3 0.0000 0.99631 0.000 0.000 1.000 0.000
#> GSM494604 1 0.1059 0.87878 0.972 0.012 0.000 0.016
#> GSM494564 4 0.2530 0.81546 0.112 0.000 0.000 0.888
#> GSM494591 3 0.0000 0.99631 0.000 0.000 1.000 0.000
#> GSM494567 2 0.4866 0.56994 0.000 0.596 0.000 0.404
#> GSM494602 1 0.3749 0.77028 0.840 0.128 0.000 0.032
#> GSM494613 2 0.3486 0.79743 0.000 0.812 0.000 0.188
#> GSM494589 4 0.1356 0.77213 0.008 0.032 0.000 0.960
#> GSM494598 2 0.5685 -0.00812 0.460 0.516 0.000 0.024
#> GSM494593 1 0.5041 0.64805 0.728 0.232 0.000 0.040
#> GSM494583 2 0.0592 0.83893 0.000 0.984 0.000 0.016
#> GSM494612 2 0.1629 0.82744 0.024 0.952 0.000 0.024
#> GSM494558 2 0.5279 0.74007 0.000 0.704 0.044 0.252
#> GSM494556 2 0.3688 0.79145 0.000 0.792 0.000 0.208
#> GSM494559 4 0.1624 0.79715 0.028 0.020 0.000 0.952
#> GSM494571 3 0.0000 0.99631 0.000 0.000 1.000 0.000
#> GSM494614 2 0.4936 0.68535 0.008 0.652 0.000 0.340
#> GSM494603 4 0.1356 0.80081 0.032 0.008 0.000 0.960
#> GSM494568 4 0.1452 0.80064 0.036 0.008 0.000 0.956
#> GSM494572 3 0.0188 0.99482 0.000 0.000 0.996 0.004
#> GSM494600 4 0.1356 0.77213 0.008 0.032 0.000 0.960
#> GSM494562 2 0.0188 0.83487 0.004 0.996 0.000 0.000
#> GSM494615 4 0.1474 0.75447 0.000 0.052 0.000 0.948
#> GSM494582 2 0.1629 0.82744 0.024 0.952 0.000 0.024
#> GSM494599 1 0.1297 0.87452 0.964 0.016 0.000 0.020
#> GSM494610 2 0.1629 0.82744 0.024 0.952 0.000 0.024
#> GSM494587 2 0.0592 0.83893 0.000 0.984 0.000 0.016
#> GSM494581 2 0.1406 0.83076 0.016 0.960 0.000 0.024
#> GSM494580 2 0.3649 0.79598 0.000 0.796 0.000 0.204
#> GSM494563 4 0.3051 0.80020 0.088 0.028 0.000 0.884
#> GSM494576 2 0.0592 0.83893 0.000 0.984 0.000 0.016
#> GSM494605 1 0.1211 0.88433 0.960 0.000 0.000 0.040
#> GSM494584 2 0.0592 0.83893 0.000 0.984 0.000 0.016
#> GSM494586 2 0.0188 0.83487 0.004 0.996 0.000 0.000
#> GSM494578 2 0.3610 0.79190 0.000 0.800 0.000 0.200
#> GSM494585 2 0.0592 0.83893 0.000 0.984 0.000 0.016
#> GSM494611 1 0.5695 0.10024 0.500 0.476 0.000 0.024
#> GSM494560 4 0.2131 0.77908 0.036 0.032 0.000 0.932
#> GSM494595 2 0.1629 0.82744 0.024 0.952 0.000 0.024
#> GSM494570 4 0.2814 0.81206 0.132 0.000 0.000 0.868
#> GSM494597 3 0.0817 0.98481 0.000 0.000 0.976 0.024
#> GSM494607 1 0.1624 0.86799 0.952 0.028 0.000 0.020
#> GSM494561 4 0.2081 0.81439 0.084 0.000 0.000 0.916
#> GSM494569 4 0.2011 0.81389 0.080 0.000 0.000 0.920
#> GSM494592 1 0.1624 0.86799 0.952 0.028 0.000 0.020
#> GSM494577 2 0.0592 0.83893 0.000 0.984 0.000 0.016
#> GSM494588 4 0.4624 0.66596 0.340 0.000 0.000 0.660
#> GSM494590 3 0.0000 0.99631 0.000 0.000 1.000 0.000
#> GSM494609 1 0.5631 0.61767 0.700 0.224 0.000 0.076
#> GSM494608 2 0.1406 0.83076 0.016 0.960 0.000 0.024
#> GSM494606 1 0.3842 0.76736 0.836 0.128 0.000 0.036
#> GSM494574 2 0.1520 0.82765 0.024 0.956 0.000 0.020
#> GSM494573 4 0.1624 0.79715 0.028 0.020 0.000 0.952
#> GSM494566 4 0.1042 0.78353 0.008 0.020 0.000 0.972
#> GSM494601 2 0.2197 0.81073 0.000 0.928 0.048 0.024
#> GSM494557 2 0.1302 0.83515 0.000 0.956 0.000 0.044
#> GSM494579 1 0.7162 0.18399 0.472 0.136 0.000 0.392
#> GSM494596 3 0.0000 0.99631 0.000 0.000 1.000 0.000
#> GSM494575 2 0.1629 0.82744 0.024 0.952 0.000 0.024
#> GSM494625 4 0.4072 0.75984 0.252 0.000 0.000 0.748
#> GSM494654 3 0.0000 0.99631 0.000 0.000 1.000 0.000
#> GSM494664 1 0.2408 0.81201 0.896 0.000 0.000 0.104
#> GSM494624 4 0.4624 0.66596 0.340 0.000 0.000 0.660
#> GSM494651 2 0.5499 0.74721 0.000 0.712 0.072 0.216
#> GSM494662 4 0.4072 0.75984 0.252 0.000 0.000 0.748
#> GSM494627 4 0.1820 0.79562 0.036 0.020 0.000 0.944
#> GSM494673 1 0.0469 0.89486 0.988 0.000 0.000 0.012
#> GSM494649 4 0.4072 0.75984 0.252 0.000 0.000 0.748
#> GSM494658 1 0.1297 0.87452 0.964 0.016 0.000 0.020
#> GSM494653 1 0.0817 0.89651 0.976 0.000 0.000 0.024
#> GSM494643 4 0.4989 -0.22976 0.000 0.472 0.000 0.528
#> GSM494672 1 0.0469 0.88592 0.988 0.012 0.000 0.000
#> GSM494618 4 0.1716 0.81037 0.064 0.000 0.000 0.936
#> GSM494631 2 0.3688 0.79059 0.000 0.792 0.000 0.208
#> GSM494619 4 0.4948 0.49221 0.440 0.000 0.000 0.560
#> GSM494674 1 0.0817 0.89651 0.976 0.000 0.000 0.024
#> GSM494616 4 0.3219 0.80251 0.164 0.000 0.000 0.836
#> GSM494663 4 0.1389 0.80642 0.048 0.000 0.000 0.952
#> GSM494628 4 0.1716 0.81037 0.064 0.000 0.000 0.936
#> GSM494632 1 0.3172 0.75438 0.840 0.000 0.000 0.160
#> GSM494660 4 0.4072 0.75984 0.252 0.000 0.000 0.748
#> GSM494622 2 0.4522 0.68602 0.000 0.680 0.000 0.320
#> GSM494642 1 0.0817 0.89651 0.976 0.000 0.000 0.024
#> GSM494647 1 0.0817 0.89651 0.976 0.000 0.000 0.024
#> GSM494659 1 0.0817 0.89651 0.976 0.000 0.000 0.024
#> GSM494670 1 0.1520 0.87020 0.956 0.020 0.000 0.024
#> GSM494675 2 0.3688 0.79145 0.000 0.792 0.000 0.208
#> GSM494641 1 0.0817 0.89651 0.976 0.000 0.000 0.024
#> GSM494636 4 0.3764 0.77862 0.216 0.000 0.000 0.784
#> GSM494640 2 0.4382 0.73005 0.000 0.704 0.000 0.296
#> GSM494623 4 0.4948 0.49221 0.440 0.000 0.000 0.560
#> GSM494644 1 0.0592 0.89548 0.984 0.000 0.000 0.016
#> GSM494646 1 0.0817 0.89651 0.976 0.000 0.000 0.024
#> GSM494665 1 0.0817 0.89651 0.976 0.000 0.000 0.024
#> GSM494638 4 0.1474 0.80770 0.052 0.000 0.000 0.948
#> GSM494645 1 0.0817 0.89651 0.976 0.000 0.000 0.024
#> GSM494671 1 0.0469 0.89486 0.988 0.000 0.000 0.012
#> GSM494655 1 0.0817 0.89651 0.976 0.000 0.000 0.024
#> GSM494620 4 0.4948 0.49221 0.440 0.000 0.000 0.560
#> GSM494630 4 0.4605 0.67151 0.336 0.000 0.000 0.664
#> GSM494657 3 0.0000 0.99631 0.000 0.000 1.000 0.000
#> GSM494667 1 0.0817 0.89651 0.976 0.000 0.000 0.024
#> GSM494621 4 0.4877 0.55608 0.408 0.000 0.000 0.592
#> GSM494629 4 0.1929 0.79320 0.036 0.024 0.000 0.940
#> GSM494637 4 0.1929 0.79320 0.036 0.024 0.000 0.940
#> GSM494652 1 0.0817 0.89651 0.976 0.000 0.000 0.024
#> GSM494648 4 0.4948 0.49221 0.440 0.000 0.000 0.560
#> GSM494650 3 0.0817 0.98481 0.000 0.000 0.976 0.024
#> GSM494669 1 0.0817 0.89651 0.976 0.000 0.000 0.024
#> GSM494666 1 0.1211 0.88433 0.960 0.000 0.000 0.040
#> GSM494668 1 0.0817 0.89651 0.976 0.000 0.000 0.024
#> GSM494633 4 0.4072 0.75984 0.252 0.000 0.000 0.748
#> GSM494634 1 0.0469 0.89486 0.988 0.000 0.000 0.012
#> GSM494639 1 0.4643 0.25405 0.656 0.000 0.000 0.344
#> GSM494661 2 0.2197 0.81073 0.000 0.928 0.048 0.024
#> GSM494617 4 0.4072 0.75984 0.252 0.000 0.000 0.748
#> GSM494626 4 0.4008 0.76426 0.244 0.000 0.000 0.756
#> GSM494656 3 0.0000 0.99631 0.000 0.000 1.000 0.000
#> GSM494635 1 0.0817 0.89651 0.976 0.000 0.000 0.024
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM494565 5 0.5819 0.51498 0.000 0.252 0.000 0.148 0.600
#> GSM494594 3 0.0000 0.98046 0.000 0.000 1.000 0.000 0.000
#> GSM494604 1 0.1502 0.85003 0.940 0.056 0.000 0.004 0.000
#> GSM494564 4 0.1942 0.70444 0.012 0.000 0.000 0.920 0.068
#> GSM494591 3 0.0000 0.98046 0.000 0.000 1.000 0.000 0.000
#> GSM494567 5 0.3932 0.61717 0.000 0.064 0.000 0.140 0.796
#> GSM494602 1 0.5321 0.45174 0.568 0.388 0.000 0.020 0.024
#> GSM494613 5 0.2852 0.56483 0.000 0.172 0.000 0.000 0.828
#> GSM494589 4 0.4341 0.34582 0.000 0.008 0.000 0.628 0.364
#> GSM494598 2 0.3308 0.63014 0.144 0.832 0.000 0.004 0.020
#> GSM494593 1 0.5355 0.41929 0.552 0.404 0.000 0.020 0.024
#> GSM494583 2 0.3661 0.74285 0.000 0.724 0.000 0.000 0.276
#> GSM494612 2 0.0162 0.77711 0.004 0.996 0.000 0.000 0.000
#> GSM494558 5 0.2358 0.59594 0.000 0.104 0.000 0.008 0.888
#> GSM494556 5 0.3630 0.56643 0.000 0.204 0.000 0.016 0.780
#> GSM494559 4 0.4142 0.53503 0.004 0.016 0.000 0.728 0.252
#> GSM494571 3 0.0162 0.97942 0.000 0.000 0.996 0.004 0.000
#> GSM494614 5 0.5770 0.51272 0.000 0.256 0.000 0.140 0.604
#> GSM494603 4 0.4610 0.22511 0.012 0.000 0.000 0.556 0.432
#> GSM494568 4 0.4546 0.11612 0.008 0.000 0.000 0.532 0.460
#> GSM494572 3 0.0162 0.97942 0.000 0.000 0.996 0.004 0.000
#> GSM494600 4 0.4444 0.33972 0.000 0.012 0.000 0.624 0.364
#> GSM494562 2 0.3143 0.77072 0.000 0.796 0.000 0.000 0.204
#> GSM494615 5 0.4555 0.35552 0.000 0.020 0.000 0.344 0.636
#> GSM494582 2 0.0162 0.77711 0.004 0.996 0.000 0.000 0.000
#> GSM494599 1 0.2206 0.83805 0.912 0.068 0.000 0.016 0.004
#> GSM494610 2 0.0162 0.77711 0.004 0.996 0.000 0.000 0.000
#> GSM494587 2 0.3561 0.75145 0.000 0.740 0.000 0.000 0.260
#> GSM494581 2 0.0955 0.76938 0.004 0.968 0.000 0.000 0.028
#> GSM494580 5 0.2377 0.57849 0.000 0.128 0.000 0.000 0.872
#> GSM494563 4 0.4124 0.55810 0.008 0.016 0.000 0.744 0.232
#> GSM494576 2 0.3684 0.74035 0.000 0.720 0.000 0.000 0.280
#> GSM494605 1 0.1792 0.81906 0.916 0.000 0.000 0.084 0.000
#> GSM494584 2 0.3752 0.73064 0.000 0.708 0.000 0.000 0.292
#> GSM494586 2 0.3143 0.77072 0.000 0.796 0.000 0.000 0.204
#> GSM494578 5 0.2852 0.56483 0.000 0.172 0.000 0.000 0.828
#> GSM494585 2 0.3452 0.75816 0.000 0.756 0.000 0.000 0.244
#> GSM494611 2 0.3055 0.63712 0.144 0.840 0.000 0.000 0.016
#> GSM494560 4 0.4763 0.34440 0.004 0.020 0.000 0.616 0.360
#> GSM494595 2 0.0566 0.76926 0.004 0.984 0.000 0.000 0.012
#> GSM494570 4 0.1741 0.71571 0.024 0.000 0.000 0.936 0.040
#> GSM494597 3 0.1894 0.93459 0.000 0.000 0.920 0.008 0.072
#> GSM494607 1 0.2935 0.80437 0.860 0.120 0.000 0.016 0.004
#> GSM494561 4 0.2069 0.70190 0.012 0.000 0.000 0.912 0.076
#> GSM494569 4 0.3165 0.69368 0.036 0.000 0.000 0.848 0.116
#> GSM494592 1 0.3124 0.79273 0.844 0.136 0.000 0.016 0.004
#> GSM494577 2 0.3661 0.74285 0.000 0.724 0.000 0.000 0.276
#> GSM494588 4 0.3535 0.69709 0.164 0.000 0.000 0.808 0.028
#> GSM494590 3 0.0000 0.98046 0.000 0.000 1.000 0.000 0.000
#> GSM494609 1 0.5638 0.39853 0.536 0.404 0.000 0.020 0.040
#> GSM494608 2 0.1285 0.76166 0.004 0.956 0.000 0.004 0.036
#> GSM494606 1 0.5330 0.44408 0.564 0.392 0.000 0.020 0.024
#> GSM494574 2 0.0162 0.77711 0.004 0.996 0.000 0.000 0.000
#> GSM494573 4 0.4116 0.53920 0.004 0.016 0.000 0.732 0.248
#> GSM494566 5 0.4747 -0.03707 0.000 0.016 0.000 0.488 0.496
#> GSM494601 2 0.4909 0.62783 0.000 0.588 0.000 0.032 0.380
#> GSM494557 2 0.4256 0.54999 0.000 0.564 0.000 0.000 0.436
#> GSM494579 1 0.8330 -0.00317 0.332 0.296 0.000 0.140 0.232
#> GSM494596 3 0.0000 0.98046 0.000 0.000 1.000 0.000 0.000
#> GSM494575 2 0.0162 0.77711 0.004 0.996 0.000 0.000 0.000
#> GSM494625 4 0.1965 0.73244 0.096 0.000 0.000 0.904 0.000
#> GSM494654 3 0.0000 0.98046 0.000 0.000 1.000 0.000 0.000
#> GSM494664 1 0.3707 0.53682 0.716 0.000 0.000 0.284 0.000
#> GSM494624 4 0.3123 0.69336 0.184 0.000 0.000 0.812 0.004
#> GSM494651 5 0.3090 0.56876 0.000 0.104 0.000 0.040 0.856
#> GSM494662 4 0.2124 0.73252 0.096 0.000 0.000 0.900 0.004
#> GSM494627 5 0.4420 0.16780 0.004 0.000 0.000 0.448 0.548
#> GSM494673 1 0.0000 0.87449 1.000 0.000 0.000 0.000 0.000
#> GSM494649 4 0.1965 0.73244 0.096 0.000 0.000 0.904 0.000
#> GSM494658 1 0.2488 0.81215 0.872 0.124 0.000 0.004 0.000
#> GSM494653 1 0.0162 0.87366 0.996 0.000 0.000 0.004 0.000
#> GSM494643 5 0.3421 0.64706 0.000 0.080 0.000 0.080 0.840
#> GSM494672 1 0.1043 0.85923 0.960 0.040 0.000 0.000 0.000
#> GSM494618 4 0.4467 0.36639 0.016 0.000 0.000 0.640 0.344
#> GSM494631 5 0.2773 0.56810 0.000 0.164 0.000 0.000 0.836
#> GSM494619 4 0.3452 0.65221 0.244 0.000 0.000 0.756 0.000
#> GSM494674 1 0.0000 0.87449 1.000 0.000 0.000 0.000 0.000
#> GSM494616 4 0.2927 0.71932 0.060 0.000 0.000 0.872 0.068
#> GSM494663 4 0.4528 0.13476 0.008 0.000 0.000 0.548 0.444
#> GSM494628 4 0.3278 0.66187 0.020 0.000 0.000 0.824 0.156
#> GSM494632 1 0.4300 0.67599 0.772 0.000 0.000 0.132 0.096
#> GSM494660 4 0.1965 0.73244 0.096 0.000 0.000 0.904 0.000
#> GSM494622 5 0.3239 0.64629 0.000 0.080 0.000 0.068 0.852
#> GSM494642 1 0.0000 0.87449 1.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.87449 1.000 0.000 0.000 0.000 0.000
#> GSM494659 1 0.0162 0.87366 0.996 0.000 0.000 0.004 0.000
#> GSM494670 1 0.2890 0.78440 0.836 0.160 0.000 0.004 0.000
#> GSM494675 5 0.3399 0.60243 0.000 0.168 0.000 0.020 0.812
#> GSM494641 1 0.0000 0.87449 1.000 0.000 0.000 0.000 0.000
#> GSM494636 4 0.4127 0.69285 0.080 0.000 0.000 0.784 0.136
#> GSM494640 5 0.2411 0.59614 0.000 0.108 0.000 0.008 0.884
#> GSM494623 4 0.3452 0.65221 0.244 0.000 0.000 0.756 0.000
#> GSM494644 1 0.0162 0.87354 0.996 0.004 0.000 0.000 0.000
#> GSM494646 1 0.0794 0.86175 0.972 0.000 0.000 0.028 0.000
#> GSM494665 1 0.0510 0.86847 0.984 0.000 0.000 0.016 0.000
#> GSM494638 5 0.4746 0.02651 0.016 0.000 0.000 0.480 0.504
#> GSM494645 1 0.0000 0.87449 1.000 0.000 0.000 0.000 0.000
#> GSM494671 1 0.0000 0.87449 1.000 0.000 0.000 0.000 0.000
#> GSM494655 1 0.0162 0.87366 0.996 0.000 0.000 0.004 0.000
#> GSM494620 4 0.3452 0.65221 0.244 0.000 0.000 0.756 0.000
#> GSM494630 4 0.3123 0.69336 0.184 0.000 0.000 0.812 0.004
#> GSM494657 3 0.0000 0.98046 0.000 0.000 1.000 0.000 0.000
#> GSM494667 1 0.0162 0.87366 0.996 0.000 0.000 0.004 0.000
#> GSM494621 4 0.3452 0.65221 0.244 0.000 0.000 0.756 0.000
#> GSM494629 5 0.4420 0.16780 0.004 0.000 0.000 0.448 0.548
#> GSM494637 5 0.4420 0.16780 0.004 0.000 0.000 0.448 0.548
#> GSM494652 1 0.0162 0.87366 0.996 0.000 0.000 0.004 0.000
#> GSM494648 4 0.3452 0.65221 0.244 0.000 0.000 0.756 0.000
#> GSM494650 3 0.3229 0.86867 0.000 0.000 0.840 0.032 0.128
#> GSM494669 1 0.0162 0.87366 0.996 0.000 0.000 0.004 0.000
#> GSM494666 1 0.1792 0.82045 0.916 0.000 0.000 0.084 0.000
#> GSM494668 1 0.0000 0.87449 1.000 0.000 0.000 0.000 0.000
#> GSM494633 4 0.2124 0.73209 0.096 0.000 0.000 0.900 0.004
#> GSM494634 1 0.0000 0.87449 1.000 0.000 0.000 0.000 0.000
#> GSM494639 1 0.3282 0.69005 0.804 0.000 0.000 0.188 0.008
#> GSM494661 2 0.4930 0.61684 0.000 0.580 0.000 0.032 0.388
#> GSM494617 4 0.3410 0.72355 0.092 0.000 0.000 0.840 0.068
#> GSM494626 4 0.3301 0.72191 0.080 0.000 0.000 0.848 0.072
#> GSM494656 3 0.0000 0.98046 0.000 0.000 1.000 0.000 0.000
#> GSM494635 1 0.0000 0.87449 1.000 0.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM494565 5 0.5248 0.32627 0.000 0.168 0.000 0.204 0.624 0.004
#> GSM494594 3 0.0000 0.96231 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494604 1 0.1297 0.87434 0.948 0.012 0.000 0.000 0.040 0.000
#> GSM494564 6 0.4477 0.21179 0.000 0.004 0.000 0.028 0.380 0.588
#> GSM494591 3 0.0000 0.96231 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494567 5 0.4096 -0.02802 0.000 0.008 0.000 0.484 0.508 0.000
#> GSM494602 1 0.5360 -0.00855 0.456 0.436 0.000 0.000 0.108 0.000
#> GSM494613 4 0.2846 0.71275 0.000 0.060 0.000 0.856 0.084 0.000
#> GSM494589 5 0.4882 0.15173 0.000 0.004 0.000 0.052 0.540 0.404
#> GSM494598 2 0.3068 0.60318 0.088 0.840 0.000 0.000 0.072 0.000
#> GSM494593 2 0.5586 0.35199 0.284 0.552 0.000 0.004 0.160 0.000
#> GSM494583 2 0.4076 0.40426 0.000 0.592 0.000 0.396 0.012 0.000
#> GSM494612 2 0.0291 0.66784 0.004 0.992 0.000 0.000 0.004 0.000
#> GSM494558 4 0.3017 0.70666 0.000 0.052 0.000 0.840 0.108 0.000
#> GSM494556 4 0.4663 0.55441 0.000 0.092 0.000 0.664 0.244 0.000
#> GSM494559 5 0.4887 -0.04137 0.000 0.004 0.000 0.048 0.476 0.472
#> GSM494571 3 0.0458 0.95884 0.000 0.000 0.984 0.000 0.016 0.000
#> GSM494614 5 0.5342 0.29781 0.000 0.156 0.000 0.236 0.604 0.004
#> GSM494603 5 0.3249 0.47857 0.000 0.004 0.000 0.044 0.824 0.128
#> GSM494568 5 0.3278 0.47709 0.000 0.000 0.000 0.040 0.808 0.152
#> GSM494572 3 0.0458 0.95884 0.000 0.000 0.984 0.000 0.016 0.000
#> GSM494600 5 0.4882 0.15173 0.000 0.004 0.000 0.052 0.540 0.404
#> GSM494562 2 0.3595 0.51306 0.000 0.704 0.000 0.288 0.008 0.000
#> GSM494615 5 0.4464 0.30786 0.000 0.008 0.000 0.340 0.624 0.028
#> GSM494582 2 0.0146 0.66824 0.004 0.996 0.000 0.000 0.000 0.000
#> GSM494599 1 0.1934 0.85343 0.916 0.040 0.000 0.000 0.044 0.000
#> GSM494610 2 0.0146 0.66820 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM494587 2 0.3945 0.42526 0.000 0.612 0.000 0.380 0.008 0.000
#> GSM494581 2 0.2263 0.64269 0.000 0.896 0.000 0.056 0.048 0.000
#> GSM494580 4 0.1984 0.69912 0.000 0.056 0.000 0.912 0.032 0.000
#> GSM494563 6 0.4932 -0.02847 0.000 0.008 0.000 0.044 0.472 0.476
#> GSM494576 2 0.4093 0.39244 0.000 0.584 0.000 0.404 0.012 0.000
#> GSM494605 1 0.2250 0.83447 0.888 0.000 0.000 0.020 0.000 0.092
#> GSM494584 2 0.4172 0.27565 0.000 0.528 0.000 0.460 0.012 0.000
#> GSM494586 2 0.3595 0.51306 0.000 0.704 0.000 0.288 0.008 0.000
#> GSM494578 4 0.2897 0.71048 0.000 0.060 0.000 0.852 0.088 0.000
#> GSM494585 2 0.3887 0.45024 0.000 0.632 0.000 0.360 0.008 0.000
#> GSM494611 2 0.2697 0.61253 0.092 0.864 0.000 0.000 0.044 0.000
#> GSM494560 5 0.5258 0.10383 0.000 0.016 0.000 0.060 0.516 0.408
#> GSM494595 2 0.0363 0.66676 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM494570 6 0.3817 0.42479 0.000 0.000 0.000 0.028 0.252 0.720
#> GSM494597 3 0.2850 0.86655 0.000 0.000 0.856 0.112 0.016 0.016
#> GSM494607 1 0.2762 0.80846 0.860 0.092 0.000 0.000 0.048 0.000
#> GSM494561 6 0.4264 0.27067 0.000 0.000 0.000 0.028 0.352 0.620
#> GSM494569 6 0.4258 0.13845 0.000 0.000 0.000 0.016 0.468 0.516
#> GSM494592 1 0.2697 0.81010 0.864 0.092 0.000 0.000 0.044 0.000
#> GSM494577 2 0.4057 0.41429 0.000 0.600 0.000 0.388 0.012 0.000
#> GSM494588 6 0.4257 0.42609 0.028 0.000 0.000 0.020 0.240 0.712
#> GSM494590 3 0.0000 0.96231 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494609 2 0.6234 0.26177 0.204 0.476 0.000 0.020 0.300 0.000
#> GSM494608 2 0.2680 0.63371 0.000 0.868 0.000 0.056 0.076 0.000
#> GSM494606 2 0.5343 0.07546 0.408 0.484 0.000 0.000 0.108 0.000
#> GSM494574 2 0.0146 0.66820 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM494573 6 0.4886 -0.03173 0.000 0.004 0.000 0.048 0.468 0.480
#> GSM494566 5 0.3083 0.48842 0.000 0.000 0.000 0.040 0.828 0.132
#> GSM494601 4 0.5383 -0.13515 0.000 0.440 0.000 0.480 0.056 0.024
#> GSM494557 4 0.3071 0.54526 0.000 0.180 0.000 0.804 0.016 0.000
#> GSM494579 5 0.5549 0.28784 0.100 0.248 0.000 0.036 0.616 0.000
#> GSM494596 3 0.0000 0.96231 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494575 2 0.0146 0.66820 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM494625 6 0.2784 0.57394 0.008 0.000 0.000 0.012 0.132 0.848
#> GSM494654 3 0.0000 0.96231 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494664 1 0.4167 0.41849 0.612 0.000 0.000 0.020 0.000 0.368
#> GSM494624 6 0.1204 0.62613 0.056 0.000 0.000 0.000 0.000 0.944
#> GSM494651 4 0.3651 0.67276 0.000 0.048 0.000 0.812 0.116 0.024
#> GSM494662 6 0.3535 0.50083 0.008 0.000 0.000 0.012 0.220 0.760
#> GSM494627 5 0.5279 0.44047 0.000 0.000 0.000 0.244 0.596 0.160
#> GSM494673 1 0.0260 0.89328 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM494649 6 0.1340 0.61922 0.008 0.000 0.000 0.004 0.040 0.948
#> GSM494658 1 0.2190 0.84069 0.900 0.060 0.000 0.000 0.040 0.000
#> GSM494653 1 0.0363 0.89423 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM494643 4 0.4406 0.13797 0.000 0.008 0.000 0.516 0.464 0.012
#> GSM494672 1 0.0405 0.89096 0.988 0.008 0.000 0.000 0.004 0.000
#> GSM494618 5 0.5242 0.06947 0.000 0.000 0.000 0.096 0.492 0.412
#> GSM494631 4 0.2740 0.71443 0.000 0.060 0.000 0.864 0.076 0.000
#> GSM494619 6 0.1556 0.62110 0.080 0.000 0.000 0.000 0.000 0.920
#> GSM494674 1 0.0405 0.89409 0.988 0.000 0.000 0.008 0.000 0.004
#> GSM494616 6 0.4264 0.32040 0.008 0.000 0.000 0.012 0.376 0.604
#> GSM494663 5 0.4646 0.30739 0.000 0.000 0.000 0.060 0.616 0.324
#> GSM494628 6 0.4405 0.10996 0.000 0.000 0.000 0.024 0.472 0.504
#> GSM494632 1 0.6229 0.08298 0.464 0.000 0.000 0.040 0.368 0.128
#> GSM494660 6 0.1340 0.61922 0.008 0.000 0.000 0.004 0.040 0.948
#> GSM494622 4 0.4122 0.12873 0.000 0.004 0.000 0.520 0.472 0.004
#> GSM494642 1 0.0146 0.89454 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM494647 1 0.0520 0.89361 0.984 0.000 0.000 0.008 0.008 0.000
#> GSM494659 1 0.0363 0.89423 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM494670 1 0.3054 0.77480 0.828 0.136 0.000 0.000 0.036 0.000
#> GSM494675 4 0.4452 0.45064 0.000 0.048 0.000 0.636 0.316 0.000
#> GSM494641 1 0.0000 0.89439 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494636 6 0.4878 0.08910 0.008 0.000 0.000 0.040 0.468 0.484
#> GSM494640 4 0.3032 0.70822 0.000 0.056 0.000 0.840 0.104 0.000
#> GSM494623 6 0.1556 0.62110 0.080 0.000 0.000 0.000 0.000 0.920
#> GSM494644 1 0.0725 0.89226 0.976 0.000 0.000 0.012 0.012 0.000
#> GSM494646 1 0.2164 0.85255 0.908 0.000 0.000 0.020 0.012 0.060
#> GSM494665 1 0.0777 0.88906 0.972 0.000 0.000 0.004 0.000 0.024
#> GSM494638 5 0.5208 0.39806 0.000 0.000 0.000 0.148 0.604 0.248
#> GSM494645 1 0.0870 0.89214 0.972 0.000 0.000 0.012 0.012 0.004
#> GSM494671 1 0.0000 0.89439 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494655 1 0.0363 0.89423 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM494620 6 0.1556 0.62110 0.080 0.000 0.000 0.000 0.000 0.920
#> GSM494630 6 0.1285 0.62551 0.052 0.000 0.000 0.000 0.004 0.944
#> GSM494657 3 0.0000 0.96231 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494667 1 0.0363 0.89423 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM494621 6 0.1556 0.62110 0.080 0.000 0.000 0.000 0.000 0.920
#> GSM494629 5 0.5259 0.44453 0.000 0.000 0.000 0.240 0.600 0.160
#> GSM494637 5 0.5279 0.44047 0.000 0.000 0.000 0.244 0.596 0.160
#> GSM494652 1 0.0363 0.89423 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM494648 6 0.1556 0.62110 0.080 0.000 0.000 0.000 0.000 0.920
#> GSM494650 3 0.4455 0.75035 0.000 0.000 0.732 0.184 0.060 0.024
#> GSM494669 1 0.0363 0.89423 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM494666 1 0.2301 0.83155 0.884 0.000 0.000 0.020 0.000 0.096
#> GSM494668 1 0.0000 0.89439 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494633 6 0.1049 0.61872 0.008 0.000 0.000 0.000 0.032 0.960
#> GSM494634 1 0.0000 0.89439 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494639 1 0.5850 0.34019 0.564 0.000 0.000 0.020 0.172 0.244
#> GSM494661 4 0.5360 -0.05690 0.000 0.412 0.000 0.508 0.056 0.024
#> GSM494617 6 0.4337 0.32268 0.008 0.000 0.000 0.016 0.372 0.604
#> GSM494626 6 0.4312 0.28535 0.008 0.000 0.000 0.012 0.396 0.584
#> GSM494656 3 0.0260 0.96082 0.000 0.000 0.992 0.000 0.008 0.000
#> GSM494635 1 0.0767 0.89318 0.976 0.000 0.000 0.008 0.012 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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 6)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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.
# code only for demonstration
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) individual(p) k
#> ATC:kmeans 120 1.05e-02 0.080773 4.26e-02 0.01630 2
#> ATC:kmeans 107 1.63e-07 0.022304 2.43e-05 0.04458 3
#> ATC:kmeans 110 1.38e-04 0.000444 2.47e-02 0.00217 4
#> ATC:kmeans 102 3.61e-07 0.007388 6.24e-06 0.00992 5
#> ATC:kmeans 72 2.39e-07 0.082830 4.48e-06 0.07407 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.
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"]
# you can also extract it by
# 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 51941 rows and 120 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 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:
k and the heatmap of
predicted classes for each k.k.k.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:
k;k, the area increased is defined as \(A_k - A_{k-1}\).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 0.958 0.983 0.4786 0.519 0.519
#> 3 3 0.944 0.946 0.975 0.4034 0.769 0.571
#> 4 4 0.953 0.909 0.962 0.1073 0.896 0.697
#> 5 5 0.864 0.819 0.912 0.0642 0.925 0.717
#> 6 6 0.794 0.705 0.809 0.0381 0.949 0.766
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
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.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM494565 2 0.5408 0.845 0.124 0.876
#> GSM494594 2 0.0000 0.973 0.000 1.000
#> GSM494604 1 0.0000 0.989 1.000 0.000
#> GSM494564 1 0.0000 0.989 1.000 0.000
#> GSM494591 2 0.0000 0.973 0.000 1.000
#> GSM494567 2 0.0000 0.973 0.000 1.000
#> GSM494602 1 0.0000 0.989 1.000 0.000
#> GSM494613 2 0.0000 0.973 0.000 1.000
#> GSM494589 1 0.9323 0.446 0.652 0.348
#> GSM494598 2 0.9833 0.281 0.424 0.576
#> GSM494593 1 0.0000 0.989 1.000 0.000
#> GSM494583 2 0.0000 0.973 0.000 1.000
#> GSM494612 2 0.0000 0.973 0.000 1.000
#> GSM494558 2 0.0000 0.973 0.000 1.000
#> GSM494556 2 0.0000 0.973 0.000 1.000
#> GSM494559 1 0.0000 0.989 1.000 0.000
#> GSM494571 2 0.0000 0.973 0.000 1.000
#> GSM494614 2 0.0672 0.966 0.008 0.992
#> GSM494603 1 0.0000 0.989 1.000 0.000
#> GSM494568 1 0.0000 0.989 1.000 0.000
#> GSM494572 2 0.0000 0.973 0.000 1.000
#> GSM494600 1 0.9427 0.416 0.640 0.360
#> GSM494562 2 0.0000 0.973 0.000 1.000
#> GSM494615 2 0.8327 0.643 0.264 0.736
#> GSM494582 2 0.0000 0.973 0.000 1.000
#> GSM494599 1 0.0000 0.989 1.000 0.000
#> GSM494610 2 0.0000 0.973 0.000 1.000
#> GSM494587 2 0.0000 0.973 0.000 1.000
#> GSM494581 2 0.0000 0.973 0.000 1.000
#> GSM494580 2 0.0000 0.973 0.000 1.000
#> GSM494563 1 0.0000 0.989 1.000 0.000
#> GSM494576 2 0.0000 0.973 0.000 1.000
#> GSM494605 1 0.0000 0.989 1.000 0.000
#> GSM494584 2 0.0000 0.973 0.000 1.000
#> GSM494586 2 0.0000 0.973 0.000 1.000
#> GSM494578 2 0.0000 0.973 0.000 1.000
#> GSM494585 2 0.0000 0.973 0.000 1.000
#> GSM494611 2 0.9775 0.315 0.412 0.588
#> GSM494560 1 0.3114 0.930 0.944 0.056
#> GSM494595 2 0.0000 0.973 0.000 1.000
#> GSM494570 1 0.0000 0.989 1.000 0.000
#> GSM494597 2 0.0000 0.973 0.000 1.000
#> GSM494607 1 0.0000 0.989 1.000 0.000
#> GSM494561 1 0.0000 0.989 1.000 0.000
#> GSM494569 1 0.0000 0.989 1.000 0.000
#> GSM494592 1 0.0000 0.989 1.000 0.000
#> GSM494577 2 0.0000 0.973 0.000 1.000
#> GSM494588 1 0.0000 0.989 1.000 0.000
#> GSM494590 2 0.0000 0.973 0.000 1.000
#> GSM494609 1 0.0000 0.989 1.000 0.000
#> GSM494608 2 0.0000 0.973 0.000 1.000
#> GSM494606 1 0.0000 0.989 1.000 0.000
#> GSM494574 2 0.0000 0.973 0.000 1.000
#> GSM494573 1 0.0000 0.989 1.000 0.000
#> GSM494566 1 0.0000 0.989 1.000 0.000
#> GSM494601 2 0.0000 0.973 0.000 1.000
#> GSM494557 2 0.0000 0.973 0.000 1.000
#> GSM494579 1 0.0000 0.989 1.000 0.000
#> GSM494596 2 0.0000 0.973 0.000 1.000
#> GSM494575 2 0.0000 0.973 0.000 1.000
#> GSM494625 1 0.0000 0.989 1.000 0.000
#> GSM494654 2 0.0000 0.973 0.000 1.000
#> GSM494664 1 0.0000 0.989 1.000 0.000
#> GSM494624 1 0.0000 0.989 1.000 0.000
#> GSM494651 2 0.0000 0.973 0.000 1.000
#> GSM494662 1 0.0000 0.989 1.000 0.000
#> GSM494627 1 0.0000 0.989 1.000 0.000
#> GSM494673 1 0.0000 0.989 1.000 0.000
#> GSM494649 1 0.0000 0.989 1.000 0.000
#> GSM494658 1 0.0000 0.989 1.000 0.000
#> GSM494653 1 0.0000 0.989 1.000 0.000
#> GSM494643 2 0.0000 0.973 0.000 1.000
#> GSM494672 1 0.0000 0.989 1.000 0.000
#> GSM494618 1 0.0000 0.989 1.000 0.000
#> GSM494631 2 0.0000 0.973 0.000 1.000
#> GSM494619 1 0.0000 0.989 1.000 0.000
#> GSM494674 1 0.0000 0.989 1.000 0.000
#> GSM494616 1 0.0000 0.989 1.000 0.000
#> GSM494663 1 0.0000 0.989 1.000 0.000
#> GSM494628 1 0.0000 0.989 1.000 0.000
#> GSM494632 1 0.0000 0.989 1.000 0.000
#> GSM494660 1 0.0000 0.989 1.000 0.000
#> GSM494622 2 0.0000 0.973 0.000 1.000
#> GSM494642 1 0.0000 0.989 1.000 0.000
#> GSM494647 1 0.0000 0.989 1.000 0.000
#> GSM494659 1 0.0000 0.989 1.000 0.000
#> GSM494670 1 0.0000 0.989 1.000 0.000
#> GSM494675 2 0.0000 0.973 0.000 1.000
#> GSM494641 1 0.0000 0.989 1.000 0.000
#> GSM494636 1 0.0000 0.989 1.000 0.000
#> GSM494640 2 0.0000 0.973 0.000 1.000
#> GSM494623 1 0.0000 0.989 1.000 0.000
#> GSM494644 1 0.0000 0.989 1.000 0.000
#> GSM494646 1 0.0000 0.989 1.000 0.000
#> GSM494665 1 0.0000 0.989 1.000 0.000
#> GSM494638 1 0.0000 0.989 1.000 0.000
#> GSM494645 1 0.0000 0.989 1.000 0.000
#> GSM494671 1 0.0000 0.989 1.000 0.000
#> GSM494655 1 0.0000 0.989 1.000 0.000
#> GSM494620 1 0.0000 0.989 1.000 0.000
#> GSM494630 1 0.0000 0.989 1.000 0.000
#> GSM494657 2 0.0000 0.973 0.000 1.000
#> GSM494667 1 0.0000 0.989 1.000 0.000
#> GSM494621 1 0.0000 0.989 1.000 0.000
#> GSM494629 1 0.0376 0.985 0.996 0.004
#> GSM494637 1 0.0000 0.989 1.000 0.000
#> GSM494652 1 0.0000 0.989 1.000 0.000
#> GSM494648 1 0.0000 0.989 1.000 0.000
#> GSM494650 2 0.0000 0.973 0.000 1.000
#> GSM494669 1 0.0000 0.989 1.000 0.000
#> GSM494666 1 0.0000 0.989 1.000 0.000
#> GSM494668 1 0.0000 0.989 1.000 0.000
#> GSM494633 1 0.0000 0.989 1.000 0.000
#> GSM494634 1 0.0000 0.989 1.000 0.000
#> GSM494639 1 0.0000 0.989 1.000 0.000
#> GSM494661 2 0.0000 0.973 0.000 1.000
#> GSM494617 1 0.0000 0.989 1.000 0.000
#> GSM494626 1 0.0000 0.989 1.000 0.000
#> GSM494656 2 0.0000 0.973 0.000 1.000
#> GSM494635 1 0.0000 0.989 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM494565 2 0.6154 0.326 0.000 0.592 0.408
#> GSM494594 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494604 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494564 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494591 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494567 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494602 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494613 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494589 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494598 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494593 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494583 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494612 1 0.4605 0.718 0.796 0.204 0.000
#> GSM494558 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494556 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494559 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494571 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494614 2 0.4178 0.783 0.000 0.828 0.172
#> GSM494603 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494568 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494572 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494600 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494562 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494615 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494582 2 0.5882 0.517 0.348 0.652 0.000
#> GSM494599 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494610 2 0.4399 0.780 0.188 0.812 0.000
#> GSM494587 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494581 2 0.0424 0.949 0.008 0.992 0.000
#> GSM494580 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494563 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494576 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494605 1 0.2711 0.896 0.912 0.000 0.088
#> GSM494584 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494586 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494578 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494585 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494611 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494560 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494595 2 0.4605 0.763 0.204 0.796 0.000
#> GSM494570 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494597 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494607 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494561 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494569 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494592 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494577 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494588 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494590 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494609 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494608 2 0.3267 0.862 0.116 0.884 0.000
#> GSM494606 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494574 2 0.4399 0.780 0.188 0.812 0.000
#> GSM494573 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494566 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494601 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494557 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494579 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494596 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494575 2 0.4605 0.761 0.204 0.796 0.000
#> GSM494625 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494654 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494664 1 0.5859 0.499 0.656 0.000 0.344
#> GSM494624 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494651 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494662 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494627 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494673 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494649 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494658 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494653 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494643 2 0.1411 0.927 0.000 0.964 0.036
#> GSM494672 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494618 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494631 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494619 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494674 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494616 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494663 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494628 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494632 1 0.2066 0.922 0.940 0.000 0.060
#> GSM494660 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494622 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494642 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494647 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494659 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494670 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494675 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494641 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494636 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494640 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494623 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494644 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494646 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494665 1 0.1753 0.932 0.952 0.000 0.048
#> GSM494638 3 0.0424 0.992 0.008 0.000 0.992
#> GSM494645 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494671 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494655 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494620 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494630 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494657 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494667 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494621 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494629 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494637 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494652 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494648 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494650 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494669 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494666 1 0.2711 0.896 0.912 0.000 0.088
#> GSM494668 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494633 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494634 1 0.0000 0.969 1.000 0.000 0.000
#> GSM494639 1 0.5291 0.648 0.732 0.000 0.268
#> GSM494661 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494617 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494626 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494656 2 0.0000 0.955 0.000 1.000 0.000
#> GSM494635 1 0.0000 0.969 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM494565 2 0.1940 0.83450 0.000 0.924 0.000 0.076
#> GSM494594 3 0.0000 0.95302 0.000 0.000 1.000 0.000
#> GSM494604 1 0.0000 0.99548 1.000 0.000 0.000 0.000
#> GSM494564 4 0.0000 0.96734 0.000 0.000 0.000 1.000
#> GSM494591 3 0.0000 0.95302 0.000 0.000 1.000 0.000
#> GSM494567 3 0.0188 0.94881 0.000 0.000 0.996 0.004
#> GSM494602 2 0.0188 0.88173 0.004 0.996 0.000 0.000
#> GSM494613 3 0.0000 0.95302 0.000 0.000 1.000 0.000
#> GSM494589 4 0.0000 0.96734 0.000 0.000 0.000 1.000
#> GSM494598 2 0.0000 0.88319 0.000 1.000 0.000 0.000
#> GSM494593 2 0.0000 0.88319 0.000 1.000 0.000 0.000
#> GSM494583 3 0.3528 0.72063 0.000 0.192 0.808 0.000
#> GSM494612 2 0.0000 0.88319 0.000 1.000 0.000 0.000
#> GSM494558 3 0.0000 0.95302 0.000 0.000 1.000 0.000
#> GSM494556 3 0.0000 0.95302 0.000 0.000 1.000 0.000
#> GSM494559 4 0.0000 0.96734 0.000 0.000 0.000 1.000
#> GSM494571 3 0.0000 0.95302 0.000 0.000 1.000 0.000
#> GSM494614 2 0.1557 0.84919 0.000 0.944 0.000 0.056
#> GSM494603 4 0.0000 0.96734 0.000 0.000 0.000 1.000
#> GSM494568 4 0.0000 0.96734 0.000 0.000 0.000 1.000
#> GSM494572 3 0.0000 0.95302 0.000 0.000 1.000 0.000
#> GSM494600 4 0.0000 0.96734 0.000 0.000 0.000 1.000
#> GSM494562 2 0.4761 0.43345 0.000 0.628 0.372 0.000
#> GSM494615 4 0.0707 0.95332 0.000 0.000 0.020 0.980
#> GSM494582 2 0.0000 0.88319 0.000 1.000 0.000 0.000
#> GSM494599 1 0.0000 0.99548 1.000 0.000 0.000 0.000
#> GSM494610 2 0.0000 0.88319 0.000 1.000 0.000 0.000
#> GSM494587 3 0.4888 0.21094 0.000 0.412 0.588 0.000
#> GSM494581 2 0.0000 0.88319 0.000 1.000 0.000 0.000
#> GSM494580 3 0.0000 0.95302 0.000 0.000 1.000 0.000
#> GSM494563 4 0.1118 0.94978 0.036 0.000 0.000 0.964
#> GSM494576 3 0.4985 0.00611 0.000 0.468 0.532 0.000
#> GSM494605 1 0.0000 0.99548 1.000 0.000 0.000 0.000
#> GSM494584 3 0.2408 0.84330 0.000 0.104 0.896 0.000
#> GSM494586 2 0.4761 0.43345 0.000 0.628 0.372 0.000
#> GSM494578 3 0.0000 0.95302 0.000 0.000 1.000 0.000
#> GSM494585 2 0.4925 0.29648 0.000 0.572 0.428 0.000
#> GSM494611 2 0.0000 0.88319 0.000 1.000 0.000 0.000
#> GSM494560 4 0.1022 0.94692 0.000 0.032 0.000 0.968
#> GSM494595 2 0.0000 0.88319 0.000 1.000 0.000 0.000
#> GSM494570 4 0.0000 0.96734 0.000 0.000 0.000 1.000
#> GSM494597 3 0.0000 0.95302 0.000 0.000 1.000 0.000
#> GSM494607 1 0.0188 0.99219 0.996 0.004 0.000 0.000
#> GSM494561 4 0.0000 0.96734 0.000 0.000 0.000 1.000
#> GSM494569 4 0.0000 0.96734 0.000 0.000 0.000 1.000
#> GSM494592 1 0.0817 0.97463 0.976 0.024 0.000 0.000
#> GSM494577 2 0.4948 0.26123 0.000 0.560 0.440 0.000
#> GSM494588 4 0.1557 0.93818 0.056 0.000 0.000 0.944
#> GSM494590 3 0.0000 0.95302 0.000 0.000 1.000 0.000
#> GSM494609 2 0.0188 0.88173 0.004 0.996 0.000 0.000
#> GSM494608 2 0.0000 0.88319 0.000 1.000 0.000 0.000
#> GSM494606 2 0.0188 0.88173 0.004 0.996 0.000 0.000
#> GSM494574 2 0.0000 0.88319 0.000 1.000 0.000 0.000
#> GSM494573 4 0.0000 0.96734 0.000 0.000 0.000 1.000
#> GSM494566 4 0.0000 0.96734 0.000 0.000 0.000 1.000
#> GSM494601 3 0.0000 0.95302 0.000 0.000 1.000 0.000
#> GSM494557 3 0.0000 0.95302 0.000 0.000 1.000 0.000
#> GSM494579 2 0.4761 0.39812 0.372 0.628 0.000 0.000
#> GSM494596 3 0.0000 0.95302 0.000 0.000 1.000 0.000
#> GSM494575 2 0.0000 0.88319 0.000 1.000 0.000 0.000
#> GSM494625 4 0.0000 0.96734 0.000 0.000 0.000 1.000
#> GSM494654 3 0.0000 0.95302 0.000 0.000 1.000 0.000
#> GSM494664 1 0.0817 0.96882 0.976 0.000 0.000 0.024
#> GSM494624 4 0.1557 0.93818 0.056 0.000 0.000 0.944
#> GSM494651 3 0.0000 0.95302 0.000 0.000 1.000 0.000
#> GSM494662 4 0.0000 0.96734 0.000 0.000 0.000 1.000
#> GSM494627 4 0.0000 0.96734 0.000 0.000 0.000 1.000
#> GSM494673 1 0.0000 0.99548 1.000 0.000 0.000 0.000
#> GSM494649 4 0.0000 0.96734 0.000 0.000 0.000 1.000
#> GSM494658 1 0.0336 0.98869 0.992 0.008 0.000 0.000
#> GSM494653 1 0.0000 0.99548 1.000 0.000 0.000 0.000
#> GSM494643 3 0.0000 0.95302 0.000 0.000 1.000 0.000
#> GSM494672 1 0.0000 0.99548 1.000 0.000 0.000 0.000
#> GSM494618 4 0.0000 0.96734 0.000 0.000 0.000 1.000
#> GSM494631 3 0.0000 0.95302 0.000 0.000 1.000 0.000
#> GSM494619 4 0.2589 0.88395 0.116 0.000 0.000 0.884
#> GSM494674 1 0.0000 0.99548 1.000 0.000 0.000 0.000
#> GSM494616 4 0.0000 0.96734 0.000 0.000 0.000 1.000
#> GSM494663 4 0.0000 0.96734 0.000 0.000 0.000 1.000
#> GSM494628 4 0.0000 0.96734 0.000 0.000 0.000 1.000
#> GSM494632 1 0.0000 0.99548 1.000 0.000 0.000 0.000
#> GSM494660 4 0.0000 0.96734 0.000 0.000 0.000 1.000
#> GSM494622 3 0.0000 0.95302 0.000 0.000 1.000 0.000
#> GSM494642 1 0.0000 0.99548 1.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.99548 1.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.99548 1.000 0.000 0.000 0.000
#> GSM494670 1 0.1716 0.92806 0.936 0.064 0.000 0.000
#> GSM494675 3 0.0000 0.95302 0.000 0.000 1.000 0.000
#> GSM494641 1 0.0000 0.99548 1.000 0.000 0.000 0.000
#> GSM494636 4 0.2081 0.91617 0.084 0.000 0.000 0.916
#> GSM494640 3 0.0000 0.95302 0.000 0.000 1.000 0.000
#> GSM494623 4 0.2011 0.91998 0.080 0.000 0.000 0.920
#> GSM494644 1 0.0000 0.99548 1.000 0.000 0.000 0.000
#> GSM494646 1 0.0000 0.99548 1.000 0.000 0.000 0.000
#> GSM494665 1 0.0000 0.99548 1.000 0.000 0.000 0.000
#> GSM494638 4 0.4585 0.54443 0.332 0.000 0.000 0.668
#> GSM494645 1 0.0000 0.99548 1.000 0.000 0.000 0.000
#> GSM494671 1 0.0000 0.99548 1.000 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.99548 1.000 0.000 0.000 0.000
#> GSM494620 4 0.2011 0.91998 0.080 0.000 0.000 0.920
#> GSM494630 4 0.1557 0.93818 0.056 0.000 0.000 0.944
#> GSM494657 3 0.0000 0.95302 0.000 0.000 1.000 0.000
#> GSM494667 1 0.0000 0.99548 1.000 0.000 0.000 0.000
#> GSM494621 4 0.1792 0.92948 0.068 0.000 0.000 0.932
#> GSM494629 4 0.0000 0.96734 0.000 0.000 0.000 1.000
#> GSM494637 4 0.0000 0.96734 0.000 0.000 0.000 1.000
#> GSM494652 1 0.0000 0.99548 1.000 0.000 0.000 0.000
#> GSM494648 4 0.2011 0.91998 0.080 0.000 0.000 0.920
#> GSM494650 3 0.0000 0.95302 0.000 0.000 1.000 0.000
#> GSM494669 1 0.0000 0.99548 1.000 0.000 0.000 0.000
#> GSM494666 1 0.0000 0.99548 1.000 0.000 0.000 0.000
#> GSM494668 1 0.0000 0.99548 1.000 0.000 0.000 0.000
#> GSM494633 4 0.0000 0.96734 0.000 0.000 0.000 1.000
#> GSM494634 1 0.0000 0.99548 1.000 0.000 0.000 0.000
#> GSM494639 1 0.0000 0.99548 1.000 0.000 0.000 0.000
#> GSM494661 3 0.0000 0.95302 0.000 0.000 1.000 0.000
#> GSM494617 4 0.0000 0.96734 0.000 0.000 0.000 1.000
#> GSM494626 4 0.0000 0.96734 0.000 0.000 0.000 1.000
#> GSM494656 3 0.0000 0.95302 0.000 0.000 1.000 0.000
#> GSM494635 1 0.0000 0.99548 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM494565 5 0.3561 0.611 0.000 0.260 0.000 0.000 0.740
#> GSM494594 3 0.0000 0.975 0.000 0.000 1.000 0.000 0.000
#> GSM494604 1 0.0000 0.978 1.000 0.000 0.000 0.000 0.000
#> GSM494564 5 0.0000 0.867 0.000 0.000 0.000 0.000 1.000
#> GSM494591 3 0.0000 0.975 0.000 0.000 1.000 0.000 0.000
#> GSM494567 3 0.0162 0.971 0.000 0.000 0.996 0.004 0.000
#> GSM494602 2 0.3143 0.640 0.204 0.796 0.000 0.000 0.000
#> GSM494613 3 0.0000 0.975 0.000 0.000 1.000 0.000 0.000
#> GSM494589 5 0.0000 0.867 0.000 0.000 0.000 0.000 1.000
#> GSM494598 2 0.0000 0.763 0.000 1.000 0.000 0.000 0.000
#> GSM494593 2 0.2377 0.694 0.128 0.872 0.000 0.000 0.000
#> GSM494583 3 0.3274 0.689 0.000 0.220 0.780 0.000 0.000
#> GSM494612 2 0.0000 0.763 0.000 1.000 0.000 0.000 0.000
#> GSM494558 3 0.0000 0.975 0.000 0.000 1.000 0.000 0.000
#> GSM494556 3 0.0000 0.975 0.000 0.000 1.000 0.000 0.000
#> GSM494559 5 0.0000 0.867 0.000 0.000 0.000 0.000 1.000
#> GSM494571 3 0.0000 0.975 0.000 0.000 1.000 0.000 0.000
#> GSM494614 5 0.3837 0.537 0.000 0.308 0.000 0.000 0.692
#> GSM494603 5 0.3074 0.717 0.000 0.000 0.000 0.196 0.804
#> GSM494568 4 0.0162 0.798 0.000 0.000 0.000 0.996 0.004
#> GSM494572 3 0.0000 0.975 0.000 0.000 1.000 0.000 0.000
#> GSM494600 5 0.0000 0.867 0.000 0.000 0.000 0.000 1.000
#> GSM494562 2 0.3730 0.589 0.000 0.712 0.288 0.000 0.000
#> GSM494615 5 0.3913 0.579 0.000 0.000 0.000 0.324 0.676
#> GSM494582 2 0.0000 0.763 0.000 1.000 0.000 0.000 0.000
#> GSM494599 1 0.0000 0.978 1.000 0.000 0.000 0.000 0.000
#> GSM494610 2 0.0000 0.763 0.000 1.000 0.000 0.000 0.000
#> GSM494587 2 0.4304 0.187 0.000 0.516 0.484 0.000 0.000
#> GSM494581 2 0.0000 0.763 0.000 1.000 0.000 0.000 0.000
#> GSM494580 3 0.0000 0.975 0.000 0.000 1.000 0.000 0.000
#> GSM494563 5 0.0000 0.867 0.000 0.000 0.000 0.000 1.000
#> GSM494576 2 0.4287 0.262 0.000 0.540 0.460 0.000 0.000
#> GSM494605 1 0.0510 0.965 0.984 0.000 0.000 0.016 0.000
#> GSM494584 3 0.2891 0.764 0.000 0.176 0.824 0.000 0.000
#> GSM494586 2 0.3774 0.579 0.000 0.704 0.296 0.000 0.000
#> GSM494578 3 0.0000 0.975 0.000 0.000 1.000 0.000 0.000
#> GSM494585 2 0.4192 0.401 0.000 0.596 0.404 0.000 0.000
#> GSM494611 2 0.0000 0.763 0.000 1.000 0.000 0.000 0.000
#> GSM494560 5 0.0000 0.867 0.000 0.000 0.000 0.000 1.000
#> GSM494595 2 0.0000 0.763 0.000 1.000 0.000 0.000 0.000
#> GSM494570 5 0.0000 0.867 0.000 0.000 0.000 0.000 1.000
#> GSM494597 3 0.0000 0.975 0.000 0.000 1.000 0.000 0.000
#> GSM494607 1 0.0162 0.975 0.996 0.004 0.000 0.000 0.000
#> GSM494561 5 0.0162 0.863 0.000 0.000 0.000 0.004 0.996
#> GSM494569 4 0.0000 0.800 0.000 0.000 0.000 1.000 0.000
#> GSM494592 1 0.0703 0.958 0.976 0.024 0.000 0.000 0.000
#> GSM494577 2 0.4219 0.374 0.000 0.584 0.416 0.000 0.000
#> GSM494588 5 0.0000 0.867 0.000 0.000 0.000 0.000 1.000
#> GSM494590 3 0.0000 0.975 0.000 0.000 1.000 0.000 0.000
#> GSM494609 2 0.2891 0.664 0.176 0.824 0.000 0.000 0.000
#> GSM494608 2 0.0000 0.763 0.000 1.000 0.000 0.000 0.000
#> GSM494606 2 0.3003 0.652 0.188 0.812 0.000 0.000 0.000
#> GSM494574 2 0.0000 0.763 0.000 1.000 0.000 0.000 0.000
#> GSM494573 5 0.0000 0.867 0.000 0.000 0.000 0.000 1.000
#> GSM494566 5 0.4235 0.431 0.000 0.000 0.000 0.424 0.576
#> GSM494601 3 0.2605 0.806 0.000 0.148 0.852 0.000 0.000
#> GSM494557 3 0.0000 0.975 0.000 0.000 1.000 0.000 0.000
#> GSM494579 2 0.6684 0.180 0.372 0.392 0.000 0.000 0.236
#> GSM494596 3 0.0000 0.975 0.000 0.000 1.000 0.000 0.000
#> GSM494575 2 0.0000 0.763 0.000 1.000 0.000 0.000 0.000
#> GSM494625 4 0.0000 0.800 0.000 0.000 0.000 1.000 0.000
#> GSM494654 3 0.0000 0.975 0.000 0.000 1.000 0.000 0.000
#> GSM494664 1 0.3274 0.687 0.780 0.000 0.000 0.220 0.000
#> GSM494624 4 0.5322 0.642 0.072 0.000 0.000 0.608 0.320
#> GSM494651 3 0.0000 0.975 0.000 0.000 1.000 0.000 0.000
#> GSM494662 4 0.2377 0.758 0.000 0.000 0.000 0.872 0.128
#> GSM494627 4 0.0000 0.800 0.000 0.000 0.000 1.000 0.000
#> GSM494673 1 0.0000 0.978 1.000 0.000 0.000 0.000 0.000
#> GSM494649 4 0.3796 0.680 0.000 0.000 0.000 0.700 0.300
#> GSM494658 1 0.0000 0.978 1.000 0.000 0.000 0.000 0.000
#> GSM494653 1 0.0000 0.978 1.000 0.000 0.000 0.000 0.000
#> GSM494643 3 0.0000 0.975 0.000 0.000 1.000 0.000 0.000
#> GSM494672 1 0.0000 0.978 1.000 0.000 0.000 0.000 0.000
#> GSM494618 4 0.0000 0.800 0.000 0.000 0.000 1.000 0.000
#> GSM494631 3 0.0000 0.975 0.000 0.000 1.000 0.000 0.000
#> GSM494619 4 0.5425 0.635 0.080 0.000 0.000 0.600 0.320
#> GSM494674 1 0.0000 0.978 1.000 0.000 0.000 0.000 0.000
#> GSM494616 4 0.0000 0.800 0.000 0.000 0.000 1.000 0.000
#> GSM494663 4 0.0000 0.800 0.000 0.000 0.000 1.000 0.000
#> GSM494628 4 0.0000 0.800 0.000 0.000 0.000 1.000 0.000
#> GSM494632 1 0.2852 0.767 0.828 0.000 0.000 0.172 0.000
#> GSM494660 4 0.3774 0.683 0.000 0.000 0.000 0.704 0.296
#> GSM494622 3 0.0794 0.947 0.000 0.000 0.972 0.028 0.000
#> GSM494642 1 0.0000 0.978 1.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.978 1.000 0.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.978 1.000 0.000 0.000 0.000 0.000
#> GSM494670 1 0.1792 0.890 0.916 0.084 0.000 0.000 0.000
#> GSM494675 3 0.0000 0.975 0.000 0.000 1.000 0.000 0.000
#> GSM494641 1 0.0000 0.978 1.000 0.000 0.000 0.000 0.000
#> GSM494636 4 0.0000 0.800 0.000 0.000 0.000 1.000 0.000
#> GSM494640 3 0.0000 0.975 0.000 0.000 1.000 0.000 0.000
#> GSM494623 4 0.5375 0.639 0.076 0.000 0.000 0.604 0.320
#> GSM494644 1 0.0000 0.978 1.000 0.000 0.000 0.000 0.000
#> GSM494646 1 0.0162 0.975 0.996 0.000 0.000 0.004 0.000
#> GSM494665 1 0.0000 0.978 1.000 0.000 0.000 0.000 0.000
#> GSM494638 4 0.0000 0.800 0.000 0.000 0.000 1.000 0.000
#> GSM494645 1 0.0000 0.978 1.000 0.000 0.000 0.000 0.000
#> GSM494671 1 0.0000 0.978 1.000 0.000 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.978 1.000 0.000 0.000 0.000 0.000
#> GSM494620 4 0.5375 0.639 0.076 0.000 0.000 0.604 0.320
#> GSM494630 4 0.5160 0.634 0.056 0.000 0.000 0.608 0.336
#> GSM494657 3 0.0000 0.975 0.000 0.000 1.000 0.000 0.000
#> GSM494667 1 0.0000 0.978 1.000 0.000 0.000 0.000 0.000
#> GSM494621 4 0.5322 0.642 0.072 0.000 0.000 0.608 0.320
#> GSM494629 4 0.0162 0.798 0.000 0.000 0.000 0.996 0.004
#> GSM494637 4 0.0000 0.800 0.000 0.000 0.000 1.000 0.000
#> GSM494652 1 0.0000 0.978 1.000 0.000 0.000 0.000 0.000
#> GSM494648 4 0.5375 0.639 0.076 0.000 0.000 0.604 0.320
#> GSM494650 3 0.0000 0.975 0.000 0.000 1.000 0.000 0.000
#> GSM494669 1 0.0000 0.978 1.000 0.000 0.000 0.000 0.000
#> GSM494666 1 0.0510 0.965 0.984 0.000 0.000 0.016 0.000
#> GSM494668 1 0.0000 0.978 1.000 0.000 0.000 0.000 0.000
#> GSM494633 4 0.3932 0.658 0.000 0.000 0.000 0.672 0.328
#> GSM494634 1 0.0000 0.978 1.000 0.000 0.000 0.000 0.000
#> GSM494639 4 0.4300 0.172 0.476 0.000 0.000 0.524 0.000
#> GSM494661 3 0.0000 0.975 0.000 0.000 1.000 0.000 0.000
#> GSM494617 4 0.0000 0.800 0.000 0.000 0.000 1.000 0.000
#> GSM494626 4 0.0000 0.800 0.000 0.000 0.000 1.000 0.000
#> GSM494656 3 0.0000 0.975 0.000 0.000 1.000 0.000 0.000
#> GSM494635 1 0.0000 0.978 1.000 0.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM494565 5 0.3555 0.640 0.000 0.176 0.000 0.000 0.780 0.044
#> GSM494594 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494604 1 0.2135 0.712 0.872 0.000 0.000 0.000 0.000 0.128
#> GSM494564 5 0.1814 0.792 0.000 0.000 0.000 0.100 0.900 0.000
#> GSM494591 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494567 3 0.3063 0.804 0.000 0.000 0.840 0.000 0.092 0.068
#> GSM494602 6 0.5594 0.662 0.272 0.168 0.000 0.000 0.004 0.556
#> GSM494613 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494589 5 0.0260 0.818 0.000 0.000 0.000 0.000 0.992 0.008
#> GSM494598 6 0.3828 0.310 0.000 0.440 0.000 0.000 0.000 0.560
#> GSM494593 6 0.5628 0.593 0.156 0.276 0.000 0.000 0.008 0.560
#> GSM494583 2 0.3428 0.680 0.000 0.696 0.304 0.000 0.000 0.000
#> GSM494612 2 0.2300 0.645 0.000 0.856 0.000 0.000 0.000 0.144
#> GSM494558 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494556 3 0.0713 0.958 0.000 0.028 0.972 0.000 0.000 0.000
#> GSM494559 5 0.0146 0.820 0.000 0.000 0.000 0.004 0.996 0.000
#> GSM494571 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494614 5 0.4352 0.487 0.000 0.280 0.000 0.000 0.668 0.052
#> GSM494603 5 0.5253 0.432 0.000 0.000 0.000 0.192 0.608 0.200
#> GSM494568 4 0.3789 0.589 0.000 0.000 0.000 0.584 0.000 0.416
#> GSM494572 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494600 5 0.0260 0.818 0.000 0.000 0.000 0.000 0.992 0.008
#> GSM494562 2 0.2260 0.746 0.000 0.860 0.140 0.000 0.000 0.000
#> GSM494615 5 0.5922 0.204 0.000 0.000 0.000 0.252 0.464 0.284
#> GSM494582 2 0.1957 0.684 0.000 0.888 0.000 0.000 0.000 0.112
#> GSM494599 1 0.3869 -0.360 0.500 0.000 0.000 0.000 0.000 0.500
#> GSM494610 2 0.1814 0.695 0.000 0.900 0.000 0.000 0.000 0.100
#> GSM494587 2 0.3198 0.716 0.000 0.740 0.260 0.000 0.000 0.000
#> GSM494581 2 0.1267 0.706 0.000 0.940 0.000 0.000 0.000 0.060
#> GSM494580 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494563 5 0.1267 0.807 0.000 0.000 0.000 0.060 0.940 0.000
#> GSM494576 2 0.3175 0.718 0.000 0.744 0.256 0.000 0.000 0.000
#> GSM494605 1 0.2597 0.709 0.824 0.000 0.000 0.176 0.000 0.000
#> GSM494584 2 0.3563 0.634 0.000 0.664 0.336 0.000 0.000 0.000
#> GSM494586 2 0.2300 0.747 0.000 0.856 0.144 0.000 0.000 0.000
#> GSM494578 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494585 2 0.2996 0.731 0.000 0.772 0.228 0.000 0.000 0.000
#> GSM494611 6 0.3828 0.310 0.000 0.440 0.000 0.000 0.000 0.560
#> GSM494560 5 0.0146 0.819 0.000 0.000 0.000 0.000 0.996 0.004
#> GSM494595 2 0.1814 0.695 0.000 0.900 0.000 0.000 0.000 0.100
#> GSM494570 5 0.2048 0.776 0.000 0.000 0.000 0.120 0.880 0.000
#> GSM494597 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494607 6 0.3864 0.333 0.480 0.000 0.000 0.000 0.000 0.520
#> GSM494561 5 0.1863 0.790 0.000 0.000 0.000 0.104 0.896 0.000
#> GSM494569 4 0.3717 0.600 0.000 0.000 0.000 0.616 0.000 0.384
#> GSM494592 6 0.3993 0.344 0.476 0.004 0.000 0.000 0.000 0.520
#> GSM494577 2 0.2793 0.740 0.000 0.800 0.200 0.000 0.000 0.000
#> GSM494588 5 0.2003 0.780 0.000 0.000 0.000 0.116 0.884 0.000
#> GSM494590 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494609 6 0.5739 0.655 0.232 0.204 0.000 0.000 0.008 0.556
#> GSM494608 2 0.1444 0.704 0.000 0.928 0.000 0.000 0.000 0.072
#> GSM494606 6 0.5643 0.663 0.248 0.192 0.000 0.000 0.004 0.556
#> GSM494574 2 0.1765 0.696 0.000 0.904 0.000 0.000 0.000 0.096
#> GSM494573 5 0.0146 0.820 0.000 0.000 0.000 0.004 0.996 0.000
#> GSM494566 6 0.5750 -0.369 0.000 0.000 0.000 0.336 0.184 0.480
#> GSM494601 2 0.3515 0.654 0.000 0.676 0.324 0.000 0.000 0.000
#> GSM494557 3 0.0260 0.975 0.000 0.008 0.992 0.000 0.000 0.000
#> GSM494579 6 0.6246 0.578 0.308 0.064 0.000 0.000 0.108 0.520
#> GSM494596 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494575 2 0.1814 0.695 0.000 0.900 0.000 0.000 0.000 0.100
#> GSM494625 4 0.1501 0.615 0.000 0.000 0.000 0.924 0.000 0.076
#> GSM494654 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494664 1 0.3923 0.481 0.620 0.000 0.000 0.372 0.008 0.000
#> GSM494624 4 0.4638 0.464 0.096 0.000 0.000 0.672 0.232 0.000
#> GSM494651 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494662 4 0.1285 0.584 0.000 0.000 0.000 0.944 0.052 0.004
#> GSM494627 4 0.3782 0.591 0.000 0.000 0.000 0.588 0.000 0.412
#> GSM494673 1 0.0000 0.845 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494649 4 0.2912 0.507 0.000 0.000 0.000 0.784 0.216 0.000
#> GSM494658 1 0.2527 0.653 0.832 0.000 0.000 0.000 0.000 0.168
#> GSM494653 1 0.0000 0.845 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494643 3 0.1124 0.947 0.000 0.000 0.956 0.008 0.000 0.036
#> GSM494672 1 0.1327 0.785 0.936 0.000 0.000 0.000 0.000 0.064
#> GSM494618 4 0.3774 0.593 0.000 0.000 0.000 0.592 0.000 0.408
#> GSM494631 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494619 4 0.4638 0.464 0.096 0.000 0.000 0.672 0.232 0.000
#> GSM494674 1 0.0000 0.845 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494616 4 0.3706 0.601 0.000 0.000 0.000 0.620 0.000 0.380
#> GSM494663 4 0.3782 0.591 0.000 0.000 0.000 0.588 0.000 0.412
#> GSM494628 4 0.3774 0.593 0.000 0.000 0.000 0.592 0.000 0.408
#> GSM494632 1 0.4460 0.519 0.644 0.000 0.000 0.304 0.000 0.052
#> GSM494660 4 0.2912 0.507 0.000 0.000 0.000 0.784 0.216 0.000
#> GSM494622 3 0.2170 0.865 0.000 0.000 0.888 0.012 0.000 0.100
#> GSM494642 1 0.0000 0.845 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0260 0.842 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM494659 1 0.0000 0.845 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494670 1 0.2431 0.702 0.860 0.008 0.000 0.000 0.000 0.132
#> GSM494675 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494641 1 0.0000 0.845 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494636 4 0.2312 0.613 0.012 0.000 0.000 0.876 0.000 0.112
#> GSM494640 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494623 4 0.4638 0.464 0.096 0.000 0.000 0.672 0.232 0.000
#> GSM494644 1 0.0458 0.839 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM494646 1 0.3606 0.608 0.728 0.000 0.000 0.256 0.000 0.016
#> GSM494665 1 0.2048 0.759 0.880 0.000 0.000 0.120 0.000 0.000
#> GSM494638 4 0.2531 0.614 0.012 0.000 0.000 0.856 0.000 0.132
#> GSM494645 1 0.0458 0.839 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM494671 1 0.0000 0.845 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.845 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494620 4 0.4638 0.464 0.096 0.000 0.000 0.672 0.232 0.000
#> GSM494630 4 0.4596 0.459 0.088 0.000 0.000 0.672 0.240 0.000
#> GSM494657 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494667 1 0.0000 0.845 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494621 4 0.4638 0.464 0.096 0.000 0.000 0.672 0.232 0.000
#> GSM494629 4 0.3782 0.591 0.000 0.000 0.000 0.588 0.000 0.412
#> GSM494637 4 0.3782 0.591 0.000 0.000 0.000 0.588 0.000 0.412
#> GSM494652 1 0.0000 0.845 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494648 4 0.4638 0.464 0.096 0.000 0.000 0.672 0.232 0.000
#> GSM494650 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494669 1 0.0000 0.845 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494666 1 0.3266 0.610 0.728 0.000 0.000 0.272 0.000 0.000
#> GSM494668 1 0.0000 0.845 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494633 4 0.3076 0.488 0.000 0.000 0.000 0.760 0.240 0.000
#> GSM494634 1 0.0000 0.845 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494639 1 0.4131 0.457 0.600 0.000 0.000 0.384 0.000 0.016
#> GSM494661 3 0.1267 0.926 0.000 0.060 0.940 0.000 0.000 0.000
#> GSM494617 4 0.3288 0.613 0.000 0.000 0.000 0.724 0.000 0.276
#> GSM494626 4 0.3672 0.604 0.000 0.000 0.000 0.632 0.000 0.368
#> GSM494656 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494635 1 0.0405 0.841 0.988 0.000 0.000 0.008 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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 3)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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.
# code only for demonstration
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) individual(p) k
#> ATC:skmeans 116 7.11e-06 0.5512 1.57e-03 0.20779 2
#> ATC:skmeans 118 5.54e-04 0.0368 4.60e-02 0.00177 3
#> ATC:skmeans 113 3.40e-07 0.0333 3.62e-05 0.01305 4
#> ATC:skmeans 113 1.02e-12 0.3047 1.99e-08 0.27779 5
#> ATC:skmeans 101 4.89e-12 0.1879 5.82e-09 0.20886 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.
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"]
# you can also extract it by
# 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 51941 rows and 120 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 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:
k and the heatmap of
predicted classes for each k.k.k.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:
k;k, the area increased is defined as \(A_k - A_{k-1}\).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 0.988 0.996 0.1759 0.832 0.832
#> 3 3 0.783 0.842 0.939 2.1103 0.649 0.578
#> 4 4 0.800 0.835 0.935 0.2775 0.815 0.615
#> 5 5 0.890 0.871 0.945 0.1081 0.910 0.706
#> 6 6 0.935 0.896 0.957 0.0456 0.957 0.811
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> 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.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM494565 1 0.000 0.995 1.000 0.000
#> GSM494594 2 0.000 1.000 0.000 1.000
#> GSM494604 1 0.000 0.995 1.000 0.000
#> GSM494564 1 0.000 0.995 1.000 0.000
#> GSM494591 2 0.000 1.000 0.000 1.000
#> GSM494567 1 0.000 0.995 1.000 0.000
#> GSM494602 1 0.000 0.995 1.000 0.000
#> GSM494613 1 0.000 0.995 1.000 0.000
#> GSM494589 1 0.000 0.995 1.000 0.000
#> GSM494598 1 0.000 0.995 1.000 0.000
#> GSM494593 1 0.000 0.995 1.000 0.000
#> GSM494583 1 0.000 0.995 1.000 0.000
#> GSM494612 1 0.000 0.995 1.000 0.000
#> GSM494558 1 0.000 0.995 1.000 0.000
#> GSM494556 1 0.000 0.995 1.000 0.000
#> GSM494559 1 0.000 0.995 1.000 0.000
#> GSM494571 2 0.000 1.000 0.000 1.000
#> GSM494614 1 0.000 0.995 1.000 0.000
#> GSM494603 1 0.000 0.995 1.000 0.000
#> GSM494568 1 0.000 0.995 1.000 0.000
#> GSM494572 2 0.000 1.000 0.000 1.000
#> GSM494600 1 0.000 0.995 1.000 0.000
#> GSM494562 1 0.000 0.995 1.000 0.000
#> GSM494615 1 0.000 0.995 1.000 0.000
#> GSM494582 1 0.000 0.995 1.000 0.000
#> GSM494599 1 0.000 0.995 1.000 0.000
#> GSM494610 1 0.000 0.995 1.000 0.000
#> GSM494587 1 0.000 0.995 1.000 0.000
#> GSM494581 1 0.000 0.995 1.000 0.000
#> GSM494580 1 0.000 0.995 1.000 0.000
#> GSM494563 1 0.000 0.995 1.000 0.000
#> GSM494576 1 0.000 0.995 1.000 0.000
#> GSM494605 1 0.000 0.995 1.000 0.000
#> GSM494584 1 0.000 0.995 1.000 0.000
#> GSM494586 1 0.000 0.995 1.000 0.000
#> GSM494578 1 0.000 0.995 1.000 0.000
#> GSM494585 1 0.000 0.995 1.000 0.000
#> GSM494611 1 0.000 0.995 1.000 0.000
#> GSM494560 1 0.000 0.995 1.000 0.000
#> GSM494595 1 0.000 0.995 1.000 0.000
#> GSM494570 1 0.000 0.995 1.000 0.000
#> GSM494597 2 0.000 1.000 0.000 1.000
#> GSM494607 1 0.000 0.995 1.000 0.000
#> GSM494561 1 0.000 0.995 1.000 0.000
#> GSM494569 1 0.000 0.995 1.000 0.000
#> GSM494592 1 0.000 0.995 1.000 0.000
#> GSM494577 1 0.000 0.995 1.000 0.000
#> GSM494588 1 0.000 0.995 1.000 0.000
#> GSM494590 2 0.000 1.000 0.000 1.000
#> GSM494609 1 0.000 0.995 1.000 0.000
#> GSM494608 1 0.000 0.995 1.000 0.000
#> GSM494606 1 0.000 0.995 1.000 0.000
#> GSM494574 1 0.000 0.995 1.000 0.000
#> GSM494573 1 0.000 0.995 1.000 0.000
#> GSM494566 1 0.000 0.995 1.000 0.000
#> GSM494601 1 0.992 0.192 0.552 0.448
#> GSM494557 1 0.000 0.995 1.000 0.000
#> GSM494579 1 0.000 0.995 1.000 0.000
#> GSM494596 2 0.000 1.000 0.000 1.000
#> GSM494575 1 0.000 0.995 1.000 0.000
#> GSM494625 1 0.000 0.995 1.000 0.000
#> GSM494654 2 0.000 1.000 0.000 1.000
#> GSM494664 1 0.000 0.995 1.000 0.000
#> GSM494624 1 0.000 0.995 1.000 0.000
#> GSM494651 1 0.000 0.995 1.000 0.000
#> GSM494662 1 0.000 0.995 1.000 0.000
#> GSM494627 1 0.000 0.995 1.000 0.000
#> GSM494673 1 0.000 0.995 1.000 0.000
#> GSM494649 1 0.000 0.995 1.000 0.000
#> GSM494658 1 0.000 0.995 1.000 0.000
#> GSM494653 1 0.000 0.995 1.000 0.000
#> GSM494643 1 0.000 0.995 1.000 0.000
#> GSM494672 1 0.000 0.995 1.000 0.000
#> GSM494618 1 0.000 0.995 1.000 0.000
#> GSM494631 1 0.000 0.995 1.000 0.000
#> GSM494619 1 0.000 0.995 1.000 0.000
#> GSM494674 1 0.000 0.995 1.000 0.000
#> GSM494616 1 0.000 0.995 1.000 0.000
#> GSM494663 1 0.000 0.995 1.000 0.000
#> GSM494628 1 0.000 0.995 1.000 0.000
#> GSM494632 1 0.000 0.995 1.000 0.000
#> GSM494660 1 0.000 0.995 1.000 0.000
#> GSM494622 1 0.000 0.995 1.000 0.000
#> GSM494642 1 0.000 0.995 1.000 0.000
#> GSM494647 1 0.000 0.995 1.000 0.000
#> GSM494659 1 0.000 0.995 1.000 0.000
#> GSM494670 1 0.000 0.995 1.000 0.000
#> GSM494675 1 0.000 0.995 1.000 0.000
#> GSM494641 1 0.000 0.995 1.000 0.000
#> GSM494636 1 0.000 0.995 1.000 0.000
#> GSM494640 1 0.000 0.995 1.000 0.000
#> GSM494623 1 0.000 0.995 1.000 0.000
#> GSM494644 1 0.000 0.995 1.000 0.000
#> GSM494646 1 0.000 0.995 1.000 0.000
#> GSM494665 1 0.000 0.995 1.000 0.000
#> GSM494638 1 0.000 0.995 1.000 0.000
#> GSM494645 1 0.000 0.995 1.000 0.000
#> GSM494671 1 0.000 0.995 1.000 0.000
#> GSM494655 1 0.000 0.995 1.000 0.000
#> GSM494620 1 0.000 0.995 1.000 0.000
#> GSM494630 1 0.000 0.995 1.000 0.000
#> GSM494657 2 0.000 1.000 0.000 1.000
#> GSM494667 1 0.000 0.995 1.000 0.000
#> GSM494621 1 0.000 0.995 1.000 0.000
#> GSM494629 1 0.000 0.995 1.000 0.000
#> GSM494637 1 0.000 0.995 1.000 0.000
#> GSM494652 1 0.000 0.995 1.000 0.000
#> GSM494648 1 0.000 0.995 1.000 0.000
#> GSM494650 2 0.000 1.000 0.000 1.000
#> GSM494669 1 0.000 0.995 1.000 0.000
#> GSM494666 1 0.000 0.995 1.000 0.000
#> GSM494668 1 0.000 0.995 1.000 0.000
#> GSM494633 1 0.000 0.995 1.000 0.000
#> GSM494634 1 0.000 0.995 1.000 0.000
#> GSM494639 1 0.000 0.995 1.000 0.000
#> GSM494661 1 0.402 0.910 0.920 0.080
#> GSM494617 1 0.000 0.995 1.000 0.000
#> GSM494626 1 0.000 0.995 1.000 0.000
#> GSM494656 2 0.000 1.000 0.000 1.000
#> GSM494635 1 0.000 0.995 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM494565 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494594 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494604 1 0.0424 0.935 0.992 0.008 0.000
#> GSM494564 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494591 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494567 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494602 1 0.0000 0.944 1.000 0.000 0.000
#> GSM494613 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494589 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494598 2 0.6252 0.277 0.444 0.556 0.000
#> GSM494593 2 0.6180 0.352 0.416 0.584 0.000
#> GSM494583 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494612 2 0.6180 0.352 0.416 0.584 0.000
#> GSM494558 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494556 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494559 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494571 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494614 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494603 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494568 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494572 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494600 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494562 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494615 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494582 2 0.6180 0.352 0.416 0.584 0.000
#> GSM494599 1 0.0000 0.944 1.000 0.000 0.000
#> GSM494610 2 0.5905 0.489 0.352 0.648 0.000
#> GSM494587 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494581 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494580 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494563 2 0.5327 0.615 0.272 0.728 0.000
#> GSM494576 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494605 1 0.0000 0.944 1.000 0.000 0.000
#> GSM494584 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494586 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494578 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494585 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494611 2 0.6299 0.179 0.476 0.524 0.000
#> GSM494560 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494595 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494570 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494597 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494607 1 0.0000 0.944 1.000 0.000 0.000
#> GSM494561 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494569 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494592 1 0.0000 0.944 1.000 0.000 0.000
#> GSM494577 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494588 1 0.5138 0.590 0.748 0.252 0.000
#> GSM494590 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494609 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494608 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494606 2 0.6180 0.352 0.416 0.584 0.000
#> GSM494574 2 0.5882 0.496 0.348 0.652 0.000
#> GSM494573 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494566 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494601 2 0.6260 0.191 0.000 0.552 0.448
#> GSM494557 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494579 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494596 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494575 2 0.5882 0.496 0.348 0.652 0.000
#> GSM494625 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494654 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494664 1 0.0000 0.944 1.000 0.000 0.000
#> GSM494624 1 0.5216 0.582 0.740 0.260 0.000
#> GSM494651 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494662 2 0.5397 0.586 0.280 0.720 0.000
#> GSM494627 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494673 1 0.0000 0.944 1.000 0.000 0.000
#> GSM494649 2 0.6140 0.314 0.404 0.596 0.000
#> GSM494658 1 0.0000 0.944 1.000 0.000 0.000
#> GSM494653 1 0.0000 0.944 1.000 0.000 0.000
#> GSM494643 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494672 1 0.0000 0.944 1.000 0.000 0.000
#> GSM494618 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494631 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494619 1 0.4346 0.702 0.816 0.184 0.000
#> GSM494674 1 0.0000 0.944 1.000 0.000 0.000
#> GSM494616 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494663 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494628 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494632 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494660 2 0.5138 0.632 0.252 0.748 0.000
#> GSM494622 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494642 1 0.0000 0.944 1.000 0.000 0.000
#> GSM494647 1 0.0000 0.944 1.000 0.000 0.000
#> GSM494659 1 0.0000 0.944 1.000 0.000 0.000
#> GSM494670 1 0.6140 0.221 0.596 0.404 0.000
#> GSM494675 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494641 1 0.0000 0.944 1.000 0.000 0.000
#> GSM494636 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494640 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494623 1 0.0000 0.944 1.000 0.000 0.000
#> GSM494644 2 0.5254 0.634 0.264 0.736 0.000
#> GSM494646 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494665 1 0.0000 0.944 1.000 0.000 0.000
#> GSM494638 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494645 2 0.3192 0.813 0.112 0.888 0.000
#> GSM494671 1 0.0000 0.944 1.000 0.000 0.000
#> GSM494655 1 0.0000 0.944 1.000 0.000 0.000
#> GSM494620 1 0.0000 0.944 1.000 0.000 0.000
#> GSM494630 2 0.6267 0.173 0.452 0.548 0.000
#> GSM494657 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494667 1 0.0000 0.944 1.000 0.000 0.000
#> GSM494621 1 0.1964 0.877 0.944 0.056 0.000
#> GSM494629 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494637 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494652 1 0.0000 0.944 1.000 0.000 0.000
#> GSM494648 1 0.0000 0.944 1.000 0.000 0.000
#> GSM494650 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494669 1 0.0000 0.944 1.000 0.000 0.000
#> GSM494666 1 0.0000 0.944 1.000 0.000 0.000
#> GSM494668 1 0.0000 0.944 1.000 0.000 0.000
#> GSM494633 2 0.0747 0.893 0.016 0.984 0.000
#> GSM494634 1 0.0000 0.944 1.000 0.000 0.000
#> GSM494639 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494661 2 0.2537 0.840 0.000 0.920 0.080
#> GSM494617 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494626 2 0.0000 0.905 0.000 1.000 0.000
#> GSM494656 3 0.0000 1.000 0.000 0.000 1.000
#> GSM494635 2 0.0000 0.905 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM494565 4 0.0336 0.911 0.000 0.008 0.000 0.992
#> GSM494594 3 0.0000 0.990 0.000 0.000 1.000 0.000
#> GSM494604 1 0.0336 0.931 0.992 0.000 0.000 0.008
#> GSM494564 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494591 3 0.0000 0.990 0.000 0.000 1.000 0.000
#> GSM494567 4 0.3873 0.644 0.000 0.228 0.000 0.772
#> GSM494602 1 0.3400 0.749 0.820 0.180 0.000 0.000
#> GSM494613 2 0.4072 0.673 0.000 0.748 0.000 0.252
#> GSM494589 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494598 2 0.4164 0.606 0.000 0.736 0.000 0.264
#> GSM494593 4 0.4252 0.576 0.004 0.252 0.000 0.744
#> GSM494583 2 0.0000 0.825 0.000 1.000 0.000 0.000
#> GSM494612 2 0.0000 0.825 0.000 1.000 0.000 0.000
#> GSM494558 2 0.4103 0.669 0.000 0.744 0.000 0.256
#> GSM494556 4 0.4008 0.625 0.000 0.244 0.000 0.756
#> GSM494559 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494571 3 0.0000 0.990 0.000 0.000 1.000 0.000
#> GSM494614 2 0.4996 0.241 0.000 0.516 0.000 0.484
#> GSM494603 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494568 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494572 3 0.0000 0.990 0.000 0.000 1.000 0.000
#> GSM494600 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494562 2 0.0000 0.825 0.000 1.000 0.000 0.000
#> GSM494615 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494582 2 0.0000 0.825 0.000 1.000 0.000 0.000
#> GSM494599 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> GSM494610 2 0.0000 0.825 0.000 1.000 0.000 0.000
#> GSM494587 2 0.0000 0.825 0.000 1.000 0.000 0.000
#> GSM494581 2 0.0000 0.825 0.000 1.000 0.000 0.000
#> GSM494580 2 0.4072 0.673 0.000 0.748 0.000 0.252
#> GSM494563 4 0.3569 0.719 0.196 0.000 0.000 0.804
#> GSM494576 2 0.0000 0.825 0.000 1.000 0.000 0.000
#> GSM494605 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> GSM494584 2 0.0000 0.825 0.000 1.000 0.000 0.000
#> GSM494586 2 0.0000 0.825 0.000 1.000 0.000 0.000
#> GSM494578 2 0.4072 0.673 0.000 0.748 0.000 0.252
#> GSM494585 2 0.0000 0.825 0.000 1.000 0.000 0.000
#> GSM494611 2 0.4830 0.408 0.000 0.608 0.000 0.392
#> GSM494560 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494595 2 0.0000 0.825 0.000 1.000 0.000 0.000
#> GSM494570 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494597 3 0.2216 0.892 0.000 0.092 0.908 0.000
#> GSM494607 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> GSM494561 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494569 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494592 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> GSM494577 2 0.0000 0.825 0.000 1.000 0.000 0.000
#> GSM494588 1 0.3837 0.663 0.776 0.000 0.000 0.224
#> GSM494590 3 0.0000 0.990 0.000 0.000 1.000 0.000
#> GSM494609 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494608 2 0.4866 0.379 0.000 0.596 0.000 0.404
#> GSM494606 4 0.4072 0.581 0.000 0.252 0.000 0.748
#> GSM494574 2 0.0000 0.825 0.000 1.000 0.000 0.000
#> GSM494573 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494566 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494601 2 0.0000 0.825 0.000 1.000 0.000 0.000
#> GSM494557 2 0.0000 0.825 0.000 1.000 0.000 0.000
#> GSM494579 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494596 3 0.0000 0.990 0.000 0.000 1.000 0.000
#> GSM494575 2 0.0000 0.825 0.000 1.000 0.000 0.000
#> GSM494625 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494654 3 0.0000 0.990 0.000 0.000 1.000 0.000
#> GSM494664 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> GSM494624 1 0.4431 0.541 0.696 0.000 0.000 0.304
#> GSM494651 4 0.4855 0.258 0.000 0.400 0.000 0.600
#> GSM494662 4 0.4277 0.603 0.280 0.000 0.000 0.720
#> GSM494627 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494673 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> GSM494649 4 0.4877 0.319 0.408 0.000 0.000 0.592
#> GSM494658 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> GSM494653 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> GSM494643 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494672 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> GSM494618 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494631 2 0.4072 0.673 0.000 0.748 0.000 0.252
#> GSM494619 1 0.4406 0.550 0.700 0.000 0.000 0.300
#> GSM494674 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> GSM494616 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494663 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494628 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494632 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494660 4 0.4072 0.650 0.252 0.000 0.000 0.748
#> GSM494622 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494642 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> GSM494670 1 0.4888 0.285 0.588 0.000 0.000 0.412
#> GSM494675 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494641 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> GSM494636 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494640 4 0.3610 0.694 0.000 0.200 0.000 0.800
#> GSM494623 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> GSM494644 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494646 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494665 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> GSM494638 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494645 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494671 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> GSM494620 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> GSM494630 4 0.4981 0.148 0.464 0.000 0.000 0.536
#> GSM494657 3 0.0000 0.990 0.000 0.000 1.000 0.000
#> GSM494667 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> GSM494621 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> GSM494629 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494637 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494652 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> GSM494648 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> GSM494650 3 0.0000 0.990 0.000 0.000 1.000 0.000
#> GSM494669 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> GSM494666 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> GSM494668 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> GSM494633 4 0.0592 0.904 0.016 0.000 0.000 0.984
#> GSM494634 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> GSM494639 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494661 2 0.4072 0.673 0.000 0.748 0.000 0.252
#> GSM494617 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494626 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> GSM494656 3 0.0000 0.990 0.000 0.000 1.000 0.000
#> GSM494635 4 0.0000 0.917 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM494565 4 0.0290 0.910 0.000 0.008 0.000 0.992 0.000
#> GSM494594 3 0.0000 0.989 0.000 0.000 1.000 0.000 0.000
#> GSM494604 1 0.0162 0.982 0.996 0.000 0.000 0.004 0.000
#> GSM494564 5 0.0000 0.989 0.000 0.000 0.000 0.000 1.000
#> GSM494591 3 0.0000 0.989 0.000 0.000 1.000 0.000 0.000
#> GSM494567 4 0.3336 0.648 0.000 0.228 0.000 0.772 0.000
#> GSM494602 1 0.2929 0.773 0.820 0.180 0.000 0.000 0.000
#> GSM494613 2 0.3508 0.675 0.000 0.748 0.000 0.252 0.000
#> GSM494589 4 0.0703 0.902 0.000 0.000 0.000 0.976 0.024
#> GSM494598 2 0.5008 0.449 0.300 0.644 0.000 0.056 0.000
#> GSM494593 4 0.6647 0.147 0.304 0.252 0.000 0.444 0.000
#> GSM494583 2 0.0000 0.838 0.000 1.000 0.000 0.000 0.000
#> GSM494612 2 0.0000 0.838 0.000 1.000 0.000 0.000 0.000
#> GSM494558 2 0.3534 0.669 0.000 0.744 0.000 0.256 0.000
#> GSM494556 4 0.3452 0.634 0.000 0.244 0.000 0.756 0.000
#> GSM494559 4 0.4074 0.454 0.000 0.000 0.000 0.636 0.364
#> GSM494571 3 0.0000 0.989 0.000 0.000 1.000 0.000 0.000
#> GSM494614 2 0.4304 0.211 0.000 0.516 0.000 0.484 0.000
#> GSM494603 4 0.0703 0.902 0.000 0.000 0.000 0.976 0.024
#> GSM494568 4 0.0000 0.916 0.000 0.000 0.000 1.000 0.000
#> GSM494572 3 0.0000 0.989 0.000 0.000 1.000 0.000 0.000
#> GSM494600 4 0.0703 0.902 0.000 0.000 0.000 0.976 0.024
#> GSM494562 2 0.0000 0.838 0.000 1.000 0.000 0.000 0.000
#> GSM494615 4 0.0000 0.916 0.000 0.000 0.000 1.000 0.000
#> GSM494582 2 0.0000 0.838 0.000 1.000 0.000 0.000 0.000
#> GSM494599 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> GSM494610 2 0.0000 0.838 0.000 1.000 0.000 0.000 0.000
#> GSM494587 2 0.0000 0.838 0.000 1.000 0.000 0.000 0.000
#> GSM494581 2 0.0000 0.838 0.000 1.000 0.000 0.000 0.000
#> GSM494580 2 0.3508 0.675 0.000 0.748 0.000 0.252 0.000
#> GSM494563 5 0.0162 0.987 0.004 0.000 0.000 0.000 0.996
#> GSM494576 2 0.0000 0.838 0.000 1.000 0.000 0.000 0.000
#> GSM494605 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> GSM494584 2 0.0000 0.838 0.000 1.000 0.000 0.000 0.000
#> GSM494586 2 0.0000 0.838 0.000 1.000 0.000 0.000 0.000
#> GSM494578 2 0.3508 0.675 0.000 0.748 0.000 0.252 0.000
#> GSM494585 2 0.0000 0.838 0.000 1.000 0.000 0.000 0.000
#> GSM494611 2 0.5748 0.389 0.300 0.584 0.000 0.116 0.000
#> GSM494560 4 0.0963 0.894 0.000 0.000 0.000 0.964 0.036
#> GSM494595 2 0.0000 0.838 0.000 1.000 0.000 0.000 0.000
#> GSM494570 5 0.0000 0.989 0.000 0.000 0.000 0.000 1.000
#> GSM494597 3 0.1908 0.891 0.000 0.092 0.908 0.000 0.000
#> GSM494607 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> GSM494561 5 0.0000 0.989 0.000 0.000 0.000 0.000 1.000
#> GSM494569 4 0.0000 0.916 0.000 0.000 0.000 1.000 0.000
#> GSM494592 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> GSM494577 2 0.0000 0.838 0.000 1.000 0.000 0.000 0.000
#> GSM494588 5 0.0000 0.989 0.000 0.000 0.000 0.000 1.000
#> GSM494590 3 0.0000 0.989 0.000 0.000 1.000 0.000 0.000
#> GSM494609 4 0.0000 0.916 0.000 0.000 0.000 1.000 0.000
#> GSM494608 2 0.4192 0.339 0.000 0.596 0.000 0.404 0.000
#> GSM494606 4 0.6637 0.154 0.300 0.252 0.000 0.448 0.000
#> GSM494574 2 0.0000 0.838 0.000 1.000 0.000 0.000 0.000
#> GSM494573 5 0.0162 0.986 0.000 0.000 0.000 0.004 0.996
#> GSM494566 4 0.0000 0.916 0.000 0.000 0.000 1.000 0.000
#> GSM494601 2 0.0000 0.838 0.000 1.000 0.000 0.000 0.000
#> GSM494557 2 0.0000 0.838 0.000 1.000 0.000 0.000 0.000
#> GSM494579 4 0.0000 0.916 0.000 0.000 0.000 1.000 0.000
#> GSM494596 3 0.0000 0.989 0.000 0.000 1.000 0.000 0.000
#> GSM494575 2 0.0000 0.838 0.000 1.000 0.000 0.000 0.000
#> GSM494625 5 0.0162 0.987 0.000 0.000 0.000 0.004 0.996
#> GSM494654 3 0.0000 0.989 0.000 0.000 1.000 0.000 0.000
#> GSM494664 1 0.0162 0.982 0.996 0.000 0.000 0.000 0.004
#> GSM494624 5 0.0000 0.989 0.000 0.000 0.000 0.000 1.000
#> GSM494651 4 0.4182 0.276 0.000 0.400 0.000 0.600 0.000
#> GSM494662 4 0.3876 0.723 0.032 0.000 0.000 0.776 0.192
#> GSM494627 4 0.0000 0.916 0.000 0.000 0.000 1.000 0.000
#> GSM494673 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> GSM494649 5 0.0000 0.989 0.000 0.000 0.000 0.000 1.000
#> GSM494658 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> GSM494653 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> GSM494643 4 0.0000 0.916 0.000 0.000 0.000 1.000 0.000
#> GSM494672 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> GSM494618 4 0.0000 0.916 0.000 0.000 0.000 1.000 0.000
#> GSM494631 2 0.3508 0.675 0.000 0.748 0.000 0.252 0.000
#> GSM494619 5 0.0703 0.976 0.024 0.000 0.000 0.000 0.976
#> GSM494674 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> GSM494616 4 0.0000 0.916 0.000 0.000 0.000 1.000 0.000
#> GSM494663 4 0.0000 0.916 0.000 0.000 0.000 1.000 0.000
#> GSM494628 4 0.0000 0.916 0.000 0.000 0.000 1.000 0.000
#> GSM494632 4 0.0000 0.916 0.000 0.000 0.000 1.000 0.000
#> GSM494660 5 0.0000 0.989 0.000 0.000 0.000 0.000 1.000
#> GSM494622 4 0.0000 0.916 0.000 0.000 0.000 1.000 0.000
#> GSM494642 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> GSM494670 1 0.2020 0.861 0.900 0.000 0.000 0.100 0.000
#> GSM494675 4 0.0000 0.916 0.000 0.000 0.000 1.000 0.000
#> GSM494641 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> GSM494636 4 0.0000 0.916 0.000 0.000 0.000 1.000 0.000
#> GSM494640 4 0.3109 0.702 0.000 0.200 0.000 0.800 0.000
#> GSM494623 5 0.0703 0.976 0.024 0.000 0.000 0.000 0.976
#> GSM494644 4 0.0000 0.916 0.000 0.000 0.000 1.000 0.000
#> GSM494646 4 0.0000 0.916 0.000 0.000 0.000 1.000 0.000
#> GSM494665 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> GSM494638 4 0.0000 0.916 0.000 0.000 0.000 1.000 0.000
#> GSM494645 4 0.0000 0.916 0.000 0.000 0.000 1.000 0.000
#> GSM494671 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> GSM494620 5 0.0703 0.976 0.024 0.000 0.000 0.000 0.976
#> GSM494630 5 0.0000 0.989 0.000 0.000 0.000 0.000 1.000
#> GSM494657 3 0.0000 0.989 0.000 0.000 1.000 0.000 0.000
#> GSM494667 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> GSM494621 5 0.0703 0.976 0.024 0.000 0.000 0.000 0.976
#> GSM494629 4 0.0000 0.916 0.000 0.000 0.000 1.000 0.000
#> GSM494637 4 0.0000 0.916 0.000 0.000 0.000 1.000 0.000
#> GSM494652 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> GSM494648 5 0.0703 0.976 0.024 0.000 0.000 0.000 0.976
#> GSM494650 3 0.0000 0.989 0.000 0.000 1.000 0.000 0.000
#> GSM494669 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> GSM494666 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> GSM494668 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> GSM494633 5 0.0000 0.989 0.000 0.000 0.000 0.000 1.000
#> GSM494634 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> GSM494639 4 0.0000 0.916 0.000 0.000 0.000 1.000 0.000
#> GSM494661 2 0.3508 0.675 0.000 0.748 0.000 0.252 0.000
#> GSM494617 4 0.0000 0.916 0.000 0.000 0.000 1.000 0.000
#> GSM494626 4 0.0000 0.916 0.000 0.000 0.000 1.000 0.000
#> GSM494656 3 0.0000 0.989 0.000 0.000 1.000 0.000 0.000
#> GSM494635 4 0.0000 0.916 0.000 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM494565 4 0.0363 0.940 0.000 0.000 0.000 0.988 0.012 0.000
#> GSM494594 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494604 1 0.0146 0.965 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM494564 6 0.0000 0.988 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494591 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494567 4 0.2562 0.769 0.000 0.000 0.000 0.828 0.172 0.000
#> GSM494602 2 0.1267 0.892 0.060 0.940 0.000 0.000 0.000 0.000
#> GSM494613 5 0.0000 0.888 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494589 4 0.0713 0.931 0.000 0.000 0.000 0.972 0.000 0.028
#> GSM494598 2 0.0000 0.941 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494593 2 0.0891 0.920 0.008 0.968 0.000 0.024 0.000 0.000
#> GSM494583 5 0.0000 0.888 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494612 2 0.0000 0.941 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494558 5 0.2823 0.655 0.000 0.000 0.000 0.204 0.796 0.000
#> GSM494556 4 0.1910 0.851 0.000 0.000 0.000 0.892 0.108 0.000
#> GSM494559 4 0.3672 0.452 0.000 0.000 0.000 0.632 0.000 0.368
#> GSM494571 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494614 5 0.3464 0.550 0.000 0.000 0.000 0.312 0.688 0.000
#> GSM494603 4 0.0713 0.931 0.000 0.000 0.000 0.972 0.000 0.028
#> GSM494568 4 0.0000 0.948 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494572 3 0.3050 0.733 0.000 0.000 0.764 0.000 0.236 0.000
#> GSM494600 4 0.0713 0.931 0.000 0.000 0.000 0.972 0.000 0.028
#> GSM494562 5 0.2048 0.819 0.000 0.120 0.000 0.000 0.880 0.000
#> GSM494615 4 0.0000 0.948 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494582 2 0.0000 0.941 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494599 1 0.0000 0.970 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494610 2 0.0000 0.941 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494587 5 0.0146 0.888 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM494581 5 0.3221 0.635 0.000 0.264 0.000 0.000 0.736 0.000
#> GSM494580 5 0.0000 0.888 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494563 6 0.0000 0.988 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494576 5 0.0146 0.888 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM494605 1 0.0000 0.970 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494584 5 0.0000 0.888 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494586 5 0.2003 0.822 0.000 0.116 0.000 0.000 0.884 0.000
#> GSM494578 5 0.0000 0.888 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494585 5 0.0363 0.884 0.000 0.012 0.000 0.000 0.988 0.000
#> GSM494611 2 0.0000 0.941 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494560 4 0.1010 0.923 0.000 0.004 0.000 0.960 0.000 0.036
#> GSM494595 2 0.0000 0.941 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494570 6 0.0000 0.988 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494597 3 0.3309 0.669 0.000 0.000 0.720 0.000 0.280 0.000
#> GSM494607 1 0.0000 0.970 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494561 6 0.0000 0.988 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494569 4 0.0000 0.948 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494592 2 0.3659 0.430 0.364 0.636 0.000 0.000 0.000 0.000
#> GSM494577 5 0.1141 0.865 0.000 0.052 0.000 0.000 0.948 0.000
#> GSM494588 6 0.0000 0.988 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494590 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494609 4 0.0000 0.948 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494608 5 0.4531 0.329 0.000 0.036 0.000 0.408 0.556 0.000
#> GSM494606 2 0.1265 0.898 0.008 0.948 0.000 0.044 0.000 0.000
#> GSM494574 2 0.0000 0.941 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494573 6 0.0000 0.988 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494566 4 0.0000 0.948 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494601 5 0.0000 0.888 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494557 5 0.0000 0.888 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494579 4 0.0000 0.948 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494596 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494575 2 0.0000 0.941 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494625 6 0.0260 0.982 0.000 0.000 0.000 0.008 0.000 0.992
#> GSM494654 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494664 1 0.0000 0.970 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494624 6 0.0000 0.988 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494651 4 0.3833 0.226 0.000 0.000 0.000 0.556 0.444 0.000
#> GSM494662 4 0.4165 0.702 0.128 0.000 0.000 0.744 0.000 0.128
#> GSM494627 4 0.0000 0.948 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494673 1 0.0000 0.970 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494649 6 0.0000 0.988 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494658 1 0.2527 0.775 0.832 0.168 0.000 0.000 0.000 0.000
#> GSM494653 1 0.0000 0.970 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494643 4 0.0000 0.948 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494672 1 0.0000 0.970 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494618 4 0.0000 0.948 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494631 5 0.0000 0.888 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494619 6 0.0713 0.972 0.028 0.000 0.000 0.000 0.000 0.972
#> GSM494674 1 0.0000 0.970 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494616 4 0.0000 0.948 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494663 4 0.0000 0.948 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494628 4 0.0000 0.948 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494632 4 0.0000 0.948 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494660 6 0.0000 0.988 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494622 4 0.0000 0.948 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494642 1 0.0000 0.970 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0000 0.970 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494659 1 0.0000 0.970 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494670 1 0.4331 0.070 0.516 0.464 0.000 0.020 0.000 0.000
#> GSM494675 4 0.0000 0.948 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494641 1 0.0000 0.970 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494636 4 0.0000 0.948 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494640 4 0.3351 0.591 0.000 0.000 0.000 0.712 0.288 0.000
#> GSM494623 6 0.0713 0.972 0.028 0.000 0.000 0.000 0.000 0.972
#> GSM494644 4 0.0000 0.948 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494646 4 0.0000 0.948 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494665 1 0.0000 0.970 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494638 4 0.0000 0.948 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494645 4 0.0000 0.948 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494671 1 0.0000 0.970 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.970 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494620 6 0.0713 0.972 0.028 0.000 0.000 0.000 0.000 0.972
#> GSM494630 6 0.0000 0.988 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494657 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494667 1 0.0000 0.970 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494621 6 0.0713 0.972 0.028 0.000 0.000 0.000 0.000 0.972
#> GSM494629 4 0.0000 0.948 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494637 4 0.0000 0.948 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494652 1 0.0000 0.970 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494648 6 0.0713 0.972 0.028 0.000 0.000 0.000 0.000 0.972
#> GSM494650 3 0.1444 0.892 0.000 0.000 0.928 0.000 0.072 0.000
#> GSM494669 1 0.0000 0.970 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494666 1 0.0000 0.970 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494668 1 0.0000 0.970 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494633 6 0.0000 0.988 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494634 1 0.0000 0.970 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494639 4 0.0000 0.948 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494661 5 0.0000 0.888 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494617 4 0.0000 0.948 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494626 4 0.0000 0.948 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM494656 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494635 4 0.0000 0.948 0.000 0.000 0.000 1.000 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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 6)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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.
# code only for demonstration
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) individual(p) k
#> ATC:pam 119 5.08e-01 0.000108 0.128878 0.000532 2
#> ATC:pam 107 3.13e-03 0.006513 0.083440 0.002141 3
#> ATC:pam 113 3.72e-06 0.027122 0.001228 0.011882 4
#> ATC:pam 112 1.51e-05 0.000879 0.003700 0.000176 5
#> ATC:pam 115 8.39e-07 0.003112 0.000129 0.000371 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.
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"]
# you can also extract it by
# 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 51941 rows and 120 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:
k and the heatmap of
predicted classes for each k.k.k.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:
k;k, the area increased is defined as \(A_k - A_{k-1}\).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 0.473 0.875 0.904 0.4597 0.497 0.497
#> 3 3 0.813 0.725 0.883 0.3400 0.861 0.731
#> 4 4 0.627 0.801 0.871 0.0841 0.858 0.662
#> 5 5 0.798 0.812 0.907 0.1697 0.881 0.622
#> 6 6 0.844 0.817 0.899 0.0255 0.929 0.705
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
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.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM494565 2 0.000 0.950 0.000 1.000
#> GSM494594 2 0.584 0.840 0.140 0.860
#> GSM494604 1 0.584 0.940 0.860 0.140
#> GSM494564 2 0.000 0.950 0.000 1.000
#> GSM494591 2 0.584 0.840 0.140 0.860
#> GSM494567 2 0.000 0.950 0.000 1.000
#> GSM494602 2 0.000 0.950 0.000 1.000
#> GSM494613 2 0.000 0.950 0.000 1.000
#> GSM494589 2 0.000 0.950 0.000 1.000
#> GSM494598 2 0.000 0.950 0.000 1.000
#> GSM494593 2 0.000 0.950 0.000 1.000
#> GSM494583 2 0.000 0.950 0.000 1.000
#> GSM494612 2 0.000 0.950 0.000 1.000
#> GSM494558 1 0.993 0.132 0.548 0.452
#> GSM494556 2 0.000 0.950 0.000 1.000
#> GSM494559 2 0.000 0.950 0.000 1.000
#> GSM494571 2 0.584 0.840 0.140 0.860
#> GSM494614 2 0.000 0.950 0.000 1.000
#> GSM494603 2 0.000 0.950 0.000 1.000
#> GSM494568 2 0.295 0.890 0.052 0.948
#> GSM494572 2 0.584 0.840 0.140 0.860
#> GSM494600 2 0.000 0.950 0.000 1.000
#> GSM494562 2 0.000 0.950 0.000 1.000
#> GSM494615 2 0.000 0.950 0.000 1.000
#> GSM494582 2 0.000 0.950 0.000 1.000
#> GSM494599 2 0.000 0.950 0.000 1.000
#> GSM494610 2 0.000 0.950 0.000 1.000
#> GSM494587 2 0.000 0.950 0.000 1.000
#> GSM494581 2 0.000 0.950 0.000 1.000
#> GSM494580 2 0.000 0.950 0.000 1.000
#> GSM494563 2 0.000 0.950 0.000 1.000
#> GSM494576 2 0.000 0.950 0.000 1.000
#> GSM494605 1 0.584 0.940 0.860 0.140
#> GSM494584 2 0.000 0.950 0.000 1.000
#> GSM494586 2 0.000 0.950 0.000 1.000
#> GSM494578 2 0.000 0.950 0.000 1.000
#> GSM494585 2 0.000 0.950 0.000 1.000
#> GSM494611 2 0.000 0.950 0.000 1.000
#> GSM494560 2 0.000 0.950 0.000 1.000
#> GSM494595 2 0.000 0.950 0.000 1.000
#> GSM494570 2 0.000 0.950 0.000 1.000
#> GSM494597 2 0.584 0.840 0.140 0.860
#> GSM494607 2 0.000 0.950 0.000 1.000
#> GSM494561 2 0.000 0.950 0.000 1.000
#> GSM494569 1 0.584 0.940 0.860 0.140
#> GSM494592 2 0.000 0.950 0.000 1.000
#> GSM494577 2 0.000 0.950 0.000 1.000
#> GSM494588 2 0.000 0.950 0.000 1.000
#> GSM494590 2 0.584 0.840 0.140 0.860
#> GSM494609 2 0.000 0.950 0.000 1.000
#> GSM494608 2 0.000 0.950 0.000 1.000
#> GSM494606 2 0.000 0.950 0.000 1.000
#> GSM494574 2 0.000 0.950 0.000 1.000
#> GSM494573 2 0.000 0.950 0.000 1.000
#> GSM494566 2 0.000 0.950 0.000 1.000
#> GSM494601 2 0.358 0.897 0.068 0.932
#> GSM494557 2 0.000 0.950 0.000 1.000
#> GSM494579 2 0.000 0.950 0.000 1.000
#> GSM494596 2 0.584 0.840 0.140 0.860
#> GSM494575 2 0.000 0.950 0.000 1.000
#> GSM494625 1 0.584 0.940 0.860 0.140
#> GSM494654 2 0.584 0.840 0.140 0.860
#> GSM494664 1 0.584 0.940 0.860 0.140
#> GSM494624 1 0.584 0.940 0.860 0.140
#> GSM494651 1 0.975 0.111 0.592 0.408
#> GSM494662 1 0.584 0.940 0.860 0.140
#> GSM494627 1 0.949 0.638 0.632 0.368
#> GSM494673 1 0.584 0.940 0.860 0.140
#> GSM494649 1 0.584 0.940 0.860 0.140
#> GSM494658 1 0.775 0.851 0.772 0.228
#> GSM494653 1 0.584 0.940 0.860 0.140
#> GSM494643 1 0.760 0.859 0.780 0.220
#> GSM494672 1 0.584 0.940 0.860 0.140
#> GSM494618 1 0.584 0.940 0.860 0.140
#> GSM494631 2 0.000 0.950 0.000 1.000
#> GSM494619 1 0.584 0.940 0.860 0.140
#> GSM494674 1 0.584 0.940 0.860 0.140
#> GSM494616 1 0.584 0.940 0.860 0.140
#> GSM494663 1 0.584 0.940 0.860 0.140
#> GSM494628 1 0.584 0.940 0.860 0.140
#> GSM494632 1 0.584 0.940 0.860 0.140
#> GSM494660 1 0.584 0.940 0.860 0.140
#> GSM494622 2 1.000 -0.300 0.488 0.512
#> GSM494642 1 0.584 0.940 0.860 0.140
#> GSM494647 1 0.584 0.940 0.860 0.140
#> GSM494659 1 0.584 0.940 0.860 0.140
#> GSM494670 1 0.625 0.926 0.844 0.156
#> GSM494675 2 0.000 0.950 0.000 1.000
#> GSM494641 1 0.584 0.940 0.860 0.140
#> GSM494636 1 0.584 0.940 0.860 0.140
#> GSM494640 1 0.993 0.132 0.548 0.452
#> GSM494623 1 0.584 0.940 0.860 0.140
#> GSM494644 1 0.584 0.940 0.860 0.140
#> GSM494646 1 0.584 0.940 0.860 0.140
#> GSM494665 1 0.584 0.940 0.860 0.140
#> GSM494638 1 0.584 0.940 0.860 0.140
#> GSM494645 1 0.584 0.940 0.860 0.140
#> GSM494671 1 0.584 0.940 0.860 0.140
#> GSM494655 1 0.584 0.940 0.860 0.140
#> GSM494620 1 0.584 0.940 0.860 0.140
#> GSM494630 1 0.584 0.940 0.860 0.140
#> GSM494657 2 0.584 0.840 0.140 0.860
#> GSM494667 1 0.584 0.940 0.860 0.140
#> GSM494621 1 0.584 0.940 0.860 0.140
#> GSM494629 2 0.993 -0.183 0.452 0.548
#> GSM494637 1 0.895 0.734 0.688 0.312
#> GSM494652 1 0.584 0.940 0.860 0.140
#> GSM494648 1 0.584 0.940 0.860 0.140
#> GSM494650 1 0.975 0.111 0.592 0.408
#> GSM494669 1 0.584 0.940 0.860 0.140
#> GSM494666 1 0.584 0.940 0.860 0.140
#> GSM494668 1 0.584 0.940 0.860 0.140
#> GSM494633 1 0.584 0.940 0.860 0.140
#> GSM494634 1 0.584 0.940 0.860 0.140
#> GSM494639 1 0.584 0.940 0.860 0.140
#> GSM494661 1 0.980 0.114 0.584 0.416
#> GSM494617 1 0.584 0.940 0.860 0.140
#> GSM494626 1 0.584 0.940 0.860 0.140
#> GSM494656 2 0.584 0.840 0.140 0.860
#> GSM494635 1 0.584 0.940 0.860 0.140
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM494565 2 0.2165 0.7450 0.064 0.936 0.000
#> GSM494594 3 0.0424 0.7816 0.000 0.008 0.992
#> GSM494604 1 0.1267 0.9390 0.972 0.024 0.004
#> GSM494564 2 0.2165 0.7450 0.064 0.936 0.000
#> GSM494591 3 0.0424 0.7816 0.000 0.008 0.992
#> GSM494567 2 0.0000 0.7208 0.000 1.000 0.000
#> GSM494602 2 0.2165 0.7450 0.064 0.936 0.000
#> GSM494613 2 0.0000 0.7208 0.000 1.000 0.000
#> GSM494589 2 0.2165 0.7450 0.064 0.936 0.000
#> GSM494598 3 0.7995 -0.2926 0.060 0.460 0.480
#> GSM494593 2 0.2165 0.7450 0.064 0.936 0.000
#> GSM494583 2 0.6302 0.2400 0.000 0.520 0.480
#> GSM494612 2 0.6302 0.2400 0.000 0.520 0.480
#> GSM494558 3 0.9816 0.1821 0.356 0.244 0.400
#> GSM494556 2 0.0000 0.7208 0.000 1.000 0.000
#> GSM494559 2 0.2165 0.7450 0.064 0.936 0.000
#> GSM494571 3 0.0424 0.7816 0.000 0.008 0.992
#> GSM494614 2 0.1031 0.7324 0.024 0.976 0.000
#> GSM494603 2 0.2165 0.7450 0.064 0.936 0.000
#> GSM494568 2 0.5292 0.6012 0.172 0.800 0.028
#> GSM494572 3 0.0424 0.7816 0.000 0.008 0.992
#> GSM494600 2 0.2165 0.7450 0.064 0.936 0.000
#> GSM494562 2 0.6302 0.2400 0.000 0.520 0.480
#> GSM494615 2 0.2165 0.7450 0.064 0.936 0.000
#> GSM494582 2 0.6302 0.2400 0.000 0.520 0.480
#> GSM494599 2 0.4346 0.6092 0.184 0.816 0.000
#> GSM494610 2 0.6302 0.2400 0.000 0.520 0.480
#> GSM494587 2 0.6302 0.2400 0.000 0.520 0.480
#> GSM494581 2 0.4452 0.5953 0.000 0.808 0.192
#> GSM494580 2 0.3116 0.6706 0.000 0.892 0.108
#> GSM494563 2 0.2165 0.7450 0.064 0.936 0.000
#> GSM494576 2 0.6302 0.2400 0.000 0.520 0.480
#> GSM494605 1 0.0237 0.9593 0.996 0.000 0.004
#> GSM494584 2 0.6302 0.2400 0.000 0.520 0.480
#> GSM494586 2 0.6302 0.2400 0.000 0.520 0.480
#> GSM494578 2 0.0000 0.7208 0.000 1.000 0.000
#> GSM494585 2 0.6302 0.2400 0.000 0.520 0.480
#> GSM494611 3 0.7586 -0.2943 0.040 0.480 0.480
#> GSM494560 2 0.2165 0.7450 0.064 0.936 0.000
#> GSM494595 2 0.6302 0.2400 0.000 0.520 0.480
#> GSM494570 2 0.2537 0.7318 0.080 0.920 0.000
#> GSM494597 3 0.0424 0.7816 0.000 0.008 0.992
#> GSM494607 2 0.4452 0.5981 0.192 0.808 0.000
#> GSM494561 2 0.2448 0.7354 0.076 0.924 0.000
#> GSM494569 1 0.0237 0.9590 0.996 0.000 0.004
#> GSM494592 2 0.2261 0.7420 0.068 0.932 0.000
#> GSM494577 2 0.6302 0.2400 0.000 0.520 0.480
#> GSM494588 2 0.2165 0.7450 0.064 0.936 0.000
#> GSM494590 3 0.0424 0.7816 0.000 0.008 0.992
#> GSM494609 2 0.2165 0.7450 0.064 0.936 0.000
#> GSM494608 2 0.0747 0.7289 0.016 0.984 0.000
#> GSM494606 2 0.2165 0.7450 0.064 0.936 0.000
#> GSM494574 2 0.6302 0.2400 0.000 0.520 0.480
#> GSM494573 2 0.2165 0.7450 0.064 0.936 0.000
#> GSM494566 2 0.2165 0.7450 0.064 0.936 0.000
#> GSM494601 3 0.3412 0.6777 0.000 0.124 0.876
#> GSM494557 2 0.6302 0.2400 0.000 0.520 0.480
#> GSM494579 2 0.2165 0.7450 0.064 0.936 0.000
#> GSM494596 3 0.0424 0.7816 0.000 0.008 0.992
#> GSM494575 2 0.6302 0.2400 0.000 0.520 0.480
#> GSM494625 1 0.0237 0.9590 0.996 0.000 0.004
#> GSM494654 3 0.0424 0.7816 0.000 0.008 0.992
#> GSM494664 1 0.0000 0.9592 1.000 0.000 0.000
#> GSM494624 1 0.0237 0.9590 0.996 0.000 0.004
#> GSM494651 1 0.8206 0.0412 0.480 0.072 0.448
#> GSM494662 1 0.0237 0.9590 0.996 0.000 0.004
#> GSM494627 1 0.1163 0.9423 0.972 0.000 0.028
#> GSM494673 1 0.0237 0.9593 0.996 0.000 0.004
#> GSM494649 1 0.0237 0.9590 0.996 0.000 0.004
#> GSM494658 1 0.1643 0.9194 0.956 0.044 0.000
#> GSM494653 1 0.0237 0.9593 0.996 0.000 0.004
#> GSM494643 1 0.2152 0.9212 0.948 0.016 0.036
#> GSM494672 1 0.0237 0.9593 0.996 0.000 0.004
#> GSM494618 1 0.1289 0.9389 0.968 0.000 0.032
#> GSM494631 2 0.1289 0.7088 0.000 0.968 0.032
#> GSM494619 1 0.0237 0.9590 0.996 0.000 0.004
#> GSM494674 1 0.0237 0.9593 0.996 0.000 0.004
#> GSM494616 1 0.0237 0.9590 0.996 0.000 0.004
#> GSM494663 1 0.0237 0.9590 0.996 0.000 0.004
#> GSM494628 1 0.0237 0.9590 0.996 0.000 0.004
#> GSM494632 1 0.0237 0.9593 0.996 0.000 0.004
#> GSM494660 1 0.0237 0.9590 0.996 0.000 0.004
#> GSM494622 1 0.2187 0.9206 0.948 0.024 0.028
#> GSM494642 1 0.0237 0.9593 0.996 0.000 0.004
#> GSM494647 1 0.0237 0.9593 0.996 0.000 0.004
#> GSM494659 1 0.0237 0.9593 0.996 0.000 0.004
#> GSM494670 1 0.0237 0.9593 0.996 0.000 0.004
#> GSM494675 2 0.0237 0.7201 0.000 0.996 0.004
#> GSM494641 1 0.0237 0.9593 0.996 0.000 0.004
#> GSM494636 1 0.0237 0.9590 0.996 0.000 0.004
#> GSM494640 1 0.7698 0.4296 0.624 0.072 0.304
#> GSM494623 1 0.0237 0.9590 0.996 0.000 0.004
#> GSM494644 1 0.0237 0.9593 0.996 0.000 0.004
#> GSM494646 1 0.0237 0.9593 0.996 0.000 0.004
#> GSM494665 1 0.0237 0.9593 0.996 0.000 0.004
#> GSM494638 1 0.0237 0.9590 0.996 0.000 0.004
#> GSM494645 1 0.0237 0.9593 0.996 0.000 0.004
#> GSM494671 1 0.0237 0.9593 0.996 0.000 0.004
#> GSM494655 1 0.0237 0.9593 0.996 0.000 0.004
#> GSM494620 1 0.0237 0.9590 0.996 0.000 0.004
#> GSM494630 1 0.0237 0.9590 0.996 0.000 0.004
#> GSM494657 3 0.0424 0.7816 0.000 0.008 0.992
#> GSM494667 1 0.0237 0.9593 0.996 0.000 0.004
#> GSM494621 1 0.0237 0.9590 0.996 0.000 0.004
#> GSM494629 1 0.6601 0.4951 0.676 0.296 0.028
#> GSM494637 1 0.1163 0.9423 0.972 0.000 0.028
#> GSM494652 1 0.0237 0.9593 0.996 0.000 0.004
#> GSM494648 1 0.0237 0.9590 0.996 0.000 0.004
#> GSM494650 3 0.6678 -0.1115 0.480 0.008 0.512
#> GSM494669 1 0.0237 0.9593 0.996 0.000 0.004
#> GSM494666 1 0.0237 0.9593 0.996 0.000 0.004
#> GSM494668 1 0.0237 0.9593 0.996 0.000 0.004
#> GSM494633 1 0.0237 0.9590 0.996 0.000 0.004
#> GSM494634 1 0.0237 0.9593 0.996 0.000 0.004
#> GSM494639 1 0.0237 0.9590 0.996 0.000 0.004
#> GSM494661 1 0.8206 0.0412 0.480 0.072 0.448
#> GSM494617 1 0.0237 0.9590 0.996 0.000 0.004
#> GSM494626 1 0.0237 0.9590 0.996 0.000 0.004
#> GSM494656 3 0.0424 0.7816 0.000 0.008 0.992
#> GSM494635 1 0.0237 0.9593 0.996 0.000 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM494565 2 0.3172 0.8820 0.000 0.840 0.000 0.160
#> GSM494594 3 0.4624 0.8819 0.340 0.000 0.660 0.000
#> GSM494604 1 0.6792 0.7098 0.476 0.096 0.000 0.428
#> GSM494564 2 0.3355 0.8809 0.004 0.836 0.000 0.160
#> GSM494591 3 0.4624 0.8819 0.340 0.000 0.660 0.000
#> GSM494567 2 0.3172 0.8820 0.000 0.840 0.000 0.160
#> GSM494602 2 0.0188 0.9000 0.004 0.996 0.000 0.000
#> GSM494613 2 0.2868 0.8914 0.000 0.864 0.000 0.136
#> GSM494589 2 0.3172 0.8820 0.000 0.840 0.000 0.160
#> GSM494598 2 0.0000 0.9008 0.000 1.000 0.000 0.000
#> GSM494593 2 0.0000 0.9008 0.000 1.000 0.000 0.000
#> GSM494583 2 0.0000 0.9008 0.000 1.000 0.000 0.000
#> GSM494612 2 0.0000 0.9008 0.000 1.000 0.000 0.000
#> GSM494558 3 0.2831 0.6900 0.004 0.000 0.876 0.120
#> GSM494556 2 0.2868 0.8914 0.000 0.864 0.000 0.136
#> GSM494559 2 0.3172 0.8820 0.000 0.840 0.000 0.160
#> GSM494571 3 0.4643 0.8814 0.344 0.000 0.656 0.000
#> GSM494614 2 0.2868 0.8914 0.000 0.864 0.000 0.136
#> GSM494603 2 0.3355 0.8809 0.004 0.836 0.000 0.160
#> GSM494568 4 0.2831 0.6545 0.004 0.120 0.000 0.876
#> GSM494572 3 0.4643 0.8814 0.344 0.000 0.656 0.000
#> GSM494600 2 0.3172 0.8820 0.000 0.840 0.000 0.160
#> GSM494562 2 0.0000 0.9008 0.000 1.000 0.000 0.000
#> GSM494615 2 0.3355 0.8809 0.004 0.836 0.000 0.160
#> GSM494582 2 0.0000 0.9008 0.000 1.000 0.000 0.000
#> GSM494599 2 0.0895 0.9010 0.004 0.976 0.000 0.020
#> GSM494610 2 0.0000 0.9008 0.000 1.000 0.000 0.000
#> GSM494587 2 0.0000 0.9008 0.000 1.000 0.000 0.000
#> GSM494581 2 0.0000 0.9008 0.000 1.000 0.000 0.000
#> GSM494580 2 0.6382 0.6730 0.004 0.664 0.196 0.136
#> GSM494563 2 0.3355 0.8809 0.004 0.836 0.000 0.160
#> GSM494576 2 0.0000 0.9008 0.000 1.000 0.000 0.000
#> GSM494605 4 0.4605 -0.0169 0.336 0.000 0.000 0.664
#> GSM494584 2 0.0000 0.9008 0.000 1.000 0.000 0.000
#> GSM494586 2 0.0000 0.9008 0.000 1.000 0.000 0.000
#> GSM494578 2 0.2868 0.8914 0.000 0.864 0.000 0.136
#> GSM494585 2 0.0000 0.9008 0.000 1.000 0.000 0.000
#> GSM494611 2 0.0000 0.9008 0.000 1.000 0.000 0.000
#> GSM494560 2 0.3172 0.8820 0.000 0.840 0.000 0.160
#> GSM494595 2 0.0000 0.9008 0.000 1.000 0.000 0.000
#> GSM494570 2 0.3355 0.8809 0.004 0.836 0.000 0.160
#> GSM494597 3 0.4643 0.8814 0.344 0.000 0.656 0.000
#> GSM494607 2 0.0376 0.9009 0.004 0.992 0.000 0.004
#> GSM494561 2 0.4283 0.7673 0.004 0.740 0.000 0.256
#> GSM494569 4 0.0188 0.8660 0.004 0.000 0.000 0.996
#> GSM494592 2 0.0188 0.9000 0.004 0.996 0.000 0.000
#> GSM494577 2 0.0000 0.9008 0.000 1.000 0.000 0.000
#> GSM494588 2 0.3355 0.8809 0.004 0.836 0.000 0.160
#> GSM494590 3 0.4624 0.8819 0.340 0.000 0.660 0.000
#> GSM494609 2 0.0376 0.9009 0.004 0.992 0.000 0.004
#> GSM494608 2 0.0000 0.9008 0.000 1.000 0.000 0.000
#> GSM494606 2 0.0188 0.9000 0.004 0.996 0.000 0.000
#> GSM494574 2 0.0000 0.9008 0.000 1.000 0.000 0.000
#> GSM494573 2 0.3355 0.8809 0.004 0.836 0.000 0.160
#> GSM494566 2 0.3052 0.8909 0.004 0.860 0.000 0.136
#> GSM494601 3 0.5271 0.5102 0.024 0.320 0.656 0.000
#> GSM494557 2 0.2281 0.8967 0.000 0.904 0.000 0.096
#> GSM494579 2 0.3052 0.8909 0.004 0.860 0.000 0.136
#> GSM494596 3 0.4624 0.8819 0.340 0.000 0.660 0.000
#> GSM494575 2 0.0000 0.9008 0.000 1.000 0.000 0.000
#> GSM494625 4 0.0000 0.8681 0.000 0.000 0.000 1.000
#> GSM494654 3 0.4624 0.8819 0.340 0.000 0.660 0.000
#> GSM494664 4 0.0817 0.8411 0.024 0.000 0.000 0.976
#> GSM494624 4 0.0000 0.8681 0.000 0.000 0.000 1.000
#> GSM494651 3 0.0376 0.7860 0.004 0.000 0.992 0.004
#> GSM494662 4 0.0188 0.8660 0.004 0.000 0.000 0.996
#> GSM494627 4 0.0188 0.8660 0.004 0.000 0.000 0.996
#> GSM494673 1 0.4713 0.9007 0.640 0.000 0.000 0.360
#> GSM494649 4 0.0000 0.8681 0.000 0.000 0.000 1.000
#> GSM494658 1 0.7423 0.6777 0.476 0.180 0.000 0.344
#> GSM494653 1 0.4661 0.9139 0.652 0.000 0.000 0.348
#> GSM494643 4 0.0000 0.8681 0.000 0.000 0.000 1.000
#> GSM494672 1 0.7261 0.7085 0.480 0.152 0.000 0.368
#> GSM494618 4 0.0000 0.8681 0.000 0.000 0.000 1.000
#> GSM494631 2 0.3052 0.8909 0.004 0.860 0.000 0.136
#> GSM494619 4 0.0000 0.8681 0.000 0.000 0.000 1.000
#> GSM494674 4 0.4989 -0.5709 0.472 0.000 0.000 0.528
#> GSM494616 4 0.0000 0.8681 0.000 0.000 0.000 1.000
#> GSM494663 4 0.0188 0.8660 0.004 0.000 0.000 0.996
#> GSM494628 4 0.0000 0.8681 0.000 0.000 0.000 1.000
#> GSM494632 4 0.0188 0.8647 0.004 0.000 0.000 0.996
#> GSM494660 4 0.0000 0.8681 0.000 0.000 0.000 1.000
#> GSM494622 4 0.0000 0.8681 0.000 0.000 0.000 1.000
#> GSM494642 1 0.4661 0.9139 0.652 0.000 0.000 0.348
#> GSM494647 4 0.4989 -0.5709 0.472 0.000 0.000 0.528
#> GSM494659 1 0.4661 0.9139 0.652 0.000 0.000 0.348
#> GSM494670 1 0.7093 0.7162 0.476 0.128 0.000 0.396
#> GSM494675 2 0.2921 0.8903 0.000 0.860 0.000 0.140
#> GSM494641 1 0.4643 0.9105 0.656 0.000 0.000 0.344
#> GSM494636 4 0.0000 0.8681 0.000 0.000 0.000 1.000
#> GSM494640 3 0.3105 0.6658 0.004 0.000 0.856 0.140
#> GSM494623 4 0.0000 0.8681 0.000 0.000 0.000 1.000
#> GSM494644 4 0.4989 -0.5709 0.472 0.000 0.000 0.528
#> GSM494646 4 0.1716 0.7784 0.064 0.000 0.000 0.936
#> GSM494665 4 0.4624 -0.0195 0.340 0.000 0.000 0.660
#> GSM494638 4 0.0188 0.8660 0.004 0.000 0.000 0.996
#> GSM494645 4 0.2973 0.5985 0.144 0.000 0.000 0.856
#> GSM494671 1 0.4661 0.9139 0.652 0.000 0.000 0.348
#> GSM494655 1 0.4661 0.9139 0.652 0.000 0.000 0.348
#> GSM494620 4 0.0000 0.8681 0.000 0.000 0.000 1.000
#> GSM494630 4 0.0188 0.8660 0.004 0.000 0.000 0.996
#> GSM494657 3 0.4624 0.8819 0.340 0.000 0.660 0.000
#> GSM494667 1 0.4661 0.9139 0.652 0.000 0.000 0.348
#> GSM494621 4 0.0000 0.8681 0.000 0.000 0.000 1.000
#> GSM494629 4 0.0188 0.8660 0.004 0.000 0.000 0.996
#> GSM494637 4 0.0188 0.8660 0.004 0.000 0.000 0.996
#> GSM494652 1 0.4661 0.9139 0.652 0.000 0.000 0.348
#> GSM494648 4 0.0000 0.8681 0.000 0.000 0.000 1.000
#> GSM494650 3 0.0188 0.7868 0.000 0.000 0.996 0.004
#> GSM494669 1 0.4661 0.9139 0.652 0.000 0.000 0.348
#> GSM494666 4 0.0921 0.8374 0.028 0.000 0.000 0.972
#> GSM494668 1 0.4643 0.9105 0.656 0.000 0.000 0.344
#> GSM494633 4 0.0188 0.8660 0.004 0.000 0.000 0.996
#> GSM494634 1 0.4661 0.9139 0.652 0.000 0.000 0.348
#> GSM494639 4 0.0000 0.8681 0.000 0.000 0.000 1.000
#> GSM494661 3 0.0188 0.7868 0.000 0.000 0.996 0.004
#> GSM494617 4 0.0000 0.8681 0.000 0.000 0.000 1.000
#> GSM494626 4 0.0000 0.8681 0.000 0.000 0.000 1.000
#> GSM494656 3 0.4624 0.8819 0.340 0.000 0.660 0.000
#> GSM494635 4 0.4907 -0.4064 0.420 0.000 0.000 0.580
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM494565 5 0.0162 0.832 0.000 0.004 0.000 0.000 0.996
#> GSM494594 3 0.0000 0.878 0.000 0.000 1.000 0.000 0.000
#> GSM494604 1 0.1732 0.879 0.920 0.000 0.000 0.000 0.080
#> GSM494564 5 0.0000 0.832 0.000 0.000 0.000 0.000 1.000
#> GSM494591 3 0.0000 0.878 0.000 0.000 1.000 0.000 0.000
#> GSM494567 5 0.0162 0.832 0.000 0.004 0.000 0.000 0.996
#> GSM494602 2 0.3177 0.782 0.000 0.792 0.000 0.000 0.208
#> GSM494613 5 0.2732 0.815 0.000 0.160 0.000 0.000 0.840
#> GSM494589 5 0.0000 0.832 0.000 0.000 0.000 0.000 1.000
#> GSM494598 2 0.0000 0.854 0.000 1.000 0.000 0.000 0.000
#> GSM494593 2 0.3177 0.782 0.000 0.792 0.000 0.000 0.208
#> GSM494583 5 0.3305 0.763 0.000 0.224 0.000 0.000 0.776
#> GSM494612 2 0.0000 0.854 0.000 1.000 0.000 0.000 0.000
#> GSM494558 3 0.3561 0.725 0.000 0.000 0.740 0.260 0.000
#> GSM494556 5 0.2732 0.815 0.000 0.160 0.000 0.000 0.840
#> GSM494559 5 0.0000 0.832 0.000 0.000 0.000 0.000 1.000
#> GSM494571 3 0.0000 0.878 0.000 0.000 1.000 0.000 0.000
#> GSM494614 5 0.0880 0.834 0.000 0.032 0.000 0.000 0.968
#> GSM494603 5 0.0000 0.832 0.000 0.000 0.000 0.000 1.000
#> GSM494568 4 0.2813 0.725 0.000 0.000 0.000 0.832 0.168
#> GSM494572 3 0.0000 0.878 0.000 0.000 1.000 0.000 0.000
#> GSM494600 5 0.0000 0.832 0.000 0.000 0.000 0.000 1.000
#> GSM494562 2 0.0703 0.855 0.000 0.976 0.000 0.000 0.024
#> GSM494615 5 0.3359 0.800 0.000 0.072 0.000 0.084 0.844
#> GSM494582 2 0.0000 0.854 0.000 1.000 0.000 0.000 0.000
#> GSM494599 2 0.3424 0.737 0.000 0.760 0.000 0.000 0.240
#> GSM494610 2 0.0000 0.854 0.000 1.000 0.000 0.000 0.000
#> GSM494587 5 0.4242 0.318 0.000 0.428 0.000 0.000 0.572
#> GSM494581 2 0.4182 0.273 0.000 0.600 0.000 0.000 0.400
#> GSM494580 5 0.5862 0.418 0.000 0.112 0.344 0.000 0.544
#> GSM494563 5 0.0000 0.832 0.000 0.000 0.000 0.000 1.000
#> GSM494576 5 0.2732 0.815 0.000 0.160 0.000 0.000 0.840
#> GSM494605 1 0.3816 0.589 0.696 0.000 0.000 0.304 0.000
#> GSM494584 5 0.2732 0.815 0.000 0.160 0.000 0.000 0.840
#> GSM494586 2 0.0963 0.853 0.000 0.964 0.000 0.000 0.036
#> GSM494578 5 0.2732 0.815 0.000 0.160 0.000 0.000 0.840
#> GSM494585 2 0.3177 0.761 0.000 0.792 0.000 0.000 0.208
#> GSM494611 2 0.0000 0.854 0.000 1.000 0.000 0.000 0.000
#> GSM494560 5 0.0000 0.832 0.000 0.000 0.000 0.000 1.000
#> GSM494595 2 0.0510 0.855 0.000 0.984 0.000 0.000 0.016
#> GSM494570 5 0.0880 0.823 0.000 0.000 0.000 0.032 0.968
#> GSM494597 3 0.0000 0.878 0.000 0.000 1.000 0.000 0.000
#> GSM494607 2 0.3210 0.778 0.000 0.788 0.000 0.000 0.212
#> GSM494561 5 0.2813 0.696 0.000 0.000 0.000 0.168 0.832
#> GSM494569 4 0.0162 0.922 0.000 0.000 0.000 0.996 0.004
#> GSM494592 2 0.3177 0.782 0.000 0.792 0.000 0.000 0.208
#> GSM494577 5 0.3949 0.606 0.000 0.332 0.000 0.000 0.668
#> GSM494588 5 0.0000 0.832 0.000 0.000 0.000 0.000 1.000
#> GSM494590 3 0.0000 0.878 0.000 0.000 1.000 0.000 0.000
#> GSM494609 5 0.4210 0.358 0.000 0.412 0.000 0.000 0.588
#> GSM494608 5 0.4227 0.350 0.000 0.420 0.000 0.000 0.580
#> GSM494606 2 0.3210 0.778 0.000 0.788 0.000 0.000 0.212
#> GSM494574 2 0.0000 0.854 0.000 1.000 0.000 0.000 0.000
#> GSM494573 5 0.0000 0.832 0.000 0.000 0.000 0.000 1.000
#> GSM494566 5 0.3359 0.800 0.000 0.072 0.000 0.084 0.844
#> GSM494601 3 0.3741 0.611 0.000 0.264 0.732 0.000 0.004
#> GSM494557 5 0.2732 0.815 0.000 0.160 0.000 0.000 0.840
#> GSM494579 5 0.2690 0.815 0.000 0.156 0.000 0.000 0.844
#> GSM494596 3 0.0000 0.878 0.000 0.000 1.000 0.000 0.000
#> GSM494575 2 0.0000 0.854 0.000 1.000 0.000 0.000 0.000
#> GSM494625 4 0.0162 0.924 0.004 0.000 0.000 0.996 0.000
#> GSM494654 3 0.0000 0.878 0.000 0.000 1.000 0.000 0.000
#> GSM494664 4 0.3586 0.615 0.264 0.000 0.000 0.736 0.000
#> GSM494624 4 0.0162 0.924 0.004 0.000 0.000 0.996 0.000
#> GSM494651 3 0.3586 0.724 0.000 0.000 0.736 0.264 0.000
#> GSM494662 4 0.0162 0.922 0.000 0.000 0.000 0.996 0.004
#> GSM494627 4 0.0162 0.922 0.000 0.000 0.000 0.996 0.004
#> GSM494673 1 0.0000 0.924 1.000 0.000 0.000 0.000 0.000
#> GSM494649 4 0.0162 0.924 0.004 0.000 0.000 0.996 0.000
#> GSM494658 1 0.2179 0.846 0.888 0.000 0.000 0.000 0.112
#> GSM494653 1 0.0000 0.924 1.000 0.000 0.000 0.000 0.000
#> GSM494643 4 0.0000 0.922 0.000 0.000 0.000 1.000 0.000
#> GSM494672 1 0.1544 0.887 0.932 0.000 0.000 0.000 0.068
#> GSM494618 4 0.0162 0.924 0.004 0.000 0.000 0.996 0.000
#> GSM494631 5 0.2732 0.815 0.000 0.160 0.000 0.000 0.840
#> GSM494619 4 0.0290 0.922 0.008 0.000 0.000 0.992 0.000
#> GSM494674 1 0.1792 0.882 0.916 0.000 0.000 0.084 0.000
#> GSM494616 4 0.0162 0.924 0.004 0.000 0.000 0.996 0.000
#> GSM494663 4 0.0162 0.922 0.000 0.000 0.000 0.996 0.004
#> GSM494628 4 0.0162 0.924 0.004 0.000 0.000 0.996 0.000
#> GSM494632 4 0.3949 0.503 0.332 0.000 0.000 0.668 0.000
#> GSM494660 4 0.0162 0.924 0.004 0.000 0.000 0.996 0.000
#> GSM494622 4 0.0000 0.922 0.000 0.000 0.000 1.000 0.000
#> GSM494642 1 0.0000 0.924 1.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.1544 0.893 0.932 0.000 0.000 0.068 0.000
#> GSM494659 1 0.0000 0.924 1.000 0.000 0.000 0.000 0.000
#> GSM494670 1 0.1732 0.879 0.920 0.000 0.000 0.000 0.080
#> GSM494675 5 0.2732 0.815 0.000 0.160 0.000 0.000 0.840
#> GSM494641 1 0.0000 0.924 1.000 0.000 0.000 0.000 0.000
#> GSM494636 4 0.0162 0.924 0.004 0.000 0.000 0.996 0.000
#> GSM494640 3 0.3586 0.724 0.000 0.000 0.736 0.264 0.000
#> GSM494623 4 0.0290 0.922 0.008 0.000 0.000 0.992 0.000
#> GSM494644 1 0.1544 0.893 0.932 0.000 0.000 0.068 0.000
#> GSM494646 4 0.4227 0.294 0.420 0.000 0.000 0.580 0.000
#> GSM494665 1 0.3508 0.686 0.748 0.000 0.000 0.252 0.000
#> GSM494638 4 0.0162 0.922 0.000 0.000 0.000 0.996 0.004
#> GSM494645 4 0.4227 0.294 0.420 0.000 0.000 0.580 0.000
#> GSM494671 1 0.0000 0.924 1.000 0.000 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.924 1.000 0.000 0.000 0.000 0.000
#> GSM494620 4 0.0290 0.922 0.008 0.000 0.000 0.992 0.000
#> GSM494630 4 0.0162 0.924 0.004 0.000 0.000 0.996 0.000
#> GSM494657 3 0.0000 0.878 0.000 0.000 1.000 0.000 0.000
#> GSM494667 1 0.0000 0.924 1.000 0.000 0.000 0.000 0.000
#> GSM494621 4 0.0162 0.924 0.004 0.000 0.000 0.996 0.000
#> GSM494629 4 0.0162 0.922 0.000 0.000 0.000 0.996 0.004
#> GSM494637 4 0.0000 0.922 0.000 0.000 0.000 1.000 0.000
#> GSM494652 1 0.0000 0.924 1.000 0.000 0.000 0.000 0.000
#> GSM494648 4 0.0162 0.924 0.004 0.000 0.000 0.996 0.000
#> GSM494650 3 0.3586 0.724 0.000 0.000 0.736 0.264 0.000
#> GSM494669 1 0.0000 0.924 1.000 0.000 0.000 0.000 0.000
#> GSM494666 4 0.3949 0.502 0.332 0.000 0.000 0.668 0.000
#> GSM494668 1 0.0000 0.924 1.000 0.000 0.000 0.000 0.000
#> GSM494633 4 0.0162 0.924 0.004 0.000 0.000 0.996 0.000
#> GSM494634 1 0.0000 0.924 1.000 0.000 0.000 0.000 0.000
#> GSM494639 4 0.0963 0.900 0.036 0.000 0.000 0.964 0.000
#> GSM494661 3 0.3715 0.599 0.260 0.000 0.736 0.004 0.000
#> GSM494617 4 0.0162 0.924 0.004 0.000 0.000 0.996 0.000
#> GSM494626 4 0.0162 0.924 0.004 0.000 0.000 0.996 0.000
#> GSM494656 3 0.0000 0.878 0.000 0.000 1.000 0.000 0.000
#> GSM494635 1 0.3003 0.770 0.812 0.000 0.000 0.188 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM494565 5 0.0000 0.750 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494594 3 0.0000 0.940 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494604 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494564 5 0.0000 0.750 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494591 3 0.0000 0.940 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494567 5 0.0146 0.751 0.000 0.000 0.000 0.004 0.996 0.000
#> GSM494602 2 0.0260 0.900 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM494613 5 0.5253 0.693 0.000 0.200 0.000 0.192 0.608 0.000
#> GSM494589 5 0.0000 0.750 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494598 2 0.0146 0.899 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM494593 2 0.0260 0.900 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM494583 5 0.5670 0.567 0.000 0.296 0.000 0.188 0.516 0.000
#> GSM494612 2 0.0000 0.898 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494558 4 0.5088 0.776 0.000 0.000 0.200 0.632 0.000 0.168
#> GSM494556 5 0.5253 0.693 0.000 0.200 0.000 0.192 0.608 0.000
#> GSM494559 5 0.0000 0.750 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494571 3 0.1714 0.904 0.000 0.000 0.908 0.092 0.000 0.000
#> GSM494614 5 0.0547 0.751 0.000 0.000 0.000 0.020 0.980 0.000
#> GSM494603 5 0.0000 0.750 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494568 6 0.1765 0.849 0.000 0.000 0.000 0.000 0.096 0.904
#> GSM494572 3 0.1714 0.904 0.000 0.000 0.908 0.092 0.000 0.000
#> GSM494600 5 0.0000 0.750 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494562 2 0.0363 0.899 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM494615 5 0.4959 0.494 0.000 0.032 0.000 0.032 0.608 0.328
#> GSM494582 2 0.0000 0.898 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494599 2 0.0363 0.899 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM494610 2 0.0000 0.898 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494587 2 0.4728 0.542 0.000 0.680 0.000 0.176 0.144 0.000
#> GSM494581 2 0.4064 0.219 0.000 0.624 0.000 0.016 0.360 0.000
#> GSM494580 5 0.5138 0.683 0.000 0.124 0.000 0.276 0.600 0.000
#> GSM494563 5 0.0000 0.750 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494576 5 0.5388 0.668 0.000 0.228 0.000 0.188 0.584 0.000
#> GSM494605 1 0.3489 0.586 0.708 0.000 0.000 0.004 0.000 0.288
#> GSM494584 5 0.5300 0.684 0.000 0.212 0.000 0.188 0.600 0.000
#> GSM494586 2 0.0363 0.899 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM494578 5 0.5254 0.695 0.000 0.196 0.000 0.196 0.608 0.000
#> GSM494585 2 0.0725 0.893 0.000 0.976 0.000 0.012 0.012 0.000
#> GSM494611 2 0.0000 0.898 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494560 5 0.0146 0.749 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM494595 2 0.0260 0.900 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM494570 5 0.0632 0.742 0.000 0.000 0.000 0.000 0.976 0.024
#> GSM494597 3 0.1714 0.904 0.000 0.000 0.908 0.092 0.000 0.000
#> GSM494607 2 0.0508 0.898 0.004 0.984 0.000 0.000 0.012 0.000
#> GSM494561 5 0.2793 0.621 0.000 0.000 0.000 0.000 0.800 0.200
#> GSM494569 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494592 2 0.0363 0.899 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM494577 5 0.5502 0.476 0.000 0.364 0.000 0.136 0.500 0.000
#> GSM494588 5 0.0000 0.750 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494590 3 0.0000 0.940 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494609 2 0.1471 0.851 0.000 0.932 0.000 0.004 0.064 0.000
#> GSM494608 2 0.4209 0.122 0.000 0.596 0.000 0.020 0.384 0.000
#> GSM494606 2 0.0363 0.899 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM494574 2 0.0000 0.898 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494573 5 0.0000 0.750 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM494566 5 0.4703 0.566 0.000 0.032 0.000 0.032 0.668 0.268
#> GSM494601 2 0.5484 0.195 0.000 0.588 0.196 0.212 0.004 0.000
#> GSM494557 5 0.5300 0.685 0.000 0.212 0.000 0.188 0.600 0.000
#> GSM494579 5 0.4745 0.568 0.000 0.348 0.000 0.028 0.604 0.020
#> GSM494596 3 0.0000 0.940 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494575 2 0.0000 0.898 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM494625 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494654 3 0.0000 0.940 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494664 1 0.3807 0.462 0.628 0.000 0.000 0.004 0.000 0.368
#> GSM494624 6 0.0146 0.969 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM494651 4 0.2793 0.767 0.000 0.000 0.200 0.800 0.000 0.000
#> GSM494662 6 0.0146 0.968 0.000 0.000 0.000 0.000 0.004 0.996
#> GSM494627 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494673 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494649 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494658 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494653 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494643 6 0.2887 0.798 0.120 0.000 0.000 0.036 0.000 0.844
#> GSM494672 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494618 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494631 5 0.5253 0.696 0.000 0.192 0.000 0.200 0.608 0.000
#> GSM494619 6 0.2191 0.825 0.120 0.000 0.000 0.004 0.000 0.876
#> GSM494674 1 0.0260 0.901 0.992 0.000 0.000 0.008 0.000 0.000
#> GSM494616 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494663 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494628 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494632 1 0.3907 0.354 0.588 0.000 0.000 0.004 0.000 0.408
#> GSM494660 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494622 6 0.0520 0.960 0.008 0.000 0.000 0.008 0.000 0.984
#> GSM494642 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494647 1 0.0260 0.901 0.992 0.000 0.000 0.008 0.000 0.000
#> GSM494659 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494670 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494675 5 0.5363 0.695 0.000 0.196 0.000 0.192 0.608 0.004
#> GSM494641 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494636 6 0.0713 0.944 0.028 0.000 0.000 0.000 0.000 0.972
#> GSM494640 4 0.5088 0.776 0.000 0.000 0.200 0.632 0.000 0.168
#> GSM494623 6 0.0405 0.963 0.008 0.000 0.000 0.004 0.000 0.988
#> GSM494644 1 0.0260 0.901 0.992 0.000 0.000 0.008 0.000 0.000
#> GSM494646 1 0.0622 0.893 0.980 0.000 0.000 0.008 0.000 0.012
#> GSM494665 1 0.3337 0.625 0.736 0.000 0.000 0.004 0.000 0.260
#> GSM494638 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494645 1 0.0622 0.893 0.980 0.000 0.000 0.008 0.000 0.012
#> GSM494671 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494655 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494620 6 0.0146 0.969 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM494630 6 0.0146 0.968 0.000 0.000 0.000 0.000 0.004 0.996
#> GSM494657 3 0.0000 0.940 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM494667 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494621 6 0.0146 0.969 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM494629 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494637 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494652 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494648 6 0.0146 0.969 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM494650 4 0.2793 0.767 0.000 0.000 0.200 0.800 0.000 0.000
#> GSM494669 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494666 1 0.2964 0.695 0.792 0.000 0.000 0.004 0.000 0.204
#> GSM494668 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494633 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494634 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM494639 6 0.2320 0.810 0.132 0.000 0.000 0.004 0.000 0.864
#> GSM494661 1 0.5624 0.178 0.536 0.000 0.200 0.264 0.000 0.000
#> GSM494617 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494626 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM494656 3 0.1714 0.904 0.000 0.000 0.908 0.092 0.000 0.000
#> GSM494635 1 0.0146 0.902 0.996 0.000 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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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.
# code only for demonstration
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) individual(p) k
#> ATC:mclust 113 4.02e-19 0.9996 1.78e-14 1.000 2
#> ATC:mclust 97 1.08e-15 0.2897 1.69e-10 0.596 3
#> ATC:mclust 114 4.40e-17 0.2582 2.18e-09 0.558 4
#> ATC:mclust 113 1.94e-15 0.2384 4.91e-09 0.269 5
#> ATC:mclust 112 1.80e-15 0.0408 2.07e-08 0.546 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.
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"]
# you can also extract it by
# 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 51941 rows and 120 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 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:
k and the heatmap of
predicted classes for each k.k.k.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:
k;k, the area increased is defined as \(A_k - A_{k-1}\).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 0.898 0.937 0.973 0.4773 0.516 0.516
#> 3 3 0.649 0.841 0.895 0.3674 0.687 0.461
#> 4 4 0.655 0.542 0.761 0.1016 0.754 0.424
#> 5 5 0.631 0.587 0.783 0.0706 0.834 0.512
#> 6 6 0.608 0.498 0.705 0.0579 0.871 0.522
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
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.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM494565 2 0.552 0.8586 0.128 0.872
#> GSM494594 2 0.000 0.9483 0.000 1.000
#> GSM494604 1 0.000 0.9865 1.000 0.000
#> GSM494564 1 0.000 0.9865 1.000 0.000
#> GSM494591 2 0.000 0.9483 0.000 1.000
#> GSM494567 2 0.000 0.9483 0.000 1.000
#> GSM494602 1 0.000 0.9865 1.000 0.000
#> GSM494613 2 0.000 0.9483 0.000 1.000
#> GSM494589 2 0.311 0.9142 0.056 0.944
#> GSM494598 1 0.000 0.9865 1.000 0.000
#> GSM494593 1 0.000 0.9865 1.000 0.000
#> GSM494583 2 0.000 0.9483 0.000 1.000
#> GSM494612 1 0.995 0.0471 0.540 0.460
#> GSM494558 2 0.000 0.9483 0.000 1.000
#> GSM494556 2 0.000 0.9483 0.000 1.000
#> GSM494559 1 0.000 0.9865 1.000 0.000
#> GSM494571 2 0.000 0.9483 0.000 1.000
#> GSM494614 2 0.000 0.9483 0.000 1.000
#> GSM494603 1 0.000 0.9865 1.000 0.000
#> GSM494568 1 0.000 0.9865 1.000 0.000
#> GSM494572 2 0.000 0.9483 0.000 1.000
#> GSM494600 2 0.118 0.9395 0.016 0.984
#> GSM494562 2 0.000 0.9483 0.000 1.000
#> GSM494615 2 0.839 0.6787 0.268 0.732
#> GSM494582 2 0.909 0.5768 0.324 0.676
#> GSM494599 1 0.000 0.9865 1.000 0.000
#> GSM494610 2 0.518 0.8686 0.116 0.884
#> GSM494587 2 0.000 0.9483 0.000 1.000
#> GSM494581 2 0.000 0.9483 0.000 1.000
#> GSM494580 2 0.000 0.9483 0.000 1.000
#> GSM494563 1 0.000 0.9865 1.000 0.000
#> GSM494576 2 0.000 0.9483 0.000 1.000
#> GSM494605 1 0.000 0.9865 1.000 0.000
#> GSM494584 2 0.000 0.9483 0.000 1.000
#> GSM494586 2 0.000 0.9483 0.000 1.000
#> GSM494578 2 0.000 0.9483 0.000 1.000
#> GSM494585 2 0.000 0.9483 0.000 1.000
#> GSM494611 1 0.000 0.9865 1.000 0.000
#> GSM494560 1 0.184 0.9571 0.972 0.028
#> GSM494595 2 0.443 0.8883 0.092 0.908
#> GSM494570 1 0.000 0.9865 1.000 0.000
#> GSM494597 2 0.000 0.9483 0.000 1.000
#> GSM494607 1 0.000 0.9865 1.000 0.000
#> GSM494561 1 0.000 0.9865 1.000 0.000
#> GSM494569 1 0.000 0.9865 1.000 0.000
#> GSM494592 1 0.000 0.9865 1.000 0.000
#> GSM494577 2 0.000 0.9483 0.000 1.000
#> GSM494588 1 0.000 0.9865 1.000 0.000
#> GSM494590 2 0.000 0.9483 0.000 1.000
#> GSM494609 1 0.000 0.9865 1.000 0.000
#> GSM494608 2 0.671 0.8076 0.176 0.824
#> GSM494606 1 0.000 0.9865 1.000 0.000
#> GSM494574 2 0.644 0.8215 0.164 0.836
#> GSM494573 1 0.000 0.9865 1.000 0.000
#> GSM494566 1 0.000 0.9865 1.000 0.000
#> GSM494601 2 0.000 0.9483 0.000 1.000
#> GSM494557 2 0.000 0.9483 0.000 1.000
#> GSM494579 1 0.000 0.9865 1.000 0.000
#> GSM494596 2 0.000 0.9483 0.000 1.000
#> GSM494575 2 0.653 0.8171 0.168 0.832
#> GSM494625 1 0.000 0.9865 1.000 0.000
#> GSM494654 2 0.000 0.9483 0.000 1.000
#> GSM494664 1 0.000 0.9865 1.000 0.000
#> GSM494624 1 0.000 0.9865 1.000 0.000
#> GSM494651 2 0.000 0.9483 0.000 1.000
#> GSM494662 1 0.000 0.9865 1.000 0.000
#> GSM494627 1 0.971 0.2694 0.600 0.400
#> GSM494673 1 0.000 0.9865 1.000 0.000
#> GSM494649 1 0.000 0.9865 1.000 0.000
#> GSM494658 1 0.000 0.9865 1.000 0.000
#> GSM494653 1 0.000 0.9865 1.000 0.000
#> GSM494643 2 0.506 0.8726 0.112 0.888
#> GSM494672 1 0.000 0.9865 1.000 0.000
#> GSM494618 1 0.000 0.9865 1.000 0.000
#> GSM494631 2 0.000 0.9483 0.000 1.000
#> GSM494619 1 0.000 0.9865 1.000 0.000
#> GSM494674 1 0.000 0.9865 1.000 0.000
#> GSM494616 1 0.000 0.9865 1.000 0.000
#> GSM494663 1 0.000 0.9865 1.000 0.000
#> GSM494628 1 0.000 0.9865 1.000 0.000
#> GSM494632 1 0.000 0.9865 1.000 0.000
#> GSM494660 1 0.000 0.9865 1.000 0.000
#> GSM494622 2 0.844 0.6730 0.272 0.728
#> GSM494642 1 0.000 0.9865 1.000 0.000
#> GSM494647 1 0.000 0.9865 1.000 0.000
#> GSM494659 1 0.000 0.9865 1.000 0.000
#> GSM494670 1 0.000 0.9865 1.000 0.000
#> GSM494675 2 0.000 0.9483 0.000 1.000
#> GSM494641 1 0.000 0.9865 1.000 0.000
#> GSM494636 1 0.000 0.9865 1.000 0.000
#> GSM494640 2 0.000 0.9483 0.000 1.000
#> GSM494623 1 0.000 0.9865 1.000 0.000
#> GSM494644 1 0.000 0.9865 1.000 0.000
#> GSM494646 1 0.000 0.9865 1.000 0.000
#> GSM494665 1 0.000 0.9865 1.000 0.000
#> GSM494638 1 0.000 0.9865 1.000 0.000
#> GSM494645 1 0.000 0.9865 1.000 0.000
#> GSM494671 1 0.000 0.9865 1.000 0.000
#> GSM494655 1 0.000 0.9865 1.000 0.000
#> GSM494620 1 0.000 0.9865 1.000 0.000
#> GSM494630 1 0.000 0.9865 1.000 0.000
#> GSM494657 2 0.000 0.9483 0.000 1.000
#> GSM494667 1 0.000 0.9865 1.000 0.000
#> GSM494621 1 0.000 0.9865 1.000 0.000
#> GSM494629 2 0.141 0.9375 0.020 0.980
#> GSM494637 2 0.998 0.1621 0.476 0.524
#> GSM494652 1 0.000 0.9865 1.000 0.000
#> GSM494648 1 0.000 0.9865 1.000 0.000
#> GSM494650 2 0.000 0.9483 0.000 1.000
#> GSM494669 1 0.000 0.9865 1.000 0.000
#> GSM494666 1 0.000 0.9865 1.000 0.000
#> GSM494668 1 0.000 0.9865 1.000 0.000
#> GSM494633 1 0.000 0.9865 1.000 0.000
#> GSM494634 1 0.000 0.9865 1.000 0.000
#> GSM494639 1 0.000 0.9865 1.000 0.000
#> GSM494661 2 0.000 0.9483 0.000 1.000
#> GSM494617 1 0.000 0.9865 1.000 0.000
#> GSM494626 1 0.000 0.9865 1.000 0.000
#> GSM494656 2 0.000 0.9483 0.000 1.000
#> GSM494635 1 0.000 0.9865 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM494565 3 0.6794 0.66204 0.028 0.324 0.648
#> GSM494594 3 0.4346 0.88760 0.000 0.184 0.816
#> GSM494604 2 0.4346 0.86082 0.184 0.816 0.000
#> GSM494564 1 0.0000 0.93793 1.000 0.000 0.000
#> GSM494591 3 0.4346 0.88760 0.000 0.184 0.816
#> GSM494567 3 0.0000 0.84512 0.000 0.000 1.000
#> GSM494602 2 0.4346 0.86082 0.184 0.816 0.000
#> GSM494613 3 0.4346 0.88760 0.000 0.184 0.816
#> GSM494589 3 0.5560 0.47966 0.300 0.000 0.700
#> GSM494598 2 0.0000 0.83669 0.000 1.000 0.000
#> GSM494593 2 0.3551 0.86502 0.132 0.868 0.000
#> GSM494583 2 0.5431 0.35048 0.000 0.716 0.284
#> GSM494612 2 0.0000 0.83669 0.000 1.000 0.000
#> GSM494558 3 0.0000 0.84512 0.000 0.000 1.000
#> GSM494556 3 0.4346 0.88760 0.000 0.184 0.816
#> GSM494559 1 0.0000 0.93793 1.000 0.000 0.000
#> GSM494571 3 0.0000 0.84512 0.000 0.000 1.000
#> GSM494614 3 0.5733 0.68987 0.000 0.324 0.676
#> GSM494603 1 0.0892 0.92970 0.980 0.000 0.020
#> GSM494568 1 0.4452 0.76961 0.808 0.000 0.192
#> GSM494572 3 0.0892 0.85387 0.000 0.020 0.980
#> GSM494600 3 0.2356 0.80540 0.072 0.000 0.928
#> GSM494562 2 0.0000 0.83669 0.000 1.000 0.000
#> GSM494615 3 0.0592 0.84375 0.012 0.000 0.988
#> GSM494582 2 0.0000 0.83669 0.000 1.000 0.000
#> GSM494599 2 0.4346 0.86082 0.184 0.816 0.000
#> GSM494610 2 0.0000 0.83669 0.000 1.000 0.000
#> GSM494587 2 0.1643 0.79702 0.000 0.956 0.044
#> GSM494581 2 0.0000 0.83669 0.000 1.000 0.000
#> GSM494580 3 0.4291 0.88836 0.000 0.180 0.820
#> GSM494563 1 0.5760 0.39490 0.672 0.328 0.000
#> GSM494576 2 0.1289 0.80955 0.000 0.968 0.032
#> GSM494605 1 0.0000 0.93793 1.000 0.000 0.000
#> GSM494584 3 0.4974 0.84764 0.000 0.236 0.764
#> GSM494586 2 0.0000 0.83669 0.000 1.000 0.000
#> GSM494578 3 0.2537 0.87611 0.000 0.080 0.920
#> GSM494585 2 0.0237 0.83409 0.000 0.996 0.004
#> GSM494611 2 0.0237 0.83799 0.004 0.996 0.000
#> GSM494560 1 0.6509 0.00275 0.524 0.004 0.472
#> GSM494595 2 0.0000 0.83669 0.000 1.000 0.000
#> GSM494570 1 0.0000 0.93793 1.000 0.000 0.000
#> GSM494597 3 0.4062 0.88998 0.000 0.164 0.836
#> GSM494607 2 0.4346 0.86082 0.184 0.816 0.000
#> GSM494561 1 0.4178 0.78794 0.828 0.000 0.172
#> GSM494569 1 0.2448 0.88861 0.924 0.000 0.076
#> GSM494592 2 0.4346 0.86082 0.184 0.816 0.000
#> GSM494577 2 0.0237 0.83409 0.000 0.996 0.004
#> GSM494588 1 0.0000 0.93793 1.000 0.000 0.000
#> GSM494590 3 0.4346 0.88760 0.000 0.184 0.816
#> GSM494609 2 0.3686 0.86569 0.140 0.860 0.000
#> GSM494608 2 0.0000 0.83669 0.000 1.000 0.000
#> GSM494606 2 0.4235 0.86285 0.176 0.824 0.000
#> GSM494574 2 0.0000 0.83669 0.000 1.000 0.000
#> GSM494573 1 0.1411 0.91981 0.964 0.000 0.036
#> GSM494566 1 0.2356 0.89648 0.928 0.000 0.072
#> GSM494601 2 0.0237 0.83409 0.000 0.996 0.004
#> GSM494557 3 0.4346 0.88760 0.000 0.184 0.816
#> GSM494579 2 0.4346 0.86082 0.184 0.816 0.000
#> GSM494596 3 0.4346 0.88760 0.000 0.184 0.816
#> GSM494575 2 0.0000 0.83669 0.000 1.000 0.000
#> GSM494625 1 0.0237 0.93671 0.996 0.000 0.004
#> GSM494654 3 0.4291 0.88836 0.000 0.180 0.820
#> GSM494664 1 0.0000 0.93793 1.000 0.000 0.000
#> GSM494624 1 0.0000 0.93793 1.000 0.000 0.000
#> GSM494651 3 0.2625 0.87711 0.000 0.084 0.916
#> GSM494662 1 0.0000 0.93793 1.000 0.000 0.000
#> GSM494627 3 0.5497 0.49574 0.292 0.000 0.708
#> GSM494673 2 0.4346 0.86082 0.184 0.816 0.000
#> GSM494649 1 0.0592 0.93347 0.988 0.000 0.012
#> GSM494658 2 0.4291 0.86206 0.180 0.820 0.000
#> GSM494653 2 0.4750 0.82991 0.216 0.784 0.000
#> GSM494643 3 0.4805 0.88717 0.012 0.176 0.812
#> GSM494672 2 0.4346 0.86082 0.184 0.816 0.000
#> GSM494618 1 0.3879 0.81869 0.848 0.000 0.152
#> GSM494631 3 0.3816 0.88914 0.000 0.148 0.852
#> GSM494619 1 0.0000 0.93793 1.000 0.000 0.000
#> GSM494674 2 0.4452 0.85441 0.192 0.808 0.000
#> GSM494616 1 0.1529 0.91707 0.960 0.000 0.040
#> GSM494663 1 0.0424 0.93530 0.992 0.000 0.008
#> GSM494628 1 0.3482 0.83816 0.872 0.000 0.128
#> GSM494632 1 0.0000 0.93793 1.000 0.000 0.000
#> GSM494660 1 0.0424 0.93529 0.992 0.000 0.008
#> GSM494622 3 0.4902 0.85880 0.064 0.092 0.844
#> GSM494642 2 0.4452 0.85453 0.192 0.808 0.000
#> GSM494647 2 0.4346 0.86082 0.184 0.816 0.000
#> GSM494659 2 0.6309 0.21903 0.500 0.500 0.000
#> GSM494670 2 0.2878 0.85888 0.096 0.904 0.000
#> GSM494675 3 0.4002 0.88991 0.000 0.160 0.840
#> GSM494641 2 0.4346 0.86082 0.184 0.816 0.000
#> GSM494636 1 0.0000 0.93793 1.000 0.000 0.000
#> GSM494640 3 0.3340 0.88553 0.000 0.120 0.880
#> GSM494623 1 0.0000 0.93793 1.000 0.000 0.000
#> GSM494644 2 0.4291 0.86206 0.180 0.820 0.000
#> GSM494646 1 0.0892 0.92493 0.980 0.020 0.000
#> GSM494665 1 0.0237 0.93580 0.996 0.004 0.000
#> GSM494638 1 0.1860 0.90483 0.948 0.000 0.052
#> GSM494645 2 0.4750 0.83000 0.216 0.784 0.000
#> GSM494671 2 0.4346 0.86082 0.184 0.816 0.000
#> GSM494655 1 0.0237 0.93580 0.996 0.004 0.000
#> GSM494620 1 0.0000 0.93793 1.000 0.000 0.000
#> GSM494630 1 0.0000 0.93793 1.000 0.000 0.000
#> GSM494657 3 0.4346 0.88760 0.000 0.184 0.816
#> GSM494667 1 0.0892 0.92436 0.980 0.020 0.000
#> GSM494621 1 0.0000 0.93793 1.000 0.000 0.000
#> GSM494629 3 0.2066 0.81507 0.060 0.000 0.940
#> GSM494637 3 0.5178 0.56487 0.256 0.000 0.744
#> GSM494652 1 0.4235 0.73046 0.824 0.176 0.000
#> GSM494648 1 0.0000 0.93793 1.000 0.000 0.000
#> GSM494650 3 0.4346 0.88760 0.000 0.184 0.816
#> GSM494669 1 0.2711 0.85457 0.912 0.088 0.000
#> GSM494666 1 0.0000 0.93793 1.000 0.000 0.000
#> GSM494668 2 0.4399 0.85780 0.188 0.812 0.000
#> GSM494633 1 0.0000 0.93793 1.000 0.000 0.000
#> GSM494634 2 0.4346 0.86082 0.184 0.816 0.000
#> GSM494639 1 0.0000 0.93793 1.000 0.000 0.000
#> GSM494661 2 0.0237 0.83409 0.000 0.996 0.004
#> GSM494617 1 0.0000 0.93793 1.000 0.000 0.000
#> GSM494626 1 0.0000 0.93793 1.000 0.000 0.000
#> GSM494656 3 0.3941 0.88979 0.000 0.156 0.844
#> GSM494635 1 0.5327 0.54672 0.728 0.272 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM494565 4 0.4955 -0.7388 0.000 0.444 0.000 0.556
#> GSM494594 3 0.4967 0.7889 0.000 0.452 0.548 0.000
#> GSM494604 4 0.7677 -0.4462 0.248 0.296 0.000 0.456
#> GSM494564 4 0.5511 0.0941 0.332 0.000 0.032 0.636
#> GSM494591 3 0.4967 0.7889 0.000 0.452 0.548 0.000
#> GSM494567 4 0.4985 0.2393 0.000 0.000 0.468 0.532
#> GSM494602 2 0.4972 0.8777 0.000 0.544 0.000 0.456
#> GSM494613 3 0.5010 0.4034 0.000 0.108 0.772 0.120
#> GSM494589 4 0.5137 0.2519 0.004 0.000 0.452 0.544
#> GSM494598 2 0.4972 0.8777 0.000 0.544 0.000 0.456
#> GSM494593 2 0.4972 0.8777 0.000 0.544 0.000 0.456
#> GSM494583 2 0.5592 0.7475 0.000 0.572 0.024 0.404
#> GSM494612 2 0.4972 0.8777 0.000 0.544 0.000 0.456
#> GSM494558 3 0.1022 0.5861 0.000 0.000 0.968 0.032
#> GSM494556 4 0.5894 -0.6865 0.000 0.428 0.036 0.536
#> GSM494559 4 0.2846 0.3301 0.028 0.012 0.052 0.908
#> GSM494571 3 0.0817 0.5930 0.000 0.000 0.976 0.024
#> GSM494614 4 0.4961 -0.7554 0.000 0.448 0.000 0.552
#> GSM494603 4 0.5444 0.3749 0.048 0.000 0.264 0.688
#> GSM494568 4 0.7197 0.2819 0.140 0.000 0.392 0.468
#> GSM494572 3 0.0672 0.6105 0.000 0.008 0.984 0.008
#> GSM494600 4 0.5126 0.2583 0.004 0.000 0.444 0.552
#> GSM494562 2 0.4972 0.8777 0.000 0.544 0.000 0.456
#> GSM494615 4 0.4998 0.2185 0.000 0.000 0.488 0.512
#> GSM494582 2 0.4972 0.8777 0.000 0.544 0.000 0.456
#> GSM494599 2 0.5850 0.8349 0.032 0.512 0.000 0.456
#> GSM494610 2 0.4972 0.8777 0.000 0.544 0.000 0.456
#> GSM494587 2 0.5132 0.8673 0.000 0.548 0.004 0.448
#> GSM494581 2 0.5155 0.8557 0.000 0.528 0.004 0.468
#> GSM494580 3 0.4948 0.7876 0.000 0.440 0.560 0.000
#> GSM494563 4 0.5217 -0.6305 0.012 0.380 0.000 0.608
#> GSM494576 2 0.4994 0.8439 0.000 0.520 0.000 0.480
#> GSM494605 1 0.0000 0.7902 1.000 0.000 0.000 0.000
#> GSM494584 2 0.7287 0.5603 0.000 0.464 0.152 0.384
#> GSM494586 2 0.4972 0.8777 0.000 0.544 0.000 0.456
#> GSM494578 3 0.0524 0.6079 0.000 0.004 0.988 0.008
#> GSM494585 2 0.5143 0.8745 0.000 0.540 0.004 0.456
#> GSM494611 2 0.4972 0.8777 0.000 0.544 0.000 0.456
#> GSM494560 4 0.3662 -0.0567 0.004 0.148 0.012 0.836
#> GSM494595 2 0.4972 0.8777 0.000 0.544 0.000 0.456
#> GSM494570 4 0.5000 -0.3475 0.496 0.000 0.000 0.504
#> GSM494597 3 0.2011 0.6577 0.000 0.080 0.920 0.000
#> GSM494607 2 0.4972 0.8777 0.000 0.544 0.000 0.456
#> GSM494561 4 0.7483 0.1283 0.288 0.000 0.216 0.496
#> GSM494569 1 0.5220 0.4275 0.568 0.000 0.008 0.424
#> GSM494592 2 0.4972 0.8777 0.000 0.544 0.000 0.456
#> GSM494577 2 0.5285 0.8438 0.000 0.524 0.008 0.468
#> GSM494588 4 0.4605 0.0985 0.336 0.000 0.000 0.664
#> GSM494590 3 0.4972 0.7874 0.000 0.456 0.544 0.000
#> GSM494609 2 0.4972 0.8777 0.000 0.544 0.000 0.456
#> GSM494608 2 0.4977 0.8745 0.000 0.540 0.000 0.460
#> GSM494606 2 0.4972 0.8777 0.000 0.544 0.000 0.456
#> GSM494574 2 0.4972 0.8777 0.000 0.544 0.000 0.456
#> GSM494573 4 0.3043 0.3687 0.008 0.004 0.112 0.876
#> GSM494566 4 0.4562 0.3653 0.036 0.028 0.116 0.820
#> GSM494601 2 0.3444 -0.3474 0.000 0.816 0.184 0.000
#> GSM494557 3 0.7244 0.5535 0.000 0.212 0.544 0.244
#> GSM494579 2 0.5137 0.8730 0.004 0.544 0.000 0.452
#> GSM494596 3 0.4967 0.7889 0.000 0.452 0.548 0.000
#> GSM494575 2 0.4972 0.8777 0.000 0.544 0.000 0.456
#> GSM494625 1 0.4888 0.4462 0.588 0.000 0.000 0.412
#> GSM494654 3 0.4967 0.7889 0.000 0.452 0.548 0.000
#> GSM494664 1 0.0000 0.7902 1.000 0.000 0.000 0.000
#> GSM494624 1 0.4643 0.5356 0.656 0.000 0.000 0.344
#> GSM494651 3 0.4967 0.7873 0.000 0.452 0.548 0.000
#> GSM494662 1 0.4431 0.5835 0.696 0.000 0.000 0.304
#> GSM494627 1 0.7888 0.0591 0.368 0.000 0.288 0.344
#> GSM494673 1 0.2984 0.7277 0.888 0.084 0.000 0.028
#> GSM494649 1 0.4977 0.3707 0.540 0.000 0.000 0.460
#> GSM494658 4 0.7692 -0.4129 0.268 0.276 0.000 0.456
#> GSM494653 1 0.1398 0.7788 0.956 0.040 0.000 0.004
#> GSM494643 3 0.5472 0.7802 0.016 0.440 0.544 0.000
#> GSM494672 1 0.6982 0.1982 0.576 0.172 0.000 0.252
#> GSM494618 1 0.4050 0.7076 0.808 0.000 0.024 0.168
#> GSM494631 3 0.3569 0.7105 0.000 0.196 0.804 0.000
#> GSM494619 1 0.0707 0.7883 0.980 0.000 0.000 0.020
#> GSM494674 1 0.1398 0.7791 0.956 0.040 0.000 0.004
#> GSM494616 1 0.4916 0.4338 0.576 0.000 0.000 0.424
#> GSM494663 1 0.4855 0.4697 0.600 0.000 0.000 0.400
#> GSM494628 1 0.5050 0.4561 0.588 0.000 0.004 0.408
#> GSM494632 1 0.0592 0.7882 0.984 0.000 0.016 0.000
#> GSM494660 1 0.4977 0.3721 0.540 0.000 0.000 0.460
#> GSM494622 3 0.7142 0.4030 0.324 0.152 0.524 0.000
#> GSM494642 1 0.1576 0.7747 0.948 0.048 0.000 0.004
#> GSM494647 1 0.1743 0.7694 0.940 0.056 0.000 0.004
#> GSM494659 1 0.0804 0.7879 0.980 0.012 0.000 0.008
#> GSM494670 1 0.7542 0.0235 0.488 0.232 0.000 0.280
#> GSM494675 4 0.7500 -0.4679 0.000 0.404 0.180 0.416
#> GSM494641 1 0.3082 0.7239 0.884 0.084 0.000 0.032
#> GSM494636 1 0.0188 0.7903 0.996 0.000 0.004 0.000
#> GSM494640 3 0.4967 0.7889 0.000 0.452 0.548 0.000
#> GSM494623 1 0.1716 0.7737 0.936 0.000 0.000 0.064
#> GSM494644 1 0.2125 0.7592 0.920 0.076 0.004 0.000
#> GSM494646 1 0.0000 0.7902 1.000 0.000 0.000 0.000
#> GSM494665 1 0.0469 0.7895 0.988 0.000 0.000 0.012
#> GSM494638 1 0.1557 0.7728 0.944 0.000 0.056 0.000
#> GSM494645 1 0.1151 0.7851 0.968 0.024 0.008 0.000
#> GSM494671 1 0.4727 0.6032 0.792 0.108 0.000 0.100
#> GSM494655 1 0.0000 0.7902 1.000 0.000 0.000 0.000
#> GSM494620 1 0.1118 0.7843 0.964 0.000 0.000 0.036
#> GSM494630 1 0.4804 0.4870 0.616 0.000 0.000 0.384
#> GSM494657 3 0.4967 0.7889 0.000 0.452 0.548 0.000
#> GSM494667 1 0.0188 0.7902 0.996 0.000 0.000 0.004
#> GSM494621 1 0.3528 0.6907 0.808 0.000 0.000 0.192
#> GSM494629 3 0.4996 -0.2519 0.000 0.000 0.516 0.484
#> GSM494637 4 0.7863 -0.0519 0.344 0.000 0.276 0.380
#> GSM494652 1 0.0336 0.7900 0.992 0.000 0.000 0.008
#> GSM494648 1 0.1792 0.7720 0.932 0.000 0.000 0.068
#> GSM494650 3 0.4972 0.7874 0.000 0.456 0.544 0.000
#> GSM494669 1 0.0188 0.7902 0.996 0.000 0.000 0.004
#> GSM494666 1 0.0000 0.7902 1.000 0.000 0.000 0.000
#> GSM494668 1 0.1854 0.7703 0.940 0.048 0.000 0.012
#> GSM494633 1 0.4977 0.3707 0.540 0.000 0.000 0.460
#> GSM494634 1 0.4411 0.6352 0.812 0.108 0.000 0.080
#> GSM494639 1 0.0000 0.7902 1.000 0.000 0.000 0.000
#> GSM494661 2 0.5000 -0.7892 0.000 0.504 0.496 0.000
#> GSM494617 1 0.1389 0.7830 0.952 0.000 0.000 0.048
#> GSM494626 1 0.3024 0.7304 0.852 0.000 0.000 0.148
#> GSM494656 3 0.4961 0.7887 0.000 0.448 0.552 0.000
#> GSM494635 1 0.0592 0.7889 0.984 0.016 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM494565 5 0.4066 0.7177 0.000 0.324 0.000 0.004 0.672
#> GSM494594 3 0.0290 0.7992 0.000 0.000 0.992 0.000 0.008
#> GSM494604 2 0.3561 0.6154 0.260 0.740 0.000 0.000 0.000
#> GSM494564 5 0.2110 0.5708 0.072 0.000 0.000 0.016 0.912
#> GSM494591 3 0.0510 0.7981 0.000 0.000 0.984 0.000 0.016
#> GSM494567 4 0.5006 0.3237 0.000 0.000 0.048 0.624 0.328
#> GSM494602 2 0.3074 0.4616 0.000 0.804 0.000 0.000 0.196
#> GSM494613 5 0.7517 0.5569 0.000 0.244 0.084 0.180 0.492
#> GSM494589 5 0.1894 0.6112 0.000 0.008 0.000 0.072 0.920
#> GSM494598 2 0.1168 0.6719 0.000 0.960 0.000 0.008 0.032
#> GSM494593 2 0.1908 0.6235 0.000 0.908 0.000 0.000 0.092
#> GSM494583 3 0.6991 -0.0244 0.000 0.264 0.436 0.012 0.288
#> GSM494612 2 0.0865 0.6819 0.000 0.972 0.000 0.024 0.004
#> GSM494558 4 0.1478 0.6985 0.000 0.000 0.064 0.936 0.000
#> GSM494556 5 0.4565 0.7022 0.000 0.352 0.008 0.008 0.632
#> GSM494559 5 0.1478 0.6673 0.000 0.064 0.000 0.000 0.936
#> GSM494571 4 0.4617 0.4356 0.000 0.000 0.224 0.716 0.060
#> GSM494614 5 0.4565 0.6989 0.000 0.352 0.008 0.008 0.632
#> GSM494603 5 0.6865 0.5506 0.012 0.200 0.012 0.240 0.536
#> GSM494568 4 0.1444 0.7113 0.012 0.000 0.040 0.948 0.000
#> GSM494572 3 0.4907 0.5288 0.000 0.000 0.656 0.292 0.052
#> GSM494600 5 0.2130 0.6786 0.000 0.080 0.000 0.012 0.908
#> GSM494562 2 0.3232 0.6066 0.000 0.864 0.084 0.016 0.036
#> GSM494615 4 0.2358 0.7093 0.012 0.004 0.048 0.916 0.020
#> GSM494582 2 0.1117 0.6767 0.000 0.964 0.000 0.016 0.020
#> GSM494599 2 0.3003 0.6653 0.188 0.812 0.000 0.000 0.000
#> GSM494610 2 0.1082 0.6812 0.000 0.964 0.000 0.028 0.008
#> GSM494587 2 0.4230 0.5565 0.000 0.780 0.164 0.012 0.044
#> GSM494581 5 0.3999 0.7060 0.000 0.344 0.000 0.000 0.656
#> GSM494580 3 0.0162 0.7980 0.000 0.000 0.996 0.004 0.000
#> GSM494563 5 0.3809 0.7361 0.008 0.256 0.000 0.000 0.736
#> GSM494576 5 0.4060 0.6947 0.000 0.360 0.000 0.000 0.640
#> GSM494605 1 0.0703 0.7248 0.976 0.024 0.000 0.000 0.000
#> GSM494584 3 0.4181 0.5656 0.000 0.268 0.712 0.000 0.020
#> GSM494586 3 0.6778 0.1484 0.000 0.400 0.448 0.032 0.120
#> GSM494578 3 0.4491 0.4867 0.000 0.000 0.652 0.328 0.020
#> GSM494585 2 0.6799 -0.4437 0.000 0.384 0.228 0.004 0.384
#> GSM494611 2 0.1012 0.6799 0.000 0.968 0.000 0.020 0.012
#> GSM494560 5 0.3480 0.7335 0.000 0.248 0.000 0.000 0.752
#> GSM494595 5 0.4291 0.5299 0.000 0.464 0.000 0.000 0.536
#> GSM494570 5 0.2997 0.4986 0.148 0.000 0.000 0.012 0.840
#> GSM494597 3 0.4615 0.5916 0.000 0.000 0.700 0.252 0.048
#> GSM494607 2 0.2280 0.6880 0.120 0.880 0.000 0.000 0.000
#> GSM494561 5 0.3736 0.4118 0.140 0.000 0.000 0.052 0.808
#> GSM494569 4 0.4426 0.3030 0.380 0.000 0.004 0.612 0.004
#> GSM494592 2 0.1478 0.6940 0.064 0.936 0.000 0.000 0.000
#> GSM494577 5 0.4418 0.7081 0.000 0.332 0.016 0.000 0.652
#> GSM494588 5 0.2124 0.5750 0.096 0.004 0.000 0.000 0.900
#> GSM494590 3 0.0290 0.7992 0.000 0.000 0.992 0.000 0.008
#> GSM494609 2 0.4310 -0.2213 0.004 0.604 0.000 0.000 0.392
#> GSM494608 5 0.4138 0.6674 0.000 0.384 0.000 0.000 0.616
#> GSM494606 2 0.1270 0.6605 0.000 0.948 0.000 0.000 0.052
#> GSM494574 2 0.0955 0.6825 0.000 0.968 0.000 0.028 0.004
#> GSM494573 5 0.1671 0.6775 0.000 0.076 0.000 0.000 0.924
#> GSM494566 2 0.6657 0.4268 0.196 0.564 0.028 0.212 0.000
#> GSM494601 3 0.1331 0.7830 0.000 0.008 0.952 0.040 0.000
#> GSM494557 3 0.5223 0.5363 0.000 0.220 0.672 0.000 0.108
#> GSM494579 2 0.2079 0.6920 0.064 0.916 0.000 0.000 0.020
#> GSM494596 3 0.0290 0.7992 0.000 0.000 0.992 0.000 0.008
#> GSM494575 2 0.0955 0.6825 0.000 0.968 0.000 0.028 0.004
#> GSM494625 1 0.4985 0.6064 0.680 0.000 0.000 0.076 0.244
#> GSM494654 3 0.0000 0.7980 0.000 0.000 1.000 0.000 0.000
#> GSM494664 1 0.1117 0.7287 0.964 0.020 0.000 0.000 0.016
#> GSM494624 1 0.3957 0.6197 0.712 0.000 0.000 0.008 0.280
#> GSM494651 3 0.1341 0.7811 0.000 0.000 0.944 0.056 0.000
#> GSM494662 1 0.2438 0.7172 0.900 0.000 0.000 0.040 0.060
#> GSM494627 4 0.6347 0.1380 0.372 0.000 0.032 0.516 0.080
#> GSM494673 2 0.4297 0.2035 0.472 0.528 0.000 0.000 0.000
#> GSM494649 1 0.4615 0.6219 0.700 0.000 0.000 0.048 0.252
#> GSM494658 2 0.3689 0.6169 0.256 0.740 0.000 0.004 0.000
#> GSM494653 1 0.1197 0.7183 0.952 0.048 0.000 0.000 0.000
#> GSM494643 3 0.6221 0.3762 0.168 0.004 0.620 0.016 0.192
#> GSM494672 2 0.3752 0.5855 0.292 0.708 0.000 0.000 0.000
#> GSM494618 1 0.4178 0.4635 0.696 0.000 0.008 0.292 0.004
#> GSM494631 3 0.2470 0.7484 0.000 0.000 0.884 0.104 0.012
#> GSM494619 1 0.3783 0.6404 0.740 0.000 0.000 0.008 0.252
#> GSM494674 1 0.2424 0.6732 0.868 0.132 0.000 0.000 0.000
#> GSM494616 1 0.3671 0.5607 0.756 0.000 0.000 0.236 0.008
#> GSM494663 1 0.5538 0.5810 0.672 0.000 0.008 0.148 0.172
#> GSM494628 1 0.4302 0.3904 0.648 0.000 0.004 0.344 0.004
#> GSM494632 1 0.1731 0.7246 0.940 0.000 0.012 0.008 0.040
#> GSM494660 1 0.4865 0.6108 0.684 0.000 0.000 0.064 0.252
#> GSM494622 1 0.5576 0.1432 0.536 0.000 0.388 0.076 0.000
#> GSM494642 1 0.3684 0.4825 0.720 0.280 0.000 0.000 0.000
#> GSM494647 1 0.1484 0.7191 0.944 0.048 0.000 0.008 0.000
#> GSM494659 1 0.4182 0.1690 0.600 0.400 0.000 0.000 0.000
#> GSM494670 2 0.4268 0.6073 0.268 0.708 0.000 0.024 0.000
#> GSM494675 2 0.5868 0.0368 0.020 0.472 0.052 0.456 0.000
#> GSM494641 1 0.3949 0.3732 0.668 0.332 0.000 0.000 0.000
#> GSM494636 1 0.3487 0.6640 0.780 0.000 0.000 0.008 0.212
#> GSM494640 3 0.1444 0.7818 0.000 0.000 0.948 0.012 0.040
#> GSM494623 1 0.3783 0.6404 0.740 0.000 0.000 0.008 0.252
#> GSM494644 1 0.1455 0.7255 0.952 0.008 0.008 0.032 0.000
#> GSM494646 1 0.0833 0.7279 0.976 0.004 0.000 0.004 0.016
#> GSM494665 1 0.3774 0.4397 0.704 0.296 0.000 0.000 0.000
#> GSM494638 1 0.0579 0.7268 0.984 0.000 0.008 0.008 0.000
#> GSM494645 1 0.1616 0.7265 0.948 0.008 0.004 0.032 0.008
#> GSM494671 2 0.3999 0.5040 0.344 0.656 0.000 0.000 0.000
#> GSM494655 1 0.0703 0.7248 0.976 0.024 0.000 0.000 0.000
#> GSM494620 1 0.3728 0.6456 0.748 0.000 0.000 0.008 0.244
#> GSM494630 1 0.4165 0.5808 0.672 0.000 0.000 0.008 0.320
#> GSM494657 3 0.0290 0.7992 0.000 0.000 0.992 0.000 0.008
#> GSM494667 1 0.2280 0.6796 0.880 0.120 0.000 0.000 0.000
#> GSM494621 1 0.3809 0.6377 0.736 0.000 0.000 0.008 0.256
#> GSM494629 4 0.1393 0.7078 0.024 0.000 0.012 0.956 0.008
#> GSM494637 1 0.6904 0.4728 0.564 0.000 0.080 0.108 0.248
#> GSM494652 1 0.2966 0.6277 0.816 0.184 0.000 0.000 0.000
#> GSM494648 1 0.3756 0.6430 0.744 0.000 0.000 0.008 0.248
#> GSM494650 3 0.1168 0.7868 0.000 0.008 0.960 0.032 0.000
#> GSM494669 1 0.2605 0.6591 0.852 0.148 0.000 0.000 0.000
#> GSM494666 1 0.0703 0.7248 0.976 0.024 0.000 0.000 0.000
#> GSM494668 1 0.4242 0.0832 0.572 0.428 0.000 0.000 0.000
#> GSM494633 1 0.4173 0.6004 0.688 0.000 0.000 0.012 0.300
#> GSM494634 2 0.4074 0.4646 0.364 0.636 0.000 0.000 0.000
#> GSM494639 1 0.0451 0.7281 0.988 0.004 0.000 0.000 0.008
#> GSM494661 3 0.1331 0.7830 0.000 0.008 0.952 0.040 0.000
#> GSM494617 1 0.2930 0.6356 0.832 0.004 0.000 0.164 0.000
#> GSM494626 1 0.3366 0.5842 0.784 0.004 0.000 0.212 0.000
#> GSM494656 3 0.0162 0.7980 0.000 0.000 0.996 0.004 0.000
#> GSM494635 1 0.0703 0.7248 0.976 0.024 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM494565 5 0.4261 0.64575 0.000 0.156 0.000 0.000 0.732 0.112
#> GSM494594 3 0.0748 0.78498 0.000 0.000 0.976 0.016 0.004 0.004
#> GSM494604 2 0.2884 0.66344 0.164 0.824 0.000 0.000 0.004 0.008
#> GSM494564 6 0.3699 0.51280 0.004 0.000 0.000 0.000 0.336 0.660
#> GSM494591 3 0.1237 0.78187 0.000 0.000 0.956 0.020 0.020 0.004
#> GSM494567 4 0.5062 0.11299 0.000 0.024 0.020 0.532 0.416 0.008
#> GSM494602 2 0.4082 -0.16111 0.004 0.560 0.000 0.000 0.432 0.004
#> GSM494613 5 0.7015 0.44245 0.000 0.140 0.100 0.220 0.520 0.020
#> GSM494589 6 0.4343 0.33934 0.000 0.004 0.008 0.004 0.456 0.528
#> GSM494598 2 0.1007 0.62943 0.000 0.956 0.000 0.000 0.044 0.000
#> GSM494593 2 0.3910 0.19479 0.008 0.660 0.000 0.000 0.328 0.004
#> GSM494583 5 0.5275 0.45351 0.000 0.088 0.316 0.000 0.584 0.012
#> GSM494612 2 0.1219 0.62543 0.000 0.948 0.000 0.000 0.048 0.004
#> GSM494558 4 0.2045 0.59731 0.000 0.000 0.052 0.916 0.016 0.016
#> GSM494556 5 0.6533 0.58010 0.004 0.284 0.024 0.064 0.548 0.076
#> GSM494559 6 0.3993 0.34857 0.000 0.004 0.000 0.000 0.476 0.520
#> GSM494571 4 0.5064 0.38660 0.000 0.000 0.232 0.668 0.056 0.044
#> GSM494614 5 0.3981 0.66375 0.000 0.144 0.000 0.008 0.772 0.076
#> GSM494603 5 0.6983 0.05062 0.060 0.052 0.000 0.356 0.452 0.080
#> GSM494568 4 0.2040 0.61579 0.004 0.004 0.000 0.904 0.004 0.084
#> GSM494572 4 0.5385 0.04513 0.000 0.000 0.412 0.504 0.064 0.020
#> GSM494600 6 0.4225 0.33354 0.000 0.004 0.008 0.000 0.480 0.508
#> GSM494562 2 0.3260 0.56162 0.000 0.832 0.092 0.000 0.072 0.004
#> GSM494615 4 0.4336 0.59635 0.096 0.016 0.004 0.792 0.060 0.032
#> GSM494582 2 0.1970 0.59538 0.000 0.900 0.000 0.000 0.092 0.008
#> GSM494599 2 0.3499 0.56008 0.264 0.728 0.000 0.000 0.004 0.004
#> GSM494610 2 0.3372 0.55866 0.000 0.816 0.000 0.000 0.100 0.084
#> GSM494587 2 0.5221 0.13471 0.004 0.544 0.380 0.000 0.064 0.008
#> GSM494581 5 0.3037 0.69588 0.000 0.160 0.004 0.000 0.820 0.016
#> GSM494580 3 0.1760 0.78290 0.004 0.000 0.936 0.028 0.012 0.020
#> GSM494563 6 0.4460 0.33480 0.000 0.028 0.000 0.000 0.452 0.520
#> GSM494576 5 0.5055 0.67340 0.000 0.164 0.048 0.000 0.700 0.088
#> GSM494605 1 0.3041 0.68916 0.832 0.128 0.000 0.000 0.000 0.040
#> GSM494584 3 0.6794 -0.10626 0.004 0.192 0.444 0.012 0.320 0.028
#> GSM494586 3 0.6742 0.08823 0.004 0.312 0.460 0.000 0.164 0.060
#> GSM494578 4 0.6229 0.28179 0.004 0.000 0.196 0.464 0.324 0.012
#> GSM494585 5 0.6278 0.46930 0.004 0.256 0.280 0.000 0.452 0.008
#> GSM494611 2 0.0713 0.63435 0.000 0.972 0.000 0.000 0.028 0.000
#> GSM494560 5 0.4632 -0.24858 0.000 0.040 0.000 0.000 0.520 0.440
#> GSM494595 5 0.3984 0.58348 0.000 0.336 0.000 0.000 0.648 0.016
#> GSM494570 6 0.4091 0.60063 0.056 0.000 0.000 0.000 0.224 0.720
#> GSM494597 3 0.5257 0.43129 0.000 0.000 0.624 0.280 0.052 0.044
#> GSM494607 2 0.2389 0.66789 0.128 0.864 0.000 0.000 0.008 0.000
#> GSM494561 6 0.4548 0.62284 0.072 0.000 0.004 0.040 0.128 0.756
#> GSM494569 4 0.2257 0.62521 0.116 0.000 0.000 0.876 0.000 0.008
#> GSM494592 2 0.2250 0.66427 0.092 0.888 0.000 0.000 0.020 0.000
#> GSM494577 5 0.3418 0.69757 0.000 0.184 0.000 0.000 0.784 0.032
#> GSM494588 6 0.4333 0.49080 0.028 0.000 0.000 0.000 0.376 0.596
#> GSM494590 3 0.0551 0.78603 0.000 0.000 0.984 0.008 0.004 0.004
#> GSM494609 5 0.4798 0.40895 0.032 0.412 0.000 0.000 0.544 0.012
#> GSM494608 5 0.3133 0.69048 0.000 0.212 0.008 0.000 0.780 0.000
#> GSM494606 2 0.3830 0.00893 0.004 0.620 0.000 0.000 0.376 0.000
#> GSM494574 2 0.2433 0.60890 0.000 0.884 0.000 0.000 0.044 0.072
#> GSM494573 6 0.4083 0.36675 0.000 0.008 0.000 0.000 0.460 0.532
#> GSM494566 4 0.4857 0.00797 0.048 0.424 0.000 0.524 0.004 0.000
#> GSM494601 3 0.3562 0.71999 0.000 0.036 0.824 0.000 0.040 0.100
#> GSM494557 5 0.6496 0.45790 0.000 0.124 0.272 0.024 0.540 0.040
#> GSM494579 2 0.4656 0.59879 0.180 0.716 0.000 0.000 0.084 0.020
#> GSM494596 3 0.1148 0.78327 0.000 0.000 0.960 0.020 0.016 0.004
#> GSM494575 2 0.1863 0.62036 0.000 0.920 0.000 0.000 0.044 0.036
#> GSM494625 1 0.4945 0.28796 0.604 0.000 0.000 0.092 0.000 0.304
#> GSM494654 3 0.0520 0.78632 0.000 0.000 0.984 0.008 0.000 0.008
#> GSM494664 1 0.1152 0.70570 0.952 0.000 0.000 0.004 0.000 0.044
#> GSM494624 6 0.3351 0.63063 0.288 0.000 0.000 0.000 0.000 0.712
#> GSM494651 3 0.3568 0.73521 0.004 0.000 0.820 0.088 0.008 0.080
#> GSM494662 1 0.3172 0.64649 0.832 0.000 0.000 0.092 0.000 0.076
#> GSM494627 4 0.5949 0.39953 0.280 0.000 0.032 0.552 0.000 0.136
#> GSM494673 2 0.4086 0.16787 0.464 0.528 0.000 0.000 0.000 0.008
#> GSM494649 6 0.4215 0.62674 0.196 0.000 0.000 0.080 0.000 0.724
#> GSM494658 2 0.2957 0.66548 0.140 0.836 0.000 0.000 0.008 0.016
#> GSM494653 1 0.1528 0.71425 0.936 0.048 0.000 0.000 0.000 0.016
#> GSM494643 3 0.5698 0.36364 0.200 0.000 0.584 0.004 0.008 0.204
#> GSM494672 2 0.3489 0.52481 0.288 0.708 0.000 0.000 0.000 0.004
#> GSM494618 4 0.5115 0.31490 0.340 0.000 0.004 0.572 0.000 0.084
#> GSM494631 3 0.4329 0.24554 0.004 0.004 0.564 0.420 0.004 0.004
#> GSM494619 6 0.3717 0.50993 0.384 0.000 0.000 0.000 0.000 0.616
#> GSM494674 1 0.3368 0.57879 0.756 0.232 0.000 0.000 0.000 0.012
#> GSM494616 1 0.4748 0.06480 0.504 0.000 0.000 0.448 0.000 0.048
#> GSM494663 1 0.5617 0.30757 0.564 0.000 0.004 0.228 0.000 0.204
#> GSM494628 4 0.5113 0.46005 0.204 0.000 0.000 0.628 0.000 0.168
#> GSM494632 1 0.3561 0.67186 0.836 0.008 0.052 0.028 0.000 0.076
#> GSM494660 6 0.4314 0.61473 0.184 0.000 0.000 0.096 0.000 0.720
#> GSM494622 4 0.6156 0.13318 0.132 0.004 0.376 0.464 0.000 0.024
#> GSM494642 1 0.3189 0.58188 0.760 0.236 0.000 0.000 0.000 0.004
#> GSM494647 1 0.3053 0.65293 0.812 0.168 0.000 0.000 0.000 0.020
#> GSM494659 1 0.3852 0.26401 0.612 0.384 0.000 0.000 0.000 0.004
#> GSM494670 2 0.4186 0.65005 0.132 0.772 0.000 0.000 0.028 0.068
#> GSM494675 2 0.6631 -0.03340 0.000 0.428 0.008 0.264 0.020 0.280
#> GSM494641 1 0.2948 0.64075 0.804 0.188 0.000 0.000 0.000 0.008
#> GSM494636 1 0.3964 0.48687 0.724 0.000 0.000 0.044 0.000 0.232
#> GSM494640 3 0.2938 0.75347 0.012 0.000 0.876 0.044 0.016 0.052
#> GSM494623 6 0.3565 0.62225 0.304 0.000 0.000 0.000 0.004 0.692
#> GSM494644 1 0.3072 0.68310 0.868 0.024 0.048 0.000 0.008 0.052
#> GSM494646 1 0.1226 0.70943 0.952 0.004 0.000 0.004 0.000 0.040
#> GSM494665 1 0.4642 0.04840 0.508 0.452 0.000 0.000 0.000 0.040
#> GSM494638 1 0.3833 0.62806 0.812 0.008 0.008 0.108 0.008 0.056
#> GSM494645 1 0.2633 0.68796 0.888 0.012 0.044 0.000 0.004 0.052
#> GSM494671 2 0.3769 0.42615 0.356 0.640 0.000 0.000 0.000 0.004
#> GSM494655 1 0.0806 0.71297 0.972 0.008 0.000 0.000 0.000 0.020
#> GSM494620 6 0.3737 0.49668 0.392 0.000 0.000 0.000 0.000 0.608
#> GSM494630 6 0.3883 0.59689 0.332 0.000 0.000 0.000 0.012 0.656
#> GSM494657 3 0.1237 0.78187 0.000 0.000 0.956 0.020 0.020 0.004
#> GSM494667 1 0.3558 0.56506 0.736 0.248 0.000 0.000 0.000 0.016
#> GSM494621 6 0.3446 0.61594 0.308 0.000 0.000 0.000 0.000 0.692
#> GSM494629 4 0.1644 0.62152 0.012 0.000 0.004 0.932 0.000 0.052
#> GSM494637 1 0.6977 0.03859 0.444 0.000 0.084 0.240 0.000 0.232
#> GSM494652 1 0.3595 0.49582 0.704 0.288 0.000 0.000 0.000 0.008
#> GSM494648 6 0.3634 0.55671 0.356 0.000 0.000 0.000 0.000 0.644
#> GSM494650 3 0.2282 0.75326 0.000 0.000 0.888 0.000 0.024 0.088
#> GSM494669 1 0.3582 0.56009 0.732 0.252 0.000 0.000 0.000 0.016
#> GSM494666 1 0.0993 0.71360 0.964 0.012 0.000 0.000 0.000 0.024
#> GSM494668 2 0.4246 0.17193 0.452 0.532 0.000 0.000 0.000 0.016
#> GSM494633 6 0.3519 0.65456 0.232 0.000 0.000 0.008 0.008 0.752
#> GSM494634 2 0.4002 0.32832 0.404 0.588 0.000 0.000 0.000 0.008
#> GSM494639 1 0.1349 0.70326 0.940 0.000 0.000 0.004 0.000 0.056
#> GSM494661 3 0.3340 0.73088 0.004 0.024 0.840 0.000 0.032 0.100
#> GSM494617 1 0.3566 0.52946 0.744 0.000 0.000 0.236 0.000 0.020
#> GSM494626 1 0.4121 0.27104 0.604 0.000 0.000 0.380 0.000 0.016
#> GSM494656 3 0.0260 0.78610 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM494635 1 0.1296 0.71082 0.952 0.012 0.000 0.004 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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
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.
# code only for demonstration
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) individual(p) k
#> ATC:NMF 117 4.64e-05 0.7546 2.76e-03 0.17200 2
#> ATC:NMF 114 9.01e-04 0.0451 7.06e-04 0.01087 3
#> ATC:NMF 81 1.98e-13 0.4838 1.39e-11 0.54995 4
#> ATC:NMF 94 1.56e-11 0.0738 3.60e-08 0.01286 5
#> ATC:NMF 74 1.36e-06 0.0070 1.94e-05 0.00218 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.
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