Date: 2019-12-25 22:12:16 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 45638 rows and 99 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] 45638 99
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 | ||
|---|---|---|---|---|---|---|
| ATC:kmeans | 2 | 1.000 | 1.000 | 1.000 | ** | |
| ATC:skmeans | 3 | 1.000 | 0.961 | 0.983 | ** | 2 |
| ATC:mclust | 5 | 1.000 | 0.973 | 0.981 | ** | 2,3 |
| ATC:NMF | 2 | 1.000 | 0.968 | 0.987 | ** | |
| MAD:skmeans | 4 | 0.997 | 0.965 | 0.981 | ** | 3 |
| ATC:pam | 3 | 0.987 | 0.941 | 0.978 | ** | |
| SD:NMF | 2 | 0.978 | 0.956 | 0.982 | ** | |
| CV:skmeans | 3 | 0.944 | 0.912 | 0.966 | * | |
| SD:skmeans | 4 | 0.920 | 0.905 | 0.955 | * | 3 |
| CV:NMF | 2 | 0.916 | 0.927 | 0.970 | * | |
| MAD:pam | 3 | 0.914 | 0.910 | 0.961 | * | |
| MAD:NMF | 3 | 0.905 | 0.904 | 0.959 | * | 2 |
| SD:kmeans | 2 | 0.902 | 0.963 | 0.982 | * | |
| CV:kmeans | 3 | 0.882 | 0.942 | 0.970 | ||
| ATC:hclust | 5 | 0.876 | 0.885 | 0.931 | ||
| SD:mclust | 5 | 0.871 | 0.881 | 0.927 | ||
| SD:pam | 3 | 0.870 | 0.857 | 0.945 | ||
| MAD:kmeans | 3 | 0.846 | 0.905 | 0.956 | ||
| MAD:mclust | 3 | 0.796 | 0.886 | 0.937 | ||
| CV:pam | 3 | 0.724 | 0.855 | 0.931 | ||
| CV:mclust | 3 | 0.479 | 0.768 | 0.837 | ||
| MAD:hclust | 2 | 0.451 | 0.737 | 0.877 | ||
| SD:hclust | 2 | 0.266 | 0.670 | 0.846 | ||
| CV:hclust | 2 | 0.253 | 0.672 | 0.837 |
**: 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 0.978 0.956 0.982 0.494 0.506 0.506
#> CV:NMF 2 0.916 0.927 0.970 0.502 0.496 0.496
#> MAD:NMF 2 0.958 0.934 0.974 0.493 0.506 0.506
#> ATC:NMF 2 1.000 0.968 0.987 0.502 0.499 0.499
#> SD:skmeans 2 0.808 0.927 0.967 0.501 0.501 0.501
#> CV:skmeans 2 0.877 0.916 0.966 0.501 0.504 0.504
#> MAD:skmeans 2 0.757 0.899 0.955 0.502 0.499 0.499
#> ATC:skmeans 2 1.000 0.992 0.997 0.501 0.499 0.499
#> SD:mclust 2 0.430 0.716 0.813 0.416 0.514 0.514
#> CV:mclust 2 0.450 0.635 0.764 0.376 0.640 0.640
#> MAD:mclust 2 0.440 0.410 0.778 0.396 0.499 0.499
#> ATC:mclust 2 1.000 0.995 0.997 0.437 0.565 0.565
#> SD:kmeans 2 0.902 0.963 0.982 0.487 0.514 0.514
#> CV:kmeans 2 0.744 0.918 0.958 0.488 0.510 0.510
#> MAD:kmeans 2 0.710 0.897 0.946 0.490 0.518 0.518
#> ATC:kmeans 2 1.000 1.000 1.000 0.497 0.504 0.504
#> SD:pam 2 0.260 0.657 0.797 0.486 0.504 0.504
#> CV:pam 2 0.265 0.413 0.751 0.495 0.504 0.504
#> MAD:pam 2 0.300 0.780 0.856 0.483 0.514 0.514
#> ATC:pam 2 0.763 0.859 0.941 0.496 0.501 0.501
#> SD:hclust 2 0.266 0.670 0.846 0.456 0.504 0.504
#> CV:hclust 2 0.253 0.672 0.837 0.453 0.538 0.538
#> MAD:hclust 2 0.451 0.737 0.877 0.452 0.558 0.558
#> ATC:hclust 2 0.867 0.860 0.946 0.494 0.496 0.496
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.863 0.882 0.949 0.358 0.744 0.531
#> CV:NMF 3 0.887 0.889 0.951 0.321 0.772 0.571
#> MAD:NMF 3 0.905 0.904 0.959 0.361 0.729 0.510
#> ATC:NMF 3 0.737 0.875 0.919 0.307 0.770 0.567
#> SD:skmeans 3 1.000 0.980 0.992 0.343 0.724 0.500
#> CV:skmeans 3 0.944 0.912 0.966 0.338 0.752 0.541
#> MAD:skmeans 3 1.000 0.985 0.994 0.340 0.728 0.505
#> ATC:skmeans 3 1.000 0.961 0.983 0.237 0.870 0.742
#> SD:mclust 3 0.757 0.846 0.925 0.581 0.793 0.605
#> CV:mclust 3 0.479 0.768 0.837 0.641 0.593 0.407
#> MAD:mclust 3 0.796 0.886 0.937 0.659 0.673 0.434
#> ATC:mclust 3 1.000 0.994 0.996 0.538 0.763 0.580
#> SD:kmeans 3 0.811 0.894 0.946 0.377 0.729 0.513
#> CV:kmeans 3 0.882 0.942 0.970 0.370 0.734 0.518
#> MAD:kmeans 3 0.846 0.905 0.956 0.372 0.725 0.509
#> ATC:kmeans 3 0.886 0.955 0.979 0.352 0.728 0.507
#> SD:pam 3 0.870 0.857 0.945 0.368 0.683 0.450
#> CV:pam 3 0.724 0.855 0.931 0.345 0.664 0.426
#> MAD:pam 3 0.914 0.910 0.961 0.378 0.716 0.499
#> ATC:pam 3 0.987 0.941 0.978 0.358 0.713 0.486
#> SD:hclust 3 0.472 0.722 0.821 0.410 0.855 0.712
#> CV:hclust 3 0.390 0.638 0.819 0.401 0.790 0.615
#> MAD:hclust 3 0.420 0.619 0.808 0.408 0.796 0.638
#> ATC:hclust 3 0.659 0.745 0.816 0.291 0.841 0.689
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.755 0.776 0.887 0.1017 0.825 0.544
#> CV:NMF 4 0.653 0.714 0.832 0.1156 0.863 0.630
#> MAD:NMF 4 0.812 0.790 0.901 0.1020 0.820 0.528
#> ATC:NMF 4 0.746 0.787 0.876 0.1107 0.864 0.633
#> SD:skmeans 4 0.920 0.905 0.955 0.1103 0.856 0.603
#> CV:skmeans 4 0.759 0.581 0.779 0.1002 0.853 0.599
#> MAD:skmeans 4 0.997 0.965 0.981 0.1111 0.856 0.603
#> ATC:skmeans 4 0.794 0.776 0.883 0.1429 0.825 0.576
#> SD:mclust 4 0.632 0.691 0.781 0.0817 0.811 0.514
#> CV:mclust 4 0.522 0.594 0.735 0.1666 0.797 0.482
#> MAD:mclust 4 0.629 0.618 0.752 0.0730 0.806 0.514
#> ATC:mclust 4 0.875 0.955 0.950 0.0665 0.955 0.862
#> SD:kmeans 4 0.711 0.689 0.826 0.1153 0.795 0.473
#> CV:kmeans 4 0.709 0.602 0.771 0.1066 0.912 0.753
#> MAD:kmeans 4 0.752 0.712 0.845 0.1113 0.810 0.504
#> ATC:kmeans 4 0.748 0.856 0.898 0.1152 0.829 0.543
#> SD:pam 4 0.755 0.741 0.870 0.1235 0.829 0.545
#> CV:pam 4 0.762 0.682 0.844 0.1120 0.879 0.657
#> MAD:pam 4 0.784 0.822 0.915 0.1187 0.867 0.632
#> ATC:pam 4 0.888 0.838 0.935 0.0978 0.881 0.662
#> SD:hclust 4 0.600 0.662 0.817 0.1461 0.822 0.546
#> CV:hclust 4 0.591 0.669 0.820 0.1655 0.843 0.578
#> MAD:hclust 4 0.619 0.661 0.806 0.1585 0.830 0.568
#> ATC:hclust 4 0.762 0.795 0.875 0.1134 0.951 0.868
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.763 0.774 0.874 0.0691 0.878 0.586
#> CV:NMF 5 0.663 0.720 0.803 0.0649 0.905 0.663
#> MAD:NMF 5 0.844 0.816 0.905 0.0669 0.892 0.625
#> ATC:NMF 5 0.801 0.773 0.878 0.0578 0.921 0.725
#> SD:skmeans 5 0.802 0.779 0.871 0.0561 0.904 0.659
#> CV:skmeans 5 0.747 0.727 0.837 0.0625 0.873 0.576
#> MAD:skmeans 5 0.841 0.853 0.919 0.0548 0.929 0.737
#> ATC:skmeans 5 0.766 0.699 0.851 0.0541 0.918 0.735
#> SD:mclust 5 0.871 0.881 0.927 0.1029 0.831 0.475
#> CV:mclust 5 0.566 0.638 0.761 0.0619 0.843 0.495
#> MAD:mclust 5 0.788 0.795 0.872 0.0976 0.801 0.422
#> ATC:mclust 5 1.000 0.973 0.981 0.0732 0.950 0.823
#> SD:kmeans 5 0.757 0.677 0.748 0.0629 0.861 0.531
#> CV:kmeans 5 0.701 0.614 0.735 0.0706 0.870 0.590
#> MAD:kmeans 5 0.772 0.741 0.850 0.0651 0.894 0.617
#> ATC:kmeans 5 0.790 0.767 0.854 0.0606 0.946 0.788
#> SD:pam 5 0.743 0.610 0.790 0.0576 0.831 0.460
#> CV:pam 5 0.859 0.839 0.915 0.0660 0.894 0.629
#> MAD:pam 5 0.815 0.779 0.874 0.0593 0.922 0.714
#> ATC:pam 5 0.810 0.813 0.906 0.0754 0.918 0.696
#> SD:hclust 5 0.706 0.670 0.815 0.0663 0.928 0.724
#> CV:hclust 5 0.667 0.653 0.797 0.0566 0.947 0.788
#> MAD:hclust 5 0.700 0.668 0.822 0.0614 0.963 0.853
#> ATC:hclust 5 0.876 0.885 0.931 0.1095 0.880 0.643
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.739 0.706 0.831 0.0325 0.943 0.744
#> CV:NMF 6 0.678 0.577 0.754 0.0386 0.959 0.817
#> MAD:NMF 6 0.713 0.657 0.804 0.0294 0.959 0.818
#> ATC:NMF 6 0.758 0.670 0.809 0.0398 0.946 0.774
#> SD:skmeans 6 0.884 0.816 0.913 0.0427 0.948 0.766
#> CV:skmeans 6 0.802 0.716 0.852 0.0442 0.957 0.806
#> MAD:skmeans 6 0.895 0.867 0.930 0.0420 0.949 0.770
#> ATC:skmeans 6 0.802 0.736 0.821 0.0496 0.905 0.671
#> SD:mclust 6 0.763 0.714 0.853 0.0430 0.877 0.510
#> CV:mclust 6 0.724 0.763 0.845 0.0650 0.852 0.449
#> MAD:mclust 6 0.793 0.791 0.885 0.0609 0.871 0.498
#> ATC:mclust 6 0.842 0.846 0.903 0.0533 0.941 0.752
#> SD:kmeans 6 0.792 0.830 0.854 0.0418 0.954 0.780
#> CV:kmeans 6 0.727 0.711 0.793 0.0423 0.925 0.669
#> MAD:kmeans 6 0.862 0.885 0.896 0.0406 0.942 0.725
#> ATC:kmeans 6 0.782 0.666 0.768 0.0371 0.973 0.877
#> SD:pam 6 0.773 0.739 0.846 0.0471 0.896 0.569
#> CV:pam 6 0.803 0.741 0.842 0.0437 0.967 0.847
#> MAD:pam 6 0.801 0.712 0.828 0.0462 0.951 0.774
#> ATC:pam 6 0.837 0.779 0.864 0.0396 0.919 0.644
#> SD:hclust 6 0.757 0.632 0.796 0.0324 0.978 0.891
#> CV:hclust 6 0.712 0.582 0.776 0.0339 0.996 0.982
#> MAD:hclust 6 0.770 0.666 0.780 0.0460 0.886 0.558
#> ATC:hclust 6 0.852 0.884 0.912 0.0259 0.977 0.893
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 tissue(p) time(p) agent(p) dose(p) individual(p) k
#> SD:NMF 98 0.01332 0.12424 0.7066 1.03e-06 2.80e-03 2
#> CV:NMF 96 0.00614 0.10600 0.8284 3.64e-06 4.26e-03 2
#> MAD:NMF 96 0.04393 0.08622 0.5448 9.92e-07 3.71e-04 2
#> ATC:NMF 98 0.82115 0.09866 0.7101 4.33e-06 8.62e-05 2
#> SD:skmeans 97 0.02132 0.09662 0.6464 1.83e-06 1.53e-03 2
#> CV:skmeans 94 0.00267 0.15640 0.7749 1.93e-06 6.19e-03 2
#> MAD:skmeans 93 0.04700 0.10906 0.7353 2.08e-06 7.18e-04 2
#> ATC:skmeans 99 0.78077 0.09173 0.6564 4.03e-06 9.83e-05 2
#> SD:mclust 97 0.31189 0.03558 0.7813 4.61e-04 4.28e-07 2
#> CV:mclust 96 0.08116 0.30546 0.0679 9.22e-07 5.12e-03 2
#> MAD:mclust 50 0.49604 0.10975 0.3229 3.16e-05 4.22e-05 2
#> ATC:mclust 99 1.00000 0.00374 0.6508 7.14e-05 1.73e-10 2
#> SD:kmeans 98 0.01755 0.19258 0.3099 4.41e-07 1.57e-03 2
#> CV:kmeans 96 0.00833 0.23703 0.1662 4.04e-07 1.28e-03 2
#> MAD:kmeans 97 0.02811 0.17663 0.2818 3.51e-07 8.99e-04 2
#> ATC:kmeans 99 0.97141 0.10095 0.6449 2.49e-05 3.97e-04 2
#> SD:pam 97 0.91470 0.05081 0.6488 1.74e-05 5.77e-07 2
#> CV:pam 50 0.00163 0.51011 0.1135 3.30e-03 2.09e-02 2
#> MAD:pam 99 0.92500 0.03967 0.6523 8.96e-06 1.16e-07 2
#> ATC:pam 86 0.90241 0.06481 0.5796 1.69e-05 1.22e-05 2
#> SD:hclust 81 0.06548 0.10792 0.1221 5.04e-08 1.27e-03 2
#> CV:hclust 84 0.05021 0.12297 0.1476 5.78e-08 4.69e-04 2
#> MAD:hclust 81 0.03903 0.18087 0.0887 7.22e-08 2.60e-03 2
#> ATC:hclust 91 0.51924 0.10241 0.7073 2.12e-05 4.11e-04 2
test_to_known_factors(res_list, k = 3)
#> n tissue(p) time(p) agent(p) dose(p) individual(p) k
#> SD:NMF 95 1.25e-03 0.20050 0.02270 2.79e-09 1.41e-05 3
#> CV:NMF 94 1.52e-03 0.06479 0.00783 6.44e-10 6.58e-07 3
#> MAD:NMF 95 8.19e-04 0.17901 0.01069 9.41e-10 5.49e-06 3
#> ATC:NMF 96 6.35e-02 0.08261 0.85556 4.35e-06 2.64e-06 3
#> SD:skmeans 98 2.15e-04 0.09482 0.00878 7.38e-09 3.95e-06 3
#> CV:skmeans 92 1.98e-04 0.14055 0.01357 1.93e-08 6.66e-07 3
#> MAD:skmeans 98 2.92e-04 0.08705 0.01369 1.07e-08 1.82e-06 3
#> ATC:skmeans 97 2.43e-01 0.12961 0.92500 5.81e-07 1.04e-06 3
#> SD:mclust 90 4.29e-04 0.04536 0.00638 3.71e-09 1.32e-07 3
#> CV:mclust 87 9.12e-04 0.10933 0.01545 7.11e-09 7.56e-06 3
#> MAD:mclust 99 5.86e-04 0.08731 0.00650 9.07e-09 1.87e-06 3
#> ATC:mclust 99 6.66e-01 0.01005 0.67836 9.51e-07 1.83e-16 3
#> SD:kmeans 98 1.71e-03 0.02278 0.00703 1.44e-09 1.50e-08 3
#> CV:kmeans 98 1.03e-03 0.12963 0.00319 1.77e-09 9.50e-08 3
#> MAD:kmeans 95 4.28e-03 0.02821 0.00738 2.66e-09 1.38e-08 3
#> ATC:kmeans 99 5.23e-01 0.00303 0.41319 1.18e-05 1.51e-06 3
#> SD:pam 92 6.16e-03 0.02361 0.01176 7.56e-08 6.82e-08 3
#> CV:pam 92 1.82e-02 0.04180 0.02753 1.28e-07 1.84e-07 3
#> MAD:pam 95 3.04e-03 0.01749 0.02335 1.40e-07 4.34e-08 3
#> ATC:pam 95 4.69e-01 0.00350 0.41251 1.53e-05 2.74e-06 3
#> SD:hclust 88 9.62e-05 0.03009 0.15850 7.96e-11 5.72e-04 3
#> CV:hclust 82 2.42e-04 0.02677 0.04893 3.65e-11 1.59e-04 3
#> MAD:hclust 66 8.72e-03 0.02112 0.21750 1.47e-08 5.19e-05 3
#> ATC:hclust 94 3.33e-01 0.13399 0.51421 2.05e-05 3.43e-04 3
test_to_known_factors(res_list, k = 4)
#> n tissue(p) time(p) agent(p) dose(p) individual(p) k
#> SD:NMF 89 1.39e-03 0.3857 0.009284 1.76e-08 8.72e-09 4
#> CV:NMF 86 1.84e-03 0.3345 0.008261 4.12e-08 4.29e-09 4
#> MAD:NMF 88 1.71e-03 0.3431 0.022341 2.08e-08 3.05e-08 4
#> ATC:NMF 91 1.10e-01 0.0575 0.563951 6.90e-07 7.61e-10 4
#> SD:skmeans 94 9.59e-05 0.2752 0.002733 3.96e-10 5.27e-05 4
#> CV:skmeans 61 3.35e-02 0.0916 0.004780 3.69e-06 1.22e-03 4
#> MAD:skmeans 99 4.18e-05 0.1178 0.003058 1.00e-10 5.61e-05 4
#> ATC:skmeans 87 1.38e-01 0.0690 0.272413 3.70e-07 3.23e-11 4
#> SD:mclust 78 7.47e-03 0.0691 0.083152 4.16e-07 2.20e-07 4
#> CV:mclust 68 1.58e-02 0.1761 0.004014 3.46e-08 2.54e-07 4
#> MAD:mclust 63 3.38e-02 0.2615 0.077091 5.41e-06 3.04e-07 4
#> ATC:mclust 99 7.21e-02 0.0201 0.203164 1.94e-09 5.15e-14 4
#> SD:kmeans 83 2.35e-05 0.5483 0.004578 7.26e-09 1.48e-04 4
#> CV:kmeans 72 1.29e-04 0.0524 0.005288 4.79e-06 1.79e-05 4
#> MAD:kmeans 81 1.00e-04 0.5558 0.006490 1.90e-09 2.22e-05 4
#> ATC:kmeans 96 5.86e-01 0.0701 0.451400 1.65e-05 5.83e-08 4
#> SD:pam 84 3.72e-05 0.0184 0.025073 7.63e-08 9.02e-05 4
#> CV:pam 77 1.40e-06 0.0353 0.092440 1.15e-05 1.01e-04 4
#> MAD:pam 95 3.28e-04 0.1725 0.015729 6.47e-08 4.03e-05 4
#> ATC:pam 86 8.95e-01 0.0912 0.082407 4.59e-07 5.16e-08 4
#> SD:hclust 70 7.61e-04 0.2827 0.002001 6.46e-07 1.34e-04 4
#> CV:hclust 79 4.77e-05 0.1667 0.012697 4.91e-09 2.64e-05 4
#> MAD:hclust 82 1.94e-03 0.1470 0.000135 8.49e-09 1.84e-04 4
#> ATC:hclust 98 1.49e-01 0.1308 0.676597 1.36e-04 2.90e-04 4
test_to_known_factors(res_list, k = 5)
#> n tissue(p) time(p) agent(p) dose(p) individual(p) k
#> SD:NMF 88 3.07e-03 0.2091 0.09137 2.98e-07 2.41e-08 5
#> CV:NMF 87 3.73e-03 0.3843 0.26943 6.49e-07 3.25e-08 5
#> MAD:NMF 91 2.65e-03 0.2701 0.17981 8.21e-08 8.09e-09 5
#> ATC:NMF 88 1.60e-01 0.0701 0.41231 2.85e-06 1.21e-07 5
#> SD:skmeans 90 1.49e-04 0.2527 0.04283 1.08e-07 8.18e-06 5
#> CV:skmeans 87 4.20e-05 0.5135 0.05125 2.07e-07 4.60e-07 5
#> MAD:skmeans 94 5.12e-05 0.3170 0.02353 7.87e-08 1.60e-05 5
#> ATC:skmeans 81 4.09e-01 0.2768 0.12452 8.59e-07 2.92e-13 5
#> SD:mclust 96 2.41e-05 0.5305 0.05245 1.66e-09 4.79e-07 5
#> CV:mclust 76 1.11e-04 0.5185 0.19781 3.27e-07 1.34e-06 5
#> MAD:mclust 93 4.57e-05 0.4726 0.02951 6.96e-10 5.63e-07 5
#> ATC:mclust 98 1.37e-02 0.0271 0.07051 5.50e-10 3.18e-10 5
#> SD:kmeans 78 2.08e-04 0.2435 0.04055 2.45e-06 5.35e-05 5
#> CV:kmeans 84 6.44e-05 0.2446 0.09096 2.60e-08 4.48e-05 5
#> MAD:kmeans 87 1.25e-04 0.3311 0.01999 5.74e-08 5.60e-05 5
#> ATC:kmeans 90 1.25e-02 0.0534 0.52117 1.64e-04 3.33e-07 5
#> SD:pam 73 7.44e-04 0.2673 0.04959 3.06e-08 5.34e-06 5
#> CV:pam 91 1.70e-05 0.1135 0.17976 8.52e-08 1.82e-08 5
#> MAD:pam 92 5.65e-04 0.1533 0.02102 1.13e-07 5.35e-05 5
#> ATC:pam 87 9.00e-03 0.2425 0.22263 3.08e-06 1.80e-06 5
#> SD:hclust 77 3.37e-04 0.2773 0.16677 6.92e-09 4.55e-06 5
#> CV:hclust 81 4.91e-04 0.1810 0.06265 1.43e-08 2.81e-08 5
#> MAD:hclust 82 1.48e-04 0.1220 0.00476 8.38e-07 1.43e-04 5
#> ATC:hclust 99 1.47e-01 0.0209 0.73084 3.70e-05 2.57e-07 5
test_to_known_factors(res_list, k = 6)
#> n tissue(p) time(p) agent(p) dose(p) individual(p) k
#> SD:NMF 84 3.29e-02 0.0655 0.08509 7.51e-09 7.60e-08 6
#> CV:NMF 72 2.23e-02 0.2778 0.12889 1.83e-08 2.83e-08 6
#> MAD:NMF 82 2.87e-02 0.1962 0.07321 2.64e-08 1.23e-07 6
#> ATC:NMF 79 9.19e-02 0.1397 0.51636 8.68e-05 5.74e-09 6
#> SD:skmeans 90 3.49e-04 0.1046 0.23699 2.70e-06 7.88e-06 6
#> CV:skmeans 84 9.19e-05 0.5458 0.14642 6.41e-07 1.18e-06 6
#> MAD:skmeans 97 2.35e-04 0.1448 0.10802 3.47e-07 3.45e-05 6
#> ATC:skmeans 82 1.38e-02 0.6681 0.26467 3.73e-06 1.52e-09 6
#> SD:mclust 85 3.06e-04 0.3664 0.30681 1.02e-06 5.15e-07 6
#> CV:mclust 91 4.17e-04 0.6910 0.37174 4.40e-07 9.00e-06 6
#> MAD:mclust 92 8.03e-04 0.3884 0.18018 5.82e-08 3.63e-06 6
#> ATC:mclust 92 1.44e-02 0.0107 0.02033 1.59e-08 3.81e-08 6
#> SD:kmeans 97 8.78e-05 0.1900 0.06137 5.51e-07 4.76e-06 6
#> CV:kmeans 84 1.12e-04 0.2974 0.20704 2.22e-08 2.66e-06 6
#> MAD:kmeans 97 6.48e-05 0.1362 0.08301 7.95e-07 7.79e-06 6
#> ATC:kmeans 84 6.85e-03 0.1171 0.43544 5.00e-05 4.62e-07 6
#> SD:pam 88 2.04e-04 0.1396 0.17381 6.20e-06 1.27e-07 6
#> CV:pam 85 1.20e-04 0.3799 0.38054 1.59e-06 1.98e-08 6
#> MAD:pam 84 4.81e-05 0.3129 0.01151 1.77e-09 6.83e-05 6
#> ATC:pam 88 4.55e-02 0.1371 0.30018 6.25e-05 6.79e-08 6
#> SD:hclust 77 1.21e-03 0.4201 0.24530 1.78e-09 9.81e-12 6
#> CV:hclust 71 2.76e-04 0.1167 0.17128 1.80e-09 6.96e-06 6
#> MAD:hclust 68 1.14e-02 0.3139 0.00947 5.73e-05 2.85e-06 6
#> ATC:hclust 98 2.11e-01 0.0234 0.83176 1.80e-04 1.40e-08 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 45638 rows and 99 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 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 0.266 0.670 0.846 0.4560 0.504 0.504
#> 3 3 0.472 0.722 0.821 0.4103 0.855 0.712
#> 4 4 0.600 0.662 0.817 0.1461 0.822 0.546
#> 5 5 0.706 0.670 0.815 0.0663 0.928 0.724
#> 6 6 0.757 0.632 0.796 0.0324 0.978 0.891
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
#> GSM1296094 2 0.8661 0.520 0.288 0.712
#> GSM1296119 2 0.0000 0.810 0.000 1.000
#> GSM1296076 2 0.0000 0.810 0.000 1.000
#> GSM1296092 2 0.0000 0.810 0.000 1.000
#> GSM1296103 2 0.8661 0.520 0.288 0.712
#> GSM1296078 2 0.0000 0.810 0.000 1.000
#> GSM1296107 2 0.0000 0.810 0.000 1.000
#> GSM1296109 2 0.0000 0.810 0.000 1.000
#> GSM1296080 1 0.9635 0.418 0.612 0.388
#> GSM1296090 2 0.0000 0.810 0.000 1.000
#> GSM1296074 2 0.0000 0.810 0.000 1.000
#> GSM1296111 2 0.0000 0.810 0.000 1.000
#> GSM1296099 2 0.7139 0.679 0.196 0.804
#> GSM1296086 2 0.5629 0.751 0.132 0.868
#> GSM1296117 2 0.0000 0.810 0.000 1.000
#> GSM1296113 2 0.0000 0.810 0.000 1.000
#> GSM1296096 2 0.7139 0.679 0.196 0.804
#> GSM1296105 1 0.9661 0.445 0.608 0.392
#> GSM1296098 1 0.9775 0.356 0.588 0.412
#> GSM1296101 2 0.6887 0.684 0.184 0.816
#> GSM1296121 2 0.0000 0.810 0.000 1.000
#> GSM1296088 2 0.6801 0.704 0.180 0.820
#> GSM1296082 2 0.0000 0.810 0.000 1.000
#> GSM1296115 2 0.0000 0.810 0.000 1.000
#> GSM1296084 1 0.4939 0.728 0.892 0.108
#> GSM1296072 2 0.7602 0.703 0.220 0.780
#> GSM1296069 2 0.6712 0.747 0.176 0.824
#> GSM1296071 2 0.6801 0.744 0.180 0.820
#> GSM1296070 2 0.6712 0.747 0.176 0.824
#> GSM1296073 2 0.0000 0.810 0.000 1.000
#> GSM1296034 1 0.1184 0.754 0.984 0.016
#> GSM1296041 2 0.0000 0.810 0.000 1.000
#> GSM1296035 2 0.7139 0.679 0.196 0.804
#> GSM1296038 2 0.3431 0.789 0.064 0.936
#> GSM1296047 2 0.7602 0.703 0.220 0.780
#> GSM1296039 2 0.0000 0.810 0.000 1.000
#> GSM1296042 2 0.0000 0.810 0.000 1.000
#> GSM1296043 2 0.6712 0.747 0.176 0.824
#> GSM1296037 1 0.0000 0.757 1.000 0.000
#> GSM1296046 2 0.6801 0.744 0.180 0.820
#> GSM1296044 2 0.6801 0.744 0.180 0.820
#> GSM1296045 2 0.6712 0.747 0.176 0.824
#> GSM1296025 1 0.0000 0.757 1.000 0.000
#> GSM1296033 1 0.7745 0.655 0.772 0.228
#> GSM1296027 1 0.0000 0.757 1.000 0.000
#> GSM1296032 1 0.0000 0.757 1.000 0.000
#> GSM1296024 1 0.0000 0.757 1.000 0.000
#> GSM1296031 1 0.8861 0.575 0.696 0.304
#> GSM1296028 1 0.0000 0.757 1.000 0.000
#> GSM1296029 1 0.4298 0.735 0.912 0.088
#> GSM1296026 2 0.9460 0.323 0.364 0.636
#> GSM1296030 1 0.9775 0.361 0.588 0.412
#> GSM1296040 1 0.9427 0.478 0.640 0.360
#> GSM1296036 1 0.9775 0.356 0.588 0.412
#> GSM1296048 2 0.0000 0.810 0.000 1.000
#> GSM1296059 2 0.8661 0.520 0.288 0.712
#> GSM1296066 2 0.0000 0.810 0.000 1.000
#> GSM1296060 2 0.7139 0.679 0.196 0.804
#> GSM1296063 2 0.3431 0.789 0.064 0.936
#> GSM1296064 2 0.0000 0.810 0.000 1.000
#> GSM1296067 2 0.8327 0.639 0.264 0.736
#> GSM1296062 1 0.9427 0.470 0.640 0.360
#> GSM1296068 2 0.6801 0.744 0.180 0.820
#> GSM1296050 1 0.1184 0.753 0.984 0.016
#> GSM1296057 1 0.8713 0.600 0.708 0.292
#> GSM1296052 1 0.0000 0.757 1.000 0.000
#> GSM1296054 1 0.0000 0.757 1.000 0.000
#> GSM1296049 1 0.0000 0.757 1.000 0.000
#> GSM1296055 1 0.8909 0.571 0.692 0.308
#> GSM1296053 1 0.0000 0.757 1.000 0.000
#> GSM1296058 1 0.9922 0.304 0.552 0.448
#> GSM1296051 2 0.9460 0.323 0.364 0.636
#> GSM1296056 2 0.0938 0.807 0.012 0.988
#> GSM1296065 1 0.9922 0.304 0.552 0.448
#> GSM1296061 1 0.9775 0.356 0.588 0.412
#> GSM1296095 2 0.9209 0.488 0.336 0.664
#> GSM1296120 2 0.7602 0.703 0.220 0.780
#> GSM1296077 1 0.0000 0.757 1.000 0.000
#> GSM1296093 1 0.0000 0.757 1.000 0.000
#> GSM1296104 1 0.9944 0.280 0.544 0.456
#> GSM1296079 1 0.0000 0.757 1.000 0.000
#> GSM1296108 2 0.6801 0.744 0.180 0.820
#> GSM1296110 2 0.8327 0.639 0.264 0.736
#> GSM1296081 1 0.0000 0.757 1.000 0.000
#> GSM1296091 1 0.9460 0.498 0.636 0.364
#> GSM1296075 1 0.9710 0.426 0.600 0.400
#> GSM1296112 2 0.6801 0.744 0.180 0.820
#> GSM1296100 1 0.0000 0.757 1.000 0.000
#> GSM1296087 1 0.0376 0.756 0.996 0.004
#> GSM1296118 2 0.8861 0.568 0.304 0.696
#> GSM1296114 2 0.6801 0.744 0.180 0.820
#> GSM1296097 1 0.9922 0.304 0.552 0.448
#> GSM1296106 1 0.9922 0.304 0.552 0.448
#> GSM1296102 1 0.4815 0.727 0.896 0.104
#> GSM1296122 2 0.9044 0.535 0.320 0.680
#> GSM1296089 1 0.8861 0.575 0.696 0.304
#> GSM1296083 1 0.0000 0.757 1.000 0.000
#> GSM1296116 2 0.6801 0.744 0.180 0.820
#> GSM1296085 1 0.0000 0.757 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1296094 3 0.6402 0.568 0.236 0.040 0.724
#> GSM1296119 3 0.4750 0.759 0.000 0.216 0.784
#> GSM1296076 3 0.2356 0.805 0.000 0.072 0.928
#> GSM1296092 3 0.2356 0.805 0.000 0.072 0.928
#> GSM1296103 3 0.6402 0.568 0.236 0.040 0.724
#> GSM1296078 3 0.2356 0.805 0.000 0.072 0.928
#> GSM1296107 3 0.4750 0.759 0.000 0.216 0.784
#> GSM1296109 3 0.3816 0.793 0.000 0.148 0.852
#> GSM1296080 1 0.7430 0.385 0.540 0.036 0.424
#> GSM1296090 3 0.2356 0.805 0.000 0.072 0.928
#> GSM1296074 3 0.2356 0.805 0.000 0.072 0.928
#> GSM1296111 3 0.4750 0.759 0.000 0.216 0.784
#> GSM1296099 3 0.5816 0.699 0.156 0.056 0.788
#> GSM1296086 3 0.5285 0.747 0.112 0.064 0.824
#> GSM1296117 3 0.4750 0.759 0.000 0.216 0.784
#> GSM1296113 3 0.4750 0.759 0.000 0.216 0.784
#> GSM1296096 3 0.5816 0.699 0.156 0.056 0.788
#> GSM1296105 1 0.9231 0.582 0.532 0.252 0.216
#> GSM1296098 1 0.7471 0.334 0.516 0.036 0.448
#> GSM1296101 3 0.5435 0.715 0.144 0.048 0.808
#> GSM1296121 3 0.4750 0.759 0.000 0.216 0.784
#> GSM1296088 3 0.5696 0.705 0.148 0.056 0.796
#> GSM1296082 3 0.2356 0.805 0.000 0.072 0.928
#> GSM1296115 3 0.4750 0.759 0.000 0.216 0.784
#> GSM1296084 1 0.5357 0.745 0.820 0.064 0.116
#> GSM1296072 2 0.2176 0.944 0.032 0.948 0.020
#> GSM1296069 2 0.1163 0.957 0.000 0.972 0.028
#> GSM1296071 2 0.1031 0.959 0.000 0.976 0.024
#> GSM1296070 2 0.1163 0.957 0.000 0.972 0.028
#> GSM1296073 3 0.4750 0.759 0.000 0.216 0.784
#> GSM1296034 1 0.1315 0.771 0.972 0.020 0.008
#> GSM1296041 3 0.4750 0.759 0.000 0.216 0.784
#> GSM1296035 3 0.5816 0.699 0.156 0.056 0.788
#> GSM1296038 3 0.2301 0.788 0.004 0.060 0.936
#> GSM1296047 2 0.2176 0.944 0.032 0.948 0.020
#> GSM1296039 3 0.2356 0.805 0.000 0.072 0.928
#> GSM1296042 3 0.4750 0.759 0.000 0.216 0.784
#> GSM1296043 2 0.1163 0.957 0.000 0.972 0.028
#> GSM1296037 1 0.0747 0.772 0.984 0.000 0.016
#> GSM1296046 2 0.1031 0.959 0.000 0.976 0.024
#> GSM1296044 2 0.1031 0.959 0.000 0.976 0.024
#> GSM1296045 2 0.1163 0.957 0.000 0.972 0.028
#> GSM1296025 1 0.0000 0.773 1.000 0.000 0.000
#> GSM1296033 1 0.7397 0.701 0.704 0.148 0.148
#> GSM1296027 1 0.0000 0.773 1.000 0.000 0.000
#> GSM1296032 1 0.0000 0.773 1.000 0.000 0.000
#> GSM1296024 1 0.0000 0.773 1.000 0.000 0.000
#> GSM1296031 1 0.7467 0.550 0.624 0.320 0.056
#> GSM1296028 1 0.0000 0.773 1.000 0.000 0.000
#> GSM1296029 1 0.4737 0.752 0.852 0.064 0.084
#> GSM1296026 3 0.6897 0.406 0.292 0.040 0.668
#> GSM1296030 1 0.7561 0.243 0.516 0.040 0.444
#> GSM1296040 1 0.8775 0.576 0.568 0.152 0.280
#> GSM1296036 1 0.7471 0.334 0.516 0.036 0.448
#> GSM1296048 3 0.4750 0.759 0.000 0.216 0.784
#> GSM1296059 3 0.6402 0.568 0.236 0.040 0.724
#> GSM1296066 3 0.4750 0.759 0.000 0.216 0.784
#> GSM1296060 3 0.5816 0.699 0.156 0.056 0.788
#> GSM1296063 3 0.2200 0.789 0.004 0.056 0.940
#> GSM1296064 3 0.2356 0.805 0.000 0.072 0.928
#> GSM1296067 2 0.3183 0.906 0.076 0.908 0.016
#> GSM1296062 1 0.7353 0.433 0.568 0.036 0.396
#> GSM1296068 2 0.1031 0.959 0.000 0.976 0.024
#> GSM1296050 1 0.0892 0.768 0.980 0.020 0.000
#> GSM1296057 1 0.8250 0.644 0.628 0.232 0.140
#> GSM1296052 1 0.0000 0.773 1.000 0.000 0.000
#> GSM1296054 1 0.0000 0.773 1.000 0.000 0.000
#> GSM1296049 1 0.0000 0.773 1.000 0.000 0.000
#> GSM1296055 1 0.7580 0.522 0.604 0.340 0.056
#> GSM1296053 1 0.0000 0.773 1.000 0.000 0.000
#> GSM1296058 1 0.9598 0.516 0.468 0.304 0.228
#> GSM1296051 3 0.6897 0.406 0.292 0.040 0.668
#> GSM1296056 3 0.1964 0.802 0.000 0.056 0.944
#> GSM1296065 1 0.9355 0.496 0.480 0.340 0.180
#> GSM1296061 1 0.7471 0.334 0.516 0.036 0.448
#> GSM1296095 3 0.9760 -0.077 0.276 0.280 0.444
#> GSM1296120 2 0.2176 0.944 0.032 0.948 0.020
#> GSM1296077 1 0.0000 0.773 1.000 0.000 0.000
#> GSM1296093 1 0.0000 0.773 1.000 0.000 0.000
#> GSM1296104 1 0.9648 0.508 0.460 0.304 0.236
#> GSM1296079 1 0.0000 0.773 1.000 0.000 0.000
#> GSM1296108 2 0.1031 0.959 0.000 0.976 0.024
#> GSM1296110 2 0.3183 0.906 0.076 0.908 0.016
#> GSM1296081 1 0.0000 0.773 1.000 0.000 0.000
#> GSM1296091 1 0.8677 0.583 0.572 0.288 0.140
#> GSM1296075 1 0.8902 0.542 0.536 0.320 0.144
#> GSM1296112 2 0.1031 0.959 0.000 0.976 0.024
#> GSM1296100 1 0.0747 0.772 0.984 0.000 0.016
#> GSM1296087 1 0.0661 0.774 0.988 0.004 0.008
#> GSM1296118 2 0.3607 0.864 0.112 0.880 0.008
#> GSM1296114 2 0.1031 0.959 0.000 0.976 0.024
#> GSM1296097 1 0.9598 0.516 0.468 0.304 0.228
#> GSM1296106 1 0.9355 0.496 0.480 0.340 0.180
#> GSM1296102 1 0.4891 0.729 0.836 0.124 0.040
#> GSM1296122 2 0.3965 0.836 0.132 0.860 0.008
#> GSM1296089 1 0.7467 0.550 0.624 0.320 0.056
#> GSM1296083 1 0.0000 0.773 1.000 0.000 0.000
#> GSM1296116 2 0.1031 0.959 0.000 0.976 0.024
#> GSM1296085 1 0.0000 0.773 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1296094 3 0.5231 0.3327 0.012 0.000 0.604 0.384
#> GSM1296119 4 0.2530 0.8110 0.000 0.112 0.000 0.888
#> GSM1296076 4 0.1389 0.7991 0.000 0.000 0.048 0.952
#> GSM1296092 4 0.1389 0.7991 0.000 0.000 0.048 0.952
#> GSM1296103 3 0.5231 0.3327 0.012 0.000 0.604 0.384
#> GSM1296078 4 0.1389 0.7991 0.000 0.000 0.048 0.952
#> GSM1296107 4 0.2530 0.8110 0.000 0.112 0.000 0.888
#> GSM1296109 4 0.4853 0.6148 0.000 0.036 0.220 0.744
#> GSM1296080 3 0.2227 0.5885 0.036 0.000 0.928 0.036
#> GSM1296090 4 0.1389 0.7991 0.000 0.000 0.048 0.952
#> GSM1296074 4 0.1389 0.7991 0.000 0.000 0.048 0.952
#> GSM1296111 4 0.2530 0.8110 0.000 0.112 0.000 0.888
#> GSM1296099 3 0.5168 0.1109 0.004 0.000 0.504 0.492
#> GSM1296086 4 0.5004 0.2218 0.004 0.000 0.392 0.604
#> GSM1296117 4 0.2530 0.8110 0.000 0.112 0.000 0.888
#> GSM1296113 4 0.2530 0.8110 0.000 0.112 0.000 0.888
#> GSM1296096 3 0.5168 0.1109 0.004 0.000 0.504 0.492
#> GSM1296105 3 0.6829 0.4808 0.140 0.200 0.644 0.016
#> GSM1296098 3 0.1938 0.5894 0.012 0.000 0.936 0.052
#> GSM1296101 4 0.5163 -0.0967 0.004 0.000 0.480 0.516
#> GSM1296121 4 0.2530 0.8110 0.000 0.112 0.000 0.888
#> GSM1296088 4 0.5360 0.0548 0.012 0.000 0.436 0.552
#> GSM1296082 4 0.1389 0.7991 0.000 0.000 0.048 0.952
#> GSM1296115 4 0.2530 0.8110 0.000 0.112 0.000 0.888
#> GSM1296084 1 0.6434 0.3183 0.560 0.032 0.384 0.024
#> GSM1296072 2 0.0992 0.9462 0.008 0.976 0.004 0.012
#> GSM1296069 2 0.1302 0.9586 0.000 0.956 0.000 0.044
#> GSM1296071 2 0.1118 0.9622 0.000 0.964 0.000 0.036
#> GSM1296070 2 0.1302 0.9586 0.000 0.956 0.000 0.044
#> GSM1296073 4 0.2530 0.8110 0.000 0.112 0.000 0.888
#> GSM1296034 1 0.4819 0.5008 0.652 0.004 0.344 0.000
#> GSM1296041 4 0.2530 0.8110 0.000 0.112 0.000 0.888
#> GSM1296035 3 0.5168 0.1109 0.004 0.000 0.504 0.492
#> GSM1296038 4 0.3443 0.7169 0.000 0.016 0.136 0.848
#> GSM1296047 2 0.0992 0.9462 0.008 0.976 0.004 0.012
#> GSM1296039 4 0.1389 0.7991 0.000 0.000 0.048 0.952
#> GSM1296042 4 0.2530 0.8110 0.000 0.112 0.000 0.888
#> GSM1296043 2 0.1302 0.9586 0.000 0.956 0.000 0.044
#> GSM1296037 1 0.1902 0.8018 0.932 0.004 0.064 0.000
#> GSM1296046 2 0.1118 0.9622 0.000 0.964 0.000 0.036
#> GSM1296044 2 0.1118 0.9622 0.000 0.964 0.000 0.036
#> GSM1296045 2 0.1302 0.9586 0.000 0.956 0.000 0.044
#> GSM1296025 1 0.0000 0.8358 1.000 0.000 0.000 0.000
#> GSM1296033 3 0.7800 -0.0318 0.416 0.120 0.436 0.028
#> GSM1296027 1 0.0000 0.8358 1.000 0.000 0.000 0.000
#> GSM1296032 1 0.0000 0.8358 1.000 0.000 0.000 0.000
#> GSM1296024 1 0.0000 0.8358 1.000 0.000 0.000 0.000
#> GSM1296031 1 0.7542 0.2773 0.472 0.320 0.208 0.000
#> GSM1296028 1 0.0000 0.8358 1.000 0.000 0.000 0.000
#> GSM1296029 1 0.5839 0.4469 0.624 0.032 0.336 0.008
#> GSM1296026 3 0.6041 0.4192 0.060 0.000 0.608 0.332
#> GSM1296030 3 0.7626 0.3698 0.304 0.000 0.464 0.232
#> GSM1296040 3 0.5820 0.5586 0.084 0.116 0.756 0.044
#> GSM1296036 3 0.1938 0.5894 0.012 0.000 0.936 0.052
#> GSM1296048 4 0.2530 0.8110 0.000 0.112 0.000 0.888
#> GSM1296059 3 0.5231 0.3327 0.012 0.000 0.604 0.384
#> GSM1296066 4 0.2530 0.8110 0.000 0.112 0.000 0.888
#> GSM1296060 3 0.5168 0.1109 0.004 0.000 0.504 0.492
#> GSM1296063 4 0.3390 0.7215 0.000 0.016 0.132 0.852
#> GSM1296064 4 0.1389 0.7991 0.000 0.000 0.048 0.952
#> GSM1296067 2 0.1624 0.9160 0.020 0.952 0.028 0.000
#> GSM1296062 3 0.2739 0.5743 0.060 0.000 0.904 0.036
#> GSM1296068 2 0.1118 0.9622 0.000 0.964 0.000 0.036
#> GSM1296050 1 0.0927 0.8254 0.976 0.008 0.016 0.000
#> GSM1296057 1 0.7978 -0.0128 0.404 0.196 0.388 0.012
#> GSM1296052 1 0.0000 0.8358 1.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.8358 1.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.8358 1.000 0.000 0.000 0.000
#> GSM1296055 1 0.7647 0.2317 0.444 0.336 0.220 0.000
#> GSM1296053 1 0.0000 0.8358 1.000 0.000 0.000 0.000
#> GSM1296058 3 0.7373 0.4830 0.112 0.256 0.596 0.036
#> GSM1296051 3 0.6041 0.4192 0.060 0.000 0.608 0.332
#> GSM1296056 4 0.1867 0.7833 0.000 0.000 0.072 0.928
#> GSM1296065 3 0.7448 0.4341 0.124 0.292 0.560 0.024
#> GSM1296061 3 0.1938 0.5894 0.012 0.000 0.936 0.052
#> GSM1296095 3 0.8069 0.3807 0.016 0.216 0.456 0.312
#> GSM1296120 2 0.0992 0.9462 0.008 0.976 0.004 0.012
#> GSM1296077 1 0.0000 0.8358 1.000 0.000 0.000 0.000
#> GSM1296093 1 0.0000 0.8358 1.000 0.000 0.000 0.000
#> GSM1296104 3 0.7354 0.4842 0.104 0.256 0.600 0.040
#> GSM1296079 1 0.0000 0.8358 1.000 0.000 0.000 0.000
#> GSM1296108 2 0.1118 0.9622 0.000 0.964 0.000 0.036
#> GSM1296110 2 0.1624 0.9160 0.020 0.952 0.028 0.000
#> GSM1296081 1 0.0000 0.8358 1.000 0.000 0.000 0.000
#> GSM1296091 3 0.7824 0.3298 0.232 0.252 0.504 0.012
#> GSM1296075 3 0.7851 0.3670 0.196 0.284 0.504 0.016
#> GSM1296112 2 0.1118 0.9622 0.000 0.964 0.000 0.036
#> GSM1296100 1 0.1902 0.8018 0.932 0.004 0.064 0.000
#> GSM1296087 1 0.1716 0.8017 0.936 0.000 0.064 0.000
#> GSM1296118 2 0.2565 0.8807 0.056 0.912 0.032 0.000
#> GSM1296114 2 0.1118 0.9622 0.000 0.964 0.000 0.036
#> GSM1296097 3 0.7373 0.4830 0.112 0.256 0.596 0.036
#> GSM1296106 3 0.7448 0.4341 0.124 0.292 0.560 0.024
#> GSM1296102 1 0.5522 0.6404 0.732 0.120 0.148 0.000
#> GSM1296122 2 0.2996 0.8588 0.064 0.892 0.044 0.000
#> GSM1296089 1 0.7542 0.2773 0.472 0.320 0.208 0.000
#> GSM1296083 1 0.0000 0.8358 1.000 0.000 0.000 0.000
#> GSM1296116 2 0.1118 0.9622 0.000 0.964 0.000 0.036
#> GSM1296085 1 0.0000 0.8358 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
#> GSM1296094 3 0.3616 0.62621 0.000 0.000 0.804 0.032 0.164
#> GSM1296119 5 0.1341 0.78909 0.000 0.056 0.000 0.000 0.944
#> GSM1296076 5 0.4333 0.71336 0.000 0.000 0.188 0.060 0.752
#> GSM1296092 5 0.4333 0.71336 0.000 0.000 0.188 0.060 0.752
#> GSM1296103 3 0.3616 0.62621 0.000 0.000 0.804 0.032 0.164
#> GSM1296078 5 0.4333 0.71336 0.000 0.000 0.188 0.060 0.752
#> GSM1296107 5 0.1341 0.78909 0.000 0.056 0.000 0.000 0.944
#> GSM1296109 5 0.4994 0.27465 0.000 0.012 0.396 0.016 0.576
#> GSM1296080 3 0.3586 0.41656 0.020 0.000 0.792 0.188 0.000
#> GSM1296090 5 0.4333 0.71336 0.000 0.000 0.188 0.060 0.752
#> GSM1296074 5 0.4333 0.71336 0.000 0.000 0.188 0.060 0.752
#> GSM1296111 5 0.1341 0.78909 0.000 0.056 0.000 0.000 0.944
#> GSM1296099 3 0.4901 0.55597 0.000 0.000 0.672 0.060 0.268
#> GSM1296086 3 0.6461 0.21194 0.000 0.000 0.444 0.184 0.372
#> GSM1296117 5 0.1341 0.78909 0.000 0.056 0.000 0.000 0.944
#> GSM1296113 5 0.1341 0.78909 0.000 0.056 0.000 0.000 0.944
#> GSM1296096 3 0.4901 0.55597 0.000 0.000 0.672 0.060 0.268
#> GSM1296105 4 0.5361 0.62484 0.076 0.044 0.144 0.732 0.004
#> GSM1296098 3 0.2891 0.44626 0.000 0.000 0.824 0.176 0.000
#> GSM1296101 3 0.5068 0.43864 0.000 0.000 0.592 0.044 0.364
#> GSM1296121 5 0.1341 0.78909 0.000 0.056 0.000 0.000 0.944
#> GSM1296088 3 0.6517 0.33351 0.000 0.000 0.468 0.212 0.320
#> GSM1296082 5 0.4333 0.71336 0.000 0.000 0.188 0.060 0.752
#> GSM1296115 5 0.1341 0.78909 0.000 0.056 0.000 0.000 0.944
#> GSM1296084 1 0.6432 -0.00641 0.484 0.000 0.128 0.376 0.012
#> GSM1296072 2 0.1117 0.93366 0.000 0.964 0.000 0.016 0.020
#> GSM1296069 2 0.1608 0.94217 0.000 0.928 0.000 0.000 0.072
#> GSM1296071 2 0.1341 0.94877 0.000 0.944 0.000 0.000 0.056
#> GSM1296070 2 0.1608 0.94217 0.000 0.928 0.000 0.000 0.072
#> GSM1296073 5 0.1341 0.78909 0.000 0.056 0.000 0.000 0.944
#> GSM1296034 1 0.5752 0.40796 0.636 0.004 0.164 0.196 0.000
#> GSM1296041 5 0.1341 0.78909 0.000 0.056 0.000 0.000 0.944
#> GSM1296035 3 0.4901 0.55597 0.000 0.000 0.672 0.060 0.268
#> GSM1296038 5 0.5185 0.64289 0.000 0.004 0.128 0.168 0.700
#> GSM1296047 2 0.1117 0.93366 0.000 0.964 0.000 0.016 0.020
#> GSM1296039 5 0.4333 0.71336 0.000 0.000 0.188 0.060 0.752
#> GSM1296042 5 0.1341 0.78909 0.000 0.056 0.000 0.000 0.944
#> GSM1296043 2 0.1608 0.94217 0.000 0.928 0.000 0.000 0.072
#> GSM1296037 1 0.2733 0.76595 0.872 0.004 0.012 0.112 0.000
#> GSM1296046 2 0.1341 0.94877 0.000 0.944 0.000 0.000 0.056
#> GSM1296044 2 0.1270 0.94952 0.000 0.948 0.000 0.000 0.052
#> GSM1296045 2 0.1608 0.94217 0.000 0.928 0.000 0.000 0.072
#> GSM1296025 1 0.0000 0.84409 1.000 0.000 0.000 0.000 0.000
#> GSM1296033 4 0.6709 0.39287 0.336 0.024 0.112 0.520 0.008
#> GSM1296027 1 0.0000 0.84409 1.000 0.000 0.000 0.000 0.000
#> GSM1296032 1 0.0000 0.84409 1.000 0.000 0.000 0.000 0.000
#> GSM1296024 1 0.0000 0.84409 1.000 0.000 0.000 0.000 0.000
#> GSM1296031 1 0.7139 -0.12294 0.396 0.228 0.008 0.360 0.008
#> GSM1296028 1 0.0000 0.84409 1.000 0.000 0.000 0.000 0.000
#> GSM1296029 1 0.5887 0.18703 0.552 0.000 0.100 0.344 0.004
#> GSM1296026 3 0.5940 0.40060 0.004 0.000 0.544 0.348 0.104
#> GSM1296030 3 0.7278 -0.02640 0.236 0.000 0.412 0.324 0.028
#> GSM1296040 3 0.6112 -0.07309 0.044 0.032 0.472 0.448 0.004
#> GSM1296036 3 0.2891 0.44626 0.000 0.000 0.824 0.176 0.000
#> GSM1296048 5 0.1341 0.78909 0.000 0.056 0.000 0.000 0.944
#> GSM1296059 3 0.3616 0.62621 0.000 0.000 0.804 0.032 0.164
#> GSM1296066 5 0.1341 0.78909 0.000 0.056 0.000 0.000 0.944
#> GSM1296060 3 0.4901 0.55597 0.000 0.000 0.672 0.060 0.268
#> GSM1296063 5 0.5150 0.64828 0.000 0.004 0.128 0.164 0.704
#> GSM1296064 5 0.4333 0.71336 0.000 0.000 0.188 0.060 0.752
#> GSM1296067 2 0.1892 0.87701 0.000 0.916 0.004 0.080 0.000
#> GSM1296062 3 0.4134 0.38984 0.044 0.000 0.760 0.196 0.000
#> GSM1296068 2 0.1270 0.94952 0.000 0.948 0.000 0.000 0.052
#> GSM1296050 1 0.1153 0.82493 0.964 0.008 0.000 0.024 0.004
#> GSM1296057 4 0.4912 0.46950 0.320 0.024 0.012 0.644 0.000
#> GSM1296052 1 0.0000 0.84409 1.000 0.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.84409 1.000 0.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.84409 1.000 0.000 0.000 0.000 0.000
#> GSM1296055 4 0.7190 0.07553 0.368 0.244 0.008 0.372 0.008
#> GSM1296053 1 0.0000 0.84409 1.000 0.000 0.000 0.000 0.000
#> GSM1296058 4 0.2234 0.69945 0.012 0.032 0.036 0.920 0.000
#> GSM1296051 3 0.5940 0.40060 0.004 0.000 0.544 0.348 0.104
#> GSM1296056 5 0.4219 0.71650 0.000 0.000 0.156 0.072 0.772
#> GSM1296065 4 0.3581 0.71139 0.040 0.092 0.016 0.848 0.004
#> GSM1296061 3 0.2891 0.44626 0.000 0.000 0.824 0.176 0.000
#> GSM1296095 4 0.6239 0.26343 0.000 0.088 0.044 0.608 0.260
#> GSM1296120 2 0.1117 0.93366 0.000 0.964 0.000 0.016 0.020
#> GSM1296077 1 0.0000 0.84409 1.000 0.000 0.000 0.000 0.000
#> GSM1296093 1 0.0000 0.84409 1.000 0.000 0.000 0.000 0.000
#> GSM1296104 4 0.2156 0.69332 0.004 0.032 0.036 0.924 0.004
#> GSM1296079 1 0.0162 0.84212 0.996 0.000 0.000 0.004 0.000
#> GSM1296108 2 0.1430 0.94944 0.000 0.944 0.000 0.004 0.052
#> GSM1296110 2 0.1892 0.87701 0.000 0.916 0.004 0.080 0.000
#> GSM1296081 1 0.0000 0.84409 1.000 0.000 0.000 0.000 0.000
#> GSM1296091 4 0.3799 0.70468 0.144 0.032 0.012 0.812 0.000
#> GSM1296075 4 0.4216 0.71450 0.120 0.072 0.012 0.796 0.000
#> GSM1296112 2 0.1430 0.94944 0.000 0.944 0.000 0.004 0.052
#> GSM1296100 1 0.2733 0.76595 0.872 0.004 0.012 0.112 0.000
#> GSM1296087 1 0.1965 0.77918 0.904 0.000 0.000 0.096 0.000
#> GSM1296118 2 0.3096 0.83739 0.032 0.872 0.004 0.084 0.008
#> GSM1296114 2 0.1270 0.94952 0.000 0.948 0.000 0.000 0.052
#> GSM1296097 4 0.2234 0.69945 0.012 0.032 0.036 0.920 0.000
#> GSM1296106 4 0.3581 0.71139 0.040 0.092 0.016 0.848 0.004
#> GSM1296102 1 0.4860 0.50890 0.668 0.028 0.012 0.292 0.000
#> GSM1296122 2 0.3452 0.81403 0.036 0.848 0.004 0.104 0.008
#> GSM1296089 1 0.7139 -0.12294 0.396 0.228 0.008 0.360 0.008
#> GSM1296083 1 0.0000 0.84409 1.000 0.000 0.000 0.000 0.000
#> GSM1296116 2 0.1270 0.94952 0.000 0.948 0.000 0.000 0.052
#> GSM1296085 1 0.0000 0.84409 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
#> GSM1296094 3 0.2723 0.6567 0.000 0.000 0.856 0.020 0.120 0.004
#> GSM1296119 5 0.1501 0.7605 0.000 0.076 0.000 0.000 0.924 0.000
#> GSM1296076 5 0.4111 0.6681 0.000 0.000 0.244 0.028 0.716 0.012
#> GSM1296092 5 0.4111 0.6681 0.000 0.000 0.244 0.028 0.716 0.012
#> GSM1296103 3 0.2723 0.6567 0.000 0.000 0.856 0.020 0.120 0.004
#> GSM1296078 5 0.4111 0.6681 0.000 0.000 0.244 0.028 0.716 0.012
#> GSM1296107 5 0.1501 0.7605 0.000 0.076 0.000 0.000 0.924 0.000
#> GSM1296109 5 0.4460 0.1902 0.000 0.028 0.452 0.000 0.520 0.000
#> GSM1296080 3 0.4053 0.4615 0.004 0.000 0.688 0.288 0.004 0.016
#> GSM1296090 5 0.4111 0.6681 0.000 0.000 0.244 0.028 0.716 0.012
#> GSM1296074 5 0.4111 0.6681 0.000 0.000 0.244 0.028 0.716 0.012
#> GSM1296111 5 0.1501 0.7605 0.000 0.076 0.000 0.000 0.924 0.000
#> GSM1296099 3 0.3384 0.5941 0.000 0.000 0.760 0.004 0.228 0.008
#> GSM1296086 3 0.6268 0.2521 0.000 0.000 0.500 0.080 0.336 0.084
#> GSM1296117 5 0.1501 0.7605 0.000 0.076 0.000 0.000 0.924 0.000
#> GSM1296113 5 0.1501 0.7605 0.000 0.076 0.000 0.000 0.924 0.000
#> GSM1296096 3 0.3384 0.5941 0.000 0.000 0.760 0.004 0.228 0.008
#> GSM1296105 6 0.4059 0.5960 0.044 0.000 0.108 0.048 0.004 0.796
#> GSM1296098 3 0.3512 0.4826 0.000 0.000 0.720 0.272 0.000 0.008
#> GSM1296101 3 0.3894 0.4872 0.000 0.000 0.664 0.004 0.324 0.008
#> GSM1296121 5 0.1501 0.7605 0.000 0.076 0.000 0.000 0.924 0.000
#> GSM1296088 3 0.6393 0.3636 0.000 0.000 0.524 0.088 0.284 0.104
#> GSM1296082 5 0.4111 0.6681 0.000 0.000 0.244 0.028 0.716 0.012
#> GSM1296115 5 0.1501 0.7605 0.000 0.076 0.000 0.000 0.924 0.000
#> GSM1296084 1 0.7554 -0.3688 0.376 0.000 0.112 0.212 0.012 0.288
#> GSM1296072 2 0.1913 0.8664 0.000 0.908 0.000 0.080 0.000 0.012
#> GSM1296069 2 0.1007 0.8940 0.000 0.956 0.000 0.000 0.044 0.000
#> GSM1296071 2 0.0547 0.9033 0.000 0.980 0.000 0.000 0.020 0.000
#> GSM1296070 2 0.1007 0.8940 0.000 0.956 0.000 0.000 0.044 0.000
#> GSM1296073 5 0.1501 0.7605 0.000 0.076 0.000 0.000 0.924 0.000
#> GSM1296034 4 0.5857 0.0201 0.400 0.000 0.108 0.472 0.004 0.016
#> GSM1296041 5 0.1501 0.7605 0.000 0.076 0.000 0.000 0.924 0.000
#> GSM1296035 3 0.3384 0.5941 0.000 0.000 0.760 0.004 0.228 0.008
#> GSM1296038 5 0.5418 0.6134 0.000 0.004 0.184 0.044 0.668 0.100
#> GSM1296047 2 0.1913 0.8664 0.000 0.908 0.000 0.080 0.000 0.012
#> GSM1296039 5 0.4111 0.6681 0.000 0.000 0.244 0.028 0.716 0.012
#> GSM1296042 5 0.1501 0.7605 0.000 0.076 0.000 0.000 0.924 0.000
#> GSM1296043 2 0.1007 0.8940 0.000 0.956 0.000 0.000 0.044 0.000
#> GSM1296037 1 0.4969 0.2799 0.636 0.000 0.000 0.260 0.004 0.100
#> GSM1296046 2 0.0547 0.9033 0.000 0.980 0.000 0.000 0.020 0.000
#> GSM1296044 2 0.0458 0.9039 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM1296045 2 0.1007 0.8940 0.000 0.956 0.000 0.000 0.044 0.000
#> GSM1296025 1 0.0000 0.8149 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296033 6 0.6872 -0.0255 0.252 0.000 0.096 0.128 0.012 0.512
#> GSM1296027 1 0.0000 0.8149 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296032 1 0.0000 0.8149 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296024 1 0.0000 0.8149 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296031 4 0.6663 0.6622 0.296 0.024 0.000 0.400 0.004 0.276
#> GSM1296028 1 0.0000 0.8149 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296029 1 0.7074 -0.2957 0.444 0.000 0.088 0.192 0.004 0.272
#> GSM1296026 3 0.6069 0.4461 0.000 0.000 0.592 0.124 0.072 0.212
#> GSM1296030 3 0.7785 0.0903 0.160 0.000 0.416 0.168 0.028 0.228
#> GSM1296040 6 0.5825 0.2199 0.040 0.000 0.408 0.064 0.004 0.484
#> GSM1296036 3 0.3512 0.4826 0.000 0.000 0.720 0.272 0.000 0.008
#> GSM1296048 5 0.1501 0.7605 0.000 0.076 0.000 0.000 0.924 0.000
#> GSM1296059 3 0.2723 0.6567 0.000 0.000 0.856 0.020 0.120 0.004
#> GSM1296066 5 0.1501 0.7605 0.000 0.076 0.000 0.000 0.924 0.000
#> GSM1296060 3 0.3384 0.5941 0.000 0.000 0.760 0.004 0.228 0.008
#> GSM1296063 5 0.5375 0.6180 0.000 0.004 0.184 0.044 0.672 0.096
#> GSM1296064 5 0.4111 0.6681 0.000 0.000 0.244 0.028 0.716 0.012
#> GSM1296067 2 0.3445 0.7306 0.000 0.732 0.000 0.260 0.000 0.008
#> GSM1296062 3 0.4804 0.4314 0.040 0.000 0.656 0.276 0.000 0.028
#> GSM1296068 2 0.0458 0.9039 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM1296050 1 0.1418 0.7575 0.944 0.000 0.000 0.024 0.000 0.032
#> GSM1296057 6 0.4837 0.1437 0.248 0.000 0.008 0.084 0.000 0.660
#> GSM1296052 1 0.0000 0.8149 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.8149 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.8149 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296055 4 0.6805 0.6277 0.268 0.036 0.000 0.404 0.004 0.288
#> GSM1296053 1 0.0000 0.8149 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296058 6 0.1931 0.6736 0.004 0.000 0.028 0.040 0.004 0.924
#> GSM1296051 3 0.6069 0.4461 0.000 0.000 0.592 0.124 0.072 0.212
#> GSM1296056 5 0.4071 0.6745 0.000 0.000 0.216 0.036 0.736 0.012
#> GSM1296065 6 0.2006 0.6626 0.004 0.044 0.008 0.016 0.004 0.924
#> GSM1296061 3 0.3512 0.4826 0.000 0.000 0.720 0.272 0.000 0.008
#> GSM1296095 6 0.5915 0.3858 0.000 0.056 0.060 0.020 0.256 0.608
#> GSM1296120 2 0.1913 0.8664 0.000 0.908 0.000 0.080 0.000 0.012
#> GSM1296077 1 0.0000 0.8149 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296093 1 0.0000 0.8149 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296104 6 0.1901 0.6726 0.000 0.000 0.028 0.040 0.008 0.924
#> GSM1296079 1 0.0146 0.8109 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1296108 2 0.0603 0.9038 0.000 0.980 0.000 0.004 0.016 0.000
#> GSM1296110 2 0.3445 0.7306 0.000 0.732 0.000 0.260 0.000 0.008
#> GSM1296081 1 0.0000 0.8149 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296091 6 0.2747 0.5737 0.096 0.000 0.000 0.044 0.000 0.860
#> GSM1296075 6 0.3327 0.5975 0.076 0.036 0.000 0.044 0.000 0.844
#> GSM1296112 2 0.0603 0.9038 0.000 0.980 0.000 0.004 0.016 0.000
#> GSM1296100 1 0.4969 0.2799 0.636 0.000 0.000 0.260 0.004 0.100
#> GSM1296087 1 0.3068 0.6227 0.840 0.000 0.000 0.088 0.000 0.072
#> GSM1296118 2 0.4407 0.6459 0.020 0.660 0.000 0.304 0.004 0.012
#> GSM1296114 2 0.0458 0.9039 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM1296097 6 0.1931 0.6736 0.004 0.000 0.028 0.040 0.004 0.924
#> GSM1296106 6 0.2006 0.6626 0.004 0.044 0.008 0.016 0.004 0.924
#> GSM1296102 1 0.6289 -0.2204 0.472 0.012 0.000 0.248 0.004 0.264
#> GSM1296122 2 0.4864 0.6145 0.024 0.636 0.000 0.304 0.004 0.032
#> GSM1296089 4 0.6663 0.6622 0.296 0.024 0.000 0.400 0.004 0.276
#> GSM1296083 1 0.0000 0.8149 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296116 2 0.0458 0.9039 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM1296085 1 0.0000 0.8149 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)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# 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 tissue(p) time(p) agent(p) dose(p) individual(p) k
#> SD:hclust 81 6.55e-02 0.1079 0.122 5.04e-08 1.27e-03 2
#> SD:hclust 88 9.62e-05 0.0301 0.158 7.96e-11 5.72e-04 3
#> SD:hclust 70 7.61e-04 0.2827 0.002 6.46e-07 1.34e-04 4
#> SD:hclust 77 3.37e-04 0.2773 0.167 6.92e-09 4.55e-06 5
#> SD:hclust 77 1.21e-03 0.4201 0.245 1.78e-09 9.81e-12 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 45638 rows and 99 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 0.902 0.963 0.982 0.4870 0.514 0.514
#> 3 3 0.811 0.894 0.946 0.3771 0.729 0.513
#> 4 4 0.711 0.689 0.826 0.1153 0.795 0.473
#> 5 5 0.757 0.677 0.748 0.0629 0.861 0.531
#> 6 6 0.792 0.830 0.854 0.0418 0.954 0.780
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
#> GSM1296094 2 0.0000 0.982 0.000 1.000
#> GSM1296119 2 0.0000 0.982 0.000 1.000
#> GSM1296076 2 0.0000 0.982 0.000 1.000
#> GSM1296092 2 0.0000 0.982 0.000 1.000
#> GSM1296103 2 0.0000 0.982 0.000 1.000
#> GSM1296078 2 0.0000 0.982 0.000 1.000
#> GSM1296107 2 0.0000 0.982 0.000 1.000
#> GSM1296109 2 0.0000 0.982 0.000 1.000
#> GSM1296080 1 0.0000 0.981 1.000 0.000
#> GSM1296090 2 0.0000 0.982 0.000 1.000
#> GSM1296074 2 0.0000 0.982 0.000 1.000
#> GSM1296111 2 0.0000 0.982 0.000 1.000
#> GSM1296099 2 0.0000 0.982 0.000 1.000
#> GSM1296086 2 0.0000 0.982 0.000 1.000
#> GSM1296117 2 0.0000 0.982 0.000 1.000
#> GSM1296113 2 0.0000 0.982 0.000 1.000
#> GSM1296096 2 0.0000 0.982 0.000 1.000
#> GSM1296105 1 0.0000 0.981 1.000 0.000
#> GSM1296098 2 0.0000 0.982 0.000 1.000
#> GSM1296101 2 0.0000 0.982 0.000 1.000
#> GSM1296121 2 0.0000 0.982 0.000 1.000
#> GSM1296088 2 0.0000 0.982 0.000 1.000
#> GSM1296082 2 0.0000 0.982 0.000 1.000
#> GSM1296115 2 0.0000 0.982 0.000 1.000
#> GSM1296084 1 0.0376 0.978 0.996 0.004
#> GSM1296072 2 0.0000 0.982 0.000 1.000
#> GSM1296069 2 0.0000 0.982 0.000 1.000
#> GSM1296071 2 0.0000 0.982 0.000 1.000
#> GSM1296070 2 0.0000 0.982 0.000 1.000
#> GSM1296073 2 0.0000 0.982 0.000 1.000
#> GSM1296034 1 0.0000 0.981 1.000 0.000
#> GSM1296041 2 0.0000 0.982 0.000 1.000
#> GSM1296035 2 0.0000 0.982 0.000 1.000
#> GSM1296038 2 0.0000 0.982 0.000 1.000
#> GSM1296047 2 0.5408 0.874 0.124 0.876
#> GSM1296039 2 0.0000 0.982 0.000 1.000
#> GSM1296042 2 0.0000 0.982 0.000 1.000
#> GSM1296043 2 0.0000 0.982 0.000 1.000
#> GSM1296037 1 0.0000 0.981 1.000 0.000
#> GSM1296046 2 0.0000 0.982 0.000 1.000
#> GSM1296044 2 0.0376 0.979 0.004 0.996
#> GSM1296045 2 0.0000 0.982 0.000 1.000
#> GSM1296025 1 0.0000 0.981 1.000 0.000
#> GSM1296033 1 0.0000 0.981 1.000 0.000
#> GSM1296027 1 0.0000 0.981 1.000 0.000
#> GSM1296032 1 0.0000 0.981 1.000 0.000
#> GSM1296024 1 0.0000 0.981 1.000 0.000
#> GSM1296031 1 0.0000 0.981 1.000 0.000
#> GSM1296028 1 0.0000 0.981 1.000 0.000
#> GSM1296029 1 0.0000 0.981 1.000 0.000
#> GSM1296026 1 0.9661 0.381 0.608 0.392
#> GSM1296030 1 0.0376 0.978 0.996 0.004
#> GSM1296040 2 0.0000 0.982 0.000 1.000
#> GSM1296036 2 0.0000 0.982 0.000 1.000
#> GSM1296048 2 0.0000 0.982 0.000 1.000
#> GSM1296059 2 0.0000 0.982 0.000 1.000
#> GSM1296066 2 0.0000 0.982 0.000 1.000
#> GSM1296060 2 0.0000 0.982 0.000 1.000
#> GSM1296063 2 0.0000 0.982 0.000 1.000
#> GSM1296064 2 0.0000 0.982 0.000 1.000
#> GSM1296067 2 0.5519 0.870 0.128 0.872
#> GSM1296062 1 0.0000 0.981 1.000 0.000
#> GSM1296068 2 0.5408 0.874 0.124 0.876
#> GSM1296050 1 0.0000 0.981 1.000 0.000
#> GSM1296057 1 0.0000 0.981 1.000 0.000
#> GSM1296052 1 0.0000 0.981 1.000 0.000
#> GSM1296054 1 0.0000 0.981 1.000 0.000
#> GSM1296049 1 0.0000 0.981 1.000 0.000
#> GSM1296055 1 0.0000 0.981 1.000 0.000
#> GSM1296053 1 0.0000 0.981 1.000 0.000
#> GSM1296058 1 0.0376 0.978 0.996 0.004
#> GSM1296051 2 0.0000 0.982 0.000 1.000
#> GSM1296056 2 0.0000 0.982 0.000 1.000
#> GSM1296065 2 0.0000 0.982 0.000 1.000
#> GSM1296061 1 0.7950 0.694 0.760 0.240
#> GSM1296095 2 0.0000 0.982 0.000 1.000
#> GSM1296120 2 0.5408 0.874 0.124 0.876
#> GSM1296077 1 0.0000 0.981 1.000 0.000
#> GSM1296093 1 0.0000 0.981 1.000 0.000
#> GSM1296104 2 0.0000 0.982 0.000 1.000
#> GSM1296079 1 0.0000 0.981 1.000 0.000
#> GSM1296108 2 0.5294 0.878 0.120 0.880
#> GSM1296110 2 0.2778 0.946 0.048 0.952
#> GSM1296081 1 0.0000 0.981 1.000 0.000
#> GSM1296091 1 0.0000 0.981 1.000 0.000
#> GSM1296075 1 0.0000 0.981 1.000 0.000
#> GSM1296112 2 0.5408 0.874 0.124 0.876
#> GSM1296100 1 0.0000 0.981 1.000 0.000
#> GSM1296087 1 0.0000 0.981 1.000 0.000
#> GSM1296118 1 0.3879 0.905 0.924 0.076
#> GSM1296114 2 0.2423 0.952 0.040 0.960
#> GSM1296097 2 0.6148 0.840 0.152 0.848
#> GSM1296106 1 0.0000 0.981 1.000 0.000
#> GSM1296102 1 0.0000 0.981 1.000 0.000
#> GSM1296122 1 0.0000 0.981 1.000 0.000
#> GSM1296089 1 0.0000 0.981 1.000 0.000
#> GSM1296083 1 0.0000 0.981 1.000 0.000
#> GSM1296116 2 0.2778 0.946 0.048 0.952
#> GSM1296085 1 0.0000 0.981 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1296094 3 0.000 0.926 0.000 0.000 1.000
#> GSM1296119 2 0.470 0.804 0.000 0.788 0.212
#> GSM1296076 3 0.000 0.926 0.000 0.000 1.000
#> GSM1296092 3 0.000 0.926 0.000 0.000 1.000
#> GSM1296103 3 0.000 0.926 0.000 0.000 1.000
#> GSM1296078 3 0.000 0.926 0.000 0.000 1.000
#> GSM1296107 2 0.254 0.896 0.000 0.920 0.080
#> GSM1296109 3 0.000 0.926 0.000 0.000 1.000
#> GSM1296080 3 0.489 0.708 0.228 0.000 0.772
#> GSM1296090 3 0.000 0.926 0.000 0.000 1.000
#> GSM1296074 3 0.000 0.926 0.000 0.000 1.000
#> GSM1296111 2 0.394 0.849 0.000 0.844 0.156
#> GSM1296099 3 0.000 0.926 0.000 0.000 1.000
#> GSM1296086 3 0.000 0.926 0.000 0.000 1.000
#> GSM1296117 2 0.475 0.800 0.000 0.784 0.216
#> GSM1296113 2 0.271 0.892 0.000 0.912 0.088
#> GSM1296096 3 0.000 0.926 0.000 0.000 1.000
#> GSM1296105 1 0.271 0.884 0.912 0.000 0.088
#> GSM1296098 3 0.000 0.926 0.000 0.000 1.000
#> GSM1296101 3 0.000 0.926 0.000 0.000 1.000
#> GSM1296121 2 0.475 0.800 0.000 0.784 0.216
#> GSM1296088 3 0.000 0.926 0.000 0.000 1.000
#> GSM1296082 3 0.000 0.926 0.000 0.000 1.000
#> GSM1296115 2 0.475 0.800 0.000 0.784 0.216
#> GSM1296084 3 0.429 0.768 0.180 0.000 0.820
#> GSM1296072 2 0.000 0.925 0.000 1.000 0.000
#> GSM1296069 2 0.000 0.925 0.000 1.000 0.000
#> GSM1296071 2 0.000 0.925 0.000 1.000 0.000
#> GSM1296070 2 0.000 0.925 0.000 1.000 0.000
#> GSM1296073 2 0.484 0.790 0.000 0.776 0.224
#> GSM1296034 1 0.000 0.969 1.000 0.000 0.000
#> GSM1296041 2 0.470 0.804 0.000 0.788 0.212
#> GSM1296035 3 0.000 0.926 0.000 0.000 1.000
#> GSM1296038 3 0.000 0.926 0.000 0.000 1.000
#> GSM1296047 2 0.000 0.925 0.000 1.000 0.000
#> GSM1296039 3 0.000 0.926 0.000 0.000 1.000
#> GSM1296042 2 0.263 0.894 0.000 0.916 0.084
#> GSM1296043 2 0.000 0.925 0.000 1.000 0.000
#> GSM1296037 1 0.000 0.969 1.000 0.000 0.000
#> GSM1296046 2 0.000 0.925 0.000 1.000 0.000
#> GSM1296044 2 0.000 0.925 0.000 1.000 0.000
#> GSM1296045 2 0.000 0.925 0.000 1.000 0.000
#> GSM1296025 1 0.000 0.969 1.000 0.000 0.000
#> GSM1296033 1 0.000 0.969 1.000 0.000 0.000
#> GSM1296027 1 0.000 0.969 1.000 0.000 0.000
#> GSM1296032 1 0.000 0.969 1.000 0.000 0.000
#> GSM1296024 1 0.000 0.969 1.000 0.000 0.000
#> GSM1296031 1 0.000 0.969 1.000 0.000 0.000
#> GSM1296028 1 0.000 0.969 1.000 0.000 0.000
#> GSM1296029 1 0.000 0.969 1.000 0.000 0.000
#> GSM1296026 3 0.000 0.926 0.000 0.000 1.000
#> GSM1296030 3 0.562 0.580 0.308 0.000 0.692
#> GSM1296040 3 0.000 0.926 0.000 0.000 1.000
#> GSM1296036 3 0.000 0.926 0.000 0.000 1.000
#> GSM1296048 2 0.475 0.800 0.000 0.784 0.216
#> GSM1296059 3 0.000 0.926 0.000 0.000 1.000
#> GSM1296066 2 0.103 0.917 0.000 0.976 0.024
#> GSM1296060 3 0.000 0.926 0.000 0.000 1.000
#> GSM1296063 3 0.406 0.759 0.000 0.164 0.836
#> GSM1296064 3 0.000 0.926 0.000 0.000 1.000
#> GSM1296067 2 0.000 0.925 0.000 1.000 0.000
#> GSM1296062 3 0.518 0.670 0.256 0.000 0.744
#> GSM1296068 2 0.000 0.925 0.000 1.000 0.000
#> GSM1296050 1 0.000 0.969 1.000 0.000 0.000
#> GSM1296057 1 0.000 0.969 1.000 0.000 0.000
#> GSM1296052 1 0.000 0.969 1.000 0.000 0.000
#> GSM1296054 1 0.000 0.969 1.000 0.000 0.000
#> GSM1296049 1 0.000 0.969 1.000 0.000 0.000
#> GSM1296055 1 0.271 0.898 0.912 0.088 0.000
#> GSM1296053 1 0.000 0.969 1.000 0.000 0.000
#> GSM1296058 1 0.533 0.599 0.728 0.000 0.272
#> GSM1296051 3 0.000 0.926 0.000 0.000 1.000
#> GSM1296056 3 0.000 0.926 0.000 0.000 1.000
#> GSM1296065 3 0.571 0.569 0.000 0.320 0.680
#> GSM1296061 3 0.000 0.926 0.000 0.000 1.000
#> GSM1296095 3 0.440 0.760 0.000 0.188 0.812
#> GSM1296120 2 0.000 0.925 0.000 1.000 0.000
#> GSM1296077 1 0.000 0.969 1.000 0.000 0.000
#> GSM1296093 1 0.000 0.969 1.000 0.000 0.000
#> GSM1296104 3 0.550 0.633 0.000 0.292 0.708
#> GSM1296079 1 0.000 0.969 1.000 0.000 0.000
#> GSM1296108 2 0.000 0.925 0.000 1.000 0.000
#> GSM1296110 2 0.000 0.925 0.000 1.000 0.000
#> GSM1296081 1 0.000 0.969 1.000 0.000 0.000
#> GSM1296091 1 0.000 0.969 1.000 0.000 0.000
#> GSM1296075 1 0.406 0.819 0.836 0.164 0.000
#> GSM1296112 2 0.000 0.925 0.000 1.000 0.000
#> GSM1296100 1 0.000 0.969 1.000 0.000 0.000
#> GSM1296087 1 0.000 0.969 1.000 0.000 0.000
#> GSM1296118 2 0.263 0.858 0.084 0.916 0.000
#> GSM1296114 2 0.000 0.925 0.000 1.000 0.000
#> GSM1296097 3 0.930 0.423 0.248 0.228 0.524
#> GSM1296106 1 0.406 0.819 0.836 0.164 0.000
#> GSM1296102 1 0.000 0.969 1.000 0.000 0.000
#> GSM1296122 1 0.406 0.819 0.836 0.164 0.000
#> GSM1296089 1 0.000 0.969 1.000 0.000 0.000
#> GSM1296083 1 0.000 0.969 1.000 0.000 0.000
#> GSM1296116 2 0.000 0.925 0.000 1.000 0.000
#> GSM1296085 1 0.000 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
#> GSM1296094 3 0.0000 0.8604 0.000 0.000 1.000 0.000
#> GSM1296119 4 0.4964 0.5535 0.000 0.380 0.004 0.616
#> GSM1296076 4 0.4948 0.1729 0.000 0.000 0.440 0.560
#> GSM1296092 3 0.3801 0.7229 0.000 0.000 0.780 0.220
#> GSM1296103 3 0.0336 0.8602 0.000 0.000 0.992 0.008
#> GSM1296078 4 0.4933 0.1904 0.000 0.000 0.432 0.568
#> GSM1296107 4 0.4877 0.5220 0.000 0.408 0.000 0.592
#> GSM1296109 4 0.4985 0.2160 0.000 0.000 0.468 0.532
#> GSM1296080 3 0.0524 0.8571 0.008 0.000 0.988 0.004
#> GSM1296090 4 0.4992 0.0767 0.000 0.000 0.476 0.524
#> GSM1296074 4 0.4992 0.0767 0.000 0.000 0.476 0.524
#> GSM1296111 4 0.4804 0.5496 0.000 0.384 0.000 0.616
#> GSM1296099 3 0.1474 0.8507 0.000 0.000 0.948 0.052
#> GSM1296086 3 0.3801 0.7229 0.000 0.000 0.780 0.220
#> GSM1296117 4 0.4964 0.5535 0.000 0.380 0.004 0.616
#> GSM1296113 4 0.4877 0.5220 0.000 0.408 0.000 0.592
#> GSM1296096 3 0.1792 0.8447 0.000 0.000 0.932 0.068
#> GSM1296105 3 0.6551 0.4859 0.136 0.000 0.624 0.240
#> GSM1296098 3 0.0188 0.8599 0.000 0.000 0.996 0.004
#> GSM1296101 3 0.0000 0.8604 0.000 0.000 1.000 0.000
#> GSM1296121 4 0.4872 0.5596 0.000 0.356 0.004 0.640
#> GSM1296088 3 0.0921 0.8589 0.000 0.000 0.972 0.028
#> GSM1296082 4 0.4992 0.0767 0.000 0.000 0.476 0.524
#> GSM1296115 4 0.4964 0.5535 0.000 0.380 0.004 0.616
#> GSM1296084 3 0.1118 0.8585 0.000 0.000 0.964 0.036
#> GSM1296072 2 0.1474 0.7309 0.000 0.948 0.000 0.052
#> GSM1296069 2 0.4008 0.4191 0.000 0.756 0.000 0.244
#> GSM1296071 2 0.0000 0.7306 0.000 1.000 0.000 0.000
#> GSM1296070 2 0.4961 -0.2264 0.000 0.552 0.000 0.448
#> GSM1296073 4 0.4420 0.5603 0.000 0.240 0.012 0.748
#> GSM1296034 1 0.1661 0.9164 0.944 0.000 0.052 0.004
#> GSM1296041 4 0.4804 0.5496 0.000 0.384 0.000 0.616
#> GSM1296035 3 0.1792 0.8447 0.000 0.000 0.932 0.068
#> GSM1296038 3 0.3688 0.7477 0.000 0.000 0.792 0.208
#> GSM1296047 2 0.2868 0.7190 0.000 0.864 0.000 0.136
#> GSM1296039 4 0.4996 0.0679 0.000 0.000 0.484 0.516
#> GSM1296042 4 0.4877 0.5220 0.000 0.408 0.000 0.592
#> GSM1296043 2 0.3172 0.5621 0.000 0.840 0.000 0.160
#> GSM1296037 1 0.2469 0.8990 0.892 0.000 0.000 0.108
#> GSM1296046 2 0.0000 0.7306 0.000 1.000 0.000 0.000
#> GSM1296044 2 0.0000 0.7306 0.000 1.000 0.000 0.000
#> GSM1296045 2 0.3400 0.5315 0.000 0.820 0.000 0.180
#> GSM1296025 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM1296033 1 0.4070 0.8440 0.824 0.000 0.044 0.132
#> GSM1296027 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM1296032 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM1296024 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM1296031 1 0.1557 0.9296 0.944 0.000 0.000 0.056
#> GSM1296028 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM1296029 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM1296026 3 0.0921 0.8598 0.000 0.000 0.972 0.028
#> GSM1296030 3 0.2300 0.8131 0.064 0.000 0.920 0.016
#> GSM1296040 3 0.0336 0.8592 0.000 0.000 0.992 0.008
#> GSM1296036 3 0.0188 0.8599 0.000 0.000 0.996 0.004
#> GSM1296048 4 0.4720 0.5623 0.000 0.324 0.004 0.672
#> GSM1296059 3 0.0336 0.8602 0.000 0.000 0.992 0.008
#> GSM1296066 4 0.4877 0.5220 0.000 0.408 0.000 0.592
#> GSM1296060 3 0.1792 0.8447 0.000 0.000 0.932 0.068
#> GSM1296063 4 0.5035 0.5616 0.000 0.196 0.056 0.748
#> GSM1296064 4 0.4948 0.1854 0.000 0.000 0.440 0.560
#> GSM1296067 2 0.2469 0.7249 0.000 0.892 0.000 0.108
#> GSM1296062 3 0.0895 0.8525 0.004 0.000 0.976 0.020
#> GSM1296068 2 0.0000 0.7306 0.000 1.000 0.000 0.000
#> GSM1296050 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM1296057 1 0.4453 0.7639 0.744 0.000 0.012 0.244
#> GSM1296052 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM1296055 2 0.7760 0.4756 0.228 0.516 0.012 0.244
#> GSM1296053 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM1296058 3 0.7005 0.4518 0.172 0.000 0.572 0.256
#> GSM1296051 3 0.3942 0.7125 0.000 0.000 0.764 0.236
#> GSM1296056 3 0.3688 0.7344 0.000 0.000 0.792 0.208
#> GSM1296065 2 0.7012 0.4788 0.000 0.504 0.124 0.372
#> GSM1296061 3 0.0188 0.8599 0.000 0.000 0.996 0.004
#> GSM1296095 3 0.7849 0.0379 0.000 0.352 0.380 0.268
#> GSM1296120 2 0.2973 0.7168 0.000 0.856 0.000 0.144
#> GSM1296077 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM1296093 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM1296104 2 0.6937 0.4911 0.000 0.508 0.116 0.376
#> GSM1296079 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM1296108 2 0.0000 0.7306 0.000 1.000 0.000 0.000
#> GSM1296110 2 0.0000 0.7306 0.000 1.000 0.000 0.000
#> GSM1296081 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM1296091 1 0.3649 0.8170 0.796 0.000 0.000 0.204
#> GSM1296075 2 0.7220 0.5147 0.212 0.548 0.000 0.240
#> GSM1296112 2 0.0000 0.7306 0.000 1.000 0.000 0.000
#> GSM1296100 1 0.2469 0.8990 0.892 0.000 0.000 0.108
#> GSM1296087 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM1296118 2 0.3257 0.7128 0.004 0.844 0.000 0.152
#> GSM1296114 2 0.0000 0.7306 0.000 1.000 0.000 0.000
#> GSM1296097 2 0.8130 0.5067 0.048 0.508 0.140 0.304
#> GSM1296106 2 0.7246 0.5713 0.160 0.584 0.012 0.244
#> GSM1296102 1 0.4328 0.7680 0.748 0.000 0.008 0.244
#> GSM1296122 2 0.6609 0.5924 0.144 0.620 0.000 0.236
#> GSM1296089 1 0.1557 0.9296 0.944 0.000 0.000 0.056
#> GSM1296083 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM1296116 2 0.0000 0.7306 0.000 1.000 0.000 0.000
#> GSM1296085 1 0.0000 0.9546 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
#> GSM1296094 3 0.3752 0.6723 0.000 0.000 0.708 0.292 0.000
#> GSM1296119 5 0.3242 0.8408 0.000 0.216 0.000 0.000 0.784
#> GSM1296076 3 0.4449 0.2355 0.000 0.000 0.512 0.004 0.484
#> GSM1296092 3 0.3910 0.5061 0.000 0.000 0.720 0.008 0.272
#> GSM1296103 3 0.3707 0.6745 0.000 0.000 0.716 0.284 0.000
#> GSM1296078 3 0.4451 0.2180 0.000 0.000 0.504 0.004 0.492
#> GSM1296107 5 0.3741 0.8187 0.000 0.264 0.000 0.004 0.732
#> GSM1296109 5 0.4836 0.2427 0.000 0.000 0.304 0.044 0.652
#> GSM1296080 3 0.4260 0.6600 0.004 0.000 0.680 0.308 0.008
#> GSM1296090 3 0.4489 0.3486 0.000 0.000 0.572 0.008 0.420
#> GSM1296074 3 0.4383 0.3456 0.000 0.000 0.572 0.004 0.424
#> GSM1296111 5 0.3274 0.8419 0.000 0.220 0.000 0.000 0.780
#> GSM1296099 3 0.4547 0.6708 0.000 0.000 0.704 0.252 0.044
#> GSM1296086 3 0.3910 0.5061 0.000 0.000 0.720 0.008 0.272
#> GSM1296117 5 0.3274 0.8419 0.000 0.220 0.000 0.000 0.780
#> GSM1296113 5 0.3586 0.8203 0.000 0.264 0.000 0.000 0.736
#> GSM1296096 3 0.4547 0.6689 0.000 0.000 0.704 0.252 0.044
#> GSM1296105 4 0.1430 0.4705 0.004 0.000 0.052 0.944 0.000
#> GSM1296098 3 0.3752 0.6723 0.000 0.000 0.708 0.292 0.000
#> GSM1296101 3 0.3774 0.6736 0.000 0.000 0.704 0.296 0.000
#> GSM1296121 5 0.3242 0.8408 0.000 0.216 0.000 0.000 0.784
#> GSM1296088 3 0.4301 0.6776 0.000 0.000 0.712 0.260 0.028
#> GSM1296082 3 0.4383 0.3456 0.000 0.000 0.572 0.004 0.424
#> GSM1296115 5 0.3274 0.8419 0.000 0.220 0.000 0.000 0.780
#> GSM1296084 3 0.4518 0.6527 0.004 0.000 0.660 0.320 0.016
#> GSM1296072 2 0.0703 0.8817 0.000 0.976 0.000 0.024 0.000
#> GSM1296069 5 0.4821 0.4030 0.000 0.464 0.000 0.020 0.516
#> GSM1296071 2 0.0000 0.8865 0.000 1.000 0.000 0.000 0.000
#> GSM1296070 5 0.4540 0.6951 0.000 0.340 0.000 0.020 0.640
#> GSM1296073 5 0.1251 0.6838 0.000 0.036 0.008 0.000 0.956
#> GSM1296034 1 0.2701 0.8608 0.896 0.000 0.048 0.044 0.012
#> GSM1296041 5 0.3274 0.8419 0.000 0.220 0.000 0.000 0.780
#> GSM1296035 3 0.4547 0.6689 0.000 0.000 0.704 0.252 0.044
#> GSM1296038 4 0.5674 -0.2225 0.000 0.000 0.324 0.576 0.100
#> GSM1296047 2 0.1544 0.8420 0.000 0.932 0.000 0.068 0.000
#> GSM1296039 3 0.4489 0.3410 0.000 0.000 0.572 0.008 0.420
#> GSM1296042 5 0.3741 0.8187 0.000 0.264 0.000 0.004 0.732
#> GSM1296043 2 0.4599 0.0892 0.000 0.624 0.000 0.020 0.356
#> GSM1296037 1 0.4067 0.5313 0.692 0.000 0.000 0.300 0.008
#> GSM1296046 2 0.0609 0.8789 0.000 0.980 0.000 0.020 0.000
#> GSM1296044 2 0.0000 0.8865 0.000 1.000 0.000 0.000 0.000
#> GSM1296045 2 0.4181 0.3954 0.000 0.712 0.000 0.020 0.268
#> GSM1296025 1 0.0671 0.9371 0.980 0.000 0.000 0.004 0.016
#> GSM1296033 4 0.4590 0.2789 0.420 0.000 0.000 0.568 0.012
#> GSM1296027 1 0.0579 0.9362 0.984 0.000 0.000 0.008 0.008
#> GSM1296032 1 0.0162 0.9377 0.996 0.000 0.000 0.004 0.000
#> GSM1296024 1 0.0671 0.9371 0.980 0.000 0.000 0.004 0.016
#> GSM1296031 1 0.2624 0.8310 0.872 0.000 0.000 0.116 0.012
#> GSM1296028 1 0.0579 0.9345 0.984 0.000 0.000 0.008 0.008
#> GSM1296029 1 0.0579 0.9345 0.984 0.000 0.000 0.008 0.008
#> GSM1296026 3 0.4603 0.6676 0.000 0.000 0.668 0.300 0.032
#> GSM1296030 3 0.5551 0.6227 0.060 0.000 0.620 0.304 0.016
#> GSM1296040 3 0.3857 0.6627 0.000 0.000 0.688 0.312 0.000
#> GSM1296036 3 0.3796 0.6691 0.000 0.000 0.700 0.300 0.000
#> GSM1296048 5 0.3143 0.8330 0.000 0.204 0.000 0.000 0.796
#> GSM1296059 3 0.3752 0.6743 0.000 0.000 0.708 0.292 0.000
#> GSM1296066 5 0.3741 0.8187 0.000 0.264 0.000 0.004 0.732
#> GSM1296060 3 0.4547 0.6700 0.000 0.000 0.704 0.252 0.044
#> GSM1296063 5 0.4454 0.3237 0.000 0.020 0.268 0.008 0.704
#> GSM1296064 3 0.4562 0.1917 0.000 0.000 0.496 0.008 0.496
#> GSM1296067 2 0.1484 0.8574 0.000 0.944 0.000 0.048 0.008
#> GSM1296062 3 0.4084 0.6477 0.000 0.000 0.668 0.328 0.004
#> GSM1296068 2 0.0000 0.8865 0.000 1.000 0.000 0.000 0.000
#> GSM1296050 1 0.0671 0.9371 0.980 0.000 0.000 0.004 0.016
#> GSM1296057 4 0.3913 0.4612 0.324 0.000 0.000 0.676 0.000
#> GSM1296052 1 0.0579 0.9362 0.984 0.000 0.000 0.008 0.008
#> GSM1296054 1 0.0162 0.9377 0.996 0.000 0.000 0.004 0.000
#> GSM1296049 1 0.0566 0.9374 0.984 0.000 0.000 0.004 0.012
#> GSM1296055 4 0.4774 0.5713 0.020 0.308 0.000 0.660 0.012
#> GSM1296053 1 0.0162 0.9377 0.996 0.000 0.000 0.004 0.000
#> GSM1296058 4 0.2376 0.5343 0.052 0.000 0.044 0.904 0.000
#> GSM1296051 3 0.4691 0.4792 0.000 0.000 0.680 0.044 0.276
#> GSM1296056 3 0.3890 0.5111 0.000 0.000 0.736 0.012 0.252
#> GSM1296065 4 0.4616 0.5827 0.000 0.288 0.028 0.680 0.004
#> GSM1296061 3 0.3796 0.6691 0.000 0.000 0.700 0.300 0.000
#> GSM1296095 4 0.8144 0.3161 0.000 0.240 0.240 0.396 0.124
#> GSM1296120 2 0.1544 0.8420 0.000 0.932 0.000 0.068 0.000
#> GSM1296077 1 0.0671 0.9371 0.980 0.000 0.000 0.004 0.016
#> GSM1296093 1 0.0162 0.9377 0.996 0.000 0.000 0.000 0.004
#> GSM1296104 4 0.4616 0.5827 0.000 0.288 0.028 0.680 0.004
#> GSM1296079 1 0.0671 0.9371 0.980 0.000 0.000 0.004 0.016
#> GSM1296108 2 0.0000 0.8865 0.000 1.000 0.000 0.000 0.000
#> GSM1296110 2 0.0000 0.8865 0.000 1.000 0.000 0.000 0.000
#> GSM1296081 1 0.0671 0.9371 0.980 0.000 0.000 0.004 0.016
#> GSM1296091 4 0.4415 0.3474 0.388 0.000 0.000 0.604 0.008
#> GSM1296075 4 0.4536 0.5612 0.016 0.324 0.000 0.656 0.004
#> GSM1296112 2 0.0404 0.8817 0.000 0.988 0.000 0.012 0.000
#> GSM1296100 1 0.4067 0.5313 0.692 0.000 0.000 0.300 0.008
#> GSM1296087 1 0.0693 0.9348 0.980 0.000 0.000 0.008 0.012
#> GSM1296118 2 0.1764 0.8382 0.000 0.928 0.000 0.064 0.008
#> GSM1296114 2 0.0000 0.8865 0.000 1.000 0.000 0.000 0.000
#> GSM1296097 4 0.4442 0.5731 0.000 0.304 0.016 0.676 0.004
#> GSM1296106 4 0.4440 0.5611 0.012 0.324 0.000 0.660 0.004
#> GSM1296102 4 0.4235 0.4491 0.336 0.000 0.000 0.656 0.008
#> GSM1296122 2 0.4604 0.3329 0.016 0.680 0.000 0.292 0.012
#> GSM1296089 1 0.2674 0.8263 0.868 0.000 0.000 0.120 0.012
#> GSM1296083 1 0.0671 0.9371 0.980 0.000 0.000 0.004 0.016
#> GSM1296116 2 0.0510 0.8808 0.000 0.984 0.000 0.016 0.000
#> GSM1296085 1 0.0162 0.9377 0.996 0.000 0.000 0.004 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1296094 3 0.0551 0.866 0.000 0.000 0.984 0.004 0.008 0.004
#> GSM1296119 5 0.1152 0.895 0.000 0.044 0.000 0.000 0.952 0.004
#> GSM1296076 4 0.4596 0.830 0.000 0.000 0.088 0.672 0.240 0.000
#> GSM1296092 4 0.4682 0.817 0.000 0.000 0.224 0.680 0.092 0.004
#> GSM1296103 3 0.0837 0.867 0.000 0.000 0.972 0.020 0.004 0.004
#> GSM1296078 4 0.4596 0.830 0.000 0.000 0.088 0.672 0.240 0.000
#> GSM1296107 5 0.1908 0.886 0.000 0.096 0.000 0.000 0.900 0.004
#> GSM1296109 5 0.4346 0.455 0.000 0.000 0.284 0.020 0.676 0.020
#> GSM1296080 3 0.1343 0.850 0.004 0.012 0.956 0.020 0.004 0.004
#> GSM1296090 4 0.5008 0.866 0.000 0.000 0.160 0.676 0.152 0.012
#> GSM1296074 4 0.4800 0.868 0.000 0.000 0.168 0.672 0.160 0.000
#> GSM1296111 5 0.1075 0.896 0.000 0.048 0.000 0.000 0.952 0.000
#> GSM1296099 3 0.3512 0.807 0.000 0.000 0.808 0.140 0.012 0.040
#> GSM1296086 4 0.4682 0.817 0.000 0.000 0.224 0.680 0.092 0.004
#> GSM1296117 5 0.1152 0.895 0.000 0.044 0.000 0.000 0.952 0.004
#> GSM1296113 5 0.1765 0.887 0.000 0.096 0.000 0.000 0.904 0.000
#> GSM1296096 3 0.3627 0.796 0.000 0.000 0.796 0.152 0.012 0.040
#> GSM1296105 6 0.1926 0.846 0.000 0.000 0.068 0.020 0.000 0.912
#> GSM1296098 3 0.0146 0.866 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM1296101 3 0.1922 0.864 0.000 0.000 0.924 0.024 0.012 0.040
#> GSM1296121 5 0.1718 0.892 0.000 0.044 0.000 0.008 0.932 0.016
#> GSM1296088 3 0.3323 0.751 0.000 0.008 0.780 0.204 0.000 0.008
#> GSM1296082 4 0.4800 0.868 0.000 0.000 0.168 0.672 0.160 0.000
#> GSM1296115 5 0.1624 0.893 0.000 0.044 0.000 0.008 0.936 0.012
#> GSM1296084 3 0.3952 0.800 0.000 0.016 0.800 0.104 0.008 0.072
#> GSM1296072 2 0.2247 0.907 0.000 0.904 0.000 0.024 0.012 0.060
#> GSM1296069 5 0.4080 0.688 0.000 0.232 0.000 0.036 0.724 0.008
#> GSM1296071 2 0.1225 0.920 0.000 0.952 0.000 0.000 0.036 0.012
#> GSM1296070 5 0.3908 0.802 0.000 0.164 0.000 0.040 0.776 0.020
#> GSM1296073 5 0.1951 0.797 0.000 0.000 0.000 0.076 0.908 0.016
#> GSM1296034 1 0.4451 0.772 0.756 0.008 0.132 0.088 0.016 0.000
#> GSM1296041 5 0.1007 0.895 0.000 0.044 0.000 0.000 0.956 0.000
#> GSM1296035 3 0.3627 0.796 0.000 0.000 0.796 0.152 0.012 0.040
#> GSM1296038 6 0.5291 0.563 0.000 0.000 0.136 0.128 0.052 0.684
#> GSM1296047 2 0.2011 0.898 0.000 0.912 0.000 0.020 0.004 0.064
#> GSM1296039 4 0.5283 0.863 0.000 0.000 0.164 0.648 0.172 0.016
#> GSM1296042 5 0.2376 0.886 0.000 0.096 0.000 0.008 0.884 0.012
#> GSM1296043 2 0.4154 0.652 0.000 0.712 0.000 0.036 0.244 0.008
#> GSM1296037 1 0.5271 0.616 0.636 0.004 0.000 0.124 0.008 0.228
#> GSM1296046 2 0.1906 0.905 0.000 0.924 0.000 0.032 0.036 0.008
#> GSM1296044 2 0.1225 0.920 0.000 0.952 0.000 0.000 0.036 0.012
#> GSM1296045 2 0.4230 0.701 0.000 0.740 0.000 0.044 0.196 0.020
#> GSM1296025 1 0.1149 0.897 0.960 0.008 0.000 0.024 0.008 0.000
#> GSM1296033 6 0.5379 0.617 0.216 0.020 0.004 0.092 0.008 0.660
#> GSM1296027 1 0.1719 0.893 0.928 0.008 0.000 0.056 0.008 0.000
#> GSM1296032 1 0.1493 0.898 0.936 0.000 0.000 0.056 0.004 0.004
#> GSM1296024 1 0.0881 0.899 0.972 0.008 0.000 0.012 0.008 0.000
#> GSM1296031 1 0.4982 0.751 0.716 0.016 0.000 0.136 0.016 0.116
#> GSM1296028 1 0.2619 0.886 0.876 0.012 0.000 0.096 0.012 0.004
#> GSM1296029 1 0.2257 0.889 0.904 0.012 0.000 0.068 0.008 0.008
#> GSM1296026 3 0.3917 0.740 0.000 0.012 0.752 0.204 0.000 0.032
#> GSM1296030 3 0.5386 0.669 0.108 0.020 0.708 0.128 0.008 0.028
#> GSM1296040 3 0.0935 0.866 0.000 0.000 0.964 0.004 0.000 0.032
#> GSM1296036 3 0.0146 0.866 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM1296048 5 0.1750 0.889 0.000 0.040 0.000 0.012 0.932 0.016
#> GSM1296059 3 0.2287 0.859 0.000 0.000 0.904 0.048 0.012 0.036
#> GSM1296066 5 0.1908 0.886 0.000 0.096 0.000 0.000 0.900 0.004
#> GSM1296060 3 0.3551 0.803 0.000 0.000 0.804 0.144 0.012 0.040
#> GSM1296063 4 0.4602 0.312 0.000 0.000 0.004 0.484 0.484 0.028
#> GSM1296064 4 0.5013 0.814 0.000 0.000 0.080 0.648 0.256 0.016
#> GSM1296067 2 0.2070 0.896 0.000 0.908 0.000 0.044 0.000 0.048
#> GSM1296062 3 0.1852 0.824 0.004 0.000 0.928 0.040 0.004 0.024
#> GSM1296068 2 0.1225 0.920 0.000 0.952 0.000 0.000 0.036 0.012
#> GSM1296050 1 0.2178 0.889 0.912 0.012 0.000 0.056 0.012 0.008
#> GSM1296057 6 0.1788 0.852 0.040 0.000 0.004 0.028 0.000 0.928
#> GSM1296052 1 0.1781 0.892 0.924 0.008 0.000 0.060 0.008 0.000
#> GSM1296054 1 0.1429 0.897 0.940 0.000 0.000 0.052 0.004 0.004
#> GSM1296049 1 0.1413 0.899 0.948 0.008 0.000 0.036 0.004 0.004
#> GSM1296055 6 0.2433 0.846 0.000 0.044 0.000 0.072 0.000 0.884
#> GSM1296053 1 0.1699 0.896 0.928 0.004 0.000 0.060 0.004 0.004
#> GSM1296058 6 0.1649 0.852 0.000 0.000 0.032 0.036 0.000 0.932
#> GSM1296051 4 0.5221 0.813 0.000 0.000 0.184 0.680 0.084 0.052
#> GSM1296056 4 0.5040 0.795 0.000 0.000 0.236 0.656 0.092 0.016
#> GSM1296065 6 0.1092 0.860 0.000 0.020 0.000 0.020 0.000 0.960
#> GSM1296061 3 0.0146 0.866 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM1296095 6 0.5165 0.637 0.000 0.016 0.104 0.116 0.044 0.720
#> GSM1296120 2 0.2011 0.898 0.000 0.912 0.000 0.020 0.004 0.064
#> GSM1296077 1 0.1453 0.896 0.944 0.008 0.000 0.040 0.008 0.000
#> GSM1296093 1 0.1440 0.897 0.944 0.004 0.000 0.044 0.004 0.004
#> GSM1296104 6 0.1261 0.860 0.000 0.024 0.000 0.024 0.000 0.952
#> GSM1296079 1 0.1382 0.896 0.948 0.008 0.000 0.036 0.008 0.000
#> GSM1296108 2 0.1370 0.920 0.000 0.948 0.000 0.004 0.036 0.012
#> GSM1296110 2 0.1370 0.920 0.000 0.948 0.000 0.004 0.036 0.012
#> GSM1296081 1 0.0951 0.900 0.968 0.008 0.000 0.020 0.004 0.000
#> GSM1296091 6 0.4763 0.685 0.172 0.012 0.000 0.084 0.012 0.720
#> GSM1296075 6 0.2487 0.840 0.004 0.076 0.000 0.028 0.004 0.888
#> GSM1296112 2 0.1649 0.909 0.000 0.932 0.000 0.032 0.036 0.000
#> GSM1296100 1 0.5271 0.616 0.636 0.004 0.000 0.124 0.008 0.228
#> GSM1296087 1 0.2312 0.885 0.896 0.012 0.000 0.080 0.008 0.004
#> GSM1296118 2 0.1844 0.901 0.000 0.924 0.000 0.024 0.004 0.048
#> GSM1296114 2 0.1225 0.920 0.000 0.952 0.000 0.000 0.036 0.012
#> GSM1296097 6 0.1151 0.860 0.000 0.032 0.000 0.012 0.000 0.956
#> GSM1296106 6 0.1779 0.849 0.000 0.064 0.000 0.016 0.000 0.920
#> GSM1296102 6 0.3345 0.823 0.052 0.004 0.000 0.112 0.004 0.828
#> GSM1296122 2 0.4390 0.691 0.000 0.724 0.000 0.096 0.004 0.176
#> GSM1296089 1 0.5023 0.746 0.712 0.016 0.000 0.136 0.016 0.120
#> GSM1296083 1 0.0951 0.900 0.968 0.008 0.000 0.020 0.004 0.000
#> GSM1296116 2 0.1867 0.906 0.000 0.924 0.000 0.036 0.036 0.004
#> GSM1296085 1 0.1555 0.897 0.932 0.000 0.000 0.060 0.004 0.004
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# 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 tissue(p) time(p) agent(p) dose(p) individual(p) k
#> SD:kmeans 98 1.75e-02 0.1926 0.30995 4.41e-07 1.57e-03 2
#> SD:kmeans 98 1.71e-03 0.0228 0.00703 1.44e-09 1.50e-08 3
#> SD:kmeans 83 2.35e-05 0.5483 0.00458 7.26e-09 1.48e-04 4
#> SD:kmeans 78 2.08e-04 0.2435 0.04055 2.45e-06 5.35e-05 5
#> SD:kmeans 97 8.78e-05 0.1900 0.06137 5.51e-07 4.76e-06 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 45638 rows and 99 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
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 0.808 0.927 0.967 0.5008 0.501 0.501
#> 3 3 1.000 0.980 0.992 0.3432 0.724 0.500
#> 4 4 0.920 0.905 0.955 0.1103 0.856 0.603
#> 5 5 0.802 0.779 0.871 0.0561 0.904 0.659
#> 6 6 0.884 0.816 0.913 0.0427 0.948 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] 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
#> GSM1296094 2 0.0000 0.958 0.000 1.000
#> GSM1296119 2 0.0000 0.958 0.000 1.000
#> GSM1296076 2 0.0000 0.958 0.000 1.000
#> GSM1296092 2 0.0000 0.958 0.000 1.000
#> GSM1296103 2 0.0000 0.958 0.000 1.000
#> GSM1296078 2 0.0000 0.958 0.000 1.000
#> GSM1296107 2 0.0000 0.958 0.000 1.000
#> GSM1296109 2 0.0000 0.958 0.000 1.000
#> GSM1296080 1 0.0000 0.971 1.000 0.000
#> GSM1296090 2 0.0000 0.958 0.000 1.000
#> GSM1296074 2 0.0000 0.958 0.000 1.000
#> GSM1296111 2 0.0000 0.958 0.000 1.000
#> GSM1296099 2 0.0000 0.958 0.000 1.000
#> GSM1296086 2 0.0000 0.958 0.000 1.000
#> GSM1296117 2 0.0000 0.958 0.000 1.000
#> GSM1296113 2 0.0000 0.958 0.000 1.000
#> GSM1296096 2 0.0000 0.958 0.000 1.000
#> GSM1296105 1 0.0000 0.971 1.000 0.000
#> GSM1296098 2 0.7745 0.696 0.228 0.772
#> GSM1296101 2 0.0000 0.958 0.000 1.000
#> GSM1296121 2 0.0000 0.958 0.000 1.000
#> GSM1296088 2 0.9286 0.460 0.344 0.656
#> GSM1296082 2 0.0000 0.958 0.000 1.000
#> GSM1296115 2 0.0000 0.958 0.000 1.000
#> GSM1296084 1 0.0000 0.971 1.000 0.000
#> GSM1296072 2 0.0000 0.958 0.000 1.000
#> GSM1296069 2 0.0000 0.958 0.000 1.000
#> GSM1296071 2 0.0672 0.953 0.008 0.992
#> GSM1296070 2 0.0000 0.958 0.000 1.000
#> GSM1296073 2 0.0000 0.958 0.000 1.000
#> GSM1296034 1 0.0000 0.971 1.000 0.000
#> GSM1296041 2 0.0000 0.958 0.000 1.000
#> GSM1296035 2 0.0000 0.958 0.000 1.000
#> GSM1296038 2 0.0000 0.958 0.000 1.000
#> GSM1296047 2 0.7299 0.771 0.204 0.796
#> GSM1296039 2 0.0000 0.958 0.000 1.000
#> GSM1296042 2 0.0000 0.958 0.000 1.000
#> GSM1296043 2 0.0000 0.958 0.000 1.000
#> GSM1296037 1 0.0000 0.971 1.000 0.000
#> GSM1296046 2 0.0000 0.958 0.000 1.000
#> GSM1296044 2 0.1633 0.942 0.024 0.976
#> GSM1296045 2 0.0000 0.958 0.000 1.000
#> GSM1296025 1 0.0000 0.971 1.000 0.000
#> GSM1296033 1 0.0000 0.971 1.000 0.000
#> GSM1296027 1 0.0000 0.971 1.000 0.000
#> GSM1296032 1 0.0000 0.971 1.000 0.000
#> GSM1296024 1 0.0000 0.971 1.000 0.000
#> GSM1296031 1 0.0000 0.971 1.000 0.000
#> GSM1296028 1 0.0000 0.971 1.000 0.000
#> GSM1296029 1 0.0000 0.971 1.000 0.000
#> GSM1296026 1 0.7219 0.755 0.800 0.200
#> GSM1296030 1 0.0672 0.964 0.992 0.008
#> GSM1296040 1 0.7299 0.749 0.796 0.204
#> GSM1296036 1 0.6973 0.771 0.812 0.188
#> GSM1296048 2 0.0000 0.958 0.000 1.000
#> GSM1296059 2 0.0000 0.958 0.000 1.000
#> GSM1296066 2 0.0000 0.958 0.000 1.000
#> GSM1296060 2 0.0000 0.958 0.000 1.000
#> GSM1296063 2 0.0000 0.958 0.000 1.000
#> GSM1296064 2 0.0000 0.958 0.000 1.000
#> GSM1296067 1 0.9795 0.225 0.584 0.416
#> GSM1296062 1 0.0000 0.971 1.000 0.000
#> GSM1296068 2 0.7299 0.771 0.204 0.796
#> GSM1296050 1 0.0000 0.971 1.000 0.000
#> GSM1296057 1 0.0000 0.971 1.000 0.000
#> GSM1296052 1 0.0000 0.971 1.000 0.000
#> GSM1296054 1 0.0000 0.971 1.000 0.000
#> GSM1296049 1 0.0000 0.971 1.000 0.000
#> GSM1296055 1 0.0000 0.971 1.000 0.000
#> GSM1296053 1 0.0000 0.971 1.000 0.000
#> GSM1296058 1 0.0000 0.971 1.000 0.000
#> GSM1296051 2 0.0000 0.958 0.000 1.000
#> GSM1296056 2 0.0000 0.958 0.000 1.000
#> GSM1296065 2 0.0000 0.958 0.000 1.000
#> GSM1296061 1 0.5946 0.825 0.856 0.144
#> GSM1296095 2 0.0000 0.958 0.000 1.000
#> GSM1296120 2 0.7299 0.771 0.204 0.796
#> GSM1296077 1 0.0000 0.971 1.000 0.000
#> GSM1296093 1 0.0000 0.971 1.000 0.000
#> GSM1296104 2 0.1843 0.939 0.028 0.972
#> GSM1296079 1 0.0000 0.971 1.000 0.000
#> GSM1296108 2 0.7219 0.776 0.200 0.800
#> GSM1296110 2 0.6343 0.822 0.160 0.840
#> GSM1296081 1 0.0000 0.971 1.000 0.000
#> GSM1296091 1 0.0000 0.971 1.000 0.000
#> GSM1296075 1 0.0000 0.971 1.000 0.000
#> GSM1296112 2 0.7299 0.771 0.204 0.796
#> GSM1296100 1 0.0000 0.971 1.000 0.000
#> GSM1296087 1 0.0000 0.971 1.000 0.000
#> GSM1296118 1 0.0000 0.971 1.000 0.000
#> GSM1296114 2 0.6048 0.834 0.148 0.852
#> GSM1296097 1 0.0000 0.971 1.000 0.000
#> GSM1296106 1 0.0000 0.971 1.000 0.000
#> GSM1296102 1 0.0000 0.971 1.000 0.000
#> GSM1296122 1 0.0000 0.971 1.000 0.000
#> GSM1296089 1 0.0000 0.971 1.000 0.000
#> GSM1296083 1 0.0000 0.971 1.000 0.000
#> GSM1296116 2 0.6343 0.822 0.160 0.840
#> GSM1296085 1 0.0000 0.971 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1296094 3 0.0000 0.983 0.000 0.000 1.000
#> GSM1296119 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296076 3 0.0000 0.983 0.000 0.000 1.000
#> GSM1296092 3 0.0000 0.983 0.000 0.000 1.000
#> GSM1296103 3 0.0000 0.983 0.000 0.000 1.000
#> GSM1296078 3 0.0000 0.983 0.000 0.000 1.000
#> GSM1296107 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296109 3 0.0000 0.983 0.000 0.000 1.000
#> GSM1296080 3 0.0000 0.983 0.000 0.000 1.000
#> GSM1296090 3 0.0000 0.983 0.000 0.000 1.000
#> GSM1296074 3 0.0000 0.983 0.000 0.000 1.000
#> GSM1296111 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296099 3 0.0000 0.983 0.000 0.000 1.000
#> GSM1296086 3 0.0000 0.983 0.000 0.000 1.000
#> GSM1296117 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296113 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296096 3 0.0000 0.983 0.000 0.000 1.000
#> GSM1296105 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296098 3 0.0000 0.983 0.000 0.000 1.000
#> GSM1296101 3 0.0000 0.983 0.000 0.000 1.000
#> GSM1296121 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296088 3 0.0000 0.983 0.000 0.000 1.000
#> GSM1296082 3 0.0000 0.983 0.000 0.000 1.000
#> GSM1296115 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296084 3 0.0000 0.983 0.000 0.000 1.000
#> GSM1296072 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296069 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296071 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296070 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296073 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296034 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296041 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296035 3 0.0000 0.983 0.000 0.000 1.000
#> GSM1296038 3 0.0000 0.983 0.000 0.000 1.000
#> GSM1296047 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296039 3 0.0000 0.983 0.000 0.000 1.000
#> GSM1296042 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296043 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296037 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296046 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296044 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296045 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296025 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296033 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296027 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296032 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296024 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296031 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296028 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296029 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296026 3 0.0000 0.983 0.000 0.000 1.000
#> GSM1296030 3 0.2356 0.911 0.072 0.000 0.928
#> GSM1296040 3 0.0000 0.983 0.000 0.000 1.000
#> GSM1296036 3 0.0000 0.983 0.000 0.000 1.000
#> GSM1296048 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296059 3 0.0000 0.983 0.000 0.000 1.000
#> GSM1296066 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296060 3 0.0000 0.983 0.000 0.000 1.000
#> GSM1296063 2 0.0424 0.984 0.000 0.992 0.008
#> GSM1296064 3 0.0000 0.983 0.000 0.000 1.000
#> GSM1296067 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296062 3 0.6235 0.227 0.436 0.000 0.564
#> GSM1296068 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296050 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296057 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296052 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296054 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296049 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296055 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296053 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296058 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296051 3 0.0000 0.983 0.000 0.000 1.000
#> GSM1296056 3 0.0000 0.983 0.000 0.000 1.000
#> GSM1296065 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296061 3 0.0000 0.983 0.000 0.000 1.000
#> GSM1296095 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296120 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296077 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296093 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296104 2 0.1411 0.956 0.036 0.964 0.000
#> GSM1296079 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296108 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296110 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296081 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296091 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296075 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296112 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296100 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296087 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296118 2 0.5058 0.677 0.244 0.756 0.000
#> GSM1296114 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296097 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296106 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296102 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296122 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296089 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296083 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1296116 2 0.0000 0.991 0.000 1.000 0.000
#> GSM1296085 1 0.0000 1.000 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1296094 3 0.0000 0.936 0.000 0.000 1.000 0.000
#> GSM1296119 4 0.1557 0.944 0.000 0.056 0.000 0.944
#> GSM1296076 4 0.0188 0.941 0.000 0.000 0.004 0.996
#> GSM1296092 3 0.4790 0.459 0.000 0.000 0.620 0.380
#> GSM1296103 3 0.0000 0.936 0.000 0.000 1.000 0.000
#> GSM1296078 4 0.0188 0.941 0.000 0.000 0.004 0.996
#> GSM1296107 4 0.2216 0.922 0.000 0.092 0.000 0.908
#> GSM1296109 4 0.2760 0.867 0.000 0.000 0.128 0.872
#> GSM1296080 3 0.0188 0.934 0.004 0.000 0.996 0.000
#> GSM1296090 4 0.1389 0.924 0.000 0.000 0.048 0.952
#> GSM1296074 4 0.1389 0.924 0.000 0.000 0.048 0.952
#> GSM1296111 4 0.1637 0.942 0.000 0.060 0.000 0.940
#> GSM1296099 3 0.0000 0.936 0.000 0.000 1.000 0.000
#> GSM1296086 3 0.4804 0.449 0.000 0.000 0.616 0.384
#> GSM1296117 4 0.1557 0.944 0.000 0.056 0.000 0.944
#> GSM1296113 4 0.2216 0.922 0.000 0.092 0.000 0.908
#> GSM1296096 3 0.0000 0.936 0.000 0.000 1.000 0.000
#> GSM1296105 1 0.3400 0.768 0.820 0.000 0.180 0.000
#> GSM1296098 3 0.0000 0.936 0.000 0.000 1.000 0.000
#> GSM1296101 3 0.0000 0.936 0.000 0.000 1.000 0.000
#> GSM1296121 4 0.1557 0.944 0.000 0.056 0.000 0.944
#> GSM1296088 3 0.0188 0.935 0.000 0.000 0.996 0.004
#> GSM1296082 4 0.1389 0.924 0.000 0.000 0.048 0.952
#> GSM1296115 4 0.1557 0.944 0.000 0.056 0.000 0.944
#> GSM1296084 3 0.0188 0.934 0.004 0.000 0.996 0.000
#> GSM1296072 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM1296069 2 0.1302 0.917 0.000 0.956 0.000 0.044
#> GSM1296071 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM1296070 2 0.4948 0.144 0.000 0.560 0.000 0.440
#> GSM1296073 4 0.0000 0.941 0.000 0.000 0.000 1.000
#> GSM1296034 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM1296041 4 0.1557 0.944 0.000 0.056 0.000 0.944
#> GSM1296035 3 0.0000 0.936 0.000 0.000 1.000 0.000
#> GSM1296038 4 0.0592 0.940 0.000 0.000 0.016 0.984
#> GSM1296047 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM1296039 4 0.1211 0.929 0.000 0.000 0.040 0.960
#> GSM1296042 4 0.2281 0.919 0.000 0.096 0.000 0.904
#> GSM1296043 2 0.0336 0.946 0.000 0.992 0.000 0.008
#> GSM1296037 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM1296046 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM1296044 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM1296045 2 0.0336 0.946 0.000 0.992 0.000 0.008
#> GSM1296025 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM1296033 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM1296027 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM1296032 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM1296024 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM1296031 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM1296028 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM1296029 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM1296026 3 0.0336 0.933 0.000 0.000 0.992 0.008
#> GSM1296030 3 0.1716 0.879 0.064 0.000 0.936 0.000
#> GSM1296040 3 0.0000 0.936 0.000 0.000 1.000 0.000
#> GSM1296036 3 0.0000 0.936 0.000 0.000 1.000 0.000
#> GSM1296048 4 0.1557 0.944 0.000 0.056 0.000 0.944
#> GSM1296059 3 0.0000 0.936 0.000 0.000 1.000 0.000
#> GSM1296066 4 0.2345 0.916 0.000 0.100 0.000 0.900
#> GSM1296060 3 0.0000 0.936 0.000 0.000 1.000 0.000
#> GSM1296063 4 0.0000 0.941 0.000 0.000 0.000 1.000
#> GSM1296064 4 0.0188 0.941 0.000 0.000 0.004 0.996
#> GSM1296067 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM1296062 3 0.0336 0.931 0.008 0.000 0.992 0.000
#> GSM1296068 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM1296050 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM1296057 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM1296052 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM1296055 1 0.0336 0.960 0.992 0.008 0.000 0.000
#> GSM1296053 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM1296058 1 0.0188 0.963 0.996 0.000 0.004 0.000
#> GSM1296051 4 0.1302 0.927 0.000 0.000 0.044 0.956
#> GSM1296056 3 0.4661 0.522 0.000 0.000 0.652 0.348
#> GSM1296065 4 0.1022 0.940 0.000 0.032 0.000 0.968
#> GSM1296061 3 0.0000 0.936 0.000 0.000 1.000 0.000
#> GSM1296095 4 0.2704 0.853 0.000 0.124 0.000 0.876
#> GSM1296120 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM1296077 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM1296093 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM1296104 2 0.2408 0.864 0.000 0.896 0.000 0.104
#> GSM1296079 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM1296108 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM1296110 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM1296081 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM1296091 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM1296075 1 0.1557 0.916 0.944 0.056 0.000 0.000
#> GSM1296112 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM1296100 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM1296087 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM1296118 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM1296114 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM1296097 1 0.4907 0.300 0.580 0.420 0.000 0.000
#> GSM1296106 1 0.4790 0.390 0.620 0.380 0.000 0.000
#> GSM1296102 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM1296122 2 0.4072 0.638 0.252 0.748 0.000 0.000
#> GSM1296089 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM1296083 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM1296116 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM1296085 1 0.0000 0.966 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
#> GSM1296094 3 0.0000 0.875 0.000 0.000 1.000 0.000 0.000
#> GSM1296119 5 0.0609 0.790 0.000 0.020 0.000 0.000 0.980
#> GSM1296076 4 0.4184 0.818 0.000 0.000 0.016 0.700 0.284
#> GSM1296092 4 0.5083 0.725 0.000 0.000 0.160 0.700 0.140
#> GSM1296103 3 0.0963 0.873 0.000 0.000 0.964 0.036 0.000
#> GSM1296078 4 0.4184 0.818 0.000 0.000 0.016 0.700 0.284
#> GSM1296107 5 0.1410 0.788 0.000 0.060 0.000 0.000 0.940
#> GSM1296109 5 0.3635 0.524 0.000 0.000 0.248 0.004 0.748
#> GSM1296080 3 0.0404 0.869 0.012 0.000 0.988 0.000 0.000
#> GSM1296090 4 0.4315 0.823 0.000 0.000 0.024 0.700 0.276
#> GSM1296074 4 0.4315 0.823 0.000 0.000 0.024 0.700 0.276
#> GSM1296111 5 0.0609 0.790 0.000 0.020 0.000 0.000 0.980
#> GSM1296099 3 0.2329 0.833 0.000 0.000 0.876 0.124 0.000
#> GSM1296086 4 0.5083 0.725 0.000 0.000 0.160 0.700 0.140
#> GSM1296117 5 0.0609 0.790 0.000 0.020 0.000 0.000 0.980
#> GSM1296113 5 0.1341 0.788 0.000 0.056 0.000 0.000 0.944
#> GSM1296096 3 0.2424 0.828 0.000 0.000 0.868 0.132 0.000
#> GSM1296105 1 0.5602 0.578 0.644 0.008 0.244 0.104 0.000
#> GSM1296098 3 0.0000 0.875 0.000 0.000 1.000 0.000 0.000
#> GSM1296101 3 0.0880 0.873 0.000 0.000 0.968 0.032 0.000
#> GSM1296121 5 0.0609 0.790 0.000 0.020 0.000 0.000 0.980
#> GSM1296088 3 0.3774 0.614 0.000 0.000 0.704 0.296 0.000
#> GSM1296082 4 0.4315 0.823 0.000 0.000 0.024 0.700 0.276
#> GSM1296115 5 0.0609 0.790 0.000 0.020 0.000 0.000 0.980
#> GSM1296084 3 0.4064 0.742 0.092 0.000 0.792 0.116 0.000
#> GSM1296072 2 0.1792 0.834 0.000 0.916 0.000 0.000 0.084
#> GSM1296069 5 0.3816 0.578 0.000 0.304 0.000 0.000 0.696
#> GSM1296071 2 0.0290 0.916 0.000 0.992 0.000 0.000 0.008
#> GSM1296070 5 0.3366 0.662 0.000 0.232 0.000 0.000 0.768
#> GSM1296073 5 0.0703 0.762 0.000 0.000 0.000 0.024 0.976
#> GSM1296034 1 0.1043 0.905 0.960 0.000 0.040 0.000 0.000
#> GSM1296041 5 0.0609 0.790 0.000 0.020 0.000 0.000 0.980
#> GSM1296035 3 0.2424 0.828 0.000 0.000 0.868 0.132 0.000
#> GSM1296038 5 0.4973 0.180 0.000 0.000 0.048 0.320 0.632
#> GSM1296047 2 0.0290 0.916 0.000 0.992 0.000 0.000 0.008
#> GSM1296039 4 0.4318 0.811 0.000 0.000 0.020 0.688 0.292
#> GSM1296042 5 0.1410 0.788 0.000 0.060 0.000 0.000 0.940
#> GSM1296043 5 0.4268 0.314 0.000 0.444 0.000 0.000 0.556
#> GSM1296037 1 0.2408 0.872 0.892 0.004 0.000 0.096 0.008
#> GSM1296046 2 0.0290 0.916 0.000 0.992 0.000 0.000 0.008
#> GSM1296044 2 0.0290 0.916 0.000 0.992 0.000 0.000 0.008
#> GSM1296045 5 0.4307 0.170 0.000 0.496 0.000 0.000 0.504
#> GSM1296025 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000
#> GSM1296033 1 0.0609 0.920 0.980 0.000 0.000 0.020 0.000
#> GSM1296027 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000
#> GSM1296032 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000
#> GSM1296024 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000
#> GSM1296031 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000
#> GSM1296028 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000
#> GSM1296029 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000
#> GSM1296026 3 0.4127 0.568 0.008 0.000 0.680 0.312 0.000
#> GSM1296030 3 0.5532 0.490 0.280 0.000 0.616 0.104 0.000
#> GSM1296040 3 0.0000 0.875 0.000 0.000 1.000 0.000 0.000
#> GSM1296036 3 0.0000 0.875 0.000 0.000 1.000 0.000 0.000
#> GSM1296048 5 0.0510 0.787 0.000 0.016 0.000 0.000 0.984
#> GSM1296059 3 0.1270 0.868 0.000 0.000 0.948 0.052 0.000
#> GSM1296066 5 0.1544 0.784 0.000 0.068 0.000 0.000 0.932
#> GSM1296060 3 0.2424 0.828 0.000 0.000 0.868 0.132 0.000
#> GSM1296063 5 0.3039 0.535 0.000 0.000 0.000 0.192 0.808
#> GSM1296064 4 0.4547 0.643 0.000 0.000 0.012 0.588 0.400
#> GSM1296067 2 0.0290 0.916 0.000 0.992 0.000 0.000 0.008
#> GSM1296062 3 0.0609 0.864 0.020 0.000 0.980 0.000 0.000
#> GSM1296068 2 0.0290 0.916 0.000 0.992 0.000 0.000 0.008
#> GSM1296050 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000
#> GSM1296057 1 0.4477 0.710 0.688 0.008 0.000 0.288 0.016
#> GSM1296052 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000
#> GSM1296055 1 0.4590 0.715 0.696 0.016 0.000 0.272 0.016
#> GSM1296053 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000
#> GSM1296058 1 0.4852 0.595 0.596 0.008 0.000 0.380 0.016
#> GSM1296051 4 0.4315 0.823 0.000 0.000 0.024 0.700 0.276
#> GSM1296056 4 0.5050 0.692 0.000 0.000 0.180 0.700 0.120
#> GSM1296065 5 0.5622 0.324 0.000 0.076 0.000 0.416 0.508
#> GSM1296061 3 0.0000 0.875 0.000 0.000 1.000 0.000 0.000
#> GSM1296095 5 0.1901 0.763 0.000 0.012 0.004 0.056 0.928
#> GSM1296120 2 0.0290 0.916 0.000 0.992 0.000 0.000 0.008
#> GSM1296077 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000
#> GSM1296093 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000
#> GSM1296104 4 0.4894 -0.317 0.000 0.456 0.000 0.520 0.024
#> GSM1296079 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000
#> GSM1296108 2 0.0290 0.916 0.000 0.992 0.000 0.000 0.008
#> GSM1296110 2 0.0290 0.916 0.000 0.992 0.000 0.000 0.008
#> GSM1296081 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000
#> GSM1296091 1 0.1121 0.909 0.956 0.000 0.000 0.044 0.000
#> GSM1296075 1 0.6433 0.299 0.516 0.296 0.000 0.184 0.004
#> GSM1296112 2 0.0290 0.916 0.000 0.992 0.000 0.000 0.008
#> GSM1296100 1 0.2408 0.872 0.892 0.004 0.000 0.096 0.008
#> GSM1296087 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000
#> GSM1296118 2 0.0290 0.916 0.000 0.992 0.000 0.000 0.008
#> GSM1296114 2 0.0290 0.916 0.000 0.992 0.000 0.000 0.008
#> GSM1296097 2 0.7170 0.160 0.292 0.392 0.000 0.300 0.016
#> GSM1296106 2 0.6318 0.467 0.128 0.556 0.000 0.300 0.016
#> GSM1296102 1 0.3154 0.835 0.836 0.004 0.000 0.148 0.012
#> GSM1296122 2 0.2456 0.831 0.064 0.904 0.000 0.024 0.008
#> GSM1296089 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000
#> GSM1296083 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000
#> GSM1296116 2 0.0290 0.916 0.000 0.992 0.000 0.000 0.008
#> GSM1296085 1 0.0000 0.929 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
#> GSM1296094 3 0.0146 0.8264 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM1296119 5 0.0717 0.8957 0.000 0.016 0.000 0.008 0.976 0.000
#> GSM1296076 4 0.0260 0.9175 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM1296092 4 0.0260 0.9175 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM1296103 3 0.1643 0.8185 0.000 0.000 0.924 0.068 0.000 0.008
#> GSM1296078 4 0.0260 0.9175 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM1296107 5 0.0632 0.8944 0.000 0.024 0.000 0.000 0.976 0.000
#> GSM1296109 5 0.3426 0.6608 0.000 0.000 0.220 0.004 0.764 0.012
#> GSM1296080 3 0.0653 0.8221 0.004 0.000 0.980 0.004 0.000 0.012
#> GSM1296090 4 0.0260 0.9175 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM1296074 4 0.0260 0.9175 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM1296111 5 0.0717 0.8957 0.000 0.016 0.000 0.008 0.976 0.000
#> GSM1296099 3 0.3421 0.7648 0.000 0.000 0.804 0.160 0.016 0.020
#> GSM1296086 4 0.0260 0.9175 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM1296117 5 0.0717 0.8957 0.000 0.016 0.000 0.008 0.976 0.000
#> GSM1296113 5 0.0632 0.8944 0.000 0.024 0.000 0.000 0.976 0.000
#> GSM1296096 3 0.3593 0.7488 0.000 0.000 0.784 0.180 0.016 0.020
#> GSM1296105 1 0.6245 0.1452 0.488 0.000 0.248 0.008 0.008 0.248
#> GSM1296098 3 0.0291 0.8261 0.000 0.000 0.992 0.004 0.000 0.004
#> GSM1296101 3 0.2068 0.8184 0.000 0.000 0.916 0.048 0.016 0.020
#> GSM1296121 5 0.0862 0.8955 0.000 0.016 0.000 0.008 0.972 0.004
#> GSM1296088 3 0.4183 0.0632 0.000 0.000 0.508 0.480 0.000 0.012
#> GSM1296082 4 0.0260 0.9175 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM1296115 5 0.0862 0.8955 0.000 0.016 0.000 0.008 0.972 0.004
#> GSM1296084 3 0.5043 0.5722 0.120 0.000 0.680 0.180 0.000 0.020
#> GSM1296072 2 0.1745 0.8843 0.000 0.920 0.000 0.000 0.068 0.012
#> GSM1296069 5 0.1970 0.8377 0.000 0.092 0.000 0.000 0.900 0.008
#> GSM1296071 2 0.0146 0.9523 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1296070 5 0.1124 0.8858 0.000 0.036 0.000 0.000 0.956 0.008
#> GSM1296073 5 0.0767 0.8909 0.000 0.008 0.000 0.012 0.976 0.004
#> GSM1296034 1 0.1471 0.8797 0.932 0.000 0.064 0.000 0.004 0.000
#> GSM1296041 5 0.0717 0.8957 0.000 0.016 0.000 0.008 0.976 0.000
#> GSM1296035 3 0.3593 0.7488 0.000 0.000 0.784 0.180 0.016 0.020
#> GSM1296038 5 0.6202 0.3168 0.000 0.000 0.044 0.284 0.528 0.144
#> GSM1296047 2 0.0146 0.9515 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1296039 4 0.1942 0.8650 0.000 0.000 0.008 0.916 0.064 0.012
#> GSM1296042 5 0.0777 0.8943 0.000 0.024 0.000 0.000 0.972 0.004
#> GSM1296043 5 0.4096 0.0216 0.000 0.484 0.000 0.000 0.508 0.008
#> GSM1296037 1 0.2883 0.7093 0.788 0.000 0.000 0.000 0.000 0.212
#> GSM1296046 2 0.0260 0.9502 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM1296044 2 0.0000 0.9533 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296045 2 0.4066 0.2816 0.000 0.596 0.000 0.000 0.392 0.012
#> GSM1296025 1 0.0146 0.9295 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM1296033 1 0.1267 0.8874 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM1296027 1 0.0146 0.9283 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1296032 1 0.0000 0.9297 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296024 1 0.0146 0.9295 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM1296031 1 0.0000 0.9297 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296028 1 0.0146 0.9283 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1296029 1 0.0000 0.9297 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296026 4 0.4234 0.0462 0.000 0.000 0.440 0.544 0.000 0.016
#> GSM1296030 3 0.5846 0.2125 0.392 0.000 0.468 0.124 0.000 0.016
#> GSM1296040 3 0.0146 0.8265 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM1296036 3 0.0291 0.8261 0.000 0.000 0.992 0.004 0.000 0.004
#> GSM1296048 5 0.0862 0.8955 0.000 0.016 0.000 0.008 0.972 0.004
#> GSM1296059 3 0.2936 0.7934 0.000 0.000 0.852 0.112 0.016 0.020
#> GSM1296066 5 0.0632 0.8944 0.000 0.024 0.000 0.000 0.976 0.000
#> GSM1296060 3 0.3492 0.7591 0.000 0.000 0.796 0.168 0.016 0.020
#> GSM1296063 5 0.3043 0.7021 0.000 0.000 0.000 0.200 0.792 0.008
#> GSM1296064 4 0.3141 0.7088 0.000 0.000 0.000 0.788 0.200 0.012
#> GSM1296067 2 0.0000 0.9533 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296062 3 0.0291 0.8261 0.000 0.000 0.992 0.004 0.000 0.004
#> GSM1296068 2 0.0000 0.9533 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296050 1 0.0146 0.9295 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM1296057 6 0.1610 0.8539 0.084 0.000 0.000 0.000 0.000 0.916
#> GSM1296052 1 0.0146 0.9283 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1296054 1 0.0000 0.9297 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296049 1 0.0146 0.9295 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM1296055 6 0.1910 0.8362 0.108 0.000 0.000 0.000 0.000 0.892
#> GSM1296053 1 0.0000 0.9297 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296058 6 0.1074 0.8672 0.028 0.000 0.000 0.012 0.000 0.960
#> GSM1296051 4 0.0260 0.9175 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM1296056 4 0.1232 0.8963 0.000 0.000 0.004 0.956 0.024 0.016
#> GSM1296065 6 0.2609 0.7974 0.000 0.000 0.000 0.036 0.096 0.868
#> GSM1296061 3 0.0291 0.8261 0.000 0.000 0.992 0.004 0.000 0.004
#> GSM1296095 5 0.2508 0.8130 0.000 0.000 0.016 0.016 0.884 0.084
#> GSM1296120 2 0.0146 0.9515 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1296077 1 0.0146 0.9295 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM1296093 1 0.0000 0.9297 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296104 6 0.0820 0.8590 0.000 0.012 0.000 0.016 0.000 0.972
#> GSM1296079 1 0.0146 0.9295 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM1296108 2 0.0000 0.9533 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296110 2 0.0146 0.9523 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1296081 1 0.0146 0.9295 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM1296091 1 0.2562 0.7615 0.828 0.000 0.000 0.000 0.000 0.172
#> GSM1296075 6 0.5114 0.4481 0.340 0.072 0.000 0.004 0.004 0.580
#> GSM1296112 2 0.0000 0.9533 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296100 1 0.2883 0.7093 0.788 0.000 0.000 0.000 0.000 0.212
#> GSM1296087 1 0.0146 0.9283 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1296118 2 0.0000 0.9533 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296114 2 0.0000 0.9533 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296097 6 0.0725 0.8669 0.012 0.012 0.000 0.000 0.000 0.976
#> GSM1296106 6 0.2044 0.8432 0.008 0.076 0.000 0.004 0.004 0.908
#> GSM1296102 1 0.3706 0.3767 0.620 0.000 0.000 0.000 0.000 0.380
#> GSM1296122 2 0.1826 0.8753 0.052 0.924 0.000 0.000 0.004 0.020
#> GSM1296089 1 0.0000 0.9297 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296083 1 0.0146 0.9295 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM1296116 2 0.0146 0.9523 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1296085 1 0.0000 0.9297 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)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# 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 tissue(p) time(p) agent(p) dose(p) individual(p) k
#> SD:skmeans 97 2.13e-02 0.0966 0.64637 1.83e-06 1.53e-03 2
#> SD:skmeans 98 2.15e-04 0.0948 0.00878 7.38e-09 3.95e-06 3
#> SD:skmeans 94 9.59e-05 0.2752 0.00273 3.96e-10 5.27e-05 4
#> SD:skmeans 90 1.49e-04 0.2527 0.04283 1.08e-07 8.18e-06 5
#> SD:skmeans 90 3.49e-04 0.1046 0.23699 2.70e-06 7.88e-06 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 45638 rows and 99 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 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 0.260 0.657 0.797 0.4857 0.504 0.504
#> 3 3 0.870 0.857 0.945 0.3675 0.683 0.450
#> 4 4 0.755 0.741 0.870 0.1235 0.829 0.545
#> 5 5 0.743 0.610 0.790 0.0576 0.831 0.460
#> 6 6 0.773 0.739 0.846 0.0471 0.896 0.569
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
#> GSM1296094 1 0.6148 0.635 0.848 0.152
#> GSM1296119 2 0.7950 0.732 0.240 0.760
#> GSM1296076 2 0.9608 0.640 0.384 0.616
#> GSM1296092 1 0.6148 0.635 0.848 0.152
#> GSM1296103 1 0.6148 0.635 0.848 0.152
#> GSM1296078 2 0.9608 0.640 0.384 0.616
#> GSM1296107 2 0.7950 0.732 0.240 0.760
#> GSM1296109 2 0.9608 0.640 0.384 0.616
#> GSM1296080 1 0.5519 0.647 0.872 0.128
#> GSM1296090 1 0.6148 0.635 0.848 0.152
#> GSM1296074 1 0.6148 0.635 0.848 0.152
#> GSM1296111 2 0.7950 0.732 0.240 0.760
#> GSM1296099 1 0.6148 0.635 0.848 0.152
#> GSM1296086 1 0.6148 0.635 0.848 0.152
#> GSM1296117 2 0.7950 0.732 0.240 0.760
#> GSM1296113 2 0.7950 0.732 0.240 0.760
#> GSM1296096 2 0.9732 0.611 0.404 0.596
#> GSM1296105 1 0.9491 0.694 0.632 0.368
#> GSM1296098 1 0.6148 0.635 0.848 0.152
#> GSM1296101 1 0.6148 0.635 0.848 0.152
#> GSM1296121 2 0.7950 0.732 0.240 0.760
#> GSM1296088 1 0.6148 0.635 0.848 0.152
#> GSM1296082 1 0.6148 0.635 0.848 0.152
#> GSM1296115 2 0.7950 0.732 0.240 0.760
#> GSM1296084 1 0.5519 0.647 0.872 0.128
#> GSM1296072 2 0.2778 0.717 0.048 0.952
#> GSM1296069 2 0.6887 0.736 0.184 0.816
#> GSM1296071 2 0.0000 0.702 0.000 1.000
#> GSM1296070 2 0.7815 0.734 0.232 0.768
#> GSM1296073 2 0.9580 0.644 0.380 0.620
#> GSM1296034 1 0.7950 0.724 0.760 0.240
#> GSM1296041 2 0.7950 0.732 0.240 0.760
#> GSM1296035 1 0.6148 0.635 0.848 0.152
#> GSM1296038 2 0.9608 0.640 0.384 0.616
#> GSM1296047 2 0.0000 0.702 0.000 1.000
#> GSM1296039 2 0.9608 0.640 0.384 0.616
#> GSM1296042 2 0.7950 0.732 0.240 0.760
#> GSM1296043 2 0.0000 0.702 0.000 1.000
#> GSM1296037 1 0.7950 0.724 0.760 0.240
#> GSM1296046 2 0.0000 0.702 0.000 1.000
#> GSM1296044 2 0.0000 0.702 0.000 1.000
#> GSM1296045 2 0.0376 0.703 0.004 0.996
#> GSM1296025 1 0.7950 0.724 0.760 0.240
#> GSM1296033 1 0.9358 0.698 0.648 0.352
#> GSM1296027 1 0.7950 0.724 0.760 0.240
#> GSM1296032 1 0.7950 0.724 0.760 0.240
#> GSM1296024 1 0.7950 0.724 0.760 0.240
#> GSM1296031 1 0.9358 0.604 0.648 0.352
#> GSM1296028 1 0.7950 0.724 0.760 0.240
#> GSM1296029 1 0.7883 0.724 0.764 0.236
#> GSM1296026 1 0.5519 0.647 0.872 0.128
#> GSM1296030 1 0.5519 0.647 0.872 0.128
#> GSM1296040 1 0.6343 0.638 0.840 0.160
#> GSM1296036 1 0.6048 0.637 0.852 0.148
#> GSM1296048 2 0.7950 0.732 0.240 0.760
#> GSM1296059 1 0.6148 0.635 0.848 0.152
#> GSM1296066 2 0.7883 0.733 0.236 0.764
#> GSM1296060 1 0.6148 0.635 0.848 0.152
#> GSM1296063 2 0.9580 0.644 0.380 0.620
#> GSM1296064 2 0.9608 0.640 0.384 0.616
#> GSM1296067 2 0.0376 0.699 0.004 0.996
#> GSM1296062 1 0.5519 0.647 0.872 0.128
#> GSM1296068 2 0.5408 0.584 0.124 0.876
#> GSM1296050 1 0.9580 0.566 0.620 0.380
#> GSM1296057 1 0.9460 0.696 0.636 0.364
#> GSM1296052 1 0.7950 0.724 0.760 0.240
#> GSM1296054 1 0.7950 0.724 0.760 0.240
#> GSM1296049 1 0.7950 0.724 0.760 0.240
#> GSM1296055 2 0.9996 -0.419 0.488 0.512
#> GSM1296053 1 0.7950 0.724 0.760 0.240
#> GSM1296058 1 0.9460 0.694 0.636 0.364
#> GSM1296051 1 0.6148 0.635 0.848 0.152
#> GSM1296056 1 0.6148 0.635 0.848 0.152
#> GSM1296065 2 0.5946 0.580 0.144 0.856
#> GSM1296061 1 0.5519 0.647 0.872 0.128
#> GSM1296095 2 0.9209 0.648 0.336 0.664
#> GSM1296120 2 0.2423 0.670 0.040 0.960
#> GSM1296077 1 0.7950 0.724 0.760 0.240
#> GSM1296093 1 0.7950 0.724 0.760 0.240
#> GSM1296104 1 0.9710 0.673 0.600 0.400
#> GSM1296079 1 0.8016 0.721 0.756 0.244
#> GSM1296108 2 0.5178 0.593 0.116 0.884
#> GSM1296110 2 0.0000 0.702 0.000 1.000
#> GSM1296081 1 0.7950 0.724 0.760 0.240
#> GSM1296091 1 0.8016 0.723 0.756 0.244
#> GSM1296075 1 0.9580 0.566 0.620 0.380
#> GSM1296112 2 0.5178 0.593 0.116 0.884
#> GSM1296100 1 0.7950 0.724 0.760 0.240
#> GSM1296087 1 0.7950 0.724 0.760 0.240
#> GSM1296118 2 0.6148 0.549 0.152 0.848
#> GSM1296114 2 0.0000 0.702 0.000 1.000
#> GSM1296097 2 0.8955 0.141 0.312 0.688
#> GSM1296106 1 0.9608 0.566 0.616 0.384
#> GSM1296102 1 0.8016 0.723 0.756 0.244
#> GSM1296122 2 0.6048 0.556 0.148 0.852
#> GSM1296089 1 0.9580 0.566 0.620 0.380
#> GSM1296083 1 0.7950 0.724 0.760 0.240
#> GSM1296116 2 0.3114 0.655 0.056 0.944
#> GSM1296085 1 0.7950 0.724 0.760 0.240
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1296094 3 0.0000 0.9474 0.000 0.000 1.000
#> GSM1296119 2 0.1411 0.9073 0.000 0.964 0.036
#> GSM1296076 3 0.0000 0.9474 0.000 0.000 1.000
#> GSM1296092 3 0.0000 0.9474 0.000 0.000 1.000
#> GSM1296103 3 0.0000 0.9474 0.000 0.000 1.000
#> GSM1296078 3 0.0747 0.9363 0.000 0.016 0.984
#> GSM1296107 2 0.0000 0.9314 0.000 1.000 0.000
#> GSM1296109 3 0.0000 0.9474 0.000 0.000 1.000
#> GSM1296080 3 0.5178 0.6389 0.256 0.000 0.744
#> GSM1296090 3 0.0000 0.9474 0.000 0.000 1.000
#> GSM1296074 3 0.0000 0.9474 0.000 0.000 1.000
#> GSM1296111 2 0.1643 0.8991 0.000 0.956 0.044
#> GSM1296099 3 0.0000 0.9474 0.000 0.000 1.000
#> GSM1296086 3 0.0000 0.9474 0.000 0.000 1.000
#> GSM1296117 2 0.1289 0.9102 0.000 0.968 0.032
#> GSM1296113 2 0.0000 0.9314 0.000 1.000 0.000
#> GSM1296096 3 0.0000 0.9474 0.000 0.000 1.000
#> GSM1296105 3 0.0747 0.9361 0.016 0.000 0.984
#> GSM1296098 3 0.0000 0.9474 0.000 0.000 1.000
#> GSM1296101 3 0.0000 0.9474 0.000 0.000 1.000
#> GSM1296121 2 0.1289 0.9108 0.000 0.968 0.032
#> GSM1296088 3 0.0000 0.9474 0.000 0.000 1.000
#> GSM1296082 3 0.0000 0.9474 0.000 0.000 1.000
#> GSM1296115 2 0.5431 0.5824 0.000 0.716 0.284
#> GSM1296084 3 0.2711 0.8685 0.088 0.000 0.912
#> GSM1296072 2 0.0000 0.9314 0.000 1.000 0.000
#> GSM1296069 2 0.0000 0.9314 0.000 1.000 0.000
#> GSM1296071 2 0.0000 0.9314 0.000 1.000 0.000
#> GSM1296070 2 0.0000 0.9314 0.000 1.000 0.000
#> GSM1296073 3 0.6309 -0.0410 0.000 0.500 0.500
#> GSM1296034 1 0.0424 0.9275 0.992 0.000 0.008
#> GSM1296041 2 0.0592 0.9243 0.000 0.988 0.012
#> GSM1296035 3 0.0000 0.9474 0.000 0.000 1.000
#> GSM1296038 3 0.0000 0.9474 0.000 0.000 1.000
#> GSM1296047 2 0.0000 0.9314 0.000 1.000 0.000
#> GSM1296039 3 0.0000 0.9474 0.000 0.000 1.000
#> GSM1296042 2 0.0000 0.9314 0.000 1.000 0.000
#> GSM1296043 2 0.0000 0.9314 0.000 1.000 0.000
#> GSM1296037 1 0.0000 0.9336 1.000 0.000 0.000
#> GSM1296046 2 0.0000 0.9314 0.000 1.000 0.000
#> GSM1296044 2 0.0000 0.9314 0.000 1.000 0.000
#> GSM1296045 2 0.0000 0.9314 0.000 1.000 0.000
#> GSM1296025 1 0.0000 0.9336 1.000 0.000 0.000
#> GSM1296033 1 0.5098 0.6619 0.752 0.000 0.248
#> GSM1296027 1 0.0000 0.9336 1.000 0.000 0.000
#> GSM1296032 1 0.0000 0.9336 1.000 0.000 0.000
#> GSM1296024 1 0.0000 0.9336 1.000 0.000 0.000
#> GSM1296031 1 0.0000 0.9336 1.000 0.000 0.000
#> GSM1296028 1 0.0000 0.9336 1.000 0.000 0.000
#> GSM1296029 1 0.0000 0.9336 1.000 0.000 0.000
#> GSM1296026 3 0.0000 0.9474 0.000 0.000 1.000
#> GSM1296030 1 0.6274 0.1667 0.544 0.000 0.456
#> GSM1296040 3 0.0000 0.9474 0.000 0.000 1.000
#> GSM1296036 3 0.0000 0.9474 0.000 0.000 1.000
#> GSM1296048 2 0.0000 0.9314 0.000 1.000 0.000
#> GSM1296059 3 0.0000 0.9474 0.000 0.000 1.000
#> GSM1296066 2 0.0000 0.9314 0.000 1.000 0.000
#> GSM1296060 3 0.0000 0.9474 0.000 0.000 1.000
#> GSM1296063 2 0.6309 -0.0157 0.000 0.500 0.500
#> GSM1296064 3 0.0000 0.9474 0.000 0.000 1.000
#> GSM1296067 2 0.0000 0.9314 0.000 1.000 0.000
#> GSM1296062 3 0.0000 0.9474 0.000 0.000 1.000
#> GSM1296068 2 0.0000 0.9314 0.000 1.000 0.000
#> GSM1296050 1 0.0000 0.9336 1.000 0.000 0.000
#> GSM1296057 3 0.5465 0.5735 0.288 0.000 0.712
#> GSM1296052 1 0.0000 0.9336 1.000 0.000 0.000
#> GSM1296054 1 0.0000 0.9336 1.000 0.000 0.000
#> GSM1296049 1 0.0000 0.9336 1.000 0.000 0.000
#> GSM1296055 3 0.7937 0.3258 0.068 0.364 0.568
#> GSM1296053 1 0.0000 0.9336 1.000 0.000 0.000
#> GSM1296058 3 0.0237 0.9448 0.004 0.000 0.996
#> GSM1296051 3 0.0000 0.9474 0.000 0.000 1.000
#> GSM1296056 3 0.0000 0.9474 0.000 0.000 1.000
#> GSM1296065 3 0.3879 0.8011 0.000 0.152 0.848
#> GSM1296061 3 0.0000 0.9474 0.000 0.000 1.000
#> GSM1296095 3 0.0237 0.9448 0.000 0.004 0.996
#> GSM1296120 2 0.0000 0.9314 0.000 1.000 0.000
#> GSM1296077 1 0.0000 0.9336 1.000 0.000 0.000
#> GSM1296093 1 0.0000 0.9336 1.000 0.000 0.000
#> GSM1296104 3 0.2448 0.8857 0.000 0.076 0.924
#> GSM1296079 1 0.0000 0.9336 1.000 0.000 0.000
#> GSM1296108 2 0.0000 0.9314 0.000 1.000 0.000
#> GSM1296110 2 0.0000 0.9314 0.000 1.000 0.000
#> GSM1296081 1 0.0000 0.9336 1.000 0.000 0.000
#> GSM1296091 1 0.5431 0.6010 0.716 0.000 0.284
#> GSM1296075 1 0.7665 0.0833 0.500 0.456 0.044
#> GSM1296112 2 0.0000 0.9314 0.000 1.000 0.000
#> GSM1296100 1 0.0000 0.9336 1.000 0.000 0.000
#> GSM1296087 1 0.0000 0.9336 1.000 0.000 0.000
#> GSM1296118 2 0.0892 0.9164 0.020 0.980 0.000
#> GSM1296114 2 0.0000 0.9314 0.000 1.000 0.000
#> GSM1296097 3 0.1289 0.9229 0.000 0.032 0.968
#> GSM1296106 2 0.7969 0.1137 0.060 0.508 0.432
#> GSM1296102 1 0.4121 0.7756 0.832 0.000 0.168
#> GSM1296122 2 0.6274 0.1010 0.456 0.544 0.000
#> GSM1296089 1 0.0000 0.9336 1.000 0.000 0.000
#> GSM1296083 1 0.0000 0.9336 1.000 0.000 0.000
#> GSM1296116 2 0.0000 0.9314 0.000 1.000 0.000
#> GSM1296085 1 0.0000 0.9336 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1296094 3 0.0000 0.9373 0.000 0.000 1.000 0.000
#> GSM1296119 2 0.0336 0.7986 0.000 0.992 0.008 0.000
#> GSM1296076 3 0.2408 0.8566 0.000 0.104 0.896 0.000
#> GSM1296092 3 0.0000 0.9373 0.000 0.000 1.000 0.000
#> GSM1296103 3 0.0000 0.9373 0.000 0.000 1.000 0.000
#> GSM1296078 3 0.4697 0.5585 0.000 0.356 0.644 0.000
#> GSM1296107 2 0.0592 0.8041 0.000 0.984 0.000 0.016
#> GSM1296109 3 0.4713 0.5494 0.000 0.360 0.640 0.000
#> GSM1296080 1 0.4855 0.3487 0.600 0.000 0.400 0.000
#> GSM1296090 3 0.0188 0.9355 0.000 0.004 0.996 0.000
#> GSM1296074 3 0.1118 0.9159 0.000 0.036 0.964 0.000
#> GSM1296111 2 0.0000 0.8014 0.000 1.000 0.000 0.000
#> GSM1296099 3 0.0000 0.9373 0.000 0.000 1.000 0.000
#> GSM1296086 3 0.0000 0.9373 0.000 0.000 1.000 0.000
#> GSM1296117 2 0.0336 0.7986 0.000 0.992 0.008 0.000
#> GSM1296113 2 0.3219 0.7871 0.000 0.836 0.000 0.164
#> GSM1296096 3 0.0000 0.9373 0.000 0.000 1.000 0.000
#> GSM1296105 4 0.4855 0.4841 0.000 0.000 0.400 0.600
#> GSM1296098 3 0.0000 0.9373 0.000 0.000 1.000 0.000
#> GSM1296101 3 0.0000 0.9373 0.000 0.000 1.000 0.000
#> GSM1296121 2 0.0000 0.8014 0.000 1.000 0.000 0.000
#> GSM1296088 3 0.0000 0.9373 0.000 0.000 1.000 0.000
#> GSM1296082 3 0.2760 0.8327 0.000 0.128 0.872 0.000
#> GSM1296115 2 0.0000 0.8014 0.000 1.000 0.000 0.000
#> GSM1296084 3 0.0000 0.9373 0.000 0.000 1.000 0.000
#> GSM1296072 2 0.4843 0.7054 0.000 0.604 0.000 0.396
#> GSM1296069 2 0.4624 0.7342 0.000 0.660 0.000 0.340
#> GSM1296071 2 0.4855 0.7029 0.000 0.600 0.000 0.400
#> GSM1296070 2 0.1637 0.8036 0.000 0.940 0.000 0.060
#> GSM1296073 2 0.0000 0.8014 0.000 1.000 0.000 0.000
#> GSM1296034 1 0.1118 0.8721 0.964 0.000 0.000 0.036
#> GSM1296041 2 0.0000 0.8014 0.000 1.000 0.000 0.000
#> GSM1296035 3 0.0000 0.9373 0.000 0.000 1.000 0.000
#> GSM1296038 3 0.0921 0.9216 0.000 0.028 0.972 0.000
#> GSM1296047 4 0.0000 0.6184 0.000 0.000 0.000 1.000
#> GSM1296039 3 0.0921 0.9214 0.000 0.028 0.972 0.000
#> GSM1296042 2 0.1022 0.8047 0.000 0.968 0.000 0.032
#> GSM1296043 2 0.4761 0.7193 0.000 0.628 0.000 0.372
#> GSM1296037 4 0.4925 0.2582 0.428 0.000 0.000 0.572
#> GSM1296046 2 0.4855 0.7029 0.000 0.600 0.000 0.400
#> GSM1296044 2 0.4855 0.7029 0.000 0.600 0.000 0.400
#> GSM1296045 2 0.3356 0.7854 0.000 0.824 0.000 0.176
#> GSM1296025 1 0.0000 0.9025 1.000 0.000 0.000 0.000
#> GSM1296033 1 0.5632 0.5689 0.712 0.000 0.196 0.092
#> GSM1296027 1 0.0000 0.9025 1.000 0.000 0.000 0.000
#> GSM1296032 1 0.0000 0.9025 1.000 0.000 0.000 0.000
#> GSM1296024 1 0.0000 0.9025 1.000 0.000 0.000 0.000
#> GSM1296031 1 0.4961 0.0541 0.552 0.000 0.000 0.448
#> GSM1296028 1 0.0000 0.9025 1.000 0.000 0.000 0.000
#> GSM1296029 1 0.0000 0.9025 1.000 0.000 0.000 0.000
#> GSM1296026 3 0.0000 0.9373 0.000 0.000 1.000 0.000
#> GSM1296030 1 0.4605 0.4719 0.664 0.000 0.336 0.000
#> GSM1296040 3 0.0336 0.9312 0.000 0.000 0.992 0.008
#> GSM1296036 3 0.0000 0.9373 0.000 0.000 1.000 0.000
#> GSM1296048 2 0.0000 0.8014 0.000 1.000 0.000 0.000
#> GSM1296059 3 0.0000 0.9373 0.000 0.000 1.000 0.000
#> GSM1296066 2 0.4134 0.7638 0.000 0.740 0.000 0.260
#> GSM1296060 3 0.0000 0.9373 0.000 0.000 1.000 0.000
#> GSM1296063 2 0.0000 0.8014 0.000 1.000 0.000 0.000
#> GSM1296064 3 0.4624 0.5817 0.000 0.340 0.660 0.000
#> GSM1296067 4 0.0000 0.6184 0.000 0.000 0.000 1.000
#> GSM1296062 3 0.0000 0.9373 0.000 0.000 1.000 0.000
#> GSM1296068 4 0.4134 0.1041 0.000 0.260 0.000 0.740
#> GSM1296050 1 0.3266 0.7149 0.832 0.000 0.000 0.168
#> GSM1296057 4 0.5016 0.4891 0.004 0.000 0.396 0.600
#> GSM1296052 1 0.0000 0.9025 1.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.9025 1.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.9025 1.000 0.000 0.000 0.000
#> GSM1296055 4 0.4228 0.6338 0.008 0.000 0.232 0.760
#> GSM1296053 1 0.0000 0.9025 1.000 0.000 0.000 0.000
#> GSM1296058 4 0.4855 0.4841 0.000 0.000 0.400 0.600
#> GSM1296051 3 0.0000 0.9373 0.000 0.000 1.000 0.000
#> GSM1296056 3 0.1022 0.9192 0.000 0.032 0.968 0.000
#> GSM1296065 4 0.4855 0.4841 0.000 0.000 0.400 0.600
#> GSM1296061 3 0.0000 0.9373 0.000 0.000 1.000 0.000
#> GSM1296095 3 0.1867 0.8631 0.000 0.000 0.928 0.072
#> GSM1296120 4 0.0000 0.6184 0.000 0.000 0.000 1.000
#> GSM1296077 1 0.0000 0.9025 1.000 0.000 0.000 0.000
#> GSM1296093 1 0.0000 0.9025 1.000 0.000 0.000 0.000
#> GSM1296104 4 0.4843 0.4901 0.000 0.000 0.396 0.604
#> GSM1296079 1 0.0000 0.9025 1.000 0.000 0.000 0.000
#> GSM1296108 4 0.1211 0.5750 0.000 0.040 0.000 0.960
#> GSM1296110 2 0.4855 0.7029 0.000 0.600 0.000 0.400
#> GSM1296081 1 0.0000 0.9025 1.000 0.000 0.000 0.000
#> GSM1296091 4 0.6852 0.5306 0.208 0.000 0.192 0.600
#> GSM1296075 4 0.1118 0.6222 0.036 0.000 0.000 0.964
#> GSM1296112 4 0.4972 -0.4835 0.000 0.456 0.000 0.544
#> GSM1296100 4 0.4866 0.3091 0.404 0.000 0.000 0.596
#> GSM1296087 1 0.0000 0.9025 1.000 0.000 0.000 0.000
#> GSM1296118 4 0.0000 0.6184 0.000 0.000 0.000 1.000
#> GSM1296114 2 0.4855 0.7029 0.000 0.600 0.000 0.400
#> GSM1296097 4 0.4830 0.4952 0.000 0.000 0.392 0.608
#> GSM1296106 4 0.0000 0.6184 0.000 0.000 0.000 1.000
#> GSM1296102 4 0.6338 0.4347 0.316 0.000 0.084 0.600
#> GSM1296122 4 0.0000 0.6184 0.000 0.000 0.000 1.000
#> GSM1296089 4 0.4855 0.3164 0.400 0.000 0.000 0.600
#> GSM1296083 1 0.0000 0.9025 1.000 0.000 0.000 0.000
#> GSM1296116 2 0.4761 0.7193 0.000 0.628 0.000 0.372
#> GSM1296085 1 0.0000 0.9025 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
#> GSM1296094 3 0.4030 0.5807 0.000 0.000 0.648 0.352 0.000
#> GSM1296119 5 0.0162 0.8424 0.000 0.000 0.000 0.004 0.996
#> GSM1296076 4 0.1638 0.7364 0.000 0.000 0.064 0.932 0.004
#> GSM1296092 4 0.1851 0.7335 0.000 0.000 0.088 0.912 0.000
#> GSM1296103 3 0.4045 0.5801 0.000 0.000 0.644 0.356 0.000
#> GSM1296078 4 0.3053 0.6144 0.000 0.000 0.008 0.828 0.164
#> GSM1296107 5 0.0000 0.8450 0.000 0.000 0.000 0.000 1.000
#> GSM1296109 3 0.6814 0.0794 0.000 0.000 0.348 0.348 0.304
#> GSM1296080 1 0.5931 -0.0568 0.460 0.000 0.436 0.104 0.000
#> GSM1296090 4 0.1704 0.7371 0.000 0.000 0.068 0.928 0.004
#> GSM1296074 4 0.1544 0.7353 0.000 0.000 0.068 0.932 0.000
#> GSM1296111 5 0.0000 0.8450 0.000 0.000 0.000 0.000 1.000
#> GSM1296099 3 0.4030 0.5805 0.000 0.000 0.648 0.352 0.000
#> GSM1296086 4 0.1544 0.7353 0.000 0.000 0.068 0.932 0.000
#> GSM1296117 5 0.0451 0.8399 0.000 0.008 0.000 0.004 0.988
#> GSM1296113 5 0.4273 0.1646 0.000 0.448 0.000 0.000 0.552
#> GSM1296096 3 0.4030 0.5805 0.000 0.000 0.648 0.352 0.000
#> GSM1296105 3 0.3086 0.3625 0.000 0.180 0.816 0.004 0.000
#> GSM1296098 3 0.3999 0.5804 0.000 0.000 0.656 0.344 0.000
#> GSM1296101 3 0.4030 0.5805 0.000 0.000 0.648 0.352 0.000
#> GSM1296121 5 0.0000 0.8450 0.000 0.000 0.000 0.000 1.000
#> GSM1296088 4 0.4060 0.1335 0.000 0.000 0.360 0.640 0.000
#> GSM1296082 4 0.1774 0.7332 0.000 0.000 0.052 0.932 0.016
#> GSM1296115 5 0.0000 0.8450 0.000 0.000 0.000 0.000 1.000
#> GSM1296084 3 0.3966 0.5607 0.000 0.000 0.664 0.336 0.000
#> GSM1296072 2 0.3561 0.5990 0.000 0.740 0.000 0.000 0.260
#> GSM1296069 5 0.4088 0.4207 0.000 0.368 0.000 0.000 0.632
#> GSM1296071 2 0.2929 0.6976 0.000 0.820 0.000 0.000 0.180
#> GSM1296070 5 0.0703 0.8328 0.000 0.024 0.000 0.000 0.976
#> GSM1296073 5 0.0000 0.8450 0.000 0.000 0.000 0.000 1.000
#> GSM1296034 1 0.1549 0.8405 0.944 0.000 0.040 0.016 0.000
#> GSM1296041 5 0.0000 0.8450 0.000 0.000 0.000 0.000 1.000
#> GSM1296035 3 0.4030 0.5805 0.000 0.000 0.648 0.352 0.000
#> GSM1296038 3 0.4880 0.5535 0.000 0.000 0.616 0.348 0.036
#> GSM1296047 2 0.2852 0.6793 0.000 0.828 0.172 0.000 0.000
#> GSM1296039 3 0.4659 0.3293 0.000 0.000 0.500 0.488 0.012
#> GSM1296042 5 0.0000 0.8450 0.000 0.000 0.000 0.000 1.000
#> GSM1296043 5 0.4210 0.3202 0.000 0.412 0.000 0.000 0.588
#> GSM1296037 1 0.6706 0.4117 0.508 0.180 0.296 0.016 0.000
#> GSM1296046 2 0.3109 0.6796 0.000 0.800 0.000 0.000 0.200
#> GSM1296044 2 0.2929 0.6976 0.000 0.820 0.000 0.000 0.180
#> GSM1296045 5 0.3242 0.6681 0.000 0.216 0.000 0.000 0.784
#> GSM1296025 1 0.1121 0.8506 0.956 0.000 0.000 0.044 0.000
#> GSM1296033 4 0.6771 0.1745 0.324 0.000 0.284 0.392 0.000
#> GSM1296027 1 0.1082 0.8510 0.964 0.000 0.028 0.008 0.000
#> GSM1296032 1 0.0579 0.8551 0.984 0.000 0.008 0.008 0.000
#> GSM1296024 1 0.1121 0.8506 0.956 0.000 0.000 0.044 0.000
#> GSM1296031 1 0.6481 0.5755 0.620 0.168 0.160 0.052 0.000
#> GSM1296028 1 0.1830 0.8341 0.924 0.000 0.068 0.008 0.000
#> GSM1296029 1 0.1082 0.8510 0.964 0.000 0.028 0.008 0.000
#> GSM1296026 3 0.4256 0.4356 0.000 0.000 0.564 0.436 0.000
#> GSM1296030 4 0.6714 0.3302 0.312 0.000 0.268 0.420 0.000
#> GSM1296040 3 0.3999 0.5822 0.000 0.000 0.656 0.344 0.000
#> GSM1296036 3 0.3999 0.5804 0.000 0.000 0.656 0.344 0.000
#> GSM1296048 5 0.0000 0.8450 0.000 0.000 0.000 0.000 1.000
#> GSM1296059 3 0.4030 0.5805 0.000 0.000 0.648 0.352 0.000
#> GSM1296066 5 0.3424 0.6343 0.000 0.240 0.000 0.000 0.760
#> GSM1296060 3 0.4030 0.5805 0.000 0.000 0.648 0.352 0.000
#> GSM1296063 5 0.0000 0.8450 0.000 0.000 0.000 0.000 1.000
#> GSM1296064 4 0.5703 0.4082 0.000 0.000 0.188 0.628 0.184
#> GSM1296067 2 0.2890 0.6841 0.000 0.836 0.160 0.004 0.000
#> GSM1296062 3 0.3857 0.5705 0.000 0.000 0.688 0.312 0.000
#> GSM1296068 2 0.2074 0.7303 0.000 0.896 0.000 0.000 0.104
#> GSM1296050 1 0.3377 0.8006 0.856 0.012 0.076 0.056 0.000
#> GSM1296057 3 0.3293 0.3488 0.008 0.160 0.824 0.008 0.000
#> GSM1296052 1 0.0992 0.8521 0.968 0.000 0.024 0.008 0.000
#> GSM1296054 1 0.0579 0.8551 0.984 0.000 0.008 0.008 0.000
#> GSM1296049 1 0.0290 0.8541 0.992 0.000 0.000 0.008 0.000
#> GSM1296055 3 0.4687 0.0919 0.000 0.336 0.636 0.028 0.000
#> GSM1296053 1 0.0579 0.8551 0.984 0.000 0.008 0.008 0.000
#> GSM1296058 3 0.2848 0.3621 0.000 0.156 0.840 0.004 0.000
#> GSM1296051 4 0.1851 0.7335 0.000 0.000 0.088 0.912 0.000
#> GSM1296056 3 0.4307 0.3152 0.000 0.000 0.500 0.500 0.000
#> GSM1296065 3 0.2929 0.3607 0.000 0.180 0.820 0.000 0.000
#> GSM1296061 3 0.3999 0.5804 0.000 0.000 0.656 0.344 0.000
#> GSM1296095 3 0.4067 0.5632 0.000 0.008 0.692 0.300 0.000
#> GSM1296120 2 0.0000 0.7392 0.000 1.000 0.000 0.000 0.000
#> GSM1296077 1 0.1121 0.8506 0.956 0.000 0.000 0.044 0.000
#> GSM1296093 1 0.0162 0.8545 0.996 0.000 0.000 0.004 0.000
#> GSM1296104 3 0.2929 0.3607 0.000 0.180 0.820 0.000 0.000
#> GSM1296079 1 0.1121 0.8506 0.956 0.000 0.000 0.044 0.000
#> GSM1296108 2 0.0671 0.7410 0.000 0.980 0.004 0.000 0.016
#> GSM1296110 2 0.2929 0.6976 0.000 0.820 0.000 0.000 0.180
#> GSM1296081 1 0.1121 0.8506 0.956 0.000 0.000 0.044 0.000
#> GSM1296091 3 0.7811 -0.1357 0.244 0.152 0.468 0.136 0.000
#> GSM1296075 2 0.6118 0.4656 0.112 0.564 0.312 0.012 0.000
#> GSM1296112 2 0.3048 0.7014 0.000 0.820 0.004 0.000 0.176
#> GSM1296100 1 0.6569 0.3917 0.500 0.180 0.312 0.008 0.000
#> GSM1296087 1 0.2522 0.8384 0.896 0.000 0.052 0.052 0.000
#> GSM1296118 2 0.0000 0.7392 0.000 1.000 0.000 0.000 0.000
#> GSM1296114 2 0.2929 0.6976 0.000 0.820 0.000 0.000 0.180
#> GSM1296097 3 0.3086 0.3624 0.000 0.180 0.816 0.004 0.000
#> GSM1296106 2 0.3857 0.5843 0.000 0.688 0.312 0.000 0.000
#> GSM1296102 3 0.6858 -0.1532 0.320 0.180 0.480 0.020 0.000
#> GSM1296122 2 0.3992 0.6121 0.000 0.720 0.268 0.012 0.000
#> GSM1296089 1 0.7314 0.3895 0.472 0.180 0.296 0.052 0.000
#> GSM1296083 1 0.0404 0.8535 0.988 0.000 0.000 0.012 0.000
#> GSM1296116 5 0.4201 0.3304 0.000 0.408 0.000 0.000 0.592
#> GSM1296085 1 0.0579 0.8551 0.984 0.000 0.008 0.008 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1296094 3 0.1321 0.844 0.000 0.004 0.952 0.020 0.000 0.024
#> GSM1296119 5 0.0000 0.893 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296076 4 0.1610 0.897 0.000 0.000 0.084 0.916 0.000 0.000
#> GSM1296092 4 0.1327 0.889 0.000 0.000 0.064 0.936 0.000 0.000
#> GSM1296103 3 0.0632 0.849 0.000 0.000 0.976 0.000 0.000 0.024
#> GSM1296078 4 0.1802 0.891 0.000 0.000 0.072 0.916 0.012 0.000
#> GSM1296107 5 0.0000 0.893 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296109 3 0.3126 0.627 0.000 0.000 0.752 0.000 0.248 0.000
#> GSM1296080 3 0.6294 0.450 0.224 0.028 0.596 0.100 0.000 0.052
#> GSM1296090 4 0.1610 0.897 0.000 0.000 0.084 0.916 0.000 0.000
#> GSM1296074 4 0.1610 0.897 0.000 0.000 0.084 0.916 0.000 0.000
#> GSM1296111 5 0.0000 0.893 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296099 3 0.0000 0.850 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296086 4 0.1610 0.897 0.000 0.000 0.084 0.916 0.000 0.000
#> GSM1296117 5 0.0260 0.888 0.000 0.008 0.000 0.000 0.992 0.000
#> GSM1296113 2 0.3464 0.500 0.000 0.688 0.000 0.000 0.312 0.000
#> GSM1296096 3 0.0000 0.850 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296105 6 0.3634 0.624 0.000 0.000 0.356 0.000 0.000 0.644
#> GSM1296098 3 0.2366 0.823 0.000 0.020 0.900 0.056 0.000 0.024
#> GSM1296101 3 0.0000 0.850 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296121 5 0.0000 0.893 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296088 3 0.3198 0.592 0.000 0.000 0.740 0.260 0.000 0.000
#> GSM1296082 4 0.1610 0.897 0.000 0.000 0.084 0.916 0.000 0.000
#> GSM1296115 5 0.0000 0.893 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296084 3 0.1333 0.833 0.000 0.008 0.944 0.048 0.000 0.000
#> GSM1296072 2 0.2941 0.652 0.000 0.780 0.000 0.000 0.220 0.000
#> GSM1296069 5 0.3531 0.556 0.000 0.328 0.000 0.000 0.672 0.000
#> GSM1296071 2 0.0713 0.860 0.000 0.972 0.000 0.000 0.028 0.000
#> GSM1296070 5 0.0146 0.891 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1296073 5 0.0000 0.893 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296034 1 0.1760 0.802 0.928 0.020 0.000 0.048 0.000 0.004
#> GSM1296041 5 0.0000 0.893 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296035 3 0.0000 0.850 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296038 3 0.0937 0.838 0.000 0.000 0.960 0.000 0.040 0.000
#> GSM1296047 2 0.3817 0.208 0.000 0.568 0.000 0.000 0.000 0.432
#> GSM1296039 3 0.2933 0.673 0.000 0.000 0.796 0.200 0.004 0.000
#> GSM1296042 5 0.0000 0.893 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296043 5 0.3659 0.488 0.000 0.364 0.000 0.000 0.636 0.000
#> GSM1296037 6 0.2854 0.518 0.208 0.000 0.000 0.000 0.000 0.792
#> GSM1296046 2 0.1387 0.837 0.000 0.932 0.000 0.000 0.068 0.000
#> GSM1296044 2 0.0713 0.860 0.000 0.972 0.000 0.000 0.028 0.000
#> GSM1296045 5 0.2562 0.762 0.000 0.172 0.000 0.000 0.828 0.000
#> GSM1296025 1 0.1910 0.815 0.892 0.000 0.000 0.000 0.000 0.108
#> GSM1296033 4 0.6246 0.512 0.068 0.008 0.128 0.592 0.000 0.204
#> GSM1296027 1 0.3626 0.822 0.776 0.008 0.000 0.028 0.000 0.188
#> GSM1296032 1 0.3198 0.830 0.796 0.008 0.000 0.008 0.000 0.188
#> GSM1296024 1 0.1910 0.815 0.892 0.000 0.000 0.000 0.000 0.108
#> GSM1296031 6 0.3757 0.173 0.272 0.008 0.000 0.008 0.000 0.712
#> GSM1296028 1 0.3688 0.816 0.768 0.008 0.000 0.028 0.000 0.196
#> GSM1296029 1 0.3626 0.822 0.776 0.008 0.000 0.028 0.000 0.188
#> GSM1296026 3 0.3023 0.653 0.000 0.000 0.768 0.232 0.000 0.000
#> GSM1296030 4 0.6158 0.497 0.044 0.008 0.200 0.588 0.000 0.160
#> GSM1296040 3 0.0000 0.850 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296036 3 0.2366 0.823 0.000 0.020 0.900 0.056 0.000 0.024
#> GSM1296048 5 0.0000 0.893 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296059 3 0.0000 0.850 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296066 5 0.2969 0.701 0.000 0.224 0.000 0.000 0.776 0.000
#> GSM1296060 3 0.0000 0.850 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296063 5 0.0000 0.893 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296064 3 0.5190 0.150 0.000 0.000 0.528 0.376 0.096 0.000
#> GSM1296067 2 0.3789 0.255 0.000 0.584 0.000 0.000 0.000 0.416
#> GSM1296062 3 0.2366 0.823 0.000 0.020 0.900 0.056 0.000 0.024
#> GSM1296068 2 0.0806 0.855 0.000 0.972 0.000 0.000 0.008 0.020
#> GSM1296050 1 0.3244 0.650 0.732 0.000 0.000 0.000 0.000 0.268
#> GSM1296057 6 0.4064 0.632 0.000 0.000 0.336 0.020 0.000 0.644
#> GSM1296052 1 0.3550 0.824 0.780 0.008 0.000 0.024 0.000 0.188
#> GSM1296054 1 0.3198 0.830 0.796 0.008 0.000 0.008 0.000 0.188
#> GSM1296049 1 0.0146 0.831 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1296055 6 0.4518 0.673 0.004 0.080 0.220 0.000 0.000 0.696
#> GSM1296053 1 0.3198 0.830 0.796 0.008 0.000 0.008 0.000 0.188
#> GSM1296058 6 0.4282 0.519 0.000 0.000 0.420 0.020 0.000 0.560
#> GSM1296051 4 0.1327 0.889 0.000 0.000 0.064 0.936 0.000 0.000
#> GSM1296056 3 0.3482 0.494 0.000 0.000 0.684 0.316 0.000 0.000
#> GSM1296065 6 0.3634 0.624 0.000 0.000 0.356 0.000 0.000 0.644
#> GSM1296061 3 0.2366 0.823 0.000 0.020 0.900 0.056 0.000 0.024
#> GSM1296095 3 0.1152 0.827 0.000 0.004 0.952 0.000 0.000 0.044
#> GSM1296120 2 0.0713 0.851 0.000 0.972 0.000 0.000 0.000 0.028
#> GSM1296077 1 0.1910 0.815 0.892 0.000 0.000 0.000 0.000 0.108
#> GSM1296093 1 0.0260 0.831 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM1296104 6 0.3647 0.620 0.000 0.000 0.360 0.000 0.000 0.640
#> GSM1296079 1 0.1910 0.815 0.892 0.000 0.000 0.000 0.000 0.108
#> GSM1296108 2 0.0865 0.848 0.000 0.964 0.000 0.000 0.000 0.036
#> GSM1296110 2 0.0713 0.860 0.000 0.972 0.000 0.000 0.028 0.000
#> GSM1296081 1 0.1910 0.815 0.892 0.000 0.000 0.000 0.000 0.108
#> GSM1296091 6 0.2372 0.615 0.036 0.008 0.024 0.024 0.000 0.908
#> GSM1296075 6 0.2454 0.573 0.000 0.160 0.000 0.000 0.000 0.840
#> GSM1296112 2 0.0972 0.859 0.000 0.964 0.000 0.000 0.028 0.008
#> GSM1296100 6 0.2664 0.569 0.184 0.000 0.000 0.000 0.000 0.816
#> GSM1296087 1 0.4464 0.772 0.624 0.008 0.000 0.028 0.000 0.340
#> GSM1296118 2 0.0790 0.850 0.000 0.968 0.000 0.000 0.000 0.032
#> GSM1296114 2 0.0713 0.860 0.000 0.972 0.000 0.000 0.028 0.000
#> GSM1296097 6 0.3756 0.567 0.000 0.000 0.400 0.000 0.000 0.600
#> GSM1296106 6 0.3634 0.339 0.000 0.356 0.000 0.000 0.000 0.644
#> GSM1296102 6 0.4410 0.687 0.120 0.000 0.164 0.000 0.000 0.716
#> GSM1296122 6 0.3547 0.369 0.000 0.332 0.000 0.000 0.000 0.668
#> GSM1296089 6 0.1075 0.617 0.048 0.000 0.000 0.000 0.000 0.952
#> GSM1296083 1 0.0000 0.830 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296116 5 0.3659 0.488 0.000 0.364 0.000 0.000 0.636 0.000
#> GSM1296085 1 0.3166 0.830 0.800 0.008 0.000 0.008 0.000 0.184
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# 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 tissue(p) time(p) agent(p) dose(p) individual(p) k
#> SD:pam 97 9.15e-01 0.0508 0.6488 1.74e-05 5.77e-07 2
#> SD:pam 92 6.16e-03 0.0236 0.0118 7.56e-08 6.82e-08 3
#> SD:pam 84 3.72e-05 0.0184 0.0251 7.63e-08 9.02e-05 4
#> SD:pam 73 7.44e-04 0.2673 0.0496 3.06e-08 5.34e-06 5
#> SD:pam 88 2.04e-04 0.1396 0.1738 6.20e-06 1.27e-07 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 45638 rows and 99 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 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 0.430 0.716 0.813 0.4163 0.514 0.514
#> 3 3 0.757 0.846 0.925 0.5810 0.793 0.605
#> 4 4 0.632 0.691 0.781 0.0817 0.811 0.514
#> 5 5 0.871 0.881 0.927 0.1029 0.831 0.475
#> 6 6 0.763 0.714 0.853 0.0430 0.877 0.510
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
#> GSM1296094 1 0.983 0.683 0.576 0.424
#> GSM1296119 2 0.000 0.919 0.000 1.000
#> GSM1296076 1 0.996 0.614 0.536 0.464
#> GSM1296092 1 0.996 0.614 0.536 0.464
#> GSM1296103 1 0.983 0.683 0.576 0.424
#> GSM1296078 1 0.996 0.614 0.536 0.464
#> GSM1296107 2 0.000 0.919 0.000 1.000
#> GSM1296109 1 0.983 0.683 0.576 0.424
#> GSM1296080 1 0.971 0.694 0.600 0.400
#> GSM1296090 1 0.996 0.614 0.536 0.464
#> GSM1296074 1 0.996 0.614 0.536 0.464
#> GSM1296111 2 0.000 0.919 0.000 1.000
#> GSM1296099 1 0.983 0.683 0.576 0.424
#> GSM1296086 1 0.996 0.614 0.536 0.464
#> GSM1296117 2 0.000 0.919 0.000 1.000
#> GSM1296113 2 0.000 0.919 0.000 1.000
#> GSM1296096 1 0.983 0.683 0.576 0.424
#> GSM1296105 1 0.983 0.683 0.576 0.424
#> GSM1296098 1 0.983 0.683 0.576 0.424
#> GSM1296101 1 0.983 0.683 0.576 0.424
#> GSM1296121 2 0.000 0.919 0.000 1.000
#> GSM1296088 1 0.971 0.694 0.600 0.400
#> GSM1296082 1 0.996 0.614 0.536 0.464
#> GSM1296115 2 0.000 0.919 0.000 1.000
#> GSM1296084 1 0.971 0.694 0.600 0.400
#> GSM1296072 2 0.000 0.919 0.000 1.000
#> GSM1296069 2 0.000 0.919 0.000 1.000
#> GSM1296071 2 0.000 0.919 0.000 1.000
#> GSM1296070 2 0.000 0.919 0.000 1.000
#> GSM1296073 2 0.000 0.919 0.000 1.000
#> GSM1296034 1 0.983 0.683 0.576 0.424
#> GSM1296041 2 0.000 0.919 0.000 1.000
#> GSM1296035 1 0.983 0.683 0.576 0.424
#> GSM1296038 2 0.999 -0.513 0.480 0.520
#> GSM1296047 2 0.000 0.919 0.000 1.000
#> GSM1296039 1 1.000 0.574 0.512 0.488
#> GSM1296042 2 0.000 0.919 0.000 1.000
#> GSM1296043 2 0.000 0.919 0.000 1.000
#> GSM1296037 1 0.000 0.619 1.000 0.000
#> GSM1296046 2 0.000 0.919 0.000 1.000
#> GSM1296044 2 0.000 0.919 0.000 1.000
#> GSM1296045 2 0.000 0.919 0.000 1.000
#> GSM1296025 1 0.000 0.619 1.000 0.000
#> GSM1296033 1 0.971 0.694 0.600 0.400
#> GSM1296027 1 0.000 0.619 1.000 0.000
#> GSM1296032 1 0.000 0.619 1.000 0.000
#> GSM1296024 1 0.000 0.619 1.000 0.000
#> GSM1296031 1 0.204 0.625 0.968 0.032
#> GSM1296028 1 0.000 0.619 1.000 0.000
#> GSM1296029 1 0.000 0.619 1.000 0.000
#> GSM1296026 1 0.971 0.694 0.600 0.400
#> GSM1296030 1 0.971 0.694 0.600 0.400
#> GSM1296040 1 0.983 0.683 0.576 0.424
#> GSM1296036 1 0.983 0.683 0.576 0.424
#> GSM1296048 2 0.000 0.919 0.000 1.000
#> GSM1296059 1 0.983 0.683 0.576 0.424
#> GSM1296066 2 0.000 0.919 0.000 1.000
#> GSM1296060 1 0.983 0.683 0.576 0.424
#> GSM1296063 2 0.311 0.861 0.056 0.944
#> GSM1296064 2 0.998 -0.502 0.476 0.524
#> GSM1296067 2 0.518 0.777 0.116 0.884
#> GSM1296062 1 0.983 0.683 0.576 0.424
#> GSM1296068 2 0.000 0.919 0.000 1.000
#> GSM1296050 1 0.788 0.647 0.764 0.236
#> GSM1296057 1 0.971 0.694 0.600 0.400
#> GSM1296052 1 0.000 0.619 1.000 0.000
#> GSM1296054 1 0.000 0.619 1.000 0.000
#> GSM1296049 1 0.000 0.619 1.000 0.000
#> GSM1296055 2 0.662 0.685 0.172 0.828
#> GSM1296053 1 0.000 0.619 1.000 0.000
#> GSM1296058 1 0.971 0.694 0.600 0.400
#> GSM1296051 1 0.993 0.634 0.548 0.452
#> GSM1296056 1 0.983 0.673 0.576 0.424
#> GSM1296065 2 0.343 0.855 0.064 0.936
#> GSM1296061 1 0.983 0.683 0.576 0.424
#> GSM1296095 2 0.529 0.771 0.120 0.880
#> GSM1296120 2 0.000 0.919 0.000 1.000
#> GSM1296077 1 0.000 0.619 1.000 0.000
#> GSM1296093 1 0.000 0.619 1.000 0.000
#> GSM1296104 2 0.653 0.686 0.168 0.832
#> GSM1296079 1 0.000 0.619 1.000 0.000
#> GSM1296108 2 0.000 0.919 0.000 1.000
#> GSM1296110 2 0.000 0.919 0.000 1.000
#> GSM1296081 1 0.000 0.619 1.000 0.000
#> GSM1296091 1 0.971 0.694 0.600 0.400
#> GSM1296075 1 0.999 0.580 0.520 0.480
#> GSM1296112 2 0.000 0.919 0.000 1.000
#> GSM1296100 1 0.000 0.619 1.000 0.000
#> GSM1296087 1 0.881 0.677 0.700 0.300
#> GSM1296118 2 0.000 0.919 0.000 1.000
#> GSM1296114 2 0.000 0.919 0.000 1.000
#> GSM1296097 2 0.738 0.577 0.208 0.792
#> GSM1296106 2 0.529 0.771 0.120 0.880
#> GSM1296102 1 0.975 0.688 0.592 0.408
#> GSM1296122 2 0.000 0.919 0.000 1.000
#> GSM1296089 1 0.529 0.626 0.880 0.120
#> GSM1296083 1 0.000 0.619 1.000 0.000
#> GSM1296116 2 0.000 0.919 0.000 1.000
#> GSM1296085 1 0.000 0.619 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1296094 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1296119 2 0.0000 0.897 0.000 1.000 0.000
#> GSM1296076 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1296092 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1296103 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1296078 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1296107 2 0.0000 0.897 0.000 1.000 0.000
#> GSM1296109 3 0.4452 0.736 0.000 0.192 0.808
#> GSM1296080 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1296090 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1296074 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1296111 2 0.0000 0.897 0.000 1.000 0.000
#> GSM1296099 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1296086 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1296117 2 0.0000 0.897 0.000 1.000 0.000
#> GSM1296113 2 0.0000 0.897 0.000 1.000 0.000
#> GSM1296096 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1296105 1 0.6192 0.448 0.580 0.000 0.420
#> GSM1296098 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1296101 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1296121 2 0.0000 0.897 0.000 1.000 0.000
#> GSM1296088 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1296082 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1296115 2 0.0000 0.897 0.000 1.000 0.000
#> GSM1296084 3 0.0237 0.986 0.004 0.000 0.996
#> GSM1296072 2 0.0000 0.897 0.000 1.000 0.000
#> GSM1296069 2 0.0000 0.897 0.000 1.000 0.000
#> GSM1296071 2 0.0000 0.897 0.000 1.000 0.000
#> GSM1296070 2 0.0000 0.897 0.000 1.000 0.000
#> GSM1296073 2 0.0000 0.897 0.000 1.000 0.000
#> GSM1296034 1 0.6274 0.360 0.544 0.000 0.456
#> GSM1296041 2 0.0000 0.897 0.000 1.000 0.000
#> GSM1296035 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1296038 2 0.6280 0.264 0.000 0.540 0.460
#> GSM1296047 2 0.0000 0.897 0.000 1.000 0.000
#> GSM1296039 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1296042 2 0.0000 0.897 0.000 1.000 0.000
#> GSM1296043 2 0.0000 0.897 0.000 1.000 0.000
#> GSM1296037 1 0.0000 0.870 1.000 0.000 0.000
#> GSM1296046 2 0.0000 0.897 0.000 1.000 0.000
#> GSM1296044 2 0.0000 0.897 0.000 1.000 0.000
#> GSM1296045 2 0.0000 0.897 0.000 1.000 0.000
#> GSM1296025 1 0.0000 0.870 1.000 0.000 0.000
#> GSM1296033 1 0.5098 0.740 0.752 0.000 0.248
#> GSM1296027 1 0.0000 0.870 1.000 0.000 0.000
#> GSM1296032 1 0.0000 0.870 1.000 0.000 0.000
#> GSM1296024 1 0.0000 0.870 1.000 0.000 0.000
#> GSM1296031 1 0.2878 0.837 0.904 0.000 0.096
#> GSM1296028 1 0.0000 0.870 1.000 0.000 0.000
#> GSM1296029 1 0.0747 0.866 0.984 0.000 0.016
#> GSM1296026 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1296030 3 0.0424 0.982 0.008 0.000 0.992
#> GSM1296040 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1296036 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1296048 2 0.0000 0.897 0.000 1.000 0.000
#> GSM1296059 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1296066 2 0.0000 0.897 0.000 1.000 0.000
#> GSM1296060 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1296063 2 0.6421 0.351 0.004 0.572 0.424
#> GSM1296064 3 0.0237 0.986 0.000 0.004 0.996
#> GSM1296067 2 0.4842 0.693 0.000 0.776 0.224
#> GSM1296062 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1296068 2 0.0000 0.897 0.000 1.000 0.000
#> GSM1296050 1 0.4750 0.767 0.784 0.000 0.216
#> GSM1296057 1 0.5098 0.740 0.752 0.000 0.248
#> GSM1296052 1 0.0000 0.870 1.000 0.000 0.000
#> GSM1296054 1 0.0000 0.870 1.000 0.000 0.000
#> GSM1296049 1 0.0000 0.870 1.000 0.000 0.000
#> GSM1296055 1 0.9800 0.158 0.412 0.344 0.244
#> GSM1296053 1 0.0000 0.870 1.000 0.000 0.000
#> GSM1296058 1 0.5098 0.740 0.752 0.000 0.248
#> GSM1296051 3 0.1411 0.948 0.036 0.000 0.964
#> GSM1296056 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1296065 2 0.9170 0.404 0.212 0.540 0.248
#> GSM1296061 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1296095 2 0.4887 0.688 0.000 0.772 0.228
#> GSM1296120 2 0.1289 0.873 0.032 0.968 0.000
#> GSM1296077 1 0.0000 0.870 1.000 0.000 0.000
#> GSM1296093 1 0.0000 0.870 1.000 0.000 0.000
#> GSM1296104 2 0.9271 0.375 0.228 0.528 0.244
#> GSM1296079 1 0.0000 0.870 1.000 0.000 0.000
#> GSM1296108 2 0.0000 0.897 0.000 1.000 0.000
#> GSM1296110 2 0.0000 0.897 0.000 1.000 0.000
#> GSM1296081 1 0.0000 0.870 1.000 0.000 0.000
#> GSM1296091 1 0.5098 0.740 0.752 0.000 0.248
#> GSM1296075 1 0.5098 0.740 0.752 0.000 0.248
#> GSM1296112 2 0.0000 0.897 0.000 1.000 0.000
#> GSM1296100 1 0.0000 0.870 1.000 0.000 0.000
#> GSM1296087 1 0.3879 0.809 0.848 0.000 0.152
#> GSM1296118 2 0.0000 0.897 0.000 1.000 0.000
#> GSM1296114 2 0.0000 0.897 0.000 1.000 0.000
#> GSM1296097 2 0.9175 0.402 0.216 0.540 0.244
#> GSM1296106 2 0.9175 0.402 0.216 0.540 0.244
#> GSM1296102 1 0.5098 0.740 0.752 0.000 0.248
#> GSM1296122 2 0.7899 0.591 0.144 0.664 0.192
#> GSM1296089 1 0.4555 0.779 0.800 0.000 0.200
#> GSM1296083 1 0.0000 0.870 1.000 0.000 0.000
#> GSM1296116 2 0.0000 0.897 0.000 1.000 0.000
#> GSM1296085 1 0.0000 0.870 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1296094 3 0.4697 0.884 0.000 0.000 0.644 0.356
#> GSM1296119 2 0.5436 0.789 0.000 0.620 0.356 0.024
#> GSM1296076 4 0.0000 0.537 0.000 0.000 0.000 1.000
#> GSM1296092 4 0.0000 0.537 0.000 0.000 0.000 1.000
#> GSM1296103 3 0.4746 0.888 0.000 0.000 0.632 0.368
#> GSM1296078 4 0.0000 0.537 0.000 0.000 0.000 1.000
#> GSM1296107 2 0.4872 0.797 0.000 0.640 0.356 0.004
#> GSM1296109 3 0.6347 0.733 0.000 0.064 0.524 0.412
#> GSM1296080 3 0.4855 0.849 0.000 0.000 0.600 0.400
#> GSM1296090 4 0.0469 0.534 0.000 0.000 0.012 0.988
#> GSM1296074 4 0.1211 0.517 0.000 0.000 0.040 0.960
#> GSM1296111 2 0.4872 0.797 0.000 0.640 0.356 0.004
#> GSM1296099 3 0.4941 0.815 0.000 0.000 0.564 0.436
#> GSM1296086 4 0.1211 0.515 0.000 0.000 0.040 0.960
#> GSM1296117 2 0.5127 0.794 0.000 0.632 0.356 0.012
#> GSM1296113 2 0.4872 0.797 0.000 0.640 0.356 0.004
#> GSM1296096 3 0.4996 0.717 0.000 0.000 0.516 0.484
#> GSM1296105 4 0.7150 0.200 0.384 0.000 0.136 0.480
#> GSM1296098 3 0.4697 0.884 0.000 0.000 0.644 0.356
#> GSM1296101 3 0.4776 0.885 0.000 0.000 0.624 0.376
#> GSM1296121 2 0.6069 0.772 0.000 0.588 0.356 0.056
#> GSM1296088 4 0.2868 0.412 0.000 0.000 0.136 0.864
#> GSM1296082 4 0.0336 0.535 0.000 0.000 0.008 0.992
#> GSM1296115 2 0.4872 0.797 0.000 0.640 0.356 0.004
#> GSM1296084 4 0.5669 0.384 0.200 0.000 0.092 0.708
#> GSM1296072 2 0.4121 0.698 0.000 0.796 0.020 0.184
#> GSM1296069 2 0.3486 0.821 0.000 0.812 0.188 0.000
#> GSM1296071 2 0.0000 0.828 0.000 1.000 0.000 0.000
#> GSM1296070 2 0.4697 0.797 0.000 0.644 0.356 0.000
#> GSM1296073 2 0.6200 0.767 0.000 0.580 0.356 0.064
#> GSM1296034 3 0.7686 0.446 0.228 0.000 0.436 0.336
#> GSM1296041 2 0.4872 0.797 0.000 0.640 0.356 0.004
#> GSM1296035 3 0.4996 0.716 0.000 0.000 0.516 0.484
#> GSM1296038 4 0.4507 0.437 0.000 0.224 0.020 0.756
#> GSM1296047 2 0.0707 0.821 0.000 0.980 0.000 0.020
#> GSM1296039 4 0.2334 0.477 0.000 0.004 0.088 0.908
#> GSM1296042 2 0.4872 0.797 0.000 0.640 0.356 0.004
#> GSM1296043 2 0.0188 0.829 0.000 0.996 0.004 0.000
#> GSM1296037 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM1296046 2 0.0000 0.828 0.000 1.000 0.000 0.000
#> GSM1296044 2 0.0000 0.828 0.000 1.000 0.000 0.000
#> GSM1296045 2 0.0336 0.829 0.000 0.992 0.008 0.000
#> GSM1296025 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM1296033 4 0.4720 0.408 0.324 0.000 0.004 0.672
#> GSM1296027 1 0.0188 0.926 0.996 0.000 0.000 0.004
#> GSM1296032 1 0.0188 0.926 0.996 0.000 0.000 0.004
#> GSM1296024 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM1296031 1 0.3356 0.757 0.824 0.000 0.000 0.176
#> GSM1296028 1 0.0817 0.913 0.976 0.000 0.000 0.024
#> GSM1296029 1 0.1118 0.903 0.964 0.000 0.000 0.036
#> GSM1296026 4 0.3435 0.458 0.036 0.000 0.100 0.864
#> GSM1296030 4 0.5775 0.373 0.212 0.000 0.092 0.696
#> GSM1296040 3 0.4761 0.887 0.000 0.000 0.628 0.372
#> GSM1296036 3 0.4697 0.884 0.000 0.000 0.644 0.356
#> GSM1296048 2 0.6136 0.770 0.000 0.584 0.356 0.060
#> GSM1296059 3 0.4761 0.887 0.000 0.000 0.628 0.372
#> GSM1296066 2 0.4872 0.797 0.000 0.640 0.356 0.004
#> GSM1296060 4 0.4955 -0.561 0.000 0.000 0.444 0.556
#> GSM1296063 4 0.3278 0.506 0.000 0.116 0.020 0.864
#> GSM1296064 4 0.2334 0.521 0.000 0.088 0.004 0.908
#> GSM1296067 2 0.2342 0.765 0.000 0.912 0.008 0.080
#> GSM1296062 3 0.4713 0.885 0.000 0.000 0.640 0.360
#> GSM1296068 2 0.0000 0.828 0.000 1.000 0.000 0.000
#> GSM1296050 1 0.3486 0.741 0.812 0.000 0.000 0.188
#> GSM1296057 4 0.5161 0.309 0.476 0.000 0.004 0.520
#> GSM1296052 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM1296055 4 0.6964 0.434 0.196 0.196 0.004 0.604
#> GSM1296053 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM1296058 4 0.5161 0.309 0.476 0.000 0.004 0.520
#> GSM1296051 4 0.0592 0.534 0.000 0.000 0.016 0.984
#> GSM1296056 4 0.1389 0.512 0.000 0.000 0.048 0.952
#> GSM1296065 4 0.4898 0.408 0.000 0.260 0.024 0.716
#> GSM1296061 3 0.4697 0.884 0.000 0.000 0.644 0.356
#> GSM1296095 4 0.5781 0.174 0.000 0.480 0.028 0.492
#> GSM1296120 2 0.1913 0.798 0.040 0.940 0.000 0.020
#> GSM1296077 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM1296093 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM1296104 4 0.5620 0.456 0.052 0.220 0.012 0.716
#> GSM1296079 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM1296108 2 0.0000 0.828 0.000 1.000 0.000 0.000
#> GSM1296110 2 0.0000 0.828 0.000 1.000 0.000 0.000
#> GSM1296081 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM1296091 4 0.5161 0.309 0.476 0.000 0.004 0.520
#> GSM1296075 4 0.4991 0.372 0.388 0.000 0.004 0.608
#> GSM1296112 2 0.0000 0.828 0.000 1.000 0.000 0.000
#> GSM1296100 1 0.0188 0.925 0.996 0.000 0.000 0.004
#> GSM1296087 1 0.3266 0.768 0.832 0.000 0.000 0.168
#> GSM1296118 2 0.0336 0.826 0.000 0.992 0.000 0.008
#> GSM1296114 2 0.0000 0.828 0.000 1.000 0.000 0.000
#> GSM1296097 4 0.6847 0.431 0.140 0.232 0.008 0.620
#> GSM1296106 4 0.6411 0.296 0.056 0.424 0.004 0.516
#> GSM1296102 1 0.5353 -0.072 0.556 0.000 0.012 0.432
#> GSM1296122 2 0.4559 0.642 0.040 0.792 0.004 0.164
#> GSM1296089 1 0.3626 0.743 0.812 0.000 0.004 0.184
#> GSM1296083 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM1296116 2 0.0000 0.828 0.000 1.000 0.000 0.000
#> GSM1296085 1 0.0188 0.926 0.996 0.000 0.000 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1296094 3 0.0566 0.9029 0.000 0.000 0.984 0.004 0.012
#> GSM1296119 5 0.1300 0.9326 0.000 0.016 0.000 0.028 0.956
#> GSM1296076 4 0.0000 0.9155 0.000 0.000 0.000 1.000 0.000
#> GSM1296092 4 0.0609 0.9199 0.000 0.000 0.020 0.980 0.000
#> GSM1296103 3 0.0510 0.9027 0.000 0.000 0.984 0.016 0.000
#> GSM1296078 4 0.0000 0.9155 0.000 0.000 0.000 1.000 0.000
#> GSM1296107 5 0.1121 0.9331 0.000 0.044 0.000 0.000 0.956
#> GSM1296109 3 0.2244 0.8748 0.000 0.016 0.920 0.024 0.040
#> GSM1296080 3 0.0566 0.9029 0.000 0.000 0.984 0.004 0.012
#> GSM1296090 4 0.0000 0.9155 0.000 0.000 0.000 1.000 0.000
#> GSM1296074 4 0.0000 0.9155 0.000 0.000 0.000 1.000 0.000
#> GSM1296111 5 0.1281 0.9345 0.000 0.032 0.000 0.012 0.956
#> GSM1296099 3 0.1732 0.8869 0.000 0.000 0.920 0.080 0.000
#> GSM1296086 4 0.1121 0.9171 0.000 0.000 0.044 0.956 0.000
#> GSM1296117 5 0.1300 0.9344 0.000 0.028 0.000 0.016 0.956
#> GSM1296113 5 0.1121 0.9331 0.000 0.044 0.000 0.000 0.956
#> GSM1296096 3 0.3636 0.6970 0.000 0.000 0.728 0.272 0.000
#> GSM1296105 1 0.3693 0.7950 0.808 0.000 0.156 0.032 0.004
#> GSM1296098 3 0.0566 0.9029 0.000 0.000 0.984 0.004 0.012
#> GSM1296101 3 0.1792 0.8846 0.000 0.000 0.916 0.084 0.000
#> GSM1296121 5 0.1300 0.9326 0.000 0.016 0.000 0.028 0.956
#> GSM1296088 4 0.3242 0.8130 0.000 0.000 0.216 0.784 0.000
#> GSM1296082 4 0.0000 0.9155 0.000 0.000 0.000 1.000 0.000
#> GSM1296115 5 0.1300 0.9326 0.000 0.016 0.000 0.028 0.956
#> GSM1296084 4 0.3431 0.8593 0.008 0.000 0.144 0.828 0.020
#> GSM1296072 5 0.2843 0.8336 0.000 0.144 0.008 0.000 0.848
#> GSM1296069 2 0.1544 0.9247 0.000 0.932 0.000 0.000 0.068
#> GSM1296071 2 0.0162 0.9835 0.000 0.996 0.000 0.000 0.004
#> GSM1296070 5 0.1270 0.9299 0.000 0.052 0.000 0.000 0.948
#> GSM1296073 5 0.1300 0.9326 0.000 0.016 0.000 0.028 0.956
#> GSM1296034 3 0.3101 0.8061 0.100 0.024 0.864 0.000 0.012
#> GSM1296041 5 0.1205 0.9340 0.000 0.040 0.000 0.004 0.956
#> GSM1296035 3 0.3684 0.6856 0.000 0.000 0.720 0.280 0.000
#> GSM1296038 5 0.5146 0.6656 0.000 0.024 0.080 0.172 0.724
#> GSM1296047 2 0.0794 0.9700 0.000 0.972 0.000 0.000 0.028
#> GSM1296039 4 0.0963 0.9203 0.000 0.000 0.036 0.964 0.000
#> GSM1296042 5 0.1121 0.9331 0.000 0.044 0.000 0.000 0.956
#> GSM1296043 2 0.0162 0.9827 0.000 0.996 0.000 0.000 0.004
#> GSM1296037 1 0.0000 0.9123 1.000 0.000 0.000 0.000 0.000
#> GSM1296046 2 0.0000 0.9847 0.000 1.000 0.000 0.000 0.000
#> GSM1296044 2 0.0000 0.9847 0.000 1.000 0.000 0.000 0.000
#> GSM1296045 2 0.0290 0.9800 0.000 0.992 0.000 0.000 0.008
#> GSM1296025 1 0.0000 0.9123 1.000 0.000 0.000 0.000 0.000
#> GSM1296033 1 0.7040 0.0442 0.444 0.024 0.084 0.416 0.032
#> GSM1296027 1 0.0000 0.9123 1.000 0.000 0.000 0.000 0.000
#> GSM1296032 1 0.0000 0.9123 1.000 0.000 0.000 0.000 0.000
#> GSM1296024 1 0.0000 0.9123 1.000 0.000 0.000 0.000 0.000
#> GSM1296031 1 0.1012 0.9068 0.968 0.000 0.000 0.012 0.020
#> GSM1296028 1 0.0510 0.9099 0.984 0.000 0.000 0.000 0.016
#> GSM1296029 1 0.0510 0.9099 0.984 0.000 0.000 0.000 0.016
#> GSM1296026 4 0.2929 0.8441 0.000 0.000 0.180 0.820 0.000
#> GSM1296030 4 0.3754 0.8346 0.008 0.000 0.176 0.796 0.020
#> GSM1296040 3 0.0880 0.9000 0.000 0.000 0.968 0.032 0.000
#> GSM1296036 3 0.0566 0.9029 0.000 0.000 0.984 0.004 0.012
#> GSM1296048 5 0.1300 0.9326 0.000 0.016 0.000 0.028 0.956
#> GSM1296059 3 0.1732 0.8867 0.000 0.000 0.920 0.080 0.000
#> GSM1296066 5 0.1121 0.9331 0.000 0.044 0.000 0.000 0.956
#> GSM1296060 3 0.3707 0.6581 0.000 0.000 0.716 0.284 0.000
#> GSM1296063 5 0.3815 0.8289 0.000 0.008 0.080 0.088 0.824
#> GSM1296064 4 0.1124 0.9197 0.000 0.000 0.036 0.960 0.004
#> GSM1296067 2 0.0671 0.9755 0.000 0.980 0.004 0.000 0.016
#> GSM1296062 3 0.0566 0.9029 0.000 0.000 0.984 0.004 0.012
#> GSM1296068 2 0.0000 0.9847 0.000 1.000 0.000 0.000 0.000
#> GSM1296050 1 0.1173 0.9058 0.964 0.004 0.000 0.012 0.020
#> GSM1296057 1 0.3732 0.8464 0.848 0.024 0.084 0.012 0.032
#> GSM1296052 1 0.0000 0.9123 1.000 0.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.9123 1.000 0.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.9123 1.000 0.000 0.000 0.000 0.000
#> GSM1296055 1 0.4132 0.8368 0.828 0.044 0.084 0.012 0.032
#> GSM1296053 1 0.0000 0.9123 1.000 0.000 0.000 0.000 0.000
#> GSM1296058 1 0.6711 0.4716 0.580 0.024 0.084 0.280 0.032
#> GSM1296051 4 0.2727 0.8725 0.000 0.000 0.116 0.868 0.016
#> GSM1296056 4 0.0963 0.9203 0.000 0.000 0.036 0.964 0.000
#> GSM1296065 5 0.3802 0.8154 0.004 0.024 0.084 0.048 0.840
#> GSM1296061 3 0.0566 0.9029 0.000 0.000 0.984 0.004 0.012
#> GSM1296095 5 0.2754 0.8530 0.000 0.040 0.080 0.000 0.880
#> GSM1296120 2 0.0794 0.9700 0.000 0.972 0.000 0.000 0.028
#> GSM1296077 1 0.0000 0.9123 1.000 0.000 0.000 0.000 0.000
#> GSM1296093 1 0.0000 0.9123 1.000 0.000 0.000 0.000 0.000
#> GSM1296104 1 0.7039 0.2950 0.496 0.024 0.084 0.036 0.360
#> GSM1296079 1 0.0000 0.9123 1.000 0.000 0.000 0.000 0.000
#> GSM1296108 2 0.0000 0.9847 0.000 1.000 0.000 0.000 0.000
#> GSM1296110 2 0.0000 0.9847 0.000 1.000 0.000 0.000 0.000
#> GSM1296081 1 0.0000 0.9123 1.000 0.000 0.000 0.000 0.000
#> GSM1296091 1 0.3732 0.8464 0.848 0.024 0.084 0.012 0.032
#> GSM1296075 1 0.4132 0.8368 0.828 0.044 0.084 0.012 0.032
#> GSM1296112 2 0.0000 0.9847 0.000 1.000 0.000 0.000 0.000
#> GSM1296100 1 0.0000 0.9123 1.000 0.000 0.000 0.000 0.000
#> GSM1296087 1 0.0771 0.9086 0.976 0.000 0.000 0.004 0.020
#> GSM1296118 2 0.0510 0.9771 0.000 0.984 0.000 0.000 0.016
#> GSM1296114 2 0.0000 0.9847 0.000 1.000 0.000 0.000 0.000
#> GSM1296097 1 0.4132 0.8368 0.828 0.044 0.084 0.012 0.032
#> GSM1296106 1 0.4171 0.8388 0.828 0.064 0.064 0.012 0.032
#> GSM1296102 1 0.3320 0.8604 0.872 0.024 0.068 0.012 0.024
#> GSM1296122 2 0.1399 0.9507 0.000 0.952 0.020 0.000 0.028
#> GSM1296089 1 0.1173 0.9058 0.964 0.004 0.000 0.012 0.020
#> GSM1296083 1 0.0000 0.9123 1.000 0.000 0.000 0.000 0.000
#> GSM1296116 2 0.0000 0.9847 0.000 1.000 0.000 0.000 0.000
#> GSM1296085 1 0.0162 0.9118 0.996 0.000 0.000 0.000 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1296094 3 0.0000 0.7919 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296119 5 0.0000 0.9790 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296076 4 0.0000 0.8199 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1296092 4 0.0000 0.8199 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1296103 3 0.0000 0.7919 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296078 4 0.0000 0.8199 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1296107 5 0.0000 0.9790 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296109 3 0.4504 0.6280 0.000 0.000 0.724 0.052 0.196 0.028
#> GSM1296080 3 0.4131 0.2615 0.000 0.000 0.624 0.020 0.000 0.356
#> GSM1296090 4 0.0000 0.8199 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1296074 4 0.0000 0.8199 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1296111 5 0.0000 0.9790 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296099 3 0.1806 0.7763 0.000 0.000 0.908 0.088 0.000 0.004
#> GSM1296086 4 0.0000 0.8199 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1296117 5 0.0000 0.9790 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296113 5 0.0000 0.9790 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296096 3 0.3290 0.6305 0.000 0.000 0.744 0.252 0.000 0.004
#> GSM1296105 6 0.3858 0.6476 0.012 0.000 0.228 0.020 0.000 0.740
#> GSM1296098 3 0.0146 0.7916 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM1296101 3 0.1556 0.7734 0.000 0.000 0.920 0.080 0.000 0.000
#> GSM1296121 5 0.0000 0.9790 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296088 3 0.5530 0.3707 0.000 0.000 0.560 0.216 0.000 0.224
#> GSM1296082 4 0.0000 0.8199 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1296115 5 0.0000 0.9790 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296084 6 0.5691 0.2526 0.000 0.000 0.368 0.164 0.000 0.468
#> GSM1296072 2 0.4570 0.6693 0.000 0.704 0.000 0.004 0.188 0.104
#> GSM1296069 2 0.3782 0.3362 0.000 0.588 0.000 0.000 0.412 0.000
#> GSM1296071 2 0.0000 0.8953 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296070 5 0.2178 0.8284 0.000 0.132 0.000 0.000 0.868 0.000
#> GSM1296073 5 0.1610 0.8891 0.000 0.000 0.000 0.084 0.916 0.000
#> GSM1296034 6 0.6179 0.2075 0.200 0.000 0.368 0.012 0.000 0.420
#> GSM1296041 5 0.0000 0.9790 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296035 3 0.3265 0.6349 0.000 0.000 0.748 0.248 0.000 0.004
#> GSM1296038 4 0.6466 -0.0626 0.000 0.000 0.288 0.364 0.016 0.332
#> GSM1296047 2 0.0260 0.8908 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM1296039 4 0.2432 0.7614 0.000 0.000 0.100 0.876 0.000 0.024
#> GSM1296042 5 0.0000 0.9790 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296043 2 0.1765 0.8339 0.000 0.904 0.000 0.000 0.096 0.000
#> GSM1296037 1 0.3847 0.6963 0.644 0.000 0.000 0.008 0.000 0.348
#> GSM1296046 2 0.0000 0.8953 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296044 2 0.0000 0.8953 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296045 2 0.2260 0.7923 0.000 0.860 0.000 0.000 0.140 0.000
#> GSM1296025 1 0.0000 0.7500 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296033 6 0.1036 0.7377 0.004 0.000 0.008 0.024 0.000 0.964
#> GSM1296027 1 0.3198 0.8177 0.740 0.000 0.000 0.000 0.000 0.260
#> GSM1296032 1 0.3198 0.8177 0.740 0.000 0.000 0.000 0.000 0.260
#> GSM1296024 1 0.0000 0.7500 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296031 6 0.2996 0.5177 0.228 0.000 0.000 0.000 0.000 0.772
#> GSM1296028 1 0.3607 0.7273 0.652 0.000 0.000 0.000 0.000 0.348
#> GSM1296029 1 0.3867 0.4452 0.512 0.000 0.000 0.000 0.000 0.488
#> GSM1296026 6 0.5758 0.2282 0.000 0.000 0.368 0.176 0.000 0.456
#> GSM1296030 6 0.5691 0.2526 0.000 0.000 0.368 0.164 0.000 0.468
#> GSM1296040 3 0.0000 0.7919 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296036 3 0.0146 0.7916 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM1296048 5 0.0000 0.9790 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296059 3 0.2178 0.7413 0.000 0.000 0.868 0.132 0.000 0.000
#> GSM1296066 5 0.0000 0.9790 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296060 3 0.3333 0.7017 0.000 0.000 0.784 0.192 0.000 0.024
#> GSM1296063 4 0.4407 0.5199 0.000 0.000 0.008 0.664 0.292 0.036
#> GSM1296064 4 0.2432 0.7614 0.000 0.000 0.100 0.876 0.000 0.024
#> GSM1296067 2 0.3804 0.3946 0.000 0.656 0.000 0.008 0.000 0.336
#> GSM1296062 3 0.4004 0.2535 0.000 0.000 0.620 0.012 0.000 0.368
#> GSM1296068 2 0.0000 0.8953 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296050 6 0.2883 0.5498 0.212 0.000 0.000 0.000 0.000 0.788
#> GSM1296057 6 0.1167 0.7390 0.012 0.000 0.008 0.020 0.000 0.960
#> GSM1296052 1 0.3198 0.8177 0.740 0.000 0.000 0.000 0.000 0.260
#> GSM1296054 1 0.0000 0.7500 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296049 1 0.2941 0.8169 0.780 0.000 0.000 0.000 0.000 0.220
#> GSM1296055 6 0.1167 0.7390 0.012 0.000 0.008 0.020 0.000 0.960
#> GSM1296053 1 0.2883 0.8162 0.788 0.000 0.000 0.000 0.000 0.212
#> GSM1296058 6 0.1167 0.7390 0.012 0.000 0.008 0.020 0.000 0.960
#> GSM1296051 4 0.4654 0.1148 0.000 0.000 0.044 0.544 0.000 0.412
#> GSM1296056 4 0.2432 0.7614 0.000 0.000 0.100 0.876 0.000 0.024
#> GSM1296065 6 0.1483 0.7303 0.000 0.000 0.008 0.036 0.012 0.944
#> GSM1296061 3 0.0260 0.7914 0.000 0.000 0.992 0.000 0.000 0.008
#> GSM1296095 6 0.7297 -0.0314 0.000 0.000 0.312 0.112 0.216 0.360
#> GSM1296120 2 0.0000 0.8953 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296077 1 0.3198 0.8177 0.740 0.000 0.000 0.000 0.000 0.260
#> GSM1296093 1 0.0000 0.7500 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296104 6 0.1167 0.7356 0.000 0.000 0.008 0.020 0.012 0.960
#> GSM1296079 1 0.3198 0.8177 0.740 0.000 0.000 0.000 0.000 0.260
#> GSM1296108 2 0.0000 0.8953 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296110 2 0.0000 0.8953 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296081 1 0.0790 0.7619 0.968 0.000 0.000 0.000 0.000 0.032
#> GSM1296091 6 0.0984 0.7348 0.012 0.000 0.008 0.012 0.000 0.968
#> GSM1296075 6 0.1167 0.7390 0.012 0.000 0.008 0.020 0.000 0.960
#> GSM1296112 2 0.0000 0.8953 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296100 1 0.3955 0.6271 0.608 0.000 0.000 0.008 0.000 0.384
#> GSM1296087 6 0.2941 0.5345 0.220 0.000 0.000 0.000 0.000 0.780
#> GSM1296118 2 0.2664 0.7124 0.000 0.816 0.000 0.000 0.000 0.184
#> GSM1296114 2 0.0000 0.8953 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296097 6 0.1167 0.7390 0.012 0.000 0.008 0.020 0.000 0.960
#> GSM1296106 6 0.1312 0.7387 0.012 0.004 0.008 0.020 0.000 0.956
#> GSM1296102 6 0.3159 0.6455 0.152 0.000 0.008 0.020 0.000 0.820
#> GSM1296122 6 0.4136 0.2217 0.000 0.428 0.000 0.012 0.000 0.560
#> GSM1296089 6 0.2883 0.5498 0.212 0.000 0.000 0.000 0.000 0.788
#> GSM1296083 1 0.0000 0.7500 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296116 2 0.0000 0.8953 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296085 1 0.3198 0.8177 0.740 0.000 0.000 0.000 0.000 0.260
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# 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 tissue(p) time(p) agent(p) dose(p) individual(p) k
#> SD:mclust 97 3.12e-01 0.0356 0.78134 4.61e-04 4.28e-07 2
#> SD:mclust 90 4.29e-04 0.0454 0.00638 3.71e-09 1.32e-07 3
#> SD:mclust 78 7.47e-03 0.0691 0.08315 4.16e-07 2.20e-07 4
#> SD:mclust 96 2.41e-05 0.5305 0.05245 1.66e-09 4.79e-07 5
#> SD:mclust 85 3.06e-04 0.3664 0.30681 1.02e-06 5.15e-07 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 45638 rows and 99 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
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 0.978 0.956 0.982 0.4939 0.506 0.506
#> 3 3 0.863 0.882 0.949 0.3578 0.744 0.531
#> 4 4 0.755 0.776 0.887 0.1017 0.825 0.544
#> 5 5 0.763 0.774 0.874 0.0691 0.878 0.586
#> 6 6 0.739 0.706 0.831 0.0325 0.943 0.744
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
#> GSM1296094 2 0.0000 0.9824 0.000 1.000
#> GSM1296119 2 0.0000 0.9824 0.000 1.000
#> GSM1296076 2 0.0000 0.9824 0.000 1.000
#> GSM1296092 2 0.0000 0.9824 0.000 1.000
#> GSM1296103 2 0.0000 0.9824 0.000 1.000
#> GSM1296078 2 0.0000 0.9824 0.000 1.000
#> GSM1296107 2 0.0000 0.9824 0.000 1.000
#> GSM1296109 2 0.0000 0.9824 0.000 1.000
#> GSM1296080 1 0.0000 0.9782 1.000 0.000
#> GSM1296090 2 0.0000 0.9824 0.000 1.000
#> GSM1296074 2 0.0000 0.9824 0.000 1.000
#> GSM1296111 2 0.0000 0.9824 0.000 1.000
#> GSM1296099 2 0.0000 0.9824 0.000 1.000
#> GSM1296086 2 0.0000 0.9824 0.000 1.000
#> GSM1296117 2 0.0000 0.9824 0.000 1.000
#> GSM1296113 2 0.0000 0.9824 0.000 1.000
#> GSM1296096 2 0.0000 0.9824 0.000 1.000
#> GSM1296105 1 0.0000 0.9782 1.000 0.000
#> GSM1296098 2 0.0376 0.9794 0.004 0.996
#> GSM1296101 2 0.0000 0.9824 0.000 1.000
#> GSM1296121 2 0.0000 0.9824 0.000 1.000
#> GSM1296088 2 0.3431 0.9213 0.064 0.936
#> GSM1296082 2 0.0000 0.9824 0.000 1.000
#> GSM1296115 2 0.0000 0.9824 0.000 1.000
#> GSM1296084 1 0.0000 0.9782 1.000 0.000
#> GSM1296072 2 0.0000 0.9824 0.000 1.000
#> GSM1296069 2 0.0000 0.9824 0.000 1.000
#> GSM1296071 2 0.0000 0.9824 0.000 1.000
#> GSM1296070 2 0.0000 0.9824 0.000 1.000
#> GSM1296073 2 0.0000 0.9824 0.000 1.000
#> GSM1296034 1 0.0000 0.9782 1.000 0.000
#> GSM1296041 2 0.0000 0.9824 0.000 1.000
#> GSM1296035 2 0.0000 0.9824 0.000 1.000
#> GSM1296038 2 0.0000 0.9824 0.000 1.000
#> GSM1296047 2 0.1184 0.9696 0.016 0.984
#> GSM1296039 2 0.0000 0.9824 0.000 1.000
#> GSM1296042 2 0.0000 0.9824 0.000 1.000
#> GSM1296043 2 0.0000 0.9824 0.000 1.000
#> GSM1296037 1 0.0000 0.9782 1.000 0.000
#> GSM1296046 2 0.0000 0.9824 0.000 1.000
#> GSM1296044 2 0.0000 0.9824 0.000 1.000
#> GSM1296045 2 0.0000 0.9824 0.000 1.000
#> GSM1296025 1 0.0000 0.9782 1.000 0.000
#> GSM1296033 1 0.0000 0.9782 1.000 0.000
#> GSM1296027 1 0.0000 0.9782 1.000 0.000
#> GSM1296032 1 0.0000 0.9782 1.000 0.000
#> GSM1296024 1 0.0000 0.9782 1.000 0.000
#> GSM1296031 1 0.0000 0.9782 1.000 0.000
#> GSM1296028 1 0.0000 0.9782 1.000 0.000
#> GSM1296029 1 0.0000 0.9782 1.000 0.000
#> GSM1296026 1 0.8016 0.6863 0.756 0.244
#> GSM1296030 1 0.0000 0.9782 1.000 0.000
#> GSM1296040 2 0.9983 0.0459 0.476 0.524
#> GSM1296036 1 0.9248 0.5001 0.660 0.340
#> GSM1296048 2 0.0000 0.9824 0.000 1.000
#> GSM1296059 2 0.0000 0.9824 0.000 1.000
#> GSM1296066 2 0.0000 0.9824 0.000 1.000
#> GSM1296060 2 0.0000 0.9824 0.000 1.000
#> GSM1296063 2 0.0000 0.9824 0.000 1.000
#> GSM1296064 2 0.0000 0.9824 0.000 1.000
#> GSM1296067 2 0.1184 0.9700 0.016 0.984
#> GSM1296062 1 0.0000 0.9782 1.000 0.000
#> GSM1296068 2 0.6712 0.7843 0.176 0.824
#> GSM1296050 1 0.0000 0.9782 1.000 0.000
#> GSM1296057 1 0.0000 0.9782 1.000 0.000
#> GSM1296052 1 0.0000 0.9782 1.000 0.000
#> GSM1296054 1 0.0000 0.9782 1.000 0.000
#> GSM1296049 1 0.0000 0.9782 1.000 0.000
#> GSM1296055 1 0.0000 0.9782 1.000 0.000
#> GSM1296053 1 0.0000 0.9782 1.000 0.000
#> GSM1296058 1 0.0000 0.9782 1.000 0.000
#> GSM1296051 2 0.0000 0.9824 0.000 1.000
#> GSM1296056 2 0.0000 0.9824 0.000 1.000
#> GSM1296065 2 0.0000 0.9824 0.000 1.000
#> GSM1296061 1 0.4022 0.9037 0.920 0.080
#> GSM1296095 2 0.0000 0.9824 0.000 1.000
#> GSM1296120 2 0.5519 0.8489 0.128 0.872
#> GSM1296077 1 0.0000 0.9782 1.000 0.000
#> GSM1296093 1 0.0000 0.9782 1.000 0.000
#> GSM1296104 2 0.0376 0.9794 0.004 0.996
#> GSM1296079 1 0.0000 0.9782 1.000 0.000
#> GSM1296108 2 0.0672 0.9763 0.008 0.992
#> GSM1296110 2 0.0000 0.9824 0.000 1.000
#> GSM1296081 1 0.0000 0.9782 1.000 0.000
#> GSM1296091 1 0.0000 0.9782 1.000 0.000
#> GSM1296075 1 0.0000 0.9782 1.000 0.000
#> GSM1296112 2 0.3114 0.9312 0.056 0.944
#> GSM1296100 1 0.0000 0.9782 1.000 0.000
#> GSM1296087 1 0.0000 0.9782 1.000 0.000
#> GSM1296118 1 0.2236 0.9469 0.964 0.036
#> GSM1296114 2 0.0000 0.9824 0.000 1.000
#> GSM1296097 1 0.6623 0.7907 0.828 0.172
#> GSM1296106 1 0.0000 0.9782 1.000 0.000
#> GSM1296102 1 0.0000 0.9782 1.000 0.000
#> GSM1296122 1 0.0000 0.9782 1.000 0.000
#> GSM1296089 1 0.0000 0.9782 1.000 0.000
#> GSM1296083 1 0.0000 0.9782 1.000 0.000
#> GSM1296116 2 0.0000 0.9824 0.000 1.000
#> GSM1296085 1 0.0000 0.9782 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1296094 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM1296119 3 0.6308 0.1244 0.000 0.492 0.508
#> GSM1296076 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM1296092 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM1296103 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM1296078 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM1296107 2 0.0237 0.9513 0.000 0.996 0.004
#> GSM1296109 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM1296080 1 0.3412 0.8467 0.876 0.000 0.124
#> GSM1296090 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM1296074 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM1296111 2 0.5560 0.5143 0.000 0.700 0.300
#> GSM1296099 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM1296086 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM1296117 3 0.3340 0.8356 0.000 0.120 0.880
#> GSM1296113 2 0.1964 0.9106 0.000 0.944 0.056
#> GSM1296096 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM1296105 1 0.0000 0.9621 1.000 0.000 0.000
#> GSM1296098 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM1296101 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM1296121 3 0.5948 0.4996 0.000 0.360 0.640
#> GSM1296088 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM1296082 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM1296115 3 0.5591 0.6031 0.000 0.304 0.696
#> GSM1296084 1 0.4346 0.7697 0.816 0.000 0.184
#> GSM1296072 2 0.0000 0.9537 0.000 1.000 0.000
#> GSM1296069 2 0.0000 0.9537 0.000 1.000 0.000
#> GSM1296071 2 0.0000 0.9537 0.000 1.000 0.000
#> GSM1296070 2 0.0000 0.9537 0.000 1.000 0.000
#> GSM1296073 3 0.2537 0.8687 0.000 0.080 0.920
#> GSM1296034 1 0.0000 0.9621 1.000 0.000 0.000
#> GSM1296041 3 0.5254 0.6654 0.000 0.264 0.736
#> GSM1296035 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM1296038 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM1296047 2 0.0000 0.9537 0.000 1.000 0.000
#> GSM1296039 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM1296042 2 0.2066 0.9066 0.000 0.940 0.060
#> GSM1296043 2 0.0000 0.9537 0.000 1.000 0.000
#> GSM1296037 1 0.0000 0.9621 1.000 0.000 0.000
#> GSM1296046 2 0.0000 0.9537 0.000 1.000 0.000
#> GSM1296044 2 0.0000 0.9537 0.000 1.000 0.000
#> GSM1296045 2 0.0000 0.9537 0.000 1.000 0.000
#> GSM1296025 1 0.0000 0.9621 1.000 0.000 0.000
#> GSM1296033 1 0.0000 0.9621 1.000 0.000 0.000
#> GSM1296027 1 0.0000 0.9621 1.000 0.000 0.000
#> GSM1296032 1 0.0000 0.9621 1.000 0.000 0.000
#> GSM1296024 1 0.0000 0.9621 1.000 0.000 0.000
#> GSM1296031 1 0.0000 0.9621 1.000 0.000 0.000
#> GSM1296028 1 0.0000 0.9621 1.000 0.000 0.000
#> GSM1296029 1 0.0000 0.9621 1.000 0.000 0.000
#> GSM1296026 3 0.5650 0.5199 0.312 0.000 0.688
#> GSM1296030 1 0.2448 0.8984 0.924 0.000 0.076
#> GSM1296040 3 0.0892 0.9043 0.020 0.000 0.980
#> GSM1296036 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM1296048 3 0.6154 0.3900 0.000 0.408 0.592
#> GSM1296059 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM1296066 2 0.0000 0.9537 0.000 1.000 0.000
#> GSM1296060 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM1296063 3 0.2711 0.8631 0.000 0.088 0.912
#> GSM1296064 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM1296067 2 0.0000 0.9537 0.000 1.000 0.000
#> GSM1296062 1 0.1289 0.9369 0.968 0.000 0.032
#> GSM1296068 2 0.0000 0.9537 0.000 1.000 0.000
#> GSM1296050 1 0.0000 0.9621 1.000 0.000 0.000
#> GSM1296057 1 0.0000 0.9621 1.000 0.000 0.000
#> GSM1296052 1 0.0000 0.9621 1.000 0.000 0.000
#> GSM1296054 1 0.0000 0.9621 1.000 0.000 0.000
#> GSM1296049 1 0.0000 0.9621 1.000 0.000 0.000
#> GSM1296055 1 0.6307 0.0321 0.512 0.488 0.000
#> GSM1296053 1 0.0000 0.9621 1.000 0.000 0.000
#> GSM1296058 1 0.0424 0.9562 0.992 0.000 0.008
#> GSM1296051 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM1296056 3 0.0000 0.9180 0.000 0.000 1.000
#> GSM1296065 2 0.2537 0.8864 0.000 0.920 0.080
#> GSM1296061 3 0.4062 0.7645 0.164 0.000 0.836
#> GSM1296095 3 0.2796 0.8607 0.000 0.092 0.908
#> GSM1296120 2 0.0000 0.9537 0.000 1.000 0.000
#> GSM1296077 1 0.0000 0.9621 1.000 0.000 0.000
#> GSM1296093 1 0.0000 0.9621 1.000 0.000 0.000
#> GSM1296104 2 0.2280 0.9150 0.052 0.940 0.008
#> GSM1296079 1 0.0000 0.9621 1.000 0.000 0.000
#> GSM1296108 2 0.0000 0.9537 0.000 1.000 0.000
#> GSM1296110 2 0.0000 0.9537 0.000 1.000 0.000
#> GSM1296081 1 0.0000 0.9621 1.000 0.000 0.000
#> GSM1296091 1 0.0000 0.9621 1.000 0.000 0.000
#> GSM1296075 2 0.4796 0.7132 0.220 0.780 0.000
#> GSM1296112 2 0.0000 0.9537 0.000 1.000 0.000
#> GSM1296100 1 0.0000 0.9621 1.000 0.000 0.000
#> GSM1296087 1 0.0000 0.9621 1.000 0.000 0.000
#> GSM1296118 2 0.0424 0.9487 0.008 0.992 0.000
#> GSM1296114 2 0.0000 0.9537 0.000 1.000 0.000
#> GSM1296097 1 0.5678 0.5267 0.684 0.316 0.000
#> GSM1296106 2 0.5216 0.6447 0.260 0.740 0.000
#> GSM1296102 1 0.0000 0.9621 1.000 0.000 0.000
#> GSM1296122 2 0.2165 0.9039 0.064 0.936 0.000
#> GSM1296089 1 0.0000 0.9621 1.000 0.000 0.000
#> GSM1296083 1 0.0000 0.9621 1.000 0.000 0.000
#> GSM1296116 2 0.0000 0.9537 0.000 1.000 0.000
#> GSM1296085 1 0.0000 0.9621 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1296094 3 0.2589 0.7909 0.000 0.000 0.884 0.116
#> GSM1296119 2 0.6442 0.0837 0.000 0.492 0.068 0.440
#> GSM1296076 4 0.0707 0.8164 0.000 0.000 0.020 0.980
#> GSM1296092 4 0.1743 0.8075 0.004 0.000 0.056 0.940
#> GSM1296103 3 0.3123 0.7664 0.000 0.000 0.844 0.156
#> GSM1296078 4 0.0000 0.8127 0.000 0.000 0.000 1.000
#> GSM1296107 2 0.0592 0.8885 0.000 0.984 0.000 0.016
#> GSM1296109 3 0.4072 0.6548 0.000 0.000 0.748 0.252
#> GSM1296080 3 0.2450 0.7933 0.072 0.000 0.912 0.016
#> GSM1296090 4 0.0937 0.8046 0.012 0.000 0.012 0.976
#> GSM1296074 4 0.2345 0.7780 0.000 0.000 0.100 0.900
#> GSM1296111 2 0.3975 0.7060 0.000 0.760 0.000 0.240
#> GSM1296099 3 0.4543 0.5193 0.000 0.000 0.676 0.324
#> GSM1296086 4 0.0895 0.8150 0.004 0.000 0.020 0.976
#> GSM1296117 4 0.4440 0.7305 0.000 0.060 0.136 0.804
#> GSM1296113 2 0.0336 0.8901 0.000 0.992 0.000 0.008
#> GSM1296096 4 0.4817 0.3502 0.000 0.000 0.388 0.612
#> GSM1296105 3 0.2773 0.7540 0.116 0.000 0.880 0.004
#> GSM1296098 3 0.2198 0.8056 0.008 0.000 0.920 0.072
#> GSM1296101 3 0.3123 0.7679 0.000 0.000 0.844 0.156
#> GSM1296121 2 0.5004 0.4417 0.000 0.604 0.004 0.392
#> GSM1296088 4 0.3726 0.6845 0.000 0.000 0.212 0.788
#> GSM1296082 4 0.1022 0.8157 0.000 0.000 0.032 0.968
#> GSM1296115 2 0.5050 0.4018 0.000 0.588 0.004 0.408
#> GSM1296084 1 0.4203 0.7983 0.824 0.000 0.068 0.108
#> GSM1296072 2 0.1109 0.8851 0.000 0.968 0.004 0.028
#> GSM1296069 2 0.0592 0.8887 0.000 0.984 0.000 0.016
#> GSM1296071 2 0.0188 0.8902 0.000 0.996 0.004 0.000
#> GSM1296070 2 0.2266 0.8510 0.000 0.912 0.004 0.084
#> GSM1296073 4 0.0937 0.8123 0.000 0.012 0.012 0.976
#> GSM1296034 3 0.3569 0.6661 0.196 0.000 0.804 0.000
#> GSM1296041 2 0.5649 0.5482 0.000 0.664 0.052 0.284
#> GSM1296035 4 0.4967 0.1624 0.000 0.000 0.452 0.548
#> GSM1296038 4 0.2156 0.8088 0.004 0.008 0.060 0.928
#> GSM1296047 2 0.0188 0.8903 0.004 0.996 0.000 0.000
#> GSM1296039 4 0.1637 0.8084 0.000 0.000 0.060 0.940
#> GSM1296042 2 0.2408 0.8375 0.000 0.896 0.000 0.104
#> GSM1296043 2 0.0000 0.8903 0.000 1.000 0.000 0.000
#> GSM1296037 1 0.2149 0.9029 0.912 0.000 0.088 0.000
#> GSM1296046 2 0.0000 0.8903 0.000 1.000 0.000 0.000
#> GSM1296044 2 0.0188 0.8902 0.000 0.996 0.004 0.000
#> GSM1296045 2 0.1398 0.8775 0.000 0.956 0.004 0.040
#> GSM1296025 1 0.1118 0.9296 0.964 0.000 0.036 0.000
#> GSM1296033 1 0.0657 0.9287 0.984 0.000 0.004 0.012
#> GSM1296027 1 0.0592 0.9328 0.984 0.000 0.016 0.000
#> GSM1296032 1 0.0188 0.9323 0.996 0.000 0.004 0.000
#> GSM1296024 1 0.1474 0.9262 0.948 0.000 0.052 0.000
#> GSM1296031 1 0.0817 0.9324 0.976 0.000 0.024 0.000
#> GSM1296028 1 0.0336 0.9319 0.992 0.000 0.008 0.000
#> GSM1296029 1 0.0817 0.9323 0.976 0.000 0.024 0.000
#> GSM1296026 4 0.5849 0.6095 0.164 0.000 0.132 0.704
#> GSM1296030 1 0.2021 0.9166 0.932 0.000 0.056 0.012
#> GSM1296040 3 0.1890 0.8085 0.008 0.000 0.936 0.056
#> GSM1296036 3 0.1722 0.8077 0.008 0.000 0.944 0.048
#> GSM1296048 4 0.4546 0.5390 0.000 0.256 0.012 0.732
#> GSM1296059 3 0.3975 0.6759 0.000 0.000 0.760 0.240
#> GSM1296066 2 0.0336 0.8901 0.000 0.992 0.000 0.008
#> GSM1296060 4 0.4999 0.0174 0.000 0.000 0.492 0.508
#> GSM1296063 4 0.1406 0.8009 0.000 0.024 0.016 0.960
#> GSM1296064 4 0.0817 0.8162 0.000 0.000 0.024 0.976
#> GSM1296067 2 0.1118 0.8799 0.000 0.964 0.036 0.000
#> GSM1296062 3 0.1824 0.7910 0.060 0.000 0.936 0.004
#> GSM1296068 2 0.0188 0.8902 0.000 0.996 0.004 0.000
#> GSM1296050 1 0.0469 0.9292 0.988 0.000 0.012 0.000
#> GSM1296057 1 0.0895 0.9298 0.976 0.000 0.020 0.004
#> GSM1296052 1 0.0707 0.9328 0.980 0.000 0.020 0.000
#> GSM1296054 1 0.1211 0.9293 0.960 0.000 0.040 0.000
#> GSM1296049 1 0.1118 0.9305 0.964 0.000 0.036 0.000
#> GSM1296055 1 0.4254 0.8428 0.848 0.056 0.036 0.060
#> GSM1296053 1 0.1474 0.9265 0.948 0.000 0.052 0.000
#> GSM1296058 1 0.3787 0.8258 0.840 0.000 0.036 0.124
#> GSM1296051 4 0.2207 0.7730 0.056 0.004 0.012 0.928
#> GSM1296056 4 0.0817 0.8156 0.000 0.000 0.024 0.976
#> GSM1296065 4 0.5476 0.1535 0.000 0.396 0.020 0.584
#> GSM1296061 3 0.1584 0.8064 0.012 0.000 0.952 0.036
#> GSM1296095 2 0.7289 0.3363 0.000 0.536 0.252 0.212
#> GSM1296120 2 0.0672 0.8884 0.008 0.984 0.008 0.000
#> GSM1296077 1 0.0469 0.9326 0.988 0.000 0.012 0.000
#> GSM1296093 1 0.1557 0.9227 0.944 0.000 0.056 0.000
#> GSM1296104 1 0.8398 0.2323 0.468 0.224 0.036 0.272
#> GSM1296079 1 0.0188 0.9323 0.996 0.000 0.004 0.000
#> GSM1296108 2 0.0188 0.8902 0.000 0.996 0.004 0.000
#> GSM1296110 2 0.0592 0.8878 0.000 0.984 0.016 0.000
#> GSM1296081 1 0.1118 0.9296 0.964 0.000 0.036 0.000
#> GSM1296091 1 0.1042 0.9252 0.972 0.000 0.020 0.008
#> GSM1296075 1 0.2923 0.8809 0.908 0.036 0.020 0.036
#> GSM1296112 2 0.0188 0.8902 0.000 0.996 0.004 0.000
#> GSM1296100 1 0.2704 0.8712 0.876 0.000 0.124 0.000
#> GSM1296087 1 0.0804 0.9298 0.980 0.000 0.012 0.008
#> GSM1296118 2 0.1042 0.8853 0.008 0.972 0.020 0.000
#> GSM1296114 2 0.0188 0.8902 0.000 0.996 0.004 0.000
#> GSM1296097 1 0.5890 0.7290 0.752 0.112 0.044 0.092
#> GSM1296106 2 0.4755 0.6762 0.200 0.760 0.040 0.000
#> GSM1296102 3 0.4999 -0.0472 0.492 0.000 0.508 0.000
#> GSM1296122 2 0.1938 0.8583 0.052 0.936 0.012 0.000
#> GSM1296089 1 0.0592 0.9317 0.984 0.000 0.016 0.000
#> GSM1296083 1 0.1474 0.9256 0.948 0.000 0.052 0.000
#> GSM1296116 2 0.0000 0.8903 0.000 1.000 0.000 0.000
#> GSM1296085 1 0.0188 0.9322 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
#> GSM1296094 3 0.1043 0.8574 0.000 0.000 0.960 0.000 0.040
#> GSM1296119 5 0.5788 0.0988 0.000 0.440 0.076 0.004 0.480
#> GSM1296076 5 0.0613 0.8105 0.000 0.004 0.004 0.008 0.984
#> GSM1296092 5 0.1251 0.8086 0.008 0.000 0.036 0.000 0.956
#> GSM1296103 3 0.1671 0.8534 0.000 0.000 0.924 0.000 0.076
#> GSM1296078 5 0.0486 0.8102 0.000 0.004 0.004 0.004 0.988
#> GSM1296107 2 0.0000 0.9097 0.000 1.000 0.000 0.000 0.000
#> GSM1296109 3 0.3167 0.7934 0.000 0.004 0.820 0.004 0.172
#> GSM1296080 3 0.3002 0.7969 0.116 0.000 0.856 0.000 0.028
#> GSM1296090 5 0.0740 0.8096 0.008 0.004 0.008 0.000 0.980
#> GSM1296074 5 0.2112 0.7775 0.000 0.004 0.084 0.004 0.908
#> GSM1296111 2 0.3317 0.7546 0.000 0.804 0.004 0.004 0.188
#> GSM1296099 3 0.3318 0.7783 0.000 0.000 0.800 0.008 0.192
#> GSM1296086 5 0.1399 0.8077 0.020 0.000 0.028 0.000 0.952
#> GSM1296117 2 0.5851 0.3226 0.000 0.572 0.104 0.004 0.320
#> GSM1296113 2 0.0740 0.9073 0.000 0.980 0.008 0.004 0.008
#> GSM1296096 5 0.4644 -0.0685 0.000 0.000 0.460 0.012 0.528
#> GSM1296105 3 0.3033 0.7993 0.084 0.000 0.864 0.052 0.000
#> GSM1296098 3 0.1082 0.8573 0.008 0.000 0.964 0.000 0.028
#> GSM1296101 3 0.3080 0.8360 0.008 0.000 0.872 0.060 0.060
#> GSM1296121 5 0.5036 0.0816 0.000 0.452 0.004 0.024 0.520
#> GSM1296088 5 0.5394 0.5646 0.160 0.000 0.144 0.008 0.688
#> GSM1296082 5 0.0794 0.8111 0.000 0.000 0.028 0.000 0.972
#> GSM1296115 2 0.4387 0.4797 0.000 0.652 0.008 0.004 0.336
#> GSM1296084 1 0.3489 0.7969 0.820 0.000 0.144 0.000 0.036
#> GSM1296072 2 0.2754 0.8436 0.000 0.880 0.000 0.040 0.080
#> GSM1296069 2 0.0510 0.9079 0.000 0.984 0.000 0.000 0.016
#> GSM1296071 2 0.0162 0.9096 0.000 0.996 0.000 0.004 0.000
#> GSM1296070 2 0.1216 0.9016 0.000 0.960 0.000 0.020 0.020
#> GSM1296073 5 0.0727 0.8060 0.000 0.012 0.004 0.004 0.980
#> GSM1296034 3 0.3339 0.7552 0.112 0.000 0.840 0.048 0.000
#> GSM1296041 2 0.2797 0.8376 0.000 0.880 0.060 0.000 0.060
#> GSM1296035 3 0.4862 0.4600 0.000 0.000 0.604 0.032 0.364
#> GSM1296038 4 0.5533 0.3802 0.012 0.000 0.052 0.580 0.356
#> GSM1296047 2 0.0510 0.9070 0.000 0.984 0.000 0.016 0.000
#> GSM1296039 5 0.0955 0.8105 0.000 0.000 0.028 0.004 0.968
#> GSM1296042 2 0.1281 0.8985 0.000 0.956 0.000 0.012 0.032
#> GSM1296043 2 0.0000 0.9097 0.000 1.000 0.000 0.000 0.000
#> GSM1296037 4 0.3495 0.7194 0.160 0.000 0.028 0.812 0.000
#> GSM1296046 2 0.0162 0.9097 0.000 0.996 0.000 0.004 0.000
#> GSM1296044 2 0.0162 0.9096 0.000 0.996 0.000 0.004 0.000
#> GSM1296045 2 0.1281 0.8999 0.000 0.956 0.000 0.032 0.012
#> GSM1296025 1 0.1168 0.9206 0.960 0.000 0.008 0.032 0.000
#> GSM1296033 1 0.1502 0.9164 0.940 0.000 0.004 0.056 0.000
#> GSM1296027 1 0.0566 0.9145 0.984 0.000 0.012 0.004 0.000
#> GSM1296032 1 0.2017 0.9081 0.912 0.000 0.008 0.080 0.000
#> GSM1296024 1 0.0794 0.9130 0.972 0.000 0.028 0.000 0.000
#> GSM1296031 1 0.1956 0.8960 0.916 0.000 0.008 0.076 0.000
#> GSM1296028 1 0.2020 0.9019 0.900 0.000 0.000 0.100 0.000
#> GSM1296029 1 0.0865 0.9120 0.972 0.000 0.024 0.004 0.000
#> GSM1296026 1 0.5322 0.5648 0.660 0.000 0.112 0.000 0.228
#> GSM1296030 1 0.1901 0.8895 0.928 0.000 0.056 0.004 0.012
#> GSM1296040 3 0.1560 0.8534 0.004 0.000 0.948 0.028 0.020
#> GSM1296036 3 0.1012 0.8536 0.020 0.000 0.968 0.000 0.012
#> GSM1296048 5 0.2609 0.7633 0.000 0.048 0.004 0.052 0.896
#> GSM1296059 3 0.2825 0.8322 0.000 0.000 0.860 0.016 0.124
#> GSM1296066 2 0.0162 0.9100 0.000 0.996 0.004 0.000 0.000
#> GSM1296060 3 0.6133 0.4332 0.000 0.000 0.540 0.160 0.300
#> GSM1296063 5 0.3053 0.6826 0.000 0.000 0.008 0.164 0.828
#> GSM1296064 5 0.0992 0.8098 0.000 0.000 0.024 0.008 0.968
#> GSM1296067 2 0.1310 0.8989 0.000 0.956 0.020 0.024 0.000
#> GSM1296062 3 0.1195 0.8451 0.028 0.000 0.960 0.012 0.000
#> GSM1296068 2 0.0162 0.9096 0.000 0.996 0.000 0.004 0.000
#> GSM1296050 1 0.2304 0.8960 0.892 0.000 0.008 0.100 0.000
#> GSM1296057 4 0.2672 0.7563 0.116 0.000 0.008 0.872 0.004
#> GSM1296052 1 0.0451 0.9154 0.988 0.000 0.008 0.004 0.000
#> GSM1296054 1 0.1764 0.9145 0.928 0.000 0.008 0.064 0.000
#> GSM1296049 1 0.0865 0.9195 0.972 0.000 0.004 0.024 0.000
#> GSM1296055 4 0.2818 0.7544 0.128 0.004 0.000 0.860 0.008
#> GSM1296053 1 0.1493 0.9168 0.948 0.000 0.028 0.024 0.000
#> GSM1296058 4 0.3174 0.7434 0.020 0.000 0.004 0.844 0.132
#> GSM1296051 5 0.3644 0.6349 0.180 0.008 0.008 0.004 0.800
#> GSM1296056 5 0.2208 0.7747 0.000 0.000 0.020 0.072 0.908
#> GSM1296065 4 0.4313 0.6680 0.004 0.020 0.008 0.744 0.224
#> GSM1296061 3 0.1281 0.8530 0.032 0.000 0.956 0.000 0.012
#> GSM1296095 4 0.7695 0.3398 0.000 0.072 0.232 0.440 0.256
#> GSM1296120 2 0.4560 0.0451 0.000 0.508 0.008 0.484 0.000
#> GSM1296077 1 0.1908 0.9079 0.908 0.000 0.000 0.092 0.000
#> GSM1296093 1 0.2563 0.8945 0.872 0.000 0.008 0.120 0.000
#> GSM1296104 4 0.2237 0.7640 0.008 0.004 0.000 0.904 0.084
#> GSM1296079 1 0.0510 0.9201 0.984 0.000 0.000 0.016 0.000
#> GSM1296108 2 0.0290 0.9097 0.000 0.992 0.000 0.008 0.000
#> GSM1296110 2 0.0898 0.9059 0.000 0.972 0.008 0.020 0.000
#> GSM1296081 1 0.1041 0.9186 0.964 0.000 0.004 0.032 0.000
#> GSM1296091 1 0.3320 0.8572 0.828 0.000 0.008 0.152 0.012
#> GSM1296075 1 0.4682 0.7363 0.728 0.016 0.012 0.228 0.016
#> GSM1296112 2 0.0290 0.9097 0.000 0.992 0.000 0.008 0.000
#> GSM1296100 4 0.3577 0.7176 0.160 0.000 0.032 0.808 0.000
#> GSM1296087 1 0.0880 0.9142 0.968 0.000 0.000 0.032 0.000
#> GSM1296118 2 0.2753 0.8304 0.008 0.876 0.012 0.104 0.000
#> GSM1296114 2 0.0290 0.9095 0.000 0.992 0.000 0.008 0.000
#> GSM1296097 4 0.2393 0.7657 0.016 0.000 0.004 0.900 0.080
#> GSM1296106 4 0.3463 0.7512 0.040 0.056 0.044 0.860 0.000
#> GSM1296102 4 0.4078 0.7295 0.148 0.000 0.068 0.784 0.000
#> GSM1296122 4 0.5381 0.2067 0.056 0.428 0.000 0.516 0.000
#> GSM1296089 1 0.2017 0.8924 0.912 0.000 0.008 0.080 0.000
#> GSM1296083 1 0.1106 0.9186 0.964 0.000 0.012 0.024 0.000
#> GSM1296116 2 0.0609 0.9069 0.000 0.980 0.000 0.020 0.000
#> GSM1296085 1 0.1792 0.9102 0.916 0.000 0.000 0.084 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1296094 3 0.1141 0.8108 0.000 0.000 0.948 0.052 0.000 0.000
#> GSM1296119 4 0.4584 0.5690 0.000 0.244 0.052 0.688 0.016 0.000
#> GSM1296076 4 0.0547 0.8006 0.000 0.000 0.020 0.980 0.000 0.000
#> GSM1296092 4 0.1672 0.7982 0.012 0.000 0.028 0.940 0.016 0.004
#> GSM1296103 3 0.2003 0.8007 0.000 0.000 0.884 0.116 0.000 0.000
#> GSM1296078 4 0.0508 0.8000 0.000 0.000 0.000 0.984 0.004 0.012
#> GSM1296107 2 0.0000 0.8911 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296109 3 0.3290 0.7012 0.000 0.004 0.744 0.252 0.000 0.000
#> GSM1296080 3 0.3382 0.7338 0.124 0.000 0.820 0.048 0.008 0.000
#> GSM1296090 4 0.1794 0.7912 0.024 0.000 0.000 0.932 0.016 0.028
#> GSM1296074 4 0.1285 0.7882 0.000 0.000 0.052 0.944 0.004 0.000
#> GSM1296111 2 0.3141 0.7223 0.000 0.788 0.000 0.200 0.012 0.000
#> GSM1296099 3 0.3593 0.7373 0.000 0.000 0.748 0.228 0.000 0.024
#> GSM1296086 4 0.2276 0.7852 0.052 0.000 0.020 0.908 0.016 0.004
#> GSM1296117 4 0.5121 0.5012 0.000 0.268 0.092 0.628 0.012 0.000
#> GSM1296113 2 0.1666 0.8757 0.000 0.936 0.008 0.036 0.020 0.000
#> GSM1296096 3 0.4841 0.4070 0.000 0.000 0.536 0.412 0.004 0.048
#> GSM1296105 3 0.3704 0.7438 0.112 0.000 0.820 0.016 0.028 0.024
#> GSM1296098 3 0.1049 0.8104 0.008 0.000 0.960 0.032 0.000 0.000
#> GSM1296101 3 0.4117 0.6660 0.000 0.000 0.740 0.044 0.012 0.204
#> GSM1296121 2 0.4346 0.1481 0.000 0.524 0.004 0.460 0.004 0.008
#> GSM1296088 4 0.6305 0.3021 0.344 0.000 0.144 0.480 0.020 0.012
#> GSM1296082 4 0.0891 0.8004 0.000 0.000 0.024 0.968 0.008 0.000
#> GSM1296115 2 0.3489 0.6039 0.000 0.708 0.000 0.288 0.000 0.004
#> GSM1296084 1 0.5249 0.6315 0.696 0.000 0.180 0.064 0.044 0.016
#> GSM1296072 2 0.4625 0.6705 0.000 0.740 0.000 0.072 0.144 0.044
#> GSM1296069 2 0.1148 0.8855 0.000 0.960 0.000 0.020 0.016 0.004
#> GSM1296071 2 0.0508 0.8909 0.000 0.984 0.004 0.000 0.012 0.000
#> GSM1296070 2 0.1245 0.8816 0.000 0.952 0.000 0.000 0.016 0.032
#> GSM1296073 4 0.0935 0.7935 0.000 0.000 0.004 0.964 0.000 0.032
#> GSM1296034 3 0.3309 0.7423 0.052 0.000 0.844 0.000 0.028 0.076
#> GSM1296041 2 0.3453 0.7502 0.000 0.808 0.040 0.144 0.008 0.000
#> GSM1296035 3 0.4969 0.6109 0.000 0.000 0.616 0.280 0.000 0.104
#> GSM1296038 6 0.3259 0.6958 0.000 0.000 0.048 0.104 0.012 0.836
#> GSM1296047 2 0.3354 0.6949 0.004 0.780 0.000 0.008 0.204 0.004
#> GSM1296039 4 0.1088 0.7973 0.000 0.000 0.016 0.960 0.000 0.024
#> GSM1296042 2 0.0777 0.8877 0.000 0.972 0.000 0.024 0.004 0.000
#> GSM1296043 2 0.0260 0.8914 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1296037 5 0.4731 0.2365 0.060 0.000 0.020 0.000 0.684 0.236
#> GSM1296046 2 0.0405 0.8918 0.000 0.988 0.000 0.004 0.008 0.000
#> GSM1296044 2 0.0603 0.8906 0.000 0.980 0.004 0.000 0.016 0.000
#> GSM1296045 2 0.1989 0.8613 0.000 0.916 0.000 0.004 0.028 0.052
#> GSM1296025 1 0.1493 0.8563 0.936 0.000 0.004 0.000 0.056 0.004
#> GSM1296033 1 0.1738 0.8556 0.928 0.000 0.004 0.016 0.052 0.000
#> GSM1296027 1 0.1151 0.8576 0.956 0.000 0.012 0.000 0.032 0.000
#> GSM1296032 1 0.2838 0.7570 0.808 0.000 0.000 0.000 0.188 0.004
#> GSM1296024 1 0.1138 0.8604 0.960 0.000 0.012 0.000 0.024 0.004
#> GSM1296031 1 0.3883 0.7756 0.804 0.000 0.032 0.000 0.080 0.084
#> GSM1296028 1 0.2520 0.8047 0.844 0.000 0.000 0.000 0.152 0.004
#> GSM1296029 1 0.2173 0.8403 0.904 0.000 0.064 0.000 0.028 0.004
#> GSM1296026 1 0.4959 0.5800 0.688 0.000 0.064 0.208 0.040 0.000
#> GSM1296030 1 0.2662 0.8341 0.888 0.000 0.048 0.016 0.044 0.004
#> GSM1296040 3 0.2044 0.8050 0.008 0.000 0.920 0.028 0.004 0.040
#> GSM1296036 3 0.0964 0.8059 0.016 0.000 0.968 0.012 0.004 0.000
#> GSM1296048 4 0.4370 0.6284 0.000 0.088 0.000 0.740 0.012 0.160
#> GSM1296059 3 0.3066 0.7920 0.000 0.000 0.832 0.124 0.000 0.044
#> GSM1296066 2 0.0551 0.8914 0.000 0.984 0.004 0.004 0.008 0.000
#> GSM1296060 3 0.5497 0.4106 0.000 0.000 0.556 0.140 0.004 0.300
#> GSM1296063 6 0.3584 0.5095 0.000 0.000 0.000 0.308 0.004 0.688
#> GSM1296064 4 0.1138 0.7970 0.000 0.004 0.012 0.960 0.000 0.024
#> GSM1296067 2 0.1871 0.8693 0.000 0.928 0.016 0.000 0.032 0.024
#> GSM1296062 3 0.1406 0.8023 0.016 0.000 0.952 0.008 0.004 0.020
#> GSM1296068 2 0.0291 0.8911 0.000 0.992 0.004 0.000 0.004 0.000
#> GSM1296050 1 0.2494 0.8380 0.864 0.000 0.000 0.000 0.120 0.016
#> GSM1296057 6 0.4661 0.6045 0.060 0.000 0.004 0.016 0.216 0.704
#> GSM1296052 1 0.1225 0.8574 0.952 0.000 0.012 0.000 0.036 0.000
#> GSM1296054 1 0.2664 0.7674 0.816 0.000 0.000 0.000 0.184 0.000
#> GSM1296049 1 0.1075 0.8558 0.952 0.000 0.000 0.000 0.048 0.000
#> GSM1296055 6 0.3509 0.6530 0.056 0.012 0.016 0.004 0.068 0.844
#> GSM1296053 1 0.2247 0.8505 0.904 0.000 0.060 0.000 0.024 0.012
#> GSM1296058 6 0.2583 0.7112 0.000 0.000 0.016 0.044 0.052 0.888
#> GSM1296051 4 0.4284 0.4925 0.304 0.000 0.004 0.664 0.024 0.004
#> GSM1296056 4 0.3999 0.5095 0.000 0.000 0.032 0.696 0.000 0.272
#> GSM1296065 6 0.4357 0.6402 0.004 0.008 0.004 0.232 0.036 0.716
#> GSM1296061 3 0.1293 0.8047 0.020 0.000 0.956 0.016 0.004 0.004
#> GSM1296095 6 0.5424 0.6262 0.000 0.024 0.188 0.088 0.024 0.676
#> GSM1296120 5 0.4311 0.4273 0.008 0.220 0.000 0.000 0.716 0.056
#> GSM1296077 1 0.2170 0.8445 0.888 0.000 0.000 0.000 0.100 0.012
#> GSM1296093 1 0.4205 0.2427 0.564 0.000 0.000 0.000 0.420 0.016
#> GSM1296104 6 0.4537 0.4117 0.000 0.000 0.000 0.036 0.412 0.552
#> GSM1296079 1 0.1471 0.8567 0.932 0.000 0.000 0.000 0.064 0.004
#> GSM1296108 2 0.0291 0.8911 0.000 0.992 0.004 0.000 0.004 0.000
#> GSM1296110 2 0.0665 0.8906 0.000 0.980 0.004 0.000 0.008 0.008
#> GSM1296081 1 0.1340 0.8602 0.948 0.000 0.004 0.000 0.040 0.008
#> GSM1296091 5 0.4450 0.2782 0.364 0.000 0.004 0.016 0.608 0.008
#> GSM1296075 5 0.4512 -0.0424 0.456 0.004 0.000 0.016 0.520 0.004
#> GSM1296112 2 0.0508 0.8914 0.000 0.984 0.004 0.000 0.012 0.000
#> GSM1296100 5 0.4599 0.3022 0.064 0.000 0.024 0.000 0.716 0.196
#> GSM1296087 1 0.2176 0.8487 0.916 0.000 0.024 0.004 0.036 0.020
#> GSM1296118 5 0.4208 0.1717 0.008 0.452 0.000 0.000 0.536 0.004
#> GSM1296114 2 0.0806 0.8895 0.000 0.972 0.008 0.000 0.020 0.000
#> GSM1296097 6 0.2202 0.7083 0.000 0.000 0.012 0.028 0.052 0.908
#> GSM1296106 6 0.5819 0.3228 0.024 0.028 0.044 0.000 0.420 0.484
#> GSM1296102 6 0.4129 0.6069 0.020 0.000 0.200 0.000 0.036 0.744
#> GSM1296122 5 0.5607 0.3926 0.028 0.340 0.000 0.000 0.548 0.084
#> GSM1296089 1 0.3680 0.7860 0.816 0.000 0.024 0.000 0.084 0.076
#> GSM1296083 1 0.1820 0.8604 0.928 0.000 0.016 0.000 0.044 0.012
#> GSM1296116 2 0.0909 0.8865 0.000 0.968 0.000 0.000 0.012 0.020
#> GSM1296085 1 0.2146 0.8319 0.880 0.000 0.000 0.000 0.116 0.004
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# 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 tissue(p) time(p) agent(p) dose(p) individual(p) k
#> SD:NMF 98 0.01332 0.1242 0.70657 1.03e-06 2.80e-03 2
#> SD:NMF 95 0.00125 0.2005 0.02270 2.79e-09 1.41e-05 3
#> SD:NMF 89 0.00139 0.3857 0.00928 1.76e-08 8.72e-09 4
#> SD:NMF 88 0.00307 0.2091 0.09137 2.98e-07 2.41e-08 5
#> SD:NMF 84 0.03292 0.0655 0.08509 7.51e-09 7.60e-08 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 45638 rows and 99 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 0.253 0.672 0.837 0.4534 0.538 0.538
#> 3 3 0.390 0.638 0.819 0.4015 0.790 0.615
#> 4 4 0.591 0.669 0.820 0.1655 0.843 0.578
#> 5 5 0.667 0.653 0.797 0.0566 0.947 0.788
#> 6 6 0.712 0.582 0.776 0.0339 0.996 0.982
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
#> GSM1296094 2 0.8861 0.581 0.304 0.696
#> GSM1296119 2 0.0000 0.763 0.000 1.000
#> GSM1296076 2 0.0000 0.763 0.000 1.000
#> GSM1296092 2 0.0000 0.763 0.000 1.000
#> GSM1296103 2 0.8499 0.617 0.276 0.724
#> GSM1296078 2 0.0000 0.763 0.000 1.000
#> GSM1296107 2 0.0000 0.763 0.000 1.000
#> GSM1296109 2 0.0000 0.763 0.000 1.000
#> GSM1296080 2 1.0000 0.144 0.500 0.500
#> GSM1296090 2 0.0000 0.763 0.000 1.000
#> GSM1296074 2 0.0000 0.763 0.000 1.000
#> GSM1296111 2 0.0000 0.763 0.000 1.000
#> GSM1296099 2 0.8955 0.573 0.312 0.688
#> GSM1296086 2 0.7219 0.700 0.200 0.800
#> GSM1296117 2 0.0000 0.763 0.000 1.000
#> GSM1296113 2 0.0000 0.763 0.000 1.000
#> GSM1296096 2 0.8813 0.586 0.300 0.700
#> GSM1296105 2 0.9775 0.426 0.412 0.588
#> GSM1296098 2 0.9993 0.207 0.484 0.516
#> GSM1296101 2 0.6623 0.704 0.172 0.828
#> GSM1296121 2 0.0000 0.763 0.000 1.000
#> GSM1296088 2 0.9580 0.435 0.380 0.620
#> GSM1296082 2 0.0000 0.763 0.000 1.000
#> GSM1296115 2 0.0000 0.763 0.000 1.000
#> GSM1296084 1 0.7815 0.655 0.768 0.232
#> GSM1296072 2 0.8386 0.677 0.268 0.732
#> GSM1296069 2 0.6048 0.741 0.148 0.852
#> GSM1296071 2 0.7602 0.707 0.220 0.780
#> GSM1296070 2 0.6048 0.741 0.148 0.852
#> GSM1296073 2 0.0000 0.763 0.000 1.000
#> GSM1296034 1 0.4431 0.800 0.908 0.092
#> GSM1296041 2 0.0000 0.763 0.000 1.000
#> GSM1296035 2 0.8813 0.586 0.300 0.700
#> GSM1296038 2 0.0000 0.763 0.000 1.000
#> GSM1296047 2 0.8386 0.677 0.268 0.732
#> GSM1296039 2 0.0000 0.763 0.000 1.000
#> GSM1296042 2 0.0000 0.763 0.000 1.000
#> GSM1296043 2 0.6048 0.741 0.148 0.852
#> GSM1296037 1 0.2603 0.824 0.956 0.044
#> GSM1296046 2 0.7602 0.707 0.220 0.780
#> GSM1296044 2 0.8016 0.691 0.244 0.756
#> GSM1296045 2 0.6048 0.741 0.148 0.852
#> GSM1296025 1 0.0000 0.834 1.000 0.000
#> GSM1296033 1 0.7602 0.676 0.780 0.220
#> GSM1296027 1 0.0000 0.834 1.000 0.000
#> GSM1296032 1 0.0000 0.834 1.000 0.000
#> GSM1296024 1 0.0000 0.834 1.000 0.000
#> GSM1296031 1 0.7815 0.666 0.768 0.232
#> GSM1296028 1 0.0000 0.834 1.000 0.000
#> GSM1296029 1 0.5946 0.761 0.856 0.144
#> GSM1296026 2 0.9795 0.366 0.416 0.584
#> GSM1296030 1 0.9795 0.141 0.584 0.416
#> GSM1296040 2 0.9635 0.494 0.388 0.612
#> GSM1296036 2 0.9993 0.207 0.484 0.516
#> GSM1296048 2 0.0000 0.763 0.000 1.000
#> GSM1296059 2 0.8909 0.574 0.308 0.692
#> GSM1296066 2 0.0000 0.763 0.000 1.000
#> GSM1296060 2 0.8813 0.586 0.300 0.700
#> GSM1296063 2 0.0000 0.763 0.000 1.000
#> GSM1296064 2 0.0000 0.763 0.000 1.000
#> GSM1296067 2 0.8909 0.627 0.308 0.692
#> GSM1296062 1 0.9850 0.148 0.572 0.428
#> GSM1296068 2 0.8016 0.691 0.244 0.756
#> GSM1296050 1 0.0000 0.834 1.000 0.000
#> GSM1296057 1 0.7815 0.661 0.768 0.232
#> GSM1296052 1 0.0000 0.834 1.000 0.000
#> GSM1296054 1 0.0000 0.834 1.000 0.000
#> GSM1296049 1 0.0000 0.834 1.000 0.000
#> GSM1296055 1 0.7815 0.666 0.768 0.232
#> GSM1296053 1 0.0000 0.834 1.000 0.000
#> GSM1296058 1 0.8813 0.554 0.700 0.300
#> GSM1296051 2 0.9661 0.405 0.392 0.608
#> GSM1296056 2 0.0376 0.762 0.004 0.996
#> GSM1296065 2 0.9754 0.436 0.408 0.592
#> GSM1296061 2 0.9993 0.207 0.484 0.516
#> GSM1296095 2 0.8207 0.685 0.256 0.744
#> GSM1296120 2 0.8386 0.677 0.268 0.732
#> GSM1296077 1 0.0000 0.834 1.000 0.000
#> GSM1296093 1 0.0000 0.834 1.000 0.000
#> GSM1296104 2 0.9087 0.606 0.324 0.676
#> GSM1296079 1 0.0000 0.834 1.000 0.000
#> GSM1296108 2 0.8016 0.691 0.244 0.756
#> GSM1296110 2 0.8909 0.627 0.308 0.692
#> GSM1296081 1 0.0000 0.834 1.000 0.000
#> GSM1296091 1 0.6712 0.739 0.824 0.176
#> GSM1296075 1 0.9635 0.320 0.612 0.388
#> GSM1296112 2 0.8016 0.691 0.244 0.756
#> GSM1296100 1 0.2603 0.824 0.956 0.044
#> GSM1296087 1 0.0672 0.833 0.992 0.008
#> GSM1296118 2 0.8207 0.681 0.256 0.744
#> GSM1296114 2 0.8016 0.691 0.244 0.756
#> GSM1296097 1 0.8909 0.540 0.692 0.308
#> GSM1296106 2 0.9754 0.436 0.408 0.592
#> GSM1296102 1 0.4022 0.809 0.920 0.080
#> GSM1296122 1 0.9087 0.478 0.676 0.324
#> GSM1296089 1 0.7815 0.666 0.768 0.232
#> GSM1296083 1 0.0000 0.834 1.000 0.000
#> GSM1296116 2 0.7528 0.712 0.216 0.784
#> GSM1296085 1 0.0000 0.834 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1296094 3 0.7128 0.5430 0.284 0.052 0.664
#> GSM1296119 3 0.3816 0.7028 0.000 0.148 0.852
#> GSM1296076 3 0.0424 0.7301 0.000 0.008 0.992
#> GSM1296092 3 0.0424 0.7301 0.000 0.008 0.992
#> GSM1296103 3 0.6894 0.5749 0.256 0.052 0.692
#> GSM1296078 3 0.0424 0.7301 0.000 0.008 0.992
#> GSM1296107 3 0.3879 0.7007 0.000 0.152 0.848
#> GSM1296109 3 0.3482 0.7161 0.000 0.128 0.872
#> GSM1296080 1 0.7996 -0.0981 0.476 0.060 0.464
#> GSM1296090 3 0.0424 0.7301 0.000 0.008 0.992
#> GSM1296074 3 0.0424 0.7301 0.000 0.008 0.992
#> GSM1296111 3 0.4121 0.6892 0.000 0.168 0.832
#> GSM1296099 3 0.7424 0.5289 0.288 0.064 0.648
#> GSM1296086 3 0.5574 0.6410 0.184 0.032 0.784
#> GSM1296117 3 0.3879 0.7007 0.000 0.152 0.848
#> GSM1296113 3 0.3879 0.7007 0.000 0.152 0.848
#> GSM1296096 3 0.7277 0.5439 0.280 0.060 0.660
#> GSM1296105 2 0.9484 0.1889 0.328 0.472 0.200
#> GSM1296098 3 0.8068 0.1190 0.456 0.064 0.480
#> GSM1296101 3 0.6719 0.6555 0.160 0.096 0.744
#> GSM1296121 3 0.3752 0.7047 0.000 0.144 0.856
#> GSM1296088 3 0.7328 0.3930 0.364 0.040 0.596
#> GSM1296082 3 0.0424 0.7301 0.000 0.008 0.992
#> GSM1296115 3 0.3879 0.7007 0.000 0.152 0.848
#> GSM1296084 1 0.6843 0.6880 0.740 0.116 0.144
#> GSM1296072 2 0.2804 0.8178 0.060 0.924 0.016
#> GSM1296069 2 0.2796 0.7775 0.000 0.908 0.092
#> GSM1296071 2 0.1491 0.8272 0.016 0.968 0.016
#> GSM1296070 2 0.3267 0.7558 0.000 0.884 0.116
#> GSM1296073 3 0.3752 0.7047 0.000 0.144 0.856
#> GSM1296034 1 0.3896 0.7784 0.888 0.060 0.052
#> GSM1296041 3 0.3879 0.7007 0.000 0.152 0.848
#> GSM1296035 3 0.7277 0.5439 0.280 0.060 0.660
#> GSM1296038 3 0.3340 0.6970 0.000 0.120 0.880
#> GSM1296047 2 0.2804 0.8178 0.060 0.924 0.016
#> GSM1296039 3 0.0424 0.7301 0.000 0.008 0.992
#> GSM1296042 3 0.4121 0.6877 0.000 0.168 0.832
#> GSM1296043 2 0.2796 0.7775 0.000 0.908 0.092
#> GSM1296037 1 0.2165 0.7934 0.936 0.064 0.000
#> GSM1296046 2 0.1491 0.8272 0.016 0.968 0.016
#> GSM1296044 2 0.1031 0.8277 0.024 0.976 0.000
#> GSM1296045 2 0.2448 0.7888 0.000 0.924 0.076
#> GSM1296025 1 0.0000 0.8115 1.000 0.000 0.000
#> GSM1296033 1 0.6911 0.6782 0.728 0.180 0.092
#> GSM1296027 1 0.0000 0.8115 1.000 0.000 0.000
#> GSM1296032 1 0.0000 0.8115 1.000 0.000 0.000
#> GSM1296024 1 0.0000 0.8115 1.000 0.000 0.000
#> GSM1296031 1 0.6104 0.5005 0.648 0.348 0.004
#> GSM1296028 1 0.0000 0.8115 1.000 0.000 0.000
#> GSM1296029 1 0.5229 0.7508 0.828 0.104 0.068
#> GSM1296026 3 0.7809 0.2870 0.396 0.056 0.548
#> GSM1296030 1 0.7438 0.1858 0.568 0.040 0.392
#> GSM1296040 3 0.9981 0.0263 0.316 0.320 0.364
#> GSM1296036 3 0.8068 0.1190 0.456 0.064 0.480
#> GSM1296048 3 0.3752 0.7047 0.000 0.144 0.856
#> GSM1296059 3 0.7159 0.5368 0.288 0.052 0.660
#> GSM1296066 3 0.3879 0.7007 0.000 0.152 0.848
#> GSM1296060 3 0.7277 0.5439 0.280 0.060 0.660
#> GSM1296063 3 0.3340 0.6970 0.000 0.120 0.880
#> GSM1296064 3 0.0424 0.7301 0.000 0.008 0.992
#> GSM1296067 2 0.3267 0.7764 0.116 0.884 0.000
#> GSM1296062 1 0.8034 0.1567 0.540 0.068 0.392
#> GSM1296068 2 0.1031 0.8277 0.024 0.976 0.000
#> GSM1296050 1 0.0000 0.8115 1.000 0.000 0.000
#> GSM1296057 1 0.7059 0.6677 0.716 0.192 0.092
#> GSM1296052 1 0.0000 0.8115 1.000 0.000 0.000
#> GSM1296054 1 0.0000 0.8115 1.000 0.000 0.000
#> GSM1296049 1 0.0000 0.8115 1.000 0.000 0.000
#> GSM1296055 1 0.6104 0.5005 0.648 0.348 0.004
#> GSM1296053 1 0.0000 0.8115 1.000 0.000 0.000
#> GSM1296058 1 0.8271 0.5820 0.632 0.212 0.156
#> GSM1296051 3 0.7726 0.3461 0.372 0.056 0.572
#> GSM1296056 3 0.0000 0.7282 0.000 0.000 1.000
#> GSM1296065 2 0.9470 0.2015 0.324 0.476 0.200
#> GSM1296061 3 0.8068 0.1190 0.456 0.064 0.480
#> GSM1296095 2 0.9162 0.2907 0.160 0.500 0.340
#> GSM1296120 2 0.2804 0.8178 0.060 0.924 0.016
#> GSM1296077 1 0.0000 0.8115 1.000 0.000 0.000
#> GSM1296093 1 0.0000 0.8115 1.000 0.000 0.000
#> GSM1296104 2 0.8069 0.4848 0.244 0.636 0.120
#> GSM1296079 1 0.0000 0.8115 1.000 0.000 0.000
#> GSM1296108 2 0.1031 0.8277 0.024 0.976 0.000
#> GSM1296110 2 0.3267 0.7764 0.116 0.884 0.000
#> GSM1296081 1 0.0000 0.8115 1.000 0.000 0.000
#> GSM1296091 1 0.5852 0.6982 0.776 0.180 0.044
#> GSM1296075 1 0.7841 0.2795 0.536 0.408 0.056
#> GSM1296112 2 0.1031 0.8277 0.024 0.976 0.000
#> GSM1296100 1 0.2165 0.7934 0.936 0.064 0.000
#> GSM1296087 1 0.0829 0.8083 0.984 0.012 0.004
#> GSM1296118 2 0.1643 0.8237 0.044 0.956 0.000
#> GSM1296114 2 0.1031 0.8277 0.024 0.976 0.000
#> GSM1296097 1 0.8361 0.5723 0.624 0.216 0.160
#> GSM1296106 2 0.9470 0.2015 0.324 0.476 0.200
#> GSM1296102 1 0.4465 0.7298 0.820 0.176 0.004
#> GSM1296122 1 0.6521 0.0831 0.504 0.492 0.004
#> GSM1296089 1 0.6081 0.5052 0.652 0.344 0.004
#> GSM1296083 1 0.0000 0.8115 1.000 0.000 0.000
#> GSM1296116 2 0.1015 0.8239 0.008 0.980 0.012
#> GSM1296085 1 0.0000 0.8115 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1296094 3 0.3801 0.7020 0.000 0.000 0.780 0.220
#> GSM1296119 4 0.2011 0.8822 0.000 0.080 0.000 0.920
#> GSM1296076 4 0.2149 0.8530 0.000 0.000 0.088 0.912
#> GSM1296092 4 0.2149 0.8530 0.000 0.000 0.088 0.912
#> GSM1296103 3 0.4193 0.6603 0.000 0.000 0.732 0.268
#> GSM1296078 4 0.2149 0.8530 0.000 0.000 0.088 0.912
#> GSM1296107 4 0.2081 0.8808 0.000 0.084 0.000 0.916
#> GSM1296109 4 0.4829 0.7581 0.000 0.068 0.156 0.776
#> GSM1296080 3 0.1871 0.6975 0.016 0.012 0.948 0.024
#> GSM1296090 4 0.2149 0.8530 0.000 0.000 0.088 0.912
#> GSM1296074 4 0.2149 0.8530 0.000 0.000 0.088 0.912
#> GSM1296111 4 0.2345 0.8714 0.000 0.100 0.000 0.900
#> GSM1296099 3 0.4137 0.7093 0.000 0.012 0.780 0.208
#> GSM1296086 3 0.4977 0.2043 0.000 0.000 0.540 0.460
#> GSM1296117 4 0.2081 0.8808 0.000 0.084 0.000 0.916
#> GSM1296113 4 0.2081 0.8808 0.000 0.084 0.000 0.916
#> GSM1296096 3 0.4228 0.6971 0.000 0.008 0.760 0.232
#> GSM1296105 2 0.8193 0.1946 0.096 0.440 0.396 0.068
#> GSM1296098 3 0.2010 0.7077 0.008 0.012 0.940 0.040
#> GSM1296101 3 0.6213 0.2280 0.000 0.052 0.484 0.464
#> GSM1296121 4 0.1940 0.8828 0.000 0.076 0.000 0.924
#> GSM1296088 3 0.3725 0.7090 0.008 0.000 0.812 0.180
#> GSM1296082 4 0.2149 0.8530 0.000 0.000 0.088 0.912
#> GSM1296115 4 0.2081 0.8808 0.000 0.084 0.000 0.916
#> GSM1296084 3 0.6790 -0.0376 0.408 0.084 0.504 0.004
#> GSM1296072 2 0.1854 0.7741 0.012 0.940 0.048 0.000
#> GSM1296069 2 0.3123 0.7119 0.000 0.844 0.000 0.156
#> GSM1296071 2 0.0817 0.7806 0.000 0.976 0.000 0.024
#> GSM1296070 2 0.3400 0.6936 0.000 0.820 0.000 0.180
#> GSM1296073 4 0.1940 0.8828 0.000 0.076 0.000 0.924
#> GSM1296034 1 0.5856 0.3575 0.556 0.036 0.408 0.000
#> GSM1296041 4 0.2081 0.8808 0.000 0.084 0.000 0.916
#> GSM1296035 3 0.4228 0.6971 0.000 0.008 0.760 0.232
#> GSM1296038 4 0.4549 0.7677 0.000 0.096 0.100 0.804
#> GSM1296047 2 0.1854 0.7741 0.012 0.940 0.048 0.000
#> GSM1296039 4 0.2149 0.8530 0.000 0.000 0.088 0.912
#> GSM1296042 4 0.2345 0.8696 0.000 0.100 0.000 0.900
#> GSM1296043 2 0.3123 0.7119 0.000 0.844 0.000 0.156
#> GSM1296037 1 0.4356 0.7057 0.804 0.048 0.148 0.000
#> GSM1296046 2 0.0817 0.7806 0.000 0.976 0.000 0.024
#> GSM1296044 2 0.0000 0.7825 0.000 1.000 0.000 0.000
#> GSM1296045 2 0.2760 0.7313 0.000 0.872 0.000 0.128
#> GSM1296025 1 0.0336 0.8135 0.992 0.000 0.008 0.000
#> GSM1296033 1 0.7383 0.1620 0.440 0.140 0.416 0.004
#> GSM1296027 1 0.0469 0.8128 0.988 0.000 0.012 0.000
#> GSM1296032 1 0.0188 0.8122 0.996 0.000 0.004 0.000
#> GSM1296024 1 0.0336 0.8135 0.992 0.000 0.008 0.000
#> GSM1296031 1 0.7239 0.3100 0.500 0.344 0.156 0.000
#> GSM1296028 1 0.0336 0.8127 0.992 0.000 0.008 0.000
#> GSM1296029 1 0.6319 0.4700 0.604 0.084 0.312 0.000
#> GSM1296026 3 0.5086 0.7135 0.060 0.012 0.776 0.152
#> GSM1296030 3 0.5312 0.5412 0.236 0.000 0.712 0.052
#> GSM1296040 3 0.6829 0.3754 0.008 0.280 0.600 0.112
#> GSM1296036 3 0.2010 0.7077 0.008 0.012 0.940 0.040
#> GSM1296048 4 0.1940 0.8828 0.000 0.076 0.000 0.924
#> GSM1296059 3 0.3764 0.7044 0.000 0.000 0.784 0.216
#> GSM1296066 4 0.2081 0.8808 0.000 0.084 0.000 0.916
#> GSM1296060 3 0.4228 0.6971 0.000 0.008 0.760 0.232
#> GSM1296063 4 0.4426 0.7773 0.000 0.096 0.092 0.812
#> GSM1296064 4 0.2149 0.8530 0.000 0.000 0.088 0.912
#> GSM1296067 2 0.2676 0.7507 0.012 0.896 0.092 0.000
#> GSM1296062 3 0.4481 0.6427 0.124 0.024 0.820 0.032
#> GSM1296068 2 0.0000 0.7825 0.000 1.000 0.000 0.000
#> GSM1296050 1 0.0336 0.8135 0.992 0.000 0.008 0.000
#> GSM1296057 1 0.7477 0.1421 0.428 0.152 0.416 0.004
#> GSM1296052 1 0.0469 0.8128 0.988 0.000 0.012 0.000
#> GSM1296054 1 0.0188 0.8122 0.996 0.000 0.004 0.000
#> GSM1296049 1 0.0336 0.8135 0.992 0.000 0.008 0.000
#> GSM1296055 1 0.7239 0.3100 0.500 0.344 0.156 0.000
#> GSM1296053 1 0.0336 0.8127 0.992 0.000 0.008 0.000
#> GSM1296058 3 0.7284 0.1925 0.276 0.172 0.548 0.004
#> GSM1296051 3 0.5123 0.7042 0.048 0.012 0.764 0.176
#> GSM1296056 4 0.2589 0.8255 0.000 0.000 0.116 0.884
#> GSM1296065 2 0.8190 0.2042 0.096 0.444 0.392 0.068
#> GSM1296061 3 0.2010 0.7077 0.008 0.012 0.940 0.040
#> GSM1296095 2 0.7683 0.2277 0.000 0.452 0.304 0.244
#> GSM1296120 2 0.1854 0.7741 0.012 0.940 0.048 0.000
#> GSM1296077 1 0.0336 0.8135 0.992 0.000 0.008 0.000
#> GSM1296093 1 0.0000 0.8118 1.000 0.000 0.000 0.000
#> GSM1296104 2 0.5990 0.4421 0.008 0.604 0.352 0.036
#> GSM1296079 1 0.0336 0.8135 0.992 0.000 0.008 0.000
#> GSM1296108 2 0.0000 0.7825 0.000 1.000 0.000 0.000
#> GSM1296110 2 0.2676 0.7507 0.012 0.896 0.092 0.000
#> GSM1296081 1 0.0188 0.8132 0.996 0.000 0.004 0.000
#> GSM1296091 1 0.6401 0.5626 0.652 0.172 0.176 0.000
#> GSM1296075 2 0.7768 0.0187 0.360 0.400 0.240 0.000
#> GSM1296112 2 0.0469 0.7826 0.000 0.988 0.000 0.012
#> GSM1296100 1 0.4356 0.7057 0.804 0.048 0.148 0.000
#> GSM1296087 1 0.1302 0.7981 0.956 0.000 0.044 0.000
#> GSM1296118 2 0.0895 0.7818 0.020 0.976 0.004 0.000
#> GSM1296114 2 0.0000 0.7825 0.000 1.000 0.000 0.000
#> GSM1296097 3 0.7428 0.1999 0.272 0.176 0.544 0.008
#> GSM1296106 2 0.8190 0.2042 0.096 0.444 0.392 0.068
#> GSM1296102 1 0.6685 0.5472 0.616 0.160 0.224 0.000
#> GSM1296122 2 0.7175 0.1231 0.360 0.496 0.144 0.000
#> GSM1296089 1 0.7133 0.3236 0.512 0.344 0.144 0.000
#> GSM1296083 1 0.0188 0.8132 0.996 0.000 0.004 0.000
#> GSM1296116 2 0.1211 0.7748 0.000 0.960 0.000 0.040
#> GSM1296085 1 0.0000 0.8118 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
#> GSM1296094 3 0.2806 0.71608 0.000 0.000 0.844 0.004 0.152
#> GSM1296119 5 0.1270 0.85485 0.000 0.052 0.000 0.000 0.948
#> GSM1296076 5 0.3579 0.81723 0.000 0.000 0.100 0.072 0.828
#> GSM1296092 5 0.3579 0.81723 0.000 0.000 0.100 0.072 0.828
#> GSM1296103 3 0.3462 0.69597 0.000 0.000 0.792 0.012 0.196
#> GSM1296078 5 0.3579 0.81723 0.000 0.000 0.100 0.072 0.828
#> GSM1296107 5 0.1341 0.85309 0.000 0.056 0.000 0.000 0.944
#> GSM1296109 5 0.3922 0.70124 0.000 0.040 0.180 0.000 0.780
#> GSM1296080 3 0.2911 0.61229 0.004 0.000 0.852 0.136 0.008
#> GSM1296090 5 0.3579 0.81723 0.000 0.000 0.100 0.072 0.828
#> GSM1296074 5 0.3579 0.81723 0.000 0.000 0.100 0.072 0.828
#> GSM1296111 5 0.1608 0.84339 0.000 0.072 0.000 0.000 0.928
#> GSM1296099 3 0.3379 0.71558 0.000 0.008 0.828 0.016 0.148
#> GSM1296086 3 0.6081 0.24192 0.000 0.000 0.476 0.124 0.400
#> GSM1296117 5 0.1341 0.85309 0.000 0.056 0.000 0.000 0.944
#> GSM1296113 5 0.1341 0.85309 0.000 0.056 0.000 0.000 0.944
#> GSM1296096 3 0.3516 0.71207 0.000 0.004 0.812 0.020 0.164
#> GSM1296105 4 0.8103 0.56409 0.076 0.244 0.160 0.484 0.036
#> GSM1296098 3 0.2189 0.63802 0.000 0.000 0.904 0.084 0.012
#> GSM1296101 3 0.6484 0.30081 0.000 0.012 0.484 0.136 0.368
#> GSM1296121 5 0.1197 0.85480 0.000 0.048 0.000 0.000 0.952
#> GSM1296088 3 0.5144 0.62628 0.000 0.000 0.692 0.176 0.132
#> GSM1296082 5 0.3579 0.81723 0.000 0.000 0.100 0.072 0.828
#> GSM1296115 5 0.1341 0.85309 0.000 0.056 0.000 0.000 0.944
#> GSM1296084 4 0.7158 0.25513 0.344 0.008 0.280 0.364 0.004
#> GSM1296072 2 0.2046 0.80252 0.000 0.916 0.016 0.068 0.000
#> GSM1296069 2 0.2966 0.73489 0.000 0.816 0.000 0.000 0.184
#> GSM1296071 2 0.0794 0.84167 0.000 0.972 0.000 0.000 0.028
#> GSM1296070 2 0.3177 0.70915 0.000 0.792 0.000 0.000 0.208
#> GSM1296073 5 0.1197 0.85480 0.000 0.048 0.000 0.000 0.952
#> GSM1296034 1 0.6615 0.11756 0.504 0.012 0.312 0.172 0.000
#> GSM1296041 5 0.1341 0.85309 0.000 0.056 0.000 0.000 0.944
#> GSM1296035 3 0.3516 0.71207 0.000 0.004 0.812 0.020 0.164
#> GSM1296038 5 0.5568 0.72965 0.000 0.060 0.096 0.128 0.716
#> GSM1296047 2 0.2046 0.80252 0.000 0.916 0.016 0.068 0.000
#> GSM1296039 5 0.3639 0.81706 0.000 0.000 0.100 0.076 0.824
#> GSM1296042 5 0.1608 0.84321 0.000 0.072 0.000 0.000 0.928
#> GSM1296043 2 0.2966 0.73489 0.000 0.816 0.000 0.000 0.184
#> GSM1296037 1 0.3628 0.61856 0.772 0.012 0.000 0.216 0.000
#> GSM1296046 2 0.0794 0.84167 0.000 0.972 0.000 0.000 0.028
#> GSM1296044 2 0.0000 0.84167 0.000 1.000 0.000 0.000 0.000
#> GSM1296045 2 0.2648 0.76397 0.000 0.848 0.000 0.000 0.152
#> GSM1296025 1 0.0162 0.79779 0.996 0.000 0.000 0.004 0.000
#> GSM1296033 4 0.6845 0.34540 0.372 0.036 0.124 0.468 0.000
#> GSM1296027 1 0.0609 0.79437 0.980 0.000 0.000 0.020 0.000
#> GSM1296032 1 0.0609 0.79539 0.980 0.000 0.000 0.020 0.000
#> GSM1296024 1 0.0162 0.79779 0.996 0.000 0.000 0.004 0.000
#> GSM1296031 1 0.6781 0.00442 0.448 0.208 0.008 0.336 0.000
#> GSM1296028 1 0.0703 0.79440 0.976 0.000 0.000 0.024 0.000
#> GSM1296029 1 0.6137 0.09099 0.540 0.008 0.116 0.336 0.000
#> GSM1296026 3 0.6151 0.51742 0.020 0.000 0.580 0.296 0.104
#> GSM1296030 3 0.6501 0.33200 0.208 0.000 0.572 0.200 0.020
#> GSM1296040 3 0.7546 0.07033 0.004 0.148 0.476 0.296 0.076
#> GSM1296036 3 0.2189 0.63802 0.000 0.000 0.904 0.084 0.012
#> GSM1296048 5 0.1197 0.85480 0.000 0.048 0.000 0.000 0.952
#> GSM1296059 3 0.2763 0.71606 0.000 0.000 0.848 0.004 0.148
#> GSM1296066 5 0.1341 0.85309 0.000 0.056 0.000 0.000 0.944
#> GSM1296060 3 0.3516 0.71207 0.000 0.004 0.812 0.020 0.164
#> GSM1296063 5 0.5508 0.73664 0.000 0.060 0.088 0.132 0.720
#> GSM1296064 5 0.3639 0.81706 0.000 0.000 0.100 0.076 0.824
#> GSM1296067 2 0.3388 0.67340 0.000 0.792 0.008 0.200 0.000
#> GSM1296062 3 0.4701 0.50677 0.108 0.004 0.764 0.116 0.008
#> GSM1296068 2 0.0000 0.84167 0.000 1.000 0.000 0.000 0.000
#> GSM1296050 1 0.0162 0.79779 0.996 0.000 0.000 0.004 0.000
#> GSM1296057 4 0.6479 0.36008 0.360 0.036 0.088 0.516 0.000
#> GSM1296052 1 0.0609 0.79437 0.980 0.000 0.000 0.020 0.000
#> GSM1296054 1 0.0609 0.79539 0.980 0.000 0.000 0.020 0.000
#> GSM1296049 1 0.0162 0.79779 0.996 0.000 0.000 0.004 0.000
#> GSM1296055 1 0.6781 0.00442 0.448 0.208 0.008 0.336 0.000
#> GSM1296053 1 0.0703 0.79440 0.976 0.000 0.000 0.024 0.000
#> GSM1296058 4 0.6219 0.54324 0.208 0.024 0.152 0.616 0.000
#> GSM1296051 3 0.6056 0.52477 0.008 0.000 0.576 0.292 0.124
#> GSM1296056 5 0.3992 0.78841 0.000 0.000 0.124 0.080 0.796
#> GSM1296065 4 0.8075 0.56482 0.076 0.244 0.156 0.488 0.036
#> GSM1296061 3 0.2189 0.63802 0.000 0.000 0.904 0.084 0.012
#> GSM1296095 4 0.8200 0.32764 0.000 0.280 0.136 0.380 0.204
#> GSM1296120 2 0.2046 0.80252 0.000 0.916 0.016 0.068 0.000
#> GSM1296077 1 0.0162 0.79779 0.996 0.000 0.000 0.004 0.000
#> GSM1296093 1 0.0162 0.79675 0.996 0.000 0.000 0.004 0.000
#> GSM1296104 4 0.5976 0.32502 0.000 0.368 0.092 0.532 0.008
#> GSM1296079 1 0.0162 0.79779 0.996 0.000 0.000 0.004 0.000
#> GSM1296108 2 0.0000 0.84167 0.000 1.000 0.000 0.000 0.000
#> GSM1296110 2 0.3388 0.67340 0.000 0.792 0.008 0.200 0.000
#> GSM1296081 1 0.0000 0.79706 1.000 0.000 0.000 0.000 0.000
#> GSM1296091 1 0.6084 0.24755 0.608 0.068 0.044 0.280 0.000
#> GSM1296075 4 0.7712 0.38382 0.312 0.280 0.052 0.356 0.000
#> GSM1296112 2 0.0510 0.84297 0.000 0.984 0.000 0.000 0.016
#> GSM1296100 1 0.3628 0.61856 0.772 0.012 0.000 0.216 0.000
#> GSM1296087 1 0.1608 0.76165 0.928 0.000 0.000 0.072 0.000
#> GSM1296118 2 0.0912 0.83565 0.012 0.972 0.000 0.016 0.000
#> GSM1296114 2 0.0000 0.84167 0.000 1.000 0.000 0.000 0.000
#> GSM1296097 4 0.6447 0.54798 0.208 0.028 0.152 0.608 0.004
#> GSM1296106 4 0.8075 0.56482 0.076 0.244 0.156 0.488 0.036
#> GSM1296102 1 0.5882 0.32901 0.572 0.084 0.012 0.332 0.000
#> GSM1296122 2 0.6941 -0.28425 0.304 0.368 0.004 0.324 0.000
#> GSM1296089 1 0.6758 0.03326 0.460 0.208 0.008 0.324 0.000
#> GSM1296083 1 0.0000 0.79706 1.000 0.000 0.000 0.000 0.000
#> GSM1296116 2 0.1121 0.83492 0.000 0.956 0.000 0.000 0.044
#> GSM1296085 1 0.0162 0.79675 0.996 0.000 0.000 0.004 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1296094 3 0.2135 0.685537 0.000 0.000 0.872 0.000 0.128 0.000
#> GSM1296119 5 0.0363 0.830670 0.000 0.012 0.000 0.000 0.988 0.000
#> GSM1296076 5 0.4120 0.788399 0.000 0.000 0.096 0.160 0.744 0.000
#> GSM1296092 5 0.4120 0.788399 0.000 0.000 0.096 0.160 0.744 0.000
#> GSM1296103 3 0.3073 0.667478 0.000 0.000 0.816 0.016 0.164 0.004
#> GSM1296078 5 0.4120 0.788399 0.000 0.000 0.096 0.160 0.744 0.000
#> GSM1296107 5 0.0748 0.827723 0.000 0.016 0.000 0.004 0.976 0.004
#> GSM1296109 5 0.2980 0.667521 0.000 0.008 0.192 0.000 0.800 0.000
#> GSM1296080 3 0.4121 0.554396 0.004 0.000 0.736 0.200 0.000 0.060
#> GSM1296090 5 0.4120 0.788399 0.000 0.000 0.096 0.160 0.744 0.000
#> GSM1296074 5 0.4120 0.788399 0.000 0.000 0.096 0.160 0.744 0.000
#> GSM1296111 5 0.1080 0.819133 0.000 0.032 0.000 0.004 0.960 0.004
#> GSM1296099 3 0.2755 0.685046 0.000 0.000 0.856 0.012 0.120 0.012
#> GSM1296086 3 0.6733 0.230086 0.000 0.000 0.444 0.136 0.336 0.084
#> GSM1296117 5 0.0748 0.827723 0.000 0.016 0.000 0.004 0.976 0.004
#> GSM1296113 5 0.0748 0.827723 0.000 0.016 0.000 0.004 0.976 0.004
#> GSM1296096 3 0.3002 0.681741 0.000 0.000 0.836 0.020 0.136 0.008
#> GSM1296105 6 0.7067 0.450179 0.032 0.092 0.092 0.172 0.028 0.584
#> GSM1296098 3 0.2398 0.601568 0.000 0.000 0.876 0.104 0.000 0.020
#> GSM1296101 3 0.6395 0.286736 0.000 0.000 0.492 0.136 0.316 0.056
#> GSM1296121 5 0.0260 0.830551 0.000 0.008 0.000 0.000 0.992 0.000
#> GSM1296088 3 0.6263 0.521939 0.000 0.000 0.580 0.196 0.092 0.132
#> GSM1296082 5 0.4120 0.788399 0.000 0.000 0.096 0.160 0.744 0.000
#> GSM1296115 5 0.0748 0.827723 0.000 0.016 0.000 0.004 0.976 0.004
#> GSM1296084 6 0.7400 0.219882 0.284 0.000 0.152 0.188 0.000 0.376
#> GSM1296072 2 0.2002 0.785029 0.000 0.908 0.004 0.012 0.000 0.076
#> GSM1296069 2 0.3043 0.707193 0.000 0.796 0.000 0.004 0.196 0.004
#> GSM1296071 2 0.0713 0.841307 0.000 0.972 0.000 0.000 0.028 0.000
#> GSM1296070 2 0.3221 0.677795 0.000 0.772 0.000 0.004 0.220 0.004
#> GSM1296073 5 0.0260 0.830551 0.000 0.008 0.000 0.000 0.992 0.000
#> GSM1296034 1 0.7045 -0.000874 0.424 0.004 0.268 0.236 0.000 0.068
#> GSM1296041 5 0.0748 0.827723 0.000 0.016 0.000 0.004 0.976 0.004
#> GSM1296035 3 0.3002 0.681741 0.000 0.000 0.836 0.020 0.136 0.008
#> GSM1296038 5 0.6002 0.697445 0.000 0.032 0.092 0.180 0.644 0.052
#> GSM1296047 2 0.2002 0.785029 0.000 0.908 0.004 0.012 0.000 0.076
#> GSM1296039 5 0.4259 0.786611 0.000 0.000 0.096 0.160 0.740 0.004
#> GSM1296042 5 0.1080 0.818387 0.000 0.032 0.000 0.004 0.960 0.004
#> GSM1296043 2 0.3043 0.707193 0.000 0.796 0.000 0.004 0.196 0.004
#> GSM1296037 1 0.4633 0.440242 0.676 0.000 0.000 0.100 0.000 0.224
#> GSM1296046 2 0.0713 0.841307 0.000 0.972 0.000 0.000 0.028 0.000
#> GSM1296044 2 0.0000 0.839366 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296045 2 0.2527 0.743260 0.000 0.832 0.000 0.000 0.168 0.000
#> GSM1296025 1 0.0146 0.744689 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1296033 6 0.5525 0.312456 0.304 0.008 0.024 0.072 0.000 0.592
#> GSM1296027 1 0.0891 0.737662 0.968 0.000 0.000 0.008 0.000 0.024
#> GSM1296032 1 0.0692 0.741460 0.976 0.000 0.000 0.004 0.000 0.020
#> GSM1296024 1 0.0146 0.744689 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1296031 1 0.7399 -0.669149 0.340 0.160 0.000 0.332 0.000 0.168
#> GSM1296028 1 0.0858 0.738866 0.968 0.000 0.000 0.004 0.000 0.028
#> GSM1296029 1 0.5900 -0.021265 0.480 0.000 0.020 0.124 0.000 0.376
#> GSM1296026 3 0.6951 0.370675 0.012 0.000 0.452 0.232 0.048 0.256
#> GSM1296030 3 0.7247 0.245195 0.176 0.000 0.464 0.180 0.004 0.176
#> GSM1296040 3 0.7137 0.003907 0.000 0.048 0.420 0.096 0.064 0.372
#> GSM1296036 3 0.2398 0.601568 0.000 0.000 0.876 0.104 0.000 0.020
#> GSM1296048 5 0.0260 0.830551 0.000 0.008 0.000 0.000 0.992 0.000
#> GSM1296059 3 0.2092 0.685545 0.000 0.000 0.876 0.000 0.124 0.000
#> GSM1296066 5 0.0748 0.827723 0.000 0.016 0.000 0.004 0.976 0.004
#> GSM1296060 3 0.3002 0.681741 0.000 0.000 0.836 0.020 0.136 0.008
#> GSM1296063 5 0.5941 0.704506 0.000 0.032 0.084 0.184 0.648 0.052
#> GSM1296064 5 0.4259 0.786611 0.000 0.000 0.096 0.160 0.740 0.004
#> GSM1296067 2 0.3720 0.496853 0.000 0.736 0.000 0.236 0.000 0.028
#> GSM1296062 3 0.4543 0.518591 0.076 0.000 0.756 0.112 0.000 0.056
#> GSM1296068 2 0.0000 0.839366 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296050 1 0.0146 0.744689 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1296057 6 0.4723 0.329678 0.292 0.004 0.016 0.036 0.000 0.652
#> GSM1296052 1 0.0891 0.737662 0.968 0.000 0.000 0.008 0.000 0.024
#> GSM1296054 1 0.0692 0.741460 0.976 0.000 0.000 0.004 0.000 0.020
#> GSM1296049 1 0.0146 0.744689 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1296055 1 0.7399 -0.669149 0.340 0.160 0.000 0.332 0.000 0.168
#> GSM1296053 1 0.0858 0.738866 0.968 0.000 0.000 0.004 0.000 0.028
#> GSM1296058 6 0.4121 0.429257 0.156 0.000 0.028 0.048 0.000 0.768
#> GSM1296051 3 0.6858 0.377992 0.000 0.000 0.448 0.232 0.068 0.252
#> GSM1296056 5 0.4617 0.767158 0.000 0.000 0.104 0.168 0.716 0.012
#> GSM1296065 6 0.7052 0.450858 0.032 0.092 0.088 0.176 0.028 0.584
#> GSM1296061 3 0.2398 0.601568 0.000 0.000 0.876 0.104 0.000 0.020
#> GSM1296095 6 0.8097 0.307933 0.000 0.112 0.080 0.200 0.192 0.416
#> GSM1296120 2 0.2002 0.785029 0.000 0.908 0.004 0.012 0.000 0.076
#> GSM1296077 1 0.0146 0.744689 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1296093 1 0.0146 0.743663 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1296104 6 0.5240 0.356530 0.000 0.132 0.020 0.192 0.000 0.656
#> GSM1296079 1 0.0146 0.744689 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1296108 2 0.0000 0.839366 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296110 2 0.3645 0.502979 0.000 0.740 0.000 0.236 0.000 0.024
#> GSM1296081 1 0.0000 0.744150 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296091 1 0.4904 0.216790 0.588 0.012 0.000 0.048 0.000 0.352
#> GSM1296075 6 0.6851 -0.224340 0.272 0.188 0.000 0.080 0.000 0.460
#> GSM1296112 2 0.0458 0.841676 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM1296100 1 0.4633 0.440242 0.676 0.000 0.000 0.100 0.000 0.224
#> GSM1296087 1 0.2250 0.688966 0.896 0.000 0.000 0.040 0.000 0.064
#> GSM1296118 2 0.1370 0.816585 0.012 0.948 0.000 0.036 0.000 0.004
#> GSM1296114 2 0.0000 0.839366 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296097 6 0.4322 0.433490 0.156 0.000 0.028 0.052 0.004 0.760
#> GSM1296106 6 0.7052 0.450858 0.032 0.092 0.088 0.176 0.028 0.584
#> GSM1296102 1 0.6841 -0.045292 0.460 0.040 0.012 0.224 0.000 0.264
#> GSM1296122 4 0.7550 0.000000 0.240 0.300 0.000 0.308 0.000 0.152
#> GSM1296089 1 0.7361 -0.646701 0.360 0.160 0.000 0.320 0.000 0.160
#> GSM1296083 1 0.0000 0.744150 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296116 2 0.1007 0.835318 0.000 0.956 0.000 0.000 0.044 0.000
#> GSM1296085 1 0.0146 0.743663 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)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# 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 tissue(p) time(p) agent(p) dose(p) individual(p) k
#> CV:hclust 84 5.02e-02 0.1230 0.1476 5.78e-08 4.69e-04 2
#> CV:hclust 82 2.42e-04 0.0268 0.0489 3.65e-11 1.59e-04 3
#> CV:hclust 79 4.77e-05 0.1667 0.0127 4.91e-09 2.64e-05 4
#> CV:hclust 81 4.91e-04 0.1810 0.0627 1.43e-08 2.81e-08 5
#> CV:hclust 71 2.76e-04 0.1167 0.1713 1.80e-09 6.96e-06 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 45638 rows and 99 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 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 0.744 0.918 0.958 0.4883 0.510 0.510
#> 3 3 0.882 0.942 0.970 0.3704 0.734 0.518
#> 4 4 0.709 0.602 0.771 0.1066 0.912 0.753
#> 5 5 0.701 0.614 0.735 0.0706 0.870 0.590
#> 6 6 0.727 0.711 0.793 0.0423 0.925 0.669
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
#> GSM1296094 2 0.5946 0.875 0.144 0.856
#> GSM1296119 2 0.0000 0.946 0.000 1.000
#> GSM1296076 2 0.0938 0.944 0.012 0.988
#> GSM1296092 2 0.5946 0.875 0.144 0.856
#> GSM1296103 2 0.5946 0.875 0.144 0.856
#> GSM1296078 2 0.0376 0.946 0.004 0.996
#> GSM1296107 2 0.0000 0.946 0.000 1.000
#> GSM1296109 2 0.0000 0.946 0.000 1.000
#> GSM1296080 1 0.0000 0.967 1.000 0.000
#> GSM1296090 2 0.1843 0.941 0.028 0.972
#> GSM1296074 2 0.1633 0.942 0.024 0.976
#> GSM1296111 2 0.0000 0.946 0.000 1.000
#> GSM1296099 2 0.3879 0.918 0.076 0.924
#> GSM1296086 2 0.5946 0.875 0.144 0.856
#> GSM1296117 2 0.0000 0.946 0.000 1.000
#> GSM1296113 2 0.0000 0.946 0.000 1.000
#> GSM1296096 2 0.1843 0.941 0.028 0.972
#> GSM1296105 1 0.0000 0.967 1.000 0.000
#> GSM1296098 2 0.5946 0.875 0.144 0.856
#> GSM1296101 2 0.5946 0.875 0.144 0.856
#> GSM1296121 2 0.0000 0.946 0.000 1.000
#> GSM1296088 2 0.6048 0.871 0.148 0.852
#> GSM1296082 2 0.1633 0.942 0.024 0.976
#> GSM1296115 2 0.0000 0.946 0.000 1.000
#> GSM1296084 1 0.0000 0.967 1.000 0.000
#> GSM1296072 2 0.0000 0.946 0.000 1.000
#> GSM1296069 2 0.0000 0.946 0.000 1.000
#> GSM1296071 2 0.0000 0.946 0.000 1.000
#> GSM1296070 2 0.0000 0.946 0.000 1.000
#> GSM1296073 2 0.0000 0.946 0.000 1.000
#> GSM1296034 1 0.0000 0.967 1.000 0.000
#> GSM1296041 2 0.0000 0.946 0.000 1.000
#> GSM1296035 2 0.5946 0.875 0.144 0.856
#> GSM1296038 2 0.1633 0.942 0.024 0.976
#> GSM1296047 2 0.4431 0.887 0.092 0.908
#> GSM1296039 2 0.1633 0.942 0.024 0.976
#> GSM1296042 2 0.0000 0.946 0.000 1.000
#> GSM1296043 2 0.0000 0.946 0.000 1.000
#> GSM1296037 1 0.0000 0.967 1.000 0.000
#> GSM1296046 2 0.0000 0.946 0.000 1.000
#> GSM1296044 2 0.0000 0.946 0.000 1.000
#> GSM1296045 2 0.0000 0.946 0.000 1.000
#> GSM1296025 1 0.0000 0.967 1.000 0.000
#> GSM1296033 1 0.0000 0.967 1.000 0.000
#> GSM1296027 1 0.0000 0.967 1.000 0.000
#> GSM1296032 1 0.0000 0.967 1.000 0.000
#> GSM1296024 1 0.0000 0.967 1.000 0.000
#> GSM1296031 1 0.0000 0.967 1.000 0.000
#> GSM1296028 1 0.0000 0.967 1.000 0.000
#> GSM1296029 1 0.0000 0.967 1.000 0.000
#> GSM1296026 1 0.9170 0.459 0.668 0.332
#> GSM1296030 1 0.0000 0.967 1.000 0.000
#> GSM1296040 2 0.5946 0.875 0.144 0.856
#> GSM1296036 2 0.5946 0.875 0.144 0.856
#> GSM1296048 2 0.0000 0.946 0.000 1.000
#> GSM1296059 2 0.5946 0.875 0.144 0.856
#> GSM1296066 2 0.0000 0.946 0.000 1.000
#> GSM1296060 2 0.5946 0.875 0.144 0.856
#> GSM1296063 2 0.0000 0.946 0.000 1.000
#> GSM1296064 2 0.0000 0.946 0.000 1.000
#> GSM1296067 2 0.3274 0.914 0.060 0.940
#> GSM1296062 1 0.0000 0.967 1.000 0.000
#> GSM1296068 2 0.2236 0.930 0.036 0.964
#> GSM1296050 1 0.0000 0.967 1.000 0.000
#> GSM1296057 1 0.0000 0.967 1.000 0.000
#> GSM1296052 1 0.0000 0.967 1.000 0.000
#> GSM1296054 1 0.0000 0.967 1.000 0.000
#> GSM1296049 1 0.0000 0.967 1.000 0.000
#> GSM1296055 1 0.0000 0.967 1.000 0.000
#> GSM1296053 1 0.0000 0.967 1.000 0.000
#> GSM1296058 1 0.0000 0.967 1.000 0.000
#> GSM1296051 2 0.5946 0.875 0.144 0.856
#> GSM1296056 2 0.5946 0.875 0.144 0.856
#> GSM1296065 2 0.1633 0.942 0.024 0.976
#> GSM1296061 1 1.0000 -0.116 0.500 0.500
#> GSM1296095 2 0.0376 0.946 0.004 0.996
#> GSM1296120 2 0.6438 0.803 0.164 0.836
#> GSM1296077 1 0.0000 0.967 1.000 0.000
#> GSM1296093 1 0.0000 0.967 1.000 0.000
#> GSM1296104 2 0.7299 0.809 0.204 0.796
#> GSM1296079 1 0.0000 0.967 1.000 0.000
#> GSM1296108 2 0.0000 0.946 0.000 1.000
#> GSM1296110 2 0.0000 0.946 0.000 1.000
#> GSM1296081 1 0.0000 0.967 1.000 0.000
#> GSM1296091 1 0.0000 0.967 1.000 0.000
#> GSM1296075 1 0.0000 0.967 1.000 0.000
#> GSM1296112 2 0.2236 0.930 0.036 0.964
#> GSM1296100 1 0.0000 0.967 1.000 0.000
#> GSM1296087 1 0.0000 0.967 1.000 0.000
#> GSM1296118 1 0.9608 0.406 0.616 0.384
#> GSM1296114 2 0.0000 0.946 0.000 1.000
#> GSM1296097 1 0.1843 0.939 0.972 0.028
#> GSM1296106 1 0.0000 0.967 1.000 0.000
#> GSM1296102 1 0.0000 0.967 1.000 0.000
#> GSM1296122 1 0.0000 0.967 1.000 0.000
#> GSM1296089 1 0.0000 0.967 1.000 0.000
#> GSM1296083 1 0.0000 0.967 1.000 0.000
#> GSM1296116 2 0.0000 0.946 0.000 1.000
#> GSM1296085 1 0.0000 0.967 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1296094 3 0.0000 0.951 0.000 0.000 1.000
#> GSM1296119 2 0.3116 0.907 0.000 0.892 0.108
#> GSM1296076 3 0.0237 0.951 0.000 0.004 0.996
#> GSM1296092 3 0.0237 0.951 0.000 0.004 0.996
#> GSM1296103 3 0.0000 0.951 0.000 0.000 1.000
#> GSM1296078 3 0.0237 0.951 0.000 0.004 0.996
#> GSM1296107 2 0.0424 0.972 0.000 0.992 0.008
#> GSM1296109 3 0.0892 0.943 0.000 0.020 0.980
#> GSM1296080 3 0.4654 0.746 0.208 0.000 0.792
#> GSM1296090 3 0.0237 0.951 0.000 0.004 0.996
#> GSM1296074 3 0.0237 0.951 0.000 0.004 0.996
#> GSM1296111 2 0.1031 0.965 0.000 0.976 0.024
#> GSM1296099 3 0.0000 0.951 0.000 0.000 1.000
#> GSM1296086 3 0.0424 0.950 0.000 0.008 0.992
#> GSM1296117 2 0.3116 0.907 0.000 0.892 0.108
#> GSM1296113 2 0.0424 0.972 0.000 0.992 0.008
#> GSM1296096 3 0.0000 0.951 0.000 0.000 1.000
#> GSM1296105 1 0.3752 0.834 0.856 0.000 0.144
#> GSM1296098 3 0.0000 0.951 0.000 0.000 1.000
#> GSM1296101 3 0.0237 0.950 0.000 0.004 0.996
#> GSM1296121 2 0.3116 0.904 0.000 0.892 0.108
#> GSM1296088 3 0.0475 0.949 0.004 0.004 0.992
#> GSM1296082 3 0.0237 0.951 0.000 0.004 0.996
#> GSM1296115 2 0.3038 0.907 0.000 0.896 0.104
#> GSM1296084 3 0.5178 0.675 0.256 0.000 0.744
#> GSM1296072 2 0.0592 0.972 0.000 0.988 0.012
#> GSM1296069 2 0.0424 0.972 0.000 0.992 0.008
#> GSM1296071 2 0.0592 0.972 0.000 0.988 0.012
#> GSM1296070 2 0.0237 0.971 0.000 0.996 0.004
#> GSM1296073 3 0.4796 0.700 0.000 0.220 0.780
#> GSM1296034 1 0.0000 0.985 1.000 0.000 0.000
#> GSM1296041 2 0.3116 0.907 0.000 0.892 0.108
#> GSM1296035 3 0.0000 0.951 0.000 0.000 1.000
#> GSM1296038 3 0.0237 0.950 0.000 0.004 0.996
#> GSM1296047 2 0.0592 0.972 0.000 0.988 0.012
#> GSM1296039 3 0.0237 0.951 0.000 0.004 0.996
#> GSM1296042 2 0.0237 0.971 0.000 0.996 0.004
#> GSM1296043 2 0.0424 0.972 0.000 0.992 0.008
#> GSM1296037 1 0.0000 0.985 1.000 0.000 0.000
#> GSM1296046 2 0.0424 0.972 0.000 0.992 0.008
#> GSM1296044 2 0.0592 0.972 0.000 0.988 0.012
#> GSM1296045 2 0.0237 0.971 0.000 0.996 0.004
#> GSM1296025 1 0.0237 0.984 0.996 0.004 0.000
#> GSM1296033 1 0.0000 0.985 1.000 0.000 0.000
#> GSM1296027 1 0.0000 0.985 1.000 0.000 0.000
#> GSM1296032 1 0.0000 0.985 1.000 0.000 0.000
#> GSM1296024 1 0.0237 0.984 0.996 0.004 0.000
#> GSM1296031 1 0.0000 0.985 1.000 0.000 0.000
#> GSM1296028 1 0.0000 0.985 1.000 0.000 0.000
#> GSM1296029 1 0.0000 0.985 1.000 0.000 0.000
#> GSM1296026 3 0.0237 0.950 0.004 0.000 0.996
#> GSM1296030 3 0.6008 0.440 0.372 0.000 0.628
#> GSM1296040 3 0.0000 0.951 0.000 0.000 1.000
#> GSM1296036 3 0.0000 0.951 0.000 0.000 1.000
#> GSM1296048 2 0.3116 0.904 0.000 0.892 0.108
#> GSM1296059 3 0.0000 0.951 0.000 0.000 1.000
#> GSM1296066 2 0.0424 0.972 0.000 0.992 0.008
#> GSM1296060 3 0.0000 0.951 0.000 0.000 1.000
#> GSM1296063 3 0.1031 0.940 0.000 0.024 0.976
#> GSM1296064 3 0.0237 0.951 0.000 0.004 0.996
#> GSM1296067 2 0.0424 0.971 0.000 0.992 0.008
#> GSM1296062 3 0.5016 0.698 0.240 0.000 0.760
#> GSM1296068 2 0.0592 0.972 0.000 0.988 0.012
#> GSM1296050 1 0.0237 0.984 0.996 0.004 0.000
#> GSM1296057 1 0.0237 0.983 0.996 0.000 0.004
#> GSM1296052 1 0.0000 0.985 1.000 0.000 0.000
#> GSM1296054 1 0.0000 0.985 1.000 0.000 0.000
#> GSM1296049 1 0.0237 0.984 0.996 0.004 0.000
#> GSM1296055 1 0.0237 0.983 0.996 0.000 0.004
#> GSM1296053 1 0.0000 0.985 1.000 0.000 0.000
#> GSM1296058 1 0.3340 0.864 0.880 0.000 0.120
#> GSM1296051 3 0.0237 0.951 0.000 0.004 0.996
#> GSM1296056 3 0.0424 0.950 0.000 0.008 0.992
#> GSM1296065 3 0.0000 0.951 0.000 0.000 1.000
#> GSM1296061 3 0.0237 0.950 0.004 0.000 0.996
#> GSM1296095 3 0.2711 0.875 0.000 0.088 0.912
#> GSM1296120 2 0.0661 0.970 0.004 0.988 0.008
#> GSM1296077 1 0.0237 0.984 0.996 0.004 0.000
#> GSM1296093 1 0.0000 0.985 1.000 0.000 0.000
#> GSM1296104 3 0.2537 0.888 0.080 0.000 0.920
#> GSM1296079 1 0.0237 0.984 0.996 0.004 0.000
#> GSM1296108 2 0.0592 0.972 0.000 0.988 0.012
#> GSM1296110 2 0.0424 0.971 0.000 0.992 0.008
#> GSM1296081 1 0.0000 0.985 1.000 0.000 0.000
#> GSM1296091 1 0.0000 0.985 1.000 0.000 0.000
#> GSM1296075 1 0.0475 0.982 0.992 0.004 0.004
#> GSM1296112 2 0.0592 0.972 0.000 0.988 0.012
#> GSM1296100 1 0.0000 0.985 1.000 0.000 0.000
#> GSM1296087 1 0.0000 0.985 1.000 0.000 0.000
#> GSM1296118 2 0.2682 0.905 0.076 0.920 0.004
#> GSM1296114 2 0.0592 0.972 0.000 0.988 0.012
#> GSM1296097 1 0.4121 0.801 0.832 0.000 0.168
#> GSM1296106 1 0.0475 0.982 0.992 0.004 0.004
#> GSM1296102 1 0.0237 0.983 0.996 0.000 0.004
#> GSM1296122 1 0.0475 0.982 0.992 0.004 0.004
#> GSM1296089 1 0.0000 0.985 1.000 0.000 0.000
#> GSM1296083 1 0.0000 0.985 1.000 0.000 0.000
#> GSM1296116 2 0.0424 0.971 0.000 0.992 0.008
#> GSM1296085 1 0.0000 0.985 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1296094 3 0.1637 0.7929 0.000 0.000 0.940 0.060
#> GSM1296119 2 0.2830 0.5121 0.000 0.900 0.040 0.060
#> GSM1296076 3 0.5670 0.7234 0.000 0.152 0.720 0.128
#> GSM1296092 3 0.4940 0.7526 0.000 0.096 0.776 0.128
#> GSM1296103 3 0.1302 0.7957 0.000 0.000 0.956 0.044
#> GSM1296078 3 0.5670 0.7234 0.000 0.152 0.720 0.128
#> GSM1296107 2 0.1302 0.5867 0.000 0.956 0.000 0.044
#> GSM1296109 3 0.5570 0.3685 0.000 0.440 0.540 0.020
#> GSM1296080 3 0.2909 0.7803 0.020 0.000 0.888 0.092
#> GSM1296090 3 0.5531 0.7309 0.000 0.140 0.732 0.128
#> GSM1296074 3 0.5670 0.7234 0.000 0.152 0.720 0.128
#> GSM1296111 2 0.0188 0.5742 0.000 0.996 0.004 0.000
#> GSM1296099 3 0.0000 0.7984 0.000 0.000 1.000 0.000
#> GSM1296086 3 0.4940 0.7526 0.000 0.096 0.776 0.128
#> GSM1296117 2 0.0804 0.5684 0.000 0.980 0.012 0.008
#> GSM1296113 2 0.1637 0.5890 0.000 0.940 0.000 0.060
#> GSM1296096 3 0.0000 0.7984 0.000 0.000 1.000 0.000
#> GSM1296105 3 0.6599 0.2701 0.080 0.000 0.488 0.432
#> GSM1296098 3 0.2011 0.7876 0.000 0.000 0.920 0.080
#> GSM1296101 3 0.1557 0.7938 0.000 0.000 0.944 0.056
#> GSM1296121 2 0.3667 0.4755 0.000 0.856 0.056 0.088
#> GSM1296088 3 0.1940 0.7967 0.000 0.000 0.924 0.076
#> GSM1296082 3 0.5670 0.7234 0.000 0.152 0.720 0.128
#> GSM1296115 2 0.0804 0.5684 0.000 0.980 0.012 0.008
#> GSM1296084 3 0.3048 0.7790 0.016 0.000 0.876 0.108
#> GSM1296072 2 0.4981 0.4284 0.000 0.536 0.000 0.464
#> GSM1296069 2 0.3528 0.5637 0.000 0.808 0.000 0.192
#> GSM1296071 2 0.4948 0.4718 0.000 0.560 0.000 0.440
#> GSM1296070 2 0.2530 0.5824 0.000 0.888 0.000 0.112
#> GSM1296073 2 0.6819 -0.0958 0.000 0.540 0.348 0.112
#> GSM1296034 1 0.4535 0.7051 0.744 0.000 0.016 0.240
#> GSM1296041 2 0.0804 0.5684 0.000 0.980 0.012 0.008
#> GSM1296035 3 0.0000 0.7984 0.000 0.000 1.000 0.000
#> GSM1296038 3 0.2089 0.7914 0.000 0.020 0.932 0.048
#> GSM1296047 4 0.4955 -0.3241 0.000 0.444 0.000 0.556
#> GSM1296039 3 0.5462 0.7274 0.000 0.152 0.736 0.112
#> GSM1296042 2 0.1637 0.5890 0.000 0.940 0.000 0.060
#> GSM1296043 2 0.4855 0.4914 0.000 0.600 0.000 0.400
#> GSM1296037 1 0.4585 0.6263 0.668 0.000 0.000 0.332
#> GSM1296046 2 0.4948 0.4718 0.000 0.560 0.000 0.440
#> GSM1296044 2 0.4948 0.4718 0.000 0.560 0.000 0.440
#> GSM1296045 2 0.4679 0.5110 0.000 0.648 0.000 0.352
#> GSM1296025 1 0.0000 0.8733 1.000 0.000 0.000 0.000
#> GSM1296033 1 0.2973 0.8018 0.856 0.000 0.000 0.144
#> GSM1296027 1 0.0000 0.8733 1.000 0.000 0.000 0.000
#> GSM1296032 1 0.0000 0.8733 1.000 0.000 0.000 0.000
#> GSM1296024 1 0.0000 0.8733 1.000 0.000 0.000 0.000
#> GSM1296031 1 0.2589 0.8191 0.884 0.000 0.000 0.116
#> GSM1296028 1 0.0000 0.8733 1.000 0.000 0.000 0.000
#> GSM1296029 1 0.0000 0.8733 1.000 0.000 0.000 0.000
#> GSM1296026 3 0.2281 0.7930 0.000 0.000 0.904 0.096
#> GSM1296030 3 0.5325 0.6626 0.160 0.000 0.744 0.096
#> GSM1296040 3 0.2081 0.7861 0.000 0.000 0.916 0.084
#> GSM1296036 3 0.2011 0.7876 0.000 0.000 0.920 0.080
#> GSM1296048 2 0.4401 0.4342 0.000 0.812 0.076 0.112
#> GSM1296059 3 0.0592 0.7985 0.000 0.000 0.984 0.016
#> GSM1296066 2 0.1940 0.5880 0.000 0.924 0.000 0.076
#> GSM1296060 3 0.0469 0.7986 0.000 0.000 0.988 0.012
#> GSM1296063 2 0.6980 -0.2329 0.000 0.484 0.400 0.116
#> GSM1296064 3 0.5462 0.7274 0.000 0.152 0.736 0.112
#> GSM1296067 2 0.4981 0.4238 0.000 0.536 0.000 0.464
#> GSM1296062 3 0.4679 0.5297 0.000 0.000 0.648 0.352
#> GSM1296068 2 0.4948 0.4718 0.000 0.560 0.000 0.440
#> GSM1296050 1 0.0000 0.8733 1.000 0.000 0.000 0.000
#> GSM1296057 1 0.5440 0.5298 0.596 0.000 0.020 0.384
#> GSM1296052 1 0.0000 0.8733 1.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.8733 1.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.8733 1.000 0.000 0.000 0.000
#> GSM1296055 1 0.5476 0.5087 0.584 0.000 0.020 0.396
#> GSM1296053 1 0.0000 0.8733 1.000 0.000 0.000 0.000
#> GSM1296058 4 0.7787 -0.1154 0.244 0.000 0.372 0.384
#> GSM1296051 3 0.4688 0.7590 0.000 0.080 0.792 0.128
#> GSM1296056 3 0.4753 0.7577 0.000 0.084 0.788 0.128
#> GSM1296065 3 0.5517 0.4393 0.000 0.020 0.568 0.412
#> GSM1296061 3 0.2011 0.7876 0.000 0.000 0.920 0.080
#> GSM1296095 3 0.4542 0.7288 0.000 0.088 0.804 0.108
#> GSM1296120 4 0.4933 -0.3022 0.000 0.432 0.000 0.568
#> GSM1296077 1 0.0000 0.8733 1.000 0.000 0.000 0.000
#> GSM1296093 1 0.0000 0.8733 1.000 0.000 0.000 0.000
#> GSM1296104 3 0.5657 0.3837 0.024 0.000 0.540 0.436
#> GSM1296079 1 0.0000 0.8733 1.000 0.000 0.000 0.000
#> GSM1296108 2 0.4948 0.4718 0.000 0.560 0.000 0.440
#> GSM1296110 2 0.4948 0.4718 0.000 0.560 0.000 0.440
#> GSM1296081 1 0.0000 0.8733 1.000 0.000 0.000 0.000
#> GSM1296091 1 0.3837 0.7331 0.776 0.000 0.000 0.224
#> GSM1296075 1 0.4661 0.6017 0.652 0.000 0.000 0.348
#> GSM1296112 2 0.4948 0.4718 0.000 0.560 0.000 0.440
#> GSM1296100 1 0.4585 0.6263 0.668 0.000 0.000 0.332
#> GSM1296087 1 0.0000 0.8733 1.000 0.000 0.000 0.000
#> GSM1296118 4 0.4989 -0.3848 0.000 0.472 0.000 0.528
#> GSM1296114 2 0.4948 0.4718 0.000 0.560 0.000 0.440
#> GSM1296097 3 0.7372 0.0554 0.160 0.000 0.420 0.420
#> GSM1296106 4 0.5417 -0.2086 0.412 0.000 0.016 0.572
#> GSM1296102 1 0.5339 0.5361 0.600 0.000 0.016 0.384
#> GSM1296122 4 0.4790 -0.0777 0.380 0.000 0.000 0.620
#> GSM1296089 1 0.2589 0.8191 0.884 0.000 0.000 0.116
#> GSM1296083 1 0.0000 0.8733 1.000 0.000 0.000 0.000
#> GSM1296116 2 0.4948 0.4718 0.000 0.560 0.000 0.440
#> GSM1296085 1 0.0000 0.8733 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
#> GSM1296094 3 0.6512 0.6482 0.000 0.348 0.452 0.200 0.000
#> GSM1296119 5 0.3231 0.6252 0.000 0.004 0.196 0.000 0.800
#> GSM1296076 3 0.1965 0.5447 0.000 0.000 0.904 0.000 0.096
#> GSM1296092 3 0.1836 0.5894 0.000 0.000 0.932 0.032 0.036
#> GSM1296103 3 0.6410 0.6638 0.000 0.320 0.488 0.192 0.000
#> GSM1296078 3 0.1965 0.5447 0.000 0.000 0.904 0.000 0.096
#> GSM1296107 5 0.0000 0.6255 0.000 0.000 0.000 0.000 1.000
#> GSM1296109 5 0.6348 0.2761 0.000 0.204 0.208 0.012 0.576
#> GSM1296080 3 0.7310 0.5996 0.032 0.344 0.404 0.220 0.000
#> GSM1296090 3 0.2362 0.5539 0.000 0.000 0.900 0.024 0.076
#> GSM1296074 3 0.1965 0.5447 0.000 0.000 0.904 0.000 0.096
#> GSM1296111 5 0.1043 0.6505 0.000 0.000 0.040 0.000 0.960
#> GSM1296099 3 0.6130 0.6759 0.000 0.264 0.556 0.180 0.000
#> GSM1296086 3 0.1836 0.5894 0.000 0.000 0.932 0.032 0.036
#> GSM1296117 5 0.1410 0.6577 0.000 0.000 0.060 0.000 0.940
#> GSM1296113 5 0.0000 0.6255 0.000 0.000 0.000 0.000 1.000
#> GSM1296096 3 0.6073 0.6762 0.000 0.264 0.564 0.172 0.000
#> GSM1296105 4 0.4322 0.5025 0.032 0.084 0.080 0.804 0.000
#> GSM1296098 3 0.6582 0.6410 0.000 0.344 0.440 0.216 0.000
#> GSM1296101 3 0.6507 0.6569 0.000 0.316 0.472 0.212 0.000
#> GSM1296121 5 0.3551 0.6148 0.000 0.008 0.220 0.000 0.772
#> GSM1296088 3 0.6166 0.6572 0.000 0.272 0.548 0.180 0.000
#> GSM1296082 3 0.1965 0.5447 0.000 0.000 0.904 0.000 0.096
#> GSM1296115 5 0.1478 0.6576 0.000 0.000 0.064 0.000 0.936
#> GSM1296084 3 0.7524 0.5749 0.044 0.296 0.408 0.252 0.000
#> GSM1296072 2 0.6021 0.7605 0.000 0.552 0.000 0.144 0.304
#> GSM1296069 5 0.3550 0.0355 0.000 0.236 0.000 0.004 0.760
#> GSM1296071 2 0.4249 0.8788 0.000 0.568 0.000 0.000 0.432
#> GSM1296070 5 0.1638 0.5221 0.000 0.064 0.000 0.004 0.932
#> GSM1296073 5 0.4505 0.4623 0.000 0.008 0.368 0.004 0.620
#> GSM1296034 1 0.5792 0.3816 0.612 0.164 0.000 0.224 0.000
#> GSM1296041 5 0.1410 0.6577 0.000 0.000 0.060 0.000 0.940
#> GSM1296035 3 0.6073 0.6762 0.000 0.264 0.564 0.172 0.000
#> GSM1296038 3 0.6590 0.5937 0.000 0.176 0.516 0.296 0.012
#> GSM1296047 2 0.6202 0.6676 0.000 0.552 0.000 0.228 0.220
#> GSM1296039 3 0.3594 0.5585 0.000 0.032 0.844 0.028 0.096
#> GSM1296042 5 0.0000 0.6255 0.000 0.000 0.000 0.000 1.000
#> GSM1296043 5 0.4448 -0.7441 0.000 0.480 0.000 0.004 0.516
#> GSM1296037 4 0.4827 0.1856 0.476 0.020 0.000 0.504 0.000
#> GSM1296046 2 0.4397 0.8770 0.000 0.564 0.000 0.004 0.432
#> GSM1296044 2 0.4397 0.8798 0.000 0.564 0.000 0.004 0.432
#> GSM1296045 5 0.4443 -0.7281 0.000 0.472 0.000 0.004 0.524
#> GSM1296025 1 0.1485 0.8685 0.948 0.032 0.000 0.020 0.000
#> GSM1296033 1 0.5274 0.3079 0.596 0.036 0.012 0.356 0.000
#> GSM1296027 1 0.1082 0.8688 0.964 0.028 0.000 0.008 0.000
#> GSM1296032 1 0.0290 0.8732 0.992 0.008 0.000 0.000 0.000
#> GSM1296024 1 0.1485 0.8685 0.948 0.032 0.000 0.020 0.000
#> GSM1296031 1 0.4141 0.5683 0.728 0.024 0.000 0.248 0.000
#> GSM1296028 1 0.0992 0.8678 0.968 0.024 0.000 0.008 0.000
#> GSM1296029 1 0.1364 0.8651 0.952 0.036 0.000 0.012 0.000
#> GSM1296026 3 0.5880 0.6531 0.000 0.228 0.600 0.172 0.000
#> GSM1296030 3 0.8235 0.4771 0.144 0.248 0.388 0.220 0.000
#> GSM1296040 3 0.6674 0.6340 0.000 0.324 0.428 0.248 0.000
#> GSM1296036 3 0.6582 0.6410 0.000 0.344 0.440 0.216 0.000
#> GSM1296048 5 0.3700 0.6041 0.000 0.008 0.240 0.000 0.752
#> GSM1296059 3 0.6155 0.6753 0.000 0.276 0.548 0.176 0.000
#> GSM1296066 5 0.0404 0.6098 0.000 0.012 0.000 0.000 0.988
#> GSM1296060 3 0.6185 0.6725 0.000 0.264 0.548 0.188 0.000
#> GSM1296063 5 0.5099 0.2547 0.000 0.012 0.484 0.016 0.488
#> GSM1296064 3 0.3594 0.5585 0.000 0.032 0.844 0.028 0.096
#> GSM1296067 2 0.5142 0.8547 0.000 0.564 0.000 0.044 0.392
#> GSM1296062 4 0.6646 -0.2830 0.004 0.356 0.196 0.444 0.000
#> GSM1296068 2 0.4397 0.8798 0.000 0.564 0.000 0.004 0.432
#> GSM1296050 1 0.1485 0.8685 0.948 0.032 0.000 0.020 0.000
#> GSM1296057 4 0.3366 0.6368 0.232 0.000 0.000 0.768 0.000
#> GSM1296052 1 0.1082 0.8688 0.964 0.028 0.000 0.008 0.000
#> GSM1296054 1 0.0290 0.8732 0.992 0.008 0.000 0.000 0.000
#> GSM1296049 1 0.1399 0.8692 0.952 0.028 0.000 0.020 0.000
#> GSM1296055 4 0.3877 0.6489 0.212 0.024 0.000 0.764 0.000
#> GSM1296053 1 0.0798 0.8704 0.976 0.016 0.000 0.008 0.000
#> GSM1296058 4 0.2569 0.6785 0.068 0.004 0.032 0.896 0.000
#> GSM1296051 3 0.2074 0.5898 0.000 0.000 0.920 0.044 0.036
#> GSM1296056 3 0.2149 0.5955 0.000 0.012 0.924 0.028 0.036
#> GSM1296065 4 0.3061 0.5885 0.000 0.020 0.136 0.844 0.000
#> GSM1296061 3 0.6582 0.6410 0.000 0.344 0.440 0.216 0.000
#> GSM1296095 3 0.7106 0.3965 0.000 0.140 0.424 0.392 0.044
#> GSM1296120 2 0.6174 0.6303 0.000 0.552 0.000 0.256 0.192
#> GSM1296077 1 0.1485 0.8685 0.948 0.032 0.000 0.020 0.000
#> GSM1296093 1 0.0609 0.8730 0.980 0.020 0.000 0.000 0.000
#> GSM1296104 4 0.2828 0.6223 0.004 0.020 0.104 0.872 0.000
#> GSM1296079 1 0.1485 0.8685 0.948 0.032 0.000 0.020 0.000
#> GSM1296108 2 0.4397 0.8798 0.000 0.564 0.000 0.004 0.432
#> GSM1296110 2 0.4249 0.8788 0.000 0.568 0.000 0.000 0.432
#> GSM1296081 1 0.0771 0.8724 0.976 0.020 0.000 0.004 0.000
#> GSM1296091 1 0.4824 -0.0951 0.512 0.020 0.000 0.468 0.000
#> GSM1296075 4 0.4227 0.5554 0.292 0.016 0.000 0.692 0.000
#> GSM1296112 2 0.4397 0.8770 0.000 0.564 0.000 0.004 0.432
#> GSM1296100 4 0.4827 0.1856 0.476 0.020 0.000 0.504 0.000
#> GSM1296087 1 0.1281 0.8659 0.956 0.032 0.000 0.012 0.000
#> GSM1296118 2 0.5820 0.7839 0.000 0.572 0.000 0.120 0.308
#> GSM1296114 2 0.4397 0.8798 0.000 0.564 0.000 0.004 0.432
#> GSM1296097 4 0.3048 0.6580 0.036 0.024 0.060 0.880 0.000
#> GSM1296106 4 0.4559 0.6523 0.100 0.152 0.000 0.748 0.000
#> GSM1296102 4 0.4014 0.6138 0.256 0.016 0.000 0.728 0.000
#> GSM1296122 4 0.5304 0.4851 0.080 0.292 0.000 0.628 0.000
#> GSM1296089 1 0.4141 0.5683 0.728 0.024 0.000 0.248 0.000
#> GSM1296083 1 0.0771 0.8724 0.976 0.020 0.000 0.004 0.000
#> GSM1296116 2 0.4397 0.8770 0.000 0.564 0.000 0.004 0.432
#> GSM1296085 1 0.0451 0.8728 0.988 0.008 0.000 0.004 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1296094 3 0.0405 0.7113 0.000 0.000 0.988 0.008 0.004 0.000
#> GSM1296119 5 0.5052 0.7482 0.000 0.096 0.000 0.176 0.692 0.036
#> GSM1296076 4 0.3154 0.9448 0.000 0.000 0.184 0.800 0.012 0.004
#> GSM1296092 4 0.3253 0.9374 0.000 0.000 0.192 0.788 0.020 0.000
#> GSM1296103 3 0.1707 0.7050 0.000 0.000 0.928 0.056 0.004 0.012
#> GSM1296078 4 0.3154 0.9448 0.000 0.000 0.184 0.800 0.012 0.004
#> GSM1296107 5 0.3853 0.7870 0.000 0.272 0.000 0.008 0.708 0.012
#> GSM1296109 5 0.4574 0.6087 0.000 0.004 0.208 0.064 0.712 0.012
#> GSM1296080 3 0.3400 0.6628 0.008 0.000 0.836 0.044 0.100 0.012
#> GSM1296090 4 0.2948 0.9462 0.000 0.000 0.188 0.804 0.008 0.000
#> GSM1296074 4 0.3089 0.9460 0.000 0.000 0.188 0.800 0.008 0.004
#> GSM1296111 5 0.4203 0.7941 0.000 0.260 0.000 0.028 0.700 0.012
#> GSM1296099 3 0.3924 0.6385 0.000 0.000 0.772 0.164 0.012 0.052
#> GSM1296086 4 0.3558 0.9275 0.000 0.000 0.184 0.780 0.032 0.004
#> GSM1296117 5 0.4107 0.7956 0.000 0.256 0.000 0.024 0.708 0.012
#> GSM1296113 5 0.3767 0.7833 0.000 0.276 0.000 0.004 0.708 0.012
#> GSM1296096 3 0.4108 0.6271 0.000 0.000 0.756 0.172 0.012 0.060
#> GSM1296105 6 0.4215 0.4702 0.016 0.000 0.300 0.004 0.008 0.672
#> GSM1296098 3 0.1152 0.7076 0.000 0.000 0.952 0.000 0.044 0.004
#> GSM1296101 3 0.2755 0.7085 0.000 0.000 0.876 0.040 0.016 0.068
#> GSM1296121 5 0.4674 0.7103 0.000 0.056 0.000 0.212 0.704 0.028
#> GSM1296088 3 0.4725 0.4384 0.000 0.000 0.648 0.264 0.088 0.000
#> GSM1296082 4 0.2948 0.9462 0.000 0.000 0.188 0.804 0.008 0.000
#> GSM1296115 5 0.3888 0.7947 0.000 0.252 0.000 0.032 0.716 0.000
#> GSM1296084 3 0.6414 0.5412 0.068 0.000 0.632 0.132 0.108 0.060
#> GSM1296072 2 0.3525 0.7574 0.000 0.800 0.000 0.012 0.032 0.156
#> GSM1296069 2 0.4696 0.1232 0.000 0.600 0.000 0.040 0.352 0.008
#> GSM1296071 2 0.0000 0.8839 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296070 5 0.4489 0.7291 0.000 0.296 0.000 0.040 0.656 0.008
#> GSM1296073 5 0.4023 0.6359 0.000 0.000 0.004 0.264 0.704 0.028
#> GSM1296034 1 0.7106 0.2447 0.456 0.000 0.324 0.028 0.096 0.096
#> GSM1296041 5 0.4107 0.7956 0.000 0.256 0.000 0.024 0.708 0.012
#> GSM1296035 3 0.4108 0.6271 0.000 0.000 0.756 0.172 0.012 0.060
#> GSM1296038 3 0.6404 0.4095 0.000 0.000 0.496 0.192 0.040 0.272
#> GSM1296047 2 0.3376 0.7315 0.000 0.792 0.000 0.004 0.024 0.180
#> GSM1296039 4 0.4447 0.8756 0.000 0.000 0.224 0.712 0.036 0.028
#> GSM1296042 5 0.3717 0.7809 0.000 0.276 0.000 0.016 0.708 0.000
#> GSM1296043 2 0.2420 0.8199 0.000 0.892 0.000 0.032 0.068 0.008
#> GSM1296037 6 0.5092 0.4204 0.352 0.000 0.000 0.028 0.040 0.580
#> GSM1296046 2 0.1036 0.8751 0.000 0.964 0.000 0.024 0.004 0.008
#> GSM1296044 2 0.0146 0.8842 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1296045 2 0.3075 0.7622 0.000 0.844 0.000 0.040 0.108 0.008
#> GSM1296025 1 0.2736 0.8154 0.876 0.000 0.000 0.028 0.076 0.020
#> GSM1296033 1 0.6305 0.1317 0.504 0.000 0.008 0.068 0.076 0.344
#> GSM1296027 1 0.2231 0.8155 0.908 0.000 0.000 0.048 0.028 0.016
#> GSM1296032 1 0.1173 0.8289 0.960 0.000 0.000 0.016 0.008 0.016
#> GSM1296024 1 0.2736 0.8154 0.876 0.000 0.000 0.028 0.076 0.020
#> GSM1296031 1 0.5159 0.4013 0.624 0.000 0.000 0.056 0.032 0.288
#> GSM1296028 1 0.2510 0.8086 0.892 0.000 0.000 0.060 0.024 0.024
#> GSM1296029 1 0.2620 0.8054 0.888 0.000 0.000 0.052 0.028 0.032
#> GSM1296026 3 0.5963 -0.0654 0.000 0.000 0.452 0.416 0.096 0.036
#> GSM1296030 3 0.7348 0.4041 0.172 0.000 0.516 0.156 0.112 0.044
#> GSM1296040 3 0.1806 0.7075 0.000 0.000 0.908 0.004 0.000 0.088
#> GSM1296036 3 0.1152 0.7076 0.000 0.000 0.952 0.000 0.044 0.004
#> GSM1296048 5 0.4454 0.6823 0.000 0.032 0.000 0.236 0.704 0.028
#> GSM1296059 3 0.3889 0.6420 0.000 0.000 0.776 0.160 0.012 0.052
#> GSM1296066 5 0.3650 0.7786 0.000 0.280 0.000 0.000 0.708 0.012
#> GSM1296060 3 0.4062 0.6395 0.000 0.000 0.764 0.160 0.012 0.064
#> GSM1296063 5 0.5320 0.1385 0.000 0.000 0.020 0.460 0.464 0.056
#> GSM1296064 4 0.4486 0.8751 0.000 0.000 0.220 0.712 0.040 0.028
#> GSM1296067 2 0.1364 0.8718 0.000 0.952 0.000 0.012 0.016 0.020
#> GSM1296062 3 0.3780 0.6054 0.000 0.000 0.788 0.012 0.052 0.148
#> GSM1296068 2 0.0146 0.8842 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1296050 1 0.2865 0.8140 0.868 0.000 0.000 0.032 0.080 0.020
#> GSM1296057 6 0.2635 0.7698 0.100 0.000 0.020 0.004 0.004 0.872
#> GSM1296052 1 0.2231 0.8155 0.908 0.000 0.000 0.048 0.028 0.016
#> GSM1296054 1 0.1173 0.8289 0.960 0.000 0.000 0.016 0.008 0.016
#> GSM1296049 1 0.2828 0.8168 0.872 0.000 0.000 0.036 0.072 0.020
#> GSM1296055 6 0.3772 0.7727 0.084 0.008 0.020 0.036 0.020 0.832
#> GSM1296053 1 0.1773 0.8245 0.932 0.000 0.000 0.036 0.016 0.016
#> GSM1296058 6 0.2557 0.7573 0.036 0.000 0.056 0.012 0.004 0.892
#> GSM1296051 4 0.4354 0.8863 0.000 0.000 0.184 0.740 0.048 0.028
#> GSM1296056 4 0.3958 0.9125 0.000 0.000 0.196 0.756 0.020 0.028
#> GSM1296065 6 0.3515 0.6889 0.000 0.012 0.104 0.032 0.020 0.832
#> GSM1296061 3 0.1152 0.7076 0.000 0.000 0.952 0.000 0.044 0.004
#> GSM1296095 3 0.6652 0.3821 0.000 0.024 0.464 0.124 0.036 0.352
#> GSM1296120 2 0.3473 0.7129 0.000 0.780 0.000 0.004 0.024 0.192
#> GSM1296077 1 0.2865 0.8140 0.868 0.000 0.000 0.032 0.080 0.020
#> GSM1296093 1 0.2032 0.8284 0.920 0.000 0.000 0.020 0.036 0.024
#> GSM1296104 6 0.3067 0.7353 0.008 0.012 0.068 0.024 0.016 0.872
#> GSM1296079 1 0.2865 0.8140 0.868 0.000 0.000 0.032 0.080 0.020
#> GSM1296108 2 0.0146 0.8842 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1296110 2 0.0146 0.8836 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM1296081 1 0.1873 0.8275 0.924 0.000 0.000 0.020 0.048 0.008
#> GSM1296091 6 0.5452 0.0627 0.456 0.000 0.000 0.056 0.028 0.460
#> GSM1296075 6 0.4286 0.7237 0.140 0.016 0.000 0.032 0.036 0.776
#> GSM1296112 2 0.0717 0.8798 0.000 0.976 0.000 0.016 0.000 0.008
#> GSM1296100 6 0.5092 0.4204 0.352 0.000 0.000 0.028 0.040 0.580
#> GSM1296087 1 0.2216 0.8116 0.908 0.000 0.000 0.052 0.024 0.016
#> GSM1296118 2 0.1745 0.8467 0.000 0.924 0.000 0.000 0.020 0.056
#> GSM1296114 2 0.0146 0.8842 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1296097 6 0.2781 0.7404 0.012 0.012 0.068 0.020 0.004 0.884
#> GSM1296106 6 0.3470 0.7554 0.036 0.076 0.016 0.004 0.020 0.848
#> GSM1296102 6 0.3999 0.7545 0.112 0.000 0.016 0.032 0.036 0.804
#> GSM1296122 6 0.5239 0.6076 0.024 0.216 0.000 0.032 0.048 0.680
#> GSM1296089 1 0.5159 0.4013 0.624 0.000 0.000 0.056 0.032 0.288
#> GSM1296083 1 0.1873 0.8275 0.924 0.000 0.000 0.020 0.048 0.008
#> GSM1296116 2 0.0972 0.8757 0.000 0.964 0.000 0.028 0.000 0.008
#> GSM1296085 1 0.1173 0.8289 0.960 0.000 0.000 0.016 0.008 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)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# 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 tissue(p) time(p) agent(p) dose(p) individual(p) k
#> CV:kmeans 96 8.33e-03 0.2370 0.16620 4.04e-07 1.28e-03 2
#> CV:kmeans 98 1.03e-03 0.1296 0.00319 1.77e-09 9.50e-08 3
#> CV:kmeans 72 1.29e-04 0.0524 0.00529 4.79e-06 1.79e-05 4
#> CV:kmeans 84 6.44e-05 0.2446 0.09096 2.60e-08 4.48e-05 5
#> CV:kmeans 84 1.12e-04 0.2974 0.20704 2.22e-08 2.66e-06 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 45638 rows and 99 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 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 0.877 0.916 0.966 0.5012 0.504 0.504
#> 3 3 0.944 0.912 0.966 0.3382 0.752 0.541
#> 4 4 0.759 0.581 0.779 0.1002 0.853 0.599
#> 5 5 0.747 0.727 0.837 0.0625 0.873 0.576
#> 6 6 0.802 0.716 0.852 0.0442 0.957 0.806
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
#> GSM1296094 2 0.0000 0.9495 0.000 1.000
#> GSM1296119 2 0.0000 0.9495 0.000 1.000
#> GSM1296076 2 0.0000 0.9495 0.000 1.000
#> GSM1296092 2 0.0000 0.9495 0.000 1.000
#> GSM1296103 2 0.0000 0.9495 0.000 1.000
#> GSM1296078 2 0.0000 0.9495 0.000 1.000
#> GSM1296107 2 0.0000 0.9495 0.000 1.000
#> GSM1296109 2 0.0000 0.9495 0.000 1.000
#> GSM1296080 1 0.0000 0.9829 1.000 0.000
#> GSM1296090 2 0.0000 0.9495 0.000 1.000
#> GSM1296074 2 0.0000 0.9495 0.000 1.000
#> GSM1296111 2 0.0000 0.9495 0.000 1.000
#> GSM1296099 2 0.0000 0.9495 0.000 1.000
#> GSM1296086 2 0.0000 0.9495 0.000 1.000
#> GSM1296117 2 0.0000 0.9495 0.000 1.000
#> GSM1296113 2 0.0000 0.9495 0.000 1.000
#> GSM1296096 2 0.0000 0.9495 0.000 1.000
#> GSM1296105 1 0.0000 0.9829 1.000 0.000
#> GSM1296098 2 0.6623 0.7733 0.172 0.828
#> GSM1296101 2 0.0000 0.9495 0.000 1.000
#> GSM1296121 2 0.0000 0.9495 0.000 1.000
#> GSM1296088 1 0.9522 0.3777 0.628 0.372
#> GSM1296082 2 0.0000 0.9495 0.000 1.000
#> GSM1296115 2 0.0000 0.9495 0.000 1.000
#> GSM1296084 1 0.0000 0.9829 1.000 0.000
#> GSM1296072 2 0.0000 0.9495 0.000 1.000
#> GSM1296069 2 0.0000 0.9495 0.000 1.000
#> GSM1296071 2 0.0000 0.9495 0.000 1.000
#> GSM1296070 2 0.0000 0.9495 0.000 1.000
#> GSM1296073 2 0.0000 0.9495 0.000 1.000
#> GSM1296034 1 0.0000 0.9829 1.000 0.000
#> GSM1296041 2 0.0000 0.9495 0.000 1.000
#> GSM1296035 2 0.0000 0.9495 0.000 1.000
#> GSM1296038 2 0.0000 0.9495 0.000 1.000
#> GSM1296047 2 0.9833 0.3077 0.424 0.576
#> GSM1296039 2 0.0000 0.9495 0.000 1.000
#> GSM1296042 2 0.0000 0.9495 0.000 1.000
#> GSM1296043 2 0.0000 0.9495 0.000 1.000
#> GSM1296037 1 0.0000 0.9829 1.000 0.000
#> GSM1296046 2 0.0000 0.9495 0.000 1.000
#> GSM1296044 2 0.0000 0.9495 0.000 1.000
#> GSM1296045 2 0.0000 0.9495 0.000 1.000
#> GSM1296025 1 0.0000 0.9829 1.000 0.000
#> GSM1296033 1 0.0000 0.9829 1.000 0.000
#> GSM1296027 1 0.0000 0.9829 1.000 0.000
#> GSM1296032 1 0.0000 0.9829 1.000 0.000
#> GSM1296024 1 0.0000 0.9829 1.000 0.000
#> GSM1296031 1 0.0000 0.9829 1.000 0.000
#> GSM1296028 1 0.0000 0.9829 1.000 0.000
#> GSM1296029 1 0.0000 0.9829 1.000 0.000
#> GSM1296026 1 0.6048 0.8114 0.852 0.148
#> GSM1296030 1 0.0000 0.9829 1.000 0.000
#> GSM1296040 2 0.9754 0.3212 0.408 0.592
#> GSM1296036 2 0.9988 0.0901 0.480 0.520
#> GSM1296048 2 0.0000 0.9495 0.000 1.000
#> GSM1296059 2 0.0000 0.9495 0.000 1.000
#> GSM1296066 2 0.0000 0.9495 0.000 1.000
#> GSM1296060 2 0.0000 0.9495 0.000 1.000
#> GSM1296063 2 0.0000 0.9495 0.000 1.000
#> GSM1296064 2 0.0000 0.9495 0.000 1.000
#> GSM1296067 2 0.9129 0.5324 0.328 0.672
#> GSM1296062 1 0.0000 0.9829 1.000 0.000
#> GSM1296068 2 0.6712 0.7744 0.176 0.824
#> GSM1296050 1 0.0000 0.9829 1.000 0.000
#> GSM1296057 1 0.0000 0.9829 1.000 0.000
#> GSM1296052 1 0.0000 0.9829 1.000 0.000
#> GSM1296054 1 0.0000 0.9829 1.000 0.000
#> GSM1296049 1 0.0000 0.9829 1.000 0.000
#> GSM1296055 1 0.0000 0.9829 1.000 0.000
#> GSM1296053 1 0.0000 0.9829 1.000 0.000
#> GSM1296058 1 0.0000 0.9829 1.000 0.000
#> GSM1296051 2 0.0376 0.9464 0.004 0.996
#> GSM1296056 2 0.0000 0.9495 0.000 1.000
#> GSM1296065 2 0.0000 0.9495 0.000 1.000
#> GSM1296061 1 0.1184 0.9679 0.984 0.016
#> GSM1296095 2 0.0000 0.9495 0.000 1.000
#> GSM1296120 2 0.9881 0.2737 0.436 0.564
#> GSM1296077 1 0.0000 0.9829 1.000 0.000
#> GSM1296093 1 0.0000 0.9829 1.000 0.000
#> GSM1296104 1 0.0376 0.9793 0.996 0.004
#> GSM1296079 1 0.0000 0.9829 1.000 0.000
#> GSM1296108 2 0.2603 0.9130 0.044 0.956
#> GSM1296110 2 0.0000 0.9495 0.000 1.000
#> GSM1296081 1 0.0000 0.9829 1.000 0.000
#> GSM1296091 1 0.0000 0.9829 1.000 0.000
#> GSM1296075 1 0.0000 0.9829 1.000 0.000
#> GSM1296112 2 0.6887 0.7643 0.184 0.816
#> GSM1296100 1 0.0000 0.9829 1.000 0.000
#> GSM1296087 1 0.0000 0.9829 1.000 0.000
#> GSM1296118 1 0.5408 0.8415 0.876 0.124
#> GSM1296114 2 0.0000 0.9495 0.000 1.000
#> GSM1296097 1 0.0000 0.9829 1.000 0.000
#> GSM1296106 1 0.0000 0.9829 1.000 0.000
#> GSM1296102 1 0.0000 0.9829 1.000 0.000
#> GSM1296122 1 0.0000 0.9829 1.000 0.000
#> GSM1296089 1 0.0000 0.9829 1.000 0.000
#> GSM1296083 1 0.0000 0.9829 1.000 0.000
#> GSM1296116 2 0.0000 0.9495 0.000 1.000
#> GSM1296085 1 0.0000 0.9829 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1296094 3 0.0000 0.952 0.000 0.000 1.000
#> GSM1296119 2 0.0000 0.943 0.000 1.000 0.000
#> GSM1296076 3 0.0000 0.952 0.000 0.000 1.000
#> GSM1296092 3 0.0000 0.952 0.000 0.000 1.000
#> GSM1296103 3 0.0000 0.952 0.000 0.000 1.000
#> GSM1296078 3 0.0000 0.952 0.000 0.000 1.000
#> GSM1296107 2 0.0000 0.943 0.000 1.000 0.000
#> GSM1296109 3 0.0424 0.944 0.000 0.008 0.992
#> GSM1296080 3 0.5810 0.493 0.336 0.000 0.664
#> GSM1296090 3 0.0000 0.952 0.000 0.000 1.000
#> GSM1296074 3 0.0000 0.952 0.000 0.000 1.000
#> GSM1296111 2 0.0000 0.943 0.000 1.000 0.000
#> GSM1296099 3 0.0000 0.952 0.000 0.000 1.000
#> GSM1296086 3 0.0000 0.952 0.000 0.000 1.000
#> GSM1296117 2 0.0000 0.943 0.000 1.000 0.000
#> GSM1296113 2 0.0000 0.943 0.000 1.000 0.000
#> GSM1296096 3 0.0000 0.952 0.000 0.000 1.000
#> GSM1296105 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296098 3 0.0000 0.952 0.000 0.000 1.000
#> GSM1296101 3 0.0000 0.952 0.000 0.000 1.000
#> GSM1296121 2 0.0000 0.943 0.000 1.000 0.000
#> GSM1296088 3 0.0000 0.952 0.000 0.000 1.000
#> GSM1296082 3 0.0000 0.952 0.000 0.000 1.000
#> GSM1296115 2 0.0000 0.943 0.000 1.000 0.000
#> GSM1296084 3 0.6280 0.167 0.460 0.000 0.540
#> GSM1296072 2 0.0000 0.943 0.000 1.000 0.000
#> GSM1296069 2 0.0000 0.943 0.000 1.000 0.000
#> GSM1296071 2 0.0000 0.943 0.000 1.000 0.000
#> GSM1296070 2 0.0000 0.943 0.000 1.000 0.000
#> GSM1296073 2 0.6225 0.321 0.000 0.568 0.432
#> GSM1296034 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296041 2 0.0000 0.943 0.000 1.000 0.000
#> GSM1296035 3 0.0000 0.952 0.000 0.000 1.000
#> GSM1296038 3 0.0000 0.952 0.000 0.000 1.000
#> GSM1296047 2 0.0000 0.943 0.000 1.000 0.000
#> GSM1296039 3 0.0000 0.952 0.000 0.000 1.000
#> GSM1296042 2 0.0000 0.943 0.000 1.000 0.000
#> GSM1296043 2 0.0000 0.943 0.000 1.000 0.000
#> GSM1296037 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296046 2 0.0000 0.943 0.000 1.000 0.000
#> GSM1296044 2 0.0000 0.943 0.000 1.000 0.000
#> GSM1296045 2 0.0000 0.943 0.000 1.000 0.000
#> GSM1296025 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296033 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296027 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296032 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296024 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296031 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296028 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296029 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296026 3 0.0000 0.952 0.000 0.000 1.000
#> GSM1296030 3 0.6192 0.291 0.420 0.000 0.580
#> GSM1296040 3 0.0000 0.952 0.000 0.000 1.000
#> GSM1296036 3 0.0000 0.952 0.000 0.000 1.000
#> GSM1296048 2 0.0000 0.943 0.000 1.000 0.000
#> GSM1296059 3 0.0000 0.952 0.000 0.000 1.000
#> GSM1296066 2 0.0000 0.943 0.000 1.000 0.000
#> GSM1296060 3 0.0000 0.952 0.000 0.000 1.000
#> GSM1296063 2 0.6307 0.160 0.000 0.512 0.488
#> GSM1296064 3 0.0000 0.952 0.000 0.000 1.000
#> GSM1296067 2 0.0000 0.943 0.000 1.000 0.000
#> GSM1296062 1 0.5706 0.496 0.680 0.000 0.320
#> GSM1296068 2 0.0000 0.943 0.000 1.000 0.000
#> GSM1296050 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296057 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296052 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296054 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296049 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296055 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296053 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296058 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296051 3 0.0000 0.952 0.000 0.000 1.000
#> GSM1296056 3 0.0000 0.952 0.000 0.000 1.000
#> GSM1296065 2 0.5650 0.574 0.000 0.688 0.312
#> GSM1296061 3 0.0000 0.952 0.000 0.000 1.000
#> GSM1296095 2 0.6126 0.400 0.000 0.600 0.400
#> GSM1296120 2 0.0000 0.943 0.000 1.000 0.000
#> GSM1296077 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296093 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296104 1 0.2261 0.913 0.932 0.000 0.068
#> GSM1296079 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296108 2 0.0000 0.943 0.000 1.000 0.000
#> GSM1296110 2 0.0000 0.943 0.000 1.000 0.000
#> GSM1296081 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296091 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296075 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296112 2 0.0000 0.943 0.000 1.000 0.000
#> GSM1296100 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296087 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296118 2 0.3752 0.799 0.144 0.856 0.000
#> GSM1296114 2 0.0000 0.943 0.000 1.000 0.000
#> GSM1296097 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296106 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296102 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296122 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296089 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296083 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296116 2 0.0000 0.943 0.000 1.000 0.000
#> GSM1296085 1 0.0000 0.988 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1296094 3 0.4898 0.3413 0.000 0.000 0.584 0.416
#> GSM1296119 3 0.7854 -0.1817 0.000 0.296 0.400 0.304
#> GSM1296076 4 0.2216 0.5344 0.000 0.000 0.092 0.908
#> GSM1296092 4 0.1389 0.5109 0.000 0.000 0.048 0.952
#> GSM1296103 3 0.4941 0.3358 0.000 0.000 0.564 0.436
#> GSM1296078 4 0.3024 0.5128 0.000 0.000 0.148 0.852
#> GSM1296107 2 0.7474 0.3389 0.000 0.424 0.400 0.176
#> GSM1296109 3 0.5453 -0.1812 0.000 0.036 0.660 0.304
#> GSM1296080 3 0.6189 0.2900 0.060 0.000 0.568 0.372
#> GSM1296090 4 0.0000 0.5488 0.000 0.000 0.000 1.000
#> GSM1296074 4 0.0000 0.5488 0.000 0.000 0.000 1.000
#> GSM1296111 3 0.7808 -0.2528 0.000 0.344 0.400 0.256
#> GSM1296099 3 0.4989 0.3099 0.000 0.000 0.528 0.472
#> GSM1296086 4 0.1389 0.5109 0.000 0.000 0.048 0.952
#> GSM1296117 3 0.7836 -0.2246 0.000 0.328 0.400 0.272
#> GSM1296113 2 0.7240 0.3918 0.000 0.456 0.400 0.144
#> GSM1296096 3 0.4998 0.2912 0.000 0.000 0.512 0.488
#> GSM1296105 1 0.3688 0.7408 0.792 0.000 0.208 0.000
#> GSM1296098 3 0.4898 0.3413 0.000 0.000 0.584 0.416
#> GSM1296101 3 0.4948 0.3340 0.000 0.000 0.560 0.440
#> GSM1296121 3 0.7816 -0.1375 0.000 0.260 0.400 0.340
#> GSM1296088 4 0.4972 -0.2346 0.000 0.000 0.456 0.544
#> GSM1296082 4 0.0000 0.5488 0.000 0.000 0.000 1.000
#> GSM1296115 3 0.7830 -0.2312 0.000 0.332 0.400 0.268
#> GSM1296084 4 0.7788 -0.1236 0.244 0.000 0.376 0.380
#> GSM1296072 2 0.1022 0.8211 0.000 0.968 0.032 0.000
#> GSM1296069 2 0.3942 0.7064 0.000 0.764 0.236 0.000
#> GSM1296071 2 0.0000 0.8320 0.000 1.000 0.000 0.000
#> GSM1296070 2 0.6417 0.4976 0.000 0.540 0.388 0.072
#> GSM1296073 4 0.5487 0.3109 0.000 0.020 0.400 0.580
#> GSM1296034 1 0.2011 0.8957 0.920 0.000 0.080 0.000
#> GSM1296041 3 0.7836 -0.2246 0.000 0.328 0.400 0.272
#> GSM1296035 3 0.4996 0.2968 0.000 0.000 0.516 0.484
#> GSM1296038 4 0.4661 0.4140 0.000 0.000 0.348 0.652
#> GSM1296047 2 0.0000 0.8320 0.000 1.000 0.000 0.000
#> GSM1296039 4 0.0921 0.5431 0.000 0.000 0.028 0.972
#> GSM1296042 2 0.7304 0.3797 0.000 0.448 0.400 0.152
#> GSM1296043 2 0.2973 0.7687 0.000 0.856 0.144 0.000
#> GSM1296037 1 0.0592 0.9533 0.984 0.000 0.016 0.000
#> GSM1296046 2 0.0000 0.8320 0.000 1.000 0.000 0.000
#> GSM1296044 2 0.0000 0.8320 0.000 1.000 0.000 0.000
#> GSM1296045 2 0.3074 0.7646 0.000 0.848 0.152 0.000
#> GSM1296025 1 0.0000 0.9592 1.000 0.000 0.000 0.000
#> GSM1296033 1 0.0000 0.9592 1.000 0.000 0.000 0.000
#> GSM1296027 1 0.0000 0.9592 1.000 0.000 0.000 0.000
#> GSM1296032 1 0.0000 0.9592 1.000 0.000 0.000 0.000
#> GSM1296024 1 0.0000 0.9592 1.000 0.000 0.000 0.000
#> GSM1296031 1 0.0000 0.9592 1.000 0.000 0.000 0.000
#> GSM1296028 1 0.0000 0.9592 1.000 0.000 0.000 0.000
#> GSM1296029 1 0.0000 0.9592 1.000 0.000 0.000 0.000
#> GSM1296026 4 0.5306 -0.0315 0.020 0.000 0.348 0.632
#> GSM1296030 4 0.7919 -0.0684 0.336 0.000 0.316 0.348
#> GSM1296040 3 0.4888 0.3404 0.000 0.000 0.588 0.412
#> GSM1296036 3 0.4898 0.3413 0.000 0.000 0.584 0.416
#> GSM1296048 4 0.7546 0.1006 0.000 0.188 0.400 0.412
#> GSM1296059 3 0.4977 0.3210 0.000 0.000 0.540 0.460
#> GSM1296066 2 0.6895 0.4405 0.000 0.492 0.400 0.108
#> GSM1296060 3 0.4992 0.3065 0.000 0.000 0.524 0.476
#> GSM1296063 4 0.5172 0.3199 0.000 0.008 0.404 0.588
#> GSM1296064 4 0.3528 0.4958 0.000 0.000 0.192 0.808
#> GSM1296067 2 0.0000 0.8320 0.000 1.000 0.000 0.000
#> GSM1296062 3 0.6908 0.1732 0.188 0.000 0.592 0.220
#> GSM1296068 2 0.0000 0.8320 0.000 1.000 0.000 0.000
#> GSM1296050 1 0.0000 0.9592 1.000 0.000 0.000 0.000
#> GSM1296057 1 0.0592 0.9533 0.984 0.000 0.016 0.000
#> GSM1296052 1 0.0000 0.9592 1.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.9592 1.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.9592 1.000 0.000 0.000 0.000
#> GSM1296055 1 0.0592 0.9533 0.984 0.000 0.016 0.000
#> GSM1296053 1 0.0000 0.9592 1.000 0.000 0.000 0.000
#> GSM1296058 1 0.0592 0.9533 0.984 0.000 0.016 0.000
#> GSM1296051 4 0.0336 0.5444 0.000 0.000 0.008 0.992
#> GSM1296056 4 0.1474 0.5083 0.000 0.000 0.052 0.948
#> GSM1296065 4 0.6617 0.2881 0.000 0.088 0.380 0.532
#> GSM1296061 3 0.5060 0.3402 0.004 0.000 0.584 0.412
#> GSM1296095 3 0.7432 -0.1991 0.000 0.184 0.480 0.336
#> GSM1296120 2 0.0000 0.8320 0.000 1.000 0.000 0.000
#> GSM1296077 1 0.0000 0.9592 1.000 0.000 0.000 0.000
#> GSM1296093 1 0.0000 0.9592 1.000 0.000 0.000 0.000
#> GSM1296104 1 0.6519 0.5762 0.672 0.048 0.052 0.228
#> GSM1296079 1 0.0000 0.9592 1.000 0.000 0.000 0.000
#> GSM1296108 2 0.0000 0.8320 0.000 1.000 0.000 0.000
#> GSM1296110 2 0.0000 0.8320 0.000 1.000 0.000 0.000
#> GSM1296081 1 0.0000 0.9592 1.000 0.000 0.000 0.000
#> GSM1296091 1 0.0000 0.9592 1.000 0.000 0.000 0.000
#> GSM1296075 1 0.0927 0.9471 0.976 0.016 0.008 0.000
#> GSM1296112 2 0.0000 0.8320 0.000 1.000 0.000 0.000
#> GSM1296100 1 0.0592 0.9533 0.984 0.000 0.016 0.000
#> GSM1296087 1 0.0000 0.9592 1.000 0.000 0.000 0.000
#> GSM1296118 2 0.0000 0.8320 0.000 1.000 0.000 0.000
#> GSM1296114 2 0.0000 0.8320 0.000 1.000 0.000 0.000
#> GSM1296097 1 0.1209 0.9418 0.964 0.004 0.032 0.000
#> GSM1296106 1 0.4012 0.7757 0.800 0.184 0.016 0.000
#> GSM1296102 1 0.0592 0.9533 0.984 0.000 0.016 0.000
#> GSM1296122 1 0.5310 0.3747 0.576 0.412 0.012 0.000
#> GSM1296089 1 0.0000 0.9592 1.000 0.000 0.000 0.000
#> GSM1296083 1 0.0000 0.9592 1.000 0.000 0.000 0.000
#> GSM1296116 2 0.0000 0.8320 0.000 1.000 0.000 0.000
#> GSM1296085 1 0.0000 0.9592 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
#> GSM1296094 3 0.2377 0.7988 0.000 0.000 0.872 0.128 0.000
#> GSM1296119 5 0.3644 0.8011 0.000 0.096 0.000 0.080 0.824
#> GSM1296076 4 0.1478 0.8077 0.000 0.000 0.000 0.936 0.064
#> GSM1296092 4 0.1469 0.7926 0.000 0.000 0.036 0.948 0.016
#> GSM1296103 3 0.3366 0.7881 0.000 0.000 0.768 0.232 0.000
#> GSM1296078 4 0.1792 0.7923 0.000 0.000 0.000 0.916 0.084
#> GSM1296107 5 0.3280 0.7868 0.000 0.176 0.000 0.012 0.812
#> GSM1296109 5 0.4166 0.6874 0.000 0.004 0.160 0.056 0.780
#> GSM1296080 3 0.4835 0.6307 0.128 0.000 0.736 0.132 0.004
#> GSM1296090 4 0.1341 0.8107 0.000 0.000 0.000 0.944 0.056
#> GSM1296074 4 0.1341 0.8107 0.000 0.000 0.000 0.944 0.056
#> GSM1296111 5 0.3565 0.8019 0.000 0.144 0.000 0.040 0.816
#> GSM1296099 3 0.3884 0.7574 0.000 0.000 0.708 0.288 0.004
#> GSM1296086 4 0.1485 0.7955 0.000 0.000 0.032 0.948 0.020
#> GSM1296117 5 0.3647 0.8044 0.000 0.132 0.000 0.052 0.816
#> GSM1296113 5 0.3003 0.7787 0.000 0.188 0.000 0.000 0.812
#> GSM1296096 3 0.3990 0.7387 0.000 0.000 0.688 0.308 0.004
#> GSM1296105 1 0.6261 0.2613 0.492 0.000 0.400 0.020 0.088
#> GSM1296098 3 0.2471 0.7982 0.000 0.000 0.864 0.136 0.000
#> GSM1296101 3 0.3333 0.7946 0.000 0.000 0.788 0.208 0.004
#> GSM1296121 5 0.3648 0.7999 0.000 0.092 0.000 0.084 0.824
#> GSM1296088 4 0.4299 0.1666 0.000 0.000 0.388 0.608 0.004
#> GSM1296082 4 0.1341 0.8107 0.000 0.000 0.000 0.944 0.056
#> GSM1296115 5 0.3622 0.8042 0.000 0.136 0.000 0.048 0.816
#> GSM1296084 3 0.7019 0.0740 0.316 0.000 0.384 0.292 0.008
#> GSM1296072 2 0.2852 0.6808 0.000 0.828 0.000 0.000 0.172
#> GSM1296069 5 0.4268 0.3404 0.000 0.444 0.000 0.000 0.556
#> GSM1296071 2 0.0000 0.8608 0.000 1.000 0.000 0.000 0.000
#> GSM1296070 5 0.3636 0.6832 0.000 0.272 0.000 0.000 0.728
#> GSM1296073 5 0.3236 0.7369 0.000 0.020 0.000 0.152 0.828
#> GSM1296034 1 0.2911 0.8078 0.852 0.000 0.136 0.004 0.008
#> GSM1296041 5 0.3622 0.8042 0.000 0.136 0.000 0.048 0.816
#> GSM1296035 3 0.3990 0.7387 0.000 0.000 0.688 0.308 0.004
#> GSM1296038 5 0.6660 -0.1169 0.000 0.000 0.228 0.384 0.388
#> GSM1296047 2 0.0290 0.8561 0.000 0.992 0.000 0.000 0.008
#> GSM1296039 4 0.3586 0.7241 0.000 0.000 0.096 0.828 0.076
#> GSM1296042 5 0.3003 0.7787 0.000 0.188 0.000 0.000 0.812
#> GSM1296043 2 0.3913 0.3645 0.000 0.676 0.000 0.000 0.324
#> GSM1296037 1 0.3561 0.8245 0.844 0.000 0.060 0.012 0.084
#> GSM1296046 2 0.0290 0.8552 0.000 0.992 0.000 0.000 0.008
#> GSM1296044 2 0.0000 0.8608 0.000 1.000 0.000 0.000 0.000
#> GSM1296045 2 0.4101 0.2204 0.000 0.628 0.000 0.000 0.372
#> GSM1296025 1 0.0000 0.9018 1.000 0.000 0.000 0.000 0.000
#> GSM1296033 1 0.0798 0.8904 0.976 0.000 0.000 0.016 0.008
#> GSM1296027 1 0.0000 0.9018 1.000 0.000 0.000 0.000 0.000
#> GSM1296032 1 0.0000 0.9018 1.000 0.000 0.000 0.000 0.000
#> GSM1296024 1 0.0000 0.9018 1.000 0.000 0.000 0.000 0.000
#> GSM1296031 1 0.0290 0.8994 0.992 0.000 0.000 0.000 0.008
#> GSM1296028 1 0.0000 0.9018 1.000 0.000 0.000 0.000 0.000
#> GSM1296029 1 0.0162 0.9004 0.996 0.000 0.000 0.000 0.004
#> GSM1296026 4 0.4231 0.6028 0.060 0.000 0.160 0.776 0.004
#> GSM1296030 1 0.6977 -0.1063 0.404 0.000 0.272 0.316 0.008
#> GSM1296040 3 0.2233 0.7877 0.000 0.000 0.892 0.104 0.004
#> GSM1296036 3 0.2471 0.7982 0.000 0.000 0.864 0.136 0.000
#> GSM1296048 5 0.3569 0.7835 0.000 0.068 0.000 0.104 0.828
#> GSM1296059 3 0.3906 0.7538 0.000 0.000 0.704 0.292 0.004
#> GSM1296066 5 0.3039 0.7752 0.000 0.192 0.000 0.000 0.808
#> GSM1296060 3 0.3969 0.7427 0.000 0.000 0.692 0.304 0.004
#> GSM1296063 5 0.4265 0.6049 0.000 0.012 0.008 0.268 0.712
#> GSM1296064 4 0.4555 0.6258 0.000 0.000 0.068 0.732 0.200
#> GSM1296067 2 0.0000 0.8608 0.000 1.000 0.000 0.000 0.000
#> GSM1296062 3 0.2460 0.6987 0.072 0.000 0.900 0.024 0.004
#> GSM1296068 2 0.0000 0.8608 0.000 1.000 0.000 0.000 0.000
#> GSM1296050 1 0.0000 0.9018 1.000 0.000 0.000 0.000 0.000
#> GSM1296057 1 0.5759 0.7068 0.688 0.000 0.120 0.040 0.152
#> GSM1296052 1 0.0000 0.9018 1.000 0.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.9018 1.000 0.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.9018 1.000 0.000 0.000 0.000 0.000
#> GSM1296055 1 0.5908 0.6945 0.676 0.000 0.124 0.044 0.156
#> GSM1296053 1 0.0000 0.9018 1.000 0.000 0.000 0.000 0.000
#> GSM1296058 1 0.6330 0.6584 0.640 0.000 0.140 0.056 0.164
#> GSM1296051 4 0.1270 0.8102 0.000 0.000 0.000 0.948 0.052
#> GSM1296056 4 0.2233 0.7640 0.000 0.000 0.080 0.904 0.016
#> GSM1296065 5 0.7349 0.1760 0.000 0.080 0.152 0.260 0.508
#> GSM1296061 3 0.2471 0.7982 0.000 0.000 0.864 0.136 0.000
#> GSM1296095 5 0.7227 0.4533 0.000 0.108 0.260 0.104 0.528
#> GSM1296120 2 0.0290 0.8561 0.000 0.992 0.000 0.000 0.008
#> GSM1296077 1 0.0000 0.9018 1.000 0.000 0.000 0.000 0.000
#> GSM1296093 1 0.0000 0.9018 1.000 0.000 0.000 0.000 0.000
#> GSM1296104 4 0.9198 0.0258 0.260 0.036 0.260 0.264 0.180
#> GSM1296079 1 0.0000 0.9018 1.000 0.000 0.000 0.000 0.000
#> GSM1296108 2 0.0000 0.8608 0.000 1.000 0.000 0.000 0.000
#> GSM1296110 2 0.0000 0.8608 0.000 1.000 0.000 0.000 0.000
#> GSM1296081 1 0.0000 0.9018 1.000 0.000 0.000 0.000 0.000
#> GSM1296091 1 0.0693 0.8956 0.980 0.000 0.008 0.000 0.012
#> GSM1296075 1 0.2596 0.8606 0.908 0.036 0.032 0.004 0.020
#> GSM1296112 2 0.0000 0.8608 0.000 1.000 0.000 0.000 0.000
#> GSM1296100 1 0.3561 0.8246 0.844 0.000 0.060 0.012 0.084
#> GSM1296087 1 0.0000 0.9018 1.000 0.000 0.000 0.000 0.000
#> GSM1296118 2 0.0000 0.8608 0.000 1.000 0.000 0.000 0.000
#> GSM1296114 2 0.0000 0.8608 0.000 1.000 0.000 0.000 0.000
#> GSM1296097 1 0.7283 0.5526 0.556 0.012 0.204 0.060 0.168
#> GSM1296106 2 0.8776 0.0174 0.316 0.364 0.116 0.044 0.160
#> GSM1296102 1 0.4584 0.7823 0.780 0.000 0.072 0.028 0.120
#> GSM1296122 2 0.5519 0.5113 0.256 0.664 0.012 0.012 0.056
#> GSM1296089 1 0.0000 0.9018 1.000 0.000 0.000 0.000 0.000
#> GSM1296083 1 0.0000 0.9018 1.000 0.000 0.000 0.000 0.000
#> GSM1296116 2 0.0000 0.8608 0.000 1.000 0.000 0.000 0.000
#> GSM1296085 1 0.0000 0.9018 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
#> GSM1296094 3 0.1408 0.7741 0.000 0.000 0.944 0.036 0.000 0.020
#> GSM1296119 5 0.1293 0.8748 0.000 0.020 0.004 0.016 0.956 0.004
#> GSM1296076 4 0.1196 0.8402 0.000 0.000 0.000 0.952 0.040 0.008
#> GSM1296092 4 0.1230 0.8401 0.000 0.000 0.008 0.956 0.028 0.008
#> GSM1296103 3 0.3557 0.7616 0.000 0.000 0.800 0.148 0.008 0.044
#> GSM1296078 4 0.1196 0.8402 0.000 0.000 0.000 0.952 0.040 0.008
#> GSM1296107 5 0.1075 0.8788 0.000 0.048 0.000 0.000 0.952 0.000
#> GSM1296109 5 0.2518 0.8022 0.000 0.004 0.096 0.012 0.880 0.008
#> GSM1296080 3 0.4513 0.5869 0.112 0.004 0.776 0.048 0.012 0.048
#> GSM1296090 4 0.0935 0.8414 0.000 0.000 0.004 0.964 0.032 0.000
#> GSM1296074 4 0.1080 0.8415 0.000 0.000 0.004 0.960 0.032 0.004
#> GSM1296111 5 0.1010 0.8810 0.000 0.036 0.000 0.004 0.960 0.000
#> GSM1296099 3 0.4498 0.7355 0.000 0.000 0.716 0.188 0.008 0.088
#> GSM1296086 4 0.1534 0.8384 0.000 0.004 0.004 0.944 0.032 0.016
#> GSM1296117 5 0.1010 0.8810 0.000 0.036 0.000 0.004 0.960 0.000
#> GSM1296113 5 0.1204 0.8754 0.000 0.056 0.000 0.000 0.944 0.000
#> GSM1296096 3 0.4685 0.7119 0.000 0.000 0.692 0.208 0.008 0.092
#> GSM1296105 1 0.6701 -0.2779 0.360 0.004 0.352 0.008 0.012 0.264
#> GSM1296098 3 0.1092 0.7558 0.000 0.000 0.960 0.020 0.000 0.020
#> GSM1296101 3 0.4233 0.7557 0.000 0.000 0.752 0.140 0.008 0.100
#> GSM1296121 5 0.1495 0.8714 0.000 0.020 0.004 0.020 0.948 0.008
#> GSM1296088 4 0.4877 0.3739 0.000 0.004 0.348 0.596 0.008 0.044
#> GSM1296082 4 0.0935 0.8414 0.000 0.000 0.004 0.964 0.032 0.000
#> GSM1296115 5 0.1268 0.8806 0.000 0.036 0.000 0.004 0.952 0.008
#> GSM1296084 3 0.7800 -0.0482 0.292 0.004 0.332 0.240 0.012 0.120
#> GSM1296072 2 0.3722 0.7210 0.000 0.764 0.000 0.004 0.196 0.036
#> GSM1296069 5 0.3850 0.4575 0.000 0.340 0.000 0.004 0.652 0.004
#> GSM1296071 2 0.0508 0.8886 0.000 0.984 0.000 0.000 0.012 0.004
#> GSM1296070 5 0.2848 0.7794 0.000 0.160 0.000 0.004 0.828 0.008
#> GSM1296073 5 0.1477 0.8486 0.000 0.000 0.004 0.048 0.940 0.008
#> GSM1296034 1 0.4141 0.5927 0.740 0.000 0.188 0.004 0.000 0.068
#> GSM1296041 5 0.1010 0.8810 0.000 0.036 0.000 0.004 0.960 0.000
#> GSM1296035 3 0.4658 0.7164 0.000 0.000 0.696 0.204 0.008 0.092
#> GSM1296038 4 0.7722 -0.0202 0.000 0.000 0.236 0.268 0.228 0.268
#> GSM1296047 2 0.0972 0.8701 0.000 0.964 0.000 0.000 0.008 0.028
#> GSM1296039 4 0.4156 0.7304 0.000 0.000 0.092 0.788 0.064 0.056
#> GSM1296042 5 0.1349 0.8757 0.000 0.056 0.000 0.000 0.940 0.004
#> GSM1296043 2 0.3976 0.3675 0.000 0.612 0.000 0.004 0.380 0.004
#> GSM1296037 1 0.3499 0.4172 0.680 0.000 0.000 0.000 0.000 0.320
#> GSM1296046 2 0.1080 0.8778 0.000 0.960 0.000 0.004 0.032 0.004
#> GSM1296044 2 0.0508 0.8891 0.000 0.984 0.000 0.000 0.012 0.004
#> GSM1296045 2 0.4100 0.3375 0.000 0.600 0.000 0.004 0.388 0.008
#> GSM1296025 1 0.0692 0.8504 0.976 0.000 0.000 0.004 0.000 0.020
#> GSM1296033 1 0.2933 0.7590 0.860 0.004 0.000 0.032 0.008 0.096
#> GSM1296027 1 0.0405 0.8506 0.988 0.000 0.000 0.000 0.004 0.008
#> GSM1296032 1 0.0000 0.8518 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296024 1 0.0777 0.8503 0.972 0.000 0.000 0.004 0.000 0.024
#> GSM1296031 1 0.1701 0.8122 0.920 0.000 0.000 0.008 0.000 0.072
#> GSM1296028 1 0.0291 0.8517 0.992 0.000 0.000 0.000 0.004 0.004
#> GSM1296029 1 0.0837 0.8474 0.972 0.000 0.000 0.004 0.004 0.020
#> GSM1296026 4 0.3896 0.7234 0.016 0.004 0.068 0.816 0.012 0.084
#> GSM1296030 1 0.7457 -0.0486 0.412 0.004 0.200 0.288 0.016 0.080
#> GSM1296040 3 0.1750 0.7640 0.000 0.000 0.932 0.016 0.012 0.040
#> GSM1296036 3 0.1092 0.7558 0.000 0.000 0.960 0.020 0.000 0.020
#> GSM1296048 5 0.1476 0.8662 0.000 0.012 0.004 0.028 0.948 0.008
#> GSM1296059 3 0.4352 0.7366 0.000 0.000 0.728 0.188 0.008 0.076
#> GSM1296066 5 0.1327 0.8700 0.000 0.064 0.000 0.000 0.936 0.000
#> GSM1296060 3 0.4662 0.7254 0.000 0.000 0.700 0.192 0.008 0.100
#> GSM1296063 5 0.4258 0.6221 0.000 0.000 0.004 0.204 0.724 0.068
#> GSM1296064 4 0.5109 0.6474 0.000 0.000 0.080 0.696 0.168 0.056
#> GSM1296067 2 0.0665 0.8854 0.000 0.980 0.000 0.004 0.008 0.008
#> GSM1296062 3 0.1707 0.7442 0.012 0.000 0.928 0.004 0.000 0.056
#> GSM1296068 2 0.0508 0.8891 0.000 0.984 0.000 0.000 0.012 0.004
#> GSM1296050 1 0.0777 0.8495 0.972 0.000 0.000 0.004 0.000 0.024
#> GSM1296057 6 0.3789 0.3757 0.416 0.000 0.000 0.000 0.000 0.584
#> GSM1296052 1 0.0363 0.8523 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM1296054 1 0.0000 0.8518 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296049 1 0.0692 0.8504 0.976 0.000 0.000 0.004 0.000 0.020
#> GSM1296055 6 0.3847 0.5059 0.348 0.000 0.000 0.008 0.000 0.644
#> GSM1296053 1 0.0260 0.8516 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM1296058 6 0.2794 0.7080 0.144 0.000 0.012 0.004 0.000 0.840
#> GSM1296051 4 0.2077 0.8291 0.000 0.004 0.004 0.916 0.044 0.032
#> GSM1296056 4 0.2985 0.7844 0.000 0.000 0.056 0.864 0.020 0.060
#> GSM1296065 6 0.6630 0.4009 0.000 0.024 0.088 0.120 0.188 0.580
#> GSM1296061 3 0.1092 0.7558 0.000 0.000 0.960 0.020 0.000 0.020
#> GSM1296095 5 0.8021 0.0892 0.000 0.068 0.228 0.092 0.396 0.216
#> GSM1296120 2 0.1124 0.8667 0.000 0.956 0.000 0.000 0.008 0.036
#> GSM1296077 1 0.0692 0.8504 0.976 0.000 0.000 0.004 0.000 0.020
#> GSM1296093 1 0.0260 0.8522 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM1296104 6 0.3229 0.6735 0.056 0.012 0.020 0.044 0.004 0.864
#> GSM1296079 1 0.0692 0.8504 0.976 0.000 0.000 0.004 0.000 0.020
#> GSM1296108 2 0.0260 0.8883 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1296110 2 0.0508 0.8886 0.000 0.984 0.000 0.004 0.012 0.000
#> GSM1296081 1 0.0146 0.8520 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1296091 1 0.1410 0.8309 0.944 0.000 0.000 0.004 0.008 0.044
#> GSM1296075 1 0.3879 0.6523 0.772 0.040 0.000 0.008 0.004 0.176
#> GSM1296112 2 0.0363 0.8887 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM1296100 1 0.3371 0.4783 0.708 0.000 0.000 0.000 0.000 0.292
#> GSM1296087 1 0.0951 0.8447 0.968 0.000 0.000 0.004 0.008 0.020
#> GSM1296118 2 0.0458 0.8805 0.000 0.984 0.000 0.000 0.000 0.016
#> GSM1296114 2 0.0508 0.8891 0.000 0.984 0.000 0.000 0.012 0.004
#> GSM1296097 6 0.2685 0.6868 0.080 0.000 0.044 0.000 0.004 0.872
#> GSM1296106 6 0.5452 0.5272 0.120 0.264 0.004 0.004 0.004 0.604
#> GSM1296102 1 0.4117 -0.0666 0.528 0.000 0.004 0.004 0.000 0.464
#> GSM1296122 2 0.5072 0.4257 0.160 0.660 0.000 0.008 0.000 0.172
#> GSM1296089 1 0.1398 0.8278 0.940 0.000 0.000 0.008 0.000 0.052
#> GSM1296083 1 0.0260 0.8517 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM1296116 2 0.0653 0.8877 0.000 0.980 0.000 0.004 0.012 0.004
#> GSM1296085 1 0.0291 0.8518 0.992 0.000 0.000 0.000 0.004 0.004
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# 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 tissue(p) time(p) agent(p) dose(p) individual(p) k
#> CV:skmeans 94 2.67e-03 0.1564 0.77486 1.93e-06 6.19e-03 2
#> CV:skmeans 92 1.98e-04 0.1405 0.01357 1.93e-08 6.66e-07 3
#> CV:skmeans 61 3.35e-02 0.0916 0.00478 3.69e-06 1.22e-03 4
#> CV:skmeans 87 4.20e-05 0.5135 0.05125 2.07e-07 4.60e-07 5
#> CV:skmeans 84 9.19e-05 0.5458 0.14642 6.41e-07 1.18e-06 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 45638 rows and 99 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 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 0.265 0.413 0.751 0.4952 0.504 0.504
#> 3 3 0.724 0.855 0.931 0.3452 0.664 0.426
#> 4 4 0.762 0.682 0.844 0.1120 0.879 0.657
#> 5 5 0.859 0.839 0.915 0.0660 0.894 0.629
#> 6 6 0.803 0.741 0.842 0.0437 0.967 0.847
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
#> GSM1296094 2 0.9850 0.3981 0.428 0.572
#> GSM1296119 2 0.0000 0.5097 0.000 1.000
#> GSM1296076 2 0.6531 0.5205 0.168 0.832
#> GSM1296092 2 0.9866 0.3922 0.432 0.568
#> GSM1296103 2 0.9833 0.4019 0.424 0.576
#> GSM1296078 2 0.6438 0.5215 0.164 0.836
#> GSM1296107 2 0.0000 0.5097 0.000 1.000
#> GSM1296109 2 0.5946 0.5239 0.144 0.856
#> GSM1296080 1 0.9998 -0.2934 0.508 0.492
#> GSM1296090 2 0.9710 0.4219 0.400 0.600
#> GSM1296074 2 0.9491 0.4414 0.368 0.632
#> GSM1296111 2 0.0000 0.5097 0.000 1.000
#> GSM1296099 2 0.9732 0.4182 0.404 0.596
#> GSM1296086 2 0.9795 0.4096 0.416 0.584
#> GSM1296117 2 0.0000 0.5097 0.000 1.000
#> GSM1296113 2 0.0000 0.5097 0.000 1.000
#> GSM1296096 2 0.9087 0.4612 0.324 0.676
#> GSM1296105 1 0.0938 0.7423 0.988 0.012
#> GSM1296098 2 0.9754 0.4169 0.408 0.592
#> GSM1296101 2 0.9850 0.3979 0.428 0.572
#> GSM1296121 2 0.2423 0.5166 0.040 0.960
#> GSM1296088 2 0.9983 0.3194 0.476 0.524
#> GSM1296082 2 0.9491 0.4414 0.368 0.632
#> GSM1296115 2 0.0000 0.5097 0.000 1.000
#> GSM1296084 1 0.9998 -0.2934 0.508 0.492
#> GSM1296072 2 0.9933 -0.0553 0.452 0.548
#> GSM1296069 2 0.8555 0.2249 0.280 0.720
#> GSM1296071 2 0.9998 -0.1082 0.492 0.508
#> GSM1296070 2 0.8081 0.2723 0.248 0.752
#> GSM1296073 2 0.5946 0.5239 0.144 0.856
#> GSM1296034 1 0.0000 0.7450 1.000 0.000
#> GSM1296041 2 0.0000 0.5097 0.000 1.000
#> GSM1296035 2 0.9866 0.3922 0.432 0.568
#> GSM1296038 2 0.6148 0.5233 0.152 0.848
#> GSM1296047 1 1.0000 0.0870 0.500 0.500
#> GSM1296039 2 0.6148 0.5234 0.152 0.848
#> GSM1296042 2 0.0000 0.5097 0.000 1.000
#> GSM1296043 2 0.9970 -0.0775 0.468 0.532
#> GSM1296037 1 0.0000 0.7450 1.000 0.000
#> GSM1296046 2 0.9998 -0.1082 0.492 0.508
#> GSM1296044 2 1.0000 -0.1142 0.496 0.504
#> GSM1296045 2 0.9998 -0.1082 0.492 0.508
#> GSM1296025 1 0.5408 0.6959 0.876 0.124
#> GSM1296033 1 0.2948 0.6871 0.948 0.052
#> GSM1296027 1 0.0000 0.7450 1.000 0.000
#> GSM1296032 1 0.0672 0.7455 0.992 0.008
#> GSM1296024 1 0.5842 0.6836 0.860 0.140
#> GSM1296031 1 0.5946 0.6802 0.856 0.144
#> GSM1296028 1 0.0000 0.7450 1.000 0.000
#> GSM1296029 1 0.0000 0.7450 1.000 0.000
#> GSM1296026 2 0.9998 0.2901 0.492 0.508
#> GSM1296030 1 0.9998 -0.2934 0.508 0.492
#> GSM1296040 2 0.9909 0.3792 0.444 0.556
#> GSM1296036 2 0.9866 0.3923 0.432 0.568
#> GSM1296048 2 0.0000 0.5097 0.000 1.000
#> GSM1296059 2 0.9833 0.4019 0.424 0.576
#> GSM1296066 2 0.4690 0.4425 0.100 0.900
#> GSM1296060 2 0.9775 0.4125 0.412 0.588
#> GSM1296063 2 0.5946 0.5239 0.144 0.856
#> GSM1296064 2 0.6048 0.5237 0.148 0.852
#> GSM1296067 2 1.0000 -0.1142 0.496 0.504
#> GSM1296062 1 0.9977 -0.2637 0.528 0.472
#> GSM1296068 2 1.0000 -0.1142 0.496 0.504
#> GSM1296050 1 0.5946 0.6802 0.856 0.144
#> GSM1296057 1 0.1184 0.7376 0.984 0.016
#> GSM1296052 1 0.0938 0.7449 0.988 0.012
#> GSM1296054 1 0.0376 0.7456 0.996 0.004
#> GSM1296049 1 0.5519 0.6930 0.872 0.128
#> GSM1296055 1 0.8763 0.4857 0.704 0.296
#> GSM1296053 1 0.0000 0.7450 1.000 0.000
#> GSM1296058 1 0.8016 0.3270 0.756 0.244
#> GSM1296051 2 0.9866 0.3922 0.432 0.568
#> GSM1296056 2 0.9795 0.4096 0.416 0.584
#> GSM1296065 1 0.8555 0.3966 0.720 0.280
#> GSM1296061 2 0.9963 0.3478 0.464 0.536
#> GSM1296095 1 0.9954 0.0320 0.540 0.460
#> GSM1296120 2 1.0000 -0.1154 0.496 0.504
#> GSM1296077 1 0.5294 0.6984 0.880 0.120
#> GSM1296093 1 0.0000 0.7450 1.000 0.000
#> GSM1296104 1 0.2043 0.7282 0.968 0.032
#> GSM1296079 1 0.5946 0.6802 0.856 0.144
#> GSM1296108 2 1.0000 -0.1227 0.500 0.500
#> GSM1296110 2 1.0000 -0.1142 0.496 0.504
#> GSM1296081 1 0.0000 0.7450 1.000 0.000
#> GSM1296091 1 0.0672 0.7422 0.992 0.008
#> GSM1296075 1 0.6343 0.6731 0.840 0.160
#> GSM1296112 2 1.0000 -0.1142 0.496 0.504
#> GSM1296100 1 0.0000 0.7450 1.000 0.000
#> GSM1296087 1 0.0000 0.7450 1.000 0.000
#> GSM1296118 1 0.9996 0.1131 0.512 0.488
#> GSM1296114 2 1.0000 -0.1142 0.496 0.504
#> GSM1296097 1 0.2778 0.7243 0.952 0.048
#> GSM1296106 1 0.7528 0.6097 0.784 0.216
#> GSM1296102 1 0.4022 0.7194 0.920 0.080
#> GSM1296122 1 0.8861 0.4752 0.696 0.304
#> GSM1296089 1 0.5946 0.6802 0.856 0.144
#> GSM1296083 1 0.0376 0.7456 0.996 0.004
#> GSM1296116 2 1.0000 -0.1142 0.496 0.504
#> GSM1296085 1 0.0000 0.7450 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1296094 3 0.0000 0.920 0.000 0.000 1.000
#> GSM1296119 2 0.3038 0.848 0.000 0.896 0.104
#> GSM1296076 3 0.0000 0.920 0.000 0.000 1.000
#> GSM1296092 3 0.0000 0.920 0.000 0.000 1.000
#> GSM1296103 3 0.0000 0.920 0.000 0.000 1.000
#> GSM1296078 3 0.0592 0.914 0.000 0.012 0.988
#> GSM1296107 2 0.0237 0.892 0.000 0.996 0.004
#> GSM1296109 3 0.0892 0.909 0.000 0.020 0.980
#> GSM1296080 3 0.5988 0.398 0.368 0.000 0.632
#> GSM1296090 3 0.0000 0.920 0.000 0.000 1.000
#> GSM1296074 3 0.0000 0.920 0.000 0.000 1.000
#> GSM1296111 2 0.0592 0.889 0.000 0.988 0.012
#> GSM1296099 3 0.0000 0.920 0.000 0.000 1.000
#> GSM1296086 3 0.0000 0.920 0.000 0.000 1.000
#> GSM1296117 2 0.1163 0.888 0.000 0.972 0.028
#> GSM1296113 2 0.0000 0.893 0.000 1.000 0.000
#> GSM1296096 3 0.0000 0.920 0.000 0.000 1.000
#> GSM1296105 3 0.3755 0.823 0.120 0.008 0.872
#> GSM1296098 3 0.0000 0.920 0.000 0.000 1.000
#> GSM1296101 3 0.0000 0.920 0.000 0.000 1.000
#> GSM1296121 2 0.4887 0.663 0.000 0.772 0.228
#> GSM1296088 3 0.0000 0.920 0.000 0.000 1.000
#> GSM1296082 3 0.0000 0.920 0.000 0.000 1.000
#> GSM1296115 2 0.6154 0.247 0.000 0.592 0.408
#> GSM1296084 3 0.4346 0.750 0.184 0.000 0.816
#> GSM1296072 2 0.0000 0.893 0.000 1.000 0.000
#> GSM1296069 2 0.0000 0.893 0.000 1.000 0.000
#> GSM1296071 2 0.2066 0.882 0.060 0.940 0.000
#> GSM1296070 2 0.0000 0.893 0.000 1.000 0.000
#> GSM1296073 3 0.6126 0.370 0.000 0.400 0.600
#> GSM1296034 1 0.3551 0.829 0.868 0.000 0.132
#> GSM1296041 2 0.2165 0.860 0.000 0.936 0.064
#> GSM1296035 3 0.0000 0.920 0.000 0.000 1.000
#> GSM1296038 3 0.1753 0.892 0.000 0.048 0.952
#> GSM1296047 2 0.3715 0.843 0.128 0.868 0.004
#> GSM1296039 3 0.0000 0.920 0.000 0.000 1.000
#> GSM1296042 2 0.0000 0.893 0.000 1.000 0.000
#> GSM1296043 2 0.0000 0.893 0.000 1.000 0.000
#> GSM1296037 1 0.0000 0.961 1.000 0.000 0.000
#> GSM1296046 2 0.0000 0.893 0.000 1.000 0.000
#> GSM1296044 2 0.2796 0.870 0.092 0.908 0.000
#> GSM1296045 2 0.0000 0.893 0.000 1.000 0.000
#> GSM1296025 1 0.0000 0.961 1.000 0.000 0.000
#> GSM1296033 1 0.0000 0.961 1.000 0.000 0.000
#> GSM1296027 1 0.0000 0.961 1.000 0.000 0.000
#> GSM1296032 1 0.0000 0.961 1.000 0.000 0.000
#> GSM1296024 1 0.0000 0.961 1.000 0.000 0.000
#> GSM1296031 1 0.0000 0.961 1.000 0.000 0.000
#> GSM1296028 1 0.0000 0.961 1.000 0.000 0.000
#> GSM1296029 1 0.0000 0.961 1.000 0.000 0.000
#> GSM1296026 3 0.0000 0.920 0.000 0.000 1.000
#> GSM1296030 1 0.5529 0.579 0.704 0.000 0.296
#> GSM1296040 3 0.0424 0.916 0.008 0.000 0.992
#> GSM1296036 3 0.0000 0.920 0.000 0.000 1.000
#> GSM1296048 2 0.2711 0.839 0.000 0.912 0.088
#> GSM1296059 3 0.0000 0.920 0.000 0.000 1.000
#> GSM1296066 2 0.0000 0.893 0.000 1.000 0.000
#> GSM1296060 3 0.0000 0.920 0.000 0.000 1.000
#> GSM1296063 3 0.6126 0.370 0.000 0.400 0.600
#> GSM1296064 3 0.0000 0.920 0.000 0.000 1.000
#> GSM1296067 2 0.2711 0.872 0.088 0.912 0.000
#> GSM1296062 3 0.0424 0.916 0.008 0.000 0.992
#> GSM1296068 2 0.2796 0.870 0.092 0.908 0.000
#> GSM1296050 1 0.0237 0.958 0.996 0.004 0.000
#> GSM1296057 1 0.4233 0.791 0.836 0.004 0.160
#> GSM1296052 1 0.0000 0.961 1.000 0.000 0.000
#> GSM1296054 1 0.0000 0.961 1.000 0.000 0.000
#> GSM1296049 1 0.0000 0.961 1.000 0.000 0.000
#> GSM1296055 2 0.8440 0.595 0.184 0.620 0.196
#> GSM1296053 1 0.0000 0.961 1.000 0.000 0.000
#> GSM1296058 3 0.2959 0.853 0.100 0.000 0.900
#> GSM1296051 3 0.0000 0.920 0.000 0.000 1.000
#> GSM1296056 3 0.0000 0.920 0.000 0.000 1.000
#> GSM1296065 3 0.8103 0.496 0.120 0.248 0.632
#> GSM1296061 3 0.0000 0.920 0.000 0.000 1.000
#> GSM1296095 3 0.7624 0.568 0.104 0.224 0.672
#> GSM1296120 2 0.3340 0.851 0.120 0.880 0.000
#> GSM1296077 1 0.0000 0.961 1.000 0.000 0.000
#> GSM1296093 1 0.0000 0.961 1.000 0.000 0.000
#> GSM1296104 3 0.5944 0.751 0.120 0.088 0.792
#> GSM1296079 1 0.0000 0.961 1.000 0.000 0.000
#> GSM1296108 2 0.3340 0.851 0.120 0.880 0.000
#> GSM1296110 2 0.0000 0.893 0.000 1.000 0.000
#> GSM1296081 1 0.0000 0.961 1.000 0.000 0.000
#> GSM1296091 1 0.0000 0.961 1.000 0.000 0.000
#> GSM1296075 1 0.5810 0.423 0.664 0.336 0.000
#> GSM1296112 2 0.2796 0.870 0.092 0.908 0.000
#> GSM1296100 1 0.0000 0.961 1.000 0.000 0.000
#> GSM1296087 1 0.0000 0.961 1.000 0.000 0.000
#> GSM1296118 2 0.3816 0.828 0.148 0.852 0.000
#> GSM1296114 2 0.2796 0.870 0.092 0.908 0.000
#> GSM1296097 3 0.5334 0.780 0.120 0.060 0.820
#> GSM1296106 2 0.8568 0.575 0.200 0.608 0.192
#> GSM1296102 1 0.1399 0.935 0.968 0.004 0.028
#> GSM1296122 2 0.6126 0.407 0.400 0.600 0.000
#> GSM1296089 1 0.0000 0.961 1.000 0.000 0.000
#> GSM1296083 1 0.0000 0.961 1.000 0.000 0.000
#> GSM1296116 2 0.0000 0.893 0.000 1.000 0.000
#> GSM1296085 1 0.0000 0.961 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1296094 3 0.0000 0.9256 0.000 0.000 1.000 0.000
#> GSM1296119 2 0.4150 0.7193 0.000 0.824 0.120 0.056
#> GSM1296076 3 0.0188 0.9230 0.000 0.004 0.996 0.000
#> GSM1296092 3 0.0000 0.9256 0.000 0.000 1.000 0.000
#> GSM1296103 3 0.0000 0.9256 0.000 0.000 1.000 0.000
#> GSM1296078 3 0.1792 0.8697 0.000 0.068 0.932 0.000
#> GSM1296107 2 0.0000 0.8698 0.000 1.000 0.000 0.000
#> GSM1296109 3 0.1557 0.8800 0.000 0.056 0.944 0.000
#> GSM1296080 3 0.4967 0.1468 0.452 0.000 0.548 0.000
#> GSM1296090 3 0.0000 0.9256 0.000 0.000 1.000 0.000
#> GSM1296074 3 0.0000 0.9256 0.000 0.000 1.000 0.000
#> GSM1296111 2 0.0000 0.8698 0.000 1.000 0.000 0.000
#> GSM1296099 3 0.0000 0.9256 0.000 0.000 1.000 0.000
#> GSM1296086 3 0.0000 0.9256 0.000 0.000 1.000 0.000
#> GSM1296117 2 0.2797 0.8223 0.000 0.900 0.032 0.068
#> GSM1296113 2 0.4164 0.6068 0.000 0.736 0.000 0.264
#> GSM1296096 3 0.0000 0.9256 0.000 0.000 1.000 0.000
#> GSM1296105 3 0.4994 0.1075 0.000 0.000 0.520 0.480
#> GSM1296098 3 0.0000 0.9256 0.000 0.000 1.000 0.000
#> GSM1296101 3 0.0000 0.9256 0.000 0.000 1.000 0.000
#> GSM1296121 2 0.0000 0.8698 0.000 1.000 0.000 0.000
#> GSM1296088 3 0.0000 0.9256 0.000 0.000 1.000 0.000
#> GSM1296082 3 0.0000 0.9256 0.000 0.000 1.000 0.000
#> GSM1296115 2 0.0000 0.8698 0.000 1.000 0.000 0.000
#> GSM1296084 3 0.2921 0.7822 0.140 0.000 0.860 0.000
#> GSM1296072 2 0.4989 0.1853 0.000 0.528 0.000 0.472
#> GSM1296069 2 0.1867 0.8506 0.000 0.928 0.000 0.072
#> GSM1296071 4 0.4916 0.0767 0.000 0.424 0.000 0.576
#> GSM1296070 2 0.1940 0.8487 0.000 0.924 0.000 0.076
#> GSM1296073 2 0.0000 0.8698 0.000 1.000 0.000 0.000
#> GSM1296034 1 0.5519 0.6491 0.684 0.000 0.052 0.264
#> GSM1296041 2 0.0000 0.8698 0.000 1.000 0.000 0.000
#> GSM1296035 3 0.0000 0.9256 0.000 0.000 1.000 0.000
#> GSM1296038 3 0.2469 0.8261 0.000 0.108 0.892 0.000
#> GSM1296047 4 0.0000 0.5883 0.000 0.000 0.000 1.000
#> GSM1296039 3 0.0000 0.9256 0.000 0.000 1.000 0.000
#> GSM1296042 2 0.0188 0.8695 0.000 0.996 0.000 0.004
#> GSM1296043 2 0.2921 0.8007 0.000 0.860 0.000 0.140
#> GSM1296037 1 0.4992 0.4445 0.524 0.000 0.000 0.476
#> GSM1296046 2 0.4989 0.1846 0.000 0.528 0.000 0.472
#> GSM1296044 4 0.4830 0.1774 0.000 0.392 0.000 0.608
#> GSM1296045 2 0.2081 0.8440 0.000 0.916 0.000 0.084
#> GSM1296025 1 0.0000 0.8318 1.000 0.000 0.000 0.000
#> GSM1296033 1 0.3015 0.7841 0.884 0.000 0.024 0.092
#> GSM1296027 1 0.0000 0.8318 1.000 0.000 0.000 0.000
#> GSM1296032 1 0.0000 0.8318 1.000 0.000 0.000 0.000
#> GSM1296024 1 0.0000 0.8318 1.000 0.000 0.000 0.000
#> GSM1296031 1 0.4697 0.6034 0.644 0.000 0.000 0.356
#> GSM1296028 1 0.1637 0.8128 0.940 0.000 0.000 0.060
#> GSM1296029 1 0.0000 0.8318 1.000 0.000 0.000 0.000
#> GSM1296026 3 0.0000 0.9256 0.000 0.000 1.000 0.000
#> GSM1296030 1 0.4304 0.5289 0.716 0.000 0.284 0.000
#> GSM1296040 3 0.0188 0.9228 0.000 0.000 0.996 0.004
#> GSM1296036 3 0.0000 0.9256 0.000 0.000 1.000 0.000
#> GSM1296048 2 0.0000 0.8698 0.000 1.000 0.000 0.000
#> GSM1296059 3 0.0000 0.9256 0.000 0.000 1.000 0.000
#> GSM1296066 2 0.0000 0.8698 0.000 1.000 0.000 0.000
#> GSM1296060 3 0.0000 0.9256 0.000 0.000 1.000 0.000
#> GSM1296063 2 0.1302 0.8423 0.000 0.956 0.044 0.000
#> GSM1296064 3 0.0000 0.9256 0.000 0.000 1.000 0.000
#> GSM1296067 4 0.4790 0.2063 0.000 0.380 0.000 0.620
#> GSM1296062 3 0.0336 0.9197 0.000 0.000 0.992 0.008
#> GSM1296068 4 0.4761 0.2192 0.000 0.372 0.000 0.628
#> GSM1296050 1 0.4713 0.5924 0.640 0.000 0.000 0.360
#> GSM1296057 4 0.6953 -0.2988 0.412 0.000 0.112 0.476
#> GSM1296052 1 0.0000 0.8318 1.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.8318 1.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.8318 1.000 0.000 0.000 0.000
#> GSM1296055 4 0.5046 0.4963 0.064 0.040 0.092 0.804
#> GSM1296053 1 0.0000 0.8318 1.000 0.000 0.000 0.000
#> GSM1296058 3 0.4360 0.6169 0.008 0.000 0.744 0.248
#> GSM1296051 3 0.0000 0.9256 0.000 0.000 1.000 0.000
#> GSM1296056 3 0.0000 0.9256 0.000 0.000 1.000 0.000
#> GSM1296065 4 0.4585 0.3165 0.000 0.000 0.332 0.668
#> GSM1296061 3 0.0000 0.9256 0.000 0.000 1.000 0.000
#> GSM1296095 3 0.4992 0.1104 0.000 0.000 0.524 0.476
#> GSM1296120 4 0.0000 0.5883 0.000 0.000 0.000 1.000
#> GSM1296077 1 0.3311 0.7569 0.828 0.000 0.000 0.172
#> GSM1296093 1 0.0817 0.8269 0.976 0.000 0.000 0.024
#> GSM1296104 4 0.4985 -0.0229 0.000 0.000 0.468 0.532
#> GSM1296079 1 0.2149 0.8048 0.912 0.000 0.000 0.088
#> GSM1296108 4 0.2281 0.5414 0.000 0.096 0.000 0.904
#> GSM1296110 4 0.4992 -0.1178 0.000 0.476 0.000 0.524
#> GSM1296081 1 0.0000 0.8318 1.000 0.000 0.000 0.000
#> GSM1296091 1 0.4164 0.6868 0.736 0.000 0.000 0.264
#> GSM1296075 4 0.4222 0.1745 0.272 0.000 0.000 0.728
#> GSM1296112 4 0.4830 0.1774 0.000 0.392 0.000 0.608
#> GSM1296100 1 0.4992 0.4445 0.524 0.000 0.000 0.476
#> GSM1296087 1 0.0000 0.8318 1.000 0.000 0.000 0.000
#> GSM1296118 4 0.0000 0.5883 0.000 0.000 0.000 1.000
#> GSM1296114 4 0.4830 0.1774 0.000 0.392 0.000 0.608
#> GSM1296097 4 0.4994 -0.0567 0.000 0.000 0.480 0.520
#> GSM1296106 4 0.0000 0.5883 0.000 0.000 0.000 1.000
#> GSM1296102 1 0.5292 0.4263 0.512 0.000 0.008 0.480
#> GSM1296122 4 0.0000 0.5883 0.000 0.000 0.000 1.000
#> GSM1296089 1 0.4972 0.4746 0.544 0.000 0.000 0.456
#> GSM1296083 1 0.0000 0.8318 1.000 0.000 0.000 0.000
#> GSM1296116 2 0.2081 0.8440 0.000 0.916 0.000 0.084
#> GSM1296085 1 0.0000 0.8318 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
#> GSM1296094 3 0.0404 0.9481 0.000 0.000 0.988 0.012 0.000
#> GSM1296119 5 0.3398 0.7759 0.000 0.024 0.144 0.004 0.828
#> GSM1296076 3 0.0794 0.9432 0.000 0.000 0.972 0.028 0.000
#> GSM1296092 3 0.1638 0.9272 0.000 0.004 0.932 0.064 0.000
#> GSM1296103 3 0.0404 0.9481 0.000 0.000 0.988 0.012 0.000
#> GSM1296078 3 0.1981 0.9143 0.000 0.000 0.924 0.028 0.048
#> GSM1296107 5 0.0000 0.9501 0.000 0.000 0.000 0.000 1.000
#> GSM1296109 3 0.1197 0.9298 0.000 0.000 0.952 0.000 0.048
#> GSM1296080 3 0.5505 0.1565 0.412 0.004 0.528 0.056 0.000
#> GSM1296090 3 0.0794 0.9432 0.000 0.000 0.972 0.028 0.000
#> GSM1296074 3 0.0794 0.9432 0.000 0.000 0.972 0.028 0.000
#> GSM1296111 5 0.0000 0.9501 0.000 0.000 0.000 0.000 1.000
#> GSM1296099 3 0.0404 0.9481 0.000 0.000 0.988 0.012 0.000
#> GSM1296086 3 0.0880 0.9424 0.000 0.000 0.968 0.032 0.000
#> GSM1296117 5 0.1725 0.9116 0.000 0.020 0.044 0.000 0.936
#> GSM1296113 2 0.2966 0.8062 0.000 0.816 0.000 0.000 0.184
#> GSM1296096 3 0.0404 0.9481 0.000 0.000 0.988 0.012 0.000
#> GSM1296105 4 0.1478 0.7812 0.000 0.000 0.064 0.936 0.000
#> GSM1296098 3 0.0404 0.9481 0.000 0.000 0.988 0.012 0.000
#> GSM1296101 3 0.0404 0.9481 0.000 0.000 0.988 0.012 0.000
#> GSM1296121 5 0.0000 0.9501 0.000 0.000 0.000 0.000 1.000
#> GSM1296088 3 0.0963 0.9361 0.000 0.000 0.964 0.036 0.000
#> GSM1296082 3 0.0794 0.9432 0.000 0.000 0.972 0.028 0.000
#> GSM1296115 5 0.0000 0.9501 0.000 0.000 0.000 0.000 1.000
#> GSM1296084 3 0.3909 0.7797 0.124 0.004 0.808 0.064 0.000
#> GSM1296072 2 0.2798 0.8484 0.000 0.852 0.000 0.008 0.140
#> GSM1296069 5 0.1544 0.9244 0.000 0.068 0.000 0.000 0.932
#> GSM1296071 2 0.0162 0.9402 0.000 0.996 0.000 0.000 0.004
#> GSM1296070 5 0.1608 0.9221 0.000 0.072 0.000 0.000 0.928
#> GSM1296073 5 0.0000 0.9501 0.000 0.000 0.000 0.000 1.000
#> GSM1296034 4 0.4283 0.2625 0.456 0.000 0.000 0.544 0.000
#> GSM1296041 5 0.0000 0.9501 0.000 0.000 0.000 0.000 1.000
#> GSM1296035 3 0.0404 0.9481 0.000 0.000 0.988 0.012 0.000
#> GSM1296038 3 0.2293 0.8858 0.000 0.000 0.900 0.016 0.084
#> GSM1296047 4 0.4287 0.1481 0.000 0.460 0.000 0.540 0.000
#> GSM1296039 3 0.0703 0.9439 0.000 0.000 0.976 0.024 0.000
#> GSM1296042 5 0.0162 0.9496 0.000 0.004 0.000 0.000 0.996
#> GSM1296043 5 0.2020 0.9004 0.000 0.100 0.000 0.000 0.900
#> GSM1296037 4 0.1908 0.7692 0.092 0.000 0.000 0.908 0.000
#> GSM1296046 2 0.2230 0.8743 0.000 0.884 0.000 0.000 0.116
#> GSM1296044 2 0.0162 0.9402 0.000 0.996 0.000 0.000 0.004
#> GSM1296045 5 0.1732 0.9161 0.000 0.080 0.000 0.000 0.920
#> GSM1296025 1 0.0000 0.8981 1.000 0.000 0.000 0.000 0.000
#> GSM1296033 1 0.3759 0.7757 0.792 0.004 0.024 0.180 0.000
#> GSM1296027 1 0.1502 0.8937 0.940 0.004 0.000 0.056 0.000
#> GSM1296032 1 0.0794 0.9015 0.972 0.000 0.000 0.028 0.000
#> GSM1296024 1 0.0000 0.8981 1.000 0.000 0.000 0.000 0.000
#> GSM1296031 4 0.4161 0.3525 0.392 0.000 0.000 0.608 0.000
#> GSM1296028 1 0.2488 0.8521 0.872 0.004 0.000 0.124 0.000
#> GSM1296029 1 0.1571 0.8918 0.936 0.004 0.000 0.060 0.000
#> GSM1296026 3 0.1502 0.9264 0.000 0.004 0.940 0.056 0.000
#> GSM1296030 1 0.5026 0.5846 0.684 0.004 0.244 0.068 0.000
#> GSM1296040 3 0.0794 0.9420 0.000 0.000 0.972 0.028 0.000
#> GSM1296036 3 0.0404 0.9481 0.000 0.000 0.988 0.012 0.000
#> GSM1296048 5 0.0000 0.9501 0.000 0.000 0.000 0.000 1.000
#> GSM1296059 3 0.0404 0.9481 0.000 0.000 0.988 0.012 0.000
#> GSM1296066 5 0.0162 0.9491 0.000 0.004 0.000 0.000 0.996
#> GSM1296060 3 0.0404 0.9481 0.000 0.000 0.988 0.012 0.000
#> GSM1296063 5 0.1121 0.9240 0.000 0.000 0.044 0.000 0.956
#> GSM1296064 3 0.0794 0.9432 0.000 0.000 0.972 0.028 0.000
#> GSM1296067 2 0.3416 0.8431 0.000 0.840 0.000 0.088 0.072
#> GSM1296062 3 0.0794 0.9409 0.000 0.000 0.972 0.028 0.000
#> GSM1296068 2 0.0162 0.9402 0.000 0.996 0.000 0.000 0.004
#> GSM1296050 4 0.4126 0.4472 0.380 0.000 0.000 0.620 0.000
#> GSM1296057 4 0.0955 0.7832 0.000 0.004 0.028 0.968 0.000
#> GSM1296052 1 0.1205 0.8991 0.956 0.004 0.000 0.040 0.000
#> GSM1296054 1 0.0794 0.9015 0.972 0.000 0.000 0.028 0.000
#> GSM1296049 1 0.0000 0.8981 1.000 0.000 0.000 0.000 0.000
#> GSM1296055 4 0.2284 0.7736 0.000 0.056 0.028 0.912 0.004
#> GSM1296053 1 0.0794 0.9015 0.972 0.000 0.000 0.028 0.000
#> GSM1296058 4 0.4440 0.0953 0.000 0.004 0.468 0.528 0.000
#> GSM1296051 3 0.1768 0.9242 0.000 0.004 0.924 0.072 0.000
#> GSM1296056 3 0.0794 0.9432 0.000 0.000 0.972 0.028 0.000
#> GSM1296065 4 0.2221 0.7779 0.000 0.036 0.052 0.912 0.000
#> GSM1296061 3 0.0404 0.9481 0.000 0.000 0.988 0.012 0.000
#> GSM1296095 4 0.4822 0.4597 0.000 0.032 0.352 0.616 0.000
#> GSM1296120 2 0.0162 0.9381 0.000 0.996 0.000 0.004 0.000
#> GSM1296077 1 0.3480 0.5951 0.752 0.000 0.000 0.248 0.000
#> GSM1296093 1 0.0880 0.8900 0.968 0.000 0.000 0.032 0.000
#> GSM1296104 4 0.1608 0.7782 0.000 0.000 0.072 0.928 0.000
#> GSM1296079 1 0.2127 0.8188 0.892 0.000 0.000 0.108 0.000
#> GSM1296108 2 0.0162 0.9381 0.000 0.996 0.000 0.004 0.000
#> GSM1296110 2 0.0162 0.9402 0.000 0.996 0.000 0.000 0.004
#> GSM1296081 1 0.0000 0.8981 1.000 0.000 0.000 0.000 0.000
#> GSM1296091 1 0.4341 0.4002 0.592 0.004 0.000 0.404 0.000
#> GSM1296075 4 0.1282 0.7766 0.004 0.044 0.000 0.952 0.000
#> GSM1296112 2 0.0510 0.9370 0.000 0.984 0.000 0.000 0.016
#> GSM1296100 4 0.1908 0.7683 0.092 0.000 0.000 0.908 0.000
#> GSM1296087 1 0.1571 0.8918 0.936 0.004 0.000 0.060 0.000
#> GSM1296118 2 0.1478 0.8987 0.000 0.936 0.000 0.064 0.000
#> GSM1296114 2 0.0162 0.9402 0.000 0.996 0.000 0.000 0.004
#> GSM1296097 4 0.1851 0.7729 0.000 0.000 0.088 0.912 0.000
#> GSM1296106 4 0.2020 0.7511 0.000 0.100 0.000 0.900 0.000
#> GSM1296102 4 0.1410 0.7788 0.060 0.000 0.000 0.940 0.000
#> GSM1296122 4 0.2929 0.6911 0.000 0.180 0.000 0.820 0.000
#> GSM1296089 4 0.2852 0.7103 0.172 0.000 0.000 0.828 0.000
#> GSM1296083 1 0.0000 0.8981 1.000 0.000 0.000 0.000 0.000
#> GSM1296116 5 0.1792 0.9135 0.000 0.084 0.000 0.000 0.916
#> GSM1296085 1 0.0703 0.9009 0.976 0.000 0.000 0.024 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1296094 3 0.0000 0.82949 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296119 5 0.3233 0.81474 0.000 0.024 0.132 0.016 0.828 0.000
#> GSM1296076 3 0.3607 0.72171 0.000 0.000 0.652 0.348 0.000 0.000
#> GSM1296092 3 0.6066 0.40595 0.268 0.000 0.392 0.340 0.000 0.000
#> GSM1296103 3 0.0000 0.82949 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296078 3 0.4251 0.70463 0.000 0.000 0.624 0.348 0.028 0.000
#> GSM1296107 5 0.0260 0.94962 0.000 0.000 0.000 0.008 0.992 0.000
#> GSM1296109 3 0.1444 0.80666 0.000 0.000 0.928 0.000 0.072 0.000
#> GSM1296080 1 0.4399 -0.00584 0.516 0.000 0.460 0.024 0.000 0.000
#> GSM1296090 3 0.3607 0.72171 0.000 0.000 0.652 0.348 0.000 0.000
#> GSM1296074 3 0.3592 0.72365 0.000 0.000 0.656 0.344 0.000 0.000
#> GSM1296111 5 0.0260 0.94962 0.000 0.000 0.000 0.008 0.992 0.000
#> GSM1296099 3 0.0000 0.82949 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296086 3 0.3512 0.75775 0.008 0.000 0.720 0.272 0.000 0.000
#> GSM1296117 5 0.1974 0.91130 0.000 0.020 0.048 0.012 0.920 0.000
#> GSM1296113 2 0.2631 0.83733 0.000 0.840 0.000 0.008 0.152 0.000
#> GSM1296096 3 0.0000 0.82949 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296105 6 0.0713 0.81230 0.000 0.000 0.028 0.000 0.000 0.972
#> GSM1296098 3 0.0458 0.82609 0.000 0.000 0.984 0.016 0.000 0.000
#> GSM1296101 3 0.0000 0.82949 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296121 5 0.0363 0.94922 0.000 0.000 0.000 0.012 0.988 0.000
#> GSM1296088 3 0.3707 0.72598 0.136 0.000 0.784 0.080 0.000 0.000
#> GSM1296082 3 0.3547 0.73041 0.000 0.000 0.668 0.332 0.000 0.000
#> GSM1296115 5 0.0000 0.94960 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296084 3 0.4209 0.34694 0.384 0.000 0.596 0.020 0.000 0.000
#> GSM1296072 2 0.2833 0.82967 0.000 0.836 0.000 0.004 0.148 0.012
#> GSM1296069 5 0.1387 0.92624 0.000 0.068 0.000 0.000 0.932 0.000
#> GSM1296071 2 0.0000 0.94022 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296070 5 0.1444 0.92390 0.000 0.072 0.000 0.000 0.928 0.000
#> GSM1296073 5 0.0260 0.94925 0.000 0.000 0.000 0.008 0.992 0.000
#> GSM1296034 6 0.5624 0.19800 0.268 0.000 0.008 0.160 0.000 0.564
#> GSM1296041 5 0.0260 0.94962 0.000 0.000 0.000 0.008 0.992 0.000
#> GSM1296035 3 0.0000 0.82949 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296038 3 0.2170 0.79015 0.000 0.000 0.888 0.000 0.100 0.012
#> GSM1296047 6 0.3804 0.24329 0.000 0.424 0.000 0.000 0.000 0.576
#> GSM1296039 3 0.2178 0.80737 0.000 0.000 0.868 0.132 0.000 0.000
#> GSM1296042 5 0.0146 0.94979 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1296043 5 0.1814 0.90404 0.000 0.100 0.000 0.000 0.900 0.000
#> GSM1296037 6 0.1124 0.80594 0.036 0.000 0.000 0.008 0.000 0.956
#> GSM1296046 2 0.2003 0.87056 0.000 0.884 0.000 0.000 0.116 0.000
#> GSM1296044 2 0.0000 0.94022 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296045 5 0.1501 0.92104 0.000 0.076 0.000 0.000 0.924 0.000
#> GSM1296025 4 0.3911 0.89966 0.368 0.000 0.000 0.624 0.000 0.008
#> GSM1296033 1 0.3727 0.56039 0.816 0.000 0.032 0.076 0.000 0.076
#> GSM1296027 1 0.0458 0.62939 0.984 0.000 0.000 0.016 0.000 0.000
#> GSM1296032 1 0.2838 0.43483 0.808 0.000 0.000 0.188 0.000 0.004
#> GSM1296024 4 0.3911 0.89966 0.368 0.000 0.000 0.624 0.000 0.008
#> GSM1296031 6 0.3608 0.52506 0.272 0.000 0.000 0.012 0.000 0.716
#> GSM1296028 1 0.1196 0.62683 0.952 0.000 0.000 0.008 0.000 0.040
#> GSM1296029 1 0.0000 0.63221 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296026 3 0.5803 0.46042 0.284 0.000 0.540 0.164 0.000 0.012
#> GSM1296030 1 0.4297 0.47038 0.724 0.000 0.176 0.100 0.000 0.000
#> GSM1296040 3 0.0363 0.82739 0.000 0.000 0.988 0.000 0.000 0.012
#> GSM1296036 3 0.0458 0.82609 0.000 0.000 0.984 0.016 0.000 0.000
#> GSM1296048 5 0.0146 0.94965 0.000 0.000 0.000 0.004 0.996 0.000
#> GSM1296059 3 0.0000 0.82949 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296066 5 0.0405 0.94931 0.000 0.004 0.000 0.008 0.988 0.000
#> GSM1296060 3 0.0000 0.82949 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296063 5 0.1429 0.91873 0.000 0.000 0.052 0.004 0.940 0.004
#> GSM1296064 3 0.2793 0.78537 0.000 0.000 0.800 0.200 0.000 0.000
#> GSM1296067 2 0.3206 0.83146 0.000 0.828 0.000 0.000 0.068 0.104
#> GSM1296062 3 0.0363 0.82709 0.000 0.000 0.988 0.012 0.000 0.000
#> GSM1296068 2 0.0000 0.94022 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296050 4 0.5214 0.58776 0.172 0.000 0.000 0.612 0.000 0.216
#> GSM1296057 6 0.3840 0.55099 0.284 0.000 0.020 0.000 0.000 0.696
#> GSM1296052 1 0.2482 0.51095 0.848 0.000 0.000 0.148 0.000 0.004
#> GSM1296054 1 0.2668 0.48392 0.828 0.000 0.000 0.168 0.000 0.004
#> GSM1296049 4 0.3819 0.89766 0.372 0.000 0.000 0.624 0.000 0.004
#> GSM1296055 6 0.0436 0.81163 0.004 0.004 0.004 0.000 0.000 0.988
#> GSM1296053 1 0.1958 0.57220 0.896 0.000 0.000 0.100 0.000 0.004
#> GSM1296058 6 0.6011 0.21095 0.272 0.000 0.296 0.000 0.000 0.432
#> GSM1296051 3 0.6381 0.37885 0.280 0.000 0.376 0.332 0.000 0.012
#> GSM1296056 3 0.2762 0.78748 0.000 0.000 0.804 0.196 0.000 0.000
#> GSM1296065 6 0.0777 0.81324 0.000 0.004 0.024 0.000 0.000 0.972
#> GSM1296061 3 0.0458 0.82609 0.000 0.000 0.984 0.016 0.000 0.000
#> GSM1296095 6 0.3874 0.43531 0.000 0.008 0.356 0.000 0.000 0.636
#> GSM1296120 2 0.0000 0.94022 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296077 4 0.4420 0.87794 0.340 0.000 0.000 0.620 0.000 0.040
#> GSM1296093 1 0.4010 -0.42896 0.584 0.000 0.000 0.408 0.000 0.008
#> GSM1296104 6 0.1141 0.80675 0.000 0.000 0.052 0.000 0.000 0.948
#> GSM1296079 4 0.4319 0.88797 0.348 0.000 0.000 0.620 0.000 0.032
#> GSM1296108 2 0.0000 0.94022 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296110 2 0.0000 0.94022 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296081 4 0.3934 0.89541 0.376 0.000 0.000 0.616 0.000 0.008
#> GSM1296091 1 0.2902 0.51044 0.800 0.000 0.000 0.004 0.000 0.196
#> GSM1296075 6 0.0717 0.81252 0.008 0.016 0.000 0.000 0.000 0.976
#> GSM1296112 2 0.0363 0.93666 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM1296100 6 0.1124 0.80532 0.036 0.000 0.000 0.008 0.000 0.956
#> GSM1296087 1 0.0547 0.63070 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM1296118 2 0.1387 0.89554 0.000 0.932 0.000 0.000 0.000 0.068
#> GSM1296114 2 0.0000 0.94022 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296097 6 0.1267 0.80227 0.000 0.000 0.060 0.000 0.000 0.940
#> GSM1296106 6 0.0713 0.81003 0.000 0.028 0.000 0.000 0.000 0.972
#> GSM1296102 6 0.0458 0.80965 0.016 0.000 0.000 0.000 0.000 0.984
#> GSM1296122 6 0.2053 0.76426 0.004 0.108 0.000 0.000 0.000 0.888
#> GSM1296089 6 0.1444 0.78226 0.072 0.000 0.000 0.000 0.000 0.928
#> GSM1296083 4 0.3930 0.82912 0.420 0.000 0.000 0.576 0.000 0.004
#> GSM1296116 5 0.1556 0.91863 0.000 0.080 0.000 0.000 0.920 0.000
#> GSM1296085 1 0.3405 0.13721 0.724 0.000 0.000 0.272 0.000 0.004
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# 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 tissue(p) time(p) agent(p) dose(p) individual(p) k
#> CV:pam 50 1.63e-03 0.5101 0.1135 3.30e-03 2.09e-02 2
#> CV:pam 92 1.82e-02 0.0418 0.0275 1.28e-07 1.84e-07 3
#> CV:pam 77 1.40e-06 0.0353 0.0924 1.15e-05 1.01e-04 4
#> CV:pam 91 1.70e-05 0.1135 0.1798 8.52e-08 1.82e-08 5
#> CV:pam 85 1.20e-04 0.3799 0.3805 1.59e-06 1.98e-08 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 45638 rows and 99 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 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 0.450 0.635 0.764 0.3760 0.640 0.640
#> 3 3 0.479 0.768 0.837 0.6407 0.593 0.407
#> 4 4 0.522 0.594 0.735 0.1666 0.797 0.482
#> 5 5 0.566 0.638 0.761 0.0619 0.843 0.495
#> 6 6 0.724 0.763 0.845 0.0650 0.852 0.449
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
#> GSM1296094 2 0.995 0.598 0.460 0.540
#> GSM1296119 2 0.456 0.647 0.096 0.904
#> GSM1296076 2 0.980 0.628 0.416 0.584
#> GSM1296092 2 0.980 0.628 0.416 0.584
#> GSM1296103 2 0.995 0.598 0.460 0.540
#> GSM1296078 2 0.980 0.628 0.416 0.584
#> GSM1296107 2 0.184 0.630 0.028 0.972
#> GSM1296109 2 0.980 0.605 0.416 0.584
#> GSM1296080 2 0.995 0.598 0.460 0.540
#> GSM1296090 2 0.980 0.628 0.416 0.584
#> GSM1296074 2 0.980 0.628 0.416 0.584
#> GSM1296111 2 0.184 0.630 0.028 0.972
#> GSM1296099 2 0.995 0.598 0.460 0.540
#> GSM1296086 2 0.969 0.619 0.396 0.604
#> GSM1296117 2 0.184 0.630 0.028 0.972
#> GSM1296113 2 0.184 0.630 0.028 0.972
#> GSM1296096 2 0.995 0.598 0.460 0.540
#> GSM1296105 2 0.995 0.598 0.460 0.540
#> GSM1296098 2 0.995 0.598 0.460 0.540
#> GSM1296101 2 0.987 0.591 0.432 0.568
#> GSM1296121 2 0.358 0.643 0.068 0.932
#> GSM1296088 2 0.987 0.591 0.432 0.568
#> GSM1296082 2 0.980 0.628 0.416 0.584
#> GSM1296115 2 0.000 0.616 0.000 1.000
#> GSM1296084 2 0.995 0.598 0.460 0.540
#> GSM1296072 2 0.456 0.647 0.096 0.904
#> GSM1296069 2 0.184 0.630 0.028 0.972
#> GSM1296071 2 0.184 0.630 0.028 0.972
#> GSM1296070 2 0.000 0.616 0.000 1.000
#> GSM1296073 2 0.358 0.643 0.068 0.932
#> GSM1296034 2 0.999 0.553 0.484 0.516
#> GSM1296041 2 0.184 0.630 0.028 0.972
#> GSM1296035 2 0.995 0.598 0.460 0.540
#> GSM1296038 2 0.980 0.608 0.416 0.584
#> GSM1296047 2 0.456 0.647 0.096 0.904
#> GSM1296039 2 0.991 0.612 0.444 0.556
#> GSM1296042 2 0.000 0.616 0.000 1.000
#> GSM1296043 2 0.184 0.630 0.028 0.972
#> GSM1296037 1 0.000 0.871 1.000 0.000
#> GSM1296046 2 0.184 0.630 0.028 0.972
#> GSM1296044 2 0.184 0.630 0.028 0.972
#> GSM1296045 2 0.000 0.616 0.000 1.000
#> GSM1296025 1 0.000 0.871 1.000 0.000
#> GSM1296033 2 0.995 0.598 0.460 0.540
#> GSM1296027 1 0.000 0.871 1.000 0.000
#> GSM1296032 1 0.000 0.871 1.000 0.000
#> GSM1296024 1 0.000 0.871 1.000 0.000
#> GSM1296031 1 0.662 0.686 0.828 0.172
#> GSM1296028 1 0.000 0.871 1.000 0.000
#> GSM1296029 1 0.388 0.792 0.924 0.076
#> GSM1296026 2 0.995 0.598 0.460 0.540
#> GSM1296030 2 0.987 0.591 0.432 0.568
#> GSM1296040 2 0.995 0.598 0.460 0.540
#> GSM1296036 2 0.995 0.598 0.460 0.540
#> GSM1296048 2 0.358 0.643 0.068 0.932
#> GSM1296059 2 0.995 0.598 0.460 0.540
#> GSM1296066 2 0.184 0.630 0.028 0.972
#> GSM1296060 2 0.995 0.598 0.460 0.540
#> GSM1296063 2 0.358 0.643 0.068 0.932
#> GSM1296064 2 0.983 0.625 0.424 0.576
#> GSM1296067 2 0.358 0.643 0.068 0.932
#> GSM1296062 2 0.995 0.598 0.460 0.540
#> GSM1296068 2 0.184 0.630 0.028 0.972
#> GSM1296050 1 0.980 -0.287 0.584 0.416
#> GSM1296057 2 0.995 0.598 0.460 0.540
#> GSM1296052 1 0.000 0.871 1.000 0.000
#> GSM1296054 1 0.000 0.871 1.000 0.000
#> GSM1296049 1 0.000 0.871 1.000 0.000
#> GSM1296055 2 0.975 0.613 0.408 0.592
#> GSM1296053 1 0.000 0.871 1.000 0.000
#> GSM1296058 2 0.988 0.593 0.436 0.564
#> GSM1296051 2 0.980 0.628 0.416 0.584
#> GSM1296056 2 0.981 0.605 0.420 0.580
#> GSM1296065 2 0.821 0.642 0.256 0.744
#> GSM1296061 2 0.995 0.598 0.460 0.540
#> GSM1296095 2 0.963 0.632 0.388 0.612
#> GSM1296120 2 0.456 0.647 0.096 0.904
#> GSM1296077 1 0.000 0.871 1.000 0.000
#> GSM1296093 1 0.000 0.871 1.000 0.000
#> GSM1296104 2 0.991 0.612 0.444 0.556
#> GSM1296079 1 0.000 0.871 1.000 0.000
#> GSM1296108 2 0.184 0.630 0.028 0.972
#> GSM1296110 2 0.000 0.616 0.000 1.000
#> GSM1296081 1 0.000 0.871 1.000 0.000
#> GSM1296091 1 0.988 -0.355 0.564 0.436
#> GSM1296075 2 0.985 0.622 0.428 0.572
#> GSM1296112 2 0.184 0.630 0.028 0.972
#> GSM1296100 1 0.343 0.805 0.936 0.064
#> GSM1296087 1 0.990 -0.266 0.560 0.440
#> GSM1296118 2 0.184 0.630 0.028 0.972
#> GSM1296114 2 0.184 0.630 0.028 0.972
#> GSM1296097 2 0.992 0.609 0.448 0.552
#> GSM1296106 2 0.827 0.640 0.260 0.740
#> GSM1296102 2 0.992 0.564 0.448 0.552
#> GSM1296122 2 0.358 0.643 0.068 0.932
#> GSM1296089 1 0.730 0.621 0.796 0.204
#> GSM1296083 1 0.000 0.871 1.000 0.000
#> GSM1296116 2 0.000 0.616 0.000 1.000
#> GSM1296085 1 0.000 0.871 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1296094 3 0.2492 0.904 0.016 0.048 0.936
#> GSM1296119 2 0.4062 0.877 0.000 0.836 0.164
#> GSM1296076 3 0.2550 0.904 0.024 0.040 0.936
#> GSM1296092 3 0.2550 0.904 0.024 0.040 0.936
#> GSM1296103 3 0.0747 0.882 0.016 0.000 0.984
#> GSM1296078 3 0.2550 0.904 0.024 0.040 0.936
#> GSM1296107 2 0.3482 0.893 0.000 0.872 0.128
#> GSM1296109 3 0.3941 0.835 0.000 0.156 0.844
#> GSM1296080 3 0.3670 0.898 0.020 0.092 0.888
#> GSM1296090 3 0.2550 0.904 0.024 0.040 0.936
#> GSM1296074 3 0.2550 0.904 0.024 0.040 0.936
#> GSM1296111 2 0.3482 0.893 0.000 0.872 0.128
#> GSM1296099 3 0.1636 0.894 0.016 0.020 0.964
#> GSM1296086 3 0.3375 0.891 0.008 0.100 0.892
#> GSM1296117 2 0.3482 0.893 0.000 0.872 0.128
#> GSM1296113 2 0.3482 0.893 0.000 0.872 0.128
#> GSM1296096 3 0.0747 0.882 0.016 0.000 0.984
#> GSM1296105 1 0.8581 0.223 0.456 0.096 0.448
#> GSM1296098 3 0.3091 0.904 0.016 0.072 0.912
#> GSM1296101 3 0.2261 0.902 0.000 0.068 0.932
#> GSM1296121 2 0.4346 0.871 0.000 0.816 0.184
#> GSM1296088 3 0.3295 0.895 0.008 0.096 0.896
#> GSM1296082 3 0.2550 0.904 0.024 0.040 0.936
#> GSM1296115 2 0.3752 0.891 0.000 0.856 0.144
#> GSM1296084 3 0.4683 0.867 0.024 0.140 0.836
#> GSM1296072 2 0.4062 0.877 0.000 0.836 0.164
#> GSM1296069 2 0.3340 0.892 0.000 0.880 0.120
#> GSM1296071 2 0.0237 0.832 0.000 0.996 0.004
#> GSM1296070 2 0.3752 0.891 0.000 0.856 0.144
#> GSM1296073 2 0.4555 0.858 0.000 0.800 0.200
#> GSM1296034 1 0.8236 0.332 0.508 0.076 0.416
#> GSM1296041 2 0.3482 0.893 0.000 0.872 0.128
#> GSM1296035 3 0.0747 0.882 0.016 0.000 0.984
#> GSM1296038 3 0.6521 -0.185 0.004 0.496 0.500
#> GSM1296047 2 0.3816 0.885 0.000 0.852 0.148
#> GSM1296039 3 0.2414 0.905 0.020 0.040 0.940
#> GSM1296042 2 0.3752 0.891 0.000 0.856 0.144
#> GSM1296043 2 0.2878 0.884 0.000 0.904 0.096
#> GSM1296037 1 0.0424 0.765 0.992 0.000 0.008
#> GSM1296046 2 0.0592 0.837 0.000 0.988 0.012
#> GSM1296044 2 0.0000 0.828 0.000 1.000 0.000
#> GSM1296045 2 0.3619 0.892 0.000 0.864 0.136
#> GSM1296025 1 0.0000 0.767 1.000 0.000 0.000
#> GSM1296033 1 0.8939 0.384 0.520 0.140 0.340
#> GSM1296027 1 0.0424 0.766 0.992 0.008 0.000
#> GSM1296032 1 0.0000 0.767 1.000 0.000 0.000
#> GSM1296024 1 0.0000 0.767 1.000 0.000 0.000
#> GSM1296031 1 0.4966 0.720 0.840 0.100 0.060
#> GSM1296028 1 0.0747 0.765 0.984 0.016 0.000
#> GSM1296029 1 0.3129 0.740 0.904 0.088 0.008
#> GSM1296026 3 0.4799 0.872 0.032 0.132 0.836
#> GSM1296030 3 0.4411 0.855 0.016 0.140 0.844
#> GSM1296040 3 0.3359 0.901 0.016 0.084 0.900
#> GSM1296036 3 0.3091 0.904 0.016 0.072 0.912
#> GSM1296048 2 0.4346 0.871 0.000 0.816 0.184
#> GSM1296059 3 0.0747 0.882 0.016 0.000 0.984
#> GSM1296066 2 0.3482 0.893 0.000 0.872 0.128
#> GSM1296060 3 0.2703 0.906 0.016 0.056 0.928
#> GSM1296063 2 0.5982 0.654 0.004 0.668 0.328
#> GSM1296064 3 0.2414 0.905 0.020 0.040 0.940
#> GSM1296067 2 0.4178 0.878 0.000 0.828 0.172
#> GSM1296062 3 0.3528 0.897 0.016 0.092 0.892
#> GSM1296068 2 0.0000 0.828 0.000 1.000 0.000
#> GSM1296050 1 0.6902 0.642 0.732 0.100 0.168
#> GSM1296057 1 0.9030 0.393 0.520 0.152 0.328
#> GSM1296052 1 0.0000 0.767 1.000 0.000 0.000
#> GSM1296054 1 0.0000 0.767 1.000 0.000 0.000
#> GSM1296049 1 0.0000 0.767 1.000 0.000 0.000
#> GSM1296055 1 0.9189 0.377 0.492 0.160 0.348
#> GSM1296053 1 0.0000 0.767 1.000 0.000 0.000
#> GSM1296058 1 0.9074 0.388 0.516 0.156 0.328
#> GSM1296051 3 0.3742 0.900 0.036 0.072 0.892
#> GSM1296056 3 0.3826 0.874 0.008 0.124 0.868
#> GSM1296065 2 0.6859 0.533 0.024 0.620 0.356
#> GSM1296061 3 0.3445 0.899 0.016 0.088 0.896
#> GSM1296095 2 0.5785 0.615 0.000 0.668 0.332
#> GSM1296120 2 0.4261 0.883 0.012 0.848 0.140
#> GSM1296077 1 0.0000 0.767 1.000 0.000 0.000
#> GSM1296093 1 0.0000 0.767 1.000 0.000 0.000
#> GSM1296104 2 0.7864 0.509 0.072 0.596 0.332
#> GSM1296079 1 0.0000 0.767 1.000 0.000 0.000
#> GSM1296108 2 0.0000 0.828 0.000 1.000 0.000
#> GSM1296110 2 0.0892 0.831 0.000 0.980 0.020
#> GSM1296081 1 0.0000 0.767 1.000 0.000 0.000
#> GSM1296091 1 0.8349 0.471 0.584 0.108 0.308
#> GSM1296075 1 0.9277 0.382 0.496 0.176 0.328
#> GSM1296112 2 0.0000 0.828 0.000 1.000 0.000
#> GSM1296100 1 0.0747 0.764 0.984 0.000 0.016
#> GSM1296087 1 0.7034 0.647 0.728 0.148 0.124
#> GSM1296118 2 0.3116 0.889 0.000 0.892 0.108
#> GSM1296114 2 0.0000 0.828 0.000 1.000 0.000
#> GSM1296097 1 0.9968 0.149 0.368 0.300 0.332
#> GSM1296106 1 0.9920 0.214 0.388 0.280 0.332
#> GSM1296102 1 0.9062 0.410 0.512 0.152 0.336
#> GSM1296122 2 0.5901 0.832 0.040 0.768 0.192
#> GSM1296089 1 0.5191 0.715 0.828 0.112 0.060
#> GSM1296083 1 0.0000 0.767 1.000 0.000 0.000
#> GSM1296116 2 0.1163 0.837 0.000 0.972 0.028
#> GSM1296085 1 0.0000 0.767 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1296094 3 0.0657 0.83581 0.000 0.004 0.984 0.012
#> GSM1296119 4 0.7154 -0.07427 0.000 0.428 0.132 0.440
#> GSM1296076 4 0.4284 0.55113 0.020 0.000 0.200 0.780
#> GSM1296092 4 0.5494 0.51617 0.076 0.000 0.208 0.716
#> GSM1296103 3 0.0592 0.83471 0.000 0.000 0.984 0.016
#> GSM1296078 4 0.4284 0.55113 0.020 0.000 0.200 0.780
#> GSM1296107 2 0.3306 0.81871 0.000 0.840 0.004 0.156
#> GSM1296109 4 0.5938 0.10795 0.008 0.024 0.424 0.544
#> GSM1296080 3 0.7725 -0.00691 0.296 0.004 0.476 0.224
#> GSM1296090 4 0.4361 0.54532 0.020 0.000 0.208 0.772
#> GSM1296074 4 0.4361 0.54532 0.020 0.000 0.208 0.772
#> GSM1296111 2 0.3157 0.82257 0.000 0.852 0.004 0.144
#> GSM1296099 3 0.0817 0.83332 0.000 0.000 0.976 0.024
#> GSM1296086 4 0.3984 0.56048 0.040 0.000 0.132 0.828
#> GSM1296117 2 0.3257 0.82026 0.000 0.844 0.004 0.152
#> GSM1296113 2 0.3157 0.82257 0.000 0.852 0.004 0.144
#> GSM1296096 3 0.1022 0.82791 0.000 0.000 0.968 0.032
#> GSM1296105 3 0.7407 -0.00798 0.176 0.004 0.524 0.296
#> GSM1296098 3 0.0524 0.83257 0.000 0.004 0.988 0.008
#> GSM1296101 3 0.2345 0.75664 0.000 0.000 0.900 0.100
#> GSM1296121 4 0.7002 0.06574 0.000 0.352 0.128 0.520
#> GSM1296088 4 0.7080 0.39503 0.236 0.000 0.196 0.568
#> GSM1296082 4 0.4361 0.54532 0.020 0.000 0.208 0.772
#> GSM1296115 2 0.3801 0.79166 0.000 0.780 0.000 0.220
#> GSM1296084 1 0.7661 0.12241 0.464 0.004 0.192 0.340
#> GSM1296072 2 0.7415 0.03163 0.008 0.500 0.140 0.352
#> GSM1296069 2 0.2593 0.83068 0.000 0.892 0.004 0.104
#> GSM1296071 2 0.0188 0.82790 0.000 0.996 0.000 0.004
#> GSM1296070 2 0.3688 0.79744 0.000 0.792 0.000 0.208
#> GSM1296073 4 0.5280 0.51787 0.000 0.120 0.128 0.752
#> GSM1296034 3 0.4949 0.58684 0.180 0.000 0.760 0.060
#> GSM1296041 2 0.3208 0.82153 0.000 0.848 0.004 0.148
#> GSM1296035 3 0.1022 0.82791 0.000 0.000 0.968 0.032
#> GSM1296038 4 0.5441 0.49571 0.016 0.008 0.332 0.644
#> GSM1296047 2 0.7332 0.00115 0.008 0.520 0.136 0.336
#> GSM1296039 4 0.4790 0.48038 0.000 0.000 0.380 0.620
#> GSM1296042 2 0.3764 0.79361 0.000 0.784 0.000 0.216
#> GSM1296043 2 0.0592 0.82921 0.000 0.984 0.000 0.016
#> GSM1296037 1 0.4168 0.65890 0.828 0.000 0.080 0.092
#> GSM1296046 2 0.0188 0.82790 0.000 0.996 0.000 0.004
#> GSM1296044 2 0.0000 0.82687 0.000 1.000 0.000 0.000
#> GSM1296045 2 0.2589 0.81359 0.000 0.884 0.000 0.116
#> GSM1296025 1 0.0000 0.75826 1.000 0.000 0.000 0.000
#> GSM1296033 1 0.6376 0.48066 0.608 0.004 0.076 0.312
#> GSM1296027 1 0.0592 0.75694 0.984 0.000 0.000 0.016
#> GSM1296032 1 0.0469 0.75748 0.988 0.000 0.000 0.012
#> GSM1296024 1 0.0000 0.75826 1.000 0.000 0.000 0.000
#> GSM1296031 1 0.4585 0.56631 0.668 0.000 0.000 0.332
#> GSM1296028 1 0.2345 0.72758 0.900 0.000 0.000 0.100
#> GSM1296029 1 0.3855 0.69263 0.820 0.004 0.012 0.164
#> GSM1296026 4 0.7487 0.33886 0.280 0.004 0.196 0.520
#> GSM1296030 1 0.7015 0.22113 0.484 0.000 0.120 0.396
#> GSM1296040 3 0.0779 0.83582 0.000 0.004 0.980 0.016
#> GSM1296036 3 0.0524 0.83257 0.000 0.004 0.988 0.008
#> GSM1296048 4 0.6939 0.11766 0.000 0.332 0.128 0.540
#> GSM1296059 3 0.0707 0.83418 0.000 0.000 0.980 0.020
#> GSM1296066 2 0.3157 0.82257 0.000 0.852 0.004 0.144
#> GSM1296060 3 0.3649 0.54423 0.000 0.000 0.796 0.204
#> GSM1296063 4 0.4100 0.54754 0.000 0.036 0.148 0.816
#> GSM1296064 4 0.4761 0.48877 0.000 0.000 0.372 0.628
#> GSM1296067 2 0.5420 0.40218 0.008 0.628 0.012 0.352
#> GSM1296062 3 0.1697 0.82437 0.016 0.004 0.952 0.028
#> GSM1296068 2 0.0000 0.82687 0.000 1.000 0.000 0.000
#> GSM1296050 1 0.5260 0.37710 0.568 0.004 0.004 0.424
#> GSM1296057 1 0.5369 0.53725 0.652 0.004 0.020 0.324
#> GSM1296052 1 0.0188 0.75717 0.996 0.000 0.000 0.004
#> GSM1296054 1 0.0000 0.75826 1.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.75826 1.000 0.000 0.000 0.000
#> GSM1296055 4 0.7389 0.36003 0.272 0.000 0.212 0.516
#> GSM1296053 1 0.0000 0.75826 1.000 0.000 0.000 0.000
#> GSM1296058 1 0.5601 0.46485 0.596 0.004 0.020 0.380
#> GSM1296051 4 0.5522 0.54773 0.080 0.000 0.204 0.716
#> GSM1296056 4 0.4100 0.57310 0.036 0.000 0.148 0.816
#> GSM1296065 4 0.6375 0.50498 0.012 0.060 0.312 0.616
#> GSM1296061 3 0.0657 0.83159 0.000 0.004 0.984 0.012
#> GSM1296095 4 0.7899 0.44729 0.016 0.188 0.312 0.484
#> GSM1296120 2 0.7230 0.09869 0.008 0.548 0.136 0.308
#> GSM1296077 1 0.0188 0.75835 0.996 0.000 0.000 0.004
#> GSM1296093 1 0.0000 0.75826 1.000 0.000 0.000 0.000
#> GSM1296104 4 0.8151 0.46132 0.204 0.032 0.260 0.504
#> GSM1296079 1 0.0000 0.75826 1.000 0.000 0.000 0.000
#> GSM1296108 2 0.0000 0.82687 0.000 1.000 0.000 0.000
#> GSM1296110 2 0.2011 0.81141 0.000 0.920 0.000 0.080
#> GSM1296081 1 0.0000 0.75826 1.000 0.000 0.000 0.000
#> GSM1296091 1 0.5679 0.46543 0.608 0.008 0.020 0.364
#> GSM1296075 1 0.6834 0.20647 0.484 0.060 0.016 0.440
#> GSM1296112 2 0.0000 0.82687 0.000 1.000 0.000 0.000
#> GSM1296100 1 0.6397 0.41409 0.652 0.000 0.164 0.184
#> GSM1296087 1 0.4477 0.58853 0.688 0.000 0.000 0.312
#> GSM1296118 2 0.2530 0.76014 0.000 0.888 0.000 0.112
#> GSM1296114 2 0.0000 0.82687 0.000 1.000 0.000 0.000
#> GSM1296097 4 0.7769 0.37669 0.264 0.008 0.232 0.496
#> GSM1296106 4 0.9248 0.43784 0.220 0.116 0.228 0.436
#> GSM1296102 4 0.7768 0.31264 0.260 0.000 0.312 0.428
#> GSM1296122 4 0.8121 0.40635 0.048 0.312 0.136 0.504
#> GSM1296089 1 0.4898 0.42682 0.584 0.000 0.000 0.416
#> GSM1296083 1 0.0000 0.75826 1.000 0.000 0.000 0.000
#> GSM1296116 2 0.2011 0.81141 0.000 0.920 0.000 0.080
#> GSM1296085 1 0.0336 0.75792 0.992 0.000 0.000 0.008
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1296094 3 0.1121 0.7571 0.000 0.000 0.956 0.044 0.000
#> GSM1296119 5 0.5218 0.6698 0.000 0.120 0.008 0.168 0.704
#> GSM1296076 4 0.1341 0.7252 0.000 0.000 0.056 0.944 0.000
#> GSM1296092 4 0.1341 0.7252 0.000 0.000 0.056 0.944 0.000
#> GSM1296103 3 0.2127 0.7445 0.000 0.000 0.892 0.108 0.000
#> GSM1296078 4 0.1341 0.7252 0.000 0.000 0.056 0.944 0.000
#> GSM1296107 5 0.3305 0.6857 0.000 0.224 0.000 0.000 0.776
#> GSM1296109 3 0.6501 0.4855 0.000 0.076 0.632 0.132 0.160
#> GSM1296080 3 0.3446 0.6493 0.048 0.000 0.840 0.108 0.004
#> GSM1296090 4 0.1341 0.7252 0.000 0.000 0.056 0.944 0.000
#> GSM1296074 4 0.1341 0.7252 0.000 0.000 0.056 0.944 0.000
#> GSM1296111 5 0.3305 0.6857 0.000 0.224 0.000 0.000 0.776
#> GSM1296099 3 0.1732 0.7538 0.000 0.000 0.920 0.080 0.000
#> GSM1296086 4 0.1965 0.6933 0.000 0.000 0.024 0.924 0.052
#> GSM1296117 5 0.3305 0.6857 0.000 0.224 0.000 0.000 0.776
#> GSM1296113 5 0.3305 0.6857 0.000 0.224 0.000 0.000 0.776
#> GSM1296096 3 0.2773 0.7098 0.000 0.000 0.836 0.164 0.000
#> GSM1296105 3 0.6448 0.0447 0.336 0.000 0.524 0.120 0.020
#> GSM1296098 3 0.0510 0.7600 0.000 0.000 0.984 0.016 0.000
#> GSM1296101 3 0.3146 0.7270 0.000 0.000 0.856 0.092 0.052
#> GSM1296121 5 0.4777 0.6575 0.000 0.064 0.012 0.188 0.736
#> GSM1296088 4 0.5938 0.4861 0.032 0.000 0.336 0.576 0.056
#> GSM1296082 4 0.1341 0.7252 0.000 0.000 0.056 0.944 0.000
#> GSM1296115 5 0.3461 0.6835 0.000 0.168 0.004 0.016 0.812
#> GSM1296084 4 0.6917 0.4464 0.056 0.000 0.332 0.504 0.108
#> GSM1296072 5 0.7591 0.4242 0.000 0.164 0.196 0.128 0.512
#> GSM1296069 2 0.4170 0.4393 0.000 0.712 0.004 0.012 0.272
#> GSM1296071 2 0.0162 0.8157 0.000 0.996 0.000 0.000 0.004
#> GSM1296070 5 0.4337 0.6044 0.000 0.284 0.004 0.016 0.696
#> GSM1296073 5 0.6290 0.5461 0.000 0.052 0.104 0.216 0.628
#> GSM1296034 3 0.5569 0.5577 0.140 0.004 0.708 0.120 0.028
#> GSM1296041 5 0.3305 0.6857 0.000 0.224 0.000 0.000 0.776
#> GSM1296035 3 0.2852 0.7023 0.000 0.000 0.828 0.172 0.000
#> GSM1296038 3 0.7603 -0.0217 0.000 0.048 0.396 0.292 0.264
#> GSM1296047 2 0.6373 0.4494 0.000 0.652 0.136 0.112 0.100
#> GSM1296039 4 0.4297 0.6550 0.000 0.000 0.236 0.728 0.036
#> GSM1296042 5 0.3556 0.6837 0.000 0.168 0.004 0.020 0.808
#> GSM1296043 2 0.0510 0.8102 0.000 0.984 0.000 0.000 0.016
#> GSM1296037 1 0.2358 0.7152 0.888 0.000 0.008 0.104 0.000
#> GSM1296046 2 0.0162 0.8150 0.000 0.996 0.000 0.000 0.004
#> GSM1296044 2 0.0000 0.8161 0.000 1.000 0.000 0.000 0.000
#> GSM1296045 2 0.2511 0.7717 0.000 0.892 0.004 0.016 0.088
#> GSM1296025 1 0.0000 0.7431 1.000 0.000 0.000 0.000 0.000
#> GSM1296033 1 0.7642 0.4824 0.492 0.000 0.240 0.140 0.128
#> GSM1296027 1 0.1251 0.7427 0.956 0.000 0.000 0.008 0.036
#> GSM1296032 1 0.0290 0.7434 0.992 0.000 0.000 0.000 0.008
#> GSM1296024 1 0.0000 0.7431 1.000 0.000 0.000 0.000 0.000
#> GSM1296031 1 0.5583 0.6560 0.680 0.000 0.016 0.132 0.172
#> GSM1296028 1 0.2124 0.7352 0.900 0.000 0.000 0.004 0.096
#> GSM1296029 1 0.4231 0.7146 0.800 0.000 0.036 0.036 0.128
#> GSM1296026 4 0.6436 0.4983 0.032 0.000 0.352 0.524 0.092
#> GSM1296030 4 0.7053 0.4784 0.052 0.000 0.268 0.524 0.156
#> GSM1296040 3 0.0162 0.7580 0.000 0.000 0.996 0.004 0.000
#> GSM1296036 3 0.0290 0.7591 0.000 0.000 0.992 0.008 0.000
#> GSM1296048 5 0.4777 0.6575 0.000 0.064 0.012 0.188 0.736
#> GSM1296059 3 0.2329 0.7375 0.000 0.000 0.876 0.124 0.000
#> GSM1296066 5 0.3305 0.6857 0.000 0.224 0.000 0.000 0.776
#> GSM1296060 3 0.3366 0.6239 0.000 0.000 0.768 0.232 0.000
#> GSM1296063 5 0.7158 0.3527 0.000 0.048 0.196 0.244 0.512
#> GSM1296064 4 0.4352 0.6474 0.000 0.000 0.244 0.720 0.036
#> GSM1296067 2 0.5519 0.5860 0.000 0.712 0.040 0.128 0.120
#> GSM1296062 3 0.1952 0.7134 0.004 0.000 0.912 0.084 0.000
#> GSM1296068 2 0.0000 0.8161 0.000 1.000 0.000 0.000 0.000
#> GSM1296050 1 0.6202 0.6470 0.664 0.000 0.076 0.132 0.128
#> GSM1296057 1 0.7690 0.4992 0.492 0.000 0.224 0.152 0.132
#> GSM1296052 1 0.0000 0.7431 1.000 0.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.7431 1.000 0.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.7431 1.000 0.000 0.000 0.000 0.000
#> GSM1296055 1 0.8100 0.4278 0.428 0.000 0.208 0.148 0.216
#> GSM1296053 1 0.0000 0.7431 1.000 0.000 0.000 0.000 0.000
#> GSM1296058 1 0.7972 0.4518 0.456 0.000 0.212 0.168 0.164
#> GSM1296051 4 0.5090 0.6097 0.000 0.004 0.264 0.668 0.064
#> GSM1296056 4 0.4305 0.6605 0.000 0.000 0.200 0.748 0.052
#> GSM1296065 5 0.7361 0.3293 0.000 0.088 0.272 0.136 0.504
#> GSM1296061 3 0.0000 0.7565 0.000 0.000 1.000 0.000 0.000
#> GSM1296095 5 0.7432 0.3639 0.000 0.108 0.264 0.124 0.504
#> GSM1296120 2 0.4952 0.6077 0.000 0.756 0.032 0.112 0.100
#> GSM1296077 1 0.0162 0.7434 0.996 0.000 0.000 0.000 0.004
#> GSM1296093 1 0.0000 0.7431 1.000 0.000 0.000 0.000 0.000
#> GSM1296104 1 0.8298 0.4083 0.412 0.004 0.240 0.172 0.172
#> GSM1296079 1 0.0000 0.7431 1.000 0.000 0.000 0.000 0.000
#> GSM1296108 2 0.0000 0.8161 0.000 1.000 0.000 0.000 0.000
#> GSM1296110 2 0.2074 0.7847 0.000 0.920 0.004 0.016 0.060
#> GSM1296081 1 0.0000 0.7431 1.000 0.000 0.000 0.000 0.000
#> GSM1296091 1 0.7532 0.5245 0.528 0.004 0.216 0.128 0.124
#> GSM1296075 1 0.8568 0.4409 0.436 0.032 0.224 0.180 0.128
#> GSM1296112 2 0.0000 0.8161 0.000 1.000 0.000 0.000 0.000
#> GSM1296100 1 0.3192 0.7015 0.848 0.000 0.040 0.112 0.000
#> GSM1296087 1 0.4319 0.7023 0.772 0.000 0.024 0.028 0.176
#> GSM1296118 2 0.1988 0.7833 0.000 0.928 0.016 0.008 0.048
#> GSM1296114 2 0.0000 0.8161 0.000 1.000 0.000 0.000 0.000
#> GSM1296097 1 0.8214 0.4355 0.428 0.004 0.236 0.160 0.172
#> GSM1296106 1 0.8616 0.4375 0.440 0.040 0.232 0.136 0.152
#> GSM1296102 1 0.7956 0.4331 0.444 0.000 0.248 0.136 0.172
#> GSM1296122 2 0.8052 0.1708 0.004 0.456 0.208 0.132 0.200
#> GSM1296089 1 0.5713 0.6507 0.672 0.000 0.020 0.136 0.172
#> GSM1296083 1 0.0000 0.7431 1.000 0.000 0.000 0.000 0.000
#> GSM1296116 2 0.2074 0.7847 0.000 0.920 0.004 0.016 0.060
#> GSM1296085 1 0.0693 0.7436 0.980 0.000 0.000 0.008 0.012
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1296094 3 0.1152 0.8682 0.000 0.000 0.952 0.044 0.000 0.004
#> GSM1296119 5 0.2898 0.8326 0.000 0.000 0.016 0.056 0.868 0.060
#> GSM1296076 4 0.1950 0.9131 0.000 0.000 0.024 0.912 0.000 0.064
#> GSM1296092 4 0.2009 0.9116 0.000 0.000 0.024 0.908 0.000 0.068
#> GSM1296103 3 0.2320 0.8576 0.000 0.000 0.864 0.132 0.000 0.004
#> GSM1296078 4 0.1950 0.9131 0.000 0.000 0.024 0.912 0.000 0.064
#> GSM1296107 5 0.0000 0.8623 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296109 3 0.5954 0.5273 0.000 0.000 0.624 0.092 0.128 0.156
#> GSM1296080 6 0.4800 0.5131 0.036 0.000 0.380 0.012 0.000 0.572
#> GSM1296090 4 0.1950 0.9131 0.000 0.000 0.024 0.912 0.000 0.064
#> GSM1296074 4 0.1950 0.9131 0.000 0.000 0.024 0.912 0.000 0.064
#> GSM1296111 5 0.0000 0.8623 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296099 3 0.1700 0.8711 0.000 0.000 0.916 0.080 0.000 0.004
#> GSM1296086 4 0.2509 0.8518 0.000 0.000 0.036 0.876 0.000 0.088
#> GSM1296117 5 0.0000 0.8623 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296113 5 0.0000 0.8623 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296096 3 0.2703 0.8298 0.000 0.000 0.824 0.172 0.000 0.004
#> GSM1296105 6 0.2520 0.7579 0.004 0.000 0.152 0.000 0.000 0.844
#> GSM1296098 3 0.1261 0.8653 0.000 0.000 0.952 0.024 0.000 0.024
#> GSM1296101 3 0.2680 0.8194 0.000 0.000 0.860 0.108 0.000 0.032
#> GSM1296121 5 0.4295 0.8139 0.000 0.000 0.048 0.080 0.776 0.096
#> GSM1296088 6 0.5778 0.4012 0.000 0.000 0.320 0.196 0.000 0.484
#> GSM1296082 4 0.1950 0.9131 0.000 0.000 0.024 0.912 0.000 0.064
#> GSM1296115 5 0.2345 0.8570 0.000 0.000 0.036 0.024 0.904 0.036
#> GSM1296084 6 0.3371 0.7668 0.036 0.000 0.116 0.020 0.000 0.828
#> GSM1296072 6 0.5959 0.5465 0.000 0.208 0.012 0.060 0.096 0.624
#> GSM1296069 5 0.4006 0.7336 0.000 0.216 0.008 0.008 0.744 0.024
#> GSM1296071 2 0.0000 0.8538 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296070 5 0.4191 0.8154 0.000 0.108 0.036 0.024 0.796 0.036
#> GSM1296073 5 0.4975 0.7741 0.000 0.000 0.052 0.128 0.716 0.104
#> GSM1296034 6 0.4148 0.7495 0.148 0.000 0.108 0.000 0.000 0.744
#> GSM1296041 5 0.0000 0.8623 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296035 3 0.2703 0.8298 0.000 0.000 0.824 0.172 0.000 0.004
#> GSM1296038 6 0.6123 0.4524 0.000 0.000 0.288 0.156 0.032 0.524
#> GSM1296047 2 0.5059 0.2047 0.000 0.540 0.012 0.052 0.000 0.396
#> GSM1296039 4 0.4203 0.7897 0.000 0.000 0.124 0.740 0.000 0.136
#> GSM1296042 5 0.2345 0.8570 0.000 0.000 0.036 0.024 0.904 0.036
#> GSM1296043 2 0.1204 0.8302 0.000 0.944 0.000 0.000 0.056 0.000
#> GSM1296037 1 0.3938 0.4528 0.672 0.000 0.012 0.004 0.000 0.312
#> GSM1296046 2 0.0146 0.8531 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1296044 2 0.0000 0.8538 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296045 2 0.4223 0.7054 0.000 0.784 0.032 0.020 0.132 0.032
#> GSM1296025 1 0.0260 0.8901 0.992 0.000 0.000 0.008 0.000 0.000
#> GSM1296033 6 0.2333 0.7581 0.120 0.000 0.004 0.004 0.000 0.872
#> GSM1296027 1 0.0891 0.8884 0.968 0.000 0.000 0.008 0.000 0.024
#> GSM1296032 1 0.0806 0.8904 0.972 0.000 0.000 0.008 0.000 0.020
#> GSM1296024 1 0.0260 0.8901 0.992 0.000 0.000 0.008 0.000 0.000
#> GSM1296031 6 0.4650 0.6378 0.260 0.000 0.040 0.024 0.000 0.676
#> GSM1296028 1 0.2389 0.7941 0.864 0.000 0.000 0.008 0.000 0.128
#> GSM1296029 1 0.4317 0.0382 0.520 0.000 0.008 0.008 0.000 0.464
#> GSM1296026 6 0.3853 0.7519 0.036 0.000 0.148 0.028 0.000 0.788
#> GSM1296030 6 0.3988 0.7685 0.036 0.000 0.140 0.040 0.000 0.784
#> GSM1296040 3 0.1141 0.8555 0.000 0.000 0.948 0.000 0.000 0.052
#> GSM1296036 3 0.1141 0.8535 0.000 0.000 0.948 0.000 0.000 0.052
#> GSM1296048 5 0.4406 0.8103 0.000 0.000 0.052 0.084 0.768 0.096
#> GSM1296059 3 0.2558 0.8438 0.000 0.000 0.840 0.156 0.000 0.004
#> GSM1296066 5 0.0000 0.8623 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296060 3 0.2762 0.8620 0.000 0.000 0.860 0.092 0.000 0.048
#> GSM1296063 5 0.6175 0.5434 0.000 0.000 0.052 0.272 0.544 0.132
#> GSM1296064 4 0.3637 0.7388 0.000 0.000 0.124 0.792 0.000 0.084
#> GSM1296067 2 0.5237 0.5791 0.000 0.644 0.036 0.072 0.000 0.248
#> GSM1296062 6 0.3867 0.3115 0.000 0.000 0.488 0.000 0.000 0.512
#> GSM1296068 2 0.0000 0.8538 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296050 6 0.3290 0.6623 0.252 0.000 0.000 0.004 0.000 0.744
#> GSM1296057 6 0.1349 0.7747 0.056 0.000 0.000 0.004 0.000 0.940
#> GSM1296052 1 0.0806 0.8904 0.972 0.000 0.000 0.008 0.000 0.020
#> GSM1296054 1 0.0260 0.8901 0.992 0.000 0.000 0.008 0.000 0.000
#> GSM1296049 1 0.0146 0.8902 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1296055 6 0.1806 0.7667 0.008 0.000 0.044 0.020 0.000 0.928
#> GSM1296053 1 0.0260 0.8901 0.992 0.000 0.000 0.008 0.000 0.000
#> GSM1296058 6 0.2629 0.7734 0.048 0.000 0.036 0.028 0.000 0.888
#> GSM1296051 6 0.4856 0.4120 0.000 0.000 0.068 0.360 0.000 0.572
#> GSM1296056 4 0.3958 0.7987 0.000 0.000 0.108 0.764 0.000 0.128
#> GSM1296065 6 0.3156 0.7595 0.004 0.004 0.076 0.052 0.008 0.856
#> GSM1296061 3 0.1444 0.8401 0.000 0.000 0.928 0.000 0.000 0.072
#> GSM1296095 6 0.6048 0.5385 0.000 0.008 0.256 0.064 0.084 0.588
#> GSM1296120 2 0.4322 0.6490 0.000 0.720 0.012 0.052 0.000 0.216
#> GSM1296077 1 0.0806 0.8904 0.972 0.000 0.000 0.008 0.000 0.020
#> GSM1296093 1 0.0260 0.8901 0.992 0.000 0.000 0.008 0.000 0.000
#> GSM1296104 6 0.0767 0.7727 0.008 0.004 0.012 0.000 0.000 0.976
#> GSM1296079 1 0.0806 0.8904 0.972 0.000 0.000 0.008 0.000 0.020
#> GSM1296108 2 0.0000 0.8538 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296110 2 0.2038 0.8231 0.000 0.920 0.032 0.020 0.000 0.028
#> GSM1296081 1 0.0260 0.8901 0.992 0.000 0.000 0.008 0.000 0.000
#> GSM1296091 6 0.2662 0.7459 0.152 0.004 0.000 0.004 0.000 0.840
#> GSM1296075 6 0.1606 0.7748 0.056 0.008 0.000 0.004 0.000 0.932
#> GSM1296112 2 0.0000 0.8538 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296100 1 0.4136 0.1082 0.560 0.000 0.012 0.000 0.000 0.428
#> GSM1296087 6 0.4634 0.6295 0.268 0.000 0.032 0.028 0.000 0.672
#> GSM1296118 2 0.3229 0.7522 0.000 0.816 0.000 0.044 0.000 0.140
#> GSM1296114 2 0.0000 0.8538 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296097 6 0.0912 0.7733 0.008 0.004 0.012 0.004 0.000 0.972
#> GSM1296106 6 0.2634 0.7679 0.032 0.012 0.012 0.052 0.000 0.892
#> GSM1296102 6 0.3228 0.7668 0.092 0.000 0.044 0.020 0.000 0.844
#> GSM1296122 6 0.4498 0.6815 0.000 0.136 0.044 0.068 0.000 0.752
#> GSM1296089 6 0.4244 0.7140 0.196 0.000 0.040 0.024 0.000 0.740
#> GSM1296083 1 0.0260 0.8901 0.992 0.000 0.000 0.008 0.000 0.000
#> GSM1296116 2 0.2115 0.8209 0.000 0.916 0.032 0.020 0.000 0.032
#> GSM1296085 1 0.0806 0.8904 0.972 0.000 0.000 0.008 0.000 0.020
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# 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 tissue(p) time(p) agent(p) dose(p) individual(p) k
#> CV:mclust 96 0.081160 0.305 0.06788 9.22e-07 5.12e-03 2
#> CV:mclust 87 0.000912 0.109 0.01545 7.11e-09 7.56e-06 3
#> CV:mclust 68 0.015782 0.176 0.00401 3.46e-08 2.54e-07 4
#> CV:mclust 76 0.000111 0.519 0.19781 3.27e-07 1.34e-06 5
#> CV:mclust 91 0.000417 0.691 0.37174 4.40e-07 9.00e-06 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 45638 rows and 99 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 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 0.916 0.927 0.970 0.5021 0.496 0.496
#> 3 3 0.887 0.889 0.951 0.3205 0.772 0.571
#> 4 4 0.653 0.714 0.832 0.1156 0.863 0.630
#> 5 5 0.663 0.720 0.803 0.0649 0.905 0.663
#> 6 6 0.678 0.577 0.754 0.0386 0.959 0.817
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
#> GSM1296094 2 0.0000 0.979 0.000 1.000
#> GSM1296119 2 0.0000 0.979 0.000 1.000
#> GSM1296076 2 0.0000 0.979 0.000 1.000
#> GSM1296092 2 0.0000 0.979 0.000 1.000
#> GSM1296103 2 0.0000 0.979 0.000 1.000
#> GSM1296078 2 0.0000 0.979 0.000 1.000
#> GSM1296107 2 0.0000 0.979 0.000 1.000
#> GSM1296109 2 0.0000 0.979 0.000 1.000
#> GSM1296080 1 0.0000 0.956 1.000 0.000
#> GSM1296090 2 0.0000 0.979 0.000 1.000
#> GSM1296074 2 0.0000 0.979 0.000 1.000
#> GSM1296111 2 0.0000 0.979 0.000 1.000
#> GSM1296099 2 0.0000 0.979 0.000 1.000
#> GSM1296086 2 0.0000 0.979 0.000 1.000
#> GSM1296117 2 0.0000 0.979 0.000 1.000
#> GSM1296113 2 0.0000 0.979 0.000 1.000
#> GSM1296096 2 0.0000 0.979 0.000 1.000
#> GSM1296105 1 0.0000 0.956 1.000 0.000
#> GSM1296098 2 0.6712 0.776 0.176 0.824
#> GSM1296101 2 0.0376 0.976 0.004 0.996
#> GSM1296121 2 0.0000 0.979 0.000 1.000
#> GSM1296088 1 0.9286 0.505 0.656 0.344
#> GSM1296082 2 0.0000 0.979 0.000 1.000
#> GSM1296115 2 0.0000 0.979 0.000 1.000
#> GSM1296084 1 0.0000 0.956 1.000 0.000
#> GSM1296072 2 0.0000 0.979 0.000 1.000
#> GSM1296069 2 0.0000 0.979 0.000 1.000
#> GSM1296071 2 0.0000 0.979 0.000 1.000
#> GSM1296070 2 0.0000 0.979 0.000 1.000
#> GSM1296073 2 0.0000 0.979 0.000 1.000
#> GSM1296034 1 0.0000 0.956 1.000 0.000
#> GSM1296041 2 0.0000 0.979 0.000 1.000
#> GSM1296035 2 0.0000 0.979 0.000 1.000
#> GSM1296038 2 0.0000 0.979 0.000 1.000
#> GSM1296047 1 0.9710 0.351 0.600 0.400
#> GSM1296039 2 0.0000 0.979 0.000 1.000
#> GSM1296042 2 0.0000 0.979 0.000 1.000
#> GSM1296043 2 0.0000 0.979 0.000 1.000
#> GSM1296037 1 0.0000 0.956 1.000 0.000
#> GSM1296046 2 0.0000 0.979 0.000 1.000
#> GSM1296044 2 0.0000 0.979 0.000 1.000
#> GSM1296045 2 0.0000 0.979 0.000 1.000
#> GSM1296025 1 0.0000 0.956 1.000 0.000
#> GSM1296033 1 0.0000 0.956 1.000 0.000
#> GSM1296027 1 0.0000 0.956 1.000 0.000
#> GSM1296032 1 0.0000 0.956 1.000 0.000
#> GSM1296024 1 0.0000 0.956 1.000 0.000
#> GSM1296031 1 0.0000 0.956 1.000 0.000
#> GSM1296028 1 0.0000 0.956 1.000 0.000
#> GSM1296029 1 0.0000 0.956 1.000 0.000
#> GSM1296026 1 0.3431 0.902 0.936 0.064
#> GSM1296030 1 0.0000 0.956 1.000 0.000
#> GSM1296040 1 0.9248 0.511 0.660 0.340
#> GSM1296036 1 0.8763 0.599 0.704 0.296
#> GSM1296048 2 0.0000 0.979 0.000 1.000
#> GSM1296059 2 0.0000 0.979 0.000 1.000
#> GSM1296066 2 0.0000 0.979 0.000 1.000
#> GSM1296060 2 0.0000 0.979 0.000 1.000
#> GSM1296063 2 0.0000 0.979 0.000 1.000
#> GSM1296064 2 0.0000 0.979 0.000 1.000
#> GSM1296067 2 0.9881 0.190 0.436 0.564
#> GSM1296062 1 0.0000 0.956 1.000 0.000
#> GSM1296068 2 0.5629 0.839 0.132 0.868
#> GSM1296050 1 0.0000 0.956 1.000 0.000
#> GSM1296057 1 0.0000 0.956 1.000 0.000
#> GSM1296052 1 0.0000 0.956 1.000 0.000
#> GSM1296054 1 0.0000 0.956 1.000 0.000
#> GSM1296049 1 0.0000 0.956 1.000 0.000
#> GSM1296055 1 0.0000 0.956 1.000 0.000
#> GSM1296053 1 0.0000 0.956 1.000 0.000
#> GSM1296058 1 0.0000 0.956 1.000 0.000
#> GSM1296051 2 0.4298 0.891 0.088 0.912
#> GSM1296056 2 0.0000 0.979 0.000 1.000
#> GSM1296065 2 0.0000 0.979 0.000 1.000
#> GSM1296061 1 0.0000 0.956 1.000 0.000
#> GSM1296095 2 0.0000 0.979 0.000 1.000
#> GSM1296120 1 0.9795 0.304 0.584 0.416
#> GSM1296077 1 0.0000 0.956 1.000 0.000
#> GSM1296093 1 0.0000 0.956 1.000 0.000
#> GSM1296104 1 0.4161 0.882 0.916 0.084
#> GSM1296079 1 0.0000 0.956 1.000 0.000
#> GSM1296108 2 0.1184 0.965 0.016 0.984
#> GSM1296110 2 0.0000 0.979 0.000 1.000
#> GSM1296081 1 0.0000 0.956 1.000 0.000
#> GSM1296091 1 0.0000 0.956 1.000 0.000
#> GSM1296075 1 0.0000 0.956 1.000 0.000
#> GSM1296112 2 0.6247 0.807 0.156 0.844
#> GSM1296100 1 0.0000 0.956 1.000 0.000
#> GSM1296087 1 0.0000 0.956 1.000 0.000
#> GSM1296118 1 0.0672 0.950 0.992 0.008
#> GSM1296114 2 0.0000 0.979 0.000 1.000
#> GSM1296097 1 0.0000 0.956 1.000 0.000
#> GSM1296106 1 0.0000 0.956 1.000 0.000
#> GSM1296102 1 0.0000 0.956 1.000 0.000
#> GSM1296122 1 0.0000 0.956 1.000 0.000
#> GSM1296089 1 0.0000 0.956 1.000 0.000
#> GSM1296083 1 0.0000 0.956 1.000 0.000
#> GSM1296116 2 0.0000 0.979 0.000 1.000
#> GSM1296085 1 0.0000 0.956 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1296094 3 0.0000 0.915 0.000 0.000 1.000
#> GSM1296119 3 0.5497 0.640 0.000 0.292 0.708
#> GSM1296076 3 0.0000 0.915 0.000 0.000 1.000
#> GSM1296092 3 0.0000 0.915 0.000 0.000 1.000
#> GSM1296103 3 0.0000 0.915 0.000 0.000 1.000
#> GSM1296078 3 0.0000 0.915 0.000 0.000 1.000
#> GSM1296107 2 0.0747 0.945 0.000 0.984 0.016
#> GSM1296109 3 0.0424 0.912 0.000 0.008 0.992
#> GSM1296080 1 0.1860 0.927 0.948 0.000 0.052
#> GSM1296090 3 0.0000 0.915 0.000 0.000 1.000
#> GSM1296074 3 0.0000 0.915 0.000 0.000 1.000
#> GSM1296111 2 0.5733 0.430 0.000 0.676 0.324
#> GSM1296099 3 0.0000 0.915 0.000 0.000 1.000
#> GSM1296086 3 0.0000 0.915 0.000 0.000 1.000
#> GSM1296117 3 0.5291 0.674 0.000 0.268 0.732
#> GSM1296113 2 0.0747 0.945 0.000 0.984 0.016
#> GSM1296096 3 0.0000 0.915 0.000 0.000 1.000
#> GSM1296105 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1296098 3 0.0000 0.915 0.000 0.000 1.000
#> GSM1296101 3 0.0000 0.915 0.000 0.000 1.000
#> GSM1296121 3 0.5678 0.602 0.000 0.316 0.684
#> GSM1296088 3 0.1163 0.896 0.028 0.000 0.972
#> GSM1296082 3 0.0000 0.915 0.000 0.000 1.000
#> GSM1296115 3 0.6305 0.186 0.000 0.484 0.516
#> GSM1296084 1 0.1289 0.944 0.968 0.000 0.032
#> GSM1296072 2 0.0000 0.956 0.000 1.000 0.000
#> GSM1296069 2 0.0000 0.956 0.000 1.000 0.000
#> GSM1296071 2 0.0000 0.956 0.000 1.000 0.000
#> GSM1296070 2 0.0000 0.956 0.000 1.000 0.000
#> GSM1296073 3 0.1031 0.904 0.000 0.024 0.976
#> GSM1296034 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1296041 3 0.6008 0.493 0.000 0.372 0.628
#> GSM1296035 3 0.0000 0.915 0.000 0.000 1.000
#> GSM1296038 3 0.0000 0.915 0.000 0.000 1.000
#> GSM1296047 2 0.0592 0.948 0.012 0.988 0.000
#> GSM1296039 3 0.0000 0.915 0.000 0.000 1.000
#> GSM1296042 2 0.2261 0.890 0.000 0.932 0.068
#> GSM1296043 2 0.0000 0.956 0.000 1.000 0.000
#> GSM1296037 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1296046 2 0.0000 0.956 0.000 1.000 0.000
#> GSM1296044 2 0.0000 0.956 0.000 1.000 0.000
#> GSM1296045 2 0.0000 0.956 0.000 1.000 0.000
#> GSM1296025 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1296033 1 0.0237 0.964 0.996 0.000 0.004
#> GSM1296027 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1296032 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1296024 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1296031 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1296028 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1296029 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1296026 1 0.6180 0.315 0.584 0.000 0.416
#> GSM1296030 1 0.1529 0.938 0.960 0.000 0.040
#> GSM1296040 3 0.2959 0.829 0.100 0.000 0.900
#> GSM1296036 3 0.1964 0.873 0.056 0.000 0.944
#> GSM1296048 3 0.5529 0.635 0.000 0.296 0.704
#> GSM1296059 3 0.0000 0.915 0.000 0.000 1.000
#> GSM1296066 2 0.0424 0.951 0.000 0.992 0.008
#> GSM1296060 3 0.0000 0.915 0.000 0.000 1.000
#> GSM1296063 3 0.1753 0.890 0.000 0.048 0.952
#> GSM1296064 3 0.0000 0.915 0.000 0.000 1.000
#> GSM1296067 2 0.0592 0.948 0.012 0.988 0.000
#> GSM1296062 1 0.0747 0.956 0.984 0.000 0.016
#> GSM1296068 2 0.0000 0.956 0.000 1.000 0.000
#> GSM1296050 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1296057 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1296052 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1296054 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1296049 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1296055 1 0.1411 0.939 0.964 0.036 0.000
#> GSM1296053 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1296058 1 0.0424 0.961 0.992 0.000 0.008
#> GSM1296051 3 0.0424 0.911 0.008 0.000 0.992
#> GSM1296056 3 0.0000 0.915 0.000 0.000 1.000
#> GSM1296065 3 0.6441 0.639 0.028 0.276 0.696
#> GSM1296061 1 0.4796 0.725 0.780 0.000 0.220
#> GSM1296095 3 0.3116 0.847 0.000 0.108 0.892
#> GSM1296120 2 0.0424 0.951 0.008 0.992 0.000
#> GSM1296077 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1296093 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1296104 1 0.1315 0.951 0.972 0.020 0.008
#> GSM1296079 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1296108 2 0.0000 0.956 0.000 1.000 0.000
#> GSM1296110 2 0.0000 0.956 0.000 1.000 0.000
#> GSM1296081 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1296091 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1296075 1 0.2711 0.887 0.912 0.088 0.000
#> GSM1296112 2 0.0000 0.956 0.000 1.000 0.000
#> GSM1296100 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1296087 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1296118 2 0.0892 0.941 0.020 0.980 0.000
#> GSM1296114 2 0.0000 0.956 0.000 1.000 0.000
#> GSM1296097 1 0.0424 0.961 0.992 0.008 0.000
#> GSM1296106 1 0.5733 0.519 0.676 0.324 0.000
#> GSM1296102 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1296122 2 0.6045 0.350 0.380 0.620 0.000
#> GSM1296089 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1296083 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1296116 2 0.0000 0.956 0.000 1.000 0.000
#> GSM1296085 1 0.0000 0.966 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1296094 3 0.4560 0.5953 0.004 0.000 0.700 0.296
#> GSM1296119 4 0.4307 0.6543 0.000 0.192 0.024 0.784
#> GSM1296076 4 0.0592 0.7517 0.000 0.000 0.016 0.984
#> GSM1296092 4 0.0895 0.7520 0.004 0.000 0.020 0.976
#> GSM1296103 3 0.4790 0.5066 0.000 0.000 0.620 0.380
#> GSM1296078 4 0.0469 0.7546 0.000 0.000 0.012 0.988
#> GSM1296107 2 0.1305 0.9160 0.000 0.960 0.004 0.036
#> GSM1296109 4 0.5535 -0.0724 0.000 0.020 0.420 0.560
#> GSM1296080 3 0.5358 0.6072 0.252 0.000 0.700 0.048
#> GSM1296090 4 0.1576 0.7504 0.004 0.000 0.048 0.948
#> GSM1296074 4 0.0817 0.7469 0.000 0.000 0.024 0.976
#> GSM1296111 2 0.3539 0.7873 0.000 0.820 0.004 0.176
#> GSM1296099 3 0.4985 0.3522 0.000 0.000 0.532 0.468
#> GSM1296086 4 0.1706 0.7488 0.016 0.000 0.036 0.948
#> GSM1296117 4 0.6111 0.3103 0.000 0.392 0.052 0.556
#> GSM1296113 2 0.0895 0.9190 0.000 0.976 0.004 0.020
#> GSM1296096 4 0.4564 0.2655 0.000 0.000 0.328 0.672
#> GSM1296105 3 0.4222 0.5236 0.272 0.000 0.728 0.000
#> GSM1296098 3 0.4781 0.6511 0.036 0.000 0.752 0.212
#> GSM1296101 3 0.5050 0.6010 0.028 0.000 0.704 0.268
#> GSM1296121 4 0.6262 0.5251 0.000 0.280 0.092 0.628
#> GSM1296088 4 0.4673 0.6504 0.076 0.000 0.132 0.792
#> GSM1296082 4 0.0336 0.7516 0.000 0.000 0.008 0.992
#> GSM1296115 2 0.5137 0.6493 0.000 0.716 0.040 0.244
#> GSM1296084 1 0.3862 0.7883 0.824 0.000 0.152 0.024
#> GSM1296072 2 0.2901 0.9063 0.016 0.908 0.040 0.036
#> GSM1296069 2 0.1042 0.9229 0.000 0.972 0.020 0.008
#> GSM1296071 2 0.0376 0.9232 0.000 0.992 0.004 0.004
#> GSM1296070 2 0.2401 0.8955 0.000 0.904 0.092 0.004
#> GSM1296073 4 0.3307 0.7279 0.000 0.028 0.104 0.868
#> GSM1296034 3 0.4277 0.5116 0.280 0.000 0.720 0.000
#> GSM1296041 2 0.5746 0.2613 0.000 0.572 0.032 0.396
#> GSM1296035 4 0.4697 0.1793 0.000 0.000 0.356 0.644
#> GSM1296038 4 0.5516 0.6658 0.040 0.032 0.180 0.748
#> GSM1296047 2 0.2908 0.8754 0.040 0.896 0.064 0.000
#> GSM1296039 4 0.1474 0.7321 0.000 0.000 0.052 0.948
#> GSM1296042 2 0.3056 0.8873 0.000 0.888 0.072 0.040
#> GSM1296043 2 0.0188 0.9237 0.000 0.996 0.000 0.004
#> GSM1296037 1 0.3873 0.7370 0.772 0.000 0.228 0.000
#> GSM1296046 2 0.0592 0.9227 0.000 0.984 0.016 0.000
#> GSM1296044 2 0.0000 0.9236 0.000 1.000 0.000 0.000
#> GSM1296045 2 0.2466 0.8934 0.000 0.900 0.096 0.004
#> GSM1296025 1 0.2469 0.8437 0.892 0.000 0.108 0.000
#> GSM1296033 1 0.1488 0.8534 0.956 0.000 0.032 0.012
#> GSM1296027 1 0.1305 0.8589 0.960 0.000 0.036 0.004
#> GSM1296032 1 0.1118 0.8595 0.964 0.000 0.036 0.000
#> GSM1296024 1 0.2589 0.8400 0.884 0.000 0.116 0.000
#> GSM1296031 1 0.1637 0.8535 0.940 0.000 0.060 0.000
#> GSM1296028 1 0.0895 0.8614 0.976 0.000 0.020 0.004
#> GSM1296029 1 0.1474 0.8585 0.948 0.000 0.052 0.000
#> GSM1296026 1 0.5780 0.0577 0.496 0.000 0.028 0.476
#> GSM1296030 1 0.3392 0.8272 0.856 0.000 0.124 0.020
#> GSM1296040 3 0.4875 0.6673 0.068 0.000 0.772 0.160
#> GSM1296036 3 0.4832 0.6642 0.056 0.000 0.768 0.176
#> GSM1296048 4 0.5689 0.6136 0.000 0.184 0.104 0.712
#> GSM1296059 3 0.4994 0.3252 0.000 0.000 0.520 0.480
#> GSM1296066 2 0.0672 0.9232 0.000 0.984 0.008 0.008
#> GSM1296060 3 0.4996 0.3134 0.000 0.000 0.516 0.484
#> GSM1296063 4 0.5022 0.6716 0.004 0.080 0.140 0.776
#> GSM1296064 4 0.0817 0.7472 0.000 0.000 0.024 0.976
#> GSM1296067 2 0.1824 0.9141 0.004 0.936 0.060 0.000
#> GSM1296062 3 0.4088 0.5922 0.232 0.000 0.764 0.004
#> GSM1296068 2 0.0336 0.9242 0.000 0.992 0.008 0.000
#> GSM1296050 1 0.1398 0.8582 0.956 0.000 0.040 0.004
#> GSM1296057 1 0.1022 0.8619 0.968 0.000 0.032 0.000
#> GSM1296052 1 0.1109 0.8616 0.968 0.000 0.028 0.004
#> GSM1296054 1 0.2530 0.8427 0.888 0.000 0.112 0.000
#> GSM1296049 1 0.1211 0.8599 0.960 0.000 0.040 0.000
#> GSM1296055 1 0.4092 0.7650 0.800 0.008 0.184 0.008
#> GSM1296053 1 0.2589 0.8386 0.884 0.000 0.116 0.000
#> GSM1296058 1 0.4182 0.7841 0.796 0.000 0.180 0.024
#> GSM1296051 4 0.3734 0.6704 0.108 0.000 0.044 0.848
#> GSM1296056 4 0.2125 0.7485 0.004 0.000 0.076 0.920
#> GSM1296065 4 0.6855 0.5852 0.060 0.152 0.104 0.684
#> GSM1296061 3 0.4784 0.6643 0.100 0.000 0.788 0.112
#> GSM1296095 3 0.7765 0.1340 0.012 0.160 0.420 0.408
#> GSM1296120 2 0.2670 0.8922 0.052 0.908 0.040 0.000
#> GSM1296077 1 0.1474 0.8572 0.948 0.000 0.052 0.000
#> GSM1296093 1 0.2704 0.8293 0.876 0.000 0.124 0.000
#> GSM1296104 1 0.4793 0.7447 0.756 0.000 0.204 0.040
#> GSM1296079 1 0.1716 0.8558 0.936 0.000 0.064 0.000
#> GSM1296108 2 0.0188 0.9236 0.000 0.996 0.004 0.000
#> GSM1296110 2 0.1302 0.9167 0.000 0.956 0.044 0.000
#> GSM1296081 1 0.1940 0.8539 0.924 0.000 0.076 0.000
#> GSM1296091 1 0.2473 0.8452 0.908 0.000 0.080 0.012
#> GSM1296075 1 0.3006 0.8382 0.888 0.008 0.092 0.012
#> GSM1296112 2 0.0469 0.9223 0.000 0.988 0.012 0.000
#> GSM1296100 1 0.4406 0.6299 0.700 0.000 0.300 0.000
#> GSM1296087 1 0.2101 0.8460 0.928 0.000 0.060 0.012
#> GSM1296118 2 0.2522 0.8811 0.016 0.908 0.076 0.000
#> GSM1296114 2 0.0000 0.9236 0.000 1.000 0.000 0.000
#> GSM1296097 1 0.3610 0.7941 0.800 0.000 0.200 0.000
#> GSM1296106 1 0.7412 0.3304 0.504 0.296 0.200 0.000
#> GSM1296102 3 0.4941 0.0712 0.436 0.000 0.564 0.000
#> GSM1296122 1 0.6549 0.3490 0.556 0.356 0.088 0.000
#> GSM1296089 1 0.1302 0.8554 0.956 0.000 0.044 0.000
#> GSM1296083 1 0.3266 0.8028 0.832 0.000 0.168 0.000
#> GSM1296116 2 0.1474 0.9143 0.000 0.948 0.052 0.000
#> GSM1296085 1 0.0707 0.8605 0.980 0.000 0.020 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1296094 3 0.2330 0.729 0.004 0.004 0.900 0.004 0.088
#> GSM1296119 5 0.4748 0.628 0.000 0.172 0.100 0.000 0.728
#> GSM1296076 5 0.2424 0.741 0.000 0.000 0.132 0.000 0.868
#> GSM1296092 5 0.2612 0.742 0.008 0.000 0.124 0.000 0.868
#> GSM1296103 3 0.3081 0.704 0.000 0.000 0.832 0.012 0.156
#> GSM1296078 5 0.1732 0.760 0.000 0.000 0.080 0.000 0.920
#> GSM1296107 2 0.0671 0.888 0.000 0.980 0.004 0.000 0.016
#> GSM1296109 3 0.4311 0.600 0.000 0.020 0.712 0.004 0.264
#> GSM1296080 3 0.5260 0.466 0.288 0.000 0.652 0.024 0.036
#> GSM1296090 5 0.2054 0.761 0.004 0.000 0.072 0.008 0.916
#> GSM1296074 5 0.2852 0.707 0.000 0.000 0.172 0.000 0.828
#> GSM1296111 2 0.3921 0.741 0.000 0.800 0.072 0.000 0.128
#> GSM1296099 3 0.4645 0.667 0.000 0.000 0.724 0.072 0.204
#> GSM1296086 5 0.3856 0.736 0.028 0.000 0.104 0.040 0.828
#> GSM1296117 2 0.6108 0.396 0.000 0.592 0.156 0.008 0.244
#> GSM1296113 2 0.1278 0.882 0.000 0.960 0.020 0.004 0.016
#> GSM1296096 3 0.5439 0.291 0.000 0.000 0.508 0.060 0.432
#> GSM1296105 3 0.4968 0.571 0.152 0.000 0.712 0.136 0.000
#> GSM1296098 3 0.2318 0.734 0.020 0.004 0.916 0.008 0.052
#> GSM1296101 3 0.5174 0.647 0.056 0.000 0.724 0.180 0.040
#> GSM1296121 5 0.5217 0.451 0.000 0.288 0.008 0.056 0.648
#> GSM1296088 5 0.7854 0.110 0.320 0.000 0.204 0.084 0.392
#> GSM1296082 5 0.1965 0.757 0.000 0.000 0.096 0.000 0.904
#> GSM1296115 2 0.3080 0.809 0.000 0.852 0.020 0.004 0.124
#> GSM1296084 1 0.3332 0.804 0.844 0.000 0.120 0.028 0.008
#> GSM1296072 2 0.6586 0.357 0.020 0.556 0.012 0.304 0.108
#> GSM1296069 2 0.0579 0.890 0.000 0.984 0.000 0.008 0.008
#> GSM1296071 2 0.0162 0.890 0.000 0.996 0.004 0.000 0.000
#> GSM1296070 2 0.0898 0.887 0.000 0.972 0.000 0.008 0.020
#> GSM1296073 5 0.1588 0.736 0.000 0.008 0.016 0.028 0.948
#> GSM1296034 3 0.4864 0.549 0.164 0.000 0.720 0.116 0.000
#> GSM1296041 2 0.5009 0.623 0.000 0.716 0.112 0.004 0.168
#> GSM1296035 3 0.5759 0.367 0.000 0.000 0.520 0.092 0.388
#> GSM1296038 4 0.6409 0.379 0.036 0.028 0.044 0.588 0.304
#> GSM1296047 2 0.3701 0.786 0.044 0.836 0.020 0.100 0.000
#> GSM1296039 5 0.2574 0.750 0.000 0.000 0.112 0.012 0.876
#> GSM1296042 2 0.1412 0.880 0.000 0.952 0.004 0.008 0.036
#> GSM1296043 2 0.0000 0.890 0.000 1.000 0.000 0.000 0.000
#> GSM1296037 4 0.5341 0.698 0.212 0.000 0.124 0.664 0.000
#> GSM1296046 2 0.0162 0.890 0.000 0.996 0.000 0.004 0.000
#> GSM1296044 2 0.0324 0.890 0.000 0.992 0.004 0.004 0.000
#> GSM1296045 2 0.1117 0.885 0.000 0.964 0.000 0.020 0.016
#> GSM1296025 1 0.1626 0.870 0.940 0.000 0.016 0.044 0.000
#> GSM1296033 1 0.1952 0.859 0.912 0.000 0.004 0.084 0.000
#> GSM1296027 1 0.1568 0.859 0.944 0.000 0.020 0.036 0.000
#> GSM1296032 1 0.2193 0.848 0.900 0.000 0.008 0.092 0.000
#> GSM1296024 1 0.1399 0.868 0.952 0.000 0.028 0.020 0.000
#> GSM1296031 1 0.4578 0.672 0.712 0.004 0.040 0.244 0.000
#> GSM1296028 1 0.1544 0.867 0.932 0.000 0.000 0.068 0.000
#> GSM1296029 1 0.2645 0.848 0.888 0.000 0.044 0.068 0.000
#> GSM1296026 1 0.5270 0.566 0.704 0.000 0.112 0.012 0.172
#> GSM1296030 1 0.3916 0.774 0.812 0.000 0.092 0.092 0.004
#> GSM1296040 3 0.2664 0.729 0.000 0.004 0.892 0.064 0.040
#> GSM1296036 3 0.2026 0.731 0.024 0.004 0.932 0.008 0.032
#> GSM1296048 5 0.4176 0.611 0.000 0.080 0.008 0.116 0.796
#> GSM1296059 3 0.4612 0.649 0.000 0.000 0.712 0.056 0.232
#> GSM1296066 2 0.0324 0.890 0.000 0.992 0.000 0.004 0.004
#> GSM1296060 3 0.5625 0.631 0.000 0.000 0.636 0.204 0.160
#> GSM1296063 5 0.4438 0.525 0.000 0.032 0.008 0.228 0.732
#> GSM1296064 5 0.2570 0.756 0.000 0.000 0.084 0.028 0.888
#> GSM1296067 2 0.2339 0.853 0.008 0.908 0.008 0.072 0.004
#> GSM1296062 3 0.3806 0.654 0.084 0.000 0.812 0.104 0.000
#> GSM1296068 2 0.0451 0.890 0.000 0.988 0.004 0.008 0.000
#> GSM1296050 1 0.2522 0.842 0.880 0.000 0.012 0.108 0.000
#> GSM1296057 4 0.4204 0.734 0.216 0.000 0.020 0.752 0.012
#> GSM1296052 1 0.1579 0.858 0.944 0.000 0.024 0.032 0.000
#> GSM1296054 1 0.1809 0.871 0.928 0.000 0.012 0.060 0.000
#> GSM1296049 1 0.1800 0.870 0.932 0.000 0.020 0.048 0.000
#> GSM1296055 4 0.3783 0.712 0.120 0.016 0.000 0.824 0.040
#> GSM1296053 1 0.2782 0.852 0.880 0.000 0.048 0.072 0.000
#> GSM1296058 4 0.4554 0.740 0.100 0.000 0.012 0.772 0.116
#> GSM1296051 5 0.6069 0.184 0.420 0.000 0.084 0.012 0.484
#> GSM1296056 5 0.2813 0.708 0.000 0.000 0.024 0.108 0.868
#> GSM1296065 4 0.6419 0.623 0.104 0.024 0.032 0.648 0.192
#> GSM1296061 3 0.2507 0.705 0.072 0.000 0.900 0.016 0.012
#> GSM1296095 3 0.7464 0.313 0.004 0.056 0.452 0.328 0.160
#> GSM1296120 2 0.6036 0.192 0.064 0.524 0.024 0.388 0.000
#> GSM1296077 1 0.2464 0.846 0.888 0.000 0.016 0.096 0.000
#> GSM1296093 1 0.3409 0.809 0.824 0.000 0.032 0.144 0.000
#> GSM1296104 4 0.4698 0.747 0.120 0.004 0.008 0.764 0.104
#> GSM1296079 1 0.1894 0.856 0.920 0.000 0.008 0.072 0.000
#> GSM1296108 2 0.0162 0.890 0.000 0.996 0.004 0.000 0.000
#> GSM1296110 2 0.0932 0.886 0.000 0.972 0.004 0.020 0.004
#> GSM1296081 1 0.1800 0.870 0.932 0.000 0.020 0.048 0.000
#> GSM1296091 1 0.3359 0.780 0.816 0.000 0.020 0.164 0.000
#> GSM1296075 1 0.3840 0.768 0.796 0.004 0.024 0.172 0.004
#> GSM1296112 2 0.0324 0.890 0.000 0.992 0.004 0.004 0.000
#> GSM1296100 4 0.5399 0.688 0.188 0.000 0.148 0.664 0.000
#> GSM1296087 1 0.2873 0.822 0.860 0.000 0.020 0.120 0.000
#> GSM1296118 2 0.3448 0.808 0.036 0.856 0.028 0.080 0.000
#> GSM1296114 2 0.0324 0.890 0.000 0.992 0.004 0.004 0.000
#> GSM1296097 4 0.4728 0.753 0.124 0.004 0.020 0.772 0.080
#> GSM1296106 4 0.5829 0.699 0.168 0.052 0.096 0.684 0.000
#> GSM1296102 4 0.5575 0.597 0.152 0.004 0.168 0.672 0.004
#> GSM1296122 4 0.6604 0.453 0.256 0.244 0.000 0.496 0.004
#> GSM1296089 1 0.4342 0.747 0.764 0.004 0.044 0.184 0.004
#> GSM1296083 1 0.2304 0.861 0.908 0.000 0.044 0.048 0.000
#> GSM1296116 2 0.0727 0.889 0.000 0.980 0.004 0.012 0.004
#> GSM1296085 1 0.1768 0.868 0.924 0.000 0.004 0.072 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1296094 3 0.2404 0.7149 0.004 0.000 0.880 0.104 0.008 0.004
#> GSM1296119 4 0.3974 0.6205 0.000 0.172 0.048 0.768 0.008 0.004
#> GSM1296076 4 0.2101 0.7728 0.000 0.000 0.052 0.912 0.028 0.008
#> GSM1296092 4 0.1584 0.7756 0.000 0.000 0.064 0.928 0.008 0.000
#> GSM1296103 3 0.2955 0.7052 0.000 0.000 0.816 0.172 0.004 0.008
#> GSM1296078 4 0.1405 0.7744 0.000 0.000 0.024 0.948 0.024 0.004
#> GSM1296107 2 0.0632 0.8494 0.000 0.976 0.000 0.024 0.000 0.000
#> GSM1296109 3 0.4749 0.6001 0.000 0.032 0.676 0.252 0.040 0.000
#> GSM1296080 3 0.5228 0.4046 0.280 0.000 0.624 0.032 0.064 0.000
#> GSM1296090 4 0.0653 0.7723 0.000 0.000 0.004 0.980 0.012 0.004
#> GSM1296074 4 0.1895 0.7710 0.000 0.000 0.072 0.912 0.016 0.000
#> GSM1296111 2 0.3197 0.7193 0.000 0.800 0.004 0.184 0.008 0.004
#> GSM1296099 3 0.4823 0.6812 0.000 0.000 0.708 0.176 0.028 0.088
#> GSM1296086 4 0.4025 0.6698 0.120 0.000 0.060 0.788 0.032 0.000
#> GSM1296117 2 0.5713 -0.0269 0.000 0.452 0.104 0.428 0.016 0.000
#> GSM1296113 2 0.1363 0.8421 0.000 0.952 0.004 0.028 0.012 0.004
#> GSM1296096 3 0.5523 0.4146 0.000 0.000 0.516 0.392 0.036 0.056
#> GSM1296105 3 0.5536 0.5994 0.076 0.000 0.676 0.004 0.100 0.144
#> GSM1296098 3 0.2563 0.6992 0.028 0.000 0.892 0.036 0.044 0.000
#> GSM1296101 3 0.5739 0.5327 0.036 0.000 0.672 0.040 0.160 0.092
#> GSM1296121 2 0.5684 0.4039 0.000 0.572 0.012 0.280 0.132 0.004
#> GSM1296088 1 0.6559 0.3165 0.528 0.000 0.220 0.172 0.080 0.000
#> GSM1296082 4 0.1398 0.7777 0.000 0.000 0.052 0.940 0.008 0.000
#> GSM1296115 2 0.1738 0.8320 0.000 0.928 0.004 0.052 0.016 0.000
#> GSM1296084 1 0.4398 0.6506 0.748 0.000 0.160 0.016 0.072 0.004
#> GSM1296072 6 0.6845 0.1115 0.000 0.360 0.000 0.088 0.144 0.408
#> GSM1296069 2 0.0837 0.8525 0.000 0.972 0.004 0.004 0.020 0.000
#> GSM1296071 2 0.0547 0.8522 0.000 0.980 0.000 0.000 0.020 0.000
#> GSM1296070 2 0.0865 0.8496 0.000 0.964 0.000 0.000 0.036 0.000
#> GSM1296073 4 0.1923 0.7542 0.000 0.000 0.016 0.916 0.064 0.004
#> GSM1296034 3 0.4350 0.6132 0.056 0.000 0.772 0.000 0.068 0.104
#> GSM1296041 2 0.4219 0.6120 0.000 0.720 0.048 0.224 0.008 0.000
#> GSM1296035 3 0.5866 0.5279 0.000 0.000 0.552 0.312 0.048 0.088
#> GSM1296038 5 0.8068 0.4665 0.036 0.020 0.112 0.140 0.396 0.296
#> GSM1296047 2 0.6201 0.2686 0.040 0.532 0.000 0.008 0.112 0.308
#> GSM1296039 4 0.2102 0.7706 0.000 0.000 0.068 0.908 0.012 0.012
#> GSM1296042 2 0.0632 0.8509 0.000 0.976 0.000 0.000 0.024 0.000
#> GSM1296043 2 0.0260 0.8524 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1296037 6 0.3190 0.3454 0.056 0.000 0.088 0.000 0.012 0.844
#> GSM1296046 2 0.0508 0.8524 0.000 0.984 0.000 0.000 0.012 0.004
#> GSM1296044 2 0.0458 0.8519 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM1296045 2 0.1219 0.8447 0.000 0.948 0.004 0.000 0.048 0.000
#> GSM1296025 1 0.4138 0.7208 0.752 0.000 0.020 0.000 0.184 0.044
#> GSM1296033 1 0.3553 0.7320 0.804 0.000 0.000 0.004 0.064 0.128
#> GSM1296027 1 0.1649 0.7387 0.936 0.000 0.008 0.000 0.040 0.016
#> GSM1296032 1 0.4275 0.6924 0.728 0.000 0.004 0.000 0.076 0.192
#> GSM1296024 1 0.3513 0.7316 0.804 0.000 0.020 0.000 0.152 0.024
#> GSM1296031 1 0.5314 0.5103 0.656 0.004 0.048 0.000 0.232 0.060
#> GSM1296028 1 0.3715 0.7053 0.764 0.000 0.000 0.000 0.048 0.188
#> GSM1296029 1 0.3316 0.7072 0.840 0.000 0.056 0.000 0.084 0.020
#> GSM1296026 1 0.4896 0.6382 0.708 0.000 0.028 0.148 0.116 0.000
#> GSM1296030 1 0.3565 0.6822 0.808 0.000 0.096 0.004 0.092 0.000
#> GSM1296040 3 0.3008 0.7018 0.000 0.000 0.864 0.032 0.036 0.068
#> GSM1296036 3 0.2100 0.6954 0.032 0.000 0.916 0.016 0.036 0.000
#> GSM1296048 4 0.5826 0.2946 0.000 0.148 0.000 0.568 0.260 0.024
#> GSM1296059 3 0.4627 0.6785 0.000 0.000 0.704 0.216 0.024 0.056
#> GSM1296066 2 0.0260 0.8524 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1296060 3 0.5903 0.5821 0.000 0.000 0.620 0.128 0.072 0.180
#> GSM1296063 4 0.5655 -0.0669 0.000 0.004 0.004 0.480 0.396 0.116
#> GSM1296064 4 0.2172 0.7768 0.000 0.000 0.044 0.912 0.024 0.020
#> GSM1296067 2 0.2376 0.8003 0.012 0.884 0.008 0.000 0.096 0.000
#> GSM1296062 3 0.2871 0.6728 0.008 0.000 0.852 0.000 0.024 0.116
#> GSM1296068 2 0.0260 0.8524 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1296050 1 0.4418 0.6890 0.700 0.000 0.004 0.000 0.228 0.068
#> GSM1296057 6 0.4887 0.1195 0.072 0.000 0.020 0.000 0.236 0.672
#> GSM1296052 1 0.1321 0.7428 0.952 0.000 0.020 0.000 0.024 0.004
#> GSM1296054 1 0.3971 0.7220 0.772 0.000 0.012 0.000 0.060 0.156
#> GSM1296049 1 0.3979 0.7298 0.772 0.000 0.016 0.000 0.160 0.052
#> GSM1296055 5 0.6201 0.4438 0.144 0.000 0.032 0.000 0.472 0.352
#> GSM1296053 1 0.3759 0.7235 0.816 0.000 0.052 0.000 0.048 0.084
#> GSM1296058 6 0.5473 -0.3231 0.008 0.000 0.032 0.040 0.388 0.532
#> GSM1296051 4 0.5775 0.0393 0.388 0.000 0.008 0.480 0.120 0.004
#> GSM1296056 4 0.4370 0.5720 0.000 0.000 0.024 0.736 0.188 0.052
#> GSM1296065 6 0.6512 0.0602 0.016 0.012 0.012 0.248 0.184 0.528
#> GSM1296061 3 0.2706 0.6749 0.060 0.000 0.876 0.008 0.056 0.000
#> GSM1296095 3 0.7557 0.1566 0.000 0.024 0.412 0.116 0.156 0.292
#> GSM1296120 6 0.5686 0.2484 0.040 0.220 0.004 0.004 0.096 0.636
#> GSM1296077 1 0.5087 0.6471 0.628 0.000 0.004 0.000 0.252 0.116
#> GSM1296093 1 0.5282 0.4890 0.548 0.000 0.012 0.000 0.076 0.364
#> GSM1296104 6 0.4283 0.1399 0.004 0.000 0.032 0.016 0.224 0.724
#> GSM1296079 1 0.4259 0.6999 0.716 0.000 0.004 0.000 0.220 0.060
#> GSM1296108 2 0.0363 0.8525 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM1296110 2 0.1075 0.8425 0.000 0.952 0.000 0.000 0.048 0.000
#> GSM1296081 1 0.2226 0.7509 0.904 0.000 0.008 0.000 0.060 0.028
#> GSM1296091 1 0.5964 0.3404 0.444 0.000 0.004 0.004 0.164 0.384
#> GSM1296075 1 0.6682 0.3192 0.400 0.008 0.004 0.012 0.284 0.292
#> GSM1296112 2 0.0260 0.8525 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1296100 6 0.3653 0.3387 0.048 0.000 0.132 0.000 0.016 0.804
#> GSM1296087 1 0.2999 0.7124 0.860 0.000 0.032 0.000 0.084 0.024
#> GSM1296118 2 0.4975 0.5761 0.020 0.692 0.004 0.000 0.092 0.192
#> GSM1296114 2 0.0547 0.8509 0.000 0.980 0.000 0.000 0.020 0.000
#> GSM1296097 6 0.5079 -0.1698 0.004 0.000 0.068 0.004 0.344 0.580
#> GSM1296106 6 0.4860 0.3291 0.052 0.004 0.092 0.000 0.116 0.736
#> GSM1296102 6 0.6869 -0.5000 0.076 0.000 0.172 0.000 0.340 0.412
#> GSM1296122 2 0.7564 -0.2772 0.128 0.344 0.008 0.000 0.200 0.320
#> GSM1296089 1 0.4743 0.5902 0.712 0.000 0.044 0.000 0.192 0.052
#> GSM1296083 1 0.2765 0.7422 0.872 0.000 0.056 0.000 0.064 0.008
#> GSM1296116 2 0.0790 0.8493 0.000 0.968 0.000 0.000 0.032 0.000
#> GSM1296085 1 0.2887 0.7402 0.844 0.000 0.000 0.000 0.036 0.120
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# 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 tissue(p) time(p) agent(p) dose(p) individual(p) k
#> CV:NMF 96 0.00614 0.1060 0.82842 3.64e-06 4.26e-03 2
#> CV:NMF 94 0.00152 0.0648 0.00783 6.44e-10 6.58e-07 3
#> CV:NMF 86 0.00184 0.3345 0.00826 4.12e-08 4.29e-09 4
#> CV:NMF 87 0.00373 0.3843 0.26943 6.49e-07 3.25e-08 5
#> CV:NMF 72 0.02235 0.2778 0.12889 1.83e-08 2.83e-08 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 45638 rows and 99 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 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 0.451 0.737 0.877 0.4518 0.558 0.558
#> 3 3 0.420 0.619 0.808 0.4079 0.796 0.638
#> 4 4 0.619 0.661 0.806 0.1585 0.830 0.568
#> 5 5 0.700 0.668 0.822 0.0614 0.963 0.853
#> 6 6 0.770 0.666 0.780 0.0460 0.886 0.558
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
#> GSM1296094 2 0.6343 0.7687 0.160 0.840
#> GSM1296119 2 0.0000 0.8404 0.000 1.000
#> GSM1296076 2 0.0000 0.8404 0.000 1.000
#> GSM1296092 2 0.0000 0.8404 0.000 1.000
#> GSM1296103 2 0.6343 0.7687 0.160 0.840
#> GSM1296078 2 0.0000 0.8404 0.000 1.000
#> GSM1296107 2 0.0000 0.8404 0.000 1.000
#> GSM1296109 2 0.1184 0.8404 0.016 0.984
#> GSM1296080 2 0.9661 0.4540 0.392 0.608
#> GSM1296090 2 0.0000 0.8404 0.000 1.000
#> GSM1296074 2 0.0000 0.8404 0.000 1.000
#> GSM1296111 2 0.0000 0.8404 0.000 1.000
#> GSM1296099 2 0.5842 0.7841 0.140 0.860
#> GSM1296086 2 0.3584 0.8330 0.068 0.932
#> GSM1296117 2 0.0000 0.8404 0.000 1.000
#> GSM1296113 2 0.0000 0.8404 0.000 1.000
#> GSM1296096 2 0.5842 0.7841 0.140 0.860
#> GSM1296105 2 0.9427 0.4896 0.360 0.640
#> GSM1296098 2 0.9552 0.4861 0.376 0.624
#> GSM1296101 2 0.6343 0.7760 0.160 0.840
#> GSM1296121 2 0.0000 0.8404 0.000 1.000
#> GSM1296088 2 0.9286 0.5493 0.344 0.656
#> GSM1296082 2 0.0000 0.8404 0.000 1.000
#> GSM1296115 2 0.0000 0.8404 0.000 1.000
#> GSM1296084 2 0.9998 0.2325 0.492 0.508
#> GSM1296072 2 0.3879 0.8344 0.076 0.924
#> GSM1296069 2 0.3879 0.8344 0.076 0.924
#> GSM1296071 2 0.3879 0.8344 0.076 0.924
#> GSM1296070 2 0.3879 0.8344 0.076 0.924
#> GSM1296073 2 0.0000 0.8404 0.000 1.000
#> GSM1296034 1 0.0000 0.8748 1.000 0.000
#> GSM1296041 2 0.0000 0.8404 0.000 1.000
#> GSM1296035 2 0.5842 0.7841 0.140 0.860
#> GSM1296038 2 0.3114 0.8349 0.056 0.944
#> GSM1296047 2 0.3879 0.8344 0.076 0.924
#> GSM1296039 2 0.0000 0.8404 0.000 1.000
#> GSM1296042 2 0.0000 0.8404 0.000 1.000
#> GSM1296043 2 0.3879 0.8344 0.076 0.924
#> GSM1296037 1 0.0000 0.8748 1.000 0.000
#> GSM1296046 2 0.3879 0.8344 0.076 0.924
#> GSM1296044 2 0.3879 0.8344 0.076 0.924
#> GSM1296045 2 0.3879 0.8344 0.076 0.924
#> GSM1296025 1 0.0000 0.8748 1.000 0.000
#> GSM1296033 2 0.9998 0.1492 0.492 0.508
#> GSM1296027 1 0.0000 0.8748 1.000 0.000
#> GSM1296032 1 0.0000 0.8748 1.000 0.000
#> GSM1296024 1 0.0000 0.8748 1.000 0.000
#> GSM1296031 1 0.4690 0.8101 0.900 0.100
#> GSM1296028 1 0.0000 0.8748 1.000 0.000
#> GSM1296029 1 0.0938 0.8689 0.988 0.012
#> GSM1296026 2 0.9323 0.5424 0.348 0.652
#> GSM1296030 2 0.9710 0.4409 0.400 0.600
#> GSM1296040 2 0.9866 0.3430 0.432 0.568
#> GSM1296036 2 0.9552 0.4861 0.376 0.624
#> GSM1296048 2 0.0000 0.8404 0.000 1.000
#> GSM1296059 2 0.6343 0.7687 0.160 0.840
#> GSM1296066 2 0.0000 0.8404 0.000 1.000
#> GSM1296060 2 0.5842 0.7841 0.140 0.860
#> GSM1296063 2 0.2236 0.8358 0.036 0.964
#> GSM1296064 2 0.0000 0.8404 0.000 1.000
#> GSM1296067 2 0.6887 0.7518 0.184 0.816
#> GSM1296062 2 0.9635 0.4590 0.388 0.612
#> GSM1296068 2 0.3879 0.8344 0.076 0.924
#> GSM1296050 1 0.3431 0.8376 0.936 0.064
#> GSM1296057 1 0.9209 0.4568 0.664 0.336
#> GSM1296052 1 0.0000 0.8748 1.000 0.000
#> GSM1296054 1 0.0000 0.8748 1.000 0.000
#> GSM1296049 1 0.0000 0.8748 1.000 0.000
#> GSM1296055 1 0.4690 0.8101 0.900 0.100
#> GSM1296053 1 0.0000 0.8748 1.000 0.000
#> GSM1296058 1 0.9635 0.3267 0.612 0.388
#> GSM1296051 2 0.9286 0.5500 0.344 0.656
#> GSM1296056 2 0.0672 0.8406 0.008 0.992
#> GSM1296065 1 0.9993 0.0457 0.516 0.484
#> GSM1296061 2 0.9552 0.4861 0.376 0.624
#> GSM1296095 2 0.5519 0.7999 0.128 0.872
#> GSM1296120 2 0.3879 0.8344 0.076 0.924
#> GSM1296077 1 0.0000 0.8748 1.000 0.000
#> GSM1296093 1 0.0000 0.8748 1.000 0.000
#> GSM1296104 2 0.9954 0.1260 0.460 0.540
#> GSM1296079 1 0.0000 0.8748 1.000 0.000
#> GSM1296108 2 0.3879 0.8344 0.076 0.924
#> GSM1296110 2 0.3879 0.8344 0.076 0.924
#> GSM1296081 1 0.0000 0.8748 1.000 0.000
#> GSM1296091 1 0.9248 0.4614 0.660 0.340
#> GSM1296075 1 0.9248 0.4614 0.660 0.340
#> GSM1296112 2 0.3879 0.8344 0.076 0.924
#> GSM1296100 1 0.0000 0.8748 1.000 0.000
#> GSM1296087 1 0.0000 0.8748 1.000 0.000
#> GSM1296118 2 0.8016 0.6738 0.244 0.756
#> GSM1296114 2 0.3879 0.8344 0.076 0.924
#> GSM1296097 1 0.9795 0.2434 0.584 0.416
#> GSM1296106 1 0.9993 0.0457 0.516 0.484
#> GSM1296102 1 0.0376 0.8730 0.996 0.004
#> GSM1296122 2 0.8016 0.6738 0.244 0.756
#> GSM1296089 1 0.4690 0.8101 0.900 0.100
#> GSM1296083 1 0.0000 0.8748 1.000 0.000
#> GSM1296116 2 0.3879 0.8344 0.076 0.924
#> GSM1296085 1 0.0000 0.8748 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1296094 3 0.4326 0.5811 0.144 0.012 0.844
#> GSM1296119 3 0.6295 0.2032 0.000 0.472 0.528
#> GSM1296076 3 0.3192 0.6328 0.000 0.112 0.888
#> GSM1296092 3 0.3192 0.6328 0.000 0.112 0.888
#> GSM1296103 3 0.4326 0.5811 0.144 0.012 0.844
#> GSM1296078 3 0.3192 0.6328 0.000 0.112 0.888
#> GSM1296107 3 0.6295 0.2032 0.000 0.472 0.528
#> GSM1296109 3 0.0892 0.6333 0.000 0.020 0.980
#> GSM1296080 3 0.6566 0.2798 0.376 0.012 0.612
#> GSM1296090 3 0.3192 0.6328 0.000 0.112 0.888
#> GSM1296074 3 0.3192 0.6328 0.000 0.112 0.888
#> GSM1296111 3 0.6295 0.2032 0.000 0.472 0.528
#> GSM1296099 3 0.6519 0.6344 0.132 0.108 0.760
#> GSM1296086 3 0.5588 0.6462 0.068 0.124 0.808
#> GSM1296117 3 0.6295 0.2032 0.000 0.472 0.528
#> GSM1296113 3 0.6295 0.2032 0.000 0.472 0.528
#> GSM1296096 3 0.6519 0.6344 0.132 0.108 0.760
#> GSM1296105 3 0.8345 0.2392 0.344 0.096 0.560
#> GSM1296098 3 0.6490 0.3125 0.360 0.012 0.628
#> GSM1296101 3 0.6823 0.6229 0.152 0.108 0.740
#> GSM1296121 3 0.6295 0.2032 0.000 0.472 0.528
#> GSM1296088 3 0.6307 0.3637 0.328 0.012 0.660
#> GSM1296082 3 0.3192 0.6328 0.000 0.112 0.888
#> GSM1296115 3 0.6295 0.2032 0.000 0.472 0.528
#> GSM1296084 3 0.6819 -0.0278 0.476 0.012 0.512
#> GSM1296072 2 0.0000 0.9678 0.000 1.000 0.000
#> GSM1296069 2 0.0000 0.9678 0.000 1.000 0.000
#> GSM1296071 2 0.0000 0.9678 0.000 1.000 0.000
#> GSM1296070 2 0.0000 0.9678 0.000 1.000 0.000
#> GSM1296073 3 0.6295 0.2032 0.000 0.472 0.528
#> GSM1296034 1 0.0000 0.8587 1.000 0.000 0.000
#> GSM1296041 3 0.6295 0.2032 0.000 0.472 0.528
#> GSM1296035 3 0.6519 0.6344 0.132 0.108 0.760
#> GSM1296038 3 0.6302 0.6194 0.048 0.208 0.744
#> GSM1296047 2 0.0000 0.9678 0.000 1.000 0.000
#> GSM1296039 3 0.3267 0.6313 0.000 0.116 0.884
#> GSM1296042 3 0.6299 0.1960 0.000 0.476 0.524
#> GSM1296043 2 0.0000 0.9678 0.000 1.000 0.000
#> GSM1296037 1 0.0000 0.8587 1.000 0.000 0.000
#> GSM1296046 2 0.0000 0.9678 0.000 1.000 0.000
#> GSM1296044 2 0.0000 0.9678 0.000 1.000 0.000
#> GSM1296045 2 0.0000 0.9678 0.000 1.000 0.000
#> GSM1296025 1 0.0000 0.8587 1.000 0.000 0.000
#> GSM1296033 3 0.7759 -0.0317 0.476 0.048 0.476
#> GSM1296027 1 0.0000 0.8587 1.000 0.000 0.000
#> GSM1296032 1 0.0000 0.8587 1.000 0.000 0.000
#> GSM1296024 1 0.0000 0.8587 1.000 0.000 0.000
#> GSM1296031 1 0.3832 0.7927 0.880 0.100 0.020
#> GSM1296028 1 0.0000 0.8587 1.000 0.000 0.000
#> GSM1296029 1 0.0592 0.8525 0.988 0.012 0.000
#> GSM1296026 3 0.7379 0.3853 0.336 0.048 0.616
#> GSM1296030 3 0.6617 0.2571 0.388 0.012 0.600
#> GSM1296040 3 0.6973 0.1747 0.416 0.020 0.564
#> GSM1296036 3 0.6490 0.3125 0.360 0.012 0.628
#> GSM1296048 3 0.6295 0.2032 0.000 0.472 0.528
#> GSM1296059 3 0.4326 0.5811 0.144 0.012 0.844
#> GSM1296066 3 0.6295 0.2032 0.000 0.472 0.528
#> GSM1296060 3 0.6519 0.6344 0.132 0.108 0.760
#> GSM1296063 3 0.5660 0.6177 0.028 0.200 0.772
#> GSM1296064 3 0.3267 0.6313 0.000 0.116 0.884
#> GSM1296067 2 0.3116 0.8601 0.108 0.892 0.000
#> GSM1296062 3 0.6548 0.2883 0.372 0.012 0.616
#> GSM1296068 2 0.0000 0.9678 0.000 1.000 0.000
#> GSM1296050 1 0.2804 0.8200 0.924 0.060 0.016
#> GSM1296057 1 0.7338 0.4795 0.652 0.060 0.288
#> GSM1296052 1 0.0000 0.8587 1.000 0.000 0.000
#> GSM1296054 1 0.0000 0.8587 1.000 0.000 0.000
#> GSM1296049 1 0.0000 0.8587 1.000 0.000 0.000
#> GSM1296055 1 0.3832 0.7927 0.880 0.100 0.020
#> GSM1296053 1 0.0000 0.8587 1.000 0.000 0.000
#> GSM1296058 1 0.7665 0.3603 0.600 0.060 0.340
#> GSM1296051 3 0.7357 0.3924 0.332 0.048 0.620
#> GSM1296056 3 0.3607 0.6358 0.008 0.112 0.880
#> GSM1296065 1 0.9029 0.2400 0.504 0.144 0.352
#> GSM1296061 3 0.6490 0.3125 0.360 0.012 0.628
#> GSM1296095 3 0.7860 0.5936 0.116 0.228 0.656
#> GSM1296120 2 0.0000 0.9678 0.000 1.000 0.000
#> GSM1296077 1 0.0000 0.8587 1.000 0.000 0.000
#> GSM1296093 1 0.0000 0.8587 1.000 0.000 0.000
#> GSM1296104 1 0.9144 0.0949 0.448 0.144 0.408
#> GSM1296079 1 0.0000 0.8587 1.000 0.000 0.000
#> GSM1296108 2 0.0000 0.9678 0.000 1.000 0.000
#> GSM1296110 2 0.0000 0.9678 0.000 1.000 0.000
#> GSM1296081 1 0.0000 0.8587 1.000 0.000 0.000
#> GSM1296091 1 0.7497 0.4938 0.652 0.072 0.276
#> GSM1296075 1 0.7497 0.4938 0.652 0.072 0.276
#> GSM1296112 2 0.0000 0.9678 0.000 1.000 0.000
#> GSM1296100 1 0.0000 0.8587 1.000 0.000 0.000
#> GSM1296087 1 0.0237 0.8570 0.996 0.000 0.004
#> GSM1296118 2 0.4121 0.7878 0.168 0.832 0.000
#> GSM1296114 2 0.0000 0.9678 0.000 1.000 0.000
#> GSM1296097 1 0.7858 0.3044 0.572 0.064 0.364
#> GSM1296106 1 0.9029 0.2400 0.504 0.144 0.352
#> GSM1296102 1 0.0424 0.8544 0.992 0.008 0.000
#> GSM1296122 2 0.4121 0.7878 0.168 0.832 0.000
#> GSM1296089 1 0.3832 0.7927 0.880 0.100 0.020
#> GSM1296083 1 0.0000 0.8587 1.000 0.000 0.000
#> GSM1296116 2 0.0000 0.9678 0.000 1.000 0.000
#> GSM1296085 1 0.0000 0.8587 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1296094 3 0.4898 0.3234 0.000 0.000 0.584 0.416
#> GSM1296119 4 0.4564 0.5225 0.000 0.328 0.000 0.672
#> GSM1296076 4 0.1022 0.5912 0.000 0.000 0.032 0.968
#> GSM1296092 4 0.1022 0.5912 0.000 0.000 0.032 0.968
#> GSM1296103 3 0.4898 0.3234 0.000 0.000 0.584 0.416
#> GSM1296078 4 0.1022 0.5912 0.000 0.000 0.032 0.968
#> GSM1296107 4 0.4564 0.5225 0.000 0.328 0.000 0.672
#> GSM1296109 4 0.5060 0.0453 0.000 0.004 0.412 0.584
#> GSM1296080 3 0.3498 0.6118 0.008 0.000 0.832 0.160
#> GSM1296090 4 0.1022 0.5912 0.000 0.000 0.032 0.968
#> GSM1296074 4 0.1022 0.5912 0.000 0.000 0.032 0.968
#> GSM1296111 4 0.4564 0.5225 0.000 0.328 0.000 0.672
#> GSM1296099 4 0.4713 0.1349 0.000 0.000 0.360 0.640
#> GSM1296086 4 0.4040 0.3205 0.000 0.000 0.248 0.752
#> GSM1296117 4 0.4564 0.5225 0.000 0.328 0.000 0.672
#> GSM1296113 4 0.4564 0.5225 0.000 0.328 0.000 0.672
#> GSM1296096 4 0.4713 0.1349 0.000 0.000 0.360 0.640
#> GSM1296105 3 0.6975 0.6118 0.088 0.084 0.680 0.148
#> GSM1296098 3 0.3266 0.6053 0.000 0.000 0.832 0.168
#> GSM1296101 4 0.4776 0.0894 0.000 0.000 0.376 0.624
#> GSM1296121 4 0.4564 0.5225 0.000 0.328 0.000 0.672
#> GSM1296088 3 0.5256 0.6001 0.040 0.000 0.700 0.260
#> GSM1296082 4 0.1022 0.5912 0.000 0.000 0.032 0.968
#> GSM1296115 4 0.4564 0.5225 0.000 0.328 0.000 0.672
#> GSM1296084 3 0.7369 0.5402 0.228 0.000 0.524 0.248
#> GSM1296072 2 0.0000 0.9521 0.000 1.000 0.000 0.000
#> GSM1296069 2 0.1557 0.9125 0.000 0.944 0.000 0.056
#> GSM1296071 2 0.0000 0.9521 0.000 1.000 0.000 0.000
#> GSM1296070 2 0.1557 0.9125 0.000 0.944 0.000 0.056
#> GSM1296073 4 0.4564 0.5225 0.000 0.328 0.000 0.672
#> GSM1296034 1 0.1792 0.8862 0.932 0.000 0.068 0.000
#> GSM1296041 4 0.4564 0.5225 0.000 0.328 0.000 0.672
#> GSM1296035 4 0.4713 0.1349 0.000 0.000 0.360 0.640
#> GSM1296038 4 0.6080 0.2638 0.000 0.100 0.236 0.664
#> GSM1296047 2 0.0000 0.9521 0.000 1.000 0.000 0.000
#> GSM1296039 4 0.0921 0.5917 0.000 0.000 0.028 0.972
#> GSM1296042 4 0.4585 0.5172 0.000 0.332 0.000 0.668
#> GSM1296043 2 0.1557 0.9125 0.000 0.944 0.000 0.056
#> GSM1296037 1 0.0000 0.9487 1.000 0.000 0.000 0.000
#> GSM1296046 2 0.0000 0.9521 0.000 1.000 0.000 0.000
#> GSM1296044 2 0.0000 0.9521 0.000 1.000 0.000 0.000
#> GSM1296045 2 0.1557 0.9125 0.000 0.944 0.000 0.056
#> GSM1296025 1 0.0000 0.9487 1.000 0.000 0.000 0.000
#> GSM1296033 3 0.8143 0.5922 0.176 0.040 0.516 0.268
#> GSM1296027 1 0.0000 0.9487 1.000 0.000 0.000 0.000
#> GSM1296032 1 0.0000 0.9487 1.000 0.000 0.000 0.000
#> GSM1296024 1 0.0000 0.9487 1.000 0.000 0.000 0.000
#> GSM1296031 1 0.5594 0.6917 0.720 0.100 0.180 0.000
#> GSM1296028 1 0.0000 0.9487 1.000 0.000 0.000 0.000
#> GSM1296029 1 0.1118 0.9188 0.964 0.000 0.036 0.000
#> GSM1296026 3 0.5929 0.5410 0.048 0.000 0.596 0.356
#> GSM1296030 3 0.5842 0.6264 0.092 0.000 0.688 0.220
#> GSM1296040 3 0.4597 0.6289 0.044 0.008 0.800 0.148
#> GSM1296036 3 0.3266 0.6053 0.000 0.000 0.832 0.168
#> GSM1296048 4 0.4564 0.5225 0.000 0.328 0.000 0.672
#> GSM1296059 3 0.4898 0.3234 0.000 0.000 0.584 0.416
#> GSM1296066 4 0.4564 0.5225 0.000 0.328 0.000 0.672
#> GSM1296060 4 0.4713 0.1349 0.000 0.000 0.360 0.640
#> GSM1296063 4 0.4359 0.5042 0.000 0.100 0.084 0.816
#> GSM1296064 4 0.0921 0.5917 0.000 0.000 0.028 0.972
#> GSM1296067 2 0.2675 0.8605 0.100 0.892 0.008 0.000
#> GSM1296062 3 0.3625 0.6134 0.012 0.000 0.828 0.160
#> GSM1296068 2 0.0000 0.9521 0.000 1.000 0.000 0.000
#> GSM1296050 1 0.4440 0.7792 0.804 0.060 0.136 0.000
#> GSM1296057 3 0.7946 0.3032 0.384 0.052 0.468 0.096
#> GSM1296052 1 0.0000 0.9487 1.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.9487 1.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.9487 1.000 0.000 0.000 0.000
#> GSM1296055 1 0.5594 0.6917 0.720 0.100 0.180 0.000
#> GSM1296053 1 0.0000 0.9487 1.000 0.000 0.000 0.000
#> GSM1296058 3 0.7685 0.4479 0.312 0.048 0.544 0.096
#> GSM1296051 3 0.5971 0.5263 0.048 0.000 0.584 0.368
#> GSM1296056 4 0.1474 0.5793 0.000 0.000 0.052 0.948
#> GSM1296065 3 0.8547 0.5317 0.212 0.132 0.536 0.120
#> GSM1296061 3 0.3266 0.6053 0.000 0.000 0.832 0.168
#> GSM1296095 4 0.7095 -0.0372 0.004 0.128 0.328 0.540
#> GSM1296120 2 0.0000 0.9521 0.000 1.000 0.000 0.000
#> GSM1296077 1 0.0000 0.9487 1.000 0.000 0.000 0.000
#> GSM1296093 1 0.0000 0.9487 1.000 0.000 0.000 0.000
#> GSM1296104 3 0.8570 0.5196 0.156 0.132 0.544 0.168
#> GSM1296079 1 0.0000 0.9487 1.000 0.000 0.000 0.000
#> GSM1296108 2 0.0000 0.9521 0.000 1.000 0.000 0.000
#> GSM1296110 2 0.0000 0.9521 0.000 1.000 0.000 0.000
#> GSM1296081 1 0.0000 0.9487 1.000 0.000 0.000 0.000
#> GSM1296091 3 0.8003 0.2333 0.400 0.064 0.452 0.084
#> GSM1296075 3 0.8003 0.2333 0.400 0.064 0.452 0.084
#> GSM1296112 2 0.0000 0.9521 0.000 1.000 0.000 0.000
#> GSM1296100 1 0.0000 0.9487 1.000 0.000 0.000 0.000
#> GSM1296087 1 0.0188 0.9456 0.996 0.000 0.004 0.000
#> GSM1296118 2 0.3577 0.7945 0.156 0.832 0.012 0.000
#> GSM1296114 2 0.0000 0.9521 0.000 1.000 0.000 0.000
#> GSM1296097 3 0.7912 0.4888 0.284 0.052 0.544 0.120
#> GSM1296106 3 0.8547 0.5317 0.212 0.132 0.536 0.120
#> GSM1296102 1 0.0524 0.9414 0.988 0.008 0.004 0.000
#> GSM1296122 2 0.3577 0.7945 0.156 0.832 0.012 0.000
#> GSM1296089 1 0.5594 0.6917 0.720 0.100 0.180 0.000
#> GSM1296083 1 0.0000 0.9487 1.000 0.000 0.000 0.000
#> GSM1296116 2 0.0000 0.9521 0.000 1.000 0.000 0.000
#> GSM1296085 1 0.0000 0.9487 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
#> GSM1296094 3 0.3774 0.55743 0.000 0.000 0.704 0.000 0.296
#> GSM1296119 5 0.5032 0.62378 0.000 0.220 0.000 0.092 0.688
#> GSM1296076 5 0.1430 0.61423 0.000 0.000 0.052 0.004 0.944
#> GSM1296092 5 0.1430 0.61423 0.000 0.000 0.052 0.004 0.944
#> GSM1296103 3 0.3774 0.55743 0.000 0.000 0.704 0.000 0.296
#> GSM1296078 5 0.1430 0.61423 0.000 0.000 0.052 0.004 0.944
#> GSM1296107 5 0.5032 0.62378 0.000 0.220 0.000 0.092 0.688
#> GSM1296109 3 0.4437 0.20122 0.000 0.000 0.532 0.004 0.464
#> GSM1296080 3 0.0960 0.65475 0.008 0.000 0.972 0.016 0.004
#> GSM1296090 5 0.1430 0.61423 0.000 0.000 0.052 0.004 0.944
#> GSM1296074 5 0.1430 0.61423 0.000 0.000 0.052 0.004 0.944
#> GSM1296111 5 0.5032 0.62378 0.000 0.220 0.000 0.092 0.688
#> GSM1296099 5 0.4367 0.11065 0.000 0.000 0.372 0.008 0.620
#> GSM1296086 5 0.3885 0.36005 0.000 0.000 0.268 0.008 0.724
#> GSM1296117 5 0.5032 0.62378 0.000 0.220 0.000 0.092 0.688
#> GSM1296113 5 0.5032 0.62378 0.000 0.220 0.000 0.092 0.688
#> GSM1296096 5 0.4367 0.11065 0.000 0.000 0.372 0.008 0.620
#> GSM1296105 3 0.6273 -0.00146 0.016 0.060 0.516 0.392 0.016
#> GSM1296098 3 0.0162 0.66538 0.000 0.000 0.996 0.000 0.004
#> GSM1296101 5 0.5505 0.11943 0.000 0.000 0.304 0.092 0.604
#> GSM1296121 5 0.5032 0.62378 0.000 0.220 0.000 0.092 0.688
#> GSM1296088 3 0.4941 0.62334 0.004 0.000 0.708 0.080 0.208
#> GSM1296082 5 0.1430 0.61423 0.000 0.000 0.052 0.004 0.944
#> GSM1296115 5 0.5032 0.62378 0.000 0.220 0.000 0.092 0.688
#> GSM1296084 3 0.7616 0.41687 0.192 0.000 0.500 0.108 0.200
#> GSM1296072 2 0.0000 0.93988 0.000 1.000 0.000 0.000 0.000
#> GSM1296069 2 0.2344 0.88133 0.000 0.904 0.000 0.032 0.064
#> GSM1296071 2 0.0000 0.93988 0.000 1.000 0.000 0.000 0.000
#> GSM1296070 2 0.2344 0.88133 0.000 0.904 0.000 0.032 0.064
#> GSM1296073 5 0.5032 0.62378 0.000 0.220 0.000 0.092 0.688
#> GSM1296034 1 0.1704 0.83959 0.928 0.000 0.068 0.004 0.000
#> GSM1296041 5 0.5032 0.62378 0.000 0.220 0.000 0.092 0.688
#> GSM1296035 5 0.4367 0.11065 0.000 0.000 0.372 0.008 0.620
#> GSM1296038 5 0.6247 0.35090 0.000 0.076 0.096 0.176 0.652
#> GSM1296047 2 0.0000 0.93988 0.000 1.000 0.000 0.000 0.000
#> GSM1296039 5 0.1357 0.61501 0.000 0.000 0.048 0.004 0.948
#> GSM1296042 5 0.5060 0.62093 0.000 0.224 0.000 0.092 0.684
#> GSM1296043 2 0.2344 0.88133 0.000 0.904 0.000 0.032 0.064
#> GSM1296037 1 0.2074 0.83337 0.896 0.000 0.000 0.104 0.000
#> GSM1296046 2 0.0000 0.93988 0.000 1.000 0.000 0.000 0.000
#> GSM1296044 2 0.0000 0.93988 0.000 1.000 0.000 0.000 0.000
#> GSM1296045 2 0.2344 0.88133 0.000 0.904 0.000 0.032 0.064
#> GSM1296025 1 0.0000 0.88893 1.000 0.000 0.000 0.000 0.000
#> GSM1296033 4 0.7880 0.28688 0.088 0.008 0.228 0.472 0.204
#> GSM1296027 1 0.0000 0.88893 1.000 0.000 0.000 0.000 0.000
#> GSM1296032 1 0.0000 0.88893 1.000 0.000 0.000 0.000 0.000
#> GSM1296024 1 0.0000 0.88893 1.000 0.000 0.000 0.000 0.000
#> GSM1296031 1 0.5320 0.17828 0.488 0.040 0.004 0.468 0.000
#> GSM1296028 1 0.0000 0.88893 1.000 0.000 0.000 0.000 0.000
#> GSM1296029 1 0.1205 0.86283 0.956 0.000 0.004 0.040 0.000
#> GSM1296026 3 0.5739 0.54408 0.008 0.000 0.596 0.088 0.308
#> GSM1296030 3 0.5825 0.60220 0.048 0.000 0.684 0.104 0.164
#> GSM1296040 3 0.4684 0.34086 0.012 0.000 0.664 0.308 0.016
#> GSM1296036 3 0.0162 0.66538 0.000 0.000 0.996 0.000 0.004
#> GSM1296048 5 0.5032 0.62378 0.000 0.220 0.000 0.092 0.688
#> GSM1296059 3 0.3774 0.55743 0.000 0.000 0.704 0.000 0.296
#> GSM1296066 5 0.5032 0.62378 0.000 0.220 0.000 0.092 0.688
#> GSM1296060 5 0.4367 0.11065 0.000 0.000 0.372 0.008 0.620
#> GSM1296063 5 0.4293 0.53498 0.000 0.076 0.028 0.092 0.804
#> GSM1296064 5 0.1357 0.61501 0.000 0.000 0.048 0.004 0.948
#> GSM1296067 2 0.2983 0.83154 0.096 0.868 0.004 0.032 0.000
#> GSM1296062 3 0.0854 0.65783 0.008 0.000 0.976 0.012 0.004
#> GSM1296068 2 0.0000 0.93988 0.000 1.000 0.000 0.000 0.000
#> GSM1296050 1 0.4801 0.41691 0.604 0.020 0.004 0.372 0.000
#> GSM1296057 4 0.4848 0.78351 0.168 0.008 0.016 0.752 0.056
#> GSM1296052 1 0.0000 0.88893 1.000 0.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.88893 1.000 0.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.88893 1.000 0.000 0.000 0.000 0.000
#> GSM1296055 1 0.5320 0.17828 0.488 0.040 0.004 0.468 0.000
#> GSM1296053 1 0.0000 0.88893 1.000 0.000 0.000 0.000 0.000
#> GSM1296058 4 0.4320 0.80986 0.096 0.004 0.028 0.808 0.064
#> GSM1296051 3 0.5786 0.53650 0.008 0.000 0.584 0.088 0.320
#> GSM1296056 5 0.1877 0.60186 0.000 0.000 0.064 0.012 0.924
#> GSM1296065 4 0.4883 0.78738 0.028 0.076 0.028 0.788 0.080
#> GSM1296061 3 0.0162 0.66538 0.000 0.000 0.996 0.000 0.004
#> GSM1296095 5 0.6336 0.07663 0.000 0.084 0.032 0.348 0.536
#> GSM1296120 2 0.0000 0.93988 0.000 1.000 0.000 0.000 0.000
#> GSM1296077 1 0.0000 0.88893 1.000 0.000 0.000 0.000 0.000
#> GSM1296093 1 0.0000 0.88893 1.000 0.000 0.000 0.000 0.000
#> GSM1296104 4 0.5161 0.74344 0.012 0.076 0.028 0.752 0.132
#> GSM1296079 1 0.1544 0.85540 0.932 0.000 0.000 0.068 0.000
#> GSM1296108 2 0.0000 0.93988 0.000 1.000 0.000 0.000 0.000
#> GSM1296110 2 0.0404 0.93465 0.000 0.988 0.000 0.012 0.000
#> GSM1296081 1 0.0000 0.88893 1.000 0.000 0.000 0.000 0.000
#> GSM1296091 4 0.4622 0.78155 0.156 0.008 0.020 0.772 0.044
#> GSM1296075 4 0.4622 0.78155 0.156 0.008 0.020 0.772 0.044
#> GSM1296112 2 0.0000 0.93988 0.000 1.000 0.000 0.000 0.000
#> GSM1296100 1 0.2074 0.83337 0.896 0.000 0.000 0.104 0.000
#> GSM1296087 1 0.0404 0.88366 0.988 0.000 0.000 0.012 0.000
#> GSM1296118 2 0.3849 0.76076 0.136 0.808 0.004 0.052 0.000
#> GSM1296114 2 0.0000 0.93988 0.000 1.000 0.000 0.000 0.000
#> GSM1296097 4 0.4397 0.80849 0.068 0.008 0.028 0.808 0.088
#> GSM1296106 4 0.4883 0.78738 0.028 0.076 0.028 0.788 0.080
#> GSM1296102 1 0.2338 0.82528 0.884 0.004 0.000 0.112 0.000
#> GSM1296122 2 0.3849 0.76076 0.136 0.808 0.004 0.052 0.000
#> GSM1296089 1 0.5320 0.17828 0.488 0.040 0.004 0.468 0.000
#> GSM1296083 1 0.0000 0.88893 1.000 0.000 0.000 0.000 0.000
#> GSM1296116 2 0.0000 0.93988 0.000 1.000 0.000 0.000 0.000
#> GSM1296085 1 0.0000 0.88893 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
#> GSM1296094 4 0.4481 -0.1981 0.000 0.000 0.296 0.648 0.056 0.000
#> GSM1296119 5 0.1556 0.9978 0.000 0.080 0.000 0.000 0.920 0.000
#> GSM1296076 4 0.4864 0.4942 0.000 0.000 0.064 0.552 0.384 0.000
#> GSM1296092 4 0.4864 0.4942 0.000 0.000 0.064 0.552 0.384 0.000
#> GSM1296103 4 0.4481 -0.1981 0.000 0.000 0.296 0.648 0.056 0.000
#> GSM1296078 4 0.4864 0.4942 0.000 0.000 0.064 0.552 0.384 0.000
#> GSM1296107 5 0.1556 0.9978 0.000 0.080 0.000 0.000 0.920 0.000
#> GSM1296109 4 0.5818 0.0818 0.000 0.000 0.280 0.492 0.228 0.000
#> GSM1296080 3 0.4189 0.7918 0.008 0.000 0.552 0.436 0.000 0.004
#> GSM1296090 4 0.4864 0.4942 0.000 0.000 0.064 0.552 0.384 0.000
#> GSM1296074 4 0.4864 0.4942 0.000 0.000 0.064 0.552 0.384 0.000
#> GSM1296111 5 0.1556 0.9978 0.000 0.080 0.000 0.000 0.920 0.000
#> GSM1296099 4 0.2588 0.4980 0.000 0.000 0.012 0.860 0.124 0.004
#> GSM1296086 4 0.4861 0.5584 0.000 0.000 0.088 0.644 0.264 0.004
#> GSM1296117 5 0.1556 0.9978 0.000 0.080 0.000 0.000 0.920 0.000
#> GSM1296113 5 0.1556 0.9978 0.000 0.080 0.000 0.000 0.920 0.000
#> GSM1296096 4 0.2588 0.4980 0.000 0.000 0.012 0.860 0.124 0.004
#> GSM1296105 3 0.5619 0.2293 0.008 0.004 0.492 0.100 0.000 0.396
#> GSM1296098 3 0.3851 0.7974 0.000 0.000 0.540 0.460 0.000 0.000
#> GSM1296101 4 0.4360 0.4927 0.000 0.000 0.048 0.768 0.116 0.068
#> GSM1296121 5 0.1700 0.9948 0.000 0.080 0.000 0.004 0.916 0.000
#> GSM1296088 4 0.4990 -0.3646 0.000 0.000 0.276 0.616 0.000 0.108
#> GSM1296082 4 0.4864 0.4942 0.000 0.000 0.064 0.552 0.384 0.000
#> GSM1296115 5 0.1556 0.9978 0.000 0.080 0.000 0.000 0.920 0.000
#> GSM1296084 4 0.7134 -0.3051 0.180 0.000 0.240 0.448 0.000 0.132
#> GSM1296072 2 0.0260 0.9260 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM1296069 2 0.2302 0.8532 0.000 0.872 0.008 0.000 0.120 0.000
#> GSM1296071 2 0.0260 0.9260 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM1296070 2 0.2302 0.8532 0.000 0.872 0.008 0.000 0.120 0.000
#> GSM1296073 5 0.1700 0.9948 0.000 0.080 0.000 0.004 0.916 0.000
#> GSM1296034 1 0.1806 0.8724 0.908 0.000 0.088 0.000 0.000 0.004
#> GSM1296041 5 0.1556 0.9978 0.000 0.080 0.000 0.000 0.920 0.000
#> GSM1296035 4 0.2588 0.4980 0.000 0.000 0.012 0.860 0.124 0.004
#> GSM1296038 4 0.6995 0.4686 0.000 0.008 0.136 0.516 0.180 0.160
#> GSM1296047 2 0.0260 0.9260 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM1296039 4 0.4872 0.4890 0.000 0.000 0.064 0.548 0.388 0.000
#> GSM1296042 5 0.1610 0.9925 0.000 0.084 0.000 0.000 0.916 0.000
#> GSM1296043 2 0.2302 0.8532 0.000 0.872 0.008 0.000 0.120 0.000
#> GSM1296037 1 0.2404 0.8531 0.872 0.000 0.016 0.000 0.000 0.112
#> GSM1296046 2 0.0260 0.9260 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM1296044 2 0.0000 0.9262 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296045 2 0.2302 0.8532 0.000 0.872 0.008 0.000 0.120 0.000
#> GSM1296025 1 0.0000 0.9401 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296033 6 0.5818 0.2431 0.056 0.000 0.080 0.292 0.000 0.572
#> GSM1296027 1 0.0000 0.9401 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296032 1 0.0000 0.9401 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296024 1 0.0000 0.9401 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296031 6 0.6908 0.4300 0.276 0.000 0.216 0.000 0.072 0.436
#> GSM1296028 1 0.0000 0.9401 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296029 1 0.1334 0.9043 0.948 0.000 0.020 0.000 0.000 0.032
#> GSM1296026 4 0.4835 -0.0926 0.000 0.000 0.180 0.692 0.012 0.116
#> GSM1296030 4 0.6081 -0.4417 0.044 0.000 0.304 0.532 0.000 0.120
#> GSM1296040 3 0.6151 0.5127 0.008 0.000 0.444 0.232 0.000 0.316
#> GSM1296036 3 0.3851 0.7974 0.000 0.000 0.540 0.460 0.000 0.000
#> GSM1296048 5 0.1700 0.9948 0.000 0.080 0.000 0.004 0.916 0.000
#> GSM1296059 4 0.4481 -0.1981 0.000 0.000 0.296 0.648 0.056 0.000
#> GSM1296066 5 0.1556 0.9978 0.000 0.080 0.000 0.000 0.920 0.000
#> GSM1296060 4 0.2588 0.4980 0.000 0.000 0.012 0.860 0.124 0.004
#> GSM1296063 4 0.6986 0.4408 0.000 0.008 0.132 0.456 0.308 0.096
#> GSM1296064 4 0.4872 0.4890 0.000 0.000 0.064 0.548 0.388 0.000
#> GSM1296067 2 0.3248 0.8013 0.000 0.804 0.164 0.000 0.032 0.000
#> GSM1296062 3 0.4296 0.7946 0.008 0.000 0.544 0.440 0.000 0.008
#> GSM1296068 2 0.0000 0.9262 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296050 1 0.5844 0.0503 0.508 0.000 0.096 0.000 0.032 0.364
#> GSM1296057 6 0.3092 0.6614 0.120 0.000 0.028 0.012 0.000 0.840
#> GSM1296052 1 0.0000 0.9401 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.9401 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.9401 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296055 6 0.6908 0.4300 0.276 0.000 0.216 0.000 0.072 0.436
#> GSM1296053 1 0.0000 0.9401 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296058 6 0.2182 0.6640 0.068 0.000 0.008 0.020 0.000 0.904
#> GSM1296051 4 0.4974 -0.0587 0.000 0.000 0.176 0.688 0.020 0.116
#> GSM1296056 4 0.4951 0.5071 0.000 0.000 0.064 0.568 0.364 0.004
#> GSM1296065 6 0.2326 0.6276 0.000 0.012 0.092 0.008 0.000 0.888
#> GSM1296061 3 0.3851 0.7974 0.000 0.000 0.540 0.460 0.000 0.000
#> GSM1296095 6 0.7423 -0.1935 0.000 0.012 0.124 0.352 0.148 0.364
#> GSM1296120 2 0.0260 0.9260 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM1296077 1 0.0000 0.9401 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296093 1 0.0000 0.9401 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296104 6 0.3094 0.6106 0.000 0.012 0.092 0.032 0.008 0.856
#> GSM1296079 1 0.2277 0.8645 0.892 0.000 0.032 0.000 0.000 0.076
#> GSM1296108 2 0.0146 0.9256 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1296110 2 0.1075 0.9080 0.000 0.952 0.048 0.000 0.000 0.000
#> GSM1296081 1 0.0000 0.9401 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296091 6 0.2830 0.6625 0.068 0.000 0.064 0.000 0.004 0.864
#> GSM1296075 6 0.2830 0.6625 0.068 0.000 0.064 0.000 0.004 0.864
#> GSM1296112 2 0.0146 0.9256 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1296100 1 0.2404 0.8531 0.872 0.000 0.016 0.000 0.000 0.112
#> GSM1296087 1 0.0405 0.9333 0.988 0.000 0.004 0.000 0.000 0.008
#> GSM1296118 2 0.4193 0.7306 0.004 0.736 0.188 0.000 0.072 0.000
#> GSM1296114 2 0.0000 0.9262 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296097 6 0.1881 0.6595 0.040 0.004 0.008 0.020 0.000 0.928
#> GSM1296106 6 0.2326 0.6276 0.000 0.012 0.092 0.008 0.000 0.888
#> GSM1296102 1 0.2618 0.8422 0.860 0.000 0.024 0.000 0.000 0.116
#> GSM1296122 2 0.4193 0.7306 0.004 0.736 0.188 0.000 0.072 0.000
#> GSM1296089 6 0.6908 0.4300 0.276 0.000 0.216 0.000 0.072 0.436
#> GSM1296083 1 0.0000 0.9401 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296116 2 0.0146 0.9256 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1296085 1 0.0000 0.9401 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)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# 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 tissue(p) time(p) agent(p) dose(p) individual(p) k
#> MAD:hclust 81 0.039026 0.1809 0.088676 7.22e-08 2.60e-03 2
#> MAD:hclust 66 0.008723 0.0211 0.217499 1.47e-08 5.19e-05 3
#> MAD:hclust 82 0.001940 0.1470 0.000135 8.49e-09 1.84e-04 4
#> MAD:hclust 82 0.000148 0.1220 0.004760 8.38e-07 1.43e-04 5
#> MAD:hclust 68 0.011383 0.3139 0.009474 5.73e-05 2.85e-06 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 45638 rows and 99 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 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 0.710 0.897 0.946 0.4895 0.518 0.518
#> 3 3 0.846 0.905 0.956 0.3720 0.725 0.509
#> 4 4 0.752 0.712 0.845 0.1113 0.810 0.504
#> 5 5 0.772 0.741 0.850 0.0651 0.894 0.617
#> 6 6 0.862 0.885 0.896 0.0406 0.942 0.725
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
#> GSM1296094 2 0.7376 0.792 0.208 0.792
#> GSM1296119 2 0.0000 0.913 0.000 1.000
#> GSM1296076 2 0.0000 0.913 0.000 1.000
#> GSM1296092 2 0.7376 0.792 0.208 0.792
#> GSM1296103 2 0.7376 0.792 0.208 0.792
#> GSM1296078 2 0.0000 0.913 0.000 1.000
#> GSM1296107 2 0.0000 0.913 0.000 1.000
#> GSM1296109 2 0.0000 0.913 0.000 1.000
#> GSM1296080 1 0.0000 0.983 1.000 0.000
#> GSM1296090 2 0.0000 0.913 0.000 1.000
#> GSM1296074 2 0.0000 0.913 0.000 1.000
#> GSM1296111 2 0.0000 0.913 0.000 1.000
#> GSM1296099 2 0.7376 0.792 0.208 0.792
#> GSM1296086 2 0.7376 0.792 0.208 0.792
#> GSM1296117 2 0.0000 0.913 0.000 1.000
#> GSM1296113 2 0.0000 0.913 0.000 1.000
#> GSM1296096 2 0.4939 0.862 0.108 0.892
#> GSM1296105 1 0.0000 0.983 1.000 0.000
#> GSM1296098 2 0.7528 0.784 0.216 0.784
#> GSM1296101 2 0.7376 0.792 0.208 0.792
#> GSM1296121 2 0.0000 0.913 0.000 1.000
#> GSM1296088 2 0.7528 0.784 0.216 0.784
#> GSM1296082 2 0.0000 0.913 0.000 1.000
#> GSM1296115 2 0.0000 0.913 0.000 1.000
#> GSM1296084 1 0.0000 0.983 1.000 0.000
#> GSM1296072 2 0.0000 0.913 0.000 1.000
#> GSM1296069 2 0.0000 0.913 0.000 1.000
#> GSM1296071 2 0.0000 0.913 0.000 1.000
#> GSM1296070 2 0.0000 0.913 0.000 1.000
#> GSM1296073 2 0.0000 0.913 0.000 1.000
#> GSM1296034 1 0.0000 0.983 1.000 0.000
#> GSM1296041 2 0.0000 0.913 0.000 1.000
#> GSM1296035 2 0.7376 0.792 0.208 0.792
#> GSM1296038 2 0.0000 0.913 0.000 1.000
#> GSM1296047 2 0.4161 0.868 0.084 0.916
#> GSM1296039 2 0.0000 0.913 0.000 1.000
#> GSM1296042 2 0.0000 0.913 0.000 1.000
#> GSM1296043 2 0.0000 0.913 0.000 1.000
#> GSM1296037 1 0.0000 0.983 1.000 0.000
#> GSM1296046 2 0.0000 0.913 0.000 1.000
#> GSM1296044 2 0.0000 0.913 0.000 1.000
#> GSM1296045 2 0.0000 0.913 0.000 1.000
#> GSM1296025 1 0.0000 0.983 1.000 0.000
#> GSM1296033 1 0.0000 0.983 1.000 0.000
#> GSM1296027 1 0.0000 0.983 1.000 0.000
#> GSM1296032 1 0.0000 0.983 1.000 0.000
#> GSM1296024 1 0.0000 0.983 1.000 0.000
#> GSM1296031 1 0.0000 0.983 1.000 0.000
#> GSM1296028 1 0.0000 0.983 1.000 0.000
#> GSM1296029 1 0.0000 0.983 1.000 0.000
#> GSM1296026 1 0.9209 0.409 0.664 0.336
#> GSM1296030 1 0.0000 0.983 1.000 0.000
#> GSM1296040 2 0.8861 0.661 0.304 0.696
#> GSM1296036 2 0.8499 0.705 0.276 0.724
#> GSM1296048 2 0.0000 0.913 0.000 1.000
#> GSM1296059 2 0.7376 0.792 0.208 0.792
#> GSM1296066 2 0.0000 0.913 0.000 1.000
#> GSM1296060 2 0.7376 0.792 0.208 0.792
#> GSM1296063 2 0.0000 0.913 0.000 1.000
#> GSM1296064 2 0.0000 0.913 0.000 1.000
#> GSM1296067 2 0.4815 0.852 0.104 0.896
#> GSM1296062 1 0.0000 0.983 1.000 0.000
#> GSM1296068 2 0.4161 0.868 0.084 0.916
#> GSM1296050 1 0.0000 0.983 1.000 0.000
#> GSM1296057 1 0.0000 0.983 1.000 0.000
#> GSM1296052 1 0.0000 0.983 1.000 0.000
#> GSM1296054 1 0.0000 0.983 1.000 0.000
#> GSM1296049 1 0.0000 0.983 1.000 0.000
#> GSM1296055 1 0.0000 0.983 1.000 0.000
#> GSM1296053 1 0.0000 0.983 1.000 0.000
#> GSM1296058 1 0.0000 0.983 1.000 0.000
#> GSM1296051 2 0.7376 0.792 0.208 0.792
#> GSM1296056 2 0.7376 0.792 0.208 0.792
#> GSM1296065 2 0.0000 0.913 0.000 1.000
#> GSM1296061 1 0.1843 0.954 0.972 0.028
#> GSM1296095 2 0.0000 0.913 0.000 1.000
#> GSM1296120 2 0.4161 0.868 0.084 0.916
#> GSM1296077 1 0.0000 0.983 1.000 0.000
#> GSM1296093 1 0.0000 0.983 1.000 0.000
#> GSM1296104 2 0.0000 0.913 0.000 1.000
#> GSM1296079 1 0.0000 0.983 1.000 0.000
#> GSM1296108 2 0.3879 0.873 0.076 0.924
#> GSM1296110 2 0.0938 0.909 0.012 0.988
#> GSM1296081 1 0.0000 0.983 1.000 0.000
#> GSM1296091 1 0.0000 0.983 1.000 0.000
#> GSM1296075 1 0.0000 0.983 1.000 0.000
#> GSM1296112 2 0.4161 0.868 0.084 0.916
#> GSM1296100 1 0.0000 0.983 1.000 0.000
#> GSM1296087 1 0.0000 0.983 1.000 0.000
#> GSM1296118 2 0.9922 0.190 0.448 0.552
#> GSM1296114 2 0.0672 0.910 0.008 0.992
#> GSM1296097 2 0.9170 0.633 0.332 0.668
#> GSM1296106 1 0.0000 0.983 1.000 0.000
#> GSM1296102 1 0.0000 0.983 1.000 0.000
#> GSM1296122 1 0.7453 0.705 0.788 0.212
#> GSM1296089 1 0.0000 0.983 1.000 0.000
#> GSM1296083 1 0.0000 0.983 1.000 0.000
#> GSM1296116 2 0.0672 0.910 0.008 0.992
#> GSM1296085 1 0.0000 0.983 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1296094 3 0.0000 0.9430 0.000 0.000 1.000
#> GSM1296119 2 0.2959 0.9199 0.000 0.900 0.100
#> GSM1296076 3 0.0000 0.9430 0.000 0.000 1.000
#> GSM1296092 3 0.0000 0.9430 0.000 0.000 1.000
#> GSM1296103 3 0.0000 0.9430 0.000 0.000 1.000
#> GSM1296078 3 0.0000 0.9430 0.000 0.000 1.000
#> GSM1296107 2 0.1964 0.9434 0.000 0.944 0.056
#> GSM1296109 3 0.0000 0.9430 0.000 0.000 1.000
#> GSM1296080 3 0.2959 0.8572 0.100 0.000 0.900
#> GSM1296090 3 0.0000 0.9430 0.000 0.000 1.000
#> GSM1296074 3 0.0000 0.9430 0.000 0.000 1.000
#> GSM1296111 2 0.2796 0.9250 0.000 0.908 0.092
#> GSM1296099 3 0.0000 0.9430 0.000 0.000 1.000
#> GSM1296086 3 0.0000 0.9430 0.000 0.000 1.000
#> GSM1296117 2 0.2959 0.9199 0.000 0.900 0.100
#> GSM1296113 2 0.2165 0.9398 0.000 0.936 0.064
#> GSM1296096 3 0.0000 0.9430 0.000 0.000 1.000
#> GSM1296105 1 0.4291 0.7632 0.820 0.000 0.180
#> GSM1296098 3 0.0000 0.9430 0.000 0.000 1.000
#> GSM1296101 3 0.0000 0.9430 0.000 0.000 1.000
#> GSM1296121 2 0.2959 0.9199 0.000 0.900 0.100
#> GSM1296088 3 0.0000 0.9430 0.000 0.000 1.000
#> GSM1296082 3 0.0000 0.9430 0.000 0.000 1.000
#> GSM1296115 2 0.2959 0.9199 0.000 0.900 0.100
#> GSM1296084 3 0.2796 0.8650 0.092 0.000 0.908
#> GSM1296072 2 0.0000 0.9619 0.000 1.000 0.000
#> GSM1296069 2 0.0000 0.9619 0.000 1.000 0.000
#> GSM1296071 2 0.0000 0.9619 0.000 1.000 0.000
#> GSM1296070 2 0.0000 0.9619 0.000 1.000 0.000
#> GSM1296073 2 0.2959 0.9199 0.000 0.900 0.100
#> GSM1296034 1 0.0000 0.9548 1.000 0.000 0.000
#> GSM1296041 2 0.2959 0.9199 0.000 0.900 0.100
#> GSM1296035 3 0.0000 0.9430 0.000 0.000 1.000
#> GSM1296038 3 0.0000 0.9430 0.000 0.000 1.000
#> GSM1296047 2 0.0000 0.9619 0.000 1.000 0.000
#> GSM1296039 3 0.0000 0.9430 0.000 0.000 1.000
#> GSM1296042 2 0.1860 0.9449 0.000 0.948 0.052
#> GSM1296043 2 0.0000 0.9619 0.000 1.000 0.000
#> GSM1296037 1 0.0000 0.9548 1.000 0.000 0.000
#> GSM1296046 2 0.0000 0.9619 0.000 1.000 0.000
#> GSM1296044 2 0.0000 0.9619 0.000 1.000 0.000
#> GSM1296045 2 0.0000 0.9619 0.000 1.000 0.000
#> GSM1296025 1 0.0000 0.9548 1.000 0.000 0.000
#> GSM1296033 1 0.0000 0.9548 1.000 0.000 0.000
#> GSM1296027 1 0.0000 0.9548 1.000 0.000 0.000
#> GSM1296032 1 0.0000 0.9548 1.000 0.000 0.000
#> GSM1296024 1 0.0000 0.9548 1.000 0.000 0.000
#> GSM1296031 1 0.0000 0.9548 1.000 0.000 0.000
#> GSM1296028 1 0.0000 0.9548 1.000 0.000 0.000
#> GSM1296029 1 0.0000 0.9548 1.000 0.000 0.000
#> GSM1296026 3 0.0000 0.9430 0.000 0.000 1.000
#> GSM1296030 3 0.4504 0.7399 0.196 0.000 0.804
#> GSM1296040 3 0.0000 0.9430 0.000 0.000 1.000
#> GSM1296036 3 0.0000 0.9430 0.000 0.000 1.000
#> GSM1296048 2 0.2959 0.9199 0.000 0.900 0.100
#> GSM1296059 3 0.0000 0.9430 0.000 0.000 1.000
#> GSM1296066 2 0.0237 0.9610 0.000 0.996 0.004
#> GSM1296060 3 0.0000 0.9430 0.000 0.000 1.000
#> GSM1296063 3 0.5926 0.4445 0.000 0.356 0.644
#> GSM1296064 3 0.0000 0.9430 0.000 0.000 1.000
#> GSM1296067 2 0.0000 0.9619 0.000 1.000 0.000
#> GSM1296062 3 0.2959 0.8572 0.100 0.000 0.900
#> GSM1296068 2 0.0000 0.9619 0.000 1.000 0.000
#> GSM1296050 1 0.0000 0.9548 1.000 0.000 0.000
#> GSM1296057 1 0.0000 0.9548 1.000 0.000 0.000
#> GSM1296052 1 0.0000 0.9548 1.000 0.000 0.000
#> GSM1296054 1 0.0000 0.9548 1.000 0.000 0.000
#> GSM1296049 1 0.0000 0.9548 1.000 0.000 0.000
#> GSM1296055 1 0.2066 0.9101 0.940 0.060 0.000
#> GSM1296053 1 0.0000 0.9548 1.000 0.000 0.000
#> GSM1296058 1 0.3551 0.8264 0.868 0.000 0.132
#> GSM1296051 3 0.0000 0.9430 0.000 0.000 1.000
#> GSM1296056 3 0.0000 0.9430 0.000 0.000 1.000
#> GSM1296065 3 0.6126 0.3509 0.000 0.400 0.600
#> GSM1296061 3 0.0000 0.9430 0.000 0.000 1.000
#> GSM1296095 3 0.4974 0.6856 0.000 0.236 0.764
#> GSM1296120 2 0.0000 0.9619 0.000 1.000 0.000
#> GSM1296077 1 0.0000 0.9548 1.000 0.000 0.000
#> GSM1296093 1 0.0000 0.9548 1.000 0.000 0.000
#> GSM1296104 3 0.5835 0.5388 0.000 0.340 0.660
#> GSM1296079 1 0.0000 0.9548 1.000 0.000 0.000
#> GSM1296108 2 0.0000 0.9619 0.000 1.000 0.000
#> GSM1296110 2 0.0000 0.9619 0.000 1.000 0.000
#> GSM1296081 1 0.0000 0.9548 1.000 0.000 0.000
#> GSM1296091 1 0.0000 0.9548 1.000 0.000 0.000
#> GSM1296075 1 0.2261 0.9034 0.932 0.068 0.000
#> GSM1296112 2 0.0000 0.9619 0.000 1.000 0.000
#> GSM1296100 1 0.0000 0.9548 1.000 0.000 0.000
#> GSM1296087 1 0.0000 0.9548 1.000 0.000 0.000
#> GSM1296118 2 0.2261 0.9049 0.068 0.932 0.000
#> GSM1296114 2 0.0000 0.9619 0.000 1.000 0.000
#> GSM1296097 1 0.9172 0.2062 0.488 0.156 0.356
#> GSM1296106 1 0.2261 0.9034 0.932 0.068 0.000
#> GSM1296102 1 0.0000 0.9548 1.000 0.000 0.000
#> GSM1296122 1 0.6309 0.0497 0.500 0.500 0.000
#> GSM1296089 1 0.0000 0.9548 1.000 0.000 0.000
#> GSM1296083 1 0.0000 0.9548 1.000 0.000 0.000
#> GSM1296116 2 0.0000 0.9619 0.000 1.000 0.000
#> GSM1296085 1 0.0000 0.9548 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1296094 3 0.0188 0.8424 0.000 0.004 0.996 0.000
#> GSM1296119 4 0.0188 0.7371 0.000 0.000 0.004 0.996
#> GSM1296076 4 0.6993 0.1555 0.000 0.124 0.364 0.512
#> GSM1296092 3 0.3694 0.7866 0.000 0.124 0.844 0.032
#> GSM1296103 3 0.0000 0.8422 0.000 0.000 1.000 0.000
#> GSM1296078 4 0.6993 0.1555 0.000 0.124 0.364 0.512
#> GSM1296107 4 0.1022 0.7121 0.000 0.032 0.000 0.968
#> GSM1296109 4 0.6197 0.1683 0.000 0.056 0.400 0.544
#> GSM1296080 3 0.0804 0.8388 0.008 0.012 0.980 0.000
#> GSM1296090 3 0.7003 0.2477 0.000 0.124 0.508 0.368
#> GSM1296074 3 0.7012 0.2372 0.000 0.124 0.504 0.372
#> GSM1296111 4 0.0188 0.7371 0.000 0.000 0.004 0.996
#> GSM1296099 3 0.1557 0.8331 0.000 0.056 0.944 0.000
#> GSM1296086 3 0.3694 0.7866 0.000 0.124 0.844 0.032
#> GSM1296117 4 0.0188 0.7371 0.000 0.000 0.004 0.996
#> GSM1296113 4 0.1022 0.7121 0.000 0.032 0.000 0.968
#> GSM1296096 3 0.1557 0.8331 0.000 0.056 0.944 0.000
#> GSM1296105 3 0.6449 0.5205 0.152 0.204 0.644 0.000
#> GSM1296098 3 0.0469 0.8419 0.000 0.012 0.988 0.000
#> GSM1296101 3 0.0336 0.8424 0.000 0.008 0.992 0.000
#> GSM1296121 4 0.0188 0.7371 0.000 0.000 0.004 0.996
#> GSM1296088 3 0.0000 0.8422 0.000 0.000 1.000 0.000
#> GSM1296082 3 0.7028 0.2150 0.000 0.124 0.496 0.380
#> GSM1296115 4 0.0188 0.7371 0.000 0.000 0.004 0.996
#> GSM1296084 3 0.0657 0.8406 0.004 0.012 0.984 0.000
#> GSM1296072 2 0.4500 0.7333 0.000 0.684 0.000 0.316
#> GSM1296069 4 0.4843 -0.2632 0.000 0.396 0.000 0.604
#> GSM1296071 2 0.4564 0.7325 0.000 0.672 0.000 0.328
#> GSM1296070 4 0.3311 0.4807 0.000 0.172 0.000 0.828
#> GSM1296073 4 0.3280 0.6677 0.000 0.124 0.016 0.860
#> GSM1296034 1 0.0000 0.9679 1.000 0.000 0.000 0.000
#> GSM1296041 4 0.0188 0.7371 0.000 0.000 0.004 0.996
#> GSM1296035 3 0.1557 0.8331 0.000 0.056 0.944 0.000
#> GSM1296038 3 0.3757 0.7879 0.000 0.152 0.828 0.020
#> GSM1296047 2 0.4431 0.7327 0.000 0.696 0.000 0.304
#> GSM1296039 3 0.7078 0.0896 0.000 0.124 0.456 0.420
#> GSM1296042 4 0.1022 0.7121 0.000 0.032 0.000 0.968
#> GSM1296043 2 0.4948 0.5871 0.000 0.560 0.000 0.440
#> GSM1296037 1 0.2216 0.9186 0.908 0.092 0.000 0.000
#> GSM1296046 2 0.4564 0.7325 0.000 0.672 0.000 0.328
#> GSM1296044 2 0.4564 0.7325 0.000 0.672 0.000 0.328
#> GSM1296045 2 0.4843 0.6538 0.000 0.604 0.000 0.396
#> GSM1296025 1 0.0000 0.9679 1.000 0.000 0.000 0.000
#> GSM1296033 1 0.1557 0.9388 0.944 0.056 0.000 0.000
#> GSM1296027 1 0.0000 0.9679 1.000 0.000 0.000 0.000
#> GSM1296032 1 0.0000 0.9679 1.000 0.000 0.000 0.000
#> GSM1296024 1 0.0000 0.9679 1.000 0.000 0.000 0.000
#> GSM1296031 1 0.1118 0.9529 0.964 0.036 0.000 0.000
#> GSM1296028 1 0.0000 0.9679 1.000 0.000 0.000 0.000
#> GSM1296029 1 0.0000 0.9679 1.000 0.000 0.000 0.000
#> GSM1296026 3 0.0469 0.8419 0.000 0.012 0.988 0.000
#> GSM1296030 3 0.2021 0.8042 0.056 0.012 0.932 0.000
#> GSM1296040 3 0.0469 0.8419 0.000 0.012 0.988 0.000
#> GSM1296036 3 0.0469 0.8419 0.000 0.012 0.988 0.000
#> GSM1296048 4 0.0188 0.7371 0.000 0.000 0.004 0.996
#> GSM1296059 3 0.0000 0.8422 0.000 0.000 1.000 0.000
#> GSM1296066 4 0.1022 0.7121 0.000 0.032 0.000 0.968
#> GSM1296060 3 0.1637 0.8338 0.000 0.060 0.940 0.000
#> GSM1296063 4 0.5141 0.6088 0.000 0.160 0.084 0.756
#> GSM1296064 4 0.6924 0.2167 0.000 0.124 0.340 0.536
#> GSM1296067 2 0.4406 0.7316 0.000 0.700 0.000 0.300
#> GSM1296062 3 0.0657 0.8406 0.004 0.012 0.984 0.000
#> GSM1296068 2 0.4564 0.7325 0.000 0.672 0.000 0.328
#> GSM1296050 1 0.1118 0.9529 0.964 0.036 0.000 0.000
#> GSM1296057 1 0.3649 0.8184 0.796 0.204 0.000 0.000
#> GSM1296052 1 0.0000 0.9679 1.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.9679 1.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.9679 1.000 0.000 0.000 0.000
#> GSM1296055 2 0.4543 0.3184 0.324 0.676 0.000 0.000
#> GSM1296053 1 0.0000 0.9679 1.000 0.000 0.000 0.000
#> GSM1296058 3 0.7529 0.1968 0.324 0.204 0.472 0.000
#> GSM1296051 3 0.3787 0.7836 0.000 0.124 0.840 0.036
#> GSM1296056 3 0.3161 0.7986 0.000 0.124 0.864 0.012
#> GSM1296065 2 0.4824 0.3906 0.000 0.780 0.144 0.076
#> GSM1296061 3 0.0469 0.8419 0.000 0.012 0.988 0.000
#> GSM1296095 2 0.6944 -0.2169 0.000 0.484 0.404 0.112
#> GSM1296120 2 0.4431 0.7327 0.000 0.696 0.000 0.304
#> GSM1296077 1 0.0000 0.9679 1.000 0.000 0.000 0.000
#> GSM1296093 1 0.0000 0.9679 1.000 0.000 0.000 0.000
#> GSM1296104 2 0.4370 0.4111 0.000 0.800 0.156 0.044
#> GSM1296079 1 0.0000 0.9679 1.000 0.000 0.000 0.000
#> GSM1296108 2 0.4564 0.7325 0.000 0.672 0.000 0.328
#> GSM1296110 2 0.4564 0.7325 0.000 0.672 0.000 0.328
#> GSM1296081 1 0.0000 0.9679 1.000 0.000 0.000 0.000
#> GSM1296091 1 0.2973 0.8768 0.856 0.144 0.000 0.000
#> GSM1296075 2 0.4406 0.3699 0.300 0.700 0.000 0.000
#> GSM1296112 2 0.4564 0.7325 0.000 0.672 0.000 0.328
#> GSM1296100 1 0.2216 0.9186 0.908 0.092 0.000 0.000
#> GSM1296087 1 0.0000 0.9679 1.000 0.000 0.000 0.000
#> GSM1296118 2 0.4382 0.7302 0.000 0.704 0.000 0.296
#> GSM1296114 2 0.4564 0.7325 0.000 0.672 0.000 0.328
#> GSM1296097 2 0.5906 0.4398 0.148 0.700 0.152 0.000
#> GSM1296106 2 0.4277 0.4074 0.280 0.720 0.000 0.000
#> GSM1296102 1 0.3649 0.8184 0.796 0.204 0.000 0.000
#> GSM1296122 2 0.3533 0.6148 0.056 0.864 0.000 0.080
#> GSM1296089 1 0.1118 0.9529 0.964 0.036 0.000 0.000
#> GSM1296083 1 0.0000 0.9679 1.000 0.000 0.000 0.000
#> GSM1296116 2 0.4564 0.7325 0.000 0.672 0.000 0.328
#> GSM1296085 1 0.0000 0.9679 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
#> GSM1296094 3 0.0000 0.879934 0.000 0.000 1.000 0.000 0.000
#> GSM1296119 5 0.2813 0.677625 0.000 0.168 0.000 0.000 0.832
#> GSM1296076 5 0.6185 0.367293 0.000 0.000 0.188 0.264 0.548
#> GSM1296092 3 0.6067 0.519066 0.000 0.000 0.560 0.276 0.164
#> GSM1296103 3 0.0162 0.880320 0.000 0.000 0.996 0.004 0.000
#> GSM1296078 5 0.6185 0.367293 0.000 0.000 0.188 0.264 0.548
#> GSM1296107 5 0.3003 0.666098 0.000 0.188 0.000 0.000 0.812
#> GSM1296109 5 0.3388 0.583852 0.000 0.000 0.200 0.008 0.792
#> GSM1296080 3 0.0486 0.876179 0.004 0.000 0.988 0.004 0.004
#> GSM1296090 5 0.6765 0.000396 0.000 0.000 0.344 0.272 0.384
#> GSM1296074 5 0.6756 0.008176 0.000 0.000 0.344 0.268 0.388
#> GSM1296111 5 0.2852 0.677652 0.000 0.172 0.000 0.000 0.828
#> GSM1296099 3 0.2694 0.842993 0.000 0.000 0.884 0.076 0.040
#> GSM1296086 3 0.6067 0.519066 0.000 0.000 0.560 0.276 0.164
#> GSM1296117 5 0.2852 0.677652 0.000 0.172 0.000 0.000 0.828
#> GSM1296113 5 0.3003 0.666098 0.000 0.188 0.000 0.000 0.812
#> GSM1296096 3 0.2694 0.842993 0.000 0.000 0.884 0.076 0.040
#> GSM1296105 4 0.4016 0.587389 0.012 0.000 0.272 0.716 0.000
#> GSM1296098 3 0.0000 0.879934 0.000 0.000 1.000 0.000 0.000
#> GSM1296101 3 0.0404 0.879719 0.000 0.000 0.988 0.012 0.000
#> GSM1296121 5 0.2813 0.677625 0.000 0.168 0.000 0.000 0.832
#> GSM1296088 3 0.0671 0.879573 0.000 0.000 0.980 0.016 0.004
#> GSM1296082 5 0.6753 0.020837 0.000 0.000 0.340 0.268 0.392
#> GSM1296115 5 0.2852 0.677652 0.000 0.172 0.000 0.000 0.828
#> GSM1296084 3 0.1082 0.873486 0.000 0.000 0.964 0.028 0.008
#> GSM1296072 2 0.0566 0.915732 0.000 0.984 0.000 0.012 0.004
#> GSM1296069 2 0.4306 -0.041891 0.000 0.508 0.000 0.000 0.492
#> GSM1296071 2 0.0162 0.920113 0.000 0.996 0.000 0.000 0.004
#> GSM1296070 5 0.3932 0.455552 0.000 0.328 0.000 0.000 0.672
#> GSM1296073 5 0.0865 0.651377 0.000 0.024 0.000 0.004 0.972
#> GSM1296034 1 0.0854 0.947616 0.976 0.000 0.012 0.008 0.004
#> GSM1296041 5 0.2852 0.677652 0.000 0.172 0.000 0.000 0.828
#> GSM1296035 3 0.2694 0.842993 0.000 0.000 0.884 0.076 0.040
#> GSM1296038 4 0.4902 0.334039 0.000 0.000 0.304 0.648 0.048
#> GSM1296047 2 0.1205 0.899008 0.000 0.956 0.000 0.040 0.004
#> GSM1296039 5 0.6692 0.147686 0.000 0.000 0.296 0.272 0.432
#> GSM1296042 5 0.3003 0.666098 0.000 0.188 0.000 0.000 0.812
#> GSM1296043 2 0.2377 0.781172 0.000 0.872 0.000 0.000 0.128
#> GSM1296037 1 0.2605 0.804500 0.852 0.000 0.000 0.148 0.000
#> GSM1296046 2 0.0000 0.920482 0.000 1.000 0.000 0.000 0.000
#> GSM1296044 2 0.0000 0.920482 0.000 1.000 0.000 0.000 0.000
#> GSM1296045 2 0.0703 0.901809 0.000 0.976 0.000 0.000 0.024
#> GSM1296025 1 0.0451 0.953546 0.988 0.000 0.000 0.008 0.004
#> GSM1296033 1 0.4565 0.116786 0.580 0.000 0.000 0.408 0.012
#> GSM1296027 1 0.0162 0.953860 0.996 0.000 0.000 0.000 0.004
#> GSM1296032 1 0.0000 0.954227 1.000 0.000 0.000 0.000 0.000
#> GSM1296024 1 0.0451 0.953546 0.988 0.000 0.000 0.008 0.004
#> GSM1296031 1 0.1018 0.946339 0.968 0.000 0.000 0.016 0.016
#> GSM1296028 1 0.0290 0.952845 0.992 0.000 0.000 0.000 0.008
#> GSM1296029 1 0.0404 0.952030 0.988 0.000 0.000 0.000 0.012
#> GSM1296026 3 0.0955 0.875075 0.000 0.000 0.968 0.028 0.004
#> GSM1296030 3 0.1498 0.865290 0.016 0.000 0.952 0.024 0.008
#> GSM1296040 3 0.0290 0.879872 0.000 0.000 0.992 0.008 0.000
#> GSM1296036 3 0.0000 0.879934 0.000 0.000 1.000 0.000 0.000
#> GSM1296048 5 0.2813 0.677625 0.000 0.168 0.000 0.000 0.832
#> GSM1296059 3 0.0510 0.879992 0.000 0.000 0.984 0.016 0.000
#> GSM1296066 5 0.3039 0.663229 0.000 0.192 0.000 0.000 0.808
#> GSM1296060 3 0.2694 0.842993 0.000 0.000 0.884 0.076 0.040
#> GSM1296063 5 0.4110 0.552960 0.000 0.012 0.008 0.244 0.736
#> GSM1296064 5 0.4924 0.518266 0.000 0.000 0.060 0.272 0.668
#> GSM1296067 2 0.1205 0.899008 0.000 0.956 0.000 0.040 0.004
#> GSM1296062 3 0.0566 0.874043 0.000 0.000 0.984 0.012 0.004
#> GSM1296068 2 0.0162 0.920883 0.000 0.996 0.000 0.004 0.000
#> GSM1296050 1 0.1018 0.946339 0.968 0.000 0.000 0.016 0.016
#> GSM1296057 4 0.3730 0.584946 0.288 0.000 0.000 0.712 0.000
#> GSM1296052 1 0.0162 0.953860 0.996 0.000 0.000 0.000 0.004
#> GSM1296054 1 0.0000 0.954227 1.000 0.000 0.000 0.000 0.000
#> GSM1296049 1 0.0162 0.953953 0.996 0.000 0.000 0.004 0.000
#> GSM1296055 4 0.4737 0.710278 0.068 0.224 0.000 0.708 0.000
#> GSM1296053 1 0.0162 0.953953 0.996 0.000 0.000 0.004 0.000
#> GSM1296058 4 0.4840 0.685593 0.124 0.000 0.152 0.724 0.000
#> GSM1296051 3 0.6273 0.456390 0.000 0.000 0.500 0.336 0.164
#> GSM1296056 3 0.5905 0.541028 0.000 0.000 0.580 0.276 0.144
#> GSM1296065 4 0.3177 0.703034 0.000 0.208 0.000 0.792 0.000
#> GSM1296061 3 0.0000 0.879934 0.000 0.000 1.000 0.000 0.000
#> GSM1296095 4 0.5917 0.608948 0.000 0.140 0.100 0.688 0.072
#> GSM1296120 2 0.1205 0.899008 0.000 0.956 0.000 0.040 0.004
#> GSM1296077 1 0.0693 0.952456 0.980 0.000 0.000 0.008 0.012
#> GSM1296093 1 0.0162 0.953953 0.996 0.000 0.000 0.004 0.000
#> GSM1296104 4 0.3274 0.699936 0.000 0.220 0.000 0.780 0.000
#> GSM1296079 1 0.0566 0.952369 0.984 0.000 0.000 0.004 0.012
#> GSM1296108 2 0.0162 0.920883 0.000 0.996 0.000 0.004 0.000
#> GSM1296110 2 0.0162 0.920883 0.000 0.996 0.000 0.004 0.000
#> GSM1296081 1 0.0451 0.953546 0.988 0.000 0.000 0.008 0.004
#> GSM1296091 4 0.4743 0.145684 0.472 0.000 0.000 0.512 0.016
#> GSM1296075 4 0.4793 0.704336 0.056 0.236 0.000 0.704 0.004
#> GSM1296112 2 0.0162 0.920883 0.000 0.996 0.000 0.004 0.000
#> GSM1296100 1 0.2605 0.804500 0.852 0.000 0.000 0.148 0.000
#> GSM1296087 1 0.0404 0.952030 0.988 0.000 0.000 0.000 0.012
#> GSM1296118 2 0.1205 0.899008 0.000 0.956 0.000 0.040 0.004
#> GSM1296114 2 0.0000 0.920482 0.000 1.000 0.000 0.000 0.000
#> GSM1296097 4 0.3720 0.702886 0.012 0.228 0.000 0.760 0.000
#> GSM1296106 4 0.4589 0.694082 0.048 0.248 0.000 0.704 0.000
#> GSM1296102 4 0.3796 0.569153 0.300 0.000 0.000 0.700 0.000
#> GSM1296122 2 0.3373 0.678361 0.008 0.816 0.000 0.168 0.008
#> GSM1296089 1 0.1018 0.946339 0.968 0.000 0.000 0.016 0.016
#> GSM1296083 1 0.0451 0.953546 0.988 0.000 0.000 0.008 0.004
#> GSM1296116 2 0.0000 0.920482 0.000 1.000 0.000 0.000 0.000
#> GSM1296085 1 0.0000 0.954227 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
#> GSM1296094 3 0.0260 0.939 0.000 0.000 0.992 0.000 0.008 0.000
#> GSM1296119 5 0.1714 0.944 0.000 0.092 0.000 0.000 0.908 0.000
#> GSM1296076 4 0.3596 0.840 0.000 0.000 0.040 0.784 0.172 0.004
#> GSM1296092 4 0.3052 0.819 0.000 0.000 0.216 0.780 0.000 0.004
#> GSM1296103 3 0.0508 0.939 0.000 0.000 0.984 0.004 0.012 0.000
#> GSM1296078 4 0.3596 0.840 0.000 0.000 0.040 0.784 0.172 0.004
#> GSM1296107 5 0.1858 0.944 0.000 0.092 0.000 0.000 0.904 0.004
#> GSM1296109 5 0.2809 0.720 0.000 0.000 0.168 0.004 0.824 0.004
#> GSM1296080 3 0.1713 0.920 0.000 0.000 0.928 0.000 0.028 0.044
#> GSM1296090 4 0.3777 0.881 0.000 0.000 0.124 0.788 0.084 0.004
#> GSM1296074 4 0.3718 0.881 0.000 0.000 0.132 0.784 0.084 0.000
#> GSM1296111 5 0.1858 0.944 0.000 0.092 0.000 0.000 0.904 0.004
#> GSM1296099 3 0.2045 0.909 0.000 0.000 0.916 0.052 0.016 0.016
#> GSM1296086 4 0.3052 0.819 0.000 0.000 0.216 0.780 0.000 0.004
#> GSM1296117 5 0.1714 0.944 0.000 0.092 0.000 0.000 0.908 0.000
#> GSM1296113 5 0.1714 0.944 0.000 0.092 0.000 0.000 0.908 0.000
#> GSM1296096 3 0.2108 0.908 0.000 0.000 0.912 0.056 0.016 0.016
#> GSM1296105 6 0.2699 0.780 0.008 0.000 0.124 0.012 0.000 0.856
#> GSM1296098 3 0.0000 0.939 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296101 3 0.0717 0.938 0.000 0.000 0.976 0.000 0.016 0.008
#> GSM1296121 5 0.1858 0.943 0.000 0.092 0.000 0.000 0.904 0.004
#> GSM1296088 3 0.2345 0.912 0.000 0.000 0.904 0.040 0.028 0.028
#> GSM1296082 4 0.3718 0.881 0.000 0.000 0.132 0.784 0.084 0.000
#> GSM1296115 5 0.1714 0.944 0.000 0.092 0.000 0.000 0.908 0.000
#> GSM1296084 3 0.3176 0.883 0.000 0.000 0.856 0.048 0.040 0.056
#> GSM1296072 2 0.1320 0.950 0.000 0.948 0.000 0.016 0.000 0.036
#> GSM1296069 5 0.4273 0.736 0.000 0.248 0.000 0.036 0.704 0.012
#> GSM1296071 2 0.0603 0.957 0.000 0.980 0.000 0.000 0.004 0.016
#> GSM1296070 5 0.3802 0.834 0.000 0.180 0.000 0.036 0.772 0.012
#> GSM1296073 5 0.2114 0.839 0.000 0.012 0.000 0.076 0.904 0.008
#> GSM1296034 1 0.1503 0.942 0.944 0.000 0.016 0.032 0.008 0.000
#> GSM1296041 5 0.1714 0.944 0.000 0.092 0.000 0.000 0.908 0.000
#> GSM1296035 3 0.2108 0.908 0.000 0.000 0.912 0.056 0.016 0.016
#> GSM1296038 6 0.3546 0.746 0.000 0.000 0.108 0.064 0.012 0.816
#> GSM1296047 2 0.1391 0.946 0.000 0.944 0.000 0.016 0.000 0.040
#> GSM1296039 4 0.4334 0.870 0.000 0.000 0.092 0.756 0.132 0.020
#> GSM1296042 5 0.1858 0.944 0.000 0.092 0.000 0.000 0.904 0.004
#> GSM1296043 2 0.2414 0.891 0.000 0.896 0.000 0.036 0.056 0.012
#> GSM1296037 1 0.3039 0.871 0.852 0.000 0.000 0.056 0.008 0.084
#> GSM1296046 2 0.1268 0.937 0.000 0.952 0.000 0.036 0.004 0.008
#> GSM1296044 2 0.0748 0.957 0.000 0.976 0.000 0.004 0.004 0.016
#> GSM1296045 2 0.2351 0.895 0.000 0.900 0.000 0.036 0.052 0.012
#> GSM1296025 1 0.1151 0.943 0.956 0.000 0.000 0.032 0.012 0.000
#> GSM1296033 6 0.5905 0.143 0.412 0.000 0.000 0.064 0.056 0.468
#> GSM1296027 1 0.1418 0.940 0.944 0.000 0.000 0.032 0.024 0.000
#> GSM1296032 1 0.0909 0.948 0.968 0.000 0.000 0.020 0.012 0.000
#> GSM1296024 1 0.0806 0.946 0.972 0.000 0.000 0.020 0.008 0.000
#> GSM1296031 1 0.2537 0.924 0.880 0.000 0.000 0.088 0.024 0.008
#> GSM1296028 1 0.2285 0.929 0.900 0.000 0.000 0.064 0.028 0.008
#> GSM1296029 1 0.2101 0.931 0.912 0.000 0.000 0.052 0.028 0.008
#> GSM1296026 3 0.2763 0.902 0.000 0.000 0.880 0.040 0.028 0.052
#> GSM1296030 3 0.3555 0.862 0.000 0.000 0.832 0.060 0.052 0.056
#> GSM1296040 3 0.0862 0.937 0.000 0.000 0.972 0.004 0.008 0.016
#> GSM1296036 3 0.0363 0.939 0.000 0.000 0.988 0.000 0.000 0.012
#> GSM1296048 5 0.1858 0.943 0.000 0.092 0.000 0.000 0.904 0.004
#> GSM1296059 3 0.0862 0.937 0.000 0.000 0.972 0.004 0.016 0.008
#> GSM1296066 5 0.1858 0.944 0.000 0.092 0.000 0.000 0.904 0.004
#> GSM1296060 3 0.2108 0.908 0.000 0.000 0.912 0.056 0.016 0.016
#> GSM1296063 4 0.3936 0.697 0.000 0.000 0.000 0.688 0.288 0.024
#> GSM1296064 4 0.3865 0.795 0.000 0.000 0.016 0.748 0.216 0.020
#> GSM1296067 2 0.1313 0.950 0.000 0.952 0.000 0.016 0.004 0.028
#> GSM1296062 3 0.0713 0.935 0.000 0.000 0.972 0.000 0.000 0.028
#> GSM1296068 2 0.0748 0.957 0.000 0.976 0.000 0.004 0.004 0.016
#> GSM1296050 1 0.2094 0.930 0.900 0.000 0.000 0.080 0.020 0.000
#> GSM1296057 6 0.1779 0.825 0.064 0.000 0.000 0.016 0.000 0.920
#> GSM1296052 1 0.1418 0.940 0.944 0.000 0.000 0.032 0.024 0.000
#> GSM1296054 1 0.0806 0.947 0.972 0.000 0.000 0.020 0.008 0.000
#> GSM1296049 1 0.0777 0.949 0.972 0.000 0.000 0.024 0.004 0.000
#> GSM1296055 6 0.2557 0.837 0.028 0.044 0.000 0.028 0.004 0.896
#> GSM1296053 1 0.1003 0.946 0.964 0.000 0.000 0.020 0.016 0.000
#> GSM1296058 6 0.1722 0.828 0.008 0.000 0.036 0.016 0.004 0.936
#> GSM1296051 4 0.3345 0.832 0.000 0.000 0.184 0.788 0.000 0.028
#> GSM1296056 4 0.3596 0.815 0.000 0.000 0.216 0.760 0.008 0.016
#> GSM1296065 6 0.1483 0.839 0.000 0.036 0.008 0.012 0.000 0.944
#> GSM1296061 3 0.0363 0.939 0.000 0.000 0.988 0.000 0.000 0.012
#> GSM1296095 6 0.3182 0.793 0.000 0.028 0.024 0.064 0.020 0.864
#> GSM1296120 2 0.1391 0.946 0.000 0.944 0.000 0.016 0.000 0.040
#> GSM1296077 1 0.1367 0.941 0.944 0.000 0.000 0.044 0.012 0.000
#> GSM1296093 1 0.0692 0.946 0.976 0.000 0.000 0.020 0.004 0.000
#> GSM1296104 6 0.1382 0.839 0.000 0.036 0.008 0.008 0.000 0.948
#> GSM1296079 1 0.1434 0.941 0.940 0.000 0.000 0.048 0.012 0.000
#> GSM1296108 2 0.0748 0.957 0.000 0.976 0.000 0.004 0.004 0.016
#> GSM1296110 2 0.0603 0.957 0.000 0.980 0.000 0.000 0.004 0.016
#> GSM1296081 1 0.0622 0.948 0.980 0.000 0.000 0.012 0.008 0.000
#> GSM1296091 6 0.5579 0.255 0.388 0.000 0.000 0.072 0.028 0.512
#> GSM1296075 6 0.2123 0.839 0.024 0.052 0.000 0.012 0.000 0.912
#> GSM1296112 2 0.1003 0.946 0.000 0.964 0.000 0.028 0.004 0.004
#> GSM1296100 1 0.3039 0.871 0.852 0.000 0.000 0.056 0.008 0.084
#> GSM1296087 1 0.2226 0.925 0.904 0.000 0.000 0.060 0.028 0.008
#> GSM1296118 2 0.1390 0.948 0.000 0.948 0.000 0.016 0.004 0.032
#> GSM1296114 2 0.0748 0.957 0.000 0.976 0.000 0.004 0.004 0.016
#> GSM1296097 6 0.1453 0.839 0.000 0.040 0.008 0.008 0.000 0.944
#> GSM1296106 6 0.1643 0.832 0.008 0.068 0.000 0.000 0.000 0.924
#> GSM1296102 6 0.2842 0.800 0.104 0.000 0.000 0.044 0.000 0.852
#> GSM1296122 2 0.3082 0.839 0.004 0.844 0.000 0.036 0.004 0.112
#> GSM1296089 1 0.2537 0.924 0.880 0.000 0.000 0.088 0.024 0.008
#> GSM1296083 1 0.0622 0.948 0.980 0.000 0.000 0.012 0.008 0.000
#> GSM1296116 2 0.1155 0.939 0.000 0.956 0.000 0.036 0.004 0.004
#> GSM1296085 1 0.1088 0.947 0.960 0.000 0.000 0.024 0.016 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# 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 tissue(p) time(p) agent(p) dose(p) individual(p) k
#> MAD:kmeans 97 2.81e-02 0.1766 0.28175 3.51e-07 8.99e-04 2
#> MAD:kmeans 95 4.28e-03 0.0282 0.00738 2.66e-09 1.38e-08 3
#> MAD:kmeans 81 1.00e-04 0.5558 0.00649 1.90e-09 2.22e-05 4
#> MAD:kmeans 87 1.25e-04 0.3311 0.01999 5.74e-08 5.60e-05 5
#> MAD:kmeans 97 6.48e-05 0.1362 0.08301 7.95e-07 7.79e-06 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 45638 rows and 99 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
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 0.757 0.899 0.955 0.5024 0.499 0.499
#> 3 3 1.000 0.985 0.994 0.3397 0.728 0.505
#> 4 4 0.997 0.965 0.981 0.1111 0.856 0.603
#> 5 5 0.841 0.853 0.919 0.0548 0.929 0.737
#> 6 6 0.895 0.867 0.930 0.0420 0.949 0.770
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] 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
#> GSM1296094 2 0.9358 0.441 0.352 0.648
#> GSM1296119 2 0.0000 0.945 0.000 1.000
#> GSM1296076 2 0.0000 0.945 0.000 1.000
#> GSM1296092 2 0.2236 0.925 0.036 0.964
#> GSM1296103 2 0.2236 0.925 0.036 0.964
#> GSM1296078 2 0.0000 0.945 0.000 1.000
#> GSM1296107 2 0.0000 0.945 0.000 1.000
#> GSM1296109 2 0.0000 0.945 0.000 1.000
#> GSM1296080 1 0.0000 0.956 1.000 0.000
#> GSM1296090 2 0.0000 0.945 0.000 1.000
#> GSM1296074 2 0.0000 0.945 0.000 1.000
#> GSM1296111 2 0.0000 0.945 0.000 1.000
#> GSM1296099 2 0.0938 0.939 0.012 0.988
#> GSM1296086 2 0.2236 0.925 0.036 0.964
#> GSM1296117 2 0.0000 0.945 0.000 1.000
#> GSM1296113 2 0.0000 0.945 0.000 1.000
#> GSM1296096 2 0.0000 0.945 0.000 1.000
#> GSM1296105 1 0.0000 0.956 1.000 0.000
#> GSM1296098 1 0.9427 0.465 0.640 0.360
#> GSM1296101 2 0.9358 0.441 0.352 0.648
#> GSM1296121 2 0.0000 0.945 0.000 1.000
#> GSM1296088 1 0.9491 0.446 0.632 0.368
#> GSM1296082 2 0.0000 0.945 0.000 1.000
#> GSM1296115 2 0.0000 0.945 0.000 1.000
#> GSM1296084 1 0.0000 0.956 1.000 0.000
#> GSM1296072 2 0.0000 0.945 0.000 1.000
#> GSM1296069 2 0.0000 0.945 0.000 1.000
#> GSM1296071 2 0.0000 0.945 0.000 1.000
#> GSM1296070 2 0.0000 0.945 0.000 1.000
#> GSM1296073 2 0.0000 0.945 0.000 1.000
#> GSM1296034 1 0.0000 0.956 1.000 0.000
#> GSM1296041 2 0.0000 0.945 0.000 1.000
#> GSM1296035 2 0.2236 0.925 0.036 0.964
#> GSM1296038 2 0.0000 0.945 0.000 1.000
#> GSM1296047 2 0.6801 0.793 0.180 0.820
#> GSM1296039 2 0.0000 0.945 0.000 1.000
#> GSM1296042 2 0.0000 0.945 0.000 1.000
#> GSM1296043 2 0.0000 0.945 0.000 1.000
#> GSM1296037 1 0.0000 0.956 1.000 0.000
#> GSM1296046 2 0.0000 0.945 0.000 1.000
#> GSM1296044 2 0.1184 0.937 0.016 0.984
#> GSM1296045 2 0.0000 0.945 0.000 1.000
#> GSM1296025 1 0.0000 0.956 1.000 0.000
#> GSM1296033 1 0.0000 0.956 1.000 0.000
#> GSM1296027 1 0.0000 0.956 1.000 0.000
#> GSM1296032 1 0.0000 0.956 1.000 0.000
#> GSM1296024 1 0.0000 0.956 1.000 0.000
#> GSM1296031 1 0.0000 0.956 1.000 0.000
#> GSM1296028 1 0.0000 0.956 1.000 0.000
#> GSM1296029 1 0.0000 0.956 1.000 0.000
#> GSM1296026 1 0.6801 0.777 0.820 0.180
#> GSM1296030 1 0.0000 0.956 1.000 0.000
#> GSM1296040 1 0.6801 0.777 0.820 0.180
#> GSM1296036 1 0.6801 0.777 0.820 0.180
#> GSM1296048 2 0.0000 0.945 0.000 1.000
#> GSM1296059 2 0.2236 0.925 0.036 0.964
#> GSM1296066 2 0.0000 0.945 0.000 1.000
#> GSM1296060 2 0.2236 0.925 0.036 0.964
#> GSM1296063 2 0.0000 0.945 0.000 1.000
#> GSM1296064 2 0.0000 0.945 0.000 1.000
#> GSM1296067 2 0.9686 0.393 0.396 0.604
#> GSM1296062 1 0.0000 0.956 1.000 0.000
#> GSM1296068 2 0.6801 0.793 0.180 0.820
#> GSM1296050 1 0.0000 0.956 1.000 0.000
#> GSM1296057 1 0.0000 0.956 1.000 0.000
#> GSM1296052 1 0.0000 0.956 1.000 0.000
#> GSM1296054 1 0.0000 0.956 1.000 0.000
#> GSM1296049 1 0.0000 0.956 1.000 0.000
#> GSM1296055 1 0.0000 0.956 1.000 0.000
#> GSM1296053 1 0.0000 0.956 1.000 0.000
#> GSM1296058 1 0.0000 0.956 1.000 0.000
#> GSM1296051 2 0.0000 0.945 0.000 1.000
#> GSM1296056 2 0.2043 0.928 0.032 0.968
#> GSM1296065 2 0.0000 0.945 0.000 1.000
#> GSM1296061 1 0.4939 0.858 0.892 0.108
#> GSM1296095 2 0.0000 0.945 0.000 1.000
#> GSM1296120 2 0.6801 0.793 0.180 0.820
#> GSM1296077 1 0.0000 0.956 1.000 0.000
#> GSM1296093 1 0.0000 0.956 1.000 0.000
#> GSM1296104 2 0.0000 0.945 0.000 1.000
#> GSM1296079 1 0.0000 0.956 1.000 0.000
#> GSM1296108 2 0.6801 0.793 0.180 0.820
#> GSM1296110 2 0.6623 0.802 0.172 0.828
#> GSM1296081 1 0.0000 0.956 1.000 0.000
#> GSM1296091 1 0.0000 0.956 1.000 0.000
#> GSM1296075 1 0.2043 0.929 0.968 0.032
#> GSM1296112 2 0.6801 0.793 0.180 0.820
#> GSM1296100 1 0.0000 0.956 1.000 0.000
#> GSM1296087 1 0.0000 0.956 1.000 0.000
#> GSM1296118 1 0.9358 0.416 0.648 0.352
#> GSM1296114 2 0.4939 0.865 0.108 0.892
#> GSM1296097 1 0.0000 0.956 1.000 0.000
#> GSM1296106 1 0.0000 0.956 1.000 0.000
#> GSM1296102 1 0.0000 0.956 1.000 0.000
#> GSM1296122 1 0.2236 0.925 0.964 0.036
#> GSM1296089 1 0.0000 0.956 1.000 0.000
#> GSM1296083 1 0.0000 0.956 1.000 0.000
#> GSM1296116 2 0.5519 0.847 0.128 0.872
#> GSM1296085 1 0.0000 0.956 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1296094 3 0.0000 0.998 0.000 0.000 1.000
#> GSM1296119 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296076 3 0.0000 0.998 0.000 0.000 1.000
#> GSM1296092 3 0.0000 0.998 0.000 0.000 1.000
#> GSM1296103 3 0.0000 0.998 0.000 0.000 1.000
#> GSM1296078 3 0.0000 0.998 0.000 0.000 1.000
#> GSM1296107 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296109 3 0.0000 0.998 0.000 0.000 1.000
#> GSM1296080 3 0.0000 0.998 0.000 0.000 1.000
#> GSM1296090 3 0.0000 0.998 0.000 0.000 1.000
#> GSM1296074 3 0.0000 0.998 0.000 0.000 1.000
#> GSM1296111 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296099 3 0.0000 0.998 0.000 0.000 1.000
#> GSM1296086 3 0.0000 0.998 0.000 0.000 1.000
#> GSM1296117 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296113 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296096 3 0.0000 0.998 0.000 0.000 1.000
#> GSM1296105 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296098 3 0.0000 0.998 0.000 0.000 1.000
#> GSM1296101 3 0.0000 0.998 0.000 0.000 1.000
#> GSM1296121 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296088 3 0.0000 0.998 0.000 0.000 1.000
#> GSM1296082 3 0.0000 0.998 0.000 0.000 1.000
#> GSM1296115 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296084 3 0.0000 0.998 0.000 0.000 1.000
#> GSM1296072 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296069 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296071 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296070 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296073 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296034 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296041 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296035 3 0.0000 0.998 0.000 0.000 1.000
#> GSM1296038 3 0.0000 0.998 0.000 0.000 1.000
#> GSM1296047 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296039 3 0.0000 0.998 0.000 0.000 1.000
#> GSM1296042 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296043 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296037 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296046 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296044 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296045 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296025 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296033 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296027 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296032 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296024 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296031 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296028 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296029 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296026 3 0.0000 0.998 0.000 0.000 1.000
#> GSM1296030 3 0.1529 0.959 0.040 0.000 0.960
#> GSM1296040 3 0.0000 0.998 0.000 0.000 1.000
#> GSM1296036 3 0.0000 0.998 0.000 0.000 1.000
#> GSM1296048 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296059 3 0.0000 0.998 0.000 0.000 1.000
#> GSM1296066 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296060 3 0.0000 0.998 0.000 0.000 1.000
#> GSM1296063 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296064 3 0.0000 0.998 0.000 0.000 1.000
#> GSM1296067 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296062 3 0.0424 0.991 0.008 0.000 0.992
#> GSM1296068 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296050 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296057 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296052 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296054 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296049 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296055 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296053 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296058 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296051 3 0.0000 0.998 0.000 0.000 1.000
#> GSM1296056 3 0.0000 0.998 0.000 0.000 1.000
#> GSM1296065 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296061 3 0.0000 0.998 0.000 0.000 1.000
#> GSM1296095 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296120 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296077 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296093 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296104 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296079 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296108 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296110 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296081 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296091 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296075 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296112 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296100 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296087 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296118 2 0.4291 0.776 0.180 0.820 0.000
#> GSM1296114 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296097 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296106 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296102 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296122 1 0.6062 0.367 0.616 0.384 0.000
#> GSM1296089 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296083 1 0.0000 0.988 1.000 0.000 0.000
#> GSM1296116 2 0.0000 0.994 0.000 1.000 0.000
#> GSM1296085 1 0.0000 0.988 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1296094 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> GSM1296119 4 0.1022 0.968 0.000 0.032 0.000 0.968
#> GSM1296076 4 0.0188 0.965 0.000 0.000 0.004 0.996
#> GSM1296092 3 0.1118 0.968 0.000 0.000 0.964 0.036
#> GSM1296103 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> GSM1296078 4 0.0188 0.965 0.000 0.000 0.004 0.996
#> GSM1296107 4 0.1118 0.966 0.000 0.036 0.000 0.964
#> GSM1296109 4 0.1022 0.960 0.000 0.000 0.032 0.968
#> GSM1296080 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> GSM1296090 4 0.1637 0.937 0.000 0.000 0.060 0.940
#> GSM1296074 4 0.1637 0.937 0.000 0.000 0.060 0.940
#> GSM1296111 4 0.1022 0.968 0.000 0.032 0.000 0.968
#> GSM1296099 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> GSM1296086 3 0.1118 0.968 0.000 0.000 0.964 0.036
#> GSM1296117 4 0.1022 0.968 0.000 0.032 0.000 0.968
#> GSM1296113 4 0.1118 0.966 0.000 0.036 0.000 0.964
#> GSM1296096 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> GSM1296105 1 0.3024 0.826 0.852 0.000 0.148 0.000
#> GSM1296098 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> GSM1296101 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> GSM1296121 4 0.1022 0.968 0.000 0.032 0.000 0.968
#> GSM1296088 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> GSM1296082 4 0.1637 0.937 0.000 0.000 0.060 0.940
#> GSM1296115 4 0.1022 0.968 0.000 0.032 0.000 0.968
#> GSM1296084 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> GSM1296072 2 0.0000 0.977 0.000 1.000 0.000 0.000
#> GSM1296069 2 0.1637 0.926 0.000 0.940 0.000 0.060
#> GSM1296071 2 0.0000 0.977 0.000 1.000 0.000 0.000
#> GSM1296070 2 0.4522 0.524 0.000 0.680 0.000 0.320
#> GSM1296073 4 0.0000 0.966 0.000 0.000 0.000 1.000
#> GSM1296034 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1296041 4 0.1022 0.968 0.000 0.032 0.000 0.968
#> GSM1296035 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> GSM1296038 4 0.0817 0.960 0.000 0.000 0.024 0.976
#> GSM1296047 2 0.0000 0.977 0.000 1.000 0.000 0.000
#> GSM1296039 4 0.1211 0.951 0.000 0.000 0.040 0.960
#> GSM1296042 4 0.1211 0.964 0.000 0.040 0.000 0.960
#> GSM1296043 2 0.0188 0.974 0.000 0.996 0.000 0.004
#> GSM1296037 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1296046 2 0.0000 0.977 0.000 1.000 0.000 0.000
#> GSM1296044 2 0.0000 0.977 0.000 1.000 0.000 0.000
#> GSM1296045 2 0.0000 0.977 0.000 1.000 0.000 0.000
#> GSM1296025 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1296033 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1296027 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1296032 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1296024 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1296031 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1296028 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1296029 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1296026 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> GSM1296030 3 0.0188 0.990 0.004 0.000 0.996 0.000
#> GSM1296040 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> GSM1296036 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> GSM1296048 4 0.1022 0.968 0.000 0.032 0.000 0.968
#> GSM1296059 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> GSM1296066 4 0.1211 0.964 0.000 0.040 0.000 0.960
#> GSM1296060 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> GSM1296063 4 0.0000 0.966 0.000 0.000 0.000 1.000
#> GSM1296064 4 0.0000 0.966 0.000 0.000 0.000 1.000
#> GSM1296067 2 0.0000 0.977 0.000 1.000 0.000 0.000
#> GSM1296062 3 0.0188 0.990 0.004 0.000 0.996 0.000
#> GSM1296068 2 0.0000 0.977 0.000 1.000 0.000 0.000
#> GSM1296050 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1296057 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1296052 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1296055 1 0.0336 0.975 0.992 0.008 0.000 0.000
#> GSM1296053 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1296058 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1296051 4 0.1940 0.923 0.000 0.000 0.076 0.924
#> GSM1296056 3 0.1389 0.958 0.000 0.000 0.952 0.048
#> GSM1296065 4 0.1211 0.951 0.000 0.040 0.000 0.960
#> GSM1296061 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> GSM1296095 4 0.0188 0.967 0.000 0.004 0.000 0.996
#> GSM1296120 2 0.0000 0.977 0.000 1.000 0.000 0.000
#> GSM1296077 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1296093 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1296104 2 0.1302 0.941 0.000 0.956 0.000 0.044
#> GSM1296079 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1296108 2 0.0000 0.977 0.000 1.000 0.000 0.000
#> GSM1296110 2 0.0000 0.977 0.000 1.000 0.000 0.000
#> GSM1296081 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1296091 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1296075 1 0.0921 0.959 0.972 0.028 0.000 0.000
#> GSM1296112 2 0.0000 0.977 0.000 1.000 0.000 0.000
#> GSM1296100 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1296087 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1296118 2 0.0000 0.977 0.000 1.000 0.000 0.000
#> GSM1296114 2 0.0000 0.977 0.000 1.000 0.000 0.000
#> GSM1296097 1 0.3764 0.736 0.784 0.216 0.000 0.000
#> GSM1296106 1 0.3444 0.780 0.816 0.184 0.000 0.000
#> GSM1296102 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1296122 2 0.0000 0.977 0.000 1.000 0.000 0.000
#> GSM1296089 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1296083 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1296116 2 0.0000 0.977 0.000 1.000 0.000 0.000
#> GSM1296085 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
#> GSM1296094 3 0.0000 0.9056 0.000 0.000 1.000 0.000 0.000
#> GSM1296119 5 0.0162 0.9182 0.000 0.004 0.000 0.000 0.996
#> GSM1296076 4 0.2471 0.8964 0.000 0.000 0.000 0.864 0.136
#> GSM1296092 4 0.2583 0.7948 0.000 0.000 0.132 0.864 0.004
#> GSM1296103 3 0.1197 0.9004 0.000 0.000 0.952 0.048 0.000
#> GSM1296078 4 0.2471 0.8964 0.000 0.000 0.000 0.864 0.136
#> GSM1296107 5 0.0162 0.9182 0.000 0.004 0.000 0.000 0.996
#> GSM1296109 5 0.3323 0.7586 0.000 0.000 0.100 0.056 0.844
#> GSM1296080 3 0.0000 0.9056 0.000 0.000 1.000 0.000 0.000
#> GSM1296090 4 0.2583 0.8982 0.000 0.000 0.004 0.864 0.132
#> GSM1296074 4 0.2583 0.8982 0.000 0.000 0.004 0.864 0.132
#> GSM1296111 5 0.0162 0.9182 0.000 0.004 0.000 0.000 0.996
#> GSM1296099 3 0.2605 0.8458 0.000 0.000 0.852 0.148 0.000
#> GSM1296086 4 0.2583 0.7948 0.000 0.000 0.132 0.864 0.004
#> GSM1296117 5 0.0162 0.9182 0.000 0.004 0.000 0.000 0.996
#> GSM1296113 5 0.0162 0.9182 0.000 0.004 0.000 0.000 0.996
#> GSM1296096 3 0.2690 0.8380 0.000 0.000 0.844 0.156 0.000
#> GSM1296105 1 0.5234 0.2992 0.560 0.000 0.396 0.040 0.004
#> GSM1296098 3 0.0000 0.9056 0.000 0.000 1.000 0.000 0.000
#> GSM1296101 3 0.1043 0.9022 0.000 0.000 0.960 0.040 0.000
#> GSM1296121 5 0.0162 0.9182 0.000 0.004 0.000 0.000 0.996
#> GSM1296088 3 0.2966 0.7922 0.000 0.000 0.816 0.184 0.000
#> GSM1296082 4 0.2660 0.8982 0.000 0.000 0.008 0.864 0.128
#> GSM1296115 5 0.0162 0.9182 0.000 0.004 0.000 0.000 0.996
#> GSM1296084 3 0.2127 0.8572 0.000 0.000 0.892 0.108 0.000
#> GSM1296072 2 0.1270 0.8759 0.000 0.948 0.000 0.000 0.052
#> GSM1296069 5 0.2732 0.7657 0.000 0.160 0.000 0.000 0.840
#> GSM1296071 2 0.0162 0.9118 0.000 0.996 0.000 0.000 0.004
#> GSM1296070 5 0.2280 0.8083 0.000 0.120 0.000 0.000 0.880
#> GSM1296073 5 0.0290 0.9113 0.000 0.000 0.000 0.008 0.992
#> GSM1296034 1 0.0000 0.9447 1.000 0.000 0.000 0.000 0.000
#> GSM1296041 5 0.0162 0.9182 0.000 0.004 0.000 0.000 0.996
#> GSM1296035 3 0.2648 0.8421 0.000 0.000 0.848 0.152 0.000
#> GSM1296038 4 0.5639 0.6639 0.000 0.000 0.124 0.616 0.260
#> GSM1296047 2 0.0162 0.9118 0.000 0.996 0.000 0.000 0.004
#> GSM1296039 4 0.2848 0.8833 0.000 0.000 0.004 0.840 0.156
#> GSM1296042 5 0.0162 0.9182 0.000 0.004 0.000 0.000 0.996
#> GSM1296043 2 0.4304 0.0291 0.000 0.516 0.000 0.000 0.484
#> GSM1296037 1 0.0771 0.9326 0.976 0.000 0.000 0.020 0.004
#> GSM1296046 2 0.0162 0.9118 0.000 0.996 0.000 0.000 0.004
#> GSM1296044 2 0.0162 0.9118 0.000 0.996 0.000 0.000 0.004
#> GSM1296045 2 0.2648 0.7750 0.000 0.848 0.000 0.000 0.152
#> GSM1296025 1 0.0000 0.9447 1.000 0.000 0.000 0.000 0.000
#> GSM1296033 1 0.0000 0.9447 1.000 0.000 0.000 0.000 0.000
#> GSM1296027 1 0.0000 0.9447 1.000 0.000 0.000 0.000 0.000
#> GSM1296032 1 0.0000 0.9447 1.000 0.000 0.000 0.000 0.000
#> GSM1296024 1 0.0000 0.9447 1.000 0.000 0.000 0.000 0.000
#> GSM1296031 1 0.0000 0.9447 1.000 0.000 0.000 0.000 0.000
#> GSM1296028 1 0.0000 0.9447 1.000 0.000 0.000 0.000 0.000
#> GSM1296029 1 0.0000 0.9447 1.000 0.000 0.000 0.000 0.000
#> GSM1296026 3 0.3177 0.7613 0.000 0.000 0.792 0.208 0.000
#> GSM1296030 3 0.3242 0.8218 0.040 0.000 0.844 0.116 0.000
#> GSM1296040 3 0.0000 0.9056 0.000 0.000 1.000 0.000 0.000
#> GSM1296036 3 0.0000 0.9056 0.000 0.000 1.000 0.000 0.000
#> GSM1296048 5 0.0162 0.9182 0.000 0.004 0.000 0.000 0.996
#> GSM1296059 3 0.1732 0.8882 0.000 0.000 0.920 0.080 0.000
#> GSM1296066 5 0.0290 0.9157 0.000 0.008 0.000 0.000 0.992
#> GSM1296060 3 0.2605 0.8458 0.000 0.000 0.852 0.148 0.000
#> GSM1296063 5 0.3999 0.3337 0.000 0.000 0.000 0.344 0.656
#> GSM1296064 4 0.3752 0.7173 0.000 0.000 0.000 0.708 0.292
#> GSM1296067 2 0.0162 0.9118 0.000 0.996 0.000 0.000 0.004
#> GSM1296062 3 0.0000 0.9056 0.000 0.000 1.000 0.000 0.000
#> GSM1296068 2 0.0162 0.9118 0.000 0.996 0.000 0.000 0.004
#> GSM1296050 1 0.0000 0.9447 1.000 0.000 0.000 0.000 0.000
#> GSM1296057 1 0.2787 0.8507 0.856 0.004 0.000 0.136 0.004
#> GSM1296052 1 0.0000 0.9447 1.000 0.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.9447 1.000 0.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.9447 1.000 0.000 0.000 0.000 0.000
#> GSM1296055 1 0.3634 0.8211 0.820 0.040 0.000 0.136 0.004
#> GSM1296053 1 0.0000 0.9447 1.000 0.000 0.000 0.000 0.000
#> GSM1296058 1 0.2787 0.8507 0.856 0.004 0.000 0.136 0.004
#> GSM1296051 4 0.2660 0.8982 0.000 0.000 0.008 0.864 0.128
#> GSM1296056 4 0.2583 0.7948 0.000 0.000 0.132 0.864 0.004
#> GSM1296065 5 0.5353 0.5597 0.000 0.092 0.000 0.272 0.636
#> GSM1296061 3 0.0000 0.9056 0.000 0.000 1.000 0.000 0.000
#> GSM1296095 5 0.0510 0.9104 0.000 0.000 0.000 0.016 0.984
#> GSM1296120 2 0.0162 0.9118 0.000 0.996 0.000 0.000 0.004
#> GSM1296077 1 0.0000 0.9447 1.000 0.000 0.000 0.000 0.000
#> GSM1296093 1 0.0000 0.9447 1.000 0.000 0.000 0.000 0.000
#> GSM1296104 2 0.4668 0.5056 0.000 0.624 0.000 0.352 0.024
#> GSM1296079 1 0.0000 0.9447 1.000 0.000 0.000 0.000 0.000
#> GSM1296108 2 0.0162 0.9118 0.000 0.996 0.000 0.000 0.004
#> GSM1296110 2 0.0162 0.9118 0.000 0.996 0.000 0.000 0.004
#> GSM1296081 1 0.0000 0.9447 1.000 0.000 0.000 0.000 0.000
#> GSM1296091 1 0.0162 0.9428 0.996 0.000 0.000 0.004 0.000
#> GSM1296075 1 0.5260 0.5378 0.648 0.264 0.000 0.088 0.000
#> GSM1296112 2 0.0162 0.9118 0.000 0.996 0.000 0.000 0.004
#> GSM1296100 1 0.0771 0.9326 0.976 0.000 0.000 0.020 0.004
#> GSM1296087 1 0.0000 0.9447 1.000 0.000 0.000 0.000 0.000
#> GSM1296118 2 0.0162 0.9118 0.000 0.996 0.000 0.000 0.004
#> GSM1296114 2 0.0162 0.9118 0.000 0.996 0.000 0.000 0.004
#> GSM1296097 1 0.6156 0.3566 0.540 0.320 0.000 0.136 0.004
#> GSM1296106 2 0.6001 0.4184 0.280 0.580 0.000 0.136 0.004
#> GSM1296102 1 0.0865 0.9305 0.972 0.000 0.000 0.024 0.004
#> GSM1296122 2 0.0000 0.9086 0.000 1.000 0.000 0.000 0.000
#> GSM1296089 1 0.0000 0.9447 1.000 0.000 0.000 0.000 0.000
#> GSM1296083 1 0.0000 0.9447 1.000 0.000 0.000 0.000 0.000
#> GSM1296116 2 0.0162 0.9118 0.000 0.996 0.000 0.000 0.004
#> GSM1296085 1 0.0000 0.9447 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
#> GSM1296094 3 0.0603 0.8124 0.000 0.000 0.980 0.004 0.000 0.016
#> GSM1296119 5 0.0000 0.9546 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296076 4 0.0632 0.9311 0.000 0.000 0.000 0.976 0.024 0.000
#> GSM1296092 4 0.0603 0.9200 0.000 0.000 0.016 0.980 0.004 0.000
#> GSM1296103 3 0.1950 0.8054 0.000 0.000 0.912 0.064 0.000 0.024
#> GSM1296078 4 0.0632 0.9311 0.000 0.000 0.000 0.976 0.024 0.000
#> GSM1296107 5 0.0146 0.9536 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1296109 5 0.2401 0.8683 0.000 0.000 0.028 0.048 0.900 0.024
#> GSM1296080 3 0.0146 0.8115 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM1296090 4 0.0632 0.9311 0.000 0.000 0.000 0.976 0.024 0.000
#> GSM1296074 4 0.0632 0.9311 0.000 0.000 0.000 0.976 0.024 0.000
#> GSM1296111 5 0.0000 0.9546 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296099 3 0.4121 0.6838 0.000 0.000 0.720 0.220 0.000 0.060
#> GSM1296086 4 0.0603 0.9200 0.000 0.000 0.016 0.980 0.004 0.000
#> GSM1296117 5 0.0000 0.9546 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296113 5 0.0146 0.9536 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1296096 3 0.4301 0.6563 0.000 0.000 0.696 0.240 0.000 0.064
#> GSM1296105 3 0.5910 0.0713 0.400 0.000 0.452 0.016 0.000 0.132
#> GSM1296098 3 0.0000 0.8124 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296101 3 0.2451 0.7983 0.000 0.000 0.884 0.056 0.000 0.060
#> GSM1296121 5 0.0000 0.9546 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296088 3 0.3221 0.6300 0.000 0.000 0.736 0.264 0.000 0.000
#> GSM1296082 4 0.0632 0.9311 0.000 0.000 0.000 0.976 0.024 0.000
#> GSM1296115 5 0.0000 0.9546 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296084 3 0.2149 0.7732 0.004 0.000 0.888 0.104 0.000 0.004
#> GSM1296072 2 0.0972 0.9407 0.000 0.964 0.000 0.000 0.028 0.008
#> GSM1296069 5 0.1219 0.9126 0.000 0.048 0.000 0.000 0.948 0.004
#> GSM1296071 2 0.0000 0.9665 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296070 5 0.0858 0.9324 0.000 0.028 0.000 0.000 0.968 0.004
#> GSM1296073 5 0.0000 0.9546 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296034 1 0.0692 0.9506 0.976 0.000 0.020 0.004 0.000 0.000
#> GSM1296041 5 0.0000 0.9546 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296035 3 0.4253 0.6672 0.000 0.000 0.704 0.232 0.000 0.064
#> GSM1296038 4 0.6202 0.5587 0.000 0.000 0.148 0.600 0.124 0.128
#> GSM1296047 2 0.0146 0.9651 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1296039 4 0.1760 0.9051 0.000 0.000 0.004 0.928 0.048 0.020
#> GSM1296042 5 0.0146 0.9536 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1296043 2 0.3383 0.6392 0.000 0.728 0.000 0.000 0.268 0.004
#> GSM1296037 1 0.2595 0.7893 0.836 0.000 0.000 0.004 0.000 0.160
#> GSM1296046 2 0.0146 0.9644 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1296044 2 0.0000 0.9665 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296045 2 0.2191 0.8413 0.000 0.876 0.000 0.000 0.120 0.004
#> GSM1296025 1 0.0146 0.9670 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM1296033 1 0.0260 0.9650 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM1296027 1 0.0146 0.9667 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1296032 1 0.0000 0.9679 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296024 1 0.0000 0.9679 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296031 1 0.0291 0.9652 0.992 0.000 0.000 0.004 0.000 0.004
#> GSM1296028 1 0.0146 0.9667 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1296029 1 0.0146 0.9667 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1296026 3 0.3409 0.5719 0.000 0.000 0.700 0.300 0.000 0.000
#> GSM1296030 3 0.4131 0.6206 0.180 0.000 0.744 0.072 0.000 0.004
#> GSM1296040 3 0.0508 0.8127 0.000 0.000 0.984 0.004 0.000 0.012
#> GSM1296036 3 0.0000 0.8124 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296048 5 0.0000 0.9546 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296059 3 0.2997 0.7802 0.000 0.000 0.844 0.096 0.000 0.060
#> GSM1296066 5 0.0146 0.9536 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1296060 3 0.4176 0.6815 0.000 0.000 0.716 0.220 0.000 0.064
#> GSM1296063 5 0.3819 0.5526 0.000 0.000 0.000 0.280 0.700 0.020
#> GSM1296064 4 0.3017 0.7884 0.000 0.000 0.000 0.816 0.164 0.020
#> GSM1296067 2 0.0000 0.9665 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296062 3 0.0000 0.8124 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296068 2 0.0000 0.9665 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296050 1 0.0291 0.9652 0.992 0.000 0.000 0.004 0.000 0.004
#> GSM1296057 6 0.2402 0.7970 0.140 0.000 0.000 0.004 0.000 0.856
#> GSM1296052 1 0.0000 0.9679 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.9679 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.9679 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296055 6 0.1806 0.8302 0.088 0.000 0.000 0.004 0.000 0.908
#> GSM1296053 1 0.0000 0.9679 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296058 6 0.1349 0.8349 0.056 0.000 0.000 0.004 0.000 0.940
#> GSM1296051 4 0.0632 0.9311 0.000 0.000 0.000 0.976 0.024 0.000
#> GSM1296056 4 0.1682 0.8903 0.000 0.000 0.020 0.928 0.000 0.052
#> GSM1296065 6 0.3441 0.6767 0.000 0.004 0.000 0.024 0.188 0.784
#> GSM1296061 3 0.0000 0.8124 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296095 5 0.2664 0.8294 0.000 0.000 0.000 0.016 0.848 0.136
#> GSM1296120 2 0.0146 0.9651 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1296077 1 0.0146 0.9670 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM1296093 1 0.0000 0.9679 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296104 6 0.0713 0.8157 0.000 0.028 0.000 0.000 0.000 0.972
#> GSM1296079 1 0.0146 0.9670 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM1296108 2 0.0000 0.9665 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296110 2 0.0000 0.9665 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296081 1 0.0000 0.9679 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296091 1 0.0458 0.9591 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM1296075 6 0.5028 0.3790 0.400 0.056 0.000 0.008 0.000 0.536
#> GSM1296112 2 0.0000 0.9665 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296100 1 0.2595 0.7893 0.836 0.000 0.000 0.004 0.000 0.160
#> GSM1296087 1 0.0146 0.9667 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1296118 2 0.0146 0.9651 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1296114 2 0.0000 0.9665 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296097 6 0.0806 0.8271 0.020 0.008 0.000 0.000 0.000 0.972
#> GSM1296106 6 0.2367 0.8039 0.016 0.088 0.000 0.008 0.000 0.888
#> GSM1296102 1 0.3448 0.5864 0.716 0.000 0.000 0.004 0.000 0.280
#> GSM1296122 2 0.0260 0.9628 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM1296089 1 0.0291 0.9652 0.992 0.000 0.000 0.004 0.000 0.004
#> GSM1296083 1 0.0000 0.9679 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296116 2 0.0000 0.9665 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296085 1 0.0000 0.9679 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)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# 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 tissue(p) time(p) agent(p) dose(p) individual(p) k
#> MAD:skmeans 93 4.70e-02 0.109 0.73529 2.08e-06 7.18e-04 2
#> MAD:skmeans 98 2.92e-04 0.087 0.01369 1.07e-08 1.82e-06 3
#> MAD:skmeans 99 4.18e-05 0.118 0.00306 1.00e-10 5.61e-05 4
#> MAD:skmeans 94 5.12e-05 0.317 0.02353 7.87e-08 1.60e-05 5
#> MAD:skmeans 97 2.35e-04 0.145 0.10802 3.47e-07 3.45e-05 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 45638 rows and 99 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 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 0.300 0.780 0.856 0.4832 0.514 0.514
#> 3 3 0.914 0.910 0.961 0.3782 0.716 0.499
#> 4 4 0.784 0.822 0.915 0.1187 0.867 0.632
#> 5 5 0.815 0.779 0.874 0.0593 0.922 0.714
#> 6 6 0.801 0.712 0.828 0.0462 0.951 0.774
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
#> GSM1296094 1 0.8909 0.749 0.692 0.308
#> GSM1296119 2 0.0000 0.827 0.000 1.000
#> GSM1296076 2 0.6148 0.724 0.152 0.848
#> GSM1296092 1 0.8909 0.749 0.692 0.308
#> GSM1296103 1 0.8909 0.749 0.692 0.308
#> GSM1296078 2 0.6148 0.724 0.152 0.848
#> GSM1296107 2 0.0000 0.827 0.000 1.000
#> GSM1296109 2 0.6148 0.724 0.152 0.848
#> GSM1296080 1 0.7139 0.780 0.804 0.196
#> GSM1296090 1 0.8909 0.749 0.692 0.308
#> GSM1296074 1 0.8909 0.749 0.692 0.308
#> GSM1296111 2 0.0000 0.827 0.000 1.000
#> GSM1296099 1 0.8909 0.749 0.692 0.308
#> GSM1296086 1 0.8909 0.749 0.692 0.308
#> GSM1296117 2 0.0000 0.827 0.000 1.000
#> GSM1296113 2 0.0000 0.827 0.000 1.000
#> GSM1296096 1 0.9248 0.708 0.660 0.340
#> GSM1296105 1 0.3733 0.811 0.928 0.072
#> GSM1296098 1 0.8909 0.749 0.692 0.308
#> GSM1296101 1 0.8909 0.749 0.692 0.308
#> GSM1296121 2 0.0000 0.827 0.000 1.000
#> GSM1296088 1 0.8813 0.753 0.700 0.300
#> GSM1296082 1 0.8909 0.749 0.692 0.308
#> GSM1296115 2 0.0000 0.827 0.000 1.000
#> GSM1296084 1 0.7883 0.778 0.764 0.236
#> GSM1296072 2 0.6438 0.824 0.164 0.836
#> GSM1296069 2 0.5059 0.832 0.112 0.888
#> GSM1296071 2 0.6438 0.824 0.164 0.836
#> GSM1296070 2 0.0938 0.829 0.012 0.988
#> GSM1296073 2 0.5842 0.736 0.140 0.860
#> GSM1296034 1 0.0000 0.815 1.000 0.000
#> GSM1296041 2 0.0000 0.827 0.000 1.000
#> GSM1296035 1 0.8909 0.749 0.692 0.308
#> GSM1296038 2 0.6148 0.724 0.152 0.848
#> GSM1296047 2 0.6438 0.824 0.164 0.836
#> GSM1296039 2 0.6148 0.724 0.152 0.848
#> GSM1296042 2 0.0000 0.827 0.000 1.000
#> GSM1296043 2 0.6438 0.824 0.164 0.836
#> GSM1296037 1 0.0000 0.815 1.000 0.000
#> GSM1296046 2 0.6438 0.824 0.164 0.836
#> GSM1296044 2 0.6438 0.824 0.164 0.836
#> GSM1296045 2 0.6438 0.824 0.164 0.836
#> GSM1296025 1 0.0000 0.815 1.000 0.000
#> GSM1296033 1 0.2603 0.815 0.956 0.044
#> GSM1296027 1 0.0000 0.815 1.000 0.000
#> GSM1296032 1 0.0000 0.815 1.000 0.000
#> GSM1296024 1 0.0000 0.815 1.000 0.000
#> GSM1296031 1 0.5178 0.714 0.884 0.116
#> GSM1296028 1 0.0000 0.815 1.000 0.000
#> GSM1296029 1 0.0000 0.815 1.000 0.000
#> GSM1296026 1 0.8016 0.776 0.756 0.244
#> GSM1296030 1 0.7883 0.778 0.764 0.236
#> GSM1296040 1 0.8861 0.751 0.696 0.304
#> GSM1296036 1 0.8813 0.754 0.700 0.300
#> GSM1296048 2 0.0000 0.827 0.000 1.000
#> GSM1296059 1 0.8909 0.749 0.692 0.308
#> GSM1296066 2 0.0000 0.827 0.000 1.000
#> GSM1296060 1 0.8909 0.749 0.692 0.308
#> GSM1296063 2 0.5842 0.736 0.140 0.860
#> GSM1296064 2 0.6148 0.724 0.152 0.848
#> GSM1296067 2 0.7139 0.804 0.196 0.804
#> GSM1296062 1 0.7453 0.781 0.788 0.212
#> GSM1296068 2 0.7139 0.804 0.196 0.804
#> GSM1296050 1 0.5842 0.684 0.860 0.140
#> GSM1296057 1 0.3584 0.811 0.932 0.068
#> GSM1296052 1 0.0000 0.815 1.000 0.000
#> GSM1296054 1 0.0000 0.815 1.000 0.000
#> GSM1296049 1 0.0000 0.815 1.000 0.000
#> GSM1296055 1 0.6531 0.710 0.832 0.168
#> GSM1296053 1 0.0000 0.815 1.000 0.000
#> GSM1296058 1 0.3733 0.811 0.928 0.072
#> GSM1296051 1 0.8909 0.749 0.692 0.308
#> GSM1296056 1 0.8909 0.749 0.692 0.308
#> GSM1296065 2 0.9000 0.700 0.316 0.684
#> GSM1296061 1 0.7883 0.778 0.764 0.236
#> GSM1296095 2 0.6712 0.730 0.176 0.824
#> GSM1296120 2 0.6438 0.824 0.164 0.836
#> GSM1296077 1 0.0000 0.815 1.000 0.000
#> GSM1296093 1 0.0000 0.815 1.000 0.000
#> GSM1296104 1 0.8713 0.562 0.708 0.292
#> GSM1296079 1 0.0000 0.815 1.000 0.000
#> GSM1296108 2 0.7139 0.804 0.196 0.804
#> GSM1296110 2 0.7056 0.807 0.192 0.808
#> GSM1296081 1 0.0000 0.815 1.000 0.000
#> GSM1296091 1 0.0000 0.815 1.000 0.000
#> GSM1296075 1 0.6801 0.685 0.820 0.180
#> GSM1296112 2 0.7219 0.800 0.200 0.800
#> GSM1296100 1 0.0000 0.815 1.000 0.000
#> GSM1296087 1 0.0000 0.815 1.000 0.000
#> GSM1296118 2 0.8608 0.710 0.284 0.716
#> GSM1296114 2 0.6623 0.820 0.172 0.828
#> GSM1296097 1 0.5629 0.786 0.868 0.132
#> GSM1296106 1 0.6887 0.685 0.816 0.184
#> GSM1296102 1 0.2423 0.812 0.960 0.040
#> GSM1296122 2 0.8713 0.699 0.292 0.708
#> GSM1296089 1 0.5842 0.684 0.860 0.140
#> GSM1296083 1 0.0000 0.815 1.000 0.000
#> GSM1296116 2 0.7056 0.807 0.192 0.808
#> GSM1296085 1 0.0000 0.815 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1296094 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1296119 2 0.0592 0.945 0.000 0.988 0.012
#> GSM1296076 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1296092 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1296103 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1296078 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1296107 2 0.0000 0.953 0.000 1.000 0.000
#> GSM1296109 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1296080 3 0.1964 0.910 0.056 0.000 0.944
#> GSM1296090 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1296074 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1296111 2 0.0000 0.953 0.000 1.000 0.000
#> GSM1296099 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1296086 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1296117 2 0.1163 0.932 0.000 0.972 0.028
#> GSM1296113 2 0.0000 0.953 0.000 1.000 0.000
#> GSM1296096 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1296105 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1296098 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1296101 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1296121 2 0.1163 0.932 0.000 0.972 0.028
#> GSM1296088 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1296082 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1296115 2 0.4452 0.749 0.000 0.808 0.192
#> GSM1296084 3 0.2796 0.875 0.092 0.000 0.908
#> GSM1296072 2 0.0000 0.953 0.000 1.000 0.000
#> GSM1296069 2 0.0000 0.953 0.000 1.000 0.000
#> GSM1296071 2 0.0000 0.953 0.000 1.000 0.000
#> GSM1296070 2 0.0000 0.953 0.000 1.000 0.000
#> GSM1296073 2 0.5560 0.571 0.000 0.700 0.300
#> GSM1296034 1 0.0000 0.978 1.000 0.000 0.000
#> GSM1296041 2 0.0237 0.950 0.000 0.996 0.004
#> GSM1296035 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1296038 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1296047 2 0.0000 0.953 0.000 1.000 0.000
#> GSM1296039 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1296042 2 0.0000 0.953 0.000 1.000 0.000
#> GSM1296043 2 0.0000 0.953 0.000 1.000 0.000
#> GSM1296037 1 0.0000 0.978 1.000 0.000 0.000
#> GSM1296046 2 0.0000 0.953 0.000 1.000 0.000
#> GSM1296044 2 0.0000 0.953 0.000 1.000 0.000
#> GSM1296045 2 0.0000 0.953 0.000 1.000 0.000
#> GSM1296025 1 0.0000 0.978 1.000 0.000 0.000
#> GSM1296033 1 0.4974 0.668 0.764 0.000 0.236
#> GSM1296027 1 0.0000 0.978 1.000 0.000 0.000
#> GSM1296032 1 0.0000 0.978 1.000 0.000 0.000
#> GSM1296024 1 0.0000 0.978 1.000 0.000 0.000
#> GSM1296031 1 0.0000 0.978 1.000 0.000 0.000
#> GSM1296028 1 0.0000 0.978 1.000 0.000 0.000
#> GSM1296029 1 0.0000 0.978 1.000 0.000 0.000
#> GSM1296026 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1296030 3 0.6095 0.387 0.392 0.000 0.608
#> GSM1296040 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1296036 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1296048 2 0.0000 0.953 0.000 1.000 0.000
#> GSM1296059 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1296066 2 0.0000 0.953 0.000 1.000 0.000
#> GSM1296060 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1296063 2 0.5591 0.563 0.000 0.696 0.304
#> GSM1296064 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1296067 2 0.0237 0.950 0.004 0.996 0.000
#> GSM1296062 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1296068 2 0.0000 0.953 0.000 1.000 0.000
#> GSM1296050 1 0.0000 0.978 1.000 0.000 0.000
#> GSM1296057 3 0.4931 0.705 0.232 0.000 0.768
#> GSM1296052 1 0.0000 0.978 1.000 0.000 0.000
#> GSM1296054 1 0.0000 0.978 1.000 0.000 0.000
#> GSM1296049 1 0.0000 0.978 1.000 0.000 0.000
#> GSM1296055 3 0.9337 0.363 0.208 0.280 0.512
#> GSM1296053 1 0.0000 0.978 1.000 0.000 0.000
#> GSM1296058 3 0.4121 0.790 0.168 0.000 0.832
#> GSM1296051 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1296056 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1296065 3 0.1289 0.928 0.000 0.032 0.968
#> GSM1296061 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1296095 3 0.0424 0.945 0.000 0.008 0.992
#> GSM1296120 2 0.0000 0.953 0.000 1.000 0.000
#> GSM1296077 1 0.0000 0.978 1.000 0.000 0.000
#> GSM1296093 1 0.0000 0.978 1.000 0.000 0.000
#> GSM1296104 3 0.1031 0.934 0.000 0.024 0.976
#> GSM1296079 1 0.0000 0.978 1.000 0.000 0.000
#> GSM1296108 2 0.0000 0.953 0.000 1.000 0.000
#> GSM1296110 2 0.0000 0.953 0.000 1.000 0.000
#> GSM1296081 1 0.0000 0.978 1.000 0.000 0.000
#> GSM1296091 1 0.1031 0.956 0.976 0.000 0.024
#> GSM1296075 1 0.6143 0.629 0.720 0.256 0.024
#> GSM1296112 2 0.0000 0.953 0.000 1.000 0.000
#> GSM1296100 1 0.0000 0.978 1.000 0.000 0.000
#> GSM1296087 1 0.0000 0.978 1.000 0.000 0.000
#> GSM1296118 2 0.0424 0.947 0.008 0.992 0.000
#> GSM1296114 2 0.0000 0.953 0.000 1.000 0.000
#> GSM1296097 3 0.1163 0.931 0.000 0.028 0.972
#> GSM1296106 3 0.8869 0.385 0.148 0.304 0.548
#> GSM1296102 1 0.0237 0.975 0.996 0.000 0.004
#> GSM1296122 2 0.6260 0.150 0.448 0.552 0.000
#> GSM1296089 1 0.0000 0.978 1.000 0.000 0.000
#> GSM1296083 1 0.0000 0.978 1.000 0.000 0.000
#> GSM1296116 2 0.0000 0.953 0.000 1.000 0.000
#> GSM1296085 1 0.0000 0.978 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1296094 3 0.0000 0.92325 0.000 0.000 1.000 0.000
#> GSM1296119 4 0.0000 0.83467 0.000 0.000 0.000 1.000
#> GSM1296076 4 0.3907 0.68511 0.000 0.000 0.232 0.768
#> GSM1296092 3 0.0000 0.92325 0.000 0.000 1.000 0.000
#> GSM1296103 3 0.0000 0.92325 0.000 0.000 1.000 0.000
#> GSM1296078 4 0.3311 0.73662 0.000 0.000 0.172 0.828
#> GSM1296107 4 0.0000 0.83467 0.000 0.000 0.000 1.000
#> GSM1296109 4 0.3610 0.71874 0.000 0.000 0.200 0.800
#> GSM1296080 3 0.2281 0.85539 0.096 0.000 0.904 0.000
#> GSM1296090 3 0.2281 0.83990 0.000 0.000 0.904 0.096
#> GSM1296074 3 0.4164 0.60019 0.000 0.000 0.736 0.264
#> GSM1296111 4 0.0000 0.83467 0.000 0.000 0.000 1.000
#> GSM1296099 3 0.0000 0.92325 0.000 0.000 1.000 0.000
#> GSM1296086 3 0.0000 0.92325 0.000 0.000 1.000 0.000
#> GSM1296117 4 0.0000 0.83467 0.000 0.000 0.000 1.000
#> GSM1296113 4 0.0188 0.83261 0.000 0.004 0.000 0.996
#> GSM1296096 3 0.0000 0.92325 0.000 0.000 1.000 0.000
#> GSM1296105 3 0.3074 0.82671 0.000 0.152 0.848 0.000
#> GSM1296098 3 0.0000 0.92325 0.000 0.000 1.000 0.000
#> GSM1296101 3 0.0000 0.92325 0.000 0.000 1.000 0.000
#> GSM1296121 4 0.0000 0.83467 0.000 0.000 0.000 1.000
#> GSM1296088 3 0.0000 0.92325 0.000 0.000 1.000 0.000
#> GSM1296082 4 0.4999 0.06477 0.000 0.000 0.492 0.508
#> GSM1296115 4 0.0000 0.83467 0.000 0.000 0.000 1.000
#> GSM1296084 3 0.0188 0.92121 0.004 0.000 0.996 0.000
#> GSM1296072 2 0.3356 0.84139 0.000 0.824 0.000 0.176
#> GSM1296069 2 0.4761 0.53899 0.000 0.628 0.000 0.372
#> GSM1296071 2 0.3219 0.84807 0.000 0.836 0.000 0.164
#> GSM1296070 4 0.4981 -0.12632 0.000 0.464 0.000 0.536
#> GSM1296073 4 0.0000 0.83467 0.000 0.000 0.000 1.000
#> GSM1296034 1 0.1118 0.93503 0.964 0.036 0.000 0.000
#> GSM1296041 4 0.0000 0.83467 0.000 0.000 0.000 1.000
#> GSM1296035 3 0.0000 0.92325 0.000 0.000 1.000 0.000
#> GSM1296038 3 0.0000 0.92325 0.000 0.000 1.000 0.000
#> GSM1296047 2 0.0000 0.81908 0.000 1.000 0.000 0.000
#> GSM1296039 3 0.4382 0.52432 0.000 0.000 0.704 0.296
#> GSM1296042 4 0.4134 0.50531 0.000 0.260 0.000 0.740
#> GSM1296043 2 0.3311 0.84430 0.000 0.828 0.000 0.172
#> GSM1296037 1 0.3074 0.85709 0.848 0.152 0.000 0.000
#> GSM1296046 2 0.3219 0.84807 0.000 0.836 0.000 0.164
#> GSM1296044 2 0.3074 0.85118 0.000 0.848 0.000 0.152
#> GSM1296045 2 0.3356 0.84147 0.000 0.824 0.000 0.176
#> GSM1296025 1 0.0000 0.95164 1.000 0.000 0.000 0.000
#> GSM1296033 1 0.4372 0.60530 0.728 0.004 0.268 0.000
#> GSM1296027 1 0.0000 0.95164 1.000 0.000 0.000 0.000
#> GSM1296032 1 0.0000 0.95164 1.000 0.000 0.000 0.000
#> GSM1296024 1 0.0000 0.95164 1.000 0.000 0.000 0.000
#> GSM1296031 1 0.0592 0.94501 0.984 0.016 0.000 0.000
#> GSM1296028 1 0.0000 0.95164 1.000 0.000 0.000 0.000
#> GSM1296029 1 0.0000 0.95164 1.000 0.000 0.000 0.000
#> GSM1296026 3 0.0000 0.92325 0.000 0.000 1.000 0.000
#> GSM1296030 3 0.4967 0.21756 0.452 0.000 0.548 0.000
#> GSM1296040 3 0.0000 0.92325 0.000 0.000 1.000 0.000
#> GSM1296036 3 0.0000 0.92325 0.000 0.000 1.000 0.000
#> GSM1296048 4 0.0000 0.83467 0.000 0.000 0.000 1.000
#> GSM1296059 3 0.0000 0.92325 0.000 0.000 1.000 0.000
#> GSM1296066 4 0.3942 0.54794 0.000 0.236 0.000 0.764
#> GSM1296060 3 0.0000 0.92325 0.000 0.000 1.000 0.000
#> GSM1296063 4 0.0000 0.83467 0.000 0.000 0.000 1.000
#> GSM1296064 4 0.3400 0.73186 0.000 0.000 0.180 0.820
#> GSM1296067 2 0.0000 0.81908 0.000 1.000 0.000 0.000
#> GSM1296062 3 0.0000 0.92325 0.000 0.000 1.000 0.000
#> GSM1296068 2 0.2647 0.85308 0.000 0.880 0.000 0.120
#> GSM1296050 1 0.1716 0.91941 0.936 0.064 0.000 0.000
#> GSM1296057 3 0.3529 0.81771 0.012 0.152 0.836 0.000
#> GSM1296052 1 0.0000 0.95164 1.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.95164 1.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.95164 1.000 0.000 0.000 0.000
#> GSM1296055 2 0.3610 0.60462 0.000 0.800 0.200 0.000
#> GSM1296053 1 0.0000 0.95164 1.000 0.000 0.000 0.000
#> GSM1296058 3 0.3401 0.82077 0.008 0.152 0.840 0.000
#> GSM1296051 3 0.0000 0.92325 0.000 0.000 1.000 0.000
#> GSM1296056 3 0.0000 0.92325 0.000 0.000 1.000 0.000
#> GSM1296065 3 0.3074 0.82671 0.000 0.152 0.848 0.000
#> GSM1296061 3 0.0000 0.92325 0.000 0.000 1.000 0.000
#> GSM1296095 3 0.0336 0.91983 0.000 0.008 0.992 0.000
#> GSM1296120 2 0.0000 0.81908 0.000 1.000 0.000 0.000
#> GSM1296077 1 0.0000 0.95164 1.000 0.000 0.000 0.000
#> GSM1296093 1 0.0000 0.95164 1.000 0.000 0.000 0.000
#> GSM1296104 3 0.3074 0.82671 0.000 0.152 0.848 0.000
#> GSM1296079 1 0.0000 0.95164 1.000 0.000 0.000 0.000
#> GSM1296108 2 0.2216 0.84833 0.000 0.908 0.000 0.092
#> GSM1296110 2 0.3074 0.85118 0.000 0.848 0.000 0.152
#> GSM1296081 1 0.0000 0.95164 1.000 0.000 0.000 0.000
#> GSM1296091 1 0.3813 0.84359 0.828 0.148 0.024 0.000
#> GSM1296075 2 0.4941 -0.00998 0.436 0.564 0.000 0.000
#> GSM1296112 2 0.2814 0.85349 0.000 0.868 0.000 0.132
#> GSM1296100 1 0.2921 0.86650 0.860 0.140 0.000 0.000
#> GSM1296087 1 0.0000 0.95164 1.000 0.000 0.000 0.000
#> GSM1296118 2 0.0000 0.81908 0.000 1.000 0.000 0.000
#> GSM1296114 2 0.3074 0.85118 0.000 0.848 0.000 0.152
#> GSM1296097 3 0.3074 0.82671 0.000 0.152 0.848 0.000
#> GSM1296106 2 0.0469 0.81344 0.000 0.988 0.012 0.000
#> GSM1296102 1 0.3074 0.85709 0.848 0.152 0.000 0.000
#> GSM1296122 2 0.0000 0.81908 0.000 1.000 0.000 0.000
#> GSM1296089 1 0.3074 0.85709 0.848 0.152 0.000 0.000
#> GSM1296083 1 0.0000 0.95164 1.000 0.000 0.000 0.000
#> GSM1296116 2 0.3266 0.84645 0.000 0.832 0.000 0.168
#> GSM1296085 1 0.0000 0.95164 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
#> GSM1296094 3 0.3707 0.841 0.000 0.000 0.716 0.284 0.000
#> GSM1296119 5 0.0000 0.903 0.000 0.000 0.000 0.000 1.000
#> GSM1296076 4 0.1965 0.772 0.000 0.000 0.000 0.904 0.096
#> GSM1296092 4 0.0794 0.768 0.000 0.000 0.028 0.972 0.000
#> GSM1296103 3 0.3707 0.841 0.000 0.000 0.716 0.284 0.000
#> GSM1296078 4 0.3636 0.492 0.000 0.000 0.000 0.728 0.272
#> GSM1296107 5 0.0000 0.903 0.000 0.000 0.000 0.000 1.000
#> GSM1296109 5 0.5256 0.395 0.000 0.000 0.116 0.212 0.672
#> GSM1296080 3 0.5029 0.773 0.060 0.000 0.648 0.292 0.000
#> GSM1296090 4 0.2388 0.776 0.000 0.000 0.028 0.900 0.072
#> GSM1296074 4 0.1041 0.779 0.000 0.000 0.004 0.964 0.032
#> GSM1296111 5 0.0000 0.903 0.000 0.000 0.000 0.000 1.000
#> GSM1296099 3 0.3707 0.841 0.000 0.000 0.716 0.284 0.000
#> GSM1296086 4 0.0794 0.768 0.000 0.000 0.028 0.972 0.000
#> GSM1296117 5 0.0000 0.903 0.000 0.000 0.000 0.000 1.000
#> GSM1296113 5 0.1608 0.850 0.000 0.072 0.000 0.000 0.928
#> GSM1296096 3 0.3707 0.841 0.000 0.000 0.716 0.284 0.000
#> GSM1296105 3 0.1661 0.622 0.000 0.036 0.940 0.024 0.000
#> GSM1296098 3 0.3707 0.841 0.000 0.000 0.716 0.284 0.000
#> GSM1296101 3 0.3707 0.841 0.000 0.000 0.716 0.284 0.000
#> GSM1296121 5 0.0000 0.903 0.000 0.000 0.000 0.000 1.000
#> GSM1296088 4 0.2605 0.602 0.000 0.000 0.148 0.852 0.000
#> GSM1296082 4 0.2233 0.761 0.000 0.000 0.004 0.892 0.104
#> GSM1296115 5 0.0000 0.903 0.000 0.000 0.000 0.000 1.000
#> GSM1296084 3 0.3816 0.829 0.000 0.000 0.696 0.304 0.000
#> GSM1296072 2 0.2605 0.854 0.000 0.852 0.000 0.000 0.148
#> GSM1296069 2 0.4045 0.545 0.000 0.644 0.000 0.000 0.356
#> GSM1296071 2 0.1341 0.896 0.000 0.944 0.000 0.000 0.056
#> GSM1296070 5 0.4060 0.294 0.000 0.360 0.000 0.000 0.640
#> GSM1296073 5 0.0000 0.903 0.000 0.000 0.000 0.000 1.000
#> GSM1296034 1 0.0955 0.889 0.968 0.000 0.004 0.028 0.000
#> GSM1296041 5 0.0000 0.903 0.000 0.000 0.000 0.000 1.000
#> GSM1296035 3 0.3707 0.841 0.000 0.000 0.716 0.284 0.000
#> GSM1296038 3 0.3707 0.841 0.000 0.000 0.716 0.284 0.000
#> GSM1296047 2 0.0510 0.883 0.000 0.984 0.016 0.000 0.000
#> GSM1296039 3 0.5558 0.662 0.000 0.000 0.620 0.268 0.112
#> GSM1296042 5 0.2424 0.784 0.000 0.132 0.000 0.000 0.868
#> GSM1296043 2 0.2516 0.860 0.000 0.860 0.000 0.000 0.140
#> GSM1296037 1 0.4479 0.676 0.700 0.036 0.264 0.000 0.000
#> GSM1296046 2 0.1851 0.886 0.000 0.912 0.000 0.000 0.088
#> GSM1296044 2 0.0963 0.900 0.000 0.964 0.000 0.000 0.036
#> GSM1296045 2 0.2690 0.847 0.000 0.844 0.000 0.000 0.156
#> GSM1296025 1 0.0794 0.890 0.972 0.000 0.000 0.028 0.000
#> GSM1296033 4 0.5992 0.139 0.416 0.000 0.112 0.472 0.000
#> GSM1296027 1 0.0000 0.892 1.000 0.000 0.000 0.000 0.000
#> GSM1296032 1 0.0000 0.892 1.000 0.000 0.000 0.000 0.000
#> GSM1296024 1 0.0794 0.890 0.972 0.000 0.000 0.028 0.000
#> GSM1296031 1 0.0771 0.884 0.976 0.004 0.020 0.000 0.000
#> GSM1296028 1 0.0290 0.890 0.992 0.000 0.008 0.000 0.000
#> GSM1296029 1 0.0000 0.892 1.000 0.000 0.000 0.000 0.000
#> GSM1296026 3 0.3837 0.826 0.000 0.000 0.692 0.308 0.000
#> GSM1296030 4 0.4508 0.488 0.332 0.000 0.020 0.648 0.000
#> GSM1296040 3 0.3707 0.841 0.000 0.000 0.716 0.284 0.000
#> GSM1296036 3 0.3707 0.841 0.000 0.000 0.716 0.284 0.000
#> GSM1296048 5 0.0000 0.903 0.000 0.000 0.000 0.000 1.000
#> GSM1296059 3 0.3707 0.841 0.000 0.000 0.716 0.284 0.000
#> GSM1296066 5 0.0703 0.886 0.000 0.024 0.000 0.000 0.976
#> GSM1296060 3 0.3707 0.841 0.000 0.000 0.716 0.284 0.000
#> GSM1296063 5 0.0000 0.903 0.000 0.000 0.000 0.000 1.000
#> GSM1296064 5 0.4072 0.675 0.000 0.000 0.100 0.108 0.792
#> GSM1296067 2 0.1197 0.869 0.000 0.952 0.048 0.000 0.000
#> GSM1296062 3 0.3707 0.841 0.000 0.000 0.716 0.284 0.000
#> GSM1296068 2 0.0963 0.900 0.000 0.964 0.000 0.000 0.036
#> GSM1296050 1 0.1750 0.877 0.936 0.000 0.036 0.028 0.000
#> GSM1296057 3 0.1251 0.589 0.008 0.036 0.956 0.000 0.000
#> GSM1296052 1 0.0000 0.892 1.000 0.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.892 1.000 0.000 0.000 0.000 0.000
#> GSM1296049 1 0.0794 0.890 0.972 0.000 0.000 0.028 0.000
#> GSM1296055 3 0.4306 -0.374 0.000 0.492 0.508 0.000 0.000
#> GSM1296053 1 0.0000 0.892 1.000 0.000 0.000 0.000 0.000
#> GSM1296058 3 0.2894 0.672 0.004 0.036 0.876 0.084 0.000
#> GSM1296051 4 0.0794 0.768 0.000 0.000 0.028 0.972 0.000
#> GSM1296056 3 0.4060 0.768 0.000 0.000 0.640 0.360 0.000
#> GSM1296065 3 0.1124 0.595 0.000 0.036 0.960 0.000 0.004
#> GSM1296061 3 0.3707 0.841 0.000 0.000 0.716 0.284 0.000
#> GSM1296095 3 0.3579 0.818 0.000 0.004 0.756 0.240 0.000
#> GSM1296120 2 0.0000 0.888 0.000 1.000 0.000 0.000 0.000
#> GSM1296077 1 0.0794 0.890 0.972 0.000 0.000 0.028 0.000
#> GSM1296093 1 0.0794 0.890 0.972 0.000 0.000 0.028 0.000
#> GSM1296104 3 0.2221 0.650 0.000 0.036 0.912 0.052 0.000
#> GSM1296079 1 0.0794 0.890 0.972 0.000 0.000 0.028 0.000
#> GSM1296108 2 0.0880 0.900 0.000 0.968 0.000 0.000 0.032
#> GSM1296110 2 0.0963 0.900 0.000 0.964 0.000 0.000 0.036
#> GSM1296081 1 0.0794 0.890 0.972 0.000 0.000 0.028 0.000
#> GSM1296091 1 0.5873 0.591 0.624 0.036 0.276 0.064 0.000
#> GSM1296075 1 0.6760 0.179 0.400 0.316 0.284 0.000 0.000
#> GSM1296112 2 0.0963 0.900 0.000 0.964 0.000 0.000 0.036
#> GSM1296100 1 0.4455 0.678 0.704 0.036 0.260 0.000 0.000
#> GSM1296087 1 0.0000 0.892 1.000 0.000 0.000 0.000 0.000
#> GSM1296118 2 0.0000 0.888 0.000 1.000 0.000 0.000 0.000
#> GSM1296114 2 0.0963 0.900 0.000 0.964 0.000 0.000 0.036
#> GSM1296097 3 0.2221 0.650 0.000 0.036 0.912 0.052 0.000
#> GSM1296106 2 0.3949 0.610 0.000 0.668 0.332 0.000 0.000
#> GSM1296102 1 0.4637 0.647 0.672 0.036 0.292 0.000 0.000
#> GSM1296122 2 0.3534 0.684 0.000 0.744 0.256 0.000 0.000
#> GSM1296089 1 0.4503 0.671 0.696 0.036 0.268 0.000 0.000
#> GSM1296083 1 0.0794 0.890 0.972 0.000 0.000 0.028 0.000
#> GSM1296116 2 0.2424 0.865 0.000 0.868 0.000 0.000 0.132
#> GSM1296085 1 0.0000 0.892 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
#> GSM1296094 3 0.2340 0.7950 0.000 0.000 0.852 0.000 0.000 0.148
#> GSM1296119 5 0.0000 0.9024 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296076 4 0.0260 0.8458 0.000 0.000 0.008 0.992 0.000 0.000
#> GSM1296092 4 0.0260 0.8458 0.000 0.000 0.008 0.992 0.000 0.000
#> GSM1296103 3 0.2300 0.7964 0.000 0.000 0.856 0.000 0.000 0.144
#> GSM1296078 4 0.0458 0.8334 0.000 0.000 0.000 0.984 0.016 0.000
#> GSM1296107 5 0.0000 0.9024 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296109 5 0.3737 0.3467 0.000 0.000 0.392 0.000 0.608 0.000
#> GSM1296080 3 0.3133 0.7626 0.008 0.000 0.804 0.008 0.000 0.180
#> GSM1296090 4 0.0260 0.8458 0.000 0.000 0.008 0.992 0.000 0.000
#> GSM1296074 4 0.0260 0.8458 0.000 0.000 0.008 0.992 0.000 0.000
#> GSM1296111 5 0.0000 0.9024 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296099 3 0.0000 0.8303 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296086 4 0.0260 0.8458 0.000 0.000 0.008 0.992 0.000 0.000
#> GSM1296117 5 0.0000 0.9024 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296113 5 0.2135 0.8023 0.000 0.128 0.000 0.000 0.872 0.000
#> GSM1296096 3 0.0000 0.8303 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296105 3 0.3997 -0.0399 0.000 0.004 0.508 0.000 0.000 0.488
#> GSM1296098 3 0.2340 0.7950 0.000 0.000 0.852 0.000 0.000 0.148
#> GSM1296101 3 0.0000 0.8303 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296121 5 0.0000 0.9024 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296088 4 0.3857 0.1002 0.000 0.000 0.468 0.532 0.000 0.000
#> GSM1296082 4 0.0260 0.8458 0.000 0.000 0.008 0.992 0.000 0.000
#> GSM1296115 5 0.0000 0.9024 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296084 3 0.1219 0.8137 0.000 0.000 0.948 0.048 0.000 0.004
#> GSM1296072 2 0.2597 0.8090 0.000 0.824 0.000 0.000 0.176 0.000
#> GSM1296069 2 0.3756 0.4444 0.000 0.600 0.000 0.000 0.400 0.000
#> GSM1296071 2 0.0632 0.8748 0.000 0.976 0.000 0.000 0.024 0.000
#> GSM1296070 5 0.3371 0.4747 0.000 0.292 0.000 0.000 0.708 0.000
#> GSM1296073 5 0.0000 0.9024 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296034 1 0.0937 0.7354 0.960 0.000 0.000 0.000 0.000 0.040
#> GSM1296041 5 0.0000 0.9024 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296035 3 0.0000 0.8303 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296038 3 0.0000 0.8303 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296047 2 0.1007 0.8562 0.000 0.956 0.000 0.000 0.000 0.044
#> GSM1296039 3 0.3862 0.6700 0.000 0.000 0.772 0.132 0.096 0.000
#> GSM1296042 5 0.2003 0.7932 0.000 0.116 0.000 0.000 0.884 0.000
#> GSM1296043 2 0.2730 0.7952 0.000 0.808 0.000 0.000 0.192 0.000
#> GSM1296037 6 0.2902 0.5134 0.196 0.004 0.000 0.000 0.000 0.800
#> GSM1296046 2 0.1501 0.8603 0.000 0.924 0.000 0.000 0.076 0.000
#> GSM1296044 2 0.0146 0.8779 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1296045 2 0.2793 0.7869 0.000 0.800 0.000 0.000 0.200 0.000
#> GSM1296025 1 0.0000 0.7716 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296033 4 0.5963 0.2430 0.072 0.000 0.056 0.488 0.000 0.384
#> GSM1296027 1 0.3547 0.7499 0.668 0.000 0.000 0.000 0.000 0.332
#> GSM1296032 1 0.3547 0.7499 0.668 0.000 0.000 0.000 0.000 0.332
#> GSM1296024 1 0.0000 0.7716 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296031 1 0.3717 0.6806 0.616 0.000 0.000 0.000 0.000 0.384
#> GSM1296028 1 0.3547 0.7499 0.668 0.000 0.000 0.000 0.000 0.332
#> GSM1296029 1 0.3547 0.7499 0.668 0.000 0.000 0.000 0.000 0.332
#> GSM1296026 3 0.0790 0.8221 0.000 0.000 0.968 0.032 0.000 0.000
#> GSM1296030 4 0.5343 0.4657 0.036 0.000 0.068 0.616 0.000 0.280
#> GSM1296040 3 0.0000 0.8303 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296036 3 0.2340 0.7950 0.000 0.000 0.852 0.000 0.000 0.148
#> GSM1296048 5 0.0000 0.9024 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296059 3 0.0000 0.8303 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296066 5 0.0000 0.9024 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296060 3 0.0000 0.8303 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296063 5 0.0000 0.9024 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296064 5 0.4403 0.6109 0.000 0.000 0.196 0.096 0.708 0.000
#> GSM1296067 2 0.2003 0.8005 0.000 0.884 0.000 0.000 0.000 0.116
#> GSM1296062 3 0.2378 0.7943 0.000 0.000 0.848 0.000 0.000 0.152
#> GSM1296068 2 0.0146 0.8779 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1296050 1 0.0632 0.7555 0.976 0.000 0.000 0.000 0.000 0.024
#> GSM1296057 6 0.4217 0.0368 0.000 0.004 0.464 0.008 0.000 0.524
#> GSM1296052 1 0.3547 0.7499 0.668 0.000 0.000 0.000 0.000 0.332
#> GSM1296054 1 0.3547 0.7499 0.668 0.000 0.000 0.000 0.000 0.332
#> GSM1296049 1 0.0000 0.7716 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296055 6 0.5853 0.3443 0.000 0.288 0.184 0.008 0.000 0.520
#> GSM1296053 1 0.3547 0.7499 0.668 0.000 0.000 0.000 0.000 0.332
#> GSM1296058 3 0.3693 0.4955 0.000 0.004 0.708 0.008 0.000 0.280
#> GSM1296051 4 0.0260 0.8458 0.000 0.000 0.008 0.992 0.000 0.000
#> GSM1296056 3 0.3101 0.6328 0.000 0.000 0.756 0.244 0.000 0.000
#> GSM1296065 6 0.4220 0.0236 0.000 0.004 0.468 0.008 0.000 0.520
#> GSM1296061 3 0.2340 0.7950 0.000 0.000 0.852 0.000 0.000 0.148
#> GSM1296095 3 0.1049 0.8157 0.000 0.000 0.960 0.008 0.000 0.032
#> GSM1296120 2 0.0000 0.8757 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296077 1 0.0000 0.7716 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296093 1 0.0865 0.7732 0.964 0.000 0.000 0.000 0.000 0.036
#> GSM1296104 3 0.4144 0.2111 0.000 0.004 0.580 0.008 0.000 0.408
#> GSM1296079 1 0.0000 0.7716 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296108 2 0.0146 0.8779 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1296110 2 0.0146 0.8779 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1296081 1 0.0000 0.7716 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296091 6 0.2454 0.5656 0.160 0.000 0.000 0.000 0.000 0.840
#> GSM1296075 6 0.3095 0.5890 0.036 0.116 0.000 0.008 0.000 0.840
#> GSM1296112 2 0.0146 0.8779 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1296100 6 0.2738 0.5500 0.176 0.004 0.000 0.000 0.000 0.820
#> GSM1296087 1 0.3547 0.7499 0.668 0.000 0.000 0.000 0.000 0.332
#> GSM1296118 2 0.0000 0.8757 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296114 2 0.0146 0.8779 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1296097 3 0.4151 0.1965 0.000 0.004 0.576 0.008 0.000 0.412
#> GSM1296106 6 0.5051 0.1029 0.000 0.416 0.056 0.008 0.000 0.520
#> GSM1296102 6 0.2876 0.5795 0.148 0.004 0.004 0.008 0.000 0.836
#> GSM1296122 2 0.3838 0.1509 0.000 0.552 0.000 0.000 0.000 0.448
#> GSM1296089 6 0.2632 0.5650 0.164 0.004 0.000 0.000 0.000 0.832
#> GSM1296083 1 0.0000 0.7716 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296116 2 0.2454 0.8186 0.000 0.840 0.000 0.000 0.160 0.000
#> GSM1296085 1 0.3547 0.7499 0.668 0.000 0.000 0.000 0.000 0.332
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# 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 tissue(p) time(p) agent(p) dose(p) individual(p) k
#> MAD:pam 99 9.25e-01 0.0397 0.6523 8.96e-06 1.16e-07 2
#> MAD:pam 95 3.04e-03 0.0175 0.0234 1.40e-07 4.34e-08 3
#> MAD:pam 95 3.28e-04 0.1725 0.0157 6.47e-08 4.03e-05 4
#> MAD:pam 92 5.65e-04 0.1533 0.0210 1.13e-07 5.35e-05 5
#> MAD:pam 84 4.81e-05 0.3129 0.0115 1.77e-09 6.83e-05 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 45638 rows and 99 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
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 0.440 0.410 0.778 0.3964 0.499 0.499
#> 3 3 0.796 0.886 0.937 0.6588 0.673 0.434
#> 4 4 0.629 0.618 0.752 0.0730 0.806 0.514
#> 5 5 0.788 0.795 0.872 0.0976 0.801 0.422
#> 6 6 0.793 0.791 0.885 0.0609 0.871 0.498
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
#> GSM1296094 2 1.000 -0.1077 0.488 0.512
#> GSM1296119 2 0.000 0.6612 0.000 1.000
#> GSM1296076 1 0.983 0.4284 0.576 0.424
#> GSM1296092 1 0.983 0.4284 0.576 0.424
#> GSM1296103 2 1.000 -0.1077 0.488 0.512
#> GSM1296078 1 0.983 0.4284 0.576 0.424
#> GSM1296107 2 0.000 0.6612 0.000 1.000
#> GSM1296109 2 1.000 -0.1077 0.488 0.512
#> GSM1296080 1 0.991 0.3663 0.556 0.444
#> GSM1296090 1 0.983 0.4284 0.576 0.424
#> GSM1296074 1 0.983 0.4284 0.576 0.424
#> GSM1296111 2 0.000 0.6612 0.000 1.000
#> GSM1296099 2 1.000 -0.1077 0.488 0.512
#> GSM1296086 1 0.983 0.4284 0.576 0.424
#> GSM1296117 2 0.000 0.6612 0.000 1.000
#> GSM1296113 2 0.000 0.6612 0.000 1.000
#> GSM1296096 2 1.000 -0.1077 0.488 0.512
#> GSM1296105 1 0.998 0.2426 0.524 0.476
#> GSM1296098 2 1.000 -0.1077 0.488 0.512
#> GSM1296101 2 1.000 -0.1077 0.488 0.512
#> GSM1296121 2 0.000 0.6612 0.000 1.000
#> GSM1296088 1 0.983 0.4284 0.576 0.424
#> GSM1296082 1 0.983 0.4284 0.576 0.424
#> GSM1296115 2 0.000 0.6612 0.000 1.000
#> GSM1296084 1 0.983 0.4284 0.576 0.424
#> GSM1296072 2 0.000 0.6612 0.000 1.000
#> GSM1296069 2 0.000 0.6612 0.000 1.000
#> GSM1296071 2 0.000 0.6612 0.000 1.000
#> GSM1296070 2 0.000 0.6612 0.000 1.000
#> GSM1296073 2 0.000 0.6612 0.000 1.000
#> GSM1296034 2 1.000 -0.1077 0.488 0.512
#> GSM1296041 2 0.000 0.6612 0.000 1.000
#> GSM1296035 2 1.000 -0.1077 0.488 0.512
#> GSM1296038 2 1.000 -0.1077 0.488 0.512
#> GSM1296047 2 0.000 0.6612 0.000 1.000
#> GSM1296039 2 1.000 -0.1452 0.496 0.504
#> GSM1296042 2 0.000 0.6612 0.000 1.000
#> GSM1296043 2 0.000 0.6612 0.000 1.000
#> GSM1296037 1 0.000 0.6033 1.000 0.000
#> GSM1296046 2 0.000 0.6612 0.000 1.000
#> GSM1296044 2 0.000 0.6612 0.000 1.000
#> GSM1296045 2 0.000 0.6612 0.000 1.000
#> GSM1296025 1 0.000 0.6033 1.000 0.000
#> GSM1296033 1 0.983 0.4284 0.576 0.424
#> GSM1296027 1 0.000 0.6033 1.000 0.000
#> GSM1296032 1 0.000 0.6033 1.000 0.000
#> GSM1296024 1 0.000 0.6033 1.000 0.000
#> GSM1296031 1 0.000 0.6033 1.000 0.000
#> GSM1296028 1 0.295 0.5927 0.948 0.052
#> GSM1296029 1 0.000 0.6033 1.000 0.000
#> GSM1296026 1 0.983 0.4284 0.576 0.424
#> GSM1296030 1 0.983 0.4284 0.576 0.424
#> GSM1296040 2 1.000 -0.1077 0.488 0.512
#> GSM1296036 2 1.000 -0.1077 0.488 0.512
#> GSM1296048 2 0.000 0.6612 0.000 1.000
#> GSM1296059 2 1.000 -0.1077 0.488 0.512
#> GSM1296066 2 0.000 0.6612 0.000 1.000
#> GSM1296060 2 1.000 -0.1077 0.488 0.512
#> GSM1296063 2 0.788 0.4374 0.236 0.764
#> GSM1296064 2 0.999 -0.0985 0.484 0.516
#> GSM1296067 2 0.900 0.3015 0.316 0.684
#> GSM1296062 2 1.000 -0.1077 0.488 0.512
#> GSM1296068 2 0.000 0.6612 0.000 1.000
#> GSM1296050 1 0.973 0.4402 0.596 0.404
#> GSM1296057 1 0.983 0.4284 0.576 0.424
#> GSM1296052 1 0.000 0.6033 1.000 0.000
#> GSM1296054 1 0.000 0.6033 1.000 0.000
#> GSM1296049 1 0.000 0.6033 1.000 0.000
#> GSM1296055 1 0.997 0.3068 0.532 0.468
#> GSM1296053 1 0.000 0.6033 1.000 0.000
#> GSM1296058 1 0.983 0.4284 0.576 0.424
#> GSM1296051 1 0.983 0.4284 0.576 0.424
#> GSM1296056 1 0.983 0.4284 0.576 0.424
#> GSM1296065 2 0.978 0.0653 0.412 0.588
#> GSM1296061 2 1.000 -0.1077 0.488 0.512
#> GSM1296095 2 0.994 -0.0290 0.456 0.544
#> GSM1296120 2 0.000 0.6612 0.000 1.000
#> GSM1296077 1 0.000 0.6033 1.000 0.000
#> GSM1296093 1 0.000 0.6033 1.000 0.000
#> GSM1296104 1 1.000 0.2292 0.508 0.492
#> GSM1296079 1 0.000 0.6033 1.000 0.000
#> GSM1296108 2 0.000 0.6612 0.000 1.000
#> GSM1296110 2 0.000 0.6612 0.000 1.000
#> GSM1296081 1 0.000 0.6033 1.000 0.000
#> GSM1296091 1 0.983 0.4284 0.576 0.424
#> GSM1296075 1 0.983 0.4284 0.576 0.424
#> GSM1296112 2 0.000 0.6612 0.000 1.000
#> GSM1296100 1 0.000 0.6033 1.000 0.000
#> GSM1296087 1 0.929 0.4682 0.656 0.344
#> GSM1296118 2 0.000 0.6612 0.000 1.000
#> GSM1296114 2 0.000 0.6612 0.000 1.000
#> GSM1296097 1 0.995 0.3160 0.540 0.460
#> GSM1296106 2 0.996 -0.0508 0.464 0.536
#> GSM1296102 2 1.000 -0.1395 0.496 0.504
#> GSM1296122 2 0.000 0.6612 0.000 1.000
#> GSM1296089 1 0.402 0.5832 0.920 0.080
#> GSM1296083 1 0.000 0.6033 1.000 0.000
#> GSM1296116 2 0.000 0.6612 0.000 1.000
#> GSM1296085 1 0.000 0.6033 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1296094 3 0.0000 0.999 0.000 0.000 1.000
#> GSM1296119 2 0.0747 0.920 0.000 0.984 0.016
#> GSM1296076 3 0.0000 0.999 0.000 0.000 1.000
#> GSM1296092 3 0.0000 0.999 0.000 0.000 1.000
#> GSM1296103 3 0.0000 0.999 0.000 0.000 1.000
#> GSM1296078 3 0.0000 0.999 0.000 0.000 1.000
#> GSM1296107 2 0.0747 0.920 0.000 0.984 0.016
#> GSM1296109 3 0.0747 0.980 0.000 0.016 0.984
#> GSM1296080 3 0.0000 0.999 0.000 0.000 1.000
#> GSM1296090 3 0.0000 0.999 0.000 0.000 1.000
#> GSM1296074 3 0.0000 0.999 0.000 0.000 1.000
#> GSM1296111 2 0.0747 0.920 0.000 0.984 0.016
#> GSM1296099 3 0.0000 0.999 0.000 0.000 1.000
#> GSM1296086 3 0.0000 0.999 0.000 0.000 1.000
#> GSM1296117 2 0.0747 0.920 0.000 0.984 0.016
#> GSM1296113 2 0.0747 0.920 0.000 0.984 0.016
#> GSM1296096 3 0.0000 0.999 0.000 0.000 1.000
#> GSM1296105 1 0.5859 0.623 0.656 0.000 0.344
#> GSM1296098 3 0.0000 0.999 0.000 0.000 1.000
#> GSM1296101 3 0.0000 0.999 0.000 0.000 1.000
#> GSM1296121 2 0.0892 0.919 0.000 0.980 0.020
#> GSM1296088 3 0.0000 0.999 0.000 0.000 1.000
#> GSM1296082 3 0.0000 0.999 0.000 0.000 1.000
#> GSM1296115 2 0.0747 0.920 0.000 0.984 0.016
#> GSM1296084 3 0.0000 0.999 0.000 0.000 1.000
#> GSM1296072 2 0.1031 0.917 0.000 0.976 0.024
#> GSM1296069 2 0.0747 0.920 0.000 0.984 0.016
#> GSM1296071 2 0.0000 0.916 0.000 1.000 0.000
#> GSM1296070 2 0.0747 0.920 0.000 0.984 0.016
#> GSM1296073 2 0.1031 0.917 0.000 0.976 0.024
#> GSM1296034 1 0.5560 0.694 0.700 0.000 0.300
#> GSM1296041 2 0.0747 0.920 0.000 0.984 0.016
#> GSM1296035 3 0.0000 0.999 0.000 0.000 1.000
#> GSM1296038 2 0.5859 0.562 0.000 0.656 0.344
#> GSM1296047 2 0.0237 0.917 0.000 0.996 0.004
#> GSM1296039 3 0.0000 0.999 0.000 0.000 1.000
#> GSM1296042 2 0.0747 0.920 0.000 0.984 0.016
#> GSM1296043 2 0.0000 0.916 0.000 1.000 0.000
#> GSM1296037 1 0.0000 0.879 1.000 0.000 0.000
#> GSM1296046 2 0.0000 0.916 0.000 1.000 0.000
#> GSM1296044 2 0.0000 0.916 0.000 1.000 0.000
#> GSM1296045 2 0.0592 0.919 0.000 0.988 0.012
#> GSM1296025 1 0.0000 0.879 1.000 0.000 0.000
#> GSM1296033 1 0.5327 0.729 0.728 0.000 0.272
#> GSM1296027 1 0.0000 0.879 1.000 0.000 0.000
#> GSM1296032 1 0.0000 0.879 1.000 0.000 0.000
#> GSM1296024 1 0.0000 0.879 1.000 0.000 0.000
#> GSM1296031 1 0.0000 0.879 1.000 0.000 0.000
#> GSM1296028 1 0.0747 0.875 0.984 0.000 0.016
#> GSM1296029 1 0.0892 0.874 0.980 0.000 0.020
#> GSM1296026 3 0.0000 0.999 0.000 0.000 1.000
#> GSM1296030 3 0.0000 0.999 0.000 0.000 1.000
#> GSM1296040 3 0.0000 0.999 0.000 0.000 1.000
#> GSM1296036 3 0.0000 0.999 0.000 0.000 1.000
#> GSM1296048 2 0.1031 0.917 0.000 0.976 0.024
#> GSM1296059 3 0.0000 0.999 0.000 0.000 1.000
#> GSM1296066 2 0.0747 0.920 0.000 0.984 0.016
#> GSM1296060 3 0.0000 0.999 0.000 0.000 1.000
#> GSM1296063 2 0.6129 0.590 0.008 0.668 0.324
#> GSM1296064 3 0.0000 0.999 0.000 0.000 1.000
#> GSM1296067 2 0.5285 0.709 0.004 0.752 0.244
#> GSM1296062 3 0.0000 0.999 0.000 0.000 1.000
#> GSM1296068 2 0.0000 0.916 0.000 1.000 0.000
#> GSM1296050 1 0.5327 0.729 0.728 0.000 0.272
#> GSM1296057 1 0.5327 0.729 0.728 0.000 0.272
#> GSM1296052 1 0.0000 0.879 1.000 0.000 0.000
#> GSM1296054 1 0.0000 0.879 1.000 0.000 0.000
#> GSM1296049 1 0.0000 0.879 1.000 0.000 0.000
#> GSM1296055 1 0.5728 0.723 0.720 0.008 0.272
#> GSM1296053 1 0.0000 0.879 1.000 0.000 0.000
#> GSM1296058 1 0.5327 0.729 0.728 0.000 0.272
#> GSM1296051 3 0.0000 0.999 0.000 0.000 1.000
#> GSM1296056 3 0.0000 0.999 0.000 0.000 1.000
#> GSM1296065 2 0.7465 0.609 0.072 0.656 0.272
#> GSM1296061 3 0.0000 0.999 0.000 0.000 1.000
#> GSM1296095 2 0.5327 0.673 0.000 0.728 0.272
#> GSM1296120 2 0.0237 0.917 0.000 0.996 0.004
#> GSM1296077 1 0.0000 0.879 1.000 0.000 0.000
#> GSM1296093 1 0.0000 0.879 1.000 0.000 0.000
#> GSM1296104 2 0.7465 0.609 0.072 0.656 0.272
#> GSM1296079 1 0.0000 0.879 1.000 0.000 0.000
#> GSM1296108 2 0.0000 0.916 0.000 1.000 0.000
#> GSM1296110 2 0.0000 0.916 0.000 1.000 0.000
#> GSM1296081 1 0.0000 0.879 1.000 0.000 0.000
#> GSM1296091 1 0.5327 0.729 0.728 0.000 0.272
#> GSM1296075 1 0.5327 0.729 0.728 0.000 0.272
#> GSM1296112 2 0.0000 0.916 0.000 1.000 0.000
#> GSM1296100 1 0.0000 0.879 1.000 0.000 0.000
#> GSM1296087 1 0.3412 0.827 0.876 0.000 0.124
#> GSM1296118 2 0.0000 0.916 0.000 1.000 0.000
#> GSM1296114 2 0.0000 0.916 0.000 1.000 0.000
#> GSM1296097 2 0.7697 0.594 0.084 0.644 0.272
#> GSM1296106 1 0.8817 0.549 0.568 0.160 0.272
#> GSM1296102 1 0.5291 0.732 0.732 0.000 0.268
#> GSM1296122 2 0.4883 0.744 0.004 0.788 0.208
#> GSM1296089 1 0.0747 0.876 0.984 0.000 0.016
#> GSM1296083 1 0.0000 0.879 1.000 0.000 0.000
#> GSM1296116 2 0.0000 0.916 0.000 1.000 0.000
#> GSM1296085 1 0.0000 0.879 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1296094 3 0.4866 0.93776 0.000 0.000 0.596 0.404
#> GSM1296119 2 0.1302 0.76181 0.000 0.956 0.000 0.044
#> GSM1296076 4 0.1109 0.45232 0.004 0.000 0.028 0.968
#> GSM1296092 4 0.1109 0.45232 0.004 0.000 0.028 0.968
#> GSM1296103 3 0.4877 0.94133 0.000 0.000 0.592 0.408
#> GSM1296078 4 0.1109 0.45232 0.004 0.000 0.028 0.968
#> GSM1296107 2 0.1118 0.76490 0.000 0.964 0.000 0.036
#> GSM1296109 4 0.5143 -0.63563 0.000 0.004 0.456 0.540
#> GSM1296080 3 0.5000 0.73810 0.000 0.000 0.500 0.500
#> GSM1296090 4 0.1109 0.45232 0.004 0.000 0.028 0.968
#> GSM1296074 4 0.1109 0.45232 0.004 0.000 0.028 0.968
#> GSM1296111 2 0.1118 0.76490 0.000 0.964 0.000 0.036
#> GSM1296099 4 0.4977 -0.64093 0.000 0.000 0.460 0.540
#> GSM1296086 4 0.0657 0.44778 0.004 0.000 0.012 0.984
#> GSM1296117 2 0.1118 0.76490 0.000 0.964 0.000 0.036
#> GSM1296113 2 0.1118 0.76490 0.000 0.964 0.000 0.036
#> GSM1296096 4 0.4985 -0.66213 0.000 0.000 0.468 0.532
#> GSM1296105 4 0.7188 0.18778 0.308 0.000 0.164 0.528
#> GSM1296098 3 0.4866 0.93776 0.000 0.000 0.596 0.404
#> GSM1296101 3 0.4888 0.94195 0.000 0.000 0.588 0.412
#> GSM1296121 2 0.1302 0.76181 0.000 0.956 0.000 0.044
#> GSM1296088 4 0.4509 -0.09951 0.004 0.000 0.288 0.708
#> GSM1296082 4 0.1109 0.45232 0.004 0.000 0.028 0.968
#> GSM1296115 2 0.1118 0.76490 0.000 0.964 0.000 0.036
#> GSM1296084 4 0.6142 0.18463 0.140 0.000 0.184 0.676
#> GSM1296072 2 0.5923 0.81045 0.000 0.580 0.376 0.044
#> GSM1296069 2 0.5062 0.81493 0.000 0.680 0.300 0.020
#> GSM1296071 2 0.5085 0.81910 0.000 0.616 0.376 0.008
#> GSM1296070 2 0.1305 0.76594 0.000 0.960 0.004 0.036
#> GSM1296073 2 0.1743 0.75134 0.000 0.940 0.004 0.056
#> GSM1296034 4 0.7927 -0.00538 0.324 0.004 0.252 0.420
#> GSM1296041 2 0.1118 0.76490 0.000 0.964 0.000 0.036
#> GSM1296035 3 0.4996 0.78553 0.000 0.000 0.516 0.484
#> GSM1296038 4 0.4584 0.34628 0.004 0.300 0.000 0.696
#> GSM1296047 2 0.5085 0.81910 0.000 0.616 0.376 0.008
#> GSM1296039 4 0.3760 0.35112 0.004 0.012 0.156 0.828
#> GSM1296042 2 0.1118 0.76490 0.000 0.964 0.000 0.036
#> GSM1296043 2 0.4776 0.82051 0.000 0.624 0.376 0.000
#> GSM1296037 1 0.0000 0.92452 1.000 0.000 0.000 0.000
#> GSM1296046 2 0.4776 0.82051 0.000 0.624 0.376 0.000
#> GSM1296044 2 0.4776 0.82051 0.000 0.624 0.376 0.000
#> GSM1296045 2 0.5313 0.81905 0.000 0.608 0.376 0.016
#> GSM1296025 1 0.0000 0.92452 1.000 0.000 0.000 0.000
#> GSM1296033 4 0.5097 0.32258 0.428 0.004 0.000 0.568
#> GSM1296027 1 0.0188 0.92322 0.996 0.000 0.000 0.004
#> GSM1296032 1 0.0336 0.92160 0.992 0.000 0.000 0.008
#> GSM1296024 1 0.0000 0.92452 1.000 0.000 0.000 0.000
#> GSM1296031 1 0.0707 0.91555 0.980 0.000 0.000 0.020
#> GSM1296028 1 0.1716 0.88098 0.936 0.000 0.000 0.064
#> GSM1296029 1 0.1637 0.88091 0.940 0.000 0.000 0.060
#> GSM1296026 4 0.5527 0.23533 0.104 0.000 0.168 0.728
#> GSM1296030 4 0.6065 0.20061 0.140 0.000 0.176 0.684
#> GSM1296040 3 0.4888 0.94195 0.000 0.000 0.588 0.412
#> GSM1296036 3 0.4866 0.93776 0.000 0.000 0.596 0.404
#> GSM1296048 2 0.1302 0.76181 0.000 0.956 0.000 0.044
#> GSM1296059 3 0.4898 0.93732 0.000 0.000 0.584 0.416
#> GSM1296066 2 0.1118 0.76490 0.000 0.964 0.000 0.036
#> GSM1296060 4 0.5060 -0.48600 0.004 0.000 0.412 0.584
#> GSM1296063 4 0.2831 0.43850 0.004 0.120 0.000 0.876
#> GSM1296064 4 0.3067 0.45054 0.004 0.084 0.024 0.888
#> GSM1296067 2 0.7563 0.66132 0.004 0.452 0.376 0.168
#> GSM1296062 3 0.4916 0.92873 0.000 0.000 0.576 0.424
#> GSM1296068 2 0.4776 0.82051 0.000 0.624 0.376 0.000
#> GSM1296050 1 0.4819 0.32738 0.652 0.004 0.000 0.344
#> GSM1296057 4 0.5126 0.31457 0.444 0.004 0.000 0.552
#> GSM1296052 1 0.0000 0.92452 1.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.92452 1.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.92452 1.000 0.000 0.000 0.000
#> GSM1296055 4 0.5618 0.40195 0.288 0.028 0.012 0.672
#> GSM1296053 1 0.0000 0.92452 1.000 0.000 0.000 0.000
#> GSM1296058 4 0.5126 0.31457 0.444 0.004 0.000 0.552
#> GSM1296051 4 0.0376 0.45172 0.004 0.004 0.000 0.992
#> GSM1296056 4 0.3402 0.33454 0.004 0.000 0.164 0.832
#> GSM1296065 4 0.4844 0.35055 0.012 0.300 0.000 0.688
#> GSM1296061 3 0.4888 0.93921 0.000 0.000 0.588 0.412
#> GSM1296095 4 0.5105 0.22329 0.004 0.432 0.000 0.564
#> GSM1296120 2 0.5085 0.81910 0.000 0.616 0.376 0.008
#> GSM1296077 1 0.0000 0.92452 1.000 0.000 0.000 0.000
#> GSM1296093 1 0.0000 0.92452 1.000 0.000 0.000 0.000
#> GSM1296104 4 0.4820 0.35331 0.012 0.296 0.000 0.692
#> GSM1296079 1 0.0000 0.92452 1.000 0.000 0.000 0.000
#> GSM1296108 2 0.4776 0.82051 0.000 0.624 0.376 0.000
#> GSM1296110 2 0.5085 0.81733 0.000 0.616 0.376 0.008
#> GSM1296081 1 0.0000 0.92452 1.000 0.000 0.000 0.000
#> GSM1296091 4 0.5119 0.31717 0.440 0.004 0.000 0.556
#> GSM1296075 4 0.4634 0.40133 0.280 0.004 0.004 0.712
#> GSM1296112 2 0.4776 0.82051 0.000 0.624 0.376 0.000
#> GSM1296100 1 0.0000 0.92452 1.000 0.000 0.000 0.000
#> GSM1296087 1 0.3494 0.73965 0.824 0.004 0.000 0.172
#> GSM1296118 2 0.5085 0.81910 0.000 0.616 0.376 0.008
#> GSM1296114 2 0.4776 0.82051 0.000 0.624 0.376 0.000
#> GSM1296097 4 0.4933 0.35370 0.016 0.296 0.000 0.688
#> GSM1296106 4 0.7056 0.40905 0.188 0.128 0.036 0.648
#> GSM1296102 1 0.5155 -0.18682 0.528 0.004 0.000 0.468
#> GSM1296122 2 0.6648 0.74680 0.000 0.536 0.372 0.092
#> GSM1296089 1 0.2530 0.82723 0.888 0.000 0.000 0.112
#> GSM1296083 1 0.0000 0.92452 1.000 0.000 0.000 0.000
#> GSM1296116 2 0.4776 0.82051 0.000 0.624 0.376 0.000
#> GSM1296085 1 0.0707 0.91525 0.980 0.000 0.000 0.020
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1296094 3 0.0000 0.887 0.000 0.000 1.000 0.000 0.000
#> GSM1296119 5 0.1544 0.884 0.000 0.068 0.000 0.000 0.932
#> GSM1296076 4 0.0324 0.809 0.000 0.000 0.004 0.992 0.004
#> GSM1296092 4 0.1041 0.814 0.000 0.000 0.032 0.964 0.004
#> GSM1296103 3 0.0000 0.887 0.000 0.000 1.000 0.000 0.000
#> GSM1296078 4 0.0324 0.809 0.000 0.000 0.004 0.992 0.004
#> GSM1296107 5 0.1544 0.884 0.000 0.068 0.000 0.000 0.932
#> GSM1296109 3 0.1908 0.870 0.000 0.000 0.908 0.092 0.000
#> GSM1296080 3 0.1908 0.870 0.000 0.000 0.908 0.092 0.000
#> GSM1296090 4 0.0324 0.809 0.000 0.000 0.004 0.992 0.004
#> GSM1296074 4 0.0324 0.809 0.000 0.000 0.004 0.992 0.004
#> GSM1296111 5 0.1544 0.884 0.000 0.068 0.000 0.000 0.932
#> GSM1296099 3 0.1908 0.870 0.000 0.000 0.908 0.092 0.000
#> GSM1296086 4 0.1121 0.814 0.000 0.000 0.044 0.956 0.000
#> GSM1296117 5 0.1544 0.884 0.000 0.068 0.000 0.000 0.932
#> GSM1296113 5 0.1544 0.884 0.000 0.068 0.000 0.000 0.932
#> GSM1296096 3 0.2329 0.844 0.000 0.000 0.876 0.124 0.000
#> GSM1296105 1 0.5615 0.474 0.584 0.000 0.320 0.096 0.000
#> GSM1296098 3 0.0000 0.887 0.000 0.000 1.000 0.000 0.000
#> GSM1296101 3 0.0000 0.887 0.000 0.000 1.000 0.000 0.000
#> GSM1296121 5 0.1544 0.884 0.000 0.068 0.000 0.000 0.932
#> GSM1296088 4 0.4114 0.558 0.000 0.000 0.376 0.624 0.000
#> GSM1296082 4 0.0324 0.809 0.000 0.000 0.004 0.992 0.004
#> GSM1296115 5 0.1544 0.884 0.000 0.068 0.000 0.000 0.932
#> GSM1296084 4 0.4003 0.692 0.000 0.000 0.288 0.704 0.008
#> GSM1296072 2 0.3809 0.602 0.000 0.736 0.000 0.008 0.256
#> GSM1296069 2 0.2966 0.769 0.000 0.816 0.000 0.000 0.184
#> GSM1296071 2 0.0162 0.967 0.000 0.996 0.000 0.000 0.004
#> GSM1296070 5 0.1671 0.878 0.000 0.076 0.000 0.000 0.924
#> GSM1296073 5 0.2046 0.876 0.000 0.068 0.000 0.016 0.916
#> GSM1296034 3 0.5192 0.515 0.244 0.000 0.664 0.092 0.000
#> GSM1296041 5 0.1544 0.884 0.000 0.068 0.000 0.000 0.932
#> GSM1296035 3 0.2424 0.836 0.000 0.000 0.868 0.132 0.000
#> GSM1296038 5 0.7236 0.404 0.000 0.068 0.220 0.184 0.528
#> GSM1296047 2 0.0162 0.962 0.000 0.996 0.000 0.004 0.000
#> GSM1296039 4 0.2848 0.803 0.000 0.000 0.156 0.840 0.004
#> GSM1296042 5 0.1544 0.884 0.000 0.068 0.000 0.000 0.932
#> GSM1296043 2 0.0162 0.967 0.000 0.996 0.000 0.000 0.004
#> GSM1296037 1 0.0000 0.823 1.000 0.000 0.000 0.000 0.000
#> GSM1296046 2 0.0162 0.967 0.000 0.996 0.000 0.000 0.004
#> GSM1296044 2 0.0162 0.967 0.000 0.996 0.000 0.000 0.004
#> GSM1296045 2 0.0162 0.967 0.000 0.996 0.000 0.000 0.004
#> GSM1296025 1 0.0000 0.823 1.000 0.000 0.000 0.000 0.000
#> GSM1296033 1 0.7653 0.369 0.472 0.004 0.208 0.248 0.068
#> GSM1296027 1 0.0000 0.823 1.000 0.000 0.000 0.000 0.000
#> GSM1296032 1 0.0880 0.815 0.968 0.000 0.000 0.000 0.032
#> GSM1296024 1 0.0000 0.823 1.000 0.000 0.000 0.000 0.000
#> GSM1296031 1 0.0000 0.823 1.000 0.000 0.000 0.000 0.000
#> GSM1296028 1 0.1704 0.803 0.928 0.004 0.000 0.000 0.068
#> GSM1296029 1 0.0000 0.823 1.000 0.000 0.000 0.000 0.000
#> GSM1296026 4 0.3928 0.684 0.000 0.000 0.296 0.700 0.004
#> GSM1296030 4 0.4046 0.682 0.000 0.000 0.296 0.696 0.008
#> GSM1296040 3 0.0000 0.887 0.000 0.000 1.000 0.000 0.000
#> GSM1296036 3 0.0000 0.887 0.000 0.000 1.000 0.000 0.000
#> GSM1296048 5 0.1830 0.880 0.000 0.068 0.000 0.008 0.924
#> GSM1296059 3 0.0000 0.887 0.000 0.000 1.000 0.000 0.000
#> GSM1296066 5 0.1544 0.884 0.000 0.068 0.000 0.000 0.932
#> GSM1296060 3 0.3074 0.750 0.000 0.000 0.804 0.196 0.000
#> GSM1296063 5 0.6022 0.456 0.000 0.008 0.204 0.176 0.612
#> GSM1296064 4 0.2929 0.803 0.000 0.000 0.152 0.840 0.008
#> GSM1296067 2 0.0324 0.963 0.000 0.992 0.004 0.004 0.000
#> GSM1296062 3 0.1908 0.870 0.000 0.000 0.908 0.092 0.000
#> GSM1296068 2 0.0162 0.967 0.000 0.996 0.000 0.000 0.004
#> GSM1296050 1 0.6052 0.661 0.684 0.004 0.144 0.100 0.068
#> GSM1296057 1 0.6706 0.585 0.608 0.004 0.208 0.112 0.068
#> GSM1296052 1 0.0000 0.823 1.000 0.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.823 1.000 0.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.823 1.000 0.000 0.000 0.000 0.000
#> GSM1296055 1 0.6980 0.580 0.608 0.052 0.200 0.112 0.028
#> GSM1296053 1 0.0000 0.823 1.000 0.000 0.000 0.000 0.000
#> GSM1296058 1 0.7341 0.496 0.532 0.004 0.208 0.188 0.068
#> GSM1296051 4 0.3398 0.756 0.000 0.004 0.216 0.780 0.000
#> GSM1296056 4 0.2806 0.805 0.000 0.000 0.152 0.844 0.004
#> GSM1296065 5 0.6610 0.536 0.000 0.072 0.208 0.112 0.608
#> GSM1296061 3 0.0000 0.887 0.000 0.000 1.000 0.000 0.000
#> GSM1296095 5 0.6540 0.537 0.000 0.068 0.212 0.108 0.612
#> GSM1296120 2 0.0162 0.962 0.000 0.996 0.000 0.004 0.000
#> GSM1296077 1 0.0000 0.823 1.000 0.000 0.000 0.000 0.000
#> GSM1296093 1 0.0000 0.823 1.000 0.000 0.000 0.000 0.000
#> GSM1296104 1 0.8273 0.178 0.340 0.004 0.208 0.116 0.332
#> GSM1296079 1 0.0000 0.823 1.000 0.000 0.000 0.000 0.000
#> GSM1296108 2 0.0162 0.967 0.000 0.996 0.000 0.000 0.004
#> GSM1296110 2 0.0162 0.967 0.000 0.996 0.000 0.000 0.004
#> GSM1296081 1 0.0000 0.823 1.000 0.000 0.000 0.000 0.000
#> GSM1296091 1 0.6706 0.585 0.608 0.004 0.208 0.112 0.068
#> GSM1296075 1 0.6706 0.585 0.608 0.004 0.208 0.112 0.068
#> GSM1296112 2 0.0162 0.967 0.000 0.996 0.000 0.000 0.004
#> GSM1296100 1 0.0000 0.823 1.000 0.000 0.000 0.000 0.000
#> GSM1296087 1 0.2206 0.798 0.912 0.004 0.000 0.016 0.068
#> GSM1296118 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000
#> GSM1296114 2 0.0162 0.967 0.000 0.996 0.000 0.000 0.004
#> GSM1296097 1 0.6874 0.583 0.608 0.020 0.208 0.112 0.052
#> GSM1296106 1 0.6691 0.570 0.608 0.092 0.196 0.104 0.000
#> GSM1296102 1 0.4803 0.650 0.720 0.000 0.184 0.096 0.000
#> GSM1296122 2 0.0955 0.932 0.000 0.968 0.004 0.028 0.000
#> GSM1296089 1 0.0000 0.823 1.000 0.000 0.000 0.000 0.000
#> GSM1296083 1 0.0000 0.823 1.000 0.000 0.000 0.000 0.000
#> GSM1296116 2 0.0162 0.967 0.000 0.996 0.000 0.000 0.004
#> GSM1296085 1 0.1544 0.804 0.932 0.000 0.000 0.000 0.068
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1296094 3 0.0000 0.7959 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296119 5 0.0000 0.9880 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296076 4 0.0632 0.8579 0.000 0.000 0.000 0.976 0.000 0.024
#> GSM1296092 4 0.0777 0.8571 0.000 0.000 0.004 0.972 0.000 0.024
#> GSM1296103 3 0.0000 0.7959 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296078 4 0.0632 0.8579 0.000 0.000 0.000 0.976 0.000 0.024
#> GSM1296107 5 0.0000 0.9880 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296109 3 0.6339 0.5364 0.000 0.000 0.576 0.180 0.100 0.144
#> GSM1296080 6 0.3838 0.3256 0.000 0.000 0.448 0.000 0.000 0.552
#> GSM1296090 4 0.0632 0.8579 0.000 0.000 0.000 0.976 0.000 0.024
#> GSM1296074 4 0.0632 0.8579 0.000 0.000 0.000 0.976 0.000 0.024
#> GSM1296111 5 0.0000 0.9880 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296099 3 0.4143 0.6639 0.000 0.000 0.736 0.180 0.000 0.084
#> GSM1296086 4 0.1492 0.8408 0.000 0.000 0.036 0.940 0.000 0.024
#> GSM1296117 5 0.0000 0.9880 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296113 5 0.0000 0.9880 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296096 3 0.4843 0.5180 0.000 0.000 0.616 0.300 0.000 0.084
#> GSM1296105 6 0.2491 0.7393 0.000 0.000 0.164 0.000 0.000 0.836
#> GSM1296098 3 0.0000 0.7959 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296101 3 0.0000 0.7959 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296121 5 0.0000 0.9880 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296088 3 0.6014 0.2142 0.000 0.000 0.428 0.264 0.000 0.308
#> GSM1296082 4 0.0632 0.8579 0.000 0.000 0.000 0.976 0.000 0.024
#> GSM1296115 5 0.0000 0.9880 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296084 6 0.4202 0.6450 0.000 0.000 0.224 0.064 0.000 0.712
#> GSM1296072 2 0.0000 0.9569 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296069 2 0.3607 0.4508 0.000 0.652 0.000 0.000 0.348 0.000
#> GSM1296071 2 0.0000 0.9569 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296070 5 0.1556 0.9049 0.000 0.080 0.000 0.000 0.920 0.000
#> GSM1296073 5 0.1007 0.9430 0.000 0.000 0.000 0.044 0.956 0.000
#> GSM1296034 6 0.3792 0.7405 0.108 0.000 0.112 0.000 0.000 0.780
#> GSM1296041 5 0.0000 0.9880 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296035 3 0.4827 0.5249 0.000 0.000 0.620 0.296 0.000 0.084
#> GSM1296038 6 0.5721 0.1657 0.000 0.000 0.176 0.344 0.000 0.480
#> GSM1296047 2 0.0000 0.9569 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296039 4 0.2846 0.8068 0.000 0.000 0.060 0.856 0.000 0.084
#> GSM1296042 5 0.0000 0.9880 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296043 2 0.0000 0.9569 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296037 1 0.2631 0.8623 0.840 0.000 0.008 0.000 0.000 0.152
#> GSM1296046 2 0.0000 0.9569 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296044 2 0.0000 0.9569 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296045 2 0.0000 0.9569 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296025 1 0.0632 0.8583 0.976 0.000 0.000 0.024 0.000 0.000
#> GSM1296033 6 0.0000 0.7936 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1296027 1 0.1957 0.8993 0.888 0.000 0.000 0.000 0.000 0.112
#> GSM1296032 1 0.1957 0.8993 0.888 0.000 0.000 0.000 0.000 0.112
#> GSM1296024 1 0.0632 0.8583 0.976 0.000 0.000 0.024 0.000 0.000
#> GSM1296031 1 0.3717 0.5724 0.616 0.000 0.000 0.000 0.000 0.384
#> GSM1296028 1 0.3446 0.7161 0.692 0.000 0.000 0.000 0.000 0.308
#> GSM1296029 1 0.3547 0.6754 0.668 0.000 0.000 0.000 0.000 0.332
#> GSM1296026 6 0.4999 0.5503 0.000 0.000 0.240 0.128 0.000 0.632
#> GSM1296030 6 0.4228 0.6409 0.000 0.000 0.228 0.064 0.000 0.708
#> GSM1296040 3 0.0000 0.7959 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296036 3 0.0000 0.7959 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296048 5 0.0000 0.9880 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296059 3 0.0458 0.7911 0.000 0.000 0.984 0.016 0.000 0.000
#> GSM1296066 5 0.0000 0.9880 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296060 3 0.4422 0.6307 0.000 0.000 0.700 0.212 0.000 0.088
#> GSM1296063 4 0.5331 0.6178 0.000 0.000 0.024 0.652 0.184 0.140
#> GSM1296064 4 0.2846 0.8068 0.000 0.000 0.060 0.856 0.000 0.084
#> GSM1296067 2 0.3373 0.5976 0.000 0.744 0.008 0.000 0.000 0.248
#> GSM1296062 6 0.3868 0.2029 0.000 0.000 0.492 0.000 0.000 0.508
#> GSM1296068 2 0.0000 0.9569 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296050 6 0.2135 0.7294 0.128 0.000 0.000 0.000 0.000 0.872
#> GSM1296057 6 0.0291 0.7944 0.004 0.000 0.004 0.000 0.000 0.992
#> GSM1296052 1 0.1957 0.8993 0.888 0.000 0.000 0.000 0.000 0.112
#> GSM1296054 1 0.0632 0.8583 0.976 0.000 0.000 0.024 0.000 0.000
#> GSM1296049 1 0.1714 0.8973 0.908 0.000 0.000 0.000 0.000 0.092
#> GSM1296055 6 0.0260 0.7961 0.000 0.000 0.008 0.000 0.000 0.992
#> GSM1296053 1 0.2006 0.8990 0.892 0.000 0.000 0.004 0.000 0.104
#> GSM1296058 6 0.0260 0.7961 0.000 0.000 0.008 0.000 0.000 0.992
#> GSM1296051 4 0.5061 0.0751 0.000 0.000 0.076 0.496 0.000 0.428
#> GSM1296056 4 0.2846 0.8068 0.000 0.000 0.060 0.856 0.000 0.084
#> GSM1296065 6 0.1950 0.7619 0.000 0.000 0.024 0.064 0.000 0.912
#> GSM1296061 3 0.0000 0.7959 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296095 6 0.6612 0.3387 0.000 0.000 0.128 0.240 0.108 0.524
#> GSM1296120 2 0.0000 0.9569 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296077 1 0.1957 0.8993 0.888 0.000 0.000 0.000 0.000 0.112
#> GSM1296093 1 0.0632 0.8583 0.976 0.000 0.000 0.024 0.000 0.000
#> GSM1296104 6 0.0363 0.7956 0.000 0.000 0.012 0.000 0.000 0.988
#> GSM1296079 1 0.1957 0.8993 0.888 0.000 0.000 0.000 0.000 0.112
#> GSM1296108 2 0.0000 0.9569 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296110 2 0.0000 0.9569 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296081 1 0.1563 0.8869 0.932 0.000 0.000 0.012 0.000 0.056
#> GSM1296091 6 0.0000 0.7936 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1296075 6 0.0146 0.7952 0.000 0.000 0.004 0.000 0.000 0.996
#> GSM1296112 2 0.0000 0.9569 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296100 1 0.2948 0.8279 0.804 0.000 0.008 0.000 0.000 0.188
#> GSM1296087 6 0.2664 0.6514 0.184 0.000 0.000 0.000 0.000 0.816
#> GSM1296118 2 0.0146 0.9534 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1296114 2 0.0000 0.9569 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296097 6 0.0260 0.7961 0.000 0.000 0.008 0.000 0.000 0.992
#> GSM1296106 6 0.0260 0.7961 0.000 0.000 0.008 0.000 0.000 0.992
#> GSM1296102 6 0.2212 0.7502 0.112 0.000 0.008 0.000 0.000 0.880
#> GSM1296122 6 0.3508 0.5780 0.000 0.292 0.004 0.000 0.000 0.704
#> GSM1296089 6 0.2664 0.6642 0.184 0.000 0.000 0.000 0.000 0.816
#> GSM1296083 1 0.0632 0.8583 0.976 0.000 0.000 0.024 0.000 0.000
#> GSM1296116 2 0.0000 0.9569 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296085 1 0.1957 0.8993 0.888 0.000 0.000 0.000 0.000 0.112
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# 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 tissue(p) time(p) agent(p) dose(p) individual(p) k
#> MAD:mclust 50 4.96e-01 0.1097 0.3229 3.16e-05 4.22e-05 2
#> MAD:mclust 99 5.86e-04 0.0873 0.0065 9.07e-09 1.87e-06 3
#> MAD:mclust 63 3.38e-02 0.2615 0.0771 5.41e-06 3.04e-07 4
#> MAD:mclust 93 4.57e-05 0.4726 0.0295 6.96e-10 5.63e-07 5
#> MAD:mclust 92 8.03e-04 0.3884 0.1802 5.82e-08 3.63e-06 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 45638 rows and 99 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 0.958 0.934 0.974 0.4929 0.506 0.506
#> 3 3 0.905 0.904 0.959 0.3613 0.729 0.510
#> 4 4 0.812 0.790 0.901 0.1020 0.820 0.528
#> 5 5 0.844 0.816 0.905 0.0669 0.892 0.625
#> 6 6 0.713 0.657 0.804 0.0294 0.959 0.818
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM1296094 2 0.0376 0.973 0.004 0.996
#> GSM1296119 2 0.0000 0.977 0.000 1.000
#> GSM1296076 2 0.0000 0.977 0.000 1.000
#> GSM1296092 2 0.0000 0.977 0.000 1.000
#> GSM1296103 2 0.0000 0.977 0.000 1.000
#> GSM1296078 2 0.0000 0.977 0.000 1.000
#> GSM1296107 2 0.0000 0.977 0.000 1.000
#> GSM1296109 2 0.0000 0.977 0.000 1.000
#> GSM1296080 1 0.0000 0.966 1.000 0.000
#> GSM1296090 2 0.0000 0.977 0.000 1.000
#> GSM1296074 2 0.0000 0.977 0.000 1.000
#> GSM1296111 2 0.0000 0.977 0.000 1.000
#> GSM1296099 2 0.0000 0.977 0.000 1.000
#> GSM1296086 2 0.0000 0.977 0.000 1.000
#> GSM1296117 2 0.0000 0.977 0.000 1.000
#> GSM1296113 2 0.0000 0.977 0.000 1.000
#> GSM1296096 2 0.0000 0.977 0.000 1.000
#> GSM1296105 1 0.0000 0.966 1.000 0.000
#> GSM1296098 2 0.8955 0.524 0.312 0.688
#> GSM1296101 2 0.2423 0.937 0.040 0.960
#> GSM1296121 2 0.0000 0.977 0.000 1.000
#> GSM1296088 2 0.9881 0.190 0.436 0.564
#> GSM1296082 2 0.0000 0.977 0.000 1.000
#> GSM1296115 2 0.0000 0.977 0.000 1.000
#> GSM1296084 1 0.0000 0.966 1.000 0.000
#> GSM1296072 2 0.0000 0.977 0.000 1.000
#> GSM1296069 2 0.0000 0.977 0.000 1.000
#> GSM1296071 2 0.0000 0.977 0.000 1.000
#> GSM1296070 2 0.0000 0.977 0.000 1.000
#> GSM1296073 2 0.0000 0.977 0.000 1.000
#> GSM1296034 1 0.0000 0.966 1.000 0.000
#> GSM1296041 2 0.0000 0.977 0.000 1.000
#> GSM1296035 2 0.0000 0.977 0.000 1.000
#> GSM1296038 2 0.0000 0.977 0.000 1.000
#> GSM1296047 2 0.0000 0.977 0.000 1.000
#> GSM1296039 2 0.0000 0.977 0.000 1.000
#> GSM1296042 2 0.0000 0.977 0.000 1.000
#> GSM1296043 2 0.0000 0.977 0.000 1.000
#> GSM1296037 1 0.0000 0.966 1.000 0.000
#> GSM1296046 2 0.0000 0.977 0.000 1.000
#> GSM1296044 2 0.0000 0.977 0.000 1.000
#> GSM1296045 2 0.0000 0.977 0.000 1.000
#> GSM1296025 1 0.0000 0.966 1.000 0.000
#> GSM1296033 1 0.0000 0.966 1.000 0.000
#> GSM1296027 1 0.0000 0.966 1.000 0.000
#> GSM1296032 1 0.0000 0.966 1.000 0.000
#> GSM1296024 1 0.0000 0.966 1.000 0.000
#> GSM1296031 1 0.0000 0.966 1.000 0.000
#> GSM1296028 1 0.0000 0.966 1.000 0.000
#> GSM1296029 1 0.0000 0.966 1.000 0.000
#> GSM1296026 1 0.7528 0.722 0.784 0.216
#> GSM1296030 1 0.0000 0.966 1.000 0.000
#> GSM1296040 1 0.9170 0.515 0.668 0.332
#> GSM1296036 1 0.8909 0.566 0.692 0.308
#> GSM1296048 2 0.0000 0.977 0.000 1.000
#> GSM1296059 2 0.0000 0.977 0.000 1.000
#> GSM1296066 2 0.0000 0.977 0.000 1.000
#> GSM1296060 2 0.0000 0.977 0.000 1.000
#> GSM1296063 2 0.0000 0.977 0.000 1.000
#> GSM1296064 2 0.0000 0.977 0.000 1.000
#> GSM1296067 2 0.0672 0.969 0.008 0.992
#> GSM1296062 1 0.0000 0.966 1.000 0.000
#> GSM1296068 2 0.0000 0.977 0.000 1.000
#> GSM1296050 1 0.0000 0.966 1.000 0.000
#> GSM1296057 1 0.0000 0.966 1.000 0.000
#> GSM1296052 1 0.0000 0.966 1.000 0.000
#> GSM1296054 1 0.0000 0.966 1.000 0.000
#> GSM1296049 1 0.0000 0.966 1.000 0.000
#> GSM1296055 1 0.0000 0.966 1.000 0.000
#> GSM1296053 1 0.0000 0.966 1.000 0.000
#> GSM1296058 1 0.0000 0.966 1.000 0.000
#> GSM1296051 2 0.0000 0.977 0.000 1.000
#> GSM1296056 2 0.0000 0.977 0.000 1.000
#> GSM1296065 2 0.0000 0.977 0.000 1.000
#> GSM1296061 1 0.1184 0.954 0.984 0.016
#> GSM1296095 2 0.0000 0.977 0.000 1.000
#> GSM1296120 2 0.0000 0.977 0.000 1.000
#> GSM1296077 1 0.0000 0.966 1.000 0.000
#> GSM1296093 1 0.0000 0.966 1.000 0.000
#> GSM1296104 2 0.0000 0.977 0.000 1.000
#> GSM1296079 1 0.0000 0.966 1.000 0.000
#> GSM1296108 2 0.0000 0.977 0.000 1.000
#> GSM1296110 2 0.0000 0.977 0.000 1.000
#> GSM1296081 1 0.0000 0.966 1.000 0.000
#> GSM1296091 1 0.0000 0.966 1.000 0.000
#> GSM1296075 1 0.1184 0.954 0.984 0.016
#> GSM1296112 2 0.0000 0.977 0.000 1.000
#> GSM1296100 1 0.0000 0.966 1.000 0.000
#> GSM1296087 1 0.0000 0.966 1.000 0.000
#> GSM1296118 2 0.9866 0.223 0.432 0.568
#> GSM1296114 2 0.0000 0.977 0.000 1.000
#> GSM1296097 1 0.9661 0.373 0.608 0.392
#> GSM1296106 1 0.0000 0.966 1.000 0.000
#> GSM1296102 1 0.0000 0.966 1.000 0.000
#> GSM1296122 1 0.2778 0.925 0.952 0.048
#> GSM1296089 1 0.0000 0.966 1.000 0.000
#> GSM1296083 1 0.0000 0.966 1.000 0.000
#> GSM1296116 2 0.0000 0.977 0.000 1.000
#> GSM1296085 1 0.0000 0.966 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1296094 3 0.0000 0.9346 0.000 0.000 1.000
#> GSM1296119 3 0.6192 0.3480 0.000 0.420 0.580
#> GSM1296076 3 0.0000 0.9346 0.000 0.000 1.000
#> GSM1296092 3 0.0000 0.9346 0.000 0.000 1.000
#> GSM1296103 3 0.0000 0.9346 0.000 0.000 1.000
#> GSM1296078 3 0.0000 0.9346 0.000 0.000 1.000
#> GSM1296107 2 0.0237 0.9558 0.000 0.996 0.004
#> GSM1296109 3 0.0000 0.9346 0.000 0.000 1.000
#> GSM1296080 1 0.2356 0.9118 0.928 0.000 0.072
#> GSM1296090 3 0.0000 0.9346 0.000 0.000 1.000
#> GSM1296074 3 0.0000 0.9346 0.000 0.000 1.000
#> GSM1296111 2 0.5968 0.3502 0.000 0.636 0.364
#> GSM1296099 3 0.0000 0.9346 0.000 0.000 1.000
#> GSM1296086 3 0.0000 0.9346 0.000 0.000 1.000
#> GSM1296117 3 0.2448 0.8883 0.000 0.076 0.924
#> GSM1296113 2 0.1964 0.9095 0.000 0.944 0.056
#> GSM1296096 3 0.0000 0.9346 0.000 0.000 1.000
#> GSM1296105 1 0.0000 0.9745 1.000 0.000 0.000
#> GSM1296098 3 0.0000 0.9346 0.000 0.000 1.000
#> GSM1296101 3 0.0000 0.9346 0.000 0.000 1.000
#> GSM1296121 3 0.4887 0.7287 0.000 0.228 0.772
#> GSM1296088 3 0.0000 0.9346 0.000 0.000 1.000
#> GSM1296082 3 0.0000 0.9346 0.000 0.000 1.000
#> GSM1296115 3 0.4796 0.7391 0.000 0.220 0.780
#> GSM1296084 1 0.4235 0.7834 0.824 0.000 0.176
#> GSM1296072 2 0.0000 0.9582 0.000 1.000 0.000
#> GSM1296069 2 0.0000 0.9582 0.000 1.000 0.000
#> GSM1296071 2 0.0000 0.9582 0.000 1.000 0.000
#> GSM1296070 2 0.0000 0.9582 0.000 1.000 0.000
#> GSM1296073 3 0.1860 0.9045 0.000 0.052 0.948
#> GSM1296034 1 0.0000 0.9745 1.000 0.000 0.000
#> GSM1296041 3 0.4750 0.7444 0.000 0.216 0.784
#> GSM1296035 3 0.0000 0.9346 0.000 0.000 1.000
#> GSM1296038 3 0.0000 0.9346 0.000 0.000 1.000
#> GSM1296047 2 0.0000 0.9582 0.000 1.000 0.000
#> GSM1296039 3 0.0000 0.9346 0.000 0.000 1.000
#> GSM1296042 2 0.0747 0.9468 0.000 0.984 0.016
#> GSM1296043 2 0.0000 0.9582 0.000 1.000 0.000
#> GSM1296037 1 0.0000 0.9745 1.000 0.000 0.000
#> GSM1296046 2 0.0000 0.9582 0.000 1.000 0.000
#> GSM1296044 2 0.0000 0.9582 0.000 1.000 0.000
#> GSM1296045 2 0.0000 0.9582 0.000 1.000 0.000
#> GSM1296025 1 0.0000 0.9745 1.000 0.000 0.000
#> GSM1296033 1 0.0000 0.9745 1.000 0.000 0.000
#> GSM1296027 1 0.0000 0.9745 1.000 0.000 0.000
#> GSM1296032 1 0.0000 0.9745 1.000 0.000 0.000
#> GSM1296024 1 0.0000 0.9745 1.000 0.000 0.000
#> GSM1296031 1 0.0000 0.9745 1.000 0.000 0.000
#> GSM1296028 1 0.0000 0.9745 1.000 0.000 0.000
#> GSM1296029 1 0.0000 0.9745 1.000 0.000 0.000
#> GSM1296026 3 0.5678 0.5246 0.316 0.000 0.684
#> GSM1296030 1 0.1529 0.9419 0.960 0.000 0.040
#> GSM1296040 3 0.0747 0.9240 0.016 0.000 0.984
#> GSM1296036 3 0.0000 0.9346 0.000 0.000 1.000
#> GSM1296048 3 0.5810 0.5459 0.000 0.336 0.664
#> GSM1296059 3 0.0000 0.9346 0.000 0.000 1.000
#> GSM1296066 2 0.0000 0.9582 0.000 1.000 0.000
#> GSM1296060 3 0.0000 0.9346 0.000 0.000 1.000
#> GSM1296063 3 0.2165 0.8969 0.000 0.064 0.936
#> GSM1296064 3 0.0000 0.9346 0.000 0.000 1.000
#> GSM1296067 2 0.0000 0.9582 0.000 1.000 0.000
#> GSM1296062 1 0.1411 0.9455 0.964 0.000 0.036
#> GSM1296068 2 0.0000 0.9582 0.000 1.000 0.000
#> GSM1296050 1 0.0424 0.9678 0.992 0.008 0.000
#> GSM1296057 1 0.0000 0.9745 1.000 0.000 0.000
#> GSM1296052 1 0.0000 0.9745 1.000 0.000 0.000
#> GSM1296054 1 0.0000 0.9745 1.000 0.000 0.000
#> GSM1296049 1 0.0000 0.9745 1.000 0.000 0.000
#> GSM1296055 2 0.6140 0.3071 0.404 0.596 0.000
#> GSM1296053 1 0.0000 0.9745 1.000 0.000 0.000
#> GSM1296058 1 0.0000 0.9745 1.000 0.000 0.000
#> GSM1296051 3 0.0000 0.9346 0.000 0.000 1.000
#> GSM1296056 3 0.0000 0.9346 0.000 0.000 1.000
#> GSM1296065 2 0.1529 0.9258 0.000 0.960 0.040
#> GSM1296061 3 0.3879 0.7938 0.152 0.000 0.848
#> GSM1296095 3 0.2537 0.8857 0.000 0.080 0.920
#> GSM1296120 2 0.0000 0.9582 0.000 1.000 0.000
#> GSM1296077 1 0.0000 0.9745 1.000 0.000 0.000
#> GSM1296093 1 0.0000 0.9745 1.000 0.000 0.000
#> GSM1296104 2 0.0237 0.9559 0.000 0.996 0.004
#> GSM1296079 1 0.0000 0.9745 1.000 0.000 0.000
#> GSM1296108 2 0.0000 0.9582 0.000 1.000 0.000
#> GSM1296110 2 0.0000 0.9582 0.000 1.000 0.000
#> GSM1296081 1 0.0000 0.9745 1.000 0.000 0.000
#> GSM1296091 1 0.0000 0.9745 1.000 0.000 0.000
#> GSM1296075 2 0.2066 0.9096 0.060 0.940 0.000
#> GSM1296112 2 0.0000 0.9582 0.000 1.000 0.000
#> GSM1296100 1 0.0000 0.9745 1.000 0.000 0.000
#> GSM1296087 1 0.0000 0.9745 1.000 0.000 0.000
#> GSM1296118 2 0.0000 0.9582 0.000 1.000 0.000
#> GSM1296114 2 0.0000 0.9582 0.000 1.000 0.000
#> GSM1296097 1 0.6302 0.0508 0.520 0.480 0.000
#> GSM1296106 2 0.3038 0.8624 0.104 0.896 0.000
#> GSM1296102 1 0.0000 0.9745 1.000 0.000 0.000
#> GSM1296122 2 0.0592 0.9497 0.012 0.988 0.000
#> GSM1296089 1 0.0000 0.9745 1.000 0.000 0.000
#> GSM1296083 1 0.0000 0.9745 1.000 0.000 0.000
#> GSM1296116 2 0.0000 0.9582 0.000 1.000 0.000
#> GSM1296085 1 0.0000 0.9745 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1296094 3 0.2408 0.82144 0.000 0.000 0.896 0.104
#> GSM1296119 2 0.5858 -0.00993 0.000 0.500 0.032 0.468
#> GSM1296076 4 0.0336 0.80708 0.000 0.000 0.008 0.992
#> GSM1296092 4 0.0895 0.80747 0.004 0.000 0.020 0.976
#> GSM1296103 3 0.2647 0.81461 0.000 0.000 0.880 0.120
#> GSM1296078 4 0.0188 0.80618 0.000 0.000 0.004 0.996
#> GSM1296107 2 0.0336 0.90557 0.000 0.992 0.000 0.008
#> GSM1296109 3 0.3074 0.79630 0.000 0.000 0.848 0.152
#> GSM1296080 3 0.2489 0.81191 0.068 0.000 0.912 0.020
#> GSM1296090 4 0.0657 0.80027 0.012 0.000 0.004 0.984
#> GSM1296074 4 0.0921 0.80627 0.000 0.000 0.028 0.972
#> GSM1296111 2 0.4454 0.53945 0.000 0.692 0.000 0.308
#> GSM1296099 3 0.3873 0.71895 0.000 0.000 0.772 0.228
#> GSM1296086 4 0.1174 0.80584 0.012 0.000 0.020 0.968
#> GSM1296117 4 0.3301 0.76592 0.000 0.048 0.076 0.876
#> GSM1296113 2 0.0524 0.90444 0.000 0.988 0.008 0.004
#> GSM1296096 4 0.4996 -0.14186 0.000 0.000 0.484 0.516
#> GSM1296105 3 0.2053 0.79842 0.072 0.000 0.924 0.004
#> GSM1296098 3 0.2081 0.82743 0.000 0.000 0.916 0.084
#> GSM1296101 3 0.2081 0.82707 0.000 0.000 0.916 0.084
#> GSM1296121 2 0.5231 0.50674 0.000 0.676 0.028 0.296
#> GSM1296088 4 0.3801 0.60685 0.000 0.000 0.220 0.780
#> GSM1296082 4 0.0707 0.80769 0.000 0.000 0.020 0.980
#> GSM1296115 4 0.5928 0.08919 0.000 0.456 0.036 0.508
#> GSM1296084 1 0.4182 0.74771 0.796 0.000 0.024 0.180
#> GSM1296072 2 0.0592 0.90179 0.000 0.984 0.000 0.016
#> GSM1296069 2 0.0188 0.90700 0.000 0.996 0.000 0.004
#> GSM1296071 2 0.0000 0.90767 0.000 1.000 0.000 0.000
#> GSM1296070 2 0.0188 0.90700 0.000 0.996 0.000 0.004
#> GSM1296073 4 0.1004 0.80764 0.000 0.004 0.024 0.972
#> GSM1296034 3 0.3400 0.69629 0.180 0.000 0.820 0.000
#> GSM1296041 2 0.5602 0.23546 0.000 0.568 0.024 0.408
#> GSM1296035 3 0.4804 0.45661 0.000 0.000 0.616 0.384
#> GSM1296038 4 0.2760 0.72981 0.000 0.000 0.128 0.872
#> GSM1296047 2 0.0000 0.90767 0.000 1.000 0.000 0.000
#> GSM1296039 4 0.1302 0.79990 0.000 0.000 0.044 0.956
#> GSM1296042 2 0.0779 0.90053 0.000 0.980 0.004 0.016
#> GSM1296043 2 0.0000 0.90767 0.000 1.000 0.000 0.000
#> GSM1296037 1 0.1940 0.93750 0.924 0.000 0.076 0.000
#> GSM1296046 2 0.0000 0.90767 0.000 1.000 0.000 0.000
#> GSM1296044 2 0.0000 0.90767 0.000 1.000 0.000 0.000
#> GSM1296045 2 0.0188 0.90700 0.000 0.996 0.000 0.004
#> GSM1296025 1 0.1302 0.95295 0.956 0.000 0.044 0.000
#> GSM1296033 1 0.0592 0.95538 0.984 0.000 0.000 0.016
#> GSM1296027 1 0.0524 0.95962 0.988 0.000 0.008 0.004
#> GSM1296032 1 0.0000 0.95986 1.000 0.000 0.000 0.000
#> GSM1296024 1 0.1211 0.95435 0.960 0.000 0.040 0.000
#> GSM1296031 1 0.0336 0.96031 0.992 0.000 0.008 0.000
#> GSM1296028 1 0.0376 0.95959 0.992 0.000 0.004 0.004
#> GSM1296029 1 0.0469 0.95992 0.988 0.000 0.012 0.000
#> GSM1296026 4 0.5550 0.53382 0.248 0.000 0.060 0.692
#> GSM1296030 1 0.0895 0.95808 0.976 0.000 0.020 0.004
#> GSM1296040 3 0.1677 0.82684 0.012 0.000 0.948 0.040
#> GSM1296036 3 0.1398 0.82583 0.004 0.000 0.956 0.040
#> GSM1296048 4 0.4800 0.43196 0.000 0.340 0.004 0.656
#> GSM1296059 3 0.2973 0.80144 0.000 0.000 0.856 0.144
#> GSM1296066 2 0.0188 0.90696 0.000 0.996 0.004 0.000
#> GSM1296060 3 0.4843 0.42534 0.000 0.000 0.604 0.396
#> GSM1296063 4 0.0469 0.80359 0.000 0.000 0.012 0.988
#> GSM1296064 4 0.0921 0.80668 0.000 0.000 0.028 0.972
#> GSM1296067 2 0.1118 0.89147 0.000 0.964 0.036 0.000
#> GSM1296062 3 0.1824 0.80430 0.060 0.000 0.936 0.004
#> GSM1296068 2 0.0000 0.90767 0.000 1.000 0.000 0.000
#> GSM1296050 1 0.0712 0.95595 0.984 0.008 0.004 0.004
#> GSM1296057 1 0.0921 0.95715 0.972 0.000 0.028 0.000
#> GSM1296052 1 0.0524 0.95962 0.988 0.000 0.008 0.004
#> GSM1296054 1 0.1118 0.95580 0.964 0.000 0.036 0.000
#> GSM1296049 1 0.0469 0.96028 0.988 0.000 0.012 0.000
#> GSM1296055 1 0.2660 0.91207 0.908 0.056 0.036 0.000
#> GSM1296053 1 0.1302 0.95296 0.956 0.000 0.044 0.000
#> GSM1296058 1 0.2363 0.93811 0.920 0.000 0.056 0.024
#> GSM1296051 4 0.1302 0.78257 0.044 0.000 0.000 0.956
#> GSM1296056 4 0.1211 0.80183 0.000 0.000 0.040 0.960
#> GSM1296065 4 0.4920 0.37469 0.000 0.368 0.004 0.628
#> GSM1296061 3 0.1629 0.82159 0.024 0.000 0.952 0.024
#> GSM1296095 2 0.7605 0.03508 0.000 0.452 0.212 0.336
#> GSM1296120 2 0.0188 0.90687 0.000 0.996 0.004 0.000
#> GSM1296077 1 0.0592 0.96045 0.984 0.000 0.016 0.000
#> GSM1296093 1 0.1474 0.94852 0.948 0.000 0.052 0.000
#> GSM1296104 4 0.7707 0.19829 0.108 0.372 0.032 0.488
#> GSM1296079 1 0.0000 0.95986 1.000 0.000 0.000 0.000
#> GSM1296108 2 0.0000 0.90767 0.000 1.000 0.000 0.000
#> GSM1296110 2 0.0817 0.89858 0.000 0.976 0.024 0.000
#> GSM1296081 1 0.1022 0.95636 0.968 0.000 0.032 0.000
#> GSM1296091 1 0.0927 0.95258 0.976 0.000 0.008 0.016
#> GSM1296075 1 0.2831 0.90321 0.908 0.040 0.008 0.044
#> GSM1296112 2 0.0000 0.90767 0.000 1.000 0.000 0.000
#> GSM1296100 1 0.2408 0.91567 0.896 0.000 0.104 0.000
#> GSM1296087 1 0.0336 0.95813 0.992 0.000 0.000 0.008
#> GSM1296118 2 0.0000 0.90767 0.000 1.000 0.000 0.000
#> GSM1296114 2 0.0188 0.90696 0.000 0.996 0.004 0.000
#> GSM1296097 1 0.5446 0.79250 0.784 0.092 0.064 0.060
#> GSM1296106 2 0.3320 0.80797 0.056 0.876 0.068 0.000
#> GSM1296102 3 0.4981 0.02772 0.464 0.000 0.536 0.000
#> GSM1296122 2 0.0336 0.90495 0.000 0.992 0.008 0.000
#> GSM1296089 1 0.0000 0.95986 1.000 0.000 0.000 0.000
#> GSM1296083 1 0.1211 0.95435 0.960 0.000 0.040 0.000
#> GSM1296116 2 0.0000 0.90767 0.000 1.000 0.000 0.000
#> GSM1296085 1 0.0000 0.95986 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
#> GSM1296094 3 0.0671 0.870 0.004 0.000 0.980 0.000 0.016
#> GSM1296119 5 0.4450 -0.110 0.000 0.488 0.004 0.000 0.508
#> GSM1296076 5 0.0671 0.868 0.000 0.004 0.016 0.000 0.980
#> GSM1296092 5 0.1408 0.871 0.008 0.000 0.044 0.000 0.948
#> GSM1296103 3 0.0955 0.870 0.000 0.000 0.968 0.004 0.028
#> GSM1296078 5 0.0566 0.867 0.000 0.004 0.012 0.000 0.984
#> GSM1296107 2 0.0404 0.907 0.000 0.988 0.000 0.000 0.012
#> GSM1296109 3 0.1571 0.863 0.000 0.000 0.936 0.004 0.060
#> GSM1296080 3 0.2719 0.766 0.144 0.000 0.852 0.000 0.004
#> GSM1296090 5 0.0566 0.868 0.004 0.000 0.012 0.000 0.984
#> GSM1296074 5 0.1341 0.867 0.000 0.000 0.056 0.000 0.944
#> GSM1296111 2 0.3534 0.684 0.000 0.744 0.000 0.000 0.256
#> GSM1296099 3 0.2389 0.828 0.000 0.000 0.880 0.004 0.116
#> GSM1296086 5 0.2054 0.858 0.028 0.000 0.052 0.000 0.920
#> GSM1296117 2 0.5580 0.362 0.000 0.576 0.088 0.000 0.336
#> GSM1296113 2 0.0404 0.907 0.000 0.988 0.000 0.000 0.012
#> GSM1296096 3 0.4383 0.305 0.000 0.000 0.572 0.004 0.424
#> GSM1296105 3 0.2300 0.833 0.024 0.000 0.904 0.072 0.000
#> GSM1296098 3 0.0771 0.868 0.020 0.000 0.976 0.000 0.004
#> GSM1296101 3 0.1582 0.867 0.000 0.000 0.944 0.028 0.028
#> GSM1296121 2 0.5091 0.348 0.000 0.584 0.044 0.000 0.372
#> GSM1296088 5 0.6323 0.331 0.220 0.000 0.252 0.000 0.528
#> GSM1296082 5 0.1197 0.870 0.000 0.000 0.048 0.000 0.952
#> GSM1296115 2 0.4132 0.638 0.000 0.720 0.020 0.000 0.260
#> GSM1296084 1 0.2554 0.878 0.892 0.000 0.036 0.000 0.072
#> GSM1296072 2 0.1408 0.895 0.000 0.948 0.000 0.008 0.044
#> GSM1296069 2 0.0992 0.903 0.000 0.968 0.000 0.008 0.024
#> GSM1296071 2 0.0613 0.905 0.000 0.984 0.008 0.004 0.004
#> GSM1296070 2 0.0794 0.904 0.000 0.972 0.000 0.000 0.028
#> GSM1296073 5 0.0880 0.871 0.000 0.000 0.032 0.000 0.968
#> GSM1296034 3 0.4073 0.732 0.104 0.000 0.792 0.104 0.000
#> GSM1296041 2 0.1894 0.875 0.000 0.920 0.008 0.000 0.072
#> GSM1296035 3 0.4404 0.635 0.000 0.000 0.704 0.032 0.264
#> GSM1296038 4 0.6127 0.114 0.000 0.000 0.128 0.456 0.416
#> GSM1296047 2 0.0404 0.907 0.000 0.988 0.000 0.000 0.012
#> GSM1296039 5 0.1697 0.863 0.000 0.000 0.060 0.008 0.932
#> GSM1296042 2 0.0404 0.907 0.000 0.988 0.000 0.000 0.012
#> GSM1296043 2 0.0404 0.907 0.000 0.988 0.000 0.000 0.012
#> GSM1296037 4 0.1697 0.826 0.060 0.000 0.008 0.932 0.000
#> GSM1296046 2 0.0404 0.907 0.000 0.988 0.000 0.000 0.012
#> GSM1296044 2 0.0613 0.905 0.000 0.984 0.008 0.004 0.004
#> GSM1296045 2 0.0865 0.904 0.000 0.972 0.000 0.004 0.024
#> GSM1296025 1 0.0404 0.952 0.988 0.000 0.000 0.012 0.000
#> GSM1296033 1 0.0162 0.951 0.996 0.000 0.004 0.000 0.000
#> GSM1296027 1 0.0162 0.951 0.996 0.000 0.004 0.000 0.000
#> GSM1296032 1 0.0609 0.950 0.980 0.000 0.000 0.020 0.000
#> GSM1296024 1 0.0451 0.951 0.988 0.000 0.008 0.004 0.000
#> GSM1296031 1 0.0880 0.947 0.968 0.000 0.000 0.032 0.000
#> GSM1296028 1 0.0000 0.952 1.000 0.000 0.000 0.000 0.000
#> GSM1296029 1 0.0404 0.949 0.988 0.000 0.012 0.000 0.000
#> GSM1296026 1 0.3412 0.785 0.820 0.000 0.028 0.000 0.152
#> GSM1296030 1 0.0609 0.945 0.980 0.000 0.020 0.000 0.000
#> GSM1296040 3 0.1560 0.869 0.004 0.000 0.948 0.028 0.020
#> GSM1296036 3 0.0794 0.864 0.028 0.000 0.972 0.000 0.000
#> GSM1296048 5 0.1914 0.827 0.000 0.056 0.008 0.008 0.928
#> GSM1296059 3 0.1444 0.867 0.000 0.000 0.948 0.012 0.040
#> GSM1296066 2 0.0404 0.907 0.000 0.988 0.000 0.000 0.012
#> GSM1296060 3 0.5703 0.563 0.000 0.000 0.616 0.140 0.244
#> GSM1296063 5 0.2700 0.811 0.000 0.004 0.024 0.088 0.884
#> GSM1296064 5 0.1557 0.867 0.000 0.000 0.052 0.008 0.940
#> GSM1296067 2 0.1059 0.900 0.000 0.968 0.020 0.004 0.008
#> GSM1296062 3 0.1211 0.866 0.024 0.000 0.960 0.016 0.000
#> GSM1296068 2 0.0162 0.906 0.000 0.996 0.000 0.000 0.004
#> GSM1296050 1 0.2074 0.897 0.896 0.000 0.000 0.104 0.000
#> GSM1296057 4 0.1205 0.841 0.040 0.000 0.000 0.956 0.004
#> GSM1296052 1 0.0162 0.951 0.996 0.000 0.004 0.000 0.000
#> GSM1296054 1 0.0162 0.952 0.996 0.000 0.004 0.000 0.000
#> GSM1296049 1 0.0162 0.951 0.996 0.000 0.004 0.000 0.000
#> GSM1296055 4 0.0609 0.844 0.020 0.000 0.000 0.980 0.000
#> GSM1296053 1 0.0798 0.949 0.976 0.000 0.016 0.008 0.000
#> GSM1296058 4 0.0912 0.844 0.012 0.000 0.000 0.972 0.016
#> GSM1296051 5 0.2991 0.747 0.120 0.012 0.004 0.004 0.860
#> GSM1296056 5 0.2221 0.852 0.000 0.000 0.052 0.036 0.912
#> GSM1296065 4 0.4010 0.647 0.004 0.008 0.004 0.744 0.240
#> GSM1296061 3 0.1043 0.858 0.040 0.000 0.960 0.000 0.000
#> GSM1296095 4 0.6908 0.111 0.000 0.036 0.128 0.428 0.408
#> GSM1296120 2 0.3508 0.661 0.000 0.748 0.000 0.252 0.000
#> GSM1296077 1 0.2230 0.890 0.884 0.000 0.000 0.116 0.000
#> GSM1296093 1 0.2286 0.895 0.888 0.000 0.004 0.108 0.000
#> GSM1296104 4 0.1041 0.836 0.000 0.004 0.000 0.964 0.032
#> GSM1296079 1 0.0290 0.951 0.992 0.000 0.000 0.008 0.000
#> GSM1296108 2 0.0613 0.905 0.000 0.984 0.008 0.004 0.004
#> GSM1296110 2 0.1059 0.900 0.000 0.968 0.020 0.004 0.008
#> GSM1296081 1 0.1043 0.943 0.960 0.000 0.000 0.040 0.000
#> GSM1296091 1 0.1996 0.927 0.928 0.004 0.000 0.036 0.032
#> GSM1296075 1 0.4703 0.763 0.768 0.032 0.000 0.140 0.060
#> GSM1296112 2 0.0162 0.906 0.000 0.996 0.000 0.000 0.004
#> GSM1296100 4 0.1894 0.818 0.072 0.000 0.008 0.920 0.000
#> GSM1296087 1 0.0162 0.951 0.996 0.000 0.004 0.000 0.000
#> GSM1296118 2 0.0955 0.900 0.000 0.968 0.000 0.028 0.004
#> GSM1296114 2 0.0613 0.905 0.000 0.984 0.008 0.004 0.004
#> GSM1296097 4 0.0290 0.842 0.008 0.000 0.000 0.992 0.000
#> GSM1296106 4 0.1278 0.839 0.016 0.020 0.004 0.960 0.000
#> GSM1296102 4 0.1800 0.821 0.020 0.000 0.048 0.932 0.000
#> GSM1296122 2 0.4565 0.323 0.012 0.580 0.000 0.408 0.000
#> GSM1296089 1 0.0794 0.948 0.972 0.000 0.000 0.028 0.000
#> GSM1296083 1 0.1341 0.935 0.944 0.000 0.000 0.056 0.000
#> GSM1296116 2 0.0162 0.907 0.000 0.996 0.000 0.000 0.004
#> GSM1296085 1 0.0609 0.950 0.980 0.000 0.000 0.020 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1296094 3 0.2006 0.7662 0.000 0.000 0.892 0.104 0.000 0.004
#> GSM1296119 4 0.4891 0.3099 0.000 0.384 0.036 0.564 0.016 0.000
#> GSM1296076 4 0.0665 0.7443 0.000 0.004 0.000 0.980 0.008 0.008
#> GSM1296092 4 0.2463 0.7308 0.068 0.000 0.020 0.892 0.020 0.000
#> GSM1296103 3 0.2595 0.7549 0.000 0.000 0.836 0.160 0.000 0.004
#> GSM1296078 4 0.1053 0.7402 0.000 0.000 0.004 0.964 0.012 0.020
#> GSM1296107 2 0.0405 0.9030 0.000 0.988 0.000 0.004 0.008 0.000
#> GSM1296109 3 0.3589 0.7222 0.000 0.000 0.752 0.228 0.008 0.012
#> GSM1296080 3 0.3957 0.5601 0.260 0.000 0.712 0.008 0.020 0.000
#> GSM1296090 4 0.3328 0.7158 0.064 0.004 0.004 0.852 0.056 0.020
#> GSM1296074 4 0.1124 0.7399 0.000 0.000 0.036 0.956 0.000 0.008
#> GSM1296111 2 0.3720 0.7030 0.000 0.760 0.012 0.208 0.020 0.000
#> GSM1296099 3 0.3819 0.6811 0.000 0.000 0.700 0.280 0.000 0.020
#> GSM1296086 4 0.3585 0.6803 0.140 0.000 0.012 0.808 0.036 0.004
#> GSM1296117 4 0.5171 0.2357 0.000 0.408 0.076 0.512 0.000 0.004
#> GSM1296113 2 0.1944 0.8884 0.000 0.924 0.016 0.036 0.024 0.000
#> GSM1296096 3 0.4892 0.3766 0.000 0.000 0.500 0.440 0.000 0.060
#> GSM1296105 3 0.5347 0.6421 0.072 0.000 0.704 0.024 0.152 0.048
#> GSM1296098 3 0.1405 0.7618 0.024 0.000 0.948 0.024 0.004 0.000
#> GSM1296101 3 0.3473 0.7130 0.000 0.000 0.804 0.048 0.004 0.144
#> GSM1296121 4 0.4703 0.0842 0.000 0.464 0.044 0.492 0.000 0.000
#> GSM1296088 1 0.6205 0.0724 0.444 0.000 0.220 0.324 0.012 0.000
#> GSM1296082 4 0.1959 0.7437 0.024 0.000 0.020 0.924 0.032 0.000
#> GSM1296115 2 0.4223 0.3712 0.000 0.612 0.016 0.368 0.004 0.000
#> GSM1296084 1 0.2502 0.7578 0.884 0.000 0.084 0.012 0.020 0.000
#> GSM1296072 2 0.3260 0.8402 0.000 0.856 0.008 0.056 0.056 0.024
#> GSM1296069 2 0.1218 0.9007 0.000 0.956 0.004 0.012 0.028 0.000
#> GSM1296071 2 0.0547 0.9017 0.000 0.980 0.020 0.000 0.000 0.000
#> GSM1296070 2 0.1232 0.8999 0.000 0.956 0.004 0.016 0.024 0.000
#> GSM1296073 4 0.0632 0.7437 0.000 0.000 0.024 0.976 0.000 0.000
#> GSM1296034 3 0.4661 0.6035 0.176 0.000 0.724 0.000 0.036 0.064
#> GSM1296041 2 0.3101 0.7986 0.000 0.832 0.020 0.136 0.012 0.000
#> GSM1296035 3 0.5288 0.5740 0.000 0.000 0.592 0.252 0.000 0.156
#> GSM1296038 6 0.4821 0.5799 0.000 0.000 0.088 0.196 0.020 0.696
#> GSM1296047 2 0.2425 0.8486 0.000 0.880 0.008 0.000 0.100 0.012
#> GSM1296039 4 0.1168 0.7416 0.000 0.000 0.028 0.956 0.000 0.016
#> GSM1296042 2 0.1409 0.8949 0.000 0.948 0.008 0.032 0.012 0.000
#> GSM1296043 2 0.0632 0.9017 0.000 0.976 0.000 0.000 0.024 0.000
#> GSM1296037 5 0.4791 -0.0582 0.020 0.000 0.020 0.000 0.516 0.444
#> GSM1296046 2 0.0820 0.9028 0.000 0.972 0.012 0.000 0.016 0.000
#> GSM1296044 2 0.1261 0.8985 0.000 0.952 0.024 0.000 0.024 0.000
#> GSM1296045 2 0.1442 0.8979 0.000 0.944 0.012 0.000 0.040 0.004
#> GSM1296025 1 0.3394 0.7307 0.752 0.000 0.000 0.000 0.236 0.012
#> GSM1296033 1 0.1851 0.7837 0.928 0.000 0.024 0.012 0.036 0.000
#> GSM1296027 1 0.1151 0.7861 0.956 0.000 0.032 0.000 0.012 0.000
#> GSM1296032 1 0.3003 0.7276 0.812 0.000 0.000 0.000 0.172 0.016
#> GSM1296024 1 0.2886 0.7760 0.836 0.000 0.016 0.000 0.144 0.004
#> GSM1296031 1 0.3817 0.7559 0.792 0.000 0.020 0.000 0.140 0.048
#> GSM1296028 1 0.1500 0.7857 0.936 0.000 0.012 0.000 0.052 0.000
#> GSM1296029 1 0.2046 0.7903 0.916 0.000 0.044 0.000 0.032 0.008
#> GSM1296026 1 0.6166 0.5510 0.612 0.000 0.072 0.156 0.152 0.008
#> GSM1296030 1 0.1549 0.7817 0.936 0.000 0.044 0.000 0.020 0.000
#> GSM1296040 3 0.2852 0.7544 0.000 0.000 0.856 0.064 0.000 0.080
#> GSM1296036 3 0.1788 0.7512 0.052 0.000 0.928 0.004 0.012 0.004
#> GSM1296048 4 0.3252 0.6787 0.000 0.128 0.004 0.832 0.020 0.016
#> GSM1296059 3 0.3492 0.7520 0.000 0.000 0.804 0.120 0.000 0.076
#> GSM1296066 2 0.0603 0.9033 0.000 0.980 0.000 0.004 0.016 0.000
#> GSM1296060 3 0.5716 0.3258 0.000 0.000 0.500 0.188 0.000 0.312
#> GSM1296063 4 0.4636 -0.0823 0.000 0.000 0.008 0.484 0.024 0.484
#> GSM1296064 4 0.1176 0.7419 0.000 0.000 0.024 0.956 0.000 0.020
#> GSM1296067 2 0.1148 0.9010 0.000 0.960 0.020 0.000 0.016 0.004
#> GSM1296062 3 0.1577 0.7576 0.036 0.000 0.940 0.008 0.000 0.016
#> GSM1296068 2 0.0806 0.9011 0.000 0.972 0.020 0.000 0.008 0.000
#> GSM1296050 1 0.4575 0.5667 0.600 0.000 0.000 0.000 0.352 0.048
#> GSM1296057 6 0.3697 0.5337 0.004 0.000 0.016 0.000 0.248 0.732
#> GSM1296052 1 0.0993 0.7884 0.964 0.000 0.024 0.000 0.012 0.000
#> GSM1296054 1 0.2263 0.7799 0.884 0.000 0.016 0.000 0.100 0.000
#> GSM1296049 1 0.2196 0.7837 0.884 0.000 0.004 0.000 0.108 0.004
#> GSM1296055 6 0.3263 0.6082 0.004 0.004 0.020 0.000 0.160 0.812
#> GSM1296053 1 0.2389 0.7899 0.888 0.000 0.060 0.000 0.052 0.000
#> GSM1296058 6 0.1408 0.6705 0.000 0.000 0.036 0.000 0.020 0.944
#> GSM1296051 4 0.4200 0.5240 0.240 0.008 0.000 0.716 0.032 0.004
#> GSM1296056 4 0.4019 0.5473 0.000 0.000 0.028 0.740 0.016 0.216
#> GSM1296065 6 0.4012 0.5590 0.000 0.008 0.000 0.232 0.032 0.728
#> GSM1296061 3 0.2067 0.7436 0.064 0.000 0.912 0.004 0.016 0.004
#> GSM1296095 6 0.5229 0.6063 0.000 0.028 0.112 0.152 0.012 0.696
#> GSM1296120 2 0.5630 0.2644 0.004 0.540 0.004 0.000 0.320 0.132
#> GSM1296077 1 0.4452 0.6234 0.636 0.000 0.000 0.000 0.316 0.048
#> GSM1296093 1 0.4862 0.2088 0.520 0.000 0.004 0.000 0.428 0.048
#> GSM1296104 6 0.4293 0.4671 0.000 0.000 0.012 0.024 0.292 0.672
#> GSM1296079 1 0.3514 0.7248 0.752 0.000 0.000 0.000 0.228 0.020
#> GSM1296108 2 0.0820 0.9018 0.000 0.972 0.016 0.000 0.012 0.000
#> GSM1296110 2 0.0692 0.9012 0.000 0.976 0.020 0.000 0.004 0.000
#> GSM1296081 1 0.3025 0.7650 0.820 0.000 0.000 0.000 0.156 0.024
#> GSM1296091 1 0.4947 0.4495 0.652 0.004 0.004 0.040 0.280 0.020
#> GSM1296075 5 0.5699 -0.3020 0.448 0.016 0.000 0.040 0.464 0.032
#> GSM1296112 2 0.0909 0.9025 0.000 0.968 0.012 0.000 0.020 0.000
#> GSM1296100 5 0.5424 0.0660 0.052 0.000 0.032 0.000 0.516 0.400
#> GSM1296087 1 0.1003 0.7923 0.964 0.000 0.016 0.000 0.020 0.000
#> GSM1296118 2 0.3654 0.6991 0.004 0.764 0.004 0.000 0.208 0.020
#> GSM1296114 2 0.1176 0.8986 0.000 0.956 0.020 0.000 0.024 0.000
#> GSM1296097 6 0.1296 0.6698 0.000 0.000 0.032 0.004 0.012 0.952
#> GSM1296106 6 0.4467 0.3834 0.000 0.000 0.048 0.000 0.320 0.632
#> GSM1296102 6 0.4495 0.5505 0.004 0.000 0.196 0.000 0.092 0.708
#> GSM1296122 5 0.5748 0.2142 0.032 0.324 0.000 0.000 0.548 0.096
#> GSM1296089 1 0.2428 0.7894 0.896 0.000 0.020 0.000 0.060 0.024
#> GSM1296083 1 0.3279 0.7537 0.796 0.000 0.000 0.000 0.176 0.028
#> GSM1296116 2 0.0972 0.9022 0.000 0.964 0.008 0.000 0.028 0.000
#> GSM1296085 1 0.2019 0.7806 0.900 0.000 0.000 0.000 0.088 0.012
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# 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 tissue(p) time(p) agent(p) dose(p) individual(p) k
#> MAD:NMF 96 0.043926 0.0862 0.5448 9.92e-07 3.71e-04 2
#> MAD:NMF 95 0.000819 0.1790 0.0107 9.41e-10 5.49e-06 3
#> MAD:NMF 88 0.001708 0.3431 0.0223 2.08e-08 3.05e-08 4
#> MAD:NMF 91 0.002645 0.2701 0.1798 8.21e-08 8.09e-09 5
#> MAD:NMF 82 0.028707 0.1962 0.0732 2.64e-08 1.23e-07 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 45638 rows and 99 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 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 0.867 0.860 0.946 0.4945 0.496 0.496
#> 3 3 0.659 0.745 0.816 0.2905 0.841 0.689
#> 4 4 0.762 0.795 0.875 0.1134 0.951 0.868
#> 5 5 0.876 0.885 0.931 0.1095 0.880 0.643
#> 6 6 0.852 0.884 0.912 0.0259 0.977 0.893
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
#> GSM1296094 1 0.204 0.9232 0.968 0.032
#> GSM1296119 2 0.000 0.9490 0.000 1.000
#> GSM1296076 2 0.000 0.9490 0.000 1.000
#> GSM1296092 2 0.430 0.8883 0.088 0.912
#> GSM1296103 1 0.242 0.9176 0.960 0.040
#> GSM1296078 2 0.000 0.9490 0.000 1.000
#> GSM1296107 2 0.000 0.9490 0.000 1.000
#> GSM1296109 2 0.141 0.9396 0.020 0.980
#> GSM1296080 1 0.000 0.9297 1.000 0.000
#> GSM1296090 2 0.430 0.8883 0.088 0.912
#> GSM1296074 2 0.000 0.9490 0.000 1.000
#> GSM1296111 2 0.000 0.9490 0.000 1.000
#> GSM1296099 2 0.430 0.8883 0.088 0.912
#> GSM1296086 2 0.456 0.8803 0.096 0.904
#> GSM1296117 2 0.000 0.9490 0.000 1.000
#> GSM1296113 2 0.000 0.9490 0.000 1.000
#> GSM1296096 2 0.430 0.8883 0.088 0.912
#> GSM1296105 1 0.204 0.9232 0.968 0.032
#> GSM1296098 1 0.000 0.9297 1.000 0.000
#> GSM1296101 1 0.224 0.9206 0.964 0.036
#> GSM1296121 2 0.000 0.9490 0.000 1.000
#> GSM1296088 1 0.242 0.9176 0.960 0.040
#> GSM1296082 2 0.184 0.9348 0.028 0.972
#> GSM1296115 2 0.000 0.9490 0.000 1.000
#> GSM1296084 1 0.184 0.9251 0.972 0.028
#> GSM1296072 2 0.000 0.9490 0.000 1.000
#> GSM1296069 2 0.000 0.9490 0.000 1.000
#> GSM1296071 2 0.000 0.9490 0.000 1.000
#> GSM1296070 2 0.000 0.9490 0.000 1.000
#> GSM1296073 2 0.000 0.9490 0.000 1.000
#> GSM1296034 1 0.000 0.9297 1.000 0.000
#> GSM1296041 2 0.000 0.9490 0.000 1.000
#> GSM1296035 2 0.430 0.8883 0.088 0.912
#> GSM1296038 2 0.184 0.9348 0.028 0.972
#> GSM1296047 2 0.000 0.9490 0.000 1.000
#> GSM1296039 2 0.000 0.9490 0.000 1.000
#> GSM1296042 2 0.000 0.9490 0.000 1.000
#> GSM1296043 2 0.000 0.9490 0.000 1.000
#> GSM1296037 1 0.141 0.9280 0.980 0.020
#> GSM1296046 2 0.000 0.9490 0.000 1.000
#> GSM1296044 2 0.000 0.9490 0.000 1.000
#> GSM1296045 2 0.000 0.9490 0.000 1.000
#> GSM1296025 1 0.000 0.9297 1.000 0.000
#> GSM1296033 1 0.184 0.9251 0.972 0.028
#> GSM1296027 1 0.000 0.9297 1.000 0.000
#> GSM1296032 1 0.000 0.9297 1.000 0.000
#> GSM1296024 1 0.000 0.9297 1.000 0.000
#> GSM1296031 1 0.141 0.9280 0.980 0.020
#> GSM1296028 1 0.000 0.9297 1.000 0.000
#> GSM1296029 1 0.000 0.9297 1.000 0.000
#> GSM1296026 1 0.204 0.9232 0.968 0.032
#> GSM1296030 1 0.000 0.9297 1.000 0.000
#> GSM1296040 1 0.224 0.9206 0.964 0.036
#> GSM1296036 1 0.000 0.9297 1.000 0.000
#> GSM1296048 2 0.000 0.9490 0.000 1.000
#> GSM1296059 1 0.242 0.9176 0.960 0.040
#> GSM1296066 2 0.000 0.9490 0.000 1.000
#> GSM1296060 2 0.430 0.8883 0.088 0.912
#> GSM1296063 2 0.000 0.9490 0.000 1.000
#> GSM1296064 2 0.000 0.9490 0.000 1.000
#> GSM1296067 1 0.999 0.0750 0.516 0.484
#> GSM1296062 1 0.141 0.9280 0.980 0.020
#> GSM1296068 2 0.000 0.9490 0.000 1.000
#> GSM1296050 1 0.999 0.0888 0.520 0.480
#> GSM1296057 1 0.163 0.9268 0.976 0.024
#> GSM1296052 1 0.000 0.9297 1.000 0.000
#> GSM1296054 1 0.000 0.9297 1.000 0.000
#> GSM1296049 1 0.000 0.9297 1.000 0.000
#> GSM1296055 2 1.000 -0.0335 0.496 0.504
#> GSM1296053 1 0.000 0.9297 1.000 0.000
#> GSM1296058 1 0.163 0.9268 0.976 0.024
#> GSM1296051 1 0.999 0.0750 0.516 0.484
#> GSM1296056 2 0.430 0.8883 0.088 0.912
#> GSM1296065 2 0.163 0.9372 0.024 0.976
#> GSM1296061 1 0.000 0.9297 1.000 0.000
#> GSM1296095 2 0.163 0.9372 0.024 0.976
#> GSM1296120 2 0.000 0.9490 0.000 1.000
#> GSM1296077 1 0.000 0.9297 1.000 0.000
#> GSM1296093 1 0.000 0.9297 1.000 0.000
#> GSM1296104 2 0.163 0.9372 0.024 0.976
#> GSM1296079 1 0.000 0.9297 1.000 0.000
#> GSM1296108 2 0.000 0.9490 0.000 1.000
#> GSM1296110 2 0.000 0.9490 0.000 1.000
#> GSM1296081 1 0.000 0.9297 1.000 0.000
#> GSM1296091 1 0.204 0.9232 0.968 0.032
#> GSM1296075 1 0.999 0.0750 0.516 0.484
#> GSM1296112 2 0.000 0.9490 0.000 1.000
#> GSM1296100 1 0.141 0.9280 0.980 0.020
#> GSM1296087 1 0.000 0.9297 1.000 0.000
#> GSM1296118 2 0.000 0.9490 0.000 1.000
#> GSM1296114 2 0.000 0.9490 0.000 1.000
#> GSM1296097 2 1.000 -0.0335 0.496 0.504
#> GSM1296106 2 1.000 -0.0335 0.496 0.504
#> GSM1296102 1 0.141 0.9280 0.980 0.020
#> GSM1296122 1 0.999 0.0750 0.516 0.484
#> GSM1296089 1 0.141 0.9280 0.980 0.020
#> GSM1296083 1 0.000 0.9297 1.000 0.000
#> GSM1296116 2 0.000 0.9490 0.000 1.000
#> GSM1296085 1 0.000 0.9297 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1296094 3 0.6267 0.685 0.452 0.000 0.548
#> GSM1296119 2 0.0000 0.848 0.000 1.000 0.000
#> GSM1296076 2 0.4974 0.806 0.000 0.764 0.236
#> GSM1296092 2 0.6215 0.680 0.000 0.572 0.428
#> GSM1296103 3 0.6244 0.687 0.440 0.000 0.560
#> GSM1296078 2 0.4974 0.806 0.000 0.764 0.236
#> GSM1296107 2 0.0000 0.848 0.000 1.000 0.000
#> GSM1296109 2 0.5560 0.777 0.000 0.700 0.300
#> GSM1296080 1 0.0000 0.920 1.000 0.000 0.000
#> GSM1296090 2 0.6215 0.680 0.000 0.572 0.428
#> GSM1296074 2 0.4974 0.806 0.000 0.764 0.236
#> GSM1296111 2 0.0000 0.848 0.000 1.000 0.000
#> GSM1296099 2 0.6215 0.680 0.000 0.572 0.428
#> GSM1296086 2 0.6235 0.672 0.000 0.564 0.436
#> GSM1296117 2 0.0000 0.848 0.000 1.000 0.000
#> GSM1296113 2 0.0000 0.848 0.000 1.000 0.000
#> GSM1296096 2 0.6215 0.680 0.000 0.572 0.428
#> GSM1296105 3 0.6260 0.687 0.448 0.000 0.552
#> GSM1296098 1 0.6309 -0.625 0.500 0.000 0.500
#> GSM1296101 3 0.6252 0.688 0.444 0.000 0.556
#> GSM1296121 2 0.0000 0.848 0.000 1.000 0.000
#> GSM1296088 3 0.6244 0.687 0.440 0.000 0.560
#> GSM1296082 2 0.5988 0.736 0.000 0.632 0.368
#> GSM1296115 2 0.0000 0.848 0.000 1.000 0.000
#> GSM1296084 3 0.6267 0.685 0.452 0.000 0.548
#> GSM1296072 2 0.4399 0.779 0.000 0.812 0.188
#> GSM1296069 2 0.0000 0.848 0.000 1.000 0.000
#> GSM1296071 2 0.1163 0.847 0.000 0.972 0.028
#> GSM1296070 2 0.0000 0.848 0.000 1.000 0.000
#> GSM1296073 2 0.0000 0.848 0.000 1.000 0.000
#> GSM1296034 1 0.0000 0.920 1.000 0.000 0.000
#> GSM1296041 2 0.0000 0.848 0.000 1.000 0.000
#> GSM1296035 2 0.6215 0.680 0.000 0.572 0.428
#> GSM1296038 2 0.5926 0.745 0.000 0.644 0.356
#> GSM1296047 2 0.4399 0.779 0.000 0.812 0.188
#> GSM1296039 2 0.4974 0.806 0.000 0.764 0.236
#> GSM1296042 2 0.0000 0.848 0.000 1.000 0.000
#> GSM1296043 2 0.0000 0.848 0.000 1.000 0.000
#> GSM1296037 3 0.6286 0.671 0.464 0.000 0.536
#> GSM1296046 2 0.0000 0.848 0.000 1.000 0.000
#> GSM1296044 2 0.0000 0.848 0.000 1.000 0.000
#> GSM1296045 2 0.0000 0.848 0.000 1.000 0.000
#> GSM1296025 1 0.0000 0.920 1.000 0.000 0.000
#> GSM1296033 3 0.6267 0.685 0.452 0.000 0.548
#> GSM1296027 1 0.0000 0.920 1.000 0.000 0.000
#> GSM1296032 1 0.0000 0.920 1.000 0.000 0.000
#> GSM1296024 1 0.0000 0.920 1.000 0.000 0.000
#> GSM1296031 3 0.6286 0.671 0.464 0.000 0.536
#> GSM1296028 1 0.0000 0.920 1.000 0.000 0.000
#> GSM1296029 1 0.0000 0.920 1.000 0.000 0.000
#> GSM1296026 3 0.6260 0.687 0.448 0.000 0.552
#> GSM1296030 1 0.0424 0.911 0.992 0.000 0.008
#> GSM1296040 3 0.6252 0.688 0.444 0.000 0.556
#> GSM1296036 1 0.6309 -0.625 0.500 0.000 0.500
#> GSM1296048 2 0.0000 0.848 0.000 1.000 0.000
#> GSM1296059 3 0.6244 0.687 0.440 0.000 0.560
#> GSM1296066 2 0.0000 0.848 0.000 1.000 0.000
#> GSM1296060 2 0.6215 0.680 0.000 0.572 0.428
#> GSM1296063 2 0.4974 0.806 0.000 0.764 0.236
#> GSM1296064 2 0.4974 0.806 0.000 0.764 0.236
#> GSM1296067 3 0.0237 0.513 0.000 0.004 0.996
#> GSM1296062 3 0.6286 0.671 0.464 0.000 0.536
#> GSM1296068 2 0.1163 0.847 0.000 0.972 0.028
#> GSM1296050 3 0.0000 0.514 0.000 0.000 1.000
#> GSM1296057 3 0.6274 0.681 0.456 0.000 0.544
#> GSM1296052 1 0.0000 0.920 1.000 0.000 0.000
#> GSM1296054 1 0.0000 0.920 1.000 0.000 0.000
#> GSM1296049 1 0.0000 0.920 1.000 0.000 0.000
#> GSM1296055 3 0.1031 0.500 0.000 0.024 0.976
#> GSM1296053 1 0.0000 0.920 1.000 0.000 0.000
#> GSM1296058 3 0.6274 0.681 0.456 0.000 0.544
#> GSM1296051 3 0.0237 0.513 0.000 0.004 0.996
#> GSM1296056 2 0.6215 0.680 0.000 0.572 0.428
#> GSM1296065 2 0.5905 0.748 0.000 0.648 0.352
#> GSM1296061 3 0.6309 0.592 0.500 0.000 0.500
#> GSM1296095 2 0.5905 0.748 0.000 0.648 0.352
#> GSM1296120 2 0.4399 0.779 0.000 0.812 0.188
#> GSM1296077 1 0.0424 0.910 0.992 0.000 0.008
#> GSM1296093 1 0.0000 0.920 1.000 0.000 0.000
#> GSM1296104 2 0.5905 0.748 0.000 0.648 0.352
#> GSM1296079 1 0.0424 0.910 0.992 0.000 0.008
#> GSM1296108 2 0.1163 0.847 0.000 0.972 0.028
#> GSM1296110 2 0.4399 0.779 0.000 0.812 0.188
#> GSM1296081 1 0.0000 0.920 1.000 0.000 0.000
#> GSM1296091 3 0.6260 0.687 0.448 0.000 0.552
#> GSM1296075 3 0.0237 0.513 0.000 0.004 0.996
#> GSM1296112 2 0.1163 0.847 0.000 0.972 0.028
#> GSM1296100 3 0.6286 0.671 0.464 0.000 0.536
#> GSM1296087 1 0.0000 0.920 1.000 0.000 0.000
#> GSM1296118 2 0.4399 0.779 0.000 0.812 0.188
#> GSM1296114 2 0.0000 0.848 0.000 1.000 0.000
#> GSM1296097 3 0.1031 0.500 0.000 0.024 0.976
#> GSM1296106 3 0.1031 0.500 0.000 0.024 0.976
#> GSM1296102 3 0.6286 0.671 0.464 0.000 0.536
#> GSM1296122 3 0.0237 0.513 0.000 0.004 0.996
#> GSM1296089 3 0.6286 0.671 0.464 0.000 0.536
#> GSM1296083 1 0.0000 0.920 1.000 0.000 0.000
#> GSM1296116 2 0.0000 0.848 0.000 1.000 0.000
#> GSM1296085 1 0.0000 0.920 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1296094 3 0.0524 0.834 0.004 0.000 0.988 0.008
#> GSM1296119 2 0.0000 0.796 0.000 1.000 0.000 0.000
#> GSM1296076 2 0.4222 0.707 0.000 0.728 0.000 0.272
#> GSM1296092 2 0.4985 0.501 0.000 0.532 0.000 0.468
#> GSM1296103 3 0.0592 0.832 0.000 0.000 0.984 0.016
#> GSM1296078 2 0.4222 0.707 0.000 0.728 0.000 0.272
#> GSM1296107 2 0.0000 0.796 0.000 1.000 0.000 0.000
#> GSM1296109 2 0.4605 0.660 0.000 0.664 0.000 0.336
#> GSM1296080 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> GSM1296090 2 0.4985 0.501 0.000 0.532 0.000 0.468
#> GSM1296074 2 0.4222 0.707 0.000 0.728 0.000 0.272
#> GSM1296111 2 0.0000 0.796 0.000 1.000 0.000 0.000
#> GSM1296099 2 0.4985 0.501 0.000 0.532 0.000 0.468
#> GSM1296086 2 0.5388 0.495 0.000 0.532 0.012 0.456
#> GSM1296117 2 0.0000 0.796 0.000 1.000 0.000 0.000
#> GSM1296113 2 0.0000 0.796 0.000 1.000 0.000 0.000
#> GSM1296096 2 0.4985 0.501 0.000 0.532 0.000 0.468
#> GSM1296105 3 0.3266 0.760 0.000 0.000 0.832 0.168
#> GSM1296098 3 0.1302 0.814 0.044 0.000 0.956 0.000
#> GSM1296101 3 0.0469 0.833 0.000 0.000 0.988 0.012
#> GSM1296121 2 0.0000 0.796 0.000 1.000 0.000 0.000
#> GSM1296088 3 0.4855 0.571 0.000 0.000 0.600 0.400
#> GSM1296082 2 0.4877 0.586 0.000 0.592 0.000 0.408
#> GSM1296115 2 0.0000 0.796 0.000 1.000 0.000 0.000
#> GSM1296084 3 0.4817 0.586 0.000 0.000 0.612 0.388
#> GSM1296072 2 0.3528 0.695 0.000 0.808 0.000 0.192
#> GSM1296069 2 0.0000 0.796 0.000 1.000 0.000 0.000
#> GSM1296071 2 0.1022 0.793 0.000 0.968 0.000 0.032
#> GSM1296070 2 0.0000 0.796 0.000 1.000 0.000 0.000
#> GSM1296073 2 0.0000 0.796 0.000 1.000 0.000 0.000
#> GSM1296034 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> GSM1296041 2 0.0000 0.796 0.000 1.000 0.000 0.000
#> GSM1296035 2 0.4985 0.501 0.000 0.532 0.000 0.468
#> GSM1296038 2 0.4843 0.600 0.000 0.604 0.000 0.396
#> GSM1296047 2 0.3528 0.695 0.000 0.808 0.000 0.192
#> GSM1296039 2 0.4222 0.707 0.000 0.728 0.000 0.272
#> GSM1296042 2 0.0000 0.796 0.000 1.000 0.000 0.000
#> GSM1296043 2 0.0000 0.796 0.000 1.000 0.000 0.000
#> GSM1296037 3 0.0336 0.834 0.008 0.000 0.992 0.000
#> GSM1296046 2 0.0000 0.796 0.000 1.000 0.000 0.000
#> GSM1296044 2 0.0000 0.796 0.000 1.000 0.000 0.000
#> GSM1296045 2 0.0000 0.796 0.000 1.000 0.000 0.000
#> GSM1296025 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> GSM1296033 3 0.4817 0.586 0.000 0.000 0.612 0.388
#> GSM1296027 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> GSM1296032 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> GSM1296024 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> GSM1296031 3 0.5112 0.591 0.008 0.000 0.608 0.384
#> GSM1296028 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> GSM1296029 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> GSM1296026 3 0.4830 0.582 0.000 0.000 0.608 0.392
#> GSM1296030 1 0.0336 0.991 0.992 0.000 0.008 0.000
#> GSM1296040 3 0.0469 0.833 0.000 0.000 0.988 0.012
#> GSM1296036 3 0.1302 0.814 0.044 0.000 0.956 0.000
#> GSM1296048 2 0.0000 0.796 0.000 1.000 0.000 0.000
#> GSM1296059 3 0.0592 0.832 0.000 0.000 0.984 0.016
#> GSM1296066 2 0.0000 0.796 0.000 1.000 0.000 0.000
#> GSM1296060 2 0.4985 0.501 0.000 0.532 0.000 0.468
#> GSM1296063 2 0.4222 0.707 0.000 0.728 0.000 0.272
#> GSM1296064 2 0.4222 0.707 0.000 0.728 0.000 0.272
#> GSM1296067 4 0.1211 0.985 0.000 0.000 0.040 0.960
#> GSM1296062 3 0.0336 0.834 0.008 0.000 0.992 0.000
#> GSM1296068 2 0.1022 0.793 0.000 0.968 0.000 0.032
#> GSM1296050 4 0.1389 0.976 0.000 0.000 0.048 0.952
#> GSM1296057 3 0.0000 0.834 0.000 0.000 1.000 0.000
#> GSM1296052 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> GSM1296055 4 0.1004 0.977 0.000 0.004 0.024 0.972
#> GSM1296053 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> GSM1296058 3 0.0000 0.834 0.000 0.000 1.000 0.000
#> GSM1296051 4 0.1211 0.985 0.000 0.000 0.040 0.960
#> GSM1296056 2 0.4985 0.501 0.000 0.532 0.000 0.468
#> GSM1296065 2 0.4830 0.605 0.000 0.608 0.000 0.392
#> GSM1296061 3 0.1302 0.814 0.044 0.000 0.956 0.000
#> GSM1296095 2 0.4830 0.605 0.000 0.608 0.000 0.392
#> GSM1296120 2 0.3528 0.695 0.000 0.808 0.000 0.192
#> GSM1296077 1 0.0469 0.987 0.988 0.000 0.012 0.000
#> GSM1296093 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> GSM1296104 2 0.4830 0.605 0.000 0.608 0.000 0.392
#> GSM1296079 1 0.0469 0.987 0.988 0.000 0.012 0.000
#> GSM1296108 2 0.1022 0.793 0.000 0.968 0.000 0.032
#> GSM1296110 2 0.3528 0.695 0.000 0.808 0.000 0.192
#> GSM1296081 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> GSM1296091 3 0.4830 0.582 0.000 0.000 0.608 0.392
#> GSM1296075 4 0.1211 0.985 0.000 0.000 0.040 0.960
#> GSM1296112 2 0.1022 0.793 0.000 0.968 0.000 0.032
#> GSM1296100 3 0.0336 0.834 0.008 0.000 0.992 0.000
#> GSM1296087 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> GSM1296118 2 0.3528 0.695 0.000 0.808 0.000 0.192
#> GSM1296114 2 0.0000 0.796 0.000 1.000 0.000 0.000
#> GSM1296097 4 0.1004 0.977 0.000 0.004 0.024 0.972
#> GSM1296106 4 0.1004 0.977 0.000 0.004 0.024 0.972
#> GSM1296102 3 0.0336 0.834 0.008 0.000 0.992 0.000
#> GSM1296122 4 0.1211 0.985 0.000 0.000 0.040 0.960
#> GSM1296089 3 0.5112 0.591 0.008 0.000 0.608 0.384
#> GSM1296083 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> GSM1296116 2 0.0000 0.796 0.000 1.000 0.000 0.000
#> GSM1296085 1 0.0000 0.998 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
#> GSM1296094 3 0.0404 0.821 0.000 0.000 0.988 0.000 0.012
#> GSM1296119 2 0.0000 0.947 0.000 1.000 0.000 0.000 0.000
#> GSM1296076 4 0.2648 0.848 0.000 0.152 0.000 0.848 0.000
#> GSM1296092 4 0.1121 0.890 0.000 0.000 0.000 0.956 0.044
#> GSM1296103 3 0.0794 0.819 0.000 0.000 0.972 0.000 0.028
#> GSM1296078 4 0.2648 0.848 0.000 0.152 0.000 0.848 0.000
#> GSM1296107 2 0.0000 0.947 0.000 1.000 0.000 0.000 0.000
#> GSM1296109 4 0.1851 0.881 0.000 0.088 0.000 0.912 0.000
#> GSM1296080 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> GSM1296090 4 0.1121 0.890 0.000 0.000 0.000 0.956 0.044
#> GSM1296074 4 0.2648 0.848 0.000 0.152 0.000 0.848 0.000
#> GSM1296111 2 0.0000 0.947 0.000 1.000 0.000 0.000 0.000
#> GSM1296099 4 0.1121 0.890 0.000 0.000 0.000 0.956 0.044
#> GSM1296086 4 0.1544 0.879 0.000 0.000 0.000 0.932 0.068
#> GSM1296117 2 0.0000 0.947 0.000 1.000 0.000 0.000 0.000
#> GSM1296113 2 0.0000 0.947 0.000 1.000 0.000 0.000 0.000
#> GSM1296096 4 0.1121 0.890 0.000 0.000 0.000 0.956 0.044
#> GSM1296105 3 0.3003 0.733 0.000 0.000 0.812 0.000 0.188
#> GSM1296098 3 0.1251 0.800 0.036 0.000 0.956 0.000 0.008
#> GSM1296101 3 0.0609 0.820 0.000 0.000 0.980 0.000 0.020
#> GSM1296121 2 0.0000 0.947 0.000 1.000 0.000 0.000 0.000
#> GSM1296088 3 0.4242 0.507 0.000 0.000 0.572 0.000 0.428
#> GSM1296082 4 0.0510 0.900 0.000 0.016 0.000 0.984 0.000
#> GSM1296115 2 0.0000 0.947 0.000 1.000 0.000 0.000 0.000
#> GSM1296084 3 0.4219 0.522 0.000 0.000 0.584 0.000 0.416
#> GSM1296072 2 0.4317 0.792 0.000 0.764 0.000 0.076 0.160
#> GSM1296069 2 0.0000 0.947 0.000 1.000 0.000 0.000 0.000
#> GSM1296071 2 0.1671 0.913 0.000 0.924 0.000 0.076 0.000
#> GSM1296070 2 0.0290 0.946 0.000 0.992 0.000 0.008 0.000
#> GSM1296073 2 0.0000 0.947 0.000 1.000 0.000 0.000 0.000
#> GSM1296034 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> GSM1296041 2 0.0000 0.947 0.000 1.000 0.000 0.000 0.000
#> GSM1296035 4 0.1121 0.890 0.000 0.000 0.000 0.956 0.044
#> GSM1296038 4 0.0794 0.902 0.000 0.028 0.000 0.972 0.000
#> GSM1296047 2 0.4317 0.792 0.000 0.764 0.000 0.076 0.160
#> GSM1296039 4 0.2648 0.848 0.000 0.152 0.000 0.848 0.000
#> GSM1296042 2 0.0000 0.947 0.000 1.000 0.000 0.000 0.000
#> GSM1296043 2 0.0290 0.946 0.000 0.992 0.000 0.008 0.000
#> GSM1296037 3 0.0162 0.819 0.000 0.000 0.996 0.000 0.004
#> GSM1296046 2 0.0404 0.945 0.000 0.988 0.000 0.012 0.000
#> GSM1296044 2 0.0404 0.945 0.000 0.988 0.000 0.012 0.000
#> GSM1296045 2 0.0290 0.946 0.000 0.992 0.000 0.008 0.000
#> GSM1296025 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> GSM1296033 3 0.4219 0.522 0.000 0.000 0.584 0.000 0.416
#> GSM1296027 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> GSM1296032 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> GSM1296024 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> GSM1296031 3 0.4192 0.530 0.000 0.000 0.596 0.000 0.404
#> GSM1296028 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> GSM1296029 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> GSM1296026 3 0.4235 0.516 0.000 0.000 0.576 0.000 0.424
#> GSM1296030 1 0.0290 0.991 0.992 0.000 0.008 0.000 0.000
#> GSM1296040 3 0.0609 0.820 0.000 0.000 0.980 0.000 0.020
#> GSM1296036 3 0.1251 0.800 0.036 0.000 0.956 0.000 0.008
#> GSM1296048 2 0.0000 0.947 0.000 1.000 0.000 0.000 0.000
#> GSM1296059 3 0.0794 0.819 0.000 0.000 0.972 0.000 0.028
#> GSM1296066 2 0.0000 0.947 0.000 1.000 0.000 0.000 0.000
#> GSM1296060 4 0.1121 0.890 0.000 0.000 0.000 0.956 0.044
#> GSM1296063 4 0.2648 0.848 0.000 0.152 0.000 0.848 0.000
#> GSM1296064 4 0.2648 0.848 0.000 0.152 0.000 0.848 0.000
#> GSM1296067 5 0.0404 0.980 0.000 0.000 0.000 0.012 0.988
#> GSM1296062 3 0.0162 0.819 0.000 0.000 0.996 0.000 0.004
#> GSM1296068 2 0.1671 0.913 0.000 0.924 0.000 0.076 0.000
#> GSM1296050 5 0.0451 0.973 0.000 0.000 0.004 0.008 0.988
#> GSM1296057 3 0.0290 0.821 0.000 0.000 0.992 0.000 0.008
#> GSM1296052 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> GSM1296055 5 0.1121 0.968 0.000 0.000 0.000 0.044 0.956
#> GSM1296053 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> GSM1296058 3 0.0290 0.821 0.000 0.000 0.992 0.000 0.008
#> GSM1296051 5 0.0404 0.980 0.000 0.000 0.000 0.012 0.988
#> GSM1296056 4 0.1121 0.890 0.000 0.000 0.000 0.956 0.044
#> GSM1296065 4 0.0880 0.903 0.000 0.032 0.000 0.968 0.000
#> GSM1296061 3 0.1251 0.800 0.036 0.000 0.956 0.000 0.008
#> GSM1296095 4 0.0880 0.903 0.000 0.032 0.000 0.968 0.000
#> GSM1296120 2 0.4317 0.792 0.000 0.764 0.000 0.076 0.160
#> GSM1296077 1 0.0451 0.989 0.988 0.000 0.004 0.000 0.008
#> GSM1296093 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> GSM1296104 4 0.0880 0.903 0.000 0.032 0.000 0.968 0.000
#> GSM1296079 1 0.0451 0.989 0.988 0.000 0.004 0.000 0.008
#> GSM1296108 2 0.1671 0.913 0.000 0.924 0.000 0.076 0.000
#> GSM1296110 2 0.4317 0.792 0.000 0.764 0.000 0.076 0.160
#> GSM1296081 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> GSM1296091 3 0.4227 0.518 0.000 0.000 0.580 0.000 0.420
#> GSM1296075 5 0.0404 0.980 0.000 0.000 0.000 0.012 0.988
#> GSM1296112 2 0.1671 0.913 0.000 0.924 0.000 0.076 0.000
#> GSM1296100 3 0.0162 0.819 0.000 0.000 0.996 0.000 0.004
#> GSM1296087 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> GSM1296118 2 0.4317 0.792 0.000 0.764 0.000 0.076 0.160
#> GSM1296114 2 0.0404 0.945 0.000 0.988 0.000 0.012 0.000
#> GSM1296097 5 0.1121 0.968 0.000 0.000 0.000 0.044 0.956
#> GSM1296106 5 0.1121 0.968 0.000 0.000 0.000 0.044 0.956
#> GSM1296102 3 0.0162 0.819 0.000 0.000 0.996 0.000 0.004
#> GSM1296122 5 0.0404 0.980 0.000 0.000 0.000 0.012 0.988
#> GSM1296089 3 0.4192 0.530 0.000 0.000 0.596 0.000 0.404
#> GSM1296083 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> GSM1296116 2 0.0404 0.945 0.000 0.988 0.000 0.012 0.000
#> GSM1296085 1 0.0000 0.998 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
#> GSM1296094 3 0.2402 0.7811 0.000 0.000 0.856 0.000 0.140 0.004
#> GSM1296119 2 0.0000 0.9326 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296076 4 0.2048 0.8386 0.000 0.120 0.000 0.880 0.000 0.000
#> GSM1296092 4 0.2263 0.8763 0.000 0.000 0.000 0.896 0.048 0.056
#> GSM1296103 3 0.2706 0.7744 0.000 0.000 0.832 0.000 0.160 0.008
#> GSM1296078 4 0.2048 0.8386 0.000 0.120 0.000 0.880 0.000 0.000
#> GSM1296107 2 0.0000 0.9326 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296109 4 0.2554 0.8731 0.000 0.076 0.000 0.876 0.048 0.000
#> GSM1296080 1 0.0146 0.9943 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM1296090 4 0.2263 0.8763 0.000 0.000 0.000 0.896 0.048 0.056
#> GSM1296074 4 0.2048 0.8386 0.000 0.120 0.000 0.880 0.000 0.000
#> GSM1296111 2 0.0000 0.9326 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296099 4 0.2263 0.8763 0.000 0.000 0.000 0.896 0.048 0.056
#> GSM1296086 4 0.2451 0.8677 0.000 0.000 0.000 0.884 0.056 0.060
#> GSM1296117 2 0.0000 0.9326 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296113 2 0.0000 0.9326 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296096 4 0.2263 0.8763 0.000 0.000 0.000 0.896 0.048 0.056
#> GSM1296105 5 0.3774 -0.0323 0.000 0.000 0.408 0.000 0.592 0.000
#> GSM1296098 3 0.0000 0.7668 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296101 3 0.2558 0.7785 0.000 0.000 0.840 0.000 0.156 0.004
#> GSM1296121 2 0.0000 0.9326 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296088 5 0.1196 0.8644 0.000 0.000 0.040 0.000 0.952 0.008
#> GSM1296082 4 0.0748 0.8899 0.000 0.004 0.000 0.976 0.016 0.004
#> GSM1296115 2 0.0000 0.9326 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296084 5 0.1267 0.8728 0.000 0.000 0.060 0.000 0.940 0.000
#> GSM1296072 2 0.4253 0.7697 0.000 0.732 0.000 0.108 0.000 0.160
#> GSM1296069 2 0.0000 0.9326 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296071 2 0.1910 0.8935 0.000 0.892 0.000 0.108 0.000 0.000
#> GSM1296070 2 0.0547 0.9319 0.000 0.980 0.000 0.020 0.000 0.000
#> GSM1296073 2 0.0000 0.9326 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296034 1 0.0865 0.9679 0.964 0.000 0.036 0.000 0.000 0.000
#> GSM1296041 2 0.0000 0.9326 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296035 4 0.2263 0.8763 0.000 0.000 0.000 0.896 0.048 0.056
#> GSM1296038 4 0.0146 0.8894 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM1296047 2 0.4253 0.7697 0.000 0.732 0.000 0.108 0.000 0.160
#> GSM1296039 4 0.2048 0.8386 0.000 0.120 0.000 0.880 0.000 0.000
#> GSM1296042 2 0.0000 0.9326 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296043 2 0.0547 0.9319 0.000 0.980 0.000 0.020 0.000 0.000
#> GSM1296037 3 0.3175 0.7152 0.000 0.000 0.744 0.000 0.256 0.000
#> GSM1296046 2 0.0865 0.9288 0.000 0.964 0.000 0.036 0.000 0.000
#> GSM1296044 2 0.0865 0.9288 0.000 0.964 0.000 0.036 0.000 0.000
#> GSM1296045 2 0.0547 0.9319 0.000 0.980 0.000 0.020 0.000 0.000
#> GSM1296025 1 0.0000 0.9965 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296033 5 0.1267 0.8728 0.000 0.000 0.060 0.000 0.940 0.000
#> GSM1296027 1 0.0000 0.9965 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296032 1 0.0000 0.9965 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296024 1 0.0000 0.9965 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296031 5 0.2454 0.8101 0.000 0.000 0.160 0.000 0.840 0.000
#> GSM1296028 1 0.0000 0.9965 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296029 1 0.0000 0.9965 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296026 5 0.1204 0.8686 0.000 0.000 0.056 0.000 0.944 0.000
#> GSM1296030 1 0.0260 0.9907 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM1296040 3 0.2558 0.7785 0.000 0.000 0.840 0.000 0.156 0.004
#> GSM1296036 3 0.0000 0.7668 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296048 2 0.0000 0.9326 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296059 3 0.2706 0.7744 0.000 0.000 0.832 0.000 0.160 0.008
#> GSM1296066 2 0.0000 0.9326 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296060 4 0.2263 0.8763 0.000 0.000 0.000 0.896 0.048 0.056
#> GSM1296063 4 0.2048 0.8386 0.000 0.120 0.000 0.880 0.000 0.000
#> GSM1296064 4 0.2048 0.8386 0.000 0.120 0.000 0.880 0.000 0.000
#> GSM1296067 6 0.0000 0.9801 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1296062 3 0.3076 0.7296 0.000 0.000 0.760 0.000 0.240 0.000
#> GSM1296068 2 0.1910 0.8935 0.000 0.892 0.000 0.108 0.000 0.000
#> GSM1296050 6 0.0260 0.9739 0.000 0.000 0.000 0.000 0.008 0.992
#> GSM1296057 3 0.3409 0.7047 0.000 0.000 0.700 0.000 0.300 0.000
#> GSM1296052 1 0.0000 0.9965 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.9965 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.9965 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296055 6 0.0790 0.9684 0.000 0.000 0.000 0.032 0.000 0.968
#> GSM1296053 1 0.0000 0.9965 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296058 3 0.3409 0.7047 0.000 0.000 0.700 0.000 0.300 0.000
#> GSM1296051 6 0.0000 0.9801 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1296056 4 0.2263 0.8763 0.000 0.000 0.000 0.896 0.048 0.056
#> GSM1296065 4 0.0291 0.8898 0.000 0.004 0.000 0.992 0.000 0.004
#> GSM1296061 3 0.0000 0.7668 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296095 4 0.0291 0.8898 0.000 0.004 0.000 0.992 0.000 0.004
#> GSM1296120 2 0.4253 0.7697 0.000 0.732 0.000 0.108 0.000 0.160
#> GSM1296077 1 0.0363 0.9886 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM1296093 1 0.0000 0.9965 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296104 4 0.0291 0.8898 0.000 0.004 0.000 0.992 0.000 0.004
#> GSM1296079 1 0.0363 0.9886 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM1296108 2 0.1910 0.8935 0.000 0.892 0.000 0.108 0.000 0.000
#> GSM1296110 2 0.4253 0.7697 0.000 0.732 0.000 0.108 0.000 0.160
#> GSM1296081 1 0.0000 0.9965 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296091 5 0.1075 0.8697 0.000 0.000 0.048 0.000 0.952 0.000
#> GSM1296075 6 0.0000 0.9801 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1296112 2 0.1910 0.8935 0.000 0.892 0.000 0.108 0.000 0.000
#> GSM1296100 3 0.3175 0.7152 0.000 0.000 0.744 0.000 0.256 0.000
#> GSM1296087 1 0.0000 0.9965 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296118 2 0.4253 0.7697 0.000 0.732 0.000 0.108 0.000 0.160
#> GSM1296114 2 0.0865 0.9288 0.000 0.964 0.000 0.036 0.000 0.000
#> GSM1296097 6 0.0790 0.9684 0.000 0.000 0.000 0.032 0.000 0.968
#> GSM1296106 6 0.0790 0.9684 0.000 0.000 0.000 0.032 0.000 0.968
#> GSM1296102 3 0.3175 0.7152 0.000 0.000 0.744 0.000 0.256 0.000
#> GSM1296122 6 0.0000 0.9801 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1296089 5 0.2454 0.8101 0.000 0.000 0.160 0.000 0.840 0.000
#> GSM1296083 1 0.0000 0.9965 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296116 2 0.0865 0.9288 0.000 0.964 0.000 0.036 0.000 0.000
#> GSM1296085 1 0.0000 0.9965 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)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# 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 tissue(p) time(p) agent(p) dose(p) individual(p) k
#> ATC:hclust 91 0.519 0.1024 0.707 2.12e-05 4.11e-04 2
#> ATC:hclust 94 0.333 0.1340 0.514 2.05e-05 3.43e-04 3
#> ATC:hclust 98 0.149 0.1308 0.677 1.36e-04 2.90e-04 4
#> ATC:hclust 99 0.147 0.0209 0.731 3.70e-05 2.57e-07 5
#> ATC:hclust 98 0.211 0.0234 0.832 1.80e-04 1.40e-08 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 45638 rows and 99 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
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 1.000 1.000 0.4969 0.504 0.504
#> 3 3 0.886 0.955 0.979 0.3517 0.728 0.507
#> 4 4 0.748 0.856 0.898 0.1152 0.829 0.543
#> 5 5 0.790 0.767 0.854 0.0606 0.946 0.788
#> 6 6 0.782 0.666 0.768 0.0371 0.973 0.877
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
#> GSM1296094 1 0.0000 1.000 1.000 0.000
#> GSM1296119 2 0.0000 1.000 0.000 1.000
#> GSM1296076 2 0.0000 1.000 0.000 1.000
#> GSM1296092 2 0.0000 1.000 0.000 1.000
#> GSM1296103 1 0.0376 0.996 0.996 0.004
#> GSM1296078 2 0.0000 1.000 0.000 1.000
#> GSM1296107 2 0.0000 1.000 0.000 1.000
#> GSM1296109 2 0.0000 1.000 0.000 1.000
#> GSM1296080 1 0.0000 1.000 1.000 0.000
#> GSM1296090 2 0.0000 1.000 0.000 1.000
#> GSM1296074 2 0.0000 1.000 0.000 1.000
#> GSM1296111 2 0.0000 1.000 0.000 1.000
#> GSM1296099 2 0.0000 1.000 0.000 1.000
#> GSM1296086 2 0.0000 1.000 0.000 1.000
#> GSM1296117 2 0.0000 1.000 0.000 1.000
#> GSM1296113 2 0.0000 1.000 0.000 1.000
#> GSM1296096 2 0.0000 1.000 0.000 1.000
#> GSM1296105 1 0.0000 1.000 1.000 0.000
#> GSM1296098 1 0.0000 1.000 1.000 0.000
#> GSM1296101 1 0.0000 1.000 1.000 0.000
#> GSM1296121 2 0.0000 1.000 0.000 1.000
#> GSM1296088 1 0.0000 1.000 1.000 0.000
#> GSM1296082 2 0.0000 1.000 0.000 1.000
#> GSM1296115 2 0.0000 1.000 0.000 1.000
#> GSM1296084 1 0.0000 1.000 1.000 0.000
#> GSM1296072 2 0.0000 1.000 0.000 1.000
#> GSM1296069 2 0.0000 1.000 0.000 1.000
#> GSM1296071 2 0.0000 1.000 0.000 1.000
#> GSM1296070 2 0.0000 1.000 0.000 1.000
#> GSM1296073 2 0.0000 1.000 0.000 1.000
#> GSM1296034 1 0.0000 1.000 1.000 0.000
#> GSM1296041 2 0.0000 1.000 0.000 1.000
#> GSM1296035 2 0.0000 1.000 0.000 1.000
#> GSM1296038 2 0.0000 1.000 0.000 1.000
#> GSM1296047 2 0.0000 1.000 0.000 1.000
#> GSM1296039 2 0.0000 1.000 0.000 1.000
#> GSM1296042 2 0.0000 1.000 0.000 1.000
#> GSM1296043 2 0.0000 1.000 0.000 1.000
#> GSM1296037 1 0.0000 1.000 1.000 0.000
#> GSM1296046 2 0.0000 1.000 0.000 1.000
#> GSM1296044 2 0.0000 1.000 0.000 1.000
#> GSM1296045 2 0.0000 1.000 0.000 1.000
#> GSM1296025 1 0.0000 1.000 1.000 0.000
#> GSM1296033 1 0.0000 1.000 1.000 0.000
#> GSM1296027 1 0.0000 1.000 1.000 0.000
#> GSM1296032 1 0.0000 1.000 1.000 0.000
#> GSM1296024 1 0.0000 1.000 1.000 0.000
#> GSM1296031 1 0.0000 1.000 1.000 0.000
#> GSM1296028 1 0.0000 1.000 1.000 0.000
#> GSM1296029 1 0.0000 1.000 1.000 0.000
#> GSM1296026 1 0.0000 1.000 1.000 0.000
#> GSM1296030 1 0.0000 1.000 1.000 0.000
#> GSM1296040 1 0.0000 1.000 1.000 0.000
#> GSM1296036 1 0.0000 1.000 1.000 0.000
#> GSM1296048 2 0.0000 1.000 0.000 1.000
#> GSM1296059 1 0.0000 1.000 1.000 0.000
#> GSM1296066 2 0.0000 1.000 0.000 1.000
#> GSM1296060 2 0.0000 1.000 0.000 1.000
#> GSM1296063 2 0.0000 1.000 0.000 1.000
#> GSM1296064 2 0.0000 1.000 0.000 1.000
#> GSM1296067 2 0.0000 1.000 0.000 1.000
#> GSM1296062 1 0.0000 1.000 1.000 0.000
#> GSM1296068 2 0.0000 1.000 0.000 1.000
#> GSM1296050 1 0.0000 1.000 1.000 0.000
#> GSM1296057 1 0.0000 1.000 1.000 0.000
#> GSM1296052 1 0.0000 1.000 1.000 0.000
#> GSM1296054 1 0.0000 1.000 1.000 0.000
#> GSM1296049 1 0.0000 1.000 1.000 0.000
#> GSM1296055 2 0.0000 1.000 0.000 1.000
#> GSM1296053 1 0.0000 1.000 1.000 0.000
#> GSM1296058 1 0.0000 1.000 1.000 0.000
#> GSM1296051 2 0.0000 1.000 0.000 1.000
#> GSM1296056 2 0.0000 1.000 0.000 1.000
#> GSM1296065 2 0.0000 1.000 0.000 1.000
#> GSM1296061 1 0.0000 1.000 1.000 0.000
#> GSM1296095 2 0.0000 1.000 0.000 1.000
#> GSM1296120 2 0.0000 1.000 0.000 1.000
#> GSM1296077 1 0.0000 1.000 1.000 0.000
#> GSM1296093 1 0.0000 1.000 1.000 0.000
#> GSM1296104 2 0.0000 1.000 0.000 1.000
#> GSM1296079 1 0.0000 1.000 1.000 0.000
#> GSM1296108 2 0.0000 1.000 0.000 1.000
#> GSM1296110 2 0.0000 1.000 0.000 1.000
#> GSM1296081 1 0.0000 1.000 1.000 0.000
#> GSM1296091 1 0.0000 1.000 1.000 0.000
#> GSM1296075 2 0.0000 1.000 0.000 1.000
#> GSM1296112 2 0.0000 1.000 0.000 1.000
#> GSM1296100 1 0.0000 1.000 1.000 0.000
#> GSM1296087 1 0.0000 1.000 1.000 0.000
#> GSM1296118 2 0.0000 1.000 0.000 1.000
#> GSM1296114 2 0.0000 1.000 0.000 1.000
#> GSM1296097 2 0.0000 1.000 0.000 1.000
#> GSM1296106 2 0.0938 0.988 0.012 0.988
#> GSM1296102 1 0.0000 1.000 1.000 0.000
#> GSM1296122 2 0.0000 1.000 0.000 1.000
#> GSM1296089 1 0.0000 1.000 1.000 0.000
#> GSM1296083 1 0.0000 1.000 1.000 0.000
#> GSM1296116 2 0.0000 1.000 0.000 1.000
#> GSM1296085 1 0.0000 1.000 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1296094 3 0.000 0.954 0.000 0.000 1.000
#> GSM1296119 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296076 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296092 3 0.000 0.954 0.000 0.000 1.000
#> GSM1296103 3 0.000 0.954 0.000 0.000 1.000
#> GSM1296078 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296107 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296109 3 0.497 0.719 0.000 0.236 0.764
#> GSM1296080 1 0.000 0.980 1.000 0.000 0.000
#> GSM1296090 3 0.000 0.954 0.000 0.000 1.000
#> GSM1296074 2 0.394 0.801 0.000 0.844 0.156
#> GSM1296111 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296099 3 0.000 0.954 0.000 0.000 1.000
#> GSM1296086 3 0.000 0.954 0.000 0.000 1.000
#> GSM1296117 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296113 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296096 3 0.000 0.954 0.000 0.000 1.000
#> GSM1296105 3 0.000 0.954 0.000 0.000 1.000
#> GSM1296098 1 0.319 0.880 0.888 0.000 0.112
#> GSM1296101 3 0.000 0.954 0.000 0.000 1.000
#> GSM1296121 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296088 3 0.000 0.954 0.000 0.000 1.000
#> GSM1296082 3 0.348 0.845 0.000 0.128 0.872
#> GSM1296115 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296084 1 0.375 0.845 0.856 0.000 0.144
#> GSM1296072 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296069 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296071 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296070 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296073 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296034 1 0.000 0.980 1.000 0.000 0.000
#> GSM1296041 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296035 3 0.000 0.954 0.000 0.000 1.000
#> GSM1296038 3 0.000 0.954 0.000 0.000 1.000
#> GSM1296047 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296039 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296042 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296043 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296037 1 0.000 0.980 1.000 0.000 0.000
#> GSM1296046 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296044 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296045 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296025 1 0.000 0.980 1.000 0.000 0.000
#> GSM1296033 1 0.000 0.980 1.000 0.000 0.000
#> GSM1296027 1 0.000 0.980 1.000 0.000 0.000
#> GSM1296032 1 0.000 0.980 1.000 0.000 0.000
#> GSM1296024 1 0.000 0.980 1.000 0.000 0.000
#> GSM1296031 1 0.000 0.980 1.000 0.000 0.000
#> GSM1296028 1 0.000 0.980 1.000 0.000 0.000
#> GSM1296029 1 0.000 0.980 1.000 0.000 0.000
#> GSM1296026 1 0.510 0.698 0.752 0.000 0.248
#> GSM1296030 1 0.000 0.980 1.000 0.000 0.000
#> GSM1296040 3 0.000 0.954 0.000 0.000 1.000
#> GSM1296036 1 0.319 0.880 0.888 0.000 0.112
#> GSM1296048 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296059 3 0.000 0.954 0.000 0.000 1.000
#> GSM1296066 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296060 3 0.000 0.954 0.000 0.000 1.000
#> GSM1296063 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296064 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296067 3 0.000 0.954 0.000 0.000 1.000
#> GSM1296062 1 0.000 0.980 1.000 0.000 0.000
#> GSM1296068 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296050 3 0.000 0.954 0.000 0.000 1.000
#> GSM1296057 3 0.000 0.954 0.000 0.000 1.000
#> GSM1296052 1 0.000 0.980 1.000 0.000 0.000
#> GSM1296054 1 0.000 0.980 1.000 0.000 0.000
#> GSM1296049 1 0.000 0.980 1.000 0.000 0.000
#> GSM1296055 3 0.000 0.954 0.000 0.000 1.000
#> GSM1296053 1 0.000 0.980 1.000 0.000 0.000
#> GSM1296058 3 0.506 0.647 0.244 0.000 0.756
#> GSM1296051 3 0.000 0.954 0.000 0.000 1.000
#> GSM1296056 3 0.000 0.954 0.000 0.000 1.000
#> GSM1296065 3 0.497 0.719 0.000 0.236 0.764
#> GSM1296061 1 0.000 0.980 1.000 0.000 0.000
#> GSM1296095 3 0.497 0.719 0.000 0.236 0.764
#> GSM1296120 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296077 1 0.000 0.980 1.000 0.000 0.000
#> GSM1296093 1 0.000 0.980 1.000 0.000 0.000
#> GSM1296104 3 0.497 0.719 0.000 0.236 0.764
#> GSM1296079 1 0.000 0.980 1.000 0.000 0.000
#> GSM1296108 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296110 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296081 1 0.000 0.980 1.000 0.000 0.000
#> GSM1296091 3 0.000 0.954 0.000 0.000 1.000
#> GSM1296075 3 0.000 0.954 0.000 0.000 1.000
#> GSM1296112 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296100 1 0.000 0.980 1.000 0.000 0.000
#> GSM1296087 1 0.000 0.980 1.000 0.000 0.000
#> GSM1296118 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296114 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296097 3 0.000 0.954 0.000 0.000 1.000
#> GSM1296106 3 0.000 0.954 0.000 0.000 1.000
#> GSM1296102 1 0.000 0.980 1.000 0.000 0.000
#> GSM1296122 3 0.000 0.954 0.000 0.000 1.000
#> GSM1296089 1 0.000 0.980 1.000 0.000 0.000
#> GSM1296083 1 0.000 0.980 1.000 0.000 0.000
#> GSM1296116 2 0.000 0.995 0.000 1.000 0.000
#> GSM1296085 1 0.000 0.980 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1296094 3 0.2973 0.840 0.000 0.000 0.856 0.144
#> GSM1296119 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM1296076 4 0.4898 0.373 0.000 0.416 0.000 0.584
#> GSM1296092 4 0.1474 0.886 0.000 0.000 0.052 0.948
#> GSM1296103 3 0.4222 0.708 0.000 0.000 0.728 0.272
#> GSM1296078 4 0.4888 0.383 0.000 0.412 0.000 0.588
#> GSM1296107 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM1296109 4 0.1151 0.882 0.000 0.024 0.008 0.968
#> GSM1296080 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM1296090 4 0.1474 0.886 0.000 0.000 0.052 0.948
#> GSM1296074 4 0.3486 0.763 0.000 0.188 0.000 0.812
#> GSM1296111 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM1296099 4 0.1389 0.886 0.000 0.000 0.048 0.952
#> GSM1296086 4 0.1474 0.886 0.000 0.000 0.052 0.948
#> GSM1296117 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM1296113 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM1296096 4 0.1389 0.886 0.000 0.000 0.048 0.952
#> GSM1296105 3 0.2973 0.840 0.000 0.000 0.856 0.144
#> GSM1296098 3 0.3123 0.849 0.156 0.000 0.844 0.000
#> GSM1296101 3 0.2973 0.840 0.000 0.000 0.856 0.144
#> GSM1296121 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM1296088 3 0.2921 0.840 0.000 0.000 0.860 0.140
#> GSM1296082 4 0.1733 0.883 0.000 0.024 0.028 0.948
#> GSM1296115 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM1296084 3 0.2973 0.852 0.144 0.000 0.856 0.000
#> GSM1296072 2 0.6353 0.695 0.000 0.652 0.140 0.208
#> GSM1296069 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM1296071 2 0.4088 0.851 0.000 0.820 0.140 0.040
#> GSM1296070 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM1296073 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM1296034 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM1296041 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM1296035 4 0.1389 0.886 0.000 0.000 0.048 0.952
#> GSM1296038 4 0.1211 0.886 0.000 0.000 0.040 0.960
#> GSM1296047 2 0.6386 0.689 0.000 0.648 0.140 0.212
#> GSM1296039 4 0.4585 0.554 0.000 0.332 0.000 0.668
#> GSM1296042 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM1296043 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM1296037 3 0.3528 0.830 0.192 0.000 0.808 0.000
#> GSM1296046 2 0.3143 0.873 0.000 0.876 0.100 0.024
#> GSM1296044 2 0.3143 0.873 0.000 0.876 0.100 0.024
#> GSM1296045 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM1296025 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM1296033 3 0.3528 0.830 0.192 0.000 0.808 0.000
#> GSM1296027 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM1296032 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM1296024 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM1296031 3 0.3528 0.830 0.192 0.000 0.808 0.000
#> GSM1296028 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM1296029 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM1296026 3 0.2921 0.852 0.140 0.000 0.860 0.000
#> GSM1296030 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM1296040 3 0.2973 0.840 0.000 0.000 0.856 0.144
#> GSM1296036 3 0.3123 0.849 0.156 0.000 0.844 0.000
#> GSM1296048 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM1296059 3 0.4193 0.713 0.000 0.000 0.732 0.268
#> GSM1296066 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM1296060 4 0.1389 0.886 0.000 0.000 0.048 0.952
#> GSM1296063 2 0.3764 0.660 0.000 0.784 0.000 0.216
#> GSM1296064 2 0.3764 0.660 0.000 0.784 0.000 0.216
#> GSM1296067 4 0.2589 0.817 0.000 0.000 0.116 0.884
#> GSM1296062 3 0.3311 0.841 0.172 0.000 0.828 0.000
#> GSM1296068 2 0.4088 0.851 0.000 0.820 0.140 0.040
#> GSM1296050 3 0.2593 0.836 0.004 0.000 0.892 0.104
#> GSM1296057 3 0.2973 0.840 0.000 0.000 0.856 0.144
#> GSM1296052 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM1296055 4 0.3172 0.750 0.000 0.000 0.160 0.840
#> GSM1296053 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM1296058 3 0.3441 0.848 0.024 0.000 0.856 0.120
#> GSM1296051 4 0.1302 0.887 0.000 0.000 0.044 0.956
#> GSM1296056 4 0.1389 0.886 0.000 0.000 0.048 0.952
#> GSM1296065 4 0.0817 0.874 0.000 0.000 0.024 0.976
#> GSM1296061 3 0.3528 0.830 0.192 0.000 0.808 0.000
#> GSM1296095 4 0.0817 0.874 0.000 0.000 0.024 0.976
#> GSM1296120 2 0.4088 0.851 0.000 0.820 0.140 0.040
#> GSM1296077 1 0.1302 0.948 0.956 0.000 0.044 0.000
#> GSM1296093 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM1296104 4 0.0817 0.874 0.000 0.000 0.024 0.976
#> GSM1296079 1 0.2081 0.900 0.916 0.000 0.084 0.000
#> GSM1296108 2 0.4088 0.851 0.000 0.820 0.140 0.040
#> GSM1296110 2 0.6386 0.689 0.000 0.648 0.140 0.212
#> GSM1296081 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM1296091 3 0.2921 0.840 0.000 0.000 0.860 0.140
#> GSM1296075 4 0.1389 0.864 0.000 0.000 0.048 0.952
#> GSM1296112 2 0.4088 0.851 0.000 0.820 0.140 0.040
#> GSM1296100 3 0.3528 0.830 0.192 0.000 0.808 0.000
#> GSM1296087 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM1296118 2 0.6320 0.700 0.000 0.656 0.140 0.204
#> GSM1296114 2 0.3143 0.873 0.000 0.876 0.100 0.024
#> GSM1296097 4 0.1211 0.886 0.000 0.000 0.040 0.960
#> GSM1296106 3 0.4955 0.363 0.000 0.000 0.556 0.444
#> GSM1296102 3 0.3528 0.830 0.192 0.000 0.808 0.000
#> GSM1296122 4 0.2814 0.804 0.000 0.000 0.132 0.868
#> GSM1296089 3 0.3528 0.830 0.192 0.000 0.808 0.000
#> GSM1296083 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM1296116 2 0.3143 0.873 0.000 0.876 0.100 0.024
#> GSM1296085 1 0.0000 0.992 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
#> GSM1296094 3 0.2843 0.8420 0.000 0.144 0.848 0.008 0.000
#> GSM1296119 5 0.0000 0.8279 0.000 0.000 0.000 0.000 1.000
#> GSM1296076 4 0.5762 0.5349 0.000 0.144 0.000 0.608 0.248
#> GSM1296092 4 0.0794 0.8668 0.000 0.028 0.000 0.972 0.000
#> GSM1296103 3 0.6071 0.5535 0.000 0.160 0.556 0.284 0.000
#> GSM1296078 4 0.5740 0.5434 0.000 0.144 0.000 0.612 0.244
#> GSM1296107 5 0.0000 0.8279 0.000 0.000 0.000 0.000 1.000
#> GSM1296109 4 0.1608 0.8648 0.000 0.072 0.000 0.928 0.000
#> GSM1296080 1 0.0162 0.9695 0.996 0.004 0.000 0.000 0.000
#> GSM1296090 4 0.1544 0.8661 0.000 0.068 0.000 0.932 0.000
#> GSM1296074 4 0.2763 0.8359 0.000 0.148 0.000 0.848 0.004
#> GSM1296111 5 0.0000 0.8279 0.000 0.000 0.000 0.000 1.000
#> GSM1296099 4 0.0290 0.8656 0.000 0.008 0.000 0.992 0.000
#> GSM1296086 4 0.1043 0.8666 0.000 0.040 0.000 0.960 0.000
#> GSM1296117 5 0.0000 0.8279 0.000 0.000 0.000 0.000 1.000
#> GSM1296113 5 0.0000 0.8279 0.000 0.000 0.000 0.000 1.000
#> GSM1296096 4 0.0290 0.8656 0.000 0.008 0.000 0.992 0.000
#> GSM1296105 3 0.1671 0.8655 0.000 0.076 0.924 0.000 0.000
#> GSM1296098 3 0.3006 0.8444 0.004 0.156 0.836 0.004 0.000
#> GSM1296101 3 0.3452 0.8335 0.000 0.148 0.820 0.032 0.000
#> GSM1296121 5 0.0000 0.8279 0.000 0.000 0.000 0.000 1.000
#> GSM1296088 3 0.3011 0.8599 0.000 0.140 0.844 0.016 0.000
#> GSM1296082 4 0.1544 0.8661 0.000 0.068 0.000 0.932 0.000
#> GSM1296115 5 0.0000 0.8279 0.000 0.000 0.000 0.000 1.000
#> GSM1296084 3 0.2563 0.8490 0.008 0.120 0.872 0.000 0.000
#> GSM1296072 2 0.4555 0.6688 0.000 0.636 0.000 0.020 0.344
#> GSM1296069 5 0.0000 0.8279 0.000 0.000 0.000 0.000 1.000
#> GSM1296071 2 0.4306 0.5797 0.000 0.508 0.000 0.000 0.492
#> GSM1296070 5 0.0000 0.8279 0.000 0.000 0.000 0.000 1.000
#> GSM1296073 5 0.0000 0.8279 0.000 0.000 0.000 0.000 1.000
#> GSM1296034 1 0.0162 0.9695 0.996 0.004 0.000 0.000 0.000
#> GSM1296041 5 0.0000 0.8279 0.000 0.000 0.000 0.000 1.000
#> GSM1296035 4 0.0609 0.8626 0.000 0.020 0.000 0.980 0.000
#> GSM1296038 4 0.0771 0.8671 0.000 0.020 0.004 0.976 0.000
#> GSM1296047 2 0.4540 0.6669 0.000 0.640 0.000 0.020 0.340
#> GSM1296039 4 0.4848 0.7082 0.000 0.144 0.000 0.724 0.132
#> GSM1296042 5 0.0000 0.8279 0.000 0.000 0.000 0.000 1.000
#> GSM1296043 5 0.0290 0.8202 0.000 0.008 0.000 0.000 0.992
#> GSM1296037 3 0.0798 0.8664 0.008 0.016 0.976 0.000 0.000
#> GSM1296046 5 0.3774 0.2142 0.000 0.296 0.000 0.000 0.704
#> GSM1296044 5 0.3816 0.1853 0.000 0.304 0.000 0.000 0.696
#> GSM1296045 5 0.0290 0.8202 0.000 0.008 0.000 0.000 0.992
#> GSM1296025 1 0.0404 0.9685 0.988 0.012 0.000 0.000 0.000
#> GSM1296033 3 0.2563 0.8490 0.008 0.120 0.872 0.000 0.000
#> GSM1296027 1 0.0162 0.9694 0.996 0.004 0.000 0.000 0.000
#> GSM1296032 1 0.0794 0.9648 0.972 0.028 0.000 0.000 0.000
#> GSM1296024 1 0.0404 0.9685 0.988 0.012 0.000 0.000 0.000
#> GSM1296031 3 0.2777 0.8461 0.016 0.120 0.864 0.000 0.000
#> GSM1296028 1 0.0794 0.9648 0.972 0.028 0.000 0.000 0.000
#> GSM1296029 1 0.0794 0.9648 0.972 0.028 0.000 0.000 0.000
#> GSM1296026 3 0.2286 0.8484 0.004 0.108 0.888 0.000 0.000
#> GSM1296030 1 0.0794 0.9648 0.972 0.028 0.000 0.000 0.000
#> GSM1296040 3 0.2843 0.8420 0.000 0.144 0.848 0.008 0.000
#> GSM1296036 3 0.2971 0.8449 0.008 0.156 0.836 0.000 0.000
#> GSM1296048 5 0.0000 0.8279 0.000 0.000 0.000 0.000 1.000
#> GSM1296059 3 0.6071 0.5535 0.000 0.160 0.556 0.284 0.000
#> GSM1296066 5 0.0000 0.8279 0.000 0.000 0.000 0.000 1.000
#> GSM1296060 4 0.0609 0.8626 0.000 0.020 0.000 0.980 0.000
#> GSM1296063 5 0.5905 0.1546 0.000 0.136 0.000 0.292 0.572
#> GSM1296064 5 0.5323 0.2434 0.000 0.080 0.000 0.296 0.624
#> GSM1296067 2 0.5245 0.0809 0.000 0.608 0.064 0.328 0.000
#> GSM1296062 3 0.2929 0.8459 0.008 0.152 0.840 0.000 0.000
#> GSM1296068 2 0.4306 0.5797 0.000 0.508 0.000 0.000 0.492
#> GSM1296050 3 0.3642 0.7636 0.000 0.232 0.760 0.008 0.000
#> GSM1296057 3 0.1124 0.8646 0.000 0.036 0.960 0.004 0.000
#> GSM1296052 1 0.0162 0.9694 0.996 0.004 0.000 0.000 0.000
#> GSM1296054 1 0.0162 0.9695 0.996 0.004 0.000 0.000 0.000
#> GSM1296049 1 0.0404 0.9685 0.988 0.012 0.000 0.000 0.000
#> GSM1296055 4 0.5725 0.5050 0.000 0.172 0.204 0.624 0.000
#> GSM1296053 1 0.0162 0.9695 0.996 0.004 0.000 0.000 0.000
#> GSM1296058 3 0.0324 0.8671 0.000 0.004 0.992 0.004 0.000
#> GSM1296051 4 0.2304 0.8611 0.000 0.100 0.008 0.892 0.000
#> GSM1296056 4 0.0290 0.8656 0.000 0.008 0.000 0.992 0.000
#> GSM1296065 4 0.2674 0.8400 0.000 0.140 0.004 0.856 0.000
#> GSM1296061 3 0.2929 0.8459 0.008 0.152 0.840 0.000 0.000
#> GSM1296095 4 0.2536 0.8426 0.000 0.128 0.004 0.868 0.000
#> GSM1296120 2 0.4306 0.5797 0.000 0.508 0.000 0.000 0.492
#> GSM1296077 1 0.4117 0.7947 0.788 0.096 0.116 0.000 0.000
#> GSM1296093 1 0.0162 0.9695 0.996 0.004 0.000 0.000 0.000
#> GSM1296104 4 0.2583 0.8415 0.000 0.132 0.004 0.864 0.000
#> GSM1296079 1 0.4478 0.7512 0.756 0.100 0.144 0.000 0.000
#> GSM1296108 2 0.4306 0.5797 0.000 0.508 0.000 0.000 0.492
#> GSM1296110 2 0.4585 0.6694 0.000 0.628 0.000 0.020 0.352
#> GSM1296081 1 0.0162 0.9695 0.996 0.004 0.000 0.000 0.000
#> GSM1296091 3 0.2753 0.8375 0.000 0.136 0.856 0.008 0.000
#> GSM1296075 4 0.3885 0.7500 0.000 0.268 0.008 0.724 0.000
#> GSM1296112 2 0.4306 0.5797 0.000 0.508 0.000 0.000 0.492
#> GSM1296100 3 0.0798 0.8664 0.008 0.016 0.976 0.000 0.000
#> GSM1296087 1 0.0794 0.9648 0.972 0.028 0.000 0.000 0.000
#> GSM1296118 2 0.4570 0.6697 0.000 0.632 0.000 0.020 0.348
#> GSM1296114 5 0.3816 0.1853 0.000 0.304 0.000 0.000 0.696
#> GSM1296097 4 0.3304 0.7635 0.000 0.168 0.016 0.816 0.000
#> GSM1296106 3 0.6219 0.4094 0.000 0.176 0.532 0.292 0.000
#> GSM1296102 3 0.0798 0.8664 0.008 0.016 0.976 0.000 0.000
#> GSM1296122 2 0.5172 0.0934 0.000 0.616 0.060 0.324 0.000
#> GSM1296089 3 0.2660 0.8467 0.008 0.128 0.864 0.000 0.000
#> GSM1296083 1 0.0162 0.9695 0.996 0.004 0.000 0.000 0.000
#> GSM1296116 5 0.3816 0.1853 0.000 0.304 0.000 0.000 0.696
#> GSM1296085 1 0.0290 0.9691 0.992 0.008 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
#> GSM1296094 3 0.3620 0.7235 0.000 0.000 0.648 0.000 0.000 NA
#> GSM1296119 5 0.0000 0.8054 0.000 0.000 0.000 0.000 1.000 NA
#> GSM1296076 4 0.6635 0.5324 0.000 0.136 0.000 0.528 0.112 NA
#> GSM1296092 4 0.1890 0.7022 0.000 0.024 0.000 0.916 0.000 NA
#> GSM1296103 4 0.6398 -0.1838 0.000 0.012 0.292 0.364 0.000 NA
#> GSM1296078 4 0.6635 0.5324 0.000 0.136 0.000 0.528 0.112 NA
#> GSM1296107 5 0.0000 0.8054 0.000 0.000 0.000 0.000 1.000 NA
#> GSM1296109 4 0.2420 0.6984 0.000 0.040 0.000 0.884 0.000 NA
#> GSM1296080 1 0.0146 0.9226 0.996 0.004 0.000 0.000 0.000 NA
#> GSM1296090 4 0.3522 0.6908 0.000 0.044 0.000 0.784 0.000 NA
#> GSM1296074 4 0.4908 0.6414 0.000 0.128 0.000 0.648 0.000 NA
#> GSM1296111 5 0.0000 0.8054 0.000 0.000 0.000 0.000 1.000 NA
#> GSM1296099 4 0.0458 0.6968 0.000 0.000 0.000 0.984 0.000 NA
#> GSM1296086 4 0.1890 0.6995 0.000 0.024 0.000 0.916 0.000 NA
#> GSM1296117 5 0.0000 0.8054 0.000 0.000 0.000 0.000 1.000 NA
#> GSM1296113 5 0.0000 0.8054 0.000 0.000 0.000 0.000 1.000 NA
#> GSM1296096 4 0.0260 0.6977 0.000 0.000 0.000 0.992 0.000 NA
#> GSM1296105 3 0.2100 0.7899 0.000 0.004 0.884 0.000 0.000 NA
#> GSM1296098 3 0.3636 0.7317 0.000 0.004 0.676 0.000 0.000 NA
#> GSM1296101 3 0.4936 0.6680 0.000 0.012 0.576 0.048 0.000 NA
#> GSM1296121 5 0.0000 0.8054 0.000 0.000 0.000 0.000 1.000 NA
#> GSM1296088 3 0.3121 0.7775 0.000 0.008 0.796 0.004 0.000 NA
#> GSM1296082 4 0.3974 0.6772 0.000 0.048 0.000 0.728 0.000 NA
#> GSM1296115 5 0.0000 0.8054 0.000 0.000 0.000 0.000 1.000 NA
#> GSM1296084 3 0.2613 0.7700 0.000 0.012 0.848 0.000 0.000 NA
#> GSM1296072 2 0.3231 0.7189 0.000 0.800 0.000 0.012 0.180 NA
#> GSM1296069 5 0.0000 0.8054 0.000 0.000 0.000 0.000 1.000 NA
#> GSM1296071 2 0.3659 0.6413 0.000 0.636 0.000 0.000 0.364 NA
#> GSM1296070 5 0.0000 0.8054 0.000 0.000 0.000 0.000 1.000 NA
#> GSM1296073 5 0.0000 0.8054 0.000 0.000 0.000 0.000 1.000 NA
#> GSM1296034 1 0.0146 0.9226 0.996 0.004 0.000 0.000 0.000 NA
#> GSM1296041 5 0.0000 0.8054 0.000 0.000 0.000 0.000 1.000 NA
#> GSM1296035 4 0.1942 0.6762 0.000 0.012 0.008 0.916 0.000 NA
#> GSM1296038 4 0.0405 0.6994 0.000 0.004 0.000 0.988 0.000 NA
#> GSM1296047 2 0.3121 0.7194 0.000 0.804 0.000 0.012 0.180 NA
#> GSM1296039 4 0.5897 0.5977 0.000 0.132 0.000 0.596 0.048 NA
#> GSM1296042 5 0.0000 0.8054 0.000 0.000 0.000 0.000 1.000 NA
#> GSM1296043 5 0.0547 0.7887 0.000 0.020 0.000 0.000 0.980 NA
#> GSM1296037 3 0.1257 0.7983 0.000 0.020 0.952 0.000 0.000 NA
#> GSM1296046 5 0.3727 0.1055 0.000 0.388 0.000 0.000 0.612 NA
#> GSM1296044 5 0.3727 0.1055 0.000 0.388 0.000 0.000 0.612 NA
#> GSM1296045 5 0.0547 0.7887 0.000 0.020 0.000 0.000 0.980 NA
#> GSM1296025 1 0.1572 0.9090 0.936 0.028 0.000 0.000 0.000 NA
#> GSM1296033 3 0.2973 0.7670 0.004 0.024 0.836 0.000 0.000 NA
#> GSM1296027 1 0.1196 0.9199 0.952 0.040 0.000 0.000 0.000 NA
#> GSM1296032 1 0.2568 0.9035 0.876 0.068 0.000 0.000 0.000 NA
#> GSM1296024 1 0.1572 0.9090 0.936 0.028 0.000 0.000 0.000 NA
#> GSM1296031 3 0.3313 0.7633 0.016 0.024 0.820 0.000 0.000 NA
#> GSM1296028 1 0.2910 0.8923 0.852 0.068 0.000 0.000 0.000 NA
#> GSM1296029 1 0.2568 0.9035 0.876 0.068 0.000 0.000 0.000 NA
#> GSM1296026 3 0.3023 0.7692 0.000 0.032 0.828 0.000 0.000 NA
#> GSM1296030 1 0.2910 0.8923 0.852 0.068 0.000 0.000 0.000 NA
#> GSM1296040 3 0.3592 0.7253 0.000 0.000 0.656 0.000 0.000 NA
#> GSM1296036 3 0.3636 0.7317 0.000 0.004 0.676 0.000 0.000 NA
#> GSM1296048 5 0.0000 0.8054 0.000 0.000 0.000 0.000 1.000 NA
#> GSM1296059 4 0.6398 -0.1838 0.000 0.012 0.292 0.364 0.000 NA
#> GSM1296066 5 0.0000 0.8054 0.000 0.000 0.000 0.000 1.000 NA
#> GSM1296060 4 0.2326 0.6673 0.000 0.012 0.008 0.888 0.000 NA
#> GSM1296063 5 0.7303 -0.1876 0.000 0.116 0.000 0.304 0.368 NA
#> GSM1296064 5 0.6897 -0.0765 0.000 0.068 0.000 0.296 0.424 NA
#> GSM1296067 2 0.5971 0.1678 0.000 0.548 0.020 0.216 0.000 NA
#> GSM1296062 3 0.3690 0.7342 0.000 0.008 0.684 0.000 0.000 NA
#> GSM1296068 2 0.3659 0.6413 0.000 0.636 0.000 0.000 0.364 NA
#> GSM1296050 3 0.6063 0.4107 0.000 0.180 0.460 0.012 0.000 NA
#> GSM1296057 3 0.2039 0.7855 0.000 0.016 0.908 0.004 0.000 NA
#> GSM1296052 1 0.1196 0.9199 0.952 0.040 0.000 0.000 0.000 NA
#> GSM1296054 1 0.0146 0.9226 0.996 0.004 0.000 0.000 0.000 NA
#> GSM1296049 1 0.1723 0.9085 0.928 0.036 0.000 0.000 0.000 NA
#> GSM1296055 4 0.7337 0.2882 0.000 0.180 0.164 0.412 0.000 NA
#> GSM1296053 1 0.0146 0.9226 0.996 0.004 0.000 0.000 0.000 NA
#> GSM1296058 3 0.0935 0.7985 0.000 0.004 0.964 0.000 0.000 NA
#> GSM1296051 4 0.4854 0.6230 0.000 0.088 0.000 0.620 0.000 NA
#> GSM1296056 4 0.0260 0.6977 0.000 0.000 0.000 0.992 0.000 NA
#> GSM1296065 4 0.4721 0.6527 0.000 0.116 0.000 0.672 0.000 NA
#> GSM1296061 3 0.3690 0.7342 0.000 0.008 0.684 0.000 0.000 NA
#> GSM1296095 4 0.4490 0.6614 0.000 0.104 0.000 0.700 0.000 NA
#> GSM1296120 2 0.3634 0.6477 0.000 0.644 0.000 0.000 0.356 NA
#> GSM1296077 1 0.6181 0.6168 0.596 0.092 0.160 0.000 0.000 NA
#> GSM1296093 1 0.0146 0.9226 0.996 0.004 0.000 0.000 0.000 NA
#> GSM1296104 4 0.4573 0.6586 0.000 0.112 0.000 0.692 0.000 NA
#> GSM1296079 1 0.6370 0.5644 0.568 0.092 0.188 0.000 0.000 NA
#> GSM1296108 2 0.3659 0.6413 0.000 0.636 0.000 0.000 0.364 NA
#> GSM1296110 2 0.3154 0.7206 0.000 0.800 0.000 0.012 0.184 NA
#> GSM1296081 1 0.0146 0.9226 0.996 0.004 0.000 0.000 0.000 NA
#> GSM1296091 3 0.4459 0.6393 0.000 0.032 0.640 0.008 0.000 NA
#> GSM1296075 4 0.6076 0.4350 0.000 0.260 0.004 0.452 0.000 NA
#> GSM1296112 2 0.3659 0.6413 0.000 0.636 0.000 0.000 0.364 NA
#> GSM1296100 3 0.1480 0.7983 0.000 0.020 0.940 0.000 0.000 NA
#> GSM1296087 1 0.2451 0.9062 0.884 0.060 0.000 0.000 0.000 NA
#> GSM1296118 2 0.3154 0.7206 0.000 0.800 0.000 0.012 0.184 NA
#> GSM1296114 5 0.3727 0.1055 0.000 0.388 0.000 0.000 0.612 NA
#> GSM1296097 4 0.5547 0.4613 0.000 0.152 0.012 0.592 0.000 NA
#> GSM1296106 4 0.7648 0.0917 0.000 0.180 0.256 0.300 0.000 NA
#> GSM1296102 3 0.1461 0.7982 0.000 0.016 0.940 0.000 0.000 NA
#> GSM1296122 2 0.5808 0.1905 0.000 0.568 0.016 0.208 0.000 NA
#> GSM1296089 3 0.2950 0.7651 0.000 0.024 0.828 0.000 0.000 NA
#> GSM1296083 1 0.0146 0.9226 0.996 0.004 0.000 0.000 0.000 NA
#> GSM1296116 5 0.3727 0.1055 0.000 0.388 0.000 0.000 0.612 NA
#> GSM1296085 1 0.1265 0.9198 0.948 0.044 0.000 0.000 0.000 NA
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# 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 tissue(p) time(p) agent(p) dose(p) individual(p) k
#> ATC:kmeans 99 0.97141 0.10095 0.645 2.49e-05 3.97e-04 2
#> ATC:kmeans 99 0.52302 0.00303 0.413 1.18e-05 1.51e-06 3
#> ATC:kmeans 96 0.58633 0.07008 0.451 1.65e-05 5.83e-08 4
#> ATC:kmeans 90 0.01248 0.05341 0.521 1.64e-04 3.33e-07 5
#> ATC:kmeans 84 0.00685 0.11714 0.435 5.00e-05 4.62e-07 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 45638 rows and 99 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 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 1.000 0.992 0.997 0.5008 0.499 0.499
#> 3 3 1.000 0.961 0.983 0.2369 0.870 0.742
#> 4 4 0.794 0.776 0.883 0.1429 0.825 0.576
#> 5 5 0.766 0.699 0.851 0.0541 0.918 0.735
#> 6 6 0.802 0.736 0.821 0.0496 0.905 0.671
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM1296094 1 0.0 0.993 1.000 0.000
#> GSM1296119 2 0.0 1.000 0.000 1.000
#> GSM1296076 2 0.0 1.000 0.000 1.000
#> GSM1296092 2 0.0 1.000 0.000 1.000
#> GSM1296103 1 0.0 0.993 1.000 0.000
#> GSM1296078 2 0.0 1.000 0.000 1.000
#> GSM1296107 2 0.0 1.000 0.000 1.000
#> GSM1296109 2 0.0 1.000 0.000 1.000
#> GSM1296080 1 0.0 0.993 1.000 0.000
#> GSM1296090 2 0.0 1.000 0.000 1.000
#> GSM1296074 2 0.0 1.000 0.000 1.000
#> GSM1296111 2 0.0 1.000 0.000 1.000
#> GSM1296099 2 0.0 1.000 0.000 1.000
#> GSM1296086 2 0.0 1.000 0.000 1.000
#> GSM1296117 2 0.0 1.000 0.000 1.000
#> GSM1296113 2 0.0 1.000 0.000 1.000
#> GSM1296096 2 0.0 1.000 0.000 1.000
#> GSM1296105 1 0.0 0.993 1.000 0.000
#> GSM1296098 1 0.0 0.993 1.000 0.000
#> GSM1296101 1 0.0 0.993 1.000 0.000
#> GSM1296121 2 0.0 1.000 0.000 1.000
#> GSM1296088 1 0.0 0.993 1.000 0.000
#> GSM1296082 2 0.0 1.000 0.000 1.000
#> GSM1296115 2 0.0 1.000 0.000 1.000
#> GSM1296084 1 0.0 0.993 1.000 0.000
#> GSM1296072 2 0.0 1.000 0.000 1.000
#> GSM1296069 2 0.0 1.000 0.000 1.000
#> GSM1296071 2 0.0 1.000 0.000 1.000
#> GSM1296070 2 0.0 1.000 0.000 1.000
#> GSM1296073 2 0.0 1.000 0.000 1.000
#> GSM1296034 1 0.0 0.993 1.000 0.000
#> GSM1296041 2 0.0 1.000 0.000 1.000
#> GSM1296035 2 0.0 1.000 0.000 1.000
#> GSM1296038 2 0.0 1.000 0.000 1.000
#> GSM1296047 2 0.0 1.000 0.000 1.000
#> GSM1296039 2 0.0 1.000 0.000 1.000
#> GSM1296042 2 0.0 1.000 0.000 1.000
#> GSM1296043 2 0.0 1.000 0.000 1.000
#> GSM1296037 1 0.0 0.993 1.000 0.000
#> GSM1296046 2 0.0 1.000 0.000 1.000
#> GSM1296044 2 0.0 1.000 0.000 1.000
#> GSM1296045 2 0.0 1.000 0.000 1.000
#> GSM1296025 1 0.0 0.993 1.000 0.000
#> GSM1296033 1 0.0 0.993 1.000 0.000
#> GSM1296027 1 0.0 0.993 1.000 0.000
#> GSM1296032 1 0.0 0.993 1.000 0.000
#> GSM1296024 1 0.0 0.993 1.000 0.000
#> GSM1296031 1 0.0 0.993 1.000 0.000
#> GSM1296028 1 0.0 0.993 1.000 0.000
#> GSM1296029 1 0.0 0.993 1.000 0.000
#> GSM1296026 1 0.0 0.993 1.000 0.000
#> GSM1296030 1 0.0 0.993 1.000 0.000
#> GSM1296040 1 0.0 0.993 1.000 0.000
#> GSM1296036 1 0.0 0.993 1.000 0.000
#> GSM1296048 2 0.0 1.000 0.000 1.000
#> GSM1296059 1 0.0 0.993 1.000 0.000
#> GSM1296066 2 0.0 1.000 0.000 1.000
#> GSM1296060 2 0.0 1.000 0.000 1.000
#> GSM1296063 2 0.0 1.000 0.000 1.000
#> GSM1296064 2 0.0 1.000 0.000 1.000
#> GSM1296067 2 0.0 1.000 0.000 1.000
#> GSM1296062 1 0.0 0.993 1.000 0.000
#> GSM1296068 2 0.0 1.000 0.000 1.000
#> GSM1296050 1 0.0 0.993 1.000 0.000
#> GSM1296057 1 0.0 0.993 1.000 0.000
#> GSM1296052 1 0.0 0.993 1.000 0.000
#> GSM1296054 1 0.0 0.993 1.000 0.000
#> GSM1296049 1 0.0 0.993 1.000 0.000
#> GSM1296055 1 0.9 0.538 0.684 0.316
#> GSM1296053 1 0.0 0.993 1.000 0.000
#> GSM1296058 1 0.0 0.993 1.000 0.000
#> GSM1296051 2 0.0 1.000 0.000 1.000
#> GSM1296056 2 0.0 1.000 0.000 1.000
#> GSM1296065 2 0.0 1.000 0.000 1.000
#> GSM1296061 1 0.0 0.993 1.000 0.000
#> GSM1296095 2 0.0 1.000 0.000 1.000
#> GSM1296120 2 0.0 1.000 0.000 1.000
#> GSM1296077 1 0.0 0.993 1.000 0.000
#> GSM1296093 1 0.0 0.993 1.000 0.000
#> GSM1296104 2 0.0 1.000 0.000 1.000
#> GSM1296079 1 0.0 0.993 1.000 0.000
#> GSM1296108 2 0.0 1.000 0.000 1.000
#> GSM1296110 2 0.0 1.000 0.000 1.000
#> GSM1296081 1 0.0 0.993 1.000 0.000
#> GSM1296091 1 0.0 0.993 1.000 0.000
#> GSM1296075 2 0.0 1.000 0.000 1.000
#> GSM1296112 2 0.0 1.000 0.000 1.000
#> GSM1296100 1 0.0 0.993 1.000 0.000
#> GSM1296087 1 0.0 0.993 1.000 0.000
#> GSM1296118 2 0.0 1.000 0.000 1.000
#> GSM1296114 2 0.0 1.000 0.000 1.000
#> GSM1296097 2 0.0 1.000 0.000 1.000
#> GSM1296106 1 0.0 0.993 1.000 0.000
#> GSM1296102 1 0.0 0.993 1.000 0.000
#> GSM1296122 2 0.0 1.000 0.000 1.000
#> GSM1296089 1 0.0 0.993 1.000 0.000
#> GSM1296083 1 0.0 0.993 1.000 0.000
#> GSM1296116 2 0.0 1.000 0.000 1.000
#> GSM1296085 1 0.0 0.993 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1296094 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296119 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296076 2 0.1031 0.968 0.000 0.976 0.024
#> GSM1296092 3 0.0000 0.953 0.000 0.000 1.000
#> GSM1296103 3 0.0000 0.953 0.000 0.000 1.000
#> GSM1296078 2 0.1031 0.968 0.000 0.976 0.024
#> GSM1296107 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296109 2 0.5988 0.411 0.000 0.632 0.368
#> GSM1296080 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296090 3 0.0000 0.953 0.000 0.000 1.000
#> GSM1296074 2 0.1753 0.949 0.000 0.952 0.048
#> GSM1296111 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296099 3 0.0000 0.953 0.000 0.000 1.000
#> GSM1296086 3 0.0000 0.953 0.000 0.000 1.000
#> GSM1296117 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296113 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296096 3 0.0000 0.953 0.000 0.000 1.000
#> GSM1296105 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296098 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296101 1 0.5968 0.407 0.636 0.000 0.364
#> GSM1296121 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296088 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296082 3 0.0000 0.953 0.000 0.000 1.000
#> GSM1296115 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296084 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296072 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296069 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296071 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296070 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296073 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296034 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296041 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296035 3 0.0000 0.953 0.000 0.000 1.000
#> GSM1296038 3 0.5465 0.581 0.000 0.288 0.712
#> GSM1296047 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296039 2 0.1753 0.949 0.000 0.952 0.048
#> GSM1296042 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296043 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296037 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296046 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296044 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296045 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296025 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296033 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296027 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296032 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296024 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296031 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296028 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296029 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296026 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296030 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296040 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296036 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296048 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296059 3 0.0000 0.953 0.000 0.000 1.000
#> GSM1296066 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296060 3 0.0000 0.953 0.000 0.000 1.000
#> GSM1296063 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296064 2 0.1753 0.949 0.000 0.952 0.048
#> GSM1296067 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296062 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296068 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296050 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296057 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296052 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296054 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296049 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296055 3 0.8278 0.584 0.248 0.132 0.620
#> GSM1296053 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296058 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296051 2 0.1031 0.968 0.000 0.976 0.024
#> GSM1296056 3 0.0000 0.953 0.000 0.000 1.000
#> GSM1296065 2 0.0892 0.971 0.000 0.980 0.020
#> GSM1296061 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296095 2 0.1753 0.949 0.000 0.952 0.048
#> GSM1296120 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296077 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296093 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296104 2 0.1753 0.949 0.000 0.952 0.048
#> GSM1296079 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296108 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296110 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296081 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296091 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296075 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296112 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296100 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296087 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296118 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296114 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296097 3 0.0000 0.953 0.000 0.000 1.000
#> GSM1296106 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296102 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296122 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296089 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296083 1 0.0000 0.991 1.000 0.000 0.000
#> GSM1296116 2 0.0000 0.983 0.000 1.000 0.000
#> GSM1296085 1 0.0000 0.991 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1296094 3 0.3266 0.793 0.168 0.000 0.832 0.000
#> GSM1296119 2 0.1118 0.948 0.000 0.964 0.000 0.036
#> GSM1296076 4 0.4500 0.607 0.000 0.316 0.000 0.684
#> GSM1296092 4 0.1118 0.613 0.000 0.000 0.036 0.964
#> GSM1296103 3 0.0707 0.656 0.000 0.000 0.980 0.020
#> GSM1296078 4 0.4500 0.607 0.000 0.316 0.000 0.684
#> GSM1296107 2 0.1118 0.948 0.000 0.964 0.000 0.036
#> GSM1296109 4 0.5165 0.384 0.000 0.484 0.004 0.512
#> GSM1296080 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> GSM1296090 4 0.0000 0.624 0.000 0.000 0.000 1.000
#> GSM1296074 4 0.4500 0.607 0.000 0.316 0.000 0.684
#> GSM1296111 2 0.1118 0.948 0.000 0.964 0.000 0.036
#> GSM1296099 4 0.3356 0.595 0.000 0.000 0.176 0.824
#> GSM1296086 4 0.1118 0.613 0.000 0.000 0.036 0.964
#> GSM1296117 2 0.1118 0.948 0.000 0.964 0.000 0.036
#> GSM1296113 2 0.1118 0.948 0.000 0.964 0.000 0.036
#> GSM1296096 4 0.3356 0.595 0.000 0.000 0.176 0.824
#> GSM1296105 3 0.4454 0.733 0.308 0.000 0.692 0.000
#> GSM1296098 3 0.3907 0.786 0.232 0.000 0.768 0.000
#> GSM1296101 3 0.1022 0.698 0.032 0.000 0.968 0.000
#> GSM1296121 2 0.1118 0.948 0.000 0.964 0.000 0.036
#> GSM1296088 1 0.1867 0.846 0.928 0.000 0.072 0.000
#> GSM1296082 4 0.0000 0.624 0.000 0.000 0.000 1.000
#> GSM1296115 2 0.1118 0.948 0.000 0.964 0.000 0.036
#> GSM1296084 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> GSM1296072 2 0.0000 0.957 0.000 1.000 0.000 0.000
#> GSM1296069 2 0.0592 0.955 0.000 0.984 0.000 0.016
#> GSM1296071 2 0.0000 0.957 0.000 1.000 0.000 0.000
#> GSM1296070 2 0.0592 0.955 0.000 0.984 0.000 0.016
#> GSM1296073 2 0.1118 0.948 0.000 0.964 0.000 0.036
#> GSM1296034 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> GSM1296041 2 0.1118 0.948 0.000 0.964 0.000 0.036
#> GSM1296035 4 0.4193 0.528 0.000 0.000 0.268 0.732
#> GSM1296038 4 0.5631 0.663 0.000 0.232 0.072 0.696
#> GSM1296047 2 0.0000 0.957 0.000 1.000 0.000 0.000
#> GSM1296039 4 0.4961 0.465 0.000 0.448 0.000 0.552
#> GSM1296042 2 0.0592 0.955 0.000 0.984 0.000 0.016
#> GSM1296043 2 0.0000 0.957 0.000 1.000 0.000 0.000
#> GSM1296037 1 0.4916 -0.142 0.576 0.000 0.424 0.000
#> GSM1296046 2 0.0000 0.957 0.000 1.000 0.000 0.000
#> GSM1296044 2 0.0000 0.957 0.000 1.000 0.000 0.000
#> GSM1296045 2 0.0000 0.957 0.000 1.000 0.000 0.000
#> GSM1296025 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> GSM1296033 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> GSM1296027 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> GSM1296032 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> GSM1296024 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> GSM1296031 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> GSM1296028 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> GSM1296029 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> GSM1296026 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> GSM1296030 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> GSM1296040 3 0.3266 0.793 0.168 0.000 0.832 0.000
#> GSM1296036 3 0.4250 0.764 0.276 0.000 0.724 0.000
#> GSM1296048 2 0.1118 0.948 0.000 0.964 0.000 0.036
#> GSM1296059 3 0.0707 0.656 0.000 0.000 0.980 0.020
#> GSM1296066 2 0.1118 0.948 0.000 0.964 0.000 0.036
#> GSM1296060 4 0.4193 0.528 0.000 0.000 0.268 0.732
#> GSM1296063 2 0.4941 -0.171 0.000 0.564 0.000 0.436
#> GSM1296064 4 0.4961 0.465 0.000 0.448 0.000 0.552
#> GSM1296067 2 0.0336 0.951 0.000 0.992 0.008 0.000
#> GSM1296062 3 0.4250 0.764 0.276 0.000 0.724 0.000
#> GSM1296068 2 0.0000 0.957 0.000 1.000 0.000 0.000
#> GSM1296050 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> GSM1296057 3 0.4989 0.398 0.472 0.000 0.528 0.000
#> GSM1296052 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> GSM1296055 3 0.6161 0.520 0.088 0.016 0.696 0.200
#> GSM1296053 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> GSM1296058 3 0.4989 0.398 0.472 0.000 0.528 0.000
#> GSM1296051 4 0.4331 0.627 0.000 0.288 0.000 0.712
#> GSM1296056 4 0.3356 0.595 0.000 0.000 0.176 0.824
#> GSM1296065 4 0.5000 0.355 0.000 0.496 0.000 0.504
#> GSM1296061 3 0.4250 0.764 0.276 0.000 0.724 0.000
#> GSM1296095 4 0.4989 0.418 0.000 0.472 0.000 0.528
#> GSM1296120 2 0.0000 0.957 0.000 1.000 0.000 0.000
#> GSM1296077 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> GSM1296093 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> GSM1296104 4 0.4994 0.399 0.000 0.480 0.000 0.520
#> GSM1296079 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> GSM1296108 2 0.0000 0.957 0.000 1.000 0.000 0.000
#> GSM1296110 2 0.0000 0.957 0.000 1.000 0.000 0.000
#> GSM1296081 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> GSM1296091 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> GSM1296075 2 0.2921 0.776 0.000 0.860 0.000 0.140
#> GSM1296112 2 0.0000 0.957 0.000 1.000 0.000 0.000
#> GSM1296100 1 0.4916 -0.142 0.576 0.000 0.424 0.000
#> GSM1296087 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> GSM1296118 2 0.0000 0.957 0.000 1.000 0.000 0.000
#> GSM1296114 2 0.0000 0.957 0.000 1.000 0.000 0.000
#> GSM1296097 4 0.4250 0.527 0.000 0.000 0.276 0.724
#> GSM1296106 3 0.3688 0.790 0.208 0.000 0.792 0.000
#> GSM1296102 1 0.4916 -0.142 0.576 0.000 0.424 0.000
#> GSM1296122 2 0.0336 0.951 0.000 0.992 0.008 0.000
#> GSM1296089 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> GSM1296083 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> GSM1296116 2 0.0000 0.957 0.000 1.000 0.000 0.000
#> GSM1296085 1 0.0000 0.937 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
#> GSM1296094 3 0.1484 0.77060 0.048 0.000 0.944 0.000 0.008
#> GSM1296119 2 0.0000 0.75625 0.000 1.000 0.000 0.000 0.000
#> GSM1296076 4 0.5998 0.67478 0.000 0.340 0.004 0.544 0.112
#> GSM1296092 4 0.4440 0.33971 0.000 0.000 0.004 0.528 0.468
#> GSM1296103 3 0.3913 0.39467 0.000 0.000 0.676 0.000 0.324
#> GSM1296078 4 0.5998 0.67478 0.000 0.340 0.004 0.544 0.112
#> GSM1296107 2 0.0000 0.75625 0.000 1.000 0.000 0.000 0.000
#> GSM1296109 2 0.3508 0.45162 0.000 0.748 0.000 0.000 0.252
#> GSM1296080 1 0.0000 0.93085 1.000 0.000 0.000 0.000 0.000
#> GSM1296090 4 0.6262 0.61958 0.000 0.168 0.004 0.548 0.280
#> GSM1296074 4 0.5998 0.67478 0.000 0.340 0.004 0.544 0.112
#> GSM1296111 2 0.0000 0.75625 0.000 1.000 0.000 0.000 0.000
#> GSM1296099 5 0.0162 0.72547 0.000 0.000 0.004 0.000 0.996
#> GSM1296086 4 0.4443 0.33301 0.000 0.000 0.004 0.524 0.472
#> GSM1296117 2 0.0000 0.75625 0.000 1.000 0.000 0.000 0.000
#> GSM1296113 2 0.0609 0.76315 0.000 0.980 0.000 0.020 0.000
#> GSM1296096 5 0.0162 0.72547 0.000 0.000 0.004 0.000 0.996
#> GSM1296105 3 0.2690 0.71709 0.156 0.000 0.844 0.000 0.000
#> GSM1296098 3 0.1410 0.77542 0.060 0.000 0.940 0.000 0.000
#> GSM1296101 3 0.1522 0.73319 0.012 0.000 0.944 0.000 0.044
#> GSM1296121 2 0.0000 0.75625 0.000 1.000 0.000 0.000 0.000
#> GSM1296088 1 0.2732 0.74477 0.840 0.000 0.160 0.000 0.000
#> GSM1296082 4 0.6262 0.61958 0.000 0.168 0.004 0.548 0.280
#> GSM1296115 2 0.0000 0.75625 0.000 1.000 0.000 0.000 0.000
#> GSM1296084 1 0.0000 0.93085 1.000 0.000 0.000 0.000 0.000
#> GSM1296072 2 0.3579 0.77373 0.000 0.756 0.004 0.240 0.000
#> GSM1296069 2 0.1908 0.77775 0.000 0.908 0.000 0.092 0.000
#> GSM1296071 2 0.3579 0.77373 0.000 0.756 0.004 0.240 0.000
#> GSM1296070 2 0.1908 0.77775 0.000 0.908 0.000 0.092 0.000
#> GSM1296073 2 0.0000 0.75625 0.000 1.000 0.000 0.000 0.000
#> GSM1296034 1 0.1851 0.83943 0.912 0.000 0.088 0.000 0.000
#> GSM1296041 2 0.0000 0.75625 0.000 1.000 0.000 0.000 0.000
#> GSM1296035 5 0.1211 0.72853 0.000 0.000 0.016 0.024 0.960
#> GSM1296038 5 0.5019 -0.04384 0.000 0.396 0.000 0.036 0.568
#> GSM1296047 2 0.3579 0.77373 0.000 0.756 0.004 0.240 0.000
#> GSM1296039 2 0.4793 0.31956 0.000 0.684 0.000 0.056 0.260
#> GSM1296042 2 0.1908 0.77775 0.000 0.908 0.000 0.092 0.000
#> GSM1296043 2 0.3333 0.77778 0.000 0.788 0.004 0.208 0.000
#> GSM1296037 1 0.5106 -0.09580 0.508 0.000 0.456 0.036 0.000
#> GSM1296046 2 0.3579 0.77373 0.000 0.756 0.004 0.240 0.000
#> GSM1296044 2 0.3579 0.77373 0.000 0.756 0.004 0.240 0.000
#> GSM1296045 2 0.3333 0.77778 0.000 0.788 0.004 0.208 0.000
#> GSM1296025 1 0.0000 0.93085 1.000 0.000 0.000 0.000 0.000
#> GSM1296033 1 0.0000 0.93085 1.000 0.000 0.000 0.000 0.000
#> GSM1296027 1 0.0000 0.93085 1.000 0.000 0.000 0.000 0.000
#> GSM1296032 1 0.0000 0.93085 1.000 0.000 0.000 0.000 0.000
#> GSM1296024 1 0.0000 0.93085 1.000 0.000 0.000 0.000 0.000
#> GSM1296031 1 0.0000 0.93085 1.000 0.000 0.000 0.000 0.000
#> GSM1296028 1 0.0000 0.93085 1.000 0.000 0.000 0.000 0.000
#> GSM1296029 1 0.0000 0.93085 1.000 0.000 0.000 0.000 0.000
#> GSM1296026 1 0.0000 0.93085 1.000 0.000 0.000 0.000 0.000
#> GSM1296030 1 0.0000 0.93085 1.000 0.000 0.000 0.000 0.000
#> GSM1296040 3 0.1484 0.77060 0.048 0.000 0.944 0.000 0.008
#> GSM1296036 3 0.1478 0.77589 0.064 0.000 0.936 0.000 0.000
#> GSM1296048 2 0.0000 0.75625 0.000 1.000 0.000 0.000 0.000
#> GSM1296059 3 0.3949 0.37873 0.000 0.000 0.668 0.000 0.332
#> GSM1296066 2 0.0609 0.76315 0.000 0.980 0.000 0.020 0.000
#> GSM1296060 5 0.1484 0.72471 0.000 0.000 0.008 0.048 0.944
#> GSM1296063 2 0.3102 0.60356 0.000 0.860 0.000 0.056 0.084
#> GSM1296064 2 0.4666 0.36053 0.000 0.704 0.000 0.056 0.240
#> GSM1296067 2 0.4935 0.64826 0.000 0.616 0.040 0.344 0.000
#> GSM1296062 3 0.1544 0.77559 0.068 0.000 0.932 0.000 0.000
#> GSM1296068 2 0.3579 0.77373 0.000 0.756 0.004 0.240 0.000
#> GSM1296050 1 0.1041 0.89633 0.964 0.000 0.032 0.004 0.000
#> GSM1296057 3 0.5836 0.27623 0.412 0.000 0.492 0.096 0.000
#> GSM1296052 1 0.0000 0.93085 1.000 0.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.93085 1.000 0.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.93085 1.000 0.000 0.000 0.000 0.000
#> GSM1296055 5 0.6846 0.18732 0.004 0.000 0.328 0.256 0.412
#> GSM1296053 1 0.0000 0.93085 1.000 0.000 0.000 0.000 0.000
#> GSM1296058 3 0.5597 0.21325 0.440 0.000 0.488 0.072 0.000
#> GSM1296051 4 0.6135 0.67736 0.000 0.304 0.004 0.552 0.140
#> GSM1296056 5 0.0162 0.72547 0.000 0.000 0.004 0.000 0.996
#> GSM1296065 2 0.4525 0.40471 0.000 0.724 0.000 0.056 0.220
#> GSM1296061 3 0.1544 0.77559 0.068 0.000 0.932 0.000 0.000
#> GSM1296095 2 0.4969 0.24969 0.000 0.652 0.000 0.056 0.292
#> GSM1296120 2 0.3579 0.77373 0.000 0.756 0.004 0.240 0.000
#> GSM1296077 1 0.0000 0.93085 1.000 0.000 0.000 0.000 0.000
#> GSM1296093 1 0.0000 0.93085 1.000 0.000 0.000 0.000 0.000
#> GSM1296104 2 0.4928 0.27091 0.000 0.660 0.000 0.056 0.284
#> GSM1296079 1 0.0000 0.93085 1.000 0.000 0.000 0.000 0.000
#> GSM1296108 2 0.3579 0.77373 0.000 0.756 0.004 0.240 0.000
#> GSM1296110 2 0.3579 0.77373 0.000 0.756 0.004 0.240 0.000
#> GSM1296081 1 0.0000 0.93085 1.000 0.000 0.000 0.000 0.000
#> GSM1296091 1 0.0000 0.93085 1.000 0.000 0.000 0.000 0.000
#> GSM1296075 4 0.3398 0.36337 0.000 0.216 0.000 0.780 0.004
#> GSM1296112 2 0.3579 0.77373 0.000 0.756 0.004 0.240 0.000
#> GSM1296100 1 0.5039 -0.08442 0.512 0.000 0.456 0.032 0.000
#> GSM1296087 1 0.0000 0.93085 1.000 0.000 0.000 0.000 0.000
#> GSM1296118 2 0.3579 0.77373 0.000 0.756 0.004 0.240 0.000
#> GSM1296114 2 0.3579 0.77373 0.000 0.756 0.004 0.240 0.000
#> GSM1296097 5 0.3863 0.64243 0.000 0.000 0.028 0.200 0.772
#> GSM1296106 3 0.3977 0.61250 0.032 0.000 0.764 0.204 0.000
#> GSM1296102 1 0.4430 -0.00277 0.540 0.000 0.456 0.004 0.000
#> GSM1296122 2 0.4921 0.65309 0.000 0.620 0.040 0.340 0.000
#> GSM1296089 1 0.0000 0.93085 1.000 0.000 0.000 0.000 0.000
#> GSM1296083 1 0.0000 0.93085 1.000 0.000 0.000 0.000 0.000
#> GSM1296116 2 0.3579 0.77373 0.000 0.756 0.004 0.240 0.000
#> GSM1296085 1 0.0000 0.93085 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
#> GSM1296094 3 0.0260 0.8210 0.008 0.000 0.992 0.000 0.000 0.000
#> GSM1296119 5 0.0000 0.8437 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296076 4 0.4129 0.4609 0.000 0.012 0.000 0.564 0.424 0.000
#> GSM1296092 4 0.1075 0.4749 0.000 0.000 0.000 0.952 0.000 0.048
#> GSM1296103 3 0.3970 0.4589 0.000 0.000 0.692 0.028 0.000 0.280
#> GSM1296078 4 0.4123 0.4690 0.000 0.012 0.000 0.568 0.420 0.000
#> GSM1296107 5 0.0000 0.8437 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296109 5 0.2212 0.7685 0.000 0.000 0.000 0.008 0.880 0.112
#> GSM1296080 1 0.0000 0.8964 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296090 4 0.1049 0.5479 0.000 0.000 0.000 0.960 0.032 0.008
#> GSM1296074 4 0.4076 0.5075 0.000 0.012 0.000 0.592 0.396 0.000
#> GSM1296111 5 0.0000 0.8437 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296099 6 0.3566 0.8506 0.000 0.000 0.020 0.236 0.000 0.744
#> GSM1296086 4 0.1075 0.4749 0.000 0.000 0.000 0.952 0.000 0.048
#> GSM1296117 5 0.0000 0.8437 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296113 5 0.0146 0.8412 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1296096 6 0.3566 0.8506 0.000 0.000 0.020 0.236 0.000 0.744
#> GSM1296105 3 0.3329 0.6353 0.184 0.020 0.792 0.000 0.000 0.004
#> GSM1296098 3 0.0713 0.8258 0.028 0.000 0.972 0.000 0.000 0.000
#> GSM1296101 3 0.0291 0.8173 0.004 0.000 0.992 0.000 0.000 0.004
#> GSM1296121 5 0.0000 0.8437 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296088 1 0.3448 0.5653 0.716 0.004 0.280 0.000 0.000 0.000
#> GSM1296082 4 0.1049 0.5479 0.000 0.000 0.000 0.960 0.032 0.008
#> GSM1296115 5 0.0000 0.8437 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296084 1 0.0146 0.8951 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM1296072 2 0.3563 0.9532 0.000 0.664 0.000 0.000 0.336 0.000
#> GSM1296069 5 0.0790 0.8160 0.000 0.032 0.000 0.000 0.968 0.000
#> GSM1296071 2 0.3563 0.9532 0.000 0.664 0.000 0.000 0.336 0.000
#> GSM1296070 5 0.0790 0.8160 0.000 0.032 0.000 0.000 0.968 0.000
#> GSM1296073 5 0.0000 0.8437 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296034 1 0.2562 0.7408 0.828 0.000 0.172 0.000 0.000 0.000
#> GSM1296041 5 0.0000 0.8437 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296035 6 0.3619 0.8495 0.000 0.000 0.024 0.232 0.000 0.744
#> GSM1296038 5 0.5566 0.1930 0.000 0.036 0.000 0.060 0.516 0.388
#> GSM1296047 2 0.3547 0.9508 0.000 0.668 0.000 0.000 0.332 0.000
#> GSM1296039 5 0.3455 0.7267 0.000 0.036 0.000 0.052 0.836 0.076
#> GSM1296042 5 0.0790 0.8160 0.000 0.032 0.000 0.000 0.968 0.000
#> GSM1296043 5 0.3756 -0.3045 0.000 0.400 0.000 0.000 0.600 0.000
#> GSM1296037 1 0.5968 0.2810 0.528 0.068 0.336 0.000 0.000 0.068
#> GSM1296046 2 0.3563 0.9532 0.000 0.664 0.000 0.000 0.336 0.000
#> GSM1296044 2 0.3563 0.9532 0.000 0.664 0.000 0.000 0.336 0.000
#> GSM1296045 5 0.3782 -0.3470 0.000 0.412 0.000 0.000 0.588 0.000
#> GSM1296025 1 0.0291 0.8940 0.992 0.004 0.004 0.000 0.000 0.000
#> GSM1296033 1 0.0146 0.8951 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM1296027 1 0.0000 0.8964 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296032 1 0.0000 0.8964 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296024 1 0.0291 0.8940 0.992 0.004 0.004 0.000 0.000 0.000
#> GSM1296031 1 0.0000 0.8964 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296028 1 0.0000 0.8964 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296029 1 0.0000 0.8964 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296026 1 0.0291 0.8940 0.992 0.004 0.004 0.000 0.000 0.000
#> GSM1296030 1 0.0146 0.8951 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM1296040 3 0.0260 0.8210 0.008 0.000 0.992 0.000 0.000 0.000
#> GSM1296036 3 0.0790 0.8255 0.032 0.000 0.968 0.000 0.000 0.000
#> GSM1296048 5 0.0000 0.8437 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296059 3 0.4029 0.4365 0.000 0.000 0.680 0.028 0.000 0.292
#> GSM1296066 5 0.0146 0.8412 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1296060 6 0.3541 0.8502 0.000 0.000 0.020 0.232 0.000 0.748
#> GSM1296063 5 0.2186 0.7890 0.000 0.036 0.000 0.048 0.908 0.008
#> GSM1296064 5 0.3345 0.7361 0.000 0.036 0.000 0.052 0.844 0.068
#> GSM1296067 2 0.2178 0.7107 0.000 0.868 0.000 0.000 0.132 0.000
#> GSM1296062 3 0.0865 0.8227 0.036 0.000 0.964 0.000 0.000 0.000
#> GSM1296068 2 0.3563 0.9532 0.000 0.664 0.000 0.000 0.336 0.000
#> GSM1296050 1 0.2145 0.8301 0.904 0.076 0.004 0.012 0.000 0.004
#> GSM1296057 1 0.7108 -0.1242 0.360 0.084 0.340 0.000 0.000 0.216
#> GSM1296052 1 0.0000 0.8964 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.8964 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296049 1 0.0291 0.8940 0.992 0.004 0.004 0.000 0.000 0.000
#> GSM1296055 6 0.4553 0.4335 0.000 0.304 0.036 0.012 0.000 0.648
#> GSM1296053 1 0.0000 0.8964 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296058 1 0.6634 0.0622 0.436 0.076 0.360 0.000 0.000 0.128
#> GSM1296051 4 0.3245 0.5996 0.000 0.008 0.000 0.764 0.228 0.000
#> GSM1296056 6 0.3566 0.8506 0.000 0.000 0.020 0.236 0.000 0.744
#> GSM1296065 5 0.3230 0.7445 0.000 0.036 0.000 0.052 0.852 0.060
#> GSM1296061 3 0.0790 0.8255 0.032 0.000 0.968 0.000 0.000 0.000
#> GSM1296095 5 0.4077 0.6651 0.000 0.036 0.000 0.048 0.780 0.136
#> GSM1296120 2 0.3563 0.9532 0.000 0.664 0.000 0.000 0.336 0.000
#> GSM1296077 1 0.0291 0.8940 0.992 0.004 0.004 0.000 0.000 0.000
#> GSM1296093 1 0.0000 0.8964 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296104 5 0.3957 0.6839 0.000 0.036 0.000 0.048 0.792 0.124
#> GSM1296079 1 0.0291 0.8940 0.992 0.004 0.004 0.000 0.000 0.000
#> GSM1296108 2 0.3563 0.9532 0.000 0.664 0.000 0.000 0.336 0.000
#> GSM1296110 2 0.3547 0.9508 0.000 0.668 0.000 0.000 0.332 0.000
#> GSM1296081 1 0.0000 0.8964 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296091 1 0.0146 0.8951 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM1296075 4 0.4570 0.2156 0.000 0.436 0.000 0.528 0.036 0.000
#> GSM1296112 2 0.3563 0.9532 0.000 0.664 0.000 0.000 0.336 0.000
#> GSM1296100 1 0.5978 0.2708 0.524 0.068 0.340 0.000 0.000 0.068
#> GSM1296087 1 0.0000 0.8964 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296118 2 0.3531 0.9474 0.000 0.672 0.000 0.000 0.328 0.000
#> GSM1296114 2 0.3563 0.9532 0.000 0.664 0.000 0.000 0.336 0.000
#> GSM1296097 6 0.1728 0.6788 0.000 0.064 0.004 0.008 0.000 0.924
#> GSM1296106 3 0.6313 0.3522 0.020 0.256 0.468 0.000 0.000 0.256
#> GSM1296102 1 0.4844 0.3957 0.596 0.052 0.344 0.000 0.000 0.008
#> GSM1296122 2 0.2178 0.7107 0.000 0.868 0.000 0.000 0.132 0.000
#> GSM1296089 1 0.0000 0.8964 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296083 1 0.0000 0.8964 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296116 2 0.3563 0.9532 0.000 0.664 0.000 0.000 0.336 0.000
#> GSM1296085 1 0.0000 0.8964 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)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# 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 tissue(p) time(p) agent(p) dose(p) individual(p) k
#> ATC:skmeans 99 0.7808 0.0917 0.656 4.03e-06 9.83e-05 2
#> ATC:skmeans 97 0.2433 0.1296 0.925 5.81e-07 1.04e-06 3
#> ATC:skmeans 87 0.1377 0.0690 0.272 3.70e-07 3.23e-11 4
#> ATC:skmeans 81 0.4093 0.2768 0.125 8.59e-07 2.92e-13 5
#> ATC:skmeans 82 0.0138 0.6681 0.265 3.73e-06 1.52e-09 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 45638 rows and 99 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 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 0.763 0.859 0.941 0.4955 0.501 0.501
#> 3 3 0.987 0.941 0.978 0.3580 0.713 0.486
#> 4 4 0.888 0.838 0.935 0.0978 0.881 0.662
#> 5 5 0.810 0.813 0.906 0.0754 0.918 0.696
#> 6 6 0.837 0.779 0.864 0.0396 0.919 0.644
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
#> GSM1296094 1 0.000 0.975 1.000 0.000
#> GSM1296119 2 0.000 0.900 0.000 1.000
#> GSM1296076 2 0.000 0.900 0.000 1.000
#> GSM1296092 2 0.971 0.453 0.400 0.600
#> GSM1296103 1 0.971 0.187 0.600 0.400
#> GSM1296078 2 0.000 0.900 0.000 1.000
#> GSM1296107 2 0.000 0.900 0.000 1.000
#> GSM1296109 2 0.000 0.900 0.000 1.000
#> GSM1296080 1 0.000 0.975 1.000 0.000
#> GSM1296090 2 0.971 0.453 0.400 0.600
#> GSM1296074 2 0.000 0.900 0.000 1.000
#> GSM1296111 2 0.000 0.900 0.000 1.000
#> GSM1296099 2 0.971 0.453 0.400 0.600
#> GSM1296086 2 0.971 0.453 0.400 0.600
#> GSM1296117 2 0.000 0.900 0.000 1.000
#> GSM1296113 2 0.000 0.900 0.000 1.000
#> GSM1296096 2 0.913 0.569 0.328 0.672
#> GSM1296105 1 0.000 0.975 1.000 0.000
#> GSM1296098 1 0.000 0.975 1.000 0.000
#> GSM1296101 1 0.000 0.975 1.000 0.000
#> GSM1296121 2 0.000 0.900 0.000 1.000
#> GSM1296088 1 0.000 0.975 1.000 0.000
#> GSM1296082 2 0.000 0.900 0.000 1.000
#> GSM1296115 2 0.000 0.900 0.000 1.000
#> GSM1296084 1 0.000 0.975 1.000 0.000
#> GSM1296072 2 0.000 0.900 0.000 1.000
#> GSM1296069 2 0.000 0.900 0.000 1.000
#> GSM1296071 2 0.000 0.900 0.000 1.000
#> GSM1296070 2 0.000 0.900 0.000 1.000
#> GSM1296073 2 0.000 0.900 0.000 1.000
#> GSM1296034 1 0.000 0.975 1.000 0.000
#> GSM1296041 2 0.000 0.900 0.000 1.000
#> GSM1296035 2 0.971 0.453 0.400 0.600
#> GSM1296038 2 0.000 0.900 0.000 1.000
#> GSM1296047 2 0.000 0.900 0.000 1.000
#> GSM1296039 2 0.000 0.900 0.000 1.000
#> GSM1296042 2 0.000 0.900 0.000 1.000
#> GSM1296043 2 0.000 0.900 0.000 1.000
#> GSM1296037 1 0.000 0.975 1.000 0.000
#> GSM1296046 2 0.000 0.900 0.000 1.000
#> GSM1296044 2 0.000 0.900 0.000 1.000
#> GSM1296045 2 0.000 0.900 0.000 1.000
#> GSM1296025 1 0.000 0.975 1.000 0.000
#> GSM1296033 1 0.000 0.975 1.000 0.000
#> GSM1296027 1 0.000 0.975 1.000 0.000
#> GSM1296032 1 0.000 0.975 1.000 0.000
#> GSM1296024 1 0.000 0.975 1.000 0.000
#> GSM1296031 1 0.000 0.975 1.000 0.000
#> GSM1296028 1 0.000 0.975 1.000 0.000
#> GSM1296029 1 0.000 0.975 1.000 0.000
#> GSM1296026 1 0.000 0.975 1.000 0.000
#> GSM1296030 1 0.000 0.975 1.000 0.000
#> GSM1296040 1 0.000 0.975 1.000 0.000
#> GSM1296036 1 0.000 0.975 1.000 0.000
#> GSM1296048 2 0.000 0.900 0.000 1.000
#> GSM1296059 1 0.278 0.920 0.952 0.048
#> GSM1296066 2 0.000 0.900 0.000 1.000
#> GSM1296060 2 0.971 0.453 0.400 0.600
#> GSM1296063 2 0.000 0.900 0.000 1.000
#> GSM1296064 2 0.000 0.900 0.000 1.000
#> GSM1296067 2 0.714 0.734 0.196 0.804
#> GSM1296062 1 0.000 0.975 1.000 0.000
#> GSM1296068 2 0.000 0.900 0.000 1.000
#> GSM1296050 1 0.000 0.975 1.000 0.000
#> GSM1296057 1 0.000 0.975 1.000 0.000
#> GSM1296052 1 0.000 0.975 1.000 0.000
#> GSM1296054 1 0.000 0.975 1.000 0.000
#> GSM1296049 1 0.000 0.975 1.000 0.000
#> GSM1296055 2 0.971 0.453 0.400 0.600
#> GSM1296053 1 0.000 0.975 1.000 0.000
#> GSM1296058 1 0.000 0.975 1.000 0.000
#> GSM1296051 2 0.971 0.453 0.400 0.600
#> GSM1296056 2 0.971 0.453 0.400 0.600
#> GSM1296065 2 0.000 0.900 0.000 1.000
#> GSM1296061 1 0.000 0.975 1.000 0.000
#> GSM1296095 2 0.000 0.900 0.000 1.000
#> GSM1296120 2 0.000 0.900 0.000 1.000
#> GSM1296077 1 0.000 0.975 1.000 0.000
#> GSM1296093 1 0.000 0.975 1.000 0.000
#> GSM1296104 2 0.000 0.900 0.000 1.000
#> GSM1296079 1 0.000 0.975 1.000 0.000
#> GSM1296108 2 0.000 0.900 0.000 1.000
#> GSM1296110 2 0.000 0.900 0.000 1.000
#> GSM1296081 1 0.000 0.975 1.000 0.000
#> GSM1296091 1 0.000 0.975 1.000 0.000
#> GSM1296075 2 0.969 0.460 0.396 0.604
#> GSM1296112 2 0.000 0.900 0.000 1.000
#> GSM1296100 1 0.000 0.975 1.000 0.000
#> GSM1296087 1 0.000 0.975 1.000 0.000
#> GSM1296118 2 0.000 0.900 0.000 1.000
#> GSM1296114 2 0.000 0.900 0.000 1.000
#> GSM1296097 2 0.971 0.453 0.400 0.600
#> GSM1296106 1 1.000 -0.152 0.512 0.488
#> GSM1296102 1 0.000 0.975 1.000 0.000
#> GSM1296122 2 0.000 0.900 0.000 1.000
#> GSM1296089 1 0.000 0.975 1.000 0.000
#> GSM1296083 1 0.000 0.975 1.000 0.000
#> GSM1296116 2 0.000 0.900 0.000 1.000
#> GSM1296085 1 0.000 0.975 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1296094 3 0.000 0.9594 0.000 0.000 1.000
#> GSM1296119 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296076 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296092 3 0.000 0.9594 0.000 0.000 1.000
#> GSM1296103 3 0.000 0.9594 0.000 0.000 1.000
#> GSM1296078 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296107 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296109 3 0.000 0.9594 0.000 0.000 1.000
#> GSM1296080 1 0.000 0.9871 1.000 0.000 0.000
#> GSM1296090 3 0.000 0.9594 0.000 0.000 1.000
#> GSM1296074 3 0.624 0.2031 0.000 0.440 0.560
#> GSM1296111 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296099 3 0.000 0.9594 0.000 0.000 1.000
#> GSM1296086 3 0.000 0.9594 0.000 0.000 1.000
#> GSM1296117 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296113 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296096 3 0.000 0.9594 0.000 0.000 1.000
#> GSM1296105 3 0.000 0.9594 0.000 0.000 1.000
#> GSM1296098 3 0.627 0.1648 0.456 0.000 0.544
#> GSM1296101 3 0.000 0.9594 0.000 0.000 1.000
#> GSM1296121 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296088 3 0.000 0.9594 0.000 0.000 1.000
#> GSM1296082 3 0.000 0.9594 0.000 0.000 1.000
#> GSM1296115 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296084 1 0.000 0.9871 1.000 0.000 0.000
#> GSM1296072 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296069 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296071 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296070 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296073 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296034 1 0.000 0.9871 1.000 0.000 0.000
#> GSM1296041 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296035 3 0.000 0.9594 0.000 0.000 1.000
#> GSM1296038 3 0.000 0.9594 0.000 0.000 1.000
#> GSM1296047 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296039 2 0.631 -0.0135 0.000 0.508 0.492
#> GSM1296042 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296043 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296037 1 0.000 0.9871 1.000 0.000 0.000
#> GSM1296046 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296044 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296045 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296025 1 0.000 0.9871 1.000 0.000 0.000
#> GSM1296033 1 0.000 0.9871 1.000 0.000 0.000
#> GSM1296027 1 0.000 0.9871 1.000 0.000 0.000
#> GSM1296032 1 0.000 0.9871 1.000 0.000 0.000
#> GSM1296024 1 0.000 0.9871 1.000 0.000 0.000
#> GSM1296031 1 0.000 0.9871 1.000 0.000 0.000
#> GSM1296028 1 0.000 0.9871 1.000 0.000 0.000
#> GSM1296029 1 0.000 0.9871 1.000 0.000 0.000
#> GSM1296026 1 0.000 0.9871 1.000 0.000 0.000
#> GSM1296030 1 0.000 0.9871 1.000 0.000 0.000
#> GSM1296040 3 0.000 0.9594 0.000 0.000 1.000
#> GSM1296036 1 0.599 0.3846 0.632 0.000 0.368
#> GSM1296048 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296059 3 0.000 0.9594 0.000 0.000 1.000
#> GSM1296066 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296060 3 0.000 0.9594 0.000 0.000 1.000
#> GSM1296063 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296064 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296067 3 0.000 0.9594 0.000 0.000 1.000
#> GSM1296062 1 0.000 0.9871 1.000 0.000 0.000
#> GSM1296068 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296050 3 0.382 0.8090 0.148 0.000 0.852
#> GSM1296057 3 0.000 0.9594 0.000 0.000 1.000
#> GSM1296052 1 0.000 0.9871 1.000 0.000 0.000
#> GSM1296054 1 0.000 0.9871 1.000 0.000 0.000
#> GSM1296049 1 0.000 0.9871 1.000 0.000 0.000
#> GSM1296055 3 0.000 0.9594 0.000 0.000 1.000
#> GSM1296053 1 0.000 0.9871 1.000 0.000 0.000
#> GSM1296058 3 0.400 0.7947 0.160 0.000 0.840
#> GSM1296051 3 0.000 0.9594 0.000 0.000 1.000
#> GSM1296056 3 0.000 0.9594 0.000 0.000 1.000
#> GSM1296065 3 0.000 0.9594 0.000 0.000 1.000
#> GSM1296061 1 0.000 0.9871 1.000 0.000 0.000
#> GSM1296095 3 0.000 0.9594 0.000 0.000 1.000
#> GSM1296120 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296077 1 0.000 0.9871 1.000 0.000 0.000
#> GSM1296093 1 0.000 0.9871 1.000 0.000 0.000
#> GSM1296104 3 0.000 0.9594 0.000 0.000 1.000
#> GSM1296079 1 0.000 0.9871 1.000 0.000 0.000
#> GSM1296108 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296110 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296081 1 0.000 0.9871 1.000 0.000 0.000
#> GSM1296091 3 0.263 0.8826 0.084 0.000 0.916
#> GSM1296075 3 0.000 0.9594 0.000 0.000 1.000
#> GSM1296112 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296100 1 0.000 0.9871 1.000 0.000 0.000
#> GSM1296087 1 0.000 0.9871 1.000 0.000 0.000
#> GSM1296118 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296114 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296097 3 0.000 0.9594 0.000 0.000 1.000
#> GSM1296106 3 0.000 0.9594 0.000 0.000 1.000
#> GSM1296102 1 0.000 0.9871 1.000 0.000 0.000
#> GSM1296122 3 0.000 0.9594 0.000 0.000 1.000
#> GSM1296089 1 0.000 0.9871 1.000 0.000 0.000
#> GSM1296083 1 0.000 0.9871 1.000 0.000 0.000
#> GSM1296116 2 0.000 0.9844 0.000 1.000 0.000
#> GSM1296085 1 0.000 0.9871 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1296094 3 0.0188 0.765 0.000 0.000 0.996 0.004
#> GSM1296119 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> GSM1296076 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> GSM1296092 4 0.0000 0.953 0.000 0.000 0.000 1.000
#> GSM1296103 4 0.0000 0.953 0.000 0.000 0.000 1.000
#> GSM1296078 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> GSM1296107 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> GSM1296109 4 0.0000 0.953 0.000 0.000 0.000 1.000
#> GSM1296080 1 0.0000 0.886 1.000 0.000 0.000 0.000
#> GSM1296090 4 0.0000 0.953 0.000 0.000 0.000 1.000
#> GSM1296074 4 0.2197 0.864 0.000 0.080 0.004 0.916
#> GSM1296111 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> GSM1296099 4 0.0000 0.953 0.000 0.000 0.000 1.000
#> GSM1296086 4 0.0000 0.953 0.000 0.000 0.000 1.000
#> GSM1296117 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> GSM1296113 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> GSM1296096 4 0.0000 0.953 0.000 0.000 0.000 1.000
#> GSM1296105 3 0.0188 0.765 0.000 0.000 0.996 0.004
#> GSM1296098 3 0.0188 0.766 0.004 0.000 0.996 0.000
#> GSM1296101 4 0.4661 0.392 0.000 0.000 0.348 0.652
#> GSM1296121 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> GSM1296088 3 0.4955 0.220 0.000 0.000 0.556 0.444
#> GSM1296082 4 0.0000 0.953 0.000 0.000 0.000 1.000
#> GSM1296115 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> GSM1296084 3 0.4948 0.344 0.440 0.000 0.560 0.000
#> GSM1296072 2 0.0188 0.991 0.000 0.996 0.004 0.000
#> GSM1296069 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> GSM1296071 2 0.0188 0.991 0.000 0.996 0.004 0.000
#> GSM1296070 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> GSM1296073 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> GSM1296034 1 0.4040 0.568 0.752 0.000 0.248 0.000
#> GSM1296041 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> GSM1296035 4 0.0000 0.953 0.000 0.000 0.000 1.000
#> GSM1296038 4 0.0000 0.953 0.000 0.000 0.000 1.000
#> GSM1296047 2 0.1978 0.921 0.000 0.928 0.004 0.068
#> GSM1296039 4 0.3172 0.765 0.000 0.160 0.000 0.840
#> GSM1296042 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> GSM1296043 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> GSM1296037 3 0.0188 0.766 0.004 0.000 0.996 0.000
#> GSM1296046 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> GSM1296044 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> GSM1296045 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> GSM1296025 1 0.0000 0.886 1.000 0.000 0.000 0.000
#> GSM1296033 3 0.4955 0.336 0.444 0.000 0.556 0.000
#> GSM1296027 1 0.0000 0.886 1.000 0.000 0.000 0.000
#> GSM1296032 1 0.0000 0.886 1.000 0.000 0.000 0.000
#> GSM1296024 1 0.0000 0.886 1.000 0.000 0.000 0.000
#> GSM1296031 3 0.4955 0.336 0.444 0.000 0.556 0.000
#> GSM1296028 1 0.3726 0.632 0.788 0.000 0.212 0.000
#> GSM1296029 1 0.0000 0.886 1.000 0.000 0.000 0.000
#> GSM1296026 3 0.4916 0.373 0.424 0.000 0.576 0.000
#> GSM1296030 1 0.4624 0.352 0.660 0.000 0.340 0.000
#> GSM1296040 3 0.0188 0.765 0.000 0.000 0.996 0.004
#> GSM1296036 3 0.0188 0.766 0.004 0.000 0.996 0.000
#> GSM1296048 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> GSM1296059 4 0.0000 0.953 0.000 0.000 0.000 1.000
#> GSM1296066 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> GSM1296060 4 0.0000 0.953 0.000 0.000 0.000 1.000
#> GSM1296063 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> GSM1296064 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> GSM1296067 4 0.0188 0.951 0.000 0.000 0.004 0.996
#> GSM1296062 3 0.0188 0.766 0.004 0.000 0.996 0.000
#> GSM1296068 2 0.0188 0.991 0.000 0.996 0.004 0.000
#> GSM1296050 3 0.7153 0.493 0.248 0.000 0.556 0.196
#> GSM1296057 4 0.4907 0.182 0.000 0.000 0.420 0.580
#> GSM1296052 1 0.0000 0.886 1.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.886 1.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.886 1.000 0.000 0.000 0.000
#> GSM1296055 4 0.0000 0.953 0.000 0.000 0.000 1.000
#> GSM1296053 1 0.0000 0.886 1.000 0.000 0.000 0.000
#> GSM1296058 3 0.0188 0.765 0.000 0.000 0.996 0.004
#> GSM1296051 4 0.0000 0.953 0.000 0.000 0.000 1.000
#> GSM1296056 4 0.0000 0.953 0.000 0.000 0.000 1.000
#> GSM1296065 4 0.0188 0.951 0.000 0.000 0.004 0.996
#> GSM1296061 3 0.0188 0.766 0.004 0.000 0.996 0.000
#> GSM1296095 4 0.0000 0.953 0.000 0.000 0.000 1.000
#> GSM1296120 2 0.0188 0.991 0.000 0.996 0.004 0.000
#> GSM1296077 1 0.4746 0.247 0.632 0.000 0.368 0.000
#> GSM1296093 1 0.0000 0.886 1.000 0.000 0.000 0.000
#> GSM1296104 4 0.0188 0.951 0.000 0.000 0.004 0.996
#> GSM1296079 1 0.4977 -0.108 0.540 0.000 0.460 0.000
#> GSM1296108 2 0.0188 0.991 0.000 0.996 0.004 0.000
#> GSM1296110 2 0.2831 0.859 0.000 0.876 0.004 0.120
#> GSM1296081 1 0.0000 0.886 1.000 0.000 0.000 0.000
#> GSM1296091 3 0.7153 0.493 0.248 0.000 0.556 0.196
#> GSM1296075 4 0.0188 0.951 0.000 0.000 0.004 0.996
#> GSM1296112 2 0.0188 0.991 0.000 0.996 0.004 0.000
#> GSM1296100 3 0.0188 0.766 0.004 0.000 0.996 0.000
#> GSM1296087 1 0.0000 0.886 1.000 0.000 0.000 0.000
#> GSM1296118 2 0.0188 0.991 0.000 0.996 0.004 0.000
#> GSM1296114 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> GSM1296097 4 0.0000 0.953 0.000 0.000 0.000 1.000
#> GSM1296106 4 0.0000 0.953 0.000 0.000 0.000 1.000
#> GSM1296102 3 0.0188 0.766 0.004 0.000 0.996 0.000
#> GSM1296122 4 0.0188 0.951 0.000 0.000 0.004 0.996
#> GSM1296089 3 0.4955 0.336 0.444 0.000 0.556 0.000
#> GSM1296083 1 0.0000 0.886 1.000 0.000 0.000 0.000
#> GSM1296116 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> GSM1296085 1 0.0000 0.886 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
#> GSM1296094 3 0.0000 0.763 0.000 0.000 1.000 0.000 0.000
#> GSM1296119 5 0.0000 0.990 0.000 0.000 0.000 0.000 1.000
#> GSM1296076 5 0.0404 0.982 0.000 0.012 0.000 0.000 0.988
#> GSM1296092 4 0.0162 0.940 0.000 0.004 0.000 0.996 0.000
#> GSM1296103 4 0.0000 0.941 0.000 0.000 0.000 1.000 0.000
#> GSM1296078 5 0.0510 0.978 0.000 0.016 0.000 0.000 0.984
#> GSM1296107 5 0.0000 0.990 0.000 0.000 0.000 0.000 1.000
#> GSM1296109 4 0.1478 0.905 0.000 0.064 0.000 0.936 0.000
#> GSM1296080 1 0.0000 0.864 1.000 0.000 0.000 0.000 0.000
#> GSM1296090 4 0.0162 0.940 0.000 0.004 0.000 0.996 0.000
#> GSM1296074 4 0.2020 0.880 0.000 0.100 0.000 0.900 0.000
#> GSM1296111 5 0.0000 0.990 0.000 0.000 0.000 0.000 1.000
#> GSM1296099 4 0.0000 0.941 0.000 0.000 0.000 1.000 0.000
#> GSM1296086 4 0.0000 0.941 0.000 0.000 0.000 1.000 0.000
#> GSM1296117 5 0.0000 0.990 0.000 0.000 0.000 0.000 1.000
#> GSM1296113 5 0.0000 0.990 0.000 0.000 0.000 0.000 1.000
#> GSM1296096 4 0.0000 0.941 0.000 0.000 0.000 1.000 0.000
#> GSM1296105 3 0.0000 0.763 0.000 0.000 1.000 0.000 0.000
#> GSM1296098 3 0.0000 0.763 0.000 0.000 1.000 0.000 0.000
#> GSM1296101 3 0.3895 0.500 0.000 0.000 0.680 0.320 0.000
#> GSM1296121 5 0.0000 0.990 0.000 0.000 0.000 0.000 1.000
#> GSM1296088 3 0.3861 0.570 0.000 0.004 0.712 0.284 0.000
#> GSM1296082 4 0.0162 0.940 0.000 0.004 0.000 0.996 0.000
#> GSM1296115 5 0.0000 0.990 0.000 0.000 0.000 0.000 1.000
#> GSM1296084 3 0.5836 0.295 0.384 0.100 0.516 0.000 0.000
#> GSM1296072 2 0.2074 0.930 0.000 0.896 0.000 0.000 0.104
#> GSM1296069 5 0.0000 0.990 0.000 0.000 0.000 0.000 1.000
#> GSM1296071 2 0.2074 0.930 0.000 0.896 0.000 0.000 0.104
#> GSM1296070 5 0.0000 0.990 0.000 0.000 0.000 0.000 1.000
#> GSM1296073 5 0.0000 0.990 0.000 0.000 0.000 0.000 1.000
#> GSM1296034 1 0.3305 0.588 0.776 0.000 0.224 0.000 0.000
#> GSM1296041 5 0.0000 0.990 0.000 0.000 0.000 0.000 1.000
#> GSM1296035 4 0.0000 0.941 0.000 0.000 0.000 1.000 0.000
#> GSM1296038 4 0.0000 0.941 0.000 0.000 0.000 1.000 0.000
#> GSM1296047 2 0.2179 0.930 0.000 0.896 0.000 0.004 0.100
#> GSM1296039 4 0.2632 0.870 0.000 0.072 0.000 0.888 0.040
#> GSM1296042 5 0.0000 0.990 0.000 0.000 0.000 0.000 1.000
#> GSM1296043 5 0.0000 0.990 0.000 0.000 0.000 0.000 1.000
#> GSM1296037 3 0.0000 0.763 0.000 0.000 1.000 0.000 0.000
#> GSM1296046 5 0.0290 0.984 0.000 0.008 0.000 0.000 0.992
#> GSM1296044 5 0.0290 0.984 0.000 0.008 0.000 0.000 0.992
#> GSM1296045 5 0.0000 0.990 0.000 0.000 0.000 0.000 1.000
#> GSM1296025 1 0.0000 0.864 1.000 0.000 0.000 0.000 0.000
#> GSM1296033 3 0.5843 0.287 0.388 0.100 0.512 0.000 0.000
#> GSM1296027 1 0.0000 0.864 1.000 0.000 0.000 0.000 0.000
#> GSM1296032 1 0.2020 0.818 0.900 0.100 0.000 0.000 0.000
#> GSM1296024 1 0.0000 0.864 1.000 0.000 0.000 0.000 0.000
#> GSM1296031 3 0.5843 0.287 0.388 0.100 0.512 0.000 0.000
#> GSM1296028 1 0.4748 0.619 0.728 0.100 0.172 0.000 0.000
#> GSM1296029 1 0.2020 0.818 0.900 0.100 0.000 0.000 0.000
#> GSM1296026 3 0.5803 0.325 0.368 0.100 0.532 0.000 0.000
#> GSM1296030 1 0.5544 0.376 0.608 0.100 0.292 0.000 0.000
#> GSM1296040 3 0.0000 0.763 0.000 0.000 1.000 0.000 0.000
#> GSM1296036 3 0.0000 0.763 0.000 0.000 1.000 0.000 0.000
#> GSM1296048 5 0.0000 0.990 0.000 0.000 0.000 0.000 1.000
#> GSM1296059 4 0.0000 0.941 0.000 0.000 0.000 1.000 0.000
#> GSM1296066 5 0.0000 0.990 0.000 0.000 0.000 0.000 1.000
#> GSM1296060 4 0.0000 0.941 0.000 0.000 0.000 1.000 0.000
#> GSM1296063 5 0.0162 0.987 0.000 0.004 0.000 0.000 0.996
#> GSM1296064 5 0.2674 0.806 0.000 0.004 0.000 0.140 0.856
#> GSM1296067 2 0.2852 0.785 0.000 0.828 0.000 0.172 0.000
#> GSM1296062 3 0.0000 0.763 0.000 0.000 1.000 0.000 0.000
#> GSM1296068 2 0.2605 0.899 0.000 0.852 0.000 0.000 0.148
#> GSM1296050 3 0.5114 0.352 0.004 0.456 0.512 0.028 0.000
#> GSM1296057 3 0.3366 0.642 0.000 0.004 0.784 0.212 0.000
#> GSM1296052 1 0.0000 0.864 1.000 0.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.864 1.000 0.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.864 1.000 0.000 0.000 0.000 0.000
#> GSM1296055 4 0.3612 0.619 0.000 0.268 0.000 0.732 0.000
#> GSM1296053 1 0.0000 0.864 1.000 0.000 0.000 0.000 0.000
#> GSM1296058 3 0.0162 0.762 0.000 0.004 0.996 0.000 0.000
#> GSM1296051 4 0.0162 0.940 0.000 0.004 0.000 0.996 0.000
#> GSM1296056 4 0.0000 0.941 0.000 0.000 0.000 1.000 0.000
#> GSM1296065 4 0.2813 0.812 0.000 0.168 0.000 0.832 0.000
#> GSM1296061 3 0.0000 0.763 0.000 0.000 1.000 0.000 0.000
#> GSM1296095 4 0.0290 0.938 0.000 0.008 0.000 0.992 0.000
#> GSM1296120 2 0.2074 0.930 0.000 0.896 0.000 0.000 0.104
#> GSM1296077 1 0.5773 0.179 0.544 0.100 0.356 0.000 0.000
#> GSM1296093 1 0.0000 0.864 1.000 0.000 0.000 0.000 0.000
#> GSM1296104 4 0.1732 0.897 0.000 0.080 0.000 0.920 0.000
#> GSM1296079 1 0.5896 -0.165 0.452 0.100 0.448 0.000 0.000
#> GSM1296108 2 0.2074 0.930 0.000 0.896 0.000 0.000 0.104
#> GSM1296110 2 0.2179 0.930 0.000 0.896 0.000 0.004 0.100
#> GSM1296081 1 0.0000 0.864 1.000 0.000 0.000 0.000 0.000
#> GSM1296091 3 0.7069 0.378 0.308 0.100 0.512 0.080 0.000
#> GSM1296075 2 0.2813 0.787 0.000 0.832 0.000 0.168 0.000
#> GSM1296112 2 0.3039 0.851 0.000 0.808 0.000 0.000 0.192
#> GSM1296100 3 0.0000 0.763 0.000 0.000 1.000 0.000 0.000
#> GSM1296087 1 0.2020 0.818 0.900 0.100 0.000 0.000 0.000
#> GSM1296118 2 0.2074 0.930 0.000 0.896 0.000 0.000 0.104
#> GSM1296114 5 0.0000 0.990 0.000 0.000 0.000 0.000 1.000
#> GSM1296097 4 0.0000 0.941 0.000 0.000 0.000 1.000 0.000
#> GSM1296106 4 0.3966 0.481 0.000 0.336 0.000 0.664 0.000
#> GSM1296102 3 0.0000 0.763 0.000 0.000 1.000 0.000 0.000
#> GSM1296122 2 0.2074 0.834 0.000 0.896 0.000 0.104 0.000
#> GSM1296089 3 0.5843 0.287 0.388 0.100 0.512 0.000 0.000
#> GSM1296083 1 0.0000 0.864 1.000 0.000 0.000 0.000 0.000
#> GSM1296116 5 0.0290 0.984 0.000 0.008 0.000 0.000 0.992
#> GSM1296085 1 0.0000 0.864 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
#> GSM1296094 3 0.0000 0.994 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296119 5 0.0000 0.943 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296076 5 0.4509 0.470 0.000 0.032 0.000 0.436 0.532 0.000
#> GSM1296092 6 0.4011 0.633 0.000 0.024 0.000 0.304 0.000 0.672
#> GSM1296103 6 0.0146 0.804 0.000 0.000 0.004 0.000 0.000 0.996
#> GSM1296078 5 0.4685 0.452 0.000 0.044 0.000 0.436 0.520 0.000
#> GSM1296107 5 0.0000 0.943 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296109 6 0.2234 0.736 0.000 0.124 0.000 0.004 0.000 0.872
#> GSM1296080 1 0.0000 0.949 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296090 6 0.4377 0.526 0.000 0.024 0.000 0.436 0.000 0.540
#> GSM1296074 4 0.5946 -0.390 0.000 0.228 0.000 0.436 0.000 0.336
#> GSM1296111 5 0.0000 0.943 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296099 6 0.0000 0.804 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1296086 6 0.3428 0.651 0.000 0.000 0.000 0.304 0.000 0.696
#> GSM1296117 5 0.0000 0.943 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296113 5 0.0000 0.943 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296096 6 0.0000 0.804 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1296105 3 0.0000 0.994 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296098 3 0.0000 0.994 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296101 6 0.2854 0.654 0.000 0.000 0.208 0.000 0.000 0.792
#> GSM1296121 5 0.0000 0.943 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296088 6 0.5764 0.210 0.000 0.000 0.216 0.280 0.000 0.504
#> GSM1296082 6 0.4377 0.526 0.000 0.024 0.000 0.436 0.000 0.540
#> GSM1296115 5 0.0000 0.943 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296084 4 0.5360 0.672 0.308 0.000 0.136 0.556 0.000 0.000
#> GSM1296072 2 0.0632 0.907 0.000 0.976 0.000 0.000 0.024 0.000
#> GSM1296069 5 0.0000 0.943 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296071 2 0.0713 0.906 0.000 0.972 0.000 0.000 0.028 0.000
#> GSM1296070 5 0.0000 0.943 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296073 5 0.0000 0.943 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296034 1 0.0000 0.949 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296041 5 0.0000 0.943 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296035 6 0.0000 0.804 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1296038 6 0.0000 0.804 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1296047 2 0.0632 0.907 0.000 0.976 0.000 0.000 0.024 0.000
#> GSM1296039 4 0.6786 -0.390 0.000 0.148 0.000 0.424 0.080 0.348
#> GSM1296042 5 0.0000 0.943 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296043 5 0.0000 0.943 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296037 3 0.0146 0.992 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM1296046 5 0.0363 0.936 0.000 0.012 0.000 0.000 0.988 0.000
#> GSM1296044 5 0.0363 0.936 0.000 0.012 0.000 0.000 0.988 0.000
#> GSM1296045 5 0.0000 0.943 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296025 1 0.0000 0.949 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296033 4 0.5296 0.675 0.308 0.000 0.128 0.564 0.000 0.000
#> GSM1296027 1 0.0000 0.949 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296032 4 0.3828 0.500 0.440 0.000 0.000 0.560 0.000 0.000
#> GSM1296024 1 0.0000 0.949 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296031 4 0.5276 0.676 0.312 0.000 0.124 0.564 0.000 0.000
#> GSM1296028 4 0.5047 0.658 0.348 0.000 0.088 0.564 0.000 0.000
#> GSM1296029 4 0.3838 0.484 0.448 0.000 0.000 0.552 0.000 0.000
#> GSM1296026 4 0.5523 0.653 0.296 0.000 0.164 0.540 0.000 0.000
#> GSM1296030 4 0.5160 0.669 0.332 0.000 0.104 0.564 0.000 0.000
#> GSM1296040 3 0.0000 0.994 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296036 3 0.0000 0.994 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296048 5 0.0000 0.943 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296059 6 0.0146 0.804 0.000 0.000 0.004 0.000 0.000 0.996
#> GSM1296066 5 0.0000 0.943 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296060 6 0.0000 0.804 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1296063 5 0.2320 0.842 0.000 0.004 0.000 0.132 0.864 0.000
#> GSM1296064 5 0.4216 0.750 0.000 0.024 0.000 0.132 0.768 0.076
#> GSM1296067 2 0.2340 0.784 0.000 0.852 0.000 0.000 0.000 0.148
#> GSM1296062 3 0.0000 0.994 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296068 2 0.1141 0.891 0.000 0.948 0.000 0.000 0.052 0.000
#> GSM1296050 2 0.7125 0.185 0.000 0.436 0.120 0.264 0.000 0.180
#> GSM1296057 6 0.4860 0.529 0.000 0.000 0.176 0.160 0.000 0.664
#> GSM1296052 1 0.0000 0.949 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.949 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296049 1 0.0547 0.925 0.980 0.000 0.000 0.020 0.000 0.000
#> GSM1296055 6 0.2053 0.742 0.000 0.108 0.004 0.000 0.000 0.888
#> GSM1296053 1 0.0000 0.949 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296058 3 0.0865 0.958 0.000 0.000 0.964 0.036 0.000 0.000
#> GSM1296051 6 0.4341 0.547 0.000 0.024 0.000 0.412 0.000 0.564
#> GSM1296056 6 0.0000 0.804 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1296065 6 0.3810 0.272 0.000 0.428 0.000 0.000 0.000 0.572
#> GSM1296061 3 0.0146 0.992 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM1296095 6 0.0547 0.799 0.000 0.020 0.000 0.000 0.000 0.980
#> GSM1296120 2 0.0632 0.907 0.000 0.976 0.000 0.000 0.024 0.000
#> GSM1296077 4 0.4923 0.634 0.368 0.000 0.072 0.560 0.000 0.000
#> GSM1296093 1 0.0000 0.949 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296104 6 0.2562 0.695 0.000 0.172 0.000 0.000 0.000 0.828
#> GSM1296079 4 0.5077 0.661 0.344 0.000 0.092 0.564 0.000 0.000
#> GSM1296108 2 0.0632 0.907 0.000 0.976 0.000 0.000 0.024 0.000
#> GSM1296110 2 0.0632 0.907 0.000 0.976 0.000 0.000 0.024 0.000
#> GSM1296081 1 0.0000 0.949 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296091 4 0.2815 0.415 0.000 0.000 0.120 0.848 0.000 0.032
#> GSM1296075 2 0.2092 0.795 0.000 0.876 0.000 0.000 0.000 0.124
#> GSM1296112 2 0.2260 0.808 0.000 0.860 0.000 0.000 0.140 0.000
#> GSM1296100 3 0.0260 0.989 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM1296087 1 0.3823 -0.236 0.564 0.000 0.000 0.436 0.000 0.000
#> GSM1296118 2 0.0632 0.907 0.000 0.976 0.000 0.000 0.024 0.000
#> GSM1296114 5 0.0000 0.943 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1296097 6 0.0000 0.804 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1296106 6 0.2191 0.732 0.000 0.120 0.004 0.000 0.000 0.876
#> GSM1296102 3 0.0000 0.994 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1296122 2 0.0790 0.885 0.000 0.968 0.000 0.000 0.000 0.032
#> GSM1296089 4 0.5276 0.676 0.312 0.000 0.124 0.564 0.000 0.000
#> GSM1296083 1 0.0000 0.949 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1296116 5 0.0363 0.936 0.000 0.012 0.000 0.000 0.988 0.000
#> GSM1296085 1 0.0000 0.949 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)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# 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 tissue(p) time(p) agent(p) dose(p) individual(p) k
#> ATC:pam 86 0.9024 0.0648 0.5796 1.69e-05 1.22e-05 2
#> ATC:pam 95 0.4686 0.0035 0.4125 1.53e-05 2.74e-06 3
#> ATC:pam 86 0.8954 0.0912 0.0824 4.59e-07 5.16e-08 4
#> ATC:pam 87 0.0090 0.2425 0.2226 3.08e-06 1.80e-06 5
#> ATC:pam 88 0.0455 0.1371 0.3002 6.25e-05 6.79e-08 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 45638 rows and 99 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 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 1.000 0.995 0.997 0.4366 0.565 0.565
#> 3 3 1.000 0.994 0.996 0.5385 0.763 0.580
#> 4 4 0.875 0.955 0.950 0.0665 0.955 0.862
#> 5 5 1.000 0.973 0.981 0.0732 0.950 0.823
#> 6 6 0.842 0.846 0.903 0.0533 0.941 0.752
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
#> 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
#> GSM1296094 1 0.0000 0.996 1.000 0.000
#> GSM1296119 2 0.0000 0.999 0.000 1.000
#> GSM1296076 1 0.0000 0.996 1.000 0.000
#> GSM1296092 1 0.0000 0.996 1.000 0.000
#> GSM1296103 1 0.0000 0.996 1.000 0.000
#> GSM1296078 1 0.0000 0.996 1.000 0.000
#> GSM1296107 2 0.0000 0.999 0.000 1.000
#> GSM1296109 1 0.0000 0.996 1.000 0.000
#> GSM1296080 1 0.0000 0.996 1.000 0.000
#> GSM1296090 1 0.0000 0.996 1.000 0.000
#> GSM1296074 1 0.0000 0.996 1.000 0.000
#> GSM1296111 2 0.0000 0.999 0.000 1.000
#> GSM1296099 1 0.0000 0.996 1.000 0.000
#> GSM1296086 1 0.0000 0.996 1.000 0.000
#> GSM1296117 2 0.0000 0.999 0.000 1.000
#> GSM1296113 2 0.0000 0.999 0.000 1.000
#> GSM1296096 1 0.0000 0.996 1.000 0.000
#> GSM1296105 1 0.0000 0.996 1.000 0.000
#> GSM1296098 1 0.0000 0.996 1.000 0.000
#> GSM1296101 1 0.0000 0.996 1.000 0.000
#> GSM1296121 2 0.0000 0.999 0.000 1.000
#> GSM1296088 1 0.0000 0.996 1.000 0.000
#> GSM1296082 1 0.0000 0.996 1.000 0.000
#> GSM1296115 2 0.0000 0.999 0.000 1.000
#> GSM1296084 1 0.0000 0.996 1.000 0.000
#> GSM1296072 2 0.0000 0.999 0.000 1.000
#> GSM1296069 2 0.0000 0.999 0.000 1.000
#> GSM1296071 2 0.0000 0.999 0.000 1.000
#> GSM1296070 2 0.0000 0.999 0.000 1.000
#> GSM1296073 2 0.0000 0.999 0.000 1.000
#> GSM1296034 1 0.0000 0.996 1.000 0.000
#> GSM1296041 2 0.0000 0.999 0.000 1.000
#> GSM1296035 1 0.0000 0.996 1.000 0.000
#> GSM1296038 1 0.0000 0.996 1.000 0.000
#> GSM1296047 2 0.0000 0.999 0.000 1.000
#> GSM1296039 1 0.1633 0.975 0.976 0.024
#> GSM1296042 2 0.0000 0.999 0.000 1.000
#> GSM1296043 2 0.0000 0.999 0.000 1.000
#> GSM1296037 1 0.0000 0.996 1.000 0.000
#> GSM1296046 2 0.0000 0.999 0.000 1.000
#> GSM1296044 2 0.0000 0.999 0.000 1.000
#> GSM1296045 2 0.0000 0.999 0.000 1.000
#> GSM1296025 1 0.0000 0.996 1.000 0.000
#> GSM1296033 1 0.0000 0.996 1.000 0.000
#> GSM1296027 1 0.0000 0.996 1.000 0.000
#> GSM1296032 1 0.0000 0.996 1.000 0.000
#> GSM1296024 1 0.0000 0.996 1.000 0.000
#> GSM1296031 1 0.0000 0.996 1.000 0.000
#> GSM1296028 1 0.0000 0.996 1.000 0.000
#> GSM1296029 1 0.0000 0.996 1.000 0.000
#> GSM1296026 1 0.0000 0.996 1.000 0.000
#> GSM1296030 1 0.0000 0.996 1.000 0.000
#> GSM1296040 1 0.0000 0.996 1.000 0.000
#> GSM1296036 1 0.0000 0.996 1.000 0.000
#> GSM1296048 2 0.0000 0.999 0.000 1.000
#> GSM1296059 1 0.0000 0.996 1.000 0.000
#> GSM1296066 2 0.0000 0.999 0.000 1.000
#> GSM1296060 1 0.0000 0.996 1.000 0.000
#> GSM1296063 1 0.3733 0.927 0.928 0.072
#> GSM1296064 1 0.2603 0.956 0.956 0.044
#> GSM1296067 2 0.0672 0.992 0.008 0.992
#> GSM1296062 1 0.0000 0.996 1.000 0.000
#> GSM1296068 2 0.0000 0.999 0.000 1.000
#> GSM1296050 1 0.0000 0.996 1.000 0.000
#> GSM1296057 1 0.0000 0.996 1.000 0.000
#> GSM1296052 1 0.0000 0.996 1.000 0.000
#> GSM1296054 1 0.0000 0.996 1.000 0.000
#> GSM1296049 1 0.0000 0.996 1.000 0.000
#> GSM1296055 1 0.0000 0.996 1.000 0.000
#> GSM1296053 1 0.0000 0.996 1.000 0.000
#> GSM1296058 1 0.0000 0.996 1.000 0.000
#> GSM1296051 1 0.0000 0.996 1.000 0.000
#> GSM1296056 1 0.0000 0.996 1.000 0.000
#> GSM1296065 1 0.2948 0.948 0.948 0.052
#> GSM1296061 1 0.0000 0.996 1.000 0.000
#> GSM1296095 1 0.0376 0.993 0.996 0.004
#> GSM1296120 2 0.0000 0.999 0.000 1.000
#> GSM1296077 1 0.0000 0.996 1.000 0.000
#> GSM1296093 1 0.0000 0.996 1.000 0.000
#> GSM1296104 1 0.2778 0.952 0.952 0.048
#> GSM1296079 1 0.0000 0.996 1.000 0.000
#> GSM1296108 2 0.0000 0.999 0.000 1.000
#> GSM1296110 2 0.0672 0.992 0.008 0.992
#> GSM1296081 1 0.0000 0.996 1.000 0.000
#> GSM1296091 1 0.0000 0.996 1.000 0.000
#> GSM1296075 1 0.0000 0.996 1.000 0.000
#> GSM1296112 2 0.0000 0.999 0.000 1.000
#> GSM1296100 1 0.0000 0.996 1.000 0.000
#> GSM1296087 1 0.0000 0.996 1.000 0.000
#> GSM1296118 2 0.0000 0.999 0.000 1.000
#> GSM1296114 2 0.0000 0.999 0.000 1.000
#> GSM1296097 1 0.0000 0.996 1.000 0.000
#> GSM1296106 1 0.0000 0.996 1.000 0.000
#> GSM1296102 1 0.0000 0.996 1.000 0.000
#> GSM1296122 2 0.0000 0.999 0.000 1.000
#> GSM1296089 1 0.0000 0.996 1.000 0.000
#> GSM1296083 1 0.0000 0.996 1.000 0.000
#> GSM1296116 2 0.0000 0.999 0.000 1.000
#> GSM1296085 1 0.0000 0.996 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1296094 3 0.0000 0.992 0.000 0.000 1.000
#> GSM1296119 2 0.0000 0.999 0.000 1.000 0.000
#> GSM1296076 1 0.0424 0.995 0.992 0.000 0.008
#> GSM1296092 1 0.0424 0.995 0.992 0.000 0.008
#> GSM1296103 3 0.0000 0.992 0.000 0.000 1.000
#> GSM1296078 1 0.0424 0.995 0.992 0.000 0.008
#> GSM1296107 2 0.0000 0.999 0.000 1.000 0.000
#> GSM1296109 3 0.0000 0.992 0.000 0.000 1.000
#> GSM1296080 1 0.0424 0.995 0.992 0.000 0.008
#> GSM1296090 1 0.0424 0.995 0.992 0.000 0.008
#> GSM1296074 1 0.0424 0.995 0.992 0.000 0.008
#> GSM1296111 2 0.0000 0.999 0.000 1.000 0.000
#> GSM1296099 3 0.0000 0.992 0.000 0.000 1.000
#> GSM1296086 1 0.0424 0.995 0.992 0.000 0.008
#> GSM1296117 2 0.0000 0.999 0.000 1.000 0.000
#> GSM1296113 2 0.0000 0.999 0.000 1.000 0.000
#> GSM1296096 3 0.0000 0.992 0.000 0.000 1.000
#> GSM1296105 3 0.0000 0.992 0.000 0.000 1.000
#> GSM1296098 3 0.0000 0.992 0.000 0.000 1.000
#> GSM1296101 3 0.0000 0.992 0.000 0.000 1.000
#> GSM1296121 2 0.0000 0.999 0.000 1.000 0.000
#> GSM1296088 1 0.0424 0.995 0.992 0.000 0.008
#> GSM1296082 1 0.0424 0.995 0.992 0.000 0.008
#> GSM1296115 2 0.0000 0.999 0.000 1.000 0.000
#> GSM1296084 1 0.0424 0.995 0.992 0.000 0.008
#> GSM1296072 2 0.0000 0.999 0.000 1.000 0.000
#> GSM1296069 2 0.0000 0.999 0.000 1.000 0.000
#> GSM1296071 2 0.0000 0.999 0.000 1.000 0.000
#> GSM1296070 2 0.0000 0.999 0.000 1.000 0.000
#> GSM1296073 2 0.0000 0.999 0.000 1.000 0.000
#> GSM1296034 3 0.0000 0.992 0.000 0.000 1.000
#> GSM1296041 2 0.0000 0.999 0.000 1.000 0.000
#> GSM1296035 3 0.0000 0.992 0.000 0.000 1.000
#> GSM1296038 3 0.0592 0.986 0.000 0.012 0.988
#> GSM1296047 2 0.0000 0.999 0.000 1.000 0.000
#> GSM1296039 3 0.1289 0.974 0.000 0.032 0.968
#> GSM1296042 2 0.0000 0.999 0.000 1.000 0.000
#> GSM1296043 2 0.0000 0.999 0.000 1.000 0.000
#> GSM1296037 3 0.0000 0.992 0.000 0.000 1.000
#> GSM1296046 2 0.0000 0.999 0.000 1.000 0.000
#> GSM1296044 2 0.0000 0.999 0.000 1.000 0.000
#> GSM1296045 2 0.0000 0.999 0.000 1.000 0.000
#> GSM1296025 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1296033 1 0.0237 0.997 0.996 0.000 0.004
#> GSM1296027 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1296032 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1296024 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1296031 1 0.0237 0.997 0.996 0.000 0.004
#> GSM1296028 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1296029 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1296026 1 0.0237 0.997 0.996 0.000 0.004
#> GSM1296030 1 0.0237 0.997 0.996 0.000 0.004
#> GSM1296040 3 0.0000 0.992 0.000 0.000 1.000
#> GSM1296036 3 0.0000 0.992 0.000 0.000 1.000
#> GSM1296048 2 0.0000 0.999 0.000 1.000 0.000
#> GSM1296059 3 0.0000 0.992 0.000 0.000 1.000
#> GSM1296066 2 0.0000 0.999 0.000 1.000 0.000
#> GSM1296060 3 0.0000 0.992 0.000 0.000 1.000
#> GSM1296063 3 0.1289 0.974 0.000 0.032 0.968
#> GSM1296064 3 0.1289 0.974 0.000 0.032 0.968
#> GSM1296067 2 0.0424 0.992 0.000 0.992 0.008
#> GSM1296062 3 0.0000 0.992 0.000 0.000 1.000
#> GSM1296068 2 0.0000 0.999 0.000 1.000 0.000
#> GSM1296050 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1296057 3 0.0424 0.987 0.008 0.000 0.992
#> GSM1296052 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1296054 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1296049 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1296055 3 0.1289 0.974 0.000 0.032 0.968
#> GSM1296053 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1296058 3 0.0424 0.987 0.008 0.000 0.992
#> GSM1296051 1 0.0424 0.995 0.992 0.000 0.008
#> GSM1296056 3 0.0000 0.992 0.000 0.000 1.000
#> GSM1296065 3 0.1289 0.974 0.000 0.032 0.968
#> GSM1296061 3 0.0000 0.992 0.000 0.000 1.000
#> GSM1296095 3 0.1289 0.974 0.000 0.032 0.968
#> GSM1296120 2 0.0000 0.999 0.000 1.000 0.000
#> GSM1296077 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1296093 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1296104 3 0.1289 0.974 0.000 0.032 0.968
#> GSM1296079 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1296108 2 0.0000 0.999 0.000 1.000 0.000
#> GSM1296110 2 0.0000 0.999 0.000 1.000 0.000
#> GSM1296081 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1296091 1 0.0237 0.997 0.996 0.000 0.004
#> GSM1296075 1 0.0237 0.997 0.996 0.000 0.004
#> GSM1296112 2 0.0000 0.999 0.000 1.000 0.000
#> GSM1296100 3 0.0000 0.992 0.000 0.000 1.000
#> GSM1296087 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1296118 2 0.0000 0.999 0.000 1.000 0.000
#> GSM1296114 2 0.0000 0.999 0.000 1.000 0.000
#> GSM1296097 3 0.0237 0.990 0.000 0.004 0.996
#> GSM1296106 3 0.0000 0.992 0.000 0.000 1.000
#> GSM1296102 3 0.0000 0.992 0.000 0.000 1.000
#> GSM1296122 2 0.0424 0.992 0.000 0.992 0.008
#> GSM1296089 1 0.0237 0.997 0.996 0.000 0.004
#> GSM1296083 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1296116 2 0.0000 0.999 0.000 1.000 0.000
#> GSM1296085 1 0.0000 0.997 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1296094 3 0.0188 0.983 0.000 0.000 0.996 0.004
#> GSM1296119 4 0.3074 1.000 0.000 0.152 0.000 0.848
#> GSM1296076 1 0.5247 0.845 0.752 0.000 0.100 0.148
#> GSM1296092 1 0.4840 0.864 0.784 0.000 0.100 0.116
#> GSM1296103 3 0.0188 0.983 0.000 0.000 0.996 0.004
#> GSM1296078 1 0.5247 0.845 0.752 0.000 0.100 0.148
#> GSM1296107 4 0.3074 1.000 0.000 0.152 0.000 0.848
#> GSM1296109 3 0.0000 0.983 0.000 0.000 1.000 0.000
#> GSM1296080 1 0.2635 0.920 0.904 0.000 0.020 0.076
#> GSM1296090 1 0.4840 0.864 0.784 0.000 0.100 0.116
#> GSM1296074 1 0.5247 0.845 0.752 0.000 0.100 0.148
#> GSM1296111 4 0.3074 1.000 0.000 0.152 0.000 0.848
#> GSM1296099 3 0.0000 0.983 0.000 0.000 1.000 0.000
#> GSM1296086 1 0.4840 0.864 0.784 0.000 0.100 0.116
#> GSM1296117 4 0.3074 1.000 0.000 0.152 0.000 0.848
#> GSM1296113 4 0.3074 1.000 0.000 0.152 0.000 0.848
#> GSM1296096 3 0.0000 0.983 0.000 0.000 1.000 0.000
#> GSM1296105 3 0.0000 0.983 0.000 0.000 1.000 0.000
#> GSM1296098 3 0.0188 0.983 0.000 0.000 0.996 0.004
#> GSM1296101 3 0.0188 0.983 0.000 0.000 0.996 0.004
#> GSM1296121 4 0.3074 1.000 0.000 0.152 0.000 0.848
#> GSM1296088 1 0.4487 0.873 0.808 0.000 0.100 0.092
#> GSM1296082 1 0.4840 0.864 0.784 0.000 0.100 0.116
#> GSM1296115 4 0.3074 1.000 0.000 0.152 0.000 0.848
#> GSM1296084 1 0.3542 0.903 0.864 0.000 0.060 0.076
#> GSM1296072 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> GSM1296069 2 0.0592 0.977 0.000 0.984 0.000 0.016
#> GSM1296071 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> GSM1296070 2 0.1022 0.962 0.000 0.968 0.000 0.032
#> GSM1296073 4 0.3074 1.000 0.000 0.152 0.000 0.848
#> GSM1296034 3 0.0000 0.983 0.000 0.000 1.000 0.000
#> GSM1296041 4 0.3074 1.000 0.000 0.152 0.000 0.848
#> GSM1296035 3 0.0000 0.983 0.000 0.000 1.000 0.000
#> GSM1296038 3 0.1211 0.964 0.000 0.040 0.960 0.000
#> GSM1296047 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> GSM1296039 3 0.1211 0.964 0.000 0.040 0.960 0.000
#> GSM1296042 2 0.0592 0.977 0.000 0.984 0.000 0.016
#> GSM1296043 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> GSM1296037 3 0.0000 0.983 0.000 0.000 1.000 0.000
#> GSM1296046 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> GSM1296044 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> GSM1296045 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> GSM1296025 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> GSM1296033 1 0.1940 0.924 0.924 0.000 0.000 0.076
#> GSM1296027 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> GSM1296032 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> GSM1296024 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> GSM1296031 1 0.1940 0.924 0.924 0.000 0.000 0.076
#> GSM1296028 1 0.1940 0.924 0.924 0.000 0.000 0.076
#> GSM1296029 1 0.1940 0.924 0.924 0.000 0.000 0.076
#> GSM1296026 1 0.1940 0.924 0.924 0.000 0.000 0.076
#> GSM1296030 1 0.1940 0.924 0.924 0.000 0.000 0.076
#> GSM1296040 3 0.0188 0.983 0.000 0.000 0.996 0.004
#> GSM1296036 3 0.0188 0.983 0.000 0.000 0.996 0.004
#> GSM1296048 4 0.3074 1.000 0.000 0.152 0.000 0.848
#> GSM1296059 3 0.0188 0.983 0.000 0.000 0.996 0.004
#> GSM1296066 4 0.3074 1.000 0.000 0.152 0.000 0.848
#> GSM1296060 3 0.0000 0.983 0.000 0.000 1.000 0.000
#> GSM1296063 3 0.1211 0.964 0.000 0.040 0.960 0.000
#> GSM1296064 3 0.1211 0.964 0.000 0.040 0.960 0.000
#> GSM1296067 2 0.1211 0.936 0.000 0.960 0.040 0.000
#> GSM1296062 3 0.0188 0.983 0.000 0.000 0.996 0.004
#> GSM1296068 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> GSM1296050 1 0.1211 0.918 0.960 0.000 0.000 0.040
#> GSM1296057 3 0.0000 0.983 0.000 0.000 1.000 0.000
#> GSM1296052 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> GSM1296054 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> GSM1296055 3 0.1211 0.964 0.000 0.040 0.960 0.000
#> GSM1296053 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> GSM1296058 3 0.0000 0.983 0.000 0.000 1.000 0.000
#> GSM1296051 1 0.4780 0.867 0.788 0.000 0.096 0.116
#> GSM1296056 3 0.0000 0.983 0.000 0.000 1.000 0.000
#> GSM1296065 3 0.1211 0.964 0.000 0.040 0.960 0.000
#> GSM1296061 3 0.0188 0.983 0.000 0.000 0.996 0.004
#> GSM1296095 3 0.1211 0.964 0.000 0.040 0.960 0.000
#> GSM1296120 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> GSM1296077 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> GSM1296093 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> GSM1296104 3 0.1211 0.964 0.000 0.040 0.960 0.000
#> GSM1296079 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> GSM1296108 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> GSM1296110 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> GSM1296081 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> GSM1296091 1 0.2011 0.923 0.920 0.000 0.000 0.080
#> GSM1296075 1 0.3581 0.904 0.852 0.000 0.032 0.116
#> GSM1296112 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> GSM1296100 3 0.0000 0.983 0.000 0.000 1.000 0.000
#> GSM1296087 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> GSM1296118 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> GSM1296114 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> GSM1296097 3 0.1211 0.964 0.000 0.040 0.960 0.000
#> GSM1296106 3 0.1211 0.964 0.000 0.040 0.960 0.000
#> GSM1296102 3 0.0000 0.983 0.000 0.000 1.000 0.000
#> GSM1296122 2 0.1302 0.930 0.000 0.956 0.044 0.000
#> GSM1296089 1 0.1940 0.924 0.924 0.000 0.000 0.076
#> GSM1296083 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> GSM1296116 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> GSM1296085 1 0.0000 0.925 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
#> GSM1296094 3 0.0162 0.991 0.000 0.000 0.996 0.000 0.004
#> GSM1296119 5 0.0162 0.999 0.000 0.004 0.000 0.000 0.996
#> GSM1296076 4 0.0000 0.995 0.000 0.000 0.000 1.000 0.000
#> GSM1296092 4 0.0162 0.997 0.004 0.000 0.000 0.996 0.000
#> GSM1296103 3 0.0162 0.991 0.000 0.000 0.996 0.000 0.004
#> GSM1296078 4 0.0000 0.995 0.000 0.000 0.000 1.000 0.000
#> GSM1296107 5 0.0162 0.999 0.000 0.004 0.000 0.000 0.996
#> GSM1296109 3 0.0000 0.992 0.000 0.000 1.000 0.000 0.000
#> GSM1296080 1 0.1544 0.966 0.932 0.000 0.000 0.068 0.000
#> GSM1296090 4 0.0162 0.997 0.004 0.000 0.000 0.996 0.000
#> GSM1296074 4 0.0000 0.995 0.000 0.000 0.000 1.000 0.000
#> GSM1296111 5 0.0162 0.999 0.000 0.004 0.000 0.000 0.996
#> GSM1296099 3 0.0000 0.992 0.000 0.000 1.000 0.000 0.000
#> GSM1296086 4 0.0162 0.997 0.004 0.000 0.000 0.996 0.000
#> GSM1296117 5 0.0162 0.999 0.000 0.004 0.000 0.000 0.996
#> GSM1296113 5 0.0162 0.999 0.000 0.004 0.000 0.000 0.996
#> GSM1296096 3 0.0000 0.992 0.000 0.000 1.000 0.000 0.000
#> GSM1296105 3 0.0404 0.994 0.000 0.000 0.988 0.012 0.000
#> GSM1296098 3 0.0162 0.991 0.000 0.000 0.996 0.000 0.004
#> GSM1296101 3 0.0162 0.991 0.000 0.000 0.996 0.000 0.004
#> GSM1296121 5 0.0162 0.999 0.000 0.004 0.000 0.000 0.996
#> GSM1296088 4 0.0162 0.997 0.004 0.000 0.000 0.996 0.000
#> GSM1296082 4 0.0162 0.997 0.004 0.000 0.000 0.996 0.000
#> GSM1296115 5 0.0162 0.999 0.000 0.004 0.000 0.000 0.996
#> GSM1296084 1 0.1544 0.966 0.932 0.000 0.000 0.068 0.000
#> GSM1296072 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM1296069 2 0.0880 0.943 0.000 0.968 0.000 0.000 0.032
#> GSM1296071 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM1296070 2 0.4291 0.151 0.000 0.536 0.000 0.000 0.464
#> GSM1296073 5 0.0451 0.988 0.000 0.004 0.008 0.000 0.988
#> GSM1296034 3 0.0404 0.994 0.000 0.000 0.988 0.012 0.000
#> GSM1296041 5 0.0162 0.999 0.000 0.004 0.000 0.000 0.996
#> GSM1296035 3 0.0000 0.992 0.000 0.000 1.000 0.000 0.000
#> GSM1296038 3 0.0404 0.994 0.000 0.000 0.988 0.012 0.000
#> GSM1296047 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM1296039 3 0.0404 0.994 0.000 0.000 0.988 0.012 0.000
#> GSM1296042 2 0.1851 0.887 0.000 0.912 0.000 0.000 0.088
#> GSM1296043 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM1296037 3 0.0404 0.994 0.000 0.000 0.988 0.012 0.000
#> GSM1296046 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM1296044 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM1296045 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM1296025 1 0.0000 0.963 1.000 0.000 0.000 0.000 0.000
#> GSM1296033 1 0.1341 0.975 0.944 0.000 0.000 0.056 0.000
#> GSM1296027 1 0.1341 0.975 0.944 0.000 0.000 0.056 0.000
#> GSM1296032 1 0.1341 0.975 0.944 0.000 0.000 0.056 0.000
#> GSM1296024 1 0.0000 0.963 1.000 0.000 0.000 0.000 0.000
#> GSM1296031 1 0.0000 0.963 1.000 0.000 0.000 0.000 0.000
#> GSM1296028 1 0.1341 0.975 0.944 0.000 0.000 0.056 0.000
#> GSM1296029 1 0.1341 0.975 0.944 0.000 0.000 0.056 0.000
#> GSM1296026 1 0.1341 0.975 0.944 0.000 0.000 0.056 0.000
#> GSM1296030 1 0.1341 0.975 0.944 0.000 0.000 0.056 0.000
#> GSM1296040 3 0.0162 0.991 0.000 0.000 0.996 0.000 0.004
#> GSM1296036 3 0.0162 0.991 0.000 0.000 0.996 0.000 0.004
#> GSM1296048 5 0.0162 0.999 0.000 0.004 0.000 0.000 0.996
#> GSM1296059 3 0.0162 0.991 0.000 0.000 0.996 0.000 0.004
#> GSM1296066 5 0.0162 0.999 0.000 0.004 0.000 0.000 0.996
#> GSM1296060 3 0.0290 0.993 0.000 0.000 0.992 0.008 0.000
#> GSM1296063 3 0.0404 0.994 0.000 0.000 0.988 0.012 0.000
#> GSM1296064 3 0.0404 0.994 0.000 0.000 0.988 0.012 0.000
#> GSM1296067 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM1296062 3 0.0162 0.991 0.000 0.000 0.996 0.000 0.004
#> GSM1296068 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM1296050 1 0.1341 0.975 0.944 0.000 0.000 0.056 0.000
#> GSM1296057 3 0.0404 0.994 0.000 0.000 0.988 0.012 0.000
#> GSM1296052 1 0.1341 0.975 0.944 0.000 0.000 0.056 0.000
#> GSM1296054 1 0.0000 0.963 1.000 0.000 0.000 0.000 0.000
#> GSM1296049 1 0.0000 0.963 1.000 0.000 0.000 0.000 0.000
#> GSM1296055 3 0.0404 0.994 0.000 0.000 0.988 0.012 0.000
#> GSM1296053 1 0.0703 0.969 0.976 0.000 0.000 0.024 0.000
#> GSM1296058 3 0.0404 0.994 0.000 0.000 0.988 0.012 0.000
#> GSM1296051 4 0.0404 0.988 0.012 0.000 0.000 0.988 0.000
#> GSM1296056 3 0.0290 0.993 0.000 0.000 0.992 0.008 0.000
#> GSM1296065 3 0.0404 0.994 0.000 0.000 0.988 0.012 0.000
#> GSM1296061 3 0.0162 0.991 0.000 0.000 0.996 0.000 0.004
#> GSM1296095 3 0.0404 0.994 0.000 0.000 0.988 0.012 0.000
#> GSM1296120 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM1296077 1 0.0000 0.963 1.000 0.000 0.000 0.000 0.000
#> GSM1296093 1 0.0000 0.963 1.000 0.000 0.000 0.000 0.000
#> GSM1296104 3 0.0404 0.994 0.000 0.000 0.988 0.012 0.000
#> GSM1296079 1 0.0000 0.963 1.000 0.000 0.000 0.000 0.000
#> GSM1296108 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM1296110 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM1296081 1 0.1341 0.975 0.944 0.000 0.000 0.056 0.000
#> GSM1296091 1 0.1341 0.975 0.944 0.000 0.000 0.056 0.000
#> GSM1296075 1 0.1341 0.975 0.944 0.000 0.000 0.056 0.000
#> GSM1296112 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM1296100 3 0.0404 0.994 0.000 0.000 0.988 0.012 0.000
#> GSM1296087 1 0.1341 0.975 0.944 0.000 0.000 0.056 0.000
#> GSM1296118 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM1296114 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM1296097 3 0.0404 0.994 0.000 0.000 0.988 0.012 0.000
#> GSM1296106 3 0.0404 0.994 0.000 0.000 0.988 0.012 0.000
#> GSM1296102 3 0.0404 0.994 0.000 0.000 0.988 0.012 0.000
#> GSM1296122 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM1296089 1 0.0290 0.965 0.992 0.000 0.000 0.008 0.000
#> GSM1296083 1 0.0000 0.963 1.000 0.000 0.000 0.000 0.000
#> GSM1296116 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM1296085 1 0.1341 0.975 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
#> GSM1296094 3 0.0865 0.838 0.000 0.000 0.964 0.000 0.000 0.036
#> GSM1296119 5 0.1814 1.000 0.000 0.100 0.000 0.000 0.900 0.000
#> GSM1296076 4 0.0146 0.938 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM1296092 4 0.0146 0.938 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM1296103 3 0.0865 0.838 0.000 0.000 0.964 0.000 0.000 0.036
#> GSM1296078 4 0.0146 0.938 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM1296107 5 0.1814 1.000 0.000 0.100 0.000 0.000 0.900 0.000
#> GSM1296109 3 0.2178 0.768 0.000 0.000 0.868 0.000 0.000 0.132
#> GSM1296080 1 0.1219 0.909 0.948 0.000 0.000 0.048 0.000 0.004
#> GSM1296090 4 0.0146 0.938 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM1296074 4 0.0146 0.938 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM1296111 5 0.1814 1.000 0.000 0.100 0.000 0.000 0.900 0.000
#> GSM1296099 6 0.3482 0.673 0.000 0.000 0.316 0.000 0.000 0.684
#> GSM1296086 4 0.0146 0.938 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM1296117 5 0.1814 1.000 0.000 0.100 0.000 0.000 0.900 0.000
#> GSM1296113 5 0.1814 1.000 0.000 0.100 0.000 0.000 0.900 0.000
#> GSM1296096 6 0.3868 0.213 0.000 0.000 0.496 0.000 0.000 0.504
#> GSM1296105 6 0.3482 0.671 0.000 0.000 0.316 0.000 0.000 0.684
#> GSM1296098 3 0.0865 0.838 0.000 0.000 0.964 0.000 0.000 0.036
#> GSM1296101 3 0.0865 0.838 0.000 0.000 0.964 0.000 0.000 0.036
#> GSM1296121 5 0.1814 1.000 0.000 0.100 0.000 0.000 0.900 0.000
#> GSM1296088 1 0.3847 0.173 0.544 0.000 0.000 0.456 0.000 0.000
#> GSM1296082 4 0.0146 0.938 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM1296115 5 0.1814 1.000 0.000 0.100 0.000 0.000 0.900 0.000
#> GSM1296084 1 0.1204 0.909 0.944 0.000 0.000 0.056 0.000 0.000
#> GSM1296072 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296069 2 0.0458 0.974 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM1296071 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296070 2 0.2491 0.783 0.000 0.836 0.000 0.000 0.164 0.000
#> GSM1296073 5 0.1814 1.000 0.000 0.100 0.000 0.000 0.900 0.000
#> GSM1296034 3 0.3499 0.495 0.000 0.000 0.680 0.000 0.000 0.320
#> GSM1296041 5 0.1814 1.000 0.000 0.100 0.000 0.000 0.900 0.000
#> GSM1296035 3 0.3851 -0.119 0.000 0.000 0.540 0.000 0.000 0.460
#> GSM1296038 6 0.0000 0.736 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1296047 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296039 6 0.0000 0.736 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1296042 2 0.0713 0.962 0.000 0.972 0.000 0.000 0.028 0.000
#> GSM1296043 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296037 6 0.3547 0.647 0.000 0.000 0.332 0.000 0.000 0.668
#> GSM1296046 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296044 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296045 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296025 1 0.1010 0.896 0.960 0.000 0.036 0.004 0.000 0.000
#> GSM1296033 1 0.2860 0.894 0.852 0.000 0.000 0.048 0.100 0.000
#> GSM1296027 1 0.2860 0.894 0.852 0.000 0.000 0.048 0.100 0.000
#> GSM1296032 1 0.2860 0.894 0.852 0.000 0.000 0.048 0.100 0.000
#> GSM1296024 1 0.1010 0.896 0.960 0.000 0.036 0.004 0.000 0.000
#> GSM1296031 1 0.0692 0.901 0.976 0.000 0.020 0.004 0.000 0.000
#> GSM1296028 1 0.2860 0.894 0.852 0.000 0.000 0.048 0.100 0.000
#> GSM1296029 1 0.1075 0.909 0.952 0.000 0.000 0.048 0.000 0.000
#> GSM1296026 1 0.1501 0.905 0.924 0.000 0.000 0.076 0.000 0.000
#> GSM1296030 1 0.2860 0.894 0.852 0.000 0.000 0.048 0.100 0.000
#> GSM1296040 3 0.0865 0.838 0.000 0.000 0.964 0.000 0.000 0.036
#> GSM1296036 3 0.0865 0.838 0.000 0.000 0.964 0.000 0.000 0.036
#> GSM1296048 5 0.1814 1.000 0.000 0.100 0.000 0.000 0.900 0.000
#> GSM1296059 3 0.0865 0.838 0.000 0.000 0.964 0.000 0.000 0.036
#> GSM1296066 5 0.1814 1.000 0.000 0.100 0.000 0.000 0.900 0.000
#> GSM1296060 6 0.3221 0.720 0.000 0.000 0.264 0.000 0.000 0.736
#> GSM1296063 6 0.0713 0.712 0.000 0.028 0.000 0.000 0.000 0.972
#> GSM1296064 6 0.0000 0.736 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1296067 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296062 3 0.1267 0.825 0.000 0.000 0.940 0.000 0.000 0.060
#> GSM1296068 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296050 1 0.2340 0.867 0.852 0.000 0.000 0.148 0.000 0.000
#> GSM1296057 6 0.3198 0.722 0.000 0.000 0.260 0.000 0.000 0.740
#> GSM1296052 1 0.2860 0.894 0.852 0.000 0.000 0.048 0.100 0.000
#> GSM1296054 1 0.0692 0.901 0.976 0.000 0.020 0.004 0.000 0.000
#> GSM1296049 1 0.1010 0.896 0.960 0.000 0.036 0.004 0.000 0.000
#> GSM1296055 6 0.2941 0.734 0.000 0.000 0.220 0.000 0.000 0.780
#> GSM1296053 1 0.0603 0.905 0.980 0.000 0.016 0.004 0.000 0.000
#> GSM1296058 6 0.3547 0.647 0.000 0.000 0.332 0.000 0.000 0.668
#> GSM1296051 4 0.3515 0.433 0.324 0.000 0.000 0.676 0.000 0.000
#> GSM1296056 6 0.3244 0.718 0.000 0.000 0.268 0.000 0.000 0.732
#> GSM1296065 6 0.0146 0.734 0.000 0.004 0.000 0.000 0.000 0.996
#> GSM1296061 3 0.0865 0.838 0.000 0.000 0.964 0.000 0.000 0.036
#> GSM1296095 6 0.0000 0.736 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1296120 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296077 1 0.1010 0.896 0.960 0.000 0.036 0.004 0.000 0.000
#> GSM1296093 1 0.0692 0.901 0.976 0.000 0.020 0.004 0.000 0.000
#> GSM1296104 6 0.0000 0.736 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1296079 1 0.1461 0.910 0.940 0.000 0.016 0.044 0.000 0.000
#> GSM1296108 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296110 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296081 1 0.0146 0.908 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM1296091 1 0.2860 0.894 0.852 0.000 0.000 0.048 0.100 0.000
#> GSM1296075 1 0.2340 0.867 0.852 0.000 0.000 0.148 0.000 0.000
#> GSM1296112 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296100 3 0.3647 0.395 0.000 0.000 0.640 0.000 0.000 0.360
#> GSM1296087 1 0.2860 0.894 0.852 0.000 0.000 0.048 0.100 0.000
#> GSM1296118 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296114 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296097 6 0.1556 0.744 0.000 0.000 0.080 0.000 0.000 0.920
#> GSM1296106 6 0.3446 0.681 0.000 0.000 0.308 0.000 0.000 0.692
#> GSM1296102 3 0.3592 0.439 0.000 0.000 0.656 0.000 0.000 0.344
#> GSM1296122 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296089 1 0.0725 0.908 0.976 0.000 0.012 0.012 0.000 0.000
#> GSM1296083 1 0.0692 0.901 0.976 0.000 0.020 0.004 0.000 0.000
#> GSM1296116 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1296085 1 0.2860 0.894 0.852 0.000 0.000 0.048 0.100 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# 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 tissue(p) time(p) agent(p) dose(p) individual(p) k
#> ATC:mclust 99 1.0000 0.00374 0.6508 7.14e-05 1.73e-10 2
#> ATC:mclust 99 0.6663 0.01005 0.6784 9.51e-07 1.83e-16 3
#> ATC:mclust 99 0.0721 0.02007 0.2032 1.94e-09 5.15e-14 4
#> ATC:mclust 98 0.0137 0.02707 0.0705 5.50e-10 3.18e-10 5
#> ATC:mclust 92 0.0144 0.01068 0.0203 1.59e-08 3.81e-08 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 45638 rows and 99 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 1.000 0.968 0.987 0.5021 0.499 0.499
#> 3 3 0.737 0.875 0.919 0.3069 0.770 0.567
#> 4 4 0.746 0.787 0.876 0.1107 0.864 0.633
#> 5 5 0.801 0.773 0.878 0.0578 0.921 0.725
#> 6 6 0.758 0.670 0.809 0.0398 0.946 0.774
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
#> GSM1296094 1 0.0000 0.9931 1.000 0.000
#> GSM1296119 2 0.0000 0.9809 0.000 1.000
#> GSM1296076 2 0.0000 0.9809 0.000 1.000
#> GSM1296092 2 0.2778 0.9374 0.048 0.952
#> GSM1296103 1 0.0000 0.9931 1.000 0.000
#> GSM1296078 2 0.0000 0.9809 0.000 1.000
#> GSM1296107 2 0.0000 0.9809 0.000 1.000
#> GSM1296109 2 0.0000 0.9809 0.000 1.000
#> GSM1296080 1 0.0000 0.9931 1.000 0.000
#> GSM1296090 2 0.0000 0.9809 0.000 1.000
#> GSM1296074 2 0.0000 0.9809 0.000 1.000
#> GSM1296111 2 0.0000 0.9809 0.000 1.000
#> GSM1296099 2 0.0000 0.9809 0.000 1.000
#> GSM1296086 2 0.9993 0.0767 0.484 0.516
#> GSM1296117 2 0.0000 0.9809 0.000 1.000
#> GSM1296113 2 0.0000 0.9809 0.000 1.000
#> GSM1296096 2 0.0000 0.9809 0.000 1.000
#> GSM1296105 1 0.0000 0.9931 1.000 0.000
#> GSM1296098 1 0.0000 0.9931 1.000 0.000
#> GSM1296101 1 0.0000 0.9931 1.000 0.000
#> GSM1296121 2 0.0000 0.9809 0.000 1.000
#> GSM1296088 1 0.0000 0.9931 1.000 0.000
#> GSM1296082 2 0.0000 0.9809 0.000 1.000
#> GSM1296115 2 0.0000 0.9809 0.000 1.000
#> GSM1296084 1 0.0000 0.9931 1.000 0.000
#> GSM1296072 2 0.0000 0.9809 0.000 1.000
#> GSM1296069 2 0.0000 0.9809 0.000 1.000
#> GSM1296071 2 0.0000 0.9809 0.000 1.000
#> GSM1296070 2 0.0000 0.9809 0.000 1.000
#> GSM1296073 2 0.0000 0.9809 0.000 1.000
#> GSM1296034 1 0.0000 0.9931 1.000 0.000
#> GSM1296041 2 0.0000 0.9809 0.000 1.000
#> GSM1296035 2 0.7602 0.7222 0.220 0.780
#> GSM1296038 2 0.0000 0.9809 0.000 1.000
#> GSM1296047 2 0.0000 0.9809 0.000 1.000
#> GSM1296039 2 0.0000 0.9809 0.000 1.000
#> GSM1296042 2 0.0000 0.9809 0.000 1.000
#> GSM1296043 2 0.0000 0.9809 0.000 1.000
#> GSM1296037 1 0.0000 0.9931 1.000 0.000
#> GSM1296046 2 0.0000 0.9809 0.000 1.000
#> GSM1296044 2 0.0000 0.9809 0.000 1.000
#> GSM1296045 2 0.0000 0.9809 0.000 1.000
#> GSM1296025 1 0.0000 0.9931 1.000 0.000
#> GSM1296033 1 0.0000 0.9931 1.000 0.000
#> GSM1296027 1 0.0000 0.9931 1.000 0.000
#> GSM1296032 1 0.0000 0.9931 1.000 0.000
#> GSM1296024 1 0.0000 0.9931 1.000 0.000
#> GSM1296031 1 0.0000 0.9931 1.000 0.000
#> GSM1296028 1 0.0000 0.9931 1.000 0.000
#> GSM1296029 1 0.0000 0.9931 1.000 0.000
#> GSM1296026 1 0.0000 0.9931 1.000 0.000
#> GSM1296030 1 0.0000 0.9931 1.000 0.000
#> GSM1296040 1 0.0000 0.9931 1.000 0.000
#> GSM1296036 1 0.0000 0.9931 1.000 0.000
#> GSM1296048 2 0.0000 0.9809 0.000 1.000
#> GSM1296059 1 0.0000 0.9931 1.000 0.000
#> GSM1296066 2 0.0000 0.9809 0.000 1.000
#> GSM1296060 2 0.6712 0.7872 0.176 0.824
#> GSM1296063 2 0.0000 0.9809 0.000 1.000
#> GSM1296064 2 0.0000 0.9809 0.000 1.000
#> GSM1296067 2 0.0376 0.9775 0.004 0.996
#> GSM1296062 1 0.0000 0.9931 1.000 0.000
#> GSM1296068 2 0.0000 0.9809 0.000 1.000
#> GSM1296050 1 0.0000 0.9931 1.000 0.000
#> GSM1296057 1 0.0000 0.9931 1.000 0.000
#> GSM1296052 1 0.0000 0.9931 1.000 0.000
#> GSM1296054 1 0.0000 0.9931 1.000 0.000
#> GSM1296049 1 0.0000 0.9931 1.000 0.000
#> GSM1296055 1 0.8443 0.6119 0.728 0.272
#> GSM1296053 1 0.0000 0.9931 1.000 0.000
#> GSM1296058 1 0.0000 0.9931 1.000 0.000
#> GSM1296051 2 0.0000 0.9809 0.000 1.000
#> GSM1296056 2 0.0000 0.9809 0.000 1.000
#> GSM1296065 2 0.0000 0.9809 0.000 1.000
#> GSM1296061 1 0.0000 0.9931 1.000 0.000
#> GSM1296095 2 0.0000 0.9809 0.000 1.000
#> GSM1296120 2 0.0000 0.9809 0.000 1.000
#> GSM1296077 1 0.0000 0.9931 1.000 0.000
#> GSM1296093 1 0.0000 0.9931 1.000 0.000
#> GSM1296104 2 0.0000 0.9809 0.000 1.000
#> GSM1296079 1 0.0000 0.9931 1.000 0.000
#> GSM1296108 2 0.0000 0.9809 0.000 1.000
#> GSM1296110 2 0.0000 0.9809 0.000 1.000
#> GSM1296081 1 0.0000 0.9931 1.000 0.000
#> GSM1296091 1 0.0000 0.9931 1.000 0.000
#> GSM1296075 2 0.0000 0.9809 0.000 1.000
#> GSM1296112 2 0.0000 0.9809 0.000 1.000
#> GSM1296100 1 0.0000 0.9931 1.000 0.000
#> GSM1296087 1 0.0000 0.9931 1.000 0.000
#> GSM1296118 2 0.0000 0.9809 0.000 1.000
#> GSM1296114 2 0.0000 0.9809 0.000 1.000
#> GSM1296097 2 0.3584 0.9173 0.068 0.932
#> GSM1296106 1 0.1414 0.9732 0.980 0.020
#> GSM1296102 1 0.0000 0.9931 1.000 0.000
#> GSM1296122 2 0.0000 0.9809 0.000 1.000
#> GSM1296089 1 0.0000 0.9931 1.000 0.000
#> GSM1296083 1 0.0000 0.9931 1.000 0.000
#> GSM1296116 2 0.0000 0.9809 0.000 1.000
#> GSM1296085 1 0.0000 0.9931 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1296094 3 0.4178 0.79923 0.172 0.000 0.828
#> GSM1296119 2 0.3340 0.92267 0.000 0.880 0.120
#> GSM1296076 2 0.4121 0.88921 0.000 0.832 0.168
#> GSM1296092 3 0.2050 0.85120 0.020 0.028 0.952
#> GSM1296103 3 0.3686 0.82018 0.140 0.000 0.860
#> GSM1296078 2 0.4291 0.87696 0.000 0.820 0.180
#> GSM1296107 2 0.3412 0.92159 0.000 0.876 0.124
#> GSM1296109 3 0.1753 0.84467 0.000 0.048 0.952
#> GSM1296080 1 0.0424 0.96359 0.992 0.000 0.008
#> GSM1296090 3 0.2448 0.82808 0.000 0.076 0.924
#> GSM1296074 3 0.6095 0.22939 0.000 0.392 0.608
#> GSM1296111 2 0.3412 0.92159 0.000 0.876 0.124
#> GSM1296099 3 0.1643 0.84598 0.000 0.044 0.956
#> GSM1296086 3 0.2400 0.85235 0.064 0.004 0.932
#> GSM1296117 2 0.3482 0.91997 0.000 0.872 0.128
#> GSM1296113 2 0.3267 0.92343 0.000 0.884 0.116
#> GSM1296096 3 0.1753 0.84467 0.000 0.048 0.952
#> GSM1296105 1 0.1031 0.95300 0.976 0.000 0.024
#> GSM1296098 3 0.4452 0.78306 0.192 0.000 0.808
#> GSM1296101 3 0.4062 0.80509 0.164 0.000 0.836
#> GSM1296121 2 0.3482 0.91997 0.000 0.872 0.128
#> GSM1296088 3 0.4974 0.74008 0.236 0.000 0.764
#> GSM1296082 3 0.1753 0.84467 0.000 0.048 0.952
#> GSM1296115 2 0.3551 0.91793 0.000 0.868 0.132
#> GSM1296084 1 0.1031 0.95292 0.976 0.000 0.024
#> GSM1296072 2 0.1031 0.91649 0.000 0.976 0.024
#> GSM1296069 2 0.2959 0.92523 0.000 0.900 0.100
#> GSM1296071 2 0.0000 0.90988 0.000 1.000 0.000
#> GSM1296070 2 0.2959 0.92523 0.000 0.900 0.100
#> GSM1296073 2 0.3551 0.91793 0.000 0.868 0.132
#> GSM1296034 1 0.0237 0.96520 0.996 0.000 0.004
#> GSM1296041 2 0.3412 0.92159 0.000 0.876 0.124
#> GSM1296035 3 0.2339 0.85454 0.048 0.012 0.940
#> GSM1296038 3 0.1753 0.84467 0.000 0.048 0.952
#> GSM1296047 2 0.1163 0.89566 0.000 0.972 0.028
#> GSM1296039 3 0.2261 0.83292 0.000 0.068 0.932
#> GSM1296042 2 0.2959 0.92523 0.000 0.900 0.100
#> GSM1296043 2 0.2796 0.92521 0.000 0.908 0.092
#> GSM1296037 1 0.0424 0.96359 0.992 0.000 0.008
#> GSM1296046 2 0.0000 0.90988 0.000 1.000 0.000
#> GSM1296044 2 0.0747 0.91470 0.000 0.984 0.016
#> GSM1296045 2 0.2625 0.92483 0.000 0.916 0.084
#> GSM1296025 1 0.0424 0.96248 0.992 0.000 0.008
#> GSM1296033 1 0.0237 0.96520 0.996 0.000 0.004
#> GSM1296027 1 0.0000 0.96560 1.000 0.000 0.000
#> GSM1296032 1 0.0424 0.96248 0.992 0.000 0.008
#> GSM1296024 1 0.0237 0.96426 0.996 0.000 0.004
#> GSM1296031 1 0.0000 0.96560 1.000 0.000 0.000
#> GSM1296028 1 0.0000 0.96560 1.000 0.000 0.000
#> GSM1296029 1 0.0000 0.96560 1.000 0.000 0.000
#> GSM1296026 1 0.0000 0.96560 1.000 0.000 0.000
#> GSM1296030 1 0.0237 0.96520 0.996 0.000 0.004
#> GSM1296040 3 0.4235 0.79640 0.176 0.000 0.824
#> GSM1296036 1 0.6274 -0.00745 0.544 0.000 0.456
#> GSM1296048 2 0.3412 0.92159 0.000 0.876 0.124
#> GSM1296059 3 0.3816 0.81570 0.148 0.000 0.852
#> GSM1296066 2 0.3340 0.92267 0.000 0.880 0.120
#> GSM1296060 3 0.2527 0.85466 0.044 0.020 0.936
#> GSM1296063 2 0.3752 0.90991 0.000 0.856 0.144
#> GSM1296064 3 0.2625 0.82029 0.000 0.084 0.916
#> GSM1296067 2 0.2383 0.87384 0.016 0.940 0.044
#> GSM1296062 3 0.5016 0.72989 0.240 0.000 0.760
#> GSM1296068 2 0.1031 0.89810 0.000 0.976 0.024
#> GSM1296050 1 0.5069 0.78983 0.828 0.128 0.044
#> GSM1296057 3 0.6280 0.27951 0.460 0.000 0.540
#> GSM1296052 1 0.0000 0.96560 1.000 0.000 0.000
#> GSM1296054 1 0.0000 0.96560 1.000 0.000 0.000
#> GSM1296049 1 0.0424 0.96248 0.992 0.000 0.008
#> GSM1296055 1 0.3845 0.82163 0.872 0.116 0.012
#> GSM1296053 1 0.0237 0.96520 0.996 0.000 0.004
#> GSM1296058 3 0.4452 0.78306 0.192 0.000 0.808
#> GSM1296051 2 0.3619 0.91565 0.000 0.864 0.136
#> GSM1296056 3 0.1643 0.84598 0.000 0.044 0.956
#> GSM1296065 2 0.3619 0.91563 0.000 0.864 0.136
#> GSM1296061 1 0.1860 0.92615 0.948 0.000 0.052
#> GSM1296095 3 0.5497 0.50749 0.000 0.292 0.708
#> GSM1296120 2 0.0424 0.90634 0.000 0.992 0.008
#> GSM1296077 1 0.0747 0.95726 0.984 0.000 0.016
#> GSM1296093 1 0.0237 0.96520 0.996 0.000 0.004
#> GSM1296104 2 0.5529 0.70795 0.000 0.704 0.296
#> GSM1296079 1 0.1129 0.95138 0.976 0.004 0.020
#> GSM1296108 2 0.0000 0.90988 0.000 1.000 0.000
#> GSM1296110 2 0.0592 0.90440 0.000 0.988 0.012
#> GSM1296081 1 0.0237 0.96520 0.996 0.000 0.004
#> GSM1296091 1 0.0000 0.96560 1.000 0.000 0.000
#> GSM1296075 2 0.2063 0.87980 0.008 0.948 0.044
#> GSM1296112 2 0.0892 0.90027 0.000 0.980 0.020
#> GSM1296100 1 0.0892 0.95609 0.980 0.000 0.020
#> GSM1296087 1 0.0000 0.96560 1.000 0.000 0.000
#> GSM1296118 2 0.1643 0.88509 0.000 0.956 0.044
#> GSM1296114 2 0.0747 0.91479 0.000 0.984 0.016
#> GSM1296097 3 0.4712 0.81147 0.044 0.108 0.848
#> GSM1296106 1 0.1289 0.94071 0.968 0.032 0.000
#> GSM1296102 1 0.0592 0.96150 0.988 0.000 0.012
#> GSM1296122 2 0.1878 0.88230 0.004 0.952 0.044
#> GSM1296089 1 0.0424 0.96248 0.992 0.000 0.008
#> GSM1296083 1 0.0237 0.96520 0.996 0.000 0.004
#> GSM1296116 2 0.0000 0.90988 0.000 1.000 0.000
#> GSM1296085 1 0.0000 0.96560 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1296094 3 0.1174 0.7859 0.012 0.000 0.968 0.020
#> GSM1296119 2 0.1792 0.8742 0.000 0.932 0.000 0.068
#> GSM1296076 4 0.4137 0.7527 0.000 0.208 0.012 0.780
#> GSM1296092 4 0.4400 0.7529 0.012 0.036 0.136 0.816
#> GSM1296103 3 0.1716 0.7778 0.000 0.000 0.936 0.064
#> GSM1296078 4 0.4098 0.7563 0.000 0.204 0.012 0.784
#> GSM1296107 2 0.1792 0.8742 0.000 0.932 0.000 0.068
#> GSM1296109 3 0.2489 0.7735 0.000 0.020 0.912 0.068
#> GSM1296080 1 0.0817 0.9407 0.976 0.000 0.024 0.000
#> GSM1296090 4 0.4327 0.7846 0.004 0.104 0.068 0.824
#> GSM1296074 4 0.3969 0.7771 0.000 0.180 0.016 0.804
#> GSM1296111 2 0.1867 0.8724 0.000 0.928 0.000 0.072
#> GSM1296099 3 0.2345 0.7623 0.000 0.000 0.900 0.100
#> GSM1296086 4 0.4441 0.7471 0.020 0.028 0.136 0.816
#> GSM1296117 2 0.2011 0.8676 0.000 0.920 0.000 0.080
#> GSM1296113 2 0.1716 0.8755 0.000 0.936 0.000 0.064
#> GSM1296096 3 0.3801 0.6224 0.000 0.000 0.780 0.220
#> GSM1296105 3 0.6107 0.6115 0.264 0.000 0.648 0.088
#> GSM1296098 3 0.2589 0.7688 0.044 0.000 0.912 0.044
#> GSM1296101 3 0.1452 0.7848 0.008 0.000 0.956 0.036
#> GSM1296121 2 0.1867 0.8724 0.000 0.928 0.000 0.072
#> GSM1296088 4 0.5267 0.5634 0.240 0.000 0.048 0.712
#> GSM1296082 4 0.4312 0.7642 0.000 0.056 0.132 0.812
#> GSM1296115 2 0.2469 0.8460 0.000 0.892 0.000 0.108
#> GSM1296084 1 0.2081 0.8977 0.916 0.000 0.000 0.084
#> GSM1296072 2 0.0336 0.8789 0.000 0.992 0.000 0.008
#> GSM1296069 2 0.1637 0.8764 0.000 0.940 0.000 0.060
#> GSM1296071 2 0.0336 0.8758 0.000 0.992 0.000 0.008
#> GSM1296070 2 0.1389 0.8786 0.000 0.952 0.000 0.048
#> GSM1296073 2 0.3172 0.7919 0.000 0.840 0.000 0.160
#> GSM1296034 1 0.4511 0.5634 0.724 0.000 0.268 0.008
#> GSM1296041 2 0.1867 0.8724 0.000 0.928 0.000 0.072
#> GSM1296035 3 0.2589 0.7493 0.000 0.000 0.884 0.116
#> GSM1296038 3 0.2530 0.7546 0.000 0.000 0.888 0.112
#> GSM1296047 2 0.1022 0.8663 0.000 0.968 0.000 0.032
#> GSM1296039 4 0.5861 0.6114 0.000 0.060 0.296 0.644
#> GSM1296042 2 0.1389 0.8786 0.000 0.952 0.000 0.048
#> GSM1296043 2 0.0921 0.8800 0.000 0.972 0.000 0.028
#> GSM1296037 3 0.6249 0.4895 0.352 0.000 0.580 0.068
#> GSM1296046 2 0.0188 0.8784 0.000 0.996 0.000 0.004
#> GSM1296044 2 0.0000 0.8777 0.000 1.000 0.000 0.000
#> GSM1296045 2 0.0921 0.8800 0.000 0.972 0.000 0.028
#> GSM1296025 1 0.0376 0.9505 0.992 0.000 0.004 0.004
#> GSM1296033 1 0.3311 0.8028 0.828 0.000 0.000 0.172
#> GSM1296027 1 0.0336 0.9505 0.992 0.000 0.000 0.008
#> GSM1296032 1 0.0336 0.9505 0.992 0.000 0.000 0.008
#> GSM1296024 1 0.0524 0.9492 0.988 0.000 0.008 0.004
#> GSM1296031 1 0.0000 0.9514 1.000 0.000 0.000 0.000
#> GSM1296028 1 0.0469 0.9491 0.988 0.000 0.000 0.012
#> GSM1296029 1 0.0000 0.9514 1.000 0.000 0.000 0.000
#> GSM1296026 1 0.2281 0.8885 0.904 0.000 0.000 0.096
#> GSM1296030 1 0.1389 0.9271 0.952 0.000 0.000 0.048
#> GSM1296040 3 0.0592 0.7845 0.016 0.000 0.984 0.000
#> GSM1296036 3 0.4411 0.7267 0.108 0.000 0.812 0.080
#> GSM1296048 2 0.1792 0.8742 0.000 0.932 0.000 0.068
#> GSM1296059 3 0.1716 0.7778 0.000 0.000 0.936 0.064
#> GSM1296066 2 0.1716 0.8755 0.000 0.936 0.000 0.064
#> GSM1296060 3 0.2149 0.7683 0.000 0.000 0.912 0.088
#> GSM1296063 2 0.4950 0.3690 0.000 0.620 0.004 0.376
#> GSM1296064 4 0.6654 0.5971 0.000 0.296 0.116 0.588
#> GSM1296067 2 0.3304 0.7817 0.008 0.864 0.008 0.120
#> GSM1296062 3 0.1637 0.7786 0.060 0.000 0.940 0.000
#> GSM1296068 2 0.1022 0.8660 0.000 0.968 0.000 0.032
#> GSM1296050 1 0.2245 0.9200 0.932 0.020 0.008 0.040
#> GSM1296057 3 0.5008 0.6277 0.040 0.000 0.732 0.228
#> GSM1296052 1 0.0336 0.9505 0.992 0.000 0.000 0.008
#> GSM1296054 1 0.0336 0.9498 0.992 0.000 0.008 0.000
#> GSM1296049 1 0.0000 0.9514 1.000 0.000 0.000 0.000
#> GSM1296055 2 0.9253 0.0456 0.308 0.408 0.144 0.140
#> GSM1296053 1 0.0469 0.9482 0.988 0.000 0.012 0.000
#> GSM1296058 3 0.2521 0.7823 0.024 0.000 0.912 0.064
#> GSM1296051 4 0.4387 0.7426 0.024 0.200 0.000 0.776
#> GSM1296056 4 0.4907 0.3318 0.000 0.000 0.420 0.580
#> GSM1296065 2 0.4313 0.6362 0.000 0.736 0.004 0.260
#> GSM1296061 3 0.5072 0.6738 0.208 0.000 0.740 0.052
#> GSM1296095 3 0.6224 0.4496 0.000 0.188 0.668 0.144
#> GSM1296120 2 0.0336 0.8758 0.000 0.992 0.000 0.008
#> GSM1296077 1 0.0188 0.9510 0.996 0.000 0.000 0.004
#> GSM1296093 1 0.0592 0.9461 0.984 0.000 0.016 0.000
#> GSM1296104 2 0.6835 0.2189 0.000 0.560 0.124 0.316
#> GSM1296079 1 0.0336 0.9508 0.992 0.000 0.000 0.008
#> GSM1296108 2 0.0921 0.8679 0.000 0.972 0.000 0.028
#> GSM1296110 2 0.1792 0.8438 0.000 0.932 0.000 0.068
#> GSM1296081 1 0.0336 0.9498 0.992 0.000 0.008 0.000
#> GSM1296091 1 0.4220 0.6922 0.748 0.000 0.004 0.248
#> GSM1296075 2 0.7330 0.3021 0.180 0.564 0.008 0.248
#> GSM1296112 2 0.1022 0.8660 0.000 0.968 0.000 0.032
#> GSM1296100 3 0.6022 0.6171 0.260 0.000 0.656 0.084
#> GSM1296087 1 0.1211 0.9340 0.960 0.000 0.000 0.040
#> GSM1296118 2 0.1792 0.8438 0.000 0.932 0.000 0.068
#> GSM1296114 2 0.0336 0.8758 0.000 0.992 0.000 0.008
#> GSM1296097 3 0.1585 0.7799 0.004 0.004 0.952 0.040
#> GSM1296106 3 0.8959 0.4466 0.236 0.132 0.484 0.148
#> GSM1296102 3 0.6366 0.6130 0.240 0.000 0.640 0.120
#> GSM1296122 2 0.1940 0.8375 0.000 0.924 0.000 0.076
#> GSM1296089 1 0.0188 0.9510 0.996 0.000 0.000 0.004
#> GSM1296083 1 0.0469 0.9482 0.988 0.000 0.012 0.000
#> GSM1296116 2 0.0592 0.8731 0.000 0.984 0.000 0.016
#> GSM1296085 1 0.0000 0.9514 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
#> GSM1296094 3 0.0510 0.7480 0.016 0.000 0.984 0.000 0.000
#> GSM1296119 2 0.0794 0.9683 0.000 0.972 0.000 0.028 0.000
#> GSM1296076 4 0.2377 0.6477 0.000 0.128 0.000 0.872 0.000
#> GSM1296092 4 0.1200 0.6818 0.012 0.008 0.016 0.964 0.000
#> GSM1296103 3 0.0290 0.7489 0.000 0.000 0.992 0.008 0.000
#> GSM1296078 4 0.1965 0.6675 0.000 0.096 0.000 0.904 0.000
#> GSM1296107 2 0.0510 0.9727 0.000 0.984 0.000 0.016 0.000
#> GSM1296109 3 0.3530 0.5260 0.000 0.204 0.784 0.012 0.000
#> GSM1296080 1 0.1914 0.9082 0.928 0.000 0.056 0.008 0.008
#> GSM1296090 4 0.1362 0.6818 0.012 0.008 0.016 0.960 0.004
#> GSM1296074 4 0.1393 0.6823 0.000 0.024 0.008 0.956 0.012
#> GSM1296111 2 0.0794 0.9680 0.000 0.972 0.000 0.028 0.000
#> GSM1296099 3 0.3339 0.7108 0.000 0.000 0.836 0.124 0.040
#> GSM1296086 4 0.1982 0.6733 0.036 0.004 0.024 0.932 0.004
#> GSM1296117 2 0.0880 0.9658 0.000 0.968 0.000 0.032 0.000
#> GSM1296113 2 0.0404 0.9750 0.000 0.988 0.000 0.012 0.000
#> GSM1296096 3 0.3650 0.6745 0.000 0.000 0.796 0.176 0.028
#> GSM1296105 3 0.3990 0.4869 0.308 0.000 0.688 0.000 0.004
#> GSM1296098 3 0.1798 0.7336 0.064 0.000 0.928 0.004 0.004
#> GSM1296101 3 0.0162 0.7480 0.004 0.000 0.996 0.000 0.000
#> GSM1296121 2 0.0880 0.9659 0.000 0.968 0.000 0.032 0.000
#> GSM1296088 4 0.3940 0.5191 0.208 0.000 0.016 0.768 0.008
#> GSM1296082 4 0.1393 0.6750 0.000 0.008 0.024 0.956 0.012
#> GSM1296115 2 0.0963 0.9643 0.000 0.964 0.000 0.036 0.000
#> GSM1296084 1 0.1697 0.9208 0.932 0.000 0.000 0.060 0.008
#> GSM1296072 2 0.0865 0.9742 0.000 0.972 0.000 0.004 0.024
#> GSM1296069 2 0.0162 0.9752 0.000 0.996 0.000 0.004 0.000
#> GSM1296071 2 0.0566 0.9737 0.000 0.984 0.000 0.004 0.012
#> GSM1296070 2 0.0162 0.9752 0.000 0.996 0.000 0.004 0.000
#> GSM1296073 2 0.1270 0.9503 0.000 0.948 0.000 0.052 0.000
#> GSM1296034 1 0.3183 0.7867 0.828 0.000 0.156 0.000 0.016
#> GSM1296041 2 0.0880 0.9658 0.000 0.968 0.000 0.032 0.000
#> GSM1296035 3 0.3714 0.6977 0.000 0.000 0.812 0.132 0.056
#> GSM1296038 3 0.3814 0.6910 0.000 0.000 0.808 0.124 0.068
#> GSM1296047 2 0.1341 0.9548 0.000 0.944 0.000 0.000 0.056
#> GSM1296039 4 0.5635 0.5142 0.000 0.112 0.156 0.696 0.036
#> GSM1296042 2 0.0162 0.9752 0.000 0.996 0.000 0.004 0.000
#> GSM1296043 2 0.0162 0.9752 0.000 0.996 0.000 0.004 0.000
#> GSM1296037 5 0.4584 0.5753 0.056 0.000 0.228 0.000 0.716
#> GSM1296046 2 0.0404 0.9746 0.000 0.988 0.000 0.000 0.012
#> GSM1296044 2 0.0566 0.9737 0.000 0.984 0.000 0.004 0.012
#> GSM1296045 2 0.0566 0.9750 0.000 0.984 0.000 0.004 0.012
#> GSM1296025 1 0.1341 0.9235 0.944 0.000 0.000 0.000 0.056
#> GSM1296033 1 0.3455 0.7518 0.784 0.000 0.000 0.208 0.008
#> GSM1296027 1 0.0955 0.9347 0.968 0.000 0.000 0.028 0.004
#> GSM1296032 1 0.1195 0.9353 0.960 0.000 0.000 0.012 0.028
#> GSM1296024 1 0.1341 0.9235 0.944 0.000 0.000 0.000 0.056
#> GSM1296031 1 0.1608 0.9227 0.928 0.000 0.000 0.000 0.072
#> GSM1296028 1 0.1043 0.9316 0.960 0.000 0.000 0.040 0.000
#> GSM1296029 1 0.0703 0.9350 0.976 0.000 0.000 0.024 0.000
#> GSM1296026 1 0.2046 0.9178 0.916 0.000 0.000 0.068 0.016
#> GSM1296030 1 0.1697 0.9202 0.932 0.000 0.000 0.060 0.008
#> GSM1296040 3 0.0290 0.7479 0.008 0.000 0.992 0.000 0.000
#> GSM1296036 3 0.3707 0.6033 0.220 0.000 0.768 0.004 0.008
#> GSM1296048 2 0.0880 0.9671 0.000 0.968 0.000 0.032 0.000
#> GSM1296059 3 0.1386 0.7466 0.000 0.000 0.952 0.032 0.016
#> GSM1296066 2 0.0290 0.9745 0.000 0.992 0.000 0.008 0.000
#> GSM1296060 3 0.3416 0.7107 0.000 0.000 0.840 0.088 0.072
#> GSM1296063 4 0.4978 0.1602 0.000 0.476 0.000 0.496 0.028
#> GSM1296064 4 0.5386 0.4030 0.000 0.332 0.028 0.612 0.028
#> GSM1296067 2 0.2286 0.8946 0.000 0.888 0.000 0.004 0.108
#> GSM1296062 3 0.1908 0.7256 0.092 0.000 0.908 0.000 0.000
#> GSM1296068 2 0.0771 0.9711 0.000 0.976 0.000 0.004 0.020
#> GSM1296050 5 0.4651 0.1822 0.372 0.000 0.000 0.020 0.608
#> GSM1296057 5 0.5088 0.5680 0.000 0.000 0.252 0.080 0.668
#> GSM1296052 1 0.0955 0.9347 0.968 0.000 0.000 0.028 0.004
#> GSM1296054 1 0.0898 0.9347 0.972 0.000 0.008 0.000 0.020
#> GSM1296049 1 0.1197 0.9288 0.952 0.000 0.000 0.000 0.048
#> GSM1296055 5 0.2507 0.6180 0.020 0.000 0.044 0.028 0.908
#> GSM1296053 1 0.0566 0.9359 0.984 0.000 0.004 0.000 0.012
#> GSM1296058 3 0.4431 0.6360 0.004 0.000 0.760 0.068 0.168
#> GSM1296051 4 0.1913 0.6763 0.024 0.020 0.000 0.936 0.020
#> GSM1296056 4 0.4885 0.3845 0.000 0.000 0.276 0.668 0.056
#> GSM1296065 5 0.6019 0.3665 0.000 0.064 0.036 0.308 0.592
#> GSM1296061 3 0.3796 0.5072 0.300 0.000 0.700 0.000 0.000
#> GSM1296095 3 0.5182 0.5413 0.000 0.000 0.680 0.208 0.112
#> GSM1296120 2 0.1041 0.9706 0.000 0.964 0.000 0.004 0.032
#> GSM1296077 1 0.1965 0.9076 0.904 0.000 0.000 0.000 0.096
#> GSM1296093 1 0.0912 0.9342 0.972 0.000 0.012 0.000 0.016
#> GSM1296104 5 0.5730 0.5472 0.000 0.008 0.136 0.212 0.644
#> GSM1296079 1 0.1908 0.9101 0.908 0.000 0.000 0.000 0.092
#> GSM1296108 2 0.0771 0.9711 0.000 0.976 0.000 0.004 0.020
#> GSM1296110 2 0.0579 0.9732 0.000 0.984 0.000 0.008 0.008
#> GSM1296081 1 0.0510 0.9348 0.984 0.000 0.000 0.000 0.016
#> GSM1296091 4 0.5165 0.2541 0.376 0.000 0.000 0.576 0.048
#> GSM1296075 4 0.7110 0.0265 0.020 0.216 0.000 0.388 0.376
#> GSM1296112 2 0.0771 0.9711 0.000 0.976 0.000 0.004 0.020
#> GSM1296100 3 0.5618 0.2403 0.088 0.000 0.564 0.000 0.348
#> GSM1296087 1 0.1484 0.9276 0.944 0.000 0.000 0.048 0.008
#> GSM1296118 2 0.1410 0.9436 0.000 0.940 0.000 0.000 0.060
#> GSM1296114 2 0.0451 0.9742 0.000 0.988 0.000 0.004 0.008
#> GSM1296097 5 0.5080 0.4930 0.000 0.000 0.316 0.056 0.628
#> GSM1296106 5 0.5253 0.5013 0.040 0.004 0.296 0.012 0.648
#> GSM1296102 3 0.4342 0.6118 0.188 0.000 0.760 0.008 0.044
#> GSM1296122 5 0.3863 0.4024 0.012 0.248 0.000 0.000 0.740
#> GSM1296089 1 0.3326 0.8188 0.824 0.000 0.000 0.024 0.152
#> GSM1296083 1 0.0671 0.9348 0.980 0.000 0.004 0.000 0.016
#> GSM1296116 2 0.0566 0.9737 0.000 0.984 0.000 0.004 0.012
#> GSM1296085 1 0.0865 0.9353 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
#> GSM1296094 3 0.1080 0.7205 0.032 0.000 0.960 0.004 0.004 0.000
#> GSM1296119 2 0.1204 0.9281 0.000 0.944 0.000 0.056 0.000 0.000
#> GSM1296076 4 0.2114 0.6881 0.000 0.076 0.008 0.904 0.012 0.000
#> GSM1296092 4 0.5034 0.5977 0.112 0.000 0.008 0.696 0.168 0.016
#> GSM1296103 3 0.1511 0.7210 0.004 0.000 0.940 0.012 0.000 0.044
#> GSM1296078 4 0.2392 0.6959 0.000 0.048 0.008 0.896 0.048 0.000
#> GSM1296107 2 0.0858 0.9385 0.000 0.968 0.000 0.028 0.004 0.000
#> GSM1296109 3 0.3602 0.5808 0.000 0.136 0.812 0.028 0.008 0.016
#> GSM1296080 1 0.3686 0.6111 0.788 0.000 0.088 0.000 0.124 0.000
#> GSM1296090 4 0.5504 0.5563 0.136 0.004 0.000 0.648 0.184 0.028
#> GSM1296074 4 0.1346 0.6867 0.000 0.024 0.016 0.952 0.000 0.008
#> GSM1296111 2 0.1082 0.9348 0.000 0.956 0.000 0.040 0.004 0.000
#> GSM1296099 3 0.4454 0.5868 0.000 0.000 0.724 0.128 0.004 0.144
#> GSM1296086 4 0.5171 0.5704 0.144 0.000 0.012 0.668 0.172 0.004
#> GSM1296117 2 0.1219 0.9324 0.000 0.948 0.000 0.048 0.000 0.004
#> GSM1296113 2 0.0862 0.9406 0.000 0.972 0.000 0.016 0.004 0.008
#> GSM1296096 3 0.5405 0.3958 0.000 0.000 0.568 0.312 0.008 0.112
#> GSM1296105 3 0.5475 0.2567 0.224 0.000 0.588 0.000 0.184 0.004
#> GSM1296098 3 0.1477 0.7166 0.048 0.000 0.940 0.000 0.008 0.004
#> GSM1296101 3 0.2068 0.7096 0.008 0.000 0.904 0.008 0.000 0.080
#> GSM1296121 2 0.1364 0.9312 0.000 0.944 0.000 0.048 0.004 0.004
#> GSM1296088 1 0.5745 0.3186 0.536 0.000 0.004 0.256 0.204 0.000
#> GSM1296082 4 0.3110 0.6782 0.020 0.000 0.016 0.836 0.128 0.000
#> GSM1296115 2 0.1285 0.9309 0.000 0.944 0.000 0.052 0.004 0.000
#> GSM1296084 1 0.4020 0.5834 0.744 0.000 0.008 0.044 0.204 0.000
#> GSM1296072 2 0.1313 0.9371 0.000 0.952 0.000 0.028 0.016 0.004
#> GSM1296069 2 0.0777 0.9392 0.000 0.972 0.000 0.024 0.004 0.000
#> GSM1296071 2 0.0551 0.9362 0.000 0.984 0.004 0.000 0.008 0.004
#> GSM1296070 2 0.0692 0.9396 0.000 0.976 0.000 0.020 0.004 0.000
#> GSM1296073 2 0.2912 0.7261 0.000 0.784 0.000 0.216 0.000 0.000
#> GSM1296034 1 0.4950 0.4355 0.652 0.000 0.184 0.000 0.164 0.000
#> GSM1296041 2 0.1152 0.9332 0.000 0.952 0.000 0.044 0.004 0.000
#> GSM1296035 3 0.5042 0.5298 0.000 0.000 0.664 0.168 0.008 0.160
#> GSM1296038 3 0.4684 0.3660 0.000 0.000 0.640 0.052 0.008 0.300
#> GSM1296047 2 0.2766 0.8369 0.000 0.852 0.004 0.000 0.124 0.020
#> GSM1296039 4 0.3680 0.6510 0.000 0.088 0.052 0.824 0.004 0.032
#> GSM1296042 2 0.0713 0.9392 0.000 0.972 0.000 0.028 0.000 0.000
#> GSM1296043 2 0.0405 0.9393 0.000 0.988 0.000 0.008 0.004 0.000
#> GSM1296037 6 0.3261 0.7875 0.024 0.000 0.144 0.000 0.012 0.820
#> GSM1296046 2 0.0260 0.9371 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1296044 2 0.0508 0.9359 0.000 0.984 0.000 0.000 0.012 0.004
#> GSM1296045 2 0.0405 0.9393 0.000 0.988 0.000 0.008 0.004 0.000
#> GSM1296025 1 0.4152 0.0470 0.548 0.000 0.012 0.000 0.440 0.000
#> GSM1296033 1 0.4236 0.5771 0.740 0.000 0.000 0.060 0.188 0.012
#> GSM1296027 1 0.1501 0.6886 0.924 0.000 0.000 0.000 0.076 0.000
#> GSM1296032 1 0.1007 0.6983 0.956 0.000 0.000 0.000 0.044 0.000
#> GSM1296024 1 0.3835 0.3938 0.668 0.000 0.012 0.000 0.320 0.000
#> GSM1296031 1 0.4067 0.6022 0.760 0.000 0.008 0.004 0.176 0.052
#> GSM1296028 1 0.2709 0.6578 0.848 0.000 0.000 0.020 0.132 0.000
#> GSM1296029 1 0.1049 0.6995 0.960 0.000 0.008 0.000 0.032 0.000
#> GSM1296026 5 0.6198 0.3414 0.360 0.000 0.008 0.176 0.448 0.008
#> GSM1296030 1 0.1059 0.6998 0.964 0.000 0.004 0.016 0.016 0.000
#> GSM1296040 3 0.1745 0.7162 0.012 0.000 0.920 0.000 0.000 0.068
#> GSM1296036 3 0.2306 0.6879 0.092 0.000 0.888 0.000 0.016 0.004
#> GSM1296048 2 0.1327 0.9225 0.000 0.936 0.000 0.064 0.000 0.000
#> GSM1296059 3 0.2630 0.6927 0.000 0.000 0.872 0.032 0.004 0.092
#> GSM1296066 2 0.0837 0.9400 0.000 0.972 0.000 0.020 0.004 0.004
#> GSM1296060 3 0.5090 0.4381 0.000 0.000 0.636 0.128 0.004 0.232
#> GSM1296063 4 0.4437 0.2141 0.000 0.436 0.000 0.540 0.004 0.020
#> GSM1296064 4 0.3647 0.5645 0.000 0.216 0.008 0.760 0.004 0.012
#> GSM1296067 2 0.4317 0.4773 0.000 0.640 0.004 0.000 0.328 0.028
#> GSM1296062 3 0.1493 0.7180 0.056 0.000 0.936 0.000 0.004 0.004
#> GSM1296068 2 0.1225 0.9225 0.000 0.952 0.000 0.000 0.036 0.012
#> GSM1296050 5 0.5000 0.5464 0.180 0.008 0.000 0.016 0.696 0.100
#> GSM1296057 6 0.2432 0.7820 0.000 0.000 0.100 0.024 0.000 0.876
#> GSM1296052 1 0.1204 0.6961 0.944 0.000 0.000 0.000 0.056 0.000
#> GSM1296054 1 0.2748 0.6560 0.848 0.000 0.024 0.000 0.128 0.000
#> GSM1296049 1 0.4136 0.0868 0.560 0.000 0.012 0.000 0.428 0.000
#> GSM1296055 6 0.1391 0.7438 0.000 0.000 0.040 0.000 0.016 0.944
#> GSM1296053 1 0.2432 0.6695 0.876 0.000 0.024 0.000 0.100 0.000
#> GSM1296058 6 0.4653 0.6584 0.032 0.000 0.304 0.020 0.000 0.644
#> GSM1296051 4 0.3277 0.6775 0.020 0.008 0.000 0.836 0.120 0.016
#> GSM1296056 4 0.4545 0.4807 0.000 0.000 0.136 0.728 0.012 0.124
#> GSM1296065 4 0.5447 0.2742 0.000 0.060 0.020 0.564 0.008 0.348
#> GSM1296061 3 0.3122 0.6174 0.160 0.000 0.816 0.000 0.020 0.004
#> GSM1296095 6 0.5474 0.3604 0.000 0.012 0.404 0.088 0.000 0.496
#> GSM1296120 2 0.1176 0.9313 0.000 0.956 0.000 0.000 0.020 0.024
#> GSM1296077 5 0.3782 0.4341 0.360 0.000 0.004 0.000 0.636 0.000
#> GSM1296093 1 0.3278 0.6196 0.808 0.000 0.040 0.000 0.152 0.000
#> GSM1296104 6 0.1970 0.7624 0.000 0.000 0.060 0.028 0.000 0.912
#> GSM1296079 5 0.3728 0.4616 0.344 0.000 0.004 0.000 0.652 0.000
#> GSM1296108 2 0.1485 0.9193 0.000 0.944 0.004 0.000 0.028 0.024
#> GSM1296110 2 0.1555 0.9204 0.000 0.940 0.008 0.000 0.040 0.012
#> GSM1296081 1 0.2527 0.6674 0.868 0.000 0.024 0.000 0.108 0.000
#> GSM1296091 1 0.6543 0.3435 0.536 0.000 0.000 0.176 0.204 0.084
#> GSM1296075 5 0.7289 0.1863 0.016 0.152 0.000 0.228 0.476 0.128
#> GSM1296112 2 0.1636 0.9143 0.000 0.936 0.004 0.000 0.036 0.024
#> GSM1296100 6 0.4572 0.6444 0.040 0.000 0.316 0.000 0.008 0.636
#> GSM1296087 1 0.2408 0.6721 0.876 0.000 0.000 0.012 0.108 0.004
#> GSM1296118 2 0.2342 0.8745 0.000 0.888 0.004 0.000 0.088 0.020
#> GSM1296114 2 0.0767 0.9337 0.000 0.976 0.004 0.000 0.012 0.008
#> GSM1296097 6 0.2814 0.7875 0.000 0.000 0.172 0.008 0.000 0.820
#> GSM1296106 6 0.4473 0.7107 0.000 0.000 0.252 0.000 0.072 0.676
#> GSM1296102 3 0.4134 0.6369 0.116 0.000 0.772 0.000 0.016 0.096
#> GSM1296122 5 0.5928 0.2145 0.004 0.264 0.000 0.000 0.496 0.236
#> GSM1296089 1 0.4890 0.5307 0.684 0.000 0.000 0.024 0.216 0.076
#> GSM1296083 1 0.2605 0.6627 0.864 0.000 0.028 0.000 0.108 0.000
#> GSM1296116 2 0.0653 0.9350 0.000 0.980 0.004 0.000 0.012 0.004
#> GSM1296085 1 0.0692 0.7010 0.976 0.000 0.004 0.000 0.020 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# 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 tissue(p) time(p) agent(p) dose(p) individual(p) k
#> ATC:NMF 98 0.8211 0.0987 0.710 4.33e-06 8.62e-05 2
#> ATC:NMF 96 0.0635 0.0826 0.856 4.35e-06 2.64e-06 3
#> ATC:NMF 91 0.1105 0.0575 0.564 6.90e-07 7.61e-10 4
#> ATC:NMF 88 0.1601 0.0701 0.412 2.85e-06 1.21e-07 5
#> ATC:NMF 79 0.0919 0.1397 0.516 8.68e-05 5.74e-09 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
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