Date: 2019-12-25 21:16:07 CET, cola version: 1.3.2
Document is loading...
All available functions which can be applied to this res_list object:
res_list
#> A 'ConsensusPartitionList' object with 24 methods.
#> On a matrix with 51941 rows and 96 columns.
#> Top rows are extracted by 'SD, CV, MAD, ATC' methods.
#> Subgroups are detected by 'hclust, kmeans, skmeans, pam, mclust, NMF' method.
#> Number of partitions are tried for k = 2, 3, 4, 5, 6.
#> Performed in total 30000 partitions by row resampling.
#>
#> Following methods can be applied to this 'ConsensusPartitionList' object:
#> [1] "cola_report" "collect_classes" "collect_plots" "collect_stats"
#> [5] "colnames" "functional_enrichment" "get_anno_col" "get_anno"
#> [9] "get_classes" "get_matrix" "get_membership" "get_stats"
#> [13] "is_best_k" "is_stable_k" "ncol" "nrow"
#> [17] "rownames" "show" "suggest_best_k" "test_to_known_factors"
#> [21] "top_rows_heatmap" "top_rows_overlap"
#>
#> You can get result for a single method by, e.g. object["SD", "hclust"] or object["SD:hclust"]
#> or a subset of methods by object[c("SD", "CV")], c("hclust", "kmeans")]
The call of run_all_consensus_partition_methods() was:
#> run_all_consensus_partition_methods(data = mat, mc.cores = 4, anno = anno)
Dimension of the input matrix:
mat = get_matrix(res_list)
dim(mat)
#> [1] 51941 96
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:mclust | 4 | 1.000 | 0.974 | 0.990 | ** | |
| CV:skmeans | 4 | 0.978 | 0.951 | 0.978 | ** | |
| ATC:kmeans | 3 | 0.969 | 0.931 | 0.963 | ** | 2 |
| ATC:pam | 5 | 0.946 | 0.920 | 0.968 | * | 2,4 |
| MAD:skmeans | 3 | 0.943 | 0.947 | 0.977 | * | |
| ATC:skmeans | 3 | 0.931 | 0.893 | 0.961 | * | 2 |
| SD:skmeans | 4 | 0.909 | 0.886 | 0.953 | * | 3 |
| CV:kmeans | 4 | 0.893 | 0.895 | 0.944 | ||
| MAD:kmeans | 4 | 0.867 | 0.849 | 0.931 | ||
| MAD:mclust | 4 | 0.859 | 0.820 | 0.928 | ||
| CV:mclust | 4 | 0.855 | 0.852 | 0.924 | ||
| CV:NMF | 2 | 0.855 | 0.923 | 0.967 | ||
| ATC:NMF | 2 | 0.854 | 0.920 | 0.966 | ||
| SD:kmeans | 4 | 0.853 | 0.879 | 0.931 | ||
| MAD:NMF | 2 | 0.851 | 0.922 | 0.967 | ||
| SD:NMF | 3 | 0.847 | 0.912 | 0.960 | ||
| ATC:hclust | 4 | 0.786 | 0.879 | 0.932 | ||
| SD:mclust | 4 | 0.783 | 0.836 | 0.923 | ||
| CV:pam | 3 | 0.678 | 0.866 | 0.906 | ||
| MAD:pam | 2 | 0.531 | 0.830 | 0.890 | ||
| SD:pam | 2 | 0.505 | 0.882 | 0.905 | ||
| SD:hclust | 2 | 0.362 | 0.757 | 0.878 | ||
| MAD:hclust | 2 | 0.341 | 0.742 | 0.864 | ||
| CV:hclust | 2 | 0.265 | 0.703 | 0.834 |
**: 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 0.594 0.864 0.920 0.498 0.498 0.498
#> CV:NMF 2 0.855 0.923 0.967 0.502 0.498 0.498
#> MAD:NMF 2 0.851 0.922 0.967 0.499 0.498 0.498
#> ATC:NMF 2 0.854 0.920 0.966 0.474 0.526 0.526
#> SD:skmeans 2 0.533 0.802 0.912 0.503 0.498 0.498
#> CV:skmeans 2 0.556 0.891 0.935 0.505 0.496 0.496
#> MAD:skmeans 2 0.580 0.851 0.929 0.505 0.496 0.496
#> ATC:skmeans 2 0.956 0.939 0.975 0.494 0.509 0.509
#> SD:mclust 2 0.231 0.673 0.776 0.372 0.667 0.667
#> CV:mclust 2 0.893 0.915 0.955 0.335 0.692 0.692
#> MAD:mclust 2 0.695 0.905 0.943 0.305 0.705 0.705
#> ATC:mclust 2 0.679 0.855 0.900 0.299 0.643 0.643
#> SD:kmeans 2 0.384 0.805 0.876 0.494 0.509 0.509
#> CV:kmeans 2 0.544 0.875 0.920 0.500 0.496 0.496
#> MAD:kmeans 2 0.455 0.809 0.891 0.496 0.500 0.500
#> ATC:kmeans 2 1.000 1.000 1.000 0.469 0.532 0.532
#> SD:pam 2 0.505 0.882 0.905 0.449 0.558 0.558
#> CV:pam 2 0.351 0.601 0.812 0.456 0.532 0.532
#> MAD:pam 2 0.531 0.830 0.890 0.456 0.558 0.558
#> ATC:pam 2 1.000 0.998 0.999 0.450 0.551 0.551
#> SD:hclust 2 0.362 0.757 0.878 0.490 0.505 0.505
#> CV:hclust 2 0.265 0.703 0.834 0.462 0.497 0.497
#> MAD:hclust 2 0.341 0.742 0.864 0.483 0.526 0.526
#> ATC:hclust 2 0.891 0.929 0.968 0.416 0.582 0.582
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.847 0.912 0.960 0.343 0.728 0.506
#> CV:NMF 3 0.870 0.859 0.940 0.329 0.741 0.525
#> MAD:NMF 3 0.839 0.877 0.949 0.339 0.740 0.524
#> ATC:NMF 3 0.819 0.891 0.946 0.376 0.745 0.547
#> SD:skmeans 3 0.901 0.917 0.965 0.337 0.734 0.512
#> CV:skmeans 3 0.889 0.895 0.958 0.329 0.708 0.477
#> MAD:skmeans 3 0.943 0.947 0.977 0.331 0.718 0.490
#> ATC:skmeans 3 0.931 0.893 0.961 0.281 0.827 0.672
#> SD:mclust 3 0.568 0.829 0.859 0.588 0.699 0.562
#> CV:mclust 3 0.606 0.846 0.890 0.726 0.668 0.537
#> MAD:mclust 3 0.680 0.858 0.925 0.958 0.679 0.557
#> ATC:mclust 3 0.637 0.847 0.926 0.743 0.668 0.541
#> SD:kmeans 3 0.640 0.837 0.885 0.343 0.734 0.518
#> CV:kmeans 3 0.597 0.825 0.871 0.326 0.714 0.487
#> MAD:kmeans 3 0.677 0.841 0.900 0.348 0.741 0.524
#> ATC:kmeans 3 0.969 0.931 0.963 0.393 0.670 0.454
#> SD:pam 3 0.632 0.870 0.907 0.429 0.792 0.630
#> CV:pam 3 0.678 0.866 0.906 0.447 0.696 0.480
#> MAD:pam 3 0.638 0.716 0.835 0.435 0.772 0.596
#> ATC:pam 3 0.868 0.880 0.955 0.455 0.697 0.494
#> SD:hclust 3 0.411 0.730 0.830 0.313 0.819 0.647
#> CV:hclust 3 0.296 0.430 0.653 0.358 0.730 0.507
#> MAD:hclust 3 0.375 0.661 0.807 0.334 0.790 0.606
#> ATC:hclust 3 0.617 0.885 0.897 0.510 0.764 0.595
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.837 0.855 0.939 0.1241 0.840 0.566
#> CV:NMF 4 0.830 0.851 0.937 0.1297 0.824 0.532
#> MAD:NMF 4 0.883 0.862 0.942 0.1274 0.832 0.549
#> ATC:NMF 4 0.612 0.655 0.837 0.0747 0.875 0.679
#> SD:skmeans 4 0.909 0.886 0.953 0.1195 0.845 0.574
#> CV:skmeans 4 0.978 0.951 0.978 0.1244 0.843 0.570
#> MAD:skmeans 4 0.893 0.898 0.953 0.1216 0.862 0.612
#> ATC:skmeans 4 0.860 0.829 0.925 0.0988 0.917 0.781
#> SD:mclust 4 0.783 0.836 0.923 0.2569 0.735 0.422
#> CV:mclust 4 0.855 0.852 0.924 0.2885 0.772 0.487
#> MAD:mclust 4 0.859 0.820 0.928 0.2601 0.713 0.399
#> ATC:mclust 4 1.000 0.974 0.990 0.2963 0.680 0.424
#> SD:kmeans 4 0.853 0.879 0.931 0.1297 0.845 0.576
#> CV:kmeans 4 0.893 0.895 0.944 0.1352 0.857 0.602
#> MAD:kmeans 4 0.867 0.849 0.931 0.1264 0.861 0.610
#> ATC:kmeans 4 0.713 0.652 0.799 0.1136 0.835 0.577
#> SD:pam 4 0.677 0.820 0.878 0.1267 0.915 0.764
#> CV:pam 4 0.558 0.587 0.767 0.1136 0.900 0.715
#> MAD:pam 4 0.567 0.477 0.728 0.1252 0.766 0.438
#> ATC:pam 4 0.910 0.881 0.958 0.0826 0.907 0.741
#> SD:hclust 4 0.529 0.665 0.790 0.1494 0.874 0.644
#> CV:hclust 4 0.510 0.576 0.780 0.1692 0.792 0.470
#> MAD:hclust 4 0.500 0.551 0.752 0.1469 0.835 0.554
#> ATC:hclust 4 0.786 0.879 0.932 0.1068 0.951 0.861
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.741 0.719 0.852 0.0557 0.919 0.698
#> CV:NMF 5 0.810 0.806 0.900 0.0558 0.932 0.740
#> MAD:NMF 5 0.729 0.693 0.847 0.0576 0.900 0.635
#> ATC:NMF 5 0.531 0.510 0.739 0.0713 0.783 0.445
#> SD:skmeans 5 0.770 0.741 0.851 0.0585 0.893 0.615
#> CV:skmeans 5 0.785 0.607 0.809 0.0546 0.961 0.848
#> MAD:skmeans 5 0.812 0.647 0.820 0.0578 0.913 0.680
#> ATC:skmeans 5 0.845 0.750 0.898 0.0365 0.941 0.822
#> SD:mclust 5 0.642 0.677 0.826 0.0363 0.947 0.796
#> CV:mclust 5 0.702 0.768 0.864 0.0490 0.923 0.713
#> MAD:mclust 5 0.830 0.826 0.895 0.0490 0.918 0.692
#> ATC:mclust 5 0.820 0.799 0.872 0.1112 0.897 0.700
#> SD:kmeans 5 0.709 0.636 0.795 0.0628 0.955 0.825
#> CV:kmeans 5 0.738 0.678 0.813 0.0572 0.971 0.884
#> MAD:kmeans 5 0.718 0.650 0.798 0.0590 0.966 0.866
#> ATC:kmeans 5 0.735 0.738 0.844 0.0546 0.896 0.667
#> SD:pam 5 0.777 0.808 0.902 0.0880 0.871 0.584
#> CV:pam 5 0.835 0.847 0.918 0.0709 0.915 0.699
#> MAD:pam 5 0.715 0.723 0.846 0.0727 0.778 0.354
#> ATC:pam 5 0.946 0.920 0.968 0.1216 0.882 0.610
#> SD:hclust 5 0.627 0.607 0.764 0.0557 0.963 0.853
#> CV:hclust 5 0.605 0.645 0.778 0.0686 0.941 0.770
#> MAD:hclust 5 0.588 0.528 0.687 0.0613 0.907 0.656
#> ATC:hclust 5 0.748 0.804 0.872 0.0771 1.000 1.000
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.678 0.591 0.767 0.0409 0.936 0.713
#> CV:NMF 6 0.726 0.661 0.801 0.0436 0.935 0.707
#> MAD:NMF 6 0.689 0.562 0.764 0.0408 0.908 0.599
#> ATC:NMF 6 0.533 0.444 0.703 0.0486 0.899 0.653
#> SD:skmeans 6 0.741 0.567 0.772 0.0410 0.965 0.834
#> CV:skmeans 6 0.757 0.655 0.797 0.0389 0.923 0.683
#> MAD:skmeans 6 0.749 0.515 0.747 0.0397 0.886 0.554
#> ATC:skmeans 6 0.848 0.788 0.902 0.0328 0.938 0.800
#> SD:mclust 6 0.759 0.715 0.804 0.0583 0.903 0.609
#> CV:mclust 6 0.780 0.613 0.764 0.0414 0.930 0.692
#> MAD:mclust 6 0.741 0.590 0.755 0.0423 0.941 0.745
#> ATC:mclust 6 0.736 0.673 0.791 0.0538 0.879 0.583
#> SD:kmeans 6 0.701 0.488 0.724 0.0402 0.938 0.737
#> CV:kmeans 6 0.713 0.544 0.728 0.0410 0.931 0.705
#> MAD:kmeans 6 0.692 0.573 0.734 0.0410 0.949 0.786
#> ATC:kmeans 6 0.781 0.664 0.819 0.0376 0.975 0.904
#> SD:pam 6 0.838 0.813 0.888 0.0434 0.937 0.723
#> CV:pam 6 0.873 0.805 0.904 0.0439 0.961 0.815
#> MAD:pam 6 0.776 0.739 0.867 0.0441 0.950 0.772
#> ATC:pam 6 0.864 0.815 0.864 0.0378 0.954 0.784
#> SD:hclust 6 0.654 0.510 0.666 0.0396 0.900 0.630
#> CV:hclust 6 0.690 0.659 0.794 0.0381 0.938 0.725
#> MAD:hclust 6 0.664 0.545 0.722 0.0457 0.944 0.744
#> ATC:hclust 6 0.759 0.749 0.862 0.0309 0.907 0.700
Following heatmap plots the partition for each combination of methods and the lightness correspond to the silhouette scores for samples in each method. On top the consensus subgroup is inferred from all methods by taking the mean silhouette scores as weight.
collect_stats(res_list, k = 2)
collect_stats(res_list, k = 3)
collect_stats(res_list, k = 4)
collect_stats(res_list, k = 5)
collect_stats(res_list, k = 6)
Collect partitions from all methods:
collect_classes(res_list, k = 2)
collect_classes(res_list, k = 3)
collect_classes(res_list, k = 4)
collect_classes(res_list, k = 5)
collect_classes(res_list, k = 6)
Overlap of top rows from different top-row methods:
top_rows_overlap(res_list, top_n = 1000, method = "euler")
top_rows_overlap(res_list, top_n = 2000, method = "euler")
top_rows_overlap(res_list, top_n = 3000, method = "euler")
top_rows_overlap(res_list, top_n = 4000, method = "euler")
top_rows_overlap(res_list, top_n = 5000, method = "euler")
Also visualize the correspondance of rankings between different top-row methods:
top_rows_overlap(res_list, top_n = 1000, method = "correspondance")
top_rows_overlap(res_list, top_n = 2000, method = "correspondance")
top_rows_overlap(res_list, top_n = 3000, method = "correspondance")
top_rows_overlap(res_list, top_n = 4000, method = "correspondance")
top_rows_overlap(res_list, top_n = 5000, method = "correspondance")
Heatmaps of the top rows:
top_rows_heatmap(res_list, top_n = 1000)
top_rows_heatmap(res_list, top_n = 2000)
top_rows_heatmap(res_list, top_n = 3000)
top_rows_heatmap(res_list, top_n = 4000)
top_rows_heatmap(res_list, top_n = 5000)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res_list, k = 2)
#> n disease.state(p) k
#> SD:NMF 93 0.15759 2
#> CV:NMF 93 0.16845 2
#> MAD:NMF 93 0.16845 2
#> ATC:NMF 93 0.05564 2
#> SD:skmeans 88 0.09371 2
#> CV:skmeans 96 0.08257 2
#> MAD:skmeans 91 0.32171 2
#> ATC:skmeans 94 0.00489 2
#> SD:mclust 86 0.03974 2
#> CV:mclust 92 0.01120 2
#> MAD:mclust 95 0.03988 2
#> ATC:mclust 91 0.15114 2
#> SD:kmeans 93 0.11088 2
#> CV:kmeans 92 0.06722 2
#> MAD:kmeans 91 0.12030 2
#> ATC:kmeans 96 0.00792 2
#> SD:pam 96 0.03175 2
#> CV:pam 79 0.00627 2
#> MAD:pam 96 0.03175 2
#> ATC:pam 96 0.01549 2
#> SD:hclust 88 0.17887 2
#> CV:hclust 78 0.02883 2
#> MAD:hclust 84 0.31190 2
#> ATC:hclust 94 0.01788 2
test_to_known_factors(res_list, k = 3)
#> n disease.state(p) k
#> SD:NMF 94 0.00109 3
#> CV:NMF 87 0.00286 3
#> MAD:NMF 89 0.00355 3
#> ATC:NMF 93 0.08296 3
#> SD:skmeans 92 0.00380 3
#> CV:skmeans 90 0.00487 3
#> MAD:skmeans 95 0.01400 3
#> ATC:skmeans 88 0.00746 3
#> SD:mclust 92 0.07742 3
#> CV:mclust 92 0.07802 3
#> MAD:mclust 92 0.05539 3
#> ATC:mclust 93 0.00722 3
#> SD:kmeans 94 0.00294 3
#> CV:kmeans 95 0.00163 3
#> MAD:kmeans 92 0.00583 3
#> ATC:kmeans 93 0.02020 3
#> SD:pam 96 0.01760 3
#> CV:pam 93 0.01307 3
#> MAD:pam 86 0.03394 3
#> ATC:pam 87 0.03242 3
#> SD:hclust 88 0.00937 3
#> CV:hclust 45 0.00364 3
#> MAD:hclust 77 0.01760 3
#> ATC:hclust 93 0.01201 3
test_to_known_factors(res_list, k = 4)
#> n disease.state(p) k
#> SD:NMF 91 0.00175 4
#> CV:NMF 90 0.00647 4
#> MAD:NMF 87 0.02678 4
#> ATC:NMF 77 0.10852 4
#> SD:skmeans 90 0.00526 4
#> CV:skmeans 94 0.00837 4
#> MAD:skmeans 91 0.00951 4
#> ATC:skmeans 85 0.01221 4
#> SD:mclust 90 0.01051 4
#> CV:mclust 90 0.00700 4
#> MAD:mclust 85 0.02228 4
#> ATC:mclust 95 0.08616 4
#> SD:kmeans 93 0.00365 4
#> CV:kmeans 92 0.00399 4
#> MAD:kmeans 90 0.02170 4
#> ATC:kmeans 78 0.02206 4
#> SD:pam 94 0.01651 4
#> CV:pam 58 0.00437 4
#> MAD:pam 42 0.23835 4
#> ATC:pam 88 0.05583 4
#> SD:hclust 80 0.00379 4
#> CV:hclust 71 0.00905 4
#> MAD:hclust 59 0.04033 4
#> ATC:hclust 95 0.04500 4
test_to_known_factors(res_list, k = 5)
#> n disease.state(p) k
#> SD:NMF 82 0.00174 5
#> CV:NMF 88 0.00720 5
#> MAD:NMF 79 0.00390 5
#> ATC:NMF 58 0.07456 5
#> SD:skmeans 80 0.01023 5
#> CV:skmeans 70 0.02696 5
#> MAD:skmeans 60 0.04834 5
#> ATC:skmeans 80 0.00487 5
#> SD:mclust 82 0.01921 5
#> CV:mclust 85 0.01969 5
#> MAD:mclust 91 0.04353 5
#> ATC:mclust 92 0.02746 5
#> SD:kmeans 80 0.02737 5
#> CV:kmeans 83 0.01015 5
#> MAD:kmeans 73 0.09287 5
#> ATC:kmeans 89 0.07282 5
#> SD:pam 91 0.05662 5
#> CV:pam 91 0.00315 5
#> MAD:pam 85 0.09371 5
#> ATC:pam 94 0.07630 5
#> SD:hclust 79 0.00935 5
#> CV:hclust 76 0.00651 5
#> MAD:hclust 65 0.02196 5
#> ATC:hclust 90 0.02573 5
test_to_known_factors(res_list, k = 6)
#> n disease.state(p) k
#> SD:NMF 63 0.012479 6
#> CV:NMF 78 0.000966 6
#> MAD:NMF 60 0.052354 6
#> ATC:NMF 55 0.008631 6
#> SD:skmeans 68 0.025212 6
#> CV:skmeans 79 0.060624 6
#> MAD:skmeans 48 0.169808 6
#> ATC:skmeans 84 0.007724 6
#> SD:mclust 86 0.017557 6
#> CV:mclust 77 0.019197 6
#> MAD:mclust 68 0.129013 6
#> ATC:mclust 83 0.054006 6
#> SD:kmeans 55 0.055655 6
#> CV:kmeans 65 0.110184 6
#> MAD:kmeans 72 0.046912 6
#> ATC:kmeans 82 0.026727 6
#> SD:pam 90 0.066235 6
#> CV:pam 88 0.009861 6
#> MAD:pam 85 0.097573 6
#> ATC:pam 91 0.074429 6
#> SD:hclust 57 0.033984 6
#> CV:hclust 73 0.022096 6
#> MAD:hclust 62 0.039231 6
#> ATC:hclust 78 0.026070 6
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "hclust"]
# you can also extract it by
# res = res_list["SD:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 96 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 0.362 0.757 0.878 0.4900 0.505 0.505
#> 3 3 0.411 0.730 0.830 0.3134 0.819 0.647
#> 4 4 0.529 0.665 0.790 0.1494 0.874 0.644
#> 5 5 0.627 0.607 0.764 0.0557 0.963 0.853
#> 6 6 0.654 0.510 0.666 0.0396 0.900 0.630
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
#> GSM531600 2 0.1414 0.8341 0.020 0.980
#> GSM531601 2 0.7674 0.7479 0.224 0.776
#> GSM531605 1 0.3879 0.8724 0.924 0.076
#> GSM531615 2 0.9491 0.5371 0.368 0.632
#> GSM531617 2 0.9522 0.5290 0.372 0.628
#> GSM531624 2 0.5842 0.8030 0.140 0.860
#> GSM531627 2 0.0376 0.8315 0.004 0.996
#> GSM531629 2 0.9552 0.5199 0.376 0.624
#> GSM531631 2 0.5842 0.8030 0.140 0.860
#> GSM531634 2 0.9522 0.5290 0.372 0.628
#> GSM531636 2 0.1414 0.8341 0.020 0.980
#> GSM531637 2 0.5842 0.8030 0.140 0.860
#> GSM531654 2 0.9000 0.6398 0.316 0.684
#> GSM531655 1 0.4562 0.8582 0.904 0.096
#> GSM531658 1 0.0938 0.8969 0.988 0.012
#> GSM531660 1 0.2778 0.8850 0.952 0.048
#> GSM531602 1 0.0000 0.8938 1.000 0.000
#> GSM531603 1 0.0000 0.8938 1.000 0.000
#> GSM531604 1 0.6343 0.7762 0.840 0.160
#> GSM531606 1 0.0672 0.8953 0.992 0.008
#> GSM531607 1 0.0000 0.8938 1.000 0.000
#> GSM531608 2 0.8267 0.7076 0.260 0.740
#> GSM531609 1 0.0938 0.8969 0.988 0.012
#> GSM531610 1 0.0938 0.8969 0.988 0.012
#> GSM531611 1 0.0938 0.8969 0.988 0.012
#> GSM531612 1 0.0938 0.8969 0.988 0.012
#> GSM531613 1 0.0938 0.8969 0.988 0.012
#> GSM531614 1 0.0938 0.8969 0.988 0.012
#> GSM531616 2 0.0376 0.8318 0.004 0.996
#> GSM531618 1 0.5178 0.8322 0.884 0.116
#> GSM531619 2 0.5842 0.8030 0.140 0.860
#> GSM531620 2 0.2778 0.8324 0.048 0.952
#> GSM531621 2 0.0938 0.8332 0.012 0.988
#> GSM531622 2 0.5842 0.8030 0.140 0.860
#> GSM531623 2 0.5842 0.8030 0.140 0.860
#> GSM531625 2 0.0000 0.8303 0.000 1.000
#> GSM531626 2 0.0376 0.8318 0.004 0.996
#> GSM531628 2 0.2423 0.8309 0.040 0.960
#> GSM531630 2 0.5842 0.8030 0.140 0.860
#> GSM531632 2 0.0376 0.8318 0.004 0.996
#> GSM531633 2 0.0938 0.8332 0.012 0.988
#> GSM531635 2 0.0376 0.8318 0.004 0.996
#> GSM531638 2 0.0376 0.8318 0.004 0.996
#> GSM531639 2 0.2948 0.8299 0.052 0.948
#> GSM531640 2 0.5519 0.8089 0.128 0.872
#> GSM531641 1 0.0938 0.8969 0.988 0.012
#> GSM531642 1 0.9000 0.5593 0.684 0.316
#> GSM531643 2 0.6712 0.7867 0.176 0.824
#> GSM531644 1 0.9000 0.5593 0.684 0.316
#> GSM531645 1 0.0938 0.8969 0.988 0.012
#> GSM531646 2 0.0376 0.8318 0.004 0.996
#> GSM531647 2 0.0376 0.8318 0.004 0.996
#> GSM531648 1 0.3879 0.8672 0.924 0.076
#> GSM531649 2 0.0376 0.8318 0.004 0.996
#> GSM531650 2 0.2423 0.8309 0.040 0.960
#> GSM531651 2 0.4562 0.8194 0.096 0.904
#> GSM531652 1 0.6801 0.7824 0.820 0.180
#> GSM531653 2 0.0376 0.8318 0.004 0.996
#> GSM531656 2 0.3431 0.8269 0.064 0.936
#> GSM531657 1 0.5059 0.8305 0.888 0.112
#> GSM531659 1 0.8861 0.5463 0.696 0.304
#> GSM531661 2 0.8207 0.7145 0.256 0.744
#> GSM531662 2 0.7376 0.7331 0.208 0.792
#> GSM531663 1 0.4690 0.8393 0.900 0.100
#> GSM531664 2 0.2423 0.8309 0.040 0.960
#> GSM531665 1 0.9815 0.2285 0.580 0.420
#> GSM531666 1 0.9491 0.4353 0.632 0.368
#> GSM531667 2 0.8144 0.7166 0.252 0.748
#> GSM531668 1 0.4431 0.8500 0.908 0.092
#> GSM531669 2 0.0672 0.8324 0.008 0.992
#> GSM531670 2 0.3431 0.8269 0.064 0.936
#> GSM531671 2 0.7376 0.7326 0.208 0.792
#> GSM531672 1 0.0938 0.8969 0.988 0.012
#> GSM531673 2 0.7376 0.7326 0.208 0.792
#> GSM531674 2 0.0672 0.8324 0.008 0.992
#> GSM531675 1 0.0376 0.8949 0.996 0.004
#> GSM531676 2 0.9996 0.0813 0.488 0.512
#> GSM531677 1 0.2778 0.8821 0.952 0.048
#> GSM531678 1 0.1184 0.8949 0.984 0.016
#> GSM531679 1 0.2948 0.8807 0.948 0.052
#> GSM531680 2 0.9815 0.2898 0.420 0.580
#> GSM531681 1 0.0000 0.8938 1.000 0.000
#> GSM531682 1 0.2948 0.8807 0.948 0.052
#> GSM531683 1 0.0000 0.8938 1.000 0.000
#> GSM531684 1 0.1843 0.8922 0.972 0.028
#> GSM531685 2 0.5519 0.7946 0.128 0.872
#> GSM531686 1 0.0000 0.8938 1.000 0.000
#> GSM531687 2 0.9996 0.0813 0.488 0.512
#> GSM531688 2 0.2423 0.8315 0.040 0.960
#> GSM531689 2 0.9996 0.0813 0.488 0.512
#> GSM531690 1 0.0000 0.8938 1.000 0.000
#> GSM531691 1 1.0000 -0.0911 0.500 0.500
#> GSM531692 2 0.6048 0.7884 0.148 0.852
#> GSM531693 2 0.4939 0.8079 0.108 0.892
#> GSM531694 1 0.0000 0.8938 1.000 0.000
#> GSM531695 2 0.9993 0.1302 0.484 0.516
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM531600 3 0.3459 0.794 0.012 0.096 0.892
#> GSM531601 2 0.3293 0.784 0.088 0.900 0.012
#> GSM531605 1 0.5974 0.814 0.784 0.148 0.068
#> GSM531615 2 0.4931 0.675 0.232 0.768 0.000
#> GSM531617 2 0.4974 0.670 0.236 0.764 0.000
#> GSM531624 2 0.0983 0.804 0.004 0.980 0.016
#> GSM531627 2 0.4605 0.712 0.000 0.796 0.204
#> GSM531629 2 0.5016 0.664 0.240 0.760 0.000
#> GSM531631 2 0.0983 0.804 0.004 0.980 0.016
#> GSM531634 2 0.4974 0.670 0.236 0.764 0.000
#> GSM531636 3 0.3459 0.794 0.012 0.096 0.892
#> GSM531637 2 0.0983 0.804 0.004 0.980 0.016
#> GSM531654 2 0.6662 0.723 0.192 0.736 0.072
#> GSM531655 1 0.6144 0.809 0.780 0.132 0.088
#> GSM531658 1 0.1015 0.845 0.980 0.008 0.012
#> GSM531660 1 0.4915 0.800 0.804 0.184 0.012
#> GSM531602 1 0.5722 0.818 0.800 0.132 0.068
#> GSM531603 1 0.5631 0.819 0.804 0.132 0.064
#> GSM531604 1 0.8241 0.687 0.636 0.160 0.204
#> GSM531606 1 0.6001 0.811 0.784 0.144 0.072
#> GSM531607 1 0.5631 0.819 0.804 0.132 0.064
#> GSM531608 2 0.6025 0.763 0.140 0.784 0.076
#> GSM531609 1 0.1015 0.845 0.980 0.008 0.012
#> GSM531610 1 0.1015 0.845 0.980 0.008 0.012
#> GSM531611 1 0.1015 0.845 0.980 0.008 0.012
#> GSM531612 1 0.1015 0.845 0.980 0.008 0.012
#> GSM531613 1 0.1015 0.845 0.980 0.008 0.012
#> GSM531614 1 0.1015 0.845 0.980 0.008 0.012
#> GSM531616 3 0.3941 0.748 0.000 0.156 0.844
#> GSM531618 1 0.4602 0.808 0.852 0.108 0.040
#> GSM531619 2 0.0983 0.804 0.004 0.980 0.016
#> GSM531620 2 0.6057 0.726 0.044 0.760 0.196
#> GSM531621 2 0.4555 0.717 0.000 0.800 0.200
#> GSM531622 2 0.0983 0.804 0.004 0.980 0.016
#> GSM531623 2 0.0983 0.804 0.004 0.980 0.016
#> GSM531625 2 0.4702 0.704 0.000 0.788 0.212
#> GSM531626 2 0.4654 0.709 0.000 0.792 0.208
#> GSM531628 3 0.3356 0.801 0.036 0.056 0.908
#> GSM531630 2 0.0983 0.804 0.004 0.980 0.016
#> GSM531632 3 0.2356 0.799 0.000 0.072 0.928
#> GSM531633 2 0.4555 0.717 0.000 0.800 0.200
#> GSM531635 3 0.3686 0.759 0.000 0.140 0.860
#> GSM531638 3 0.3941 0.748 0.000 0.156 0.844
#> GSM531639 3 0.6905 0.554 0.044 0.280 0.676
#> GSM531640 2 0.2050 0.803 0.020 0.952 0.028
#> GSM531641 1 0.1015 0.845 0.980 0.008 0.012
#> GSM531642 1 0.6653 0.544 0.680 0.032 0.288
#> GSM531643 3 0.6229 0.706 0.172 0.064 0.764
#> GSM531644 1 0.6653 0.544 0.680 0.032 0.288
#> GSM531645 1 0.1015 0.845 0.980 0.008 0.012
#> GSM531646 3 0.2537 0.797 0.000 0.080 0.920
#> GSM531647 3 0.2356 0.799 0.000 0.072 0.928
#> GSM531648 1 0.3589 0.834 0.900 0.052 0.048
#> GSM531649 3 0.2356 0.799 0.000 0.072 0.928
#> GSM531650 3 0.3356 0.801 0.036 0.056 0.908
#> GSM531651 2 0.2066 0.795 0.000 0.940 0.060
#> GSM531652 1 0.5173 0.763 0.816 0.036 0.148
#> GSM531653 3 0.2356 0.799 0.000 0.072 0.928
#> GSM531656 3 0.4807 0.787 0.060 0.092 0.848
#> GSM531657 1 0.4418 0.790 0.848 0.132 0.020
#> GSM531659 1 0.8043 0.569 0.644 0.228 0.128
#> GSM531661 2 0.5677 0.772 0.124 0.804 0.072
#> GSM531662 2 0.8896 0.557 0.172 0.564 0.264
#> GSM531663 1 0.3973 0.809 0.880 0.088 0.032
#> GSM531664 3 0.3356 0.801 0.036 0.056 0.908
#> GSM531665 1 0.9217 0.258 0.492 0.344 0.164
#> GSM531666 1 0.7084 0.419 0.628 0.036 0.336
#> GSM531667 2 0.5810 0.770 0.132 0.796 0.072
#> GSM531668 1 0.4139 0.815 0.860 0.124 0.016
#> GSM531669 3 0.2590 0.800 0.004 0.072 0.924
#> GSM531670 3 0.4807 0.787 0.060 0.092 0.848
#> GSM531671 2 0.8950 0.545 0.172 0.556 0.272
#> GSM531672 1 0.1015 0.845 0.980 0.008 0.012
#> GSM531673 2 0.8924 0.550 0.172 0.560 0.268
#> GSM531674 3 0.2590 0.800 0.004 0.072 0.924
#> GSM531675 1 0.2280 0.848 0.940 0.008 0.052
#> GSM531676 3 0.7490 0.231 0.380 0.044 0.576
#> GSM531677 1 0.4636 0.836 0.852 0.044 0.104
#> GSM531678 1 0.6122 0.807 0.776 0.152 0.072
#> GSM531679 1 0.4994 0.832 0.836 0.052 0.112
#> GSM531680 3 0.6333 0.388 0.332 0.012 0.656
#> GSM531681 1 0.2301 0.846 0.936 0.004 0.060
#> GSM531682 1 0.4994 0.832 0.836 0.052 0.112
#> GSM531683 1 0.5377 0.827 0.820 0.112 0.068
#> GSM531684 1 0.6295 0.802 0.764 0.164 0.072
#> GSM531685 3 0.2599 0.759 0.052 0.016 0.932
#> GSM531686 1 0.2301 0.846 0.936 0.004 0.060
#> GSM531687 3 0.7490 0.231 0.380 0.044 0.576
#> GSM531688 3 0.1765 0.797 0.004 0.040 0.956
#> GSM531689 3 0.7490 0.231 0.380 0.044 0.576
#> GSM531690 1 0.2486 0.846 0.932 0.008 0.060
#> GSM531691 3 0.8131 0.198 0.376 0.076 0.548
#> GSM531692 3 0.3797 0.738 0.056 0.052 0.892
#> GSM531693 3 0.2793 0.769 0.044 0.028 0.928
#> GSM531694 1 0.5722 0.818 0.800 0.132 0.068
#> GSM531695 3 0.6769 0.237 0.392 0.016 0.592
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM531600 3 0.2718 0.8520 0.020 0.056 0.912 0.012
#> GSM531601 2 0.3272 0.7868 0.052 0.884 0.004 0.060
#> GSM531605 1 0.7219 -0.1080 0.456 0.068 0.028 0.448
#> GSM531615 2 0.5484 0.7065 0.104 0.732 0.000 0.164
#> GSM531617 2 0.5527 0.7035 0.104 0.728 0.000 0.168
#> GSM531624 2 0.0921 0.8135 0.028 0.972 0.000 0.000
#> GSM531627 2 0.3946 0.7532 0.020 0.812 0.168 0.000
#> GSM531629 2 0.5569 0.6986 0.104 0.724 0.000 0.172
#> GSM531631 2 0.0921 0.8135 0.028 0.972 0.000 0.000
#> GSM531634 2 0.5527 0.7035 0.104 0.728 0.000 0.168
#> GSM531636 3 0.2718 0.8520 0.020 0.056 0.912 0.012
#> GSM531637 2 0.0921 0.8135 0.028 0.972 0.000 0.000
#> GSM531654 2 0.6760 0.7108 0.184 0.680 0.056 0.080
#> GSM531655 4 0.7496 0.0405 0.440 0.072 0.040 0.448
#> GSM531658 4 0.1557 0.7736 0.056 0.000 0.000 0.944
#> GSM531660 4 0.6889 0.4832 0.252 0.144 0.004 0.600
#> GSM531602 1 0.2483 0.6965 0.916 0.052 0.000 0.032
#> GSM531603 1 0.3088 0.6899 0.888 0.052 0.000 0.060
#> GSM531604 1 0.5048 0.6531 0.788 0.068 0.128 0.016
#> GSM531606 1 0.2443 0.6963 0.916 0.060 0.000 0.024
#> GSM531607 1 0.3088 0.6899 0.888 0.052 0.000 0.060
#> GSM531608 2 0.5557 0.7701 0.088 0.776 0.048 0.088
#> GSM531609 4 0.0469 0.7852 0.012 0.000 0.000 0.988
#> GSM531610 4 0.0469 0.7852 0.012 0.000 0.000 0.988
#> GSM531611 4 0.0921 0.7817 0.028 0.000 0.000 0.972
#> GSM531612 4 0.0469 0.7852 0.012 0.000 0.000 0.988
#> GSM531613 4 0.0469 0.7852 0.012 0.000 0.000 0.988
#> GSM531614 4 0.0469 0.7852 0.012 0.000 0.000 0.988
#> GSM531616 3 0.3501 0.8043 0.020 0.132 0.848 0.000
#> GSM531618 4 0.6652 0.6424 0.172 0.076 0.060 0.692
#> GSM531619 2 0.0921 0.8135 0.028 0.972 0.000 0.000
#> GSM531620 2 0.5120 0.7619 0.036 0.776 0.160 0.028
#> GSM531621 2 0.3900 0.7562 0.020 0.816 0.164 0.000
#> GSM531622 2 0.0921 0.8135 0.028 0.972 0.000 0.000
#> GSM531623 2 0.0921 0.8135 0.028 0.972 0.000 0.000
#> GSM531625 2 0.4035 0.7452 0.020 0.804 0.176 0.000
#> GSM531626 2 0.3991 0.7495 0.020 0.808 0.172 0.000
#> GSM531628 3 0.1118 0.8571 0.000 0.000 0.964 0.036
#> GSM531630 2 0.0921 0.8135 0.028 0.972 0.000 0.000
#> GSM531632 3 0.0000 0.8634 0.000 0.000 1.000 0.000
#> GSM531633 2 0.3900 0.7562 0.020 0.816 0.164 0.000
#> GSM531635 3 0.2988 0.8226 0.012 0.112 0.876 0.000
#> GSM531638 3 0.3501 0.8043 0.020 0.132 0.848 0.000
#> GSM531639 3 0.5992 0.6068 0.020 0.260 0.676 0.044
#> GSM531640 2 0.1297 0.8136 0.016 0.964 0.000 0.020
#> GSM531641 4 0.0469 0.7852 0.012 0.000 0.000 0.988
#> GSM531642 4 0.6002 0.5623 0.044 0.012 0.296 0.648
#> GSM531643 3 0.4360 0.7408 0.032 0.012 0.816 0.140
#> GSM531644 4 0.6002 0.5623 0.044 0.012 0.296 0.648
#> GSM531645 4 0.0469 0.7852 0.012 0.000 0.000 0.988
#> GSM531646 3 0.0336 0.8634 0.000 0.008 0.992 0.000
#> GSM531647 3 0.0000 0.8634 0.000 0.000 1.000 0.000
#> GSM531648 4 0.4291 0.7522 0.068 0.028 0.060 0.844
#> GSM531649 3 0.0000 0.8634 0.000 0.000 1.000 0.000
#> GSM531650 3 0.1118 0.8571 0.000 0.000 0.964 0.036
#> GSM531651 2 0.1256 0.8142 0.008 0.964 0.028 0.000
#> GSM531652 4 0.5460 0.6923 0.072 0.016 0.156 0.756
#> GSM531653 3 0.0000 0.8634 0.000 0.000 1.000 0.000
#> GSM531656 3 0.4085 0.8321 0.020 0.068 0.852 0.060
#> GSM531657 4 0.7138 0.3496 0.316 0.124 0.008 0.552
#> GSM531659 1 0.9147 0.1887 0.420 0.212 0.092 0.276
#> GSM531661 2 0.5303 0.7793 0.088 0.792 0.052 0.068
#> GSM531662 2 0.8192 0.5968 0.168 0.560 0.200 0.072
#> GSM531663 4 0.6479 0.4466 0.256 0.080 0.016 0.648
#> GSM531664 3 0.1118 0.8571 0.000 0.000 0.964 0.036
#> GSM531665 1 0.9231 0.0990 0.384 0.332 0.108 0.176
#> GSM531666 4 0.6161 0.4474 0.044 0.008 0.356 0.592
#> GSM531667 2 0.5490 0.7771 0.092 0.780 0.048 0.080
#> GSM531668 4 0.5079 0.6818 0.116 0.104 0.004 0.776
#> GSM531669 3 0.0188 0.8633 0.000 0.000 0.996 0.004
#> GSM531670 3 0.4085 0.8321 0.020 0.068 0.852 0.060
#> GSM531671 2 0.8250 0.5882 0.168 0.552 0.208 0.072
#> GSM531672 4 0.1637 0.7731 0.060 0.000 0.000 0.940
#> GSM531673 2 0.8222 0.5921 0.168 0.556 0.204 0.072
#> GSM531674 3 0.0188 0.8633 0.000 0.000 0.996 0.004
#> GSM531675 1 0.4072 0.5839 0.748 0.000 0.000 0.252
#> GSM531676 1 0.5447 0.0689 0.528 0.008 0.460 0.004
#> GSM531677 1 0.3463 0.6739 0.864 0.000 0.040 0.096
#> GSM531678 1 0.2222 0.6958 0.924 0.060 0.000 0.016
#> GSM531679 1 0.3128 0.6806 0.884 0.000 0.040 0.076
#> GSM531680 3 0.6166 0.1916 0.412 0.008 0.544 0.036
#> GSM531681 1 0.4522 0.4988 0.680 0.000 0.000 0.320
#> GSM531682 1 0.3128 0.6806 0.884 0.000 0.040 0.076
#> GSM531683 1 0.3081 0.6934 0.888 0.048 0.000 0.064
#> GSM531684 1 0.2450 0.6947 0.912 0.072 0.000 0.016
#> GSM531685 3 0.3770 0.7749 0.136 0.016 0.840 0.008
#> GSM531686 1 0.4522 0.4988 0.680 0.000 0.000 0.320
#> GSM531687 1 0.5447 0.0689 0.528 0.008 0.460 0.004
#> GSM531688 3 0.1489 0.8513 0.044 0.000 0.952 0.004
#> GSM531689 1 0.5447 0.0689 0.528 0.008 0.460 0.004
#> GSM531690 1 0.3837 0.6087 0.776 0.000 0.000 0.224
#> GSM531691 1 0.5908 0.1093 0.536 0.028 0.432 0.004
#> GSM531692 3 0.4781 0.7315 0.188 0.032 0.772 0.008
#> GSM531693 3 0.3494 0.7945 0.116 0.016 0.860 0.008
#> GSM531694 1 0.2483 0.6965 0.916 0.052 0.000 0.032
#> GSM531695 3 0.6265 0.0559 0.444 0.000 0.500 0.056
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM531600 3 0.2685 0.78946 0.000 0.028 0.880 0.000 0.092
#> GSM531601 2 0.3005 0.76841 0.040 0.884 0.000 0.028 0.048
#> GSM531605 1 0.6845 0.02520 0.460 0.016 0.000 0.340 0.184
#> GSM531615 2 0.5864 0.68956 0.064 0.692 0.000 0.116 0.128
#> GSM531617 2 0.5908 0.68651 0.064 0.688 0.000 0.120 0.128
#> GSM531624 2 0.0290 0.79640 0.008 0.992 0.000 0.000 0.000
#> GSM531627 2 0.4169 0.72565 0.000 0.784 0.100 0.000 0.116
#> GSM531629 2 0.5950 0.68154 0.064 0.684 0.000 0.124 0.128
#> GSM531631 2 0.0290 0.79640 0.008 0.992 0.000 0.000 0.000
#> GSM531634 2 0.5908 0.68651 0.064 0.688 0.000 0.120 0.128
#> GSM531636 3 0.2685 0.78946 0.000 0.028 0.880 0.000 0.092
#> GSM531637 2 0.0290 0.79640 0.008 0.992 0.000 0.000 0.000
#> GSM531654 2 0.6912 0.66226 0.176 0.592 0.012 0.044 0.176
#> GSM531655 1 0.7218 0.01044 0.440 0.020 0.008 0.340 0.192
#> GSM531658 4 0.3578 0.73635 0.048 0.000 0.000 0.820 0.132
#> GSM531660 4 0.7752 0.45797 0.232 0.128 0.000 0.480 0.160
#> GSM531602 1 0.0324 0.64194 0.992 0.004 0.000 0.004 0.000
#> GSM531603 1 0.1116 0.64248 0.964 0.004 0.000 0.028 0.004
#> GSM531604 1 0.3816 0.56319 0.828 0.016 0.056 0.000 0.100
#> GSM531606 1 0.0566 0.63989 0.984 0.012 0.000 0.000 0.004
#> GSM531607 1 0.1116 0.64248 0.964 0.004 0.000 0.028 0.004
#> GSM531608 2 0.4956 0.74393 0.008 0.736 0.012 0.060 0.184
#> GSM531609 4 0.0000 0.76167 0.000 0.000 0.000 1.000 0.000
#> GSM531610 4 0.0000 0.76167 0.000 0.000 0.000 1.000 0.000
#> GSM531611 4 0.0510 0.75647 0.016 0.000 0.000 0.984 0.000
#> GSM531612 4 0.0000 0.76167 0.000 0.000 0.000 1.000 0.000
#> GSM531613 4 0.0000 0.76167 0.000 0.000 0.000 1.000 0.000
#> GSM531614 4 0.0000 0.76167 0.000 0.000 0.000 1.000 0.000
#> GSM531616 3 0.3970 0.69169 0.000 0.104 0.800 0.000 0.096
#> GSM531618 4 0.7763 0.58839 0.168 0.060 0.056 0.556 0.160
#> GSM531619 2 0.0290 0.79640 0.008 0.992 0.000 0.000 0.000
#> GSM531620 2 0.4970 0.73000 0.000 0.744 0.100 0.020 0.136
#> GSM531621 2 0.4111 0.72981 0.000 0.788 0.092 0.000 0.120
#> GSM531622 2 0.0290 0.79640 0.008 0.992 0.000 0.000 0.000
#> GSM531623 2 0.0290 0.79640 0.008 0.992 0.000 0.000 0.000
#> GSM531625 2 0.4266 0.71906 0.000 0.776 0.104 0.000 0.120
#> GSM531626 2 0.4216 0.72362 0.000 0.780 0.100 0.000 0.120
#> GSM531628 3 0.0992 0.81531 0.000 0.000 0.968 0.008 0.024
#> GSM531630 2 0.0290 0.79640 0.008 0.992 0.000 0.000 0.000
#> GSM531632 3 0.0162 0.82398 0.000 0.000 0.996 0.000 0.004
#> GSM531633 2 0.4111 0.72981 0.000 0.788 0.092 0.000 0.120
#> GSM531635 3 0.3354 0.74161 0.000 0.088 0.844 0.000 0.068
#> GSM531638 3 0.3970 0.69169 0.000 0.104 0.800 0.000 0.096
#> GSM531639 3 0.5988 0.40863 0.000 0.232 0.628 0.020 0.120
#> GSM531640 2 0.1211 0.79612 0.000 0.960 0.000 0.016 0.024
#> GSM531641 4 0.0000 0.76167 0.000 0.000 0.000 1.000 0.000
#> GSM531642 4 0.6396 0.48744 0.004 0.004 0.284 0.544 0.164
#> GSM531643 3 0.4063 0.62997 0.004 0.004 0.808 0.080 0.104
#> GSM531644 4 0.6396 0.48744 0.004 0.004 0.284 0.544 0.164
#> GSM531645 4 0.0000 0.76167 0.000 0.000 0.000 1.000 0.000
#> GSM531646 3 0.0579 0.82498 0.000 0.008 0.984 0.000 0.008
#> GSM531647 3 0.0162 0.82398 0.000 0.000 0.996 0.000 0.004
#> GSM531648 4 0.5640 0.71424 0.052 0.016 0.056 0.720 0.156
#> GSM531649 3 0.0290 0.82471 0.000 0.000 0.992 0.000 0.008
#> GSM531650 3 0.0992 0.81531 0.000 0.000 0.968 0.008 0.024
#> GSM531651 2 0.1281 0.79321 0.000 0.956 0.012 0.000 0.032
#> GSM531652 4 0.6071 0.64390 0.016 0.004 0.144 0.636 0.200
#> GSM531653 3 0.0290 0.82471 0.000 0.000 0.992 0.000 0.008
#> GSM531656 3 0.4136 0.74873 0.000 0.040 0.808 0.032 0.120
#> GSM531657 4 0.7735 0.30454 0.304 0.104 0.004 0.456 0.132
#> GSM531659 1 0.9099 0.17137 0.364 0.180 0.044 0.216 0.196
#> GSM531661 2 0.4507 0.75545 0.012 0.772 0.008 0.044 0.164
#> GSM531662 2 0.7685 0.55176 0.068 0.500 0.084 0.044 0.304
#> GSM531663 4 0.6394 0.41247 0.196 0.072 0.000 0.632 0.100
#> GSM531664 3 0.0992 0.81531 0.000 0.000 0.968 0.008 0.024
#> GSM531665 1 0.9096 0.00541 0.304 0.296 0.048 0.124 0.228
#> GSM531666 4 0.6471 0.36650 0.004 0.000 0.348 0.480 0.168
#> GSM531667 2 0.4711 0.75266 0.008 0.760 0.012 0.056 0.164
#> GSM531668 4 0.6144 0.64763 0.104 0.048 0.000 0.644 0.204
#> GSM531669 3 0.0510 0.81552 0.000 0.000 0.984 0.000 0.016
#> GSM531670 3 0.4136 0.74873 0.000 0.040 0.808 0.032 0.120
#> GSM531671 2 0.7741 0.54256 0.068 0.492 0.088 0.044 0.308
#> GSM531672 4 0.3692 0.73483 0.052 0.000 0.000 0.812 0.136
#> GSM531673 2 0.7697 0.54813 0.068 0.496 0.084 0.044 0.308
#> GSM531674 3 0.0510 0.81552 0.000 0.000 0.984 0.000 0.016
#> GSM531675 1 0.3491 0.59133 0.768 0.000 0.000 0.228 0.004
#> GSM531676 1 0.6618 -0.25509 0.400 0.000 0.216 0.000 0.384
#> GSM531677 1 0.3725 0.62712 0.840 0.000 0.024 0.056 0.080
#> GSM531678 1 0.0807 0.63909 0.976 0.012 0.000 0.000 0.012
#> GSM531679 1 0.3477 0.62749 0.852 0.000 0.024 0.036 0.088
#> GSM531680 5 0.7298 0.31443 0.328 0.000 0.320 0.020 0.332
#> GSM531681 1 0.3816 0.51540 0.696 0.000 0.000 0.304 0.000
#> GSM531682 1 0.3477 0.62749 0.852 0.000 0.024 0.036 0.088
#> GSM531683 1 0.1329 0.64653 0.956 0.008 0.000 0.032 0.004
#> GSM531684 1 0.1117 0.63720 0.964 0.016 0.000 0.000 0.020
#> GSM531685 5 0.4949 0.67793 0.032 0.000 0.396 0.000 0.572
#> GSM531686 1 0.3816 0.51540 0.696 0.000 0.000 0.304 0.000
#> GSM531687 1 0.6618 -0.25509 0.400 0.000 0.216 0.000 0.384
#> GSM531688 3 0.2416 0.68554 0.012 0.000 0.888 0.000 0.100
#> GSM531689 1 0.6618 -0.25509 0.400 0.000 0.216 0.000 0.384
#> GSM531690 1 0.3266 0.60255 0.796 0.000 0.000 0.200 0.004
#> GSM531691 1 0.6900 -0.22976 0.412 0.012 0.204 0.000 0.372
#> GSM531692 5 0.4844 0.65289 0.044 0.008 0.256 0.000 0.692
#> GSM531693 5 0.4824 0.58631 0.020 0.000 0.468 0.000 0.512
#> GSM531694 1 0.0324 0.64194 0.992 0.004 0.000 0.004 0.000
#> GSM531695 1 0.7288 -0.37148 0.344 0.000 0.300 0.020 0.336
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM531600 3 0.3442 0.8314 0.000 0.020 0.848 0.036 0.072 NA
#> GSM531601 2 0.2218 0.7109 0.000 0.884 0.000 0.104 0.000 NA
#> GSM531605 4 0.6964 0.2353 0.076 0.012 0.000 0.452 0.320 NA
#> GSM531615 2 0.5279 0.5928 0.016 0.576 0.000 0.332 0.000 NA
#> GSM531617 2 0.5291 0.5899 0.016 0.572 0.000 0.336 0.000 NA
#> GSM531624 2 0.0000 0.7453 0.000 1.000 0.000 0.000 0.000 NA
#> GSM531627 2 0.4892 0.6859 0.000 0.760 0.060 0.044 0.084 NA
#> GSM531629 2 0.5303 0.5850 0.016 0.568 0.000 0.340 0.000 NA
#> GSM531631 2 0.0146 0.7444 0.000 0.996 0.000 0.004 0.000 NA
#> GSM531634 2 0.5291 0.5899 0.016 0.572 0.000 0.336 0.000 NA
#> GSM531636 3 0.3442 0.8314 0.000 0.020 0.848 0.036 0.072 NA
#> GSM531637 2 0.0000 0.7453 0.000 1.000 0.000 0.000 0.000 NA
#> GSM531654 2 0.7151 0.5461 0.024 0.480 0.000 0.240 0.068 NA
#> GSM531655 4 0.7008 0.2356 0.072 0.016 0.000 0.448 0.324 NA
#> GSM531658 4 0.5133 0.5797 0.332 0.000 0.000 0.576 0.004 NA
#> GSM531660 4 0.6677 0.4449 0.108 0.124 0.000 0.612 0.088 NA
#> GSM531602 1 0.5890 0.2982 0.468 0.000 0.000 0.012 0.376 NA
#> GSM531603 1 0.6140 0.2998 0.476 0.000 0.000 0.028 0.352 NA
#> GSM531604 5 0.5816 -0.1645 0.324 0.004 0.000 0.016 0.536 NA
#> GSM531606 1 0.5928 0.2881 0.452 0.000 0.000 0.012 0.388 NA
#> GSM531607 1 0.6140 0.2998 0.476 0.000 0.000 0.028 0.352 NA
#> GSM531608 2 0.5849 0.6554 0.020 0.620 0.000 0.240 0.040 NA
#> GSM531609 1 0.4735 -0.0138 0.520 0.000 0.000 0.048 0.000 NA
#> GSM531610 1 0.4735 -0.0138 0.520 0.000 0.000 0.048 0.000 NA
#> GSM531611 1 0.4671 -0.0163 0.532 0.000 0.000 0.044 0.000 NA
#> GSM531612 1 0.4735 -0.0138 0.520 0.000 0.000 0.048 0.000 NA
#> GSM531613 1 0.4735 -0.0138 0.520 0.000 0.000 0.048 0.000 NA
#> GSM531614 1 0.4735 -0.0138 0.520 0.000 0.000 0.048 0.000 NA
#> GSM531616 3 0.4632 0.7600 0.000 0.092 0.768 0.032 0.084 NA
#> GSM531618 4 0.7437 0.5819 0.224 0.052 0.052 0.540 0.076 NA
#> GSM531619 2 0.0000 0.7453 0.000 1.000 0.000 0.000 0.000 NA
#> GSM531620 2 0.5826 0.6851 0.000 0.684 0.064 0.120 0.084 NA
#> GSM531621 2 0.4835 0.6886 0.000 0.764 0.056 0.044 0.084 NA
#> GSM531622 2 0.0260 0.7456 0.000 0.992 0.000 0.008 0.000 NA
#> GSM531623 2 0.0000 0.7453 0.000 1.000 0.000 0.000 0.000 NA
#> GSM531625 2 0.4997 0.6798 0.000 0.752 0.064 0.044 0.088 NA
#> GSM531626 2 0.4948 0.6833 0.000 0.756 0.064 0.044 0.084 NA
#> GSM531628 3 0.0972 0.8559 0.000 0.000 0.964 0.028 0.008 NA
#> GSM531630 2 0.0146 0.7444 0.000 0.996 0.000 0.004 0.000 NA
#> GSM531632 3 0.0000 0.8622 0.000 0.000 1.000 0.000 0.000 NA
#> GSM531633 2 0.4835 0.6886 0.000 0.764 0.056 0.044 0.084 NA
#> GSM531635 3 0.3936 0.7974 0.000 0.076 0.812 0.020 0.076 NA
#> GSM531638 3 0.4632 0.7600 0.000 0.092 0.768 0.032 0.084 NA
#> GSM531639 3 0.6512 0.5464 0.000 0.216 0.588 0.076 0.084 NA
#> GSM531640 2 0.1508 0.7430 0.016 0.948 0.000 0.020 0.004 NA
#> GSM531641 1 0.4735 -0.0138 0.520 0.000 0.000 0.048 0.000 NA
#> GSM531642 4 0.6771 0.5532 0.180 0.000 0.272 0.480 0.004 NA
#> GSM531643 3 0.3752 0.7274 0.024 0.000 0.792 0.160 0.008 NA
#> GSM531644 4 0.6771 0.5532 0.180 0.000 0.272 0.480 0.004 NA
#> GSM531645 1 0.4735 -0.0138 0.520 0.000 0.000 0.048 0.000 NA
#> GSM531646 3 0.0405 0.8628 0.000 0.008 0.988 0.000 0.000 NA
#> GSM531647 3 0.0000 0.8622 0.000 0.000 1.000 0.000 0.000 NA
#> GSM531648 4 0.5640 0.6339 0.252 0.008 0.056 0.624 0.000 NA
#> GSM531649 3 0.0146 0.8629 0.000 0.000 0.996 0.000 0.004 NA
#> GSM531650 3 0.0972 0.8559 0.000 0.000 0.964 0.028 0.008 NA
#> GSM531651 2 0.1508 0.7426 0.000 0.948 0.004 0.016 0.020 NA
#> GSM531652 4 0.6077 0.6202 0.188 0.000 0.132 0.608 0.004 NA
#> GSM531653 3 0.0146 0.8629 0.000 0.000 0.996 0.000 0.004 NA
#> GSM531656 3 0.4696 0.7979 0.000 0.032 0.768 0.072 0.092 NA
#> GSM531657 4 0.7676 0.3531 0.280 0.096 0.000 0.440 0.116 NA
#> GSM531659 5 0.8657 0.0683 0.212 0.156 0.008 0.264 0.292 NA
#> GSM531661 2 0.5210 0.6680 0.004 0.664 0.000 0.228 0.036 NA
#> GSM531662 2 0.8225 0.4641 0.028 0.388 0.032 0.264 0.188 NA
#> GSM531663 1 0.7835 -0.0449 0.432 0.056 0.000 0.140 0.124 NA
#> GSM531664 3 0.0972 0.8559 0.000 0.000 0.964 0.028 0.008 NA
#> GSM531665 5 0.8541 0.0885 0.136 0.256 0.008 0.236 0.304 NA
#> GSM531666 4 0.6841 0.4428 0.168 0.000 0.332 0.440 0.012 NA
#> GSM531667 2 0.5495 0.6653 0.016 0.652 0.000 0.228 0.036 NA
#> GSM531668 4 0.4389 0.5391 0.156 0.008 0.000 0.748 0.008 NA
#> GSM531669 3 0.0692 0.8561 0.000 0.000 0.976 0.000 0.020 NA
#> GSM531670 3 0.4696 0.7979 0.000 0.032 0.768 0.072 0.092 NA
#> GSM531671 2 0.8330 0.4544 0.028 0.380 0.040 0.264 0.188 NA
#> GSM531672 4 0.5108 0.5839 0.324 0.000 0.000 0.584 0.004 NA
#> GSM531673 2 0.8279 0.4596 0.028 0.384 0.036 0.264 0.188 NA
#> GSM531674 3 0.0692 0.8561 0.000 0.000 0.976 0.000 0.020 NA
#> GSM531675 1 0.4257 0.3143 0.652 0.000 0.000 0.012 0.320 NA
#> GSM531676 5 0.1267 0.5725 0.000 0.000 0.060 0.000 0.940 NA
#> GSM531677 1 0.5459 0.2657 0.520 0.000 0.004 0.068 0.392 NA
#> GSM531678 1 0.5747 0.2874 0.460 0.000 0.000 0.008 0.400 NA
#> GSM531679 1 0.5621 0.2563 0.500 0.000 0.004 0.068 0.404 NA
#> GSM531680 5 0.4684 0.5211 0.020 0.000 0.188 0.020 0.728 NA
#> GSM531681 1 0.4670 0.3040 0.636 0.000 0.000 0.000 0.292 NA
#> GSM531682 1 0.5621 0.2563 0.500 0.000 0.004 0.068 0.404 NA
#> GSM531683 1 0.5735 0.3087 0.504 0.000 0.000 0.024 0.376 NA
#> GSM531684 1 0.6010 0.2739 0.452 0.004 0.000 0.016 0.400 NA
#> GSM531685 5 0.5946 0.3938 0.000 0.000 0.204 0.004 0.472 NA
#> GSM531686 1 0.4670 0.3040 0.636 0.000 0.000 0.000 0.292 NA
#> GSM531687 5 0.1267 0.5725 0.000 0.000 0.060 0.000 0.940 NA
#> GSM531688 3 0.2302 0.7720 0.000 0.000 0.872 0.000 0.120 NA
#> GSM531689 5 0.1267 0.5725 0.000 0.000 0.060 0.000 0.940 NA
#> GSM531690 1 0.3805 0.3199 0.664 0.000 0.000 0.004 0.328 NA
#> GSM531691 5 0.2302 0.5613 0.004 0.004 0.056 0.012 0.908 NA
#> GSM531692 5 0.5131 0.4043 0.000 0.004 0.048 0.016 0.576 NA
#> GSM531693 5 0.6227 0.3331 0.000 0.000 0.300 0.004 0.376 NA
#> GSM531694 1 0.5890 0.2982 0.468 0.000 0.000 0.012 0.376 NA
#> GSM531695 5 0.5922 0.4534 0.028 0.000 0.196 0.072 0.644 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)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> SD:hclust 88 0.17887 2
#> SD:hclust 88 0.00937 3
#> SD:hclust 80 0.00379 4
#> SD:hclust 79 0.00935 5
#> SD:hclust 57 0.03398 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "kmeans"]
# you can also extract it by
# res = res_list["SD:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 96 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 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 0.384 0.805 0.876 0.4945 0.509 0.509
#> 3 3 0.640 0.837 0.885 0.3431 0.734 0.518
#> 4 4 0.853 0.879 0.931 0.1297 0.845 0.576
#> 5 5 0.709 0.636 0.795 0.0628 0.955 0.825
#> 6 6 0.701 0.488 0.724 0.0402 0.938 0.737
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM531600 2 0.3431 0.825 0.064 0.936
#> GSM531601 2 0.7602 0.754 0.220 0.780
#> GSM531605 1 0.3733 0.887 0.928 0.072
#> GSM531615 2 0.7602 0.754 0.220 0.780
#> GSM531617 2 0.7674 0.752 0.224 0.776
#> GSM531624 2 0.7299 0.768 0.204 0.796
#> GSM531627 2 0.2778 0.829 0.048 0.952
#> GSM531629 2 0.9833 0.383 0.424 0.576
#> GSM531631 2 0.7299 0.768 0.204 0.796
#> GSM531634 2 0.7602 0.754 0.220 0.780
#> GSM531636 2 0.1414 0.823 0.020 0.980
#> GSM531637 2 0.7299 0.768 0.204 0.796
#> GSM531654 2 0.7602 0.754 0.220 0.780
#> GSM531655 2 0.7883 0.766 0.236 0.764
#> GSM531658 1 0.1184 0.914 0.984 0.016
#> GSM531660 1 0.2948 0.895 0.948 0.052
#> GSM531602 1 0.2778 0.898 0.952 0.048
#> GSM531603 1 0.2778 0.898 0.952 0.048
#> GSM531604 1 0.5946 0.830 0.856 0.144
#> GSM531606 1 0.2778 0.898 0.952 0.048
#> GSM531607 1 0.0672 0.912 0.992 0.008
#> GSM531608 2 0.7602 0.754 0.220 0.780
#> GSM531609 1 0.1184 0.914 0.984 0.016
#> GSM531610 1 0.1184 0.914 0.984 0.016
#> GSM531611 1 0.0000 0.911 1.000 0.000
#> GSM531612 1 0.1184 0.914 0.984 0.016
#> GSM531613 1 0.1184 0.914 0.984 0.016
#> GSM531614 1 0.1184 0.914 0.984 0.016
#> GSM531616 2 0.0672 0.826 0.008 0.992
#> GSM531618 2 0.9460 0.628 0.364 0.636
#> GSM531619 2 0.7299 0.768 0.204 0.796
#> GSM531620 2 0.2778 0.829 0.048 0.952
#> GSM531621 2 0.2778 0.829 0.048 0.952
#> GSM531622 2 0.7299 0.768 0.204 0.796
#> GSM531623 2 0.4022 0.824 0.080 0.920
#> GSM531625 2 0.0000 0.826 0.000 1.000
#> GSM531626 2 0.0376 0.826 0.004 0.996
#> GSM531628 2 0.7376 0.750 0.208 0.792
#> GSM531630 2 0.6973 0.778 0.188 0.812
#> GSM531632 2 0.4690 0.816 0.100 0.900
#> GSM531633 2 0.2778 0.829 0.048 0.952
#> GSM531635 2 0.2948 0.823 0.052 0.948
#> GSM531638 2 0.0000 0.826 0.000 1.000
#> GSM531639 2 0.0000 0.826 0.000 1.000
#> GSM531640 2 0.7299 0.768 0.204 0.796
#> GSM531641 1 0.1633 0.912 0.976 0.024
#> GSM531642 2 0.5519 0.805 0.128 0.872
#> GSM531643 2 0.6148 0.792 0.152 0.848
#> GSM531644 2 0.7376 0.750 0.208 0.792
#> GSM531645 1 0.1184 0.914 0.984 0.016
#> GSM531646 2 0.3584 0.823 0.068 0.932
#> GSM531647 2 0.4690 0.816 0.100 0.900
#> GSM531648 1 0.6048 0.762 0.852 0.148
#> GSM531649 2 0.2043 0.823 0.032 0.968
#> GSM531650 2 0.7139 0.760 0.196 0.804
#> GSM531651 2 0.2778 0.829 0.048 0.952
#> GSM531652 2 0.7056 0.766 0.192 0.808
#> GSM531653 2 0.4161 0.820 0.084 0.916
#> GSM531656 2 0.3431 0.825 0.064 0.936
#> GSM531657 1 0.1414 0.913 0.980 0.020
#> GSM531659 1 0.1184 0.914 0.984 0.016
#> GSM531661 2 0.7299 0.768 0.204 0.796
#> GSM531662 2 0.4161 0.823 0.084 0.916
#> GSM531663 1 0.1414 0.913 0.980 0.020
#> GSM531664 2 0.7376 0.750 0.208 0.792
#> GSM531665 2 0.7528 0.741 0.216 0.784
#> GSM531666 2 0.9427 0.508 0.360 0.640
#> GSM531667 2 0.7299 0.768 0.204 0.796
#> GSM531668 1 0.2778 0.898 0.952 0.048
#> GSM531669 2 0.7376 0.750 0.208 0.792
#> GSM531670 2 0.3431 0.825 0.064 0.936
#> GSM531671 2 0.3584 0.825 0.068 0.932
#> GSM531672 1 0.1633 0.912 0.976 0.024
#> GSM531673 2 0.3431 0.835 0.064 0.936
#> GSM531674 2 0.7376 0.750 0.208 0.792
#> GSM531675 1 0.0000 0.911 1.000 0.000
#> GSM531676 1 0.7674 0.701 0.776 0.224
#> GSM531677 1 0.1633 0.898 0.976 0.024
#> GSM531678 1 0.0938 0.912 0.988 0.012
#> GSM531679 1 0.2423 0.887 0.960 0.040
#> GSM531680 1 0.7602 0.706 0.780 0.220
#> GSM531681 1 0.0000 0.911 1.000 0.000
#> GSM531682 1 0.1414 0.901 0.980 0.020
#> GSM531683 1 0.1633 0.912 0.976 0.024
#> GSM531684 1 0.6343 0.777 0.840 0.160
#> GSM531685 2 0.9491 0.470 0.368 0.632
#> GSM531686 1 0.0000 0.911 1.000 0.000
#> GSM531687 1 0.7602 0.706 0.780 0.220
#> GSM531688 1 0.9866 0.217 0.568 0.432
#> GSM531689 1 0.6247 0.782 0.844 0.156
#> GSM531690 1 0.0000 0.911 1.000 0.000
#> GSM531691 1 0.7299 0.752 0.796 0.204
#> GSM531692 2 0.6438 0.769 0.164 0.836
#> GSM531693 2 0.7376 0.750 0.208 0.792
#> GSM531694 1 0.2778 0.898 0.952 0.048
#> GSM531695 1 0.7602 0.706 0.780 0.220
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM531600 3 0.3816 0.8940 0.000 0.148 0.852
#> GSM531601 2 0.2200 0.8680 0.056 0.940 0.004
#> GSM531605 1 0.7766 0.7569 0.676 0.176 0.148
#> GSM531615 2 0.0424 0.9081 0.008 0.992 0.000
#> GSM531617 2 0.1411 0.8906 0.036 0.964 0.000
#> GSM531624 2 0.0000 0.9122 0.000 1.000 0.000
#> GSM531627 2 0.0747 0.9070 0.000 0.984 0.016
#> GSM531629 2 0.4291 0.7456 0.180 0.820 0.000
#> GSM531631 2 0.0000 0.9122 0.000 1.000 0.000
#> GSM531634 2 0.0424 0.9081 0.008 0.992 0.000
#> GSM531636 3 0.4121 0.8800 0.000 0.168 0.832
#> GSM531637 2 0.0000 0.9122 0.000 1.000 0.000
#> GSM531654 2 0.0237 0.9114 0.004 0.996 0.000
#> GSM531655 2 0.7839 -0.1523 0.052 0.484 0.464
#> GSM531658 1 0.2152 0.8773 0.948 0.036 0.016
#> GSM531660 1 0.3349 0.8577 0.888 0.108 0.004
#> GSM531602 1 0.5913 0.8527 0.788 0.068 0.144
#> GSM531603 1 0.5913 0.8527 0.788 0.068 0.144
#> GSM531604 1 0.7821 0.7540 0.672 0.176 0.152
#> GSM531606 1 0.5913 0.8527 0.788 0.068 0.144
#> GSM531607 1 0.5823 0.8543 0.792 0.064 0.144
#> GSM531608 2 0.0237 0.9103 0.004 0.996 0.000
#> GSM531609 1 0.2152 0.8773 0.948 0.036 0.016
#> GSM531610 1 0.1832 0.8788 0.956 0.036 0.008
#> GSM531611 1 0.1636 0.8801 0.964 0.020 0.016
#> GSM531612 1 0.2152 0.8773 0.948 0.036 0.016
#> GSM531613 1 0.1315 0.8810 0.972 0.020 0.008
#> GSM531614 1 0.2152 0.8773 0.948 0.036 0.016
#> GSM531616 2 0.6274 -0.0441 0.000 0.544 0.456
#> GSM531618 1 0.5595 0.7432 0.756 0.228 0.016
#> GSM531619 2 0.0000 0.9122 0.000 1.000 0.000
#> GSM531620 2 0.0747 0.9070 0.000 0.984 0.016
#> GSM531621 2 0.0747 0.9070 0.000 0.984 0.016
#> GSM531622 2 0.0000 0.9122 0.000 1.000 0.000
#> GSM531623 2 0.0000 0.9122 0.000 1.000 0.000
#> GSM531625 2 0.3412 0.8040 0.000 0.876 0.124
#> GSM531626 2 0.3686 0.7843 0.000 0.860 0.140
#> GSM531628 3 0.4489 0.8968 0.036 0.108 0.856
#> GSM531630 2 0.0000 0.9122 0.000 1.000 0.000
#> GSM531632 3 0.3816 0.8940 0.000 0.148 0.852
#> GSM531633 2 0.0747 0.9070 0.000 0.984 0.016
#> GSM531635 3 0.3816 0.8940 0.000 0.148 0.852
#> GSM531638 2 0.3412 0.8040 0.000 0.876 0.124
#> GSM531639 3 0.5178 0.7736 0.000 0.256 0.744
#> GSM531640 2 0.0000 0.9122 0.000 1.000 0.000
#> GSM531641 1 0.2152 0.8773 0.948 0.036 0.016
#> GSM531642 3 0.4586 0.8927 0.048 0.096 0.856
#> GSM531643 3 0.4489 0.8968 0.036 0.108 0.856
#> GSM531644 3 0.4652 0.8852 0.064 0.080 0.856
#> GSM531645 1 0.2152 0.8773 0.948 0.036 0.016
#> GSM531646 3 0.3816 0.8940 0.000 0.148 0.852
#> GSM531647 3 0.3918 0.8961 0.004 0.140 0.856
#> GSM531648 1 0.2152 0.8773 0.948 0.036 0.016
#> GSM531649 3 0.3879 0.8915 0.000 0.152 0.848
#> GSM531650 3 0.4489 0.8968 0.036 0.108 0.856
#> GSM531651 2 0.0592 0.9088 0.000 0.988 0.012
#> GSM531652 3 0.4749 0.8810 0.072 0.076 0.852
#> GSM531653 3 0.3752 0.8952 0.000 0.144 0.856
#> GSM531656 3 0.3816 0.8940 0.000 0.148 0.852
#> GSM531657 1 0.1832 0.8788 0.956 0.036 0.008
#> GSM531659 1 0.1711 0.8798 0.960 0.032 0.008
#> GSM531661 2 0.0237 0.9114 0.004 0.996 0.000
#> GSM531662 2 0.0829 0.9076 0.004 0.984 0.012
#> GSM531663 1 0.1832 0.8788 0.956 0.036 0.008
#> GSM531664 3 0.4423 0.8927 0.048 0.088 0.864
#> GSM531665 3 0.4121 0.8949 0.024 0.108 0.868
#> GSM531666 3 0.4658 0.8832 0.068 0.076 0.856
#> GSM531667 2 0.0000 0.9122 0.000 1.000 0.000
#> GSM531668 1 0.3129 0.8665 0.904 0.088 0.008
#> GSM531669 3 0.3995 0.9001 0.016 0.116 0.868
#> GSM531670 3 0.3816 0.8940 0.000 0.148 0.852
#> GSM531671 3 0.6282 0.5260 0.004 0.384 0.612
#> GSM531672 1 0.1315 0.8810 0.972 0.020 0.008
#> GSM531673 2 0.3445 0.8445 0.016 0.896 0.088
#> GSM531674 3 0.3995 0.9001 0.016 0.116 0.868
#> GSM531675 1 0.3412 0.8702 0.876 0.000 0.124
#> GSM531676 3 0.1643 0.8113 0.044 0.000 0.956
#> GSM531677 1 0.3752 0.8642 0.856 0.000 0.144
#> GSM531678 1 0.5730 0.8557 0.796 0.060 0.144
#> GSM531679 1 0.4605 0.8373 0.796 0.000 0.204
#> GSM531680 3 0.1411 0.8167 0.036 0.000 0.964
#> GSM531681 1 0.2261 0.8793 0.932 0.000 0.068
#> GSM531682 1 0.4605 0.8373 0.796 0.000 0.204
#> GSM531683 1 0.4995 0.8631 0.824 0.032 0.144
#> GSM531684 2 0.7505 0.5632 0.160 0.696 0.144
#> GSM531685 3 0.1411 0.8173 0.036 0.000 0.964
#> GSM531686 1 0.2356 0.8789 0.928 0.000 0.072
#> GSM531687 3 0.1643 0.8113 0.044 0.000 0.956
#> GSM531688 3 0.0424 0.8332 0.008 0.000 0.992
#> GSM531689 1 0.5760 0.7000 0.672 0.000 0.328
#> GSM531690 1 0.2261 0.8793 0.932 0.000 0.068
#> GSM531691 1 0.6396 0.7006 0.664 0.016 0.320
#> GSM531692 3 0.6481 0.5645 0.048 0.224 0.728
#> GSM531693 3 0.1170 0.8439 0.008 0.016 0.976
#> GSM531694 1 0.5913 0.8527 0.788 0.068 0.144
#> GSM531695 3 0.1289 0.8190 0.032 0.000 0.968
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM531600 3 0.1510 0.911 0.028 0.016 0.956 0.000
#> GSM531601 2 0.0592 0.969 0.016 0.984 0.000 0.000
#> GSM531605 1 0.1082 0.880 0.972 0.004 0.004 0.020
#> GSM531615 2 0.0592 0.969 0.016 0.984 0.000 0.000
#> GSM531617 2 0.1406 0.955 0.016 0.960 0.000 0.024
#> GSM531624 2 0.0336 0.972 0.008 0.992 0.000 0.000
#> GSM531627 2 0.0817 0.967 0.024 0.976 0.000 0.000
#> GSM531629 2 0.2623 0.909 0.028 0.908 0.000 0.064
#> GSM531631 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> GSM531634 2 0.0592 0.969 0.016 0.984 0.000 0.000
#> GSM531636 3 0.2319 0.896 0.036 0.040 0.924 0.000
#> GSM531637 2 0.0336 0.972 0.008 0.992 0.000 0.000
#> GSM531654 2 0.2647 0.885 0.120 0.880 0.000 0.000
#> GSM531655 3 0.6833 0.490 0.144 0.272 0.584 0.000
#> GSM531658 4 0.0000 0.934 0.000 0.000 0.000 1.000
#> GSM531660 4 0.4471 0.746 0.212 0.016 0.004 0.768
#> GSM531602 1 0.1953 0.874 0.940 0.012 0.004 0.044
#> GSM531603 1 0.1863 0.873 0.944 0.012 0.004 0.040
#> GSM531604 1 0.0967 0.878 0.976 0.004 0.004 0.016
#> GSM531606 1 0.1863 0.876 0.944 0.012 0.004 0.040
#> GSM531607 1 0.1953 0.874 0.940 0.012 0.004 0.044
#> GSM531608 2 0.0336 0.973 0.008 0.992 0.000 0.000
#> GSM531609 4 0.0000 0.934 0.000 0.000 0.000 1.000
#> GSM531610 4 0.0000 0.934 0.000 0.000 0.000 1.000
#> GSM531611 4 0.0000 0.934 0.000 0.000 0.000 1.000
#> GSM531612 4 0.0000 0.934 0.000 0.000 0.000 1.000
#> GSM531613 4 0.0000 0.934 0.000 0.000 0.000 1.000
#> GSM531614 4 0.0000 0.934 0.000 0.000 0.000 1.000
#> GSM531616 3 0.5915 0.336 0.040 0.400 0.560 0.000
#> GSM531618 4 0.5234 0.633 0.032 0.256 0.004 0.708
#> GSM531619 2 0.0336 0.972 0.008 0.992 0.000 0.000
#> GSM531620 2 0.0707 0.969 0.020 0.980 0.000 0.000
#> GSM531621 2 0.0707 0.969 0.020 0.980 0.000 0.000
#> GSM531622 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> GSM531623 2 0.0336 0.972 0.008 0.992 0.000 0.000
#> GSM531625 2 0.1929 0.943 0.036 0.940 0.024 0.000
#> GSM531626 2 0.2411 0.926 0.040 0.920 0.040 0.000
#> GSM531628 3 0.0336 0.922 0.000 0.008 0.992 0.000
#> GSM531630 2 0.0188 0.973 0.004 0.996 0.000 0.000
#> GSM531632 3 0.0376 0.921 0.004 0.004 0.992 0.000
#> GSM531633 2 0.0707 0.969 0.020 0.980 0.000 0.000
#> GSM531635 3 0.0804 0.922 0.012 0.008 0.980 0.000
#> GSM531638 2 0.2021 0.942 0.040 0.936 0.024 0.000
#> GSM531639 3 0.4332 0.776 0.040 0.160 0.800 0.000
#> GSM531640 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> GSM531641 4 0.0000 0.934 0.000 0.000 0.000 1.000
#> GSM531642 3 0.1724 0.909 0.032 0.020 0.948 0.000
#> GSM531643 3 0.0524 0.921 0.004 0.008 0.988 0.000
#> GSM531644 3 0.0524 0.921 0.004 0.008 0.988 0.000
#> GSM531645 4 0.0000 0.934 0.000 0.000 0.000 1.000
#> GSM531646 3 0.0524 0.922 0.004 0.008 0.988 0.000
#> GSM531647 3 0.0524 0.922 0.004 0.008 0.988 0.000
#> GSM531648 4 0.0895 0.927 0.020 0.000 0.004 0.976
#> GSM531649 3 0.0804 0.922 0.012 0.008 0.980 0.000
#> GSM531650 3 0.0336 0.922 0.000 0.008 0.992 0.000
#> GSM531651 2 0.0336 0.972 0.008 0.992 0.000 0.000
#> GSM531652 3 0.0672 0.921 0.008 0.008 0.984 0.000
#> GSM531653 3 0.0524 0.922 0.004 0.008 0.988 0.000
#> GSM531656 3 0.1724 0.909 0.032 0.020 0.948 0.000
#> GSM531657 4 0.0592 0.929 0.016 0.000 0.000 0.984
#> GSM531659 4 0.0469 0.931 0.012 0.000 0.000 0.988
#> GSM531661 2 0.0469 0.972 0.012 0.988 0.000 0.000
#> GSM531662 2 0.2125 0.931 0.076 0.920 0.004 0.000
#> GSM531663 4 0.0000 0.934 0.000 0.000 0.000 1.000
#> GSM531664 3 0.0188 0.919 0.004 0.000 0.996 0.000
#> GSM531665 3 0.3208 0.804 0.148 0.004 0.848 0.000
#> GSM531666 3 0.0336 0.919 0.008 0.000 0.992 0.000
#> GSM531667 2 0.0336 0.973 0.008 0.992 0.000 0.000
#> GSM531668 4 0.4077 0.784 0.184 0.012 0.004 0.800
#> GSM531669 3 0.0376 0.921 0.004 0.004 0.992 0.000
#> GSM531670 3 0.1724 0.909 0.032 0.020 0.948 0.000
#> GSM531671 3 0.5056 0.689 0.044 0.224 0.732 0.000
#> GSM531672 4 0.0779 0.928 0.016 0.000 0.004 0.980
#> GSM531673 1 0.4706 0.643 0.732 0.248 0.020 0.000
#> GSM531674 3 0.0376 0.921 0.004 0.004 0.992 0.000
#> GSM531675 1 0.4950 0.435 0.620 0.000 0.004 0.376
#> GSM531676 1 0.3801 0.727 0.780 0.000 0.220 0.000
#> GSM531677 1 0.2589 0.835 0.884 0.000 0.000 0.116
#> GSM531678 1 0.1396 0.880 0.960 0.004 0.004 0.032
#> GSM531679 1 0.1584 0.880 0.952 0.000 0.012 0.036
#> GSM531680 1 0.4454 0.618 0.692 0.000 0.308 0.000
#> GSM531681 4 0.2647 0.843 0.120 0.000 0.000 0.880
#> GSM531682 1 0.1584 0.880 0.952 0.000 0.012 0.036
#> GSM531683 1 0.1743 0.871 0.940 0.000 0.004 0.056
#> GSM531684 1 0.2010 0.861 0.932 0.060 0.004 0.004
#> GSM531685 1 0.4331 0.629 0.712 0.000 0.288 0.000
#> GSM531686 4 0.2760 0.836 0.128 0.000 0.000 0.872
#> GSM531687 1 0.3907 0.716 0.768 0.000 0.232 0.000
#> GSM531688 3 0.0336 0.919 0.008 0.000 0.992 0.000
#> GSM531689 1 0.1911 0.875 0.944 0.004 0.032 0.020
#> GSM531690 4 0.3157 0.827 0.144 0.000 0.004 0.852
#> GSM531691 1 0.1871 0.874 0.948 0.012 0.024 0.016
#> GSM531692 1 0.1661 0.862 0.944 0.004 0.052 0.000
#> GSM531693 3 0.0336 0.919 0.008 0.000 0.992 0.000
#> GSM531694 1 0.1953 0.874 0.940 0.012 0.004 0.044
#> GSM531695 3 0.3569 0.701 0.196 0.000 0.804 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM531600 3 0.3948 0.715 0.012 0.016 0.776 0.000 0.196
#> GSM531601 2 0.3242 0.636 0.000 0.784 0.000 0.000 0.216
#> GSM531605 1 0.3906 0.643 0.704 0.000 0.000 0.004 0.292
#> GSM531615 2 0.2561 0.757 0.000 0.856 0.000 0.000 0.144
#> GSM531617 2 0.3452 0.668 0.000 0.756 0.000 0.000 0.244
#> GSM531624 2 0.0703 0.798 0.000 0.976 0.000 0.000 0.024
#> GSM531627 2 0.3196 0.739 0.004 0.804 0.000 0.000 0.192
#> GSM531629 2 0.5215 0.347 0.000 0.576 0.000 0.052 0.372
#> GSM531631 2 0.0510 0.802 0.000 0.984 0.000 0.000 0.016
#> GSM531634 2 0.2329 0.777 0.000 0.876 0.000 0.000 0.124
#> GSM531636 3 0.4665 0.682 0.012 0.040 0.724 0.000 0.224
#> GSM531637 2 0.0703 0.798 0.000 0.976 0.000 0.000 0.024
#> GSM531654 2 0.5408 0.313 0.060 0.532 0.000 0.000 0.408
#> GSM531655 5 0.6086 0.109 0.020 0.112 0.264 0.000 0.604
#> GSM531658 4 0.2929 0.717 0.000 0.000 0.000 0.820 0.180
#> GSM531660 5 0.6585 0.226 0.080 0.052 0.000 0.340 0.528
#> GSM531602 1 0.4491 0.603 0.624 0.008 0.000 0.004 0.364
#> GSM531603 1 0.4505 0.600 0.620 0.008 0.000 0.004 0.368
#> GSM531604 1 0.1831 0.679 0.920 0.000 0.000 0.004 0.076
#> GSM531606 1 0.4449 0.611 0.636 0.008 0.000 0.004 0.352
#> GSM531607 1 0.4211 0.612 0.636 0.000 0.000 0.004 0.360
#> GSM531608 2 0.2424 0.779 0.000 0.868 0.000 0.000 0.132
#> GSM531609 4 0.0000 0.832 0.000 0.000 0.000 1.000 0.000
#> GSM531610 4 0.0000 0.832 0.000 0.000 0.000 1.000 0.000
#> GSM531611 4 0.0000 0.832 0.000 0.000 0.000 1.000 0.000
#> GSM531612 4 0.0000 0.832 0.000 0.000 0.000 1.000 0.000
#> GSM531613 4 0.0000 0.832 0.000 0.000 0.000 1.000 0.000
#> GSM531614 4 0.0000 0.832 0.000 0.000 0.000 1.000 0.000
#> GSM531616 2 0.6884 0.153 0.008 0.444 0.288 0.000 0.260
#> GSM531618 5 0.6836 0.136 0.008 0.212 0.000 0.364 0.416
#> GSM531619 2 0.0703 0.798 0.000 0.976 0.000 0.000 0.024
#> GSM531620 2 0.3333 0.764 0.004 0.788 0.000 0.000 0.208
#> GSM531621 2 0.2583 0.773 0.004 0.864 0.000 0.000 0.132
#> GSM531622 2 0.0510 0.803 0.000 0.984 0.000 0.000 0.016
#> GSM531623 2 0.0963 0.802 0.000 0.964 0.000 0.000 0.036
#> GSM531625 2 0.3888 0.693 0.008 0.756 0.008 0.000 0.228
#> GSM531626 2 0.4850 0.636 0.016 0.660 0.020 0.000 0.304
#> GSM531628 3 0.0290 0.783 0.000 0.000 0.992 0.000 0.008
#> GSM531630 2 0.0963 0.802 0.000 0.964 0.000 0.000 0.036
#> GSM531632 3 0.1197 0.776 0.000 0.000 0.952 0.000 0.048
#> GSM531633 2 0.2629 0.771 0.004 0.860 0.000 0.000 0.136
#> GSM531635 3 0.3421 0.725 0.008 0.000 0.788 0.000 0.204
#> GSM531638 2 0.4216 0.662 0.008 0.720 0.012 0.000 0.260
#> GSM531639 3 0.6006 0.513 0.012 0.124 0.604 0.000 0.260
#> GSM531640 2 0.0510 0.802 0.000 0.984 0.000 0.000 0.016
#> GSM531641 4 0.0000 0.832 0.000 0.000 0.000 1.000 0.000
#> GSM531642 3 0.5339 0.467 0.020 0.024 0.560 0.000 0.396
#> GSM531643 3 0.1270 0.779 0.000 0.000 0.948 0.000 0.052
#> GSM531644 3 0.2605 0.724 0.000 0.000 0.852 0.000 0.148
#> GSM531645 4 0.0000 0.832 0.000 0.000 0.000 1.000 0.000
#> GSM531646 3 0.1121 0.779 0.000 0.000 0.956 0.000 0.044
#> GSM531647 3 0.0510 0.783 0.000 0.000 0.984 0.000 0.016
#> GSM531648 4 0.3809 0.635 0.008 0.000 0.000 0.736 0.256
#> GSM531649 3 0.3160 0.730 0.004 0.000 0.808 0.000 0.188
#> GSM531650 3 0.0290 0.783 0.000 0.000 0.992 0.000 0.008
#> GSM531651 2 0.1121 0.801 0.000 0.956 0.000 0.000 0.044
#> GSM531652 3 0.3957 0.570 0.008 0.000 0.712 0.000 0.280
#> GSM531653 3 0.0510 0.783 0.000 0.000 0.984 0.000 0.016
#> GSM531656 3 0.4164 0.707 0.012 0.024 0.764 0.000 0.200
#> GSM531657 4 0.4106 0.629 0.020 0.000 0.000 0.724 0.256
#> GSM531659 4 0.4161 0.603 0.016 0.000 0.000 0.704 0.280
#> GSM531661 2 0.2280 0.785 0.000 0.880 0.000 0.000 0.120
#> GSM531662 2 0.5099 0.600 0.052 0.612 0.000 0.000 0.336
#> GSM531663 4 0.0000 0.832 0.000 0.000 0.000 1.000 0.000
#> GSM531664 3 0.0290 0.783 0.000 0.000 0.992 0.000 0.008
#> GSM531665 1 0.6912 -0.084 0.392 0.004 0.300 0.000 0.304
#> GSM531666 3 0.3861 0.599 0.008 0.000 0.728 0.000 0.264
#> GSM531667 2 0.2074 0.788 0.000 0.896 0.000 0.000 0.104
#> GSM531668 5 0.6358 0.295 0.080 0.048 0.000 0.292 0.580
#> GSM531669 3 0.0162 0.783 0.000 0.000 0.996 0.000 0.004
#> GSM531670 3 0.4164 0.707 0.012 0.024 0.764 0.000 0.200
#> GSM531671 5 0.7852 -0.169 0.236 0.072 0.328 0.000 0.364
#> GSM531672 4 0.3519 0.686 0.008 0.000 0.000 0.776 0.216
#> GSM531673 1 0.6113 0.168 0.528 0.108 0.008 0.000 0.356
#> GSM531674 3 0.0162 0.783 0.000 0.000 0.996 0.000 0.004
#> GSM531675 1 0.6186 0.300 0.512 0.000 0.000 0.336 0.152
#> GSM531676 1 0.3506 0.612 0.832 0.000 0.104 0.000 0.064
#> GSM531677 1 0.3527 0.667 0.828 0.000 0.000 0.056 0.116
#> GSM531678 1 0.1768 0.690 0.924 0.000 0.000 0.004 0.072
#> GSM531679 1 0.2233 0.687 0.892 0.000 0.000 0.004 0.104
#> GSM531680 1 0.4905 0.489 0.696 0.000 0.224 0.000 0.080
#> GSM531681 4 0.3532 0.680 0.092 0.000 0.000 0.832 0.076
#> GSM531682 1 0.2124 0.689 0.900 0.000 0.000 0.004 0.096
#> GSM531683 1 0.4196 0.615 0.640 0.000 0.000 0.004 0.356
#> GSM531684 1 0.4708 0.620 0.668 0.040 0.000 0.000 0.292
#> GSM531685 1 0.5222 0.459 0.680 0.000 0.196 0.000 0.124
#> GSM531686 4 0.3586 0.678 0.096 0.000 0.000 0.828 0.076
#> GSM531687 1 0.3641 0.601 0.820 0.000 0.120 0.000 0.060
#> GSM531688 3 0.3970 0.555 0.224 0.000 0.752 0.000 0.024
#> GSM531689 1 0.1443 0.668 0.948 0.000 0.004 0.004 0.044
#> GSM531690 4 0.5680 0.434 0.240 0.000 0.000 0.620 0.140
#> GSM531691 1 0.1798 0.663 0.928 0.000 0.004 0.004 0.064
#> GSM531692 1 0.2669 0.638 0.876 0.000 0.020 0.000 0.104
#> GSM531693 3 0.4100 0.593 0.192 0.000 0.764 0.000 0.044
#> GSM531694 1 0.4491 0.603 0.624 0.008 0.000 0.004 0.364
#> GSM531695 3 0.5267 0.125 0.428 0.000 0.524 0.000 0.048
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM531600 3 0.4408 0.4213 0.000 0.000 0.664 0.000 0.056 0.280
#> GSM531601 2 0.4019 0.4340 0.004 0.652 0.000 0.000 0.012 0.332
#> GSM531605 1 0.2553 0.4451 0.848 0.000 0.000 0.000 0.144 0.008
#> GSM531615 2 0.3998 0.6894 0.036 0.788 0.000 0.000 0.048 0.128
#> GSM531617 2 0.5005 0.6109 0.052 0.688 0.000 0.000 0.056 0.204
#> GSM531624 2 0.0909 0.7493 0.000 0.968 0.000 0.000 0.012 0.020
#> GSM531627 2 0.4513 0.6496 0.004 0.700 0.000 0.000 0.084 0.212
#> GSM531629 2 0.6730 0.2612 0.072 0.476 0.000 0.036 0.064 0.352
#> GSM531631 2 0.1074 0.7482 0.000 0.960 0.000 0.000 0.012 0.028
#> GSM531634 2 0.3424 0.7107 0.008 0.816 0.000 0.000 0.048 0.128
#> GSM531636 3 0.5412 0.2704 0.000 0.024 0.548 0.000 0.068 0.360
#> GSM531637 2 0.0909 0.7493 0.000 0.968 0.000 0.000 0.012 0.020
#> GSM531654 1 0.7354 -0.1030 0.368 0.312 0.000 0.000 0.160 0.160
#> GSM531655 6 0.6119 0.4919 0.092 0.056 0.148 0.000 0.052 0.652
#> GSM531658 4 0.4373 0.5452 0.016 0.000 0.000 0.640 0.016 0.328
#> GSM531660 1 0.6324 0.0807 0.480 0.000 0.000 0.164 0.036 0.320
#> GSM531602 1 0.0603 0.5500 0.980 0.000 0.000 0.004 0.016 0.000
#> GSM531603 1 0.0508 0.5497 0.984 0.000 0.000 0.004 0.012 0.000
#> GSM531604 5 0.4634 0.3183 0.400 0.000 0.000 0.000 0.556 0.044
#> GSM531606 1 0.2212 0.4942 0.880 0.000 0.000 0.000 0.112 0.008
#> GSM531607 1 0.0508 0.5497 0.984 0.000 0.000 0.004 0.012 0.000
#> GSM531608 2 0.4086 0.6896 0.000 0.752 0.000 0.000 0.124 0.124
#> GSM531609 4 0.0146 0.7670 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM531610 4 0.0000 0.7665 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531611 4 0.0146 0.7670 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM531612 4 0.0146 0.7670 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM531613 4 0.0000 0.7665 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531614 4 0.0146 0.7670 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM531616 2 0.7296 0.1778 0.004 0.396 0.176 0.000 0.116 0.308
#> GSM531618 6 0.6282 0.2806 0.072 0.112 0.012 0.132 0.024 0.648
#> GSM531619 2 0.0909 0.7493 0.000 0.968 0.000 0.000 0.012 0.020
#> GSM531620 2 0.5145 0.6436 0.004 0.612 0.000 0.000 0.112 0.272
#> GSM531621 2 0.3786 0.6946 0.004 0.772 0.000 0.000 0.052 0.172
#> GSM531622 2 0.1218 0.7510 0.004 0.956 0.000 0.000 0.012 0.028
#> GSM531623 2 0.1138 0.7529 0.004 0.960 0.000 0.000 0.012 0.024
#> GSM531625 2 0.5234 0.6046 0.004 0.644 0.012 0.000 0.108 0.232
#> GSM531626 2 0.5990 0.5190 0.004 0.532 0.020 0.000 0.140 0.304
#> GSM531628 3 0.0692 0.6234 0.000 0.000 0.976 0.000 0.004 0.020
#> GSM531630 2 0.1390 0.7486 0.004 0.948 0.000 0.000 0.016 0.032
#> GSM531632 3 0.1461 0.6192 0.000 0.000 0.940 0.000 0.044 0.016
#> GSM531633 2 0.3915 0.6852 0.004 0.756 0.000 0.000 0.052 0.188
#> GSM531635 3 0.4801 0.4041 0.000 0.000 0.632 0.000 0.088 0.280
#> GSM531638 2 0.5525 0.5163 0.004 0.568 0.008 0.000 0.112 0.308
#> GSM531639 3 0.6238 0.1376 0.000 0.064 0.472 0.000 0.092 0.372
#> GSM531640 2 0.1074 0.7482 0.000 0.960 0.000 0.000 0.012 0.028
#> GSM531641 4 0.0146 0.7670 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM531642 6 0.4550 0.4524 0.000 0.008 0.296 0.000 0.044 0.652
#> GSM531643 3 0.2848 0.4975 0.000 0.000 0.816 0.000 0.008 0.176
#> GSM531644 3 0.3992 -0.0345 0.000 0.000 0.624 0.000 0.012 0.364
#> GSM531645 4 0.0146 0.7670 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM531646 3 0.1341 0.6244 0.000 0.000 0.948 0.000 0.024 0.028
#> GSM531647 3 0.0405 0.6300 0.000 0.000 0.988 0.000 0.008 0.004
#> GSM531648 4 0.5288 0.3503 0.048 0.000 0.000 0.476 0.024 0.452
#> GSM531649 3 0.4024 0.4954 0.000 0.000 0.744 0.000 0.072 0.184
#> GSM531650 3 0.0692 0.6234 0.000 0.000 0.976 0.000 0.004 0.020
#> GSM531651 2 0.1138 0.7529 0.004 0.960 0.000 0.000 0.012 0.024
#> GSM531652 6 0.4195 0.4616 0.000 0.000 0.440 0.004 0.008 0.548
#> GSM531653 3 0.0405 0.6300 0.000 0.000 0.988 0.000 0.008 0.004
#> GSM531656 3 0.5272 0.2869 0.000 0.020 0.564 0.000 0.064 0.352
#> GSM531657 4 0.5519 0.4371 0.072 0.000 0.000 0.512 0.024 0.392
#> GSM531659 4 0.5710 0.4026 0.048 0.000 0.000 0.488 0.056 0.408
#> GSM531661 2 0.3873 0.7020 0.000 0.772 0.000 0.000 0.124 0.104
#> GSM531662 2 0.6282 0.3933 0.008 0.400 0.000 0.000 0.304 0.288
#> GSM531663 4 0.0000 0.7665 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531664 3 0.0692 0.6234 0.000 0.000 0.976 0.000 0.004 0.020
#> GSM531665 5 0.4491 0.4371 0.008 0.004 0.076 0.000 0.724 0.188
#> GSM531666 6 0.4456 0.4287 0.004 0.000 0.456 0.000 0.020 0.520
#> GSM531667 2 0.2733 0.7356 0.000 0.864 0.000 0.000 0.056 0.080
#> GSM531668 1 0.6322 0.0598 0.464 0.000 0.000 0.136 0.044 0.356
#> GSM531669 3 0.0713 0.6268 0.000 0.000 0.972 0.000 0.028 0.000
#> GSM531670 3 0.5243 0.2871 0.000 0.016 0.564 0.000 0.068 0.352
#> GSM531671 5 0.6913 -0.0330 0.000 0.080 0.180 0.000 0.424 0.316
#> GSM531672 4 0.5386 0.4743 0.072 0.000 0.000 0.548 0.020 0.360
#> GSM531673 5 0.5623 0.3026 0.052 0.064 0.000 0.000 0.580 0.304
#> GSM531674 3 0.0632 0.6279 0.000 0.000 0.976 0.000 0.024 0.000
#> GSM531675 1 0.6743 0.2184 0.524 0.000 0.000 0.196 0.132 0.148
#> GSM531676 5 0.3494 0.5817 0.252 0.000 0.012 0.000 0.736 0.000
#> GSM531677 1 0.5303 -0.0840 0.532 0.000 0.000 0.008 0.376 0.084
#> GSM531678 1 0.4648 -0.2659 0.496 0.000 0.000 0.000 0.464 0.040
#> GSM531679 1 0.4837 -0.1926 0.512 0.000 0.000 0.000 0.432 0.056
#> GSM531680 5 0.4923 0.5275 0.248 0.000 0.076 0.000 0.660 0.016
#> GSM531681 4 0.4060 0.5871 0.188 0.000 0.000 0.752 0.012 0.048
#> GSM531682 5 0.4945 0.1880 0.452 0.000 0.000 0.000 0.484 0.064
#> GSM531683 1 0.1577 0.5350 0.940 0.000 0.000 0.008 0.016 0.036
#> GSM531684 1 0.5259 0.3005 0.656 0.068 0.000 0.000 0.228 0.048
#> GSM531685 5 0.3840 0.5613 0.104 0.000 0.076 0.000 0.800 0.020
#> GSM531686 4 0.4475 0.5642 0.192 0.000 0.000 0.728 0.028 0.052
#> GSM531687 5 0.4002 0.5580 0.284 0.000 0.016 0.000 0.692 0.008
#> GSM531688 3 0.3584 0.3776 0.000 0.000 0.688 0.000 0.308 0.004
#> GSM531689 5 0.3741 0.5328 0.320 0.000 0.000 0.000 0.672 0.008
#> GSM531690 4 0.6402 0.1493 0.392 0.000 0.000 0.416 0.040 0.152
#> GSM531691 5 0.3778 0.5641 0.288 0.000 0.000 0.000 0.696 0.016
#> GSM531692 5 0.3807 0.5687 0.192 0.000 0.000 0.000 0.756 0.052
#> GSM531693 3 0.3534 0.4141 0.000 0.000 0.716 0.000 0.276 0.008
#> GSM531694 1 0.0603 0.5500 0.980 0.000 0.000 0.004 0.016 0.000
#> GSM531695 3 0.6061 -0.0823 0.148 0.000 0.460 0.000 0.372 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)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> SD:kmeans 93 0.11088 2
#> SD:kmeans 94 0.00294 3
#> SD:kmeans 93 0.00365 4
#> SD:kmeans 80 0.02737 5
#> SD:kmeans 55 0.05566 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "skmeans"]
# you can also extract it by
# res = res_list["SD:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 96 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 0.533 0.802 0.912 0.5031 0.498 0.498
#> 3 3 0.901 0.917 0.965 0.3372 0.734 0.512
#> 4 4 0.909 0.886 0.953 0.1195 0.845 0.574
#> 5 5 0.770 0.741 0.851 0.0585 0.893 0.615
#> 6 6 0.741 0.567 0.772 0.0410 0.965 0.834
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
#> GSM531600 2 0.0000 0.88751 0.000 1.000
#> GSM531601 2 0.7219 0.77476 0.200 0.800
#> GSM531605 1 0.5842 0.80348 0.860 0.140
#> GSM531615 2 0.7950 0.73051 0.240 0.760
#> GSM531617 2 0.7219 0.77476 0.200 0.800
#> GSM531624 2 0.7219 0.77476 0.200 0.800
#> GSM531627 2 0.0000 0.88751 0.000 1.000
#> GSM531629 1 0.9754 0.17241 0.592 0.408
#> GSM531631 2 0.7219 0.77476 0.200 0.800
#> GSM531634 2 0.7219 0.77476 0.200 0.800
#> GSM531636 2 0.0000 0.88751 0.000 1.000
#> GSM531637 2 0.7219 0.77476 0.200 0.800
#> GSM531654 2 0.8909 0.64331 0.308 0.692
#> GSM531655 2 0.9286 0.38337 0.344 0.656
#> GSM531658 1 0.0000 0.89938 1.000 0.000
#> GSM531660 1 0.0000 0.89938 1.000 0.000
#> GSM531602 1 0.0000 0.89938 1.000 0.000
#> GSM531603 1 0.0000 0.89938 1.000 0.000
#> GSM531604 1 0.7219 0.75224 0.800 0.200
#> GSM531606 1 0.0000 0.89938 1.000 0.000
#> GSM531607 1 0.0000 0.89938 1.000 0.000
#> GSM531608 2 0.8016 0.72645 0.244 0.756
#> GSM531609 1 0.0000 0.89938 1.000 0.000
#> GSM531610 1 0.0000 0.89938 1.000 0.000
#> GSM531611 1 0.0000 0.89938 1.000 0.000
#> GSM531612 1 0.0000 0.89938 1.000 0.000
#> GSM531613 1 0.0000 0.89938 1.000 0.000
#> GSM531614 1 0.0000 0.89938 1.000 0.000
#> GSM531616 2 0.0000 0.88751 0.000 1.000
#> GSM531618 1 0.9954 -0.02335 0.540 0.460
#> GSM531619 2 0.7219 0.77476 0.200 0.800
#> GSM531620 2 0.0000 0.88751 0.000 1.000
#> GSM531621 2 0.0000 0.88751 0.000 1.000
#> GSM531622 2 0.7219 0.77476 0.200 0.800
#> GSM531623 2 0.0000 0.88751 0.000 1.000
#> GSM531625 2 0.0000 0.88751 0.000 1.000
#> GSM531626 2 0.0000 0.88751 0.000 1.000
#> GSM531628 2 0.0000 0.88751 0.000 1.000
#> GSM531630 2 0.5842 0.81492 0.140 0.860
#> GSM531632 2 0.0000 0.88751 0.000 1.000
#> GSM531633 2 0.0000 0.88751 0.000 1.000
#> GSM531635 2 0.0000 0.88751 0.000 1.000
#> GSM531638 2 0.0000 0.88751 0.000 1.000
#> GSM531639 2 0.0000 0.88751 0.000 1.000
#> GSM531640 2 0.7219 0.77476 0.200 0.800
#> GSM531641 1 0.0000 0.89938 1.000 0.000
#> GSM531642 2 0.0000 0.88751 0.000 1.000
#> GSM531643 2 0.0000 0.88751 0.000 1.000
#> GSM531644 2 0.0000 0.88751 0.000 1.000
#> GSM531645 1 0.0000 0.89938 1.000 0.000
#> GSM531646 2 0.0000 0.88751 0.000 1.000
#> GSM531647 2 0.0000 0.88751 0.000 1.000
#> GSM531648 1 0.4939 0.79997 0.892 0.108
#> GSM531649 2 0.0000 0.88751 0.000 1.000
#> GSM531650 2 0.0000 0.88751 0.000 1.000
#> GSM531651 2 0.0000 0.88751 0.000 1.000
#> GSM531652 2 0.0672 0.88441 0.008 0.992
#> GSM531653 2 0.0000 0.88751 0.000 1.000
#> GSM531656 2 0.0000 0.88751 0.000 1.000
#> GSM531657 1 0.0000 0.89938 1.000 0.000
#> GSM531659 1 0.0000 0.89938 1.000 0.000
#> GSM531661 2 0.7219 0.77476 0.200 0.800
#> GSM531662 2 0.0000 0.88751 0.000 1.000
#> GSM531663 1 0.0000 0.89938 1.000 0.000
#> GSM531664 2 0.2778 0.85505 0.048 0.952
#> GSM531665 1 0.9732 0.41892 0.596 0.404
#> GSM531666 1 0.9850 0.38216 0.572 0.428
#> GSM531667 2 0.7219 0.77476 0.200 0.800
#> GSM531668 1 0.0000 0.89938 1.000 0.000
#> GSM531669 2 0.0000 0.88751 0.000 1.000
#> GSM531670 2 0.0000 0.88751 0.000 1.000
#> GSM531671 2 0.0000 0.88751 0.000 1.000
#> GSM531672 1 0.0000 0.89938 1.000 0.000
#> GSM531673 2 0.8909 0.47148 0.308 0.692
#> GSM531674 2 0.0000 0.88751 0.000 1.000
#> GSM531675 1 0.0000 0.89938 1.000 0.000
#> GSM531676 1 0.7219 0.75224 0.800 0.200
#> GSM531677 1 0.0000 0.89938 1.000 0.000
#> GSM531678 1 0.0000 0.89938 1.000 0.000
#> GSM531679 1 0.0000 0.89938 1.000 0.000
#> GSM531680 1 0.7219 0.75224 0.800 0.200
#> GSM531681 1 0.0000 0.89938 1.000 0.000
#> GSM531682 1 0.0000 0.89938 1.000 0.000
#> GSM531683 1 0.0000 0.89938 1.000 0.000
#> GSM531684 1 0.0000 0.89938 1.000 0.000
#> GSM531685 2 0.9954 -0.00187 0.460 0.540
#> GSM531686 1 0.0000 0.89938 1.000 0.000
#> GSM531687 1 0.7219 0.75224 0.800 0.200
#> GSM531688 1 0.9286 0.54909 0.656 0.344
#> GSM531689 1 0.7219 0.75224 0.800 0.200
#> GSM531690 1 0.0000 0.89938 1.000 0.000
#> GSM531691 1 0.7219 0.75224 0.800 0.200
#> GSM531692 2 0.9881 0.09095 0.436 0.564
#> GSM531693 2 0.2603 0.85803 0.044 0.956
#> GSM531694 1 0.0000 0.89938 1.000 0.000
#> GSM531695 1 0.7219 0.75224 0.800 0.200
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM531600 3 0.0000 0.976 0.000 0.000 1.000
#> GSM531601 2 0.0000 0.942 0.000 1.000 0.000
#> GSM531605 1 0.4346 0.783 0.816 0.184 0.000
#> GSM531615 2 0.0000 0.942 0.000 1.000 0.000
#> GSM531617 2 0.3482 0.825 0.128 0.872 0.000
#> GSM531624 2 0.0000 0.942 0.000 1.000 0.000
#> GSM531627 2 0.0000 0.942 0.000 1.000 0.000
#> GSM531629 2 0.4291 0.761 0.180 0.820 0.000
#> GSM531631 2 0.0000 0.942 0.000 1.000 0.000
#> GSM531634 2 0.0000 0.942 0.000 1.000 0.000
#> GSM531636 3 0.0237 0.972 0.000 0.004 0.996
#> GSM531637 2 0.0000 0.942 0.000 1.000 0.000
#> GSM531654 2 0.0000 0.942 0.000 1.000 0.000
#> GSM531655 3 0.6140 0.309 0.000 0.404 0.596
#> GSM531658 1 0.0000 0.969 1.000 0.000 0.000
#> GSM531660 1 0.0000 0.969 1.000 0.000 0.000
#> GSM531602 1 0.0000 0.969 1.000 0.000 0.000
#> GSM531603 1 0.0000 0.969 1.000 0.000 0.000
#> GSM531604 1 0.4555 0.761 0.800 0.200 0.000
#> GSM531606 1 0.0000 0.969 1.000 0.000 0.000
#> GSM531607 1 0.0000 0.969 1.000 0.000 0.000
#> GSM531608 2 0.0000 0.942 0.000 1.000 0.000
#> GSM531609 1 0.0000 0.969 1.000 0.000 0.000
#> GSM531610 1 0.0000 0.969 1.000 0.000 0.000
#> GSM531611 1 0.0000 0.969 1.000 0.000 0.000
#> GSM531612 1 0.0000 0.969 1.000 0.000 0.000
#> GSM531613 1 0.0000 0.969 1.000 0.000 0.000
#> GSM531614 1 0.0000 0.969 1.000 0.000 0.000
#> GSM531616 2 0.6140 0.366 0.000 0.596 0.404
#> GSM531618 1 0.4931 0.692 0.768 0.232 0.000
#> GSM531619 2 0.0000 0.942 0.000 1.000 0.000
#> GSM531620 2 0.0000 0.942 0.000 1.000 0.000
#> GSM531621 2 0.0000 0.942 0.000 1.000 0.000
#> GSM531622 2 0.0000 0.942 0.000 1.000 0.000
#> GSM531623 2 0.0000 0.942 0.000 1.000 0.000
#> GSM531625 2 0.0747 0.931 0.000 0.984 0.016
#> GSM531626 2 0.1643 0.909 0.000 0.956 0.044
#> GSM531628 3 0.0000 0.976 0.000 0.000 1.000
#> GSM531630 2 0.0000 0.942 0.000 1.000 0.000
#> GSM531632 3 0.0000 0.976 0.000 0.000 1.000
#> GSM531633 2 0.0000 0.942 0.000 1.000 0.000
#> GSM531635 3 0.0000 0.976 0.000 0.000 1.000
#> GSM531638 2 0.0892 0.928 0.000 0.980 0.020
#> GSM531639 3 0.5058 0.656 0.000 0.244 0.756
#> GSM531640 2 0.0000 0.942 0.000 1.000 0.000
#> GSM531641 1 0.0000 0.969 1.000 0.000 0.000
#> GSM531642 3 0.0000 0.976 0.000 0.000 1.000
#> GSM531643 3 0.0000 0.976 0.000 0.000 1.000
#> GSM531644 3 0.0000 0.976 0.000 0.000 1.000
#> GSM531645 1 0.0000 0.969 1.000 0.000 0.000
#> GSM531646 3 0.0000 0.976 0.000 0.000 1.000
#> GSM531647 3 0.0000 0.976 0.000 0.000 1.000
#> GSM531648 1 0.0000 0.969 1.000 0.000 0.000
#> GSM531649 3 0.0000 0.976 0.000 0.000 1.000
#> GSM531650 3 0.0000 0.976 0.000 0.000 1.000
#> GSM531651 2 0.0000 0.942 0.000 1.000 0.000
#> GSM531652 3 0.0000 0.976 0.000 0.000 1.000
#> GSM531653 3 0.0000 0.976 0.000 0.000 1.000
#> GSM531656 3 0.0000 0.976 0.000 0.000 1.000
#> GSM531657 1 0.0000 0.969 1.000 0.000 0.000
#> GSM531659 1 0.0000 0.969 1.000 0.000 0.000
#> GSM531661 2 0.0000 0.942 0.000 1.000 0.000
#> GSM531662 2 0.0000 0.942 0.000 1.000 0.000
#> GSM531663 1 0.0000 0.969 1.000 0.000 0.000
#> GSM531664 3 0.0000 0.976 0.000 0.000 1.000
#> GSM531665 3 0.0000 0.976 0.000 0.000 1.000
#> GSM531666 3 0.0000 0.976 0.000 0.000 1.000
#> GSM531667 2 0.0000 0.942 0.000 1.000 0.000
#> GSM531668 1 0.0000 0.969 1.000 0.000 0.000
#> GSM531669 3 0.0000 0.976 0.000 0.000 1.000
#> GSM531670 3 0.0000 0.976 0.000 0.000 1.000
#> GSM531671 2 0.6244 0.270 0.000 0.560 0.440
#> GSM531672 1 0.0000 0.969 1.000 0.000 0.000
#> GSM531673 2 0.0000 0.942 0.000 1.000 0.000
#> GSM531674 3 0.0000 0.976 0.000 0.000 1.000
#> GSM531675 1 0.0000 0.969 1.000 0.000 0.000
#> GSM531676 3 0.0000 0.976 0.000 0.000 1.000
#> GSM531677 1 0.0000 0.969 1.000 0.000 0.000
#> GSM531678 1 0.0000 0.969 1.000 0.000 0.000
#> GSM531679 1 0.0000 0.969 1.000 0.000 0.000
#> GSM531680 3 0.0000 0.976 0.000 0.000 1.000
#> GSM531681 1 0.0000 0.969 1.000 0.000 0.000
#> GSM531682 1 0.0000 0.969 1.000 0.000 0.000
#> GSM531683 1 0.0000 0.969 1.000 0.000 0.000
#> GSM531684 2 0.0237 0.939 0.004 0.996 0.000
#> GSM531685 3 0.0000 0.976 0.000 0.000 1.000
#> GSM531686 1 0.0000 0.969 1.000 0.000 0.000
#> GSM531687 3 0.0000 0.976 0.000 0.000 1.000
#> GSM531688 3 0.0000 0.976 0.000 0.000 1.000
#> GSM531689 1 0.4555 0.753 0.800 0.000 0.200
#> GSM531690 1 0.0000 0.969 1.000 0.000 0.000
#> GSM531691 1 0.4733 0.756 0.800 0.004 0.196
#> GSM531692 2 0.6180 0.336 0.000 0.584 0.416
#> GSM531693 3 0.0000 0.976 0.000 0.000 1.000
#> GSM531694 1 0.0000 0.969 1.000 0.000 0.000
#> GSM531695 3 0.0000 0.976 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM531600 3 0.0000 0.8994 0.000 0.000 1.000 0.000
#> GSM531601 2 0.0000 0.9786 0.000 1.000 0.000 0.000
#> GSM531605 1 0.0000 0.9279 1.000 0.000 0.000 0.000
#> GSM531615 2 0.0000 0.9786 0.000 1.000 0.000 0.000
#> GSM531617 2 0.3219 0.8043 0.000 0.836 0.000 0.164
#> GSM531624 2 0.0000 0.9786 0.000 1.000 0.000 0.000
#> GSM531627 2 0.0000 0.9786 0.000 1.000 0.000 0.000
#> GSM531629 2 0.3873 0.7168 0.000 0.772 0.000 0.228
#> GSM531631 2 0.0000 0.9786 0.000 1.000 0.000 0.000
#> GSM531634 2 0.0000 0.9786 0.000 1.000 0.000 0.000
#> GSM531636 3 0.0469 0.8910 0.000 0.012 0.988 0.000
#> GSM531637 2 0.0000 0.9786 0.000 1.000 0.000 0.000
#> GSM531654 2 0.1637 0.9275 0.060 0.940 0.000 0.000
#> GSM531655 3 0.6347 0.3147 0.068 0.384 0.548 0.000
#> GSM531658 4 0.0000 0.9896 0.000 0.000 0.000 1.000
#> GSM531660 4 0.0188 0.9867 0.004 0.000 0.000 0.996
#> GSM531602 1 0.0188 0.9270 0.996 0.000 0.000 0.004
#> GSM531603 1 0.0188 0.9270 0.996 0.000 0.000 0.004
#> GSM531604 1 0.0000 0.9279 1.000 0.000 0.000 0.000
#> GSM531606 1 0.0000 0.9279 1.000 0.000 0.000 0.000
#> GSM531607 1 0.0000 0.9279 1.000 0.000 0.000 0.000
#> GSM531608 2 0.0000 0.9786 0.000 1.000 0.000 0.000
#> GSM531609 4 0.0000 0.9896 0.000 0.000 0.000 1.000
#> GSM531610 4 0.0000 0.9896 0.000 0.000 0.000 1.000
#> GSM531611 4 0.0000 0.9896 0.000 0.000 0.000 1.000
#> GSM531612 4 0.0000 0.9896 0.000 0.000 0.000 1.000
#> GSM531613 4 0.0000 0.9896 0.000 0.000 0.000 1.000
#> GSM531614 4 0.0000 0.9896 0.000 0.000 0.000 1.000
#> GSM531616 3 0.4977 0.1790 0.000 0.460 0.540 0.000
#> GSM531618 4 0.0000 0.9896 0.000 0.000 0.000 1.000
#> GSM531619 2 0.0000 0.9786 0.000 1.000 0.000 0.000
#> GSM531620 2 0.0000 0.9786 0.000 1.000 0.000 0.000
#> GSM531621 2 0.0000 0.9786 0.000 1.000 0.000 0.000
#> GSM531622 2 0.0000 0.9786 0.000 1.000 0.000 0.000
#> GSM531623 2 0.0000 0.9786 0.000 1.000 0.000 0.000
#> GSM531625 2 0.0000 0.9786 0.000 1.000 0.000 0.000
#> GSM531626 2 0.1118 0.9462 0.000 0.964 0.036 0.000
#> GSM531628 3 0.0000 0.8994 0.000 0.000 1.000 0.000
#> GSM531630 2 0.0000 0.9786 0.000 1.000 0.000 0.000
#> GSM531632 3 0.0000 0.8994 0.000 0.000 1.000 0.000
#> GSM531633 2 0.0000 0.9786 0.000 1.000 0.000 0.000
#> GSM531635 3 0.0000 0.8994 0.000 0.000 1.000 0.000
#> GSM531638 2 0.0707 0.9618 0.000 0.980 0.020 0.000
#> GSM531639 3 0.3444 0.7284 0.000 0.184 0.816 0.000
#> GSM531640 2 0.0000 0.9786 0.000 1.000 0.000 0.000
#> GSM531641 4 0.0000 0.9896 0.000 0.000 0.000 1.000
#> GSM531642 3 0.0000 0.8994 0.000 0.000 1.000 0.000
#> GSM531643 3 0.0000 0.8994 0.000 0.000 1.000 0.000
#> GSM531644 3 0.0000 0.8994 0.000 0.000 1.000 0.000
#> GSM531645 4 0.0000 0.9896 0.000 0.000 0.000 1.000
#> GSM531646 3 0.0000 0.8994 0.000 0.000 1.000 0.000
#> GSM531647 3 0.0000 0.8994 0.000 0.000 1.000 0.000
#> GSM531648 4 0.0000 0.9896 0.000 0.000 0.000 1.000
#> GSM531649 3 0.0000 0.8994 0.000 0.000 1.000 0.000
#> GSM531650 3 0.0000 0.8994 0.000 0.000 1.000 0.000
#> GSM531651 2 0.0000 0.9786 0.000 1.000 0.000 0.000
#> GSM531652 3 0.1211 0.8672 0.000 0.000 0.960 0.040
#> GSM531653 3 0.0000 0.8994 0.000 0.000 1.000 0.000
#> GSM531656 3 0.0000 0.8994 0.000 0.000 1.000 0.000
#> GSM531657 4 0.0000 0.9896 0.000 0.000 0.000 1.000
#> GSM531659 4 0.0000 0.9896 0.000 0.000 0.000 1.000
#> GSM531661 2 0.0000 0.9786 0.000 1.000 0.000 0.000
#> GSM531662 2 0.0000 0.9786 0.000 1.000 0.000 0.000
#> GSM531663 4 0.0000 0.9896 0.000 0.000 0.000 1.000
#> GSM531664 3 0.0000 0.8994 0.000 0.000 1.000 0.000
#> GSM531665 3 0.4977 0.0659 0.460 0.000 0.540 0.000
#> GSM531666 3 0.0000 0.8994 0.000 0.000 1.000 0.000
#> GSM531667 2 0.0000 0.9786 0.000 1.000 0.000 0.000
#> GSM531668 4 0.0000 0.9896 0.000 0.000 0.000 1.000
#> GSM531669 3 0.0000 0.8994 0.000 0.000 1.000 0.000
#> GSM531670 3 0.0000 0.8994 0.000 0.000 1.000 0.000
#> GSM531671 3 0.4916 0.2843 0.000 0.424 0.576 0.000
#> GSM531672 4 0.0000 0.9896 0.000 0.000 0.000 1.000
#> GSM531673 1 0.4040 0.6801 0.752 0.248 0.000 0.000
#> GSM531674 3 0.0000 0.8994 0.000 0.000 1.000 0.000
#> GSM531675 1 0.4898 0.3162 0.584 0.000 0.000 0.416
#> GSM531676 1 0.3266 0.7958 0.832 0.000 0.168 0.000
#> GSM531677 1 0.0592 0.9200 0.984 0.000 0.000 0.016
#> GSM531678 1 0.0000 0.9279 1.000 0.000 0.000 0.000
#> GSM531679 1 0.0000 0.9279 1.000 0.000 0.000 0.000
#> GSM531680 1 0.3569 0.7630 0.804 0.000 0.196 0.000
#> GSM531681 4 0.1474 0.9456 0.052 0.000 0.000 0.948
#> GSM531682 1 0.0000 0.9279 1.000 0.000 0.000 0.000
#> GSM531683 1 0.0188 0.9270 0.996 0.000 0.000 0.004
#> GSM531684 1 0.0188 0.9263 0.996 0.004 0.000 0.000
#> GSM531685 1 0.3569 0.7630 0.804 0.000 0.196 0.000
#> GSM531686 4 0.2011 0.9189 0.080 0.000 0.000 0.920
#> GSM531687 1 0.3311 0.7916 0.828 0.000 0.172 0.000
#> GSM531688 3 0.0000 0.8994 0.000 0.000 1.000 0.000
#> GSM531689 1 0.0000 0.9279 1.000 0.000 0.000 0.000
#> GSM531690 4 0.1474 0.9456 0.052 0.000 0.000 0.948
#> GSM531691 1 0.0000 0.9279 1.000 0.000 0.000 0.000
#> GSM531692 1 0.0000 0.9279 1.000 0.000 0.000 0.000
#> GSM531693 3 0.0000 0.8994 0.000 0.000 1.000 0.000
#> GSM531694 1 0.0188 0.9270 0.996 0.000 0.000 0.004
#> GSM531695 3 0.4843 0.2764 0.396 0.000 0.604 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM531600 3 0.2020 0.901 0.100 0.000 0.900 0.000 0.000
#> GSM531601 2 0.3195 0.816 0.040 0.856 0.004 0.000 0.100
#> GSM531605 5 0.2516 0.471 0.140 0.000 0.000 0.000 0.860
#> GSM531615 2 0.2471 0.808 0.000 0.864 0.000 0.000 0.136
#> GSM531617 2 0.2798 0.799 0.008 0.852 0.000 0.000 0.140
#> GSM531624 2 0.0000 0.892 0.000 1.000 0.000 0.000 0.000
#> GSM531627 2 0.1671 0.881 0.076 0.924 0.000 0.000 0.000
#> GSM531629 2 0.5426 0.605 0.012 0.692 0.000 0.144 0.152
#> GSM531631 2 0.0000 0.892 0.000 1.000 0.000 0.000 0.000
#> GSM531634 2 0.2304 0.836 0.008 0.892 0.000 0.000 0.100
#> GSM531636 3 0.2677 0.878 0.112 0.016 0.872 0.000 0.000
#> GSM531637 2 0.0000 0.892 0.000 1.000 0.000 0.000 0.000
#> GSM531654 5 0.5670 0.159 0.084 0.388 0.000 0.000 0.528
#> GSM531655 5 0.7977 0.181 0.116 0.176 0.304 0.000 0.404
#> GSM531658 4 0.0290 0.893 0.008 0.000 0.000 0.992 0.000
#> GSM531660 5 0.5638 0.256 0.016 0.048 0.000 0.384 0.552
#> GSM531602 5 0.0510 0.650 0.016 0.000 0.000 0.000 0.984
#> GSM531603 5 0.0000 0.649 0.000 0.000 0.000 0.000 1.000
#> GSM531604 1 0.4227 0.457 0.580 0.000 0.000 0.000 0.420
#> GSM531606 5 0.0880 0.647 0.032 0.000 0.000 0.000 0.968
#> GSM531607 5 0.0510 0.650 0.016 0.000 0.000 0.000 0.984
#> GSM531608 2 0.1831 0.861 0.076 0.920 0.000 0.000 0.004
#> GSM531609 4 0.0000 0.897 0.000 0.000 0.000 1.000 0.000
#> GSM531610 4 0.0000 0.897 0.000 0.000 0.000 1.000 0.000
#> GSM531611 4 0.0000 0.897 0.000 0.000 0.000 1.000 0.000
#> GSM531612 4 0.0000 0.897 0.000 0.000 0.000 1.000 0.000
#> GSM531613 4 0.0000 0.897 0.000 0.000 0.000 1.000 0.000
#> GSM531614 4 0.0000 0.897 0.000 0.000 0.000 1.000 0.000
#> GSM531616 2 0.5884 0.139 0.100 0.480 0.420 0.000 0.000
#> GSM531618 4 0.4748 0.658 0.040 0.056 0.000 0.768 0.136
#> GSM531619 2 0.0000 0.892 0.000 1.000 0.000 0.000 0.000
#> GSM531620 2 0.1478 0.885 0.064 0.936 0.000 0.000 0.000
#> GSM531621 2 0.1608 0.882 0.072 0.928 0.000 0.000 0.000
#> GSM531622 2 0.0000 0.892 0.000 1.000 0.000 0.000 0.000
#> GSM531623 2 0.0963 0.891 0.036 0.964 0.000 0.000 0.000
#> GSM531625 2 0.2361 0.865 0.096 0.892 0.012 0.000 0.000
#> GSM531626 2 0.3130 0.843 0.096 0.856 0.048 0.000 0.000
#> GSM531628 3 0.0290 0.929 0.008 0.000 0.992 0.000 0.000
#> GSM531630 2 0.0703 0.892 0.024 0.976 0.000 0.000 0.000
#> GSM531632 3 0.2280 0.849 0.120 0.000 0.880 0.000 0.000
#> GSM531633 2 0.1608 0.882 0.072 0.928 0.000 0.000 0.000
#> GSM531635 3 0.1121 0.926 0.044 0.000 0.956 0.000 0.000
#> GSM531638 2 0.2983 0.847 0.096 0.864 0.040 0.000 0.000
#> GSM531639 3 0.4593 0.727 0.124 0.128 0.748 0.000 0.000
#> GSM531640 2 0.0000 0.892 0.000 1.000 0.000 0.000 0.000
#> GSM531641 4 0.0000 0.897 0.000 0.000 0.000 1.000 0.000
#> GSM531642 3 0.1121 0.926 0.044 0.000 0.956 0.000 0.000
#> GSM531643 3 0.0162 0.930 0.004 0.000 0.996 0.000 0.000
#> GSM531644 3 0.0963 0.926 0.036 0.000 0.964 0.000 0.000
#> GSM531645 4 0.0000 0.897 0.000 0.000 0.000 1.000 0.000
#> GSM531646 3 0.0510 0.928 0.016 0.000 0.984 0.000 0.000
#> GSM531647 3 0.0404 0.928 0.012 0.000 0.988 0.000 0.000
#> GSM531648 4 0.1041 0.876 0.032 0.000 0.000 0.964 0.004
#> GSM531649 3 0.1908 0.904 0.092 0.000 0.908 0.000 0.000
#> GSM531650 3 0.0162 0.929 0.004 0.000 0.996 0.000 0.000
#> GSM531651 2 0.1043 0.891 0.040 0.960 0.000 0.000 0.000
#> GSM531652 3 0.1282 0.921 0.044 0.000 0.952 0.000 0.004
#> GSM531653 3 0.0794 0.929 0.028 0.000 0.972 0.000 0.000
#> GSM531656 3 0.1965 0.900 0.096 0.000 0.904 0.000 0.000
#> GSM531657 4 0.0000 0.897 0.000 0.000 0.000 1.000 0.000
#> GSM531659 4 0.0000 0.897 0.000 0.000 0.000 1.000 0.000
#> GSM531661 2 0.1671 0.862 0.076 0.924 0.000 0.000 0.000
#> GSM531662 2 0.4161 0.755 0.208 0.752 0.000 0.000 0.040
#> GSM531663 4 0.0000 0.897 0.000 0.000 0.000 1.000 0.000
#> GSM531664 3 0.0510 0.926 0.016 0.000 0.984 0.000 0.000
#> GSM531665 1 0.3728 0.547 0.748 0.000 0.244 0.000 0.008
#> GSM531666 3 0.0963 0.926 0.036 0.000 0.964 0.000 0.000
#> GSM531667 2 0.0000 0.892 0.000 1.000 0.000 0.000 0.000
#> GSM531668 5 0.5262 0.235 0.016 0.024 0.000 0.400 0.560
#> GSM531669 3 0.2127 0.862 0.108 0.000 0.892 0.000 0.000
#> GSM531670 3 0.1965 0.900 0.096 0.000 0.904 0.000 0.000
#> GSM531671 1 0.5814 0.367 0.584 0.128 0.288 0.000 0.000
#> GSM531672 4 0.0290 0.893 0.008 0.000 0.000 0.992 0.000
#> GSM531673 1 0.4486 0.439 0.748 0.080 0.000 0.000 0.172
#> GSM531674 3 0.1270 0.907 0.052 0.000 0.948 0.000 0.000
#> GSM531675 4 0.5026 0.366 0.040 0.000 0.000 0.588 0.372
#> GSM531676 1 0.4372 0.661 0.756 0.000 0.072 0.000 0.172
#> GSM531677 1 0.6000 0.445 0.460 0.000 0.000 0.112 0.428
#> GSM531678 1 0.4305 0.490 0.512 0.000 0.000 0.000 0.488
#> GSM531679 1 0.4304 0.497 0.516 0.000 0.000 0.000 0.484
#> GSM531680 1 0.4933 0.659 0.688 0.000 0.076 0.000 0.236
#> GSM531681 4 0.3636 0.623 0.000 0.000 0.000 0.728 0.272
#> GSM531682 1 0.4304 0.497 0.516 0.000 0.000 0.000 0.484
#> GSM531683 5 0.0510 0.650 0.016 0.000 0.000 0.000 0.984
#> GSM531684 5 0.4254 0.510 0.148 0.080 0.000 0.000 0.772
#> GSM531685 1 0.3575 0.650 0.824 0.000 0.056 0.000 0.120
#> GSM531686 4 0.3636 0.623 0.000 0.000 0.000 0.728 0.272
#> GSM531687 1 0.4877 0.660 0.692 0.000 0.072 0.000 0.236
#> GSM531688 1 0.4126 0.398 0.620 0.000 0.380 0.000 0.000
#> GSM531689 1 0.4015 0.615 0.652 0.000 0.000 0.000 0.348
#> GSM531690 4 0.3661 0.618 0.000 0.000 0.000 0.724 0.276
#> GSM531691 1 0.3999 0.617 0.656 0.000 0.000 0.000 0.344
#> GSM531692 1 0.2690 0.623 0.844 0.000 0.000 0.000 0.156
#> GSM531693 1 0.4227 0.303 0.580 0.000 0.420 0.000 0.000
#> GSM531694 5 0.0510 0.650 0.016 0.000 0.000 0.000 0.984
#> GSM531695 1 0.5904 0.616 0.600 0.000 0.200 0.000 0.200
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM531600 3 0.3565 0.6088 0.000 0.000 0.692 0.000 0.004 0.304
#> GSM531601 2 0.3558 0.4877 0.000 0.736 0.000 0.000 0.016 0.248
#> GSM531605 1 0.2053 0.5924 0.888 0.000 0.000 0.000 0.108 0.004
#> GSM531615 2 0.3599 0.6399 0.076 0.820 0.000 0.000 0.020 0.084
#> GSM531617 2 0.4589 0.5787 0.104 0.752 0.000 0.004 0.032 0.108
#> GSM531624 2 0.0000 0.7330 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531627 2 0.3351 0.5455 0.000 0.712 0.000 0.000 0.000 0.288
#> GSM531629 2 0.6373 0.3952 0.128 0.628 0.000 0.084 0.036 0.124
#> GSM531631 2 0.0000 0.7330 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531634 2 0.2203 0.6865 0.004 0.896 0.000 0.000 0.016 0.084
#> GSM531636 3 0.3841 0.5510 0.000 0.004 0.616 0.000 0.000 0.380
#> GSM531637 2 0.0000 0.7330 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531654 1 0.6882 0.1373 0.492 0.152 0.000 0.000 0.124 0.232
#> GSM531655 6 0.7706 0.0641 0.304 0.120 0.216 0.000 0.016 0.344
#> GSM531658 4 0.1838 0.8206 0.000 0.000 0.000 0.916 0.016 0.068
#> GSM531660 1 0.5577 0.4130 0.612 0.008 0.000 0.272 0.032 0.076
#> GSM531602 1 0.0000 0.7023 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531603 1 0.0260 0.7018 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM531604 5 0.5428 0.3074 0.320 0.000 0.000 0.000 0.540 0.140
#> GSM531606 1 0.0993 0.6938 0.964 0.000 0.000 0.000 0.012 0.024
#> GSM531607 1 0.0000 0.7023 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531608 2 0.4441 0.4251 0.000 0.700 0.000 0.000 0.092 0.208
#> GSM531609 4 0.0146 0.8470 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM531610 4 0.0000 0.8470 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531611 4 0.0000 0.8470 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531612 4 0.0146 0.8470 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM531613 4 0.0000 0.8470 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531614 4 0.0000 0.8470 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531616 6 0.5833 -0.1109 0.000 0.416 0.144 0.000 0.008 0.432
#> GSM531618 4 0.7129 0.4006 0.084 0.084 0.012 0.512 0.032 0.276
#> GSM531619 2 0.0000 0.7330 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531620 2 0.3634 0.5651 0.000 0.696 0.000 0.000 0.008 0.296
#> GSM531621 2 0.3175 0.5816 0.000 0.744 0.000 0.000 0.000 0.256
#> GSM531622 2 0.0146 0.7329 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM531623 2 0.1556 0.7113 0.000 0.920 0.000 0.000 0.000 0.080
#> GSM531625 2 0.4161 0.2549 0.000 0.540 0.012 0.000 0.000 0.448
#> GSM531626 2 0.4456 0.2149 0.000 0.524 0.028 0.000 0.000 0.448
#> GSM531628 3 0.0820 0.7392 0.000 0.000 0.972 0.000 0.016 0.012
#> GSM531630 2 0.0260 0.7329 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM531632 3 0.2568 0.6963 0.000 0.000 0.876 0.000 0.068 0.056
#> GSM531633 2 0.3175 0.5816 0.000 0.744 0.000 0.000 0.000 0.256
#> GSM531635 3 0.3398 0.6563 0.000 0.000 0.740 0.000 0.008 0.252
#> GSM531638 2 0.4736 0.2197 0.000 0.528 0.032 0.000 0.008 0.432
#> GSM531639 3 0.4338 0.3850 0.000 0.020 0.492 0.000 0.000 0.488
#> GSM531640 2 0.0146 0.7328 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM531641 4 0.0146 0.8470 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM531642 3 0.3874 0.5867 0.000 0.000 0.636 0.000 0.008 0.356
#> GSM531643 3 0.0458 0.7400 0.000 0.000 0.984 0.000 0.000 0.016
#> GSM531644 3 0.3052 0.6400 0.000 0.000 0.780 0.000 0.004 0.216
#> GSM531645 4 0.0146 0.8470 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM531646 3 0.1967 0.7298 0.000 0.000 0.904 0.000 0.012 0.084
#> GSM531647 3 0.0603 0.7396 0.000 0.000 0.980 0.000 0.016 0.004
#> GSM531648 4 0.4062 0.6759 0.000 0.000 0.012 0.724 0.028 0.236
#> GSM531649 3 0.3707 0.6018 0.000 0.000 0.680 0.000 0.008 0.312
#> GSM531650 3 0.0363 0.7404 0.000 0.000 0.988 0.000 0.000 0.012
#> GSM531651 2 0.1556 0.7113 0.000 0.920 0.000 0.000 0.000 0.080
#> GSM531652 3 0.3819 0.5688 0.000 0.000 0.700 0.000 0.020 0.280
#> GSM531653 3 0.1398 0.7397 0.000 0.000 0.940 0.000 0.008 0.052
#> GSM531656 3 0.3867 0.6054 0.000 0.000 0.660 0.000 0.012 0.328
#> GSM531657 4 0.1391 0.8358 0.000 0.000 0.000 0.944 0.016 0.040
#> GSM531659 4 0.1151 0.8388 0.000 0.000 0.000 0.956 0.012 0.032
#> GSM531661 2 0.4406 0.4441 0.000 0.696 0.000 0.000 0.080 0.224
#> GSM531662 6 0.5803 -0.0575 0.008 0.404 0.000 0.000 0.140 0.448
#> GSM531663 4 0.0000 0.8470 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531664 3 0.0909 0.7382 0.000 0.000 0.968 0.000 0.020 0.012
#> GSM531665 5 0.3384 0.5057 0.000 0.000 0.120 0.000 0.812 0.068
#> GSM531666 3 0.3558 0.6083 0.000 0.000 0.736 0.000 0.016 0.248
#> GSM531667 2 0.0260 0.7316 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM531668 1 0.5812 0.4374 0.620 0.004 0.000 0.208 0.044 0.124
#> GSM531669 3 0.1745 0.7142 0.000 0.000 0.920 0.000 0.068 0.012
#> GSM531670 3 0.3867 0.6054 0.000 0.000 0.660 0.000 0.012 0.328
#> GSM531671 6 0.6743 0.2318 0.000 0.044 0.300 0.000 0.244 0.412
#> GSM531672 4 0.2375 0.8122 0.016 0.000 0.000 0.896 0.020 0.068
#> GSM531673 6 0.5619 0.1529 0.084 0.024 0.000 0.000 0.376 0.516
#> GSM531674 3 0.1204 0.7265 0.000 0.000 0.944 0.000 0.056 0.000
#> GSM531675 4 0.5741 0.1437 0.428 0.000 0.000 0.460 0.084 0.028
#> GSM531676 5 0.2911 0.6768 0.144 0.000 0.024 0.000 0.832 0.000
#> GSM531677 1 0.5434 -0.3867 0.460 0.000 0.000 0.060 0.456 0.024
#> GSM531678 5 0.4399 0.3793 0.460 0.000 0.000 0.000 0.516 0.024
#> GSM531679 5 0.4338 0.3427 0.488 0.000 0.000 0.000 0.492 0.020
#> GSM531680 5 0.3317 0.6756 0.156 0.000 0.032 0.000 0.808 0.004
#> GSM531681 4 0.4260 0.5657 0.268 0.000 0.000 0.692 0.024 0.016
#> GSM531682 5 0.4408 0.3393 0.488 0.000 0.000 0.000 0.488 0.024
#> GSM531683 1 0.0748 0.6896 0.976 0.000 0.000 0.004 0.004 0.016
#> GSM531684 1 0.5879 0.4275 0.636 0.108 0.000 0.000 0.116 0.140
#> GSM531685 5 0.3083 0.6315 0.052 0.000 0.060 0.000 0.860 0.028
#> GSM531686 4 0.4491 0.5459 0.272 0.000 0.000 0.676 0.036 0.016
#> GSM531687 5 0.3203 0.6754 0.160 0.000 0.024 0.000 0.812 0.004
#> GSM531688 5 0.4381 0.1205 0.004 0.000 0.456 0.000 0.524 0.016
#> GSM531689 5 0.3217 0.6528 0.224 0.000 0.000 0.000 0.768 0.008
#> GSM531690 4 0.4919 0.4788 0.320 0.000 0.000 0.616 0.040 0.024
#> GSM531691 5 0.3348 0.6563 0.216 0.000 0.000 0.000 0.768 0.016
#> GSM531692 5 0.3548 0.5500 0.068 0.000 0.000 0.000 0.796 0.136
#> GSM531693 3 0.4333 -0.0368 0.000 0.000 0.512 0.000 0.468 0.020
#> GSM531694 1 0.0000 0.7023 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531695 5 0.5541 0.4567 0.144 0.000 0.304 0.000 0.548 0.004
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> SD:skmeans 88 0.09371 2
#> SD:skmeans 92 0.00380 3
#> SD:skmeans 90 0.00526 4
#> SD:skmeans 80 0.01023 5
#> SD:skmeans 68 0.02521 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "pam"]
# you can also extract it by
# res = res_list["SD:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 96 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.505 0.882 0.905 0.4491 0.558 0.558
#> 3 3 0.632 0.870 0.907 0.4293 0.792 0.630
#> 4 4 0.677 0.820 0.878 0.1267 0.915 0.764
#> 5 5 0.777 0.808 0.902 0.0880 0.871 0.584
#> 6 6 0.838 0.813 0.888 0.0434 0.937 0.723
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
#> GSM531600 2 0.6148 0.907 0.152 0.848
#> GSM531601 2 0.3114 0.867 0.056 0.944
#> GSM531605 1 0.1184 0.870 0.984 0.016
#> GSM531615 2 0.2778 0.862 0.048 0.952
#> GSM531617 2 0.1184 0.871 0.016 0.984
#> GSM531624 2 0.6887 0.901 0.184 0.816
#> GSM531627 2 0.6887 0.902 0.184 0.816
#> GSM531629 2 0.2778 0.862 0.048 0.952
#> GSM531631 2 0.6887 0.902 0.184 0.816
#> GSM531634 2 0.6712 0.902 0.176 0.824
#> GSM531636 2 0.6148 0.907 0.152 0.848
#> GSM531637 2 0.6887 0.902 0.184 0.816
#> GSM531654 2 0.2778 0.862 0.048 0.952
#> GSM531655 2 0.6148 0.907 0.152 0.848
#> GSM531658 2 0.1184 0.871 0.016 0.984
#> GSM531660 2 0.2778 0.862 0.048 0.952
#> GSM531602 1 0.6148 0.874 0.848 0.152
#> GSM531603 1 0.6148 0.874 0.848 0.152
#> GSM531604 1 0.0000 0.875 1.000 0.000
#> GSM531606 1 0.6148 0.874 0.848 0.152
#> GSM531607 1 0.6148 0.874 0.848 0.152
#> GSM531608 2 0.2778 0.862 0.048 0.952
#> GSM531609 2 0.1184 0.871 0.016 0.984
#> GSM531610 2 0.1184 0.871 0.016 0.984
#> GSM531611 2 0.0000 0.877 0.000 1.000
#> GSM531612 2 0.1184 0.871 0.016 0.984
#> GSM531613 2 0.1414 0.868 0.020 0.980
#> GSM531614 2 0.1184 0.871 0.016 0.984
#> GSM531616 2 0.6148 0.907 0.152 0.848
#> GSM531618 2 0.1184 0.871 0.016 0.984
#> GSM531619 2 0.6801 0.903 0.180 0.820
#> GSM531620 2 0.6801 0.903 0.180 0.820
#> GSM531621 2 0.6887 0.902 0.184 0.816
#> GSM531622 2 0.5737 0.907 0.136 0.864
#> GSM531623 2 0.6887 0.902 0.184 0.816
#> GSM531625 2 0.6887 0.902 0.184 0.816
#> GSM531626 2 0.6148 0.907 0.152 0.848
#> GSM531628 2 0.6148 0.907 0.152 0.848
#> GSM531630 2 0.6887 0.902 0.184 0.816
#> GSM531632 2 0.6148 0.907 0.152 0.848
#> GSM531633 2 0.6148 0.907 0.152 0.848
#> GSM531635 2 0.6148 0.907 0.152 0.848
#> GSM531638 2 0.6148 0.907 0.152 0.848
#> GSM531639 2 0.6148 0.907 0.152 0.848
#> GSM531640 2 0.1184 0.881 0.016 0.984
#> GSM531641 2 0.1184 0.871 0.016 0.984
#> GSM531642 2 0.6148 0.907 0.152 0.848
#> GSM531643 2 0.6148 0.907 0.152 0.848
#> GSM531644 2 0.3274 0.896 0.060 0.940
#> GSM531645 2 0.1184 0.871 0.016 0.984
#> GSM531646 2 0.6148 0.907 0.152 0.848
#> GSM531647 2 0.6148 0.907 0.152 0.848
#> GSM531648 2 0.1184 0.871 0.016 0.984
#> GSM531649 2 0.6148 0.907 0.152 0.848
#> GSM531650 2 0.6148 0.907 0.152 0.848
#> GSM531651 2 0.6887 0.902 0.184 0.816
#> GSM531652 2 0.0000 0.877 0.000 1.000
#> GSM531653 2 0.6148 0.907 0.152 0.848
#> GSM531656 2 0.6148 0.907 0.152 0.848
#> GSM531657 2 0.1184 0.871 0.016 0.984
#> GSM531659 2 0.0938 0.872 0.012 0.988
#> GSM531661 2 0.6887 0.902 0.184 0.816
#> GSM531662 2 0.6887 0.902 0.184 0.816
#> GSM531663 2 0.1184 0.871 0.016 0.984
#> GSM531664 1 0.6438 0.759 0.836 0.164
#> GSM531665 1 0.6973 0.883 0.812 0.188
#> GSM531666 2 0.6048 0.907 0.148 0.852
#> GSM531667 2 0.1184 0.881 0.016 0.984
#> GSM531668 2 0.2778 0.862 0.048 0.952
#> GSM531669 1 0.8386 0.566 0.732 0.268
#> GSM531670 2 0.6148 0.907 0.152 0.848
#> GSM531671 2 0.6531 0.903 0.168 0.832
#> GSM531672 2 0.1184 0.871 0.016 0.984
#> GSM531673 2 0.6887 0.902 0.184 0.816
#> GSM531674 1 0.2948 0.877 0.948 0.052
#> GSM531675 1 0.6887 0.878 0.816 0.184
#> GSM531676 1 0.2778 0.878 0.952 0.048
#> GSM531677 1 0.6887 0.878 0.816 0.184
#> GSM531678 1 0.6623 0.886 0.828 0.172
#> GSM531679 1 0.6887 0.878 0.816 0.184
#> GSM531680 1 0.2948 0.877 0.948 0.052
#> GSM531681 1 0.6887 0.878 0.816 0.184
#> GSM531682 1 0.6887 0.878 0.816 0.184
#> GSM531683 1 0.6148 0.874 0.848 0.152
#> GSM531684 1 0.1414 0.879 0.980 0.020
#> GSM531685 1 0.2778 0.878 0.952 0.048
#> GSM531686 1 0.6887 0.878 0.816 0.184
#> GSM531687 1 0.2948 0.877 0.948 0.052
#> GSM531688 1 0.2778 0.878 0.952 0.048
#> GSM531689 1 0.2778 0.878 0.952 0.048
#> GSM531690 1 0.6887 0.878 0.816 0.184
#> GSM531691 1 0.2778 0.878 0.952 0.048
#> GSM531692 1 0.1184 0.870 0.984 0.016
#> GSM531693 1 0.2948 0.877 0.948 0.052
#> GSM531694 1 0.6148 0.874 0.848 0.152
#> GSM531695 1 0.3274 0.881 0.940 0.060
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM531600 3 0.4235 0.850 0.176 0.000 0.824
#> GSM531601 3 0.4654 0.785 0.000 0.208 0.792
#> GSM531605 1 0.5223 0.866 0.800 0.024 0.176
#> GSM531615 2 0.2711 0.872 0.000 0.912 0.088
#> GSM531617 3 0.0237 0.890 0.000 0.004 0.996
#> GSM531624 2 0.0000 0.962 0.000 1.000 0.000
#> GSM531627 2 0.0000 0.962 0.000 1.000 0.000
#> GSM531629 3 0.1031 0.883 0.000 0.024 0.976
#> GSM531631 2 0.0000 0.962 0.000 1.000 0.000
#> GSM531634 2 0.0747 0.951 0.000 0.984 0.016
#> GSM531636 3 0.4802 0.823 0.020 0.156 0.824
#> GSM531637 2 0.0000 0.962 0.000 1.000 0.000
#> GSM531654 2 0.4235 0.752 0.000 0.824 0.176
#> GSM531655 3 0.0592 0.891 0.000 0.012 0.988
#> GSM531658 3 0.1031 0.886 0.024 0.000 0.976
#> GSM531660 3 0.1031 0.883 0.000 0.024 0.976
#> GSM531602 1 0.5223 0.866 0.800 0.024 0.176
#> GSM531603 1 0.5223 0.866 0.800 0.024 0.176
#> GSM531604 1 0.4968 0.755 0.800 0.188 0.012
#> GSM531606 1 0.5223 0.866 0.800 0.024 0.176
#> GSM531607 1 0.5223 0.866 0.800 0.024 0.176
#> GSM531608 2 0.0000 0.962 0.000 1.000 0.000
#> GSM531609 3 0.1031 0.886 0.024 0.000 0.976
#> GSM531610 3 0.1031 0.886 0.024 0.000 0.976
#> GSM531611 3 0.0592 0.889 0.012 0.000 0.988
#> GSM531612 3 0.1031 0.886 0.024 0.000 0.976
#> GSM531613 3 0.1163 0.884 0.028 0.000 0.972
#> GSM531614 3 0.1031 0.886 0.024 0.000 0.976
#> GSM531616 3 0.4409 0.814 0.004 0.172 0.824
#> GSM531618 3 0.0000 0.890 0.000 0.000 1.000
#> GSM531619 2 0.0000 0.962 0.000 1.000 0.000
#> GSM531620 2 0.0237 0.960 0.000 0.996 0.004
#> GSM531621 2 0.0000 0.962 0.000 1.000 0.000
#> GSM531622 2 0.1031 0.941 0.000 0.976 0.024
#> GSM531623 2 0.0000 0.962 0.000 1.000 0.000
#> GSM531625 2 0.0000 0.962 0.000 1.000 0.000
#> GSM531626 3 0.4235 0.811 0.000 0.176 0.824
#> GSM531628 3 0.4235 0.850 0.176 0.000 0.824
#> GSM531630 2 0.0000 0.962 0.000 1.000 0.000
#> GSM531632 3 0.4235 0.850 0.176 0.000 0.824
#> GSM531633 2 0.1031 0.941 0.000 0.976 0.024
#> GSM531635 3 0.4235 0.850 0.176 0.000 0.824
#> GSM531638 3 0.4409 0.814 0.004 0.172 0.824
#> GSM531639 3 0.4235 0.811 0.000 0.176 0.824
#> GSM531640 3 0.3267 0.856 0.000 0.116 0.884
#> GSM531641 3 0.1031 0.886 0.024 0.000 0.976
#> GSM531642 3 0.0000 0.890 0.000 0.000 1.000
#> GSM531643 3 0.4235 0.850 0.176 0.000 0.824
#> GSM531644 3 0.4235 0.850 0.176 0.000 0.824
#> GSM531645 3 0.1031 0.886 0.024 0.000 0.976
#> GSM531646 3 0.4235 0.850 0.176 0.000 0.824
#> GSM531647 3 0.4235 0.850 0.176 0.000 0.824
#> GSM531648 3 0.0000 0.890 0.000 0.000 1.000
#> GSM531649 3 0.4235 0.850 0.176 0.000 0.824
#> GSM531650 3 0.4235 0.850 0.176 0.000 0.824
#> GSM531651 2 0.0000 0.962 0.000 1.000 0.000
#> GSM531652 3 0.0000 0.890 0.000 0.000 1.000
#> GSM531653 3 0.4235 0.850 0.176 0.000 0.824
#> GSM531656 3 0.4235 0.850 0.176 0.000 0.824
#> GSM531657 3 0.0000 0.890 0.000 0.000 1.000
#> GSM531659 3 0.0000 0.890 0.000 0.000 1.000
#> GSM531661 2 0.0000 0.962 0.000 1.000 0.000
#> GSM531662 2 0.0000 0.962 0.000 1.000 0.000
#> GSM531663 3 0.0237 0.890 0.004 0.000 0.996
#> GSM531664 1 0.3686 0.764 0.860 0.000 0.140
#> GSM531665 1 0.1289 0.852 0.968 0.000 0.032
#> GSM531666 3 0.0000 0.890 0.000 0.000 1.000
#> GSM531667 3 0.4235 0.734 0.000 0.176 0.824
#> GSM531668 3 0.1031 0.883 0.000 0.024 0.976
#> GSM531669 1 0.5058 0.593 0.756 0.000 0.244
#> GSM531670 3 0.4235 0.850 0.176 0.000 0.824
#> GSM531671 3 0.4994 0.855 0.160 0.024 0.816
#> GSM531672 3 0.0000 0.890 0.000 0.000 1.000
#> GSM531673 2 0.5216 0.599 0.000 0.740 0.260
#> GSM531674 1 0.1163 0.850 0.972 0.000 0.028
#> GSM531675 1 0.4555 0.870 0.800 0.000 0.200
#> GSM531676 1 0.1031 0.850 0.976 0.000 0.024
#> GSM531677 1 0.4555 0.870 0.800 0.000 0.200
#> GSM531678 1 0.4733 0.870 0.800 0.004 0.196
#> GSM531679 1 0.4555 0.870 0.800 0.000 0.200
#> GSM531680 1 0.1163 0.850 0.972 0.000 0.028
#> GSM531681 1 0.4235 0.863 0.824 0.000 0.176
#> GSM531682 1 0.4555 0.870 0.800 0.000 0.200
#> GSM531683 1 0.5223 0.866 0.800 0.024 0.176
#> GSM531684 2 0.0000 0.962 0.000 1.000 0.000
#> GSM531685 1 0.1031 0.850 0.976 0.000 0.024
#> GSM531686 1 0.4235 0.863 0.824 0.000 0.176
#> GSM531687 1 0.1163 0.850 0.972 0.000 0.028
#> GSM531688 1 0.1031 0.850 0.976 0.000 0.024
#> GSM531689 1 0.1163 0.852 0.972 0.000 0.028
#> GSM531690 1 0.4452 0.869 0.808 0.000 0.192
#> GSM531691 1 0.1163 0.852 0.972 0.000 0.028
#> GSM531692 1 0.4504 0.745 0.804 0.196 0.000
#> GSM531693 1 0.1163 0.850 0.972 0.000 0.028
#> GSM531694 1 0.5223 0.866 0.800 0.024 0.176
#> GSM531695 1 0.1031 0.850 0.976 0.000 0.024
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM531600 3 0.1661 0.83657 0.004 0.000 0.944 0.052
#> GSM531601 3 0.3688 0.74859 0.000 0.208 0.792 0.000
#> GSM531605 1 0.4008 0.80513 0.820 0.032 0.148 0.000
#> GSM531615 2 0.2149 0.86256 0.088 0.912 0.000 0.000
#> GSM531617 3 0.3829 0.79956 0.152 0.004 0.828 0.016
#> GSM531624 2 0.0000 0.95889 0.000 1.000 0.000 0.000
#> GSM531627 2 0.0000 0.95889 0.000 1.000 0.000 0.000
#> GSM531629 3 0.4625 0.78721 0.152 0.032 0.800 0.016
#> GSM531631 2 0.0000 0.95889 0.000 1.000 0.000 0.000
#> GSM531634 2 0.0592 0.94669 0.000 0.984 0.016 0.000
#> GSM531636 3 0.0895 0.84717 0.000 0.020 0.976 0.004
#> GSM531637 2 0.0000 0.95889 0.000 1.000 0.000 0.000
#> GSM531654 2 0.3647 0.74025 0.152 0.832 0.000 0.016
#> GSM531655 3 0.0707 0.84828 0.020 0.000 0.980 0.000
#> GSM531658 4 0.7292 0.32573 0.152 0.000 0.388 0.460
#> GSM531660 3 0.4625 0.78721 0.152 0.032 0.800 0.016
#> GSM531602 1 0.1610 0.79706 0.952 0.032 0.000 0.016
#> GSM531603 1 0.2807 0.77724 0.912 0.032 0.040 0.016
#> GSM531604 1 0.4552 0.79597 0.800 0.072 0.128 0.000
#> GSM531606 1 0.1610 0.79706 0.952 0.032 0.000 0.016
#> GSM531607 1 0.1610 0.79706 0.952 0.032 0.000 0.016
#> GSM531608 2 0.0000 0.95889 0.000 1.000 0.000 0.000
#> GSM531609 4 0.4153 0.90583 0.132 0.000 0.048 0.820
#> GSM531610 4 0.4153 0.90583 0.132 0.000 0.048 0.820
#> GSM531611 4 0.4817 0.88002 0.128 0.000 0.088 0.784
#> GSM531612 4 0.4153 0.90583 0.132 0.000 0.048 0.820
#> GSM531613 4 0.3539 0.86437 0.176 0.000 0.004 0.820
#> GSM531614 4 0.4153 0.90583 0.132 0.000 0.048 0.820
#> GSM531616 3 0.0707 0.84694 0.000 0.020 0.980 0.000
#> GSM531618 3 0.3647 0.80092 0.152 0.000 0.832 0.016
#> GSM531619 2 0.0000 0.95889 0.000 1.000 0.000 0.000
#> GSM531620 2 0.0188 0.95591 0.000 0.996 0.004 0.000
#> GSM531621 2 0.0000 0.95889 0.000 1.000 0.000 0.000
#> GSM531622 2 0.1022 0.92913 0.000 0.968 0.032 0.000
#> GSM531623 2 0.0000 0.95889 0.000 1.000 0.000 0.000
#> GSM531625 2 0.0000 0.95889 0.000 1.000 0.000 0.000
#> GSM531626 3 0.1302 0.84499 0.000 0.044 0.956 0.000
#> GSM531628 3 0.4719 0.78920 0.048 0.000 0.772 0.180
#> GSM531630 2 0.0000 0.95889 0.000 1.000 0.000 0.000
#> GSM531632 3 0.4719 0.78920 0.048 0.000 0.772 0.180
#> GSM531633 2 0.1022 0.92913 0.000 0.968 0.032 0.000
#> GSM531635 3 0.0188 0.84807 0.000 0.000 0.996 0.004
#> GSM531638 3 0.0707 0.84694 0.000 0.020 0.980 0.000
#> GSM531639 3 0.0707 0.84694 0.000 0.020 0.980 0.000
#> GSM531640 3 0.0804 0.84832 0.008 0.012 0.980 0.000
#> GSM531641 4 0.4153 0.90583 0.132 0.000 0.048 0.820
#> GSM531642 3 0.0707 0.84828 0.020 0.000 0.980 0.000
#> GSM531643 3 0.2814 0.83376 0.000 0.000 0.868 0.132
#> GSM531644 3 0.2760 0.83452 0.000 0.000 0.872 0.128
#> GSM531645 4 0.4153 0.90583 0.132 0.000 0.048 0.820
#> GSM531646 3 0.4719 0.78920 0.048 0.000 0.772 0.180
#> GSM531647 3 0.4719 0.78920 0.048 0.000 0.772 0.180
#> GSM531648 3 0.3647 0.80092 0.152 0.000 0.832 0.016
#> GSM531649 3 0.3144 0.82300 0.044 0.000 0.884 0.072
#> GSM531650 3 0.4719 0.78920 0.048 0.000 0.772 0.180
#> GSM531651 2 0.0000 0.95889 0.000 1.000 0.000 0.000
#> GSM531652 3 0.3024 0.81173 0.148 0.000 0.852 0.000
#> GSM531653 3 0.4719 0.78920 0.048 0.000 0.772 0.180
#> GSM531656 3 0.0000 0.84792 0.000 0.000 1.000 0.000
#> GSM531657 3 0.3647 0.80092 0.152 0.000 0.832 0.016
#> GSM531659 3 0.3647 0.80092 0.152 0.000 0.832 0.016
#> GSM531661 2 0.0000 0.95889 0.000 1.000 0.000 0.000
#> GSM531662 2 0.0000 0.95889 0.000 1.000 0.000 0.000
#> GSM531663 4 0.6821 0.66447 0.152 0.000 0.256 0.592
#> GSM531664 1 0.6011 0.68386 0.688 0.000 0.132 0.180
#> GSM531665 1 0.4353 0.78497 0.756 0.000 0.232 0.012
#> GSM531666 3 0.0707 0.84828 0.020 0.000 0.980 0.000
#> GSM531667 3 0.4798 0.68807 0.052 0.180 0.768 0.000
#> GSM531668 3 0.4625 0.78721 0.152 0.032 0.800 0.016
#> GSM531669 1 0.6869 0.54742 0.596 0.000 0.224 0.180
#> GSM531670 3 0.0000 0.84792 0.000 0.000 1.000 0.000
#> GSM531671 3 0.4958 0.79553 0.040 0.012 0.772 0.176
#> GSM531672 3 0.3647 0.80092 0.152 0.000 0.832 0.016
#> GSM531673 2 0.4250 0.57697 0.000 0.724 0.276 0.000
#> GSM531674 1 0.4079 0.77587 0.800 0.000 0.020 0.180
#> GSM531675 1 0.1610 0.79689 0.952 0.000 0.032 0.016
#> GSM531676 1 0.3208 0.81005 0.848 0.000 0.148 0.004
#> GSM531677 1 0.0927 0.80264 0.976 0.000 0.008 0.016
#> GSM531678 1 0.3539 0.80023 0.820 0.004 0.176 0.000
#> GSM531679 1 0.0188 0.80816 0.996 0.000 0.004 0.000
#> GSM531680 1 0.3903 0.80937 0.844 0.000 0.080 0.076
#> GSM531681 4 0.3400 0.85954 0.180 0.000 0.000 0.820
#> GSM531682 1 0.1610 0.79689 0.952 0.000 0.032 0.016
#> GSM531683 1 0.1610 0.79706 0.952 0.032 0.000 0.016
#> GSM531684 2 0.0000 0.95889 0.000 1.000 0.000 0.000
#> GSM531685 1 0.4035 0.77818 0.804 0.000 0.020 0.176
#> GSM531686 1 0.4907 0.00463 0.580 0.000 0.000 0.420
#> GSM531687 1 0.4008 0.77854 0.756 0.000 0.244 0.000
#> GSM531688 1 0.4035 0.77818 0.804 0.000 0.020 0.176
#> GSM531689 1 0.3172 0.80975 0.840 0.000 0.160 0.000
#> GSM531690 1 0.1610 0.79509 0.952 0.000 0.016 0.032
#> GSM531691 1 0.3610 0.79844 0.800 0.000 0.200 0.000
#> GSM531692 1 0.4440 0.80790 0.820 0.028 0.128 0.024
#> GSM531693 1 0.4035 0.77818 0.804 0.000 0.020 0.176
#> GSM531694 1 0.1610 0.79706 0.952 0.032 0.000 0.016
#> GSM531695 1 0.4035 0.77818 0.804 0.000 0.020 0.176
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM531600 3 0.3424 0.7003 0.000 0.000 0.760 0.000 0.240
#> GSM531601 5 0.3305 0.6994 0.000 0.224 0.000 0.000 0.776
#> GSM531605 1 0.2471 0.7893 0.864 0.000 0.000 0.000 0.136
#> GSM531615 2 0.1732 0.8830 0.080 0.920 0.000 0.000 0.000
#> GSM531617 5 0.2629 0.8296 0.136 0.004 0.000 0.000 0.860
#> GSM531624 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM531627 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM531629 5 0.3003 0.8044 0.188 0.000 0.000 0.000 0.812
#> GSM531631 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM531634 2 0.0510 0.9510 0.000 0.984 0.000 0.000 0.016
#> GSM531636 5 0.0703 0.8451 0.000 0.000 0.024 0.000 0.976
#> GSM531637 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM531654 2 0.3074 0.7495 0.196 0.804 0.000 0.000 0.000
#> GSM531655 5 0.0000 0.8476 0.000 0.000 0.000 0.000 1.000
#> GSM531658 4 0.6150 0.0399 0.132 0.000 0.000 0.464 0.404
#> GSM531660 5 0.3109 0.7966 0.200 0.000 0.000 0.000 0.800
#> GSM531602 1 0.0609 0.8174 0.980 0.000 0.000 0.000 0.020
#> GSM531603 5 0.4304 0.3425 0.484 0.000 0.000 0.000 0.516
#> GSM531604 1 0.2583 0.7900 0.864 0.004 0.000 0.000 0.132
#> GSM531606 1 0.0609 0.8174 0.980 0.000 0.000 0.000 0.020
#> GSM531607 1 0.0000 0.8206 1.000 0.000 0.000 0.000 0.000
#> GSM531608 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM531609 4 0.0000 0.8307 0.000 0.000 0.000 1.000 0.000
#> GSM531610 4 0.0000 0.8307 0.000 0.000 0.000 1.000 0.000
#> GSM531611 4 0.0794 0.8118 0.000 0.000 0.000 0.972 0.028
#> GSM531612 4 0.0000 0.8307 0.000 0.000 0.000 1.000 0.000
#> GSM531613 4 0.0000 0.8307 0.000 0.000 0.000 1.000 0.000
#> GSM531614 4 0.0000 0.8307 0.000 0.000 0.000 1.000 0.000
#> GSM531616 5 0.1608 0.8268 0.000 0.000 0.072 0.000 0.928
#> GSM531618 5 0.2471 0.8306 0.136 0.000 0.000 0.000 0.864
#> GSM531619 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM531620 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM531621 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM531622 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM531623 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM531625 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM531626 5 0.1568 0.8433 0.000 0.036 0.020 0.000 0.944
#> GSM531628 3 0.0000 0.9240 0.000 0.000 1.000 0.000 0.000
#> GSM531630 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM531632 3 0.0000 0.9240 0.000 0.000 1.000 0.000 0.000
#> GSM531633 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM531635 5 0.1792 0.8203 0.000 0.000 0.084 0.000 0.916
#> GSM531638 5 0.0703 0.8451 0.000 0.000 0.024 0.000 0.976
#> GSM531639 5 0.0609 0.8454 0.000 0.000 0.020 0.000 0.980
#> GSM531640 5 0.0609 0.8446 0.000 0.020 0.000 0.000 0.980
#> GSM531641 4 0.0000 0.8307 0.000 0.000 0.000 1.000 0.000
#> GSM531642 5 0.0290 0.8468 0.008 0.000 0.000 0.000 0.992
#> GSM531643 5 0.3177 0.7552 0.000 0.000 0.208 0.000 0.792
#> GSM531644 5 0.2648 0.7949 0.000 0.000 0.152 0.000 0.848
#> GSM531645 4 0.0000 0.8307 0.000 0.000 0.000 1.000 0.000
#> GSM531646 3 0.0000 0.9240 0.000 0.000 1.000 0.000 0.000
#> GSM531647 3 0.0000 0.9240 0.000 0.000 1.000 0.000 0.000
#> GSM531648 5 0.2471 0.8306 0.136 0.000 0.000 0.000 0.864
#> GSM531649 3 0.2230 0.8310 0.000 0.000 0.884 0.000 0.116
#> GSM531650 3 0.0000 0.9240 0.000 0.000 1.000 0.000 0.000
#> GSM531651 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM531652 5 0.2424 0.8319 0.132 0.000 0.000 0.000 0.868
#> GSM531653 3 0.0000 0.9240 0.000 0.000 1.000 0.000 0.000
#> GSM531656 5 0.0609 0.8454 0.000 0.000 0.020 0.000 0.980
#> GSM531657 5 0.2471 0.8306 0.136 0.000 0.000 0.000 0.864
#> GSM531659 5 0.2471 0.8306 0.136 0.000 0.000 0.000 0.864
#> GSM531661 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM531662 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM531663 4 0.5921 0.3669 0.136 0.000 0.000 0.568 0.296
#> GSM531664 3 0.0290 0.9214 0.008 0.000 0.992 0.000 0.000
#> GSM531665 5 0.4581 0.6030 0.196 0.000 0.072 0.000 0.732
#> GSM531666 5 0.0609 0.8432 0.020 0.000 0.000 0.000 0.980
#> GSM531667 5 0.3650 0.7027 0.028 0.176 0.000 0.000 0.796
#> GSM531668 5 0.3231 0.7967 0.196 0.000 0.000 0.004 0.800
#> GSM531669 3 0.0000 0.9240 0.000 0.000 1.000 0.000 0.000
#> GSM531670 5 0.0609 0.8432 0.020 0.000 0.000 0.000 0.980
#> GSM531671 3 0.0162 0.9221 0.000 0.000 0.996 0.000 0.004
#> GSM531672 5 0.2471 0.8306 0.136 0.000 0.000 0.000 0.864
#> GSM531673 2 0.3612 0.6109 0.000 0.732 0.000 0.000 0.268
#> GSM531674 3 0.0000 0.9240 0.000 0.000 1.000 0.000 0.000
#> GSM531675 1 0.1671 0.8101 0.924 0.000 0.000 0.000 0.076
#> GSM531676 1 0.4848 0.7284 0.724 0.000 0.144 0.000 0.132
#> GSM531677 1 0.1943 0.8162 0.924 0.000 0.056 0.000 0.020
#> GSM531678 1 0.3074 0.7676 0.804 0.000 0.000 0.000 0.196
#> GSM531679 1 0.1341 0.8182 0.944 0.000 0.056 0.000 0.000
#> GSM531680 1 0.5252 0.5087 0.616 0.000 0.316 0.000 0.068
#> GSM531681 4 0.4294 -0.0267 0.468 0.000 0.000 0.532 0.000
#> GSM531682 1 0.1732 0.8083 0.920 0.000 0.000 0.000 0.080
#> GSM531683 1 0.0609 0.8174 0.980 0.000 0.000 0.000 0.020
#> GSM531684 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM531685 3 0.3074 0.7412 0.196 0.000 0.804 0.000 0.000
#> GSM531686 1 0.3837 0.5278 0.692 0.000 0.000 0.308 0.000
#> GSM531687 5 0.3074 0.6850 0.196 0.000 0.000 0.000 0.804
#> GSM531688 3 0.1671 0.8788 0.076 0.000 0.924 0.000 0.000
#> GSM531689 1 0.3803 0.7788 0.804 0.000 0.056 0.000 0.140
#> GSM531690 1 0.1997 0.8118 0.924 0.000 0.000 0.040 0.036
#> GSM531691 1 0.3109 0.7652 0.800 0.000 0.000 0.000 0.200
#> GSM531692 1 0.6220 0.3168 0.508 0.004 0.356 0.000 0.132
#> GSM531693 3 0.1341 0.8928 0.056 0.000 0.944 0.000 0.000
#> GSM531694 1 0.0609 0.8174 0.980 0.000 0.000 0.000 0.020
#> GSM531695 3 0.3534 0.6456 0.256 0.000 0.744 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM531600 6 0.3458 0.748 0.000 0.000 0.112 0.000 0.080 0.808
#> GSM531601 3 0.2854 0.731 0.000 0.208 0.792 0.000 0.000 0.000
#> GSM531605 5 0.2178 0.755 0.132 0.000 0.000 0.000 0.868 0.000
#> GSM531615 2 0.2002 0.892 0.028 0.920 0.040 0.000 0.012 0.000
#> GSM531617 3 0.2138 0.824 0.052 0.004 0.908 0.000 0.036 0.000
#> GSM531624 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531627 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531629 3 0.2404 0.811 0.080 0.000 0.884 0.000 0.036 0.000
#> GSM531631 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531634 2 0.0458 0.950 0.000 0.984 0.016 0.000 0.000 0.000
#> GSM531636 3 0.1700 0.858 0.000 0.000 0.916 0.000 0.080 0.004
#> GSM531637 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531654 2 0.2946 0.781 0.176 0.812 0.000 0.000 0.012 0.000
#> GSM531655 3 0.1556 0.859 0.000 0.000 0.920 0.000 0.080 0.000
#> GSM531658 3 0.5473 -0.061 0.048 0.000 0.464 0.452 0.036 0.000
#> GSM531660 3 0.4285 0.557 0.320 0.000 0.644 0.000 0.036 0.000
#> GSM531602 1 0.0865 0.704 0.964 0.000 0.000 0.000 0.036 0.000
#> GSM531603 5 0.3756 0.412 0.400 0.000 0.000 0.000 0.600 0.000
#> GSM531604 1 0.3578 0.580 0.660 0.000 0.000 0.000 0.340 0.000
#> GSM531606 1 0.0865 0.704 0.964 0.000 0.000 0.000 0.036 0.000
#> GSM531607 1 0.0865 0.704 0.964 0.000 0.000 0.000 0.036 0.000
#> GSM531608 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531609 4 0.0000 0.922 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531610 4 0.0000 0.922 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531611 4 0.1204 0.867 0.000 0.000 0.056 0.944 0.000 0.000
#> GSM531612 4 0.0000 0.922 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531613 4 0.0000 0.922 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531614 4 0.0000 0.922 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531616 3 0.2965 0.831 0.000 0.000 0.848 0.000 0.080 0.072
#> GSM531618 3 0.1995 0.825 0.052 0.000 0.912 0.000 0.036 0.000
#> GSM531619 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531620 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531621 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531622 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531623 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531625 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531626 3 0.1779 0.857 0.000 0.016 0.920 0.000 0.064 0.000
#> GSM531628 6 0.0000 0.942 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531630 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531632 6 0.0000 0.942 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531633 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531635 3 0.2965 0.831 0.000 0.000 0.848 0.000 0.080 0.072
#> GSM531638 3 0.1812 0.858 0.000 0.000 0.912 0.000 0.080 0.008
#> GSM531639 3 0.1556 0.859 0.000 0.000 0.920 0.000 0.080 0.000
#> GSM531640 3 0.1556 0.859 0.000 0.000 0.920 0.000 0.080 0.000
#> GSM531641 4 0.0000 0.922 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531642 3 0.1556 0.859 0.000 0.000 0.920 0.000 0.080 0.000
#> GSM531643 3 0.2416 0.792 0.000 0.000 0.844 0.000 0.000 0.156
#> GSM531644 3 0.1556 0.838 0.000 0.000 0.920 0.000 0.000 0.080
#> GSM531645 4 0.0000 0.922 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531646 6 0.0000 0.942 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531647 6 0.0000 0.942 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531648 3 0.1995 0.825 0.052 0.000 0.912 0.000 0.036 0.000
#> GSM531649 6 0.1471 0.881 0.000 0.000 0.004 0.000 0.064 0.932
#> GSM531650 6 0.0000 0.942 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531651 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531652 3 0.0000 0.850 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531653 6 0.0000 0.942 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531656 3 0.1556 0.859 0.000 0.000 0.920 0.000 0.080 0.000
#> GSM531657 3 0.1995 0.825 0.052 0.000 0.912 0.000 0.036 0.000
#> GSM531659 3 0.1713 0.832 0.044 0.000 0.928 0.000 0.028 0.000
#> GSM531661 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531662 2 0.0865 0.937 0.000 0.964 0.000 0.000 0.036 0.000
#> GSM531663 4 0.5332 0.385 0.052 0.000 0.332 0.580 0.036 0.000
#> GSM531664 6 0.0260 0.937 0.000 0.000 0.000 0.000 0.008 0.992
#> GSM531665 5 0.2048 0.789 0.000 0.000 0.120 0.000 0.880 0.000
#> GSM531666 3 0.1556 0.859 0.000 0.000 0.920 0.000 0.080 0.000
#> GSM531667 3 0.3907 0.703 0.000 0.176 0.756 0.000 0.068 0.000
#> GSM531668 3 0.3823 0.484 0.436 0.000 0.564 0.000 0.000 0.000
#> GSM531669 6 0.0000 0.942 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531670 3 0.1556 0.859 0.000 0.000 0.920 0.000 0.080 0.000
#> GSM531671 6 0.0146 0.939 0.000 0.000 0.004 0.000 0.000 0.996
#> GSM531672 3 0.1995 0.825 0.052 0.000 0.912 0.000 0.036 0.000
#> GSM531673 2 0.4165 0.544 0.000 0.672 0.292 0.000 0.036 0.000
#> GSM531674 6 0.0000 0.942 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531675 1 0.4843 0.658 0.616 0.000 0.084 0.000 0.300 0.000
#> GSM531676 5 0.1387 0.848 0.000 0.000 0.000 0.000 0.932 0.068
#> GSM531677 1 0.4814 0.657 0.616 0.000 0.080 0.000 0.304 0.000
#> GSM531678 5 0.1075 0.840 0.000 0.000 0.048 0.000 0.952 0.000
#> GSM531679 1 0.5571 0.614 0.568 0.000 0.080 0.000 0.320 0.032
#> GSM531680 5 0.2092 0.821 0.000 0.000 0.000 0.000 0.876 0.124
#> GSM531681 1 0.3823 0.332 0.564 0.000 0.000 0.436 0.000 0.000
#> GSM531682 1 0.4843 0.658 0.616 0.000 0.084 0.000 0.300 0.000
#> GSM531683 1 0.1010 0.705 0.960 0.000 0.004 0.000 0.036 0.000
#> GSM531684 2 0.0865 0.937 0.000 0.964 0.000 0.000 0.036 0.000
#> GSM531685 5 0.2260 0.796 0.000 0.000 0.000 0.000 0.860 0.140
#> GSM531686 1 0.4680 0.389 0.564 0.000 0.008 0.396 0.032 0.000
#> GSM531687 5 0.1765 0.811 0.000 0.000 0.096 0.000 0.904 0.000
#> GSM531688 6 0.1501 0.873 0.000 0.000 0.000 0.000 0.076 0.924
#> GSM531689 5 0.0935 0.850 0.000 0.000 0.004 0.000 0.964 0.032
#> GSM531690 1 0.4933 0.659 0.616 0.000 0.080 0.004 0.300 0.000
#> GSM531691 5 0.1075 0.840 0.000 0.000 0.048 0.000 0.952 0.000
#> GSM531692 5 0.1196 0.850 0.000 0.008 0.000 0.000 0.952 0.040
#> GSM531693 6 0.3309 0.559 0.000 0.000 0.000 0.000 0.280 0.720
#> GSM531694 1 0.0458 0.702 0.984 0.000 0.000 0.000 0.016 0.000
#> GSM531695 5 0.2730 0.758 0.000 0.000 0.000 0.000 0.808 0.192
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> SD:pam 96 0.0318 2
#> SD:pam 96 0.0176 3
#> SD:pam 94 0.0165 4
#> SD:pam 91 0.0566 5
#> SD:pam 90 0.0662 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "mclust"]
# you can also extract it by
# res = res_list["SD:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 96 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
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 0.231 0.673 0.776 0.3718 0.667 0.667
#> 3 3 0.568 0.829 0.859 0.5885 0.699 0.562
#> 4 4 0.783 0.836 0.923 0.2569 0.735 0.422
#> 5 5 0.642 0.677 0.826 0.0363 0.947 0.796
#> 6 6 0.759 0.715 0.804 0.0583 0.903 0.609
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM531600 1 0.991 0.574 0.556 0.444
#> GSM531601 1 0.992 0.429 0.552 0.448
#> GSM531605 1 0.000 0.722 1.000 0.000
#> GSM531615 2 0.494 0.898 0.108 0.892
#> GSM531617 1 0.929 0.615 0.656 0.344
#> GSM531624 2 0.494 0.898 0.108 0.892
#> GSM531627 2 0.494 0.898 0.108 0.892
#> GSM531629 1 0.987 0.459 0.568 0.432
#> GSM531631 2 0.494 0.898 0.108 0.892
#> GSM531634 2 0.494 0.898 0.108 0.892
#> GSM531636 1 0.993 0.436 0.548 0.452
#> GSM531637 2 0.494 0.898 0.108 0.892
#> GSM531654 1 0.909 0.631 0.676 0.324
#> GSM531655 1 0.921 0.622 0.664 0.336
#> GSM531658 1 0.430 0.740 0.912 0.088
#> GSM531660 1 0.416 0.740 0.916 0.084
#> GSM531602 1 0.000 0.722 1.000 0.000
#> GSM531603 1 0.000 0.722 1.000 0.000
#> GSM531604 1 0.000 0.722 1.000 0.000
#> GSM531606 1 0.000 0.722 1.000 0.000
#> GSM531607 1 0.000 0.722 1.000 0.000
#> GSM531608 1 0.990 0.447 0.560 0.440
#> GSM531609 1 0.430 0.740 0.912 0.088
#> GSM531610 1 0.430 0.740 0.912 0.088
#> GSM531611 1 0.430 0.740 0.912 0.088
#> GSM531612 1 0.430 0.740 0.912 0.088
#> GSM531613 1 0.430 0.740 0.912 0.088
#> GSM531614 1 0.430 0.740 0.912 0.088
#> GSM531616 1 0.991 0.439 0.556 0.444
#> GSM531618 1 0.745 0.703 0.788 0.212
#> GSM531619 2 0.494 0.898 0.108 0.892
#> GSM531620 2 0.494 0.898 0.108 0.892
#> GSM531621 2 0.494 0.898 0.108 0.892
#> GSM531622 2 0.494 0.898 0.108 0.892
#> GSM531623 2 0.494 0.898 0.108 0.892
#> GSM531625 2 0.788 0.699 0.236 0.764
#> GSM531626 2 1.000 -0.272 0.492 0.508
#> GSM531628 1 0.993 0.568 0.548 0.452
#> GSM531630 2 0.494 0.898 0.108 0.892
#> GSM531632 1 0.993 0.568 0.548 0.452
#> GSM531633 2 0.494 0.898 0.108 0.892
#> GSM531635 1 0.987 0.582 0.568 0.432
#> GSM531638 1 0.994 0.407 0.544 0.456
#> GSM531639 1 0.991 0.439 0.556 0.444
#> GSM531640 2 0.563 0.872 0.132 0.868
#> GSM531641 1 0.430 0.740 0.912 0.088
#> GSM531642 1 0.929 0.615 0.656 0.344
#> GSM531643 1 0.952 0.611 0.628 0.372
#> GSM531644 1 0.936 0.619 0.648 0.352
#> GSM531645 1 0.430 0.740 0.912 0.088
#> GSM531646 1 0.993 0.568 0.548 0.452
#> GSM531647 1 0.993 0.568 0.548 0.452
#> GSM531648 1 0.430 0.740 0.912 0.088
#> GSM531649 2 0.998 -0.446 0.476 0.524
#> GSM531650 1 0.993 0.568 0.548 0.452
#> GSM531651 2 0.494 0.898 0.108 0.892
#> GSM531652 1 0.482 0.739 0.896 0.104
#> GSM531653 1 0.993 0.568 0.548 0.452
#> GSM531656 1 0.995 0.441 0.540 0.460
#> GSM531657 1 0.416 0.740 0.916 0.084
#> GSM531659 1 0.430 0.740 0.912 0.088
#> GSM531661 2 0.802 0.699 0.244 0.756
#> GSM531662 1 0.904 0.634 0.680 0.320
#> GSM531663 1 0.430 0.740 0.912 0.088
#> GSM531664 1 0.993 0.568 0.548 0.452
#> GSM531665 1 0.891 0.650 0.692 0.308
#> GSM531666 1 0.850 0.675 0.724 0.276
#> GSM531667 2 0.541 0.882 0.124 0.876
#> GSM531668 1 0.416 0.740 0.916 0.084
#> GSM531669 1 0.991 0.575 0.556 0.444
#> GSM531670 1 0.943 0.613 0.640 0.360
#> GSM531671 1 0.900 0.636 0.684 0.316
#> GSM531672 1 0.416 0.740 0.916 0.084
#> GSM531673 1 0.871 0.661 0.708 0.292
#> GSM531674 1 0.993 0.568 0.548 0.452
#> GSM531675 1 0.000 0.722 1.000 0.000
#> GSM531676 1 0.662 0.703 0.828 0.172
#> GSM531677 1 0.000 0.722 1.000 0.000
#> GSM531678 1 0.000 0.722 1.000 0.000
#> GSM531679 1 0.000 0.722 1.000 0.000
#> GSM531680 1 0.795 0.662 0.760 0.240
#> GSM531681 1 0.000 0.722 1.000 0.000
#> GSM531682 1 0.000 0.722 1.000 0.000
#> GSM531683 1 0.000 0.722 1.000 0.000
#> GSM531684 1 0.000 0.722 1.000 0.000
#> GSM531685 1 0.871 0.638 0.708 0.292
#> GSM531686 1 0.000 0.722 1.000 0.000
#> GSM531687 1 0.541 0.719 0.876 0.124
#> GSM531688 1 0.936 0.589 0.648 0.352
#> GSM531689 1 0.000 0.722 1.000 0.000
#> GSM531690 1 0.000 0.722 1.000 0.000
#> GSM531691 1 0.000 0.722 1.000 0.000
#> GSM531692 1 0.000 0.722 1.000 0.000
#> GSM531693 1 0.943 0.580 0.640 0.360
#> GSM531694 1 0.000 0.722 1.000 0.000
#> GSM531695 1 0.871 0.635 0.708 0.292
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM531600 3 0.1453 0.8871 0.024 0.008 0.968
#> GSM531601 2 0.9075 0.2224 0.388 0.472 0.140
#> GSM531605 1 0.0000 0.8757 1.000 0.000 0.000
#> GSM531615 2 0.3816 0.8952 0.000 0.852 0.148
#> GSM531617 2 0.7878 0.6740 0.160 0.668 0.172
#> GSM531624 2 0.3551 0.8991 0.000 0.868 0.132
#> GSM531627 2 0.3816 0.8891 0.000 0.852 0.148
#> GSM531629 1 0.6324 0.7876 0.764 0.076 0.160
#> GSM531631 2 0.3551 0.8991 0.000 0.868 0.132
#> GSM531634 2 0.3686 0.8969 0.000 0.860 0.140
#> GSM531636 3 0.1774 0.8889 0.024 0.016 0.960
#> GSM531637 2 0.3551 0.8991 0.000 0.868 0.132
#> GSM531654 1 0.6231 0.8553 0.772 0.148 0.080
#> GSM531655 1 0.6037 0.8577 0.788 0.112 0.100
#> GSM531658 1 0.6083 0.8516 0.772 0.168 0.060
#> GSM531660 1 0.5571 0.8639 0.804 0.140 0.056
#> GSM531602 1 0.0000 0.8757 1.000 0.000 0.000
#> GSM531603 1 0.0237 0.8765 0.996 0.004 0.000
#> GSM531604 1 0.0237 0.8752 0.996 0.000 0.004
#> GSM531606 1 0.0000 0.8757 1.000 0.000 0.000
#> GSM531607 1 0.0000 0.8757 1.000 0.000 0.000
#> GSM531608 2 0.7829 0.6578 0.164 0.672 0.164
#> GSM531609 1 0.6044 0.8519 0.772 0.172 0.056
#> GSM531610 1 0.5526 0.8610 0.792 0.172 0.036
#> GSM531611 1 0.5883 0.8582 0.796 0.112 0.092
#> GSM531612 1 0.6044 0.8519 0.772 0.172 0.056
#> GSM531613 1 0.5659 0.8622 0.796 0.152 0.052
#> GSM531614 1 0.6044 0.8519 0.772 0.172 0.056
#> GSM531616 3 0.6859 0.2210 0.024 0.356 0.620
#> GSM531618 1 0.5852 0.8135 0.788 0.060 0.152
#> GSM531619 2 0.3686 0.8969 0.000 0.860 0.140
#> GSM531620 2 0.4682 0.8596 0.004 0.804 0.192
#> GSM531621 2 0.3551 0.8991 0.000 0.868 0.132
#> GSM531622 2 0.3551 0.8991 0.000 0.868 0.132
#> GSM531623 2 0.3551 0.8991 0.000 0.868 0.132
#> GSM531625 2 0.5062 0.8523 0.016 0.800 0.184
#> GSM531626 2 0.6625 0.6710 0.024 0.660 0.316
#> GSM531628 3 0.1453 0.8872 0.024 0.008 0.968
#> GSM531630 2 0.3551 0.8991 0.000 0.868 0.132
#> GSM531632 3 0.1453 0.8871 0.024 0.008 0.968
#> GSM531633 2 0.3551 0.8991 0.000 0.868 0.132
#> GSM531635 3 0.1774 0.8889 0.024 0.016 0.960
#> GSM531638 2 0.5633 0.8227 0.024 0.768 0.208
#> GSM531639 3 0.7069 -0.0957 0.472 0.020 0.508
#> GSM531640 2 0.3686 0.8969 0.000 0.860 0.140
#> GSM531641 1 0.6044 0.8519 0.772 0.172 0.056
#> GSM531642 1 0.5292 0.7682 0.764 0.008 0.228
#> GSM531643 3 0.1453 0.8872 0.024 0.008 0.968
#> GSM531644 1 0.5896 0.6815 0.700 0.008 0.292
#> GSM531645 1 0.6044 0.8519 0.772 0.172 0.056
#> GSM531646 3 0.1774 0.8889 0.024 0.016 0.960
#> GSM531647 3 0.1774 0.8889 0.024 0.016 0.960
#> GSM531648 1 0.5412 0.8623 0.796 0.172 0.032
#> GSM531649 3 0.1774 0.8889 0.024 0.016 0.960
#> GSM531650 3 0.1453 0.8872 0.024 0.008 0.968
#> GSM531651 2 0.3551 0.8991 0.000 0.868 0.132
#> GSM531652 1 0.5597 0.7805 0.764 0.020 0.216
#> GSM531653 3 0.1774 0.8889 0.024 0.016 0.960
#> GSM531656 3 0.1774 0.8889 0.024 0.016 0.960
#> GSM531657 1 0.5730 0.8616 0.796 0.144 0.060
#> GSM531659 1 0.5874 0.8592 0.796 0.116 0.088
#> GSM531661 2 0.2845 0.7557 0.012 0.920 0.068
#> GSM531662 1 0.6299 0.8529 0.772 0.132 0.096
#> GSM531663 1 0.5730 0.8616 0.796 0.144 0.060
#> GSM531664 3 0.1031 0.8847 0.024 0.000 0.976
#> GSM531665 1 0.6181 0.8531 0.780 0.104 0.116
#> GSM531666 1 0.5951 0.8089 0.764 0.040 0.196
#> GSM531667 2 0.4178 0.8783 0.000 0.828 0.172
#> GSM531668 1 0.5730 0.8616 0.796 0.144 0.060
#> GSM531669 3 0.1753 0.8618 0.048 0.000 0.952
#> GSM531670 3 0.1453 0.8871 0.024 0.008 0.968
#> GSM531671 1 0.6936 0.8194 0.732 0.108 0.160
#> GSM531672 1 0.5730 0.8616 0.796 0.144 0.060
#> GSM531673 1 0.6107 0.8568 0.784 0.116 0.100
#> GSM531674 3 0.1163 0.8825 0.028 0.000 0.972
#> GSM531675 1 0.0237 0.8759 0.996 0.004 0.000
#> GSM531676 1 0.1289 0.8670 0.968 0.000 0.032
#> GSM531677 1 0.0000 0.8757 1.000 0.000 0.000
#> GSM531678 1 0.0000 0.8757 1.000 0.000 0.000
#> GSM531679 1 0.0000 0.8757 1.000 0.000 0.000
#> GSM531680 1 0.0892 0.8715 0.980 0.000 0.020
#> GSM531681 1 0.0237 0.8759 0.996 0.004 0.000
#> GSM531682 1 0.0000 0.8757 1.000 0.000 0.000
#> GSM531683 1 0.0000 0.8757 1.000 0.000 0.000
#> GSM531684 1 0.3213 0.8778 0.900 0.092 0.008
#> GSM531685 1 0.1964 0.8530 0.944 0.000 0.056
#> GSM531686 1 0.0237 0.8759 0.996 0.004 0.000
#> GSM531687 1 0.0892 0.8715 0.980 0.000 0.020
#> GSM531688 1 0.4504 0.6867 0.804 0.000 0.196
#> GSM531689 1 0.0000 0.8757 1.000 0.000 0.000
#> GSM531690 1 0.0237 0.8759 0.996 0.004 0.000
#> GSM531691 1 0.0424 0.8744 0.992 0.000 0.008
#> GSM531692 1 0.1289 0.8670 0.968 0.000 0.032
#> GSM531693 3 0.6274 0.3025 0.456 0.000 0.544
#> GSM531694 1 0.0000 0.8757 1.000 0.000 0.000
#> GSM531695 1 0.1289 0.8670 0.968 0.000 0.032
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM531600 3 0.1474 0.8826 0.000 0.052 0.948 0.000
#> GSM531601 2 0.0188 0.9396 0.000 0.996 0.000 0.004
#> GSM531605 1 0.0000 0.9334 1.000 0.000 0.000 0.000
#> GSM531615 2 0.0000 0.9415 0.000 1.000 0.000 0.000
#> GSM531617 2 0.0336 0.9392 0.000 0.992 0.008 0.000
#> GSM531624 2 0.0000 0.9415 0.000 1.000 0.000 0.000
#> GSM531627 2 0.0000 0.9415 0.000 1.000 0.000 0.000
#> GSM531629 2 0.1022 0.9249 0.000 0.968 0.032 0.000
#> GSM531631 2 0.0000 0.9415 0.000 1.000 0.000 0.000
#> GSM531634 2 0.0000 0.9415 0.000 1.000 0.000 0.000
#> GSM531636 3 0.2345 0.8510 0.000 0.100 0.900 0.000
#> GSM531637 2 0.0000 0.9415 0.000 1.000 0.000 0.000
#> GSM531654 2 0.4436 0.7941 0.052 0.800 0.148 0.000
#> GSM531655 3 0.5130 0.5067 0.020 0.312 0.668 0.000
#> GSM531658 4 0.0469 0.8462 0.000 0.000 0.012 0.988
#> GSM531660 4 0.4597 0.8208 0.008 0.044 0.148 0.800
#> GSM531602 1 0.0000 0.9334 1.000 0.000 0.000 0.000
#> GSM531603 1 0.0000 0.9334 1.000 0.000 0.000 0.000
#> GSM531604 1 0.0000 0.9334 1.000 0.000 0.000 0.000
#> GSM531606 1 0.0000 0.9334 1.000 0.000 0.000 0.000
#> GSM531607 1 0.0000 0.9334 1.000 0.000 0.000 0.000
#> GSM531608 2 0.2704 0.8421 0.000 0.876 0.124 0.000
#> GSM531609 4 0.0000 0.8447 0.000 0.000 0.000 1.000
#> GSM531610 4 0.0000 0.8447 0.000 0.000 0.000 1.000
#> GSM531611 4 0.4436 0.8223 0.000 0.052 0.148 0.800
#> GSM531612 4 0.0000 0.8447 0.000 0.000 0.000 1.000
#> GSM531613 4 0.1022 0.8453 0.000 0.000 0.032 0.968
#> GSM531614 4 0.0000 0.8447 0.000 0.000 0.000 1.000
#> GSM531616 2 0.4967 0.0859 0.000 0.548 0.452 0.000
#> GSM531618 4 0.4872 0.8046 0.000 0.076 0.148 0.776
#> GSM531619 2 0.0000 0.9415 0.000 1.000 0.000 0.000
#> GSM531620 2 0.0707 0.9329 0.000 0.980 0.020 0.000
#> GSM531621 2 0.0000 0.9415 0.000 1.000 0.000 0.000
#> GSM531622 2 0.0000 0.9415 0.000 1.000 0.000 0.000
#> GSM531623 2 0.0000 0.9415 0.000 1.000 0.000 0.000
#> GSM531625 2 0.0188 0.9406 0.000 0.996 0.004 0.000
#> GSM531626 2 0.2011 0.8872 0.000 0.920 0.080 0.000
#> GSM531628 3 0.0000 0.8982 0.000 0.000 1.000 0.000
#> GSM531630 2 0.0000 0.9415 0.000 1.000 0.000 0.000
#> GSM531632 3 0.0000 0.8982 0.000 0.000 1.000 0.000
#> GSM531633 2 0.0000 0.9415 0.000 1.000 0.000 0.000
#> GSM531635 3 0.0000 0.8982 0.000 0.000 1.000 0.000
#> GSM531638 2 0.0336 0.9392 0.000 0.992 0.008 0.000
#> GSM531639 3 0.1557 0.8805 0.000 0.056 0.944 0.000
#> GSM531640 2 0.0000 0.9415 0.000 1.000 0.000 0.000
#> GSM531641 4 0.0000 0.8447 0.000 0.000 0.000 1.000
#> GSM531642 3 0.1474 0.8826 0.000 0.052 0.948 0.000
#> GSM531643 3 0.0336 0.8968 0.000 0.008 0.992 0.000
#> GSM531644 3 0.1042 0.8898 0.000 0.008 0.972 0.020
#> GSM531645 4 0.0000 0.8447 0.000 0.000 0.000 1.000
#> GSM531646 3 0.0000 0.8982 0.000 0.000 1.000 0.000
#> GSM531647 3 0.0000 0.8982 0.000 0.000 1.000 0.000
#> GSM531648 4 0.0000 0.8447 0.000 0.000 0.000 1.000
#> GSM531649 3 0.0000 0.8982 0.000 0.000 1.000 0.000
#> GSM531650 3 0.0000 0.8982 0.000 0.000 1.000 0.000
#> GSM531651 2 0.0000 0.9415 0.000 1.000 0.000 0.000
#> GSM531652 4 0.6315 0.1009 0.000 0.060 0.432 0.508
#> GSM531653 3 0.0000 0.8982 0.000 0.000 1.000 0.000
#> GSM531656 3 0.1474 0.8826 0.000 0.052 0.948 0.000
#> GSM531657 4 0.4436 0.8223 0.000 0.052 0.148 0.800
#> GSM531659 4 0.4436 0.8223 0.000 0.052 0.148 0.800
#> GSM531661 2 0.4436 0.7941 0.052 0.800 0.148 0.000
#> GSM531662 2 0.4436 0.7941 0.052 0.800 0.148 0.000
#> GSM531663 4 0.4436 0.8223 0.000 0.052 0.148 0.800
#> GSM531664 3 0.0000 0.8982 0.000 0.000 1.000 0.000
#> GSM531665 3 0.6061 0.2629 0.400 0.048 0.552 0.000
#> GSM531666 3 0.1474 0.8826 0.000 0.052 0.948 0.000
#> GSM531667 2 0.0921 0.9278 0.000 0.972 0.028 0.000
#> GSM531668 4 0.4436 0.8223 0.000 0.052 0.148 0.800
#> GSM531669 3 0.0000 0.8982 0.000 0.000 1.000 0.000
#> GSM531670 3 0.1474 0.8826 0.000 0.052 0.948 0.000
#> GSM531671 3 0.4998 0.6861 0.052 0.200 0.748 0.000
#> GSM531672 4 0.4436 0.8223 0.000 0.052 0.148 0.800
#> GSM531673 1 0.3024 0.7602 0.852 0.000 0.148 0.000
#> GSM531674 3 0.0000 0.8982 0.000 0.000 1.000 0.000
#> GSM531675 1 0.1118 0.9082 0.964 0.000 0.000 0.036
#> GSM531676 1 0.0000 0.9334 1.000 0.000 0.000 0.000
#> GSM531677 1 0.0000 0.9334 1.000 0.000 0.000 0.000
#> GSM531678 1 0.0000 0.9334 1.000 0.000 0.000 0.000
#> GSM531679 1 0.0000 0.9334 1.000 0.000 0.000 0.000
#> GSM531680 1 0.3873 0.6793 0.772 0.000 0.228 0.000
#> GSM531681 1 0.4643 0.4602 0.656 0.000 0.000 0.344
#> GSM531682 1 0.0000 0.9334 1.000 0.000 0.000 0.000
#> GSM531683 1 0.0000 0.9334 1.000 0.000 0.000 0.000
#> GSM531684 1 0.0000 0.9334 1.000 0.000 0.000 0.000
#> GSM531685 1 0.4866 0.2830 0.596 0.000 0.404 0.000
#> GSM531686 1 0.2589 0.8333 0.884 0.000 0.000 0.116
#> GSM531687 1 0.1211 0.9039 0.960 0.000 0.040 0.000
#> GSM531688 3 0.3610 0.7301 0.200 0.000 0.800 0.000
#> GSM531689 1 0.0000 0.9334 1.000 0.000 0.000 0.000
#> GSM531690 4 0.5000 -0.0426 0.496 0.000 0.000 0.504
#> GSM531691 1 0.0000 0.9334 1.000 0.000 0.000 0.000
#> GSM531692 1 0.0000 0.9334 1.000 0.000 0.000 0.000
#> GSM531693 3 0.3610 0.7301 0.200 0.000 0.800 0.000
#> GSM531694 1 0.0000 0.9334 1.000 0.000 0.000 0.000
#> GSM531695 3 0.3610 0.7301 0.200 0.000 0.800 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM531600 3 0.2812 0.7640 0.004 0.096 0.876 0.000 0.024
#> GSM531601 2 0.1579 0.9061 0.000 0.944 0.000 0.032 0.024
#> GSM531605 1 0.0963 0.6770 0.964 0.000 0.000 0.000 0.036
#> GSM531615 2 0.0324 0.9253 0.004 0.992 0.000 0.000 0.004
#> GSM531617 2 0.1794 0.9168 0.012 0.944 0.012 0.008 0.024
#> GSM531624 2 0.0000 0.9258 0.000 1.000 0.000 0.000 0.000
#> GSM531627 2 0.0609 0.9216 0.000 0.980 0.020 0.000 0.000
#> GSM531629 2 0.4695 0.7505 0.112 0.784 0.040 0.060 0.004
#> GSM531631 2 0.0000 0.9258 0.000 1.000 0.000 0.000 0.000
#> GSM531634 2 0.0703 0.9213 0.000 0.976 0.000 0.000 0.024
#> GSM531636 3 0.3862 0.7346 0.000 0.104 0.808 0.000 0.088
#> GSM531637 2 0.0000 0.9258 0.000 1.000 0.000 0.000 0.000
#> GSM531654 2 0.5543 0.6663 0.124 0.716 0.052 0.000 0.108
#> GSM531655 3 0.8413 0.1482 0.144 0.296 0.428 0.044 0.088
#> GSM531658 4 0.0162 0.7763 0.000 0.000 0.004 0.996 0.000
#> GSM531660 4 0.6747 0.6928 0.136 0.040 0.044 0.648 0.132
#> GSM531602 1 0.0000 0.6964 1.000 0.000 0.000 0.000 0.000
#> GSM531603 1 0.0880 0.6860 0.968 0.000 0.000 0.000 0.032
#> GSM531604 1 0.4030 -0.0297 0.648 0.000 0.000 0.000 0.352
#> GSM531606 1 0.0794 0.6838 0.972 0.000 0.000 0.000 0.028
#> GSM531607 1 0.0000 0.6964 1.000 0.000 0.000 0.000 0.000
#> GSM531608 2 0.3265 0.8607 0.036 0.868 0.068 0.000 0.028
#> GSM531609 4 0.0000 0.7749 0.000 0.000 0.000 1.000 0.000
#> GSM531610 4 0.0865 0.7776 0.024 0.000 0.000 0.972 0.004
#> GSM531611 4 0.6060 0.6936 0.136 0.000 0.152 0.664 0.048
#> GSM531612 4 0.0000 0.7749 0.000 0.000 0.000 1.000 0.000
#> GSM531613 4 0.4006 0.7529 0.116 0.000 0.028 0.816 0.040
#> GSM531614 4 0.0000 0.7749 0.000 0.000 0.000 1.000 0.000
#> GSM531616 3 0.5847 0.1527 0.000 0.424 0.480 0.000 0.096
#> GSM531618 4 0.6484 0.7023 0.140 0.112 0.076 0.660 0.012
#> GSM531619 2 0.0000 0.9258 0.000 1.000 0.000 0.000 0.000
#> GSM531620 2 0.1597 0.9063 0.012 0.940 0.048 0.000 0.000
#> GSM531621 2 0.0000 0.9258 0.000 1.000 0.000 0.000 0.000
#> GSM531622 2 0.0000 0.9258 0.000 1.000 0.000 0.000 0.000
#> GSM531623 2 0.0000 0.9258 0.000 1.000 0.000 0.000 0.000
#> GSM531625 2 0.1153 0.9173 0.008 0.964 0.024 0.000 0.004
#> GSM531626 2 0.4169 0.7983 0.000 0.784 0.116 0.000 0.100
#> GSM531628 3 0.0963 0.7872 0.000 0.000 0.964 0.000 0.036
#> GSM531630 2 0.0000 0.9258 0.000 1.000 0.000 0.000 0.000
#> GSM531632 3 0.1544 0.7822 0.000 0.000 0.932 0.000 0.068
#> GSM531633 2 0.0000 0.9258 0.000 1.000 0.000 0.000 0.000
#> GSM531635 3 0.1124 0.7918 0.004 0.000 0.960 0.000 0.036
#> GSM531638 2 0.2653 0.8771 0.000 0.880 0.024 0.000 0.096
#> GSM531639 3 0.3919 0.7401 0.008 0.100 0.816 0.000 0.076
#> GSM531640 2 0.0703 0.9213 0.000 0.976 0.000 0.000 0.024
#> GSM531641 4 0.0162 0.7765 0.004 0.000 0.000 0.996 0.000
#> GSM531642 3 0.3757 0.7115 0.088 0.076 0.828 0.000 0.008
#> GSM531643 3 0.0960 0.7923 0.016 0.000 0.972 0.004 0.008
#> GSM531644 3 0.2580 0.7649 0.044 0.000 0.892 0.064 0.000
#> GSM531645 4 0.0000 0.7749 0.000 0.000 0.000 1.000 0.000
#> GSM531646 3 0.0000 0.7937 0.000 0.000 1.000 0.000 0.000
#> GSM531647 3 0.0963 0.7872 0.000 0.000 0.964 0.000 0.036
#> GSM531648 4 0.0404 0.7777 0.012 0.000 0.000 0.988 0.000
#> GSM531649 3 0.0566 0.7939 0.004 0.000 0.984 0.000 0.012
#> GSM531650 3 0.0880 0.7890 0.000 0.000 0.968 0.000 0.032
#> GSM531651 2 0.0000 0.9258 0.000 1.000 0.000 0.000 0.000
#> GSM531652 4 0.6182 0.3907 0.088 0.016 0.336 0.556 0.004
#> GSM531653 3 0.0880 0.7890 0.000 0.000 0.968 0.000 0.032
#> GSM531656 3 0.3058 0.7575 0.000 0.096 0.860 0.000 0.044
#> GSM531657 4 0.6784 0.7304 0.116 0.092 0.040 0.660 0.092
#> GSM531659 4 0.6749 0.7155 0.140 0.088 0.056 0.660 0.056
#> GSM531661 2 0.3640 0.8301 0.008 0.832 0.052 0.000 0.108
#> GSM531662 2 0.6525 0.4872 0.144 0.612 0.052 0.000 0.192
#> GSM531663 4 0.6784 0.7304 0.116 0.092 0.040 0.660 0.092
#> GSM531664 3 0.1732 0.7807 0.000 0.000 0.920 0.000 0.080
#> GSM531665 5 0.6150 0.6140 0.148 0.028 0.192 0.000 0.632
#> GSM531666 3 0.4126 0.7103 0.092 0.052 0.824 0.012 0.020
#> GSM531667 2 0.1571 0.9056 0.000 0.936 0.060 0.000 0.004
#> GSM531668 4 0.6783 0.7308 0.116 0.088 0.040 0.660 0.096
#> GSM531669 3 0.1732 0.7807 0.000 0.000 0.920 0.000 0.080
#> GSM531670 3 0.3622 0.7542 0.004 0.096 0.832 0.000 0.068
#> GSM531671 5 0.6120 0.5998 0.144 0.024 0.204 0.000 0.628
#> GSM531672 4 0.6553 0.7313 0.124 0.092 0.040 0.676 0.068
#> GSM531673 5 0.6009 0.5703 0.300 0.048 0.052 0.000 0.600
#> GSM531674 3 0.1732 0.7807 0.000 0.000 0.920 0.000 0.080
#> GSM531675 1 0.3366 0.6047 0.768 0.000 0.000 0.000 0.232
#> GSM531676 5 0.4743 0.4893 0.472 0.000 0.016 0.000 0.512
#> GSM531677 1 0.3366 0.6047 0.768 0.000 0.000 0.000 0.232
#> GSM531678 1 0.0404 0.6919 0.988 0.000 0.000 0.000 0.012
#> GSM531679 1 0.0000 0.6964 1.000 0.000 0.000 0.000 0.000
#> GSM531680 1 0.6818 -0.3651 0.356 0.000 0.328 0.000 0.316
#> GSM531681 1 0.3366 0.6047 0.768 0.000 0.000 0.000 0.232
#> GSM531682 1 0.2377 0.6504 0.872 0.000 0.000 0.000 0.128
#> GSM531683 1 0.1270 0.6862 0.948 0.000 0.000 0.000 0.052
#> GSM531684 1 0.3837 0.1457 0.692 0.000 0.000 0.000 0.308
#> GSM531685 5 0.6122 0.6180 0.348 0.000 0.140 0.000 0.512
#> GSM531686 1 0.3366 0.6047 0.768 0.000 0.000 0.000 0.232
#> GSM531687 5 0.5988 0.5124 0.364 0.000 0.120 0.000 0.516
#> GSM531688 3 0.6348 0.0698 0.196 0.000 0.512 0.000 0.292
#> GSM531689 1 0.3707 0.1911 0.716 0.000 0.000 0.000 0.284
#> GSM531690 1 0.3521 0.6006 0.764 0.000 0.000 0.004 0.232
#> GSM531691 1 0.4307 -0.5247 0.500 0.000 0.000 0.000 0.500
#> GSM531692 5 0.4305 0.4487 0.488 0.000 0.000 0.000 0.512
#> GSM531693 3 0.5287 0.4546 0.092 0.000 0.648 0.000 0.260
#> GSM531694 1 0.0000 0.6964 1.000 0.000 0.000 0.000 0.000
#> GSM531695 3 0.6637 -0.1734 0.252 0.000 0.448 0.000 0.300
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM531600 3 0.3006 0.7777 0.000 0.028 0.856 0.100 0.004 0.012
#> GSM531601 2 0.0291 0.9274 0.000 0.992 0.000 0.004 0.004 0.000
#> GSM531605 1 0.0725 0.7711 0.976 0.000 0.000 0.000 0.012 0.012
#> GSM531615 2 0.0000 0.9292 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531617 2 0.1585 0.8963 0.004 0.940 0.004 0.044 0.004 0.004
#> GSM531624 2 0.0000 0.9292 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531627 2 0.1078 0.9166 0.000 0.964 0.008 0.012 0.000 0.016
#> GSM531629 2 0.3440 0.7763 0.036 0.828 0.000 0.108 0.000 0.028
#> GSM531631 2 0.0000 0.9292 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531634 2 0.0146 0.9288 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM531636 3 0.4089 0.7378 0.000 0.076 0.800 0.088 0.016 0.020
#> GSM531637 2 0.0146 0.9292 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM531654 4 0.5577 0.5343 0.044 0.292 0.008 0.612 0.012 0.032
#> GSM531655 4 0.5834 0.5498 0.056 0.044 0.212 0.652 0.008 0.028
#> GSM531658 6 0.3531 0.9410 0.000 0.000 0.000 0.328 0.000 0.672
#> GSM531660 4 0.1970 0.5746 0.060 0.000 0.000 0.912 0.000 0.028
#> GSM531602 1 0.0603 0.7751 0.980 0.000 0.000 0.004 0.000 0.016
#> GSM531603 1 0.4437 -0.0959 0.540 0.000 0.000 0.436 0.004 0.020
#> GSM531604 1 0.3175 0.6968 0.832 0.000 0.000 0.000 0.080 0.088
#> GSM531606 1 0.1701 0.7523 0.920 0.000 0.000 0.000 0.008 0.072
#> GSM531607 1 0.0547 0.7754 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM531608 4 0.5042 0.2771 0.008 0.440 0.028 0.512 0.004 0.008
#> GSM531609 6 0.3499 0.9499 0.000 0.000 0.000 0.320 0.000 0.680
#> GSM531610 4 0.3742 -0.1876 0.004 0.000 0.000 0.648 0.000 0.348
#> GSM531611 4 0.3483 0.5208 0.040 0.000 0.120 0.820 0.000 0.020
#> GSM531612 6 0.3499 0.9499 0.000 0.000 0.000 0.320 0.000 0.680
#> GSM531613 4 0.3136 0.3372 0.016 0.000 0.000 0.796 0.000 0.188
#> GSM531614 6 0.3499 0.9499 0.000 0.000 0.000 0.320 0.000 0.680
#> GSM531616 3 0.6106 0.4039 0.000 0.292 0.564 0.084 0.016 0.044
#> GSM531618 6 0.6687 0.6010 0.048 0.020 0.112 0.380 0.000 0.440
#> GSM531619 2 0.0000 0.9292 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531620 2 0.2267 0.8833 0.004 0.912 0.036 0.036 0.008 0.004
#> GSM531621 2 0.0458 0.9256 0.000 0.984 0.000 0.000 0.000 0.016
#> GSM531622 2 0.0000 0.9292 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531623 2 0.0146 0.9292 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM531625 2 0.3150 0.8056 0.000 0.840 0.112 0.012 0.000 0.036
#> GSM531626 2 0.6310 0.3068 0.000 0.516 0.332 0.092 0.016 0.044
#> GSM531628 3 0.2917 0.7707 0.000 0.000 0.840 0.008 0.136 0.016
#> GSM531630 2 0.0146 0.9292 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM531632 3 0.3348 0.7234 0.000 0.000 0.768 0.000 0.216 0.016
#> GSM531633 2 0.0363 0.9271 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM531635 3 0.1408 0.7975 0.000 0.000 0.944 0.036 0.000 0.020
#> GSM531638 2 0.4816 0.7082 0.000 0.740 0.148 0.052 0.016 0.044
#> GSM531639 3 0.3582 0.7550 0.000 0.076 0.828 0.072 0.004 0.020
#> GSM531640 2 0.0146 0.9288 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM531641 6 0.3499 0.9499 0.000 0.000 0.000 0.320 0.000 0.680
#> GSM531642 3 0.2589 0.7831 0.024 0.000 0.888 0.060 0.000 0.028
#> GSM531643 3 0.1495 0.8013 0.008 0.000 0.948 0.020 0.020 0.004
#> GSM531644 3 0.3342 0.7866 0.012 0.000 0.848 0.020 0.036 0.084
#> GSM531645 6 0.3499 0.9499 0.000 0.000 0.000 0.320 0.000 0.680
#> GSM531646 3 0.2367 0.7870 0.000 0.000 0.888 0.008 0.088 0.016
#> GSM531647 3 0.2917 0.7707 0.000 0.000 0.840 0.008 0.136 0.016
#> GSM531648 6 0.3636 0.9454 0.004 0.000 0.000 0.320 0.000 0.676
#> GSM531649 3 0.2342 0.7979 0.000 0.032 0.904 0.040 0.000 0.024
#> GSM531650 3 0.2791 0.7744 0.000 0.000 0.852 0.008 0.124 0.016
#> GSM531651 2 0.0146 0.9292 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM531652 3 0.5766 0.4638 0.020 0.016 0.604 0.104 0.000 0.256
#> GSM531653 3 0.2876 0.7720 0.000 0.000 0.844 0.008 0.132 0.016
#> GSM531656 3 0.3358 0.7666 0.000 0.060 0.844 0.072 0.004 0.020
#> GSM531657 4 0.2825 0.5652 0.040 0.000 0.056 0.876 0.000 0.028
#> GSM531659 4 0.2925 0.5809 0.052 0.000 0.080 0.860 0.000 0.008
#> GSM531661 4 0.4673 0.3976 0.004 0.392 0.008 0.576 0.012 0.008
#> GSM531662 4 0.6740 0.5536 0.084 0.160 0.008 0.608 0.048 0.092
#> GSM531663 4 0.2886 0.5632 0.040 0.000 0.060 0.872 0.000 0.028
#> GSM531664 3 0.3516 0.7203 0.000 0.000 0.760 0.004 0.220 0.016
#> GSM531665 4 0.6317 0.4608 0.076 0.000 0.064 0.584 0.248 0.028
#> GSM531666 3 0.2420 0.7828 0.032 0.000 0.892 0.068 0.000 0.008
#> GSM531667 2 0.1500 0.8897 0.000 0.936 0.012 0.052 0.000 0.000
#> GSM531668 4 0.2137 0.5732 0.048 0.000 0.012 0.912 0.000 0.028
#> GSM531669 3 0.3895 0.6510 0.000 0.000 0.696 0.004 0.284 0.016
#> GSM531670 3 0.3475 0.7562 0.004 0.008 0.816 0.144 0.008 0.020
#> GSM531671 4 0.6943 0.5034 0.056 0.000 0.184 0.568 0.096 0.096
#> GSM531672 4 0.2763 0.5667 0.040 0.000 0.052 0.880 0.000 0.028
#> GSM531673 4 0.6253 0.4870 0.200 0.008 0.008 0.612 0.076 0.096
#> GSM531674 3 0.3790 0.6763 0.000 0.000 0.716 0.004 0.264 0.016
#> GSM531675 1 0.4873 0.5918 0.676 0.000 0.000 0.004 0.164 0.156
#> GSM531676 5 0.4219 0.5401 0.388 0.000 0.000 0.000 0.592 0.020
#> GSM531677 1 0.4533 0.6191 0.704 0.000 0.000 0.000 0.140 0.156
#> GSM531678 1 0.0000 0.7746 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531679 1 0.1461 0.7712 0.940 0.000 0.000 0.000 0.044 0.016
#> GSM531680 5 0.3136 0.7954 0.188 0.000 0.016 0.000 0.796 0.000
#> GSM531681 1 0.4873 0.5918 0.676 0.000 0.000 0.004 0.164 0.156
#> GSM531682 1 0.2790 0.7174 0.840 0.000 0.000 0.000 0.140 0.020
#> GSM531683 1 0.0632 0.7752 0.976 0.000 0.000 0.000 0.000 0.024
#> GSM531684 1 0.2647 0.7219 0.868 0.000 0.000 0.000 0.044 0.088
#> GSM531685 5 0.3916 0.6922 0.300 0.000 0.000 0.000 0.680 0.020
#> GSM531686 1 0.4873 0.5918 0.676 0.000 0.000 0.004 0.164 0.156
#> GSM531687 5 0.3345 0.7664 0.204 0.000 0.000 0.000 0.776 0.020
#> GSM531688 5 0.3936 0.7770 0.124 0.000 0.088 0.000 0.780 0.008
#> GSM531689 1 0.2412 0.7327 0.880 0.000 0.000 0.000 0.092 0.028
#> GSM531690 1 0.4873 0.5918 0.676 0.000 0.000 0.004 0.164 0.156
#> GSM531691 1 0.3328 0.6836 0.816 0.000 0.000 0.000 0.120 0.064
#> GSM531692 1 0.3469 0.6818 0.808 0.000 0.000 0.000 0.104 0.088
#> GSM531693 5 0.3139 0.6780 0.036 0.000 0.120 0.000 0.836 0.008
#> GSM531694 1 0.0603 0.7751 0.980 0.000 0.000 0.004 0.000 0.016
#> GSM531695 5 0.3506 0.8021 0.156 0.000 0.052 0.000 0.792 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> SD:mclust 86 0.0397 2
#> SD:mclust 92 0.0774 3
#> SD:mclust 90 0.0105 4
#> SD:mclust 82 0.0192 5
#> SD:mclust 86 0.0176 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "NMF"]
# you can also extract it by
# res = res_list["SD:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 96 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.594 0.864 0.920 0.4985 0.498 0.498
#> 3 3 0.847 0.912 0.960 0.3432 0.728 0.506
#> 4 4 0.837 0.855 0.939 0.1241 0.840 0.566
#> 5 5 0.741 0.719 0.852 0.0557 0.919 0.698
#> 6 6 0.678 0.591 0.767 0.0409 0.936 0.713
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
#> GSM531600 2 0.1414 0.9130 0.020 0.980
#> GSM531601 2 0.6801 0.8389 0.180 0.820
#> GSM531605 1 0.1184 0.9143 0.984 0.016
#> GSM531615 2 0.6801 0.8389 0.180 0.820
#> GSM531617 2 0.6801 0.8389 0.180 0.820
#> GSM531624 2 0.6801 0.8389 0.180 0.820
#> GSM531627 2 0.2948 0.9000 0.052 0.948
#> GSM531629 1 0.1414 0.9118 0.980 0.020
#> GSM531631 2 0.6801 0.8389 0.180 0.820
#> GSM531634 2 0.6801 0.8389 0.180 0.820
#> GSM531636 2 0.0000 0.9110 0.000 1.000
#> GSM531637 2 0.6801 0.8389 0.180 0.820
#> GSM531654 2 0.6887 0.8351 0.184 0.816
#> GSM531655 2 0.8499 0.7153 0.276 0.724
#> GSM531658 1 0.0000 0.9211 1.000 0.000
#> GSM531660 1 0.1414 0.9118 0.980 0.020
#> GSM531602 1 0.1414 0.9118 0.980 0.020
#> GSM531603 1 0.1414 0.9118 0.980 0.020
#> GSM531604 1 0.1184 0.9143 0.984 0.016
#> GSM531606 1 0.1414 0.9118 0.980 0.020
#> GSM531607 1 0.0000 0.9211 1.000 0.000
#> GSM531608 2 0.6801 0.8389 0.180 0.820
#> GSM531609 1 0.0000 0.9211 1.000 0.000
#> GSM531610 1 0.0000 0.9211 1.000 0.000
#> GSM531611 1 0.0000 0.9211 1.000 0.000
#> GSM531612 1 0.0000 0.9211 1.000 0.000
#> GSM531613 1 0.0000 0.9211 1.000 0.000
#> GSM531614 1 0.0000 0.9211 1.000 0.000
#> GSM531616 2 0.0000 0.9110 0.000 1.000
#> GSM531618 1 0.9977 -0.0398 0.528 0.472
#> GSM531619 2 0.6801 0.8389 0.180 0.820
#> GSM531620 2 0.0376 0.9114 0.004 0.996
#> GSM531621 2 0.0000 0.9110 0.000 1.000
#> GSM531622 2 0.6801 0.8389 0.180 0.820
#> GSM531623 2 0.6343 0.8507 0.160 0.840
#> GSM531625 2 0.0000 0.9110 0.000 1.000
#> GSM531626 2 0.0000 0.9110 0.000 1.000
#> GSM531628 2 0.1414 0.9130 0.020 0.980
#> GSM531630 2 0.6712 0.8414 0.176 0.824
#> GSM531632 2 0.1414 0.9130 0.020 0.980
#> GSM531633 2 0.0376 0.9114 0.004 0.996
#> GSM531635 2 0.1414 0.9130 0.020 0.980
#> GSM531638 2 0.0000 0.9110 0.000 1.000
#> GSM531639 2 0.0000 0.9110 0.000 1.000
#> GSM531640 2 0.6801 0.8389 0.180 0.820
#> GSM531641 1 0.0000 0.9211 1.000 0.000
#> GSM531642 2 0.1414 0.9130 0.020 0.980
#> GSM531643 2 0.1414 0.9130 0.020 0.980
#> GSM531644 2 0.1633 0.9113 0.024 0.976
#> GSM531645 1 0.0000 0.9211 1.000 0.000
#> GSM531646 2 0.1414 0.9130 0.020 0.980
#> GSM531647 2 0.1414 0.9130 0.020 0.980
#> GSM531648 1 0.0000 0.9211 1.000 0.000
#> GSM531649 2 0.1414 0.9130 0.020 0.980
#> GSM531650 2 0.1414 0.9130 0.020 0.980
#> GSM531651 2 0.4022 0.8897 0.080 0.920
#> GSM531652 2 0.1414 0.9130 0.020 0.980
#> GSM531653 2 0.1414 0.9130 0.020 0.980
#> GSM531656 2 0.1414 0.9130 0.020 0.980
#> GSM531657 1 0.0000 0.9211 1.000 0.000
#> GSM531659 1 0.0000 0.9211 1.000 0.000
#> GSM531661 2 0.6801 0.8389 0.180 0.820
#> GSM531662 2 0.4815 0.8793 0.104 0.896
#> GSM531663 1 0.0000 0.9211 1.000 0.000
#> GSM531664 2 0.5178 0.8347 0.116 0.884
#> GSM531665 1 0.9977 0.2532 0.528 0.472
#> GSM531666 1 0.9427 0.5309 0.640 0.360
#> GSM531667 2 0.6801 0.8389 0.180 0.820
#> GSM531668 1 0.1184 0.9143 0.984 0.016
#> GSM531669 2 0.1414 0.9130 0.020 0.980
#> GSM531670 2 0.1184 0.9129 0.016 0.984
#> GSM531671 2 0.1414 0.9130 0.020 0.980
#> GSM531672 1 0.0376 0.9197 0.996 0.004
#> GSM531673 2 0.0672 0.9112 0.008 0.992
#> GSM531674 2 0.1414 0.9130 0.020 0.980
#> GSM531675 1 0.0000 0.9211 1.000 0.000
#> GSM531676 1 0.7950 0.7249 0.760 0.240
#> GSM531677 1 0.0672 0.9176 0.992 0.008
#> GSM531678 1 0.0000 0.9211 1.000 0.000
#> GSM531679 1 0.2778 0.8922 0.952 0.048
#> GSM531680 1 0.6801 0.7812 0.820 0.180
#> GSM531681 1 0.0000 0.9211 1.000 0.000
#> GSM531682 1 0.0376 0.9195 0.996 0.004
#> GSM531683 1 0.0000 0.9211 1.000 0.000
#> GSM531684 1 0.4939 0.8396 0.892 0.108
#> GSM531685 2 0.1633 0.9114 0.024 0.976
#> GSM531686 1 0.0000 0.9211 1.000 0.000
#> GSM531687 1 0.6801 0.7812 0.820 0.180
#> GSM531688 1 0.9710 0.4532 0.600 0.400
#> GSM531689 1 0.5629 0.8254 0.868 0.132
#> GSM531690 1 0.0000 0.9211 1.000 0.000
#> GSM531691 1 0.6712 0.7852 0.824 0.176
#> GSM531692 2 0.1414 0.9130 0.020 0.980
#> GSM531693 2 0.1414 0.9130 0.020 0.980
#> GSM531694 1 0.1184 0.9143 0.984 0.016
#> GSM531695 1 0.6801 0.7812 0.820 0.180
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM531600 3 0.0000 0.937 0.000 0.000 1.000
#> GSM531601 2 0.0237 0.956 0.000 0.996 0.004
#> GSM531605 1 0.3686 0.842 0.860 0.140 0.000
#> GSM531615 2 0.0000 0.959 0.000 1.000 0.000
#> GSM531617 2 0.0000 0.959 0.000 1.000 0.000
#> GSM531624 2 0.0000 0.959 0.000 1.000 0.000
#> GSM531627 2 0.0000 0.959 0.000 1.000 0.000
#> GSM531629 2 0.0592 0.950 0.012 0.988 0.000
#> GSM531631 2 0.0000 0.959 0.000 1.000 0.000
#> GSM531634 2 0.0000 0.959 0.000 1.000 0.000
#> GSM531636 3 0.1031 0.922 0.000 0.024 0.976
#> GSM531637 2 0.0000 0.959 0.000 1.000 0.000
#> GSM531654 2 0.0000 0.959 0.000 1.000 0.000
#> GSM531655 2 0.7996 0.245 0.380 0.552 0.068
#> GSM531658 1 0.0424 0.970 0.992 0.000 0.008
#> GSM531660 1 0.0237 0.971 0.996 0.004 0.000
#> GSM531602 1 0.0000 0.973 1.000 0.000 0.000
#> GSM531603 1 0.0000 0.973 1.000 0.000 0.000
#> GSM531604 1 0.4555 0.762 0.800 0.200 0.000
#> GSM531606 1 0.1411 0.948 0.964 0.036 0.000
#> GSM531607 1 0.0000 0.973 1.000 0.000 0.000
#> GSM531608 2 0.0000 0.959 0.000 1.000 0.000
#> GSM531609 1 0.0237 0.972 0.996 0.000 0.004
#> GSM531610 1 0.0000 0.973 1.000 0.000 0.000
#> GSM531611 1 0.0424 0.970 0.992 0.000 0.008
#> GSM531612 1 0.0424 0.970 0.992 0.000 0.008
#> GSM531613 1 0.0000 0.973 1.000 0.000 0.000
#> GSM531614 1 0.0237 0.972 0.996 0.000 0.004
#> GSM531616 3 0.0592 0.930 0.000 0.012 0.988
#> GSM531618 1 0.4862 0.797 0.820 0.160 0.020
#> GSM531619 2 0.0000 0.959 0.000 1.000 0.000
#> GSM531620 2 0.0000 0.959 0.000 1.000 0.000
#> GSM531621 2 0.0000 0.959 0.000 1.000 0.000
#> GSM531622 2 0.0000 0.959 0.000 1.000 0.000
#> GSM531623 2 0.0000 0.959 0.000 1.000 0.000
#> GSM531625 2 0.3551 0.822 0.000 0.868 0.132
#> GSM531626 2 0.5760 0.473 0.000 0.672 0.328
#> GSM531628 3 0.0000 0.937 0.000 0.000 1.000
#> GSM531630 2 0.0000 0.959 0.000 1.000 0.000
#> GSM531632 3 0.0000 0.937 0.000 0.000 1.000
#> GSM531633 2 0.0000 0.959 0.000 1.000 0.000
#> GSM531635 3 0.0237 0.935 0.000 0.004 0.996
#> GSM531638 3 0.5529 0.601 0.000 0.296 0.704
#> GSM531639 3 0.4842 0.702 0.000 0.224 0.776
#> GSM531640 2 0.0000 0.959 0.000 1.000 0.000
#> GSM531641 1 0.0237 0.972 0.996 0.000 0.004
#> GSM531642 3 0.0000 0.937 0.000 0.000 1.000
#> GSM531643 3 0.0000 0.937 0.000 0.000 1.000
#> GSM531644 3 0.0000 0.937 0.000 0.000 1.000
#> GSM531645 1 0.0592 0.968 0.988 0.000 0.012
#> GSM531646 3 0.0000 0.937 0.000 0.000 1.000
#> GSM531647 3 0.0000 0.937 0.000 0.000 1.000
#> GSM531648 1 0.1163 0.956 0.972 0.000 0.028
#> GSM531649 3 0.0237 0.935 0.000 0.004 0.996
#> GSM531650 3 0.0000 0.937 0.000 0.000 1.000
#> GSM531651 2 0.0000 0.959 0.000 1.000 0.000
#> GSM531652 3 0.0000 0.937 0.000 0.000 1.000
#> GSM531653 3 0.0000 0.937 0.000 0.000 1.000
#> GSM531656 3 0.0000 0.937 0.000 0.000 1.000
#> GSM531657 1 0.0000 0.973 1.000 0.000 0.000
#> GSM531659 1 0.0237 0.972 0.996 0.000 0.004
#> GSM531661 2 0.0000 0.959 0.000 1.000 0.000
#> GSM531662 2 0.0000 0.959 0.000 1.000 0.000
#> GSM531663 1 0.0000 0.973 1.000 0.000 0.000
#> GSM531664 3 0.0000 0.937 0.000 0.000 1.000
#> GSM531665 3 0.3879 0.813 0.152 0.000 0.848
#> GSM531666 3 0.0000 0.937 0.000 0.000 1.000
#> GSM531667 2 0.0000 0.959 0.000 1.000 0.000
#> GSM531668 1 0.0000 0.973 1.000 0.000 0.000
#> GSM531669 3 0.0000 0.937 0.000 0.000 1.000
#> GSM531670 3 0.0000 0.937 0.000 0.000 1.000
#> GSM531671 3 0.4555 0.749 0.000 0.200 0.800
#> GSM531672 1 0.0000 0.973 1.000 0.000 0.000
#> GSM531673 2 0.3375 0.857 0.008 0.892 0.100
#> GSM531674 3 0.0000 0.937 0.000 0.000 1.000
#> GSM531675 1 0.0000 0.973 1.000 0.000 0.000
#> GSM531676 3 0.3340 0.849 0.120 0.000 0.880
#> GSM531677 1 0.0000 0.973 1.000 0.000 0.000
#> GSM531678 1 0.0000 0.973 1.000 0.000 0.000
#> GSM531679 1 0.0000 0.973 1.000 0.000 0.000
#> GSM531680 3 0.4346 0.772 0.184 0.000 0.816
#> GSM531681 1 0.0000 0.973 1.000 0.000 0.000
#> GSM531682 1 0.0000 0.973 1.000 0.000 0.000
#> GSM531683 1 0.0000 0.973 1.000 0.000 0.000
#> GSM531684 2 0.0592 0.949 0.012 0.988 0.000
#> GSM531685 3 0.0475 0.934 0.004 0.004 0.992
#> GSM531686 1 0.0000 0.973 1.000 0.000 0.000
#> GSM531687 3 0.5706 0.548 0.320 0.000 0.680
#> GSM531688 3 0.0000 0.937 0.000 0.000 1.000
#> GSM531689 1 0.1643 0.939 0.956 0.000 0.044
#> GSM531690 1 0.0000 0.973 1.000 0.000 0.000
#> GSM531691 1 0.4555 0.742 0.800 0.000 0.200
#> GSM531692 3 0.7616 0.535 0.072 0.292 0.636
#> GSM531693 3 0.0000 0.937 0.000 0.000 1.000
#> GSM531694 1 0.0000 0.973 1.000 0.000 0.000
#> GSM531695 3 0.0000 0.937 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM531600 3 0.0000 0.9214 0.000 0.000 1.000 0.000
#> GSM531601 2 0.4713 0.4721 0.000 0.640 0.000 0.360
#> GSM531605 1 0.0000 0.9310 1.000 0.000 0.000 0.000
#> GSM531615 2 0.0000 0.9404 0.000 1.000 0.000 0.000
#> GSM531617 2 0.0592 0.9302 0.000 0.984 0.000 0.016
#> GSM531624 2 0.0000 0.9404 0.000 1.000 0.000 0.000
#> GSM531627 2 0.0000 0.9404 0.000 1.000 0.000 0.000
#> GSM531629 2 0.4222 0.6375 0.000 0.728 0.000 0.272
#> GSM531631 2 0.0000 0.9404 0.000 1.000 0.000 0.000
#> GSM531634 2 0.0000 0.9404 0.000 1.000 0.000 0.000
#> GSM531636 3 0.2704 0.8032 0.000 0.124 0.876 0.000
#> GSM531637 2 0.0000 0.9404 0.000 1.000 0.000 0.000
#> GSM531654 2 0.2647 0.8323 0.120 0.880 0.000 0.000
#> GSM531655 2 0.4643 0.5029 0.000 0.656 0.000 0.344
#> GSM531658 4 0.0000 0.9246 0.000 0.000 0.000 1.000
#> GSM531660 4 0.4713 0.4645 0.360 0.000 0.000 0.640
#> GSM531602 1 0.0000 0.9310 1.000 0.000 0.000 0.000
#> GSM531603 1 0.0000 0.9310 1.000 0.000 0.000 0.000
#> GSM531604 1 0.0000 0.9310 1.000 0.000 0.000 0.000
#> GSM531606 1 0.0000 0.9310 1.000 0.000 0.000 0.000
#> GSM531607 1 0.0000 0.9310 1.000 0.000 0.000 0.000
#> GSM531608 2 0.0000 0.9404 0.000 1.000 0.000 0.000
#> GSM531609 4 0.0000 0.9246 0.000 0.000 0.000 1.000
#> GSM531610 4 0.0000 0.9246 0.000 0.000 0.000 1.000
#> GSM531611 4 0.0000 0.9246 0.000 0.000 0.000 1.000
#> GSM531612 4 0.0000 0.9246 0.000 0.000 0.000 1.000
#> GSM531613 4 0.0000 0.9246 0.000 0.000 0.000 1.000
#> GSM531614 4 0.0000 0.9246 0.000 0.000 0.000 1.000
#> GSM531616 3 0.0000 0.9214 0.000 0.000 1.000 0.000
#> GSM531618 4 0.0000 0.9246 0.000 0.000 0.000 1.000
#> GSM531619 2 0.0000 0.9404 0.000 1.000 0.000 0.000
#> GSM531620 2 0.0000 0.9404 0.000 1.000 0.000 0.000
#> GSM531621 2 0.0000 0.9404 0.000 1.000 0.000 0.000
#> GSM531622 2 0.0000 0.9404 0.000 1.000 0.000 0.000
#> GSM531623 2 0.0000 0.9404 0.000 1.000 0.000 0.000
#> GSM531625 2 0.0188 0.9376 0.000 0.996 0.004 0.000
#> GSM531626 2 0.2530 0.8405 0.000 0.888 0.112 0.000
#> GSM531628 3 0.0000 0.9214 0.000 0.000 1.000 0.000
#> GSM531630 2 0.0000 0.9404 0.000 1.000 0.000 0.000
#> GSM531632 3 0.0000 0.9214 0.000 0.000 1.000 0.000
#> GSM531633 2 0.0000 0.9404 0.000 1.000 0.000 0.000
#> GSM531635 3 0.0000 0.9214 0.000 0.000 1.000 0.000
#> GSM531638 2 0.3801 0.6984 0.000 0.780 0.220 0.000
#> GSM531639 3 0.5000 -0.0439 0.000 0.496 0.504 0.000
#> GSM531640 2 0.0000 0.9404 0.000 1.000 0.000 0.000
#> GSM531641 4 0.0000 0.9246 0.000 0.000 0.000 1.000
#> GSM531642 4 0.3831 0.7203 0.000 0.004 0.204 0.792
#> GSM531643 3 0.0000 0.9214 0.000 0.000 1.000 0.000
#> GSM531644 3 0.2216 0.8399 0.000 0.000 0.908 0.092
#> GSM531645 4 0.0000 0.9246 0.000 0.000 0.000 1.000
#> GSM531646 3 0.0000 0.9214 0.000 0.000 1.000 0.000
#> GSM531647 3 0.0000 0.9214 0.000 0.000 1.000 0.000
#> GSM531648 4 0.0000 0.9246 0.000 0.000 0.000 1.000
#> GSM531649 3 0.0000 0.9214 0.000 0.000 1.000 0.000
#> GSM531650 3 0.0000 0.9214 0.000 0.000 1.000 0.000
#> GSM531651 2 0.0000 0.9404 0.000 1.000 0.000 0.000
#> GSM531652 4 0.0188 0.9220 0.000 0.000 0.004 0.996
#> GSM531653 3 0.0000 0.9214 0.000 0.000 1.000 0.000
#> GSM531656 3 0.0000 0.9214 0.000 0.000 1.000 0.000
#> GSM531657 4 0.0000 0.9246 0.000 0.000 0.000 1.000
#> GSM531659 4 0.4304 0.5611 0.284 0.000 0.000 0.716
#> GSM531661 2 0.0000 0.9404 0.000 1.000 0.000 0.000
#> GSM531662 2 0.0000 0.9404 0.000 1.000 0.000 0.000
#> GSM531663 4 0.2081 0.8517 0.084 0.000 0.000 0.916
#> GSM531664 3 0.0000 0.9214 0.000 0.000 1.000 0.000
#> GSM531665 3 0.4830 0.3364 0.392 0.000 0.608 0.000
#> GSM531666 4 0.4431 0.5597 0.000 0.000 0.304 0.696
#> GSM531667 2 0.0000 0.9404 0.000 1.000 0.000 0.000
#> GSM531668 1 0.4431 0.5171 0.696 0.000 0.000 0.304
#> GSM531669 3 0.0000 0.9214 0.000 0.000 1.000 0.000
#> GSM531670 3 0.0000 0.9214 0.000 0.000 1.000 0.000
#> GSM531671 3 0.2002 0.8831 0.044 0.020 0.936 0.000
#> GSM531672 4 0.0000 0.9246 0.000 0.000 0.000 1.000
#> GSM531673 1 0.4163 0.7500 0.792 0.188 0.020 0.000
#> GSM531674 3 0.0000 0.9214 0.000 0.000 1.000 0.000
#> GSM531675 1 0.0000 0.9310 1.000 0.000 0.000 0.000
#> GSM531676 1 0.3444 0.7686 0.816 0.000 0.184 0.000
#> GSM531677 1 0.0000 0.9310 1.000 0.000 0.000 0.000
#> GSM531678 1 0.0000 0.9310 1.000 0.000 0.000 0.000
#> GSM531679 1 0.0000 0.9310 1.000 0.000 0.000 0.000
#> GSM531680 3 0.4985 0.0867 0.468 0.000 0.532 0.000
#> GSM531681 1 0.3528 0.7675 0.808 0.000 0.000 0.192
#> GSM531682 1 0.0000 0.9310 1.000 0.000 0.000 0.000
#> GSM531683 1 0.0000 0.9310 1.000 0.000 0.000 0.000
#> GSM531684 1 0.1716 0.8867 0.936 0.064 0.000 0.000
#> GSM531685 3 0.2081 0.8584 0.084 0.000 0.916 0.000
#> GSM531686 1 0.3172 0.8058 0.840 0.000 0.000 0.160
#> GSM531687 1 0.2973 0.8197 0.856 0.000 0.144 0.000
#> GSM531688 3 0.0000 0.9214 0.000 0.000 1.000 0.000
#> GSM531689 1 0.0000 0.9310 1.000 0.000 0.000 0.000
#> GSM531690 1 0.1389 0.8999 0.952 0.000 0.000 0.048
#> GSM531691 1 0.0000 0.9310 1.000 0.000 0.000 0.000
#> GSM531692 1 0.2589 0.8485 0.884 0.000 0.116 0.000
#> GSM531693 3 0.0000 0.9214 0.000 0.000 1.000 0.000
#> GSM531694 1 0.0000 0.9310 1.000 0.000 0.000 0.000
#> GSM531695 3 0.0592 0.9114 0.016 0.000 0.984 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM531600 3 0.0324 0.8429 0.000 0.004 0.992 0.000 0.004
#> GSM531601 5 0.4203 0.5685 0.000 0.188 0.000 0.052 0.760
#> GSM531605 1 0.4268 0.2922 0.556 0.000 0.000 0.000 0.444
#> GSM531615 2 0.3010 0.7852 0.000 0.824 0.000 0.004 0.172
#> GSM531617 5 0.6747 0.1178 0.000 0.364 0.000 0.260 0.376
#> GSM531624 2 0.1341 0.8643 0.000 0.944 0.000 0.000 0.056
#> GSM531627 2 0.1270 0.8590 0.000 0.948 0.000 0.000 0.052
#> GSM531629 5 0.5960 -0.0462 0.004 0.444 0.000 0.092 0.460
#> GSM531631 2 0.2280 0.8502 0.000 0.880 0.000 0.000 0.120
#> GSM531634 2 0.3534 0.6892 0.000 0.744 0.000 0.000 0.256
#> GSM531636 3 0.2189 0.8109 0.000 0.012 0.904 0.000 0.084
#> GSM531637 2 0.2377 0.8486 0.000 0.872 0.000 0.000 0.128
#> GSM531654 2 0.4960 0.5564 0.064 0.668 0.000 0.000 0.268
#> GSM531655 5 0.3475 0.5856 0.004 0.180 0.000 0.012 0.804
#> GSM531658 5 0.4306 0.1560 0.000 0.000 0.000 0.492 0.508
#> GSM531660 5 0.4590 0.6015 0.136 0.020 0.000 0.072 0.772
#> GSM531602 1 0.2690 0.7951 0.844 0.000 0.000 0.000 0.156
#> GSM531603 5 0.3707 0.3953 0.284 0.000 0.000 0.000 0.716
#> GSM531604 1 0.0000 0.8459 1.000 0.000 0.000 0.000 0.000
#> GSM531606 1 0.0703 0.8452 0.976 0.000 0.000 0.000 0.024
#> GSM531607 1 0.2732 0.7910 0.840 0.000 0.000 0.000 0.160
#> GSM531608 2 0.1179 0.8639 0.004 0.964 0.000 0.016 0.016
#> GSM531609 4 0.0000 0.9114 0.000 0.000 0.000 1.000 0.000
#> GSM531610 4 0.0000 0.9114 0.000 0.000 0.000 1.000 0.000
#> GSM531611 4 0.0000 0.9114 0.000 0.000 0.000 1.000 0.000
#> GSM531612 4 0.0162 0.9101 0.000 0.000 0.000 0.996 0.004
#> GSM531613 4 0.0000 0.9114 0.000 0.000 0.000 1.000 0.000
#> GSM531614 4 0.0000 0.9114 0.000 0.000 0.000 1.000 0.000
#> GSM531616 3 0.1502 0.8155 0.000 0.056 0.940 0.000 0.004
#> GSM531618 5 0.4329 0.4985 0.000 0.016 0.000 0.312 0.672
#> GSM531619 2 0.2179 0.8527 0.000 0.888 0.000 0.000 0.112
#> GSM531620 2 0.3282 0.7714 0.000 0.804 0.008 0.000 0.188
#> GSM531621 2 0.0703 0.8624 0.000 0.976 0.000 0.000 0.024
#> GSM531622 2 0.1341 0.8670 0.000 0.944 0.000 0.000 0.056
#> GSM531623 2 0.0404 0.8678 0.000 0.988 0.000 0.000 0.012
#> GSM531625 2 0.1997 0.8452 0.000 0.924 0.036 0.000 0.040
#> GSM531626 2 0.4714 0.4609 0.000 0.644 0.324 0.000 0.032
#> GSM531628 3 0.0794 0.8376 0.000 0.000 0.972 0.000 0.028
#> GSM531630 2 0.1608 0.8511 0.000 0.928 0.000 0.000 0.072
#> GSM531632 3 0.0162 0.8425 0.000 0.004 0.996 0.000 0.000
#> GSM531633 2 0.0880 0.8686 0.000 0.968 0.000 0.000 0.032
#> GSM531635 3 0.0290 0.8421 0.000 0.000 0.992 0.000 0.008
#> GSM531638 2 0.4213 0.5021 0.000 0.680 0.308 0.000 0.012
#> GSM531639 3 0.4977 0.0381 0.000 0.472 0.500 0.000 0.028
#> GSM531640 2 0.2110 0.8439 0.000 0.912 0.000 0.016 0.072
#> GSM531641 4 0.0162 0.9101 0.000 0.000 0.000 0.996 0.004
#> GSM531642 3 0.6380 0.2069 0.000 0.000 0.508 0.288 0.204
#> GSM531643 3 0.0794 0.8379 0.000 0.000 0.972 0.000 0.028
#> GSM531644 3 0.4275 0.5741 0.000 0.000 0.696 0.020 0.284
#> GSM531645 4 0.0162 0.9101 0.000 0.000 0.000 0.996 0.004
#> GSM531646 3 0.0000 0.8428 0.000 0.000 1.000 0.000 0.000
#> GSM531647 3 0.0000 0.8428 0.000 0.000 1.000 0.000 0.000
#> GSM531648 5 0.4262 0.3275 0.000 0.000 0.000 0.440 0.560
#> GSM531649 3 0.0000 0.8428 0.000 0.000 1.000 0.000 0.000
#> GSM531650 3 0.1043 0.8338 0.000 0.000 0.960 0.000 0.040
#> GSM531651 2 0.0000 0.8677 0.000 1.000 0.000 0.000 0.000
#> GSM531652 5 0.5268 0.5529 0.000 0.000 0.148 0.172 0.680
#> GSM531653 3 0.0000 0.8428 0.000 0.000 1.000 0.000 0.000
#> GSM531656 3 0.3814 0.7410 0.000 0.124 0.808 0.000 0.068
#> GSM531657 4 0.0865 0.8892 0.004 0.000 0.000 0.972 0.024
#> GSM531659 4 0.1571 0.8634 0.060 0.000 0.000 0.936 0.004
#> GSM531661 2 0.1197 0.8650 0.000 0.952 0.000 0.000 0.048
#> GSM531662 2 0.1216 0.8570 0.020 0.960 0.000 0.000 0.020
#> GSM531663 4 0.1281 0.8826 0.032 0.012 0.000 0.956 0.000
#> GSM531664 3 0.1732 0.8152 0.000 0.000 0.920 0.000 0.080
#> GSM531665 3 0.4347 0.4487 0.356 0.004 0.636 0.000 0.004
#> GSM531666 5 0.4355 0.5760 0.000 0.000 0.164 0.076 0.760
#> GSM531667 2 0.1478 0.8619 0.000 0.936 0.000 0.000 0.064
#> GSM531668 5 0.4211 0.5802 0.148 0.032 0.000 0.028 0.792
#> GSM531669 3 0.0000 0.8428 0.000 0.000 1.000 0.000 0.000
#> GSM531670 3 0.2260 0.8115 0.000 0.064 0.908 0.000 0.028
#> GSM531671 3 0.4191 0.7195 0.096 0.084 0.804 0.000 0.016
#> GSM531672 5 0.4435 0.4587 0.016 0.000 0.000 0.336 0.648
#> GSM531673 1 0.4883 0.6351 0.712 0.228 0.040 0.000 0.020
#> GSM531674 3 0.0000 0.8428 0.000 0.000 1.000 0.000 0.000
#> GSM531675 1 0.1965 0.8251 0.904 0.000 0.000 0.000 0.096
#> GSM531676 1 0.2280 0.7809 0.880 0.000 0.120 0.000 0.000
#> GSM531677 1 0.0451 0.8458 0.988 0.000 0.000 0.008 0.004
#> GSM531678 1 0.2740 0.8074 0.888 0.044 0.000 0.064 0.004
#> GSM531679 1 0.0000 0.8459 1.000 0.000 0.000 0.000 0.000
#> GSM531680 3 0.6622 0.1053 0.328 0.000 0.440 0.000 0.232
#> GSM531681 4 0.3636 0.6072 0.272 0.000 0.000 0.728 0.000
#> GSM531682 1 0.0000 0.8459 1.000 0.000 0.000 0.000 0.000
#> GSM531683 1 0.1478 0.8387 0.936 0.000 0.000 0.000 0.064
#> GSM531684 1 0.3521 0.6900 0.764 0.232 0.000 0.000 0.004
#> GSM531685 3 0.4512 0.6707 0.192 0.040 0.752 0.000 0.016
#> GSM531686 4 0.3752 0.5811 0.292 0.000 0.000 0.708 0.000
#> GSM531687 1 0.4364 0.6680 0.736 0.000 0.216 0.000 0.048
#> GSM531688 3 0.0290 0.8421 0.008 0.000 0.992 0.000 0.000
#> GSM531689 1 0.1565 0.8407 0.952 0.008 0.016 0.020 0.004
#> GSM531690 1 0.4038 0.7576 0.792 0.000 0.000 0.080 0.128
#> GSM531691 1 0.2331 0.8151 0.900 0.080 0.000 0.000 0.020
#> GSM531692 1 0.3653 0.7752 0.840 0.056 0.088 0.000 0.016
#> GSM531693 3 0.0290 0.8421 0.008 0.000 0.992 0.000 0.000
#> GSM531694 1 0.2329 0.8129 0.876 0.000 0.000 0.000 0.124
#> GSM531695 3 0.5151 0.3340 0.044 0.000 0.560 0.000 0.396
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM531600 3 0.3279 0.7409 0.000 0.004 0.828 0.000 0.060 0.108
#> GSM531601 5 0.5894 0.3895 0.000 0.260 0.000 0.028 0.564 0.148
#> GSM531605 5 0.4651 -0.1326 0.480 0.000 0.000 0.000 0.480 0.040
#> GSM531615 6 0.4486 0.4225 0.000 0.208 0.000 0.000 0.096 0.696
#> GSM531617 6 0.3947 0.5098 0.000 0.016 0.000 0.032 0.196 0.756
#> GSM531624 2 0.3950 0.3002 0.000 0.564 0.000 0.000 0.004 0.432
#> GSM531627 2 0.2378 0.5831 0.000 0.848 0.000 0.000 0.000 0.152
#> GSM531629 6 0.4077 0.3569 0.000 0.012 0.000 0.008 0.320 0.660
#> GSM531631 2 0.0858 0.6329 0.000 0.968 0.000 0.000 0.004 0.028
#> GSM531634 6 0.4643 0.4871 0.000 0.128 0.000 0.000 0.184 0.688
#> GSM531636 3 0.4693 0.6622 0.000 0.004 0.692 0.000 0.188 0.116
#> GSM531637 2 0.1588 0.6311 0.000 0.924 0.000 0.000 0.004 0.072
#> GSM531654 6 0.4063 0.4871 0.072 0.012 0.000 0.000 0.148 0.768
#> GSM531655 5 0.4262 0.4984 0.004 0.088 0.016 0.000 0.768 0.124
#> GSM531658 5 0.3887 0.3246 0.000 0.000 0.000 0.360 0.632 0.008
#> GSM531660 5 0.4868 0.3881 0.076 0.000 0.000 0.000 0.592 0.332
#> GSM531602 1 0.4120 0.6992 0.744 0.000 0.000 0.000 0.160 0.096
#> GSM531603 5 0.5047 0.4771 0.208 0.000 0.000 0.000 0.636 0.156
#> GSM531604 1 0.0146 0.8106 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM531606 1 0.1500 0.8067 0.936 0.000 0.000 0.000 0.012 0.052
#> GSM531607 1 0.3445 0.7446 0.796 0.000 0.000 0.000 0.156 0.048
#> GSM531608 6 0.5347 0.1620 0.004 0.304 0.000 0.120 0.000 0.572
#> GSM531609 4 0.0000 0.9440 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531610 4 0.0000 0.9440 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531611 4 0.0000 0.9440 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531612 4 0.0000 0.9440 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531613 4 0.0000 0.9440 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531614 4 0.0000 0.9440 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531616 3 0.2455 0.7494 0.000 0.012 0.872 0.000 0.004 0.112
#> GSM531618 5 0.5730 0.4384 0.000 0.032 0.000 0.124 0.592 0.252
#> GSM531619 2 0.0713 0.6349 0.000 0.972 0.000 0.000 0.000 0.028
#> GSM531620 6 0.3821 0.5283 0.000 0.032 0.068 0.000 0.092 0.808
#> GSM531621 2 0.3789 0.3974 0.000 0.584 0.000 0.000 0.000 0.416
#> GSM531622 2 0.2300 0.6265 0.000 0.856 0.000 0.000 0.000 0.144
#> GSM531623 2 0.3833 0.3071 0.000 0.556 0.000 0.000 0.000 0.444
#> GSM531625 2 0.5272 0.3200 0.000 0.584 0.140 0.000 0.000 0.276
#> GSM531626 6 0.4593 0.2602 0.000 0.044 0.380 0.000 0.000 0.576
#> GSM531628 3 0.1910 0.7522 0.000 0.000 0.892 0.000 0.108 0.000
#> GSM531630 2 0.0363 0.6310 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM531632 3 0.1957 0.7556 0.000 0.000 0.888 0.000 0.000 0.112
#> GSM531633 6 0.3634 0.0570 0.000 0.356 0.000 0.000 0.000 0.644
#> GSM531635 3 0.1007 0.7775 0.000 0.000 0.956 0.000 0.000 0.044
#> GSM531638 2 0.4147 0.1007 0.000 0.552 0.436 0.000 0.000 0.012
#> GSM531639 3 0.5761 0.6049 0.000 0.156 0.644 0.000 0.088 0.112
#> GSM531640 2 0.1858 0.5909 0.000 0.904 0.000 0.092 0.000 0.004
#> GSM531641 4 0.0000 0.9440 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531642 5 0.6071 0.0476 0.000 0.000 0.360 0.220 0.416 0.004
#> GSM531643 3 0.1983 0.7707 0.000 0.000 0.908 0.000 0.072 0.020
#> GSM531644 3 0.3854 0.2926 0.000 0.000 0.536 0.000 0.464 0.000
#> GSM531645 4 0.0000 0.9440 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531646 3 0.1610 0.7680 0.000 0.000 0.916 0.000 0.000 0.084
#> GSM531647 3 0.1387 0.7727 0.000 0.000 0.932 0.000 0.000 0.068
#> GSM531648 5 0.4264 0.5423 0.000 0.000 0.000 0.196 0.720 0.084
#> GSM531649 3 0.2454 0.7309 0.000 0.000 0.840 0.000 0.000 0.160
#> GSM531650 3 0.1700 0.7641 0.000 0.000 0.916 0.000 0.080 0.004
#> GSM531651 2 0.3428 0.5187 0.000 0.696 0.000 0.000 0.000 0.304
#> GSM531652 5 0.4395 0.5404 0.000 0.000 0.132 0.068 0.760 0.040
#> GSM531653 3 0.1141 0.7797 0.000 0.000 0.948 0.000 0.000 0.052
#> GSM531656 3 0.4770 0.6487 0.000 0.132 0.696 0.000 0.164 0.008
#> GSM531657 4 0.2402 0.8287 0.000 0.000 0.000 0.868 0.120 0.012
#> GSM531659 4 0.3362 0.8046 0.076 0.000 0.000 0.824 0.096 0.004
#> GSM531661 2 0.3864 0.2005 0.000 0.520 0.000 0.000 0.000 0.480
#> GSM531662 6 0.4486 0.3308 0.112 0.184 0.000 0.000 0.000 0.704
#> GSM531663 4 0.0458 0.9349 0.016 0.000 0.000 0.984 0.000 0.000
#> GSM531664 3 0.2378 0.7308 0.000 0.000 0.848 0.000 0.152 0.000
#> GSM531665 3 0.4906 0.1066 0.460 0.004 0.492 0.000 0.040 0.004
#> GSM531666 5 0.3314 0.4051 0.000 0.000 0.256 0.000 0.740 0.004
#> GSM531667 2 0.4282 0.4845 0.000 0.656 0.000 0.000 0.040 0.304
#> GSM531668 5 0.4806 0.1814 0.052 0.000 0.000 0.000 0.488 0.460
#> GSM531669 3 0.1556 0.7694 0.000 0.000 0.920 0.000 0.000 0.080
#> GSM531670 3 0.4874 0.6295 0.000 0.168 0.684 0.000 0.140 0.008
#> GSM531671 6 0.4301 0.3023 0.024 0.000 0.392 0.000 0.000 0.584
#> GSM531672 5 0.4323 0.5615 0.028 0.000 0.000 0.188 0.740 0.044
#> GSM531673 6 0.5830 0.2907 0.292 0.068 0.068 0.000 0.000 0.572
#> GSM531674 3 0.0363 0.7774 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM531675 1 0.2595 0.7760 0.836 0.000 0.000 0.000 0.160 0.004
#> GSM531676 1 0.3073 0.6595 0.788 0.000 0.204 0.000 0.000 0.008
#> GSM531677 1 0.0713 0.8093 0.972 0.000 0.000 0.000 0.028 0.000
#> GSM531678 1 0.2959 0.7433 0.844 0.024 0.000 0.124 0.000 0.008
#> GSM531679 1 0.0363 0.8111 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM531680 3 0.6650 0.2559 0.296 0.000 0.456 0.000 0.196 0.052
#> GSM531681 4 0.1957 0.8529 0.112 0.000 0.000 0.888 0.000 0.000
#> GSM531682 1 0.1320 0.8107 0.948 0.000 0.016 0.000 0.000 0.036
#> GSM531683 1 0.2436 0.7906 0.880 0.000 0.000 0.000 0.088 0.032
#> GSM531684 1 0.3653 0.6058 0.692 0.300 0.000 0.000 0.000 0.008
#> GSM531685 3 0.5459 0.4302 0.312 0.012 0.568 0.000 0.000 0.108
#> GSM531686 4 0.2178 0.8299 0.132 0.000 0.000 0.868 0.000 0.000
#> GSM531687 1 0.5348 0.1879 0.508 0.000 0.392 0.000 0.096 0.004
#> GSM531688 3 0.0551 0.7788 0.008 0.000 0.984 0.000 0.004 0.004
#> GSM531689 1 0.0924 0.8108 0.972 0.000 0.004 0.008 0.008 0.008
#> GSM531690 1 0.4141 0.7075 0.740 0.000 0.000 0.040 0.204 0.016
#> GSM531691 1 0.3456 0.7424 0.828 0.068 0.004 0.000 0.008 0.092
#> GSM531692 1 0.3509 0.7251 0.816 0.024 0.128 0.000 0.000 0.032
#> GSM531693 3 0.1549 0.7779 0.020 0.000 0.936 0.000 0.000 0.044
#> GSM531694 1 0.3014 0.7667 0.832 0.000 0.000 0.000 0.132 0.036
#> GSM531695 3 0.5014 0.3290 0.056 0.000 0.544 0.000 0.392 0.008
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> SD:NMF 93 0.15759 2
#> SD:NMF 94 0.00109 3
#> SD:NMF 91 0.00175 4
#> SD:NMF 82 0.00174 5
#> SD:NMF 63 0.01248 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "hclust"]
# you can also extract it by
# res = res_list["CV:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 96 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 0.265 0.703 0.834 0.4615 0.497 0.497
#> 3 3 0.296 0.430 0.653 0.3580 0.730 0.507
#> 4 4 0.510 0.576 0.780 0.1692 0.792 0.470
#> 5 5 0.605 0.645 0.778 0.0686 0.941 0.770
#> 6 6 0.690 0.659 0.794 0.0381 0.938 0.725
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
#> GSM531600 2 0.529 0.8495 0.120 0.880
#> GSM531601 2 0.833 0.6749 0.264 0.736
#> GSM531605 1 0.958 0.4658 0.620 0.380
#> GSM531615 2 0.311 0.8571 0.056 0.944
#> GSM531617 2 0.311 0.8571 0.056 0.944
#> GSM531624 2 0.224 0.8613 0.036 0.964
#> GSM531627 2 0.224 0.8613 0.036 0.964
#> GSM531629 2 0.311 0.8571 0.056 0.944
#> GSM531631 2 0.224 0.8613 0.036 0.964
#> GSM531634 2 0.311 0.8571 0.056 0.944
#> GSM531636 2 0.529 0.8495 0.120 0.880
#> GSM531637 2 0.224 0.8613 0.036 0.964
#> GSM531654 2 0.584 0.7902 0.140 0.860
#> GSM531655 1 0.958 0.4658 0.620 0.380
#> GSM531658 1 0.563 0.7440 0.868 0.132
#> GSM531660 1 0.983 0.3559 0.576 0.424
#> GSM531602 1 0.242 0.7459 0.960 0.040
#> GSM531603 1 0.714 0.7225 0.804 0.196
#> GSM531604 1 0.939 0.5750 0.644 0.356
#> GSM531606 1 0.653 0.7375 0.832 0.168
#> GSM531607 1 0.714 0.7225 0.804 0.196
#> GSM531608 2 0.416 0.8423 0.084 0.916
#> GSM531609 1 0.163 0.7577 0.976 0.024
#> GSM531610 1 0.163 0.7577 0.976 0.024
#> GSM531611 1 0.163 0.7577 0.976 0.024
#> GSM531612 1 0.163 0.7577 0.976 0.024
#> GSM531613 1 0.163 0.7577 0.976 0.024
#> GSM531614 1 0.163 0.7577 0.976 0.024
#> GSM531616 2 0.278 0.8565 0.048 0.952
#> GSM531618 2 0.994 0.0393 0.456 0.544
#> GSM531619 2 0.224 0.8613 0.036 0.964
#> GSM531620 2 0.469 0.8384 0.100 0.900
#> GSM531621 2 0.204 0.8606 0.032 0.968
#> GSM531622 2 0.224 0.8613 0.036 0.964
#> GSM531623 2 0.224 0.8613 0.036 0.964
#> GSM531625 2 0.000 0.8470 0.000 1.000
#> GSM531626 2 0.000 0.8470 0.000 1.000
#> GSM531628 2 0.482 0.8443 0.104 0.896
#> GSM531630 2 0.224 0.8613 0.036 0.964
#> GSM531632 2 0.456 0.8454 0.096 0.904
#> GSM531633 2 0.204 0.8606 0.032 0.968
#> GSM531635 2 0.278 0.8565 0.048 0.952
#> GSM531638 2 0.278 0.8565 0.048 0.952
#> GSM531639 2 0.697 0.8030 0.188 0.812
#> GSM531640 2 0.224 0.8613 0.036 0.964
#> GSM531641 1 0.163 0.7577 0.976 0.024
#> GSM531642 1 0.997 0.2449 0.532 0.468
#> GSM531643 2 0.706 0.7743 0.192 0.808
#> GSM531644 1 0.997 0.2449 0.532 0.468
#> GSM531645 1 0.163 0.7577 0.976 0.024
#> GSM531646 2 0.373 0.8526 0.072 0.928
#> GSM531647 2 0.456 0.8454 0.096 0.904
#> GSM531648 1 0.992 0.2978 0.552 0.448
#> GSM531649 2 0.456 0.8454 0.096 0.904
#> GSM531650 2 0.469 0.8447 0.100 0.900
#> GSM531651 2 0.224 0.8613 0.036 0.964
#> GSM531652 1 0.993 0.2878 0.548 0.452
#> GSM531653 2 0.456 0.8454 0.096 0.904
#> GSM531656 2 0.563 0.8344 0.132 0.868
#> GSM531657 1 0.925 0.5701 0.660 0.340
#> GSM531659 1 0.814 0.6983 0.748 0.252
#> GSM531661 2 0.416 0.8424 0.084 0.916
#> GSM531662 2 0.802 0.6622 0.244 0.756
#> GSM531663 1 0.595 0.7486 0.856 0.144
#> GSM531664 2 0.958 0.3298 0.380 0.620
#> GSM531665 1 0.833 0.6885 0.736 0.264
#> GSM531666 1 0.963 0.4647 0.612 0.388
#> GSM531667 2 0.430 0.8402 0.088 0.912
#> GSM531668 1 0.998 0.1987 0.528 0.472
#> GSM531669 2 0.552 0.8340 0.128 0.872
#> GSM531670 2 0.563 0.8344 0.132 0.868
#> GSM531671 2 0.833 0.6973 0.264 0.736
#> GSM531672 1 0.563 0.7440 0.868 0.132
#> GSM531673 2 0.850 0.5938 0.276 0.724
#> GSM531674 2 0.541 0.8363 0.124 0.876
#> GSM531675 1 0.278 0.7497 0.952 0.048
#> GSM531676 1 0.985 0.4338 0.572 0.428
#> GSM531677 1 0.311 0.7495 0.944 0.056
#> GSM531678 1 0.653 0.7375 0.832 0.168
#> GSM531679 1 0.311 0.7495 0.944 0.056
#> GSM531680 1 0.946 0.4834 0.636 0.364
#> GSM531681 1 0.163 0.7577 0.976 0.024
#> GSM531682 1 0.311 0.7495 0.944 0.056
#> GSM531683 1 0.242 0.7459 0.960 0.040
#> GSM531684 1 0.706 0.7261 0.808 0.192
#> GSM531685 2 0.909 0.5377 0.324 0.676
#> GSM531686 1 0.163 0.7577 0.976 0.024
#> GSM531687 1 0.985 0.4338 0.572 0.428
#> GSM531688 2 0.552 0.8340 0.128 0.872
#> GSM531689 1 0.985 0.4338 0.572 0.428
#> GSM531690 1 0.163 0.7577 0.976 0.024
#> GSM531691 1 0.988 0.4134 0.564 0.436
#> GSM531692 2 0.995 -0.1250 0.460 0.540
#> GSM531693 2 0.595 0.8232 0.144 0.856
#> GSM531694 1 0.242 0.7459 0.960 0.040
#> GSM531695 1 0.946 0.4831 0.636 0.364
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM531600 3 0.6308 0.263951 0.000 0.492 0.508
#> GSM531601 2 0.7190 0.161431 0.044 0.636 0.320
#> GSM531605 1 0.9908 -0.104942 0.372 0.268 0.360
#> GSM531615 2 0.0892 0.765996 0.020 0.980 0.000
#> GSM531617 2 0.0892 0.765996 0.020 0.980 0.000
#> GSM531624 2 0.0000 0.768657 0.000 1.000 0.000
#> GSM531627 2 0.1964 0.738556 0.000 0.944 0.056
#> GSM531629 2 0.0892 0.765996 0.020 0.980 0.000
#> GSM531631 2 0.0000 0.768657 0.000 1.000 0.000
#> GSM531634 2 0.0892 0.765996 0.020 0.980 0.000
#> GSM531636 3 0.6308 0.263951 0.000 0.492 0.508
#> GSM531637 2 0.0000 0.768657 0.000 1.000 0.000
#> GSM531654 2 0.4479 0.680137 0.096 0.860 0.044
#> GSM531655 1 0.9908 -0.104942 0.372 0.268 0.360
#> GSM531658 3 0.7484 -0.420287 0.460 0.036 0.504
#> GSM531660 3 0.9968 0.135989 0.332 0.300 0.368
#> GSM531602 1 0.1482 0.594817 0.968 0.020 0.012
#> GSM531603 1 0.7245 0.481818 0.712 0.120 0.168
#> GSM531604 1 0.8765 0.353994 0.588 0.212 0.200
#> GSM531606 1 0.5408 0.539831 0.812 0.136 0.052
#> GSM531607 1 0.7245 0.481818 0.712 0.120 0.168
#> GSM531608 2 0.1989 0.747729 0.048 0.948 0.004
#> GSM531609 1 0.6359 0.538961 0.592 0.004 0.404
#> GSM531610 1 0.6359 0.538961 0.592 0.004 0.404
#> GSM531611 1 0.6359 0.538961 0.592 0.004 0.404
#> GSM531612 1 0.6359 0.538961 0.592 0.004 0.404
#> GSM531613 1 0.6359 0.538961 0.592 0.004 0.404
#> GSM531614 1 0.6359 0.538961 0.592 0.004 0.404
#> GSM531616 2 0.6235 0.000492 0.000 0.564 0.436
#> GSM531618 2 0.9625 -0.220939 0.212 0.440 0.348
#> GSM531619 2 0.0000 0.768657 0.000 1.000 0.000
#> GSM531620 2 0.4339 0.698579 0.048 0.868 0.084
#> GSM531621 2 0.0892 0.763308 0.000 0.980 0.020
#> GSM531622 2 0.0000 0.768657 0.000 1.000 0.000
#> GSM531623 2 0.0000 0.768657 0.000 1.000 0.000
#> GSM531625 2 0.4346 0.600319 0.000 0.816 0.184
#> GSM531626 2 0.4346 0.600319 0.000 0.816 0.184
#> GSM531628 3 0.6264 0.443390 0.004 0.380 0.616
#> GSM531630 2 0.0000 0.768657 0.000 1.000 0.000
#> GSM531632 3 0.6079 0.435239 0.000 0.388 0.612
#> GSM531633 2 0.0892 0.763308 0.000 0.980 0.020
#> GSM531635 2 0.6244 -0.010525 0.000 0.560 0.440
#> GSM531638 2 0.6235 0.000492 0.000 0.564 0.436
#> GSM531639 2 0.7392 -0.230400 0.032 0.500 0.468
#> GSM531640 2 0.0000 0.768657 0.000 1.000 0.000
#> GSM531641 1 0.6359 0.538961 0.592 0.004 0.404
#> GSM531642 3 0.9820 0.238686 0.276 0.296 0.428
#> GSM531643 3 0.7980 0.436013 0.072 0.356 0.572
#> GSM531644 3 0.9820 0.238686 0.276 0.296 0.428
#> GSM531645 1 0.6359 0.538961 0.592 0.004 0.404
#> GSM531646 3 0.6286 0.267206 0.000 0.464 0.536
#> GSM531647 3 0.6079 0.435239 0.000 0.388 0.612
#> GSM531648 3 0.9910 0.201315 0.292 0.308 0.400
#> GSM531649 3 0.6079 0.435239 0.000 0.388 0.612
#> GSM531650 3 0.6282 0.439534 0.004 0.384 0.612
#> GSM531651 2 0.0000 0.768657 0.000 1.000 0.000
#> GSM531652 3 0.9898 0.208024 0.288 0.308 0.404
#> GSM531653 3 0.6079 0.435239 0.000 0.388 0.612
#> GSM531656 3 0.6924 0.419437 0.020 0.400 0.580
#> GSM531657 1 0.9582 0.215459 0.480 0.256 0.264
#> GSM531659 1 0.7179 0.497909 0.712 0.184 0.104
#> GSM531661 2 0.1989 0.748305 0.048 0.948 0.004
#> GSM531662 2 0.7524 0.491044 0.196 0.688 0.116
#> GSM531663 1 0.6229 0.578549 0.764 0.064 0.172
#> GSM531664 3 0.6599 0.451419 0.084 0.168 0.748
#> GSM531665 1 0.7323 0.483870 0.700 0.196 0.104
#> GSM531666 3 0.8835 0.085147 0.268 0.164 0.568
#> GSM531667 2 0.2096 0.745803 0.052 0.944 0.004
#> GSM531668 3 0.9967 0.213782 0.296 0.340 0.364
#> GSM531669 3 0.6318 0.446460 0.008 0.356 0.636
#> GSM531670 3 0.6924 0.419437 0.020 0.400 0.580
#> GSM531671 2 0.8700 0.147838 0.148 0.576 0.276
#> GSM531672 3 0.7484 -0.420287 0.460 0.036 0.504
#> GSM531673 2 0.7898 0.433875 0.232 0.652 0.116
#> GSM531674 3 0.6169 0.445069 0.004 0.360 0.636
#> GSM531675 1 0.5291 0.576192 0.732 0.000 0.268
#> GSM531676 1 0.9153 0.268882 0.520 0.172 0.308
#> GSM531677 1 0.2636 0.599877 0.932 0.020 0.048
#> GSM531678 1 0.5408 0.539831 0.812 0.136 0.052
#> GSM531679 1 0.2636 0.599877 0.932 0.020 0.048
#> GSM531680 3 0.6805 -0.004842 0.268 0.044 0.688
#> GSM531681 1 0.6126 0.541291 0.600 0.000 0.400
#> GSM531682 1 0.2636 0.599877 0.932 0.020 0.048
#> GSM531683 1 0.1170 0.599763 0.976 0.008 0.016
#> GSM531684 1 0.5947 0.515019 0.776 0.172 0.052
#> GSM531685 3 0.8853 0.328597 0.176 0.252 0.572
#> GSM531686 1 0.6126 0.541291 0.600 0.000 0.400
#> GSM531687 1 0.9153 0.268882 0.520 0.172 0.308
#> GSM531688 3 0.6339 0.442716 0.008 0.360 0.632
#> GSM531689 1 0.9153 0.268882 0.520 0.172 0.308
#> GSM531690 1 0.6330 0.540946 0.600 0.004 0.396
#> GSM531691 1 0.9228 0.251650 0.508 0.176 0.316
#> GSM531692 1 0.9574 0.094481 0.412 0.196 0.392
#> GSM531693 3 0.6696 0.434217 0.020 0.348 0.632
#> GSM531694 1 0.1482 0.594817 0.968 0.020 0.012
#> GSM531695 3 0.6597 0.000235 0.268 0.036 0.696
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM531600 3 0.3166 0.6920 0.016 0.116 0.868 0.000
#> GSM531601 2 0.7971 -0.0453 0.044 0.488 0.352 0.116
#> GSM531605 3 0.9436 -0.1142 0.280 0.100 0.348 0.272
#> GSM531615 2 0.0804 0.8973 0.012 0.980 0.000 0.008
#> GSM531617 2 0.0804 0.8973 0.012 0.980 0.000 0.008
#> GSM531624 2 0.0000 0.8997 0.000 1.000 0.000 0.000
#> GSM531627 2 0.2021 0.8640 0.012 0.932 0.056 0.000
#> GSM531629 2 0.0804 0.8973 0.012 0.980 0.000 0.008
#> GSM531631 2 0.0000 0.8997 0.000 1.000 0.000 0.000
#> GSM531634 2 0.0804 0.8973 0.012 0.980 0.000 0.008
#> GSM531636 3 0.3166 0.6920 0.016 0.116 0.868 0.000
#> GSM531637 2 0.0000 0.8997 0.000 1.000 0.000 0.000
#> GSM531654 2 0.4100 0.8257 0.036 0.852 0.032 0.080
#> GSM531655 3 0.9465 -0.1136 0.276 0.104 0.348 0.272
#> GSM531658 4 0.5787 0.5655 0.124 0.020 0.112 0.744
#> GSM531660 3 0.9534 -0.2105 0.176 0.148 0.344 0.332
#> GSM531602 1 0.4222 0.6234 0.728 0.000 0.000 0.272
#> GSM531603 1 0.7566 0.4314 0.584 0.028 0.176 0.212
#> GSM531604 1 0.3822 0.6307 0.844 0.032 0.120 0.004
#> GSM531606 1 0.2589 0.6650 0.884 0.000 0.000 0.116
#> GSM531607 1 0.7566 0.4314 0.584 0.028 0.176 0.212
#> GSM531608 2 0.1675 0.8821 0.004 0.948 0.004 0.044
#> GSM531609 4 0.0469 0.6398 0.012 0.000 0.000 0.988
#> GSM531610 4 0.0469 0.6398 0.012 0.000 0.000 0.988
#> GSM531611 4 0.0817 0.6350 0.024 0.000 0.000 0.976
#> GSM531612 4 0.0469 0.6398 0.012 0.000 0.000 0.988
#> GSM531613 4 0.0469 0.6398 0.012 0.000 0.000 0.988
#> GSM531614 4 0.0469 0.6398 0.012 0.000 0.000 0.988
#> GSM531616 3 0.4164 0.5582 0.000 0.264 0.736 0.000
#> GSM531618 3 0.8930 -0.1381 0.048 0.304 0.328 0.320
#> GSM531619 2 0.0000 0.8997 0.000 1.000 0.000 0.000
#> GSM531620 2 0.3927 0.8232 0.012 0.856 0.072 0.060
#> GSM531621 2 0.0895 0.8929 0.004 0.976 0.020 0.000
#> GSM531622 2 0.0000 0.8997 0.000 1.000 0.000 0.000
#> GSM531623 2 0.0000 0.8997 0.000 1.000 0.000 0.000
#> GSM531625 2 0.3751 0.7198 0.004 0.800 0.196 0.000
#> GSM531626 2 0.3751 0.7198 0.004 0.800 0.196 0.000
#> GSM531628 3 0.0336 0.7166 0.000 0.000 0.992 0.008
#> GSM531630 2 0.0000 0.8997 0.000 1.000 0.000 0.000
#> GSM531632 3 0.0188 0.7167 0.004 0.000 0.996 0.000
#> GSM531633 2 0.0895 0.8929 0.004 0.976 0.020 0.000
#> GSM531635 3 0.4103 0.5661 0.000 0.256 0.744 0.000
#> GSM531638 3 0.4164 0.5582 0.000 0.264 0.736 0.000
#> GSM531639 3 0.5806 0.5953 0.016 0.196 0.720 0.068
#> GSM531640 2 0.0000 0.8997 0.000 1.000 0.000 0.000
#> GSM531641 4 0.0469 0.6398 0.012 0.000 0.000 0.988
#> GSM531642 4 0.8482 0.1671 0.064 0.132 0.400 0.404
#> GSM531643 3 0.3190 0.6739 0.008 0.016 0.880 0.096
#> GSM531644 4 0.8482 0.1671 0.064 0.132 0.400 0.404
#> GSM531645 4 0.0469 0.6398 0.012 0.000 0.000 0.988
#> GSM531646 3 0.2345 0.6843 0.000 0.100 0.900 0.000
#> GSM531647 3 0.0188 0.7167 0.004 0.000 0.996 0.000
#> GSM531648 4 0.8695 0.2115 0.072 0.148 0.368 0.412
#> GSM531649 3 0.0188 0.7167 0.004 0.000 0.996 0.000
#> GSM531650 3 0.0188 0.7162 0.000 0.000 0.996 0.004
#> GSM531651 2 0.0000 0.8997 0.000 1.000 0.000 0.000
#> GSM531652 4 0.8698 0.2042 0.072 0.148 0.372 0.408
#> GSM531653 3 0.0188 0.7167 0.004 0.000 0.996 0.000
#> GSM531656 3 0.2975 0.7082 0.008 0.060 0.900 0.032
#> GSM531657 4 0.9195 0.3278 0.192 0.172 0.168 0.468
#> GSM531659 1 0.8550 0.3495 0.496 0.152 0.076 0.276
#> GSM531661 2 0.1732 0.8835 0.008 0.948 0.004 0.040
#> GSM531662 2 0.7477 0.5687 0.188 0.632 0.100 0.080
#> GSM531663 4 0.7396 -0.2164 0.424 0.048 0.056 0.472
#> GSM531664 3 0.4776 0.4646 0.016 0.000 0.712 0.272
#> GSM531665 1 0.8632 0.3375 0.488 0.164 0.076 0.272
#> GSM531666 4 0.7441 0.1790 0.064 0.044 0.416 0.476
#> GSM531667 2 0.1822 0.8816 0.008 0.944 0.004 0.044
#> GSM531668 4 0.9112 0.1718 0.088 0.188 0.348 0.376
#> GSM531669 3 0.1398 0.7074 0.040 0.000 0.956 0.004
#> GSM531670 3 0.2975 0.7082 0.008 0.060 0.900 0.032
#> GSM531671 3 0.8260 0.2460 0.084 0.360 0.468 0.088
#> GSM531672 4 0.5787 0.5655 0.124 0.020 0.112 0.744
#> GSM531673 2 0.7904 0.4890 0.228 0.584 0.096 0.092
#> GSM531674 3 0.1209 0.7098 0.032 0.000 0.964 0.004
#> GSM531675 4 0.4431 0.2780 0.304 0.000 0.000 0.696
#> GSM531676 1 0.4158 0.5801 0.768 0.000 0.224 0.008
#> GSM531677 1 0.4936 0.5790 0.652 0.000 0.008 0.340
#> GSM531678 1 0.2589 0.6650 0.884 0.000 0.000 0.116
#> GSM531679 1 0.4936 0.5790 0.652 0.000 0.008 0.340
#> GSM531680 3 0.7763 0.0580 0.332 0.000 0.420 0.248
#> GSM531681 4 0.0921 0.6314 0.028 0.000 0.000 0.972
#> GSM531682 1 0.4936 0.5790 0.652 0.000 0.008 0.340
#> GSM531683 1 0.4713 0.5484 0.640 0.000 0.000 0.360
#> GSM531684 1 0.3037 0.6627 0.880 0.020 0.000 0.100
#> GSM531685 3 0.4500 0.4381 0.316 0.000 0.684 0.000
#> GSM531686 4 0.0921 0.6314 0.028 0.000 0.000 0.972
#> GSM531687 1 0.4158 0.5801 0.768 0.000 0.224 0.008
#> GSM531688 3 0.1489 0.7060 0.044 0.000 0.952 0.004
#> GSM531689 1 0.4158 0.5801 0.768 0.000 0.224 0.008
#> GSM531690 4 0.3074 0.5337 0.152 0.000 0.000 0.848
#> GSM531691 1 0.4408 0.5678 0.756 0.004 0.232 0.008
#> GSM531692 1 0.5026 0.4213 0.672 0.016 0.312 0.000
#> GSM531693 3 0.1792 0.6952 0.068 0.000 0.932 0.000
#> GSM531694 1 0.4222 0.6234 0.728 0.000 0.000 0.272
#> GSM531695 3 0.7756 0.0737 0.320 0.000 0.428 0.252
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM531600 3 0.3166 0.72474 0.012 0.112 0.856 0.000 0.020
#> GSM531601 5 0.5322 0.41076 0.000 0.392 0.056 0.000 0.552
#> GSM531605 5 0.5193 0.59430 0.128 0.032 0.052 0.028 0.760
#> GSM531615 2 0.1357 0.89276 0.000 0.948 0.000 0.004 0.048
#> GSM531617 2 0.1357 0.89276 0.000 0.948 0.000 0.004 0.048
#> GSM531624 2 0.0000 0.90171 0.000 1.000 0.000 0.000 0.000
#> GSM531627 2 0.2067 0.86908 0.012 0.928 0.032 0.000 0.028
#> GSM531629 2 0.1357 0.89276 0.000 0.948 0.000 0.004 0.048
#> GSM531631 2 0.0000 0.90171 0.000 1.000 0.000 0.000 0.000
#> GSM531634 2 0.1357 0.89276 0.000 0.948 0.000 0.004 0.048
#> GSM531636 3 0.3166 0.72474 0.012 0.112 0.856 0.000 0.020
#> GSM531637 2 0.0000 0.90171 0.000 1.000 0.000 0.000 0.000
#> GSM531654 2 0.4132 0.79931 0.020 0.804 0.016 0.016 0.144
#> GSM531655 5 0.5270 0.59830 0.128 0.036 0.052 0.028 0.756
#> GSM531658 4 0.6449 -0.00805 0.064 0.020 0.016 0.472 0.428
#> GSM531660 5 0.5452 0.68129 0.056 0.048 0.052 0.084 0.760
#> GSM531602 1 0.5083 0.64864 0.696 0.000 0.000 0.120 0.184
#> GSM531603 1 0.6024 0.41288 0.516 0.000 0.028 0.056 0.400
#> GSM531604 1 0.4932 0.62681 0.752 0.028 0.084 0.000 0.136
#> GSM531606 1 0.2723 0.67976 0.864 0.000 0.000 0.012 0.124
#> GSM531607 1 0.6024 0.41288 0.516 0.000 0.028 0.056 0.400
#> GSM531608 2 0.2052 0.87390 0.000 0.912 0.004 0.004 0.080
#> GSM531609 4 0.0000 0.76273 0.000 0.000 0.000 1.000 0.000
#> GSM531610 4 0.0000 0.76273 0.000 0.000 0.000 1.000 0.000
#> GSM531611 4 0.0451 0.75849 0.008 0.000 0.000 0.988 0.004
#> GSM531612 4 0.0000 0.76273 0.000 0.000 0.000 1.000 0.000
#> GSM531613 4 0.0000 0.76273 0.000 0.000 0.000 1.000 0.000
#> GSM531614 4 0.0000 0.76273 0.000 0.000 0.000 1.000 0.000
#> GSM531616 3 0.4347 0.60269 0.012 0.264 0.712 0.000 0.012
#> GSM531618 5 0.7296 0.62735 0.000 0.208 0.136 0.112 0.544
#> GSM531619 2 0.0000 0.90171 0.000 1.000 0.000 0.000 0.000
#> GSM531620 2 0.4210 0.80519 0.012 0.804 0.040 0.012 0.132
#> GSM531621 2 0.0771 0.89467 0.000 0.976 0.020 0.000 0.004
#> GSM531622 2 0.0000 0.90171 0.000 1.000 0.000 0.000 0.000
#> GSM531623 2 0.0000 0.90171 0.000 1.000 0.000 0.000 0.000
#> GSM531625 2 0.3787 0.74113 0.012 0.800 0.168 0.000 0.020
#> GSM531626 2 0.3787 0.74113 0.012 0.800 0.168 0.000 0.020
#> GSM531628 3 0.1282 0.75248 0.000 0.000 0.952 0.004 0.044
#> GSM531630 2 0.0000 0.90171 0.000 1.000 0.000 0.000 0.000
#> GSM531632 3 0.0510 0.75624 0.000 0.000 0.984 0.000 0.016
#> GSM531633 2 0.0771 0.89467 0.000 0.976 0.020 0.000 0.004
#> GSM531635 3 0.4296 0.60841 0.012 0.256 0.720 0.000 0.012
#> GSM531638 3 0.4347 0.60269 0.012 0.264 0.712 0.000 0.012
#> GSM531639 3 0.6454 0.52365 0.012 0.180 0.624 0.024 0.160
#> GSM531640 2 0.0000 0.90171 0.000 1.000 0.000 0.000 0.000
#> GSM531641 4 0.0000 0.76273 0.000 0.000 0.000 1.000 0.000
#> GSM531642 5 0.6263 0.71144 0.000 0.064 0.112 0.176 0.648
#> GSM531643 3 0.3783 0.68437 0.000 0.016 0.824 0.040 0.120
#> GSM531644 5 0.6263 0.71144 0.000 0.064 0.112 0.176 0.648
#> GSM531645 4 0.0000 0.76273 0.000 0.000 0.000 1.000 0.000
#> GSM531646 3 0.2179 0.73268 0.000 0.100 0.896 0.000 0.004
#> GSM531647 3 0.0510 0.75624 0.000 0.000 0.984 0.000 0.016
#> GSM531648 5 0.6112 0.71371 0.000 0.076 0.080 0.184 0.660
#> GSM531649 3 0.0162 0.75777 0.004 0.000 0.996 0.000 0.000
#> GSM531650 3 0.1121 0.75252 0.000 0.000 0.956 0.000 0.044
#> GSM531651 2 0.0000 0.90171 0.000 1.000 0.000 0.000 0.000
#> GSM531652 5 0.6131 0.71588 0.000 0.076 0.084 0.180 0.660
#> GSM531653 3 0.0162 0.75777 0.004 0.000 0.996 0.000 0.000
#> GSM531656 3 0.4165 0.71976 0.012 0.060 0.820 0.016 0.092
#> GSM531657 5 0.8253 0.40182 0.148 0.100 0.032 0.256 0.464
#> GSM531659 1 0.8172 0.32517 0.448 0.152 0.008 0.156 0.236
#> GSM531661 2 0.2052 0.87491 0.000 0.912 0.004 0.004 0.080
#> GSM531662 2 0.7004 0.49139 0.172 0.584 0.048 0.012 0.184
#> GSM531663 4 0.7468 -0.17618 0.384 0.048 0.004 0.396 0.168
#> GSM531664 3 0.5568 0.41848 0.004 0.000 0.644 0.116 0.236
#> GSM531665 1 0.8207 0.31017 0.444 0.164 0.008 0.152 0.232
#> GSM531666 5 0.7421 0.39271 0.004 0.024 0.316 0.264 0.392
#> GSM531667 2 0.2177 0.87310 0.000 0.908 0.004 0.008 0.080
#> GSM531668 5 0.5481 0.71706 0.008 0.080 0.056 0.120 0.736
#> GSM531669 3 0.1743 0.75310 0.028 0.000 0.940 0.004 0.028
#> GSM531670 3 0.4165 0.71976 0.012 0.060 0.820 0.016 0.092
#> GSM531671 3 0.7892 0.19422 0.068 0.312 0.440 0.016 0.164
#> GSM531672 4 0.6449 -0.00805 0.064 0.020 0.016 0.472 0.428
#> GSM531673 2 0.7416 0.40522 0.196 0.536 0.044 0.020 0.204
#> GSM531674 3 0.1560 0.75450 0.020 0.000 0.948 0.004 0.028
#> GSM531675 4 0.6752 0.06051 0.316 0.000 0.000 0.404 0.280
#> GSM531676 1 0.4002 0.60949 0.796 0.000 0.120 0.000 0.084
#> GSM531677 1 0.5301 0.61076 0.676 0.000 0.000 0.176 0.148
#> GSM531678 1 0.2723 0.67976 0.864 0.000 0.000 0.012 0.124
#> GSM531679 1 0.5301 0.61076 0.676 0.000 0.000 0.176 0.148
#> GSM531680 3 0.8175 -0.02786 0.336 0.000 0.340 0.128 0.196
#> GSM531681 4 0.0510 0.75772 0.016 0.000 0.000 0.984 0.000
#> GSM531682 1 0.5301 0.61076 0.676 0.000 0.000 0.176 0.148
#> GSM531683 1 0.5602 0.59429 0.640 0.000 0.000 0.196 0.164
#> GSM531684 1 0.3218 0.67637 0.848 0.016 0.000 0.012 0.124
#> GSM531685 3 0.5658 0.38797 0.332 0.000 0.572 0.000 0.096
#> GSM531686 4 0.0510 0.75772 0.016 0.000 0.000 0.984 0.000
#> GSM531687 1 0.4002 0.60949 0.796 0.000 0.120 0.000 0.084
#> GSM531688 3 0.1828 0.75240 0.028 0.000 0.936 0.004 0.032
#> GSM531689 1 0.4002 0.60949 0.796 0.000 0.120 0.000 0.084
#> GSM531690 4 0.6144 0.31814 0.172 0.000 0.000 0.548 0.280
#> GSM531691 1 0.4094 0.60291 0.788 0.000 0.128 0.000 0.084
#> GSM531692 1 0.5456 0.48629 0.684 0.016 0.200 0.000 0.100
#> GSM531693 3 0.2300 0.74130 0.052 0.000 0.908 0.000 0.040
#> GSM531694 1 0.5083 0.64864 0.696 0.000 0.000 0.120 0.184
#> GSM531695 3 0.8224 -0.00744 0.324 0.000 0.340 0.132 0.204
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM531600 3 0.3396 0.7454 0.000 0.108 0.828 0.000 0.048 0.016
#> GSM531601 6 0.3659 0.3778 0.000 0.364 0.000 0.000 0.000 0.636
#> GSM531605 6 0.4198 0.5166 0.212 0.016 0.000 0.000 0.040 0.732
#> GSM531615 2 0.1806 0.8768 0.000 0.908 0.000 0.000 0.004 0.088
#> GSM531617 2 0.1806 0.8768 0.000 0.908 0.000 0.000 0.004 0.088
#> GSM531624 2 0.0000 0.8922 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531627 2 0.1850 0.8651 0.000 0.924 0.008 0.000 0.052 0.016
#> GSM531629 2 0.1806 0.8768 0.000 0.908 0.000 0.000 0.004 0.088
#> GSM531631 2 0.0000 0.8922 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531634 2 0.1806 0.8768 0.000 0.908 0.000 0.000 0.004 0.088
#> GSM531636 3 0.3459 0.7439 0.000 0.108 0.824 0.000 0.052 0.016
#> GSM531637 2 0.0000 0.8922 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531654 2 0.3997 0.7754 0.012 0.756 0.004 0.000 0.032 0.196
#> GSM531655 6 0.4253 0.5204 0.208 0.020 0.000 0.000 0.040 0.732
#> GSM531658 6 0.7399 0.2740 0.168 0.000 0.000 0.268 0.176 0.388
#> GSM531660 6 0.3248 0.6040 0.136 0.016 0.000 0.016 0.004 0.828
#> GSM531602 1 0.0632 0.5358 0.976 0.000 0.000 0.000 0.024 0.000
#> GSM531603 1 0.3512 0.4542 0.720 0.000 0.000 0.000 0.008 0.272
#> GSM531604 5 0.4375 0.3049 0.432 0.012 0.000 0.000 0.548 0.008
#> GSM531606 1 0.3101 0.3146 0.756 0.000 0.000 0.000 0.244 0.000
#> GSM531607 1 0.3512 0.4542 0.720 0.000 0.000 0.000 0.008 0.272
#> GSM531608 2 0.2243 0.8597 0.004 0.880 0.000 0.000 0.004 0.112
#> GSM531609 4 0.0000 0.9921 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531610 4 0.0000 0.9921 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531611 4 0.0363 0.9846 0.012 0.000 0.000 0.988 0.000 0.000
#> GSM531612 4 0.0000 0.9921 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531613 4 0.0000 0.9921 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531614 4 0.0000 0.9921 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531616 3 0.4607 0.6113 0.000 0.264 0.672 0.000 0.052 0.012
#> GSM531618 6 0.5666 0.5559 0.012 0.172 0.100 0.044 0.004 0.668
#> GSM531619 2 0.0000 0.8922 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531620 2 0.3976 0.7900 0.000 0.768 0.020 0.000 0.040 0.172
#> GSM531621 2 0.0767 0.8865 0.000 0.976 0.004 0.000 0.008 0.012
#> GSM531622 2 0.0000 0.8922 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531623 2 0.0000 0.8922 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531625 2 0.3767 0.7538 0.000 0.800 0.120 0.000 0.064 0.016
#> GSM531626 2 0.3767 0.7538 0.000 0.800 0.120 0.000 0.064 0.016
#> GSM531628 3 0.1003 0.7802 0.004 0.000 0.964 0.000 0.004 0.028
#> GSM531630 2 0.0000 0.8922 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531632 3 0.0146 0.7794 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM531633 2 0.0767 0.8865 0.000 0.976 0.004 0.000 0.008 0.012
#> GSM531635 3 0.4565 0.6175 0.000 0.256 0.680 0.000 0.052 0.012
#> GSM531638 3 0.4607 0.6113 0.000 0.264 0.672 0.000 0.052 0.012
#> GSM531639 3 0.6211 0.5709 0.012 0.172 0.596 0.000 0.048 0.172
#> GSM531640 2 0.0000 0.8922 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531641 4 0.0000 0.9921 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531642 6 0.3597 0.6715 0.000 0.028 0.048 0.076 0.012 0.836
#> GSM531643 3 0.3473 0.7173 0.012 0.008 0.812 0.004 0.012 0.152
#> GSM531644 6 0.3597 0.6715 0.000 0.028 0.048 0.076 0.012 0.836
#> GSM531645 4 0.0000 0.9921 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531646 3 0.2686 0.7582 0.000 0.100 0.868 0.000 0.024 0.008
#> GSM531647 3 0.0146 0.7794 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM531648 6 0.3046 0.6730 0.000 0.036 0.016 0.084 0.004 0.860
#> GSM531649 3 0.1074 0.7811 0.000 0.000 0.960 0.000 0.028 0.012
#> GSM531650 3 0.0858 0.7806 0.000 0.000 0.968 0.000 0.004 0.028
#> GSM531651 2 0.0000 0.8922 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531652 6 0.3079 0.6738 0.000 0.036 0.020 0.080 0.004 0.860
#> GSM531653 3 0.1074 0.7811 0.000 0.000 0.960 0.000 0.028 0.012
#> GSM531656 3 0.4467 0.7466 0.012 0.052 0.776 0.000 0.056 0.104
#> GSM531657 6 0.6865 0.3861 0.208 0.080 0.004 0.120 0.028 0.560
#> GSM531659 1 0.6878 0.3735 0.540 0.136 0.004 0.036 0.048 0.236
#> GSM531661 2 0.2243 0.8606 0.004 0.880 0.000 0.000 0.004 0.112
#> GSM531662 2 0.6646 0.4835 0.076 0.544 0.008 0.000 0.176 0.196
#> GSM531663 1 0.6384 0.3270 0.500 0.044 0.000 0.336 0.012 0.108
#> GSM531664 3 0.5359 0.4871 0.024 0.000 0.648 0.000 0.188 0.140
#> GSM531665 1 0.6967 0.3592 0.528 0.148 0.004 0.036 0.048 0.236
#> GSM531666 6 0.7488 0.3021 0.020 0.008 0.296 0.068 0.192 0.416
#> GSM531667 2 0.2288 0.8590 0.004 0.876 0.000 0.000 0.004 0.116
#> GSM531668 6 0.1946 0.6514 0.024 0.020 0.000 0.024 0.004 0.928
#> GSM531669 3 0.1285 0.7660 0.004 0.000 0.944 0.000 0.052 0.000
#> GSM531670 3 0.4467 0.7466 0.012 0.052 0.776 0.000 0.056 0.104
#> GSM531671 3 0.7557 0.2389 0.044 0.280 0.416 0.000 0.064 0.196
#> GSM531672 6 0.7399 0.2740 0.168 0.000 0.000 0.268 0.176 0.388
#> GSM531673 2 0.6942 0.3949 0.100 0.496 0.004 0.000 0.196 0.204
#> GSM531674 3 0.1152 0.7696 0.004 0.000 0.952 0.000 0.044 0.000
#> GSM531675 1 0.6668 0.2960 0.528 0.000 0.000 0.104 0.196 0.172
#> GSM531676 5 0.3756 0.5666 0.316 0.000 0.004 0.000 0.676 0.004
#> GSM531677 1 0.3308 0.5466 0.836 0.000 0.000 0.012 0.088 0.064
#> GSM531678 1 0.3151 0.3032 0.748 0.000 0.000 0.000 0.252 0.000
#> GSM531679 1 0.3308 0.5466 0.836 0.000 0.000 0.012 0.088 0.064
#> GSM531680 5 0.6215 0.3249 0.184 0.000 0.224 0.000 0.548 0.044
#> GSM531681 4 0.0632 0.9756 0.024 0.000 0.000 0.976 0.000 0.000
#> GSM531682 1 0.3308 0.5466 0.836 0.000 0.000 0.012 0.088 0.064
#> GSM531683 1 0.3181 0.5653 0.840 0.000 0.000 0.020 0.028 0.112
#> GSM531684 1 0.3787 0.2758 0.720 0.012 0.000 0.000 0.260 0.008
#> GSM531685 5 0.4814 0.0942 0.056 0.000 0.412 0.000 0.532 0.000
#> GSM531686 4 0.0632 0.9756 0.024 0.000 0.000 0.976 0.000 0.000
#> GSM531687 5 0.3756 0.5666 0.316 0.000 0.004 0.000 0.676 0.004
#> GSM531688 3 0.1349 0.7647 0.004 0.000 0.940 0.000 0.056 0.000
#> GSM531689 5 0.3756 0.5666 0.316 0.000 0.004 0.000 0.676 0.004
#> GSM531690 1 0.7482 0.0385 0.376 0.000 0.000 0.252 0.180 0.192
#> GSM531691 5 0.3584 0.5676 0.308 0.000 0.004 0.000 0.688 0.000
#> GSM531692 5 0.3377 0.5347 0.188 0.000 0.028 0.000 0.784 0.000
#> GSM531693 3 0.2562 0.6851 0.000 0.000 0.828 0.000 0.172 0.000
#> GSM531694 1 0.0632 0.5358 0.976 0.000 0.000 0.000 0.024 0.000
#> GSM531695 5 0.6419 0.3000 0.180 0.000 0.264 0.000 0.508 0.048
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> CV:hclust 78 0.02883 2
#> CV:hclust 45 0.00364 3
#> CV:hclust 71 0.00905 4
#> CV:hclust 76 0.00651 5
#> CV:hclust 73 0.02210 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "kmeans"]
# you can also extract it by
# res = res_list["CV:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 96 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.544 0.875 0.920 0.4998 0.496 0.496
#> 3 3 0.597 0.825 0.871 0.3259 0.714 0.487
#> 4 4 0.893 0.895 0.944 0.1352 0.857 0.602
#> 5 5 0.738 0.678 0.813 0.0572 0.971 0.884
#> 6 6 0.713 0.544 0.728 0.0410 0.931 0.705
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM531600 2 0.1414 0.898 0.020 0.980
#> GSM531601 2 0.5059 0.890 0.112 0.888
#> GSM531605 1 0.0938 0.935 0.988 0.012
#> GSM531615 2 0.5294 0.886 0.120 0.880
#> GSM531617 2 0.5294 0.886 0.120 0.880
#> GSM531624 2 0.4815 0.896 0.104 0.896
#> GSM531627 2 0.2948 0.907 0.052 0.948
#> GSM531629 2 0.6247 0.856 0.156 0.844
#> GSM531631 2 0.4815 0.896 0.104 0.896
#> GSM531634 2 0.5178 0.889 0.116 0.884
#> GSM531636 2 0.0000 0.899 0.000 1.000
#> GSM531637 2 0.4815 0.896 0.104 0.896
#> GSM531654 2 0.5178 0.889 0.116 0.884
#> GSM531655 1 0.6801 0.794 0.820 0.180
#> GSM531658 1 0.0672 0.936 0.992 0.008
#> GSM531660 1 0.0938 0.933 0.988 0.012
#> GSM531602 1 0.0938 0.933 0.988 0.012
#> GSM531603 1 0.0938 0.933 0.988 0.012
#> GSM531604 1 0.0938 0.935 0.988 0.012
#> GSM531606 1 0.0938 0.933 0.988 0.012
#> GSM531607 1 0.0000 0.936 1.000 0.000
#> GSM531608 2 0.5178 0.889 0.116 0.884
#> GSM531609 1 0.0672 0.936 0.992 0.008
#> GSM531610 1 0.0376 0.936 0.996 0.004
#> GSM531611 1 0.0376 0.935 0.996 0.004
#> GSM531612 1 0.0672 0.936 0.992 0.008
#> GSM531613 1 0.0376 0.936 0.996 0.004
#> GSM531614 1 0.0376 0.936 0.996 0.004
#> GSM531616 2 0.0938 0.898 0.012 0.988
#> GSM531618 2 0.7602 0.811 0.220 0.780
#> GSM531619 2 0.4815 0.896 0.104 0.896
#> GSM531620 2 0.2948 0.907 0.052 0.948
#> GSM531621 2 0.2948 0.907 0.052 0.948
#> GSM531622 2 0.4815 0.896 0.104 0.896
#> GSM531623 2 0.3114 0.906 0.056 0.944
#> GSM531625 2 0.0376 0.900 0.004 0.996
#> GSM531626 2 0.0376 0.900 0.004 0.996
#> GSM531628 2 0.4562 0.865 0.096 0.904
#> GSM531630 2 0.4690 0.897 0.100 0.900
#> GSM531632 2 0.3114 0.888 0.056 0.944
#> GSM531633 2 0.2948 0.907 0.052 0.948
#> GSM531635 2 0.0938 0.898 0.012 0.988
#> GSM531638 2 0.0000 0.899 0.000 1.000
#> GSM531639 2 0.0000 0.899 0.000 1.000
#> GSM531640 2 0.4815 0.896 0.104 0.896
#> GSM531641 1 0.1184 0.934 0.984 0.016
#> GSM531642 2 0.3114 0.890 0.056 0.944
#> GSM531643 2 0.3431 0.885 0.064 0.936
#> GSM531644 2 0.4562 0.865 0.096 0.904
#> GSM531645 1 0.0672 0.936 0.992 0.008
#> GSM531646 2 0.0938 0.898 0.012 0.988
#> GSM531647 2 0.2948 0.890 0.052 0.948
#> GSM531648 2 0.9552 0.552 0.376 0.624
#> GSM531649 2 0.0938 0.898 0.012 0.988
#> GSM531650 2 0.4562 0.865 0.096 0.904
#> GSM531651 2 0.2948 0.907 0.052 0.948
#> GSM531652 2 0.4815 0.870 0.104 0.896
#> GSM531653 2 0.1414 0.898 0.020 0.980
#> GSM531656 2 0.1843 0.897 0.028 0.972
#> GSM531657 1 0.0376 0.936 0.996 0.004
#> GSM531659 1 0.0376 0.936 0.996 0.004
#> GSM531661 2 0.4815 0.896 0.104 0.896
#> GSM531662 2 0.3431 0.905 0.064 0.936
#> GSM531663 1 0.0376 0.936 0.996 0.004
#> GSM531664 1 0.8207 0.731 0.744 0.256
#> GSM531665 1 0.8016 0.743 0.756 0.244
#> GSM531666 1 0.6973 0.815 0.812 0.188
#> GSM531667 2 0.4939 0.894 0.108 0.892
#> GSM531668 1 0.0938 0.933 0.988 0.012
#> GSM531669 2 0.9286 0.461 0.344 0.656
#> GSM531670 2 0.1414 0.898 0.020 0.980
#> GSM531671 2 0.3114 0.894 0.056 0.944
#> GSM531672 1 0.0938 0.933 0.988 0.012
#> GSM531673 1 0.8081 0.730 0.752 0.248
#> GSM531674 2 0.9286 0.461 0.344 0.656
#> GSM531675 1 0.0000 0.936 1.000 0.000
#> GSM531676 1 0.5842 0.853 0.860 0.140
#> GSM531677 1 0.0376 0.935 0.996 0.004
#> GSM531678 1 0.0000 0.936 1.000 0.000
#> GSM531679 1 0.0376 0.935 0.996 0.004
#> GSM531680 1 0.5294 0.866 0.880 0.120
#> GSM531681 1 0.0000 0.936 1.000 0.000
#> GSM531682 1 0.0376 0.935 0.996 0.004
#> GSM531683 1 0.0376 0.936 0.996 0.004
#> GSM531684 1 0.2948 0.906 0.948 0.052
#> GSM531685 1 0.8081 0.739 0.752 0.248
#> GSM531686 1 0.0000 0.936 1.000 0.000
#> GSM531687 1 0.5294 0.866 0.880 0.120
#> GSM531688 1 0.6343 0.839 0.840 0.160
#> GSM531689 1 0.2603 0.916 0.956 0.044
#> GSM531690 1 0.0000 0.936 1.000 0.000
#> GSM531691 1 0.4022 0.895 0.920 0.080
#> GSM531692 1 0.9608 0.473 0.616 0.384
#> GSM531693 2 0.9286 0.461 0.344 0.656
#> GSM531694 1 0.0938 0.933 0.988 0.012
#> GSM531695 1 0.5294 0.866 0.880 0.120
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM531600 3 0.4654 0.791 0.000 0.208 0.792
#> GSM531601 2 0.2711 0.881 0.000 0.912 0.088
#> GSM531605 1 0.3340 0.832 0.880 0.000 0.120
#> GSM531615 2 0.0424 0.972 0.000 0.992 0.008
#> GSM531617 2 0.0424 0.972 0.000 0.992 0.008
#> GSM531624 2 0.0237 0.981 0.000 0.996 0.004
#> GSM531627 2 0.0237 0.981 0.000 0.996 0.004
#> GSM531629 2 0.1031 0.955 0.000 0.976 0.024
#> GSM531631 2 0.0237 0.981 0.000 0.996 0.004
#> GSM531634 2 0.0424 0.972 0.000 0.992 0.008
#> GSM531636 3 0.6079 0.556 0.000 0.388 0.612
#> GSM531637 2 0.0237 0.981 0.000 0.996 0.004
#> GSM531654 2 0.0237 0.981 0.000 0.996 0.004
#> GSM531655 1 0.9752 0.363 0.424 0.236 0.340
#> GSM531658 1 0.4261 0.829 0.848 0.012 0.140
#> GSM531660 1 0.4261 0.829 0.848 0.012 0.140
#> GSM531602 1 0.3340 0.832 0.880 0.000 0.120
#> GSM531603 1 0.3340 0.832 0.880 0.000 0.120
#> GSM531604 1 0.3340 0.832 0.880 0.000 0.120
#> GSM531606 1 0.3340 0.832 0.880 0.000 0.120
#> GSM531607 1 0.3340 0.832 0.880 0.000 0.120
#> GSM531608 2 0.0000 0.979 0.000 1.000 0.000
#> GSM531609 1 0.4261 0.829 0.848 0.012 0.140
#> GSM531610 1 0.4261 0.829 0.848 0.012 0.140
#> GSM531611 1 0.3918 0.830 0.856 0.004 0.140
#> GSM531612 1 0.4261 0.829 0.848 0.012 0.140
#> GSM531613 1 0.3272 0.837 0.892 0.004 0.104
#> GSM531614 1 0.4261 0.829 0.848 0.012 0.140
#> GSM531616 2 0.4291 0.714 0.000 0.820 0.180
#> GSM531618 1 0.8763 0.582 0.588 0.196 0.216
#> GSM531619 2 0.0237 0.981 0.000 0.996 0.004
#> GSM531620 2 0.0237 0.981 0.000 0.996 0.004
#> GSM531621 2 0.0237 0.981 0.000 0.996 0.004
#> GSM531622 2 0.0237 0.981 0.000 0.996 0.004
#> GSM531623 2 0.0237 0.981 0.000 0.996 0.004
#> GSM531625 2 0.0747 0.971 0.000 0.984 0.016
#> GSM531626 2 0.0747 0.971 0.000 0.984 0.016
#> GSM531628 3 0.3551 0.809 0.000 0.132 0.868
#> GSM531630 2 0.0237 0.981 0.000 0.996 0.004
#> GSM531632 3 0.5016 0.771 0.000 0.240 0.760
#> GSM531633 2 0.0237 0.981 0.000 0.996 0.004
#> GSM531635 3 0.5216 0.754 0.000 0.260 0.740
#> GSM531638 2 0.0747 0.971 0.000 0.984 0.016
#> GSM531639 3 0.6079 0.556 0.000 0.388 0.612
#> GSM531640 2 0.0000 0.979 0.000 1.000 0.000
#> GSM531641 1 0.4261 0.829 0.848 0.012 0.140
#> GSM531642 3 0.3995 0.800 0.016 0.116 0.868
#> GSM531643 3 0.3551 0.809 0.000 0.132 0.868
#> GSM531644 3 0.3846 0.802 0.016 0.108 0.876
#> GSM531645 1 0.4261 0.829 0.848 0.012 0.140
#> GSM531646 3 0.5216 0.754 0.000 0.260 0.740
#> GSM531647 3 0.4452 0.798 0.000 0.192 0.808
#> GSM531648 1 0.5167 0.783 0.792 0.016 0.192
#> GSM531649 3 0.5216 0.754 0.000 0.260 0.740
#> GSM531650 3 0.3551 0.809 0.000 0.132 0.868
#> GSM531651 2 0.0237 0.981 0.000 0.996 0.004
#> GSM531652 3 0.3987 0.795 0.020 0.108 0.872
#> GSM531653 3 0.4654 0.791 0.000 0.208 0.792
#> GSM531656 3 0.4178 0.804 0.000 0.172 0.828
#> GSM531657 1 0.4261 0.829 0.848 0.012 0.140
#> GSM531659 1 0.4453 0.830 0.836 0.012 0.152
#> GSM531661 2 0.0237 0.981 0.000 0.996 0.004
#> GSM531662 2 0.0237 0.981 0.000 0.996 0.004
#> GSM531663 1 0.3618 0.836 0.884 0.012 0.104
#> GSM531664 3 0.1643 0.799 0.000 0.044 0.956
#> GSM531665 3 0.5817 0.651 0.236 0.020 0.744
#> GSM531666 3 0.2926 0.784 0.040 0.036 0.924
#> GSM531667 2 0.0000 0.979 0.000 1.000 0.000
#> GSM531668 1 0.4453 0.830 0.836 0.012 0.152
#> GSM531669 3 0.2804 0.809 0.016 0.060 0.924
#> GSM531670 3 0.4654 0.791 0.000 0.208 0.792
#> GSM531671 3 0.6062 0.579 0.000 0.384 0.616
#> GSM531672 1 0.4261 0.829 0.848 0.012 0.140
#> GSM531673 1 0.7620 0.662 0.684 0.188 0.128
#> GSM531674 3 0.2804 0.809 0.016 0.060 0.924
#> GSM531675 1 0.2959 0.835 0.900 0.000 0.100
#> GSM531676 3 0.5363 0.598 0.276 0.000 0.724
#> GSM531677 1 0.3340 0.832 0.880 0.000 0.120
#> GSM531678 1 0.3340 0.832 0.880 0.000 0.120
#> GSM531679 1 0.3340 0.832 0.880 0.000 0.120
#> GSM531680 3 0.5465 0.577 0.288 0.000 0.712
#> GSM531681 1 0.0000 0.840 1.000 0.000 0.000
#> GSM531682 1 0.3340 0.832 0.880 0.000 0.120
#> GSM531683 1 0.3192 0.833 0.888 0.000 0.112
#> GSM531684 1 0.7297 0.681 0.708 0.172 0.120
#> GSM531685 3 0.3752 0.734 0.144 0.000 0.856
#> GSM531686 1 0.0000 0.840 1.000 0.000 0.000
#> GSM531687 3 0.5363 0.598 0.276 0.000 0.724
#> GSM531688 3 0.3752 0.734 0.144 0.000 0.856
#> GSM531689 1 0.3340 0.832 0.880 0.000 0.120
#> GSM531690 1 0.0237 0.840 0.996 0.000 0.004
#> GSM531691 1 0.3879 0.807 0.848 0.000 0.152
#> GSM531692 3 0.5363 0.598 0.276 0.000 0.724
#> GSM531693 3 0.4397 0.758 0.116 0.028 0.856
#> GSM531694 1 0.3340 0.832 0.880 0.000 0.120
#> GSM531695 3 0.3879 0.729 0.152 0.000 0.848
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM531600 3 0.0376 0.941 0.004 0.004 0.992 0.000
#> GSM531601 2 0.1118 0.945 0.000 0.964 0.000 0.036
#> GSM531605 1 0.0469 0.903 0.988 0.000 0.000 0.012
#> GSM531615 2 0.0188 0.975 0.004 0.996 0.000 0.000
#> GSM531617 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> GSM531624 2 0.0188 0.975 0.004 0.996 0.000 0.000
#> GSM531627 2 0.0188 0.975 0.004 0.996 0.000 0.000
#> GSM531629 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> GSM531631 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> GSM531634 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> GSM531636 3 0.4049 0.763 0.008 0.180 0.804 0.008
#> GSM531637 2 0.0188 0.975 0.004 0.996 0.000 0.000
#> GSM531654 2 0.0592 0.968 0.016 0.984 0.000 0.000
#> GSM531655 1 0.8893 0.225 0.444 0.164 0.304 0.088
#> GSM531658 4 0.0524 0.949 0.008 0.000 0.004 0.988
#> GSM531660 4 0.3479 0.862 0.148 0.012 0.000 0.840
#> GSM531602 1 0.0592 0.903 0.984 0.000 0.000 0.016
#> GSM531603 1 0.0592 0.903 0.984 0.000 0.000 0.016
#> GSM531604 1 0.0336 0.902 0.992 0.000 0.000 0.008
#> GSM531606 1 0.0592 0.903 0.984 0.000 0.000 0.016
#> GSM531607 1 0.0592 0.903 0.984 0.000 0.000 0.016
#> GSM531608 2 0.0188 0.975 0.004 0.996 0.000 0.000
#> GSM531609 4 0.0524 0.949 0.008 0.000 0.004 0.988
#> GSM531610 4 0.0336 0.949 0.008 0.000 0.000 0.992
#> GSM531611 4 0.0336 0.949 0.008 0.000 0.000 0.992
#> GSM531612 4 0.0524 0.949 0.008 0.000 0.004 0.988
#> GSM531613 4 0.0336 0.949 0.008 0.000 0.000 0.992
#> GSM531614 4 0.0524 0.949 0.008 0.000 0.004 0.988
#> GSM531616 2 0.5497 0.338 0.012 0.608 0.372 0.008
#> GSM531618 4 0.4011 0.738 0.000 0.208 0.008 0.784
#> GSM531619 2 0.0188 0.975 0.004 0.996 0.000 0.000
#> GSM531620 2 0.0188 0.975 0.004 0.996 0.000 0.000
#> GSM531621 2 0.0336 0.975 0.008 0.992 0.000 0.000
#> GSM531622 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> GSM531623 2 0.0188 0.975 0.004 0.996 0.000 0.000
#> GSM531625 2 0.1406 0.953 0.016 0.960 0.024 0.000
#> GSM531626 2 0.1284 0.953 0.012 0.964 0.024 0.000
#> GSM531628 3 0.0188 0.941 0.000 0.000 0.996 0.004
#> GSM531630 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> GSM531632 3 0.0376 0.941 0.004 0.004 0.992 0.000
#> GSM531633 2 0.0188 0.975 0.004 0.996 0.000 0.000
#> GSM531635 3 0.1139 0.936 0.012 0.008 0.972 0.008
#> GSM531638 2 0.1617 0.947 0.012 0.956 0.024 0.008
#> GSM531639 3 0.4049 0.763 0.008 0.180 0.804 0.008
#> GSM531640 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> GSM531641 4 0.0524 0.949 0.008 0.000 0.004 0.988
#> GSM531642 3 0.2520 0.889 0.004 0.004 0.904 0.088
#> GSM531643 3 0.0469 0.940 0.000 0.000 0.988 0.012
#> GSM531644 3 0.0469 0.940 0.000 0.000 0.988 0.012
#> GSM531645 4 0.0524 0.949 0.008 0.000 0.004 0.988
#> GSM531646 3 0.0376 0.941 0.004 0.004 0.992 0.000
#> GSM531647 3 0.0188 0.942 0.000 0.004 0.996 0.000
#> GSM531648 4 0.0804 0.937 0.000 0.012 0.008 0.980
#> GSM531649 3 0.0657 0.939 0.012 0.004 0.984 0.000
#> GSM531650 3 0.0188 0.941 0.000 0.000 0.996 0.004
#> GSM531651 2 0.0188 0.975 0.004 0.996 0.000 0.000
#> GSM531652 3 0.2334 0.889 0.000 0.004 0.908 0.088
#> GSM531653 3 0.0188 0.942 0.000 0.004 0.996 0.000
#> GSM531656 3 0.0657 0.940 0.004 0.000 0.984 0.012
#> GSM531657 4 0.1389 0.938 0.048 0.000 0.000 0.952
#> GSM531659 4 0.1867 0.925 0.072 0.000 0.000 0.928
#> GSM531661 2 0.0188 0.975 0.004 0.996 0.000 0.000
#> GSM531662 2 0.0592 0.971 0.016 0.984 0.000 0.000
#> GSM531663 4 0.0336 0.949 0.008 0.000 0.000 0.992
#> GSM531664 3 0.0188 0.941 0.000 0.000 0.996 0.004
#> GSM531665 1 0.4991 0.455 0.608 0.000 0.388 0.004
#> GSM531666 3 0.2149 0.890 0.000 0.000 0.912 0.088
#> GSM531667 2 0.0188 0.975 0.004 0.996 0.000 0.000
#> GSM531668 4 0.3428 0.866 0.144 0.012 0.000 0.844
#> GSM531669 3 0.0000 0.941 0.000 0.000 1.000 0.000
#> GSM531670 3 0.0712 0.940 0.004 0.004 0.984 0.008
#> GSM531671 3 0.5435 0.242 0.016 0.420 0.564 0.000
#> GSM531672 4 0.1389 0.939 0.048 0.000 0.000 0.952
#> GSM531673 1 0.1824 0.861 0.936 0.060 0.000 0.004
#> GSM531674 3 0.0000 0.941 0.000 0.000 1.000 0.000
#> GSM531675 1 0.1109 0.900 0.968 0.000 0.004 0.028
#> GSM531676 1 0.3052 0.836 0.860 0.000 0.136 0.004
#> GSM531677 1 0.1004 0.901 0.972 0.000 0.004 0.024
#> GSM531678 1 0.0895 0.903 0.976 0.000 0.004 0.020
#> GSM531679 1 0.0895 0.903 0.976 0.000 0.004 0.020
#> GSM531680 1 0.3052 0.836 0.860 0.000 0.136 0.004
#> GSM531681 4 0.2266 0.901 0.084 0.000 0.004 0.912
#> GSM531682 1 0.0895 0.903 0.976 0.000 0.004 0.020
#> GSM531683 1 0.0592 0.903 0.984 0.000 0.000 0.016
#> GSM531684 1 0.0524 0.901 0.988 0.004 0.000 0.008
#> GSM531685 1 0.3831 0.766 0.792 0.000 0.204 0.004
#> GSM531686 4 0.2266 0.901 0.084 0.000 0.004 0.912
#> GSM531687 1 0.3052 0.836 0.860 0.000 0.136 0.004
#> GSM531688 3 0.0188 0.939 0.000 0.000 0.996 0.004
#> GSM531689 1 0.0779 0.903 0.980 0.000 0.004 0.016
#> GSM531690 4 0.2530 0.905 0.100 0.000 0.004 0.896
#> GSM531691 1 0.0779 0.903 0.980 0.000 0.004 0.016
#> GSM531692 1 0.2197 0.867 0.916 0.000 0.080 0.004
#> GSM531693 3 0.0376 0.939 0.004 0.000 0.992 0.004
#> GSM531694 1 0.0592 0.903 0.984 0.000 0.000 0.016
#> GSM531695 1 0.4950 0.504 0.620 0.000 0.376 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM531600 3 0.2179 0.8193 0.000 0.000 0.888 0.000 0.112
#> GSM531601 2 0.3639 0.6771 0.000 0.792 0.000 0.024 0.184
#> GSM531605 1 0.4138 0.5492 0.616 0.000 0.000 0.000 0.384
#> GSM531615 2 0.1043 0.8699 0.000 0.960 0.000 0.000 0.040
#> GSM531617 2 0.1121 0.8690 0.000 0.956 0.000 0.000 0.044
#> GSM531624 2 0.0000 0.8791 0.000 1.000 0.000 0.000 0.000
#> GSM531627 2 0.2179 0.8445 0.000 0.888 0.000 0.000 0.112
#> GSM531629 2 0.3109 0.7230 0.000 0.800 0.000 0.000 0.200
#> GSM531631 2 0.0000 0.8791 0.000 1.000 0.000 0.000 0.000
#> GSM531634 2 0.0794 0.8741 0.000 0.972 0.000 0.000 0.028
#> GSM531636 3 0.4803 0.7007 0.000 0.096 0.720 0.000 0.184
#> GSM531637 2 0.0000 0.8791 0.000 1.000 0.000 0.000 0.000
#> GSM531654 2 0.4225 0.4505 0.004 0.632 0.000 0.000 0.364
#> GSM531655 5 0.6107 0.4146 0.048 0.064 0.148 0.040 0.700
#> GSM531658 4 0.3424 0.6340 0.000 0.000 0.000 0.760 0.240
#> GSM531660 5 0.5799 0.2705 0.048 0.040 0.000 0.296 0.616
#> GSM531602 1 0.4182 0.5363 0.600 0.000 0.000 0.000 0.400
#> GSM531603 5 0.4256 -0.3142 0.436 0.000 0.000 0.000 0.564
#> GSM531604 1 0.2561 0.6936 0.856 0.000 0.000 0.000 0.144
#> GSM531606 1 0.4114 0.5739 0.624 0.000 0.000 0.000 0.376
#> GSM531607 1 0.4182 0.5363 0.600 0.000 0.000 0.000 0.400
#> GSM531608 2 0.1121 0.8708 0.000 0.956 0.000 0.000 0.044
#> GSM531609 4 0.0162 0.7933 0.000 0.000 0.000 0.996 0.004
#> GSM531610 4 0.0000 0.7930 0.000 0.000 0.000 1.000 0.000
#> GSM531611 4 0.0000 0.7930 0.000 0.000 0.000 1.000 0.000
#> GSM531612 4 0.0162 0.7933 0.000 0.000 0.000 0.996 0.004
#> GSM531613 4 0.0000 0.7930 0.000 0.000 0.000 1.000 0.000
#> GSM531614 4 0.0162 0.7933 0.000 0.000 0.000 0.996 0.004
#> GSM531616 2 0.5941 0.4455 0.000 0.592 0.228 0.000 0.180
#> GSM531618 5 0.7706 0.0508 0.000 0.212 0.064 0.352 0.372
#> GSM531619 2 0.0000 0.8791 0.000 1.000 0.000 0.000 0.000
#> GSM531620 2 0.2127 0.8582 0.000 0.892 0.000 0.000 0.108
#> GSM531621 2 0.1908 0.8564 0.000 0.908 0.000 0.000 0.092
#> GSM531622 2 0.0290 0.8796 0.000 0.992 0.000 0.000 0.008
#> GSM531623 2 0.0510 0.8794 0.000 0.984 0.000 0.000 0.016
#> GSM531625 2 0.3304 0.7931 0.000 0.816 0.016 0.000 0.168
#> GSM531626 2 0.3476 0.7822 0.000 0.804 0.020 0.000 0.176
#> GSM531628 3 0.0000 0.8361 0.000 0.000 1.000 0.000 0.000
#> GSM531630 2 0.0404 0.8795 0.000 0.988 0.000 0.000 0.012
#> GSM531632 3 0.1478 0.8245 0.000 0.000 0.936 0.000 0.064
#> GSM531633 2 0.1908 0.8564 0.000 0.908 0.000 0.000 0.092
#> GSM531635 3 0.2852 0.7891 0.000 0.000 0.828 0.000 0.172
#> GSM531638 2 0.3513 0.7783 0.000 0.800 0.020 0.000 0.180
#> GSM531639 3 0.5264 0.6441 0.000 0.128 0.676 0.000 0.196
#> GSM531640 2 0.0000 0.8791 0.000 1.000 0.000 0.000 0.000
#> GSM531641 4 0.0162 0.7933 0.000 0.000 0.000 0.996 0.004
#> GSM531642 3 0.5030 0.5254 0.000 0.000 0.604 0.044 0.352
#> GSM531643 3 0.1544 0.8277 0.000 0.000 0.932 0.000 0.068
#> GSM531644 3 0.2280 0.8052 0.000 0.000 0.880 0.000 0.120
#> GSM531645 4 0.0162 0.7933 0.000 0.000 0.000 0.996 0.004
#> GSM531646 3 0.1341 0.8270 0.000 0.000 0.944 0.000 0.056
#> GSM531647 3 0.0290 0.8366 0.000 0.000 0.992 0.000 0.008
#> GSM531648 4 0.4470 0.5071 0.000 0.008 0.008 0.656 0.328
#> GSM531649 3 0.2690 0.7937 0.000 0.000 0.844 0.000 0.156
#> GSM531650 3 0.0290 0.8368 0.000 0.000 0.992 0.000 0.008
#> GSM531651 2 0.0510 0.8794 0.000 0.984 0.000 0.000 0.016
#> GSM531652 3 0.4953 0.5519 0.000 0.004 0.664 0.048 0.284
#> GSM531653 3 0.0609 0.8373 0.000 0.000 0.980 0.000 0.020
#> GSM531656 3 0.2929 0.8054 0.000 0.008 0.840 0.000 0.152
#> GSM531657 4 0.4449 0.5603 0.020 0.004 0.000 0.688 0.288
#> GSM531659 4 0.6331 0.2573 0.152 0.004 0.000 0.508 0.336
#> GSM531661 2 0.1544 0.8615 0.000 0.932 0.000 0.000 0.068
#> GSM531662 2 0.4946 0.6386 0.060 0.664 0.000 0.000 0.276
#> GSM531663 4 0.0000 0.7930 0.000 0.000 0.000 1.000 0.000
#> GSM531664 3 0.0290 0.8368 0.000 0.000 0.992 0.000 0.008
#> GSM531665 1 0.5770 0.4032 0.604 0.000 0.140 0.000 0.256
#> GSM531666 3 0.4687 0.5625 0.000 0.000 0.672 0.040 0.288
#> GSM531667 2 0.1792 0.8508 0.000 0.916 0.000 0.000 0.084
#> GSM531668 5 0.5477 0.3348 0.044 0.040 0.000 0.248 0.668
#> GSM531669 3 0.0404 0.8348 0.000 0.000 0.988 0.000 0.012
#> GSM531670 3 0.2971 0.8046 0.000 0.008 0.836 0.000 0.156
#> GSM531671 5 0.8228 -0.0830 0.124 0.224 0.312 0.000 0.340
#> GSM531672 4 0.4249 0.5722 0.016 0.000 0.000 0.688 0.296
#> GSM531673 1 0.4946 0.4624 0.596 0.036 0.000 0.000 0.368
#> GSM531674 3 0.0404 0.8348 0.000 0.000 0.988 0.000 0.012
#> GSM531675 1 0.3921 0.6589 0.784 0.000 0.000 0.044 0.172
#> GSM531676 1 0.2388 0.6826 0.900 0.000 0.072 0.000 0.028
#> GSM531677 1 0.2763 0.6944 0.848 0.000 0.000 0.004 0.148
#> GSM531678 1 0.1341 0.7157 0.944 0.000 0.000 0.000 0.056
#> GSM531679 1 0.2020 0.7090 0.900 0.000 0.000 0.000 0.100
#> GSM531680 1 0.3169 0.6808 0.856 0.000 0.084 0.000 0.060
#> GSM531681 4 0.3844 0.6219 0.132 0.000 0.000 0.804 0.064
#> GSM531682 1 0.1732 0.7149 0.920 0.000 0.000 0.000 0.080
#> GSM531683 1 0.4288 0.5481 0.612 0.000 0.000 0.004 0.384
#> GSM531684 1 0.4150 0.5628 0.612 0.000 0.000 0.000 0.388
#> GSM531685 1 0.4609 0.5768 0.744 0.000 0.104 0.000 0.152
#> GSM531686 4 0.3844 0.6219 0.132 0.000 0.000 0.804 0.064
#> GSM531687 1 0.2130 0.6826 0.908 0.000 0.080 0.000 0.012
#> GSM531688 3 0.4090 0.5273 0.268 0.000 0.716 0.000 0.016
#> GSM531689 1 0.0000 0.7121 1.000 0.000 0.000 0.000 0.000
#> GSM531690 4 0.6407 0.2826 0.244 0.000 0.000 0.512 0.244
#> GSM531691 1 0.1043 0.7082 0.960 0.000 0.000 0.000 0.040
#> GSM531692 1 0.3692 0.6375 0.812 0.000 0.052 0.000 0.136
#> GSM531693 3 0.3359 0.7451 0.108 0.000 0.840 0.000 0.052
#> GSM531694 1 0.4182 0.5363 0.600 0.000 0.000 0.000 0.400
#> GSM531695 1 0.5199 0.4342 0.636 0.000 0.292 0.000 0.072
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM531600 3 0.3652 0.69146 0.000 0.000 0.768 0.000 0.044 0.188
#> GSM531601 2 0.4622 0.34931 0.036 0.608 0.000 0.008 0.000 0.348
#> GSM531605 1 0.3364 0.51566 0.780 0.000 0.000 0.000 0.196 0.024
#> GSM531615 2 0.2333 0.79298 0.024 0.884 0.000 0.000 0.000 0.092
#> GSM531617 2 0.2412 0.79034 0.028 0.880 0.000 0.000 0.000 0.092
#> GSM531624 2 0.0508 0.81840 0.004 0.984 0.000 0.000 0.000 0.012
#> GSM531627 2 0.3605 0.72964 0.016 0.776 0.000 0.000 0.016 0.192
#> GSM531629 2 0.4356 0.44586 0.032 0.608 0.000 0.000 0.000 0.360
#> GSM531631 2 0.0717 0.81848 0.016 0.976 0.000 0.000 0.000 0.008
#> GSM531634 2 0.2331 0.79505 0.032 0.888 0.000 0.000 0.000 0.080
#> GSM531636 3 0.5152 0.58131 0.000 0.036 0.608 0.000 0.044 0.312
#> GSM531637 2 0.0508 0.81840 0.004 0.984 0.000 0.000 0.000 0.012
#> GSM531654 2 0.7125 0.14179 0.276 0.412 0.000 0.000 0.096 0.216
#> GSM531655 6 0.5953 0.23728 0.284 0.008 0.056 0.012 0.048 0.592
#> GSM531658 4 0.5892 -0.08582 0.180 0.000 0.000 0.460 0.004 0.356
#> GSM531660 1 0.5145 0.02166 0.572 0.004 0.000 0.088 0.000 0.336
#> GSM531602 1 0.2300 0.56929 0.856 0.000 0.000 0.000 0.144 0.000
#> GSM531603 1 0.2897 0.56168 0.852 0.000 0.000 0.000 0.088 0.060
#> GSM531604 5 0.4716 0.41071 0.224 0.000 0.000 0.000 0.668 0.108
#> GSM531606 1 0.4420 0.37235 0.620 0.000 0.000 0.000 0.340 0.040
#> GSM531607 1 0.2553 0.56915 0.848 0.000 0.000 0.000 0.144 0.008
#> GSM531608 2 0.2501 0.79063 0.016 0.872 0.000 0.000 0.004 0.108
#> GSM531609 4 0.0146 0.81491 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM531610 4 0.0146 0.81439 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM531611 4 0.0260 0.81404 0.008 0.000 0.000 0.992 0.000 0.000
#> GSM531612 4 0.0146 0.81491 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM531613 4 0.0405 0.81260 0.008 0.000 0.000 0.988 0.000 0.004
#> GSM531614 4 0.0146 0.81491 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM531616 2 0.5857 0.53902 0.012 0.612 0.104 0.000 0.036 0.236
#> GSM531618 6 0.7013 0.40177 0.136 0.084 0.048 0.156 0.004 0.572
#> GSM531619 2 0.0508 0.81840 0.004 0.984 0.000 0.000 0.000 0.012
#> GSM531620 2 0.3349 0.78357 0.024 0.804 0.000 0.000 0.008 0.164
#> GSM531621 2 0.2466 0.78969 0.008 0.872 0.000 0.000 0.008 0.112
#> GSM531622 2 0.0603 0.81857 0.016 0.980 0.000 0.000 0.000 0.004
#> GSM531623 2 0.0790 0.81885 0.000 0.968 0.000 0.000 0.000 0.032
#> GSM531625 2 0.4300 0.67478 0.008 0.720 0.008 0.000 0.036 0.228
#> GSM531626 2 0.4470 0.66945 0.012 0.704 0.008 0.000 0.036 0.240
#> GSM531628 3 0.0146 0.76014 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM531630 2 0.0914 0.81892 0.016 0.968 0.000 0.000 0.000 0.016
#> GSM531632 3 0.1296 0.75219 0.004 0.000 0.952 0.000 0.012 0.032
#> GSM531633 2 0.2466 0.78969 0.008 0.872 0.000 0.000 0.008 0.112
#> GSM531635 3 0.4396 0.66063 0.012 0.008 0.716 0.000 0.036 0.228
#> GSM531638 2 0.4494 0.65952 0.012 0.700 0.008 0.000 0.036 0.244
#> GSM531639 3 0.5740 0.53692 0.008 0.060 0.572 0.000 0.044 0.316
#> GSM531640 2 0.0717 0.81848 0.016 0.976 0.000 0.000 0.000 0.008
#> GSM531641 4 0.0146 0.81491 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM531642 6 0.5099 -0.02195 0.036 0.000 0.372 0.016 0.008 0.568
#> GSM531643 3 0.2092 0.72527 0.000 0.000 0.876 0.000 0.000 0.124
#> GSM531644 3 0.4438 0.43153 0.036 0.000 0.664 0.004 0.004 0.292
#> GSM531645 4 0.0146 0.81491 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM531646 3 0.1296 0.75934 0.004 0.000 0.948 0.000 0.004 0.044
#> GSM531647 3 0.0363 0.76039 0.000 0.000 0.988 0.000 0.000 0.012
#> GSM531648 6 0.6137 0.21736 0.152 0.008 0.008 0.332 0.004 0.496
#> GSM531649 3 0.3839 0.67766 0.004 0.000 0.748 0.000 0.036 0.212
#> GSM531650 3 0.0260 0.76009 0.000 0.000 0.992 0.000 0.000 0.008
#> GSM531651 2 0.0790 0.81885 0.000 0.968 0.000 0.000 0.000 0.032
#> GSM531652 6 0.5623 -0.01830 0.072 0.000 0.448 0.020 0.004 0.456
#> GSM531653 3 0.0622 0.76127 0.000 0.000 0.980 0.000 0.008 0.012
#> GSM531656 3 0.4087 0.65252 0.000 0.000 0.688 0.000 0.036 0.276
#> GSM531657 6 0.6079 0.10543 0.200 0.000 0.000 0.352 0.008 0.440
#> GSM531659 6 0.7029 0.26975 0.200 0.000 0.000 0.204 0.124 0.472
#> GSM531661 2 0.3680 0.73779 0.016 0.796 0.000 0.000 0.040 0.148
#> GSM531662 6 0.6454 -0.23030 0.020 0.364 0.000 0.000 0.240 0.376
#> GSM531663 4 0.0405 0.81260 0.008 0.000 0.000 0.988 0.000 0.004
#> GSM531664 3 0.0458 0.75972 0.000 0.000 0.984 0.000 0.000 0.016
#> GSM531665 5 0.4362 0.43251 0.012 0.000 0.036 0.000 0.688 0.264
#> GSM531666 3 0.5837 -0.12547 0.084 0.000 0.448 0.020 0.008 0.440
#> GSM531667 2 0.2581 0.78535 0.016 0.856 0.000 0.000 0.000 0.128
#> GSM531668 1 0.4729 -0.04100 0.544 0.004 0.000 0.040 0.000 0.412
#> GSM531669 3 0.0820 0.75427 0.000 0.000 0.972 0.000 0.016 0.012
#> GSM531670 3 0.4087 0.65252 0.000 0.000 0.688 0.000 0.036 0.276
#> GSM531671 6 0.7286 -0.00437 0.016 0.100 0.144 0.000 0.324 0.416
#> GSM531672 4 0.6189 -0.18448 0.264 0.000 0.000 0.380 0.004 0.352
#> GSM531673 5 0.4923 0.34025 0.052 0.012 0.000 0.000 0.596 0.340
#> GSM531674 3 0.0717 0.75549 0.000 0.000 0.976 0.000 0.016 0.008
#> GSM531675 1 0.5762 -0.03303 0.528 0.000 0.000 0.036 0.352 0.084
#> GSM531676 5 0.2913 0.62865 0.116 0.000 0.032 0.000 0.848 0.004
#> GSM531677 5 0.5077 0.15890 0.464 0.000 0.000 0.004 0.468 0.064
#> GSM531678 5 0.4252 0.47157 0.312 0.000 0.000 0.000 0.652 0.036
#> GSM531679 5 0.4610 0.36576 0.388 0.000 0.000 0.000 0.568 0.044
#> GSM531680 5 0.4253 0.57378 0.196 0.000 0.036 0.000 0.740 0.028
#> GSM531681 4 0.4927 0.57355 0.160 0.000 0.000 0.712 0.080 0.048
#> GSM531682 5 0.4587 0.41261 0.356 0.000 0.000 0.000 0.596 0.048
#> GSM531683 1 0.3621 0.51133 0.788 0.000 0.000 0.004 0.160 0.048
#> GSM531684 1 0.5350 0.13913 0.476 0.000 0.000 0.000 0.416 0.108
#> GSM531685 5 0.2350 0.56717 0.000 0.000 0.036 0.000 0.888 0.076
#> GSM531686 4 0.5023 0.56309 0.160 0.000 0.000 0.704 0.088 0.048
#> GSM531687 5 0.3323 0.62437 0.128 0.000 0.036 0.000 0.824 0.012
#> GSM531688 3 0.4018 0.41419 0.000 0.000 0.656 0.000 0.324 0.020
#> GSM531689 5 0.2706 0.61931 0.160 0.000 0.000 0.000 0.832 0.008
#> GSM531690 1 0.6624 0.23139 0.524 0.000 0.000 0.232 0.100 0.144
#> GSM531691 5 0.3163 0.62420 0.140 0.000 0.000 0.000 0.820 0.040
#> GSM531692 5 0.2356 0.56143 0.016 0.000 0.004 0.000 0.884 0.096
#> GSM531693 3 0.3794 0.56725 0.000 0.000 0.744 0.000 0.216 0.040
#> GSM531694 1 0.2300 0.56929 0.856 0.000 0.000 0.000 0.144 0.000
#> GSM531695 5 0.6164 0.34957 0.184 0.000 0.268 0.000 0.520 0.028
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> CV:kmeans 92 0.06722 2
#> CV:kmeans 95 0.00163 3
#> CV:kmeans 92 0.00399 4
#> CV:kmeans 83 0.01015 5
#> CV:kmeans 65 0.11018 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "skmeans"]
# you can also extract it by
# res = res_list["CV:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 96 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
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 0.556 0.891 0.935 0.5048 0.496 0.496
#> 3 3 0.889 0.895 0.958 0.3289 0.708 0.477
#> 4 4 0.978 0.951 0.978 0.1244 0.843 0.570
#> 5 5 0.785 0.607 0.809 0.0546 0.961 0.848
#> 6 6 0.757 0.655 0.797 0.0389 0.923 0.683
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM531600 2 0.0000 0.915 0.000 1.000
#> GSM531601 2 0.6712 0.848 0.176 0.824
#> GSM531605 1 0.0000 0.937 1.000 0.000
#> GSM531615 2 0.6712 0.848 0.176 0.824
#> GSM531617 2 0.6712 0.848 0.176 0.824
#> GSM531624 2 0.6712 0.848 0.176 0.824
#> GSM531627 2 0.0000 0.915 0.000 1.000
#> GSM531629 2 0.7219 0.826 0.200 0.800
#> GSM531631 2 0.6712 0.848 0.176 0.824
#> GSM531634 2 0.6712 0.848 0.176 0.824
#> GSM531636 2 0.0000 0.915 0.000 1.000
#> GSM531637 2 0.6712 0.848 0.176 0.824
#> GSM531654 2 0.6712 0.848 0.176 0.824
#> GSM531655 1 0.2778 0.913 0.952 0.048
#> GSM531658 1 0.0000 0.937 1.000 0.000
#> GSM531660 1 0.0000 0.937 1.000 0.000
#> GSM531602 1 0.0000 0.937 1.000 0.000
#> GSM531603 1 0.0000 0.937 1.000 0.000
#> GSM531604 1 0.0376 0.936 0.996 0.004
#> GSM531606 1 0.0000 0.937 1.000 0.000
#> GSM531607 1 0.0000 0.937 1.000 0.000
#> GSM531608 2 0.6712 0.848 0.176 0.824
#> GSM531609 1 0.0000 0.937 1.000 0.000
#> GSM531610 1 0.0000 0.937 1.000 0.000
#> GSM531611 1 0.0000 0.937 1.000 0.000
#> GSM531612 1 0.0000 0.937 1.000 0.000
#> GSM531613 1 0.0000 0.937 1.000 0.000
#> GSM531614 1 0.0000 0.937 1.000 0.000
#> GSM531616 2 0.0000 0.915 0.000 1.000
#> GSM531618 2 0.7219 0.826 0.200 0.800
#> GSM531619 2 0.6712 0.848 0.176 0.824
#> GSM531620 2 0.0000 0.915 0.000 1.000
#> GSM531621 2 0.0000 0.915 0.000 1.000
#> GSM531622 2 0.6623 0.850 0.172 0.828
#> GSM531623 2 0.0376 0.913 0.004 0.996
#> GSM531625 2 0.0000 0.915 0.000 1.000
#> GSM531626 2 0.0000 0.915 0.000 1.000
#> GSM531628 2 0.0000 0.915 0.000 1.000
#> GSM531630 2 0.4815 0.880 0.104 0.896
#> GSM531632 2 0.0000 0.915 0.000 1.000
#> GSM531633 2 0.0000 0.915 0.000 1.000
#> GSM531635 2 0.0000 0.915 0.000 1.000
#> GSM531638 2 0.0000 0.915 0.000 1.000
#> GSM531639 2 0.0000 0.915 0.000 1.000
#> GSM531640 2 0.6712 0.848 0.176 0.824
#> GSM531641 1 0.0000 0.937 1.000 0.000
#> GSM531642 2 0.0000 0.915 0.000 1.000
#> GSM531643 2 0.0000 0.915 0.000 1.000
#> GSM531644 2 0.0000 0.915 0.000 1.000
#> GSM531645 1 0.0000 0.937 1.000 0.000
#> GSM531646 2 0.0000 0.915 0.000 1.000
#> GSM531647 2 0.0000 0.915 0.000 1.000
#> GSM531648 2 0.9661 0.523 0.392 0.608
#> GSM531649 2 0.0000 0.915 0.000 1.000
#> GSM531650 2 0.0000 0.915 0.000 1.000
#> GSM531651 2 0.0000 0.915 0.000 1.000
#> GSM531652 2 0.0000 0.915 0.000 1.000
#> GSM531653 2 0.0000 0.915 0.000 1.000
#> GSM531656 2 0.0000 0.915 0.000 1.000
#> GSM531657 1 0.0000 0.937 1.000 0.000
#> GSM531659 1 0.0000 0.937 1.000 0.000
#> GSM531661 2 0.6712 0.848 0.176 0.824
#> GSM531662 2 0.0000 0.915 0.000 1.000
#> GSM531663 1 0.0000 0.937 1.000 0.000
#> GSM531664 1 0.7219 0.815 0.800 0.200
#> GSM531665 1 0.7219 0.815 0.800 0.200
#> GSM531666 1 0.6712 0.834 0.824 0.176
#> GSM531667 2 0.6712 0.848 0.176 0.824
#> GSM531668 1 0.0000 0.937 1.000 0.000
#> GSM531669 2 0.5408 0.813 0.124 0.876
#> GSM531670 2 0.0000 0.915 0.000 1.000
#> GSM531671 2 0.0000 0.915 0.000 1.000
#> GSM531672 1 0.0000 0.937 1.000 0.000
#> GSM531673 1 0.7219 0.815 0.800 0.200
#> GSM531674 2 0.5408 0.813 0.124 0.876
#> GSM531675 1 0.0000 0.937 1.000 0.000
#> GSM531676 1 0.6712 0.834 0.824 0.176
#> GSM531677 1 0.0000 0.937 1.000 0.000
#> GSM531678 1 0.0000 0.937 1.000 0.000
#> GSM531679 1 0.0376 0.936 0.996 0.004
#> GSM531680 1 0.6712 0.834 0.824 0.176
#> GSM531681 1 0.0000 0.937 1.000 0.000
#> GSM531682 1 0.0376 0.936 0.996 0.004
#> GSM531683 1 0.0000 0.937 1.000 0.000
#> GSM531684 1 0.0000 0.937 1.000 0.000
#> GSM531685 1 0.7219 0.815 0.800 0.200
#> GSM531686 1 0.0000 0.937 1.000 0.000
#> GSM531687 1 0.6712 0.834 0.824 0.176
#> GSM531688 1 0.7139 0.819 0.804 0.196
#> GSM531689 1 0.6531 0.839 0.832 0.168
#> GSM531690 1 0.0000 0.937 1.000 0.000
#> GSM531691 1 0.6712 0.834 0.824 0.176
#> GSM531692 1 0.7219 0.815 0.800 0.200
#> GSM531693 2 0.5408 0.813 0.124 0.876
#> GSM531694 1 0.0000 0.937 1.000 0.000
#> GSM531695 1 0.6712 0.834 0.824 0.176
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM531600 3 0.0000 0.9236 0.000 0.000 1.000
#> GSM531601 2 0.0000 0.9527 0.000 1.000 0.000
#> GSM531605 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531615 2 0.0000 0.9527 0.000 1.000 0.000
#> GSM531617 2 0.0000 0.9527 0.000 1.000 0.000
#> GSM531624 2 0.0000 0.9527 0.000 1.000 0.000
#> GSM531627 2 0.0000 0.9527 0.000 1.000 0.000
#> GSM531629 2 0.0000 0.9527 0.000 1.000 0.000
#> GSM531631 2 0.0000 0.9527 0.000 1.000 0.000
#> GSM531634 2 0.0000 0.9527 0.000 1.000 0.000
#> GSM531636 3 0.4504 0.7198 0.000 0.196 0.804
#> GSM531637 2 0.0000 0.9527 0.000 1.000 0.000
#> GSM531654 2 0.0000 0.9527 0.000 1.000 0.000
#> GSM531655 3 0.9599 0.0836 0.388 0.200 0.412
#> GSM531658 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531660 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531602 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531603 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531604 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531606 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531607 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531608 2 0.0000 0.9527 0.000 1.000 0.000
#> GSM531609 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531610 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531611 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531612 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531613 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531614 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531616 2 0.4555 0.7130 0.000 0.800 0.200
#> GSM531618 2 0.5254 0.6317 0.264 0.736 0.000
#> GSM531619 2 0.0000 0.9527 0.000 1.000 0.000
#> GSM531620 2 0.0000 0.9527 0.000 1.000 0.000
#> GSM531621 2 0.0000 0.9527 0.000 1.000 0.000
#> GSM531622 2 0.0000 0.9527 0.000 1.000 0.000
#> GSM531623 2 0.0000 0.9527 0.000 1.000 0.000
#> GSM531625 2 0.0000 0.9527 0.000 1.000 0.000
#> GSM531626 2 0.0000 0.9527 0.000 1.000 0.000
#> GSM531628 3 0.0000 0.9236 0.000 0.000 1.000
#> GSM531630 2 0.0000 0.9527 0.000 1.000 0.000
#> GSM531632 3 0.0000 0.9236 0.000 0.000 1.000
#> GSM531633 2 0.0000 0.9527 0.000 1.000 0.000
#> GSM531635 3 0.0237 0.9209 0.000 0.004 0.996
#> GSM531638 2 0.0000 0.9527 0.000 1.000 0.000
#> GSM531639 3 0.6192 0.2847 0.000 0.420 0.580
#> GSM531640 2 0.0000 0.9527 0.000 1.000 0.000
#> GSM531641 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531642 3 0.0000 0.9236 0.000 0.000 1.000
#> GSM531643 3 0.0000 0.9236 0.000 0.000 1.000
#> GSM531644 3 0.0000 0.9236 0.000 0.000 1.000
#> GSM531645 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531646 3 0.0000 0.9236 0.000 0.000 1.000
#> GSM531647 3 0.0000 0.9236 0.000 0.000 1.000
#> GSM531648 1 0.2446 0.9202 0.936 0.012 0.052
#> GSM531649 3 0.0000 0.9236 0.000 0.000 1.000
#> GSM531650 3 0.0000 0.9236 0.000 0.000 1.000
#> GSM531651 2 0.0000 0.9527 0.000 1.000 0.000
#> GSM531652 3 0.0000 0.9236 0.000 0.000 1.000
#> GSM531653 3 0.0000 0.9236 0.000 0.000 1.000
#> GSM531656 3 0.0000 0.9236 0.000 0.000 1.000
#> GSM531657 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531659 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531661 2 0.0000 0.9527 0.000 1.000 0.000
#> GSM531662 2 0.0000 0.9527 0.000 1.000 0.000
#> GSM531663 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531664 3 0.0000 0.9236 0.000 0.000 1.000
#> GSM531665 3 0.3267 0.8399 0.116 0.000 0.884
#> GSM531666 3 0.0000 0.9236 0.000 0.000 1.000
#> GSM531667 2 0.0000 0.9527 0.000 1.000 0.000
#> GSM531668 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531669 3 0.0000 0.9236 0.000 0.000 1.000
#> GSM531670 3 0.0000 0.9236 0.000 0.000 1.000
#> GSM531671 2 0.6111 0.3105 0.000 0.604 0.396
#> GSM531672 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531673 2 0.5968 0.4121 0.364 0.636 0.000
#> GSM531674 3 0.0000 0.9236 0.000 0.000 1.000
#> GSM531675 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531676 3 0.3482 0.8300 0.128 0.000 0.872
#> GSM531677 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531678 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531679 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531680 3 0.3619 0.8219 0.136 0.000 0.864
#> GSM531681 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531682 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531683 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531684 1 0.6280 0.1224 0.540 0.460 0.000
#> GSM531685 3 0.0000 0.9236 0.000 0.000 1.000
#> GSM531686 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531687 3 0.3551 0.8261 0.132 0.000 0.868
#> GSM531688 3 0.0000 0.9236 0.000 0.000 1.000
#> GSM531689 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531690 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531691 1 0.2356 0.9054 0.928 0.000 0.072
#> GSM531692 3 0.6654 0.1293 0.008 0.456 0.536
#> GSM531693 3 0.0000 0.9236 0.000 0.000 1.000
#> GSM531694 1 0.0000 0.9815 1.000 0.000 0.000
#> GSM531695 3 0.0000 0.9236 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM531600 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> GSM531601 2 0.0000 0.969 0.000 1.000 0.000 0.000
#> GSM531605 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM531615 2 0.0000 0.969 0.000 1.000 0.000 0.000
#> GSM531617 2 0.0000 0.969 0.000 1.000 0.000 0.000
#> GSM531624 2 0.0000 0.969 0.000 1.000 0.000 0.000
#> GSM531627 2 0.0000 0.969 0.000 1.000 0.000 0.000
#> GSM531629 2 0.0000 0.969 0.000 1.000 0.000 0.000
#> GSM531631 2 0.0000 0.969 0.000 1.000 0.000 0.000
#> GSM531634 2 0.0000 0.969 0.000 1.000 0.000 0.000
#> GSM531636 3 0.3024 0.820 0.000 0.148 0.852 0.000
#> GSM531637 2 0.0000 0.969 0.000 1.000 0.000 0.000
#> GSM531654 2 0.0000 0.969 0.000 1.000 0.000 0.000
#> GSM531655 1 0.6724 0.546 0.616 0.192 0.192 0.000
#> GSM531658 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM531660 4 0.0469 0.989 0.012 0.000 0.000 0.988
#> GSM531602 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM531603 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM531604 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM531606 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM531607 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM531608 2 0.0000 0.969 0.000 1.000 0.000 0.000
#> GSM531609 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM531610 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM531611 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM531612 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM531613 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM531614 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM531616 2 0.4713 0.460 0.000 0.640 0.360 0.000
#> GSM531618 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM531619 2 0.0000 0.969 0.000 1.000 0.000 0.000
#> GSM531620 2 0.0000 0.969 0.000 1.000 0.000 0.000
#> GSM531621 2 0.0000 0.969 0.000 1.000 0.000 0.000
#> GSM531622 2 0.0000 0.969 0.000 1.000 0.000 0.000
#> GSM531623 2 0.0000 0.969 0.000 1.000 0.000 0.000
#> GSM531625 2 0.0592 0.957 0.000 0.984 0.016 0.000
#> GSM531626 2 0.0921 0.947 0.000 0.972 0.028 0.000
#> GSM531628 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> GSM531630 2 0.0000 0.969 0.000 1.000 0.000 0.000
#> GSM531632 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> GSM531633 2 0.0000 0.969 0.000 1.000 0.000 0.000
#> GSM531635 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> GSM531638 2 0.0817 0.950 0.000 0.976 0.024 0.000
#> GSM531639 3 0.3444 0.769 0.000 0.184 0.816 0.000
#> GSM531640 2 0.0000 0.969 0.000 1.000 0.000 0.000
#> GSM531641 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM531642 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> GSM531643 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> GSM531644 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> GSM531645 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM531646 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> GSM531647 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> GSM531648 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM531649 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> GSM531650 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> GSM531651 2 0.0000 0.969 0.000 1.000 0.000 0.000
#> GSM531652 3 0.0469 0.972 0.000 0.000 0.988 0.012
#> GSM531653 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> GSM531656 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> GSM531657 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM531659 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM531661 2 0.0000 0.969 0.000 1.000 0.000 0.000
#> GSM531662 2 0.0000 0.969 0.000 1.000 0.000 0.000
#> GSM531663 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM531664 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> GSM531665 1 0.3801 0.739 0.780 0.000 0.220 0.000
#> GSM531666 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> GSM531667 2 0.0000 0.969 0.000 1.000 0.000 0.000
#> GSM531668 4 0.0188 0.995 0.004 0.000 0.000 0.996
#> GSM531669 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> GSM531670 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> GSM531671 2 0.4817 0.394 0.000 0.612 0.388 0.000
#> GSM531672 4 0.0000 0.998 0.000 0.000 0.000 1.000
#> GSM531673 1 0.0469 0.956 0.988 0.012 0.000 0.000
#> GSM531674 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> GSM531675 1 0.1792 0.910 0.932 0.000 0.000 0.068
#> GSM531676 1 0.0817 0.952 0.976 0.000 0.024 0.000
#> GSM531677 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM531678 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM531679 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM531680 1 0.0817 0.952 0.976 0.000 0.024 0.000
#> GSM531681 4 0.0469 0.989 0.012 0.000 0.000 0.988
#> GSM531682 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM531683 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM531684 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM531685 1 0.1118 0.944 0.964 0.000 0.036 0.000
#> GSM531686 4 0.0592 0.985 0.016 0.000 0.000 0.984
#> GSM531687 1 0.0817 0.952 0.976 0.000 0.024 0.000
#> GSM531688 3 0.0469 0.971 0.012 0.000 0.988 0.000
#> GSM531689 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM531690 4 0.0188 0.995 0.004 0.000 0.000 0.996
#> GSM531691 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM531692 1 0.0188 0.961 0.996 0.000 0.004 0.000
#> GSM531693 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> GSM531694 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM531695 1 0.2704 0.862 0.876 0.000 0.124 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM531600 3 0.2280 0.8358 0.000 0.000 0.880 0.000 0.120
#> GSM531601 2 0.3814 0.6683 0.000 0.720 0.004 0.000 0.276
#> GSM531605 1 0.0162 0.3957 0.996 0.000 0.000 0.000 0.004
#> GSM531615 2 0.0290 0.9071 0.000 0.992 0.000 0.000 0.008
#> GSM531617 2 0.0290 0.9071 0.000 0.992 0.000 0.000 0.008
#> GSM531624 2 0.0000 0.9081 0.000 1.000 0.000 0.000 0.000
#> GSM531627 2 0.1270 0.8952 0.000 0.948 0.000 0.000 0.052
#> GSM531629 2 0.0404 0.9061 0.000 0.988 0.000 0.000 0.012
#> GSM531631 2 0.0000 0.9081 0.000 1.000 0.000 0.000 0.000
#> GSM531634 2 0.0290 0.9071 0.000 0.992 0.000 0.000 0.008
#> GSM531636 3 0.3236 0.8180 0.000 0.020 0.828 0.000 0.152
#> GSM531637 2 0.0000 0.9081 0.000 1.000 0.000 0.000 0.000
#> GSM531654 2 0.5535 0.3385 0.392 0.536 0.000 0.000 0.072
#> GSM531655 1 0.6299 0.1791 0.556 0.080 0.036 0.000 0.328
#> GSM531658 4 0.1341 0.9058 0.000 0.000 0.000 0.944 0.056
#> GSM531660 1 0.5804 -0.1912 0.476 0.012 0.000 0.452 0.060
#> GSM531602 1 0.0000 0.3967 1.000 0.000 0.000 0.000 0.000
#> GSM531603 1 0.0404 0.3941 0.988 0.000 0.000 0.000 0.012
#> GSM531604 1 0.4150 -0.3012 0.612 0.000 0.000 0.000 0.388
#> GSM531606 1 0.1341 0.3722 0.944 0.000 0.000 0.000 0.056
#> GSM531607 1 0.0162 0.3957 0.996 0.000 0.000 0.000 0.004
#> GSM531608 2 0.0162 0.9077 0.000 0.996 0.000 0.000 0.004
#> GSM531609 4 0.0000 0.9312 0.000 0.000 0.000 1.000 0.000
#> GSM531610 4 0.0000 0.9312 0.000 0.000 0.000 1.000 0.000
#> GSM531611 4 0.0000 0.9312 0.000 0.000 0.000 1.000 0.000
#> GSM531612 4 0.0000 0.9312 0.000 0.000 0.000 1.000 0.000
#> GSM531613 4 0.0000 0.9312 0.000 0.000 0.000 1.000 0.000
#> GSM531614 4 0.0000 0.9312 0.000 0.000 0.000 1.000 0.000
#> GSM531616 2 0.5076 0.6325 0.000 0.692 0.200 0.000 0.108
#> GSM531618 4 0.4703 0.6851 0.000 0.036 0.004 0.684 0.276
#> GSM531619 2 0.0000 0.9081 0.000 1.000 0.000 0.000 0.000
#> GSM531620 2 0.0880 0.9039 0.000 0.968 0.000 0.000 0.032
#> GSM531621 2 0.1121 0.8981 0.000 0.956 0.000 0.000 0.044
#> GSM531622 2 0.0000 0.9081 0.000 1.000 0.000 0.000 0.000
#> GSM531623 2 0.0404 0.9071 0.000 0.988 0.000 0.000 0.012
#> GSM531625 2 0.3214 0.8312 0.000 0.844 0.036 0.000 0.120
#> GSM531626 2 0.3507 0.8176 0.000 0.828 0.052 0.000 0.120
#> GSM531628 3 0.0000 0.8516 0.000 0.000 1.000 0.000 0.000
#> GSM531630 2 0.0404 0.9071 0.000 0.988 0.000 0.000 0.012
#> GSM531632 3 0.1270 0.8369 0.000 0.000 0.948 0.000 0.052
#> GSM531633 2 0.1043 0.8995 0.000 0.960 0.000 0.000 0.040
#> GSM531635 3 0.2179 0.8378 0.000 0.000 0.888 0.000 0.112
#> GSM531638 2 0.3543 0.8156 0.000 0.828 0.060 0.000 0.112
#> GSM531639 3 0.5145 0.6925 0.000 0.056 0.612 0.000 0.332
#> GSM531640 2 0.0000 0.9081 0.000 1.000 0.000 0.000 0.000
#> GSM531641 4 0.0000 0.9312 0.000 0.000 0.000 1.000 0.000
#> GSM531642 3 0.4318 0.7130 0.000 0.000 0.688 0.020 0.292
#> GSM531643 3 0.0162 0.8519 0.000 0.000 0.996 0.000 0.004
#> GSM531644 3 0.3534 0.7370 0.000 0.000 0.744 0.000 0.256
#> GSM531645 4 0.0000 0.9312 0.000 0.000 0.000 1.000 0.000
#> GSM531646 3 0.0510 0.8523 0.000 0.000 0.984 0.000 0.016
#> GSM531647 3 0.0162 0.8512 0.000 0.000 0.996 0.000 0.004
#> GSM531648 4 0.3766 0.7264 0.000 0.000 0.004 0.728 0.268
#> GSM531649 3 0.2280 0.8358 0.000 0.000 0.880 0.000 0.120
#> GSM531650 3 0.0000 0.8516 0.000 0.000 1.000 0.000 0.000
#> GSM531651 2 0.0404 0.9071 0.000 0.988 0.000 0.000 0.012
#> GSM531652 3 0.4229 0.7087 0.000 0.000 0.704 0.020 0.276
#> GSM531653 3 0.1544 0.8479 0.000 0.000 0.932 0.000 0.068
#> GSM531656 3 0.2280 0.8377 0.000 0.000 0.880 0.000 0.120
#> GSM531657 4 0.0290 0.9290 0.000 0.000 0.000 0.992 0.008
#> GSM531659 4 0.0566 0.9278 0.004 0.000 0.000 0.984 0.012
#> GSM531661 2 0.0880 0.8976 0.000 0.968 0.000 0.000 0.032
#> GSM531662 2 0.4049 0.7764 0.056 0.780 0.000 0.000 0.164
#> GSM531663 4 0.0000 0.9312 0.000 0.000 0.000 1.000 0.000
#> GSM531664 3 0.0290 0.8506 0.000 0.000 0.992 0.000 0.008
#> GSM531665 5 0.5795 0.6393 0.268 0.000 0.136 0.000 0.596
#> GSM531666 3 0.4170 0.7148 0.004 0.000 0.712 0.012 0.272
#> GSM531667 2 0.0000 0.9081 0.000 1.000 0.000 0.000 0.000
#> GSM531668 1 0.5904 -0.1990 0.468 0.012 0.000 0.452 0.068
#> GSM531669 3 0.1270 0.8369 0.000 0.000 0.948 0.000 0.052
#> GSM531670 3 0.2280 0.8377 0.000 0.000 0.880 0.000 0.120
#> GSM531671 2 0.7343 0.1022 0.036 0.380 0.208 0.000 0.376
#> GSM531672 4 0.2036 0.8980 0.024 0.000 0.000 0.920 0.056
#> GSM531673 1 0.3774 0.1979 0.704 0.000 0.000 0.000 0.296
#> GSM531674 3 0.0703 0.8465 0.000 0.000 0.976 0.000 0.024
#> GSM531675 1 0.6038 -0.1776 0.576 0.000 0.000 0.184 0.240
#> GSM531676 5 0.4905 0.6044 0.476 0.000 0.024 0.000 0.500
#> GSM531677 1 0.4350 -0.4322 0.588 0.000 0.000 0.004 0.408
#> GSM531678 1 0.4235 -0.4488 0.576 0.000 0.000 0.000 0.424
#> GSM531679 1 0.4227 -0.4429 0.580 0.000 0.000 0.000 0.420
#> GSM531680 1 0.4974 -0.6416 0.508 0.000 0.028 0.000 0.464
#> GSM531681 4 0.2563 0.8362 0.120 0.000 0.000 0.872 0.008
#> GSM531682 1 0.4235 -0.4488 0.576 0.000 0.000 0.000 0.424
#> GSM531683 1 0.0290 0.3941 0.992 0.000 0.000 0.000 0.008
#> GSM531684 1 0.2020 0.3465 0.900 0.000 0.000 0.000 0.100
#> GSM531685 5 0.4371 0.6991 0.344 0.000 0.012 0.000 0.644
#> GSM531686 4 0.3182 0.8090 0.124 0.000 0.000 0.844 0.032
#> GSM531687 1 0.4980 -0.6707 0.488 0.000 0.028 0.000 0.484
#> GSM531688 3 0.4440 0.0586 0.004 0.000 0.528 0.000 0.468
#> GSM531689 1 0.4294 -0.5584 0.532 0.000 0.000 0.000 0.468
#> GSM531690 4 0.2583 0.8342 0.132 0.000 0.000 0.864 0.004
#> GSM531691 1 0.4304 -0.5873 0.516 0.000 0.000 0.000 0.484
#> GSM531692 5 0.4192 0.6795 0.404 0.000 0.000 0.000 0.596
#> GSM531693 3 0.3895 0.4887 0.000 0.000 0.680 0.000 0.320
#> GSM531694 1 0.0000 0.3967 1.000 0.000 0.000 0.000 0.000
#> GSM531695 5 0.6127 0.6406 0.416 0.000 0.128 0.000 0.456
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM531600 3 0.2632 0.6681 0.004 0.000 0.832 0.000 0.000 0.164
#> GSM531601 2 0.4072 0.2162 0.008 0.544 0.000 0.000 0.000 0.448
#> GSM531605 1 0.3136 0.6842 0.768 0.000 0.000 0.000 0.228 0.004
#> GSM531615 2 0.1644 0.8499 0.028 0.932 0.000 0.000 0.000 0.040
#> GSM531617 2 0.1700 0.8474 0.024 0.928 0.000 0.000 0.000 0.048
#> GSM531624 2 0.0000 0.8656 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531627 2 0.2442 0.8248 0.004 0.852 0.000 0.000 0.000 0.144
#> GSM531629 2 0.1921 0.8425 0.032 0.916 0.000 0.000 0.000 0.052
#> GSM531631 2 0.0405 0.8664 0.004 0.988 0.000 0.000 0.000 0.008
#> GSM531634 2 0.1297 0.8540 0.012 0.948 0.000 0.000 0.000 0.040
#> GSM531636 3 0.3803 0.6061 0.004 0.020 0.724 0.000 0.000 0.252
#> GSM531637 2 0.0000 0.8656 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531654 1 0.4990 0.3722 0.616 0.276 0.000 0.000 0.000 0.108
#> GSM531655 1 0.5180 0.3956 0.580 0.016 0.028 0.000 0.020 0.356
#> GSM531658 4 0.3248 0.7512 0.032 0.000 0.000 0.804 0.000 0.164
#> GSM531660 1 0.4213 0.5868 0.744 0.004 0.000 0.160 0.000 0.092
#> GSM531602 1 0.2883 0.6891 0.788 0.000 0.000 0.000 0.212 0.000
#> GSM531603 1 0.2933 0.6910 0.796 0.000 0.000 0.000 0.200 0.004
#> GSM531604 5 0.4855 0.4915 0.328 0.000 0.000 0.000 0.596 0.076
#> GSM531606 1 0.3558 0.6360 0.736 0.000 0.000 0.000 0.248 0.016
#> GSM531607 1 0.2883 0.6891 0.788 0.000 0.000 0.000 0.212 0.000
#> GSM531608 2 0.2003 0.8461 0.044 0.912 0.000 0.000 0.000 0.044
#> GSM531609 4 0.0547 0.8631 0.000 0.000 0.000 0.980 0.000 0.020
#> GSM531610 4 0.0000 0.8649 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531611 4 0.0000 0.8649 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531612 4 0.0547 0.8631 0.000 0.000 0.000 0.980 0.000 0.020
#> GSM531613 4 0.0000 0.8649 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531614 4 0.0458 0.8639 0.000 0.000 0.000 0.984 0.000 0.016
#> GSM531616 2 0.5624 0.5439 0.012 0.584 0.168 0.000 0.000 0.236
#> GSM531618 6 0.5161 0.2924 0.012 0.068 0.000 0.352 0.000 0.568
#> GSM531619 2 0.0000 0.8656 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531620 2 0.2100 0.8455 0.004 0.884 0.000 0.000 0.000 0.112
#> GSM531621 2 0.2234 0.8354 0.004 0.872 0.000 0.000 0.000 0.124
#> GSM531622 2 0.0405 0.8664 0.004 0.988 0.000 0.000 0.000 0.008
#> GSM531623 2 0.0632 0.8661 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM531625 2 0.4599 0.6847 0.012 0.680 0.056 0.000 0.000 0.252
#> GSM531626 2 0.4654 0.6807 0.012 0.676 0.060 0.000 0.000 0.252
#> GSM531628 3 0.1334 0.6884 0.000 0.000 0.948 0.000 0.020 0.032
#> GSM531630 2 0.0508 0.8664 0.004 0.984 0.000 0.000 0.000 0.012
#> GSM531632 3 0.2358 0.6869 0.012 0.000 0.900 0.000 0.040 0.048
#> GSM531633 2 0.2234 0.8354 0.004 0.872 0.000 0.000 0.000 0.124
#> GSM531635 3 0.3693 0.6331 0.012 0.016 0.756 0.000 0.000 0.216
#> GSM531638 2 0.4715 0.6777 0.012 0.676 0.068 0.000 0.000 0.244
#> GSM531639 3 0.5167 0.2281 0.012 0.056 0.480 0.000 0.000 0.452
#> GSM531640 2 0.0291 0.8662 0.004 0.992 0.000 0.000 0.000 0.004
#> GSM531641 4 0.0547 0.8631 0.000 0.000 0.000 0.980 0.000 0.020
#> GSM531642 6 0.4245 0.4657 0.004 0.000 0.376 0.016 0.000 0.604
#> GSM531643 3 0.1838 0.6708 0.000 0.000 0.916 0.000 0.016 0.068
#> GSM531644 3 0.4227 -0.4186 0.004 0.000 0.500 0.000 0.008 0.488
#> GSM531645 4 0.0547 0.8631 0.000 0.000 0.000 0.980 0.000 0.020
#> GSM531646 3 0.1668 0.7010 0.008 0.000 0.928 0.000 0.004 0.060
#> GSM531647 3 0.0547 0.6992 0.000 0.000 0.980 0.000 0.020 0.000
#> GSM531648 6 0.4300 0.1260 0.020 0.000 0.000 0.432 0.000 0.548
#> GSM531649 3 0.3245 0.6382 0.008 0.000 0.764 0.000 0.000 0.228
#> GSM531650 3 0.1461 0.6840 0.000 0.000 0.940 0.000 0.016 0.044
#> GSM531651 2 0.0632 0.8661 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM531652 6 0.4325 0.4962 0.004 0.000 0.412 0.016 0.000 0.568
#> GSM531653 3 0.1219 0.7027 0.000 0.000 0.948 0.000 0.004 0.048
#> GSM531656 3 0.2994 0.6493 0.004 0.000 0.788 0.000 0.000 0.208
#> GSM531657 4 0.2858 0.7976 0.032 0.000 0.000 0.844 0.000 0.124
#> GSM531659 4 0.4104 0.7646 0.040 0.000 0.000 0.784 0.056 0.120
#> GSM531661 2 0.3123 0.7920 0.076 0.836 0.000 0.000 0.000 0.088
#> GSM531662 2 0.6069 0.4981 0.212 0.548 0.000 0.000 0.028 0.212
#> GSM531663 4 0.0260 0.8634 0.000 0.000 0.000 0.992 0.000 0.008
#> GSM531664 3 0.1713 0.6833 0.000 0.000 0.928 0.000 0.028 0.044
#> GSM531665 5 0.5089 0.5834 0.164 0.000 0.068 0.000 0.700 0.068
#> GSM531666 6 0.4093 0.4471 0.004 0.000 0.440 0.004 0.000 0.552
#> GSM531667 2 0.0820 0.8625 0.016 0.972 0.000 0.000 0.000 0.012
#> GSM531668 1 0.4379 0.5724 0.732 0.004 0.000 0.140 0.000 0.124
#> GSM531669 3 0.1624 0.6905 0.008 0.000 0.936 0.000 0.044 0.012
#> GSM531670 3 0.2964 0.6518 0.004 0.000 0.792 0.000 0.000 0.204
#> GSM531671 3 0.8807 0.0213 0.192 0.180 0.268 0.000 0.124 0.236
#> GSM531672 4 0.3943 0.7172 0.084 0.000 0.000 0.760 0.000 0.156
#> GSM531673 1 0.5716 0.0407 0.500 0.000 0.000 0.000 0.312 0.188
#> GSM531674 3 0.1265 0.6940 0.000 0.000 0.948 0.000 0.044 0.008
#> GSM531675 5 0.6550 0.2709 0.260 0.000 0.000 0.188 0.496 0.056
#> GSM531676 5 0.0146 0.7416 0.000 0.000 0.000 0.000 0.996 0.004
#> GSM531677 5 0.4331 0.6028 0.228 0.000 0.000 0.024 0.716 0.032
#> GSM531678 5 0.3178 0.6701 0.176 0.000 0.000 0.004 0.804 0.016
#> GSM531679 5 0.3480 0.6578 0.200 0.000 0.000 0.008 0.776 0.016
#> GSM531680 5 0.1219 0.7332 0.048 0.000 0.000 0.000 0.948 0.004
#> GSM531681 4 0.3721 0.7324 0.064 0.000 0.000 0.808 0.108 0.020
#> GSM531682 5 0.3799 0.6549 0.196 0.000 0.000 0.016 0.764 0.024
#> GSM531683 1 0.4222 0.5973 0.700 0.000 0.000 0.020 0.260 0.020
#> GSM531684 1 0.3803 0.5528 0.760 0.000 0.000 0.000 0.184 0.056
#> GSM531685 5 0.3150 0.6728 0.104 0.000 0.016 0.000 0.844 0.036
#> GSM531686 4 0.3935 0.7097 0.064 0.000 0.000 0.788 0.128 0.020
#> GSM531687 5 0.0862 0.7410 0.016 0.000 0.004 0.000 0.972 0.008
#> GSM531688 3 0.4481 0.1614 0.008 0.000 0.520 0.000 0.456 0.016
#> GSM531689 5 0.1075 0.7404 0.048 0.000 0.000 0.000 0.952 0.000
#> GSM531690 4 0.5832 0.5643 0.172 0.000 0.000 0.628 0.132 0.068
#> GSM531691 5 0.1967 0.7364 0.084 0.000 0.000 0.000 0.904 0.012
#> GSM531692 5 0.3663 0.6357 0.148 0.000 0.000 0.000 0.784 0.068
#> GSM531693 3 0.4178 0.4832 0.008 0.000 0.700 0.000 0.260 0.032
#> GSM531694 1 0.2883 0.6891 0.788 0.000 0.000 0.000 0.212 0.000
#> GSM531695 5 0.4034 0.5827 0.064 0.000 0.168 0.000 0.760 0.008
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> CV:skmeans 96 0.08257 2
#> CV:skmeans 90 0.00487 3
#> CV:skmeans 94 0.00837 4
#> CV:skmeans 70 0.02696 5
#> CV:skmeans 79 0.06062 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "pam"]
# you can also extract it by
# res = res_list["CV:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 96 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 0.351 0.601 0.812 0.4563 0.532 0.532
#> 3 3 0.678 0.866 0.906 0.4473 0.696 0.480
#> 4 4 0.558 0.587 0.767 0.1136 0.900 0.715
#> 5 5 0.835 0.847 0.918 0.0709 0.915 0.699
#> 6 6 0.873 0.805 0.904 0.0439 0.961 0.815
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
#> GSM531600 1 0.973 0.514 0.596 0.404
#> GSM531601 2 0.482 0.772 0.104 0.896
#> GSM531605 1 0.722 0.511 0.800 0.200
#> GSM531615 2 0.000 0.836 0.000 1.000
#> GSM531617 2 0.311 0.811 0.056 0.944
#> GSM531624 2 0.000 0.836 0.000 1.000
#> GSM531627 2 0.000 0.836 0.000 1.000
#> GSM531629 2 0.000 0.836 0.000 1.000
#> GSM531631 2 0.000 0.836 0.000 1.000
#> GSM531634 2 0.000 0.836 0.000 1.000
#> GSM531636 1 0.988 0.465 0.564 0.436
#> GSM531637 2 0.000 0.836 0.000 1.000
#> GSM531654 2 0.000 0.836 0.000 1.000
#> GSM531655 2 0.980 -0.167 0.416 0.584
#> GSM531658 1 0.969 0.524 0.604 0.396
#> GSM531660 2 0.973 -0.138 0.404 0.596
#> GSM531602 1 0.722 0.511 0.800 0.200
#> GSM531603 1 0.722 0.511 0.800 0.200
#> GSM531604 1 0.722 0.511 0.800 0.200
#> GSM531606 1 0.722 0.511 0.800 0.200
#> GSM531607 1 0.722 0.511 0.800 0.200
#> GSM531608 2 0.000 0.836 0.000 1.000
#> GSM531609 1 0.969 0.524 0.604 0.396
#> GSM531610 1 0.969 0.524 0.604 0.396
#> GSM531611 1 0.966 0.526 0.608 0.392
#> GSM531612 1 0.969 0.524 0.604 0.396
#> GSM531613 1 0.482 0.624 0.896 0.104
#> GSM531614 1 0.969 0.524 0.604 0.396
#> GSM531616 2 0.722 0.663 0.200 0.800
#> GSM531618 1 0.988 0.465 0.564 0.436
#> GSM531619 2 0.000 0.836 0.000 1.000
#> GSM531620 2 0.358 0.804 0.068 0.932
#> GSM531621 2 0.000 0.836 0.000 1.000
#> GSM531622 2 0.373 0.801 0.072 0.928
#> GSM531623 2 0.000 0.836 0.000 1.000
#> GSM531625 2 0.000 0.836 0.000 1.000
#> GSM531626 2 0.722 0.663 0.200 0.800
#> GSM531628 1 0.973 0.514 0.596 0.404
#> GSM531630 2 0.000 0.836 0.000 1.000
#> GSM531632 1 0.988 0.465 0.564 0.436
#> GSM531633 2 0.722 0.663 0.200 0.800
#> GSM531635 2 0.775 0.617 0.228 0.772
#> GSM531638 2 0.722 0.663 0.200 0.800
#> GSM531639 1 0.988 0.465 0.564 0.436
#> GSM531640 2 0.722 0.663 0.200 0.800
#> GSM531641 1 0.969 0.524 0.604 0.396
#> GSM531642 1 0.983 0.485 0.576 0.424
#> GSM531643 1 0.988 0.465 0.564 0.436
#> GSM531644 1 0.969 0.524 0.604 0.396
#> GSM531645 1 0.969 0.524 0.604 0.396
#> GSM531646 1 0.988 0.465 0.564 0.436
#> GSM531647 1 0.988 0.465 0.564 0.436
#> GSM531648 1 0.980 0.497 0.584 0.416
#> GSM531649 2 0.722 0.663 0.200 0.800
#> GSM531650 1 0.969 0.524 0.604 0.396
#> GSM531651 2 0.000 0.836 0.000 1.000
#> GSM531652 1 0.988 0.465 0.564 0.436
#> GSM531653 1 0.988 0.465 0.564 0.436
#> GSM531656 1 0.985 0.478 0.572 0.428
#> GSM531657 1 0.969 0.524 0.604 0.396
#> GSM531659 1 0.969 0.524 0.604 0.396
#> GSM531661 2 0.000 0.836 0.000 1.000
#> GSM531662 2 0.000 0.836 0.000 1.000
#> GSM531663 1 0.969 0.524 0.604 0.396
#> GSM531664 1 0.000 0.643 1.000 0.000
#> GSM531665 1 0.000 0.643 1.000 0.000
#> GSM531666 1 0.969 0.524 0.604 0.396
#> GSM531667 2 0.327 0.808 0.060 0.940
#> GSM531668 2 0.973 -0.138 0.404 0.596
#> GSM531669 1 0.000 0.643 1.000 0.000
#> GSM531670 1 0.983 0.484 0.576 0.424
#> GSM531671 2 0.000 0.836 0.000 1.000
#> GSM531672 1 0.969 0.524 0.604 0.396
#> GSM531673 2 0.242 0.801 0.040 0.960
#> GSM531674 1 0.000 0.643 1.000 0.000
#> GSM531675 1 0.000 0.643 1.000 0.000
#> GSM531676 1 0.000 0.643 1.000 0.000
#> GSM531677 1 0.000 0.643 1.000 0.000
#> GSM531678 1 0.563 0.564 0.868 0.132
#> GSM531679 1 0.000 0.643 1.000 0.000
#> GSM531680 1 0.000 0.643 1.000 0.000
#> GSM531681 1 0.000 0.643 1.000 0.000
#> GSM531682 1 0.000 0.643 1.000 0.000
#> GSM531683 1 0.722 0.511 0.800 0.200
#> GSM531684 2 0.988 0.155 0.436 0.564
#> GSM531685 1 0.000 0.643 1.000 0.000
#> GSM531686 1 0.000 0.643 1.000 0.000
#> GSM531687 1 0.000 0.643 1.000 0.000
#> GSM531688 1 0.000 0.643 1.000 0.000
#> GSM531689 1 0.000 0.643 1.000 0.000
#> GSM531690 1 0.000 0.643 1.000 0.000
#> GSM531691 1 0.722 0.511 0.800 0.200
#> GSM531692 1 0.722 0.511 0.800 0.200
#> GSM531693 1 0.000 0.643 1.000 0.000
#> GSM531694 1 0.722 0.511 0.800 0.200
#> GSM531695 1 0.000 0.643 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM531600 3 0.0000 0.865 0.000 0.000 1.000
#> GSM531601 3 0.4235 0.797 0.000 0.176 0.824
#> GSM531605 1 0.1643 0.885 0.956 0.044 0.000
#> GSM531615 2 0.0000 0.926 0.000 1.000 0.000
#> GSM531617 2 0.5058 0.771 0.148 0.820 0.032
#> GSM531624 2 0.0000 0.926 0.000 1.000 0.000
#> GSM531627 2 0.0000 0.926 0.000 1.000 0.000
#> GSM531629 3 0.5514 0.883 0.156 0.044 0.800
#> GSM531631 2 0.0000 0.926 0.000 1.000 0.000
#> GSM531634 2 0.0000 0.926 0.000 1.000 0.000
#> GSM531636 3 0.4002 0.807 0.000 0.160 0.840
#> GSM531637 2 0.0000 0.926 0.000 1.000 0.000
#> GSM531654 2 0.0237 0.923 0.004 0.996 0.000
#> GSM531655 3 0.5407 0.885 0.156 0.040 0.804
#> GSM531658 3 0.4121 0.899 0.168 0.000 0.832
#> GSM531660 3 0.5514 0.883 0.156 0.044 0.800
#> GSM531602 1 0.1643 0.885 0.956 0.044 0.000
#> GSM531603 1 0.1643 0.885 0.956 0.044 0.000
#> GSM531604 1 0.2066 0.880 0.940 0.060 0.000
#> GSM531606 1 0.1643 0.885 0.956 0.044 0.000
#> GSM531607 1 0.1529 0.885 0.960 0.040 0.000
#> GSM531608 2 0.0000 0.926 0.000 1.000 0.000
#> GSM531609 3 0.4291 0.896 0.180 0.000 0.820
#> GSM531610 3 0.4291 0.896 0.180 0.000 0.820
#> GSM531611 3 0.4399 0.893 0.188 0.000 0.812
#> GSM531612 3 0.4291 0.896 0.180 0.000 0.820
#> GSM531613 1 0.5098 0.541 0.752 0.000 0.248
#> GSM531614 3 0.4291 0.896 0.180 0.000 0.820
#> GSM531616 2 0.5327 0.645 0.000 0.728 0.272
#> GSM531618 3 0.4002 0.900 0.160 0.000 0.840
#> GSM531619 2 0.0000 0.926 0.000 1.000 0.000
#> GSM531620 2 0.1031 0.913 0.000 0.976 0.024
#> GSM531621 2 0.0000 0.926 0.000 1.000 0.000
#> GSM531622 2 0.0747 0.918 0.000 0.984 0.016
#> GSM531623 2 0.0000 0.926 0.000 1.000 0.000
#> GSM531625 2 0.0000 0.926 0.000 1.000 0.000
#> GSM531626 2 0.1753 0.899 0.000 0.952 0.048
#> GSM531628 3 0.0892 0.855 0.020 0.000 0.980
#> GSM531630 2 0.0000 0.926 0.000 1.000 0.000
#> GSM531632 3 0.0892 0.855 0.020 0.000 0.980
#> GSM531633 2 0.1529 0.902 0.000 0.960 0.040
#> GSM531635 3 0.0000 0.865 0.000 0.000 1.000
#> GSM531638 2 0.5926 0.473 0.000 0.644 0.356
#> GSM531639 3 0.4002 0.807 0.000 0.160 0.840
#> GSM531640 3 0.4002 0.807 0.000 0.160 0.840
#> GSM531641 3 0.4291 0.896 0.180 0.000 0.820
#> GSM531642 3 0.4002 0.900 0.160 0.000 0.840
#> GSM531643 3 0.0000 0.865 0.000 0.000 1.000
#> GSM531644 3 0.0000 0.865 0.000 0.000 1.000
#> GSM531645 3 0.4291 0.896 0.180 0.000 0.820
#> GSM531646 3 0.0892 0.855 0.020 0.000 0.980
#> GSM531647 3 0.0892 0.855 0.020 0.000 0.980
#> GSM531648 3 0.4002 0.900 0.160 0.000 0.840
#> GSM531649 3 0.0000 0.865 0.000 0.000 1.000
#> GSM531650 3 0.0000 0.865 0.000 0.000 1.000
#> GSM531651 2 0.0000 0.926 0.000 1.000 0.000
#> GSM531652 3 0.4002 0.900 0.160 0.000 0.840
#> GSM531653 3 0.0892 0.855 0.020 0.000 0.980
#> GSM531656 3 0.0000 0.865 0.000 0.000 1.000
#> GSM531657 3 0.4002 0.900 0.160 0.000 0.840
#> GSM531659 3 0.4002 0.900 0.160 0.000 0.840
#> GSM531661 2 0.0000 0.926 0.000 1.000 0.000
#> GSM531662 2 0.0000 0.926 0.000 1.000 0.000
#> GSM531663 3 0.4235 0.897 0.176 0.000 0.824
#> GSM531664 1 0.4291 0.869 0.820 0.000 0.180
#> GSM531665 1 0.4555 0.867 0.800 0.000 0.200
#> GSM531666 3 0.4002 0.900 0.160 0.000 0.840
#> GSM531667 2 0.8444 0.477 0.152 0.612 0.236
#> GSM531668 3 0.5514 0.883 0.156 0.044 0.800
#> GSM531669 1 0.4291 0.869 0.820 0.000 0.180
#> GSM531670 3 0.0237 0.863 0.004 0.000 0.996
#> GSM531671 2 0.8087 0.371 0.076 0.560 0.364
#> GSM531672 3 0.4002 0.900 0.160 0.000 0.840
#> GSM531673 2 0.1753 0.897 0.000 0.952 0.048
#> GSM531674 1 0.4291 0.869 0.820 0.000 0.180
#> GSM531675 1 0.1529 0.882 0.960 0.000 0.040
#> GSM531676 1 0.4291 0.869 0.820 0.000 0.180
#> GSM531677 1 0.0892 0.889 0.980 0.000 0.020
#> GSM531678 1 0.1529 0.885 0.960 0.040 0.000
#> GSM531679 1 0.0892 0.889 0.980 0.000 0.020
#> GSM531680 1 0.4291 0.869 0.820 0.000 0.180
#> GSM531681 1 0.0892 0.880 0.980 0.000 0.020
#> GSM531682 1 0.1529 0.882 0.960 0.000 0.040
#> GSM531683 1 0.1529 0.885 0.960 0.040 0.000
#> GSM531684 2 0.0000 0.926 0.000 1.000 0.000
#> GSM531685 1 0.4291 0.869 0.820 0.000 0.180
#> GSM531686 1 0.0000 0.886 1.000 0.000 0.000
#> GSM531687 1 0.4452 0.868 0.808 0.000 0.192
#> GSM531688 1 0.4291 0.869 0.820 0.000 0.180
#> GSM531689 1 0.2537 0.891 0.920 0.000 0.080
#> GSM531690 1 0.1031 0.881 0.976 0.000 0.024
#> GSM531691 1 0.4786 0.878 0.844 0.044 0.112
#> GSM531692 1 0.4934 0.865 0.820 0.024 0.156
#> GSM531693 1 0.4291 0.869 0.820 0.000 0.180
#> GSM531694 1 0.1529 0.885 0.960 0.040 0.000
#> GSM531695 1 0.4291 0.869 0.820 0.000 0.180
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM531600 3 0.5184 0.4480 0.060 0.000 0.736 0.204
#> GSM531601 3 0.5511 0.2651 0.000 0.332 0.636 0.032
#> GSM531605 1 0.3486 0.7318 0.812 0.188 0.000 0.000
#> GSM531615 2 0.0000 0.9338 0.000 1.000 0.000 0.000
#> GSM531617 2 0.2101 0.8850 0.012 0.928 0.060 0.000
#> GSM531624 2 0.0000 0.9338 0.000 1.000 0.000 0.000
#> GSM531627 2 0.0000 0.9338 0.000 1.000 0.000 0.000
#> GSM531629 3 0.7555 0.1030 0.012 0.256 0.544 0.188
#> GSM531631 2 0.0000 0.9338 0.000 1.000 0.000 0.000
#> GSM531634 2 0.0000 0.9338 0.000 1.000 0.000 0.000
#> GSM531636 3 0.0469 0.4939 0.000 0.012 0.988 0.000
#> GSM531637 2 0.0000 0.9338 0.000 1.000 0.000 0.000
#> GSM531654 2 0.0000 0.9338 0.000 1.000 0.000 0.000
#> GSM531655 3 0.7216 0.2439 0.180 0.176 0.620 0.024
#> GSM531658 3 0.4868 0.1249 0.012 0.000 0.684 0.304
#> GSM531660 3 0.8064 0.0629 0.044 0.188 0.544 0.224
#> GSM531602 1 0.4636 0.7194 0.772 0.188 0.000 0.040
#> GSM531603 1 0.4546 0.7236 0.780 0.188 0.004 0.028
#> GSM531604 1 0.3486 0.7318 0.812 0.188 0.000 0.000
#> GSM531606 1 0.4880 0.7135 0.760 0.188 0.000 0.052
#> GSM531607 1 0.3486 0.7318 0.812 0.188 0.000 0.000
#> GSM531608 2 0.0000 0.9338 0.000 1.000 0.000 0.000
#> GSM531609 4 0.4643 0.7329 0.000 0.000 0.344 0.656
#> GSM531610 4 0.4643 0.7329 0.000 0.000 0.344 0.656
#> GSM531611 4 0.6156 0.6685 0.064 0.000 0.344 0.592
#> GSM531612 4 0.4643 0.7329 0.000 0.000 0.344 0.656
#> GSM531613 4 0.6265 0.5201 0.220 0.000 0.124 0.656
#> GSM531614 4 0.4643 0.7329 0.000 0.000 0.344 0.656
#> GSM531616 3 0.7904 0.2529 0.000 0.300 0.360 0.340
#> GSM531618 3 0.4319 0.2786 0.012 0.000 0.760 0.228
#> GSM531619 2 0.0000 0.9338 0.000 1.000 0.000 0.000
#> GSM531620 2 0.1792 0.8781 0.000 0.932 0.068 0.000
#> GSM531621 2 0.0000 0.9338 0.000 1.000 0.000 0.000
#> GSM531622 2 0.1867 0.8740 0.000 0.928 0.072 0.000
#> GSM531623 2 0.0000 0.9338 0.000 1.000 0.000 0.000
#> GSM531625 2 0.0000 0.9338 0.000 1.000 0.000 0.000
#> GSM531626 2 0.7520 0.1672 0.000 0.464 0.196 0.340
#> GSM531628 3 0.7478 0.3502 0.188 0.000 0.468 0.344
#> GSM531630 2 0.0000 0.9338 0.000 1.000 0.000 0.000
#> GSM531632 3 0.7478 0.3502 0.188 0.000 0.468 0.344
#> GSM531633 2 0.3486 0.7104 0.000 0.812 0.188 0.000
#> GSM531635 3 0.4624 0.4181 0.000 0.000 0.660 0.340
#> GSM531638 3 0.7892 0.2722 0.000 0.292 0.368 0.340
#> GSM531639 3 0.1890 0.4854 0.008 0.056 0.936 0.000
#> GSM531640 3 0.4193 0.3393 0.000 0.268 0.732 0.000
#> GSM531641 4 0.4643 0.7329 0.000 0.000 0.344 0.656
#> GSM531642 3 0.1284 0.4831 0.012 0.000 0.964 0.024
#> GSM531643 3 0.0188 0.4953 0.000 0.000 0.996 0.004
#> GSM531644 3 0.0000 0.4943 0.000 0.000 1.000 0.000
#> GSM531645 4 0.4661 0.7286 0.000 0.000 0.348 0.652
#> GSM531646 3 0.7478 0.3502 0.188 0.000 0.468 0.344
#> GSM531647 3 0.7478 0.3502 0.188 0.000 0.468 0.344
#> GSM531648 3 0.4319 0.2786 0.012 0.000 0.760 0.228
#> GSM531649 3 0.6340 0.3972 0.076 0.000 0.580 0.344
#> GSM531650 3 0.2402 0.4678 0.076 0.000 0.912 0.012
#> GSM531651 2 0.0000 0.9338 0.000 1.000 0.000 0.000
#> GSM531652 3 0.0469 0.4931 0.012 0.000 0.988 0.000
#> GSM531653 3 0.7478 0.3502 0.188 0.000 0.468 0.344
#> GSM531656 3 0.0188 0.4945 0.004 0.000 0.996 0.000
#> GSM531657 3 0.5522 0.3055 0.120 0.000 0.732 0.148
#> GSM531659 3 0.5515 0.3135 0.152 0.000 0.732 0.116
#> GSM531661 2 0.0000 0.9338 0.000 1.000 0.000 0.000
#> GSM531662 2 0.0000 0.9338 0.000 1.000 0.000 0.000
#> GSM531663 4 0.5016 0.6538 0.004 0.000 0.396 0.600
#> GSM531664 1 0.4372 0.5270 0.728 0.000 0.268 0.004
#> GSM531665 1 0.3791 0.6859 0.796 0.000 0.200 0.004
#> GSM531666 3 0.3907 0.3638 0.232 0.000 0.768 0.000
#> GSM531667 2 0.4248 0.6548 0.012 0.768 0.220 0.000
#> GSM531668 3 0.7784 0.0407 0.024 0.188 0.544 0.244
#> GSM531669 1 0.7843 -0.0105 0.388 0.000 0.268 0.344
#> GSM531670 3 0.4103 0.3309 0.256 0.000 0.744 0.000
#> GSM531671 4 0.8590 -0.2342 0.056 0.308 0.180 0.456
#> GSM531672 3 0.5184 0.2689 0.056 0.000 0.732 0.212
#> GSM531673 2 0.3647 0.7813 0.000 0.852 0.040 0.108
#> GSM531674 1 0.7843 -0.0105 0.388 0.000 0.268 0.344
#> GSM531675 1 0.5889 0.6089 0.696 0.000 0.188 0.116
#> GSM531676 1 0.0469 0.7483 0.988 0.000 0.012 0.000
#> GSM531677 1 0.2589 0.6969 0.884 0.000 0.000 0.116
#> GSM531678 1 0.4356 0.7396 0.812 0.124 0.064 0.000
#> GSM531679 1 0.0000 0.7494 1.000 0.000 0.000 0.000
#> GSM531680 1 0.0469 0.7483 0.988 0.000 0.012 0.000
#> GSM531681 4 0.4679 0.1933 0.352 0.000 0.000 0.648
#> GSM531682 1 0.5889 0.6089 0.696 0.000 0.188 0.116
#> GSM531683 1 0.3668 0.7314 0.808 0.188 0.000 0.004
#> GSM531684 2 0.0000 0.9338 0.000 1.000 0.000 0.000
#> GSM531685 1 0.0657 0.7475 0.984 0.000 0.012 0.004
#> GSM531686 1 0.4522 0.5218 0.680 0.000 0.000 0.320
#> GSM531687 1 0.3400 0.7015 0.820 0.000 0.180 0.000
#> GSM531688 1 0.0657 0.7475 0.984 0.000 0.012 0.004
#> GSM531689 1 0.1792 0.7442 0.932 0.000 0.068 0.000
#> GSM531690 1 0.5169 0.5915 0.696 0.000 0.032 0.272
#> GSM531691 1 0.3668 0.7323 0.808 0.188 0.004 0.000
#> GSM531692 1 0.0844 0.7491 0.980 0.004 0.012 0.004
#> GSM531693 1 0.5075 0.4080 0.644 0.000 0.012 0.344
#> GSM531694 1 0.5102 0.7050 0.748 0.188 0.000 0.064
#> GSM531695 1 0.0657 0.7475 0.984 0.000 0.012 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM531600 5 0.4278 0.169 0.000 0.000 0.452 0.000 0.548
#> GSM531601 5 0.3143 0.728 0.000 0.204 0.000 0.000 0.796
#> GSM531605 1 0.0880 0.873 0.968 0.032 0.000 0.000 0.000
#> GSM531615 2 0.0000 0.967 0.000 1.000 0.000 0.000 0.000
#> GSM531617 2 0.1341 0.922 0.000 0.944 0.000 0.000 0.056
#> GSM531624 2 0.0000 0.967 0.000 1.000 0.000 0.000 0.000
#> GSM531627 2 0.0000 0.967 0.000 1.000 0.000 0.000 0.000
#> GSM531629 5 0.0963 0.896 0.000 0.036 0.000 0.000 0.964
#> GSM531631 2 0.0000 0.967 0.000 1.000 0.000 0.000 0.000
#> GSM531634 2 0.0000 0.967 0.000 1.000 0.000 0.000 0.000
#> GSM531636 5 0.0898 0.907 0.000 0.020 0.008 0.000 0.972
#> GSM531637 2 0.0000 0.967 0.000 1.000 0.000 0.000 0.000
#> GSM531654 2 0.1965 0.876 0.096 0.904 0.000 0.000 0.000
#> GSM531655 5 0.0880 0.898 0.000 0.032 0.000 0.000 0.968
#> GSM531658 5 0.1952 0.861 0.004 0.000 0.000 0.084 0.912
#> GSM531660 5 0.3134 0.801 0.120 0.032 0.000 0.000 0.848
#> GSM531602 1 0.1668 0.869 0.940 0.032 0.000 0.000 0.028
#> GSM531603 1 0.2209 0.864 0.912 0.032 0.000 0.000 0.056
#> GSM531604 1 0.0880 0.873 0.968 0.032 0.000 0.000 0.000
#> GSM531606 1 0.1668 0.869 0.940 0.032 0.000 0.000 0.028
#> GSM531607 1 0.0609 0.875 0.980 0.000 0.000 0.000 0.020
#> GSM531608 2 0.0000 0.967 0.000 1.000 0.000 0.000 0.000
#> GSM531609 4 0.0000 0.918 0.000 0.000 0.000 1.000 0.000
#> GSM531610 4 0.0000 0.918 0.000 0.000 0.000 1.000 0.000
#> GSM531611 4 0.0290 0.913 0.000 0.000 0.000 0.992 0.008
#> GSM531612 4 0.0000 0.918 0.000 0.000 0.000 1.000 0.000
#> GSM531613 4 0.0000 0.918 0.000 0.000 0.000 1.000 0.000
#> GSM531614 4 0.0000 0.918 0.000 0.000 0.000 1.000 0.000
#> GSM531616 3 0.3977 0.708 0.000 0.032 0.764 0.000 0.204
#> GSM531618 5 0.0000 0.910 0.000 0.000 0.000 0.000 1.000
#> GSM531619 2 0.0000 0.967 0.000 1.000 0.000 0.000 0.000
#> GSM531620 2 0.0162 0.964 0.000 0.996 0.000 0.000 0.004
#> GSM531621 2 0.0000 0.967 0.000 1.000 0.000 0.000 0.000
#> GSM531622 2 0.0162 0.964 0.000 0.996 0.000 0.000 0.004
#> GSM531623 2 0.0000 0.967 0.000 1.000 0.000 0.000 0.000
#> GSM531625 2 0.0162 0.964 0.000 0.996 0.004 0.000 0.000
#> GSM531626 3 0.6133 0.453 0.000 0.300 0.540 0.000 0.160
#> GSM531628 3 0.0000 0.856 0.000 0.000 1.000 0.000 0.000
#> GSM531630 2 0.0000 0.967 0.000 1.000 0.000 0.000 0.000
#> GSM531632 3 0.0000 0.856 0.000 0.000 1.000 0.000 0.000
#> GSM531633 2 0.0880 0.937 0.000 0.968 0.000 0.000 0.032
#> GSM531635 3 0.3242 0.704 0.000 0.000 0.784 0.000 0.216
#> GSM531638 3 0.4054 0.705 0.000 0.036 0.760 0.000 0.204
#> GSM531639 5 0.0794 0.905 0.000 0.028 0.000 0.000 0.972
#> GSM531640 5 0.0794 0.905 0.000 0.028 0.000 0.000 0.972
#> GSM531641 4 0.0000 0.918 0.000 0.000 0.000 1.000 0.000
#> GSM531642 5 0.0898 0.907 0.020 0.000 0.008 0.000 0.972
#> GSM531643 5 0.3561 0.604 0.000 0.000 0.260 0.000 0.740
#> GSM531644 5 0.0794 0.904 0.000 0.000 0.028 0.000 0.972
#> GSM531645 4 0.0162 0.916 0.000 0.000 0.000 0.996 0.004
#> GSM531646 3 0.0000 0.856 0.000 0.000 1.000 0.000 0.000
#> GSM531647 3 0.0000 0.856 0.000 0.000 1.000 0.000 0.000
#> GSM531648 5 0.0000 0.910 0.000 0.000 0.000 0.000 1.000
#> GSM531649 3 0.0404 0.852 0.000 0.000 0.988 0.000 0.012
#> GSM531650 3 0.4227 0.191 0.000 0.000 0.580 0.000 0.420
#> GSM531651 2 0.0000 0.967 0.000 1.000 0.000 0.000 0.000
#> GSM531652 5 0.0404 0.910 0.000 0.000 0.012 0.000 0.988
#> GSM531653 3 0.0000 0.856 0.000 0.000 1.000 0.000 0.000
#> GSM531656 5 0.0865 0.905 0.004 0.000 0.024 0.000 0.972
#> GSM531657 5 0.0162 0.910 0.004 0.000 0.000 0.000 0.996
#> GSM531659 5 0.0000 0.910 0.000 0.000 0.000 0.000 1.000
#> GSM531661 2 0.0000 0.967 0.000 1.000 0.000 0.000 0.000
#> GSM531662 2 0.0000 0.967 0.000 1.000 0.000 0.000 0.000
#> GSM531663 4 0.3612 0.613 0.000 0.000 0.000 0.732 0.268
#> GSM531664 1 0.3508 0.779 0.748 0.000 0.252 0.000 0.000
#> GSM531665 1 0.3427 0.789 0.796 0.000 0.012 0.000 0.192
#> GSM531666 5 0.0794 0.905 0.028 0.000 0.000 0.000 0.972
#> GSM531667 2 0.4150 0.384 0.000 0.612 0.000 0.000 0.388
#> GSM531668 5 0.3292 0.798 0.120 0.032 0.000 0.004 0.844
#> GSM531669 3 0.0000 0.856 0.000 0.000 1.000 0.000 0.000
#> GSM531670 5 0.0880 0.903 0.032 0.000 0.000 0.000 0.968
#> GSM531671 3 0.1106 0.837 0.000 0.024 0.964 0.000 0.012
#> GSM531672 5 0.0000 0.910 0.000 0.000 0.000 0.000 1.000
#> GSM531673 2 0.1430 0.920 0.004 0.944 0.000 0.000 0.052
#> GSM531674 3 0.0000 0.856 0.000 0.000 1.000 0.000 0.000
#> GSM531675 1 0.1341 0.875 0.944 0.000 0.000 0.000 0.056
#> GSM531676 1 0.2966 0.830 0.816 0.000 0.184 0.000 0.000
#> GSM531677 1 0.1399 0.877 0.952 0.000 0.020 0.000 0.028
#> GSM531678 1 0.2488 0.838 0.872 0.004 0.000 0.000 0.124
#> GSM531679 1 0.0609 0.877 0.980 0.000 0.020 0.000 0.000
#> GSM531680 1 0.3039 0.826 0.808 0.000 0.192 0.000 0.000
#> GSM531681 4 0.4047 0.455 0.320 0.000 0.000 0.676 0.004
#> GSM531682 1 0.2648 0.834 0.848 0.000 0.000 0.000 0.152
#> GSM531683 1 0.0404 0.876 0.988 0.000 0.000 0.000 0.012
#> GSM531684 2 0.0162 0.964 0.004 0.996 0.000 0.000 0.000
#> GSM531685 1 0.3109 0.820 0.800 0.000 0.200 0.000 0.000
#> GSM531686 1 0.3796 0.608 0.700 0.000 0.000 0.300 0.000
#> GSM531687 1 0.3266 0.784 0.796 0.000 0.004 0.000 0.200
#> GSM531688 1 0.3336 0.796 0.772 0.000 0.228 0.000 0.000
#> GSM531689 1 0.0693 0.878 0.980 0.000 0.012 0.000 0.008
#> GSM531690 1 0.1997 0.871 0.924 0.000 0.000 0.036 0.040
#> GSM531691 1 0.3134 0.837 0.848 0.032 0.000 0.000 0.120
#> GSM531692 1 0.3013 0.843 0.832 0.008 0.160 0.000 0.000
#> GSM531693 3 0.0404 0.850 0.012 0.000 0.988 0.000 0.000
#> GSM531694 1 0.0794 0.873 0.972 0.000 0.000 0.000 0.028
#> GSM531695 1 0.3074 0.823 0.804 0.000 0.196 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM531600 3 0.3838 0.183 0.000 0.000 0.552 0.000 0.000 0.448
#> GSM531601 3 0.2793 0.709 0.000 0.200 0.800 0.000 0.000 0.000
#> GSM531605 5 0.0790 0.826 0.032 0.000 0.000 0.000 0.968 0.000
#> GSM531615 2 0.0000 0.972 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531617 2 0.1327 0.911 0.000 0.936 0.064 0.000 0.000 0.000
#> GSM531624 2 0.0000 0.972 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531627 2 0.0000 0.972 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531629 3 0.0146 0.896 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM531631 2 0.0000 0.972 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531634 2 0.0000 0.972 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531636 3 0.0713 0.889 0.000 0.028 0.972 0.000 0.000 0.000
#> GSM531637 2 0.0000 0.972 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531654 2 0.0146 0.969 0.004 0.996 0.000 0.000 0.000 0.000
#> GSM531655 3 0.0000 0.896 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531658 3 0.1918 0.835 0.000 0.000 0.904 0.088 0.008 0.000
#> GSM531660 3 0.3620 0.544 0.352 0.000 0.648 0.000 0.000 0.000
#> GSM531602 1 0.0000 0.659 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531603 5 0.3706 0.477 0.380 0.000 0.000 0.000 0.620 0.000
#> GSM531604 5 0.1556 0.802 0.080 0.000 0.000 0.000 0.920 0.000
#> GSM531606 5 0.3862 0.310 0.476 0.000 0.000 0.000 0.524 0.000
#> GSM531607 1 0.3151 0.366 0.748 0.000 0.000 0.000 0.252 0.000
#> GSM531608 2 0.0000 0.972 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531609 4 0.0000 0.950 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531610 4 0.0000 0.950 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531611 4 0.0260 0.943 0.000 0.000 0.008 0.992 0.000 0.000
#> GSM531612 4 0.0000 0.950 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531613 4 0.0000 0.950 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531614 4 0.0000 0.950 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531616 6 0.3394 0.718 0.000 0.024 0.200 0.000 0.000 0.776
#> GSM531618 3 0.0000 0.896 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531619 2 0.0000 0.972 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531620 2 0.0000 0.972 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531621 2 0.0000 0.972 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531622 2 0.0000 0.972 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531623 2 0.0000 0.972 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531625 2 0.0000 0.972 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531626 6 0.5463 0.442 0.000 0.312 0.148 0.000 0.000 0.540
#> GSM531628 6 0.0000 0.861 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531630 2 0.0000 0.972 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531632 6 0.0000 0.861 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531633 2 0.0000 0.972 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531635 6 0.2941 0.706 0.000 0.000 0.220 0.000 0.000 0.780
#> GSM531638 6 0.3512 0.717 0.000 0.032 0.196 0.000 0.000 0.772
#> GSM531639 3 0.0713 0.889 0.000 0.028 0.972 0.000 0.000 0.000
#> GSM531640 3 0.0713 0.889 0.000 0.028 0.972 0.000 0.000 0.000
#> GSM531641 4 0.0000 0.950 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531642 3 0.0713 0.890 0.000 0.000 0.972 0.000 0.028 0.000
#> GSM531643 3 0.3221 0.595 0.000 0.000 0.736 0.000 0.000 0.264
#> GSM531644 3 0.0713 0.888 0.000 0.000 0.972 0.000 0.000 0.028
#> GSM531645 4 0.0146 0.947 0.000 0.000 0.004 0.996 0.000 0.000
#> GSM531646 6 0.0000 0.861 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531647 6 0.0000 0.861 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531648 3 0.0000 0.896 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531649 6 0.0000 0.861 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531650 6 0.3774 0.202 0.000 0.000 0.408 0.000 0.000 0.592
#> GSM531651 2 0.0000 0.972 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531652 3 0.0146 0.896 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM531653 6 0.0000 0.861 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531656 3 0.0777 0.889 0.000 0.000 0.972 0.000 0.004 0.024
#> GSM531657 3 0.0260 0.894 0.000 0.000 0.992 0.000 0.008 0.000
#> GSM531659 3 0.0000 0.896 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531661 2 0.0000 0.972 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531662 2 0.0000 0.972 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531663 4 0.3244 0.595 0.000 0.000 0.268 0.732 0.000 0.000
#> GSM531664 5 0.3050 0.697 0.000 0.000 0.000 0.000 0.764 0.236
#> GSM531665 5 0.2562 0.686 0.000 0.000 0.172 0.000 0.828 0.000
#> GSM531666 3 0.0790 0.889 0.000 0.000 0.968 0.000 0.032 0.000
#> GSM531667 2 0.3789 0.320 0.000 0.584 0.416 0.000 0.000 0.000
#> GSM531668 3 0.3706 0.521 0.380 0.000 0.620 0.000 0.000 0.000
#> GSM531669 6 0.0000 0.861 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531670 3 0.0865 0.887 0.000 0.000 0.964 0.000 0.036 0.000
#> GSM531671 6 0.0146 0.859 0.000 0.000 0.004 0.000 0.000 0.996
#> GSM531672 3 0.0000 0.896 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531673 2 0.1075 0.925 0.000 0.952 0.048 0.000 0.000 0.000
#> GSM531674 6 0.0000 0.861 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531675 1 0.4264 0.675 0.620 0.000 0.028 0.000 0.352 0.000
#> GSM531676 5 0.0713 0.834 0.000 0.000 0.000 0.000 0.972 0.028
#> GSM531677 1 0.4264 0.675 0.620 0.000 0.028 0.000 0.352 0.000
#> GSM531678 5 0.0146 0.825 0.000 0.000 0.004 0.000 0.996 0.000
#> GSM531679 1 0.3706 0.644 0.620 0.000 0.000 0.000 0.380 0.000
#> GSM531680 5 0.0713 0.834 0.000 0.000 0.000 0.000 0.972 0.028
#> GSM531681 1 0.3830 0.392 0.620 0.000 0.000 0.376 0.004 0.000
#> GSM531682 1 0.4264 0.675 0.620 0.000 0.028 0.000 0.352 0.000
#> GSM531683 1 0.0000 0.659 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531684 2 0.0000 0.972 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531685 5 0.1267 0.826 0.000 0.000 0.000 0.000 0.940 0.060
#> GSM531686 1 0.4798 0.516 0.620 0.000 0.000 0.300 0.080 0.000
#> GSM531687 5 0.0713 0.825 0.000 0.000 0.028 0.000 0.972 0.000
#> GSM531688 5 0.2941 0.713 0.000 0.000 0.000 0.000 0.780 0.220
#> GSM531689 5 0.0000 0.823 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM531690 1 0.4264 0.675 0.620 0.000 0.028 0.000 0.352 0.000
#> GSM531691 5 0.0713 0.825 0.000 0.000 0.028 0.000 0.972 0.000
#> GSM531692 5 0.0713 0.834 0.000 0.000 0.000 0.000 0.972 0.028
#> GSM531693 6 0.0146 0.859 0.000 0.000 0.000 0.000 0.004 0.996
#> GSM531694 1 0.0000 0.659 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531695 5 0.2597 0.752 0.000 0.000 0.000 0.000 0.824 0.176
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> CV:pam 79 0.00627 2
#> CV:pam 93 0.01307 3
#> CV:pam 58 0.00437 4
#> CV:pam 91 0.00315 5
#> CV:pam 88 0.00986 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "mclust"]
# you can also extract it by
# res = res_list["CV:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 96 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 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 0.893 0.915 0.955 0.3350 0.692 0.692
#> 3 3 0.606 0.846 0.890 0.7263 0.668 0.537
#> 4 4 0.855 0.852 0.924 0.2885 0.772 0.487
#> 5 5 0.702 0.768 0.864 0.0490 0.923 0.713
#> 6 6 0.780 0.613 0.764 0.0414 0.930 0.692
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM531600 1 0.2043 0.948 0.968 0.032
#> GSM531601 1 0.9661 0.356 0.608 0.392
#> GSM531605 1 0.0000 0.956 1.000 0.000
#> GSM531615 2 0.1633 0.969 0.024 0.976
#> GSM531617 2 0.6887 0.810 0.184 0.816
#> GSM531624 2 0.1633 0.969 0.024 0.976
#> GSM531627 2 0.1633 0.969 0.024 0.976
#> GSM531629 1 0.6973 0.766 0.812 0.188
#> GSM531631 2 0.1633 0.969 0.024 0.976
#> GSM531634 2 0.1633 0.969 0.024 0.976
#> GSM531636 1 0.2603 0.945 0.956 0.044
#> GSM531637 2 0.1633 0.969 0.024 0.976
#> GSM531654 1 0.0672 0.955 0.992 0.008
#> GSM531655 1 0.0672 0.955 0.992 0.008
#> GSM531658 1 0.0672 0.955 0.992 0.008
#> GSM531660 1 0.0000 0.956 1.000 0.000
#> GSM531602 1 0.0000 0.956 1.000 0.000
#> GSM531603 1 0.0000 0.956 1.000 0.000
#> GSM531604 1 0.0000 0.956 1.000 0.000
#> GSM531606 1 0.0000 0.956 1.000 0.000
#> GSM531607 1 0.0000 0.956 1.000 0.000
#> GSM531608 2 0.2603 0.956 0.044 0.956
#> GSM531609 1 0.0672 0.955 0.992 0.008
#> GSM531610 1 0.0672 0.955 0.992 0.008
#> GSM531611 1 0.0000 0.956 1.000 0.000
#> GSM531612 1 0.0672 0.955 0.992 0.008
#> GSM531613 1 0.0000 0.956 1.000 0.000
#> GSM531614 1 0.0672 0.955 0.992 0.008
#> GSM531616 1 0.7139 0.752 0.804 0.196
#> GSM531618 1 0.0938 0.954 0.988 0.012
#> GSM531619 2 0.1633 0.969 0.024 0.976
#> GSM531620 2 0.6148 0.850 0.152 0.848
#> GSM531621 2 0.1633 0.969 0.024 0.976
#> GSM531622 2 0.1633 0.969 0.024 0.976
#> GSM531623 2 0.1633 0.969 0.024 0.976
#> GSM531625 1 0.9833 0.256 0.576 0.424
#> GSM531626 1 0.9896 0.202 0.560 0.440
#> GSM531628 1 0.2778 0.943 0.952 0.048
#> GSM531630 2 0.1633 0.969 0.024 0.976
#> GSM531632 1 0.2778 0.943 0.952 0.048
#> GSM531633 2 0.1633 0.969 0.024 0.976
#> GSM531635 1 0.2778 0.943 0.952 0.048
#> GSM531638 1 0.9815 0.268 0.580 0.420
#> GSM531639 1 0.1184 0.953 0.984 0.016
#> GSM531640 2 0.1633 0.969 0.024 0.976
#> GSM531641 1 0.0672 0.955 0.992 0.008
#> GSM531642 1 0.1633 0.948 0.976 0.024
#> GSM531643 1 0.2423 0.946 0.960 0.040
#> GSM531644 1 0.2236 0.949 0.964 0.036
#> GSM531645 1 0.0672 0.955 0.992 0.008
#> GSM531646 1 0.2778 0.943 0.952 0.048
#> GSM531647 1 0.2778 0.943 0.952 0.048
#> GSM531648 1 0.0672 0.955 0.992 0.008
#> GSM531649 1 0.2948 0.941 0.948 0.052
#> GSM531650 1 0.2778 0.943 0.952 0.048
#> GSM531651 2 0.1633 0.969 0.024 0.976
#> GSM531652 1 0.0938 0.954 0.988 0.012
#> GSM531653 1 0.2778 0.943 0.952 0.048
#> GSM531656 1 0.2603 0.946 0.956 0.044
#> GSM531657 1 0.0000 0.956 1.000 0.000
#> GSM531659 1 0.0000 0.956 1.000 0.000
#> GSM531661 1 0.8386 0.621 0.732 0.268
#> GSM531662 1 0.0672 0.955 0.992 0.008
#> GSM531663 1 0.0000 0.956 1.000 0.000
#> GSM531664 1 0.2043 0.948 0.968 0.032
#> GSM531665 1 0.0376 0.956 0.996 0.004
#> GSM531666 1 0.1414 0.952 0.980 0.020
#> GSM531667 2 0.7139 0.793 0.196 0.804
#> GSM531668 1 0.0000 0.956 1.000 0.000
#> GSM531669 1 0.1843 0.948 0.972 0.028
#> GSM531670 1 0.1843 0.950 0.972 0.028
#> GSM531671 1 0.0376 0.956 0.996 0.004
#> GSM531672 1 0.0000 0.956 1.000 0.000
#> GSM531673 1 0.0376 0.956 0.996 0.004
#> GSM531674 1 0.1843 0.948 0.972 0.028
#> GSM531675 1 0.0000 0.956 1.000 0.000
#> GSM531676 1 0.1414 0.952 0.980 0.020
#> GSM531677 1 0.0000 0.956 1.000 0.000
#> GSM531678 1 0.0000 0.956 1.000 0.000
#> GSM531679 1 0.0000 0.956 1.000 0.000
#> GSM531680 1 0.1843 0.948 0.972 0.028
#> GSM531681 1 0.0000 0.956 1.000 0.000
#> GSM531682 1 0.0000 0.956 1.000 0.000
#> GSM531683 1 0.0000 0.956 1.000 0.000
#> GSM531684 1 0.0000 0.956 1.000 0.000
#> GSM531685 1 0.1633 0.950 0.976 0.024
#> GSM531686 1 0.0000 0.956 1.000 0.000
#> GSM531687 1 0.1184 0.953 0.984 0.016
#> GSM531688 1 0.1843 0.948 0.972 0.028
#> GSM531689 1 0.0000 0.956 1.000 0.000
#> GSM531690 1 0.0000 0.956 1.000 0.000
#> GSM531691 1 0.0000 0.956 1.000 0.000
#> GSM531692 1 0.0376 0.956 0.996 0.004
#> GSM531693 1 0.1843 0.948 0.972 0.028
#> GSM531694 1 0.0000 0.956 1.000 0.000
#> GSM531695 1 0.1843 0.948 0.972 0.028
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM531600 3 0.4045 0.8826 0.104 0.024 0.872
#> GSM531601 2 0.6235 0.1113 0.436 0.564 0.000
#> GSM531605 1 0.2356 0.9034 0.928 0.000 0.072
#> GSM531615 2 0.0000 0.8836 0.000 1.000 0.000
#> GSM531617 2 0.1163 0.8668 0.028 0.972 0.000
#> GSM531624 2 0.0237 0.8859 0.000 0.996 0.004
#> GSM531627 2 0.0237 0.8859 0.000 0.996 0.004
#> GSM531629 1 0.4369 0.8701 0.864 0.096 0.040
#> GSM531631 2 0.0237 0.8859 0.000 0.996 0.004
#> GSM531634 2 0.0000 0.8836 0.000 1.000 0.000
#> GSM531636 3 0.5339 0.9004 0.096 0.080 0.824
#> GSM531637 2 0.0237 0.8859 0.000 0.996 0.004
#> GSM531654 1 0.3618 0.9041 0.884 0.012 0.104
#> GSM531655 1 0.3349 0.9038 0.888 0.004 0.108
#> GSM531658 1 0.4033 0.8507 0.856 0.008 0.136
#> GSM531660 1 0.1950 0.9095 0.952 0.008 0.040
#> GSM531602 1 0.0424 0.9132 0.992 0.000 0.008
#> GSM531603 1 0.1399 0.9147 0.968 0.004 0.028
#> GSM531604 1 0.2356 0.9034 0.928 0.000 0.072
#> GSM531606 1 0.1289 0.9127 0.968 0.000 0.032
#> GSM531607 1 0.0424 0.9132 0.992 0.000 0.008
#> GSM531608 2 0.1950 0.8559 0.008 0.952 0.040
#> GSM531609 1 0.4345 0.8500 0.848 0.016 0.136
#> GSM531610 1 0.3918 0.8644 0.868 0.012 0.120
#> GSM531611 1 0.1950 0.9095 0.952 0.008 0.040
#> GSM531612 1 0.4345 0.8500 0.848 0.016 0.136
#> GSM531613 1 0.1950 0.9095 0.952 0.008 0.040
#> GSM531614 1 0.4345 0.8500 0.848 0.016 0.136
#> GSM531616 2 0.8363 -0.0224 0.084 0.504 0.412
#> GSM531618 1 0.3856 0.8822 0.888 0.072 0.040
#> GSM531619 2 0.0237 0.8859 0.000 0.996 0.004
#> GSM531620 2 0.1031 0.8761 0.000 0.976 0.024
#> GSM531621 2 0.0237 0.8859 0.000 0.996 0.004
#> GSM531622 2 0.0237 0.8859 0.000 0.996 0.004
#> GSM531623 2 0.0237 0.8859 0.000 0.996 0.004
#> GSM531625 2 0.6463 0.6392 0.080 0.756 0.164
#> GSM531626 2 0.6239 0.6591 0.072 0.768 0.160
#> GSM531628 3 0.5165 0.9014 0.096 0.072 0.832
#> GSM531630 2 0.0237 0.8859 0.000 0.996 0.004
#> GSM531632 3 0.4982 0.9002 0.096 0.064 0.840
#> GSM531633 2 0.0237 0.8859 0.000 0.996 0.004
#> GSM531635 3 0.5339 0.9004 0.096 0.080 0.824
#> GSM531638 2 0.6380 0.6468 0.076 0.760 0.164
#> GSM531639 3 0.8018 0.3734 0.416 0.064 0.520
#> GSM531640 2 0.0237 0.8859 0.000 0.996 0.004
#> GSM531641 1 0.4345 0.8500 0.848 0.016 0.136
#> GSM531642 1 0.5481 0.8118 0.816 0.076 0.108
#> GSM531643 3 0.5331 0.9012 0.100 0.076 0.824
#> GSM531644 1 0.7742 0.4293 0.632 0.080 0.288
#> GSM531645 1 0.4345 0.8500 0.848 0.016 0.136
#> GSM531646 3 0.5339 0.9004 0.096 0.080 0.824
#> GSM531647 3 0.5339 0.9004 0.096 0.080 0.824
#> GSM531648 1 0.2998 0.9006 0.916 0.016 0.068
#> GSM531649 3 0.5339 0.9004 0.096 0.080 0.824
#> GSM531650 3 0.5165 0.9014 0.096 0.072 0.832
#> GSM531651 2 0.0237 0.8859 0.000 0.996 0.004
#> GSM531652 1 0.4269 0.8681 0.872 0.076 0.052
#> GSM531653 3 0.5253 0.9012 0.096 0.076 0.828
#> GSM531656 3 0.5137 0.9004 0.104 0.064 0.832
#> GSM531657 1 0.1950 0.9095 0.952 0.008 0.040
#> GSM531659 1 0.1950 0.9095 0.952 0.008 0.040
#> GSM531661 2 0.6693 0.6361 0.148 0.748 0.104
#> GSM531662 1 0.3532 0.9032 0.884 0.008 0.108
#> GSM531663 1 0.1950 0.9095 0.952 0.008 0.040
#> GSM531664 3 0.6927 0.6688 0.296 0.040 0.664
#> GSM531665 1 0.4047 0.8795 0.848 0.004 0.148
#> GSM531666 1 0.3993 0.8772 0.884 0.064 0.052
#> GSM531667 2 0.1950 0.8559 0.008 0.952 0.040
#> GSM531668 1 0.1950 0.9095 0.952 0.008 0.040
#> GSM531669 3 0.3112 0.8666 0.096 0.004 0.900
#> GSM531670 3 0.3910 0.8799 0.104 0.020 0.876
#> GSM531671 1 0.4733 0.8288 0.800 0.004 0.196
#> GSM531672 1 0.1950 0.9095 0.952 0.008 0.040
#> GSM531673 1 0.2959 0.9064 0.900 0.000 0.100
#> GSM531674 3 0.3459 0.8740 0.096 0.012 0.892
#> GSM531675 1 0.0424 0.9132 0.992 0.000 0.008
#> GSM531676 1 0.3482 0.8699 0.872 0.000 0.128
#> GSM531677 1 0.0424 0.9132 0.992 0.000 0.008
#> GSM531678 1 0.0592 0.9133 0.988 0.000 0.012
#> GSM531679 1 0.0747 0.9133 0.984 0.000 0.016
#> GSM531680 1 0.2711 0.8977 0.912 0.000 0.088
#> GSM531681 1 0.0237 0.9133 0.996 0.004 0.000
#> GSM531682 1 0.1163 0.9131 0.972 0.000 0.028
#> GSM531683 1 0.0424 0.9132 0.992 0.000 0.008
#> GSM531684 1 0.2165 0.9057 0.936 0.000 0.064
#> GSM531685 1 0.4002 0.8363 0.840 0.000 0.160
#> GSM531686 1 0.0661 0.9134 0.988 0.004 0.008
#> GSM531687 1 0.2448 0.9023 0.924 0.000 0.076
#> GSM531688 1 0.4062 0.8327 0.836 0.000 0.164
#> GSM531689 1 0.2356 0.9034 0.928 0.000 0.072
#> GSM531690 1 0.0237 0.9133 0.996 0.004 0.000
#> GSM531691 1 0.2356 0.9034 0.928 0.000 0.072
#> GSM531692 1 0.3340 0.8769 0.880 0.000 0.120
#> GSM531693 3 0.5529 0.6458 0.296 0.000 0.704
#> GSM531694 1 0.0424 0.9132 0.992 0.000 0.008
#> GSM531695 1 0.2711 0.8977 0.912 0.000 0.088
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM531600 3 0.1022 0.87810 0.000 0.000 0.968 0.032
#> GSM531601 2 0.0707 0.94336 0.000 0.980 0.000 0.020
#> GSM531605 1 0.0000 0.90473 1.000 0.000 0.000 0.000
#> GSM531615 2 0.0000 0.95360 0.000 1.000 0.000 0.000
#> GSM531617 2 0.0000 0.95360 0.000 1.000 0.000 0.000
#> GSM531624 2 0.0000 0.95360 0.000 1.000 0.000 0.000
#> GSM531627 2 0.0000 0.95360 0.000 1.000 0.000 0.000
#> GSM531629 2 0.1118 0.93317 0.000 0.964 0.000 0.036
#> GSM531631 2 0.0000 0.95360 0.000 1.000 0.000 0.000
#> GSM531634 2 0.0000 0.95360 0.000 1.000 0.000 0.000
#> GSM531636 3 0.2814 0.85589 0.000 0.132 0.868 0.000
#> GSM531637 2 0.0000 0.95360 0.000 1.000 0.000 0.000
#> GSM531654 2 0.3991 0.85994 0.044 0.860 0.064 0.032
#> GSM531655 1 0.7004 0.47551 0.620 0.048 0.064 0.268
#> GSM531658 4 0.0336 0.90843 0.000 0.000 0.008 0.992
#> GSM531660 4 0.2921 0.86795 0.140 0.000 0.000 0.860
#> GSM531602 1 0.0000 0.90473 1.000 0.000 0.000 0.000
#> GSM531603 1 0.0592 0.89781 0.984 0.000 0.000 0.016
#> GSM531604 1 0.0000 0.90473 1.000 0.000 0.000 0.000
#> GSM531606 1 0.0000 0.90473 1.000 0.000 0.000 0.000
#> GSM531607 1 0.0000 0.90473 1.000 0.000 0.000 0.000
#> GSM531608 2 0.2376 0.90022 0.000 0.916 0.016 0.068
#> GSM531609 4 0.0469 0.90882 0.000 0.000 0.012 0.988
#> GSM531610 4 0.0469 0.90882 0.000 0.000 0.012 0.988
#> GSM531611 4 0.2281 0.90080 0.096 0.000 0.000 0.904
#> GSM531612 4 0.0469 0.90882 0.000 0.000 0.012 0.988
#> GSM531613 4 0.2081 0.90415 0.084 0.000 0.000 0.916
#> GSM531614 4 0.0469 0.90882 0.000 0.000 0.012 0.988
#> GSM531616 2 0.3688 0.72061 0.000 0.792 0.208 0.000
#> GSM531618 4 0.3919 0.88165 0.096 0.040 0.012 0.852
#> GSM531619 2 0.0000 0.95360 0.000 1.000 0.000 0.000
#> GSM531620 2 0.0000 0.95360 0.000 1.000 0.000 0.000
#> GSM531621 2 0.0000 0.95360 0.000 1.000 0.000 0.000
#> GSM531622 2 0.0000 0.95360 0.000 1.000 0.000 0.000
#> GSM531623 2 0.0000 0.95360 0.000 1.000 0.000 0.000
#> GSM531625 2 0.2149 0.88722 0.000 0.912 0.088 0.000
#> GSM531626 2 0.2081 0.89095 0.000 0.916 0.084 0.000
#> GSM531628 3 0.1716 0.89296 0.000 0.064 0.936 0.000
#> GSM531630 2 0.0000 0.95360 0.000 1.000 0.000 0.000
#> GSM531632 3 0.0000 0.88358 0.000 0.000 1.000 0.000
#> GSM531633 2 0.0000 0.95360 0.000 1.000 0.000 0.000
#> GSM531635 3 0.2081 0.89181 0.000 0.084 0.916 0.000
#> GSM531638 2 0.2216 0.88329 0.000 0.908 0.092 0.000
#> GSM531639 3 0.4361 0.69121 0.000 0.208 0.772 0.020
#> GSM531640 2 0.0000 0.95360 0.000 1.000 0.000 0.000
#> GSM531641 4 0.0469 0.90882 0.000 0.000 0.012 0.988
#> GSM531642 3 0.2699 0.88715 0.000 0.068 0.904 0.028
#> GSM531643 3 0.2081 0.89181 0.000 0.084 0.916 0.000
#> GSM531644 3 0.2081 0.89181 0.000 0.084 0.916 0.000
#> GSM531645 4 0.0469 0.90882 0.000 0.000 0.012 0.988
#> GSM531646 3 0.2081 0.89181 0.000 0.084 0.916 0.000
#> GSM531647 3 0.2081 0.89181 0.000 0.084 0.916 0.000
#> GSM531648 4 0.0804 0.90988 0.008 0.000 0.012 0.980
#> GSM531649 3 0.2216 0.88827 0.000 0.092 0.908 0.000
#> GSM531650 3 0.1716 0.89296 0.000 0.064 0.936 0.000
#> GSM531651 2 0.0000 0.95360 0.000 1.000 0.000 0.000
#> GSM531652 4 0.6592 0.13286 0.000 0.084 0.392 0.524
#> GSM531653 3 0.2081 0.89181 0.000 0.084 0.916 0.000
#> GSM531656 3 0.0707 0.88041 0.000 0.000 0.980 0.020
#> GSM531657 4 0.2281 0.90080 0.096 0.000 0.000 0.904
#> GSM531659 1 0.4585 0.51627 0.668 0.000 0.000 0.332
#> GSM531661 2 0.2722 0.88949 0.000 0.904 0.064 0.032
#> GSM531662 2 0.4803 0.81370 0.088 0.816 0.064 0.032
#> GSM531663 4 0.2281 0.90080 0.096 0.000 0.000 0.904
#> GSM531664 3 0.0469 0.88274 0.000 0.000 0.988 0.012
#> GSM531665 1 0.4289 0.78208 0.796 0.000 0.172 0.032
#> GSM531666 3 0.5861 -0.00699 0.032 0.000 0.492 0.476
#> GSM531667 2 0.2101 0.91016 0.000 0.928 0.012 0.060
#> GSM531668 4 0.2345 0.89877 0.100 0.000 0.000 0.900
#> GSM531669 3 0.0469 0.88274 0.000 0.000 0.988 0.012
#> GSM531670 3 0.1022 0.87810 0.000 0.000 0.968 0.032
#> GSM531671 3 0.6724 0.18965 0.036 0.400 0.532 0.032
#> GSM531672 4 0.2281 0.90080 0.096 0.000 0.000 0.904
#> GSM531673 1 0.3182 0.84353 0.892 0.012 0.064 0.032
#> GSM531674 3 0.0469 0.88274 0.000 0.000 0.988 0.012
#> GSM531675 1 0.0000 0.90473 1.000 0.000 0.000 0.000
#> GSM531676 1 0.2281 0.85154 0.904 0.000 0.096 0.000
#> GSM531677 1 0.0000 0.90473 1.000 0.000 0.000 0.000
#> GSM531678 1 0.0000 0.90473 1.000 0.000 0.000 0.000
#> GSM531679 1 0.0000 0.90473 1.000 0.000 0.000 0.000
#> GSM531680 1 0.2081 0.86185 0.916 0.000 0.084 0.000
#> GSM531681 1 0.2469 0.83603 0.892 0.000 0.000 0.108
#> GSM531682 1 0.0000 0.90473 1.000 0.000 0.000 0.000
#> GSM531683 1 0.0000 0.90473 1.000 0.000 0.000 0.000
#> GSM531684 1 0.0000 0.90473 1.000 0.000 0.000 0.000
#> GSM531685 1 0.4250 0.63979 0.724 0.000 0.276 0.000
#> GSM531686 1 0.0469 0.90025 0.988 0.000 0.000 0.012
#> GSM531687 1 0.1557 0.88140 0.944 0.000 0.056 0.000
#> GSM531688 3 0.0592 0.87975 0.016 0.000 0.984 0.000
#> GSM531689 1 0.0000 0.90473 1.000 0.000 0.000 0.000
#> GSM531690 1 0.4776 0.38636 0.624 0.000 0.000 0.376
#> GSM531691 1 0.0000 0.90473 1.000 0.000 0.000 0.000
#> GSM531692 1 0.1792 0.86947 0.932 0.000 0.068 0.000
#> GSM531693 3 0.0469 0.88274 0.000 0.000 0.988 0.012
#> GSM531694 1 0.0000 0.90473 1.000 0.000 0.000 0.000
#> GSM531695 1 0.4697 0.48315 0.644 0.000 0.356 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM531600 3 0.2719 0.867 0.000 0.004 0.852 0.000 0.144
#> GSM531601 2 0.2280 0.829 0.000 0.880 0.000 0.120 0.000
#> GSM531605 1 0.0404 0.845 0.988 0.000 0.000 0.000 0.012
#> GSM531615 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> GSM531617 2 0.0162 0.931 0.004 0.996 0.000 0.000 0.000
#> GSM531624 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> GSM531627 2 0.0404 0.930 0.000 0.988 0.000 0.000 0.012
#> GSM531629 2 0.1116 0.914 0.028 0.964 0.004 0.000 0.004
#> GSM531631 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> GSM531634 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> GSM531636 3 0.2694 0.868 0.000 0.040 0.884 0.000 0.076
#> GSM531637 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> GSM531654 2 0.4398 0.719 0.100 0.780 0.008 0.000 0.112
#> GSM531655 4 0.7979 0.243 0.272 0.048 0.016 0.400 0.264
#> GSM531658 4 0.0290 0.774 0.000 0.000 0.000 0.992 0.008
#> GSM531660 4 0.5861 0.667 0.228 0.012 0.000 0.632 0.128
#> GSM531602 1 0.0162 0.849 0.996 0.000 0.000 0.000 0.004
#> GSM531603 1 0.2074 0.713 0.896 0.000 0.000 0.000 0.104
#> GSM531604 1 0.4291 -0.129 0.536 0.000 0.000 0.000 0.464
#> GSM531606 1 0.3424 0.559 0.760 0.000 0.000 0.000 0.240
#> GSM531607 1 0.0000 0.850 1.000 0.000 0.000 0.000 0.000
#> GSM531608 2 0.1282 0.914 0.000 0.952 0.044 0.000 0.004
#> GSM531609 4 0.0000 0.772 0.000 0.000 0.000 1.000 0.000
#> GSM531610 4 0.0000 0.772 0.000 0.000 0.000 1.000 0.000
#> GSM531611 4 0.4410 0.775 0.112 0.000 0.000 0.764 0.124
#> GSM531612 4 0.0000 0.772 0.000 0.000 0.000 1.000 0.000
#> GSM531613 4 0.4317 0.775 0.112 0.000 0.000 0.772 0.116
#> GSM531614 4 0.0000 0.772 0.000 0.000 0.000 1.000 0.000
#> GSM531616 2 0.5110 0.631 0.000 0.680 0.224 0.000 0.096
#> GSM531618 4 0.5221 0.728 0.112 0.036 0.116 0.736 0.000
#> GSM531619 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> GSM531620 2 0.0671 0.929 0.000 0.980 0.004 0.000 0.016
#> GSM531621 2 0.0404 0.930 0.000 0.988 0.000 0.000 0.012
#> GSM531622 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> GSM531623 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> GSM531625 2 0.2429 0.894 0.008 0.904 0.020 0.000 0.068
#> GSM531626 2 0.3090 0.864 0.000 0.856 0.040 0.000 0.104
#> GSM531628 3 0.0162 0.908 0.000 0.004 0.996 0.000 0.000
#> GSM531630 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> GSM531632 3 0.0000 0.907 0.000 0.000 1.000 0.000 0.000
#> GSM531633 2 0.0404 0.930 0.000 0.988 0.000 0.000 0.012
#> GSM531635 3 0.1205 0.903 0.000 0.004 0.956 0.000 0.040
#> GSM531638 2 0.2470 0.882 0.000 0.884 0.012 0.000 0.104
#> GSM531639 3 0.3780 0.760 0.000 0.132 0.808 0.000 0.060
#> GSM531640 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> GSM531641 4 0.0162 0.774 0.004 0.000 0.000 0.996 0.000
#> GSM531642 3 0.3412 0.799 0.048 0.008 0.848 0.096 0.000
#> GSM531643 3 0.1205 0.892 0.040 0.004 0.956 0.000 0.000
#> GSM531644 3 0.1282 0.888 0.044 0.004 0.952 0.000 0.000
#> GSM531645 4 0.0000 0.772 0.000 0.000 0.000 1.000 0.000
#> GSM531646 3 0.0162 0.908 0.000 0.004 0.996 0.000 0.000
#> GSM531647 3 0.0162 0.908 0.000 0.004 0.996 0.000 0.000
#> GSM531648 4 0.1121 0.778 0.044 0.000 0.000 0.956 0.000
#> GSM531649 3 0.1124 0.904 0.000 0.004 0.960 0.000 0.036
#> GSM531650 3 0.0162 0.908 0.000 0.004 0.996 0.000 0.000
#> GSM531651 2 0.0162 0.932 0.000 0.996 0.000 0.000 0.004
#> GSM531652 4 0.6011 0.440 0.052 0.036 0.356 0.556 0.000
#> GSM531653 3 0.0162 0.908 0.000 0.004 0.996 0.000 0.000
#> GSM531656 3 0.1282 0.901 0.000 0.004 0.952 0.000 0.044
#> GSM531657 4 0.4454 0.773 0.112 0.000 0.000 0.760 0.128
#> GSM531659 4 0.5102 0.726 0.176 0.000 0.000 0.696 0.128
#> GSM531661 2 0.3115 0.829 0.020 0.860 0.012 0.000 0.108
#> GSM531662 2 0.5923 0.457 0.120 0.616 0.012 0.000 0.252
#> GSM531663 4 0.4444 0.775 0.104 0.000 0.000 0.760 0.136
#> GSM531664 3 0.2127 0.863 0.000 0.000 0.892 0.000 0.108
#> GSM531665 5 0.2813 0.637 0.108 0.000 0.024 0.000 0.868
#> GSM531666 4 0.7452 0.351 0.100 0.000 0.352 0.440 0.108
#> GSM531667 2 0.1443 0.913 0.004 0.948 0.044 0.000 0.004
#> GSM531668 4 0.4733 0.770 0.116 0.008 0.000 0.752 0.124
#> GSM531669 3 0.2127 0.863 0.000 0.000 0.892 0.000 0.108
#> GSM531670 3 0.2953 0.864 0.000 0.012 0.844 0.000 0.144
#> GSM531671 5 0.6553 0.500 0.116 0.156 0.096 0.000 0.632
#> GSM531672 4 0.4454 0.773 0.112 0.000 0.000 0.760 0.128
#> GSM531673 5 0.4512 0.498 0.300 0.020 0.004 0.000 0.676
#> GSM531674 3 0.2127 0.863 0.000 0.000 0.892 0.000 0.108
#> GSM531675 1 0.0404 0.847 0.988 0.000 0.000 0.000 0.012
#> GSM531676 5 0.3579 0.676 0.240 0.000 0.004 0.000 0.756
#> GSM531677 1 0.0404 0.847 0.988 0.000 0.000 0.000 0.012
#> GSM531678 1 0.0000 0.850 1.000 0.000 0.000 0.000 0.000
#> GSM531679 1 0.0000 0.850 1.000 0.000 0.000 0.000 0.000
#> GSM531680 5 0.4549 0.384 0.464 0.000 0.008 0.000 0.528
#> GSM531681 1 0.1106 0.830 0.964 0.000 0.000 0.024 0.012
#> GSM531682 1 0.1908 0.772 0.908 0.000 0.000 0.000 0.092
#> GSM531683 1 0.0000 0.850 1.000 0.000 0.000 0.000 0.000
#> GSM531684 1 0.4030 0.312 0.648 0.000 0.000 0.000 0.352
#> GSM531685 5 0.3671 0.677 0.236 0.000 0.008 0.000 0.756
#> GSM531686 1 0.0566 0.845 0.984 0.000 0.000 0.004 0.012
#> GSM531687 5 0.4074 0.568 0.364 0.000 0.000 0.000 0.636
#> GSM531688 5 0.6536 0.308 0.196 0.000 0.396 0.000 0.408
#> GSM531689 1 0.3999 0.260 0.656 0.000 0.000 0.000 0.344
#> GSM531690 1 0.0912 0.837 0.972 0.000 0.000 0.016 0.012
#> GSM531691 5 0.4074 0.539 0.364 0.000 0.000 0.000 0.636
#> GSM531692 5 0.3579 0.676 0.240 0.000 0.004 0.000 0.756
#> GSM531693 3 0.3728 0.715 0.008 0.000 0.748 0.000 0.244
#> GSM531694 1 0.0162 0.849 0.996 0.000 0.000 0.000 0.004
#> GSM531695 5 0.6557 0.510 0.240 0.000 0.288 0.000 0.472
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM531600 3 0.0964 0.72956 0.000 0.004 0.968 0.016 0.012 0.000
#> GSM531601 2 0.1605 0.88380 0.012 0.940 0.000 0.032 0.016 0.000
#> GSM531605 6 0.3860 -0.76152 0.472 0.000 0.000 0.000 0.000 0.528
#> GSM531615 2 0.0260 0.91411 0.008 0.992 0.000 0.000 0.000 0.000
#> GSM531617 2 0.0260 0.91411 0.008 0.992 0.000 0.000 0.000 0.000
#> GSM531624 2 0.0000 0.91502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531627 2 0.0260 0.91276 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM531629 2 0.1540 0.89816 0.012 0.948 0.012 0.000 0.016 0.012
#> GSM531631 2 0.0000 0.91502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531634 2 0.0260 0.91411 0.008 0.992 0.000 0.000 0.000 0.000
#> GSM531636 3 0.0820 0.72872 0.000 0.012 0.972 0.000 0.016 0.000
#> GSM531637 2 0.0000 0.91502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531654 2 0.5963 0.35182 0.000 0.564 0.052 0.308 0.012 0.064
#> GSM531655 4 0.7355 0.11068 0.036 0.052 0.076 0.504 0.048 0.284
#> GSM531658 4 0.3409 0.69449 0.300 0.000 0.000 0.700 0.000 0.000
#> GSM531660 4 0.5790 0.50136 0.128 0.008 0.052 0.696 0.052 0.064
#> GSM531602 1 0.3868 0.76599 0.504 0.000 0.000 0.000 0.000 0.496
#> GSM531603 1 0.6990 -0.08893 0.396 0.000 0.012 0.348 0.048 0.196
#> GSM531604 6 0.0865 0.30347 0.036 0.000 0.000 0.000 0.000 0.964
#> GSM531606 6 0.2854 -0.06528 0.208 0.000 0.000 0.000 0.000 0.792
#> GSM531607 1 0.3868 0.76599 0.504 0.000 0.000 0.000 0.000 0.496
#> GSM531608 2 0.0717 0.90895 0.008 0.976 0.016 0.000 0.000 0.000
#> GSM531609 4 0.3499 0.68928 0.320 0.000 0.000 0.680 0.000 0.000
#> GSM531610 4 0.2003 0.70637 0.116 0.000 0.000 0.884 0.000 0.000
#> GSM531611 4 0.1625 0.68354 0.012 0.000 0.000 0.928 0.000 0.060
#> GSM531612 4 0.3499 0.68928 0.320 0.000 0.000 0.680 0.000 0.000
#> GSM531613 4 0.1616 0.68707 0.020 0.000 0.000 0.932 0.000 0.048
#> GSM531614 4 0.3499 0.68928 0.320 0.000 0.000 0.680 0.000 0.000
#> GSM531616 3 0.4692 -0.09571 0.000 0.444 0.512 0.000 0.044 0.000
#> GSM531618 4 0.7136 0.66072 0.280 0.016 0.108 0.508 0.032 0.056
#> GSM531619 2 0.0000 0.91502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531620 2 0.1663 0.86724 0.000 0.912 0.088 0.000 0.000 0.000
#> GSM531621 2 0.0000 0.91502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531622 2 0.0146 0.91465 0.004 0.996 0.000 0.000 0.000 0.000
#> GSM531623 2 0.0000 0.91502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531625 2 0.2877 0.78375 0.000 0.820 0.168 0.000 0.012 0.000
#> GSM531626 2 0.4460 0.58528 0.000 0.644 0.304 0.000 0.052 0.000
#> GSM531628 3 0.3126 0.70808 0.000 0.000 0.752 0.000 0.248 0.000
#> GSM531630 2 0.0000 0.91502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531632 3 0.3126 0.70838 0.000 0.000 0.752 0.000 0.248 0.000
#> GSM531633 2 0.0000 0.91502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531635 3 0.0146 0.73564 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM531638 2 0.4227 0.65739 0.000 0.692 0.256 0.000 0.052 0.000
#> GSM531639 3 0.0363 0.73310 0.000 0.012 0.988 0.000 0.000 0.000
#> GSM531640 2 0.0260 0.91411 0.008 0.992 0.000 0.000 0.000 0.000
#> GSM531641 4 0.3499 0.68928 0.320 0.000 0.000 0.680 0.000 0.000
#> GSM531642 3 0.2145 0.69890 0.016 0.000 0.916 0.044 0.004 0.020
#> GSM531643 3 0.2664 0.73281 0.000 0.000 0.884 0.040 0.056 0.020
#> GSM531644 3 0.3436 0.72540 0.000 0.000 0.828 0.048 0.104 0.020
#> GSM531645 4 0.3499 0.68928 0.320 0.000 0.000 0.680 0.000 0.000
#> GSM531646 3 0.3126 0.70838 0.000 0.000 0.752 0.000 0.248 0.000
#> GSM531647 3 0.3126 0.70808 0.000 0.000 0.752 0.000 0.248 0.000
#> GSM531648 4 0.4002 0.69155 0.320 0.000 0.000 0.660 0.000 0.020
#> GSM531649 3 0.0363 0.73794 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM531650 3 0.3126 0.70808 0.000 0.000 0.752 0.000 0.248 0.000
#> GSM531651 2 0.0000 0.91502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531652 3 0.6743 -0.17344 0.272 0.004 0.384 0.316 0.004 0.020
#> GSM531653 3 0.3126 0.70808 0.000 0.000 0.752 0.000 0.248 0.000
#> GSM531656 3 0.0291 0.73602 0.000 0.004 0.992 0.000 0.004 0.000
#> GSM531657 4 0.3688 0.66032 0.016 0.000 0.044 0.836 0.052 0.052
#> GSM531659 4 0.3946 0.63969 0.024 0.000 0.048 0.820 0.036 0.072
#> GSM531661 2 0.4424 0.50356 0.000 0.656 0.004 0.308 0.012 0.020
#> GSM531662 6 0.7125 0.16904 0.000 0.252 0.048 0.308 0.012 0.380
#> GSM531663 4 0.3530 0.66455 0.012 0.000 0.040 0.844 0.052 0.052
#> GSM531664 3 0.3636 0.65433 0.000 0.000 0.676 0.004 0.320 0.000
#> GSM531665 6 0.5797 0.24839 0.000 0.000 0.088 0.308 0.044 0.560
#> GSM531666 3 0.4835 0.10546 0.000 0.000 0.540 0.408 0.004 0.048
#> GSM531667 2 0.1462 0.88500 0.008 0.936 0.056 0.000 0.000 0.000
#> GSM531668 4 0.4042 0.65005 0.020 0.004 0.052 0.820 0.052 0.052
#> GSM531669 5 0.3915 -0.27422 0.000 0.000 0.412 0.004 0.584 0.000
#> GSM531670 3 0.1078 0.72801 0.000 0.008 0.964 0.016 0.012 0.000
#> GSM531671 6 0.6929 0.22500 0.000 0.028 0.208 0.308 0.024 0.432
#> GSM531672 4 0.2906 0.67179 0.032 0.000 0.044 0.872 0.000 0.052
#> GSM531673 6 0.4904 0.30680 0.004 0.004 0.040 0.308 0.012 0.632
#> GSM531674 3 0.3995 0.41064 0.000 0.000 0.516 0.004 0.480 0.000
#> GSM531675 1 0.3898 0.75169 0.652 0.000 0.000 0.012 0.000 0.336
#> GSM531676 5 0.5064 0.64838 0.060 0.000 0.008 0.000 0.540 0.392
#> GSM531677 1 0.3907 0.77055 0.588 0.000 0.000 0.000 0.004 0.408
#> GSM531678 1 0.3868 0.76599 0.504 0.000 0.000 0.000 0.000 0.496
#> GSM531679 1 0.4072 0.76427 0.544 0.000 0.000 0.000 0.008 0.448
#> GSM531680 5 0.5289 0.66704 0.092 0.000 0.008 0.000 0.564 0.336
#> GSM531681 1 0.3969 0.74591 0.652 0.000 0.000 0.016 0.000 0.332
#> GSM531682 1 0.4076 0.76142 0.540 0.000 0.000 0.000 0.008 0.452
#> GSM531683 1 0.3868 0.76599 0.504 0.000 0.000 0.000 0.000 0.496
#> GSM531684 6 0.2697 -0.00245 0.188 0.000 0.000 0.000 0.000 0.812
#> GSM531685 5 0.4637 0.63413 0.028 0.000 0.008 0.000 0.556 0.408
#> GSM531686 1 0.3883 0.74805 0.656 0.000 0.000 0.012 0.000 0.332
#> GSM531687 5 0.5105 0.66595 0.080 0.000 0.004 0.000 0.564 0.352
#> GSM531688 5 0.5121 0.62991 0.060 0.000 0.100 0.000 0.704 0.136
#> GSM531689 6 0.3619 -0.42149 0.316 0.000 0.000 0.000 0.004 0.680
#> GSM531690 1 0.3969 0.74591 0.652 0.000 0.000 0.016 0.000 0.332
#> GSM531691 6 0.0363 0.33084 0.000 0.000 0.000 0.000 0.012 0.988
#> GSM531692 6 0.0458 0.33321 0.000 0.000 0.000 0.000 0.016 0.984
#> GSM531693 5 0.3877 0.52353 0.048 0.000 0.124 0.004 0.800 0.024
#> GSM531694 1 0.3868 0.76599 0.504 0.000 0.000 0.000 0.000 0.496
#> GSM531695 5 0.5681 0.68172 0.060 0.000 0.056 0.000 0.564 0.320
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> CV:mclust 92 0.0112 2
#> CV:mclust 92 0.0780 3
#> CV:mclust 90 0.0070 4
#> CV:mclust 85 0.0197 5
#> CV:mclust 77 0.0192 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "NMF"]
# you can also extract it by
# res = res_list["CV:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 96 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 0.855 0.923 0.967 0.5015 0.498 0.498
#> 3 3 0.870 0.859 0.940 0.3289 0.741 0.525
#> 4 4 0.830 0.851 0.937 0.1297 0.824 0.532
#> 5 5 0.810 0.806 0.900 0.0558 0.932 0.740
#> 6 6 0.726 0.661 0.801 0.0436 0.935 0.707
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
#> GSM531600 2 0.0000 0.963 0.000 1.000
#> GSM531601 2 0.0672 0.957 0.008 0.992
#> GSM531605 1 0.0000 0.966 1.000 0.000
#> GSM531615 2 0.6148 0.817 0.152 0.848
#> GSM531617 2 0.4022 0.898 0.080 0.920
#> GSM531624 2 0.0000 0.963 0.000 1.000
#> GSM531627 2 0.0000 0.963 0.000 1.000
#> GSM531629 1 0.0000 0.966 1.000 0.000
#> GSM531631 2 0.0000 0.963 0.000 1.000
#> GSM531634 2 0.2423 0.933 0.040 0.960
#> GSM531636 2 0.0000 0.963 0.000 1.000
#> GSM531637 2 0.0000 0.963 0.000 1.000
#> GSM531654 2 0.4939 0.871 0.108 0.892
#> GSM531655 2 0.9866 0.228 0.432 0.568
#> GSM531658 1 0.0000 0.966 1.000 0.000
#> GSM531660 1 0.0000 0.966 1.000 0.000
#> GSM531602 1 0.0000 0.966 1.000 0.000
#> GSM531603 1 0.0000 0.966 1.000 0.000
#> GSM531604 1 0.0376 0.964 0.996 0.004
#> GSM531606 1 0.0000 0.966 1.000 0.000
#> GSM531607 1 0.0000 0.966 1.000 0.000
#> GSM531608 2 0.4562 0.882 0.096 0.904
#> GSM531609 1 0.0000 0.966 1.000 0.000
#> GSM531610 1 0.0000 0.966 1.000 0.000
#> GSM531611 1 0.0000 0.966 1.000 0.000
#> GSM531612 1 0.0000 0.966 1.000 0.000
#> GSM531613 1 0.0000 0.966 1.000 0.000
#> GSM531614 1 0.0000 0.966 1.000 0.000
#> GSM531616 2 0.0000 0.963 0.000 1.000
#> GSM531618 1 0.7453 0.725 0.788 0.212
#> GSM531619 2 0.0000 0.963 0.000 1.000
#> GSM531620 2 0.0000 0.963 0.000 1.000
#> GSM531621 2 0.0000 0.963 0.000 1.000
#> GSM531622 2 0.0000 0.963 0.000 1.000
#> GSM531623 2 0.0000 0.963 0.000 1.000
#> GSM531625 2 0.0000 0.963 0.000 1.000
#> GSM531626 2 0.0000 0.963 0.000 1.000
#> GSM531628 2 0.0000 0.963 0.000 1.000
#> GSM531630 2 0.0000 0.963 0.000 1.000
#> GSM531632 2 0.0000 0.963 0.000 1.000
#> GSM531633 2 0.0000 0.963 0.000 1.000
#> GSM531635 2 0.0000 0.963 0.000 1.000
#> GSM531638 2 0.0000 0.963 0.000 1.000
#> GSM531639 2 0.0000 0.963 0.000 1.000
#> GSM531640 2 0.0000 0.963 0.000 1.000
#> GSM531641 1 0.0000 0.966 1.000 0.000
#> GSM531642 2 0.0000 0.963 0.000 1.000
#> GSM531643 2 0.0000 0.963 0.000 1.000
#> GSM531644 2 0.4690 0.871 0.100 0.900
#> GSM531645 1 0.0000 0.966 1.000 0.000
#> GSM531646 2 0.0000 0.963 0.000 1.000
#> GSM531647 2 0.0000 0.963 0.000 1.000
#> GSM531648 1 0.0000 0.966 1.000 0.000
#> GSM531649 2 0.0000 0.963 0.000 1.000
#> GSM531650 2 0.0000 0.963 0.000 1.000
#> GSM531651 2 0.0000 0.963 0.000 1.000
#> GSM531652 2 0.0000 0.963 0.000 1.000
#> GSM531653 2 0.0000 0.963 0.000 1.000
#> GSM531656 2 0.0000 0.963 0.000 1.000
#> GSM531657 1 0.0000 0.966 1.000 0.000
#> GSM531659 1 0.0000 0.966 1.000 0.000
#> GSM531661 2 0.0000 0.963 0.000 1.000
#> GSM531662 2 0.0000 0.963 0.000 1.000
#> GSM531663 1 0.0000 0.966 1.000 0.000
#> GSM531664 2 0.9881 0.202 0.436 0.564
#> GSM531665 1 0.7745 0.722 0.772 0.228
#> GSM531666 1 0.5408 0.859 0.876 0.124
#> GSM531667 2 0.0938 0.955 0.012 0.988
#> GSM531668 1 0.0000 0.966 1.000 0.000
#> GSM531669 2 0.0000 0.963 0.000 1.000
#> GSM531670 2 0.0000 0.963 0.000 1.000
#> GSM531671 2 0.0000 0.963 0.000 1.000
#> GSM531672 1 0.0000 0.966 1.000 0.000
#> GSM531673 2 0.8861 0.550 0.304 0.696
#> GSM531674 2 0.0000 0.963 0.000 1.000
#> GSM531675 1 0.0000 0.966 1.000 0.000
#> GSM531676 1 0.7299 0.758 0.796 0.204
#> GSM531677 1 0.0000 0.966 1.000 0.000
#> GSM531678 1 0.0000 0.966 1.000 0.000
#> GSM531679 1 0.0000 0.966 1.000 0.000
#> GSM531680 1 0.1414 0.953 0.980 0.020
#> GSM531681 1 0.0000 0.966 1.000 0.000
#> GSM531682 1 0.0000 0.966 1.000 0.000
#> GSM531683 1 0.0000 0.966 1.000 0.000
#> GSM531684 1 0.0672 0.961 0.992 0.008
#> GSM531685 2 0.1633 0.945 0.024 0.976
#> GSM531686 1 0.0000 0.966 1.000 0.000
#> GSM531687 1 0.3879 0.907 0.924 0.076
#> GSM531688 1 0.9552 0.418 0.624 0.376
#> GSM531689 1 0.0000 0.966 1.000 0.000
#> GSM531690 1 0.0000 0.966 1.000 0.000
#> GSM531691 1 0.4815 0.880 0.896 0.104
#> GSM531692 2 0.0000 0.963 0.000 1.000
#> GSM531693 2 0.0000 0.963 0.000 1.000
#> GSM531694 1 0.0000 0.966 1.000 0.000
#> GSM531695 1 0.1843 0.947 0.972 0.028
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM531600 3 0.1163 0.91253 0.000 0.028 0.972
#> GSM531601 2 0.1643 0.90577 0.000 0.956 0.044
#> GSM531605 1 0.0000 0.94721 1.000 0.000 0.000
#> GSM531615 2 0.0000 0.93500 0.000 1.000 0.000
#> GSM531617 2 0.0000 0.93500 0.000 1.000 0.000
#> GSM531624 2 0.0000 0.93500 0.000 1.000 0.000
#> GSM531627 2 0.0000 0.93500 0.000 1.000 0.000
#> GSM531629 2 0.3340 0.83145 0.120 0.880 0.000
#> GSM531631 2 0.0000 0.93500 0.000 1.000 0.000
#> GSM531634 2 0.0000 0.93500 0.000 1.000 0.000
#> GSM531636 3 0.4555 0.72333 0.000 0.200 0.800
#> GSM531637 2 0.0000 0.93500 0.000 1.000 0.000
#> GSM531654 2 0.1031 0.91763 0.024 0.976 0.000
#> GSM531655 1 0.7422 0.43044 0.608 0.344 0.048
#> GSM531658 1 0.1289 0.93622 0.968 0.000 0.032
#> GSM531660 1 0.0000 0.94721 1.000 0.000 0.000
#> GSM531602 1 0.0000 0.94721 1.000 0.000 0.000
#> GSM531603 1 0.0000 0.94721 1.000 0.000 0.000
#> GSM531604 1 0.0747 0.93831 0.984 0.016 0.000
#> GSM531606 1 0.0000 0.94721 1.000 0.000 0.000
#> GSM531607 1 0.0000 0.94721 1.000 0.000 0.000
#> GSM531608 2 0.0000 0.93500 0.000 1.000 0.000
#> GSM531609 1 0.1529 0.93154 0.960 0.000 0.040
#> GSM531610 1 0.1031 0.93970 0.976 0.000 0.024
#> GSM531611 1 0.1411 0.93408 0.964 0.000 0.036
#> GSM531612 1 0.1964 0.92001 0.944 0.000 0.056
#> GSM531613 1 0.1031 0.93970 0.976 0.000 0.024
#> GSM531614 1 0.1411 0.93408 0.964 0.000 0.036
#> GSM531616 3 0.2537 0.87343 0.000 0.080 0.920
#> GSM531618 1 0.5393 0.81404 0.820 0.108 0.072
#> GSM531619 2 0.0000 0.93500 0.000 1.000 0.000
#> GSM531620 2 0.0000 0.93500 0.000 1.000 0.000
#> GSM531621 2 0.0000 0.93500 0.000 1.000 0.000
#> GSM531622 2 0.0000 0.93500 0.000 1.000 0.000
#> GSM531623 2 0.0000 0.93500 0.000 1.000 0.000
#> GSM531625 2 0.2796 0.85080 0.000 0.908 0.092
#> GSM531626 2 0.0892 0.92088 0.000 0.980 0.020
#> GSM531628 3 0.0000 0.91594 0.000 0.000 1.000
#> GSM531630 2 0.0000 0.93500 0.000 1.000 0.000
#> GSM531632 3 0.1031 0.91425 0.000 0.024 0.976
#> GSM531633 2 0.0000 0.93500 0.000 1.000 0.000
#> GSM531635 3 0.1163 0.91253 0.000 0.028 0.972
#> GSM531638 2 0.6307 -0.02407 0.000 0.512 0.488
#> GSM531639 2 0.6295 0.10009 0.000 0.528 0.472
#> GSM531640 2 0.0000 0.93500 0.000 1.000 0.000
#> GSM531641 1 0.1289 0.93622 0.968 0.000 0.032
#> GSM531642 3 0.0000 0.91594 0.000 0.000 1.000
#> GSM531643 3 0.0000 0.91594 0.000 0.000 1.000
#> GSM531644 3 0.0000 0.91594 0.000 0.000 1.000
#> GSM531645 1 0.2165 0.91381 0.936 0.000 0.064
#> GSM531646 3 0.1031 0.91425 0.000 0.024 0.976
#> GSM531647 3 0.0592 0.91671 0.000 0.012 0.988
#> GSM531648 1 0.2448 0.90369 0.924 0.000 0.076
#> GSM531649 3 0.1163 0.91253 0.000 0.028 0.972
#> GSM531650 3 0.0000 0.91594 0.000 0.000 1.000
#> GSM531651 2 0.0000 0.93500 0.000 1.000 0.000
#> GSM531652 3 0.0000 0.91594 0.000 0.000 1.000
#> GSM531653 3 0.0892 0.91536 0.000 0.020 0.980
#> GSM531656 3 0.0424 0.91682 0.000 0.008 0.992
#> GSM531657 1 0.0237 0.94624 0.996 0.000 0.004
#> GSM531659 1 0.0000 0.94721 1.000 0.000 0.000
#> GSM531661 2 0.0000 0.93500 0.000 1.000 0.000
#> GSM531662 2 0.0000 0.93500 0.000 1.000 0.000
#> GSM531663 1 0.0000 0.94721 1.000 0.000 0.000
#> GSM531664 3 0.0000 0.91594 0.000 0.000 1.000
#> GSM531665 1 0.6260 0.13015 0.552 0.000 0.448
#> GSM531666 3 0.2878 0.84589 0.096 0.000 0.904
#> GSM531667 2 0.0000 0.93500 0.000 1.000 0.000
#> GSM531668 1 0.0000 0.94721 1.000 0.000 0.000
#> GSM531669 3 0.0592 0.91671 0.000 0.012 0.988
#> GSM531670 3 0.1163 0.91253 0.000 0.028 0.972
#> GSM531671 3 0.5882 0.47617 0.000 0.348 0.652
#> GSM531672 1 0.0892 0.94123 0.980 0.000 0.020
#> GSM531673 2 0.5835 0.47549 0.340 0.660 0.000
#> GSM531674 3 0.0237 0.91653 0.000 0.004 0.996
#> GSM531675 1 0.0000 0.94721 1.000 0.000 0.000
#> GSM531676 3 0.5016 0.68988 0.240 0.000 0.760
#> GSM531677 1 0.0000 0.94721 1.000 0.000 0.000
#> GSM531678 1 0.0000 0.94721 1.000 0.000 0.000
#> GSM531679 1 0.0000 0.94721 1.000 0.000 0.000
#> GSM531680 3 0.6305 0.00699 0.484 0.000 0.516
#> GSM531681 1 0.0000 0.94721 1.000 0.000 0.000
#> GSM531682 1 0.0000 0.94721 1.000 0.000 0.000
#> GSM531683 1 0.0000 0.94721 1.000 0.000 0.000
#> GSM531684 2 0.2066 0.88985 0.060 0.940 0.000
#> GSM531685 3 0.1753 0.89466 0.048 0.000 0.952
#> GSM531686 1 0.0000 0.94721 1.000 0.000 0.000
#> GSM531687 1 0.6168 0.26128 0.588 0.000 0.412
#> GSM531688 3 0.0424 0.91531 0.008 0.000 0.992
#> GSM531689 1 0.0000 0.94721 1.000 0.000 0.000
#> GSM531690 1 0.0000 0.94721 1.000 0.000 0.000
#> GSM531691 1 0.0592 0.94129 0.988 0.000 0.012
#> GSM531692 3 0.8866 0.49999 0.248 0.180 0.572
#> GSM531693 3 0.1031 0.91425 0.000 0.024 0.976
#> GSM531694 1 0.0000 0.94721 1.000 0.000 0.000
#> GSM531695 3 0.1163 0.90152 0.028 0.000 0.972
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM531600 3 0.0000 0.9501 0.000 0.000 1.000 0.000
#> GSM531601 4 0.4955 0.1508 0.000 0.444 0.000 0.556
#> GSM531605 1 0.0000 0.9013 1.000 0.000 0.000 0.000
#> GSM531615 2 0.0000 0.9556 0.000 1.000 0.000 0.000
#> GSM531617 2 0.0188 0.9525 0.000 0.996 0.000 0.004
#> GSM531624 2 0.0000 0.9556 0.000 1.000 0.000 0.000
#> GSM531627 2 0.0000 0.9556 0.000 1.000 0.000 0.000
#> GSM531629 2 0.3688 0.7146 0.000 0.792 0.000 0.208
#> GSM531631 2 0.0000 0.9556 0.000 1.000 0.000 0.000
#> GSM531634 2 0.0000 0.9556 0.000 1.000 0.000 0.000
#> GSM531636 3 0.3610 0.7122 0.000 0.200 0.800 0.000
#> GSM531637 2 0.0000 0.9556 0.000 1.000 0.000 0.000
#> GSM531654 2 0.0592 0.9418 0.016 0.984 0.000 0.000
#> GSM531655 2 0.5402 0.0474 0.012 0.516 0.000 0.472
#> GSM531658 4 0.0000 0.9010 0.000 0.000 0.000 1.000
#> GSM531660 4 0.4817 0.4013 0.388 0.000 0.000 0.612
#> GSM531602 1 0.0000 0.9013 1.000 0.000 0.000 0.000
#> GSM531603 1 0.0000 0.9013 1.000 0.000 0.000 0.000
#> GSM531604 1 0.0000 0.9013 1.000 0.000 0.000 0.000
#> GSM531606 1 0.0000 0.9013 1.000 0.000 0.000 0.000
#> GSM531607 1 0.0000 0.9013 1.000 0.000 0.000 0.000
#> GSM531608 2 0.0000 0.9556 0.000 1.000 0.000 0.000
#> GSM531609 4 0.0000 0.9010 0.000 0.000 0.000 1.000
#> GSM531610 4 0.0000 0.9010 0.000 0.000 0.000 1.000
#> GSM531611 4 0.0000 0.9010 0.000 0.000 0.000 1.000
#> GSM531612 4 0.0000 0.9010 0.000 0.000 0.000 1.000
#> GSM531613 4 0.0000 0.9010 0.000 0.000 0.000 1.000
#> GSM531614 4 0.0000 0.9010 0.000 0.000 0.000 1.000
#> GSM531616 3 0.0000 0.9501 0.000 0.000 1.000 0.000
#> GSM531618 4 0.0000 0.9010 0.000 0.000 0.000 1.000
#> GSM531619 2 0.0000 0.9556 0.000 1.000 0.000 0.000
#> GSM531620 2 0.0000 0.9556 0.000 1.000 0.000 0.000
#> GSM531621 2 0.0000 0.9556 0.000 1.000 0.000 0.000
#> GSM531622 2 0.0000 0.9556 0.000 1.000 0.000 0.000
#> GSM531623 2 0.0000 0.9556 0.000 1.000 0.000 0.000
#> GSM531625 2 0.0000 0.9556 0.000 1.000 0.000 0.000
#> GSM531626 2 0.0000 0.9556 0.000 1.000 0.000 0.000
#> GSM531628 3 0.0000 0.9501 0.000 0.000 1.000 0.000
#> GSM531630 2 0.0000 0.9556 0.000 1.000 0.000 0.000
#> GSM531632 3 0.0000 0.9501 0.000 0.000 1.000 0.000
#> GSM531633 2 0.0000 0.9556 0.000 1.000 0.000 0.000
#> GSM531635 3 0.0000 0.9501 0.000 0.000 1.000 0.000
#> GSM531638 2 0.3311 0.7787 0.000 0.828 0.172 0.000
#> GSM531639 2 0.3486 0.7620 0.000 0.812 0.188 0.000
#> GSM531640 2 0.0000 0.9556 0.000 1.000 0.000 0.000
#> GSM531641 4 0.0000 0.9010 0.000 0.000 0.000 1.000
#> GSM531642 4 0.2216 0.8329 0.000 0.000 0.092 0.908
#> GSM531643 3 0.0000 0.9501 0.000 0.000 1.000 0.000
#> GSM531644 3 0.2760 0.8164 0.000 0.000 0.872 0.128
#> GSM531645 4 0.0000 0.9010 0.000 0.000 0.000 1.000
#> GSM531646 3 0.0000 0.9501 0.000 0.000 1.000 0.000
#> GSM531647 3 0.0000 0.9501 0.000 0.000 1.000 0.000
#> GSM531648 4 0.0000 0.9010 0.000 0.000 0.000 1.000
#> GSM531649 3 0.0000 0.9501 0.000 0.000 1.000 0.000
#> GSM531650 3 0.0000 0.9501 0.000 0.000 1.000 0.000
#> GSM531651 2 0.0000 0.9556 0.000 1.000 0.000 0.000
#> GSM531652 4 0.0000 0.9010 0.000 0.000 0.000 1.000
#> GSM531653 3 0.0000 0.9501 0.000 0.000 1.000 0.000
#> GSM531656 3 0.0000 0.9501 0.000 0.000 1.000 0.000
#> GSM531657 4 0.0000 0.9010 0.000 0.000 0.000 1.000
#> GSM531659 1 0.4250 0.6542 0.724 0.000 0.000 0.276
#> GSM531661 2 0.0000 0.9556 0.000 1.000 0.000 0.000
#> GSM531662 2 0.0000 0.9556 0.000 1.000 0.000 0.000
#> GSM531663 4 0.2216 0.8193 0.092 0.000 0.000 0.908
#> GSM531664 3 0.0000 0.9501 0.000 0.000 1.000 0.000
#> GSM531665 1 0.4134 0.6713 0.740 0.000 0.260 0.000
#> GSM531666 4 0.3801 0.6806 0.000 0.000 0.220 0.780
#> GSM531667 2 0.0000 0.9556 0.000 1.000 0.000 0.000
#> GSM531668 4 0.4999 0.1076 0.492 0.000 0.000 0.508
#> GSM531669 3 0.0000 0.9501 0.000 0.000 1.000 0.000
#> GSM531670 3 0.0000 0.9501 0.000 0.000 1.000 0.000
#> GSM531671 3 0.6077 -0.0250 0.460 0.044 0.496 0.000
#> GSM531672 4 0.0000 0.9010 0.000 0.000 0.000 1.000
#> GSM531673 1 0.3172 0.7778 0.840 0.160 0.000 0.000
#> GSM531674 3 0.0000 0.9501 0.000 0.000 1.000 0.000
#> GSM531675 1 0.0000 0.9013 1.000 0.000 0.000 0.000
#> GSM531676 1 0.2647 0.8342 0.880 0.000 0.120 0.000
#> GSM531677 1 0.0000 0.9013 1.000 0.000 0.000 0.000
#> GSM531678 1 0.0000 0.9013 1.000 0.000 0.000 0.000
#> GSM531679 1 0.0000 0.9013 1.000 0.000 0.000 0.000
#> GSM531680 1 0.3801 0.7206 0.780 0.000 0.220 0.000
#> GSM531681 1 0.4134 0.6749 0.740 0.000 0.000 0.260
#> GSM531682 1 0.0000 0.9013 1.000 0.000 0.000 0.000
#> GSM531683 1 0.0000 0.9013 1.000 0.000 0.000 0.000
#> GSM531684 1 0.3837 0.7074 0.776 0.224 0.000 0.000
#> GSM531685 3 0.0592 0.9374 0.016 0.000 0.984 0.000
#> GSM531686 1 0.4730 0.4940 0.636 0.000 0.000 0.364
#> GSM531687 1 0.0921 0.8889 0.972 0.000 0.028 0.000
#> GSM531688 3 0.0000 0.9501 0.000 0.000 1.000 0.000
#> GSM531689 1 0.0000 0.9013 1.000 0.000 0.000 0.000
#> GSM531690 1 0.3610 0.7104 0.800 0.000 0.000 0.200
#> GSM531691 1 0.0000 0.9013 1.000 0.000 0.000 0.000
#> GSM531692 1 0.2760 0.8281 0.872 0.000 0.128 0.000
#> GSM531693 3 0.0000 0.9501 0.000 0.000 1.000 0.000
#> GSM531694 1 0.0000 0.9013 1.000 0.000 0.000 0.000
#> GSM531695 3 0.2973 0.8068 0.144 0.000 0.856 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM531600 3 0.0162 0.9277 0.000 0.000 0.996 0.000 0.004
#> GSM531601 5 0.2110 0.7351 0.000 0.072 0.000 0.016 0.912
#> GSM531605 1 0.4045 0.4658 0.644 0.000 0.000 0.000 0.356
#> GSM531615 2 0.2891 0.8154 0.000 0.824 0.000 0.000 0.176
#> GSM531617 2 0.5831 0.4954 0.000 0.580 0.000 0.128 0.292
#> GSM531624 2 0.1270 0.8966 0.000 0.948 0.000 0.000 0.052
#> GSM531627 2 0.0404 0.9011 0.000 0.988 0.000 0.000 0.012
#> GSM531629 5 0.4826 -0.1732 0.000 0.472 0.000 0.020 0.508
#> GSM531631 2 0.1608 0.8969 0.000 0.928 0.000 0.000 0.072
#> GSM531634 2 0.3143 0.7874 0.000 0.796 0.000 0.000 0.204
#> GSM531636 3 0.3615 0.7520 0.000 0.156 0.808 0.000 0.036
#> GSM531637 2 0.1608 0.8962 0.000 0.928 0.000 0.000 0.072
#> GSM531654 2 0.4096 0.7453 0.040 0.760 0.000 0.000 0.200
#> GSM531655 5 0.2104 0.7571 0.024 0.044 0.000 0.008 0.924
#> GSM531658 5 0.3730 0.5555 0.000 0.000 0.000 0.288 0.712
#> GSM531660 5 0.2304 0.7570 0.068 0.004 0.000 0.020 0.908
#> GSM531602 1 0.1270 0.8531 0.948 0.000 0.000 0.000 0.052
#> GSM531603 5 0.2439 0.7326 0.120 0.000 0.000 0.004 0.876
#> GSM531604 1 0.0000 0.8635 1.000 0.000 0.000 0.000 0.000
#> GSM531606 1 0.0000 0.8635 1.000 0.000 0.000 0.000 0.000
#> GSM531607 1 0.1608 0.8424 0.928 0.000 0.000 0.000 0.072
#> GSM531608 2 0.0324 0.9020 0.000 0.992 0.000 0.004 0.004
#> GSM531609 4 0.0000 0.9713 0.000 0.000 0.000 1.000 0.000
#> GSM531610 4 0.0000 0.9713 0.000 0.000 0.000 1.000 0.000
#> GSM531611 4 0.0000 0.9713 0.000 0.000 0.000 1.000 0.000
#> GSM531612 4 0.0000 0.9713 0.000 0.000 0.000 1.000 0.000
#> GSM531613 4 0.0000 0.9713 0.000 0.000 0.000 1.000 0.000
#> GSM531614 4 0.0000 0.9713 0.000 0.000 0.000 1.000 0.000
#> GSM531616 3 0.0992 0.9099 0.000 0.024 0.968 0.000 0.008
#> GSM531618 5 0.2068 0.7411 0.000 0.004 0.000 0.092 0.904
#> GSM531619 2 0.1544 0.8974 0.000 0.932 0.000 0.000 0.068
#> GSM531620 2 0.2516 0.8523 0.000 0.860 0.000 0.000 0.140
#> GSM531621 2 0.0566 0.9001 0.000 0.984 0.004 0.000 0.012
#> GSM531622 2 0.0609 0.9030 0.000 0.980 0.000 0.000 0.020
#> GSM531623 2 0.0000 0.9020 0.000 1.000 0.000 0.000 0.000
#> GSM531625 2 0.1012 0.8947 0.000 0.968 0.020 0.000 0.012
#> GSM531626 2 0.2136 0.8450 0.000 0.904 0.088 0.000 0.008
#> GSM531628 3 0.0404 0.9254 0.000 0.000 0.988 0.000 0.012
#> GSM531630 2 0.0609 0.8999 0.000 0.980 0.000 0.000 0.020
#> GSM531632 3 0.0162 0.9268 0.000 0.000 0.996 0.000 0.004
#> GSM531633 2 0.0162 0.9018 0.000 0.996 0.000 0.000 0.004
#> GSM531635 3 0.0162 0.9272 0.000 0.004 0.996 0.000 0.000
#> GSM531638 2 0.3861 0.5815 0.000 0.712 0.284 0.000 0.004
#> GSM531639 2 0.3106 0.7790 0.000 0.840 0.140 0.000 0.020
#> GSM531640 2 0.1493 0.8953 0.000 0.948 0.000 0.028 0.024
#> GSM531641 4 0.0000 0.9713 0.000 0.000 0.000 1.000 0.000
#> GSM531642 5 0.5971 0.2929 0.000 0.000 0.396 0.112 0.492
#> GSM531643 3 0.0404 0.9254 0.000 0.000 0.988 0.000 0.012
#> GSM531644 3 0.4276 0.2825 0.000 0.000 0.616 0.004 0.380
#> GSM531645 4 0.0000 0.9713 0.000 0.000 0.000 1.000 0.000
#> GSM531646 3 0.0000 0.9278 0.000 0.000 1.000 0.000 0.000
#> GSM531647 3 0.0162 0.9277 0.000 0.000 0.996 0.000 0.004
#> GSM531648 5 0.2690 0.7139 0.000 0.000 0.000 0.156 0.844
#> GSM531649 3 0.0324 0.9259 0.000 0.004 0.992 0.000 0.004
#> GSM531650 3 0.0510 0.9235 0.000 0.000 0.984 0.000 0.016
#> GSM531651 2 0.0000 0.9020 0.000 1.000 0.000 0.000 0.000
#> GSM531652 5 0.3336 0.7405 0.000 0.000 0.096 0.060 0.844
#> GSM531653 3 0.0162 0.9277 0.000 0.000 0.996 0.000 0.004
#> GSM531656 3 0.2390 0.8549 0.000 0.084 0.896 0.000 0.020
#> GSM531657 4 0.2230 0.8517 0.000 0.000 0.000 0.884 0.116
#> GSM531659 1 0.4430 0.4504 0.628 0.000 0.000 0.360 0.012
#> GSM531661 2 0.1043 0.9002 0.000 0.960 0.000 0.000 0.040
#> GSM531662 2 0.0579 0.8992 0.008 0.984 0.000 0.000 0.008
#> GSM531663 4 0.0404 0.9625 0.012 0.000 0.000 0.988 0.000
#> GSM531664 3 0.1043 0.9076 0.000 0.000 0.960 0.000 0.040
#> GSM531665 1 0.3424 0.6724 0.760 0.000 0.240 0.000 0.000
#> GSM531666 5 0.2669 0.7452 0.000 0.000 0.104 0.020 0.876
#> GSM531667 2 0.1965 0.8801 0.000 0.904 0.000 0.000 0.096
#> GSM531668 5 0.2238 0.7581 0.064 0.004 0.000 0.020 0.912
#> GSM531669 3 0.0000 0.9278 0.000 0.000 1.000 0.000 0.000
#> GSM531670 3 0.0898 0.9182 0.000 0.020 0.972 0.000 0.008
#> GSM531671 3 0.4774 0.5693 0.264 0.044 0.688 0.000 0.004
#> GSM531672 5 0.3655 0.7002 0.036 0.000 0.000 0.160 0.804
#> GSM531673 1 0.3421 0.7209 0.788 0.204 0.000 0.000 0.008
#> GSM531674 3 0.0162 0.9277 0.000 0.000 0.996 0.000 0.004
#> GSM531675 1 0.1197 0.8548 0.952 0.000 0.000 0.000 0.048
#> GSM531676 1 0.1608 0.8379 0.928 0.000 0.072 0.000 0.000
#> GSM531677 1 0.0000 0.8635 1.000 0.000 0.000 0.000 0.000
#> GSM531678 1 0.2299 0.8386 0.912 0.032 0.000 0.052 0.004
#> GSM531679 1 0.0000 0.8635 1.000 0.000 0.000 0.000 0.000
#> GSM531680 1 0.4571 0.6860 0.736 0.000 0.188 0.000 0.076
#> GSM531681 4 0.1671 0.9036 0.076 0.000 0.000 0.924 0.000
#> GSM531682 1 0.0000 0.8635 1.000 0.000 0.000 0.000 0.000
#> GSM531683 1 0.0510 0.8618 0.984 0.000 0.000 0.000 0.016
#> GSM531684 1 0.4425 0.4207 0.600 0.392 0.000 0.000 0.008
#> GSM531685 3 0.3465 0.7972 0.104 0.052 0.840 0.000 0.004
#> GSM531686 4 0.1341 0.9258 0.056 0.000 0.000 0.944 0.000
#> GSM531687 1 0.2046 0.8414 0.916 0.000 0.068 0.000 0.016
#> GSM531688 3 0.0000 0.9278 0.000 0.000 1.000 0.000 0.000
#> GSM531689 1 0.0000 0.8635 1.000 0.000 0.000 0.000 0.000
#> GSM531690 1 0.4766 0.6352 0.708 0.000 0.000 0.072 0.220
#> GSM531691 1 0.1282 0.8509 0.952 0.044 0.000 0.000 0.004
#> GSM531692 1 0.2536 0.8338 0.900 0.044 0.052 0.000 0.004
#> GSM531693 3 0.0000 0.9278 0.000 0.000 1.000 0.000 0.000
#> GSM531694 1 0.1197 0.8539 0.952 0.000 0.000 0.000 0.048
#> GSM531695 5 0.5115 0.0517 0.036 0.000 0.480 0.000 0.484
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM531600 3 0.2505 0.8081 0.000 0.008 0.888 0.000 0.040 0.064
#> GSM531601 5 0.5155 0.5542 0.000 0.132 0.000 0.020 0.668 0.180
#> GSM531605 1 0.5147 0.3966 0.568 0.000 0.000 0.000 0.328 0.104
#> GSM531615 6 0.3938 0.4623 0.000 0.228 0.000 0.000 0.044 0.728
#> GSM531617 6 0.4098 0.5747 0.000 0.040 0.000 0.028 0.168 0.764
#> GSM531624 2 0.3563 0.5606 0.000 0.664 0.000 0.000 0.000 0.336
#> GSM531627 2 0.1444 0.7129 0.000 0.928 0.000 0.000 0.000 0.072
#> GSM531629 6 0.2762 0.5456 0.000 0.000 0.000 0.000 0.196 0.804
#> GSM531631 2 0.0363 0.7254 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM531634 6 0.4396 0.4863 0.000 0.208 0.000 0.000 0.088 0.704
#> GSM531636 3 0.5826 0.5597 0.000 0.104 0.628 0.000 0.188 0.080
#> GSM531637 2 0.0632 0.7276 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM531654 6 0.3414 0.5808 0.028 0.032 0.000 0.000 0.112 0.828
#> GSM531655 5 0.4307 0.5974 0.000 0.080 0.012 0.000 0.744 0.164
#> GSM531658 5 0.2664 0.6557 0.000 0.000 0.000 0.136 0.848 0.016
#> GSM531660 6 0.4388 0.0873 0.028 0.000 0.000 0.000 0.400 0.572
#> GSM531602 1 0.3928 0.7358 0.760 0.000 0.000 0.000 0.080 0.160
#> GSM531603 5 0.4842 0.3879 0.076 0.000 0.000 0.000 0.600 0.324
#> GSM531604 1 0.0363 0.8121 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM531606 1 0.1588 0.8114 0.924 0.000 0.000 0.000 0.004 0.072
#> GSM531607 1 0.3481 0.7670 0.804 0.000 0.000 0.000 0.072 0.124
#> GSM531608 2 0.5383 0.2828 0.000 0.472 0.000 0.112 0.000 0.416
#> GSM531609 4 0.0000 0.9646 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531610 4 0.0000 0.9646 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531611 4 0.0000 0.9646 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531612 4 0.0000 0.9646 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531613 4 0.0000 0.9646 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531614 4 0.0000 0.9646 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531616 3 0.1888 0.8286 0.000 0.012 0.916 0.000 0.004 0.068
#> GSM531618 5 0.4605 0.3429 0.000 0.004 0.000 0.040 0.600 0.356
#> GSM531619 2 0.0458 0.7263 0.000 0.984 0.000 0.000 0.000 0.016
#> GSM531620 6 0.3582 0.5114 0.000 0.096 0.064 0.000 0.020 0.820
#> GSM531621 2 0.2697 0.7088 0.000 0.812 0.000 0.000 0.000 0.188
#> GSM531622 2 0.1387 0.7320 0.000 0.932 0.000 0.000 0.000 0.068
#> GSM531623 2 0.3266 0.6502 0.000 0.728 0.000 0.000 0.000 0.272
#> GSM531625 2 0.3874 0.6199 0.000 0.760 0.068 0.000 0.000 0.172
#> GSM531626 3 0.5728 0.0921 0.000 0.168 0.452 0.000 0.000 0.380
#> GSM531628 3 0.1267 0.8121 0.000 0.000 0.940 0.000 0.060 0.000
#> GSM531630 2 0.0000 0.7252 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531632 3 0.1387 0.8293 0.000 0.000 0.932 0.000 0.000 0.068
#> GSM531633 2 0.3672 0.5792 0.000 0.632 0.000 0.000 0.000 0.368
#> GSM531635 3 0.1327 0.8324 0.000 0.000 0.936 0.000 0.000 0.064
#> GSM531638 2 0.3468 0.5462 0.000 0.728 0.264 0.000 0.000 0.008
#> GSM531639 2 0.5865 0.2332 0.000 0.516 0.360 0.000 0.048 0.076
#> GSM531640 2 0.0777 0.7208 0.000 0.972 0.000 0.024 0.000 0.004
#> GSM531641 4 0.0000 0.9646 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531642 5 0.3763 0.6555 0.000 0.000 0.172 0.060 0.768 0.000
#> GSM531643 3 0.0790 0.8243 0.000 0.000 0.968 0.000 0.032 0.000
#> GSM531644 5 0.3288 0.5747 0.000 0.000 0.276 0.000 0.724 0.000
#> GSM531645 4 0.0000 0.9646 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531646 3 0.1387 0.8293 0.000 0.000 0.932 0.000 0.000 0.068
#> GSM531647 3 0.1141 0.8325 0.000 0.000 0.948 0.000 0.000 0.052
#> GSM531648 5 0.3055 0.6529 0.000 0.000 0.000 0.096 0.840 0.064
#> GSM531649 3 0.2135 0.8001 0.000 0.000 0.872 0.000 0.000 0.128
#> GSM531650 3 0.1219 0.8212 0.000 0.000 0.948 0.000 0.048 0.004
#> GSM531651 2 0.2912 0.6886 0.000 0.784 0.000 0.000 0.000 0.216
#> GSM531652 5 0.3679 0.6899 0.000 0.000 0.088 0.040 0.820 0.052
#> GSM531653 3 0.1267 0.8276 0.000 0.000 0.940 0.000 0.000 0.060
#> GSM531656 3 0.4806 0.5931 0.000 0.148 0.684 0.000 0.164 0.004
#> GSM531657 4 0.3509 0.6475 0.000 0.000 0.000 0.744 0.240 0.016
#> GSM531659 1 0.5067 0.5779 0.640 0.000 0.000 0.128 0.228 0.004
#> GSM531661 2 0.3804 0.4078 0.000 0.576 0.000 0.000 0.000 0.424
#> GSM531662 6 0.5071 -0.2194 0.080 0.400 0.000 0.000 0.000 0.520
#> GSM531663 4 0.0000 0.9646 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531664 3 0.2454 0.7332 0.000 0.000 0.840 0.000 0.160 0.000
#> GSM531665 1 0.3315 0.7576 0.820 0.000 0.104 0.000 0.076 0.000
#> GSM531666 5 0.2513 0.6743 0.000 0.000 0.140 0.000 0.852 0.008
#> GSM531667 2 0.4947 0.5603 0.000 0.636 0.000 0.000 0.120 0.244
#> GSM531668 6 0.3725 0.3271 0.008 0.000 0.000 0.000 0.316 0.676
#> GSM531669 3 0.1471 0.8306 0.000 0.000 0.932 0.000 0.004 0.064
#> GSM531670 3 0.5419 0.4154 0.000 0.296 0.568 0.000 0.132 0.004
#> GSM531671 6 0.4970 0.1309 0.060 0.000 0.396 0.000 0.004 0.540
#> GSM531672 5 0.3350 0.6663 0.036 0.000 0.000 0.064 0.844 0.056
#> GSM531673 6 0.5271 0.0558 0.408 0.068 0.012 0.000 0.000 0.512
#> GSM531674 3 0.0865 0.8228 0.000 0.000 0.964 0.000 0.036 0.000
#> GSM531675 1 0.2513 0.7971 0.852 0.000 0.000 0.000 0.140 0.008
#> GSM531676 1 0.1806 0.7934 0.908 0.000 0.088 0.000 0.000 0.004
#> GSM531677 1 0.1007 0.8117 0.956 0.000 0.000 0.000 0.044 0.000
#> GSM531678 1 0.2048 0.7761 0.880 0.000 0.000 0.120 0.000 0.000
#> GSM531679 1 0.0547 0.8132 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM531680 1 0.6092 0.4940 0.572 0.000 0.220 0.000 0.160 0.048
#> GSM531681 4 0.1141 0.9181 0.052 0.000 0.000 0.948 0.000 0.000
#> GSM531682 1 0.1152 0.8120 0.952 0.000 0.000 0.000 0.004 0.044
#> GSM531683 1 0.2579 0.7978 0.872 0.000 0.000 0.000 0.040 0.088
#> GSM531684 1 0.4099 0.4544 0.612 0.372 0.000 0.000 0.000 0.016
#> GSM531685 3 0.4920 0.5165 0.296 0.012 0.628 0.000 0.000 0.064
#> GSM531686 4 0.0865 0.9342 0.036 0.000 0.000 0.964 0.000 0.000
#> GSM531687 1 0.4749 0.6792 0.716 0.000 0.144 0.000 0.120 0.020
#> GSM531688 3 0.0603 0.8280 0.004 0.000 0.980 0.000 0.016 0.000
#> GSM531689 1 0.0291 0.8121 0.992 0.000 0.000 0.004 0.000 0.004
#> GSM531690 1 0.4821 0.5761 0.616 0.000 0.000 0.024 0.328 0.032
#> GSM531691 1 0.2030 0.7967 0.908 0.028 0.000 0.000 0.000 0.064
#> GSM531692 1 0.2126 0.7983 0.904 0.004 0.072 0.000 0.000 0.020
#> GSM531693 3 0.1204 0.8322 0.000 0.000 0.944 0.000 0.000 0.056
#> GSM531694 1 0.2997 0.7858 0.844 0.000 0.000 0.000 0.060 0.096
#> GSM531695 5 0.5278 0.2729 0.040 0.000 0.412 0.000 0.516 0.032
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> CV:NMF 93 0.168450 2
#> CV:NMF 87 0.002865 3
#> CV:NMF 90 0.006466 4
#> CV:NMF 88 0.007205 5
#> CV:NMF 78 0.000966 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "hclust"]
# you can also extract it by
# res = res_list["MAD:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 96 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 0.341 0.742 0.864 0.4826 0.526 0.526
#> 3 3 0.375 0.661 0.807 0.3340 0.790 0.606
#> 4 4 0.500 0.551 0.752 0.1469 0.835 0.554
#> 5 5 0.588 0.528 0.687 0.0613 0.907 0.656
#> 6 6 0.664 0.545 0.722 0.0457 0.944 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
#> GSM531600 2 0.2603 0.817 0.044 0.956
#> GSM531601 2 0.9954 0.196 0.460 0.540
#> GSM531605 1 0.5629 0.837 0.868 0.132
#> GSM531615 2 0.7219 0.737 0.200 0.800
#> GSM531617 2 0.7299 0.735 0.204 0.796
#> GSM531624 2 0.3733 0.812 0.072 0.928
#> GSM531627 2 0.0672 0.815 0.008 0.992
#> GSM531629 2 0.7299 0.735 0.204 0.796
#> GSM531631 2 0.3733 0.812 0.072 0.928
#> GSM531634 2 0.7299 0.735 0.204 0.796
#> GSM531636 2 0.2603 0.817 0.044 0.956
#> GSM531637 2 0.3733 0.812 0.072 0.928
#> GSM531654 2 0.8763 0.624 0.296 0.704
#> GSM531655 1 0.5629 0.837 0.868 0.132
#> GSM531658 1 0.2236 0.893 0.964 0.036
#> GSM531660 1 0.4161 0.874 0.916 0.084
#> GSM531602 1 0.1843 0.884 0.972 0.028
#> GSM531603 1 0.3114 0.893 0.944 0.056
#> GSM531604 1 0.9754 0.225 0.592 0.408
#> GSM531606 1 0.4690 0.864 0.900 0.100
#> GSM531607 1 0.2948 0.893 0.948 0.052
#> GSM531608 2 0.7219 0.738 0.200 0.800
#> GSM531609 1 0.1633 0.894 0.976 0.024
#> GSM531610 1 0.1633 0.894 0.976 0.024
#> GSM531611 1 0.1843 0.895 0.972 0.028
#> GSM531612 1 0.1843 0.895 0.972 0.028
#> GSM531613 1 0.1633 0.894 0.976 0.024
#> GSM531614 1 0.1633 0.894 0.976 0.024
#> GSM531616 2 0.1414 0.817 0.020 0.980
#> GSM531618 1 0.6247 0.816 0.844 0.156
#> GSM531619 2 0.3733 0.812 0.072 0.928
#> GSM531620 2 0.1414 0.820 0.020 0.980
#> GSM531621 2 0.0672 0.815 0.008 0.992
#> GSM531622 2 0.3733 0.812 0.072 0.928
#> GSM531623 2 0.3733 0.812 0.072 0.928
#> GSM531625 2 0.0672 0.815 0.008 0.992
#> GSM531626 2 0.0672 0.815 0.008 0.992
#> GSM531628 2 0.3114 0.817 0.056 0.944
#> GSM531630 2 0.3733 0.812 0.072 0.928
#> GSM531632 2 0.1843 0.816 0.028 0.972
#> GSM531633 2 0.0672 0.815 0.008 0.992
#> GSM531635 2 0.1414 0.817 0.020 0.980
#> GSM531638 2 0.1414 0.817 0.020 0.980
#> GSM531639 2 0.2778 0.817 0.048 0.952
#> GSM531640 2 0.3879 0.812 0.076 0.924
#> GSM531641 1 0.1843 0.895 0.972 0.028
#> GSM531642 2 0.9970 0.161 0.468 0.532
#> GSM531643 2 0.4815 0.805 0.104 0.896
#> GSM531644 2 0.9988 0.116 0.480 0.520
#> GSM531645 1 0.1843 0.895 0.972 0.028
#> GSM531646 2 0.1843 0.816 0.028 0.972
#> GSM531647 2 0.1843 0.816 0.028 0.972
#> GSM531648 1 0.8955 0.540 0.688 0.312
#> GSM531649 2 0.1843 0.816 0.028 0.972
#> GSM531650 2 0.3114 0.817 0.056 0.944
#> GSM531651 2 0.1843 0.818 0.028 0.972
#> GSM531652 1 0.8955 0.540 0.688 0.312
#> GSM531653 2 0.1843 0.816 0.028 0.972
#> GSM531656 2 0.2778 0.817 0.048 0.952
#> GSM531657 1 0.3431 0.889 0.936 0.064
#> GSM531659 1 0.8267 0.653 0.740 0.260
#> GSM531661 2 0.7528 0.723 0.216 0.784
#> GSM531662 2 0.8555 0.641 0.280 0.720
#> GSM531663 1 0.6973 0.767 0.812 0.188
#> GSM531664 2 0.3114 0.817 0.056 0.944
#> GSM531665 2 0.9866 0.326 0.432 0.568
#> GSM531666 2 0.9686 0.423 0.396 0.604
#> GSM531667 2 0.7139 0.742 0.196 0.804
#> GSM531668 1 0.7602 0.722 0.780 0.220
#> GSM531669 2 0.3274 0.818 0.060 0.940
#> GSM531670 2 0.2778 0.817 0.048 0.952
#> GSM531671 2 0.8386 0.658 0.268 0.732
#> GSM531672 1 0.2236 0.893 0.964 0.036
#> GSM531673 2 0.8555 0.641 0.280 0.720
#> GSM531674 2 0.3274 0.818 0.060 0.940
#> GSM531675 1 0.1414 0.886 0.980 0.020
#> GSM531676 2 0.9954 0.245 0.460 0.540
#> GSM531677 1 0.4562 0.872 0.904 0.096
#> GSM531678 1 0.4939 0.858 0.892 0.108
#> GSM531679 1 0.4562 0.872 0.904 0.096
#> GSM531680 2 0.9635 0.453 0.388 0.612
#> GSM531681 1 0.0938 0.890 0.988 0.012
#> GSM531682 1 0.2948 0.888 0.948 0.052
#> GSM531683 1 0.1843 0.884 0.972 0.028
#> GSM531684 1 0.5178 0.851 0.884 0.116
#> GSM531685 2 0.6623 0.759 0.172 0.828
#> GSM531686 1 0.1184 0.892 0.984 0.016
#> GSM531687 2 0.9954 0.245 0.460 0.540
#> GSM531688 2 0.4690 0.805 0.100 0.900
#> GSM531689 2 0.9954 0.245 0.460 0.540
#> GSM531690 1 0.1633 0.885 0.976 0.024
#> GSM531691 2 0.9996 0.158 0.488 0.512
#> GSM531692 2 0.8861 0.607 0.304 0.696
#> GSM531693 2 0.5946 0.776 0.144 0.856
#> GSM531694 1 0.1843 0.884 0.972 0.028
#> GSM531695 2 0.9635 0.453 0.388 0.612
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM531600 3 0.2998 0.71361 0.016 0.068 0.916
#> GSM531601 2 0.7996 0.10867 0.380 0.552 0.068
#> GSM531605 1 0.6031 0.78898 0.788 0.116 0.096
#> GSM531615 2 0.4209 0.75733 0.128 0.856 0.016
#> GSM531617 2 0.4277 0.75563 0.132 0.852 0.016
#> GSM531624 2 0.0424 0.79220 0.000 0.992 0.008
#> GSM531627 2 0.3752 0.73538 0.000 0.856 0.144
#> GSM531629 2 0.4277 0.75563 0.132 0.852 0.016
#> GSM531631 2 0.0424 0.79220 0.000 0.992 0.008
#> GSM531634 2 0.4277 0.75563 0.132 0.852 0.016
#> GSM531636 3 0.2998 0.71361 0.016 0.068 0.916
#> GSM531637 2 0.0424 0.79220 0.000 0.992 0.008
#> GSM531654 2 0.6964 0.62957 0.264 0.684 0.052
#> GSM531655 1 0.6031 0.78898 0.788 0.116 0.096
#> GSM531658 1 0.2651 0.83812 0.928 0.012 0.060
#> GSM531660 1 0.5212 0.81933 0.828 0.108 0.064
#> GSM531602 1 0.3896 0.81608 0.888 0.060 0.052
#> GSM531603 1 0.4146 0.83569 0.876 0.044 0.080
#> GSM531604 1 0.8930 0.24002 0.536 0.148 0.316
#> GSM531606 1 0.5343 0.77474 0.816 0.132 0.052
#> GSM531607 1 0.4357 0.83284 0.868 0.052 0.080
#> GSM531608 2 0.5276 0.74701 0.128 0.820 0.052
#> GSM531609 1 0.2280 0.84120 0.940 0.008 0.052
#> GSM531610 1 0.2280 0.84120 0.940 0.008 0.052
#> GSM531611 1 0.2384 0.84112 0.936 0.008 0.056
#> GSM531612 1 0.2384 0.84044 0.936 0.008 0.056
#> GSM531613 1 0.2280 0.84120 0.940 0.008 0.052
#> GSM531614 1 0.2280 0.84120 0.940 0.008 0.052
#> GSM531616 3 0.6359 0.33503 0.004 0.404 0.592
#> GSM531618 1 0.5944 0.78138 0.792 0.120 0.088
#> GSM531619 2 0.0424 0.79220 0.000 0.992 0.008
#> GSM531620 2 0.4679 0.73921 0.020 0.832 0.148
#> GSM531621 2 0.3752 0.73538 0.000 0.856 0.144
#> GSM531622 2 0.0592 0.79184 0.000 0.988 0.012
#> GSM531623 2 0.0424 0.79220 0.000 0.992 0.008
#> GSM531625 2 0.3752 0.73538 0.000 0.856 0.144
#> GSM531626 2 0.3752 0.73538 0.000 0.856 0.144
#> GSM531628 3 0.3009 0.71766 0.028 0.052 0.920
#> GSM531630 2 0.0424 0.79220 0.000 0.992 0.008
#> GSM531632 3 0.2448 0.70665 0.000 0.076 0.924
#> GSM531633 2 0.3752 0.73538 0.000 0.856 0.144
#> GSM531635 3 0.6359 0.33503 0.004 0.404 0.592
#> GSM531638 3 0.6359 0.33503 0.004 0.404 0.592
#> GSM531639 3 0.5356 0.64121 0.020 0.196 0.784
#> GSM531640 2 0.0661 0.79265 0.004 0.988 0.008
#> GSM531641 1 0.2384 0.84044 0.936 0.008 0.056
#> GSM531642 3 0.8842 0.09179 0.432 0.116 0.452
#> GSM531643 3 0.4469 0.70521 0.076 0.060 0.864
#> GSM531644 3 0.8337 0.07068 0.444 0.080 0.476
#> GSM531645 1 0.2384 0.84044 0.936 0.008 0.056
#> GSM531646 3 0.3116 0.69991 0.000 0.108 0.892
#> GSM531647 3 0.1860 0.71106 0.000 0.052 0.948
#> GSM531648 1 0.8265 0.59153 0.636 0.180 0.184
#> GSM531649 3 0.1860 0.71106 0.000 0.052 0.948
#> GSM531650 3 0.3009 0.71766 0.028 0.052 0.920
#> GSM531651 2 0.2066 0.77812 0.000 0.940 0.060
#> GSM531652 1 0.8265 0.59153 0.636 0.180 0.184
#> GSM531653 3 0.1860 0.71106 0.000 0.052 0.948
#> GSM531656 3 0.5036 0.66235 0.020 0.172 0.808
#> GSM531657 1 0.3461 0.83472 0.900 0.024 0.076
#> GSM531659 1 0.6920 0.67824 0.732 0.164 0.104
#> GSM531661 2 0.5598 0.73214 0.148 0.800 0.052
#> GSM531662 2 0.9536 0.34758 0.232 0.484 0.284
#> GSM531663 1 0.5631 0.74107 0.792 0.164 0.044
#> GSM531664 3 0.3009 0.71766 0.028 0.052 0.920
#> GSM531665 1 0.9867 -0.00576 0.412 0.312 0.276
#> GSM531666 3 0.7534 0.37212 0.368 0.048 0.584
#> GSM531667 2 0.5276 0.74576 0.128 0.820 0.052
#> GSM531668 1 0.6746 0.71532 0.732 0.192 0.076
#> GSM531669 3 0.3310 0.71596 0.028 0.064 0.908
#> GSM531670 3 0.5036 0.66235 0.020 0.172 0.808
#> GSM531671 2 0.9641 0.27702 0.224 0.452 0.324
#> GSM531672 1 0.2651 0.83812 0.928 0.012 0.060
#> GSM531673 2 0.9536 0.34758 0.232 0.484 0.284
#> GSM531674 3 0.3310 0.71596 0.028 0.064 0.908
#> GSM531675 1 0.1860 0.82874 0.948 0.000 0.052
#> GSM531676 3 0.8507 0.20495 0.424 0.092 0.484
#> GSM531677 1 0.3412 0.79773 0.876 0.000 0.124
#> GSM531678 1 0.5536 0.76486 0.804 0.144 0.052
#> GSM531679 1 0.3412 0.79773 0.876 0.000 0.124
#> GSM531680 3 0.5859 0.46586 0.344 0.000 0.656
#> GSM531681 1 0.2280 0.84275 0.940 0.008 0.052
#> GSM531682 1 0.2448 0.82338 0.924 0.000 0.076
#> GSM531683 1 0.3692 0.81909 0.896 0.056 0.048
#> GSM531684 1 0.5659 0.75724 0.796 0.152 0.052
#> GSM531685 3 0.5848 0.65332 0.124 0.080 0.796
#> GSM531686 1 0.2384 0.84277 0.936 0.008 0.056
#> GSM531687 3 0.8507 0.20495 0.424 0.092 0.484
#> GSM531688 3 0.3375 0.70553 0.048 0.044 0.908
#> GSM531689 3 0.8507 0.20495 0.424 0.092 0.484
#> GSM531690 1 0.1753 0.82706 0.952 0.000 0.048
#> GSM531691 3 0.8524 0.12363 0.452 0.092 0.456
#> GSM531692 3 0.9347 0.34402 0.276 0.212 0.512
#> GSM531693 3 0.4335 0.68070 0.100 0.036 0.864
#> GSM531694 1 0.3896 0.81608 0.888 0.060 0.052
#> GSM531695 3 0.5859 0.46586 0.344 0.000 0.656
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM531600 3 0.1411 0.7792 0.000 0.020 0.960 0.020
#> GSM531601 2 0.7415 0.1759 0.076 0.532 0.040 0.352
#> GSM531605 4 0.6982 0.0879 0.448 0.052 0.028 0.472
#> GSM531615 2 0.3731 0.7789 0.036 0.844 0.000 0.120
#> GSM531617 2 0.3787 0.7764 0.036 0.840 0.000 0.124
#> GSM531624 2 0.0000 0.8385 0.000 1.000 0.000 0.000
#> GSM531627 2 0.4144 0.7844 0.068 0.828 0.104 0.000
#> GSM531629 2 0.3787 0.7764 0.036 0.840 0.000 0.124
#> GSM531631 2 0.0188 0.8382 0.004 0.996 0.000 0.000
#> GSM531634 2 0.3787 0.7764 0.036 0.840 0.000 0.124
#> GSM531636 3 0.1411 0.7792 0.000 0.020 0.960 0.020
#> GSM531637 2 0.0000 0.8385 0.000 1.000 0.000 0.000
#> GSM531654 2 0.7121 0.5669 0.236 0.620 0.028 0.116
#> GSM531655 4 0.6982 0.0879 0.448 0.052 0.028 0.472
#> GSM531658 4 0.0524 0.6779 0.000 0.004 0.008 0.988
#> GSM531660 4 0.5047 0.5991 0.136 0.076 0.008 0.780
#> GSM531602 1 0.5085 0.4328 0.708 0.032 0.000 0.260
#> GSM531603 1 0.6144 0.2307 0.576 0.028 0.016 0.380
#> GSM531604 1 0.5716 0.4847 0.740 0.052 0.176 0.032
#> GSM531606 1 0.4244 0.4911 0.800 0.032 0.000 0.168
#> GSM531607 1 0.6149 0.2841 0.596 0.032 0.016 0.356
#> GSM531608 2 0.5329 0.7190 0.188 0.752 0.028 0.032
#> GSM531609 4 0.1867 0.6844 0.072 0.000 0.000 0.928
#> GSM531610 4 0.1867 0.6844 0.072 0.000 0.000 0.928
#> GSM531611 4 0.2125 0.6829 0.076 0.000 0.004 0.920
#> GSM531612 4 0.1902 0.6864 0.064 0.000 0.004 0.932
#> GSM531613 4 0.1867 0.6844 0.072 0.000 0.000 0.928
#> GSM531614 4 0.1867 0.6844 0.072 0.000 0.000 0.928
#> GSM531616 3 0.6360 0.3559 0.060 0.372 0.564 0.004
#> GSM531618 4 0.5213 0.6122 0.060 0.108 0.040 0.792
#> GSM531619 2 0.0000 0.8385 0.000 1.000 0.000 0.000
#> GSM531620 2 0.4967 0.7860 0.068 0.804 0.100 0.028
#> GSM531621 2 0.4144 0.7844 0.068 0.828 0.104 0.000
#> GSM531622 2 0.0469 0.8381 0.012 0.988 0.000 0.000
#> GSM531623 2 0.0000 0.8385 0.000 1.000 0.000 0.000
#> GSM531625 2 0.4144 0.7844 0.068 0.828 0.104 0.000
#> GSM531626 2 0.4144 0.7844 0.068 0.828 0.104 0.000
#> GSM531628 3 0.1109 0.7763 0.004 0.000 0.968 0.028
#> GSM531630 2 0.0188 0.8382 0.004 0.996 0.000 0.000
#> GSM531632 3 0.1022 0.7747 0.032 0.000 0.968 0.000
#> GSM531633 2 0.4144 0.7844 0.068 0.828 0.104 0.000
#> GSM531635 3 0.6360 0.3559 0.060 0.372 0.564 0.004
#> GSM531638 3 0.6360 0.3559 0.060 0.372 0.564 0.004
#> GSM531639 3 0.4883 0.7082 0.048 0.128 0.800 0.024
#> GSM531640 2 0.0376 0.8380 0.004 0.992 0.000 0.004
#> GSM531641 4 0.1902 0.6864 0.064 0.000 0.004 0.932
#> GSM531642 4 0.7868 0.0534 0.052 0.088 0.396 0.464
#> GSM531643 3 0.2311 0.7494 0.004 0.004 0.916 0.076
#> GSM531644 4 0.7100 0.0530 0.028 0.060 0.436 0.476
#> GSM531645 4 0.1902 0.6864 0.064 0.000 0.004 0.932
#> GSM531646 3 0.2124 0.7741 0.028 0.040 0.932 0.000
#> GSM531647 3 0.0000 0.7782 0.000 0.000 1.000 0.000
#> GSM531648 4 0.7711 0.4899 0.084 0.160 0.136 0.620
#> GSM531649 3 0.0000 0.7782 0.000 0.000 1.000 0.000
#> GSM531650 3 0.1109 0.7763 0.004 0.000 0.968 0.028
#> GSM531651 2 0.1936 0.8285 0.032 0.940 0.028 0.000
#> GSM531652 4 0.7711 0.4899 0.084 0.160 0.136 0.620
#> GSM531653 3 0.0000 0.7782 0.000 0.000 1.000 0.000
#> GSM531656 3 0.4546 0.7249 0.048 0.104 0.824 0.024
#> GSM531657 4 0.4020 0.6310 0.128 0.016 0.020 0.836
#> GSM531659 4 0.8241 0.1586 0.296 0.156 0.048 0.500
#> GSM531661 2 0.5568 0.6847 0.224 0.720 0.028 0.028
#> GSM531662 1 0.8309 -0.0276 0.412 0.376 0.180 0.032
#> GSM531663 4 0.5361 0.5356 0.108 0.148 0.000 0.744
#> GSM531664 3 0.1109 0.7763 0.004 0.000 0.968 0.028
#> GSM531665 1 0.8900 0.3758 0.480 0.240 0.180 0.100
#> GSM531666 3 0.6326 0.2433 0.028 0.024 0.572 0.376
#> GSM531667 2 0.5289 0.7220 0.184 0.756 0.028 0.032
#> GSM531668 4 0.6313 0.5437 0.100 0.176 0.024 0.700
#> GSM531669 3 0.2469 0.7439 0.108 0.000 0.892 0.000
#> GSM531670 3 0.4546 0.7249 0.048 0.104 0.824 0.024
#> GSM531671 1 0.8516 0.0303 0.400 0.340 0.228 0.032
#> GSM531672 4 0.0524 0.6779 0.000 0.004 0.008 0.988
#> GSM531673 1 0.8309 -0.0276 0.412 0.376 0.180 0.032
#> GSM531674 3 0.2469 0.7439 0.108 0.000 0.892 0.000
#> GSM531675 4 0.4985 -0.0575 0.468 0.000 0.000 0.532
#> GSM531676 1 0.5830 0.3138 0.620 0.000 0.332 0.048
#> GSM531677 1 0.5866 0.3882 0.624 0.000 0.052 0.324
#> GSM531678 1 0.4290 0.4947 0.800 0.036 0.000 0.164
#> GSM531679 1 0.5866 0.3882 0.624 0.000 0.052 0.324
#> GSM531680 3 0.6688 0.2191 0.368 0.000 0.536 0.096
#> GSM531681 4 0.3942 0.5342 0.236 0.000 0.000 0.764
#> GSM531682 1 0.5400 0.3226 0.608 0.000 0.020 0.372
#> GSM531683 1 0.5344 0.3992 0.668 0.032 0.000 0.300
#> GSM531684 1 0.4100 0.4965 0.816 0.036 0.000 0.148
#> GSM531685 3 0.4679 0.5113 0.352 0.000 0.648 0.000
#> GSM531686 4 0.4155 0.5281 0.240 0.000 0.004 0.756
#> GSM531687 1 0.5830 0.3138 0.620 0.000 0.332 0.048
#> GSM531688 3 0.3266 0.7019 0.168 0.000 0.832 0.000
#> GSM531689 1 0.5830 0.3138 0.620 0.000 0.332 0.048
#> GSM531690 1 0.4981 0.1643 0.536 0.000 0.000 0.464
#> GSM531691 1 0.6013 0.3535 0.624 0.000 0.312 0.064
#> GSM531692 1 0.6712 0.1076 0.552 0.104 0.344 0.000
#> GSM531693 3 0.4072 0.6159 0.252 0.000 0.748 0.000
#> GSM531694 1 0.5085 0.4328 0.708 0.032 0.000 0.260
#> GSM531695 3 0.6688 0.2191 0.368 0.000 0.536 0.096
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM531600 3 0.0898 0.6819 0.000 0.020 0.972 0.000 0.008
#> GSM531601 2 0.6907 0.1358 0.016 0.452 0.036 0.080 0.416
#> GSM531605 1 0.6775 0.3858 0.512 0.012 0.008 0.160 0.308
#> GSM531615 2 0.4216 0.7236 0.012 0.796 0.000 0.072 0.120
#> GSM531617 2 0.4275 0.7204 0.012 0.792 0.000 0.076 0.120
#> GSM531624 2 0.0162 0.8078 0.000 0.996 0.000 0.000 0.004
#> GSM531627 2 0.3445 0.7477 0.000 0.824 0.036 0.000 0.140
#> GSM531629 2 0.4275 0.7204 0.012 0.792 0.000 0.076 0.120
#> GSM531631 2 0.0290 0.8072 0.000 0.992 0.000 0.000 0.008
#> GSM531634 2 0.4275 0.7204 0.012 0.792 0.000 0.076 0.120
#> GSM531636 3 0.0898 0.6819 0.000 0.020 0.972 0.000 0.008
#> GSM531637 2 0.0162 0.8078 0.000 0.996 0.000 0.000 0.004
#> GSM531654 2 0.7265 0.4842 0.208 0.580 0.024 0.068 0.120
#> GSM531655 1 0.6775 0.3858 0.512 0.012 0.008 0.160 0.308
#> GSM531658 4 0.4114 0.6702 0.044 0.000 0.004 0.776 0.176
#> GSM531660 4 0.7106 0.3756 0.260 0.020 0.004 0.480 0.236
#> GSM531602 1 0.1341 0.7108 0.944 0.000 0.000 0.056 0.000
#> GSM531603 1 0.4553 0.6770 0.772 0.008 0.020 0.164 0.036
#> GSM531604 1 0.5960 0.1213 0.588 0.028 0.068 0.000 0.316
#> GSM531606 1 0.1644 0.6697 0.940 0.004 0.000 0.008 0.048
#> GSM531607 1 0.4186 0.6874 0.804 0.008 0.020 0.136 0.032
#> GSM531608 2 0.5508 0.6683 0.116 0.728 0.024 0.016 0.116
#> GSM531609 4 0.0880 0.7663 0.032 0.000 0.000 0.968 0.000
#> GSM531610 4 0.0880 0.7663 0.032 0.000 0.000 0.968 0.000
#> GSM531611 4 0.1357 0.7593 0.048 0.000 0.000 0.948 0.004
#> GSM531612 4 0.1082 0.7668 0.028 0.000 0.000 0.964 0.008
#> GSM531613 4 0.0880 0.7663 0.032 0.000 0.000 0.968 0.000
#> GSM531614 4 0.0880 0.7663 0.032 0.000 0.000 0.968 0.000
#> GSM531616 3 0.6040 0.2472 0.000 0.372 0.504 0.000 0.124
#> GSM531618 4 0.7201 0.4949 0.076 0.064 0.028 0.548 0.284
#> GSM531619 2 0.0162 0.8078 0.000 0.996 0.000 0.000 0.004
#> GSM531620 2 0.4004 0.7483 0.000 0.792 0.032 0.012 0.164
#> GSM531621 2 0.3445 0.7477 0.000 0.824 0.036 0.000 0.140
#> GSM531622 2 0.0404 0.8078 0.000 0.988 0.000 0.000 0.012
#> GSM531623 2 0.0162 0.8078 0.000 0.996 0.000 0.000 0.004
#> GSM531625 2 0.3445 0.7477 0.000 0.824 0.036 0.000 0.140
#> GSM531626 2 0.3445 0.7477 0.000 0.824 0.036 0.000 0.140
#> GSM531628 3 0.0703 0.6803 0.000 0.000 0.976 0.000 0.024
#> GSM531630 2 0.0290 0.8072 0.000 0.992 0.000 0.000 0.008
#> GSM531632 3 0.1341 0.6719 0.000 0.000 0.944 0.000 0.056
#> GSM531633 2 0.3445 0.7477 0.000 0.824 0.036 0.000 0.140
#> GSM531635 3 0.6040 0.2472 0.000 0.372 0.504 0.000 0.124
#> GSM531638 3 0.6040 0.2472 0.000 0.372 0.504 0.000 0.124
#> GSM531639 3 0.4593 0.5598 0.000 0.128 0.748 0.000 0.124
#> GSM531640 2 0.0451 0.8070 0.000 0.988 0.000 0.004 0.008
#> GSM531641 4 0.1082 0.7668 0.028 0.000 0.000 0.964 0.008
#> GSM531642 3 0.7643 0.1108 0.000 0.056 0.368 0.220 0.356
#> GSM531643 3 0.2074 0.6613 0.000 0.004 0.920 0.060 0.016
#> GSM531644 3 0.7181 0.1616 0.000 0.024 0.428 0.232 0.316
#> GSM531645 4 0.1082 0.7668 0.028 0.000 0.000 0.964 0.008
#> GSM531646 3 0.2228 0.6709 0.000 0.040 0.912 0.000 0.048
#> GSM531647 3 0.0404 0.6806 0.000 0.000 0.988 0.000 0.012
#> GSM531648 5 0.7988 -0.2439 0.024 0.080 0.128 0.344 0.424
#> GSM531649 3 0.0404 0.6806 0.000 0.000 0.988 0.000 0.012
#> GSM531650 3 0.0703 0.6803 0.000 0.000 0.976 0.000 0.024
#> GSM531651 2 0.1628 0.7955 0.000 0.936 0.008 0.000 0.056
#> GSM531652 5 0.7988 -0.2439 0.024 0.080 0.128 0.344 0.424
#> GSM531653 3 0.0404 0.6806 0.000 0.000 0.988 0.000 0.012
#> GSM531656 3 0.4312 0.5780 0.000 0.104 0.772 0.000 0.124
#> GSM531657 4 0.6317 0.5027 0.200 0.008 0.004 0.588 0.200
#> GSM531659 4 0.8508 0.0165 0.280 0.152 0.012 0.380 0.176
#> GSM531661 2 0.5798 0.6324 0.128 0.696 0.024 0.012 0.140
#> GSM531662 5 0.7896 0.1307 0.184 0.352 0.080 0.004 0.380
#> GSM531663 4 0.5277 0.5725 0.092 0.144 0.000 0.728 0.036
#> GSM531664 3 0.0703 0.6803 0.000 0.000 0.976 0.000 0.024
#> GSM531665 5 0.8638 0.2457 0.320 0.232 0.068 0.044 0.336
#> GSM531666 3 0.6633 0.3338 0.004 0.016 0.560 0.224 0.196
#> GSM531667 2 0.5461 0.6753 0.108 0.732 0.024 0.016 0.120
#> GSM531668 5 0.8429 -0.3235 0.164 0.096 0.028 0.344 0.368
#> GSM531669 3 0.3492 0.5841 0.016 0.000 0.796 0.000 0.188
#> GSM531670 3 0.4312 0.5780 0.000 0.104 0.772 0.000 0.124
#> GSM531671 5 0.8192 0.1918 0.176 0.316 0.124 0.004 0.380
#> GSM531672 4 0.4114 0.6702 0.044 0.000 0.004 0.776 0.176
#> GSM531673 5 0.7896 0.1307 0.184 0.352 0.080 0.004 0.380
#> GSM531674 3 0.3492 0.5841 0.016 0.000 0.796 0.000 0.188
#> GSM531675 1 0.4227 0.3557 0.580 0.000 0.000 0.420 0.000
#> GSM531676 5 0.6338 0.2145 0.388 0.000 0.140 0.004 0.468
#> GSM531677 1 0.4834 0.6799 0.748 0.000 0.020 0.160 0.072
#> GSM531678 1 0.2228 0.6710 0.916 0.008 0.000 0.020 0.056
#> GSM531679 1 0.4834 0.6799 0.748 0.000 0.020 0.160 0.072
#> GSM531680 3 0.7374 0.0259 0.288 0.000 0.412 0.032 0.268
#> GSM531681 4 0.3395 0.5653 0.236 0.000 0.000 0.764 0.000
#> GSM531682 1 0.4099 0.6864 0.764 0.000 0.004 0.200 0.032
#> GSM531683 1 0.2439 0.7169 0.876 0.000 0.000 0.120 0.004
#> GSM531684 1 0.2115 0.6569 0.916 0.008 0.000 0.008 0.068
#> GSM531685 5 0.5816 -0.1745 0.092 0.000 0.440 0.000 0.468
#> GSM531686 4 0.3607 0.5533 0.244 0.000 0.000 0.752 0.004
#> GSM531687 5 0.6338 0.2145 0.388 0.000 0.140 0.004 0.468
#> GSM531688 3 0.4352 0.5144 0.036 0.000 0.720 0.000 0.244
#> GSM531689 5 0.6338 0.2145 0.388 0.000 0.140 0.004 0.468
#> GSM531690 1 0.3895 0.5581 0.680 0.000 0.000 0.320 0.000
#> GSM531691 5 0.6530 0.1736 0.412 0.000 0.136 0.012 0.440
#> GSM531692 5 0.7269 0.3571 0.220 0.096 0.144 0.000 0.540
#> GSM531693 3 0.5385 0.3946 0.088 0.000 0.624 0.000 0.288
#> GSM531694 1 0.1341 0.7108 0.944 0.000 0.000 0.056 0.000
#> GSM531695 3 0.7363 0.0336 0.288 0.000 0.416 0.032 0.264
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM531600 3 0.1059 0.72184 0.000 0.016 0.964 0.000 0.004 0.016
#> GSM531601 6 0.3975 0.26630 0.000 0.392 0.008 0.000 0.000 0.600
#> GSM531605 1 0.6026 0.31636 0.520 0.000 0.000 0.052 0.092 0.336
#> GSM531615 2 0.4821 0.62699 0.004 0.684 0.000 0.036 0.036 0.240
#> GSM531617 2 0.4908 0.62012 0.004 0.676 0.000 0.040 0.036 0.244
#> GSM531624 2 0.0000 0.77155 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531627 2 0.4304 0.69758 0.000 0.740 0.004 0.000 0.128 0.128
#> GSM531629 2 0.4908 0.62012 0.004 0.676 0.000 0.040 0.036 0.244
#> GSM531631 2 0.0363 0.76979 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM531634 2 0.4908 0.62012 0.004 0.676 0.000 0.040 0.036 0.244
#> GSM531636 3 0.1059 0.72184 0.000 0.016 0.964 0.000 0.004 0.016
#> GSM531637 2 0.0000 0.77155 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531654 2 0.7759 0.39305 0.144 0.448 0.000 0.040 0.192 0.176
#> GSM531655 1 0.6026 0.31636 0.520 0.000 0.000 0.052 0.092 0.336
#> GSM531658 4 0.4085 0.46794 0.044 0.000 0.000 0.704 0.000 0.252
#> GSM531660 4 0.6437 -0.15741 0.264 0.016 0.000 0.364 0.000 0.356
#> GSM531602 1 0.0000 0.72554 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531603 1 0.3297 0.69834 0.820 0.000 0.000 0.068 0.000 0.112
#> GSM531604 5 0.4226 -0.00483 0.484 0.004 0.000 0.000 0.504 0.008
#> GSM531606 1 0.2053 0.68746 0.888 0.004 0.000 0.000 0.108 0.000
#> GSM531607 1 0.2776 0.70941 0.860 0.000 0.000 0.052 0.000 0.088
#> GSM531608 2 0.5984 0.58787 0.060 0.616 0.000 0.004 0.184 0.136
#> GSM531609 4 0.0000 0.72879 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531610 4 0.0000 0.72879 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531611 4 0.0603 0.72455 0.016 0.000 0.000 0.980 0.004 0.000
#> GSM531612 4 0.0363 0.72782 0.000 0.000 0.000 0.988 0.000 0.012
#> GSM531613 4 0.0000 0.72879 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531614 4 0.0000 0.72879 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531616 3 0.6476 0.30732 0.000 0.316 0.492 0.000 0.112 0.080
#> GSM531618 6 0.5860 0.07953 0.052 0.024 0.024 0.424 0.000 0.476
#> GSM531619 2 0.0000 0.77155 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531620 2 0.5001 0.69201 0.000 0.680 0.004 0.008 0.132 0.176
#> GSM531621 2 0.4304 0.69758 0.000 0.740 0.004 0.000 0.128 0.128
#> GSM531622 2 0.0820 0.77141 0.000 0.972 0.000 0.000 0.012 0.016
#> GSM531623 2 0.0000 0.77155 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531625 2 0.4304 0.69758 0.000 0.740 0.004 0.000 0.128 0.128
#> GSM531626 2 0.4304 0.69758 0.000 0.740 0.004 0.000 0.128 0.128
#> GSM531628 3 0.0865 0.71716 0.000 0.000 0.964 0.000 0.000 0.036
#> GSM531630 2 0.0363 0.76979 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM531632 3 0.1644 0.70415 0.000 0.000 0.920 0.000 0.076 0.004
#> GSM531633 2 0.4304 0.69758 0.000 0.740 0.004 0.000 0.128 0.128
#> GSM531635 3 0.6476 0.30732 0.000 0.316 0.492 0.000 0.112 0.080
#> GSM531638 3 0.6476 0.30732 0.000 0.316 0.492 0.000 0.112 0.080
#> GSM531639 3 0.4877 0.60265 0.000 0.084 0.732 0.000 0.080 0.104
#> GSM531640 2 0.0458 0.76953 0.000 0.984 0.000 0.000 0.000 0.016
#> GSM531641 4 0.0363 0.72782 0.000 0.000 0.000 0.988 0.000 0.012
#> GSM531642 6 0.6234 0.38823 0.000 0.016 0.332 0.096 0.036 0.520
#> GSM531643 3 0.2144 0.68698 0.000 0.000 0.908 0.040 0.004 0.048
#> GSM531644 6 0.5688 0.34930 0.000 0.008 0.396 0.108 0.004 0.484
#> GSM531645 4 0.0363 0.72782 0.000 0.000 0.000 0.988 0.000 0.012
#> GSM531646 3 0.2432 0.70448 0.000 0.024 0.888 0.000 0.080 0.008
#> GSM531647 3 0.0146 0.72204 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM531648 6 0.4777 0.59033 0.000 0.024 0.096 0.168 0.000 0.712
#> GSM531649 3 0.0146 0.72204 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM531650 3 0.0865 0.71716 0.000 0.000 0.964 0.000 0.000 0.036
#> GSM531651 2 0.1921 0.75713 0.000 0.916 0.000 0.000 0.032 0.052
#> GSM531652 6 0.4777 0.59033 0.000 0.024 0.096 0.168 0.000 0.712
#> GSM531653 3 0.0146 0.72204 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM531656 3 0.4575 0.61913 0.000 0.064 0.756 0.000 0.080 0.100
#> GSM531657 4 0.6386 0.19560 0.172 0.000 0.000 0.500 0.044 0.284
#> GSM531659 4 0.8528 0.00945 0.236 0.112 0.000 0.344 0.168 0.140
#> GSM531661 2 0.6326 0.52959 0.072 0.572 0.000 0.004 0.216 0.136
#> GSM531662 5 0.6202 0.27613 0.068 0.204 0.000 0.000 0.576 0.152
#> GSM531663 4 0.5020 0.51950 0.060 0.112 0.000 0.744 0.048 0.036
#> GSM531664 3 0.0865 0.71716 0.000 0.000 0.964 0.000 0.000 0.036
#> GSM531665 5 0.7371 0.36785 0.220 0.148 0.004 0.020 0.488 0.120
#> GSM531666 3 0.5822 -0.07937 0.000 0.008 0.528 0.128 0.008 0.328
#> GSM531667 2 0.5989 0.57399 0.052 0.608 0.000 0.004 0.196 0.140
#> GSM531668 6 0.6123 0.40792 0.156 0.024 0.000 0.168 0.036 0.616
#> GSM531669 3 0.3952 0.41608 0.000 0.000 0.672 0.000 0.308 0.020
#> GSM531670 3 0.4575 0.61913 0.000 0.064 0.756 0.000 0.080 0.100
#> GSM531671 5 0.6944 0.31322 0.068 0.192 0.044 0.000 0.552 0.144
#> GSM531672 4 0.4085 0.46794 0.044 0.000 0.000 0.704 0.000 0.252
#> GSM531673 5 0.6202 0.27613 0.068 0.204 0.000 0.000 0.576 0.152
#> GSM531674 3 0.3952 0.41608 0.000 0.000 0.672 0.000 0.308 0.020
#> GSM531675 1 0.4302 0.42024 0.608 0.000 0.000 0.368 0.004 0.020
#> GSM531676 5 0.3483 0.47267 0.236 0.000 0.016 0.000 0.748 0.000
#> GSM531677 1 0.4547 0.68768 0.752 0.000 0.000 0.076 0.124 0.048
#> GSM531678 1 0.2611 0.68568 0.864 0.008 0.000 0.012 0.116 0.000
#> GSM531679 1 0.4547 0.68768 0.752 0.000 0.000 0.076 0.124 0.048
#> GSM531680 5 0.7305 0.30976 0.248 0.000 0.296 0.016 0.380 0.060
#> GSM531681 4 0.3081 0.59096 0.220 0.000 0.000 0.776 0.000 0.004
#> GSM531682 1 0.4262 0.70581 0.776 0.000 0.000 0.112 0.064 0.048
#> GSM531683 1 0.1728 0.73512 0.924 0.000 0.000 0.064 0.004 0.008
#> GSM531684 1 0.2389 0.67155 0.864 0.008 0.000 0.000 0.128 0.000
#> GSM531685 5 0.3790 0.31960 0.004 0.000 0.264 0.000 0.716 0.016
#> GSM531686 4 0.3276 0.58401 0.228 0.000 0.000 0.764 0.004 0.004
#> GSM531687 5 0.3483 0.47267 0.236 0.000 0.016 0.000 0.748 0.000
#> GSM531688 3 0.4205 0.20848 0.000 0.000 0.564 0.000 0.420 0.016
#> GSM531689 5 0.3483 0.47267 0.236 0.000 0.016 0.000 0.748 0.000
#> GSM531690 1 0.3711 0.60941 0.720 0.000 0.000 0.260 0.000 0.020
#> GSM531691 5 0.3695 0.44840 0.272 0.000 0.016 0.000 0.712 0.000
#> GSM531692 5 0.2315 0.51145 0.040 0.016 0.004 0.000 0.908 0.032
#> GSM531693 5 0.4386 -0.07922 0.004 0.000 0.464 0.000 0.516 0.016
#> GSM531694 1 0.0000 0.72554 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531695 5 0.7310 0.30750 0.248 0.000 0.300 0.016 0.376 0.060
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> MAD:hclust 84 0.3119 2
#> MAD:hclust 77 0.0176 3
#> MAD:hclust 59 0.0403 4
#> MAD:hclust 65 0.0220 5
#> MAD:hclust 62 0.0392 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "kmeans"]
# you can also extract it by
# res = res_list["MAD:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 96 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.455 0.809 0.891 0.496 0.500 0.500
#> 3 3 0.677 0.841 0.900 0.348 0.741 0.524
#> 4 4 0.867 0.849 0.931 0.126 0.861 0.610
#> 5 5 0.718 0.650 0.798 0.059 0.966 0.866
#> 6 6 0.692 0.573 0.734 0.041 0.949 0.786
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM531600 2 0.3114 0.854 0.056 0.944
#> GSM531601 2 0.6712 0.782 0.176 0.824
#> GSM531605 1 0.1843 0.907 0.972 0.028
#> GSM531615 2 0.6801 0.778 0.180 0.820
#> GSM531617 2 0.6801 0.778 0.180 0.820
#> GSM531624 2 0.5408 0.823 0.124 0.876
#> GSM531627 2 0.0000 0.856 0.000 1.000
#> GSM531629 2 0.9866 0.312 0.432 0.568
#> GSM531631 2 0.5294 0.825 0.120 0.880
#> GSM531634 2 0.6712 0.782 0.176 0.824
#> GSM531636 2 0.0672 0.856 0.008 0.992
#> GSM531637 2 0.5294 0.825 0.120 0.880
#> GSM531654 2 0.6801 0.779 0.180 0.820
#> GSM531655 1 0.9580 0.349 0.620 0.380
#> GSM531658 1 0.0000 0.914 1.000 0.000
#> GSM531660 1 0.1633 0.905 0.976 0.024
#> GSM531602 1 0.1633 0.905 0.976 0.024
#> GSM531603 1 0.1633 0.905 0.976 0.024
#> GSM531604 1 0.4431 0.864 0.908 0.092
#> GSM531606 1 0.1633 0.905 0.976 0.024
#> GSM531607 1 0.0672 0.913 0.992 0.008
#> GSM531608 2 0.6712 0.782 0.176 0.824
#> GSM531609 1 0.0000 0.914 1.000 0.000
#> GSM531610 1 0.0672 0.913 0.992 0.008
#> GSM531611 1 0.0000 0.914 1.000 0.000
#> GSM531612 1 0.0000 0.914 1.000 0.000
#> GSM531613 1 0.0000 0.914 1.000 0.000
#> GSM531614 1 0.0000 0.914 1.000 0.000
#> GSM531616 2 0.0672 0.856 0.008 0.992
#> GSM531618 1 0.9970 -0.142 0.532 0.468
#> GSM531619 2 0.5408 0.823 0.124 0.876
#> GSM531620 2 0.0000 0.856 0.000 1.000
#> GSM531621 2 0.0000 0.856 0.000 1.000
#> GSM531622 2 0.5178 0.827 0.116 0.884
#> GSM531623 2 0.0672 0.856 0.008 0.992
#> GSM531625 2 0.0000 0.856 0.000 1.000
#> GSM531626 2 0.0672 0.856 0.008 0.992
#> GSM531628 2 0.8144 0.700 0.252 0.748
#> GSM531630 2 0.4298 0.839 0.088 0.912
#> GSM531632 2 0.3733 0.850 0.072 0.928
#> GSM531633 2 0.0000 0.856 0.000 1.000
#> GSM531635 2 0.2043 0.856 0.032 0.968
#> GSM531638 2 0.0000 0.856 0.000 1.000
#> GSM531639 2 0.0672 0.856 0.008 0.992
#> GSM531640 2 0.5408 0.823 0.124 0.876
#> GSM531641 1 0.1184 0.909 0.984 0.016
#> GSM531642 2 0.7674 0.731 0.224 0.776
#> GSM531643 2 0.4939 0.833 0.108 0.892
#> GSM531644 2 0.8144 0.700 0.252 0.748
#> GSM531645 1 0.0000 0.914 1.000 0.000
#> GSM531646 2 0.2043 0.856 0.032 0.968
#> GSM531647 2 0.3584 0.851 0.068 0.932
#> GSM531648 1 0.2423 0.891 0.960 0.040
#> GSM531649 2 0.1414 0.856 0.020 0.980
#> GSM531650 2 0.8081 0.705 0.248 0.752
#> GSM531651 2 0.0000 0.856 0.000 1.000
#> GSM531652 2 0.8144 0.703 0.252 0.748
#> GSM531653 2 0.3274 0.853 0.060 0.940
#> GSM531656 2 0.3114 0.854 0.056 0.944
#> GSM531657 1 0.0672 0.913 0.992 0.008
#> GSM531659 1 0.0000 0.914 1.000 0.000
#> GSM531661 2 0.5294 0.825 0.120 0.880
#> GSM531662 2 0.1843 0.855 0.028 0.972
#> GSM531663 1 0.0672 0.913 0.992 0.008
#> GSM531664 2 0.8443 0.670 0.272 0.728
#> GSM531665 2 0.9170 0.565 0.332 0.668
#> GSM531666 1 0.9044 0.546 0.680 0.320
#> GSM531667 2 0.6438 0.792 0.164 0.836
#> GSM531668 1 0.1414 0.907 0.980 0.020
#> GSM531669 2 0.8144 0.700 0.252 0.748
#> GSM531670 2 0.3114 0.854 0.056 0.944
#> GSM531671 2 0.3431 0.852 0.064 0.936
#> GSM531672 1 0.0672 0.913 0.992 0.008
#> GSM531673 2 0.5294 0.841 0.120 0.880
#> GSM531674 2 0.8144 0.700 0.252 0.748
#> GSM531675 1 0.0000 0.914 1.000 0.000
#> GSM531676 1 0.6887 0.761 0.816 0.184
#> GSM531677 1 0.0000 0.914 1.000 0.000
#> GSM531678 1 0.0672 0.913 0.992 0.008
#> GSM531679 1 0.0000 0.914 1.000 0.000
#> GSM531680 1 0.6801 0.765 0.820 0.180
#> GSM531681 1 0.0000 0.914 1.000 0.000
#> GSM531682 1 0.0000 0.914 1.000 0.000
#> GSM531683 1 0.0672 0.913 0.992 0.008
#> GSM531684 1 0.4939 0.840 0.892 0.108
#> GSM531685 2 0.9522 0.467 0.372 0.628
#> GSM531686 1 0.0000 0.914 1.000 0.000
#> GSM531687 1 0.6887 0.761 0.816 0.184
#> GSM531688 1 0.9608 0.378 0.616 0.384
#> GSM531689 1 0.6148 0.794 0.848 0.152
#> GSM531690 1 0.0000 0.914 1.000 0.000
#> GSM531691 1 0.6438 0.788 0.836 0.164
#> GSM531692 2 0.4431 0.843 0.092 0.908
#> GSM531693 2 0.8144 0.700 0.252 0.748
#> GSM531694 1 0.1633 0.905 0.976 0.024
#> GSM531695 1 0.6801 0.765 0.820 0.180
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM531600 3 0.3879 0.8652 0.000 0.152 0.848
#> GSM531601 2 0.2878 0.8333 0.096 0.904 0.000
#> GSM531605 1 0.3619 0.8992 0.864 0.000 0.136
#> GSM531615 2 0.0424 0.9114 0.008 0.992 0.000
#> GSM531617 2 0.0424 0.9114 0.008 0.992 0.000
#> GSM531624 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM531627 2 0.0424 0.9126 0.000 0.992 0.008
#> GSM531629 2 0.5560 0.6136 0.300 0.700 0.000
#> GSM531631 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM531634 2 0.0424 0.9114 0.008 0.992 0.000
#> GSM531636 3 0.3879 0.8652 0.000 0.152 0.848
#> GSM531637 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM531654 2 0.1620 0.8960 0.012 0.964 0.024
#> GSM531655 3 0.9053 0.5310 0.220 0.224 0.556
#> GSM531658 1 0.0661 0.9095 0.988 0.008 0.004
#> GSM531660 1 0.0424 0.9097 0.992 0.008 0.000
#> GSM531602 1 0.3551 0.9008 0.868 0.000 0.132
#> GSM531603 1 0.3784 0.9018 0.864 0.004 0.132
#> GSM531604 1 0.7666 0.7465 0.680 0.128 0.192
#> GSM531606 1 0.3816 0.8941 0.852 0.000 0.148
#> GSM531607 1 0.3551 0.9008 0.868 0.000 0.132
#> GSM531608 2 0.1182 0.9046 0.012 0.976 0.012
#> GSM531609 1 0.0661 0.9095 0.988 0.008 0.004
#> GSM531610 1 0.0661 0.9095 0.988 0.008 0.004
#> GSM531611 1 0.0475 0.9098 0.992 0.004 0.004
#> GSM531612 1 0.0661 0.9095 0.988 0.008 0.004
#> GSM531613 1 0.0661 0.9095 0.988 0.008 0.004
#> GSM531614 1 0.0661 0.9095 0.988 0.008 0.004
#> GSM531616 2 0.5431 0.5146 0.000 0.716 0.284
#> GSM531618 1 0.1267 0.9015 0.972 0.024 0.004
#> GSM531619 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM531620 2 0.0424 0.9126 0.000 0.992 0.008
#> GSM531621 2 0.0424 0.9126 0.000 0.992 0.008
#> GSM531622 2 0.0000 0.9139 0.000 1.000 0.000
#> GSM531623 2 0.0237 0.9135 0.000 0.996 0.004
#> GSM531625 2 0.0424 0.9126 0.000 0.992 0.008
#> GSM531626 2 0.0892 0.9047 0.000 0.980 0.020
#> GSM531628 3 0.4745 0.8749 0.080 0.068 0.852
#> GSM531630 2 0.0237 0.9135 0.000 0.996 0.004
#> GSM531632 3 0.3752 0.8676 0.000 0.144 0.856
#> GSM531633 2 0.0424 0.9126 0.000 0.992 0.008
#> GSM531635 3 0.3879 0.8652 0.000 0.152 0.848
#> GSM531638 2 0.0424 0.9126 0.000 0.992 0.008
#> GSM531639 3 0.3941 0.8625 0.000 0.156 0.844
#> GSM531640 2 0.0237 0.9129 0.004 0.996 0.000
#> GSM531641 1 0.0661 0.9095 0.988 0.008 0.004
#> GSM531642 3 0.4768 0.8666 0.100 0.052 0.848
#> GSM531643 3 0.4745 0.8749 0.080 0.068 0.852
#> GSM531644 3 0.4602 0.8648 0.108 0.040 0.852
#> GSM531645 1 0.0661 0.9095 0.988 0.008 0.004
#> GSM531646 3 0.3879 0.8652 0.000 0.152 0.848
#> GSM531647 3 0.3816 0.8665 0.000 0.148 0.852
#> GSM531648 1 0.0661 0.9095 0.988 0.008 0.004
#> GSM531649 3 0.3879 0.8652 0.000 0.152 0.848
#> GSM531650 3 0.4745 0.8749 0.080 0.068 0.852
#> GSM531651 2 0.0424 0.9126 0.000 0.992 0.008
#> GSM531652 3 0.4636 0.8593 0.116 0.036 0.848
#> GSM531653 3 0.3879 0.8652 0.000 0.152 0.848
#> GSM531656 3 0.4712 0.8749 0.044 0.108 0.848
#> GSM531657 1 0.0424 0.9097 0.992 0.008 0.000
#> GSM531659 1 0.0475 0.9098 0.992 0.004 0.004
#> GSM531661 2 0.0829 0.9080 0.004 0.984 0.012
#> GSM531662 2 0.1453 0.9012 0.008 0.968 0.024
#> GSM531663 1 0.0424 0.9097 0.992 0.008 0.000
#> GSM531664 3 0.4335 0.8676 0.100 0.036 0.864
#> GSM531665 3 0.3461 0.8158 0.076 0.024 0.900
#> GSM531666 3 0.4483 0.8528 0.128 0.024 0.848
#> GSM531667 2 0.0424 0.9124 0.008 0.992 0.000
#> GSM531668 1 0.0424 0.9097 0.992 0.008 0.000
#> GSM531669 3 0.3791 0.8798 0.048 0.060 0.892
#> GSM531670 3 0.3879 0.8652 0.000 0.152 0.848
#> GSM531671 2 0.6421 0.1604 0.004 0.572 0.424
#> GSM531672 1 0.0424 0.9097 0.992 0.008 0.000
#> GSM531673 2 0.7622 0.4710 0.060 0.608 0.332
#> GSM531674 3 0.3983 0.8805 0.048 0.068 0.884
#> GSM531675 1 0.3192 0.9061 0.888 0.000 0.112
#> GSM531676 3 0.2537 0.7920 0.080 0.000 0.920
#> GSM531677 1 0.3686 0.8998 0.860 0.000 0.140
#> GSM531678 1 0.3816 0.8941 0.852 0.000 0.148
#> GSM531679 1 0.3816 0.8941 0.852 0.000 0.148
#> GSM531680 3 0.2165 0.8142 0.064 0.000 0.936
#> GSM531681 1 0.3192 0.9061 0.888 0.000 0.112
#> GSM531682 1 0.3816 0.8941 0.852 0.000 0.148
#> GSM531683 1 0.3551 0.9008 0.868 0.000 0.132
#> GSM531684 2 0.9074 0.1034 0.352 0.500 0.148
#> GSM531685 3 0.0424 0.8396 0.008 0.000 0.992
#> GSM531686 1 0.3192 0.9061 0.888 0.000 0.112
#> GSM531687 3 0.2448 0.7935 0.076 0.000 0.924
#> GSM531688 3 0.0237 0.8406 0.004 0.000 0.996
#> GSM531689 1 0.5835 0.6635 0.660 0.000 0.340
#> GSM531690 1 0.3192 0.9061 0.888 0.000 0.112
#> GSM531691 1 0.6168 0.5202 0.588 0.000 0.412
#> GSM531692 3 0.6793 -0.0585 0.012 0.452 0.536
#> GSM531693 3 0.1031 0.8544 0.000 0.024 0.976
#> GSM531694 1 0.3551 0.9008 0.868 0.000 0.132
#> GSM531695 3 0.0892 0.8397 0.020 0.000 0.980
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM531600 3 0.0469 0.9531 0.012 0.000 0.988 0.000
#> GSM531601 2 0.1661 0.8872 0.004 0.944 0.000 0.052
#> GSM531605 1 0.0707 0.8839 0.980 0.000 0.000 0.020
#> GSM531615 2 0.0336 0.9267 0.008 0.992 0.000 0.000
#> GSM531617 2 0.0000 0.9264 0.000 1.000 0.000 0.000
#> GSM531624 2 0.0469 0.9264 0.012 0.988 0.000 0.000
#> GSM531627 2 0.0336 0.9254 0.008 0.992 0.000 0.000
#> GSM531629 2 0.4920 0.4053 0.004 0.628 0.000 0.368
#> GSM531631 2 0.0188 0.9262 0.004 0.996 0.000 0.000
#> GSM531634 2 0.0188 0.9262 0.004 0.996 0.000 0.000
#> GSM531636 3 0.0592 0.9519 0.016 0.000 0.984 0.000
#> GSM531637 2 0.0469 0.9264 0.012 0.988 0.000 0.000
#> GSM531654 2 0.4916 0.2605 0.424 0.576 0.000 0.000
#> GSM531655 3 0.5244 0.3636 0.388 0.000 0.600 0.012
#> GSM531658 4 0.0000 0.9162 0.000 0.000 0.000 1.000
#> GSM531660 4 0.2921 0.8085 0.140 0.000 0.000 0.860
#> GSM531602 1 0.2216 0.8563 0.908 0.000 0.000 0.092
#> GSM531603 1 0.2216 0.8563 0.908 0.000 0.000 0.092
#> GSM531604 1 0.0469 0.8819 0.988 0.000 0.000 0.012
#> GSM531606 1 0.1022 0.8834 0.968 0.000 0.000 0.032
#> GSM531607 1 0.2216 0.8563 0.908 0.000 0.000 0.092
#> GSM531608 2 0.0336 0.9267 0.008 0.992 0.000 0.000
#> GSM531609 4 0.0000 0.9162 0.000 0.000 0.000 1.000
#> GSM531610 4 0.0000 0.9162 0.000 0.000 0.000 1.000
#> GSM531611 4 0.0000 0.9162 0.000 0.000 0.000 1.000
#> GSM531612 4 0.0000 0.9162 0.000 0.000 0.000 1.000
#> GSM531613 4 0.0000 0.9162 0.000 0.000 0.000 1.000
#> GSM531614 4 0.0000 0.9162 0.000 0.000 0.000 1.000
#> GSM531616 2 0.4284 0.7063 0.020 0.780 0.200 0.000
#> GSM531618 4 0.2647 0.8165 0.000 0.120 0.000 0.880
#> GSM531619 2 0.0469 0.9264 0.012 0.988 0.000 0.000
#> GSM531620 2 0.0336 0.9254 0.008 0.992 0.000 0.000
#> GSM531621 2 0.0592 0.9252 0.016 0.984 0.000 0.000
#> GSM531622 2 0.0188 0.9262 0.004 0.996 0.000 0.000
#> GSM531623 2 0.0336 0.9267 0.008 0.992 0.000 0.000
#> GSM531625 2 0.0921 0.9196 0.028 0.972 0.000 0.000
#> GSM531626 2 0.0707 0.9199 0.020 0.980 0.000 0.000
#> GSM531628 3 0.0000 0.9575 0.000 0.000 1.000 0.000
#> GSM531630 2 0.0188 0.9262 0.004 0.996 0.000 0.000
#> GSM531632 3 0.0188 0.9564 0.004 0.000 0.996 0.000
#> GSM531633 2 0.0336 0.9254 0.008 0.992 0.000 0.000
#> GSM531635 3 0.0672 0.9508 0.008 0.008 0.984 0.000
#> GSM531638 2 0.0707 0.9199 0.020 0.980 0.000 0.000
#> GSM531639 3 0.0927 0.9484 0.016 0.008 0.976 0.000
#> GSM531640 2 0.0188 0.9262 0.004 0.996 0.000 0.000
#> GSM531641 4 0.0000 0.9162 0.000 0.000 0.000 1.000
#> GSM531642 3 0.1721 0.9317 0.012 0.008 0.952 0.028
#> GSM531643 3 0.0000 0.9575 0.000 0.000 1.000 0.000
#> GSM531644 3 0.0000 0.9575 0.000 0.000 1.000 0.000
#> GSM531645 4 0.0000 0.9162 0.000 0.000 0.000 1.000
#> GSM531646 3 0.0188 0.9564 0.004 0.000 0.996 0.000
#> GSM531647 3 0.0000 0.9575 0.000 0.000 1.000 0.000
#> GSM531648 4 0.0336 0.9101 0.000 0.008 0.000 0.992
#> GSM531649 3 0.0336 0.9549 0.008 0.000 0.992 0.000
#> GSM531650 3 0.0000 0.9575 0.000 0.000 1.000 0.000
#> GSM531651 2 0.0336 0.9267 0.008 0.992 0.000 0.000
#> GSM531652 3 0.1807 0.9128 0.000 0.008 0.940 0.052
#> GSM531653 3 0.0000 0.9575 0.000 0.000 1.000 0.000
#> GSM531656 3 0.0469 0.9531 0.012 0.000 0.988 0.000
#> GSM531657 4 0.0000 0.9162 0.000 0.000 0.000 1.000
#> GSM531659 4 0.0000 0.9162 0.000 0.000 0.000 1.000
#> GSM531661 2 0.0336 0.9267 0.008 0.992 0.000 0.000
#> GSM531662 1 0.4994 -0.0799 0.520 0.480 0.000 0.000
#> GSM531663 4 0.0000 0.9162 0.000 0.000 0.000 1.000
#> GSM531664 3 0.0000 0.9575 0.000 0.000 1.000 0.000
#> GSM531665 1 0.3688 0.7270 0.792 0.000 0.208 0.000
#> GSM531666 3 0.0336 0.9538 0.000 0.000 0.992 0.008
#> GSM531667 2 0.0336 0.9267 0.008 0.992 0.000 0.000
#> GSM531668 4 0.2589 0.8325 0.116 0.000 0.000 0.884
#> GSM531669 3 0.0000 0.9575 0.000 0.000 1.000 0.000
#> GSM531670 3 0.0469 0.9531 0.012 0.000 0.988 0.000
#> GSM531671 2 0.7894 -0.0326 0.344 0.364 0.292 0.000
#> GSM531672 4 0.0000 0.9162 0.000 0.000 0.000 1.000
#> GSM531673 1 0.0336 0.8758 0.992 0.008 0.000 0.000
#> GSM531674 3 0.0000 0.9575 0.000 0.000 1.000 0.000
#> GSM531675 4 0.4605 0.5107 0.336 0.000 0.000 0.664
#> GSM531676 1 0.3486 0.7532 0.812 0.000 0.188 0.000
#> GSM531677 1 0.3764 0.7260 0.784 0.000 0.000 0.216
#> GSM531678 1 0.0921 0.8840 0.972 0.000 0.000 0.028
#> GSM531679 1 0.1022 0.8834 0.968 0.000 0.000 0.032
#> GSM531680 3 0.4830 0.2819 0.392 0.000 0.608 0.000
#> GSM531681 4 0.4040 0.6792 0.248 0.000 0.000 0.752
#> GSM531682 1 0.1022 0.8834 0.968 0.000 0.000 0.032
#> GSM531683 1 0.2216 0.8563 0.908 0.000 0.000 0.092
#> GSM531684 1 0.0927 0.8784 0.976 0.016 0.000 0.008
#> GSM531685 1 0.3837 0.7080 0.776 0.000 0.224 0.000
#> GSM531686 4 0.4040 0.6792 0.248 0.000 0.000 0.752
#> GSM531687 1 0.3528 0.7488 0.808 0.000 0.192 0.000
#> GSM531688 3 0.0000 0.9575 0.000 0.000 1.000 0.000
#> GSM531689 1 0.0707 0.8839 0.980 0.000 0.000 0.020
#> GSM531690 4 0.4040 0.6792 0.248 0.000 0.000 0.752
#> GSM531691 1 0.0707 0.8839 0.980 0.000 0.000 0.020
#> GSM531692 1 0.0188 0.8771 0.996 0.004 0.000 0.000
#> GSM531693 3 0.0000 0.9575 0.000 0.000 1.000 0.000
#> GSM531694 1 0.2216 0.8563 0.908 0.000 0.000 0.092
#> GSM531695 3 0.0000 0.9575 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM531600 3 0.2516 0.8276 0.000 0.000 0.860 0.000 0.140
#> GSM531601 2 0.3541 0.7535 0.000 0.824 0.012 0.020 0.144
#> GSM531605 1 0.1732 0.5390 0.920 0.000 0.000 0.000 0.080
#> GSM531615 2 0.2127 0.8298 0.000 0.892 0.000 0.000 0.108
#> GSM531617 2 0.2612 0.8221 0.000 0.868 0.000 0.008 0.124
#> GSM531624 2 0.0404 0.8494 0.000 0.988 0.000 0.000 0.012
#> GSM531627 2 0.2561 0.8155 0.000 0.856 0.000 0.000 0.144
#> GSM531629 2 0.7685 0.2863 0.100 0.468 0.000 0.264 0.168
#> GSM531631 2 0.0290 0.8497 0.000 0.992 0.000 0.000 0.008
#> GSM531634 2 0.2020 0.8329 0.000 0.900 0.000 0.000 0.100
#> GSM531636 3 0.3003 0.8103 0.000 0.000 0.812 0.000 0.188
#> GSM531637 2 0.0404 0.8494 0.000 0.988 0.000 0.000 0.012
#> GSM531654 2 0.6736 0.0210 0.360 0.384 0.000 0.000 0.256
#> GSM531655 3 0.6909 0.2199 0.280 0.004 0.448 0.004 0.264
#> GSM531658 4 0.1557 0.8384 0.008 0.000 0.000 0.940 0.052
#> GSM531660 4 0.5878 0.4892 0.336 0.000 0.000 0.548 0.116
#> GSM531602 1 0.0963 0.5529 0.964 0.000 0.000 0.036 0.000
#> GSM531603 1 0.2426 0.5077 0.900 0.000 0.000 0.036 0.064
#> GSM531604 1 0.4297 -0.1566 0.528 0.000 0.000 0.000 0.472
#> GSM531606 1 0.2719 0.4299 0.852 0.000 0.000 0.004 0.144
#> GSM531607 1 0.1041 0.5524 0.964 0.000 0.000 0.032 0.004
#> GSM531608 2 0.3109 0.7649 0.000 0.800 0.000 0.000 0.200
#> GSM531609 4 0.0000 0.8492 0.000 0.000 0.000 1.000 0.000
#> GSM531610 4 0.0290 0.8483 0.000 0.000 0.000 0.992 0.008
#> GSM531611 4 0.0290 0.8483 0.000 0.000 0.000 0.992 0.008
#> GSM531612 4 0.0000 0.8492 0.000 0.000 0.000 1.000 0.000
#> GSM531613 4 0.0290 0.8483 0.000 0.000 0.000 0.992 0.008
#> GSM531614 4 0.0000 0.8492 0.000 0.000 0.000 1.000 0.000
#> GSM531616 2 0.5668 0.5672 0.000 0.624 0.144 0.000 0.232
#> GSM531618 4 0.6151 0.6479 0.036 0.076 0.020 0.664 0.204
#> GSM531619 2 0.0404 0.8494 0.000 0.988 0.000 0.000 0.012
#> GSM531620 2 0.2813 0.8231 0.000 0.832 0.000 0.000 0.168
#> GSM531621 2 0.2230 0.8272 0.000 0.884 0.000 0.000 0.116
#> GSM531622 2 0.0404 0.8497 0.000 0.988 0.000 0.000 0.012
#> GSM531623 2 0.0794 0.8500 0.000 0.972 0.000 0.000 0.028
#> GSM531625 2 0.3430 0.7550 0.000 0.776 0.004 0.000 0.220
#> GSM531626 2 0.3662 0.7484 0.000 0.744 0.004 0.000 0.252
#> GSM531628 3 0.0000 0.8404 0.000 0.000 1.000 0.000 0.000
#> GSM531630 2 0.0404 0.8497 0.000 0.988 0.000 0.000 0.012
#> GSM531632 3 0.2377 0.8080 0.000 0.000 0.872 0.000 0.128
#> GSM531633 2 0.2230 0.8272 0.000 0.884 0.000 0.000 0.116
#> GSM531635 3 0.3519 0.7805 0.000 0.008 0.776 0.000 0.216
#> GSM531638 2 0.3491 0.7514 0.000 0.768 0.004 0.000 0.228
#> GSM531639 3 0.3812 0.7878 0.000 0.024 0.772 0.000 0.204
#> GSM531640 2 0.0290 0.8497 0.000 0.992 0.000 0.000 0.008
#> GSM531641 4 0.0000 0.8492 0.000 0.000 0.000 1.000 0.000
#> GSM531642 3 0.3789 0.7840 0.000 0.000 0.760 0.016 0.224
#> GSM531643 3 0.1043 0.8390 0.000 0.000 0.960 0.000 0.040
#> GSM531644 3 0.2011 0.8290 0.000 0.000 0.908 0.004 0.088
#> GSM531645 4 0.0000 0.8492 0.000 0.000 0.000 1.000 0.000
#> GSM531646 3 0.2329 0.8191 0.000 0.000 0.876 0.000 0.124
#> GSM531647 3 0.0794 0.8384 0.000 0.000 0.972 0.000 0.028
#> GSM531648 4 0.2674 0.8088 0.012 0.000 0.000 0.868 0.120
#> GSM531649 3 0.3109 0.7952 0.000 0.000 0.800 0.000 0.200
#> GSM531650 3 0.0000 0.8404 0.000 0.000 1.000 0.000 0.000
#> GSM531651 2 0.0794 0.8500 0.000 0.972 0.000 0.000 0.028
#> GSM531652 3 0.3535 0.7812 0.000 0.000 0.808 0.028 0.164
#> GSM531653 3 0.0794 0.8384 0.000 0.000 0.972 0.000 0.028
#> GSM531656 3 0.3010 0.8150 0.004 0.000 0.824 0.000 0.172
#> GSM531657 4 0.2504 0.8324 0.040 0.000 0.000 0.896 0.064
#> GSM531659 4 0.2331 0.8335 0.020 0.000 0.000 0.900 0.080
#> GSM531661 2 0.3266 0.7584 0.004 0.796 0.000 0.000 0.200
#> GSM531662 5 0.5763 0.4605 0.188 0.192 0.000 0.000 0.620
#> GSM531663 4 0.0404 0.8481 0.000 0.000 0.000 0.988 0.012
#> GSM531664 3 0.0162 0.8403 0.000 0.000 0.996 0.000 0.004
#> GSM531665 5 0.4989 0.5790 0.296 0.000 0.056 0.000 0.648
#> GSM531666 3 0.2536 0.8180 0.000 0.000 0.868 0.004 0.128
#> GSM531667 2 0.1908 0.8372 0.000 0.908 0.000 0.000 0.092
#> GSM531668 4 0.6291 0.4229 0.344 0.000 0.000 0.492 0.164
#> GSM531669 3 0.1121 0.8355 0.000 0.000 0.956 0.000 0.044
#> GSM531670 3 0.3010 0.8150 0.004 0.000 0.824 0.000 0.172
#> GSM531671 5 0.5360 0.5056 0.068 0.088 0.108 0.000 0.736
#> GSM531672 4 0.2522 0.8287 0.052 0.000 0.000 0.896 0.052
#> GSM531673 5 0.4151 0.5333 0.344 0.004 0.000 0.000 0.652
#> GSM531674 3 0.1121 0.8355 0.000 0.000 0.956 0.000 0.044
#> GSM531675 1 0.5115 -0.1658 0.484 0.000 0.000 0.480 0.036
#> GSM531676 1 0.5551 -0.1342 0.488 0.000 0.068 0.000 0.444
#> GSM531677 1 0.5478 0.4198 0.656 0.000 0.000 0.164 0.180
#> GSM531678 1 0.3707 0.4102 0.716 0.000 0.000 0.000 0.284
#> GSM531679 1 0.3336 0.4738 0.772 0.000 0.000 0.000 0.228
#> GSM531680 3 0.6690 -0.1710 0.300 0.000 0.432 0.000 0.268
#> GSM531681 4 0.4770 0.4920 0.320 0.000 0.000 0.644 0.036
#> GSM531682 1 0.3636 0.4340 0.728 0.000 0.000 0.000 0.272
#> GSM531683 1 0.1568 0.5470 0.944 0.000 0.000 0.036 0.020
#> GSM531684 1 0.3689 0.3075 0.740 0.004 0.000 0.000 0.256
#> GSM531685 5 0.5551 0.5079 0.304 0.000 0.096 0.000 0.600
#> GSM531686 4 0.4770 0.4920 0.320 0.000 0.000 0.644 0.036
#> GSM531687 1 0.5626 -0.0336 0.504 0.000 0.076 0.000 0.420
#> GSM531688 3 0.2813 0.7584 0.000 0.000 0.832 0.000 0.168
#> GSM531689 1 0.4171 0.1861 0.604 0.000 0.000 0.000 0.396
#> GSM531690 4 0.4866 0.4707 0.344 0.000 0.000 0.620 0.036
#> GSM531691 1 0.4249 0.0886 0.568 0.000 0.000 0.000 0.432
#> GSM531692 5 0.4227 0.3878 0.420 0.000 0.000 0.000 0.580
#> GSM531693 3 0.3210 0.7270 0.000 0.000 0.788 0.000 0.212
#> GSM531694 1 0.0963 0.5529 0.964 0.000 0.000 0.036 0.000
#> GSM531695 3 0.4171 0.7016 0.112 0.000 0.784 0.000 0.104
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM531600 3 0.3709 0.70100 0.000 0.000 0.756 0.000 0.040 0.204
#> GSM531601 2 0.4493 0.52389 0.016 0.620 0.000 0.012 0.004 0.348
#> GSM531605 1 0.2945 0.44095 0.824 0.000 0.000 0.000 0.156 0.020
#> GSM531615 2 0.3857 0.71294 0.004 0.776 0.000 0.000 0.072 0.148
#> GSM531617 2 0.4550 0.70200 0.016 0.732 0.000 0.004 0.076 0.172
#> GSM531624 2 0.0363 0.76348 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM531627 2 0.4124 0.68115 0.000 0.644 0.000 0.000 0.024 0.332
#> GSM531629 2 0.7733 0.34798 0.096 0.448 0.000 0.104 0.088 0.264
#> GSM531631 2 0.1082 0.76563 0.000 0.956 0.000 0.000 0.004 0.040
#> GSM531634 2 0.3692 0.72180 0.012 0.792 0.000 0.000 0.044 0.152
#> GSM531636 3 0.4260 0.67772 0.000 0.004 0.640 0.000 0.024 0.332
#> GSM531637 2 0.0363 0.76348 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM531654 1 0.7486 0.03458 0.380 0.240 0.000 0.000 0.192 0.188
#> GSM531655 3 0.7446 0.21088 0.168 0.000 0.332 0.000 0.172 0.328
#> GSM531658 4 0.2944 0.74574 0.008 0.000 0.000 0.832 0.012 0.148
#> GSM531660 1 0.6318 -0.08128 0.460 0.000 0.000 0.332 0.028 0.180
#> GSM531602 1 0.0858 0.53579 0.968 0.000 0.000 0.004 0.028 0.000
#> GSM531603 1 0.1320 0.52443 0.948 0.000 0.000 0.000 0.016 0.036
#> GSM531604 5 0.4493 0.44445 0.344 0.000 0.000 0.000 0.612 0.044
#> GSM531606 1 0.3210 0.42334 0.804 0.000 0.000 0.000 0.168 0.028
#> GSM531607 1 0.1218 0.53478 0.956 0.000 0.000 0.004 0.028 0.012
#> GSM531608 2 0.4997 0.62819 0.004 0.660 0.000 0.000 0.160 0.176
#> GSM531609 4 0.0000 0.79601 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531610 4 0.0405 0.79496 0.004 0.000 0.000 0.988 0.000 0.008
#> GSM531611 4 0.0508 0.79568 0.004 0.000 0.000 0.984 0.000 0.012
#> GSM531612 4 0.0000 0.79601 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531613 4 0.0405 0.79496 0.004 0.000 0.000 0.988 0.000 0.008
#> GSM531614 4 0.0000 0.79601 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531616 2 0.5947 0.50682 0.000 0.456 0.072 0.000 0.052 0.420
#> GSM531618 4 0.7502 0.32424 0.040 0.120 0.024 0.404 0.044 0.368
#> GSM531619 2 0.0363 0.76348 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM531620 2 0.4763 0.66610 0.000 0.536 0.000 0.000 0.052 0.412
#> GSM531621 2 0.3816 0.69889 0.000 0.688 0.000 0.000 0.016 0.296
#> GSM531622 2 0.1349 0.76800 0.000 0.940 0.000 0.000 0.004 0.056
#> GSM531623 2 0.1411 0.76763 0.000 0.936 0.000 0.000 0.004 0.060
#> GSM531625 2 0.4625 0.62750 0.000 0.572 0.004 0.000 0.036 0.388
#> GSM531626 2 0.5104 0.60137 0.000 0.512 0.008 0.000 0.060 0.420
#> GSM531628 3 0.0909 0.73174 0.000 0.000 0.968 0.000 0.012 0.020
#> GSM531630 2 0.1411 0.76635 0.000 0.936 0.000 0.000 0.004 0.060
#> GSM531632 3 0.3790 0.67435 0.000 0.000 0.780 0.000 0.116 0.104
#> GSM531633 2 0.3816 0.69856 0.000 0.688 0.000 0.000 0.016 0.296
#> GSM531635 3 0.4912 0.58514 0.000 0.004 0.568 0.000 0.060 0.368
#> GSM531638 2 0.4792 0.60881 0.000 0.536 0.004 0.000 0.044 0.416
#> GSM531639 3 0.4877 0.63297 0.000 0.012 0.560 0.000 0.040 0.388
#> GSM531640 2 0.1082 0.76563 0.000 0.956 0.000 0.000 0.004 0.040
#> GSM531641 4 0.0291 0.79486 0.004 0.000 0.000 0.992 0.000 0.004
#> GSM531642 3 0.5116 0.56702 0.004 0.000 0.476 0.016 0.036 0.468
#> GSM531643 3 0.2302 0.72291 0.000 0.000 0.872 0.000 0.008 0.120
#> GSM531644 3 0.3535 0.68415 0.000 0.000 0.760 0.008 0.012 0.220
#> GSM531645 4 0.0291 0.79486 0.004 0.000 0.000 0.992 0.000 0.004
#> GSM531646 3 0.3680 0.69069 0.000 0.000 0.784 0.000 0.072 0.144
#> GSM531647 3 0.1921 0.72576 0.000 0.000 0.916 0.000 0.052 0.032
#> GSM531648 4 0.3998 0.68246 0.016 0.000 0.000 0.728 0.020 0.236
#> GSM531649 3 0.4463 0.65010 0.000 0.000 0.652 0.000 0.056 0.292
#> GSM531650 3 0.0777 0.73309 0.000 0.000 0.972 0.000 0.004 0.024
#> GSM531651 2 0.1411 0.76763 0.000 0.936 0.000 0.000 0.004 0.060
#> GSM531652 3 0.4939 0.60756 0.012 0.000 0.616 0.024 0.020 0.328
#> GSM531653 3 0.1921 0.72576 0.000 0.000 0.916 0.000 0.052 0.032
#> GSM531656 3 0.3993 0.68702 0.000 0.000 0.676 0.000 0.024 0.300
#> GSM531657 4 0.4655 0.70012 0.072 0.000 0.000 0.700 0.016 0.212
#> GSM531659 4 0.4673 0.70772 0.044 0.000 0.000 0.708 0.040 0.208
#> GSM531661 2 0.4570 0.65348 0.000 0.700 0.000 0.000 0.148 0.152
#> GSM531662 5 0.5927 0.38768 0.064 0.136 0.000 0.000 0.612 0.188
#> GSM531663 4 0.0692 0.79310 0.004 0.000 0.000 0.976 0.000 0.020
#> GSM531664 3 0.0909 0.73174 0.000 0.000 0.968 0.000 0.012 0.020
#> GSM531665 5 0.2807 0.63063 0.088 0.000 0.016 0.000 0.868 0.028
#> GSM531666 3 0.4495 0.63128 0.008 0.000 0.656 0.008 0.024 0.304
#> GSM531667 2 0.3756 0.72073 0.004 0.788 0.000 0.000 0.076 0.132
#> GSM531668 1 0.6946 0.11521 0.448 0.000 0.000 0.208 0.084 0.260
#> GSM531669 3 0.1643 0.72193 0.000 0.000 0.924 0.000 0.068 0.008
#> GSM531670 3 0.3993 0.68702 0.000 0.000 0.676 0.000 0.024 0.300
#> GSM531671 5 0.4740 0.44653 0.000 0.032 0.052 0.000 0.696 0.220
#> GSM531672 4 0.4451 0.70499 0.092 0.000 0.000 0.732 0.012 0.164
#> GSM531673 5 0.4243 0.53567 0.104 0.000 0.000 0.000 0.732 0.164
#> GSM531674 3 0.1643 0.72193 0.000 0.000 0.924 0.000 0.068 0.008
#> GSM531675 1 0.6150 0.03215 0.488 0.000 0.000 0.360 0.052 0.100
#> GSM531676 5 0.3645 0.59484 0.236 0.000 0.024 0.000 0.740 0.000
#> GSM531677 1 0.6153 0.28084 0.584 0.000 0.000 0.100 0.220 0.096
#> GSM531678 1 0.4565 -0.08747 0.532 0.000 0.000 0.000 0.432 0.036
#> GSM531679 1 0.4931 0.16892 0.592 0.000 0.000 0.000 0.324 0.084
#> GSM531680 5 0.6744 0.24602 0.152 0.000 0.328 0.000 0.444 0.076
#> GSM531681 4 0.5349 0.35006 0.340 0.000 0.000 0.560 0.012 0.088
#> GSM531682 1 0.5152 0.00841 0.512 0.000 0.000 0.000 0.400 0.088
#> GSM531683 1 0.1672 0.52844 0.932 0.000 0.000 0.004 0.016 0.048
#> GSM531684 1 0.4609 0.06900 0.588 0.000 0.000 0.000 0.364 0.048
#> GSM531685 5 0.3160 0.62640 0.104 0.000 0.048 0.000 0.840 0.008
#> GSM531686 4 0.5432 0.34322 0.340 0.000 0.000 0.556 0.016 0.088
#> GSM531687 5 0.4907 0.54296 0.244 0.000 0.064 0.000 0.668 0.024
#> GSM531688 3 0.3774 0.48007 0.000 0.000 0.664 0.000 0.328 0.008
#> GSM531689 5 0.4002 0.51418 0.320 0.000 0.000 0.000 0.660 0.020
#> GSM531690 4 0.5725 0.24202 0.392 0.000 0.000 0.488 0.020 0.100
#> GSM531691 5 0.3672 0.55450 0.304 0.000 0.000 0.000 0.688 0.008
#> GSM531692 5 0.3555 0.62413 0.176 0.000 0.000 0.000 0.780 0.044
#> GSM531693 3 0.4466 0.46653 0.000 0.000 0.620 0.000 0.336 0.044
#> GSM531694 1 0.0858 0.53579 0.968 0.000 0.000 0.004 0.028 0.000
#> GSM531695 3 0.6101 0.36249 0.104 0.000 0.576 0.000 0.244 0.076
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> MAD:kmeans 91 0.12030 2
#> MAD:kmeans 92 0.00583 3
#> MAD:kmeans 90 0.02170 4
#> MAD:kmeans 73 0.09287 5
#> MAD:kmeans 72 0.04691 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "skmeans"]
# you can also extract it by
# res = res_list["MAD:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 96 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 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 0.580 0.851 0.929 0.5050 0.496 0.496
#> 3 3 0.943 0.947 0.977 0.3312 0.718 0.490
#> 4 4 0.893 0.898 0.953 0.1216 0.862 0.612
#> 5 5 0.812 0.647 0.820 0.0578 0.913 0.680
#> 6 6 0.749 0.515 0.747 0.0397 0.886 0.554
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
#> GSM531600 2 0.0000 0.909 0.000 1.000
#> GSM531601 2 0.7219 0.787 0.200 0.800
#> GSM531605 1 0.0000 0.921 1.000 0.000
#> GSM531615 2 0.7219 0.787 0.200 0.800
#> GSM531617 2 0.7219 0.787 0.200 0.800
#> GSM531624 2 0.7219 0.787 0.200 0.800
#> GSM531627 2 0.0000 0.909 0.000 1.000
#> GSM531629 1 0.9129 0.431 0.672 0.328
#> GSM531631 2 0.7219 0.787 0.200 0.800
#> GSM531634 2 0.7219 0.787 0.200 0.800
#> GSM531636 2 0.0000 0.909 0.000 1.000
#> GSM531637 2 0.7219 0.787 0.200 0.800
#> GSM531654 1 0.5737 0.792 0.864 0.136
#> GSM531655 1 0.7376 0.768 0.792 0.208
#> GSM531658 1 0.0000 0.921 1.000 0.000
#> GSM531660 1 0.0000 0.921 1.000 0.000
#> GSM531602 1 0.0000 0.921 1.000 0.000
#> GSM531603 1 0.0000 0.921 1.000 0.000
#> GSM531604 1 0.2603 0.895 0.956 0.044
#> GSM531606 1 0.0000 0.921 1.000 0.000
#> GSM531607 1 0.0000 0.921 1.000 0.000
#> GSM531608 2 0.7950 0.743 0.240 0.760
#> GSM531609 1 0.0000 0.921 1.000 0.000
#> GSM531610 1 0.0000 0.921 1.000 0.000
#> GSM531611 1 0.0000 0.921 1.000 0.000
#> GSM531612 1 0.0000 0.921 1.000 0.000
#> GSM531613 1 0.0000 0.921 1.000 0.000
#> GSM531614 1 0.0000 0.921 1.000 0.000
#> GSM531616 2 0.0000 0.909 0.000 1.000
#> GSM531618 1 0.9323 0.381 0.652 0.348
#> GSM531619 2 0.7219 0.787 0.200 0.800
#> GSM531620 2 0.0000 0.909 0.000 1.000
#> GSM531621 2 0.0000 0.909 0.000 1.000
#> GSM531622 2 0.7219 0.787 0.200 0.800
#> GSM531623 2 0.0000 0.909 0.000 1.000
#> GSM531625 2 0.0000 0.909 0.000 1.000
#> GSM531626 2 0.0000 0.909 0.000 1.000
#> GSM531628 2 0.0000 0.909 0.000 1.000
#> GSM531630 2 0.1633 0.898 0.024 0.976
#> GSM531632 2 0.0000 0.909 0.000 1.000
#> GSM531633 2 0.0000 0.909 0.000 1.000
#> GSM531635 2 0.0000 0.909 0.000 1.000
#> GSM531638 2 0.0000 0.909 0.000 1.000
#> GSM531639 2 0.0000 0.909 0.000 1.000
#> GSM531640 2 0.7219 0.787 0.200 0.800
#> GSM531641 1 0.0000 0.921 1.000 0.000
#> GSM531642 2 0.0000 0.909 0.000 1.000
#> GSM531643 2 0.0000 0.909 0.000 1.000
#> GSM531644 2 0.0000 0.909 0.000 1.000
#> GSM531645 1 0.0000 0.921 1.000 0.000
#> GSM531646 2 0.0000 0.909 0.000 1.000
#> GSM531647 2 0.0000 0.909 0.000 1.000
#> GSM531648 1 0.0672 0.916 0.992 0.008
#> GSM531649 2 0.0000 0.909 0.000 1.000
#> GSM531650 2 0.0000 0.909 0.000 1.000
#> GSM531651 2 0.0000 0.909 0.000 1.000
#> GSM531652 2 0.0000 0.909 0.000 1.000
#> GSM531653 2 0.0000 0.909 0.000 1.000
#> GSM531656 2 0.0000 0.909 0.000 1.000
#> GSM531657 1 0.0000 0.921 1.000 0.000
#> GSM531659 1 0.0000 0.921 1.000 0.000
#> GSM531661 2 0.7219 0.787 0.200 0.800
#> GSM531662 2 0.0000 0.909 0.000 1.000
#> GSM531663 1 0.0000 0.921 1.000 0.000
#> GSM531664 2 0.4815 0.825 0.104 0.896
#> GSM531665 1 0.7219 0.772 0.800 0.200
#> GSM531666 1 0.7815 0.741 0.768 0.232
#> GSM531667 2 0.7219 0.787 0.200 0.800
#> GSM531668 1 0.0000 0.921 1.000 0.000
#> GSM531669 2 0.0000 0.909 0.000 1.000
#> GSM531670 2 0.0000 0.909 0.000 1.000
#> GSM531671 2 0.0000 0.909 0.000 1.000
#> GSM531672 1 0.0000 0.921 1.000 0.000
#> GSM531673 2 0.9209 0.426 0.336 0.664
#> GSM531674 2 0.0000 0.909 0.000 1.000
#> GSM531675 1 0.0000 0.921 1.000 0.000
#> GSM531676 1 0.7219 0.772 0.800 0.200
#> GSM531677 1 0.0000 0.921 1.000 0.000
#> GSM531678 1 0.0000 0.921 1.000 0.000
#> GSM531679 1 0.0000 0.921 1.000 0.000
#> GSM531680 1 0.7219 0.772 0.800 0.200
#> GSM531681 1 0.0000 0.921 1.000 0.000
#> GSM531682 1 0.0000 0.921 1.000 0.000
#> GSM531683 1 0.0000 0.921 1.000 0.000
#> GSM531684 1 0.0000 0.921 1.000 0.000
#> GSM531685 2 0.9323 0.396 0.348 0.652
#> GSM531686 1 0.0000 0.921 1.000 0.000
#> GSM531687 1 0.7219 0.772 0.800 0.200
#> GSM531688 1 0.9170 0.579 0.668 0.332
#> GSM531689 1 0.7219 0.772 0.800 0.200
#> GSM531690 1 0.0000 0.921 1.000 0.000
#> GSM531691 1 0.7219 0.772 0.800 0.200
#> GSM531692 2 0.9129 0.445 0.328 0.672
#> GSM531693 2 0.0000 0.909 0.000 1.000
#> GSM531694 1 0.0000 0.921 1.000 0.000
#> GSM531695 1 0.7219 0.772 0.800 0.200
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM531600 3 0.0000 0.990 0.000 0.000 1.000
#> GSM531601 2 0.0000 0.956 0.000 1.000 0.000
#> GSM531605 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531615 2 0.0000 0.956 0.000 1.000 0.000
#> GSM531617 2 0.0000 0.956 0.000 1.000 0.000
#> GSM531624 2 0.0000 0.956 0.000 1.000 0.000
#> GSM531627 2 0.0000 0.956 0.000 1.000 0.000
#> GSM531629 2 0.4504 0.753 0.196 0.804 0.000
#> GSM531631 2 0.0000 0.956 0.000 1.000 0.000
#> GSM531634 2 0.0000 0.956 0.000 1.000 0.000
#> GSM531636 3 0.0000 0.990 0.000 0.000 1.000
#> GSM531637 2 0.0000 0.956 0.000 1.000 0.000
#> GSM531654 2 0.0000 0.956 0.000 1.000 0.000
#> GSM531655 3 0.5708 0.707 0.028 0.204 0.768
#> GSM531658 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531660 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531602 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531603 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531604 1 0.4750 0.724 0.784 0.216 0.000
#> GSM531606 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531607 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531608 2 0.0000 0.956 0.000 1.000 0.000
#> GSM531609 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531610 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531611 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531612 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531613 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531614 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531616 2 0.4605 0.743 0.000 0.796 0.204
#> GSM531618 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531619 2 0.0000 0.956 0.000 1.000 0.000
#> GSM531620 2 0.0000 0.956 0.000 1.000 0.000
#> GSM531621 2 0.0000 0.956 0.000 1.000 0.000
#> GSM531622 2 0.0000 0.956 0.000 1.000 0.000
#> GSM531623 2 0.0000 0.956 0.000 1.000 0.000
#> GSM531625 2 0.0000 0.956 0.000 1.000 0.000
#> GSM531626 2 0.0000 0.956 0.000 1.000 0.000
#> GSM531628 3 0.0000 0.990 0.000 0.000 1.000
#> GSM531630 2 0.0000 0.956 0.000 1.000 0.000
#> GSM531632 3 0.0000 0.990 0.000 0.000 1.000
#> GSM531633 2 0.0000 0.956 0.000 1.000 0.000
#> GSM531635 3 0.0000 0.990 0.000 0.000 1.000
#> GSM531638 2 0.0000 0.956 0.000 1.000 0.000
#> GSM531639 3 0.1860 0.936 0.000 0.052 0.948
#> GSM531640 2 0.0000 0.956 0.000 1.000 0.000
#> GSM531641 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531642 3 0.0000 0.990 0.000 0.000 1.000
#> GSM531643 3 0.0000 0.990 0.000 0.000 1.000
#> GSM531644 3 0.0000 0.990 0.000 0.000 1.000
#> GSM531645 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531646 3 0.0000 0.990 0.000 0.000 1.000
#> GSM531647 3 0.0000 0.990 0.000 0.000 1.000
#> GSM531648 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531649 3 0.0000 0.990 0.000 0.000 1.000
#> GSM531650 3 0.0000 0.990 0.000 0.000 1.000
#> GSM531651 2 0.0000 0.956 0.000 1.000 0.000
#> GSM531652 3 0.0000 0.990 0.000 0.000 1.000
#> GSM531653 3 0.0000 0.990 0.000 0.000 1.000
#> GSM531656 3 0.0000 0.990 0.000 0.000 1.000
#> GSM531657 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531659 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531661 2 0.0000 0.956 0.000 1.000 0.000
#> GSM531662 2 0.0000 0.956 0.000 1.000 0.000
#> GSM531663 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531664 3 0.0000 0.990 0.000 0.000 1.000
#> GSM531665 3 0.0000 0.990 0.000 0.000 1.000
#> GSM531666 3 0.0000 0.990 0.000 0.000 1.000
#> GSM531667 2 0.0000 0.956 0.000 1.000 0.000
#> GSM531668 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531669 3 0.0000 0.990 0.000 0.000 1.000
#> GSM531670 3 0.0000 0.990 0.000 0.000 1.000
#> GSM531671 2 0.6126 0.367 0.000 0.600 0.400
#> GSM531672 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531673 2 0.0237 0.953 0.000 0.996 0.004
#> GSM531674 3 0.0000 0.990 0.000 0.000 1.000
#> GSM531675 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531676 3 0.0000 0.990 0.000 0.000 1.000
#> GSM531677 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531678 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531679 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531680 3 0.0000 0.990 0.000 0.000 1.000
#> GSM531681 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531682 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531683 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531684 2 0.4931 0.682 0.232 0.768 0.000
#> GSM531685 3 0.0000 0.990 0.000 0.000 1.000
#> GSM531686 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531687 3 0.0000 0.990 0.000 0.000 1.000
#> GSM531688 3 0.0000 0.990 0.000 0.000 1.000
#> GSM531689 1 0.4555 0.750 0.800 0.000 0.200
#> GSM531690 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531691 1 0.5254 0.649 0.736 0.000 0.264
#> GSM531692 2 0.4796 0.721 0.000 0.780 0.220
#> GSM531693 3 0.0000 0.990 0.000 0.000 1.000
#> GSM531694 1 0.0000 0.979 1.000 0.000 0.000
#> GSM531695 3 0.0000 0.990 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM531600 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM531601 2 0.1474 0.900 0.000 0.948 0.000 0.052
#> GSM531605 1 0.0000 0.920 1.000 0.000 0.000 0.000
#> GSM531615 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM531617 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM531624 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM531627 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM531629 2 0.4907 0.312 0.000 0.580 0.000 0.420
#> GSM531631 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM531634 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM531636 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM531637 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM531654 2 0.3266 0.791 0.168 0.832 0.000 0.000
#> GSM531655 3 0.4920 0.423 0.368 0.004 0.628 0.000
#> GSM531658 4 0.0000 0.946 0.000 0.000 0.000 1.000
#> GSM531660 4 0.0469 0.938 0.012 0.000 0.000 0.988
#> GSM531602 1 0.1118 0.911 0.964 0.000 0.000 0.036
#> GSM531603 1 0.1118 0.911 0.964 0.000 0.000 0.036
#> GSM531604 1 0.0000 0.920 1.000 0.000 0.000 0.000
#> GSM531606 1 0.0817 0.915 0.976 0.000 0.000 0.024
#> GSM531607 1 0.1118 0.911 0.964 0.000 0.000 0.036
#> GSM531608 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM531609 4 0.0000 0.946 0.000 0.000 0.000 1.000
#> GSM531610 4 0.0000 0.946 0.000 0.000 0.000 1.000
#> GSM531611 4 0.0000 0.946 0.000 0.000 0.000 1.000
#> GSM531612 4 0.0000 0.946 0.000 0.000 0.000 1.000
#> GSM531613 4 0.0000 0.946 0.000 0.000 0.000 1.000
#> GSM531614 4 0.0000 0.946 0.000 0.000 0.000 1.000
#> GSM531616 2 0.3486 0.755 0.000 0.812 0.188 0.000
#> GSM531618 4 0.0000 0.946 0.000 0.000 0.000 1.000
#> GSM531619 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM531620 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM531621 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM531622 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM531623 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM531625 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM531626 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM531628 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM531630 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM531632 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM531633 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM531635 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM531638 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM531639 3 0.2011 0.893 0.000 0.080 0.920 0.000
#> GSM531640 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM531641 4 0.0000 0.946 0.000 0.000 0.000 1.000
#> GSM531642 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM531643 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM531644 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM531645 4 0.0000 0.946 0.000 0.000 0.000 1.000
#> GSM531646 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM531647 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM531648 4 0.0000 0.946 0.000 0.000 0.000 1.000
#> GSM531649 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM531650 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM531651 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM531652 3 0.1637 0.916 0.000 0.000 0.940 0.060
#> GSM531653 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM531656 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM531657 4 0.0000 0.946 0.000 0.000 0.000 1.000
#> GSM531659 4 0.0000 0.946 0.000 0.000 0.000 1.000
#> GSM531661 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM531662 2 0.4730 0.489 0.364 0.636 0.000 0.000
#> GSM531663 4 0.0000 0.946 0.000 0.000 0.000 1.000
#> GSM531664 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM531665 1 0.3266 0.815 0.832 0.000 0.168 0.000
#> GSM531666 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM531667 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM531668 4 0.0188 0.944 0.004 0.000 0.000 0.996
#> GSM531669 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM531670 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM531671 2 0.6854 0.497 0.172 0.596 0.232 0.000
#> GSM531672 4 0.0000 0.946 0.000 0.000 0.000 1.000
#> GSM531673 1 0.0000 0.920 1.000 0.000 0.000 0.000
#> GSM531674 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM531675 4 0.4431 0.587 0.304 0.000 0.000 0.696
#> GSM531676 1 0.3266 0.815 0.832 0.000 0.168 0.000
#> GSM531677 1 0.3569 0.745 0.804 0.000 0.000 0.196
#> GSM531678 1 0.0000 0.920 1.000 0.000 0.000 0.000
#> GSM531679 1 0.0000 0.920 1.000 0.000 0.000 0.000
#> GSM531680 1 0.4776 0.474 0.624 0.000 0.376 0.000
#> GSM531681 4 0.3801 0.732 0.220 0.000 0.000 0.780
#> GSM531682 1 0.0707 0.916 0.980 0.000 0.000 0.020
#> GSM531683 1 0.1211 0.909 0.960 0.000 0.000 0.040
#> GSM531684 1 0.0188 0.919 0.996 0.004 0.000 0.000
#> GSM531685 1 0.3266 0.815 0.832 0.000 0.168 0.000
#> GSM531686 4 0.3801 0.732 0.220 0.000 0.000 0.780
#> GSM531687 1 0.3266 0.815 0.832 0.000 0.168 0.000
#> GSM531688 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM531689 1 0.0000 0.920 1.000 0.000 0.000 0.000
#> GSM531690 4 0.3764 0.737 0.216 0.000 0.000 0.784
#> GSM531691 1 0.0000 0.920 1.000 0.000 0.000 0.000
#> GSM531692 1 0.0000 0.920 1.000 0.000 0.000 0.000
#> GSM531693 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM531694 1 0.1118 0.911 0.964 0.000 0.000 0.036
#> GSM531695 3 0.0000 0.977 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM531600 3 0.1544 0.8839 0.000 0.000 0.932 0.000 0.068
#> GSM531601 2 0.1836 0.8981 0.000 0.932 0.036 0.000 0.032
#> GSM531605 1 0.3857 0.3578 0.688 0.000 0.000 0.000 0.312
#> GSM531615 2 0.0404 0.9404 0.000 0.988 0.000 0.000 0.012
#> GSM531617 2 0.0404 0.9404 0.000 0.988 0.000 0.000 0.012
#> GSM531624 2 0.0000 0.9444 0.000 1.000 0.000 0.000 0.000
#> GSM531627 2 0.0794 0.9365 0.000 0.972 0.000 0.000 0.028
#> GSM531629 4 0.6633 0.2070 0.000 0.304 0.000 0.448 0.248
#> GSM531631 2 0.0000 0.9444 0.000 1.000 0.000 0.000 0.000
#> GSM531634 2 0.0290 0.9420 0.000 0.992 0.000 0.000 0.008
#> GSM531636 3 0.1121 0.8805 0.000 0.000 0.956 0.000 0.044
#> GSM531637 2 0.0000 0.9444 0.000 1.000 0.000 0.000 0.000
#> GSM531654 5 0.5406 0.1637 0.068 0.360 0.000 0.000 0.572
#> GSM531655 3 0.6309 0.1843 0.168 0.000 0.492 0.000 0.340
#> GSM531658 4 0.0404 0.8940 0.000 0.000 0.000 0.988 0.012
#> GSM531660 4 0.5091 0.4373 0.044 0.000 0.000 0.584 0.372
#> GSM531602 1 0.4402 0.3659 0.636 0.000 0.000 0.012 0.352
#> GSM531603 1 0.4491 0.3561 0.624 0.004 0.000 0.008 0.364
#> GSM531604 1 0.4235 -0.3414 0.576 0.000 0.000 0.000 0.424
#> GSM531606 5 0.4390 -0.1180 0.428 0.004 0.000 0.000 0.568
#> GSM531607 1 0.4402 0.3659 0.636 0.000 0.000 0.012 0.352
#> GSM531608 2 0.3177 0.7133 0.000 0.792 0.000 0.000 0.208
#> GSM531609 4 0.0000 0.8992 0.000 0.000 0.000 1.000 0.000
#> GSM531610 4 0.0000 0.8992 0.000 0.000 0.000 1.000 0.000
#> GSM531611 4 0.0000 0.8992 0.000 0.000 0.000 1.000 0.000
#> GSM531612 4 0.0000 0.8992 0.000 0.000 0.000 1.000 0.000
#> GSM531613 4 0.0000 0.8992 0.000 0.000 0.000 1.000 0.000
#> GSM531614 4 0.0000 0.8992 0.000 0.000 0.000 1.000 0.000
#> GSM531616 2 0.4394 0.6326 0.000 0.732 0.220 0.000 0.048
#> GSM531618 4 0.1942 0.8490 0.000 0.012 0.000 0.920 0.068
#> GSM531619 2 0.0000 0.9444 0.000 1.000 0.000 0.000 0.000
#> GSM531620 2 0.0609 0.9400 0.000 0.980 0.000 0.000 0.020
#> GSM531621 2 0.0703 0.9381 0.000 0.976 0.000 0.000 0.024
#> GSM531622 2 0.0000 0.9444 0.000 1.000 0.000 0.000 0.000
#> GSM531623 2 0.0000 0.9444 0.000 1.000 0.000 0.000 0.000
#> GSM531625 2 0.1800 0.9113 0.000 0.932 0.020 0.000 0.048
#> GSM531626 2 0.1981 0.9048 0.000 0.924 0.028 0.000 0.048
#> GSM531628 3 0.0510 0.8881 0.000 0.000 0.984 0.000 0.016
#> GSM531630 2 0.0000 0.9444 0.000 1.000 0.000 0.000 0.000
#> GSM531632 3 0.3424 0.7356 0.000 0.000 0.760 0.000 0.240
#> GSM531633 2 0.0703 0.9381 0.000 0.976 0.000 0.000 0.024
#> GSM531635 3 0.1792 0.8794 0.000 0.000 0.916 0.000 0.084
#> GSM531638 2 0.1981 0.9048 0.000 0.924 0.028 0.000 0.048
#> GSM531639 3 0.2153 0.8539 0.000 0.040 0.916 0.000 0.044
#> GSM531640 2 0.0000 0.9444 0.000 1.000 0.000 0.000 0.000
#> GSM531641 4 0.0000 0.8992 0.000 0.000 0.000 1.000 0.000
#> GSM531642 3 0.1341 0.8776 0.000 0.000 0.944 0.000 0.056
#> GSM531643 3 0.0404 0.8885 0.000 0.000 0.988 0.000 0.012
#> GSM531644 3 0.0794 0.8870 0.000 0.000 0.972 0.000 0.028
#> GSM531645 4 0.0000 0.8992 0.000 0.000 0.000 1.000 0.000
#> GSM531646 3 0.1270 0.8830 0.000 0.000 0.948 0.000 0.052
#> GSM531647 3 0.1197 0.8834 0.000 0.000 0.952 0.000 0.048
#> GSM531648 4 0.0609 0.8903 0.000 0.000 0.000 0.980 0.020
#> GSM531649 3 0.1671 0.8820 0.000 0.000 0.924 0.000 0.076
#> GSM531650 3 0.0404 0.8885 0.000 0.000 0.988 0.000 0.012
#> GSM531651 2 0.0162 0.9438 0.000 0.996 0.000 0.000 0.004
#> GSM531652 3 0.1485 0.8747 0.000 0.000 0.948 0.020 0.032
#> GSM531653 3 0.0963 0.8859 0.000 0.000 0.964 0.000 0.036
#> GSM531656 3 0.1043 0.8820 0.000 0.000 0.960 0.000 0.040
#> GSM531657 4 0.0000 0.8992 0.000 0.000 0.000 1.000 0.000
#> GSM531659 4 0.0000 0.8992 0.000 0.000 0.000 1.000 0.000
#> GSM531661 2 0.3210 0.7070 0.000 0.788 0.000 0.000 0.212
#> GSM531662 5 0.6750 0.3488 0.300 0.292 0.000 0.000 0.408
#> GSM531663 4 0.0000 0.8992 0.000 0.000 0.000 1.000 0.000
#> GSM531664 3 0.0510 0.8881 0.000 0.000 0.984 0.000 0.016
#> GSM531665 5 0.4866 0.4027 0.392 0.000 0.028 0.000 0.580
#> GSM531666 3 0.1043 0.8847 0.000 0.000 0.960 0.000 0.040
#> GSM531667 2 0.0000 0.9444 0.000 1.000 0.000 0.000 0.000
#> GSM531668 4 0.5331 0.4121 0.060 0.000 0.000 0.568 0.372
#> GSM531669 3 0.2462 0.8469 0.008 0.000 0.880 0.000 0.112
#> GSM531670 3 0.1043 0.8820 0.000 0.000 0.960 0.000 0.040
#> GSM531671 5 0.6107 0.4136 0.344 0.044 0.052 0.000 0.560
#> GSM531672 4 0.0693 0.8907 0.008 0.000 0.000 0.980 0.012
#> GSM531673 1 0.4559 -0.4350 0.512 0.008 0.000 0.000 0.480
#> GSM531674 3 0.2304 0.8545 0.008 0.000 0.892 0.000 0.100
#> GSM531675 1 0.4291 0.2445 0.536 0.000 0.000 0.464 0.000
#> GSM531676 1 0.4565 -0.0313 0.664 0.000 0.028 0.000 0.308
#> GSM531677 1 0.2852 0.4018 0.828 0.000 0.000 0.172 0.000
#> GSM531678 1 0.0794 0.3665 0.972 0.000 0.000 0.000 0.028
#> GSM531679 1 0.0000 0.3745 1.000 0.000 0.000 0.000 0.000
#> GSM531680 1 0.5547 0.2130 0.644 0.000 0.208 0.000 0.148
#> GSM531681 1 0.4306 0.1855 0.508 0.000 0.000 0.492 0.000
#> GSM531682 1 0.0992 0.3672 0.968 0.000 0.000 0.008 0.024
#> GSM531683 1 0.4402 0.3659 0.636 0.000 0.000 0.012 0.352
#> GSM531684 5 0.4425 -0.0834 0.452 0.004 0.000 0.000 0.544
#> GSM531685 5 0.4942 0.3693 0.432 0.000 0.028 0.000 0.540
#> GSM531686 1 0.4306 0.1855 0.508 0.000 0.000 0.492 0.000
#> GSM531687 1 0.3650 0.2300 0.796 0.000 0.028 0.000 0.176
#> GSM531688 3 0.5903 0.3908 0.120 0.000 0.548 0.000 0.332
#> GSM531689 1 0.2377 0.2835 0.872 0.000 0.000 0.000 0.128
#> GSM531690 1 0.4306 0.1855 0.508 0.000 0.000 0.492 0.000
#> GSM531691 1 0.2561 0.2637 0.856 0.000 0.000 0.000 0.144
#> GSM531692 5 0.4300 0.3570 0.476 0.000 0.000 0.000 0.524
#> GSM531693 3 0.5834 0.3909 0.108 0.000 0.544 0.000 0.348
#> GSM531694 1 0.4402 0.3659 0.636 0.000 0.000 0.012 0.352
#> GSM531695 1 0.6032 0.1331 0.508 0.000 0.368 0.000 0.124
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM531600 3 0.0260 0.4740 0.000 0.000 0.992 0.000 0.000 0.008
#> GSM531601 2 0.3728 0.5349 0.000 0.652 0.000 0.004 0.000 0.344
#> GSM531605 1 0.1584 0.5287 0.928 0.000 0.000 0.000 0.064 0.008
#> GSM531615 2 0.1643 0.8027 0.000 0.924 0.000 0.000 0.008 0.068
#> GSM531617 2 0.2001 0.7902 0.000 0.900 0.000 0.004 0.004 0.092
#> GSM531624 2 0.0000 0.8225 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531627 2 0.4053 0.7530 0.000 0.764 0.128 0.000 0.004 0.104
#> GSM531629 2 0.6474 0.3698 0.092 0.540 0.000 0.260 0.004 0.104
#> GSM531631 2 0.0146 0.8230 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM531634 2 0.1387 0.8041 0.000 0.932 0.000 0.000 0.000 0.068
#> GSM531636 3 0.2320 0.3673 0.000 0.000 0.864 0.000 0.004 0.132
#> GSM531637 2 0.0000 0.8225 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531654 1 0.7301 0.1242 0.424 0.176 0.000 0.000 0.168 0.232
#> GSM531655 1 0.7017 -0.1776 0.404 0.004 0.232 0.000 0.060 0.300
#> GSM531658 4 0.1387 0.8355 0.000 0.000 0.000 0.932 0.000 0.068
#> GSM531660 1 0.4602 0.1476 0.572 0.000 0.000 0.384 0.000 0.044
#> GSM531602 1 0.0000 0.5537 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531603 1 0.0260 0.5529 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM531604 5 0.4923 0.3538 0.176 0.000 0.000 0.000 0.656 0.168
#> GSM531606 1 0.3746 0.4461 0.780 0.000 0.000 0.000 0.140 0.080
#> GSM531607 1 0.0146 0.5532 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM531608 2 0.5209 0.4671 0.000 0.612 0.000 0.000 0.168 0.220
#> GSM531609 4 0.0146 0.8594 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM531610 4 0.0146 0.8589 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM531611 4 0.0146 0.8592 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM531612 4 0.0363 0.8585 0.000 0.000 0.000 0.988 0.000 0.012
#> GSM531613 4 0.0291 0.8581 0.004 0.000 0.000 0.992 0.000 0.004
#> GSM531614 4 0.0146 0.8594 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM531616 2 0.5146 0.6323 0.000 0.616 0.264 0.000 0.004 0.116
#> GSM531618 4 0.4774 0.5758 0.020 0.044 0.000 0.648 0.000 0.288
#> GSM531619 2 0.0146 0.8220 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM531620 2 0.3726 0.7730 0.000 0.792 0.080 0.000 0.004 0.124
#> GSM531621 2 0.3753 0.7684 0.000 0.792 0.100 0.000 0.004 0.104
#> GSM531622 2 0.0260 0.8233 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM531623 2 0.0632 0.8230 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM531625 2 0.4896 0.6827 0.000 0.664 0.212 0.000 0.004 0.120
#> GSM531626 2 0.4921 0.6799 0.000 0.660 0.216 0.000 0.004 0.120
#> GSM531628 3 0.3126 0.4137 0.000 0.000 0.752 0.000 0.000 0.248
#> GSM531630 2 0.0458 0.8233 0.000 0.984 0.000 0.000 0.000 0.016
#> GSM531632 3 0.4684 0.3289 0.000 0.000 0.656 0.000 0.088 0.256
#> GSM531633 2 0.3706 0.7703 0.000 0.796 0.096 0.000 0.004 0.104
#> GSM531635 3 0.1858 0.4277 0.000 0.000 0.904 0.000 0.004 0.092
#> GSM531638 2 0.4921 0.6794 0.000 0.660 0.216 0.000 0.004 0.120
#> GSM531639 3 0.3481 0.2042 0.000 0.012 0.756 0.000 0.004 0.228
#> GSM531640 2 0.0146 0.8230 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM531641 4 0.0458 0.8576 0.000 0.000 0.000 0.984 0.000 0.016
#> GSM531642 3 0.3934 -0.3671 0.000 0.000 0.616 0.008 0.000 0.376
#> GSM531643 3 0.3198 0.3903 0.000 0.000 0.740 0.000 0.000 0.260
#> GSM531644 6 0.3991 0.8183 0.000 0.000 0.472 0.004 0.000 0.524
#> GSM531645 4 0.0458 0.8576 0.000 0.000 0.000 0.984 0.000 0.016
#> GSM531646 3 0.2994 0.4719 0.000 0.000 0.788 0.000 0.004 0.208
#> GSM531647 3 0.2793 0.4592 0.000 0.000 0.800 0.000 0.000 0.200
#> GSM531648 4 0.3221 0.6668 0.000 0.000 0.000 0.736 0.000 0.264
#> GSM531649 3 0.1644 0.4420 0.000 0.000 0.920 0.000 0.004 0.076
#> GSM531650 3 0.3126 0.4137 0.000 0.000 0.752 0.000 0.000 0.248
#> GSM531651 2 0.0632 0.8230 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM531652 6 0.4569 0.7711 0.000 0.000 0.396 0.040 0.000 0.564
#> GSM531653 3 0.2664 0.4719 0.000 0.000 0.816 0.000 0.000 0.184
#> GSM531656 3 0.1814 0.3914 0.000 0.000 0.900 0.000 0.000 0.100
#> GSM531657 4 0.1720 0.8243 0.040 0.000 0.000 0.928 0.000 0.032
#> GSM531659 4 0.1010 0.8464 0.004 0.000 0.000 0.960 0.000 0.036
#> GSM531661 2 0.5273 0.4722 0.004 0.620 0.000 0.000 0.168 0.208
#> GSM531662 5 0.5587 0.3474 0.012 0.140 0.004 0.000 0.600 0.244
#> GSM531663 4 0.0405 0.8570 0.004 0.000 0.000 0.988 0.000 0.008
#> GSM531664 3 0.3126 0.4137 0.000 0.000 0.752 0.000 0.000 0.248
#> GSM531665 5 0.1901 0.5144 0.004 0.000 0.008 0.000 0.912 0.076
#> GSM531666 6 0.3975 0.8515 0.000 0.000 0.452 0.004 0.000 0.544
#> GSM531667 2 0.0806 0.8167 0.000 0.972 0.000 0.000 0.008 0.020
#> GSM531668 1 0.4871 0.2873 0.616 0.000 0.000 0.296 0.000 0.088
#> GSM531669 3 0.3892 0.4072 0.000 0.000 0.740 0.000 0.048 0.212
#> GSM531670 3 0.1814 0.3914 0.000 0.000 0.900 0.000 0.000 0.100
#> GSM531671 5 0.5417 0.4070 0.000 0.060 0.040 0.000 0.592 0.308
#> GSM531672 4 0.2201 0.8202 0.048 0.000 0.000 0.900 0.000 0.052
#> GSM531673 5 0.4033 0.4340 0.052 0.000 0.000 0.000 0.724 0.224
#> GSM531674 3 0.3630 0.4231 0.000 0.000 0.756 0.000 0.032 0.212
#> GSM531675 1 0.5228 0.0839 0.504 0.000 0.000 0.424 0.016 0.056
#> GSM531676 5 0.2814 0.4685 0.172 0.000 0.000 0.000 0.820 0.008
#> GSM531677 1 0.6236 0.2762 0.548 0.000 0.000 0.144 0.252 0.056
#> GSM531678 1 0.4649 0.0157 0.492 0.000 0.000 0.000 0.468 0.040
#> GSM531679 1 0.4905 0.1580 0.552 0.000 0.000 0.004 0.388 0.056
#> GSM531680 5 0.7315 0.2963 0.240 0.000 0.136 0.000 0.408 0.216
#> GSM531681 4 0.5215 0.0542 0.412 0.000 0.000 0.516 0.016 0.056
#> GSM531682 1 0.5028 0.1358 0.536 0.000 0.000 0.008 0.400 0.056
#> GSM531683 1 0.1453 0.5429 0.944 0.000 0.000 0.008 0.008 0.040
#> GSM531684 1 0.5612 0.2268 0.564 0.008 0.000 0.000 0.272 0.156
#> GSM531685 5 0.2982 0.5163 0.060 0.000 0.068 0.000 0.860 0.012
#> GSM531686 4 0.5378 -0.0217 0.432 0.000 0.000 0.488 0.024 0.056
#> GSM531687 5 0.4280 0.4017 0.232 0.000 0.004 0.000 0.708 0.056
#> GSM531688 5 0.6038 -0.0931 0.004 0.000 0.356 0.000 0.428 0.212
#> GSM531689 5 0.3817 0.3832 0.252 0.000 0.000 0.000 0.720 0.028
#> GSM531690 1 0.5247 -0.0166 0.468 0.000 0.000 0.460 0.016 0.056
#> GSM531691 5 0.3470 0.4015 0.248 0.000 0.000 0.000 0.740 0.012
#> GSM531692 5 0.2877 0.4831 0.012 0.000 0.000 0.000 0.820 0.168
#> GSM531693 5 0.5954 -0.1215 0.000 0.000 0.372 0.000 0.408 0.220
#> GSM531694 1 0.0000 0.5537 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531695 5 0.7640 0.1504 0.220 0.000 0.204 0.000 0.328 0.248
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> MAD:skmeans 91 0.32171 2
#> MAD:skmeans 95 0.01400 3
#> MAD:skmeans 91 0.00951 4
#> MAD:skmeans 60 0.04834 5
#> MAD:skmeans 48 0.16981 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "pam"]
# you can also extract it by
# res = res_list["MAD:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 96 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
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 0.531 0.830 0.890 0.4565 0.558 0.558
#> 3 3 0.638 0.716 0.835 0.4352 0.772 0.596
#> 4 4 0.567 0.477 0.728 0.1252 0.766 0.438
#> 5 5 0.715 0.723 0.846 0.0727 0.778 0.354
#> 6 6 0.776 0.739 0.867 0.0441 0.950 0.772
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
#> GSM531600 2 0.0000 0.872 0.000 1.000
#> GSM531601 2 0.3274 0.862 0.060 0.940
#> GSM531605 1 0.7883 0.809 0.764 0.236
#> GSM531615 2 0.8207 0.784 0.256 0.744
#> GSM531617 2 0.8207 0.784 0.256 0.744
#> GSM531624 2 0.3274 0.862 0.060 0.940
#> GSM531627 2 0.0000 0.872 0.000 1.000
#> GSM531629 2 0.8207 0.784 0.256 0.744
#> GSM531631 2 0.0000 0.872 0.000 1.000
#> GSM531634 2 0.3274 0.862 0.060 0.940
#> GSM531636 2 0.0000 0.872 0.000 1.000
#> GSM531637 2 0.0000 0.872 0.000 1.000
#> GSM531654 2 0.8443 0.769 0.272 0.728
#> GSM531655 2 0.0000 0.872 0.000 1.000
#> GSM531658 2 0.8207 0.784 0.256 0.744
#> GSM531660 2 0.8608 0.757 0.284 0.716
#> GSM531602 1 0.0000 0.848 1.000 0.000
#> GSM531603 1 0.1184 0.840 0.984 0.016
#> GSM531604 1 0.2043 0.847 0.968 0.032
#> GSM531606 1 0.0000 0.848 1.000 0.000
#> GSM531607 1 0.0000 0.848 1.000 0.000
#> GSM531608 2 0.8207 0.784 0.256 0.744
#> GSM531609 2 0.8207 0.784 0.256 0.744
#> GSM531610 2 0.8207 0.784 0.256 0.744
#> GSM531611 2 0.8207 0.784 0.256 0.744
#> GSM531612 2 0.8207 0.784 0.256 0.744
#> GSM531613 2 0.9710 0.589 0.400 0.600
#> GSM531614 2 0.8207 0.784 0.256 0.744
#> GSM531616 2 0.0000 0.872 0.000 1.000
#> GSM531618 2 0.8207 0.784 0.256 0.744
#> GSM531619 2 0.0000 0.872 0.000 1.000
#> GSM531620 2 0.0000 0.872 0.000 1.000
#> GSM531621 2 0.0000 0.872 0.000 1.000
#> GSM531622 2 0.2948 0.864 0.052 0.948
#> GSM531623 2 0.0000 0.872 0.000 1.000
#> GSM531625 2 0.0000 0.872 0.000 1.000
#> GSM531626 2 0.0000 0.872 0.000 1.000
#> GSM531628 2 0.0000 0.872 0.000 1.000
#> GSM531630 2 0.0000 0.872 0.000 1.000
#> GSM531632 2 0.0000 0.872 0.000 1.000
#> GSM531633 2 0.0000 0.872 0.000 1.000
#> GSM531635 2 0.0000 0.872 0.000 1.000
#> GSM531638 2 0.0000 0.872 0.000 1.000
#> GSM531639 2 0.0000 0.872 0.000 1.000
#> GSM531640 2 0.0376 0.872 0.004 0.996
#> GSM531641 2 0.8207 0.784 0.256 0.744
#> GSM531642 2 0.0000 0.872 0.000 1.000
#> GSM531643 2 0.0000 0.872 0.000 1.000
#> GSM531644 2 0.1843 0.869 0.028 0.972
#> GSM531645 2 0.8207 0.784 0.256 0.744
#> GSM531646 2 0.0000 0.872 0.000 1.000
#> GSM531647 2 0.0000 0.872 0.000 1.000
#> GSM531648 2 0.8207 0.784 0.256 0.744
#> GSM531649 2 0.0000 0.872 0.000 1.000
#> GSM531650 2 0.0000 0.872 0.000 1.000
#> GSM531651 2 0.0000 0.872 0.000 1.000
#> GSM531652 2 0.7139 0.810 0.196 0.804
#> GSM531653 2 0.0000 0.872 0.000 1.000
#> GSM531656 2 0.0000 0.872 0.000 1.000
#> GSM531657 2 0.8207 0.784 0.256 0.744
#> GSM531659 2 0.8207 0.784 0.256 0.744
#> GSM531661 2 0.0000 0.872 0.000 1.000
#> GSM531662 2 0.1843 0.869 0.028 0.972
#> GSM531663 2 0.8386 0.773 0.268 0.732
#> GSM531664 1 0.9129 0.732 0.672 0.328
#> GSM531665 1 0.4298 0.836 0.912 0.088
#> GSM531666 2 0.0376 0.872 0.004 0.996
#> GSM531667 2 0.8144 0.785 0.252 0.748
#> GSM531668 2 0.8207 0.784 0.256 0.744
#> GSM531669 1 0.8955 0.753 0.688 0.312
#> GSM531670 2 0.3584 0.810 0.068 0.932
#> GSM531671 2 0.2043 0.865 0.032 0.968
#> GSM531672 2 0.8207 0.784 0.256 0.744
#> GSM531673 2 0.2423 0.867 0.040 0.960
#> GSM531674 1 0.8608 0.782 0.716 0.284
#> GSM531675 1 0.0000 0.848 1.000 0.000
#> GSM531676 1 0.8207 0.800 0.744 0.256
#> GSM531677 1 0.0000 0.848 1.000 0.000
#> GSM531678 1 0.0672 0.848 0.992 0.008
#> GSM531679 1 0.0000 0.848 1.000 0.000
#> GSM531680 1 0.8207 0.800 0.744 0.256
#> GSM531681 1 0.0000 0.848 1.000 0.000
#> GSM531682 1 0.0000 0.848 1.000 0.000
#> GSM531683 1 0.0000 0.848 1.000 0.000
#> GSM531684 1 0.0000 0.848 1.000 0.000
#> GSM531685 1 0.8207 0.800 0.744 0.256
#> GSM531686 1 0.0000 0.848 1.000 0.000
#> GSM531687 1 0.8608 0.782 0.716 0.284
#> GSM531688 1 0.8207 0.800 0.744 0.256
#> GSM531689 1 0.7376 0.817 0.792 0.208
#> GSM531690 1 0.0000 0.848 1.000 0.000
#> GSM531691 1 0.8207 0.800 0.744 0.256
#> GSM531692 1 0.8207 0.800 0.744 0.256
#> GSM531693 1 0.8608 0.782 0.716 0.284
#> GSM531694 1 0.0000 0.848 1.000 0.000
#> GSM531695 1 0.8144 0.802 0.748 0.252
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM531600 3 0.5859 0.6599 0.344 0.000 0.656
#> GSM531601 3 0.5859 0.4752 0.000 0.344 0.656
#> GSM531605 1 0.5859 0.7550 0.656 0.000 0.344
#> GSM531615 2 0.0000 0.8839 0.000 1.000 0.000
#> GSM531617 2 0.6299 0.2496 0.000 0.524 0.476
#> GSM531624 2 0.0000 0.8839 0.000 1.000 0.000
#> GSM531627 2 0.0000 0.8839 0.000 1.000 0.000
#> GSM531629 3 0.0000 0.7615 0.000 0.000 1.000
#> GSM531631 2 0.0000 0.8839 0.000 1.000 0.000
#> GSM531634 2 0.0000 0.8839 0.000 1.000 0.000
#> GSM531636 3 0.7906 0.6521 0.220 0.124 0.656
#> GSM531637 2 0.0000 0.8839 0.000 1.000 0.000
#> GSM531654 2 0.5859 0.4790 0.000 0.656 0.344
#> GSM531655 3 0.0000 0.7615 0.000 0.000 1.000
#> GSM531658 3 0.0000 0.7615 0.000 0.000 1.000
#> GSM531660 3 0.0747 0.7470 0.016 0.000 0.984
#> GSM531602 1 0.5859 0.7550 0.656 0.000 0.344
#> GSM531603 1 0.5905 0.7476 0.648 0.000 0.352
#> GSM531604 1 0.5859 0.7550 0.656 0.000 0.344
#> GSM531606 1 0.5859 0.7550 0.656 0.000 0.344
#> GSM531607 1 0.5859 0.7550 0.656 0.000 0.344
#> GSM531608 2 0.0000 0.8839 0.000 1.000 0.000
#> GSM531609 3 0.0000 0.7615 0.000 0.000 1.000
#> GSM531610 3 0.0000 0.7615 0.000 0.000 1.000
#> GSM531611 3 0.0000 0.7615 0.000 0.000 1.000
#> GSM531612 3 0.0000 0.7615 0.000 0.000 1.000
#> GSM531613 3 0.3038 0.6371 0.104 0.000 0.896
#> GSM531614 3 0.0000 0.7615 0.000 0.000 1.000
#> GSM531616 2 0.6244 0.1028 0.000 0.560 0.440
#> GSM531618 3 0.0000 0.7615 0.000 0.000 1.000
#> GSM531619 2 0.0000 0.8839 0.000 1.000 0.000
#> GSM531620 2 0.0000 0.8839 0.000 1.000 0.000
#> GSM531621 2 0.0000 0.8839 0.000 1.000 0.000
#> GSM531622 2 0.0000 0.8839 0.000 1.000 0.000
#> GSM531623 2 0.0000 0.8839 0.000 1.000 0.000
#> GSM531625 2 0.0000 0.8839 0.000 1.000 0.000
#> GSM531626 2 0.5058 0.5915 0.000 0.756 0.244
#> GSM531628 3 0.5859 0.6599 0.344 0.000 0.656
#> GSM531630 2 0.0000 0.8839 0.000 1.000 0.000
#> GSM531632 3 0.5859 0.6599 0.344 0.000 0.656
#> GSM531633 2 0.0000 0.8839 0.000 1.000 0.000
#> GSM531635 3 0.5859 0.6599 0.344 0.000 0.656
#> GSM531638 3 0.5859 0.4752 0.000 0.344 0.656
#> GSM531639 3 0.5859 0.4752 0.000 0.344 0.656
#> GSM531640 3 0.5859 0.4752 0.000 0.344 0.656
#> GSM531641 3 0.0000 0.7615 0.000 0.000 1.000
#> GSM531642 3 0.0000 0.7615 0.000 0.000 1.000
#> GSM531643 3 0.5859 0.6599 0.344 0.000 0.656
#> GSM531644 3 0.5859 0.6599 0.344 0.000 0.656
#> GSM531645 3 0.0000 0.7615 0.000 0.000 1.000
#> GSM531646 3 0.5859 0.6599 0.344 0.000 0.656
#> GSM531647 3 0.5859 0.6599 0.344 0.000 0.656
#> GSM531648 3 0.0000 0.7615 0.000 0.000 1.000
#> GSM531649 3 0.5859 0.6599 0.344 0.000 0.656
#> GSM531650 3 0.5859 0.6599 0.344 0.000 0.656
#> GSM531651 2 0.0000 0.8839 0.000 1.000 0.000
#> GSM531652 3 0.0000 0.7615 0.000 0.000 1.000
#> GSM531653 3 0.5859 0.6599 0.344 0.000 0.656
#> GSM531656 3 0.5859 0.6599 0.344 0.000 0.656
#> GSM531657 3 0.0000 0.7615 0.000 0.000 1.000
#> GSM531659 3 0.0000 0.7615 0.000 0.000 1.000
#> GSM531661 2 0.0000 0.8839 0.000 1.000 0.000
#> GSM531662 2 0.0000 0.8839 0.000 1.000 0.000
#> GSM531663 3 0.0424 0.7545 0.008 0.000 0.992
#> GSM531664 1 0.2066 0.6785 0.940 0.000 0.060
#> GSM531665 1 0.0892 0.7216 0.980 0.000 0.020
#> GSM531666 3 0.2625 0.7443 0.084 0.000 0.916
#> GSM531667 3 0.5926 0.2030 0.000 0.356 0.644
#> GSM531668 3 0.0000 0.7615 0.000 0.000 1.000
#> GSM531669 1 0.1643 0.6958 0.956 0.000 0.044
#> GSM531670 3 0.5948 0.6433 0.360 0.000 0.640
#> GSM531671 2 0.9816 0.0472 0.356 0.400 0.244
#> GSM531672 3 0.0000 0.7615 0.000 0.000 1.000
#> GSM531673 2 0.6062 0.4331 0.000 0.616 0.384
#> GSM531674 1 0.0747 0.7193 0.984 0.000 0.016
#> GSM531675 1 0.5859 0.7550 0.656 0.000 0.344
#> GSM531676 1 0.0000 0.7263 1.000 0.000 0.000
#> GSM531677 1 0.5859 0.7550 0.656 0.000 0.344
#> GSM531678 1 0.5859 0.7550 0.656 0.000 0.344
#> GSM531679 1 0.5859 0.7550 0.656 0.000 0.344
#> GSM531680 1 0.0000 0.7263 1.000 0.000 0.000
#> GSM531681 1 0.5859 0.7550 0.656 0.000 0.344
#> GSM531682 1 0.5859 0.7550 0.656 0.000 0.344
#> GSM531683 1 0.5859 0.7550 0.656 0.000 0.344
#> GSM531684 2 0.0000 0.8839 0.000 1.000 0.000
#> GSM531685 1 0.0000 0.7263 1.000 0.000 0.000
#> GSM531686 1 0.5859 0.7550 0.656 0.000 0.344
#> GSM531687 1 0.0747 0.7193 0.984 0.000 0.016
#> GSM531688 1 0.0000 0.7263 1.000 0.000 0.000
#> GSM531689 1 0.0424 0.7293 0.992 0.000 0.008
#> GSM531690 1 0.5859 0.7550 0.656 0.000 0.344
#> GSM531691 1 0.0237 0.7281 0.996 0.000 0.004
#> GSM531692 1 0.1031 0.7234 0.976 0.024 0.000
#> GSM531693 1 0.0747 0.7193 0.984 0.000 0.016
#> GSM531694 1 0.5859 0.7550 0.656 0.000 0.344
#> GSM531695 1 0.0000 0.7263 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM531600 3 0.4996 0.372 0.484 0.000 0.516 0.000
#> GSM531601 3 0.6400 0.205 0.000 0.408 0.524 0.068
#> GSM531605 1 0.4781 0.497 0.660 0.000 0.004 0.336
#> GSM531615 2 0.0000 0.894 0.000 1.000 0.000 0.000
#> GSM531617 4 0.6520 0.301 0.000 0.384 0.080 0.536
#> GSM531624 2 0.0000 0.894 0.000 1.000 0.000 0.000
#> GSM531627 2 0.0000 0.894 0.000 1.000 0.000 0.000
#> GSM531629 4 0.4977 0.427 0.000 0.000 0.460 0.540
#> GSM531631 2 0.0000 0.894 0.000 1.000 0.000 0.000
#> GSM531634 2 0.0000 0.894 0.000 1.000 0.000 0.000
#> GSM531636 3 0.3726 0.525 0.212 0.000 0.788 0.000
#> GSM531637 2 0.0000 0.894 0.000 1.000 0.000 0.000
#> GSM531654 4 0.4981 0.158 0.000 0.464 0.000 0.536
#> GSM531655 3 0.4950 0.448 0.376 0.000 0.620 0.004
#> GSM531658 4 0.4222 0.549 0.000 0.000 0.272 0.728
#> GSM531660 4 0.4977 0.427 0.000 0.000 0.460 0.540
#> GSM531602 4 0.3528 0.473 0.192 0.000 0.000 0.808
#> GSM531603 4 0.7593 0.455 0.300 0.000 0.228 0.472
#> GSM531604 1 0.4817 0.426 0.612 0.000 0.000 0.388
#> GSM531606 4 0.3528 0.473 0.192 0.000 0.000 0.808
#> GSM531607 4 0.4967 -0.103 0.452 0.000 0.000 0.548
#> GSM531608 2 0.0000 0.894 0.000 1.000 0.000 0.000
#> GSM531609 4 0.4250 0.547 0.000 0.000 0.276 0.724
#> GSM531610 4 0.4250 0.547 0.000 0.000 0.276 0.724
#> GSM531611 4 0.7193 0.353 0.152 0.000 0.340 0.508
#> GSM531612 4 0.4624 0.484 0.000 0.000 0.340 0.660
#> GSM531613 4 0.1389 0.555 0.000 0.000 0.048 0.952
#> GSM531614 4 0.4250 0.547 0.000 0.000 0.276 0.724
#> GSM531616 2 0.7784 -0.160 0.252 0.412 0.336 0.000
#> GSM531618 3 0.4992 -0.351 0.000 0.000 0.524 0.476
#> GSM531619 2 0.0000 0.894 0.000 1.000 0.000 0.000
#> GSM531620 2 0.0000 0.894 0.000 1.000 0.000 0.000
#> GSM531621 2 0.0000 0.894 0.000 1.000 0.000 0.000
#> GSM531622 2 0.0000 0.894 0.000 1.000 0.000 0.000
#> GSM531623 2 0.0000 0.894 0.000 1.000 0.000 0.000
#> GSM531625 2 0.0000 0.894 0.000 1.000 0.000 0.000
#> GSM531626 2 0.5448 0.502 0.056 0.700 0.244 0.000
#> GSM531628 3 0.4522 0.282 0.320 0.000 0.680 0.000
#> GSM531630 2 0.0000 0.894 0.000 1.000 0.000 0.000
#> GSM531632 3 0.4624 0.268 0.340 0.000 0.660 0.000
#> GSM531633 2 0.0000 0.894 0.000 1.000 0.000 0.000
#> GSM531635 3 0.4103 0.519 0.256 0.000 0.744 0.000
#> GSM531638 3 0.7500 0.424 0.252 0.248 0.500 0.000
#> GSM531639 3 0.4837 0.473 0.348 0.004 0.648 0.000
#> GSM531640 3 0.6991 0.326 0.128 0.348 0.524 0.000
#> GSM531641 4 0.4250 0.547 0.000 0.000 0.276 0.724
#> GSM531642 3 0.7344 0.186 0.224 0.000 0.528 0.248
#> GSM531643 3 0.0188 0.506 0.004 0.000 0.996 0.000
#> GSM531644 3 0.1022 0.506 0.032 0.000 0.968 0.000
#> GSM531645 4 0.4624 0.484 0.000 0.000 0.340 0.660
#> GSM531646 3 0.4624 0.268 0.340 0.000 0.660 0.000
#> GSM531647 3 0.4624 0.268 0.340 0.000 0.660 0.000
#> GSM531648 4 0.4977 0.427 0.000 0.000 0.460 0.540
#> GSM531649 3 0.4996 0.372 0.484 0.000 0.516 0.000
#> GSM531650 3 0.3907 0.356 0.232 0.000 0.768 0.000
#> GSM531651 2 0.0000 0.894 0.000 1.000 0.000 0.000
#> GSM531652 3 0.3907 0.241 0.000 0.000 0.768 0.232
#> GSM531653 3 0.4624 0.268 0.340 0.000 0.660 0.000
#> GSM531656 3 0.4730 0.463 0.364 0.000 0.636 0.000
#> GSM531657 4 0.4977 0.427 0.000 0.000 0.460 0.540
#> GSM531659 4 0.5147 0.428 0.004 0.000 0.460 0.536
#> GSM531661 2 0.0000 0.894 0.000 1.000 0.000 0.000
#> GSM531662 2 0.0188 0.891 0.004 0.996 0.000 0.000
#> GSM531663 4 0.4222 0.549 0.000 0.000 0.272 0.728
#> GSM531664 1 0.6005 0.297 0.500 0.000 0.460 0.040
#> GSM531665 1 0.2996 0.511 0.892 0.000 0.064 0.044
#> GSM531666 3 0.4741 0.484 0.328 0.000 0.668 0.004
#> GSM531667 2 0.8392 -0.155 0.020 0.376 0.260 0.344
#> GSM531668 4 0.4972 0.431 0.000 0.000 0.456 0.544
#> GSM531669 1 0.4989 0.225 0.528 0.000 0.472 0.000
#> GSM531670 3 0.4992 0.331 0.476 0.000 0.524 0.000
#> GSM531671 3 0.8325 0.170 0.340 0.020 0.400 0.240
#> GSM531672 4 0.4977 0.427 0.000 0.000 0.460 0.540
#> GSM531673 2 0.6777 -0.108 0.080 0.460 0.004 0.456
#> GSM531674 1 0.4981 0.236 0.536 0.000 0.464 0.000
#> GSM531675 4 0.3528 0.473 0.192 0.000 0.000 0.808
#> GSM531676 1 0.4222 0.580 0.728 0.000 0.000 0.272
#> GSM531677 4 0.3528 0.473 0.192 0.000 0.000 0.808
#> GSM531678 4 0.4989 -0.158 0.472 0.000 0.000 0.528
#> GSM531679 4 0.4967 -0.103 0.452 0.000 0.000 0.548
#> GSM531680 1 0.7153 0.594 0.556 0.000 0.196 0.248
#> GSM531681 4 0.0188 0.533 0.000 0.000 0.004 0.996
#> GSM531682 4 0.3528 0.473 0.192 0.000 0.000 0.808
#> GSM531683 4 0.3528 0.473 0.192 0.000 0.000 0.808
#> GSM531684 2 0.0188 0.891 0.000 0.996 0.000 0.004
#> GSM531685 1 0.4914 0.567 0.748 0.000 0.208 0.044
#> GSM531686 4 0.4605 0.221 0.336 0.000 0.000 0.664
#> GSM531687 1 0.5090 0.316 0.728 0.000 0.228 0.044
#> GSM531688 1 0.4914 0.567 0.748 0.000 0.208 0.044
#> GSM531689 1 0.4222 0.580 0.728 0.000 0.000 0.272
#> GSM531690 4 0.3528 0.473 0.192 0.000 0.000 0.808
#> GSM531691 1 0.4222 0.580 0.728 0.000 0.000 0.272
#> GSM531692 1 0.5280 0.511 0.748 0.156 0.000 0.096
#> GSM531693 1 0.3873 0.515 0.772 0.000 0.228 0.000
#> GSM531694 4 0.3528 0.473 0.192 0.000 0.000 0.808
#> GSM531695 1 0.4914 0.567 0.748 0.000 0.208 0.044
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM531600 3 0.3242 0.698 0.000 0.000 0.784 0.000 0.216
#> GSM531601 5 0.3684 0.581 0.000 0.280 0.000 0.000 0.720
#> GSM531605 1 0.3684 0.659 0.720 0.000 0.000 0.000 0.280
#> GSM531615 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> GSM531617 2 0.6836 0.308 0.280 0.544 0.000 0.052 0.124
#> GSM531624 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> GSM531627 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> GSM531629 5 0.4844 0.660 0.280 0.000 0.000 0.052 0.668
#> GSM531631 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> GSM531634 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> GSM531636 5 0.1732 0.699 0.000 0.000 0.080 0.000 0.920
#> GSM531637 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> GSM531654 2 0.4844 0.513 0.280 0.668 0.000 0.052 0.000
#> GSM531655 5 0.0000 0.703 0.000 0.000 0.000 0.000 1.000
#> GSM531658 4 0.3957 0.567 0.280 0.000 0.000 0.712 0.008
#> GSM531660 5 0.5302 0.486 0.412 0.000 0.000 0.052 0.536
#> GSM531602 1 0.1270 0.781 0.948 0.000 0.000 0.052 0.000
#> GSM531603 5 0.5314 0.492 0.420 0.000 0.000 0.052 0.528
#> GSM531604 1 0.3424 0.693 0.760 0.000 0.000 0.000 0.240
#> GSM531606 1 0.1270 0.781 0.948 0.000 0.000 0.052 0.000
#> GSM531607 1 0.1732 0.772 0.920 0.000 0.000 0.000 0.080
#> GSM531608 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> GSM531609 4 0.0000 0.886 0.000 0.000 0.000 1.000 0.000
#> GSM531610 4 0.0000 0.886 0.000 0.000 0.000 1.000 0.000
#> GSM531611 4 0.1430 0.863 0.004 0.000 0.000 0.944 0.052
#> GSM531612 4 0.1270 0.862 0.000 0.000 0.000 0.948 0.052
#> GSM531613 4 0.0000 0.886 0.000 0.000 0.000 1.000 0.000
#> GSM531614 4 0.0000 0.886 0.000 0.000 0.000 1.000 0.000
#> GSM531616 5 0.5752 0.445 0.000 0.240 0.148 0.000 0.612
#> GSM531618 5 0.3684 0.682 0.280 0.000 0.000 0.000 0.720
#> GSM531619 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> GSM531620 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> GSM531621 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> GSM531622 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> GSM531623 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> GSM531625 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> GSM531626 2 0.5991 0.302 0.000 0.564 0.148 0.000 0.288
#> GSM531628 3 0.1043 0.814 0.000 0.000 0.960 0.000 0.040
#> GSM531630 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> GSM531632 3 0.0000 0.823 0.000 0.000 1.000 0.000 0.000
#> GSM531633 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> GSM531635 5 0.3274 0.601 0.000 0.000 0.220 0.000 0.780
#> GSM531638 5 0.3953 0.630 0.000 0.060 0.148 0.000 0.792
#> GSM531639 5 0.0162 0.704 0.000 0.000 0.004 0.000 0.996
#> GSM531640 5 0.2732 0.672 0.000 0.160 0.000 0.000 0.840
#> GSM531641 4 0.0000 0.886 0.000 0.000 0.000 1.000 0.000
#> GSM531642 5 0.1608 0.718 0.072 0.000 0.000 0.000 0.928
#> GSM531643 5 0.3707 0.579 0.000 0.000 0.284 0.000 0.716
#> GSM531644 5 0.3561 0.592 0.000 0.000 0.260 0.000 0.740
#> GSM531645 4 0.1270 0.862 0.000 0.000 0.000 0.948 0.052
#> GSM531646 3 0.0000 0.823 0.000 0.000 1.000 0.000 0.000
#> GSM531647 3 0.0000 0.823 0.000 0.000 1.000 0.000 0.000
#> GSM531648 5 0.4844 0.660 0.280 0.000 0.000 0.052 0.668
#> GSM531649 3 0.3109 0.705 0.000 0.000 0.800 0.000 0.200
#> GSM531650 3 0.2377 0.761 0.000 0.000 0.872 0.000 0.128
#> GSM531651 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> GSM531652 5 0.4588 0.695 0.220 0.000 0.060 0.000 0.720
#> GSM531653 3 0.0000 0.823 0.000 0.000 1.000 0.000 0.000
#> GSM531656 5 0.0000 0.703 0.000 0.000 0.000 0.000 1.000
#> GSM531657 5 0.4844 0.660 0.280 0.000 0.000 0.052 0.668
#> GSM531659 5 0.4844 0.660 0.280 0.000 0.000 0.052 0.668
#> GSM531661 2 0.0000 0.916 0.000 1.000 0.000 0.000 0.000
#> GSM531662 2 0.0162 0.913 0.000 0.996 0.000 0.000 0.004
#> GSM531663 4 0.5083 0.508 0.280 0.000 0.000 0.652 0.068
#> GSM531664 3 0.3051 0.779 0.120 0.000 0.852 0.000 0.028
#> GSM531665 3 0.6460 0.352 0.180 0.000 0.416 0.000 0.404
#> GSM531666 5 0.0000 0.703 0.000 0.000 0.000 0.000 1.000
#> GSM531667 5 0.6744 0.242 0.260 0.356 0.000 0.000 0.384
#> GSM531668 5 0.5778 0.611 0.280 0.000 0.000 0.128 0.592
#> GSM531669 3 0.0000 0.823 0.000 0.000 1.000 0.000 0.000
#> GSM531670 5 0.0404 0.699 0.012 0.000 0.000 0.000 0.988
#> GSM531671 3 0.1410 0.800 0.060 0.000 0.940 0.000 0.000
#> GSM531672 5 0.4844 0.660 0.280 0.000 0.000 0.052 0.668
#> GSM531673 2 0.6651 0.447 0.200 0.596 0.000 0.052 0.152
#> GSM531674 3 0.0162 0.824 0.000 0.000 0.996 0.000 0.004
#> GSM531675 1 0.1270 0.781 0.948 0.000 0.000 0.052 0.000
#> GSM531676 1 0.6115 0.424 0.552 0.000 0.168 0.000 0.280
#> GSM531677 1 0.1270 0.781 0.948 0.000 0.000 0.052 0.000
#> GSM531678 1 0.2020 0.767 0.900 0.000 0.000 0.000 0.100
#> GSM531679 1 0.1732 0.772 0.920 0.000 0.000 0.000 0.080
#> GSM531680 3 0.6158 0.260 0.384 0.000 0.480 0.000 0.136
#> GSM531681 1 0.4227 0.306 0.580 0.000 0.000 0.420 0.000
#> GSM531682 1 0.1270 0.781 0.948 0.000 0.000 0.052 0.000
#> GSM531683 1 0.1270 0.781 0.948 0.000 0.000 0.052 0.000
#> GSM531684 2 0.0566 0.903 0.012 0.984 0.000 0.004 0.000
#> GSM531685 3 0.4558 0.708 0.180 0.000 0.740 0.000 0.080
#> GSM531686 1 0.1043 0.781 0.960 0.000 0.000 0.040 0.000
#> GSM531687 5 0.2929 0.536 0.180 0.000 0.000 0.000 0.820
#> GSM531688 3 0.4038 0.745 0.128 0.000 0.792 0.000 0.080
#> GSM531689 1 0.3684 0.659 0.720 0.000 0.000 0.000 0.280
#> GSM531690 1 0.1270 0.781 0.948 0.000 0.000 0.052 0.000
#> GSM531691 1 0.3684 0.659 0.720 0.000 0.000 0.000 0.280
#> GSM531692 1 0.6726 0.506 0.544 0.140 0.036 0.000 0.280
#> GSM531693 3 0.1410 0.816 0.000 0.000 0.940 0.000 0.060
#> GSM531694 1 0.1270 0.781 0.948 0.000 0.000 0.052 0.000
#> GSM531695 3 0.4558 0.708 0.180 0.000 0.740 0.000 0.080
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM531600 6 0.2793 0.7400 0.000 0.000 0.200 0.000 0.000 0.800
#> GSM531601 3 0.2871 0.6743 0.000 0.192 0.804 0.000 0.004 0.000
#> GSM531605 5 0.0000 0.7866 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM531615 2 0.0000 0.8979 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531617 2 0.5339 0.1694 0.404 0.488 0.108 0.000 0.000 0.000
#> GSM531624 2 0.0000 0.8979 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531627 2 0.0000 0.8979 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531629 3 0.3765 0.5584 0.404 0.000 0.596 0.000 0.000 0.000
#> GSM531631 2 0.0000 0.8979 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531634 2 0.0000 0.8979 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531636 3 0.0000 0.7397 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531637 2 0.0000 0.8979 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531654 2 0.5296 0.4614 0.236 0.596 0.000 0.000 0.168 0.000
#> GSM531655 3 0.0000 0.7397 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531658 4 0.3993 0.3353 0.400 0.000 0.008 0.592 0.000 0.000
#> GSM531660 3 0.3774 0.5528 0.408 0.000 0.592 0.000 0.000 0.000
#> GSM531602 1 0.2664 0.7596 0.816 0.000 0.000 0.000 0.184 0.000
#> GSM531603 5 0.3607 0.3231 0.348 0.000 0.000 0.000 0.652 0.000
#> GSM531604 1 0.3765 0.4817 0.596 0.000 0.000 0.000 0.404 0.000
#> GSM531606 1 0.2823 0.7434 0.796 0.000 0.000 0.000 0.204 0.000
#> GSM531607 1 0.3774 0.4745 0.592 0.000 0.000 0.000 0.408 0.000
#> GSM531608 2 0.0000 0.8979 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531609 4 0.0000 0.8814 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531610 4 0.0000 0.8814 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531611 4 0.0146 0.8785 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM531612 4 0.0000 0.8814 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531613 4 0.0000 0.8814 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531614 4 0.0000 0.8814 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531616 3 0.4687 0.4905 0.000 0.180 0.684 0.000 0.000 0.136
#> GSM531618 3 0.2664 0.7108 0.184 0.000 0.816 0.000 0.000 0.000
#> GSM531619 2 0.0000 0.8979 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531620 2 0.0000 0.8979 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531621 2 0.0000 0.8979 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531622 2 0.0000 0.8979 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531623 2 0.0000 0.8979 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531625 2 0.0000 0.8979 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531626 2 0.5517 0.0672 0.000 0.472 0.396 0.000 0.000 0.132
#> GSM531628 6 0.0937 0.8818 0.000 0.000 0.040 0.000 0.000 0.960
#> GSM531630 2 0.0000 0.8979 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531632 6 0.0000 0.9010 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531633 2 0.0000 0.8979 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531635 3 0.2260 0.6633 0.000 0.000 0.860 0.000 0.000 0.140
#> GSM531638 3 0.2219 0.6650 0.000 0.000 0.864 0.000 0.000 0.136
#> GSM531639 3 0.0000 0.7397 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531640 3 0.1444 0.7317 0.000 0.072 0.928 0.000 0.000 0.000
#> GSM531641 4 0.0000 0.8814 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531642 3 0.0000 0.7397 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531643 3 0.2597 0.6776 0.000 0.000 0.824 0.000 0.000 0.176
#> GSM531644 3 0.2527 0.6818 0.000 0.000 0.832 0.000 0.000 0.168
#> GSM531645 4 0.0000 0.8814 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531646 6 0.0000 0.9010 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531647 6 0.0000 0.9010 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531648 3 0.3765 0.5584 0.404 0.000 0.596 0.000 0.000 0.000
#> GSM531649 6 0.2597 0.7510 0.000 0.000 0.176 0.000 0.000 0.824
#> GSM531650 6 0.2178 0.7944 0.000 0.000 0.132 0.000 0.000 0.868
#> GSM531651 2 0.0000 0.8979 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531652 3 0.2527 0.7173 0.168 0.000 0.832 0.000 0.000 0.000
#> GSM531653 6 0.0000 0.9010 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531656 3 0.0000 0.7397 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531657 3 0.3765 0.5584 0.404 0.000 0.596 0.000 0.000 0.000
#> GSM531659 3 0.3765 0.5584 0.404 0.000 0.596 0.000 0.000 0.000
#> GSM531661 2 0.0000 0.8979 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531662 2 0.2300 0.7909 0.000 0.856 0.000 0.000 0.144 0.000
#> GSM531663 4 0.4946 0.2495 0.404 0.000 0.068 0.528 0.000 0.000
#> GSM531664 6 0.2389 0.8069 0.000 0.000 0.008 0.000 0.128 0.864
#> GSM531665 5 0.3773 0.7642 0.000 0.000 0.204 0.000 0.752 0.044
#> GSM531666 3 0.0000 0.7397 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531667 3 0.5598 0.2614 0.152 0.356 0.492 0.000 0.000 0.000
#> GSM531668 3 0.4672 0.5505 0.348 0.000 0.596 0.000 0.056 0.000
#> GSM531669 6 0.0000 0.9010 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531670 3 0.2092 0.6644 0.000 0.000 0.876 0.000 0.124 0.000
#> GSM531671 6 0.0260 0.8978 0.000 0.000 0.000 0.000 0.008 0.992
#> GSM531672 3 0.3765 0.5584 0.404 0.000 0.596 0.000 0.000 0.000
#> GSM531673 2 0.5680 0.4776 0.236 0.596 0.024 0.000 0.144 0.000
#> GSM531674 6 0.0000 0.9010 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531675 1 0.0000 0.8155 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531676 5 0.3354 0.7925 0.000 0.000 0.168 0.000 0.796 0.036
#> GSM531677 1 0.0146 0.8177 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM531678 5 0.1333 0.7919 0.048 0.000 0.008 0.000 0.944 0.000
#> GSM531679 1 0.2823 0.6630 0.796 0.000 0.000 0.000 0.204 0.000
#> GSM531680 5 0.3012 0.7498 0.000 0.000 0.008 0.000 0.796 0.196
#> GSM531681 1 0.3426 0.5581 0.720 0.000 0.000 0.276 0.004 0.000
#> GSM531682 1 0.0146 0.8177 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM531683 1 0.0146 0.8177 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM531684 2 0.2988 0.7719 0.028 0.828 0.000 0.000 0.144 0.000
#> GSM531685 5 0.2823 0.7424 0.000 0.000 0.000 0.000 0.796 0.204
#> GSM531686 1 0.0725 0.8169 0.976 0.000 0.000 0.012 0.012 0.000
#> GSM531687 5 0.3012 0.7817 0.000 0.000 0.196 0.000 0.796 0.008
#> GSM531688 6 0.2219 0.8013 0.000 0.000 0.000 0.000 0.136 0.864
#> GSM531689 5 0.1267 0.8129 0.000 0.000 0.060 0.000 0.940 0.000
#> GSM531690 1 0.0000 0.8155 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531691 5 0.1204 0.8119 0.000 0.000 0.056 0.000 0.944 0.000
#> GSM531692 5 0.2733 0.7646 0.080 0.000 0.056 0.000 0.864 0.000
#> GSM531693 6 0.2664 0.7284 0.000 0.000 0.000 0.000 0.184 0.816
#> GSM531694 1 0.1267 0.8073 0.940 0.000 0.000 0.000 0.060 0.000
#> GSM531695 5 0.2823 0.7424 0.000 0.000 0.000 0.000 0.796 0.204
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> MAD:pam 96 0.0318 2
#> MAD:pam 86 0.0339 3
#> MAD:pam 42 0.2384 4
#> MAD:pam 85 0.0937 5
#> MAD:pam 85 0.0976 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "mclust"]
# you can also extract it by
# res = res_list["MAD:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 96 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 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 0.695 0.905 0.943 0.3053 0.705 0.705
#> 3 3 0.680 0.858 0.925 0.9577 0.679 0.557
#> 4 4 0.859 0.820 0.928 0.2601 0.713 0.399
#> 5 5 0.830 0.826 0.895 0.0490 0.918 0.692
#> 6 6 0.741 0.590 0.755 0.0423 0.941 0.745
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM531600 1 0.4431 0.9037 0.908 0.092
#> GSM531601 1 0.6712 0.7985 0.824 0.176
#> GSM531605 1 0.0000 0.9540 1.000 0.000
#> GSM531615 2 0.6623 0.8474 0.172 0.828
#> GSM531617 1 0.3584 0.9177 0.932 0.068
#> GSM531624 2 0.2778 0.9088 0.048 0.952
#> GSM531627 2 0.4815 0.8879 0.104 0.896
#> GSM531629 1 0.0672 0.9514 0.992 0.008
#> GSM531631 2 0.2778 0.9088 0.048 0.952
#> GSM531634 2 0.5737 0.8712 0.136 0.864
#> GSM531636 1 0.5737 0.8593 0.864 0.136
#> GSM531637 2 0.2778 0.9088 0.048 0.952
#> GSM531654 1 0.0376 0.9529 0.996 0.004
#> GSM531655 1 0.0376 0.9529 0.996 0.004
#> GSM531658 1 0.0000 0.9540 1.000 0.000
#> GSM531660 1 0.0000 0.9540 1.000 0.000
#> GSM531602 1 0.0000 0.9540 1.000 0.000
#> GSM531603 1 0.0000 0.9540 1.000 0.000
#> GSM531604 1 0.0000 0.9540 1.000 0.000
#> GSM531606 1 0.0000 0.9540 1.000 0.000
#> GSM531607 1 0.0000 0.9540 1.000 0.000
#> GSM531608 1 0.0938 0.9498 0.988 0.012
#> GSM531609 1 0.0000 0.9540 1.000 0.000
#> GSM531610 1 0.0000 0.9540 1.000 0.000
#> GSM531611 1 0.0000 0.9540 1.000 0.000
#> GSM531612 1 0.0376 0.9530 0.996 0.004
#> GSM531613 1 0.0000 0.9540 1.000 0.000
#> GSM531614 1 0.0000 0.9540 1.000 0.000
#> GSM531616 1 0.5737 0.8593 0.864 0.136
#> GSM531618 1 0.0000 0.9540 1.000 0.000
#> GSM531619 2 0.4939 0.8866 0.108 0.892
#> GSM531620 2 0.7745 0.7581 0.228 0.772
#> GSM531621 2 0.2778 0.9088 0.048 0.952
#> GSM531622 2 0.2778 0.9088 0.048 0.952
#> GSM531623 2 0.2778 0.9088 0.048 0.952
#> GSM531625 2 0.8955 0.6197 0.312 0.688
#> GSM531626 2 0.9998 0.0832 0.492 0.508
#> GSM531628 1 0.3584 0.9233 0.932 0.068
#> GSM531630 2 0.2778 0.9088 0.048 0.952
#> GSM531632 1 0.5519 0.8781 0.872 0.128
#> GSM531633 2 0.2778 0.9088 0.048 0.952
#> GSM531635 1 0.6148 0.8522 0.848 0.152
#> GSM531638 1 0.9044 0.5396 0.680 0.320
#> GSM531639 1 0.3733 0.9131 0.928 0.072
#> GSM531640 2 0.5178 0.8806 0.116 0.884
#> GSM531641 1 0.0376 0.9530 0.996 0.004
#> GSM531642 1 0.2603 0.9353 0.956 0.044
#> GSM531643 1 0.3584 0.9177 0.932 0.068
#> GSM531644 1 0.3431 0.9196 0.936 0.064
#> GSM531645 1 0.3114 0.9224 0.944 0.056
#> GSM531646 1 0.6887 0.8281 0.816 0.184
#> GSM531647 1 0.6887 0.8281 0.816 0.184
#> GSM531648 1 0.3114 0.9224 0.944 0.056
#> GSM531649 1 0.6148 0.8522 0.848 0.152
#> GSM531650 1 0.5178 0.8926 0.884 0.116
#> GSM531651 2 0.2778 0.9088 0.048 0.952
#> GSM531652 1 0.0000 0.9540 1.000 0.000
#> GSM531653 1 0.6887 0.8281 0.816 0.184
#> GSM531656 1 0.3431 0.9198 0.936 0.064
#> GSM531657 1 0.0000 0.9540 1.000 0.000
#> GSM531659 1 0.0000 0.9540 1.000 0.000
#> GSM531661 1 0.7674 0.6758 0.776 0.224
#> GSM531662 1 0.0376 0.9529 0.996 0.004
#> GSM531663 1 0.0000 0.9540 1.000 0.000
#> GSM531664 1 0.3274 0.9253 0.940 0.060
#> GSM531665 1 0.0000 0.9540 1.000 0.000
#> GSM531666 1 0.0000 0.9540 1.000 0.000
#> GSM531667 1 0.6148 0.8036 0.848 0.152
#> GSM531668 1 0.0000 0.9540 1.000 0.000
#> GSM531669 1 0.3274 0.9253 0.940 0.060
#> GSM531670 1 0.3584 0.9165 0.932 0.068
#> GSM531671 1 0.0938 0.9498 0.988 0.012
#> GSM531672 1 0.0000 0.9540 1.000 0.000
#> GSM531673 1 0.0000 0.9540 1.000 0.000
#> GSM531674 1 0.3274 0.9253 0.940 0.060
#> GSM531675 1 0.0000 0.9540 1.000 0.000
#> GSM531676 1 0.0938 0.9490 0.988 0.012
#> GSM531677 1 0.0000 0.9540 1.000 0.000
#> GSM531678 1 0.0000 0.9540 1.000 0.000
#> GSM531679 1 0.0000 0.9540 1.000 0.000
#> GSM531680 1 0.1184 0.9490 0.984 0.016
#> GSM531681 1 0.0000 0.9540 1.000 0.000
#> GSM531682 1 0.0000 0.9540 1.000 0.000
#> GSM531683 1 0.0000 0.9540 1.000 0.000
#> GSM531684 1 0.0000 0.9540 1.000 0.000
#> GSM531685 1 0.1633 0.9470 0.976 0.024
#> GSM531686 1 0.0000 0.9540 1.000 0.000
#> GSM531687 1 0.0672 0.9510 0.992 0.008
#> GSM531688 1 0.3114 0.9282 0.944 0.056
#> GSM531689 1 0.0000 0.9540 1.000 0.000
#> GSM531690 1 0.0000 0.9540 1.000 0.000
#> GSM531691 1 0.0000 0.9540 1.000 0.000
#> GSM531692 1 0.0000 0.9540 1.000 0.000
#> GSM531693 1 0.3274 0.9253 0.940 0.060
#> GSM531694 1 0.0000 0.9540 1.000 0.000
#> GSM531695 1 0.1633 0.9470 0.976 0.024
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM531600 3 0.0424 0.961 0.008 0.000 0.992
#> GSM531601 1 0.6192 0.421 0.580 0.420 0.000
#> GSM531605 1 0.0000 0.892 1.000 0.000 0.000
#> GSM531615 2 0.0424 0.938 0.008 0.992 0.000
#> GSM531617 2 0.3340 0.824 0.120 0.880 0.000
#> GSM531624 2 0.0000 0.943 0.000 1.000 0.000
#> GSM531627 2 0.0424 0.938 0.008 0.992 0.000
#> GSM531629 1 0.2261 0.860 0.932 0.068 0.000
#> GSM531631 2 0.0000 0.943 0.000 1.000 0.000
#> GSM531634 2 0.0000 0.943 0.000 1.000 0.000
#> GSM531636 3 0.0424 0.962 0.000 0.008 0.992
#> GSM531637 2 0.0000 0.943 0.000 1.000 0.000
#> GSM531654 1 0.5216 0.709 0.740 0.260 0.000
#> GSM531655 1 0.4861 0.778 0.800 0.192 0.008
#> GSM531658 1 0.0237 0.891 0.996 0.000 0.004
#> GSM531660 1 0.0000 0.892 1.000 0.000 0.000
#> GSM531602 1 0.0000 0.892 1.000 0.000 0.000
#> GSM531603 1 0.0000 0.892 1.000 0.000 0.000
#> GSM531604 1 0.3031 0.862 0.912 0.012 0.076
#> GSM531606 1 0.0000 0.892 1.000 0.000 0.000
#> GSM531607 1 0.0000 0.892 1.000 0.000 0.000
#> GSM531608 1 0.5216 0.709 0.740 0.260 0.000
#> GSM531609 1 0.0237 0.891 0.996 0.000 0.004
#> GSM531610 1 0.0237 0.891 0.996 0.000 0.004
#> GSM531611 1 0.0000 0.892 1.000 0.000 0.000
#> GSM531612 1 0.0237 0.891 0.996 0.000 0.004
#> GSM531613 1 0.0237 0.891 0.996 0.000 0.004
#> GSM531614 1 0.0237 0.891 0.996 0.000 0.004
#> GSM531616 2 0.6215 0.230 0.000 0.572 0.428
#> GSM531618 1 0.0000 0.892 1.000 0.000 0.000
#> GSM531619 2 0.0424 0.938 0.008 0.992 0.000
#> GSM531620 2 0.0000 0.943 0.000 1.000 0.000
#> GSM531621 2 0.0000 0.943 0.000 1.000 0.000
#> GSM531622 2 0.0000 0.943 0.000 1.000 0.000
#> GSM531623 2 0.0000 0.943 0.000 1.000 0.000
#> GSM531625 2 0.0000 0.943 0.000 1.000 0.000
#> GSM531626 2 0.3619 0.787 0.000 0.864 0.136
#> GSM531628 3 0.0237 0.963 0.004 0.000 0.996
#> GSM531630 2 0.0000 0.943 0.000 1.000 0.000
#> GSM531632 3 0.0237 0.963 0.004 0.000 0.996
#> GSM531633 2 0.0000 0.943 0.000 1.000 0.000
#> GSM531635 3 0.0424 0.962 0.000 0.008 0.992
#> GSM531638 2 0.0000 0.943 0.000 1.000 0.000
#> GSM531639 3 0.1753 0.915 0.048 0.000 0.952
#> GSM531640 2 0.0000 0.943 0.000 1.000 0.000
#> GSM531641 1 0.0237 0.891 0.996 0.000 0.004
#> GSM531642 1 0.5216 0.738 0.740 0.000 0.260
#> GSM531643 3 0.0475 0.963 0.004 0.004 0.992
#> GSM531644 1 0.5845 0.667 0.688 0.004 0.308
#> GSM531645 1 0.0237 0.891 0.996 0.000 0.004
#> GSM531646 3 0.0424 0.962 0.000 0.008 0.992
#> GSM531647 3 0.0424 0.962 0.000 0.008 0.992
#> GSM531648 1 0.0237 0.891 0.996 0.000 0.004
#> GSM531649 3 0.0424 0.962 0.000 0.008 0.992
#> GSM531650 3 0.0237 0.962 0.000 0.004 0.996
#> GSM531651 2 0.0000 0.943 0.000 1.000 0.000
#> GSM531652 1 0.5216 0.738 0.740 0.000 0.260
#> GSM531653 3 0.0424 0.962 0.000 0.008 0.992
#> GSM531656 3 0.0424 0.961 0.008 0.000 0.992
#> GSM531657 1 0.0000 0.892 1.000 0.000 0.000
#> GSM531659 1 0.0000 0.892 1.000 0.000 0.000
#> GSM531661 2 0.3192 0.835 0.112 0.888 0.000
#> GSM531662 1 0.5216 0.709 0.740 0.260 0.000
#> GSM531663 1 0.0000 0.892 1.000 0.000 0.000
#> GSM531664 3 0.0237 0.963 0.004 0.000 0.996
#> GSM531665 1 0.4504 0.797 0.804 0.000 0.196
#> GSM531666 1 0.4796 0.779 0.780 0.000 0.220
#> GSM531667 2 0.4002 0.773 0.160 0.840 0.000
#> GSM531668 1 0.0000 0.892 1.000 0.000 0.000
#> GSM531669 3 0.0237 0.963 0.004 0.000 0.996
#> GSM531670 3 0.0424 0.961 0.008 0.000 0.992
#> GSM531671 1 0.6449 0.758 0.740 0.056 0.204
#> GSM531672 1 0.0000 0.892 1.000 0.000 0.000
#> GSM531673 1 0.4452 0.800 0.808 0.000 0.192
#> GSM531674 3 0.0237 0.963 0.004 0.000 0.996
#> GSM531675 1 0.0237 0.891 0.996 0.000 0.004
#> GSM531676 1 0.4750 0.782 0.784 0.000 0.216
#> GSM531677 1 0.0000 0.892 1.000 0.000 0.000
#> GSM531678 1 0.0000 0.892 1.000 0.000 0.000
#> GSM531679 1 0.0000 0.892 1.000 0.000 0.000
#> GSM531680 1 0.4702 0.785 0.788 0.000 0.212
#> GSM531681 1 0.0237 0.891 0.996 0.000 0.004
#> GSM531682 1 0.0000 0.892 1.000 0.000 0.000
#> GSM531683 1 0.0000 0.892 1.000 0.000 0.000
#> GSM531684 1 0.4452 0.781 0.808 0.192 0.000
#> GSM531685 1 0.5216 0.738 0.740 0.000 0.260
#> GSM531686 1 0.0000 0.892 1.000 0.000 0.000
#> GSM531687 1 0.4504 0.797 0.804 0.000 0.196
#> GSM531688 1 0.6267 0.360 0.548 0.000 0.452
#> GSM531689 1 0.4291 0.808 0.820 0.000 0.180
#> GSM531690 1 0.0237 0.891 0.996 0.000 0.004
#> GSM531691 1 0.4452 0.800 0.808 0.000 0.192
#> GSM531692 1 0.5536 0.784 0.776 0.024 0.200
#> GSM531693 3 0.5859 0.337 0.344 0.000 0.656
#> GSM531694 1 0.0000 0.892 1.000 0.000 0.000
#> GSM531695 1 0.5216 0.738 0.740 0.000 0.260
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM531600 3 0.0188 0.9206 0.004 0.000 0.996 0.000
#> GSM531601 2 0.4790 0.3768 0.000 0.620 0.000 0.380
#> GSM531605 1 0.0000 0.9089 1.000 0.000 0.000 0.000
#> GSM531615 2 0.0188 0.9182 0.004 0.996 0.000 0.000
#> GSM531617 2 0.0000 0.9198 0.000 1.000 0.000 0.000
#> GSM531624 2 0.0000 0.9198 0.000 1.000 0.000 0.000
#> GSM531627 2 0.0188 0.9182 0.004 0.996 0.000 0.000
#> GSM531629 2 0.5167 0.0663 0.004 0.508 0.000 0.488
#> GSM531631 2 0.0000 0.9198 0.000 1.000 0.000 0.000
#> GSM531634 2 0.0000 0.9198 0.000 1.000 0.000 0.000
#> GSM531636 3 0.0188 0.9215 0.000 0.004 0.996 0.000
#> GSM531637 2 0.0000 0.9198 0.000 1.000 0.000 0.000
#> GSM531654 2 0.3688 0.7264 0.208 0.792 0.000 0.000
#> GSM531655 1 0.7849 0.0133 0.400 0.000 0.284 0.316
#> GSM531658 4 0.0000 0.9194 0.000 0.000 0.000 1.000
#> GSM531660 4 0.1867 0.8682 0.072 0.000 0.000 0.928
#> GSM531602 1 0.0000 0.9089 1.000 0.000 0.000 0.000
#> GSM531603 1 0.0188 0.9063 0.996 0.000 0.000 0.004
#> GSM531604 1 0.0000 0.9089 1.000 0.000 0.000 0.000
#> GSM531606 1 0.0000 0.9089 1.000 0.000 0.000 0.000
#> GSM531607 1 0.0000 0.9089 1.000 0.000 0.000 0.000
#> GSM531608 2 0.0188 0.9182 0.004 0.996 0.000 0.000
#> GSM531609 4 0.0000 0.9194 0.000 0.000 0.000 1.000
#> GSM531610 4 0.0000 0.9194 0.000 0.000 0.000 1.000
#> GSM531611 4 0.0188 0.9197 0.004 0.000 0.000 0.996
#> GSM531612 4 0.0000 0.9194 0.000 0.000 0.000 1.000
#> GSM531613 4 0.0188 0.9197 0.004 0.000 0.000 0.996
#> GSM531614 4 0.0000 0.9194 0.000 0.000 0.000 1.000
#> GSM531616 2 0.4817 0.3260 0.000 0.612 0.388 0.000
#> GSM531618 4 0.0376 0.9178 0.004 0.004 0.000 0.992
#> GSM531619 2 0.0188 0.9182 0.004 0.996 0.000 0.000
#> GSM531620 2 0.0000 0.9198 0.000 1.000 0.000 0.000
#> GSM531621 2 0.0000 0.9198 0.000 1.000 0.000 0.000
#> GSM531622 2 0.0000 0.9198 0.000 1.000 0.000 0.000
#> GSM531623 2 0.0000 0.9198 0.000 1.000 0.000 0.000
#> GSM531625 2 0.0000 0.9198 0.000 1.000 0.000 0.000
#> GSM531626 2 0.1867 0.8594 0.000 0.928 0.072 0.000
#> GSM531628 3 0.0000 0.9214 0.000 0.000 1.000 0.000
#> GSM531630 2 0.0000 0.9198 0.000 1.000 0.000 0.000
#> GSM531632 3 0.0000 0.9214 0.000 0.000 1.000 0.000
#> GSM531633 2 0.0000 0.9198 0.000 1.000 0.000 0.000
#> GSM531635 3 0.0188 0.9215 0.000 0.004 0.996 0.000
#> GSM531638 2 0.0000 0.9198 0.000 1.000 0.000 0.000
#> GSM531639 3 0.0779 0.9100 0.004 0.016 0.980 0.000
#> GSM531640 2 0.0000 0.9198 0.000 1.000 0.000 0.000
#> GSM531641 4 0.0000 0.9194 0.000 0.000 0.000 1.000
#> GSM531642 3 0.2125 0.8577 0.004 0.000 0.920 0.076
#> GSM531643 3 0.0188 0.9215 0.000 0.004 0.996 0.000
#> GSM531644 3 0.0188 0.9215 0.000 0.004 0.996 0.000
#> GSM531645 4 0.0000 0.9194 0.000 0.000 0.000 1.000
#> GSM531646 3 0.0188 0.9215 0.000 0.004 0.996 0.000
#> GSM531647 3 0.0188 0.9215 0.000 0.004 0.996 0.000
#> GSM531648 4 0.0188 0.9197 0.004 0.000 0.000 0.996
#> GSM531649 3 0.0188 0.9215 0.000 0.004 0.996 0.000
#> GSM531650 3 0.0000 0.9214 0.000 0.000 1.000 0.000
#> GSM531651 2 0.0000 0.9198 0.000 1.000 0.000 0.000
#> GSM531652 4 0.4018 0.6452 0.004 0.000 0.224 0.772
#> GSM531653 3 0.0188 0.9215 0.000 0.004 0.996 0.000
#> GSM531656 3 0.0188 0.9206 0.004 0.000 0.996 0.000
#> GSM531657 4 0.0188 0.9197 0.004 0.000 0.000 0.996
#> GSM531659 4 0.3569 0.7276 0.196 0.000 0.000 0.804
#> GSM531661 2 0.0188 0.9182 0.004 0.996 0.000 0.000
#> GSM531662 2 0.4817 0.4162 0.388 0.612 0.000 0.000
#> GSM531663 4 0.4406 0.5346 0.300 0.000 0.000 0.700
#> GSM531664 3 0.0000 0.9214 0.000 0.000 1.000 0.000
#> GSM531665 1 0.2216 0.8389 0.908 0.000 0.092 0.000
#> GSM531666 3 0.4855 0.4523 0.004 0.000 0.644 0.352
#> GSM531667 2 0.0188 0.9182 0.004 0.996 0.000 0.000
#> GSM531668 4 0.0188 0.9197 0.004 0.000 0.000 0.996
#> GSM531669 3 0.0000 0.9214 0.000 0.000 1.000 0.000
#> GSM531670 3 0.0188 0.9206 0.004 0.000 0.996 0.000
#> GSM531671 3 0.5028 0.3030 0.004 0.400 0.596 0.000
#> GSM531672 4 0.0188 0.9197 0.004 0.000 0.000 0.996
#> GSM531673 1 0.0000 0.9089 1.000 0.000 0.000 0.000
#> GSM531674 3 0.0000 0.9214 0.000 0.000 1.000 0.000
#> GSM531675 1 0.3975 0.6651 0.760 0.000 0.000 0.240
#> GSM531676 1 0.3569 0.7250 0.804 0.000 0.196 0.000
#> GSM531677 1 0.0188 0.9067 0.996 0.000 0.000 0.004
#> GSM531678 1 0.0000 0.9089 1.000 0.000 0.000 0.000
#> GSM531679 1 0.0000 0.9089 1.000 0.000 0.000 0.000
#> GSM531680 3 0.4925 0.2182 0.428 0.000 0.572 0.000
#> GSM531681 1 0.4713 0.4314 0.640 0.000 0.000 0.360
#> GSM531682 1 0.0000 0.9089 1.000 0.000 0.000 0.000
#> GSM531683 1 0.0000 0.9089 1.000 0.000 0.000 0.000
#> GSM531684 1 0.0000 0.9089 1.000 0.000 0.000 0.000
#> GSM531685 3 0.4985 0.0886 0.468 0.000 0.532 0.000
#> GSM531686 1 0.3219 0.7658 0.836 0.000 0.000 0.164
#> GSM531687 1 0.3801 0.6900 0.780 0.000 0.220 0.000
#> GSM531688 3 0.0000 0.9214 0.000 0.000 1.000 0.000
#> GSM531689 1 0.0000 0.9089 1.000 0.000 0.000 0.000
#> GSM531690 4 0.4933 0.1837 0.432 0.000 0.000 0.568
#> GSM531691 1 0.0000 0.9089 1.000 0.000 0.000 0.000
#> GSM531692 1 0.0000 0.9089 1.000 0.000 0.000 0.000
#> GSM531693 3 0.0188 0.9206 0.004 0.000 0.996 0.000
#> GSM531694 1 0.0000 0.9089 1.000 0.000 0.000 0.000
#> GSM531695 3 0.0188 0.9206 0.004 0.000 0.996 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM531600 3 0.0451 0.926 0.008 0.000 0.988 0.000 0.004
#> GSM531601 2 0.4182 0.243 0.000 0.600 0.000 0.400 0.000
#> GSM531605 1 0.2648 0.820 0.848 0.000 0.000 0.000 0.152
#> GSM531615 2 0.0000 0.931 0.000 1.000 0.000 0.000 0.000
#> GSM531617 2 0.2344 0.879 0.032 0.904 0.000 0.064 0.000
#> GSM531624 2 0.0000 0.931 0.000 1.000 0.000 0.000 0.000
#> GSM531627 2 0.0290 0.930 0.000 0.992 0.000 0.000 0.008
#> GSM531629 4 0.4754 0.628 0.052 0.264 0.000 0.684 0.000
#> GSM531631 2 0.0000 0.931 0.000 1.000 0.000 0.000 0.000
#> GSM531634 2 0.0000 0.931 0.000 1.000 0.000 0.000 0.000
#> GSM531636 3 0.2278 0.885 0.000 0.032 0.908 0.000 0.060
#> GSM531637 2 0.0000 0.931 0.000 1.000 0.000 0.000 0.000
#> GSM531654 2 0.4593 0.650 0.080 0.736 0.000 0.000 0.184
#> GSM531655 4 0.8192 0.103 0.092 0.012 0.196 0.404 0.296
#> GSM531658 4 0.0000 0.835 0.000 0.000 0.000 1.000 0.000
#> GSM531660 4 0.3612 0.744 0.268 0.000 0.000 0.732 0.000
#> GSM531602 1 0.2074 0.863 0.896 0.000 0.000 0.000 0.104
#> GSM531603 1 0.1197 0.872 0.952 0.000 0.000 0.000 0.048
#> GSM531604 5 0.1908 0.911 0.092 0.000 0.000 0.000 0.908
#> GSM531606 1 0.3913 0.528 0.676 0.000 0.000 0.000 0.324
#> GSM531607 1 0.2074 0.863 0.896 0.000 0.000 0.000 0.104
#> GSM531608 2 0.1197 0.900 0.048 0.952 0.000 0.000 0.000
#> GSM531609 4 0.0000 0.835 0.000 0.000 0.000 1.000 0.000
#> GSM531610 4 0.0609 0.835 0.020 0.000 0.000 0.980 0.000
#> GSM531611 4 0.2852 0.806 0.172 0.000 0.000 0.828 0.000
#> GSM531612 4 0.0000 0.835 0.000 0.000 0.000 1.000 0.000
#> GSM531613 4 0.4306 0.166 0.492 0.000 0.000 0.508 0.000
#> GSM531614 4 0.0000 0.835 0.000 0.000 0.000 1.000 0.000
#> GSM531616 2 0.5010 0.594 0.000 0.676 0.248 0.000 0.076
#> GSM531618 4 0.3555 0.778 0.052 0.124 0.000 0.824 0.000
#> GSM531619 2 0.0000 0.931 0.000 1.000 0.000 0.000 0.000
#> GSM531620 2 0.0992 0.923 0.008 0.968 0.000 0.000 0.024
#> GSM531621 2 0.0290 0.930 0.000 0.992 0.000 0.000 0.008
#> GSM531622 2 0.0000 0.931 0.000 1.000 0.000 0.000 0.000
#> GSM531623 2 0.0000 0.931 0.000 1.000 0.000 0.000 0.000
#> GSM531625 2 0.1280 0.920 0.008 0.960 0.008 0.000 0.024
#> GSM531626 2 0.1831 0.902 0.000 0.920 0.004 0.000 0.076
#> GSM531628 3 0.0000 0.928 0.000 0.000 1.000 0.000 0.000
#> GSM531630 2 0.0000 0.931 0.000 1.000 0.000 0.000 0.000
#> GSM531632 3 0.0000 0.928 0.000 0.000 1.000 0.000 0.000
#> GSM531633 2 0.0510 0.927 0.000 0.984 0.000 0.000 0.016
#> GSM531635 3 0.0162 0.928 0.000 0.000 0.996 0.000 0.004
#> GSM531638 2 0.1831 0.902 0.000 0.920 0.004 0.000 0.076
#> GSM531639 3 0.3616 0.802 0.004 0.116 0.828 0.000 0.052
#> GSM531640 2 0.0000 0.931 0.000 1.000 0.000 0.000 0.000
#> GSM531641 4 0.0000 0.835 0.000 0.000 0.000 1.000 0.000
#> GSM531642 3 0.4528 0.683 0.028 0.000 0.748 0.200 0.024
#> GSM531643 3 0.1043 0.912 0.000 0.000 0.960 0.040 0.000
#> GSM531644 3 0.2074 0.865 0.000 0.000 0.896 0.104 0.000
#> GSM531645 4 0.0000 0.835 0.000 0.000 0.000 1.000 0.000
#> GSM531646 3 0.0000 0.928 0.000 0.000 1.000 0.000 0.000
#> GSM531647 3 0.0000 0.928 0.000 0.000 1.000 0.000 0.000
#> GSM531648 4 0.0000 0.835 0.000 0.000 0.000 1.000 0.000
#> GSM531649 3 0.0510 0.924 0.000 0.000 0.984 0.000 0.016
#> GSM531650 3 0.0000 0.928 0.000 0.000 1.000 0.000 0.000
#> GSM531651 2 0.0000 0.931 0.000 1.000 0.000 0.000 0.000
#> GSM531652 4 0.2193 0.814 0.028 0.000 0.060 0.912 0.000
#> GSM531653 3 0.0000 0.928 0.000 0.000 1.000 0.000 0.000
#> GSM531656 3 0.0992 0.920 0.008 0.000 0.968 0.000 0.024
#> GSM531657 4 0.3336 0.774 0.228 0.000 0.000 0.772 0.000
#> GSM531659 4 0.3612 0.738 0.268 0.000 0.000 0.732 0.000
#> GSM531661 2 0.3012 0.807 0.036 0.860 0.000 0.000 0.104
#> GSM531662 5 0.2068 0.909 0.092 0.004 0.000 0.000 0.904
#> GSM531663 1 0.2690 0.723 0.844 0.000 0.000 0.156 0.000
#> GSM531664 3 0.0000 0.928 0.000 0.000 1.000 0.000 0.000
#> GSM531665 5 0.1792 0.909 0.084 0.000 0.000 0.000 0.916
#> GSM531666 3 0.5086 0.219 0.040 0.000 0.564 0.396 0.000
#> GSM531667 2 0.0963 0.910 0.036 0.964 0.000 0.000 0.000
#> GSM531668 4 0.3003 0.800 0.188 0.000 0.000 0.812 0.000
#> GSM531669 3 0.0290 0.927 0.000 0.000 0.992 0.000 0.008
#> GSM531670 3 0.0992 0.920 0.008 0.000 0.968 0.000 0.024
#> GSM531671 5 0.2228 0.877 0.040 0.000 0.048 0.000 0.912
#> GSM531672 4 0.3003 0.800 0.188 0.000 0.000 0.812 0.000
#> GSM531673 5 0.1908 0.911 0.092 0.000 0.000 0.000 0.908
#> GSM531674 3 0.0162 0.928 0.000 0.000 0.996 0.000 0.004
#> GSM531675 1 0.1043 0.850 0.960 0.000 0.000 0.040 0.000
#> GSM531676 5 0.2074 0.886 0.044 0.000 0.036 0.000 0.920
#> GSM531677 1 0.0324 0.863 0.992 0.000 0.000 0.004 0.004
#> GSM531678 1 0.2813 0.804 0.832 0.000 0.000 0.000 0.168
#> GSM531679 1 0.2179 0.860 0.888 0.000 0.000 0.000 0.112
#> GSM531680 3 0.4167 0.629 0.252 0.000 0.724 0.000 0.024
#> GSM531681 1 0.1043 0.850 0.960 0.000 0.000 0.040 0.000
#> GSM531682 1 0.0794 0.868 0.972 0.000 0.000 0.000 0.028
#> GSM531683 1 0.1851 0.868 0.912 0.000 0.000 0.000 0.088
#> GSM531684 5 0.2127 0.902 0.108 0.000 0.000 0.000 0.892
#> GSM531685 5 0.2139 0.870 0.032 0.000 0.052 0.000 0.916
#> GSM531686 1 0.1043 0.850 0.960 0.000 0.000 0.040 0.000
#> GSM531687 5 0.5538 0.127 0.428 0.000 0.068 0.000 0.504
#> GSM531688 3 0.0798 0.921 0.008 0.000 0.976 0.000 0.016
#> GSM531689 5 0.3395 0.747 0.236 0.000 0.000 0.000 0.764
#> GSM531690 1 0.1121 0.847 0.956 0.000 0.000 0.044 0.000
#> GSM531691 5 0.1908 0.911 0.092 0.000 0.000 0.000 0.908
#> GSM531692 5 0.1908 0.911 0.092 0.000 0.000 0.000 0.908
#> GSM531693 3 0.0510 0.924 0.000 0.000 0.984 0.000 0.016
#> GSM531694 1 0.2074 0.863 0.896 0.000 0.000 0.000 0.104
#> GSM531695 3 0.1579 0.896 0.032 0.000 0.944 0.000 0.024
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM531600 3 0.1934 0.7470 0.000 0.000 0.916 0.000 0.044 NA
#> GSM531601 2 0.4709 0.5026 0.000 0.680 0.000 0.188 0.000 NA
#> GSM531605 1 0.2562 0.6485 0.828 0.000 0.000 0.000 0.172 NA
#> GSM531615 2 0.0146 0.8492 0.004 0.996 0.000 0.000 0.000 NA
#> GSM531617 2 0.1471 0.8263 0.004 0.932 0.000 0.000 0.000 NA
#> GSM531624 2 0.0000 0.8505 0.000 1.000 0.000 0.000 0.000 NA
#> GSM531627 2 0.1644 0.8349 0.000 0.932 0.028 0.000 0.000 NA
#> GSM531629 2 0.4881 0.3665 0.004 0.604 0.000 0.324 0.000 NA
#> GSM531631 2 0.0000 0.8505 0.000 1.000 0.000 0.000 0.000 NA
#> GSM531634 2 0.0260 0.8490 0.000 0.992 0.000 0.000 0.000 NA
#> GSM531636 3 0.1367 0.7204 0.000 0.044 0.944 0.000 0.000 NA
#> GSM531637 2 0.0000 0.8505 0.000 1.000 0.000 0.000 0.000 NA
#> GSM531654 2 0.7626 0.1117 0.044 0.416 0.000 0.296 0.156 NA
#> GSM531655 4 0.6861 0.0403 0.044 0.004 0.152 0.496 0.284 NA
#> GSM531658 4 0.3867 0.6022 0.000 0.000 0.000 0.512 0.000 NA
#> GSM531660 4 0.2762 0.5264 0.196 0.000 0.000 0.804 0.000 NA
#> GSM531602 1 0.0260 0.8303 0.992 0.000 0.000 0.000 0.000 NA
#> GSM531603 1 0.3860 0.1270 0.528 0.000 0.000 0.472 0.000 NA
#> GSM531604 5 0.5036 0.2557 0.344 0.000 0.000 0.000 0.568 NA
#> GSM531606 1 0.4871 0.3228 0.616 0.000 0.000 0.000 0.296 NA
#> GSM531607 1 0.0146 0.8306 0.996 0.000 0.000 0.000 0.004 NA
#> GSM531608 2 0.3528 0.5695 0.004 0.700 0.000 0.296 0.000 NA
#> GSM531609 4 0.3867 0.6022 0.000 0.000 0.000 0.512 0.000 NA
#> GSM531610 4 0.1075 0.5839 0.000 0.000 0.000 0.952 0.000 NA
#> GSM531611 4 0.1657 0.5818 0.056 0.000 0.000 0.928 0.000 NA
#> GSM531612 4 0.3867 0.6022 0.000 0.000 0.000 0.512 0.000 NA
#> GSM531613 4 0.3023 0.4143 0.232 0.000 0.000 0.768 0.000 NA
#> GSM531614 4 0.3782 0.6078 0.000 0.000 0.000 0.588 0.000 NA
#> GSM531616 3 0.4712 0.0168 0.000 0.384 0.564 0.000 0.000 NA
#> GSM531618 4 0.5298 0.5805 0.004 0.100 0.000 0.548 0.000 NA
#> GSM531619 2 0.0000 0.8505 0.000 1.000 0.000 0.000 0.000 NA
#> GSM531620 2 0.2362 0.7921 0.000 0.860 0.136 0.000 0.000 NA
#> GSM531621 2 0.2066 0.8259 0.000 0.908 0.052 0.000 0.000 NA
#> GSM531622 2 0.0000 0.8505 0.000 1.000 0.000 0.000 0.000 NA
#> GSM531623 2 0.0000 0.8505 0.000 1.000 0.000 0.000 0.000 NA
#> GSM531625 2 0.4059 0.7031 0.004 0.732 0.216 0.000 0.000 NA
#> GSM531626 2 0.4408 0.6132 0.000 0.656 0.292 0.000 0.000 NA
#> GSM531628 3 0.2854 0.7708 0.000 0.000 0.792 0.000 0.000 NA
#> GSM531630 2 0.0000 0.8505 0.000 1.000 0.000 0.000 0.000 NA
#> GSM531632 3 0.2854 0.7708 0.000 0.000 0.792 0.000 0.000 NA
#> GSM531633 2 0.2308 0.8184 0.000 0.892 0.068 0.000 0.000 NA
#> GSM531635 3 0.0260 0.7383 0.000 0.000 0.992 0.000 0.000 NA
#> GSM531638 2 0.3980 0.7019 0.000 0.732 0.216 0.000 0.000 NA
#> GSM531639 3 0.1367 0.7204 0.000 0.044 0.944 0.000 0.000 NA
#> GSM531640 2 0.0000 0.8505 0.000 1.000 0.000 0.000 0.000 NA
#> GSM531641 4 0.3867 0.6022 0.000 0.000 0.000 0.512 0.000 NA
#> GSM531642 3 0.2884 0.5979 0.004 0.000 0.824 0.164 0.000 NA
#> GSM531643 3 0.2854 0.7708 0.000 0.000 0.792 0.000 0.000 NA
#> GSM531644 3 0.2996 0.7655 0.000 0.000 0.772 0.000 0.000 NA
#> GSM531645 4 0.3867 0.6022 0.000 0.000 0.000 0.512 0.000 NA
#> GSM531646 3 0.2664 0.7716 0.000 0.000 0.816 0.000 0.000 NA
#> GSM531647 3 0.2854 0.7708 0.000 0.000 0.792 0.000 0.000 NA
#> GSM531648 4 0.3867 0.6022 0.000 0.000 0.000 0.512 0.000 NA
#> GSM531649 3 0.0260 0.7383 0.000 0.000 0.992 0.000 0.000 NA
#> GSM531650 3 0.2854 0.7708 0.000 0.000 0.792 0.000 0.000 NA
#> GSM531651 2 0.0260 0.8495 0.000 0.992 0.000 0.000 0.000 NA
#> GSM531652 4 0.6078 0.4594 0.004 0.004 0.220 0.480 0.000 NA
#> GSM531653 3 0.2854 0.7708 0.000 0.000 0.792 0.000 0.000 NA
#> GSM531656 3 0.0865 0.7306 0.000 0.036 0.964 0.000 0.000 NA
#> GSM531657 4 0.2562 0.5491 0.172 0.000 0.000 0.828 0.000 NA
#> GSM531659 4 0.2631 0.5418 0.180 0.000 0.000 0.820 0.000 NA
#> GSM531661 2 0.4609 0.5106 0.048 0.648 0.000 0.296 0.008 NA
#> GSM531662 5 0.5811 0.3515 0.048 0.000 0.000 0.296 0.568 NA
#> GSM531663 4 0.3838 -0.0203 0.448 0.000 0.000 0.552 0.000 NA
#> GSM531664 3 0.2994 0.7687 0.000 0.000 0.788 0.000 0.004 NA
#> GSM531665 5 0.3482 0.4793 0.004 0.000 0.000 0.116 0.812 NA
#> GSM531666 3 0.6058 0.2601 0.004 0.000 0.456 0.292 0.000 NA
#> GSM531667 2 0.1411 0.8242 0.004 0.936 0.000 0.060 0.000 NA
#> GSM531668 4 0.2378 0.5592 0.152 0.000 0.000 0.848 0.000 NA
#> GSM531669 3 0.6034 0.3064 0.000 0.000 0.416 0.000 0.320 NA
#> GSM531670 3 0.1152 0.7323 0.000 0.000 0.952 0.000 0.044 NA
#> GSM531671 5 0.4751 0.4103 0.004 0.000 0.000 0.228 0.672 NA
#> GSM531672 4 0.2378 0.5592 0.152 0.000 0.000 0.848 0.000 NA
#> GSM531673 5 0.5766 0.3639 0.048 0.000 0.000 0.284 0.580 NA
#> GSM531674 3 0.5979 0.3525 0.000 0.000 0.440 0.000 0.308 NA
#> GSM531675 1 0.1219 0.8263 0.948 0.000 0.000 0.048 0.004 NA
#> GSM531676 5 0.5048 0.3748 0.264 0.000 0.000 0.000 0.616 NA
#> GSM531677 1 0.1075 0.8270 0.952 0.000 0.000 0.048 0.000 NA
#> GSM531678 1 0.1863 0.7558 0.896 0.000 0.000 0.000 0.104 NA
#> GSM531679 1 0.0458 0.8259 0.984 0.000 0.000 0.000 0.016 NA
#> GSM531680 5 0.6809 0.1082 0.072 0.000 0.172 0.000 0.432 NA
#> GSM531681 1 0.1219 0.8263 0.948 0.000 0.000 0.048 0.004 NA
#> GSM531682 1 0.1649 0.8280 0.932 0.000 0.000 0.036 0.032 NA
#> GSM531683 1 0.0146 0.8321 0.996 0.000 0.000 0.004 0.000 NA
#> GSM531684 5 0.5600 0.2435 0.348 0.024 0.000 0.000 0.540 NA
#> GSM531685 5 0.3164 0.4952 0.044 0.000 0.004 0.000 0.832 NA
#> GSM531686 1 0.1075 0.8270 0.952 0.000 0.000 0.048 0.000 NA
#> GSM531687 5 0.5132 0.3866 0.172 0.000 0.028 0.000 0.680 NA
#> GSM531688 5 0.5989 -0.0112 0.000 0.000 0.244 0.000 0.428 NA
#> GSM531689 1 0.3828 0.1432 0.560 0.000 0.000 0.000 0.440 NA
#> GSM531690 1 0.1219 0.8263 0.948 0.000 0.000 0.048 0.004 NA
#> GSM531691 5 0.4306 0.2712 0.344 0.000 0.000 0.000 0.624 NA
#> GSM531692 5 0.5036 0.2557 0.344 0.000 0.000 0.000 0.568 NA
#> GSM531693 5 0.5989 -0.0112 0.000 0.000 0.244 0.000 0.428 NA
#> GSM531694 1 0.0260 0.8303 0.992 0.000 0.000 0.000 0.000 NA
#> GSM531695 5 0.5989 -0.0112 0.000 0.000 0.244 0.000 0.428 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)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> MAD:mclust 95 0.0399 2
#> MAD:mclust 92 0.0554 3
#> MAD:mclust 85 0.0223 4
#> MAD:mclust 91 0.0435 5
#> MAD:mclust 68 0.1290 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "NMF"]
# you can also extract it by
# res = res_list["MAD:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 96 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
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 0.851 0.922 0.967 0.4994 0.498 0.498
#> 3 3 0.839 0.877 0.949 0.3387 0.740 0.524
#> 4 4 0.883 0.862 0.942 0.1274 0.832 0.549
#> 5 5 0.729 0.693 0.847 0.0576 0.900 0.635
#> 6 6 0.689 0.562 0.764 0.0408 0.908 0.599
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
#> GSM531600 2 0.0000 0.9677 0.000 1.000
#> GSM531601 2 0.0000 0.9677 0.000 1.000
#> GSM531605 1 0.0000 0.9604 1.000 0.000
#> GSM531615 2 0.2423 0.9342 0.040 0.960
#> GSM531617 2 0.0672 0.9613 0.008 0.992
#> GSM531624 2 0.0000 0.9677 0.000 1.000
#> GSM531627 2 0.0000 0.9677 0.000 1.000
#> GSM531629 1 0.0000 0.9604 1.000 0.000
#> GSM531631 2 0.0000 0.9677 0.000 1.000
#> GSM531634 2 0.0000 0.9677 0.000 1.000
#> GSM531636 2 0.0000 0.9677 0.000 1.000
#> GSM531637 2 0.0000 0.9677 0.000 1.000
#> GSM531654 2 0.8386 0.6282 0.268 0.732
#> GSM531655 2 0.9998 -0.0246 0.492 0.508
#> GSM531658 1 0.0000 0.9604 1.000 0.000
#> GSM531660 1 0.0000 0.9604 1.000 0.000
#> GSM531602 1 0.0000 0.9604 1.000 0.000
#> GSM531603 1 0.0000 0.9604 1.000 0.000
#> GSM531604 1 0.4562 0.8882 0.904 0.096
#> GSM531606 1 0.0000 0.9604 1.000 0.000
#> GSM531607 1 0.0000 0.9604 1.000 0.000
#> GSM531608 2 0.2778 0.9286 0.048 0.952
#> GSM531609 1 0.0000 0.9604 1.000 0.000
#> GSM531610 1 0.0000 0.9604 1.000 0.000
#> GSM531611 1 0.0000 0.9604 1.000 0.000
#> GSM531612 1 0.0000 0.9604 1.000 0.000
#> GSM531613 1 0.0000 0.9604 1.000 0.000
#> GSM531614 1 0.0000 0.9604 1.000 0.000
#> GSM531616 2 0.0000 0.9677 0.000 1.000
#> GSM531618 1 0.5946 0.8308 0.856 0.144
#> GSM531619 2 0.0000 0.9677 0.000 1.000
#> GSM531620 2 0.0000 0.9677 0.000 1.000
#> GSM531621 2 0.0000 0.9677 0.000 1.000
#> GSM531622 2 0.0000 0.9677 0.000 1.000
#> GSM531623 2 0.0000 0.9677 0.000 1.000
#> GSM531625 2 0.0000 0.9677 0.000 1.000
#> GSM531626 2 0.0000 0.9677 0.000 1.000
#> GSM531628 2 0.0000 0.9677 0.000 1.000
#> GSM531630 2 0.0000 0.9677 0.000 1.000
#> GSM531632 2 0.0000 0.9677 0.000 1.000
#> GSM531633 2 0.0000 0.9677 0.000 1.000
#> GSM531635 2 0.0000 0.9677 0.000 1.000
#> GSM531638 2 0.0000 0.9677 0.000 1.000
#> GSM531639 2 0.0000 0.9677 0.000 1.000
#> GSM531640 2 0.0000 0.9677 0.000 1.000
#> GSM531641 1 0.0000 0.9604 1.000 0.000
#> GSM531642 2 0.0000 0.9677 0.000 1.000
#> GSM531643 2 0.0000 0.9677 0.000 1.000
#> GSM531644 2 0.5294 0.8476 0.120 0.880
#> GSM531645 1 0.0000 0.9604 1.000 0.000
#> GSM531646 2 0.0000 0.9677 0.000 1.000
#> GSM531647 2 0.0000 0.9677 0.000 1.000
#> GSM531648 1 0.0000 0.9604 1.000 0.000
#> GSM531649 2 0.0000 0.9677 0.000 1.000
#> GSM531650 2 0.0000 0.9677 0.000 1.000
#> GSM531651 2 0.0000 0.9677 0.000 1.000
#> GSM531652 2 0.0000 0.9677 0.000 1.000
#> GSM531653 2 0.0000 0.9677 0.000 1.000
#> GSM531656 2 0.0000 0.9677 0.000 1.000
#> GSM531657 1 0.0000 0.9604 1.000 0.000
#> GSM531659 1 0.0000 0.9604 1.000 0.000
#> GSM531661 2 0.0000 0.9677 0.000 1.000
#> GSM531662 2 0.0000 0.9677 0.000 1.000
#> GSM531663 1 0.0000 0.9604 1.000 0.000
#> GSM531664 2 0.9850 0.2149 0.428 0.572
#> GSM531665 1 0.7950 0.7114 0.760 0.240
#> GSM531666 1 0.4690 0.8850 0.900 0.100
#> GSM531667 2 0.0000 0.9677 0.000 1.000
#> GSM531668 1 0.0000 0.9604 1.000 0.000
#> GSM531669 2 0.0000 0.9677 0.000 1.000
#> GSM531670 2 0.0000 0.9677 0.000 1.000
#> GSM531671 2 0.0000 0.9677 0.000 1.000
#> GSM531672 1 0.0000 0.9604 1.000 0.000
#> GSM531673 2 0.4562 0.8759 0.096 0.904
#> GSM531674 2 0.0000 0.9677 0.000 1.000
#> GSM531675 1 0.0000 0.9604 1.000 0.000
#> GSM531676 1 0.8016 0.7040 0.756 0.244
#> GSM531677 1 0.0000 0.9604 1.000 0.000
#> GSM531678 1 0.0000 0.9604 1.000 0.000
#> GSM531679 1 0.0000 0.9604 1.000 0.000
#> GSM531680 1 0.1184 0.9502 0.984 0.016
#> GSM531681 1 0.0000 0.9604 1.000 0.000
#> GSM531682 1 0.0000 0.9604 1.000 0.000
#> GSM531683 1 0.0000 0.9604 1.000 0.000
#> GSM531684 1 0.4939 0.8771 0.892 0.108
#> GSM531685 2 0.2948 0.9225 0.052 0.948
#> GSM531686 1 0.0000 0.9604 1.000 0.000
#> GSM531687 1 0.5519 0.8572 0.872 0.128
#> GSM531688 1 0.9323 0.4978 0.652 0.348
#> GSM531689 1 0.0000 0.9604 1.000 0.000
#> GSM531690 1 0.0000 0.9604 1.000 0.000
#> GSM531691 1 0.7219 0.7694 0.800 0.200
#> GSM531692 2 0.0000 0.9677 0.000 1.000
#> GSM531693 2 0.0000 0.9677 0.000 1.000
#> GSM531694 1 0.0000 0.9604 1.000 0.000
#> GSM531695 1 0.0376 0.9580 0.996 0.004
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM531600 3 0.0000 0.9405 0.000 0.000 1.000
#> GSM531601 2 0.2165 0.9029 0.000 0.936 0.064
#> GSM531605 1 0.0000 0.9398 1.000 0.000 0.000
#> GSM531615 2 0.0000 0.9549 0.000 1.000 0.000
#> GSM531617 2 0.0000 0.9549 0.000 1.000 0.000
#> GSM531624 2 0.0000 0.9549 0.000 1.000 0.000
#> GSM531627 2 0.0000 0.9549 0.000 1.000 0.000
#> GSM531629 2 0.3816 0.8134 0.148 0.852 0.000
#> GSM531631 2 0.0000 0.9549 0.000 1.000 0.000
#> GSM531634 2 0.0000 0.9549 0.000 1.000 0.000
#> GSM531636 3 0.0000 0.9405 0.000 0.000 1.000
#> GSM531637 2 0.0000 0.9549 0.000 1.000 0.000
#> GSM531654 2 0.0000 0.9549 0.000 1.000 0.000
#> GSM531655 1 0.9213 0.4269 0.536 0.232 0.232
#> GSM531658 1 0.0000 0.9398 1.000 0.000 0.000
#> GSM531660 1 0.0000 0.9398 1.000 0.000 0.000
#> GSM531602 1 0.0000 0.9398 1.000 0.000 0.000
#> GSM531603 1 0.0000 0.9398 1.000 0.000 0.000
#> GSM531604 1 0.3941 0.7899 0.844 0.156 0.000
#> GSM531606 1 0.0000 0.9398 1.000 0.000 0.000
#> GSM531607 1 0.0000 0.9398 1.000 0.000 0.000
#> GSM531608 2 0.0000 0.9549 0.000 1.000 0.000
#> GSM531609 1 0.0000 0.9398 1.000 0.000 0.000
#> GSM531610 1 0.0000 0.9398 1.000 0.000 0.000
#> GSM531611 1 0.0000 0.9398 1.000 0.000 0.000
#> GSM531612 1 0.0592 0.9316 0.988 0.000 0.012
#> GSM531613 1 0.0000 0.9398 1.000 0.000 0.000
#> GSM531614 1 0.0000 0.9398 1.000 0.000 0.000
#> GSM531616 3 0.4399 0.7403 0.000 0.188 0.812
#> GSM531618 1 0.5689 0.7419 0.780 0.036 0.184
#> GSM531619 2 0.0000 0.9549 0.000 1.000 0.000
#> GSM531620 2 0.0000 0.9549 0.000 1.000 0.000
#> GSM531621 2 0.0000 0.9549 0.000 1.000 0.000
#> GSM531622 2 0.0000 0.9549 0.000 1.000 0.000
#> GSM531623 2 0.0000 0.9549 0.000 1.000 0.000
#> GSM531625 2 0.1411 0.9273 0.000 0.964 0.036
#> GSM531626 2 0.4002 0.7930 0.000 0.840 0.160
#> GSM531628 3 0.0000 0.9405 0.000 0.000 1.000
#> GSM531630 2 0.0000 0.9549 0.000 1.000 0.000
#> GSM531632 3 0.0000 0.9405 0.000 0.000 1.000
#> GSM531633 2 0.0000 0.9549 0.000 1.000 0.000
#> GSM531635 3 0.0000 0.9405 0.000 0.000 1.000
#> GSM531638 3 0.6295 0.1093 0.000 0.472 0.528
#> GSM531639 3 0.1031 0.9218 0.000 0.024 0.976
#> GSM531640 2 0.0000 0.9549 0.000 1.000 0.000
#> GSM531641 1 0.0000 0.9398 1.000 0.000 0.000
#> GSM531642 3 0.0000 0.9405 0.000 0.000 1.000
#> GSM531643 3 0.0000 0.9405 0.000 0.000 1.000
#> GSM531644 3 0.0000 0.9405 0.000 0.000 1.000
#> GSM531645 1 0.3412 0.8337 0.876 0.000 0.124
#> GSM531646 3 0.0000 0.9405 0.000 0.000 1.000
#> GSM531647 3 0.0000 0.9405 0.000 0.000 1.000
#> GSM531648 1 0.4555 0.7462 0.800 0.000 0.200
#> GSM531649 3 0.0000 0.9405 0.000 0.000 1.000
#> GSM531650 3 0.0000 0.9405 0.000 0.000 1.000
#> GSM531651 2 0.0000 0.9549 0.000 1.000 0.000
#> GSM531652 3 0.0000 0.9405 0.000 0.000 1.000
#> GSM531653 3 0.0000 0.9405 0.000 0.000 1.000
#> GSM531656 3 0.0000 0.9405 0.000 0.000 1.000
#> GSM531657 1 0.0000 0.9398 1.000 0.000 0.000
#> GSM531659 1 0.0000 0.9398 1.000 0.000 0.000
#> GSM531661 2 0.0000 0.9549 0.000 1.000 0.000
#> GSM531662 2 0.0000 0.9549 0.000 1.000 0.000
#> GSM531663 1 0.0000 0.9398 1.000 0.000 0.000
#> GSM531664 3 0.0000 0.9405 0.000 0.000 1.000
#> GSM531665 1 0.6252 0.1910 0.556 0.000 0.444
#> GSM531666 3 0.1289 0.9145 0.032 0.000 0.968
#> GSM531667 2 0.0000 0.9549 0.000 1.000 0.000
#> GSM531668 1 0.0000 0.9398 1.000 0.000 0.000
#> GSM531669 3 0.0000 0.9405 0.000 0.000 1.000
#> GSM531670 3 0.0000 0.9405 0.000 0.000 1.000
#> GSM531671 3 0.6140 0.3224 0.000 0.404 0.596
#> GSM531672 1 0.0000 0.9398 1.000 0.000 0.000
#> GSM531673 2 0.6354 0.6972 0.056 0.748 0.196
#> GSM531674 3 0.0000 0.9405 0.000 0.000 1.000
#> GSM531675 1 0.0000 0.9398 1.000 0.000 0.000
#> GSM531676 3 0.5835 0.4660 0.340 0.000 0.660
#> GSM531677 1 0.0000 0.9398 1.000 0.000 0.000
#> GSM531678 1 0.0000 0.9398 1.000 0.000 0.000
#> GSM531679 1 0.0000 0.9398 1.000 0.000 0.000
#> GSM531680 3 0.3482 0.8150 0.128 0.000 0.872
#> GSM531681 1 0.0000 0.9398 1.000 0.000 0.000
#> GSM531682 1 0.0000 0.9398 1.000 0.000 0.000
#> GSM531683 1 0.0000 0.9398 1.000 0.000 0.000
#> GSM531684 2 0.2878 0.8700 0.096 0.904 0.000
#> GSM531685 3 0.0592 0.9317 0.012 0.000 0.988
#> GSM531686 1 0.0000 0.9398 1.000 0.000 0.000
#> GSM531687 1 0.6308 0.0288 0.508 0.000 0.492
#> GSM531688 3 0.0000 0.9405 0.000 0.000 1.000
#> GSM531689 1 0.0000 0.9398 1.000 0.000 0.000
#> GSM531690 1 0.0000 0.9398 1.000 0.000 0.000
#> GSM531691 1 0.2537 0.8726 0.920 0.000 0.080
#> GSM531692 2 0.6209 0.3874 0.004 0.628 0.368
#> GSM531693 3 0.0000 0.9405 0.000 0.000 1.000
#> GSM531694 1 0.0000 0.9398 1.000 0.000 0.000
#> GSM531695 3 0.0000 0.9405 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM531600 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM531601 4 0.4500 0.547 0.000 0.316 0.000 0.684
#> GSM531605 1 0.0000 0.900 1.000 0.000 0.000 0.000
#> GSM531615 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM531617 2 0.0707 0.936 0.000 0.980 0.000 0.020
#> GSM531624 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM531627 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM531629 2 0.4925 0.175 0.000 0.572 0.000 0.428
#> GSM531631 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM531634 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM531636 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM531637 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM531654 2 0.1211 0.916 0.040 0.960 0.000 0.000
#> GSM531655 4 0.4804 0.400 0.000 0.384 0.000 0.616
#> GSM531658 4 0.0000 0.916 0.000 0.000 0.000 1.000
#> GSM531660 4 0.3219 0.781 0.164 0.000 0.000 0.836
#> GSM531602 1 0.0000 0.900 1.000 0.000 0.000 0.000
#> GSM531603 1 0.0469 0.894 0.988 0.000 0.000 0.012
#> GSM531604 1 0.0000 0.900 1.000 0.000 0.000 0.000
#> GSM531606 1 0.0000 0.900 1.000 0.000 0.000 0.000
#> GSM531607 1 0.0000 0.900 1.000 0.000 0.000 0.000
#> GSM531608 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM531609 4 0.0000 0.916 0.000 0.000 0.000 1.000
#> GSM531610 4 0.0188 0.914 0.004 0.000 0.000 0.996
#> GSM531611 4 0.0000 0.916 0.000 0.000 0.000 1.000
#> GSM531612 4 0.0000 0.916 0.000 0.000 0.000 1.000
#> GSM531613 4 0.0188 0.914 0.004 0.000 0.000 0.996
#> GSM531614 4 0.0000 0.916 0.000 0.000 0.000 1.000
#> GSM531616 3 0.0188 0.965 0.000 0.004 0.996 0.000
#> GSM531618 4 0.0000 0.916 0.000 0.000 0.000 1.000
#> GSM531619 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM531620 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM531621 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM531622 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM531623 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM531625 2 0.0707 0.934 0.000 0.980 0.020 0.000
#> GSM531626 2 0.3074 0.792 0.000 0.848 0.152 0.000
#> GSM531628 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM531630 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM531632 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM531633 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM531635 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM531638 2 0.4776 0.403 0.000 0.624 0.376 0.000
#> GSM531639 3 0.1004 0.945 0.000 0.024 0.972 0.004
#> GSM531640 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM531641 4 0.0000 0.916 0.000 0.000 0.000 1.000
#> GSM531642 4 0.2973 0.793 0.000 0.000 0.144 0.856
#> GSM531643 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM531644 3 0.4961 0.157 0.000 0.000 0.552 0.448
#> GSM531645 4 0.0000 0.916 0.000 0.000 0.000 1.000
#> GSM531646 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM531647 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM531648 4 0.0000 0.916 0.000 0.000 0.000 1.000
#> GSM531649 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM531650 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM531651 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM531652 4 0.0000 0.916 0.000 0.000 0.000 1.000
#> GSM531653 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM531656 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM531657 4 0.0921 0.897 0.028 0.000 0.000 0.972
#> GSM531659 1 0.4877 0.364 0.592 0.000 0.000 0.408
#> GSM531661 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM531662 2 0.0592 0.939 0.016 0.984 0.000 0.000
#> GSM531663 1 0.4790 0.452 0.620 0.000 0.000 0.380
#> GSM531664 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM531665 1 0.4008 0.674 0.756 0.000 0.244 0.000
#> GSM531666 4 0.1940 0.862 0.000 0.000 0.076 0.924
#> GSM531667 2 0.0000 0.951 0.000 1.000 0.000 0.000
#> GSM531668 4 0.4776 0.434 0.376 0.000 0.000 0.624
#> GSM531669 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM531670 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM531671 3 0.1211 0.935 0.040 0.000 0.960 0.000
#> GSM531672 4 0.0000 0.916 0.000 0.000 0.000 1.000
#> GSM531673 1 0.1004 0.887 0.972 0.004 0.024 0.000
#> GSM531674 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM531675 1 0.0000 0.900 1.000 0.000 0.000 0.000
#> GSM531676 1 0.4697 0.479 0.644 0.000 0.356 0.000
#> GSM531677 1 0.0000 0.900 1.000 0.000 0.000 0.000
#> GSM531678 1 0.0000 0.900 1.000 0.000 0.000 0.000
#> GSM531679 1 0.0000 0.900 1.000 0.000 0.000 0.000
#> GSM531680 3 0.3311 0.764 0.172 0.000 0.828 0.000
#> GSM531681 1 0.0817 0.890 0.976 0.000 0.000 0.024
#> GSM531682 1 0.0000 0.900 1.000 0.000 0.000 0.000
#> GSM531683 1 0.0000 0.900 1.000 0.000 0.000 0.000
#> GSM531684 1 0.4543 0.518 0.676 0.324 0.000 0.000
#> GSM531685 3 0.1022 0.942 0.032 0.000 0.968 0.000
#> GSM531686 1 0.1557 0.870 0.944 0.000 0.000 0.056
#> GSM531687 1 0.4761 0.444 0.628 0.000 0.372 0.000
#> GSM531688 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM531689 1 0.0000 0.900 1.000 0.000 0.000 0.000
#> GSM531690 1 0.1474 0.870 0.948 0.000 0.000 0.052
#> GSM531691 1 0.0000 0.900 1.000 0.000 0.000 0.000
#> GSM531692 1 0.1302 0.876 0.956 0.000 0.044 0.000
#> GSM531693 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM531694 1 0.0000 0.900 1.000 0.000 0.000 0.000
#> GSM531695 3 0.0000 0.968 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM531600 3 0.0880 0.8573 0.000 0.000 0.968 0.000 0.032
#> GSM531601 5 0.4149 0.5796 0.000 0.088 0.000 0.128 0.784
#> GSM531605 1 0.4440 0.2117 0.528 0.000 0.004 0.000 0.468
#> GSM531615 2 0.3612 0.8210 0.000 0.800 0.000 0.028 0.172
#> GSM531617 4 0.5798 0.4319 0.000 0.148 0.000 0.604 0.248
#> GSM531624 2 0.1197 0.9026 0.000 0.952 0.000 0.000 0.048
#> GSM531627 2 0.0865 0.9007 0.000 0.972 0.004 0.000 0.024
#> GSM531629 4 0.6273 0.2771 0.000 0.164 0.000 0.500 0.336
#> GSM531631 2 0.1410 0.9025 0.000 0.940 0.000 0.000 0.060
#> GSM531634 2 0.2929 0.8454 0.000 0.840 0.000 0.008 0.152
#> GSM531636 3 0.3388 0.7230 0.000 0.008 0.792 0.000 0.200
#> GSM531637 2 0.1671 0.8977 0.000 0.924 0.000 0.000 0.076
#> GSM531654 2 0.4693 0.7104 0.056 0.700 0.000 0.000 0.244
#> GSM531655 5 0.2235 0.6351 0.004 0.032 0.004 0.040 0.920
#> GSM531658 5 0.4161 0.3597 0.000 0.000 0.000 0.392 0.608
#> GSM531660 5 0.3849 0.5938 0.052 0.004 0.000 0.136 0.808
#> GSM531602 1 0.2605 0.7581 0.852 0.000 0.000 0.000 0.148
#> GSM531603 5 0.3548 0.5620 0.188 0.004 0.000 0.012 0.796
#> GSM531604 1 0.0000 0.8078 1.000 0.000 0.000 0.000 0.000
#> GSM531606 1 0.0703 0.8077 0.976 0.000 0.000 0.000 0.024
#> GSM531607 1 0.2471 0.7656 0.864 0.000 0.000 0.000 0.136
#> GSM531608 2 0.3307 0.8504 0.012 0.864 0.004 0.080 0.040
#> GSM531609 4 0.0162 0.8030 0.000 0.000 0.000 0.996 0.004
#> GSM531610 4 0.0404 0.7989 0.000 0.000 0.000 0.988 0.012
#> GSM531611 4 0.0000 0.8034 0.000 0.000 0.000 1.000 0.000
#> GSM531612 4 0.0609 0.7961 0.000 0.000 0.000 0.980 0.020
#> GSM531613 4 0.0000 0.8034 0.000 0.000 0.000 1.000 0.000
#> GSM531614 4 0.0000 0.8034 0.000 0.000 0.000 1.000 0.000
#> GSM531616 3 0.1764 0.8213 0.000 0.064 0.928 0.000 0.008
#> GSM531618 5 0.4452 -0.0784 0.000 0.004 0.000 0.496 0.500
#> GSM531619 2 0.1197 0.9039 0.000 0.952 0.000 0.000 0.048
#> GSM531620 2 0.4584 0.7869 0.000 0.752 0.036 0.024 0.188
#> GSM531621 2 0.1300 0.8964 0.000 0.956 0.016 0.000 0.028
#> GSM531622 2 0.1168 0.9028 0.000 0.960 0.000 0.008 0.032
#> GSM531623 2 0.0510 0.9044 0.000 0.984 0.000 0.000 0.016
#> GSM531625 2 0.3163 0.7676 0.000 0.824 0.164 0.000 0.012
#> GSM531626 3 0.5175 0.3289 0.000 0.372 0.584 0.004 0.040
#> GSM531628 3 0.1671 0.8392 0.000 0.000 0.924 0.000 0.076
#> GSM531630 2 0.0703 0.9017 0.000 0.976 0.000 0.000 0.024
#> GSM531632 3 0.0162 0.8596 0.000 0.000 0.996 0.000 0.004
#> GSM531633 2 0.0794 0.9018 0.000 0.972 0.000 0.000 0.028
#> GSM531635 3 0.0290 0.8585 0.000 0.000 0.992 0.000 0.008
#> GSM531638 3 0.4415 0.3600 0.000 0.388 0.604 0.000 0.008
#> GSM531639 3 0.2989 0.8143 0.000 0.072 0.868 0.000 0.060
#> GSM531640 2 0.0865 0.9009 0.000 0.972 0.000 0.004 0.024
#> GSM531641 4 0.0404 0.8007 0.000 0.000 0.000 0.988 0.012
#> GSM531642 4 0.6744 -0.2543 0.000 0.000 0.268 0.400 0.332
#> GSM531643 3 0.1544 0.8433 0.000 0.000 0.932 0.000 0.068
#> GSM531644 5 0.5204 0.2906 0.000 0.000 0.392 0.048 0.560
#> GSM531645 4 0.0510 0.7993 0.000 0.000 0.000 0.984 0.016
#> GSM531646 3 0.0162 0.8596 0.000 0.000 0.996 0.000 0.004
#> GSM531647 3 0.0404 0.8613 0.000 0.000 0.988 0.000 0.012
#> GSM531648 4 0.4182 0.1719 0.000 0.000 0.000 0.600 0.400
#> GSM531649 3 0.0162 0.8597 0.000 0.000 0.996 0.000 0.004
#> GSM531650 3 0.1544 0.8433 0.000 0.000 0.932 0.000 0.068
#> GSM531651 2 0.0794 0.9033 0.000 0.972 0.000 0.000 0.028
#> GSM531652 5 0.4683 0.5983 0.000 0.000 0.092 0.176 0.732
#> GSM531653 3 0.0404 0.8613 0.000 0.000 0.988 0.000 0.012
#> GSM531656 3 0.5892 0.2729 0.000 0.108 0.520 0.000 0.372
#> GSM531657 4 0.0671 0.7989 0.004 0.000 0.000 0.980 0.016
#> GSM531659 4 0.2280 0.7042 0.120 0.000 0.000 0.880 0.000
#> GSM531661 2 0.0880 0.9065 0.000 0.968 0.000 0.000 0.032
#> GSM531662 2 0.4358 0.5395 0.284 0.696 0.008 0.000 0.012
#> GSM531663 4 0.2800 0.7325 0.072 0.016 0.000 0.888 0.024
#> GSM531664 3 0.3336 0.6866 0.000 0.000 0.772 0.000 0.228
#> GSM531665 1 0.4505 0.4061 0.620 0.008 0.368 0.000 0.004
#> GSM531666 5 0.3181 0.6422 0.000 0.000 0.072 0.072 0.856
#> GSM531667 2 0.1270 0.9044 0.000 0.948 0.000 0.000 0.052
#> GSM531668 5 0.4062 0.5749 0.168 0.016 0.000 0.028 0.788
#> GSM531669 3 0.0162 0.8608 0.000 0.000 0.996 0.000 0.004
#> GSM531670 3 0.3237 0.8109 0.000 0.048 0.848 0.000 0.104
#> GSM531671 3 0.3184 0.7795 0.068 0.028 0.872 0.000 0.032
#> GSM531672 5 0.3774 0.4968 0.000 0.000 0.000 0.296 0.704
#> GSM531673 1 0.2917 0.7776 0.884 0.076 0.024 0.004 0.012
#> GSM531674 3 0.0510 0.8610 0.000 0.000 0.984 0.000 0.016
#> GSM531675 1 0.1502 0.7994 0.940 0.000 0.000 0.004 0.056
#> GSM531676 1 0.3783 0.6073 0.740 0.000 0.252 0.000 0.008
#> GSM531677 1 0.0000 0.8078 1.000 0.000 0.000 0.000 0.000
#> GSM531678 1 0.2067 0.7986 0.924 0.044 0.000 0.028 0.004
#> GSM531679 1 0.0000 0.8078 1.000 0.000 0.000 0.000 0.000
#> GSM531680 5 0.6289 0.1616 0.152 0.000 0.396 0.000 0.452
#> GSM531681 1 0.4300 0.1980 0.524 0.000 0.000 0.476 0.000
#> GSM531682 1 0.0324 0.8078 0.992 0.000 0.004 0.004 0.000
#> GSM531683 1 0.1341 0.7993 0.944 0.000 0.000 0.000 0.056
#> GSM531684 1 0.4218 0.5149 0.660 0.332 0.000 0.000 0.008
#> GSM531685 3 0.3706 0.6704 0.184 0.020 0.792 0.000 0.004
#> GSM531686 1 0.4287 0.2457 0.540 0.000 0.000 0.460 0.000
#> GSM531687 1 0.5139 0.5118 0.648 0.000 0.280 0.000 0.072
#> GSM531688 3 0.0404 0.8614 0.000 0.000 0.988 0.000 0.012
#> GSM531689 1 0.0290 0.8083 0.992 0.000 0.000 0.000 0.008
#> GSM531690 1 0.3639 0.7521 0.824 0.000 0.000 0.100 0.076
#> GSM531691 1 0.1364 0.8045 0.952 0.036 0.000 0.000 0.012
#> GSM531692 1 0.2605 0.7803 0.896 0.056 0.044 0.000 0.004
#> GSM531693 3 0.0000 0.8603 0.000 0.000 1.000 0.000 0.000
#> GSM531694 1 0.1965 0.7861 0.904 0.000 0.000 0.000 0.096
#> GSM531695 5 0.4599 0.2890 0.016 0.000 0.384 0.000 0.600
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM531600 3 0.2537 0.78170 0.000 0.008 0.880 0.000 0.024 0.088
#> GSM531601 5 0.5627 0.43803 0.000 0.208 0.000 0.096 0.636 0.060
#> GSM531605 5 0.4845 -0.06991 0.400 0.000 0.000 0.000 0.540 0.060
#> GSM531615 6 0.5101 0.30798 0.000 0.264 0.000 0.032 0.060 0.644
#> GSM531617 6 0.5010 0.39575 0.000 0.008 0.000 0.156 0.168 0.668
#> GSM531624 2 0.3727 0.41769 0.000 0.612 0.000 0.000 0.000 0.388
#> GSM531627 2 0.2178 0.66219 0.000 0.868 0.000 0.000 0.000 0.132
#> GSM531629 6 0.4659 0.33978 0.000 0.000 0.000 0.084 0.260 0.656
#> GSM531631 2 0.0363 0.70709 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM531634 6 0.5291 0.34834 0.000 0.224 0.000 0.016 0.124 0.636
#> GSM531636 3 0.5315 0.61403 0.000 0.028 0.656 0.000 0.192 0.124
#> GSM531637 2 0.1327 0.69886 0.000 0.936 0.000 0.000 0.000 0.064
#> GSM531654 6 0.4923 0.39573 0.072 0.040 0.000 0.000 0.188 0.700
#> GSM531655 5 0.2815 0.54810 0.000 0.024 0.044 0.000 0.876 0.056
#> GSM531658 5 0.3489 0.45199 0.000 0.000 0.000 0.288 0.708 0.004
#> GSM531660 5 0.4099 0.30519 0.016 0.000 0.000 0.000 0.612 0.372
#> GSM531602 1 0.4871 0.64446 0.656 0.000 0.000 0.000 0.212 0.132
#> GSM531603 5 0.3551 0.47801 0.036 0.000 0.000 0.000 0.772 0.192
#> GSM531604 1 0.0363 0.78555 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM531606 1 0.2544 0.77017 0.864 0.004 0.000 0.000 0.012 0.120
#> GSM531607 1 0.4357 0.67867 0.700 0.000 0.000 0.000 0.224 0.076
#> GSM531608 6 0.5492 0.07972 0.036 0.352 0.000 0.060 0.000 0.552
#> GSM531609 4 0.0000 0.84939 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531610 4 0.0260 0.84746 0.000 0.000 0.000 0.992 0.000 0.008
#> GSM531611 4 0.0146 0.84900 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM531612 4 0.0547 0.84248 0.000 0.000 0.000 0.980 0.020 0.000
#> GSM531613 4 0.0146 0.84926 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM531614 4 0.0000 0.84939 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531616 3 0.2544 0.75281 0.000 0.004 0.852 0.000 0.004 0.140
#> GSM531618 5 0.6219 0.31063 0.000 0.020 0.008 0.360 0.472 0.140
#> GSM531619 2 0.0713 0.70987 0.000 0.972 0.000 0.000 0.000 0.028
#> GSM531620 6 0.4774 0.44823 0.000 0.056 0.108 0.016 0.064 0.756
#> GSM531621 2 0.3695 0.50531 0.000 0.624 0.000 0.000 0.000 0.376
#> GSM531622 2 0.2048 0.70746 0.000 0.880 0.000 0.000 0.000 0.120
#> GSM531623 2 0.3672 0.47456 0.000 0.632 0.000 0.000 0.000 0.368
#> GSM531625 2 0.5600 0.33316 0.000 0.548 0.160 0.000 0.004 0.288
#> GSM531626 6 0.4389 -0.00352 0.000 0.024 0.448 0.000 0.000 0.528
#> GSM531628 3 0.1757 0.77702 0.000 0.000 0.916 0.000 0.076 0.008
#> GSM531630 2 0.0547 0.70707 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM531632 3 0.2320 0.76170 0.000 0.000 0.864 0.000 0.004 0.132
#> GSM531633 6 0.4039 -0.23425 0.000 0.424 0.008 0.000 0.000 0.568
#> GSM531635 3 0.1411 0.80376 0.000 0.004 0.936 0.000 0.000 0.060
#> GSM531638 2 0.4241 0.31251 0.000 0.608 0.368 0.000 0.000 0.024
#> GSM531639 3 0.3992 0.74234 0.000 0.056 0.800 0.000 0.056 0.088
#> GSM531640 2 0.1010 0.69158 0.000 0.960 0.000 0.036 0.000 0.004
#> GSM531641 4 0.0405 0.84866 0.000 0.000 0.000 0.988 0.008 0.004
#> GSM531642 4 0.5843 0.14816 0.000 0.000 0.224 0.536 0.232 0.008
#> GSM531643 3 0.0935 0.79860 0.000 0.000 0.964 0.000 0.032 0.004
#> GSM531644 5 0.4559 0.01020 0.000 0.000 0.444 0.012 0.528 0.016
#> GSM531645 4 0.0405 0.84676 0.000 0.000 0.000 0.988 0.008 0.004
#> GSM531646 3 0.2092 0.76968 0.000 0.000 0.876 0.000 0.000 0.124
#> GSM531647 3 0.1531 0.79795 0.000 0.000 0.928 0.000 0.004 0.068
#> GSM531648 5 0.4666 0.30560 0.000 0.000 0.000 0.388 0.564 0.048
#> GSM531649 3 0.2340 0.75999 0.000 0.000 0.852 0.000 0.000 0.148
#> GSM531650 3 0.1265 0.79419 0.000 0.000 0.948 0.000 0.044 0.008
#> GSM531651 2 0.2300 0.69740 0.000 0.856 0.000 0.000 0.000 0.144
#> GSM531652 5 0.4756 0.53741 0.000 0.000 0.100 0.180 0.704 0.016
#> GSM531653 3 0.0858 0.80378 0.000 0.000 0.968 0.000 0.004 0.028
#> GSM531656 3 0.5809 0.49172 0.000 0.144 0.608 0.008 0.216 0.024
#> GSM531657 4 0.1657 0.81414 0.000 0.000 0.000 0.928 0.056 0.016
#> GSM531659 4 0.2786 0.78142 0.100 0.000 0.000 0.864 0.024 0.012
#> GSM531661 2 0.3862 0.21754 0.000 0.524 0.000 0.000 0.000 0.476
#> GSM531662 6 0.5824 0.24111 0.332 0.140 0.008 0.000 0.004 0.516
#> GSM531663 4 0.1757 0.80874 0.076 0.000 0.000 0.916 0.000 0.008
#> GSM531664 3 0.3217 0.65389 0.000 0.000 0.768 0.000 0.224 0.008
#> GSM531665 1 0.2913 0.66150 0.812 0.000 0.180 0.000 0.004 0.004
#> GSM531666 5 0.3329 0.49831 0.000 0.000 0.220 0.004 0.768 0.008
#> GSM531667 2 0.2948 0.66248 0.000 0.804 0.000 0.000 0.008 0.188
#> GSM531668 5 0.4161 0.15955 0.012 0.000 0.000 0.000 0.540 0.448
#> GSM531669 3 0.1663 0.78928 0.000 0.000 0.912 0.000 0.000 0.088
#> GSM531670 3 0.5537 0.57860 0.008 0.152 0.660 0.000 0.148 0.032
#> GSM531671 6 0.4351 0.13255 0.008 0.000 0.416 0.000 0.012 0.564
#> GSM531672 5 0.3748 0.55897 0.004 0.000 0.000 0.148 0.784 0.064
#> GSM531673 6 0.5057 -0.21767 0.472 0.016 0.032 0.000 0.004 0.476
#> GSM531674 3 0.0725 0.80063 0.000 0.000 0.976 0.000 0.012 0.012
#> GSM531675 1 0.2912 0.75480 0.816 0.000 0.000 0.000 0.172 0.012
#> GSM531676 1 0.3481 0.61554 0.756 0.000 0.228 0.000 0.004 0.012
#> GSM531677 1 0.0508 0.78847 0.984 0.000 0.000 0.004 0.012 0.000
#> GSM531678 1 0.2070 0.74596 0.896 0.000 0.000 0.092 0.000 0.012
#> GSM531679 1 0.1713 0.78900 0.928 0.000 0.000 0.000 0.028 0.044
#> GSM531680 3 0.7165 -0.11851 0.264 0.004 0.348 0.016 0.336 0.032
#> GSM531681 4 0.3887 0.49481 0.360 0.000 0.000 0.632 0.000 0.008
#> GSM531682 1 0.1230 0.78862 0.956 0.000 0.008 0.000 0.008 0.028
#> GSM531683 1 0.3963 0.72877 0.756 0.000 0.000 0.000 0.164 0.080
#> GSM531684 1 0.4367 0.42604 0.604 0.364 0.000 0.000 0.000 0.032
#> GSM531685 3 0.5477 0.26356 0.384 0.000 0.500 0.000 0.004 0.112
#> GSM531686 4 0.3934 0.46144 0.376 0.000 0.000 0.616 0.000 0.008
#> GSM531687 1 0.5765 0.29461 0.532 0.004 0.368 0.012 0.064 0.020
#> GSM531688 3 0.1453 0.79494 0.040 0.000 0.944 0.000 0.008 0.008
#> GSM531689 1 0.0717 0.78187 0.976 0.000 0.000 0.008 0.000 0.016
#> GSM531690 1 0.4594 0.70472 0.708 0.000 0.000 0.044 0.216 0.032
#> GSM531691 1 0.2635 0.75750 0.880 0.036 0.004 0.000 0.004 0.076
#> GSM531692 1 0.1375 0.77970 0.952 0.008 0.028 0.000 0.004 0.008
#> GSM531693 3 0.2461 0.79256 0.044 0.000 0.888 0.000 0.004 0.064
#> GSM531694 1 0.4195 0.70369 0.724 0.000 0.000 0.000 0.200 0.076
#> GSM531695 5 0.4607 0.20104 0.008 0.000 0.392 0.000 0.572 0.028
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> MAD:NMF 93 0.16845 2
#> MAD:NMF 89 0.00355 3
#> MAD:NMF 87 0.02678 4
#> MAD:NMF 79 0.00390 5
#> MAD:NMF 60 0.05235 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "hclust"]
# you can also extract it by
# res = res_list["ATC:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 96 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 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 0.891 0.929 0.968 0.4161 0.582 0.582
#> 3 3 0.617 0.885 0.897 0.5096 0.764 0.595
#> 4 4 0.786 0.879 0.932 0.1068 0.951 0.861
#> 5 5 0.748 0.804 0.872 0.0771 1.000 1.000
#> 6 6 0.759 0.749 0.862 0.0309 0.907 0.700
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM531600 1 0.0376 0.974 0.996 0.004
#> GSM531601 1 0.7745 0.703 0.772 0.228
#> GSM531605 1 0.0000 0.974 1.000 0.000
#> GSM531615 2 0.0000 0.945 0.000 1.000
#> GSM531617 2 0.0376 0.946 0.004 0.996
#> GSM531624 2 0.0000 0.945 0.000 1.000
#> GSM531627 2 0.1184 0.942 0.016 0.984
#> GSM531629 2 0.2603 0.924 0.044 0.956
#> GSM531631 2 0.3114 0.915 0.056 0.944
#> GSM531634 2 0.0376 0.946 0.004 0.996
#> GSM531636 1 0.6438 0.800 0.836 0.164
#> GSM531637 2 0.0000 0.945 0.000 1.000
#> GSM531654 2 0.7674 0.726 0.224 0.776
#> GSM531655 1 0.0000 0.974 1.000 0.000
#> GSM531658 1 0.0376 0.974 0.996 0.004
#> GSM531660 1 0.0000 0.974 1.000 0.000
#> GSM531602 1 0.0000 0.974 1.000 0.000
#> GSM531603 1 0.0000 0.974 1.000 0.000
#> GSM531604 1 0.0376 0.974 0.996 0.004
#> GSM531606 1 0.0376 0.974 0.996 0.004
#> GSM531607 1 0.0000 0.974 1.000 0.000
#> GSM531608 2 0.0000 0.945 0.000 1.000
#> GSM531609 1 0.2603 0.939 0.956 0.044
#> GSM531610 1 0.0376 0.974 0.996 0.004
#> GSM531611 1 0.0000 0.974 1.000 0.000
#> GSM531612 1 0.0000 0.974 1.000 0.000
#> GSM531613 1 0.0000 0.974 1.000 0.000
#> GSM531614 1 0.2603 0.939 0.956 0.044
#> GSM531616 2 0.0376 0.946 0.004 0.996
#> GSM531618 1 0.0376 0.974 0.996 0.004
#> GSM531619 2 0.0000 0.945 0.000 1.000
#> GSM531620 2 0.1184 0.942 0.016 0.984
#> GSM531621 2 0.0000 0.945 0.000 1.000
#> GSM531622 2 0.0376 0.946 0.004 0.996
#> GSM531623 2 0.0376 0.946 0.004 0.996
#> GSM531625 2 0.0000 0.945 0.000 1.000
#> GSM531626 2 0.6048 0.828 0.148 0.852
#> GSM531628 1 0.9944 0.124 0.544 0.456
#> GSM531630 2 0.0376 0.946 0.004 0.996
#> GSM531632 1 0.8443 0.621 0.728 0.272
#> GSM531633 2 0.0000 0.945 0.000 1.000
#> GSM531635 2 0.0376 0.946 0.004 0.996
#> GSM531638 2 0.9522 0.442 0.372 0.628
#> GSM531639 1 0.0376 0.974 0.996 0.004
#> GSM531640 2 0.0376 0.946 0.004 0.996
#> GSM531641 1 0.0376 0.974 0.996 0.004
#> GSM531642 1 0.0376 0.974 0.996 0.004
#> GSM531643 1 0.0376 0.974 0.996 0.004
#> GSM531644 1 0.0376 0.974 0.996 0.004
#> GSM531645 1 0.0376 0.974 0.996 0.004
#> GSM531646 2 0.9248 0.516 0.340 0.660
#> GSM531647 1 0.6712 0.784 0.824 0.176
#> GSM531648 1 0.0376 0.974 0.996 0.004
#> GSM531649 2 0.6048 0.828 0.148 0.852
#> GSM531650 1 0.0376 0.974 0.996 0.004
#> GSM531651 2 0.0000 0.945 0.000 1.000
#> GSM531652 1 0.0376 0.974 0.996 0.004
#> GSM531653 1 0.4161 0.898 0.916 0.084
#> GSM531656 1 0.0376 0.974 0.996 0.004
#> GSM531657 1 0.0000 0.974 1.000 0.000
#> GSM531659 1 0.0376 0.974 0.996 0.004
#> GSM531661 2 0.0672 0.945 0.008 0.992
#> GSM531662 1 0.0672 0.971 0.992 0.008
#> GSM531663 1 0.0376 0.974 0.996 0.004
#> GSM531664 1 0.0000 0.974 1.000 0.000
#> GSM531665 1 0.0376 0.974 0.996 0.004
#> GSM531666 1 0.0000 0.974 1.000 0.000
#> GSM531667 2 0.0672 0.945 0.008 0.992
#> GSM531668 1 0.0376 0.974 0.996 0.004
#> GSM531669 1 0.0376 0.974 0.996 0.004
#> GSM531670 1 0.0376 0.974 0.996 0.004
#> GSM531671 1 0.3274 0.924 0.940 0.060
#> GSM531672 1 0.0000 0.974 1.000 0.000
#> GSM531673 1 0.0376 0.974 0.996 0.004
#> GSM531674 1 0.0376 0.974 0.996 0.004
#> GSM531675 1 0.0000 0.974 1.000 0.000
#> GSM531676 1 0.0000 0.974 1.000 0.000
#> GSM531677 1 0.0000 0.974 1.000 0.000
#> GSM531678 1 0.0000 0.974 1.000 0.000
#> GSM531679 1 0.0000 0.974 1.000 0.000
#> GSM531680 1 0.0000 0.974 1.000 0.000
#> GSM531681 1 0.0000 0.974 1.000 0.000
#> GSM531682 1 0.0000 0.974 1.000 0.000
#> GSM531683 1 0.0000 0.974 1.000 0.000
#> GSM531684 1 0.0376 0.974 0.996 0.004
#> GSM531685 1 0.0000 0.974 1.000 0.000
#> GSM531686 1 0.0000 0.974 1.000 0.000
#> GSM531687 1 0.0000 0.974 1.000 0.000
#> GSM531688 1 0.0000 0.974 1.000 0.000
#> GSM531689 1 0.0000 0.974 1.000 0.000
#> GSM531690 1 0.0000 0.974 1.000 0.000
#> GSM531691 1 0.0000 0.974 1.000 0.000
#> GSM531692 1 0.0376 0.974 0.996 0.004
#> GSM531693 1 0.0000 0.974 1.000 0.000
#> GSM531694 1 0.0000 0.974 1.000 0.000
#> GSM531695 1 0.0000 0.974 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM531600 3 0.400 0.913 0.160 0.000 0.840
#> GSM531601 3 0.312 0.674 0.000 0.108 0.892
#> GSM531605 1 0.216 0.918 0.936 0.000 0.064
#> GSM531615 2 0.141 0.866 0.000 0.964 0.036
#> GSM531617 2 0.341 0.905 0.000 0.876 0.124
#> GSM531624 2 0.141 0.866 0.000 0.964 0.036
#> GSM531627 2 0.362 0.903 0.000 0.864 0.136
#> GSM531629 2 0.406 0.891 0.000 0.836 0.164
#> GSM531631 2 0.424 0.886 0.000 0.824 0.176
#> GSM531634 2 0.341 0.905 0.000 0.876 0.124
#> GSM531636 3 0.164 0.754 0.000 0.044 0.956
#> GSM531637 2 0.141 0.866 0.000 0.964 0.036
#> GSM531654 2 0.586 0.698 0.000 0.656 0.344
#> GSM531655 3 0.529 0.848 0.268 0.000 0.732
#> GSM531658 3 0.412 0.916 0.168 0.000 0.832
#> GSM531660 3 0.550 0.823 0.292 0.000 0.708
#> GSM531602 1 0.000 0.990 1.000 0.000 0.000
#> GSM531603 1 0.216 0.918 0.936 0.000 0.064
#> GSM531604 3 0.435 0.911 0.184 0.000 0.816
#> GSM531606 3 0.440 0.910 0.188 0.000 0.812
#> GSM531607 1 0.216 0.918 0.936 0.000 0.064
#> GSM531608 2 0.245 0.896 0.000 0.924 0.076
#> GSM531609 3 0.319 0.884 0.112 0.000 0.888
#> GSM531610 3 0.394 0.911 0.156 0.000 0.844
#> GSM531611 1 0.000 0.990 1.000 0.000 0.000
#> GSM531612 1 0.000 0.990 1.000 0.000 0.000
#> GSM531613 1 0.000 0.990 1.000 0.000 0.000
#> GSM531614 3 0.319 0.884 0.112 0.000 0.888
#> GSM531616 2 0.341 0.905 0.000 0.876 0.124
#> GSM531618 3 0.412 0.916 0.168 0.000 0.832
#> GSM531619 2 0.141 0.866 0.000 0.964 0.036
#> GSM531620 2 0.362 0.903 0.000 0.864 0.136
#> GSM531621 2 0.141 0.866 0.000 0.964 0.036
#> GSM531622 2 0.341 0.905 0.000 0.876 0.124
#> GSM531623 2 0.341 0.905 0.000 0.876 0.124
#> GSM531625 2 0.141 0.866 0.000 0.964 0.036
#> GSM531626 2 0.529 0.809 0.000 0.732 0.268
#> GSM531628 3 0.581 0.134 0.000 0.336 0.664
#> GSM531630 2 0.341 0.905 0.000 0.876 0.124
#> GSM531632 3 0.522 0.606 0.024 0.176 0.800
#> GSM531633 2 0.141 0.866 0.000 0.964 0.036
#> GSM531635 2 0.341 0.905 0.000 0.876 0.124
#> GSM531638 2 0.631 0.425 0.000 0.508 0.492
#> GSM531639 3 0.455 0.903 0.200 0.000 0.800
#> GSM531640 2 0.341 0.905 0.000 0.876 0.124
#> GSM531641 3 0.518 0.861 0.256 0.000 0.744
#> GSM531642 3 0.484 0.888 0.224 0.000 0.776
#> GSM531643 3 0.412 0.916 0.168 0.000 0.832
#> GSM531644 3 0.412 0.916 0.168 0.000 0.832
#> GSM531645 3 0.412 0.916 0.168 0.000 0.832
#> GSM531646 2 0.628 0.498 0.000 0.540 0.460
#> GSM531647 3 0.196 0.741 0.000 0.056 0.944
#> GSM531648 3 0.412 0.916 0.168 0.000 0.832
#> GSM531649 2 0.529 0.809 0.000 0.732 0.268
#> GSM531650 3 0.412 0.916 0.168 0.000 0.832
#> GSM531651 2 0.141 0.866 0.000 0.964 0.036
#> GSM531652 3 0.412 0.916 0.168 0.000 0.832
#> GSM531653 3 0.259 0.848 0.072 0.004 0.924
#> GSM531656 3 0.412 0.916 0.168 0.000 0.832
#> GSM531657 3 0.550 0.823 0.292 0.000 0.708
#> GSM531659 3 0.484 0.888 0.224 0.000 0.776
#> GSM531661 2 0.348 0.905 0.000 0.872 0.128
#> GSM531662 3 0.388 0.910 0.152 0.000 0.848
#> GSM531663 3 0.412 0.916 0.168 0.000 0.832
#> GSM531664 3 0.550 0.823 0.292 0.000 0.708
#> GSM531665 3 0.412 0.916 0.168 0.000 0.832
#> GSM531666 3 0.550 0.823 0.292 0.000 0.708
#> GSM531667 2 0.348 0.905 0.000 0.872 0.128
#> GSM531668 3 0.412 0.916 0.168 0.000 0.832
#> GSM531669 3 0.418 0.914 0.172 0.000 0.828
#> GSM531670 3 0.400 0.913 0.160 0.000 0.840
#> GSM531671 3 0.377 0.878 0.112 0.012 0.876
#> GSM531672 3 0.536 0.841 0.276 0.000 0.724
#> GSM531673 3 0.412 0.916 0.168 0.000 0.832
#> GSM531674 3 0.400 0.913 0.160 0.000 0.840
#> GSM531675 1 0.000 0.990 1.000 0.000 0.000
#> GSM531676 1 0.000 0.990 1.000 0.000 0.000
#> GSM531677 1 0.000 0.990 1.000 0.000 0.000
#> GSM531678 1 0.000 0.990 1.000 0.000 0.000
#> GSM531679 1 0.000 0.990 1.000 0.000 0.000
#> GSM531680 1 0.000 0.990 1.000 0.000 0.000
#> GSM531681 1 0.000 0.990 1.000 0.000 0.000
#> GSM531682 1 0.000 0.990 1.000 0.000 0.000
#> GSM531683 1 0.000 0.990 1.000 0.000 0.000
#> GSM531684 3 0.435 0.911 0.184 0.000 0.816
#> GSM531685 1 0.000 0.990 1.000 0.000 0.000
#> GSM531686 1 0.000 0.990 1.000 0.000 0.000
#> GSM531687 1 0.000 0.990 1.000 0.000 0.000
#> GSM531688 1 0.000 0.990 1.000 0.000 0.000
#> GSM531689 1 0.000 0.990 1.000 0.000 0.000
#> GSM531690 1 0.000 0.990 1.000 0.000 0.000
#> GSM531691 1 0.000 0.990 1.000 0.000 0.000
#> GSM531692 3 0.435 0.911 0.184 0.000 0.816
#> GSM531693 3 0.573 0.763 0.324 0.000 0.676
#> GSM531694 1 0.000 0.990 1.000 0.000 0.000
#> GSM531695 1 0.000 0.990 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM531600 3 0.1004 0.9038 0.004 0.000 0.972 0.024
#> GSM531601 3 0.5550 0.6017 0.000 0.248 0.692 0.060
#> GSM531605 1 0.2401 0.8777 0.904 0.000 0.092 0.004
#> GSM531615 4 0.1792 1.0000 0.000 0.068 0.000 0.932
#> GSM531617 2 0.0592 0.8607 0.000 0.984 0.000 0.016
#> GSM531624 4 0.1792 1.0000 0.000 0.068 0.000 0.932
#> GSM531627 2 0.0524 0.8594 0.000 0.988 0.004 0.008
#> GSM531629 2 0.0937 0.8508 0.000 0.976 0.012 0.012
#> GSM531631 2 0.1256 0.8438 0.000 0.964 0.028 0.008
#> GSM531634 2 0.0592 0.8607 0.000 0.984 0.000 0.016
#> GSM531636 3 0.4990 0.7087 0.000 0.184 0.756 0.060
#> GSM531637 4 0.1792 1.0000 0.000 0.068 0.000 0.932
#> GSM531654 2 0.4462 0.6921 0.000 0.792 0.164 0.044
#> GSM531655 3 0.2760 0.8518 0.128 0.000 0.872 0.000
#> GSM531658 3 0.0336 0.9105 0.008 0.000 0.992 0.000
#> GSM531660 3 0.3444 0.8060 0.184 0.000 0.816 0.000
#> GSM531602 1 0.0000 0.9841 1.000 0.000 0.000 0.000
#> GSM531603 1 0.2401 0.8777 0.904 0.000 0.092 0.004
#> GSM531604 3 0.1042 0.9086 0.020 0.000 0.972 0.008
#> GSM531606 3 0.1151 0.9079 0.024 0.000 0.968 0.008
#> GSM531607 1 0.2401 0.8777 0.904 0.000 0.092 0.004
#> GSM531608 2 0.3024 0.7244 0.000 0.852 0.000 0.148
#> GSM531609 3 0.2759 0.8644 0.000 0.044 0.904 0.052
#> GSM531610 3 0.1661 0.8914 0.004 0.000 0.944 0.052
#> GSM531611 1 0.0000 0.9841 1.000 0.000 0.000 0.000
#> GSM531612 1 0.0000 0.9841 1.000 0.000 0.000 0.000
#> GSM531613 1 0.0000 0.9841 1.000 0.000 0.000 0.000
#> GSM531614 3 0.2759 0.8644 0.000 0.044 0.904 0.052
#> GSM531616 2 0.0592 0.8607 0.000 0.984 0.000 0.016
#> GSM531618 3 0.0336 0.9105 0.008 0.000 0.992 0.000
#> GSM531619 4 0.1792 1.0000 0.000 0.068 0.000 0.932
#> GSM531620 2 0.0524 0.8594 0.000 0.988 0.004 0.008
#> GSM531621 4 0.1792 1.0000 0.000 0.068 0.000 0.932
#> GSM531622 2 0.0592 0.8607 0.000 0.984 0.000 0.016
#> GSM531623 2 0.0592 0.8607 0.000 0.984 0.000 0.016
#> GSM531625 4 0.1792 1.0000 0.000 0.068 0.000 0.932
#> GSM531626 2 0.3160 0.7780 0.000 0.872 0.108 0.020
#> GSM531628 2 0.6337 0.0374 0.000 0.476 0.464 0.060
#> GSM531630 2 0.0592 0.8607 0.000 0.984 0.000 0.016
#> GSM531632 3 0.5791 0.5171 0.000 0.284 0.656 0.060
#> GSM531633 4 0.1792 1.0000 0.000 0.068 0.000 0.932
#> GSM531635 2 0.0592 0.8607 0.000 0.984 0.000 0.016
#> GSM531638 2 0.5742 0.5376 0.000 0.648 0.300 0.052
#> GSM531639 3 0.1211 0.9031 0.040 0.000 0.960 0.000
#> GSM531640 2 0.0592 0.8607 0.000 0.984 0.000 0.016
#> GSM531641 3 0.2760 0.8546 0.128 0.000 0.872 0.000
#> GSM531642 3 0.2281 0.8770 0.096 0.000 0.904 0.000
#> GSM531643 3 0.0336 0.9105 0.008 0.000 0.992 0.000
#> GSM531644 3 0.0336 0.9105 0.008 0.000 0.992 0.000
#> GSM531645 3 0.0336 0.9105 0.008 0.000 0.992 0.000
#> GSM531646 2 0.5498 0.5729 0.000 0.680 0.272 0.048
#> GSM531647 3 0.5109 0.6907 0.000 0.196 0.744 0.060
#> GSM531648 3 0.0336 0.9105 0.008 0.000 0.992 0.000
#> GSM531649 2 0.3160 0.7780 0.000 0.872 0.108 0.020
#> GSM531650 3 0.0336 0.9105 0.008 0.000 0.992 0.000
#> GSM531651 4 0.1792 1.0000 0.000 0.068 0.000 0.932
#> GSM531652 3 0.0336 0.9105 0.008 0.000 0.992 0.000
#> GSM531653 3 0.3935 0.8095 0.000 0.100 0.840 0.060
#> GSM531656 3 0.0336 0.9105 0.008 0.000 0.992 0.000
#> GSM531657 3 0.3356 0.8131 0.176 0.000 0.824 0.000
#> GSM531659 3 0.2281 0.8770 0.096 0.000 0.904 0.000
#> GSM531661 2 0.0707 0.8602 0.000 0.980 0.000 0.020
#> GSM531662 3 0.1305 0.8959 0.000 0.004 0.960 0.036
#> GSM531663 3 0.0524 0.9101 0.008 0.000 0.988 0.004
#> GSM531664 3 0.3444 0.8060 0.184 0.000 0.816 0.000
#> GSM531665 3 0.0524 0.9101 0.008 0.000 0.988 0.004
#> GSM531666 3 0.3444 0.8060 0.184 0.000 0.816 0.000
#> GSM531667 2 0.0707 0.8602 0.000 0.980 0.000 0.020
#> GSM531668 3 0.0336 0.9105 0.008 0.000 0.992 0.000
#> GSM531669 3 0.1256 0.9078 0.028 0.000 0.964 0.008
#> GSM531670 3 0.0524 0.9078 0.004 0.000 0.988 0.008
#> GSM531671 3 0.2908 0.8577 0.000 0.064 0.896 0.040
#> GSM531672 3 0.2868 0.8458 0.136 0.000 0.864 0.000
#> GSM531673 3 0.0524 0.9101 0.008 0.000 0.988 0.004
#> GSM531674 3 0.0524 0.9078 0.004 0.000 0.988 0.008
#> GSM531675 1 0.0000 0.9841 1.000 0.000 0.000 0.000
#> GSM531676 1 0.0000 0.9841 1.000 0.000 0.000 0.000
#> GSM531677 1 0.0000 0.9841 1.000 0.000 0.000 0.000
#> GSM531678 1 0.0000 0.9841 1.000 0.000 0.000 0.000
#> GSM531679 1 0.0000 0.9841 1.000 0.000 0.000 0.000
#> GSM531680 1 0.0000 0.9841 1.000 0.000 0.000 0.000
#> GSM531681 1 0.0000 0.9841 1.000 0.000 0.000 0.000
#> GSM531682 1 0.0000 0.9841 1.000 0.000 0.000 0.000
#> GSM531683 1 0.0000 0.9841 1.000 0.000 0.000 0.000
#> GSM531684 3 0.1042 0.9086 0.020 0.000 0.972 0.008
#> GSM531685 1 0.0000 0.9841 1.000 0.000 0.000 0.000
#> GSM531686 1 0.0000 0.9841 1.000 0.000 0.000 0.000
#> GSM531687 1 0.0000 0.9841 1.000 0.000 0.000 0.000
#> GSM531688 1 0.0000 0.9841 1.000 0.000 0.000 0.000
#> GSM531689 1 0.0000 0.9841 1.000 0.000 0.000 0.000
#> GSM531690 1 0.0000 0.9841 1.000 0.000 0.000 0.000
#> GSM531691 1 0.0000 0.9841 1.000 0.000 0.000 0.000
#> GSM531692 3 0.1042 0.9086 0.020 0.000 0.972 0.008
#> GSM531693 3 0.4011 0.7600 0.208 0.000 0.784 0.008
#> GSM531694 1 0.0000 0.9841 1.000 0.000 0.000 0.000
#> GSM531695 1 0.0000 0.9841 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
#> GSM531600 3 0.2773 0.738 0.000 0.000 0.836 NA 0.000
#> GSM531601 3 0.6526 0.319 0.000 0.204 0.452 NA 0.000
#> GSM531605 1 0.2535 0.881 0.892 0.000 0.076 NA 0.000
#> GSM531615 5 0.0290 1.000 0.000 0.008 0.000 NA 0.992
#> GSM531617 2 0.0290 0.884 0.000 0.992 0.000 NA 0.008
#> GSM531624 5 0.0290 1.000 0.000 0.008 0.000 NA 0.992
#> GSM531627 2 0.0960 0.883 0.000 0.972 0.004 NA 0.008
#> GSM531629 2 0.1168 0.878 0.000 0.960 0.008 NA 0.000
#> GSM531631 2 0.1518 0.874 0.000 0.944 0.004 NA 0.004
#> GSM531634 2 0.0290 0.884 0.000 0.992 0.000 NA 0.008
#> GSM531636 3 0.6112 0.446 0.000 0.140 0.516 NA 0.000
#> GSM531637 5 0.0290 1.000 0.000 0.008 0.000 NA 0.992
#> GSM531654 2 0.4417 0.729 0.000 0.760 0.092 NA 0.000
#> GSM531655 3 0.3427 0.739 0.108 0.000 0.836 NA 0.000
#> GSM531658 3 0.0510 0.778 0.000 0.000 0.984 NA 0.000
#> GSM531660 3 0.4724 0.686 0.164 0.000 0.732 NA 0.000
#> GSM531602 1 0.0000 0.984 1.000 0.000 0.000 NA 0.000
#> GSM531603 1 0.2535 0.881 0.892 0.000 0.076 NA 0.000
#> GSM531604 3 0.4434 0.502 0.000 0.000 0.536 NA 0.004
#> GSM531606 3 0.4580 0.501 0.004 0.000 0.532 NA 0.004
#> GSM531607 1 0.2535 0.881 0.892 0.000 0.076 NA 0.000
#> GSM531608 2 0.3875 0.719 0.000 0.804 0.000 NA 0.124
#> GSM531609 3 0.4299 0.581 0.000 0.000 0.608 NA 0.004
#> GSM531610 3 0.4347 0.652 0.004 0.000 0.636 NA 0.004
#> GSM531611 1 0.0000 0.984 1.000 0.000 0.000 NA 0.000
#> GSM531612 1 0.0000 0.984 1.000 0.000 0.000 NA 0.000
#> GSM531613 1 0.0000 0.984 1.000 0.000 0.000 NA 0.000
#> GSM531614 3 0.4299 0.581 0.000 0.000 0.608 NA 0.004
#> GSM531616 2 0.0290 0.884 0.000 0.992 0.000 NA 0.008
#> GSM531618 3 0.0880 0.778 0.000 0.000 0.968 NA 0.000
#> GSM531619 5 0.0290 1.000 0.000 0.008 0.000 NA 0.992
#> GSM531620 2 0.0960 0.883 0.000 0.972 0.004 NA 0.008
#> GSM531621 5 0.0290 1.000 0.000 0.008 0.000 NA 0.992
#> GSM531622 2 0.0290 0.884 0.000 0.992 0.000 NA 0.008
#> GSM531623 2 0.0290 0.884 0.000 0.992 0.000 NA 0.008
#> GSM531625 5 0.0290 1.000 0.000 0.008 0.000 NA 0.992
#> GSM531626 2 0.3035 0.819 0.000 0.856 0.032 NA 0.000
#> GSM531628 2 0.6645 0.289 0.000 0.432 0.236 NA 0.000
#> GSM531630 2 0.0290 0.884 0.000 0.992 0.000 NA 0.008
#> GSM531632 3 0.6660 0.233 0.000 0.244 0.432 NA 0.000
#> GSM531633 5 0.0290 1.000 0.000 0.008 0.000 NA 0.992
#> GSM531635 2 0.0290 0.884 0.000 0.992 0.000 NA 0.008
#> GSM531638 2 0.5594 0.627 0.000 0.632 0.136 NA 0.000
#> GSM531639 3 0.1872 0.768 0.020 0.000 0.928 NA 0.000
#> GSM531640 2 0.0290 0.884 0.000 0.992 0.000 NA 0.008
#> GSM531641 3 0.4123 0.723 0.108 0.000 0.788 NA 0.000
#> GSM531642 3 0.3639 0.742 0.076 0.000 0.824 NA 0.000
#> GSM531643 3 0.0162 0.779 0.000 0.000 0.996 NA 0.000
#> GSM531644 3 0.0290 0.780 0.000 0.000 0.992 NA 0.000
#> GSM531645 3 0.0290 0.780 0.000 0.000 0.992 NA 0.000
#> GSM531646 2 0.5354 0.655 0.000 0.664 0.128 NA 0.000
#> GSM531647 3 0.6206 0.425 0.000 0.152 0.504 NA 0.000
#> GSM531648 3 0.0290 0.780 0.000 0.000 0.992 NA 0.000
#> GSM531649 2 0.3035 0.819 0.000 0.856 0.032 NA 0.000
#> GSM531650 3 0.0290 0.780 0.000 0.000 0.992 NA 0.000
#> GSM531651 5 0.0290 1.000 0.000 0.008 0.000 NA 0.992
#> GSM531652 3 0.0290 0.780 0.000 0.000 0.992 NA 0.000
#> GSM531653 3 0.5029 0.618 0.000 0.060 0.648 NA 0.000
#> GSM531656 3 0.0880 0.778 0.000 0.000 0.968 NA 0.000
#> GSM531657 3 0.4648 0.692 0.156 0.000 0.740 NA 0.000
#> GSM531659 3 0.3691 0.740 0.076 0.000 0.820 NA 0.000
#> GSM531661 2 0.1168 0.877 0.000 0.960 0.000 NA 0.008
#> GSM531662 3 0.3177 0.719 0.000 0.000 0.792 NA 0.000
#> GSM531663 3 0.0794 0.780 0.000 0.000 0.972 NA 0.000
#> GSM531664 3 0.4724 0.686 0.164 0.000 0.732 NA 0.000
#> GSM531665 3 0.0794 0.780 0.000 0.000 0.972 NA 0.000
#> GSM531666 3 0.4724 0.686 0.164 0.000 0.732 NA 0.000
#> GSM531667 2 0.1168 0.877 0.000 0.960 0.000 NA 0.008
#> GSM531668 3 0.0510 0.778 0.000 0.000 0.984 NA 0.000
#> GSM531669 3 0.3409 0.752 0.024 0.000 0.816 NA 0.000
#> GSM531670 3 0.2516 0.747 0.000 0.000 0.860 NA 0.000
#> GSM531671 3 0.4197 0.683 0.000 0.028 0.728 NA 0.000
#> GSM531672 3 0.4317 0.713 0.116 0.000 0.772 NA 0.000
#> GSM531673 3 0.0794 0.780 0.000 0.000 0.972 NA 0.000
#> GSM531674 3 0.2516 0.747 0.000 0.000 0.860 NA 0.000
#> GSM531675 1 0.0000 0.984 1.000 0.000 0.000 NA 0.000
#> GSM531676 1 0.0290 0.978 0.992 0.000 0.000 NA 0.000
#> GSM531677 1 0.0000 0.984 1.000 0.000 0.000 NA 0.000
#> GSM531678 1 0.0000 0.984 1.000 0.000 0.000 NA 0.000
#> GSM531679 1 0.0000 0.984 1.000 0.000 0.000 NA 0.000
#> GSM531680 1 0.0000 0.984 1.000 0.000 0.000 NA 0.000
#> GSM531681 1 0.0000 0.984 1.000 0.000 0.000 NA 0.000
#> GSM531682 1 0.0000 0.984 1.000 0.000 0.000 NA 0.000
#> GSM531683 1 0.0000 0.984 1.000 0.000 0.000 NA 0.000
#> GSM531684 3 0.4434 0.502 0.000 0.000 0.536 NA 0.004
#> GSM531685 1 0.0290 0.978 0.992 0.000 0.000 NA 0.000
#> GSM531686 1 0.0000 0.984 1.000 0.000 0.000 NA 0.000
#> GSM531687 1 0.0000 0.984 1.000 0.000 0.000 NA 0.000
#> GSM531688 1 0.0000 0.984 1.000 0.000 0.000 NA 0.000
#> GSM531689 1 0.0000 0.984 1.000 0.000 0.000 NA 0.000
#> GSM531690 1 0.0000 0.984 1.000 0.000 0.000 NA 0.000
#> GSM531691 1 0.0000 0.984 1.000 0.000 0.000 NA 0.000
#> GSM531692 3 0.4434 0.502 0.000 0.000 0.536 NA 0.004
#> GSM531693 3 0.6634 0.368 0.188 0.000 0.424 NA 0.004
#> GSM531694 1 0.0000 0.984 1.000 0.000 0.000 NA 0.000
#> GSM531695 1 0.0000 0.984 1.000 0.000 0.000 NA 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM531600 3 0.3789 0.4023 0.000 0.000 0.716 0.260 0.024 0.000
#> GSM531601 4 0.5791 0.5627 0.000 0.192 0.336 0.472 0.000 0.000
#> GSM531605 1 0.2331 0.8660 0.888 0.000 0.080 0.000 0.032 0.000
#> GSM531615 6 0.0146 1.0000 0.000 0.004 0.000 0.000 0.000 0.996
#> GSM531617 2 0.0000 0.8877 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531624 6 0.0146 1.0000 0.000 0.004 0.000 0.000 0.000 0.996
#> GSM531627 2 0.0603 0.8849 0.000 0.980 0.004 0.016 0.000 0.000
#> GSM531629 2 0.1152 0.8764 0.000 0.952 0.004 0.044 0.000 0.000
#> GSM531631 2 0.1204 0.8715 0.000 0.944 0.000 0.056 0.000 0.000
#> GSM531634 2 0.0000 0.8877 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531636 4 0.5492 0.5028 0.000 0.128 0.400 0.472 0.000 0.000
#> GSM531637 6 0.0146 1.0000 0.000 0.004 0.000 0.000 0.000 0.996
#> GSM531654 2 0.4046 0.6281 0.000 0.748 0.084 0.168 0.000 0.000
#> GSM531655 3 0.3270 0.5773 0.092 0.000 0.840 0.016 0.052 0.000
#> GSM531658 3 0.0363 0.7072 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM531660 3 0.4866 0.4247 0.144 0.000 0.708 0.024 0.124 0.000
#> GSM531602 1 0.0146 0.9798 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM531603 1 0.2331 0.8660 0.888 0.000 0.080 0.000 0.032 0.000
#> GSM531604 5 0.3695 0.8602 0.000 0.000 0.376 0.000 0.624 0.000
#> GSM531606 5 0.3684 0.8608 0.000 0.000 0.372 0.000 0.628 0.000
#> GSM531607 1 0.2331 0.8660 0.888 0.000 0.080 0.000 0.032 0.000
#> GSM531608 2 0.5161 0.5460 0.000 0.656 0.000 0.152 0.180 0.012
#> GSM531609 4 0.5306 0.3977 0.000 0.000 0.284 0.588 0.124 0.004
#> GSM531610 4 0.5853 0.2506 0.000 0.000 0.292 0.504 0.200 0.004
#> GSM531611 1 0.0000 0.9826 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531612 1 0.0000 0.9826 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531613 1 0.0000 0.9826 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531614 4 0.5306 0.3977 0.000 0.000 0.284 0.588 0.124 0.004
#> GSM531616 2 0.0000 0.8877 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531618 3 0.1387 0.6943 0.000 0.000 0.932 0.068 0.000 0.000
#> GSM531619 6 0.0146 1.0000 0.000 0.004 0.000 0.000 0.000 0.996
#> GSM531620 2 0.0603 0.8849 0.000 0.980 0.004 0.016 0.000 0.000
#> GSM531621 6 0.0146 1.0000 0.000 0.004 0.000 0.000 0.000 0.996
#> GSM531622 2 0.0000 0.8877 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531623 2 0.0000 0.8877 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531625 6 0.0146 1.0000 0.000 0.004 0.000 0.000 0.000 0.996
#> GSM531626 2 0.2815 0.7851 0.000 0.848 0.032 0.120 0.000 0.000
#> GSM531628 4 0.5447 0.1488 0.000 0.420 0.120 0.460 0.000 0.000
#> GSM531630 2 0.0000 0.8877 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531632 4 0.5934 0.5727 0.000 0.232 0.320 0.448 0.000 0.000
#> GSM531633 6 0.0146 1.0000 0.000 0.004 0.000 0.000 0.000 0.996
#> GSM531635 2 0.0000 0.8877 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531638 2 0.5055 0.4189 0.000 0.624 0.132 0.244 0.000 0.000
#> GSM531639 3 0.1972 0.6676 0.004 0.000 0.916 0.024 0.056 0.000
#> GSM531640 2 0.0000 0.8877 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531641 3 0.4286 0.5159 0.088 0.000 0.764 0.024 0.124 0.000
#> GSM531642 3 0.4008 0.5674 0.056 0.000 0.788 0.032 0.124 0.000
#> GSM531643 3 0.0458 0.7141 0.000 0.000 0.984 0.016 0.000 0.000
#> GSM531644 3 0.0547 0.7145 0.000 0.000 0.980 0.020 0.000 0.000
#> GSM531645 3 0.0547 0.7145 0.000 0.000 0.980 0.020 0.000 0.000
#> GSM531646 2 0.4860 0.4793 0.000 0.656 0.128 0.216 0.000 0.000
#> GSM531647 4 0.5564 0.5239 0.000 0.140 0.388 0.472 0.000 0.000
#> GSM531648 3 0.0547 0.7145 0.000 0.000 0.980 0.020 0.000 0.000
#> GSM531649 2 0.2815 0.7851 0.000 0.848 0.032 0.120 0.000 0.000
#> GSM531650 3 0.0547 0.7145 0.000 0.000 0.980 0.020 0.000 0.000
#> GSM531651 6 0.0146 1.0000 0.000 0.004 0.000 0.000 0.000 0.996
#> GSM531652 3 0.0547 0.7145 0.000 0.000 0.980 0.020 0.000 0.000
#> GSM531653 3 0.4721 -0.2170 0.000 0.048 0.532 0.420 0.000 0.000
#> GSM531656 3 0.1387 0.6943 0.000 0.000 0.932 0.068 0.000 0.000
#> GSM531657 3 0.4794 0.4371 0.136 0.000 0.716 0.024 0.124 0.000
#> GSM531659 3 0.3859 0.5610 0.056 0.000 0.796 0.024 0.124 0.000
#> GSM531661 2 0.0865 0.8750 0.000 0.964 0.000 0.036 0.000 0.000
#> GSM531662 3 0.4083 0.2910 0.000 0.000 0.668 0.304 0.028 0.000
#> GSM531663 3 0.0806 0.7106 0.000 0.000 0.972 0.020 0.008 0.000
#> GSM531664 3 0.4866 0.4247 0.144 0.000 0.708 0.024 0.124 0.000
#> GSM531665 3 0.0806 0.7106 0.000 0.000 0.972 0.020 0.008 0.000
#> GSM531666 3 0.4866 0.4247 0.144 0.000 0.708 0.024 0.124 0.000
#> GSM531667 2 0.0865 0.8750 0.000 0.964 0.000 0.036 0.000 0.000
#> GSM531668 3 0.0363 0.7072 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM531669 3 0.4594 0.4556 0.020 0.000 0.696 0.232 0.052 0.000
#> GSM531670 3 0.3645 0.4546 0.000 0.000 0.740 0.236 0.024 0.000
#> GSM531671 3 0.4749 0.0747 0.000 0.024 0.604 0.348 0.024 0.000
#> GSM531672 3 0.4459 0.4777 0.096 0.000 0.748 0.024 0.132 0.000
#> GSM531673 3 0.0806 0.7106 0.000 0.000 0.972 0.020 0.008 0.000
#> GSM531674 3 0.3619 0.4609 0.000 0.000 0.744 0.232 0.024 0.000
#> GSM531675 1 0.0000 0.9826 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531676 1 0.0260 0.9764 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM531677 1 0.0000 0.9826 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531678 1 0.0000 0.9826 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531679 1 0.0000 0.9826 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531680 1 0.0000 0.9826 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531681 1 0.0000 0.9826 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531682 1 0.0000 0.9826 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531683 1 0.0000 0.9826 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531684 5 0.3563 0.8723 0.000 0.000 0.336 0.000 0.664 0.000
#> GSM531685 1 0.0260 0.9764 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM531686 1 0.0000 0.9826 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531687 1 0.0000 0.9826 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531688 1 0.0000 0.9826 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531689 1 0.0000 0.9826 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531690 1 0.0000 0.9826 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531691 1 0.0000 0.9826 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531692 5 0.3563 0.8723 0.000 0.000 0.336 0.000 0.664 0.000
#> GSM531693 5 0.5522 0.6426 0.188 0.000 0.256 0.000 0.556 0.000
#> GSM531694 1 0.0000 0.9826 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531695 1 0.0000 0.9826 1.000 0.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> ATC:hclust 94 0.0179 2
#> ATC:hclust 93 0.0120 3
#> ATC:hclust 95 0.0450 4
#> ATC:hclust 90 0.0257 5
#> ATC:hclust 78 0.0261 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "kmeans"]
# you can also extract it by
# res = res_list["ATC:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 96 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 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 1.000 1.000 0.4687 0.532 0.532
#> 3 3 0.969 0.931 0.963 0.3933 0.670 0.454
#> 4 4 0.713 0.652 0.799 0.1136 0.835 0.577
#> 5 5 0.735 0.738 0.844 0.0546 0.896 0.667
#> 6 6 0.781 0.664 0.819 0.0376 0.975 0.904
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
#> GSM531600 1 0.0000 1.000 1.000 0.000
#> GSM531601 2 0.0000 1.000 0.000 1.000
#> GSM531605 1 0.0000 1.000 1.000 0.000
#> GSM531615 2 0.0000 1.000 0.000 1.000
#> GSM531617 2 0.0000 1.000 0.000 1.000
#> GSM531624 2 0.0000 1.000 0.000 1.000
#> GSM531627 2 0.0000 1.000 0.000 1.000
#> GSM531629 2 0.0000 1.000 0.000 1.000
#> GSM531631 2 0.0000 1.000 0.000 1.000
#> GSM531634 2 0.0000 1.000 0.000 1.000
#> GSM531636 2 0.0000 1.000 0.000 1.000
#> GSM531637 2 0.0000 1.000 0.000 1.000
#> GSM531654 2 0.0000 1.000 0.000 1.000
#> GSM531655 1 0.0000 1.000 1.000 0.000
#> GSM531658 1 0.0000 1.000 1.000 0.000
#> GSM531660 1 0.0000 1.000 1.000 0.000
#> GSM531602 1 0.0000 1.000 1.000 0.000
#> GSM531603 1 0.0000 1.000 1.000 0.000
#> GSM531604 1 0.0000 1.000 1.000 0.000
#> GSM531606 1 0.0000 1.000 1.000 0.000
#> GSM531607 1 0.0000 1.000 1.000 0.000
#> GSM531608 2 0.0000 1.000 0.000 1.000
#> GSM531609 1 0.0000 1.000 1.000 0.000
#> GSM531610 1 0.0000 1.000 1.000 0.000
#> GSM531611 1 0.0000 1.000 1.000 0.000
#> GSM531612 1 0.0000 1.000 1.000 0.000
#> GSM531613 1 0.0000 1.000 1.000 0.000
#> GSM531614 2 0.0000 1.000 0.000 1.000
#> GSM531616 2 0.0000 1.000 0.000 1.000
#> GSM531618 1 0.0000 1.000 1.000 0.000
#> GSM531619 2 0.0000 1.000 0.000 1.000
#> GSM531620 2 0.0000 1.000 0.000 1.000
#> GSM531621 2 0.0000 1.000 0.000 1.000
#> GSM531622 2 0.0000 1.000 0.000 1.000
#> GSM531623 2 0.0000 1.000 0.000 1.000
#> GSM531625 2 0.0000 1.000 0.000 1.000
#> GSM531626 2 0.0000 1.000 0.000 1.000
#> GSM531628 2 0.0000 1.000 0.000 1.000
#> GSM531630 2 0.0000 1.000 0.000 1.000
#> GSM531632 2 0.0000 1.000 0.000 1.000
#> GSM531633 2 0.0000 1.000 0.000 1.000
#> GSM531635 2 0.0000 1.000 0.000 1.000
#> GSM531638 2 0.0000 1.000 0.000 1.000
#> GSM531639 1 0.0000 1.000 1.000 0.000
#> GSM531640 2 0.0000 1.000 0.000 1.000
#> GSM531641 1 0.0000 1.000 1.000 0.000
#> GSM531642 1 0.0000 1.000 1.000 0.000
#> GSM531643 1 0.0000 1.000 1.000 0.000
#> GSM531644 1 0.0000 1.000 1.000 0.000
#> GSM531645 1 0.0000 1.000 1.000 0.000
#> GSM531646 2 0.0000 1.000 0.000 1.000
#> GSM531647 2 0.0000 1.000 0.000 1.000
#> GSM531648 1 0.0000 1.000 1.000 0.000
#> GSM531649 2 0.0000 1.000 0.000 1.000
#> GSM531650 1 0.0000 1.000 1.000 0.000
#> GSM531651 2 0.0000 1.000 0.000 1.000
#> GSM531652 1 0.0000 1.000 1.000 0.000
#> GSM531653 1 0.0000 1.000 1.000 0.000
#> GSM531656 1 0.0000 1.000 1.000 0.000
#> GSM531657 1 0.0000 1.000 1.000 0.000
#> GSM531659 1 0.0000 1.000 1.000 0.000
#> GSM531661 2 0.0000 1.000 0.000 1.000
#> GSM531662 1 0.0000 1.000 1.000 0.000
#> GSM531663 1 0.0000 1.000 1.000 0.000
#> GSM531664 1 0.0000 1.000 1.000 0.000
#> GSM531665 1 0.0000 1.000 1.000 0.000
#> GSM531666 1 0.0000 1.000 1.000 0.000
#> GSM531667 2 0.0000 1.000 0.000 1.000
#> GSM531668 1 0.0000 1.000 1.000 0.000
#> GSM531669 1 0.0000 1.000 1.000 0.000
#> GSM531670 1 0.0000 1.000 1.000 0.000
#> GSM531671 2 0.0672 0.992 0.008 0.992
#> GSM531672 1 0.0000 1.000 1.000 0.000
#> GSM531673 1 0.0000 1.000 1.000 0.000
#> GSM531674 1 0.0000 1.000 1.000 0.000
#> GSM531675 1 0.0000 1.000 1.000 0.000
#> GSM531676 1 0.0000 1.000 1.000 0.000
#> GSM531677 1 0.0000 1.000 1.000 0.000
#> GSM531678 1 0.0000 1.000 1.000 0.000
#> GSM531679 1 0.0000 1.000 1.000 0.000
#> GSM531680 1 0.0000 1.000 1.000 0.000
#> GSM531681 1 0.0000 1.000 1.000 0.000
#> GSM531682 1 0.0000 1.000 1.000 0.000
#> GSM531683 1 0.0000 1.000 1.000 0.000
#> GSM531684 1 0.0000 1.000 1.000 0.000
#> GSM531685 1 0.0000 1.000 1.000 0.000
#> GSM531686 1 0.0000 1.000 1.000 0.000
#> GSM531687 1 0.0000 1.000 1.000 0.000
#> GSM531688 1 0.0000 1.000 1.000 0.000
#> GSM531689 1 0.0000 1.000 1.000 0.000
#> GSM531690 1 0.0000 1.000 1.000 0.000
#> GSM531691 1 0.0000 1.000 1.000 0.000
#> GSM531692 1 0.0000 1.000 1.000 0.000
#> GSM531693 1 0.0000 1.000 1.000 0.000
#> GSM531694 1 0.0000 1.000 1.000 0.000
#> GSM531695 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
#> GSM531600 3 0.0237 0.941 0.004 0.000 0.996
#> GSM531601 3 0.0424 0.936 0.000 0.008 0.992
#> GSM531605 1 0.0237 0.974 0.996 0.000 0.004
#> GSM531615 2 0.0000 0.987 0.000 1.000 0.000
#> GSM531617 2 0.1031 0.991 0.000 0.976 0.024
#> GSM531624 2 0.0000 0.987 0.000 1.000 0.000
#> GSM531627 2 0.1031 0.991 0.000 0.976 0.024
#> GSM531629 2 0.1031 0.991 0.000 0.976 0.024
#> GSM531631 2 0.1031 0.991 0.000 0.976 0.024
#> GSM531634 2 0.0000 0.987 0.000 1.000 0.000
#> GSM531636 3 0.0424 0.936 0.000 0.008 0.992
#> GSM531637 2 0.0000 0.987 0.000 1.000 0.000
#> GSM531654 3 0.0892 0.929 0.000 0.020 0.980
#> GSM531655 3 0.1163 0.950 0.028 0.000 0.972
#> GSM531658 3 0.0892 0.947 0.020 0.000 0.980
#> GSM531660 1 0.6299 0.030 0.524 0.000 0.476
#> GSM531602 1 0.0237 0.974 0.996 0.000 0.004
#> GSM531603 1 0.0237 0.974 0.996 0.000 0.004
#> GSM531604 3 0.1163 0.950 0.028 0.000 0.972
#> GSM531606 3 0.6225 0.270 0.432 0.000 0.568
#> GSM531607 1 0.0237 0.974 0.996 0.000 0.004
#> GSM531608 2 0.1399 0.988 0.004 0.968 0.028
#> GSM531609 3 0.1163 0.947 0.028 0.000 0.972
#> GSM531610 3 0.1031 0.949 0.024 0.000 0.976
#> GSM531611 1 0.0237 0.974 0.996 0.000 0.004
#> GSM531612 1 0.0237 0.974 0.996 0.000 0.004
#> GSM531613 1 0.0237 0.974 0.996 0.000 0.004
#> GSM531614 3 0.0237 0.937 0.004 0.000 0.996
#> GSM531616 2 0.1031 0.991 0.000 0.976 0.024
#> GSM531618 3 0.1163 0.950 0.028 0.000 0.972
#> GSM531619 2 0.0000 0.987 0.000 1.000 0.000
#> GSM531620 2 0.1031 0.991 0.000 0.976 0.024
#> GSM531621 2 0.0000 0.987 0.000 1.000 0.000
#> GSM531622 2 0.1031 0.991 0.000 0.976 0.024
#> GSM531623 2 0.1031 0.991 0.000 0.976 0.024
#> GSM531625 2 0.0000 0.987 0.000 1.000 0.000
#> GSM531626 2 0.1031 0.991 0.000 0.976 0.024
#> GSM531628 3 0.4842 0.689 0.000 0.224 0.776
#> GSM531630 2 0.1031 0.991 0.000 0.976 0.024
#> GSM531632 3 0.0424 0.936 0.000 0.008 0.992
#> GSM531633 2 0.0000 0.987 0.000 1.000 0.000
#> GSM531635 2 0.1031 0.991 0.000 0.976 0.024
#> GSM531638 3 0.5363 0.605 0.000 0.276 0.724
#> GSM531639 3 0.1163 0.950 0.028 0.000 0.972
#> GSM531640 2 0.0000 0.987 0.000 1.000 0.000
#> GSM531641 3 0.1163 0.950 0.028 0.000 0.972
#> GSM531642 3 0.1964 0.929 0.056 0.000 0.944
#> GSM531643 3 0.1163 0.950 0.028 0.000 0.972
#> GSM531644 3 0.1163 0.950 0.028 0.000 0.972
#> GSM531645 3 0.1163 0.950 0.028 0.000 0.972
#> GSM531646 3 0.6026 0.387 0.000 0.376 0.624
#> GSM531647 3 0.0424 0.936 0.000 0.008 0.992
#> GSM531648 3 0.1163 0.950 0.028 0.000 0.972
#> GSM531649 2 0.1031 0.991 0.000 0.976 0.024
#> GSM531650 3 0.1163 0.950 0.028 0.000 0.972
#> GSM531651 2 0.0000 0.987 0.000 1.000 0.000
#> GSM531652 3 0.1163 0.950 0.028 0.000 0.972
#> GSM531653 3 0.0237 0.941 0.004 0.000 0.996
#> GSM531656 3 0.1163 0.950 0.028 0.000 0.972
#> GSM531657 3 0.1163 0.950 0.028 0.000 0.972
#> GSM531659 3 0.1163 0.950 0.028 0.000 0.972
#> GSM531661 2 0.1031 0.991 0.000 0.976 0.024
#> GSM531662 3 0.0237 0.941 0.004 0.000 0.996
#> GSM531663 3 0.1163 0.950 0.028 0.000 0.972
#> GSM531664 3 0.1163 0.950 0.028 0.000 0.972
#> GSM531665 3 0.0424 0.943 0.008 0.000 0.992
#> GSM531666 3 0.1964 0.929 0.056 0.000 0.944
#> GSM531667 2 0.1031 0.991 0.000 0.976 0.024
#> GSM531668 3 0.1163 0.950 0.028 0.000 0.972
#> GSM531669 3 0.1163 0.950 0.028 0.000 0.972
#> GSM531670 3 0.1163 0.950 0.028 0.000 0.972
#> GSM531671 3 0.0424 0.936 0.000 0.008 0.992
#> GSM531672 3 0.5254 0.656 0.264 0.000 0.736
#> GSM531673 3 0.1163 0.950 0.028 0.000 0.972
#> GSM531674 3 0.1163 0.950 0.028 0.000 0.972
#> GSM531675 1 0.0237 0.974 0.996 0.000 0.004
#> GSM531676 1 0.0237 0.974 0.996 0.000 0.004
#> GSM531677 1 0.0237 0.974 0.996 0.000 0.004
#> GSM531678 1 0.0237 0.974 0.996 0.000 0.004
#> GSM531679 1 0.0237 0.974 0.996 0.000 0.004
#> GSM531680 1 0.0237 0.974 0.996 0.000 0.004
#> GSM531681 1 0.0237 0.974 0.996 0.000 0.004
#> GSM531682 1 0.0237 0.974 0.996 0.000 0.004
#> GSM531683 1 0.0237 0.974 0.996 0.000 0.004
#> GSM531684 1 0.3816 0.808 0.852 0.000 0.148
#> GSM531685 1 0.0237 0.974 0.996 0.000 0.004
#> GSM531686 1 0.0237 0.974 0.996 0.000 0.004
#> GSM531687 1 0.0237 0.974 0.996 0.000 0.004
#> GSM531688 1 0.0237 0.974 0.996 0.000 0.004
#> GSM531689 1 0.0237 0.974 0.996 0.000 0.004
#> GSM531690 1 0.0237 0.974 0.996 0.000 0.004
#> GSM531691 1 0.0237 0.974 0.996 0.000 0.004
#> GSM531692 3 0.1163 0.950 0.028 0.000 0.972
#> GSM531693 1 0.0237 0.974 0.996 0.000 0.004
#> GSM531694 1 0.0237 0.974 0.996 0.000 0.004
#> GSM531695 1 0.0237 0.974 0.996 0.000 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM531600 4 0.4697 0.6585 0.000 0.000 0.356 0.644
#> GSM531601 3 0.4134 0.4512 0.000 0.000 0.740 0.260
#> GSM531605 1 0.1637 0.9289 0.940 0.000 0.000 0.060
#> GSM531615 2 0.0000 0.6574 0.000 1.000 0.000 0.000
#> GSM531617 3 0.5000 -0.3835 0.000 0.500 0.500 0.000
#> GSM531624 2 0.0000 0.6574 0.000 1.000 0.000 0.000
#> GSM531627 3 0.5000 -0.3835 0.000 0.500 0.500 0.000
#> GSM531629 3 0.3024 0.5083 0.000 0.148 0.852 0.000
#> GSM531631 3 0.5000 -0.3835 0.000 0.500 0.500 0.000
#> GSM531634 2 0.3528 0.6132 0.000 0.808 0.192 0.000
#> GSM531636 3 0.3356 0.6074 0.000 0.000 0.824 0.176
#> GSM531637 2 0.0000 0.6574 0.000 1.000 0.000 0.000
#> GSM531654 3 0.2973 0.6194 0.000 0.000 0.856 0.144
#> GSM531655 4 0.0707 0.7708 0.000 0.000 0.020 0.980
#> GSM531658 4 0.3311 0.7428 0.000 0.000 0.172 0.828
#> GSM531660 4 0.1302 0.7474 0.044 0.000 0.000 0.956
#> GSM531602 1 0.0000 0.9824 1.000 0.000 0.000 0.000
#> GSM531603 4 0.6221 0.3104 0.316 0.000 0.076 0.608
#> GSM531604 4 0.2345 0.7371 0.000 0.000 0.100 0.900
#> GSM531606 4 0.3903 0.6721 0.076 0.000 0.080 0.844
#> GSM531607 1 0.4855 0.6391 0.712 0.000 0.020 0.268
#> GSM531608 2 0.4981 0.3892 0.000 0.536 0.464 0.000
#> GSM531609 4 0.4907 0.6227 0.000 0.000 0.420 0.580
#> GSM531610 4 0.1716 0.7415 0.000 0.000 0.064 0.936
#> GSM531611 1 0.0188 0.9805 0.996 0.000 0.004 0.000
#> GSM531612 1 0.0188 0.9805 0.996 0.000 0.004 0.000
#> GSM531613 1 0.0188 0.9805 0.996 0.000 0.004 0.000
#> GSM531614 3 0.2814 0.5936 0.000 0.000 0.868 0.132
#> GSM531616 2 0.5000 0.3064 0.000 0.500 0.500 0.000
#> GSM531618 4 0.4697 0.6585 0.000 0.000 0.356 0.644
#> GSM531619 2 0.0000 0.6574 0.000 1.000 0.000 0.000
#> GSM531620 3 0.4888 -0.1238 0.000 0.412 0.588 0.000
#> GSM531621 2 0.0000 0.6574 0.000 1.000 0.000 0.000
#> GSM531622 2 0.4972 0.3929 0.000 0.544 0.456 0.000
#> GSM531623 2 0.4888 0.4473 0.000 0.588 0.412 0.000
#> GSM531625 2 0.0000 0.6574 0.000 1.000 0.000 0.000
#> GSM531626 3 0.5000 -0.3835 0.000 0.500 0.500 0.000
#> GSM531628 3 0.3441 0.6155 0.000 0.024 0.856 0.120
#> GSM531630 2 0.5000 0.3064 0.000 0.500 0.500 0.000
#> GSM531632 3 0.2973 0.6194 0.000 0.000 0.856 0.144
#> GSM531633 2 0.0000 0.6574 0.000 1.000 0.000 0.000
#> GSM531635 2 0.5000 0.3064 0.000 0.500 0.500 0.000
#> GSM531638 3 0.3687 0.5868 0.000 0.080 0.856 0.064
#> GSM531639 4 0.1557 0.7681 0.000 0.000 0.056 0.944
#> GSM531640 2 0.3569 0.6115 0.000 0.804 0.196 0.000
#> GSM531641 4 0.0000 0.7687 0.000 0.000 0.000 1.000
#> GSM531642 4 0.0000 0.7687 0.000 0.000 0.000 1.000
#> GSM531643 4 0.4522 0.6784 0.000 0.000 0.320 0.680
#> GSM531644 4 0.4697 0.6585 0.000 0.000 0.356 0.644
#> GSM531645 4 0.4697 0.6585 0.000 0.000 0.356 0.644
#> GSM531646 3 0.3667 0.5799 0.000 0.088 0.856 0.056
#> GSM531647 3 0.3266 0.6120 0.000 0.000 0.832 0.168
#> GSM531648 4 0.4697 0.6585 0.000 0.000 0.356 0.644
#> GSM531649 3 0.4761 0.0192 0.000 0.372 0.628 0.000
#> GSM531650 4 0.4697 0.6585 0.000 0.000 0.356 0.644
#> GSM531651 2 0.0000 0.6574 0.000 1.000 0.000 0.000
#> GSM531652 4 0.4697 0.6585 0.000 0.000 0.356 0.644
#> GSM531653 4 0.4697 0.6585 0.000 0.000 0.356 0.644
#> GSM531656 4 0.4697 0.6585 0.000 0.000 0.356 0.644
#> GSM531657 4 0.0000 0.7687 0.000 0.000 0.000 1.000
#> GSM531659 4 0.0000 0.7687 0.000 0.000 0.000 1.000
#> GSM531661 2 0.4972 0.3929 0.000 0.544 0.456 0.000
#> GSM531662 4 0.4679 0.6612 0.000 0.000 0.352 0.648
#> GSM531663 4 0.2408 0.7603 0.000 0.000 0.104 0.896
#> GSM531664 4 0.0000 0.7687 0.000 0.000 0.000 1.000
#> GSM531665 4 0.3444 0.7388 0.000 0.000 0.184 0.816
#> GSM531666 4 0.0000 0.7687 0.000 0.000 0.000 1.000
#> GSM531667 2 0.5000 0.3064 0.000 0.500 0.500 0.000
#> GSM531668 4 0.0707 0.7708 0.000 0.000 0.020 0.980
#> GSM531669 4 0.0000 0.7687 0.000 0.000 0.000 1.000
#> GSM531670 4 0.4697 0.6585 0.000 0.000 0.356 0.644
#> GSM531671 3 0.4454 0.3122 0.000 0.000 0.692 0.308
#> GSM531672 4 0.0707 0.7610 0.020 0.000 0.000 0.980
#> GSM531673 4 0.0592 0.7706 0.000 0.000 0.016 0.984
#> GSM531674 4 0.4250 0.7031 0.000 0.000 0.276 0.724
#> GSM531675 1 0.0000 0.9824 1.000 0.000 0.000 0.000
#> GSM531676 1 0.0707 0.9699 0.980 0.000 0.020 0.000
#> GSM531677 1 0.0000 0.9824 1.000 0.000 0.000 0.000
#> GSM531678 1 0.0000 0.9824 1.000 0.000 0.000 0.000
#> GSM531679 1 0.0000 0.9824 1.000 0.000 0.000 0.000
#> GSM531680 1 0.0000 0.9824 1.000 0.000 0.000 0.000
#> GSM531681 1 0.0000 0.9824 1.000 0.000 0.000 0.000
#> GSM531682 1 0.0000 0.9824 1.000 0.000 0.000 0.000
#> GSM531683 1 0.0000 0.9824 1.000 0.000 0.000 0.000
#> GSM531684 4 0.5208 0.5756 0.172 0.000 0.080 0.748
#> GSM531685 1 0.0707 0.9699 0.980 0.000 0.020 0.000
#> GSM531686 1 0.0000 0.9824 1.000 0.000 0.000 0.000
#> GSM531687 1 0.0000 0.9824 1.000 0.000 0.000 0.000
#> GSM531688 1 0.0000 0.9824 1.000 0.000 0.000 0.000
#> GSM531689 1 0.0000 0.9824 1.000 0.000 0.000 0.000
#> GSM531690 1 0.0000 0.9824 1.000 0.000 0.000 0.000
#> GSM531691 1 0.0000 0.9824 1.000 0.000 0.000 0.000
#> GSM531692 4 0.2011 0.7269 0.000 0.000 0.080 0.920
#> GSM531693 4 0.6015 0.4221 0.268 0.000 0.080 0.652
#> GSM531694 1 0.0000 0.9824 1.000 0.000 0.000 0.000
#> GSM531695 1 0.0000 0.9824 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
#> GSM531600 3 0.2390 0.6480 0.000 0.000 0.896 0.020 0.084
#> GSM531601 3 0.6400 -0.0506 0.000 0.320 0.540 0.020 0.120
#> GSM531605 1 0.3779 0.7208 0.776 0.000 0.000 0.024 0.200
#> GSM531615 4 0.2773 1.0000 0.000 0.164 0.000 0.836 0.000
#> GSM531617 2 0.0000 0.7481 0.000 1.000 0.000 0.000 0.000
#> GSM531624 4 0.2773 1.0000 0.000 0.164 0.000 0.836 0.000
#> GSM531627 2 0.0000 0.7481 0.000 1.000 0.000 0.000 0.000
#> GSM531629 2 0.3624 0.7218 0.000 0.836 0.112 0.020 0.032
#> GSM531631 2 0.0290 0.7483 0.000 0.992 0.000 0.000 0.008
#> GSM531634 2 0.4045 0.1116 0.000 0.644 0.000 0.356 0.000
#> GSM531636 2 0.6459 0.5173 0.000 0.520 0.340 0.020 0.120
#> GSM531637 4 0.2773 1.0000 0.000 0.164 0.000 0.836 0.000
#> GSM531654 2 0.5291 0.6805 0.000 0.716 0.140 0.020 0.124
#> GSM531655 3 0.3496 0.6820 0.000 0.000 0.788 0.012 0.200
#> GSM531658 3 0.2280 0.7154 0.000 0.000 0.880 0.000 0.120
#> GSM531660 3 0.4384 0.5517 0.000 0.000 0.660 0.016 0.324
#> GSM531602 1 0.0703 0.9408 0.976 0.000 0.000 0.024 0.000
#> GSM531603 5 0.5597 0.7811 0.168 0.000 0.128 0.020 0.684
#> GSM531604 5 0.4063 0.7189 0.000 0.000 0.280 0.012 0.708
#> GSM531606 5 0.4617 0.8554 0.056 0.000 0.184 0.012 0.748
#> GSM531607 1 0.5703 0.1646 0.540 0.000 0.040 0.024 0.396
#> GSM531608 2 0.3421 0.6473 0.000 0.840 0.000 0.080 0.080
#> GSM531609 3 0.4058 0.5620 0.000 0.000 0.784 0.064 0.152
#> GSM531610 3 0.5304 0.3846 0.000 0.000 0.560 0.056 0.384
#> GSM531611 1 0.2193 0.9082 0.912 0.000 0.000 0.060 0.028
#> GSM531612 1 0.2193 0.9082 0.912 0.000 0.000 0.060 0.028
#> GSM531613 1 0.1965 0.9162 0.924 0.000 0.000 0.052 0.024
#> GSM531614 2 0.7696 0.4341 0.000 0.420 0.272 0.064 0.244
#> GSM531616 2 0.0000 0.7481 0.000 1.000 0.000 0.000 0.000
#> GSM531618 3 0.0609 0.7184 0.000 0.000 0.980 0.020 0.000
#> GSM531619 4 0.2773 1.0000 0.000 0.164 0.000 0.836 0.000
#> GSM531620 2 0.0798 0.7496 0.000 0.976 0.016 0.000 0.008
#> GSM531621 4 0.2773 1.0000 0.000 0.164 0.000 0.836 0.000
#> GSM531622 2 0.1121 0.7196 0.000 0.956 0.000 0.044 0.000
#> GSM531623 2 0.1965 0.6686 0.000 0.904 0.000 0.096 0.000
#> GSM531625 4 0.2773 1.0000 0.000 0.164 0.000 0.836 0.000
#> GSM531626 2 0.0162 0.7483 0.000 0.996 0.000 0.000 0.004
#> GSM531628 2 0.5159 0.6832 0.000 0.724 0.160 0.020 0.096
#> GSM531630 2 0.0000 0.7481 0.000 1.000 0.000 0.000 0.000
#> GSM531632 2 0.6396 0.5502 0.000 0.548 0.308 0.020 0.124
#> GSM531633 4 0.2773 1.0000 0.000 0.164 0.000 0.836 0.000
#> GSM531635 2 0.0000 0.7481 0.000 1.000 0.000 0.000 0.000
#> GSM531638 2 0.5093 0.6875 0.000 0.732 0.148 0.020 0.100
#> GSM531639 3 0.3010 0.6856 0.000 0.000 0.824 0.004 0.172
#> GSM531640 2 0.4045 0.1116 0.000 0.644 0.000 0.356 0.000
#> GSM531641 3 0.4016 0.6289 0.000 0.000 0.716 0.012 0.272
#> GSM531642 3 0.4016 0.6289 0.000 0.000 0.716 0.012 0.272
#> GSM531643 3 0.0771 0.7198 0.000 0.000 0.976 0.020 0.004
#> GSM531644 3 0.0609 0.7184 0.000 0.000 0.980 0.020 0.000
#> GSM531645 3 0.0000 0.7234 0.000 0.000 1.000 0.000 0.000
#> GSM531646 2 0.4794 0.6991 0.000 0.760 0.120 0.020 0.100
#> GSM531647 2 0.6448 0.5226 0.000 0.524 0.336 0.020 0.120
#> GSM531648 3 0.0609 0.7184 0.000 0.000 0.980 0.020 0.000
#> GSM531649 2 0.1753 0.7455 0.000 0.936 0.032 0.000 0.032
#> GSM531650 3 0.0609 0.7184 0.000 0.000 0.980 0.020 0.000
#> GSM531651 4 0.2773 1.0000 0.000 0.164 0.000 0.836 0.000
#> GSM531652 3 0.0609 0.7184 0.000 0.000 0.980 0.020 0.000
#> GSM531653 3 0.2722 0.6211 0.000 0.000 0.872 0.020 0.108
#> GSM531656 3 0.0162 0.7228 0.000 0.000 0.996 0.004 0.000
#> GSM531657 3 0.4152 0.6049 0.000 0.000 0.692 0.012 0.296
#> GSM531659 3 0.3969 0.6015 0.000 0.000 0.692 0.004 0.304
#> GSM531661 2 0.1121 0.7196 0.000 0.956 0.000 0.044 0.000
#> GSM531662 3 0.3621 0.5940 0.000 0.000 0.788 0.020 0.192
#> GSM531663 3 0.2966 0.6943 0.000 0.000 0.816 0.000 0.184
#> GSM531664 3 0.3992 0.6314 0.000 0.000 0.720 0.012 0.268
#> GSM531665 3 0.3336 0.6578 0.000 0.000 0.772 0.000 0.228
#> GSM531666 3 0.4016 0.6289 0.000 0.000 0.716 0.012 0.272
#> GSM531667 2 0.0000 0.7481 0.000 1.000 0.000 0.000 0.000
#> GSM531668 3 0.3395 0.6677 0.000 0.000 0.764 0.000 0.236
#> GSM531669 3 0.3838 0.6281 0.000 0.000 0.716 0.004 0.280
#> GSM531670 3 0.1310 0.7032 0.000 0.000 0.956 0.020 0.024
#> GSM531671 2 0.7007 0.3652 0.000 0.416 0.372 0.020 0.192
#> GSM531672 3 0.4227 0.6026 0.000 0.000 0.692 0.016 0.292
#> GSM531673 3 0.3452 0.6626 0.000 0.000 0.756 0.000 0.244
#> GSM531674 3 0.0609 0.7251 0.000 0.000 0.980 0.000 0.020
#> GSM531675 1 0.0162 0.9427 0.996 0.000 0.000 0.004 0.000
#> GSM531676 1 0.1403 0.9303 0.952 0.000 0.000 0.024 0.024
#> GSM531677 1 0.0609 0.9401 0.980 0.000 0.000 0.020 0.000
#> GSM531678 1 0.0703 0.9408 0.976 0.000 0.000 0.024 0.000
#> GSM531679 1 0.0290 0.9420 0.992 0.000 0.000 0.008 0.000
#> GSM531680 1 0.0880 0.9380 0.968 0.000 0.000 0.032 0.000
#> GSM531681 1 0.0609 0.9416 0.980 0.000 0.000 0.020 0.000
#> GSM531682 1 0.0880 0.9380 0.968 0.000 0.000 0.032 0.000
#> GSM531683 1 0.0703 0.9408 0.976 0.000 0.000 0.024 0.000
#> GSM531684 5 0.4738 0.8601 0.076 0.000 0.164 0.012 0.748
#> GSM531685 1 0.1579 0.9327 0.944 0.000 0.000 0.032 0.024
#> GSM531686 1 0.0794 0.9388 0.972 0.000 0.000 0.028 0.000
#> GSM531687 1 0.0963 0.9419 0.964 0.000 0.000 0.036 0.000
#> GSM531688 1 0.0880 0.9380 0.968 0.000 0.000 0.032 0.000
#> GSM531689 1 0.0703 0.9408 0.976 0.000 0.000 0.024 0.000
#> GSM531690 1 0.0162 0.9427 0.996 0.000 0.000 0.004 0.000
#> GSM531691 1 0.0703 0.9424 0.976 0.000 0.000 0.024 0.000
#> GSM531692 5 0.3628 0.7966 0.000 0.000 0.216 0.012 0.772
#> GSM531693 5 0.4730 0.8244 0.128 0.000 0.112 0.008 0.752
#> GSM531694 1 0.0703 0.9408 0.976 0.000 0.000 0.024 0.000
#> GSM531695 1 0.0880 0.9380 0.968 0.000 0.000 0.032 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM531600 3 0.2896 0.450732 0.000 0.000 0.824 0.160 0.016 0.000
#> GSM531601 3 0.6084 -0.200419 0.000 0.228 0.512 0.244 0.016 0.000
#> GSM531605 1 0.5434 0.557823 0.616 0.000 0.000 0.160 0.212 0.012
#> GSM531615 6 0.1007 0.999138 0.000 0.044 0.000 0.000 0.000 0.956
#> GSM531617 2 0.0000 0.780929 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531624 6 0.1007 0.999138 0.000 0.044 0.000 0.000 0.000 0.956
#> GSM531627 2 0.0000 0.780929 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531629 2 0.2412 0.742890 0.000 0.880 0.028 0.092 0.000 0.000
#> GSM531631 2 0.0632 0.779777 0.000 0.976 0.000 0.024 0.000 0.000
#> GSM531634 2 0.3476 0.518144 0.000 0.732 0.000 0.004 0.004 0.260
#> GSM531636 2 0.6381 0.079854 0.000 0.408 0.328 0.248 0.016 0.000
#> GSM531637 6 0.1152 0.997410 0.000 0.044 0.000 0.000 0.004 0.952
#> GSM531654 2 0.4512 0.614223 0.000 0.696 0.032 0.248 0.020 0.004
#> GSM531655 3 0.3650 0.617558 0.000 0.000 0.792 0.116 0.092 0.000
#> GSM531658 3 0.2705 0.635658 0.000 0.000 0.872 0.072 0.052 0.004
#> GSM531660 3 0.5296 0.473565 0.000 0.000 0.600 0.184 0.216 0.000
#> GSM531602 1 0.2312 0.885508 0.876 0.000 0.000 0.112 0.000 0.012
#> GSM531603 5 0.5174 0.712682 0.076 0.000 0.048 0.180 0.692 0.004
#> GSM531604 5 0.2494 0.760562 0.000 0.000 0.120 0.016 0.864 0.000
#> GSM531606 5 0.2085 0.863423 0.024 0.000 0.056 0.008 0.912 0.000
#> GSM531607 1 0.6108 0.000728 0.432 0.000 0.004 0.156 0.396 0.012
#> GSM531608 2 0.4436 0.470373 0.000 0.680 0.000 0.272 0.020 0.028
#> GSM531609 3 0.5160 -0.244225 0.000 0.000 0.532 0.396 0.060 0.012
#> GSM531610 4 0.6090 -0.092829 0.000 0.000 0.312 0.488 0.184 0.016
#> GSM531611 1 0.2850 0.835438 0.856 0.000 0.000 0.112 0.016 0.016
#> GSM531612 1 0.2850 0.835438 0.856 0.000 0.000 0.112 0.016 0.016
#> GSM531613 1 0.2207 0.862833 0.900 0.000 0.000 0.076 0.008 0.016
#> GSM531614 4 0.6612 0.115742 0.000 0.172 0.212 0.544 0.060 0.012
#> GSM531616 2 0.0000 0.780929 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531618 3 0.0363 0.634773 0.000 0.000 0.988 0.012 0.000 0.000
#> GSM531619 6 0.1007 0.999138 0.000 0.044 0.000 0.000 0.000 0.956
#> GSM531620 2 0.0790 0.778235 0.000 0.968 0.000 0.032 0.000 0.000
#> GSM531621 6 0.1007 0.999138 0.000 0.044 0.000 0.000 0.000 0.956
#> GSM531622 2 0.0922 0.770404 0.000 0.968 0.000 0.004 0.004 0.024
#> GSM531623 2 0.1155 0.763283 0.000 0.956 0.000 0.004 0.004 0.036
#> GSM531625 6 0.1007 0.999138 0.000 0.044 0.000 0.000 0.000 0.956
#> GSM531626 2 0.0458 0.780688 0.000 0.984 0.000 0.016 0.000 0.000
#> GSM531628 2 0.4382 0.605610 0.000 0.696 0.076 0.228 0.000 0.000
#> GSM531630 2 0.0000 0.780929 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531632 2 0.6355 0.128342 0.000 0.428 0.304 0.252 0.016 0.000
#> GSM531633 6 0.1007 0.999138 0.000 0.044 0.000 0.000 0.000 0.956
#> GSM531635 2 0.0000 0.780929 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531638 2 0.4503 0.588240 0.000 0.680 0.080 0.240 0.000 0.000
#> GSM531639 3 0.3473 0.596093 0.000 0.000 0.808 0.096 0.096 0.000
#> GSM531640 2 0.3452 0.525359 0.000 0.736 0.000 0.004 0.004 0.256
#> GSM531641 3 0.4982 0.530586 0.000 0.000 0.648 0.172 0.180 0.000
#> GSM531642 3 0.4923 0.534732 0.000 0.000 0.656 0.172 0.172 0.000
#> GSM531643 3 0.0508 0.636495 0.000 0.000 0.984 0.012 0.004 0.000
#> GSM531644 3 0.0458 0.633242 0.000 0.000 0.984 0.016 0.000 0.000
#> GSM531645 3 0.0146 0.637622 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM531646 2 0.3884 0.637290 0.000 0.724 0.036 0.240 0.000 0.000
#> GSM531647 2 0.6376 0.090010 0.000 0.412 0.324 0.248 0.016 0.000
#> GSM531648 3 0.0458 0.633242 0.000 0.000 0.984 0.016 0.000 0.000
#> GSM531649 2 0.1913 0.757728 0.000 0.908 0.012 0.080 0.000 0.000
#> GSM531650 3 0.0458 0.633242 0.000 0.000 0.984 0.016 0.000 0.000
#> GSM531651 6 0.1152 0.997410 0.000 0.044 0.000 0.000 0.004 0.952
#> GSM531652 3 0.0458 0.633242 0.000 0.000 0.984 0.016 0.000 0.000
#> GSM531653 3 0.3104 0.411358 0.000 0.000 0.800 0.184 0.016 0.000
#> GSM531656 3 0.0146 0.637644 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM531657 3 0.5065 0.522275 0.000 0.000 0.636 0.172 0.192 0.000
#> GSM531659 3 0.5066 0.525036 0.000 0.000 0.636 0.176 0.188 0.000
#> GSM531661 2 0.1149 0.770529 0.000 0.960 0.000 0.008 0.008 0.024
#> GSM531662 3 0.4619 0.338752 0.000 0.000 0.704 0.168 0.124 0.004
#> GSM531663 3 0.3419 0.622873 0.000 0.000 0.820 0.088 0.088 0.004
#> GSM531664 3 0.4923 0.537186 0.000 0.000 0.656 0.172 0.172 0.000
#> GSM531665 3 0.4151 0.555656 0.000 0.000 0.748 0.084 0.164 0.004
#> GSM531666 3 0.5011 0.527074 0.000 0.000 0.644 0.176 0.180 0.000
#> GSM531667 2 0.0520 0.779704 0.000 0.984 0.000 0.008 0.008 0.000
#> GSM531668 3 0.3885 0.614243 0.000 0.000 0.780 0.100 0.116 0.004
#> GSM531669 3 0.4863 0.546749 0.000 0.000 0.664 0.168 0.168 0.000
#> GSM531670 3 0.1088 0.617560 0.000 0.000 0.960 0.024 0.016 0.000
#> GSM531671 3 0.7491 -0.350615 0.000 0.304 0.328 0.252 0.112 0.004
#> GSM531672 3 0.5122 0.513546 0.000 0.000 0.628 0.180 0.192 0.000
#> GSM531673 3 0.4225 0.600517 0.000 0.000 0.748 0.124 0.124 0.004
#> GSM531674 3 0.0405 0.639478 0.000 0.000 0.988 0.008 0.004 0.000
#> GSM531675 1 0.0935 0.903594 0.964 0.000 0.000 0.032 0.000 0.004
#> GSM531676 1 0.2656 0.882624 0.860 0.000 0.000 0.120 0.012 0.008
#> GSM531677 1 0.0458 0.899018 0.984 0.000 0.000 0.016 0.000 0.000
#> GSM531678 1 0.2070 0.892317 0.892 0.000 0.000 0.100 0.000 0.008
#> GSM531679 1 0.0405 0.901912 0.988 0.000 0.000 0.008 0.000 0.004
#> GSM531680 1 0.0713 0.897112 0.972 0.000 0.000 0.028 0.000 0.000
#> GSM531681 1 0.1462 0.901476 0.936 0.000 0.000 0.056 0.000 0.008
#> GSM531682 1 0.0713 0.897112 0.972 0.000 0.000 0.028 0.000 0.000
#> GSM531683 1 0.1524 0.901051 0.932 0.000 0.000 0.060 0.000 0.008
#> GSM531684 5 0.1829 0.863836 0.024 0.000 0.056 0.000 0.920 0.000
#> GSM531685 1 0.2588 0.885062 0.860 0.000 0.000 0.124 0.012 0.004
#> GSM531686 1 0.0547 0.898416 0.980 0.000 0.000 0.020 0.000 0.000
#> GSM531687 1 0.2234 0.890400 0.872 0.000 0.000 0.124 0.000 0.004
#> GSM531688 1 0.1556 0.896363 0.920 0.000 0.000 0.080 0.000 0.000
#> GSM531689 1 0.1970 0.894291 0.900 0.000 0.000 0.092 0.000 0.008
#> GSM531690 1 0.0603 0.903419 0.980 0.000 0.000 0.016 0.000 0.004
#> GSM531691 1 0.1349 0.904035 0.940 0.000 0.000 0.056 0.000 0.004
#> GSM531692 5 0.1719 0.844718 0.000 0.000 0.060 0.016 0.924 0.000
#> GSM531693 5 0.3944 0.804135 0.032 0.000 0.044 0.136 0.788 0.000
#> GSM531694 1 0.1524 0.901051 0.932 0.000 0.000 0.060 0.000 0.008
#> GSM531695 1 0.0713 0.897112 0.972 0.000 0.000 0.028 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> ATC:kmeans 96 0.00792 2
#> ATC:kmeans 93 0.02020 3
#> ATC:kmeans 78 0.02206 4
#> ATC:kmeans 89 0.07282 5
#> ATC:kmeans 82 0.02673 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "skmeans"]
# you can also extract it by
# res = res_list["ATC:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 96 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 0.956 0.939 0.975 0.4935 0.509 0.509
#> 3 3 0.931 0.893 0.961 0.2810 0.827 0.672
#> 4 4 0.860 0.829 0.925 0.0988 0.917 0.781
#> 5 5 0.845 0.750 0.898 0.0365 0.941 0.822
#> 6 6 0.848 0.788 0.902 0.0328 0.938 0.800
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
#> GSM531600 2 0.000 0.979 0.000 1.000
#> GSM531601 2 0.000 0.979 0.000 1.000
#> GSM531605 1 0.000 0.969 1.000 0.000
#> GSM531615 2 0.000 0.979 0.000 1.000
#> GSM531617 2 0.000 0.979 0.000 1.000
#> GSM531624 2 0.000 0.979 0.000 1.000
#> GSM531627 2 0.000 0.979 0.000 1.000
#> GSM531629 2 0.000 0.979 0.000 1.000
#> GSM531631 2 0.000 0.979 0.000 1.000
#> GSM531634 2 0.000 0.979 0.000 1.000
#> GSM531636 2 0.000 0.979 0.000 1.000
#> GSM531637 2 0.000 0.979 0.000 1.000
#> GSM531654 2 0.000 0.979 0.000 1.000
#> GSM531655 1 0.000 0.969 1.000 0.000
#> GSM531658 1 0.224 0.938 0.964 0.036
#> GSM531660 1 0.000 0.969 1.000 0.000
#> GSM531602 1 0.000 0.969 1.000 0.000
#> GSM531603 1 0.000 0.969 1.000 0.000
#> GSM531604 1 0.000 0.969 1.000 0.000
#> GSM531606 1 0.000 0.969 1.000 0.000
#> GSM531607 1 0.000 0.969 1.000 0.000
#> GSM531608 2 0.000 0.979 0.000 1.000
#> GSM531609 1 0.886 0.581 0.696 0.304
#> GSM531610 1 0.000 0.969 1.000 0.000
#> GSM531611 1 0.000 0.969 1.000 0.000
#> GSM531612 1 0.000 0.969 1.000 0.000
#> GSM531613 1 0.000 0.969 1.000 0.000
#> GSM531614 2 0.000 0.979 0.000 1.000
#> GSM531616 2 0.000 0.979 0.000 1.000
#> GSM531618 1 0.689 0.771 0.816 0.184
#> GSM531619 2 0.000 0.979 0.000 1.000
#> GSM531620 2 0.000 0.979 0.000 1.000
#> GSM531621 2 0.000 0.979 0.000 1.000
#> GSM531622 2 0.000 0.979 0.000 1.000
#> GSM531623 2 0.000 0.979 0.000 1.000
#> GSM531625 2 0.000 0.979 0.000 1.000
#> GSM531626 2 0.000 0.979 0.000 1.000
#> GSM531628 2 0.000 0.979 0.000 1.000
#> GSM531630 2 0.000 0.979 0.000 1.000
#> GSM531632 2 0.000 0.979 0.000 1.000
#> GSM531633 2 0.000 0.979 0.000 1.000
#> GSM531635 2 0.000 0.979 0.000 1.000
#> GSM531638 2 0.000 0.979 0.000 1.000
#> GSM531639 1 0.000 0.969 1.000 0.000
#> GSM531640 2 0.000 0.979 0.000 1.000
#> GSM531641 1 0.000 0.969 1.000 0.000
#> GSM531642 1 0.000 0.969 1.000 0.000
#> GSM531643 1 0.141 0.953 0.980 0.020
#> GSM531644 1 0.295 0.924 0.948 0.052
#> GSM531645 1 0.000 0.969 1.000 0.000
#> GSM531646 2 0.000 0.979 0.000 1.000
#> GSM531647 2 0.000 0.979 0.000 1.000
#> GSM531648 1 0.925 0.509 0.660 0.340
#> GSM531649 2 0.000 0.979 0.000 1.000
#> GSM531650 1 0.921 0.517 0.664 0.336
#> GSM531651 2 0.000 0.979 0.000 1.000
#> GSM531652 1 0.925 0.509 0.660 0.340
#> GSM531653 2 0.000 0.979 0.000 1.000
#> GSM531656 1 0.000 0.969 1.000 0.000
#> GSM531657 1 0.000 0.969 1.000 0.000
#> GSM531659 1 0.000 0.969 1.000 0.000
#> GSM531661 2 0.000 0.979 0.000 1.000
#> GSM531662 2 0.000 0.979 0.000 1.000
#> GSM531663 1 0.000 0.969 1.000 0.000
#> GSM531664 1 0.000 0.969 1.000 0.000
#> GSM531665 2 0.958 0.380 0.380 0.620
#> GSM531666 1 0.000 0.969 1.000 0.000
#> GSM531667 2 0.000 0.979 0.000 1.000
#> GSM531668 1 0.000 0.969 1.000 0.000
#> GSM531669 1 0.000 0.969 1.000 0.000
#> GSM531670 2 0.971 0.288 0.400 0.600
#> GSM531671 2 0.000 0.979 0.000 1.000
#> GSM531672 1 0.000 0.969 1.000 0.000
#> GSM531673 1 0.000 0.969 1.000 0.000
#> GSM531674 1 0.000 0.969 1.000 0.000
#> GSM531675 1 0.000 0.969 1.000 0.000
#> GSM531676 1 0.000 0.969 1.000 0.000
#> GSM531677 1 0.000 0.969 1.000 0.000
#> GSM531678 1 0.000 0.969 1.000 0.000
#> GSM531679 1 0.000 0.969 1.000 0.000
#> GSM531680 1 0.000 0.969 1.000 0.000
#> GSM531681 1 0.000 0.969 1.000 0.000
#> GSM531682 1 0.000 0.969 1.000 0.000
#> GSM531683 1 0.000 0.969 1.000 0.000
#> GSM531684 1 0.000 0.969 1.000 0.000
#> GSM531685 1 0.000 0.969 1.000 0.000
#> GSM531686 1 0.000 0.969 1.000 0.000
#> GSM531687 1 0.000 0.969 1.000 0.000
#> GSM531688 1 0.000 0.969 1.000 0.000
#> GSM531689 1 0.000 0.969 1.000 0.000
#> GSM531690 1 0.000 0.969 1.000 0.000
#> GSM531691 1 0.000 0.969 1.000 0.000
#> GSM531692 1 0.000 0.969 1.000 0.000
#> GSM531693 1 0.000 0.969 1.000 0.000
#> GSM531694 1 0.000 0.969 1.000 0.000
#> GSM531695 1 0.000 0.969 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM531600 3 0.000 0.919 0.000 0.000 1.000
#> GSM531601 2 0.000 1.000 0.000 1.000 0.000
#> GSM531605 1 0.000 0.928 1.000 0.000 0.000
#> GSM531615 2 0.000 1.000 0.000 1.000 0.000
#> GSM531617 2 0.000 1.000 0.000 1.000 0.000
#> GSM531624 2 0.000 1.000 0.000 1.000 0.000
#> GSM531627 2 0.000 1.000 0.000 1.000 0.000
#> GSM531629 2 0.000 1.000 0.000 1.000 0.000
#> GSM531631 2 0.000 1.000 0.000 1.000 0.000
#> GSM531634 2 0.000 1.000 0.000 1.000 0.000
#> GSM531636 2 0.000 1.000 0.000 1.000 0.000
#> GSM531637 2 0.000 1.000 0.000 1.000 0.000
#> GSM531654 2 0.000 1.000 0.000 1.000 0.000
#> GSM531655 3 0.620 0.258 0.424 0.000 0.576
#> GSM531658 3 0.669 0.296 0.408 0.012 0.580
#> GSM531660 1 0.000 0.928 1.000 0.000 0.000
#> GSM531602 1 0.000 0.928 1.000 0.000 0.000
#> GSM531603 1 0.000 0.928 1.000 0.000 0.000
#> GSM531604 1 0.000 0.928 1.000 0.000 0.000
#> GSM531606 1 0.000 0.928 1.000 0.000 0.000
#> GSM531607 1 0.000 0.928 1.000 0.000 0.000
#> GSM531608 2 0.000 1.000 0.000 1.000 0.000
#> GSM531609 1 0.665 0.469 0.656 0.320 0.024
#> GSM531610 1 0.000 0.928 1.000 0.000 0.000
#> GSM531611 1 0.000 0.928 1.000 0.000 0.000
#> GSM531612 1 0.000 0.928 1.000 0.000 0.000
#> GSM531613 1 0.000 0.928 1.000 0.000 0.000
#> GSM531614 2 0.000 1.000 0.000 1.000 0.000
#> GSM531616 2 0.000 1.000 0.000 1.000 0.000
#> GSM531618 3 0.000 0.919 0.000 0.000 1.000
#> GSM531619 2 0.000 1.000 0.000 1.000 0.000
#> GSM531620 2 0.000 1.000 0.000 1.000 0.000
#> GSM531621 2 0.000 1.000 0.000 1.000 0.000
#> GSM531622 2 0.000 1.000 0.000 1.000 0.000
#> GSM531623 2 0.000 1.000 0.000 1.000 0.000
#> GSM531625 2 0.000 1.000 0.000 1.000 0.000
#> GSM531626 2 0.000 1.000 0.000 1.000 0.000
#> GSM531628 2 0.000 1.000 0.000 1.000 0.000
#> GSM531630 2 0.000 1.000 0.000 1.000 0.000
#> GSM531632 2 0.000 1.000 0.000 1.000 0.000
#> GSM531633 2 0.000 1.000 0.000 1.000 0.000
#> GSM531635 2 0.000 1.000 0.000 1.000 0.000
#> GSM531638 2 0.000 1.000 0.000 1.000 0.000
#> GSM531639 3 0.000 0.919 0.000 0.000 1.000
#> GSM531640 2 0.000 1.000 0.000 1.000 0.000
#> GSM531641 1 0.620 0.204 0.576 0.000 0.424
#> GSM531642 3 0.529 0.613 0.268 0.000 0.732
#> GSM531643 3 0.000 0.919 0.000 0.000 1.000
#> GSM531644 3 0.000 0.919 0.000 0.000 1.000
#> GSM531645 3 0.000 0.919 0.000 0.000 1.000
#> GSM531646 2 0.000 1.000 0.000 1.000 0.000
#> GSM531647 2 0.000 1.000 0.000 1.000 0.000
#> GSM531648 3 0.000 0.919 0.000 0.000 1.000
#> GSM531649 2 0.000 1.000 0.000 1.000 0.000
#> GSM531650 3 0.000 0.919 0.000 0.000 1.000
#> GSM531651 2 0.000 1.000 0.000 1.000 0.000
#> GSM531652 3 0.000 0.919 0.000 0.000 1.000
#> GSM531653 3 0.000 0.919 0.000 0.000 1.000
#> GSM531656 3 0.000 0.919 0.000 0.000 1.000
#> GSM531657 1 0.000 0.928 1.000 0.000 0.000
#> GSM531659 1 0.000 0.928 1.000 0.000 0.000
#> GSM531661 2 0.000 1.000 0.000 1.000 0.000
#> GSM531662 2 0.000 1.000 0.000 1.000 0.000
#> GSM531663 1 0.375 0.770 0.856 0.000 0.144
#> GSM531664 1 0.620 0.204 0.576 0.000 0.424
#> GSM531665 1 0.621 0.243 0.572 0.428 0.000
#> GSM531666 1 0.620 0.204 0.576 0.000 0.424
#> GSM531667 2 0.000 1.000 0.000 1.000 0.000
#> GSM531668 1 0.000 0.928 1.000 0.000 0.000
#> GSM531669 1 0.620 0.204 0.576 0.000 0.424
#> GSM531670 3 0.000 0.919 0.000 0.000 1.000
#> GSM531671 2 0.000 1.000 0.000 1.000 0.000
#> GSM531672 1 0.000 0.928 1.000 0.000 0.000
#> GSM531673 1 0.000 0.928 1.000 0.000 0.000
#> GSM531674 3 0.000 0.919 0.000 0.000 1.000
#> GSM531675 1 0.000 0.928 1.000 0.000 0.000
#> GSM531676 1 0.000 0.928 1.000 0.000 0.000
#> GSM531677 1 0.000 0.928 1.000 0.000 0.000
#> GSM531678 1 0.000 0.928 1.000 0.000 0.000
#> GSM531679 1 0.000 0.928 1.000 0.000 0.000
#> GSM531680 1 0.000 0.928 1.000 0.000 0.000
#> GSM531681 1 0.000 0.928 1.000 0.000 0.000
#> GSM531682 1 0.000 0.928 1.000 0.000 0.000
#> GSM531683 1 0.000 0.928 1.000 0.000 0.000
#> GSM531684 1 0.000 0.928 1.000 0.000 0.000
#> GSM531685 1 0.000 0.928 1.000 0.000 0.000
#> GSM531686 1 0.000 0.928 1.000 0.000 0.000
#> GSM531687 1 0.000 0.928 1.000 0.000 0.000
#> GSM531688 1 0.000 0.928 1.000 0.000 0.000
#> GSM531689 1 0.000 0.928 1.000 0.000 0.000
#> GSM531690 1 0.000 0.928 1.000 0.000 0.000
#> GSM531691 1 0.000 0.928 1.000 0.000 0.000
#> GSM531692 1 0.000 0.928 1.000 0.000 0.000
#> GSM531693 1 0.000 0.928 1.000 0.000 0.000
#> GSM531694 1 0.000 0.928 1.000 0.000 0.000
#> GSM531695 1 0.000 0.928 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM531600 3 0.1867 0.8856 0.000 0.000 0.928 0.072
#> GSM531601 2 0.4193 0.6347 0.000 0.732 0.268 0.000
#> GSM531605 1 0.0000 0.9048 1.000 0.000 0.000 0.000
#> GSM531615 2 0.0000 0.9837 0.000 1.000 0.000 0.000
#> GSM531617 2 0.0000 0.9837 0.000 1.000 0.000 0.000
#> GSM531624 2 0.0000 0.9837 0.000 1.000 0.000 0.000
#> GSM531627 2 0.0000 0.9837 0.000 1.000 0.000 0.000
#> GSM531629 2 0.0000 0.9837 0.000 1.000 0.000 0.000
#> GSM531631 2 0.0000 0.9837 0.000 1.000 0.000 0.000
#> GSM531634 2 0.0000 0.9837 0.000 1.000 0.000 0.000
#> GSM531636 2 0.0707 0.9662 0.000 0.980 0.020 0.000
#> GSM531637 2 0.0000 0.9837 0.000 1.000 0.000 0.000
#> GSM531654 2 0.0000 0.9837 0.000 1.000 0.000 0.000
#> GSM531655 4 0.5119 0.5945 0.112 0.000 0.124 0.764
#> GSM531658 4 0.4499 0.5717 0.072 0.000 0.124 0.804
#> GSM531660 1 0.4406 0.4498 0.700 0.000 0.000 0.300
#> GSM531602 1 0.0000 0.9048 1.000 0.000 0.000 0.000
#> GSM531603 1 0.0000 0.9048 1.000 0.000 0.000 0.000
#> GSM531604 1 0.4382 0.4731 0.704 0.000 0.000 0.296
#> GSM531606 1 0.0188 0.9015 0.996 0.000 0.000 0.004
#> GSM531607 1 0.0000 0.9048 1.000 0.000 0.000 0.000
#> GSM531608 2 0.0000 0.9837 0.000 1.000 0.000 0.000
#> GSM531609 1 0.7493 0.3939 0.640 0.092 0.160 0.108
#> GSM531610 1 0.2408 0.8229 0.896 0.000 0.000 0.104
#> GSM531611 1 0.1867 0.8448 0.928 0.000 0.000 0.072
#> GSM531612 1 0.1867 0.8448 0.928 0.000 0.000 0.072
#> GSM531613 1 0.1867 0.8448 0.928 0.000 0.000 0.072
#> GSM531614 2 0.0921 0.9605 0.000 0.972 0.000 0.028
#> GSM531616 2 0.0000 0.9837 0.000 1.000 0.000 0.000
#> GSM531618 3 0.0000 0.9360 0.000 0.000 1.000 0.000
#> GSM531619 2 0.0000 0.9837 0.000 1.000 0.000 0.000
#> GSM531620 2 0.0000 0.9837 0.000 1.000 0.000 0.000
#> GSM531621 2 0.0000 0.9837 0.000 1.000 0.000 0.000
#> GSM531622 2 0.0000 0.9837 0.000 1.000 0.000 0.000
#> GSM531623 2 0.0000 0.9837 0.000 1.000 0.000 0.000
#> GSM531625 2 0.0000 0.9837 0.000 1.000 0.000 0.000
#> GSM531626 2 0.0000 0.9837 0.000 1.000 0.000 0.000
#> GSM531628 2 0.0000 0.9837 0.000 1.000 0.000 0.000
#> GSM531630 2 0.0000 0.9837 0.000 1.000 0.000 0.000
#> GSM531632 2 0.0000 0.9837 0.000 1.000 0.000 0.000
#> GSM531633 2 0.0000 0.9837 0.000 1.000 0.000 0.000
#> GSM531635 2 0.0000 0.9837 0.000 1.000 0.000 0.000
#> GSM531638 2 0.0000 0.9837 0.000 1.000 0.000 0.000
#> GSM531639 3 0.0000 0.9360 0.000 0.000 1.000 0.000
#> GSM531640 2 0.0000 0.9837 0.000 1.000 0.000 0.000
#> GSM531641 4 0.7186 0.4079 0.420 0.000 0.136 0.444
#> GSM531642 3 0.5088 0.3368 0.288 0.000 0.688 0.024
#> GSM531643 3 0.0000 0.9360 0.000 0.000 1.000 0.000
#> GSM531644 3 0.0000 0.9360 0.000 0.000 1.000 0.000
#> GSM531645 3 0.0000 0.9360 0.000 0.000 1.000 0.000
#> GSM531646 2 0.0000 0.9837 0.000 1.000 0.000 0.000
#> GSM531647 2 0.0707 0.9662 0.000 0.980 0.020 0.000
#> GSM531648 3 0.0000 0.9360 0.000 0.000 1.000 0.000
#> GSM531649 2 0.0000 0.9837 0.000 1.000 0.000 0.000
#> GSM531650 3 0.0000 0.9360 0.000 0.000 1.000 0.000
#> GSM531651 2 0.0000 0.9837 0.000 1.000 0.000 0.000
#> GSM531652 3 0.0000 0.9360 0.000 0.000 1.000 0.000
#> GSM531653 3 0.0000 0.9360 0.000 0.000 1.000 0.000
#> GSM531656 3 0.0000 0.9360 0.000 0.000 1.000 0.000
#> GSM531657 1 0.4955 -0.0511 0.556 0.000 0.000 0.444
#> GSM531659 1 0.3873 0.6142 0.772 0.000 0.000 0.228
#> GSM531661 2 0.0000 0.9837 0.000 1.000 0.000 0.000
#> GSM531662 4 0.4985 -0.1593 0.000 0.468 0.000 0.532
#> GSM531663 4 0.0895 0.5605 0.020 0.000 0.004 0.976
#> GSM531664 4 0.7279 0.4275 0.408 0.000 0.148 0.444
#> GSM531665 4 0.0927 0.5576 0.016 0.008 0.000 0.976
#> GSM531666 4 0.7186 0.4079 0.420 0.000 0.136 0.444
#> GSM531667 2 0.0000 0.9837 0.000 1.000 0.000 0.000
#> GSM531668 4 0.4188 0.6100 0.244 0.000 0.004 0.752
#> GSM531669 4 0.7179 0.4283 0.408 0.000 0.136 0.456
#> GSM531670 3 0.1557 0.8985 0.000 0.000 0.944 0.056
#> GSM531671 2 0.3486 0.7800 0.000 0.812 0.000 0.188
#> GSM531672 1 0.4961 -0.0655 0.552 0.000 0.000 0.448
#> GSM531673 4 0.2011 0.6027 0.080 0.000 0.000 0.920
#> GSM531674 3 0.3356 0.7378 0.000 0.000 0.824 0.176
#> GSM531675 1 0.0000 0.9048 1.000 0.000 0.000 0.000
#> GSM531676 1 0.0000 0.9048 1.000 0.000 0.000 0.000
#> GSM531677 1 0.0000 0.9048 1.000 0.000 0.000 0.000
#> GSM531678 1 0.0000 0.9048 1.000 0.000 0.000 0.000
#> GSM531679 1 0.0000 0.9048 1.000 0.000 0.000 0.000
#> GSM531680 1 0.0000 0.9048 1.000 0.000 0.000 0.000
#> GSM531681 1 0.0000 0.9048 1.000 0.000 0.000 0.000
#> GSM531682 1 0.0000 0.9048 1.000 0.000 0.000 0.000
#> GSM531683 1 0.0000 0.9048 1.000 0.000 0.000 0.000
#> GSM531684 1 0.0000 0.9048 1.000 0.000 0.000 0.000
#> GSM531685 1 0.0000 0.9048 1.000 0.000 0.000 0.000
#> GSM531686 1 0.0000 0.9048 1.000 0.000 0.000 0.000
#> GSM531687 1 0.0000 0.9048 1.000 0.000 0.000 0.000
#> GSM531688 1 0.0000 0.9048 1.000 0.000 0.000 0.000
#> GSM531689 1 0.0000 0.9048 1.000 0.000 0.000 0.000
#> GSM531690 1 0.0000 0.9048 1.000 0.000 0.000 0.000
#> GSM531691 1 0.0000 0.9048 1.000 0.000 0.000 0.000
#> GSM531692 1 0.2704 0.7715 0.876 0.000 0.000 0.124
#> GSM531693 1 0.0000 0.9048 1.000 0.000 0.000 0.000
#> GSM531694 1 0.0000 0.9048 1.000 0.000 0.000 0.000
#> GSM531695 1 0.0000 0.9048 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
#> GSM531600 3 0.4342 0.6754 0.000 0.000 0.728 0.040 0.232
#> GSM531601 2 0.3774 0.5054 0.000 0.704 0.296 0.000 0.000
#> GSM531605 1 0.0000 0.8396 1.000 0.000 0.000 0.000 0.000
#> GSM531615 2 0.0000 0.9686 0.000 1.000 0.000 0.000 0.000
#> GSM531617 2 0.0000 0.9686 0.000 1.000 0.000 0.000 0.000
#> GSM531624 2 0.0000 0.9686 0.000 1.000 0.000 0.000 0.000
#> GSM531627 2 0.0000 0.9686 0.000 1.000 0.000 0.000 0.000
#> GSM531629 2 0.0000 0.9686 0.000 1.000 0.000 0.000 0.000
#> GSM531631 2 0.0000 0.9686 0.000 1.000 0.000 0.000 0.000
#> GSM531634 2 0.0000 0.9686 0.000 1.000 0.000 0.000 0.000
#> GSM531636 2 0.0963 0.9309 0.000 0.964 0.036 0.000 0.000
#> GSM531637 2 0.0000 0.9686 0.000 1.000 0.000 0.000 0.000
#> GSM531654 2 0.0000 0.9686 0.000 1.000 0.000 0.000 0.000
#> GSM531655 5 0.7230 0.5238 0.116 0.000 0.092 0.272 0.520
#> GSM531658 5 0.5982 0.5024 0.016 0.000 0.072 0.392 0.520
#> GSM531660 1 0.5687 0.4348 0.628 0.000 0.000 0.208 0.164
#> GSM531602 1 0.0000 0.8396 1.000 0.000 0.000 0.000 0.000
#> GSM531603 1 0.0000 0.8396 1.000 0.000 0.000 0.000 0.000
#> GSM531604 1 0.4765 0.4970 0.704 0.000 0.000 0.068 0.228
#> GSM531606 1 0.1300 0.8077 0.956 0.000 0.000 0.016 0.028
#> GSM531607 1 0.0000 0.8396 1.000 0.000 0.000 0.000 0.000
#> GSM531608 2 0.0880 0.9381 0.000 0.968 0.000 0.032 0.000
#> GSM531609 4 0.4333 0.4799 0.104 0.020 0.080 0.796 0.000
#> GSM531610 4 0.3143 0.4331 0.204 0.000 0.000 0.796 0.000
#> GSM531611 1 0.2488 0.7524 0.872 0.000 0.000 0.124 0.004
#> GSM531612 1 0.2674 0.7400 0.856 0.000 0.000 0.140 0.004
#> GSM531613 1 0.1908 0.7801 0.908 0.000 0.000 0.092 0.000
#> GSM531614 4 0.3949 0.3182 0.000 0.332 0.000 0.668 0.000
#> GSM531616 2 0.0000 0.9686 0.000 1.000 0.000 0.000 0.000
#> GSM531618 3 0.0000 0.8922 0.000 0.000 1.000 0.000 0.000
#> GSM531619 2 0.0000 0.9686 0.000 1.000 0.000 0.000 0.000
#> GSM531620 2 0.0000 0.9686 0.000 1.000 0.000 0.000 0.000
#> GSM531621 2 0.0000 0.9686 0.000 1.000 0.000 0.000 0.000
#> GSM531622 2 0.0000 0.9686 0.000 1.000 0.000 0.000 0.000
#> GSM531623 2 0.0000 0.9686 0.000 1.000 0.000 0.000 0.000
#> GSM531625 2 0.0000 0.9686 0.000 1.000 0.000 0.000 0.000
#> GSM531626 2 0.0000 0.9686 0.000 1.000 0.000 0.000 0.000
#> GSM531628 2 0.0000 0.9686 0.000 1.000 0.000 0.000 0.000
#> GSM531630 2 0.0000 0.9686 0.000 1.000 0.000 0.000 0.000
#> GSM531632 2 0.0162 0.9652 0.000 0.996 0.000 0.004 0.000
#> GSM531633 2 0.0000 0.9686 0.000 1.000 0.000 0.000 0.000
#> GSM531635 2 0.0000 0.9686 0.000 1.000 0.000 0.000 0.000
#> GSM531638 2 0.0000 0.9686 0.000 1.000 0.000 0.000 0.000
#> GSM531639 3 0.0486 0.8868 0.004 0.000 0.988 0.004 0.004
#> GSM531640 2 0.0000 0.9686 0.000 1.000 0.000 0.000 0.000
#> GSM531641 1 0.7927 -0.1156 0.404 0.000 0.088 0.264 0.244
#> GSM531642 3 0.5781 0.2130 0.292 0.000 0.596 0.108 0.004
#> GSM531643 3 0.0162 0.8909 0.000 0.000 0.996 0.000 0.004
#> GSM531644 3 0.0000 0.8922 0.000 0.000 1.000 0.000 0.000
#> GSM531645 3 0.0000 0.8922 0.000 0.000 1.000 0.000 0.000
#> GSM531646 2 0.0000 0.9686 0.000 1.000 0.000 0.000 0.000
#> GSM531647 2 0.1282 0.9182 0.000 0.952 0.044 0.004 0.000
#> GSM531648 3 0.0000 0.8922 0.000 0.000 1.000 0.000 0.000
#> GSM531649 2 0.0000 0.9686 0.000 1.000 0.000 0.000 0.000
#> GSM531650 3 0.0000 0.8922 0.000 0.000 1.000 0.000 0.000
#> GSM531651 2 0.0000 0.9686 0.000 1.000 0.000 0.000 0.000
#> GSM531652 3 0.0000 0.8922 0.000 0.000 1.000 0.000 0.000
#> GSM531653 3 0.0451 0.8879 0.000 0.000 0.988 0.008 0.004
#> GSM531656 3 0.0000 0.8922 0.000 0.000 1.000 0.000 0.000
#> GSM531657 1 0.6584 0.0510 0.468 0.000 0.000 0.272 0.260
#> GSM531659 1 0.4401 0.6423 0.764 0.000 0.000 0.132 0.104
#> GSM531661 2 0.0000 0.9686 0.000 1.000 0.000 0.000 0.000
#> GSM531662 5 0.4527 0.0265 0.000 0.260 0.000 0.040 0.700
#> GSM531663 5 0.2471 0.5571 0.000 0.000 0.000 0.136 0.864
#> GSM531664 1 0.7915 -0.1642 0.388 0.000 0.080 0.268 0.264
#> GSM531665 5 0.0000 0.4710 0.000 0.000 0.000 0.000 1.000
#> GSM531666 1 0.7905 -0.1523 0.392 0.000 0.080 0.268 0.260
#> GSM531667 2 0.0000 0.9686 0.000 1.000 0.000 0.000 0.000
#> GSM531668 5 0.6725 0.4502 0.216 0.000 0.012 0.268 0.504
#> GSM531669 1 0.7891 -0.1480 0.396 0.000 0.080 0.248 0.276
#> GSM531670 3 0.3608 0.7628 0.000 0.000 0.812 0.040 0.148
#> GSM531671 2 0.5111 0.2002 0.000 0.552 0.000 0.040 0.408
#> GSM531672 1 0.6673 -0.0420 0.440 0.000 0.000 0.276 0.284
#> GSM531673 5 0.4734 0.5877 0.088 0.000 0.000 0.188 0.724
#> GSM531674 3 0.4276 0.6293 0.000 0.000 0.764 0.068 0.168
#> GSM531675 1 0.0000 0.8396 1.000 0.000 0.000 0.000 0.000
#> GSM531676 1 0.0000 0.8396 1.000 0.000 0.000 0.000 0.000
#> GSM531677 1 0.0000 0.8396 1.000 0.000 0.000 0.000 0.000
#> GSM531678 1 0.0000 0.8396 1.000 0.000 0.000 0.000 0.000
#> GSM531679 1 0.0000 0.8396 1.000 0.000 0.000 0.000 0.000
#> GSM531680 1 0.0000 0.8396 1.000 0.000 0.000 0.000 0.000
#> GSM531681 1 0.0000 0.8396 1.000 0.000 0.000 0.000 0.000
#> GSM531682 1 0.0000 0.8396 1.000 0.000 0.000 0.000 0.000
#> GSM531683 1 0.0000 0.8396 1.000 0.000 0.000 0.000 0.000
#> GSM531684 1 0.0807 0.8230 0.976 0.000 0.000 0.012 0.012
#> GSM531685 1 0.0000 0.8396 1.000 0.000 0.000 0.000 0.000
#> GSM531686 1 0.0000 0.8396 1.000 0.000 0.000 0.000 0.000
#> GSM531687 1 0.0000 0.8396 1.000 0.000 0.000 0.000 0.000
#> GSM531688 1 0.0000 0.8396 1.000 0.000 0.000 0.000 0.000
#> GSM531689 1 0.0000 0.8396 1.000 0.000 0.000 0.000 0.000
#> GSM531690 1 0.0000 0.8396 1.000 0.000 0.000 0.000 0.000
#> GSM531691 1 0.0000 0.8396 1.000 0.000 0.000 0.000 0.000
#> GSM531692 1 0.3229 0.7002 0.840 0.000 0.000 0.032 0.128
#> GSM531693 1 0.0000 0.8396 1.000 0.000 0.000 0.000 0.000
#> GSM531694 1 0.0000 0.8396 1.000 0.000 0.000 0.000 0.000
#> GSM531695 1 0.0000 0.8396 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
#> GSM531600 6 0.4854 0.00774 0.000 0.000 0.408 0.036 0.012 0.544
#> GSM531601 2 0.4479 0.52442 0.000 0.684 0.268 0.020 0.004 0.024
#> GSM531605 1 0.0000 0.89655 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531615 2 0.0000 0.97306 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531617 2 0.0146 0.97224 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM531624 2 0.0000 0.97306 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531627 2 0.0146 0.97224 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM531629 2 0.0000 0.97306 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531631 2 0.0000 0.97306 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531634 2 0.0000 0.97306 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531636 2 0.3437 0.82223 0.000 0.848 0.056 0.028 0.012 0.056
#> GSM531637 2 0.0000 0.97306 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531654 2 0.0000 0.97306 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531655 4 0.3205 0.54870 0.080 0.000 0.064 0.844 0.000 0.012
#> GSM531658 4 0.3960 0.41764 0.024 0.000 0.048 0.820 0.068 0.040
#> GSM531660 1 0.3872 0.02580 0.604 0.000 0.000 0.392 0.004 0.000
#> GSM531602 1 0.0000 0.89655 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531603 1 0.0000 0.89655 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531604 1 0.6152 0.28081 0.548 0.000 0.000 0.140 0.048 0.264
#> GSM531606 1 0.4403 0.63141 0.760 0.000 0.000 0.096 0.032 0.112
#> GSM531607 1 0.0000 0.89655 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531608 2 0.0790 0.94745 0.000 0.968 0.000 0.000 0.032 0.000
#> GSM531609 5 0.1528 0.89128 0.012 0.000 0.028 0.016 0.944 0.000
#> GSM531610 5 0.1391 0.88363 0.040 0.000 0.000 0.016 0.944 0.000
#> GSM531611 1 0.2442 0.72919 0.852 0.000 0.000 0.144 0.004 0.000
#> GSM531612 1 0.2871 0.64710 0.804 0.000 0.000 0.192 0.004 0.000
#> GSM531613 1 0.1285 0.84660 0.944 0.000 0.000 0.052 0.004 0.000
#> GSM531614 5 0.1267 0.84168 0.000 0.060 0.000 0.000 0.940 0.000
#> GSM531616 2 0.0146 0.97224 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM531618 3 0.0000 0.85498 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531619 2 0.0000 0.97306 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531620 2 0.0146 0.97224 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM531621 2 0.0000 0.97306 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531622 2 0.0000 0.97306 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531623 2 0.0000 0.97306 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531625 2 0.0000 0.97306 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531626 2 0.0146 0.97224 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM531628 2 0.0405 0.96783 0.000 0.988 0.000 0.004 0.000 0.008
#> GSM531630 2 0.0146 0.97224 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM531632 2 0.1367 0.93000 0.000 0.944 0.000 0.012 0.000 0.044
#> GSM531633 2 0.0000 0.97306 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531635 2 0.0146 0.97224 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM531638 2 0.0146 0.97224 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM531639 3 0.0870 0.83803 0.012 0.000 0.972 0.012 0.000 0.004
#> GSM531640 2 0.0000 0.97306 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531641 4 0.5209 0.68034 0.344 0.000 0.092 0.560 0.004 0.000
#> GSM531642 3 0.5827 -0.02650 0.272 0.000 0.536 0.184 0.004 0.004
#> GSM531643 3 0.0291 0.85110 0.000 0.000 0.992 0.004 0.000 0.004
#> GSM531644 3 0.0000 0.85498 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531645 3 0.0000 0.85498 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531646 2 0.0405 0.96793 0.000 0.988 0.000 0.004 0.000 0.008
#> GSM531647 2 0.3841 0.78619 0.000 0.820 0.072 0.028 0.012 0.068
#> GSM531648 3 0.0000 0.85498 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531649 2 0.0291 0.97029 0.000 0.992 0.000 0.004 0.000 0.004
#> GSM531650 3 0.0000 0.85498 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531651 2 0.0000 0.97306 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531652 3 0.0000 0.85498 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531653 3 0.2686 0.75022 0.000 0.000 0.876 0.032 0.012 0.080
#> GSM531656 3 0.0000 0.85498 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531657 4 0.3945 0.64145 0.380 0.000 0.000 0.612 0.008 0.000
#> GSM531659 1 0.3271 0.55295 0.760 0.000 0.000 0.232 0.008 0.000
#> GSM531661 2 0.0000 0.97306 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531662 6 0.2266 0.47499 0.000 0.012 0.000 0.108 0.000 0.880
#> GSM531663 4 0.4253 -0.07584 0.000 0.000 0.008 0.608 0.012 0.372
#> GSM531664 4 0.4780 0.70922 0.324 0.000 0.060 0.612 0.004 0.000
#> GSM531665 6 0.3690 0.41256 0.000 0.000 0.000 0.288 0.012 0.700
#> GSM531666 4 0.4832 0.70827 0.324 0.000 0.064 0.608 0.004 0.000
#> GSM531667 2 0.0000 0.97306 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM531668 4 0.4302 0.59364 0.172 0.000 0.000 0.748 0.024 0.056
#> GSM531669 4 0.5260 0.68848 0.348 0.000 0.048 0.576 0.004 0.024
#> GSM531670 3 0.4651 0.26018 0.000 0.000 0.588 0.028 0.012 0.372
#> GSM531671 6 0.3151 0.32553 0.000 0.252 0.000 0.000 0.000 0.748
#> GSM531672 4 0.3482 0.69978 0.316 0.000 0.000 0.684 0.000 0.000
#> GSM531673 4 0.4372 0.41258 0.080 0.000 0.000 0.728 0.008 0.184
#> GSM531674 3 0.3877 0.57689 0.000 0.000 0.748 0.208 0.004 0.040
#> GSM531675 1 0.0146 0.89578 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM531676 1 0.0000 0.89655 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531677 1 0.0146 0.89578 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM531678 1 0.0000 0.89655 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531679 1 0.0000 0.89655 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531680 1 0.0146 0.89578 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM531681 1 0.0000 0.89655 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531682 1 0.0146 0.89578 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM531683 1 0.0000 0.89655 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531684 1 0.4069 0.66843 0.788 0.000 0.000 0.076 0.032 0.104
#> GSM531685 1 0.0000 0.89655 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531686 1 0.0146 0.89578 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM531687 1 0.0146 0.89578 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM531688 1 0.0146 0.89578 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM531689 1 0.0000 0.89655 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531690 1 0.0146 0.89578 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM531691 1 0.0000 0.89655 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531692 1 0.5762 0.29058 0.552 0.000 0.000 0.100 0.032 0.316
#> GSM531693 1 0.0000 0.89655 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531694 1 0.0000 0.89655 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531695 1 0.0146 0.89578 0.996 0.000 0.000 0.004 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> ATC:skmeans 94 0.00489 2
#> ATC:skmeans 88 0.00746 3
#> ATC:skmeans 85 0.01221 4
#> ATC:skmeans 80 0.00487 5
#> ATC:skmeans 84 0.00772 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "pam"]
# you can also extract it by
# res = res_list["ATC:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 96 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 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 0.998 0.999 0.4500 0.551 0.551
#> 3 3 0.868 0.880 0.955 0.4555 0.697 0.494
#> 4 4 0.910 0.881 0.958 0.0826 0.907 0.741
#> 5 5 0.946 0.920 0.968 0.1216 0.882 0.610
#> 6 6 0.864 0.815 0.864 0.0378 0.954 0.784
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 4
There is also optional best \(k\) = 2 4 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM531600 1 0.0938 0.988 0.988 0.012
#> GSM531601 1 0.1184 0.985 0.984 0.016
#> GSM531605 1 0.0000 0.999 1.000 0.000
#> GSM531615 2 0.0000 0.999 0.000 1.000
#> GSM531617 2 0.0000 0.999 0.000 1.000
#> GSM531624 2 0.0000 0.999 0.000 1.000
#> GSM531627 2 0.0000 0.999 0.000 1.000
#> GSM531629 2 0.0000 0.999 0.000 1.000
#> GSM531631 2 0.0000 0.999 0.000 1.000
#> GSM531634 2 0.0000 0.999 0.000 1.000
#> GSM531636 2 0.0376 0.996 0.004 0.996
#> GSM531637 2 0.0000 0.999 0.000 1.000
#> GSM531654 2 0.1414 0.980 0.020 0.980
#> GSM531655 1 0.0000 0.999 1.000 0.000
#> GSM531658 1 0.0000 0.999 1.000 0.000
#> GSM531660 1 0.0000 0.999 1.000 0.000
#> GSM531602 1 0.0000 0.999 1.000 0.000
#> GSM531603 1 0.0000 0.999 1.000 0.000
#> GSM531604 1 0.0000 0.999 1.000 0.000
#> GSM531606 1 0.0000 0.999 1.000 0.000
#> GSM531607 1 0.0000 0.999 1.000 0.000
#> GSM531608 2 0.0000 0.999 0.000 1.000
#> GSM531609 1 0.0000 0.999 1.000 0.000
#> GSM531610 1 0.0000 0.999 1.000 0.000
#> GSM531611 1 0.0000 0.999 1.000 0.000
#> GSM531612 1 0.0000 0.999 1.000 0.000
#> GSM531613 1 0.0000 0.999 1.000 0.000
#> GSM531614 1 0.1843 0.973 0.972 0.028
#> GSM531616 2 0.0000 0.999 0.000 1.000
#> GSM531618 1 0.0000 0.999 1.000 0.000
#> GSM531619 2 0.0000 0.999 0.000 1.000
#> GSM531620 2 0.0000 0.999 0.000 1.000
#> GSM531621 2 0.0000 0.999 0.000 1.000
#> GSM531622 2 0.0000 0.999 0.000 1.000
#> GSM531623 2 0.0000 0.999 0.000 1.000
#> GSM531625 2 0.0000 0.999 0.000 1.000
#> GSM531626 2 0.0000 0.999 0.000 1.000
#> GSM531628 2 0.0000 0.999 0.000 1.000
#> GSM531630 2 0.0000 0.999 0.000 1.000
#> GSM531632 2 0.0000 0.999 0.000 1.000
#> GSM531633 2 0.0000 0.999 0.000 1.000
#> GSM531635 2 0.0000 0.999 0.000 1.000
#> GSM531638 2 0.0000 0.999 0.000 1.000
#> GSM531639 1 0.0000 0.999 1.000 0.000
#> GSM531640 2 0.0000 0.999 0.000 1.000
#> GSM531641 1 0.0000 0.999 1.000 0.000
#> GSM531642 1 0.0000 0.999 1.000 0.000
#> GSM531643 1 0.0000 0.999 1.000 0.000
#> GSM531644 1 0.0000 0.999 1.000 0.000
#> GSM531645 1 0.0000 0.999 1.000 0.000
#> GSM531646 2 0.0000 0.999 0.000 1.000
#> GSM531647 2 0.0000 0.999 0.000 1.000
#> GSM531648 1 0.0000 0.999 1.000 0.000
#> GSM531649 2 0.0000 0.999 0.000 1.000
#> GSM531650 1 0.0000 0.999 1.000 0.000
#> GSM531651 2 0.0000 0.999 0.000 1.000
#> GSM531652 1 0.0000 0.999 1.000 0.000
#> GSM531653 1 0.1184 0.985 0.984 0.016
#> GSM531656 1 0.0000 0.999 1.000 0.000
#> GSM531657 1 0.0000 0.999 1.000 0.000
#> GSM531659 1 0.0000 0.999 1.000 0.000
#> GSM531661 2 0.0000 0.999 0.000 1.000
#> GSM531662 1 0.0000 0.999 1.000 0.000
#> GSM531663 1 0.0000 0.999 1.000 0.000
#> GSM531664 1 0.0000 0.999 1.000 0.000
#> GSM531665 1 0.0000 0.999 1.000 0.000
#> GSM531666 1 0.0000 0.999 1.000 0.000
#> GSM531667 2 0.0000 0.999 0.000 1.000
#> GSM531668 1 0.0000 0.999 1.000 0.000
#> GSM531669 1 0.0000 0.999 1.000 0.000
#> GSM531670 1 0.0000 0.999 1.000 0.000
#> GSM531671 1 0.1184 0.985 0.984 0.016
#> GSM531672 1 0.0000 0.999 1.000 0.000
#> GSM531673 1 0.0000 0.999 1.000 0.000
#> GSM531674 1 0.0000 0.999 1.000 0.000
#> GSM531675 1 0.0000 0.999 1.000 0.000
#> GSM531676 1 0.0000 0.999 1.000 0.000
#> GSM531677 1 0.0000 0.999 1.000 0.000
#> GSM531678 1 0.0000 0.999 1.000 0.000
#> GSM531679 1 0.0000 0.999 1.000 0.000
#> GSM531680 1 0.0000 0.999 1.000 0.000
#> GSM531681 1 0.0000 0.999 1.000 0.000
#> GSM531682 1 0.0000 0.999 1.000 0.000
#> GSM531683 1 0.0000 0.999 1.000 0.000
#> GSM531684 1 0.0000 0.999 1.000 0.000
#> GSM531685 1 0.0000 0.999 1.000 0.000
#> GSM531686 1 0.0000 0.999 1.000 0.000
#> GSM531687 1 0.0000 0.999 1.000 0.000
#> GSM531688 1 0.0000 0.999 1.000 0.000
#> GSM531689 1 0.0000 0.999 1.000 0.000
#> GSM531690 1 0.0000 0.999 1.000 0.000
#> GSM531691 1 0.0000 0.999 1.000 0.000
#> GSM531692 1 0.0000 0.999 1.000 0.000
#> GSM531693 1 0.0000 0.999 1.000 0.000
#> GSM531694 1 0.0000 0.999 1.000 0.000
#> GSM531695 1 0.0000 0.999 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM531600 3 0.000 0.940 0.000 0.000 1.000
#> GSM531601 3 0.000 0.940 0.000 0.000 1.000
#> GSM531605 1 0.000 0.922 1.000 0.000 0.000
#> GSM531615 2 0.000 0.987 0.000 1.000 0.000
#> GSM531617 2 0.000 0.987 0.000 1.000 0.000
#> GSM531624 2 0.000 0.987 0.000 1.000 0.000
#> GSM531627 2 0.000 0.987 0.000 1.000 0.000
#> GSM531629 2 0.000 0.987 0.000 1.000 0.000
#> GSM531631 2 0.000 0.987 0.000 1.000 0.000
#> GSM531634 2 0.000 0.987 0.000 1.000 0.000
#> GSM531636 3 0.000 0.940 0.000 0.000 1.000
#> GSM531637 2 0.000 0.987 0.000 1.000 0.000
#> GSM531654 3 0.581 0.485 0.000 0.336 0.664
#> GSM531655 3 0.000 0.940 0.000 0.000 1.000
#> GSM531658 3 0.000 0.940 0.000 0.000 1.000
#> GSM531660 3 0.613 0.266 0.400 0.000 0.600
#> GSM531602 1 0.000 0.922 1.000 0.000 0.000
#> GSM531603 1 0.618 0.331 0.584 0.000 0.416
#> GSM531604 3 0.601 0.344 0.372 0.000 0.628
#> GSM531606 1 0.618 0.331 0.584 0.000 0.416
#> GSM531607 1 0.445 0.734 0.808 0.000 0.192
#> GSM531608 2 0.000 0.987 0.000 1.000 0.000
#> GSM531609 3 0.000 0.940 0.000 0.000 1.000
#> GSM531610 3 0.000 0.940 0.000 0.000 1.000
#> GSM531611 1 0.000 0.922 1.000 0.000 0.000
#> GSM531612 1 0.000 0.922 1.000 0.000 0.000
#> GSM531613 1 0.000 0.922 1.000 0.000 0.000
#> GSM531614 3 0.000 0.940 0.000 0.000 1.000
#> GSM531616 2 0.000 0.987 0.000 1.000 0.000
#> GSM531618 3 0.000 0.940 0.000 0.000 1.000
#> GSM531619 2 0.000 0.987 0.000 1.000 0.000
#> GSM531620 2 0.000 0.987 0.000 1.000 0.000
#> GSM531621 2 0.000 0.987 0.000 1.000 0.000
#> GSM531622 2 0.000 0.987 0.000 1.000 0.000
#> GSM531623 2 0.000 0.987 0.000 1.000 0.000
#> GSM531625 2 0.000 0.987 0.000 1.000 0.000
#> GSM531626 2 0.000 0.987 0.000 1.000 0.000
#> GSM531628 3 0.429 0.746 0.000 0.180 0.820
#> GSM531630 2 0.000 0.987 0.000 1.000 0.000
#> GSM531632 3 0.000 0.940 0.000 0.000 1.000
#> GSM531633 2 0.000 0.987 0.000 1.000 0.000
#> GSM531635 2 0.000 0.987 0.000 1.000 0.000
#> GSM531638 3 0.630 0.111 0.000 0.480 0.520
#> GSM531639 3 0.000 0.940 0.000 0.000 1.000
#> GSM531640 2 0.000 0.987 0.000 1.000 0.000
#> GSM531641 3 0.000 0.940 0.000 0.000 1.000
#> GSM531642 3 0.000 0.940 0.000 0.000 1.000
#> GSM531643 3 0.000 0.940 0.000 0.000 1.000
#> GSM531644 3 0.000 0.940 0.000 0.000 1.000
#> GSM531645 3 0.000 0.940 0.000 0.000 1.000
#> GSM531646 2 0.536 0.583 0.000 0.724 0.276
#> GSM531647 3 0.000 0.940 0.000 0.000 1.000
#> GSM531648 3 0.000 0.940 0.000 0.000 1.000
#> GSM531649 2 0.000 0.987 0.000 1.000 0.000
#> GSM531650 3 0.000 0.940 0.000 0.000 1.000
#> GSM531651 2 0.000 0.987 0.000 1.000 0.000
#> GSM531652 3 0.000 0.940 0.000 0.000 1.000
#> GSM531653 3 0.000 0.940 0.000 0.000 1.000
#> GSM531656 3 0.000 0.940 0.000 0.000 1.000
#> GSM531657 3 0.000 0.940 0.000 0.000 1.000
#> GSM531659 3 0.000 0.940 0.000 0.000 1.000
#> GSM531661 2 0.000 0.987 0.000 1.000 0.000
#> GSM531662 3 0.000 0.940 0.000 0.000 1.000
#> GSM531663 3 0.000 0.940 0.000 0.000 1.000
#> GSM531664 3 0.000 0.940 0.000 0.000 1.000
#> GSM531665 3 0.000 0.940 0.000 0.000 1.000
#> GSM531666 3 0.000 0.940 0.000 0.000 1.000
#> GSM531667 2 0.000 0.987 0.000 1.000 0.000
#> GSM531668 3 0.000 0.940 0.000 0.000 1.000
#> GSM531669 3 0.000 0.940 0.000 0.000 1.000
#> GSM531670 3 0.000 0.940 0.000 0.000 1.000
#> GSM531671 3 0.000 0.940 0.000 0.000 1.000
#> GSM531672 3 0.000 0.940 0.000 0.000 1.000
#> GSM531673 3 0.000 0.940 0.000 0.000 1.000
#> GSM531674 3 0.000 0.940 0.000 0.000 1.000
#> GSM531675 1 0.000 0.922 1.000 0.000 0.000
#> GSM531676 1 0.000 0.922 1.000 0.000 0.000
#> GSM531677 1 0.000 0.922 1.000 0.000 0.000
#> GSM531678 1 0.000 0.922 1.000 0.000 0.000
#> GSM531679 1 0.000 0.922 1.000 0.000 0.000
#> GSM531680 1 0.000 0.922 1.000 0.000 0.000
#> GSM531681 1 0.000 0.922 1.000 0.000 0.000
#> GSM531682 1 0.000 0.922 1.000 0.000 0.000
#> GSM531683 1 0.000 0.922 1.000 0.000 0.000
#> GSM531684 1 0.618 0.331 0.584 0.000 0.416
#> GSM531685 1 0.000 0.922 1.000 0.000 0.000
#> GSM531686 1 0.000 0.922 1.000 0.000 0.000
#> GSM531687 1 0.000 0.922 1.000 0.000 0.000
#> GSM531688 1 0.000 0.922 1.000 0.000 0.000
#> GSM531689 1 0.000 0.922 1.000 0.000 0.000
#> GSM531690 1 0.000 0.922 1.000 0.000 0.000
#> GSM531691 1 0.000 0.922 1.000 0.000 0.000
#> GSM531692 3 0.626 0.105 0.448 0.000 0.552
#> GSM531693 1 0.610 0.388 0.608 0.000 0.392
#> GSM531694 1 0.000 0.922 1.000 0.000 0.000
#> GSM531695 1 0.000 0.922 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM531600 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531601 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531605 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> GSM531615 4 0.0000 0.967 0.000 0.000 0.000 1.000
#> GSM531617 2 0.0000 0.953 0.000 1.000 0.000 0.000
#> GSM531624 4 0.0000 0.967 0.000 0.000 0.000 1.000
#> GSM531627 2 0.0000 0.953 0.000 1.000 0.000 0.000
#> GSM531629 2 0.0000 0.953 0.000 1.000 0.000 0.000
#> GSM531631 2 0.0000 0.953 0.000 1.000 0.000 0.000
#> GSM531634 4 0.0817 0.950 0.000 0.024 0.000 0.976
#> GSM531636 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531637 4 0.0000 0.967 0.000 0.000 0.000 1.000
#> GSM531654 2 0.0000 0.953 0.000 1.000 0.000 0.000
#> GSM531655 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531658 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531660 3 0.4955 0.115 0.444 0.000 0.556 0.000
#> GSM531602 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> GSM531603 1 0.4804 0.417 0.616 0.000 0.384 0.000
#> GSM531604 3 0.4855 0.257 0.400 0.000 0.600 0.000
#> GSM531606 1 0.4804 0.417 0.616 0.000 0.384 0.000
#> GSM531607 1 0.3569 0.715 0.804 0.000 0.196 0.000
#> GSM531608 2 0.0000 0.953 0.000 1.000 0.000 0.000
#> GSM531609 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531610 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531611 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> GSM531612 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> GSM531613 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> GSM531614 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531616 2 0.0000 0.953 0.000 1.000 0.000 0.000
#> GSM531618 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531619 4 0.0000 0.967 0.000 0.000 0.000 1.000
#> GSM531620 2 0.0000 0.953 0.000 1.000 0.000 0.000
#> GSM531621 4 0.0000 0.967 0.000 0.000 0.000 1.000
#> GSM531622 2 0.0000 0.953 0.000 1.000 0.000 0.000
#> GSM531623 2 0.0000 0.953 0.000 1.000 0.000 0.000
#> GSM531625 4 0.0000 0.967 0.000 0.000 0.000 1.000
#> GSM531626 2 0.0000 0.953 0.000 1.000 0.000 0.000
#> GSM531628 2 0.4830 0.383 0.000 0.608 0.392 0.000
#> GSM531630 2 0.0000 0.953 0.000 1.000 0.000 0.000
#> GSM531632 2 0.0000 0.953 0.000 1.000 0.000 0.000
#> GSM531633 4 0.0000 0.967 0.000 0.000 0.000 1.000
#> GSM531635 2 0.0000 0.953 0.000 1.000 0.000 0.000
#> GSM531638 2 0.0000 0.953 0.000 1.000 0.000 0.000
#> GSM531639 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531640 4 0.4164 0.644 0.000 0.264 0.000 0.736
#> GSM531641 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531642 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531643 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531644 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531645 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531646 2 0.0000 0.953 0.000 1.000 0.000 0.000
#> GSM531647 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531648 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531649 2 0.0000 0.953 0.000 1.000 0.000 0.000
#> GSM531650 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531651 4 0.0000 0.967 0.000 0.000 0.000 1.000
#> GSM531652 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531653 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531656 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531657 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531659 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531661 2 0.0000 0.953 0.000 1.000 0.000 0.000
#> GSM531662 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531663 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531664 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531665 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531666 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531667 2 0.0000 0.953 0.000 1.000 0.000 0.000
#> GSM531668 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531669 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531670 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531671 2 0.4103 0.576 0.000 0.744 0.256 0.000
#> GSM531672 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531673 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531674 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> GSM531675 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> GSM531676 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> GSM531677 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> GSM531678 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> GSM531679 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> GSM531680 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> GSM531681 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> GSM531682 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> GSM531683 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> GSM531684 1 0.4804 0.417 0.616 0.000 0.384 0.000
#> GSM531685 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> GSM531686 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> GSM531687 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> GSM531688 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> GSM531689 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> GSM531690 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> GSM531691 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> GSM531692 3 0.5000 -0.102 0.500 0.000 0.500 0.000
#> GSM531693 1 0.4713 0.468 0.640 0.000 0.360 0.000
#> GSM531694 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> GSM531695 1 0.0000 0.914 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
#> GSM531600 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM531601 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM531605 1 0.3837 0.567 0.692 0.000 0.000 0.308 0.000
#> GSM531615 5 0.0000 0.967 0.000 0.000 0.000 0.000 1.000
#> GSM531617 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> GSM531624 5 0.0000 0.967 0.000 0.000 0.000 0.000 1.000
#> GSM531627 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> GSM531629 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> GSM531631 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> GSM531634 5 0.0703 0.949 0.000 0.024 0.000 0.000 0.976
#> GSM531636 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM531637 5 0.0000 0.967 0.000 0.000 0.000 0.000 1.000
#> GSM531654 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> GSM531655 4 0.3177 0.738 0.000 0.000 0.208 0.792 0.000
#> GSM531658 3 0.3730 0.577 0.000 0.000 0.712 0.288 0.000
#> GSM531660 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> GSM531602 1 0.0000 0.951 1.000 0.000 0.000 0.000 0.000
#> GSM531603 4 0.0162 0.961 0.004 0.000 0.000 0.996 0.000
#> GSM531604 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> GSM531606 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> GSM531607 4 0.0162 0.961 0.004 0.000 0.000 0.996 0.000
#> GSM531608 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> GSM531609 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM531610 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> GSM531611 1 0.3796 0.585 0.700 0.000 0.000 0.300 0.000
#> GSM531612 1 0.3730 0.607 0.712 0.000 0.000 0.288 0.000
#> GSM531613 1 0.0000 0.951 1.000 0.000 0.000 0.000 0.000
#> GSM531614 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM531616 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> GSM531618 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM531619 5 0.0000 0.967 0.000 0.000 0.000 0.000 1.000
#> GSM531620 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> GSM531621 5 0.0000 0.967 0.000 0.000 0.000 0.000 1.000
#> GSM531622 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> GSM531623 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> GSM531625 5 0.0000 0.967 0.000 0.000 0.000 0.000 1.000
#> GSM531626 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> GSM531628 2 0.4138 0.381 0.000 0.616 0.384 0.000 0.000
#> GSM531630 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> GSM531632 2 0.0162 0.961 0.000 0.996 0.004 0.000 0.000
#> GSM531633 5 0.0000 0.967 0.000 0.000 0.000 0.000 1.000
#> GSM531635 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> GSM531638 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> GSM531639 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM531640 5 0.3612 0.636 0.000 0.268 0.000 0.000 0.732
#> GSM531641 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> GSM531642 4 0.2773 0.789 0.000 0.000 0.164 0.836 0.000
#> GSM531643 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM531644 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM531645 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM531646 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> GSM531647 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM531648 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM531649 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> GSM531650 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM531651 5 0.0000 0.967 0.000 0.000 0.000 0.000 1.000
#> GSM531652 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM531653 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM531656 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM531657 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> GSM531659 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> GSM531661 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> GSM531662 3 0.3999 0.449 0.000 0.000 0.656 0.344 0.000
#> GSM531663 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> GSM531664 4 0.0290 0.958 0.000 0.000 0.008 0.992 0.000
#> GSM531665 4 0.3109 0.750 0.000 0.000 0.200 0.800 0.000
#> GSM531666 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> GSM531667 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> GSM531668 4 0.1197 0.925 0.000 0.000 0.048 0.952 0.000
#> GSM531669 4 0.0162 0.961 0.000 0.000 0.004 0.996 0.000
#> GSM531670 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM531671 2 0.3003 0.736 0.000 0.812 0.188 0.000 0.000
#> GSM531672 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> GSM531673 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> GSM531674 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM531675 1 0.0000 0.951 1.000 0.000 0.000 0.000 0.000
#> GSM531676 1 0.0000 0.951 1.000 0.000 0.000 0.000 0.000
#> GSM531677 1 0.0000 0.951 1.000 0.000 0.000 0.000 0.000
#> GSM531678 1 0.0000 0.951 1.000 0.000 0.000 0.000 0.000
#> GSM531679 1 0.0000 0.951 1.000 0.000 0.000 0.000 0.000
#> GSM531680 1 0.0000 0.951 1.000 0.000 0.000 0.000 0.000
#> GSM531681 1 0.0000 0.951 1.000 0.000 0.000 0.000 0.000
#> GSM531682 1 0.0000 0.951 1.000 0.000 0.000 0.000 0.000
#> GSM531683 1 0.0000 0.951 1.000 0.000 0.000 0.000 0.000
#> GSM531684 4 0.0162 0.961 0.004 0.000 0.000 0.996 0.000
#> GSM531685 1 0.0000 0.951 1.000 0.000 0.000 0.000 0.000
#> GSM531686 1 0.0000 0.951 1.000 0.000 0.000 0.000 0.000
#> GSM531687 1 0.0000 0.951 1.000 0.000 0.000 0.000 0.000
#> GSM531688 1 0.0000 0.951 1.000 0.000 0.000 0.000 0.000
#> GSM531689 1 0.0000 0.951 1.000 0.000 0.000 0.000 0.000
#> GSM531690 1 0.0000 0.951 1.000 0.000 0.000 0.000 0.000
#> GSM531691 1 0.0000 0.951 1.000 0.000 0.000 0.000 0.000
#> GSM531692 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> GSM531693 4 0.0162 0.961 0.004 0.000 0.000 0.996 0.000
#> GSM531694 1 0.0000 0.951 1.000 0.000 0.000 0.000 0.000
#> GSM531695 1 0.0000 0.951 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
#> GSM531600 3 0.0000 0.955 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531601 3 0.0000 0.955 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531605 2 0.6094 -0.442 0.312 0.388 0.000 0.300 0.000 0.000
#> GSM531615 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM531617 2 0.3727 0.803 0.000 0.612 0.000 0.000 0.000 0.388
#> GSM531624 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM531627 2 0.3727 0.803 0.000 0.612 0.000 0.000 0.000 0.388
#> GSM531629 6 0.0000 0.865 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531631 6 0.0000 0.865 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531634 2 0.4230 0.271 0.000 0.612 0.000 0.000 0.364 0.024
#> GSM531636 3 0.1610 0.878 0.000 0.000 0.916 0.000 0.000 0.084
#> GSM531637 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM531654 6 0.0000 0.865 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531655 4 0.2793 0.710 0.000 0.000 0.200 0.800 0.000 0.000
#> GSM531658 3 0.2003 0.842 0.000 0.000 0.884 0.116 0.000 0.000
#> GSM531660 4 0.0000 0.880 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531602 1 0.0000 0.826 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531603 4 0.3727 0.581 0.000 0.388 0.000 0.612 0.000 0.000
#> GSM531604 4 0.0000 0.880 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531606 4 0.0000 0.880 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531607 4 0.3727 0.581 0.000 0.388 0.000 0.612 0.000 0.000
#> GSM531608 2 0.3727 0.803 0.000 0.612 0.000 0.000 0.000 0.388
#> GSM531609 3 0.0000 0.955 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531610 4 0.0000 0.880 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531611 1 0.4292 0.733 0.588 0.388 0.000 0.024 0.000 0.000
#> GSM531612 1 0.3927 0.762 0.644 0.344 0.000 0.012 0.000 0.000
#> GSM531613 1 0.0000 0.826 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531614 3 0.2300 0.806 0.000 0.000 0.856 0.000 0.000 0.144
#> GSM531616 2 0.3727 0.803 0.000 0.612 0.000 0.000 0.000 0.388
#> GSM531618 3 0.0000 0.955 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531619 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM531620 2 0.3727 0.803 0.000 0.612 0.000 0.000 0.000 0.388
#> GSM531621 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM531622 2 0.3727 0.803 0.000 0.612 0.000 0.000 0.000 0.388
#> GSM531623 2 0.3727 0.803 0.000 0.612 0.000 0.000 0.000 0.388
#> GSM531625 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM531626 6 0.0000 0.865 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531628 6 0.3659 0.375 0.000 0.000 0.364 0.000 0.000 0.636
#> GSM531630 2 0.3727 0.803 0.000 0.612 0.000 0.000 0.000 0.388
#> GSM531632 6 0.0000 0.865 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531633 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM531635 2 0.3727 0.803 0.000 0.612 0.000 0.000 0.000 0.388
#> GSM531638 6 0.0000 0.865 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531639 3 0.0000 0.955 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531640 2 0.5156 0.657 0.000 0.612 0.000 0.000 0.144 0.244
#> GSM531641 4 0.0000 0.880 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531642 4 0.2491 0.754 0.000 0.000 0.164 0.836 0.000 0.000
#> GSM531643 3 0.0000 0.955 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531644 3 0.0000 0.955 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531645 3 0.0000 0.955 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531646 6 0.0000 0.865 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531647 6 0.3727 0.312 0.000 0.000 0.388 0.000 0.000 0.612
#> GSM531648 3 0.0000 0.955 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531649 6 0.0000 0.865 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531650 3 0.0000 0.955 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531651 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM531652 3 0.0000 0.955 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531653 3 0.0000 0.955 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531656 3 0.0000 0.955 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531657 4 0.0000 0.880 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531659 4 0.0000 0.880 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531661 2 0.3727 0.803 0.000 0.612 0.000 0.000 0.000 0.388
#> GSM531662 3 0.3592 0.460 0.000 0.000 0.656 0.344 0.000 0.000
#> GSM531663 4 0.0000 0.880 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531664 4 0.0260 0.877 0.000 0.000 0.008 0.992 0.000 0.000
#> GSM531665 4 0.2793 0.710 0.000 0.000 0.200 0.800 0.000 0.000
#> GSM531666 4 0.0000 0.880 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531667 6 0.0000 0.865 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531668 4 0.1814 0.810 0.000 0.000 0.100 0.900 0.000 0.000
#> GSM531669 4 0.0146 0.879 0.000 0.000 0.004 0.996 0.000 0.000
#> GSM531670 3 0.0000 0.955 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531671 6 0.0000 0.865 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM531672 4 0.0000 0.880 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531673 4 0.0000 0.880 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531674 3 0.0000 0.955 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM531675 1 0.0000 0.826 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531676 1 0.3727 0.752 0.612 0.388 0.000 0.000 0.000 0.000
#> GSM531677 1 0.0000 0.826 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531678 1 0.2631 0.799 0.820 0.180 0.000 0.000 0.000 0.000
#> GSM531679 1 0.0000 0.826 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531680 1 0.0000 0.826 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531681 1 0.0000 0.826 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531682 1 0.0146 0.826 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM531683 1 0.0000 0.826 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531684 4 0.3727 0.581 0.000 0.388 0.000 0.612 0.000 0.000
#> GSM531685 1 0.3727 0.752 0.612 0.388 0.000 0.000 0.000 0.000
#> GSM531686 1 0.0000 0.826 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531687 1 0.3727 0.752 0.612 0.388 0.000 0.000 0.000 0.000
#> GSM531688 1 0.3727 0.752 0.612 0.388 0.000 0.000 0.000 0.000
#> GSM531689 1 0.3727 0.752 0.612 0.388 0.000 0.000 0.000 0.000
#> GSM531690 1 0.0000 0.826 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531691 1 0.3695 0.757 0.624 0.376 0.000 0.000 0.000 0.000
#> GSM531692 4 0.0000 0.880 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM531693 4 0.3727 0.581 0.000 0.388 0.000 0.612 0.000 0.000
#> GSM531694 1 0.0000 0.826 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM531695 1 0.3578 0.767 0.660 0.340 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> ATC:pam 96 0.0155 2
#> ATC:pam 87 0.0324 3
#> ATC:pam 88 0.0558 4
#> ATC:pam 94 0.0763 5
#> ATC:pam 91 0.0744 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "mclust"]
# you can also extract it by
# res = res_list["ATC:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 96 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 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 0.679 0.855 0.900 0.2991 0.643 0.643
#> 3 3 0.637 0.847 0.926 0.7433 0.668 0.541
#> 4 4 1.000 0.974 0.990 0.2963 0.680 0.424
#> 5 5 0.820 0.799 0.872 0.1112 0.897 0.700
#> 6 6 0.736 0.673 0.791 0.0538 0.879 0.583
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM531600 1 0.0000 0.933 1.000 0.000
#> GSM531601 1 0.0000 0.933 1.000 0.000
#> GSM531605 1 0.2778 0.923 0.952 0.048
#> GSM531615 2 0.9286 0.892 0.344 0.656
#> GSM531617 2 0.9286 0.892 0.344 0.656
#> GSM531624 2 0.9286 0.892 0.344 0.656
#> GSM531627 2 0.9522 0.859 0.372 0.628
#> GSM531629 1 0.9881 -0.374 0.564 0.436
#> GSM531631 1 0.9635 -0.178 0.612 0.388
#> GSM531634 2 0.9286 0.892 0.344 0.656
#> GSM531636 1 0.0376 0.929 0.996 0.004
#> GSM531637 2 0.9286 0.892 0.344 0.656
#> GSM531654 1 0.0000 0.933 1.000 0.000
#> GSM531655 1 0.0000 0.933 1.000 0.000
#> GSM531658 1 0.0000 0.933 1.000 0.000
#> GSM531660 1 0.0000 0.933 1.000 0.000
#> GSM531602 1 0.2778 0.923 0.952 0.048
#> GSM531603 1 0.2778 0.923 0.952 0.048
#> GSM531604 1 0.2778 0.923 0.952 0.048
#> GSM531606 1 0.2778 0.923 0.952 0.048
#> GSM531607 1 0.2778 0.923 0.952 0.048
#> GSM531608 2 0.0000 0.666 0.000 1.000
#> GSM531609 2 0.0000 0.666 0.000 1.000
#> GSM531610 2 0.0000 0.666 0.000 1.000
#> GSM531611 1 0.2778 0.923 0.952 0.048
#> GSM531612 1 0.2778 0.923 0.952 0.048
#> GSM531613 1 0.2778 0.923 0.952 0.048
#> GSM531614 2 0.0000 0.666 0.000 1.000
#> GSM531616 2 0.9323 0.889 0.348 0.652
#> GSM531618 1 0.0000 0.933 1.000 0.000
#> GSM531619 2 0.9286 0.892 0.344 0.656
#> GSM531620 2 0.9460 0.870 0.364 0.636
#> GSM531621 2 0.9286 0.892 0.344 0.656
#> GSM531622 2 0.9552 0.853 0.376 0.624
#> GSM531623 1 0.9977 -0.497 0.528 0.472
#> GSM531625 2 0.9286 0.892 0.344 0.656
#> GSM531626 1 0.9044 0.159 0.680 0.320
#> GSM531628 2 0.9286 0.892 0.344 0.656
#> GSM531630 2 0.9552 0.853 0.376 0.624
#> GSM531632 1 0.0000 0.933 1.000 0.000
#> GSM531633 2 0.9286 0.892 0.344 0.656
#> GSM531635 2 0.9323 0.889 0.348 0.652
#> GSM531638 1 0.0000 0.933 1.000 0.000
#> GSM531639 1 0.0000 0.933 1.000 0.000
#> GSM531640 2 0.9286 0.892 0.344 0.656
#> GSM531641 1 0.0000 0.933 1.000 0.000
#> GSM531642 1 0.0000 0.933 1.000 0.000
#> GSM531643 1 0.0000 0.933 1.000 0.000
#> GSM531644 1 0.0000 0.933 1.000 0.000
#> GSM531645 1 0.0000 0.933 1.000 0.000
#> GSM531646 1 0.0000 0.933 1.000 0.000
#> GSM531647 1 0.0000 0.933 1.000 0.000
#> GSM531648 1 0.0000 0.933 1.000 0.000
#> GSM531649 1 0.8555 0.323 0.720 0.280
#> GSM531650 1 0.0000 0.933 1.000 0.000
#> GSM531651 2 0.9286 0.892 0.344 0.656
#> GSM531652 1 0.0000 0.933 1.000 0.000
#> GSM531653 1 0.0000 0.933 1.000 0.000
#> GSM531656 1 0.0000 0.933 1.000 0.000
#> GSM531657 1 0.0000 0.933 1.000 0.000
#> GSM531659 1 0.0000 0.933 1.000 0.000
#> GSM531661 1 0.0000 0.933 1.000 0.000
#> GSM531662 1 0.0000 0.933 1.000 0.000
#> GSM531663 1 0.0000 0.933 1.000 0.000
#> GSM531664 1 0.0000 0.933 1.000 0.000
#> GSM531665 1 0.0000 0.933 1.000 0.000
#> GSM531666 1 0.0000 0.933 1.000 0.000
#> GSM531667 1 0.0000 0.933 1.000 0.000
#> GSM531668 1 0.0000 0.933 1.000 0.000
#> GSM531669 1 0.0000 0.933 1.000 0.000
#> GSM531670 1 0.0000 0.933 1.000 0.000
#> GSM531671 1 0.0000 0.933 1.000 0.000
#> GSM531672 1 0.2236 0.926 0.964 0.036
#> GSM531673 1 0.0000 0.933 1.000 0.000
#> GSM531674 1 0.0000 0.933 1.000 0.000
#> GSM531675 1 0.2778 0.923 0.952 0.048
#> GSM531676 1 0.2778 0.923 0.952 0.048
#> GSM531677 1 0.2778 0.923 0.952 0.048
#> GSM531678 1 0.2778 0.923 0.952 0.048
#> GSM531679 1 0.2778 0.923 0.952 0.048
#> GSM531680 1 0.2778 0.923 0.952 0.048
#> GSM531681 1 0.2778 0.923 0.952 0.048
#> GSM531682 1 0.2778 0.923 0.952 0.048
#> GSM531683 1 0.2778 0.923 0.952 0.048
#> GSM531684 1 0.2778 0.923 0.952 0.048
#> GSM531685 1 0.2778 0.923 0.952 0.048
#> GSM531686 1 0.2778 0.923 0.952 0.048
#> GSM531687 1 0.2778 0.923 0.952 0.048
#> GSM531688 1 0.2778 0.923 0.952 0.048
#> GSM531689 1 0.2778 0.923 0.952 0.048
#> GSM531690 1 0.2778 0.923 0.952 0.048
#> GSM531691 1 0.2778 0.923 0.952 0.048
#> GSM531692 1 0.2778 0.923 0.952 0.048
#> GSM531693 1 0.0000 0.933 1.000 0.000
#> GSM531694 1 0.2778 0.923 0.952 0.048
#> GSM531695 1 0.2778 0.923 0.952 0.048
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM531600 2 0.1411 0.8939 0.036 0.964 0.000
#> GSM531601 2 0.0424 0.9172 0.008 0.992 0.000
#> GSM531605 1 0.0000 0.8767 1.000 0.000 0.000
#> GSM531615 2 0.0237 0.9236 0.000 0.996 0.004
#> GSM531617 2 0.0237 0.9236 0.000 0.996 0.004
#> GSM531624 2 0.0237 0.9236 0.000 0.996 0.004
#> GSM531627 2 0.0237 0.9236 0.000 0.996 0.004
#> GSM531629 2 0.0237 0.9236 0.000 0.996 0.004
#> GSM531631 2 0.0000 0.9225 0.000 1.000 0.000
#> GSM531634 2 0.0237 0.9236 0.000 0.996 0.004
#> GSM531636 2 0.0000 0.9225 0.000 1.000 0.000
#> GSM531637 2 0.0237 0.9236 0.000 0.996 0.004
#> GSM531654 2 0.6282 0.2678 0.384 0.612 0.004
#> GSM531655 1 0.4733 0.8121 0.800 0.196 0.004
#> GSM531658 1 0.3112 0.8492 0.900 0.096 0.004
#> GSM531660 1 0.0475 0.8757 0.992 0.004 0.004
#> GSM531602 1 0.0000 0.8767 1.000 0.000 0.000
#> GSM531603 1 0.0237 0.8765 0.996 0.004 0.000
#> GSM531604 1 0.0000 0.8767 1.000 0.000 0.000
#> GSM531606 1 0.0000 0.8767 1.000 0.000 0.000
#> GSM531607 1 0.0237 0.8765 0.996 0.004 0.000
#> GSM531608 3 0.0000 1.0000 0.000 0.000 1.000
#> GSM531609 3 0.0000 1.0000 0.000 0.000 1.000
#> GSM531610 3 0.0000 1.0000 0.000 0.000 1.000
#> GSM531611 1 0.0475 0.8757 0.992 0.004 0.004
#> GSM531612 1 0.0475 0.8757 0.992 0.004 0.004
#> GSM531613 1 0.0237 0.8765 0.996 0.004 0.000
#> GSM531614 3 0.0000 1.0000 0.000 0.000 1.000
#> GSM531616 2 0.0237 0.9236 0.000 0.996 0.004
#> GSM531618 1 0.4974 0.7781 0.764 0.236 0.000
#> GSM531619 2 0.0237 0.9236 0.000 0.996 0.004
#> GSM531620 2 0.0237 0.9236 0.000 0.996 0.004
#> GSM531621 2 0.0237 0.9236 0.000 0.996 0.004
#> GSM531622 2 0.0237 0.9236 0.000 0.996 0.004
#> GSM531623 2 0.0237 0.9236 0.000 0.996 0.004
#> GSM531625 2 0.0237 0.9236 0.000 0.996 0.004
#> GSM531626 2 0.0000 0.9225 0.000 1.000 0.000
#> GSM531628 2 0.0237 0.9236 0.000 0.996 0.004
#> GSM531630 2 0.0237 0.9236 0.000 0.996 0.004
#> GSM531632 2 0.0000 0.9225 0.000 1.000 0.000
#> GSM531633 2 0.0237 0.9236 0.000 0.996 0.004
#> GSM531635 2 0.0237 0.9236 0.000 0.996 0.004
#> GSM531638 2 0.0000 0.9225 0.000 1.000 0.000
#> GSM531639 2 0.2878 0.8217 0.096 0.904 0.000
#> GSM531640 2 0.0237 0.9236 0.000 0.996 0.004
#> GSM531641 1 0.4555 0.8105 0.800 0.200 0.000
#> GSM531642 2 0.6095 0.2461 0.392 0.608 0.000
#> GSM531643 2 0.4062 0.7325 0.164 0.836 0.000
#> GSM531644 1 0.5291 0.7458 0.732 0.268 0.000
#> GSM531645 1 0.5291 0.7458 0.732 0.268 0.000
#> GSM531646 2 0.0000 0.9225 0.000 1.000 0.000
#> GSM531647 1 0.5678 0.6738 0.684 0.316 0.000
#> GSM531648 1 0.5291 0.7458 0.732 0.268 0.000
#> GSM531649 2 0.0000 0.9225 0.000 1.000 0.000
#> GSM531650 1 0.5529 0.7068 0.704 0.296 0.000
#> GSM531651 2 0.0237 0.9236 0.000 0.996 0.004
#> GSM531652 2 0.6215 0.0905 0.428 0.572 0.000
#> GSM531653 1 0.5497 0.7128 0.708 0.292 0.000
#> GSM531656 1 0.4974 0.7781 0.764 0.236 0.000
#> GSM531657 1 0.4110 0.8300 0.844 0.152 0.004
#> GSM531659 1 0.4733 0.8121 0.800 0.196 0.004
#> GSM531661 2 0.1525 0.8946 0.032 0.964 0.004
#> GSM531662 2 0.5404 0.6069 0.256 0.740 0.004
#> GSM531663 1 0.4733 0.8121 0.800 0.196 0.004
#> GSM531664 1 0.4931 0.7820 0.768 0.232 0.000
#> GSM531665 1 0.4733 0.8121 0.800 0.196 0.004
#> GSM531666 1 0.4555 0.8105 0.800 0.200 0.000
#> GSM531667 2 0.1525 0.8946 0.032 0.964 0.004
#> GSM531668 1 0.4733 0.8121 0.800 0.196 0.004
#> GSM531669 1 0.4974 0.7781 0.764 0.236 0.000
#> GSM531670 1 0.5733 0.6492 0.676 0.324 0.000
#> GSM531671 2 0.1525 0.8946 0.032 0.964 0.004
#> GSM531672 1 0.0475 0.8757 0.992 0.004 0.004
#> GSM531673 1 0.4733 0.8121 0.800 0.196 0.004
#> GSM531674 1 0.4974 0.7781 0.764 0.236 0.000
#> GSM531675 1 0.0000 0.8767 1.000 0.000 0.000
#> GSM531676 1 0.0000 0.8767 1.000 0.000 0.000
#> GSM531677 1 0.0000 0.8767 1.000 0.000 0.000
#> GSM531678 1 0.0000 0.8767 1.000 0.000 0.000
#> GSM531679 1 0.0000 0.8767 1.000 0.000 0.000
#> GSM531680 1 0.0000 0.8767 1.000 0.000 0.000
#> GSM531681 1 0.0000 0.8767 1.000 0.000 0.000
#> GSM531682 1 0.0000 0.8767 1.000 0.000 0.000
#> GSM531683 1 0.0000 0.8767 1.000 0.000 0.000
#> GSM531684 1 0.0000 0.8767 1.000 0.000 0.000
#> GSM531685 1 0.0000 0.8767 1.000 0.000 0.000
#> GSM531686 1 0.0000 0.8767 1.000 0.000 0.000
#> GSM531687 1 0.0000 0.8767 1.000 0.000 0.000
#> GSM531688 1 0.0000 0.8767 1.000 0.000 0.000
#> GSM531689 1 0.0000 0.8767 1.000 0.000 0.000
#> GSM531690 1 0.0000 0.8767 1.000 0.000 0.000
#> GSM531691 1 0.0000 0.8767 1.000 0.000 0.000
#> GSM531692 1 0.0000 0.8767 1.000 0.000 0.000
#> GSM531693 1 0.0237 0.8765 0.996 0.004 0.000
#> GSM531694 1 0.0000 0.8767 1.000 0.000 0.000
#> GSM531695 1 0.0000 0.8767 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM531600 3 0.0000 0.988 0.000 0.000 1.000 0
#> GSM531601 3 0.0817 0.982 0.000 0.024 0.976 0
#> GSM531605 1 0.0000 0.990 1.000 0.000 0.000 0
#> GSM531615 2 0.0000 0.967 0.000 1.000 0.000 0
#> GSM531617 2 0.0000 0.967 0.000 1.000 0.000 0
#> GSM531624 2 0.0000 0.967 0.000 1.000 0.000 0
#> GSM531627 2 0.0000 0.967 0.000 1.000 0.000 0
#> GSM531629 2 0.4730 0.345 0.000 0.636 0.364 0
#> GSM531631 3 0.0921 0.979 0.000 0.028 0.972 0
#> GSM531634 2 0.0000 0.967 0.000 1.000 0.000 0
#> GSM531636 3 0.0817 0.982 0.000 0.024 0.976 0
#> GSM531637 2 0.0000 0.967 0.000 1.000 0.000 0
#> GSM531654 3 0.0000 0.988 0.000 0.000 1.000 0
#> GSM531655 3 0.0000 0.988 0.000 0.000 1.000 0
#> GSM531658 3 0.0000 0.988 0.000 0.000 1.000 0
#> GSM531660 1 0.2760 0.812 0.872 0.000 0.128 0
#> GSM531602 1 0.0000 0.990 1.000 0.000 0.000 0
#> GSM531603 1 0.0000 0.990 1.000 0.000 0.000 0
#> GSM531604 1 0.0000 0.990 1.000 0.000 0.000 0
#> GSM531606 1 0.0000 0.990 1.000 0.000 0.000 0
#> GSM531607 1 0.0000 0.990 1.000 0.000 0.000 0
#> GSM531608 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM531609 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM531610 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM531611 1 0.0469 0.979 0.988 0.000 0.012 0
#> GSM531612 1 0.0469 0.979 0.988 0.000 0.012 0
#> GSM531613 1 0.0000 0.990 1.000 0.000 0.000 0
#> GSM531614 4 0.0000 1.000 0.000 0.000 0.000 1
#> GSM531616 2 0.0000 0.967 0.000 1.000 0.000 0
#> GSM531618 3 0.0000 0.988 0.000 0.000 1.000 0
#> GSM531619 2 0.0000 0.967 0.000 1.000 0.000 0
#> GSM531620 2 0.0000 0.967 0.000 1.000 0.000 0
#> GSM531621 2 0.0000 0.967 0.000 1.000 0.000 0
#> GSM531622 2 0.0000 0.967 0.000 1.000 0.000 0
#> GSM531623 2 0.0921 0.928 0.000 0.972 0.028 0
#> GSM531625 2 0.0000 0.967 0.000 1.000 0.000 0
#> GSM531626 3 0.0817 0.982 0.000 0.024 0.976 0
#> GSM531628 2 0.0000 0.967 0.000 1.000 0.000 0
#> GSM531630 2 0.0000 0.967 0.000 1.000 0.000 0
#> GSM531632 3 0.0817 0.982 0.000 0.024 0.976 0
#> GSM531633 2 0.0000 0.967 0.000 1.000 0.000 0
#> GSM531635 2 0.0000 0.967 0.000 1.000 0.000 0
#> GSM531638 3 0.0817 0.982 0.000 0.024 0.976 0
#> GSM531639 3 0.0000 0.988 0.000 0.000 1.000 0
#> GSM531640 2 0.0000 0.967 0.000 1.000 0.000 0
#> GSM531641 3 0.0000 0.988 0.000 0.000 1.000 0
#> GSM531642 3 0.0000 0.988 0.000 0.000 1.000 0
#> GSM531643 3 0.0469 0.985 0.000 0.012 0.988 0
#> GSM531644 3 0.0817 0.982 0.000 0.024 0.976 0
#> GSM531645 3 0.0817 0.982 0.000 0.024 0.976 0
#> GSM531646 3 0.0817 0.982 0.000 0.024 0.976 0
#> GSM531647 3 0.0817 0.982 0.000 0.024 0.976 0
#> GSM531648 3 0.0817 0.982 0.000 0.024 0.976 0
#> GSM531649 3 0.0817 0.982 0.000 0.024 0.976 0
#> GSM531650 3 0.0817 0.982 0.000 0.024 0.976 0
#> GSM531651 2 0.0000 0.967 0.000 1.000 0.000 0
#> GSM531652 3 0.0817 0.982 0.000 0.024 0.976 0
#> GSM531653 3 0.0817 0.982 0.000 0.024 0.976 0
#> GSM531656 3 0.0000 0.988 0.000 0.000 1.000 0
#> GSM531657 3 0.0000 0.988 0.000 0.000 1.000 0
#> GSM531659 3 0.0000 0.988 0.000 0.000 1.000 0
#> GSM531661 3 0.0188 0.986 0.000 0.004 0.996 0
#> GSM531662 3 0.0000 0.988 0.000 0.000 1.000 0
#> GSM531663 3 0.0000 0.988 0.000 0.000 1.000 0
#> GSM531664 3 0.0000 0.988 0.000 0.000 1.000 0
#> GSM531665 3 0.0000 0.988 0.000 0.000 1.000 0
#> GSM531666 3 0.0000 0.988 0.000 0.000 1.000 0
#> GSM531667 3 0.0000 0.988 0.000 0.000 1.000 0
#> GSM531668 3 0.0000 0.988 0.000 0.000 1.000 0
#> GSM531669 3 0.0000 0.988 0.000 0.000 1.000 0
#> GSM531670 3 0.0000 0.988 0.000 0.000 1.000 0
#> GSM531671 3 0.0000 0.988 0.000 0.000 1.000 0
#> GSM531672 1 0.1118 0.952 0.964 0.000 0.036 0
#> GSM531673 3 0.0000 0.988 0.000 0.000 1.000 0
#> GSM531674 3 0.0000 0.988 0.000 0.000 1.000 0
#> GSM531675 1 0.0000 0.990 1.000 0.000 0.000 0
#> GSM531676 1 0.0000 0.990 1.000 0.000 0.000 0
#> GSM531677 1 0.0000 0.990 1.000 0.000 0.000 0
#> GSM531678 1 0.0000 0.990 1.000 0.000 0.000 0
#> GSM531679 1 0.0000 0.990 1.000 0.000 0.000 0
#> GSM531680 1 0.0000 0.990 1.000 0.000 0.000 0
#> GSM531681 1 0.0000 0.990 1.000 0.000 0.000 0
#> GSM531682 1 0.0000 0.990 1.000 0.000 0.000 0
#> GSM531683 1 0.0000 0.990 1.000 0.000 0.000 0
#> GSM531684 1 0.0000 0.990 1.000 0.000 0.000 0
#> GSM531685 1 0.0000 0.990 1.000 0.000 0.000 0
#> GSM531686 1 0.0000 0.990 1.000 0.000 0.000 0
#> GSM531687 1 0.0000 0.990 1.000 0.000 0.000 0
#> GSM531688 1 0.0000 0.990 1.000 0.000 0.000 0
#> GSM531689 1 0.0000 0.990 1.000 0.000 0.000 0
#> GSM531690 1 0.0000 0.990 1.000 0.000 0.000 0
#> GSM531691 1 0.0000 0.990 1.000 0.000 0.000 0
#> GSM531692 1 0.0000 0.990 1.000 0.000 0.000 0
#> GSM531693 1 0.1118 0.947 0.964 0.000 0.036 0
#> GSM531694 1 0.0000 0.990 1.000 0.000 0.000 0
#> GSM531695 1 0.0000 0.990 1.000 0.000 0.000 0
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM531600 3 0.3774 0.791 0.000 0.296 0.704 0 0.000
#> GSM531601 3 0.4171 0.748 0.000 0.396 0.604 0 0.000
#> GSM531605 1 0.0000 0.921 1.000 0.000 0.000 0 0.000
#> GSM531615 5 0.0000 0.996 0.000 0.000 0.000 0 1.000
#> GSM531617 2 0.4126 0.576 0.000 0.620 0.000 0 0.380
#> GSM531624 5 0.0000 0.996 0.000 0.000 0.000 0 1.000
#> GSM531627 2 0.3983 0.623 0.000 0.660 0.000 0 0.340
#> GSM531629 2 0.1792 0.696 0.000 0.916 0.000 0 0.084
#> GSM531631 2 0.0000 0.674 0.000 1.000 0.000 0 0.000
#> GSM531634 5 0.0290 0.990 0.000 0.008 0.000 0 0.992
#> GSM531636 2 0.1168 0.653 0.000 0.960 0.032 0 0.008
#> GSM531637 5 0.0000 0.996 0.000 0.000 0.000 0 1.000
#> GSM531654 2 0.4227 0.431 0.000 0.580 0.420 0 0.000
#> GSM531655 3 0.0162 0.750 0.000 0.004 0.996 0 0.000
#> GSM531658 3 0.0000 0.747 0.000 0.000 1.000 0 0.000
#> GSM531660 1 0.4227 0.458 0.580 0.000 0.420 0 0.000
#> GSM531602 1 0.0000 0.921 1.000 0.000 0.000 0 0.000
#> GSM531603 1 0.0000 0.921 1.000 0.000 0.000 0 0.000
#> GSM531604 1 0.0000 0.921 1.000 0.000 0.000 0 0.000
#> GSM531606 1 0.0000 0.921 1.000 0.000 0.000 0 0.000
#> GSM531607 1 0.0000 0.921 1.000 0.000 0.000 0 0.000
#> GSM531608 4 0.0000 1.000 0.000 0.000 0.000 1 0.000
#> GSM531609 4 0.0000 1.000 0.000 0.000 0.000 1 0.000
#> GSM531610 4 0.0000 1.000 0.000 0.000 0.000 1 0.000
#> GSM531611 1 0.4045 0.551 0.644 0.000 0.356 0 0.000
#> GSM531612 1 0.4074 0.540 0.636 0.000 0.364 0 0.000
#> GSM531613 1 0.0290 0.916 0.992 0.000 0.008 0 0.000
#> GSM531614 4 0.0000 1.000 0.000 0.000 0.000 1 0.000
#> GSM531616 2 0.4060 0.607 0.000 0.640 0.000 0 0.360
#> GSM531618 3 0.3534 0.797 0.000 0.256 0.744 0 0.000
#> GSM531619 5 0.0000 0.996 0.000 0.000 0.000 0 1.000
#> GSM531620 2 0.4045 0.611 0.000 0.644 0.000 0 0.356
#> GSM531621 5 0.0000 0.996 0.000 0.000 0.000 0 1.000
#> GSM531622 2 0.3966 0.626 0.000 0.664 0.000 0 0.336
#> GSM531623 2 0.1908 0.691 0.000 0.908 0.000 0 0.092
#> GSM531625 5 0.0000 0.996 0.000 0.000 0.000 0 1.000
#> GSM531626 2 0.0000 0.674 0.000 1.000 0.000 0 0.000
#> GSM531628 2 0.4101 0.588 0.000 0.628 0.000 0 0.372
#> GSM531630 2 0.4045 0.611 0.000 0.644 0.000 0 0.356
#> GSM531632 3 0.4235 0.717 0.000 0.424 0.576 0 0.000
#> GSM531633 5 0.0290 0.990 0.000 0.008 0.000 0 0.992
#> GSM531635 2 0.4060 0.607 0.000 0.640 0.000 0 0.360
#> GSM531638 2 0.0000 0.674 0.000 1.000 0.000 0 0.000
#> GSM531639 3 0.3857 0.787 0.000 0.312 0.688 0 0.000
#> GSM531640 5 0.0290 0.990 0.000 0.008 0.000 0 0.992
#> GSM531641 3 0.1341 0.778 0.000 0.056 0.944 0 0.000
#> GSM531642 3 0.3039 0.802 0.000 0.192 0.808 0 0.000
#> GSM531643 3 0.4114 0.763 0.000 0.376 0.624 0 0.000
#> GSM531644 3 0.4138 0.759 0.000 0.384 0.616 0 0.000
#> GSM531645 3 0.4138 0.759 0.000 0.384 0.616 0 0.000
#> GSM531646 2 0.0510 0.662 0.000 0.984 0.016 0 0.000
#> GSM531647 3 0.4249 0.708 0.000 0.432 0.568 0 0.000
#> GSM531648 3 0.4138 0.759 0.000 0.384 0.616 0 0.000
#> GSM531649 2 0.0000 0.674 0.000 1.000 0.000 0 0.000
#> GSM531650 3 0.4138 0.759 0.000 0.384 0.616 0 0.000
#> GSM531651 5 0.0000 0.996 0.000 0.000 0.000 0 1.000
#> GSM531652 3 0.4150 0.756 0.000 0.388 0.612 0 0.000
#> GSM531653 3 0.4161 0.752 0.000 0.392 0.608 0 0.000
#> GSM531656 3 0.4126 0.761 0.000 0.380 0.620 0 0.000
#> GSM531657 3 0.0000 0.747 0.000 0.000 1.000 0 0.000
#> GSM531659 3 0.0000 0.747 0.000 0.000 1.000 0 0.000
#> GSM531661 2 0.4665 0.565 0.000 0.692 0.260 0 0.048
#> GSM531662 3 0.2179 0.799 0.000 0.112 0.888 0 0.000
#> GSM531663 3 0.0000 0.747 0.000 0.000 1.000 0 0.000
#> GSM531664 3 0.2179 0.799 0.000 0.112 0.888 0 0.000
#> GSM531665 3 0.0000 0.747 0.000 0.000 1.000 0 0.000
#> GSM531666 3 0.1851 0.791 0.000 0.088 0.912 0 0.000
#> GSM531667 2 0.4713 0.549 0.000 0.676 0.280 0 0.044
#> GSM531668 3 0.0000 0.747 0.000 0.000 1.000 0 0.000
#> GSM531669 3 0.2179 0.799 0.000 0.112 0.888 0 0.000
#> GSM531670 3 0.2230 0.800 0.000 0.116 0.884 0 0.000
#> GSM531671 3 0.2179 0.799 0.000 0.112 0.888 0 0.000
#> GSM531672 1 0.4182 0.489 0.600 0.000 0.400 0 0.000
#> GSM531673 3 0.0000 0.747 0.000 0.000 1.000 0 0.000
#> GSM531674 3 0.3932 0.782 0.000 0.328 0.672 0 0.000
#> GSM531675 1 0.0000 0.921 1.000 0.000 0.000 0 0.000
#> GSM531676 1 0.0000 0.921 1.000 0.000 0.000 0 0.000
#> GSM531677 1 0.0000 0.921 1.000 0.000 0.000 0 0.000
#> GSM531678 1 0.0000 0.921 1.000 0.000 0.000 0 0.000
#> GSM531679 1 0.0000 0.921 1.000 0.000 0.000 0 0.000
#> GSM531680 1 0.0000 0.921 1.000 0.000 0.000 0 0.000
#> GSM531681 1 0.0000 0.921 1.000 0.000 0.000 0 0.000
#> GSM531682 1 0.0000 0.921 1.000 0.000 0.000 0 0.000
#> GSM531683 1 0.0000 0.921 1.000 0.000 0.000 0 0.000
#> GSM531684 1 0.0000 0.921 1.000 0.000 0.000 0 0.000
#> GSM531685 1 0.0703 0.907 0.976 0.000 0.024 0 0.000
#> GSM531686 1 0.0000 0.921 1.000 0.000 0.000 0 0.000
#> GSM531687 1 0.1043 0.896 0.960 0.000 0.040 0 0.000
#> GSM531688 1 0.1043 0.896 0.960 0.000 0.040 0 0.000
#> GSM531689 1 0.0000 0.921 1.000 0.000 0.000 0 0.000
#> GSM531690 1 0.0000 0.921 1.000 0.000 0.000 0 0.000
#> GSM531691 1 0.0000 0.921 1.000 0.000 0.000 0 0.000
#> GSM531692 1 0.0000 0.921 1.000 0.000 0.000 0 0.000
#> GSM531693 1 0.4262 0.416 0.560 0.000 0.440 0 0.000
#> GSM531694 1 0.0000 0.921 1.000 0.000 0.000 0 0.000
#> GSM531695 1 0.0794 0.905 0.972 0.000 0.028 0 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM531600 3 0.5733 0.588 0.000 0.184 0.488 0.328 0 0.000
#> GSM531601 3 0.3630 0.731 0.000 0.032 0.756 0.212 0 0.000
#> GSM531605 1 0.0000 0.896 1.000 0.000 0.000 0.000 0 0.000
#> GSM531615 6 0.0000 0.937 0.000 0.000 0.000 0.000 0 1.000
#> GSM531617 2 0.3620 0.520 0.000 0.648 0.000 0.000 0 0.352
#> GSM531624 6 0.0000 0.937 0.000 0.000 0.000 0.000 0 1.000
#> GSM531627 2 0.4075 0.682 0.000 0.740 0.184 0.000 0 0.076
#> GSM531629 2 0.3725 0.643 0.000 0.676 0.316 0.000 0 0.008
#> GSM531631 2 0.3563 0.623 0.000 0.664 0.336 0.000 0 0.000
#> GSM531634 6 0.2730 0.703 0.000 0.192 0.000 0.000 0 0.808
#> GSM531636 3 0.5149 0.624 0.000 0.184 0.624 0.192 0 0.000
#> GSM531637 6 0.0000 0.937 0.000 0.000 0.000 0.000 0 1.000
#> GSM531654 2 0.3937 0.218 0.000 0.572 0.004 0.424 0 0.000
#> GSM531655 4 0.2852 0.546 0.000 0.080 0.064 0.856 0 0.000
#> GSM531658 4 0.0146 0.684 0.000 0.004 0.000 0.996 0 0.000
#> GSM531660 4 0.3269 0.531 0.184 0.024 0.000 0.792 0 0.000
#> GSM531602 1 0.0000 0.896 1.000 0.000 0.000 0.000 0 0.000
#> GSM531603 1 0.2450 0.807 0.868 0.016 0.000 0.116 0 0.000
#> GSM531604 1 0.2941 0.729 0.780 0.000 0.220 0.000 0 0.000
#> GSM531606 1 0.2941 0.729 0.780 0.000 0.220 0.000 0 0.000
#> GSM531607 1 0.0146 0.894 0.996 0.004 0.000 0.000 0 0.000
#> GSM531608 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> GSM531609 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> GSM531610 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> GSM531611 4 0.4626 0.404 0.272 0.076 0.000 0.652 0 0.000
#> GSM531612 4 0.4544 0.417 0.256 0.076 0.000 0.668 0 0.000
#> GSM531613 1 0.1327 0.858 0.936 0.064 0.000 0.000 0 0.000
#> GSM531614 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> GSM531616 2 0.3175 0.630 0.000 0.744 0.000 0.000 0 0.256
#> GSM531618 3 0.5631 0.574 0.000 0.156 0.484 0.360 0 0.000
#> GSM531619 6 0.0000 0.937 0.000 0.000 0.000 0.000 0 1.000
#> GSM531620 2 0.3175 0.630 0.000 0.744 0.000 0.000 0 0.256
#> GSM531621 6 0.0146 0.936 0.000 0.004 0.000 0.000 0 0.996
#> GSM531622 2 0.4516 0.681 0.000 0.700 0.188 0.000 0 0.112
#> GSM531623 2 0.4687 0.647 0.000 0.624 0.308 0.000 0 0.068
#> GSM531625 6 0.0000 0.937 0.000 0.000 0.000 0.000 0 1.000
#> GSM531626 2 0.3563 0.623 0.000 0.664 0.336 0.000 0 0.000
#> GSM531628 2 0.4101 0.579 0.000 0.664 0.000 0.028 0 0.308
#> GSM531630 2 0.3175 0.630 0.000 0.744 0.000 0.000 0 0.256
#> GSM531632 3 0.4059 0.636 0.000 0.148 0.752 0.100 0 0.000
#> GSM531633 6 0.0260 0.933 0.000 0.008 0.000 0.000 0 0.992
#> GSM531635 2 0.3175 0.630 0.000 0.744 0.000 0.000 0 0.256
#> GSM531638 3 0.3409 0.334 0.000 0.300 0.700 0.000 0 0.000
#> GSM531639 3 0.5624 0.578 0.000 0.156 0.488 0.356 0 0.000
#> GSM531640 6 0.2883 0.674 0.000 0.212 0.000 0.000 0 0.788
#> GSM531641 4 0.5574 -0.358 0.000 0.152 0.344 0.504 0 0.000
#> GSM531642 3 0.5653 0.551 0.000 0.156 0.468 0.376 0 0.000
#> GSM531643 3 0.3420 0.738 0.000 0.012 0.748 0.240 0 0.000
#> GSM531644 3 0.3215 0.739 0.000 0.004 0.756 0.240 0 0.000
#> GSM531645 3 0.3215 0.739 0.000 0.004 0.756 0.240 0 0.000
#> GSM531646 3 0.3175 0.425 0.000 0.256 0.744 0.000 0 0.000
#> GSM531647 3 0.3923 0.720 0.000 0.060 0.748 0.192 0 0.000
#> GSM531648 3 0.3215 0.739 0.000 0.004 0.756 0.240 0 0.000
#> GSM531649 3 0.3547 0.262 0.000 0.332 0.668 0.000 0 0.000
#> GSM531650 3 0.3215 0.739 0.000 0.004 0.756 0.240 0 0.000
#> GSM531651 6 0.0000 0.937 0.000 0.000 0.000 0.000 0 1.000
#> GSM531652 3 0.3215 0.739 0.000 0.004 0.756 0.240 0 0.000
#> GSM531653 3 0.3487 0.736 0.000 0.020 0.756 0.224 0 0.000
#> GSM531656 3 0.3076 0.738 0.000 0.000 0.760 0.240 0 0.000
#> GSM531657 4 0.0000 0.685 0.000 0.000 0.000 1.000 0 0.000
#> GSM531659 4 0.0000 0.685 0.000 0.000 0.000 1.000 0 0.000
#> GSM531661 2 0.4702 0.474 0.000 0.708 0.028 0.200 0 0.064
#> GSM531662 4 0.5774 -0.463 0.000 0.176 0.384 0.440 0 0.000
#> GSM531663 4 0.0713 0.673 0.000 0.028 0.000 0.972 0 0.000
#> GSM531664 3 0.5673 0.522 0.000 0.156 0.448 0.396 0 0.000
#> GSM531665 4 0.0363 0.681 0.000 0.012 0.000 0.988 0 0.000
#> GSM531666 4 0.5681 -0.526 0.000 0.156 0.420 0.424 0 0.000
#> GSM531667 2 0.4827 0.461 0.000 0.696 0.032 0.208 0 0.064
#> GSM531668 4 0.0260 0.683 0.000 0.008 0.000 0.992 0 0.000
#> GSM531669 3 0.5675 0.514 0.000 0.156 0.444 0.400 0 0.000
#> GSM531670 3 0.5662 0.539 0.000 0.156 0.460 0.384 0 0.000
#> GSM531671 3 0.5855 0.469 0.000 0.192 0.408 0.400 0 0.000
#> GSM531672 4 0.4431 0.436 0.236 0.076 0.000 0.688 0 0.000
#> GSM531673 4 0.0000 0.685 0.000 0.000 0.000 1.000 0 0.000
#> GSM531674 3 0.5454 0.633 0.000 0.152 0.548 0.300 0 0.000
#> GSM531675 1 0.0000 0.896 1.000 0.000 0.000 0.000 0 0.000
#> GSM531676 1 0.0000 0.896 1.000 0.000 0.000 0.000 0 0.000
#> GSM531677 1 0.0000 0.896 1.000 0.000 0.000 0.000 0 0.000
#> GSM531678 1 0.0000 0.896 1.000 0.000 0.000 0.000 0 0.000
#> GSM531679 1 0.0000 0.896 1.000 0.000 0.000 0.000 0 0.000
#> GSM531680 1 0.0000 0.896 1.000 0.000 0.000 0.000 0 0.000
#> GSM531681 1 0.0000 0.896 1.000 0.000 0.000 0.000 0 0.000
#> GSM531682 1 0.0000 0.896 1.000 0.000 0.000 0.000 0 0.000
#> GSM531683 1 0.0000 0.896 1.000 0.000 0.000 0.000 0 0.000
#> GSM531684 1 0.2941 0.729 0.780 0.000 0.220 0.000 0 0.000
#> GSM531685 1 0.3464 0.574 0.688 0.000 0.000 0.312 0 0.000
#> GSM531686 1 0.0000 0.896 1.000 0.000 0.000 0.000 0 0.000
#> GSM531687 1 0.3592 0.519 0.656 0.000 0.000 0.344 0 0.000
#> GSM531688 1 0.3592 0.519 0.656 0.000 0.000 0.344 0 0.000
#> GSM531689 1 0.0000 0.896 1.000 0.000 0.000 0.000 0 0.000
#> GSM531690 1 0.0000 0.896 1.000 0.000 0.000 0.000 0 0.000
#> GSM531691 1 0.0000 0.896 1.000 0.000 0.000 0.000 0 0.000
#> GSM531692 1 0.2941 0.729 0.780 0.000 0.220 0.000 0 0.000
#> GSM531693 4 0.1584 0.661 0.064 0.008 0.000 0.928 0 0.000
#> GSM531694 1 0.0000 0.896 1.000 0.000 0.000 0.000 0 0.000
#> GSM531695 1 0.3446 0.582 0.692 0.000 0.000 0.308 0 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> ATC:mclust 91 0.15114 2
#> ATC:mclust 93 0.00722 3
#> ATC:mclust 95 0.08616 4
#> ATC:mclust 92 0.02746 5
#> ATC:mclust 83 0.05401 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "NMF"]
# you can also extract it by
# res = res_list["ATC:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 96 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.854 0.920 0.966 0.4745 0.526 0.526
#> 3 3 0.819 0.891 0.946 0.3760 0.745 0.547
#> 4 4 0.612 0.655 0.837 0.0747 0.875 0.679
#> 5 5 0.531 0.510 0.739 0.0713 0.783 0.445
#> 6 6 0.533 0.444 0.703 0.0486 0.899 0.653
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
#> GSM531600 2 0.0000 0.9637 0.000 1.000
#> GSM531601 2 0.0000 0.9637 0.000 1.000
#> GSM531605 1 0.0000 0.9624 1.000 0.000
#> GSM531615 2 0.0000 0.9637 0.000 1.000
#> GSM531617 2 0.0000 0.9637 0.000 1.000
#> GSM531624 2 0.0000 0.9637 0.000 1.000
#> GSM531627 2 0.0000 0.9637 0.000 1.000
#> GSM531629 2 0.0000 0.9637 0.000 1.000
#> GSM531631 2 0.0000 0.9637 0.000 1.000
#> GSM531634 2 0.0000 0.9637 0.000 1.000
#> GSM531636 2 0.0000 0.9637 0.000 1.000
#> GSM531637 2 0.0000 0.9637 0.000 1.000
#> GSM531654 2 0.0000 0.9637 0.000 1.000
#> GSM531655 2 0.9850 0.2447 0.428 0.572
#> GSM531658 2 0.5842 0.8200 0.140 0.860
#> GSM531660 1 0.0000 0.9624 1.000 0.000
#> GSM531602 1 0.0000 0.9624 1.000 0.000
#> GSM531603 1 0.0000 0.9624 1.000 0.000
#> GSM531604 2 0.9977 0.0912 0.472 0.528
#> GSM531606 1 0.7056 0.7796 0.808 0.192
#> GSM531607 1 0.0000 0.9624 1.000 0.000
#> GSM531608 2 0.0000 0.9637 0.000 1.000
#> GSM531609 2 0.0000 0.9637 0.000 1.000
#> GSM531610 1 0.8386 0.6553 0.732 0.268
#> GSM531611 1 0.0000 0.9624 1.000 0.000
#> GSM531612 1 0.0000 0.9624 1.000 0.000
#> GSM531613 1 0.0000 0.9624 1.000 0.000
#> GSM531614 2 0.0000 0.9637 0.000 1.000
#> GSM531616 2 0.0000 0.9637 0.000 1.000
#> GSM531618 2 0.0000 0.9637 0.000 1.000
#> GSM531619 2 0.0000 0.9637 0.000 1.000
#> GSM531620 2 0.0000 0.9637 0.000 1.000
#> GSM531621 2 0.0000 0.9637 0.000 1.000
#> GSM531622 2 0.0000 0.9637 0.000 1.000
#> GSM531623 2 0.0000 0.9637 0.000 1.000
#> GSM531625 2 0.0000 0.9637 0.000 1.000
#> GSM531626 2 0.0000 0.9637 0.000 1.000
#> GSM531628 2 0.0000 0.9637 0.000 1.000
#> GSM531630 2 0.0000 0.9637 0.000 1.000
#> GSM531632 2 0.0000 0.9637 0.000 1.000
#> GSM531633 2 0.0000 0.9637 0.000 1.000
#> GSM531635 2 0.0000 0.9637 0.000 1.000
#> GSM531638 2 0.0000 0.9637 0.000 1.000
#> GSM531639 2 0.0000 0.9637 0.000 1.000
#> GSM531640 2 0.0000 0.9637 0.000 1.000
#> GSM531641 1 0.4562 0.8882 0.904 0.096
#> GSM531642 2 0.9460 0.4255 0.364 0.636
#> GSM531643 2 0.0000 0.9637 0.000 1.000
#> GSM531644 2 0.0000 0.9637 0.000 1.000
#> GSM531645 2 0.0000 0.9637 0.000 1.000
#> GSM531646 2 0.0000 0.9637 0.000 1.000
#> GSM531647 2 0.0000 0.9637 0.000 1.000
#> GSM531648 2 0.0000 0.9637 0.000 1.000
#> GSM531649 2 0.0000 0.9637 0.000 1.000
#> GSM531650 2 0.0000 0.9637 0.000 1.000
#> GSM531651 2 0.0000 0.9637 0.000 1.000
#> GSM531652 2 0.0000 0.9637 0.000 1.000
#> GSM531653 2 0.0000 0.9637 0.000 1.000
#> GSM531656 2 0.0000 0.9637 0.000 1.000
#> GSM531657 1 0.6531 0.8111 0.832 0.168
#> GSM531659 1 0.7674 0.7319 0.776 0.224
#> GSM531661 2 0.0000 0.9637 0.000 1.000
#> GSM531662 2 0.0000 0.9637 0.000 1.000
#> GSM531663 2 0.4690 0.8666 0.100 0.900
#> GSM531664 1 0.6623 0.8062 0.828 0.172
#> GSM531665 2 0.0000 0.9637 0.000 1.000
#> GSM531666 1 0.0000 0.9624 1.000 0.000
#> GSM531667 2 0.0000 0.9637 0.000 1.000
#> GSM531668 2 0.0938 0.9537 0.012 0.988
#> GSM531669 2 0.8555 0.6058 0.280 0.720
#> GSM531670 2 0.0000 0.9637 0.000 1.000
#> GSM531671 2 0.0000 0.9637 0.000 1.000
#> GSM531672 1 0.0000 0.9624 1.000 0.000
#> GSM531673 2 0.7299 0.7330 0.204 0.796
#> GSM531674 2 0.0000 0.9637 0.000 1.000
#> GSM531675 1 0.0000 0.9624 1.000 0.000
#> GSM531676 1 0.0000 0.9624 1.000 0.000
#> GSM531677 1 0.0000 0.9624 1.000 0.000
#> GSM531678 1 0.0000 0.9624 1.000 0.000
#> GSM531679 1 0.0000 0.9624 1.000 0.000
#> GSM531680 1 0.0000 0.9624 1.000 0.000
#> GSM531681 1 0.0000 0.9624 1.000 0.000
#> GSM531682 1 0.0000 0.9624 1.000 0.000
#> GSM531683 1 0.0000 0.9624 1.000 0.000
#> GSM531684 1 0.2236 0.9377 0.964 0.036
#> GSM531685 1 0.0000 0.9624 1.000 0.000
#> GSM531686 1 0.0000 0.9624 1.000 0.000
#> GSM531687 1 0.0000 0.9624 1.000 0.000
#> GSM531688 1 0.0000 0.9624 1.000 0.000
#> GSM531689 1 0.0000 0.9624 1.000 0.000
#> GSM531690 1 0.0000 0.9624 1.000 0.000
#> GSM531691 1 0.0000 0.9624 1.000 0.000
#> GSM531692 2 0.0672 0.9571 0.008 0.992
#> GSM531693 1 0.4022 0.9028 0.920 0.080
#> GSM531694 1 0.0000 0.9624 1.000 0.000
#> GSM531695 1 0.0000 0.9624 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM531600 2 0.2066 0.923 0.000 0.940 0.060
#> GSM531601 3 0.3879 0.834 0.000 0.152 0.848
#> GSM531605 1 0.0000 0.948 1.000 0.000 0.000
#> GSM531615 2 0.0000 0.948 0.000 1.000 0.000
#> GSM531617 2 0.1411 0.941 0.000 0.964 0.036
#> GSM531624 2 0.0000 0.948 0.000 1.000 0.000
#> GSM531627 2 0.0892 0.949 0.000 0.980 0.020
#> GSM531629 2 0.0747 0.950 0.000 0.984 0.016
#> GSM531631 2 0.0747 0.950 0.000 0.984 0.016
#> GSM531634 2 0.0592 0.950 0.000 0.988 0.012
#> GSM531636 3 0.4842 0.733 0.000 0.224 0.776
#> GSM531637 2 0.0000 0.948 0.000 1.000 0.000
#> GSM531654 2 0.0000 0.948 0.000 1.000 0.000
#> GSM531655 3 0.2356 0.898 0.000 0.072 0.928
#> GSM531658 3 0.5621 0.585 0.000 0.308 0.692
#> GSM531660 3 0.5905 0.427 0.352 0.000 0.648
#> GSM531602 1 0.0000 0.948 1.000 0.000 0.000
#> GSM531603 1 0.2096 0.910 0.944 0.052 0.004
#> GSM531604 2 0.6204 0.216 0.424 0.576 0.000
#> GSM531606 1 0.5178 0.677 0.744 0.256 0.000
#> GSM531607 1 0.0000 0.948 1.000 0.000 0.000
#> GSM531608 2 0.0000 0.948 0.000 1.000 0.000
#> GSM531609 3 0.3412 0.853 0.000 0.124 0.876
#> GSM531610 3 0.0000 0.922 0.000 0.000 1.000
#> GSM531611 3 0.2261 0.875 0.068 0.000 0.932
#> GSM531612 3 0.3038 0.841 0.104 0.000 0.896
#> GSM531613 1 0.3038 0.861 0.896 0.000 0.104
#> GSM531614 2 0.1163 0.945 0.000 0.972 0.028
#> GSM531616 2 0.0747 0.950 0.000 0.984 0.016
#> GSM531618 3 0.3551 0.855 0.000 0.132 0.868
#> GSM531619 2 0.0000 0.948 0.000 1.000 0.000
#> GSM531620 2 0.2066 0.922 0.000 0.940 0.060
#> GSM531621 2 0.0237 0.949 0.000 0.996 0.004
#> GSM531622 2 0.0747 0.950 0.000 0.984 0.016
#> GSM531623 2 0.0000 0.948 0.000 1.000 0.000
#> GSM531625 2 0.0000 0.948 0.000 1.000 0.000
#> GSM531626 2 0.0592 0.950 0.000 0.988 0.012
#> GSM531628 2 0.3267 0.868 0.000 0.884 0.116
#> GSM531630 2 0.0892 0.949 0.000 0.980 0.020
#> GSM531632 2 0.1163 0.946 0.000 0.972 0.028
#> GSM531633 2 0.0424 0.950 0.000 0.992 0.008
#> GSM531635 2 0.0747 0.950 0.000 0.984 0.016
#> GSM531638 2 0.1163 0.946 0.000 0.972 0.028
#> GSM531639 3 0.0592 0.925 0.000 0.012 0.988
#> GSM531640 2 0.0747 0.950 0.000 0.984 0.016
#> GSM531641 3 0.0237 0.921 0.004 0.000 0.996
#> GSM531642 3 0.0000 0.922 0.000 0.000 1.000
#> GSM531643 3 0.0747 0.925 0.000 0.016 0.984
#> GSM531644 3 0.0592 0.925 0.000 0.012 0.988
#> GSM531645 3 0.0237 0.923 0.000 0.004 0.996
#> GSM531646 2 0.1031 0.948 0.000 0.976 0.024
#> GSM531647 2 0.5621 0.566 0.000 0.692 0.308
#> GSM531648 3 0.0747 0.925 0.000 0.016 0.984
#> GSM531649 2 0.0747 0.950 0.000 0.984 0.016
#> GSM531650 3 0.0747 0.925 0.000 0.016 0.984
#> GSM531651 2 0.0000 0.948 0.000 1.000 0.000
#> GSM531652 3 0.0424 0.924 0.000 0.008 0.992
#> GSM531653 3 0.1753 0.914 0.000 0.048 0.952
#> GSM531656 3 0.2066 0.907 0.000 0.060 0.940
#> GSM531657 3 0.0237 0.923 0.000 0.004 0.996
#> GSM531659 1 0.7990 0.211 0.532 0.064 0.404
#> GSM531661 2 0.0000 0.948 0.000 1.000 0.000
#> GSM531662 2 0.0592 0.950 0.000 0.988 0.012
#> GSM531663 2 0.5435 0.798 0.048 0.808 0.144
#> GSM531664 3 0.0000 0.922 0.000 0.000 1.000
#> GSM531665 2 0.0983 0.950 0.004 0.980 0.016
#> GSM531666 3 0.0237 0.921 0.004 0.000 0.996
#> GSM531667 2 0.0000 0.948 0.000 1.000 0.000
#> GSM531668 2 0.0892 0.948 0.000 0.980 0.020
#> GSM531669 3 0.0747 0.924 0.000 0.016 0.984
#> GSM531670 2 0.1964 0.926 0.000 0.944 0.056
#> GSM531671 2 0.0000 0.948 0.000 1.000 0.000
#> GSM531672 3 0.0237 0.921 0.004 0.000 0.996
#> GSM531673 2 0.8120 0.562 0.136 0.640 0.224
#> GSM531674 3 0.1753 0.914 0.000 0.048 0.952
#> GSM531675 1 0.0000 0.948 1.000 0.000 0.000
#> GSM531676 1 0.0237 0.946 0.996 0.004 0.000
#> GSM531677 1 0.0237 0.946 0.996 0.000 0.004
#> GSM531678 1 0.0000 0.948 1.000 0.000 0.000
#> GSM531679 1 0.0000 0.948 1.000 0.000 0.000
#> GSM531680 1 0.0424 0.944 0.992 0.000 0.008
#> GSM531681 1 0.0000 0.948 1.000 0.000 0.000
#> GSM531682 1 0.0000 0.948 1.000 0.000 0.000
#> GSM531683 1 0.0000 0.948 1.000 0.000 0.000
#> GSM531684 1 0.4178 0.794 0.828 0.172 0.000
#> GSM531685 1 0.0000 0.948 1.000 0.000 0.000
#> GSM531686 1 0.0424 0.944 0.992 0.000 0.008
#> GSM531687 1 0.0000 0.948 1.000 0.000 0.000
#> GSM531688 1 0.0237 0.946 0.996 0.000 0.004
#> GSM531689 1 0.0000 0.948 1.000 0.000 0.000
#> GSM531690 1 0.0000 0.948 1.000 0.000 0.000
#> GSM531691 1 0.0000 0.948 1.000 0.000 0.000
#> GSM531692 2 0.3412 0.835 0.124 0.876 0.000
#> GSM531693 1 0.4291 0.784 0.820 0.180 0.000
#> GSM531694 1 0.0000 0.948 1.000 0.000 0.000
#> GSM531695 1 0.0424 0.944 0.992 0.000 0.008
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM531600 2 0.3681 0.6926 0.000 0.816 0.176 0.008
#> GSM531601 3 0.4522 0.5330 0.000 0.320 0.680 0.000
#> GSM531605 1 0.1042 0.8900 0.972 0.008 0.000 0.020
#> GSM531615 2 0.4406 0.4816 0.000 0.700 0.000 0.300
#> GSM531617 2 0.4228 0.6796 0.000 0.760 0.008 0.232
#> GSM531624 2 0.3123 0.6723 0.000 0.844 0.000 0.156
#> GSM531627 2 0.2987 0.7560 0.000 0.880 0.016 0.104
#> GSM531629 2 0.1610 0.7693 0.000 0.952 0.032 0.016
#> GSM531631 2 0.1510 0.7693 0.000 0.956 0.028 0.016
#> GSM531634 4 0.4761 0.2627 0.000 0.372 0.000 0.628
#> GSM531636 2 0.5147 0.0910 0.000 0.536 0.460 0.004
#> GSM531637 2 0.4981 0.0673 0.000 0.536 0.000 0.464
#> GSM531654 2 0.1022 0.7575 0.000 0.968 0.000 0.032
#> GSM531655 3 0.4889 0.4675 0.000 0.360 0.636 0.004
#> GSM531658 3 0.5263 0.2233 0.000 0.448 0.544 0.008
#> GSM531660 1 0.5935 0.1136 0.496 0.000 0.468 0.036
#> GSM531602 1 0.0817 0.8885 0.976 0.000 0.000 0.024
#> GSM531603 1 0.1929 0.8789 0.940 0.024 0.000 0.036
#> GSM531604 1 0.5432 0.6847 0.740 0.136 0.000 0.124
#> GSM531606 1 0.3984 0.7834 0.828 0.132 0.000 0.040
#> GSM531607 1 0.0817 0.8860 0.976 0.000 0.000 0.024
#> GSM531608 4 0.2345 0.7377 0.000 0.100 0.000 0.900
#> GSM531609 4 0.4056 0.7013 0.004 0.060 0.096 0.840
#> GSM531610 4 0.5821 0.2963 0.040 0.000 0.368 0.592
#> GSM531611 3 0.4387 0.5098 0.144 0.000 0.804 0.052
#> GSM531612 3 0.4286 0.5210 0.136 0.000 0.812 0.052
#> GSM531613 3 0.5848 0.2367 0.336 0.000 0.616 0.048
#> GSM531614 4 0.2081 0.7428 0.000 0.084 0.000 0.916
#> GSM531616 2 0.2924 0.7563 0.000 0.884 0.016 0.100
#> GSM531618 3 0.4585 0.5069 0.000 0.332 0.668 0.000
#> GSM531619 2 0.3649 0.6226 0.000 0.796 0.000 0.204
#> GSM531620 2 0.3497 0.7498 0.000 0.860 0.036 0.104
#> GSM531621 2 0.4967 0.1516 0.000 0.548 0.000 0.452
#> GSM531622 2 0.1151 0.7694 0.000 0.968 0.024 0.008
#> GSM531623 2 0.1867 0.7394 0.000 0.928 0.000 0.072
#> GSM531625 2 0.4250 0.5534 0.000 0.724 0.000 0.276
#> GSM531626 2 0.1004 0.7692 0.000 0.972 0.024 0.004
#> GSM531628 2 0.5332 0.6979 0.000 0.748 0.124 0.128
#> GSM531630 2 0.3279 0.7604 0.000 0.872 0.032 0.096
#> GSM531632 2 0.2909 0.7532 0.000 0.888 0.092 0.020
#> GSM531633 2 0.4955 0.2181 0.000 0.556 0.000 0.444
#> GSM531635 2 0.2909 0.7588 0.000 0.888 0.020 0.092
#> GSM531638 2 0.1488 0.7694 0.000 0.956 0.032 0.012
#> GSM531639 3 0.4220 0.6300 0.000 0.248 0.748 0.004
#> GSM531640 2 0.4857 0.5416 0.000 0.668 0.008 0.324
#> GSM531641 3 0.0657 0.7115 0.000 0.004 0.984 0.012
#> GSM531642 3 0.0469 0.7235 0.000 0.012 0.988 0.000
#> GSM531643 3 0.5016 0.3653 0.000 0.396 0.600 0.004
#> GSM531644 3 0.1474 0.7294 0.000 0.052 0.948 0.000
#> GSM531645 3 0.0592 0.7260 0.000 0.016 0.984 0.000
#> GSM531646 2 0.2473 0.7593 0.000 0.908 0.080 0.012
#> GSM531647 2 0.4391 0.6026 0.000 0.740 0.252 0.008
#> GSM531648 3 0.1022 0.7318 0.000 0.032 0.968 0.000
#> GSM531649 2 0.1305 0.7692 0.000 0.960 0.036 0.004
#> GSM531650 3 0.1211 0.7319 0.000 0.040 0.960 0.000
#> GSM531651 2 0.4955 0.1066 0.000 0.556 0.000 0.444
#> GSM531652 3 0.1022 0.7317 0.000 0.032 0.968 0.000
#> GSM531653 3 0.5168 0.0393 0.000 0.492 0.504 0.004
#> GSM531656 3 0.3024 0.6887 0.000 0.148 0.852 0.000
#> GSM531657 3 0.1004 0.7296 0.004 0.024 0.972 0.000
#> GSM531659 1 0.7455 0.2662 0.548 0.196 0.248 0.008
#> GSM531661 2 0.0592 0.7604 0.000 0.984 0.000 0.016
#> GSM531662 2 0.3303 0.7470 0.028 0.892 0.032 0.048
#> GSM531663 2 0.4845 0.6473 0.020 0.756 0.212 0.012
#> GSM531664 3 0.1209 0.7309 0.000 0.032 0.964 0.004
#> GSM531665 2 0.3943 0.7382 0.048 0.864 0.040 0.048
#> GSM531666 3 0.0000 0.7141 0.000 0.000 1.000 0.000
#> GSM531667 2 0.1716 0.7404 0.000 0.936 0.000 0.064
#> GSM531668 2 0.3188 0.7412 0.008 0.872 0.112 0.008
#> GSM531669 2 0.4964 0.3540 0.000 0.616 0.380 0.004
#> GSM531670 2 0.3725 0.6889 0.000 0.812 0.180 0.008
#> GSM531671 2 0.1732 0.7590 0.004 0.948 0.008 0.040
#> GSM531672 3 0.2313 0.6521 0.044 0.000 0.924 0.032
#> GSM531673 2 0.6730 0.4819 0.104 0.624 0.260 0.012
#> GSM531674 2 0.5040 0.3923 0.000 0.628 0.364 0.008
#> GSM531675 1 0.0469 0.8890 0.988 0.000 0.000 0.012
#> GSM531676 1 0.1820 0.8779 0.944 0.020 0.000 0.036
#> GSM531677 1 0.1388 0.8801 0.960 0.000 0.012 0.028
#> GSM531678 1 0.0188 0.8898 0.996 0.000 0.000 0.004
#> GSM531679 1 0.0469 0.8890 0.988 0.000 0.000 0.012
#> GSM531680 1 0.1510 0.8782 0.956 0.000 0.016 0.028
#> GSM531681 1 0.0707 0.8890 0.980 0.000 0.000 0.020
#> GSM531682 1 0.0707 0.8874 0.980 0.000 0.000 0.020
#> GSM531683 1 0.0469 0.8890 0.988 0.000 0.000 0.012
#> GSM531684 1 0.4017 0.7848 0.828 0.128 0.000 0.044
#> GSM531685 1 0.1118 0.8856 0.964 0.000 0.000 0.036
#> GSM531686 1 0.1510 0.8782 0.956 0.000 0.016 0.028
#> GSM531687 1 0.1118 0.8856 0.964 0.000 0.000 0.036
#> GSM531688 1 0.0672 0.8904 0.984 0.000 0.008 0.008
#> GSM531689 1 0.1118 0.8856 0.964 0.000 0.000 0.036
#> GSM531690 1 0.0817 0.8860 0.976 0.000 0.000 0.024
#> GSM531691 1 0.1118 0.8856 0.964 0.000 0.000 0.036
#> GSM531692 1 0.7808 -0.0204 0.416 0.272 0.000 0.312
#> GSM531693 1 0.3372 0.8225 0.868 0.096 0.000 0.036
#> GSM531694 1 0.1118 0.8856 0.964 0.000 0.000 0.036
#> GSM531695 1 0.1109 0.8832 0.968 0.000 0.004 0.028
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM531600 3 0.3700 0.55647 0.000 0.240 0.752 0.008 0.000
#> GSM531601 4 0.6687 0.16115 0.000 0.248 0.332 0.420 0.000
#> GSM531605 1 0.3623 0.75366 0.828 0.008 0.020 0.136 0.008
#> GSM531615 2 0.2193 0.58989 0.000 0.912 0.028 0.000 0.060
#> GSM531617 5 0.6837 -0.17674 0.000 0.244 0.352 0.004 0.400
#> GSM531624 2 0.0898 0.59188 0.000 0.972 0.008 0.000 0.020
#> GSM531627 3 0.6210 -0.06197 0.000 0.428 0.448 0.004 0.120
#> GSM531629 2 0.4706 -0.00700 0.000 0.496 0.492 0.004 0.008
#> GSM531631 2 0.1557 0.60941 0.000 0.940 0.052 0.008 0.000
#> GSM531634 5 0.5439 -0.00889 0.000 0.432 0.060 0.000 0.508
#> GSM531636 3 0.2054 0.63286 0.000 0.028 0.920 0.052 0.000
#> GSM531637 2 0.3151 0.52700 0.000 0.836 0.020 0.000 0.144
#> GSM531654 2 0.5136 0.37283 0.000 0.608 0.352 0.020 0.020
#> GSM531655 3 0.6206 0.48076 0.084 0.052 0.656 0.200 0.008
#> GSM531658 3 0.7205 0.28683 0.088 0.084 0.524 0.296 0.008
#> GSM531660 4 0.5860 0.45069 0.340 0.000 0.080 0.568 0.012
#> GSM531602 1 0.1538 0.83992 0.948 0.000 0.008 0.036 0.008
#> GSM531603 1 0.6180 0.46105 0.632 0.092 0.024 0.240 0.012
#> GSM531604 1 0.6971 0.61730 0.628 0.068 0.032 0.164 0.108
#> GSM531606 1 0.6130 0.67453 0.684 0.116 0.036 0.144 0.020
#> GSM531607 1 0.3864 0.74556 0.820 0.012 0.028 0.132 0.008
#> GSM531608 5 0.1043 0.66389 0.000 0.040 0.000 0.000 0.960
#> GSM531609 5 0.0794 0.65679 0.000 0.000 0.000 0.028 0.972
#> GSM531610 5 0.1251 0.64600 0.000 0.000 0.008 0.036 0.956
#> GSM531611 4 0.5872 0.59155 0.156 0.000 0.076 0.688 0.080
#> GSM531612 4 0.4950 0.61588 0.148 0.000 0.096 0.740 0.016
#> GSM531613 4 0.5309 0.50363 0.312 0.000 0.012 0.628 0.048
#> GSM531614 5 0.0671 0.66550 0.000 0.016 0.000 0.004 0.980
#> GSM531616 3 0.6397 0.11453 0.000 0.352 0.488 0.004 0.156
#> GSM531618 3 0.4477 0.50626 0.000 0.040 0.708 0.252 0.000
#> GSM531619 2 0.0609 0.58539 0.000 0.980 0.000 0.000 0.020
#> GSM531620 3 0.6273 0.38424 0.000 0.208 0.576 0.008 0.208
#> GSM531621 2 0.5739 0.39369 0.000 0.596 0.124 0.000 0.280
#> GSM531622 2 0.3910 0.51704 0.000 0.740 0.248 0.004 0.008
#> GSM531623 2 0.0898 0.59811 0.000 0.972 0.020 0.008 0.000
#> GSM531625 2 0.4450 0.57532 0.000 0.760 0.132 0.000 0.108
#> GSM531626 2 0.4298 0.35268 0.000 0.640 0.352 0.008 0.000
#> GSM531628 3 0.5379 0.56529 0.000 0.080 0.696 0.024 0.200
#> GSM531630 2 0.5367 0.36310 0.000 0.600 0.336 0.004 0.060
#> GSM531632 3 0.3461 0.55458 0.000 0.224 0.772 0.004 0.000
#> GSM531633 2 0.6246 0.36114 0.000 0.528 0.180 0.000 0.292
#> GSM531635 3 0.6253 0.15388 0.000 0.356 0.504 0.004 0.136
#> GSM531638 2 0.4522 0.12411 0.000 0.552 0.440 0.008 0.000
#> GSM531639 3 0.3766 0.37796 0.000 0.004 0.728 0.268 0.000
#> GSM531640 2 0.6893 0.19660 0.000 0.392 0.272 0.004 0.332
#> GSM531641 4 0.3815 0.60626 0.012 0.000 0.220 0.764 0.004
#> GSM531642 4 0.4126 0.43688 0.000 0.000 0.380 0.620 0.000
#> GSM531643 3 0.2304 0.59922 0.000 0.008 0.892 0.100 0.000
#> GSM531644 3 0.2329 0.58436 0.000 0.000 0.876 0.124 0.000
#> GSM531645 3 0.4242 0.02914 0.000 0.000 0.572 0.428 0.000
#> GSM531646 3 0.4047 0.44446 0.000 0.320 0.676 0.004 0.000
#> GSM531647 3 0.2677 0.64096 0.000 0.112 0.872 0.016 0.000
#> GSM531648 3 0.3508 0.44490 0.000 0.000 0.748 0.252 0.000
#> GSM531649 3 0.4420 0.16558 0.000 0.448 0.548 0.004 0.000
#> GSM531650 3 0.2732 0.55855 0.000 0.000 0.840 0.160 0.000
#> GSM531651 2 0.2230 0.52946 0.000 0.884 0.000 0.000 0.116
#> GSM531652 3 0.4451 -0.22229 0.000 0.000 0.504 0.492 0.004
#> GSM531653 3 0.3060 0.59755 0.000 0.024 0.848 0.128 0.000
#> GSM531656 3 0.2561 0.62123 0.000 0.020 0.884 0.096 0.000
#> GSM531657 3 0.4953 0.47176 0.136 0.000 0.732 0.124 0.008
#> GSM531659 3 0.6647 0.02460 0.312 0.004 0.516 0.156 0.012
#> GSM531661 2 0.1857 0.61393 0.000 0.928 0.060 0.004 0.008
#> GSM531662 3 0.6653 0.36921 0.024 0.248 0.576 0.144 0.008
#> GSM531663 3 0.4090 0.63078 0.028 0.104 0.824 0.032 0.012
#> GSM531664 3 0.2775 0.60288 0.020 0.004 0.876 0.100 0.000
#> GSM531665 3 0.6436 0.49389 0.068 0.144 0.656 0.124 0.008
#> GSM531666 4 0.4639 0.45068 0.020 0.000 0.368 0.612 0.000
#> GSM531667 2 0.0968 0.59048 0.000 0.972 0.012 0.012 0.004
#> GSM531668 3 0.6860 0.10577 0.036 0.384 0.476 0.096 0.008
#> GSM531669 3 0.2388 0.62873 0.000 0.028 0.900 0.072 0.000
#> GSM531670 3 0.4264 0.36237 0.000 0.376 0.620 0.004 0.000
#> GSM531671 2 0.6140 0.17299 0.000 0.492 0.372 0.136 0.000
#> GSM531672 4 0.6927 0.38074 0.328 0.000 0.248 0.416 0.008
#> GSM531673 3 0.2856 0.64274 0.004 0.040 0.892 0.052 0.012
#> GSM531674 3 0.1281 0.64075 0.000 0.032 0.956 0.012 0.000
#> GSM531675 1 0.0703 0.84364 0.976 0.000 0.000 0.024 0.000
#> GSM531676 1 0.3067 0.79717 0.844 0.004 0.000 0.140 0.012
#> GSM531677 1 0.1121 0.83797 0.956 0.000 0.000 0.044 0.000
#> GSM531678 1 0.0609 0.84725 0.980 0.000 0.000 0.020 0.000
#> GSM531679 1 0.0703 0.84600 0.976 0.000 0.000 0.024 0.000
#> GSM531680 1 0.2230 0.79612 0.884 0.000 0.000 0.116 0.000
#> GSM531681 1 0.0703 0.84645 0.976 0.000 0.000 0.024 0.000
#> GSM531682 1 0.2416 0.82698 0.888 0.000 0.000 0.100 0.012
#> GSM531683 1 0.0609 0.84607 0.980 0.000 0.000 0.020 0.000
#> GSM531684 2 0.6665 0.01738 0.308 0.508 0.000 0.168 0.016
#> GSM531685 1 0.3111 0.79645 0.840 0.004 0.000 0.144 0.012
#> GSM531686 1 0.1544 0.82674 0.932 0.000 0.000 0.068 0.000
#> GSM531687 1 0.1544 0.84576 0.932 0.000 0.000 0.068 0.000
#> GSM531688 1 0.2771 0.80943 0.860 0.000 0.000 0.128 0.012
#> GSM531689 1 0.2818 0.80373 0.856 0.000 0.000 0.132 0.012
#> GSM531690 1 0.1671 0.82252 0.924 0.000 0.000 0.076 0.000
#> GSM531691 1 0.3022 0.79816 0.848 0.004 0.000 0.136 0.012
#> GSM531692 2 0.5742 0.25425 0.152 0.664 0.000 0.168 0.016
#> GSM531693 1 0.6431 0.46159 0.564 0.272 0.004 0.148 0.012
#> GSM531694 1 0.0609 0.84754 0.980 0.000 0.000 0.020 0.000
#> GSM531695 1 0.2280 0.80104 0.880 0.000 0.000 0.120 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM531600 3 0.3743 0.61092 0.000 0.044 0.812 0.040 0.104 0.000
#> GSM531601 4 0.5900 0.40643 0.000 0.188 0.176 0.596 0.040 0.000
#> GSM531605 1 0.5217 0.28524 0.568 0.000 0.008 0.084 0.340 0.000
#> GSM531615 2 0.2332 0.67864 0.000 0.904 0.040 0.000 0.020 0.036
#> GSM531617 6 0.5886 0.10354 0.000 0.132 0.376 0.000 0.016 0.476
#> GSM531624 2 0.0951 0.68308 0.000 0.968 0.008 0.000 0.020 0.004
#> GSM531627 3 0.5361 0.44930 0.000 0.300 0.600 0.000 0.032 0.068
#> GSM531629 3 0.5083 0.41814 0.000 0.344 0.584 0.004 0.060 0.008
#> GSM531631 2 0.1168 0.67747 0.000 0.956 0.028 0.000 0.016 0.000
#> GSM531634 6 0.5991 0.25524 0.000 0.292 0.128 0.000 0.036 0.544
#> GSM531636 3 0.1749 0.63031 0.000 0.012 0.936 0.032 0.016 0.004
#> GSM531637 2 0.2432 0.66272 0.000 0.892 0.020 0.000 0.016 0.072
#> GSM531654 2 0.6681 0.00838 0.000 0.376 0.332 0.024 0.264 0.004
#> GSM531655 3 0.6722 -0.05461 0.084 0.008 0.448 0.100 0.360 0.000
#> GSM531658 5 0.7425 0.13038 0.100 0.036 0.356 0.096 0.408 0.004
#> GSM531660 4 0.6812 0.18972 0.332 0.000 0.080 0.432 0.156 0.000
#> GSM531602 1 0.3136 0.56914 0.768 0.000 0.000 0.004 0.228 0.000
#> GSM531603 1 0.7218 -0.04738 0.392 0.068 0.020 0.140 0.376 0.004
#> GSM531604 5 0.5901 -0.16258 0.384 0.088 0.008 0.000 0.496 0.024
#> GSM531606 1 0.6425 -0.03159 0.424 0.176 0.012 0.004 0.376 0.008
#> GSM531607 1 0.5333 0.31746 0.588 0.004 0.008 0.076 0.320 0.004
#> GSM531608 6 0.0405 0.57112 0.000 0.008 0.000 0.000 0.004 0.988
#> GSM531609 6 0.0713 0.56130 0.000 0.000 0.000 0.028 0.000 0.972
#> GSM531610 6 0.1897 0.52461 0.000 0.000 0.004 0.084 0.004 0.908
#> GSM531611 4 0.4307 0.55889 0.156 0.000 0.008 0.764 0.028 0.044
#> GSM531612 4 0.3376 0.58084 0.120 0.000 0.020 0.832 0.012 0.016
#> GSM531613 4 0.4693 0.42216 0.312 0.000 0.000 0.636 0.024 0.028
#> GSM531614 6 0.0146 0.56935 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM531616 3 0.5490 0.50243 0.000 0.216 0.636 0.000 0.036 0.112
#> GSM531618 3 0.4987 0.41256 0.000 0.040 0.596 0.340 0.024 0.000
#> GSM531619 2 0.1167 0.68170 0.000 0.960 0.008 0.000 0.020 0.012
#> GSM531620 3 0.5332 0.48836 0.000 0.136 0.652 0.004 0.016 0.192
#> GSM531621 2 0.5869 0.20720 0.000 0.504 0.208 0.000 0.004 0.284
#> GSM531622 2 0.4521 0.06247 0.000 0.568 0.400 0.000 0.028 0.004
#> GSM531623 2 0.0862 0.67914 0.000 0.972 0.008 0.000 0.016 0.004
#> GSM531625 2 0.5071 0.48750 0.000 0.676 0.204 0.000 0.028 0.092
#> GSM531626 3 0.4876 0.24813 0.000 0.444 0.504 0.000 0.048 0.004
#> GSM531628 6 0.5010 0.18287 0.000 0.016 0.416 0.016 0.016 0.536
#> GSM531630 3 0.5536 0.14374 0.000 0.460 0.460 0.008 0.032 0.040
#> GSM531632 3 0.3955 0.62362 0.000 0.088 0.796 0.016 0.096 0.004
#> GSM531633 2 0.6337 -0.00605 0.000 0.400 0.260 0.000 0.012 0.328
#> GSM531635 3 0.5420 0.51156 0.000 0.208 0.640 0.000 0.028 0.124
#> GSM531638 3 0.5214 0.30277 0.000 0.412 0.520 0.012 0.052 0.004
#> GSM531639 3 0.4539 0.31118 0.000 0.004 0.644 0.304 0.048 0.000
#> GSM531640 6 0.6644 0.22725 0.000 0.240 0.264 0.000 0.044 0.452
#> GSM531641 4 0.3124 0.59793 0.016 0.000 0.108 0.844 0.032 0.000
#> GSM531642 4 0.3671 0.56668 0.000 0.000 0.208 0.756 0.036 0.000
#> GSM531643 3 0.2762 0.59938 0.000 0.000 0.860 0.092 0.048 0.000
#> GSM531644 3 0.3094 0.58615 0.000 0.000 0.824 0.140 0.036 0.000
#> GSM531645 3 0.5149 -0.02885 0.000 0.000 0.476 0.440 0.084 0.000
#> GSM531646 3 0.3889 0.60724 0.000 0.160 0.776 0.012 0.052 0.000
#> GSM531647 3 0.2973 0.63469 0.000 0.056 0.868 0.040 0.036 0.000
#> GSM531648 3 0.4788 0.25648 0.000 0.000 0.568 0.372 0.060 0.000
#> GSM531649 3 0.4565 0.54265 0.000 0.268 0.672 0.004 0.052 0.004
#> GSM531650 3 0.3551 0.54248 0.000 0.000 0.772 0.192 0.036 0.000
#> GSM531651 2 0.1932 0.67683 0.000 0.924 0.016 0.000 0.020 0.040
#> GSM531652 4 0.4150 0.33067 0.000 0.000 0.392 0.592 0.016 0.000
#> GSM531653 3 0.3493 0.57901 0.000 0.004 0.812 0.072 0.112 0.000
#> GSM531656 3 0.4094 0.55232 0.000 0.000 0.744 0.168 0.088 0.000
#> GSM531657 3 0.6758 0.21497 0.140 0.000 0.524 0.148 0.188 0.000
#> GSM531659 3 0.6095 0.14584 0.204 0.000 0.532 0.024 0.240 0.000
#> GSM531661 2 0.2568 0.66120 0.000 0.876 0.068 0.000 0.056 0.000
#> GSM531662 3 0.5191 0.55286 0.016 0.064 0.644 0.012 0.264 0.000
#> GSM531663 3 0.5572 0.51410 0.060 0.016 0.676 0.076 0.172 0.000
#> GSM531664 3 0.4980 0.46120 0.012 0.000 0.676 0.128 0.184 0.000
#> GSM531665 3 0.5580 0.50666 0.064 0.032 0.644 0.024 0.236 0.000
#> GSM531666 4 0.6150 0.33526 0.032 0.000 0.228 0.540 0.200 0.000
#> GSM531667 2 0.0865 0.66784 0.000 0.964 0.000 0.000 0.036 0.000
#> GSM531668 5 0.7598 0.17520 0.096 0.116 0.304 0.056 0.428 0.000
#> GSM531669 3 0.3123 0.58038 0.000 0.000 0.824 0.040 0.136 0.000
#> GSM531670 3 0.5194 0.56297 0.000 0.236 0.656 0.044 0.064 0.000
#> GSM531671 3 0.6122 0.30472 0.000 0.296 0.460 0.008 0.236 0.000
#> GSM531672 5 0.7559 -0.01480 0.292 0.000 0.152 0.248 0.308 0.000
#> GSM531673 3 0.4650 0.55341 0.040 0.008 0.736 0.044 0.172 0.000
#> GSM531674 3 0.2001 0.62125 0.000 0.000 0.912 0.040 0.048 0.000
#> GSM531675 1 0.0993 0.71753 0.964 0.000 0.000 0.012 0.024 0.000
#> GSM531676 1 0.3969 0.52705 0.700 0.012 0.000 0.012 0.276 0.000
#> GSM531677 1 0.0547 0.71644 0.980 0.000 0.000 0.020 0.000 0.000
#> GSM531678 1 0.0937 0.71298 0.960 0.000 0.000 0.000 0.040 0.000
#> GSM531679 1 0.0146 0.71793 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM531680 1 0.3013 0.66133 0.844 0.000 0.000 0.088 0.068 0.000
#> GSM531681 1 0.1700 0.70248 0.916 0.000 0.000 0.004 0.080 0.000
#> GSM531682 1 0.2219 0.67105 0.864 0.000 0.000 0.000 0.136 0.000
#> GSM531683 1 0.0146 0.71793 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM531684 2 0.5190 0.32157 0.136 0.664 0.000 0.012 0.184 0.004
#> GSM531685 1 0.3940 0.45683 0.640 0.000 0.000 0.012 0.348 0.000
#> GSM531686 1 0.1124 0.71014 0.956 0.000 0.000 0.036 0.008 0.000
#> GSM531687 1 0.2983 0.68168 0.832 0.000 0.000 0.032 0.136 0.000
#> GSM531688 1 0.4319 0.54208 0.696 0.000 0.012 0.036 0.256 0.000
#> GSM531689 1 0.2442 0.66664 0.852 0.000 0.000 0.004 0.144 0.000
#> GSM531690 1 0.2954 0.64968 0.844 0.000 0.000 0.048 0.108 0.000
#> GSM531691 1 0.3426 0.54766 0.720 0.000 0.000 0.004 0.276 0.000
#> GSM531692 2 0.5348 0.25721 0.064 0.564 0.000 0.012 0.352 0.008
#> GSM531693 5 0.6730 -0.21507 0.400 0.112 0.032 0.036 0.420 0.000
#> GSM531694 1 0.0508 0.71845 0.984 0.000 0.000 0.004 0.012 0.000
#> GSM531695 1 0.4717 0.58157 0.720 0.000 0.020 0.128 0.132 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 3, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 4, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 5, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6, scale_rows = FALSE)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> ATC:NMF 93 0.05564 2
#> ATC:NMF 93 0.08296 3
#> ATC:NMF 77 0.10852 4
#> ATC:NMF 58 0.07456 5
#> ATC:NMF 55 0.00863 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
sessionInfo()
#> R version 3.6.0 (2019-04-26)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: CentOS Linux 7 (Core)
#>
#> Matrix products: default
#> BLAS: /usr/lib64/libblas.so.3.4.2
#> LAPACK: /usr/lib64/liblapack.so.3.4.2
#>
#> locale:
#> [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C LC_TIME=en_GB.UTF-8
#> [4] LC_COLLATE=en_GB.UTF-8 LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
#> [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] grid stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] genefilter_1.66.0 ComplexHeatmap_2.3.1 markdown_1.1 knitr_1.26
#> [5] GetoptLong_0.1.7 cola_1.3.2
#>
#> loaded via a namespace (and not attached):
#> [1] circlize_0.4.8 shape_1.4.4 xfun_0.11 slam_0.1-46
#> [5] lattice_0.20-38 splines_3.6.0 colorspace_1.4-1 vctrs_0.2.0
#> [9] stats4_3.6.0 blob_1.2.0 XML_3.98-1.20 survival_2.44-1.1
#> [13] rlang_0.4.2 pillar_1.4.2 DBI_1.0.0 BiocGenerics_0.30.0
#> [17] bit64_0.9-7 RColorBrewer_1.1-2 matrixStats_0.55.0 stringr_1.4.0
#> [21] GlobalOptions_0.1.1 evaluate_0.14 memoise_1.1.0 Biobase_2.44.0
#> [25] IRanges_2.18.3 parallel_3.6.0 AnnotationDbi_1.46.1 highr_0.8
#> [29] Rcpp_1.0.3 xtable_1.8-4 backports_1.1.5 S4Vectors_0.22.1
#> [33] annotate_1.62.0 skmeans_0.2-11 bit_1.1-14 microbenchmark_1.4-7
#> [37] brew_1.0-6 impute_1.58.0 rjson_0.2.20 png_0.1-7
#> [41] digest_0.6.23 stringi_1.4.3 polyclip_1.10-0 clue_0.3-57
#> [45] tools_3.6.0 bitops_1.0-6 magrittr_1.5 eulerr_6.0.0
#> [49] RCurl_1.95-4.12 RSQLite_2.1.4 tibble_2.1.3 cluster_2.1.0
#> [53] crayon_1.3.4 pkgconfig_2.0.3 zeallot_0.1.0 Matrix_1.2-17
#> [57] xml2_1.2.2 httr_1.4.1 R6_2.4.1 mclust_5.4.5
#> [61] compiler_3.6.0