Date: 2019-12-25 20:17:21 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 21512 rows and 119 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] 21512 119
The density distribution for each sample is visualized as in one column in the following heatmap. The clustering is based on the distance which is the Kolmogorov-Smirnov statistic between two distributions.
library(ComplexHeatmap)
densityHeatmap(mat, top_annotation = HeatmapAnnotation(df = get_anno(res_list),
col = get_anno_col(res_list)), ylab = "value", cluster_columns = TRUE, show_column_names = FALSE,
mc.cores = 4)
Folowing table shows the best k (number of partitions) for each combination
of top-value methods and partition methods. Clicking on the method name in
the table goes to the section for a single combination of methods.
The cola vignette explains the definition of the metrics used for determining the best number of partitions.
suggest_best_k(res_list)
| The best k | 1-PAC | Mean silhouette | Concordance | Optional k | ||
|---|---|---|---|---|---|---|
| ATC:kmeans | 2 | 1.000 | 0.992 | 0.997 | ** | |
| ATC:NMF | 2 | 0.983 | 0.971 | 0.987 | ** | |
| MAD:skmeans | 2 | 0.982 | 0.975 | 0.987 | ** | |
| ATC:pam | 3 | 0.955 | 0.941 | 0.974 | ** | |
| ATC:skmeans | 4 | 0.952 | 0.938 | 0.972 | ** | 2,3 |
| MAD:NMF | 2 | 0.930 | 0.932 | 0.972 | * | |
| SD:NMF | 2 | 0.914 | 0.924 | 0.970 | * | |
| MAD:mclust | 3 | 0.904 | 0.902 | 0.954 | * | |
| SD:skmeans | 2 | 0.899 | 0.943 | 0.975 | ||
| MAD:kmeans | 2 | 0.896 | 0.920 | 0.961 | ||
| CV:skmeans | 2 | 0.865 | 0.926 | 0.968 | ||
| CV:NMF | 2 | 0.817 | 0.904 | 0.959 | ||
| SD:kmeans | 2 | 0.787 | 0.882 | 0.950 | ||
| CV:kmeans | 2 | 0.755 | 0.853 | 0.940 | ||
| SD:mclust | 2 | 0.736 | 0.889 | 0.952 | ||
| CV:mclust | 2 | 0.704 | 0.865 | 0.934 | ||
| ATC:mclust | 2 | 0.647 | 0.847 | 0.925 | ||
| MAD:pam | 2 | 0.556 | 0.827 | 0.907 | ||
| MAD:hclust | 2 | 0.554 | 0.684 | 0.871 | ||
| CV:pam | 2 | 0.539 | 0.782 | 0.901 | ||
| ATC:hclust | 2 | 0.513 | 0.799 | 0.906 | ||
| SD:pam | 2 | 0.465 | 0.743 | 0.877 | ||
| SD:hclust | 2 | 0.321 | 0.776 | 0.861 | ||
| CV:hclust | 2 | 0.144 | 0.592 | 0.791 |
**: 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 0.914 0.924 0.970 0.503 0.496 0.496
#> CV:NMF 2 0.817 0.904 0.959 0.497 0.499 0.499
#> MAD:NMF 2 0.930 0.932 0.972 0.502 0.496 0.496
#> ATC:NMF 2 0.983 0.971 0.987 0.504 0.497 0.497
#> SD:skmeans 2 0.899 0.943 0.975 0.502 0.496 0.496
#> CV:skmeans 2 0.865 0.926 0.968 0.501 0.497 0.497
#> MAD:skmeans 2 0.982 0.975 0.987 0.503 0.496 0.496
#> ATC:skmeans 2 1.000 1.000 1.000 0.504 0.496 0.496
#> SD:mclust 2 0.736 0.889 0.952 0.489 0.506 0.506
#> CV:mclust 2 0.704 0.865 0.934 0.475 0.545 0.545
#> MAD:mclust 2 0.732 0.939 0.967 0.497 0.496 0.496
#> ATC:mclust 2 0.647 0.847 0.925 0.475 0.499 0.499
#> SD:kmeans 2 0.787 0.882 0.950 0.489 0.499 0.499
#> CV:kmeans 2 0.755 0.853 0.940 0.485 0.500 0.500
#> MAD:kmeans 2 0.896 0.920 0.961 0.494 0.496 0.496
#> ATC:kmeans 2 1.000 0.992 0.997 0.504 0.497 0.497
#> SD:pam 2 0.465 0.743 0.877 0.468 0.526 0.526
#> CV:pam 2 0.539 0.782 0.901 0.483 0.499 0.499
#> MAD:pam 2 0.556 0.827 0.907 0.486 0.500 0.500
#> ATC:pam 2 0.791 0.812 0.928 0.486 0.497 0.497
#> SD:hclust 2 0.321 0.776 0.861 0.444 0.504 0.504
#> CV:hclust 2 0.144 0.592 0.791 0.416 0.496 0.496
#> MAD:hclust 2 0.554 0.684 0.871 0.468 0.522 0.522
#> ATC:hclust 2 0.513 0.799 0.906 0.479 0.498 0.498
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.535 0.705 0.846 0.315 0.742 0.527
#> CV:NMF 3 0.502 0.650 0.821 0.316 0.741 0.529
#> MAD:NMF 3 0.573 0.680 0.847 0.308 0.753 0.544
#> ATC:NMF 3 0.659 0.635 0.784 0.288 0.819 0.647
#> SD:skmeans 3 0.768 0.849 0.923 0.279 0.830 0.669
#> CV:skmeans 3 0.827 0.872 0.935 0.281 0.841 0.688
#> MAD:skmeans 3 0.895 0.905 0.955 0.265 0.845 0.695
#> ATC:skmeans 3 0.997 0.949 0.977 0.210 0.902 0.802
#> SD:mclust 3 0.679 0.764 0.849 0.289 0.781 0.590
#> CV:mclust 3 0.674 0.871 0.878 0.323 0.778 0.599
#> MAD:mclust 3 0.904 0.902 0.954 0.309 0.787 0.593
#> ATC:mclust 3 0.730 0.834 0.896 0.350 0.773 0.577
#> SD:kmeans 3 0.553 0.646 0.771 0.333 0.790 0.602
#> CV:kmeans 3 0.584 0.737 0.840 0.340 0.770 0.571
#> MAD:kmeans 3 0.739 0.899 0.925 0.319 0.799 0.615
#> ATC:kmeans 3 0.795 0.886 0.940 0.296 0.816 0.642
#> SD:pam 3 0.382 0.522 0.765 0.406 0.759 0.562
#> CV:pam 3 0.350 0.427 0.639 0.315 0.945 0.891
#> MAD:pam 3 0.658 0.789 0.848 0.374 0.714 0.487
#> ATC:pam 3 0.955 0.941 0.974 0.322 0.824 0.659
#> SD:hclust 3 0.381 0.676 0.797 0.336 0.909 0.819
#> CV:hclust 3 0.224 0.462 0.681 0.357 0.716 0.522
#> MAD:hclust 3 0.414 0.598 0.740 0.301 0.639 0.436
#> ATC:hclust 3 0.645 0.678 0.860 0.347 0.774 0.573
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.595 0.583 0.764 0.1252 0.815 0.522
#> CV:NMF 4 0.577 0.529 0.716 0.1312 0.775 0.469
#> MAD:NMF 4 0.601 0.538 0.746 0.1221 0.782 0.477
#> ATC:NMF 4 0.745 0.777 0.881 0.1246 0.835 0.579
#> SD:skmeans 4 0.687 0.668 0.848 0.1371 0.870 0.664
#> CV:skmeans 4 0.654 0.637 0.809 0.1340 0.862 0.647
#> MAD:skmeans 4 0.694 0.688 0.830 0.1473 0.880 0.680
#> ATC:skmeans 4 0.952 0.938 0.972 0.1378 0.905 0.764
#> SD:mclust 4 0.658 0.764 0.831 0.1148 0.885 0.696
#> CV:mclust 4 0.760 0.848 0.918 0.1235 0.900 0.728
#> MAD:mclust 4 0.796 0.868 0.906 0.0822 0.959 0.878
#> ATC:mclust 4 0.604 0.549 0.718 0.1291 0.885 0.688
#> SD:kmeans 4 0.679 0.682 0.844 0.1406 0.823 0.543
#> CV:kmeans 4 0.651 0.638 0.822 0.1392 0.797 0.498
#> MAD:kmeans 4 0.680 0.736 0.805 0.1340 0.865 0.630
#> ATC:kmeans 4 0.807 0.884 0.916 0.1419 0.837 0.569
#> SD:pam 4 0.531 0.663 0.785 0.1275 0.761 0.426
#> CV:pam 4 0.506 0.470 0.758 0.1598 0.704 0.410
#> MAD:pam 4 0.701 0.749 0.877 0.0994 0.907 0.728
#> ATC:pam 4 0.677 0.767 0.879 0.1570 0.807 0.520
#> SD:hclust 4 0.417 0.633 0.746 0.0870 0.997 0.993
#> CV:hclust 4 0.332 0.360 0.653 0.1140 0.661 0.412
#> MAD:hclust 4 0.525 0.656 0.758 0.1034 0.833 0.598
#> ATC:hclust 4 0.602 0.599 0.779 0.1002 0.805 0.513
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.619 0.521 0.748 0.0617 0.874 0.572
#> CV:NMF 5 0.607 0.523 0.705 0.0640 0.833 0.500
#> MAD:NMF 5 0.619 0.526 0.750 0.0564 0.886 0.636
#> ATC:NMF 5 0.673 0.560 0.750 0.0622 0.951 0.827
#> SD:skmeans 5 0.698 0.698 0.833 0.0638 0.894 0.650
#> CV:skmeans 5 0.628 0.617 0.780 0.0655 0.923 0.732
#> MAD:skmeans 5 0.688 0.647 0.803 0.0656 0.932 0.754
#> ATC:skmeans 5 0.862 0.872 0.932 0.0415 0.964 0.886
#> SD:mclust 5 0.691 0.615 0.801 0.0914 0.863 0.585
#> CV:mclust 5 0.687 0.633 0.806 0.1040 0.917 0.720
#> MAD:mclust 5 0.762 0.707 0.844 0.0878 0.913 0.717
#> ATC:mclust 5 0.691 0.751 0.851 0.0799 0.806 0.415
#> SD:kmeans 5 0.663 0.548 0.757 0.0540 0.886 0.632
#> CV:kmeans 5 0.673 0.667 0.758 0.0561 0.895 0.640
#> MAD:kmeans 5 0.724 0.680 0.768 0.0628 0.945 0.799
#> ATC:kmeans 5 0.718 0.660 0.772 0.0597 0.912 0.676
#> SD:pam 5 0.640 0.586 0.769 0.0738 0.904 0.658
#> CV:pam 5 0.568 0.553 0.743 0.0739 0.839 0.479
#> MAD:pam 5 0.715 0.643 0.796 0.0720 0.877 0.593
#> ATC:pam 5 0.747 0.738 0.845 0.0678 0.921 0.700
#> SD:hclust 5 0.450 0.535 0.685 0.0521 0.831 0.608
#> CV:hclust 5 0.394 0.400 0.607 0.0633 0.727 0.433
#> MAD:hclust 5 0.503 0.686 0.762 0.0569 0.937 0.783
#> ATC:hclust 5 0.590 0.641 0.733 0.0401 0.901 0.671
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.675 0.632 0.766 0.0455 0.839 0.413
#> CV:NMF 6 0.662 0.610 0.753 0.0469 0.883 0.548
#> MAD:NMF 6 0.643 0.587 0.751 0.0502 0.839 0.442
#> ATC:NMF 6 0.670 0.491 0.737 0.0356 0.860 0.513
#> SD:skmeans 6 0.737 0.734 0.834 0.0427 0.965 0.845
#> CV:skmeans 6 0.672 0.667 0.779 0.0461 0.963 0.837
#> MAD:skmeans 6 0.716 0.695 0.810 0.0386 0.936 0.736
#> ATC:skmeans 6 0.783 0.764 0.870 0.0421 1.000 1.000
#> SD:mclust 6 0.708 0.643 0.785 0.0607 0.909 0.623
#> CV:mclust 6 0.712 0.644 0.780 0.0436 0.878 0.528
#> MAD:mclust 6 0.705 0.571 0.777 0.0580 0.871 0.521
#> ATC:mclust 6 0.791 0.734 0.852 0.0518 0.939 0.713
#> SD:kmeans 6 0.718 0.666 0.737 0.0403 0.935 0.749
#> CV:kmeans 6 0.717 0.606 0.728 0.0434 0.930 0.699
#> MAD:kmeans 6 0.722 0.583 0.696 0.0407 0.929 0.714
#> ATC:kmeans 6 0.698 0.430 0.680 0.0393 0.864 0.486
#> SD:pam 6 0.725 0.644 0.795 0.0401 0.898 0.571
#> CV:pam 6 0.684 0.646 0.769 0.0425 0.916 0.623
#> MAD:pam 6 0.793 0.775 0.874 0.0492 0.919 0.662
#> ATC:pam 6 0.816 0.771 0.881 0.0454 0.948 0.747
#> SD:hclust 6 0.482 0.522 0.680 0.0337 0.847 0.560
#> CV:hclust 6 0.412 0.488 0.655 0.0343 0.823 0.505
#> MAD:hclust 6 0.575 0.671 0.801 0.0507 0.996 0.983
#> ATC:hclust 6 0.676 0.643 0.789 0.0568 0.923 0.712
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 114 7.01e-09 2
#> CV:NMF 114 4.29e-09 2
#> MAD:NMF 114 2.75e-09 2
#> ATC:NMF 117 5.52e-10 2
#> SD:skmeans 117 5.29e-10 2
#> CV:skmeans 117 5.29e-10 2
#> MAD:skmeans 119 2.79e-10 2
#> ATC:skmeans 119 5.01e-10 2
#> SD:mclust 117 9.38e-12 2
#> CV:mclust 119 4.04e-11 2
#> MAD:mclust 118 1.49e-11 2
#> ATC:mclust 104 2.27e-10 2
#> SD:kmeans 111 2.81e-09 2
#> CV:kmeans 109 4.67e-09 2
#> MAD:kmeans 117 4.14e-10 2
#> ATC:kmeans 118 7.87e-10 2
#> SD:pam 109 7.45e-10 2
#> CV:pam 109 2.86e-09 2
#> MAD:pam 116 1.04e-12 2
#> ATC:pam 102 8.89e-11 2
#> SD:hclust 111 1.28e-12 2
#> CV:hclust 94 9.82e-10 2
#> MAD:hclust 90 1.24e-11 2
#> ATC:hclust 113 2.24e-12 2
test_to_known_factors(res_list, k = 3)
#> n disease.state(p) k
#> SD:NMF 105 3.24e-13 3
#> CV:NMF 97 2.32e-15 3
#> MAD:NMF 99 8.65e-13 3
#> ATC:NMF 96 9.72e-18 3
#> SD:skmeans 112 3.67e-18 3
#> CV:skmeans 113 1.79e-18 3
#> MAD:skmeans 116 3.26e-19 3
#> ATC:skmeans 116 1.48e-14 3
#> SD:mclust 113 2.34e-19 3
#> CV:mclust 115 8.21e-20 3
#> MAD:mclust 113 9.63e-21 3
#> ATC:mclust 113 5.88e-20 3
#> SD:kmeans 100 5.05e-19 3
#> CV:kmeans 107 6.87e-18 3
#> MAD:kmeans 117 3.92e-19 3
#> ATC:kmeans 116 1.04e-17 3
#> SD:pam 77 2.51e-14 3
#> CV:pam 57 NA 3
#> MAD:pam 114 3.11e-21 3
#> ATC:pam 116 1.04e-17 3
#> SD:hclust 105 2.15e-18 3
#> CV:hclust 53 1.54e-08 3
#> MAD:hclust 90 1.40e-16 3
#> ATC:hclust 99 1.15e-09 3
test_to_known_factors(res_list, k = 4)
#> n disease.state(p) k
#> SD:NMF 77 8.69e-16 4
#> CV:NMF 68 1.18e-08 4
#> MAD:NMF 77 2.66e-14 4
#> ATC:NMF 108 5.11e-22 4
#> SD:skmeans 95 4.54e-22 4
#> CV:skmeans 96 1.17e-19 4
#> MAD:skmeans 100 5.17e-21 4
#> ATC:skmeans 116 6.80e-17 4
#> SD:mclust 112 1.08e-27 4
#> CV:mclust 112 9.16e-29 4
#> MAD:mclust 116 9.09e-21 4
#> ATC:mclust 90 6.90e-19 4
#> SD:kmeans 100 1.50e-22 4
#> CV:kmeans 92 4.33e-26 4
#> MAD:kmeans 109 2.52e-25 4
#> ATC:kmeans 116 3.14e-21 4
#> SD:pam 100 8.14e-25 4
#> CV:pam 53 1.39e-11 4
#> MAD:pam 103 2.60e-23 4
#> ATC:pam 104 2.33e-18 4
#> SD:hclust 101 9.48e-19 4
#> CV:hclust 54 6.64e-14 4
#> MAD:hclust 103 1.13e-27 4
#> ATC:hclust 78 3.98e-17 4
test_to_known_factors(res_list, k = 5)
#> n disease.state(p) k
#> SD:NMF 74 2.15e-16 5
#> CV:NMF 78 3.29e-24 5
#> MAD:NMF 69 1.15e-15 5
#> ATC:NMF 75 5.24e-18 5
#> SD:skmeans 98 7.05e-30 5
#> CV:skmeans 93 6.15e-26 5
#> MAD:skmeans 95 1.65e-24 5
#> ATC:skmeans 117 3.50e-24 5
#> SD:mclust 79 1.93e-26 5
#> CV:mclust 81 4.06e-20 5
#> MAD:mclust 92 1.92e-27 5
#> ATC:mclust 108 2.60e-28 5
#> SD:kmeans 80 1.19e-20 5
#> CV:kmeans 92 1.39e-25 5
#> MAD:kmeans 102 1.80e-25 5
#> ATC:kmeans 102 1.72e-21 5
#> SD:pam 72 5.14e-29 5
#> CV:pam 71 5.82e-17 5
#> MAD:pam 94 9.48e-27 5
#> ATC:pam 106 1.98e-23 5
#> SD:hclust 80 8.86e-27 5
#> CV:hclust 58 6.84e-21 5
#> MAD:hclust 104 4.61e-29 5
#> ATC:hclust 97 1.02e-20 5
test_to_known_factors(res_list, k = 6)
#> n disease.state(p) k
#> SD:NMF 97 4.04e-31 6
#> CV:NMF 91 7.84e-30 6
#> MAD:NMF 88 5.75e-29 6
#> ATC:NMF 65 6.63e-13 6
#> SD:skmeans 103 1.04e-33 6
#> CV:skmeans 98 7.70e-33 6
#> MAD:skmeans 100 5.86e-33 6
#> ATC:skmeans 109 9.40e-26 6
#> SD:mclust 91 4.12e-33 6
#> CV:mclust 91 2.14e-29 6
#> MAD:mclust 82 4.30e-31 6
#> ATC:mclust 98 5.69e-32 6
#> SD:kmeans 94 5.79e-25 6
#> CV:kmeans 83 4.51e-22 6
#> MAD:kmeans 95 3.19e-42 6
#> ATC:kmeans 55 5.76e-12 6
#> SD:pam 90 1.24e-29 6
#> CV:pam 90 1.78e-28 6
#> MAD:pam 105 1.45e-34 6
#> ATC:pam 110 1.58e-27 6
#> SD:hclust 85 3.11e-24 6
#> CV:hclust 75 2.37e-18 6
#> MAD:hclust 107 3.50e-27 6
#> ATC:hclust 96 2.03e-18 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 21512 rows and 119 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 0.321 0.776 0.861 0.4436 0.504 0.504
#> 3 3 0.381 0.676 0.797 0.3356 0.909 0.819
#> 4 4 0.417 0.633 0.746 0.0870 0.997 0.993
#> 5 5 0.450 0.535 0.685 0.0521 0.831 0.608
#> 6 6 0.482 0.522 0.680 0.0337 0.847 0.560
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
#> GSM120719 1 0.2603 0.821 0.956 0.044
#> GSM120720 1 0.2603 0.821 0.956 0.044
#> GSM120765 2 0.2778 0.864 0.048 0.952
#> GSM120767 2 0.3733 0.872 0.072 0.928
#> GSM120784 2 0.3274 0.868 0.060 0.940
#> GSM121400 1 0.7745 0.748 0.772 0.228
#> GSM121401 1 0.4161 0.813 0.916 0.084
#> GSM121402 2 0.1843 0.853 0.028 0.972
#> GSM121403 1 0.7745 0.748 0.772 0.228
#> GSM121404 2 0.5408 0.859 0.124 0.876
#> GSM121405 1 0.4161 0.813 0.916 0.084
#> GSM121406 2 0.0000 0.837 0.000 1.000
#> GSM121408 2 0.1414 0.850 0.020 0.980
#> GSM121409 1 0.7745 0.748 0.772 0.228
#> GSM121410 1 0.7745 0.748 0.772 0.228
#> GSM121412 2 0.0000 0.837 0.000 1.000
#> GSM121413 2 0.0000 0.837 0.000 1.000
#> GSM121414 2 0.0000 0.837 0.000 1.000
#> GSM121415 2 0.1414 0.850 0.020 0.980
#> GSM121416 2 0.1414 0.850 0.020 0.980
#> GSM120591 1 0.2603 0.821 0.956 0.044
#> GSM120594 1 0.2603 0.821 0.956 0.044
#> GSM120718 1 0.2603 0.821 0.956 0.044
#> GSM121205 1 0.0000 0.811 1.000 0.000
#> GSM121206 1 0.0000 0.811 1.000 0.000
#> GSM121207 1 0.0000 0.811 1.000 0.000
#> GSM121208 1 0.0000 0.811 1.000 0.000
#> GSM121209 1 0.0000 0.811 1.000 0.000
#> GSM121210 1 0.0000 0.811 1.000 0.000
#> GSM121211 1 0.0000 0.811 1.000 0.000
#> GSM121212 1 0.0000 0.811 1.000 0.000
#> GSM121213 1 0.0000 0.811 1.000 0.000
#> GSM121214 1 0.0000 0.811 1.000 0.000
#> GSM121215 1 0.0000 0.811 1.000 0.000
#> GSM121216 1 0.0000 0.811 1.000 0.000
#> GSM121217 1 0.0000 0.811 1.000 0.000
#> GSM121218 1 0.0000 0.811 1.000 0.000
#> GSM121234 1 0.0000 0.811 1.000 0.000
#> GSM121243 1 0.0000 0.811 1.000 0.000
#> GSM121245 1 0.0000 0.811 1.000 0.000
#> GSM121246 1 0.0672 0.814 0.992 0.008
#> GSM121247 1 0.0376 0.812 0.996 0.004
#> GSM121248 1 0.0000 0.811 1.000 0.000
#> GSM120744 1 0.8661 0.698 0.712 0.288
#> GSM120745 1 0.8661 0.698 0.712 0.288
#> GSM120746 1 0.8661 0.698 0.712 0.288
#> GSM120747 1 0.8661 0.698 0.712 0.288
#> GSM120748 1 0.8661 0.698 0.712 0.288
#> GSM120749 1 0.8661 0.698 0.712 0.288
#> GSM120750 1 0.8661 0.698 0.712 0.288
#> GSM120751 1 0.8661 0.698 0.712 0.288
#> GSM120752 1 0.8661 0.698 0.712 0.288
#> GSM121336 2 0.0000 0.837 0.000 1.000
#> GSM121339 2 0.9427 0.487 0.360 0.640
#> GSM121349 2 0.0000 0.837 0.000 1.000
#> GSM121355 2 0.0000 0.837 0.000 1.000
#> GSM120757 1 0.8909 0.665 0.692 0.308
#> GSM120766 1 0.9000 0.652 0.684 0.316
#> GSM120770 2 0.9608 0.321 0.384 0.616
#> GSM120779 1 0.8499 0.702 0.724 0.276
#> GSM120780 1 0.9000 0.652 0.684 0.316
#> GSM121102 1 0.9850 0.427 0.572 0.428
#> GSM121203 1 0.9732 0.487 0.596 0.404
#> GSM121204 1 0.6343 0.783 0.840 0.160
#> GSM121330 1 0.2236 0.821 0.964 0.036
#> GSM121335 1 0.2236 0.821 0.964 0.036
#> GSM121337 1 0.9896 0.368 0.560 0.440
#> GSM121338 1 0.9909 0.362 0.556 0.444
#> GSM121341 1 0.2236 0.821 0.964 0.036
#> GSM121342 1 0.2236 0.821 0.964 0.036
#> GSM121343 1 0.9909 0.362 0.556 0.444
#> GSM121344 1 0.2236 0.821 0.964 0.036
#> GSM121346 1 0.2236 0.821 0.964 0.036
#> GSM121347 1 0.9922 0.351 0.552 0.448
#> GSM121348 1 0.9044 0.645 0.680 0.320
#> GSM121350 1 0.2236 0.821 0.964 0.036
#> GSM121352 1 0.2236 0.821 0.964 0.036
#> GSM121354 1 0.2236 0.821 0.964 0.036
#> GSM120753 2 0.5408 0.874 0.124 0.876
#> GSM120761 2 0.5294 0.874 0.120 0.880
#> GSM120768 2 0.5629 0.872 0.132 0.868
#> GSM120781 2 0.4690 0.875 0.100 0.900
#> GSM120788 2 0.8267 0.765 0.260 0.740
#> GSM120760 2 0.7528 0.822 0.216 0.784
#> GSM120763 2 0.7219 0.836 0.200 0.800
#> GSM120764 2 0.7745 0.808 0.228 0.772
#> GSM120777 2 0.8861 0.681 0.304 0.696
#> GSM120786 2 0.7602 0.817 0.220 0.780
#> GSM121329 1 0.4939 0.806 0.892 0.108
#> GSM121331 1 0.8555 0.698 0.720 0.280
#> GSM121333 1 0.8555 0.698 0.720 0.280
#> GSM121345 1 0.8555 0.698 0.720 0.280
#> GSM121356 1 0.8555 0.698 0.720 0.280
#> GSM120754 2 0.7299 0.829 0.204 0.796
#> GSM120759 2 0.0000 0.837 0.000 1.000
#> GSM120762 2 0.4161 0.874 0.084 0.916
#> GSM120775 2 0.7299 0.832 0.204 0.796
#> GSM120776 1 0.8016 0.729 0.756 0.244
#> GSM120782 2 0.7056 0.840 0.192 0.808
#> GSM120789 2 0.2948 0.864 0.052 0.948
#> GSM120790 2 0.0000 0.837 0.000 1.000
#> GSM120791 2 0.6887 0.849 0.184 0.816
#> GSM120755 2 0.4161 0.874 0.084 0.916
#> GSM120756 2 0.8016 0.788 0.244 0.756
#> GSM120769 2 0.4022 0.873 0.080 0.920
#> GSM120778 2 0.6531 0.859 0.168 0.832
#> GSM120792 2 0.6712 0.854 0.176 0.824
#> GSM121332 2 0.4161 0.874 0.084 0.916
#> GSM121334 2 0.6048 0.867 0.148 0.852
#> GSM121340 2 0.7602 0.820 0.220 0.780
#> GSM121351 2 0.0000 0.837 0.000 1.000
#> GSM121353 2 0.7883 0.800 0.236 0.764
#> GSM120758 2 0.5629 0.872 0.132 0.868
#> GSM120771 2 0.5059 0.875 0.112 0.888
#> GSM120772 2 0.5737 0.871 0.136 0.864
#> GSM120773 2 0.7602 0.816 0.220 0.780
#> GSM120774 2 0.6623 0.857 0.172 0.828
#> GSM120783 2 0.7602 0.816 0.220 0.780
#> GSM120787 2 0.6438 0.862 0.164 0.836
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM120719 1 0.3349 0.7760 0.888 0.108 0.004
#> GSM120720 1 0.3349 0.7760 0.888 0.108 0.004
#> GSM120765 3 0.5785 0.5247 0.000 0.332 0.668
#> GSM120767 3 0.6302 0.0862 0.000 0.480 0.520
#> GSM120784 3 0.6126 0.3572 0.000 0.400 0.600
#> GSM121400 1 0.7713 0.6894 0.656 0.248 0.096
#> GSM121401 1 0.4891 0.7676 0.836 0.124 0.040
#> GSM121402 3 0.2878 0.7694 0.000 0.096 0.904
#> GSM121403 1 0.7713 0.6894 0.656 0.248 0.096
#> GSM121404 3 0.6341 0.5546 0.032 0.252 0.716
#> GSM121405 1 0.4891 0.7676 0.836 0.124 0.040
#> GSM121406 3 0.1289 0.7864 0.000 0.032 0.968
#> GSM121408 3 0.4750 0.6987 0.000 0.216 0.784
#> GSM121409 1 0.7713 0.6894 0.656 0.248 0.096
#> GSM121410 1 0.7713 0.6894 0.656 0.248 0.096
#> GSM121412 3 0.0747 0.7859 0.000 0.016 0.984
#> GSM121413 3 0.0747 0.7859 0.000 0.016 0.984
#> GSM121414 3 0.0747 0.7859 0.000 0.016 0.984
#> GSM121415 3 0.4121 0.7406 0.000 0.168 0.832
#> GSM121416 3 0.4399 0.7321 0.000 0.188 0.812
#> GSM120591 1 0.3349 0.7760 0.888 0.108 0.004
#> GSM120594 1 0.3349 0.7760 0.888 0.108 0.004
#> GSM120718 1 0.3349 0.7760 0.888 0.108 0.004
#> GSM121205 1 0.0000 0.7573 1.000 0.000 0.000
#> GSM121206 1 0.0000 0.7573 1.000 0.000 0.000
#> GSM121207 1 0.0000 0.7573 1.000 0.000 0.000
#> GSM121208 1 0.0000 0.7573 1.000 0.000 0.000
#> GSM121209 1 0.0000 0.7573 1.000 0.000 0.000
#> GSM121210 1 0.0000 0.7573 1.000 0.000 0.000
#> GSM121211 1 0.0000 0.7573 1.000 0.000 0.000
#> GSM121212 1 0.0000 0.7573 1.000 0.000 0.000
#> GSM121213 1 0.0000 0.7573 1.000 0.000 0.000
#> GSM121214 1 0.0000 0.7573 1.000 0.000 0.000
#> GSM121215 1 0.0000 0.7573 1.000 0.000 0.000
#> GSM121216 1 0.0000 0.7573 1.000 0.000 0.000
#> GSM121217 1 0.0000 0.7573 1.000 0.000 0.000
#> GSM121218 1 0.0000 0.7573 1.000 0.000 0.000
#> GSM121234 1 0.0000 0.7573 1.000 0.000 0.000
#> GSM121243 1 0.0000 0.7573 1.000 0.000 0.000
#> GSM121245 1 0.0000 0.7573 1.000 0.000 0.000
#> GSM121246 1 0.0892 0.7626 0.980 0.020 0.000
#> GSM121247 1 0.0237 0.7578 0.996 0.004 0.000
#> GSM121248 1 0.0000 0.7573 1.000 0.000 0.000
#> GSM120744 1 0.7787 0.6353 0.588 0.348 0.064
#> GSM120745 1 0.7787 0.6353 0.588 0.348 0.064
#> GSM120746 1 0.7787 0.6353 0.588 0.348 0.064
#> GSM120747 1 0.7787 0.6353 0.588 0.348 0.064
#> GSM120748 1 0.7787 0.6353 0.588 0.348 0.064
#> GSM120749 1 0.7787 0.6353 0.588 0.348 0.064
#> GSM120750 1 0.7787 0.6353 0.588 0.348 0.064
#> GSM120751 1 0.7787 0.6353 0.588 0.348 0.064
#> GSM120752 1 0.7787 0.6353 0.588 0.348 0.064
#> GSM121336 3 0.0747 0.7859 0.000 0.016 0.984
#> GSM121339 2 0.9818 0.1437 0.248 0.408 0.344
#> GSM121349 3 0.0747 0.7859 0.000 0.016 0.984
#> GSM121355 3 0.0747 0.7859 0.000 0.016 0.984
#> GSM120757 1 0.7223 0.5803 0.548 0.424 0.028
#> GSM120766 1 0.7337 0.5722 0.540 0.428 0.032
#> GSM120770 2 0.9849 0.2125 0.300 0.420 0.280
#> GSM120779 1 0.6675 0.6063 0.584 0.404 0.012
#> GSM120780 1 0.7337 0.5722 0.540 0.428 0.032
#> GSM121102 1 0.9514 0.4174 0.468 0.328 0.204
#> GSM121203 1 0.9350 0.4725 0.488 0.328 0.184
#> GSM121204 1 0.5763 0.7137 0.716 0.276 0.008
#> GSM121330 1 0.2959 0.7758 0.900 0.100 0.000
#> GSM121335 1 0.2878 0.7758 0.904 0.096 0.000
#> GSM121337 1 0.9713 0.3581 0.444 0.316 0.240
#> GSM121338 1 0.9722 0.3547 0.444 0.312 0.244
#> GSM121341 1 0.2878 0.7758 0.904 0.096 0.000
#> GSM121342 1 0.2959 0.7758 0.900 0.100 0.000
#> GSM121343 1 0.9722 0.3547 0.444 0.312 0.244
#> GSM121344 1 0.2878 0.7758 0.904 0.096 0.000
#> GSM121346 1 0.2878 0.7758 0.904 0.096 0.000
#> GSM121347 1 0.9690 0.3449 0.444 0.324 0.232
#> GSM121348 1 0.7438 0.5587 0.536 0.428 0.036
#> GSM121350 1 0.2878 0.7758 0.904 0.096 0.000
#> GSM121352 1 0.2878 0.7758 0.904 0.096 0.000
#> GSM121354 1 0.2878 0.7758 0.904 0.096 0.000
#> GSM120753 2 0.4555 0.7214 0.000 0.800 0.200
#> GSM120761 2 0.4062 0.7430 0.000 0.836 0.164
#> GSM120768 2 0.4452 0.7268 0.000 0.808 0.192
#> GSM120781 2 0.5650 0.5160 0.000 0.688 0.312
#> GSM120788 2 0.2400 0.7625 0.064 0.932 0.004
#> GSM120760 2 0.1453 0.7873 0.024 0.968 0.008
#> GSM120763 2 0.1491 0.7904 0.016 0.968 0.016
#> GSM120764 2 0.1289 0.7816 0.032 0.968 0.000
#> GSM120777 2 0.3500 0.7078 0.116 0.880 0.004
#> GSM120786 2 0.1585 0.7861 0.028 0.964 0.008
#> GSM121329 1 0.4682 0.7576 0.804 0.192 0.004
#> GSM121331 1 0.6688 0.6014 0.580 0.408 0.012
#> GSM121333 1 0.6688 0.6014 0.580 0.408 0.012
#> GSM121345 1 0.6701 0.5943 0.576 0.412 0.012
#> GSM121356 1 0.6688 0.6014 0.580 0.408 0.012
#> GSM120754 2 0.4479 0.7709 0.044 0.860 0.096
#> GSM120759 3 0.2878 0.7643 0.000 0.096 0.904
#> GSM120762 2 0.5882 0.4602 0.000 0.652 0.348
#> GSM120775 2 0.3921 0.7812 0.036 0.884 0.080
#> GSM120776 1 0.6696 0.6517 0.632 0.348 0.020
#> GSM120782 2 0.5497 0.7505 0.048 0.804 0.148
#> GSM120789 3 0.5902 0.5881 0.004 0.316 0.680
#> GSM120790 3 0.4974 0.6704 0.000 0.236 0.764
#> GSM120791 2 0.2772 0.7898 0.004 0.916 0.080
#> GSM120755 2 0.6192 0.2495 0.000 0.580 0.420
#> GSM120756 2 0.1753 0.7749 0.048 0.952 0.000
#> GSM120769 2 0.5905 0.4329 0.000 0.648 0.352
#> GSM120778 2 0.2796 0.7843 0.000 0.908 0.092
#> GSM120792 2 0.4540 0.7792 0.028 0.848 0.124
#> GSM121332 3 0.6683 0.0496 0.008 0.492 0.500
#> GSM121334 2 0.4121 0.7445 0.000 0.832 0.168
#> GSM121340 2 0.1999 0.7839 0.036 0.952 0.012
#> GSM121351 3 0.0892 0.7856 0.000 0.020 0.980
#> GSM121353 2 0.2663 0.7805 0.044 0.932 0.024
#> GSM120758 2 0.4452 0.7268 0.000 0.808 0.192
#> GSM120771 2 0.4750 0.6918 0.000 0.784 0.216
#> GSM120772 2 0.4702 0.7028 0.000 0.788 0.212
#> GSM120773 2 0.1711 0.7856 0.032 0.960 0.008
#> GSM120774 2 0.2955 0.7888 0.008 0.912 0.080
#> GSM120783 2 0.1711 0.7856 0.032 0.960 0.008
#> GSM120787 2 0.2860 0.7878 0.004 0.912 0.084
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM120719 1 0.2222 0.7655 0.924 0.000 0.016 0.060
#> GSM120720 1 0.2222 0.7655 0.924 0.000 0.016 0.060
#> GSM120765 2 0.5972 0.4724 0.000 0.640 0.068 0.292
#> GSM120767 2 0.6834 0.0640 0.000 0.476 0.100 0.424
#> GSM120784 2 0.6482 0.3576 0.000 0.564 0.084 0.352
#> GSM121400 1 0.6934 0.7073 0.680 0.060 0.124 0.136
#> GSM121401 1 0.3761 0.7586 0.868 0.020 0.068 0.044
#> GSM121402 2 0.4371 0.5389 0.008 0.820 0.124 0.048
#> GSM121403 1 0.6934 0.7073 0.680 0.060 0.124 0.136
#> GSM121404 2 0.7141 0.3853 0.056 0.660 0.140 0.144
#> GSM121405 1 0.3761 0.7586 0.868 0.020 0.068 0.044
#> GSM121406 2 0.1452 0.6042 0.000 0.956 0.036 0.008
#> GSM121408 2 0.5021 0.5441 0.000 0.756 0.064 0.180
#> GSM121409 1 0.6934 0.7073 0.680 0.060 0.124 0.136
#> GSM121410 1 0.6934 0.7073 0.680 0.060 0.124 0.136
#> GSM121412 2 0.0469 0.5887 0.000 0.988 0.012 0.000
#> GSM121413 2 0.0469 0.5887 0.000 0.988 0.012 0.000
#> GSM121414 2 0.0469 0.5887 0.000 0.988 0.012 0.000
#> GSM121415 2 0.4781 0.5842 0.004 0.796 0.088 0.112
#> GSM121416 2 0.5121 0.5761 0.004 0.772 0.096 0.128
#> GSM120591 1 0.2222 0.7655 0.924 0.000 0.016 0.060
#> GSM120594 1 0.2222 0.7655 0.924 0.000 0.016 0.060
#> GSM120718 1 0.2222 0.7655 0.924 0.000 0.016 0.060
#> GSM121205 1 0.2216 0.7251 0.908 0.000 0.092 0.000
#> GSM121206 1 0.2216 0.7251 0.908 0.000 0.092 0.000
#> GSM121207 1 0.2216 0.7251 0.908 0.000 0.092 0.000
#> GSM121208 1 0.2216 0.7251 0.908 0.000 0.092 0.000
#> GSM121209 1 0.2216 0.7251 0.908 0.000 0.092 0.000
#> GSM121210 1 0.2216 0.7251 0.908 0.000 0.092 0.000
#> GSM121211 1 0.2216 0.7251 0.908 0.000 0.092 0.000
#> GSM121212 1 0.2216 0.7251 0.908 0.000 0.092 0.000
#> GSM121213 1 0.2216 0.7251 0.908 0.000 0.092 0.000
#> GSM121214 1 0.2216 0.7251 0.908 0.000 0.092 0.000
#> GSM121215 1 0.2216 0.7251 0.908 0.000 0.092 0.000
#> GSM121216 1 0.2216 0.7251 0.908 0.000 0.092 0.000
#> GSM121217 1 0.2216 0.7251 0.908 0.000 0.092 0.000
#> GSM121218 1 0.2216 0.7251 0.908 0.000 0.092 0.000
#> GSM121234 1 0.2216 0.7251 0.908 0.000 0.092 0.000
#> GSM121243 1 0.2216 0.7251 0.908 0.000 0.092 0.000
#> GSM121245 1 0.2216 0.7251 0.908 0.000 0.092 0.000
#> GSM121246 1 0.2125 0.7343 0.920 0.000 0.076 0.004
#> GSM121247 1 0.2401 0.7256 0.904 0.000 0.092 0.004
#> GSM121248 1 0.2216 0.7251 0.908 0.000 0.092 0.000
#> GSM120744 1 0.7216 0.6725 0.612 0.020 0.168 0.200
#> GSM120745 1 0.7216 0.6725 0.612 0.020 0.168 0.200
#> GSM120746 1 0.7216 0.6725 0.612 0.020 0.168 0.200
#> GSM120747 1 0.7216 0.6725 0.612 0.020 0.168 0.200
#> GSM120748 1 0.7216 0.6725 0.612 0.020 0.168 0.200
#> GSM120749 1 0.7216 0.6725 0.612 0.020 0.168 0.200
#> GSM120750 1 0.7216 0.6725 0.612 0.020 0.168 0.200
#> GSM120751 1 0.7216 0.6725 0.612 0.020 0.168 0.200
#> GSM120752 1 0.7216 0.6725 0.612 0.020 0.168 0.200
#> GSM121336 2 0.0921 0.5942 0.000 0.972 0.028 0.000
#> GSM121339 4 0.9608 -0.0136 0.268 0.300 0.120 0.312
#> GSM121349 2 0.0921 0.5942 0.000 0.972 0.028 0.000
#> GSM121355 2 0.0921 0.5942 0.000 0.972 0.028 0.000
#> GSM120757 1 0.6897 0.6201 0.572 0.000 0.144 0.284
#> GSM120766 1 0.7146 0.6160 0.560 0.004 0.152 0.284
#> GSM120770 4 0.9619 0.0596 0.308 0.240 0.128 0.324
#> GSM120779 1 0.6661 0.6368 0.604 0.000 0.132 0.264
#> GSM120780 1 0.7146 0.6160 0.560 0.004 0.152 0.284
#> GSM121102 1 0.9102 0.5025 0.476 0.156 0.156 0.212
#> GSM121203 1 0.8885 0.5465 0.504 0.136 0.160 0.200
#> GSM121204 1 0.5226 0.7241 0.744 0.000 0.076 0.180
#> GSM121330 1 0.2032 0.7619 0.936 0.000 0.028 0.036
#> GSM121335 1 0.1936 0.7613 0.940 0.000 0.028 0.032
#> GSM121337 1 0.9198 0.4547 0.464 0.200 0.148 0.188
#> GSM121338 1 0.9228 0.4527 0.460 0.204 0.152 0.184
#> GSM121341 1 0.1936 0.7613 0.940 0.000 0.028 0.032
#> GSM121342 1 0.2032 0.7619 0.936 0.000 0.028 0.036
#> GSM121343 1 0.9228 0.4527 0.460 0.204 0.152 0.184
#> GSM121344 1 0.1936 0.7613 0.940 0.000 0.028 0.032
#> GSM121346 1 0.1936 0.7613 0.940 0.000 0.028 0.032
#> GSM121347 1 0.9239 0.4393 0.456 0.196 0.144 0.204
#> GSM121348 1 0.7311 0.6029 0.556 0.008 0.160 0.276
#> GSM121350 1 0.1936 0.7613 0.940 0.000 0.028 0.032
#> GSM121352 1 0.1936 0.7613 0.940 0.000 0.028 0.032
#> GSM121354 1 0.1936 0.7613 0.940 0.000 0.028 0.032
#> GSM120753 4 0.5007 0.6801 0.000 0.172 0.068 0.760
#> GSM120761 4 0.4534 0.7072 0.000 0.132 0.068 0.800
#> GSM120768 4 0.5029 0.6900 0.004 0.164 0.064 0.768
#> GSM120781 4 0.6293 0.4959 0.000 0.276 0.096 0.628
#> GSM120788 4 0.3392 0.7078 0.072 0.000 0.056 0.872
#> GSM120760 4 0.2669 0.7443 0.032 0.004 0.052 0.912
#> GSM120763 4 0.2966 0.7496 0.020 0.008 0.076 0.896
#> GSM120764 4 0.2319 0.7361 0.036 0.000 0.040 0.924
#> GSM120777 4 0.4356 0.6348 0.124 0.000 0.064 0.812
#> GSM120786 4 0.2131 0.7420 0.032 0.000 0.036 0.932
#> GSM121329 1 0.4150 0.7564 0.824 0.000 0.056 0.120
#> GSM121331 1 0.6685 0.6325 0.600 0.000 0.132 0.268
#> GSM121333 1 0.6685 0.6325 0.600 0.000 0.132 0.268
#> GSM121345 1 0.6639 0.6202 0.596 0.000 0.120 0.284
#> GSM121356 1 0.6685 0.6325 0.600 0.000 0.132 0.268
#> GSM120754 4 0.4623 0.7227 0.040 0.080 0.052 0.828
#> GSM120759 2 0.4212 0.2329 0.000 0.772 0.216 0.012
#> GSM120762 4 0.6674 0.4088 0.000 0.300 0.116 0.584
#> GSM120775 4 0.3991 0.7373 0.032 0.064 0.044 0.860
#> GSM120776 1 0.6297 0.6575 0.652 0.012 0.072 0.264
#> GSM120782 4 0.5254 0.6994 0.040 0.128 0.048 0.784
#> GSM120789 2 0.6476 0.4043 0.004 0.632 0.104 0.260
#> GSM120790 3 0.5639 0.0000 0.000 0.324 0.636 0.040
#> GSM120791 4 0.3974 0.7488 0.016 0.060 0.068 0.856
#> GSM120755 4 0.6875 0.2349 0.000 0.368 0.112 0.520
#> GSM120756 4 0.2840 0.7296 0.056 0.000 0.044 0.900
#> GSM120769 4 0.6501 0.4225 0.000 0.316 0.096 0.588
#> GSM120778 4 0.3873 0.7270 0.000 0.060 0.096 0.844
#> GSM120792 4 0.4584 0.7398 0.024 0.096 0.056 0.824
#> GSM121332 2 0.6672 0.0803 0.004 0.468 0.072 0.456
#> GSM121334 4 0.4824 0.7090 0.000 0.144 0.076 0.780
#> GSM121340 4 0.3160 0.7180 0.020 0.000 0.108 0.872
#> GSM121351 2 0.1389 0.5612 0.000 0.952 0.048 0.000
#> GSM121353 4 0.3619 0.7339 0.052 0.020 0.052 0.876
#> GSM120758 4 0.5029 0.6900 0.004 0.164 0.064 0.768
#> GSM120771 4 0.5406 0.6540 0.004 0.180 0.076 0.740
#> GSM120772 4 0.5292 0.6597 0.000 0.168 0.088 0.744
#> GSM120773 4 0.2408 0.7380 0.036 0.000 0.044 0.920
#> GSM120774 4 0.3421 0.7385 0.000 0.044 0.088 0.868
#> GSM120783 4 0.2408 0.7380 0.036 0.000 0.044 0.920
#> GSM120787 4 0.3958 0.7291 0.000 0.052 0.112 0.836
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM120719 3 0.395 -0.04361 0.300 0.000 0.696 0.000 0.004
#> GSM120720 3 0.395 -0.04361 0.300 0.000 0.696 0.000 0.004
#> GSM120765 2 0.641 0.42988 0.064 0.576 0.052 0.304 0.004
#> GSM120767 4 0.665 0.03811 0.068 0.420 0.024 0.468 0.020
#> GSM120784 2 0.672 0.21562 0.064 0.500 0.060 0.372 0.004
#> GSM121400 3 0.325 0.52015 0.080 0.040 0.864 0.016 0.000
#> GSM121401 3 0.404 0.11216 0.252 0.012 0.732 0.004 0.000
#> GSM121402 2 0.590 0.62749 0.092 0.724 0.092 0.028 0.064
#> GSM121403 3 0.325 0.52015 0.080 0.040 0.864 0.016 0.000
#> GSM121404 2 0.713 0.45977 0.100 0.564 0.248 0.076 0.012
#> GSM121405 3 0.404 0.11216 0.252 0.012 0.732 0.004 0.000
#> GSM121406 2 0.242 0.70492 0.036 0.916 0.024 0.020 0.004
#> GSM121408 2 0.496 0.61365 0.028 0.736 0.028 0.196 0.012
#> GSM121409 3 0.325 0.52015 0.080 0.040 0.864 0.016 0.000
#> GSM121410 3 0.325 0.52015 0.080 0.040 0.864 0.016 0.000
#> GSM121412 2 0.096 0.69719 0.004 0.972 0.008 0.000 0.016
#> GSM121413 2 0.096 0.69719 0.004 0.972 0.008 0.000 0.016
#> GSM121414 2 0.096 0.69719 0.004 0.972 0.008 0.000 0.016
#> GSM121415 2 0.591 0.65012 0.092 0.708 0.088 0.104 0.008
#> GSM121416 2 0.615 0.64071 0.092 0.692 0.084 0.120 0.012
#> GSM120591 3 0.395 -0.04361 0.300 0.000 0.696 0.000 0.004
#> GSM120594 3 0.395 -0.04361 0.300 0.000 0.696 0.000 0.004
#> GSM120718 3 0.395 -0.04361 0.300 0.000 0.696 0.000 0.004
#> GSM121205 1 0.428 0.99477 0.544 0.000 0.456 0.000 0.000
#> GSM121206 1 0.428 0.99477 0.544 0.000 0.456 0.000 0.000
#> GSM121207 1 0.428 0.99477 0.544 0.000 0.456 0.000 0.000
#> GSM121208 1 0.428 0.99477 0.544 0.000 0.456 0.000 0.000
#> GSM121209 1 0.428 0.99477 0.544 0.000 0.456 0.000 0.000
#> GSM121210 1 0.428 0.99477 0.544 0.000 0.456 0.000 0.000
#> GSM121211 1 0.428 0.99477 0.544 0.000 0.456 0.000 0.000
#> GSM121212 1 0.428 0.99477 0.544 0.000 0.456 0.000 0.000
#> GSM121213 1 0.428 0.99477 0.544 0.000 0.456 0.000 0.000
#> GSM121214 1 0.428 0.99477 0.544 0.000 0.456 0.000 0.000
#> GSM121215 1 0.428 0.99477 0.544 0.000 0.456 0.000 0.000
#> GSM121216 1 0.428 0.99477 0.544 0.000 0.456 0.000 0.000
#> GSM121217 1 0.428 0.99477 0.544 0.000 0.456 0.000 0.000
#> GSM121218 1 0.428 0.99477 0.544 0.000 0.456 0.000 0.000
#> GSM121234 1 0.428 0.99477 0.544 0.000 0.456 0.000 0.000
#> GSM121243 1 0.428 0.99477 0.544 0.000 0.456 0.000 0.000
#> GSM121245 1 0.428 0.99477 0.544 0.000 0.456 0.000 0.000
#> GSM121246 1 0.431 0.90400 0.508 0.000 0.492 0.000 0.000
#> GSM121247 1 0.428 0.98606 0.548 0.000 0.452 0.000 0.000
#> GSM121248 1 0.428 0.99477 0.544 0.000 0.456 0.000 0.000
#> GSM120744 3 0.104 0.57873 0.004 0.000 0.964 0.032 0.000
#> GSM120745 3 0.104 0.57873 0.004 0.000 0.964 0.032 0.000
#> GSM120746 3 0.104 0.57873 0.004 0.000 0.964 0.032 0.000
#> GSM120747 3 0.104 0.57873 0.004 0.000 0.964 0.032 0.000
#> GSM120748 3 0.104 0.57873 0.004 0.000 0.964 0.032 0.000
#> GSM120749 3 0.104 0.57873 0.004 0.000 0.964 0.032 0.000
#> GSM120750 3 0.104 0.57873 0.004 0.000 0.964 0.032 0.000
#> GSM120751 3 0.104 0.57873 0.004 0.000 0.964 0.032 0.000
#> GSM120752 3 0.104 0.57873 0.004 0.000 0.964 0.032 0.000
#> GSM121336 2 0.178 0.69520 0.024 0.940 0.000 0.008 0.028
#> GSM121339 3 0.797 -0.14103 0.080 0.244 0.440 0.228 0.008
#> GSM121349 2 0.178 0.69520 0.024 0.940 0.000 0.008 0.028
#> GSM121355 2 0.187 0.69578 0.028 0.936 0.000 0.008 0.028
#> GSM120757 3 0.399 0.54889 0.052 0.000 0.820 0.104 0.024
#> GSM120766 3 0.382 0.55297 0.032 0.004 0.832 0.108 0.024
#> GSM120770 3 0.718 0.07622 0.032 0.200 0.496 0.268 0.004
#> GSM120779 3 0.486 0.51430 0.100 0.000 0.764 0.104 0.032
#> GSM120780 3 0.382 0.55297 0.032 0.004 0.832 0.108 0.024
#> GSM121102 3 0.443 0.49077 0.008 0.128 0.776 0.088 0.000
#> GSM121203 3 0.410 0.51461 0.004 0.112 0.804 0.076 0.004
#> GSM121204 3 0.447 0.41965 0.168 0.000 0.764 0.056 0.012
#> GSM121330 3 0.389 -0.14649 0.320 0.000 0.680 0.000 0.000
#> GSM121335 3 0.393 -0.18270 0.328 0.000 0.672 0.000 0.000
#> GSM121337 3 0.624 0.44795 0.048 0.176 0.672 0.084 0.020
#> GSM121338 3 0.613 0.44563 0.048 0.180 0.676 0.080 0.016
#> GSM121341 3 0.393 -0.18270 0.328 0.000 0.672 0.000 0.000
#> GSM121342 3 0.389 -0.14649 0.320 0.000 0.680 0.000 0.000
#> GSM121343 3 0.613 0.44563 0.048 0.180 0.676 0.080 0.016
#> GSM121344 3 0.393 -0.18270 0.328 0.000 0.672 0.000 0.000
#> GSM121346 3 0.393 -0.18270 0.328 0.000 0.672 0.000 0.000
#> GSM121347 3 0.641 0.44214 0.048 0.172 0.656 0.108 0.016
#> GSM121348 3 0.438 0.54061 0.048 0.008 0.804 0.112 0.028
#> GSM121350 3 0.393 -0.18270 0.328 0.000 0.672 0.000 0.000
#> GSM121352 3 0.393 -0.18270 0.328 0.000 0.672 0.000 0.000
#> GSM121354 3 0.393 -0.18270 0.328 0.000 0.672 0.000 0.000
#> GSM120753 4 0.491 0.68865 0.032 0.152 0.048 0.760 0.008
#> GSM120761 4 0.471 0.71289 0.028 0.112 0.068 0.784 0.008
#> GSM120768 4 0.516 0.69993 0.036 0.144 0.072 0.744 0.004
#> GSM120781 4 0.562 0.53265 0.052 0.252 0.020 0.664 0.012
#> GSM120788 4 0.471 0.68006 0.048 0.000 0.188 0.744 0.020
#> GSM120760 4 0.372 0.72731 0.036 0.000 0.140 0.816 0.008
#> GSM120763 4 0.415 0.73339 0.044 0.012 0.120 0.812 0.012
#> GSM120764 4 0.384 0.71407 0.032 0.000 0.148 0.808 0.012
#> GSM120777 4 0.544 0.60273 0.064 0.000 0.240 0.672 0.024
#> GSM120786 4 0.386 0.72027 0.028 0.000 0.148 0.808 0.016
#> GSM121329 3 0.498 0.14477 0.256 0.000 0.684 0.052 0.008
#> GSM121331 3 0.490 0.51311 0.100 0.000 0.760 0.108 0.032
#> GSM121333 3 0.490 0.51311 0.100 0.000 0.760 0.108 0.032
#> GSM121345 3 0.523 0.49794 0.108 0.000 0.732 0.128 0.032
#> GSM121356 3 0.490 0.51311 0.100 0.000 0.760 0.108 0.032
#> GSM120754 4 0.497 0.70948 0.016 0.064 0.156 0.752 0.012
#> GSM120759 2 0.452 0.31544 0.016 0.680 0.008 0.000 0.296
#> GSM120762 4 0.646 0.45712 0.068 0.256 0.024 0.616 0.036
#> GSM120775 4 0.479 0.72574 0.020 0.048 0.128 0.780 0.024
#> GSM120776 3 0.561 0.43283 0.132 0.008 0.692 0.156 0.012
#> GSM120782 4 0.566 0.69023 0.016 0.112 0.152 0.704 0.016
#> GSM120789 2 0.781 0.42816 0.072 0.524 0.044 0.248 0.112
#> GSM120790 5 0.234 0.00000 0.000 0.100 0.004 0.004 0.892
#> GSM120791 4 0.452 0.73875 0.040 0.044 0.120 0.792 0.004
#> GSM120755 4 0.641 0.33489 0.056 0.336 0.024 0.560 0.024
#> GSM120756 4 0.444 0.70282 0.040 0.000 0.164 0.772 0.024
#> GSM120769 4 0.607 0.47846 0.060 0.280 0.016 0.620 0.024
#> GSM120778 4 0.362 0.70187 0.048 0.052 0.012 0.860 0.028
#> GSM120792 4 0.546 0.73293 0.032 0.088 0.112 0.744 0.024
#> GSM121332 4 0.711 0.00472 0.044 0.420 0.088 0.432 0.016
#> GSM121334 4 0.542 0.71040 0.044 0.128 0.084 0.736 0.008
#> GSM121340 4 0.526 0.42903 0.284 0.000 0.008 0.648 0.060
#> GSM121351 2 0.177 0.68112 0.008 0.936 0.008 0.000 0.048
#> GSM121353 4 0.486 0.70564 0.036 0.020 0.156 0.764 0.024
#> GSM120758 4 0.516 0.69993 0.036 0.144 0.072 0.744 0.004
#> GSM120771 4 0.501 0.67285 0.020 0.156 0.068 0.748 0.008
#> GSM120772 4 0.463 0.67738 0.024 0.152 0.036 0.776 0.012
#> GSM120773 4 0.373 0.72066 0.028 0.000 0.156 0.808 0.008
#> GSM120774 4 0.476 0.69294 0.080 0.040 0.040 0.800 0.040
#> GSM120783 4 0.369 0.72017 0.028 0.000 0.152 0.812 0.008
#> GSM120787 4 0.551 0.61987 0.144 0.032 0.032 0.736 0.056
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM120719 1 0.3314 0.5663 0.740 0.000 0.004 0.000 0.000 0.256
#> GSM120720 1 0.3314 0.5663 0.740 0.000 0.004 0.000 0.000 0.256
#> GSM120765 2 0.7319 0.2527 0.000 0.396 0.116 0.300 0.004 0.184
#> GSM120767 4 0.7058 0.1603 0.000 0.280 0.148 0.456 0.004 0.112
#> GSM120784 4 0.7151 -0.0949 0.000 0.364 0.100 0.372 0.004 0.160
#> GSM121400 6 0.5252 0.4907 0.460 0.020 0.016 0.016 0.004 0.484
#> GSM121401 1 0.3859 0.4842 0.692 0.008 0.008 0.000 0.000 0.292
#> GSM121402 2 0.6751 0.4426 0.000 0.484 0.140 0.036 0.028 0.312
#> GSM121403 6 0.5252 0.4907 0.460 0.020 0.016 0.016 0.004 0.484
#> GSM121404 6 0.6572 -0.5406 0.000 0.316 0.136 0.060 0.004 0.484
#> GSM121405 1 0.3859 0.4842 0.692 0.008 0.008 0.000 0.000 0.292
#> GSM121406 2 0.4520 0.6329 0.000 0.752 0.080 0.028 0.004 0.136
#> GSM121408 2 0.5846 0.5133 0.000 0.632 0.088 0.196 0.004 0.080
#> GSM121409 6 0.5252 0.4907 0.460 0.020 0.016 0.016 0.004 0.484
#> GSM121410 6 0.5252 0.4907 0.460 0.020 0.016 0.016 0.004 0.484
#> GSM121412 2 0.1262 0.6542 0.000 0.956 0.016 0.000 0.008 0.020
#> GSM121413 2 0.1173 0.6538 0.000 0.960 0.016 0.000 0.008 0.016
#> GSM121414 2 0.1173 0.6538 0.000 0.960 0.016 0.000 0.008 0.016
#> GSM121415 2 0.6770 0.5062 0.000 0.484 0.120 0.096 0.004 0.296
#> GSM121416 2 0.6983 0.4942 0.000 0.460 0.132 0.112 0.004 0.292
#> GSM120591 1 0.3314 0.5663 0.740 0.000 0.004 0.000 0.000 0.256
#> GSM120594 1 0.3314 0.5663 0.740 0.000 0.004 0.000 0.000 0.256
#> GSM120718 1 0.3314 0.5663 0.740 0.000 0.004 0.000 0.000 0.256
#> GSM121205 1 0.0146 0.7554 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM121206 1 0.0000 0.7549 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121207 1 0.0146 0.7554 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM121208 1 0.0000 0.7549 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121209 1 0.0000 0.7549 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.0146 0.7554 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM121211 1 0.0146 0.7554 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM121212 1 0.0146 0.7554 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM121213 1 0.0146 0.7554 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM121214 1 0.0146 0.7554 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM121215 1 0.0146 0.7554 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM121216 1 0.0146 0.7554 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM121217 1 0.0146 0.7554 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM121218 1 0.0146 0.7554 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM121234 1 0.0146 0.7554 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM121243 1 0.0146 0.7554 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM121245 1 0.0146 0.7554 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM121246 1 0.0865 0.7442 0.964 0.000 0.000 0.000 0.000 0.036
#> GSM121247 1 0.0291 0.7520 0.992 0.000 0.000 0.000 0.004 0.004
#> GSM121248 1 0.0146 0.7554 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM120744 6 0.4263 0.6525 0.376 0.000 0.000 0.024 0.000 0.600
#> GSM120745 6 0.4263 0.6525 0.376 0.000 0.000 0.024 0.000 0.600
#> GSM120746 6 0.4263 0.6525 0.376 0.000 0.000 0.024 0.000 0.600
#> GSM120747 6 0.4263 0.6525 0.376 0.000 0.000 0.024 0.000 0.600
#> GSM120748 6 0.4263 0.6525 0.376 0.000 0.000 0.024 0.000 0.600
#> GSM120749 6 0.4263 0.6525 0.376 0.000 0.000 0.024 0.000 0.600
#> GSM120750 6 0.4263 0.6525 0.376 0.000 0.000 0.024 0.000 0.600
#> GSM120751 6 0.4263 0.6525 0.376 0.000 0.000 0.024 0.000 0.600
#> GSM120752 6 0.4263 0.6525 0.376 0.000 0.000 0.024 0.000 0.600
#> GSM121336 2 0.2374 0.6467 0.000 0.904 0.048 0.016 0.004 0.028
#> GSM121339 6 0.7940 0.0132 0.124 0.168 0.044 0.236 0.004 0.424
#> GSM121349 2 0.2374 0.6467 0.000 0.904 0.048 0.016 0.004 0.028
#> GSM121355 2 0.2450 0.6471 0.000 0.900 0.048 0.016 0.004 0.032
#> GSM120757 6 0.6238 0.6380 0.368 0.000 0.048 0.080 0.012 0.492
#> GSM120766 6 0.6257 0.6471 0.356 0.000 0.048 0.084 0.012 0.500
#> GSM120770 6 0.8252 0.2524 0.216 0.152 0.048 0.260 0.000 0.324
#> GSM120779 6 0.6460 0.5760 0.404 0.000 0.060 0.076 0.016 0.444
#> GSM120780 6 0.6257 0.6471 0.356 0.000 0.048 0.084 0.012 0.500
#> GSM121102 6 0.6872 0.6061 0.296 0.092 0.024 0.076 0.004 0.508
#> GSM121203 6 0.6724 0.6257 0.312 0.076 0.024 0.064 0.008 0.516
#> GSM121204 1 0.5317 -0.3284 0.524 0.000 0.044 0.032 0.000 0.400
#> GSM121330 1 0.3189 0.6251 0.760 0.000 0.004 0.000 0.000 0.236
#> GSM121335 1 0.3109 0.6426 0.772 0.000 0.004 0.000 0.000 0.224
#> GSM121337 6 0.7708 0.5696 0.312 0.108 0.080 0.072 0.004 0.424
#> GSM121338 6 0.7662 0.5672 0.312 0.112 0.072 0.072 0.004 0.428
#> GSM121341 1 0.3109 0.6426 0.772 0.000 0.004 0.000 0.000 0.224
#> GSM121342 1 0.3189 0.6251 0.760 0.000 0.004 0.000 0.000 0.236
#> GSM121343 6 0.7662 0.5672 0.312 0.112 0.072 0.072 0.004 0.428
#> GSM121344 1 0.3109 0.6426 0.772 0.000 0.004 0.000 0.000 0.224
#> GSM121346 1 0.3109 0.6426 0.772 0.000 0.004 0.000 0.000 0.224
#> GSM121347 6 0.7844 0.5632 0.312 0.112 0.072 0.092 0.004 0.408
#> GSM121348 6 0.6349 0.6395 0.344 0.004 0.044 0.080 0.016 0.512
#> GSM121350 1 0.3109 0.6426 0.772 0.000 0.004 0.000 0.000 0.224
#> GSM121352 1 0.3109 0.6426 0.772 0.000 0.004 0.000 0.000 0.224
#> GSM121354 1 0.3109 0.6426 0.772 0.000 0.004 0.000 0.000 0.224
#> GSM120753 4 0.5000 0.5443 0.000 0.112 0.084 0.728 0.004 0.072
#> GSM120761 4 0.4335 0.5654 0.000 0.084 0.064 0.776 0.000 0.076
#> GSM120768 4 0.4734 0.5622 0.000 0.100 0.080 0.744 0.000 0.076
#> GSM120781 4 0.5415 0.4262 0.000 0.196 0.132 0.644 0.000 0.028
#> GSM120788 4 0.4461 0.4448 0.016 0.000 0.048 0.720 0.004 0.212
#> GSM120760 4 0.3527 0.5180 0.000 0.000 0.040 0.792 0.004 0.164
#> GSM120763 4 0.3810 0.5213 0.000 0.004 0.056 0.784 0.004 0.152
#> GSM120764 4 0.3695 0.4940 0.000 0.000 0.044 0.776 0.004 0.176
#> GSM120777 4 0.5456 0.3364 0.064 0.000 0.056 0.648 0.004 0.228
#> GSM120786 4 0.3695 0.5083 0.000 0.000 0.044 0.776 0.004 0.176
#> GSM121329 1 0.4728 0.4398 0.672 0.000 0.036 0.032 0.000 0.260
#> GSM121331 6 0.6499 0.5738 0.404 0.000 0.060 0.080 0.016 0.440
#> GSM121333 6 0.6499 0.5738 0.404 0.000 0.060 0.080 0.016 0.440
#> GSM121345 6 0.6674 0.5520 0.404 0.000 0.060 0.100 0.016 0.420
#> GSM121356 6 0.6499 0.5738 0.404 0.000 0.060 0.080 0.016 0.440
#> GSM120754 4 0.4299 0.5177 0.004 0.028 0.028 0.752 0.004 0.184
#> GSM120759 2 0.4636 0.3023 0.000 0.648 0.020 0.000 0.300 0.032
#> GSM120762 4 0.6223 0.3586 0.000 0.184 0.160 0.588 0.004 0.064
#> GSM120775 4 0.4110 0.5245 0.004 0.020 0.044 0.780 0.004 0.148
#> GSM120776 1 0.6242 -0.4096 0.476 0.004 0.024 0.152 0.000 0.344
#> GSM120782 4 0.5201 0.5161 0.004 0.072 0.044 0.696 0.004 0.180
#> GSM120789 2 0.7907 0.2601 0.000 0.376 0.036 0.268 0.120 0.200
#> GSM120790 5 0.0748 0.0000 0.000 0.016 0.000 0.004 0.976 0.004
#> GSM120791 4 0.3655 0.5448 0.000 0.012 0.044 0.796 0.000 0.148
#> GSM120755 4 0.6548 0.3276 0.000 0.200 0.160 0.552 0.004 0.084
#> GSM120756 4 0.4225 0.4769 0.016 0.000 0.048 0.752 0.004 0.180
#> GSM120769 4 0.5785 0.3728 0.000 0.224 0.148 0.596 0.000 0.032
#> GSM120778 4 0.3559 0.4386 0.000 0.016 0.180 0.788 0.004 0.012
#> GSM120792 4 0.4880 0.5585 0.004 0.056 0.068 0.740 0.004 0.128
#> GSM121332 4 0.7028 0.1132 0.000 0.316 0.056 0.436 0.016 0.176
#> GSM121334 4 0.4656 0.5612 0.000 0.096 0.044 0.752 0.004 0.104
#> GSM121340 3 0.4856 0.0000 0.000 0.000 0.592 0.348 0.008 0.052
#> GSM121351 2 0.1944 0.6395 0.000 0.924 0.024 0.000 0.036 0.016
#> GSM121353 4 0.4593 0.4748 0.016 0.016 0.048 0.744 0.004 0.172
#> GSM120758 4 0.4734 0.5622 0.000 0.100 0.080 0.744 0.000 0.076
#> GSM120771 4 0.4574 0.5456 0.000 0.120 0.056 0.752 0.000 0.072
#> GSM120772 4 0.4237 0.5313 0.000 0.100 0.072 0.780 0.000 0.048
#> GSM120773 4 0.3424 0.5065 0.000 0.000 0.032 0.796 0.004 0.168
#> GSM120774 4 0.4948 0.2020 0.000 0.024 0.280 0.648 0.004 0.044
#> GSM120783 4 0.3460 0.5053 0.000 0.000 0.036 0.796 0.004 0.164
#> GSM120787 4 0.5523 -0.2192 0.000 0.012 0.392 0.516 0.008 0.072
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> SD:hclust 111 1.28e-12 2
#> SD:hclust 105 2.15e-18 3
#> SD:hclust 101 9.48e-19 4
#> SD:hclust 80 8.86e-27 5
#> SD:hclust 85 3.11e-24 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 21512 rows and 119 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.787 0.882 0.950 0.4894 0.499 0.499
#> 3 3 0.553 0.646 0.771 0.3335 0.790 0.602
#> 4 4 0.679 0.682 0.844 0.1406 0.823 0.543
#> 5 5 0.663 0.548 0.757 0.0540 0.886 0.632
#> 6 6 0.718 0.666 0.737 0.0403 0.935 0.749
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
#> GSM120719 1 0.0000 0.9097 1.000 0.000
#> GSM120720 1 0.0000 0.9097 1.000 0.000
#> GSM120765 2 0.0000 0.9762 0.000 1.000
#> GSM120767 2 0.0000 0.9762 0.000 1.000
#> GSM120784 2 0.0000 0.9762 0.000 1.000
#> GSM121400 1 0.0000 0.9097 1.000 0.000
#> GSM121401 1 0.0000 0.9097 1.000 0.000
#> GSM121402 2 0.0000 0.9762 0.000 1.000
#> GSM121403 2 0.8555 0.5473 0.280 0.720
#> GSM121404 2 0.0000 0.9762 0.000 1.000
#> GSM121405 1 0.0672 0.9055 0.992 0.008
#> GSM121406 2 0.0000 0.9762 0.000 1.000
#> GSM121408 2 0.0000 0.9762 0.000 1.000
#> GSM121409 1 0.5737 0.8192 0.864 0.136
#> GSM121410 1 0.2236 0.8892 0.964 0.036
#> GSM121412 2 0.0000 0.9762 0.000 1.000
#> GSM121413 2 0.0000 0.9762 0.000 1.000
#> GSM121414 2 0.0000 0.9762 0.000 1.000
#> GSM121415 2 0.0000 0.9762 0.000 1.000
#> GSM121416 2 0.0000 0.9762 0.000 1.000
#> GSM120591 1 0.0000 0.9097 1.000 0.000
#> GSM120594 1 0.0000 0.9097 1.000 0.000
#> GSM120718 1 0.0000 0.9097 1.000 0.000
#> GSM121205 1 0.0000 0.9097 1.000 0.000
#> GSM121206 1 0.0000 0.9097 1.000 0.000
#> GSM121207 1 0.0000 0.9097 1.000 0.000
#> GSM121208 1 0.0000 0.9097 1.000 0.000
#> GSM121209 1 0.0000 0.9097 1.000 0.000
#> GSM121210 1 0.0000 0.9097 1.000 0.000
#> GSM121211 1 0.0000 0.9097 1.000 0.000
#> GSM121212 1 0.0000 0.9097 1.000 0.000
#> GSM121213 1 0.0000 0.9097 1.000 0.000
#> GSM121214 1 0.0000 0.9097 1.000 0.000
#> GSM121215 1 0.0000 0.9097 1.000 0.000
#> GSM121216 1 0.0000 0.9097 1.000 0.000
#> GSM121217 1 0.0000 0.9097 1.000 0.000
#> GSM121218 1 0.0000 0.9097 1.000 0.000
#> GSM121234 1 0.0000 0.9097 1.000 0.000
#> GSM121243 1 0.0000 0.9097 1.000 0.000
#> GSM121245 1 0.0000 0.9097 1.000 0.000
#> GSM121246 1 0.0000 0.9097 1.000 0.000
#> GSM121247 1 0.0000 0.9097 1.000 0.000
#> GSM121248 1 0.0000 0.9097 1.000 0.000
#> GSM120744 2 0.9686 0.2269 0.396 0.604
#> GSM120745 1 0.6712 0.7852 0.824 0.176
#> GSM120746 1 0.9933 0.3025 0.548 0.452
#> GSM120747 1 0.9944 0.2913 0.544 0.456
#> GSM120748 2 0.5294 0.8320 0.120 0.880
#> GSM120749 1 0.8144 0.6966 0.748 0.252
#> GSM120750 1 0.9944 0.2913 0.544 0.456
#> GSM120751 1 0.9944 0.2913 0.544 0.456
#> GSM120752 1 0.7602 0.7377 0.780 0.220
#> GSM121336 2 0.0000 0.9762 0.000 1.000
#> GSM121339 2 0.0000 0.9762 0.000 1.000
#> GSM121349 2 0.0000 0.9762 0.000 1.000
#> GSM121355 2 0.0000 0.9762 0.000 1.000
#> GSM120757 1 0.9922 0.3133 0.552 0.448
#> GSM120766 2 0.9988 -0.0983 0.480 0.520
#> GSM120770 2 0.0000 0.9762 0.000 1.000
#> GSM120779 1 0.6887 0.7776 0.816 0.184
#> GSM120780 2 0.0672 0.9683 0.008 0.992
#> GSM121102 2 0.0000 0.9762 0.000 1.000
#> GSM121203 1 0.9970 0.2542 0.532 0.468
#> GSM121204 1 0.0000 0.9097 1.000 0.000
#> GSM121330 1 0.0000 0.9097 1.000 0.000
#> GSM121335 1 0.0000 0.9097 1.000 0.000
#> GSM121337 2 0.0000 0.9762 0.000 1.000
#> GSM121338 2 0.0000 0.9762 0.000 1.000
#> GSM121341 1 0.0000 0.9097 1.000 0.000
#> GSM121342 1 0.0000 0.9097 1.000 0.000
#> GSM121343 2 0.0000 0.9762 0.000 1.000
#> GSM121344 1 0.0000 0.9097 1.000 0.000
#> GSM121346 1 0.0000 0.9097 1.000 0.000
#> GSM121347 2 0.0000 0.9762 0.000 1.000
#> GSM121348 2 0.0000 0.9762 0.000 1.000
#> GSM121350 1 0.0000 0.9097 1.000 0.000
#> GSM121352 1 0.0000 0.9097 1.000 0.000
#> GSM121354 1 0.0000 0.9097 1.000 0.000
#> GSM120753 2 0.0000 0.9762 0.000 1.000
#> GSM120761 2 0.0000 0.9762 0.000 1.000
#> GSM120768 2 0.0000 0.9762 0.000 1.000
#> GSM120781 2 0.0000 0.9762 0.000 1.000
#> GSM120788 2 0.0000 0.9762 0.000 1.000
#> GSM120760 2 0.0000 0.9762 0.000 1.000
#> GSM120763 2 0.0000 0.9762 0.000 1.000
#> GSM120764 2 0.0000 0.9762 0.000 1.000
#> GSM120777 2 0.0000 0.9762 0.000 1.000
#> GSM120786 2 0.0000 0.9762 0.000 1.000
#> GSM121329 1 0.0000 0.9097 1.000 0.000
#> GSM121331 1 0.8555 0.6558 0.720 0.280
#> GSM121333 1 0.7219 0.7608 0.800 0.200
#> GSM121345 1 0.6801 0.7815 0.820 0.180
#> GSM121356 1 0.7056 0.7694 0.808 0.192
#> GSM120754 2 0.0000 0.9762 0.000 1.000
#> GSM120759 2 0.0000 0.9762 0.000 1.000
#> GSM120762 2 0.0000 0.9762 0.000 1.000
#> GSM120775 2 0.0000 0.9762 0.000 1.000
#> GSM120776 2 0.1414 0.9557 0.020 0.980
#> GSM120782 2 0.0000 0.9762 0.000 1.000
#> GSM120789 2 0.0000 0.9762 0.000 1.000
#> GSM120790 2 0.0000 0.9762 0.000 1.000
#> GSM120791 2 0.0000 0.9762 0.000 1.000
#> GSM120755 2 0.0000 0.9762 0.000 1.000
#> GSM120756 2 0.0000 0.9762 0.000 1.000
#> GSM120769 2 0.0000 0.9762 0.000 1.000
#> GSM120778 2 0.0000 0.9762 0.000 1.000
#> GSM120792 2 0.0000 0.9762 0.000 1.000
#> GSM121332 2 0.0000 0.9762 0.000 1.000
#> GSM121334 2 0.0000 0.9762 0.000 1.000
#> GSM121340 2 0.0000 0.9762 0.000 1.000
#> GSM121351 2 0.0000 0.9762 0.000 1.000
#> GSM121353 2 0.0000 0.9762 0.000 1.000
#> GSM120758 2 0.0000 0.9762 0.000 1.000
#> GSM120771 2 0.0000 0.9762 0.000 1.000
#> GSM120772 2 0.0000 0.9762 0.000 1.000
#> GSM120773 2 0.0000 0.9762 0.000 1.000
#> GSM120774 2 0.0000 0.9762 0.000 1.000
#> GSM120783 2 0.0000 0.9762 0.000 1.000
#> GSM120787 2 0.0000 0.9762 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM120719 1 0.3192 0.82874 0.888 0.000 0.112
#> GSM120720 1 0.3551 0.81677 0.868 0.000 0.132
#> GSM120765 2 0.5216 0.73915 0.000 0.740 0.260
#> GSM120767 2 0.4931 0.74511 0.000 0.768 0.232
#> GSM120784 2 0.5216 0.73915 0.000 0.740 0.260
#> GSM121400 3 0.6680 0.11258 0.484 0.008 0.508
#> GSM121401 1 0.6509 -0.00465 0.524 0.004 0.472
#> GSM121402 2 0.5216 0.73915 0.000 0.740 0.260
#> GSM121403 3 0.5178 0.26291 0.000 0.256 0.744
#> GSM121404 2 0.5785 0.67854 0.000 0.668 0.332
#> GSM121405 3 0.7178 0.18341 0.464 0.024 0.512
#> GSM121406 2 0.5216 0.73915 0.000 0.740 0.260
#> GSM121408 2 0.5178 0.74032 0.000 0.744 0.256
#> GSM121409 3 0.6855 0.56966 0.316 0.032 0.652
#> GSM121410 3 0.7278 0.20894 0.456 0.028 0.516
#> GSM121412 2 0.5216 0.73915 0.000 0.740 0.260
#> GSM121413 2 0.5216 0.73915 0.000 0.740 0.260
#> GSM121414 2 0.5216 0.73915 0.000 0.740 0.260
#> GSM121415 2 0.5216 0.73915 0.000 0.740 0.260
#> GSM121416 2 0.5216 0.73915 0.000 0.740 0.260
#> GSM120591 1 0.6045 0.36141 0.620 0.000 0.380
#> GSM120594 1 0.3551 0.81677 0.868 0.000 0.132
#> GSM120718 1 0.3192 0.82874 0.888 0.000 0.112
#> GSM121205 1 0.0000 0.86278 1.000 0.000 0.000
#> GSM121206 1 0.0000 0.86278 1.000 0.000 0.000
#> GSM121207 1 0.0000 0.86278 1.000 0.000 0.000
#> GSM121208 1 0.0000 0.86278 1.000 0.000 0.000
#> GSM121209 1 0.0000 0.86278 1.000 0.000 0.000
#> GSM121210 1 0.0000 0.86278 1.000 0.000 0.000
#> GSM121211 1 0.0000 0.86278 1.000 0.000 0.000
#> GSM121212 1 0.0000 0.86278 1.000 0.000 0.000
#> GSM121213 1 0.0000 0.86278 1.000 0.000 0.000
#> GSM121214 1 0.0000 0.86278 1.000 0.000 0.000
#> GSM121215 1 0.0000 0.86278 1.000 0.000 0.000
#> GSM121216 1 0.0000 0.86278 1.000 0.000 0.000
#> GSM121217 1 0.0000 0.86278 1.000 0.000 0.000
#> GSM121218 1 0.0000 0.86278 1.000 0.000 0.000
#> GSM121234 1 0.0000 0.86278 1.000 0.000 0.000
#> GSM121243 1 0.0000 0.86278 1.000 0.000 0.000
#> GSM121245 1 0.0000 0.86278 1.000 0.000 0.000
#> GSM121246 1 0.2066 0.84745 0.940 0.000 0.060
#> GSM121247 1 0.0000 0.86278 1.000 0.000 0.000
#> GSM121248 1 0.0000 0.86278 1.000 0.000 0.000
#> GSM120744 3 0.6079 0.69421 0.216 0.036 0.748
#> GSM120745 3 0.5058 0.67674 0.244 0.000 0.756
#> GSM120746 3 0.6056 0.69344 0.224 0.032 0.744
#> GSM120747 3 0.6056 0.69344 0.224 0.032 0.744
#> GSM120748 3 0.1289 0.55987 0.000 0.032 0.968
#> GSM120749 3 0.6099 0.69176 0.228 0.032 0.740
#> GSM120750 3 0.6056 0.69344 0.224 0.032 0.744
#> GSM120751 3 0.6056 0.69344 0.224 0.032 0.744
#> GSM120752 3 0.5058 0.67674 0.244 0.000 0.756
#> GSM121336 2 0.5216 0.73915 0.000 0.740 0.260
#> GSM121339 2 0.6180 0.57033 0.000 0.584 0.416
#> GSM121349 2 0.5216 0.73915 0.000 0.740 0.260
#> GSM121355 2 0.5216 0.73915 0.000 0.740 0.260
#> GSM120757 3 0.5945 0.67634 0.236 0.024 0.740
#> GSM120766 3 0.6630 0.67818 0.220 0.056 0.724
#> GSM120770 2 0.5926 0.65909 0.000 0.644 0.356
#> GSM120779 3 0.5938 0.67069 0.248 0.020 0.732
#> GSM120780 3 0.2537 0.50013 0.000 0.080 0.920
#> GSM121102 3 0.5948 -0.03797 0.000 0.360 0.640
#> GSM121203 3 0.6168 0.69206 0.224 0.036 0.740
#> GSM121204 3 0.5443 0.65947 0.260 0.004 0.736
#> GSM121330 1 0.4654 0.73289 0.792 0.000 0.208
#> GSM121335 1 0.3340 0.82452 0.880 0.000 0.120
#> GSM121337 2 0.6062 0.62560 0.000 0.616 0.384
#> GSM121338 3 0.5621 0.13329 0.000 0.308 0.692
#> GSM121341 1 0.3340 0.82452 0.880 0.000 0.120
#> GSM121342 1 0.3340 0.82452 0.880 0.000 0.120
#> GSM121343 3 0.5621 0.13329 0.000 0.308 0.692
#> GSM121344 1 0.4121 0.78283 0.832 0.000 0.168
#> GSM121346 1 0.5733 0.51850 0.676 0.000 0.324
#> GSM121347 3 0.6299 -0.40362 0.000 0.476 0.524
#> GSM121348 3 0.3879 0.32503 0.000 0.152 0.848
#> GSM121350 1 0.5733 0.51850 0.676 0.000 0.324
#> GSM121352 1 0.4750 0.72148 0.784 0.000 0.216
#> GSM121354 1 0.4121 0.78283 0.832 0.000 0.168
#> GSM120753 2 0.0424 0.74361 0.000 0.992 0.008
#> GSM120761 2 0.0592 0.73900 0.000 0.988 0.012
#> GSM120768 2 0.0747 0.73765 0.000 0.984 0.016
#> GSM120781 2 0.1163 0.74666 0.000 0.972 0.028
#> GSM120788 2 0.6252 0.23683 0.000 0.556 0.444
#> GSM120760 2 0.3551 0.67345 0.000 0.868 0.132
#> GSM120763 2 0.3482 0.67646 0.000 0.872 0.128
#> GSM120764 2 0.5216 0.55188 0.000 0.740 0.260
#> GSM120777 2 0.6235 0.25672 0.000 0.564 0.436
#> GSM120786 2 0.5178 0.55692 0.000 0.744 0.256
#> GSM121329 1 0.6244 0.13736 0.560 0.000 0.440
#> GSM121331 3 0.5938 0.67069 0.248 0.020 0.732
#> GSM121333 3 0.5938 0.67069 0.248 0.020 0.732
#> GSM121345 3 0.5803 0.66917 0.248 0.016 0.736
#> GSM121356 3 0.5723 0.67474 0.240 0.016 0.744
#> GSM120754 2 0.5988 0.39677 0.000 0.632 0.368
#> GSM120759 2 0.5216 0.73915 0.000 0.740 0.260
#> GSM120762 2 0.1529 0.74825 0.000 0.960 0.040
#> GSM120775 2 0.6180 0.30244 0.000 0.584 0.416
#> GSM120776 3 0.5431 0.46255 0.000 0.284 0.716
#> GSM120782 2 0.6192 0.30281 0.000 0.580 0.420
#> GSM120789 2 0.4931 0.74460 0.000 0.768 0.232
#> GSM120790 2 0.5138 0.74174 0.000 0.748 0.252
#> GSM120791 2 0.2625 0.70566 0.000 0.916 0.084
#> GSM120755 2 0.2537 0.74995 0.000 0.920 0.080
#> GSM120756 2 0.6291 0.17181 0.000 0.532 0.468
#> GSM120769 2 0.0424 0.74361 0.000 0.992 0.008
#> GSM120778 2 0.0747 0.73765 0.000 0.984 0.016
#> GSM120792 2 0.1411 0.73034 0.000 0.964 0.036
#> GSM121332 2 0.4974 0.74426 0.000 0.764 0.236
#> GSM121334 2 0.0424 0.74015 0.000 0.992 0.008
#> GSM121340 2 0.5016 0.57491 0.000 0.760 0.240
#> GSM121351 2 0.5216 0.73915 0.000 0.740 0.260
#> GSM121353 2 0.5497 0.51325 0.000 0.708 0.292
#> GSM120758 2 0.0747 0.74515 0.000 0.984 0.016
#> GSM120771 2 0.5098 0.74192 0.000 0.752 0.248
#> GSM120772 2 0.0424 0.74361 0.000 0.992 0.008
#> GSM120773 2 0.5138 0.56151 0.000 0.748 0.252
#> GSM120774 2 0.1163 0.73410 0.000 0.972 0.028
#> GSM120783 2 0.5216 0.55188 0.000 0.740 0.260
#> GSM120787 2 0.0592 0.73900 0.000 0.988 0.012
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM120719 1 0.5186 0.58130 0.640 0.000 0.344 0.016
#> GSM120720 1 0.5444 0.44873 0.560 0.000 0.424 0.016
#> GSM120765 2 0.0336 0.86770 0.000 0.992 0.008 0.000
#> GSM120767 2 0.0376 0.86696 0.004 0.992 0.004 0.000
#> GSM120784 2 0.0469 0.86581 0.000 0.988 0.012 0.000
#> GSM121400 3 0.1118 0.72550 0.036 0.000 0.964 0.000
#> GSM121401 3 0.1975 0.71200 0.048 0.000 0.936 0.016
#> GSM121402 2 0.0000 0.86851 0.000 1.000 0.000 0.000
#> GSM121403 3 0.1716 0.72324 0.000 0.064 0.936 0.000
#> GSM121404 2 0.2704 0.75992 0.000 0.876 0.124 0.000
#> GSM121405 3 0.1706 0.71881 0.036 0.000 0.948 0.016
#> GSM121406 2 0.0000 0.86851 0.000 1.000 0.000 0.000
#> GSM121408 2 0.0336 0.86770 0.000 0.992 0.008 0.000
#> GSM121409 3 0.0000 0.73804 0.000 0.000 1.000 0.000
#> GSM121410 3 0.1109 0.72838 0.028 0.004 0.968 0.000
#> GSM121412 2 0.0336 0.86595 0.000 0.992 0.008 0.000
#> GSM121413 2 0.0336 0.86595 0.000 0.992 0.008 0.000
#> GSM121414 2 0.0336 0.86595 0.000 0.992 0.008 0.000
#> GSM121415 2 0.0336 0.86770 0.000 0.992 0.008 0.000
#> GSM121416 2 0.0188 0.86802 0.000 0.996 0.004 0.000
#> GSM120591 3 0.3711 0.62693 0.140 0.000 0.836 0.024
#> GSM120594 1 0.5444 0.44873 0.560 0.000 0.424 0.016
#> GSM120718 1 0.5253 0.56153 0.624 0.000 0.360 0.016
#> GSM121205 1 0.0188 0.86884 0.996 0.000 0.004 0.000
#> GSM121206 1 0.0188 0.86884 0.996 0.000 0.004 0.000
#> GSM121207 1 0.0188 0.86884 0.996 0.000 0.004 0.000
#> GSM121208 1 0.0469 0.86494 0.988 0.000 0.012 0.000
#> GSM121209 1 0.0188 0.86884 0.996 0.000 0.004 0.000
#> GSM121210 1 0.0188 0.86884 0.996 0.000 0.004 0.000
#> GSM121211 1 0.0188 0.86884 0.996 0.000 0.004 0.000
#> GSM121212 1 0.0188 0.86884 0.996 0.000 0.004 0.000
#> GSM121213 1 0.0188 0.86884 0.996 0.000 0.004 0.000
#> GSM121214 1 0.0188 0.86884 0.996 0.000 0.004 0.000
#> GSM121215 1 0.0188 0.86884 0.996 0.000 0.004 0.000
#> GSM121216 1 0.0188 0.86884 0.996 0.000 0.004 0.000
#> GSM121217 1 0.0188 0.86884 0.996 0.000 0.004 0.000
#> GSM121218 1 0.0188 0.86884 0.996 0.000 0.004 0.000
#> GSM121234 1 0.0188 0.86884 0.996 0.000 0.004 0.000
#> GSM121243 1 0.0188 0.86884 0.996 0.000 0.004 0.000
#> GSM121245 1 0.0188 0.86884 0.996 0.000 0.004 0.000
#> GSM121246 1 0.4690 0.67144 0.724 0.000 0.260 0.016
#> GSM121247 1 0.0188 0.86884 0.996 0.000 0.004 0.000
#> GSM121248 1 0.0188 0.86884 0.996 0.000 0.004 0.000
#> GSM120744 3 0.1716 0.75337 0.000 0.000 0.936 0.064
#> GSM120745 3 0.1716 0.75337 0.000 0.000 0.936 0.064
#> GSM120746 3 0.1716 0.75337 0.000 0.000 0.936 0.064
#> GSM120747 3 0.1637 0.75285 0.000 0.000 0.940 0.060
#> GSM120748 3 0.1807 0.75213 0.000 0.008 0.940 0.052
#> GSM120749 3 0.1637 0.75285 0.000 0.000 0.940 0.060
#> GSM120750 3 0.1716 0.75337 0.000 0.000 0.936 0.064
#> GSM120751 3 0.1716 0.75337 0.000 0.000 0.936 0.064
#> GSM120752 3 0.1716 0.75337 0.000 0.000 0.936 0.064
#> GSM121336 2 0.0000 0.86851 0.000 1.000 0.000 0.000
#> GSM121339 2 0.3649 0.66286 0.000 0.796 0.204 0.000
#> GSM121349 2 0.0000 0.86851 0.000 1.000 0.000 0.000
#> GSM121355 2 0.0000 0.86851 0.000 1.000 0.000 0.000
#> GSM120757 3 0.4697 0.59222 0.000 0.000 0.644 0.356
#> GSM120766 3 0.4697 0.59222 0.000 0.000 0.644 0.356
#> GSM120770 2 0.1211 0.84666 0.000 0.960 0.040 0.000
#> GSM120779 3 0.4697 0.59222 0.000 0.000 0.644 0.356
#> GSM120780 3 0.5820 0.61750 0.000 0.080 0.680 0.240
#> GSM121102 3 0.5168 0.00189 0.000 0.496 0.500 0.004
#> GSM121203 3 0.1389 0.75189 0.000 0.000 0.952 0.048
#> GSM121204 3 0.4543 0.62381 0.000 0.000 0.676 0.324
#> GSM121330 3 0.4831 0.38685 0.280 0.000 0.704 0.016
#> GSM121335 1 0.5298 0.54314 0.612 0.000 0.372 0.016
#> GSM121337 2 0.6976 0.35531 0.000 0.580 0.240 0.180
#> GSM121338 3 0.4889 0.39618 0.000 0.360 0.636 0.004
#> GSM121341 1 0.5298 0.54314 0.612 0.000 0.372 0.016
#> GSM121342 1 0.5313 0.53668 0.608 0.000 0.376 0.016
#> GSM121343 3 0.4855 0.41390 0.000 0.352 0.644 0.004
#> GSM121344 3 0.5427 -0.04340 0.416 0.000 0.568 0.016
#> GSM121346 3 0.3048 0.66629 0.108 0.000 0.876 0.016
#> GSM121347 3 0.7873 0.14239 0.000 0.320 0.388 0.292
#> GSM121348 3 0.6797 0.47094 0.000 0.108 0.536 0.356
#> GSM121350 3 0.2861 0.67643 0.096 0.000 0.888 0.016
#> GSM121352 3 0.4690 0.43145 0.260 0.000 0.724 0.016
#> GSM121354 3 0.5408 -0.01658 0.408 0.000 0.576 0.016
#> GSM120753 2 0.5325 -0.09181 0.004 0.524 0.004 0.468
#> GSM120761 4 0.5095 0.51139 0.004 0.368 0.004 0.624
#> GSM120768 4 0.4917 0.57738 0.004 0.328 0.004 0.664
#> GSM120781 2 0.4786 0.45396 0.004 0.688 0.004 0.304
#> GSM120788 4 0.0592 0.79980 0.000 0.016 0.000 0.984
#> GSM120760 4 0.1792 0.80404 0.000 0.068 0.000 0.932
#> GSM120763 4 0.2216 0.79424 0.000 0.092 0.000 0.908
#> GSM120764 4 0.0921 0.80679 0.000 0.028 0.000 0.972
#> GSM120777 4 0.0592 0.79980 0.000 0.016 0.000 0.984
#> GSM120786 4 0.1211 0.81079 0.000 0.040 0.000 0.960
#> GSM121329 3 0.5151 0.63456 0.140 0.000 0.760 0.100
#> GSM121331 3 0.4697 0.59222 0.000 0.000 0.644 0.356
#> GSM121333 3 0.4697 0.59222 0.000 0.000 0.644 0.356
#> GSM121345 3 0.4746 0.57839 0.000 0.000 0.632 0.368
#> GSM121356 3 0.4697 0.59222 0.000 0.000 0.644 0.356
#> GSM120754 4 0.0817 0.80537 0.000 0.024 0.000 0.976
#> GSM120759 2 0.0000 0.86851 0.000 1.000 0.000 0.000
#> GSM120762 2 0.3631 0.70480 0.004 0.824 0.004 0.168
#> GSM120775 4 0.0707 0.80352 0.000 0.020 0.000 0.980
#> GSM120776 4 0.3486 0.53309 0.000 0.000 0.188 0.812
#> GSM120782 4 0.1557 0.80691 0.000 0.056 0.000 0.944
#> GSM120789 2 0.0188 0.86716 0.004 0.996 0.000 0.000
#> GSM120790 2 0.0000 0.86851 0.000 1.000 0.000 0.000
#> GSM120791 4 0.3649 0.71964 0.000 0.204 0.000 0.796
#> GSM120755 2 0.3072 0.76049 0.004 0.868 0.004 0.124
#> GSM120756 4 0.0592 0.79980 0.000 0.016 0.000 0.984
#> GSM120769 2 0.5276 0.05729 0.004 0.560 0.004 0.432
#> GSM120778 4 0.4995 0.55342 0.004 0.344 0.004 0.648
#> GSM120792 4 0.4343 0.65934 0.000 0.264 0.004 0.732
#> GSM121332 2 0.0188 0.86802 0.000 0.996 0.004 0.000
#> GSM121334 4 0.5252 0.39172 0.004 0.420 0.004 0.572
#> GSM121340 4 0.1211 0.81079 0.000 0.040 0.000 0.960
#> GSM121351 2 0.0000 0.86851 0.000 1.000 0.000 0.000
#> GSM121353 4 0.1118 0.80996 0.000 0.036 0.000 0.964
#> GSM120758 2 0.5297 0.01034 0.004 0.548 0.004 0.444
#> GSM120771 2 0.3257 0.73161 0.000 0.844 0.004 0.152
#> GSM120772 4 0.5317 0.27182 0.004 0.460 0.004 0.532
#> GSM120773 4 0.1302 0.81043 0.000 0.044 0.000 0.956
#> GSM120774 4 0.5047 0.53391 0.004 0.356 0.004 0.636
#> GSM120783 4 0.1211 0.81079 0.000 0.040 0.000 0.960
#> GSM120787 4 0.5125 0.49351 0.004 0.376 0.004 0.616
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM120719 3 0.6740 0.430 0.316 0.000 0.448 0.004 0.232
#> GSM120720 3 0.6511 0.516 0.252 0.000 0.516 0.004 0.228
#> GSM120765 2 0.1410 0.757 0.000 0.940 0.000 0.000 0.060
#> GSM120767 2 0.2873 0.735 0.000 0.860 0.000 0.020 0.120
#> GSM120784 2 0.1410 0.757 0.000 0.940 0.000 0.000 0.060
#> GSM121400 3 0.4026 0.548 0.020 0.000 0.736 0.000 0.244
#> GSM121401 3 0.4666 0.570 0.056 0.000 0.704 0.000 0.240
#> GSM121402 2 0.3280 0.755 0.004 0.808 0.000 0.004 0.184
#> GSM121403 3 0.5287 0.451 0.000 0.092 0.648 0.000 0.260
#> GSM121404 2 0.3821 0.673 0.000 0.800 0.148 0.000 0.052
#> GSM121405 3 0.4710 0.558 0.020 0.024 0.708 0.000 0.248
#> GSM121406 2 0.2516 0.756 0.000 0.860 0.000 0.000 0.140
#> GSM121408 2 0.1478 0.769 0.000 0.936 0.000 0.000 0.064
#> GSM121409 3 0.3807 0.542 0.012 0.000 0.748 0.000 0.240
#> GSM121410 3 0.3999 0.546 0.020 0.000 0.740 0.000 0.240
#> GSM121412 2 0.2561 0.756 0.000 0.856 0.000 0.000 0.144
#> GSM121413 2 0.2648 0.755 0.000 0.848 0.000 0.000 0.152
#> GSM121414 2 0.2561 0.756 0.000 0.856 0.000 0.000 0.144
#> GSM121415 2 0.1478 0.763 0.000 0.936 0.000 0.000 0.064
#> GSM121416 2 0.2046 0.756 0.000 0.916 0.000 0.016 0.068
#> GSM120591 3 0.5429 0.570 0.108 0.000 0.660 0.004 0.228
#> GSM120594 3 0.6511 0.516 0.252 0.000 0.516 0.004 0.228
#> GSM120718 3 0.6672 0.462 0.296 0.000 0.472 0.004 0.228
#> GSM121205 1 0.0162 0.960 0.996 0.000 0.004 0.000 0.000
#> GSM121206 1 0.0162 0.960 0.996 0.000 0.004 0.000 0.000
#> GSM121207 1 0.0162 0.960 0.996 0.000 0.004 0.000 0.000
#> GSM121208 1 0.0566 0.950 0.984 0.000 0.004 0.000 0.012
#> GSM121209 1 0.0162 0.960 0.996 0.000 0.004 0.000 0.000
#> GSM121210 1 0.0162 0.960 0.996 0.000 0.004 0.000 0.000
#> GSM121211 1 0.0162 0.960 0.996 0.000 0.004 0.000 0.000
#> GSM121212 1 0.0162 0.960 0.996 0.000 0.004 0.000 0.000
#> GSM121213 1 0.0162 0.960 0.996 0.000 0.004 0.000 0.000
#> GSM121214 1 0.0162 0.960 0.996 0.000 0.004 0.000 0.000
#> GSM121215 1 0.0566 0.955 0.984 0.000 0.004 0.000 0.012
#> GSM121216 1 0.0566 0.955 0.984 0.000 0.004 0.000 0.012
#> GSM121217 1 0.0162 0.960 0.996 0.000 0.004 0.000 0.000
#> GSM121218 1 0.0162 0.960 0.996 0.000 0.004 0.000 0.000
#> GSM121234 1 0.0566 0.955 0.984 0.000 0.004 0.000 0.012
#> GSM121243 1 0.0566 0.955 0.984 0.000 0.004 0.000 0.012
#> GSM121245 1 0.0162 0.960 0.996 0.000 0.004 0.000 0.000
#> GSM121246 1 0.6721 -0.293 0.404 0.000 0.340 0.000 0.256
#> GSM121247 1 0.0162 0.960 0.996 0.000 0.004 0.000 0.000
#> GSM121248 1 0.0162 0.960 0.996 0.000 0.004 0.000 0.000
#> GSM120744 3 0.0727 0.457 0.000 0.012 0.980 0.004 0.004
#> GSM120745 3 0.0771 0.469 0.000 0.000 0.976 0.004 0.020
#> GSM120746 3 0.0162 0.473 0.000 0.000 0.996 0.004 0.000
#> GSM120747 3 0.0162 0.473 0.000 0.000 0.996 0.004 0.000
#> GSM120748 3 0.0960 0.448 0.000 0.016 0.972 0.004 0.008
#> GSM120749 3 0.0162 0.473 0.000 0.000 0.996 0.004 0.000
#> GSM120750 3 0.0162 0.473 0.000 0.000 0.996 0.004 0.000
#> GSM120751 3 0.0162 0.473 0.000 0.000 0.996 0.004 0.000
#> GSM120752 3 0.0771 0.469 0.000 0.000 0.976 0.004 0.020
#> GSM121336 2 0.2732 0.758 0.000 0.840 0.000 0.000 0.160
#> GSM121339 2 0.3953 0.664 0.000 0.792 0.148 0.000 0.060
#> GSM121349 2 0.2732 0.758 0.000 0.840 0.000 0.000 0.160
#> GSM121355 2 0.2690 0.759 0.000 0.844 0.000 0.000 0.156
#> GSM120757 3 0.6261 -0.551 0.000 0.000 0.488 0.156 0.356
#> GSM120766 3 0.6240 -0.578 0.000 0.000 0.488 0.152 0.360
#> GSM120770 2 0.2888 0.725 0.000 0.880 0.060 0.004 0.056
#> GSM120779 3 0.6405 -0.552 0.004 0.000 0.484 0.156 0.356
#> GSM120780 3 0.7320 -0.693 0.000 0.136 0.476 0.072 0.316
#> GSM121102 2 0.5112 0.457 0.000 0.664 0.268 0.004 0.064
#> GSM121203 3 0.1124 0.444 0.000 0.000 0.960 0.004 0.036
#> GSM121204 3 0.5799 -0.469 0.004 0.000 0.560 0.092 0.344
#> GSM121330 3 0.5941 0.556 0.168 0.000 0.588 0.000 0.244
#> GSM121335 3 0.6605 0.477 0.288 0.000 0.460 0.000 0.252
#> GSM121337 2 0.6446 0.490 0.000 0.644 0.136 0.128 0.092
#> GSM121338 2 0.5751 0.238 0.000 0.552 0.348 0.000 0.100
#> GSM121341 3 0.6593 0.484 0.284 0.000 0.464 0.000 0.252
#> GSM121342 3 0.6620 0.476 0.288 0.000 0.456 0.000 0.256
#> GSM121343 2 0.5851 0.237 0.000 0.548 0.340 0.000 0.112
#> GSM121344 3 0.6323 0.535 0.220 0.000 0.528 0.000 0.252
#> GSM121346 3 0.4873 0.571 0.068 0.000 0.688 0.000 0.244
#> GSM121347 2 0.7887 0.061 0.004 0.456 0.180 0.260 0.100
#> GSM121348 5 0.8070 0.000 0.004 0.128 0.304 0.152 0.412
#> GSM121350 3 0.4815 0.571 0.064 0.000 0.692 0.000 0.244
#> GSM121352 3 0.5844 0.560 0.156 0.000 0.600 0.000 0.244
#> GSM121354 3 0.6216 0.541 0.208 0.000 0.548 0.000 0.244
#> GSM120753 4 0.6047 0.382 0.000 0.376 0.000 0.500 0.124
#> GSM120761 4 0.5329 0.642 0.000 0.236 0.000 0.656 0.108
#> GSM120768 4 0.3767 0.732 0.000 0.120 0.000 0.812 0.068
#> GSM120781 2 0.5932 0.208 0.000 0.560 0.000 0.308 0.132
#> GSM120788 4 0.1571 0.715 0.000 0.004 0.000 0.936 0.060
#> GSM120760 4 0.2079 0.753 0.000 0.020 0.000 0.916 0.064
#> GSM120763 4 0.2260 0.753 0.000 0.028 0.000 0.908 0.064
#> GSM120764 4 0.0865 0.739 0.000 0.004 0.000 0.972 0.024
#> GSM120777 4 0.1704 0.707 0.000 0.004 0.000 0.928 0.068
#> GSM120786 4 0.0771 0.741 0.000 0.004 0.000 0.976 0.020
#> GSM121329 3 0.6127 0.539 0.096 0.000 0.572 0.020 0.312
#> GSM121331 3 0.6275 -0.566 0.000 0.000 0.480 0.156 0.364
#> GSM121333 3 0.6405 -0.552 0.004 0.000 0.484 0.156 0.356
#> GSM121345 3 0.6626 -0.595 0.004 0.000 0.448 0.192 0.356
#> GSM121356 3 0.6261 -0.551 0.000 0.000 0.488 0.156 0.356
#> GSM120754 4 0.0771 0.741 0.000 0.004 0.000 0.976 0.020
#> GSM120759 2 0.3585 0.734 0.004 0.772 0.000 0.004 0.220
#> GSM120762 2 0.5477 0.441 0.000 0.648 0.000 0.220 0.132
#> GSM120775 4 0.1282 0.726 0.000 0.004 0.000 0.952 0.044
#> GSM120776 4 0.6237 -0.284 0.000 0.000 0.196 0.540 0.264
#> GSM120782 4 0.2362 0.744 0.000 0.040 0.040 0.912 0.008
#> GSM120789 2 0.3691 0.757 0.004 0.804 0.000 0.028 0.164
#> GSM120790 2 0.3647 0.730 0.004 0.764 0.000 0.004 0.228
#> GSM120791 4 0.1670 0.757 0.000 0.052 0.000 0.936 0.012
#> GSM120755 2 0.4929 0.584 0.000 0.716 0.000 0.148 0.136
#> GSM120756 4 0.1768 0.704 0.000 0.004 0.000 0.924 0.072
#> GSM120769 4 0.6254 0.342 0.000 0.368 0.000 0.480 0.152
#> GSM120778 4 0.4916 0.699 0.000 0.160 0.000 0.716 0.124
#> GSM120792 4 0.3359 0.740 0.000 0.108 0.000 0.840 0.052
#> GSM121332 2 0.2929 0.764 0.004 0.876 0.000 0.044 0.076
#> GSM121334 4 0.5491 0.598 0.000 0.272 0.000 0.624 0.104
#> GSM121340 4 0.0671 0.744 0.000 0.004 0.000 0.980 0.016
#> GSM121351 2 0.3160 0.744 0.004 0.808 0.000 0.000 0.188
#> GSM121353 4 0.1430 0.722 0.000 0.004 0.000 0.944 0.052
#> GSM120758 4 0.6092 0.288 0.000 0.412 0.000 0.464 0.124
#> GSM120771 2 0.4845 0.609 0.000 0.724 0.000 0.148 0.128
#> GSM120772 4 0.5915 0.493 0.000 0.324 0.000 0.552 0.124
#> GSM120773 4 0.0324 0.746 0.000 0.004 0.000 0.992 0.004
#> GSM120774 4 0.5150 0.689 0.000 0.172 0.000 0.692 0.136
#> GSM120783 4 0.0324 0.746 0.000 0.004 0.000 0.992 0.004
#> GSM120787 4 0.5496 0.656 0.000 0.196 0.000 0.652 0.152
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM120719 3 0.4783 0.7431 0.152 0.000 0.724 0.000 0.084 NA
#> GSM120720 3 0.4385 0.7696 0.124 0.000 0.760 0.000 0.084 NA
#> GSM120765 2 0.1511 0.7010 0.000 0.944 0.000 0.012 0.012 NA
#> GSM120767 2 0.3627 0.6541 0.000 0.796 0.000 0.028 0.020 NA
#> GSM120784 2 0.1448 0.7008 0.000 0.948 0.000 0.012 0.016 NA
#> GSM121400 3 0.3001 0.6831 0.008 0.004 0.852 0.000 0.108 NA
#> GSM121401 3 0.0779 0.7904 0.008 0.000 0.976 0.000 0.008 NA
#> GSM121402 2 0.4249 0.6944 0.000 0.688 0.000 0.000 0.052 NA
#> GSM121403 3 0.5121 0.4647 0.000 0.112 0.696 0.000 0.148 NA
#> GSM121404 2 0.5020 0.6187 0.000 0.716 0.064 0.004 0.152 NA
#> GSM121405 3 0.0810 0.7876 0.004 0.004 0.976 0.000 0.008 NA
#> GSM121406 2 0.3565 0.6952 0.000 0.716 0.000 0.004 0.004 NA
#> GSM121408 2 0.2656 0.7094 0.000 0.860 0.000 0.012 0.008 NA
#> GSM121409 3 0.3266 0.6357 0.004 0.004 0.824 0.000 0.136 NA
#> GSM121410 3 0.3075 0.6730 0.004 0.008 0.848 0.000 0.108 NA
#> GSM121412 2 0.3555 0.6950 0.000 0.712 0.000 0.000 0.008 NA
#> GSM121413 2 0.3555 0.6950 0.000 0.712 0.000 0.000 0.008 NA
#> GSM121414 2 0.3555 0.6950 0.000 0.712 0.000 0.000 0.008 NA
#> GSM121415 2 0.1552 0.7093 0.000 0.940 0.000 0.004 0.020 NA
#> GSM121416 2 0.2383 0.6850 0.000 0.900 0.000 0.028 0.020 NA
#> GSM120591 3 0.2969 0.7500 0.020 0.000 0.860 0.000 0.088 NA
#> GSM120594 3 0.4385 0.7696 0.124 0.000 0.760 0.000 0.084 NA
#> GSM120718 3 0.4577 0.7557 0.144 0.000 0.740 0.000 0.084 NA
#> GSM121205 1 0.0146 0.9840 0.996 0.000 0.000 0.000 0.000 NA
#> GSM121206 1 0.0000 0.9852 1.000 0.000 0.000 0.000 0.000 NA
#> GSM121207 1 0.0146 0.9840 0.996 0.000 0.000 0.000 0.000 NA
#> GSM121208 1 0.1007 0.9459 0.956 0.000 0.044 0.000 0.000 NA
#> GSM121209 1 0.0000 0.9852 1.000 0.000 0.000 0.000 0.000 NA
#> GSM121210 1 0.0000 0.9852 1.000 0.000 0.000 0.000 0.000 NA
#> GSM121211 1 0.0000 0.9852 1.000 0.000 0.000 0.000 0.000 NA
#> GSM121212 1 0.0000 0.9852 1.000 0.000 0.000 0.000 0.000 NA
#> GSM121213 1 0.0000 0.9852 1.000 0.000 0.000 0.000 0.000 NA
#> GSM121214 1 0.0000 0.9852 1.000 0.000 0.000 0.000 0.000 NA
#> GSM121215 1 0.1524 0.9548 0.932 0.000 0.000 0.000 0.008 NA
#> GSM121216 1 0.1524 0.9548 0.932 0.000 0.000 0.000 0.008 NA
#> GSM121217 1 0.0000 0.9852 1.000 0.000 0.000 0.000 0.000 NA
#> GSM121218 1 0.0000 0.9852 1.000 0.000 0.000 0.000 0.000 NA
#> GSM121234 1 0.1524 0.9548 0.932 0.000 0.000 0.000 0.008 NA
#> GSM121243 1 0.1398 0.9589 0.940 0.000 0.000 0.000 0.008 NA
#> GSM121245 1 0.0000 0.9852 1.000 0.000 0.000 0.000 0.000 NA
#> GSM121246 3 0.4110 0.6441 0.248 0.000 0.712 0.000 0.008 NA
#> GSM121247 1 0.0146 0.9840 0.996 0.000 0.000 0.000 0.000 NA
#> GSM121248 1 0.0000 0.9852 1.000 0.000 0.000 0.000 0.000 NA
#> GSM120744 5 0.3991 0.3950 0.000 0.004 0.472 0.000 0.524 NA
#> GSM120745 5 0.3862 0.3932 0.000 0.000 0.476 0.000 0.524 NA
#> GSM120746 5 0.3862 0.3932 0.000 0.000 0.476 0.000 0.524 NA
#> GSM120747 5 0.3862 0.3932 0.000 0.000 0.476 0.000 0.524 NA
#> GSM120748 5 0.4080 0.3963 0.000 0.008 0.456 0.000 0.536 NA
#> GSM120749 5 0.3862 0.3932 0.000 0.000 0.476 0.000 0.524 NA
#> GSM120750 5 0.3862 0.3932 0.000 0.000 0.476 0.000 0.524 NA
#> GSM120751 5 0.3862 0.3932 0.000 0.000 0.476 0.000 0.524 NA
#> GSM120752 5 0.3862 0.3932 0.000 0.000 0.476 0.000 0.524 NA
#> GSM121336 2 0.3733 0.6933 0.000 0.700 0.000 0.004 0.008 NA
#> GSM121339 2 0.4545 0.6427 0.000 0.760 0.104 0.004 0.092 NA
#> GSM121349 2 0.3733 0.6933 0.000 0.700 0.000 0.004 0.008 NA
#> GSM121355 2 0.3733 0.6933 0.000 0.700 0.000 0.004 0.008 NA
#> GSM120757 5 0.6382 0.5855 0.000 0.000 0.144 0.068 0.536 NA
#> GSM120766 5 0.6304 0.5829 0.000 0.000 0.136 0.068 0.548 NA
#> GSM120770 2 0.2939 0.6869 0.000 0.868 0.012 0.012 0.084 NA
#> GSM120779 5 0.6411 0.5849 0.000 0.000 0.148 0.068 0.532 NA
#> GSM120780 5 0.6498 0.5349 0.000 0.080 0.128 0.032 0.608 NA
#> GSM121102 2 0.5306 0.5091 0.000 0.652 0.112 0.000 0.208 NA
#> GSM121203 5 0.4229 0.3931 0.000 0.000 0.436 0.000 0.548 NA
#> GSM121204 5 0.6183 0.5825 0.000 0.000 0.140 0.060 0.564 NA
#> GSM121330 3 0.1141 0.8189 0.052 0.000 0.948 0.000 0.000 NA
#> GSM121335 3 0.2320 0.8009 0.132 0.000 0.864 0.000 0.000 NA
#> GSM121337 2 0.6692 0.4707 0.000 0.584 0.048 0.196 0.100 NA
#> GSM121338 2 0.6517 0.3735 0.000 0.536 0.184 0.004 0.216 NA
#> GSM121341 3 0.2320 0.8009 0.132 0.000 0.864 0.000 0.000 NA
#> GSM121342 3 0.2320 0.8009 0.132 0.000 0.864 0.000 0.000 NA
#> GSM121343 2 0.6608 0.3549 0.000 0.524 0.188 0.004 0.220 NA
#> GSM121344 3 0.1908 0.8170 0.096 0.000 0.900 0.000 0.000 NA
#> GSM121346 3 0.0547 0.8042 0.020 0.000 0.980 0.000 0.000 NA
#> GSM121347 2 0.7467 0.2738 0.000 0.460 0.064 0.280 0.124 NA
#> GSM121348 5 0.6980 0.4567 0.000 0.092 0.064 0.052 0.508 NA
#> GSM121350 3 0.0458 0.8011 0.016 0.000 0.984 0.000 0.000 NA
#> GSM121352 3 0.1007 0.8168 0.044 0.000 0.956 0.000 0.000 NA
#> GSM121354 3 0.1610 0.8191 0.084 0.000 0.916 0.000 0.000 NA
#> GSM120753 4 0.6241 0.3617 0.000 0.340 0.000 0.452 0.020 NA
#> GSM120761 4 0.5808 0.5720 0.000 0.220 0.000 0.576 0.020 NA
#> GSM120768 4 0.3896 0.7163 0.000 0.068 0.000 0.784 0.012 NA
#> GSM120781 2 0.6239 0.0563 0.000 0.480 0.000 0.288 0.020 NA
#> GSM120788 4 0.3032 0.6519 0.000 0.000 0.000 0.840 0.056 NA
#> GSM120760 4 0.3195 0.7375 0.000 0.036 0.000 0.836 0.012 NA
#> GSM120763 4 0.3111 0.7357 0.000 0.032 0.000 0.836 0.008 NA
#> GSM120764 4 0.0622 0.7410 0.000 0.000 0.000 0.980 0.008 NA
#> GSM120777 4 0.3078 0.6479 0.000 0.000 0.000 0.836 0.056 NA
#> GSM120786 4 0.0436 0.7443 0.000 0.004 0.000 0.988 0.004 NA
#> GSM121329 3 0.2926 0.7713 0.040 0.000 0.876 0.004 0.040 NA
#> GSM121331 5 0.6411 0.5849 0.000 0.000 0.148 0.068 0.532 NA
#> GSM121333 5 0.6411 0.5849 0.000 0.000 0.148 0.068 0.532 NA
#> GSM121345 5 0.6673 0.5619 0.000 0.000 0.132 0.104 0.508 NA
#> GSM121356 5 0.6411 0.5849 0.000 0.000 0.148 0.068 0.532 NA
#> GSM120754 4 0.0520 0.7421 0.000 0.000 0.000 0.984 0.008 NA
#> GSM120759 2 0.5196 0.6407 0.000 0.548 0.004 0.004 0.072 NA
#> GSM120762 2 0.6070 0.2196 0.000 0.528 0.000 0.232 0.020 NA
#> GSM120775 4 0.1480 0.7250 0.000 0.000 0.000 0.940 0.020 NA
#> GSM120776 4 0.6200 -0.0727 0.000 0.000 0.016 0.452 0.336 NA
#> GSM120782 4 0.2186 0.7439 0.000 0.056 0.000 0.908 0.024 NA
#> GSM120789 2 0.5578 0.6379 0.000 0.628 0.004 0.088 0.040 NA
#> GSM120790 2 0.5147 0.6256 0.000 0.528 0.004 0.000 0.076 NA
#> GSM120791 4 0.2007 0.7454 0.000 0.044 0.000 0.916 0.004 NA
#> GSM120755 2 0.5839 0.3268 0.000 0.580 0.000 0.188 0.024 NA
#> GSM120756 4 0.3078 0.6479 0.000 0.000 0.000 0.836 0.056 NA
#> GSM120769 4 0.6369 0.3522 0.000 0.308 0.000 0.440 0.020 NA
#> GSM120778 4 0.4953 0.6753 0.000 0.088 0.000 0.684 0.024 NA
#> GSM120792 4 0.3307 0.7334 0.000 0.064 0.000 0.832 0.008 NA
#> GSM121332 2 0.3307 0.6908 0.000 0.820 0.000 0.072 0.000 NA
#> GSM121334 4 0.6040 0.5275 0.000 0.248 0.000 0.540 0.024 NA
#> GSM121340 4 0.1218 0.7433 0.000 0.004 0.000 0.956 0.012 NA
#> GSM121351 2 0.4679 0.6731 0.000 0.628 0.004 0.004 0.044 NA
#> GSM121353 4 0.1624 0.7272 0.000 0.004 0.000 0.936 0.020 NA
#> GSM120758 4 0.6345 0.2764 0.000 0.376 0.000 0.412 0.024 NA
#> GSM120771 2 0.5400 0.3691 0.000 0.624 0.000 0.180 0.012 NA
#> GSM120772 4 0.6284 0.4629 0.000 0.284 0.000 0.492 0.028 NA
#> GSM120773 4 0.0976 0.7449 0.000 0.008 0.000 0.968 0.008 NA
#> GSM120774 4 0.5843 0.6356 0.000 0.148 0.004 0.612 0.036 NA
#> GSM120783 4 0.0767 0.7435 0.000 0.004 0.000 0.976 0.008 NA
#> GSM120787 4 0.6138 0.5989 0.000 0.176 0.004 0.568 0.036 NA
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> SD:kmeans 111 2.81e-09 2
#> SD:kmeans 100 5.05e-19 3
#> SD:kmeans 100 1.50e-22 4
#> SD:kmeans 80 1.19e-20 5
#> SD:kmeans 94 5.79e-25 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 21512 rows and 119 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 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 0.899 0.943 0.975 0.5022 0.496 0.496
#> 3 3 0.768 0.849 0.923 0.2790 0.830 0.669
#> 4 4 0.687 0.668 0.848 0.1371 0.870 0.664
#> 5 5 0.698 0.698 0.833 0.0638 0.894 0.650
#> 6 6 0.737 0.734 0.834 0.0427 0.965 0.845
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
#> GSM120719 1 0.0000 0.958 1.000 0.000
#> GSM120720 1 0.0000 0.958 1.000 0.000
#> GSM120765 2 0.0000 0.988 0.000 1.000
#> GSM120767 2 0.0000 0.988 0.000 1.000
#> GSM120784 2 0.0000 0.988 0.000 1.000
#> GSM121400 1 0.0000 0.958 1.000 0.000
#> GSM121401 1 0.0000 0.958 1.000 0.000
#> GSM121402 2 0.0000 0.988 0.000 1.000
#> GSM121403 1 0.8016 0.701 0.756 0.244
#> GSM121404 2 0.0000 0.988 0.000 1.000
#> GSM121405 1 0.0000 0.958 1.000 0.000
#> GSM121406 2 0.0000 0.988 0.000 1.000
#> GSM121408 2 0.0000 0.988 0.000 1.000
#> GSM121409 1 0.0000 0.958 1.000 0.000
#> GSM121410 1 0.0000 0.958 1.000 0.000
#> GSM121412 2 0.0000 0.988 0.000 1.000
#> GSM121413 2 0.0000 0.988 0.000 1.000
#> GSM121414 2 0.0000 0.988 0.000 1.000
#> GSM121415 2 0.0000 0.988 0.000 1.000
#> GSM121416 2 0.0000 0.988 0.000 1.000
#> GSM120591 1 0.0000 0.958 1.000 0.000
#> GSM120594 1 0.0000 0.958 1.000 0.000
#> GSM120718 1 0.0000 0.958 1.000 0.000
#> GSM121205 1 0.0000 0.958 1.000 0.000
#> GSM121206 1 0.0000 0.958 1.000 0.000
#> GSM121207 1 0.0000 0.958 1.000 0.000
#> GSM121208 1 0.0000 0.958 1.000 0.000
#> GSM121209 1 0.0000 0.958 1.000 0.000
#> GSM121210 1 0.0000 0.958 1.000 0.000
#> GSM121211 1 0.0000 0.958 1.000 0.000
#> GSM121212 1 0.0000 0.958 1.000 0.000
#> GSM121213 1 0.0000 0.958 1.000 0.000
#> GSM121214 1 0.0000 0.958 1.000 0.000
#> GSM121215 1 0.0000 0.958 1.000 0.000
#> GSM121216 1 0.0000 0.958 1.000 0.000
#> GSM121217 1 0.0000 0.958 1.000 0.000
#> GSM121218 1 0.0000 0.958 1.000 0.000
#> GSM121234 1 0.0000 0.958 1.000 0.000
#> GSM121243 1 0.0000 0.958 1.000 0.000
#> GSM121245 1 0.0000 0.958 1.000 0.000
#> GSM121246 1 0.0000 0.958 1.000 0.000
#> GSM121247 1 0.0000 0.958 1.000 0.000
#> GSM121248 1 0.0000 0.958 1.000 0.000
#> GSM120744 1 0.9944 0.236 0.544 0.456
#> GSM120745 1 0.0000 0.958 1.000 0.000
#> GSM120746 1 0.7056 0.776 0.808 0.192
#> GSM120747 1 0.7056 0.776 0.808 0.192
#> GSM120748 2 0.7453 0.710 0.212 0.788
#> GSM120749 1 0.0000 0.958 1.000 0.000
#> GSM120750 1 0.7528 0.746 0.784 0.216
#> GSM120751 1 0.6973 0.781 0.812 0.188
#> GSM120752 1 0.0000 0.958 1.000 0.000
#> GSM121336 2 0.0000 0.988 0.000 1.000
#> GSM121339 2 0.0000 0.988 0.000 1.000
#> GSM121349 2 0.0000 0.988 0.000 1.000
#> GSM121355 2 0.0000 0.988 0.000 1.000
#> GSM120757 1 0.7139 0.772 0.804 0.196
#> GSM120766 1 0.9661 0.416 0.608 0.392
#> GSM120770 2 0.0000 0.988 0.000 1.000
#> GSM120779 1 0.0000 0.958 1.000 0.000
#> GSM120780 2 0.0376 0.985 0.004 0.996
#> GSM121102 2 0.0000 0.988 0.000 1.000
#> GSM121203 1 0.8555 0.648 0.720 0.280
#> GSM121204 1 0.0000 0.958 1.000 0.000
#> GSM121330 1 0.0000 0.958 1.000 0.000
#> GSM121335 1 0.0000 0.958 1.000 0.000
#> GSM121337 2 0.0000 0.988 0.000 1.000
#> GSM121338 2 0.0000 0.988 0.000 1.000
#> GSM121341 1 0.0000 0.958 1.000 0.000
#> GSM121342 1 0.0000 0.958 1.000 0.000
#> GSM121343 2 0.0000 0.988 0.000 1.000
#> GSM121344 1 0.0000 0.958 1.000 0.000
#> GSM121346 1 0.0000 0.958 1.000 0.000
#> GSM121347 2 0.0000 0.988 0.000 1.000
#> GSM121348 2 0.0000 0.988 0.000 1.000
#> GSM121350 1 0.0000 0.958 1.000 0.000
#> GSM121352 1 0.0000 0.958 1.000 0.000
#> GSM121354 1 0.0000 0.958 1.000 0.000
#> GSM120753 2 0.0000 0.988 0.000 1.000
#> GSM120761 2 0.0000 0.988 0.000 1.000
#> GSM120768 2 0.0000 0.988 0.000 1.000
#> GSM120781 2 0.0000 0.988 0.000 1.000
#> GSM120788 2 0.0000 0.988 0.000 1.000
#> GSM120760 2 0.0000 0.988 0.000 1.000
#> GSM120763 2 0.0000 0.988 0.000 1.000
#> GSM120764 2 0.0000 0.988 0.000 1.000
#> GSM120777 2 0.0000 0.988 0.000 1.000
#> GSM120786 2 0.0000 0.988 0.000 1.000
#> GSM121329 1 0.0000 0.958 1.000 0.000
#> GSM121331 1 0.0000 0.958 1.000 0.000
#> GSM121333 1 0.0000 0.958 1.000 0.000
#> GSM121345 1 0.0000 0.958 1.000 0.000
#> GSM121356 1 0.0000 0.958 1.000 0.000
#> GSM120754 2 0.0000 0.988 0.000 1.000
#> GSM120759 2 0.0000 0.988 0.000 1.000
#> GSM120762 2 0.0000 0.988 0.000 1.000
#> GSM120775 2 0.0000 0.988 0.000 1.000
#> GSM120776 2 0.0000 0.988 0.000 1.000
#> GSM120782 2 0.0000 0.988 0.000 1.000
#> GSM120789 2 0.0000 0.988 0.000 1.000
#> GSM120790 2 0.0000 0.988 0.000 1.000
#> GSM120791 2 0.0000 0.988 0.000 1.000
#> GSM120755 2 0.0000 0.988 0.000 1.000
#> GSM120756 2 0.8207 0.651 0.256 0.744
#> GSM120769 2 0.0000 0.988 0.000 1.000
#> GSM120778 2 0.0000 0.988 0.000 1.000
#> GSM120792 2 0.0000 0.988 0.000 1.000
#> GSM121332 2 0.0000 0.988 0.000 1.000
#> GSM121334 2 0.0000 0.988 0.000 1.000
#> GSM121340 2 0.0000 0.988 0.000 1.000
#> GSM121351 2 0.0000 0.988 0.000 1.000
#> GSM121353 2 0.6712 0.777 0.176 0.824
#> GSM120758 2 0.0000 0.988 0.000 1.000
#> GSM120771 2 0.0000 0.988 0.000 1.000
#> GSM120772 2 0.0000 0.988 0.000 1.000
#> GSM120773 2 0.0000 0.988 0.000 1.000
#> GSM120774 2 0.0000 0.988 0.000 1.000
#> GSM120783 2 0.0000 0.988 0.000 1.000
#> GSM120787 2 0.0000 0.988 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM120719 1 0.0000 0.932 1.000 0.000 0.000
#> GSM120720 1 0.0424 0.930 0.992 0.000 0.008
#> GSM120765 2 0.0000 0.943 0.000 1.000 0.000
#> GSM120767 2 0.0000 0.943 0.000 1.000 0.000
#> GSM120784 2 0.0000 0.943 0.000 1.000 0.000
#> GSM121400 1 0.5291 0.710 0.732 0.000 0.268
#> GSM121401 1 0.4605 0.781 0.796 0.000 0.204
#> GSM121402 2 0.0000 0.943 0.000 1.000 0.000
#> GSM121403 1 0.9612 0.173 0.452 0.216 0.332
#> GSM121404 2 0.3340 0.829 0.000 0.880 0.120
#> GSM121405 1 0.4605 0.781 0.796 0.000 0.204
#> GSM121406 2 0.0000 0.943 0.000 1.000 0.000
#> GSM121408 2 0.0000 0.943 0.000 1.000 0.000
#> GSM121409 1 0.5465 0.683 0.712 0.000 0.288
#> GSM121410 1 0.5291 0.709 0.732 0.000 0.268
#> GSM121412 2 0.0000 0.943 0.000 1.000 0.000
#> GSM121413 2 0.0000 0.943 0.000 1.000 0.000
#> GSM121414 2 0.0000 0.943 0.000 1.000 0.000
#> GSM121415 2 0.0000 0.943 0.000 1.000 0.000
#> GSM121416 2 0.0000 0.943 0.000 1.000 0.000
#> GSM120591 1 0.0592 0.928 0.988 0.000 0.012
#> GSM120594 1 0.0592 0.928 0.988 0.000 0.012
#> GSM120718 1 0.0000 0.932 1.000 0.000 0.000
#> GSM121205 1 0.0000 0.932 1.000 0.000 0.000
#> GSM121206 1 0.0000 0.932 1.000 0.000 0.000
#> GSM121207 1 0.0000 0.932 1.000 0.000 0.000
#> GSM121208 1 0.0000 0.932 1.000 0.000 0.000
#> GSM121209 1 0.0000 0.932 1.000 0.000 0.000
#> GSM121210 1 0.0000 0.932 1.000 0.000 0.000
#> GSM121211 1 0.0000 0.932 1.000 0.000 0.000
#> GSM121212 1 0.0000 0.932 1.000 0.000 0.000
#> GSM121213 1 0.0000 0.932 1.000 0.000 0.000
#> GSM121214 1 0.0000 0.932 1.000 0.000 0.000
#> GSM121215 1 0.0000 0.932 1.000 0.000 0.000
#> GSM121216 1 0.0000 0.932 1.000 0.000 0.000
#> GSM121217 1 0.0000 0.932 1.000 0.000 0.000
#> GSM121218 1 0.0000 0.932 1.000 0.000 0.000
#> GSM121234 1 0.0000 0.932 1.000 0.000 0.000
#> GSM121243 1 0.0000 0.932 1.000 0.000 0.000
#> GSM121245 1 0.0000 0.932 1.000 0.000 0.000
#> GSM121246 1 0.0000 0.932 1.000 0.000 0.000
#> GSM121247 1 0.0000 0.932 1.000 0.000 0.000
#> GSM121248 1 0.0000 0.932 1.000 0.000 0.000
#> GSM120744 3 0.1585 0.814 0.008 0.028 0.964
#> GSM120745 3 0.1289 0.805 0.032 0.000 0.968
#> GSM120746 3 0.1585 0.814 0.008 0.028 0.964
#> GSM120747 3 0.1832 0.812 0.008 0.036 0.956
#> GSM120748 3 0.2448 0.799 0.000 0.076 0.924
#> GSM120749 3 0.1289 0.805 0.032 0.000 0.968
#> GSM120750 3 0.1585 0.814 0.008 0.028 0.964
#> GSM120751 3 0.1453 0.813 0.008 0.024 0.968
#> GSM120752 3 0.1289 0.805 0.032 0.000 0.968
#> GSM121336 2 0.0000 0.943 0.000 1.000 0.000
#> GSM121339 2 0.2356 0.883 0.000 0.928 0.072
#> GSM121349 2 0.0000 0.943 0.000 1.000 0.000
#> GSM121355 2 0.0000 0.943 0.000 1.000 0.000
#> GSM120757 3 0.3500 0.787 0.116 0.004 0.880
#> GSM120766 3 0.0848 0.810 0.008 0.008 0.984
#> GSM120770 2 0.0592 0.936 0.000 0.988 0.012
#> GSM120779 3 0.4605 0.740 0.204 0.000 0.796
#> GSM120780 3 0.3267 0.782 0.000 0.116 0.884
#> GSM121102 2 0.6008 0.371 0.000 0.628 0.372
#> GSM121203 3 0.3129 0.791 0.008 0.088 0.904
#> GSM121204 3 0.5016 0.710 0.240 0.000 0.760
#> GSM121330 1 0.3412 0.855 0.876 0.000 0.124
#> GSM121335 1 0.0424 0.930 0.992 0.000 0.008
#> GSM121337 2 0.0000 0.943 0.000 1.000 0.000
#> GSM121338 2 0.5291 0.607 0.000 0.732 0.268
#> GSM121341 1 0.0424 0.930 0.992 0.000 0.008
#> GSM121342 1 0.0424 0.930 0.992 0.000 0.008
#> GSM121343 2 0.4974 0.662 0.000 0.764 0.236
#> GSM121344 1 0.0424 0.930 0.992 0.000 0.008
#> GSM121346 1 0.4062 0.821 0.836 0.000 0.164
#> GSM121347 2 0.0592 0.939 0.000 0.988 0.012
#> GSM121348 3 0.5988 0.455 0.000 0.368 0.632
#> GSM121350 1 0.4002 0.825 0.840 0.000 0.160
#> GSM121352 1 0.3412 0.855 0.876 0.000 0.124
#> GSM121354 1 0.3116 0.866 0.892 0.000 0.108
#> GSM120753 2 0.0000 0.943 0.000 1.000 0.000
#> GSM120761 2 0.0000 0.943 0.000 1.000 0.000
#> GSM120768 2 0.0592 0.937 0.000 0.988 0.012
#> GSM120781 2 0.0000 0.943 0.000 1.000 0.000
#> GSM120788 3 0.6410 0.318 0.004 0.420 0.576
#> GSM120760 2 0.1964 0.911 0.000 0.944 0.056
#> GSM120763 2 0.1643 0.918 0.000 0.956 0.044
#> GSM120764 2 0.3941 0.816 0.000 0.844 0.156
#> GSM120777 3 0.6244 0.259 0.000 0.440 0.560
#> GSM120786 2 0.3551 0.843 0.000 0.868 0.132
#> GSM121329 1 0.0000 0.932 1.000 0.000 0.000
#> GSM121331 3 0.4605 0.740 0.204 0.000 0.796
#> GSM121333 3 0.4605 0.740 0.204 0.000 0.796
#> GSM121345 3 0.5178 0.684 0.256 0.000 0.744
#> GSM121356 3 0.4452 0.748 0.192 0.000 0.808
#> GSM120754 2 0.3752 0.831 0.000 0.856 0.144
#> GSM120759 2 0.0000 0.943 0.000 1.000 0.000
#> GSM120762 2 0.0000 0.943 0.000 1.000 0.000
#> GSM120775 2 0.6026 0.372 0.000 0.624 0.376
#> GSM120776 3 0.5678 0.713 0.032 0.192 0.776
#> GSM120782 2 0.4121 0.784 0.000 0.832 0.168
#> GSM120789 2 0.0000 0.943 0.000 1.000 0.000
#> GSM120790 2 0.0237 0.941 0.000 0.996 0.004
#> GSM120791 2 0.1529 0.921 0.000 0.960 0.040
#> GSM120755 2 0.0000 0.943 0.000 1.000 0.000
#> GSM120756 3 0.8395 0.433 0.096 0.356 0.548
#> GSM120769 2 0.0000 0.943 0.000 1.000 0.000
#> GSM120778 2 0.0747 0.935 0.000 0.984 0.016
#> GSM120792 2 0.0424 0.939 0.000 0.992 0.008
#> GSM121332 2 0.0000 0.943 0.000 1.000 0.000
#> GSM121334 2 0.0000 0.943 0.000 1.000 0.000
#> GSM121340 2 0.2711 0.885 0.000 0.912 0.088
#> GSM121351 2 0.0000 0.943 0.000 1.000 0.000
#> GSM121353 2 0.4453 0.811 0.012 0.836 0.152
#> GSM120758 2 0.0000 0.943 0.000 1.000 0.000
#> GSM120771 2 0.0000 0.943 0.000 1.000 0.000
#> GSM120772 2 0.0000 0.943 0.000 1.000 0.000
#> GSM120773 2 0.2261 0.902 0.000 0.932 0.068
#> GSM120774 2 0.0424 0.939 0.000 0.992 0.008
#> GSM120783 2 0.2959 0.875 0.000 0.900 0.100
#> GSM120787 2 0.0000 0.943 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM120719 1 0.0000 0.90011 1.000 0.000 0.000 0.000
#> GSM120720 1 0.1209 0.88810 0.964 0.000 0.032 0.004
#> GSM120765 2 0.0000 0.81034 0.000 1.000 0.000 0.000
#> GSM120767 2 0.1474 0.80132 0.000 0.948 0.000 0.052
#> GSM120784 2 0.0000 0.81034 0.000 1.000 0.000 0.000
#> GSM121400 1 0.5856 0.26308 0.504 0.000 0.464 0.032
#> GSM121401 1 0.5815 0.35942 0.540 0.000 0.428 0.032
#> GSM121402 2 0.0000 0.81034 0.000 1.000 0.000 0.000
#> GSM121403 2 0.6461 0.00497 0.020 0.488 0.460 0.032
#> GSM121404 2 0.3450 0.68844 0.000 0.836 0.156 0.008
#> GSM121405 1 0.5827 0.34026 0.532 0.000 0.436 0.032
#> GSM121406 2 0.0000 0.81034 0.000 1.000 0.000 0.000
#> GSM121408 2 0.0000 0.81034 0.000 1.000 0.000 0.000
#> GSM121409 3 0.6291 0.00190 0.404 0.016 0.548 0.032
#> GSM121410 1 0.6547 0.23963 0.492 0.024 0.452 0.032
#> GSM121412 2 0.0000 0.81034 0.000 1.000 0.000 0.000
#> GSM121413 2 0.0000 0.81034 0.000 1.000 0.000 0.000
#> GSM121414 2 0.0000 0.81034 0.000 1.000 0.000 0.000
#> GSM121415 2 0.0000 0.81034 0.000 1.000 0.000 0.000
#> GSM121416 2 0.0336 0.81032 0.000 0.992 0.000 0.008
#> GSM120591 1 0.2125 0.86689 0.920 0.000 0.076 0.004
#> GSM120594 1 0.1209 0.88810 0.964 0.000 0.032 0.004
#> GSM120718 1 0.0376 0.89782 0.992 0.000 0.004 0.004
#> GSM121205 1 0.0000 0.90011 1.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.90011 1.000 0.000 0.000 0.000
#> GSM121207 1 0.0000 0.90011 1.000 0.000 0.000 0.000
#> GSM121208 1 0.0000 0.90011 1.000 0.000 0.000 0.000
#> GSM121209 1 0.0000 0.90011 1.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.90011 1.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.90011 1.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.90011 1.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.90011 1.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.90011 1.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.90011 1.000 0.000 0.000 0.000
#> GSM121216 1 0.0000 0.90011 1.000 0.000 0.000 0.000
#> GSM121217 1 0.0000 0.90011 1.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.90011 1.000 0.000 0.000 0.000
#> GSM121234 1 0.0000 0.90011 1.000 0.000 0.000 0.000
#> GSM121243 1 0.0000 0.90011 1.000 0.000 0.000 0.000
#> GSM121245 1 0.0000 0.90011 1.000 0.000 0.000 0.000
#> GSM121246 1 0.0000 0.90011 1.000 0.000 0.000 0.000
#> GSM121247 1 0.0000 0.90011 1.000 0.000 0.000 0.000
#> GSM121248 1 0.0000 0.90011 1.000 0.000 0.000 0.000
#> GSM120744 3 0.0000 0.83240 0.000 0.000 1.000 0.000
#> GSM120745 3 0.0000 0.83240 0.000 0.000 1.000 0.000
#> GSM120746 3 0.0000 0.83240 0.000 0.000 1.000 0.000
#> GSM120747 3 0.0000 0.83240 0.000 0.000 1.000 0.000
#> GSM120748 3 0.0000 0.83240 0.000 0.000 1.000 0.000
#> GSM120749 3 0.0000 0.83240 0.000 0.000 1.000 0.000
#> GSM120750 3 0.0000 0.83240 0.000 0.000 1.000 0.000
#> GSM120751 3 0.0000 0.83240 0.000 0.000 1.000 0.000
#> GSM120752 3 0.0000 0.83240 0.000 0.000 1.000 0.000
#> GSM121336 2 0.0000 0.81034 0.000 1.000 0.000 0.000
#> GSM121339 2 0.3257 0.69590 0.000 0.844 0.152 0.004
#> GSM121349 2 0.0000 0.81034 0.000 1.000 0.000 0.000
#> GSM121355 2 0.0000 0.81034 0.000 1.000 0.000 0.000
#> GSM120757 4 0.5780 -0.00167 0.028 0.000 0.476 0.496
#> GSM120766 3 0.5461 -0.02822 0.004 0.008 0.508 0.480
#> GSM120770 2 0.0188 0.80891 0.000 0.996 0.004 0.000
#> GSM120779 4 0.6074 0.03005 0.044 0.000 0.456 0.500
#> GSM120780 3 0.5560 0.52964 0.000 0.156 0.728 0.116
#> GSM121102 2 0.4356 0.53826 0.000 0.708 0.292 0.000
#> GSM121203 3 0.0000 0.83240 0.000 0.000 1.000 0.000
#> GSM121204 3 0.7382 0.24415 0.208 0.000 0.516 0.276
#> GSM121330 1 0.4375 0.75718 0.788 0.000 0.180 0.032
#> GSM121335 1 0.2124 0.87415 0.932 0.000 0.040 0.028
#> GSM121337 2 0.0817 0.80850 0.000 0.976 0.000 0.024
#> GSM121338 2 0.5511 0.34846 0.000 0.620 0.352 0.028
#> GSM121341 1 0.2214 0.87192 0.928 0.000 0.044 0.028
#> GSM121342 1 0.1174 0.89028 0.968 0.000 0.012 0.020
#> GSM121343 2 0.5105 0.50229 0.000 0.696 0.276 0.028
#> GSM121344 1 0.2699 0.85691 0.904 0.000 0.068 0.028
#> GSM121346 1 0.5141 0.65053 0.700 0.000 0.268 0.032
#> GSM121347 2 0.3428 0.73356 0.000 0.844 0.012 0.144
#> GSM121348 4 0.7139 0.26365 0.000 0.360 0.140 0.500
#> GSM121350 1 0.5321 0.60936 0.672 0.000 0.296 0.032
#> GSM121352 1 0.4764 0.71207 0.748 0.000 0.220 0.032
#> GSM121354 1 0.3958 0.79248 0.824 0.000 0.144 0.032
#> GSM120753 2 0.4040 0.67139 0.000 0.752 0.000 0.248
#> GSM120761 2 0.4500 0.58250 0.000 0.684 0.000 0.316
#> GSM120768 2 0.5000 0.18545 0.000 0.504 0.000 0.496
#> GSM120781 2 0.3726 0.70453 0.000 0.788 0.000 0.212
#> GSM120788 4 0.1182 0.63622 0.000 0.016 0.016 0.968
#> GSM120760 4 0.4382 0.44189 0.000 0.296 0.000 0.704
#> GSM120763 4 0.4522 0.39362 0.000 0.320 0.000 0.680
#> GSM120764 4 0.2216 0.66810 0.000 0.092 0.000 0.908
#> GSM120777 4 0.1151 0.64172 0.000 0.024 0.008 0.968
#> GSM120786 4 0.3123 0.64415 0.000 0.156 0.000 0.844
#> GSM121329 1 0.0188 0.89829 0.996 0.000 0.000 0.004
#> GSM121331 4 0.6192 0.06426 0.052 0.000 0.436 0.512
#> GSM121333 4 0.6261 0.05038 0.056 0.000 0.440 0.504
#> GSM121345 4 0.6774 0.18866 0.120 0.000 0.312 0.568
#> GSM121356 4 0.6204 0.03807 0.052 0.000 0.448 0.500
#> GSM120754 4 0.2589 0.67020 0.000 0.116 0.000 0.884
#> GSM120759 2 0.0188 0.81020 0.000 0.996 0.000 0.004
#> GSM120762 2 0.3172 0.74374 0.000 0.840 0.000 0.160
#> GSM120775 4 0.1824 0.65852 0.000 0.060 0.004 0.936
#> GSM120776 4 0.4008 0.43914 0.000 0.000 0.244 0.756
#> GSM120782 4 0.6497 0.34846 0.000 0.304 0.100 0.596
#> GSM120789 2 0.1637 0.79997 0.000 0.940 0.000 0.060
#> GSM120790 2 0.0469 0.80895 0.000 0.988 0.000 0.012
#> GSM120791 4 0.4730 0.27662 0.000 0.364 0.000 0.636
#> GSM120755 2 0.2647 0.76981 0.000 0.880 0.000 0.120
#> GSM120756 4 0.1262 0.63098 0.016 0.008 0.008 0.968
#> GSM120769 2 0.4356 0.61914 0.000 0.708 0.000 0.292
#> GSM120778 2 0.4941 0.34775 0.000 0.564 0.000 0.436
#> GSM120792 2 0.4985 0.26611 0.000 0.532 0.000 0.468
#> GSM121332 2 0.1792 0.79733 0.000 0.932 0.000 0.068
#> GSM121334 2 0.4277 0.63381 0.000 0.720 0.000 0.280
#> GSM121340 4 0.2868 0.65944 0.000 0.136 0.000 0.864
#> GSM121351 2 0.0000 0.81034 0.000 1.000 0.000 0.000
#> GSM121353 4 0.3142 0.66099 0.008 0.132 0.000 0.860
#> GSM120758 2 0.3837 0.69485 0.000 0.776 0.000 0.224
#> GSM120771 2 0.1302 0.80453 0.000 0.956 0.000 0.044
#> GSM120772 2 0.4304 0.62891 0.000 0.716 0.000 0.284
#> GSM120773 4 0.3688 0.58620 0.000 0.208 0.000 0.792
#> GSM120774 2 0.4697 0.51419 0.000 0.644 0.000 0.356
#> GSM120783 4 0.3266 0.63198 0.000 0.168 0.000 0.832
#> GSM120787 2 0.4356 0.61802 0.000 0.708 0.000 0.292
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM120719 1 0.0932 0.9113 0.972 0.000 0.004 0.004 0.020
#> GSM120720 1 0.4583 0.4341 0.672 0.000 0.296 0.000 0.032
#> GSM120765 2 0.0162 0.8317 0.000 0.996 0.000 0.000 0.004
#> GSM120767 2 0.1043 0.8270 0.000 0.960 0.000 0.040 0.000
#> GSM120784 2 0.0162 0.8317 0.000 0.996 0.000 0.000 0.004
#> GSM121400 3 0.2554 0.7795 0.072 0.000 0.892 0.000 0.036
#> GSM121401 3 0.3003 0.8001 0.092 0.000 0.864 0.000 0.044
#> GSM121402 2 0.0324 0.8312 0.000 0.992 0.004 0.000 0.004
#> GSM121403 3 0.3924 0.6258 0.004 0.120 0.808 0.000 0.068
#> GSM121404 2 0.3791 0.7200 0.000 0.812 0.076 0.000 0.112
#> GSM121405 3 0.3055 0.7795 0.072 0.000 0.864 0.000 0.064
#> GSM121406 2 0.0324 0.8325 0.000 0.992 0.000 0.004 0.004
#> GSM121408 2 0.0404 0.8322 0.000 0.988 0.000 0.012 0.000
#> GSM121409 3 0.3146 0.7241 0.052 0.000 0.856 0.000 0.092
#> GSM121410 3 0.2754 0.7828 0.080 0.004 0.884 0.000 0.032
#> GSM121412 2 0.0162 0.8317 0.000 0.996 0.000 0.000 0.004
#> GSM121413 2 0.0000 0.8321 0.000 1.000 0.000 0.000 0.000
#> GSM121414 2 0.0000 0.8321 0.000 1.000 0.000 0.000 0.000
#> GSM121415 2 0.0162 0.8317 0.000 0.996 0.000 0.000 0.004
#> GSM121416 2 0.0771 0.8324 0.000 0.976 0.000 0.020 0.004
#> GSM120591 1 0.5717 0.3479 0.608 0.000 0.260 0.000 0.132
#> GSM120594 1 0.4576 0.4955 0.692 0.000 0.268 0.000 0.040
#> GSM120718 1 0.3304 0.7242 0.816 0.000 0.168 0.000 0.016
#> GSM121205 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000
#> GSM121207 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000
#> GSM121208 1 0.0290 0.9262 0.992 0.000 0.008 0.000 0.000
#> GSM121209 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000
#> GSM121216 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000
#> GSM121217 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000
#> GSM121234 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000
#> GSM121243 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000
#> GSM121245 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000
#> GSM121246 1 0.1341 0.8834 0.944 0.000 0.056 0.000 0.000
#> GSM121247 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000
#> GSM121248 1 0.0000 0.9317 1.000 0.000 0.000 0.000 0.000
#> GSM120744 5 0.2439 0.6950 0.000 0.004 0.120 0.000 0.876
#> GSM120745 5 0.2280 0.6959 0.000 0.000 0.120 0.000 0.880
#> GSM120746 5 0.2329 0.6949 0.000 0.000 0.124 0.000 0.876
#> GSM120747 5 0.2471 0.6870 0.000 0.000 0.136 0.000 0.864
#> GSM120748 5 0.2488 0.6933 0.000 0.004 0.124 0.000 0.872
#> GSM120749 5 0.2329 0.6949 0.000 0.000 0.124 0.000 0.876
#> GSM120750 5 0.2329 0.6949 0.000 0.000 0.124 0.000 0.876
#> GSM120751 5 0.2329 0.6949 0.000 0.000 0.124 0.000 0.876
#> GSM120752 5 0.2179 0.6970 0.000 0.000 0.112 0.000 0.888
#> GSM121336 2 0.0000 0.8321 0.000 1.000 0.000 0.000 0.000
#> GSM121339 2 0.3390 0.7356 0.000 0.840 0.100 0.000 0.060
#> GSM121349 2 0.0162 0.8323 0.000 0.996 0.000 0.004 0.000
#> GSM121355 2 0.0162 0.8323 0.000 0.996 0.000 0.004 0.000
#> GSM120757 5 0.6529 0.4672 0.024 0.000 0.124 0.324 0.528
#> GSM120766 5 0.6000 0.4962 0.000 0.004 0.132 0.288 0.576
#> GSM120770 2 0.0992 0.8262 0.000 0.968 0.008 0.000 0.024
#> GSM120779 5 0.7662 0.4194 0.108 0.000 0.128 0.336 0.428
#> GSM120780 5 0.5591 0.5951 0.000 0.072 0.144 0.072 0.712
#> GSM121102 2 0.4902 0.5042 0.000 0.648 0.048 0.000 0.304
#> GSM121203 5 0.2516 0.6758 0.000 0.000 0.140 0.000 0.860
#> GSM121204 5 0.7270 0.3729 0.336 0.000 0.072 0.124 0.468
#> GSM121330 3 0.3074 0.8392 0.196 0.000 0.804 0.000 0.000
#> GSM121335 3 0.4126 0.6118 0.380 0.000 0.620 0.000 0.000
#> GSM121337 2 0.2987 0.7974 0.000 0.880 0.056 0.052 0.012
#> GSM121338 2 0.5678 0.4587 0.000 0.600 0.284 0.000 0.116
#> GSM121341 3 0.3966 0.6930 0.336 0.000 0.664 0.000 0.000
#> GSM121342 3 0.4235 0.5079 0.424 0.000 0.576 0.000 0.000
#> GSM121343 2 0.5486 0.3770 0.000 0.572 0.352 0.000 0.076
#> GSM121344 3 0.3586 0.7855 0.264 0.000 0.736 0.000 0.000
#> GSM121346 3 0.3171 0.8397 0.176 0.000 0.816 0.000 0.008
#> GSM121347 2 0.5195 0.6481 0.000 0.732 0.060 0.160 0.048
#> GSM121348 4 0.8335 -0.0779 0.000 0.280 0.144 0.344 0.232
#> GSM121350 3 0.3048 0.8400 0.176 0.000 0.820 0.000 0.004
#> GSM121352 3 0.3266 0.8371 0.200 0.000 0.796 0.000 0.004
#> GSM121354 3 0.3177 0.8328 0.208 0.000 0.792 0.000 0.000
#> GSM120753 2 0.3884 0.6013 0.000 0.708 0.000 0.288 0.004
#> GSM120761 2 0.4201 0.3306 0.000 0.592 0.000 0.408 0.000
#> GSM120768 4 0.4114 0.3623 0.000 0.376 0.000 0.624 0.000
#> GSM120781 2 0.3003 0.7344 0.000 0.812 0.000 0.188 0.000
#> GSM120788 4 0.0451 0.6812 0.000 0.000 0.008 0.988 0.004
#> GSM120760 4 0.3242 0.6697 0.000 0.216 0.000 0.784 0.000
#> GSM120763 4 0.3143 0.6811 0.000 0.204 0.000 0.796 0.000
#> GSM120764 4 0.1168 0.7100 0.000 0.032 0.000 0.960 0.008
#> GSM120777 4 0.0451 0.6817 0.000 0.000 0.008 0.988 0.004
#> GSM120786 4 0.1908 0.7344 0.000 0.092 0.000 0.908 0.000
#> GSM121329 1 0.2170 0.8411 0.904 0.000 0.088 0.004 0.004
#> GSM121331 5 0.7880 0.3959 0.132 0.000 0.132 0.340 0.396
#> GSM121333 5 0.7879 0.3944 0.136 0.000 0.128 0.340 0.396
#> GSM121345 4 0.8099 -0.2199 0.248 0.000 0.124 0.408 0.220
#> GSM121356 5 0.7843 0.4060 0.120 0.000 0.140 0.336 0.404
#> GSM120754 4 0.1410 0.7255 0.000 0.060 0.000 0.940 0.000
#> GSM120759 2 0.0324 0.8325 0.000 0.992 0.000 0.004 0.004
#> GSM120762 2 0.2179 0.7955 0.000 0.888 0.000 0.112 0.000
#> GSM120775 4 0.0324 0.6894 0.000 0.004 0.000 0.992 0.004
#> GSM120776 4 0.4735 0.2126 0.000 0.000 0.044 0.672 0.284
#> GSM120782 4 0.5899 0.4903 0.000 0.160 0.000 0.592 0.248
#> GSM120789 2 0.2020 0.8053 0.000 0.900 0.000 0.100 0.000
#> GSM120790 2 0.1372 0.8282 0.000 0.956 0.016 0.024 0.004
#> GSM120791 4 0.3586 0.6040 0.000 0.264 0.000 0.736 0.000
#> GSM120755 2 0.2424 0.7807 0.000 0.868 0.000 0.132 0.000
#> GSM120756 4 0.0451 0.6805 0.000 0.000 0.004 0.988 0.008
#> GSM120769 2 0.3774 0.5874 0.000 0.704 0.000 0.296 0.000
#> GSM120778 4 0.4297 0.0591 0.000 0.472 0.000 0.528 0.000
#> GSM120792 4 0.4088 0.3842 0.000 0.368 0.000 0.632 0.000
#> GSM121332 2 0.1704 0.8204 0.000 0.928 0.000 0.068 0.004
#> GSM121334 2 0.3949 0.5158 0.000 0.668 0.000 0.332 0.000
#> GSM121340 4 0.1792 0.7342 0.000 0.084 0.000 0.916 0.000
#> GSM121351 2 0.0000 0.8321 0.000 1.000 0.000 0.000 0.000
#> GSM121353 4 0.1798 0.7287 0.000 0.064 0.004 0.928 0.004
#> GSM120758 2 0.3210 0.7092 0.000 0.788 0.000 0.212 0.000
#> GSM120771 2 0.1792 0.8133 0.000 0.916 0.000 0.084 0.000
#> GSM120772 2 0.3636 0.6303 0.000 0.728 0.000 0.272 0.000
#> GSM120773 4 0.2561 0.7244 0.000 0.144 0.000 0.856 0.000
#> GSM120774 2 0.4278 0.1646 0.000 0.548 0.000 0.452 0.000
#> GSM120783 4 0.1851 0.7345 0.000 0.088 0.000 0.912 0.000
#> GSM120787 2 0.3876 0.5444 0.000 0.684 0.000 0.316 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM120719 1 0.2898 0.8276 0.876 0.000 0.028 0.004 0.056 0.036
#> GSM120720 1 0.6107 0.3835 0.564 0.000 0.276 0.004 0.060 0.096
#> GSM120765 2 0.1121 0.7984 0.000 0.964 0.008 0.016 0.004 0.008
#> GSM120767 2 0.2060 0.7832 0.000 0.900 0.016 0.084 0.000 0.000
#> GSM120784 2 0.1425 0.7966 0.000 0.952 0.012 0.008 0.020 0.008
#> GSM121400 3 0.2893 0.8132 0.032 0.000 0.868 0.000 0.076 0.024
#> GSM121401 3 0.2186 0.8478 0.048 0.000 0.908 0.000 0.008 0.036
#> GSM121402 2 0.1148 0.8000 0.000 0.960 0.004 0.016 0.020 0.000
#> GSM121403 3 0.4680 0.6381 0.000 0.092 0.740 0.000 0.124 0.044
#> GSM121404 2 0.5512 0.6255 0.000 0.684 0.084 0.008 0.080 0.144
#> GSM121405 3 0.2081 0.8403 0.036 0.000 0.916 0.000 0.012 0.036
#> GSM121406 2 0.0951 0.7977 0.000 0.968 0.004 0.008 0.020 0.000
#> GSM121408 2 0.0964 0.7977 0.000 0.968 0.004 0.016 0.012 0.000
#> GSM121409 3 0.4562 0.7301 0.048 0.000 0.744 0.000 0.148 0.060
#> GSM121410 3 0.3228 0.7932 0.028 0.000 0.844 0.000 0.096 0.032
#> GSM121412 2 0.1390 0.7919 0.000 0.948 0.016 0.004 0.032 0.000
#> GSM121413 2 0.0891 0.7942 0.000 0.968 0.008 0.000 0.024 0.000
#> GSM121414 2 0.1194 0.7931 0.000 0.956 0.008 0.004 0.032 0.000
#> GSM121415 2 0.1534 0.7931 0.000 0.944 0.016 0.004 0.032 0.004
#> GSM121416 2 0.2495 0.7949 0.000 0.896 0.012 0.052 0.036 0.004
#> GSM120591 1 0.6941 0.2011 0.448 0.000 0.228 0.004 0.064 0.256
#> GSM120594 1 0.6187 0.4171 0.572 0.000 0.248 0.004 0.064 0.112
#> GSM120718 1 0.5171 0.6074 0.688 0.000 0.192 0.004 0.056 0.060
#> GSM121205 1 0.0000 0.9098 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.9098 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121207 1 0.0000 0.9098 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121208 1 0.0260 0.9047 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM121209 1 0.0000 0.9098 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.9098 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.9098 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.9098 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.9098 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.9098 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.9098 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121216 1 0.0000 0.9098 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121217 1 0.0000 0.9098 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.9098 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121234 1 0.0000 0.9098 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121243 1 0.0000 0.9098 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121245 1 0.0000 0.9098 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121246 1 0.1663 0.8388 0.912 0.000 0.088 0.000 0.000 0.000
#> GSM121247 1 0.0547 0.8957 0.980 0.000 0.000 0.000 0.020 0.000
#> GSM121248 1 0.0000 0.9098 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM120744 6 0.0692 0.9600 0.000 0.000 0.020 0.000 0.004 0.976
#> GSM120745 6 0.1003 0.9554 0.000 0.000 0.020 0.000 0.016 0.964
#> GSM120746 6 0.0820 0.9596 0.000 0.000 0.016 0.000 0.012 0.972
#> GSM120747 6 0.0935 0.9525 0.000 0.000 0.032 0.000 0.004 0.964
#> GSM120748 6 0.1082 0.9486 0.000 0.000 0.040 0.000 0.004 0.956
#> GSM120749 6 0.1176 0.9511 0.000 0.000 0.024 0.000 0.020 0.956
#> GSM120750 6 0.0909 0.9582 0.000 0.000 0.020 0.000 0.012 0.968
#> GSM120751 6 0.0692 0.9598 0.000 0.000 0.020 0.000 0.004 0.976
#> GSM120752 6 0.1003 0.9554 0.000 0.000 0.020 0.000 0.016 0.964
#> GSM121336 2 0.0665 0.7959 0.000 0.980 0.004 0.008 0.008 0.000
#> GSM121339 2 0.4150 0.7313 0.000 0.804 0.076 0.016 0.060 0.044
#> GSM121349 2 0.0665 0.7959 0.000 0.980 0.004 0.008 0.008 0.000
#> GSM121355 2 0.0653 0.7965 0.000 0.980 0.004 0.012 0.004 0.000
#> GSM120757 5 0.4270 0.7963 0.008 0.000 0.000 0.100 0.748 0.144
#> GSM120766 5 0.3993 0.7692 0.000 0.004 0.012 0.052 0.776 0.156
#> GSM120770 2 0.3246 0.7742 0.000 0.856 0.024 0.012 0.076 0.032
#> GSM120779 5 0.4498 0.8241 0.040 0.000 0.000 0.104 0.756 0.100
#> GSM120780 5 0.5427 0.5719 0.000 0.048 0.032 0.024 0.648 0.248
#> GSM121102 2 0.5575 0.4508 0.000 0.584 0.056 0.000 0.056 0.304
#> GSM121203 6 0.4064 0.7576 0.000 0.012 0.084 0.000 0.132 0.772
#> GSM121204 5 0.6604 0.4374 0.288 0.000 0.008 0.036 0.480 0.188
#> GSM121330 3 0.2282 0.8576 0.088 0.000 0.888 0.000 0.000 0.024
#> GSM121335 3 0.3831 0.7136 0.268 0.000 0.712 0.000 0.008 0.012
#> GSM121337 2 0.5231 0.6491 0.000 0.684 0.064 0.076 0.176 0.000
#> GSM121338 2 0.6403 0.4766 0.000 0.568 0.220 0.004 0.104 0.104
#> GSM121341 3 0.3642 0.7571 0.236 0.000 0.744 0.000 0.008 0.012
#> GSM121342 3 0.3878 0.6684 0.296 0.000 0.688 0.000 0.008 0.008
#> GSM121343 2 0.6757 0.3100 0.000 0.468 0.292 0.008 0.184 0.048
#> GSM121344 3 0.3230 0.8007 0.192 0.000 0.792 0.000 0.008 0.008
#> GSM121346 3 0.2237 0.8557 0.068 0.000 0.896 0.000 0.000 0.036
#> GSM121347 2 0.7564 0.3160 0.000 0.452 0.068 0.212 0.216 0.052
#> GSM121348 5 0.4072 0.6990 0.000 0.116 0.020 0.052 0.796 0.016
#> GSM121350 3 0.2209 0.8568 0.072 0.000 0.900 0.000 0.004 0.024
#> GSM121352 3 0.2333 0.8573 0.092 0.000 0.884 0.000 0.000 0.024
#> GSM121354 3 0.2312 0.8511 0.112 0.000 0.876 0.000 0.000 0.012
#> GSM120753 2 0.4330 0.5343 0.000 0.660 0.004 0.308 0.020 0.008
#> GSM120761 4 0.4635 0.1138 0.000 0.444 0.012 0.524 0.020 0.000
#> GSM120768 4 0.3539 0.6726 0.000 0.208 0.008 0.768 0.016 0.000
#> GSM120781 2 0.4173 0.6160 0.000 0.708 0.016 0.256 0.016 0.004
#> GSM120788 4 0.2734 0.6631 0.000 0.000 0.008 0.840 0.148 0.004
#> GSM120760 4 0.2971 0.7482 0.000 0.116 0.012 0.848 0.024 0.000
#> GSM120763 4 0.3219 0.7399 0.000 0.132 0.012 0.828 0.028 0.000
#> GSM120764 4 0.1462 0.7390 0.000 0.008 0.000 0.936 0.056 0.000
#> GSM120777 4 0.3152 0.6044 0.000 0.000 0.008 0.792 0.196 0.004
#> GSM120786 4 0.1562 0.7578 0.000 0.032 0.000 0.940 0.024 0.004
#> GSM121329 1 0.3746 0.7105 0.780 0.000 0.140 0.000 0.080 0.000
#> GSM121331 5 0.4185 0.8278 0.044 0.000 0.000 0.116 0.780 0.060
#> GSM121333 5 0.4468 0.8258 0.060 0.000 0.000 0.120 0.760 0.060
#> GSM121345 5 0.4800 0.7901 0.088 0.000 0.008 0.144 0.732 0.028
#> GSM121356 5 0.4339 0.8295 0.052 0.000 0.004 0.096 0.780 0.068
#> GSM120754 4 0.2763 0.7557 0.000 0.052 0.012 0.880 0.052 0.004
#> GSM120759 2 0.1620 0.7984 0.000 0.940 0.012 0.024 0.024 0.000
#> GSM120762 2 0.3163 0.7180 0.000 0.808 0.008 0.172 0.012 0.000
#> GSM120775 4 0.2417 0.7177 0.000 0.004 0.012 0.888 0.088 0.008
#> GSM120776 4 0.6045 -0.0566 0.000 0.000 0.024 0.472 0.368 0.136
#> GSM120782 4 0.6178 0.5824 0.000 0.132 0.028 0.620 0.044 0.176
#> GSM120789 2 0.3252 0.7599 0.000 0.828 0.012 0.128 0.032 0.000
#> GSM120790 2 0.3529 0.7551 0.000 0.816 0.016 0.048 0.120 0.000
#> GSM120791 4 0.3344 0.7302 0.000 0.160 0.004 0.808 0.024 0.004
#> GSM120755 2 0.2900 0.7667 0.000 0.856 0.012 0.112 0.016 0.004
#> GSM120756 4 0.2944 0.6592 0.000 0.000 0.012 0.832 0.148 0.008
#> GSM120769 2 0.4180 0.4736 0.000 0.632 0.008 0.348 0.012 0.000
#> GSM120778 4 0.4494 0.4580 0.000 0.324 0.016 0.640 0.016 0.004
#> GSM120792 4 0.3990 0.6328 0.000 0.240 0.012 0.728 0.016 0.004
#> GSM121332 2 0.3226 0.7687 0.000 0.836 0.020 0.116 0.028 0.000
#> GSM121334 2 0.4643 0.3970 0.000 0.596 0.016 0.364 0.024 0.000
#> GSM121340 4 0.1989 0.7533 0.000 0.028 0.004 0.916 0.052 0.000
#> GSM121351 2 0.1059 0.7968 0.000 0.964 0.000 0.016 0.016 0.004
#> GSM121353 4 0.2594 0.7314 0.000 0.012 0.020 0.888 0.072 0.008
#> GSM120758 2 0.4014 0.6106 0.000 0.704 0.012 0.268 0.016 0.000
#> GSM120771 2 0.2520 0.7711 0.000 0.872 0.008 0.108 0.012 0.000
#> GSM120772 2 0.4701 0.3005 0.000 0.556 0.008 0.408 0.024 0.004
#> GSM120773 4 0.1682 0.7596 0.000 0.052 0.000 0.928 0.020 0.000
#> GSM120774 4 0.5235 0.2761 0.000 0.380 0.016 0.552 0.044 0.008
#> GSM120783 4 0.1088 0.7556 0.000 0.024 0.000 0.960 0.016 0.000
#> GSM120787 2 0.4943 0.1549 0.000 0.516 0.016 0.440 0.020 0.008
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> SD:skmeans 117 5.29e-10 2
#> SD:skmeans 112 3.67e-18 3
#> SD:skmeans 95 4.54e-22 4
#> SD:skmeans 98 7.05e-30 5
#> SD:skmeans 103 1.04e-33 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 21512 rows and 119 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 0.465 0.743 0.877 0.4676 0.526 0.526
#> 3 3 0.382 0.522 0.765 0.4062 0.759 0.562
#> 4 4 0.531 0.663 0.785 0.1275 0.761 0.426
#> 5 5 0.640 0.586 0.769 0.0738 0.904 0.658
#> 6 6 0.725 0.644 0.795 0.0401 0.898 0.571
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
#> GSM120719 1 0.0000 0.912 1.000 0.000
#> GSM120720 1 0.0376 0.910 0.996 0.004
#> GSM120765 2 0.0000 0.809 0.000 1.000
#> GSM120767 2 0.0000 0.809 0.000 1.000
#> GSM120784 2 0.0000 0.809 0.000 1.000
#> GSM121400 2 0.9850 0.474 0.428 0.572
#> GSM121401 1 0.9977 -0.213 0.528 0.472
#> GSM121402 2 0.0000 0.809 0.000 1.000
#> GSM121403 2 0.9710 0.530 0.400 0.600
#> GSM121404 2 0.9522 0.574 0.372 0.628
#> GSM121405 1 0.9998 -0.279 0.508 0.492
#> GSM121406 2 0.0000 0.809 0.000 1.000
#> GSM121408 2 0.0376 0.807 0.004 0.996
#> GSM121409 2 0.9881 0.456 0.436 0.564
#> GSM121410 2 0.9850 0.472 0.428 0.572
#> GSM121412 2 0.3431 0.786 0.064 0.936
#> GSM121413 2 0.0000 0.809 0.000 1.000
#> GSM121414 2 0.0672 0.807 0.008 0.992
#> GSM121415 2 0.5408 0.760 0.124 0.876
#> GSM121416 2 0.0000 0.809 0.000 1.000
#> GSM120591 1 0.4298 0.842 0.912 0.088
#> GSM120594 1 0.0938 0.905 0.988 0.012
#> GSM120718 1 0.0000 0.912 1.000 0.000
#> GSM121205 1 0.0000 0.912 1.000 0.000
#> GSM121206 1 0.0000 0.912 1.000 0.000
#> GSM121207 1 0.0000 0.912 1.000 0.000
#> GSM121208 1 0.0000 0.912 1.000 0.000
#> GSM121209 1 0.0000 0.912 1.000 0.000
#> GSM121210 1 0.0000 0.912 1.000 0.000
#> GSM121211 1 0.0000 0.912 1.000 0.000
#> GSM121212 1 0.0000 0.912 1.000 0.000
#> GSM121213 1 0.0000 0.912 1.000 0.000
#> GSM121214 1 0.0000 0.912 1.000 0.000
#> GSM121215 1 0.0000 0.912 1.000 0.000
#> GSM121216 1 0.0000 0.912 1.000 0.000
#> GSM121217 1 0.0000 0.912 1.000 0.000
#> GSM121218 1 0.0000 0.912 1.000 0.000
#> GSM121234 1 0.0000 0.912 1.000 0.000
#> GSM121243 1 0.0000 0.912 1.000 0.000
#> GSM121245 1 0.0000 0.912 1.000 0.000
#> GSM121246 1 0.0000 0.912 1.000 0.000
#> GSM121247 1 0.0000 0.912 1.000 0.000
#> GSM121248 1 0.0000 0.912 1.000 0.000
#> GSM120744 2 0.9427 0.587 0.360 0.640
#> GSM120745 2 0.9833 0.483 0.424 0.576
#> GSM120746 2 0.9608 0.555 0.384 0.616
#> GSM120747 2 0.9635 0.549 0.388 0.612
#> GSM120748 2 0.9286 0.605 0.344 0.656
#> GSM120749 2 0.9754 0.516 0.408 0.592
#> GSM120750 2 0.9635 0.549 0.388 0.612
#> GSM120751 2 0.9661 0.543 0.392 0.608
#> GSM120752 2 0.9661 0.543 0.392 0.608
#> GSM121336 2 0.0000 0.809 0.000 1.000
#> GSM121339 2 0.9209 0.610 0.336 0.664
#> GSM121349 2 0.0000 0.809 0.000 1.000
#> GSM121355 2 0.0000 0.809 0.000 1.000
#> GSM120757 2 0.9732 0.520 0.404 0.596
#> GSM120766 2 0.9522 0.572 0.372 0.628
#> GSM120770 2 0.6973 0.729 0.188 0.812
#> GSM120779 1 0.6973 0.717 0.812 0.188
#> GSM120780 2 0.9000 0.632 0.316 0.684
#> GSM121102 2 0.7602 0.710 0.220 0.780
#> GSM121203 2 0.9608 0.555 0.384 0.616
#> GSM121204 1 0.4939 0.824 0.892 0.108
#> GSM121330 1 0.1633 0.897 0.976 0.024
#> GSM121335 1 0.0000 0.912 1.000 0.000
#> GSM121337 2 0.9460 0.582 0.364 0.636
#> GSM121338 2 0.9522 0.573 0.372 0.628
#> GSM121341 1 0.0000 0.912 1.000 0.000
#> GSM121342 1 0.0000 0.912 1.000 0.000
#> GSM121343 2 0.9635 0.550 0.388 0.612
#> GSM121344 1 0.0000 0.912 1.000 0.000
#> GSM121346 1 0.7056 0.693 0.808 0.192
#> GSM121347 2 0.9686 0.536 0.396 0.604
#> GSM121348 2 0.9580 0.561 0.380 0.620
#> GSM121350 1 0.8608 0.482 0.716 0.284
#> GSM121352 1 0.1184 0.903 0.984 0.016
#> GSM121354 1 0.0000 0.912 1.000 0.000
#> GSM120753 2 0.0000 0.809 0.000 1.000
#> GSM120761 2 0.0000 0.809 0.000 1.000
#> GSM120768 2 0.0000 0.809 0.000 1.000
#> GSM120781 2 0.0000 0.809 0.000 1.000
#> GSM120788 2 0.9754 0.429 0.408 0.592
#> GSM120760 2 0.0000 0.809 0.000 1.000
#> GSM120763 2 0.0000 0.809 0.000 1.000
#> GSM120764 2 0.0672 0.807 0.008 0.992
#> GSM120777 2 0.9866 0.343 0.432 0.568
#> GSM120786 2 0.0000 0.809 0.000 1.000
#> GSM121329 1 0.0000 0.912 1.000 0.000
#> GSM121331 1 0.6887 0.720 0.816 0.184
#> GSM121333 1 0.6712 0.735 0.824 0.176
#> GSM121345 1 0.6148 0.765 0.848 0.152
#> GSM121356 1 0.7815 0.633 0.768 0.232
#> GSM120754 2 0.0000 0.809 0.000 1.000
#> GSM120759 2 0.0000 0.809 0.000 1.000
#> GSM120762 2 0.0000 0.809 0.000 1.000
#> GSM120775 2 0.6247 0.746 0.156 0.844
#> GSM120776 2 0.8555 0.661 0.280 0.720
#> GSM120782 2 0.6712 0.735 0.176 0.824
#> GSM120789 2 0.0000 0.809 0.000 1.000
#> GSM120790 2 0.0000 0.809 0.000 1.000
#> GSM120791 2 0.0000 0.809 0.000 1.000
#> GSM120755 2 0.0000 0.809 0.000 1.000
#> GSM120756 1 0.6623 0.748 0.828 0.172
#> GSM120769 2 0.0000 0.809 0.000 1.000
#> GSM120778 2 0.0000 0.809 0.000 1.000
#> GSM120792 2 0.0376 0.808 0.004 0.996
#> GSM121332 2 0.0000 0.809 0.000 1.000
#> GSM121334 2 0.0000 0.809 0.000 1.000
#> GSM121340 2 0.9358 0.471 0.352 0.648
#> GSM121351 2 0.0000 0.809 0.000 1.000
#> GSM121353 1 0.5737 0.792 0.864 0.136
#> GSM120758 2 0.0000 0.809 0.000 1.000
#> GSM120771 2 0.0000 0.809 0.000 1.000
#> GSM120772 2 0.0000 0.809 0.000 1.000
#> GSM120773 2 0.0000 0.809 0.000 1.000
#> GSM120774 2 0.0000 0.809 0.000 1.000
#> GSM120783 2 0.0000 0.809 0.000 1.000
#> GSM120787 2 0.0672 0.807 0.008 0.992
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM120719 1 0.0592 0.7856 0.988 0.000 0.012
#> GSM120720 1 0.5016 0.6628 0.760 0.000 0.240
#> GSM120765 2 0.5254 0.6255 0.000 0.736 0.264
#> GSM120767 3 0.5760 0.1309 0.000 0.328 0.672
#> GSM120784 2 0.6154 0.4557 0.000 0.592 0.408
#> GSM121400 3 0.6187 0.5369 0.028 0.248 0.724
#> GSM121401 3 0.1163 0.6147 0.028 0.000 0.972
#> GSM121402 2 0.4178 0.6606 0.000 0.828 0.172
#> GSM121403 3 0.4840 0.5441 0.016 0.168 0.816
#> GSM121404 3 0.4963 0.5341 0.008 0.200 0.792
#> GSM121405 3 0.4196 0.5796 0.024 0.112 0.864
#> GSM121406 2 0.5733 0.5463 0.000 0.676 0.324
#> GSM121408 3 0.6180 0.1118 0.000 0.416 0.584
#> GSM121409 3 0.4563 0.6030 0.036 0.112 0.852
#> GSM121410 3 0.6742 0.4811 0.028 0.316 0.656
#> GSM121412 3 0.6274 -0.0223 0.000 0.456 0.544
#> GSM121413 2 0.5431 0.6015 0.000 0.716 0.284
#> GSM121414 2 0.6225 0.2945 0.000 0.568 0.432
#> GSM121415 2 0.5443 0.5870 0.004 0.736 0.260
#> GSM121416 2 0.2796 0.6675 0.000 0.908 0.092
#> GSM120591 3 0.6235 -0.1176 0.436 0.000 0.564
#> GSM120594 1 0.5760 0.5850 0.672 0.000 0.328
#> GSM120718 1 0.0747 0.7843 0.984 0.000 0.016
#> GSM121205 1 0.0000 0.7900 1.000 0.000 0.000
#> GSM121206 1 0.0000 0.7900 1.000 0.000 0.000
#> GSM121207 1 0.0000 0.7900 1.000 0.000 0.000
#> GSM121208 1 0.0000 0.7900 1.000 0.000 0.000
#> GSM121209 1 0.0000 0.7900 1.000 0.000 0.000
#> GSM121210 1 0.0000 0.7900 1.000 0.000 0.000
#> GSM121211 1 0.0000 0.7900 1.000 0.000 0.000
#> GSM121212 1 0.0000 0.7900 1.000 0.000 0.000
#> GSM121213 1 0.0000 0.7900 1.000 0.000 0.000
#> GSM121214 1 0.0000 0.7900 1.000 0.000 0.000
#> GSM121215 1 0.0000 0.7900 1.000 0.000 0.000
#> GSM121216 1 0.0000 0.7900 1.000 0.000 0.000
#> GSM121217 1 0.0000 0.7900 1.000 0.000 0.000
#> GSM121218 1 0.0000 0.7900 1.000 0.000 0.000
#> GSM121234 1 0.0000 0.7900 1.000 0.000 0.000
#> GSM121243 1 0.0000 0.7900 1.000 0.000 0.000
#> GSM121245 1 0.0000 0.7900 1.000 0.000 0.000
#> GSM121246 1 0.4654 0.6810 0.792 0.000 0.208
#> GSM121247 1 0.0000 0.7900 1.000 0.000 0.000
#> GSM121248 1 0.0000 0.7900 1.000 0.000 0.000
#> GSM120744 3 0.4399 0.5052 0.000 0.188 0.812
#> GSM120745 3 0.2651 0.6261 0.012 0.060 0.928
#> GSM120746 3 0.2066 0.6259 0.000 0.060 0.940
#> GSM120747 3 0.1753 0.6255 0.000 0.048 0.952
#> GSM120748 3 0.2066 0.6236 0.000 0.060 0.940
#> GSM120749 3 0.1753 0.6255 0.000 0.048 0.952
#> GSM120750 3 0.3038 0.6039 0.000 0.104 0.896
#> GSM120751 3 0.2066 0.6254 0.000 0.060 0.940
#> GSM120752 3 0.4206 0.6036 0.040 0.088 0.872
#> GSM121336 2 0.6095 0.4141 0.000 0.608 0.392
#> GSM121339 3 0.2356 0.6204 0.000 0.072 0.928
#> GSM121349 2 0.5058 0.6393 0.000 0.756 0.244
#> GSM121355 2 0.6062 0.4317 0.000 0.616 0.384
#> GSM120757 3 0.9256 0.1001 0.156 0.400 0.444
#> GSM120766 3 0.7043 0.1897 0.020 0.448 0.532
#> GSM120770 3 0.6309 -0.0283 0.000 0.496 0.504
#> GSM120779 1 0.9389 0.1905 0.468 0.352 0.180
#> GSM120780 2 0.6804 -0.0814 0.012 0.528 0.460
#> GSM121102 3 0.4605 0.5431 0.000 0.204 0.796
#> GSM121203 3 0.5178 0.4902 0.000 0.256 0.744
#> GSM121204 1 0.8853 0.3624 0.572 0.176 0.252
#> GSM121330 1 0.6235 0.4377 0.564 0.000 0.436
#> GSM121335 1 0.6079 0.5050 0.612 0.000 0.388
#> GSM121337 3 0.6950 0.1245 0.016 0.476 0.508
#> GSM121338 3 0.5020 0.5378 0.012 0.192 0.796
#> GSM121341 1 0.6154 0.4783 0.592 0.000 0.408
#> GSM121342 1 0.6215 0.4500 0.572 0.000 0.428
#> GSM121343 3 0.6027 0.5118 0.016 0.272 0.712
#> GSM121344 1 0.6235 0.4377 0.564 0.000 0.436
#> GSM121346 3 0.5291 0.3276 0.268 0.000 0.732
#> GSM121347 2 0.6937 0.0270 0.020 0.576 0.404
#> GSM121348 2 0.6341 0.2771 0.016 0.672 0.312
#> GSM121350 3 0.5785 0.2078 0.332 0.000 0.668
#> GSM121352 1 0.6280 0.3938 0.540 0.000 0.460
#> GSM121354 1 0.6235 0.4377 0.564 0.000 0.436
#> GSM120753 2 0.5016 0.6591 0.000 0.760 0.240
#> GSM120761 2 0.1860 0.6760 0.000 0.948 0.052
#> GSM120768 2 0.3412 0.6811 0.000 0.876 0.124
#> GSM120781 2 0.4178 0.6675 0.000 0.828 0.172
#> GSM120788 2 0.7147 0.4292 0.076 0.696 0.228
#> GSM120760 2 0.0747 0.6568 0.000 0.984 0.016
#> GSM120763 2 0.0237 0.6536 0.000 0.996 0.004
#> GSM120764 2 0.3686 0.6003 0.000 0.860 0.140
#> GSM120777 2 0.8839 0.2133 0.256 0.572 0.172
#> GSM120786 2 0.3116 0.6163 0.000 0.892 0.108
#> GSM121329 1 0.6155 0.5731 0.664 0.008 0.328
#> GSM121331 1 0.9520 0.1611 0.452 0.352 0.196
#> GSM121333 1 0.7925 0.3948 0.584 0.344 0.072
#> GSM121345 1 0.7116 0.4647 0.636 0.324 0.040
#> GSM121356 3 0.9996 0.0982 0.320 0.336 0.344
#> GSM120754 2 0.5621 0.4037 0.000 0.692 0.308
#> GSM120759 2 0.5098 0.6324 0.000 0.752 0.248
#> GSM120762 2 0.4062 0.6688 0.000 0.836 0.164
#> GSM120775 2 0.5553 0.4544 0.004 0.724 0.272
#> GSM120776 3 0.8891 -0.0262 0.120 0.432 0.448
#> GSM120782 3 0.6154 -0.1377 0.000 0.408 0.592
#> GSM120789 2 0.6302 0.1878 0.000 0.520 0.480
#> GSM120790 2 0.3879 0.6184 0.000 0.848 0.152
#> GSM120791 2 0.0747 0.6603 0.000 0.984 0.016
#> GSM120755 2 0.5926 0.4896 0.000 0.644 0.356
#> GSM120756 1 0.6402 0.5901 0.724 0.236 0.040
#> GSM120769 2 0.4346 0.6609 0.000 0.816 0.184
#> GSM120778 2 0.3340 0.6784 0.000 0.880 0.120
#> GSM120792 2 0.5968 0.5271 0.000 0.636 0.364
#> GSM121332 2 0.6126 0.3215 0.000 0.600 0.400
#> GSM121334 2 0.0592 0.6580 0.000 0.988 0.012
#> GSM121340 2 0.6192 0.5133 0.060 0.764 0.176
#> GSM121351 2 0.4842 0.6506 0.000 0.776 0.224
#> GSM121353 1 0.9153 0.3567 0.524 0.176 0.300
#> GSM120758 2 0.2959 0.6834 0.000 0.900 0.100
#> GSM120771 2 0.3340 0.6583 0.000 0.880 0.120
#> GSM120772 2 0.3551 0.6883 0.000 0.868 0.132
#> GSM120773 2 0.4062 0.5872 0.000 0.836 0.164
#> GSM120774 2 0.6274 0.3950 0.000 0.544 0.456
#> GSM120783 2 0.3116 0.6255 0.000 0.892 0.108
#> GSM120787 2 0.5733 0.5913 0.000 0.676 0.324
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM120719 1 0.0188 0.967 0.996 0.000 0.000 0.004
#> GSM120720 4 0.6743 0.282 0.392 0.000 0.096 0.512
#> GSM120765 2 0.3448 0.730 0.000 0.828 0.168 0.004
#> GSM120767 2 0.4720 0.512 0.000 0.672 0.324 0.004
#> GSM120784 2 0.5169 0.590 0.000 0.696 0.272 0.032
#> GSM121400 4 0.0000 0.809 0.000 0.000 0.000 1.000
#> GSM121401 4 0.0188 0.809 0.000 0.000 0.004 0.996
#> GSM121402 2 0.4988 0.690 0.000 0.692 0.288 0.020
#> GSM121403 4 0.0336 0.806 0.000 0.008 0.000 0.992
#> GSM121404 4 0.6921 0.267 0.000 0.160 0.260 0.580
#> GSM121405 4 0.0336 0.808 0.000 0.000 0.008 0.992
#> GSM121406 2 0.3450 0.724 0.000 0.836 0.156 0.008
#> GSM121408 2 0.6751 0.271 0.000 0.508 0.096 0.396
#> GSM121409 4 0.2281 0.749 0.000 0.000 0.096 0.904
#> GSM121410 4 0.0707 0.799 0.000 0.020 0.000 0.980
#> GSM121412 2 0.5902 0.619 0.000 0.700 0.140 0.160
#> GSM121413 2 0.4707 0.714 0.000 0.760 0.204 0.036
#> GSM121414 2 0.5427 0.664 0.000 0.736 0.164 0.100
#> GSM121415 2 0.5426 0.721 0.000 0.708 0.232 0.060
#> GSM121416 2 0.4584 0.696 0.000 0.696 0.300 0.004
#> GSM120591 3 0.7552 0.198 0.164 0.004 0.440 0.392
#> GSM120594 4 0.6688 0.224 0.420 0.000 0.088 0.492
#> GSM120718 1 0.1557 0.917 0.944 0.000 0.000 0.056
#> GSM121205 1 0.0000 0.971 1.000 0.000 0.000 0.000
#> GSM121206 1 0.1022 0.940 0.968 0.000 0.000 0.032
#> GSM121207 1 0.0000 0.971 1.000 0.000 0.000 0.000
#> GSM121208 1 0.1022 0.940 0.968 0.000 0.000 0.032
#> GSM121209 1 0.0000 0.971 1.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.971 1.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.971 1.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.971 1.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.971 1.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.971 1.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.971 1.000 0.000 0.000 0.000
#> GSM121216 1 0.0000 0.971 1.000 0.000 0.000 0.000
#> GSM121217 1 0.0000 0.971 1.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.971 1.000 0.000 0.000 0.000
#> GSM121234 1 0.0000 0.971 1.000 0.000 0.000 0.000
#> GSM121243 1 0.0000 0.971 1.000 0.000 0.000 0.000
#> GSM121245 1 0.0000 0.971 1.000 0.000 0.000 0.000
#> GSM121246 4 0.4998 0.128 0.488 0.000 0.000 0.512
#> GSM121247 1 0.0000 0.971 1.000 0.000 0.000 0.000
#> GSM121248 1 0.0000 0.971 1.000 0.000 0.000 0.000
#> GSM120744 3 0.4636 0.608 0.000 0.140 0.792 0.068
#> GSM120745 3 0.6027 0.568 0.000 0.124 0.684 0.192
#> GSM120746 3 0.6281 0.540 0.000 0.128 0.656 0.216
#> GSM120747 3 0.6374 0.528 0.000 0.128 0.644 0.228
#> GSM120748 3 0.6313 0.535 0.000 0.128 0.652 0.220
#> GSM120749 3 0.6374 0.528 0.000 0.128 0.644 0.228
#> GSM120750 3 0.5332 0.607 0.000 0.128 0.748 0.124
#> GSM120751 3 0.6181 0.555 0.000 0.128 0.668 0.204
#> GSM120752 3 0.5651 0.616 0.008 0.128 0.740 0.124
#> GSM121336 2 0.2376 0.746 0.000 0.916 0.068 0.016
#> GSM121339 3 0.7214 0.298 0.000 0.144 0.476 0.380
#> GSM121349 2 0.2125 0.753 0.000 0.920 0.076 0.004
#> GSM121355 2 0.2918 0.743 0.000 0.876 0.116 0.008
#> GSM120757 3 0.2940 0.629 0.036 0.028 0.908 0.028
#> GSM120766 3 0.2928 0.620 0.000 0.052 0.896 0.052
#> GSM120770 3 0.3552 0.592 0.000 0.128 0.848 0.024
#> GSM120779 3 0.5490 0.582 0.180 0.024 0.748 0.048
#> GSM120780 3 0.3598 0.596 0.000 0.124 0.848 0.028
#> GSM121102 3 0.6798 0.527 0.000 0.172 0.604 0.224
#> GSM121203 3 0.4144 0.635 0.000 0.068 0.828 0.104
#> GSM121204 3 0.5216 0.536 0.272 0.012 0.700 0.016
#> GSM121330 4 0.0188 0.810 0.004 0.000 0.000 0.996
#> GSM121335 4 0.1022 0.799 0.032 0.000 0.000 0.968
#> GSM121337 4 0.6781 0.304 0.000 0.148 0.256 0.596
#> GSM121338 4 0.6823 0.307 0.000 0.160 0.244 0.596
#> GSM121341 4 0.0469 0.809 0.012 0.000 0.000 0.988
#> GSM121342 4 0.0592 0.807 0.016 0.000 0.000 0.984
#> GSM121343 4 0.2699 0.747 0.000 0.028 0.068 0.904
#> GSM121344 4 0.0188 0.810 0.004 0.000 0.000 0.996
#> GSM121346 4 0.0188 0.809 0.000 0.000 0.004 0.996
#> GSM121347 3 0.5247 0.604 0.004 0.112 0.764 0.120
#> GSM121348 3 0.5151 0.558 0.000 0.140 0.760 0.100
#> GSM121350 4 0.0188 0.809 0.000 0.000 0.004 0.996
#> GSM121352 4 0.0188 0.810 0.004 0.000 0.000 0.996
#> GSM121354 4 0.0188 0.810 0.004 0.000 0.000 0.996
#> GSM120753 2 0.2530 0.762 0.000 0.888 0.112 0.000
#> GSM120761 2 0.3074 0.728 0.000 0.848 0.152 0.000
#> GSM120768 2 0.2081 0.753 0.000 0.916 0.084 0.000
#> GSM120781 2 0.0469 0.755 0.000 0.988 0.012 0.000
#> GSM120788 3 0.4993 0.475 0.000 0.260 0.712 0.028
#> GSM120760 2 0.4431 0.603 0.000 0.696 0.304 0.000
#> GSM120763 2 0.4522 0.596 0.000 0.680 0.320 0.000
#> GSM120764 2 0.4761 0.501 0.000 0.628 0.372 0.000
#> GSM120777 3 0.6018 0.524 0.064 0.204 0.708 0.024
#> GSM120786 2 0.4746 0.507 0.000 0.632 0.368 0.000
#> GSM121329 4 0.5673 0.236 0.448 0.000 0.024 0.528
#> GSM121331 3 0.5781 0.576 0.184 0.028 0.732 0.056
#> GSM121333 3 0.6697 0.284 0.388 0.032 0.544 0.036
#> GSM121345 3 0.7170 0.159 0.424 0.032 0.484 0.060
#> GSM121356 3 0.6097 0.579 0.132 0.020 0.720 0.128
#> GSM120754 3 0.4356 0.437 0.000 0.292 0.708 0.000
#> GSM120759 2 0.3105 0.743 0.000 0.856 0.140 0.004
#> GSM120762 2 0.1557 0.757 0.000 0.944 0.056 0.000
#> GSM120775 3 0.4564 0.370 0.000 0.328 0.672 0.000
#> GSM120776 3 0.2611 0.615 0.008 0.096 0.896 0.000
#> GSM120782 3 0.4722 0.510 0.000 0.300 0.692 0.008
#> GSM120789 2 0.5102 0.668 0.000 0.732 0.220 0.048
#> GSM120790 3 0.3982 0.440 0.000 0.220 0.776 0.004
#> GSM120791 2 0.3400 0.700 0.000 0.820 0.180 0.000
#> GSM120755 2 0.2266 0.745 0.000 0.912 0.084 0.004
#> GSM120756 1 0.7273 0.292 0.572 0.148 0.268 0.012
#> GSM120769 2 0.0592 0.752 0.000 0.984 0.016 0.000
#> GSM120778 2 0.2814 0.727 0.000 0.868 0.132 0.000
#> GSM120792 2 0.3873 0.667 0.000 0.772 0.228 0.000
#> GSM121332 2 0.6248 0.523 0.000 0.644 0.104 0.252
#> GSM121334 2 0.3710 0.729 0.000 0.804 0.192 0.004
#> GSM121340 3 0.5150 0.178 0.008 0.396 0.596 0.000
#> GSM121351 2 0.2976 0.746 0.000 0.872 0.120 0.008
#> GSM121353 3 0.9390 0.397 0.160 0.172 0.428 0.240
#> GSM120758 2 0.2408 0.758 0.000 0.896 0.104 0.000
#> GSM120771 2 0.4855 0.652 0.000 0.644 0.352 0.004
#> GSM120772 2 0.3311 0.759 0.000 0.828 0.172 0.000
#> GSM120773 2 0.4040 0.669 0.000 0.752 0.248 0.000
#> GSM120774 2 0.4356 0.502 0.000 0.708 0.292 0.000
#> GSM120783 2 0.4624 0.560 0.000 0.660 0.340 0.000
#> GSM120787 2 0.3356 0.709 0.000 0.824 0.176 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM120719 1 0.0000 0.9700 1.000 0.000 0.000 0.000 0.000
#> GSM120720 3 0.5825 0.4221 0.320 0.000 0.564 0.000 0.116
#> GSM120765 2 0.2905 0.6554 0.000 0.868 0.000 0.036 0.096
#> GSM120767 2 0.4803 0.2396 0.000 0.536 0.000 0.020 0.444
#> GSM120784 2 0.4863 0.4512 0.000 0.656 0.000 0.048 0.296
#> GSM121400 3 0.0566 0.8230 0.000 0.012 0.984 0.004 0.000
#> GSM121401 3 0.0000 0.8290 0.000 0.000 1.000 0.000 0.000
#> GSM121402 2 0.5377 0.5195 0.000 0.680 0.004 0.176 0.140
#> GSM121403 3 0.0880 0.8146 0.000 0.032 0.968 0.000 0.000
#> GSM121404 3 0.5952 0.3039 0.000 0.136 0.560 0.000 0.304
#> GSM121405 3 0.0000 0.8290 0.000 0.000 1.000 0.000 0.000
#> GSM121406 2 0.2873 0.6391 0.000 0.860 0.000 0.020 0.120
#> GSM121408 2 0.6000 0.4539 0.000 0.620 0.248 0.020 0.112
#> GSM121409 3 0.3530 0.6324 0.000 0.000 0.784 0.012 0.204
#> GSM121410 3 0.2969 0.7346 0.000 0.128 0.852 0.020 0.000
#> GSM121412 2 0.4266 0.6197 0.000 0.800 0.072 0.020 0.108
#> GSM121413 2 0.4601 0.5962 0.000 0.768 0.012 0.100 0.120
#> GSM121414 2 0.3963 0.6224 0.000 0.820 0.032 0.036 0.112
#> GSM121415 2 0.4191 0.6235 0.000 0.804 0.016 0.084 0.096
#> GSM121416 2 0.4073 0.5386 0.000 0.752 0.000 0.216 0.032
#> GSM120591 5 0.5348 0.4906 0.112 0.000 0.232 0.000 0.656
#> GSM120594 3 0.5931 0.1299 0.436 0.000 0.460 0.000 0.104
#> GSM120718 1 0.1478 0.9035 0.936 0.000 0.064 0.000 0.000
#> GSM121205 1 0.0000 0.9700 1.000 0.000 0.000 0.000 0.000
#> GSM121206 1 0.0290 0.9627 0.992 0.000 0.008 0.000 0.000
#> GSM121207 1 0.0000 0.9700 1.000 0.000 0.000 0.000 0.000
#> GSM121208 1 0.0290 0.9627 0.992 0.000 0.008 0.000 0.000
#> GSM121209 1 0.0000 0.9700 1.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.9700 1.000 0.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.9700 1.000 0.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.9700 1.000 0.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.9700 1.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.9700 1.000 0.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.9700 1.000 0.000 0.000 0.000 0.000
#> GSM121216 1 0.0000 0.9700 1.000 0.000 0.000 0.000 0.000
#> GSM121217 1 0.0000 0.9700 1.000 0.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.9700 1.000 0.000 0.000 0.000 0.000
#> GSM121234 1 0.0000 0.9700 1.000 0.000 0.000 0.000 0.000
#> GSM121243 1 0.0000 0.9700 1.000 0.000 0.000 0.000 0.000
#> GSM121245 1 0.0000 0.9700 1.000 0.000 0.000 0.000 0.000
#> GSM121246 3 0.4278 0.2124 0.452 0.000 0.548 0.000 0.000
#> GSM121247 1 0.0000 0.9700 1.000 0.000 0.000 0.000 0.000
#> GSM121248 1 0.0000 0.9700 1.000 0.000 0.000 0.000 0.000
#> GSM120744 5 0.0000 0.7646 0.000 0.000 0.000 0.000 1.000
#> GSM120745 5 0.0162 0.7622 0.000 0.000 0.000 0.004 0.996
#> GSM120746 5 0.0000 0.7646 0.000 0.000 0.000 0.000 1.000
#> GSM120747 5 0.0000 0.7646 0.000 0.000 0.000 0.000 1.000
#> GSM120748 5 0.0404 0.7610 0.000 0.000 0.012 0.000 0.988
#> GSM120749 5 0.0000 0.7646 0.000 0.000 0.000 0.000 1.000
#> GSM120750 5 0.0000 0.7646 0.000 0.000 0.000 0.000 1.000
#> GSM120751 5 0.0000 0.7646 0.000 0.000 0.000 0.000 1.000
#> GSM120752 5 0.0000 0.7646 0.000 0.000 0.000 0.000 1.000
#> GSM121336 2 0.2171 0.6614 0.000 0.912 0.000 0.024 0.064
#> GSM121339 5 0.6043 0.4614 0.000 0.176 0.252 0.000 0.572
#> GSM121349 2 0.1568 0.6573 0.000 0.944 0.000 0.020 0.036
#> GSM121355 2 0.2011 0.6565 0.000 0.908 0.000 0.004 0.088
#> GSM120757 4 0.4709 0.3366 0.004 0.024 0.004 0.676 0.292
#> GSM120766 4 0.5021 0.3513 0.000 0.044 0.012 0.676 0.268
#> GSM120770 5 0.5620 0.3776 0.000 0.116 0.000 0.272 0.612
#> GSM120779 4 0.5596 0.3843 0.060 0.024 0.008 0.676 0.232
#> GSM120780 4 0.6253 0.0987 0.000 0.124 0.008 0.516 0.352
#> GSM121102 5 0.5682 0.6047 0.000 0.080 0.100 0.108 0.712
#> GSM121203 5 0.3969 0.6362 0.000 0.008 0.040 0.156 0.796
#> GSM121204 5 0.5737 0.0867 0.076 0.000 0.004 0.396 0.524
#> GSM121330 3 0.0000 0.8290 0.000 0.000 1.000 0.000 0.000
#> GSM121335 3 0.0510 0.8221 0.016 0.000 0.984 0.000 0.000
#> GSM121337 3 0.7419 0.1849 0.000 0.208 0.460 0.280 0.052
#> GSM121338 5 0.6642 0.1940 0.000 0.148 0.356 0.016 0.480
#> GSM121341 3 0.0000 0.8290 0.000 0.000 1.000 0.000 0.000
#> GSM121342 3 0.0162 0.8277 0.004 0.000 0.996 0.000 0.000
#> GSM121343 3 0.4267 0.6823 0.000 0.144 0.788 0.052 0.016
#> GSM121344 3 0.0000 0.8290 0.000 0.000 1.000 0.000 0.000
#> GSM121346 3 0.0000 0.8290 0.000 0.000 1.000 0.000 0.000
#> GSM121347 5 0.7684 0.1260 0.000 0.220 0.068 0.284 0.428
#> GSM121348 4 0.5526 0.4035 0.000 0.180 0.020 0.688 0.112
#> GSM121350 3 0.0000 0.8290 0.000 0.000 1.000 0.000 0.000
#> GSM121352 3 0.0000 0.8290 0.000 0.000 1.000 0.000 0.000
#> GSM121354 3 0.0000 0.8290 0.000 0.000 1.000 0.000 0.000
#> GSM120753 2 0.4769 0.5466 0.000 0.688 0.000 0.256 0.056
#> GSM120761 2 0.4537 0.3979 0.000 0.592 0.000 0.396 0.012
#> GSM120768 2 0.4380 0.4300 0.000 0.616 0.000 0.376 0.008
#> GSM120781 2 0.2674 0.6386 0.000 0.868 0.000 0.120 0.012
#> GSM120788 4 0.4231 0.4678 0.012 0.132 0.000 0.792 0.064
#> GSM120760 4 0.3990 0.2258 0.000 0.308 0.000 0.688 0.004
#> GSM120763 4 0.4210 0.0484 0.000 0.412 0.000 0.588 0.000
#> GSM120764 4 0.3796 0.2559 0.000 0.300 0.000 0.700 0.000
#> GSM120777 4 0.1915 0.5228 0.000 0.032 0.000 0.928 0.040
#> GSM120786 4 0.4182 0.1408 0.000 0.352 0.000 0.644 0.004
#> GSM121329 1 0.5498 -0.0379 0.496 0.000 0.440 0.064 0.000
#> GSM121331 4 0.5739 0.3825 0.068 0.024 0.008 0.664 0.236
#> GSM121333 4 0.5562 0.3833 0.200 0.000 0.000 0.644 0.156
#> GSM121345 4 0.5998 0.3632 0.220 0.000 0.012 0.620 0.148
#> GSM121356 4 0.5699 0.3714 0.060 0.016 0.012 0.652 0.260
#> GSM120754 4 0.4818 0.4433 0.000 0.080 0.000 0.708 0.212
#> GSM120759 2 0.2989 0.6588 0.000 0.868 0.000 0.060 0.072
#> GSM120762 2 0.2648 0.6152 0.000 0.848 0.000 0.152 0.000
#> GSM120775 4 0.4884 0.4300 0.000 0.128 0.000 0.720 0.152
#> GSM120776 4 0.4538 0.0561 0.000 0.008 0.000 0.540 0.452
#> GSM120782 5 0.1877 0.7079 0.000 0.012 0.000 0.064 0.924
#> GSM120789 2 0.6849 0.3630 0.000 0.476 0.016 0.196 0.312
#> GSM120790 4 0.4251 0.3397 0.000 0.316 0.000 0.672 0.012
#> GSM120791 2 0.4641 0.2881 0.000 0.532 0.000 0.456 0.012
#> GSM120755 2 0.1914 0.6604 0.000 0.924 0.000 0.016 0.060
#> GSM120756 4 0.5678 0.3993 0.248 0.048 0.000 0.656 0.048
#> GSM120769 2 0.3752 0.5179 0.000 0.708 0.000 0.292 0.000
#> GSM120778 2 0.4505 0.4089 0.000 0.604 0.000 0.384 0.012
#> GSM120792 2 0.5834 0.3558 0.000 0.532 0.000 0.364 0.104
#> GSM121332 2 0.6513 0.4459 0.000 0.624 0.172 0.064 0.140
#> GSM121334 2 0.3282 0.6132 0.000 0.804 0.000 0.188 0.008
#> GSM121340 4 0.3970 0.3584 0.000 0.236 0.000 0.744 0.020
#> GSM121351 2 0.2448 0.6487 0.000 0.892 0.000 0.020 0.088
#> GSM121353 4 0.8429 0.2204 0.084 0.088 0.088 0.444 0.296
#> GSM120758 2 0.4161 0.6059 0.000 0.752 0.000 0.208 0.040
#> GSM120771 2 0.5167 0.5071 0.000 0.684 0.000 0.200 0.116
#> GSM120772 2 0.4409 0.6100 0.000 0.752 0.000 0.176 0.072
#> GSM120773 2 0.5406 0.2318 0.000 0.480 0.000 0.464 0.056
#> GSM120774 2 0.6783 0.2037 0.000 0.380 0.000 0.284 0.336
#> GSM120783 4 0.4818 -0.1823 0.000 0.460 0.000 0.520 0.020
#> GSM120787 2 0.6262 0.4256 0.000 0.504 0.000 0.332 0.164
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM120719 1 0.3979 0.6864 0.712 0.256 0.000 0.004 0.028 0.000
#> GSM120720 3 0.7168 0.3901 0.188 0.256 0.476 0.004 0.028 0.048
#> GSM120765 2 0.4512 0.7993 0.000 0.708 0.000 0.224 0.028 0.040
#> GSM120767 2 0.5420 0.6438 0.000 0.572 0.000 0.172 0.000 0.256
#> GSM120784 2 0.5563 0.7174 0.000 0.608 0.000 0.196 0.016 0.180
#> GSM121400 3 0.0458 0.8358 0.000 0.000 0.984 0.000 0.016 0.000
#> GSM121401 3 0.0000 0.8412 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121402 2 0.5999 0.6473 0.000 0.616 0.000 0.100 0.176 0.108
#> GSM121403 3 0.1007 0.8215 0.000 0.044 0.956 0.000 0.000 0.000
#> GSM121404 3 0.5383 0.4298 0.000 0.156 0.596 0.004 0.000 0.244
#> GSM121405 3 0.0000 0.8412 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121406 2 0.4105 0.8001 0.000 0.732 0.000 0.216 0.008 0.044
#> GSM121408 2 0.5034 0.6373 0.000 0.688 0.200 0.056 0.000 0.056
#> GSM121409 3 0.3729 0.4908 0.000 0.000 0.692 0.000 0.012 0.296
#> GSM121410 3 0.2748 0.7492 0.000 0.128 0.848 0.000 0.024 0.000
#> GSM121412 2 0.4506 0.7944 0.000 0.728 0.044 0.200 0.008 0.020
#> GSM121413 2 0.4812 0.7819 0.000 0.728 0.000 0.140 0.080 0.052
#> GSM121414 2 0.4192 0.7996 0.000 0.740 0.004 0.200 0.008 0.048
#> GSM121415 2 0.4932 0.7898 0.000 0.704 0.000 0.176 0.080 0.040
#> GSM121416 2 0.5601 0.5775 0.000 0.564 0.000 0.244 0.188 0.004
#> GSM120591 6 0.7240 0.4107 0.092 0.256 0.124 0.004 0.028 0.496
#> GSM120594 1 0.7697 0.0272 0.360 0.256 0.284 0.004 0.028 0.068
#> GSM120718 1 0.4838 0.6492 0.672 0.256 0.040 0.004 0.028 0.000
#> GSM121205 1 0.0000 0.9221 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.9221 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121207 1 0.0000 0.9221 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121208 1 0.0000 0.9221 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121209 1 0.0000 0.9221 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.9221 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.9221 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.9221 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.9221 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.9221 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.9221 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121216 1 0.0000 0.9221 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121217 1 0.0000 0.9221 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.9221 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121234 1 0.0000 0.9221 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121243 1 0.0000 0.9221 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121245 1 0.0000 0.9221 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121246 3 0.4082 0.1966 0.432 0.004 0.560 0.004 0.000 0.000
#> GSM121247 1 0.0000 0.9221 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121248 1 0.0000 0.9221 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM120744 6 0.0000 0.7682 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120745 6 0.1152 0.7476 0.000 0.044 0.000 0.000 0.004 0.952
#> GSM120746 6 0.0000 0.7682 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120747 6 0.0000 0.7682 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120748 6 0.0363 0.7660 0.000 0.000 0.012 0.000 0.000 0.988
#> GSM120749 6 0.0000 0.7682 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120750 6 0.0000 0.7682 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120751 6 0.0000 0.7682 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120752 6 0.0713 0.7579 0.000 0.028 0.000 0.000 0.000 0.972
#> GSM121336 2 0.3794 0.7877 0.000 0.724 0.000 0.248 0.000 0.028
#> GSM121339 6 0.6245 0.3742 0.000 0.244 0.252 0.008 0.008 0.488
#> GSM121349 2 0.3330 0.7633 0.000 0.716 0.000 0.284 0.000 0.000
#> GSM121355 2 0.3894 0.7986 0.000 0.740 0.000 0.220 0.004 0.036
#> GSM120757 5 0.1121 0.8788 0.004 0.008 0.000 0.016 0.964 0.008
#> GSM120766 5 0.1078 0.8776 0.000 0.008 0.000 0.016 0.964 0.012
#> GSM120770 6 0.5850 0.4218 0.000 0.156 0.004 0.016 0.256 0.568
#> GSM120779 5 0.0820 0.8786 0.012 0.000 0.000 0.016 0.972 0.000
#> GSM120780 5 0.3441 0.7610 0.000 0.048 0.000 0.016 0.824 0.112
#> GSM121102 6 0.3761 0.7081 0.000 0.036 0.056 0.004 0.084 0.820
#> GSM121203 6 0.3424 0.6664 0.000 0.004 0.036 0.000 0.160 0.800
#> GSM121204 5 0.5355 0.5483 0.020 0.152 0.000 0.000 0.644 0.184
#> GSM121330 3 0.0000 0.8412 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121335 3 0.2288 0.7679 0.004 0.116 0.876 0.000 0.004 0.000
#> GSM121337 3 0.7204 0.2435 0.000 0.196 0.460 0.052 0.260 0.032
#> GSM121338 6 0.6367 0.3258 0.000 0.176 0.284 0.004 0.032 0.504
#> GSM121341 3 0.0000 0.8412 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121342 3 0.0891 0.8302 0.008 0.024 0.968 0.000 0.000 0.000
#> GSM121343 3 0.3777 0.7150 0.000 0.132 0.800 0.008 0.052 0.008
#> GSM121344 3 0.0000 0.8412 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121346 3 0.0000 0.8412 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121347 6 0.7604 0.2856 0.000 0.144 0.036 0.120 0.256 0.444
#> GSM121348 5 0.1341 0.8658 0.000 0.028 0.000 0.024 0.948 0.000
#> GSM121350 3 0.0000 0.8412 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121352 3 0.0000 0.8412 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121354 3 0.0000 0.8412 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM120753 4 0.5118 0.1457 0.000 0.252 0.000 0.644 0.020 0.084
#> GSM120761 4 0.2282 0.5922 0.000 0.068 0.000 0.900 0.020 0.012
#> GSM120768 4 0.1204 0.5845 0.000 0.056 0.000 0.944 0.000 0.000
#> GSM120781 4 0.3843 -0.3436 0.000 0.452 0.000 0.548 0.000 0.000
#> GSM120788 4 0.4138 0.4247 0.000 0.008 0.000 0.620 0.364 0.008
#> GSM120760 4 0.3690 0.5234 0.000 0.008 0.000 0.684 0.308 0.000
#> GSM120763 4 0.3885 0.5903 0.000 0.044 0.000 0.736 0.220 0.000
#> GSM120764 4 0.3288 0.5432 0.000 0.000 0.000 0.724 0.276 0.000
#> GSM120777 4 0.3789 0.3414 0.000 0.000 0.000 0.584 0.416 0.000
#> GSM120786 4 0.2823 0.5918 0.000 0.000 0.000 0.796 0.204 0.000
#> GSM121329 1 0.6041 -0.0163 0.448 0.060 0.420 0.000 0.072 0.000
#> GSM121331 5 0.0964 0.8796 0.012 0.000 0.000 0.016 0.968 0.004
#> GSM121333 5 0.1794 0.8715 0.024 0.000 0.000 0.016 0.932 0.028
#> GSM121345 5 0.2072 0.8690 0.024 0.012 0.000 0.016 0.924 0.024
#> GSM121356 5 0.1528 0.8771 0.012 0.000 0.000 0.016 0.944 0.028
#> GSM120754 4 0.4799 0.4549 0.000 0.012 0.000 0.620 0.320 0.048
#> GSM120759 2 0.5754 0.7195 0.000 0.576 0.000 0.292 0.048 0.084
#> GSM120762 4 0.3864 -0.3893 0.000 0.480 0.000 0.520 0.000 0.000
#> GSM120775 4 0.4238 0.4506 0.000 0.016 0.000 0.636 0.340 0.008
#> GSM120776 5 0.4298 0.6606 0.000 0.016 0.000 0.052 0.732 0.200
#> GSM120782 6 0.2964 0.6129 0.000 0.004 0.000 0.204 0.000 0.792
#> GSM120789 6 0.7091 0.0365 0.000 0.152 0.016 0.380 0.068 0.384
#> GSM120790 5 0.2704 0.7545 0.000 0.140 0.000 0.016 0.844 0.000
#> GSM120791 4 0.1334 0.6142 0.000 0.020 0.000 0.948 0.032 0.000
#> GSM120755 2 0.4150 0.6435 0.000 0.592 0.000 0.392 0.000 0.016
#> GSM120756 4 0.5741 0.4129 0.012 0.196 0.000 0.588 0.200 0.004
#> GSM120769 4 0.2597 0.4468 0.000 0.176 0.000 0.824 0.000 0.000
#> GSM120778 4 0.0865 0.5978 0.000 0.036 0.000 0.964 0.000 0.000
#> GSM120792 4 0.1340 0.6078 0.000 0.008 0.000 0.948 0.004 0.040
#> GSM121332 2 0.6830 0.4029 0.000 0.472 0.148 0.280 0.000 0.100
#> GSM121334 4 0.4535 -0.4739 0.000 0.484 0.000 0.484 0.032 0.000
#> GSM121340 4 0.3357 0.5742 0.000 0.008 0.000 0.764 0.224 0.004
#> GSM121351 2 0.3807 0.7967 0.000 0.740 0.000 0.228 0.004 0.028
#> GSM121353 4 0.6289 0.4219 0.016 0.248 0.004 0.584 0.096 0.052
#> GSM120758 4 0.3916 0.1275 0.000 0.300 0.000 0.680 0.020 0.000
#> GSM120771 2 0.5925 0.6274 0.000 0.604 0.000 0.136 0.204 0.056
#> GSM120772 4 0.5855 -0.1932 0.000 0.308 0.000 0.520 0.012 0.160
#> GSM120773 4 0.1605 0.6313 0.000 0.004 0.000 0.936 0.044 0.016
#> GSM120774 4 0.4029 0.4063 0.000 0.028 0.000 0.680 0.000 0.292
#> GSM120783 4 0.1866 0.6387 0.000 0.000 0.000 0.908 0.084 0.008
#> GSM120787 4 0.4117 0.4893 0.000 0.128 0.000 0.764 0.008 0.100
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> SD:pam 109 7.45e-10 2
#> SD:pam 77 2.51e-14 3
#> SD:pam 100 8.14e-25 4
#> SD:pam 72 5.14e-29 5
#> SD:pam 90 1.24e-29 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 21512 rows and 119 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 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 0.736 0.889 0.952 0.4890 0.506 0.506
#> 3 3 0.679 0.764 0.849 0.2885 0.781 0.590
#> 4 4 0.658 0.764 0.831 0.1148 0.885 0.696
#> 5 5 0.691 0.615 0.801 0.0914 0.863 0.585
#> 6 6 0.708 0.643 0.785 0.0607 0.909 0.623
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
#> GSM120719 1 0.0000 0.943 1.000 0.000
#> GSM120720 1 0.0000 0.943 1.000 0.000
#> GSM120765 2 0.0000 0.947 0.000 1.000
#> GSM120767 2 0.0000 0.947 0.000 1.000
#> GSM120784 2 0.0000 0.947 0.000 1.000
#> GSM121400 1 0.0000 0.943 1.000 0.000
#> GSM121401 1 0.0000 0.943 1.000 0.000
#> GSM121402 2 0.0000 0.947 0.000 1.000
#> GSM121403 1 0.0672 0.936 0.992 0.008
#> GSM121404 2 0.1633 0.928 0.024 0.976
#> GSM121405 1 0.0000 0.943 1.000 0.000
#> GSM121406 2 0.0000 0.947 0.000 1.000
#> GSM121408 2 0.0000 0.947 0.000 1.000
#> GSM121409 1 0.0000 0.943 1.000 0.000
#> GSM121410 1 0.0000 0.943 1.000 0.000
#> GSM121412 2 0.0000 0.947 0.000 1.000
#> GSM121413 2 0.0000 0.947 0.000 1.000
#> GSM121414 2 0.0000 0.947 0.000 1.000
#> GSM121415 2 0.0000 0.947 0.000 1.000
#> GSM121416 2 0.0000 0.947 0.000 1.000
#> GSM120591 1 0.0000 0.943 1.000 0.000
#> GSM120594 1 0.0000 0.943 1.000 0.000
#> GSM120718 1 0.0000 0.943 1.000 0.000
#> GSM121205 1 0.0000 0.943 1.000 0.000
#> GSM121206 1 0.0000 0.943 1.000 0.000
#> GSM121207 1 0.0000 0.943 1.000 0.000
#> GSM121208 1 0.0000 0.943 1.000 0.000
#> GSM121209 1 0.0000 0.943 1.000 0.000
#> GSM121210 1 0.0000 0.943 1.000 0.000
#> GSM121211 1 0.0000 0.943 1.000 0.000
#> GSM121212 1 0.0000 0.943 1.000 0.000
#> GSM121213 1 0.0000 0.943 1.000 0.000
#> GSM121214 1 0.0000 0.943 1.000 0.000
#> GSM121215 1 0.0000 0.943 1.000 0.000
#> GSM121216 1 0.0000 0.943 1.000 0.000
#> GSM121217 1 0.0000 0.943 1.000 0.000
#> GSM121218 1 0.0000 0.943 1.000 0.000
#> GSM121234 1 0.0000 0.943 1.000 0.000
#> GSM121243 1 0.0000 0.943 1.000 0.000
#> GSM121245 1 0.0000 0.943 1.000 0.000
#> GSM121246 1 0.0000 0.943 1.000 0.000
#> GSM121247 1 0.0000 0.943 1.000 0.000
#> GSM121248 1 0.0000 0.943 1.000 0.000
#> GSM120744 1 0.8144 0.694 0.748 0.252
#> GSM120745 1 0.8144 0.694 0.748 0.252
#> GSM120746 1 0.8144 0.694 0.748 0.252
#> GSM120747 1 0.8144 0.694 0.748 0.252
#> GSM120748 1 0.8144 0.694 0.748 0.252
#> GSM120749 1 0.8144 0.694 0.748 0.252
#> GSM120750 1 0.8144 0.694 0.748 0.252
#> GSM120751 1 0.8144 0.694 0.748 0.252
#> GSM120752 1 0.8144 0.694 0.748 0.252
#> GSM121336 2 0.0000 0.947 0.000 1.000
#> GSM121339 2 0.6247 0.792 0.156 0.844
#> GSM121349 2 0.0000 0.947 0.000 1.000
#> GSM121355 2 0.0000 0.947 0.000 1.000
#> GSM120757 2 0.8661 0.599 0.288 0.712
#> GSM120766 2 0.8861 0.567 0.304 0.696
#> GSM120770 2 0.0000 0.947 0.000 1.000
#> GSM120779 2 0.8661 0.599 0.288 0.712
#> GSM120780 2 0.9963 0.115 0.464 0.536
#> GSM121102 2 0.0000 0.947 0.000 1.000
#> GSM121203 1 0.8443 0.658 0.728 0.272
#> GSM121204 2 0.9732 0.326 0.404 0.596
#> GSM121330 1 0.0000 0.943 1.000 0.000
#> GSM121335 1 0.0000 0.943 1.000 0.000
#> GSM121337 2 0.0000 0.947 0.000 1.000
#> GSM121338 2 0.1633 0.928 0.024 0.976
#> GSM121341 1 0.0000 0.943 1.000 0.000
#> GSM121342 1 0.0000 0.943 1.000 0.000
#> GSM121343 2 0.1414 0.932 0.020 0.980
#> GSM121344 1 0.0000 0.943 1.000 0.000
#> GSM121346 1 0.0000 0.943 1.000 0.000
#> GSM121347 2 0.0000 0.947 0.000 1.000
#> GSM121348 2 0.4161 0.872 0.084 0.916
#> GSM121350 1 0.0000 0.943 1.000 0.000
#> GSM121352 1 0.0000 0.943 1.000 0.000
#> GSM121354 1 0.0000 0.943 1.000 0.000
#> GSM120753 2 0.0000 0.947 0.000 1.000
#> GSM120761 2 0.0000 0.947 0.000 1.000
#> GSM120768 2 0.0000 0.947 0.000 1.000
#> GSM120781 2 0.0000 0.947 0.000 1.000
#> GSM120788 2 0.0000 0.947 0.000 1.000
#> GSM120760 2 0.0000 0.947 0.000 1.000
#> GSM120763 2 0.0000 0.947 0.000 1.000
#> GSM120764 2 0.0000 0.947 0.000 1.000
#> GSM120777 2 0.0000 0.947 0.000 1.000
#> GSM120786 2 0.0000 0.947 0.000 1.000
#> GSM121329 1 0.0000 0.943 1.000 0.000
#> GSM121331 2 0.8661 0.599 0.288 0.712
#> GSM121333 2 0.8661 0.599 0.288 0.712
#> GSM121345 2 0.8499 0.619 0.276 0.724
#> GSM121356 2 0.8661 0.599 0.288 0.712
#> GSM120754 2 0.0000 0.947 0.000 1.000
#> GSM120759 2 0.0000 0.947 0.000 1.000
#> GSM120762 2 0.0000 0.947 0.000 1.000
#> GSM120775 2 0.0000 0.947 0.000 1.000
#> GSM120776 2 0.0000 0.947 0.000 1.000
#> GSM120782 2 0.0000 0.947 0.000 1.000
#> GSM120789 2 0.0000 0.947 0.000 1.000
#> GSM120790 2 0.0000 0.947 0.000 1.000
#> GSM120791 2 0.0000 0.947 0.000 1.000
#> GSM120755 2 0.0000 0.947 0.000 1.000
#> GSM120756 2 0.0000 0.947 0.000 1.000
#> GSM120769 2 0.0000 0.947 0.000 1.000
#> GSM120778 2 0.0000 0.947 0.000 1.000
#> GSM120792 2 0.0000 0.947 0.000 1.000
#> GSM121332 2 0.0000 0.947 0.000 1.000
#> GSM121334 2 0.0000 0.947 0.000 1.000
#> GSM121340 2 0.0000 0.947 0.000 1.000
#> GSM121351 2 0.0000 0.947 0.000 1.000
#> GSM121353 2 0.0000 0.947 0.000 1.000
#> GSM120758 2 0.0000 0.947 0.000 1.000
#> GSM120771 2 0.0000 0.947 0.000 1.000
#> GSM120772 2 0.0000 0.947 0.000 1.000
#> GSM120773 2 0.0000 0.947 0.000 1.000
#> GSM120774 2 0.0000 0.947 0.000 1.000
#> GSM120783 2 0.0000 0.947 0.000 1.000
#> GSM120787 2 0.0000 0.947 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM120719 1 0.6579 0.7065 0.652 0.020 0.328
#> GSM120720 1 0.6045 0.7164 0.620 0.000 0.380
#> GSM120765 2 0.1860 0.9148 0.000 0.948 0.052
#> GSM120767 2 0.1411 0.9305 0.000 0.964 0.036
#> GSM120784 2 0.2537 0.8920 0.000 0.920 0.080
#> GSM121400 1 0.6305 0.6048 0.516 0.000 0.484
#> GSM121401 1 0.6299 0.6164 0.524 0.000 0.476
#> GSM121402 2 0.0000 0.9550 0.000 1.000 0.000
#> GSM121403 3 0.5926 -0.2275 0.356 0.000 0.644
#> GSM121404 3 0.3482 0.7343 0.000 0.128 0.872
#> GSM121405 1 0.6305 0.6048 0.516 0.000 0.484
#> GSM121406 2 0.0424 0.9517 0.000 0.992 0.008
#> GSM121408 2 0.0424 0.9527 0.000 0.992 0.008
#> GSM121409 3 0.6307 -0.5785 0.488 0.000 0.512
#> GSM121410 1 0.6308 0.5918 0.508 0.000 0.492
#> GSM121412 2 0.1411 0.9306 0.000 0.964 0.036
#> GSM121413 2 0.1031 0.9405 0.000 0.976 0.024
#> GSM121414 2 0.1163 0.9374 0.000 0.972 0.028
#> GSM121415 2 0.1411 0.9306 0.000 0.964 0.036
#> GSM121416 2 0.0000 0.9550 0.000 1.000 0.000
#> GSM120591 1 0.6154 0.6950 0.592 0.000 0.408
#> GSM120594 1 0.6045 0.7164 0.620 0.000 0.380
#> GSM120718 1 0.6026 0.7183 0.624 0.000 0.376
#> GSM121205 1 0.0000 0.7286 1.000 0.000 0.000
#> GSM121206 1 0.0000 0.7286 1.000 0.000 0.000
#> GSM121207 1 0.0424 0.7293 0.992 0.000 0.008
#> GSM121208 1 0.5948 0.7218 0.640 0.000 0.360
#> GSM121209 1 0.0000 0.7286 1.000 0.000 0.000
#> GSM121210 1 0.0424 0.7293 0.992 0.000 0.008
#> GSM121211 1 0.0000 0.7286 1.000 0.000 0.000
#> GSM121212 1 0.0000 0.7286 1.000 0.000 0.000
#> GSM121213 1 0.0000 0.7286 1.000 0.000 0.000
#> GSM121214 1 0.0000 0.7286 1.000 0.000 0.000
#> GSM121215 1 0.0000 0.7286 1.000 0.000 0.000
#> GSM121216 1 0.3752 0.7300 0.856 0.000 0.144
#> GSM121217 1 0.0000 0.7286 1.000 0.000 0.000
#> GSM121218 1 0.0000 0.7286 1.000 0.000 0.000
#> GSM121234 1 0.0000 0.7286 1.000 0.000 0.000
#> GSM121243 1 0.0000 0.7286 1.000 0.000 0.000
#> GSM121245 1 0.0000 0.7286 1.000 0.000 0.000
#> GSM121246 1 0.6008 0.7191 0.628 0.000 0.372
#> GSM121247 1 0.4796 0.7245 0.780 0.000 0.220
#> GSM121248 1 0.0000 0.7286 1.000 0.000 0.000
#> GSM120744 3 0.1182 0.7092 0.012 0.012 0.976
#> GSM120745 3 0.1182 0.7092 0.012 0.012 0.976
#> GSM120746 3 0.1182 0.7092 0.012 0.012 0.976
#> GSM120747 3 0.1182 0.7092 0.012 0.012 0.976
#> GSM120748 3 0.1182 0.7092 0.012 0.012 0.976
#> GSM120749 3 0.1182 0.7092 0.012 0.012 0.976
#> GSM120750 3 0.1182 0.7092 0.012 0.012 0.976
#> GSM120751 3 0.1182 0.7092 0.012 0.012 0.976
#> GSM120752 3 0.1182 0.7092 0.012 0.012 0.976
#> GSM121336 2 0.0237 0.9536 0.000 0.996 0.004
#> GSM121339 3 0.3295 0.7273 0.008 0.096 0.896
#> GSM121349 2 0.0000 0.9550 0.000 1.000 0.000
#> GSM121355 2 0.0237 0.9536 0.000 0.996 0.004
#> GSM120757 3 0.6193 0.6969 0.016 0.292 0.692
#> GSM120766 3 0.5656 0.7071 0.004 0.284 0.712
#> GSM120770 3 0.6280 0.3437 0.000 0.460 0.540
#> GSM120779 3 0.6193 0.6969 0.016 0.292 0.692
#> GSM120780 3 0.2682 0.7344 0.004 0.076 0.920
#> GSM121102 3 0.4452 0.7307 0.000 0.192 0.808
#> GSM121203 3 0.1031 0.7086 0.000 0.024 0.976
#> GSM121204 3 0.6337 0.7100 0.028 0.264 0.708
#> GSM121330 1 0.6168 0.6951 0.588 0.000 0.412
#> GSM121335 1 0.6045 0.7164 0.620 0.000 0.380
#> GSM121337 2 0.6192 0.0308 0.000 0.580 0.420
#> GSM121338 3 0.2711 0.7296 0.000 0.088 0.912
#> GSM121341 1 0.6045 0.7164 0.620 0.000 0.380
#> GSM121342 1 0.6026 0.7178 0.624 0.000 0.376
#> GSM121343 3 0.3619 0.7361 0.000 0.136 0.864
#> GSM121344 1 0.6045 0.7164 0.620 0.000 0.380
#> GSM121346 1 0.6111 0.7091 0.604 0.000 0.396
#> GSM121347 3 0.6309 0.2653 0.000 0.496 0.504
#> GSM121348 2 0.6308 -0.2482 0.000 0.508 0.492
#> GSM121350 1 0.6215 0.6781 0.572 0.000 0.428
#> GSM121352 1 0.6111 0.7091 0.604 0.000 0.396
#> GSM121354 1 0.6079 0.7136 0.612 0.000 0.388
#> GSM120753 2 0.0000 0.9550 0.000 1.000 0.000
#> GSM120761 2 0.0424 0.9529 0.000 0.992 0.008
#> GSM120768 2 0.0424 0.9529 0.000 0.992 0.008
#> GSM120781 2 0.0000 0.9550 0.000 1.000 0.000
#> GSM120788 2 0.1289 0.9342 0.000 0.968 0.032
#> GSM120760 2 0.0424 0.9529 0.000 0.992 0.008
#> GSM120763 2 0.0424 0.9529 0.000 0.992 0.008
#> GSM120764 2 0.0424 0.9529 0.000 0.992 0.008
#> GSM120777 2 0.0424 0.9529 0.000 0.992 0.008
#> GSM120786 2 0.0424 0.9529 0.000 0.992 0.008
#> GSM121329 1 0.6677 0.7058 0.652 0.024 0.324
#> GSM121331 3 0.6193 0.6962 0.016 0.292 0.692
#> GSM121333 3 0.6193 0.6969 0.016 0.292 0.692
#> GSM121345 3 0.6262 0.7028 0.020 0.284 0.696
#> GSM121356 3 0.6229 0.7059 0.020 0.280 0.700
#> GSM120754 2 0.1860 0.9118 0.000 0.948 0.052
#> GSM120759 2 0.0000 0.9550 0.000 1.000 0.000
#> GSM120762 2 0.0000 0.9550 0.000 1.000 0.000
#> GSM120775 2 0.1031 0.9419 0.000 0.976 0.024
#> GSM120776 3 0.5948 0.5981 0.000 0.360 0.640
#> GSM120782 2 0.4062 0.7316 0.000 0.836 0.164
#> GSM120789 2 0.0000 0.9550 0.000 1.000 0.000
#> GSM120790 2 0.0000 0.9550 0.000 1.000 0.000
#> GSM120791 2 0.0424 0.9529 0.000 0.992 0.008
#> GSM120755 2 0.0000 0.9550 0.000 1.000 0.000
#> GSM120756 2 0.2711 0.8685 0.000 0.912 0.088
#> GSM120769 2 0.0000 0.9550 0.000 1.000 0.000
#> GSM120778 2 0.0000 0.9550 0.000 1.000 0.000
#> GSM120792 2 0.0000 0.9550 0.000 1.000 0.000
#> GSM121332 2 0.0000 0.9550 0.000 1.000 0.000
#> GSM121334 2 0.0000 0.9550 0.000 1.000 0.000
#> GSM121340 2 0.0424 0.9529 0.000 0.992 0.008
#> GSM121351 2 0.0000 0.9550 0.000 1.000 0.000
#> GSM121353 2 0.3340 0.8112 0.000 0.880 0.120
#> GSM120758 2 0.0000 0.9550 0.000 1.000 0.000
#> GSM120771 2 0.0000 0.9550 0.000 1.000 0.000
#> GSM120772 2 0.0000 0.9550 0.000 1.000 0.000
#> GSM120773 2 0.0424 0.9529 0.000 0.992 0.008
#> GSM120774 2 0.0000 0.9550 0.000 1.000 0.000
#> GSM120783 2 0.0424 0.9529 0.000 0.992 0.008
#> GSM120787 2 0.0000 0.9550 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM120719 1 0.5850 0.6391 0.700 0.000 0.184 0.116
#> GSM120720 1 0.2450 0.8501 0.912 0.000 0.016 0.072
#> GSM120765 2 0.0927 0.8668 0.008 0.976 0.016 0.000
#> GSM120767 2 0.0592 0.8675 0.000 0.984 0.016 0.000
#> GSM120784 2 0.1284 0.8650 0.012 0.964 0.024 0.000
#> GSM121400 1 0.1854 0.7648 0.940 0.000 0.048 0.012
#> GSM121401 1 0.0707 0.8157 0.980 0.000 0.000 0.020
#> GSM121402 2 0.0657 0.8663 0.004 0.984 0.012 0.000
#> GSM121403 1 0.3791 0.4794 0.796 0.000 0.200 0.004
#> GSM121404 2 0.7165 0.0096 0.372 0.488 0.140 0.000
#> GSM121405 1 0.0592 0.8116 0.984 0.000 0.000 0.016
#> GSM121406 2 0.1109 0.8630 0.004 0.968 0.028 0.000
#> GSM121408 2 0.0895 0.8649 0.004 0.976 0.020 0.000
#> GSM121409 1 0.2714 0.6793 0.884 0.000 0.112 0.004
#> GSM121410 1 0.2271 0.7365 0.916 0.000 0.076 0.008
#> GSM121412 2 0.1356 0.8600 0.008 0.960 0.032 0.000
#> GSM121413 2 0.1209 0.8612 0.004 0.964 0.032 0.000
#> GSM121414 2 0.1209 0.8612 0.004 0.964 0.032 0.000
#> GSM121415 2 0.1004 0.8632 0.004 0.972 0.024 0.000
#> GSM121416 2 0.0376 0.8679 0.004 0.992 0.004 0.000
#> GSM120591 1 0.2816 0.8322 0.900 0.000 0.036 0.064
#> GSM120594 1 0.2198 0.8503 0.920 0.000 0.008 0.072
#> GSM120718 1 0.2593 0.8471 0.904 0.000 0.016 0.080
#> GSM121205 4 0.0000 0.9610 0.000 0.000 0.000 1.000
#> GSM121206 4 0.0000 0.9610 0.000 0.000 0.000 1.000
#> GSM121207 4 0.1389 0.9413 0.048 0.000 0.000 0.952
#> GSM121208 1 0.2987 0.8290 0.880 0.000 0.016 0.104
#> GSM121209 4 0.0000 0.9610 0.000 0.000 0.000 1.000
#> GSM121210 4 0.3907 0.7152 0.232 0.000 0.000 0.768
#> GSM121211 4 0.0000 0.9610 0.000 0.000 0.000 1.000
#> GSM121212 4 0.1389 0.9413 0.048 0.000 0.000 0.952
#> GSM121213 4 0.0000 0.9610 0.000 0.000 0.000 1.000
#> GSM121214 4 0.0000 0.9610 0.000 0.000 0.000 1.000
#> GSM121215 4 0.0000 0.9610 0.000 0.000 0.000 1.000
#> GSM121216 4 0.2589 0.8716 0.116 0.000 0.000 0.884
#> GSM121217 4 0.0000 0.9610 0.000 0.000 0.000 1.000
#> GSM121218 4 0.0000 0.9610 0.000 0.000 0.000 1.000
#> GSM121234 4 0.0000 0.9610 0.000 0.000 0.000 1.000
#> GSM121243 4 0.2011 0.9181 0.080 0.000 0.000 0.920
#> GSM121245 4 0.1716 0.9308 0.064 0.000 0.000 0.936
#> GSM121246 1 0.2924 0.8330 0.884 0.000 0.016 0.100
#> GSM121247 1 0.6640 0.3712 0.552 0.000 0.096 0.352
#> GSM121248 4 0.0000 0.9610 0.000 0.000 0.000 1.000
#> GSM120744 3 0.4989 0.6280 0.472 0.000 0.528 0.000
#> GSM120745 3 0.4985 0.6292 0.468 0.000 0.532 0.000
#> GSM120746 3 0.4985 0.6292 0.468 0.000 0.532 0.000
#> GSM120747 3 0.4989 0.6280 0.472 0.000 0.528 0.000
#> GSM120748 3 0.4989 0.6280 0.472 0.000 0.528 0.000
#> GSM120749 3 0.4985 0.6292 0.468 0.000 0.532 0.000
#> GSM120750 3 0.4985 0.6292 0.468 0.000 0.532 0.000
#> GSM120751 3 0.4985 0.6292 0.468 0.000 0.532 0.000
#> GSM120752 3 0.4985 0.6292 0.468 0.000 0.532 0.000
#> GSM121336 2 0.1022 0.8622 0.000 0.968 0.032 0.000
#> GSM121339 2 0.7073 -0.0528 0.412 0.464 0.124 0.000
#> GSM121349 2 0.0817 0.8652 0.000 0.976 0.024 0.000
#> GSM121355 2 0.0921 0.8637 0.000 0.972 0.028 0.000
#> GSM120757 3 0.4841 0.6752 0.140 0.080 0.780 0.000
#> GSM120766 3 0.5670 0.6630 0.152 0.128 0.720 0.000
#> GSM120770 3 0.6979 0.5106 0.128 0.344 0.528 0.000
#> GSM120779 3 0.3813 0.6738 0.148 0.024 0.828 0.000
#> GSM120780 3 0.6523 0.6647 0.348 0.088 0.564 0.000
#> GSM121102 3 0.7182 0.6319 0.248 0.200 0.552 0.000
#> GSM121203 3 0.4989 0.6244 0.472 0.000 0.528 0.000
#> GSM121204 3 0.4050 0.6747 0.168 0.024 0.808 0.000
#> GSM121330 1 0.1792 0.8493 0.932 0.000 0.000 0.068
#> GSM121335 1 0.2450 0.8501 0.912 0.000 0.016 0.072
#> GSM121337 2 0.3612 0.7915 0.044 0.856 0.100 0.000
#> GSM121338 1 0.7716 -0.5079 0.396 0.224 0.380 0.000
#> GSM121341 1 0.2450 0.8501 0.912 0.000 0.016 0.072
#> GSM121342 1 0.2662 0.8451 0.900 0.000 0.016 0.084
#> GSM121343 3 0.7822 0.4686 0.364 0.256 0.380 0.000
#> GSM121344 1 0.2450 0.8501 0.912 0.000 0.016 0.072
#> GSM121346 1 0.1792 0.8493 0.932 0.000 0.000 0.068
#> GSM121347 2 0.5123 0.7148 0.044 0.724 0.232 0.000
#> GSM121348 3 0.4737 0.4439 0.020 0.252 0.728 0.000
#> GSM121350 1 0.1557 0.8437 0.944 0.000 0.000 0.056
#> GSM121352 1 0.1792 0.8493 0.932 0.000 0.000 0.068
#> GSM121354 1 0.1978 0.8501 0.928 0.000 0.004 0.068
#> GSM120753 2 0.0817 0.8688 0.000 0.976 0.024 0.000
#> GSM120761 2 0.3649 0.8201 0.000 0.796 0.204 0.000
#> GSM120768 2 0.3528 0.8250 0.000 0.808 0.192 0.000
#> GSM120781 2 0.0336 0.8684 0.000 0.992 0.008 0.000
#> GSM120788 2 0.5530 0.6902 0.032 0.632 0.336 0.000
#> GSM120760 2 0.3907 0.8064 0.000 0.768 0.232 0.000
#> GSM120763 2 0.4008 0.7998 0.000 0.756 0.244 0.000
#> GSM120764 2 0.4283 0.7918 0.004 0.740 0.256 0.000
#> GSM120777 2 0.5344 0.7293 0.032 0.668 0.300 0.000
#> GSM120786 2 0.4072 0.7954 0.000 0.748 0.252 0.000
#> GSM121329 1 0.6167 0.6091 0.664 0.000 0.220 0.116
#> GSM121331 3 0.3266 0.6537 0.108 0.024 0.868 0.000
#> GSM121333 3 0.3659 0.6704 0.136 0.024 0.840 0.000
#> GSM121345 3 0.4224 0.6702 0.144 0.044 0.812 0.000
#> GSM121356 3 0.3763 0.6730 0.144 0.024 0.832 0.000
#> GSM120754 2 0.4295 0.8023 0.008 0.752 0.240 0.000
#> GSM120759 2 0.0707 0.8658 0.000 0.980 0.020 0.000
#> GSM120762 2 0.0469 0.8688 0.000 0.988 0.012 0.000
#> GSM120775 2 0.5062 0.7444 0.020 0.680 0.300 0.000
#> GSM120776 3 0.5670 0.6217 0.128 0.152 0.720 0.000
#> GSM120782 2 0.5857 0.7055 0.108 0.696 0.196 0.000
#> GSM120789 2 0.0188 0.8675 0.000 0.996 0.004 0.000
#> GSM120790 2 0.2053 0.8624 0.004 0.924 0.072 0.000
#> GSM120791 2 0.3837 0.8104 0.000 0.776 0.224 0.000
#> GSM120755 2 0.0707 0.8651 0.000 0.980 0.020 0.000
#> GSM120756 2 0.6111 0.5752 0.052 0.556 0.392 0.000
#> GSM120769 2 0.0707 0.8694 0.000 0.980 0.020 0.000
#> GSM120778 2 0.1118 0.8690 0.000 0.964 0.036 0.000
#> GSM120792 2 0.2814 0.8484 0.000 0.868 0.132 0.000
#> GSM121332 2 0.0469 0.8666 0.000 0.988 0.012 0.000
#> GSM121334 2 0.1940 0.8612 0.000 0.924 0.076 0.000
#> GSM121340 2 0.4193 0.7860 0.000 0.732 0.268 0.000
#> GSM121351 2 0.0817 0.8652 0.000 0.976 0.024 0.000
#> GSM121353 2 0.6000 0.6053 0.052 0.592 0.356 0.000
#> GSM120758 2 0.0592 0.8688 0.000 0.984 0.016 0.000
#> GSM120771 2 0.1004 0.8694 0.004 0.972 0.024 0.000
#> GSM120772 2 0.0707 0.8688 0.000 0.980 0.020 0.000
#> GSM120773 2 0.4008 0.7998 0.000 0.756 0.244 0.000
#> GSM120774 2 0.2469 0.8555 0.000 0.892 0.108 0.000
#> GSM120783 2 0.4072 0.7954 0.000 0.748 0.252 0.000
#> GSM120787 2 0.1302 0.8679 0.000 0.956 0.044 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM120719 3 0.5732 0.6848 0.096 0.000 0.688 0.172 0.044
#> GSM120720 3 0.0579 0.9293 0.008 0.000 0.984 0.000 0.008
#> GSM120765 4 0.5008 -0.2607 0.000 0.476 0.012 0.500 0.012
#> GSM120767 2 0.4189 0.5626 0.000 0.736 0.012 0.240 0.012
#> GSM120784 4 0.4548 0.2867 0.000 0.300 0.012 0.676 0.012
#> GSM121400 3 0.0865 0.9196 0.000 0.000 0.972 0.004 0.024
#> GSM121401 3 0.0000 0.9314 0.000 0.000 1.000 0.000 0.000
#> GSM121402 2 0.3171 0.6362 0.000 0.816 0.000 0.176 0.008
#> GSM121403 3 0.4437 0.7340 0.000 0.000 0.760 0.140 0.100
#> GSM121404 4 0.5531 0.4303 0.000 0.228 0.036 0.676 0.060
#> GSM121405 3 0.0162 0.9307 0.000 0.000 0.996 0.004 0.000
#> GSM121406 2 0.3141 0.6373 0.000 0.832 0.000 0.152 0.016
#> GSM121408 2 0.0404 0.7250 0.000 0.988 0.000 0.000 0.012
#> GSM121409 3 0.3861 0.7811 0.000 0.000 0.804 0.128 0.068
#> GSM121410 3 0.1800 0.8944 0.000 0.000 0.932 0.020 0.048
#> GSM121412 2 0.3264 0.6266 0.000 0.820 0.000 0.164 0.016
#> GSM121413 2 0.3381 0.6246 0.000 0.808 0.000 0.176 0.016
#> GSM121414 2 0.3381 0.6246 0.000 0.808 0.000 0.176 0.016
#> GSM121415 2 0.4422 0.5453 0.000 0.664 0.004 0.320 0.012
#> GSM121416 2 0.4114 0.5217 0.000 0.624 0.000 0.376 0.000
#> GSM120591 3 0.3794 0.7799 0.000 0.000 0.800 0.152 0.048
#> GSM120594 3 0.0290 0.9304 0.000 0.000 0.992 0.000 0.008
#> GSM120718 3 0.0992 0.9203 0.024 0.000 0.968 0.000 0.008
#> GSM121205 1 0.0000 0.9619 1.000 0.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.9619 1.000 0.000 0.000 0.000 0.000
#> GSM121207 1 0.0404 0.9580 0.988 0.000 0.012 0.000 0.000
#> GSM121208 3 0.0579 0.9298 0.008 0.000 0.984 0.000 0.008
#> GSM121209 1 0.0000 0.9619 1.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.2377 0.8466 0.872 0.000 0.128 0.000 0.000
#> GSM121211 1 0.0000 0.9619 1.000 0.000 0.000 0.000 0.000
#> GSM121212 1 0.0404 0.9580 0.988 0.000 0.012 0.000 0.000
#> GSM121213 1 0.0000 0.9619 1.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.9619 1.000 0.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.9619 1.000 0.000 0.000 0.000 0.000
#> GSM121216 1 0.0510 0.9559 0.984 0.000 0.016 0.000 0.000
#> GSM121217 1 0.0000 0.9619 1.000 0.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.9619 1.000 0.000 0.000 0.000 0.000
#> GSM121234 1 0.0000 0.9619 1.000 0.000 0.000 0.000 0.000
#> GSM121243 1 0.0510 0.9559 0.984 0.000 0.016 0.000 0.000
#> GSM121245 1 0.1197 0.9292 0.952 0.000 0.048 0.000 0.000
#> GSM121246 3 0.0579 0.9298 0.008 0.000 0.984 0.000 0.008
#> GSM121247 1 0.5769 0.4894 0.632 0.000 0.276 0.048 0.044
#> GSM121248 1 0.0000 0.9619 1.000 0.000 0.000 0.000 0.000
#> GSM120744 5 0.0880 0.8890 0.000 0.000 0.032 0.000 0.968
#> GSM120745 5 0.0880 0.8890 0.000 0.000 0.032 0.000 0.968
#> GSM120746 5 0.0880 0.8890 0.000 0.000 0.032 0.000 0.968
#> GSM120747 5 0.0880 0.8890 0.000 0.000 0.032 0.000 0.968
#> GSM120748 5 0.0880 0.8890 0.000 0.000 0.032 0.000 0.968
#> GSM120749 5 0.0880 0.8890 0.000 0.000 0.032 0.000 0.968
#> GSM120750 5 0.0880 0.8890 0.000 0.000 0.032 0.000 0.968
#> GSM120751 5 0.0880 0.8890 0.000 0.000 0.032 0.000 0.968
#> GSM120752 5 0.0880 0.8890 0.000 0.000 0.032 0.000 0.968
#> GSM121336 2 0.0510 0.7242 0.000 0.984 0.000 0.000 0.016
#> GSM121339 4 0.7234 0.2970 0.000 0.212 0.208 0.520 0.060
#> GSM121349 2 0.0510 0.7242 0.000 0.984 0.000 0.000 0.016
#> GSM121355 2 0.0510 0.7242 0.000 0.984 0.000 0.000 0.016
#> GSM120757 4 0.4383 0.2387 0.000 0.000 0.004 0.572 0.424
#> GSM120766 4 0.4410 0.2130 0.000 0.000 0.004 0.556 0.440
#> GSM120770 4 0.5927 0.3054 0.000 0.056 0.028 0.560 0.356
#> GSM120779 4 0.4383 0.2387 0.000 0.000 0.004 0.572 0.424
#> GSM120780 5 0.4871 0.3009 0.000 0.008 0.028 0.316 0.648
#> GSM121102 4 0.6041 0.2473 0.000 0.048 0.036 0.520 0.396
#> GSM121203 5 0.5439 -0.0432 0.000 0.020 0.028 0.408 0.544
#> GSM121204 4 0.4855 0.2058 0.000 0.000 0.024 0.552 0.424
#> GSM121330 3 0.0000 0.9314 0.000 0.000 1.000 0.000 0.000
#> GSM121335 3 0.0290 0.9304 0.000 0.000 0.992 0.000 0.008
#> GSM121337 4 0.5336 0.3429 0.000 0.304 0.012 0.632 0.052
#> GSM121338 4 0.6770 0.4208 0.000 0.188 0.036 0.560 0.216
#> GSM121341 3 0.0451 0.9305 0.004 0.000 0.988 0.000 0.008
#> GSM121342 3 0.0451 0.9305 0.004 0.000 0.988 0.000 0.008
#> GSM121343 4 0.6750 0.4281 0.000 0.196 0.036 0.564 0.204
#> GSM121344 3 0.0290 0.9304 0.000 0.000 0.992 0.000 0.008
#> GSM121346 3 0.0000 0.9314 0.000 0.000 1.000 0.000 0.000
#> GSM121347 4 0.5455 0.1954 0.000 0.364 0.008 0.576 0.052
#> GSM121348 4 0.5123 0.3046 0.000 0.044 0.000 0.572 0.384
#> GSM121350 3 0.0000 0.9314 0.000 0.000 1.000 0.000 0.000
#> GSM121352 3 0.0000 0.9314 0.000 0.000 1.000 0.000 0.000
#> GSM121354 3 0.0000 0.9314 0.000 0.000 1.000 0.000 0.000
#> GSM120753 2 0.3642 0.6355 0.000 0.760 0.000 0.232 0.008
#> GSM120761 2 0.4557 0.3680 0.000 0.516 0.000 0.476 0.008
#> GSM120768 2 0.4555 0.3539 0.000 0.520 0.000 0.472 0.008
#> GSM120781 2 0.2971 0.6963 0.000 0.836 0.000 0.156 0.008
#> GSM120788 4 0.4300 -0.2734 0.000 0.476 0.000 0.524 0.000
#> GSM120760 2 0.4452 0.3211 0.000 0.500 0.000 0.496 0.004
#> GSM120763 2 0.4306 0.3388 0.000 0.508 0.000 0.492 0.000
#> GSM120764 4 0.4273 -0.2515 0.000 0.448 0.000 0.552 0.000
#> GSM120777 4 0.4306 -0.3088 0.000 0.492 0.000 0.508 0.000
#> GSM120786 4 0.4287 -0.2781 0.000 0.460 0.000 0.540 0.000
#> GSM121329 3 0.5401 0.7023 0.064 0.000 0.708 0.184 0.044
#> GSM121331 4 0.4235 0.2411 0.000 0.000 0.000 0.576 0.424
#> GSM121333 4 0.4383 0.2387 0.000 0.000 0.004 0.572 0.424
#> GSM121345 4 0.4389 0.2947 0.000 0.004 0.004 0.624 0.368
#> GSM121356 4 0.4497 0.2331 0.000 0.000 0.008 0.568 0.424
#> GSM120754 4 0.2605 0.3680 0.000 0.148 0.000 0.852 0.000
#> GSM120759 2 0.1768 0.7211 0.000 0.924 0.000 0.072 0.004
#> GSM120762 2 0.1408 0.7311 0.000 0.948 0.000 0.044 0.008
#> GSM120775 2 0.4297 0.3133 0.000 0.528 0.000 0.472 0.000
#> GSM120776 4 0.3720 0.3848 0.000 0.012 0.000 0.760 0.228
#> GSM120782 4 0.3634 0.4256 0.000 0.184 0.008 0.796 0.012
#> GSM120789 2 0.0324 0.7281 0.000 0.992 0.000 0.004 0.004
#> GSM120790 2 0.4341 0.5917 0.000 0.628 0.000 0.364 0.008
#> GSM120791 2 0.4561 0.3315 0.000 0.504 0.000 0.488 0.008
#> GSM120755 2 0.0566 0.7259 0.000 0.984 0.000 0.004 0.012
#> GSM120756 2 0.4273 0.3353 0.000 0.552 0.000 0.448 0.000
#> GSM120769 2 0.1331 0.7310 0.000 0.952 0.000 0.040 0.008
#> GSM120778 2 0.1331 0.7310 0.000 0.952 0.000 0.040 0.008
#> GSM120792 2 0.2077 0.7266 0.000 0.908 0.000 0.084 0.008
#> GSM121332 2 0.0162 0.7273 0.000 0.996 0.000 0.004 0.000
#> GSM121334 2 0.3910 0.6233 0.000 0.720 0.000 0.272 0.008
#> GSM121340 2 0.4074 0.4809 0.000 0.636 0.000 0.364 0.000
#> GSM121351 2 0.1845 0.7043 0.000 0.928 0.000 0.056 0.016
#> GSM121353 2 0.4182 0.4621 0.000 0.644 0.004 0.352 0.000
#> GSM120758 2 0.3246 0.6706 0.000 0.808 0.000 0.184 0.008
#> GSM120771 2 0.4455 0.5643 0.000 0.588 0.000 0.404 0.008
#> GSM120772 2 0.2304 0.7248 0.000 0.892 0.000 0.100 0.008
#> GSM120773 4 0.4306 -0.3271 0.000 0.492 0.000 0.508 0.000
#> GSM120774 2 0.2136 0.7275 0.000 0.904 0.000 0.088 0.008
#> GSM120783 4 0.4287 -0.2750 0.000 0.460 0.000 0.540 0.000
#> GSM120787 2 0.1331 0.7310 0.000 0.952 0.000 0.040 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM120719 3 0.5261 0.6055 0.092 0.008 0.660 0.004 0.224 0.012
#> GSM120720 3 0.0363 0.9286 0.012 0.000 0.988 0.000 0.000 0.000
#> GSM120765 5 0.5482 0.2787 0.000 0.300 0.000 0.156 0.544 0.000
#> GSM120767 2 0.5173 0.4160 0.000 0.596 0.000 0.128 0.276 0.000
#> GSM120784 5 0.5177 0.4053 0.000 0.236 0.000 0.152 0.612 0.000
#> GSM121400 3 0.0862 0.9224 0.000 0.008 0.972 0.000 0.004 0.016
#> GSM121401 3 0.0000 0.9337 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121402 2 0.4533 0.5761 0.000 0.704 0.000 0.156 0.140 0.000
#> GSM121403 3 0.3589 0.7951 0.000 0.012 0.800 0.000 0.148 0.040
#> GSM121404 5 0.4030 0.5392 0.000 0.196 0.000 0.020 0.752 0.032
#> GSM121405 3 0.0291 0.9315 0.000 0.004 0.992 0.000 0.000 0.004
#> GSM121406 2 0.3586 0.6423 0.000 0.796 0.000 0.080 0.124 0.000
#> GSM121408 2 0.2662 0.6689 0.000 0.856 0.000 0.120 0.024 0.000
#> GSM121409 3 0.3419 0.7720 0.000 0.008 0.796 0.000 0.172 0.024
#> GSM121410 3 0.1173 0.9156 0.000 0.008 0.960 0.000 0.016 0.016
#> GSM121412 2 0.3542 0.6118 0.000 0.788 0.000 0.052 0.160 0.000
#> GSM121413 2 0.3786 0.5992 0.000 0.768 0.000 0.064 0.168 0.000
#> GSM121414 2 0.3578 0.6073 0.000 0.784 0.000 0.052 0.164 0.000
#> GSM121415 2 0.4854 0.5079 0.000 0.636 0.000 0.100 0.264 0.000
#> GSM121416 2 0.5890 0.2564 0.000 0.472 0.000 0.240 0.288 0.000
#> GSM120591 3 0.3594 0.7389 0.000 0.008 0.780 0.000 0.184 0.028
#> GSM120594 3 0.0000 0.9337 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM120718 3 0.1141 0.9006 0.052 0.000 0.948 0.000 0.000 0.000
#> GSM121205 1 0.0000 0.9633 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.9633 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121207 1 0.0260 0.9611 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM121208 3 0.0000 0.9337 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121209 1 0.0000 0.9633 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.1610 0.8853 0.916 0.000 0.084 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.9633 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121212 1 0.0260 0.9611 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.9633 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.0146 0.9624 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.9633 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121216 1 0.0260 0.9611 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM121217 1 0.0000 0.9633 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.9633 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121234 1 0.0000 0.9633 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121243 1 0.0363 0.9583 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM121245 1 0.0713 0.9445 0.972 0.000 0.028 0.000 0.000 0.000
#> GSM121246 3 0.0146 0.9324 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM121247 1 0.5973 0.4245 0.576 0.008 0.232 0.004 0.168 0.012
#> GSM121248 1 0.0000 0.9633 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM120744 6 0.0458 0.8794 0.000 0.000 0.000 0.000 0.016 0.984
#> GSM120745 6 0.1075 0.8596 0.000 0.000 0.000 0.000 0.048 0.952
#> GSM120746 6 0.0000 0.8845 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120747 6 0.0260 0.8835 0.000 0.000 0.000 0.000 0.008 0.992
#> GSM120748 6 0.0260 0.8833 0.000 0.000 0.000 0.000 0.008 0.992
#> GSM120749 6 0.0000 0.8845 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120750 6 0.0000 0.8845 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120751 6 0.0000 0.8845 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120752 6 0.0632 0.8763 0.000 0.000 0.000 0.000 0.024 0.976
#> GSM121336 2 0.2771 0.6664 0.000 0.852 0.000 0.116 0.032 0.000
#> GSM121339 5 0.5997 0.3992 0.000 0.180 0.236 0.004 0.560 0.020
#> GSM121349 2 0.2662 0.6689 0.000 0.856 0.000 0.120 0.024 0.000
#> GSM121355 2 0.2618 0.6692 0.000 0.860 0.000 0.116 0.024 0.000
#> GSM120757 5 0.5491 0.5498 0.000 0.016 0.000 0.132 0.604 0.248
#> GSM120766 5 0.5876 0.4594 0.000 0.016 0.000 0.136 0.496 0.352
#> GSM120770 5 0.5705 0.5093 0.000 0.100 0.000 0.040 0.592 0.268
#> GSM120779 5 0.5529 0.5449 0.000 0.016 0.000 0.132 0.596 0.256
#> GSM120780 6 0.4265 0.1857 0.000 0.004 0.000 0.016 0.384 0.596
#> GSM121102 5 0.5215 0.4391 0.000 0.064 0.000 0.020 0.584 0.332
#> GSM121203 6 0.4231 0.2029 0.000 0.008 0.000 0.012 0.364 0.616
#> GSM121204 5 0.5406 0.5510 0.000 0.012 0.000 0.132 0.608 0.248
#> GSM121330 3 0.0000 0.9337 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121335 3 0.0000 0.9337 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121337 5 0.4667 0.4138 0.000 0.292 0.000 0.036 0.652 0.020
#> GSM121338 5 0.4885 0.5462 0.000 0.172 0.008 0.020 0.712 0.088
#> GSM121341 3 0.0146 0.9326 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM121342 3 0.0146 0.9326 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM121343 5 0.4544 0.5473 0.000 0.172 0.000 0.020 0.728 0.080
#> GSM121344 3 0.0000 0.9337 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121346 3 0.0000 0.9337 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121347 5 0.5422 0.2987 0.000 0.316 0.000 0.080 0.580 0.024
#> GSM121348 5 0.6202 0.5437 0.000 0.024 0.000 0.212 0.508 0.256
#> GSM121350 3 0.0000 0.9337 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121352 3 0.0000 0.9337 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121354 3 0.0000 0.9337 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM120753 4 0.4717 0.2915 0.000 0.364 0.000 0.580 0.056 0.000
#> GSM120761 4 0.2404 0.6682 0.000 0.036 0.000 0.884 0.080 0.000
#> GSM120768 4 0.2003 0.6690 0.000 0.044 0.000 0.912 0.044 0.000
#> GSM120781 4 0.4756 0.1833 0.000 0.408 0.000 0.540 0.052 0.000
#> GSM120788 4 0.3641 0.5576 0.000 0.020 0.000 0.732 0.248 0.000
#> GSM120760 4 0.1285 0.6740 0.000 0.004 0.000 0.944 0.052 0.000
#> GSM120763 4 0.1461 0.6752 0.000 0.016 0.000 0.940 0.044 0.000
#> GSM120764 4 0.1556 0.6708 0.000 0.000 0.000 0.920 0.080 0.000
#> GSM120777 4 0.3071 0.6387 0.000 0.016 0.000 0.804 0.180 0.000
#> GSM120786 4 0.1141 0.6736 0.000 0.000 0.000 0.948 0.052 0.000
#> GSM121329 3 0.4799 0.6352 0.044 0.008 0.692 0.004 0.236 0.016
#> GSM121331 5 0.5620 0.5486 0.000 0.016 0.000 0.148 0.588 0.248
#> GSM121333 5 0.5491 0.5498 0.000 0.016 0.000 0.132 0.604 0.248
#> GSM121345 5 0.5230 0.5631 0.000 0.008 0.000 0.132 0.628 0.232
#> GSM121356 5 0.5524 0.5526 0.000 0.016 0.000 0.136 0.600 0.248
#> GSM120754 4 0.4897 -0.0705 0.000 0.060 0.000 0.492 0.448 0.000
#> GSM120759 2 0.4408 0.5648 0.000 0.656 0.000 0.292 0.052 0.000
#> GSM120762 2 0.4097 0.1092 0.000 0.504 0.000 0.488 0.008 0.000
#> GSM120775 4 0.3364 0.6148 0.000 0.024 0.000 0.780 0.196 0.000
#> GSM120776 5 0.5316 0.5932 0.000 0.044 0.000 0.172 0.672 0.112
#> GSM120782 5 0.5067 0.3168 0.000 0.088 0.000 0.356 0.556 0.000
#> GSM120789 2 0.2730 0.6374 0.000 0.808 0.000 0.192 0.000 0.000
#> GSM120790 4 0.4780 0.3737 0.000 0.228 0.000 0.660 0.112 0.000
#> GSM120791 4 0.1461 0.6733 0.000 0.016 0.000 0.940 0.044 0.000
#> GSM120755 2 0.2830 0.6627 0.000 0.836 0.000 0.144 0.020 0.000
#> GSM120756 4 0.4024 0.5338 0.000 0.036 0.000 0.700 0.264 0.000
#> GSM120769 2 0.3995 0.1336 0.000 0.516 0.000 0.480 0.004 0.000
#> GSM120778 2 0.3868 0.0940 0.000 0.504 0.000 0.496 0.000 0.000
#> GSM120792 2 0.4184 0.0560 0.000 0.500 0.000 0.488 0.012 0.000
#> GSM121332 2 0.2527 0.6531 0.000 0.832 0.000 0.168 0.000 0.000
#> GSM121334 4 0.3088 0.5202 0.000 0.172 0.000 0.808 0.020 0.000
#> GSM121340 4 0.4767 0.4263 0.000 0.304 0.000 0.620 0.076 0.000
#> GSM121351 2 0.3295 0.6695 0.000 0.816 0.000 0.128 0.056 0.000
#> GSM121353 4 0.6068 0.1421 0.000 0.360 0.000 0.376 0.264 0.000
#> GSM120758 4 0.4544 0.1043 0.000 0.416 0.000 0.548 0.036 0.000
#> GSM120771 4 0.5309 0.1777 0.000 0.312 0.000 0.560 0.128 0.000
#> GSM120772 4 0.4025 0.0800 0.000 0.416 0.000 0.576 0.008 0.000
#> GSM120773 4 0.1471 0.6757 0.000 0.004 0.000 0.932 0.064 0.000
#> GSM120774 4 0.3868 -0.1370 0.000 0.492 0.000 0.508 0.000 0.000
#> GSM120783 4 0.1141 0.6736 0.000 0.000 0.000 0.948 0.052 0.000
#> GSM120787 2 0.3868 0.0940 0.000 0.504 0.000 0.496 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> SD:mclust 117 9.38e-12 2
#> SD:mclust 113 2.34e-19 3
#> SD:mclust 112 1.08e-27 4
#> SD:mclust 79 1.93e-26 5
#> SD:mclust 91 4.12e-33 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 21512 rows and 119 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.914 0.924 0.970 0.5028 0.496 0.496
#> 3 3 0.535 0.705 0.846 0.3149 0.742 0.527
#> 4 4 0.595 0.583 0.764 0.1252 0.815 0.522
#> 5 5 0.619 0.521 0.748 0.0617 0.874 0.572
#> 6 6 0.675 0.632 0.766 0.0455 0.839 0.413
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
#> GSM120719 1 0.0000 0.963 1.000 0.000
#> GSM120720 1 0.0000 0.963 1.000 0.000
#> GSM120765 2 0.0000 0.972 0.000 1.000
#> GSM120767 2 0.0000 0.972 0.000 1.000
#> GSM120784 2 0.0000 0.972 0.000 1.000
#> GSM121400 1 0.0000 0.963 1.000 0.000
#> GSM121401 1 0.0000 0.963 1.000 0.000
#> GSM121402 2 0.0000 0.972 0.000 1.000
#> GSM121403 1 0.7745 0.706 0.772 0.228
#> GSM121404 2 0.0000 0.972 0.000 1.000
#> GSM121405 1 0.0000 0.963 1.000 0.000
#> GSM121406 2 0.0000 0.972 0.000 1.000
#> GSM121408 2 0.0000 0.972 0.000 1.000
#> GSM121409 1 0.0000 0.963 1.000 0.000
#> GSM121410 1 0.0000 0.963 1.000 0.000
#> GSM121412 2 0.0000 0.972 0.000 1.000
#> GSM121413 2 0.0000 0.972 0.000 1.000
#> GSM121414 2 0.0000 0.972 0.000 1.000
#> GSM121415 2 0.0000 0.972 0.000 1.000
#> GSM121416 2 0.0000 0.972 0.000 1.000
#> GSM120591 1 0.0000 0.963 1.000 0.000
#> GSM120594 1 0.0000 0.963 1.000 0.000
#> GSM120718 1 0.0000 0.963 1.000 0.000
#> GSM121205 1 0.0000 0.963 1.000 0.000
#> GSM121206 1 0.0000 0.963 1.000 0.000
#> GSM121207 1 0.0000 0.963 1.000 0.000
#> GSM121208 1 0.0000 0.963 1.000 0.000
#> GSM121209 1 0.0000 0.963 1.000 0.000
#> GSM121210 1 0.0000 0.963 1.000 0.000
#> GSM121211 1 0.0000 0.963 1.000 0.000
#> GSM121212 1 0.0000 0.963 1.000 0.000
#> GSM121213 1 0.0000 0.963 1.000 0.000
#> GSM121214 1 0.0000 0.963 1.000 0.000
#> GSM121215 1 0.0000 0.963 1.000 0.000
#> GSM121216 1 0.0000 0.963 1.000 0.000
#> GSM121217 1 0.0000 0.963 1.000 0.000
#> GSM121218 1 0.0000 0.963 1.000 0.000
#> GSM121234 1 0.0000 0.963 1.000 0.000
#> GSM121243 1 0.0000 0.963 1.000 0.000
#> GSM121245 1 0.0000 0.963 1.000 0.000
#> GSM121246 1 0.0000 0.963 1.000 0.000
#> GSM121247 1 0.0000 0.963 1.000 0.000
#> GSM121248 1 0.0000 0.963 1.000 0.000
#> GSM120744 1 0.9954 0.171 0.540 0.460
#> GSM120745 1 0.0000 0.963 1.000 0.000
#> GSM120746 1 0.4022 0.897 0.920 0.080
#> GSM120747 1 0.8327 0.654 0.736 0.264
#> GSM120748 2 0.2603 0.930 0.044 0.956
#> GSM120749 1 0.1184 0.952 0.984 0.016
#> GSM120750 1 0.7602 0.725 0.780 0.220
#> GSM120751 1 0.5629 0.841 0.868 0.132
#> GSM120752 1 0.0000 0.963 1.000 0.000
#> GSM121336 2 0.0000 0.972 0.000 1.000
#> GSM121339 2 0.0672 0.965 0.008 0.992
#> GSM121349 2 0.0000 0.972 0.000 1.000
#> GSM121355 2 0.0000 0.972 0.000 1.000
#> GSM120757 1 0.2423 0.933 0.960 0.040
#> GSM120766 1 0.9881 0.248 0.564 0.436
#> GSM120770 2 0.0000 0.972 0.000 1.000
#> GSM120779 1 0.0000 0.963 1.000 0.000
#> GSM120780 2 0.0000 0.972 0.000 1.000
#> GSM121102 2 0.0000 0.972 0.000 1.000
#> GSM121203 2 0.9358 0.435 0.352 0.648
#> GSM121204 1 0.0000 0.963 1.000 0.000
#> GSM121330 1 0.0000 0.963 1.000 0.000
#> GSM121335 1 0.0000 0.963 1.000 0.000
#> GSM121337 2 0.0000 0.972 0.000 1.000
#> GSM121338 2 0.0000 0.972 0.000 1.000
#> GSM121341 1 0.0000 0.963 1.000 0.000
#> GSM121342 1 0.0000 0.963 1.000 0.000
#> GSM121343 2 0.0000 0.972 0.000 1.000
#> GSM121344 1 0.0000 0.963 1.000 0.000
#> GSM121346 1 0.0000 0.963 1.000 0.000
#> GSM121347 2 0.0000 0.972 0.000 1.000
#> GSM121348 2 0.0000 0.972 0.000 1.000
#> GSM121350 1 0.0000 0.963 1.000 0.000
#> GSM121352 1 0.0000 0.963 1.000 0.000
#> GSM121354 1 0.0000 0.963 1.000 0.000
#> GSM120753 2 0.0000 0.972 0.000 1.000
#> GSM120761 2 0.0000 0.972 0.000 1.000
#> GSM120768 2 0.0000 0.972 0.000 1.000
#> GSM120781 2 0.0000 0.972 0.000 1.000
#> GSM120788 2 0.9635 0.353 0.388 0.612
#> GSM120760 2 0.0000 0.972 0.000 1.000
#> GSM120763 2 0.0000 0.972 0.000 1.000
#> GSM120764 2 0.0000 0.972 0.000 1.000
#> GSM120777 2 0.0672 0.965 0.008 0.992
#> GSM120786 2 0.0000 0.972 0.000 1.000
#> GSM121329 1 0.0000 0.963 1.000 0.000
#> GSM121331 1 0.2043 0.940 0.968 0.032
#> GSM121333 1 0.0376 0.961 0.996 0.004
#> GSM121345 1 0.0000 0.963 1.000 0.000
#> GSM121356 1 0.0376 0.961 0.996 0.004
#> GSM120754 2 0.0000 0.972 0.000 1.000
#> GSM120759 2 0.0000 0.972 0.000 1.000
#> GSM120762 2 0.0000 0.972 0.000 1.000
#> GSM120775 2 0.1633 0.950 0.024 0.976
#> GSM120776 2 0.9933 0.150 0.452 0.548
#> GSM120782 2 0.0000 0.972 0.000 1.000
#> GSM120789 2 0.0000 0.972 0.000 1.000
#> GSM120790 2 0.0000 0.972 0.000 1.000
#> GSM120791 2 0.0000 0.972 0.000 1.000
#> GSM120755 2 0.0000 0.972 0.000 1.000
#> GSM120756 1 0.4690 0.876 0.900 0.100
#> GSM120769 2 0.0000 0.972 0.000 1.000
#> GSM120778 2 0.0000 0.972 0.000 1.000
#> GSM120792 2 0.0000 0.972 0.000 1.000
#> GSM121332 2 0.0000 0.972 0.000 1.000
#> GSM121334 2 0.0000 0.972 0.000 1.000
#> GSM121340 2 0.0000 0.972 0.000 1.000
#> GSM121351 2 0.0000 0.972 0.000 1.000
#> GSM121353 2 0.9044 0.523 0.320 0.680
#> GSM120758 2 0.0000 0.972 0.000 1.000
#> GSM120771 2 0.0000 0.972 0.000 1.000
#> GSM120772 2 0.0000 0.972 0.000 1.000
#> GSM120773 2 0.0000 0.972 0.000 1.000
#> GSM120774 2 0.0000 0.972 0.000 1.000
#> GSM120783 2 0.0000 0.972 0.000 1.000
#> GSM120787 2 0.0000 0.972 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM120719 1 0.3551 0.7513 0.868 0.000 0.132
#> GSM120720 3 0.3267 0.7324 0.116 0.000 0.884
#> GSM120765 2 0.2711 0.8463 0.000 0.912 0.088
#> GSM120767 2 0.2356 0.8541 0.000 0.928 0.072
#> GSM120784 2 0.4235 0.7720 0.000 0.824 0.176
#> GSM121400 3 0.1163 0.7746 0.000 0.028 0.972
#> GSM121401 3 0.0475 0.7753 0.004 0.004 0.992
#> GSM121402 2 0.2165 0.8573 0.000 0.936 0.064
#> GSM121403 3 0.2537 0.7580 0.000 0.080 0.920
#> GSM121404 3 0.6308 -0.0119 0.000 0.492 0.508
#> GSM121405 3 0.1529 0.7721 0.000 0.040 0.960
#> GSM121406 2 0.3116 0.8345 0.000 0.892 0.108
#> GSM121408 2 0.3116 0.8359 0.000 0.892 0.108
#> GSM121409 3 0.1163 0.7749 0.000 0.028 0.972
#> GSM121410 3 0.1289 0.7740 0.000 0.032 0.968
#> GSM121412 2 0.4750 0.7220 0.000 0.784 0.216
#> GSM121413 2 0.3412 0.8236 0.000 0.876 0.124
#> GSM121414 2 0.4399 0.7580 0.000 0.812 0.188
#> GSM121415 2 0.3412 0.8235 0.000 0.876 0.124
#> GSM121416 2 0.2356 0.8541 0.000 0.928 0.072
#> GSM120591 3 0.3038 0.7412 0.104 0.000 0.896
#> GSM120594 3 0.3192 0.7355 0.112 0.000 0.888
#> GSM120718 3 0.6026 0.2357 0.376 0.000 0.624
#> GSM121205 1 0.5098 0.6929 0.752 0.000 0.248
#> GSM121206 1 0.6079 0.4946 0.612 0.000 0.388
#> GSM121207 1 0.3340 0.7527 0.880 0.000 0.120
#> GSM121208 3 0.5465 0.4716 0.288 0.000 0.712
#> GSM121209 1 0.6286 0.3134 0.536 0.000 0.464
#> GSM121210 1 0.5098 0.6922 0.752 0.000 0.248
#> GSM121211 1 0.6192 0.4303 0.580 0.000 0.420
#> GSM121212 1 0.4931 0.7063 0.768 0.000 0.232
#> GSM121213 1 0.5497 0.6430 0.708 0.000 0.292
#> GSM121214 1 0.4399 0.7344 0.812 0.000 0.188
#> GSM121215 1 0.6095 0.4887 0.608 0.000 0.392
#> GSM121216 1 0.5497 0.6478 0.708 0.000 0.292
#> GSM121217 1 0.5948 0.5480 0.640 0.000 0.360
#> GSM121218 1 0.4399 0.7344 0.812 0.000 0.188
#> GSM121234 3 0.6168 0.1053 0.412 0.000 0.588
#> GSM121243 1 0.4654 0.7236 0.792 0.000 0.208
#> GSM121245 1 0.3879 0.7471 0.848 0.000 0.152
#> GSM121246 3 0.4555 0.6337 0.200 0.000 0.800
#> GSM121247 1 0.1529 0.7545 0.960 0.000 0.040
#> GSM121248 1 0.4504 0.7302 0.804 0.000 0.196
#> GSM120744 3 0.5216 0.6149 0.000 0.260 0.740
#> GSM120745 3 0.4121 0.7020 0.168 0.000 0.832
#> GSM120746 3 0.2356 0.7630 0.000 0.072 0.928
#> GSM120747 3 0.2711 0.7542 0.000 0.088 0.912
#> GSM120748 3 0.5178 0.6177 0.000 0.256 0.744
#> GSM120749 3 0.1170 0.7760 0.008 0.016 0.976
#> GSM120750 3 0.3686 0.7270 0.000 0.140 0.860
#> GSM120751 3 0.3918 0.7293 0.004 0.140 0.856
#> GSM120752 3 0.4605 0.6686 0.204 0.000 0.796
#> GSM121336 2 0.2356 0.8540 0.000 0.928 0.072
#> GSM121339 3 0.5465 0.5532 0.000 0.288 0.712
#> GSM121349 2 0.2066 0.8579 0.000 0.940 0.060
#> GSM121355 2 0.2796 0.8444 0.000 0.908 0.092
#> GSM120757 1 0.0747 0.7481 0.984 0.016 0.000
#> GSM120766 1 0.7485 0.5102 0.680 0.224 0.096
#> GSM120770 2 0.3412 0.8234 0.000 0.876 0.124
#> GSM120779 1 0.0892 0.7469 0.980 0.020 0.000
#> GSM120780 2 0.6062 0.3889 0.000 0.616 0.384
#> GSM121102 2 0.6140 0.3362 0.000 0.596 0.404
#> GSM121203 3 0.5363 0.5861 0.000 0.276 0.724
#> GSM121204 1 0.0983 0.7534 0.980 0.004 0.016
#> GSM121330 3 0.1643 0.7689 0.044 0.000 0.956
#> GSM121335 3 0.3267 0.7324 0.116 0.000 0.884
#> GSM121337 2 0.3412 0.8235 0.000 0.876 0.124
#> GSM121338 3 0.5859 0.4472 0.000 0.344 0.656
#> GSM121341 3 0.3340 0.7289 0.120 0.000 0.880
#> GSM121342 3 0.4842 0.5959 0.224 0.000 0.776
#> GSM121343 3 0.5678 0.5033 0.000 0.316 0.684
#> GSM121344 3 0.2959 0.7432 0.100 0.000 0.900
#> GSM121346 3 0.1529 0.7700 0.040 0.000 0.960
#> GSM121347 2 0.3237 0.8665 0.056 0.912 0.032
#> GSM121348 2 0.4094 0.8495 0.100 0.872 0.028
#> GSM121350 3 0.1289 0.7716 0.032 0.000 0.968
#> GSM121352 3 0.1860 0.7661 0.052 0.000 0.948
#> GSM121354 3 0.2261 0.7591 0.068 0.000 0.932
#> GSM120753 2 0.1031 0.8656 0.024 0.976 0.000
#> GSM120761 2 0.2165 0.8542 0.064 0.936 0.000
#> GSM120768 2 0.3267 0.8261 0.116 0.884 0.000
#> GSM120781 2 0.1015 0.8667 0.012 0.980 0.008
#> GSM120788 1 0.3482 0.6885 0.872 0.128 0.000
#> GSM120760 2 0.4887 0.7213 0.228 0.772 0.000
#> GSM120763 2 0.4452 0.7621 0.192 0.808 0.000
#> GSM120764 1 0.6307 -0.1562 0.512 0.488 0.000
#> GSM120777 1 0.4346 0.6382 0.816 0.184 0.000
#> GSM120786 2 0.6192 0.3837 0.420 0.580 0.000
#> GSM121329 1 0.3879 0.7481 0.848 0.000 0.152
#> GSM121331 1 0.1031 0.7454 0.976 0.024 0.000
#> GSM121333 1 0.0424 0.7498 0.992 0.008 0.000
#> GSM121345 1 0.1031 0.7455 0.976 0.024 0.000
#> GSM121356 1 0.0983 0.7537 0.980 0.004 0.016
#> GSM120754 2 0.4399 0.7655 0.188 0.812 0.000
#> GSM120759 2 0.1529 0.8628 0.000 0.960 0.040
#> GSM120762 2 0.1163 0.8650 0.028 0.972 0.000
#> GSM120775 1 0.4654 0.6041 0.792 0.208 0.000
#> GSM120776 1 0.3192 0.7009 0.888 0.112 0.000
#> GSM120782 2 0.3752 0.8048 0.144 0.856 0.000
#> GSM120789 2 0.0892 0.8655 0.000 0.980 0.020
#> GSM120790 2 0.1129 0.8666 0.020 0.976 0.004
#> GSM120791 2 0.3816 0.8011 0.148 0.852 0.000
#> GSM120755 2 0.1411 0.8635 0.000 0.964 0.036
#> GSM120756 1 0.3038 0.7042 0.896 0.104 0.000
#> GSM120769 2 0.1163 0.8653 0.028 0.972 0.000
#> GSM120778 2 0.2261 0.8528 0.068 0.932 0.000
#> GSM120792 2 0.2796 0.8417 0.092 0.908 0.000
#> GSM121332 2 0.1289 0.8639 0.000 0.968 0.032
#> GSM121334 2 0.1529 0.8625 0.040 0.960 0.000
#> GSM121340 2 0.6286 0.2668 0.464 0.536 0.000
#> GSM121351 2 0.2356 0.8540 0.000 0.928 0.072
#> GSM121353 1 0.4346 0.6389 0.816 0.184 0.000
#> GSM120758 2 0.0892 0.8661 0.020 0.980 0.000
#> GSM120771 2 0.0983 0.8662 0.004 0.980 0.016
#> GSM120772 2 0.1411 0.8635 0.036 0.964 0.000
#> GSM120773 2 0.4605 0.7506 0.204 0.796 0.000
#> GSM120774 2 0.2537 0.8466 0.080 0.920 0.000
#> GSM120783 2 0.5882 0.5382 0.348 0.652 0.000
#> GSM120787 2 0.2165 0.8549 0.064 0.936 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM120719 1 0.4483 0.5094 0.712 0.000 0.004 0.284
#> GSM120720 3 0.5163 -0.1160 0.480 0.000 0.516 0.004
#> GSM120765 2 0.2589 0.8407 0.000 0.884 0.116 0.000
#> GSM120767 2 0.1398 0.8777 0.000 0.956 0.040 0.004
#> GSM120784 2 0.4391 0.6867 0.000 0.740 0.252 0.008
#> GSM121400 3 0.3161 0.5244 0.124 0.012 0.864 0.000
#> GSM121401 3 0.3975 0.4344 0.240 0.000 0.760 0.000
#> GSM121402 2 0.1637 0.8696 0.000 0.940 0.060 0.000
#> GSM121403 3 0.4337 0.5071 0.140 0.052 0.808 0.000
#> GSM121404 3 0.4713 0.3893 0.004 0.292 0.700 0.004
#> GSM121405 3 0.4194 0.4455 0.228 0.008 0.764 0.000
#> GSM121406 2 0.1940 0.8619 0.000 0.924 0.076 0.000
#> GSM121408 2 0.1118 0.8761 0.000 0.964 0.036 0.000
#> GSM121409 3 0.4538 0.4510 0.216 0.024 0.760 0.000
#> GSM121410 3 0.4898 0.3980 0.260 0.024 0.716 0.000
#> GSM121412 2 0.3123 0.8059 0.000 0.844 0.156 0.000
#> GSM121413 2 0.2704 0.8332 0.000 0.876 0.124 0.000
#> GSM121414 2 0.3123 0.8053 0.000 0.844 0.156 0.000
#> GSM121415 2 0.2704 0.8327 0.000 0.876 0.124 0.000
#> GSM121416 2 0.2198 0.8670 0.000 0.920 0.072 0.008
#> GSM120591 3 0.5250 0.5008 0.176 0.000 0.744 0.080
#> GSM120594 3 0.5167 -0.1429 0.488 0.000 0.508 0.004
#> GSM120718 1 0.4401 0.5852 0.724 0.000 0.272 0.004
#> GSM121205 1 0.1302 0.7461 0.956 0.000 0.000 0.044
#> GSM121206 1 0.2345 0.7205 0.900 0.000 0.100 0.000
#> GSM121207 1 0.3668 0.6504 0.808 0.000 0.004 0.188
#> GSM121208 1 0.4040 0.5991 0.752 0.000 0.248 0.000
#> GSM121209 1 0.2921 0.6959 0.860 0.000 0.140 0.000
#> GSM121210 1 0.2198 0.7440 0.920 0.000 0.008 0.072
#> GSM121211 1 0.2480 0.7295 0.904 0.000 0.088 0.008
#> GSM121212 1 0.1978 0.7434 0.928 0.000 0.004 0.068
#> GSM121213 1 0.2089 0.7448 0.932 0.000 0.048 0.020
#> GSM121214 1 0.2281 0.7311 0.904 0.000 0.000 0.096
#> GSM121215 1 0.2342 0.7329 0.912 0.000 0.080 0.008
#> GSM121216 1 0.1109 0.7450 0.968 0.000 0.028 0.004
#> GSM121217 1 0.2142 0.7425 0.928 0.000 0.056 0.016
#> GSM121218 1 0.2081 0.7363 0.916 0.000 0.000 0.084
#> GSM121234 1 0.3123 0.6839 0.844 0.000 0.156 0.000
#> GSM121243 1 0.1824 0.7454 0.936 0.000 0.004 0.060
#> GSM121245 1 0.3400 0.6646 0.820 0.000 0.000 0.180
#> GSM121246 1 0.4277 0.5606 0.720 0.000 0.280 0.000
#> GSM121247 1 0.4819 0.3891 0.652 0.000 0.004 0.344
#> GSM121248 1 0.1867 0.7410 0.928 0.000 0.000 0.072
#> GSM120744 3 0.4916 0.3133 0.000 0.000 0.576 0.424
#> GSM120745 3 0.4981 0.2483 0.000 0.000 0.536 0.464
#> GSM120746 3 0.4661 0.4187 0.000 0.000 0.652 0.348
#> GSM120747 3 0.4049 0.5109 0.008 0.000 0.780 0.212
#> GSM120748 3 0.4250 0.4729 0.000 0.000 0.724 0.276
#> GSM120749 3 0.4800 0.4295 0.004 0.000 0.656 0.340
#> GSM120750 3 0.4804 0.3790 0.000 0.000 0.616 0.384
#> GSM120751 3 0.4804 0.3780 0.000 0.000 0.616 0.384
#> GSM120752 3 0.4998 0.1860 0.000 0.000 0.512 0.488
#> GSM121336 2 0.1022 0.8769 0.000 0.968 0.032 0.000
#> GSM121339 3 0.6663 0.2855 0.100 0.344 0.556 0.000
#> GSM121349 2 0.0817 0.8777 0.000 0.976 0.024 0.000
#> GSM121355 2 0.1302 0.8742 0.000 0.956 0.044 0.000
#> GSM120757 4 0.3710 0.5238 0.004 0.000 0.192 0.804
#> GSM120766 4 0.4722 0.3467 0.000 0.008 0.300 0.692
#> GSM120770 3 0.6980 0.1997 0.000 0.116 0.484 0.400
#> GSM120779 4 0.2999 0.5853 0.004 0.000 0.132 0.864
#> GSM120780 3 0.5292 0.1920 0.000 0.008 0.512 0.480
#> GSM121102 3 0.5566 0.4722 0.000 0.072 0.704 0.224
#> GSM121203 3 0.4661 0.4221 0.000 0.000 0.652 0.348
#> GSM121204 4 0.3335 0.5942 0.016 0.000 0.128 0.856
#> GSM121330 1 0.5000 0.0941 0.500 0.000 0.500 0.000
#> GSM121335 1 0.4972 0.2282 0.544 0.000 0.456 0.000
#> GSM121337 2 0.4054 0.7714 0.000 0.796 0.188 0.016
#> GSM121338 3 0.4052 0.5459 0.012 0.124 0.836 0.028
#> GSM121341 1 0.4907 0.3162 0.580 0.000 0.420 0.000
#> GSM121342 1 0.4543 0.4955 0.676 0.000 0.324 0.000
#> GSM121343 3 0.4678 0.5412 0.012 0.120 0.808 0.060
#> GSM121344 3 0.4907 0.0766 0.420 0.000 0.580 0.000
#> GSM121346 3 0.4122 0.4416 0.236 0.000 0.760 0.004
#> GSM121347 2 0.6394 0.4013 0.000 0.596 0.088 0.316
#> GSM121348 4 0.5530 0.4658 0.000 0.076 0.212 0.712
#> GSM121350 3 0.4040 0.4277 0.248 0.000 0.752 0.000
#> GSM121352 3 0.4535 0.3590 0.292 0.000 0.704 0.004
#> GSM121354 3 0.4972 -0.0271 0.456 0.000 0.544 0.000
#> GSM120753 2 0.0921 0.8714 0.000 0.972 0.000 0.028
#> GSM120761 2 0.3975 0.6261 0.000 0.760 0.000 0.240
#> GSM120768 2 0.3726 0.6670 0.000 0.788 0.000 0.212
#> GSM120781 2 0.0707 0.8738 0.000 0.980 0.000 0.020
#> GSM120788 4 0.4815 0.5946 0.216 0.028 0.004 0.752
#> GSM120760 4 0.5151 0.2059 0.004 0.464 0.000 0.532
#> GSM120763 2 0.5004 0.2275 0.004 0.604 0.000 0.392
#> GSM120764 4 0.6163 0.6337 0.140 0.168 0.004 0.688
#> GSM120777 4 0.4517 0.6374 0.168 0.036 0.004 0.792
#> GSM120786 4 0.5854 0.6009 0.064 0.256 0.004 0.676
#> GSM121329 1 0.3539 0.6723 0.820 0.000 0.004 0.176
#> GSM121331 4 0.2739 0.6423 0.036 0.000 0.060 0.904
#> GSM121333 4 0.2282 0.6425 0.024 0.000 0.052 0.924
#> GSM121345 4 0.3105 0.6452 0.140 0.000 0.004 0.856
#> GSM121356 4 0.3032 0.5938 0.008 0.000 0.124 0.868
#> GSM120754 4 0.4261 0.6235 0.000 0.112 0.068 0.820
#> GSM120759 2 0.0921 0.8783 0.000 0.972 0.028 0.000
#> GSM120762 2 0.0592 0.8747 0.000 0.984 0.000 0.016
#> GSM120775 4 0.6109 0.5759 0.224 0.096 0.004 0.676
#> GSM120776 4 0.3052 0.6457 0.032 0.008 0.064 0.896
#> GSM120782 4 0.6508 0.4421 0.000 0.360 0.084 0.556
#> GSM120789 2 0.0469 0.8779 0.000 0.988 0.012 0.000
#> GSM120790 2 0.5170 0.6186 0.000 0.724 0.048 0.228
#> GSM120791 2 0.4500 0.4533 0.000 0.684 0.000 0.316
#> GSM120755 2 0.0376 0.8770 0.000 0.992 0.004 0.004
#> GSM120756 4 0.5822 0.4826 0.296 0.048 0.004 0.652
#> GSM120769 2 0.0707 0.8738 0.000 0.980 0.000 0.020
#> GSM120778 2 0.1302 0.8638 0.000 0.956 0.000 0.044
#> GSM120792 2 0.1557 0.8563 0.000 0.944 0.000 0.056
#> GSM121332 2 0.0524 0.8775 0.000 0.988 0.008 0.004
#> GSM121334 2 0.1557 0.8577 0.000 0.944 0.000 0.056
#> GSM121340 4 0.7392 0.4950 0.172 0.300 0.004 0.524
#> GSM121351 2 0.1022 0.8772 0.000 0.968 0.032 0.000
#> GSM121353 1 0.7685 -0.2410 0.412 0.184 0.004 0.400
#> GSM120758 2 0.0707 0.8736 0.000 0.980 0.000 0.020
#> GSM120771 2 0.2319 0.8700 0.000 0.924 0.036 0.040
#> GSM120772 2 0.1022 0.8698 0.000 0.968 0.000 0.032
#> GSM120773 4 0.5039 0.3587 0.004 0.404 0.000 0.592
#> GSM120774 2 0.1389 0.8622 0.000 0.952 0.000 0.048
#> GSM120783 4 0.5778 0.4645 0.040 0.356 0.000 0.604
#> GSM120787 2 0.0921 0.8708 0.000 0.972 0.000 0.028
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM120719 1 0.5773 0.48985 0.592 0.008 0.064 0.328 0.008
#> GSM120720 3 0.1410 0.70536 0.060 0.000 0.940 0.000 0.000
#> GSM120765 2 0.4040 0.54341 0.000 0.712 0.012 0.000 0.276
#> GSM120767 2 0.2214 0.68709 0.000 0.916 0.028 0.004 0.052
#> GSM120784 2 0.5905 0.28478 0.000 0.556 0.104 0.004 0.336
#> GSM121400 5 0.5295 0.40856 0.128 0.000 0.200 0.000 0.672
#> GSM121401 3 0.4541 0.63935 0.172 0.000 0.744 0.000 0.084
#> GSM121402 5 0.4446 -0.08951 0.000 0.476 0.000 0.004 0.520
#> GSM121403 5 0.4792 0.51070 0.128 0.004 0.128 0.000 0.740
#> GSM121404 3 0.5240 0.45659 0.000 0.112 0.672 0.000 0.216
#> GSM121405 3 0.4818 0.62737 0.180 0.000 0.720 0.000 0.100
#> GSM121406 2 0.4288 0.37590 0.000 0.612 0.004 0.000 0.384
#> GSM121408 2 0.3395 0.59752 0.000 0.764 0.000 0.000 0.236
#> GSM121409 5 0.5013 0.47933 0.204 0.000 0.100 0.000 0.696
#> GSM121410 5 0.5447 0.44762 0.200 0.000 0.128 0.004 0.668
#> GSM121412 5 0.4594 0.24983 0.004 0.364 0.012 0.000 0.620
#> GSM121413 5 0.4196 0.26981 0.000 0.356 0.004 0.000 0.640
#> GSM121414 5 0.4470 0.24164 0.000 0.372 0.012 0.000 0.616
#> GSM121415 2 0.4622 0.24729 0.000 0.548 0.012 0.000 0.440
#> GSM121416 2 0.4473 0.33720 0.000 0.580 0.000 0.008 0.412
#> GSM120591 3 0.0703 0.71001 0.000 0.000 0.976 0.024 0.000
#> GSM120594 3 0.1571 0.70610 0.060 0.000 0.936 0.004 0.000
#> GSM120718 3 0.4003 0.51173 0.288 0.000 0.704 0.008 0.000
#> GSM121205 1 0.1608 0.82032 0.928 0.000 0.000 0.072 0.000
#> GSM121206 1 0.0404 0.81312 0.988 0.000 0.012 0.000 0.000
#> GSM121207 1 0.3398 0.72747 0.780 0.000 0.000 0.216 0.004
#> GSM121208 1 0.2278 0.77808 0.908 0.000 0.032 0.000 0.060
#> GSM121209 1 0.0912 0.80797 0.972 0.000 0.016 0.000 0.012
#> GSM121210 1 0.2305 0.81787 0.896 0.000 0.000 0.092 0.012
#> GSM121211 1 0.0451 0.81586 0.988 0.000 0.008 0.004 0.000
#> GSM121212 1 0.1908 0.81604 0.908 0.000 0.000 0.092 0.000
#> GSM121213 1 0.1124 0.82234 0.960 0.000 0.000 0.036 0.004
#> GSM121214 1 0.2629 0.79336 0.860 0.000 0.000 0.136 0.004
#> GSM121215 1 0.0671 0.81479 0.980 0.000 0.000 0.004 0.016
#> GSM121216 1 0.1579 0.82063 0.944 0.000 0.000 0.032 0.024
#> GSM121217 1 0.0703 0.82101 0.976 0.000 0.000 0.024 0.000
#> GSM121218 1 0.1908 0.81514 0.908 0.000 0.000 0.092 0.000
#> GSM121234 1 0.1653 0.79717 0.944 0.000 0.028 0.004 0.024
#> GSM121243 1 0.2236 0.82079 0.908 0.000 0.000 0.068 0.024
#> GSM121245 1 0.3109 0.74522 0.800 0.000 0.000 0.200 0.000
#> GSM121246 1 0.2863 0.75274 0.876 0.000 0.060 0.000 0.064
#> GSM121247 1 0.5143 0.30101 0.532 0.000 0.000 0.428 0.040
#> GSM121248 1 0.2020 0.81357 0.900 0.000 0.000 0.100 0.000
#> GSM120744 3 0.3023 0.68065 0.000 0.004 0.860 0.112 0.024
#> GSM120745 3 0.2727 0.68481 0.000 0.000 0.868 0.116 0.016
#> GSM120746 3 0.1831 0.70463 0.000 0.000 0.920 0.076 0.004
#> GSM120747 3 0.0955 0.71007 0.000 0.000 0.968 0.028 0.004
#> GSM120748 3 0.2036 0.70646 0.000 0.000 0.920 0.056 0.024
#> GSM120749 3 0.1704 0.70633 0.000 0.000 0.928 0.068 0.004
#> GSM120750 3 0.2712 0.69111 0.000 0.000 0.880 0.088 0.032
#> GSM120751 3 0.2136 0.69925 0.000 0.000 0.904 0.088 0.008
#> GSM120752 3 0.3224 0.64825 0.000 0.000 0.824 0.160 0.016
#> GSM121336 2 0.3336 0.60440 0.000 0.772 0.000 0.000 0.228
#> GSM121339 3 0.7723 -0.11922 0.060 0.288 0.396 0.000 0.256
#> GSM121349 2 0.3424 0.59461 0.000 0.760 0.000 0.000 0.240
#> GSM121355 2 0.3534 0.57688 0.000 0.744 0.000 0.000 0.256
#> GSM120757 4 0.5169 0.54515 0.008 0.000 0.048 0.640 0.304
#> GSM120766 4 0.5222 0.36405 0.008 0.000 0.028 0.512 0.452
#> GSM120770 5 0.6927 0.35184 0.000 0.068 0.184 0.172 0.576
#> GSM120779 4 0.4560 0.55971 0.008 0.000 0.016 0.672 0.304
#> GSM120780 5 0.5256 0.04391 0.000 0.008 0.048 0.324 0.620
#> GSM121102 3 0.5730 0.12787 0.000 0.020 0.512 0.044 0.424
#> GSM121203 3 0.5565 0.47881 0.000 0.000 0.640 0.144 0.216
#> GSM121204 4 0.4998 0.58182 0.008 0.000 0.132 0.728 0.132
#> GSM121330 3 0.5600 0.14999 0.456 0.000 0.480 0.004 0.060
#> GSM121335 1 0.5272 0.11860 0.552 0.000 0.396 0.000 0.052
#> GSM121337 5 0.4583 0.42840 0.008 0.264 0.004 0.020 0.704
#> GSM121338 5 0.5426 0.15272 0.016 0.032 0.408 0.000 0.544
#> GSM121341 1 0.5219 0.32039 0.616 0.000 0.328 0.004 0.052
#> GSM121342 1 0.4382 0.61238 0.760 0.000 0.176 0.004 0.060
#> GSM121343 5 0.3801 0.54922 0.016 0.024 0.136 0.004 0.820
#> GSM121344 1 0.5504 -0.11087 0.488 0.000 0.448 0.000 0.064
#> GSM121346 3 0.5299 0.59758 0.212 0.000 0.668 0.000 0.120
#> GSM121347 5 0.4400 0.38530 0.000 0.060 0.000 0.196 0.744
#> GSM121348 5 0.4370 0.04680 0.008 0.004 0.000 0.332 0.656
#> GSM121350 3 0.5831 0.52582 0.268 0.000 0.592 0.000 0.140
#> GSM121352 3 0.5635 0.55361 0.252 0.000 0.620 0.000 0.128
#> GSM121354 3 0.5714 0.27020 0.412 0.000 0.512 0.004 0.072
#> GSM120753 2 0.1121 0.69397 0.000 0.956 0.000 0.000 0.044
#> GSM120761 2 0.2625 0.65450 0.000 0.876 0.000 0.108 0.016
#> GSM120768 2 0.2389 0.62642 0.000 0.880 0.000 0.116 0.004
#> GSM120781 2 0.0510 0.69316 0.000 0.984 0.000 0.000 0.016
#> GSM120788 4 0.3642 0.61560 0.048 0.124 0.000 0.824 0.004
#> GSM120760 4 0.5165 0.20982 0.000 0.448 0.000 0.512 0.040
#> GSM120763 2 0.4763 0.26958 0.000 0.632 0.000 0.336 0.032
#> GSM120764 4 0.3861 0.52095 0.004 0.284 0.000 0.712 0.000
#> GSM120777 4 0.3103 0.62782 0.044 0.072 0.000 0.872 0.012
#> GSM120786 4 0.4323 0.46250 0.000 0.332 0.000 0.656 0.012
#> GSM121329 1 0.3336 0.71923 0.772 0.000 0.000 0.228 0.000
#> GSM121331 4 0.4789 0.48089 0.024 0.000 0.000 0.584 0.392
#> GSM121333 4 0.4445 0.56550 0.024 0.000 0.000 0.676 0.300
#> GSM121345 4 0.4019 0.61489 0.052 0.004 0.000 0.792 0.152
#> GSM121356 4 0.4872 0.42075 0.024 0.000 0.000 0.540 0.436
#> GSM120754 4 0.5509 0.60283 0.000 0.188 0.056 0.700 0.056
#> GSM120759 2 0.4549 0.19021 0.000 0.528 0.000 0.008 0.464
#> GSM120762 2 0.1043 0.69407 0.000 0.960 0.000 0.000 0.040
#> GSM120775 4 0.5656 0.46047 0.024 0.316 0.036 0.616 0.008
#> GSM120776 4 0.5374 0.56234 0.000 0.064 0.208 0.696 0.032
#> GSM120782 2 0.6821 -0.12932 0.000 0.420 0.328 0.248 0.004
#> GSM120789 2 0.3143 0.62267 0.000 0.796 0.000 0.000 0.204
#> GSM120790 5 0.4021 0.41202 0.000 0.052 0.000 0.168 0.780
#> GSM120791 2 0.4000 0.48950 0.000 0.748 0.000 0.228 0.024
#> GSM120755 2 0.1270 0.69203 0.000 0.948 0.000 0.000 0.052
#> GSM120756 4 0.5104 0.57044 0.104 0.172 0.004 0.716 0.004
#> GSM120769 2 0.0609 0.69420 0.000 0.980 0.000 0.000 0.020
#> GSM120778 2 0.1205 0.67658 0.000 0.956 0.000 0.040 0.004
#> GSM120792 2 0.1341 0.67101 0.000 0.944 0.000 0.056 0.000
#> GSM121332 2 0.3003 0.63037 0.000 0.812 0.000 0.000 0.188
#> GSM121334 2 0.2248 0.68700 0.000 0.900 0.000 0.012 0.088
#> GSM121340 2 0.4530 0.19026 0.008 0.612 0.000 0.376 0.004
#> GSM121351 2 0.4268 0.26817 0.000 0.556 0.000 0.000 0.444
#> GSM121353 2 0.6070 0.04588 0.108 0.544 0.000 0.340 0.008
#> GSM120758 2 0.0794 0.69455 0.000 0.972 0.000 0.000 0.028
#> GSM120771 2 0.4638 0.46519 0.000 0.648 0.000 0.028 0.324
#> GSM120772 2 0.1117 0.69054 0.000 0.964 0.000 0.020 0.016
#> GSM120773 2 0.4562 -0.00039 0.000 0.548 0.004 0.444 0.004
#> GSM120774 2 0.1124 0.67827 0.000 0.960 0.000 0.036 0.004
#> GSM120783 2 0.4722 0.07127 0.000 0.572 0.012 0.412 0.004
#> GSM120787 2 0.0671 0.69308 0.000 0.980 0.000 0.004 0.016
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM120719 1 0.5496 0.5466 0.640 0.000 0.016 0.088 0.020 0.236
#> GSM120720 6 0.4016 0.6860 0.056 0.004 0.128 0.004 0.016 0.792
#> GSM120765 2 0.1956 0.6871 0.000 0.928 0.008 0.016 0.016 0.032
#> GSM120767 2 0.4526 0.5913 0.000 0.728 0.004 0.092 0.008 0.168
#> GSM120784 2 0.4322 0.5251 0.000 0.720 0.012 0.000 0.052 0.216
#> GSM121400 3 0.4687 0.6599 0.060 0.036 0.740 0.000 0.156 0.008
#> GSM121401 3 0.3837 0.7632 0.060 0.000 0.780 0.000 0.008 0.152
#> GSM121402 2 0.4503 0.6300 0.000 0.728 0.056 0.028 0.188 0.000
#> GSM121403 3 0.5658 0.5796 0.060 0.116 0.656 0.000 0.164 0.004
#> GSM121404 3 0.4286 0.7049 0.000 0.072 0.792 0.016 0.036 0.084
#> GSM121405 3 0.3331 0.7742 0.044 0.000 0.816 0.000 0.004 0.136
#> GSM121406 2 0.2772 0.6796 0.000 0.876 0.060 0.016 0.048 0.000
#> GSM121408 2 0.1572 0.6913 0.000 0.936 0.028 0.036 0.000 0.000
#> GSM121409 3 0.7906 0.1275 0.136 0.216 0.336 0.004 0.292 0.016
#> GSM121410 3 0.6158 0.5382 0.108 0.092 0.604 0.000 0.192 0.004
#> GSM121412 2 0.4751 0.5655 0.004 0.712 0.116 0.004 0.160 0.004
#> GSM121413 2 0.4799 0.5011 0.000 0.668 0.080 0.004 0.244 0.004
#> GSM121414 2 0.5014 0.5309 0.000 0.676 0.124 0.008 0.188 0.004
#> GSM121415 2 0.5772 0.5074 0.000 0.604 0.264 0.044 0.080 0.008
#> GSM121416 2 0.6684 0.4250 0.000 0.500 0.284 0.144 0.064 0.008
#> GSM120591 6 0.1924 0.7659 0.008 0.008 0.044 0.004 0.008 0.928
#> GSM120594 6 0.3564 0.7127 0.084 0.004 0.076 0.000 0.012 0.824
#> GSM120718 6 0.4504 0.6066 0.204 0.000 0.068 0.004 0.008 0.716
#> GSM121205 1 0.0436 0.9246 0.988 0.000 0.000 0.004 0.004 0.004
#> GSM121206 1 0.0777 0.9228 0.972 0.000 0.024 0.000 0.000 0.004
#> GSM121207 1 0.2307 0.8864 0.896 0.000 0.004 0.068 0.032 0.000
#> GSM121208 1 0.2404 0.8458 0.872 0.000 0.112 0.000 0.016 0.000
#> GSM121209 1 0.1152 0.9129 0.952 0.000 0.044 0.000 0.004 0.000
#> GSM121210 1 0.0935 0.9206 0.964 0.000 0.000 0.004 0.032 0.000
#> GSM121211 1 0.0777 0.9219 0.972 0.000 0.024 0.000 0.004 0.000
#> GSM121212 1 0.1307 0.9220 0.952 0.000 0.008 0.032 0.008 0.000
#> GSM121213 1 0.0858 0.9227 0.968 0.000 0.028 0.004 0.000 0.000
#> GSM121214 1 0.1542 0.9094 0.936 0.000 0.004 0.052 0.008 0.000
#> GSM121215 1 0.0632 0.9231 0.976 0.000 0.024 0.000 0.000 0.000
#> GSM121216 1 0.0806 0.9250 0.972 0.000 0.020 0.000 0.008 0.000
#> GSM121217 1 0.0458 0.9240 0.984 0.000 0.016 0.000 0.000 0.000
#> GSM121218 1 0.0909 0.9223 0.968 0.000 0.000 0.020 0.012 0.000
#> GSM121234 1 0.1364 0.9103 0.944 0.000 0.048 0.000 0.004 0.004
#> GSM121243 1 0.1261 0.9203 0.956 0.000 0.004 0.008 0.028 0.004
#> GSM121245 1 0.2468 0.8824 0.888 0.000 0.004 0.048 0.060 0.000
#> GSM121246 1 0.2234 0.8419 0.872 0.000 0.124 0.000 0.004 0.000
#> GSM121247 1 0.4137 0.7438 0.756 0.000 0.004 0.108 0.132 0.000
#> GSM121248 1 0.1036 0.9213 0.964 0.000 0.004 0.024 0.008 0.000
#> GSM120744 6 0.1862 0.7710 0.000 0.008 0.016 0.004 0.044 0.928
#> GSM120745 6 0.1615 0.7614 0.000 0.000 0.004 0.004 0.064 0.928
#> GSM120746 6 0.1338 0.7761 0.000 0.004 0.032 0.004 0.008 0.952
#> GSM120747 6 0.2062 0.7594 0.000 0.004 0.088 0.000 0.008 0.900
#> GSM120748 6 0.1914 0.7704 0.000 0.016 0.056 0.000 0.008 0.920
#> GSM120749 6 0.1573 0.7735 0.000 0.004 0.052 0.004 0.004 0.936
#> GSM120750 6 0.1788 0.7735 0.000 0.000 0.028 0.004 0.040 0.928
#> GSM120751 6 0.1321 0.7757 0.000 0.000 0.024 0.004 0.020 0.952
#> GSM120752 6 0.1493 0.7617 0.000 0.000 0.004 0.004 0.056 0.936
#> GSM121336 2 0.0779 0.6888 0.000 0.976 0.008 0.008 0.008 0.000
#> GSM121339 2 0.5323 0.4348 0.028 0.652 0.036 0.008 0.016 0.260
#> GSM121349 2 0.0520 0.6897 0.000 0.984 0.000 0.008 0.008 0.000
#> GSM121355 2 0.0881 0.6897 0.000 0.972 0.008 0.012 0.008 0.000
#> GSM120757 5 0.4937 0.6754 0.000 0.000 0.012 0.140 0.684 0.164
#> GSM120766 5 0.4278 0.7413 0.000 0.008 0.052 0.056 0.788 0.096
#> GSM120770 6 0.6516 -0.0558 0.000 0.272 0.020 0.000 0.348 0.360
#> GSM120779 5 0.4493 0.7262 0.016 0.000 0.004 0.120 0.748 0.112
#> GSM120780 5 0.4380 0.6759 0.000 0.028 0.108 0.004 0.768 0.092
#> GSM121102 6 0.6243 0.3938 0.000 0.252 0.052 0.000 0.152 0.544
#> GSM121203 6 0.4645 0.5386 0.000 0.008 0.060 0.000 0.268 0.664
#> GSM121204 6 0.5654 0.0643 0.024 0.000 0.000 0.084 0.400 0.492
#> GSM121330 3 0.3949 0.7765 0.112 0.000 0.788 0.004 0.008 0.088
#> GSM121335 3 0.4175 0.7648 0.120 0.000 0.768 0.004 0.008 0.100
#> GSM121337 3 0.5764 0.4479 0.000 0.064 0.648 0.152 0.132 0.004
#> GSM121338 3 0.3833 0.7052 0.004 0.056 0.824 0.004 0.064 0.048
#> GSM121341 3 0.3853 0.7754 0.112 0.000 0.800 0.008 0.008 0.072
#> GSM121342 3 0.4289 0.6930 0.220 0.000 0.720 0.012 0.000 0.048
#> GSM121343 3 0.3577 0.6678 0.000 0.028 0.820 0.016 0.124 0.012
#> GSM121344 3 0.3597 0.7798 0.104 0.000 0.812 0.004 0.004 0.076
#> GSM121346 3 0.3000 0.7780 0.032 0.000 0.840 0.000 0.004 0.124
#> GSM121347 5 0.7156 0.1671 0.004 0.056 0.364 0.204 0.364 0.008
#> GSM121348 5 0.2738 0.7211 0.000 0.028 0.052 0.020 0.888 0.012
#> GSM121350 3 0.2776 0.7865 0.052 0.000 0.860 0.000 0.000 0.088
#> GSM121352 3 0.2586 0.7805 0.032 0.000 0.868 0.000 0.000 0.100
#> GSM121354 3 0.3318 0.7820 0.084 0.000 0.828 0.004 0.000 0.084
#> GSM120753 2 0.4234 0.3409 0.000 0.576 0.012 0.408 0.004 0.000
#> GSM120761 4 0.4967 0.1204 0.000 0.412 0.012 0.540 0.028 0.008
#> GSM120768 4 0.4076 0.3113 0.000 0.348 0.012 0.636 0.004 0.000
#> GSM120781 2 0.4200 0.3774 0.000 0.592 0.012 0.392 0.004 0.000
#> GSM120788 4 0.3661 0.5374 0.012 0.000 0.008 0.796 0.160 0.024
#> GSM120760 4 0.5167 0.5757 0.000 0.216 0.000 0.632 0.148 0.004
#> GSM120763 4 0.4345 0.6160 0.000 0.188 0.012 0.732 0.068 0.000
#> GSM120764 4 0.2477 0.6384 0.000 0.012 0.008 0.888 0.084 0.008
#> GSM120777 4 0.4351 0.4211 0.020 0.000 0.000 0.704 0.244 0.032
#> GSM120786 4 0.2939 0.6591 0.000 0.044 0.004 0.864 0.080 0.008
#> GSM121329 4 0.6284 0.1420 0.280 0.000 0.176 0.508 0.036 0.000
#> GSM121331 5 0.3164 0.7443 0.020 0.000 0.000 0.104 0.844 0.032
#> GSM121333 5 0.4209 0.6687 0.016 0.000 0.000 0.196 0.740 0.048
#> GSM121345 4 0.5249 -0.1527 0.028 0.000 0.012 0.488 0.452 0.020
#> GSM121356 5 0.3123 0.7534 0.004 0.000 0.008 0.100 0.848 0.040
#> GSM120754 4 0.5756 0.3363 0.000 0.032 0.004 0.600 0.252 0.112
#> GSM120759 2 0.4217 0.6471 0.000 0.760 0.052 0.028 0.160 0.000
#> GSM120762 2 0.2909 0.6534 0.000 0.828 0.000 0.156 0.004 0.012
#> GSM120775 4 0.2935 0.6406 0.008 0.020 0.004 0.880 0.040 0.048
#> GSM120776 6 0.5685 0.4263 0.016 0.008 0.004 0.140 0.204 0.628
#> GSM120782 6 0.4346 0.6378 0.000 0.124 0.004 0.088 0.020 0.764
#> GSM120789 2 0.4604 0.6237 0.000 0.720 0.056 0.192 0.032 0.000
#> GSM120790 5 0.4393 0.5093 0.000 0.248 0.024 0.016 0.704 0.008
#> GSM120791 4 0.4418 0.6045 0.000 0.184 0.048 0.740 0.024 0.004
#> GSM120755 2 0.3722 0.5764 0.000 0.724 0.004 0.260 0.004 0.008
#> GSM120756 4 0.2332 0.6177 0.016 0.000 0.008 0.904 0.060 0.012
#> GSM120769 2 0.3822 0.5277 0.000 0.688 0.004 0.300 0.004 0.004
#> GSM120778 4 0.4128 -0.1625 0.000 0.492 0.004 0.500 0.004 0.000
#> GSM120792 2 0.4195 0.2585 0.000 0.548 0.000 0.440 0.008 0.004
#> GSM121332 2 0.3053 0.6693 0.000 0.828 0.024 0.144 0.004 0.000
#> GSM121334 2 0.3672 0.6079 0.000 0.744 0.004 0.236 0.012 0.004
#> GSM121340 4 0.3776 0.6097 0.008 0.188 0.008 0.776 0.016 0.004
#> GSM121351 2 0.3225 0.6478 0.000 0.828 0.024 0.008 0.136 0.004
#> GSM121353 4 0.3267 0.6478 0.024 0.120 0.008 0.836 0.012 0.000
#> GSM120758 2 0.4150 0.4197 0.000 0.612 0.012 0.372 0.004 0.000
#> GSM120771 2 0.3979 0.6609 0.000 0.796 0.016 0.036 0.132 0.020
#> GSM120772 2 0.4208 0.2499 0.000 0.536 0.008 0.452 0.004 0.000
#> GSM120773 4 0.3584 0.6757 0.000 0.104 0.012 0.820 0.060 0.004
#> GSM120774 2 0.4077 0.4207 0.000 0.620 0.004 0.368 0.004 0.004
#> GSM120783 4 0.2326 0.6749 0.000 0.092 0.012 0.888 0.008 0.000
#> GSM120787 2 0.3037 0.6475 0.000 0.820 0.000 0.160 0.004 0.016
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> SD:NMF 114 7.01e-09 2
#> SD:NMF 105 3.24e-13 3
#> SD:NMF 77 8.69e-16 4
#> SD:NMF 74 2.15e-16 5
#> SD:NMF 97 4.04e-31 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 21512 rows and 119 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 0.144 0.592 0.791 0.4162 0.496 0.496
#> 3 3 0.224 0.462 0.681 0.3570 0.716 0.522
#> 4 4 0.332 0.360 0.653 0.1140 0.661 0.412
#> 5 5 0.394 0.400 0.607 0.0633 0.727 0.433
#> 6 6 0.412 0.488 0.655 0.0343 0.823 0.505
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
#> GSM120719 1 0.5408 0.71674 0.876 0.124
#> GSM120720 1 0.5408 0.71674 0.876 0.124
#> GSM120765 2 0.5178 0.74463 0.116 0.884
#> GSM120767 2 0.4431 0.73696 0.092 0.908
#> GSM120784 2 0.5946 0.75178 0.144 0.856
#> GSM121400 1 0.9393 0.45465 0.644 0.356
#> GSM121401 1 0.6048 0.70325 0.852 0.148
#> GSM121402 2 0.0938 0.69899 0.012 0.988
#> GSM121403 1 0.9491 0.43045 0.632 0.368
#> GSM121404 2 0.4939 0.73242 0.108 0.892
#> GSM121405 1 0.5946 0.70590 0.856 0.144
#> GSM121406 2 0.0000 0.69160 0.000 1.000
#> GSM121408 2 0.2236 0.71088 0.036 0.964
#> GSM121409 1 0.9460 0.43821 0.636 0.364
#> GSM121410 1 0.9460 0.43821 0.636 0.364
#> GSM121412 2 0.0000 0.69160 0.000 1.000
#> GSM121413 2 0.0000 0.69160 0.000 1.000
#> GSM121414 2 0.0000 0.69160 0.000 1.000
#> GSM121415 2 0.1633 0.70625 0.024 0.976
#> GSM121416 2 0.2423 0.71444 0.040 0.960
#> GSM120591 1 0.5408 0.71674 0.876 0.124
#> GSM120594 1 0.5408 0.71674 0.876 0.124
#> GSM120718 1 0.5408 0.71674 0.876 0.124
#> GSM121205 1 0.0000 0.70163 1.000 0.000
#> GSM121206 1 0.0000 0.70163 1.000 0.000
#> GSM121207 1 0.0000 0.70163 1.000 0.000
#> GSM121208 1 0.0000 0.70163 1.000 0.000
#> GSM121209 1 0.0000 0.70163 1.000 0.000
#> GSM121210 1 0.0000 0.70163 1.000 0.000
#> GSM121211 1 0.0000 0.70163 1.000 0.000
#> GSM121212 1 0.0000 0.70163 1.000 0.000
#> GSM121213 1 0.0000 0.70163 1.000 0.000
#> GSM121214 1 0.0000 0.70163 1.000 0.000
#> GSM121215 1 0.0000 0.70163 1.000 0.000
#> GSM121216 1 0.0000 0.70163 1.000 0.000
#> GSM121217 1 0.0000 0.70163 1.000 0.000
#> GSM121218 1 0.0000 0.70163 1.000 0.000
#> GSM121234 1 0.0000 0.70163 1.000 0.000
#> GSM121243 1 0.0000 0.70163 1.000 0.000
#> GSM121245 1 0.0000 0.70163 1.000 0.000
#> GSM121246 1 0.0000 0.70163 1.000 0.000
#> GSM121247 1 0.0000 0.70163 1.000 0.000
#> GSM121248 1 0.0000 0.70163 1.000 0.000
#> GSM120744 1 0.9983 0.12949 0.524 0.476
#> GSM120745 1 0.9970 0.16182 0.532 0.468
#> GSM120746 1 0.9983 0.12949 0.524 0.476
#> GSM120747 1 0.9983 0.12949 0.524 0.476
#> GSM120748 1 0.9983 0.12949 0.524 0.476
#> GSM120749 1 0.9970 0.16182 0.532 0.468
#> GSM120750 1 0.9983 0.12949 0.524 0.476
#> GSM120751 1 0.9983 0.12949 0.524 0.476
#> GSM120752 1 0.9970 0.16182 0.532 0.468
#> GSM121336 2 0.0000 0.69160 0.000 1.000
#> GSM121339 2 0.8144 0.68729 0.252 0.748
#> GSM121349 2 0.0000 0.69160 0.000 1.000
#> GSM121355 2 0.0000 0.69160 0.000 1.000
#> GSM120757 1 0.9933 0.18130 0.548 0.452
#> GSM120766 2 0.9850 0.29954 0.428 0.572
#> GSM120770 2 0.8207 0.71274 0.256 0.744
#> GSM120779 1 0.9954 0.14818 0.540 0.460
#> GSM120780 2 0.9850 0.29954 0.428 0.572
#> GSM121102 2 0.9427 0.51452 0.360 0.640
#> GSM121203 2 0.9993 0.03063 0.484 0.516
#> GSM121204 1 0.8267 0.58849 0.740 0.260
#> GSM121330 1 0.5629 0.71361 0.868 0.132
#> GSM121335 1 0.5408 0.71656 0.876 0.124
#> GSM121337 2 0.8813 0.67131 0.300 0.700
#> GSM121338 2 0.8861 0.66282 0.304 0.696
#> GSM121341 1 0.5408 0.71656 0.876 0.124
#> GSM121342 1 0.5629 0.71361 0.868 0.132
#> GSM121343 2 0.8861 0.66282 0.304 0.696
#> GSM121344 1 0.5519 0.71553 0.872 0.128
#> GSM121346 1 0.5519 0.71553 0.872 0.128
#> GSM121347 2 0.8763 0.68037 0.296 0.704
#> GSM121348 2 0.9933 0.21377 0.452 0.548
#> GSM121350 1 0.5519 0.71553 0.872 0.128
#> GSM121352 1 0.5519 0.71553 0.872 0.128
#> GSM121354 1 0.5519 0.71553 0.872 0.128
#> GSM120753 2 0.7219 0.75209 0.200 0.800
#> GSM120761 2 0.7950 0.74329 0.240 0.760
#> GSM120768 2 0.7528 0.74893 0.216 0.784
#> GSM120781 2 0.6973 0.75413 0.188 0.812
#> GSM120788 2 0.9686 0.55240 0.396 0.604
#> GSM120760 2 0.9460 0.61419 0.364 0.636
#> GSM120763 2 0.9323 0.63801 0.348 0.652
#> GSM120764 2 0.9608 0.57813 0.384 0.616
#> GSM120777 2 0.9815 0.48941 0.420 0.580
#> GSM120786 2 0.9552 0.59260 0.376 0.624
#> GSM121329 1 0.6712 0.68061 0.824 0.176
#> GSM121331 1 0.9970 0.11193 0.532 0.468
#> GSM121333 1 0.9983 0.07314 0.524 0.476
#> GSM121345 1 0.8909 0.50741 0.692 0.308
#> GSM121356 1 0.9970 0.11193 0.532 0.468
#> GSM120754 2 0.8499 0.71943 0.276 0.724
#> GSM120759 2 0.0000 0.69160 0.000 1.000
#> GSM120762 2 0.6531 0.75457 0.168 0.832
#> GSM120775 2 0.8661 0.70990 0.288 0.712
#> GSM120776 1 0.9491 0.38674 0.632 0.368
#> GSM120782 2 0.8327 0.72603 0.264 0.736
#> GSM120789 2 0.4431 0.73839 0.092 0.908
#> GSM120790 2 0.0000 0.69160 0.000 1.000
#> GSM120791 2 0.8443 0.72482 0.272 0.728
#> GSM120755 2 0.4939 0.74182 0.108 0.892
#> GSM120756 2 0.9754 0.52503 0.408 0.592
#> GSM120769 2 0.6887 0.75476 0.184 0.816
#> GSM120778 2 0.8955 0.69049 0.312 0.688
#> GSM120792 2 0.8661 0.71433 0.288 0.712
#> GSM121332 2 0.5629 0.74986 0.132 0.868
#> GSM121334 2 0.8386 0.72642 0.268 0.732
#> GSM121340 1 0.9922 -0.00677 0.552 0.448
#> GSM121351 2 0.0000 0.69160 0.000 1.000
#> GSM121353 2 0.9580 0.58472 0.380 0.620
#> GSM120758 2 0.7528 0.74893 0.216 0.784
#> GSM120771 2 0.7376 0.75257 0.208 0.792
#> GSM120772 2 0.8207 0.73662 0.256 0.744
#> GSM120773 2 0.9608 0.57816 0.384 0.616
#> GSM120774 2 0.9000 0.68745 0.316 0.684
#> GSM120783 2 0.9635 0.57041 0.388 0.612
#> GSM120787 2 0.8861 0.68690 0.304 0.696
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM120719 1 0.4399 0.7640 0.812 0.188 0.000
#> GSM120720 1 0.4399 0.7640 0.812 0.188 0.000
#> GSM120765 2 0.6859 -0.3689 0.016 0.564 0.420
#> GSM120767 2 0.6950 -0.4574 0.016 0.508 0.476
#> GSM120784 2 0.7192 -0.2245 0.032 0.588 0.380
#> GSM121400 1 0.7796 0.2826 0.552 0.392 0.056
#> GSM121401 1 0.4702 0.7387 0.788 0.212 0.000
#> GSM121402 3 0.6209 0.7806 0.004 0.368 0.628
#> GSM121403 1 0.7969 0.2409 0.540 0.396 0.064
#> GSM121404 2 0.7138 -0.3417 0.024 0.540 0.436
#> GSM121405 1 0.4654 0.7428 0.792 0.208 0.000
#> GSM121406 3 0.5905 0.7872 0.000 0.352 0.648
#> GSM121408 3 0.6527 0.7005 0.008 0.404 0.588
#> GSM121409 1 0.7958 0.2525 0.544 0.392 0.064
#> GSM121410 1 0.7958 0.2525 0.544 0.392 0.064
#> GSM121412 3 0.5327 0.8492 0.000 0.272 0.728
#> GSM121413 3 0.5291 0.8486 0.000 0.268 0.732
#> GSM121414 3 0.5327 0.8492 0.000 0.272 0.728
#> GSM121415 3 0.6460 0.6381 0.004 0.440 0.556
#> GSM121416 3 0.6680 0.5288 0.008 0.484 0.508
#> GSM120591 1 0.4399 0.7640 0.812 0.188 0.000
#> GSM120594 1 0.4399 0.7640 0.812 0.188 0.000
#> GSM120718 1 0.4399 0.7640 0.812 0.188 0.000
#> GSM121205 1 0.0237 0.7837 0.996 0.000 0.004
#> GSM121206 1 0.0237 0.7837 0.996 0.000 0.004
#> GSM121207 1 0.0237 0.7837 0.996 0.000 0.004
#> GSM121208 1 0.0237 0.7837 0.996 0.000 0.004
#> GSM121209 1 0.0237 0.7837 0.996 0.000 0.004
#> GSM121210 1 0.0237 0.7837 0.996 0.000 0.004
#> GSM121211 1 0.0237 0.7837 0.996 0.000 0.004
#> GSM121212 1 0.0237 0.7837 0.996 0.000 0.004
#> GSM121213 1 0.0237 0.7837 0.996 0.000 0.004
#> GSM121214 1 0.0237 0.7837 0.996 0.000 0.004
#> GSM121215 1 0.0237 0.7837 0.996 0.000 0.004
#> GSM121216 1 0.0237 0.7837 0.996 0.000 0.004
#> GSM121217 1 0.0237 0.7837 0.996 0.000 0.004
#> GSM121218 1 0.0237 0.7837 0.996 0.000 0.004
#> GSM121234 1 0.0237 0.7837 0.996 0.000 0.004
#> GSM121243 1 0.0237 0.7837 0.996 0.000 0.004
#> GSM121245 1 0.0237 0.7837 0.996 0.000 0.004
#> GSM121246 1 0.0237 0.7837 0.996 0.000 0.004
#> GSM121247 1 0.0237 0.7837 0.996 0.000 0.004
#> GSM121248 1 0.0237 0.7837 0.996 0.000 0.004
#> GSM120744 2 0.8310 0.1764 0.420 0.500 0.080
#> GSM120745 2 0.8255 0.1512 0.428 0.496 0.076
#> GSM120746 2 0.8310 0.1764 0.420 0.500 0.080
#> GSM120747 2 0.8310 0.1764 0.420 0.500 0.080
#> GSM120748 2 0.8310 0.1764 0.420 0.500 0.080
#> GSM120749 2 0.8255 0.1512 0.428 0.496 0.076
#> GSM120750 2 0.8310 0.1764 0.420 0.500 0.080
#> GSM120751 2 0.8310 0.1764 0.420 0.500 0.080
#> GSM120752 2 0.8255 0.1512 0.428 0.496 0.076
#> GSM121336 3 0.5291 0.8420 0.000 0.268 0.732
#> GSM121339 2 0.8957 0.1845 0.152 0.536 0.312
#> GSM121349 3 0.5291 0.8420 0.000 0.268 0.732
#> GSM121355 3 0.5291 0.8420 0.000 0.268 0.732
#> GSM120757 2 0.7901 0.1112 0.440 0.504 0.056
#> GSM120766 2 0.8738 0.3851 0.328 0.544 0.128
#> GSM120770 2 0.8953 0.3426 0.180 0.560 0.260
#> GSM120779 2 0.7883 0.1568 0.428 0.516 0.056
#> GSM120780 2 0.8738 0.3851 0.328 0.544 0.128
#> GSM121102 2 0.8835 0.4605 0.268 0.568 0.164
#> GSM121203 2 0.8559 0.2571 0.388 0.512 0.100
#> GSM121204 1 0.6381 0.5069 0.648 0.340 0.012
#> GSM121330 1 0.4504 0.7572 0.804 0.196 0.000
#> GSM121335 1 0.4399 0.7637 0.812 0.188 0.000
#> GSM121337 2 0.8813 0.3642 0.184 0.580 0.236
#> GSM121338 2 0.8957 0.3503 0.192 0.564 0.244
#> GSM121341 1 0.4399 0.7637 0.812 0.188 0.000
#> GSM121342 1 0.4504 0.7572 0.804 0.196 0.000
#> GSM121343 2 0.8957 0.3503 0.192 0.564 0.244
#> GSM121344 1 0.4452 0.7611 0.808 0.192 0.000
#> GSM121346 1 0.4452 0.7611 0.808 0.192 0.000
#> GSM121347 2 0.8550 0.3830 0.176 0.608 0.216
#> GSM121348 2 0.8571 0.3509 0.340 0.548 0.112
#> GSM121350 1 0.4452 0.7611 0.808 0.192 0.000
#> GSM121352 1 0.4452 0.7611 0.808 0.192 0.000
#> GSM121354 1 0.4452 0.7611 0.808 0.192 0.000
#> GSM120753 2 0.6388 0.0773 0.024 0.692 0.284
#> GSM120761 2 0.5947 0.3053 0.052 0.776 0.172
#> GSM120768 2 0.5746 0.2658 0.040 0.780 0.180
#> GSM120781 2 0.6062 0.0902 0.016 0.708 0.276
#> GSM120788 2 0.5173 0.4974 0.148 0.816 0.036
#> GSM120760 2 0.4591 0.4865 0.120 0.848 0.032
#> GSM120763 2 0.4015 0.4730 0.096 0.876 0.028
#> GSM120764 2 0.4982 0.4917 0.136 0.828 0.036
#> GSM120777 2 0.5455 0.5077 0.184 0.788 0.028
#> GSM120786 2 0.4799 0.4939 0.132 0.836 0.032
#> GSM121329 1 0.5058 0.7063 0.756 0.244 0.000
#> GSM121331 2 0.7868 0.1815 0.420 0.524 0.056
#> GSM121333 2 0.7851 0.2046 0.412 0.532 0.056
#> GSM121345 1 0.6661 0.3664 0.588 0.400 0.012
#> GSM121356 2 0.7868 0.1815 0.420 0.524 0.056
#> GSM120754 2 0.6452 0.4040 0.088 0.760 0.152
#> GSM120759 3 0.5016 0.7762 0.000 0.240 0.760
#> GSM120762 2 0.6566 -0.0444 0.016 0.636 0.348
#> GSM120775 2 0.6425 0.4164 0.096 0.764 0.140
#> GSM120776 1 0.7248 0.2176 0.536 0.436 0.028
#> GSM120782 2 0.7360 0.3329 0.096 0.692 0.212
#> GSM120789 2 0.6678 -0.5132 0.008 0.512 0.480
#> GSM120790 3 0.4974 0.6562 0.000 0.236 0.764
#> GSM120791 2 0.5730 0.3810 0.060 0.796 0.144
#> GSM120755 2 0.6675 -0.2490 0.012 0.584 0.404
#> GSM120756 2 0.4934 0.4998 0.156 0.820 0.024
#> GSM120769 2 0.6282 0.0082 0.012 0.664 0.324
#> GSM120778 2 0.6354 0.2757 0.056 0.748 0.196
#> GSM120792 2 0.5998 0.4022 0.084 0.788 0.128
#> GSM121332 2 0.7114 -0.2296 0.028 0.584 0.388
#> GSM121334 2 0.5571 0.3775 0.056 0.804 0.140
#> GSM121340 2 0.8683 0.3232 0.236 0.592 0.172
#> GSM121351 3 0.5291 0.8462 0.000 0.268 0.732
#> GSM121353 2 0.5473 0.4924 0.140 0.808 0.052
#> GSM120758 2 0.5798 0.2616 0.040 0.776 0.184
#> GSM120771 2 0.6375 0.1693 0.036 0.720 0.244
#> GSM120772 2 0.6586 0.2342 0.056 0.728 0.216
#> GSM120773 2 0.5241 0.4929 0.132 0.820 0.048
#> GSM120774 2 0.6765 0.3456 0.068 0.724 0.208
#> GSM120783 2 0.5202 0.4937 0.136 0.820 0.044
#> GSM120787 2 0.7564 0.2411 0.068 0.636 0.296
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM120719 1 0.0657 0.6856 0.984 0.004 0.000 0.012
#> GSM120720 1 0.0657 0.6856 0.984 0.004 0.000 0.012
#> GSM120765 2 0.5003 0.4136 0.028 0.792 0.044 0.136
#> GSM120767 2 0.3998 0.3693 0.012 0.852 0.060 0.076
#> GSM120784 2 0.5417 0.3810 0.048 0.760 0.028 0.164
#> GSM121400 1 0.5936 0.5416 0.724 0.084 0.020 0.172
#> GSM121401 1 0.1059 0.6804 0.972 0.016 0.000 0.012
#> GSM121402 2 0.6656 -0.1459 0.008 0.616 0.276 0.100
#> GSM121403 1 0.6173 0.5286 0.708 0.100 0.020 0.172
#> GSM121404 2 0.6466 0.3583 0.148 0.712 0.064 0.076
#> GSM121405 1 0.0937 0.6819 0.976 0.012 0.000 0.012
#> GSM121406 2 0.4434 0.0203 0.004 0.772 0.208 0.016
#> GSM121408 2 0.4611 0.1407 0.008 0.788 0.172 0.032
#> GSM121409 1 0.6116 0.5324 0.712 0.096 0.020 0.172
#> GSM121410 1 0.6116 0.5324 0.712 0.096 0.020 0.172
#> GSM121412 2 0.4857 -0.2494 0.000 0.668 0.324 0.008
#> GSM121413 2 0.5057 -0.2873 0.000 0.648 0.340 0.012
#> GSM121414 2 0.4877 -0.2541 0.000 0.664 0.328 0.008
#> GSM121415 2 0.3895 0.3063 0.012 0.856 0.084 0.048
#> GSM121416 2 0.4964 0.3845 0.052 0.812 0.072 0.064
#> GSM120591 1 0.0657 0.6856 0.984 0.004 0.000 0.012
#> GSM120594 1 0.0657 0.6856 0.984 0.004 0.000 0.012
#> GSM120718 1 0.0657 0.6856 0.984 0.004 0.000 0.012
#> GSM121205 1 0.4290 0.6456 0.800 0.000 0.036 0.164
#> GSM121206 1 0.4290 0.6456 0.800 0.000 0.036 0.164
#> GSM121207 1 0.4290 0.6456 0.800 0.000 0.036 0.164
#> GSM121208 1 0.4290 0.6456 0.800 0.000 0.036 0.164
#> GSM121209 1 0.4290 0.6456 0.800 0.000 0.036 0.164
#> GSM121210 1 0.4290 0.6456 0.800 0.000 0.036 0.164
#> GSM121211 1 0.4290 0.6456 0.800 0.000 0.036 0.164
#> GSM121212 1 0.4290 0.6456 0.800 0.000 0.036 0.164
#> GSM121213 1 0.4290 0.6456 0.800 0.000 0.036 0.164
#> GSM121214 1 0.4290 0.6456 0.800 0.000 0.036 0.164
#> GSM121215 1 0.4290 0.6456 0.800 0.000 0.036 0.164
#> GSM121216 1 0.4290 0.6456 0.800 0.000 0.036 0.164
#> GSM121217 1 0.4290 0.6456 0.800 0.000 0.036 0.164
#> GSM121218 1 0.4290 0.6456 0.800 0.000 0.036 0.164
#> GSM121234 1 0.4290 0.6456 0.800 0.000 0.036 0.164
#> GSM121243 1 0.4290 0.6456 0.800 0.000 0.036 0.164
#> GSM121245 1 0.4290 0.6456 0.800 0.000 0.036 0.164
#> GSM121246 1 0.4290 0.6456 0.800 0.000 0.036 0.164
#> GSM121247 1 0.4290 0.6456 0.800 0.000 0.036 0.164
#> GSM121248 1 0.4290 0.6456 0.800 0.000 0.036 0.164
#> GSM120744 1 0.7447 0.3666 0.572 0.112 0.032 0.284
#> GSM120745 1 0.7379 0.3795 0.580 0.108 0.032 0.280
#> GSM120746 1 0.7447 0.3666 0.572 0.112 0.032 0.284
#> GSM120747 1 0.7447 0.3666 0.572 0.112 0.032 0.284
#> GSM120748 1 0.7447 0.3666 0.572 0.112 0.032 0.284
#> GSM120749 1 0.7379 0.3795 0.580 0.108 0.032 0.280
#> GSM120750 1 0.7447 0.3666 0.572 0.112 0.032 0.284
#> GSM120751 1 0.7447 0.3666 0.572 0.112 0.032 0.284
#> GSM120752 1 0.7379 0.3795 0.580 0.108 0.032 0.280
#> GSM121336 2 0.4608 -0.1837 0.000 0.692 0.304 0.004
#> GSM121339 2 0.6933 0.1659 0.216 0.624 0.012 0.148
#> GSM121349 2 0.4608 -0.1837 0.000 0.692 0.304 0.004
#> GSM121355 2 0.4608 -0.1837 0.000 0.692 0.304 0.004
#> GSM120757 1 0.7162 0.3813 0.600 0.112 0.024 0.264
#> GSM120766 1 0.8430 0.1054 0.452 0.212 0.036 0.300
#> GSM120770 2 0.8308 -0.0564 0.220 0.488 0.036 0.256
#> GSM120779 1 0.7320 0.3437 0.564 0.084 0.036 0.316
#> GSM120780 1 0.8430 0.1054 0.452 0.212 0.036 0.300
#> GSM121102 1 0.8855 -0.1677 0.356 0.300 0.044 0.300
#> GSM121203 1 0.7943 0.2917 0.528 0.168 0.032 0.272
#> GSM121204 1 0.4643 0.5881 0.788 0.028 0.012 0.172
#> GSM121330 1 0.0524 0.6849 0.988 0.008 0.000 0.004
#> GSM121335 1 0.0524 0.6857 0.988 0.004 0.000 0.008
#> GSM121337 2 0.8089 -0.0466 0.264 0.504 0.028 0.204
#> GSM121338 2 0.7900 -0.0223 0.272 0.520 0.024 0.184
#> GSM121341 1 0.0524 0.6857 0.988 0.004 0.000 0.008
#> GSM121342 1 0.0524 0.6849 0.988 0.008 0.000 0.004
#> GSM121343 2 0.7900 -0.0223 0.272 0.520 0.024 0.184
#> GSM121344 1 0.0376 0.6856 0.992 0.004 0.000 0.004
#> GSM121346 1 0.0376 0.6856 0.992 0.004 0.000 0.004
#> GSM121347 2 0.8214 -0.0995 0.276 0.480 0.028 0.216
#> GSM121348 1 0.8255 0.1541 0.476 0.180 0.036 0.308
#> GSM121350 1 0.0376 0.6856 0.992 0.004 0.000 0.004
#> GSM121352 1 0.0376 0.6856 0.992 0.004 0.000 0.004
#> GSM121354 1 0.0376 0.6856 0.992 0.004 0.000 0.004
#> GSM120753 2 0.7028 0.1777 0.052 0.600 0.052 0.296
#> GSM120761 2 0.6630 -0.0945 0.060 0.548 0.012 0.380
#> GSM120768 2 0.6771 -0.0629 0.084 0.560 0.008 0.348
#> GSM120781 2 0.6392 0.2162 0.032 0.636 0.040 0.292
#> GSM120788 4 0.7411 0.7389 0.196 0.264 0.004 0.536
#> GSM120760 4 0.7358 0.7213 0.172 0.292 0.004 0.532
#> GSM120763 4 0.7023 0.7148 0.164 0.272 0.000 0.564
#> GSM120764 4 0.7381 0.7416 0.192 0.264 0.004 0.540
#> GSM120777 4 0.7540 0.6756 0.244 0.232 0.004 0.520
#> GSM120786 4 0.7417 0.7322 0.184 0.284 0.004 0.528
#> GSM121329 1 0.2297 0.6620 0.928 0.024 0.004 0.044
#> GSM121331 1 0.7445 0.3370 0.560 0.096 0.036 0.308
#> GSM121333 1 0.7512 0.3181 0.552 0.100 0.036 0.312
#> GSM121345 1 0.5623 0.5213 0.728 0.064 0.012 0.196
#> GSM121356 1 0.7445 0.3370 0.560 0.096 0.036 0.308
#> GSM120754 2 0.7648 -0.3751 0.164 0.460 0.008 0.368
#> GSM120759 3 0.5581 0.4291 0.000 0.448 0.532 0.020
#> GSM120762 2 0.6370 0.3068 0.024 0.664 0.064 0.248
#> GSM120775 2 0.7743 -0.4256 0.176 0.440 0.008 0.376
#> GSM120776 1 0.6187 0.4172 0.672 0.104 0.004 0.220
#> GSM120782 2 0.7275 -0.0336 0.156 0.560 0.008 0.276
#> GSM120789 2 0.5891 0.3393 0.024 0.740 0.120 0.116
#> GSM120790 3 0.5369 0.5398 0.000 0.144 0.744 0.112
#> GSM120791 2 0.7560 -0.4043 0.136 0.464 0.012 0.388
#> GSM120755 2 0.4763 0.3988 0.016 0.792 0.036 0.156
#> GSM120756 4 0.7471 0.7111 0.228 0.236 0.004 0.532
#> GSM120769 2 0.6664 0.2730 0.016 0.624 0.084 0.276
#> GSM120778 4 0.7351 0.0319 0.032 0.444 0.072 0.452
#> GSM120792 2 0.7555 -0.4198 0.148 0.452 0.008 0.392
#> GSM121332 2 0.5542 0.3951 0.052 0.760 0.036 0.152
#> GSM121334 2 0.7515 -0.3608 0.092 0.452 0.028 0.428
#> GSM121340 4 0.8097 0.1809 0.120 0.136 0.152 0.592
#> GSM121351 2 0.5085 -0.3546 0.000 0.616 0.376 0.008
#> GSM121353 4 0.7565 0.7118 0.216 0.268 0.004 0.512
#> GSM120758 2 0.6888 -0.0602 0.084 0.556 0.012 0.348
#> GSM120771 2 0.6758 0.0688 0.072 0.600 0.020 0.308
#> GSM120772 2 0.6591 0.0648 0.056 0.596 0.020 0.328
#> GSM120773 4 0.7570 0.7252 0.168 0.292 0.012 0.528
#> GSM120774 4 0.8121 0.2217 0.076 0.412 0.080 0.432
#> GSM120783 4 0.7336 0.7364 0.176 0.280 0.004 0.540
#> GSM120787 2 0.7706 -0.0172 0.020 0.432 0.128 0.420
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM120719 3 0.430 -0.1748 0.476 0.000 0.524 0.000 0.000
#> GSM120720 3 0.430 -0.1748 0.476 0.000 0.524 0.000 0.000
#> GSM120765 2 0.675 0.2356 0.012 0.480 0.140 0.360 0.008
#> GSM120767 2 0.633 0.4778 0.028 0.560 0.056 0.340 0.016
#> GSM120784 2 0.712 0.1332 0.012 0.432 0.204 0.344 0.008
#> GSM121400 3 0.369 0.4608 0.200 0.020 0.780 0.000 0.000
#> GSM121401 3 0.443 -0.1290 0.460 0.004 0.536 0.000 0.000
#> GSM121402 2 0.633 0.4263 0.032 0.672 0.176 0.068 0.052
#> GSM121403 3 0.395 0.4710 0.192 0.028 0.776 0.004 0.000
#> GSM121404 2 0.707 0.3857 0.028 0.524 0.256 0.184 0.008
#> GSM121405 3 0.429 -0.1329 0.460 0.000 0.540 0.000 0.000
#> GSM121406 2 0.432 0.6163 0.016 0.804 0.048 0.120 0.012
#> GSM121408 2 0.518 0.6269 0.020 0.736 0.056 0.172 0.016
#> GSM121409 3 0.379 0.4705 0.192 0.028 0.780 0.000 0.000
#> GSM121410 3 0.379 0.4705 0.192 0.028 0.780 0.000 0.000
#> GSM121412 2 0.290 0.5440 0.016 0.896 0.048 0.016 0.024
#> GSM121413 2 0.296 0.5258 0.016 0.892 0.048 0.012 0.032
#> GSM121414 2 0.298 0.5414 0.016 0.892 0.048 0.016 0.028
#> GSM121415 2 0.602 0.5467 0.012 0.620 0.100 0.260 0.008
#> GSM121416 2 0.657 0.4392 0.016 0.540 0.140 0.300 0.004
#> GSM120591 3 0.430 -0.1748 0.476 0.000 0.524 0.000 0.000
#> GSM120594 3 0.430 -0.1748 0.476 0.000 0.524 0.000 0.000
#> GSM120718 3 0.430 -0.1748 0.476 0.000 0.524 0.000 0.000
#> GSM121205 1 0.366 1.0000 0.724 0.000 0.276 0.000 0.000
#> GSM121206 1 0.366 1.0000 0.724 0.000 0.276 0.000 0.000
#> GSM121207 1 0.366 1.0000 0.724 0.000 0.276 0.000 0.000
#> GSM121208 1 0.366 1.0000 0.724 0.000 0.276 0.000 0.000
#> GSM121209 1 0.366 1.0000 0.724 0.000 0.276 0.000 0.000
#> GSM121210 1 0.366 1.0000 0.724 0.000 0.276 0.000 0.000
#> GSM121211 1 0.366 1.0000 0.724 0.000 0.276 0.000 0.000
#> GSM121212 1 0.366 1.0000 0.724 0.000 0.276 0.000 0.000
#> GSM121213 1 0.366 1.0000 0.724 0.000 0.276 0.000 0.000
#> GSM121214 1 0.366 1.0000 0.724 0.000 0.276 0.000 0.000
#> GSM121215 1 0.366 1.0000 0.724 0.000 0.276 0.000 0.000
#> GSM121216 1 0.366 1.0000 0.724 0.000 0.276 0.000 0.000
#> GSM121217 1 0.366 1.0000 0.724 0.000 0.276 0.000 0.000
#> GSM121218 1 0.366 1.0000 0.724 0.000 0.276 0.000 0.000
#> GSM121234 1 0.366 1.0000 0.724 0.000 0.276 0.000 0.000
#> GSM121243 1 0.366 1.0000 0.724 0.000 0.276 0.000 0.000
#> GSM121245 1 0.366 1.0000 0.724 0.000 0.276 0.000 0.000
#> GSM121246 1 0.366 1.0000 0.724 0.000 0.276 0.000 0.000
#> GSM121247 1 0.366 1.0000 0.724 0.000 0.276 0.000 0.000
#> GSM121248 1 0.366 1.0000 0.724 0.000 0.276 0.000 0.000
#> GSM120744 3 0.136 0.5648 0.036 0.012 0.952 0.000 0.000
#> GSM120745 3 0.141 0.5675 0.044 0.008 0.948 0.000 0.000
#> GSM120746 3 0.136 0.5648 0.036 0.012 0.952 0.000 0.000
#> GSM120747 3 0.136 0.5648 0.036 0.012 0.952 0.000 0.000
#> GSM120748 3 0.136 0.5648 0.036 0.012 0.952 0.000 0.000
#> GSM120749 3 0.141 0.5675 0.044 0.008 0.948 0.000 0.000
#> GSM120750 3 0.136 0.5648 0.036 0.012 0.952 0.000 0.000
#> GSM120751 3 0.136 0.5648 0.036 0.012 0.952 0.000 0.000
#> GSM120752 3 0.141 0.5675 0.044 0.008 0.948 0.000 0.000
#> GSM121336 2 0.365 0.5540 0.032 0.852 0.008 0.080 0.028
#> GSM121339 3 0.773 -0.4322 0.032 0.320 0.380 0.256 0.012
#> GSM121349 2 0.365 0.5540 0.032 0.852 0.008 0.080 0.028
#> GSM121355 2 0.365 0.5540 0.032 0.852 0.008 0.080 0.028
#> GSM120757 3 0.295 0.5496 0.088 0.004 0.876 0.028 0.004
#> GSM120766 3 0.259 0.4195 0.000 0.060 0.892 0.048 0.000
#> GSM120770 3 0.620 -0.2746 0.004 0.260 0.564 0.172 0.000
#> GSM120779 3 0.192 0.5478 0.040 0.000 0.928 0.032 0.000
#> GSM120780 3 0.259 0.4195 0.000 0.060 0.892 0.048 0.000
#> GSM121102 3 0.439 0.2304 0.004 0.148 0.776 0.068 0.004
#> GSM121203 3 0.297 0.5242 0.044 0.052 0.884 0.020 0.000
#> GSM121204 3 0.452 0.3432 0.284 0.000 0.684 0.032 0.000
#> GSM121330 3 0.430 -0.1664 0.472 0.000 0.528 0.000 0.000
#> GSM121335 3 0.430 -0.1972 0.480 0.000 0.520 0.000 0.000
#> GSM121337 3 0.662 -0.3012 0.004 0.252 0.492 0.252 0.000
#> GSM121338 3 0.641 -0.2479 0.000 0.272 0.508 0.220 0.000
#> GSM121341 3 0.430 -0.1972 0.480 0.000 0.520 0.000 0.000
#> GSM121342 3 0.430 -0.1664 0.472 0.000 0.528 0.000 0.000
#> GSM121343 3 0.641 -0.2479 0.000 0.272 0.508 0.220 0.000
#> GSM121344 3 0.430 -0.1779 0.476 0.000 0.524 0.000 0.000
#> GSM121346 3 0.430 -0.1779 0.476 0.000 0.524 0.000 0.000
#> GSM121347 3 0.655 -0.3260 0.004 0.208 0.500 0.288 0.000
#> GSM121348 3 0.207 0.4537 0.000 0.048 0.920 0.032 0.000
#> GSM121350 3 0.430 -0.1779 0.476 0.000 0.524 0.000 0.000
#> GSM121352 3 0.430 -0.1779 0.476 0.000 0.524 0.000 0.000
#> GSM121354 3 0.430 -0.1779 0.476 0.000 0.524 0.000 0.000
#> GSM120753 4 0.712 0.2805 0.008 0.324 0.180 0.468 0.020
#> GSM120761 4 0.678 0.4903 0.008 0.220 0.264 0.504 0.004
#> GSM120768 4 0.637 0.4896 0.008 0.208 0.224 0.560 0.000
#> GSM120781 4 0.587 0.1920 0.036 0.248 0.064 0.648 0.004
#> GSM120788 4 0.504 0.5988 0.008 0.004 0.448 0.528 0.012
#> GSM120760 4 0.572 0.6083 0.008 0.032 0.432 0.512 0.016
#> GSM120763 4 0.547 0.6072 0.008 0.024 0.424 0.532 0.012
#> GSM120764 4 0.512 0.6001 0.012 0.004 0.436 0.536 0.012
#> GSM120777 3 0.491 -0.5488 0.008 0.000 0.512 0.468 0.012
#> GSM120786 4 0.530 0.6051 0.004 0.020 0.440 0.524 0.012
#> GSM121329 3 0.510 -0.0862 0.448 0.000 0.516 0.036 0.000
#> GSM121331 3 0.192 0.5447 0.040 0.000 0.928 0.032 0.000
#> GSM121333 3 0.192 0.5354 0.036 0.000 0.928 0.036 0.000
#> GSM121345 3 0.498 0.4086 0.244 0.000 0.680 0.076 0.000
#> GSM121356 3 0.192 0.5447 0.040 0.000 0.928 0.032 0.000
#> GSM120754 4 0.680 0.5730 0.012 0.140 0.336 0.500 0.012
#> GSM120759 2 0.482 -0.0885 0.008 0.688 0.040 0.000 0.264
#> GSM120762 4 0.650 0.0256 0.052 0.300 0.056 0.580 0.012
#> GSM120775 4 0.673 0.5770 0.012 0.128 0.348 0.500 0.012
#> GSM120776 3 0.652 0.3805 0.244 0.024 0.584 0.144 0.004
#> GSM120782 4 0.708 0.4120 0.012 0.236 0.308 0.440 0.004
#> GSM120789 2 0.680 0.4811 0.020 0.560 0.112 0.284 0.024
#> GSM120790 5 0.471 0.0000 0.000 0.256 0.052 0.000 0.692
#> GSM120791 4 0.634 0.5858 0.012 0.124 0.348 0.516 0.000
#> GSM120755 4 0.662 -0.2629 0.032 0.424 0.068 0.464 0.012
#> GSM120756 4 0.518 0.5672 0.020 0.000 0.476 0.492 0.012
#> GSM120769 4 0.616 0.0530 0.040 0.288 0.044 0.612 0.016
#> GSM120778 4 0.545 0.3111 0.048 0.084 0.056 0.760 0.052
#> GSM120792 4 0.654 0.5824 0.008 0.136 0.296 0.548 0.012
#> GSM121332 2 0.712 0.1619 0.008 0.436 0.168 0.368 0.020
#> GSM121334 4 0.674 0.5572 0.008 0.152 0.356 0.476 0.008
#> GSM121340 4 0.716 -0.3479 0.208 0.004 0.024 0.472 0.292
#> GSM121351 2 0.363 0.4956 0.012 0.856 0.036 0.024 0.072
#> GSM121353 4 0.599 0.5840 0.024 0.032 0.452 0.480 0.012
#> GSM120758 4 0.642 0.4883 0.008 0.212 0.228 0.552 0.000
#> GSM120771 4 0.719 0.3837 0.016 0.300 0.248 0.432 0.004
#> GSM120772 4 0.653 0.3957 0.008 0.212 0.164 0.596 0.020
#> GSM120773 4 0.591 0.6065 0.016 0.028 0.424 0.512 0.020
#> GSM120774 4 0.786 0.4306 0.064 0.096 0.208 0.548 0.084
#> GSM120783 4 0.542 0.6048 0.012 0.012 0.432 0.528 0.016
#> GSM120787 4 0.807 0.1180 0.092 0.124 0.108 0.556 0.120
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM120719 1 0.363 0.63338 0.704 0.000 0.000 0.004 0.004 0.288
#> GSM120720 1 0.363 0.63338 0.704 0.000 0.000 0.004 0.004 0.288
#> GSM120765 4 0.618 -0.01688 0.000 0.388 0.008 0.440 0.012 0.152
#> GSM120767 2 0.570 0.34547 0.000 0.460 0.012 0.440 0.012 0.076
#> GSM120784 4 0.645 0.09738 0.000 0.340 0.008 0.420 0.012 0.220
#> GSM121400 6 0.467 0.35380 0.420 0.016 0.000 0.008 0.008 0.548
#> GSM121401 1 0.393 0.60928 0.684 0.004 0.000 0.004 0.008 0.300
#> GSM121402 2 0.600 0.41441 0.000 0.608 0.012 0.076 0.068 0.236
#> GSM121403 6 0.490 0.38252 0.412 0.024 0.000 0.008 0.012 0.544
#> GSM121404 2 0.695 0.31057 0.004 0.440 0.016 0.232 0.028 0.280
#> GSM121405 1 0.379 0.61313 0.688 0.000 0.000 0.004 0.008 0.300
#> GSM121406 2 0.423 0.63965 0.000 0.752 0.008 0.168 0.004 0.068
#> GSM121408 2 0.489 0.62950 0.000 0.684 0.008 0.224 0.012 0.072
#> GSM121409 6 0.471 0.38783 0.412 0.024 0.000 0.004 0.008 0.552
#> GSM121410 6 0.481 0.38212 0.412 0.024 0.000 0.008 0.008 0.548
#> GSM121412 2 0.225 0.60632 0.000 0.912 0.004 0.024 0.020 0.040
#> GSM121413 2 0.224 0.59743 0.000 0.912 0.004 0.016 0.028 0.040
#> GSM121414 2 0.225 0.60608 0.000 0.912 0.004 0.020 0.024 0.040
#> GSM121415 2 0.599 0.45629 0.000 0.540 0.016 0.308 0.012 0.124
#> GSM121416 2 0.639 0.30218 0.000 0.452 0.016 0.364 0.016 0.152
#> GSM120591 1 0.363 0.63338 0.704 0.000 0.000 0.004 0.004 0.288
#> GSM120594 1 0.363 0.63338 0.704 0.000 0.000 0.004 0.004 0.288
#> GSM120718 1 0.363 0.63338 0.704 0.000 0.000 0.004 0.004 0.288
#> GSM121205 1 0.000 0.75347 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121206 1 0.000 0.75347 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121207 1 0.000 0.75347 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121208 1 0.000 0.75347 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121209 1 0.000 0.75347 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.000 0.75347 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121211 1 0.000 0.75347 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121212 1 0.000 0.75347 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121213 1 0.000 0.75347 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.000 0.75347 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121215 1 0.000 0.75347 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121216 1 0.000 0.75347 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121217 1 0.000 0.75347 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121218 1 0.000 0.75347 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121234 1 0.000 0.75347 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121243 1 0.000 0.75347 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121245 1 0.000 0.75347 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121246 1 0.000 0.75347 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121247 1 0.000 0.75347 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121248 1 0.000 0.75347 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM120744 6 0.356 0.70163 0.256 0.008 0.000 0.004 0.000 0.732
#> GSM120745 6 0.350 0.69415 0.264 0.004 0.000 0.004 0.000 0.728
#> GSM120746 6 0.356 0.70163 0.256 0.008 0.000 0.004 0.000 0.732
#> GSM120747 6 0.356 0.70163 0.256 0.008 0.000 0.004 0.000 0.732
#> GSM120748 6 0.356 0.70163 0.256 0.008 0.000 0.004 0.000 0.732
#> GSM120749 6 0.350 0.69415 0.264 0.004 0.000 0.004 0.000 0.728
#> GSM120750 6 0.356 0.70163 0.256 0.008 0.000 0.004 0.000 0.732
#> GSM120751 6 0.356 0.70163 0.256 0.008 0.000 0.004 0.000 0.732
#> GSM120752 6 0.350 0.69415 0.264 0.004 0.000 0.004 0.000 0.728
#> GSM121336 2 0.338 0.60382 0.000 0.832 0.016 0.120 0.012 0.020
#> GSM121339 4 0.743 0.19254 0.076 0.236 0.004 0.344 0.008 0.332
#> GSM121349 2 0.338 0.60382 0.000 0.832 0.016 0.120 0.012 0.020
#> GSM121355 2 0.338 0.60382 0.000 0.832 0.016 0.120 0.012 0.020
#> GSM120757 6 0.441 0.63883 0.300 0.004 0.000 0.032 0.004 0.660
#> GSM120766 6 0.480 0.64736 0.184 0.060 0.000 0.044 0.000 0.712
#> GSM120770 6 0.730 -0.02376 0.116 0.228 0.000 0.200 0.008 0.448
#> GSM120779 6 0.412 0.68392 0.256 0.000 0.004 0.036 0.000 0.704
#> GSM120780 6 0.480 0.64736 0.184 0.060 0.000 0.044 0.000 0.712
#> GSM121102 6 0.598 0.49756 0.156 0.136 0.000 0.076 0.004 0.628
#> GSM121203 6 0.467 0.70055 0.240 0.044 0.000 0.028 0.000 0.688
#> GSM121204 1 0.475 -0.10641 0.508 0.000 0.008 0.032 0.000 0.452
#> GSM121330 1 0.373 0.63291 0.700 0.000 0.000 0.004 0.008 0.288
#> GSM121335 1 0.369 0.64153 0.708 0.000 0.000 0.004 0.008 0.280
#> GSM121337 6 0.746 -0.08683 0.100 0.192 0.004 0.304 0.008 0.392
#> GSM121338 6 0.749 -0.00509 0.104 0.212 0.004 0.264 0.008 0.408
#> GSM121341 1 0.369 0.64153 0.708 0.000 0.000 0.004 0.008 0.280
#> GSM121342 1 0.373 0.63291 0.700 0.000 0.000 0.004 0.008 0.288
#> GSM121343 6 0.749 -0.00509 0.104 0.212 0.004 0.264 0.008 0.408
#> GSM121344 1 0.371 0.63782 0.704 0.000 0.000 0.004 0.008 0.284
#> GSM121346 1 0.371 0.63782 0.704 0.000 0.000 0.004 0.008 0.284
#> GSM121347 6 0.727 -0.12535 0.096 0.148 0.004 0.348 0.008 0.396
#> GSM121348 6 0.451 0.67078 0.184 0.040 0.000 0.036 0.004 0.736
#> GSM121350 1 0.371 0.63782 0.704 0.000 0.000 0.004 0.008 0.284
#> GSM121352 1 0.371 0.63782 0.704 0.000 0.000 0.004 0.008 0.284
#> GSM121354 1 0.371 0.63782 0.704 0.000 0.000 0.004 0.008 0.284
#> GSM120753 4 0.615 0.38307 0.000 0.256 0.012 0.544 0.016 0.172
#> GSM120761 4 0.561 0.54217 0.000 0.148 0.000 0.576 0.012 0.264
#> GSM120768 4 0.503 0.54209 0.000 0.120 0.004 0.660 0.004 0.212
#> GSM120781 4 0.471 0.27241 0.000 0.180 0.012 0.724 0.016 0.068
#> GSM120788 4 0.488 0.47748 0.012 0.000 0.016 0.520 0.012 0.440
#> GSM120760 4 0.533 0.51386 0.012 0.012 0.020 0.520 0.016 0.420
#> GSM120763 4 0.481 0.51882 0.000 0.004 0.024 0.540 0.012 0.420
#> GSM120764 4 0.486 0.49302 0.008 0.000 0.020 0.524 0.012 0.436
#> GSM120777 6 0.531 -0.38581 0.032 0.000 0.020 0.452 0.012 0.484
#> GSM120786 4 0.491 0.49911 0.008 0.004 0.016 0.528 0.012 0.432
#> GSM121329 1 0.460 0.56291 0.664 0.000 0.004 0.036 0.012 0.284
#> GSM121331 6 0.404 0.68706 0.256 0.000 0.000 0.040 0.000 0.704
#> GSM121333 6 0.406 0.68877 0.248 0.000 0.000 0.044 0.000 0.708
#> GSM121345 6 0.524 0.25444 0.440 0.000 0.004 0.080 0.000 0.476
#> GSM121356 6 0.404 0.68706 0.256 0.000 0.000 0.040 0.000 0.704
#> GSM120754 4 0.576 0.58455 0.008 0.080 0.016 0.568 0.008 0.320
#> GSM120759 2 0.492 0.08274 0.000 0.632 0.004 0.020 0.304 0.040
#> GSM120762 4 0.498 0.14786 0.000 0.220 0.016 0.688 0.016 0.060
#> GSM120775 4 0.578 0.58433 0.012 0.072 0.016 0.564 0.008 0.328
#> GSM120776 1 0.637 -0.27157 0.428 0.012 0.008 0.164 0.004 0.384
#> GSM120782 4 0.620 0.48536 0.024 0.152 0.004 0.536 0.004 0.280
#> GSM120789 2 0.601 0.40430 0.000 0.520 0.008 0.332 0.020 0.120
#> GSM120790 5 0.239 0.00000 0.000 0.128 0.000 0.000 0.864 0.008
#> GSM120791 4 0.542 0.59019 0.000 0.076 0.008 0.576 0.012 0.328
#> GSM120755 4 0.573 -0.09378 0.000 0.312 0.016 0.576 0.024 0.072
#> GSM120756 6 0.496 -0.43240 0.020 0.000 0.016 0.476 0.008 0.480
#> GSM120769 4 0.523 0.16512 0.000 0.200 0.032 0.688 0.020 0.060
#> GSM120778 4 0.526 0.21593 0.000 0.016 0.128 0.712 0.048 0.096
#> GSM120792 4 0.585 0.59191 0.008 0.088 0.024 0.592 0.008 0.280
#> GSM121332 4 0.619 0.04448 0.000 0.356 0.008 0.464 0.012 0.160
#> GSM121334 4 0.609 0.56905 0.000 0.120 0.012 0.488 0.016 0.364
#> GSM121340 3 0.166 0.00000 0.000 0.000 0.928 0.056 0.000 0.016
#> GSM121351 2 0.331 0.58250 0.000 0.852 0.004 0.036 0.060 0.048
#> GSM121353 4 0.556 0.44352 0.020 0.028 0.016 0.476 0.008 0.452
#> GSM120758 4 0.509 0.54175 0.000 0.124 0.004 0.652 0.004 0.216
#> GSM120771 4 0.617 0.47647 0.000 0.216 0.000 0.504 0.020 0.260
#> GSM120772 4 0.560 0.45389 0.000 0.152 0.008 0.648 0.028 0.164
#> GSM120773 4 0.543 0.50278 0.008 0.004 0.044 0.508 0.016 0.420
#> GSM120774 4 0.733 0.32188 0.000 0.056 0.188 0.468 0.044 0.244
#> GSM120783 4 0.517 0.49379 0.008 0.000 0.040 0.512 0.012 0.428
#> GSM120787 4 0.743 -0.24184 0.000 0.048 0.252 0.464 0.068 0.168
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> CV:hclust 94 9.82e-10 2
#> CV:hclust 53 1.54e-08 3
#> CV:hclust 54 6.64e-14 4
#> CV:hclust 58 6.84e-21 5
#> CV:hclust 75 2.37e-18 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 21512 rows and 119 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
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 0.755 0.853 0.940 0.4854 0.500 0.500
#> 3 3 0.584 0.737 0.840 0.3400 0.770 0.571
#> 4 4 0.651 0.638 0.822 0.1392 0.797 0.498
#> 5 5 0.673 0.667 0.758 0.0561 0.895 0.640
#> 6 6 0.717 0.606 0.728 0.0434 0.930 0.699
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
#> GSM120719 1 0.0000 0.8969 1.000 0.000
#> GSM120720 1 0.0000 0.8969 1.000 0.000
#> GSM120765 2 0.0000 0.9625 0.000 1.000
#> GSM120767 2 0.0000 0.9625 0.000 1.000
#> GSM120784 2 0.0000 0.9625 0.000 1.000
#> GSM121400 1 0.0672 0.8933 0.992 0.008
#> GSM121401 1 0.0376 0.8952 0.996 0.004
#> GSM121402 2 0.0000 0.9625 0.000 1.000
#> GSM121403 2 0.9552 0.2984 0.376 0.624
#> GSM121404 2 0.0000 0.9625 0.000 1.000
#> GSM121405 1 0.3431 0.8617 0.936 0.064
#> GSM121406 2 0.0000 0.9625 0.000 1.000
#> GSM121408 2 0.0000 0.9625 0.000 1.000
#> GSM121409 1 0.7299 0.7496 0.796 0.204
#> GSM121410 1 0.6531 0.7842 0.832 0.168
#> GSM121412 2 0.0000 0.9625 0.000 1.000
#> GSM121413 2 0.0000 0.9625 0.000 1.000
#> GSM121414 2 0.0000 0.9625 0.000 1.000
#> GSM121415 2 0.0000 0.9625 0.000 1.000
#> GSM121416 2 0.0000 0.9625 0.000 1.000
#> GSM120591 1 0.0000 0.8969 1.000 0.000
#> GSM120594 1 0.0000 0.8969 1.000 0.000
#> GSM120718 1 0.0000 0.8969 1.000 0.000
#> GSM121205 1 0.0000 0.8969 1.000 0.000
#> GSM121206 1 0.0000 0.8969 1.000 0.000
#> GSM121207 1 0.0000 0.8969 1.000 0.000
#> GSM121208 1 0.0000 0.8969 1.000 0.000
#> GSM121209 1 0.0000 0.8969 1.000 0.000
#> GSM121210 1 0.0000 0.8969 1.000 0.000
#> GSM121211 1 0.0000 0.8969 1.000 0.000
#> GSM121212 1 0.0000 0.8969 1.000 0.000
#> GSM121213 1 0.0000 0.8969 1.000 0.000
#> GSM121214 1 0.0000 0.8969 1.000 0.000
#> GSM121215 1 0.0000 0.8969 1.000 0.000
#> GSM121216 1 0.0000 0.8969 1.000 0.000
#> GSM121217 1 0.0000 0.8969 1.000 0.000
#> GSM121218 1 0.0000 0.8969 1.000 0.000
#> GSM121234 1 0.0000 0.8969 1.000 0.000
#> GSM121243 1 0.0000 0.8969 1.000 0.000
#> GSM121245 1 0.0000 0.8969 1.000 0.000
#> GSM121246 1 0.0000 0.8969 1.000 0.000
#> GSM121247 1 0.0000 0.8969 1.000 0.000
#> GSM121248 1 0.0000 0.8969 1.000 0.000
#> GSM120744 2 0.9491 0.3222 0.368 0.632
#> GSM120745 1 0.7056 0.7620 0.808 0.192
#> GSM120746 1 0.9993 0.1947 0.516 0.484
#> GSM120747 1 1.0000 0.1533 0.504 0.496
#> GSM120748 2 0.7139 0.7088 0.196 0.804
#> GSM120749 1 0.9580 0.4703 0.620 0.380
#> GSM120750 1 0.9998 0.1676 0.508 0.492
#> GSM120751 1 0.9996 0.1814 0.512 0.488
#> GSM120752 1 0.8909 0.6060 0.692 0.308
#> GSM121336 2 0.0000 0.9625 0.000 1.000
#> GSM121339 2 0.0000 0.9625 0.000 1.000
#> GSM121349 2 0.0000 0.9625 0.000 1.000
#> GSM121355 2 0.0000 0.9625 0.000 1.000
#> GSM120757 1 0.9977 0.2326 0.528 0.472
#> GSM120766 2 0.9881 0.0898 0.436 0.564
#> GSM120770 2 0.0000 0.9625 0.000 1.000
#> GSM120779 1 0.6973 0.7658 0.812 0.188
#> GSM120780 2 0.6623 0.7477 0.172 0.828
#> GSM121102 2 0.0000 0.9625 0.000 1.000
#> GSM121203 2 0.9963 -0.0260 0.464 0.536
#> GSM121204 1 0.4022 0.8513 0.920 0.080
#> GSM121330 1 0.0000 0.8969 1.000 0.000
#> GSM121335 1 0.0000 0.8969 1.000 0.000
#> GSM121337 2 0.0000 0.9625 0.000 1.000
#> GSM121338 2 0.0000 0.9625 0.000 1.000
#> GSM121341 1 0.0000 0.8969 1.000 0.000
#> GSM121342 1 0.0000 0.8969 1.000 0.000
#> GSM121343 2 0.0000 0.9625 0.000 1.000
#> GSM121344 1 0.0000 0.8969 1.000 0.000
#> GSM121346 1 0.0000 0.8969 1.000 0.000
#> GSM121347 2 0.0000 0.9625 0.000 1.000
#> GSM121348 2 0.0000 0.9625 0.000 1.000
#> GSM121350 1 0.0000 0.8969 1.000 0.000
#> GSM121352 1 0.0000 0.8969 1.000 0.000
#> GSM121354 1 0.0000 0.8969 1.000 0.000
#> GSM120753 2 0.0000 0.9625 0.000 1.000
#> GSM120761 2 0.0000 0.9625 0.000 1.000
#> GSM120768 2 0.0000 0.9625 0.000 1.000
#> GSM120781 2 0.0000 0.9625 0.000 1.000
#> GSM120788 2 0.0000 0.9625 0.000 1.000
#> GSM120760 2 0.0000 0.9625 0.000 1.000
#> GSM120763 2 0.0000 0.9625 0.000 1.000
#> GSM120764 2 0.0000 0.9625 0.000 1.000
#> GSM120777 2 0.0000 0.9625 0.000 1.000
#> GSM120786 2 0.0000 0.9625 0.000 1.000
#> GSM121329 1 0.0376 0.8952 0.996 0.004
#> GSM121331 1 0.9460 0.5038 0.636 0.364
#> GSM121333 1 0.7299 0.7496 0.796 0.204
#> GSM121345 1 0.7299 0.7496 0.796 0.204
#> GSM121356 1 0.8081 0.6953 0.752 0.248
#> GSM120754 2 0.0000 0.9625 0.000 1.000
#> GSM120759 2 0.0000 0.9625 0.000 1.000
#> GSM120762 2 0.0000 0.9625 0.000 1.000
#> GSM120775 2 0.0000 0.9625 0.000 1.000
#> GSM120776 2 0.1843 0.9341 0.028 0.972
#> GSM120782 2 0.0000 0.9625 0.000 1.000
#> GSM120789 2 0.0000 0.9625 0.000 1.000
#> GSM120790 2 0.0000 0.9625 0.000 1.000
#> GSM120791 2 0.0000 0.9625 0.000 1.000
#> GSM120755 2 0.0000 0.9625 0.000 1.000
#> GSM120756 2 0.0376 0.9587 0.004 0.996
#> GSM120769 2 0.0000 0.9625 0.000 1.000
#> GSM120778 2 0.0000 0.9625 0.000 1.000
#> GSM120792 2 0.0000 0.9625 0.000 1.000
#> GSM121332 2 0.0000 0.9625 0.000 1.000
#> GSM121334 2 0.0000 0.9625 0.000 1.000
#> GSM121340 2 0.0000 0.9625 0.000 1.000
#> GSM121351 2 0.0000 0.9625 0.000 1.000
#> GSM121353 2 0.0000 0.9625 0.000 1.000
#> GSM120758 2 0.0000 0.9625 0.000 1.000
#> GSM120771 2 0.0000 0.9625 0.000 1.000
#> GSM120772 2 0.0000 0.9625 0.000 1.000
#> GSM120773 2 0.0000 0.9625 0.000 1.000
#> GSM120774 2 0.0000 0.9625 0.000 1.000
#> GSM120783 2 0.0000 0.9625 0.000 1.000
#> GSM120787 2 0.0000 0.9625 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM120719 1 0.4346 0.788 0.816 0.000 0.184
#> GSM120720 1 0.4796 0.762 0.780 0.000 0.220
#> GSM120765 2 0.4346 0.817 0.000 0.816 0.184
#> GSM120767 2 0.3116 0.827 0.000 0.892 0.108
#> GSM120784 2 0.4291 0.817 0.000 0.820 0.180
#> GSM121400 3 0.5903 0.732 0.232 0.024 0.744
#> GSM121401 3 0.6313 0.602 0.308 0.016 0.676
#> GSM121402 2 0.4346 0.817 0.000 0.816 0.184
#> GSM121403 3 0.3043 0.701 0.008 0.084 0.908
#> GSM121404 2 0.4931 0.786 0.000 0.768 0.232
#> GSM121405 3 0.6099 0.736 0.228 0.032 0.740
#> GSM121406 2 0.4399 0.816 0.000 0.812 0.188
#> GSM121408 2 0.4346 0.817 0.000 0.816 0.184
#> GSM121409 3 0.6098 0.784 0.176 0.056 0.768
#> GSM121410 3 0.6254 0.769 0.188 0.056 0.756
#> GSM121412 2 0.4399 0.816 0.000 0.812 0.188
#> GSM121413 2 0.4399 0.816 0.000 0.812 0.188
#> GSM121414 2 0.4399 0.816 0.000 0.812 0.188
#> GSM121415 2 0.4399 0.816 0.000 0.812 0.188
#> GSM121416 2 0.4178 0.819 0.000 0.828 0.172
#> GSM120591 1 0.6045 0.473 0.620 0.000 0.380
#> GSM120594 1 0.4796 0.762 0.780 0.000 0.220
#> GSM120718 1 0.4291 0.790 0.820 0.000 0.180
#> GSM121205 1 0.0000 0.857 1.000 0.000 0.000
#> GSM121206 1 0.0000 0.857 1.000 0.000 0.000
#> GSM121207 1 0.0000 0.857 1.000 0.000 0.000
#> GSM121208 1 0.0000 0.857 1.000 0.000 0.000
#> GSM121209 1 0.0000 0.857 1.000 0.000 0.000
#> GSM121210 1 0.0000 0.857 1.000 0.000 0.000
#> GSM121211 1 0.0000 0.857 1.000 0.000 0.000
#> GSM121212 1 0.0000 0.857 1.000 0.000 0.000
#> GSM121213 1 0.0000 0.857 1.000 0.000 0.000
#> GSM121214 1 0.0000 0.857 1.000 0.000 0.000
#> GSM121215 1 0.0000 0.857 1.000 0.000 0.000
#> GSM121216 1 0.0000 0.857 1.000 0.000 0.000
#> GSM121217 1 0.0000 0.857 1.000 0.000 0.000
#> GSM121218 1 0.0000 0.857 1.000 0.000 0.000
#> GSM121234 1 0.0000 0.857 1.000 0.000 0.000
#> GSM121243 1 0.0000 0.857 1.000 0.000 0.000
#> GSM121245 1 0.0000 0.857 1.000 0.000 0.000
#> GSM121246 1 0.3116 0.822 0.892 0.000 0.108
#> GSM121247 1 0.0000 0.857 1.000 0.000 0.000
#> GSM121248 1 0.0000 0.857 1.000 0.000 0.000
#> GSM120744 3 0.5598 0.799 0.148 0.052 0.800
#> GSM120745 3 0.5092 0.789 0.176 0.020 0.804
#> GSM120746 3 0.4999 0.802 0.152 0.028 0.820
#> GSM120747 3 0.4999 0.802 0.152 0.028 0.820
#> GSM120748 3 0.2339 0.726 0.012 0.048 0.940
#> GSM120749 3 0.5111 0.795 0.168 0.024 0.808
#> GSM120750 3 0.4999 0.802 0.152 0.028 0.820
#> GSM120751 3 0.4999 0.802 0.152 0.028 0.820
#> GSM120752 3 0.5111 0.795 0.168 0.024 0.808
#> GSM121336 2 0.4399 0.816 0.000 0.812 0.188
#> GSM121339 2 0.6235 0.497 0.000 0.564 0.436
#> GSM121349 2 0.4399 0.816 0.000 0.812 0.188
#> GSM121355 2 0.4399 0.816 0.000 0.812 0.188
#> GSM120757 3 0.5295 0.792 0.156 0.036 0.808
#> GSM120766 3 0.5285 0.792 0.148 0.040 0.812
#> GSM120770 2 0.6079 0.590 0.000 0.612 0.388
#> GSM120779 3 0.4953 0.780 0.176 0.016 0.808
#> GSM120780 3 0.2774 0.712 0.008 0.072 0.920
#> GSM121102 3 0.5591 0.302 0.000 0.304 0.696
#> GSM121203 3 0.5497 0.797 0.148 0.048 0.804
#> GSM121204 3 0.4805 0.777 0.176 0.012 0.812
#> GSM121330 1 0.5363 0.691 0.724 0.000 0.276
#> GSM121335 1 0.4750 0.766 0.784 0.000 0.216
#> GSM121337 2 0.6225 0.510 0.000 0.568 0.432
#> GSM121338 3 0.4235 0.598 0.000 0.176 0.824
#> GSM121341 1 0.4750 0.766 0.784 0.000 0.216
#> GSM121342 1 0.4750 0.766 0.784 0.000 0.216
#> GSM121343 3 0.4062 0.618 0.000 0.164 0.836
#> GSM121344 1 0.5058 0.736 0.756 0.000 0.244
#> GSM121346 1 0.6140 0.412 0.596 0.000 0.404
#> GSM121347 3 0.6252 -0.312 0.000 0.444 0.556
#> GSM121348 3 0.3619 0.613 0.000 0.136 0.864
#> GSM121350 1 0.6126 0.429 0.600 0.000 0.400
#> GSM121352 1 0.5397 0.685 0.720 0.000 0.280
#> GSM121354 1 0.5058 0.736 0.756 0.000 0.244
#> GSM120753 2 0.0424 0.827 0.000 0.992 0.008
#> GSM120761 2 0.1031 0.822 0.000 0.976 0.024
#> GSM120768 2 0.1860 0.813 0.000 0.948 0.052
#> GSM120781 2 0.0424 0.827 0.000 0.992 0.008
#> GSM120788 2 0.6252 0.205 0.000 0.556 0.444
#> GSM120760 2 0.2356 0.805 0.000 0.928 0.072
#> GSM120763 2 0.2066 0.810 0.000 0.940 0.060
#> GSM120764 2 0.2796 0.793 0.000 0.908 0.092
#> GSM120777 2 0.6252 0.206 0.000 0.556 0.444
#> GSM120786 2 0.2796 0.793 0.000 0.908 0.092
#> GSM121329 3 0.5864 0.638 0.288 0.008 0.704
#> GSM121331 3 0.5581 0.789 0.168 0.040 0.792
#> GSM121333 3 0.4953 0.780 0.176 0.016 0.808
#> GSM121345 3 0.4953 0.780 0.176 0.016 0.808
#> GSM121356 3 0.4840 0.786 0.168 0.016 0.816
#> GSM120754 2 0.6095 0.349 0.000 0.608 0.392
#> GSM120759 2 0.4346 0.817 0.000 0.816 0.184
#> GSM120762 2 0.0424 0.827 0.000 0.992 0.008
#> GSM120775 2 0.6215 0.254 0.000 0.572 0.428
#> GSM120776 3 0.4504 0.677 0.000 0.196 0.804
#> GSM120782 2 0.6192 0.237 0.000 0.580 0.420
#> GSM120789 2 0.4121 0.821 0.000 0.832 0.168
#> GSM120790 2 0.4291 0.819 0.000 0.820 0.180
#> GSM120791 2 0.1964 0.812 0.000 0.944 0.056
#> GSM120755 2 0.0747 0.827 0.000 0.984 0.016
#> GSM120756 3 0.6302 0.035 0.000 0.480 0.520
#> GSM120769 2 0.0424 0.827 0.000 0.992 0.008
#> GSM120778 2 0.1860 0.813 0.000 0.948 0.052
#> GSM120792 2 0.1411 0.820 0.000 0.964 0.036
#> GSM121332 2 0.4178 0.820 0.000 0.828 0.172
#> GSM121334 2 0.0747 0.823 0.000 0.984 0.016
#> GSM121340 2 0.2448 0.803 0.000 0.924 0.076
#> GSM121351 2 0.4399 0.816 0.000 0.812 0.188
#> GSM121353 2 0.3816 0.751 0.000 0.852 0.148
#> GSM120758 2 0.0237 0.826 0.000 0.996 0.004
#> GSM120771 2 0.4235 0.819 0.000 0.824 0.176
#> GSM120772 2 0.0000 0.826 0.000 1.000 0.000
#> GSM120773 2 0.2537 0.800 0.000 0.920 0.080
#> GSM120774 2 0.1964 0.812 0.000 0.944 0.056
#> GSM120783 2 0.2625 0.798 0.000 0.916 0.084
#> GSM120787 2 0.1289 0.819 0.000 0.968 0.032
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM120719 1 0.5273 0.1939 0.536 0.000 0.456 0.008
#> GSM120720 3 0.5263 0.0314 0.448 0.000 0.544 0.008
#> GSM120765 2 0.0188 0.8778 0.000 0.996 0.000 0.004
#> GSM120767 2 0.0336 0.8764 0.000 0.992 0.000 0.008
#> GSM120784 2 0.0188 0.8753 0.000 0.996 0.004 0.000
#> GSM121400 3 0.0657 0.6924 0.000 0.004 0.984 0.012
#> GSM121401 3 0.1639 0.6728 0.036 0.004 0.952 0.008
#> GSM121402 2 0.0524 0.8760 0.008 0.988 0.000 0.004
#> GSM121403 3 0.1284 0.6918 0.000 0.024 0.964 0.012
#> GSM121404 2 0.2281 0.7864 0.000 0.904 0.096 0.000
#> GSM121405 3 0.0992 0.6841 0.012 0.004 0.976 0.008
#> GSM121406 2 0.0188 0.8778 0.000 0.996 0.000 0.004
#> GSM121408 2 0.0188 0.8778 0.000 0.996 0.000 0.004
#> GSM121409 3 0.0779 0.6935 0.000 0.004 0.980 0.016
#> GSM121410 3 0.0657 0.6924 0.000 0.004 0.984 0.012
#> GSM121412 2 0.0188 0.8753 0.000 0.996 0.004 0.000
#> GSM121413 2 0.0188 0.8753 0.000 0.996 0.004 0.000
#> GSM121414 2 0.0188 0.8753 0.000 0.996 0.004 0.000
#> GSM121415 2 0.0376 0.8770 0.000 0.992 0.004 0.004
#> GSM121416 2 0.0336 0.8767 0.000 0.992 0.000 0.008
#> GSM120591 3 0.4814 0.3507 0.316 0.000 0.676 0.008
#> GSM120594 3 0.5263 0.0314 0.448 0.000 0.544 0.008
#> GSM120718 1 0.5277 0.1835 0.532 0.000 0.460 0.008
#> GSM121205 1 0.0336 0.9267 0.992 0.000 0.008 0.000
#> GSM121206 1 0.0336 0.9267 0.992 0.000 0.008 0.000
#> GSM121207 1 0.0336 0.9267 0.992 0.000 0.008 0.000
#> GSM121208 1 0.1716 0.8799 0.936 0.000 0.064 0.000
#> GSM121209 1 0.0336 0.9267 0.992 0.000 0.008 0.000
#> GSM121210 1 0.0336 0.9267 0.992 0.000 0.008 0.000
#> GSM121211 1 0.0336 0.9267 0.992 0.000 0.008 0.000
#> GSM121212 1 0.0336 0.9267 0.992 0.000 0.008 0.000
#> GSM121213 1 0.0336 0.9267 0.992 0.000 0.008 0.000
#> GSM121214 1 0.0336 0.9267 0.992 0.000 0.008 0.000
#> GSM121215 1 0.0524 0.9253 0.988 0.000 0.008 0.004
#> GSM121216 1 0.0524 0.9253 0.988 0.000 0.008 0.004
#> GSM121217 1 0.0336 0.9267 0.992 0.000 0.008 0.000
#> GSM121218 1 0.0336 0.9267 0.992 0.000 0.008 0.000
#> GSM121234 1 0.0524 0.9253 0.988 0.000 0.008 0.004
#> GSM121243 1 0.0524 0.9253 0.988 0.000 0.008 0.004
#> GSM121245 1 0.0336 0.9267 0.992 0.000 0.008 0.000
#> GSM121246 1 0.4868 0.5176 0.684 0.000 0.304 0.012
#> GSM121247 1 0.0336 0.9267 0.992 0.000 0.008 0.000
#> GSM121248 1 0.0336 0.9267 0.992 0.000 0.008 0.000
#> GSM120744 3 0.2965 0.6972 0.000 0.036 0.892 0.072
#> GSM120745 3 0.1716 0.7023 0.000 0.000 0.936 0.064
#> GSM120746 3 0.1716 0.7023 0.000 0.000 0.936 0.064
#> GSM120747 3 0.1716 0.7023 0.000 0.000 0.936 0.064
#> GSM120748 3 0.2816 0.6981 0.000 0.036 0.900 0.064
#> GSM120749 3 0.1716 0.7023 0.000 0.000 0.936 0.064
#> GSM120750 3 0.1716 0.7023 0.000 0.000 0.936 0.064
#> GSM120751 3 0.1716 0.7023 0.000 0.000 0.936 0.064
#> GSM120752 3 0.1716 0.7023 0.000 0.000 0.936 0.064
#> GSM121336 2 0.0188 0.8778 0.000 0.996 0.000 0.004
#> GSM121339 2 0.3402 0.6946 0.000 0.832 0.164 0.004
#> GSM121349 2 0.0188 0.8778 0.000 0.996 0.000 0.004
#> GSM121355 2 0.0188 0.8778 0.000 0.996 0.000 0.004
#> GSM120757 3 0.4761 0.5370 0.000 0.000 0.628 0.372
#> GSM120766 3 0.4761 0.5370 0.000 0.000 0.628 0.372
#> GSM120770 2 0.4462 0.6563 0.000 0.792 0.164 0.044
#> GSM120779 3 0.4761 0.5370 0.000 0.000 0.628 0.372
#> GSM120780 3 0.5658 0.5444 0.000 0.040 0.632 0.328
#> GSM121102 3 0.6082 -0.0232 0.000 0.476 0.480 0.044
#> GSM121203 3 0.2342 0.7012 0.000 0.008 0.912 0.080
#> GSM121204 3 0.4746 0.5409 0.000 0.000 0.632 0.368
#> GSM121330 3 0.4973 0.2852 0.348 0.000 0.644 0.008
#> GSM121335 3 0.5281 -0.0202 0.464 0.000 0.528 0.008
#> GSM121337 2 0.6874 0.3257 0.000 0.568 0.296 0.136
#> GSM121338 3 0.5510 0.2890 0.000 0.376 0.600 0.024
#> GSM121341 3 0.5281 -0.0202 0.464 0.000 0.528 0.008
#> GSM121342 3 0.5281 -0.0202 0.464 0.000 0.528 0.008
#> GSM121343 3 0.5682 0.3333 0.000 0.352 0.612 0.036
#> GSM121344 3 0.5172 0.1549 0.404 0.000 0.588 0.008
#> GSM121346 3 0.2342 0.6470 0.080 0.000 0.912 0.008
#> GSM121347 3 0.7812 0.0705 0.000 0.252 0.376 0.372
#> GSM121348 3 0.6983 0.4240 0.000 0.124 0.516 0.360
#> GSM121350 3 0.2342 0.6469 0.080 0.000 0.912 0.008
#> GSM121352 3 0.4936 0.3015 0.340 0.000 0.652 0.008
#> GSM121354 3 0.5172 0.1549 0.404 0.000 0.588 0.008
#> GSM120753 4 0.4996 0.3339 0.000 0.484 0.000 0.516
#> GSM120761 4 0.4866 0.5098 0.000 0.404 0.000 0.596
#> GSM120768 4 0.3726 0.7455 0.000 0.212 0.000 0.788
#> GSM120781 2 0.4981 -0.2133 0.000 0.536 0.000 0.464
#> GSM120788 4 0.1059 0.7314 0.000 0.016 0.012 0.972
#> GSM120760 4 0.2921 0.7732 0.000 0.140 0.000 0.860
#> GSM120763 4 0.2921 0.7732 0.000 0.140 0.000 0.860
#> GSM120764 4 0.2281 0.7728 0.000 0.096 0.000 0.904
#> GSM120777 4 0.1059 0.7314 0.000 0.016 0.012 0.972
#> GSM120786 4 0.2345 0.7742 0.000 0.100 0.000 0.900
#> GSM121329 3 0.4374 0.6629 0.068 0.000 0.812 0.120
#> GSM121331 3 0.4776 0.5324 0.000 0.000 0.624 0.376
#> GSM121333 3 0.4776 0.5324 0.000 0.000 0.624 0.376
#> GSM121345 3 0.4790 0.5288 0.000 0.000 0.620 0.380
#> GSM121356 3 0.4761 0.5370 0.000 0.000 0.628 0.372
#> GSM120754 4 0.0927 0.7340 0.000 0.016 0.008 0.976
#> GSM120759 2 0.0804 0.8731 0.008 0.980 0.000 0.012
#> GSM120762 2 0.4643 0.2577 0.000 0.656 0.000 0.344
#> GSM120775 4 0.0927 0.7340 0.000 0.016 0.008 0.976
#> GSM120776 4 0.4843 -0.1022 0.000 0.000 0.396 0.604
#> GSM120782 4 0.2300 0.7539 0.000 0.064 0.016 0.920
#> GSM120789 2 0.0672 0.8748 0.008 0.984 0.000 0.008
#> GSM120790 2 0.0804 0.8731 0.008 0.980 0.000 0.012
#> GSM120791 4 0.3764 0.7433 0.000 0.216 0.000 0.784
#> GSM120755 2 0.4522 0.3366 0.000 0.680 0.000 0.320
#> GSM120756 4 0.1059 0.7314 0.000 0.016 0.012 0.972
#> GSM120769 4 0.4996 0.3347 0.000 0.484 0.000 0.516
#> GSM120778 4 0.3942 0.7303 0.000 0.236 0.000 0.764
#> GSM120792 4 0.4193 0.6987 0.000 0.268 0.000 0.732
#> GSM121332 2 0.0336 0.8765 0.000 0.992 0.000 0.008
#> GSM121334 4 0.4941 0.4468 0.000 0.436 0.000 0.564
#> GSM121340 4 0.2408 0.7754 0.000 0.104 0.000 0.896
#> GSM121351 2 0.0524 0.8760 0.008 0.988 0.000 0.004
#> GSM121353 4 0.2408 0.7753 0.000 0.104 0.000 0.896
#> GSM120758 4 0.4992 0.3557 0.000 0.476 0.000 0.524
#> GSM120771 2 0.3311 0.6764 0.000 0.828 0.000 0.172
#> GSM120772 4 0.4992 0.3562 0.000 0.476 0.000 0.524
#> GSM120773 4 0.2589 0.7758 0.000 0.116 0.000 0.884
#> GSM120774 4 0.4103 0.7138 0.000 0.256 0.000 0.744
#> GSM120783 4 0.2469 0.7756 0.000 0.108 0.000 0.892
#> GSM120787 4 0.4730 0.5778 0.000 0.364 0.000 0.636
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM120719 3 0.4617 0.5580 0.304 0.000 0.668 0.004 0.024
#> GSM120720 3 0.4347 0.5965 0.256 0.000 0.716 0.004 0.024
#> GSM120765 2 0.1597 0.7685 0.000 0.940 0.000 0.012 0.048
#> GSM120767 2 0.2171 0.7527 0.000 0.912 0.000 0.024 0.064
#> GSM120784 2 0.1914 0.7693 0.000 0.924 0.000 0.016 0.060
#> GSM121400 3 0.1704 0.4939 0.000 0.004 0.928 0.000 0.068
#> GSM121401 3 0.0794 0.5733 0.028 0.000 0.972 0.000 0.000
#> GSM121402 2 0.3727 0.7456 0.000 0.768 0.000 0.016 0.216
#> GSM121403 3 0.3691 0.3994 0.000 0.040 0.804 0.000 0.156
#> GSM121404 2 0.4139 0.7488 0.000 0.804 0.052 0.020 0.124
#> GSM121405 3 0.0510 0.5638 0.016 0.000 0.984 0.000 0.000
#> GSM121406 2 0.2233 0.7728 0.000 0.892 0.000 0.004 0.104
#> GSM121408 2 0.1701 0.7772 0.000 0.936 0.000 0.016 0.048
#> GSM121409 3 0.2583 0.4417 0.000 0.004 0.864 0.000 0.132
#> GSM121410 3 0.2233 0.4655 0.000 0.004 0.892 0.000 0.104
#> GSM121412 2 0.2674 0.7697 0.000 0.856 0.000 0.004 0.140
#> GSM121413 2 0.2674 0.7697 0.000 0.856 0.000 0.004 0.140
#> GSM121414 2 0.2674 0.7697 0.000 0.856 0.000 0.004 0.140
#> GSM121415 2 0.1282 0.7757 0.000 0.952 0.000 0.004 0.044
#> GSM121416 2 0.2473 0.7639 0.000 0.896 0.000 0.032 0.072
#> GSM120591 3 0.3684 0.6155 0.172 0.000 0.800 0.004 0.024
#> GSM120594 3 0.4347 0.5965 0.256 0.000 0.716 0.004 0.024
#> GSM120718 3 0.4617 0.5580 0.304 0.000 0.668 0.004 0.024
#> GSM121205 1 0.0000 0.9864 1.000 0.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.9864 1.000 0.000 0.000 0.000 0.000
#> GSM121207 1 0.0162 0.9851 0.996 0.000 0.000 0.000 0.004
#> GSM121208 1 0.2732 0.7886 0.840 0.000 0.160 0.000 0.000
#> GSM121209 1 0.0000 0.9864 1.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.9864 1.000 0.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.9864 1.000 0.000 0.000 0.000 0.000
#> GSM121212 1 0.0162 0.9851 0.996 0.000 0.000 0.000 0.004
#> GSM121213 1 0.0000 0.9864 1.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.9864 1.000 0.000 0.000 0.000 0.000
#> GSM121215 1 0.0579 0.9799 0.984 0.000 0.000 0.008 0.008
#> GSM121216 1 0.0579 0.9799 0.984 0.000 0.000 0.008 0.008
#> GSM121217 1 0.0000 0.9864 1.000 0.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.9864 1.000 0.000 0.000 0.000 0.000
#> GSM121234 1 0.0579 0.9799 0.984 0.000 0.000 0.008 0.008
#> GSM121243 1 0.0579 0.9799 0.984 0.000 0.000 0.008 0.008
#> GSM121245 1 0.0000 0.9864 1.000 0.000 0.000 0.000 0.000
#> GSM121246 3 0.4692 0.2433 0.460 0.000 0.528 0.004 0.008
#> GSM121247 1 0.0162 0.9851 0.996 0.000 0.000 0.000 0.004
#> GSM121248 1 0.0000 0.9864 1.000 0.000 0.000 0.000 0.000
#> GSM120744 3 0.4305 0.1985 0.000 0.012 0.688 0.004 0.296
#> GSM120745 3 0.3884 0.2411 0.000 0.000 0.708 0.004 0.288
#> GSM120746 3 0.3752 0.2421 0.000 0.000 0.708 0.000 0.292
#> GSM120747 3 0.3752 0.2421 0.000 0.000 0.708 0.000 0.292
#> GSM120748 3 0.4220 0.2008 0.000 0.008 0.688 0.004 0.300
#> GSM120749 3 0.3752 0.2421 0.000 0.000 0.708 0.000 0.292
#> GSM120750 3 0.3774 0.2350 0.000 0.000 0.704 0.000 0.296
#> GSM120751 3 0.3752 0.2421 0.000 0.000 0.708 0.000 0.292
#> GSM120752 3 0.3884 0.2411 0.000 0.000 0.708 0.004 0.288
#> GSM121336 2 0.1704 0.7749 0.000 0.928 0.000 0.004 0.068
#> GSM121339 2 0.3934 0.7350 0.000 0.820 0.060 0.016 0.104
#> GSM121349 2 0.2011 0.7735 0.000 0.908 0.000 0.004 0.088
#> GSM121355 2 0.1704 0.7749 0.000 0.928 0.000 0.004 0.068
#> GSM120757 5 0.6085 0.8816 0.000 0.000 0.404 0.124 0.472
#> GSM120766 5 0.6047 0.8798 0.000 0.000 0.400 0.120 0.480
#> GSM120770 2 0.4851 0.6738 0.000 0.744 0.096 0.012 0.148
#> GSM120779 5 0.6085 0.8816 0.000 0.000 0.404 0.124 0.472
#> GSM120780 5 0.5576 0.7644 0.000 0.016 0.384 0.044 0.556
#> GSM121102 2 0.6778 0.2090 0.000 0.496 0.248 0.012 0.244
#> GSM121203 3 0.4166 0.0189 0.000 0.004 0.648 0.000 0.348
#> GSM121204 5 0.5906 0.8591 0.000 0.000 0.404 0.104 0.492
#> GSM121330 3 0.3003 0.6170 0.188 0.000 0.812 0.000 0.000
#> GSM121335 3 0.3586 0.5915 0.264 0.000 0.736 0.000 0.000
#> GSM121337 2 0.6789 0.5356 0.000 0.608 0.156 0.096 0.140
#> GSM121338 2 0.6692 0.2473 0.000 0.496 0.304 0.012 0.188
#> GSM121341 3 0.3586 0.5915 0.264 0.000 0.736 0.000 0.000
#> GSM121342 3 0.3561 0.5941 0.260 0.000 0.740 0.000 0.000
#> GSM121343 2 0.6642 0.2128 0.000 0.488 0.312 0.008 0.192
#> GSM121344 3 0.3305 0.6103 0.224 0.000 0.776 0.000 0.000
#> GSM121346 3 0.1671 0.5990 0.076 0.000 0.924 0.000 0.000
#> GSM121347 2 0.8198 0.0957 0.000 0.380 0.188 0.288 0.144
#> GSM121348 5 0.7041 0.7205 0.000 0.080 0.288 0.104 0.528
#> GSM121350 3 0.1608 0.5977 0.072 0.000 0.928 0.000 0.000
#> GSM121352 3 0.3039 0.6166 0.192 0.000 0.808 0.000 0.000
#> GSM121354 3 0.3305 0.6103 0.224 0.000 0.776 0.000 0.000
#> GSM120753 4 0.5831 0.3475 0.000 0.408 0.000 0.496 0.096
#> GSM120761 4 0.5461 0.5896 0.000 0.284 0.000 0.620 0.096
#> GSM120768 4 0.2491 0.8204 0.000 0.036 0.000 0.896 0.068
#> GSM120781 2 0.5985 -0.1575 0.000 0.480 0.000 0.408 0.112
#> GSM120788 4 0.0963 0.7963 0.000 0.000 0.000 0.964 0.036
#> GSM120760 4 0.1877 0.8219 0.000 0.012 0.000 0.924 0.064
#> GSM120763 4 0.2172 0.8216 0.000 0.016 0.000 0.908 0.076
#> GSM120764 4 0.0807 0.8145 0.000 0.012 0.000 0.976 0.012
#> GSM120777 4 0.1341 0.7854 0.000 0.000 0.000 0.944 0.056
#> GSM120786 4 0.0807 0.8145 0.000 0.012 0.000 0.976 0.012
#> GSM121329 3 0.3511 0.5490 0.072 0.000 0.848 0.068 0.012
#> GSM121331 5 0.6257 0.8815 0.000 0.004 0.400 0.128 0.468
#> GSM121333 5 0.6257 0.8815 0.000 0.004 0.400 0.128 0.468
#> GSM121345 5 0.6434 0.8574 0.000 0.004 0.400 0.152 0.444
#> GSM121356 5 0.6229 0.8816 0.000 0.004 0.404 0.124 0.468
#> GSM120754 4 0.1106 0.8119 0.000 0.012 0.000 0.964 0.024
#> GSM120759 2 0.4003 0.7134 0.000 0.704 0.000 0.008 0.288
#> GSM120762 2 0.5493 0.3330 0.000 0.628 0.000 0.264 0.108
#> GSM120775 4 0.0703 0.8042 0.000 0.000 0.000 0.976 0.024
#> GSM120776 5 0.6320 0.4905 0.000 0.000 0.156 0.404 0.440
#> GSM120782 4 0.3096 0.7695 0.000 0.024 0.008 0.860 0.108
#> GSM120789 2 0.3284 0.7669 0.000 0.828 0.000 0.024 0.148
#> GSM120790 2 0.4029 0.6937 0.000 0.680 0.000 0.004 0.316
#> GSM120791 4 0.1915 0.8264 0.000 0.032 0.000 0.928 0.040
#> GSM120755 2 0.5660 0.3306 0.000 0.612 0.000 0.264 0.124
#> GSM120756 4 0.0963 0.7963 0.000 0.000 0.000 0.964 0.036
#> GSM120769 4 0.6085 0.3178 0.000 0.404 0.000 0.472 0.124
#> GSM120778 4 0.4254 0.7755 0.000 0.080 0.000 0.772 0.148
#> GSM120792 4 0.2189 0.8115 0.000 0.084 0.000 0.904 0.012
#> GSM121332 2 0.2067 0.7738 0.000 0.920 0.000 0.032 0.048
#> GSM121334 4 0.5656 0.5556 0.000 0.308 0.000 0.588 0.104
#> GSM121340 4 0.2069 0.8167 0.000 0.012 0.000 0.912 0.076
#> GSM121351 2 0.2890 0.7637 0.000 0.836 0.000 0.004 0.160
#> GSM121353 4 0.0579 0.8141 0.000 0.008 0.000 0.984 0.008
#> GSM120758 4 0.5840 0.3284 0.000 0.416 0.000 0.488 0.096
#> GSM120771 2 0.4164 0.6620 0.000 0.784 0.000 0.120 0.096
#> GSM120772 4 0.5977 0.4776 0.000 0.332 0.000 0.540 0.128
#> GSM120773 4 0.1493 0.8237 0.000 0.024 0.000 0.948 0.028
#> GSM120774 4 0.4493 0.7618 0.000 0.108 0.000 0.756 0.136
#> GSM120783 4 0.1281 0.8195 0.000 0.012 0.000 0.956 0.032
#> GSM120787 4 0.5162 0.7123 0.000 0.160 0.000 0.692 0.148
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM120719 3 0.4305 0.7865 0.116 0.000 0.772 0.000 0.064 0.048
#> GSM120720 3 0.4190 0.7949 0.100 0.000 0.784 0.000 0.064 0.052
#> GSM120765 2 0.3810 0.5512 0.000 0.708 0.004 0.004 0.008 0.276
#> GSM120767 2 0.4227 0.4281 0.000 0.632 0.004 0.020 0.000 0.344
#> GSM120784 2 0.4053 0.5534 0.000 0.700 0.004 0.004 0.020 0.272
#> GSM121400 3 0.3660 0.6523 0.000 0.000 0.780 0.000 0.160 0.060
#> GSM121401 3 0.1444 0.7931 0.000 0.000 0.928 0.000 0.072 0.000
#> GSM121402 2 0.4783 0.4800 0.000 0.728 0.032 0.020 0.040 0.180
#> GSM121403 3 0.6257 0.3444 0.000 0.076 0.576 0.000 0.188 0.160
#> GSM121404 2 0.5434 0.4512 0.000 0.572 0.036 0.008 0.040 0.344
#> GSM121405 3 0.1444 0.7931 0.000 0.000 0.928 0.000 0.072 0.000
#> GSM121406 2 0.1674 0.6163 0.000 0.924 0.004 0.004 0.000 0.068
#> GSM121408 2 0.3130 0.6082 0.000 0.808 0.004 0.008 0.004 0.176
#> GSM121409 3 0.4282 0.5813 0.000 0.004 0.732 0.000 0.180 0.084
#> GSM121410 3 0.4051 0.6208 0.000 0.004 0.756 0.000 0.164 0.076
#> GSM121412 2 0.0881 0.6094 0.000 0.972 0.012 0.000 0.008 0.008
#> GSM121413 2 0.1180 0.6057 0.000 0.960 0.012 0.000 0.016 0.012
#> GSM121414 2 0.0881 0.6094 0.000 0.972 0.012 0.000 0.008 0.008
#> GSM121415 2 0.3925 0.5621 0.000 0.716 0.008 0.004 0.012 0.260
#> GSM121416 2 0.5175 0.4346 0.000 0.596 0.008 0.056 0.012 0.328
#> GSM120591 3 0.3947 0.7935 0.064 0.000 0.804 0.000 0.080 0.052
#> GSM120594 3 0.4190 0.7949 0.100 0.000 0.784 0.000 0.064 0.052
#> GSM120718 3 0.4305 0.7865 0.116 0.000 0.772 0.000 0.064 0.048
#> GSM121205 1 0.0000 0.9753 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.9753 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121207 1 0.0000 0.9753 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121208 1 0.3244 0.5871 0.732 0.000 0.268 0.000 0.000 0.000
#> GSM121209 1 0.0000 0.9753 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.9753 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.9753 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.9753 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.9753 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.9753 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121215 1 0.1155 0.9556 0.956 0.000 0.000 0.004 0.004 0.036
#> GSM121216 1 0.1155 0.9556 0.956 0.000 0.000 0.004 0.004 0.036
#> GSM121217 1 0.0000 0.9753 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.9753 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121234 1 0.1155 0.9556 0.956 0.000 0.000 0.004 0.004 0.036
#> GSM121243 1 0.1155 0.9556 0.956 0.000 0.000 0.004 0.004 0.036
#> GSM121245 1 0.0000 0.9753 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121246 3 0.4146 0.6044 0.288 0.000 0.680 0.000 0.004 0.028
#> GSM121247 1 0.0000 0.9753 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121248 1 0.0000 0.9753 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM120744 5 0.6046 0.4762 0.000 0.008 0.372 0.000 0.432 0.188
#> GSM120745 5 0.5774 0.4660 0.000 0.000 0.384 0.000 0.440 0.176
#> GSM120746 5 0.5761 0.4627 0.000 0.000 0.396 0.000 0.432 0.172
#> GSM120747 5 0.5761 0.4627 0.000 0.000 0.396 0.000 0.432 0.172
#> GSM120748 5 0.6064 0.4746 0.000 0.008 0.372 0.000 0.428 0.192
#> GSM120749 5 0.5781 0.4605 0.000 0.000 0.396 0.000 0.428 0.176
#> GSM120750 5 0.5761 0.4627 0.000 0.000 0.396 0.000 0.432 0.172
#> GSM120751 5 0.5761 0.4627 0.000 0.000 0.396 0.000 0.432 0.172
#> GSM120752 5 0.5774 0.4660 0.000 0.000 0.384 0.000 0.440 0.176
#> GSM121336 2 0.3037 0.5934 0.000 0.820 0.016 0.000 0.004 0.160
#> GSM121339 2 0.5153 0.4624 0.000 0.564 0.028 0.004 0.032 0.372
#> GSM121349 2 0.2454 0.5904 0.000 0.876 0.016 0.000 0.004 0.104
#> GSM121355 2 0.3037 0.5960 0.000 0.820 0.016 0.000 0.004 0.160
#> GSM120757 5 0.2744 0.6458 0.000 0.000 0.072 0.064 0.864 0.000
#> GSM120766 5 0.3008 0.6418 0.000 0.000 0.072 0.052 0.860 0.016
#> GSM120770 2 0.5928 0.3737 0.000 0.532 0.008 0.004 0.184 0.272
#> GSM120779 5 0.2801 0.6449 0.000 0.000 0.072 0.068 0.860 0.000
#> GSM120780 5 0.3766 0.6078 0.000 0.016 0.060 0.020 0.824 0.080
#> GSM121102 6 0.6963 -0.1509 0.000 0.292 0.048 0.004 0.268 0.388
#> GSM121203 5 0.5986 0.5228 0.000 0.012 0.288 0.000 0.508 0.192
#> GSM121204 5 0.3254 0.6495 0.000 0.000 0.072 0.052 0.848 0.028
#> GSM121330 3 0.1444 0.8338 0.072 0.000 0.928 0.000 0.000 0.000
#> GSM121335 3 0.1814 0.8271 0.100 0.000 0.900 0.000 0.000 0.000
#> GSM121337 2 0.7198 0.2184 0.000 0.440 0.020 0.072 0.168 0.300
#> GSM121338 2 0.7283 0.1166 0.000 0.364 0.096 0.008 0.176 0.356
#> GSM121341 3 0.1814 0.8271 0.100 0.000 0.900 0.000 0.000 0.000
#> GSM121342 3 0.1814 0.8271 0.100 0.000 0.900 0.000 0.000 0.000
#> GSM121343 2 0.7420 0.1079 0.000 0.364 0.100 0.012 0.184 0.340
#> GSM121344 3 0.1663 0.8324 0.088 0.000 0.912 0.000 0.000 0.000
#> GSM121346 3 0.1657 0.8086 0.016 0.000 0.928 0.000 0.056 0.000
#> GSM121347 6 0.8245 0.0402 0.000 0.256 0.032 0.252 0.196 0.264
#> GSM121348 5 0.4583 0.4963 0.000 0.072 0.036 0.040 0.780 0.072
#> GSM121350 3 0.1657 0.8086 0.016 0.000 0.928 0.000 0.056 0.000
#> GSM121352 3 0.1444 0.8338 0.072 0.000 0.928 0.000 0.000 0.000
#> GSM121354 3 0.1663 0.8324 0.088 0.000 0.912 0.000 0.000 0.000
#> GSM120753 4 0.5922 -0.2378 0.000 0.212 0.000 0.420 0.000 0.368
#> GSM120761 4 0.5411 0.0734 0.000 0.124 0.000 0.512 0.000 0.364
#> GSM120768 4 0.3136 0.6483 0.000 0.016 0.000 0.796 0.000 0.188
#> GSM120781 6 0.5935 0.4294 0.000 0.244 0.000 0.300 0.000 0.456
#> GSM120788 4 0.1644 0.6832 0.000 0.000 0.000 0.920 0.076 0.004
#> GSM120760 4 0.3348 0.6734 0.000 0.016 0.000 0.812 0.020 0.152
#> GSM120763 4 0.3385 0.6702 0.000 0.016 0.000 0.808 0.020 0.156
#> GSM120764 4 0.0858 0.7058 0.000 0.004 0.000 0.968 0.028 0.000
#> GSM120777 4 0.2320 0.6428 0.000 0.000 0.000 0.864 0.132 0.004
#> GSM120786 4 0.0858 0.7058 0.000 0.004 0.000 0.968 0.028 0.000
#> GSM121329 3 0.3330 0.7868 0.032 0.000 0.848 0.040 0.076 0.004
#> GSM121331 5 0.2801 0.6449 0.000 0.000 0.072 0.068 0.860 0.000
#> GSM121333 5 0.2801 0.6449 0.000 0.000 0.072 0.068 0.860 0.000
#> GSM121345 5 0.3307 0.6150 0.000 0.000 0.072 0.108 0.820 0.000
#> GSM121356 5 0.2801 0.6449 0.000 0.000 0.072 0.068 0.860 0.000
#> GSM120754 4 0.1327 0.6928 0.000 0.000 0.000 0.936 0.064 0.000
#> GSM120759 2 0.5027 0.4391 0.000 0.692 0.052 0.004 0.048 0.204
#> GSM120762 6 0.5870 0.4307 0.000 0.328 0.000 0.212 0.000 0.460
#> GSM120775 4 0.1471 0.6910 0.000 0.000 0.000 0.932 0.064 0.004
#> GSM120776 5 0.4751 0.2804 0.000 0.000 0.008 0.372 0.580 0.040
#> GSM120782 4 0.3858 0.5603 0.000 0.004 0.000 0.780 0.084 0.132
#> GSM120789 2 0.3782 0.5367 0.000 0.804 0.012 0.040 0.012 0.132
#> GSM120790 2 0.5233 0.4248 0.000 0.688 0.052 0.004 0.076 0.180
#> GSM120791 4 0.2658 0.6916 0.000 0.016 0.000 0.864 0.008 0.112
#> GSM120755 6 0.5830 0.4646 0.000 0.284 0.000 0.228 0.000 0.488
#> GSM120756 4 0.1753 0.6781 0.000 0.000 0.000 0.912 0.084 0.004
#> GSM120769 4 0.5855 -0.2310 0.000 0.192 0.000 0.408 0.000 0.400
#> GSM120778 4 0.4368 0.5201 0.000 0.020 0.000 0.640 0.012 0.328
#> GSM120792 4 0.2384 0.6849 0.000 0.032 0.000 0.884 0.000 0.084
#> GSM121332 2 0.4275 0.5729 0.000 0.728 0.012 0.040 0.004 0.216
#> GSM121334 4 0.5337 0.1514 0.000 0.116 0.000 0.524 0.000 0.360
#> GSM121340 4 0.2237 0.6882 0.000 0.004 0.000 0.896 0.020 0.080
#> GSM121351 2 0.2763 0.5715 0.000 0.876 0.028 0.000 0.024 0.072
#> GSM121353 4 0.0922 0.7058 0.000 0.004 0.000 0.968 0.024 0.004
#> GSM120758 6 0.5673 0.2191 0.000 0.156 0.000 0.396 0.000 0.448
#> GSM120771 2 0.5765 0.0175 0.000 0.440 0.004 0.104 0.012 0.440
#> GSM120772 4 0.5361 -0.0563 0.000 0.108 0.000 0.448 0.000 0.444
#> GSM120773 4 0.1226 0.7054 0.000 0.004 0.000 0.952 0.004 0.040
#> GSM120774 4 0.4393 0.5316 0.000 0.016 0.004 0.648 0.012 0.320
#> GSM120783 4 0.0922 0.7068 0.000 0.004 0.000 0.968 0.004 0.024
#> GSM120787 4 0.4923 0.4177 0.000 0.036 0.004 0.572 0.012 0.376
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> CV:kmeans 109 4.67e-09 2
#> CV:kmeans 107 6.87e-18 3
#> CV:kmeans 92 4.33e-26 4
#> CV:kmeans 92 1.39e-25 5
#> CV:kmeans 83 4.51e-22 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 21512 rows and 119 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 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 0.865 0.926 0.968 0.5011 0.497 0.497
#> 3 3 0.827 0.872 0.935 0.2806 0.841 0.688
#> 4 4 0.654 0.637 0.809 0.1340 0.862 0.647
#> 5 5 0.628 0.617 0.780 0.0655 0.923 0.732
#> 6 6 0.672 0.667 0.779 0.0461 0.963 0.837
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
#> GSM120719 1 0.000 0.954 1.000 0.000
#> GSM120720 1 0.000 0.954 1.000 0.000
#> GSM120765 2 0.000 0.977 0.000 1.000
#> GSM120767 2 0.000 0.977 0.000 1.000
#> GSM120784 2 0.000 0.977 0.000 1.000
#> GSM121400 1 0.000 0.954 1.000 0.000
#> GSM121401 1 0.000 0.954 1.000 0.000
#> GSM121402 2 0.000 0.977 0.000 1.000
#> GSM121403 1 0.855 0.644 0.720 0.280
#> GSM121404 2 0.000 0.977 0.000 1.000
#> GSM121405 1 0.000 0.954 1.000 0.000
#> GSM121406 2 0.000 0.977 0.000 1.000
#> GSM121408 2 0.000 0.977 0.000 1.000
#> GSM121409 1 0.000 0.954 1.000 0.000
#> GSM121410 1 0.000 0.954 1.000 0.000
#> GSM121412 2 0.000 0.977 0.000 1.000
#> GSM121413 2 0.000 0.977 0.000 1.000
#> GSM121414 2 0.000 0.977 0.000 1.000
#> GSM121415 2 0.000 0.977 0.000 1.000
#> GSM121416 2 0.000 0.977 0.000 1.000
#> GSM120591 1 0.000 0.954 1.000 0.000
#> GSM120594 1 0.000 0.954 1.000 0.000
#> GSM120718 1 0.000 0.954 1.000 0.000
#> GSM121205 1 0.000 0.954 1.000 0.000
#> GSM121206 1 0.000 0.954 1.000 0.000
#> GSM121207 1 0.000 0.954 1.000 0.000
#> GSM121208 1 0.000 0.954 1.000 0.000
#> GSM121209 1 0.000 0.954 1.000 0.000
#> GSM121210 1 0.000 0.954 1.000 0.000
#> GSM121211 1 0.000 0.954 1.000 0.000
#> GSM121212 1 0.000 0.954 1.000 0.000
#> GSM121213 1 0.000 0.954 1.000 0.000
#> GSM121214 1 0.000 0.954 1.000 0.000
#> GSM121215 1 0.000 0.954 1.000 0.000
#> GSM121216 1 0.000 0.954 1.000 0.000
#> GSM121217 1 0.000 0.954 1.000 0.000
#> GSM121218 1 0.000 0.954 1.000 0.000
#> GSM121234 1 0.000 0.954 1.000 0.000
#> GSM121243 1 0.000 0.954 1.000 0.000
#> GSM121245 1 0.000 0.954 1.000 0.000
#> GSM121246 1 0.000 0.954 1.000 0.000
#> GSM121247 1 0.000 0.954 1.000 0.000
#> GSM121248 1 0.000 0.954 1.000 0.000
#> GSM120744 2 0.978 0.242 0.412 0.588
#> GSM120745 1 0.000 0.954 1.000 0.000
#> GSM120746 1 0.714 0.763 0.804 0.196
#> GSM120747 1 0.808 0.694 0.752 0.248
#> GSM120748 2 0.814 0.640 0.252 0.748
#> GSM120749 1 0.000 0.954 1.000 0.000
#> GSM120750 1 0.904 0.571 0.680 0.320
#> GSM120751 1 0.781 0.717 0.768 0.232
#> GSM120752 1 0.000 0.954 1.000 0.000
#> GSM121336 2 0.000 0.977 0.000 1.000
#> GSM121339 2 0.000 0.977 0.000 1.000
#> GSM121349 2 0.000 0.977 0.000 1.000
#> GSM121355 2 0.000 0.977 0.000 1.000
#> GSM120757 1 0.850 0.651 0.724 0.276
#> GSM120766 1 0.992 0.247 0.552 0.448
#> GSM120770 2 0.000 0.977 0.000 1.000
#> GSM120779 1 0.000 0.954 1.000 0.000
#> GSM120780 2 0.697 0.749 0.188 0.812
#> GSM121102 2 0.000 0.977 0.000 1.000
#> GSM121203 1 0.909 0.563 0.676 0.324
#> GSM121204 1 0.000 0.954 1.000 0.000
#> GSM121330 1 0.000 0.954 1.000 0.000
#> GSM121335 1 0.000 0.954 1.000 0.000
#> GSM121337 2 0.000 0.977 0.000 1.000
#> GSM121338 2 0.000 0.977 0.000 1.000
#> GSM121341 1 0.000 0.954 1.000 0.000
#> GSM121342 1 0.000 0.954 1.000 0.000
#> GSM121343 2 0.000 0.977 0.000 1.000
#> GSM121344 1 0.000 0.954 1.000 0.000
#> GSM121346 1 0.000 0.954 1.000 0.000
#> GSM121347 2 0.000 0.977 0.000 1.000
#> GSM121348 2 0.000 0.977 0.000 1.000
#> GSM121350 1 0.000 0.954 1.000 0.000
#> GSM121352 1 0.000 0.954 1.000 0.000
#> GSM121354 1 0.000 0.954 1.000 0.000
#> GSM120753 2 0.000 0.977 0.000 1.000
#> GSM120761 2 0.000 0.977 0.000 1.000
#> GSM120768 2 0.000 0.977 0.000 1.000
#> GSM120781 2 0.000 0.977 0.000 1.000
#> GSM120788 2 0.000 0.977 0.000 1.000
#> GSM120760 2 0.000 0.977 0.000 1.000
#> GSM120763 2 0.000 0.977 0.000 1.000
#> GSM120764 2 0.000 0.977 0.000 1.000
#> GSM120777 2 0.000 0.977 0.000 1.000
#> GSM120786 2 0.000 0.977 0.000 1.000
#> GSM121329 1 0.000 0.954 1.000 0.000
#> GSM121331 1 0.605 0.819 0.852 0.148
#> GSM121333 1 0.000 0.954 1.000 0.000
#> GSM121345 1 0.000 0.954 1.000 0.000
#> GSM121356 1 0.000 0.954 1.000 0.000
#> GSM120754 2 0.000 0.977 0.000 1.000
#> GSM120759 2 0.000 0.977 0.000 1.000
#> GSM120762 2 0.000 0.977 0.000 1.000
#> GSM120775 2 0.000 0.977 0.000 1.000
#> GSM120776 2 0.000 0.977 0.000 1.000
#> GSM120782 2 0.000 0.977 0.000 1.000
#> GSM120789 2 0.000 0.977 0.000 1.000
#> GSM120790 2 0.000 0.977 0.000 1.000
#> GSM120791 2 0.000 0.977 0.000 1.000
#> GSM120755 2 0.000 0.977 0.000 1.000
#> GSM120756 2 0.866 0.588 0.288 0.712
#> GSM120769 2 0.000 0.977 0.000 1.000
#> GSM120778 2 0.000 0.977 0.000 1.000
#> GSM120792 2 0.000 0.977 0.000 1.000
#> GSM121332 2 0.000 0.977 0.000 1.000
#> GSM121334 2 0.000 0.977 0.000 1.000
#> GSM121340 2 0.000 0.977 0.000 1.000
#> GSM121351 2 0.000 0.977 0.000 1.000
#> GSM121353 2 0.644 0.789 0.164 0.836
#> GSM120758 2 0.000 0.977 0.000 1.000
#> GSM120771 2 0.000 0.977 0.000 1.000
#> GSM120772 2 0.000 0.977 0.000 1.000
#> GSM120773 2 0.000 0.977 0.000 1.000
#> GSM120774 2 0.000 0.977 0.000 1.000
#> GSM120783 2 0.000 0.977 0.000 1.000
#> GSM120787 2 0.000 0.977 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM120719 1 0.0000 0.948 1.000 0.000 0.000
#> GSM120720 1 0.1163 0.938 0.972 0.000 0.028
#> GSM120765 2 0.0000 0.942 0.000 1.000 0.000
#> GSM120767 2 0.0000 0.942 0.000 1.000 0.000
#> GSM120784 2 0.0000 0.942 0.000 1.000 0.000
#> GSM121400 1 0.5621 0.627 0.692 0.000 0.308
#> GSM121401 1 0.3551 0.865 0.868 0.000 0.132
#> GSM121402 2 0.0237 0.942 0.000 0.996 0.004
#> GSM121403 3 0.8538 0.194 0.380 0.100 0.520
#> GSM121404 2 0.2448 0.885 0.000 0.924 0.076
#> GSM121405 1 0.3686 0.857 0.860 0.000 0.140
#> GSM121406 2 0.0000 0.942 0.000 1.000 0.000
#> GSM121408 2 0.0000 0.942 0.000 1.000 0.000
#> GSM121409 1 0.6235 0.327 0.564 0.000 0.436
#> GSM121410 1 0.5216 0.709 0.740 0.000 0.260
#> GSM121412 2 0.0000 0.942 0.000 1.000 0.000
#> GSM121413 2 0.0000 0.942 0.000 1.000 0.000
#> GSM121414 2 0.0000 0.942 0.000 1.000 0.000
#> GSM121415 2 0.0000 0.942 0.000 1.000 0.000
#> GSM121416 2 0.0000 0.942 0.000 1.000 0.000
#> GSM120591 1 0.1860 0.927 0.948 0.000 0.052
#> GSM120594 1 0.1529 0.933 0.960 0.000 0.040
#> GSM120718 1 0.0237 0.947 0.996 0.000 0.004
#> GSM121205 1 0.0000 0.948 1.000 0.000 0.000
#> GSM121206 1 0.0000 0.948 1.000 0.000 0.000
#> GSM121207 1 0.0000 0.948 1.000 0.000 0.000
#> GSM121208 1 0.0000 0.948 1.000 0.000 0.000
#> GSM121209 1 0.0000 0.948 1.000 0.000 0.000
#> GSM121210 1 0.0000 0.948 1.000 0.000 0.000
#> GSM121211 1 0.0000 0.948 1.000 0.000 0.000
#> GSM121212 1 0.0000 0.948 1.000 0.000 0.000
#> GSM121213 1 0.0000 0.948 1.000 0.000 0.000
#> GSM121214 1 0.0000 0.948 1.000 0.000 0.000
#> GSM121215 1 0.0000 0.948 1.000 0.000 0.000
#> GSM121216 1 0.0000 0.948 1.000 0.000 0.000
#> GSM121217 1 0.0000 0.948 1.000 0.000 0.000
#> GSM121218 1 0.0000 0.948 1.000 0.000 0.000
#> GSM121234 1 0.0000 0.948 1.000 0.000 0.000
#> GSM121243 1 0.0000 0.948 1.000 0.000 0.000
#> GSM121245 1 0.0000 0.948 1.000 0.000 0.000
#> GSM121246 1 0.0000 0.948 1.000 0.000 0.000
#> GSM121247 1 0.0000 0.948 1.000 0.000 0.000
#> GSM121248 1 0.0000 0.948 1.000 0.000 0.000
#> GSM120744 3 0.1289 0.876 0.000 0.032 0.968
#> GSM120745 3 0.1031 0.868 0.024 0.000 0.976
#> GSM120746 3 0.1031 0.876 0.000 0.024 0.976
#> GSM120747 3 0.1031 0.876 0.000 0.024 0.976
#> GSM120748 3 0.1163 0.876 0.000 0.028 0.972
#> GSM120749 3 0.1129 0.871 0.020 0.004 0.976
#> GSM120750 3 0.1031 0.876 0.000 0.024 0.976
#> GSM120751 3 0.0892 0.876 0.000 0.020 0.980
#> GSM120752 3 0.1129 0.871 0.020 0.004 0.976
#> GSM121336 2 0.0000 0.942 0.000 1.000 0.000
#> GSM121339 2 0.2448 0.886 0.000 0.924 0.076
#> GSM121349 2 0.0000 0.942 0.000 1.000 0.000
#> GSM121355 2 0.0000 0.942 0.000 1.000 0.000
#> GSM120757 3 0.3337 0.861 0.032 0.060 0.908
#> GSM120766 3 0.2066 0.864 0.000 0.060 0.940
#> GSM120770 2 0.4178 0.775 0.000 0.828 0.172
#> GSM120779 3 0.3482 0.835 0.128 0.000 0.872
#> GSM120780 3 0.2537 0.862 0.000 0.080 0.920
#> GSM121102 3 0.6244 0.241 0.000 0.440 0.560
#> GSM121203 3 0.1289 0.875 0.000 0.032 0.968
#> GSM121204 3 0.4062 0.809 0.164 0.000 0.836
#> GSM121330 1 0.2878 0.897 0.904 0.000 0.096
#> GSM121335 1 0.0592 0.944 0.988 0.000 0.012
#> GSM121337 2 0.0424 0.941 0.000 0.992 0.008
#> GSM121338 2 0.5098 0.671 0.000 0.752 0.248
#> GSM121341 1 0.0424 0.946 0.992 0.000 0.008
#> GSM121342 1 0.0892 0.942 0.980 0.000 0.020
#> GSM121343 2 0.5363 0.620 0.000 0.724 0.276
#> GSM121344 1 0.1860 0.927 0.948 0.000 0.052
#> GSM121346 1 0.2959 0.894 0.900 0.000 0.100
#> GSM121347 2 0.1411 0.930 0.000 0.964 0.036
#> GSM121348 3 0.3941 0.797 0.000 0.156 0.844
#> GSM121350 1 0.3116 0.887 0.892 0.000 0.108
#> GSM121352 1 0.2796 0.900 0.908 0.000 0.092
#> GSM121354 1 0.2448 0.911 0.924 0.000 0.076
#> GSM120753 2 0.0000 0.942 0.000 1.000 0.000
#> GSM120761 2 0.0237 0.942 0.000 0.996 0.004
#> GSM120768 2 0.0747 0.938 0.000 0.984 0.016
#> GSM120781 2 0.0000 0.942 0.000 1.000 0.000
#> GSM120788 2 0.6180 0.317 0.000 0.584 0.416
#> GSM120760 2 0.1529 0.927 0.000 0.960 0.040
#> GSM120763 2 0.1163 0.933 0.000 0.972 0.028
#> GSM120764 2 0.1860 0.919 0.000 0.948 0.052
#> GSM120777 2 0.6189 0.448 0.004 0.632 0.364
#> GSM120786 2 0.1411 0.929 0.000 0.964 0.036
#> GSM121329 1 0.0000 0.948 1.000 0.000 0.000
#> GSM121331 3 0.3573 0.839 0.120 0.004 0.876
#> GSM121333 3 0.3619 0.829 0.136 0.000 0.864
#> GSM121345 3 0.5835 0.555 0.340 0.000 0.660
#> GSM121356 3 0.3482 0.835 0.128 0.000 0.872
#> GSM120754 2 0.2537 0.897 0.000 0.920 0.080
#> GSM120759 2 0.0000 0.942 0.000 1.000 0.000
#> GSM120762 2 0.0000 0.942 0.000 1.000 0.000
#> GSM120775 2 0.4235 0.793 0.000 0.824 0.176
#> GSM120776 3 0.3910 0.836 0.020 0.104 0.876
#> GSM120782 2 0.3879 0.819 0.000 0.848 0.152
#> GSM120789 2 0.0000 0.942 0.000 1.000 0.000
#> GSM120790 2 0.0000 0.942 0.000 1.000 0.000
#> GSM120791 2 0.0892 0.936 0.000 0.980 0.020
#> GSM120755 2 0.0000 0.942 0.000 1.000 0.000
#> GSM120756 2 0.9054 0.139 0.144 0.496 0.360
#> GSM120769 2 0.0000 0.942 0.000 1.000 0.000
#> GSM120778 2 0.0747 0.938 0.000 0.984 0.016
#> GSM120792 2 0.0237 0.942 0.000 0.996 0.004
#> GSM121332 2 0.0000 0.942 0.000 1.000 0.000
#> GSM121334 2 0.0237 0.942 0.000 0.996 0.004
#> GSM121340 2 0.1163 0.932 0.000 0.972 0.028
#> GSM121351 2 0.0000 0.942 0.000 1.000 0.000
#> GSM121353 2 0.3967 0.864 0.072 0.884 0.044
#> GSM120758 2 0.0000 0.942 0.000 1.000 0.000
#> GSM120771 2 0.0000 0.942 0.000 1.000 0.000
#> GSM120772 2 0.0000 0.942 0.000 1.000 0.000
#> GSM120773 2 0.1031 0.934 0.000 0.976 0.024
#> GSM120774 2 0.0592 0.939 0.000 0.988 0.012
#> GSM120783 2 0.1411 0.929 0.000 0.964 0.036
#> GSM120787 2 0.0592 0.939 0.000 0.988 0.012
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM120719 1 0.0336 0.8743 0.992 0.000 0.008 0.000
#> GSM120720 1 0.2840 0.8500 0.900 0.000 0.056 0.044
#> GSM120765 2 0.0000 0.7369 0.000 1.000 0.000 0.000
#> GSM120767 2 0.2081 0.7019 0.000 0.916 0.000 0.084
#> GSM120784 2 0.0188 0.7368 0.000 0.996 0.004 0.000
#> GSM121400 1 0.7441 0.3934 0.468 0.004 0.376 0.152
#> GSM121401 1 0.7054 0.5249 0.536 0.000 0.320 0.144
#> GSM121402 2 0.0469 0.7373 0.000 0.988 0.000 0.012
#> GSM121403 2 0.7863 -0.0295 0.020 0.432 0.400 0.148
#> GSM121404 2 0.3647 0.6277 0.000 0.852 0.108 0.040
#> GSM121405 1 0.7378 0.4437 0.488 0.004 0.360 0.148
#> GSM121406 2 0.0000 0.7369 0.000 1.000 0.000 0.000
#> GSM121408 2 0.0707 0.7357 0.000 0.980 0.000 0.020
#> GSM121409 3 0.7554 -0.0715 0.348 0.012 0.496 0.144
#> GSM121410 1 0.8088 0.3464 0.444 0.032 0.376 0.148
#> GSM121412 2 0.0336 0.7348 0.000 0.992 0.000 0.008
#> GSM121413 2 0.0188 0.7361 0.000 0.996 0.000 0.004
#> GSM121414 2 0.0336 0.7348 0.000 0.992 0.000 0.008
#> GSM121415 2 0.0188 0.7361 0.000 0.996 0.000 0.004
#> GSM121416 2 0.1118 0.7312 0.000 0.964 0.000 0.036
#> GSM120591 1 0.4181 0.8126 0.820 0.000 0.128 0.052
#> GSM120594 1 0.2996 0.8478 0.892 0.000 0.064 0.044
#> GSM120718 1 0.1724 0.8643 0.948 0.000 0.032 0.020
#> GSM121205 1 0.0000 0.8753 1.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.8753 1.000 0.000 0.000 0.000
#> GSM121207 1 0.0000 0.8753 1.000 0.000 0.000 0.000
#> GSM121208 1 0.0000 0.8753 1.000 0.000 0.000 0.000
#> GSM121209 1 0.0000 0.8753 1.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.8753 1.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.8753 1.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.8753 1.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.8753 1.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.8753 1.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.8753 1.000 0.000 0.000 0.000
#> GSM121216 1 0.0000 0.8753 1.000 0.000 0.000 0.000
#> GSM121217 1 0.0000 0.8753 1.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.8753 1.000 0.000 0.000 0.000
#> GSM121234 1 0.0000 0.8753 1.000 0.000 0.000 0.000
#> GSM121243 1 0.0000 0.8753 1.000 0.000 0.000 0.000
#> GSM121245 1 0.0000 0.8753 1.000 0.000 0.000 0.000
#> GSM121246 1 0.0657 0.8732 0.984 0.000 0.004 0.012
#> GSM121247 1 0.0188 0.8737 0.996 0.000 0.000 0.004
#> GSM121248 1 0.0000 0.8753 1.000 0.000 0.000 0.000
#> GSM120744 3 0.1151 0.7801 0.000 0.024 0.968 0.008
#> GSM120745 3 0.0592 0.7877 0.000 0.000 0.984 0.016
#> GSM120746 3 0.0376 0.7865 0.000 0.004 0.992 0.004
#> GSM120747 3 0.0524 0.7814 0.000 0.004 0.988 0.008
#> GSM120748 3 0.1042 0.7816 0.000 0.020 0.972 0.008
#> GSM120749 3 0.0336 0.7846 0.000 0.000 0.992 0.008
#> GSM120750 3 0.0376 0.7865 0.000 0.004 0.992 0.004
#> GSM120751 3 0.0524 0.7875 0.000 0.004 0.988 0.008
#> GSM120752 3 0.0469 0.7873 0.000 0.000 0.988 0.012
#> GSM121336 2 0.0188 0.7375 0.000 0.996 0.000 0.004
#> GSM121339 2 0.4152 0.5785 0.000 0.808 0.160 0.032
#> GSM121349 2 0.0336 0.7372 0.000 0.992 0.000 0.008
#> GSM121355 2 0.0188 0.7375 0.000 0.996 0.000 0.004
#> GSM120757 3 0.5558 0.6380 0.028 0.000 0.608 0.364
#> GSM120766 3 0.5047 0.6504 0.004 0.004 0.636 0.356
#> GSM120770 2 0.2376 0.6888 0.000 0.916 0.068 0.016
#> GSM120779 3 0.6121 0.6412 0.060 0.000 0.588 0.352
#> GSM120780 3 0.5304 0.6884 0.000 0.104 0.748 0.148
#> GSM121102 2 0.5203 0.3708 0.000 0.636 0.348 0.016
#> GSM121203 3 0.1724 0.7756 0.000 0.032 0.948 0.020
#> GSM121204 3 0.7065 0.6033 0.216 0.000 0.572 0.212
#> GSM121330 1 0.6025 0.7212 0.688 0.000 0.172 0.140
#> GSM121335 1 0.4015 0.8233 0.832 0.000 0.052 0.116
#> GSM121337 2 0.1970 0.7243 0.000 0.932 0.008 0.060
#> GSM121338 2 0.6323 0.3709 0.000 0.628 0.272 0.100
#> GSM121341 1 0.4046 0.8215 0.828 0.000 0.048 0.124
#> GSM121342 1 0.4127 0.8193 0.824 0.000 0.052 0.124
#> GSM121343 2 0.6215 0.4163 0.000 0.664 0.208 0.128
#> GSM121344 1 0.5116 0.7818 0.764 0.000 0.108 0.128
#> GSM121346 1 0.6251 0.6970 0.664 0.000 0.196 0.140
#> GSM121347 2 0.4542 0.5308 0.000 0.752 0.020 0.228
#> GSM121348 2 0.7918 -0.3187 0.000 0.352 0.332 0.316
#> GSM121350 1 0.6621 0.6385 0.616 0.000 0.244 0.140
#> GSM121352 1 0.6065 0.7163 0.684 0.000 0.176 0.140
#> GSM121354 1 0.5630 0.7520 0.724 0.000 0.136 0.140
#> GSM120753 2 0.4103 0.4766 0.000 0.744 0.000 0.256
#> GSM120761 2 0.4605 0.2637 0.000 0.664 0.000 0.336
#> GSM120768 4 0.4999 0.3569 0.000 0.492 0.000 0.508
#> GSM120781 2 0.4040 0.4948 0.000 0.752 0.000 0.248
#> GSM120788 4 0.3706 0.6205 0.000 0.112 0.040 0.848
#> GSM120760 4 0.4925 0.5268 0.000 0.428 0.000 0.572
#> GSM120763 4 0.4776 0.6122 0.000 0.376 0.000 0.624
#> GSM120764 4 0.3907 0.6801 0.000 0.232 0.000 0.768
#> GSM120777 4 0.3653 0.6386 0.000 0.128 0.028 0.844
#> GSM120786 4 0.4454 0.6709 0.000 0.308 0.000 0.692
#> GSM121329 1 0.1256 0.8668 0.964 0.000 0.008 0.028
#> GSM121331 3 0.6110 0.6288 0.056 0.000 0.576 0.368
#> GSM121333 3 0.6163 0.6300 0.060 0.000 0.576 0.364
#> GSM121345 4 0.7754 -0.4189 0.244 0.000 0.336 0.420
#> GSM121356 3 0.6263 0.6350 0.068 0.000 0.576 0.356
#> GSM120754 4 0.4477 0.6663 0.000 0.312 0.000 0.688
#> GSM120759 2 0.0188 0.7373 0.000 0.996 0.000 0.004
#> GSM120762 2 0.3528 0.5862 0.000 0.808 0.000 0.192
#> GSM120775 4 0.3937 0.6691 0.000 0.188 0.012 0.800
#> GSM120776 4 0.5016 -0.2538 0.004 0.000 0.396 0.600
#> GSM120782 4 0.7119 0.5159 0.000 0.352 0.140 0.508
#> GSM120789 2 0.2281 0.6975 0.000 0.904 0.000 0.096
#> GSM120790 2 0.1474 0.7250 0.000 0.948 0.000 0.052
#> GSM120791 4 0.4998 0.3722 0.000 0.488 0.000 0.512
#> GSM120755 2 0.3123 0.6332 0.000 0.844 0.000 0.156
#> GSM120756 4 0.4153 0.5749 0.084 0.076 0.004 0.836
#> GSM120769 2 0.4477 0.3426 0.000 0.688 0.000 0.312
#> GSM120778 2 0.4989 -0.2885 0.000 0.528 0.000 0.472
#> GSM120792 4 0.5000 0.3346 0.000 0.496 0.000 0.504
#> GSM121332 2 0.1792 0.7171 0.000 0.932 0.000 0.068
#> GSM121334 2 0.4250 0.4319 0.000 0.724 0.000 0.276
#> GSM121340 4 0.4431 0.6718 0.000 0.304 0.000 0.696
#> GSM121351 2 0.0000 0.7369 0.000 1.000 0.000 0.000
#> GSM121353 4 0.4671 0.6783 0.028 0.220 0.000 0.752
#> GSM120758 2 0.3764 0.5494 0.000 0.784 0.000 0.216
#> GSM120771 2 0.1118 0.7312 0.000 0.964 0.000 0.036
#> GSM120772 2 0.4250 0.4367 0.000 0.724 0.000 0.276
#> GSM120773 4 0.4843 0.5753 0.000 0.396 0.000 0.604
#> GSM120774 2 0.4804 0.0758 0.000 0.616 0.000 0.384
#> GSM120783 4 0.4605 0.6509 0.000 0.336 0.000 0.664
#> GSM120787 2 0.4817 0.0616 0.000 0.612 0.000 0.388
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM120719 1 0.1830 0.8195 0.932 0.000 0.040 0.000 0.028
#> GSM120720 1 0.4754 0.4602 0.684 0.000 0.264 0.000 0.052
#> GSM120765 2 0.0566 0.7603 0.000 0.984 0.012 0.004 0.000
#> GSM120767 2 0.1731 0.7540 0.000 0.932 0.004 0.060 0.004
#> GSM120784 2 0.0740 0.7603 0.000 0.980 0.008 0.004 0.008
#> GSM121400 3 0.3134 0.6983 0.120 0.000 0.848 0.000 0.032
#> GSM121401 3 0.3958 0.7529 0.176 0.000 0.780 0.000 0.044
#> GSM121402 2 0.1282 0.7608 0.000 0.952 0.004 0.044 0.000
#> GSM121403 3 0.4739 0.4052 0.004 0.160 0.756 0.012 0.068
#> GSM121404 2 0.4346 0.6551 0.000 0.800 0.096 0.028 0.076
#> GSM121405 3 0.3960 0.7251 0.140 0.004 0.800 0.000 0.056
#> GSM121406 2 0.0162 0.7602 0.000 0.996 0.000 0.004 0.000
#> GSM121408 2 0.1597 0.7625 0.000 0.940 0.012 0.048 0.000
#> GSM121409 3 0.5418 0.5325 0.120 0.008 0.704 0.008 0.160
#> GSM121410 3 0.4040 0.6632 0.112 0.016 0.820 0.008 0.044
#> GSM121412 2 0.0798 0.7569 0.000 0.976 0.016 0.008 0.000
#> GSM121413 2 0.0992 0.7551 0.000 0.968 0.024 0.008 0.000
#> GSM121414 2 0.0898 0.7558 0.000 0.972 0.020 0.008 0.000
#> GSM121415 2 0.0451 0.7602 0.000 0.988 0.008 0.004 0.000
#> GSM121416 2 0.2006 0.7551 0.000 0.916 0.012 0.072 0.000
#> GSM120591 1 0.5868 0.2046 0.576 0.000 0.292 0.000 0.132
#> GSM120594 1 0.5040 0.4131 0.660 0.000 0.272 0.000 0.068
#> GSM120718 1 0.3910 0.6223 0.772 0.000 0.196 0.000 0.032
#> GSM121205 1 0.0000 0.8684 1.000 0.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.8684 1.000 0.000 0.000 0.000 0.000
#> GSM121207 1 0.0000 0.8684 1.000 0.000 0.000 0.000 0.000
#> GSM121208 1 0.0880 0.8462 0.968 0.000 0.032 0.000 0.000
#> GSM121209 1 0.0000 0.8684 1.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.8684 1.000 0.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.8684 1.000 0.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.8684 1.000 0.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.8684 1.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.8684 1.000 0.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.8684 1.000 0.000 0.000 0.000 0.000
#> GSM121216 1 0.0000 0.8684 1.000 0.000 0.000 0.000 0.000
#> GSM121217 1 0.0000 0.8684 1.000 0.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.8684 1.000 0.000 0.000 0.000 0.000
#> GSM121234 1 0.0000 0.8684 1.000 0.000 0.000 0.000 0.000
#> GSM121243 1 0.0000 0.8684 1.000 0.000 0.000 0.000 0.000
#> GSM121245 1 0.0000 0.8684 1.000 0.000 0.000 0.000 0.000
#> GSM121246 1 0.1608 0.8074 0.928 0.000 0.072 0.000 0.000
#> GSM121247 1 0.0404 0.8569 0.988 0.000 0.012 0.000 0.000
#> GSM121248 1 0.0000 0.8684 1.000 0.000 0.000 0.000 0.000
#> GSM120744 5 0.3565 0.5941 0.000 0.024 0.176 0.000 0.800
#> GSM120745 5 0.2966 0.6002 0.000 0.000 0.184 0.000 0.816
#> GSM120746 5 0.2966 0.5980 0.000 0.000 0.184 0.000 0.816
#> GSM120747 5 0.3242 0.5774 0.000 0.000 0.216 0.000 0.784
#> GSM120748 5 0.3910 0.5794 0.000 0.032 0.196 0.000 0.772
#> GSM120749 5 0.3210 0.5797 0.000 0.000 0.212 0.000 0.788
#> GSM120750 5 0.3039 0.5924 0.000 0.000 0.192 0.000 0.808
#> GSM120751 5 0.3039 0.5939 0.000 0.000 0.192 0.000 0.808
#> GSM120752 5 0.2929 0.5990 0.000 0.000 0.180 0.000 0.820
#> GSM121336 2 0.0324 0.7603 0.000 0.992 0.004 0.004 0.000
#> GSM121339 2 0.3956 0.6574 0.000 0.808 0.108 0.004 0.080
#> GSM121349 2 0.0324 0.7606 0.000 0.992 0.004 0.004 0.000
#> GSM121355 2 0.0162 0.7613 0.000 0.996 0.004 0.000 0.000
#> GSM120757 5 0.6408 0.5401 0.020 0.000 0.132 0.292 0.556
#> GSM120766 5 0.6215 0.5416 0.004 0.000 0.164 0.276 0.556
#> GSM120770 2 0.4249 0.6631 0.000 0.800 0.056 0.024 0.120
#> GSM120779 5 0.7537 0.5147 0.088 0.000 0.152 0.288 0.472
#> GSM120780 5 0.6511 0.5684 0.000 0.048 0.204 0.140 0.608
#> GSM121102 2 0.6024 0.2869 0.000 0.548 0.092 0.012 0.348
#> GSM121203 5 0.4196 0.5902 0.000 0.024 0.192 0.016 0.768
#> GSM121204 5 0.7352 0.3655 0.324 0.000 0.088 0.116 0.472
#> GSM121330 3 0.3969 0.7418 0.304 0.000 0.692 0.000 0.004
#> GSM121335 1 0.4302 -0.3100 0.520 0.000 0.480 0.000 0.000
#> GSM121337 2 0.5163 0.6205 0.000 0.740 0.084 0.136 0.040
#> GSM121338 2 0.6259 0.3748 0.000 0.560 0.304 0.016 0.120
#> GSM121341 3 0.4307 0.3125 0.500 0.000 0.500 0.000 0.000
#> GSM121342 1 0.4306 -0.3548 0.508 0.000 0.492 0.000 0.000
#> GSM121343 2 0.6338 0.2328 0.000 0.488 0.400 0.024 0.088
#> GSM121344 3 0.4126 0.6330 0.380 0.000 0.620 0.000 0.000
#> GSM121346 3 0.3796 0.7465 0.300 0.000 0.700 0.000 0.000
#> GSM121347 2 0.7030 0.2536 0.000 0.516 0.112 0.304 0.068
#> GSM121348 5 0.8480 0.3178 0.000 0.200 0.200 0.280 0.320
#> GSM121350 3 0.3586 0.7605 0.264 0.000 0.736 0.000 0.000
#> GSM121352 3 0.3895 0.7311 0.320 0.000 0.680 0.000 0.000
#> GSM121354 3 0.3966 0.7103 0.336 0.000 0.664 0.000 0.000
#> GSM120753 2 0.3969 0.4999 0.000 0.692 0.004 0.304 0.000
#> GSM120761 2 0.4403 0.4097 0.000 0.648 0.008 0.340 0.004
#> GSM120768 4 0.4238 0.5311 0.000 0.368 0.004 0.628 0.000
#> GSM120781 2 0.3579 0.6092 0.000 0.756 0.004 0.240 0.000
#> GSM120788 4 0.1982 0.6206 0.000 0.028 0.012 0.932 0.028
#> GSM120760 4 0.4387 0.6032 0.000 0.336 0.004 0.652 0.008
#> GSM120763 4 0.4081 0.6543 0.000 0.296 0.004 0.696 0.004
#> GSM120764 4 0.2597 0.7271 0.000 0.120 0.004 0.872 0.004
#> GSM120777 4 0.2770 0.5549 0.000 0.016 0.020 0.888 0.076
#> GSM120786 4 0.3492 0.7293 0.000 0.188 0.000 0.796 0.016
#> GSM121329 1 0.4119 0.6527 0.792 0.000 0.156 0.032 0.020
#> GSM121331 5 0.7928 0.4877 0.104 0.004 0.160 0.304 0.428
#> GSM121333 5 0.7875 0.4806 0.124 0.000 0.148 0.304 0.424
#> GSM121345 4 0.8402 -0.3671 0.244 0.000 0.164 0.348 0.244
#> GSM121356 5 0.7851 0.4924 0.108 0.000 0.168 0.296 0.428
#> GSM120754 4 0.4321 0.7088 0.000 0.152 0.024 0.784 0.040
#> GSM120759 2 0.1357 0.7620 0.000 0.948 0.004 0.048 0.000
#> GSM120762 2 0.3333 0.6463 0.000 0.788 0.004 0.208 0.000
#> GSM120775 4 0.2289 0.7032 0.000 0.080 0.004 0.904 0.012
#> GSM120776 5 0.5494 0.2527 0.000 0.004 0.052 0.460 0.484
#> GSM120782 4 0.7058 0.4312 0.000 0.256 0.024 0.476 0.244
#> GSM120789 2 0.2583 0.7233 0.000 0.864 0.004 0.132 0.000
#> GSM120790 2 0.3441 0.7169 0.000 0.852 0.020 0.092 0.036
#> GSM120791 4 0.4236 0.6070 0.000 0.328 0.004 0.664 0.004
#> GSM120755 2 0.3160 0.6683 0.000 0.808 0.004 0.188 0.000
#> GSM120756 4 0.2631 0.5961 0.036 0.012 0.004 0.904 0.044
#> GSM120769 2 0.4182 0.3794 0.000 0.644 0.004 0.352 0.000
#> GSM120778 4 0.4383 0.3913 0.000 0.424 0.004 0.572 0.000
#> GSM120792 4 0.4714 0.5265 0.000 0.372 0.004 0.608 0.016
#> GSM121332 2 0.2674 0.7238 0.000 0.856 0.004 0.140 0.000
#> GSM121334 2 0.4084 0.4456 0.000 0.668 0.004 0.328 0.000
#> GSM121340 4 0.3487 0.7214 0.000 0.212 0.000 0.780 0.008
#> GSM121351 2 0.0451 0.7607 0.000 0.988 0.008 0.004 0.000
#> GSM121353 4 0.3958 0.7240 0.040 0.140 0.004 0.808 0.008
#> GSM120758 2 0.3814 0.5568 0.000 0.720 0.004 0.276 0.000
#> GSM120771 2 0.2068 0.7460 0.000 0.904 0.004 0.092 0.000
#> GSM120772 2 0.3949 0.5096 0.000 0.696 0.004 0.300 0.000
#> GSM120773 4 0.3844 0.6885 0.000 0.256 0.004 0.736 0.004
#> GSM120774 2 0.4567 0.0303 0.000 0.544 0.004 0.448 0.004
#> GSM120783 4 0.3074 0.7227 0.000 0.196 0.000 0.804 0.000
#> GSM120787 2 0.4264 0.3156 0.000 0.620 0.004 0.376 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM120719 1 0.3978 0.7624 0.820 0.000 0.068 0.032 0.036 0.044
#> GSM120720 1 0.6328 0.2327 0.524 0.000 0.320 0.028 0.028 0.100
#> GSM120765 2 0.2050 0.7189 0.000 0.920 0.008 0.032 0.036 0.004
#> GSM120767 2 0.3077 0.7073 0.000 0.864 0.008 0.068 0.044 0.016
#> GSM120784 2 0.1881 0.7197 0.000 0.928 0.008 0.012 0.044 0.008
#> GSM121400 3 0.3968 0.7775 0.052 0.000 0.800 0.000 0.096 0.052
#> GSM121401 3 0.3267 0.8199 0.064 0.000 0.852 0.004 0.024 0.056
#> GSM121402 2 0.2829 0.7160 0.000 0.864 0.016 0.096 0.024 0.000
#> GSM121403 3 0.5951 0.5271 0.000 0.124 0.632 0.004 0.160 0.080
#> GSM121404 2 0.6118 0.5644 0.000 0.664 0.100 0.068 0.064 0.104
#> GSM121405 3 0.3053 0.7966 0.036 0.000 0.864 0.004 0.024 0.072
#> GSM121406 2 0.1452 0.7160 0.000 0.948 0.012 0.020 0.020 0.000
#> GSM121408 2 0.2415 0.7211 0.000 0.904 0.024 0.032 0.036 0.004
#> GSM121409 3 0.6791 0.6237 0.104 0.032 0.600 0.008 0.124 0.132
#> GSM121410 3 0.5496 0.6738 0.024 0.028 0.700 0.016 0.164 0.068
#> GSM121412 2 0.1950 0.7103 0.000 0.924 0.020 0.008 0.044 0.004
#> GSM121413 2 0.1769 0.7105 0.000 0.924 0.012 0.004 0.060 0.000
#> GSM121414 2 0.2002 0.7078 0.000 0.916 0.020 0.008 0.056 0.000
#> GSM121415 2 0.2239 0.7190 0.000 0.908 0.020 0.024 0.048 0.000
#> GSM121416 2 0.3916 0.6931 0.000 0.792 0.028 0.128 0.052 0.000
#> GSM120591 1 0.6969 0.0745 0.448 0.000 0.312 0.028 0.036 0.176
#> GSM120594 1 0.6364 0.3015 0.544 0.000 0.288 0.028 0.032 0.108
#> GSM120718 1 0.5489 0.5318 0.660 0.000 0.220 0.028 0.028 0.064
#> GSM121205 1 0.0000 0.8923 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.8923 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121207 1 0.0000 0.8923 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121208 1 0.1075 0.8612 0.952 0.000 0.048 0.000 0.000 0.000
#> GSM121209 1 0.0000 0.8923 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.8923 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.8923 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.8923 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.8923 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.8923 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.8923 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121216 1 0.0146 0.8907 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM121217 1 0.0000 0.8923 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.8923 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121234 1 0.0146 0.8907 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM121243 1 0.0146 0.8907 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM121245 1 0.0000 0.8923 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121246 1 0.2488 0.7806 0.864 0.000 0.124 0.004 0.008 0.000
#> GSM121247 1 0.0146 0.8903 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM121248 1 0.0000 0.8923 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM120744 6 0.1570 0.9136 0.000 0.004 0.016 0.008 0.028 0.944
#> GSM120745 6 0.1321 0.9163 0.000 0.000 0.020 0.004 0.024 0.952
#> GSM120746 6 0.0891 0.9252 0.000 0.000 0.024 0.000 0.008 0.968
#> GSM120747 6 0.1320 0.9162 0.000 0.000 0.036 0.000 0.016 0.948
#> GSM120748 6 0.1320 0.9153 0.000 0.000 0.036 0.000 0.016 0.948
#> GSM120749 6 0.1296 0.9120 0.000 0.000 0.044 0.004 0.004 0.948
#> GSM120750 6 0.1092 0.9242 0.000 0.000 0.020 0.000 0.020 0.960
#> GSM120751 6 0.1257 0.9229 0.000 0.000 0.028 0.000 0.020 0.952
#> GSM120752 6 0.1777 0.9006 0.000 0.000 0.024 0.004 0.044 0.928
#> GSM121336 2 0.1148 0.7144 0.000 0.960 0.004 0.020 0.016 0.000
#> GSM121339 2 0.5338 0.6133 0.000 0.720 0.056 0.032 0.084 0.108
#> GSM121349 2 0.1251 0.7157 0.000 0.956 0.008 0.024 0.012 0.000
#> GSM121355 2 0.1257 0.7174 0.000 0.952 0.000 0.028 0.020 0.000
#> GSM120757 5 0.5002 0.6594 0.008 0.000 0.004 0.076 0.640 0.272
#> GSM120766 5 0.4136 0.6534 0.000 0.004 0.000 0.032 0.692 0.272
#> GSM120770 2 0.4966 0.6197 0.000 0.728 0.020 0.024 0.136 0.092
#> GSM120779 5 0.4852 0.7254 0.064 0.000 0.000 0.044 0.708 0.184
#> GSM120780 5 0.5298 0.5015 0.000 0.036 0.028 0.012 0.592 0.332
#> GSM121102 2 0.6445 0.3514 0.000 0.512 0.040 0.020 0.108 0.320
#> GSM121203 6 0.5423 0.5224 0.000 0.040 0.084 0.008 0.204 0.664
#> GSM121204 5 0.6751 0.3366 0.340 0.000 0.004 0.028 0.360 0.268
#> GSM121330 3 0.2358 0.8421 0.108 0.000 0.876 0.000 0.000 0.016
#> GSM121335 3 0.3855 0.6993 0.276 0.000 0.704 0.004 0.000 0.016
#> GSM121337 2 0.7007 0.4169 0.000 0.512 0.064 0.168 0.228 0.028
#> GSM121338 2 0.7747 0.2944 0.000 0.440 0.216 0.036 0.152 0.156
#> GSM121341 3 0.3656 0.7342 0.256 0.000 0.728 0.000 0.004 0.012
#> GSM121342 3 0.3404 0.7383 0.248 0.000 0.744 0.004 0.000 0.004
#> GSM121343 2 0.7753 0.0879 0.000 0.356 0.308 0.036 0.216 0.084
#> GSM121344 3 0.3086 0.8258 0.156 0.000 0.820 0.004 0.000 0.020
#> GSM121346 3 0.2605 0.8426 0.108 0.000 0.864 0.000 0.000 0.028
#> GSM121347 2 0.7610 0.1063 0.000 0.348 0.076 0.224 0.324 0.028
#> GSM121348 5 0.5239 0.5369 0.000 0.164 0.028 0.040 0.708 0.060
#> GSM121350 3 0.2781 0.8419 0.108 0.000 0.860 0.000 0.008 0.024
#> GSM121352 3 0.2678 0.8420 0.116 0.000 0.860 0.004 0.000 0.020
#> GSM121354 3 0.2531 0.8368 0.132 0.000 0.856 0.000 0.000 0.012
#> GSM120753 2 0.5039 0.4510 0.000 0.612 0.020 0.320 0.044 0.004
#> GSM120761 2 0.5168 0.0541 0.000 0.480 0.020 0.456 0.044 0.000
#> GSM120768 4 0.4152 0.5988 0.000 0.264 0.012 0.700 0.024 0.000
#> GSM120781 2 0.4520 0.5078 0.000 0.656 0.004 0.296 0.040 0.004
#> GSM120788 4 0.3354 0.6017 0.000 0.000 0.016 0.792 0.184 0.008
#> GSM120760 4 0.5174 0.6233 0.000 0.244 0.020 0.656 0.072 0.008
#> GSM120763 4 0.4795 0.6620 0.000 0.212 0.016 0.700 0.064 0.008
#> GSM120764 4 0.3511 0.7094 0.000 0.048 0.012 0.828 0.104 0.008
#> GSM120777 4 0.3834 0.4966 0.000 0.000 0.024 0.708 0.268 0.000
#> GSM120786 4 0.3622 0.7453 0.000 0.088 0.024 0.820 0.068 0.000
#> GSM121329 1 0.5107 0.5930 0.704 0.000 0.176 0.036 0.072 0.012
#> GSM121331 5 0.5000 0.7305 0.072 0.004 0.000 0.060 0.720 0.144
#> GSM121333 5 0.5083 0.7251 0.092 0.000 0.000 0.056 0.704 0.148
#> GSM121345 5 0.6458 0.6284 0.152 0.000 0.032 0.140 0.608 0.068
#> GSM121356 5 0.5184 0.7284 0.064 0.000 0.016 0.048 0.708 0.164
#> GSM120754 4 0.5432 0.6827 0.000 0.172 0.020 0.664 0.132 0.012
#> GSM120759 2 0.2857 0.7168 0.000 0.876 0.020 0.064 0.036 0.004
#> GSM120762 2 0.4233 0.6089 0.000 0.724 0.008 0.224 0.040 0.004
#> GSM120775 4 0.3633 0.6901 0.000 0.028 0.020 0.832 0.092 0.028
#> GSM120776 5 0.6591 0.3238 0.000 0.004 0.016 0.336 0.356 0.288
#> GSM120782 4 0.7540 0.3652 0.000 0.204 0.032 0.412 0.076 0.276
#> GSM120789 2 0.4271 0.6542 0.000 0.752 0.016 0.180 0.044 0.008
#> GSM120790 2 0.4206 0.6612 0.000 0.764 0.028 0.056 0.152 0.000
#> GSM120791 4 0.4741 0.6077 0.000 0.252 0.016 0.672 0.060 0.000
#> GSM120755 2 0.4511 0.6127 0.000 0.712 0.024 0.224 0.036 0.004
#> GSM120756 4 0.3753 0.6026 0.012 0.000 0.020 0.780 0.180 0.008
#> GSM120769 2 0.5032 0.2806 0.000 0.556 0.016 0.388 0.036 0.004
#> GSM120778 4 0.4885 0.4342 0.000 0.340 0.012 0.604 0.040 0.004
#> GSM120792 4 0.5195 0.5803 0.000 0.264 0.020 0.648 0.052 0.016
#> GSM121332 2 0.3811 0.6743 0.000 0.788 0.012 0.160 0.032 0.008
#> GSM121334 2 0.5313 0.3910 0.000 0.572 0.036 0.344 0.048 0.000
#> GSM121340 4 0.4264 0.7378 0.000 0.108 0.008 0.760 0.120 0.004
#> GSM121351 2 0.1679 0.7166 0.000 0.936 0.016 0.012 0.036 0.000
#> GSM121353 4 0.4244 0.7043 0.032 0.040 0.032 0.804 0.088 0.004
#> GSM120758 2 0.5134 0.4189 0.000 0.596 0.028 0.328 0.048 0.000
#> GSM120771 2 0.4210 0.6759 0.000 0.768 0.024 0.152 0.052 0.004
#> GSM120772 2 0.5327 0.3208 0.000 0.552 0.016 0.372 0.052 0.008
#> GSM120773 4 0.4241 0.7335 0.000 0.140 0.024 0.764 0.072 0.000
#> GSM120774 4 0.5151 0.1467 0.000 0.436 0.004 0.500 0.052 0.008
#> GSM120783 4 0.3235 0.7405 0.000 0.076 0.012 0.848 0.060 0.004
#> GSM120787 2 0.5133 0.2506 0.000 0.540 0.012 0.400 0.040 0.008
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> CV:skmeans 117 5.29e-10 2
#> CV:skmeans 113 1.79e-18 3
#> CV:skmeans 96 1.17e-19 4
#> CV:skmeans 93 6.15e-26 5
#> CV:skmeans 98 7.70e-33 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 21512 rows and 119 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.539 0.782 0.901 0.4830 0.499 0.499
#> 3 3 0.350 0.427 0.639 0.3153 0.945 0.891
#> 4 4 0.506 0.470 0.758 0.1598 0.704 0.410
#> 5 5 0.568 0.553 0.743 0.0739 0.839 0.479
#> 6 6 0.684 0.646 0.769 0.0425 0.916 0.623
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
#> GSM120719 1 0.0000 0.8733 1.000 0.000
#> GSM120720 1 0.0376 0.8728 0.996 0.004
#> GSM120765 2 0.0000 0.8874 0.000 1.000
#> GSM120767 2 0.0000 0.8874 0.000 1.000
#> GSM120784 2 0.0000 0.8874 0.000 1.000
#> GSM121400 1 0.9000 0.5632 0.684 0.316
#> GSM121401 1 0.9129 0.5408 0.672 0.328
#> GSM121402 2 0.0000 0.8874 0.000 1.000
#> GSM121403 1 0.9044 0.5558 0.680 0.320
#> GSM121404 2 0.9686 0.3718 0.396 0.604
#> GSM121405 1 0.9998 0.0354 0.508 0.492
#> GSM121406 2 0.0000 0.8874 0.000 1.000
#> GSM121408 2 0.1184 0.8855 0.016 0.984
#> GSM121409 1 0.9909 0.2228 0.556 0.444
#> GSM121410 1 0.9000 0.5635 0.684 0.316
#> GSM121412 2 0.2603 0.8729 0.044 0.956
#> GSM121413 2 0.0000 0.8874 0.000 1.000
#> GSM121414 2 0.1633 0.8834 0.024 0.976
#> GSM121415 2 0.8861 0.5396 0.304 0.696
#> GSM121416 2 0.0000 0.8874 0.000 1.000
#> GSM120591 1 0.2603 0.8614 0.956 0.044
#> GSM120594 1 0.1843 0.8669 0.972 0.028
#> GSM120718 1 0.0000 0.8733 1.000 0.000
#> GSM121205 1 0.0000 0.8733 1.000 0.000
#> GSM121206 1 0.0000 0.8733 1.000 0.000
#> GSM121207 1 0.0000 0.8733 1.000 0.000
#> GSM121208 1 0.0000 0.8733 1.000 0.000
#> GSM121209 1 0.0000 0.8733 1.000 0.000
#> GSM121210 1 0.0000 0.8733 1.000 0.000
#> GSM121211 1 0.0000 0.8733 1.000 0.000
#> GSM121212 1 0.0000 0.8733 1.000 0.000
#> GSM121213 1 0.0000 0.8733 1.000 0.000
#> GSM121214 1 0.0000 0.8733 1.000 0.000
#> GSM121215 1 0.0000 0.8733 1.000 0.000
#> GSM121216 1 0.0000 0.8733 1.000 0.000
#> GSM121217 1 0.0000 0.8733 1.000 0.000
#> GSM121218 1 0.0000 0.8733 1.000 0.000
#> GSM121234 1 0.0000 0.8733 1.000 0.000
#> GSM121243 1 0.0000 0.8733 1.000 0.000
#> GSM121245 1 0.0000 0.8733 1.000 0.000
#> GSM121246 1 0.0000 0.8733 1.000 0.000
#> GSM121247 1 0.0000 0.8733 1.000 0.000
#> GSM121248 1 0.0000 0.8733 1.000 0.000
#> GSM120744 2 0.7602 0.7177 0.220 0.780
#> GSM120745 2 0.9087 0.5559 0.324 0.676
#> GSM120746 2 0.7815 0.7037 0.232 0.768
#> GSM120747 2 0.7815 0.7038 0.232 0.768
#> GSM120748 2 0.7528 0.7226 0.216 0.784
#> GSM120749 2 0.8016 0.6906 0.244 0.756
#> GSM120750 2 0.7950 0.6953 0.240 0.760
#> GSM120751 2 0.8016 0.6906 0.244 0.756
#> GSM120752 2 0.8207 0.6736 0.256 0.744
#> GSM121336 2 0.0000 0.8874 0.000 1.000
#> GSM121339 2 0.4939 0.8306 0.108 0.892
#> GSM121349 2 0.0000 0.8874 0.000 1.000
#> GSM121355 2 0.0000 0.8874 0.000 1.000
#> GSM120757 2 0.9358 0.4880 0.352 0.648
#> GSM120766 2 0.9686 0.3424 0.396 0.604
#> GSM120770 2 0.0376 0.8871 0.004 0.996
#> GSM120779 1 0.3879 0.8444 0.924 0.076
#> GSM120780 2 0.4161 0.8479 0.084 0.916
#> GSM121102 2 0.1184 0.8846 0.016 0.984
#> GSM121203 2 0.7745 0.7077 0.228 0.772
#> GSM121204 1 0.8955 0.5401 0.688 0.312
#> GSM121330 1 0.4431 0.8342 0.908 0.092
#> GSM121335 1 0.0000 0.8733 1.000 0.000
#> GSM121337 1 0.9608 0.4110 0.616 0.384
#> GSM121338 2 0.9775 0.3229 0.412 0.588
#> GSM121341 1 0.0000 0.8733 1.000 0.000
#> GSM121342 1 0.0000 0.8733 1.000 0.000
#> GSM121343 1 0.9608 0.4152 0.616 0.384
#> GSM121344 1 0.0672 0.8721 0.992 0.008
#> GSM121346 1 0.6247 0.7797 0.844 0.156
#> GSM121347 1 0.9044 0.5558 0.680 0.320
#> GSM121348 1 0.9323 0.5073 0.652 0.348
#> GSM121350 1 0.7950 0.6817 0.760 0.240
#> GSM121352 1 0.3114 0.8559 0.944 0.056
#> GSM121354 1 0.0376 0.8728 0.996 0.004
#> GSM120753 2 0.0000 0.8874 0.000 1.000
#> GSM120761 2 0.0000 0.8874 0.000 1.000
#> GSM120768 2 0.0000 0.8874 0.000 1.000
#> GSM120781 2 0.0000 0.8874 0.000 1.000
#> GSM120788 1 0.9323 0.5261 0.652 0.348
#> GSM120760 2 0.0672 0.8865 0.008 0.992
#> GSM120763 2 0.0000 0.8874 0.000 1.000
#> GSM120764 2 0.1414 0.8840 0.020 0.980
#> GSM120777 2 0.9866 0.2338 0.432 0.568
#> GSM120786 2 0.1414 0.8847 0.020 0.980
#> GSM121329 1 0.0672 0.8721 0.992 0.008
#> GSM121331 1 0.4022 0.8458 0.920 0.080
#> GSM121333 1 0.3733 0.8504 0.928 0.072
#> GSM121345 1 0.2423 0.8637 0.960 0.040
#> GSM121356 1 0.4298 0.8406 0.912 0.088
#> GSM120754 2 0.0000 0.8874 0.000 1.000
#> GSM120759 2 0.0000 0.8874 0.000 1.000
#> GSM120762 2 0.1414 0.8832 0.020 0.980
#> GSM120775 2 0.9608 0.3744 0.384 0.616
#> GSM120776 2 0.7139 0.7411 0.196 0.804
#> GSM120782 2 0.1633 0.8833 0.024 0.976
#> GSM120789 2 0.0938 0.8863 0.012 0.988
#> GSM120790 2 0.0000 0.8874 0.000 1.000
#> GSM120791 2 0.0000 0.8874 0.000 1.000
#> GSM120755 2 0.0000 0.8874 0.000 1.000
#> GSM120756 1 0.4690 0.8245 0.900 0.100
#> GSM120769 2 0.0000 0.8874 0.000 1.000
#> GSM120778 2 0.2043 0.8770 0.032 0.968
#> GSM120792 2 0.0000 0.8874 0.000 1.000
#> GSM121332 2 0.1414 0.8844 0.020 0.980
#> GSM121334 2 0.3114 0.8602 0.056 0.944
#> GSM121340 1 0.8081 0.6877 0.752 0.248
#> GSM121351 2 0.1633 0.8815 0.024 0.976
#> GSM121353 1 0.6048 0.7877 0.852 0.148
#> GSM120758 2 0.0000 0.8874 0.000 1.000
#> GSM120771 2 0.0000 0.8874 0.000 1.000
#> GSM120772 2 0.0000 0.8874 0.000 1.000
#> GSM120773 2 0.1843 0.8794 0.028 0.972
#> GSM120774 2 0.0000 0.8874 0.000 1.000
#> GSM120783 2 0.1843 0.8818 0.028 0.972
#> GSM120787 2 0.1843 0.8796 0.028 0.972
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM120719 1 0.6305 0.25744 0.516 0.000 0.484
#> GSM120720 1 0.4413 0.35184 0.832 0.008 0.160
#> GSM120765 2 0.1643 0.74524 0.000 0.956 0.044
#> GSM120767 2 0.6361 0.67804 0.040 0.728 0.232
#> GSM120784 2 0.2796 0.74928 0.000 0.908 0.092
#> GSM121400 1 0.6719 0.19518 0.744 0.096 0.160
#> GSM121401 1 0.6714 0.19645 0.748 0.140 0.112
#> GSM121402 2 0.4062 0.73335 0.000 0.836 0.164
#> GSM121403 1 0.6375 0.16984 0.720 0.244 0.036
#> GSM121404 2 0.7961 0.32537 0.336 0.588 0.076
#> GSM121405 1 0.7997 0.01873 0.600 0.316 0.084
#> GSM121406 2 0.1031 0.74381 0.000 0.976 0.024
#> GSM121408 2 0.4233 0.65741 0.160 0.836 0.004
#> GSM121409 1 0.9076 -0.06208 0.552 0.208 0.240
#> GSM121410 1 0.7004 0.16570 0.728 0.112 0.160
#> GSM121412 2 0.4095 0.72804 0.056 0.880 0.064
#> GSM121413 2 0.3272 0.74145 0.004 0.892 0.104
#> GSM121414 2 0.5722 0.72972 0.068 0.800 0.132
#> GSM121415 2 0.7391 0.54512 0.196 0.696 0.108
#> GSM121416 2 0.5061 0.70924 0.008 0.784 0.208
#> GSM120591 1 0.4483 0.34437 0.848 0.024 0.128
#> GSM120594 1 0.1182 0.38702 0.976 0.012 0.012
#> GSM120718 1 0.5733 0.30611 0.676 0.000 0.324
#> GSM121205 1 0.6309 0.25528 0.504 0.000 0.496
#> GSM121206 1 0.5016 0.32998 0.760 0.000 0.240
#> GSM121207 1 0.6309 0.25528 0.504 0.000 0.496
#> GSM121208 1 0.4887 0.33277 0.772 0.000 0.228
#> GSM121209 1 0.6235 0.27431 0.564 0.000 0.436
#> GSM121210 1 0.6309 0.25528 0.504 0.000 0.496
#> GSM121211 1 0.6308 0.25646 0.508 0.000 0.492
#> GSM121212 1 0.6244 0.27573 0.560 0.000 0.440
#> GSM121213 1 0.6309 0.25528 0.504 0.000 0.496
#> GSM121214 1 0.6309 0.25528 0.504 0.000 0.496
#> GSM121215 1 0.6309 0.25528 0.504 0.000 0.496
#> GSM121216 1 0.6309 0.25528 0.504 0.000 0.496
#> GSM121217 1 0.6309 0.25528 0.504 0.000 0.496
#> GSM121218 1 0.6309 0.25528 0.504 0.000 0.496
#> GSM121234 1 0.6309 0.25528 0.504 0.000 0.496
#> GSM121243 1 0.6309 0.25528 0.504 0.000 0.496
#> GSM121245 1 0.6309 0.25528 0.504 0.000 0.496
#> GSM121246 1 0.3192 0.37316 0.888 0.000 0.112
#> GSM121247 3 0.6309 -0.36871 0.500 0.000 0.500
#> GSM121248 1 0.6309 0.25528 0.504 0.000 0.496
#> GSM120744 2 0.8067 0.59249 0.100 0.616 0.284
#> GSM120745 2 0.9304 0.46548 0.204 0.516 0.280
#> GSM120746 2 0.8258 0.58447 0.112 0.604 0.284
#> GSM120747 2 0.8291 0.58139 0.116 0.604 0.280
#> GSM120748 2 0.8258 0.58447 0.112 0.604 0.284
#> GSM120749 2 0.8291 0.58139 0.116 0.604 0.280
#> GSM120750 2 0.8104 0.59045 0.104 0.616 0.280
#> GSM120751 2 0.8291 0.58139 0.116 0.604 0.280
#> GSM120752 2 0.8291 0.58037 0.116 0.604 0.280
#> GSM121336 2 0.1989 0.73233 0.048 0.948 0.004
#> GSM121339 2 0.7728 0.62629 0.084 0.640 0.276
#> GSM121349 2 0.0892 0.74372 0.000 0.980 0.020
#> GSM121355 2 0.2339 0.74764 0.012 0.940 0.048
#> GSM120757 3 0.9120 -0.32846 0.156 0.340 0.504
#> GSM120766 2 0.9992 -0.03670 0.328 0.352 0.320
#> GSM120770 2 0.5465 0.71312 0.000 0.712 0.288
#> GSM120779 3 0.7807 0.13527 0.432 0.052 0.516
#> GSM120780 2 0.7442 0.65020 0.056 0.628 0.316
#> GSM121102 2 0.6761 0.67239 0.048 0.700 0.252
#> GSM121203 2 0.8573 0.56362 0.104 0.524 0.372
#> GSM121204 3 0.8241 0.25737 0.204 0.160 0.636
#> GSM121330 1 0.1129 0.37978 0.976 0.020 0.004
#> GSM121335 1 0.1031 0.38895 0.976 0.000 0.024
#> GSM121337 1 0.7876 -0.09200 0.520 0.424 0.056
#> GSM121338 2 0.8604 0.34856 0.312 0.564 0.124
#> GSM121341 1 0.1529 0.38690 0.960 0.000 0.040
#> GSM121342 1 0.0000 0.38737 1.000 0.000 0.000
#> GSM121343 1 0.7844 0.08003 0.660 0.220 0.120
#> GSM121344 1 0.0424 0.38607 0.992 0.008 0.000
#> GSM121346 1 0.5377 0.26626 0.820 0.068 0.112
#> GSM121347 1 0.8637 -0.02116 0.596 0.236 0.168
#> GSM121348 1 0.9485 -0.17405 0.484 0.304 0.212
#> GSM121350 1 0.4914 0.28777 0.844 0.068 0.088
#> GSM121352 1 0.1585 0.37370 0.964 0.028 0.008
#> GSM121354 1 0.0424 0.38607 0.992 0.008 0.000
#> GSM120753 2 0.3851 0.75575 0.004 0.860 0.136
#> GSM120761 2 0.3340 0.73668 0.000 0.880 0.120
#> GSM120768 2 0.3686 0.73485 0.000 0.860 0.140
#> GSM120781 2 0.2537 0.73946 0.000 0.920 0.080
#> GSM120788 1 0.9577 -0.25765 0.404 0.196 0.400
#> GSM120760 2 0.5138 0.69982 0.000 0.748 0.252
#> GSM120763 2 0.5158 0.67429 0.004 0.764 0.232
#> GSM120764 2 0.6608 0.66081 0.016 0.628 0.356
#> GSM120777 3 0.9756 0.14581 0.248 0.316 0.436
#> GSM120786 2 0.6172 0.67032 0.012 0.680 0.308
#> GSM121329 1 0.1529 0.38349 0.960 0.000 0.040
#> GSM121331 1 0.7756 -0.06028 0.564 0.056 0.380
#> GSM121333 3 0.7759 0.04495 0.472 0.048 0.480
#> GSM121345 1 0.7192 -0.00934 0.588 0.032 0.380
#> GSM121356 1 0.7095 0.05283 0.660 0.048 0.292
#> GSM120754 2 0.5431 0.70475 0.000 0.716 0.284
#> GSM120759 2 0.2772 0.75124 0.004 0.916 0.080
#> GSM120762 2 0.2229 0.74659 0.012 0.944 0.044
#> GSM120775 2 0.9674 0.26270 0.212 0.396 0.392
#> GSM120776 2 0.8037 0.55977 0.076 0.572 0.352
#> GSM120782 2 0.5843 0.67875 0.016 0.732 0.252
#> GSM120789 2 0.5746 0.71011 0.040 0.780 0.180
#> GSM120790 2 0.5285 0.68486 0.004 0.752 0.244
#> GSM120791 2 0.5115 0.70410 0.004 0.768 0.228
#> GSM120755 2 0.2806 0.73714 0.032 0.928 0.040
#> GSM120756 3 0.8050 -0.11520 0.436 0.064 0.500
#> GSM120769 2 0.1453 0.74185 0.008 0.968 0.024
#> GSM120778 2 0.4397 0.73694 0.028 0.856 0.116
#> GSM120792 2 0.5578 0.70864 0.012 0.748 0.240
#> GSM121332 2 0.5060 0.71281 0.100 0.836 0.064
#> GSM121334 2 0.4968 0.68682 0.012 0.800 0.188
#> GSM121340 1 0.9663 -0.22410 0.416 0.212 0.372
#> GSM121351 2 0.3910 0.74209 0.020 0.876 0.104
#> GSM121353 1 0.7898 0.05676 0.616 0.084 0.300
#> GSM120758 2 0.3551 0.72539 0.000 0.868 0.132
#> GSM120771 2 0.4796 0.72497 0.000 0.780 0.220
#> GSM120772 2 0.4654 0.75116 0.000 0.792 0.208
#> GSM120773 2 0.6168 0.71107 0.036 0.740 0.224
#> GSM120774 2 0.4654 0.72979 0.000 0.792 0.208
#> GSM120783 2 0.6319 0.70361 0.040 0.732 0.228
#> GSM120787 2 0.4068 0.75537 0.016 0.864 0.120
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM120719 1 0.0188 0.91085 0.996 0.000 0.000 0.004
#> GSM120720 4 0.4456 0.58749 0.280 0.000 0.004 0.716
#> GSM120765 2 0.5511 -0.01006 0.000 0.500 0.484 0.016
#> GSM120767 3 0.2408 0.59010 0.000 0.104 0.896 0.000
#> GSM120784 3 0.4830 0.21895 0.000 0.392 0.608 0.000
#> GSM121400 4 0.0657 0.80842 0.000 0.004 0.012 0.984
#> GSM121401 4 0.0469 0.80860 0.000 0.000 0.012 0.988
#> GSM121402 2 0.5298 0.29475 0.000 0.612 0.372 0.016
#> GSM121403 4 0.0927 0.80044 0.000 0.008 0.016 0.976
#> GSM121404 3 0.5388 0.17391 0.000 0.012 0.532 0.456
#> GSM121405 4 0.1867 0.77299 0.000 0.000 0.072 0.928
#> GSM121406 3 0.5310 0.17303 0.000 0.412 0.576 0.012
#> GSM121408 2 0.7748 0.03811 0.000 0.428 0.324 0.248
#> GSM121409 4 0.5132 0.18565 0.000 0.004 0.448 0.548
#> GSM121410 4 0.3015 0.74991 0.000 0.092 0.024 0.884
#> GSM121412 3 0.5639 0.30942 0.000 0.324 0.636 0.040
#> GSM121413 2 0.5329 0.17189 0.000 0.568 0.420 0.012
#> GSM121414 3 0.6336 0.02497 0.000 0.460 0.480 0.060
#> GSM121415 2 0.7166 0.28917 0.000 0.544 0.280 0.176
#> GSM121416 2 0.4610 0.40156 0.000 0.744 0.236 0.020
#> GSM120591 4 0.5006 0.71899 0.124 0.000 0.104 0.772
#> GSM120594 4 0.3790 0.72619 0.164 0.000 0.016 0.820
#> GSM120718 1 0.4222 0.59319 0.728 0.000 0.000 0.272
#> GSM121205 1 0.0000 0.91313 1.000 0.000 0.000 0.000
#> GSM121206 1 0.4776 0.29402 0.624 0.000 0.000 0.376
#> GSM121207 1 0.0000 0.91313 1.000 0.000 0.000 0.000
#> GSM121208 1 0.4866 0.21697 0.596 0.000 0.000 0.404
#> GSM121209 1 0.1302 0.88025 0.956 0.000 0.000 0.044
#> GSM121210 1 0.0000 0.91313 1.000 0.000 0.000 0.000
#> GSM121211 1 0.0336 0.90800 0.992 0.000 0.000 0.008
#> GSM121212 1 0.1302 0.87928 0.956 0.000 0.000 0.044
#> GSM121213 1 0.0000 0.91313 1.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.91313 1.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.91313 1.000 0.000 0.000 0.000
#> GSM121216 1 0.0000 0.91313 1.000 0.000 0.000 0.000
#> GSM121217 1 0.0000 0.91313 1.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.91313 1.000 0.000 0.000 0.000
#> GSM121234 1 0.0000 0.91313 1.000 0.000 0.000 0.000
#> GSM121243 1 0.0000 0.91313 1.000 0.000 0.000 0.000
#> GSM121245 1 0.0000 0.91313 1.000 0.000 0.000 0.000
#> GSM121246 4 0.4356 0.57761 0.292 0.000 0.000 0.708
#> GSM121247 1 0.0000 0.91313 1.000 0.000 0.000 0.000
#> GSM121248 1 0.0000 0.91313 1.000 0.000 0.000 0.000
#> GSM120744 3 0.0376 0.62362 0.000 0.004 0.992 0.004
#> GSM120745 3 0.2345 0.56350 0.000 0.000 0.900 0.100
#> GSM120746 3 0.0376 0.62362 0.000 0.004 0.992 0.004
#> GSM120747 3 0.0336 0.62423 0.000 0.000 0.992 0.008
#> GSM120748 3 0.0376 0.62362 0.000 0.004 0.992 0.004
#> GSM120749 3 0.0336 0.62423 0.000 0.000 0.992 0.008
#> GSM120750 3 0.0336 0.62423 0.000 0.000 0.992 0.008
#> GSM120751 3 0.0336 0.62423 0.000 0.000 0.992 0.008
#> GSM120752 3 0.0524 0.62267 0.000 0.004 0.988 0.008
#> GSM121336 2 0.5695 -0.02788 0.000 0.500 0.476 0.024
#> GSM121339 3 0.2443 0.60573 0.000 0.024 0.916 0.060
#> GSM121349 2 0.5402 0.00298 0.000 0.516 0.472 0.012
#> GSM121355 3 0.5550 0.14502 0.000 0.428 0.552 0.020
#> GSM120757 3 0.6595 -0.07496 0.000 0.428 0.492 0.080
#> GSM120766 2 0.7289 0.25668 0.000 0.532 0.200 0.268
#> GSM120770 2 0.4830 0.26954 0.000 0.608 0.392 0.000
#> GSM120779 2 0.9180 -0.03946 0.164 0.428 0.120 0.288
#> GSM120780 2 0.5720 0.29853 0.000 0.652 0.296 0.052
#> GSM121102 3 0.2949 0.59437 0.000 0.088 0.888 0.024
#> GSM121203 3 0.4638 0.40404 0.000 0.180 0.776 0.044
#> GSM121204 3 0.8354 0.03934 0.216 0.272 0.476 0.036
#> GSM121330 4 0.0469 0.81101 0.012 0.000 0.000 0.988
#> GSM121335 4 0.1792 0.79461 0.068 0.000 0.000 0.932
#> GSM121337 4 0.6351 0.26588 0.000 0.080 0.332 0.588
#> GSM121338 3 0.4539 0.41224 0.000 0.008 0.720 0.272
#> GSM121341 4 0.1867 0.78654 0.072 0.000 0.000 0.928
#> GSM121342 4 0.0592 0.81122 0.016 0.000 0.000 0.984
#> GSM121343 4 0.3286 0.74450 0.000 0.080 0.044 0.876
#> GSM121344 4 0.0469 0.81101 0.012 0.000 0.000 0.988
#> GSM121346 4 0.0469 0.80860 0.000 0.000 0.012 0.988
#> GSM121347 4 0.7335 0.05308 0.000 0.400 0.156 0.444
#> GSM121348 2 0.5800 -0.00245 0.000 0.548 0.032 0.420
#> GSM121350 4 0.0524 0.80985 0.004 0.000 0.008 0.988
#> GSM121352 4 0.0469 0.81101 0.012 0.000 0.000 0.988
#> GSM121354 4 0.0469 0.81101 0.012 0.000 0.000 0.988
#> GSM120753 2 0.4872 0.29983 0.000 0.640 0.356 0.004
#> GSM120761 2 0.4564 0.29060 0.000 0.672 0.328 0.000
#> GSM120768 2 0.4477 0.32741 0.000 0.688 0.312 0.000
#> GSM120781 2 0.4564 0.27384 0.000 0.672 0.328 0.000
#> GSM120788 2 0.6461 0.28467 0.000 0.632 0.128 0.240
#> GSM120760 2 0.2921 0.45351 0.000 0.860 0.140 0.000
#> GSM120763 2 0.1970 0.45449 0.000 0.932 0.060 0.008
#> GSM120764 2 0.4406 0.31836 0.000 0.700 0.300 0.000
#> GSM120777 2 0.7322 0.32722 0.064 0.648 0.140 0.148
#> GSM120786 2 0.3801 0.41039 0.000 0.780 0.220 0.000
#> GSM121329 4 0.3893 0.69796 0.196 0.008 0.000 0.796
#> GSM121331 2 0.8308 -0.18045 0.088 0.428 0.084 0.400
#> GSM121333 2 0.8846 -0.07161 0.236 0.432 0.060 0.272
#> GSM121345 4 0.8531 0.30818 0.224 0.316 0.036 0.424
#> GSM121356 2 0.8093 -0.19163 0.060 0.424 0.096 0.420
#> GSM120754 2 0.4134 0.41017 0.000 0.740 0.260 0.000
#> GSM120759 2 0.5399 0.03369 0.000 0.520 0.468 0.012
#> GSM120762 2 0.4991 0.15758 0.000 0.608 0.388 0.004
#> GSM120775 2 0.6147 0.22328 0.000 0.564 0.380 0.056
#> GSM120776 3 0.4051 0.45277 0.004 0.208 0.784 0.004
#> GSM120782 3 0.1940 0.59021 0.000 0.076 0.924 0.000
#> GSM120789 3 0.3881 0.54182 0.000 0.172 0.812 0.016
#> GSM120790 2 0.4137 0.39387 0.000 0.780 0.208 0.012
#> GSM120791 2 0.3569 0.43926 0.000 0.804 0.196 0.000
#> GSM120755 3 0.5204 0.24903 0.000 0.376 0.612 0.012
#> GSM120756 1 0.7754 0.42709 0.568 0.236 0.036 0.160
#> GSM120769 2 0.4907 0.08521 0.000 0.580 0.420 0.000
#> GSM120778 2 0.4313 0.36697 0.000 0.736 0.260 0.004
#> GSM120792 3 0.4936 0.24327 0.000 0.372 0.624 0.004
#> GSM121332 3 0.7221 0.04927 0.000 0.428 0.432 0.140
#> GSM121334 2 0.2530 0.45644 0.000 0.896 0.100 0.004
#> GSM121340 2 0.6410 0.34852 0.028 0.692 0.092 0.188
#> GSM121351 2 0.5339 0.26561 0.000 0.624 0.356 0.020
#> GSM121353 4 0.9213 0.34421 0.256 0.136 0.164 0.444
#> GSM120758 2 0.4516 0.39949 0.000 0.736 0.252 0.012
#> GSM120771 2 0.5174 0.28172 0.000 0.620 0.368 0.012
#> GSM120772 3 0.5099 0.26602 0.000 0.380 0.612 0.008
#> GSM120773 2 0.4833 0.40921 0.000 0.740 0.228 0.032
#> GSM120774 3 0.4605 0.35169 0.000 0.336 0.664 0.000
#> GSM120783 2 0.4610 0.39992 0.000 0.744 0.236 0.020
#> GSM120787 2 0.5097 0.12727 0.000 0.568 0.428 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM120719 1 0.2694 0.8504 0.876 0.008 0.008 0.000 0.108
#> GSM120720 3 0.6057 0.4479 0.312 0.008 0.564 0.000 0.116
#> GSM120765 2 0.3264 0.6645 0.000 0.836 0.004 0.020 0.140
#> GSM120767 5 0.4682 0.2005 0.000 0.420 0.000 0.016 0.564
#> GSM120784 2 0.4227 0.5335 0.000 0.692 0.000 0.016 0.292
#> GSM121400 3 0.1012 0.8046 0.000 0.020 0.968 0.012 0.000
#> GSM121401 3 0.0000 0.8140 0.000 0.000 1.000 0.000 0.000
#> GSM121402 2 0.6219 0.4882 0.000 0.548 0.000 0.212 0.240
#> GSM121403 3 0.2011 0.7728 0.000 0.088 0.908 0.000 0.004
#> GSM121404 3 0.6312 0.1591 0.000 0.200 0.524 0.000 0.276
#> GSM121405 3 0.0162 0.8131 0.000 0.004 0.996 0.000 0.000
#> GSM121406 2 0.3098 0.6487 0.000 0.836 0.000 0.016 0.148
#> GSM121408 2 0.5489 0.5629 0.000 0.704 0.172 0.036 0.088
#> GSM121409 5 0.5271 0.2221 0.000 0.036 0.392 0.008 0.564
#> GSM121410 3 0.4189 0.7053 0.000 0.108 0.808 0.056 0.028
#> GSM121412 2 0.4403 0.5563 0.000 0.724 0.032 0.004 0.240
#> GSM121413 2 0.4355 0.6442 0.000 0.760 0.000 0.076 0.164
#> GSM121414 2 0.4981 0.6200 0.000 0.744 0.036 0.060 0.160
#> GSM121415 2 0.5351 0.6503 0.000 0.708 0.020 0.112 0.160
#> GSM121416 2 0.5815 0.4040 0.000 0.588 0.004 0.300 0.108
#> GSM120591 3 0.6300 0.5100 0.156 0.008 0.552 0.000 0.284
#> GSM120594 3 0.6022 0.5182 0.268 0.008 0.592 0.000 0.132
#> GSM120718 1 0.5082 0.6518 0.716 0.008 0.168 0.000 0.108
#> GSM121205 1 0.0000 0.9470 1.000 0.000 0.000 0.000 0.000
#> GSM121206 1 0.3424 0.6195 0.760 0.000 0.240 0.000 0.000
#> GSM121207 1 0.0000 0.9470 1.000 0.000 0.000 0.000 0.000
#> GSM121208 1 0.3534 0.5914 0.744 0.000 0.256 0.000 0.000
#> GSM121209 1 0.0000 0.9470 1.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.9470 1.000 0.000 0.000 0.000 0.000
#> GSM121211 1 0.0162 0.9443 0.996 0.000 0.004 0.000 0.000
#> GSM121212 1 0.0404 0.9375 0.988 0.000 0.012 0.000 0.000
#> GSM121213 1 0.0000 0.9470 1.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.9470 1.000 0.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.9470 1.000 0.000 0.000 0.000 0.000
#> GSM121216 1 0.0000 0.9470 1.000 0.000 0.000 0.000 0.000
#> GSM121217 1 0.0000 0.9470 1.000 0.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.9470 1.000 0.000 0.000 0.000 0.000
#> GSM121234 1 0.0000 0.9470 1.000 0.000 0.000 0.000 0.000
#> GSM121243 1 0.0162 0.9443 0.996 0.000 0.004 0.000 0.000
#> GSM121245 1 0.0000 0.9470 1.000 0.000 0.000 0.000 0.000
#> GSM121246 3 0.4481 0.5404 0.312 0.004 0.668 0.000 0.016
#> GSM121247 1 0.0000 0.9470 1.000 0.000 0.000 0.000 0.000
#> GSM121248 1 0.0000 0.9470 1.000 0.000 0.000 0.000 0.000
#> GSM120744 5 0.2127 0.6981 0.000 0.108 0.000 0.000 0.892
#> GSM120745 5 0.1300 0.6554 0.000 0.016 0.028 0.000 0.956
#> GSM120746 5 0.2127 0.6981 0.000 0.108 0.000 0.000 0.892
#> GSM120747 5 0.2127 0.6981 0.000 0.108 0.000 0.000 0.892
#> GSM120748 5 0.2127 0.6981 0.000 0.108 0.000 0.000 0.892
#> GSM120749 5 0.1608 0.6932 0.000 0.072 0.000 0.000 0.928
#> GSM120750 5 0.2127 0.6981 0.000 0.108 0.000 0.000 0.892
#> GSM120751 5 0.2127 0.6981 0.000 0.108 0.000 0.000 0.892
#> GSM120752 5 0.0609 0.6715 0.000 0.020 0.000 0.000 0.980
#> GSM121336 2 0.2969 0.6617 0.000 0.852 0.000 0.020 0.128
#> GSM121339 5 0.4461 0.5639 0.000 0.220 0.052 0.000 0.728
#> GSM121349 2 0.2361 0.6668 0.000 0.892 0.000 0.012 0.096
#> GSM121355 2 0.2787 0.6560 0.000 0.856 0.004 0.004 0.136
#> GSM120757 4 0.5234 0.2221 0.000 0.020 0.032 0.632 0.316
#> GSM120766 4 0.6486 0.3900 0.000 0.068 0.176 0.628 0.128
#> GSM120770 5 0.6758 -0.1579 0.000 0.304 0.000 0.292 0.404
#> GSM120779 4 0.6696 0.3774 0.084 0.016 0.200 0.628 0.072
#> GSM120780 4 0.6108 0.2398 0.000 0.216 0.012 0.608 0.164
#> GSM121102 5 0.3869 0.6704 0.000 0.140 0.028 0.020 0.812
#> GSM121203 5 0.5389 0.5319 0.000 0.076 0.032 0.188 0.704
#> GSM121204 5 0.5884 0.0421 0.100 0.000 0.000 0.420 0.480
#> GSM121330 3 0.0000 0.8140 0.000 0.000 1.000 0.000 0.000
#> GSM121335 3 0.0955 0.8057 0.028 0.000 0.968 0.000 0.004
#> GSM121337 3 0.7390 0.1152 0.000 0.324 0.452 0.060 0.164
#> GSM121338 5 0.5611 0.5099 0.000 0.196 0.148 0.004 0.652
#> GSM121341 3 0.0703 0.8055 0.024 0.000 0.976 0.000 0.000
#> GSM121342 3 0.0324 0.8130 0.004 0.000 0.992 0.000 0.004
#> GSM121343 3 0.3574 0.7283 0.000 0.108 0.840 0.032 0.020
#> GSM121344 3 0.0000 0.8140 0.000 0.000 1.000 0.000 0.000
#> GSM121346 3 0.0000 0.8140 0.000 0.000 1.000 0.000 0.000
#> GSM121347 4 0.8362 0.1822 0.000 0.148 0.296 0.328 0.228
#> GSM121348 4 0.6030 0.3624 0.000 0.116 0.232 0.628 0.024
#> GSM121350 3 0.0000 0.8140 0.000 0.000 1.000 0.000 0.000
#> GSM121352 3 0.0000 0.8140 0.000 0.000 1.000 0.000 0.000
#> GSM121354 3 0.0000 0.8140 0.000 0.000 1.000 0.000 0.000
#> GSM120753 2 0.6403 0.3443 0.000 0.512 0.000 0.256 0.232
#> GSM120761 2 0.6171 0.2711 0.000 0.488 0.000 0.372 0.140
#> GSM120768 4 0.5216 0.1376 0.000 0.436 0.000 0.520 0.044
#> GSM120781 2 0.3888 0.6041 0.000 0.800 0.000 0.136 0.064
#> GSM120788 4 0.4644 0.4424 0.000 0.156 0.016 0.760 0.068
#> GSM120760 4 0.5960 0.1238 0.000 0.352 0.000 0.528 0.120
#> GSM120763 4 0.4824 0.2949 0.000 0.376 0.000 0.596 0.028
#> GSM120764 4 0.4629 0.3983 0.000 0.244 0.000 0.704 0.052
#> GSM120777 4 0.2073 0.4650 0.008 0.044 0.004 0.928 0.016
#> GSM120786 4 0.4975 0.3623 0.000 0.276 0.004 0.668 0.052
#> GSM121329 3 0.4997 0.5776 0.276 0.000 0.672 0.012 0.040
#> GSM121331 4 0.6380 0.3513 0.064 0.012 0.232 0.632 0.060
#> GSM121333 4 0.6402 0.3538 0.152 0.000 0.192 0.616 0.040
#> GSM121345 4 0.7245 0.2009 0.160 0.000 0.268 0.508 0.064
#> GSM121356 4 0.6300 0.3456 0.044 0.004 0.244 0.620 0.088
#> GSM120754 4 0.6108 0.2664 0.000 0.248 0.000 0.564 0.188
#> GSM120759 2 0.5392 0.5893 0.000 0.668 0.004 0.112 0.216
#> GSM120762 2 0.3163 0.5329 0.000 0.824 0.000 0.164 0.012
#> GSM120775 4 0.5913 0.3644 0.000 0.188 0.004 0.616 0.192
#> GSM120776 5 0.5550 0.2501 0.000 0.076 0.000 0.376 0.548
#> GSM120782 5 0.3752 0.6582 0.000 0.124 0.000 0.064 0.812
#> GSM120789 5 0.4949 0.4805 0.000 0.296 0.004 0.044 0.656
#> GSM120790 4 0.4651 0.1043 0.000 0.372 0.000 0.608 0.020
#> GSM120791 4 0.5252 0.2632 0.000 0.364 0.000 0.580 0.056
#> GSM120755 2 0.4181 0.5441 0.000 0.712 0.000 0.020 0.268
#> GSM120756 4 0.7032 0.3279 0.236 0.040 0.036 0.588 0.100
#> GSM120769 2 0.4761 0.1976 0.000 0.616 0.000 0.356 0.028
#> GSM120778 4 0.6100 0.0928 0.000 0.416 0.004 0.472 0.108
#> GSM120792 4 0.6742 -0.0387 0.000 0.292 0.000 0.412 0.296
#> GSM121332 2 0.6119 0.5393 0.000 0.652 0.084 0.064 0.200
#> GSM121334 2 0.4713 0.3947 0.000 0.676 0.000 0.280 0.044
#> GSM121340 4 0.2392 0.4577 0.000 0.104 0.004 0.888 0.004
#> GSM121351 2 0.4022 0.6680 0.000 0.796 0.000 0.100 0.104
#> GSM121353 4 0.9427 0.2141 0.160 0.092 0.160 0.344 0.244
#> GSM120758 2 0.5163 0.4349 0.000 0.636 0.000 0.296 0.068
#> GSM120771 2 0.6665 0.3861 0.000 0.440 0.000 0.260 0.300
#> GSM120772 5 0.5891 0.2678 0.000 0.328 0.000 0.120 0.552
#> GSM120773 4 0.5992 0.2896 0.000 0.316 0.008 0.568 0.108
#> GSM120774 5 0.6491 0.1683 0.000 0.228 0.000 0.284 0.488
#> GSM120783 4 0.5225 0.3631 0.000 0.268 0.004 0.656 0.072
#> GSM120787 2 0.6361 0.3068 0.000 0.484 0.000 0.340 0.176
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM120719 1 0.6144 0.5444 0.580 0.020 0.000 0.048 0.268 0.084
#> GSM120720 3 0.8370 0.1867 0.240 0.020 0.336 0.048 0.268 0.088
#> GSM120765 2 0.3171 0.7366 0.000 0.844 0.004 0.056 0.004 0.092
#> GSM120767 2 0.4338 0.1090 0.000 0.496 0.000 0.020 0.000 0.484
#> GSM120784 2 0.4198 0.6344 0.000 0.716 0.000 0.052 0.004 0.228
#> GSM121400 3 0.0632 0.8266 0.000 0.024 0.976 0.000 0.000 0.000
#> GSM121401 3 0.0000 0.8342 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121402 2 0.5999 0.5074 0.000 0.552 0.000 0.220 0.024 0.204
#> GSM121403 3 0.2100 0.7716 0.000 0.112 0.884 0.000 0.000 0.004
#> GSM121404 3 0.5439 0.4178 0.000 0.200 0.608 0.008 0.000 0.184
#> GSM121405 3 0.0000 0.8342 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121406 2 0.2176 0.7398 0.000 0.896 0.000 0.024 0.000 0.080
#> GSM121408 2 0.5090 0.6259 0.000 0.700 0.152 0.100 0.000 0.048
#> GSM121409 6 0.4932 0.4103 0.000 0.024 0.304 0.016 0.020 0.636
#> GSM121410 3 0.4286 0.7120 0.000 0.140 0.776 0.016 0.036 0.032
#> GSM121412 2 0.3110 0.7185 0.000 0.836 0.020 0.016 0.000 0.128
#> GSM121413 2 0.2317 0.7362 0.000 0.900 0.000 0.016 0.020 0.064
#> GSM121414 2 0.2396 0.7363 0.000 0.904 0.012 0.020 0.012 0.052
#> GSM121415 2 0.3372 0.7320 0.000 0.848 0.012 0.036 0.024 0.080
#> GSM121416 2 0.5684 0.4716 0.000 0.588 0.004 0.292 0.040 0.076
#> GSM120591 6 0.8604 0.0107 0.148 0.020 0.192 0.048 0.268 0.324
#> GSM120594 1 0.8323 0.0493 0.348 0.020 0.232 0.048 0.268 0.084
#> GSM120718 1 0.6957 0.4867 0.540 0.020 0.040 0.048 0.268 0.084
#> GSM121205 1 0.0000 0.9300 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121206 1 0.0458 0.9168 0.984 0.000 0.016 0.000 0.000 0.000
#> GSM121207 1 0.0000 0.9300 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121208 1 0.0547 0.9133 0.980 0.000 0.020 0.000 0.000 0.000
#> GSM121209 1 0.0000 0.9300 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.9300 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.9300 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.9300 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.9300 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.9300 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.9300 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121216 1 0.0000 0.9300 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121217 1 0.0000 0.9300 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.9300 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121234 1 0.0000 0.9300 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121243 1 0.0000 0.9300 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121245 1 0.0000 0.9300 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121246 3 0.4943 0.5081 0.292 0.008 0.648 0.008 0.020 0.024
#> GSM121247 1 0.0000 0.9300 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121248 1 0.0000 0.9300 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM120744 6 0.1663 0.7198 0.000 0.088 0.000 0.000 0.000 0.912
#> GSM120745 6 0.1686 0.6732 0.000 0.004 0.008 0.004 0.052 0.932
#> GSM120746 6 0.1663 0.7198 0.000 0.088 0.000 0.000 0.000 0.912
#> GSM120747 6 0.1663 0.7198 0.000 0.088 0.000 0.000 0.000 0.912
#> GSM120748 6 0.1663 0.7198 0.000 0.088 0.000 0.000 0.000 0.912
#> GSM120749 6 0.1398 0.7157 0.000 0.052 0.000 0.000 0.008 0.940
#> GSM120750 6 0.1610 0.7195 0.000 0.084 0.000 0.000 0.000 0.916
#> GSM120751 6 0.1610 0.7195 0.000 0.084 0.000 0.000 0.000 0.916
#> GSM120752 6 0.2321 0.6905 0.000 0.040 0.000 0.008 0.052 0.900
#> GSM121336 2 0.2350 0.7267 0.000 0.888 0.000 0.076 0.000 0.036
#> GSM121339 6 0.5936 0.4522 0.000 0.252 0.044 0.016 0.084 0.604
#> GSM121349 2 0.1719 0.7246 0.000 0.924 0.000 0.060 0.000 0.016
#> GSM121355 2 0.1594 0.7274 0.000 0.932 0.000 0.052 0.000 0.016
#> GSM120757 5 0.4412 0.8507 0.000 0.028 0.000 0.200 0.728 0.044
#> GSM120766 5 0.4492 0.8512 0.000 0.044 0.004 0.192 0.732 0.028
#> GSM120770 6 0.6579 0.0422 0.000 0.340 0.000 0.196 0.040 0.424
#> GSM120779 5 0.4581 0.8556 0.028 0.020 0.000 0.200 0.728 0.024
#> GSM120780 5 0.4525 0.8294 0.000 0.080 0.000 0.180 0.724 0.016
#> GSM121102 6 0.2986 0.7079 0.000 0.112 0.020 0.012 0.004 0.852
#> GSM121203 6 0.4735 0.6271 0.000 0.104 0.020 0.124 0.012 0.740
#> GSM121204 5 0.3872 0.6992 0.048 0.000 0.000 0.036 0.800 0.116
#> GSM121330 3 0.0000 0.8342 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121335 3 0.1088 0.8190 0.024 0.000 0.960 0.000 0.016 0.000
#> GSM121337 3 0.7568 -0.0827 0.000 0.312 0.360 0.116 0.012 0.200
#> GSM121338 6 0.4035 0.6105 0.000 0.204 0.052 0.004 0.000 0.740
#> GSM121341 3 0.0000 0.8342 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121342 3 0.0405 0.8306 0.008 0.000 0.988 0.000 0.004 0.000
#> GSM121343 3 0.3157 0.7381 0.000 0.136 0.832 0.012 0.004 0.016
#> GSM121344 3 0.0000 0.8342 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121346 3 0.0000 0.8342 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121347 6 0.7743 0.2448 0.000 0.216 0.068 0.212 0.068 0.436
#> GSM121348 5 0.4875 0.8200 0.000 0.092 0.008 0.200 0.692 0.008
#> GSM121350 3 0.0000 0.8342 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121352 3 0.0000 0.8342 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121354 3 0.0000 0.8342 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM120753 4 0.6023 0.0501 0.000 0.280 0.000 0.428 0.000 0.292
#> GSM120761 4 0.5454 0.4494 0.000 0.288 0.000 0.588 0.016 0.108
#> GSM120768 4 0.2740 0.6835 0.000 0.076 0.000 0.864 0.000 0.060
#> GSM120781 2 0.4775 0.5197 0.000 0.636 0.000 0.296 0.008 0.060
#> GSM120788 4 0.3861 0.6105 0.004 0.016 0.004 0.808 0.108 0.060
#> GSM120760 4 0.5567 0.5278 0.000 0.216 0.000 0.636 0.052 0.096
#> GSM120763 4 0.3591 0.6486 0.000 0.120 0.000 0.812 0.052 0.016
#> GSM120764 4 0.2123 0.6676 0.000 0.012 0.000 0.912 0.052 0.024
#> GSM120777 4 0.4087 0.2795 0.008 0.008 0.000 0.668 0.312 0.004
#> GSM120786 4 0.2038 0.6803 0.000 0.032 0.000 0.920 0.028 0.020
#> GSM121329 3 0.7066 0.3172 0.304 0.008 0.488 0.048 0.116 0.036
#> GSM121331 5 0.4645 0.8566 0.024 0.024 0.004 0.200 0.728 0.020
#> GSM121333 5 0.4644 0.8455 0.044 0.008 0.004 0.200 0.724 0.020
#> GSM121345 5 0.4957 0.7794 0.060 0.008 0.044 0.144 0.736 0.008
#> GSM121356 5 0.4667 0.8563 0.020 0.016 0.008 0.200 0.728 0.028
#> GSM120754 4 0.4709 0.6324 0.000 0.140 0.000 0.716 0.016 0.128
#> GSM120759 2 0.5384 0.5436 0.000 0.616 0.000 0.176 0.008 0.200
#> GSM120762 2 0.3833 0.4096 0.000 0.648 0.000 0.344 0.000 0.008
#> GSM120775 4 0.3441 0.6651 0.000 0.016 0.000 0.824 0.048 0.112
#> GSM120776 5 0.4894 0.4853 0.000 0.008 0.000 0.068 0.624 0.300
#> GSM120782 6 0.3808 0.6703 0.000 0.088 0.000 0.112 0.008 0.792
#> GSM120789 6 0.5012 0.4937 0.000 0.236 0.000 0.132 0.000 0.632
#> GSM120790 5 0.5253 0.6856 0.000 0.200 0.000 0.192 0.608 0.000
#> GSM120791 4 0.2641 0.6930 0.000 0.072 0.000 0.876 0.004 0.048
#> GSM120755 2 0.5150 0.5984 0.000 0.620 0.000 0.160 0.000 0.220
#> GSM120756 4 0.5699 0.3808 0.032 0.004 0.020 0.540 0.372 0.032
#> GSM120769 4 0.3584 0.4809 0.000 0.308 0.000 0.688 0.000 0.004
#> GSM120778 4 0.3649 0.6492 0.000 0.112 0.004 0.800 0.000 0.084
#> GSM120792 4 0.4813 0.5561 0.000 0.092 0.000 0.672 0.008 0.228
#> GSM121332 2 0.5955 0.6007 0.000 0.608 0.060 0.156 0.000 0.176
#> GSM121334 2 0.4912 0.4284 0.000 0.588 0.000 0.356 0.028 0.028
#> GSM121340 4 0.3265 0.4416 0.000 0.004 0.000 0.748 0.248 0.000
#> GSM121351 2 0.2274 0.7273 0.000 0.908 0.000 0.036 0.028 0.028
#> GSM121353 4 0.6875 0.4527 0.032 0.008 0.068 0.560 0.224 0.108
#> GSM120758 4 0.5065 -0.1393 0.000 0.448 0.000 0.492 0.012 0.048
#> GSM120771 2 0.6022 0.4006 0.000 0.548 0.000 0.152 0.032 0.268
#> GSM120772 6 0.5857 0.3999 0.000 0.220 0.000 0.192 0.020 0.568
#> GSM120773 4 0.3385 0.6980 0.000 0.064 0.004 0.840 0.016 0.076
#> GSM120774 6 0.5867 -0.0859 0.000 0.136 0.000 0.416 0.012 0.436
#> GSM120783 4 0.2985 0.7006 0.000 0.028 0.004 0.864 0.020 0.084
#> GSM120787 4 0.5752 0.2255 0.000 0.372 0.000 0.472 0.004 0.152
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> CV:pam 109 2.86e-09 2
#> CV:pam 57 NA 3
#> CV:pam 53 1.39e-11 4
#> CV:pam 71 5.82e-17 5
#> CV:pam 90 1.78e-28 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 21512 rows and 119 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.704 0.865 0.934 0.4753 0.545 0.545
#> 3 3 0.674 0.871 0.878 0.3228 0.778 0.599
#> 4 4 0.760 0.848 0.918 0.1235 0.900 0.728
#> 5 5 0.687 0.633 0.806 0.1040 0.917 0.720
#> 6 6 0.712 0.644 0.780 0.0436 0.878 0.528
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
#> GSM120719 1 0.0000 0.999 1.000 0.000
#> GSM120720 1 0.0000 0.999 1.000 0.000
#> GSM120765 2 0.0000 0.889 0.000 1.000
#> GSM120767 2 0.0000 0.889 0.000 1.000
#> GSM120784 2 0.0000 0.889 0.000 1.000
#> GSM121400 1 0.0000 0.999 1.000 0.000
#> GSM121401 1 0.0000 0.999 1.000 0.000
#> GSM121402 2 0.0000 0.889 0.000 1.000
#> GSM121403 1 0.0000 0.999 1.000 0.000
#> GSM121404 2 0.0000 0.889 0.000 1.000
#> GSM121405 1 0.0000 0.999 1.000 0.000
#> GSM121406 2 0.0000 0.889 0.000 1.000
#> GSM121408 2 0.0000 0.889 0.000 1.000
#> GSM121409 1 0.0000 0.999 1.000 0.000
#> GSM121410 1 0.0000 0.999 1.000 0.000
#> GSM121412 2 0.0000 0.889 0.000 1.000
#> GSM121413 2 0.0000 0.889 0.000 1.000
#> GSM121414 2 0.0000 0.889 0.000 1.000
#> GSM121415 2 0.0000 0.889 0.000 1.000
#> GSM121416 2 0.0000 0.889 0.000 1.000
#> GSM120591 1 0.0000 0.999 1.000 0.000
#> GSM120594 1 0.0000 0.999 1.000 0.000
#> GSM120718 1 0.0000 0.999 1.000 0.000
#> GSM121205 1 0.0000 0.999 1.000 0.000
#> GSM121206 1 0.0000 0.999 1.000 0.000
#> GSM121207 1 0.0000 0.999 1.000 0.000
#> GSM121208 1 0.0000 0.999 1.000 0.000
#> GSM121209 1 0.0000 0.999 1.000 0.000
#> GSM121210 1 0.0000 0.999 1.000 0.000
#> GSM121211 1 0.0000 0.999 1.000 0.000
#> GSM121212 1 0.0000 0.999 1.000 0.000
#> GSM121213 1 0.0000 0.999 1.000 0.000
#> GSM121214 1 0.0000 0.999 1.000 0.000
#> GSM121215 1 0.0000 0.999 1.000 0.000
#> GSM121216 1 0.0000 0.999 1.000 0.000
#> GSM121217 1 0.0000 0.999 1.000 0.000
#> GSM121218 1 0.0000 0.999 1.000 0.000
#> GSM121234 1 0.0000 0.999 1.000 0.000
#> GSM121243 1 0.0000 0.999 1.000 0.000
#> GSM121245 1 0.0000 0.999 1.000 0.000
#> GSM121246 1 0.0000 0.999 1.000 0.000
#> GSM121247 1 0.0000 0.999 1.000 0.000
#> GSM121248 1 0.0000 0.999 1.000 0.000
#> GSM120744 2 0.9635 0.521 0.388 0.612
#> GSM120745 2 0.9635 0.521 0.388 0.612
#> GSM120746 2 0.9635 0.521 0.388 0.612
#> GSM120747 2 0.9635 0.521 0.388 0.612
#> GSM120748 2 0.9635 0.521 0.388 0.612
#> GSM120749 2 0.9635 0.521 0.388 0.612
#> GSM120750 2 0.9635 0.521 0.388 0.612
#> GSM120751 2 0.9635 0.521 0.388 0.612
#> GSM120752 2 0.9635 0.521 0.388 0.612
#> GSM121336 2 0.0000 0.889 0.000 1.000
#> GSM121339 2 0.4939 0.799 0.108 0.892
#> GSM121349 2 0.0000 0.889 0.000 1.000
#> GSM121355 2 0.0000 0.889 0.000 1.000
#> GSM120757 2 0.9635 0.521 0.388 0.612
#> GSM120766 2 0.9635 0.521 0.388 0.612
#> GSM120770 2 0.0000 0.889 0.000 1.000
#> GSM120779 2 0.9635 0.521 0.388 0.612
#> GSM120780 2 0.9635 0.521 0.388 0.612
#> GSM121102 2 0.0000 0.889 0.000 1.000
#> GSM121203 2 0.9635 0.521 0.388 0.612
#> GSM121204 2 0.9635 0.521 0.388 0.612
#> GSM121330 1 0.0000 0.999 1.000 0.000
#> GSM121335 1 0.0000 0.999 1.000 0.000
#> GSM121337 2 0.0000 0.889 0.000 1.000
#> GSM121338 2 0.0672 0.883 0.008 0.992
#> GSM121341 1 0.0000 0.999 1.000 0.000
#> GSM121342 1 0.0000 0.999 1.000 0.000
#> GSM121343 2 0.0000 0.889 0.000 1.000
#> GSM121344 1 0.0000 0.999 1.000 0.000
#> GSM121346 1 0.0000 0.999 1.000 0.000
#> GSM121347 2 0.0000 0.889 0.000 1.000
#> GSM121348 2 0.9170 0.594 0.332 0.668
#> GSM121350 1 0.0000 0.999 1.000 0.000
#> GSM121352 1 0.0000 0.999 1.000 0.000
#> GSM121354 1 0.0000 0.999 1.000 0.000
#> GSM120753 2 0.0000 0.889 0.000 1.000
#> GSM120761 2 0.0000 0.889 0.000 1.000
#> GSM120768 2 0.0000 0.889 0.000 1.000
#> GSM120781 2 0.0000 0.889 0.000 1.000
#> GSM120788 2 0.0000 0.889 0.000 1.000
#> GSM120760 2 0.0000 0.889 0.000 1.000
#> GSM120763 2 0.0000 0.889 0.000 1.000
#> GSM120764 2 0.0000 0.889 0.000 1.000
#> GSM120777 2 0.0000 0.889 0.000 1.000
#> GSM120786 2 0.0000 0.889 0.000 1.000
#> GSM121329 1 0.1843 0.965 0.972 0.028
#> GSM121331 2 0.9635 0.521 0.388 0.612
#> GSM121333 2 0.9635 0.521 0.388 0.612
#> GSM121345 2 0.9635 0.521 0.388 0.612
#> GSM121356 2 0.9635 0.521 0.388 0.612
#> GSM120754 2 0.0000 0.889 0.000 1.000
#> GSM120759 2 0.0000 0.889 0.000 1.000
#> GSM120762 2 0.0000 0.889 0.000 1.000
#> GSM120775 2 0.0000 0.889 0.000 1.000
#> GSM120776 2 0.0000 0.889 0.000 1.000
#> GSM120782 2 0.0000 0.889 0.000 1.000
#> GSM120789 2 0.0000 0.889 0.000 1.000
#> GSM120790 2 0.0000 0.889 0.000 1.000
#> GSM120791 2 0.0000 0.889 0.000 1.000
#> GSM120755 2 0.0000 0.889 0.000 1.000
#> GSM120756 2 0.0000 0.889 0.000 1.000
#> GSM120769 2 0.0000 0.889 0.000 1.000
#> GSM120778 2 0.0000 0.889 0.000 1.000
#> GSM120792 2 0.0000 0.889 0.000 1.000
#> GSM121332 2 0.0000 0.889 0.000 1.000
#> GSM121334 2 0.0000 0.889 0.000 1.000
#> GSM121340 2 0.0000 0.889 0.000 1.000
#> GSM121351 2 0.0000 0.889 0.000 1.000
#> GSM121353 2 0.0938 0.881 0.012 0.988
#> GSM120758 2 0.0000 0.889 0.000 1.000
#> GSM120771 2 0.0000 0.889 0.000 1.000
#> GSM120772 2 0.0000 0.889 0.000 1.000
#> GSM120773 2 0.0000 0.889 0.000 1.000
#> GSM120774 2 0.0000 0.889 0.000 1.000
#> GSM120783 2 0.0000 0.889 0.000 1.000
#> GSM120787 2 0.0000 0.889 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM120719 1 0.5465 0.846 0.712 0.000 0.288
#> GSM120720 1 0.5529 0.851 0.704 0.000 0.296
#> GSM120765 2 0.1031 0.969 0.000 0.976 0.024
#> GSM120767 2 0.1031 0.969 0.000 0.976 0.024
#> GSM120784 2 0.1411 0.961 0.000 0.964 0.036
#> GSM121400 1 0.5988 0.786 0.632 0.000 0.368
#> GSM121401 1 0.5560 0.849 0.700 0.000 0.300
#> GSM121402 2 0.0747 0.973 0.000 0.984 0.016
#> GSM121403 3 0.6027 0.121 0.272 0.016 0.712
#> GSM121404 3 0.5058 0.844 0.000 0.244 0.756
#> GSM121405 1 0.5650 0.841 0.688 0.000 0.312
#> GSM121406 2 0.0892 0.971 0.000 0.980 0.020
#> GSM121408 2 0.1031 0.969 0.000 0.976 0.024
#> GSM121409 3 0.6468 -0.468 0.444 0.004 0.552
#> GSM121410 1 0.6111 0.752 0.604 0.000 0.396
#> GSM121412 2 0.1163 0.966 0.000 0.972 0.028
#> GSM121413 2 0.1031 0.969 0.000 0.976 0.024
#> GSM121414 2 0.1031 0.969 0.000 0.976 0.024
#> GSM121415 2 0.1163 0.966 0.000 0.972 0.028
#> GSM121416 2 0.0592 0.974 0.000 0.988 0.012
#> GSM120591 1 0.5591 0.846 0.696 0.000 0.304
#> GSM120594 1 0.5497 0.852 0.708 0.000 0.292
#> GSM120718 1 0.5465 0.853 0.712 0.000 0.288
#> GSM121205 1 0.0000 0.842 1.000 0.000 0.000
#> GSM121206 1 0.0000 0.842 1.000 0.000 0.000
#> GSM121207 1 0.0424 0.843 0.992 0.000 0.008
#> GSM121208 1 0.5431 0.854 0.716 0.000 0.284
#> GSM121209 1 0.0000 0.842 1.000 0.000 0.000
#> GSM121210 1 0.0424 0.843 0.992 0.000 0.008
#> GSM121211 1 0.0000 0.842 1.000 0.000 0.000
#> GSM121212 1 0.0000 0.842 1.000 0.000 0.000
#> GSM121213 1 0.0000 0.842 1.000 0.000 0.000
#> GSM121214 1 0.0000 0.842 1.000 0.000 0.000
#> GSM121215 1 0.0000 0.842 1.000 0.000 0.000
#> GSM121216 1 0.1031 0.840 0.976 0.000 0.024
#> GSM121217 1 0.0000 0.842 1.000 0.000 0.000
#> GSM121218 1 0.0000 0.842 1.000 0.000 0.000
#> GSM121234 1 0.0000 0.842 1.000 0.000 0.000
#> GSM121243 1 0.0237 0.843 0.996 0.000 0.004
#> GSM121245 1 0.0237 0.843 0.996 0.000 0.004
#> GSM121246 1 0.5397 0.854 0.720 0.000 0.280
#> GSM121247 1 0.3375 0.837 0.892 0.008 0.100
#> GSM121248 1 0.0000 0.842 1.000 0.000 0.000
#> GSM120744 3 0.3879 0.877 0.000 0.152 0.848
#> GSM120745 3 0.3816 0.876 0.000 0.148 0.852
#> GSM120746 3 0.3816 0.876 0.000 0.148 0.852
#> GSM120747 3 0.3816 0.876 0.000 0.148 0.852
#> GSM120748 3 0.3816 0.876 0.000 0.148 0.852
#> GSM120749 3 0.3816 0.876 0.000 0.148 0.852
#> GSM120750 3 0.3816 0.876 0.000 0.148 0.852
#> GSM120751 3 0.3816 0.876 0.000 0.148 0.852
#> GSM120752 3 0.3816 0.876 0.000 0.148 0.852
#> GSM121336 2 0.1031 0.969 0.000 0.976 0.024
#> GSM121339 3 0.3941 0.848 0.000 0.156 0.844
#> GSM121349 2 0.0747 0.973 0.000 0.984 0.016
#> GSM121355 2 0.1163 0.966 0.000 0.972 0.028
#> GSM120757 3 0.4974 0.869 0.000 0.236 0.764
#> GSM120766 3 0.4974 0.869 0.000 0.236 0.764
#> GSM120770 3 0.6274 0.473 0.000 0.456 0.544
#> GSM120779 3 0.4974 0.869 0.000 0.236 0.764
#> GSM120780 3 0.4178 0.879 0.000 0.172 0.828
#> GSM121102 3 0.4702 0.869 0.000 0.212 0.788
#> GSM121203 3 0.3816 0.876 0.000 0.148 0.852
#> GSM121204 3 0.4974 0.869 0.000 0.236 0.764
#> GSM121330 1 0.5529 0.851 0.704 0.000 0.296
#> GSM121335 1 0.5497 0.852 0.708 0.000 0.292
#> GSM121337 3 0.6244 0.524 0.000 0.440 0.560
#> GSM121338 3 0.4178 0.878 0.000 0.172 0.828
#> GSM121341 1 0.5465 0.853 0.712 0.000 0.288
#> GSM121342 1 0.5465 0.853 0.712 0.000 0.288
#> GSM121343 3 0.4121 0.879 0.000 0.168 0.832
#> GSM121344 1 0.5497 0.852 0.708 0.000 0.292
#> GSM121346 1 0.5497 0.852 0.708 0.000 0.292
#> GSM121347 3 0.6308 0.430 0.000 0.492 0.508
#> GSM121348 3 0.4931 0.868 0.000 0.232 0.768
#> GSM121350 1 0.5560 0.849 0.700 0.000 0.300
#> GSM121352 1 0.5529 0.851 0.704 0.000 0.296
#> GSM121354 1 0.5497 0.852 0.708 0.000 0.292
#> GSM120753 2 0.0747 0.973 0.000 0.984 0.016
#> GSM120761 2 0.0424 0.973 0.000 0.992 0.008
#> GSM120768 2 0.0592 0.971 0.000 0.988 0.012
#> GSM120781 2 0.0424 0.974 0.000 0.992 0.008
#> GSM120788 2 0.1529 0.945 0.000 0.960 0.040
#> GSM120760 2 0.0424 0.973 0.000 0.992 0.008
#> GSM120763 2 0.0424 0.973 0.000 0.992 0.008
#> GSM120764 2 0.0592 0.971 0.000 0.988 0.012
#> GSM120777 2 0.1529 0.945 0.000 0.960 0.040
#> GSM120786 2 0.0592 0.971 0.000 0.988 0.012
#> GSM121329 1 0.6326 0.822 0.688 0.020 0.292
#> GSM121331 3 0.4974 0.869 0.000 0.236 0.764
#> GSM121333 3 0.4974 0.869 0.000 0.236 0.764
#> GSM121345 3 0.4974 0.869 0.000 0.236 0.764
#> GSM121356 3 0.4974 0.869 0.000 0.236 0.764
#> GSM120754 2 0.1860 0.929 0.000 0.948 0.052
#> GSM120759 2 0.0237 0.974 0.000 0.996 0.004
#> GSM120762 2 0.0000 0.973 0.000 1.000 0.000
#> GSM120775 2 0.1529 0.945 0.000 0.960 0.040
#> GSM120776 3 0.5254 0.842 0.000 0.264 0.736
#> GSM120782 2 0.4605 0.687 0.000 0.796 0.204
#> GSM120789 2 0.0237 0.974 0.000 0.996 0.004
#> GSM120790 2 0.0237 0.973 0.000 0.996 0.004
#> GSM120791 2 0.0592 0.971 0.000 0.988 0.012
#> GSM120755 2 0.0892 0.971 0.000 0.980 0.020
#> GSM120756 2 0.1529 0.945 0.000 0.960 0.040
#> GSM120769 2 0.0000 0.973 0.000 1.000 0.000
#> GSM120778 2 0.0000 0.973 0.000 1.000 0.000
#> GSM120792 2 0.0592 0.971 0.000 0.988 0.012
#> GSM121332 2 0.0747 0.973 0.000 0.984 0.016
#> GSM121334 2 0.0237 0.973 0.000 0.996 0.004
#> GSM121340 2 0.0424 0.973 0.000 0.992 0.008
#> GSM121351 2 0.0747 0.973 0.000 0.984 0.016
#> GSM121353 2 0.1411 0.947 0.000 0.964 0.036
#> GSM120758 2 0.0237 0.974 0.000 0.996 0.004
#> GSM120771 2 0.0747 0.974 0.000 0.984 0.016
#> GSM120772 2 0.0000 0.973 0.000 1.000 0.000
#> GSM120773 2 0.0424 0.973 0.000 0.992 0.008
#> GSM120774 2 0.0000 0.973 0.000 1.000 0.000
#> GSM120783 2 0.0424 0.973 0.000 0.992 0.008
#> GSM120787 2 0.0000 0.973 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM120719 1 0.3791 0.7500 0.796 0.000 0.200 0.004
#> GSM120720 1 0.0188 0.8793 0.996 0.000 0.000 0.004
#> GSM120765 2 0.2760 0.8754 0.000 0.872 0.128 0.000
#> GSM120767 2 0.0336 0.8996 0.000 0.992 0.008 0.000
#> GSM120784 2 0.3356 0.8466 0.000 0.824 0.176 0.000
#> GSM121400 1 0.3356 0.7709 0.824 0.000 0.176 0.000
#> GSM121401 1 0.0000 0.8790 1.000 0.000 0.000 0.000
#> GSM121402 2 0.0188 0.9000 0.000 0.996 0.004 0.000
#> GSM121403 1 0.4985 0.2462 0.532 0.000 0.468 0.000
#> GSM121404 3 0.5404 -0.0401 0.012 0.476 0.512 0.000
#> GSM121405 1 0.0000 0.8790 1.000 0.000 0.000 0.000
#> GSM121406 2 0.0524 0.8989 0.000 0.988 0.008 0.004
#> GSM121408 2 0.0524 0.8989 0.000 0.988 0.008 0.004
#> GSM121409 1 0.4866 0.4157 0.596 0.000 0.404 0.000
#> GSM121410 1 0.4304 0.6429 0.716 0.000 0.284 0.000
#> GSM121412 2 0.0524 0.8989 0.000 0.988 0.008 0.004
#> GSM121413 2 0.0336 0.8996 0.000 0.992 0.008 0.000
#> GSM121414 2 0.0524 0.8989 0.000 0.988 0.008 0.004
#> GSM121415 2 0.0779 0.9008 0.000 0.980 0.016 0.004
#> GSM121416 2 0.1716 0.8955 0.000 0.936 0.064 0.000
#> GSM120591 1 0.1004 0.8691 0.972 0.000 0.024 0.004
#> GSM120594 1 0.0188 0.8793 0.996 0.000 0.000 0.004
#> GSM120718 1 0.0592 0.8724 0.984 0.000 0.000 0.016
#> GSM121205 4 0.0188 0.9440 0.004 0.000 0.000 0.996
#> GSM121206 4 0.0188 0.9440 0.004 0.000 0.000 0.996
#> GSM121207 4 0.2589 0.8982 0.116 0.000 0.000 0.884
#> GSM121208 1 0.0188 0.8793 0.996 0.000 0.000 0.004
#> GSM121209 4 0.0188 0.9440 0.004 0.000 0.000 0.996
#> GSM121210 4 0.3486 0.8301 0.188 0.000 0.000 0.812
#> GSM121211 4 0.0188 0.9440 0.004 0.000 0.000 0.996
#> GSM121212 4 0.2704 0.8936 0.124 0.000 0.000 0.876
#> GSM121213 4 0.0188 0.9440 0.004 0.000 0.000 0.996
#> GSM121214 4 0.0188 0.9440 0.004 0.000 0.000 0.996
#> GSM121215 4 0.0188 0.9440 0.004 0.000 0.000 0.996
#> GSM121216 4 0.3074 0.8723 0.152 0.000 0.000 0.848
#> GSM121217 4 0.0188 0.9440 0.004 0.000 0.000 0.996
#> GSM121218 4 0.0188 0.9440 0.004 0.000 0.000 0.996
#> GSM121234 4 0.0336 0.9429 0.008 0.000 0.000 0.992
#> GSM121243 4 0.3172 0.8645 0.160 0.000 0.000 0.840
#> GSM121245 4 0.2921 0.8822 0.140 0.000 0.000 0.860
#> GSM121246 1 0.0188 0.8793 0.996 0.000 0.000 0.004
#> GSM121247 1 0.7357 0.3948 0.524 0.000 0.216 0.260
#> GSM121248 4 0.0188 0.9440 0.004 0.000 0.000 0.996
#> GSM120744 3 0.0592 0.9308 0.016 0.000 0.984 0.000
#> GSM120745 3 0.0592 0.9308 0.016 0.000 0.984 0.000
#> GSM120746 3 0.0592 0.9308 0.016 0.000 0.984 0.000
#> GSM120747 3 0.0592 0.9308 0.016 0.000 0.984 0.000
#> GSM120748 3 0.0592 0.9308 0.016 0.000 0.984 0.000
#> GSM120749 3 0.0592 0.9308 0.016 0.000 0.984 0.000
#> GSM120750 3 0.0592 0.9308 0.016 0.000 0.984 0.000
#> GSM120751 3 0.0592 0.9308 0.016 0.000 0.984 0.000
#> GSM120752 3 0.0592 0.9308 0.016 0.000 0.984 0.000
#> GSM121336 2 0.0336 0.8996 0.000 0.992 0.008 0.000
#> GSM121339 2 0.6840 0.0125 0.100 0.468 0.432 0.000
#> GSM121349 2 0.0336 0.8996 0.000 0.992 0.008 0.000
#> GSM121355 2 0.0524 0.8989 0.000 0.988 0.008 0.004
#> GSM120757 3 0.0336 0.9300 0.000 0.008 0.992 0.000
#> GSM120766 3 0.0336 0.9300 0.000 0.008 0.992 0.000
#> GSM120770 3 0.1940 0.8760 0.000 0.076 0.924 0.000
#> GSM120779 3 0.0336 0.9300 0.000 0.008 0.992 0.000
#> GSM120780 3 0.0592 0.9308 0.016 0.000 0.984 0.000
#> GSM121102 3 0.2125 0.8772 0.004 0.076 0.920 0.000
#> GSM121203 3 0.1182 0.9215 0.016 0.016 0.968 0.000
#> GSM121204 3 0.0336 0.9300 0.000 0.008 0.992 0.000
#> GSM121330 1 0.0000 0.8790 1.000 0.000 0.000 0.000
#> GSM121335 1 0.0188 0.8793 0.996 0.000 0.000 0.004
#> GSM121337 2 0.4877 0.3992 0.000 0.592 0.408 0.000
#> GSM121338 3 0.4720 0.6258 0.016 0.264 0.720 0.000
#> GSM121341 1 0.0188 0.8793 0.996 0.000 0.000 0.004
#> GSM121342 1 0.0188 0.8793 0.996 0.000 0.000 0.004
#> GSM121343 3 0.3695 0.7872 0.016 0.156 0.828 0.000
#> GSM121344 1 0.0188 0.8793 0.996 0.000 0.000 0.004
#> GSM121346 1 0.0000 0.8790 1.000 0.000 0.000 0.000
#> GSM121347 2 0.4933 0.3130 0.000 0.568 0.432 0.000
#> GSM121348 3 0.0336 0.9300 0.000 0.008 0.992 0.000
#> GSM121350 1 0.0000 0.8790 1.000 0.000 0.000 0.000
#> GSM121352 1 0.0188 0.8793 0.996 0.000 0.000 0.004
#> GSM121354 1 0.0188 0.8793 0.996 0.000 0.000 0.004
#> GSM120753 2 0.0188 0.9000 0.000 0.996 0.004 0.000
#> GSM120761 2 0.3172 0.8583 0.000 0.840 0.160 0.000
#> GSM120768 2 0.3219 0.8573 0.000 0.836 0.164 0.000
#> GSM120781 2 0.1389 0.8995 0.000 0.952 0.048 0.000
#> GSM120788 2 0.3400 0.8467 0.000 0.820 0.180 0.000
#> GSM120760 2 0.2921 0.8689 0.000 0.860 0.140 0.000
#> GSM120763 2 0.3219 0.8573 0.000 0.836 0.164 0.000
#> GSM120764 2 0.3266 0.8546 0.000 0.832 0.168 0.000
#> GSM120777 2 0.3400 0.8462 0.000 0.820 0.180 0.000
#> GSM120786 2 0.3266 0.8546 0.000 0.832 0.168 0.000
#> GSM121329 1 0.4372 0.6684 0.728 0.000 0.268 0.004
#> GSM121331 3 0.0336 0.9300 0.000 0.008 0.992 0.000
#> GSM121333 3 0.0336 0.9300 0.000 0.008 0.992 0.000
#> GSM121345 3 0.0592 0.9273 0.000 0.016 0.984 0.000
#> GSM121356 3 0.0592 0.9284 0.000 0.016 0.984 0.000
#> GSM120754 2 0.3356 0.8494 0.000 0.824 0.176 0.000
#> GSM120759 2 0.0000 0.9000 0.000 1.000 0.000 0.000
#> GSM120762 2 0.0000 0.9000 0.000 1.000 0.000 0.000
#> GSM120775 2 0.3400 0.8467 0.000 0.820 0.180 0.000
#> GSM120776 3 0.0592 0.9261 0.000 0.016 0.984 0.000
#> GSM120782 2 0.4917 0.6197 0.008 0.656 0.336 0.000
#> GSM120789 2 0.0188 0.8996 0.000 0.996 0.000 0.004
#> GSM120790 2 0.0336 0.9011 0.000 0.992 0.008 0.000
#> GSM120791 2 0.3219 0.8573 0.000 0.836 0.164 0.000
#> GSM120755 2 0.0524 0.8989 0.000 0.988 0.008 0.004
#> GSM120756 2 0.3444 0.8434 0.000 0.816 0.184 0.000
#> GSM120769 2 0.0000 0.9000 0.000 1.000 0.000 0.000
#> GSM120778 2 0.0779 0.9002 0.000 0.980 0.016 0.004
#> GSM120792 2 0.1022 0.9009 0.000 0.968 0.032 0.000
#> GSM121332 2 0.0376 0.8996 0.000 0.992 0.004 0.004
#> GSM121334 2 0.0000 0.9000 0.000 1.000 0.000 0.000
#> GSM121340 2 0.2197 0.8903 0.000 0.916 0.080 0.004
#> GSM121351 2 0.0188 0.9000 0.000 0.996 0.004 0.000
#> GSM121353 2 0.2831 0.8761 0.000 0.876 0.120 0.004
#> GSM120758 2 0.1302 0.8991 0.000 0.956 0.044 0.000
#> GSM120771 2 0.1211 0.9007 0.000 0.960 0.040 0.000
#> GSM120772 2 0.0000 0.9000 0.000 1.000 0.000 0.000
#> GSM120773 2 0.3172 0.8596 0.000 0.840 0.160 0.000
#> GSM120774 2 0.0817 0.9009 0.000 0.976 0.024 0.000
#> GSM120783 2 0.3219 0.8573 0.000 0.836 0.164 0.000
#> GSM120787 2 0.0188 0.9002 0.000 0.996 0.004 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM120719 3 0.7085 0.454 0.204 0.048 0.528 0.000 0.220
#> GSM120720 3 0.0794 0.879 0.028 0.000 0.972 0.000 0.000
#> GSM120765 2 0.4251 0.459 0.000 0.672 0.000 0.316 0.012
#> GSM120767 4 0.2280 0.548 0.000 0.120 0.000 0.880 0.000
#> GSM120784 2 0.4761 0.540 0.000 0.728 0.000 0.168 0.104
#> GSM121400 3 0.2773 0.761 0.000 0.000 0.836 0.000 0.164
#> GSM121401 3 0.0000 0.890 0.000 0.000 1.000 0.000 0.000
#> GSM121402 4 0.4242 -0.233 0.000 0.428 0.000 0.572 0.000
#> GSM121403 3 0.5243 0.399 0.000 0.048 0.596 0.004 0.352
#> GSM121404 2 0.7277 0.371 0.000 0.456 0.036 0.228 0.280
#> GSM121405 3 0.0000 0.890 0.000 0.000 1.000 0.000 0.000
#> GSM121406 4 0.4268 -0.227 0.000 0.444 0.000 0.556 0.000
#> GSM121408 4 0.1478 0.592 0.000 0.064 0.000 0.936 0.000
#> GSM121409 3 0.4891 0.490 0.000 0.044 0.640 0.000 0.316
#> GSM121410 3 0.3845 0.688 0.000 0.024 0.768 0.000 0.208
#> GSM121412 4 0.4287 -0.257 0.000 0.460 0.000 0.540 0.000
#> GSM121413 2 0.4307 0.261 0.000 0.504 0.000 0.496 0.000
#> GSM121414 4 0.4287 -0.257 0.000 0.460 0.000 0.540 0.000
#> GSM121415 2 0.4304 0.278 0.000 0.516 0.000 0.484 0.000
#> GSM121416 2 0.4211 0.442 0.000 0.636 0.000 0.360 0.004
#> GSM120591 3 0.0324 0.888 0.000 0.004 0.992 0.000 0.004
#> GSM120594 3 0.0000 0.890 0.000 0.000 1.000 0.000 0.000
#> GSM120718 3 0.2230 0.812 0.116 0.000 0.884 0.000 0.000
#> GSM121205 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> GSM121207 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> GSM121208 3 0.0290 0.888 0.000 0.008 0.992 0.000 0.000
#> GSM121209 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.0162 0.969 0.996 0.000 0.004 0.000 0.000
#> GSM121211 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> GSM121216 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> GSM121217 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> GSM121234 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> GSM121243 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> GSM121245 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> GSM121246 3 0.0451 0.887 0.004 0.008 0.988 0.000 0.000
#> GSM121247 1 0.6700 0.354 0.564 0.032 0.188 0.000 0.216
#> GSM121248 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> GSM120744 5 0.1648 0.820 0.000 0.020 0.040 0.000 0.940
#> GSM120745 5 0.2153 0.811 0.000 0.040 0.044 0.000 0.916
#> GSM120746 5 0.2153 0.811 0.000 0.040 0.044 0.000 0.916
#> GSM120747 5 0.2153 0.811 0.000 0.040 0.044 0.000 0.916
#> GSM120748 5 0.2230 0.814 0.000 0.044 0.044 0.000 0.912
#> GSM120749 5 0.2153 0.811 0.000 0.040 0.044 0.000 0.916
#> GSM120750 5 0.2153 0.811 0.000 0.040 0.044 0.000 0.916
#> GSM120751 5 0.2153 0.811 0.000 0.040 0.044 0.000 0.916
#> GSM120752 5 0.2153 0.811 0.000 0.040 0.044 0.000 0.916
#> GSM121336 4 0.1544 0.589 0.000 0.068 0.000 0.932 0.000
#> GSM121339 2 0.8222 0.299 0.000 0.380 0.132 0.240 0.248
#> GSM121349 4 0.1197 0.603 0.000 0.048 0.000 0.952 0.000
#> GSM121355 4 0.1732 0.578 0.000 0.080 0.000 0.920 0.000
#> GSM120757 5 0.2605 0.811 0.000 0.148 0.000 0.000 0.852
#> GSM120766 5 0.2605 0.811 0.000 0.148 0.000 0.000 0.852
#> GSM120770 5 0.4614 0.719 0.000 0.224 0.032 0.016 0.728
#> GSM120779 5 0.2773 0.807 0.000 0.164 0.000 0.000 0.836
#> GSM120780 5 0.1981 0.823 0.000 0.048 0.028 0.000 0.924
#> GSM121102 5 0.4925 0.720 0.000 0.152 0.020 0.084 0.744
#> GSM121203 5 0.3549 0.811 0.000 0.076 0.040 0.032 0.852
#> GSM121204 5 0.2930 0.810 0.000 0.164 0.004 0.000 0.832
#> GSM121330 3 0.0000 0.890 0.000 0.000 1.000 0.000 0.000
#> GSM121335 3 0.0000 0.890 0.000 0.000 1.000 0.000 0.000
#> GSM121337 2 0.6521 0.484 0.000 0.512 0.004 0.220 0.264
#> GSM121338 5 0.7401 0.287 0.000 0.224 0.068 0.208 0.500
#> GSM121341 3 0.0290 0.888 0.008 0.000 0.992 0.000 0.000
#> GSM121342 3 0.0794 0.879 0.028 0.000 0.972 0.000 0.000
#> GSM121343 5 0.6932 0.377 0.000 0.248 0.044 0.168 0.540
#> GSM121344 3 0.0000 0.890 0.000 0.000 1.000 0.000 0.000
#> GSM121346 3 0.0000 0.890 0.000 0.000 1.000 0.000 0.000
#> GSM121347 2 0.6328 0.521 0.000 0.528 0.000 0.228 0.244
#> GSM121348 5 0.3132 0.803 0.000 0.172 0.000 0.008 0.820
#> GSM121350 3 0.0000 0.890 0.000 0.000 1.000 0.000 0.000
#> GSM121352 3 0.0000 0.890 0.000 0.000 1.000 0.000 0.000
#> GSM121354 3 0.0000 0.890 0.000 0.000 1.000 0.000 0.000
#> GSM120753 4 0.1671 0.605 0.000 0.076 0.000 0.924 0.000
#> GSM120761 2 0.5109 -0.189 0.000 0.504 0.000 0.460 0.036
#> GSM120768 4 0.4731 0.440 0.000 0.328 0.000 0.640 0.032
#> GSM120781 4 0.1608 0.611 0.000 0.072 0.000 0.928 0.000
#> GSM120788 4 0.5527 0.331 0.000 0.388 0.000 0.540 0.072
#> GSM120760 4 0.4835 0.405 0.000 0.380 0.000 0.592 0.028
#> GSM120763 4 0.4856 0.401 0.000 0.388 0.000 0.584 0.028
#> GSM120764 4 0.5028 0.384 0.000 0.400 0.000 0.564 0.036
#> GSM120777 4 0.5476 0.344 0.000 0.388 0.000 0.544 0.068
#> GSM120786 4 0.4966 0.383 0.000 0.404 0.000 0.564 0.032
#> GSM121329 3 0.6989 0.489 0.164 0.060 0.556 0.000 0.220
#> GSM121331 5 0.2891 0.802 0.000 0.176 0.000 0.000 0.824
#> GSM121333 5 0.2773 0.807 0.000 0.164 0.000 0.000 0.836
#> GSM121345 5 0.3242 0.777 0.000 0.216 0.000 0.000 0.784
#> GSM121356 5 0.2970 0.805 0.000 0.168 0.000 0.004 0.828
#> GSM120754 2 0.5060 0.399 0.000 0.684 0.000 0.224 0.092
#> GSM120759 4 0.2891 0.367 0.000 0.176 0.000 0.824 0.000
#> GSM120762 4 0.0162 0.613 0.000 0.004 0.000 0.996 0.000
#> GSM120775 4 0.5510 0.340 0.000 0.380 0.000 0.548 0.072
#> GSM120776 5 0.4302 0.312 0.000 0.480 0.000 0.000 0.520
#> GSM120782 2 0.5824 0.468 0.000 0.660 0.020 0.176 0.144
#> GSM120789 4 0.0290 0.612 0.000 0.008 0.000 0.992 0.000
#> GSM120790 2 0.4262 0.261 0.000 0.560 0.000 0.440 0.000
#> GSM120791 4 0.4930 0.398 0.000 0.388 0.000 0.580 0.032
#> GSM120755 4 0.1270 0.599 0.000 0.052 0.000 0.948 0.000
#> GSM120756 4 0.5579 0.334 0.000 0.368 0.000 0.552 0.080
#> GSM120769 4 0.0000 0.614 0.000 0.000 0.000 1.000 0.000
#> GSM120778 4 0.0912 0.619 0.000 0.016 0.000 0.972 0.012
#> GSM120792 4 0.1399 0.616 0.000 0.028 0.000 0.952 0.020
#> GSM121332 4 0.0880 0.603 0.000 0.032 0.000 0.968 0.000
#> GSM121334 4 0.3305 0.504 0.000 0.224 0.000 0.776 0.000
#> GSM121340 4 0.2388 0.603 0.000 0.072 0.000 0.900 0.028
#> GSM121351 4 0.3424 0.259 0.000 0.240 0.000 0.760 0.000
#> GSM121353 4 0.3410 0.540 0.000 0.092 0.000 0.840 0.068
#> GSM120758 4 0.2074 0.590 0.000 0.104 0.000 0.896 0.000
#> GSM120771 2 0.4300 0.245 0.000 0.524 0.000 0.476 0.000
#> GSM120772 4 0.0963 0.617 0.000 0.036 0.000 0.964 0.000
#> GSM120773 4 0.4798 0.392 0.000 0.396 0.000 0.580 0.024
#> GSM120774 4 0.0798 0.618 0.000 0.016 0.000 0.976 0.008
#> GSM120783 4 0.4856 0.401 0.000 0.388 0.000 0.584 0.028
#> GSM120787 4 0.0451 0.616 0.000 0.008 0.000 0.988 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM120719 3 0.6715 0.38335 0.244 0.000 0.488 0.072 0.196 0.000
#> GSM120720 3 0.0458 0.87359 0.016 0.000 0.984 0.000 0.000 0.000
#> GSM120765 2 0.4060 0.46882 0.000 0.760 0.000 0.180 0.032 0.028
#> GSM120767 2 0.2234 0.67040 0.000 0.872 0.000 0.124 0.004 0.000
#> GSM120784 2 0.5211 0.38449 0.000 0.680 0.000 0.188 0.076 0.056
#> GSM121400 3 0.3910 0.71815 0.000 0.000 0.784 0.012 0.072 0.132
#> GSM121401 3 0.0146 0.88050 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM121402 2 0.2538 0.65493 0.000 0.860 0.000 0.124 0.000 0.016
#> GSM121403 3 0.6895 0.13295 0.000 0.000 0.400 0.076 0.176 0.348
#> GSM121404 2 0.7100 -0.01097 0.000 0.376 0.000 0.080 0.232 0.312
#> GSM121405 3 0.0146 0.88050 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM121406 2 0.0692 0.65470 0.000 0.976 0.000 0.020 0.000 0.004
#> GSM121408 2 0.2260 0.66757 0.000 0.860 0.000 0.140 0.000 0.000
#> GSM121409 3 0.6645 0.35096 0.000 0.000 0.504 0.072 0.192 0.232
#> GSM121410 3 0.5370 0.62917 0.000 0.000 0.684 0.072 0.128 0.116
#> GSM121412 2 0.0806 0.64931 0.000 0.972 0.000 0.008 0.000 0.020
#> GSM121413 2 0.0909 0.64688 0.000 0.968 0.000 0.012 0.000 0.020
#> GSM121414 2 0.0806 0.64722 0.000 0.972 0.000 0.008 0.000 0.020
#> GSM121415 2 0.1346 0.64308 0.000 0.952 0.000 0.024 0.008 0.016
#> GSM121416 2 0.3780 0.49559 0.000 0.760 0.000 0.204 0.016 0.020
#> GSM120591 3 0.0692 0.87165 0.000 0.000 0.976 0.020 0.004 0.000
#> GSM120594 3 0.0000 0.88089 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM120718 3 0.1814 0.81201 0.100 0.000 0.900 0.000 0.000 0.000
#> GSM121205 1 0.0000 0.97319 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.97319 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121207 1 0.0000 0.97319 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121208 3 0.0146 0.88050 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM121209 1 0.0000 0.97319 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.97319 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.97319 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.97319 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.97319 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.97319 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.97319 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121216 1 0.0000 0.97319 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121217 1 0.0000 0.97319 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.97319 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121234 1 0.0000 0.97319 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121243 1 0.0000 0.97319 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121245 1 0.0000 0.97319 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121246 3 0.0146 0.88050 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM121247 1 0.6322 0.35483 0.560 0.000 0.176 0.068 0.196 0.000
#> GSM121248 1 0.0000 0.97319 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM120744 6 0.3659 0.59541 0.000 0.000 0.000 0.000 0.364 0.636
#> GSM120745 6 0.3864 0.35807 0.000 0.000 0.000 0.000 0.480 0.520
#> GSM120746 6 0.3050 0.73746 0.000 0.000 0.000 0.000 0.236 0.764
#> GSM120747 6 0.3076 0.73681 0.000 0.000 0.000 0.000 0.240 0.760
#> GSM120748 6 0.3101 0.73517 0.000 0.000 0.000 0.000 0.244 0.756
#> GSM120749 6 0.3050 0.73746 0.000 0.000 0.000 0.000 0.236 0.764
#> GSM120750 6 0.3050 0.73746 0.000 0.000 0.000 0.000 0.236 0.764
#> GSM120751 6 0.3050 0.73746 0.000 0.000 0.000 0.000 0.236 0.764
#> GSM120752 6 0.3428 0.68106 0.000 0.000 0.000 0.000 0.304 0.696
#> GSM121336 2 0.2300 0.66738 0.000 0.856 0.000 0.144 0.000 0.000
#> GSM121339 2 0.8306 -0.04755 0.000 0.328 0.104 0.080 0.216 0.272
#> GSM121349 2 0.2664 0.65116 0.000 0.816 0.000 0.184 0.000 0.000
#> GSM121355 2 0.2135 0.67006 0.000 0.872 0.000 0.128 0.000 0.000
#> GSM120757 5 0.0806 0.77025 0.000 0.000 0.000 0.008 0.972 0.020
#> GSM120766 5 0.1349 0.74242 0.000 0.000 0.000 0.004 0.940 0.056
#> GSM120770 5 0.6038 -0.05375 0.000 0.044 0.000 0.092 0.444 0.420
#> GSM120779 5 0.0405 0.76571 0.000 0.000 0.000 0.004 0.988 0.008
#> GSM120780 5 0.3907 -0.07794 0.000 0.000 0.000 0.004 0.588 0.408
#> GSM121102 5 0.6062 0.00329 0.000 0.148 0.000 0.024 0.496 0.332
#> GSM121203 6 0.4874 0.61914 0.000 0.072 0.000 0.008 0.284 0.636
#> GSM121204 5 0.0603 0.76999 0.000 0.000 0.000 0.004 0.980 0.016
#> GSM121330 3 0.0000 0.88089 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121335 3 0.0000 0.88089 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121337 2 0.6773 0.12845 0.000 0.432 0.000 0.052 0.236 0.280
#> GSM121338 6 0.6728 0.05281 0.000 0.180 0.000 0.080 0.248 0.492
#> GSM121341 3 0.0000 0.88089 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121342 3 0.0146 0.87976 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM121343 6 0.6626 0.05929 0.000 0.152 0.000 0.084 0.256 0.508
#> GSM121344 3 0.0000 0.88089 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121346 3 0.0000 0.88089 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121347 4 0.7637 -0.04828 0.000 0.176 0.000 0.300 0.252 0.272
#> GSM121348 5 0.3438 0.57401 0.000 0.008 0.000 0.020 0.788 0.184
#> GSM121350 3 0.0000 0.88089 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121352 3 0.0000 0.88089 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121354 3 0.0000 0.88089 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM120753 2 0.3684 0.52055 0.000 0.664 0.000 0.332 0.004 0.000
#> GSM120761 4 0.4804 0.69761 0.000 0.140 0.000 0.700 0.148 0.012
#> GSM120768 4 0.4787 0.67864 0.000 0.184 0.000 0.672 0.144 0.000
#> GSM120781 2 0.3938 0.51511 0.000 0.660 0.000 0.324 0.016 0.000
#> GSM120788 4 0.4795 0.70508 0.000 0.088 0.000 0.692 0.204 0.016
#> GSM120760 4 0.3464 0.71436 0.000 0.084 0.000 0.808 0.108 0.000
#> GSM120763 4 0.3930 0.72056 0.000 0.092 0.000 0.764 0.144 0.000
#> GSM120764 4 0.4105 0.71791 0.000 0.080 0.000 0.760 0.152 0.008
#> GSM120777 4 0.4767 0.70689 0.000 0.088 0.000 0.696 0.200 0.016
#> GSM120786 4 0.3961 0.71910 0.000 0.080 0.000 0.768 0.148 0.004
#> GSM121329 3 0.5702 0.59157 0.040 0.004 0.656 0.072 0.208 0.020
#> GSM121331 5 0.0520 0.76871 0.000 0.000 0.000 0.008 0.984 0.008
#> GSM121333 5 0.0717 0.77083 0.000 0.000 0.000 0.008 0.976 0.016
#> GSM121345 5 0.0914 0.76576 0.000 0.000 0.000 0.016 0.968 0.016
#> GSM121356 5 0.0909 0.76573 0.000 0.000 0.000 0.020 0.968 0.012
#> GSM120754 4 0.5979 0.56948 0.000 0.212 0.000 0.556 0.208 0.024
#> GSM120759 2 0.3309 0.57591 0.000 0.720 0.000 0.280 0.000 0.000
#> GSM120762 2 0.3756 0.42602 0.000 0.600 0.000 0.400 0.000 0.000
#> GSM120775 4 0.4767 0.70644 0.000 0.088 0.000 0.696 0.200 0.016
#> GSM120776 5 0.2818 0.66970 0.000 0.028 0.000 0.076 0.872 0.024
#> GSM120782 4 0.7205 0.41139 0.000 0.224 0.000 0.440 0.196 0.140
#> GSM120789 2 0.3371 0.58041 0.000 0.708 0.000 0.292 0.000 0.000
#> GSM120790 4 0.3876 0.53579 0.000 0.244 0.000 0.728 0.012 0.016
#> GSM120791 4 0.4277 0.71388 0.000 0.124 0.000 0.732 0.144 0.000
#> GSM120755 2 0.2883 0.62785 0.000 0.788 0.000 0.212 0.000 0.000
#> GSM120756 4 0.4929 0.70091 0.000 0.088 0.000 0.680 0.212 0.020
#> GSM120769 2 0.3797 0.38227 0.000 0.580 0.000 0.420 0.000 0.000
#> GSM120778 4 0.3672 0.31833 0.000 0.368 0.000 0.632 0.000 0.000
#> GSM120792 4 0.4018 0.25557 0.000 0.412 0.000 0.580 0.008 0.000
#> GSM121332 2 0.2697 0.65257 0.000 0.812 0.000 0.188 0.000 0.000
#> GSM121334 4 0.3050 0.52027 0.000 0.236 0.000 0.764 0.000 0.000
#> GSM121340 4 0.4337 0.42159 0.000 0.320 0.000 0.648 0.020 0.012
#> GSM121351 2 0.2933 0.63240 0.000 0.796 0.000 0.200 0.000 0.004
#> GSM121353 4 0.5135 0.43312 0.000 0.304 0.000 0.608 0.072 0.016
#> GSM120758 2 0.3916 0.56001 0.000 0.680 0.000 0.300 0.020 0.000
#> GSM120771 2 0.4915 0.26539 0.000 0.552 0.000 0.396 0.036 0.016
#> GSM120772 2 0.3828 0.35152 0.000 0.560 0.000 0.440 0.000 0.000
#> GSM120773 4 0.3746 0.72028 0.000 0.080 0.000 0.780 0.140 0.000
#> GSM120774 4 0.3899 0.33414 0.000 0.364 0.000 0.628 0.008 0.000
#> GSM120783 4 0.3923 0.71940 0.000 0.080 0.000 0.772 0.144 0.004
#> GSM120787 4 0.3717 0.27333 0.000 0.384 0.000 0.616 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> CV:mclust 119 4.04e-11 2
#> CV:mclust 115 8.21e-20 3
#> CV:mclust 112 9.16e-29 4
#> CV:mclust 81 4.06e-20 5
#> CV:mclust 91 2.14e-29 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 21512 rows and 119 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 0.817 0.904 0.959 0.4972 0.499 0.499
#> 3 3 0.502 0.650 0.821 0.3161 0.741 0.529
#> 4 4 0.577 0.529 0.716 0.1312 0.775 0.469
#> 5 5 0.607 0.523 0.705 0.0640 0.833 0.500
#> 6 6 0.662 0.610 0.753 0.0469 0.883 0.548
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
#> GSM120719 1 0.0000 0.9421 1.000 0.000
#> GSM120720 1 0.0000 0.9421 1.000 0.000
#> GSM120765 2 0.0000 0.9685 0.000 1.000
#> GSM120767 2 0.0000 0.9685 0.000 1.000
#> GSM120784 2 0.0000 0.9685 0.000 1.000
#> GSM121400 1 0.0000 0.9421 1.000 0.000
#> GSM121401 1 0.0000 0.9421 1.000 0.000
#> GSM121402 2 0.0000 0.9685 0.000 1.000
#> GSM121403 1 0.9963 0.1596 0.536 0.464
#> GSM121404 2 0.0000 0.9685 0.000 1.000
#> GSM121405 1 0.0000 0.9421 1.000 0.000
#> GSM121406 2 0.0000 0.9685 0.000 1.000
#> GSM121408 2 0.0000 0.9685 0.000 1.000
#> GSM121409 1 0.4022 0.8892 0.920 0.080
#> GSM121410 1 0.0000 0.9421 1.000 0.000
#> GSM121412 2 0.0000 0.9685 0.000 1.000
#> GSM121413 2 0.0000 0.9685 0.000 1.000
#> GSM121414 2 0.0000 0.9685 0.000 1.000
#> GSM121415 2 0.0000 0.9685 0.000 1.000
#> GSM121416 2 0.0000 0.9685 0.000 1.000
#> GSM120591 1 0.0000 0.9421 1.000 0.000
#> GSM120594 1 0.0000 0.9421 1.000 0.000
#> GSM120718 1 0.0000 0.9421 1.000 0.000
#> GSM121205 1 0.0000 0.9421 1.000 0.000
#> GSM121206 1 0.0000 0.9421 1.000 0.000
#> GSM121207 1 0.0000 0.9421 1.000 0.000
#> GSM121208 1 0.0000 0.9421 1.000 0.000
#> GSM121209 1 0.0000 0.9421 1.000 0.000
#> GSM121210 1 0.0000 0.9421 1.000 0.000
#> GSM121211 1 0.0000 0.9421 1.000 0.000
#> GSM121212 1 0.0000 0.9421 1.000 0.000
#> GSM121213 1 0.0000 0.9421 1.000 0.000
#> GSM121214 1 0.0000 0.9421 1.000 0.000
#> GSM121215 1 0.0000 0.9421 1.000 0.000
#> GSM121216 1 0.0000 0.9421 1.000 0.000
#> GSM121217 1 0.0000 0.9421 1.000 0.000
#> GSM121218 1 0.0000 0.9421 1.000 0.000
#> GSM121234 1 0.0000 0.9421 1.000 0.000
#> GSM121243 1 0.0000 0.9421 1.000 0.000
#> GSM121245 1 0.0000 0.9421 1.000 0.000
#> GSM121246 1 0.0000 0.9421 1.000 0.000
#> GSM121247 1 0.0000 0.9421 1.000 0.000
#> GSM121248 1 0.0000 0.9421 1.000 0.000
#> GSM120744 2 0.7299 0.7284 0.204 0.796
#> GSM120745 1 0.0672 0.9374 0.992 0.008
#> GSM120746 1 0.7299 0.7569 0.796 0.204
#> GSM120747 2 0.9993 -0.0206 0.484 0.516
#> GSM120748 2 0.0938 0.9583 0.012 0.988
#> GSM120749 1 0.6048 0.8237 0.852 0.148
#> GSM120750 1 0.9522 0.4560 0.628 0.372
#> GSM120751 1 0.8909 0.5922 0.692 0.308
#> GSM120752 1 0.2948 0.9096 0.948 0.052
#> GSM121336 2 0.0000 0.9685 0.000 1.000
#> GSM121339 2 0.0000 0.9685 0.000 1.000
#> GSM121349 2 0.0000 0.9685 0.000 1.000
#> GSM121355 2 0.0000 0.9685 0.000 1.000
#> GSM120757 1 0.8909 0.5933 0.692 0.308
#> GSM120766 2 0.9393 0.4123 0.356 0.644
#> GSM120770 2 0.0000 0.9685 0.000 1.000
#> GSM120779 1 0.3584 0.8984 0.932 0.068
#> GSM120780 2 0.0672 0.9618 0.008 0.992
#> GSM121102 2 0.0000 0.9685 0.000 1.000
#> GSM121203 2 0.7528 0.7094 0.216 0.784
#> GSM121204 1 0.0000 0.9421 1.000 0.000
#> GSM121330 1 0.0000 0.9421 1.000 0.000
#> GSM121335 1 0.0000 0.9421 1.000 0.000
#> GSM121337 2 0.0000 0.9685 0.000 1.000
#> GSM121338 2 0.0000 0.9685 0.000 1.000
#> GSM121341 1 0.0000 0.9421 1.000 0.000
#> GSM121342 1 0.0000 0.9421 1.000 0.000
#> GSM121343 2 0.0000 0.9685 0.000 1.000
#> GSM121344 1 0.0000 0.9421 1.000 0.000
#> GSM121346 1 0.0000 0.9421 1.000 0.000
#> GSM121347 2 0.0000 0.9685 0.000 1.000
#> GSM121348 2 0.0000 0.9685 0.000 1.000
#> GSM121350 1 0.0000 0.9421 1.000 0.000
#> GSM121352 1 0.0000 0.9421 1.000 0.000
#> GSM121354 1 0.0000 0.9421 1.000 0.000
#> GSM120753 2 0.0000 0.9685 0.000 1.000
#> GSM120761 2 0.0000 0.9685 0.000 1.000
#> GSM120768 2 0.0000 0.9685 0.000 1.000
#> GSM120781 2 0.0000 0.9685 0.000 1.000
#> GSM120788 2 0.4939 0.8613 0.108 0.892
#> GSM120760 2 0.0000 0.9685 0.000 1.000
#> GSM120763 2 0.0000 0.9685 0.000 1.000
#> GSM120764 2 0.0000 0.9685 0.000 1.000
#> GSM120777 2 0.0000 0.9685 0.000 1.000
#> GSM120786 2 0.0000 0.9685 0.000 1.000
#> GSM121329 1 0.0000 0.9421 1.000 0.000
#> GSM121331 1 0.7815 0.7189 0.768 0.232
#> GSM121333 1 0.4690 0.8717 0.900 0.100
#> GSM121345 1 0.3733 0.8953 0.928 0.072
#> GSM121356 1 0.5408 0.8488 0.876 0.124
#> GSM120754 2 0.0000 0.9685 0.000 1.000
#> GSM120759 2 0.0000 0.9685 0.000 1.000
#> GSM120762 2 0.0000 0.9685 0.000 1.000
#> GSM120775 2 0.1414 0.9511 0.020 0.980
#> GSM120776 2 0.7602 0.7088 0.220 0.780
#> GSM120782 2 0.0000 0.9685 0.000 1.000
#> GSM120789 2 0.0000 0.9685 0.000 1.000
#> GSM120790 2 0.0000 0.9685 0.000 1.000
#> GSM120791 2 0.0000 0.9685 0.000 1.000
#> GSM120755 2 0.0000 0.9685 0.000 1.000
#> GSM120756 1 0.9933 0.1854 0.548 0.452
#> GSM120769 2 0.0000 0.9685 0.000 1.000
#> GSM120778 2 0.0000 0.9685 0.000 1.000
#> GSM120792 2 0.0000 0.9685 0.000 1.000
#> GSM121332 2 0.0000 0.9685 0.000 1.000
#> GSM121334 2 0.0000 0.9685 0.000 1.000
#> GSM121340 2 0.0000 0.9685 0.000 1.000
#> GSM121351 2 0.0000 0.9685 0.000 1.000
#> GSM121353 2 0.7219 0.7402 0.200 0.800
#> GSM120758 2 0.0000 0.9685 0.000 1.000
#> GSM120771 2 0.0000 0.9685 0.000 1.000
#> GSM120772 2 0.0000 0.9685 0.000 1.000
#> GSM120773 2 0.0000 0.9685 0.000 1.000
#> GSM120774 2 0.0000 0.9685 0.000 1.000
#> GSM120783 2 0.0000 0.9685 0.000 1.000
#> GSM120787 2 0.0000 0.9685 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM120719 1 0.4750 0.7342 0.784 0.000 0.216
#> GSM120720 3 0.4931 0.5023 0.232 0.000 0.768
#> GSM120765 2 0.3619 0.7925 0.000 0.864 0.136
#> GSM120767 2 0.3116 0.8124 0.000 0.892 0.108
#> GSM120784 2 0.5058 0.6871 0.000 0.756 0.244
#> GSM121400 3 0.0424 0.7155 0.000 0.008 0.992
#> GSM121401 3 0.0237 0.7133 0.004 0.000 0.996
#> GSM121402 2 0.2261 0.8308 0.000 0.932 0.068
#> GSM121403 3 0.3038 0.6999 0.000 0.104 0.896
#> GSM121404 2 0.6295 0.1674 0.000 0.528 0.472
#> GSM121405 3 0.1163 0.7172 0.000 0.028 0.972
#> GSM121406 2 0.3482 0.7983 0.000 0.872 0.128
#> GSM121408 2 0.3038 0.8142 0.000 0.896 0.104
#> GSM121409 3 0.1411 0.7181 0.000 0.036 0.964
#> GSM121410 3 0.1753 0.7165 0.000 0.048 0.952
#> GSM121412 2 0.5431 0.6262 0.000 0.716 0.284
#> GSM121413 2 0.4399 0.7492 0.000 0.812 0.188
#> GSM121414 2 0.5058 0.6862 0.000 0.756 0.244
#> GSM121415 2 0.4605 0.7340 0.000 0.796 0.204
#> GSM121416 2 0.2537 0.8262 0.000 0.920 0.080
#> GSM120591 3 0.3752 0.6201 0.144 0.000 0.856
#> GSM120594 3 0.4504 0.5548 0.196 0.000 0.804
#> GSM120718 3 0.6154 0.0418 0.408 0.000 0.592
#> GSM121205 1 0.4931 0.7262 0.768 0.000 0.232
#> GSM121206 1 0.5760 0.6325 0.672 0.000 0.328
#> GSM121207 1 0.2625 0.7339 0.916 0.000 0.084
#> GSM121208 3 0.6095 0.1106 0.392 0.000 0.608
#> GSM121209 1 0.5988 0.5675 0.632 0.000 0.368
#> GSM121210 1 0.5431 0.6845 0.716 0.000 0.284
#> GSM121211 1 0.5835 0.6153 0.660 0.000 0.340
#> GSM121212 1 0.4842 0.7303 0.776 0.000 0.224
#> GSM121213 1 0.5529 0.6719 0.704 0.000 0.296
#> GSM121214 1 0.4931 0.7263 0.768 0.000 0.232
#> GSM121215 1 0.5706 0.6442 0.680 0.000 0.320
#> GSM121216 1 0.4605 0.7372 0.796 0.000 0.204
#> GSM121217 1 0.5497 0.6766 0.708 0.000 0.292
#> GSM121218 1 0.4346 0.7408 0.816 0.000 0.184
#> GSM121234 1 0.6140 0.4999 0.596 0.000 0.404
#> GSM121243 1 0.4931 0.7264 0.768 0.000 0.232
#> GSM121245 1 0.4121 0.7421 0.832 0.000 0.168
#> GSM121246 3 0.5948 0.2209 0.360 0.000 0.640
#> GSM121247 1 0.1411 0.7245 0.964 0.000 0.036
#> GSM121248 1 0.4750 0.7331 0.784 0.000 0.216
#> GSM120744 3 0.6209 0.3318 0.004 0.368 0.628
#> GSM120745 3 0.3272 0.6907 0.080 0.016 0.904
#> GSM120746 3 0.2682 0.7100 0.004 0.076 0.920
#> GSM120747 3 0.3644 0.6903 0.004 0.124 0.872
#> GSM120748 3 0.5588 0.5162 0.004 0.276 0.720
#> GSM120749 3 0.2584 0.7150 0.008 0.064 0.928
#> GSM120750 3 0.3715 0.6894 0.004 0.128 0.868
#> GSM120751 3 0.3715 0.6893 0.004 0.128 0.868
#> GSM120752 3 0.3692 0.7127 0.056 0.048 0.896
#> GSM121336 2 0.2448 0.8271 0.000 0.924 0.076
#> GSM121339 3 0.6274 0.0613 0.000 0.456 0.544
#> GSM121349 2 0.1753 0.8353 0.000 0.952 0.048
#> GSM121355 2 0.2959 0.8163 0.000 0.900 0.100
#> GSM120757 1 0.6144 0.6363 0.780 0.132 0.088
#> GSM120766 2 0.8370 0.2536 0.084 0.500 0.416
#> GSM120770 2 0.5244 0.6897 0.004 0.756 0.240
#> GSM120779 1 0.2050 0.7173 0.952 0.020 0.028
#> GSM120780 3 0.6676 -0.0290 0.008 0.476 0.516
#> GSM121102 2 0.6505 0.1736 0.004 0.528 0.468
#> GSM121203 3 0.5690 0.4977 0.004 0.288 0.708
#> GSM121204 1 0.2173 0.7266 0.944 0.008 0.048
#> GSM121330 3 0.1964 0.6928 0.056 0.000 0.944
#> GSM121335 3 0.5016 0.4929 0.240 0.000 0.760
#> GSM121337 2 0.4605 0.7338 0.000 0.796 0.204
#> GSM121338 3 0.6126 0.2464 0.000 0.400 0.600
#> GSM121341 3 0.5098 0.4754 0.248 0.000 0.752
#> GSM121342 3 0.5591 0.3569 0.304 0.000 0.696
#> GSM121343 3 0.6140 0.2352 0.000 0.404 0.596
#> GSM121344 3 0.3752 0.6197 0.144 0.000 0.856
#> GSM121346 3 0.1163 0.7056 0.028 0.000 0.972
#> GSM121347 2 0.4475 0.8238 0.064 0.864 0.072
#> GSM121348 2 0.5493 0.7005 0.012 0.756 0.232
#> GSM121350 3 0.0592 0.7113 0.012 0.000 0.988
#> GSM121352 3 0.2066 0.6903 0.060 0.000 0.940
#> GSM121354 3 0.3038 0.6566 0.104 0.000 0.896
#> GSM120753 2 0.0475 0.8404 0.004 0.992 0.004
#> GSM120761 2 0.1289 0.8367 0.032 0.968 0.000
#> GSM120768 2 0.2711 0.8152 0.088 0.912 0.000
#> GSM120781 2 0.0661 0.8404 0.004 0.988 0.008
#> GSM120788 1 0.4931 0.5481 0.768 0.232 0.000
#> GSM120760 2 0.4346 0.7382 0.184 0.816 0.000
#> GSM120763 2 0.4452 0.7301 0.192 0.808 0.000
#> GSM120764 2 0.6026 0.4541 0.376 0.624 0.000
#> GSM120777 1 0.6095 0.2084 0.608 0.392 0.000
#> GSM120786 2 0.5785 0.5392 0.332 0.668 0.000
#> GSM121329 1 0.4062 0.7428 0.836 0.000 0.164
#> GSM121331 1 0.3966 0.6763 0.876 0.100 0.024
#> GSM121333 1 0.2050 0.7119 0.952 0.028 0.020
#> GSM121345 1 0.1267 0.7057 0.972 0.024 0.004
#> GSM121356 1 0.6171 0.6724 0.776 0.080 0.144
#> GSM120754 2 0.3192 0.8041 0.112 0.888 0.000
#> GSM120759 2 0.1289 0.8385 0.000 0.968 0.032
#> GSM120762 2 0.1289 0.8362 0.032 0.968 0.000
#> GSM120775 1 0.6111 0.2001 0.604 0.396 0.000
#> GSM120776 1 0.5397 0.4715 0.720 0.280 0.000
#> GSM120782 2 0.1399 0.8390 0.028 0.968 0.004
#> GSM120789 2 0.0892 0.8402 0.000 0.980 0.020
#> GSM120790 2 0.1453 0.8415 0.008 0.968 0.024
#> GSM120791 2 0.3116 0.8035 0.108 0.892 0.000
#> GSM120755 2 0.0592 0.8405 0.000 0.988 0.012
#> GSM120756 1 0.4235 0.6053 0.824 0.176 0.000
#> GSM120769 2 0.1289 0.8368 0.032 0.968 0.000
#> GSM120778 2 0.2625 0.8172 0.084 0.916 0.000
#> GSM120792 2 0.2261 0.8254 0.068 0.932 0.000
#> GSM121332 2 0.0747 0.8403 0.000 0.984 0.016
#> GSM121334 2 0.1031 0.8382 0.024 0.976 0.000
#> GSM121340 2 0.6062 0.4384 0.384 0.616 0.000
#> GSM121351 2 0.2356 0.8289 0.000 0.928 0.072
#> GSM121353 1 0.5465 0.4536 0.712 0.288 0.000
#> GSM120758 2 0.0747 0.8393 0.016 0.984 0.000
#> GSM120771 2 0.1267 0.8414 0.004 0.972 0.024
#> GSM120772 2 0.1163 0.8373 0.028 0.972 0.000
#> GSM120773 2 0.3879 0.7704 0.152 0.848 0.000
#> GSM120774 2 0.2448 0.8210 0.076 0.924 0.000
#> GSM120783 2 0.5431 0.6181 0.284 0.716 0.000
#> GSM120787 2 0.2356 0.8232 0.072 0.928 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM120719 4 0.5125 0.1340 0.388 0.000 0.008 0.604
#> GSM120720 1 0.3105 0.5714 0.868 0.000 0.120 0.012
#> GSM120765 2 0.2563 0.8073 0.000 0.908 0.072 0.020
#> GSM120767 2 0.0592 0.8320 0.000 0.984 0.016 0.000
#> GSM120784 2 0.5228 0.6072 0.004 0.700 0.268 0.028
#> GSM121400 1 0.6473 0.1352 0.476 0.024 0.472 0.028
#> GSM121401 1 0.5858 0.4531 0.660 0.020 0.292 0.028
#> GSM121402 2 0.1398 0.8274 0.000 0.956 0.040 0.004
#> GSM121403 1 0.7415 0.1090 0.468 0.084 0.420 0.028
#> GSM121404 2 0.8178 0.1844 0.200 0.480 0.292 0.028
#> GSM121405 1 0.6225 0.4213 0.632 0.032 0.308 0.028
#> GSM121406 2 0.2021 0.8190 0.000 0.936 0.040 0.024
#> GSM121408 2 0.1174 0.8279 0.000 0.968 0.012 0.020
#> GSM121409 1 0.7049 0.2226 0.516 0.060 0.396 0.028
#> GSM121410 1 0.6809 0.3388 0.580 0.056 0.336 0.028
#> GSM121412 2 0.3301 0.7893 0.004 0.876 0.092 0.028
#> GSM121413 2 0.3427 0.7807 0.000 0.860 0.112 0.028
#> GSM121414 2 0.3366 0.7872 0.004 0.872 0.096 0.028
#> GSM121415 2 0.3245 0.7892 0.000 0.872 0.100 0.028
#> GSM121416 2 0.2300 0.8218 0.000 0.920 0.064 0.016
#> GSM120591 1 0.4699 0.4654 0.676 0.000 0.320 0.004
#> GSM120594 1 0.3105 0.5704 0.856 0.000 0.140 0.004
#> GSM120718 1 0.3399 0.5383 0.868 0.000 0.040 0.092
#> GSM121205 1 0.4925 0.2584 0.572 0.000 0.000 0.428
#> GSM121206 1 0.4304 0.4243 0.716 0.000 0.000 0.284
#> GSM121207 4 0.4804 0.1439 0.384 0.000 0.000 0.616
#> GSM121208 1 0.2589 0.5194 0.884 0.000 0.000 0.116
#> GSM121209 1 0.4040 0.4506 0.752 0.000 0.000 0.248
#> GSM121210 1 0.4961 0.2236 0.552 0.000 0.000 0.448
#> GSM121211 1 0.4406 0.4127 0.700 0.000 0.000 0.300
#> GSM121212 1 0.4992 0.1643 0.524 0.000 0.000 0.476
#> GSM121213 1 0.4855 0.3008 0.600 0.000 0.000 0.400
#> GSM121214 1 0.4996 0.1480 0.516 0.000 0.000 0.484
#> GSM121215 1 0.4500 0.3973 0.684 0.000 0.000 0.316
#> GSM121216 1 0.4855 0.2988 0.600 0.000 0.000 0.400
#> GSM121217 1 0.4746 0.3402 0.632 0.000 0.000 0.368
#> GSM121218 1 0.4999 0.1250 0.508 0.000 0.000 0.492
#> GSM121234 1 0.3764 0.4697 0.784 0.000 0.000 0.216
#> GSM121243 1 0.4967 0.2123 0.548 0.000 0.000 0.452
#> GSM121245 4 0.4877 0.0787 0.408 0.000 0.000 0.592
#> GSM121246 1 0.1557 0.5395 0.944 0.000 0.000 0.056
#> GSM121247 4 0.3837 0.3750 0.224 0.000 0.000 0.776
#> GSM121248 1 0.4994 0.1557 0.520 0.000 0.000 0.480
#> GSM120744 3 0.1743 0.7096 0.004 0.000 0.940 0.056
#> GSM120745 3 0.2662 0.7049 0.016 0.000 0.900 0.084
#> GSM120746 3 0.1545 0.6947 0.040 0.000 0.952 0.008
#> GSM120747 3 0.3734 0.6055 0.116 0.012 0.852 0.020
#> GSM120748 3 0.2641 0.6657 0.064 0.012 0.912 0.012
#> GSM120749 3 0.1978 0.6801 0.068 0.000 0.928 0.004
#> GSM120750 3 0.0927 0.7037 0.016 0.000 0.976 0.008
#> GSM120751 3 0.1733 0.7064 0.024 0.000 0.948 0.028
#> GSM120752 3 0.2266 0.7050 0.004 0.000 0.912 0.084
#> GSM121336 2 0.0804 0.8305 0.000 0.980 0.012 0.008
#> GSM121339 2 0.7755 0.3511 0.252 0.552 0.168 0.028
#> GSM121349 2 0.0376 0.8313 0.000 0.992 0.004 0.004
#> GSM121355 2 0.1406 0.8258 0.000 0.960 0.016 0.024
#> GSM120757 3 0.4605 0.5448 0.000 0.000 0.664 0.336
#> GSM120766 3 0.3975 0.6251 0.000 0.000 0.760 0.240
#> GSM120770 3 0.3198 0.6868 0.000 0.080 0.880 0.040
#> GSM120779 3 0.4746 0.5081 0.000 0.000 0.632 0.368
#> GSM120780 3 0.2530 0.7007 0.000 0.004 0.896 0.100
#> GSM121102 3 0.4208 0.6421 0.048 0.096 0.840 0.016
#> GSM121203 3 0.1114 0.7025 0.016 0.004 0.972 0.008
#> GSM121204 3 0.4981 0.3480 0.000 0.000 0.536 0.464
#> GSM121330 1 0.4419 0.5582 0.792 0.004 0.176 0.028
#> GSM121335 1 0.2593 0.5724 0.892 0.000 0.104 0.004
#> GSM121337 2 0.5486 0.6803 0.000 0.720 0.200 0.080
#> GSM121338 3 0.8099 0.1914 0.272 0.200 0.500 0.028
#> GSM121341 1 0.2859 0.5726 0.880 0.000 0.112 0.008
#> GSM121342 1 0.2882 0.5680 0.892 0.000 0.084 0.024
#> GSM121343 3 0.7229 0.3604 0.208 0.144 0.620 0.028
#> GSM121344 1 0.4072 0.5336 0.748 0.000 0.252 0.000
#> GSM121346 1 0.5540 0.4404 0.648 0.004 0.320 0.028
#> GSM121347 3 0.7789 0.1482 0.000 0.352 0.400 0.248
#> GSM121348 3 0.4713 0.6501 0.000 0.052 0.776 0.172
#> GSM121350 1 0.5454 0.4572 0.664 0.004 0.304 0.028
#> GSM121352 1 0.5498 0.4525 0.656 0.004 0.312 0.028
#> GSM121354 1 0.3870 0.5578 0.788 0.000 0.208 0.004
#> GSM120753 2 0.1109 0.8308 0.000 0.968 0.004 0.028
#> GSM120761 2 0.4127 0.7679 0.000 0.824 0.052 0.124
#> GSM120768 2 0.3428 0.7639 0.000 0.844 0.012 0.144
#> GSM120781 2 0.1109 0.8308 0.000 0.968 0.004 0.028
#> GSM120788 4 0.3308 0.5045 0.000 0.036 0.092 0.872
#> GSM120760 2 0.6188 0.3624 0.000 0.548 0.056 0.396
#> GSM120763 2 0.5173 0.5502 0.000 0.660 0.020 0.320
#> GSM120764 4 0.5535 0.3164 0.000 0.304 0.040 0.656
#> GSM120777 4 0.4364 0.4472 0.000 0.056 0.136 0.808
#> GSM120786 4 0.6089 0.2430 0.000 0.328 0.064 0.608
#> GSM121329 4 0.4916 0.0186 0.424 0.000 0.000 0.576
#> GSM121331 3 0.4898 0.4445 0.000 0.000 0.584 0.416
#> GSM121333 3 0.4989 0.3398 0.000 0.000 0.528 0.472
#> GSM121345 4 0.4406 0.1807 0.000 0.000 0.300 0.700
#> GSM121356 3 0.4661 0.5351 0.000 0.000 0.652 0.348
#> GSM120754 3 0.7242 0.2940 0.000 0.148 0.476 0.376
#> GSM120759 2 0.1356 0.8320 0.000 0.960 0.032 0.008
#> GSM120762 2 0.1022 0.8295 0.000 0.968 0.000 0.032
#> GSM120775 4 0.3907 0.5212 0.000 0.120 0.044 0.836
#> GSM120776 4 0.5378 -0.2434 0.000 0.012 0.448 0.540
#> GSM120782 2 0.6910 0.4539 0.000 0.584 0.164 0.252
#> GSM120789 2 0.0779 0.8313 0.000 0.980 0.004 0.016
#> GSM120790 2 0.5970 0.3819 0.000 0.600 0.348 0.052
#> GSM120791 2 0.4019 0.7165 0.000 0.792 0.012 0.196
#> GSM120755 2 0.0707 0.8307 0.000 0.980 0.000 0.020
#> GSM120756 4 0.2636 0.5560 0.012 0.052 0.020 0.916
#> GSM120769 2 0.1211 0.8277 0.000 0.960 0.000 0.040
#> GSM120778 2 0.1867 0.8159 0.000 0.928 0.000 0.072
#> GSM120792 2 0.1867 0.8168 0.000 0.928 0.000 0.072
#> GSM121332 2 0.0469 0.8314 0.000 0.988 0.000 0.012
#> GSM121334 2 0.1576 0.8271 0.000 0.948 0.004 0.048
#> GSM121340 2 0.4961 0.2892 0.000 0.552 0.000 0.448
#> GSM121351 2 0.1109 0.8302 0.000 0.968 0.028 0.004
#> GSM121353 4 0.5721 0.4454 0.056 0.284 0.000 0.660
#> GSM120758 2 0.1305 0.8298 0.000 0.960 0.004 0.036
#> GSM120771 2 0.3523 0.7947 0.000 0.856 0.112 0.032
#> GSM120772 2 0.1398 0.8290 0.000 0.956 0.004 0.040
#> GSM120773 2 0.5970 0.4766 0.000 0.600 0.052 0.348
#> GSM120774 2 0.1637 0.8213 0.000 0.940 0.000 0.060
#> GSM120783 2 0.5856 0.2132 0.000 0.504 0.032 0.464
#> GSM120787 2 0.1637 0.8214 0.000 0.940 0.000 0.060
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM120719 1 0.6133 0.35771 0.512 0.000 0.120 0.364 0.004
#> GSM120720 3 0.2818 0.63404 0.128 0.000 0.860 0.008 0.004
#> GSM120765 2 0.2753 0.69867 0.000 0.856 0.008 0.000 0.136
#> GSM120767 2 0.1471 0.71173 0.000 0.952 0.020 0.024 0.004
#> GSM120784 2 0.5348 0.55989 0.000 0.684 0.108 0.008 0.200
#> GSM121400 5 0.7273 0.25028 0.180 0.008 0.248 0.044 0.520
#> GSM121401 3 0.5098 0.55615 0.248 0.004 0.692 0.036 0.020
#> GSM121402 2 0.3999 0.51578 0.000 0.656 0.000 0.000 0.344
#> GSM121403 5 0.7839 0.41541 0.144 0.112 0.140 0.044 0.560
#> GSM121404 3 0.6190 0.12388 0.000 0.356 0.520 0.008 0.116
#> GSM121405 3 0.5362 0.54856 0.252 0.004 0.676 0.040 0.028
#> GSM121406 2 0.3766 0.59955 0.000 0.728 0.004 0.000 0.268
#> GSM121408 2 0.2179 0.71216 0.000 0.888 0.000 0.000 0.112
#> GSM121409 5 0.7584 0.39686 0.236 0.072 0.096 0.044 0.552
#> GSM121410 5 0.7621 0.40688 0.208 0.076 0.104 0.048 0.564
#> GSM121412 2 0.4951 0.34283 0.000 0.556 0.012 0.012 0.420
#> GSM121413 5 0.4434 -0.14171 0.000 0.460 0.004 0.000 0.536
#> GSM121414 2 0.4782 0.31239 0.000 0.544 0.008 0.008 0.440
#> GSM121415 2 0.4313 0.50132 0.000 0.636 0.008 0.000 0.356
#> GSM121416 2 0.4211 0.50520 0.000 0.636 0.000 0.004 0.360
#> GSM120591 3 0.2196 0.63644 0.024 0.000 0.916 0.056 0.004
#> GSM120594 3 0.2304 0.63689 0.100 0.000 0.892 0.008 0.000
#> GSM120718 3 0.3934 0.55911 0.244 0.000 0.740 0.016 0.000
#> GSM121205 1 0.1732 0.80813 0.920 0.000 0.000 0.080 0.000
#> GSM121206 1 0.1106 0.79556 0.964 0.000 0.024 0.012 0.000
#> GSM121207 1 0.3885 0.66061 0.724 0.000 0.000 0.268 0.008
#> GSM121208 1 0.3134 0.73246 0.876 0.000 0.056 0.044 0.024
#> GSM121209 1 0.1522 0.77616 0.944 0.000 0.044 0.012 0.000
#> GSM121210 1 0.2248 0.80680 0.900 0.000 0.000 0.088 0.012
#> GSM121211 1 0.1012 0.80050 0.968 0.000 0.020 0.012 0.000
#> GSM121212 1 0.2439 0.79657 0.876 0.000 0.000 0.120 0.004
#> GSM121213 1 0.1041 0.80765 0.964 0.000 0.004 0.032 0.000
#> GSM121214 1 0.2605 0.78352 0.852 0.000 0.000 0.148 0.000
#> GSM121215 1 0.0798 0.80342 0.976 0.000 0.008 0.016 0.000
#> GSM121216 1 0.1557 0.80691 0.940 0.000 0.000 0.052 0.008
#> GSM121217 1 0.1041 0.80806 0.964 0.000 0.004 0.032 0.000
#> GSM121218 1 0.2439 0.79495 0.876 0.000 0.000 0.120 0.004
#> GSM121234 1 0.1907 0.76779 0.928 0.000 0.044 0.028 0.000
#> GSM121243 1 0.2616 0.80161 0.880 0.000 0.000 0.100 0.020
#> GSM121245 1 0.3642 0.70079 0.760 0.000 0.000 0.232 0.008
#> GSM121246 1 0.3270 0.70379 0.852 0.000 0.100 0.044 0.004
#> GSM121247 1 0.5338 0.37406 0.544 0.000 0.000 0.400 0.056
#> GSM121248 1 0.2377 0.79578 0.872 0.000 0.000 0.128 0.000
#> GSM120744 3 0.4098 0.56977 0.000 0.000 0.780 0.156 0.064
#> GSM120745 3 0.4075 0.56830 0.000 0.000 0.780 0.160 0.060
#> GSM120746 3 0.3437 0.60687 0.000 0.000 0.832 0.120 0.048
#> GSM120747 3 0.2300 0.62821 0.000 0.000 0.904 0.072 0.024
#> GSM120748 3 0.3112 0.61749 0.000 0.000 0.856 0.100 0.044
#> GSM120749 3 0.2873 0.61256 0.000 0.000 0.856 0.128 0.016
#> GSM120750 3 0.3767 0.59612 0.000 0.000 0.812 0.120 0.068
#> GSM120751 3 0.3477 0.59783 0.000 0.000 0.824 0.136 0.040
#> GSM120752 3 0.4360 0.54006 0.000 0.000 0.752 0.184 0.064
#> GSM121336 2 0.2127 0.71197 0.000 0.892 0.000 0.000 0.108
#> GSM121339 2 0.6237 0.28678 0.000 0.544 0.332 0.016 0.108
#> GSM121349 2 0.2230 0.71048 0.000 0.884 0.000 0.000 0.116
#> GSM121355 2 0.2280 0.70941 0.000 0.880 0.000 0.000 0.120
#> GSM120757 5 0.5868 0.17057 0.000 0.000 0.104 0.380 0.516
#> GSM120766 5 0.4974 0.43570 0.000 0.000 0.092 0.212 0.696
#> GSM120770 5 0.5432 0.51090 0.000 0.044 0.140 0.096 0.720
#> GSM120779 5 0.5240 0.25300 0.000 0.000 0.056 0.360 0.584
#> GSM120780 5 0.4454 0.49538 0.000 0.000 0.112 0.128 0.760
#> GSM121102 3 0.6974 -0.13936 0.000 0.108 0.432 0.052 0.408
#> GSM121203 3 0.5815 0.21397 0.000 0.000 0.540 0.104 0.356
#> GSM121204 4 0.6184 0.23211 0.004 0.000 0.160 0.560 0.276
#> GSM121330 3 0.5629 0.29303 0.416 0.004 0.528 0.040 0.012
#> GSM121335 1 0.5299 0.17174 0.576 0.000 0.376 0.040 0.008
#> GSM121337 5 0.4623 0.20328 0.000 0.340 0.008 0.012 0.640
#> GSM121338 5 0.7574 0.25048 0.020 0.164 0.328 0.036 0.452
#> GSM121341 1 0.5294 0.24033 0.596 0.000 0.352 0.044 0.008
#> GSM121342 1 0.4729 0.45391 0.688 0.000 0.268 0.040 0.004
#> GSM121343 5 0.5902 0.52138 0.016 0.144 0.104 0.036 0.700
#> GSM121344 3 0.5447 0.21959 0.440 0.000 0.512 0.036 0.012
#> GSM121346 3 0.5244 0.54295 0.260 0.004 0.676 0.040 0.020
#> GSM121347 5 0.3416 0.53804 0.000 0.072 0.000 0.088 0.840
#> GSM121348 5 0.2552 0.54155 0.004 0.004 0.016 0.080 0.896
#> GSM121350 3 0.5947 0.44331 0.328 0.004 0.588 0.044 0.036
#> GSM121352 3 0.5780 0.49944 0.288 0.004 0.628 0.040 0.040
#> GSM121354 3 0.5347 0.33908 0.396 0.000 0.556 0.040 0.008
#> GSM120753 2 0.1522 0.71119 0.000 0.944 0.000 0.044 0.012
#> GSM120761 2 0.4215 0.59292 0.000 0.772 0.004 0.172 0.052
#> GSM120768 2 0.3826 0.52408 0.000 0.752 0.004 0.236 0.008
#> GSM120781 2 0.1774 0.70961 0.000 0.932 0.000 0.052 0.016
#> GSM120788 4 0.4281 0.60248 0.104 0.048 0.008 0.812 0.028
#> GSM120760 2 0.5337 0.00654 0.000 0.508 0.000 0.440 0.052
#> GSM120763 2 0.4707 0.24262 0.000 0.588 0.000 0.392 0.020
#> GSM120764 4 0.4845 0.55959 0.012 0.276 0.004 0.684 0.024
#> GSM120777 4 0.4434 0.57593 0.084 0.024 0.008 0.804 0.080
#> GSM120786 4 0.4607 0.48626 0.000 0.320 0.004 0.656 0.020
#> GSM121329 1 0.3817 0.68588 0.740 0.004 0.000 0.252 0.004
#> GSM121331 5 0.4288 0.43476 0.016 0.000 0.012 0.240 0.732
#> GSM121333 5 0.5230 0.18455 0.020 0.000 0.020 0.384 0.576
#> GSM121345 4 0.5458 0.31475 0.076 0.004 0.000 0.616 0.304
#> GSM121356 5 0.3944 0.46581 0.004 0.000 0.020 0.212 0.764
#> GSM120754 4 0.6513 0.52793 0.000 0.136 0.088 0.636 0.140
#> GSM120759 2 0.3816 0.56622 0.000 0.696 0.000 0.000 0.304
#> GSM120762 2 0.1041 0.71255 0.000 0.964 0.000 0.032 0.004
#> GSM120775 4 0.4841 0.63475 0.040 0.200 0.028 0.732 0.000
#> GSM120776 4 0.5745 0.44410 0.004 0.020 0.252 0.648 0.076
#> GSM120782 4 0.6856 0.35828 0.000 0.248 0.368 0.380 0.004
#> GSM120789 2 0.2583 0.70707 0.000 0.864 0.000 0.004 0.132
#> GSM120790 5 0.3918 0.52451 0.008 0.144 0.000 0.044 0.804
#> GSM120791 2 0.4209 0.53073 0.000 0.744 0.004 0.224 0.028
#> GSM120755 2 0.1251 0.71125 0.000 0.956 0.000 0.036 0.008
#> GSM120756 4 0.4775 0.61312 0.112 0.108 0.008 0.764 0.008
#> GSM120769 2 0.1502 0.70591 0.000 0.940 0.000 0.056 0.004
#> GSM120778 2 0.2389 0.66251 0.000 0.880 0.000 0.116 0.004
#> GSM120792 2 0.2971 0.62485 0.000 0.836 0.000 0.156 0.008
#> GSM121332 2 0.2124 0.71459 0.000 0.900 0.000 0.004 0.096
#> GSM121334 2 0.1444 0.71296 0.000 0.948 0.000 0.040 0.012
#> GSM121340 2 0.4837 0.09620 0.016 0.556 0.000 0.424 0.004
#> GSM121351 2 0.3983 0.52319 0.000 0.660 0.000 0.000 0.340
#> GSM121353 4 0.5869 0.25626 0.084 0.428 0.000 0.484 0.004
#> GSM120758 2 0.1412 0.71077 0.000 0.952 0.004 0.036 0.008
#> GSM120771 2 0.4550 0.58058 0.000 0.692 0.004 0.028 0.276
#> GSM120772 2 0.1704 0.69551 0.000 0.928 0.000 0.068 0.004
#> GSM120773 2 0.4489 0.14451 0.000 0.572 0.008 0.420 0.000
#> GSM120774 2 0.1965 0.67939 0.000 0.904 0.000 0.096 0.000
#> GSM120783 2 0.4803 -0.12525 0.000 0.500 0.012 0.484 0.004
#> GSM120787 2 0.1768 0.69814 0.000 0.924 0.000 0.072 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM120719 1 0.5818 0.3167 0.548 0.000 0.032 0.068 0.012 0.340
#> GSM120720 6 0.4807 0.6241 0.112 0.008 0.176 0.000 0.004 0.700
#> GSM120765 2 0.2479 0.6483 0.000 0.896 0.012 0.008 0.020 0.064
#> GSM120767 2 0.3875 0.5728 0.000 0.772 0.008 0.020 0.016 0.184
#> GSM120784 2 0.5232 0.3907 0.000 0.628 0.016 0.000 0.100 0.256
#> GSM121400 3 0.5303 0.5923 0.068 0.016 0.652 0.000 0.244 0.020
#> GSM121401 3 0.3367 0.7821 0.080 0.000 0.816 0.000 0.000 0.104
#> GSM121402 2 0.4939 0.5930 0.000 0.688 0.044 0.056 0.212 0.000
#> GSM121403 3 0.6324 0.3938 0.056 0.124 0.536 0.000 0.280 0.004
#> GSM121404 3 0.4860 0.6334 0.000 0.112 0.752 0.064 0.024 0.048
#> GSM121405 3 0.3138 0.7788 0.060 0.000 0.832 0.000 0.000 0.108
#> GSM121406 2 0.3337 0.6436 0.000 0.840 0.044 0.028 0.088 0.000
#> GSM121408 2 0.2663 0.6572 0.000 0.880 0.040 0.068 0.012 0.000
#> GSM121409 5 0.7892 0.0350 0.160 0.176 0.252 0.004 0.388 0.020
#> GSM121410 3 0.6595 0.2618 0.108 0.088 0.448 0.000 0.356 0.000
#> GSM121412 2 0.4873 0.4910 0.004 0.660 0.072 0.008 0.256 0.000
#> GSM121413 2 0.4798 0.3386 0.000 0.580 0.052 0.004 0.364 0.000
#> GSM121414 2 0.4818 0.4520 0.000 0.636 0.076 0.004 0.284 0.000
#> GSM121415 2 0.5157 0.5701 0.000 0.680 0.140 0.028 0.152 0.000
#> GSM121416 2 0.7140 0.3169 0.000 0.452 0.204 0.212 0.132 0.000
#> GSM120591 6 0.2538 0.7528 0.020 0.012 0.076 0.000 0.004 0.888
#> GSM120594 6 0.4300 0.6837 0.092 0.012 0.132 0.000 0.004 0.760
#> GSM120718 6 0.4986 0.5028 0.284 0.000 0.104 0.000 0.000 0.612
#> GSM121205 1 0.0603 0.9038 0.980 0.000 0.004 0.016 0.000 0.000
#> GSM121206 1 0.1080 0.8974 0.960 0.000 0.032 0.004 0.000 0.004
#> GSM121207 1 0.2821 0.8102 0.832 0.000 0.000 0.152 0.016 0.000
#> GSM121208 1 0.2933 0.7308 0.796 0.000 0.200 0.000 0.004 0.000
#> GSM121209 1 0.1141 0.8892 0.948 0.000 0.052 0.000 0.000 0.000
#> GSM121210 1 0.1148 0.9009 0.960 0.000 0.004 0.016 0.020 0.000
#> GSM121211 1 0.0865 0.8972 0.964 0.000 0.036 0.000 0.000 0.000
#> GSM121212 1 0.1367 0.9010 0.944 0.000 0.012 0.044 0.000 0.000
#> GSM121213 1 0.0692 0.9030 0.976 0.000 0.020 0.004 0.000 0.000
#> GSM121214 1 0.1204 0.8939 0.944 0.000 0.000 0.056 0.000 0.000
#> GSM121215 1 0.0692 0.9015 0.976 0.000 0.020 0.004 0.000 0.000
#> GSM121216 1 0.0653 0.9032 0.980 0.004 0.012 0.004 0.000 0.000
#> GSM121217 1 0.0603 0.9031 0.980 0.000 0.016 0.004 0.000 0.000
#> GSM121218 1 0.1082 0.8998 0.956 0.000 0.004 0.040 0.000 0.000
#> GSM121234 1 0.1349 0.8840 0.940 0.000 0.056 0.004 0.000 0.000
#> GSM121243 1 0.0951 0.9000 0.968 0.000 0.004 0.008 0.020 0.000
#> GSM121245 1 0.2633 0.8405 0.864 0.000 0.000 0.104 0.032 0.000
#> GSM121246 1 0.2738 0.7665 0.820 0.000 0.176 0.000 0.004 0.000
#> GSM121247 1 0.4599 0.6714 0.700 0.000 0.004 0.192 0.104 0.000
#> GSM121248 1 0.0865 0.9007 0.964 0.000 0.000 0.036 0.000 0.000
#> GSM120744 6 0.2569 0.7685 0.000 0.008 0.036 0.008 0.056 0.892
#> GSM120745 6 0.2394 0.7667 0.000 0.000 0.032 0.020 0.048 0.900
#> GSM120746 6 0.2351 0.7740 0.000 0.000 0.052 0.012 0.036 0.900
#> GSM120747 6 0.2706 0.7337 0.000 0.000 0.160 0.000 0.008 0.832
#> GSM120748 6 0.3220 0.7644 0.000 0.028 0.084 0.004 0.032 0.852
#> GSM120749 6 0.2174 0.7678 0.000 0.000 0.088 0.008 0.008 0.896
#> GSM120750 6 0.2787 0.7655 0.000 0.000 0.044 0.012 0.072 0.872
#> GSM120751 6 0.2152 0.7738 0.000 0.000 0.040 0.012 0.036 0.912
#> GSM120752 6 0.2108 0.7626 0.000 0.000 0.016 0.016 0.056 0.912
#> GSM121336 2 0.1708 0.6503 0.000 0.932 0.004 0.000 0.024 0.040
#> GSM121339 2 0.4581 0.4867 0.008 0.700 0.016 0.004 0.028 0.244
#> GSM121349 2 0.1434 0.6532 0.000 0.948 0.008 0.000 0.024 0.020
#> GSM121355 2 0.1636 0.6517 0.000 0.936 0.004 0.000 0.024 0.036
#> GSM120757 5 0.5128 0.5785 0.004 0.000 0.012 0.112 0.664 0.208
#> GSM120766 5 0.3479 0.6812 0.000 0.000 0.024 0.024 0.812 0.140
#> GSM120770 5 0.5963 0.4018 0.000 0.176 0.020 0.000 0.540 0.264
#> GSM120779 5 0.4209 0.6487 0.004 0.000 0.004 0.092 0.756 0.144
#> GSM120780 5 0.3220 0.6903 0.000 0.004 0.056 0.004 0.840 0.096
#> GSM121102 6 0.6485 0.1586 0.000 0.196 0.040 0.000 0.292 0.472
#> GSM121203 6 0.5329 0.3294 0.000 0.012 0.084 0.000 0.348 0.556
#> GSM121204 6 0.5894 0.2660 0.016 0.000 0.008 0.120 0.316 0.540
#> GSM121330 3 0.3274 0.7881 0.096 0.000 0.824 0.000 0.000 0.080
#> GSM121335 3 0.3062 0.7831 0.112 0.000 0.836 0.000 0.000 0.052
#> GSM121337 3 0.6776 0.1624 0.000 0.096 0.472 0.140 0.292 0.000
#> GSM121338 3 0.4622 0.6580 0.000 0.084 0.760 0.008 0.104 0.044
#> GSM121341 3 0.2839 0.7861 0.092 0.000 0.860 0.004 0.000 0.044
#> GSM121342 3 0.3315 0.7520 0.156 0.000 0.804 0.000 0.000 0.040
#> GSM121343 3 0.4561 0.5682 0.000 0.036 0.724 0.036 0.200 0.004
#> GSM121344 3 0.3118 0.7896 0.092 0.000 0.836 0.000 0.000 0.072
#> GSM121346 3 0.2896 0.7849 0.044 0.000 0.864 0.000 0.012 0.080
#> GSM121347 5 0.6798 0.2925 0.000 0.068 0.248 0.220 0.464 0.000
#> GSM121348 5 0.2215 0.6922 0.000 0.024 0.032 0.008 0.916 0.020
#> GSM121350 3 0.3419 0.7925 0.088 0.000 0.828 0.000 0.012 0.072
#> GSM121352 3 0.2644 0.7867 0.052 0.000 0.880 0.000 0.008 0.060
#> GSM121354 3 0.3006 0.7913 0.092 0.000 0.844 0.000 0.000 0.064
#> GSM120753 2 0.4370 0.4261 0.000 0.616 0.020 0.356 0.008 0.000
#> GSM120761 2 0.5703 0.2146 0.000 0.512 0.008 0.392 0.052 0.036
#> GSM120768 4 0.4334 0.2393 0.000 0.392 0.012 0.588 0.004 0.004
#> GSM120781 2 0.4178 0.5004 0.000 0.680 0.008 0.292 0.004 0.016
#> GSM120788 4 0.3308 0.6003 0.036 0.004 0.000 0.852 0.060 0.048
#> GSM120760 4 0.5550 0.3134 0.000 0.360 0.008 0.548 0.060 0.024
#> GSM120763 4 0.4342 0.5197 0.000 0.272 0.016 0.688 0.020 0.004
#> GSM120764 4 0.2649 0.6705 0.000 0.064 0.000 0.884 0.024 0.028
#> GSM120777 4 0.4394 0.5304 0.044 0.000 0.004 0.772 0.112 0.068
#> GSM120786 4 0.3142 0.6683 0.000 0.084 0.004 0.856 0.024 0.032
#> GSM121329 4 0.6032 0.2087 0.284 0.000 0.164 0.528 0.024 0.000
#> GSM121331 5 0.2856 0.7105 0.016 0.000 0.004 0.052 0.876 0.052
#> GSM121333 5 0.5034 0.6174 0.028 0.000 0.004 0.192 0.692 0.084
#> GSM121345 4 0.5738 0.0341 0.024 0.000 0.024 0.552 0.348 0.052
#> GSM121356 5 0.2941 0.7124 0.004 0.000 0.020 0.056 0.872 0.048
#> GSM120754 4 0.7147 0.2372 0.000 0.092 0.008 0.448 0.180 0.272
#> GSM120759 2 0.4562 0.6105 0.000 0.732 0.048 0.044 0.176 0.000
#> GSM120762 2 0.2678 0.6378 0.000 0.860 0.000 0.116 0.004 0.020
#> GSM120775 4 0.4305 0.6229 0.028 0.044 0.008 0.788 0.012 0.120
#> GSM120776 6 0.5257 0.5771 0.008 0.028 0.012 0.112 0.128 0.712
#> GSM120782 6 0.3813 0.6517 0.000 0.132 0.012 0.048 0.008 0.800
#> GSM120789 2 0.4889 0.6047 0.000 0.696 0.052 0.204 0.048 0.000
#> GSM120790 5 0.4135 0.5494 0.004 0.204 0.028 0.008 0.748 0.008
#> GSM120791 4 0.4737 0.4788 0.000 0.276 0.064 0.652 0.008 0.000
#> GSM120755 2 0.3894 0.5685 0.000 0.732 0.008 0.240 0.004 0.016
#> GSM120756 4 0.2244 0.6367 0.036 0.000 0.004 0.912 0.016 0.032
#> GSM120769 2 0.3627 0.5603 0.000 0.740 0.008 0.244 0.004 0.004
#> GSM120778 2 0.4374 0.1829 0.000 0.532 0.016 0.448 0.000 0.004
#> GSM120792 2 0.4539 0.3543 0.000 0.600 0.008 0.368 0.004 0.020
#> GSM121332 2 0.3495 0.6425 0.000 0.812 0.036 0.136 0.016 0.000
#> GSM121334 2 0.3990 0.5582 0.000 0.716 0.008 0.256 0.016 0.004
#> GSM121340 4 0.3859 0.5355 0.012 0.256 0.012 0.720 0.000 0.000
#> GSM121351 2 0.4236 0.5712 0.008 0.748 0.024 0.008 0.200 0.012
#> GSM121353 4 0.3638 0.6265 0.036 0.156 0.008 0.796 0.004 0.000
#> GSM120758 2 0.3830 0.5242 0.000 0.704 0.004 0.280 0.004 0.008
#> GSM120771 2 0.4756 0.5469 0.000 0.672 0.012 0.024 0.268 0.024
#> GSM120772 2 0.4789 0.3149 0.000 0.552 0.036 0.404 0.004 0.004
#> GSM120773 4 0.3678 0.5834 0.000 0.228 0.008 0.748 0.000 0.016
#> GSM120774 2 0.4376 0.3984 0.000 0.604 0.024 0.368 0.000 0.004
#> GSM120783 4 0.3844 0.6156 0.000 0.192 0.016 0.764 0.000 0.028
#> GSM120787 2 0.3265 0.6344 0.000 0.836 0.012 0.116 0.004 0.032
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> CV:NMF 114 4.29e-09 2
#> CV:NMF 97 2.32e-15 3
#> CV:NMF 68 1.18e-08 4
#> CV:NMF 78 3.29e-24 5
#> CV:NMF 91 7.84e-30 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 21512 rows and 119 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 0.554 0.684 0.871 0.4682 0.522 0.522
#> 3 3 0.414 0.598 0.740 0.3011 0.639 0.436
#> 4 4 0.525 0.656 0.758 0.1034 0.833 0.598
#> 5 5 0.503 0.686 0.762 0.0569 0.937 0.783
#> 6 6 0.575 0.671 0.801 0.0507 0.996 0.983
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
#> GSM120719 1 0.1184 0.880 0.984 0.016
#> GSM120720 1 0.1184 0.880 0.984 0.016
#> GSM120765 2 0.0000 0.813 0.000 1.000
#> GSM120767 2 0.0376 0.813 0.004 0.996
#> GSM120784 2 0.0000 0.813 0.000 1.000
#> GSM121400 1 0.9129 0.433 0.672 0.328
#> GSM121401 1 0.3879 0.831 0.924 0.076
#> GSM121402 2 0.0000 0.813 0.000 1.000
#> GSM121403 1 0.9129 0.433 0.672 0.328
#> GSM121404 2 0.3114 0.800 0.056 0.944
#> GSM121405 1 0.3879 0.831 0.924 0.076
#> GSM121406 2 0.0000 0.813 0.000 1.000
#> GSM121408 2 0.0000 0.813 0.000 1.000
#> GSM121409 1 0.9170 0.422 0.668 0.332
#> GSM121410 1 0.9129 0.433 0.672 0.328
#> GSM121412 2 0.0000 0.813 0.000 1.000
#> GSM121413 2 0.0000 0.813 0.000 1.000
#> GSM121414 2 0.0000 0.813 0.000 1.000
#> GSM121415 2 0.0000 0.813 0.000 1.000
#> GSM121416 2 0.0000 0.813 0.000 1.000
#> GSM120591 1 0.1184 0.880 0.984 0.016
#> GSM120594 1 0.1184 0.880 0.984 0.016
#> GSM120718 1 0.1184 0.880 0.984 0.016
#> GSM121205 1 0.0000 0.883 1.000 0.000
#> GSM121206 1 0.0000 0.883 1.000 0.000
#> GSM121207 1 0.0000 0.883 1.000 0.000
#> GSM121208 1 0.0000 0.883 1.000 0.000
#> GSM121209 1 0.0000 0.883 1.000 0.000
#> GSM121210 1 0.0000 0.883 1.000 0.000
#> GSM121211 1 0.0000 0.883 1.000 0.000
#> GSM121212 1 0.0000 0.883 1.000 0.000
#> GSM121213 1 0.0000 0.883 1.000 0.000
#> GSM121214 1 0.0000 0.883 1.000 0.000
#> GSM121215 1 0.0000 0.883 1.000 0.000
#> GSM121216 1 0.0000 0.883 1.000 0.000
#> GSM121217 1 0.0000 0.883 1.000 0.000
#> GSM121218 1 0.0000 0.883 1.000 0.000
#> GSM121234 1 0.0000 0.883 1.000 0.000
#> GSM121243 1 0.0000 0.883 1.000 0.000
#> GSM121245 1 0.0000 0.883 1.000 0.000
#> GSM121246 1 0.0672 0.882 0.992 0.008
#> GSM121247 1 0.0000 0.883 1.000 0.000
#> GSM121248 1 0.0000 0.883 1.000 0.000
#> GSM120744 2 1.0000 0.177 0.496 0.504
#> GSM120745 2 1.0000 0.177 0.496 0.504
#> GSM120746 2 1.0000 0.177 0.496 0.504
#> GSM120747 2 1.0000 0.177 0.496 0.504
#> GSM120748 2 1.0000 0.177 0.496 0.504
#> GSM120749 2 1.0000 0.177 0.496 0.504
#> GSM120750 2 1.0000 0.177 0.496 0.504
#> GSM120751 2 1.0000 0.177 0.496 0.504
#> GSM120752 2 1.0000 0.177 0.496 0.504
#> GSM121336 2 0.0000 0.813 0.000 1.000
#> GSM121339 2 0.8081 0.635 0.248 0.752
#> GSM121349 2 0.0000 0.813 0.000 1.000
#> GSM121355 2 0.0000 0.813 0.000 1.000
#> GSM120757 1 0.9775 0.156 0.588 0.412
#> GSM120766 2 0.9881 0.329 0.436 0.564
#> GSM120770 2 0.9044 0.536 0.320 0.680
#> GSM120779 1 0.9983 -0.102 0.524 0.476
#> GSM120780 2 0.9881 0.329 0.436 0.564
#> GSM121102 2 0.9933 0.291 0.452 0.548
#> GSM121203 2 0.9933 0.291 0.452 0.548
#> GSM121204 1 0.9580 0.261 0.620 0.380
#> GSM121330 1 0.1184 0.880 0.984 0.016
#> GSM121335 1 0.0672 0.883 0.992 0.008
#> GSM121337 2 0.9491 0.466 0.368 0.632
#> GSM121338 2 0.9427 0.476 0.360 0.640
#> GSM121341 1 0.0672 0.883 0.992 0.008
#> GSM121342 1 0.0938 0.882 0.988 0.012
#> GSM121343 2 0.9491 0.462 0.368 0.632
#> GSM121344 1 0.0672 0.883 0.992 0.008
#> GSM121346 1 0.0672 0.883 0.992 0.008
#> GSM121347 2 0.9491 0.464 0.368 0.632
#> GSM121348 2 0.9988 0.217 0.480 0.520
#> GSM121350 1 0.0938 0.882 0.988 0.012
#> GSM121352 1 0.0938 0.882 0.988 0.012
#> GSM121354 1 0.0672 0.883 0.992 0.008
#> GSM120753 2 0.0000 0.813 0.000 1.000
#> GSM120761 2 0.0376 0.813 0.004 0.996
#> GSM120768 2 0.0376 0.813 0.004 0.996
#> GSM120781 2 0.0000 0.813 0.000 1.000
#> GSM120788 2 0.5178 0.767 0.116 0.884
#> GSM120760 2 0.2043 0.809 0.032 0.968
#> GSM120763 2 0.2423 0.807 0.040 0.960
#> GSM120764 2 0.3584 0.797 0.068 0.932
#> GSM120777 2 0.5737 0.754 0.136 0.864
#> GSM120786 2 0.2948 0.803 0.052 0.948
#> GSM121329 1 0.6623 0.715 0.828 0.172
#> GSM121331 2 1.0000 0.165 0.496 0.504
#> GSM121333 2 1.0000 0.165 0.496 0.504
#> GSM121345 1 0.9988 -0.109 0.520 0.480
#> GSM121356 1 1.0000 -0.181 0.500 0.500
#> GSM120754 2 0.2948 0.802 0.052 0.948
#> GSM120759 2 0.0000 0.813 0.000 1.000
#> GSM120762 2 0.0376 0.813 0.004 0.996
#> GSM120775 2 0.3431 0.798 0.064 0.936
#> GSM120776 2 0.9170 0.530 0.332 0.668
#> GSM120782 2 0.2948 0.802 0.052 0.948
#> GSM120789 2 0.0672 0.813 0.008 0.992
#> GSM120790 2 0.0376 0.813 0.004 0.996
#> GSM120791 2 0.2043 0.809 0.032 0.968
#> GSM120755 2 0.0376 0.813 0.004 0.996
#> GSM120756 2 0.6048 0.743 0.148 0.852
#> GSM120769 2 0.0000 0.813 0.000 1.000
#> GSM120778 2 0.0672 0.813 0.008 0.992
#> GSM120792 2 0.0376 0.812 0.004 0.996
#> GSM121332 2 0.0376 0.813 0.004 0.996
#> GSM121334 2 0.0376 0.813 0.004 0.996
#> GSM121340 2 0.3274 0.797 0.060 0.940
#> GSM121351 2 0.0000 0.813 0.000 1.000
#> GSM121353 2 0.3431 0.797 0.064 0.936
#> GSM120758 2 0.0376 0.813 0.004 0.996
#> GSM120771 2 0.0938 0.813 0.012 0.988
#> GSM120772 2 0.0672 0.813 0.008 0.992
#> GSM120773 2 0.2423 0.807 0.040 0.960
#> GSM120774 2 0.0938 0.813 0.012 0.988
#> GSM120783 2 0.2423 0.807 0.040 0.960
#> GSM120787 2 0.0672 0.813 0.008 0.992
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM120719 1 0.1315 0.75739 0.972 0.020 0.008
#> GSM120720 1 0.1315 0.75739 0.972 0.020 0.008
#> GSM120765 3 0.6286 0.10341 0.000 0.464 0.536
#> GSM120767 2 0.6302 0.04755 0.000 0.520 0.480
#> GSM120784 2 0.6309 0.09227 0.000 0.504 0.496
#> GSM121400 1 0.7844 0.62552 0.652 0.108 0.240
#> GSM121401 1 0.3310 0.73726 0.908 0.028 0.064
#> GSM121402 3 0.5216 0.64115 0.000 0.260 0.740
#> GSM121403 1 0.7844 0.62552 0.652 0.108 0.240
#> GSM121404 3 0.6894 0.57691 0.052 0.256 0.692
#> GSM121405 1 0.3310 0.73726 0.908 0.028 0.064
#> GSM121406 3 0.4887 0.66038 0.000 0.228 0.772
#> GSM121408 3 0.5397 0.60798 0.000 0.280 0.720
#> GSM121409 1 0.7909 0.62325 0.648 0.112 0.240
#> GSM121410 1 0.7844 0.62552 0.652 0.108 0.240
#> GSM121412 3 0.4842 0.66109 0.000 0.224 0.776
#> GSM121413 3 0.4842 0.66109 0.000 0.224 0.776
#> GSM121414 3 0.4842 0.66109 0.000 0.224 0.776
#> GSM121415 3 0.4842 0.66145 0.000 0.224 0.776
#> GSM121416 3 0.4842 0.66145 0.000 0.224 0.776
#> GSM120591 1 0.1315 0.75739 0.972 0.020 0.008
#> GSM120594 1 0.1315 0.75739 0.972 0.020 0.008
#> GSM120718 1 0.1315 0.75739 0.972 0.020 0.008
#> GSM121205 1 0.0237 0.75505 0.996 0.000 0.004
#> GSM121206 1 0.0237 0.75505 0.996 0.000 0.004
#> GSM121207 1 0.0237 0.75505 0.996 0.000 0.004
#> GSM121208 1 0.0237 0.75505 0.996 0.000 0.004
#> GSM121209 1 0.0237 0.75505 0.996 0.000 0.004
#> GSM121210 1 0.0237 0.75505 0.996 0.000 0.004
#> GSM121211 1 0.0237 0.75505 0.996 0.000 0.004
#> GSM121212 1 0.0237 0.75505 0.996 0.000 0.004
#> GSM121213 1 0.0237 0.75505 0.996 0.000 0.004
#> GSM121214 1 0.0237 0.75505 0.996 0.000 0.004
#> GSM121215 1 0.0237 0.75505 0.996 0.000 0.004
#> GSM121216 1 0.0237 0.75505 0.996 0.000 0.004
#> GSM121217 1 0.0237 0.75505 0.996 0.000 0.004
#> GSM121218 1 0.0237 0.75505 0.996 0.000 0.004
#> GSM121234 1 0.0237 0.75505 0.996 0.000 0.004
#> GSM121243 1 0.0237 0.75505 0.996 0.000 0.004
#> GSM121245 1 0.0237 0.75505 0.996 0.000 0.004
#> GSM121246 1 0.0424 0.75582 0.992 0.000 0.008
#> GSM121247 1 0.0237 0.75505 0.996 0.000 0.004
#> GSM121248 1 0.0237 0.75505 0.996 0.000 0.004
#> GSM120744 1 0.9624 0.49406 0.472 0.272 0.256
#> GSM120745 1 0.9624 0.49406 0.472 0.272 0.256
#> GSM120746 1 0.9624 0.49406 0.472 0.272 0.256
#> GSM120747 1 0.9624 0.49406 0.472 0.272 0.256
#> GSM120748 1 0.9624 0.49406 0.472 0.272 0.256
#> GSM120749 1 0.9624 0.49406 0.472 0.272 0.256
#> GSM120750 1 0.9624 0.49406 0.472 0.272 0.256
#> GSM120751 1 0.9624 0.49406 0.472 0.272 0.256
#> GSM120752 1 0.9624 0.49406 0.472 0.272 0.256
#> GSM121336 3 0.4931 0.65958 0.000 0.232 0.768
#> GSM121339 3 0.9756 0.11730 0.240 0.332 0.428
#> GSM121349 3 0.4887 0.66158 0.000 0.228 0.772
#> GSM121355 3 0.4887 0.66158 0.000 0.228 0.772
#> GSM120757 1 0.8940 0.58034 0.568 0.232 0.200
#> GSM120766 1 0.9846 0.41645 0.420 0.276 0.304
#> GSM120770 3 0.9970 -0.08576 0.312 0.316 0.372
#> GSM120779 1 0.9304 0.53169 0.508 0.300 0.192
#> GSM120780 1 0.9846 0.41645 0.420 0.276 0.304
#> GSM121102 1 0.9790 0.43748 0.436 0.272 0.292
#> GSM121203 1 0.9790 0.43748 0.436 0.272 0.292
#> GSM121204 1 0.8528 0.59919 0.604 0.240 0.156
#> GSM121330 1 0.1337 0.75761 0.972 0.016 0.012
#> GSM121335 1 0.1015 0.75758 0.980 0.012 0.008
#> GSM121337 1 0.9964 0.16214 0.356 0.292 0.352
#> GSM121338 3 0.9904 -0.14320 0.352 0.268 0.380
#> GSM121341 1 0.1015 0.75758 0.980 0.012 0.008
#> GSM121342 1 0.1170 0.75732 0.976 0.016 0.008
#> GSM121343 3 0.9880 -0.16694 0.356 0.260 0.384
#> GSM121344 1 0.1015 0.75758 0.980 0.012 0.008
#> GSM121346 1 0.1015 0.75758 0.980 0.012 0.008
#> GSM121347 1 0.9940 0.17172 0.360 0.280 0.360
#> GSM121348 1 0.9641 0.48612 0.464 0.296 0.240
#> GSM121350 1 0.1182 0.75777 0.976 0.012 0.012
#> GSM121352 1 0.1182 0.75777 0.976 0.012 0.012
#> GSM121354 1 0.1015 0.75758 0.980 0.012 0.008
#> GSM120753 2 0.4654 0.68589 0.000 0.792 0.208
#> GSM120761 2 0.4062 0.72372 0.000 0.836 0.164
#> GSM120768 2 0.4654 0.68597 0.000 0.792 0.208
#> GSM120781 2 0.5098 0.64829 0.000 0.752 0.248
#> GSM120788 2 0.3213 0.65225 0.092 0.900 0.008
#> GSM120760 2 0.2313 0.72884 0.024 0.944 0.032
#> GSM120763 2 0.2689 0.72750 0.032 0.932 0.036
#> GSM120764 2 0.2269 0.70358 0.040 0.944 0.016
#> GSM120777 2 0.4249 0.60903 0.108 0.864 0.028
#> GSM120786 2 0.1399 0.71313 0.028 0.968 0.004
#> GSM121329 1 0.5239 0.68978 0.808 0.160 0.032
#> GSM121331 1 0.9498 0.50740 0.484 0.300 0.216
#> GSM121333 1 0.9498 0.50740 0.484 0.300 0.216
#> GSM121345 1 0.9160 0.48051 0.492 0.352 0.156
#> GSM121356 1 0.9519 0.51306 0.484 0.292 0.224
#> GSM120754 2 0.5514 0.68355 0.044 0.800 0.156
#> GSM120759 3 0.5497 0.59274 0.000 0.292 0.708
#> GSM120762 2 0.5560 0.56308 0.000 0.700 0.300
#> GSM120775 2 0.5136 0.68557 0.044 0.824 0.132
#> GSM120776 2 0.8570 0.19615 0.316 0.564 0.120
#> GSM120782 2 0.5798 0.67590 0.044 0.780 0.176
#> GSM120789 3 0.6577 0.30811 0.008 0.420 0.572
#> GSM120790 3 0.5397 0.23469 0.000 0.280 0.720
#> GSM120791 2 0.2269 0.73128 0.016 0.944 0.040
#> GSM120755 2 0.5988 0.42373 0.000 0.632 0.368
#> GSM120756 2 0.3918 0.61289 0.120 0.868 0.012
#> GSM120769 2 0.4931 0.66800 0.000 0.768 0.232
#> GSM120778 2 0.3038 0.73239 0.000 0.896 0.104
#> GSM120792 2 0.3983 0.73206 0.004 0.852 0.144
#> GSM121332 2 0.6516 -0.00739 0.004 0.516 0.480
#> GSM121334 2 0.4504 0.69901 0.000 0.804 0.196
#> GSM121340 2 0.3310 0.69845 0.028 0.908 0.064
#> GSM121351 3 0.4931 0.65857 0.000 0.232 0.768
#> GSM121353 2 0.6565 0.59320 0.048 0.720 0.232
#> GSM120758 2 0.4702 0.68262 0.000 0.788 0.212
#> GSM120771 2 0.4861 0.71360 0.008 0.800 0.192
#> GSM120772 2 0.4409 0.71932 0.004 0.824 0.172
#> GSM120773 2 0.1781 0.72476 0.020 0.960 0.020
#> GSM120774 2 0.3918 0.73062 0.004 0.856 0.140
#> GSM120783 2 0.1781 0.72476 0.020 0.960 0.020
#> GSM120787 2 0.4178 0.71089 0.000 0.828 0.172
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM120719 1 0.2216 0.8048 0.908 0.000 0.092 0.000
#> GSM120720 1 0.2216 0.8048 0.908 0.000 0.092 0.000
#> GSM120765 2 0.5069 0.4185 0.000 0.664 0.016 0.320
#> GSM120767 2 0.5028 0.1945 0.000 0.596 0.004 0.400
#> GSM120784 2 0.5905 0.1578 0.000 0.564 0.040 0.396
#> GSM121400 1 0.6436 -0.2142 0.556 0.064 0.376 0.004
#> GSM121401 1 0.3813 0.7082 0.828 0.024 0.148 0.000
#> GSM121402 2 0.2919 0.7766 0.000 0.896 0.060 0.044
#> GSM121403 1 0.6436 -0.2142 0.556 0.064 0.376 0.004
#> GSM121404 2 0.4401 0.7350 0.016 0.832 0.084 0.068
#> GSM121405 1 0.3813 0.7082 0.828 0.024 0.148 0.000
#> GSM121406 2 0.0817 0.8066 0.000 0.976 0.000 0.024
#> GSM121408 2 0.2334 0.7750 0.000 0.908 0.004 0.088
#> GSM121409 1 0.6447 -0.2323 0.552 0.064 0.380 0.004
#> GSM121410 1 0.6436 -0.2142 0.556 0.064 0.376 0.004
#> GSM121412 2 0.0657 0.8045 0.000 0.984 0.004 0.012
#> GSM121413 2 0.0657 0.8045 0.000 0.984 0.004 0.012
#> GSM121414 2 0.0657 0.8045 0.000 0.984 0.004 0.012
#> GSM121415 2 0.1520 0.8043 0.000 0.956 0.020 0.024
#> GSM121416 2 0.1520 0.8043 0.000 0.956 0.020 0.024
#> GSM120591 1 0.2216 0.8048 0.908 0.000 0.092 0.000
#> GSM120594 1 0.2216 0.8048 0.908 0.000 0.092 0.000
#> GSM120718 1 0.2216 0.8048 0.908 0.000 0.092 0.000
#> GSM121205 1 0.0000 0.8211 1.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.8211 1.000 0.000 0.000 0.000
#> GSM121207 1 0.0000 0.8211 1.000 0.000 0.000 0.000
#> GSM121208 1 0.0000 0.8211 1.000 0.000 0.000 0.000
#> GSM121209 1 0.0000 0.8211 1.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.8211 1.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.8211 1.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.8211 1.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.8211 1.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.8211 1.000 0.000 0.000 0.000
#> GSM121215 1 0.0336 0.8212 0.992 0.000 0.008 0.000
#> GSM121216 1 0.0000 0.8211 1.000 0.000 0.000 0.000
#> GSM121217 1 0.0000 0.8211 1.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.8211 1.000 0.000 0.000 0.000
#> GSM121234 1 0.0336 0.8212 0.992 0.000 0.008 0.000
#> GSM121243 1 0.0000 0.8211 1.000 0.000 0.000 0.000
#> GSM121245 1 0.0000 0.8211 1.000 0.000 0.000 0.000
#> GSM121246 1 0.1489 0.8169 0.952 0.004 0.044 0.000
#> GSM121247 1 0.0000 0.8211 1.000 0.000 0.000 0.000
#> GSM121248 1 0.0000 0.8211 1.000 0.000 0.000 0.000
#> GSM120744 3 0.7354 0.8072 0.372 0.068 0.520 0.040
#> GSM120745 3 0.7354 0.8072 0.372 0.068 0.520 0.040
#> GSM120746 3 0.7354 0.8072 0.372 0.068 0.520 0.040
#> GSM120747 3 0.7354 0.8072 0.372 0.068 0.520 0.040
#> GSM120748 3 0.7354 0.8072 0.372 0.068 0.520 0.040
#> GSM120749 3 0.7354 0.8072 0.372 0.068 0.520 0.040
#> GSM120750 3 0.7354 0.8072 0.372 0.068 0.520 0.040
#> GSM120751 3 0.7354 0.8072 0.372 0.068 0.520 0.040
#> GSM120752 3 0.7354 0.8072 0.372 0.068 0.520 0.040
#> GSM121336 2 0.1004 0.8062 0.000 0.972 0.004 0.024
#> GSM121339 2 0.9163 0.2028 0.192 0.460 0.128 0.220
#> GSM121349 2 0.0895 0.8062 0.000 0.976 0.004 0.020
#> GSM121355 2 0.0895 0.8062 0.000 0.976 0.004 0.020
#> GSM120757 1 0.6342 -0.5587 0.488 0.012 0.464 0.036
#> GSM120766 3 0.7542 0.8007 0.320 0.088 0.548 0.044
#> GSM120770 3 0.8937 0.6475 0.240 0.248 0.440 0.072
#> GSM120779 3 0.6155 0.6866 0.412 0.000 0.536 0.052
#> GSM120780 3 0.7542 0.8007 0.320 0.088 0.548 0.044
#> GSM121102 3 0.7564 0.8057 0.340 0.096 0.528 0.036
#> GSM121203 3 0.7564 0.8057 0.340 0.096 0.528 0.036
#> GSM121204 1 0.6229 -0.4333 0.528 0.000 0.416 0.056
#> GSM121330 1 0.2216 0.8055 0.908 0.000 0.092 0.000
#> GSM121335 1 0.2011 0.8113 0.920 0.000 0.080 0.000
#> GSM121337 3 0.8657 0.7108 0.280 0.216 0.452 0.052
#> GSM121338 3 0.8842 0.6441 0.280 0.280 0.392 0.048
#> GSM121341 1 0.2011 0.8113 0.920 0.000 0.080 0.000
#> GSM121342 1 0.2081 0.8098 0.916 0.000 0.084 0.000
#> GSM121343 3 0.8769 0.6518 0.280 0.276 0.400 0.044
#> GSM121344 1 0.2011 0.8113 0.920 0.000 0.080 0.000
#> GSM121346 1 0.2011 0.8113 0.920 0.000 0.080 0.000
#> GSM121347 3 0.8697 0.7082 0.280 0.212 0.452 0.056
#> GSM121348 3 0.6681 0.7565 0.364 0.024 0.564 0.048
#> GSM121350 1 0.2149 0.8073 0.912 0.000 0.088 0.000
#> GSM121352 1 0.2081 0.8096 0.916 0.000 0.084 0.000
#> GSM121354 1 0.2011 0.8113 0.920 0.000 0.080 0.000
#> GSM120753 4 0.4343 0.6877 0.000 0.264 0.004 0.732
#> GSM120761 4 0.5144 0.7343 0.000 0.216 0.052 0.732
#> GSM120768 4 0.4452 0.6928 0.000 0.260 0.008 0.732
#> GSM120781 4 0.4980 0.6354 0.000 0.304 0.016 0.680
#> GSM120788 4 0.4657 0.7084 0.056 0.012 0.124 0.808
#> GSM120760 4 0.3550 0.7578 0.000 0.044 0.096 0.860
#> GSM120763 4 0.3372 0.7557 0.000 0.036 0.096 0.868
#> GSM120764 4 0.3965 0.7379 0.016 0.020 0.124 0.840
#> GSM120777 4 0.4989 0.6528 0.072 0.000 0.164 0.764
#> GSM120786 4 0.3462 0.7448 0.004 0.020 0.116 0.860
#> GSM121329 1 0.5631 0.5233 0.732 0.004 0.156 0.108
#> GSM121331 3 0.6554 0.7112 0.396 0.008 0.536 0.060
#> GSM121333 3 0.6554 0.7112 0.396 0.008 0.536 0.060
#> GSM121345 1 0.7662 -0.5746 0.416 0.004 0.400 0.180
#> GSM121356 3 0.6586 0.7219 0.388 0.012 0.544 0.056
#> GSM120754 4 0.6263 0.7124 0.036 0.156 0.092 0.716
#> GSM120759 2 0.4057 0.7152 0.000 0.812 0.160 0.028
#> GSM120762 4 0.4964 0.4924 0.000 0.380 0.004 0.616
#> GSM120775 4 0.6156 0.7160 0.036 0.132 0.104 0.728
#> GSM120776 4 0.8617 -0.0642 0.264 0.040 0.264 0.432
#> GSM120782 4 0.6490 0.7043 0.036 0.172 0.096 0.696
#> GSM120789 2 0.5491 0.5526 0.000 0.688 0.052 0.260
#> GSM120790 3 0.4904 -0.0462 0.000 0.216 0.744 0.040
#> GSM120791 4 0.4083 0.7622 0.000 0.068 0.100 0.832
#> GSM120755 4 0.5132 0.3246 0.000 0.448 0.004 0.548
#> GSM120756 4 0.4791 0.6700 0.080 0.000 0.136 0.784
#> GSM120769 4 0.4647 0.6606 0.000 0.288 0.008 0.704
#> GSM120778 4 0.3047 0.7487 0.000 0.116 0.012 0.872
#> GSM120792 4 0.4303 0.7524 0.004 0.184 0.020 0.792
#> GSM121332 2 0.5805 0.2153 0.000 0.576 0.036 0.388
#> GSM121334 4 0.5055 0.7089 0.000 0.256 0.032 0.712
#> GSM121340 4 0.1938 0.6904 0.000 0.012 0.052 0.936
#> GSM121351 2 0.0817 0.8065 0.000 0.976 0.000 0.024
#> GSM121353 4 0.7106 0.5536 0.032 0.268 0.092 0.608
#> GSM120758 4 0.4482 0.6897 0.000 0.264 0.008 0.728
#> GSM120771 4 0.5559 0.7124 0.000 0.240 0.064 0.696
#> GSM120772 4 0.4049 0.7267 0.000 0.212 0.008 0.780
#> GSM120773 4 0.3716 0.7603 0.000 0.052 0.096 0.852
#> GSM120774 4 0.3757 0.7504 0.000 0.152 0.020 0.828
#> GSM120783 4 0.3716 0.7603 0.000 0.052 0.096 0.852
#> GSM120787 4 0.3893 0.7280 0.000 0.196 0.008 0.796
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM120719 1 0.4613 0.779161 0.620 0.000 0.020 0.000 0.360
#> GSM120720 1 0.4613 0.779161 0.620 0.000 0.020 0.000 0.360
#> GSM120765 2 0.5559 0.370539 0.004 0.612 0.040 0.324 0.020
#> GSM120767 2 0.5503 0.143294 0.008 0.548 0.040 0.400 0.004
#> GSM120784 2 0.6582 0.080729 0.004 0.488 0.044 0.396 0.068
#> GSM121400 5 0.4803 0.454768 0.252 0.020 0.020 0.004 0.704
#> GSM121401 1 0.5101 0.670050 0.552 0.008 0.024 0.000 0.416
#> GSM121402 2 0.3864 0.668285 0.004 0.840 0.032 0.052 0.072
#> GSM121403 5 0.4803 0.454768 0.252 0.020 0.020 0.004 0.704
#> GSM121404 2 0.5073 0.622732 0.012 0.768 0.044 0.068 0.108
#> GSM121405 1 0.5101 0.670050 0.552 0.008 0.024 0.000 0.416
#> GSM121406 2 0.0955 0.733286 0.004 0.968 0.000 0.028 0.000
#> GSM121408 2 0.3002 0.704600 0.004 0.872 0.028 0.092 0.004
#> GSM121409 5 0.4777 0.465875 0.248 0.020 0.020 0.004 0.708
#> GSM121410 5 0.4803 0.454768 0.252 0.020 0.020 0.004 0.704
#> GSM121412 2 0.0968 0.721574 0.000 0.972 0.012 0.004 0.012
#> GSM121413 2 0.0968 0.721574 0.000 0.972 0.012 0.004 0.012
#> GSM121414 2 0.0968 0.721574 0.000 0.972 0.012 0.004 0.012
#> GSM121415 2 0.1954 0.727440 0.000 0.932 0.008 0.028 0.032
#> GSM121416 2 0.1954 0.727440 0.000 0.932 0.008 0.028 0.032
#> GSM120591 1 0.4613 0.779161 0.620 0.000 0.020 0.000 0.360
#> GSM120594 1 0.4613 0.779161 0.620 0.000 0.020 0.000 0.360
#> GSM120718 1 0.4613 0.779161 0.620 0.000 0.020 0.000 0.360
#> GSM121205 1 0.3003 0.846886 0.812 0.000 0.000 0.000 0.188
#> GSM121206 1 0.3003 0.846886 0.812 0.000 0.000 0.000 0.188
#> GSM121207 1 0.3003 0.846886 0.812 0.000 0.000 0.000 0.188
#> GSM121208 1 0.3003 0.846886 0.812 0.000 0.000 0.000 0.188
#> GSM121209 1 0.3003 0.846886 0.812 0.000 0.000 0.000 0.188
#> GSM121210 1 0.3003 0.846886 0.812 0.000 0.000 0.000 0.188
#> GSM121211 1 0.3003 0.846886 0.812 0.000 0.000 0.000 0.188
#> GSM121212 1 0.3003 0.846886 0.812 0.000 0.000 0.000 0.188
#> GSM121213 1 0.3003 0.846886 0.812 0.000 0.000 0.000 0.188
#> GSM121214 1 0.3003 0.846886 0.812 0.000 0.000 0.000 0.188
#> GSM121215 1 0.3366 0.843215 0.768 0.000 0.000 0.000 0.232
#> GSM121216 1 0.3003 0.846886 0.812 0.000 0.000 0.000 0.188
#> GSM121217 1 0.3003 0.846886 0.812 0.000 0.000 0.000 0.188
#> GSM121218 1 0.3003 0.846886 0.812 0.000 0.000 0.000 0.188
#> GSM121234 1 0.3366 0.843215 0.768 0.000 0.000 0.000 0.232
#> GSM121243 1 0.3003 0.846886 0.812 0.000 0.000 0.000 0.188
#> GSM121245 1 0.3003 0.846886 0.812 0.000 0.000 0.000 0.188
#> GSM121246 1 0.3838 0.830060 0.716 0.000 0.004 0.000 0.280
#> GSM121247 1 0.3003 0.846886 0.812 0.000 0.000 0.000 0.188
#> GSM121248 1 0.3003 0.846886 0.812 0.000 0.000 0.000 0.188
#> GSM120744 5 0.1808 0.803236 0.044 0.012 0.000 0.008 0.936
#> GSM120745 5 0.1808 0.803236 0.044 0.012 0.000 0.008 0.936
#> GSM120746 5 0.1808 0.803236 0.044 0.012 0.000 0.008 0.936
#> GSM120747 5 0.1808 0.803236 0.044 0.012 0.000 0.008 0.936
#> GSM120748 5 0.1808 0.803236 0.044 0.012 0.000 0.008 0.936
#> GSM120749 5 0.1808 0.803236 0.044 0.012 0.000 0.008 0.936
#> GSM120750 5 0.1808 0.803236 0.044 0.012 0.000 0.008 0.936
#> GSM120751 5 0.1808 0.803236 0.044 0.012 0.000 0.008 0.936
#> GSM120752 5 0.1808 0.803236 0.044 0.012 0.000 0.008 0.936
#> GSM121336 2 0.1200 0.731691 0.000 0.964 0.012 0.016 0.008
#> GSM121339 2 0.8223 0.121902 0.036 0.388 0.044 0.228 0.304
#> GSM121349 2 0.0960 0.731370 0.000 0.972 0.008 0.016 0.004
#> GSM121355 2 0.0960 0.731370 0.000 0.972 0.008 0.016 0.004
#> GSM120757 5 0.4280 0.649530 0.192 0.000 0.028 0.016 0.764
#> GSM120766 5 0.2559 0.769048 0.008 0.032 0.024 0.024 0.912
#> GSM120770 5 0.4959 0.508225 0.000 0.184 0.016 0.072 0.728
#> GSM120779 5 0.4233 0.732870 0.108 0.000 0.064 0.024 0.804
#> GSM120780 5 0.2559 0.769048 0.008 0.032 0.024 0.024 0.912
#> GSM121102 5 0.1729 0.782088 0.012 0.032 0.004 0.008 0.944
#> GSM121203 5 0.1729 0.782088 0.012 0.032 0.004 0.008 0.944
#> GSM121204 5 0.5149 0.553924 0.232 0.000 0.044 0.028 0.696
#> GSM121330 1 0.4639 0.794898 0.632 0.000 0.024 0.000 0.344
#> GSM121335 1 0.4524 0.803857 0.644 0.000 0.020 0.000 0.336
#> GSM121337 5 0.4590 0.638958 0.004 0.140 0.032 0.044 0.780
#> GSM121338 5 0.5211 0.543665 0.004 0.212 0.024 0.052 0.708
#> GSM121341 1 0.4524 0.803857 0.644 0.000 0.020 0.000 0.336
#> GSM121342 1 0.4608 0.801592 0.640 0.000 0.024 0.000 0.336
#> GSM121343 5 0.5115 0.556242 0.004 0.208 0.024 0.048 0.716
#> GSM121344 1 0.4524 0.803857 0.644 0.000 0.020 0.000 0.336
#> GSM121346 1 0.4524 0.803857 0.644 0.000 0.020 0.000 0.336
#> GSM121347 5 0.4648 0.633498 0.004 0.140 0.028 0.052 0.776
#> GSM121348 5 0.3373 0.760224 0.052 0.008 0.056 0.016 0.868
#> GSM121350 1 0.4555 0.797606 0.636 0.000 0.020 0.000 0.344
#> GSM121352 1 0.4540 0.801084 0.640 0.000 0.020 0.000 0.340
#> GSM121354 1 0.4524 0.803857 0.644 0.000 0.020 0.000 0.336
#> GSM120753 4 0.4530 0.683531 0.004 0.216 0.028 0.740 0.012
#> GSM120761 4 0.4636 0.725551 0.000 0.172 0.020 0.756 0.052
#> GSM120768 4 0.4292 0.686825 0.000 0.216 0.024 0.748 0.012
#> GSM120781 4 0.5289 0.631303 0.008 0.248 0.040 0.684 0.020
#> GSM120788 4 0.4634 0.681640 0.044 0.004 0.028 0.772 0.152
#> GSM120760 4 0.3549 0.750905 0.004 0.024 0.024 0.848 0.100
#> GSM120763 4 0.3527 0.747819 0.004 0.024 0.028 0.852 0.092
#> GSM120764 4 0.4202 0.719789 0.016 0.008 0.040 0.804 0.132
#> GSM120777 4 0.5028 0.619573 0.040 0.000 0.036 0.720 0.204
#> GSM120786 4 0.3189 0.728046 0.004 0.004 0.016 0.848 0.128
#> GSM121329 1 0.6500 0.494164 0.492 0.000 0.032 0.092 0.384
#> GSM121331 5 0.4093 0.740359 0.088 0.000 0.072 0.024 0.816
#> GSM121333 5 0.4093 0.740359 0.088 0.000 0.072 0.024 0.816
#> GSM121345 5 0.6057 0.598656 0.156 0.000 0.048 0.132 0.664
#> GSM121356 5 0.3828 0.746494 0.080 0.000 0.068 0.020 0.832
#> GSM120754 4 0.5342 0.708855 0.000 0.100 0.032 0.720 0.148
#> GSM120759 2 0.4671 0.508588 0.000 0.740 0.200 0.020 0.040
#> GSM120762 4 0.5663 0.518467 0.008 0.304 0.056 0.620 0.012
#> GSM120775 4 0.5096 0.711258 0.000 0.084 0.028 0.736 0.152
#> GSM120776 4 0.7248 0.000222 0.120 0.012 0.040 0.416 0.412
#> GSM120782 4 0.5366 0.705196 0.000 0.112 0.028 0.716 0.144
#> GSM120789 2 0.6534 0.486801 0.004 0.596 0.080 0.260 0.060
#> GSM120790 3 0.5538 0.000000 0.000 0.100 0.688 0.024 0.188
#> GSM120791 4 0.3802 0.753223 0.000 0.036 0.020 0.824 0.120
#> GSM120755 4 0.5716 0.355724 0.008 0.388 0.048 0.548 0.008
#> GSM120756 4 0.5177 0.638975 0.068 0.000 0.040 0.732 0.160
#> GSM120769 4 0.5174 0.657550 0.008 0.224 0.048 0.704 0.016
#> GSM120778 4 0.3606 0.737051 0.012 0.072 0.064 0.848 0.004
#> GSM120792 4 0.4449 0.740800 0.004 0.156 0.028 0.780 0.032
#> GSM121332 2 0.6376 0.119991 0.004 0.496 0.040 0.404 0.056
#> GSM121334 4 0.4929 0.704023 0.004 0.216 0.016 0.720 0.044
#> GSM121340 4 0.6164 0.297066 0.156 0.000 0.236 0.596 0.012
#> GSM121351 2 0.1267 0.731360 0.000 0.960 0.024 0.012 0.004
#> GSM121353 4 0.7092 0.562295 0.040 0.220 0.040 0.592 0.108
#> GSM120758 4 0.4323 0.685888 0.000 0.220 0.024 0.744 0.012
#> GSM120771 4 0.5196 0.707649 0.000 0.188 0.028 0.716 0.068
#> GSM120772 4 0.4429 0.719985 0.008 0.156 0.044 0.780 0.012
#> GSM120773 4 0.3739 0.753023 0.004 0.028 0.024 0.836 0.108
#> GSM120774 4 0.4491 0.737843 0.016 0.100 0.056 0.804 0.024
#> GSM120783 4 0.3739 0.753023 0.004 0.028 0.024 0.836 0.108
#> GSM120787 4 0.5178 0.705416 0.016 0.124 0.096 0.748 0.016
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM120719 1 0.3828 0.7396 0.724 0.000 0.016 0.000 0.008 0.252
#> GSM120720 1 0.3828 0.7396 0.724 0.000 0.016 0.000 0.008 0.252
#> GSM120765 2 0.5436 0.3630 0.000 0.596 0.012 0.316 0.040 0.036
#> GSM120767 2 0.5461 0.1103 0.000 0.524 0.016 0.396 0.052 0.012
#> GSM120784 2 0.6404 0.0845 0.000 0.464 0.020 0.388 0.044 0.084
#> GSM121400 6 0.4512 0.5074 0.304 0.008 0.020 0.000 0.012 0.656
#> GSM121401 1 0.4216 0.6801 0.676 0.000 0.020 0.000 0.012 0.292
#> GSM121402 2 0.4204 0.6457 0.000 0.804 0.056 0.044 0.024 0.072
#> GSM121403 6 0.4512 0.5074 0.304 0.008 0.020 0.000 0.012 0.656
#> GSM121404 2 0.5173 0.5907 0.012 0.736 0.044 0.036 0.036 0.136
#> GSM121405 1 0.4216 0.6801 0.676 0.000 0.020 0.000 0.012 0.292
#> GSM121406 2 0.1129 0.7176 0.000 0.964 0.012 0.012 0.004 0.008
#> GSM121408 2 0.3350 0.6826 0.000 0.844 0.024 0.096 0.024 0.012
#> GSM121409 6 0.4495 0.5164 0.300 0.008 0.020 0.000 0.012 0.660
#> GSM121410 6 0.4512 0.5074 0.304 0.008 0.020 0.000 0.012 0.656
#> GSM121412 2 0.0891 0.7054 0.000 0.968 0.024 0.000 0.000 0.008
#> GSM121413 2 0.0891 0.7054 0.000 0.968 0.024 0.000 0.000 0.008
#> GSM121414 2 0.0891 0.7054 0.000 0.968 0.024 0.000 0.000 0.008
#> GSM121415 2 0.2007 0.7089 0.000 0.924 0.012 0.016 0.008 0.040
#> GSM121416 2 0.2007 0.7089 0.000 0.924 0.012 0.016 0.008 0.040
#> GSM120591 1 0.3828 0.7396 0.724 0.000 0.016 0.000 0.008 0.252
#> GSM120594 1 0.3828 0.7396 0.724 0.000 0.016 0.000 0.008 0.252
#> GSM120718 1 0.3828 0.7396 0.724 0.000 0.016 0.000 0.008 0.252
#> GSM121205 1 0.0363 0.8304 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM121206 1 0.0363 0.8304 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM121207 1 0.0363 0.8304 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM121208 1 0.0363 0.8304 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM121209 1 0.0363 0.8304 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM121210 1 0.0363 0.8304 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM121211 1 0.0363 0.8304 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM121212 1 0.0363 0.8304 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM121213 1 0.0363 0.8304 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM121214 1 0.0363 0.8304 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM121215 1 0.1501 0.8289 0.924 0.000 0.000 0.000 0.000 0.076
#> GSM121216 1 0.0363 0.8304 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM121217 1 0.0363 0.8304 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM121218 1 0.0363 0.8304 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM121234 1 0.1501 0.8289 0.924 0.000 0.000 0.000 0.000 0.076
#> GSM121243 1 0.0363 0.8304 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM121245 1 0.0363 0.8304 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM121246 1 0.2320 0.8157 0.864 0.000 0.000 0.000 0.004 0.132
#> GSM121247 1 0.0363 0.8304 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM121248 1 0.0363 0.8304 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM120744 6 0.1732 0.8060 0.072 0.004 0.000 0.004 0.000 0.920
#> GSM120745 6 0.1732 0.8060 0.072 0.004 0.000 0.004 0.000 0.920
#> GSM120746 6 0.1732 0.8060 0.072 0.004 0.000 0.004 0.000 0.920
#> GSM120747 6 0.1732 0.8060 0.072 0.004 0.000 0.004 0.000 0.920
#> GSM120748 6 0.1732 0.8060 0.072 0.004 0.000 0.004 0.000 0.920
#> GSM120749 6 0.1732 0.8060 0.072 0.004 0.000 0.004 0.000 0.920
#> GSM120750 6 0.1732 0.8060 0.072 0.004 0.000 0.004 0.000 0.920
#> GSM120751 6 0.1732 0.8060 0.072 0.004 0.000 0.004 0.000 0.920
#> GSM120752 6 0.1732 0.8060 0.072 0.004 0.000 0.004 0.000 0.920
#> GSM121336 2 0.1325 0.7176 0.000 0.956 0.004 0.016 0.012 0.012
#> GSM121339 2 0.7925 0.1463 0.040 0.348 0.028 0.236 0.036 0.312
#> GSM121349 2 0.1121 0.7170 0.000 0.964 0.004 0.016 0.008 0.008
#> GSM121355 2 0.1121 0.7170 0.000 0.964 0.004 0.016 0.008 0.008
#> GSM120757 6 0.4861 0.6746 0.236 0.000 0.036 0.028 0.012 0.688
#> GSM120766 6 0.2743 0.7783 0.036 0.024 0.036 0.008 0.004 0.892
#> GSM120770 6 0.4763 0.6018 0.016 0.176 0.012 0.056 0.008 0.732
#> GSM120779 6 0.5057 0.7230 0.140 0.000 0.072 0.032 0.028 0.728
#> GSM120780 6 0.2743 0.7783 0.036 0.024 0.036 0.008 0.004 0.892
#> GSM121102 6 0.1624 0.7891 0.040 0.020 0.000 0.004 0.000 0.936
#> GSM121203 6 0.1624 0.7891 0.040 0.020 0.000 0.004 0.000 0.936
#> GSM121204 6 0.5727 0.5889 0.280 0.000 0.044 0.036 0.032 0.608
#> GSM121330 1 0.3748 0.7771 0.756 0.000 0.020 0.000 0.012 0.212
#> GSM121335 1 0.3607 0.7849 0.768 0.000 0.016 0.000 0.012 0.204
#> GSM121337 6 0.4502 0.6768 0.020 0.108 0.028 0.028 0.028 0.788
#> GSM121338 6 0.5046 0.6022 0.020 0.184 0.032 0.020 0.024 0.720
#> GSM121341 1 0.3607 0.7849 0.768 0.000 0.016 0.000 0.012 0.204
#> GSM121342 1 0.3691 0.7829 0.764 0.000 0.020 0.000 0.012 0.204
#> GSM121343 6 0.4984 0.6127 0.020 0.176 0.032 0.020 0.024 0.728
#> GSM121344 1 0.3607 0.7849 0.768 0.000 0.016 0.000 0.012 0.204
#> GSM121346 1 0.3607 0.7849 0.768 0.000 0.016 0.000 0.012 0.204
#> GSM121347 6 0.4603 0.6724 0.020 0.104 0.028 0.032 0.032 0.784
#> GSM121348 6 0.4021 0.7646 0.080 0.004 0.068 0.020 0.016 0.812
#> GSM121350 1 0.3665 0.7800 0.760 0.000 0.016 0.000 0.012 0.212
#> GSM121352 1 0.3636 0.7825 0.764 0.000 0.016 0.000 0.012 0.208
#> GSM121354 1 0.3607 0.7849 0.768 0.000 0.016 0.000 0.012 0.204
#> GSM120753 4 0.4322 0.6826 0.000 0.176 0.008 0.752 0.044 0.020
#> GSM120761 4 0.4412 0.7083 0.000 0.144 0.016 0.764 0.020 0.056
#> GSM120768 4 0.4141 0.6852 0.000 0.172 0.008 0.764 0.040 0.016
#> GSM120781 4 0.5019 0.6392 0.000 0.200 0.016 0.700 0.056 0.028
#> GSM120788 4 0.4762 0.6112 0.044 0.000 0.012 0.752 0.084 0.108
#> GSM120760 4 0.3733 0.7010 0.000 0.020 0.012 0.824 0.064 0.080
#> GSM120763 4 0.3693 0.6957 0.000 0.020 0.008 0.824 0.076 0.072
#> GSM120764 4 0.3996 0.6525 0.004 0.004 0.008 0.792 0.084 0.108
#> GSM120777 4 0.5271 0.5498 0.040 0.000 0.012 0.696 0.088 0.164
#> GSM120786 4 0.3299 0.6696 0.000 0.000 0.012 0.836 0.060 0.092
#> GSM121329 1 0.6136 0.5504 0.600 0.000 0.020 0.068 0.072 0.240
#> GSM121331 6 0.4625 0.7436 0.112 0.000 0.072 0.020 0.032 0.764
#> GSM121333 6 0.4625 0.7436 0.112 0.000 0.072 0.020 0.032 0.764
#> GSM121345 6 0.6618 0.5736 0.184 0.000 0.024 0.108 0.100 0.584
#> GSM121356 6 0.4465 0.7483 0.104 0.000 0.072 0.020 0.028 0.776
#> GSM120754 4 0.4873 0.6590 0.004 0.068 0.016 0.740 0.028 0.144
#> GSM120759 2 0.4023 0.4844 0.000 0.704 0.264 0.004 0.000 0.028
#> GSM120762 4 0.5601 0.5285 0.000 0.268 0.020 0.620 0.064 0.028
#> GSM120775 4 0.4847 0.6527 0.004 0.056 0.016 0.744 0.036 0.144
#> GSM120776 4 0.7070 0.0696 0.148 0.004 0.032 0.420 0.036 0.360
#> GSM120782 4 0.4772 0.6638 0.004 0.072 0.016 0.744 0.020 0.144
#> GSM120789 2 0.6527 0.4582 0.000 0.556 0.104 0.248 0.016 0.076
#> GSM120790 3 0.2177 0.0000 0.000 0.032 0.908 0.008 0.000 0.052
#> GSM120791 4 0.3811 0.7087 0.000 0.032 0.012 0.820 0.044 0.092
#> GSM120755 4 0.5637 0.3902 0.000 0.340 0.012 0.560 0.064 0.024
#> GSM120756 4 0.5390 0.5531 0.060 0.000 0.012 0.700 0.104 0.124
#> GSM120769 4 0.4939 0.6545 0.000 0.184 0.016 0.712 0.064 0.024
#> GSM120778 4 0.3178 0.6804 0.000 0.028 0.008 0.836 0.124 0.004
#> GSM120792 4 0.4091 0.7162 0.000 0.116 0.012 0.796 0.036 0.040
#> GSM121332 2 0.6056 0.0730 0.000 0.460 0.020 0.420 0.024 0.076
#> GSM121334 4 0.4714 0.6871 0.000 0.192 0.020 0.724 0.020 0.044
#> GSM121340 5 0.2219 0.0000 0.000 0.000 0.000 0.136 0.864 0.000
#> GSM121351 2 0.1741 0.7153 0.000 0.936 0.036 0.012 0.008 0.008
#> GSM121353 4 0.7040 0.4967 0.024 0.208 0.020 0.560 0.080 0.108
#> GSM120758 4 0.4174 0.6854 0.000 0.176 0.008 0.760 0.040 0.016
#> GSM120771 4 0.4839 0.6968 0.000 0.160 0.028 0.728 0.012 0.072
#> GSM120772 4 0.4065 0.6980 0.000 0.108 0.024 0.800 0.052 0.016
#> GSM120773 4 0.3681 0.7009 0.000 0.016 0.016 0.828 0.060 0.080
#> GSM120774 4 0.4676 0.6399 0.000 0.040 0.020 0.752 0.144 0.044
#> GSM120783 4 0.3681 0.7006 0.000 0.016 0.016 0.828 0.060 0.080
#> GSM120787 4 0.5315 0.5712 0.000 0.032 0.036 0.688 0.196 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)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> MAD:hclust 90 1.24e-11 2
#> MAD:hclust 90 1.40e-16 3
#> MAD:hclust 103 1.13e-27 4
#> MAD:hclust 104 4.61e-29 5
#> MAD:hclust 107 3.50e-27 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 21512 rows and 119 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
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 0.896 0.920 0.961 0.4945 0.496 0.496
#> 3 3 0.739 0.899 0.925 0.3194 0.799 0.615
#> 4 4 0.680 0.736 0.805 0.1340 0.865 0.630
#> 5 5 0.724 0.680 0.768 0.0628 0.945 0.799
#> 6 6 0.722 0.583 0.696 0.0407 0.929 0.714
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
#> GSM120719 1 0.0000 0.919 1.000 0.000
#> GSM120720 1 0.0000 0.919 1.000 0.000
#> GSM120765 2 0.0000 0.997 0.000 1.000
#> GSM120767 2 0.0000 0.997 0.000 1.000
#> GSM120784 2 0.0000 0.997 0.000 1.000
#> GSM121400 1 0.0000 0.919 1.000 0.000
#> GSM121401 1 0.0000 0.919 1.000 0.000
#> GSM121402 2 0.0000 0.997 0.000 1.000
#> GSM121403 1 0.9129 0.603 0.672 0.328
#> GSM121404 2 0.0000 0.997 0.000 1.000
#> GSM121405 1 0.0000 0.919 1.000 0.000
#> GSM121406 2 0.0000 0.997 0.000 1.000
#> GSM121408 2 0.0000 0.997 0.000 1.000
#> GSM121409 1 0.3274 0.888 0.940 0.060
#> GSM121410 1 0.0938 0.913 0.988 0.012
#> GSM121412 2 0.0000 0.997 0.000 1.000
#> GSM121413 2 0.0000 0.997 0.000 1.000
#> GSM121414 2 0.0000 0.997 0.000 1.000
#> GSM121415 2 0.0000 0.997 0.000 1.000
#> GSM121416 2 0.0000 0.997 0.000 1.000
#> GSM120591 1 0.0000 0.919 1.000 0.000
#> GSM120594 1 0.0000 0.919 1.000 0.000
#> GSM120718 1 0.0000 0.919 1.000 0.000
#> GSM121205 1 0.0000 0.919 1.000 0.000
#> GSM121206 1 0.0000 0.919 1.000 0.000
#> GSM121207 1 0.0000 0.919 1.000 0.000
#> GSM121208 1 0.0000 0.919 1.000 0.000
#> GSM121209 1 0.0000 0.919 1.000 0.000
#> GSM121210 1 0.0000 0.919 1.000 0.000
#> GSM121211 1 0.0000 0.919 1.000 0.000
#> GSM121212 1 0.0000 0.919 1.000 0.000
#> GSM121213 1 0.0000 0.919 1.000 0.000
#> GSM121214 1 0.0000 0.919 1.000 0.000
#> GSM121215 1 0.0000 0.919 1.000 0.000
#> GSM121216 1 0.0000 0.919 1.000 0.000
#> GSM121217 1 0.0000 0.919 1.000 0.000
#> GSM121218 1 0.0000 0.919 1.000 0.000
#> GSM121234 1 0.0000 0.919 1.000 0.000
#> GSM121243 1 0.0000 0.919 1.000 0.000
#> GSM121245 1 0.0000 0.919 1.000 0.000
#> GSM121246 1 0.0000 0.919 1.000 0.000
#> GSM121247 1 0.0000 0.919 1.000 0.000
#> GSM121248 1 0.0000 0.919 1.000 0.000
#> GSM120744 1 0.9580 0.512 0.620 0.380
#> GSM120745 1 0.4161 0.873 0.916 0.084
#> GSM120746 1 0.9491 0.535 0.632 0.368
#> GSM120747 1 0.9552 0.520 0.624 0.376
#> GSM120748 2 0.4431 0.886 0.092 0.908
#> GSM120749 1 0.9129 0.603 0.672 0.328
#> GSM120750 1 0.9608 0.503 0.616 0.384
#> GSM120751 1 0.9522 0.527 0.628 0.372
#> GSM120752 1 0.8207 0.702 0.744 0.256
#> GSM121336 2 0.0000 0.997 0.000 1.000
#> GSM121339 2 0.0000 0.997 0.000 1.000
#> GSM121349 2 0.0000 0.997 0.000 1.000
#> GSM121355 2 0.0000 0.997 0.000 1.000
#> GSM120757 1 0.9358 0.564 0.648 0.352
#> GSM120766 1 0.9661 0.486 0.608 0.392
#> GSM120770 2 0.0000 0.997 0.000 1.000
#> GSM120779 1 0.4022 0.876 0.920 0.080
#> GSM120780 2 0.3879 0.907 0.076 0.924
#> GSM121102 2 0.0000 0.997 0.000 1.000
#> GSM121203 1 0.9661 0.486 0.608 0.392
#> GSM121204 1 0.0000 0.919 1.000 0.000
#> GSM121330 1 0.0000 0.919 1.000 0.000
#> GSM121335 1 0.0000 0.919 1.000 0.000
#> GSM121337 2 0.0000 0.997 0.000 1.000
#> GSM121338 2 0.0000 0.997 0.000 1.000
#> GSM121341 1 0.0000 0.919 1.000 0.000
#> GSM121342 1 0.0000 0.919 1.000 0.000
#> GSM121343 2 0.0000 0.997 0.000 1.000
#> GSM121344 1 0.0000 0.919 1.000 0.000
#> GSM121346 1 0.0000 0.919 1.000 0.000
#> GSM121347 2 0.0000 0.997 0.000 1.000
#> GSM121348 2 0.0000 0.997 0.000 1.000
#> GSM121350 1 0.0000 0.919 1.000 0.000
#> GSM121352 1 0.0000 0.919 1.000 0.000
#> GSM121354 1 0.0000 0.919 1.000 0.000
#> GSM120753 2 0.0000 0.997 0.000 1.000
#> GSM120761 2 0.0000 0.997 0.000 1.000
#> GSM120768 2 0.0000 0.997 0.000 1.000
#> GSM120781 2 0.0000 0.997 0.000 1.000
#> GSM120788 2 0.0000 0.997 0.000 1.000
#> GSM120760 2 0.0000 0.997 0.000 1.000
#> GSM120763 2 0.0000 0.997 0.000 1.000
#> GSM120764 2 0.0000 0.997 0.000 1.000
#> GSM120777 2 0.0000 0.997 0.000 1.000
#> GSM120786 2 0.0000 0.997 0.000 1.000
#> GSM121329 1 0.0000 0.919 1.000 0.000
#> GSM121331 1 0.4690 0.861 0.900 0.100
#> GSM121333 1 0.3879 0.878 0.924 0.076
#> GSM121345 1 0.4161 0.873 0.916 0.084
#> GSM121356 1 0.3879 0.878 0.924 0.076
#> GSM120754 2 0.0000 0.997 0.000 1.000
#> GSM120759 2 0.0000 0.997 0.000 1.000
#> GSM120762 2 0.0000 0.997 0.000 1.000
#> GSM120775 2 0.0000 0.997 0.000 1.000
#> GSM120776 2 0.0000 0.997 0.000 1.000
#> GSM120782 2 0.0000 0.997 0.000 1.000
#> GSM120789 2 0.0000 0.997 0.000 1.000
#> GSM120790 2 0.0000 0.997 0.000 1.000
#> GSM120791 2 0.0000 0.997 0.000 1.000
#> GSM120755 2 0.0000 0.997 0.000 1.000
#> GSM120756 2 0.0000 0.997 0.000 1.000
#> GSM120769 2 0.0000 0.997 0.000 1.000
#> GSM120778 2 0.0000 0.997 0.000 1.000
#> GSM120792 2 0.0000 0.997 0.000 1.000
#> GSM121332 2 0.0000 0.997 0.000 1.000
#> GSM121334 2 0.0000 0.997 0.000 1.000
#> GSM121340 2 0.0000 0.997 0.000 1.000
#> GSM121351 2 0.0000 0.997 0.000 1.000
#> GSM121353 2 0.0000 0.997 0.000 1.000
#> GSM120758 2 0.0000 0.997 0.000 1.000
#> GSM120771 2 0.0000 0.997 0.000 1.000
#> GSM120772 2 0.0000 0.997 0.000 1.000
#> GSM120773 2 0.0000 0.997 0.000 1.000
#> GSM120774 2 0.0000 0.997 0.000 1.000
#> GSM120783 2 0.0000 0.997 0.000 1.000
#> GSM120787 2 0.0000 0.997 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM120719 1 0.0747 0.957 0.984 0.000 0.016
#> GSM120720 1 0.1643 0.939 0.956 0.000 0.044
#> GSM120765 2 0.3941 0.896 0.000 0.844 0.156
#> GSM120767 2 0.2711 0.917 0.000 0.912 0.088
#> GSM120784 2 0.3941 0.896 0.000 0.844 0.156
#> GSM121400 3 0.3267 0.902 0.116 0.000 0.884
#> GSM121401 3 0.5016 0.775 0.240 0.000 0.760
#> GSM121402 2 0.3752 0.900 0.000 0.856 0.144
#> GSM121403 3 0.0848 0.859 0.008 0.008 0.984
#> GSM121404 2 0.4605 0.857 0.000 0.796 0.204
#> GSM121405 3 0.3752 0.885 0.144 0.000 0.856
#> GSM121406 2 0.3941 0.896 0.000 0.844 0.156
#> GSM121408 2 0.3551 0.905 0.000 0.868 0.132
#> GSM121409 3 0.3349 0.905 0.108 0.004 0.888
#> GSM121410 3 0.3425 0.903 0.112 0.004 0.884
#> GSM121412 2 0.3941 0.896 0.000 0.844 0.156
#> GSM121413 2 0.3941 0.896 0.000 0.844 0.156
#> GSM121414 2 0.3941 0.896 0.000 0.844 0.156
#> GSM121415 2 0.3941 0.896 0.000 0.844 0.156
#> GSM121416 2 0.3941 0.896 0.000 0.844 0.156
#> GSM120591 1 0.5948 0.374 0.640 0.000 0.360
#> GSM120594 1 0.1643 0.939 0.956 0.000 0.044
#> GSM120718 1 0.0747 0.957 0.984 0.000 0.016
#> GSM121205 1 0.0000 0.960 1.000 0.000 0.000
#> GSM121206 1 0.0000 0.960 1.000 0.000 0.000
#> GSM121207 1 0.0000 0.960 1.000 0.000 0.000
#> GSM121208 1 0.0000 0.960 1.000 0.000 0.000
#> GSM121209 1 0.0000 0.960 1.000 0.000 0.000
#> GSM121210 1 0.0000 0.960 1.000 0.000 0.000
#> GSM121211 1 0.0000 0.960 1.000 0.000 0.000
#> GSM121212 1 0.0000 0.960 1.000 0.000 0.000
#> GSM121213 1 0.0000 0.960 1.000 0.000 0.000
#> GSM121214 1 0.0000 0.960 1.000 0.000 0.000
#> GSM121215 1 0.0000 0.960 1.000 0.000 0.000
#> GSM121216 1 0.0000 0.960 1.000 0.000 0.000
#> GSM121217 1 0.0000 0.960 1.000 0.000 0.000
#> GSM121218 1 0.0000 0.960 1.000 0.000 0.000
#> GSM121234 1 0.0000 0.960 1.000 0.000 0.000
#> GSM121243 1 0.0000 0.960 1.000 0.000 0.000
#> GSM121245 1 0.0000 0.960 1.000 0.000 0.000
#> GSM121246 1 0.0424 0.959 0.992 0.000 0.008
#> GSM121247 1 0.0000 0.960 1.000 0.000 0.000
#> GSM121248 1 0.0000 0.960 1.000 0.000 0.000
#> GSM120744 3 0.3193 0.907 0.100 0.004 0.896
#> GSM120745 3 0.3551 0.891 0.132 0.000 0.868
#> GSM120746 3 0.3193 0.907 0.100 0.004 0.896
#> GSM120747 3 0.3193 0.907 0.100 0.004 0.896
#> GSM120748 3 0.0237 0.859 0.000 0.004 0.996
#> GSM120749 3 0.3193 0.907 0.100 0.004 0.896
#> GSM120750 3 0.3193 0.907 0.100 0.004 0.896
#> GSM120751 3 0.3193 0.907 0.100 0.004 0.896
#> GSM120752 3 0.3192 0.903 0.112 0.000 0.888
#> GSM121336 2 0.3941 0.896 0.000 0.844 0.156
#> GSM121339 2 0.4002 0.894 0.000 0.840 0.160
#> GSM121349 2 0.3941 0.896 0.000 0.844 0.156
#> GSM121355 2 0.3941 0.896 0.000 0.844 0.156
#> GSM120757 3 0.4045 0.900 0.104 0.024 0.872
#> GSM120766 3 0.3415 0.902 0.080 0.020 0.900
#> GSM120770 2 0.5835 0.662 0.000 0.660 0.340
#> GSM120779 3 0.4811 0.877 0.148 0.024 0.828
#> GSM120780 3 0.0000 0.860 0.000 0.000 1.000
#> GSM121102 3 0.1163 0.847 0.000 0.028 0.972
#> GSM121203 3 0.2496 0.897 0.068 0.004 0.928
#> GSM121204 3 0.4178 0.861 0.172 0.000 0.828
#> GSM121330 1 0.2066 0.927 0.940 0.000 0.060
#> GSM121335 1 0.0747 0.957 0.984 0.000 0.016
#> GSM121337 2 0.5760 0.673 0.000 0.672 0.328
#> GSM121338 3 0.2261 0.821 0.000 0.068 0.932
#> GSM121341 1 0.0747 0.957 0.984 0.000 0.016
#> GSM121342 1 0.0747 0.957 0.984 0.000 0.016
#> GSM121343 3 0.2261 0.821 0.000 0.068 0.932
#> GSM121344 1 0.0747 0.957 0.984 0.000 0.016
#> GSM121346 1 0.4931 0.692 0.768 0.000 0.232
#> GSM121347 3 0.5591 0.439 0.000 0.304 0.696
#> GSM121348 3 0.1643 0.849 0.000 0.044 0.956
#> GSM121350 1 0.4750 0.720 0.784 0.000 0.216
#> GSM121352 1 0.2625 0.903 0.916 0.000 0.084
#> GSM121354 1 0.1289 0.948 0.968 0.000 0.032
#> GSM120753 2 0.0000 0.929 0.000 1.000 0.000
#> GSM120761 2 0.0000 0.929 0.000 1.000 0.000
#> GSM120768 2 0.0000 0.929 0.000 1.000 0.000
#> GSM120781 2 0.0000 0.929 0.000 1.000 0.000
#> GSM120788 2 0.0747 0.925 0.000 0.984 0.016
#> GSM120760 2 0.0237 0.928 0.000 0.996 0.004
#> GSM120763 2 0.0237 0.928 0.000 0.996 0.004
#> GSM120764 2 0.0747 0.925 0.000 0.984 0.016
#> GSM120777 2 0.0747 0.925 0.000 0.984 0.016
#> GSM120786 2 0.0747 0.925 0.000 0.984 0.016
#> GSM121329 1 0.1753 0.935 0.952 0.000 0.048
#> GSM121331 3 0.4811 0.877 0.148 0.024 0.828
#> GSM121333 3 0.4811 0.877 0.148 0.024 0.828
#> GSM121345 3 0.5028 0.880 0.132 0.040 0.828
#> GSM121356 3 0.4618 0.883 0.136 0.024 0.840
#> GSM120754 2 0.0747 0.925 0.000 0.984 0.016
#> GSM120759 2 0.3686 0.902 0.000 0.860 0.140
#> GSM120762 2 0.0000 0.929 0.000 1.000 0.000
#> GSM120775 2 0.0747 0.925 0.000 0.984 0.016
#> GSM120776 3 0.5098 0.725 0.000 0.248 0.752
#> GSM120782 2 0.0747 0.927 0.000 0.984 0.016
#> GSM120789 2 0.1031 0.928 0.000 0.976 0.024
#> GSM120790 2 0.3686 0.903 0.000 0.860 0.140
#> GSM120791 2 0.0237 0.928 0.000 0.996 0.004
#> GSM120755 2 0.0892 0.927 0.000 0.980 0.020
#> GSM120756 2 0.0747 0.925 0.000 0.984 0.016
#> GSM120769 2 0.0000 0.929 0.000 1.000 0.000
#> GSM120778 2 0.0000 0.929 0.000 1.000 0.000
#> GSM120792 2 0.0237 0.929 0.000 0.996 0.004
#> GSM121332 2 0.2959 0.914 0.000 0.900 0.100
#> GSM121334 2 0.0000 0.929 0.000 1.000 0.000
#> GSM121340 2 0.0424 0.928 0.000 0.992 0.008
#> GSM121351 2 0.3941 0.896 0.000 0.844 0.156
#> GSM121353 2 0.0747 0.925 0.000 0.984 0.016
#> GSM120758 2 0.0000 0.929 0.000 1.000 0.000
#> GSM120771 2 0.2878 0.915 0.000 0.904 0.096
#> GSM120772 2 0.0000 0.929 0.000 1.000 0.000
#> GSM120773 2 0.0237 0.928 0.000 0.996 0.004
#> GSM120774 2 0.0000 0.929 0.000 1.000 0.000
#> GSM120783 2 0.0747 0.925 0.000 0.984 0.016
#> GSM120787 2 0.0000 0.929 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM120719 1 0.4036 0.8601 0.836 0.000 0.076 0.088
#> GSM120720 1 0.5080 0.8064 0.764 0.000 0.144 0.092
#> GSM120765 2 0.0000 0.8799 0.000 1.000 0.000 0.000
#> GSM120767 2 0.0336 0.8748 0.000 0.992 0.008 0.000
#> GSM120784 2 0.0000 0.8799 0.000 1.000 0.000 0.000
#> GSM121400 3 0.3232 0.7598 0.016 0.004 0.872 0.108
#> GSM121401 3 0.4961 0.6589 0.116 0.004 0.784 0.096
#> GSM121402 2 0.0000 0.8799 0.000 1.000 0.000 0.000
#> GSM121403 3 0.3160 0.7635 0.000 0.020 0.872 0.108
#> GSM121404 2 0.3306 0.6876 0.000 0.840 0.156 0.004
#> GSM121405 3 0.4205 0.7119 0.068 0.004 0.832 0.096
#> GSM121406 2 0.0000 0.8799 0.000 1.000 0.000 0.000
#> GSM121408 2 0.0000 0.8799 0.000 1.000 0.000 0.000
#> GSM121409 3 0.2983 0.7640 0.008 0.004 0.880 0.108
#> GSM121410 3 0.3232 0.7598 0.016 0.004 0.872 0.108
#> GSM121412 2 0.0376 0.8754 0.000 0.992 0.004 0.004
#> GSM121413 2 0.0376 0.8754 0.000 0.992 0.004 0.004
#> GSM121414 2 0.0376 0.8754 0.000 0.992 0.004 0.004
#> GSM121415 2 0.0000 0.8799 0.000 1.000 0.000 0.000
#> GSM121416 2 0.0000 0.8799 0.000 1.000 0.000 0.000
#> GSM120591 3 0.6737 -0.0617 0.420 0.000 0.488 0.092
#> GSM120594 1 0.5080 0.8064 0.764 0.000 0.144 0.092
#> GSM120718 1 0.4235 0.8541 0.824 0.000 0.084 0.092
#> GSM121205 1 0.0000 0.9079 1.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.9079 1.000 0.000 0.000 0.000
#> GSM121207 1 0.0000 0.9079 1.000 0.000 0.000 0.000
#> GSM121208 1 0.0469 0.9052 0.988 0.000 0.000 0.012
#> GSM121209 1 0.0000 0.9079 1.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.9079 1.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.9079 1.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.9079 1.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.9079 1.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.9079 1.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.9079 1.000 0.000 0.000 0.000
#> GSM121216 1 0.0000 0.9079 1.000 0.000 0.000 0.000
#> GSM121217 1 0.0000 0.9079 1.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.9079 1.000 0.000 0.000 0.000
#> GSM121234 1 0.0000 0.9079 1.000 0.000 0.000 0.000
#> GSM121243 1 0.0000 0.9079 1.000 0.000 0.000 0.000
#> GSM121245 1 0.0000 0.9079 1.000 0.000 0.000 0.000
#> GSM121246 1 0.3900 0.8609 0.844 0.000 0.072 0.084
#> GSM121247 1 0.0000 0.9079 1.000 0.000 0.000 0.000
#> GSM121248 1 0.0000 0.9079 1.000 0.000 0.000 0.000
#> GSM120744 3 0.0712 0.7902 0.008 0.004 0.984 0.004
#> GSM120745 3 0.0927 0.7897 0.008 0.000 0.976 0.016
#> GSM120746 3 0.0712 0.7902 0.008 0.004 0.984 0.004
#> GSM120747 3 0.0712 0.7902 0.008 0.004 0.984 0.004
#> GSM120748 3 0.0657 0.7897 0.000 0.012 0.984 0.004
#> GSM120749 3 0.0712 0.7902 0.008 0.004 0.984 0.004
#> GSM120750 3 0.0712 0.7902 0.008 0.004 0.984 0.004
#> GSM120751 3 0.0712 0.7902 0.008 0.004 0.984 0.004
#> GSM120752 3 0.0927 0.7897 0.008 0.000 0.976 0.016
#> GSM121336 2 0.0000 0.8799 0.000 1.000 0.000 0.000
#> GSM121339 2 0.1398 0.8399 0.000 0.956 0.040 0.004
#> GSM121349 2 0.0188 0.8771 0.000 0.996 0.000 0.004
#> GSM121355 2 0.0000 0.8799 0.000 1.000 0.000 0.000
#> GSM120757 3 0.4621 0.7096 0.008 0.000 0.708 0.284
#> GSM120766 3 0.4579 0.7154 0.004 0.004 0.720 0.272
#> GSM120770 2 0.1902 0.8160 0.000 0.932 0.064 0.004
#> GSM120779 3 0.4963 0.7061 0.020 0.000 0.696 0.284
#> GSM120780 3 0.3547 0.7626 0.000 0.016 0.840 0.144
#> GSM121102 3 0.3392 0.7295 0.000 0.124 0.856 0.020
#> GSM121203 3 0.1114 0.7894 0.004 0.008 0.972 0.016
#> GSM121204 3 0.4882 0.7095 0.020 0.000 0.708 0.272
#> GSM121330 1 0.5907 0.6940 0.680 0.000 0.228 0.092
#> GSM121335 1 0.4168 0.8543 0.828 0.000 0.080 0.092
#> GSM121337 2 0.7747 0.0277 0.000 0.432 0.316 0.252
#> GSM121338 3 0.5738 0.2336 0.000 0.432 0.540 0.028
#> GSM121341 1 0.4168 0.8543 0.828 0.000 0.080 0.092
#> GSM121342 1 0.4168 0.8543 0.828 0.000 0.080 0.092
#> GSM121343 3 0.5630 0.3985 0.000 0.360 0.608 0.032
#> GSM121344 1 0.4235 0.8519 0.824 0.000 0.084 0.092
#> GSM121346 3 0.6752 -0.1383 0.440 0.000 0.468 0.092
#> GSM121347 3 0.7796 0.3089 0.000 0.292 0.424 0.284
#> GSM121348 3 0.5522 0.6837 0.000 0.044 0.668 0.288
#> GSM121350 3 0.6750 -0.1248 0.436 0.000 0.472 0.092
#> GSM121352 1 0.6483 0.5184 0.584 0.000 0.324 0.092
#> GSM121354 1 0.5669 0.7351 0.708 0.000 0.200 0.092
#> GSM120753 4 0.5290 0.6222 0.000 0.476 0.008 0.516
#> GSM120761 4 0.5203 0.7109 0.000 0.416 0.008 0.576
#> GSM120768 4 0.5112 0.7409 0.000 0.384 0.008 0.608
#> GSM120781 4 0.5296 0.5889 0.000 0.492 0.008 0.500
#> GSM120788 4 0.2918 0.6991 0.000 0.116 0.008 0.876
#> GSM120760 4 0.4422 0.7909 0.000 0.256 0.008 0.736
#> GSM120763 4 0.4697 0.7872 0.000 0.296 0.008 0.696
#> GSM120764 4 0.3649 0.7767 0.000 0.204 0.000 0.796
#> GSM120777 4 0.2859 0.6948 0.000 0.112 0.008 0.880
#> GSM120786 4 0.3649 0.7767 0.000 0.204 0.000 0.796
#> GSM121329 1 0.4931 0.8212 0.776 0.000 0.092 0.132
#> GSM121331 3 0.4963 0.7061 0.020 0.000 0.696 0.284
#> GSM121333 3 0.4963 0.7061 0.020 0.000 0.696 0.284
#> GSM121345 3 0.5173 0.6768 0.020 0.000 0.660 0.320
#> GSM121356 3 0.4621 0.7096 0.008 0.000 0.708 0.284
#> GSM120754 4 0.3907 0.7873 0.000 0.232 0.000 0.768
#> GSM120759 2 0.0188 0.8771 0.000 0.996 0.000 0.004
#> GSM120762 2 0.4836 0.0662 0.000 0.672 0.008 0.320
#> GSM120775 4 0.3751 0.7707 0.000 0.196 0.004 0.800
#> GSM120776 4 0.4560 0.1536 0.000 0.004 0.296 0.700
#> GSM120782 4 0.4331 0.7890 0.000 0.288 0.000 0.712
#> GSM120789 2 0.2859 0.7283 0.000 0.880 0.008 0.112
#> GSM120790 2 0.2197 0.7948 0.000 0.916 0.004 0.080
#> GSM120791 4 0.4746 0.7844 0.000 0.304 0.008 0.688
#> GSM120755 2 0.3401 0.6375 0.000 0.840 0.008 0.152
#> GSM120756 4 0.2918 0.6991 0.000 0.116 0.008 0.876
#> GSM120769 4 0.5290 0.6222 0.000 0.476 0.008 0.516
#> GSM120778 4 0.5125 0.7379 0.000 0.388 0.008 0.604
#> GSM120792 4 0.4761 0.7527 0.000 0.372 0.000 0.628
#> GSM121332 2 0.0592 0.8684 0.000 0.984 0.000 0.016
#> GSM121334 4 0.5294 0.6083 0.000 0.484 0.008 0.508
#> GSM121340 4 0.3688 0.7787 0.000 0.208 0.000 0.792
#> GSM121351 2 0.0000 0.8799 0.000 1.000 0.000 0.000
#> GSM121353 4 0.3688 0.7789 0.000 0.208 0.000 0.792
#> GSM120758 4 0.5296 0.5918 0.000 0.492 0.008 0.500
#> GSM120771 2 0.4955 -0.0585 0.000 0.648 0.008 0.344
#> GSM120772 4 0.5257 0.6747 0.000 0.444 0.008 0.548
#> GSM120773 4 0.4008 0.7894 0.000 0.244 0.000 0.756
#> GSM120774 4 0.5070 0.7513 0.000 0.372 0.008 0.620
#> GSM120783 4 0.3873 0.7862 0.000 0.228 0.000 0.772
#> GSM120787 4 0.5183 0.7193 0.000 0.408 0.008 0.584
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM120719 1 0.3692 0.6088 0.812 0.008 0.152 0.000 NA
#> GSM120720 1 0.4485 0.5378 0.732 0.008 0.224 0.000 NA
#> GSM120765 2 0.1830 0.8368 0.000 0.932 0.000 0.040 NA
#> GSM120767 2 0.3176 0.7921 0.000 0.856 0.000 0.080 NA
#> GSM120784 2 0.1741 0.8366 0.000 0.936 0.000 0.040 NA
#> GSM121400 3 0.5727 0.5688 0.220 0.012 0.648 0.000 NA
#> GSM121401 3 0.5635 0.4580 0.284 0.012 0.624 0.000 NA
#> GSM121402 2 0.3432 0.8293 0.000 0.828 0.000 0.040 NA
#> GSM121403 3 0.6265 0.5679 0.208 0.032 0.620 0.000 NA
#> GSM121404 2 0.4596 0.7935 0.000 0.784 0.096 0.032 NA
#> GSM121405 3 0.5574 0.4797 0.272 0.012 0.636 0.000 NA
#> GSM121406 2 0.1836 0.8421 0.000 0.932 0.000 0.036 NA
#> GSM121408 2 0.2300 0.8367 0.000 0.908 0.000 0.040 NA
#> GSM121409 3 0.5672 0.5776 0.212 0.012 0.656 0.000 NA
#> GSM121410 3 0.5700 0.5729 0.216 0.012 0.652 0.000 NA
#> GSM121412 2 0.2616 0.8375 0.000 0.888 0.000 0.036 NA
#> GSM121413 2 0.2616 0.8375 0.000 0.888 0.000 0.036 NA
#> GSM121414 2 0.2616 0.8375 0.000 0.888 0.000 0.036 NA
#> GSM121415 2 0.2221 0.8408 0.000 0.912 0.000 0.036 NA
#> GSM121416 2 0.2300 0.8411 0.000 0.908 0.000 0.040 NA
#> GSM120591 3 0.5368 0.0352 0.476 0.008 0.480 0.000 NA
#> GSM120594 1 0.4485 0.5378 0.732 0.008 0.224 0.000 NA
#> GSM120718 1 0.3768 0.6112 0.812 0.008 0.144 0.000 NA
#> GSM121205 1 0.3395 0.7521 0.764 0.000 0.000 0.000 NA
#> GSM121206 1 0.3395 0.7521 0.764 0.000 0.000 0.000 NA
#> GSM121207 1 0.3395 0.7521 0.764 0.000 0.000 0.000 NA
#> GSM121208 1 0.2732 0.7338 0.840 0.000 0.000 0.000 NA
#> GSM121209 1 0.3395 0.7521 0.764 0.000 0.000 0.000 NA
#> GSM121210 1 0.3395 0.7521 0.764 0.000 0.000 0.000 NA
#> GSM121211 1 0.3395 0.7521 0.764 0.000 0.000 0.000 NA
#> GSM121212 1 0.3395 0.7521 0.764 0.000 0.000 0.000 NA
#> GSM121213 1 0.3395 0.7521 0.764 0.000 0.000 0.000 NA
#> GSM121214 1 0.3395 0.7521 0.764 0.000 0.000 0.000 NA
#> GSM121215 1 0.3819 0.7484 0.756 0.016 0.000 0.000 NA
#> GSM121216 1 0.3819 0.7484 0.756 0.016 0.000 0.000 NA
#> GSM121217 1 0.3395 0.7521 0.764 0.000 0.000 0.000 NA
#> GSM121218 1 0.3395 0.7521 0.764 0.000 0.000 0.000 NA
#> GSM121234 1 0.3819 0.7484 0.756 0.016 0.000 0.000 NA
#> GSM121243 1 0.3750 0.7491 0.756 0.012 0.000 0.000 NA
#> GSM121245 1 0.3395 0.7521 0.764 0.000 0.000 0.000 NA
#> GSM121246 1 0.4129 0.6149 0.808 0.016 0.100 0.000 NA
#> GSM121247 1 0.3395 0.7521 0.764 0.000 0.000 0.000 NA
#> GSM121248 1 0.3395 0.7521 0.764 0.000 0.000 0.000 NA
#> GSM120744 3 0.0162 0.7502 0.004 0.000 0.996 0.000 NA
#> GSM120745 3 0.0771 0.7489 0.004 0.000 0.976 0.000 NA
#> GSM120746 3 0.0162 0.7502 0.004 0.000 0.996 0.000 NA
#> GSM120747 3 0.0162 0.7502 0.004 0.000 0.996 0.000 NA
#> GSM120748 3 0.0162 0.7499 0.000 0.004 0.996 0.000 NA
#> GSM120749 3 0.0162 0.7502 0.004 0.000 0.996 0.000 NA
#> GSM120750 3 0.0162 0.7502 0.004 0.000 0.996 0.000 NA
#> GSM120751 3 0.0162 0.7502 0.004 0.000 0.996 0.000 NA
#> GSM120752 3 0.0771 0.7489 0.004 0.000 0.976 0.000 NA
#> GSM121336 2 0.2228 0.8293 0.000 0.912 0.000 0.040 NA
#> GSM121339 2 0.1822 0.8376 0.000 0.936 0.004 0.036 NA
#> GSM121349 2 0.2300 0.8307 0.000 0.908 0.000 0.040 NA
#> GSM121355 2 0.2228 0.8293 0.000 0.912 0.000 0.040 NA
#> GSM120757 3 0.4940 0.6945 0.004 0.004 0.640 0.028 NA
#> GSM120766 3 0.4773 0.6930 0.000 0.008 0.656 0.024 NA
#> GSM120770 2 0.3841 0.8137 0.000 0.836 0.072 0.032 NA
#> GSM120779 3 0.5033 0.6931 0.004 0.004 0.632 0.032 NA
#> GSM120780 3 0.4607 0.6811 0.000 0.020 0.656 0.004 NA
#> GSM121102 3 0.4822 0.4192 0.000 0.288 0.664 0.000 NA
#> GSM121203 3 0.1408 0.7489 0.000 0.008 0.948 0.000 NA
#> GSM121204 3 0.4754 0.6954 0.004 0.000 0.652 0.028 NA
#> GSM121330 1 0.5704 0.3822 0.616 0.012 0.288 0.000 NA
#> GSM121335 1 0.4403 0.6014 0.784 0.012 0.112 0.000 NA
#> GSM121337 2 0.7780 0.4855 0.000 0.480 0.176 0.216 NA
#> GSM121338 2 0.5818 0.4980 0.000 0.592 0.316 0.016 NA
#> GSM121341 1 0.4403 0.6014 0.784 0.012 0.112 0.000 NA
#> GSM121342 1 0.4403 0.6014 0.784 0.012 0.112 0.000 NA
#> GSM121343 2 0.6169 0.4545 0.000 0.564 0.312 0.016 NA
#> GSM121344 1 0.4718 0.5778 0.756 0.012 0.140 0.000 NA
#> GSM121346 1 0.6081 -0.0200 0.464 0.012 0.440 0.000 NA
#> GSM121347 2 0.8269 0.2706 0.000 0.380 0.260 0.208 NA
#> GSM121348 3 0.5867 0.6162 0.000 0.040 0.552 0.036 NA
#> GSM121350 1 0.6081 -0.0196 0.464 0.012 0.440 0.000 NA
#> GSM121352 1 0.5908 0.2792 0.564 0.012 0.340 0.000 NA
#> GSM121354 1 0.5625 0.4104 0.632 0.012 0.272 0.000 NA
#> GSM120753 4 0.4537 0.7155 0.000 0.184 0.000 0.740 NA
#> GSM120761 4 0.3995 0.7534 0.000 0.152 0.000 0.788 NA
#> GSM120768 4 0.2153 0.8138 0.000 0.040 0.000 0.916 NA
#> GSM120781 4 0.4933 0.6594 0.000 0.228 0.000 0.692 NA
#> GSM120788 4 0.1908 0.8185 0.000 0.000 0.000 0.908 NA
#> GSM120760 4 0.1211 0.8226 0.000 0.016 0.000 0.960 NA
#> GSM120763 4 0.1549 0.8200 0.000 0.016 0.000 0.944 NA
#> GSM120764 4 0.1851 0.8202 0.000 0.000 0.000 0.912 NA
#> GSM120777 4 0.1908 0.8185 0.000 0.000 0.000 0.908 NA
#> GSM120786 4 0.1851 0.8202 0.000 0.000 0.000 0.912 NA
#> GSM121329 1 0.4890 0.5786 0.752 0.012 0.132 0.004 NA
#> GSM121331 3 0.5033 0.6931 0.004 0.004 0.632 0.032 NA
#> GSM121333 3 0.5033 0.6931 0.004 0.004 0.632 0.032 NA
#> GSM121345 3 0.6185 0.5922 0.000 0.004 0.500 0.124 NA
#> GSM121356 3 0.5033 0.6931 0.004 0.004 0.632 0.032 NA
#> GSM120754 4 0.2193 0.8218 0.000 0.008 0.000 0.900 NA
#> GSM120759 2 0.3983 0.8158 0.000 0.784 0.000 0.052 NA
#> GSM120762 4 0.5663 0.3349 0.000 0.384 0.000 0.532 NA
#> GSM120775 4 0.1908 0.8197 0.000 0.000 0.000 0.908 NA
#> GSM120776 4 0.6235 0.1959 0.000 0.000 0.344 0.500 NA
#> GSM120782 4 0.2505 0.8238 0.000 0.020 0.000 0.888 NA
#> GSM120789 2 0.5990 0.5113 0.000 0.568 0.000 0.280 NA
#> GSM120790 2 0.4989 0.7284 0.000 0.648 0.000 0.056 NA
#> GSM120791 4 0.0798 0.8257 0.000 0.016 0.000 0.976 NA
#> GSM120755 2 0.5426 0.3979 0.000 0.608 0.000 0.308 NA
#> GSM120756 4 0.1965 0.8162 0.000 0.000 0.000 0.904 NA
#> GSM120769 4 0.4373 0.7368 0.000 0.160 0.000 0.760 NA
#> GSM120778 4 0.3181 0.7958 0.000 0.072 0.000 0.856 NA
#> GSM120792 4 0.3058 0.8248 0.000 0.044 0.000 0.860 NA
#> GSM121332 2 0.3055 0.8301 0.000 0.864 0.000 0.072 NA
#> GSM121334 4 0.4558 0.6970 0.000 0.208 0.000 0.728 NA
#> GSM121340 4 0.2068 0.8206 0.000 0.000 0.004 0.904 NA
#> GSM121351 2 0.2813 0.8351 0.000 0.876 0.000 0.040 NA
#> GSM121353 4 0.1908 0.8197 0.000 0.000 0.000 0.908 NA
#> GSM120758 4 0.5028 0.6206 0.000 0.260 0.000 0.668 NA
#> GSM120771 4 0.5501 0.1473 0.000 0.444 0.000 0.492 NA
#> GSM120772 4 0.4199 0.7443 0.000 0.160 0.000 0.772 NA
#> GSM120773 4 0.2177 0.8240 0.000 0.008 0.004 0.908 NA
#> GSM120774 4 0.2885 0.8089 0.000 0.052 0.004 0.880 NA
#> GSM120783 4 0.2295 0.8224 0.000 0.008 0.004 0.900 NA
#> GSM120787 4 0.3898 0.7801 0.000 0.108 0.004 0.812 NA
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM120719 1 0.6423 -0.319 0.472 0.000 0.284 0.000 0.032 0.212
#> GSM120720 1 0.6717 -0.557 0.348 0.000 0.304 0.000 0.032 0.316
#> GSM120765 2 0.1657 0.768 0.000 0.928 0.016 0.000 0.056 0.000
#> GSM120767 2 0.3014 0.694 0.000 0.804 0.012 0.000 0.184 0.000
#> GSM120784 2 0.1657 0.770 0.000 0.928 0.016 0.000 0.056 0.000
#> GSM121400 6 0.4731 0.184 0.000 0.000 0.428 0.000 0.048 0.524
#> GSM121401 6 0.4097 -0.227 0.008 0.000 0.488 0.000 0.000 0.504
#> GSM121402 2 0.4737 0.699 0.000 0.676 0.192 0.000 0.132 0.000
#> GSM121403 6 0.4962 0.194 0.000 0.008 0.428 0.000 0.048 0.516
#> GSM121404 2 0.5629 0.657 0.000 0.656 0.140 0.000 0.072 0.132
#> GSM121405 6 0.4128 -0.210 0.004 0.004 0.492 0.000 0.000 0.500
#> GSM121406 2 0.0935 0.775 0.000 0.964 0.032 0.000 0.004 0.000
#> GSM121408 2 0.1984 0.774 0.000 0.912 0.032 0.000 0.056 0.000
#> GSM121409 6 0.4721 0.203 0.000 0.000 0.420 0.000 0.048 0.532
#> GSM121410 6 0.4731 0.184 0.000 0.000 0.428 0.000 0.048 0.524
#> GSM121412 2 0.2030 0.769 0.000 0.908 0.064 0.000 0.028 0.000
#> GSM121413 2 0.1890 0.770 0.000 0.916 0.060 0.000 0.024 0.000
#> GSM121414 2 0.1890 0.770 0.000 0.916 0.060 0.000 0.024 0.000
#> GSM121415 2 0.1245 0.776 0.000 0.952 0.032 0.000 0.016 0.000
#> GSM121416 2 0.1794 0.776 0.000 0.924 0.036 0.000 0.040 0.000
#> GSM120591 6 0.6090 -0.256 0.144 0.000 0.304 0.000 0.032 0.520
#> GSM120594 1 0.6721 -0.567 0.336 0.000 0.304 0.000 0.032 0.328
#> GSM120718 1 0.6497 -0.387 0.448 0.000 0.300 0.000 0.032 0.220
#> GSM121205 1 0.0000 0.847 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.847 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121207 1 0.0000 0.847 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121208 1 0.1556 0.756 0.920 0.000 0.080 0.000 0.000 0.000
#> GSM121209 1 0.0000 0.847 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.847 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.847 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.847 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.847 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.847 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121215 1 0.0909 0.832 0.968 0.000 0.012 0.000 0.020 0.000
#> GSM121216 1 0.0909 0.832 0.968 0.000 0.012 0.000 0.020 0.000
#> GSM121217 1 0.0000 0.847 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.847 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121234 1 0.0909 0.832 0.968 0.000 0.012 0.000 0.020 0.000
#> GSM121243 1 0.0622 0.838 0.980 0.000 0.008 0.000 0.012 0.000
#> GSM121245 1 0.0000 0.847 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121246 3 0.5603 0.716 0.376 0.000 0.476 0.000 0.000 0.148
#> GSM121247 1 0.0000 0.847 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121248 1 0.0000 0.847 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM120744 6 0.0000 0.643 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120745 6 0.0405 0.634 0.000 0.000 0.004 0.000 0.008 0.988
#> GSM120746 6 0.0000 0.643 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120747 6 0.0000 0.643 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120748 6 0.0632 0.624 0.000 0.000 0.024 0.000 0.000 0.976
#> GSM120749 6 0.0000 0.643 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120750 6 0.0000 0.643 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120751 6 0.0000 0.643 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120752 6 0.0405 0.634 0.000 0.000 0.004 0.000 0.008 0.988
#> GSM121336 2 0.1524 0.767 0.000 0.932 0.008 0.000 0.060 0.000
#> GSM121339 2 0.2034 0.770 0.000 0.912 0.024 0.000 0.060 0.004
#> GSM121349 2 0.1563 0.768 0.000 0.932 0.012 0.000 0.056 0.000
#> GSM121355 2 0.1462 0.767 0.000 0.936 0.008 0.000 0.056 0.000
#> GSM120757 5 0.6165 0.640 0.000 0.000 0.152 0.024 0.440 0.384
#> GSM120766 5 0.6043 0.632 0.000 0.000 0.156 0.016 0.452 0.376
#> GSM120770 2 0.3497 0.751 0.000 0.832 0.084 0.000 0.048 0.036
#> GSM120779 5 0.6204 0.639 0.000 0.000 0.148 0.028 0.440 0.384
#> GSM120780 5 0.5904 0.596 0.000 0.000 0.176 0.004 0.432 0.388
#> GSM121102 6 0.5674 0.224 0.000 0.256 0.072 0.000 0.064 0.608
#> GSM121203 6 0.2197 0.561 0.000 0.000 0.056 0.000 0.044 0.900
#> GSM121204 6 0.6150 -0.633 0.000 0.000 0.148 0.024 0.400 0.428
#> GSM121330 3 0.5675 0.671 0.168 0.000 0.488 0.000 0.000 0.344
#> GSM121335 3 0.5649 0.754 0.356 0.000 0.484 0.000 0.000 0.160
#> GSM121337 2 0.8289 0.393 0.000 0.408 0.200 0.136 0.128 0.128
#> GSM121338 2 0.6648 0.473 0.000 0.508 0.160 0.000 0.084 0.248
#> GSM121341 3 0.5666 0.758 0.352 0.000 0.484 0.000 0.000 0.164
#> GSM121342 3 0.5649 0.754 0.356 0.000 0.484 0.000 0.000 0.160
#> GSM121343 2 0.7010 0.419 0.000 0.460 0.188 0.000 0.108 0.244
#> GSM121344 3 0.5720 0.770 0.332 0.000 0.488 0.000 0.000 0.180
#> GSM121346 3 0.5246 0.513 0.096 0.000 0.488 0.000 0.000 0.416
#> GSM121347 2 0.8697 0.257 0.000 0.324 0.208 0.132 0.164 0.172
#> GSM121348 5 0.6688 0.540 0.000 0.032 0.212 0.020 0.508 0.228
#> GSM121350 3 0.5178 0.487 0.088 0.000 0.488 0.000 0.000 0.424
#> GSM121352 3 0.5449 0.590 0.124 0.000 0.488 0.000 0.000 0.388
#> GSM121354 3 0.5706 0.681 0.176 0.000 0.488 0.000 0.000 0.336
#> GSM120753 4 0.5402 0.653 0.000 0.104 0.004 0.512 0.380 0.000
#> GSM120761 4 0.5463 0.680 0.000 0.088 0.016 0.544 0.352 0.000
#> GSM120768 4 0.4045 0.735 0.000 0.024 0.000 0.664 0.312 0.000
#> GSM120781 4 0.5970 0.572 0.000 0.172 0.008 0.444 0.376 0.000
#> GSM120788 4 0.0146 0.757 0.000 0.000 0.004 0.996 0.000 0.000
#> GSM120760 4 0.3764 0.750 0.000 0.008 0.012 0.724 0.256 0.000
#> GSM120763 4 0.3954 0.741 0.000 0.008 0.012 0.688 0.292 0.000
#> GSM120764 4 0.0291 0.759 0.000 0.004 0.004 0.992 0.000 0.000
#> GSM120777 4 0.0146 0.757 0.000 0.000 0.004 0.996 0.000 0.000
#> GSM120786 4 0.0291 0.759 0.000 0.004 0.004 0.992 0.000 0.000
#> GSM121329 3 0.5949 0.748 0.348 0.000 0.480 0.000 0.012 0.160
#> GSM121331 5 0.6225 0.642 0.000 0.000 0.152 0.028 0.440 0.380
#> GSM121333 5 0.6225 0.642 0.000 0.000 0.152 0.028 0.440 0.380
#> GSM121345 5 0.7182 0.562 0.000 0.000 0.156 0.152 0.436 0.256
#> GSM121356 5 0.6185 0.641 0.000 0.000 0.156 0.024 0.440 0.380
#> GSM120754 4 0.0862 0.763 0.000 0.008 0.004 0.972 0.016 0.000
#> GSM120759 2 0.5196 0.646 0.000 0.604 0.252 0.000 0.144 0.000
#> GSM120762 5 0.6300 -0.409 0.000 0.332 0.008 0.280 0.380 0.000
#> GSM120775 4 0.0405 0.760 0.000 0.004 0.008 0.988 0.000 0.000
#> GSM120776 4 0.4937 0.316 0.000 0.000 0.016 0.676 0.096 0.212
#> GSM120782 4 0.1321 0.764 0.000 0.020 0.004 0.952 0.024 0.000
#> GSM120789 2 0.7466 0.197 0.000 0.360 0.172 0.180 0.288 0.000
#> GSM120790 2 0.6173 0.483 0.000 0.472 0.268 0.012 0.248 0.000
#> GSM120791 4 0.2765 0.768 0.000 0.016 0.004 0.848 0.132 0.000
#> GSM120755 2 0.5798 0.206 0.000 0.476 0.008 0.144 0.372 0.000
#> GSM120756 4 0.0260 0.757 0.000 0.000 0.008 0.992 0.000 0.000
#> GSM120769 4 0.5471 0.647 0.000 0.112 0.004 0.504 0.380 0.000
#> GSM120778 4 0.4582 0.713 0.000 0.032 0.008 0.604 0.356 0.000
#> GSM120792 4 0.2781 0.768 0.000 0.024 0.008 0.860 0.108 0.000
#> GSM121332 2 0.3671 0.760 0.000 0.820 0.088 0.036 0.056 0.000
#> GSM121334 4 0.6152 0.636 0.000 0.140 0.032 0.488 0.340 0.000
#> GSM121340 4 0.1138 0.758 0.000 0.004 0.024 0.960 0.012 0.000
#> GSM121351 2 0.2837 0.761 0.000 0.856 0.088 0.000 0.056 0.000
#> GSM121353 4 0.0508 0.760 0.000 0.004 0.012 0.984 0.000 0.000
#> GSM120758 4 0.6033 0.526 0.000 0.208 0.004 0.420 0.368 0.000
#> GSM120771 5 0.6693 -0.415 0.000 0.308 0.032 0.288 0.372 0.000
#> GSM120772 4 0.5635 0.669 0.000 0.096 0.020 0.524 0.360 0.000
#> GSM120773 4 0.0748 0.764 0.000 0.004 0.004 0.976 0.016 0.000
#> GSM120774 4 0.4653 0.734 0.000 0.020 0.032 0.644 0.304 0.000
#> GSM120783 4 0.0291 0.760 0.000 0.004 0.004 0.992 0.000 0.000
#> GSM120787 4 0.5149 0.716 0.000 0.040 0.036 0.596 0.328 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> MAD:kmeans 117 4.14e-10 2
#> MAD:kmeans 117 3.92e-19 3
#> MAD:kmeans 109 2.52e-25 4
#> MAD:kmeans 102 1.80e-25 5
#> MAD:kmeans 95 3.19e-42 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 21512 rows and 119 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 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 0.982 0.975 0.987 0.5029 0.496 0.496
#> 3 3 0.895 0.905 0.955 0.2654 0.845 0.695
#> 4 4 0.694 0.688 0.830 0.1473 0.880 0.680
#> 5 5 0.688 0.647 0.803 0.0656 0.932 0.754
#> 6 6 0.716 0.695 0.810 0.0386 0.936 0.736
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
#> GSM120719 1 0.0000 0.978 1.000 0.000
#> GSM120720 1 0.0000 0.978 1.000 0.000
#> GSM120765 2 0.0000 0.996 0.000 1.000
#> GSM120767 2 0.0000 0.996 0.000 1.000
#> GSM120784 2 0.0000 0.996 0.000 1.000
#> GSM121400 1 0.0000 0.978 1.000 0.000
#> GSM121401 1 0.0000 0.978 1.000 0.000
#> GSM121402 2 0.0000 0.996 0.000 1.000
#> GSM121403 1 0.0000 0.978 1.000 0.000
#> GSM121404 2 0.0000 0.996 0.000 1.000
#> GSM121405 1 0.0000 0.978 1.000 0.000
#> GSM121406 2 0.0000 0.996 0.000 1.000
#> GSM121408 2 0.0000 0.996 0.000 1.000
#> GSM121409 1 0.0000 0.978 1.000 0.000
#> GSM121410 1 0.0000 0.978 1.000 0.000
#> GSM121412 2 0.0000 0.996 0.000 1.000
#> GSM121413 2 0.0000 0.996 0.000 1.000
#> GSM121414 2 0.0000 0.996 0.000 1.000
#> GSM121415 2 0.0000 0.996 0.000 1.000
#> GSM121416 2 0.0000 0.996 0.000 1.000
#> GSM120591 1 0.0000 0.978 1.000 0.000
#> GSM120594 1 0.0000 0.978 1.000 0.000
#> GSM120718 1 0.0000 0.978 1.000 0.000
#> GSM121205 1 0.0000 0.978 1.000 0.000
#> GSM121206 1 0.0000 0.978 1.000 0.000
#> GSM121207 1 0.0000 0.978 1.000 0.000
#> GSM121208 1 0.0000 0.978 1.000 0.000
#> GSM121209 1 0.0000 0.978 1.000 0.000
#> GSM121210 1 0.0000 0.978 1.000 0.000
#> GSM121211 1 0.0000 0.978 1.000 0.000
#> GSM121212 1 0.0000 0.978 1.000 0.000
#> GSM121213 1 0.0000 0.978 1.000 0.000
#> GSM121214 1 0.0000 0.978 1.000 0.000
#> GSM121215 1 0.0000 0.978 1.000 0.000
#> GSM121216 1 0.0000 0.978 1.000 0.000
#> GSM121217 1 0.0000 0.978 1.000 0.000
#> GSM121218 1 0.0000 0.978 1.000 0.000
#> GSM121234 1 0.0000 0.978 1.000 0.000
#> GSM121243 1 0.0000 0.978 1.000 0.000
#> GSM121245 1 0.0000 0.978 1.000 0.000
#> GSM121246 1 0.0000 0.978 1.000 0.000
#> GSM121247 1 0.0000 0.978 1.000 0.000
#> GSM121248 1 0.0000 0.978 1.000 0.000
#> GSM120744 1 0.6973 0.793 0.812 0.188
#> GSM120745 1 0.0000 0.978 1.000 0.000
#> GSM120746 1 0.6531 0.818 0.832 0.168
#> GSM120747 1 0.6148 0.837 0.848 0.152
#> GSM120748 2 0.5408 0.855 0.124 0.876
#> GSM120749 1 0.0938 0.969 0.988 0.012
#> GSM120750 1 0.6973 0.793 0.812 0.188
#> GSM120751 1 0.3733 0.919 0.928 0.072
#> GSM120752 1 0.0000 0.978 1.000 0.000
#> GSM121336 2 0.0000 0.996 0.000 1.000
#> GSM121339 2 0.0000 0.996 0.000 1.000
#> GSM121349 2 0.0000 0.996 0.000 1.000
#> GSM121355 2 0.0000 0.996 0.000 1.000
#> GSM120757 1 0.4298 0.904 0.912 0.088
#> GSM120766 1 0.7139 0.782 0.804 0.196
#> GSM120770 2 0.0000 0.996 0.000 1.000
#> GSM120779 1 0.0000 0.978 1.000 0.000
#> GSM120780 2 0.3114 0.938 0.056 0.944
#> GSM121102 2 0.0000 0.996 0.000 1.000
#> GSM121203 1 0.6973 0.793 0.812 0.188
#> GSM121204 1 0.0000 0.978 1.000 0.000
#> GSM121330 1 0.0000 0.978 1.000 0.000
#> GSM121335 1 0.0000 0.978 1.000 0.000
#> GSM121337 2 0.0000 0.996 0.000 1.000
#> GSM121338 2 0.0000 0.996 0.000 1.000
#> GSM121341 1 0.0000 0.978 1.000 0.000
#> GSM121342 1 0.0000 0.978 1.000 0.000
#> GSM121343 2 0.0000 0.996 0.000 1.000
#> GSM121344 1 0.0000 0.978 1.000 0.000
#> GSM121346 1 0.0000 0.978 1.000 0.000
#> GSM121347 2 0.0000 0.996 0.000 1.000
#> GSM121348 2 0.0000 0.996 0.000 1.000
#> GSM121350 1 0.0000 0.978 1.000 0.000
#> GSM121352 1 0.0000 0.978 1.000 0.000
#> GSM121354 1 0.0000 0.978 1.000 0.000
#> GSM120753 2 0.0000 0.996 0.000 1.000
#> GSM120761 2 0.0000 0.996 0.000 1.000
#> GSM120768 2 0.0000 0.996 0.000 1.000
#> GSM120781 2 0.0000 0.996 0.000 1.000
#> GSM120788 2 0.0000 0.996 0.000 1.000
#> GSM120760 2 0.0000 0.996 0.000 1.000
#> GSM120763 2 0.0000 0.996 0.000 1.000
#> GSM120764 2 0.0000 0.996 0.000 1.000
#> GSM120777 2 0.0000 0.996 0.000 1.000
#> GSM120786 2 0.0000 0.996 0.000 1.000
#> GSM121329 1 0.0000 0.978 1.000 0.000
#> GSM121331 1 0.0000 0.978 1.000 0.000
#> GSM121333 1 0.0000 0.978 1.000 0.000
#> GSM121345 1 0.0000 0.978 1.000 0.000
#> GSM121356 1 0.0000 0.978 1.000 0.000
#> GSM120754 2 0.0000 0.996 0.000 1.000
#> GSM120759 2 0.0000 0.996 0.000 1.000
#> GSM120762 2 0.0000 0.996 0.000 1.000
#> GSM120775 2 0.0000 0.996 0.000 1.000
#> GSM120776 2 0.0000 0.996 0.000 1.000
#> GSM120782 2 0.0000 0.996 0.000 1.000
#> GSM120789 2 0.0000 0.996 0.000 1.000
#> GSM120790 2 0.0000 0.996 0.000 1.000
#> GSM120791 2 0.0000 0.996 0.000 1.000
#> GSM120755 2 0.0000 0.996 0.000 1.000
#> GSM120756 2 0.3733 0.921 0.072 0.928
#> GSM120769 2 0.0000 0.996 0.000 1.000
#> GSM120778 2 0.0000 0.996 0.000 1.000
#> GSM120792 2 0.0000 0.996 0.000 1.000
#> GSM121332 2 0.0000 0.996 0.000 1.000
#> GSM121334 2 0.0000 0.996 0.000 1.000
#> GSM121340 2 0.0000 0.996 0.000 1.000
#> GSM121351 2 0.0000 0.996 0.000 1.000
#> GSM121353 2 0.0000 0.996 0.000 1.000
#> GSM120758 2 0.0000 0.996 0.000 1.000
#> GSM120771 2 0.0000 0.996 0.000 1.000
#> GSM120772 2 0.0000 0.996 0.000 1.000
#> GSM120773 2 0.0000 0.996 0.000 1.000
#> GSM120774 2 0.0000 0.996 0.000 1.000
#> GSM120783 2 0.0000 0.996 0.000 1.000
#> GSM120787 2 0.0000 0.996 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM120719 1 0.0000 0.950 1.000 0.000 0.000
#> GSM120720 1 0.0000 0.950 1.000 0.000 0.000
#> GSM120765 2 0.0424 0.976 0.000 0.992 0.008
#> GSM120767 2 0.0237 0.977 0.000 0.996 0.004
#> GSM120784 2 0.0424 0.976 0.000 0.992 0.008
#> GSM121400 1 0.6062 0.426 0.616 0.000 0.384
#> GSM121401 1 0.4121 0.796 0.832 0.000 0.168
#> GSM121402 2 0.0424 0.976 0.000 0.992 0.008
#> GSM121403 3 0.6235 0.127 0.436 0.000 0.564
#> GSM121404 2 0.2796 0.901 0.000 0.908 0.092
#> GSM121405 1 0.4178 0.791 0.828 0.000 0.172
#> GSM121406 2 0.0237 0.977 0.000 0.996 0.004
#> GSM121408 2 0.0000 0.978 0.000 1.000 0.000
#> GSM121409 1 0.6305 0.122 0.516 0.000 0.484
#> GSM121410 1 0.5291 0.659 0.732 0.000 0.268
#> GSM121412 2 0.0592 0.974 0.000 0.988 0.012
#> GSM121413 2 0.0592 0.974 0.000 0.988 0.012
#> GSM121414 2 0.0424 0.976 0.000 0.992 0.008
#> GSM121415 2 0.0424 0.976 0.000 0.992 0.008
#> GSM121416 2 0.0237 0.977 0.000 0.996 0.004
#> GSM120591 1 0.0237 0.947 0.996 0.000 0.004
#> GSM120594 1 0.0000 0.950 1.000 0.000 0.000
#> GSM120718 1 0.0000 0.950 1.000 0.000 0.000
#> GSM121205 1 0.0000 0.950 1.000 0.000 0.000
#> GSM121206 1 0.0000 0.950 1.000 0.000 0.000
#> GSM121207 1 0.0000 0.950 1.000 0.000 0.000
#> GSM121208 1 0.0000 0.950 1.000 0.000 0.000
#> GSM121209 1 0.0000 0.950 1.000 0.000 0.000
#> GSM121210 1 0.0000 0.950 1.000 0.000 0.000
#> GSM121211 1 0.0000 0.950 1.000 0.000 0.000
#> GSM121212 1 0.0000 0.950 1.000 0.000 0.000
#> GSM121213 1 0.0000 0.950 1.000 0.000 0.000
#> GSM121214 1 0.0000 0.950 1.000 0.000 0.000
#> GSM121215 1 0.0000 0.950 1.000 0.000 0.000
#> GSM121216 1 0.0000 0.950 1.000 0.000 0.000
#> GSM121217 1 0.0000 0.950 1.000 0.000 0.000
#> GSM121218 1 0.0000 0.950 1.000 0.000 0.000
#> GSM121234 1 0.0000 0.950 1.000 0.000 0.000
#> GSM121243 1 0.0000 0.950 1.000 0.000 0.000
#> GSM121245 1 0.0000 0.950 1.000 0.000 0.000
#> GSM121246 1 0.0000 0.950 1.000 0.000 0.000
#> GSM121247 1 0.0000 0.950 1.000 0.000 0.000
#> GSM121248 1 0.0000 0.950 1.000 0.000 0.000
#> GSM120744 3 0.0000 0.885 0.000 0.000 1.000
#> GSM120745 3 0.0424 0.883 0.008 0.000 0.992
#> GSM120746 3 0.0000 0.885 0.000 0.000 1.000
#> GSM120747 3 0.0000 0.885 0.000 0.000 1.000
#> GSM120748 3 0.0000 0.885 0.000 0.000 1.000
#> GSM120749 3 0.0000 0.885 0.000 0.000 1.000
#> GSM120750 3 0.0000 0.885 0.000 0.000 1.000
#> GSM120751 3 0.0000 0.885 0.000 0.000 1.000
#> GSM120752 3 0.0424 0.883 0.008 0.000 0.992
#> GSM121336 2 0.0237 0.977 0.000 0.996 0.004
#> GSM121339 2 0.1643 0.949 0.000 0.956 0.044
#> GSM121349 2 0.0237 0.977 0.000 0.996 0.004
#> GSM121355 2 0.0237 0.977 0.000 0.996 0.004
#> GSM120757 3 0.2066 0.866 0.060 0.000 0.940
#> GSM120766 3 0.0000 0.885 0.000 0.000 1.000
#> GSM120770 2 0.2448 0.919 0.000 0.924 0.076
#> GSM120779 3 0.4291 0.797 0.180 0.000 0.820
#> GSM120780 3 0.0000 0.885 0.000 0.000 1.000
#> GSM121102 3 0.2711 0.826 0.000 0.088 0.912
#> GSM121203 3 0.0000 0.885 0.000 0.000 1.000
#> GSM121204 3 0.4974 0.736 0.236 0.000 0.764
#> GSM121330 1 0.1753 0.916 0.952 0.000 0.048
#> GSM121335 1 0.0000 0.950 1.000 0.000 0.000
#> GSM121337 2 0.1289 0.957 0.000 0.968 0.032
#> GSM121338 2 0.5591 0.593 0.000 0.696 0.304
#> GSM121341 1 0.0000 0.950 1.000 0.000 0.000
#> GSM121342 1 0.0000 0.950 1.000 0.000 0.000
#> GSM121343 2 0.5560 0.600 0.000 0.700 0.300
#> GSM121344 1 0.0000 0.950 1.000 0.000 0.000
#> GSM121346 1 0.2625 0.886 0.916 0.000 0.084
#> GSM121347 2 0.3267 0.872 0.000 0.884 0.116
#> GSM121348 3 0.3192 0.816 0.000 0.112 0.888
#> GSM121350 1 0.3038 0.866 0.896 0.000 0.104
#> GSM121352 1 0.2066 0.907 0.940 0.000 0.060
#> GSM121354 1 0.1289 0.928 0.968 0.000 0.032
#> GSM120753 2 0.0000 0.978 0.000 1.000 0.000
#> GSM120761 2 0.0000 0.978 0.000 1.000 0.000
#> GSM120768 2 0.0000 0.978 0.000 1.000 0.000
#> GSM120781 2 0.0000 0.978 0.000 1.000 0.000
#> GSM120788 2 0.1643 0.944 0.000 0.956 0.044
#> GSM120760 2 0.0000 0.978 0.000 1.000 0.000
#> GSM120763 2 0.0000 0.978 0.000 1.000 0.000
#> GSM120764 2 0.0000 0.978 0.000 1.000 0.000
#> GSM120777 2 0.1031 0.962 0.000 0.976 0.024
#> GSM120786 2 0.0000 0.978 0.000 1.000 0.000
#> GSM121329 1 0.0000 0.950 1.000 0.000 0.000
#> GSM121331 3 0.4291 0.797 0.180 0.000 0.820
#> GSM121333 3 0.4399 0.790 0.188 0.000 0.812
#> GSM121345 3 0.5926 0.542 0.356 0.000 0.644
#> GSM121356 3 0.4235 0.801 0.176 0.000 0.824
#> GSM120754 2 0.0000 0.978 0.000 1.000 0.000
#> GSM120759 2 0.0237 0.977 0.000 0.996 0.004
#> GSM120762 2 0.0000 0.978 0.000 1.000 0.000
#> GSM120775 2 0.0000 0.978 0.000 1.000 0.000
#> GSM120776 3 0.5843 0.654 0.016 0.252 0.732
#> GSM120782 2 0.0000 0.978 0.000 1.000 0.000
#> GSM120789 2 0.0000 0.978 0.000 1.000 0.000
#> GSM120790 2 0.0592 0.974 0.000 0.988 0.012
#> GSM120791 2 0.0000 0.978 0.000 1.000 0.000
#> GSM120755 2 0.0000 0.978 0.000 1.000 0.000
#> GSM120756 2 0.1999 0.939 0.036 0.952 0.012
#> GSM120769 2 0.0000 0.978 0.000 1.000 0.000
#> GSM120778 2 0.0000 0.978 0.000 1.000 0.000
#> GSM120792 2 0.0000 0.978 0.000 1.000 0.000
#> GSM121332 2 0.0000 0.978 0.000 1.000 0.000
#> GSM121334 2 0.0000 0.978 0.000 1.000 0.000
#> GSM121340 2 0.0000 0.978 0.000 1.000 0.000
#> GSM121351 2 0.0424 0.976 0.000 0.992 0.008
#> GSM121353 2 0.0000 0.978 0.000 1.000 0.000
#> GSM120758 2 0.0000 0.978 0.000 1.000 0.000
#> GSM120771 2 0.0000 0.978 0.000 1.000 0.000
#> GSM120772 2 0.0000 0.978 0.000 1.000 0.000
#> GSM120773 2 0.0000 0.978 0.000 1.000 0.000
#> GSM120774 2 0.0000 0.978 0.000 1.000 0.000
#> GSM120783 2 0.0000 0.978 0.000 1.000 0.000
#> GSM120787 2 0.0000 0.978 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM120719 1 0.0188 0.9179 0.996 0.000 0.004 0.000
#> GSM120720 1 0.0779 0.9139 0.980 0.000 0.016 0.004
#> GSM120765 2 0.0000 0.7820 0.000 1.000 0.000 0.000
#> GSM120767 2 0.1557 0.7586 0.000 0.944 0.000 0.056
#> GSM120784 2 0.0000 0.7820 0.000 1.000 0.000 0.000
#> GSM121400 1 0.6919 0.3162 0.504 0.004 0.396 0.096
#> GSM121401 1 0.6307 0.5629 0.620 0.000 0.288 0.092
#> GSM121402 2 0.0000 0.7820 0.000 1.000 0.000 0.000
#> GSM121403 3 0.8198 0.1588 0.068 0.404 0.432 0.096
#> GSM121404 2 0.3239 0.6859 0.000 0.880 0.068 0.052
#> GSM121405 1 0.6656 0.5079 0.588 0.004 0.312 0.096
#> GSM121406 2 0.0000 0.7820 0.000 1.000 0.000 0.000
#> GSM121408 2 0.0336 0.7802 0.000 0.992 0.000 0.008
#> GSM121409 3 0.7252 -0.0260 0.396 0.012 0.488 0.104
#> GSM121410 1 0.6883 0.4219 0.548 0.004 0.344 0.104
#> GSM121412 2 0.0000 0.7820 0.000 1.000 0.000 0.000
#> GSM121413 2 0.0000 0.7820 0.000 1.000 0.000 0.000
#> GSM121414 2 0.0000 0.7820 0.000 1.000 0.000 0.000
#> GSM121415 2 0.0000 0.7820 0.000 1.000 0.000 0.000
#> GSM121416 2 0.0188 0.7813 0.000 0.996 0.000 0.004
#> GSM120591 1 0.1398 0.9013 0.956 0.000 0.040 0.004
#> GSM120594 1 0.0779 0.9139 0.980 0.000 0.016 0.004
#> GSM120718 1 0.0336 0.9171 0.992 0.000 0.008 0.000
#> GSM121205 1 0.0000 0.9192 1.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.9192 1.000 0.000 0.000 0.000
#> GSM121207 1 0.0000 0.9192 1.000 0.000 0.000 0.000
#> GSM121208 1 0.0000 0.9192 1.000 0.000 0.000 0.000
#> GSM121209 1 0.0000 0.9192 1.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.9192 1.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.9192 1.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.9192 1.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.9192 1.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.9192 1.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.9192 1.000 0.000 0.000 0.000
#> GSM121216 1 0.0000 0.9192 1.000 0.000 0.000 0.000
#> GSM121217 1 0.0000 0.9192 1.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.9192 1.000 0.000 0.000 0.000
#> GSM121234 1 0.0000 0.9192 1.000 0.000 0.000 0.000
#> GSM121243 1 0.0000 0.9192 1.000 0.000 0.000 0.000
#> GSM121245 1 0.0000 0.9192 1.000 0.000 0.000 0.000
#> GSM121246 1 0.0336 0.9174 0.992 0.000 0.000 0.008
#> GSM121247 1 0.0188 0.9177 0.996 0.000 0.000 0.004
#> GSM121248 1 0.0000 0.9192 1.000 0.000 0.000 0.000
#> GSM120744 3 0.0000 0.7889 0.000 0.000 1.000 0.000
#> GSM120745 3 0.0336 0.7892 0.000 0.000 0.992 0.008
#> GSM120746 3 0.0000 0.7889 0.000 0.000 1.000 0.000
#> GSM120747 3 0.0188 0.7877 0.000 0.000 0.996 0.004
#> GSM120748 3 0.0336 0.7874 0.000 0.000 0.992 0.008
#> GSM120749 3 0.0000 0.7889 0.000 0.000 1.000 0.000
#> GSM120750 3 0.0000 0.7889 0.000 0.000 1.000 0.000
#> GSM120751 3 0.0188 0.7892 0.000 0.000 0.996 0.004
#> GSM120752 3 0.0336 0.7892 0.000 0.000 0.992 0.008
#> GSM121336 2 0.0000 0.7820 0.000 1.000 0.000 0.000
#> GSM121339 2 0.2002 0.7403 0.000 0.936 0.044 0.020
#> GSM121349 2 0.0000 0.7820 0.000 1.000 0.000 0.000
#> GSM121355 2 0.0000 0.7820 0.000 1.000 0.000 0.000
#> GSM120757 3 0.5309 0.6957 0.044 0.000 0.700 0.256
#> GSM120766 3 0.4126 0.7247 0.004 0.004 0.776 0.216
#> GSM120770 2 0.1022 0.7647 0.000 0.968 0.032 0.000
#> GSM120779 3 0.6040 0.6720 0.080 0.000 0.648 0.272
#> GSM120780 3 0.2593 0.7727 0.000 0.016 0.904 0.080
#> GSM121102 3 0.5268 0.1683 0.000 0.452 0.540 0.008
#> GSM121203 3 0.0188 0.7885 0.000 0.000 0.996 0.004
#> GSM121204 3 0.6651 0.6185 0.236 0.000 0.616 0.148
#> GSM121330 1 0.4106 0.8238 0.832 0.000 0.084 0.084
#> GSM121335 1 0.1545 0.9024 0.952 0.000 0.008 0.040
#> GSM121337 2 0.4010 0.6500 0.000 0.816 0.028 0.156
#> GSM121338 2 0.5910 0.4272 0.000 0.672 0.244 0.084
#> GSM121341 1 0.1890 0.8943 0.936 0.000 0.008 0.056
#> GSM121342 1 0.1211 0.9054 0.960 0.000 0.000 0.040
#> GSM121343 2 0.5454 0.5145 0.000 0.732 0.172 0.096
#> GSM121344 1 0.2255 0.8848 0.920 0.000 0.012 0.068
#> GSM121346 1 0.4969 0.7669 0.772 0.000 0.140 0.088
#> GSM121347 2 0.6148 0.3967 0.000 0.636 0.084 0.280
#> GSM121348 3 0.7149 0.5450 0.000 0.156 0.528 0.316
#> GSM121350 1 0.5457 0.7158 0.728 0.000 0.184 0.088
#> GSM121352 1 0.4477 0.8026 0.808 0.000 0.108 0.084
#> GSM121354 1 0.3900 0.8337 0.844 0.000 0.072 0.084
#> GSM120753 2 0.4679 0.2730 0.000 0.648 0.000 0.352
#> GSM120761 2 0.4866 0.0543 0.000 0.596 0.000 0.404
#> GSM120768 4 0.4907 0.5462 0.000 0.420 0.000 0.580
#> GSM120781 2 0.4454 0.3999 0.000 0.692 0.000 0.308
#> GSM120788 4 0.2530 0.6781 0.000 0.100 0.004 0.896
#> GSM120760 4 0.4585 0.6896 0.000 0.332 0.000 0.668
#> GSM120763 4 0.4585 0.6887 0.000 0.332 0.000 0.668
#> GSM120764 4 0.3311 0.7252 0.000 0.172 0.000 0.828
#> GSM120777 4 0.2469 0.6843 0.000 0.108 0.000 0.892
#> GSM120786 4 0.3726 0.7397 0.000 0.212 0.000 0.788
#> GSM121329 1 0.0188 0.9185 0.996 0.000 0.000 0.004
#> GSM121331 3 0.6293 0.6629 0.096 0.000 0.628 0.276
#> GSM121333 3 0.6347 0.6613 0.100 0.000 0.624 0.276
#> GSM121345 4 0.7608 -0.3538 0.216 0.000 0.328 0.456
#> GSM121356 3 0.6269 0.6676 0.096 0.000 0.632 0.272
#> GSM120754 4 0.4164 0.7338 0.000 0.264 0.000 0.736
#> GSM120759 2 0.0188 0.7813 0.000 0.996 0.000 0.004
#> GSM120762 2 0.3873 0.5694 0.000 0.772 0.000 0.228
#> GSM120775 4 0.3400 0.7293 0.000 0.180 0.000 0.820
#> GSM120776 4 0.4406 0.1071 0.000 0.000 0.300 0.700
#> GSM120782 4 0.5159 0.6454 0.000 0.364 0.012 0.624
#> GSM120789 2 0.3219 0.6658 0.000 0.836 0.000 0.164
#> GSM120790 2 0.2737 0.7323 0.000 0.888 0.008 0.104
#> GSM120791 4 0.4761 0.6379 0.000 0.372 0.000 0.628
#> GSM120755 2 0.3172 0.6699 0.000 0.840 0.000 0.160
#> GSM120756 4 0.2796 0.6727 0.016 0.092 0.000 0.892
#> GSM120769 2 0.4843 0.1007 0.000 0.604 0.000 0.396
#> GSM120778 4 0.4981 0.4284 0.000 0.464 0.000 0.536
#> GSM120792 4 0.4817 0.6087 0.000 0.388 0.000 0.612
#> GSM121332 2 0.2345 0.7318 0.000 0.900 0.000 0.100
#> GSM121334 2 0.4564 0.3485 0.000 0.672 0.000 0.328
#> GSM121340 4 0.3837 0.7405 0.000 0.224 0.000 0.776
#> GSM121351 2 0.0000 0.7820 0.000 1.000 0.000 0.000
#> GSM121353 4 0.3764 0.7405 0.000 0.216 0.000 0.784
#> GSM120758 2 0.4477 0.3906 0.000 0.688 0.000 0.312
#> GSM120771 2 0.3311 0.6567 0.000 0.828 0.000 0.172
#> GSM120772 2 0.4713 0.2414 0.000 0.640 0.000 0.360
#> GSM120773 4 0.4406 0.7144 0.000 0.300 0.000 0.700
#> GSM120774 4 0.5000 0.3362 0.000 0.496 0.000 0.504
#> GSM120783 4 0.4222 0.7297 0.000 0.272 0.000 0.728
#> GSM120787 2 0.4981 -0.2340 0.000 0.536 0.000 0.464
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM120719 1 0.0932 0.87611 0.972 0.000 0.020 0.004 0.004
#> GSM120720 1 0.2681 0.80131 0.876 0.000 0.108 0.004 0.012
#> GSM120765 2 0.0324 0.77590 0.000 0.992 0.004 0.004 0.000
#> GSM120767 2 0.1764 0.75805 0.000 0.928 0.008 0.064 0.000
#> GSM120784 2 0.0324 0.77590 0.000 0.992 0.004 0.004 0.000
#> GSM121400 3 0.4273 0.64405 0.116 0.004 0.784 0.000 0.096
#> GSM121401 3 0.2971 0.67236 0.156 0.000 0.836 0.000 0.008
#> GSM121402 2 0.0404 0.77564 0.000 0.988 0.000 0.012 0.000
#> GSM121403 3 0.4013 0.45523 0.004 0.108 0.804 0.000 0.084
#> GSM121404 2 0.2712 0.72417 0.000 0.880 0.088 0.000 0.032
#> GSM121405 3 0.2392 0.62680 0.104 0.004 0.888 0.000 0.004
#> GSM121406 2 0.0000 0.77470 0.000 1.000 0.000 0.000 0.000
#> GSM121408 2 0.0703 0.77391 0.000 0.976 0.000 0.024 0.000
#> GSM121409 3 0.4268 0.53481 0.084 0.000 0.772 0.000 0.144
#> GSM121410 3 0.4953 0.66580 0.164 0.000 0.712 0.000 0.124
#> GSM121412 2 0.0162 0.77507 0.000 0.996 0.004 0.000 0.000
#> GSM121413 2 0.0162 0.77507 0.000 0.996 0.004 0.000 0.000
#> GSM121414 2 0.0290 0.77493 0.000 0.992 0.008 0.000 0.000
#> GSM121415 2 0.0162 0.77501 0.000 0.996 0.004 0.000 0.000
#> GSM121416 2 0.0290 0.77606 0.000 0.992 0.000 0.008 0.000
#> GSM120591 1 0.4184 0.65066 0.772 0.000 0.176 0.004 0.048
#> GSM120594 1 0.2731 0.80014 0.876 0.000 0.104 0.004 0.016
#> GSM120718 1 0.1857 0.84781 0.928 0.000 0.060 0.004 0.008
#> GSM121205 1 0.0000 0.89349 1.000 0.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.89349 1.000 0.000 0.000 0.000 0.000
#> GSM121207 1 0.0000 0.89349 1.000 0.000 0.000 0.000 0.000
#> GSM121208 1 0.0162 0.89112 0.996 0.000 0.004 0.000 0.000
#> GSM121209 1 0.0000 0.89349 1.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.89349 1.000 0.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.89349 1.000 0.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.89349 1.000 0.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.89349 1.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.89349 1.000 0.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.89349 1.000 0.000 0.000 0.000 0.000
#> GSM121216 1 0.0000 0.89349 1.000 0.000 0.000 0.000 0.000
#> GSM121217 1 0.0000 0.89349 1.000 0.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.89349 1.000 0.000 0.000 0.000 0.000
#> GSM121234 1 0.0000 0.89349 1.000 0.000 0.000 0.000 0.000
#> GSM121243 1 0.0000 0.89349 1.000 0.000 0.000 0.000 0.000
#> GSM121245 1 0.0000 0.89349 1.000 0.000 0.000 0.000 0.000
#> GSM121246 1 0.1965 0.80879 0.904 0.000 0.096 0.000 0.000
#> GSM121247 1 0.0404 0.88343 0.988 0.000 0.000 0.000 0.012
#> GSM121248 1 0.0000 0.89349 1.000 0.000 0.000 0.000 0.000
#> GSM120744 5 0.4410 0.61281 0.000 0.000 0.440 0.004 0.556
#> GSM120745 5 0.4350 0.62115 0.000 0.000 0.408 0.004 0.588
#> GSM120746 5 0.4403 0.61376 0.000 0.000 0.436 0.004 0.560
#> GSM120747 5 0.4420 0.60491 0.000 0.000 0.448 0.004 0.548
#> GSM120748 5 0.4430 0.60157 0.000 0.000 0.456 0.004 0.540
#> GSM120749 5 0.4410 0.61164 0.000 0.000 0.440 0.004 0.556
#> GSM120750 5 0.4410 0.61281 0.000 0.000 0.440 0.004 0.556
#> GSM120751 5 0.4410 0.61281 0.000 0.000 0.440 0.004 0.556
#> GSM120752 5 0.4331 0.62251 0.000 0.000 0.400 0.004 0.596
#> GSM121336 2 0.0290 0.77565 0.000 0.992 0.000 0.008 0.000
#> GSM121339 2 0.1970 0.75090 0.000 0.924 0.060 0.004 0.012
#> GSM121349 2 0.0162 0.77560 0.000 0.996 0.000 0.004 0.000
#> GSM121355 2 0.0162 0.77560 0.000 0.996 0.000 0.004 0.000
#> GSM120757 5 0.2722 0.59107 0.004 0.000 0.008 0.120 0.868
#> GSM120766 5 0.2464 0.59709 0.000 0.000 0.016 0.096 0.888
#> GSM120770 2 0.2990 0.72165 0.000 0.868 0.024 0.008 0.100
#> GSM120779 5 0.3339 0.57944 0.040 0.000 0.000 0.124 0.836
#> GSM120780 5 0.2484 0.60380 0.000 0.004 0.068 0.028 0.900
#> GSM121102 2 0.6868 -0.13286 0.000 0.408 0.268 0.004 0.320
#> GSM121203 5 0.4264 0.61150 0.000 0.000 0.376 0.004 0.620
#> GSM121204 5 0.6021 0.25130 0.364 0.000 0.032 0.056 0.548
#> GSM121330 3 0.4235 0.54359 0.424 0.000 0.576 0.000 0.000
#> GSM121335 1 0.3983 0.27996 0.660 0.000 0.340 0.000 0.000
#> GSM121337 2 0.6825 0.46191 0.000 0.596 0.100 0.196 0.108
#> GSM121338 2 0.5174 0.43549 0.000 0.604 0.340 0.000 0.056
#> GSM121341 1 0.4201 -0.00737 0.592 0.000 0.408 0.000 0.000
#> GSM121342 1 0.3774 0.42483 0.704 0.000 0.296 0.000 0.000
#> GSM121343 2 0.5748 0.42050 0.000 0.584 0.300 0.000 0.116
#> GSM121344 1 0.4278 -0.20486 0.548 0.000 0.452 0.000 0.000
#> GSM121346 3 0.4264 0.62386 0.376 0.000 0.620 0.000 0.004
#> GSM121347 2 0.7470 0.19074 0.000 0.444 0.048 0.248 0.260
#> GSM121348 5 0.6283 0.44458 0.000 0.100 0.064 0.196 0.640
#> GSM121350 3 0.4182 0.65018 0.352 0.000 0.644 0.000 0.004
#> GSM121352 3 0.4242 0.53627 0.428 0.000 0.572 0.000 0.000
#> GSM121354 3 0.4287 0.44901 0.460 0.000 0.540 0.000 0.000
#> GSM120753 2 0.3949 0.44009 0.000 0.668 0.000 0.332 0.000
#> GSM120761 4 0.4306 0.13936 0.000 0.492 0.000 0.508 0.000
#> GSM120768 4 0.3661 0.68656 0.000 0.276 0.000 0.724 0.000
#> GSM120781 2 0.3895 0.46629 0.000 0.680 0.000 0.320 0.000
#> GSM120788 4 0.0693 0.75911 0.000 0.008 0.000 0.980 0.012
#> GSM120760 4 0.3242 0.75784 0.000 0.216 0.000 0.784 0.000
#> GSM120763 4 0.3210 0.76200 0.000 0.212 0.000 0.788 0.000
#> GSM120764 4 0.1282 0.79562 0.000 0.044 0.000 0.952 0.004
#> GSM120777 4 0.0898 0.75478 0.000 0.008 0.000 0.972 0.020
#> GSM120786 4 0.1671 0.80973 0.000 0.076 0.000 0.924 0.000
#> GSM121329 1 0.2228 0.83448 0.916 0.000 0.056 0.008 0.020
#> GSM121331 5 0.4772 0.53295 0.092 0.000 0.008 0.156 0.744
#> GSM121333 5 0.4514 0.54613 0.076 0.000 0.008 0.152 0.764
#> GSM121345 5 0.6603 0.32120 0.192 0.000 0.012 0.272 0.524
#> GSM121356 5 0.4576 0.54214 0.096 0.000 0.008 0.132 0.764
#> GSM120754 4 0.2470 0.80762 0.000 0.104 0.000 0.884 0.012
#> GSM120759 2 0.0609 0.77469 0.000 0.980 0.000 0.020 0.000
#> GSM120762 2 0.3671 0.61146 0.000 0.756 0.008 0.236 0.000
#> GSM120775 4 0.1430 0.79995 0.000 0.052 0.000 0.944 0.004
#> GSM120776 4 0.5034 0.26604 0.000 0.004 0.048 0.648 0.300
#> GSM120782 4 0.5213 0.65333 0.000 0.264 0.032 0.672 0.032
#> GSM120789 2 0.3074 0.66582 0.000 0.804 0.000 0.196 0.000
#> GSM120790 2 0.4295 0.65984 0.000 0.780 0.004 0.084 0.132
#> GSM120791 4 0.3109 0.77304 0.000 0.200 0.000 0.800 0.000
#> GSM120755 2 0.2329 0.72475 0.000 0.876 0.000 0.124 0.000
#> GSM120756 4 0.0671 0.77349 0.000 0.016 0.000 0.980 0.004
#> GSM120769 2 0.4256 0.12240 0.000 0.564 0.000 0.436 0.000
#> GSM120778 4 0.4030 0.55125 0.000 0.352 0.000 0.648 0.000
#> GSM120792 4 0.3534 0.71535 0.000 0.256 0.000 0.744 0.000
#> GSM121332 2 0.2424 0.72563 0.000 0.868 0.000 0.132 0.000
#> GSM121334 2 0.3932 0.44382 0.000 0.672 0.000 0.328 0.000
#> GSM121340 4 0.1851 0.81112 0.000 0.088 0.000 0.912 0.000
#> GSM121351 2 0.0324 0.77590 0.000 0.992 0.004 0.004 0.000
#> GSM121353 4 0.1952 0.81101 0.000 0.084 0.000 0.912 0.004
#> GSM120758 2 0.3949 0.43439 0.000 0.668 0.000 0.332 0.000
#> GSM120771 2 0.3143 0.65003 0.000 0.796 0.000 0.204 0.000
#> GSM120772 2 0.4415 0.07330 0.000 0.552 0.004 0.444 0.000
#> GSM120773 4 0.2536 0.80544 0.000 0.128 0.000 0.868 0.004
#> GSM120774 4 0.4235 0.38744 0.000 0.424 0.000 0.576 0.000
#> GSM120783 4 0.2233 0.80988 0.000 0.104 0.000 0.892 0.004
#> GSM120787 2 0.4294 -0.02278 0.000 0.532 0.000 0.468 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM120719 1 0.4004 0.7664 0.816 0.000 0.040 0.024 0.044 0.076
#> GSM120720 1 0.5959 0.5338 0.640 0.000 0.184 0.024 0.048 0.104
#> GSM120765 2 0.0622 0.7621 0.000 0.980 0.012 0.000 0.008 0.000
#> GSM120767 2 0.2001 0.7576 0.000 0.920 0.016 0.044 0.020 0.000
#> GSM120784 2 0.0964 0.7613 0.000 0.968 0.012 0.004 0.016 0.000
#> GSM121400 3 0.3909 0.6786 0.024 0.000 0.816 0.016 0.072 0.072
#> GSM121401 3 0.3093 0.7497 0.076 0.000 0.852 0.000 0.012 0.060
#> GSM121402 2 0.2078 0.7645 0.000 0.916 0.012 0.032 0.040 0.000
#> GSM121403 3 0.4158 0.6192 0.000 0.024 0.796 0.016 0.096 0.068
#> GSM121404 2 0.4849 0.6775 0.000 0.756 0.100 0.024 0.060 0.060
#> GSM121405 3 0.2934 0.7308 0.048 0.008 0.872 0.000 0.012 0.060
#> GSM121406 2 0.1230 0.7621 0.000 0.956 0.008 0.008 0.028 0.000
#> GSM121408 2 0.1515 0.7640 0.000 0.944 0.008 0.028 0.020 0.000
#> GSM121409 3 0.6066 0.5326 0.060 0.000 0.632 0.016 0.144 0.148
#> GSM121410 3 0.4349 0.6703 0.032 0.004 0.788 0.012 0.100 0.064
#> GSM121412 2 0.2016 0.7547 0.000 0.920 0.024 0.016 0.040 0.000
#> GSM121413 2 0.1585 0.7572 0.000 0.940 0.012 0.012 0.036 0.000
#> GSM121414 2 0.1511 0.7578 0.000 0.944 0.012 0.012 0.032 0.000
#> GSM121415 2 0.1518 0.7597 0.000 0.944 0.024 0.008 0.024 0.000
#> GSM121416 2 0.1787 0.7643 0.000 0.932 0.020 0.016 0.032 0.000
#> GSM120591 1 0.6682 0.3575 0.540 0.000 0.180 0.024 0.044 0.212
#> GSM120594 1 0.6045 0.5166 0.628 0.000 0.188 0.024 0.044 0.116
#> GSM120718 1 0.5048 0.6793 0.736 0.000 0.116 0.024 0.044 0.080
#> GSM121205 1 0.0000 0.9120 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.9120 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121207 1 0.0000 0.9120 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121208 1 0.0937 0.8827 0.960 0.000 0.040 0.000 0.000 0.000
#> GSM121209 1 0.0000 0.9120 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.9120 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.9120 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.9120 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.9120 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.9120 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.9120 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121216 1 0.0000 0.9120 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121217 1 0.0000 0.9120 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.9120 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121234 1 0.0000 0.9120 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121243 1 0.0000 0.9120 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121245 1 0.0000 0.9120 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121246 1 0.2260 0.7672 0.860 0.000 0.140 0.000 0.000 0.000
#> GSM121247 1 0.0547 0.8955 0.980 0.000 0.000 0.000 0.020 0.000
#> GSM121248 1 0.0000 0.9120 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM120744 6 0.0806 0.8719 0.000 0.000 0.020 0.000 0.008 0.972
#> GSM120745 6 0.0603 0.8587 0.000 0.000 0.004 0.000 0.016 0.980
#> GSM120746 6 0.0993 0.8720 0.000 0.000 0.024 0.000 0.012 0.964
#> GSM120747 6 0.1265 0.8609 0.000 0.000 0.044 0.000 0.008 0.948
#> GSM120748 6 0.1408 0.8598 0.000 0.000 0.036 0.000 0.020 0.944
#> GSM120749 6 0.0622 0.8688 0.000 0.000 0.008 0.000 0.012 0.980
#> GSM120750 6 0.0993 0.8721 0.000 0.000 0.024 0.000 0.012 0.964
#> GSM120751 6 0.0622 0.8698 0.000 0.000 0.012 0.000 0.008 0.980
#> GSM120752 6 0.0547 0.8608 0.000 0.000 0.000 0.000 0.020 0.980
#> GSM121336 2 0.0520 0.7610 0.000 0.984 0.008 0.008 0.000 0.000
#> GSM121339 2 0.4004 0.7077 0.000 0.816 0.080 0.036 0.032 0.036
#> GSM121349 2 0.0725 0.7619 0.000 0.976 0.012 0.012 0.000 0.000
#> GSM121355 2 0.0767 0.7613 0.000 0.976 0.008 0.012 0.004 0.000
#> GSM120757 5 0.4034 0.6966 0.012 0.000 0.008 0.012 0.724 0.244
#> GSM120766 5 0.3686 0.6931 0.000 0.000 0.012 0.012 0.748 0.228
#> GSM120770 2 0.4357 0.7029 0.000 0.788 0.048 0.016 0.092 0.056
#> GSM120779 5 0.3866 0.7363 0.036 0.000 0.000 0.012 0.764 0.188
#> GSM120780 5 0.4474 0.6156 0.000 0.004 0.032 0.012 0.680 0.272
#> GSM121102 6 0.7018 0.1628 0.000 0.364 0.088 0.016 0.112 0.420
#> GSM121203 6 0.4260 0.6178 0.000 0.000 0.064 0.012 0.184 0.740
#> GSM121204 5 0.6507 0.3331 0.328 0.000 0.016 0.004 0.404 0.248
#> GSM121330 3 0.3281 0.7797 0.200 0.000 0.784 0.000 0.004 0.012
#> GSM121335 3 0.3961 0.4465 0.440 0.000 0.556 0.000 0.004 0.000
#> GSM121337 2 0.7163 0.4012 0.000 0.500 0.116 0.164 0.204 0.016
#> GSM121338 2 0.7326 0.2636 0.000 0.468 0.260 0.024 0.120 0.128
#> GSM121341 3 0.3878 0.6358 0.348 0.000 0.644 0.000 0.004 0.004
#> GSM121342 3 0.3993 0.3401 0.476 0.000 0.520 0.000 0.004 0.000
#> GSM121343 2 0.7139 0.2051 0.000 0.440 0.288 0.028 0.200 0.044
#> GSM121344 3 0.3601 0.6846 0.312 0.000 0.684 0.000 0.004 0.000
#> GSM121346 3 0.3527 0.7808 0.164 0.000 0.792 0.000 0.004 0.040
#> GSM121347 5 0.7788 0.0550 0.000 0.308 0.084 0.196 0.368 0.044
#> GSM121348 5 0.3512 0.6922 0.000 0.036 0.012 0.044 0.844 0.064
#> GSM121350 3 0.3090 0.7771 0.140 0.000 0.828 0.000 0.004 0.028
#> GSM121352 3 0.3250 0.7810 0.196 0.000 0.788 0.000 0.004 0.012
#> GSM121354 3 0.3248 0.7680 0.224 0.000 0.768 0.000 0.004 0.004
#> GSM120753 2 0.4675 0.3877 0.000 0.584 0.016 0.376 0.024 0.000
#> GSM120761 2 0.4605 0.2887 0.000 0.552 0.016 0.416 0.016 0.000
#> GSM120768 4 0.4116 0.5691 0.000 0.288 0.016 0.684 0.012 0.000
#> GSM120781 2 0.4499 0.4711 0.000 0.620 0.024 0.344 0.012 0.000
#> GSM120788 4 0.2174 0.7700 0.000 0.008 0.008 0.896 0.088 0.000
#> GSM120760 4 0.3988 0.7262 0.000 0.200 0.020 0.752 0.028 0.000
#> GSM120763 4 0.3799 0.7115 0.000 0.196 0.016 0.764 0.024 0.000
#> GSM120764 4 0.2619 0.8115 0.000 0.056 0.012 0.884 0.048 0.000
#> GSM120777 4 0.2755 0.7278 0.000 0.004 0.012 0.844 0.140 0.000
#> GSM120786 4 0.2247 0.8146 0.000 0.060 0.012 0.904 0.024 0.000
#> GSM121329 1 0.2412 0.8134 0.880 0.000 0.092 0.000 0.028 0.000
#> GSM121331 5 0.3892 0.7514 0.080 0.000 0.000 0.012 0.788 0.120
#> GSM121333 5 0.4073 0.7486 0.084 0.000 0.000 0.016 0.776 0.124
#> GSM121345 5 0.4820 0.6844 0.132 0.000 0.000 0.092 0.728 0.048
#> GSM121356 5 0.3869 0.7483 0.080 0.000 0.008 0.008 0.800 0.104
#> GSM120754 4 0.3943 0.7963 0.000 0.104 0.028 0.796 0.072 0.000
#> GSM120759 2 0.2186 0.7636 0.000 0.908 0.008 0.048 0.036 0.000
#> GSM120762 2 0.3962 0.6305 0.000 0.732 0.024 0.232 0.012 0.000
#> GSM120775 4 0.2521 0.7955 0.000 0.032 0.020 0.892 0.056 0.000
#> GSM120776 4 0.6424 0.1846 0.004 0.000 0.032 0.500 0.268 0.196
#> GSM120782 4 0.6517 0.5787 0.000 0.232 0.032 0.576 0.064 0.096
#> GSM120789 2 0.4051 0.6927 0.000 0.756 0.028 0.188 0.028 0.000
#> GSM120790 2 0.4707 0.6278 0.000 0.688 0.016 0.068 0.228 0.000
#> GSM120791 4 0.4133 0.6978 0.000 0.220 0.024 0.732 0.024 0.000
#> GSM120755 2 0.3492 0.6973 0.000 0.796 0.016 0.168 0.020 0.000
#> GSM120756 4 0.2039 0.7718 0.000 0.004 0.016 0.908 0.072 0.000
#> GSM120769 2 0.4738 0.3132 0.000 0.556 0.020 0.404 0.020 0.000
#> GSM120778 4 0.4632 0.3898 0.000 0.360 0.024 0.600 0.016 0.000
#> GSM120792 4 0.4159 0.6966 0.000 0.224 0.024 0.728 0.024 0.000
#> GSM121332 2 0.3310 0.7238 0.000 0.816 0.016 0.148 0.020 0.000
#> GSM121334 2 0.4794 0.4743 0.000 0.608 0.024 0.340 0.028 0.000
#> GSM121340 4 0.2556 0.8131 0.000 0.052 0.012 0.888 0.048 0.000
#> GSM121351 2 0.1599 0.7635 0.000 0.940 0.008 0.024 0.028 0.000
#> GSM121353 4 0.2629 0.8111 0.000 0.048 0.028 0.888 0.036 0.000
#> GSM120758 2 0.4476 0.5593 0.000 0.672 0.020 0.280 0.028 0.000
#> GSM120771 2 0.4060 0.6725 0.000 0.748 0.020 0.200 0.032 0.000
#> GSM120772 2 0.4835 0.4754 0.000 0.612 0.032 0.332 0.024 0.000
#> GSM120773 4 0.2698 0.8058 0.000 0.092 0.016 0.872 0.020 0.000
#> GSM120774 2 0.4874 0.0419 0.000 0.484 0.020 0.472 0.024 0.000
#> GSM120783 4 0.2036 0.8118 0.000 0.064 0.008 0.912 0.016 0.000
#> GSM120787 2 0.4979 0.1908 0.000 0.524 0.024 0.424 0.028 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> MAD:skmeans 119 2.79e-10 2
#> MAD:skmeans 116 3.26e-19 3
#> MAD:skmeans 100 5.17e-21 4
#> MAD:skmeans 95 1.65e-24 5
#> MAD:skmeans 100 5.86e-33 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 21512 rows and 119 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 0.556 0.827 0.907 0.4859 0.500 0.500
#> 3 3 0.658 0.789 0.848 0.3741 0.714 0.487
#> 4 4 0.701 0.749 0.877 0.0994 0.907 0.728
#> 5 5 0.715 0.643 0.796 0.0720 0.877 0.593
#> 6 6 0.793 0.775 0.874 0.0492 0.919 0.662
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
#> GSM120719 1 0.0000 0.840 1.000 0.000
#> GSM120720 1 0.0000 0.840 1.000 0.000
#> GSM120765 2 0.0000 0.950 0.000 1.000
#> GSM120767 2 0.0000 0.950 0.000 1.000
#> GSM120784 2 0.0000 0.950 0.000 1.000
#> GSM121400 1 0.9044 0.682 0.680 0.320
#> GSM121401 1 0.9248 0.666 0.660 0.340
#> GSM121402 2 0.0000 0.950 0.000 1.000
#> GSM121403 1 0.9323 0.658 0.652 0.348
#> GSM121404 1 0.9427 0.642 0.640 0.360
#> GSM121405 1 0.9323 0.658 0.652 0.348
#> GSM121406 2 0.0000 0.950 0.000 1.000
#> GSM121408 2 0.0000 0.950 0.000 1.000
#> GSM121409 1 0.8813 0.698 0.700 0.300
#> GSM121410 1 0.7453 0.755 0.788 0.212
#> GSM121412 2 0.0376 0.947 0.004 0.996
#> GSM121413 2 0.0000 0.950 0.000 1.000
#> GSM121414 2 0.0000 0.950 0.000 1.000
#> GSM121415 2 0.4431 0.852 0.092 0.908
#> GSM121416 2 0.0000 0.950 0.000 1.000
#> GSM120591 1 0.2236 0.835 0.964 0.036
#> GSM120594 1 0.0000 0.840 1.000 0.000
#> GSM120718 1 0.0000 0.840 1.000 0.000
#> GSM121205 1 0.0000 0.840 1.000 0.000
#> GSM121206 1 0.0000 0.840 1.000 0.000
#> GSM121207 1 0.0000 0.840 1.000 0.000
#> GSM121208 1 0.0000 0.840 1.000 0.000
#> GSM121209 1 0.0000 0.840 1.000 0.000
#> GSM121210 1 0.0000 0.840 1.000 0.000
#> GSM121211 1 0.0000 0.840 1.000 0.000
#> GSM121212 1 0.0000 0.840 1.000 0.000
#> GSM121213 1 0.0000 0.840 1.000 0.000
#> GSM121214 1 0.0000 0.840 1.000 0.000
#> GSM121215 1 0.0000 0.840 1.000 0.000
#> GSM121216 1 0.0000 0.840 1.000 0.000
#> GSM121217 1 0.0000 0.840 1.000 0.000
#> GSM121218 1 0.0000 0.840 1.000 0.000
#> GSM121234 1 0.0000 0.840 1.000 0.000
#> GSM121243 1 0.0000 0.840 1.000 0.000
#> GSM121245 1 0.0000 0.840 1.000 0.000
#> GSM121246 1 0.0000 0.840 1.000 0.000
#> GSM121247 1 0.0000 0.840 1.000 0.000
#> GSM121248 1 0.0000 0.840 1.000 0.000
#> GSM120744 1 0.9460 0.636 0.636 0.364
#> GSM120745 1 0.9323 0.658 0.652 0.348
#> GSM120746 1 0.9358 0.654 0.648 0.352
#> GSM120747 1 0.9358 0.654 0.648 0.352
#> GSM120748 2 0.9358 0.261 0.352 0.648
#> GSM120749 1 0.9323 0.658 0.652 0.348
#> GSM120750 1 0.9358 0.654 0.648 0.352
#> GSM120751 1 0.9358 0.654 0.648 0.352
#> GSM120752 1 0.9358 0.654 0.648 0.352
#> GSM121336 2 0.0000 0.950 0.000 1.000
#> GSM121339 2 0.6887 0.709 0.184 0.816
#> GSM121349 2 0.0000 0.950 0.000 1.000
#> GSM121355 2 0.0000 0.950 0.000 1.000
#> GSM120757 1 0.9710 0.555 0.600 0.400
#> GSM120766 1 0.9522 0.622 0.628 0.372
#> GSM120770 2 0.4815 0.836 0.104 0.896
#> GSM120779 1 0.4690 0.814 0.900 0.100
#> GSM120780 1 0.9358 0.654 0.648 0.352
#> GSM121102 2 0.2423 0.913 0.040 0.960
#> GSM121203 1 0.9358 0.654 0.648 0.352
#> GSM121204 1 0.1184 0.838 0.984 0.016
#> GSM121330 1 0.0000 0.840 1.000 0.000
#> GSM121335 1 0.0000 0.840 1.000 0.000
#> GSM121337 1 0.9358 0.654 0.648 0.352
#> GSM121338 1 0.9460 0.636 0.636 0.364
#> GSM121341 1 0.0000 0.840 1.000 0.000
#> GSM121342 1 0.0000 0.840 1.000 0.000
#> GSM121343 1 0.9393 0.648 0.644 0.356
#> GSM121344 1 0.0000 0.840 1.000 0.000
#> GSM121346 1 0.2603 0.833 0.956 0.044
#> GSM121347 1 0.9248 0.666 0.660 0.340
#> GSM121348 1 0.9358 0.654 0.648 0.352
#> GSM121350 1 0.3733 0.824 0.928 0.072
#> GSM121352 1 0.1184 0.838 0.984 0.016
#> GSM121354 1 0.0000 0.840 1.000 0.000
#> GSM120753 2 0.0000 0.950 0.000 1.000
#> GSM120761 2 0.0000 0.950 0.000 1.000
#> GSM120768 2 0.0000 0.950 0.000 1.000
#> GSM120781 2 0.0000 0.950 0.000 1.000
#> GSM120788 2 0.5408 0.827 0.124 0.876
#> GSM120760 2 0.0000 0.950 0.000 1.000
#> GSM120763 2 0.0000 0.950 0.000 1.000
#> GSM120764 2 0.0000 0.950 0.000 1.000
#> GSM120777 2 0.5178 0.834 0.116 0.884
#> GSM120786 2 0.0000 0.950 0.000 1.000
#> GSM121329 1 0.0000 0.840 1.000 0.000
#> GSM121331 1 0.4939 0.811 0.892 0.108
#> GSM121333 1 0.5294 0.805 0.880 0.120
#> GSM121345 1 0.2948 0.830 0.948 0.052
#> GSM121356 1 0.5408 0.804 0.876 0.124
#> GSM120754 2 0.0000 0.950 0.000 1.000
#> GSM120759 2 0.0000 0.950 0.000 1.000
#> GSM120762 2 0.0000 0.950 0.000 1.000
#> GSM120775 2 0.0000 0.950 0.000 1.000
#> GSM120776 2 0.6973 0.703 0.188 0.812
#> GSM120782 2 0.0000 0.950 0.000 1.000
#> GSM120789 2 0.0000 0.950 0.000 1.000
#> GSM120790 2 0.0672 0.943 0.008 0.992
#> GSM120791 2 0.0000 0.950 0.000 1.000
#> GSM120755 2 0.0000 0.950 0.000 1.000
#> GSM120756 2 0.9460 0.442 0.364 0.636
#> GSM120769 2 0.0000 0.950 0.000 1.000
#> GSM120778 2 0.0000 0.950 0.000 1.000
#> GSM120792 2 0.0000 0.950 0.000 1.000
#> GSM121332 2 0.0000 0.950 0.000 1.000
#> GSM121334 2 0.0000 0.950 0.000 1.000
#> GSM121340 2 0.7219 0.714 0.200 0.800
#> GSM121351 2 0.0000 0.950 0.000 1.000
#> GSM121353 2 0.9323 0.471 0.348 0.652
#> GSM120758 2 0.0000 0.950 0.000 1.000
#> GSM120771 2 0.0000 0.950 0.000 1.000
#> GSM120772 2 0.0000 0.950 0.000 1.000
#> GSM120773 2 0.0000 0.950 0.000 1.000
#> GSM120774 2 0.0000 0.950 0.000 1.000
#> GSM120783 2 0.0000 0.950 0.000 1.000
#> GSM120787 2 0.0000 0.950 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM120719 1 0.0000 0.9404 1.000 0.000 0.000
#> GSM120720 1 0.0000 0.9404 1.000 0.000 0.000
#> GSM120765 2 0.5988 0.5166 0.000 0.632 0.368
#> GSM120767 3 0.2537 0.8250 0.000 0.080 0.920
#> GSM120784 3 0.3551 0.7856 0.000 0.132 0.868
#> GSM121400 3 0.2663 0.8307 0.044 0.024 0.932
#> GSM121401 3 0.0747 0.8529 0.016 0.000 0.984
#> GSM121402 3 0.5529 0.5660 0.000 0.296 0.704
#> GSM121403 3 0.0000 0.8588 0.000 0.000 1.000
#> GSM121404 3 0.0000 0.8588 0.000 0.000 1.000
#> GSM121405 3 0.0000 0.8588 0.000 0.000 1.000
#> GSM121406 3 0.5497 0.5639 0.000 0.292 0.708
#> GSM121408 2 0.5291 0.6836 0.000 0.732 0.268
#> GSM121409 3 0.3434 0.8098 0.064 0.032 0.904
#> GSM121410 3 0.5355 0.7131 0.160 0.036 0.804
#> GSM121412 3 0.5363 0.5688 0.000 0.276 0.724
#> GSM121413 3 0.4002 0.7658 0.000 0.160 0.840
#> GSM121414 3 0.3816 0.7624 0.000 0.148 0.852
#> GSM121415 3 0.5138 0.6291 0.000 0.252 0.748
#> GSM121416 2 0.5785 0.5493 0.000 0.668 0.332
#> GSM120591 1 0.3879 0.8288 0.848 0.000 0.152
#> GSM120594 1 0.0237 0.9394 0.996 0.000 0.004
#> GSM120718 1 0.0000 0.9404 1.000 0.000 0.000
#> GSM121205 1 0.0000 0.9404 1.000 0.000 0.000
#> GSM121206 1 0.0000 0.9404 1.000 0.000 0.000
#> GSM121207 1 0.0000 0.9404 1.000 0.000 0.000
#> GSM121208 1 0.0000 0.9404 1.000 0.000 0.000
#> GSM121209 1 0.0000 0.9404 1.000 0.000 0.000
#> GSM121210 1 0.0000 0.9404 1.000 0.000 0.000
#> GSM121211 1 0.0000 0.9404 1.000 0.000 0.000
#> GSM121212 1 0.0000 0.9404 1.000 0.000 0.000
#> GSM121213 1 0.0000 0.9404 1.000 0.000 0.000
#> GSM121214 1 0.0000 0.9404 1.000 0.000 0.000
#> GSM121215 1 0.0000 0.9404 1.000 0.000 0.000
#> GSM121216 1 0.0000 0.9404 1.000 0.000 0.000
#> GSM121217 1 0.0000 0.9404 1.000 0.000 0.000
#> GSM121218 1 0.0000 0.9404 1.000 0.000 0.000
#> GSM121234 1 0.0000 0.9404 1.000 0.000 0.000
#> GSM121243 1 0.0000 0.9404 1.000 0.000 0.000
#> GSM121245 1 0.0000 0.9404 1.000 0.000 0.000
#> GSM121246 1 0.0237 0.9394 0.996 0.000 0.004
#> GSM121247 1 0.0000 0.9404 1.000 0.000 0.000
#> GSM121248 1 0.0000 0.9404 1.000 0.000 0.000
#> GSM120744 3 0.0000 0.8588 0.000 0.000 1.000
#> GSM120745 3 0.0000 0.8588 0.000 0.000 1.000
#> GSM120746 3 0.0000 0.8588 0.000 0.000 1.000
#> GSM120747 3 0.0000 0.8588 0.000 0.000 1.000
#> GSM120748 3 0.0000 0.8588 0.000 0.000 1.000
#> GSM120749 3 0.0000 0.8588 0.000 0.000 1.000
#> GSM120750 3 0.0237 0.8585 0.000 0.004 0.996
#> GSM120751 3 0.0000 0.8588 0.000 0.000 1.000
#> GSM120752 3 0.0237 0.8585 0.000 0.004 0.996
#> GSM121336 2 0.5327 0.6779 0.000 0.728 0.272
#> GSM121339 3 0.0592 0.8574 0.000 0.012 0.988
#> GSM121349 2 0.5835 0.5556 0.000 0.660 0.340
#> GSM121355 2 0.5948 0.5236 0.000 0.640 0.360
#> GSM120757 3 0.3998 0.8094 0.056 0.060 0.884
#> GSM120766 3 0.1753 0.8439 0.000 0.048 0.952
#> GSM120770 3 0.5706 0.5636 0.000 0.320 0.680
#> GSM120779 1 0.6423 0.6969 0.728 0.044 0.228
#> GSM120780 3 0.1529 0.8444 0.000 0.040 0.960
#> GSM121102 3 0.0237 0.8585 0.000 0.004 0.996
#> GSM121203 3 0.0237 0.8585 0.000 0.004 0.996
#> GSM121204 1 0.3573 0.8547 0.876 0.004 0.120
#> GSM121330 1 0.0237 0.9394 0.996 0.000 0.004
#> GSM121335 1 0.0237 0.9394 0.996 0.000 0.004
#> GSM121337 3 0.5859 0.5204 0.000 0.344 0.656
#> GSM121338 3 0.0000 0.8588 0.000 0.000 1.000
#> GSM121341 1 0.0237 0.9394 0.996 0.000 0.004
#> GSM121342 1 0.0237 0.9394 0.996 0.000 0.004
#> GSM121343 3 0.0892 0.8570 0.000 0.020 0.980
#> GSM121344 1 0.0237 0.9394 0.996 0.000 0.004
#> GSM121346 1 0.5497 0.6367 0.708 0.000 0.292
#> GSM121347 3 0.1765 0.8456 0.004 0.040 0.956
#> GSM121348 3 0.5785 0.5428 0.000 0.332 0.668
#> GSM121350 1 0.5216 0.6810 0.740 0.000 0.260
#> GSM121352 1 0.2448 0.8937 0.924 0.000 0.076
#> GSM121354 1 0.0237 0.9394 0.996 0.000 0.004
#> GSM120753 2 0.2878 0.8220 0.000 0.904 0.096
#> GSM120761 2 0.2261 0.8365 0.000 0.932 0.068
#> GSM120768 2 0.1753 0.8380 0.000 0.952 0.048
#> GSM120781 2 0.2066 0.8371 0.000 0.940 0.060
#> GSM120788 2 0.2187 0.8238 0.024 0.948 0.028
#> GSM120760 2 0.0000 0.8312 0.000 1.000 0.000
#> GSM120763 2 0.0000 0.8312 0.000 1.000 0.000
#> GSM120764 2 0.0237 0.8314 0.000 0.996 0.004
#> GSM120777 2 0.1031 0.8354 0.000 0.976 0.024
#> GSM120786 2 0.0000 0.8312 0.000 1.000 0.000
#> GSM121329 1 0.0000 0.9404 1.000 0.000 0.000
#> GSM121331 1 0.6247 0.7156 0.744 0.044 0.212
#> GSM121333 1 0.4902 0.8338 0.844 0.064 0.092
#> GSM121345 1 0.4384 0.8593 0.868 0.068 0.064
#> GSM121356 1 0.5635 0.7628 0.784 0.036 0.180
#> GSM120754 2 0.3752 0.7647 0.000 0.856 0.144
#> GSM120759 2 0.6244 0.3242 0.000 0.560 0.440
#> GSM120762 2 0.1643 0.8365 0.000 0.956 0.044
#> GSM120775 2 0.1289 0.8321 0.000 0.968 0.032
#> GSM120776 3 0.6225 0.2141 0.000 0.432 0.568
#> GSM120782 3 0.6299 0.0685 0.000 0.476 0.524
#> GSM120789 2 0.5363 0.6807 0.000 0.724 0.276
#> GSM120790 3 0.5706 0.5735 0.000 0.320 0.680
#> GSM120791 2 0.0747 0.8355 0.000 0.984 0.016
#> GSM120755 2 0.4974 0.7158 0.000 0.764 0.236
#> GSM120756 1 0.6307 0.0807 0.512 0.488 0.000
#> GSM120769 2 0.1289 0.8353 0.000 0.968 0.032
#> GSM120778 2 0.1031 0.8360 0.000 0.976 0.024
#> GSM120792 2 0.1753 0.8378 0.000 0.952 0.048
#> GSM121332 2 0.1964 0.8378 0.000 0.944 0.056
#> GSM121334 2 0.2625 0.8251 0.000 0.916 0.084
#> GSM121340 2 0.0000 0.8312 0.000 1.000 0.000
#> GSM121351 2 0.5733 0.5847 0.000 0.676 0.324
#> GSM121353 2 0.4702 0.6643 0.212 0.788 0.000
#> GSM120758 2 0.0892 0.8377 0.000 0.980 0.020
#> GSM120771 2 0.5621 0.5693 0.000 0.692 0.308
#> GSM120772 2 0.4931 0.6907 0.000 0.768 0.232
#> GSM120773 2 0.1529 0.8369 0.000 0.960 0.040
#> GSM120774 2 0.3752 0.8056 0.000 0.856 0.144
#> GSM120783 2 0.1411 0.8364 0.000 0.964 0.036
#> GSM120787 2 0.6252 0.2151 0.000 0.556 0.444
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM120719 1 0.0000 0.9283 1.000 0.000 0.000 0.000
#> GSM120720 1 0.1661 0.8962 0.944 0.000 0.004 0.052
#> GSM120765 2 0.4356 0.5973 0.000 0.708 0.292 0.000
#> GSM120767 3 0.3569 0.6700 0.000 0.196 0.804 0.000
#> GSM120784 3 0.4331 0.5764 0.000 0.288 0.712 0.000
#> GSM121400 4 0.0707 0.9417 0.000 0.000 0.020 0.980
#> GSM121401 4 0.1302 0.9396 0.000 0.000 0.044 0.956
#> GSM121402 3 0.4661 0.4927 0.000 0.348 0.652 0.000
#> GSM121403 4 0.1557 0.9331 0.000 0.000 0.056 0.944
#> GSM121404 3 0.5088 0.2284 0.000 0.004 0.572 0.424
#> GSM121405 4 0.1302 0.9396 0.000 0.000 0.044 0.956
#> GSM121406 3 0.4992 0.1112 0.000 0.476 0.524 0.000
#> GSM121408 2 0.3764 0.7015 0.000 0.784 0.216 0.000
#> GSM121409 3 0.5407 -0.0451 0.012 0.000 0.504 0.484
#> GSM121410 4 0.0859 0.9402 0.008 0.004 0.008 0.980
#> GSM121412 3 0.4699 0.4623 0.000 0.320 0.676 0.004
#> GSM121413 3 0.4522 0.5414 0.000 0.320 0.680 0.000
#> GSM121414 3 0.4134 0.5918 0.000 0.260 0.740 0.000
#> GSM121415 3 0.4961 0.1880 0.000 0.448 0.552 0.000
#> GSM121416 2 0.5321 0.5418 0.000 0.672 0.296 0.032
#> GSM120591 1 0.6401 0.6033 0.652 0.000 0.176 0.172
#> GSM120594 1 0.2334 0.8680 0.908 0.000 0.004 0.088
#> GSM120718 1 0.0000 0.9283 1.000 0.000 0.000 0.000
#> GSM121205 1 0.0000 0.9283 1.000 0.000 0.000 0.000
#> GSM121206 1 0.0188 0.9264 0.996 0.000 0.000 0.004
#> GSM121207 1 0.0000 0.9283 1.000 0.000 0.000 0.000
#> GSM121208 1 0.0188 0.9264 0.996 0.000 0.000 0.004
#> GSM121209 1 0.0000 0.9283 1.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.9283 1.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.9283 1.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.9283 1.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.9283 1.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.9283 1.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.9283 1.000 0.000 0.000 0.000
#> GSM121216 1 0.0000 0.9283 1.000 0.000 0.000 0.000
#> GSM121217 1 0.0000 0.9283 1.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.9283 1.000 0.000 0.000 0.000
#> GSM121234 1 0.0000 0.9283 1.000 0.000 0.000 0.000
#> GSM121243 1 0.0000 0.9283 1.000 0.000 0.000 0.000
#> GSM121245 1 0.0000 0.9283 1.000 0.000 0.000 0.000
#> GSM121246 1 0.4730 0.4475 0.636 0.000 0.000 0.364
#> GSM121247 1 0.0000 0.9283 1.000 0.000 0.000 0.000
#> GSM121248 1 0.0000 0.9283 1.000 0.000 0.000 0.000
#> GSM120744 3 0.0000 0.7667 0.000 0.000 1.000 0.000
#> GSM120745 3 0.0000 0.7667 0.000 0.000 1.000 0.000
#> GSM120746 3 0.0000 0.7667 0.000 0.000 1.000 0.000
#> GSM120747 3 0.0000 0.7667 0.000 0.000 1.000 0.000
#> GSM120748 3 0.0000 0.7667 0.000 0.000 1.000 0.000
#> GSM120749 3 0.0000 0.7667 0.000 0.000 1.000 0.000
#> GSM120750 3 0.0000 0.7667 0.000 0.000 1.000 0.000
#> GSM120751 3 0.0000 0.7667 0.000 0.000 1.000 0.000
#> GSM120752 3 0.0000 0.7667 0.000 0.000 1.000 0.000
#> GSM121336 2 0.3726 0.7098 0.000 0.788 0.212 0.000
#> GSM121339 3 0.2473 0.7263 0.000 0.012 0.908 0.080
#> GSM121349 2 0.3726 0.7056 0.000 0.788 0.212 0.000
#> GSM121355 2 0.4103 0.6557 0.000 0.744 0.256 0.000
#> GSM120757 3 0.3595 0.7348 0.036 0.040 0.880 0.044
#> GSM120766 3 0.2399 0.7492 0.000 0.032 0.920 0.048
#> GSM120770 3 0.5582 0.4912 0.000 0.348 0.620 0.032
#> GSM120779 1 0.5954 0.6483 0.688 0.020 0.244 0.048
#> GSM120780 3 0.1798 0.7539 0.000 0.016 0.944 0.040
#> GSM121102 3 0.0000 0.7667 0.000 0.000 1.000 0.000
#> GSM121203 3 0.0188 0.7661 0.000 0.000 0.996 0.004
#> GSM121204 1 0.4673 0.7132 0.748 0.008 0.232 0.012
#> GSM121330 4 0.1302 0.9568 0.044 0.000 0.000 0.956
#> GSM121335 4 0.1302 0.9568 0.044 0.000 0.000 0.956
#> GSM121337 4 0.4417 0.7352 0.000 0.044 0.160 0.796
#> GSM121338 3 0.3257 0.6846 0.000 0.004 0.844 0.152
#> GSM121341 4 0.1389 0.9545 0.048 0.000 0.000 0.952
#> GSM121342 4 0.1389 0.9543 0.048 0.000 0.000 0.952
#> GSM121343 4 0.2401 0.8958 0.000 0.004 0.092 0.904
#> GSM121344 4 0.1302 0.9568 0.044 0.000 0.000 0.956
#> GSM121346 4 0.1302 0.9568 0.044 0.000 0.000 0.956
#> GSM121347 3 0.2179 0.7478 0.000 0.012 0.924 0.064
#> GSM121348 3 0.6097 0.4429 0.000 0.364 0.580 0.056
#> GSM121350 4 0.1452 0.9558 0.036 0.000 0.008 0.956
#> GSM121352 4 0.1302 0.9568 0.044 0.000 0.000 0.956
#> GSM121354 4 0.1302 0.9568 0.044 0.000 0.000 0.956
#> GSM120753 2 0.1637 0.8212 0.000 0.940 0.060 0.000
#> GSM120761 2 0.1474 0.8291 0.000 0.948 0.052 0.000
#> GSM120768 2 0.0707 0.8307 0.000 0.980 0.020 0.000
#> GSM120781 2 0.1118 0.8307 0.000 0.964 0.036 0.000
#> GSM120788 2 0.2733 0.8069 0.020 0.916 0.032 0.032
#> GSM120760 2 0.0188 0.8278 0.000 0.996 0.000 0.004
#> GSM120763 2 0.0336 0.8286 0.000 0.992 0.000 0.008
#> GSM120764 2 0.0524 0.8284 0.000 0.988 0.004 0.008
#> GSM120777 2 0.2310 0.8185 0.004 0.928 0.028 0.040
#> GSM120786 2 0.0524 0.8282 0.000 0.988 0.004 0.008
#> GSM121329 1 0.0592 0.9206 0.984 0.000 0.000 0.016
#> GSM121331 1 0.5691 0.6767 0.712 0.016 0.224 0.048
#> GSM121333 1 0.3410 0.8630 0.888 0.032 0.036 0.044
#> GSM121345 1 0.3507 0.8648 0.884 0.040 0.036 0.040
#> GSM121356 1 0.5165 0.7406 0.760 0.012 0.180 0.048
#> GSM120754 2 0.3088 0.7653 0.000 0.864 0.128 0.008
#> GSM120759 2 0.4713 0.4487 0.000 0.640 0.360 0.000
#> GSM120762 2 0.0592 0.8299 0.000 0.984 0.016 0.000
#> GSM120775 2 0.2021 0.8196 0.000 0.936 0.040 0.024
#> GSM120776 3 0.5329 0.3417 0.000 0.420 0.568 0.012
#> GSM120782 3 0.4999 0.1538 0.000 0.492 0.508 0.000
#> GSM120789 2 0.4843 0.3647 0.000 0.604 0.396 0.000
#> GSM120790 3 0.5855 0.4659 0.000 0.356 0.600 0.044
#> GSM120791 2 0.0188 0.8286 0.000 0.996 0.004 0.000
#> GSM120755 2 0.3400 0.7406 0.000 0.820 0.180 0.000
#> GSM120756 2 0.5163 0.0249 0.480 0.516 0.000 0.004
#> GSM120769 2 0.0469 0.8286 0.000 0.988 0.012 0.000
#> GSM120778 2 0.0336 0.8290 0.000 0.992 0.008 0.000
#> GSM120792 2 0.1118 0.8323 0.000 0.964 0.036 0.000
#> GSM121332 2 0.0921 0.8319 0.000 0.972 0.028 0.000
#> GSM121334 2 0.2048 0.8198 0.000 0.928 0.064 0.008
#> GSM121340 2 0.0376 0.8281 0.000 0.992 0.004 0.004
#> GSM121351 2 0.3873 0.6803 0.000 0.772 0.228 0.000
#> GSM121353 2 0.3441 0.7093 0.152 0.840 0.004 0.004
#> GSM120758 2 0.0779 0.8324 0.000 0.980 0.016 0.004
#> GSM120771 2 0.4908 0.5333 0.000 0.692 0.292 0.016
#> GSM120772 2 0.4382 0.5301 0.000 0.704 0.296 0.000
#> GSM120773 2 0.1004 0.8319 0.000 0.972 0.024 0.004
#> GSM120774 2 0.2814 0.7896 0.000 0.868 0.132 0.000
#> GSM120783 2 0.0592 0.8305 0.000 0.984 0.016 0.000
#> GSM120787 2 0.4843 0.2329 0.000 0.604 0.396 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM120719 1 0.0000 0.9486 1.000 0.000 0.000 0.000 0.000
#> GSM120720 1 0.1270 0.8989 0.948 0.000 0.052 0.000 0.000
#> GSM120765 2 0.4410 -0.0440 0.000 0.556 0.000 0.440 0.004
#> GSM120767 2 0.2264 0.5771 0.000 0.912 0.004 0.060 0.024
#> GSM120784 2 0.1894 0.5679 0.000 0.920 0.000 0.072 0.008
#> GSM121400 3 0.0162 0.9319 0.000 0.000 0.996 0.000 0.004
#> GSM121401 3 0.0000 0.9330 0.000 0.000 1.000 0.000 0.000
#> GSM121402 2 0.4428 0.5441 0.000 0.756 0.000 0.160 0.084
#> GSM121403 3 0.0510 0.9247 0.000 0.016 0.984 0.000 0.000
#> GSM121404 2 0.5777 0.0216 0.000 0.468 0.444 0.000 0.088
#> GSM121405 3 0.0000 0.9330 0.000 0.000 1.000 0.000 0.000
#> GSM121406 2 0.3424 0.3968 0.000 0.760 0.000 0.240 0.000
#> GSM121408 4 0.4302 0.2068 0.000 0.480 0.000 0.520 0.000
#> GSM121409 3 0.6768 0.1138 0.004 0.272 0.452 0.000 0.272
#> GSM121410 3 0.1774 0.8874 0.000 0.016 0.932 0.000 0.052
#> GSM121412 2 0.4268 0.3796 0.000 0.708 0.000 0.268 0.024
#> GSM121413 2 0.2513 0.5352 0.000 0.876 0.000 0.116 0.008
#> GSM121414 2 0.3171 0.4693 0.000 0.816 0.000 0.176 0.008
#> GSM121415 2 0.4033 0.4264 0.000 0.760 0.004 0.212 0.024
#> GSM121416 2 0.6219 -0.0831 0.000 0.472 0.000 0.384 0.144
#> GSM120591 1 0.6515 0.4157 0.624 0.080 0.192 0.000 0.104
#> GSM120594 1 0.2193 0.8453 0.900 0.000 0.092 0.000 0.008
#> GSM120718 1 0.0000 0.9486 1.000 0.000 0.000 0.000 0.000
#> GSM121205 1 0.0000 0.9486 1.000 0.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.9486 1.000 0.000 0.000 0.000 0.000
#> GSM121207 1 0.0000 0.9486 1.000 0.000 0.000 0.000 0.000
#> GSM121208 1 0.0000 0.9486 1.000 0.000 0.000 0.000 0.000
#> GSM121209 1 0.0000 0.9486 1.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.9486 1.000 0.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.9486 1.000 0.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.9486 1.000 0.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.9486 1.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.9486 1.000 0.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.9486 1.000 0.000 0.000 0.000 0.000
#> GSM121216 1 0.0000 0.9486 1.000 0.000 0.000 0.000 0.000
#> GSM121217 1 0.0000 0.9486 1.000 0.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.9486 1.000 0.000 0.000 0.000 0.000
#> GSM121234 1 0.0162 0.9452 0.996 0.000 0.004 0.000 0.000
#> GSM121243 1 0.0000 0.9486 1.000 0.000 0.000 0.000 0.000
#> GSM121245 1 0.0000 0.9486 1.000 0.000 0.000 0.000 0.000
#> GSM121246 1 0.4262 0.2506 0.560 0.000 0.440 0.000 0.000
#> GSM121247 1 0.0000 0.9486 1.000 0.000 0.000 0.000 0.000
#> GSM121248 1 0.0000 0.9486 1.000 0.000 0.000 0.000 0.000
#> GSM120744 2 0.4066 0.5501 0.000 0.672 0.004 0.000 0.324
#> GSM120745 2 0.4066 0.5501 0.000 0.672 0.004 0.000 0.324
#> GSM120746 2 0.4066 0.5501 0.000 0.672 0.004 0.000 0.324
#> GSM120747 2 0.4066 0.5501 0.000 0.672 0.004 0.000 0.324
#> GSM120748 2 0.4066 0.5501 0.000 0.672 0.004 0.000 0.324
#> GSM120749 2 0.4066 0.5501 0.000 0.672 0.004 0.000 0.324
#> GSM120750 2 0.4066 0.5501 0.000 0.672 0.004 0.000 0.324
#> GSM120751 2 0.4066 0.5501 0.000 0.672 0.004 0.000 0.324
#> GSM120752 2 0.4066 0.5501 0.000 0.672 0.004 0.000 0.324
#> GSM121336 2 0.4307 -0.2034 0.000 0.504 0.000 0.496 0.000
#> GSM121339 2 0.3274 0.5614 0.000 0.856 0.064 0.004 0.076
#> GSM121349 4 0.4294 0.2415 0.000 0.468 0.000 0.532 0.000
#> GSM121355 2 0.4306 -0.1915 0.000 0.508 0.000 0.492 0.000
#> GSM120757 5 0.0290 0.6667 0.008 0.000 0.000 0.000 0.992
#> GSM120766 5 0.0000 0.6573 0.000 0.000 0.000 0.000 1.000
#> GSM120770 2 0.5726 0.4677 0.000 0.612 0.000 0.140 0.248
#> GSM120779 5 0.3109 0.7708 0.200 0.000 0.000 0.000 0.800
#> GSM120780 5 0.0880 0.6442 0.000 0.032 0.000 0.000 0.968
#> GSM121102 2 0.4118 0.5446 0.000 0.660 0.004 0.000 0.336
#> GSM121203 2 0.4151 0.5370 0.000 0.652 0.004 0.000 0.344
#> GSM121204 5 0.4206 0.7192 0.272 0.020 0.000 0.000 0.708
#> GSM121330 3 0.0162 0.9346 0.004 0.000 0.996 0.000 0.000
#> GSM121335 3 0.0162 0.9346 0.004 0.000 0.996 0.000 0.000
#> GSM121337 3 0.3817 0.7525 0.000 0.152 0.808 0.016 0.024
#> GSM121338 2 0.5150 0.5166 0.000 0.692 0.136 0.000 0.172
#> GSM121341 3 0.0162 0.9346 0.004 0.000 0.996 0.000 0.000
#> GSM121342 3 0.0290 0.9318 0.008 0.000 0.992 0.000 0.000
#> GSM121343 3 0.2325 0.8615 0.000 0.068 0.904 0.000 0.028
#> GSM121344 3 0.0162 0.9346 0.004 0.000 0.996 0.000 0.000
#> GSM121346 3 0.0162 0.9346 0.004 0.000 0.996 0.000 0.000
#> GSM121347 2 0.4545 0.4369 0.000 0.560 0.004 0.004 0.432
#> GSM121348 5 0.4791 0.6158 0.000 0.072 0.012 0.176 0.740
#> GSM121350 3 0.0162 0.9346 0.004 0.000 0.996 0.000 0.000
#> GSM121352 3 0.0162 0.9346 0.004 0.000 0.996 0.000 0.000
#> GSM121354 3 0.0162 0.9346 0.004 0.000 0.996 0.000 0.000
#> GSM120753 4 0.3534 0.5997 0.000 0.256 0.000 0.744 0.000
#> GSM120761 4 0.2338 0.7399 0.000 0.112 0.000 0.884 0.004
#> GSM120768 4 0.0727 0.7643 0.000 0.012 0.004 0.980 0.004
#> GSM120781 4 0.3455 0.6540 0.000 0.208 0.000 0.784 0.008
#> GSM120788 4 0.3336 0.6881 0.016 0.008 0.000 0.832 0.144
#> GSM120760 4 0.0566 0.7632 0.000 0.004 0.000 0.984 0.012
#> GSM120763 4 0.1168 0.7635 0.000 0.008 0.000 0.960 0.032
#> GSM120764 4 0.0880 0.7620 0.000 0.000 0.000 0.968 0.032
#> GSM120777 4 0.4299 0.2862 0.000 0.004 0.000 0.608 0.388
#> GSM120786 4 0.0609 0.7626 0.000 0.000 0.000 0.980 0.020
#> GSM121329 1 0.1197 0.9013 0.952 0.000 0.048 0.000 0.000
#> GSM121331 5 0.3452 0.7535 0.244 0.000 0.000 0.000 0.756
#> GSM121333 5 0.3876 0.6767 0.316 0.000 0.000 0.000 0.684
#> GSM121345 5 0.5297 0.5792 0.360 0.000 0.000 0.060 0.580
#> GSM121356 5 0.3684 0.7224 0.280 0.000 0.000 0.000 0.720
#> GSM120754 4 0.3445 0.6647 0.000 0.140 0.000 0.824 0.036
#> GSM120759 2 0.4818 -0.0679 0.000 0.520 0.000 0.460 0.020
#> GSM120762 4 0.3661 0.5861 0.000 0.276 0.000 0.724 0.000
#> GSM120775 4 0.2300 0.7366 0.000 0.024 0.000 0.904 0.072
#> GSM120776 4 0.6381 -0.0211 0.000 0.168 0.000 0.448 0.384
#> GSM120782 4 0.5190 -0.0309 0.000 0.468 0.004 0.496 0.032
#> GSM120789 4 0.5642 0.4123 0.000 0.180 0.000 0.636 0.184
#> GSM120790 5 0.4522 0.5865 0.000 0.176 0.000 0.080 0.744
#> GSM120791 4 0.0000 0.7617 0.000 0.000 0.000 1.000 0.000
#> GSM120755 4 0.4227 0.3438 0.000 0.420 0.000 0.580 0.000
#> GSM120756 4 0.4643 0.5492 0.192 0.004 0.000 0.736 0.068
#> GSM120769 4 0.0794 0.7615 0.000 0.028 0.000 0.972 0.000
#> GSM120778 4 0.0162 0.7618 0.000 0.004 0.000 0.996 0.000
#> GSM120792 4 0.0671 0.7639 0.000 0.016 0.000 0.980 0.004
#> GSM121332 4 0.1410 0.7591 0.000 0.060 0.000 0.940 0.000
#> GSM121334 4 0.4315 0.6005 0.000 0.276 0.000 0.700 0.024
#> GSM121340 4 0.0510 0.7631 0.000 0.000 0.000 0.984 0.016
#> GSM121351 2 0.4302 -0.1599 0.000 0.520 0.000 0.480 0.000
#> GSM121353 4 0.1282 0.7502 0.044 0.004 0.000 0.952 0.000
#> GSM120758 4 0.3521 0.6277 0.000 0.232 0.000 0.764 0.004
#> GSM120771 2 0.5329 -0.1321 0.000 0.516 0.000 0.432 0.052
#> GSM120772 4 0.4649 0.5912 0.000 0.220 0.000 0.716 0.064
#> GSM120773 4 0.0771 0.7632 0.000 0.020 0.000 0.976 0.004
#> GSM120774 4 0.2172 0.7373 0.000 0.076 0.000 0.908 0.016
#> GSM120783 4 0.0290 0.7620 0.000 0.008 0.000 0.992 0.000
#> GSM120787 4 0.4350 0.2482 0.000 0.408 0.000 0.588 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM120719 1 0.2667 0.842 0.852 0.020 0.000 0.000 0.000 0.128
#> GSM120720 1 0.3820 0.803 0.796 0.020 0.056 0.000 0.000 0.128
#> GSM120765 2 0.2536 0.779 0.000 0.864 0.000 0.116 0.000 0.020
#> GSM120767 2 0.2980 0.650 0.000 0.808 0.000 0.012 0.000 0.180
#> GSM120784 2 0.1867 0.743 0.000 0.916 0.000 0.020 0.000 0.064
#> GSM121400 3 0.0146 0.951 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM121401 3 0.0000 0.953 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121402 2 0.4472 0.561 0.000 0.700 0.000 0.064 0.008 0.228
#> GSM121403 3 0.0260 0.948 0.000 0.008 0.992 0.000 0.000 0.000
#> GSM121404 3 0.4972 0.112 0.000 0.072 0.536 0.000 0.000 0.392
#> GSM121405 3 0.0000 0.953 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121406 2 0.0820 0.755 0.000 0.972 0.000 0.012 0.000 0.016
#> GSM121408 2 0.2473 0.768 0.000 0.856 0.000 0.136 0.000 0.008
#> GSM121409 6 0.4077 0.510 0.000 0.008 0.320 0.000 0.012 0.660
#> GSM121410 3 0.1367 0.915 0.000 0.012 0.944 0.000 0.044 0.000
#> GSM121412 2 0.1934 0.755 0.000 0.916 0.000 0.040 0.000 0.044
#> GSM121413 2 0.0820 0.755 0.000 0.972 0.000 0.012 0.000 0.016
#> GSM121414 2 0.0717 0.754 0.000 0.976 0.000 0.008 0.000 0.016
#> GSM121415 2 0.2798 0.735 0.000 0.852 0.000 0.036 0.000 0.112
#> GSM121416 2 0.5670 0.680 0.000 0.640 0.000 0.176 0.128 0.056
#> GSM120591 1 0.6119 0.360 0.480 0.020 0.168 0.000 0.000 0.332
#> GSM120594 1 0.4294 0.771 0.760 0.020 0.092 0.000 0.000 0.128
#> GSM120718 1 0.2667 0.842 0.852 0.020 0.000 0.000 0.000 0.128
#> GSM121205 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121207 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121208 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121209 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121216 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121217 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121234 1 0.0146 0.927 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM121243 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121245 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121246 1 0.3866 0.114 0.516 0.000 0.484 0.000 0.000 0.000
#> GSM121247 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121248 1 0.0000 0.929 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM120744 6 0.2135 0.909 0.000 0.128 0.000 0.000 0.000 0.872
#> GSM120745 6 0.0260 0.832 0.000 0.008 0.000 0.000 0.000 0.992
#> GSM120746 6 0.2135 0.909 0.000 0.128 0.000 0.000 0.000 0.872
#> GSM120747 6 0.2135 0.909 0.000 0.128 0.000 0.000 0.000 0.872
#> GSM120748 6 0.2135 0.909 0.000 0.128 0.000 0.000 0.000 0.872
#> GSM120749 6 0.2135 0.909 0.000 0.128 0.000 0.000 0.000 0.872
#> GSM120750 6 0.2135 0.909 0.000 0.128 0.000 0.000 0.000 0.872
#> GSM120751 6 0.2135 0.909 0.000 0.128 0.000 0.000 0.000 0.872
#> GSM120752 6 0.0458 0.839 0.000 0.016 0.000 0.000 0.000 0.984
#> GSM121336 2 0.2278 0.765 0.000 0.868 0.000 0.128 0.000 0.004
#> GSM121339 2 0.3896 0.570 0.000 0.748 0.056 0.000 0.000 0.196
#> GSM121349 2 0.2003 0.769 0.000 0.884 0.000 0.116 0.000 0.000
#> GSM121355 2 0.1814 0.776 0.000 0.900 0.000 0.100 0.000 0.000
#> GSM120757 5 0.0000 0.963 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM120766 5 0.0000 0.963 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM120770 2 0.6879 0.133 0.000 0.424 0.000 0.092 0.148 0.336
#> GSM120779 5 0.0000 0.963 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM120780 5 0.0458 0.954 0.000 0.000 0.000 0.000 0.984 0.016
#> GSM121102 6 0.2489 0.905 0.000 0.128 0.000 0.000 0.012 0.860
#> GSM121203 6 0.2972 0.893 0.000 0.128 0.000 0.000 0.036 0.836
#> GSM121204 5 0.2563 0.872 0.040 0.004 0.000 0.000 0.880 0.076
#> GSM121330 3 0.0000 0.953 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121335 3 0.0000 0.953 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121337 3 0.2663 0.855 0.000 0.032 0.884 0.012 0.004 0.068
#> GSM121338 6 0.5305 0.677 0.000 0.204 0.176 0.000 0.004 0.616
#> GSM121341 3 0.0000 0.953 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121342 3 0.0291 0.949 0.004 0.004 0.992 0.000 0.000 0.000
#> GSM121343 3 0.1718 0.911 0.000 0.020 0.936 0.000 0.024 0.020
#> GSM121344 3 0.0000 0.953 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121346 3 0.0000 0.953 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121347 6 0.4520 0.805 0.000 0.132 0.004 0.004 0.132 0.728
#> GSM121348 5 0.0146 0.961 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM121350 3 0.0000 0.953 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121352 3 0.0000 0.953 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121354 3 0.0000 0.953 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM120753 4 0.3864 -0.270 0.000 0.480 0.000 0.520 0.000 0.000
#> GSM120761 4 0.3453 0.700 0.000 0.132 0.000 0.804 0.000 0.064
#> GSM120768 4 0.0260 0.798 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM120781 4 0.4224 -0.103 0.000 0.432 0.000 0.552 0.000 0.016
#> GSM120788 4 0.2543 0.764 0.004 0.004 0.000 0.868 0.116 0.008
#> GSM120760 4 0.0291 0.799 0.000 0.004 0.000 0.992 0.004 0.000
#> GSM120763 4 0.1594 0.787 0.000 0.016 0.000 0.932 0.052 0.000
#> GSM120764 4 0.0935 0.800 0.000 0.004 0.000 0.964 0.032 0.000
#> GSM120777 4 0.3151 0.640 0.000 0.000 0.000 0.748 0.252 0.000
#> GSM120786 4 0.0508 0.800 0.000 0.004 0.000 0.984 0.012 0.000
#> GSM121329 1 0.2683 0.863 0.880 0.012 0.056 0.000 0.000 0.052
#> GSM121331 5 0.0000 0.963 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM121333 5 0.0260 0.960 0.008 0.000 0.000 0.000 0.992 0.000
#> GSM121345 5 0.3223 0.832 0.104 0.008 0.000 0.052 0.836 0.000
#> GSM121356 5 0.0260 0.960 0.008 0.000 0.000 0.000 0.992 0.000
#> GSM120754 4 0.2686 0.764 0.000 0.080 0.000 0.876 0.032 0.012
#> GSM120759 2 0.5378 0.497 0.000 0.552 0.000 0.340 0.008 0.100
#> GSM120762 2 0.3765 0.489 0.000 0.596 0.000 0.404 0.000 0.000
#> GSM120775 4 0.1845 0.791 0.000 0.000 0.000 0.920 0.052 0.028
#> GSM120776 4 0.5862 0.414 0.000 0.008 0.000 0.532 0.224 0.236
#> GSM120782 4 0.4949 0.537 0.000 0.144 0.000 0.648 0.000 0.208
#> GSM120789 4 0.4199 0.338 0.000 0.016 0.000 0.568 0.000 0.416
#> GSM120790 5 0.1003 0.939 0.000 0.016 0.000 0.020 0.964 0.000
#> GSM120791 4 0.0146 0.798 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM120755 2 0.3742 0.566 0.000 0.648 0.000 0.348 0.000 0.004
#> GSM120756 4 0.4892 0.669 0.072 0.012 0.000 0.744 0.060 0.112
#> GSM120769 4 0.1610 0.770 0.000 0.084 0.000 0.916 0.000 0.000
#> GSM120778 4 0.0260 0.798 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM120792 4 0.0146 0.799 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM121332 4 0.2346 0.746 0.000 0.124 0.000 0.868 0.000 0.008
#> GSM121334 2 0.4801 0.362 0.000 0.520 0.000 0.436 0.008 0.036
#> GSM121340 4 0.1049 0.795 0.000 0.000 0.000 0.960 0.008 0.032
#> GSM121351 2 0.1663 0.779 0.000 0.912 0.000 0.088 0.000 0.000
#> GSM121353 4 0.2890 0.733 0.012 0.016 0.000 0.848 0.000 0.124
#> GSM120758 2 0.3868 0.276 0.000 0.508 0.000 0.492 0.000 0.000
#> GSM120771 2 0.3456 0.752 0.000 0.800 0.000 0.156 0.040 0.004
#> GSM120772 4 0.4791 0.452 0.000 0.244 0.000 0.652 0.000 0.104
#> GSM120773 4 0.0260 0.800 0.000 0.000 0.000 0.992 0.000 0.008
#> GSM120774 4 0.1700 0.784 0.000 0.004 0.000 0.916 0.000 0.080
#> GSM120783 4 0.0000 0.798 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM120787 4 0.4766 0.438 0.000 0.316 0.000 0.612 0.000 0.072
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> MAD:pam 116 1.04e-12 2
#> MAD:pam 114 3.11e-21 3
#> MAD:pam 103 2.60e-23 4
#> MAD:pam 94 9.48e-27 5
#> MAD:pam 105 1.45e-34 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 21512 rows and 119 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.732 0.939 0.967 0.4974 0.496 0.496
#> 3 3 0.904 0.902 0.954 0.3095 0.787 0.593
#> 4 4 0.796 0.868 0.906 0.0822 0.959 0.878
#> 5 5 0.762 0.707 0.844 0.0878 0.913 0.717
#> 6 6 0.705 0.571 0.777 0.0580 0.871 0.521
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
#> GSM120719 1 0.0376 0.950 0.996 0.004
#> GSM120720 1 0.0000 0.951 1.000 0.000
#> GSM120765 2 0.0000 0.979 0.000 1.000
#> GSM120767 2 0.0000 0.979 0.000 1.000
#> GSM120784 2 0.0000 0.979 0.000 1.000
#> GSM121400 1 0.0376 0.950 0.996 0.004
#> GSM121401 1 0.0000 0.951 1.000 0.000
#> GSM121402 2 0.0000 0.979 0.000 1.000
#> GSM121403 1 0.0938 0.948 0.988 0.012
#> GSM121404 2 0.4161 0.905 0.084 0.916
#> GSM121405 1 0.0000 0.951 1.000 0.000
#> GSM121406 2 0.0000 0.979 0.000 1.000
#> GSM121408 2 0.0000 0.979 0.000 1.000
#> GSM121409 1 0.1184 0.946 0.984 0.016
#> GSM121410 1 0.0672 0.949 0.992 0.008
#> GSM121412 2 0.0000 0.979 0.000 1.000
#> GSM121413 2 0.0000 0.979 0.000 1.000
#> GSM121414 2 0.0000 0.979 0.000 1.000
#> GSM121415 2 0.0000 0.979 0.000 1.000
#> GSM121416 2 0.0000 0.979 0.000 1.000
#> GSM120591 1 0.0000 0.951 1.000 0.000
#> GSM120594 1 0.0000 0.951 1.000 0.000
#> GSM120718 1 0.0000 0.951 1.000 0.000
#> GSM121205 1 0.0000 0.951 1.000 0.000
#> GSM121206 1 0.0000 0.951 1.000 0.000
#> GSM121207 1 0.0000 0.951 1.000 0.000
#> GSM121208 1 0.0000 0.951 1.000 0.000
#> GSM121209 1 0.0000 0.951 1.000 0.000
#> GSM121210 1 0.0000 0.951 1.000 0.000
#> GSM121211 1 0.0000 0.951 1.000 0.000
#> GSM121212 1 0.0000 0.951 1.000 0.000
#> GSM121213 1 0.0000 0.951 1.000 0.000
#> GSM121214 1 0.0000 0.951 1.000 0.000
#> GSM121215 1 0.0000 0.951 1.000 0.000
#> GSM121216 1 0.0000 0.951 1.000 0.000
#> GSM121217 1 0.0000 0.951 1.000 0.000
#> GSM121218 1 0.0000 0.951 1.000 0.000
#> GSM121234 1 0.0000 0.951 1.000 0.000
#> GSM121243 1 0.0000 0.951 1.000 0.000
#> GSM121245 1 0.0000 0.951 1.000 0.000
#> GSM121246 1 0.0000 0.951 1.000 0.000
#> GSM121247 1 0.0672 0.949 0.992 0.008
#> GSM121248 1 0.0000 0.951 1.000 0.000
#> GSM120744 1 0.5059 0.902 0.888 0.112
#> GSM120745 1 0.5059 0.902 0.888 0.112
#> GSM120746 1 0.5059 0.902 0.888 0.112
#> GSM120747 1 0.4815 0.906 0.896 0.104
#> GSM120748 1 0.5059 0.902 0.888 0.112
#> GSM120749 1 0.4939 0.904 0.892 0.108
#> GSM120750 1 0.5059 0.902 0.888 0.112
#> GSM120751 1 0.5059 0.902 0.888 0.112
#> GSM120752 1 0.5059 0.902 0.888 0.112
#> GSM121336 2 0.0000 0.979 0.000 1.000
#> GSM121339 2 0.4298 0.903 0.088 0.912
#> GSM121349 2 0.0000 0.979 0.000 1.000
#> GSM121355 2 0.0000 0.979 0.000 1.000
#> GSM120757 1 0.5408 0.892 0.876 0.124
#> GSM120766 1 0.5408 0.892 0.876 0.124
#> GSM120770 2 0.4431 0.897 0.092 0.908
#> GSM120779 1 0.5408 0.892 0.876 0.124
#> GSM120780 1 0.5294 0.896 0.880 0.120
#> GSM121102 2 0.8443 0.618 0.272 0.728
#> GSM121203 1 0.5059 0.902 0.888 0.112
#> GSM121204 1 0.4939 0.904 0.892 0.108
#> GSM121330 1 0.0000 0.951 1.000 0.000
#> GSM121335 1 0.0000 0.951 1.000 0.000
#> GSM121337 2 0.4161 0.905 0.084 0.916
#> GSM121338 2 0.6531 0.802 0.168 0.832
#> GSM121341 1 0.0000 0.951 1.000 0.000
#> GSM121342 1 0.0000 0.951 1.000 0.000
#> GSM121343 2 0.6887 0.778 0.184 0.816
#> GSM121344 1 0.0000 0.951 1.000 0.000
#> GSM121346 1 0.0000 0.951 1.000 0.000
#> GSM121347 2 0.4161 0.905 0.084 0.916
#> GSM121348 1 0.6623 0.837 0.828 0.172
#> GSM121350 1 0.0000 0.951 1.000 0.000
#> GSM121352 1 0.0000 0.951 1.000 0.000
#> GSM121354 1 0.0000 0.951 1.000 0.000
#> GSM120753 2 0.0000 0.979 0.000 1.000
#> GSM120761 2 0.0000 0.979 0.000 1.000
#> GSM120768 2 0.0000 0.979 0.000 1.000
#> GSM120781 2 0.0000 0.979 0.000 1.000
#> GSM120788 2 0.0000 0.979 0.000 1.000
#> GSM120760 2 0.0000 0.979 0.000 1.000
#> GSM120763 2 0.0000 0.979 0.000 1.000
#> GSM120764 2 0.0000 0.979 0.000 1.000
#> GSM120777 2 0.0000 0.979 0.000 1.000
#> GSM120786 2 0.0000 0.979 0.000 1.000
#> GSM121329 1 0.1414 0.944 0.980 0.020
#> GSM121331 1 0.5408 0.892 0.876 0.124
#> GSM121333 1 0.5408 0.892 0.876 0.124
#> GSM121345 1 0.9996 0.110 0.512 0.488
#> GSM121356 1 0.5408 0.892 0.876 0.124
#> GSM120754 2 0.0000 0.979 0.000 1.000
#> GSM120759 2 0.0000 0.979 0.000 1.000
#> GSM120762 2 0.0000 0.979 0.000 1.000
#> GSM120775 2 0.0000 0.979 0.000 1.000
#> GSM120776 2 0.2778 0.939 0.048 0.952
#> GSM120782 2 0.0000 0.979 0.000 1.000
#> GSM120789 2 0.0000 0.979 0.000 1.000
#> GSM120790 2 0.0000 0.979 0.000 1.000
#> GSM120791 2 0.0000 0.979 0.000 1.000
#> GSM120755 2 0.0000 0.979 0.000 1.000
#> GSM120756 2 0.0000 0.979 0.000 1.000
#> GSM120769 2 0.0000 0.979 0.000 1.000
#> GSM120778 2 0.0000 0.979 0.000 1.000
#> GSM120792 2 0.0000 0.979 0.000 1.000
#> GSM121332 2 0.0000 0.979 0.000 1.000
#> GSM121334 2 0.0000 0.979 0.000 1.000
#> GSM121340 2 0.0000 0.979 0.000 1.000
#> GSM121351 2 0.0000 0.979 0.000 1.000
#> GSM121353 2 0.0000 0.979 0.000 1.000
#> GSM120758 2 0.0000 0.979 0.000 1.000
#> GSM120771 2 0.0000 0.979 0.000 1.000
#> GSM120772 2 0.0000 0.979 0.000 1.000
#> GSM120773 2 0.0000 0.979 0.000 1.000
#> GSM120774 2 0.0000 0.979 0.000 1.000
#> GSM120783 2 0.0000 0.979 0.000 1.000
#> GSM120787 2 0.0000 0.979 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM120719 1 0.4291 0.835 0.820 0.000 0.180
#> GSM120720 1 0.3038 0.909 0.896 0.000 0.104
#> GSM120765 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120767 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120784 2 0.0000 1.000 0.000 1.000 0.000
#> GSM121400 1 0.5760 0.602 0.672 0.000 0.328
#> GSM121401 1 0.2878 0.914 0.904 0.000 0.096
#> GSM121402 2 0.0000 1.000 0.000 1.000 0.000
#> GSM121403 3 0.6168 0.147 0.412 0.000 0.588
#> GSM121404 3 0.6291 0.226 0.000 0.468 0.532
#> GSM121405 1 0.3038 0.909 0.896 0.000 0.104
#> GSM121406 2 0.0000 1.000 0.000 1.000 0.000
#> GSM121408 2 0.0000 1.000 0.000 1.000 0.000
#> GSM121409 3 0.6008 0.271 0.372 0.000 0.628
#> GSM121410 1 0.5327 0.707 0.728 0.000 0.272
#> GSM121412 2 0.0000 1.000 0.000 1.000 0.000
#> GSM121413 2 0.0000 1.000 0.000 1.000 0.000
#> GSM121414 2 0.0000 1.000 0.000 1.000 0.000
#> GSM121415 2 0.0000 1.000 0.000 1.000 0.000
#> GSM121416 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120591 1 0.3619 0.881 0.864 0.000 0.136
#> GSM120594 1 0.2959 0.912 0.900 0.000 0.100
#> GSM120718 1 0.1529 0.944 0.960 0.000 0.040
#> GSM121205 1 0.0000 0.940 1.000 0.000 0.000
#> GSM121206 1 0.0000 0.940 1.000 0.000 0.000
#> GSM121207 1 0.1289 0.945 0.968 0.000 0.032
#> GSM121208 1 0.1411 0.945 0.964 0.000 0.036
#> GSM121209 1 0.0000 0.940 1.000 0.000 0.000
#> GSM121210 1 0.1411 0.945 0.964 0.000 0.036
#> GSM121211 1 0.0000 0.940 1.000 0.000 0.000
#> GSM121212 1 0.0237 0.941 0.996 0.000 0.004
#> GSM121213 1 0.0000 0.940 1.000 0.000 0.000
#> GSM121214 1 0.0000 0.940 1.000 0.000 0.000
#> GSM121215 1 0.0000 0.940 1.000 0.000 0.000
#> GSM121216 1 0.2959 0.912 0.900 0.000 0.100
#> GSM121217 1 0.0000 0.940 1.000 0.000 0.000
#> GSM121218 1 0.0000 0.940 1.000 0.000 0.000
#> GSM121234 1 0.0424 0.943 0.992 0.000 0.008
#> GSM121243 1 0.0424 0.943 0.992 0.000 0.008
#> GSM121245 1 0.0424 0.943 0.992 0.000 0.008
#> GSM121246 1 0.1411 0.945 0.964 0.000 0.036
#> GSM121247 1 0.4291 0.835 0.820 0.000 0.180
#> GSM121248 1 0.0000 0.940 1.000 0.000 0.000
#> GSM120744 3 0.0000 0.873 0.000 0.000 1.000
#> GSM120745 3 0.0000 0.873 0.000 0.000 1.000
#> GSM120746 3 0.0000 0.873 0.000 0.000 1.000
#> GSM120747 3 0.0000 0.873 0.000 0.000 1.000
#> GSM120748 3 0.0000 0.873 0.000 0.000 1.000
#> GSM120749 3 0.0000 0.873 0.000 0.000 1.000
#> GSM120750 3 0.0000 0.873 0.000 0.000 1.000
#> GSM120751 3 0.0000 0.873 0.000 0.000 1.000
#> GSM120752 3 0.0000 0.873 0.000 0.000 1.000
#> GSM121336 2 0.0000 1.000 0.000 1.000 0.000
#> GSM121339 3 0.6309 0.135 0.000 0.496 0.504
#> GSM121349 2 0.0000 1.000 0.000 1.000 0.000
#> GSM121355 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120757 3 0.0000 0.873 0.000 0.000 1.000
#> GSM120766 3 0.0000 0.873 0.000 0.000 1.000
#> GSM120770 3 0.6062 0.441 0.000 0.384 0.616
#> GSM120779 3 0.0000 0.873 0.000 0.000 1.000
#> GSM120780 3 0.0000 0.873 0.000 0.000 1.000
#> GSM121102 3 0.0000 0.873 0.000 0.000 1.000
#> GSM121203 3 0.0000 0.873 0.000 0.000 1.000
#> GSM121204 3 0.0000 0.873 0.000 0.000 1.000
#> GSM121330 1 0.1411 0.945 0.964 0.000 0.036
#> GSM121335 1 0.1411 0.945 0.964 0.000 0.036
#> GSM121337 3 0.6244 0.307 0.000 0.440 0.560
#> GSM121338 3 0.1529 0.850 0.000 0.040 0.960
#> GSM121341 1 0.0747 0.944 0.984 0.000 0.016
#> GSM121342 1 0.0592 0.944 0.988 0.000 0.012
#> GSM121343 3 0.1163 0.857 0.000 0.028 0.972
#> GSM121344 1 0.1411 0.945 0.964 0.000 0.036
#> GSM121346 1 0.1411 0.945 0.964 0.000 0.036
#> GSM121347 3 0.4887 0.693 0.000 0.228 0.772
#> GSM121348 3 0.0237 0.871 0.000 0.004 0.996
#> GSM121350 1 0.1411 0.945 0.964 0.000 0.036
#> GSM121352 1 0.1411 0.945 0.964 0.000 0.036
#> GSM121354 1 0.1411 0.945 0.964 0.000 0.036
#> GSM120753 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120761 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120768 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120781 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120788 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120760 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120763 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120764 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120777 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120786 2 0.0000 1.000 0.000 1.000 0.000
#> GSM121329 1 0.4452 0.821 0.808 0.000 0.192
#> GSM121331 3 0.0000 0.873 0.000 0.000 1.000
#> GSM121333 3 0.0000 0.873 0.000 0.000 1.000
#> GSM121345 3 0.0000 0.873 0.000 0.000 1.000
#> GSM121356 3 0.0000 0.873 0.000 0.000 1.000
#> GSM120754 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120759 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120762 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120775 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120776 3 0.5859 0.513 0.000 0.344 0.656
#> GSM120782 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120789 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120790 2 0.0592 0.987 0.000 0.988 0.012
#> GSM120791 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120755 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120756 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120769 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120778 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120792 2 0.0000 1.000 0.000 1.000 0.000
#> GSM121332 2 0.0000 1.000 0.000 1.000 0.000
#> GSM121334 2 0.0000 1.000 0.000 1.000 0.000
#> GSM121340 2 0.0000 1.000 0.000 1.000 0.000
#> GSM121351 2 0.0000 1.000 0.000 1.000 0.000
#> GSM121353 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120758 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120771 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120772 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120773 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120774 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120783 2 0.0000 1.000 0.000 1.000 0.000
#> GSM120787 2 0.0000 1.000 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM120719 1 0.3925 0.653 0.808 0.000 0.176 0.016
#> GSM120720 1 0.0000 0.849 1.000 0.000 0.000 0.000
#> GSM120765 2 0.2281 0.919 0.000 0.904 0.000 0.096
#> GSM120767 2 0.0188 0.962 0.000 0.996 0.000 0.004
#> GSM120784 2 0.3172 0.878 0.000 0.840 0.000 0.160
#> GSM121400 1 0.5193 0.260 0.580 0.000 0.412 0.008
#> GSM121401 1 0.0000 0.849 1.000 0.000 0.000 0.000
#> GSM121402 2 0.3074 0.883 0.000 0.848 0.000 0.152
#> GSM121403 3 0.3224 0.800 0.120 0.000 0.864 0.016
#> GSM121404 3 0.6875 0.580 0.000 0.220 0.596 0.184
#> GSM121405 1 0.0336 0.845 0.992 0.000 0.008 0.000
#> GSM121406 2 0.1716 0.936 0.000 0.936 0.000 0.064
#> GSM121408 2 0.0188 0.962 0.000 0.996 0.000 0.004
#> GSM121409 3 0.3048 0.812 0.108 0.000 0.876 0.016
#> GSM121410 1 0.4883 0.479 0.696 0.000 0.288 0.016
#> GSM121412 2 0.3266 0.872 0.000 0.832 0.000 0.168
#> GSM121413 2 0.3172 0.878 0.000 0.840 0.000 0.160
#> GSM121414 2 0.3172 0.878 0.000 0.840 0.000 0.160
#> GSM121415 2 0.3400 0.862 0.000 0.820 0.000 0.180
#> GSM121416 2 0.3400 0.862 0.000 0.820 0.000 0.180
#> GSM120591 1 0.1406 0.819 0.960 0.000 0.024 0.016
#> GSM120594 1 0.0000 0.849 1.000 0.000 0.000 0.000
#> GSM120718 1 0.0817 0.841 0.976 0.000 0.000 0.024
#> GSM121205 4 0.4356 1.000 0.292 0.000 0.000 0.708
#> GSM121206 4 0.4356 1.000 0.292 0.000 0.000 0.708
#> GSM121207 1 0.3266 0.689 0.832 0.000 0.000 0.168
#> GSM121208 1 0.0336 0.848 0.992 0.000 0.000 0.008
#> GSM121209 4 0.4356 1.000 0.292 0.000 0.000 0.708
#> GSM121210 1 0.3172 0.702 0.840 0.000 0.000 0.160
#> GSM121211 4 0.4356 1.000 0.292 0.000 0.000 0.708
#> GSM121212 1 0.3123 0.708 0.844 0.000 0.000 0.156
#> GSM121213 4 0.4356 1.000 0.292 0.000 0.000 0.708
#> GSM121214 4 0.4356 1.000 0.292 0.000 0.000 0.708
#> GSM121215 4 0.4356 1.000 0.292 0.000 0.000 0.708
#> GSM121216 1 0.1637 0.813 0.940 0.000 0.000 0.060
#> GSM121217 4 0.4356 1.000 0.292 0.000 0.000 0.708
#> GSM121218 4 0.4356 1.000 0.292 0.000 0.000 0.708
#> GSM121234 1 0.4643 0.173 0.656 0.000 0.000 0.344
#> GSM121243 1 0.3074 0.713 0.848 0.000 0.000 0.152
#> GSM121245 1 0.3219 0.696 0.836 0.000 0.000 0.164
#> GSM121246 1 0.0000 0.849 1.000 0.000 0.000 0.000
#> GSM121247 1 0.3300 0.712 0.848 0.000 0.144 0.008
#> GSM121248 4 0.4356 1.000 0.292 0.000 0.000 0.708
#> GSM120744 3 0.0000 0.884 0.000 0.000 1.000 0.000
#> GSM120745 3 0.0188 0.884 0.000 0.000 0.996 0.004
#> GSM120746 3 0.0000 0.884 0.000 0.000 1.000 0.000
#> GSM120747 3 0.0188 0.884 0.000 0.000 0.996 0.004
#> GSM120748 3 0.0188 0.884 0.000 0.000 0.996 0.004
#> GSM120749 3 0.0188 0.884 0.000 0.000 0.996 0.004
#> GSM120750 3 0.0188 0.884 0.000 0.000 0.996 0.004
#> GSM120751 3 0.0188 0.884 0.000 0.000 0.996 0.004
#> GSM120752 3 0.0188 0.884 0.000 0.000 0.996 0.004
#> GSM121336 2 0.0188 0.962 0.000 0.996 0.000 0.004
#> GSM121339 3 0.6958 0.563 0.000 0.232 0.584 0.184
#> GSM121349 2 0.0188 0.962 0.000 0.996 0.000 0.004
#> GSM121355 2 0.0188 0.962 0.000 0.996 0.000 0.004
#> GSM120757 3 0.1867 0.874 0.000 0.000 0.928 0.072
#> GSM120766 3 0.2149 0.871 0.000 0.000 0.912 0.088
#> GSM120770 3 0.6548 0.643 0.000 0.188 0.636 0.176
#> GSM120779 3 0.2149 0.871 0.000 0.000 0.912 0.088
#> GSM120780 3 0.2081 0.871 0.000 0.000 0.916 0.084
#> GSM121102 3 0.0817 0.881 0.000 0.000 0.976 0.024
#> GSM121203 3 0.0188 0.884 0.000 0.000 0.996 0.004
#> GSM121204 3 0.0592 0.884 0.000 0.000 0.984 0.016
#> GSM121330 1 0.0000 0.849 1.000 0.000 0.000 0.000
#> GSM121335 1 0.0592 0.845 0.984 0.000 0.000 0.016
#> GSM121337 3 0.6908 0.580 0.000 0.220 0.592 0.188
#> GSM121338 3 0.3037 0.847 0.000 0.036 0.888 0.076
#> GSM121341 1 0.0336 0.848 0.992 0.000 0.000 0.008
#> GSM121342 1 0.0336 0.848 0.992 0.000 0.000 0.008
#> GSM121343 3 0.2089 0.868 0.000 0.020 0.932 0.048
#> GSM121344 1 0.0188 0.849 0.996 0.000 0.000 0.004
#> GSM121346 1 0.0000 0.849 1.000 0.000 0.000 0.000
#> GSM121347 3 0.5728 0.719 0.000 0.104 0.708 0.188
#> GSM121348 3 0.2149 0.871 0.000 0.000 0.912 0.088
#> GSM121350 1 0.0000 0.849 1.000 0.000 0.000 0.000
#> GSM121352 1 0.0000 0.849 1.000 0.000 0.000 0.000
#> GSM121354 1 0.0336 0.848 0.992 0.000 0.000 0.008
#> GSM120753 2 0.0188 0.962 0.000 0.996 0.000 0.004
#> GSM120761 2 0.0000 0.962 0.000 1.000 0.000 0.000
#> GSM120768 2 0.0188 0.962 0.000 0.996 0.000 0.004
#> GSM120781 2 0.0336 0.962 0.000 0.992 0.000 0.008
#> GSM120788 2 0.0000 0.962 0.000 1.000 0.000 0.000
#> GSM120760 2 0.0188 0.962 0.000 0.996 0.000 0.004
#> GSM120763 2 0.0188 0.962 0.000 0.996 0.000 0.004
#> GSM120764 2 0.0188 0.962 0.000 0.996 0.000 0.004
#> GSM120777 2 0.0000 0.962 0.000 1.000 0.000 0.000
#> GSM120786 2 0.0188 0.962 0.000 0.996 0.000 0.004
#> GSM121329 1 0.4468 0.571 0.752 0.000 0.232 0.016
#> GSM121331 3 0.2149 0.871 0.000 0.000 0.912 0.088
#> GSM121333 3 0.2149 0.871 0.000 0.000 0.912 0.088
#> GSM121345 3 0.3048 0.861 0.000 0.016 0.876 0.108
#> GSM121356 3 0.2149 0.871 0.000 0.000 0.912 0.088
#> GSM120754 2 0.3024 0.884 0.000 0.852 0.000 0.148
#> GSM120759 2 0.1389 0.945 0.000 0.952 0.000 0.048
#> GSM120762 2 0.0188 0.962 0.000 0.996 0.000 0.004
#> GSM120775 2 0.0000 0.962 0.000 1.000 0.000 0.000
#> GSM120776 3 0.6198 0.682 0.000 0.152 0.672 0.176
#> GSM120782 2 0.2654 0.909 0.000 0.888 0.004 0.108
#> GSM120789 2 0.0188 0.962 0.000 0.996 0.000 0.004
#> GSM120790 2 0.3545 0.866 0.000 0.828 0.008 0.164
#> GSM120791 2 0.0188 0.962 0.000 0.996 0.000 0.004
#> GSM120755 2 0.0188 0.962 0.000 0.996 0.000 0.004
#> GSM120756 2 0.0188 0.961 0.000 0.996 0.000 0.004
#> GSM120769 2 0.0188 0.962 0.000 0.996 0.000 0.004
#> GSM120778 2 0.0188 0.962 0.000 0.996 0.000 0.004
#> GSM120792 2 0.0188 0.962 0.000 0.996 0.000 0.004
#> GSM121332 2 0.0188 0.962 0.000 0.996 0.000 0.004
#> GSM121334 2 0.0188 0.962 0.000 0.996 0.000 0.004
#> GSM121340 2 0.0188 0.962 0.000 0.996 0.000 0.004
#> GSM121351 2 0.0188 0.962 0.000 0.996 0.000 0.004
#> GSM121353 2 0.0336 0.961 0.000 0.992 0.000 0.008
#> GSM120758 2 0.0188 0.962 0.000 0.996 0.000 0.004
#> GSM120771 2 0.2345 0.915 0.000 0.900 0.000 0.100
#> GSM120772 2 0.0188 0.962 0.000 0.996 0.000 0.004
#> GSM120773 2 0.0188 0.962 0.000 0.996 0.000 0.004
#> GSM120774 2 0.0188 0.962 0.000 0.996 0.000 0.004
#> GSM120783 2 0.0188 0.962 0.000 0.996 0.000 0.004
#> GSM120787 2 0.0188 0.962 0.000 0.996 0.000 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM120719 3 0.6589 0.4447 0.264 0.008 0.516 0.000 0.212
#> GSM120720 3 0.3916 0.6106 0.256 0.000 0.732 0.000 0.012
#> GSM120765 2 0.3949 0.5367 0.000 0.668 0.000 0.332 0.000
#> GSM120767 4 0.1197 0.8985 0.000 0.048 0.000 0.952 0.000
#> GSM120784 2 0.2516 0.7006 0.000 0.860 0.000 0.140 0.000
#> GSM121400 3 0.4150 0.1711 0.000 0.000 0.612 0.000 0.388
#> GSM121401 3 0.0000 0.7010 0.000 0.000 1.000 0.000 0.000
#> GSM121402 2 0.4045 0.4858 0.000 0.644 0.000 0.356 0.000
#> GSM121403 5 0.4425 0.4311 0.000 0.008 0.392 0.000 0.600
#> GSM121404 2 0.4359 0.2692 0.000 0.584 0.000 0.004 0.412
#> GSM121405 3 0.0510 0.7001 0.000 0.000 0.984 0.000 0.016
#> GSM121406 4 0.4305 -0.0714 0.000 0.488 0.000 0.512 0.000
#> GSM121408 4 0.1121 0.9001 0.000 0.044 0.000 0.956 0.000
#> GSM121409 5 0.4444 0.4773 0.000 0.012 0.364 0.000 0.624
#> GSM121410 3 0.3928 0.4200 0.000 0.004 0.700 0.000 0.296
#> GSM121412 2 0.3231 0.6889 0.000 0.800 0.000 0.196 0.004
#> GSM121413 2 0.3177 0.6699 0.000 0.792 0.000 0.208 0.000
#> GSM121414 2 0.3305 0.6553 0.000 0.776 0.000 0.224 0.000
#> GSM121415 2 0.2424 0.7007 0.000 0.868 0.000 0.132 0.000
#> GSM121416 2 0.2561 0.7025 0.000 0.856 0.000 0.144 0.000
#> GSM120591 3 0.1750 0.7001 0.028 0.000 0.936 0.000 0.036
#> GSM120594 3 0.3628 0.6406 0.216 0.000 0.772 0.000 0.012
#> GSM120718 3 0.3999 0.5240 0.344 0.000 0.656 0.000 0.000
#> GSM121205 1 0.1478 0.9604 0.936 0.000 0.064 0.000 0.000
#> GSM121206 1 0.1478 0.9604 0.936 0.000 0.064 0.000 0.000
#> GSM121207 3 0.4307 0.2299 0.500 0.000 0.500 0.000 0.000
#> GSM121208 3 0.0290 0.7026 0.008 0.000 0.992 0.000 0.000
#> GSM121209 1 0.1478 0.9604 0.936 0.000 0.064 0.000 0.000
#> GSM121210 3 0.4305 0.2632 0.488 0.000 0.512 0.000 0.000
#> GSM121211 1 0.1478 0.9604 0.936 0.000 0.064 0.000 0.000
#> GSM121212 3 0.4307 0.2426 0.496 0.000 0.504 0.000 0.000
#> GSM121213 1 0.1478 0.9604 0.936 0.000 0.064 0.000 0.000
#> GSM121214 1 0.1478 0.9604 0.936 0.000 0.064 0.000 0.000
#> GSM121215 1 0.1478 0.9604 0.936 0.000 0.064 0.000 0.000
#> GSM121216 3 0.5014 0.3636 0.432 0.004 0.540 0.000 0.024
#> GSM121217 1 0.1478 0.9604 0.936 0.000 0.064 0.000 0.000
#> GSM121218 1 0.1478 0.9604 0.936 0.000 0.064 0.000 0.000
#> GSM121234 1 0.3876 0.4185 0.684 0.000 0.316 0.000 0.000
#> GSM121243 3 0.4302 0.2822 0.480 0.000 0.520 0.000 0.000
#> GSM121245 3 0.4307 0.2426 0.496 0.000 0.504 0.000 0.000
#> GSM121246 3 0.1851 0.6970 0.088 0.000 0.912 0.000 0.000
#> GSM121247 3 0.6697 0.4299 0.284 0.012 0.504 0.000 0.200
#> GSM121248 1 0.1478 0.9604 0.936 0.000 0.064 0.000 0.000
#> GSM120744 5 0.0404 0.8714 0.000 0.012 0.000 0.000 0.988
#> GSM120745 5 0.0162 0.8712 0.000 0.004 0.000 0.000 0.996
#> GSM120746 5 0.0404 0.8714 0.000 0.012 0.000 0.000 0.988
#> GSM120747 5 0.0404 0.8714 0.000 0.012 0.000 0.000 0.988
#> GSM120748 5 0.0000 0.8711 0.000 0.000 0.000 0.000 1.000
#> GSM120749 5 0.0404 0.8714 0.000 0.012 0.000 0.000 0.988
#> GSM120750 5 0.0404 0.8714 0.000 0.012 0.000 0.000 0.988
#> GSM120751 5 0.0404 0.8714 0.000 0.012 0.000 0.000 0.988
#> GSM120752 5 0.0404 0.8714 0.000 0.012 0.000 0.000 0.988
#> GSM121336 4 0.1121 0.9001 0.000 0.044 0.000 0.956 0.000
#> GSM121339 2 0.4574 0.2691 0.000 0.576 0.000 0.012 0.412
#> GSM121349 4 0.0963 0.9029 0.000 0.036 0.000 0.964 0.000
#> GSM121355 4 0.1121 0.9001 0.000 0.044 0.000 0.956 0.000
#> GSM120757 5 0.1168 0.8678 0.032 0.008 0.000 0.000 0.960
#> GSM120766 5 0.1764 0.8604 0.064 0.008 0.000 0.000 0.928
#> GSM120770 5 0.4249 0.2225 0.000 0.432 0.000 0.000 0.568
#> GSM120779 5 0.1764 0.8604 0.064 0.008 0.000 0.000 0.928
#> GSM120780 5 0.1764 0.8604 0.064 0.008 0.000 0.000 0.928
#> GSM121102 5 0.2179 0.8050 0.000 0.112 0.000 0.000 0.888
#> GSM121203 5 0.0290 0.8716 0.000 0.008 0.000 0.000 0.992
#> GSM121204 5 0.0451 0.8708 0.008 0.004 0.000 0.000 0.988
#> GSM121330 3 0.0880 0.7051 0.032 0.000 0.968 0.000 0.000
#> GSM121335 3 0.0963 0.7045 0.036 0.000 0.964 0.000 0.000
#> GSM121337 2 0.4341 0.2831 0.000 0.592 0.000 0.004 0.404
#> GSM121338 5 0.3534 0.6250 0.000 0.256 0.000 0.000 0.744
#> GSM121341 3 0.2516 0.6803 0.140 0.000 0.860 0.000 0.000
#> GSM121342 3 0.3480 0.6210 0.248 0.000 0.752 0.000 0.000
#> GSM121343 5 0.3210 0.6898 0.000 0.212 0.000 0.000 0.788
#> GSM121344 3 0.0162 0.7024 0.004 0.000 0.996 0.000 0.000
#> GSM121346 3 0.0162 0.7024 0.004 0.000 0.996 0.000 0.000
#> GSM121347 2 0.4507 0.2673 0.004 0.580 0.000 0.004 0.412
#> GSM121348 5 0.2079 0.8576 0.064 0.020 0.000 0.000 0.916
#> GSM121350 3 0.0162 0.7024 0.004 0.000 0.996 0.000 0.000
#> GSM121352 3 0.0162 0.7024 0.004 0.000 0.996 0.000 0.000
#> GSM121354 3 0.0162 0.7024 0.004 0.000 0.996 0.000 0.000
#> GSM120753 4 0.0000 0.9084 0.000 0.000 0.000 1.000 0.000
#> GSM120761 4 0.2329 0.8023 0.000 0.124 0.000 0.876 0.000
#> GSM120768 4 0.0000 0.9084 0.000 0.000 0.000 1.000 0.000
#> GSM120781 4 0.0290 0.9085 0.000 0.008 0.000 0.992 0.000
#> GSM120788 4 0.2074 0.8511 0.000 0.104 0.000 0.896 0.000
#> GSM120760 4 0.0162 0.9082 0.000 0.004 0.000 0.996 0.000
#> GSM120763 4 0.0000 0.9084 0.000 0.000 0.000 1.000 0.000
#> GSM120764 4 0.0404 0.9080 0.000 0.012 0.000 0.988 0.000
#> GSM120777 4 0.1732 0.8692 0.000 0.080 0.000 0.920 0.000
#> GSM120786 4 0.0290 0.9075 0.000 0.008 0.000 0.992 0.000
#> GSM121329 3 0.6717 0.4336 0.256 0.012 0.508 0.000 0.224
#> GSM121331 5 0.1877 0.8596 0.064 0.012 0.000 0.000 0.924
#> GSM121333 5 0.1764 0.8604 0.064 0.008 0.000 0.000 0.928
#> GSM121345 5 0.4482 0.3710 0.012 0.376 0.000 0.000 0.612
#> GSM121356 5 0.1764 0.8604 0.064 0.008 0.000 0.000 0.928
#> GSM120754 2 0.4088 0.4977 0.000 0.632 0.000 0.368 0.000
#> GSM120759 4 0.4088 0.2909 0.000 0.368 0.000 0.632 0.000
#> GSM120762 4 0.0000 0.9084 0.000 0.000 0.000 1.000 0.000
#> GSM120775 4 0.1197 0.8964 0.000 0.048 0.000 0.952 0.000
#> GSM120776 2 0.4359 0.2616 0.004 0.584 0.000 0.000 0.412
#> GSM120782 4 0.4517 0.0818 0.000 0.436 0.000 0.556 0.008
#> GSM120789 4 0.0963 0.9028 0.000 0.036 0.000 0.964 0.000
#> GSM120790 2 0.5126 0.6068 0.000 0.636 0.000 0.300 0.064
#> GSM120791 4 0.0404 0.9080 0.000 0.012 0.000 0.988 0.000
#> GSM120755 4 0.1121 0.9001 0.000 0.044 0.000 0.956 0.000
#> GSM120756 4 0.2230 0.8371 0.000 0.116 0.000 0.884 0.000
#> GSM120769 4 0.0000 0.9084 0.000 0.000 0.000 1.000 0.000
#> GSM120778 4 0.0000 0.9084 0.000 0.000 0.000 1.000 0.000
#> GSM120792 4 0.0794 0.9050 0.000 0.028 0.000 0.972 0.000
#> GSM121332 4 0.1121 0.9001 0.000 0.044 0.000 0.956 0.000
#> GSM121334 4 0.0162 0.9082 0.000 0.004 0.000 0.996 0.000
#> GSM121340 4 0.0510 0.9051 0.000 0.016 0.000 0.984 0.000
#> GSM121351 4 0.2690 0.7853 0.000 0.156 0.000 0.844 0.000
#> GSM121353 4 0.2280 0.8401 0.000 0.120 0.000 0.880 0.000
#> GSM120758 4 0.0290 0.9087 0.000 0.008 0.000 0.992 0.000
#> GSM120771 4 0.4305 -0.1511 0.000 0.488 0.000 0.512 0.000
#> GSM120772 4 0.0000 0.9084 0.000 0.000 0.000 1.000 0.000
#> GSM120773 4 0.0162 0.9082 0.000 0.004 0.000 0.996 0.000
#> GSM120774 4 0.0609 0.9067 0.000 0.020 0.000 0.980 0.000
#> GSM120783 4 0.0000 0.9084 0.000 0.000 0.000 1.000 0.000
#> GSM120787 4 0.0000 0.9084 0.000 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM120719 1 0.6312 0.4583 0.544 0.012 0.152 0.000 0.260 0.032
#> GSM120720 3 0.4198 0.6253 0.232 0.000 0.708 0.000 0.060 0.000
#> GSM120765 2 0.2051 0.6259 0.000 0.896 0.004 0.096 0.004 0.000
#> GSM120767 2 0.4310 0.0969 0.000 0.512 0.004 0.472 0.012 0.000
#> GSM120784 2 0.0937 0.6273 0.000 0.960 0.000 0.040 0.000 0.000
#> GSM121400 3 0.4695 0.2081 0.004 0.004 0.572 0.000 0.032 0.388
#> GSM121401 3 0.0405 0.8681 0.004 0.000 0.988 0.000 0.008 0.000
#> GSM121402 2 0.2092 0.6204 0.000 0.876 0.000 0.124 0.000 0.000
#> GSM121403 6 0.5913 0.3239 0.000 0.012 0.320 0.000 0.164 0.504
#> GSM121404 2 0.4621 0.2368 0.000 0.632 0.000 0.000 0.304 0.064
#> GSM121405 3 0.0777 0.8636 0.004 0.000 0.972 0.000 0.024 0.000
#> GSM121406 2 0.2902 0.5749 0.000 0.800 0.004 0.196 0.000 0.000
#> GSM121408 2 0.4537 0.0531 0.000 0.492 0.004 0.480 0.024 0.000
#> GSM121409 6 0.6110 0.3351 0.000 0.016 0.220 0.000 0.260 0.504
#> GSM121410 3 0.3903 0.6892 0.004 0.008 0.784 0.000 0.060 0.144
#> GSM121412 2 0.1398 0.6307 0.000 0.940 0.000 0.052 0.008 0.000
#> GSM121413 2 0.1387 0.6320 0.000 0.932 0.000 0.068 0.000 0.000
#> GSM121414 2 0.1501 0.6314 0.000 0.924 0.000 0.076 0.000 0.000
#> GSM121415 2 0.1124 0.6245 0.000 0.956 0.000 0.036 0.008 0.000
#> GSM121416 2 0.1462 0.6290 0.000 0.936 0.000 0.056 0.008 0.000
#> GSM120591 3 0.2164 0.8346 0.020 0.000 0.908 0.000 0.060 0.012
#> GSM120594 3 0.3440 0.7038 0.196 0.000 0.776 0.000 0.028 0.000
#> GSM120718 1 0.3309 0.6387 0.720 0.000 0.280 0.000 0.000 0.000
#> GSM121205 1 0.0000 0.8462 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.8462 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121207 1 0.2362 0.8071 0.860 0.000 0.136 0.000 0.004 0.000
#> GSM121208 3 0.1556 0.8368 0.080 0.000 0.920 0.000 0.000 0.000
#> GSM121209 1 0.0000 0.8462 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.2527 0.7880 0.832 0.000 0.168 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.8462 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121212 1 0.2219 0.8075 0.864 0.000 0.136 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.8462 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.8462 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.8462 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121216 1 0.4460 0.7243 0.736 0.008 0.160 0.000 0.092 0.004
#> GSM121217 1 0.0000 0.8462 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.8462 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121234 1 0.1387 0.8328 0.932 0.000 0.068 0.000 0.000 0.000
#> GSM121243 1 0.2340 0.8002 0.852 0.000 0.148 0.000 0.000 0.000
#> GSM121245 1 0.2300 0.8029 0.856 0.000 0.144 0.000 0.000 0.000
#> GSM121246 3 0.1075 0.8596 0.048 0.000 0.952 0.000 0.000 0.000
#> GSM121247 1 0.6137 0.4796 0.556 0.012 0.144 0.000 0.264 0.024
#> GSM121248 1 0.0000 0.8462 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM120744 6 0.0000 0.6245 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120745 6 0.3668 0.2731 0.000 0.004 0.000 0.000 0.328 0.668
#> GSM120746 6 0.0000 0.6245 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120747 6 0.1757 0.6227 0.000 0.008 0.000 0.000 0.076 0.916
#> GSM120748 6 0.1610 0.6204 0.000 0.000 0.000 0.000 0.084 0.916
#> GSM120749 6 0.0777 0.6295 0.000 0.004 0.000 0.000 0.024 0.972
#> GSM120750 6 0.0000 0.6245 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120751 6 0.0000 0.6245 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120752 6 0.2053 0.5980 0.000 0.004 0.000 0.000 0.108 0.888
#> GSM121336 2 0.4227 0.0577 0.000 0.496 0.004 0.492 0.008 0.000
#> GSM121339 2 0.4621 0.2368 0.000 0.632 0.000 0.000 0.304 0.064
#> GSM121349 4 0.4172 0.1148 0.000 0.424 0.004 0.564 0.008 0.000
#> GSM121355 2 0.4227 0.0577 0.000 0.496 0.004 0.492 0.008 0.000
#> GSM120757 5 0.3428 0.8567 0.000 0.000 0.000 0.000 0.696 0.304
#> GSM120766 5 0.3050 0.8821 0.000 0.000 0.000 0.000 0.764 0.236
#> GSM120770 6 0.6111 0.2373 0.000 0.284 0.000 0.004 0.272 0.440
#> GSM120779 5 0.3023 0.8810 0.000 0.000 0.000 0.000 0.768 0.232
#> GSM120780 6 0.3862 -0.1274 0.000 0.000 0.000 0.000 0.476 0.524
#> GSM121102 6 0.4332 0.4214 0.000 0.048 0.000 0.000 0.288 0.664
#> GSM121203 6 0.0363 0.6261 0.000 0.000 0.000 0.000 0.012 0.988
#> GSM121204 5 0.3725 0.8301 0.000 0.008 0.000 0.000 0.676 0.316
#> GSM121330 3 0.0508 0.8701 0.012 0.000 0.984 0.000 0.004 0.000
#> GSM121335 3 0.0790 0.8651 0.032 0.000 0.968 0.000 0.000 0.000
#> GSM121337 2 0.6766 -0.2215 0.000 0.436 0.000 0.052 0.272 0.240
#> GSM121338 6 0.5411 0.3960 0.000 0.148 0.000 0.000 0.296 0.556
#> GSM121341 3 0.2300 0.7833 0.144 0.000 0.856 0.000 0.000 0.000
#> GSM121342 3 0.3684 0.3716 0.372 0.000 0.628 0.000 0.000 0.000
#> GSM121343 6 0.5219 0.4137 0.000 0.124 0.000 0.000 0.296 0.580
#> GSM121344 3 0.0260 0.8697 0.008 0.000 0.992 0.000 0.000 0.000
#> GSM121346 3 0.0260 0.8697 0.008 0.000 0.992 0.000 0.000 0.000
#> GSM121347 6 0.7063 0.1198 0.000 0.316 0.000 0.064 0.296 0.324
#> GSM121348 5 0.3483 0.8819 0.000 0.016 0.000 0.000 0.748 0.236
#> GSM121350 3 0.0260 0.8697 0.008 0.000 0.992 0.000 0.000 0.000
#> GSM121352 3 0.0260 0.8697 0.008 0.000 0.992 0.000 0.000 0.000
#> GSM121354 3 0.0260 0.8697 0.008 0.000 0.992 0.000 0.000 0.000
#> GSM120753 4 0.3707 0.3651 0.000 0.312 0.000 0.680 0.008 0.000
#> GSM120761 4 0.2871 0.6082 0.000 0.192 0.000 0.804 0.004 0.000
#> GSM120768 4 0.1594 0.6660 0.000 0.052 0.000 0.932 0.016 0.000
#> GSM120781 4 0.3892 0.2916 0.000 0.352 0.004 0.640 0.004 0.000
#> GSM120788 4 0.3626 0.6209 0.000 0.068 0.000 0.788 0.144 0.000
#> GSM120760 4 0.0717 0.6714 0.000 0.008 0.000 0.976 0.016 0.000
#> GSM120763 4 0.1124 0.6700 0.000 0.008 0.000 0.956 0.036 0.000
#> GSM120764 4 0.2218 0.6543 0.000 0.012 0.000 0.884 0.104 0.000
#> GSM120777 4 0.3455 0.6232 0.000 0.056 0.000 0.800 0.144 0.000
#> GSM120786 4 0.2218 0.6543 0.000 0.012 0.000 0.884 0.104 0.000
#> GSM121329 1 0.6969 0.0919 0.368 0.012 0.320 0.000 0.268 0.032
#> GSM121331 5 0.3151 0.8921 0.000 0.000 0.000 0.000 0.748 0.252
#> GSM121333 5 0.3198 0.8924 0.000 0.000 0.000 0.000 0.740 0.260
#> GSM121345 5 0.4701 0.5585 0.000 0.192 0.000 0.000 0.680 0.128
#> GSM121356 5 0.3448 0.8806 0.000 0.004 0.000 0.000 0.716 0.280
#> GSM120754 4 0.4175 0.0528 0.000 0.464 0.000 0.524 0.012 0.000
#> GSM120759 2 0.3672 0.3825 0.000 0.632 0.000 0.368 0.000 0.000
#> GSM120762 4 0.3713 0.4134 0.000 0.284 0.004 0.704 0.008 0.000
#> GSM120775 4 0.3475 0.6293 0.000 0.060 0.000 0.800 0.140 0.000
#> GSM120776 4 0.7026 -0.2806 0.000 0.296 0.000 0.324 0.320 0.060
#> GSM120782 4 0.4782 0.2225 0.000 0.380 0.004 0.568 0.048 0.000
#> GSM120789 4 0.4313 0.2731 0.000 0.372 0.004 0.604 0.020 0.000
#> GSM120790 2 0.5635 0.0748 0.000 0.468 0.000 0.408 0.116 0.008
#> GSM120791 4 0.1151 0.6725 0.000 0.032 0.000 0.956 0.012 0.000
#> GSM120755 4 0.4195 0.0727 0.000 0.440 0.004 0.548 0.008 0.000
#> GSM120756 4 0.3672 0.6148 0.000 0.056 0.000 0.776 0.168 0.000
#> GSM120769 4 0.4274 0.4503 0.000 0.276 0.000 0.676 0.048 0.000
#> GSM120778 4 0.2649 0.6710 0.000 0.052 0.004 0.876 0.068 0.000
#> GSM120792 4 0.2451 0.6678 0.000 0.068 0.004 0.888 0.040 0.000
#> GSM121332 4 0.4529 -0.0077 0.000 0.460 0.004 0.512 0.024 0.000
#> GSM121334 4 0.2520 0.6012 0.000 0.152 0.000 0.844 0.004 0.000
#> GSM121340 4 0.2553 0.6448 0.000 0.008 0.000 0.848 0.144 0.000
#> GSM121351 2 0.4070 0.2487 0.000 0.568 0.004 0.424 0.004 0.000
#> GSM121353 4 0.3707 0.6266 0.000 0.056 0.004 0.784 0.156 0.000
#> GSM120758 4 0.3864 0.3096 0.000 0.344 0.004 0.648 0.004 0.000
#> GSM120771 2 0.3126 0.5209 0.000 0.752 0.000 0.248 0.000 0.000
#> GSM120772 4 0.3468 0.4281 0.000 0.284 0.000 0.712 0.004 0.000
#> GSM120773 4 0.0858 0.6718 0.000 0.028 0.000 0.968 0.004 0.000
#> GSM120774 4 0.1923 0.6636 0.000 0.064 0.004 0.916 0.016 0.000
#> GSM120783 4 0.1863 0.6556 0.000 0.000 0.000 0.896 0.104 0.000
#> GSM120787 4 0.1829 0.6568 0.000 0.064 0.004 0.920 0.012 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> MAD:mclust 118 1.49e-11 2
#> MAD:mclust 113 9.63e-21 3
#> MAD:mclust 116 9.09e-21 4
#> MAD:mclust 92 1.92e-27 5
#> MAD:mclust 82 4.30e-31 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 21512 rows and 119 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 0.930 0.932 0.972 0.5018 0.496 0.496
#> 3 3 0.573 0.680 0.847 0.3080 0.753 0.544
#> 4 4 0.601 0.538 0.746 0.1221 0.782 0.477
#> 5 5 0.619 0.526 0.750 0.0564 0.886 0.636
#> 6 6 0.643 0.587 0.751 0.0502 0.839 0.442
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
#> GSM120719 1 0.0000 0.953 1.000 0.000
#> GSM120720 1 0.0000 0.953 1.000 0.000
#> GSM120765 2 0.0000 0.987 0.000 1.000
#> GSM120767 2 0.0000 0.987 0.000 1.000
#> GSM120784 2 0.0000 0.987 0.000 1.000
#> GSM121400 1 0.0000 0.953 1.000 0.000
#> GSM121401 1 0.0000 0.953 1.000 0.000
#> GSM121402 2 0.0000 0.987 0.000 1.000
#> GSM121403 1 0.8267 0.657 0.740 0.260
#> GSM121404 2 0.0000 0.987 0.000 1.000
#> GSM121405 1 0.0000 0.953 1.000 0.000
#> GSM121406 2 0.0000 0.987 0.000 1.000
#> GSM121408 2 0.0000 0.987 0.000 1.000
#> GSM121409 1 0.0000 0.953 1.000 0.000
#> GSM121410 1 0.0000 0.953 1.000 0.000
#> GSM121412 2 0.0000 0.987 0.000 1.000
#> GSM121413 2 0.0000 0.987 0.000 1.000
#> GSM121414 2 0.0000 0.987 0.000 1.000
#> GSM121415 2 0.0000 0.987 0.000 1.000
#> GSM121416 2 0.0000 0.987 0.000 1.000
#> GSM120591 1 0.0000 0.953 1.000 0.000
#> GSM120594 1 0.0000 0.953 1.000 0.000
#> GSM120718 1 0.0000 0.953 1.000 0.000
#> GSM121205 1 0.0000 0.953 1.000 0.000
#> GSM121206 1 0.0000 0.953 1.000 0.000
#> GSM121207 1 0.0000 0.953 1.000 0.000
#> GSM121208 1 0.0000 0.953 1.000 0.000
#> GSM121209 1 0.0000 0.953 1.000 0.000
#> GSM121210 1 0.0000 0.953 1.000 0.000
#> GSM121211 1 0.0000 0.953 1.000 0.000
#> GSM121212 1 0.0000 0.953 1.000 0.000
#> GSM121213 1 0.0000 0.953 1.000 0.000
#> GSM121214 1 0.0000 0.953 1.000 0.000
#> GSM121215 1 0.0000 0.953 1.000 0.000
#> GSM121216 1 0.0000 0.953 1.000 0.000
#> GSM121217 1 0.0000 0.953 1.000 0.000
#> GSM121218 1 0.0000 0.953 1.000 0.000
#> GSM121234 1 0.0000 0.953 1.000 0.000
#> GSM121243 1 0.0000 0.953 1.000 0.000
#> GSM121245 1 0.0000 0.953 1.000 0.000
#> GSM121246 1 0.0000 0.953 1.000 0.000
#> GSM121247 1 0.0000 0.953 1.000 0.000
#> GSM121248 1 0.0000 0.953 1.000 0.000
#> GSM120744 1 0.9754 0.359 0.592 0.408
#> GSM120745 1 0.0000 0.953 1.000 0.000
#> GSM120746 1 0.6887 0.768 0.816 0.184
#> GSM120747 1 0.9775 0.347 0.588 0.412
#> GSM120748 2 0.0000 0.987 0.000 1.000
#> GSM120749 1 0.1184 0.941 0.984 0.016
#> GSM120750 1 0.9754 0.359 0.592 0.408
#> GSM120751 1 0.4298 0.876 0.912 0.088
#> GSM120752 1 0.0000 0.953 1.000 0.000
#> GSM121336 2 0.0000 0.987 0.000 1.000
#> GSM121339 2 0.0000 0.987 0.000 1.000
#> GSM121349 2 0.0000 0.987 0.000 1.000
#> GSM121355 2 0.0000 0.987 0.000 1.000
#> GSM120757 1 0.0376 0.950 0.996 0.004
#> GSM120766 1 0.9460 0.466 0.636 0.364
#> GSM120770 2 0.0000 0.987 0.000 1.000
#> GSM120779 1 0.0000 0.953 1.000 0.000
#> GSM120780 2 0.0000 0.987 0.000 1.000
#> GSM121102 2 0.0000 0.987 0.000 1.000
#> GSM121203 2 0.9000 0.507 0.316 0.684
#> GSM121204 1 0.0000 0.953 1.000 0.000
#> GSM121330 1 0.0000 0.953 1.000 0.000
#> GSM121335 1 0.0000 0.953 1.000 0.000
#> GSM121337 2 0.0000 0.987 0.000 1.000
#> GSM121338 2 0.0000 0.987 0.000 1.000
#> GSM121341 1 0.0000 0.953 1.000 0.000
#> GSM121342 1 0.0000 0.953 1.000 0.000
#> GSM121343 2 0.0000 0.987 0.000 1.000
#> GSM121344 1 0.0000 0.953 1.000 0.000
#> GSM121346 1 0.0000 0.953 1.000 0.000
#> GSM121347 2 0.0938 0.976 0.012 0.988
#> GSM121348 2 0.0000 0.987 0.000 1.000
#> GSM121350 1 0.0000 0.953 1.000 0.000
#> GSM121352 1 0.0000 0.953 1.000 0.000
#> GSM121354 1 0.0000 0.953 1.000 0.000
#> GSM120753 2 0.0000 0.987 0.000 1.000
#> GSM120761 2 0.0000 0.987 0.000 1.000
#> GSM120768 2 0.0000 0.987 0.000 1.000
#> GSM120781 2 0.0000 0.987 0.000 1.000
#> GSM120788 2 0.2948 0.936 0.052 0.948
#> GSM120760 2 0.0000 0.987 0.000 1.000
#> GSM120763 2 0.0000 0.987 0.000 1.000
#> GSM120764 2 0.0000 0.987 0.000 1.000
#> GSM120777 2 0.0000 0.987 0.000 1.000
#> GSM120786 2 0.0000 0.987 0.000 1.000
#> GSM121329 1 0.0000 0.953 1.000 0.000
#> GSM121331 1 0.0000 0.953 1.000 0.000
#> GSM121333 1 0.0000 0.953 1.000 0.000
#> GSM121345 1 0.0000 0.953 1.000 0.000
#> GSM121356 1 0.0000 0.953 1.000 0.000
#> GSM120754 2 0.0000 0.987 0.000 1.000
#> GSM120759 2 0.0000 0.987 0.000 1.000
#> GSM120762 2 0.0000 0.987 0.000 1.000
#> GSM120775 2 0.0000 0.987 0.000 1.000
#> GSM120776 2 0.7056 0.747 0.192 0.808
#> GSM120782 2 0.0000 0.987 0.000 1.000
#> GSM120789 2 0.0000 0.987 0.000 1.000
#> GSM120790 2 0.0000 0.987 0.000 1.000
#> GSM120791 2 0.0000 0.987 0.000 1.000
#> GSM120755 2 0.0000 0.987 0.000 1.000
#> GSM120756 1 0.9970 0.161 0.532 0.468
#> GSM120769 2 0.0000 0.987 0.000 1.000
#> GSM120778 2 0.0000 0.987 0.000 1.000
#> GSM120792 2 0.0000 0.987 0.000 1.000
#> GSM121332 2 0.0000 0.987 0.000 1.000
#> GSM121334 2 0.0000 0.987 0.000 1.000
#> GSM121340 2 0.0000 0.987 0.000 1.000
#> GSM121351 2 0.0000 0.987 0.000 1.000
#> GSM121353 2 0.5946 0.823 0.144 0.856
#> GSM120758 2 0.0000 0.987 0.000 1.000
#> GSM120771 2 0.0000 0.987 0.000 1.000
#> GSM120772 2 0.0000 0.987 0.000 1.000
#> GSM120773 2 0.0000 0.987 0.000 1.000
#> GSM120774 2 0.0000 0.987 0.000 1.000
#> GSM120783 2 0.0000 0.987 0.000 1.000
#> GSM120787 2 0.0000 0.987 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM120719 1 0.2625 0.7093 0.916 0.000 0.084
#> GSM120720 3 0.4887 0.6424 0.228 0.000 0.772
#> GSM120765 2 0.2796 0.8683 0.000 0.908 0.092
#> GSM120767 2 0.2537 0.8738 0.000 0.920 0.080
#> GSM120784 2 0.4062 0.8171 0.000 0.836 0.164
#> GSM121400 3 0.0237 0.7713 0.000 0.004 0.996
#> GSM121401 3 0.0424 0.7724 0.008 0.000 0.992
#> GSM121402 2 0.2066 0.8792 0.000 0.940 0.060
#> GSM121403 3 0.1411 0.7586 0.000 0.036 0.964
#> GSM121404 2 0.6267 0.3137 0.000 0.548 0.452
#> GSM121405 3 0.0592 0.7690 0.000 0.012 0.988
#> GSM121406 2 0.3192 0.8575 0.000 0.888 0.112
#> GSM121408 2 0.2711 0.8705 0.000 0.912 0.088
#> GSM121409 3 0.0661 0.7724 0.008 0.004 0.988
#> GSM121410 3 0.0237 0.7713 0.000 0.004 0.996
#> GSM121412 2 0.5397 0.6731 0.000 0.720 0.280
#> GSM121413 2 0.4178 0.8097 0.000 0.828 0.172
#> GSM121414 2 0.4796 0.7562 0.000 0.780 0.220
#> GSM121415 2 0.4346 0.7984 0.000 0.816 0.184
#> GSM121416 2 0.2448 0.8747 0.000 0.924 0.076
#> GSM120591 3 0.4062 0.7121 0.164 0.000 0.836
#> GSM120594 3 0.4750 0.6581 0.216 0.000 0.784
#> GSM120718 3 0.6244 0.1737 0.440 0.000 0.560
#> GSM121205 1 0.4702 0.6436 0.788 0.000 0.212
#> GSM121206 1 0.6280 0.1482 0.540 0.000 0.460
#> GSM121207 1 0.2448 0.7096 0.924 0.000 0.076
#> GSM121208 3 0.5591 0.5182 0.304 0.000 0.696
#> GSM121209 3 0.6302 0.0299 0.480 0.000 0.520
#> GSM121210 1 0.5098 0.6063 0.752 0.000 0.248
#> GSM121211 1 0.6215 0.2528 0.572 0.000 0.428
#> GSM121212 1 0.5138 0.6017 0.748 0.000 0.252
#> GSM121213 1 0.5591 0.5239 0.696 0.000 0.304
#> GSM121214 1 0.3816 0.6882 0.852 0.000 0.148
#> GSM121215 1 0.6192 0.2747 0.580 0.000 0.420
#> GSM121216 1 0.5254 0.5872 0.736 0.000 0.264
#> GSM121217 1 0.5988 0.4042 0.632 0.000 0.368
#> GSM121218 1 0.3879 0.6861 0.848 0.000 0.152
#> GSM121234 3 0.6267 0.1336 0.452 0.000 0.548
#> GSM121243 1 0.4555 0.6550 0.800 0.000 0.200
#> GSM121245 1 0.3038 0.7044 0.896 0.000 0.104
#> GSM121246 3 0.4346 0.6944 0.184 0.000 0.816
#> GSM121247 1 0.1031 0.7123 0.976 0.000 0.024
#> GSM121248 1 0.4346 0.6663 0.816 0.000 0.184
#> GSM120744 3 0.3412 0.6904 0.000 0.124 0.876
#> GSM120745 3 0.3686 0.7303 0.140 0.000 0.860
#> GSM120746 3 0.1411 0.7597 0.000 0.036 0.964
#> GSM120747 3 0.1529 0.7559 0.000 0.040 0.960
#> GSM120748 3 0.4750 0.5970 0.000 0.216 0.784
#> GSM120749 3 0.0237 0.7713 0.000 0.004 0.996
#> GSM120750 3 0.2356 0.7337 0.000 0.072 0.928
#> GSM120751 3 0.0892 0.7666 0.000 0.020 0.980
#> GSM120752 3 0.2796 0.7577 0.092 0.000 0.908
#> GSM121336 2 0.2356 0.8756 0.000 0.928 0.072
#> GSM121339 3 0.6168 0.1193 0.000 0.412 0.588
#> GSM121349 2 0.2066 0.8789 0.000 0.940 0.060
#> GSM121355 2 0.3038 0.8624 0.000 0.896 0.104
#> GSM120757 1 0.1399 0.7064 0.968 0.028 0.004
#> GSM120766 1 0.9913 0.0795 0.388 0.336 0.276
#> GSM120770 2 0.3816 0.8310 0.000 0.852 0.148
#> GSM120779 1 0.1129 0.7088 0.976 0.020 0.004
#> GSM120780 2 0.6204 0.3878 0.000 0.576 0.424
#> GSM121102 2 0.6192 0.3989 0.000 0.580 0.420
#> GSM121203 3 0.4504 0.6202 0.000 0.196 0.804
#> GSM121204 1 0.0747 0.7123 0.984 0.000 0.016
#> GSM121330 3 0.1860 0.7690 0.052 0.000 0.948
#> GSM121335 3 0.4178 0.7053 0.172 0.000 0.828
#> GSM121337 2 0.3500 0.8568 0.004 0.880 0.116
#> GSM121338 3 0.5650 0.4089 0.000 0.312 0.688
#> GSM121341 3 0.4291 0.6983 0.180 0.000 0.820
#> GSM121342 3 0.5327 0.5760 0.272 0.000 0.728
#> GSM121343 3 0.6215 0.0645 0.000 0.428 0.572
#> GSM121344 3 0.3879 0.7206 0.152 0.000 0.848
#> GSM121346 3 0.1753 0.7696 0.048 0.000 0.952
#> GSM121347 2 0.2903 0.8753 0.048 0.924 0.028
#> GSM121348 2 0.3752 0.8402 0.096 0.884 0.020
#> GSM121350 3 0.1529 0.7709 0.040 0.000 0.960
#> GSM121352 3 0.1860 0.7687 0.052 0.000 0.948
#> GSM121354 3 0.2537 0.7595 0.080 0.000 0.920
#> GSM120753 2 0.0424 0.8832 0.000 0.992 0.008
#> GSM120761 2 0.0892 0.8769 0.020 0.980 0.000
#> GSM120768 2 0.1289 0.8727 0.032 0.968 0.000
#> GSM120781 2 0.0747 0.8837 0.000 0.984 0.016
#> GSM120788 1 0.5016 0.5741 0.760 0.240 0.000
#> GSM120760 2 0.2959 0.8275 0.100 0.900 0.000
#> GSM120763 2 0.3038 0.8239 0.104 0.896 0.000
#> GSM120764 2 0.6026 0.3869 0.376 0.624 0.000
#> GSM120777 1 0.5650 0.4618 0.688 0.312 0.000
#> GSM120786 2 0.5988 0.4062 0.368 0.632 0.000
#> GSM121329 1 0.3482 0.6974 0.872 0.000 0.128
#> GSM121331 1 0.0892 0.7076 0.980 0.020 0.000
#> GSM121333 1 0.0592 0.7092 0.988 0.012 0.000
#> GSM121345 1 0.1643 0.6976 0.956 0.044 0.000
#> GSM121356 1 0.0983 0.7123 0.980 0.004 0.016
#> GSM120754 2 0.2066 0.8570 0.060 0.940 0.000
#> GSM120759 2 0.1753 0.8812 0.000 0.952 0.048
#> GSM120762 2 0.0475 0.8821 0.004 0.992 0.004
#> GSM120775 1 0.6252 0.1455 0.556 0.444 0.000
#> GSM120776 1 0.5058 0.5716 0.756 0.244 0.000
#> GSM120782 2 0.1163 0.8743 0.028 0.972 0.000
#> GSM120789 2 0.0892 0.8837 0.000 0.980 0.020
#> GSM120790 2 0.1015 0.8835 0.008 0.980 0.012
#> GSM120791 2 0.2066 0.8571 0.060 0.940 0.000
#> GSM120755 2 0.1529 0.8828 0.000 0.960 0.040
#> GSM120756 1 0.4002 0.6370 0.840 0.160 0.000
#> GSM120769 2 0.0424 0.8803 0.008 0.992 0.000
#> GSM120778 2 0.1163 0.8743 0.028 0.972 0.000
#> GSM120792 2 0.1163 0.8743 0.028 0.972 0.000
#> GSM121332 2 0.1289 0.8830 0.000 0.968 0.032
#> GSM121334 2 0.0424 0.8804 0.008 0.992 0.000
#> GSM121340 1 0.6308 -0.0204 0.508 0.492 0.000
#> GSM121351 2 0.1860 0.8805 0.000 0.948 0.052
#> GSM121353 1 0.5882 0.3903 0.652 0.348 0.000
#> GSM120758 2 0.0661 0.8828 0.004 0.988 0.008
#> GSM120771 2 0.0747 0.8837 0.000 0.984 0.016
#> GSM120772 2 0.0424 0.8804 0.008 0.992 0.000
#> GSM120773 2 0.2796 0.8339 0.092 0.908 0.000
#> GSM120774 2 0.1411 0.8709 0.036 0.964 0.000
#> GSM120783 2 0.4974 0.6617 0.236 0.764 0.000
#> GSM120787 2 0.1031 0.8757 0.024 0.976 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM120719 4 0.4804 0.1293 0.384 0.000 0.000 0.616
#> GSM120720 1 0.1209 0.6744 0.964 0.000 0.004 0.032
#> GSM120765 2 0.3937 0.7385 0.000 0.800 0.188 0.012
#> GSM120767 2 0.1488 0.8238 0.000 0.956 0.032 0.012
#> GSM120784 2 0.4883 0.6294 0.000 0.696 0.288 0.016
#> GSM121400 3 0.5630 0.3889 0.376 0.012 0.600 0.012
#> GSM121401 1 0.4813 0.4174 0.716 0.004 0.268 0.012
#> GSM121402 2 0.3498 0.7647 0.000 0.832 0.160 0.008
#> GSM121403 3 0.6060 0.2826 0.428 0.024 0.536 0.012
#> GSM121404 2 0.7924 0.1518 0.176 0.452 0.356 0.016
#> GSM121405 1 0.5512 0.3425 0.680 0.020 0.284 0.016
#> GSM121406 2 0.3790 0.7531 0.000 0.820 0.164 0.016
#> GSM121408 2 0.1488 0.8238 0.000 0.956 0.032 0.012
#> GSM121409 3 0.5909 0.3376 0.400 0.020 0.568 0.012
#> GSM121410 1 0.5914 -0.1445 0.504 0.016 0.468 0.012
#> GSM121412 2 0.5613 0.5616 0.016 0.648 0.320 0.016
#> GSM121413 2 0.5075 0.5525 0.000 0.644 0.344 0.012
#> GSM121414 2 0.5481 0.5970 0.016 0.672 0.296 0.016
#> GSM121415 2 0.5496 0.5375 0.008 0.632 0.344 0.016
#> GSM121416 2 0.4123 0.7159 0.000 0.772 0.220 0.008
#> GSM120591 1 0.2197 0.6520 0.916 0.000 0.080 0.004
#> GSM120594 1 0.1042 0.6746 0.972 0.000 0.008 0.020
#> GSM120718 1 0.3024 0.6402 0.852 0.000 0.000 0.148
#> GSM121205 1 0.5088 0.3179 0.572 0.000 0.004 0.424
#> GSM121206 1 0.4040 0.5774 0.752 0.000 0.000 0.248
#> GSM121207 4 0.5004 0.1117 0.392 0.000 0.004 0.604
#> GSM121208 1 0.2142 0.6715 0.928 0.000 0.016 0.056
#> GSM121209 1 0.3569 0.6148 0.804 0.000 0.000 0.196
#> GSM121210 1 0.5290 0.1797 0.516 0.000 0.008 0.476
#> GSM121211 1 0.4134 0.5672 0.740 0.000 0.000 0.260
#> GSM121212 1 0.5088 0.3179 0.572 0.000 0.004 0.424
#> GSM121213 1 0.4741 0.4860 0.668 0.000 0.004 0.328
#> GSM121214 4 0.5155 -0.1020 0.468 0.000 0.004 0.528
#> GSM121215 1 0.4428 0.5487 0.720 0.000 0.004 0.276
#> GSM121216 1 0.4872 0.4446 0.640 0.000 0.004 0.356
#> GSM121217 1 0.4560 0.5273 0.700 0.000 0.004 0.296
#> GSM121218 4 0.5163 -0.1400 0.480 0.000 0.004 0.516
#> GSM121234 1 0.3219 0.6326 0.836 0.000 0.000 0.164
#> GSM121243 1 0.5158 0.2021 0.524 0.000 0.004 0.472
#> GSM121245 4 0.4950 0.1432 0.376 0.000 0.004 0.620
#> GSM121246 1 0.0779 0.6742 0.980 0.000 0.004 0.016
#> GSM121247 4 0.3539 0.3952 0.176 0.000 0.004 0.820
#> GSM121248 1 0.5163 0.1761 0.516 0.000 0.004 0.480
#> GSM120744 3 0.1637 0.6927 0.000 0.000 0.940 0.060
#> GSM120745 3 0.3597 0.6647 0.016 0.000 0.836 0.148
#> GSM120746 3 0.2124 0.6914 0.068 0.000 0.924 0.008
#> GSM120747 3 0.5188 0.5413 0.268 0.012 0.704 0.016
#> GSM120748 3 0.3257 0.6705 0.108 0.012 0.872 0.008
#> GSM120749 3 0.2805 0.6837 0.100 0.000 0.888 0.012
#> GSM120750 3 0.1488 0.6975 0.012 0.000 0.956 0.032
#> GSM120751 3 0.2111 0.6969 0.044 0.000 0.932 0.024
#> GSM120752 3 0.3300 0.6638 0.008 0.000 0.848 0.144
#> GSM121336 2 0.2021 0.8168 0.000 0.932 0.056 0.012
#> GSM121339 2 0.8177 0.1509 0.276 0.444 0.264 0.016
#> GSM121349 2 0.1677 0.8217 0.000 0.948 0.040 0.012
#> GSM121355 2 0.2222 0.8137 0.000 0.924 0.060 0.016
#> GSM120757 3 0.4877 0.3800 0.000 0.000 0.592 0.408
#> GSM120766 3 0.4222 0.5566 0.000 0.000 0.728 0.272
#> GSM120770 3 0.3198 0.6828 0.000 0.040 0.880 0.080
#> GSM120779 3 0.4996 0.2333 0.000 0.000 0.516 0.484
#> GSM120780 3 0.3024 0.6581 0.000 0.000 0.852 0.148
#> GSM121102 3 0.2313 0.6865 0.044 0.032 0.924 0.000
#> GSM121203 3 0.1661 0.6949 0.004 0.000 0.944 0.052
#> GSM121204 4 0.4920 0.0455 0.004 0.000 0.368 0.628
#> GSM121330 1 0.2737 0.6266 0.888 0.000 0.104 0.008
#> GSM121335 1 0.0592 0.6700 0.984 0.000 0.016 0.000
#> GSM121337 2 0.5207 0.6172 0.000 0.680 0.292 0.028
#> GSM121338 3 0.7098 0.4111 0.320 0.100 0.564 0.016
#> GSM121341 1 0.0469 0.6709 0.988 0.000 0.012 0.000
#> GSM121342 1 0.1118 0.6735 0.964 0.000 0.000 0.036
#> GSM121343 3 0.6005 0.5622 0.224 0.072 0.692 0.012
#> GSM121344 1 0.1211 0.6637 0.960 0.000 0.040 0.000
#> GSM121346 1 0.4482 0.4375 0.728 0.000 0.264 0.008
#> GSM121347 3 0.7366 0.3866 0.000 0.224 0.524 0.252
#> GSM121348 3 0.4957 0.5025 0.000 0.012 0.668 0.320
#> GSM121350 1 0.4452 0.4405 0.732 0.000 0.260 0.008
#> GSM121352 1 0.4011 0.5234 0.784 0.000 0.208 0.008
#> GSM121354 1 0.2149 0.6413 0.912 0.000 0.088 0.000
#> GSM120753 2 0.0469 0.8283 0.000 0.988 0.000 0.012
#> GSM120761 2 0.1209 0.8255 0.000 0.964 0.004 0.032
#> GSM120768 2 0.0921 0.8250 0.000 0.972 0.000 0.028
#> GSM120781 2 0.0336 0.8286 0.000 0.992 0.000 0.008
#> GSM120788 4 0.3708 0.4919 0.000 0.148 0.020 0.832
#> GSM120760 2 0.3219 0.7679 0.000 0.868 0.020 0.112
#> GSM120763 2 0.2401 0.7911 0.000 0.904 0.004 0.092
#> GSM120764 4 0.5607 -0.1700 0.000 0.488 0.020 0.492
#> GSM120777 4 0.4150 0.4644 0.000 0.120 0.056 0.824
#> GSM120786 2 0.5503 0.1638 0.000 0.516 0.016 0.468
#> GSM121329 4 0.4933 0.0073 0.432 0.000 0.000 0.568
#> GSM121331 4 0.4955 -0.1489 0.000 0.000 0.444 0.556
#> GSM121333 4 0.4907 -0.0857 0.000 0.000 0.420 0.580
#> GSM121345 4 0.3441 0.3882 0.004 0.004 0.152 0.840
#> GSM121356 3 0.4916 0.3577 0.000 0.000 0.576 0.424
#> GSM120754 2 0.5845 0.5556 0.000 0.672 0.076 0.252
#> GSM120759 2 0.1902 0.8193 0.000 0.932 0.064 0.004
#> GSM120762 2 0.0592 0.8277 0.000 0.984 0.000 0.016
#> GSM120775 4 0.4989 -0.0495 0.000 0.472 0.000 0.528
#> GSM120776 4 0.4418 0.3476 0.000 0.032 0.184 0.784
#> GSM120782 2 0.1722 0.8180 0.000 0.944 0.008 0.048
#> GSM120789 2 0.0336 0.8286 0.000 0.992 0.000 0.008
#> GSM120790 3 0.6147 0.5471 0.000 0.112 0.664 0.224
#> GSM120791 2 0.1474 0.8158 0.000 0.948 0.000 0.052
#> GSM120755 2 0.0188 0.8285 0.000 0.996 0.000 0.004
#> GSM120756 4 0.3529 0.4848 0.012 0.152 0.000 0.836
#> GSM120769 2 0.0707 0.8270 0.000 0.980 0.000 0.020
#> GSM120778 2 0.0707 0.8270 0.000 0.980 0.000 0.020
#> GSM120792 2 0.1022 0.8241 0.000 0.968 0.000 0.032
#> GSM121332 2 0.0336 0.8283 0.000 0.992 0.000 0.008
#> GSM121334 2 0.0707 0.8270 0.000 0.980 0.000 0.020
#> GSM121340 2 0.4804 0.3678 0.000 0.616 0.000 0.384
#> GSM121351 2 0.1854 0.8197 0.000 0.940 0.048 0.012
#> GSM121353 2 0.5329 0.2212 0.012 0.568 0.000 0.420
#> GSM120758 2 0.0469 0.8283 0.000 0.988 0.000 0.012
#> GSM120771 2 0.0895 0.8294 0.000 0.976 0.020 0.004
#> GSM120772 2 0.0707 0.8270 0.000 0.980 0.000 0.020
#> GSM120773 2 0.3271 0.7631 0.000 0.856 0.012 0.132
#> GSM120774 2 0.1022 0.8237 0.000 0.968 0.000 0.032
#> GSM120783 2 0.3444 0.7122 0.000 0.816 0.000 0.184
#> GSM120787 2 0.0707 0.8270 0.000 0.980 0.000 0.020
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM120719 4 0.6610 0.02238 0.364 0.016 0.124 0.492 0.004
#> GSM120720 3 0.3696 0.65216 0.212 0.000 0.772 0.016 0.000
#> GSM120765 2 0.3670 0.69546 0.000 0.796 0.020 0.004 0.180
#> GSM120767 2 0.2238 0.76628 0.000 0.912 0.064 0.004 0.020
#> GSM120784 2 0.5127 0.60265 0.000 0.692 0.092 0.004 0.212
#> GSM121400 5 0.5320 0.43739 0.264 0.000 0.072 0.008 0.656
#> GSM121401 1 0.5878 0.14634 0.504 0.000 0.416 0.012 0.068
#> GSM121402 2 0.4375 0.37468 0.000 0.576 0.004 0.000 0.420
#> GSM121403 5 0.5745 0.45915 0.256 0.024 0.060 0.008 0.652
#> GSM121404 2 0.7505 0.02417 0.024 0.396 0.256 0.008 0.316
#> GSM121405 1 0.6476 0.21742 0.500 0.000 0.348 0.012 0.140
#> GSM121406 2 0.4135 0.51961 0.000 0.656 0.004 0.000 0.340
#> GSM121408 2 0.2646 0.74336 0.000 0.868 0.004 0.004 0.124
#> GSM121409 5 0.4873 0.40267 0.316 0.012 0.016 0.004 0.652
#> GSM121410 5 0.5161 0.36251 0.336 0.012 0.020 0.008 0.624
#> GSM121412 5 0.5062 0.01488 0.020 0.420 0.004 0.004 0.552
#> GSM121413 5 0.4350 0.06244 0.000 0.408 0.004 0.000 0.588
#> GSM121414 5 0.4443 -0.14437 0.000 0.472 0.004 0.000 0.524
#> GSM121415 2 0.4504 0.34461 0.000 0.564 0.008 0.000 0.428
#> GSM121416 2 0.4449 0.50261 0.000 0.636 0.004 0.008 0.352
#> GSM120591 3 0.2266 0.79299 0.064 0.000 0.912 0.016 0.008
#> GSM120594 3 0.3340 0.71706 0.156 0.000 0.824 0.016 0.004
#> GSM120718 3 0.4958 0.30412 0.372 0.000 0.592 0.036 0.000
#> GSM121205 1 0.3395 0.66941 0.764 0.000 0.000 0.236 0.000
#> GSM121206 1 0.1544 0.74027 0.932 0.000 0.000 0.068 0.000
#> GSM121207 4 0.4450 -0.29660 0.488 0.000 0.004 0.508 0.000
#> GSM121208 1 0.1329 0.73525 0.956 0.000 0.004 0.008 0.032
#> GSM121209 1 0.1197 0.74069 0.952 0.000 0.000 0.048 0.000
#> GSM121210 1 0.4326 0.62500 0.708 0.000 0.000 0.264 0.028
#> GSM121211 1 0.1908 0.73785 0.908 0.000 0.000 0.092 0.000
#> GSM121212 1 0.3366 0.67042 0.768 0.000 0.000 0.232 0.000
#> GSM121213 1 0.2536 0.72768 0.868 0.000 0.000 0.128 0.004
#> GSM121214 1 0.4182 0.46768 0.600 0.000 0.000 0.400 0.000
#> GSM121215 1 0.1965 0.73681 0.904 0.000 0.000 0.096 0.000
#> GSM121216 1 0.2624 0.73191 0.872 0.000 0.000 0.116 0.012
#> GSM121217 1 0.2424 0.72576 0.868 0.000 0.000 0.132 0.000
#> GSM121218 1 0.3949 0.60546 0.696 0.000 0.000 0.300 0.004
#> GSM121234 1 0.0794 0.73978 0.972 0.000 0.000 0.028 0.000
#> GSM121243 1 0.3779 0.66347 0.752 0.000 0.000 0.236 0.012
#> GSM121245 1 0.4549 0.32060 0.528 0.000 0.000 0.464 0.008
#> GSM121246 1 0.1648 0.72636 0.940 0.000 0.020 0.000 0.040
#> GSM121247 4 0.4503 0.26214 0.268 0.000 0.000 0.696 0.036
#> GSM121248 1 0.3774 0.61368 0.704 0.000 0.000 0.296 0.000
#> GSM120744 3 0.2313 0.81992 0.000 0.004 0.912 0.044 0.040
#> GSM120745 3 0.2569 0.80986 0.004 0.000 0.896 0.068 0.032
#> GSM120746 3 0.0960 0.83547 0.000 0.004 0.972 0.016 0.008
#> GSM120747 3 0.0451 0.82742 0.004 0.008 0.988 0.000 0.000
#> GSM120748 3 0.1280 0.83197 0.000 0.008 0.960 0.008 0.024
#> GSM120749 3 0.1187 0.83524 0.004 0.004 0.964 0.024 0.004
#> GSM120750 3 0.2313 0.82060 0.000 0.004 0.912 0.040 0.044
#> GSM120751 3 0.1471 0.83322 0.000 0.004 0.952 0.024 0.020
#> GSM120752 3 0.2694 0.80299 0.004 0.000 0.888 0.076 0.032
#> GSM121336 2 0.2877 0.73116 0.000 0.848 0.004 0.004 0.144
#> GSM121339 2 0.7637 0.29609 0.064 0.516 0.212 0.016 0.192
#> GSM121349 2 0.2833 0.73426 0.000 0.852 0.004 0.004 0.140
#> GSM121355 2 0.2833 0.73343 0.000 0.852 0.004 0.004 0.140
#> GSM120757 4 0.6434 0.08676 0.000 0.000 0.176 0.432 0.392
#> GSM120766 5 0.5885 0.13676 0.000 0.000 0.132 0.296 0.572
#> GSM120770 5 0.5779 0.34139 0.000 0.016 0.248 0.100 0.636
#> GSM120779 4 0.5867 0.12280 0.000 0.000 0.100 0.496 0.404
#> GSM120780 5 0.5163 0.33034 0.000 0.000 0.152 0.156 0.692
#> GSM121102 5 0.5662 0.15561 0.000 0.020 0.384 0.044 0.552
#> GSM121203 3 0.5688 0.35381 0.000 0.000 0.572 0.100 0.328
#> GSM121204 4 0.6023 0.28628 0.000 0.000 0.248 0.576 0.176
#> GSM121330 1 0.4290 0.64120 0.780 0.000 0.156 0.012 0.052
#> GSM121335 1 0.2914 0.70778 0.872 0.000 0.100 0.012 0.016
#> GSM121337 5 0.4633 0.21581 0.004 0.348 0.000 0.016 0.632
#> GSM121338 5 0.6913 0.45095 0.144 0.084 0.152 0.008 0.612
#> GSM121341 1 0.2906 0.71159 0.880 0.000 0.080 0.012 0.028
#> GSM121342 1 0.1522 0.72978 0.944 0.000 0.044 0.000 0.012
#> GSM121343 5 0.3736 0.50728 0.080 0.080 0.004 0.004 0.832
#> GSM121344 1 0.3054 0.70531 0.876 0.000 0.060 0.012 0.052
#> GSM121346 1 0.5625 0.46049 0.632 0.000 0.272 0.012 0.084
#> GSM121347 5 0.4108 0.45130 0.012 0.068 0.000 0.116 0.804
#> GSM121348 5 0.4162 0.34008 0.004 0.004 0.020 0.220 0.752
#> GSM121350 1 0.5887 0.47537 0.632 0.000 0.220 0.012 0.136
#> GSM121352 1 0.5223 0.52525 0.680 0.000 0.240 0.012 0.068
#> GSM121354 1 0.4330 0.64058 0.776 0.000 0.160 0.012 0.052
#> GSM120753 2 0.0693 0.78029 0.000 0.980 0.000 0.008 0.012
#> GSM120761 2 0.1630 0.77878 0.000 0.944 0.004 0.036 0.016
#> GSM120768 2 0.1282 0.77035 0.000 0.952 0.000 0.044 0.004
#> GSM120781 2 0.0968 0.78023 0.000 0.972 0.004 0.012 0.012
#> GSM120788 4 0.3821 0.49638 0.020 0.144 0.012 0.816 0.008
#> GSM120760 2 0.2338 0.74763 0.000 0.884 0.000 0.112 0.004
#> GSM120763 2 0.2233 0.75570 0.000 0.892 0.000 0.104 0.004
#> GSM120764 4 0.4700 -0.04350 0.000 0.472 0.004 0.516 0.008
#> GSM120777 4 0.3649 0.49594 0.012 0.112 0.012 0.840 0.024
#> GSM120786 2 0.4446 0.09164 0.000 0.520 0.004 0.476 0.000
#> GSM121329 1 0.4321 0.46455 0.600 0.000 0.000 0.396 0.004
#> GSM121331 5 0.5258 -0.10965 0.004 0.000 0.036 0.472 0.488
#> GSM121333 4 0.5281 0.18567 0.004 0.000 0.044 0.564 0.388
#> GSM121345 4 0.3521 0.42754 0.024 0.000 0.008 0.824 0.144
#> GSM121356 5 0.5265 0.01073 0.004 0.000 0.040 0.412 0.544
#> GSM120754 2 0.5533 0.50172 0.000 0.660 0.060 0.252 0.028
#> GSM120759 2 0.4126 0.45912 0.000 0.620 0.000 0.000 0.380
#> GSM120762 2 0.0740 0.78094 0.000 0.980 0.004 0.008 0.008
#> GSM120775 4 0.5083 0.00702 0.000 0.476 0.020 0.496 0.008
#> GSM120776 4 0.6423 0.18913 0.004 0.068 0.348 0.540 0.040
#> GSM120782 2 0.6287 0.17644 0.000 0.500 0.368 0.124 0.008
#> GSM120789 2 0.1410 0.77126 0.000 0.940 0.000 0.000 0.060
#> GSM120790 5 0.3394 0.44198 0.000 0.028 0.012 0.116 0.844
#> GSM120791 2 0.1478 0.76741 0.000 0.936 0.000 0.064 0.000
#> GSM120755 2 0.1285 0.77609 0.000 0.956 0.004 0.004 0.036
#> GSM120756 4 0.4873 0.47672 0.056 0.196 0.012 0.732 0.004
#> GSM120769 2 0.0290 0.77982 0.000 0.992 0.000 0.008 0.000
#> GSM120778 2 0.1124 0.77232 0.000 0.960 0.000 0.036 0.004
#> GSM120792 2 0.1408 0.76783 0.000 0.948 0.000 0.044 0.008
#> GSM121332 2 0.1908 0.76005 0.000 0.908 0.000 0.000 0.092
#> GSM121334 2 0.0794 0.77819 0.000 0.972 0.000 0.000 0.028
#> GSM121340 2 0.4347 0.37606 0.000 0.636 0.004 0.356 0.004
#> GSM121351 2 0.4182 0.50730 0.000 0.644 0.004 0.000 0.352
#> GSM121353 2 0.4673 0.40565 0.012 0.660 0.008 0.316 0.004
#> GSM120758 2 0.0865 0.77826 0.000 0.972 0.004 0.000 0.024
#> GSM120771 2 0.2280 0.75151 0.000 0.880 0.000 0.000 0.120
#> GSM120772 2 0.1153 0.77931 0.000 0.964 0.004 0.024 0.008
#> GSM120773 2 0.2956 0.70646 0.000 0.848 0.004 0.140 0.008
#> GSM120774 2 0.1285 0.77326 0.000 0.956 0.004 0.036 0.004
#> GSM120783 2 0.3001 0.69581 0.000 0.844 0.004 0.144 0.008
#> GSM120787 2 0.1026 0.77713 0.000 0.968 0.004 0.024 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM120719 1 0.5929 0.3560 0.564 0.000 0.044 0.092 0.004 0.296
#> GSM120720 6 0.5541 0.5351 0.252 0.012 0.104 0.008 0.004 0.620
#> GSM120765 2 0.2399 0.6773 0.000 0.904 0.024 0.004 0.044 0.024
#> GSM120767 2 0.3283 0.6296 0.000 0.832 0.004 0.048 0.004 0.112
#> GSM120784 2 0.3373 0.6459 0.000 0.844 0.024 0.004 0.052 0.076
#> GSM121400 3 0.5967 0.4063 0.080 0.012 0.520 0.012 0.364 0.012
#> GSM121401 3 0.3968 0.7162 0.100 0.004 0.772 0.000 0.000 0.124
#> GSM121402 2 0.4830 0.5940 0.000 0.704 0.044 0.056 0.196 0.000
#> GSM121403 3 0.6877 0.3672 0.076 0.076 0.496 0.024 0.320 0.008
#> GSM121404 3 0.4644 0.5870 0.000 0.140 0.756 0.024 0.032 0.048
#> GSM121405 3 0.3867 0.7373 0.124 0.004 0.788 0.004 0.000 0.080
#> GSM121406 2 0.3200 0.6534 0.000 0.844 0.036 0.012 0.104 0.004
#> GSM121408 2 0.1767 0.6878 0.000 0.932 0.012 0.020 0.036 0.000
#> GSM121409 5 0.7674 0.0171 0.128 0.164 0.240 0.024 0.440 0.004
#> GSM121410 3 0.7158 0.2965 0.148 0.064 0.400 0.024 0.364 0.000
#> GSM121412 2 0.5683 0.4277 0.008 0.604 0.104 0.024 0.260 0.000
#> GSM121413 2 0.4900 0.4630 0.000 0.632 0.056 0.016 0.296 0.000
#> GSM121414 2 0.4671 0.5331 0.000 0.688 0.072 0.012 0.228 0.000
#> GSM121415 2 0.5146 0.5624 0.000 0.696 0.148 0.024 0.124 0.008
#> GSM121416 2 0.5744 0.5336 0.000 0.636 0.156 0.060 0.148 0.000
#> GSM120591 6 0.3488 0.7042 0.052 0.020 0.064 0.008 0.008 0.848
#> GSM120594 6 0.5109 0.5892 0.200 0.012 0.084 0.012 0.004 0.688
#> GSM120718 6 0.5531 0.3315 0.360 0.000 0.088 0.012 0.004 0.536
#> GSM121205 1 0.0508 0.8611 0.984 0.000 0.004 0.012 0.000 0.000
#> GSM121206 1 0.1692 0.8507 0.932 0.000 0.048 0.012 0.008 0.000
#> GSM121207 1 0.3776 0.7351 0.760 0.000 0.012 0.208 0.016 0.004
#> GSM121208 1 0.2632 0.7550 0.832 0.000 0.164 0.000 0.004 0.000
#> GSM121209 1 0.1584 0.8450 0.928 0.000 0.064 0.008 0.000 0.000
#> GSM121210 1 0.1572 0.8528 0.936 0.000 0.000 0.028 0.036 0.000
#> GSM121211 1 0.1285 0.8547 0.944 0.000 0.052 0.004 0.000 0.000
#> GSM121212 1 0.2067 0.8534 0.912 0.000 0.016 0.064 0.004 0.004
#> GSM121213 1 0.0935 0.8597 0.964 0.000 0.032 0.004 0.000 0.000
#> GSM121214 1 0.2196 0.8297 0.884 0.000 0.004 0.108 0.000 0.004
#> GSM121215 1 0.1462 0.8501 0.936 0.000 0.056 0.008 0.000 0.000
#> GSM121216 1 0.2318 0.8505 0.904 0.000 0.048 0.028 0.020 0.000
#> GSM121217 1 0.1124 0.8598 0.956 0.000 0.036 0.008 0.000 0.000
#> GSM121218 1 0.2253 0.8459 0.896 0.000 0.012 0.084 0.004 0.004
#> GSM121234 1 0.2262 0.8308 0.896 0.000 0.080 0.016 0.008 0.000
#> GSM121243 1 0.1977 0.8468 0.920 0.000 0.008 0.032 0.040 0.000
#> GSM121245 1 0.3155 0.7956 0.828 0.000 0.004 0.132 0.036 0.000
#> GSM121246 1 0.2810 0.7574 0.832 0.000 0.156 0.008 0.004 0.000
#> GSM121247 1 0.5719 0.4421 0.552 0.000 0.012 0.320 0.108 0.008
#> GSM121248 1 0.1267 0.8537 0.940 0.000 0.000 0.060 0.000 0.000
#> GSM120744 6 0.1168 0.7357 0.000 0.000 0.028 0.000 0.016 0.956
#> GSM120745 6 0.1245 0.7352 0.000 0.000 0.032 0.000 0.016 0.952
#> GSM120746 6 0.1010 0.7377 0.000 0.000 0.036 0.000 0.004 0.960
#> GSM120747 6 0.1910 0.7145 0.000 0.000 0.108 0.000 0.000 0.892
#> GSM120748 6 0.1707 0.7336 0.000 0.012 0.056 0.000 0.004 0.928
#> GSM120749 6 0.1411 0.7341 0.000 0.000 0.060 0.000 0.004 0.936
#> GSM120750 6 0.1418 0.7338 0.000 0.000 0.032 0.000 0.024 0.944
#> GSM120751 6 0.0790 0.7376 0.000 0.000 0.032 0.000 0.000 0.968
#> GSM120752 6 0.0914 0.7324 0.000 0.000 0.016 0.000 0.016 0.968
#> GSM121336 2 0.1836 0.6801 0.000 0.928 0.008 0.004 0.048 0.012
#> GSM121339 2 0.5394 0.5315 0.040 0.732 0.036 0.024 0.048 0.120
#> GSM121349 2 0.1718 0.6815 0.000 0.932 0.016 0.000 0.044 0.008
#> GSM121355 2 0.2158 0.6770 0.000 0.912 0.016 0.004 0.056 0.012
#> GSM120757 5 0.5663 0.5870 0.012 0.000 0.012 0.144 0.616 0.216
#> GSM120766 5 0.4131 0.6620 0.000 0.000 0.032 0.044 0.768 0.156
#> GSM120770 5 0.6559 0.1766 0.000 0.184 0.032 0.004 0.424 0.356
#> GSM120779 5 0.4816 0.6715 0.020 0.000 0.004 0.132 0.720 0.124
#> GSM120780 5 0.4150 0.6446 0.000 0.004 0.084 0.004 0.760 0.148
#> GSM121102 6 0.6239 -0.1457 0.000 0.164 0.024 0.000 0.400 0.412
#> GSM121203 6 0.4561 0.1813 0.000 0.000 0.040 0.000 0.392 0.568
#> GSM121204 6 0.6102 0.1223 0.036 0.000 0.008 0.100 0.332 0.524
#> GSM121330 3 0.3660 0.7318 0.188 0.000 0.772 0.000 0.004 0.036
#> GSM121335 3 0.4431 0.6633 0.248 0.000 0.688 0.000 0.004 0.060
#> GSM121337 3 0.6432 0.3582 0.004 0.096 0.576 0.144 0.180 0.000
#> GSM121338 3 0.4638 0.5875 0.000 0.064 0.744 0.008 0.152 0.032
#> GSM121341 3 0.3853 0.7224 0.196 0.000 0.756 0.000 0.004 0.044
#> GSM121342 3 0.4481 0.5444 0.336 0.000 0.628 0.004 0.004 0.028
#> GSM121343 3 0.4491 0.4941 0.000 0.040 0.680 0.008 0.268 0.004
#> GSM121344 3 0.3640 0.7273 0.204 0.000 0.764 0.000 0.004 0.028
#> GSM121346 3 0.3265 0.7351 0.088 0.000 0.836 0.000 0.008 0.068
#> GSM121347 5 0.6065 0.4349 0.000 0.040 0.228 0.148 0.580 0.004
#> GSM121348 5 0.2421 0.6879 0.004 0.000 0.028 0.032 0.904 0.032
#> GSM121350 3 0.3033 0.7400 0.136 0.000 0.836 0.004 0.004 0.020
#> GSM121352 3 0.3308 0.7339 0.088 0.000 0.836 0.000 0.012 0.064
#> GSM121354 3 0.3445 0.7391 0.156 0.000 0.796 0.000 0.000 0.048
#> GSM120753 2 0.3629 0.5299 0.000 0.712 0.012 0.276 0.000 0.000
#> GSM120761 2 0.3867 0.4352 0.000 0.660 0.012 0.328 0.000 0.000
#> GSM120768 2 0.4203 0.2603 0.000 0.596 0.008 0.388 0.000 0.008
#> GSM120781 2 0.3702 0.5341 0.000 0.720 0.012 0.264 0.004 0.000
#> GSM120788 4 0.2407 0.5461 0.036 0.012 0.000 0.904 0.040 0.008
#> GSM120760 2 0.4464 0.1875 0.004 0.560 0.004 0.416 0.016 0.000
#> GSM120763 4 0.4326 0.0379 0.000 0.484 0.008 0.500 0.008 0.000
#> GSM120764 4 0.3073 0.6467 0.004 0.140 0.004 0.832 0.020 0.000
#> GSM120777 4 0.3409 0.4626 0.048 0.004 0.008 0.844 0.084 0.012
#> GSM120786 4 0.3673 0.6344 0.004 0.196 0.008 0.772 0.020 0.000
#> GSM121329 4 0.5761 0.0942 0.300 0.000 0.144 0.544 0.008 0.004
#> GSM121331 5 0.4471 0.6880 0.036 0.000 0.004 0.144 0.756 0.060
#> GSM121333 5 0.5556 0.5627 0.040 0.000 0.004 0.296 0.596 0.064
#> GSM121345 4 0.5260 0.0736 0.056 0.000 0.012 0.640 0.268 0.024
#> GSM121356 5 0.4084 0.6961 0.016 0.000 0.008 0.132 0.784 0.060
#> GSM120754 4 0.5987 0.3893 0.000 0.360 0.000 0.504 0.048 0.088
#> GSM120759 2 0.4565 0.5928 0.000 0.728 0.040 0.028 0.196 0.008
#> GSM120762 2 0.2631 0.6634 0.000 0.860 0.008 0.124 0.004 0.004
#> GSM120775 4 0.3630 0.6394 0.008 0.164 0.004 0.796 0.004 0.024
#> GSM120776 6 0.5964 0.5237 0.048 0.028 0.020 0.120 0.100 0.684
#> GSM120782 6 0.5570 0.3659 0.000 0.296 0.024 0.088 0.004 0.588
#> GSM120789 2 0.3568 0.6429 0.000 0.788 0.032 0.172 0.008 0.000
#> GSM120790 5 0.3875 0.5870 0.000 0.128 0.028 0.024 0.804 0.016
#> GSM120791 4 0.4625 0.2538 0.000 0.424 0.032 0.540 0.004 0.000
#> GSM120755 2 0.2320 0.6609 0.000 0.864 0.004 0.132 0.000 0.000
#> GSM120756 4 0.2594 0.5506 0.068 0.012 0.008 0.892 0.016 0.004
#> GSM120769 2 0.3023 0.5977 0.000 0.784 0.004 0.212 0.000 0.000
#> GSM120778 2 0.3905 0.3733 0.000 0.636 0.000 0.356 0.004 0.004
#> GSM120792 2 0.4008 0.4576 0.000 0.672 0.000 0.308 0.004 0.016
#> GSM121332 2 0.2972 0.6815 0.000 0.852 0.016 0.108 0.024 0.000
#> GSM121334 2 0.2529 0.6831 0.000 0.884 0.008 0.088 0.012 0.008
#> GSM121340 4 0.4689 0.5508 0.024 0.296 0.012 0.656 0.008 0.004
#> GSM121351 2 0.4405 0.5737 0.000 0.728 0.032 0.012 0.212 0.016
#> GSM121353 4 0.4771 0.5356 0.032 0.304 0.008 0.644 0.004 0.008
#> GSM120758 2 0.3200 0.6157 0.000 0.788 0.016 0.196 0.000 0.000
#> GSM120771 2 0.2645 0.6898 0.000 0.880 0.008 0.056 0.056 0.000
#> GSM120772 2 0.3565 0.4823 0.000 0.692 0.004 0.304 0.000 0.000
#> GSM120773 4 0.4499 0.4738 0.000 0.344 0.012 0.624 0.012 0.008
#> GSM120774 2 0.4164 0.4556 0.000 0.668 0.008 0.308 0.004 0.012
#> GSM120783 4 0.3985 0.5564 0.004 0.292 0.012 0.688 0.000 0.004
#> GSM120787 2 0.3381 0.6475 0.000 0.816 0.008 0.144 0.004 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)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> MAD:NMF 114 2.75e-09 2
#> MAD:NMF 99 8.65e-13 3
#> MAD:NMF 77 2.66e-14 4
#> MAD:NMF 69 1.15e-15 5
#> MAD:NMF 88 5.75e-29 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 21512 rows and 119 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.513 0.799 0.906 0.4789 0.498 0.498
#> 3 3 0.645 0.678 0.860 0.3471 0.774 0.573
#> 4 4 0.602 0.599 0.779 0.1002 0.805 0.513
#> 5 5 0.590 0.641 0.733 0.0401 0.901 0.671
#> 6 6 0.676 0.643 0.789 0.0568 0.923 0.712
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
#> GSM120719 1 0.0000 0.9249 1.000 0.000
#> GSM120720 1 0.0000 0.9249 1.000 0.000
#> GSM120765 2 0.0000 0.8512 0.000 1.000
#> GSM120767 2 0.0000 0.8512 0.000 1.000
#> GSM120784 2 0.0000 0.8512 0.000 1.000
#> GSM121400 2 0.9754 0.4683 0.408 0.592
#> GSM121401 1 0.9866 0.0983 0.568 0.432
#> GSM121402 2 0.0376 0.8506 0.004 0.996
#> GSM121403 2 0.9552 0.5419 0.376 0.624
#> GSM121404 2 0.7950 0.7120 0.240 0.760
#> GSM121405 1 0.9866 0.0983 0.568 0.432
#> GSM121406 2 0.0000 0.8512 0.000 1.000
#> GSM121408 2 0.0000 0.8512 0.000 1.000
#> GSM121409 2 0.9754 0.4683 0.408 0.592
#> GSM121410 2 0.9552 0.5419 0.376 0.624
#> GSM121412 2 0.0000 0.8512 0.000 1.000
#> GSM121413 2 0.0000 0.8512 0.000 1.000
#> GSM121414 2 0.0000 0.8512 0.000 1.000
#> GSM121415 2 0.0000 0.8512 0.000 1.000
#> GSM121416 2 0.2236 0.8418 0.036 0.964
#> GSM120591 1 0.0000 0.9249 1.000 0.000
#> GSM120594 1 0.0000 0.9249 1.000 0.000
#> GSM120718 1 0.0000 0.9249 1.000 0.000
#> GSM121205 1 0.0000 0.9249 1.000 0.000
#> GSM121206 1 0.0000 0.9249 1.000 0.000
#> GSM121207 1 0.0000 0.9249 1.000 0.000
#> GSM121208 1 0.0000 0.9249 1.000 0.000
#> GSM121209 1 0.0000 0.9249 1.000 0.000
#> GSM121210 1 0.0000 0.9249 1.000 0.000
#> GSM121211 1 0.0000 0.9249 1.000 0.000
#> GSM121212 1 0.0000 0.9249 1.000 0.000
#> GSM121213 1 0.0000 0.9249 1.000 0.000
#> GSM121214 1 0.0000 0.9249 1.000 0.000
#> GSM121215 1 0.0000 0.9249 1.000 0.000
#> GSM121216 1 0.0000 0.9249 1.000 0.000
#> GSM121217 1 0.0000 0.9249 1.000 0.000
#> GSM121218 1 0.0000 0.9249 1.000 0.000
#> GSM121234 1 0.0000 0.9249 1.000 0.000
#> GSM121243 1 0.0000 0.9249 1.000 0.000
#> GSM121245 1 0.0000 0.9249 1.000 0.000
#> GSM121246 1 0.0000 0.9249 1.000 0.000
#> GSM121247 1 0.0000 0.9249 1.000 0.000
#> GSM121248 1 0.0000 0.9249 1.000 0.000
#> GSM120744 1 0.6973 0.7415 0.812 0.188
#> GSM120745 1 0.4690 0.8533 0.900 0.100
#> GSM120746 1 0.4939 0.8457 0.892 0.108
#> GSM120747 1 0.9580 0.2929 0.620 0.380
#> GSM120748 1 0.9580 0.2929 0.620 0.380
#> GSM120749 1 0.4939 0.8457 0.892 0.108
#> GSM120750 1 0.4939 0.8457 0.892 0.108
#> GSM120751 1 0.4939 0.8457 0.892 0.108
#> GSM120752 1 0.4690 0.8533 0.900 0.100
#> GSM121336 2 0.0000 0.8512 0.000 1.000
#> GSM121339 2 0.9522 0.5485 0.372 0.628
#> GSM121349 2 0.0000 0.8512 0.000 1.000
#> GSM121355 2 0.0000 0.8512 0.000 1.000
#> GSM120757 1 0.4022 0.8701 0.920 0.080
#> GSM120766 1 0.7056 0.7353 0.808 0.192
#> GSM120770 2 0.9393 0.5729 0.356 0.644
#> GSM120779 1 0.0000 0.9249 1.000 0.000
#> GSM120780 1 0.7056 0.7353 0.808 0.192
#> GSM121102 2 0.9393 0.5717 0.356 0.644
#> GSM121203 1 0.8016 0.6423 0.756 0.244
#> GSM121204 1 0.0000 0.9249 1.000 0.000
#> GSM121330 1 0.1633 0.9112 0.976 0.024
#> GSM121335 1 0.0000 0.9249 1.000 0.000
#> GSM121337 2 0.7528 0.7302 0.216 0.784
#> GSM121338 2 0.8016 0.7064 0.244 0.756
#> GSM121341 1 0.0000 0.9249 1.000 0.000
#> GSM121342 1 0.0000 0.9249 1.000 0.000
#> GSM121343 2 0.8016 0.7064 0.244 0.756
#> GSM121344 1 0.0000 0.9249 1.000 0.000
#> GSM121346 1 0.0376 0.9230 0.996 0.004
#> GSM121347 2 0.8016 0.7064 0.244 0.756
#> GSM121348 2 0.2423 0.8406 0.040 0.960
#> GSM121350 1 0.0938 0.9185 0.988 0.012
#> GSM121352 1 0.0000 0.9249 1.000 0.000
#> GSM121354 1 0.0376 0.9230 0.996 0.004
#> GSM120753 2 0.0000 0.8512 0.000 1.000
#> GSM120761 2 0.0000 0.8512 0.000 1.000
#> GSM120768 2 0.0000 0.8512 0.000 1.000
#> GSM120781 2 0.0000 0.8512 0.000 1.000
#> GSM120788 2 0.9580 0.5338 0.380 0.620
#> GSM120760 2 0.0000 0.8512 0.000 1.000
#> GSM120763 2 0.0000 0.8512 0.000 1.000
#> GSM120764 2 0.9552 0.5419 0.376 0.624
#> GSM120777 2 0.9552 0.5419 0.376 0.624
#> GSM120786 2 0.4815 0.8113 0.104 0.896
#> GSM121329 1 0.5059 0.8420 0.888 0.112
#> GSM121331 1 0.0000 0.9249 1.000 0.000
#> GSM121333 1 0.0000 0.9249 1.000 0.000
#> GSM121345 1 0.0000 0.9249 1.000 0.000
#> GSM121356 1 0.0000 0.9249 1.000 0.000
#> GSM120754 2 0.9552 0.5419 0.376 0.624
#> GSM120759 2 0.0000 0.8512 0.000 1.000
#> GSM120762 2 0.0000 0.8512 0.000 1.000
#> GSM120775 2 0.9552 0.5419 0.376 0.624
#> GSM120776 2 0.9580 0.5338 0.380 0.620
#> GSM120782 2 0.9552 0.5419 0.376 0.624
#> GSM120789 2 0.0000 0.8512 0.000 1.000
#> GSM120790 2 0.0000 0.8512 0.000 1.000
#> GSM120791 2 0.1633 0.8455 0.024 0.976
#> GSM120755 2 0.0000 0.8512 0.000 1.000
#> GSM120756 2 0.9580 0.5338 0.380 0.620
#> GSM120769 2 0.0000 0.8512 0.000 1.000
#> GSM120778 2 0.0000 0.8512 0.000 1.000
#> GSM120792 2 0.4815 0.8113 0.104 0.896
#> GSM121332 2 0.0672 0.8495 0.008 0.992
#> GSM121334 2 0.0000 0.8512 0.000 1.000
#> GSM121340 2 0.9358 0.5785 0.352 0.648
#> GSM121351 2 0.0000 0.8512 0.000 1.000
#> GSM121353 2 0.3733 0.8280 0.072 0.928
#> GSM120758 2 0.0000 0.8512 0.000 1.000
#> GSM120771 2 0.0000 0.8512 0.000 1.000
#> GSM120772 2 0.0376 0.8506 0.004 0.996
#> GSM120773 2 0.5519 0.7979 0.128 0.872
#> GSM120774 2 0.0376 0.8506 0.004 0.996
#> GSM120783 2 0.5519 0.7979 0.128 0.872
#> GSM120787 2 0.0000 0.8512 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM120719 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM120720 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM120765 2 0.2261 0.7518 0.000 0.932 0.068
#> GSM120767 2 0.2959 0.7520 0.000 0.900 0.100
#> GSM120784 2 0.2959 0.7520 0.000 0.900 0.100
#> GSM121400 3 0.1289 0.7112 0.032 0.000 0.968
#> GSM121401 3 0.4702 0.6048 0.212 0.000 0.788
#> GSM121402 2 0.6225 0.5133 0.000 0.568 0.432
#> GSM121403 3 0.0000 0.7201 0.000 0.000 1.000
#> GSM121404 3 0.4121 0.5888 0.000 0.168 0.832
#> GSM121405 3 0.4702 0.6048 0.212 0.000 0.788
#> GSM121406 2 0.0000 0.7460 0.000 1.000 0.000
#> GSM121408 2 0.4291 0.7373 0.000 0.820 0.180
#> GSM121409 3 0.1289 0.7118 0.032 0.000 0.968
#> GSM121410 3 0.0000 0.7201 0.000 0.000 1.000
#> GSM121412 2 0.0000 0.7460 0.000 1.000 0.000
#> GSM121413 2 0.0000 0.7460 0.000 1.000 0.000
#> GSM121414 2 0.0000 0.7460 0.000 1.000 0.000
#> GSM121415 2 0.6140 0.5538 0.000 0.596 0.404
#> GSM121416 3 0.6095 -0.0260 0.000 0.392 0.608
#> GSM120591 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM120594 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM120718 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM121205 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM121206 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM121207 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM121208 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM121209 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM121210 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM121211 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM121212 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM121213 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM121214 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM121215 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM121216 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM121217 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM121218 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM121234 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM121243 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM121245 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM121246 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM121247 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM121248 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM120744 3 0.6274 0.0387 0.456 0.000 0.544
#> GSM120745 1 0.6274 0.2306 0.544 0.000 0.456
#> GSM120746 1 0.6286 0.2064 0.536 0.000 0.464
#> GSM120747 3 0.5216 0.5465 0.260 0.000 0.740
#> GSM120748 3 0.5216 0.5465 0.260 0.000 0.740
#> GSM120749 1 0.6286 0.2064 0.536 0.000 0.464
#> GSM120750 1 0.6286 0.2064 0.536 0.000 0.464
#> GSM120751 1 0.6286 0.2064 0.536 0.000 0.464
#> GSM120752 1 0.6274 0.2306 0.544 0.000 0.456
#> GSM121336 2 0.0000 0.7460 0.000 1.000 0.000
#> GSM121339 3 0.0237 0.7190 0.000 0.004 0.996
#> GSM121349 2 0.0000 0.7460 0.000 1.000 0.000
#> GSM121355 2 0.0000 0.7460 0.000 1.000 0.000
#> GSM120757 1 0.6079 0.3883 0.612 0.000 0.388
#> GSM120766 3 0.6267 0.0552 0.452 0.000 0.548
#> GSM120770 3 0.1289 0.7084 0.000 0.032 0.968
#> GSM120779 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM120780 3 0.6267 0.0552 0.452 0.000 0.548
#> GSM121102 3 0.1860 0.6999 0.000 0.052 0.948
#> GSM121203 3 0.6126 0.2221 0.400 0.000 0.600
#> GSM121204 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM121330 1 0.2537 0.8475 0.920 0.000 0.080
#> GSM121335 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM121337 3 0.5098 0.4776 0.000 0.248 0.752
#> GSM121338 3 0.3752 0.6159 0.000 0.144 0.856
#> GSM121341 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM121342 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM121343 3 0.3752 0.6159 0.000 0.144 0.856
#> GSM121344 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM121346 1 0.0237 0.9124 0.996 0.000 0.004
#> GSM121347 3 0.3752 0.6159 0.000 0.144 0.856
#> GSM121348 2 0.4504 0.6905 0.000 0.804 0.196
#> GSM121350 1 0.1031 0.8969 0.976 0.000 0.024
#> GSM121352 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM121354 1 0.0237 0.9124 0.996 0.000 0.004
#> GSM120753 2 0.6045 0.6252 0.000 0.620 0.380
#> GSM120761 2 0.5835 0.6662 0.000 0.660 0.340
#> GSM120768 2 0.6062 0.6200 0.000 0.616 0.384
#> GSM120781 2 0.0424 0.7471 0.000 0.992 0.008
#> GSM120788 3 0.0237 0.7201 0.004 0.000 0.996
#> GSM120760 2 0.6126 0.5943 0.000 0.600 0.400
#> GSM120763 2 0.6126 0.5943 0.000 0.600 0.400
#> GSM120764 3 0.0000 0.7201 0.000 0.000 1.000
#> GSM120777 3 0.0000 0.7201 0.000 0.000 1.000
#> GSM120786 3 0.5988 0.0996 0.000 0.368 0.632
#> GSM121329 1 0.4887 0.6695 0.772 0.000 0.228
#> GSM121331 1 0.0237 0.9126 0.996 0.000 0.004
#> GSM121333 1 0.0000 0.9149 1.000 0.000 0.000
#> GSM121345 1 0.0237 0.9126 0.996 0.000 0.004
#> GSM121356 1 0.0237 0.9126 0.996 0.000 0.004
#> GSM120754 3 0.0000 0.7201 0.000 0.000 1.000
#> GSM120759 2 0.0000 0.7460 0.000 1.000 0.000
#> GSM120762 2 0.0000 0.7460 0.000 1.000 0.000
#> GSM120775 3 0.0000 0.7201 0.000 0.000 1.000
#> GSM120776 3 0.0237 0.7201 0.004 0.000 0.996
#> GSM120782 3 0.0000 0.7201 0.000 0.000 1.000
#> GSM120789 2 0.5497 0.6980 0.000 0.708 0.292
#> GSM120790 2 0.0000 0.7460 0.000 1.000 0.000
#> GSM120791 3 0.6154 -0.0925 0.000 0.408 0.592
#> GSM120755 2 0.5591 0.6934 0.000 0.696 0.304
#> GSM120756 3 0.0237 0.7201 0.004 0.000 0.996
#> GSM120769 2 0.0000 0.7460 0.000 1.000 0.000
#> GSM120778 2 0.5706 0.6844 0.000 0.680 0.320
#> GSM120792 3 0.5968 0.1158 0.000 0.364 0.636
#> GSM121332 2 0.5905 0.6370 0.000 0.648 0.352
#> GSM121334 2 0.5785 0.6736 0.000 0.668 0.332
#> GSM121340 3 0.1529 0.7045 0.000 0.040 0.960
#> GSM121351 2 0.0000 0.7460 0.000 1.000 0.000
#> GSM121353 3 0.5733 0.2131 0.000 0.324 0.676
#> GSM120758 2 0.6045 0.6252 0.000 0.620 0.380
#> GSM120771 2 0.5591 0.6927 0.000 0.696 0.304
#> GSM120772 2 0.6045 0.6263 0.000 0.620 0.380
#> GSM120773 3 0.5733 0.2496 0.000 0.324 0.676
#> GSM120774 2 0.6267 0.4879 0.000 0.548 0.452
#> GSM120783 3 0.5733 0.2496 0.000 0.324 0.676
#> GSM120787 2 0.6192 0.5583 0.000 0.580 0.420
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM120719 1 0.2281 0.90934 0.904 0.000 0.096 0.000
#> GSM120720 1 0.2281 0.90934 0.904 0.000 0.096 0.000
#> GSM120765 2 0.4564 0.62022 0.000 0.672 0.000 0.328
#> GSM120767 2 0.4746 0.55819 0.000 0.632 0.000 0.368
#> GSM120784 2 0.4746 0.55819 0.000 0.632 0.000 0.368
#> GSM121400 3 0.4776 0.23613 0.000 0.000 0.624 0.376
#> GSM121401 3 0.5147 0.51451 0.060 0.000 0.740 0.200
#> GSM121402 4 0.3925 0.48869 0.000 0.176 0.016 0.808
#> GSM121403 3 0.4877 0.16341 0.000 0.000 0.592 0.408
#> GSM121404 4 0.4883 0.40916 0.000 0.016 0.288 0.696
#> GSM121405 3 0.5147 0.51451 0.060 0.000 0.740 0.200
#> GSM121406 2 0.0188 0.80822 0.000 0.996 0.000 0.004
#> GSM121408 2 0.5183 0.43147 0.000 0.584 0.008 0.408
#> GSM121409 3 0.4761 0.23752 0.000 0.000 0.628 0.372
#> GSM121410 3 0.4877 0.16341 0.000 0.000 0.592 0.408
#> GSM121412 2 0.0188 0.80822 0.000 0.996 0.000 0.004
#> GSM121413 2 0.0188 0.80822 0.000 0.996 0.000 0.004
#> GSM121414 2 0.0188 0.80822 0.000 0.996 0.000 0.004
#> GSM121415 4 0.4214 0.44481 0.000 0.204 0.016 0.780
#> GSM121416 4 0.2256 0.58510 0.000 0.020 0.056 0.924
#> GSM120591 1 0.2281 0.90934 0.904 0.000 0.096 0.000
#> GSM120594 1 0.2281 0.90934 0.904 0.000 0.096 0.000
#> GSM120718 1 0.2281 0.90934 0.904 0.000 0.096 0.000
#> GSM121205 1 0.0000 0.92557 1.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.92557 1.000 0.000 0.000 0.000
#> GSM121207 1 0.0000 0.92557 1.000 0.000 0.000 0.000
#> GSM121208 1 0.0188 0.92566 0.996 0.000 0.004 0.000
#> GSM121209 1 0.0000 0.92557 1.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.92557 1.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.92557 1.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.92557 1.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.92557 1.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.92557 1.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.92557 1.000 0.000 0.000 0.000
#> GSM121216 1 0.1022 0.92232 0.968 0.000 0.032 0.000
#> GSM121217 1 0.0000 0.92557 1.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.92557 1.000 0.000 0.000 0.000
#> GSM121234 1 0.0000 0.92557 1.000 0.000 0.000 0.000
#> GSM121243 1 0.0000 0.92557 1.000 0.000 0.000 0.000
#> GSM121245 1 0.0000 0.92557 1.000 0.000 0.000 0.000
#> GSM121246 1 0.0000 0.92557 1.000 0.000 0.000 0.000
#> GSM121247 1 0.0000 0.92557 1.000 0.000 0.000 0.000
#> GSM121248 1 0.0000 0.92557 1.000 0.000 0.000 0.000
#> GSM120744 3 0.4262 0.63634 0.236 0.000 0.756 0.008
#> GSM120745 3 0.4564 0.54024 0.328 0.000 0.672 0.000
#> GSM120746 3 0.4500 0.55826 0.316 0.000 0.684 0.000
#> GSM120747 3 0.5186 0.55640 0.084 0.000 0.752 0.164
#> GSM120748 3 0.5186 0.55640 0.084 0.000 0.752 0.164
#> GSM120749 3 0.4500 0.55826 0.316 0.000 0.684 0.000
#> GSM120750 3 0.4500 0.55826 0.316 0.000 0.684 0.000
#> GSM120751 3 0.4500 0.55826 0.316 0.000 0.684 0.000
#> GSM120752 3 0.4564 0.54024 0.328 0.000 0.672 0.000
#> GSM121336 2 0.0000 0.80678 0.000 1.000 0.000 0.000
#> GSM121339 3 0.4977 0.06345 0.000 0.000 0.540 0.460
#> GSM121349 2 0.0000 0.80678 0.000 1.000 0.000 0.000
#> GSM121355 2 0.0000 0.80678 0.000 1.000 0.000 0.000
#> GSM120757 3 0.4961 0.28581 0.448 0.000 0.552 0.000
#> GSM120766 3 0.4228 0.63915 0.232 0.000 0.760 0.008
#> GSM120770 3 0.4998 -0.00931 0.000 0.000 0.512 0.488
#> GSM120779 1 0.0469 0.92489 0.988 0.000 0.012 0.000
#> GSM120780 3 0.4228 0.63915 0.232 0.000 0.760 0.008
#> GSM121102 3 0.5760 0.00458 0.000 0.028 0.524 0.448
#> GSM121203 3 0.4761 0.62859 0.192 0.000 0.764 0.044
#> GSM121204 1 0.0817 0.92443 0.976 0.000 0.024 0.000
#> GSM121330 1 0.3610 0.80517 0.800 0.000 0.200 0.000
#> GSM121335 1 0.2469 0.90410 0.892 0.000 0.108 0.000
#> GSM121337 4 0.6217 0.42458 0.000 0.084 0.292 0.624
#> GSM121338 4 0.4661 0.30361 0.000 0.000 0.348 0.652
#> GSM121341 1 0.2469 0.90410 0.892 0.000 0.108 0.000
#> GSM121342 1 0.2469 0.90410 0.892 0.000 0.108 0.000
#> GSM121343 4 0.4661 0.30361 0.000 0.000 0.348 0.652
#> GSM121344 1 0.2469 0.90410 0.892 0.000 0.108 0.000
#> GSM121346 1 0.2530 0.90183 0.888 0.000 0.112 0.000
#> GSM121347 4 0.4661 0.30361 0.000 0.000 0.348 0.652
#> GSM121348 4 0.7497 -0.30344 0.000 0.396 0.180 0.424
#> GSM121350 1 0.2921 0.88158 0.860 0.000 0.140 0.000
#> GSM121352 1 0.2530 0.90231 0.888 0.000 0.112 0.000
#> GSM121354 1 0.2530 0.90183 0.888 0.000 0.112 0.000
#> GSM120753 4 0.4040 0.42779 0.000 0.248 0.000 0.752
#> GSM120761 4 0.4356 0.35607 0.000 0.292 0.000 0.708
#> GSM120768 4 0.4008 0.43363 0.000 0.244 0.000 0.756
#> GSM120781 2 0.3123 0.77010 0.000 0.844 0.000 0.156
#> GSM120788 4 0.4994 0.09775 0.000 0.000 0.480 0.520
#> GSM120760 4 0.3726 0.46246 0.000 0.212 0.000 0.788
#> GSM120763 4 0.3726 0.46246 0.000 0.212 0.000 0.788
#> GSM120764 4 0.4992 0.10798 0.000 0.000 0.476 0.524
#> GSM120777 4 0.4992 0.10798 0.000 0.000 0.476 0.524
#> GSM120786 4 0.4114 0.59126 0.000 0.060 0.112 0.828
#> GSM121329 1 0.5398 0.35824 0.580 0.000 0.404 0.016
#> GSM121331 1 0.3266 0.84964 0.832 0.000 0.168 0.000
#> GSM121333 1 0.2973 0.86593 0.856 0.000 0.144 0.000
#> GSM121345 1 0.3266 0.84964 0.832 0.000 0.168 0.000
#> GSM121356 1 0.3266 0.84964 0.832 0.000 0.168 0.000
#> GSM120754 4 0.4994 0.10229 0.000 0.000 0.480 0.520
#> GSM120759 2 0.0707 0.80697 0.000 0.980 0.000 0.020
#> GSM120762 2 0.3486 0.75172 0.000 0.812 0.000 0.188
#> GSM120775 4 0.4994 0.10229 0.000 0.000 0.480 0.520
#> GSM120776 4 0.4996 0.09137 0.000 0.000 0.484 0.516
#> GSM120782 4 0.4994 0.10229 0.000 0.000 0.480 0.520
#> GSM120789 2 0.4948 0.23397 0.000 0.560 0.000 0.440
#> GSM120790 2 0.6747 0.60347 0.000 0.596 0.140 0.264
#> GSM120791 4 0.1406 0.57896 0.000 0.016 0.024 0.960
#> GSM120755 4 0.4624 0.25227 0.000 0.340 0.000 0.660
#> GSM120756 4 0.4994 0.09775 0.000 0.000 0.480 0.520
#> GSM120769 2 0.2973 0.77482 0.000 0.856 0.000 0.144
#> GSM120778 4 0.4679 0.23574 0.000 0.352 0.000 0.648
#> GSM120792 4 0.4171 0.58928 0.000 0.060 0.116 0.824
#> GSM121332 4 0.4883 0.33514 0.000 0.288 0.016 0.696
#> GSM121334 4 0.4382 0.33806 0.000 0.296 0.000 0.704
#> GSM121340 4 0.4898 0.19801 0.000 0.000 0.416 0.584
#> GSM121351 2 0.0188 0.80772 0.000 0.996 0.000 0.004
#> GSM121353 4 0.2704 0.56090 0.000 0.000 0.124 0.876
#> GSM120758 4 0.4040 0.42779 0.000 0.248 0.000 0.752
#> GSM120771 4 0.4564 0.27131 0.000 0.328 0.000 0.672
#> GSM120772 4 0.4103 0.42066 0.000 0.256 0.000 0.744
#> GSM120773 4 0.3803 0.56707 0.000 0.032 0.132 0.836
#> GSM120774 4 0.3024 0.51422 0.000 0.148 0.000 0.852
#> GSM120783 4 0.3803 0.56707 0.000 0.032 0.132 0.836
#> GSM120787 4 0.3486 0.48329 0.000 0.188 0.000 0.812
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM120719 1 0.2516 0.8585 0.860 0.000 0.140 0.000 0.000
#> GSM120720 1 0.2516 0.8585 0.860 0.000 0.140 0.000 0.000
#> GSM120765 2 0.4030 0.4842 0.000 0.648 0.000 0.352 0.000
#> GSM120767 2 0.4171 0.3901 0.000 0.604 0.000 0.396 0.000
#> GSM120784 2 0.4171 0.3901 0.000 0.604 0.000 0.396 0.000
#> GSM121400 5 0.4401 0.5801 0.000 0.000 0.104 0.132 0.764
#> GSM121401 5 0.6127 0.1896 0.040 0.000 0.292 0.072 0.596
#> GSM121402 4 0.4961 0.7494 0.000 0.140 0.004 0.724 0.132
#> GSM121403 5 0.4010 0.6046 0.000 0.000 0.072 0.136 0.792
#> GSM121404 5 0.4731 0.1645 0.000 0.000 0.016 0.456 0.528
#> GSM121405 5 0.6127 0.1896 0.040 0.000 0.292 0.072 0.596
#> GSM121406 2 0.0162 0.7918 0.000 0.996 0.000 0.004 0.000
#> GSM121408 2 0.5204 0.2137 0.000 0.560 0.000 0.392 0.048
#> GSM121409 5 0.4364 0.5765 0.000 0.000 0.112 0.120 0.768
#> GSM121410 5 0.4010 0.6046 0.000 0.000 0.072 0.136 0.792
#> GSM121412 2 0.0404 0.7918 0.000 0.988 0.000 0.012 0.000
#> GSM121413 2 0.0162 0.7918 0.000 0.996 0.000 0.004 0.000
#> GSM121414 2 0.0404 0.7918 0.000 0.988 0.000 0.012 0.000
#> GSM121415 4 0.5009 0.7136 0.000 0.168 0.004 0.716 0.112
#> GSM121416 4 0.3969 0.6015 0.000 0.000 0.004 0.692 0.304
#> GSM120591 1 0.2516 0.8585 0.860 0.000 0.140 0.000 0.000
#> GSM120594 1 0.2516 0.8585 0.860 0.000 0.140 0.000 0.000
#> GSM120718 1 0.2516 0.8585 0.860 0.000 0.140 0.000 0.000
#> GSM121205 1 0.0000 0.8839 1.000 0.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.8839 1.000 0.000 0.000 0.000 0.000
#> GSM121207 1 0.0000 0.8839 1.000 0.000 0.000 0.000 0.000
#> GSM121208 1 0.0609 0.8837 0.980 0.000 0.020 0.000 0.000
#> GSM121209 1 0.0000 0.8839 1.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.8839 1.000 0.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.8839 1.000 0.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.8839 1.000 0.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.8839 1.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.8839 1.000 0.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.8839 1.000 0.000 0.000 0.000 0.000
#> GSM121216 1 0.1043 0.8812 0.960 0.000 0.040 0.000 0.000
#> GSM121217 1 0.0000 0.8839 1.000 0.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.8839 1.000 0.000 0.000 0.000 0.000
#> GSM121234 1 0.0000 0.8839 1.000 0.000 0.000 0.000 0.000
#> GSM121243 1 0.0162 0.8842 0.996 0.000 0.004 0.000 0.000
#> GSM121245 1 0.0000 0.8839 1.000 0.000 0.000 0.000 0.000
#> GSM121246 1 0.0162 0.8839 0.996 0.000 0.004 0.000 0.000
#> GSM121247 1 0.0000 0.8839 1.000 0.000 0.000 0.000 0.000
#> GSM121248 1 0.0000 0.8839 1.000 0.000 0.000 0.000 0.000
#> GSM120744 3 0.6068 0.5908 0.120 0.000 0.452 0.000 0.428
#> GSM120745 3 0.6586 0.6677 0.208 0.000 0.408 0.000 0.384
#> GSM120746 3 0.6532 0.6771 0.196 0.000 0.420 0.000 0.384
#> GSM120747 5 0.6145 0.0313 0.036 0.000 0.344 0.064 0.556
#> GSM120748 5 0.6145 0.0313 0.036 0.000 0.344 0.064 0.556
#> GSM120749 3 0.6532 0.6771 0.196 0.000 0.420 0.000 0.384
#> GSM120750 3 0.6532 0.6771 0.196 0.000 0.420 0.000 0.384
#> GSM120751 3 0.6532 0.6771 0.196 0.000 0.420 0.000 0.384
#> GSM120752 3 0.6586 0.6677 0.208 0.000 0.408 0.000 0.384
#> GSM121336 2 0.0000 0.7903 0.000 1.000 0.000 0.000 0.000
#> GSM121339 5 0.3370 0.6361 0.000 0.000 0.028 0.148 0.824
#> GSM121349 2 0.0000 0.7903 0.000 1.000 0.000 0.000 0.000
#> GSM121355 2 0.0162 0.7908 0.000 0.996 0.000 0.004 0.000
#> GSM120757 5 0.6955 -0.5611 0.324 0.000 0.320 0.004 0.352
#> GSM120766 3 0.6036 0.5839 0.116 0.000 0.452 0.000 0.432
#> GSM120770 5 0.3829 0.6261 0.000 0.000 0.028 0.196 0.776
#> GSM120779 1 0.1831 0.8536 0.920 0.000 0.076 0.004 0.000
#> GSM120780 3 0.6036 0.5839 0.116 0.000 0.452 0.000 0.432
#> GSM121102 5 0.4196 0.6191 0.000 0.016 0.024 0.192 0.768
#> GSM121203 5 0.6283 -0.5083 0.096 0.000 0.424 0.016 0.464
#> GSM121204 1 0.2068 0.8542 0.904 0.000 0.092 0.004 0.000
#> GSM121330 1 0.4298 0.7472 0.756 0.000 0.184 0.000 0.060
#> GSM121335 1 0.2648 0.8525 0.848 0.000 0.152 0.000 0.000
#> GSM121337 5 0.5539 0.1112 0.000 0.048 0.008 0.444 0.500
#> GSM121338 5 0.4298 0.4273 0.000 0.000 0.008 0.352 0.640
#> GSM121341 1 0.2648 0.8525 0.848 0.000 0.152 0.000 0.000
#> GSM121342 1 0.2648 0.8525 0.848 0.000 0.152 0.000 0.000
#> GSM121343 5 0.4298 0.4273 0.000 0.000 0.008 0.352 0.640
#> GSM121344 1 0.2648 0.8525 0.848 0.000 0.152 0.000 0.000
#> GSM121346 1 0.2806 0.8498 0.844 0.000 0.152 0.000 0.004
#> GSM121347 5 0.4298 0.4273 0.000 0.000 0.008 0.352 0.640
#> GSM121348 3 0.6933 -0.0815 0.000 0.052 0.524 0.296 0.128
#> GSM121350 1 0.3171 0.8279 0.816 0.000 0.176 0.000 0.008
#> GSM121352 1 0.2690 0.8504 0.844 0.000 0.156 0.000 0.000
#> GSM121354 1 0.2806 0.8498 0.844 0.000 0.152 0.000 0.004
#> GSM120753 4 0.5210 0.7573 0.000 0.200 0.000 0.680 0.120
#> GSM120761 4 0.5423 0.7213 0.000 0.244 0.000 0.644 0.112
#> GSM120768 4 0.5222 0.7602 0.000 0.196 0.000 0.680 0.124
#> GSM120781 2 0.2813 0.7256 0.000 0.832 0.000 0.168 0.000
#> GSM120788 5 0.2669 0.6270 0.000 0.000 0.020 0.104 0.876
#> GSM120760 4 0.4996 0.7714 0.000 0.164 0.000 0.708 0.128
#> GSM120763 4 0.4996 0.7714 0.000 0.164 0.000 0.708 0.128
#> GSM120764 5 0.2727 0.6314 0.000 0.000 0.016 0.116 0.868
#> GSM120777 5 0.2625 0.6280 0.000 0.000 0.016 0.108 0.876
#> GSM120786 4 0.4703 0.5777 0.000 0.028 0.000 0.632 0.340
#> GSM121329 1 0.6232 0.0845 0.492 0.000 0.356 0.000 0.152
#> GSM121331 1 0.3766 0.7094 0.728 0.000 0.268 0.004 0.000
#> GSM121333 1 0.3607 0.7309 0.752 0.000 0.244 0.004 0.000
#> GSM121345 1 0.3766 0.7094 0.728 0.000 0.268 0.004 0.000
#> GSM121356 1 0.3766 0.7094 0.728 0.000 0.268 0.004 0.000
#> GSM120754 5 0.2825 0.6370 0.000 0.000 0.016 0.124 0.860
#> GSM120759 2 0.0807 0.7898 0.000 0.976 0.012 0.012 0.000
#> GSM120762 2 0.3109 0.6965 0.000 0.800 0.000 0.200 0.000
#> GSM120775 5 0.2825 0.6370 0.000 0.000 0.016 0.124 0.860
#> GSM120776 5 0.2722 0.6354 0.000 0.000 0.020 0.108 0.872
#> GSM120782 5 0.2777 0.6378 0.000 0.000 0.016 0.120 0.864
#> GSM120789 2 0.6099 -0.0460 0.000 0.516 0.012 0.380 0.092
#> GSM120790 3 0.6365 -0.2997 0.000 0.252 0.520 0.228 0.000
#> GSM120791 4 0.3838 0.6267 0.000 0.000 0.004 0.716 0.280
#> GSM120755 4 0.5613 0.6320 0.000 0.308 0.000 0.592 0.100
#> GSM120756 5 0.2669 0.6270 0.000 0.000 0.020 0.104 0.876
#> GSM120769 2 0.2605 0.7363 0.000 0.852 0.000 0.148 0.000
#> GSM120778 4 0.5714 0.6158 0.000 0.312 0.000 0.580 0.108
#> GSM120792 4 0.4718 0.5678 0.000 0.028 0.000 0.628 0.344
#> GSM121332 4 0.5699 0.6533 0.000 0.264 0.000 0.608 0.128
#> GSM121334 4 0.5490 0.7130 0.000 0.248 0.000 0.636 0.116
#> GSM121340 5 0.3586 0.5633 0.000 0.000 0.020 0.188 0.792
#> GSM121351 2 0.0162 0.7916 0.000 0.996 0.000 0.004 0.000
#> GSM121353 4 0.4644 0.3312 0.000 0.000 0.012 0.528 0.460
#> GSM120758 4 0.5253 0.7586 0.000 0.200 0.000 0.676 0.124
#> GSM120771 4 0.5487 0.6667 0.000 0.280 0.000 0.620 0.100
#> GSM120772 4 0.5394 0.7536 0.000 0.208 0.000 0.660 0.132
#> GSM120773 4 0.4114 0.4783 0.000 0.000 0.000 0.624 0.376
#> GSM120774 4 0.4637 0.7680 0.000 0.100 0.000 0.740 0.160
#> GSM120783 4 0.4114 0.4783 0.000 0.000 0.000 0.624 0.376
#> GSM120787 4 0.4890 0.7733 0.000 0.140 0.000 0.720 0.140
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM120719 1 0.3767 0.7711 0.708 0.000 0.000 0.004 0.012 0.276
#> GSM120720 1 0.3767 0.7711 0.708 0.000 0.000 0.004 0.012 0.276
#> GSM120765 2 0.3756 0.4853 0.000 0.644 0.000 0.352 0.004 0.000
#> GSM120767 2 0.3890 0.3926 0.000 0.596 0.000 0.400 0.004 0.000
#> GSM120784 2 0.3890 0.3926 0.000 0.596 0.000 0.400 0.004 0.000
#> GSM121400 6 0.7007 0.4802 0.000 0.000 0.168 0.224 0.132 0.476
#> GSM121401 6 0.5036 0.6127 0.000 0.000 0.096 0.144 0.052 0.708
#> GSM121402 4 0.3144 0.6282 0.000 0.136 0.008 0.832 0.020 0.004
#> GSM121403 6 0.7203 0.4471 0.000 0.000 0.180 0.224 0.152 0.444
#> GSM121404 4 0.5889 0.3032 0.000 0.000 0.140 0.588 0.040 0.232
#> GSM121405 6 0.5036 0.6127 0.000 0.000 0.096 0.144 0.052 0.708
#> GSM121406 2 0.0146 0.7358 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM121408 2 0.4400 0.2437 0.000 0.552 0.004 0.428 0.012 0.004
#> GSM121409 6 0.6977 0.4851 0.000 0.000 0.172 0.212 0.132 0.484
#> GSM121410 6 0.7203 0.4471 0.000 0.000 0.180 0.224 0.152 0.444
#> GSM121412 2 0.0363 0.7370 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM121413 2 0.0146 0.7358 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM121414 2 0.0363 0.7370 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM121415 4 0.3409 0.5961 0.000 0.164 0.008 0.804 0.020 0.004
#> GSM121416 4 0.3301 0.5980 0.000 0.000 0.124 0.828 0.032 0.016
#> GSM120591 1 0.3767 0.7711 0.708 0.000 0.000 0.004 0.012 0.276
#> GSM120594 1 0.3767 0.7711 0.708 0.000 0.000 0.004 0.012 0.276
#> GSM120718 1 0.3767 0.7711 0.708 0.000 0.000 0.004 0.012 0.276
#> GSM121205 1 0.0000 0.8186 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.8186 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121207 1 0.0000 0.8186 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121208 1 0.1501 0.8156 0.924 0.000 0.000 0.000 0.000 0.076
#> GSM121209 1 0.0000 0.8186 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.8186 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.8186 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.8186 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.8186 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.8186 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121215 1 0.0146 0.8191 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM121216 1 0.1204 0.8177 0.944 0.000 0.000 0.000 0.000 0.056
#> GSM121217 1 0.0000 0.8186 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.8186 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121234 1 0.0146 0.8191 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM121243 1 0.0260 0.8197 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM121245 1 0.0000 0.8186 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121246 1 0.0363 0.8200 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM121247 1 0.0000 0.8186 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121248 1 0.0000 0.8186 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM120744 6 0.1772 0.6441 0.008 0.000 0.020 0.008 0.028 0.936
#> GSM120745 6 0.1370 0.6220 0.036 0.000 0.004 0.000 0.012 0.948
#> GSM120746 6 0.1010 0.6280 0.036 0.000 0.004 0.000 0.000 0.960
#> GSM120747 6 0.4473 0.6298 0.000 0.000 0.072 0.128 0.044 0.756
#> GSM120748 6 0.4473 0.6298 0.000 0.000 0.072 0.128 0.044 0.756
#> GSM120749 6 0.1010 0.6280 0.036 0.000 0.004 0.000 0.000 0.960
#> GSM120750 6 0.1010 0.6280 0.036 0.000 0.004 0.000 0.000 0.960
#> GSM120751 6 0.1010 0.6280 0.036 0.000 0.004 0.000 0.000 0.960
#> GSM120752 6 0.1370 0.6220 0.036 0.000 0.004 0.000 0.012 0.948
#> GSM121336 2 0.0000 0.7330 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121339 6 0.6747 0.3556 0.000 0.000 0.284 0.240 0.048 0.428
#> GSM121349 2 0.0000 0.7330 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121355 2 0.0146 0.7341 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM120757 6 0.3517 0.5175 0.132 0.000 0.008 0.008 0.036 0.816
#> GSM120766 6 0.1856 0.6442 0.008 0.000 0.024 0.008 0.028 0.932
#> GSM120770 6 0.7011 0.3018 0.000 0.000 0.176 0.324 0.092 0.408
#> GSM120779 1 0.3407 0.7569 0.820 0.000 0.004 0.008 0.036 0.132
#> GSM120780 6 0.1856 0.6442 0.008 0.000 0.024 0.008 0.028 0.932
#> GSM121102 6 0.7427 0.3686 0.000 0.016 0.164 0.288 0.112 0.420
#> GSM121203 6 0.2888 0.6467 0.008 0.000 0.028 0.044 0.040 0.880
#> GSM121204 1 0.3812 0.7554 0.776 0.000 0.004 0.008 0.036 0.176
#> GSM121330 1 0.3819 0.6679 0.624 0.000 0.000 0.000 0.004 0.372
#> GSM121335 1 0.3351 0.7693 0.712 0.000 0.000 0.000 0.000 0.288
#> GSM121337 4 0.6848 0.2722 0.000 0.040 0.100 0.540 0.072 0.248
#> GSM121338 4 0.6868 0.0725 0.000 0.000 0.172 0.468 0.092 0.268
#> GSM121341 1 0.3351 0.7693 0.712 0.000 0.000 0.000 0.000 0.288
#> GSM121342 1 0.3351 0.7693 0.712 0.000 0.000 0.000 0.000 0.288
#> GSM121343 4 0.6868 0.0725 0.000 0.000 0.172 0.468 0.092 0.268
#> GSM121344 1 0.3351 0.7693 0.712 0.000 0.000 0.000 0.000 0.288
#> GSM121346 1 0.3371 0.7664 0.708 0.000 0.000 0.000 0.000 0.292
#> GSM121347 4 0.6868 0.0725 0.000 0.000 0.172 0.468 0.092 0.268
#> GSM121348 5 0.2361 0.6957 0.000 0.000 0.032 0.064 0.896 0.008
#> GSM121350 1 0.3601 0.7462 0.684 0.000 0.000 0.000 0.004 0.312
#> GSM121352 1 0.3371 0.7670 0.708 0.000 0.000 0.000 0.000 0.292
#> GSM121354 1 0.3371 0.7664 0.708 0.000 0.000 0.000 0.000 0.292
#> GSM120753 4 0.3121 0.6219 0.000 0.192 0.008 0.796 0.004 0.000
#> GSM120761 4 0.3076 0.5814 0.000 0.240 0.000 0.760 0.000 0.000
#> GSM120768 4 0.3089 0.6242 0.000 0.188 0.008 0.800 0.004 0.000
#> GSM120781 2 0.2527 0.7080 0.000 0.832 0.000 0.168 0.000 0.000
#> GSM120788 3 0.1564 0.9236 0.000 0.000 0.936 0.040 0.000 0.024
#> GSM120760 4 0.2896 0.6391 0.000 0.160 0.016 0.824 0.000 0.000
#> GSM120763 4 0.3037 0.6392 0.000 0.160 0.016 0.820 0.004 0.000
#> GSM120764 3 0.1616 0.9225 0.000 0.000 0.932 0.048 0.000 0.020
#> GSM120777 3 0.1480 0.9229 0.000 0.000 0.940 0.040 0.000 0.020
#> GSM120786 4 0.4191 0.5003 0.000 0.024 0.284 0.684 0.004 0.004
#> GSM121329 6 0.4693 -0.0988 0.372 0.000 0.004 0.008 0.028 0.588
#> GSM121331 1 0.4842 0.5907 0.572 0.000 0.004 0.008 0.036 0.380
#> GSM121333 1 0.4769 0.6155 0.600 0.000 0.004 0.008 0.036 0.352
#> GSM121345 1 0.4842 0.5907 0.572 0.000 0.004 0.008 0.036 0.380
#> GSM121356 1 0.4842 0.5907 0.572 0.000 0.004 0.008 0.036 0.380
#> GSM120754 3 0.2973 0.9158 0.000 0.000 0.868 0.056 0.036 0.040
#> GSM120759 2 0.0820 0.7318 0.000 0.972 0.000 0.012 0.016 0.000
#> GSM120762 2 0.2933 0.6882 0.000 0.796 0.000 0.200 0.004 0.000
#> GSM120775 3 0.2973 0.9158 0.000 0.000 0.868 0.056 0.036 0.040
#> GSM120776 3 0.2843 0.9134 0.000 0.000 0.876 0.048 0.032 0.044
#> GSM120782 3 0.2901 0.9140 0.000 0.000 0.872 0.056 0.032 0.040
#> GSM120789 2 0.4589 0.0879 0.000 0.504 0.000 0.460 0.036 0.000
#> GSM120790 5 0.3168 0.7089 0.000 0.192 0.000 0.016 0.792 0.000
#> GSM120791 4 0.3235 0.6181 0.000 0.000 0.136 0.824 0.032 0.008
#> GSM120755 4 0.3547 0.5005 0.000 0.300 0.000 0.696 0.004 0.000
#> GSM120756 3 0.1564 0.9236 0.000 0.000 0.936 0.040 0.000 0.024
#> GSM120769 2 0.2340 0.7132 0.000 0.852 0.000 0.148 0.000 0.000
#> GSM120778 4 0.3703 0.4743 0.000 0.304 0.004 0.688 0.004 0.000
#> GSM120792 4 0.4317 0.4918 0.000 0.024 0.288 0.676 0.008 0.004
#> GSM121332 4 0.4245 0.5287 0.000 0.256 0.024 0.704 0.012 0.004
#> GSM121334 4 0.3354 0.5744 0.000 0.240 0.004 0.752 0.004 0.000
#> GSM121340 3 0.2494 0.7711 0.000 0.000 0.864 0.120 0.016 0.000
#> GSM121351 2 0.0146 0.7358 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM121353 4 0.4599 0.1506 0.000 0.000 0.428 0.540 0.024 0.008
#> GSM120758 4 0.3219 0.6229 0.000 0.192 0.012 0.792 0.004 0.000
#> GSM120771 4 0.3405 0.5369 0.000 0.272 0.000 0.724 0.004 0.000
#> GSM120772 4 0.3534 0.6189 0.000 0.200 0.024 0.772 0.004 0.000
#> GSM120773 4 0.3713 0.4940 0.000 0.000 0.284 0.704 0.008 0.004
#> GSM120774 4 0.2890 0.6532 0.000 0.096 0.032 0.860 0.012 0.000
#> GSM120783 4 0.3713 0.4940 0.000 0.000 0.284 0.704 0.008 0.004
#> GSM120787 4 0.3233 0.6456 0.000 0.132 0.024 0.828 0.016 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> ATC:hclust 113 2.24e-12 2
#> ATC:hclust 99 1.15e-09 3
#> ATC:hclust 78 3.98e-17 4
#> ATC:hclust 97 1.02e-20 5
#> ATC:hclust 96 2.03e-18 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 21512 rows and 119 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.992 0.997 0.5041 0.497 0.497
#> 3 3 0.795 0.886 0.940 0.2964 0.816 0.642
#> 4 4 0.807 0.884 0.916 0.1419 0.837 0.569
#> 5 5 0.718 0.660 0.772 0.0597 0.912 0.676
#> 6 6 0.698 0.430 0.680 0.0393 0.864 0.486
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
#> GSM120719 1 0.000 0.994 1.000 0.000
#> GSM120720 1 0.000 0.994 1.000 0.000
#> GSM120765 2 0.000 1.000 0.000 1.000
#> GSM120767 2 0.000 1.000 0.000 1.000
#> GSM120784 2 0.000 1.000 0.000 1.000
#> GSM121400 1 0.000 0.994 1.000 0.000
#> GSM121401 1 0.000 0.994 1.000 0.000
#> GSM121402 2 0.000 1.000 0.000 1.000
#> GSM121403 2 0.000 1.000 0.000 1.000
#> GSM121404 2 0.000 1.000 0.000 1.000
#> GSM121405 1 0.000 0.994 1.000 0.000
#> GSM121406 2 0.000 1.000 0.000 1.000
#> GSM121408 2 0.000 1.000 0.000 1.000
#> GSM121409 1 0.000 0.994 1.000 0.000
#> GSM121410 1 0.000 0.994 1.000 0.000
#> GSM121412 2 0.000 1.000 0.000 1.000
#> GSM121413 2 0.000 1.000 0.000 1.000
#> GSM121414 2 0.000 1.000 0.000 1.000
#> GSM121415 2 0.000 1.000 0.000 1.000
#> GSM121416 2 0.000 1.000 0.000 1.000
#> GSM120591 1 0.000 0.994 1.000 0.000
#> GSM120594 1 0.000 0.994 1.000 0.000
#> GSM120718 1 0.000 0.994 1.000 0.000
#> GSM121205 1 0.000 0.994 1.000 0.000
#> GSM121206 1 0.000 0.994 1.000 0.000
#> GSM121207 1 0.000 0.994 1.000 0.000
#> GSM121208 1 0.000 0.994 1.000 0.000
#> GSM121209 1 0.000 0.994 1.000 0.000
#> GSM121210 1 0.000 0.994 1.000 0.000
#> GSM121211 1 0.000 0.994 1.000 0.000
#> GSM121212 1 0.000 0.994 1.000 0.000
#> GSM121213 1 0.000 0.994 1.000 0.000
#> GSM121214 1 0.000 0.994 1.000 0.000
#> GSM121215 1 0.000 0.994 1.000 0.000
#> GSM121216 1 0.000 0.994 1.000 0.000
#> GSM121217 1 0.000 0.994 1.000 0.000
#> GSM121218 1 0.000 0.994 1.000 0.000
#> GSM121234 1 0.000 0.994 1.000 0.000
#> GSM121243 1 0.000 0.994 1.000 0.000
#> GSM121245 1 0.000 0.994 1.000 0.000
#> GSM121246 1 0.000 0.994 1.000 0.000
#> GSM121247 1 0.000 0.994 1.000 0.000
#> GSM121248 1 0.000 0.994 1.000 0.000
#> GSM120744 1 0.000 0.994 1.000 0.000
#> GSM120745 1 0.000 0.994 1.000 0.000
#> GSM120746 1 0.000 0.994 1.000 0.000
#> GSM120747 1 0.000 0.994 1.000 0.000
#> GSM120748 1 0.000 0.994 1.000 0.000
#> GSM120749 1 0.000 0.994 1.000 0.000
#> GSM120750 1 0.000 0.994 1.000 0.000
#> GSM120751 1 0.000 0.994 1.000 0.000
#> GSM120752 1 0.000 0.994 1.000 0.000
#> GSM121336 2 0.000 1.000 0.000 1.000
#> GSM121339 2 0.000 1.000 0.000 1.000
#> GSM121349 2 0.000 1.000 0.000 1.000
#> GSM121355 2 0.000 1.000 0.000 1.000
#> GSM120757 1 0.000 0.994 1.000 0.000
#> GSM120766 1 0.000 0.994 1.000 0.000
#> GSM120770 2 0.000 1.000 0.000 1.000
#> GSM120779 1 0.000 0.994 1.000 0.000
#> GSM120780 1 0.000 0.994 1.000 0.000
#> GSM121102 2 0.000 1.000 0.000 1.000
#> GSM121203 1 0.000 0.994 1.000 0.000
#> GSM121204 1 0.000 0.994 1.000 0.000
#> GSM121330 1 0.000 0.994 1.000 0.000
#> GSM121335 1 0.000 0.994 1.000 0.000
#> GSM121337 2 0.000 1.000 0.000 1.000
#> GSM121338 2 0.000 1.000 0.000 1.000
#> GSM121341 1 0.000 0.994 1.000 0.000
#> GSM121342 1 0.000 0.994 1.000 0.000
#> GSM121343 2 0.000 1.000 0.000 1.000
#> GSM121344 1 0.000 0.994 1.000 0.000
#> GSM121346 1 0.000 0.994 1.000 0.000
#> GSM121347 2 0.000 1.000 0.000 1.000
#> GSM121348 2 0.000 1.000 0.000 1.000
#> GSM121350 1 0.000 0.994 1.000 0.000
#> GSM121352 1 0.000 0.994 1.000 0.000
#> GSM121354 1 0.000 0.994 1.000 0.000
#> GSM120753 2 0.000 1.000 0.000 1.000
#> GSM120761 2 0.000 1.000 0.000 1.000
#> GSM120768 2 0.000 1.000 0.000 1.000
#> GSM120781 2 0.000 1.000 0.000 1.000
#> GSM120788 1 0.961 0.377 0.616 0.384
#> GSM120760 2 0.000 1.000 0.000 1.000
#> GSM120763 2 0.000 1.000 0.000 1.000
#> GSM120764 2 0.000 1.000 0.000 1.000
#> GSM120777 2 0.000 1.000 0.000 1.000
#> GSM120786 2 0.000 1.000 0.000 1.000
#> GSM121329 1 0.000 0.994 1.000 0.000
#> GSM121331 1 0.000 0.994 1.000 0.000
#> GSM121333 1 0.000 0.994 1.000 0.000
#> GSM121345 1 0.000 0.994 1.000 0.000
#> GSM121356 1 0.000 0.994 1.000 0.000
#> GSM120754 2 0.000 1.000 0.000 1.000
#> GSM120759 2 0.000 1.000 0.000 1.000
#> GSM120762 2 0.000 1.000 0.000 1.000
#> GSM120775 2 0.000 1.000 0.000 1.000
#> GSM120776 1 0.000 0.994 1.000 0.000
#> GSM120782 2 0.000 1.000 0.000 1.000
#> GSM120789 2 0.000 1.000 0.000 1.000
#> GSM120790 2 0.000 1.000 0.000 1.000
#> GSM120791 2 0.000 1.000 0.000 1.000
#> GSM120755 2 0.000 1.000 0.000 1.000
#> GSM120756 1 0.000 0.994 1.000 0.000
#> GSM120769 2 0.000 1.000 0.000 1.000
#> GSM120778 2 0.000 1.000 0.000 1.000
#> GSM120792 2 0.000 1.000 0.000 1.000
#> GSM121332 2 0.000 1.000 0.000 1.000
#> GSM121334 2 0.000 1.000 0.000 1.000
#> GSM121340 2 0.000 1.000 0.000 1.000
#> GSM121351 2 0.000 1.000 0.000 1.000
#> GSM121353 2 0.000 1.000 0.000 1.000
#> GSM120758 2 0.000 1.000 0.000 1.000
#> GSM120771 2 0.000 1.000 0.000 1.000
#> GSM120772 2 0.000 1.000 0.000 1.000
#> GSM120773 2 0.000 1.000 0.000 1.000
#> GSM120774 2 0.000 1.000 0.000 1.000
#> GSM120783 2 0.000 1.000 0.000 1.000
#> GSM120787 2 0.000 1.000 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM120719 1 0.3482 0.8650 0.872 0.000 0.128
#> GSM120720 1 0.2165 0.9337 0.936 0.000 0.064
#> GSM120765 2 0.0000 0.9384 0.000 1.000 0.000
#> GSM120767 2 0.0000 0.9384 0.000 1.000 0.000
#> GSM120784 2 0.0000 0.9384 0.000 1.000 0.000
#> GSM121400 3 0.0592 0.8931 0.012 0.000 0.988
#> GSM121401 3 0.0592 0.8931 0.012 0.000 0.988
#> GSM121402 2 0.0000 0.9384 0.000 1.000 0.000
#> GSM121403 3 0.6299 -0.1186 0.000 0.476 0.524
#> GSM121404 2 0.5216 0.7464 0.000 0.740 0.260
#> GSM121405 3 0.0592 0.8931 0.012 0.000 0.988
#> GSM121406 2 0.0000 0.9384 0.000 1.000 0.000
#> GSM121408 2 0.0000 0.9384 0.000 1.000 0.000
#> GSM121409 3 0.0592 0.8931 0.012 0.000 0.988
#> GSM121410 3 0.0592 0.8931 0.012 0.000 0.988
#> GSM121412 2 0.0000 0.9384 0.000 1.000 0.000
#> GSM121413 2 0.0000 0.9384 0.000 1.000 0.000
#> GSM121414 2 0.0000 0.9384 0.000 1.000 0.000
#> GSM121415 2 0.0000 0.9384 0.000 1.000 0.000
#> GSM121416 2 0.3116 0.8919 0.000 0.892 0.108
#> GSM120591 1 0.3340 0.8746 0.880 0.000 0.120
#> GSM120594 1 0.2066 0.9369 0.940 0.000 0.060
#> GSM120718 1 0.0000 0.9758 1.000 0.000 0.000
#> GSM121205 1 0.0000 0.9758 1.000 0.000 0.000
#> GSM121206 1 0.0000 0.9758 1.000 0.000 0.000
#> GSM121207 1 0.0000 0.9758 1.000 0.000 0.000
#> GSM121208 1 0.0000 0.9758 1.000 0.000 0.000
#> GSM121209 1 0.0000 0.9758 1.000 0.000 0.000
#> GSM121210 1 0.0000 0.9758 1.000 0.000 0.000
#> GSM121211 1 0.0000 0.9758 1.000 0.000 0.000
#> GSM121212 1 0.0000 0.9758 1.000 0.000 0.000
#> GSM121213 1 0.0000 0.9758 1.000 0.000 0.000
#> GSM121214 1 0.0000 0.9758 1.000 0.000 0.000
#> GSM121215 1 0.0000 0.9758 1.000 0.000 0.000
#> GSM121216 1 0.0000 0.9758 1.000 0.000 0.000
#> GSM121217 1 0.0000 0.9758 1.000 0.000 0.000
#> GSM121218 1 0.0000 0.9758 1.000 0.000 0.000
#> GSM121234 1 0.0000 0.9758 1.000 0.000 0.000
#> GSM121243 1 0.0000 0.9758 1.000 0.000 0.000
#> GSM121245 1 0.0000 0.9758 1.000 0.000 0.000
#> GSM121246 1 0.0000 0.9758 1.000 0.000 0.000
#> GSM121247 1 0.0000 0.9758 1.000 0.000 0.000
#> GSM121248 1 0.0000 0.9758 1.000 0.000 0.000
#> GSM120744 3 0.0592 0.8931 0.012 0.000 0.988
#> GSM120745 3 0.5291 0.6537 0.268 0.000 0.732
#> GSM120746 3 0.2066 0.8789 0.060 0.000 0.940
#> GSM120747 3 0.0592 0.8931 0.012 0.000 0.988
#> GSM120748 3 0.0592 0.8931 0.012 0.000 0.988
#> GSM120749 3 0.2796 0.8618 0.092 0.000 0.908
#> GSM120750 3 0.1411 0.8877 0.036 0.000 0.964
#> GSM120751 3 0.0592 0.8931 0.012 0.000 0.988
#> GSM120752 3 0.3941 0.7986 0.156 0.000 0.844
#> GSM121336 2 0.0000 0.9384 0.000 1.000 0.000
#> GSM121339 2 0.4796 0.7987 0.000 0.780 0.220
#> GSM121349 2 0.0000 0.9384 0.000 1.000 0.000
#> GSM121355 2 0.0000 0.9384 0.000 1.000 0.000
#> GSM120757 3 0.2959 0.8562 0.100 0.000 0.900
#> GSM120766 3 0.0592 0.8931 0.012 0.000 0.988
#> GSM120770 2 0.4002 0.8591 0.000 0.840 0.160
#> GSM120779 1 0.0000 0.9758 1.000 0.000 0.000
#> GSM120780 3 0.0592 0.8931 0.012 0.000 0.988
#> GSM121102 2 0.4002 0.8591 0.000 0.840 0.160
#> GSM121203 3 0.0592 0.8931 0.012 0.000 0.988
#> GSM121204 1 0.2165 0.9337 0.936 0.000 0.064
#> GSM121330 3 0.2625 0.8667 0.084 0.000 0.916
#> GSM121335 1 0.0000 0.9758 1.000 0.000 0.000
#> GSM121337 2 0.3816 0.8675 0.000 0.852 0.148
#> GSM121338 2 0.5178 0.7521 0.000 0.744 0.256
#> GSM121341 1 0.0000 0.9758 1.000 0.000 0.000
#> GSM121342 1 0.2066 0.9370 0.940 0.000 0.060
#> GSM121343 2 0.5016 0.7737 0.000 0.760 0.240
#> GSM121344 1 0.2711 0.9115 0.912 0.000 0.088
#> GSM121346 3 0.5291 0.6575 0.268 0.000 0.732
#> GSM121347 3 0.0424 0.8821 0.000 0.008 0.992
#> GSM121348 2 0.4002 0.8591 0.000 0.840 0.160
#> GSM121350 3 0.3116 0.8491 0.108 0.000 0.892
#> GSM121352 3 0.6204 0.3198 0.424 0.000 0.576
#> GSM121354 1 0.2878 0.9032 0.904 0.000 0.096
#> GSM120753 2 0.0000 0.9384 0.000 1.000 0.000
#> GSM120761 2 0.0000 0.9384 0.000 1.000 0.000
#> GSM120768 2 0.0592 0.9339 0.000 0.988 0.012
#> GSM120781 2 0.0000 0.9384 0.000 1.000 0.000
#> GSM120788 3 0.0000 0.8869 0.000 0.000 1.000
#> GSM120760 2 0.0592 0.9339 0.000 0.988 0.012
#> GSM120763 2 0.0592 0.9339 0.000 0.988 0.012
#> GSM120764 2 0.5291 0.7443 0.000 0.732 0.268
#> GSM120777 3 0.0000 0.8869 0.000 0.000 1.000
#> GSM120786 2 0.1411 0.9273 0.000 0.964 0.036
#> GSM121329 3 0.1163 0.8900 0.028 0.000 0.972
#> GSM121331 3 0.2878 0.8591 0.096 0.000 0.904
#> GSM121333 3 0.5591 0.5939 0.304 0.000 0.696
#> GSM121345 3 0.2959 0.8562 0.100 0.000 0.900
#> GSM121356 3 0.2261 0.8753 0.068 0.000 0.932
#> GSM120754 2 0.4178 0.8541 0.000 0.828 0.172
#> GSM120759 2 0.0000 0.9384 0.000 1.000 0.000
#> GSM120762 2 0.0000 0.9384 0.000 1.000 0.000
#> GSM120775 3 0.6215 0.0461 0.000 0.428 0.572
#> GSM120776 3 0.0000 0.8869 0.000 0.000 1.000
#> GSM120782 2 0.4346 0.8436 0.000 0.816 0.184
#> GSM120789 2 0.0000 0.9384 0.000 1.000 0.000
#> GSM120790 2 0.0000 0.9384 0.000 1.000 0.000
#> GSM120791 2 0.2625 0.9070 0.000 0.916 0.084
#> GSM120755 2 0.0000 0.9384 0.000 1.000 0.000
#> GSM120756 3 0.0000 0.8869 0.000 0.000 1.000
#> GSM120769 2 0.0000 0.9384 0.000 1.000 0.000
#> GSM120778 2 0.0000 0.9384 0.000 1.000 0.000
#> GSM120792 2 0.2066 0.9179 0.000 0.940 0.060
#> GSM121332 2 0.0000 0.9384 0.000 1.000 0.000
#> GSM121334 2 0.0000 0.9384 0.000 1.000 0.000
#> GSM121340 2 0.4002 0.8636 0.000 0.840 0.160
#> GSM121351 2 0.0000 0.9384 0.000 1.000 0.000
#> GSM121353 2 0.4399 0.8397 0.000 0.812 0.188
#> GSM120758 2 0.0000 0.9384 0.000 1.000 0.000
#> GSM120771 2 0.0000 0.9384 0.000 1.000 0.000
#> GSM120772 2 0.0000 0.9384 0.000 1.000 0.000
#> GSM120773 2 0.4062 0.8607 0.000 0.836 0.164
#> GSM120774 2 0.0000 0.9384 0.000 1.000 0.000
#> GSM120783 2 0.4062 0.8607 0.000 0.836 0.164
#> GSM120787 2 0.0000 0.9384 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM120719 1 0.6538 0.335 0.528 0.000 0.392 0.080
#> GSM120720 1 0.5392 0.622 0.680 0.000 0.280 0.040
#> GSM120765 2 0.0000 0.958 0.000 1.000 0.000 0.000
#> GSM120767 2 0.1302 0.947 0.000 0.956 0.000 0.044
#> GSM120784 2 0.0469 0.956 0.000 0.988 0.000 0.012
#> GSM121400 3 0.0336 0.943 0.000 0.000 0.992 0.008
#> GSM121401 3 0.0336 0.943 0.000 0.000 0.992 0.008
#> GSM121402 2 0.3907 0.680 0.000 0.768 0.000 0.232
#> GSM121403 4 0.5143 0.797 0.000 0.076 0.172 0.752
#> GSM121404 4 0.4227 0.888 0.000 0.120 0.060 0.820
#> GSM121405 3 0.0336 0.943 0.000 0.000 0.992 0.008
#> GSM121406 2 0.0336 0.958 0.008 0.992 0.000 0.000
#> GSM121408 2 0.1302 0.947 0.000 0.956 0.000 0.044
#> GSM121409 3 0.0336 0.943 0.000 0.000 0.992 0.008
#> GSM121410 3 0.0336 0.943 0.000 0.000 0.992 0.008
#> GSM121412 2 0.0336 0.958 0.008 0.992 0.000 0.000
#> GSM121413 2 0.0336 0.958 0.008 0.992 0.000 0.000
#> GSM121414 2 0.0336 0.958 0.008 0.992 0.000 0.000
#> GSM121415 2 0.0000 0.958 0.000 1.000 0.000 0.000
#> GSM121416 4 0.3400 0.892 0.000 0.180 0.000 0.820
#> GSM120591 1 0.6521 0.285 0.512 0.000 0.412 0.076
#> GSM120594 1 0.5284 0.647 0.696 0.000 0.264 0.040
#> GSM120718 1 0.1452 0.901 0.956 0.000 0.008 0.036
#> GSM121205 1 0.0336 0.911 0.992 0.000 0.008 0.000
#> GSM121206 1 0.0336 0.911 0.992 0.000 0.008 0.000
#> GSM121207 1 0.0336 0.911 0.992 0.000 0.008 0.000
#> GSM121208 1 0.0927 0.908 0.976 0.000 0.008 0.016
#> GSM121209 1 0.0336 0.911 0.992 0.000 0.008 0.000
#> GSM121210 1 0.0336 0.911 0.992 0.000 0.008 0.000
#> GSM121211 1 0.0336 0.911 0.992 0.000 0.008 0.000
#> GSM121212 1 0.0336 0.911 0.992 0.000 0.008 0.000
#> GSM121213 1 0.0336 0.911 0.992 0.000 0.008 0.000
#> GSM121214 1 0.0336 0.911 0.992 0.000 0.008 0.000
#> GSM121215 1 0.0524 0.910 0.988 0.000 0.008 0.004
#> GSM121216 1 0.1151 0.907 0.968 0.000 0.008 0.024
#> GSM121217 1 0.0336 0.911 0.992 0.000 0.008 0.000
#> GSM121218 1 0.0336 0.911 0.992 0.000 0.008 0.000
#> GSM121234 1 0.1042 0.908 0.972 0.000 0.008 0.020
#> GSM121243 1 0.0524 0.910 0.988 0.000 0.008 0.004
#> GSM121245 1 0.0336 0.911 0.992 0.000 0.008 0.000
#> GSM121246 1 0.1452 0.901 0.956 0.000 0.008 0.036
#> GSM121247 1 0.0336 0.911 0.992 0.000 0.008 0.000
#> GSM121248 1 0.0336 0.911 0.992 0.000 0.008 0.000
#> GSM120744 3 0.0000 0.943 0.000 0.000 1.000 0.000
#> GSM120745 3 0.1867 0.926 0.000 0.000 0.928 0.072
#> GSM120746 3 0.0000 0.943 0.000 0.000 1.000 0.000
#> GSM120747 3 0.0336 0.943 0.000 0.000 0.992 0.008
#> GSM120748 3 0.0336 0.943 0.000 0.000 0.992 0.008
#> GSM120749 3 0.0592 0.941 0.000 0.000 0.984 0.016
#> GSM120750 3 0.0000 0.943 0.000 0.000 1.000 0.000
#> GSM120751 3 0.0000 0.943 0.000 0.000 1.000 0.000
#> GSM120752 3 0.1716 0.930 0.000 0.000 0.936 0.064
#> GSM121336 2 0.0336 0.958 0.008 0.992 0.000 0.000
#> GSM121339 4 0.4227 0.888 0.000 0.120 0.060 0.820
#> GSM121349 2 0.0336 0.958 0.008 0.992 0.000 0.000
#> GSM121355 2 0.0336 0.958 0.008 0.992 0.000 0.000
#> GSM120757 3 0.1637 0.928 0.000 0.000 0.940 0.060
#> GSM120766 3 0.1302 0.933 0.000 0.000 0.956 0.044
#> GSM120770 4 0.3400 0.892 0.000 0.180 0.000 0.820
#> GSM120779 1 0.2222 0.877 0.924 0.000 0.016 0.060
#> GSM120780 3 0.0469 0.943 0.000 0.000 0.988 0.012
#> GSM121102 4 0.3725 0.886 0.000 0.180 0.008 0.812
#> GSM121203 3 0.0336 0.943 0.000 0.000 0.992 0.008
#> GSM121204 1 0.6329 0.547 0.616 0.000 0.292 0.092
#> GSM121330 3 0.1211 0.934 0.000 0.000 0.960 0.040
#> GSM121335 1 0.2578 0.877 0.912 0.000 0.052 0.036
#> GSM121337 4 0.3400 0.892 0.000 0.180 0.000 0.820
#> GSM121338 4 0.4227 0.888 0.000 0.120 0.060 0.820
#> GSM121341 1 0.2578 0.877 0.912 0.000 0.052 0.036
#> GSM121342 1 0.5308 0.626 0.684 0.000 0.280 0.036
#> GSM121343 4 0.4205 0.889 0.000 0.124 0.056 0.820
#> GSM121344 3 0.4956 0.639 0.232 0.000 0.732 0.036
#> GSM121346 3 0.1398 0.931 0.004 0.000 0.956 0.040
#> GSM121347 4 0.2654 0.850 0.000 0.004 0.108 0.888
#> GSM121348 4 0.2814 0.912 0.000 0.132 0.000 0.868
#> GSM121350 3 0.1302 0.932 0.000 0.000 0.956 0.044
#> GSM121352 3 0.1584 0.928 0.012 0.000 0.952 0.036
#> GSM121354 3 0.4290 0.758 0.164 0.000 0.800 0.036
#> GSM120753 2 0.1940 0.925 0.000 0.924 0.000 0.076
#> GSM120761 2 0.2011 0.922 0.000 0.920 0.000 0.080
#> GSM120768 4 0.2589 0.916 0.000 0.116 0.000 0.884
#> GSM120781 2 0.0000 0.958 0.000 1.000 0.000 0.000
#> GSM120788 4 0.2216 0.817 0.000 0.000 0.092 0.908
#> GSM120760 4 0.2408 0.918 0.000 0.104 0.000 0.896
#> GSM120763 4 0.2408 0.918 0.000 0.104 0.000 0.896
#> GSM120764 4 0.2142 0.893 0.000 0.056 0.016 0.928
#> GSM120777 4 0.1389 0.853 0.000 0.000 0.048 0.952
#> GSM120786 4 0.2408 0.918 0.000 0.104 0.000 0.896
#> GSM121329 3 0.1474 0.930 0.000 0.000 0.948 0.052
#> GSM121331 3 0.1557 0.928 0.000 0.000 0.944 0.056
#> GSM121333 3 0.2450 0.916 0.016 0.000 0.912 0.072
#> GSM121345 3 0.1557 0.928 0.000 0.000 0.944 0.056
#> GSM121356 3 0.1557 0.928 0.000 0.000 0.944 0.056
#> GSM120754 4 0.2408 0.918 0.000 0.104 0.000 0.896
#> GSM120759 2 0.0336 0.958 0.008 0.992 0.000 0.000
#> GSM120762 2 0.0000 0.958 0.000 1.000 0.000 0.000
#> GSM120775 4 0.1635 0.860 0.000 0.008 0.044 0.948
#> GSM120776 3 0.4941 0.332 0.000 0.000 0.564 0.436
#> GSM120782 4 0.2408 0.918 0.000 0.104 0.000 0.896
#> GSM120789 2 0.1557 0.940 0.000 0.944 0.000 0.056
#> GSM120790 2 0.0336 0.958 0.008 0.992 0.000 0.000
#> GSM120791 4 0.2408 0.918 0.000 0.104 0.000 0.896
#> GSM120755 2 0.1302 0.947 0.000 0.956 0.000 0.044
#> GSM120756 4 0.4356 0.502 0.000 0.000 0.292 0.708
#> GSM120769 2 0.0336 0.958 0.008 0.992 0.000 0.000
#> GSM120778 2 0.1940 0.925 0.000 0.924 0.000 0.076
#> GSM120792 4 0.2408 0.918 0.000 0.104 0.000 0.896
#> GSM121332 2 0.1940 0.925 0.000 0.924 0.000 0.076
#> GSM121334 2 0.1940 0.925 0.000 0.924 0.000 0.076
#> GSM121340 4 0.2408 0.918 0.000 0.104 0.000 0.896
#> GSM121351 2 0.0336 0.958 0.008 0.992 0.000 0.000
#> GSM121353 4 0.2408 0.918 0.000 0.104 0.000 0.896
#> GSM120758 4 0.3400 0.892 0.000 0.180 0.000 0.820
#> GSM120771 2 0.1302 0.947 0.000 0.956 0.000 0.044
#> GSM120772 4 0.3400 0.892 0.000 0.180 0.000 0.820
#> GSM120773 4 0.2408 0.918 0.000 0.104 0.000 0.896
#> GSM120774 4 0.3400 0.892 0.000 0.180 0.000 0.820
#> GSM120783 4 0.2408 0.918 0.000 0.104 0.000 0.896
#> GSM120787 4 0.4072 0.810 0.000 0.252 0.000 0.748
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM120719 3 0.6593 0.1663 0.340 0.000 0.440 0.220 0.000
#> GSM120720 3 0.6436 0.0470 0.396 0.000 0.428 0.176 0.000
#> GSM120765 2 0.2654 0.8034 0.000 0.888 0.000 0.064 0.048
#> GSM120767 2 0.5251 0.7617 0.000 0.680 0.000 0.136 0.184
#> GSM120784 2 0.5127 0.7662 0.000 0.692 0.000 0.124 0.184
#> GSM121400 3 0.4382 0.5990 0.000 0.000 0.688 0.024 0.288
#> GSM121401 3 0.4206 0.6122 0.000 0.000 0.708 0.020 0.272
#> GSM121402 2 0.6342 0.3985 0.000 0.464 0.000 0.164 0.372
#> GSM121403 5 0.3018 0.5066 0.000 0.012 0.116 0.012 0.860
#> GSM121404 5 0.1018 0.6290 0.000 0.016 0.016 0.000 0.968
#> GSM121405 3 0.4400 0.5790 0.000 0.000 0.672 0.020 0.308
#> GSM121406 2 0.0290 0.7936 0.000 0.992 0.000 0.008 0.000
#> GSM121408 2 0.5329 0.7616 0.000 0.672 0.000 0.144 0.184
#> GSM121409 3 0.4465 0.5778 0.000 0.000 0.672 0.024 0.304
#> GSM121410 3 0.4484 0.5775 0.000 0.000 0.668 0.024 0.308
#> GSM121412 2 0.0290 0.7936 0.000 0.992 0.000 0.008 0.000
#> GSM121413 2 0.0290 0.7936 0.000 0.992 0.000 0.008 0.000
#> GSM121414 2 0.0290 0.7936 0.000 0.992 0.000 0.008 0.000
#> GSM121415 2 0.4417 0.7850 0.000 0.760 0.000 0.092 0.148
#> GSM121416 5 0.4192 0.1387 0.000 0.032 0.000 0.232 0.736
#> GSM120591 3 0.6420 0.2281 0.324 0.000 0.484 0.192 0.000
#> GSM120594 3 0.6436 0.0470 0.396 0.000 0.428 0.176 0.000
#> GSM120718 1 0.5653 0.5589 0.632 0.000 0.208 0.160 0.000
#> GSM121205 1 0.0000 0.9091 1.000 0.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.9091 1.000 0.000 0.000 0.000 0.000
#> GSM121207 1 0.0000 0.9091 1.000 0.000 0.000 0.000 0.000
#> GSM121208 1 0.3169 0.8125 0.856 0.000 0.084 0.060 0.000
#> GSM121209 1 0.0000 0.9091 1.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.9091 1.000 0.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.9091 1.000 0.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.9091 1.000 0.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.9091 1.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.9091 1.000 0.000 0.000 0.000 0.000
#> GSM121215 1 0.0798 0.9013 0.976 0.000 0.000 0.016 0.008
#> GSM121216 1 0.1764 0.8792 0.928 0.000 0.000 0.064 0.008
#> GSM121217 1 0.0000 0.9091 1.000 0.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.9091 1.000 0.000 0.000 0.000 0.000
#> GSM121234 1 0.1082 0.8974 0.964 0.000 0.000 0.028 0.008
#> GSM121243 1 0.0798 0.9013 0.976 0.000 0.000 0.016 0.008
#> GSM121245 1 0.0000 0.9091 1.000 0.000 0.000 0.000 0.000
#> GSM121246 1 0.4743 0.7185 0.744 0.000 0.116 0.136 0.004
#> GSM121247 1 0.0000 0.9091 1.000 0.000 0.000 0.000 0.000
#> GSM121248 1 0.0000 0.9091 1.000 0.000 0.000 0.000 0.000
#> GSM120744 3 0.2707 0.7005 0.000 0.000 0.860 0.008 0.132
#> GSM120745 3 0.2732 0.7233 0.000 0.000 0.840 0.160 0.000
#> GSM120746 3 0.1168 0.7373 0.000 0.000 0.960 0.008 0.032
#> GSM120747 3 0.2798 0.6967 0.000 0.000 0.852 0.008 0.140
#> GSM120748 3 0.3957 0.6043 0.000 0.000 0.712 0.008 0.280
#> GSM120749 3 0.0451 0.7412 0.000 0.000 0.988 0.008 0.004
#> GSM120750 3 0.1251 0.7366 0.000 0.000 0.956 0.008 0.036
#> GSM120751 3 0.1251 0.7366 0.000 0.000 0.956 0.008 0.036
#> GSM120752 3 0.2471 0.7321 0.000 0.000 0.864 0.136 0.000
#> GSM121336 2 0.0000 0.7940 0.000 1.000 0.000 0.000 0.000
#> GSM121339 5 0.0912 0.6295 0.000 0.016 0.012 0.000 0.972
#> GSM121349 2 0.0000 0.7940 0.000 1.000 0.000 0.000 0.000
#> GSM121355 2 0.0000 0.7940 0.000 1.000 0.000 0.000 0.000
#> GSM120757 3 0.2424 0.7259 0.000 0.000 0.868 0.132 0.000
#> GSM120766 3 0.4155 0.6816 0.000 0.000 0.780 0.076 0.144
#> GSM120770 5 0.1818 0.5940 0.000 0.024 0.000 0.044 0.932
#> GSM120779 1 0.5309 0.6269 0.676 0.000 0.164 0.160 0.000
#> GSM120780 3 0.4206 0.5947 0.000 0.000 0.696 0.016 0.288
#> GSM121102 5 0.1617 0.6276 0.000 0.020 0.012 0.020 0.948
#> GSM121203 3 0.4130 0.5929 0.000 0.000 0.696 0.012 0.292
#> GSM121204 3 0.6724 0.1021 0.356 0.000 0.392 0.252 0.000
#> GSM121330 3 0.2727 0.7337 0.000 0.000 0.868 0.116 0.016
#> GSM121335 1 0.5912 0.5014 0.604 0.000 0.248 0.144 0.004
#> GSM121337 5 0.1981 0.5878 0.000 0.028 0.000 0.048 0.924
#> GSM121338 5 0.1018 0.6290 0.000 0.016 0.016 0.000 0.968
#> GSM121341 1 0.5912 0.5014 0.604 0.000 0.248 0.144 0.004
#> GSM121342 3 0.6529 0.0736 0.392 0.000 0.436 0.168 0.004
#> GSM121343 5 0.1018 0.6290 0.000 0.016 0.016 0.000 0.968
#> GSM121344 3 0.4962 0.6398 0.108 0.000 0.720 0.168 0.004
#> GSM121346 3 0.2953 0.7284 0.000 0.000 0.844 0.144 0.012
#> GSM121347 5 0.2653 0.4531 0.000 0.000 0.024 0.096 0.880
#> GSM121348 5 0.3016 0.5324 0.000 0.020 0.000 0.132 0.848
#> GSM121350 3 0.2777 0.7330 0.000 0.000 0.864 0.120 0.016
#> GSM121352 3 0.3124 0.7258 0.008 0.000 0.840 0.144 0.008
#> GSM121354 3 0.4374 0.6854 0.072 0.000 0.776 0.144 0.008
#> GSM120753 2 0.5590 0.7292 0.000 0.640 0.000 0.156 0.204
#> GSM120761 2 0.5653 0.7183 0.000 0.632 0.000 0.160 0.208
#> GSM120768 4 0.5049 0.1309 0.000 0.032 0.000 0.488 0.480
#> GSM120781 2 0.2889 0.8026 0.000 0.872 0.000 0.084 0.044
#> GSM120788 4 0.5606 0.5444 0.000 0.000 0.084 0.556 0.360
#> GSM120760 4 0.4613 0.5655 0.000 0.020 0.000 0.620 0.360
#> GSM120763 4 0.4898 0.4841 0.000 0.032 0.000 0.592 0.376
#> GSM120764 4 0.4430 0.7383 0.000 0.004 0.000 0.540 0.456
#> GSM120777 4 0.4367 0.6798 0.000 0.000 0.004 0.580 0.416
#> GSM120786 4 0.4744 0.6971 0.000 0.020 0.000 0.572 0.408
#> GSM121329 3 0.1608 0.7418 0.000 0.000 0.928 0.072 0.000
#> GSM121331 3 0.2690 0.7205 0.000 0.000 0.844 0.156 0.000
#> GSM121333 3 0.3750 0.7078 0.012 0.000 0.756 0.232 0.000
#> GSM121345 3 0.2891 0.7191 0.000 0.000 0.824 0.176 0.000
#> GSM121356 3 0.2605 0.7221 0.000 0.000 0.852 0.148 0.000
#> GSM120754 4 0.4655 0.7376 0.000 0.012 0.000 0.512 0.476
#> GSM120759 2 0.0290 0.7936 0.000 0.992 0.000 0.008 0.000
#> GSM120762 2 0.2580 0.8032 0.000 0.892 0.000 0.064 0.044
#> GSM120775 4 0.4440 0.7238 0.000 0.000 0.004 0.528 0.468
#> GSM120776 4 0.6508 0.1978 0.000 0.000 0.264 0.488 0.248
#> GSM120782 4 0.4655 0.7376 0.000 0.012 0.000 0.512 0.476
#> GSM120789 2 0.5532 0.7447 0.000 0.648 0.000 0.156 0.196
#> GSM120790 2 0.1121 0.7775 0.000 0.956 0.000 0.044 0.000
#> GSM120791 4 0.4829 0.6980 0.000 0.020 0.000 0.496 0.484
#> GSM120755 2 0.5251 0.7617 0.000 0.680 0.000 0.136 0.184
#> GSM120756 4 0.6056 0.4541 0.000 0.000 0.152 0.552 0.296
#> GSM120769 2 0.0000 0.7940 0.000 1.000 0.000 0.000 0.000
#> GSM120778 2 0.5597 0.7285 0.000 0.640 0.000 0.160 0.200
#> GSM120792 4 0.4829 0.6786 0.000 0.020 0.000 0.496 0.484
#> GSM121332 2 0.5590 0.7360 0.000 0.640 0.000 0.156 0.204
#> GSM121334 2 0.5567 0.7334 0.000 0.644 0.000 0.160 0.196
#> GSM121340 4 0.4807 0.7452 0.000 0.020 0.000 0.532 0.448
#> GSM121351 2 0.0290 0.7936 0.000 0.992 0.000 0.008 0.000
#> GSM121353 4 0.4738 0.7419 0.000 0.016 0.000 0.520 0.464
#> GSM120758 5 0.6119 0.2596 0.000 0.160 0.000 0.296 0.544
#> GSM120771 2 0.5251 0.7617 0.000 0.680 0.000 0.136 0.184
#> GSM120772 5 0.5601 -0.0725 0.000 0.072 0.000 0.448 0.480
#> GSM120773 4 0.4735 0.7426 0.000 0.016 0.000 0.524 0.460
#> GSM120774 5 0.6316 0.1591 0.000 0.164 0.000 0.356 0.480
#> GSM120783 4 0.4815 0.7421 0.000 0.020 0.000 0.524 0.456
#> GSM120787 5 0.6491 0.1860 0.000 0.200 0.000 0.336 0.464
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM120719 3 0.3973 0.59505 0.144 0.004 0.768 0.000 0.084 0.000
#> GSM120720 3 0.3821 0.59185 0.156 0.004 0.776 0.000 0.064 0.000
#> GSM120765 2 0.3133 0.71272 0.000 0.780 0.000 0.212 0.000 0.008
#> GSM120767 2 0.4389 0.48589 0.000 0.536 0.000 0.444 0.008 0.012
#> GSM120784 2 0.4375 0.50482 0.000 0.548 0.000 0.432 0.008 0.012
#> GSM121400 6 0.2738 0.47428 0.000 0.000 0.176 0.000 0.004 0.820
#> GSM121401 6 0.2902 0.45732 0.000 0.000 0.196 0.004 0.000 0.800
#> GSM121402 4 0.5448 -0.09597 0.000 0.320 0.000 0.580 0.064 0.036
#> GSM121403 6 0.5065 0.48695 0.000 0.004 0.004 0.148 0.180 0.664
#> GSM121404 6 0.5711 0.43856 0.000 0.004 0.000 0.264 0.192 0.540
#> GSM121405 6 0.2772 0.47562 0.000 0.000 0.180 0.000 0.004 0.816
#> GSM121406 2 0.0363 0.77764 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM121408 2 0.4389 0.48782 0.000 0.536 0.000 0.444 0.008 0.012
#> GSM121409 6 0.2848 0.47644 0.000 0.000 0.176 0.000 0.008 0.816
#> GSM121410 6 0.2848 0.47644 0.000 0.000 0.176 0.000 0.008 0.816
#> GSM121412 2 0.0363 0.77764 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM121413 2 0.0363 0.77764 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM121414 2 0.0363 0.77764 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM121415 2 0.4194 0.58404 0.000 0.628 0.000 0.352 0.008 0.012
#> GSM121416 4 0.4868 0.18672 0.000 0.004 0.000 0.676 0.172 0.148
#> GSM120591 3 0.3672 0.60484 0.140 0.004 0.792 0.000 0.064 0.000
#> GSM120594 3 0.3821 0.59185 0.156 0.004 0.776 0.000 0.064 0.000
#> GSM120718 3 0.4559 0.42513 0.272 0.004 0.664 0.000 0.060 0.000
#> GSM121205 1 0.0260 0.90467 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM121206 1 0.0000 0.90540 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121207 1 0.0260 0.90467 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM121208 1 0.3373 0.62935 0.744 0.000 0.248 0.000 0.008 0.000
#> GSM121209 1 0.0000 0.90540 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.0260 0.90467 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM121211 1 0.0000 0.90540 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.90540 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.90540 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.0260 0.90467 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM121215 1 0.1555 0.88178 0.940 0.008 0.000 0.000 0.040 0.012
#> GSM121216 1 0.3582 0.78624 0.820 0.008 0.116 0.000 0.044 0.012
#> GSM121217 1 0.0000 0.90540 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.90540 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121234 1 0.2403 0.86088 0.904 0.008 0.032 0.000 0.044 0.012
#> GSM121243 1 0.1555 0.88178 0.940 0.008 0.000 0.000 0.040 0.012
#> GSM121245 1 0.0260 0.90467 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM121246 1 0.4712 0.17759 0.512 0.000 0.452 0.000 0.024 0.012
#> GSM121247 1 0.0260 0.90326 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM121248 1 0.0260 0.90467 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM120744 6 0.5065 -0.16276 0.000 0.000 0.396 0.000 0.080 0.524
#> GSM120745 3 0.3254 0.62688 0.000 0.000 0.816 0.000 0.136 0.048
#> GSM120746 3 0.5112 0.29845 0.000 0.000 0.476 0.000 0.080 0.444
#> GSM120747 6 0.5020 -0.11828 0.000 0.000 0.372 0.000 0.080 0.548
#> GSM120748 6 0.3168 0.44465 0.000 0.000 0.172 0.000 0.024 0.804
#> GSM120749 3 0.5120 0.40316 0.000 0.000 0.532 0.000 0.088 0.380
#> GSM120750 3 0.5112 0.29845 0.000 0.000 0.476 0.000 0.080 0.444
#> GSM120751 3 0.5113 0.29503 0.000 0.000 0.472 0.000 0.080 0.448
#> GSM120752 3 0.3845 0.61429 0.000 0.000 0.772 0.000 0.140 0.088
#> GSM121336 2 0.0725 0.77700 0.000 0.976 0.000 0.012 0.000 0.012
#> GSM121339 6 0.5742 0.43287 0.000 0.004 0.000 0.272 0.192 0.532
#> GSM121349 2 0.0725 0.77700 0.000 0.976 0.000 0.012 0.000 0.012
#> GSM121355 2 0.0725 0.77700 0.000 0.976 0.000 0.012 0.000 0.012
#> GSM120757 3 0.5658 0.49417 0.000 0.000 0.508 0.000 0.316 0.176
#> GSM120766 6 0.5863 -0.15287 0.000 0.000 0.284 0.000 0.236 0.480
#> GSM120770 6 0.5934 0.34698 0.000 0.004 0.000 0.344 0.192 0.460
#> GSM120779 1 0.6419 -0.23025 0.340 0.000 0.324 0.000 0.324 0.012
#> GSM120780 6 0.2988 0.45524 0.000 0.000 0.152 0.000 0.024 0.824
#> GSM121102 6 0.5729 0.42947 0.000 0.004 0.000 0.288 0.180 0.528
#> GSM121203 6 0.2595 0.47094 0.000 0.000 0.160 0.000 0.004 0.836
#> GSM121204 3 0.5551 0.55306 0.112 0.000 0.568 0.000 0.304 0.016
#> GSM121330 3 0.3329 0.54600 0.000 0.000 0.768 0.004 0.008 0.220
#> GSM121335 3 0.4145 0.28163 0.356 0.000 0.628 0.004 0.008 0.004
#> GSM121337 6 0.5934 0.34320 0.000 0.004 0.000 0.344 0.192 0.460
#> GSM121338 6 0.5711 0.43856 0.000 0.004 0.000 0.264 0.192 0.540
#> GSM121341 3 0.4145 0.28163 0.356 0.000 0.628 0.004 0.008 0.004
#> GSM121342 3 0.3339 0.55929 0.188 0.000 0.792 0.004 0.008 0.008
#> GSM121343 6 0.5711 0.43856 0.000 0.004 0.000 0.264 0.192 0.540
#> GSM121344 3 0.2113 0.62416 0.028 0.000 0.916 0.004 0.008 0.044
#> GSM121346 3 0.2573 0.59513 0.000 0.000 0.856 0.004 0.008 0.132
#> GSM121347 6 0.5549 0.40663 0.000 0.000 0.000 0.212 0.232 0.556
#> GSM121348 6 0.6005 0.31225 0.000 0.004 0.000 0.260 0.260 0.476
#> GSM121350 3 0.3301 0.54825 0.000 0.000 0.772 0.004 0.008 0.216
#> GSM121352 3 0.2841 0.59881 0.012 0.000 0.852 0.004 0.008 0.124
#> GSM121354 3 0.3019 0.60052 0.020 0.000 0.844 0.004 0.008 0.124
#> GSM120753 4 0.3804 -0.26123 0.000 0.424 0.000 0.576 0.000 0.000
#> GSM120761 4 0.3797 -0.25091 0.000 0.420 0.000 0.580 0.000 0.000
#> GSM120768 4 0.0146 0.33548 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM120781 2 0.3652 0.67728 0.000 0.720 0.000 0.264 0.000 0.016
#> GSM120788 5 0.4794 0.78972 0.000 0.000 0.004 0.344 0.596 0.056
#> GSM120760 4 0.2738 0.11896 0.000 0.004 0.000 0.820 0.176 0.000
#> GSM120763 4 0.1285 0.28973 0.000 0.004 0.000 0.944 0.052 0.000
#> GSM120764 4 0.3982 -0.43291 0.000 0.000 0.000 0.536 0.460 0.004
#> GSM120777 5 0.3930 0.65352 0.000 0.000 0.004 0.420 0.576 0.000
#> GSM120786 4 0.3699 -0.22118 0.000 0.004 0.000 0.660 0.336 0.000
#> GSM121329 3 0.5400 0.49425 0.000 0.000 0.572 0.000 0.164 0.264
#> GSM121331 3 0.5609 0.49539 0.000 0.000 0.504 0.000 0.336 0.160
#> GSM121333 3 0.4884 0.54970 0.004 0.000 0.592 0.000 0.340 0.064
#> GSM121345 3 0.5314 0.52038 0.000 0.000 0.544 0.000 0.336 0.120
#> GSM121356 3 0.5733 0.47909 0.000 0.000 0.488 0.000 0.328 0.184
#> GSM120754 4 0.3966 -0.38454 0.000 0.000 0.000 0.552 0.444 0.004
#> GSM120759 2 0.0363 0.77764 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM120762 2 0.3290 0.71293 0.000 0.776 0.000 0.208 0.000 0.016
#> GSM120775 4 0.3982 -0.43291 0.000 0.000 0.000 0.536 0.460 0.004
#> GSM120776 5 0.5341 0.65275 0.000 0.000 0.024 0.176 0.652 0.148
#> GSM120782 4 0.3966 -0.38454 0.000 0.000 0.000 0.552 0.444 0.004
#> GSM120789 4 0.4097 -0.42522 0.000 0.492 0.000 0.500 0.008 0.000
#> GSM120790 2 0.1930 0.74280 0.000 0.924 0.000 0.012 0.036 0.028
#> GSM120791 4 0.3360 -0.03632 0.000 0.004 0.000 0.732 0.264 0.000
#> GSM120755 2 0.4411 0.42466 0.000 0.504 0.000 0.476 0.008 0.012
#> GSM120756 5 0.5081 0.79641 0.000 0.000 0.012 0.312 0.604 0.072
#> GSM120769 2 0.0725 0.77700 0.000 0.976 0.000 0.012 0.000 0.012
#> GSM120778 4 0.3810 -0.27076 0.000 0.428 0.000 0.572 0.000 0.000
#> GSM120792 4 0.3240 0.00564 0.000 0.004 0.000 0.752 0.244 0.000
#> GSM121332 4 0.4291 -0.30778 0.000 0.436 0.000 0.548 0.008 0.008
#> GSM121334 4 0.3828 -0.30110 0.000 0.440 0.000 0.560 0.000 0.000
#> GSM121340 4 0.3955 -0.37998 0.000 0.000 0.000 0.560 0.436 0.004
#> GSM121351 2 0.0363 0.77764 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM121353 4 0.3971 -0.39454 0.000 0.000 0.000 0.548 0.448 0.004
#> GSM120758 4 0.3809 0.37575 0.000 0.116 0.000 0.800 0.064 0.020
#> GSM120771 2 0.4325 0.42251 0.000 0.504 0.000 0.480 0.008 0.008
#> GSM120772 4 0.0632 0.35113 0.000 0.024 0.000 0.976 0.000 0.000
#> GSM120773 4 0.3944 -0.36490 0.000 0.000 0.000 0.568 0.428 0.004
#> GSM120774 4 0.2234 0.39850 0.000 0.124 0.000 0.872 0.004 0.000
#> GSM120783 4 0.3950 -0.37041 0.000 0.000 0.000 0.564 0.432 0.004
#> GSM120787 4 0.2402 0.39378 0.000 0.140 0.000 0.856 0.004 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# 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 118 7.87e-10 2
#> ATC:kmeans 116 1.04e-17 3
#> ATC:kmeans 116 3.14e-21 4
#> ATC:kmeans 102 1.72e-21 5
#> ATC:kmeans 55 5.76e-12 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 21512 rows and 119 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.5044 0.496 0.496
#> 3 3 0.997 0.949 0.977 0.2100 0.902 0.802
#> 4 4 0.952 0.938 0.972 0.1378 0.905 0.764
#> 5 5 0.862 0.872 0.932 0.0415 0.964 0.886
#> 6 6 0.783 0.764 0.870 0.0421 1.000 1.000
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM120719 1 0.0000 1.000 1.000 0.000
#> GSM120720 1 0.0000 1.000 1.000 0.000
#> GSM120765 2 0.0000 1.000 0.000 1.000
#> GSM120767 2 0.0000 1.000 0.000 1.000
#> GSM120784 2 0.0000 1.000 0.000 1.000
#> GSM121400 1 0.0000 1.000 1.000 0.000
#> GSM121401 1 0.0000 1.000 1.000 0.000
#> GSM121402 2 0.0000 1.000 0.000 1.000
#> GSM121403 2 0.0000 1.000 0.000 1.000
#> GSM121404 2 0.0000 1.000 0.000 1.000
#> GSM121405 1 0.0376 0.996 0.996 0.004
#> GSM121406 2 0.0000 1.000 0.000 1.000
#> GSM121408 2 0.0000 1.000 0.000 1.000
#> GSM121409 1 0.0000 1.000 1.000 0.000
#> GSM121410 1 0.0000 1.000 1.000 0.000
#> GSM121412 2 0.0000 1.000 0.000 1.000
#> GSM121413 2 0.0000 1.000 0.000 1.000
#> GSM121414 2 0.0000 1.000 0.000 1.000
#> GSM121415 2 0.0000 1.000 0.000 1.000
#> GSM121416 2 0.0000 1.000 0.000 1.000
#> GSM120591 1 0.0000 1.000 1.000 0.000
#> GSM120594 1 0.0000 1.000 1.000 0.000
#> GSM120718 1 0.0000 1.000 1.000 0.000
#> GSM121205 1 0.0000 1.000 1.000 0.000
#> GSM121206 1 0.0000 1.000 1.000 0.000
#> GSM121207 1 0.0000 1.000 1.000 0.000
#> GSM121208 1 0.0000 1.000 1.000 0.000
#> GSM121209 1 0.0000 1.000 1.000 0.000
#> GSM121210 1 0.0000 1.000 1.000 0.000
#> GSM121211 1 0.0000 1.000 1.000 0.000
#> GSM121212 1 0.0000 1.000 1.000 0.000
#> GSM121213 1 0.0000 1.000 1.000 0.000
#> GSM121214 1 0.0000 1.000 1.000 0.000
#> GSM121215 1 0.0000 1.000 1.000 0.000
#> GSM121216 1 0.0000 1.000 1.000 0.000
#> GSM121217 1 0.0000 1.000 1.000 0.000
#> GSM121218 1 0.0000 1.000 1.000 0.000
#> GSM121234 1 0.0000 1.000 1.000 0.000
#> GSM121243 1 0.0000 1.000 1.000 0.000
#> GSM121245 1 0.0000 1.000 1.000 0.000
#> GSM121246 1 0.0000 1.000 1.000 0.000
#> GSM121247 1 0.0000 1.000 1.000 0.000
#> GSM121248 1 0.0000 1.000 1.000 0.000
#> GSM120744 1 0.0000 1.000 1.000 0.000
#> GSM120745 1 0.0000 1.000 1.000 0.000
#> GSM120746 1 0.0000 1.000 1.000 0.000
#> GSM120747 1 0.0000 1.000 1.000 0.000
#> GSM120748 1 0.0000 1.000 1.000 0.000
#> GSM120749 1 0.0000 1.000 1.000 0.000
#> GSM120750 1 0.0000 1.000 1.000 0.000
#> GSM120751 1 0.0000 1.000 1.000 0.000
#> GSM120752 1 0.0000 1.000 1.000 0.000
#> GSM121336 2 0.0000 1.000 0.000 1.000
#> GSM121339 2 0.0000 1.000 0.000 1.000
#> GSM121349 2 0.0000 1.000 0.000 1.000
#> GSM121355 2 0.0000 1.000 0.000 1.000
#> GSM120757 1 0.0000 1.000 1.000 0.000
#> GSM120766 1 0.0000 1.000 1.000 0.000
#> GSM120770 2 0.0000 1.000 0.000 1.000
#> GSM120779 1 0.0000 1.000 1.000 0.000
#> GSM120780 1 0.0000 1.000 1.000 0.000
#> GSM121102 2 0.0000 1.000 0.000 1.000
#> GSM121203 1 0.0000 1.000 1.000 0.000
#> GSM121204 1 0.0000 1.000 1.000 0.000
#> GSM121330 1 0.0000 1.000 1.000 0.000
#> GSM121335 1 0.0000 1.000 1.000 0.000
#> GSM121337 2 0.0000 1.000 0.000 1.000
#> GSM121338 2 0.0000 1.000 0.000 1.000
#> GSM121341 1 0.0000 1.000 1.000 0.000
#> GSM121342 1 0.0000 1.000 1.000 0.000
#> GSM121343 2 0.0000 1.000 0.000 1.000
#> GSM121344 1 0.0000 1.000 1.000 0.000
#> GSM121346 1 0.0000 1.000 1.000 0.000
#> GSM121347 2 0.0000 1.000 0.000 1.000
#> GSM121348 2 0.0000 1.000 0.000 1.000
#> GSM121350 1 0.0000 1.000 1.000 0.000
#> GSM121352 1 0.0000 1.000 1.000 0.000
#> GSM121354 1 0.0000 1.000 1.000 0.000
#> GSM120753 2 0.0000 1.000 0.000 1.000
#> GSM120761 2 0.0000 1.000 0.000 1.000
#> GSM120768 2 0.0000 1.000 0.000 1.000
#> GSM120781 2 0.0000 1.000 0.000 1.000
#> GSM120788 2 0.0672 0.992 0.008 0.992
#> GSM120760 2 0.0000 1.000 0.000 1.000
#> GSM120763 2 0.0000 1.000 0.000 1.000
#> GSM120764 2 0.0000 1.000 0.000 1.000
#> GSM120777 2 0.0000 1.000 0.000 1.000
#> GSM120786 2 0.0000 1.000 0.000 1.000
#> GSM121329 1 0.0000 1.000 1.000 0.000
#> GSM121331 1 0.0000 1.000 1.000 0.000
#> GSM121333 1 0.0000 1.000 1.000 0.000
#> GSM121345 1 0.0000 1.000 1.000 0.000
#> GSM121356 1 0.0000 1.000 1.000 0.000
#> GSM120754 2 0.0000 1.000 0.000 1.000
#> GSM120759 2 0.0000 1.000 0.000 1.000
#> GSM120762 2 0.0000 1.000 0.000 1.000
#> GSM120775 2 0.0000 1.000 0.000 1.000
#> GSM120776 1 0.0000 1.000 1.000 0.000
#> GSM120782 2 0.0000 1.000 0.000 1.000
#> GSM120789 2 0.0000 1.000 0.000 1.000
#> GSM120790 2 0.0000 1.000 0.000 1.000
#> GSM120791 2 0.0000 1.000 0.000 1.000
#> GSM120755 2 0.0000 1.000 0.000 1.000
#> GSM120756 1 0.0000 1.000 1.000 0.000
#> GSM120769 2 0.0000 1.000 0.000 1.000
#> GSM120778 2 0.0000 1.000 0.000 1.000
#> GSM120792 2 0.0000 1.000 0.000 1.000
#> GSM121332 2 0.0000 1.000 0.000 1.000
#> GSM121334 2 0.0000 1.000 0.000 1.000
#> GSM121340 2 0.0000 1.000 0.000 1.000
#> GSM121351 2 0.0000 1.000 0.000 1.000
#> GSM121353 2 0.0000 1.000 0.000 1.000
#> GSM120758 2 0.0000 1.000 0.000 1.000
#> GSM120771 2 0.0000 1.000 0.000 1.000
#> GSM120772 2 0.0000 1.000 0.000 1.000
#> GSM120773 2 0.0000 1.000 0.000 1.000
#> GSM120774 2 0.0000 1.000 0.000 1.000
#> GSM120783 2 0.0000 1.000 0.000 1.000
#> GSM120787 2 0.0000 1.000 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM120719 1 0.0000 0.957 1.000 0.000 0.000
#> GSM120720 1 0.0000 0.957 1.000 0.000 0.000
#> GSM120765 2 0.0000 0.988 0.000 1.000 0.000
#> GSM120767 2 0.0000 0.988 0.000 1.000 0.000
#> GSM120784 2 0.0000 0.988 0.000 1.000 0.000
#> GSM121400 3 0.0747 0.993 0.016 0.000 0.984
#> GSM121401 3 0.0747 0.993 0.016 0.000 0.984
#> GSM121402 2 0.0000 0.988 0.000 1.000 0.000
#> GSM121403 2 0.2537 0.907 0.000 0.920 0.080
#> GSM121404 2 0.0000 0.988 0.000 1.000 0.000
#> GSM121405 3 0.0747 0.993 0.016 0.000 0.984
#> GSM121406 2 0.0000 0.988 0.000 1.000 0.000
#> GSM121408 2 0.0000 0.988 0.000 1.000 0.000
#> GSM121409 3 0.0747 0.993 0.016 0.000 0.984
#> GSM121410 3 0.0747 0.993 0.016 0.000 0.984
#> GSM121412 2 0.0000 0.988 0.000 1.000 0.000
#> GSM121413 2 0.0000 0.988 0.000 1.000 0.000
#> GSM121414 2 0.0000 0.988 0.000 1.000 0.000
#> GSM121415 2 0.0000 0.988 0.000 1.000 0.000
#> GSM121416 2 0.0000 0.988 0.000 1.000 0.000
#> GSM120591 1 0.0000 0.957 1.000 0.000 0.000
#> GSM120594 1 0.0000 0.957 1.000 0.000 0.000
#> GSM120718 1 0.0000 0.957 1.000 0.000 0.000
#> GSM121205 1 0.0000 0.957 1.000 0.000 0.000
#> GSM121206 1 0.0000 0.957 1.000 0.000 0.000
#> GSM121207 1 0.0000 0.957 1.000 0.000 0.000
#> GSM121208 1 0.0000 0.957 1.000 0.000 0.000
#> GSM121209 1 0.0000 0.957 1.000 0.000 0.000
#> GSM121210 1 0.0000 0.957 1.000 0.000 0.000
#> GSM121211 1 0.0000 0.957 1.000 0.000 0.000
#> GSM121212 1 0.0000 0.957 1.000 0.000 0.000
#> GSM121213 1 0.0000 0.957 1.000 0.000 0.000
#> GSM121214 1 0.0000 0.957 1.000 0.000 0.000
#> GSM121215 1 0.0000 0.957 1.000 0.000 0.000
#> GSM121216 1 0.0000 0.957 1.000 0.000 0.000
#> GSM121217 1 0.0000 0.957 1.000 0.000 0.000
#> GSM121218 1 0.0000 0.957 1.000 0.000 0.000
#> GSM121234 1 0.0000 0.957 1.000 0.000 0.000
#> GSM121243 1 0.0000 0.957 1.000 0.000 0.000
#> GSM121245 1 0.0000 0.957 1.000 0.000 0.000
#> GSM121246 1 0.0000 0.957 1.000 0.000 0.000
#> GSM121247 1 0.0000 0.957 1.000 0.000 0.000
#> GSM121248 1 0.0000 0.957 1.000 0.000 0.000
#> GSM120744 3 0.0592 0.994 0.012 0.000 0.988
#> GSM120745 1 0.2796 0.879 0.908 0.000 0.092
#> GSM120746 3 0.0592 0.994 0.012 0.000 0.988
#> GSM120747 3 0.0592 0.994 0.012 0.000 0.988
#> GSM120748 3 0.0592 0.994 0.012 0.000 0.988
#> GSM120749 3 0.2356 0.930 0.072 0.000 0.928
#> GSM120750 3 0.0592 0.994 0.012 0.000 0.988
#> GSM120751 3 0.0592 0.994 0.012 0.000 0.988
#> GSM120752 1 0.3879 0.814 0.848 0.000 0.152
#> GSM121336 2 0.0000 0.988 0.000 1.000 0.000
#> GSM121339 2 0.0000 0.988 0.000 1.000 0.000
#> GSM121349 2 0.0000 0.988 0.000 1.000 0.000
#> GSM121355 2 0.0000 0.988 0.000 1.000 0.000
#> GSM120757 1 0.0237 0.955 0.996 0.000 0.004
#> GSM120766 3 0.0592 0.994 0.012 0.000 0.988
#> GSM120770 2 0.0000 0.988 0.000 1.000 0.000
#> GSM120779 1 0.0237 0.955 0.996 0.000 0.004
#> GSM120780 3 0.0592 0.994 0.012 0.000 0.988
#> GSM121102 2 0.0000 0.988 0.000 1.000 0.000
#> GSM121203 3 0.0592 0.994 0.012 0.000 0.988
#> GSM121204 1 0.0237 0.955 0.996 0.000 0.004
#> GSM121330 1 0.6180 0.329 0.584 0.000 0.416
#> GSM121335 1 0.0000 0.957 1.000 0.000 0.000
#> GSM121337 2 0.0000 0.988 0.000 1.000 0.000
#> GSM121338 2 0.0000 0.988 0.000 1.000 0.000
#> GSM121341 1 0.0000 0.957 1.000 0.000 0.000
#> GSM121342 1 0.0000 0.957 1.000 0.000 0.000
#> GSM121343 2 0.0000 0.988 0.000 1.000 0.000
#> GSM121344 1 0.0000 0.957 1.000 0.000 0.000
#> GSM121346 1 0.5621 0.576 0.692 0.000 0.308
#> GSM121347 2 0.0000 0.988 0.000 1.000 0.000
#> GSM121348 2 0.0000 0.988 0.000 1.000 0.000
#> GSM121350 1 0.6274 0.210 0.544 0.000 0.456
#> GSM121352 1 0.5591 0.583 0.696 0.000 0.304
#> GSM121354 1 0.2066 0.909 0.940 0.000 0.060
#> GSM120753 2 0.0000 0.988 0.000 1.000 0.000
#> GSM120761 2 0.0000 0.988 0.000 1.000 0.000
#> GSM120768 2 0.0237 0.986 0.000 0.996 0.004
#> GSM120781 2 0.0000 0.988 0.000 1.000 0.000
#> GSM120788 2 0.6753 0.350 0.388 0.596 0.016
#> GSM120760 2 0.0424 0.985 0.000 0.992 0.008
#> GSM120763 2 0.0424 0.985 0.000 0.992 0.008
#> GSM120764 2 0.0592 0.983 0.000 0.988 0.012
#> GSM120777 2 0.0592 0.983 0.000 0.988 0.012
#> GSM120786 2 0.0592 0.983 0.000 0.988 0.012
#> GSM121329 1 0.0000 0.957 1.000 0.000 0.000
#> GSM121331 1 0.0237 0.955 0.996 0.000 0.004
#> GSM121333 1 0.0237 0.955 0.996 0.000 0.004
#> GSM121345 1 0.0237 0.955 0.996 0.000 0.004
#> GSM121356 1 0.0237 0.955 0.996 0.000 0.004
#> GSM120754 2 0.0592 0.983 0.000 0.988 0.012
#> GSM120759 2 0.0000 0.988 0.000 1.000 0.000
#> GSM120762 2 0.0000 0.988 0.000 1.000 0.000
#> GSM120775 2 0.0592 0.983 0.000 0.988 0.012
#> GSM120776 1 0.0747 0.946 0.984 0.000 0.016
#> GSM120782 2 0.0592 0.983 0.000 0.988 0.012
#> GSM120789 2 0.0000 0.988 0.000 1.000 0.000
#> GSM120790 2 0.0000 0.988 0.000 1.000 0.000
#> GSM120791 2 0.0424 0.985 0.000 0.992 0.008
#> GSM120755 2 0.0000 0.988 0.000 1.000 0.000
#> GSM120756 1 0.0747 0.946 0.984 0.000 0.016
#> GSM120769 2 0.0000 0.988 0.000 1.000 0.000
#> GSM120778 2 0.0000 0.988 0.000 1.000 0.000
#> GSM120792 2 0.0424 0.985 0.000 0.992 0.008
#> GSM121332 2 0.0000 0.988 0.000 1.000 0.000
#> GSM121334 2 0.0000 0.988 0.000 1.000 0.000
#> GSM121340 2 0.0592 0.983 0.000 0.988 0.012
#> GSM121351 2 0.0000 0.988 0.000 1.000 0.000
#> GSM121353 2 0.0592 0.983 0.000 0.988 0.012
#> GSM120758 2 0.0000 0.988 0.000 1.000 0.000
#> GSM120771 2 0.0000 0.988 0.000 1.000 0.000
#> GSM120772 2 0.0000 0.988 0.000 1.000 0.000
#> GSM120773 2 0.0592 0.983 0.000 0.988 0.012
#> GSM120774 2 0.0000 0.988 0.000 1.000 0.000
#> GSM120783 2 0.0592 0.983 0.000 0.988 0.012
#> GSM120787 2 0.0000 0.988 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM120719 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> GSM120720 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> GSM120765 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM120767 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM120784 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM121400 3 0.0657 0.979 0.004 0.000 0.984 0.012
#> GSM121401 3 0.0657 0.979 0.004 0.000 0.984 0.012
#> GSM121402 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM121403 2 0.0469 0.959 0.000 0.988 0.000 0.012
#> GSM121404 2 0.0188 0.966 0.000 0.996 0.000 0.004
#> GSM121405 3 0.0657 0.979 0.004 0.000 0.984 0.012
#> GSM121406 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM121408 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM121409 3 0.0657 0.979 0.004 0.000 0.984 0.012
#> GSM121410 3 0.0657 0.979 0.004 0.000 0.984 0.012
#> GSM121412 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM121413 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM121414 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM121415 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM121416 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM120591 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> GSM120594 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> GSM120718 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> GSM121205 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> GSM121207 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> GSM121208 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> GSM121209 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> GSM121216 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> GSM121217 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> GSM121234 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> GSM121243 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> GSM121245 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> GSM121246 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> GSM121247 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> GSM121248 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> GSM120744 3 0.0000 0.981 0.000 0.000 1.000 0.000
#> GSM120745 1 0.2814 0.862 0.868 0.000 0.132 0.000
#> GSM120746 3 0.0000 0.981 0.000 0.000 1.000 0.000
#> GSM120747 3 0.0000 0.981 0.000 0.000 1.000 0.000
#> GSM120748 3 0.0000 0.981 0.000 0.000 1.000 0.000
#> GSM120749 3 0.2647 0.819 0.120 0.000 0.880 0.000
#> GSM120750 3 0.0000 0.981 0.000 0.000 1.000 0.000
#> GSM120751 3 0.0000 0.981 0.000 0.000 1.000 0.000
#> GSM120752 1 0.3726 0.758 0.788 0.000 0.212 0.000
#> GSM121336 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM121339 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM121349 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM121355 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM120757 1 0.2611 0.895 0.896 0.000 0.096 0.008
#> GSM120766 3 0.0336 0.977 0.000 0.000 0.992 0.008
#> GSM120770 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM120779 1 0.0672 0.969 0.984 0.000 0.008 0.008
#> GSM120780 3 0.0000 0.981 0.000 0.000 1.000 0.000
#> GSM121102 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM121203 3 0.0336 0.980 0.000 0.000 0.992 0.008
#> GSM121204 1 0.0188 0.975 0.996 0.000 0.004 0.000
#> GSM121330 1 0.2593 0.887 0.892 0.000 0.104 0.004
#> GSM121335 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> GSM121337 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM121338 2 0.0188 0.966 0.000 0.996 0.000 0.004
#> GSM121341 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> GSM121342 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> GSM121343 2 0.0188 0.966 0.000 0.996 0.000 0.004
#> GSM121344 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> GSM121346 1 0.1792 0.925 0.932 0.000 0.068 0.000
#> GSM121347 2 0.0188 0.966 0.000 0.996 0.000 0.004
#> GSM121348 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM121350 1 0.3982 0.735 0.776 0.000 0.220 0.004
#> GSM121352 1 0.1716 0.929 0.936 0.000 0.064 0.000
#> GSM121354 1 0.0188 0.975 0.996 0.000 0.004 0.000
#> GSM120753 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM120761 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM120768 2 0.3172 0.786 0.000 0.840 0.000 0.160
#> GSM120781 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM120788 4 0.0524 0.916 0.000 0.004 0.008 0.988
#> GSM120760 4 0.4730 0.452 0.000 0.364 0.000 0.636
#> GSM120763 2 0.4888 0.256 0.000 0.588 0.000 0.412
#> GSM120764 4 0.0707 0.928 0.000 0.020 0.000 0.980
#> GSM120777 4 0.0469 0.923 0.000 0.012 0.000 0.988
#> GSM120786 4 0.2647 0.847 0.000 0.120 0.000 0.880
#> GSM121329 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> GSM121331 1 0.0672 0.969 0.984 0.000 0.008 0.008
#> GSM121333 1 0.0672 0.969 0.984 0.000 0.008 0.008
#> GSM121345 1 0.0672 0.969 0.984 0.000 0.008 0.008
#> GSM121356 1 0.0672 0.969 0.984 0.000 0.008 0.008
#> GSM120754 4 0.2149 0.883 0.000 0.088 0.000 0.912
#> GSM120759 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM120762 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM120775 4 0.0707 0.928 0.000 0.020 0.000 0.980
#> GSM120776 4 0.0524 0.911 0.004 0.000 0.008 0.988
#> GSM120782 4 0.1118 0.921 0.000 0.036 0.000 0.964
#> GSM120789 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM120790 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM120791 2 0.4624 0.457 0.000 0.660 0.000 0.340
#> GSM120755 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM120756 4 0.0524 0.911 0.008 0.000 0.004 0.988
#> GSM120769 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM120778 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM120792 2 0.4500 0.513 0.000 0.684 0.000 0.316
#> GSM121332 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM121334 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM121340 4 0.0707 0.928 0.000 0.020 0.000 0.980
#> GSM121351 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM121353 4 0.0707 0.928 0.000 0.020 0.000 0.980
#> GSM120758 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM120771 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM120772 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM120773 4 0.1940 0.894 0.000 0.076 0.000 0.924
#> GSM120774 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> GSM120783 4 0.0707 0.928 0.000 0.020 0.000 0.980
#> GSM120787 2 0.0000 0.970 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM120719 1 0.0451 0.9447 0.988 0.000 0.008 0.000 0.004
#> GSM120720 1 0.0451 0.9450 0.988 0.000 0.008 0.000 0.004
#> GSM120765 2 0.0000 0.9535 0.000 1.000 0.000 0.000 0.000
#> GSM120767 2 0.0566 0.9524 0.000 0.984 0.004 0.012 0.000
#> GSM120784 2 0.0000 0.9535 0.000 1.000 0.000 0.000 0.000
#> GSM121400 3 0.2690 0.8902 0.000 0.000 0.844 0.000 0.156
#> GSM121401 3 0.3003 0.8708 0.000 0.000 0.812 0.000 0.188
#> GSM121402 2 0.0324 0.9540 0.000 0.992 0.004 0.004 0.000
#> GSM121403 3 0.3210 0.5855 0.000 0.212 0.788 0.000 0.000
#> GSM121404 2 0.1341 0.9263 0.000 0.944 0.056 0.000 0.000
#> GSM121405 3 0.2773 0.8888 0.000 0.000 0.836 0.000 0.164
#> GSM121406 2 0.0290 0.9526 0.000 0.992 0.008 0.000 0.000
#> GSM121408 2 0.0290 0.9534 0.000 0.992 0.000 0.008 0.000
#> GSM121409 3 0.2648 0.8912 0.000 0.000 0.848 0.000 0.152
#> GSM121410 3 0.2516 0.8870 0.000 0.000 0.860 0.000 0.140
#> GSM121412 2 0.0290 0.9526 0.000 0.992 0.008 0.000 0.000
#> GSM121413 2 0.0290 0.9526 0.000 0.992 0.008 0.000 0.000
#> GSM121414 2 0.0290 0.9526 0.000 0.992 0.008 0.000 0.000
#> GSM121415 2 0.0404 0.9514 0.000 0.988 0.012 0.000 0.000
#> GSM121416 2 0.1195 0.9486 0.000 0.960 0.012 0.028 0.000
#> GSM120591 1 0.0566 0.9442 0.984 0.000 0.012 0.000 0.004
#> GSM120594 1 0.0566 0.9442 0.984 0.000 0.012 0.000 0.004
#> GSM120718 1 0.0451 0.9450 0.988 0.000 0.008 0.000 0.004
#> GSM121205 1 0.0000 0.9478 1.000 0.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.9478 1.000 0.000 0.000 0.000 0.000
#> GSM121207 1 0.0000 0.9478 1.000 0.000 0.000 0.000 0.000
#> GSM121208 1 0.0000 0.9478 1.000 0.000 0.000 0.000 0.000
#> GSM121209 1 0.0000 0.9478 1.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.9478 1.000 0.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.9478 1.000 0.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.9478 1.000 0.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.9478 1.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.9478 1.000 0.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.9478 1.000 0.000 0.000 0.000 0.000
#> GSM121216 1 0.0000 0.9478 1.000 0.000 0.000 0.000 0.000
#> GSM121217 1 0.0000 0.9478 1.000 0.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.9478 1.000 0.000 0.000 0.000 0.000
#> GSM121234 1 0.0000 0.9478 1.000 0.000 0.000 0.000 0.000
#> GSM121243 1 0.0000 0.9478 1.000 0.000 0.000 0.000 0.000
#> GSM121245 1 0.0000 0.9478 1.000 0.000 0.000 0.000 0.000
#> GSM121246 1 0.0000 0.9478 1.000 0.000 0.000 0.000 0.000
#> GSM121247 1 0.0000 0.9478 1.000 0.000 0.000 0.000 0.000
#> GSM121248 1 0.0000 0.9478 1.000 0.000 0.000 0.000 0.000
#> GSM120744 5 0.0290 0.8311 0.000 0.000 0.008 0.000 0.992
#> GSM120745 5 0.4086 0.4944 0.284 0.000 0.012 0.000 0.704
#> GSM120746 5 0.0000 0.8326 0.000 0.000 0.000 0.000 1.000
#> GSM120747 5 0.0609 0.8263 0.000 0.000 0.020 0.000 0.980
#> GSM120748 5 0.0880 0.8198 0.000 0.000 0.032 0.000 0.968
#> GSM120749 5 0.0898 0.8222 0.020 0.000 0.008 0.000 0.972
#> GSM120750 5 0.0000 0.8326 0.000 0.000 0.000 0.000 1.000
#> GSM120751 5 0.0000 0.8326 0.000 0.000 0.000 0.000 1.000
#> GSM120752 5 0.2921 0.7170 0.124 0.000 0.020 0.000 0.856
#> GSM121336 2 0.0000 0.9535 0.000 1.000 0.000 0.000 0.000
#> GSM121339 2 0.0290 0.9530 0.000 0.992 0.008 0.000 0.000
#> GSM121349 2 0.0000 0.9535 0.000 1.000 0.000 0.000 0.000
#> GSM121355 2 0.0000 0.9535 0.000 1.000 0.000 0.000 0.000
#> GSM120757 5 0.4948 0.5641 0.224 0.000 0.072 0.004 0.700
#> GSM120766 5 0.2536 0.7828 0.000 0.000 0.128 0.004 0.868
#> GSM120770 2 0.0510 0.9501 0.000 0.984 0.016 0.000 0.000
#> GSM120779 1 0.2632 0.8812 0.892 0.000 0.072 0.004 0.032
#> GSM120780 5 0.2605 0.7626 0.000 0.000 0.148 0.000 0.852
#> GSM121102 2 0.1121 0.9353 0.000 0.956 0.044 0.000 0.000
#> GSM121203 5 0.3395 0.5949 0.000 0.000 0.236 0.000 0.764
#> GSM121204 1 0.1399 0.9236 0.952 0.000 0.028 0.000 0.020
#> GSM121330 1 0.4823 0.5917 0.672 0.000 0.276 0.000 0.052
#> GSM121335 1 0.1701 0.9153 0.936 0.000 0.048 0.000 0.016
#> GSM121337 2 0.0703 0.9469 0.000 0.976 0.024 0.000 0.000
#> GSM121338 2 0.2074 0.8853 0.000 0.896 0.104 0.000 0.000
#> GSM121341 1 0.1701 0.9153 0.936 0.000 0.048 0.000 0.016
#> GSM121342 1 0.0162 0.9466 0.996 0.000 0.004 0.000 0.000
#> GSM121343 2 0.1792 0.9033 0.000 0.916 0.084 0.000 0.000
#> GSM121344 1 0.1484 0.9202 0.944 0.000 0.048 0.000 0.008
#> GSM121346 1 0.3339 0.8292 0.836 0.000 0.124 0.000 0.040
#> GSM121347 2 0.2685 0.8778 0.000 0.880 0.092 0.028 0.000
#> GSM121348 2 0.0794 0.9450 0.000 0.972 0.028 0.000 0.000
#> GSM121350 1 0.4885 0.5840 0.668 0.000 0.276 0.000 0.056
#> GSM121352 1 0.3339 0.8286 0.836 0.000 0.124 0.000 0.040
#> GSM121354 1 0.3012 0.8447 0.852 0.000 0.124 0.000 0.024
#> GSM120753 2 0.1124 0.9434 0.000 0.960 0.004 0.036 0.000
#> GSM120761 2 0.1205 0.9412 0.000 0.956 0.004 0.040 0.000
#> GSM120768 2 0.2583 0.8526 0.000 0.864 0.004 0.132 0.000
#> GSM120781 2 0.0566 0.9524 0.000 0.984 0.004 0.012 0.000
#> GSM120788 4 0.1410 0.8051 0.000 0.000 0.060 0.940 0.000
#> GSM120760 4 0.4452 0.0112 0.000 0.496 0.004 0.500 0.000
#> GSM120763 2 0.3884 0.5986 0.000 0.708 0.004 0.288 0.000
#> GSM120764 4 0.0865 0.8187 0.000 0.004 0.024 0.972 0.000
#> GSM120777 4 0.1270 0.8090 0.000 0.000 0.052 0.948 0.000
#> GSM120786 4 0.3010 0.7186 0.000 0.172 0.004 0.824 0.000
#> GSM121329 1 0.0566 0.9446 0.984 0.000 0.012 0.000 0.004
#> GSM121331 1 0.2940 0.8667 0.876 0.000 0.072 0.004 0.048
#> GSM121333 1 0.3148 0.8548 0.864 0.000 0.072 0.004 0.060
#> GSM121345 1 0.2792 0.8757 0.884 0.000 0.072 0.004 0.040
#> GSM121356 1 0.3683 0.8141 0.828 0.000 0.072 0.004 0.096
#> GSM120754 4 0.2719 0.7504 0.000 0.144 0.004 0.852 0.000
#> GSM120759 2 0.0290 0.9526 0.000 0.992 0.008 0.000 0.000
#> GSM120762 2 0.0566 0.9524 0.000 0.984 0.004 0.012 0.000
#> GSM120775 4 0.1205 0.8158 0.000 0.004 0.040 0.956 0.000
#> GSM120776 4 0.2017 0.7891 0.000 0.000 0.080 0.912 0.008
#> GSM120782 4 0.2124 0.7894 0.000 0.096 0.004 0.900 0.000
#> GSM120789 2 0.0162 0.9532 0.000 0.996 0.004 0.000 0.000
#> GSM120790 2 0.0290 0.9526 0.000 0.992 0.008 0.000 0.000
#> GSM120791 2 0.3835 0.6587 0.000 0.732 0.008 0.260 0.000
#> GSM120755 2 0.0566 0.9524 0.000 0.984 0.004 0.012 0.000
#> GSM120756 4 0.1410 0.8051 0.000 0.000 0.060 0.940 0.000
#> GSM120769 2 0.0566 0.9524 0.000 0.984 0.004 0.012 0.000
#> GSM120778 2 0.1430 0.9332 0.000 0.944 0.004 0.052 0.000
#> GSM120792 2 0.3579 0.6956 0.000 0.756 0.004 0.240 0.000
#> GSM121332 2 0.0566 0.9524 0.000 0.984 0.004 0.012 0.000
#> GSM121334 2 0.1282 0.9384 0.000 0.952 0.004 0.044 0.000
#> GSM121340 4 0.0404 0.8220 0.000 0.012 0.000 0.988 0.000
#> GSM121351 2 0.0000 0.9535 0.000 1.000 0.000 0.000 0.000
#> GSM121353 4 0.0880 0.8232 0.000 0.032 0.000 0.968 0.000
#> GSM120758 2 0.0865 0.9485 0.000 0.972 0.004 0.024 0.000
#> GSM120771 2 0.0671 0.9513 0.000 0.980 0.004 0.016 0.000
#> GSM120772 2 0.1430 0.9333 0.000 0.944 0.004 0.052 0.000
#> GSM120773 4 0.3123 0.7035 0.000 0.184 0.004 0.812 0.000
#> GSM120774 2 0.1205 0.9413 0.000 0.956 0.004 0.040 0.000
#> GSM120783 4 0.0955 0.8228 0.000 0.028 0.004 0.968 0.000
#> GSM120787 2 0.1124 0.9431 0.000 0.960 0.004 0.036 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM120719 1 0.1219 0.8398 0.948 0.000 0.000 0.000 NA 0.004
#> GSM120720 1 0.0713 0.8484 0.972 0.000 0.000 0.000 NA 0.000
#> GSM120765 2 0.0520 0.8876 0.000 0.984 0.000 0.008 NA 0.000
#> GSM120767 2 0.1700 0.8792 0.000 0.928 0.000 0.048 NA 0.000
#> GSM120784 2 0.0520 0.8876 0.000 0.984 0.000 0.008 NA 0.000
#> GSM121400 3 0.1124 0.9075 0.000 0.000 0.956 0.000 NA 0.036
#> GSM121401 3 0.3235 0.8056 0.000 0.000 0.820 0.000 NA 0.052
#> GSM121402 2 0.0820 0.8889 0.000 0.972 0.000 0.012 NA 0.000
#> GSM121403 3 0.3062 0.7384 0.000 0.112 0.836 0.000 NA 0.000
#> GSM121404 2 0.3130 0.7744 0.000 0.828 0.048 0.000 NA 0.000
#> GSM121405 3 0.1168 0.9081 0.000 0.000 0.956 0.000 NA 0.028
#> GSM121406 2 0.0363 0.8841 0.000 0.988 0.000 0.000 NA 0.000
#> GSM121408 2 0.0972 0.8864 0.000 0.964 0.000 0.028 NA 0.000
#> GSM121409 3 0.0713 0.9103 0.000 0.000 0.972 0.000 NA 0.028
#> GSM121410 3 0.0632 0.9099 0.000 0.000 0.976 0.000 NA 0.024
#> GSM121412 2 0.0508 0.8835 0.000 0.984 0.004 0.000 NA 0.000
#> GSM121413 2 0.0508 0.8835 0.000 0.984 0.004 0.000 NA 0.000
#> GSM121414 2 0.0508 0.8835 0.000 0.984 0.004 0.000 NA 0.000
#> GSM121415 2 0.0363 0.8841 0.000 0.988 0.000 0.000 NA 0.000
#> GSM121416 2 0.1934 0.8788 0.000 0.916 0.000 0.044 NA 0.000
#> GSM120591 1 0.1333 0.8381 0.944 0.000 0.000 0.000 NA 0.008
#> GSM120594 1 0.1082 0.8427 0.956 0.000 0.000 0.000 NA 0.004
#> GSM120718 1 0.0713 0.8484 0.972 0.000 0.000 0.000 NA 0.000
#> GSM121205 1 0.0000 0.8570 1.000 0.000 0.000 0.000 NA 0.000
#> GSM121206 1 0.0000 0.8570 1.000 0.000 0.000 0.000 NA 0.000
#> GSM121207 1 0.0000 0.8570 1.000 0.000 0.000 0.000 NA 0.000
#> GSM121208 1 0.0000 0.8570 1.000 0.000 0.000 0.000 NA 0.000
#> GSM121209 1 0.0000 0.8570 1.000 0.000 0.000 0.000 NA 0.000
#> GSM121210 1 0.0000 0.8570 1.000 0.000 0.000 0.000 NA 0.000
#> GSM121211 1 0.0000 0.8570 1.000 0.000 0.000 0.000 NA 0.000
#> GSM121212 1 0.0000 0.8570 1.000 0.000 0.000 0.000 NA 0.000
#> GSM121213 1 0.0000 0.8570 1.000 0.000 0.000 0.000 NA 0.000
#> GSM121214 1 0.0000 0.8570 1.000 0.000 0.000 0.000 NA 0.000
#> GSM121215 1 0.0000 0.8570 1.000 0.000 0.000 0.000 NA 0.000
#> GSM121216 1 0.0000 0.8570 1.000 0.000 0.000 0.000 NA 0.000
#> GSM121217 1 0.0000 0.8570 1.000 0.000 0.000 0.000 NA 0.000
#> GSM121218 1 0.0000 0.8570 1.000 0.000 0.000 0.000 NA 0.000
#> GSM121234 1 0.0000 0.8570 1.000 0.000 0.000 0.000 NA 0.000
#> GSM121243 1 0.0000 0.8570 1.000 0.000 0.000 0.000 NA 0.000
#> GSM121245 1 0.0000 0.8570 1.000 0.000 0.000 0.000 NA 0.000
#> GSM121246 1 0.0000 0.8570 1.000 0.000 0.000 0.000 NA 0.000
#> GSM121247 1 0.0000 0.8570 1.000 0.000 0.000 0.000 NA 0.000
#> GSM121248 1 0.0000 0.8570 1.000 0.000 0.000 0.000 NA 0.000
#> GSM120744 6 0.0146 0.8238 0.000 0.000 0.000 0.000 NA 0.996
#> GSM120745 6 0.3454 0.5930 0.208 0.000 0.000 0.000 NA 0.768
#> GSM120746 6 0.0000 0.8242 0.000 0.000 0.000 0.000 NA 1.000
#> GSM120747 6 0.0692 0.8159 0.000 0.000 0.020 0.000 NA 0.976
#> GSM120748 6 0.0858 0.8120 0.000 0.000 0.028 0.000 NA 0.968
#> GSM120749 6 0.0146 0.8233 0.004 0.000 0.000 0.000 NA 0.996
#> GSM120750 6 0.0000 0.8242 0.000 0.000 0.000 0.000 NA 1.000
#> GSM120751 6 0.0000 0.8242 0.000 0.000 0.000 0.000 NA 1.000
#> GSM120752 6 0.1983 0.7698 0.072 0.000 0.000 0.000 NA 0.908
#> GSM121336 2 0.0000 0.8864 0.000 1.000 0.000 0.000 NA 0.000
#> GSM121339 2 0.0603 0.8861 0.000 0.980 0.016 0.004 NA 0.000
#> GSM121349 2 0.0000 0.8864 0.000 1.000 0.000 0.000 NA 0.000
#> GSM121355 2 0.0000 0.8864 0.000 1.000 0.000 0.000 NA 0.000
#> GSM120757 6 0.5694 0.4681 0.156 0.000 0.000 0.004 NA 0.512
#> GSM120766 6 0.4405 0.6208 0.000 0.000 0.036 0.004 NA 0.644
#> GSM120770 2 0.0603 0.8822 0.000 0.980 0.004 0.000 NA 0.000
#> GSM120779 1 0.4500 0.4778 0.592 0.000 0.000 0.008 NA 0.024
#> GSM120780 6 0.4475 0.6752 0.000 0.000 0.100 0.000 NA 0.700
#> GSM121102 2 0.1649 0.8599 0.000 0.932 0.032 0.000 NA 0.000
#> GSM121203 6 0.4229 0.5308 0.000 0.000 0.292 0.000 NA 0.668
#> GSM121204 1 0.2070 0.8004 0.896 0.000 0.000 0.000 NA 0.012
#> GSM121330 1 0.6216 0.4249 0.536 0.000 0.216 0.000 NA 0.036
#> GSM121335 1 0.4043 0.6920 0.740 0.000 0.036 0.000 NA 0.012
#> GSM121337 2 0.2170 0.8269 0.000 0.888 0.012 0.000 NA 0.000
#> GSM121338 2 0.3845 0.7125 0.000 0.772 0.088 0.000 NA 0.000
#> GSM121341 1 0.4043 0.6920 0.740 0.000 0.036 0.000 NA 0.012
#> GSM121342 1 0.3136 0.7383 0.796 0.000 0.016 0.000 NA 0.000
#> GSM121343 2 0.3834 0.7133 0.000 0.772 0.084 0.000 NA 0.000
#> GSM121344 1 0.4257 0.6777 0.724 0.000 0.056 0.000 NA 0.008
#> GSM121346 1 0.5619 0.5592 0.620 0.000 0.136 0.000 NA 0.032
#> GSM121347 2 0.4652 0.6755 0.000 0.728 0.076 0.032 NA 0.000
#> GSM121348 2 0.2311 0.8248 0.000 0.880 0.016 0.000 NA 0.000
#> GSM121350 1 0.6256 0.4081 0.528 0.000 0.224 0.000 NA 0.036
#> GSM121352 1 0.5480 0.5697 0.628 0.000 0.136 0.000 NA 0.024
#> GSM121354 1 0.5370 0.5806 0.636 0.000 0.132 0.000 NA 0.020
#> GSM120753 2 0.2706 0.8487 0.000 0.860 0.000 0.104 NA 0.000
#> GSM120761 2 0.2752 0.8453 0.000 0.856 0.000 0.108 NA 0.000
#> GSM120768 2 0.4002 0.6660 0.000 0.704 0.000 0.260 NA 0.000
#> GSM120781 2 0.1970 0.8741 0.000 0.912 0.000 0.060 NA 0.000
#> GSM120788 4 0.3043 0.6491 0.000 0.000 0.008 0.792 NA 0.000
#> GSM120760 4 0.4593 -0.0808 0.000 0.472 0.000 0.492 NA 0.000
#> GSM120763 2 0.4408 0.4689 0.000 0.608 0.000 0.356 NA 0.000
#> GSM120764 4 0.1728 0.7050 0.000 0.008 0.004 0.924 NA 0.000
#> GSM120777 4 0.2848 0.6597 0.000 0.000 0.008 0.816 NA 0.000
#> GSM120786 4 0.3481 0.6462 0.000 0.192 0.000 0.776 NA 0.000
#> GSM121329 1 0.1349 0.8373 0.940 0.000 0.004 0.000 NA 0.000
#> GSM121331 1 0.4770 0.4296 0.560 0.000 0.000 0.012 NA 0.032
#> GSM121333 1 0.4745 0.4477 0.572 0.000 0.000 0.012 NA 0.032
#> GSM121345 1 0.4652 0.4360 0.560 0.000 0.000 0.012 NA 0.024
#> GSM121356 1 0.5201 0.3719 0.532 0.000 0.000 0.012 NA 0.064
#> GSM120754 4 0.3551 0.6488 0.000 0.192 0.000 0.772 NA 0.000
#> GSM120759 2 0.0508 0.8835 0.000 0.984 0.004 0.000 NA 0.000
#> GSM120762 2 0.1845 0.8773 0.000 0.920 0.000 0.052 NA 0.000
#> GSM120775 4 0.2466 0.6958 0.000 0.008 0.008 0.872 NA 0.000
#> GSM120776 4 0.4413 0.5189 0.004 0.000 0.008 0.620 NA 0.016
#> GSM120782 4 0.3558 0.6557 0.000 0.184 0.004 0.780 NA 0.000
#> GSM120789 2 0.0622 0.8868 0.000 0.980 0.000 0.008 NA 0.000
#> GSM120790 2 0.0508 0.8835 0.000 0.984 0.004 0.000 NA 0.000
#> GSM120791 2 0.4466 0.5112 0.000 0.620 0.000 0.336 NA 0.000
#> GSM120755 2 0.2088 0.8707 0.000 0.904 0.000 0.068 NA 0.000
#> GSM120756 4 0.3161 0.6387 0.000 0.000 0.008 0.776 NA 0.000
#> GSM120769 2 0.1780 0.8781 0.000 0.924 0.000 0.048 NA 0.000
#> GSM120778 2 0.2972 0.8304 0.000 0.836 0.000 0.128 NA 0.000
#> GSM120792 2 0.4269 0.5604 0.000 0.648 0.000 0.316 NA 0.000
#> GSM121332 2 0.1334 0.8851 0.000 0.948 0.000 0.032 NA 0.000
#> GSM121334 2 0.3014 0.8268 0.000 0.832 0.000 0.132 NA 0.000
#> GSM121340 4 0.1977 0.7212 0.000 0.032 0.008 0.920 NA 0.000
#> GSM121351 2 0.0146 0.8869 0.000 0.996 0.000 0.004 NA 0.000
#> GSM121353 4 0.2279 0.7239 0.000 0.048 0.004 0.900 NA 0.000
#> GSM120758 2 0.2436 0.8598 0.000 0.880 0.000 0.088 NA 0.000
#> GSM120771 2 0.2058 0.8736 0.000 0.908 0.000 0.056 NA 0.000
#> GSM120772 2 0.3094 0.8199 0.000 0.824 0.000 0.140 NA 0.000
#> GSM120773 4 0.3678 0.6204 0.000 0.220 0.004 0.752 NA 0.000
#> GSM120774 2 0.2752 0.8520 0.000 0.864 0.004 0.096 NA 0.000
#> GSM120783 4 0.1410 0.7145 0.000 0.044 0.004 0.944 NA 0.000
#> GSM120787 2 0.3029 0.8348 0.000 0.840 0.004 0.120 NA 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> ATC:skmeans 119 5.01e-10 2
#> ATC:skmeans 116 1.48e-14 3
#> ATC:skmeans 116 6.80e-17 4
#> ATC:skmeans 117 3.50e-24 5
#> ATC:skmeans 109 9.40e-26 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 21512 rows and 119 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.791 0.812 0.928 0.4862 0.497 0.497
#> 3 3 0.955 0.941 0.974 0.3215 0.824 0.659
#> 4 4 0.677 0.767 0.879 0.1570 0.807 0.520
#> 5 5 0.747 0.738 0.845 0.0678 0.921 0.700
#> 6 6 0.816 0.771 0.881 0.0454 0.948 0.747
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
#> GSM120719 1 0.0000 0.8681 1.000 0.000
#> GSM120720 1 0.0000 0.8681 1.000 0.000
#> GSM120765 2 0.0000 0.9621 0.000 1.000
#> GSM120767 2 0.0000 0.9621 0.000 1.000
#> GSM120784 2 0.0000 0.9621 0.000 1.000
#> GSM121400 1 0.9993 0.2332 0.516 0.484
#> GSM121401 1 0.9993 0.2332 0.516 0.484
#> GSM121402 2 0.0000 0.9621 0.000 1.000
#> GSM121403 2 0.0000 0.9621 0.000 1.000
#> GSM121404 2 0.0000 0.9621 0.000 1.000
#> GSM121405 2 0.9998 -0.1803 0.492 0.508
#> GSM121406 2 0.0000 0.9621 0.000 1.000
#> GSM121408 2 0.0000 0.9621 0.000 1.000
#> GSM121409 1 0.9996 0.2208 0.512 0.488
#> GSM121410 1 0.9993 0.2332 0.516 0.484
#> GSM121412 2 0.0000 0.9621 0.000 1.000
#> GSM121413 2 0.0000 0.9621 0.000 1.000
#> GSM121414 2 0.0000 0.9621 0.000 1.000
#> GSM121415 2 0.0000 0.9621 0.000 1.000
#> GSM121416 2 0.0000 0.9621 0.000 1.000
#> GSM120591 1 0.0000 0.8681 1.000 0.000
#> GSM120594 1 0.0000 0.8681 1.000 0.000
#> GSM120718 1 0.0000 0.8681 1.000 0.000
#> GSM121205 1 0.0000 0.8681 1.000 0.000
#> GSM121206 1 0.0000 0.8681 1.000 0.000
#> GSM121207 1 0.0000 0.8681 1.000 0.000
#> GSM121208 1 0.0000 0.8681 1.000 0.000
#> GSM121209 1 0.0000 0.8681 1.000 0.000
#> GSM121210 1 0.0000 0.8681 1.000 0.000
#> GSM121211 1 0.0000 0.8681 1.000 0.000
#> GSM121212 1 0.0000 0.8681 1.000 0.000
#> GSM121213 1 0.0000 0.8681 1.000 0.000
#> GSM121214 1 0.0000 0.8681 1.000 0.000
#> GSM121215 1 0.0000 0.8681 1.000 0.000
#> GSM121216 1 0.0000 0.8681 1.000 0.000
#> GSM121217 1 0.0000 0.8681 1.000 0.000
#> GSM121218 1 0.0000 0.8681 1.000 0.000
#> GSM121234 1 0.0000 0.8681 1.000 0.000
#> GSM121243 1 0.0000 0.8681 1.000 0.000
#> GSM121245 1 0.0000 0.8681 1.000 0.000
#> GSM121246 1 0.0000 0.8681 1.000 0.000
#> GSM121247 1 0.0000 0.8681 1.000 0.000
#> GSM121248 1 0.0000 0.8681 1.000 0.000
#> GSM120744 1 0.9993 0.2332 0.516 0.484
#> GSM120745 1 0.0000 0.8681 1.000 0.000
#> GSM120746 1 0.2423 0.8419 0.960 0.040
#> GSM120747 1 0.9993 0.2332 0.516 0.484
#> GSM120748 2 0.9775 0.1257 0.412 0.588
#> GSM120749 1 0.0000 0.8681 1.000 0.000
#> GSM120750 1 0.9977 0.2632 0.528 0.472
#> GSM120751 1 0.9993 0.2332 0.516 0.484
#> GSM120752 1 0.0000 0.8681 1.000 0.000
#> GSM121336 2 0.0000 0.9621 0.000 1.000
#> GSM121339 2 0.0000 0.9621 0.000 1.000
#> GSM121349 2 0.0000 0.9621 0.000 1.000
#> GSM121355 2 0.0000 0.9621 0.000 1.000
#> GSM120757 1 0.0000 0.8681 1.000 0.000
#> GSM120766 1 0.9993 0.2332 0.516 0.484
#> GSM120770 2 0.0000 0.9621 0.000 1.000
#> GSM120779 1 0.0000 0.8681 1.000 0.000
#> GSM120780 1 0.9993 0.2332 0.516 0.484
#> GSM121102 2 0.0000 0.9621 0.000 1.000
#> GSM121203 2 0.9983 -0.1231 0.476 0.524
#> GSM121204 1 0.0000 0.8681 1.000 0.000
#> GSM121330 1 0.9710 0.4170 0.600 0.400
#> GSM121335 1 0.0000 0.8681 1.000 0.000
#> GSM121337 2 0.0000 0.9621 0.000 1.000
#> GSM121338 2 0.0000 0.9621 0.000 1.000
#> GSM121341 1 0.0000 0.8681 1.000 0.000
#> GSM121342 1 0.0000 0.8681 1.000 0.000
#> GSM121343 2 0.0000 0.9621 0.000 1.000
#> GSM121344 1 0.0000 0.8681 1.000 0.000
#> GSM121346 1 0.0000 0.8681 1.000 0.000
#> GSM121347 2 0.0000 0.9621 0.000 1.000
#> GSM121348 2 0.0000 0.9621 0.000 1.000
#> GSM121350 1 0.0376 0.8657 0.996 0.004
#> GSM121352 1 0.0000 0.8681 1.000 0.000
#> GSM121354 1 0.0000 0.8681 1.000 0.000
#> GSM120753 2 0.0000 0.9621 0.000 1.000
#> GSM120761 2 0.0000 0.9621 0.000 1.000
#> GSM120768 2 0.0000 0.9621 0.000 1.000
#> GSM120781 2 0.0000 0.9621 0.000 1.000
#> GSM120788 2 0.4161 0.8618 0.084 0.916
#> GSM120760 2 0.0000 0.9621 0.000 1.000
#> GSM120763 2 0.0000 0.9621 0.000 1.000
#> GSM120764 2 0.0000 0.9621 0.000 1.000
#> GSM120777 2 0.0000 0.9621 0.000 1.000
#> GSM120786 2 0.0000 0.9621 0.000 1.000
#> GSM121329 1 0.8661 0.5963 0.712 0.288
#> GSM121331 1 0.6973 0.7149 0.812 0.188
#> GSM121333 1 0.0000 0.8681 1.000 0.000
#> GSM121345 1 0.0000 0.8681 1.000 0.000
#> GSM121356 1 0.9850 0.3621 0.572 0.428
#> GSM120754 2 0.0000 0.9621 0.000 1.000
#> GSM120759 2 0.0000 0.9621 0.000 1.000
#> GSM120762 2 0.0000 0.9621 0.000 1.000
#> GSM120775 2 0.0000 0.9621 0.000 1.000
#> GSM120776 2 0.9896 0.0184 0.440 0.560
#> GSM120782 2 0.0000 0.9621 0.000 1.000
#> GSM120789 2 0.0000 0.9621 0.000 1.000
#> GSM120790 2 0.0000 0.9621 0.000 1.000
#> GSM120791 2 0.0000 0.9621 0.000 1.000
#> GSM120755 2 0.0000 0.9621 0.000 1.000
#> GSM120756 1 0.9996 0.2207 0.512 0.488
#> GSM120769 2 0.0000 0.9621 0.000 1.000
#> GSM120778 2 0.0000 0.9621 0.000 1.000
#> GSM120792 2 0.0000 0.9621 0.000 1.000
#> GSM121332 2 0.0000 0.9621 0.000 1.000
#> GSM121334 2 0.0000 0.9621 0.000 1.000
#> GSM121340 2 0.0000 0.9621 0.000 1.000
#> GSM121351 2 0.0000 0.9621 0.000 1.000
#> GSM121353 2 0.0000 0.9621 0.000 1.000
#> GSM120758 2 0.0000 0.9621 0.000 1.000
#> GSM120771 2 0.0000 0.9621 0.000 1.000
#> GSM120772 2 0.0000 0.9621 0.000 1.000
#> GSM120773 2 0.0000 0.9621 0.000 1.000
#> GSM120774 2 0.0000 0.9621 0.000 1.000
#> GSM120783 2 0.0000 0.9621 0.000 1.000
#> GSM120787 2 0.0000 0.9621 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM120719 3 0.6140 0.304 0.404 0.000 0.596
#> GSM120720 1 0.3686 0.844 0.860 0.000 0.140
#> GSM120765 2 0.0000 0.980 0.000 1.000 0.000
#> GSM120767 2 0.0000 0.980 0.000 1.000 0.000
#> GSM120784 2 0.0000 0.980 0.000 1.000 0.000
#> GSM121400 3 0.0424 0.954 0.000 0.008 0.992
#> GSM121401 3 0.0424 0.954 0.000 0.008 0.992
#> GSM121402 2 0.0000 0.980 0.000 1.000 0.000
#> GSM121403 2 0.2711 0.907 0.000 0.912 0.088
#> GSM121404 2 0.4178 0.808 0.000 0.828 0.172
#> GSM121405 3 0.0424 0.954 0.000 0.008 0.992
#> GSM121406 2 0.0000 0.980 0.000 1.000 0.000
#> GSM121408 2 0.0000 0.980 0.000 1.000 0.000
#> GSM121409 3 0.0424 0.954 0.000 0.008 0.992
#> GSM121410 3 0.0424 0.954 0.000 0.008 0.992
#> GSM121412 2 0.0000 0.980 0.000 1.000 0.000
#> GSM121413 2 0.0000 0.980 0.000 1.000 0.000
#> GSM121414 2 0.0000 0.980 0.000 1.000 0.000
#> GSM121415 2 0.0000 0.980 0.000 1.000 0.000
#> GSM121416 2 0.0000 0.980 0.000 1.000 0.000
#> GSM120591 3 0.2066 0.909 0.060 0.000 0.940
#> GSM120594 1 0.4750 0.742 0.784 0.000 0.216
#> GSM120718 1 0.0892 0.958 0.980 0.000 0.020
#> GSM121205 1 0.0000 0.973 1.000 0.000 0.000
#> GSM121206 1 0.0000 0.973 1.000 0.000 0.000
#> GSM121207 1 0.0000 0.973 1.000 0.000 0.000
#> GSM121208 1 0.0000 0.973 1.000 0.000 0.000
#> GSM121209 1 0.0000 0.973 1.000 0.000 0.000
#> GSM121210 1 0.0000 0.973 1.000 0.000 0.000
#> GSM121211 1 0.0000 0.973 1.000 0.000 0.000
#> GSM121212 1 0.0000 0.973 1.000 0.000 0.000
#> GSM121213 1 0.0000 0.973 1.000 0.000 0.000
#> GSM121214 1 0.0000 0.973 1.000 0.000 0.000
#> GSM121215 1 0.0000 0.973 1.000 0.000 0.000
#> GSM121216 1 0.0000 0.973 1.000 0.000 0.000
#> GSM121217 1 0.0000 0.973 1.000 0.000 0.000
#> GSM121218 1 0.0000 0.973 1.000 0.000 0.000
#> GSM121234 1 0.0000 0.973 1.000 0.000 0.000
#> GSM121243 1 0.0000 0.973 1.000 0.000 0.000
#> GSM121245 1 0.0000 0.973 1.000 0.000 0.000
#> GSM121246 1 0.0000 0.973 1.000 0.000 0.000
#> GSM121247 1 0.0000 0.973 1.000 0.000 0.000
#> GSM121248 1 0.0000 0.973 1.000 0.000 0.000
#> GSM120744 3 0.0000 0.957 0.000 0.000 1.000
#> GSM120745 3 0.0000 0.957 0.000 0.000 1.000
#> GSM120746 3 0.0000 0.957 0.000 0.000 1.000
#> GSM120747 3 0.0000 0.957 0.000 0.000 1.000
#> GSM120748 3 0.0000 0.957 0.000 0.000 1.000
#> GSM120749 3 0.0000 0.957 0.000 0.000 1.000
#> GSM120750 3 0.0000 0.957 0.000 0.000 1.000
#> GSM120751 3 0.0000 0.957 0.000 0.000 1.000
#> GSM120752 3 0.0000 0.957 0.000 0.000 1.000
#> GSM121336 2 0.0000 0.980 0.000 1.000 0.000
#> GSM121339 2 0.4002 0.824 0.000 0.840 0.160
#> GSM121349 2 0.0000 0.980 0.000 1.000 0.000
#> GSM121355 2 0.0000 0.980 0.000 1.000 0.000
#> GSM120757 3 0.0000 0.957 0.000 0.000 1.000
#> GSM120766 3 0.0000 0.957 0.000 0.000 1.000
#> GSM120770 2 0.0424 0.975 0.000 0.992 0.008
#> GSM120779 1 0.2878 0.891 0.904 0.000 0.096
#> GSM120780 3 0.0424 0.954 0.000 0.008 0.992
#> GSM121102 2 0.1529 0.951 0.000 0.960 0.040
#> GSM121203 3 0.0424 0.954 0.000 0.008 0.992
#> GSM121204 3 0.6235 0.207 0.436 0.000 0.564
#> GSM121330 3 0.0000 0.957 0.000 0.000 1.000
#> GSM121335 1 0.0000 0.973 1.000 0.000 0.000
#> GSM121337 2 0.0000 0.980 0.000 1.000 0.000
#> GSM121338 2 0.2625 0.912 0.000 0.916 0.084
#> GSM121341 1 0.0000 0.973 1.000 0.000 0.000
#> GSM121342 1 0.0000 0.973 1.000 0.000 0.000
#> GSM121343 2 0.1529 0.951 0.000 0.960 0.040
#> GSM121344 1 0.2165 0.922 0.936 0.000 0.064
#> GSM121346 3 0.0592 0.951 0.012 0.000 0.988
#> GSM121347 2 0.6079 0.400 0.000 0.612 0.388
#> GSM121348 2 0.0000 0.980 0.000 1.000 0.000
#> GSM121350 3 0.0000 0.957 0.000 0.000 1.000
#> GSM121352 3 0.3116 0.863 0.108 0.000 0.892
#> GSM121354 1 0.4555 0.757 0.800 0.000 0.200
#> GSM120753 2 0.0000 0.980 0.000 1.000 0.000
#> GSM120761 2 0.0000 0.980 0.000 1.000 0.000
#> GSM120768 2 0.0000 0.980 0.000 1.000 0.000
#> GSM120781 2 0.0000 0.980 0.000 1.000 0.000
#> GSM120788 3 0.3038 0.845 0.000 0.104 0.896
#> GSM120760 2 0.0000 0.980 0.000 1.000 0.000
#> GSM120763 2 0.0000 0.980 0.000 1.000 0.000
#> GSM120764 2 0.0000 0.980 0.000 1.000 0.000
#> GSM120777 2 0.2796 0.901 0.000 0.908 0.092
#> GSM120786 2 0.0000 0.980 0.000 1.000 0.000
#> GSM121329 3 0.0000 0.957 0.000 0.000 1.000
#> GSM121331 3 0.0000 0.957 0.000 0.000 1.000
#> GSM121333 3 0.2066 0.909 0.060 0.000 0.940
#> GSM121345 3 0.0592 0.950 0.012 0.000 0.988
#> GSM121356 3 0.0000 0.957 0.000 0.000 1.000
#> GSM120754 2 0.0000 0.980 0.000 1.000 0.000
#> GSM120759 2 0.0000 0.980 0.000 1.000 0.000
#> GSM120762 2 0.0000 0.980 0.000 1.000 0.000
#> GSM120775 2 0.1860 0.940 0.000 0.948 0.052
#> GSM120776 3 0.0000 0.957 0.000 0.000 1.000
#> GSM120782 2 0.0237 0.977 0.000 0.996 0.004
#> GSM120789 2 0.0000 0.980 0.000 1.000 0.000
#> GSM120790 2 0.0000 0.980 0.000 1.000 0.000
#> GSM120791 2 0.0000 0.980 0.000 1.000 0.000
#> GSM120755 2 0.0000 0.980 0.000 1.000 0.000
#> GSM120756 3 0.0000 0.957 0.000 0.000 1.000
#> GSM120769 2 0.0000 0.980 0.000 1.000 0.000
#> GSM120778 2 0.0000 0.980 0.000 1.000 0.000
#> GSM120792 2 0.0000 0.980 0.000 1.000 0.000
#> GSM121332 2 0.0000 0.980 0.000 1.000 0.000
#> GSM121334 2 0.0000 0.980 0.000 1.000 0.000
#> GSM121340 2 0.0000 0.980 0.000 1.000 0.000
#> GSM121351 2 0.0000 0.980 0.000 1.000 0.000
#> GSM121353 2 0.0000 0.980 0.000 1.000 0.000
#> GSM120758 2 0.0000 0.980 0.000 1.000 0.000
#> GSM120771 2 0.0000 0.980 0.000 1.000 0.000
#> GSM120772 2 0.0000 0.980 0.000 1.000 0.000
#> GSM120773 2 0.0000 0.980 0.000 1.000 0.000
#> GSM120774 2 0.0000 0.980 0.000 1.000 0.000
#> GSM120783 2 0.0000 0.980 0.000 1.000 0.000
#> GSM120787 2 0.0000 0.980 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM120719 3 0.2345 0.86896 0.100 0.000 0.900 0.000
#> GSM120720 3 0.4804 0.40720 0.384 0.000 0.616 0.000
#> GSM120765 2 0.2704 0.83499 0.000 0.876 0.000 0.124
#> GSM120767 2 0.2973 0.83089 0.000 0.856 0.000 0.144
#> GSM120784 2 0.4103 0.73327 0.000 0.744 0.000 0.256
#> GSM121400 4 0.4040 0.65037 0.000 0.000 0.248 0.752
#> GSM121401 4 0.4713 0.49050 0.000 0.000 0.360 0.640
#> GSM121402 4 0.3688 0.65755 0.000 0.208 0.000 0.792
#> GSM121403 4 0.2466 0.73760 0.000 0.004 0.096 0.900
#> GSM121404 4 0.2345 0.73643 0.000 0.000 0.100 0.900
#> GSM121405 4 0.4040 0.65037 0.000 0.000 0.248 0.752
#> GSM121406 2 0.0469 0.81792 0.000 0.988 0.000 0.012
#> GSM121408 2 0.2814 0.83394 0.000 0.868 0.000 0.132
#> GSM121409 4 0.4040 0.65037 0.000 0.000 0.248 0.752
#> GSM121410 4 0.4040 0.65037 0.000 0.000 0.248 0.752
#> GSM121412 2 0.4072 0.69771 0.000 0.748 0.000 0.252
#> GSM121413 2 0.0469 0.81803 0.000 0.988 0.000 0.012
#> GSM121414 2 0.4222 0.64836 0.000 0.728 0.000 0.272
#> GSM121415 4 0.3486 0.68171 0.000 0.188 0.000 0.812
#> GSM121416 4 0.2868 0.70805 0.000 0.136 0.000 0.864
#> GSM120591 3 0.2281 0.87238 0.096 0.000 0.904 0.000
#> GSM120594 3 0.3649 0.74454 0.204 0.000 0.796 0.000
#> GSM120718 1 0.1716 0.90240 0.936 0.000 0.064 0.000
#> GSM121205 1 0.0000 0.96098 1.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.96098 1.000 0.000 0.000 0.000
#> GSM121207 1 0.0000 0.96098 1.000 0.000 0.000 0.000
#> GSM121208 1 0.0000 0.96098 1.000 0.000 0.000 0.000
#> GSM121209 1 0.0000 0.96098 1.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.96098 1.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.96098 1.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.96098 1.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.96098 1.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.96098 1.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.96098 1.000 0.000 0.000 0.000
#> GSM121216 1 0.0000 0.96098 1.000 0.000 0.000 0.000
#> GSM121217 1 0.0000 0.96098 1.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.96098 1.000 0.000 0.000 0.000
#> GSM121234 1 0.0000 0.96098 1.000 0.000 0.000 0.000
#> GSM121243 1 0.0000 0.96098 1.000 0.000 0.000 0.000
#> GSM121245 1 0.0000 0.96098 1.000 0.000 0.000 0.000
#> GSM121246 1 0.0000 0.96098 1.000 0.000 0.000 0.000
#> GSM121247 1 0.0000 0.96098 1.000 0.000 0.000 0.000
#> GSM121248 1 0.0000 0.96098 1.000 0.000 0.000 0.000
#> GSM120744 3 0.0000 0.92622 0.000 0.000 1.000 0.000
#> GSM120745 3 0.0000 0.92622 0.000 0.000 1.000 0.000
#> GSM120746 3 0.0000 0.92622 0.000 0.000 1.000 0.000
#> GSM120747 3 0.0592 0.92113 0.000 0.000 0.984 0.016
#> GSM120748 3 0.0592 0.92113 0.000 0.000 0.984 0.016
#> GSM120749 3 0.0000 0.92622 0.000 0.000 1.000 0.000
#> GSM120750 3 0.0000 0.92622 0.000 0.000 1.000 0.000
#> GSM120751 3 0.0000 0.92622 0.000 0.000 1.000 0.000
#> GSM120752 3 0.0000 0.92622 0.000 0.000 1.000 0.000
#> GSM121336 2 0.0000 0.82209 0.000 1.000 0.000 0.000
#> GSM121339 4 0.2675 0.73863 0.000 0.008 0.100 0.892
#> GSM121349 2 0.0000 0.82209 0.000 1.000 0.000 0.000
#> GSM121355 2 0.0000 0.82209 0.000 1.000 0.000 0.000
#> GSM120757 3 0.0000 0.92622 0.000 0.000 1.000 0.000
#> GSM120766 3 0.0000 0.92622 0.000 0.000 1.000 0.000
#> GSM120770 4 0.2635 0.73326 0.000 0.076 0.020 0.904
#> GSM120779 1 0.4898 0.23344 0.584 0.000 0.416 0.000
#> GSM120780 4 0.4356 0.60612 0.000 0.000 0.292 0.708
#> GSM121102 4 0.2611 0.73844 0.000 0.008 0.096 0.896
#> GSM121203 4 0.4040 0.65037 0.000 0.000 0.248 0.752
#> GSM121204 3 0.2704 0.84631 0.124 0.000 0.876 0.000
#> GSM121330 3 0.0707 0.91938 0.000 0.000 0.980 0.020
#> GSM121335 1 0.0000 0.96098 1.000 0.000 0.000 0.000
#> GSM121337 4 0.0921 0.73637 0.000 0.028 0.000 0.972
#> GSM121338 4 0.2345 0.73643 0.000 0.000 0.100 0.900
#> GSM121341 1 0.0000 0.96098 1.000 0.000 0.000 0.000
#> GSM121342 1 0.0000 0.96098 1.000 0.000 0.000 0.000
#> GSM121343 4 0.2546 0.73835 0.000 0.008 0.092 0.900
#> GSM121344 1 0.3356 0.76604 0.824 0.000 0.176 0.000
#> GSM121346 3 0.1042 0.91732 0.008 0.000 0.972 0.020
#> GSM121347 4 0.2760 0.72606 0.000 0.000 0.128 0.872
#> GSM121348 4 0.1211 0.73702 0.000 0.040 0.000 0.960
#> GSM121350 3 0.0707 0.91938 0.000 0.000 0.980 0.020
#> GSM121352 3 0.4855 0.61448 0.268 0.000 0.712 0.020
#> GSM121354 1 0.4690 0.60391 0.724 0.000 0.260 0.016
#> GSM120753 2 0.3024 0.82953 0.000 0.852 0.000 0.148
#> GSM120761 2 0.3266 0.81675 0.000 0.832 0.000 0.168
#> GSM120768 4 0.4746 0.35951 0.000 0.368 0.000 0.632
#> GSM120781 2 0.1716 0.83419 0.000 0.936 0.000 0.064
#> GSM120788 4 0.4677 0.49303 0.000 0.004 0.316 0.680
#> GSM120760 4 0.4985 -0.00199 0.000 0.468 0.000 0.532
#> GSM120763 2 0.4304 0.69591 0.000 0.716 0.000 0.284
#> GSM120764 4 0.3444 0.64963 0.000 0.184 0.000 0.816
#> GSM120777 4 0.3862 0.67209 0.000 0.024 0.152 0.824
#> GSM120786 4 0.4564 0.44033 0.000 0.328 0.000 0.672
#> GSM121329 3 0.0707 0.91938 0.000 0.000 0.980 0.020
#> GSM121331 3 0.1716 0.89187 0.000 0.000 0.936 0.064
#> GSM121333 3 0.2345 0.86896 0.100 0.000 0.900 0.000
#> GSM121345 3 0.0188 0.92511 0.000 0.000 0.996 0.004
#> GSM121356 3 0.0000 0.92622 0.000 0.000 1.000 0.000
#> GSM120754 4 0.1474 0.73272 0.000 0.052 0.000 0.948
#> GSM120759 2 0.0000 0.82209 0.000 1.000 0.000 0.000
#> GSM120762 2 0.0921 0.82862 0.000 0.972 0.000 0.028
#> GSM120775 4 0.1302 0.73417 0.000 0.044 0.000 0.956
#> GSM120776 3 0.2345 0.86443 0.000 0.000 0.900 0.100
#> GSM120782 4 0.2909 0.72051 0.000 0.092 0.020 0.888
#> GSM120789 4 0.4866 0.33887 0.000 0.404 0.000 0.596
#> GSM120790 2 0.4477 0.48494 0.000 0.688 0.000 0.312
#> GSM120791 4 0.2149 0.72483 0.000 0.088 0.000 0.912
#> GSM120755 2 0.3024 0.82953 0.000 0.852 0.000 0.148
#> GSM120756 3 0.2345 0.86443 0.000 0.000 0.900 0.100
#> GSM120769 2 0.0000 0.82209 0.000 1.000 0.000 0.000
#> GSM120778 2 0.3024 0.82953 0.000 0.852 0.000 0.148
#> GSM120792 4 0.4605 0.43245 0.000 0.336 0.000 0.664
#> GSM121332 2 0.4804 0.45950 0.000 0.616 0.000 0.384
#> GSM121334 2 0.3024 0.82953 0.000 0.852 0.000 0.148
#> GSM121340 4 0.3311 0.65973 0.000 0.172 0.000 0.828
#> GSM121351 2 0.0000 0.82209 0.000 1.000 0.000 0.000
#> GSM121353 4 0.1022 0.73931 0.000 0.032 0.000 0.968
#> GSM120758 4 0.4898 0.29886 0.000 0.416 0.000 0.584
#> GSM120771 2 0.3024 0.82953 0.000 0.852 0.000 0.148
#> GSM120772 4 0.4967 0.19848 0.000 0.452 0.000 0.548
#> GSM120773 4 0.1716 0.73063 0.000 0.064 0.000 0.936
#> GSM120774 4 0.4585 0.44063 0.000 0.332 0.000 0.668
#> GSM120783 4 0.4304 0.52015 0.000 0.284 0.000 0.716
#> GSM120787 2 0.5000 0.06360 0.000 0.500 0.000 0.500
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM120719 3 0.0290 0.9158 0.008 0.000 0.992 0.000 0.000
#> GSM120720 3 0.5666 0.5079 0.244 0.000 0.620 0.136 0.000
#> GSM120765 2 0.3639 0.8091 0.000 0.792 0.000 0.184 0.024
#> GSM120767 2 0.3845 0.8015 0.000 0.768 0.000 0.208 0.024
#> GSM120784 2 0.4302 0.7900 0.000 0.744 0.000 0.208 0.048
#> GSM121400 5 0.3409 0.6699 0.000 0.000 0.032 0.144 0.824
#> GSM121401 5 0.5720 0.4605 0.000 0.000 0.168 0.208 0.624
#> GSM121402 5 0.4370 0.5997 0.000 0.056 0.000 0.200 0.744
#> GSM121403 5 0.0162 0.7854 0.000 0.000 0.004 0.000 0.996
#> GSM121404 5 0.0290 0.7848 0.000 0.000 0.008 0.000 0.992
#> GSM121405 5 0.1012 0.7738 0.000 0.000 0.012 0.020 0.968
#> GSM121406 2 0.0162 0.7943 0.000 0.996 0.000 0.000 0.004
#> GSM121408 2 0.3779 0.8049 0.000 0.776 0.000 0.200 0.024
#> GSM121409 5 0.0807 0.7778 0.000 0.000 0.012 0.012 0.976
#> GSM121410 5 0.0807 0.7778 0.000 0.000 0.012 0.012 0.976
#> GSM121412 2 0.4036 0.7918 0.000 0.788 0.000 0.144 0.068
#> GSM121413 2 0.0162 0.7943 0.000 0.996 0.000 0.000 0.004
#> GSM121414 2 0.5245 0.5163 0.000 0.640 0.000 0.080 0.280
#> GSM121415 5 0.3601 0.6930 0.000 0.052 0.000 0.128 0.820
#> GSM121416 5 0.3628 0.6205 0.000 0.012 0.000 0.216 0.772
#> GSM120591 3 0.0290 0.9158 0.008 0.000 0.992 0.000 0.000
#> GSM120594 3 0.0703 0.9048 0.024 0.000 0.976 0.000 0.000
#> GSM120718 1 0.3242 0.7714 0.816 0.000 0.172 0.012 0.000
#> GSM121205 1 0.0000 0.9024 1.000 0.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.9024 1.000 0.000 0.000 0.000 0.000
#> GSM121207 1 0.0000 0.9024 1.000 0.000 0.000 0.000 0.000
#> GSM121208 1 0.2127 0.8643 0.892 0.000 0.000 0.108 0.000
#> GSM121209 1 0.0000 0.9024 1.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.9024 1.000 0.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.9024 1.000 0.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.9024 1.000 0.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.9024 1.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.9024 1.000 0.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.9024 1.000 0.000 0.000 0.000 0.000
#> GSM121216 1 0.2966 0.8293 0.816 0.000 0.000 0.184 0.000
#> GSM121217 1 0.0000 0.9024 1.000 0.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.9024 1.000 0.000 0.000 0.000 0.000
#> GSM121234 1 0.0794 0.8941 0.972 0.000 0.000 0.028 0.000
#> GSM121243 1 0.0000 0.9024 1.000 0.000 0.000 0.000 0.000
#> GSM121245 1 0.0000 0.9024 1.000 0.000 0.000 0.000 0.000
#> GSM121246 1 0.2966 0.8293 0.816 0.000 0.000 0.184 0.000
#> GSM121247 1 0.0000 0.9024 1.000 0.000 0.000 0.000 0.000
#> GSM121248 1 0.0000 0.9024 1.000 0.000 0.000 0.000 0.000
#> GSM120744 3 0.0000 0.9185 0.000 0.000 1.000 0.000 0.000
#> GSM120745 3 0.0000 0.9185 0.000 0.000 1.000 0.000 0.000
#> GSM120746 3 0.0000 0.9185 0.000 0.000 1.000 0.000 0.000
#> GSM120747 3 0.0609 0.9088 0.000 0.000 0.980 0.000 0.020
#> GSM120748 3 0.0609 0.9088 0.000 0.000 0.980 0.000 0.020
#> GSM120749 3 0.0000 0.9185 0.000 0.000 1.000 0.000 0.000
#> GSM120750 3 0.0000 0.9185 0.000 0.000 1.000 0.000 0.000
#> GSM120751 3 0.0000 0.9185 0.000 0.000 1.000 0.000 0.000
#> GSM120752 3 0.0000 0.9185 0.000 0.000 1.000 0.000 0.000
#> GSM121336 2 0.0000 0.7967 0.000 1.000 0.000 0.000 0.000
#> GSM121339 5 0.0451 0.7855 0.000 0.000 0.008 0.004 0.988
#> GSM121349 2 0.0000 0.7967 0.000 1.000 0.000 0.000 0.000
#> GSM121355 2 0.0000 0.7967 0.000 1.000 0.000 0.000 0.000
#> GSM120757 3 0.0000 0.9185 0.000 0.000 1.000 0.000 0.000
#> GSM120766 3 0.0000 0.9185 0.000 0.000 1.000 0.000 0.000
#> GSM120770 5 0.0693 0.7801 0.000 0.008 0.000 0.012 0.980
#> GSM120779 1 0.4300 0.0355 0.524 0.000 0.476 0.000 0.000
#> GSM120780 5 0.2377 0.7027 0.000 0.000 0.128 0.000 0.872
#> GSM121102 5 0.0451 0.7847 0.000 0.000 0.008 0.004 0.988
#> GSM121203 5 0.1270 0.7630 0.000 0.000 0.052 0.000 0.948
#> GSM121204 3 0.0404 0.9136 0.012 0.000 0.988 0.000 0.000
#> GSM121330 3 0.3789 0.7714 0.000 0.000 0.768 0.212 0.020
#> GSM121335 1 0.3210 0.8135 0.788 0.000 0.000 0.212 0.000
#> GSM121337 5 0.0693 0.7801 0.000 0.008 0.000 0.012 0.980
#> GSM121338 5 0.0290 0.7848 0.000 0.000 0.008 0.000 0.992
#> GSM121341 1 0.3210 0.8135 0.788 0.000 0.000 0.212 0.000
#> GSM121342 1 0.3210 0.8135 0.788 0.000 0.000 0.212 0.000
#> GSM121343 5 0.0000 0.7850 0.000 0.000 0.000 0.000 1.000
#> GSM121344 1 0.5956 0.5751 0.592 0.000 0.196 0.212 0.000
#> GSM121346 3 0.4173 0.7622 0.012 0.000 0.756 0.212 0.020
#> GSM121347 5 0.0404 0.7834 0.000 0.000 0.012 0.000 0.988
#> GSM121348 5 0.0798 0.7807 0.000 0.016 0.000 0.008 0.976
#> GSM121350 3 0.3789 0.7714 0.000 0.000 0.768 0.212 0.020
#> GSM121352 3 0.7003 0.1967 0.308 0.000 0.460 0.212 0.020
#> GSM121354 1 0.6465 0.5348 0.568 0.000 0.204 0.212 0.016
#> GSM120753 2 0.3940 0.7939 0.000 0.756 0.000 0.220 0.024
#> GSM120761 2 0.3940 0.7939 0.000 0.756 0.000 0.220 0.024
#> GSM120768 5 0.6208 0.0666 0.000 0.144 0.000 0.376 0.480
#> GSM120781 2 0.2674 0.8144 0.000 0.868 0.000 0.120 0.012
#> GSM120788 4 0.5263 0.5150 0.000 0.000 0.056 0.576 0.368
#> GSM120760 4 0.4350 0.6550 0.000 0.152 0.000 0.764 0.084
#> GSM120763 4 0.4104 0.5445 0.000 0.220 0.000 0.748 0.032
#> GSM120764 4 0.3848 0.7300 0.000 0.040 0.000 0.788 0.172
#> GSM120777 4 0.4725 0.6495 0.000 0.004 0.036 0.680 0.280
#> GSM120786 4 0.4221 0.7032 0.000 0.112 0.000 0.780 0.108
#> GSM121329 3 0.3194 0.8187 0.000 0.000 0.832 0.148 0.020
#> GSM121331 3 0.0290 0.9145 0.000 0.000 0.992 0.008 0.000
#> GSM121333 3 0.0290 0.9158 0.008 0.000 0.992 0.000 0.000
#> GSM121345 3 0.0000 0.9185 0.000 0.000 1.000 0.000 0.000
#> GSM121356 3 0.0000 0.9185 0.000 0.000 1.000 0.000 0.000
#> GSM120754 4 0.3421 0.7208 0.000 0.008 0.000 0.788 0.204
#> GSM120759 2 0.0703 0.8040 0.000 0.976 0.000 0.024 0.000
#> GSM120762 2 0.1628 0.8090 0.000 0.936 0.000 0.056 0.008
#> GSM120775 4 0.3861 0.6687 0.000 0.004 0.000 0.712 0.284
#> GSM120776 4 0.4249 0.2109 0.000 0.000 0.432 0.568 0.000
#> GSM120782 4 0.3831 0.7283 0.000 0.024 0.004 0.784 0.188
#> GSM120789 5 0.5125 0.5719 0.000 0.148 0.000 0.156 0.696
#> GSM120790 2 0.4696 0.0767 0.000 0.556 0.000 0.016 0.428
#> GSM120791 5 0.4134 0.5928 0.000 0.032 0.000 0.224 0.744
#> GSM120755 2 0.3909 0.7973 0.000 0.760 0.000 0.216 0.024
#> GSM120756 4 0.4249 0.2109 0.000 0.000 0.432 0.568 0.000
#> GSM120769 2 0.0162 0.7979 0.000 0.996 0.000 0.000 0.004
#> GSM120778 2 0.3940 0.7939 0.000 0.756 0.000 0.220 0.024
#> GSM120792 4 0.4411 0.6924 0.000 0.120 0.000 0.764 0.116
#> GSM121332 5 0.6127 0.1021 0.000 0.384 0.000 0.132 0.484
#> GSM121334 2 0.3909 0.7973 0.000 0.760 0.000 0.216 0.024
#> GSM121340 4 0.4525 0.7161 0.000 0.056 0.000 0.724 0.220
#> GSM121351 2 0.0000 0.7967 0.000 1.000 0.000 0.000 0.000
#> GSM121353 4 0.4666 0.4986 0.000 0.016 0.000 0.572 0.412
#> GSM120758 5 0.5504 0.4695 0.000 0.132 0.000 0.224 0.644
#> GSM120771 2 0.3909 0.7973 0.000 0.760 0.000 0.216 0.024
#> GSM120772 5 0.5886 0.4019 0.000 0.176 0.000 0.224 0.600
#> GSM120773 4 0.3696 0.7204 0.000 0.016 0.000 0.772 0.212
#> GSM120774 4 0.6069 0.1013 0.000 0.120 0.000 0.448 0.432
#> GSM120783 4 0.4390 0.7274 0.000 0.084 0.000 0.760 0.156
#> GSM120787 4 0.6472 0.3114 0.000 0.284 0.000 0.492 0.224
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM120719 6 0.0000 0.9682 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120720 6 0.4242 0.1145 0.016 0.000 0.448 0.000 0.000 0.536
#> GSM120765 2 0.4121 0.7704 0.000 0.720 0.000 0.220 0.060 0.000
#> GSM120767 2 0.4294 0.7611 0.000 0.692 0.000 0.248 0.060 0.000
#> GSM120784 2 0.4348 0.7612 0.000 0.688 0.000 0.248 0.064 0.000
#> GSM121400 3 0.3862 0.2200 0.000 0.000 0.608 0.000 0.388 0.004
#> GSM121401 3 0.0291 0.8635 0.000 0.000 0.992 0.000 0.004 0.004
#> GSM121402 5 0.3445 0.6371 0.000 0.012 0.000 0.244 0.744 0.000
#> GSM121403 5 0.1267 0.8034 0.000 0.000 0.060 0.000 0.940 0.000
#> GSM121404 5 0.1267 0.8034 0.000 0.000 0.060 0.000 0.940 0.000
#> GSM121405 5 0.2300 0.7474 0.000 0.000 0.144 0.000 0.856 0.000
#> GSM121406 2 0.0146 0.7573 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM121408 2 0.4247 0.7647 0.000 0.700 0.000 0.240 0.060 0.000
#> GSM121409 5 0.1411 0.8028 0.000 0.000 0.060 0.000 0.936 0.004
#> GSM121410 5 0.1267 0.8034 0.000 0.000 0.060 0.000 0.940 0.000
#> GSM121412 2 0.3245 0.7722 0.000 0.800 0.000 0.172 0.028 0.000
#> GSM121413 2 0.0146 0.7573 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM121414 2 0.4832 0.5323 0.000 0.648 0.000 0.108 0.244 0.000
#> GSM121415 5 0.3819 0.7069 0.000 0.064 0.000 0.172 0.764 0.000
#> GSM121416 5 0.3314 0.6287 0.000 0.004 0.000 0.256 0.740 0.000
#> GSM120591 6 0.0000 0.9682 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120594 6 0.0146 0.9647 0.000 0.000 0.004 0.000 0.000 0.996
#> GSM120718 1 0.3883 0.4974 0.656 0.000 0.012 0.000 0.000 0.332
#> GSM121205 1 0.0000 0.9510 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.9510 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121207 1 0.0000 0.9510 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121208 3 0.3860 0.1863 0.472 0.000 0.528 0.000 0.000 0.000
#> GSM121209 1 0.0000 0.9510 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.9510 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.9510 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.9510 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.9510 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.9510 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.9510 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121216 3 0.2527 0.7898 0.168 0.000 0.832 0.000 0.000 0.000
#> GSM121217 1 0.0000 0.9510 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.9510 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121234 1 0.2664 0.7271 0.816 0.000 0.184 0.000 0.000 0.000
#> GSM121243 1 0.0000 0.9510 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121245 1 0.0000 0.9510 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121246 3 0.2527 0.7898 0.168 0.000 0.832 0.000 0.000 0.000
#> GSM121247 1 0.0000 0.9510 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121248 1 0.0000 0.9510 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM120744 6 0.0000 0.9682 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120745 6 0.0000 0.9682 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120746 6 0.0000 0.9682 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120747 6 0.0937 0.9312 0.000 0.000 0.040 0.000 0.000 0.960
#> GSM120748 6 0.1204 0.9145 0.000 0.000 0.056 0.000 0.000 0.944
#> GSM120749 6 0.0000 0.9682 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120750 6 0.0000 0.9682 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120751 6 0.0000 0.9682 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120752 6 0.0000 0.9682 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM121336 2 0.0146 0.7573 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM121339 5 0.1007 0.8044 0.000 0.000 0.044 0.000 0.956 0.000
#> GSM121349 2 0.0146 0.7573 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM121355 2 0.0146 0.7573 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM120757 6 0.0000 0.9682 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120766 6 0.0000 0.9682 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120770 5 0.0000 0.7947 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM120779 1 0.2854 0.7223 0.792 0.000 0.000 0.000 0.000 0.208
#> GSM120780 5 0.3138 0.7418 0.000 0.000 0.060 0.000 0.832 0.108
#> GSM121102 5 0.1141 0.8046 0.000 0.000 0.052 0.000 0.948 0.000
#> GSM121203 5 0.1807 0.7966 0.000 0.000 0.060 0.000 0.920 0.020
#> GSM121204 6 0.0000 0.9682 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM121330 3 0.0146 0.8655 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM121335 3 0.1327 0.8637 0.064 0.000 0.936 0.000 0.000 0.000
#> GSM121337 5 0.0000 0.7947 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM121338 5 0.1267 0.8034 0.000 0.000 0.060 0.000 0.940 0.000
#> GSM121341 3 0.1327 0.8637 0.064 0.000 0.936 0.000 0.000 0.000
#> GSM121342 3 0.1327 0.8637 0.064 0.000 0.936 0.000 0.000 0.000
#> GSM121343 5 0.1267 0.8034 0.000 0.000 0.060 0.000 0.940 0.000
#> GSM121344 3 0.1528 0.8638 0.048 0.000 0.936 0.000 0.000 0.016
#> GSM121346 3 0.0146 0.8655 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM121347 5 0.1267 0.8034 0.000 0.000 0.060 0.000 0.940 0.000
#> GSM121348 5 0.0713 0.7953 0.000 0.028 0.000 0.000 0.972 0.000
#> GSM121350 3 0.0146 0.8655 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM121352 3 0.0146 0.8655 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM121354 3 0.0547 0.8685 0.020 0.000 0.980 0.000 0.000 0.000
#> GSM120753 2 0.4443 0.7405 0.000 0.664 0.000 0.276 0.060 0.000
#> GSM120761 2 0.4443 0.7405 0.000 0.664 0.000 0.276 0.060 0.000
#> GSM120768 5 0.4513 0.2407 0.000 0.032 0.000 0.440 0.528 0.000
#> GSM120781 2 0.3435 0.7793 0.000 0.804 0.000 0.136 0.060 0.000
#> GSM120788 4 0.3518 0.6068 0.000 0.000 0.000 0.732 0.256 0.012
#> GSM120760 4 0.1984 0.7353 0.000 0.032 0.000 0.912 0.056 0.000
#> GSM120763 4 0.2571 0.6977 0.000 0.064 0.000 0.876 0.060 0.000
#> GSM120764 4 0.0260 0.7883 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM120777 4 0.2260 0.7404 0.000 0.000 0.000 0.860 0.140 0.000
#> GSM120786 4 0.0993 0.7685 0.000 0.024 0.000 0.964 0.012 0.000
#> GSM121329 3 0.2664 0.7101 0.000 0.000 0.816 0.000 0.000 0.184
#> GSM121331 6 0.0000 0.9682 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM121333 6 0.0000 0.9682 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM121345 6 0.0000 0.9682 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM121356 6 0.0000 0.9682 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120754 4 0.0260 0.7883 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM120759 2 0.0806 0.7659 0.000 0.972 0.000 0.020 0.008 0.000
#> GSM120762 2 0.2325 0.7737 0.000 0.892 0.000 0.048 0.060 0.000
#> GSM120775 4 0.1863 0.7682 0.000 0.000 0.000 0.896 0.104 0.000
#> GSM120776 4 0.3288 0.5373 0.000 0.000 0.000 0.724 0.000 0.276
#> GSM120782 4 0.0363 0.7898 0.000 0.000 0.000 0.988 0.012 0.000
#> GSM120789 5 0.4475 0.6586 0.000 0.100 0.000 0.200 0.700 0.000
#> GSM120790 2 0.4276 0.0808 0.000 0.564 0.000 0.020 0.416 0.000
#> GSM120791 5 0.3383 0.6164 0.000 0.004 0.000 0.268 0.728 0.000
#> GSM120755 2 0.4402 0.7481 0.000 0.672 0.000 0.268 0.060 0.000
#> GSM120756 4 0.3288 0.5373 0.000 0.000 0.000 0.724 0.000 0.276
#> GSM120769 2 0.0405 0.7596 0.000 0.988 0.004 0.000 0.008 0.000
#> GSM120778 2 0.4443 0.7405 0.000 0.664 0.000 0.276 0.060 0.000
#> GSM120792 4 0.2046 0.7318 0.000 0.032 0.000 0.908 0.060 0.000
#> GSM121332 5 0.5547 0.1785 0.000 0.312 0.000 0.160 0.528 0.000
#> GSM121334 2 0.4360 0.7540 0.000 0.680 0.000 0.260 0.060 0.000
#> GSM121340 4 0.1327 0.7886 0.000 0.000 0.000 0.936 0.064 0.000
#> GSM121351 2 0.0146 0.7573 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM121353 4 0.3198 0.6087 0.000 0.000 0.000 0.740 0.260 0.000
#> GSM120758 5 0.4087 0.5738 0.000 0.036 0.000 0.276 0.688 0.000
#> GSM120771 2 0.4423 0.7446 0.000 0.668 0.000 0.272 0.060 0.000
#> GSM120772 5 0.4738 0.5178 0.000 0.084 0.000 0.276 0.640 0.000
#> GSM120773 4 0.0713 0.7928 0.000 0.000 0.000 0.972 0.028 0.000
#> GSM120774 4 0.4523 -0.0606 0.000 0.032 0.000 0.516 0.452 0.000
#> GSM120783 4 0.0937 0.7926 0.000 0.000 0.000 0.960 0.040 0.000
#> GSM120787 4 0.5486 0.2774 0.000 0.188 0.000 0.564 0.248 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> ATC:pam 102 8.89e-11 2
#> ATC:pam 116 1.04e-17 3
#> ATC:pam 104 2.33e-18 4
#> ATC:pam 106 1.98e-23 5
#> ATC:pam 110 1.58e-27 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 21512 rows and 119 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.647 0.847 0.925 0.4745 0.499 0.499
#> 3 3 0.730 0.834 0.896 0.3498 0.773 0.577
#> 4 4 0.604 0.549 0.718 0.1291 0.885 0.688
#> 5 5 0.691 0.751 0.851 0.0799 0.806 0.415
#> 6 6 0.791 0.734 0.852 0.0518 0.939 0.713
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
#> GSM120719 1 0.5842 0.788 0.860 0.140
#> GSM120720 1 0.2043 0.837 0.968 0.032
#> GSM120765 2 0.0000 0.977 0.000 1.000
#> GSM120767 2 0.0000 0.977 0.000 1.000
#> GSM120784 2 0.0000 0.977 0.000 1.000
#> GSM121400 1 0.6531 0.768 0.832 0.168
#> GSM121401 1 0.2043 0.837 0.968 0.032
#> GSM121402 2 0.0000 0.977 0.000 1.000
#> GSM121403 2 0.8016 0.608 0.244 0.756
#> GSM121404 2 0.0376 0.974 0.004 0.996
#> GSM121405 1 0.5294 0.798 0.880 0.120
#> GSM121406 2 0.0000 0.977 0.000 1.000
#> GSM121408 2 0.0000 0.977 0.000 1.000
#> GSM121409 1 0.8861 0.631 0.696 0.304
#> GSM121410 1 0.6247 0.776 0.844 0.156
#> GSM121412 2 0.0000 0.977 0.000 1.000
#> GSM121413 2 0.0000 0.977 0.000 1.000
#> GSM121414 2 0.0000 0.977 0.000 1.000
#> GSM121415 2 0.0000 0.977 0.000 1.000
#> GSM121416 2 0.0000 0.977 0.000 1.000
#> GSM120591 1 0.7950 0.712 0.760 0.240
#> GSM120594 1 0.2236 0.835 0.964 0.036
#> GSM120718 1 0.2043 0.837 0.968 0.032
#> GSM121205 1 0.0000 0.844 1.000 0.000
#> GSM121206 1 0.0000 0.844 1.000 0.000
#> GSM121207 1 0.0000 0.844 1.000 0.000
#> GSM121208 1 0.0000 0.844 1.000 0.000
#> GSM121209 1 0.0000 0.844 1.000 0.000
#> GSM121210 1 0.0000 0.844 1.000 0.000
#> GSM121211 1 0.0000 0.844 1.000 0.000
#> GSM121212 1 0.0000 0.844 1.000 0.000
#> GSM121213 1 0.0000 0.844 1.000 0.000
#> GSM121214 1 0.0000 0.844 1.000 0.000
#> GSM121215 1 0.0000 0.844 1.000 0.000
#> GSM121216 1 0.0000 0.844 1.000 0.000
#> GSM121217 1 0.0000 0.844 1.000 0.000
#> GSM121218 1 0.0000 0.844 1.000 0.000
#> GSM121234 1 0.0000 0.844 1.000 0.000
#> GSM121243 1 0.0000 0.844 1.000 0.000
#> GSM121245 1 0.0000 0.844 1.000 0.000
#> GSM121246 1 0.0000 0.844 1.000 0.000
#> GSM121247 1 0.1414 0.840 0.980 0.020
#> GSM121248 1 0.0000 0.844 1.000 0.000
#> GSM120744 1 0.9850 0.484 0.572 0.428
#> GSM120745 1 0.9850 0.484 0.572 0.428
#> GSM120746 1 0.9850 0.484 0.572 0.428
#> GSM120747 1 0.9850 0.484 0.572 0.428
#> GSM120748 1 0.9850 0.484 0.572 0.428
#> GSM120749 1 0.9850 0.484 0.572 0.428
#> GSM120750 1 0.9850 0.484 0.572 0.428
#> GSM120751 1 0.9850 0.484 0.572 0.428
#> GSM120752 1 0.9850 0.484 0.572 0.428
#> GSM121336 2 0.0000 0.977 0.000 1.000
#> GSM121339 2 0.6048 0.780 0.148 0.852
#> GSM121349 2 0.0000 0.977 0.000 1.000
#> GSM121355 2 0.0000 0.977 0.000 1.000
#> GSM120757 1 0.9850 0.484 0.572 0.428
#> GSM120766 1 0.9850 0.484 0.572 0.428
#> GSM120770 2 0.0000 0.977 0.000 1.000
#> GSM120779 1 0.9988 0.349 0.520 0.480
#> GSM120780 1 0.9850 0.484 0.572 0.428
#> GSM121102 2 0.0376 0.974 0.004 0.996
#> GSM121203 1 0.9850 0.484 0.572 0.428
#> GSM121204 1 0.9427 0.581 0.640 0.360
#> GSM121330 1 0.0000 0.844 1.000 0.000
#> GSM121335 1 0.0000 0.844 1.000 0.000
#> GSM121337 2 0.0000 0.977 0.000 1.000
#> GSM121338 2 0.0376 0.974 0.004 0.996
#> GSM121341 1 0.0000 0.844 1.000 0.000
#> GSM121342 1 0.0000 0.844 1.000 0.000
#> GSM121343 2 0.0376 0.974 0.004 0.996
#> GSM121344 1 0.0000 0.844 1.000 0.000
#> GSM121346 1 0.0000 0.844 1.000 0.000
#> GSM121347 2 0.0376 0.974 0.004 0.996
#> GSM121348 2 0.0376 0.974 0.004 0.996
#> GSM121350 1 0.0000 0.844 1.000 0.000
#> GSM121352 1 0.0000 0.844 1.000 0.000
#> GSM121354 1 0.0000 0.844 1.000 0.000
#> GSM120753 2 0.0000 0.977 0.000 1.000
#> GSM120761 2 0.0000 0.977 0.000 1.000
#> GSM120768 2 0.0000 0.977 0.000 1.000
#> GSM120781 2 0.0000 0.977 0.000 1.000
#> GSM120788 2 0.0000 0.977 0.000 1.000
#> GSM120760 2 0.0000 0.977 0.000 1.000
#> GSM120763 2 0.0000 0.977 0.000 1.000
#> GSM120764 2 0.0000 0.977 0.000 1.000
#> GSM120777 2 0.0000 0.977 0.000 1.000
#> GSM120786 2 0.0000 0.977 0.000 1.000
#> GSM121329 1 0.5294 0.798 0.880 0.120
#> GSM121331 2 0.2948 0.917 0.052 0.948
#> GSM121333 2 0.9933 -0.142 0.452 0.548
#> GSM121345 2 0.0672 0.970 0.008 0.992
#> GSM121356 2 0.7602 0.632 0.220 0.780
#> GSM120754 2 0.0000 0.977 0.000 1.000
#> GSM120759 2 0.0000 0.977 0.000 1.000
#> GSM120762 2 0.0000 0.977 0.000 1.000
#> GSM120775 2 0.0000 0.977 0.000 1.000
#> GSM120776 2 0.0000 0.977 0.000 1.000
#> GSM120782 2 0.0000 0.977 0.000 1.000
#> GSM120789 2 0.0000 0.977 0.000 1.000
#> GSM120790 2 0.0000 0.977 0.000 1.000
#> GSM120791 2 0.0000 0.977 0.000 1.000
#> GSM120755 2 0.0000 0.977 0.000 1.000
#> GSM120756 2 0.0000 0.977 0.000 1.000
#> GSM120769 2 0.0000 0.977 0.000 1.000
#> GSM120778 2 0.0000 0.977 0.000 1.000
#> GSM120792 2 0.0000 0.977 0.000 1.000
#> GSM121332 2 0.0000 0.977 0.000 1.000
#> GSM121334 2 0.0000 0.977 0.000 1.000
#> GSM121340 2 0.0000 0.977 0.000 1.000
#> GSM121351 2 0.0000 0.977 0.000 1.000
#> GSM121353 2 0.0000 0.977 0.000 1.000
#> GSM120758 2 0.0000 0.977 0.000 1.000
#> GSM120771 2 0.0000 0.977 0.000 1.000
#> GSM120772 2 0.0000 0.977 0.000 1.000
#> GSM120773 2 0.0000 0.977 0.000 1.000
#> GSM120774 2 0.0000 0.977 0.000 1.000
#> GSM120783 2 0.0000 0.977 0.000 1.000
#> GSM120787 2 0.0000 0.977 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM120719 3 0.4821 0.828 0.064 0.088 0.848
#> GSM120720 1 0.7874 0.475 0.568 0.064 0.368
#> GSM120765 2 0.1964 0.943 0.000 0.944 0.056
#> GSM120767 2 0.2066 0.943 0.000 0.940 0.060
#> GSM120784 2 0.2066 0.943 0.000 0.940 0.060
#> GSM121400 3 0.7190 0.305 0.356 0.036 0.608
#> GSM121401 3 0.7032 0.261 0.368 0.028 0.604
#> GSM121402 2 0.2165 0.942 0.000 0.936 0.064
#> GSM121403 3 0.5428 0.766 0.120 0.064 0.816
#> GSM121404 3 0.3091 0.839 0.016 0.072 0.912
#> GSM121405 3 0.6994 0.288 0.360 0.028 0.612
#> GSM121406 2 0.2356 0.940 0.000 0.928 0.072
#> GSM121408 2 0.2261 0.941 0.000 0.932 0.068
#> GSM121409 3 0.4369 0.794 0.096 0.040 0.864
#> GSM121410 3 0.6912 0.337 0.344 0.028 0.628
#> GSM121412 2 0.2356 0.940 0.000 0.928 0.072
#> GSM121413 2 0.2356 0.940 0.000 0.928 0.072
#> GSM121414 2 0.2356 0.940 0.000 0.928 0.072
#> GSM121415 2 0.2066 0.943 0.000 0.940 0.060
#> GSM121416 2 0.2625 0.939 0.000 0.916 0.084
#> GSM120591 3 0.3713 0.845 0.032 0.076 0.892
#> GSM120594 3 0.7945 0.172 0.388 0.064 0.548
#> GSM120718 1 0.7748 0.542 0.596 0.064 0.340
#> GSM121205 1 0.0000 0.830 1.000 0.000 0.000
#> GSM121206 1 0.0000 0.830 1.000 0.000 0.000
#> GSM121207 1 0.0424 0.830 0.992 0.000 0.008
#> GSM121208 1 0.4702 0.777 0.788 0.000 0.212
#> GSM121209 1 0.0237 0.830 0.996 0.000 0.004
#> GSM121210 1 0.0000 0.830 1.000 0.000 0.000
#> GSM121211 1 0.0000 0.830 1.000 0.000 0.000
#> GSM121212 1 0.0592 0.829 0.988 0.000 0.012
#> GSM121213 1 0.0000 0.830 1.000 0.000 0.000
#> GSM121214 1 0.0000 0.830 1.000 0.000 0.000
#> GSM121215 1 0.0000 0.830 1.000 0.000 0.000
#> GSM121216 1 0.4504 0.782 0.804 0.000 0.196
#> GSM121217 1 0.0237 0.830 0.996 0.000 0.004
#> GSM121218 1 0.0000 0.830 1.000 0.000 0.000
#> GSM121234 1 0.0237 0.830 0.996 0.000 0.004
#> GSM121243 1 0.0237 0.830 0.996 0.000 0.004
#> GSM121245 1 0.0000 0.830 1.000 0.000 0.000
#> GSM121246 1 0.5254 0.750 0.736 0.000 0.264
#> GSM121247 1 0.6082 0.658 0.692 0.012 0.296
#> GSM121248 1 0.0000 0.830 1.000 0.000 0.000
#> GSM120744 3 0.1919 0.861 0.024 0.020 0.956
#> GSM120745 3 0.1919 0.861 0.024 0.020 0.956
#> GSM120746 3 0.1919 0.861 0.024 0.020 0.956
#> GSM120747 3 0.1781 0.860 0.020 0.020 0.960
#> GSM120748 3 0.1781 0.860 0.020 0.020 0.960
#> GSM120749 3 0.1919 0.861 0.024 0.020 0.956
#> GSM120750 3 0.1919 0.861 0.024 0.020 0.956
#> GSM120751 3 0.1919 0.861 0.024 0.020 0.956
#> GSM120752 3 0.1919 0.861 0.024 0.020 0.956
#> GSM121336 2 0.2356 0.940 0.000 0.928 0.072
#> GSM121339 3 0.3690 0.801 0.016 0.100 0.884
#> GSM121349 2 0.2356 0.940 0.000 0.928 0.072
#> GSM121355 2 0.2356 0.940 0.000 0.928 0.072
#> GSM120757 3 0.1919 0.861 0.024 0.020 0.956
#> GSM120766 3 0.1919 0.861 0.024 0.020 0.956
#> GSM120770 2 0.5327 0.710 0.000 0.728 0.272
#> GSM120779 3 0.3670 0.846 0.020 0.092 0.888
#> GSM120780 3 0.1919 0.861 0.024 0.020 0.956
#> GSM121102 3 0.4931 0.693 0.004 0.212 0.784
#> GSM121203 3 0.1950 0.855 0.008 0.040 0.952
#> GSM121204 3 0.3590 0.847 0.028 0.076 0.896
#> GSM121330 1 0.5690 0.738 0.708 0.004 0.288
#> GSM121335 1 0.5363 0.746 0.724 0.000 0.276
#> GSM121337 2 0.5058 0.750 0.000 0.756 0.244
#> GSM121338 3 0.2998 0.833 0.016 0.068 0.916
#> GSM121341 1 0.5291 0.749 0.732 0.000 0.268
#> GSM121342 1 0.5291 0.747 0.732 0.000 0.268
#> GSM121343 3 0.4749 0.739 0.012 0.172 0.816
#> GSM121344 1 0.5363 0.742 0.724 0.000 0.276
#> GSM121346 1 0.5497 0.739 0.708 0.000 0.292
#> GSM121347 3 0.3941 0.749 0.000 0.156 0.844
#> GSM121348 2 0.4346 0.823 0.000 0.816 0.184
#> GSM121350 1 0.5529 0.734 0.704 0.000 0.296
#> GSM121352 1 0.5497 0.739 0.708 0.000 0.292
#> GSM121354 1 0.5497 0.739 0.708 0.000 0.292
#> GSM120753 2 0.0747 0.941 0.000 0.984 0.016
#> GSM120761 2 0.0747 0.941 0.000 0.984 0.016
#> GSM120768 2 0.0747 0.941 0.000 0.984 0.016
#> GSM120781 2 0.2066 0.943 0.000 0.940 0.060
#> GSM120788 2 0.2066 0.913 0.000 0.940 0.060
#> GSM120760 2 0.0747 0.941 0.000 0.984 0.016
#> GSM120763 2 0.0747 0.941 0.000 0.984 0.016
#> GSM120764 2 0.2066 0.913 0.000 0.940 0.060
#> GSM120777 2 0.2066 0.913 0.000 0.940 0.060
#> GSM120786 2 0.0747 0.941 0.000 0.984 0.016
#> GSM121329 3 0.4443 0.838 0.052 0.084 0.864
#> GSM121331 3 0.3532 0.842 0.008 0.108 0.884
#> GSM121333 3 0.3587 0.846 0.020 0.088 0.892
#> GSM121345 3 0.3349 0.840 0.004 0.108 0.888
#> GSM121356 3 0.3528 0.845 0.016 0.092 0.892
#> GSM120754 2 0.0747 0.941 0.000 0.984 0.016
#> GSM120759 2 0.2165 0.942 0.000 0.936 0.064
#> GSM120762 2 0.2165 0.942 0.000 0.936 0.064
#> GSM120775 2 0.1860 0.919 0.000 0.948 0.052
#> GSM120776 2 0.4931 0.686 0.000 0.768 0.232
#> GSM120782 2 0.0892 0.940 0.000 0.980 0.020
#> GSM120789 2 0.2165 0.942 0.000 0.936 0.064
#> GSM120790 2 0.2261 0.943 0.000 0.932 0.068
#> GSM120791 2 0.0747 0.941 0.000 0.984 0.016
#> GSM120755 2 0.1753 0.945 0.000 0.952 0.048
#> GSM120756 2 0.2066 0.913 0.000 0.940 0.060
#> GSM120769 2 0.2165 0.942 0.000 0.936 0.064
#> GSM120778 2 0.0237 0.942 0.000 0.996 0.004
#> GSM120792 2 0.0592 0.941 0.000 0.988 0.012
#> GSM121332 2 0.2165 0.942 0.000 0.936 0.064
#> GSM121334 2 0.0747 0.941 0.000 0.984 0.016
#> GSM121340 2 0.0747 0.941 0.000 0.984 0.016
#> GSM121351 2 0.2261 0.941 0.000 0.932 0.068
#> GSM121353 2 0.0592 0.941 0.000 0.988 0.012
#> GSM120758 2 0.1643 0.945 0.000 0.956 0.044
#> GSM120771 2 0.1031 0.943 0.000 0.976 0.024
#> GSM120772 2 0.0747 0.941 0.000 0.984 0.016
#> GSM120773 2 0.0747 0.941 0.000 0.984 0.016
#> GSM120774 2 0.1753 0.945 0.000 0.952 0.048
#> GSM120783 2 0.0747 0.941 0.000 0.984 0.016
#> GSM120787 2 0.1163 0.945 0.000 0.972 0.028
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM120719 3 0.2956 0.5003 0.048 0.012 0.904 0.036
#> GSM120720 3 0.5689 -0.0884 0.412 0.004 0.564 0.020
#> GSM120765 2 0.2814 0.7333 0.000 0.868 0.000 0.132
#> GSM120767 2 0.1637 0.7319 0.000 0.940 0.000 0.060
#> GSM120784 2 0.4008 0.6727 0.000 0.756 0.000 0.244
#> GSM121400 4 0.7589 -0.0222 0.164 0.008 0.340 0.488
#> GSM121401 4 0.7593 -0.0339 0.228 0.000 0.300 0.472
#> GSM121402 2 0.0336 0.7226 0.000 0.992 0.000 0.008
#> GSM121403 4 0.8343 0.1076 0.088 0.104 0.304 0.504
#> GSM121404 4 0.7345 0.1405 0.000 0.184 0.308 0.508
#> GSM121405 4 0.8142 0.0714 0.120 0.060 0.308 0.512
#> GSM121406 2 0.0188 0.7183 0.000 0.996 0.000 0.004
#> GSM121408 2 0.0188 0.7183 0.000 0.996 0.000 0.004
#> GSM121409 4 0.8082 0.0990 0.068 0.100 0.320 0.512
#> GSM121410 4 0.8104 0.0545 0.116 0.056 0.328 0.500
#> GSM121412 2 0.0188 0.7183 0.000 0.996 0.000 0.004
#> GSM121413 2 0.0188 0.7183 0.000 0.996 0.000 0.004
#> GSM121414 2 0.0188 0.7183 0.000 0.996 0.000 0.004
#> GSM121415 2 0.0707 0.7221 0.000 0.980 0.000 0.020
#> GSM121416 2 0.5996 0.5528 0.000 0.512 0.040 0.448
#> GSM120591 3 0.3187 0.5074 0.052 0.024 0.896 0.028
#> GSM120594 3 0.5112 0.3660 0.252 0.004 0.716 0.028
#> GSM120718 3 0.5643 -0.1687 0.440 0.004 0.540 0.016
#> GSM121205 1 0.0000 0.8640 1.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.8640 1.000 0.000 0.000 0.000
#> GSM121207 1 0.0804 0.8633 0.980 0.000 0.012 0.008
#> GSM121208 1 0.4281 0.8053 0.792 0.000 0.180 0.028
#> GSM121209 1 0.0188 0.8640 0.996 0.000 0.004 0.000
#> GSM121210 1 0.0336 0.8643 0.992 0.000 0.008 0.000
#> GSM121211 1 0.0000 0.8640 1.000 0.000 0.000 0.000
#> GSM121212 1 0.0707 0.8630 0.980 0.000 0.020 0.000
#> GSM121213 1 0.0000 0.8640 1.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.8640 1.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.8640 1.000 0.000 0.000 0.000
#> GSM121216 1 0.3108 0.8221 0.872 0.000 0.112 0.016
#> GSM121217 1 0.0000 0.8640 1.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.8640 1.000 0.000 0.000 0.000
#> GSM121234 1 0.0000 0.8640 1.000 0.000 0.000 0.000
#> GSM121243 1 0.0336 0.8643 0.992 0.000 0.000 0.008
#> GSM121245 1 0.0188 0.8643 0.996 0.000 0.004 0.000
#> GSM121246 1 0.4524 0.7906 0.768 0.000 0.204 0.028
#> GSM121247 1 0.5580 0.2918 0.572 0.004 0.408 0.016
#> GSM121248 1 0.0000 0.8640 1.000 0.000 0.000 0.000
#> GSM120744 3 0.5400 0.5459 0.020 0.000 0.608 0.372
#> GSM120745 3 0.5386 0.5444 0.020 0.000 0.612 0.368
#> GSM120746 3 0.5400 0.5459 0.020 0.000 0.608 0.372
#> GSM120747 3 0.5400 0.5459 0.020 0.000 0.608 0.372
#> GSM120748 3 0.5400 0.5459 0.020 0.000 0.608 0.372
#> GSM120749 3 0.5400 0.5459 0.020 0.000 0.608 0.372
#> GSM120750 3 0.5400 0.5459 0.020 0.000 0.608 0.372
#> GSM120751 3 0.5400 0.5459 0.020 0.000 0.608 0.372
#> GSM120752 3 0.5400 0.5459 0.020 0.000 0.608 0.372
#> GSM121336 2 0.0188 0.7183 0.000 0.996 0.000 0.004
#> GSM121339 4 0.7344 0.1273 0.000 0.180 0.316 0.504
#> GSM121349 2 0.0188 0.7183 0.000 0.996 0.000 0.004
#> GSM121355 2 0.0188 0.7183 0.000 0.996 0.000 0.004
#> GSM120757 3 0.5598 0.5411 0.020 0.008 0.628 0.344
#> GSM120766 3 0.5400 0.5459 0.020 0.000 0.608 0.372
#> GSM120770 4 0.7439 0.1998 0.000 0.272 0.220 0.508
#> GSM120779 3 0.1396 0.5121 0.004 0.032 0.960 0.004
#> GSM120780 3 0.5400 0.5459 0.020 0.000 0.608 0.372
#> GSM121102 4 0.7203 0.1500 0.000 0.176 0.288 0.536
#> GSM121203 3 0.6154 0.4812 0.020 0.024 0.576 0.380
#> GSM121204 3 0.1443 0.5136 0.008 0.028 0.960 0.004
#> GSM121330 1 0.6373 0.6912 0.652 0.000 0.200 0.148
#> GSM121335 1 0.5111 0.7828 0.740 0.000 0.204 0.056
#> GSM121337 2 0.6753 0.0516 0.000 0.608 0.164 0.228
#> GSM121338 4 0.7340 0.1191 0.000 0.176 0.324 0.500
#> GSM121341 1 0.4793 0.7882 0.756 0.000 0.204 0.040
#> GSM121342 1 0.4524 0.7906 0.768 0.000 0.204 0.028
#> GSM121343 4 0.7299 0.1363 0.000 0.176 0.312 0.512
#> GSM121344 1 0.4745 0.7853 0.756 0.000 0.208 0.036
#> GSM121346 1 0.5361 0.7720 0.724 0.000 0.208 0.068
#> GSM121347 4 0.7664 0.1617 0.000 0.248 0.292 0.460
#> GSM121348 2 0.5815 0.5122 0.000 0.708 0.152 0.140
#> GSM121350 1 0.5522 0.7642 0.716 0.000 0.204 0.080
#> GSM121352 1 0.5361 0.7720 0.724 0.000 0.208 0.068
#> GSM121354 1 0.5361 0.7720 0.724 0.000 0.208 0.068
#> GSM120753 2 0.4898 0.6806 0.000 0.584 0.000 0.416
#> GSM120761 2 0.4961 0.6671 0.000 0.552 0.000 0.448
#> GSM120768 2 0.4955 0.6690 0.000 0.556 0.000 0.444
#> GSM120781 2 0.2530 0.7349 0.000 0.888 0.000 0.112
#> GSM120788 4 0.7597 -0.1990 0.000 0.224 0.308 0.468
#> GSM120760 2 0.4961 0.6671 0.000 0.552 0.000 0.448
#> GSM120763 2 0.4961 0.6671 0.000 0.552 0.000 0.448
#> GSM120764 2 0.5132 0.6649 0.000 0.548 0.004 0.448
#> GSM120777 4 0.7551 -0.4246 0.000 0.356 0.196 0.448
#> GSM120786 2 0.4961 0.6671 0.000 0.552 0.000 0.448
#> GSM121329 3 0.3082 0.4979 0.056 0.008 0.896 0.040
#> GSM121331 3 0.2750 0.4684 0.004 0.032 0.908 0.056
#> GSM121333 3 0.1396 0.5121 0.004 0.032 0.960 0.004
#> GSM121345 3 0.3858 0.3982 0.004 0.036 0.844 0.116
#> GSM121356 3 0.1674 0.5143 0.004 0.032 0.952 0.012
#> GSM120754 2 0.4961 0.6671 0.000 0.552 0.000 0.448
#> GSM120759 2 0.0188 0.7212 0.000 0.996 0.000 0.004
#> GSM120762 2 0.0817 0.7251 0.000 0.976 0.000 0.024
#> GSM120775 4 0.7216 -0.5169 0.000 0.412 0.140 0.448
#> GSM120776 3 0.7528 -0.1477 0.004 0.188 0.504 0.304
#> GSM120782 2 0.4961 0.6671 0.000 0.552 0.000 0.448
#> GSM120789 2 0.0000 0.7195 0.000 1.000 0.000 0.000
#> GSM120790 2 0.2011 0.7262 0.000 0.920 0.000 0.080
#> GSM120791 2 0.4961 0.6671 0.000 0.552 0.000 0.448
#> GSM120755 2 0.3219 0.7310 0.000 0.836 0.000 0.164
#> GSM120756 4 0.7670 -0.2121 0.000 0.232 0.320 0.448
#> GSM120769 2 0.0592 0.7227 0.000 0.984 0.000 0.016
#> GSM120778 2 0.4164 0.7165 0.000 0.736 0.000 0.264
#> GSM120792 2 0.4877 0.6807 0.000 0.592 0.000 0.408
#> GSM121332 2 0.0000 0.7195 0.000 1.000 0.000 0.000
#> GSM121334 2 0.4955 0.6693 0.000 0.556 0.000 0.444
#> GSM121340 4 0.7854 -0.2806 0.000 0.304 0.296 0.400
#> GSM121351 2 0.0188 0.7183 0.000 0.996 0.000 0.004
#> GSM121353 2 0.7098 0.3087 0.000 0.536 0.312 0.152
#> GSM120758 2 0.4925 0.6763 0.000 0.572 0.000 0.428
#> GSM120771 2 0.4907 0.6791 0.000 0.580 0.000 0.420
#> GSM120772 2 0.4961 0.6671 0.000 0.552 0.000 0.448
#> GSM120773 2 0.4961 0.6671 0.000 0.552 0.000 0.448
#> GSM120774 2 0.2081 0.7350 0.000 0.916 0.000 0.084
#> GSM120783 2 0.4955 0.6690 0.000 0.556 0.000 0.444
#> GSM120787 2 0.3837 0.7250 0.000 0.776 0.000 0.224
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM120719 3 0.6062 -0.0053 0.000 0.004 0.540 0.120 0.336
#> GSM120720 3 0.6491 0.6757 0.140 0.036 0.624 0.008 0.192
#> GSM120765 2 0.4268 0.4085 0.000 0.556 0.000 0.444 0.000
#> GSM120767 2 0.3424 0.7690 0.000 0.760 0.000 0.240 0.000
#> GSM120784 4 0.4276 0.1985 0.000 0.380 0.004 0.616 0.000
#> GSM121400 3 0.2677 0.7201 0.000 0.016 0.872 0.000 0.112
#> GSM121401 3 0.2673 0.7332 0.016 0.016 0.892 0.000 0.076
#> GSM121402 2 0.3561 0.7605 0.000 0.740 0.000 0.260 0.000
#> GSM121403 3 0.1671 0.7193 0.000 0.000 0.924 0.000 0.076
#> GSM121404 5 0.6600 0.5784 0.000 0.004 0.236 0.264 0.496
#> GSM121405 3 0.1671 0.7193 0.000 0.000 0.924 0.000 0.076
#> GSM121406 2 0.1792 0.8650 0.000 0.916 0.000 0.084 0.000
#> GSM121408 2 0.1792 0.8650 0.000 0.916 0.000 0.084 0.000
#> GSM121409 3 0.1732 0.7169 0.000 0.000 0.920 0.000 0.080
#> GSM121410 3 0.1671 0.7193 0.000 0.000 0.924 0.000 0.076
#> GSM121412 2 0.1851 0.8648 0.000 0.912 0.000 0.088 0.000
#> GSM121413 2 0.1792 0.8650 0.000 0.916 0.000 0.084 0.000
#> GSM121414 2 0.1851 0.8648 0.000 0.912 0.000 0.088 0.000
#> GSM121415 2 0.3796 0.6994 0.000 0.700 0.000 0.300 0.000
#> GSM121416 4 0.2472 0.8320 0.000 0.052 0.020 0.908 0.020
#> GSM120591 5 0.6094 0.5323 0.000 0.004 0.312 0.132 0.552
#> GSM120594 3 0.6293 0.5753 0.092 0.024 0.612 0.012 0.260
#> GSM120718 3 0.6448 0.6940 0.168 0.036 0.632 0.008 0.156
#> GSM121205 1 0.0000 0.9507 1.000 0.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.9507 1.000 0.000 0.000 0.000 0.000
#> GSM121207 1 0.0000 0.9507 1.000 0.000 0.000 0.000 0.000
#> GSM121208 1 0.5708 -0.0748 0.516 0.072 0.408 0.000 0.004
#> GSM121209 1 0.0000 0.9507 1.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.9507 1.000 0.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.9507 1.000 0.000 0.000 0.000 0.000
#> GSM121212 1 0.0000 0.9507 1.000 0.000 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.9507 1.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.9507 1.000 0.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.9507 1.000 0.000 0.000 0.000 0.000
#> GSM121216 1 0.2576 0.8388 0.900 0.008 0.036 0.000 0.056
#> GSM121217 1 0.0000 0.9507 1.000 0.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.9507 1.000 0.000 0.000 0.000 0.000
#> GSM121234 1 0.0000 0.9507 1.000 0.000 0.000 0.000 0.000
#> GSM121243 1 0.0162 0.9465 0.996 0.000 0.000 0.000 0.004
#> GSM121245 1 0.0000 0.9507 1.000 0.000 0.000 0.000 0.000
#> GSM121246 3 0.5293 0.7101 0.212 0.080 0.692 0.000 0.016
#> GSM121247 1 0.3457 0.7698 0.848 0.008 0.064 0.000 0.080
#> GSM121248 1 0.0000 0.9507 1.000 0.000 0.000 0.000 0.000
#> GSM120744 5 0.0000 0.7698 0.000 0.000 0.000 0.000 1.000
#> GSM120745 5 0.0404 0.7693 0.000 0.000 0.012 0.000 0.988
#> GSM120746 5 0.0000 0.7698 0.000 0.000 0.000 0.000 1.000
#> GSM120747 5 0.0000 0.7698 0.000 0.000 0.000 0.000 1.000
#> GSM120748 5 0.0000 0.7698 0.000 0.000 0.000 0.000 1.000
#> GSM120749 5 0.0000 0.7698 0.000 0.000 0.000 0.000 1.000
#> GSM120750 5 0.0000 0.7698 0.000 0.000 0.000 0.000 1.000
#> GSM120751 5 0.0000 0.7698 0.000 0.000 0.000 0.000 1.000
#> GSM120752 5 0.0000 0.7698 0.000 0.000 0.000 0.000 1.000
#> GSM121336 2 0.1792 0.8650 0.000 0.916 0.000 0.084 0.000
#> GSM121339 3 0.6366 0.0555 0.000 0.020 0.556 0.124 0.300
#> GSM121349 2 0.1792 0.8650 0.000 0.916 0.000 0.084 0.000
#> GSM121355 2 0.1792 0.8650 0.000 0.916 0.000 0.084 0.000
#> GSM120757 5 0.1830 0.7745 0.000 0.000 0.008 0.068 0.924
#> GSM120766 5 0.0404 0.7721 0.000 0.000 0.000 0.012 0.988
#> GSM120770 4 0.4415 0.6177 0.000 0.008 0.064 0.768 0.160
#> GSM120779 5 0.4295 0.7599 0.000 0.004 0.084 0.132 0.780
#> GSM120780 5 0.0000 0.7698 0.000 0.000 0.000 0.000 1.000
#> GSM121102 5 0.6568 0.5822 0.000 0.004 0.220 0.276 0.500
#> GSM121203 5 0.3578 0.7657 0.000 0.000 0.048 0.132 0.820
#> GSM121204 5 0.4403 0.7566 0.000 0.004 0.092 0.132 0.772
#> GSM121330 3 0.4083 0.7484 0.132 0.080 0.788 0.000 0.000
#> GSM121335 3 0.4334 0.7402 0.156 0.080 0.764 0.000 0.000
#> GSM121337 2 0.6872 0.5353 0.000 0.588 0.184 0.152 0.076
#> GSM121338 5 0.6604 0.4990 0.000 0.004 0.332 0.196 0.468
#> GSM121341 3 0.4334 0.7402 0.156 0.080 0.764 0.000 0.000
#> GSM121342 3 0.4522 0.7304 0.176 0.080 0.744 0.000 0.000
#> GSM121343 5 0.6573 0.5830 0.000 0.004 0.224 0.272 0.500
#> GSM121344 3 0.4254 0.7437 0.148 0.080 0.772 0.000 0.000
#> GSM121346 3 0.4212 0.7453 0.144 0.080 0.776 0.000 0.000
#> GSM121347 5 0.6463 0.5664 0.000 0.000 0.212 0.300 0.488
#> GSM121348 4 0.5979 0.2456 0.000 0.328 0.024 0.576 0.072
#> GSM121350 3 0.4127 0.7478 0.136 0.080 0.784 0.000 0.000
#> GSM121352 3 0.4254 0.7437 0.148 0.080 0.772 0.000 0.000
#> GSM121354 3 0.4254 0.7437 0.148 0.080 0.772 0.000 0.000
#> GSM120753 4 0.0290 0.8888 0.000 0.008 0.000 0.992 0.000
#> GSM120761 4 0.0000 0.8926 0.000 0.000 0.000 1.000 0.000
#> GSM120768 4 0.0162 0.8911 0.000 0.004 0.000 0.996 0.000
#> GSM120781 2 0.3752 0.7187 0.000 0.708 0.000 0.292 0.000
#> GSM120788 4 0.0324 0.8883 0.000 0.004 0.004 0.992 0.000
#> GSM120760 4 0.0000 0.8926 0.000 0.000 0.000 1.000 0.000
#> GSM120763 4 0.0000 0.8926 0.000 0.000 0.000 1.000 0.000
#> GSM120764 4 0.0000 0.8926 0.000 0.000 0.000 1.000 0.000
#> GSM120777 4 0.0000 0.8926 0.000 0.000 0.000 1.000 0.000
#> GSM120786 4 0.0000 0.8926 0.000 0.000 0.000 1.000 0.000
#> GSM121329 3 0.4291 0.4366 0.000 0.004 0.704 0.016 0.276
#> GSM121331 5 0.5620 0.6448 0.000 0.004 0.092 0.296 0.608
#> GSM121333 5 0.4295 0.7599 0.000 0.004 0.084 0.132 0.780
#> GSM121345 5 0.4957 0.7299 0.000 0.004 0.096 0.184 0.716
#> GSM121356 5 0.4295 0.7599 0.000 0.004 0.084 0.132 0.780
#> GSM120754 4 0.0000 0.8926 0.000 0.000 0.000 1.000 0.000
#> GSM120759 2 0.1851 0.8648 0.000 0.912 0.000 0.088 0.000
#> GSM120762 2 0.2230 0.8558 0.000 0.884 0.000 0.116 0.000
#> GSM120775 4 0.0000 0.8926 0.000 0.000 0.000 1.000 0.000
#> GSM120776 4 0.4054 0.6583 0.000 0.004 0.080 0.800 0.116
#> GSM120782 4 0.0000 0.8926 0.000 0.000 0.000 1.000 0.000
#> GSM120789 2 0.1792 0.8650 0.000 0.916 0.000 0.084 0.000
#> GSM120790 2 0.3932 0.6281 0.000 0.672 0.000 0.328 0.000
#> GSM120791 4 0.0000 0.8926 0.000 0.000 0.000 1.000 0.000
#> GSM120755 4 0.4302 -0.1926 0.000 0.480 0.000 0.520 0.000
#> GSM120756 4 0.0162 0.8901 0.000 0.004 0.000 0.996 0.000
#> GSM120769 2 0.2377 0.8510 0.000 0.872 0.000 0.128 0.000
#> GSM120778 4 0.3816 0.4265 0.000 0.304 0.000 0.696 0.000
#> GSM120792 4 0.0609 0.8781 0.000 0.020 0.000 0.980 0.000
#> GSM121332 2 0.1965 0.8624 0.000 0.904 0.000 0.096 0.000
#> GSM121334 4 0.0162 0.8910 0.000 0.004 0.000 0.996 0.000
#> GSM121340 4 0.3143 0.6457 0.000 0.204 0.000 0.796 0.000
#> GSM121351 2 0.1792 0.8650 0.000 0.916 0.000 0.084 0.000
#> GSM121353 2 0.3730 0.7214 0.000 0.712 0.000 0.288 0.000
#> GSM120758 4 0.0510 0.8831 0.000 0.016 0.000 0.984 0.000
#> GSM120771 4 0.0162 0.8909 0.000 0.004 0.000 0.996 0.000
#> GSM120772 4 0.0000 0.8926 0.000 0.000 0.000 1.000 0.000
#> GSM120773 4 0.0000 0.8926 0.000 0.000 0.000 1.000 0.000
#> GSM120774 2 0.3242 0.8029 0.000 0.784 0.000 0.216 0.000
#> GSM120783 4 0.0000 0.8926 0.000 0.000 0.000 1.000 0.000
#> GSM120787 2 0.4249 0.4622 0.000 0.568 0.000 0.432 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM120719 5 0.3888 0.45401 0.000 0.000 0.208 0.016 0.752 0.024
#> GSM120720 3 0.5642 0.51339 0.084 0.000 0.568 0.008 0.320 0.020
#> GSM120765 2 0.3925 0.57555 0.000 0.656 0.008 0.332 0.004 0.000
#> GSM120767 2 0.2218 0.83767 0.000 0.884 0.000 0.104 0.012 0.000
#> GSM120784 4 0.5281 -0.14909 0.000 0.448 0.004 0.464 0.084 0.000
#> GSM121400 3 0.5184 0.44575 0.000 0.000 0.584 0.000 0.296 0.120
#> GSM121401 3 0.4633 0.51005 0.016 0.004 0.636 0.000 0.320 0.024
#> GSM121402 2 0.2593 0.81928 0.000 0.844 0.008 0.148 0.000 0.000
#> GSM121403 3 0.4456 0.29979 0.000 0.000 0.524 0.000 0.448 0.028
#> GSM121404 5 0.4484 0.51157 0.000 0.000 0.012 0.028 0.640 0.320
#> GSM121405 3 0.4386 0.44644 0.000 0.004 0.600 0.000 0.372 0.024
#> GSM121406 2 0.0551 0.86129 0.000 0.984 0.004 0.004 0.008 0.000
#> GSM121408 2 0.0806 0.86109 0.000 0.972 0.000 0.008 0.020 0.000
#> GSM121409 3 0.4385 0.31345 0.000 0.000 0.532 0.000 0.444 0.024
#> GSM121410 3 0.4513 0.44806 0.000 0.004 0.596 0.000 0.368 0.032
#> GSM121412 2 0.1785 0.85636 0.000 0.928 0.008 0.048 0.016 0.000
#> GSM121413 2 0.1268 0.86087 0.000 0.952 0.004 0.036 0.008 0.000
#> GSM121414 2 0.1829 0.85235 0.000 0.920 0.012 0.064 0.004 0.000
#> GSM121415 2 0.2643 0.82772 0.000 0.856 0.008 0.128 0.008 0.000
#> GSM121416 4 0.5487 0.39874 0.000 0.036 0.000 0.624 0.244 0.096
#> GSM120591 5 0.4142 0.59182 0.004 0.000 0.096 0.012 0.776 0.112
#> GSM120594 3 0.5403 0.42086 0.048 0.000 0.556 0.008 0.364 0.024
#> GSM120718 3 0.5769 0.53480 0.132 0.000 0.552 0.008 0.300 0.008
#> GSM121205 1 0.0000 0.93516 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121206 1 0.0000 0.93516 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121207 1 0.0000 0.93516 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121208 1 0.3995 0.00641 0.516 0.000 0.480 0.000 0.000 0.004
#> GSM121209 1 0.0000 0.93516 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121210 1 0.0000 0.93516 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121211 1 0.0000 0.93516 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121212 1 0.0146 0.93178 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM121213 1 0.0000 0.93516 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.0000 0.93516 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121215 1 0.0000 0.93516 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121216 1 0.3648 0.65737 0.776 0.000 0.024 0.000 0.188 0.012
#> GSM121217 1 0.0000 0.93516 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121218 1 0.0000 0.93516 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121234 1 0.0000 0.93516 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121243 1 0.0000 0.93516 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121245 1 0.0000 0.93516 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121246 3 0.2378 0.71748 0.152 0.000 0.848 0.000 0.000 0.000
#> GSM121247 1 0.4579 0.56049 0.708 0.000 0.060 0.000 0.212 0.020
#> GSM121248 1 0.0000 0.93516 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM120744 6 0.0000 0.96263 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120745 6 0.0146 0.95946 0.000 0.000 0.000 0.000 0.004 0.996
#> GSM120746 6 0.0000 0.96263 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120747 6 0.0146 0.95937 0.000 0.000 0.000 0.000 0.004 0.996
#> GSM120748 6 0.0000 0.96263 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120749 6 0.0000 0.96263 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120750 6 0.0000 0.96263 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120751 6 0.0000 0.96263 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM120752 6 0.0000 0.96263 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM121336 2 0.0551 0.86129 0.000 0.984 0.004 0.004 0.008 0.000
#> GSM121339 5 0.6248 0.44879 0.000 0.168 0.152 0.012 0.604 0.064
#> GSM121349 2 0.0551 0.86129 0.000 0.984 0.004 0.004 0.008 0.000
#> GSM121355 2 0.0520 0.86244 0.000 0.984 0.000 0.008 0.008 0.000
#> GSM120757 6 0.0622 0.94268 0.000 0.000 0.000 0.012 0.008 0.980
#> GSM120766 6 0.0665 0.94619 0.000 0.000 0.008 0.008 0.004 0.980
#> GSM120770 5 0.6882 0.31772 0.000 0.016 0.024 0.344 0.376 0.240
#> GSM120779 5 0.4286 0.46342 0.000 0.000 0.012 0.012 0.624 0.352
#> GSM120780 6 0.0000 0.96263 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM121102 5 0.5542 0.56776 0.000 0.040 0.020 0.048 0.632 0.260
#> GSM121203 6 0.3290 0.45928 0.000 0.000 0.004 0.000 0.252 0.744
#> GSM121204 5 0.3740 0.58647 0.000 0.000 0.008 0.012 0.728 0.252
#> GSM121330 3 0.2121 0.73790 0.096 0.000 0.892 0.000 0.012 0.000
#> GSM121335 3 0.1814 0.73774 0.100 0.000 0.900 0.000 0.000 0.000
#> GSM121337 5 0.4597 0.10666 0.000 0.484 0.004 0.004 0.488 0.020
#> GSM121338 5 0.5112 0.58674 0.000 0.056 0.012 0.024 0.668 0.240
#> GSM121341 3 0.2135 0.73109 0.128 0.000 0.872 0.000 0.000 0.000
#> GSM121342 3 0.2135 0.73064 0.128 0.000 0.872 0.000 0.000 0.000
#> GSM121343 5 0.4884 0.58376 0.000 0.052 0.000 0.032 0.672 0.244
#> GSM121344 3 0.1765 0.73846 0.096 0.000 0.904 0.000 0.000 0.000
#> GSM121346 3 0.1765 0.73846 0.096 0.000 0.904 0.000 0.000 0.000
#> GSM121347 5 0.6368 0.59194 0.000 0.104 0.024 0.080 0.608 0.184
#> GSM121348 2 0.6491 0.20666 0.000 0.460 0.024 0.368 0.124 0.024
#> GSM121350 3 0.1765 0.73846 0.096 0.000 0.904 0.000 0.000 0.000
#> GSM121352 3 0.1765 0.73846 0.096 0.000 0.904 0.000 0.000 0.000
#> GSM121354 3 0.1765 0.73846 0.096 0.000 0.904 0.000 0.000 0.000
#> GSM120753 4 0.0865 0.87682 0.000 0.036 0.000 0.964 0.000 0.000
#> GSM120761 4 0.0146 0.88812 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM120768 4 0.0865 0.87585 0.000 0.036 0.000 0.964 0.000 0.000
#> GSM120781 2 0.2896 0.78749 0.000 0.824 0.000 0.160 0.016 0.000
#> GSM120788 4 0.1890 0.83637 0.000 0.000 0.024 0.916 0.060 0.000
#> GSM120760 4 0.0000 0.88908 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM120763 4 0.0632 0.88081 0.000 0.024 0.000 0.976 0.000 0.000
#> GSM120764 4 0.0000 0.88908 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM120777 4 0.0000 0.88908 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM120786 4 0.0000 0.88908 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM121329 5 0.3582 0.45074 0.000 0.000 0.192 0.008 0.776 0.024
#> GSM121331 5 0.4010 0.62369 0.000 0.000 0.020 0.040 0.764 0.176
#> GSM121333 5 0.4202 0.49754 0.000 0.000 0.012 0.012 0.648 0.328
#> GSM121345 5 0.3564 0.63204 0.000 0.000 0.040 0.016 0.808 0.136
#> GSM121356 5 0.3836 0.57540 0.000 0.000 0.012 0.012 0.724 0.252
#> GSM120754 4 0.0000 0.88908 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM120759 2 0.1901 0.84960 0.000 0.912 0.008 0.076 0.004 0.000
#> GSM120762 2 0.1321 0.86301 0.000 0.952 0.004 0.024 0.020 0.000
#> GSM120775 4 0.0000 0.88908 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM120776 4 0.4665 0.24906 0.000 0.000 0.024 0.588 0.372 0.016
#> GSM120782 4 0.0000 0.88908 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM120789 2 0.0405 0.86296 0.000 0.988 0.000 0.008 0.004 0.000
#> GSM120790 2 0.3452 0.69712 0.000 0.736 0.004 0.256 0.004 0.000
#> GSM120791 4 0.0000 0.88908 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM120755 2 0.4064 0.44350 0.000 0.624 0.000 0.360 0.016 0.000
#> GSM120756 4 0.1411 0.84380 0.000 0.000 0.004 0.936 0.060 0.000
#> GSM120769 2 0.1760 0.85902 0.000 0.928 0.004 0.048 0.020 0.000
#> GSM120778 4 0.4039 0.22530 0.000 0.424 0.000 0.568 0.008 0.000
#> GSM120792 4 0.1267 0.85679 0.000 0.060 0.000 0.940 0.000 0.000
#> GSM121332 2 0.0806 0.86109 0.000 0.972 0.000 0.008 0.020 0.000
#> GSM121334 4 0.1387 0.85616 0.000 0.068 0.000 0.932 0.000 0.000
#> GSM121340 4 0.3468 0.57749 0.000 0.284 0.000 0.712 0.004 0.000
#> GSM121351 2 0.0951 0.86180 0.000 0.968 0.004 0.008 0.020 0.000
#> GSM121353 2 0.1895 0.85365 0.000 0.912 0.000 0.072 0.016 0.000
#> GSM120758 4 0.0405 0.88455 0.000 0.008 0.000 0.988 0.004 0.000
#> GSM120771 4 0.0146 0.88812 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM120772 4 0.0000 0.88908 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM120773 4 0.0000 0.88908 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM120774 2 0.1500 0.86170 0.000 0.936 0.000 0.052 0.012 0.000
#> GSM120783 4 0.0363 0.88620 0.000 0.012 0.000 0.988 0.000 0.000
#> GSM120787 2 0.3468 0.65285 0.000 0.712 0.004 0.284 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> ATC:mclust 104 2.27e-10 2
#> ATC:mclust 113 5.88e-20 3
#> ATC:mclust 90 6.90e-19 4
#> ATC:mclust 108 2.60e-28 5
#> ATC:mclust 98 5.69e-32 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 21512 rows and 119 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 0.983 0.971 0.987 0.5039 0.497 0.497
#> 3 3 0.659 0.635 0.784 0.2882 0.819 0.647
#> 4 4 0.745 0.777 0.881 0.1246 0.835 0.579
#> 5 5 0.673 0.560 0.750 0.0622 0.951 0.827
#> 6 6 0.670 0.491 0.737 0.0356 0.860 0.513
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
#> GSM120719 1 0.0000 0.976 1.000 0.000
#> GSM120720 1 0.0000 0.976 1.000 0.000
#> GSM120765 2 0.0000 0.998 0.000 1.000
#> GSM120767 2 0.0000 0.998 0.000 1.000
#> GSM120784 2 0.0000 0.998 0.000 1.000
#> GSM121400 1 0.0000 0.976 1.000 0.000
#> GSM121401 1 0.0000 0.976 1.000 0.000
#> GSM121402 2 0.0000 0.998 0.000 1.000
#> GSM121403 2 0.0000 0.998 0.000 1.000
#> GSM121404 2 0.0000 0.998 0.000 1.000
#> GSM121405 1 0.9358 0.490 0.648 0.352
#> GSM121406 2 0.0000 0.998 0.000 1.000
#> GSM121408 2 0.0000 0.998 0.000 1.000
#> GSM121409 1 0.7219 0.762 0.800 0.200
#> GSM121410 1 0.5842 0.839 0.860 0.140
#> GSM121412 2 0.0000 0.998 0.000 1.000
#> GSM121413 2 0.0000 0.998 0.000 1.000
#> GSM121414 2 0.0000 0.998 0.000 1.000
#> GSM121415 2 0.0000 0.998 0.000 1.000
#> GSM121416 2 0.0000 0.998 0.000 1.000
#> GSM120591 1 0.0000 0.976 1.000 0.000
#> GSM120594 1 0.0000 0.976 1.000 0.000
#> GSM120718 1 0.0000 0.976 1.000 0.000
#> GSM121205 1 0.0000 0.976 1.000 0.000
#> GSM121206 1 0.0000 0.976 1.000 0.000
#> GSM121207 1 0.0000 0.976 1.000 0.000
#> GSM121208 1 0.0000 0.976 1.000 0.000
#> GSM121209 1 0.0000 0.976 1.000 0.000
#> GSM121210 1 0.0000 0.976 1.000 0.000
#> GSM121211 1 0.0000 0.976 1.000 0.000
#> GSM121212 1 0.0000 0.976 1.000 0.000
#> GSM121213 1 0.0000 0.976 1.000 0.000
#> GSM121214 1 0.0000 0.976 1.000 0.000
#> GSM121215 1 0.0000 0.976 1.000 0.000
#> GSM121216 1 0.0000 0.976 1.000 0.000
#> GSM121217 1 0.0000 0.976 1.000 0.000
#> GSM121218 1 0.0000 0.976 1.000 0.000
#> GSM121234 1 0.0000 0.976 1.000 0.000
#> GSM121243 1 0.0000 0.976 1.000 0.000
#> GSM121245 1 0.0000 0.976 1.000 0.000
#> GSM121246 1 0.0000 0.976 1.000 0.000
#> GSM121247 1 0.0000 0.976 1.000 0.000
#> GSM121248 1 0.0000 0.976 1.000 0.000
#> GSM120744 1 0.0000 0.976 1.000 0.000
#> GSM120745 1 0.0000 0.976 1.000 0.000
#> GSM120746 1 0.0000 0.976 1.000 0.000
#> GSM120747 1 0.0000 0.976 1.000 0.000
#> GSM120748 1 0.7815 0.714 0.768 0.232
#> GSM120749 1 0.0000 0.976 1.000 0.000
#> GSM120750 1 0.0000 0.976 1.000 0.000
#> GSM120751 1 0.0000 0.976 1.000 0.000
#> GSM120752 1 0.0000 0.976 1.000 0.000
#> GSM121336 2 0.0000 0.998 0.000 1.000
#> GSM121339 2 0.0000 0.998 0.000 1.000
#> GSM121349 2 0.0000 0.998 0.000 1.000
#> GSM121355 2 0.0000 0.998 0.000 1.000
#> GSM120757 1 0.0000 0.976 1.000 0.000
#> GSM120766 1 0.0000 0.976 1.000 0.000
#> GSM120770 2 0.0000 0.998 0.000 1.000
#> GSM120779 1 0.0000 0.976 1.000 0.000
#> GSM120780 1 0.0672 0.970 0.992 0.008
#> GSM121102 2 0.0000 0.998 0.000 1.000
#> GSM121203 1 0.4815 0.880 0.896 0.104
#> GSM121204 1 0.0000 0.976 1.000 0.000
#> GSM121330 1 0.0000 0.976 1.000 0.000
#> GSM121335 1 0.0000 0.976 1.000 0.000
#> GSM121337 2 0.0000 0.998 0.000 1.000
#> GSM121338 2 0.0000 0.998 0.000 1.000
#> GSM121341 1 0.0000 0.976 1.000 0.000
#> GSM121342 1 0.0000 0.976 1.000 0.000
#> GSM121343 2 0.0000 0.998 0.000 1.000
#> GSM121344 1 0.0000 0.976 1.000 0.000
#> GSM121346 1 0.0000 0.976 1.000 0.000
#> GSM121347 2 0.0000 0.998 0.000 1.000
#> GSM121348 2 0.0000 0.998 0.000 1.000
#> GSM121350 1 0.0000 0.976 1.000 0.000
#> GSM121352 1 0.0000 0.976 1.000 0.000
#> GSM121354 1 0.0000 0.976 1.000 0.000
#> GSM120753 2 0.0000 0.998 0.000 1.000
#> GSM120761 2 0.0000 0.998 0.000 1.000
#> GSM120768 2 0.0000 0.998 0.000 1.000
#> GSM120781 2 0.0000 0.998 0.000 1.000
#> GSM120788 1 0.9460 0.461 0.636 0.364
#> GSM120760 2 0.0000 0.998 0.000 1.000
#> GSM120763 2 0.0000 0.998 0.000 1.000
#> GSM120764 2 0.0000 0.998 0.000 1.000
#> GSM120777 2 0.5059 0.868 0.112 0.888
#> GSM120786 2 0.0000 0.998 0.000 1.000
#> GSM121329 1 0.0000 0.976 1.000 0.000
#> GSM121331 1 0.0000 0.976 1.000 0.000
#> GSM121333 1 0.0000 0.976 1.000 0.000
#> GSM121345 1 0.0000 0.976 1.000 0.000
#> GSM121356 1 0.0000 0.976 1.000 0.000
#> GSM120754 2 0.0000 0.998 0.000 1.000
#> GSM120759 2 0.0000 0.998 0.000 1.000
#> GSM120762 2 0.0000 0.998 0.000 1.000
#> GSM120775 2 0.0000 0.998 0.000 1.000
#> GSM120776 1 0.2778 0.935 0.952 0.048
#> GSM120782 2 0.0000 0.998 0.000 1.000
#> GSM120789 2 0.0000 0.998 0.000 1.000
#> GSM120790 2 0.0000 0.998 0.000 1.000
#> GSM120791 2 0.0000 0.998 0.000 1.000
#> GSM120755 2 0.0000 0.998 0.000 1.000
#> GSM120756 1 0.0000 0.976 1.000 0.000
#> GSM120769 2 0.0000 0.998 0.000 1.000
#> GSM120778 2 0.0000 0.998 0.000 1.000
#> GSM120792 2 0.0000 0.998 0.000 1.000
#> GSM121332 2 0.0000 0.998 0.000 1.000
#> GSM121334 2 0.0000 0.998 0.000 1.000
#> GSM121340 2 0.0000 0.998 0.000 1.000
#> GSM121351 2 0.0000 0.998 0.000 1.000
#> GSM121353 2 0.0000 0.998 0.000 1.000
#> GSM120758 2 0.0000 0.998 0.000 1.000
#> GSM120771 2 0.0000 0.998 0.000 1.000
#> GSM120772 2 0.0000 0.998 0.000 1.000
#> GSM120773 2 0.0000 0.998 0.000 1.000
#> GSM120774 2 0.0000 0.998 0.000 1.000
#> GSM120783 2 0.0000 0.998 0.000 1.000
#> GSM120787 2 0.0000 0.998 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM120719 1 0.6154 0.497 0.592 0.000 0.408
#> GSM120720 1 0.3038 0.582 0.896 0.000 0.104
#> GSM120765 2 0.0000 0.878 0.000 1.000 0.000
#> GSM120767 2 0.0000 0.878 0.000 1.000 0.000
#> GSM120784 2 0.0424 0.875 0.000 0.992 0.008
#> GSM121400 3 0.7878 0.655 0.392 0.060 0.548
#> GSM121401 3 0.7223 0.661 0.424 0.028 0.548
#> GSM121402 2 0.0000 0.878 0.000 1.000 0.000
#> GSM121403 3 0.6274 0.152 0.000 0.456 0.544
#> GSM121404 3 0.6274 0.152 0.000 0.456 0.544
#> GSM121405 3 0.8571 0.628 0.340 0.112 0.548
#> GSM121406 2 0.0237 0.877 0.000 0.996 0.004
#> GSM121408 2 0.0000 0.878 0.000 1.000 0.000
#> GSM121409 3 0.8962 0.588 0.304 0.156 0.540
#> GSM121410 3 0.8395 0.640 0.356 0.096 0.548
#> GSM121412 2 0.1163 0.863 0.000 0.972 0.028
#> GSM121413 2 0.0237 0.877 0.000 0.996 0.004
#> GSM121414 2 0.0592 0.873 0.000 0.988 0.012
#> GSM121415 2 0.0424 0.875 0.000 0.992 0.008
#> GSM121416 2 0.0592 0.873 0.000 0.988 0.012
#> GSM120591 1 0.1289 0.692 0.968 0.000 0.032
#> GSM120594 1 0.1643 0.670 0.956 0.000 0.044
#> GSM120718 1 0.1411 0.679 0.964 0.000 0.036
#> GSM121205 1 0.0000 0.698 1.000 0.000 0.000
#> GSM121206 1 0.0892 0.691 0.980 0.000 0.020
#> GSM121207 1 0.2796 0.669 0.908 0.000 0.092
#> GSM121208 1 0.4702 0.354 0.788 0.000 0.212
#> GSM121209 1 0.1529 0.675 0.960 0.000 0.040
#> GSM121210 1 0.1860 0.686 0.948 0.000 0.052
#> GSM121211 1 0.1163 0.686 0.972 0.000 0.028
#> GSM121212 1 0.0747 0.693 0.984 0.000 0.016
#> GSM121213 1 0.0747 0.693 0.984 0.000 0.016
#> GSM121214 1 0.0000 0.698 1.000 0.000 0.000
#> GSM121215 1 0.0000 0.698 1.000 0.000 0.000
#> GSM121216 1 0.0424 0.697 0.992 0.000 0.008
#> GSM121217 1 0.1289 0.683 0.968 0.000 0.032
#> GSM121218 1 0.0000 0.698 1.000 0.000 0.000
#> GSM121234 1 0.1411 0.679 0.964 0.000 0.036
#> GSM121243 1 0.1964 0.685 0.944 0.000 0.056
#> GSM121245 1 0.0237 0.698 0.996 0.000 0.004
#> GSM121246 1 0.5291 0.167 0.732 0.000 0.268
#> GSM121247 1 0.5948 0.524 0.640 0.000 0.360
#> GSM121248 1 0.0000 0.698 1.000 0.000 0.000
#> GSM120744 3 0.6267 0.658 0.452 0.000 0.548
#> GSM120745 1 0.5138 0.225 0.748 0.000 0.252
#> GSM120746 3 0.6267 0.658 0.452 0.000 0.548
#> GSM120747 3 0.7013 0.661 0.432 0.020 0.548
#> GSM120748 3 0.8295 0.644 0.364 0.088 0.548
#> GSM120749 3 0.6286 0.643 0.464 0.000 0.536
#> GSM120750 3 0.6267 0.658 0.452 0.000 0.548
#> GSM120751 3 0.6274 0.654 0.456 0.000 0.544
#> GSM120752 1 0.5760 -0.082 0.672 0.000 0.328
#> GSM121336 2 0.0000 0.878 0.000 1.000 0.000
#> GSM121339 2 0.6204 0.178 0.000 0.576 0.424
#> GSM121349 2 0.0000 0.878 0.000 1.000 0.000
#> GSM121355 2 0.0237 0.877 0.000 0.996 0.004
#> GSM120757 1 0.4178 0.638 0.828 0.000 0.172
#> GSM120766 1 0.5216 0.314 0.740 0.000 0.260
#> GSM120770 2 0.0892 0.868 0.000 0.980 0.020
#> GSM120779 1 0.6079 0.509 0.612 0.000 0.388
#> GSM120780 3 0.7660 0.659 0.404 0.048 0.548
#> GSM121102 2 0.5706 0.445 0.000 0.680 0.320
#> GSM121203 3 0.8530 0.631 0.344 0.108 0.548
#> GSM121204 1 0.5397 0.567 0.720 0.000 0.280
#> GSM121330 3 0.6267 0.658 0.452 0.000 0.548
#> GSM121335 3 0.6302 0.617 0.480 0.000 0.520
#> GSM121337 2 0.0424 0.875 0.000 0.992 0.008
#> GSM121338 3 0.6286 0.128 0.000 0.464 0.536
#> GSM121341 3 0.6302 0.617 0.480 0.000 0.520
#> GSM121342 1 0.6252 -0.473 0.556 0.000 0.444
#> GSM121343 2 0.6192 0.198 0.000 0.580 0.420
#> GSM121344 3 0.6274 0.654 0.456 0.000 0.544
#> GSM121346 3 0.6267 0.658 0.452 0.000 0.548
#> GSM121347 2 0.0424 0.875 0.000 0.992 0.008
#> GSM121348 2 0.1753 0.871 0.000 0.952 0.048
#> GSM121350 3 0.6267 0.658 0.452 0.000 0.548
#> GSM121352 3 0.6267 0.658 0.452 0.000 0.548
#> GSM121354 3 0.6274 0.654 0.456 0.000 0.544
#> GSM120753 2 0.1411 0.874 0.000 0.964 0.036
#> GSM120761 2 0.2261 0.864 0.000 0.932 0.068
#> GSM120768 2 0.2959 0.849 0.000 0.900 0.100
#> GSM120781 2 0.0592 0.878 0.000 0.988 0.012
#> GSM120788 3 0.8691 -0.385 0.444 0.104 0.452
#> GSM120760 2 0.5988 0.644 0.000 0.632 0.368
#> GSM120763 2 0.5591 0.703 0.000 0.696 0.304
#> GSM120764 3 0.8582 -0.454 0.096 0.452 0.452
#> GSM120777 3 0.9247 -0.327 0.392 0.156 0.452
#> GSM120786 2 0.6215 0.580 0.000 0.572 0.428
#> GSM121329 1 0.1031 0.689 0.976 0.000 0.024
#> GSM121331 1 0.6260 0.467 0.552 0.000 0.448
#> GSM121333 1 0.6215 0.483 0.572 0.000 0.428
#> GSM121345 1 0.6267 0.463 0.548 0.000 0.452
#> GSM121356 1 0.6180 0.492 0.584 0.000 0.416
#> GSM120754 2 0.5733 0.685 0.000 0.676 0.324
#> GSM120759 2 0.0000 0.878 0.000 1.000 0.000
#> GSM120762 2 0.0424 0.878 0.000 0.992 0.008
#> GSM120775 2 0.8844 0.385 0.116 0.444 0.440
#> GSM120776 1 0.7274 0.436 0.520 0.028 0.452
#> GSM120782 2 0.4002 0.813 0.000 0.840 0.160
#> GSM120789 2 0.0000 0.878 0.000 1.000 0.000
#> GSM120790 2 0.0892 0.877 0.000 0.980 0.020
#> GSM120791 2 0.4399 0.794 0.000 0.812 0.188
#> GSM120755 2 0.0000 0.878 0.000 1.000 0.000
#> GSM120756 1 0.6647 0.456 0.540 0.008 0.452
#> GSM120769 2 0.0424 0.878 0.000 0.992 0.008
#> GSM120778 2 0.2165 0.865 0.000 0.936 0.064
#> GSM120792 2 0.2448 0.861 0.000 0.924 0.076
#> GSM121332 2 0.0592 0.878 0.000 0.988 0.012
#> GSM121334 2 0.2261 0.864 0.000 0.932 0.068
#> GSM121340 2 0.7164 0.523 0.024 0.524 0.452
#> GSM121351 2 0.0000 0.878 0.000 1.000 0.000
#> GSM121353 2 0.6651 0.652 0.020 0.640 0.340
#> GSM120758 2 0.0237 0.878 0.000 0.996 0.004
#> GSM120771 2 0.1031 0.877 0.000 0.976 0.024
#> GSM120772 2 0.1964 0.868 0.000 0.944 0.056
#> GSM120773 2 0.5948 0.652 0.000 0.640 0.360
#> GSM120774 2 0.2066 0.867 0.000 0.940 0.060
#> GSM120783 2 0.6587 0.576 0.008 0.568 0.424
#> GSM120787 2 0.2261 0.864 0.000 0.932 0.068
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM120719 1 0.2149 0.8995 0.912 0.000 0.000 0.088
#> GSM120720 1 0.1356 0.9380 0.960 0.000 0.032 0.008
#> GSM120765 2 0.0592 0.8902 0.000 0.984 0.016 0.000
#> GSM120767 2 0.0376 0.8912 0.000 0.992 0.004 0.004
#> GSM120784 2 0.1557 0.8742 0.000 0.944 0.056 0.000
#> GSM121400 3 0.1404 0.8093 0.012 0.012 0.964 0.012
#> GSM121401 3 0.2853 0.7966 0.076 0.016 0.900 0.008
#> GSM121402 2 0.1256 0.8843 0.000 0.964 0.028 0.008
#> GSM121403 3 0.4044 0.7356 0.004 0.152 0.820 0.024
#> GSM121404 3 0.2021 0.8053 0.012 0.056 0.932 0.000
#> GSM121405 3 0.2877 0.8012 0.060 0.028 0.904 0.008
#> GSM121406 2 0.1635 0.8775 0.000 0.948 0.044 0.008
#> GSM121408 2 0.0336 0.8909 0.000 0.992 0.008 0.000
#> GSM121409 3 0.4211 0.7782 0.064 0.092 0.836 0.008
#> GSM121410 3 0.3349 0.7944 0.052 0.064 0.880 0.004
#> GSM121412 2 0.2198 0.8593 0.000 0.920 0.072 0.008
#> GSM121413 2 0.1635 0.8775 0.000 0.948 0.044 0.008
#> GSM121414 2 0.2124 0.8617 0.000 0.924 0.068 0.008
#> GSM121415 2 0.1824 0.8702 0.000 0.936 0.060 0.004
#> GSM121416 2 0.5466 0.1678 0.000 0.548 0.436 0.016
#> GSM120591 1 0.1798 0.9390 0.944 0.000 0.016 0.040
#> GSM120594 1 0.1388 0.9411 0.960 0.000 0.028 0.012
#> GSM120718 1 0.0657 0.9488 0.984 0.000 0.012 0.004
#> GSM121205 1 0.0592 0.9489 0.984 0.000 0.000 0.016
#> GSM121206 1 0.0188 0.9498 0.996 0.000 0.004 0.000
#> GSM121207 1 0.1211 0.9373 0.960 0.000 0.000 0.040
#> GSM121208 1 0.1807 0.9261 0.940 0.000 0.052 0.008
#> GSM121209 1 0.0779 0.9462 0.980 0.000 0.016 0.004
#> GSM121210 1 0.1022 0.9423 0.968 0.000 0.000 0.032
#> GSM121211 1 0.0188 0.9498 0.996 0.000 0.004 0.000
#> GSM121212 1 0.0469 0.9495 0.988 0.000 0.000 0.012
#> GSM121213 1 0.0188 0.9501 0.996 0.000 0.000 0.004
#> GSM121214 1 0.0592 0.9489 0.984 0.000 0.000 0.016
#> GSM121215 1 0.0336 0.9499 0.992 0.000 0.000 0.008
#> GSM121216 1 0.0336 0.9499 0.992 0.000 0.000 0.008
#> GSM121217 1 0.0336 0.9489 0.992 0.000 0.008 0.000
#> GSM121218 1 0.0469 0.9495 0.988 0.000 0.000 0.012
#> GSM121234 1 0.0779 0.9459 0.980 0.000 0.016 0.004
#> GSM121243 1 0.0707 0.9473 0.980 0.000 0.000 0.020
#> GSM121245 1 0.1022 0.9423 0.968 0.000 0.000 0.032
#> GSM121246 1 0.2021 0.9218 0.932 0.000 0.056 0.012
#> GSM121247 1 0.2216 0.8917 0.908 0.000 0.000 0.092
#> GSM121248 1 0.0592 0.9489 0.984 0.000 0.000 0.016
#> GSM120744 3 0.3450 0.7685 0.008 0.000 0.836 0.156
#> GSM120745 3 0.5903 0.5479 0.052 0.000 0.616 0.332
#> GSM120746 3 0.3051 0.8015 0.028 0.000 0.884 0.088
#> GSM120747 3 0.2467 0.8092 0.024 0.004 0.920 0.052
#> GSM120748 3 0.2124 0.8045 0.008 0.000 0.924 0.068
#> GSM120749 3 0.3215 0.8002 0.032 0.000 0.876 0.092
#> GSM120750 3 0.3271 0.7846 0.012 0.000 0.856 0.132
#> GSM120751 3 0.3658 0.7790 0.020 0.000 0.836 0.144
#> GSM120752 3 0.5364 0.5812 0.028 0.000 0.652 0.320
#> GSM121336 2 0.0524 0.8907 0.000 0.988 0.008 0.004
#> GSM121339 2 0.4795 0.6715 0.024 0.768 0.196 0.012
#> GSM121349 2 0.0657 0.8903 0.000 0.984 0.012 0.004
#> GSM121355 2 0.0592 0.8902 0.000 0.984 0.016 0.000
#> GSM120757 4 0.4464 0.5489 0.024 0.000 0.208 0.768
#> GSM120766 4 0.5396 -0.1092 0.012 0.000 0.464 0.524
#> GSM120770 3 0.6449 0.5612 0.000 0.220 0.640 0.140
#> GSM120779 4 0.3367 0.7032 0.108 0.000 0.028 0.864
#> GSM120780 3 0.3123 0.7699 0.000 0.000 0.844 0.156
#> GSM121102 3 0.4205 0.7605 0.000 0.124 0.820 0.056
#> GSM121203 3 0.2589 0.7898 0.000 0.000 0.884 0.116
#> GSM121204 4 0.3215 0.7195 0.092 0.000 0.032 0.876
#> GSM121330 3 0.4516 0.6520 0.252 0.000 0.736 0.012
#> GSM121335 1 0.3764 0.8061 0.816 0.000 0.172 0.012
#> GSM121337 2 0.1807 0.8729 0.000 0.940 0.052 0.008
#> GSM121338 3 0.2197 0.7936 0.000 0.080 0.916 0.004
#> GSM121341 1 0.3529 0.8303 0.836 0.000 0.152 0.012
#> GSM121342 1 0.2867 0.8804 0.884 0.000 0.104 0.012
#> GSM121343 3 0.2796 0.7876 0.000 0.092 0.892 0.016
#> GSM121344 1 0.4059 0.7669 0.788 0.000 0.200 0.012
#> GSM121346 3 0.3545 0.7425 0.164 0.000 0.828 0.008
#> GSM121347 3 0.7049 0.4574 0.000 0.236 0.572 0.192
#> GSM121348 2 0.4261 0.7759 0.000 0.820 0.068 0.112
#> GSM121350 3 0.2888 0.7735 0.124 0.000 0.872 0.004
#> GSM121352 3 0.4453 0.6607 0.244 0.000 0.744 0.012
#> GSM121354 3 0.5279 0.3275 0.400 0.000 0.588 0.012
#> GSM120753 2 0.0707 0.8879 0.000 0.980 0.000 0.020
#> GSM120761 2 0.1474 0.8747 0.000 0.948 0.000 0.052
#> GSM120768 2 0.2281 0.8426 0.000 0.904 0.000 0.096
#> GSM120781 2 0.0336 0.8903 0.000 0.992 0.000 0.008
#> GSM120788 4 0.2773 0.7440 0.072 0.028 0.000 0.900
#> GSM120760 2 0.4222 0.5970 0.000 0.728 0.000 0.272
#> GSM120763 2 0.2868 0.8030 0.000 0.864 0.000 0.136
#> GSM120764 4 0.2814 0.7077 0.000 0.132 0.000 0.868
#> GSM120777 4 0.3009 0.7406 0.056 0.052 0.000 0.892
#> GSM120786 4 0.4967 0.2032 0.000 0.452 0.000 0.548
#> GSM121329 1 0.1209 0.9439 0.964 0.000 0.004 0.032
#> GSM121331 4 0.2816 0.7331 0.064 0.000 0.036 0.900
#> GSM121333 4 0.2224 0.7250 0.040 0.000 0.032 0.928
#> GSM121345 4 0.2611 0.7353 0.096 0.000 0.008 0.896
#> GSM121356 4 0.2489 0.7024 0.020 0.000 0.068 0.912
#> GSM120754 4 0.4967 0.1812 0.000 0.452 0.000 0.548
#> GSM120759 2 0.1677 0.8782 0.000 0.948 0.040 0.012
#> GSM120762 2 0.0336 0.8903 0.000 0.992 0.000 0.008
#> GSM120775 4 0.5320 0.2965 0.012 0.416 0.000 0.572
#> GSM120776 4 0.2380 0.7428 0.064 0.008 0.008 0.920
#> GSM120782 2 0.3311 0.7593 0.000 0.828 0.000 0.172
#> GSM120789 2 0.0779 0.8894 0.000 0.980 0.016 0.004
#> GSM120790 2 0.1624 0.8835 0.000 0.952 0.028 0.020
#> GSM120791 2 0.4955 0.1570 0.000 0.556 0.000 0.444
#> GSM120755 2 0.0188 0.8908 0.000 0.996 0.000 0.004
#> GSM120756 4 0.3324 0.7142 0.136 0.012 0.000 0.852
#> GSM120769 2 0.0336 0.8904 0.000 0.992 0.000 0.008
#> GSM120778 2 0.1389 0.8763 0.000 0.952 0.000 0.048
#> GSM120792 2 0.1792 0.8645 0.000 0.932 0.000 0.068
#> GSM121332 2 0.0336 0.8904 0.000 0.992 0.000 0.008
#> GSM121334 2 0.1557 0.8722 0.000 0.944 0.000 0.056
#> GSM121340 2 0.5290 0.2508 0.012 0.584 0.000 0.404
#> GSM121351 2 0.0524 0.8910 0.000 0.988 0.008 0.004
#> GSM121353 2 0.2255 0.8596 0.012 0.920 0.000 0.068
#> GSM120758 2 0.0524 0.8915 0.000 0.988 0.008 0.004
#> GSM120771 2 0.0524 0.8917 0.000 0.988 0.008 0.004
#> GSM120772 2 0.1867 0.8623 0.000 0.928 0.000 0.072
#> GSM120773 2 0.4977 0.0785 0.000 0.540 0.000 0.460
#> GSM120774 2 0.0921 0.8847 0.000 0.972 0.000 0.028
#> GSM120783 4 0.4888 0.3122 0.000 0.412 0.000 0.588
#> GSM120787 2 0.1022 0.8835 0.000 0.968 0.000 0.032
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM120719 1 0.2349 0.88648 0.900 0.000 0.004 0.084 0.012
#> GSM120720 1 0.3768 0.80552 0.808 0.000 0.156 0.020 0.016
#> GSM120765 2 0.1410 0.64272 0.000 0.940 0.000 0.000 0.060
#> GSM120767 2 0.3632 0.62789 0.000 0.800 0.004 0.176 0.020
#> GSM120784 2 0.1924 0.64812 0.000 0.924 0.004 0.008 0.064
#> GSM121400 5 0.4892 -0.19545 0.004 0.016 0.484 0.000 0.496
#> GSM121401 3 0.3051 0.79429 0.028 0.000 0.852 0.000 0.120
#> GSM121402 2 0.3074 0.52468 0.000 0.804 0.000 0.000 0.196
#> GSM121403 5 0.5792 0.43496 0.004 0.376 0.084 0.000 0.536
#> GSM121404 3 0.2550 0.80563 0.004 0.020 0.892 0.000 0.084
#> GSM121405 3 0.3451 0.78121 0.024 0.012 0.836 0.000 0.128
#> GSM121406 2 0.3424 0.45929 0.000 0.760 0.000 0.000 0.240
#> GSM121408 2 0.1205 0.65059 0.000 0.956 0.000 0.004 0.040
#> GSM121409 5 0.6676 0.48643 0.016 0.332 0.160 0.000 0.492
#> GSM121410 5 0.6529 0.48346 0.008 0.272 0.196 0.000 0.524
#> GSM121412 2 0.4114 0.18013 0.000 0.624 0.000 0.000 0.376
#> GSM121413 2 0.3913 0.31784 0.000 0.676 0.000 0.000 0.324
#> GSM121414 2 0.4201 0.08432 0.000 0.592 0.000 0.000 0.408
#> GSM121415 2 0.3171 0.53948 0.000 0.816 0.008 0.000 0.176
#> GSM121416 2 0.6118 0.47053 0.000 0.652 0.196 0.096 0.056
#> GSM120591 1 0.6650 0.38801 0.528 0.000 0.316 0.124 0.032
#> GSM120594 1 0.5094 0.63517 0.676 0.000 0.264 0.044 0.016
#> GSM120718 1 0.3023 0.86814 0.872 0.000 0.088 0.028 0.012
#> GSM121205 1 0.0510 0.92035 0.984 0.000 0.000 0.016 0.000
#> GSM121206 1 0.0162 0.92044 0.996 0.000 0.000 0.000 0.004
#> GSM121207 1 0.1043 0.91579 0.960 0.000 0.000 0.040 0.000
#> GSM121208 1 0.0162 0.92044 0.996 0.000 0.000 0.000 0.004
#> GSM121209 1 0.0162 0.92044 0.996 0.000 0.000 0.000 0.004
#> GSM121210 1 0.0880 0.91785 0.968 0.000 0.000 0.032 0.000
#> GSM121211 1 0.0162 0.92044 0.996 0.000 0.000 0.000 0.004
#> GSM121212 1 0.0566 0.92119 0.984 0.000 0.000 0.012 0.004
#> GSM121213 1 0.0000 0.92051 1.000 0.000 0.000 0.000 0.000
#> GSM121214 1 0.0880 0.91826 0.968 0.000 0.000 0.032 0.000
#> GSM121215 1 0.0404 0.92034 0.988 0.000 0.000 0.012 0.000
#> GSM121216 1 0.0404 0.92034 0.988 0.000 0.000 0.012 0.000
#> GSM121217 1 0.0162 0.92044 0.996 0.000 0.000 0.000 0.004
#> GSM121218 1 0.0510 0.92035 0.984 0.000 0.000 0.016 0.000
#> GSM121234 1 0.0162 0.92044 0.996 0.000 0.000 0.000 0.004
#> GSM121243 1 0.0510 0.92035 0.984 0.000 0.000 0.016 0.000
#> GSM121245 1 0.1043 0.91579 0.960 0.000 0.000 0.040 0.000
#> GSM121246 1 0.0771 0.91395 0.976 0.000 0.020 0.000 0.004
#> GSM121247 1 0.1830 0.89462 0.924 0.000 0.000 0.068 0.008
#> GSM121248 1 0.0963 0.91717 0.964 0.000 0.000 0.036 0.000
#> GSM120744 3 0.1956 0.80090 0.000 0.000 0.916 0.008 0.076
#> GSM120745 3 0.3517 0.73893 0.000 0.000 0.832 0.068 0.100
#> GSM120746 3 0.1041 0.82092 0.000 0.000 0.964 0.004 0.032
#> GSM120747 3 0.0671 0.82135 0.000 0.000 0.980 0.004 0.016
#> GSM120748 3 0.0955 0.82141 0.000 0.000 0.968 0.004 0.028
#> GSM120749 3 0.1205 0.82000 0.000 0.000 0.956 0.004 0.040
#> GSM120750 3 0.1282 0.81802 0.000 0.000 0.952 0.004 0.044
#> GSM120751 3 0.1597 0.81437 0.000 0.000 0.940 0.012 0.048
#> GSM120752 3 0.3705 0.71956 0.000 0.000 0.816 0.064 0.120
#> GSM121336 2 0.2074 0.61490 0.000 0.896 0.000 0.000 0.104
#> GSM121339 2 0.4819 0.31712 0.004 0.624 0.352 0.008 0.012
#> GSM121349 2 0.2127 0.61133 0.000 0.892 0.000 0.000 0.108
#> GSM121355 2 0.1792 0.62816 0.000 0.916 0.000 0.000 0.084
#> GSM120757 4 0.6062 0.26778 0.000 0.000 0.120 0.464 0.416
#> GSM120766 5 0.5691 -0.23989 0.000 0.000 0.088 0.376 0.536
#> GSM120770 2 0.6247 -0.30398 0.000 0.432 0.144 0.000 0.424
#> GSM120779 4 0.5437 0.37378 0.048 0.000 0.008 0.564 0.380
#> GSM120780 5 0.4890 0.00179 0.000 0.000 0.332 0.040 0.628
#> GSM121102 5 0.6309 0.48248 0.000 0.340 0.168 0.000 0.492
#> GSM121203 3 0.3861 0.66997 0.000 0.000 0.712 0.004 0.284
#> GSM121204 4 0.5956 0.37769 0.040 0.000 0.044 0.560 0.356
#> GSM121330 3 0.4022 0.76450 0.100 0.004 0.804 0.000 0.092
#> GSM121335 1 0.4166 0.49128 0.648 0.000 0.348 0.000 0.004
#> GSM121337 2 0.3796 0.35744 0.000 0.700 0.000 0.000 0.300
#> GSM121338 3 0.6579 -0.15758 0.000 0.220 0.448 0.000 0.332
#> GSM121341 1 0.3838 0.63207 0.716 0.000 0.280 0.000 0.004
#> GSM121342 1 0.1082 0.91075 0.964 0.000 0.028 0.000 0.008
#> GSM121343 5 0.6519 0.48230 0.000 0.340 0.204 0.000 0.456
#> GSM121344 1 0.2674 0.82458 0.856 0.000 0.140 0.000 0.004
#> GSM121346 3 0.3281 0.79331 0.060 0.000 0.848 0.000 0.092
#> GSM121347 5 0.5287 0.47295 0.000 0.292 0.032 0.028 0.648
#> GSM121348 5 0.4465 0.44864 0.000 0.212 0.000 0.056 0.732
#> GSM121350 3 0.3844 0.74938 0.044 0.000 0.792 0.000 0.164
#> GSM121352 3 0.3267 0.77141 0.112 0.000 0.844 0.000 0.044
#> GSM121354 3 0.3656 0.68104 0.196 0.000 0.784 0.000 0.020
#> GSM120753 2 0.2891 0.63694 0.000 0.824 0.000 0.176 0.000
#> GSM120761 2 0.2077 0.66566 0.000 0.908 0.000 0.084 0.008
#> GSM120768 2 0.4182 0.48011 0.000 0.644 0.000 0.352 0.004
#> GSM120781 2 0.2848 0.64415 0.000 0.840 0.000 0.156 0.004
#> GSM120788 4 0.3123 0.50190 0.004 0.000 0.000 0.812 0.184
#> GSM120760 2 0.5486 0.34889 0.000 0.572 0.000 0.352 0.076
#> GSM120763 2 0.3999 0.49368 0.000 0.656 0.000 0.344 0.000
#> GSM120764 4 0.2448 0.50012 0.000 0.088 0.000 0.892 0.020
#> GSM120777 4 0.3088 0.50280 0.004 0.004 0.000 0.828 0.164
#> GSM120786 4 0.5768 -0.02776 0.000 0.428 0.000 0.484 0.088
#> GSM121329 1 0.1492 0.91491 0.948 0.000 0.008 0.040 0.004
#> GSM121331 5 0.5159 -0.35500 0.024 0.000 0.008 0.472 0.496
#> GSM121333 4 0.5046 0.34336 0.020 0.000 0.008 0.540 0.432
#> GSM121345 4 0.4585 0.40666 0.020 0.000 0.000 0.628 0.352
#> GSM121356 5 0.4989 -0.33494 0.008 0.000 0.016 0.456 0.520
#> GSM120754 4 0.5741 0.16295 0.000 0.360 0.000 0.544 0.096
#> GSM120759 2 0.3895 0.32735 0.000 0.680 0.000 0.000 0.320
#> GSM120762 2 0.3053 0.63926 0.000 0.828 0.000 0.164 0.008
#> GSM120775 4 0.4824 -0.20887 0.000 0.468 0.000 0.512 0.020
#> GSM120776 4 0.4699 0.49949 0.004 0.016 0.052 0.756 0.172
#> GSM120782 2 0.5311 0.35731 0.000 0.560 0.012 0.396 0.032
#> GSM120789 2 0.2069 0.63833 0.000 0.912 0.000 0.012 0.076
#> GSM120790 2 0.4538 -0.04557 0.000 0.540 0.000 0.008 0.452
#> GSM120791 2 0.5283 0.21872 0.000 0.508 0.000 0.444 0.048
#> GSM120755 2 0.2890 0.64255 0.000 0.836 0.000 0.160 0.004
#> GSM120756 4 0.1808 0.49680 0.008 0.044 0.000 0.936 0.012
#> GSM120769 2 0.1121 0.66914 0.000 0.956 0.000 0.044 0.000
#> GSM120778 2 0.3990 0.52968 0.000 0.688 0.000 0.308 0.004
#> GSM120792 2 0.4047 0.51835 0.000 0.676 0.000 0.320 0.004
#> GSM121332 2 0.0693 0.66068 0.000 0.980 0.000 0.008 0.012
#> GSM121334 2 0.2773 0.64257 0.000 0.836 0.000 0.164 0.000
#> GSM121340 2 0.4897 0.26003 0.000 0.516 0.000 0.460 0.024
#> GSM121351 2 0.2690 0.56924 0.000 0.844 0.000 0.000 0.156
#> GSM121353 2 0.4567 0.46169 0.004 0.628 0.000 0.356 0.012
#> GSM120758 2 0.2124 0.66170 0.000 0.900 0.000 0.096 0.004
#> GSM120771 2 0.2020 0.62472 0.000 0.900 0.000 0.000 0.100
#> GSM120772 2 0.4101 0.50368 0.000 0.664 0.000 0.332 0.004
#> GSM120773 2 0.4291 0.29378 0.000 0.536 0.000 0.464 0.000
#> GSM120774 2 0.3461 0.60395 0.000 0.772 0.000 0.224 0.004
#> GSM120783 4 0.4978 -0.22891 0.000 0.476 0.000 0.496 0.028
#> GSM120787 2 0.3550 0.59505 0.000 0.760 0.000 0.236 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM120719 1 0.4625 0.6777 0.724 0.000 0.004 0.048 0.032 0.192
#> GSM120720 1 0.4821 0.4114 0.600 0.000 0.060 0.000 0.004 0.336
#> GSM120765 4 0.4423 0.2492 0.000 0.420 0.000 0.552 0.000 0.028
#> GSM120767 4 0.5421 0.4437 0.000 0.204 0.000 0.580 0.000 0.216
#> GSM120784 4 0.5737 0.0568 0.000 0.392 0.000 0.440 0.000 0.168
#> GSM121400 2 0.5159 -0.2834 0.000 0.532 0.392 0.000 0.008 0.068
#> GSM121401 3 0.1480 0.5538 0.000 0.020 0.940 0.000 0.000 0.040
#> GSM121402 2 0.3986 0.0538 0.000 0.532 0.000 0.464 0.000 0.004
#> GSM121403 2 0.2460 0.5502 0.000 0.896 0.064 0.020 0.004 0.016
#> GSM121404 3 0.2016 0.5432 0.000 0.016 0.920 0.024 0.000 0.040
#> GSM121405 3 0.2201 0.5377 0.000 0.028 0.896 0.000 0.000 0.076
#> GSM121406 2 0.4457 0.1490 0.000 0.544 0.016 0.432 0.000 0.008
#> GSM121408 4 0.4269 0.2867 0.000 0.412 0.000 0.568 0.000 0.020
#> GSM121409 2 0.4378 0.5025 0.056 0.788 0.044 0.020 0.000 0.092
#> GSM121410 2 0.3669 0.4705 0.020 0.832 0.072 0.004 0.008 0.064
#> GSM121412 2 0.3952 0.4418 0.000 0.672 0.020 0.308 0.000 0.000
#> GSM121413 2 0.3615 0.4629 0.000 0.700 0.008 0.292 0.000 0.000
#> GSM121414 2 0.3934 0.4843 0.000 0.708 0.032 0.260 0.000 0.000
#> GSM121415 4 0.6060 0.1932 0.000 0.320 0.152 0.504 0.000 0.024
#> GSM121416 3 0.5624 -0.0205 0.000 0.052 0.476 0.428 0.000 0.044
#> GSM120591 6 0.4141 0.4320 0.144 0.008 0.040 0.016 0.008 0.784
#> GSM120594 6 0.4941 0.3380 0.284 0.004 0.064 0.004 0.004 0.640
#> GSM120718 1 0.3794 0.7455 0.788 0.004 0.060 0.000 0.004 0.144
#> GSM121205 1 0.0000 0.9015 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121206 1 0.0291 0.9017 0.992 0.000 0.004 0.000 0.004 0.000
#> GSM121207 1 0.1364 0.8894 0.944 0.004 0.000 0.000 0.048 0.004
#> GSM121208 1 0.1458 0.8963 0.948 0.000 0.016 0.000 0.020 0.016
#> GSM121209 1 0.0508 0.9020 0.984 0.000 0.012 0.000 0.000 0.004
#> GSM121210 1 0.0547 0.9000 0.980 0.000 0.000 0.000 0.020 0.000
#> GSM121211 1 0.0405 0.9015 0.988 0.000 0.008 0.000 0.000 0.004
#> GSM121212 1 0.1483 0.8953 0.944 0.000 0.008 0.000 0.036 0.012
#> GSM121213 1 0.0436 0.9020 0.988 0.000 0.004 0.000 0.004 0.004
#> GSM121214 1 0.0922 0.8993 0.968 0.000 0.004 0.000 0.024 0.004
#> GSM121215 1 0.0146 0.9014 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM121216 1 0.0291 0.9017 0.992 0.000 0.000 0.000 0.004 0.004
#> GSM121217 1 0.0363 0.9016 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM121218 1 0.0436 0.9020 0.988 0.000 0.004 0.000 0.004 0.004
#> GSM121234 1 0.0291 0.9014 0.992 0.000 0.004 0.000 0.004 0.000
#> GSM121243 1 0.0146 0.9014 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM121245 1 0.1010 0.8956 0.960 0.000 0.000 0.000 0.036 0.004
#> GSM121246 1 0.0405 0.9015 0.988 0.000 0.008 0.000 0.004 0.000
#> GSM121247 1 0.2500 0.8403 0.868 0.004 0.000 0.000 0.116 0.012
#> GSM121248 1 0.0777 0.8985 0.972 0.000 0.000 0.000 0.024 0.004
#> GSM120744 6 0.4952 0.3335 0.000 0.016 0.360 0.000 0.044 0.580
#> GSM120745 6 0.5360 0.3869 0.004 0.016 0.316 0.000 0.076 0.588
#> GSM120746 3 0.4225 -0.1557 0.000 0.008 0.508 0.000 0.004 0.480
#> GSM120747 3 0.3984 0.0608 0.000 0.008 0.596 0.000 0.000 0.396
#> GSM120748 3 0.4051 -0.0256 0.000 0.008 0.560 0.000 0.000 0.432
#> GSM120749 3 0.4304 -0.0865 0.000 0.008 0.536 0.000 0.008 0.448
#> GSM120750 6 0.4720 0.1157 0.000 0.012 0.468 0.000 0.024 0.496
#> GSM120751 6 0.4695 0.2692 0.000 0.008 0.404 0.000 0.032 0.556
#> GSM120752 6 0.5267 0.4105 0.000 0.016 0.272 0.000 0.096 0.616
#> GSM121336 4 0.3966 0.2098 0.000 0.444 0.000 0.552 0.000 0.004
#> GSM121339 6 0.6317 0.2297 0.008 0.124 0.064 0.232 0.000 0.572
#> GSM121349 4 0.3966 0.2217 0.000 0.444 0.000 0.552 0.000 0.004
#> GSM121355 4 0.4045 0.2587 0.000 0.428 0.000 0.564 0.000 0.008
#> GSM120757 5 0.5674 0.4682 0.004 0.084 0.056 0.000 0.632 0.224
#> GSM120766 5 0.5608 0.5233 0.000 0.252 0.044 0.000 0.612 0.092
#> GSM120770 2 0.6331 0.4135 0.000 0.616 0.048 0.092 0.052 0.192
#> GSM120779 5 0.2607 0.6517 0.028 0.028 0.000 0.000 0.888 0.056
#> GSM120780 3 0.7268 -0.0206 0.000 0.316 0.320 0.000 0.272 0.092
#> GSM121102 2 0.4212 0.5298 0.000 0.788 0.048 0.044 0.008 0.112
#> GSM121203 3 0.6037 0.1138 0.000 0.108 0.524 0.000 0.044 0.324
#> GSM121204 5 0.5023 0.2524 0.032 0.016 0.004 0.000 0.528 0.420
#> GSM121330 3 0.1226 0.5494 0.040 0.004 0.952 0.000 0.000 0.004
#> GSM121335 1 0.3937 0.3718 0.572 0.000 0.424 0.000 0.000 0.004
#> GSM121337 2 0.5204 0.2479 0.000 0.528 0.072 0.392 0.000 0.008
#> GSM121338 3 0.4056 0.4302 0.000 0.264 0.704 0.024 0.000 0.008
#> GSM121341 1 0.4067 0.3211 0.548 0.000 0.444 0.000 0.000 0.008
#> GSM121342 1 0.1863 0.8745 0.920 0.000 0.060 0.000 0.004 0.016
#> GSM121343 3 0.4979 0.3611 0.000 0.316 0.612 0.056 0.000 0.016
#> GSM121344 1 0.2445 0.8303 0.868 0.000 0.120 0.000 0.004 0.008
#> GSM121346 3 0.1078 0.5581 0.012 0.008 0.964 0.000 0.000 0.016
#> GSM121347 3 0.7092 0.1619 0.000 0.336 0.444 0.088 0.108 0.024
#> GSM121348 2 0.4427 0.0655 0.000 0.676 0.016 0.016 0.284 0.008
#> GSM121350 3 0.1218 0.5606 0.004 0.028 0.956 0.000 0.000 0.012
#> GSM121352 3 0.1074 0.5538 0.028 0.000 0.960 0.000 0.000 0.012
#> GSM121354 3 0.2100 0.4875 0.112 0.000 0.884 0.000 0.000 0.004
#> GSM120753 4 0.2653 0.6266 0.000 0.144 0.000 0.844 0.000 0.012
#> GSM120761 4 0.3626 0.5322 0.000 0.288 0.000 0.704 0.004 0.004
#> GSM120768 4 0.1442 0.6083 0.000 0.012 0.000 0.944 0.004 0.040
#> GSM120781 4 0.2932 0.6185 0.000 0.164 0.000 0.820 0.000 0.016
#> GSM120788 5 0.4454 0.5927 0.000 0.016 0.004 0.220 0.716 0.044
#> GSM120760 4 0.4414 0.5268 0.000 0.028 0.000 0.736 0.184 0.052
#> GSM120763 4 0.1340 0.6175 0.000 0.008 0.000 0.948 0.040 0.004
#> GSM120764 5 0.5293 0.4446 0.000 0.024 0.000 0.400 0.524 0.052
#> GSM120777 5 0.4406 0.5924 0.000 0.012 0.000 0.220 0.712 0.056
#> GSM120786 4 0.4235 0.3850 0.000 0.012 0.000 0.684 0.280 0.024
#> GSM121329 1 0.4785 0.7813 0.780 0.020 0.048 0.028 0.080 0.044
#> GSM121331 5 0.3348 0.6292 0.000 0.216 0.000 0.000 0.768 0.016
#> GSM121333 5 0.1895 0.6641 0.000 0.072 0.000 0.000 0.912 0.016
#> GSM121345 5 0.1465 0.6603 0.004 0.004 0.000 0.020 0.948 0.024
#> GSM121356 5 0.3073 0.6358 0.000 0.204 0.000 0.000 0.788 0.008
#> GSM120754 4 0.6306 -0.0689 0.000 0.016 0.000 0.424 0.340 0.220
#> GSM120759 2 0.3955 0.4308 0.000 0.668 0.012 0.316 0.004 0.000
#> GSM120762 4 0.4570 0.5330 0.000 0.252 0.000 0.668 0.000 0.080
#> GSM120775 4 0.3764 0.5036 0.000 0.008 0.000 0.796 0.088 0.108
#> GSM120776 6 0.5282 0.0817 0.012 0.008 0.000 0.096 0.244 0.640
#> GSM120782 6 0.4364 0.1699 0.000 0.024 0.000 0.364 0.004 0.608
#> GSM120789 4 0.4732 0.4653 0.000 0.304 0.028 0.640 0.000 0.028
#> GSM120790 2 0.2563 0.5450 0.000 0.880 0.000 0.084 0.028 0.008
#> GSM120791 4 0.4152 0.5390 0.000 0.024 0.044 0.808 0.060 0.064
#> GSM120755 4 0.3572 0.5927 0.000 0.204 0.000 0.764 0.000 0.032
#> GSM120756 5 0.5772 0.4736 0.004 0.020 0.016 0.356 0.544 0.060
#> GSM120769 4 0.3758 0.4775 0.000 0.324 0.000 0.668 0.000 0.008
#> GSM120778 4 0.1074 0.6300 0.000 0.028 0.000 0.960 0.000 0.012
#> GSM120792 4 0.1003 0.6270 0.000 0.016 0.000 0.964 0.000 0.020
#> GSM121332 4 0.3833 0.4542 0.000 0.344 0.000 0.648 0.000 0.008
#> GSM121334 4 0.3717 0.5448 0.000 0.276 0.000 0.708 0.000 0.016
#> GSM121340 4 0.3459 0.5301 0.000 0.012 0.004 0.832 0.072 0.080
#> GSM121351 2 0.4141 0.1531 0.000 0.556 0.000 0.432 0.000 0.012
#> GSM121353 4 0.1982 0.5973 0.000 0.020 0.004 0.924 0.012 0.040
#> GSM120758 4 0.3298 0.5771 0.000 0.236 0.000 0.756 0.000 0.008
#> GSM120771 2 0.4535 -0.0705 0.000 0.488 0.000 0.480 0.000 0.032
#> GSM120772 4 0.1930 0.6330 0.000 0.048 0.000 0.916 0.000 0.036
#> GSM120773 4 0.2247 0.5853 0.000 0.012 0.000 0.904 0.060 0.024
#> GSM120774 4 0.2266 0.6333 0.000 0.108 0.000 0.880 0.000 0.012
#> GSM120783 4 0.3647 0.5321 0.000 0.016 0.012 0.828 0.080 0.064
#> GSM120787 4 0.2191 0.6332 0.000 0.120 0.000 0.876 0.000 0.004
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> ATC:NMF 117 5.52e-10 2
#> ATC:NMF 96 9.72e-18 3
#> ATC:NMF 108 5.11e-22 4
#> ATC:NMF 75 5.24e-18 5
#> ATC:NMF 65 6.63e-13 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