Date: 2019-12-25 20:17:12 CET, cola version: 1.3.2
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All available functions which can be applied to this res_list object:
res_list
#> A 'ConsensusPartitionList' object with 24 methods.
#> On a matrix with 21168 rows and 121 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] 21168 121
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 | 0.994 | 0.997 | ** | |
| ATC:skmeans | 4 | 0.968 | 0.952 | 0.981 | ** | 2,3 |
| ATC:NMF | 2 | 0.966 | 0.950 | 0.980 | ** | |
| MAD:NMF | 2 | 0.963 | 0.954 | 0.980 | ** | |
| CV:kmeans | 2 | 0.956 | 0.957 | 0.976 | ** | |
| SD:mclust | 2 | 0.948 | 0.931 | 0.974 | * | |
| CV:NMF | 2 | 0.932 | 0.940 | 0.975 | * | |
| MAD:kmeans | 2 | 0.919 | 0.956 | 0.980 | * | |
| CV:skmeans | 3 | 0.917 | 0.895 | 0.953 | * | 2 |
| MAD:skmeans | 4 | 0.917 | 0.899 | 0.948 | * | 2 |
| ATC:pam | 6 | 0.906 | 0.858 | 0.940 | * | 2,3 |
| MAD:mclust | 3 | 0.882 | 0.915 | 0.950 | ||
| SD:skmeans | 2 | 0.874 | 0.952 | 0.972 | ||
| SD:NMF | 2 | 0.866 | 0.929 | 0.968 | ||
| MAD:pam | 6 | 0.856 | 0.817 | 0.908 | ||
| CV:mclust | 5 | 0.841 | 0.860 | 0.929 | ||
| CV:pam | 5 | 0.744 | 0.762 | 0.884 | ||
| SD:pam | 5 | 0.742 | 0.752 | 0.877 | ||
| ATC:mclust | 3 | 0.710 | 0.931 | 0.945 | ||
| SD:kmeans | 2 | 0.698 | 0.927 | 0.952 | ||
| ATC:hclust | 2 | 0.674 | 0.887 | 0.943 | ||
| MAD:hclust | 5 | 0.641 | 0.763 | 0.829 | ||
| SD:hclust | 4 | 0.592 | 0.629 | 0.747 | ||
| CV:hclust | 2 | 0.305 | 0.759 | 0.868 |
**: 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 0.866 0.929 0.968 0.501 0.498 0.498
#> CV:NMF 2 0.932 0.940 0.975 0.500 0.500 0.500
#> MAD:NMF 2 0.963 0.954 0.980 0.502 0.497 0.497
#> ATC:NMF 2 0.966 0.950 0.980 0.504 0.496 0.496
#> SD:skmeans 2 0.874 0.952 0.972 0.503 0.497 0.497
#> CV:skmeans 2 1.000 0.983 0.993 0.503 0.497 0.497
#> MAD:skmeans 2 1.000 0.989 0.995 0.503 0.497 0.497
#> ATC:skmeans 2 0.983 0.956 0.983 0.504 0.496 0.496
#> SD:mclust 2 0.948 0.931 0.974 0.499 0.500 0.500
#> CV:mclust 2 0.590 0.903 0.928 0.464 0.497 0.497
#> MAD:mclust 2 0.805 0.914 0.957 0.497 0.502 0.502
#> ATC:mclust 2 0.533 0.895 0.915 0.442 0.506 0.506
#> SD:kmeans 2 0.698 0.927 0.952 0.489 0.497 0.497
#> CV:kmeans 2 0.956 0.957 0.976 0.497 0.497 0.497
#> MAD:kmeans 2 0.919 0.956 0.980 0.500 0.499 0.499
#> ATC:kmeans 2 1.000 0.994 0.997 0.503 0.498 0.498
#> SD:pam 2 0.295 0.648 0.840 0.470 0.508 0.508
#> CV:pam 2 0.408 0.732 0.872 0.480 0.506 0.506
#> MAD:pam 2 0.474 0.813 0.895 0.485 0.521 0.521
#> ATC:pam 2 1.000 0.976 0.990 0.499 0.504 0.504
#> SD:hclust 2 0.248 0.684 0.821 0.425 0.521 0.521
#> CV:hclust 2 0.305 0.759 0.868 0.437 0.543 0.543
#> MAD:hclust 2 0.204 0.583 0.805 0.433 0.521 0.521
#> ATC:hclust 2 0.674 0.887 0.943 0.481 0.514 0.514
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.539 0.634 0.834 0.325 0.727 0.507
#> CV:NMF 3 0.577 0.667 0.849 0.322 0.728 0.511
#> MAD:NMF 3 0.537 0.679 0.842 0.319 0.760 0.555
#> ATC:NMF 3 0.652 0.738 0.884 0.317 0.749 0.537
#> SD:skmeans 3 0.751 0.872 0.935 0.322 0.729 0.507
#> CV:skmeans 3 0.917 0.895 0.953 0.316 0.770 0.569
#> MAD:skmeans 3 0.866 0.911 0.960 0.331 0.739 0.521
#> ATC:skmeans 3 0.922 0.939 0.971 0.257 0.822 0.658
#> SD:mclust 3 0.599 0.652 0.823 0.277 0.780 0.593
#> CV:mclust 3 0.702 0.766 0.863 0.371 0.804 0.625
#> MAD:mclust 3 0.882 0.915 0.950 0.310 0.821 0.651
#> ATC:mclust 3 0.710 0.931 0.945 0.387 0.809 0.648
#> SD:kmeans 3 0.604 0.761 0.871 0.336 0.684 0.449
#> CV:kmeans 3 0.663 0.823 0.903 0.325 0.725 0.501
#> MAD:kmeans 3 0.841 0.844 0.925 0.335 0.691 0.458
#> ATC:kmeans 3 0.563 0.579 0.791 0.301 0.736 0.515
#> SD:pam 3 0.525 0.651 0.803 0.384 0.555 0.317
#> CV:pam 3 0.566 0.604 0.817 0.361 0.801 0.618
#> MAD:pam 3 0.673 0.794 0.903 0.371 0.720 0.505
#> ATC:pam 3 0.920 0.904 0.960 0.281 0.806 0.634
#> SD:hclust 3 0.366 0.411 0.698 0.459 0.699 0.480
#> CV:hclust 3 0.354 0.678 0.777 0.416 0.825 0.682
#> MAD:hclust 3 0.312 0.573 0.680 0.440 0.688 0.471
#> ATC:hclust 3 0.747 0.856 0.912 0.362 0.811 0.635
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.546 0.627 0.769 0.117 0.821 0.537
#> CV:NMF 4 0.555 0.509 0.716 0.123 0.806 0.517
#> MAD:NMF 4 0.578 0.476 0.692 0.125 0.831 0.559
#> ATC:NMF 4 0.690 0.776 0.869 0.116 0.840 0.577
#> SD:skmeans 4 0.829 0.900 0.944 0.129 0.844 0.576
#> CV:skmeans 4 0.858 0.846 0.931 0.134 0.817 0.524
#> MAD:skmeans 4 0.917 0.899 0.948 0.121 0.850 0.590
#> ATC:skmeans 4 0.968 0.952 0.981 0.120 0.895 0.724
#> SD:mclust 4 0.630 0.774 0.851 0.104 0.840 0.593
#> CV:mclust 4 0.657 0.745 0.832 0.132 0.847 0.603
#> MAD:mclust 4 0.707 0.806 0.879 0.107 0.924 0.786
#> ATC:mclust 4 0.787 0.897 0.921 0.196 0.844 0.613
#> SD:kmeans 4 0.705 0.788 0.881 0.132 0.834 0.556
#> CV:kmeans 4 0.710 0.719 0.864 0.130 0.802 0.487
#> MAD:kmeans 4 0.767 0.768 0.870 0.120 0.906 0.725
#> ATC:kmeans 4 0.849 0.905 0.939 0.141 0.808 0.501
#> SD:pam 4 0.648 0.667 0.836 0.130 0.910 0.746
#> CV:pam 4 0.547 0.536 0.730 0.136 0.689 0.310
#> MAD:pam 4 0.646 0.629 0.805 0.124 0.800 0.488
#> ATC:pam 4 0.780 0.845 0.902 0.162 0.844 0.596
#> SD:hclust 4 0.592 0.629 0.747 0.142 0.774 0.449
#> CV:hclust 4 0.505 0.395 0.709 0.107 0.945 0.860
#> MAD:hclust 4 0.554 0.739 0.838 0.168 0.886 0.682
#> ATC:hclust 4 0.717 0.796 0.858 0.107 0.929 0.789
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.620 0.564 0.731 0.0679 0.898 0.639
#> CV:NMF 5 0.623 0.547 0.731 0.0650 0.853 0.537
#> MAD:NMF 5 0.612 0.538 0.738 0.0634 0.819 0.436
#> ATC:NMF 5 0.746 0.744 0.860 0.0690 0.887 0.606
#> SD:skmeans 5 0.784 0.799 0.884 0.0434 0.944 0.786
#> CV:skmeans 5 0.762 0.721 0.853 0.0463 0.951 0.806
#> MAD:skmeans 5 0.769 0.749 0.856 0.0467 0.960 0.843
#> ATC:skmeans 5 0.812 0.743 0.862 0.0702 0.946 0.820
#> SD:mclust 5 0.831 0.810 0.891 0.1028 0.916 0.703
#> CV:mclust 5 0.841 0.860 0.929 0.0797 0.913 0.698
#> MAD:mclust 5 0.838 0.845 0.916 0.0862 0.899 0.666
#> ATC:mclust 5 0.850 0.830 0.913 0.0657 0.908 0.675
#> SD:kmeans 5 0.731 0.730 0.815 0.0623 0.956 0.829
#> CV:kmeans 5 0.694 0.598 0.750 0.0616 0.913 0.677
#> MAD:kmeans 5 0.748 0.642 0.811 0.0541 0.898 0.656
#> ATC:kmeans 5 0.787 0.707 0.836 0.0584 0.978 0.912
#> SD:pam 5 0.742 0.752 0.877 0.0645 0.869 0.574
#> CV:pam 5 0.744 0.762 0.884 0.0740 0.888 0.603
#> MAD:pam 5 0.769 0.764 0.881 0.0657 0.902 0.642
#> ATC:pam 5 0.805 0.840 0.917 0.0699 0.879 0.582
#> SD:hclust 5 0.569 0.568 0.717 0.0638 0.868 0.578
#> CV:hclust 5 0.522 0.509 0.670 0.0586 0.842 0.573
#> MAD:hclust 5 0.641 0.763 0.829 0.0644 0.944 0.791
#> ATC:hclust 5 0.734 0.536 0.781 0.0639 0.879 0.594
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.727 0.712 0.819 0.0387 0.946 0.752
#> CV:NMF 6 0.709 0.669 0.799 0.0435 0.895 0.582
#> MAD:NMF 6 0.736 0.695 0.824 0.0406 0.927 0.683
#> ATC:NMF 6 0.757 0.756 0.850 0.0419 0.925 0.661
#> SD:skmeans 6 0.778 0.626 0.752 0.0436 0.921 0.663
#> CV:skmeans 6 0.753 0.594 0.764 0.0407 0.884 0.543
#> MAD:skmeans 6 0.749 0.545 0.739 0.0405 0.927 0.695
#> ATC:skmeans 6 0.776 0.762 0.832 0.0549 0.890 0.597
#> SD:mclust 6 0.795 0.610 0.783 0.0511 0.936 0.717
#> CV:mclust 6 0.815 0.741 0.859 0.0593 0.913 0.638
#> MAD:mclust 6 0.802 0.669 0.813 0.0435 0.925 0.672
#> ATC:mclust 6 0.820 0.791 0.847 0.0321 0.932 0.720
#> SD:kmeans 6 0.770 0.531 0.745 0.0398 0.924 0.689
#> CV:kmeans 6 0.726 0.597 0.698 0.0366 0.896 0.572
#> MAD:kmeans 6 0.756 0.647 0.789 0.0380 0.899 0.607
#> ATC:kmeans 6 0.781 0.647 0.756 0.0422 0.898 0.595
#> SD:pam 6 0.805 0.675 0.827 0.0475 0.925 0.685
#> CV:pam 6 0.734 0.623 0.806 0.0384 0.924 0.663
#> MAD:pam 6 0.856 0.817 0.908 0.0351 0.958 0.800
#> ATC:pam 6 0.906 0.858 0.940 0.0391 0.963 0.820
#> SD:hclust 6 0.697 0.631 0.778 0.0555 0.944 0.776
#> CV:hclust 6 0.547 0.601 0.734 0.0596 0.920 0.702
#> MAD:hclust 6 0.706 0.722 0.829 0.0412 0.965 0.841
#> ATC:hclust 6 0.787 0.695 0.821 0.0533 0.928 0.705
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 119 1.77e-09 2
#> CV:NMF 118 2.16e-09 2
#> MAD:NMF 119 2.13e-09 2
#> ATC:NMF 117 1.51e-11 2
#> SD:skmeans 121 1.64e-11 2
#> CV:skmeans 119 4.11e-11 2
#> MAD:skmeans 121 1.64e-11 2
#> ATC:skmeans 118 2.26e-12 2
#> SD:mclust 115 2.16e-12 2
#> CV:mclust 119 2.00e-11 2
#> MAD:mclust 115 4.59e-13 2
#> ATC:mclust 120 1.97e-13 2
#> SD:kmeans 121 1.64e-11 2
#> CV:kmeans 119 4.11e-11 2
#> MAD:kmeans 119 4.11e-11 2
#> ATC:kmeans 121 1.03e-12 2
#> SD:pam 100 1.70e-07 2
#> CV:pam 105 2.16e-07 2
#> MAD:pam 117 4.39e-11 2
#> ATC:pam 119 9.88e-12 2
#> SD:hclust 108 2.55e-07 2
#> CV:hclust 112 1.59e-07 2
#> MAD:hclust 91 3.47e-13 2
#> ATC:hclust 114 5.47e-10 2
test_to_known_factors(res_list, k = 3)
#> n disease.state(p) k
#> SD:NMF 95 4.31e-15 3
#> CV:NMF 96 5.04e-18 3
#> MAD:NMF 100 1.57e-13 3
#> ATC:NMF 107 3.98e-15 3
#> SD:skmeans 119 4.44e-25 3
#> CV:skmeans 115 3.53e-24 3
#> MAD:skmeans 117 9.06e-25 3
#> ATC:skmeans 120 2.66e-15 3
#> SD:mclust 100 1.65e-22 3
#> CV:mclust 112 4.74e-26 3
#> MAD:mclust 117 2.26e-23 3
#> ATC:mclust 120 5.94e-26 3
#> SD:kmeans 104 2.49e-24 3
#> CV:kmeans 118 4.50e-24 3
#> MAD:kmeans 106 4.12e-25 3
#> ATC:kmeans 71 1.25e-06 3
#> SD:pam 107 3.79e-30 3
#> CV:pam 100 3.72e-15 3
#> MAD:pam 107 9.17e-24 3
#> ATC:pam 116 1.99e-18 3
#> SD:hclust 56 9.31e-09 3
#> CV:hclust 110 9.28e-15 3
#> MAD:hclust 90 5.69e-24 3
#> ATC:hclust 117 3.22e-14 3
test_to_known_factors(res_list, k = 4)
#> n disease.state(p) k
#> SD:NMF 97 7.14e-25 4
#> CV:NMF 73 3.18e-18 4
#> MAD:NMF 64 6.35e-14 4
#> ATC:NMF 112 7.37e-29 4
#> SD:skmeans 120 5.03e-32 4
#> CV:skmeans 114 2.92e-32 4
#> MAD:skmeans 114 1.20e-29 4
#> ATC:skmeans 119 3.26e-18 4
#> SD:mclust 116 3.19e-34 4
#> CV:mclust 103 4.17e-33 4
#> MAD:mclust 115 1.30e-28 4
#> ATC:mclust 118 2.68e-31 4
#> SD:kmeans 110 2.30e-33 4
#> CV:kmeans 106 1.87e-32 4
#> MAD:kmeans 107 2.49e-29 4
#> ATC:kmeans 116 7.41e-26 4
#> SD:pam 96 2.55e-35 4
#> CV:pam 72 4.64e-18 4
#> MAD:pam 84 1.16e-28 4
#> ATC:pam 113 1.64e-25 4
#> SD:hclust 94 1.06e-28 4
#> CV:hclust 69 4.80e-20 4
#> MAD:hclust 110 2.57e-33 4
#> ATC:hclust 110 4.29e-17 4
test_to_known_factors(res_list, k = 5)
#> n disease.state(p) k
#> SD:NMF 82 2.70e-33 5
#> CV:NMF 73 1.17e-18 5
#> MAD:NMF 73 2.37e-23 5
#> ATC:NMF 102 3.44e-31 5
#> SD:skmeans 111 8.59e-43 5
#> CV:skmeans 105 7.43e-42 5
#> MAD:skmeans 110 3.19e-42 5
#> ATC:skmeans 104 7.19e-22 5
#> SD:mclust 113 9.45e-45 5
#> CV:mclust 115 1.41e-43 5
#> MAD:mclust 116 5.98e-42 5
#> ATC:mclust 113 1.40e-34 5
#> SD:kmeans 105 3.35e-37 5
#> CV:kmeans 87 1.90e-28 5
#> MAD:kmeans 96 5.51e-30 5
#> ATC:kmeans 103 2.03e-20 5
#> SD:pam 106 3.02e-45 5
#> CV:pam 110 1.01e-33 5
#> MAD:pam 104 5.26e-42 5
#> ATC:pam 112 2.20e-27 5
#> SD:hclust 91 9.57e-31 5
#> CV:hclust 82 1.06e-22 5
#> MAD:hclust 115 1.30e-33 5
#> ATC:hclust 71 7.26e-13 5
test_to_known_factors(res_list, k = 6)
#> n disease.state(p) k
#> SD:NMF 105 1.88e-43 6
#> CV:NMF 98 1.30e-39 6
#> MAD:NMF 102 8.44e-42 6
#> ATC:NMF 112 4.38e-45 6
#> SD:skmeans 89 2.00e-36 6
#> CV:skmeans 85 6.27e-33 6
#> MAD:skmeans 74 3.02e-25 6
#> ATC:skmeans 109 2.94e-30 6
#> SD:mclust 88 5.61e-29 6
#> CV:mclust 104 1.88e-35 6
#> MAD:mclust 91 3.80e-29 6
#> ATC:mclust 110 2.94e-36 6
#> SD:kmeans 81 6.33e-29 6
#> CV:kmeans 93 5.65e-30 6
#> MAD:kmeans 94 3.94e-30 6
#> ATC:kmeans 101 1.16e-28 6
#> SD:pam 97 1.16e-51 6
#> CV:pam 88 3.06e-33 6
#> MAD:pam 111 4.15e-37 6
#> ATC:pam 112 2.66e-32 6
#> SD:hclust 96 1.37e-30 6
#> CV:hclust 92 5.23e-26 6
#> MAD:hclust 106 2.64e-31 6
#> ATC:hclust 109 2.14e-23 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 21168 rows and 121 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 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.248 0.684 0.821 0.4254 0.521 0.521
#> 3 3 0.366 0.411 0.698 0.4595 0.699 0.480
#> 4 4 0.592 0.629 0.747 0.1418 0.774 0.449
#> 5 5 0.569 0.568 0.717 0.0638 0.868 0.578
#> 6 6 0.697 0.631 0.778 0.0555 0.944 0.776
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM74356 2 0.9393 0.5906 0.356 0.644
#> GSM74357 2 0.9323 0.6071 0.348 0.652
#> GSM74358 2 0.9323 0.6071 0.348 0.652
#> GSM74359 1 0.4939 0.7651 0.892 0.108
#> GSM74360 1 0.4939 0.7651 0.892 0.108
#> GSM74361 1 0.9993 -0.0645 0.516 0.484
#> GSM74362 1 0.9993 -0.0645 0.516 0.484
#> GSM74363 2 0.9323 0.6071 0.348 0.652
#> GSM74402 1 0.0000 0.7733 1.000 0.000
#> GSM74403 1 0.0000 0.7733 1.000 0.000
#> GSM74404 1 0.0000 0.7733 1.000 0.000
#> GSM74406 1 0.0000 0.7733 1.000 0.000
#> GSM74407 1 0.0672 0.7745 0.992 0.008
#> GSM74408 1 0.0000 0.7733 1.000 0.000
#> GSM74409 1 0.0000 0.7733 1.000 0.000
#> GSM74410 1 0.0000 0.7733 1.000 0.000
#> GSM119936 1 0.0000 0.7733 1.000 0.000
#> GSM119937 1 0.1633 0.7750 0.976 0.024
#> GSM74411 2 0.8443 0.7201 0.272 0.728
#> GSM74412 2 0.8443 0.7201 0.272 0.728
#> GSM74413 2 0.8443 0.7201 0.272 0.728
#> GSM74414 2 0.7528 0.7508 0.216 0.784
#> GSM74415 2 0.8443 0.7201 0.272 0.728
#> GSM121379 2 0.0000 0.7517 0.000 1.000
#> GSM121380 2 0.0000 0.7517 0.000 1.000
#> GSM121381 2 0.0000 0.7517 0.000 1.000
#> GSM121382 2 0.0000 0.7517 0.000 1.000
#> GSM121383 2 0.0000 0.7517 0.000 1.000
#> GSM121384 2 0.0000 0.7517 0.000 1.000
#> GSM121385 2 0.0000 0.7517 0.000 1.000
#> GSM121386 2 0.0000 0.7517 0.000 1.000
#> GSM121387 2 0.0000 0.7517 0.000 1.000
#> GSM121388 2 0.0000 0.7517 0.000 1.000
#> GSM121389 2 0.0000 0.7517 0.000 1.000
#> GSM121390 2 0.0000 0.7517 0.000 1.000
#> GSM121391 2 0.0000 0.7517 0.000 1.000
#> GSM121392 2 0.0000 0.7517 0.000 1.000
#> GSM121393 2 0.0000 0.7517 0.000 1.000
#> GSM121394 2 0.0000 0.7517 0.000 1.000
#> GSM121395 2 0.0000 0.7517 0.000 1.000
#> GSM121396 2 0.1633 0.7573 0.024 0.976
#> GSM121397 2 0.0000 0.7517 0.000 1.000
#> GSM121398 2 0.0000 0.7517 0.000 1.000
#> GSM121399 2 0.0000 0.7517 0.000 1.000
#> GSM74240 2 0.8661 0.7051 0.288 0.712
#> GSM74241 2 0.8661 0.7051 0.288 0.712
#> GSM74242 2 0.8661 0.7051 0.288 0.712
#> GSM74243 2 0.8661 0.7051 0.288 0.712
#> GSM74244 2 0.8661 0.7051 0.288 0.712
#> GSM74245 2 0.8661 0.7051 0.288 0.712
#> GSM74246 2 0.8661 0.7051 0.288 0.712
#> GSM74247 2 0.8661 0.7051 0.288 0.712
#> GSM74248 2 0.8661 0.7051 0.288 0.712
#> GSM74416 1 0.0000 0.7733 1.000 0.000
#> GSM74417 1 0.0000 0.7733 1.000 0.000
#> GSM74418 1 0.0000 0.7733 1.000 0.000
#> GSM74419 1 0.0376 0.7740 0.996 0.004
#> GSM121358 2 0.8555 0.7141 0.280 0.720
#> GSM121359 2 0.8555 0.7141 0.280 0.720
#> GSM121360 1 0.5059 0.7635 0.888 0.112
#> GSM121362 1 0.5059 0.7635 0.888 0.112
#> GSM121364 1 0.4939 0.7651 0.892 0.108
#> GSM121365 2 0.8661 0.7056 0.288 0.712
#> GSM121366 2 0.8555 0.7141 0.280 0.720
#> GSM121367 2 0.8555 0.7141 0.280 0.720
#> GSM121370 2 0.8555 0.7141 0.280 0.720
#> GSM121371 2 0.8555 0.7141 0.280 0.720
#> GSM121372 2 0.8555 0.7141 0.280 0.720
#> GSM121373 1 0.4939 0.7651 0.892 0.108
#> GSM121374 1 0.4939 0.7651 0.892 0.108
#> GSM121407 2 0.7815 0.7443 0.232 0.768
#> GSM74387 2 0.4161 0.7645 0.084 0.916
#> GSM74388 2 0.1184 0.7576 0.016 0.984
#> GSM74389 1 0.9209 0.4836 0.664 0.336
#> GSM74390 2 0.7883 0.7446 0.236 0.764
#> GSM74391 1 0.7299 0.6882 0.796 0.204
#> GSM74392 1 0.9710 0.3083 0.600 0.400
#> GSM74393 1 0.9710 0.3083 0.600 0.400
#> GSM74394 2 0.1843 0.7601 0.028 0.972
#> GSM74239 1 0.4431 0.7667 0.908 0.092
#> GSM74364 1 0.4431 0.7654 0.908 0.092
#> GSM74365 1 0.9815 0.2335 0.580 0.420
#> GSM74366 2 0.7815 0.7208 0.232 0.768
#> GSM74367 1 0.9286 0.4696 0.656 0.344
#> GSM74377 2 0.8016 0.7115 0.244 0.756
#> GSM74378 2 0.8016 0.7115 0.244 0.756
#> GSM74379 2 0.9044 0.6058 0.320 0.680
#> GSM74380 2 0.8661 0.6702 0.288 0.712
#> GSM74381 2 0.8144 0.7033 0.252 0.748
#> GSM121357 2 0.5294 0.7619 0.120 0.880
#> GSM121361 2 0.1184 0.7576 0.016 0.984
#> GSM121363 2 0.1184 0.7576 0.016 0.984
#> GSM121368 2 0.1184 0.7576 0.016 0.984
#> GSM121369 2 0.1414 0.7586 0.020 0.980
#> GSM74368 1 0.8661 0.5963 0.712 0.288
#> GSM74369 1 0.8661 0.5963 0.712 0.288
#> GSM74370 1 0.5178 0.7593 0.884 0.116
#> GSM74371 1 0.0000 0.7733 1.000 0.000
#> GSM74372 1 0.9129 0.5017 0.672 0.328
#> GSM74373 2 0.9608 0.4303 0.384 0.616
#> GSM74374 1 0.9427 0.4199 0.640 0.360
#> GSM74375 2 0.8267 0.6976 0.260 0.740
#> GSM74376 2 0.8016 0.7061 0.244 0.756
#> GSM74405 2 0.8267 0.6987 0.260 0.740
#> GSM74351 1 0.0000 0.7733 1.000 0.000
#> GSM74352 2 0.8016 0.7100 0.244 0.756
#> GSM74353 1 0.7745 0.6709 0.772 0.228
#> GSM74354 1 0.9460 0.4120 0.636 0.364
#> GSM74355 2 0.7950 0.7131 0.240 0.760
#> GSM74382 1 0.0376 0.7736 0.996 0.004
#> GSM74383 1 0.7745 0.6597 0.772 0.228
#> GSM74384 2 0.7950 0.7131 0.240 0.760
#> GSM74385 1 0.0000 0.7733 1.000 0.000
#> GSM74386 1 0.9522 0.4047 0.628 0.372
#> GSM74395 1 0.9170 0.4999 0.668 0.332
#> GSM74396 1 0.9170 0.4999 0.668 0.332
#> GSM74397 1 0.9000 0.5346 0.684 0.316
#> GSM74398 2 0.8267 0.6929 0.260 0.740
#> GSM74399 2 0.8016 0.7094 0.244 0.756
#> GSM74400 2 0.8608 0.6765 0.284 0.716
#> GSM74401 2 0.8608 0.6765 0.284 0.716
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM74356 3 0.9464 0.05545 0.180 0.408 0.412
#> GSM74357 3 0.9334 0.05714 0.164 0.408 0.428
#> GSM74358 3 0.9334 0.05714 0.164 0.408 0.428
#> GSM74359 1 0.5016 0.68464 0.760 0.000 0.240
#> GSM74360 1 0.5016 0.68464 0.760 0.000 0.240
#> GSM74361 3 0.9953 0.15168 0.344 0.288 0.368
#> GSM74362 3 0.9955 0.15249 0.348 0.288 0.364
#> GSM74363 3 0.9334 0.05714 0.164 0.408 0.428
#> GSM74402 1 0.0592 0.77389 0.988 0.000 0.012
#> GSM74403 1 0.0000 0.77210 1.000 0.000 0.000
#> GSM74404 1 0.0000 0.77210 1.000 0.000 0.000
#> GSM74406 1 0.0592 0.77389 0.988 0.000 0.012
#> GSM74407 1 0.1289 0.77050 0.968 0.000 0.032
#> GSM74408 1 0.0237 0.77317 0.996 0.000 0.004
#> GSM74409 1 0.0237 0.77317 0.996 0.000 0.004
#> GSM74410 1 0.0237 0.77317 0.996 0.000 0.004
#> GSM119936 1 0.0237 0.77317 0.996 0.000 0.004
#> GSM119937 1 0.2448 0.75594 0.924 0.000 0.076
#> GSM74411 2 0.8403 0.01157 0.084 0.468 0.448
#> GSM74412 2 0.8403 0.01157 0.084 0.468 0.448
#> GSM74413 2 0.8403 0.01157 0.084 0.468 0.448
#> GSM74414 2 0.8056 0.16719 0.068 0.532 0.400
#> GSM74415 2 0.8403 0.01157 0.084 0.468 0.448
#> GSM121379 2 0.0000 0.75634 0.000 1.000 0.000
#> GSM121380 2 0.0000 0.75634 0.000 1.000 0.000
#> GSM121381 2 0.0237 0.75447 0.000 0.996 0.004
#> GSM121382 2 0.0000 0.75634 0.000 1.000 0.000
#> GSM121383 2 0.0000 0.75634 0.000 1.000 0.000
#> GSM121384 2 0.0000 0.75634 0.000 1.000 0.000
#> GSM121385 2 0.0000 0.75634 0.000 1.000 0.000
#> GSM121386 2 0.0000 0.75634 0.000 1.000 0.000
#> GSM121387 2 0.0000 0.75634 0.000 1.000 0.000
#> GSM121388 2 0.0000 0.75634 0.000 1.000 0.000
#> GSM121389 2 0.0000 0.75634 0.000 1.000 0.000
#> GSM121390 2 0.0000 0.75634 0.000 1.000 0.000
#> GSM121391 2 0.0000 0.75634 0.000 1.000 0.000
#> GSM121392 2 0.0000 0.75634 0.000 1.000 0.000
#> GSM121393 2 0.0000 0.75634 0.000 1.000 0.000
#> GSM121394 2 0.0237 0.75447 0.000 0.996 0.004
#> GSM121395 2 0.0000 0.75634 0.000 1.000 0.000
#> GSM121396 2 0.1989 0.73083 0.004 0.948 0.048
#> GSM121397 2 0.0000 0.75634 0.000 1.000 0.000
#> GSM121398 2 0.0000 0.75634 0.000 1.000 0.000
#> GSM121399 2 0.0000 0.75634 0.000 1.000 0.000
#> GSM74240 3 0.8395 0.02193 0.084 0.436 0.480
#> GSM74241 3 0.8395 0.02193 0.084 0.436 0.480
#> GSM74242 3 0.8395 0.02193 0.084 0.436 0.480
#> GSM74243 3 0.8395 0.02193 0.084 0.436 0.480
#> GSM74244 3 0.8395 0.02193 0.084 0.436 0.480
#> GSM74245 3 0.8395 0.02193 0.084 0.436 0.480
#> GSM74246 3 0.8395 0.02193 0.084 0.436 0.480
#> GSM74247 3 0.8395 0.02193 0.084 0.436 0.480
#> GSM74248 3 0.8395 0.02193 0.084 0.436 0.480
#> GSM74416 1 0.0424 0.77163 0.992 0.000 0.008
#> GSM74417 1 0.0424 0.77163 0.992 0.000 0.008
#> GSM74418 1 0.0424 0.77163 0.992 0.000 0.008
#> GSM74419 1 0.0829 0.77364 0.984 0.004 0.012
#> GSM121358 3 0.8404 -0.01185 0.084 0.452 0.464
#> GSM121359 3 0.8404 -0.01185 0.084 0.452 0.464
#> GSM121360 1 0.5058 0.68053 0.756 0.000 0.244
#> GSM121362 1 0.5058 0.68053 0.756 0.000 0.244
#> GSM121364 1 0.5016 0.68464 0.760 0.000 0.240
#> GSM121365 3 0.8581 -0.00120 0.096 0.448 0.456
#> GSM121366 3 0.8404 -0.01185 0.084 0.452 0.464
#> GSM121367 3 0.8404 -0.01185 0.084 0.452 0.464
#> GSM121370 3 0.8404 -0.01185 0.084 0.452 0.464
#> GSM121371 3 0.8404 -0.01185 0.084 0.452 0.464
#> GSM121372 3 0.8404 -0.01185 0.084 0.452 0.464
#> GSM121373 1 0.5016 0.68464 0.760 0.000 0.240
#> GSM121374 1 0.5016 0.68464 0.760 0.000 0.240
#> GSM121407 2 0.8093 0.12742 0.068 0.516 0.416
#> GSM74387 2 0.6143 0.49289 0.012 0.684 0.304
#> GSM74388 2 0.5016 0.57987 0.000 0.760 0.240
#> GSM74389 1 0.8965 0.27666 0.564 0.240 0.196
#> GSM74390 2 0.8936 0.03488 0.128 0.484 0.388
#> GSM74391 1 0.6705 0.57272 0.740 0.176 0.084
#> GSM74392 1 0.9474 0.11770 0.496 0.272 0.232
#> GSM74393 1 0.9474 0.11770 0.496 0.272 0.232
#> GSM74394 2 0.5098 0.57030 0.000 0.752 0.248
#> GSM74239 1 0.5591 0.60662 0.696 0.000 0.304
#> GSM74364 1 0.5621 0.60179 0.692 0.000 0.308
#> GSM74365 3 0.7310 0.00815 0.324 0.048 0.628
#> GSM74366 3 0.5115 0.39788 0.004 0.228 0.768
#> GSM74367 3 0.7309 -0.18824 0.416 0.032 0.552
#> GSM74377 3 0.4978 0.40553 0.004 0.216 0.780
#> GSM74378 3 0.4978 0.40553 0.004 0.216 0.780
#> GSM74379 3 0.6318 0.41796 0.068 0.172 0.760
#> GSM74380 3 0.6007 0.41998 0.044 0.192 0.764
#> GSM74381 3 0.5109 0.40878 0.008 0.212 0.780
#> GSM121357 2 0.6677 0.45179 0.024 0.652 0.324
#> GSM121361 2 0.4931 0.59329 0.000 0.768 0.232
#> GSM121363 2 0.4931 0.59329 0.000 0.768 0.232
#> GSM121368 2 0.4931 0.59329 0.000 0.768 0.232
#> GSM121369 2 0.4974 0.58826 0.000 0.764 0.236
#> GSM74368 1 0.7575 0.34125 0.504 0.040 0.456
#> GSM74369 1 0.7575 0.34125 0.504 0.040 0.456
#> GSM74370 1 0.5480 0.66732 0.732 0.004 0.264
#> GSM74371 1 0.3192 0.72876 0.888 0.000 0.112
#> GSM74372 3 0.6442 -0.23515 0.432 0.004 0.564
#> GSM74373 3 0.7202 0.35369 0.124 0.160 0.716
#> GSM74374 3 0.6282 -0.13703 0.384 0.004 0.612
#> GSM74375 3 0.5366 0.41267 0.016 0.208 0.776
#> GSM74376 3 0.5551 0.40250 0.016 0.224 0.760
#> GSM74405 3 0.5455 0.41422 0.020 0.204 0.776
#> GSM74351 1 0.0237 0.77195 0.996 0.000 0.004
#> GSM74352 3 0.5360 0.40605 0.012 0.220 0.768
#> GSM74353 1 0.7158 0.47036 0.596 0.032 0.372
#> GSM74354 3 0.6264 -0.13302 0.380 0.004 0.616
#> GSM74355 3 0.5024 0.40328 0.004 0.220 0.776
#> GSM74382 1 0.4605 0.68103 0.796 0.000 0.204
#> GSM74383 1 0.6295 0.37642 0.528 0.000 0.472
#> GSM74384 3 0.5070 0.40059 0.004 0.224 0.772
#> GSM74385 1 0.3816 0.70559 0.852 0.000 0.148
#> GSM74386 3 0.7969 -0.15895 0.396 0.064 0.540
#> GSM74395 3 0.7232 -0.20842 0.428 0.028 0.544
#> GSM74396 3 0.7232 -0.20842 0.428 0.028 0.544
#> GSM74397 3 0.7274 -0.26266 0.452 0.028 0.520
#> GSM74398 3 0.5772 0.40912 0.024 0.220 0.756
#> GSM74399 3 0.5202 0.40509 0.008 0.220 0.772
#> GSM74400 3 0.5951 0.41801 0.040 0.196 0.764
#> GSM74401 3 0.5951 0.41801 0.040 0.196 0.764
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM74356 3 0.2530 0.6661 0.000 0.004 0.896 0.100
#> GSM74357 3 0.2197 0.6817 0.000 0.004 0.916 0.080
#> GSM74358 3 0.2197 0.6817 0.000 0.004 0.916 0.080
#> GSM74359 4 0.7718 0.5477 0.004 0.240 0.272 0.484
#> GSM74360 4 0.7718 0.5477 0.004 0.240 0.272 0.484
#> GSM74361 3 0.5394 0.5049 0.000 0.060 0.712 0.228
#> GSM74362 3 0.5426 0.4980 0.000 0.060 0.708 0.232
#> GSM74363 3 0.2197 0.6817 0.000 0.004 0.916 0.080
#> GSM74402 4 0.0592 0.7203 0.000 0.000 0.016 0.984
#> GSM74403 4 0.1209 0.7101 0.004 0.032 0.000 0.964
#> GSM74404 4 0.1209 0.7101 0.004 0.032 0.000 0.964
#> GSM74406 4 0.0707 0.7206 0.000 0.000 0.020 0.980
#> GSM74407 4 0.1302 0.7160 0.000 0.000 0.044 0.956
#> GSM74408 4 0.0672 0.7199 0.000 0.008 0.008 0.984
#> GSM74409 4 0.0672 0.7199 0.000 0.008 0.008 0.984
#> GSM74410 4 0.0672 0.7199 0.000 0.008 0.008 0.984
#> GSM119936 4 0.0672 0.7199 0.000 0.008 0.008 0.984
#> GSM119937 4 0.2334 0.6965 0.000 0.004 0.088 0.908
#> GSM74411 3 0.0921 0.7115 0.000 0.028 0.972 0.000
#> GSM74412 3 0.0921 0.7115 0.000 0.028 0.972 0.000
#> GSM74413 3 0.0921 0.7115 0.000 0.028 0.972 0.000
#> GSM74414 3 0.3323 0.6321 0.060 0.064 0.876 0.000
#> GSM74415 3 0.0921 0.7115 0.000 0.028 0.972 0.000
#> GSM121379 2 0.5007 0.9945 0.008 0.636 0.356 0.000
#> GSM121380 2 0.5007 0.9945 0.008 0.636 0.356 0.000
#> GSM121381 2 0.5024 0.9888 0.008 0.632 0.360 0.000
#> GSM121382 2 0.5007 0.9945 0.008 0.636 0.356 0.000
#> GSM121383 2 0.5007 0.9945 0.008 0.636 0.356 0.000
#> GSM121384 2 0.5007 0.9945 0.008 0.636 0.356 0.000
#> GSM121385 2 0.5007 0.9945 0.008 0.636 0.356 0.000
#> GSM121386 2 0.5007 0.9945 0.008 0.636 0.356 0.000
#> GSM121387 2 0.5007 0.9945 0.008 0.636 0.356 0.000
#> GSM121388 2 0.5007 0.9945 0.008 0.636 0.356 0.000
#> GSM121389 2 0.5007 0.9945 0.008 0.636 0.356 0.000
#> GSM121390 2 0.5007 0.9945 0.008 0.636 0.356 0.000
#> GSM121391 2 0.5007 0.9945 0.008 0.636 0.356 0.000
#> GSM121392 2 0.5007 0.9945 0.008 0.636 0.356 0.000
#> GSM121393 2 0.5007 0.9945 0.008 0.636 0.356 0.000
#> GSM121394 2 0.5024 0.9888 0.008 0.632 0.360 0.000
#> GSM121395 2 0.5007 0.9945 0.008 0.636 0.356 0.000
#> GSM121396 2 0.5193 0.8973 0.008 0.580 0.412 0.000
#> GSM121397 2 0.5007 0.9945 0.008 0.636 0.356 0.000
#> GSM121398 2 0.5007 0.9945 0.008 0.636 0.356 0.000
#> GSM121399 2 0.5007 0.9945 0.008 0.636 0.356 0.000
#> GSM74240 3 0.0188 0.7232 0.000 0.004 0.996 0.000
#> GSM74241 3 0.0188 0.7232 0.000 0.004 0.996 0.000
#> GSM74242 3 0.0188 0.7232 0.000 0.004 0.996 0.000
#> GSM74243 3 0.0188 0.7232 0.000 0.004 0.996 0.000
#> GSM74244 3 0.0188 0.7232 0.000 0.004 0.996 0.000
#> GSM74245 3 0.0188 0.7232 0.000 0.004 0.996 0.000
#> GSM74246 3 0.0188 0.7232 0.000 0.004 0.996 0.000
#> GSM74247 3 0.0188 0.7232 0.000 0.004 0.996 0.000
#> GSM74248 3 0.0188 0.7232 0.000 0.004 0.996 0.000
#> GSM74416 4 0.2345 0.6906 0.000 0.100 0.000 0.900
#> GSM74417 4 0.2345 0.6906 0.000 0.100 0.000 0.900
#> GSM74418 4 0.2345 0.6906 0.000 0.100 0.000 0.900
#> GSM74419 4 0.0707 0.7207 0.000 0.000 0.020 0.980
#> GSM121358 3 0.0469 0.7214 0.000 0.012 0.988 0.000
#> GSM121359 3 0.0469 0.7214 0.000 0.012 0.988 0.000
#> GSM121360 4 0.7845 0.5445 0.008 0.240 0.272 0.480
#> GSM121362 4 0.7845 0.5445 0.008 0.240 0.272 0.480
#> GSM121364 4 0.7718 0.5477 0.004 0.240 0.272 0.484
#> GSM121365 3 0.0804 0.7216 0.000 0.008 0.980 0.012
#> GSM121366 3 0.0469 0.7214 0.000 0.012 0.988 0.000
#> GSM121367 3 0.0469 0.7214 0.000 0.012 0.988 0.000
#> GSM121370 3 0.0469 0.7214 0.000 0.012 0.988 0.000
#> GSM121371 3 0.0469 0.7214 0.000 0.012 0.988 0.000
#> GSM121372 3 0.0469 0.7214 0.000 0.012 0.988 0.000
#> GSM121373 4 0.7718 0.5477 0.004 0.240 0.272 0.484
#> GSM121374 4 0.7718 0.5477 0.004 0.240 0.272 0.484
#> GSM121407 3 0.2363 0.6734 0.024 0.056 0.920 0.000
#> GSM74387 3 0.6977 0.0610 0.212 0.204 0.584 0.000
#> GSM74388 3 0.7754 -0.2031 0.336 0.244 0.420 0.000
#> GSM74389 3 0.6443 -0.0932 0.004 0.056 0.472 0.468
#> GSM74390 3 0.5880 0.4598 0.232 0.008 0.692 0.068
#> GSM74391 4 0.5523 0.5487 0.012 0.032 0.260 0.696
#> GSM74392 3 0.6212 0.1832 0.000 0.060 0.560 0.380
#> GSM74393 3 0.6212 0.1832 0.000 0.060 0.560 0.380
#> GSM74394 3 0.7705 -0.1812 0.312 0.244 0.444 0.000
#> GSM74239 4 0.7285 0.2378 0.300 0.180 0.000 0.520
#> GSM74364 4 0.7254 0.2429 0.300 0.176 0.000 0.524
#> GSM74365 1 0.5826 0.5835 0.680 0.064 0.004 0.252
#> GSM74366 1 0.1174 0.7540 0.968 0.020 0.012 0.000
#> GSM74367 1 0.6265 0.4702 0.588 0.072 0.000 0.340
#> GSM74377 1 0.0779 0.7590 0.980 0.016 0.004 0.000
#> GSM74378 1 0.0779 0.7590 0.980 0.016 0.004 0.000
#> GSM74379 1 0.2317 0.7521 0.928 0.036 0.004 0.032
#> GSM74380 1 0.1697 0.7598 0.952 0.016 0.004 0.028
#> GSM74381 1 0.0779 0.7607 0.980 0.016 0.004 0.000
#> GSM121357 3 0.6303 0.2071 0.148 0.192 0.660 0.000
#> GSM121361 3 0.7758 -0.2193 0.308 0.260 0.432 0.000
#> GSM121363 3 0.7758 -0.2193 0.308 0.260 0.432 0.000
#> GSM121368 3 0.7758 -0.2193 0.308 0.260 0.432 0.000
#> GSM121369 3 0.7733 -0.2078 0.304 0.256 0.440 0.000
#> GSM74368 1 0.7276 0.2830 0.496 0.120 0.008 0.376
#> GSM74369 1 0.7276 0.2830 0.496 0.120 0.008 0.376
#> GSM74370 4 0.7627 0.3358 0.252 0.240 0.004 0.504
#> GSM74371 4 0.6010 0.5914 0.104 0.220 0.000 0.676
#> GSM74372 1 0.6536 0.4305 0.560 0.088 0.000 0.352
#> GSM74373 1 0.3836 0.7254 0.852 0.092 0.004 0.052
#> GSM74374 1 0.6300 0.5126 0.608 0.084 0.000 0.308
#> GSM74375 1 0.0524 0.7615 0.988 0.008 0.000 0.004
#> GSM74376 1 0.1396 0.7571 0.960 0.032 0.004 0.004
#> GSM74405 1 0.1114 0.7624 0.972 0.016 0.004 0.008
#> GSM74351 4 0.1305 0.7140 0.004 0.036 0.000 0.960
#> GSM74352 1 0.1229 0.7596 0.968 0.020 0.004 0.008
#> GSM74353 4 0.6677 0.0304 0.400 0.060 0.012 0.528
#> GSM74354 1 0.6242 0.5153 0.612 0.080 0.000 0.308
#> GSM74355 1 0.0895 0.7578 0.976 0.020 0.004 0.000
#> GSM74382 4 0.6719 0.4501 0.204 0.180 0.000 0.616
#> GSM74383 1 0.7037 0.2240 0.464 0.120 0.000 0.416
#> GSM74384 1 0.1004 0.7568 0.972 0.024 0.004 0.000
#> GSM74385 4 0.6673 0.5284 0.140 0.252 0.000 0.608
#> GSM74386 1 0.6762 0.4673 0.596 0.088 0.012 0.304
#> GSM74395 1 0.6310 0.4534 0.576 0.072 0.000 0.352
#> GSM74396 1 0.6310 0.4534 0.576 0.072 0.000 0.352
#> GSM74397 1 0.6698 0.3832 0.540 0.072 0.008 0.380
#> GSM74398 1 0.1362 0.7625 0.964 0.020 0.004 0.012
#> GSM74399 1 0.0779 0.7592 0.980 0.016 0.004 0.000
#> GSM74400 1 0.1510 0.7601 0.956 0.028 0.000 0.016
#> GSM74401 1 0.1510 0.7601 0.956 0.028 0.000 0.016
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM74356 3 0.5579 0.6888 0.000 0.216 0.676 0.080 0.028
#> GSM74357 3 0.5273 0.6975 0.000 0.216 0.696 0.064 0.024
#> GSM74358 3 0.5273 0.6975 0.000 0.216 0.696 0.064 0.024
#> GSM74359 3 0.6526 -0.1116 0.000 0.000 0.452 0.344 0.204
#> GSM74360 3 0.6526 -0.1116 0.000 0.000 0.452 0.344 0.204
#> GSM74361 3 0.6443 0.5915 0.000 0.136 0.632 0.168 0.064
#> GSM74362 3 0.6475 0.5873 0.000 0.136 0.628 0.172 0.064
#> GSM74363 3 0.5273 0.6975 0.000 0.216 0.696 0.064 0.024
#> GSM74402 4 0.1211 0.7745 0.000 0.000 0.016 0.960 0.024
#> GSM74403 4 0.2136 0.7276 0.000 0.000 0.008 0.904 0.088
#> GSM74404 4 0.2136 0.7276 0.000 0.000 0.008 0.904 0.088
#> GSM74406 4 0.1216 0.7731 0.000 0.000 0.020 0.960 0.020
#> GSM74407 4 0.2446 0.7247 0.000 0.000 0.044 0.900 0.056
#> GSM74408 4 0.0290 0.7788 0.000 0.000 0.008 0.992 0.000
#> GSM74409 4 0.0290 0.7788 0.000 0.000 0.008 0.992 0.000
#> GSM74410 4 0.0290 0.7788 0.000 0.000 0.008 0.992 0.000
#> GSM119936 4 0.0290 0.7788 0.000 0.000 0.008 0.992 0.000
#> GSM119937 4 0.3033 0.6788 0.000 0.000 0.084 0.864 0.052
#> GSM74411 3 0.3534 0.7020 0.000 0.256 0.744 0.000 0.000
#> GSM74412 3 0.3534 0.7020 0.000 0.256 0.744 0.000 0.000
#> GSM74413 3 0.3534 0.7020 0.000 0.256 0.744 0.000 0.000
#> GSM74414 3 0.5120 0.6166 0.056 0.292 0.648 0.000 0.004
#> GSM74415 3 0.3534 0.7020 0.000 0.256 0.744 0.000 0.000
#> GSM121379 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000
#> GSM121380 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000
#> GSM121381 2 0.0404 0.8455 0.000 0.988 0.012 0.000 0.000
#> GSM121382 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000
#> GSM121383 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000
#> GSM121384 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000
#> GSM121385 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000
#> GSM121386 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000
#> GSM121387 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000
#> GSM121388 2 0.0162 0.8516 0.000 0.996 0.004 0.000 0.000
#> GSM121389 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000
#> GSM121390 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000
#> GSM121391 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000
#> GSM121392 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000
#> GSM121393 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000
#> GSM121394 2 0.0404 0.8455 0.000 0.988 0.012 0.000 0.000
#> GSM121395 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000
#> GSM121396 2 0.1410 0.8070 0.000 0.940 0.060 0.000 0.000
#> GSM121397 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000
#> GSM121398 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000
#> GSM121399 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000
#> GSM74240 3 0.4355 0.7130 0.000 0.224 0.732 0.000 0.044
#> GSM74241 3 0.4355 0.7130 0.000 0.224 0.732 0.000 0.044
#> GSM74242 3 0.4355 0.7130 0.000 0.224 0.732 0.000 0.044
#> GSM74243 3 0.4355 0.7130 0.000 0.224 0.732 0.000 0.044
#> GSM74244 3 0.4355 0.7130 0.000 0.224 0.732 0.000 0.044
#> GSM74245 3 0.4355 0.7130 0.000 0.224 0.732 0.000 0.044
#> GSM74246 3 0.4355 0.7130 0.000 0.224 0.732 0.000 0.044
#> GSM74247 3 0.4355 0.7130 0.000 0.224 0.732 0.000 0.044
#> GSM74248 3 0.4355 0.7130 0.000 0.224 0.732 0.000 0.044
#> GSM74416 4 0.3098 0.6779 0.000 0.000 0.016 0.836 0.148
#> GSM74417 4 0.3141 0.6747 0.000 0.000 0.016 0.832 0.152
#> GSM74418 4 0.3141 0.6747 0.000 0.000 0.016 0.832 0.152
#> GSM74419 4 0.1117 0.7738 0.000 0.000 0.020 0.964 0.016
#> GSM121358 3 0.3424 0.7117 0.000 0.240 0.760 0.000 0.000
#> GSM121359 3 0.3424 0.7117 0.000 0.240 0.760 0.000 0.000
#> GSM121360 3 0.6661 -0.1088 0.004 0.000 0.452 0.340 0.204
#> GSM121362 3 0.6661 -0.1088 0.004 0.000 0.452 0.340 0.204
#> GSM121364 3 0.6526 -0.1116 0.000 0.000 0.452 0.344 0.204
#> GSM121365 3 0.3779 0.7136 0.000 0.236 0.752 0.012 0.000
#> GSM121366 3 0.3424 0.7117 0.000 0.240 0.760 0.000 0.000
#> GSM121367 3 0.3424 0.7117 0.000 0.240 0.760 0.000 0.000
#> GSM121370 3 0.3424 0.7117 0.000 0.240 0.760 0.000 0.000
#> GSM121371 3 0.3424 0.7117 0.000 0.240 0.760 0.000 0.000
#> GSM121372 3 0.3424 0.7117 0.000 0.240 0.760 0.000 0.000
#> GSM121373 3 0.6526 -0.1116 0.000 0.000 0.452 0.344 0.204
#> GSM121374 3 0.6526 -0.1116 0.000 0.000 0.452 0.344 0.204
#> GSM121407 3 0.4442 0.6605 0.028 0.284 0.688 0.000 0.000
#> GSM74387 2 0.6746 0.2373 0.204 0.492 0.292 0.000 0.012
#> GSM74388 2 0.5756 0.5396 0.324 0.588 0.076 0.000 0.012
#> GSM74389 3 0.7691 0.2085 0.004 0.132 0.392 0.384 0.088
#> GSM74390 3 0.8010 0.4470 0.216 0.224 0.468 0.020 0.072
#> GSM74391 4 0.6825 0.3017 0.012 0.104 0.184 0.620 0.080
#> GSM74392 3 0.7345 0.3966 0.000 0.136 0.480 0.308 0.076
#> GSM74393 3 0.7345 0.3966 0.000 0.136 0.480 0.308 0.076
#> GSM74394 2 0.5945 0.5371 0.300 0.588 0.100 0.000 0.012
#> GSM74239 5 0.6129 0.6440 0.160 0.000 0.004 0.264 0.572
#> GSM74364 5 0.6129 0.6421 0.160 0.000 0.004 0.264 0.572
#> GSM74365 1 0.5821 0.3387 0.564 0.000 0.008 0.084 0.344
#> GSM74366 1 0.0693 0.6821 0.980 0.000 0.012 0.000 0.008
#> GSM74367 1 0.6452 0.1191 0.480 0.000 0.004 0.164 0.352
#> GSM74377 1 0.0671 0.6909 0.980 0.000 0.004 0.000 0.016
#> GSM74378 1 0.0566 0.6876 0.984 0.000 0.004 0.000 0.012
#> GSM74379 1 0.2672 0.6616 0.872 0.000 0.008 0.004 0.116
#> GSM74380 1 0.2054 0.6813 0.916 0.000 0.008 0.004 0.072
#> GSM74381 1 0.0898 0.6913 0.972 0.000 0.008 0.000 0.020
#> GSM121357 2 0.6502 0.0381 0.136 0.472 0.380 0.000 0.012
#> GSM121361 2 0.5790 0.5583 0.296 0.604 0.088 0.000 0.012
#> GSM121363 2 0.5790 0.5583 0.296 0.604 0.088 0.000 0.012
#> GSM121368 2 0.5790 0.5583 0.296 0.604 0.088 0.000 0.012
#> GSM121369 2 0.5865 0.5526 0.292 0.600 0.096 0.000 0.012
#> GSM74368 1 0.7608 -0.1232 0.384 0.000 0.072 0.168 0.376
#> GSM74369 1 0.7608 -0.1232 0.384 0.000 0.072 0.168 0.376
#> GSM74370 5 0.8098 0.3841 0.132 0.000 0.200 0.256 0.412
#> GSM74371 5 0.4787 0.2747 0.004 0.000 0.012 0.456 0.528
#> GSM74372 1 0.6768 0.0258 0.440 0.000 0.020 0.148 0.392
#> GSM74373 1 0.3880 0.5994 0.772 0.000 0.020 0.004 0.204
#> GSM74374 1 0.6507 0.1811 0.488 0.000 0.016 0.128 0.368
#> GSM74375 1 0.1341 0.6806 0.944 0.000 0.000 0.000 0.056
#> GSM74376 1 0.1356 0.6874 0.956 0.012 0.004 0.000 0.028
#> GSM74405 1 0.1124 0.6912 0.960 0.000 0.004 0.000 0.036
#> GSM74351 4 0.3656 0.6036 0.000 0.000 0.020 0.784 0.196
#> GSM74352 1 0.0992 0.6862 0.968 0.000 0.008 0.000 0.024
#> GSM74353 4 0.7521 -0.4298 0.324 0.000 0.040 0.380 0.256
#> GSM74354 1 0.6575 0.1914 0.492 0.000 0.020 0.128 0.360
#> GSM74355 1 0.0324 0.6873 0.992 0.000 0.004 0.000 0.004
#> GSM74382 5 0.5674 0.5675 0.072 0.000 0.004 0.388 0.536
#> GSM74383 5 0.6928 0.2549 0.320 0.000 0.020 0.192 0.468
#> GSM74384 1 0.0613 0.6850 0.984 0.004 0.004 0.000 0.008
#> GSM74385 5 0.4517 0.4576 0.008 0.000 0.004 0.372 0.616
#> GSM74386 1 0.6993 0.1080 0.476 0.000 0.048 0.124 0.352
#> GSM74395 1 0.6453 0.1035 0.468 0.000 0.004 0.160 0.368
#> GSM74396 1 0.6453 0.1035 0.468 0.000 0.004 0.160 0.368
#> GSM74397 1 0.6894 -0.0274 0.440 0.000 0.012 0.212 0.336
#> GSM74398 1 0.1059 0.6918 0.968 0.000 0.004 0.008 0.020
#> GSM74399 1 0.0451 0.6889 0.988 0.000 0.004 0.000 0.008
#> GSM74400 1 0.2953 0.6353 0.844 0.000 0.012 0.000 0.144
#> GSM74401 1 0.2953 0.6353 0.844 0.000 0.012 0.000 0.144
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM74356 3 0.2487 0.72919 0.000 0.000 0.876 0.032 0.092 0.000
#> GSM74357 3 0.2147 0.74411 0.000 0.000 0.896 0.020 0.084 0.000
#> GSM74358 3 0.2147 0.74411 0.000 0.000 0.896 0.020 0.084 0.000
#> GSM74359 5 0.4536 0.90753 0.000 0.000 0.180 0.120 0.700 0.000
#> GSM74360 5 0.4536 0.90753 0.000 0.000 0.180 0.120 0.700 0.000
#> GSM74361 3 0.4408 0.36877 0.000 0.000 0.656 0.052 0.292 0.000
#> GSM74362 3 0.4463 0.36061 0.000 0.000 0.652 0.056 0.292 0.000
#> GSM74363 3 0.2147 0.74411 0.000 0.000 0.896 0.020 0.084 0.000
#> GSM74402 4 0.2355 0.81471 0.008 0.000 0.004 0.876 0.112 0.000
#> GSM74403 4 0.1524 0.74111 0.060 0.000 0.000 0.932 0.008 0.000
#> GSM74404 4 0.1643 0.73518 0.068 0.000 0.000 0.924 0.008 0.000
#> GSM74406 4 0.2400 0.81328 0.004 0.000 0.008 0.872 0.116 0.000
#> GSM74407 4 0.3343 0.75550 0.004 0.000 0.024 0.796 0.176 0.000
#> GSM74408 4 0.1814 0.82056 0.000 0.000 0.000 0.900 0.100 0.000
#> GSM74409 4 0.1814 0.82056 0.000 0.000 0.000 0.900 0.100 0.000
#> GSM74410 4 0.1814 0.82056 0.000 0.000 0.000 0.900 0.100 0.000
#> GSM119936 4 0.1814 0.82056 0.000 0.000 0.000 0.900 0.100 0.000
#> GSM119937 4 0.4062 0.71268 0.004 0.000 0.060 0.744 0.192 0.000
#> GSM74411 3 0.0937 0.79778 0.000 0.040 0.960 0.000 0.000 0.000
#> GSM74412 3 0.0937 0.79778 0.000 0.040 0.960 0.000 0.000 0.000
#> GSM74413 3 0.0937 0.79778 0.000 0.040 0.960 0.000 0.000 0.000
#> GSM74414 3 0.2822 0.74310 0.000 0.076 0.864 0.000 0.004 0.056
#> GSM74415 3 0.0937 0.79778 0.000 0.040 0.960 0.000 0.000 0.000
#> GSM121379 2 0.0865 0.89131 0.000 0.964 0.036 0.000 0.000 0.000
#> GSM121380 2 0.0865 0.89131 0.000 0.964 0.036 0.000 0.000 0.000
#> GSM121381 2 0.1267 0.87504 0.000 0.940 0.060 0.000 0.000 0.000
#> GSM121382 2 0.0865 0.89131 0.000 0.964 0.036 0.000 0.000 0.000
#> GSM121383 2 0.0865 0.89131 0.000 0.964 0.036 0.000 0.000 0.000
#> GSM121384 2 0.0865 0.89131 0.000 0.964 0.036 0.000 0.000 0.000
#> GSM121385 2 0.0937 0.88940 0.000 0.960 0.040 0.000 0.000 0.000
#> GSM121386 2 0.0937 0.88940 0.000 0.960 0.040 0.000 0.000 0.000
#> GSM121387 2 0.0865 0.89131 0.000 0.964 0.036 0.000 0.000 0.000
#> GSM121388 2 0.1141 0.88051 0.000 0.948 0.052 0.000 0.000 0.000
#> GSM121389 2 0.0865 0.89131 0.000 0.964 0.036 0.000 0.000 0.000
#> GSM121390 2 0.0865 0.89131 0.000 0.964 0.036 0.000 0.000 0.000
#> GSM121391 2 0.0865 0.89131 0.000 0.964 0.036 0.000 0.000 0.000
#> GSM121392 2 0.0865 0.89131 0.000 0.964 0.036 0.000 0.000 0.000
#> GSM121393 2 0.0865 0.89131 0.000 0.964 0.036 0.000 0.000 0.000
#> GSM121394 2 0.1267 0.87504 0.000 0.940 0.060 0.000 0.000 0.000
#> GSM121395 2 0.0865 0.89131 0.000 0.964 0.036 0.000 0.000 0.000
#> GSM121396 2 0.1910 0.83862 0.000 0.892 0.108 0.000 0.000 0.000
#> GSM121397 2 0.0865 0.89131 0.000 0.964 0.036 0.000 0.000 0.000
#> GSM121398 2 0.0865 0.89131 0.000 0.964 0.036 0.000 0.000 0.000
#> GSM121399 2 0.0865 0.89131 0.000 0.964 0.036 0.000 0.000 0.000
#> GSM74240 3 0.2604 0.78242 0.076 0.008 0.880 0.000 0.036 0.000
#> GSM74241 3 0.2604 0.78242 0.076 0.008 0.880 0.000 0.036 0.000
#> GSM74242 3 0.2604 0.78242 0.076 0.008 0.880 0.000 0.036 0.000
#> GSM74243 3 0.2604 0.78242 0.076 0.008 0.880 0.000 0.036 0.000
#> GSM74244 3 0.2604 0.78242 0.076 0.008 0.880 0.000 0.036 0.000
#> GSM74245 3 0.2604 0.78242 0.076 0.008 0.880 0.000 0.036 0.000
#> GSM74246 3 0.2604 0.78242 0.076 0.008 0.880 0.000 0.036 0.000
#> GSM74247 3 0.2604 0.78242 0.076 0.008 0.880 0.000 0.036 0.000
#> GSM74248 3 0.2604 0.78242 0.076 0.008 0.880 0.000 0.036 0.000
#> GSM74416 4 0.2163 0.70992 0.092 0.000 0.000 0.892 0.016 0.000
#> GSM74417 4 0.2214 0.70678 0.096 0.000 0.000 0.888 0.016 0.000
#> GSM74418 4 0.2214 0.70678 0.096 0.000 0.000 0.888 0.016 0.000
#> GSM74419 4 0.2445 0.81340 0.004 0.000 0.008 0.868 0.120 0.000
#> GSM121358 3 0.0547 0.80195 0.000 0.020 0.980 0.000 0.000 0.000
#> GSM121359 3 0.0547 0.80195 0.000 0.020 0.980 0.000 0.000 0.000
#> GSM121360 5 0.4496 0.90443 0.000 0.000 0.180 0.116 0.704 0.000
#> GSM121362 5 0.4496 0.90443 0.000 0.000 0.180 0.116 0.704 0.000
#> GSM121364 5 0.4536 0.90753 0.000 0.000 0.180 0.120 0.700 0.000
#> GSM121365 3 0.0909 0.80007 0.000 0.020 0.968 0.012 0.000 0.000
#> GSM121366 3 0.0547 0.80195 0.000 0.020 0.980 0.000 0.000 0.000
#> GSM121367 3 0.0547 0.80195 0.000 0.020 0.980 0.000 0.000 0.000
#> GSM121370 3 0.0547 0.80195 0.000 0.020 0.980 0.000 0.000 0.000
#> GSM121371 3 0.0547 0.80195 0.000 0.020 0.980 0.000 0.000 0.000
#> GSM121372 3 0.0547 0.80195 0.000 0.020 0.980 0.000 0.000 0.000
#> GSM121373 5 0.4536 0.90753 0.000 0.000 0.180 0.120 0.700 0.000
#> GSM121374 5 0.4536 0.90753 0.000 0.000 0.180 0.120 0.700 0.000
#> GSM121407 3 0.2152 0.77483 0.000 0.068 0.904 0.000 0.004 0.024
#> GSM74387 3 0.6374 -0.10129 0.000 0.368 0.412 0.000 0.024 0.196
#> GSM74388 2 0.5644 0.53281 0.000 0.552 0.108 0.000 0.020 0.320
#> GSM74389 3 0.6563 -0.24686 0.020 0.000 0.416 0.256 0.304 0.004
#> GSM74390 3 0.5271 0.53332 0.064 0.008 0.680 0.000 0.048 0.200
#> GSM74391 4 0.6408 0.17109 0.020 0.000 0.228 0.512 0.228 0.012
#> GSM74392 3 0.5780 -0.06655 0.004 0.000 0.496 0.168 0.332 0.000
#> GSM74393 3 0.5780 -0.06655 0.004 0.000 0.496 0.168 0.332 0.000
#> GSM74394 2 0.5828 0.54639 0.000 0.552 0.132 0.000 0.024 0.292
#> GSM74239 1 0.6081 0.66217 0.604 0.000 0.000 0.188 0.104 0.104
#> GSM74364 1 0.6145 0.66473 0.596 0.000 0.000 0.192 0.108 0.104
#> GSM74365 6 0.5884 0.30624 0.348 0.000 0.000 0.032 0.104 0.516
#> GSM74366 6 0.0622 0.63969 0.000 0.000 0.008 0.000 0.012 0.980
#> GSM74367 6 0.6385 0.09759 0.396 0.000 0.000 0.092 0.076 0.436
#> GSM74377 6 0.0820 0.64802 0.016 0.000 0.000 0.000 0.012 0.972
#> GSM74378 6 0.0717 0.64564 0.008 0.000 0.000 0.000 0.016 0.976
#> GSM74379 6 0.2909 0.61146 0.136 0.000 0.000 0.000 0.028 0.836
#> GSM74380 6 0.2176 0.63581 0.080 0.000 0.000 0.000 0.024 0.896
#> GSM74381 6 0.1092 0.64791 0.020 0.000 0.000 0.000 0.020 0.960
#> GSM121357 3 0.5784 0.26715 0.000 0.300 0.548 0.000 0.020 0.132
#> GSM121361 2 0.5714 0.56503 0.000 0.568 0.120 0.000 0.024 0.288
#> GSM121363 2 0.5714 0.56503 0.000 0.568 0.120 0.000 0.024 0.288
#> GSM121368 2 0.5714 0.56503 0.000 0.568 0.120 0.000 0.024 0.288
#> GSM121369 2 0.5766 0.56231 0.000 0.564 0.128 0.000 0.024 0.284
#> GSM74368 6 0.7107 -0.01428 0.300 0.000 0.004 0.056 0.308 0.332
#> GSM74369 6 0.7107 -0.01428 0.300 0.000 0.004 0.056 0.308 0.332
#> GSM74370 5 0.5973 -0.04582 0.284 0.000 0.000 0.064 0.564 0.088
#> GSM74371 1 0.5442 0.40085 0.496 0.016 0.000 0.412 0.076 0.000
#> GSM74372 6 0.6899 0.04277 0.356 0.000 0.000 0.092 0.148 0.404
#> GSM74373 6 0.3963 0.56043 0.164 0.000 0.000 0.000 0.080 0.756
#> GSM74374 6 0.6390 0.17546 0.368 0.000 0.000 0.044 0.144 0.444
#> GSM74375 6 0.1995 0.62470 0.052 0.000 0.000 0.000 0.036 0.912
#> GSM74376 6 0.1180 0.64550 0.016 0.012 0.000 0.000 0.012 0.960
#> GSM74405 6 0.1168 0.64815 0.028 0.000 0.000 0.000 0.016 0.956
#> GSM74351 4 0.4175 0.57140 0.136 0.000 0.004 0.752 0.108 0.000
#> GSM74352 6 0.0820 0.64493 0.016 0.000 0.000 0.000 0.012 0.972
#> GSM74353 6 0.7820 -0.23370 0.212 0.000 0.004 0.272 0.236 0.276
#> GSM74354 6 0.6346 0.17831 0.380 0.000 0.000 0.048 0.128 0.444
#> GSM74355 6 0.0260 0.64429 0.000 0.000 0.000 0.000 0.008 0.992
#> GSM74382 1 0.5363 0.63885 0.580 0.000 0.000 0.320 0.080 0.020
#> GSM74383 1 0.6644 0.23375 0.504 0.000 0.000 0.096 0.132 0.268
#> GSM74384 6 0.0508 0.64206 0.000 0.004 0.000 0.000 0.012 0.984
#> GSM74385 1 0.5207 0.54091 0.588 0.016 0.000 0.324 0.072 0.000
#> GSM74386 6 0.6785 0.15695 0.300 0.000 0.008 0.040 0.208 0.444
#> GSM74395 6 0.6391 0.08631 0.412 0.000 0.000 0.076 0.092 0.420
#> GSM74396 6 0.6391 0.08631 0.412 0.000 0.000 0.076 0.092 0.420
#> GSM74397 6 0.7014 -0.00771 0.372 0.000 0.004 0.116 0.116 0.392
#> GSM74398 6 0.0713 0.64880 0.028 0.000 0.000 0.000 0.000 0.972
#> GSM74399 6 0.0260 0.64593 0.008 0.000 0.000 0.000 0.000 0.992
#> GSM74400 6 0.5356 0.45246 0.184 0.020 0.004 0.000 0.136 0.656
#> GSM74401 6 0.5356 0.45246 0.184 0.020 0.004 0.000 0.136 0.656
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 108 2.55e-07 2
#> SD:hclust 56 9.31e-09 3
#> SD:hclust 94 1.06e-28 4
#> SD:hclust 91 9.57e-31 5
#> SD:hclust 96 1.37e-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["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 21168 rows and 121 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 0.698 0.927 0.952 0.4895 0.497 0.497
#> 3 3 0.604 0.761 0.871 0.3365 0.684 0.449
#> 4 4 0.705 0.788 0.881 0.1317 0.834 0.556
#> 5 5 0.731 0.730 0.815 0.0623 0.956 0.829
#> 6 6 0.770 0.531 0.745 0.0398 0.924 0.689
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
#> GSM74356 2 0.6623 0.853 0.172 0.828
#> GSM74357 2 0.8081 0.783 0.248 0.752
#> GSM74358 2 0.8081 0.783 0.248 0.752
#> GSM74359 1 0.0000 0.987 1.000 0.000
#> GSM74360 1 0.0000 0.987 1.000 0.000
#> GSM74361 2 0.7139 0.835 0.196 0.804
#> GSM74362 2 0.8081 0.783 0.248 0.752
#> GSM74363 2 0.6623 0.853 0.172 0.828
#> GSM74402 1 0.0000 0.987 1.000 0.000
#> GSM74403 1 0.0000 0.987 1.000 0.000
#> GSM74404 1 0.0000 0.987 1.000 0.000
#> GSM74406 1 0.0000 0.987 1.000 0.000
#> GSM74407 1 0.0000 0.987 1.000 0.000
#> GSM74408 1 0.0000 0.987 1.000 0.000
#> GSM74409 1 0.0000 0.987 1.000 0.000
#> GSM74410 1 0.0000 0.987 1.000 0.000
#> GSM119936 1 0.0000 0.987 1.000 0.000
#> GSM119937 1 0.0000 0.987 1.000 0.000
#> GSM74411 2 0.1184 0.918 0.016 0.984
#> GSM74412 2 0.0938 0.919 0.012 0.988
#> GSM74413 2 0.0938 0.919 0.012 0.988
#> GSM74414 2 0.0938 0.919 0.012 0.988
#> GSM74415 2 0.5946 0.870 0.144 0.856
#> GSM121379 2 0.0938 0.919 0.012 0.988
#> GSM121380 2 0.0938 0.919 0.012 0.988
#> GSM121381 2 0.0938 0.919 0.012 0.988
#> GSM121382 2 0.0938 0.919 0.012 0.988
#> GSM121383 2 0.0938 0.919 0.012 0.988
#> GSM121384 2 0.0938 0.919 0.012 0.988
#> GSM121385 2 0.0938 0.919 0.012 0.988
#> GSM121386 2 0.0938 0.919 0.012 0.988
#> GSM121387 2 0.0938 0.919 0.012 0.988
#> GSM121388 2 0.0938 0.919 0.012 0.988
#> GSM121389 2 0.0938 0.919 0.012 0.988
#> GSM121390 2 0.0938 0.919 0.012 0.988
#> GSM121391 2 0.0938 0.919 0.012 0.988
#> GSM121392 2 0.0938 0.919 0.012 0.988
#> GSM121393 2 0.0938 0.919 0.012 0.988
#> GSM121394 2 0.0938 0.919 0.012 0.988
#> GSM121395 2 0.0938 0.919 0.012 0.988
#> GSM121396 2 0.0938 0.919 0.012 0.988
#> GSM121397 2 0.0938 0.919 0.012 0.988
#> GSM121398 2 0.0938 0.919 0.012 0.988
#> GSM121399 2 0.0938 0.919 0.012 0.988
#> GSM74240 2 0.7815 0.790 0.232 0.768
#> GSM74241 2 0.7299 0.819 0.204 0.796
#> GSM74242 2 0.9608 0.529 0.384 0.616
#> GSM74243 2 0.9608 0.529 0.384 0.616
#> GSM74244 2 0.7056 0.831 0.192 0.808
#> GSM74245 2 0.7815 0.790 0.232 0.768
#> GSM74246 2 0.7056 0.831 0.192 0.808
#> GSM74247 2 0.7056 0.831 0.192 0.808
#> GSM74248 2 0.7950 0.781 0.240 0.760
#> GSM74416 1 0.0000 0.987 1.000 0.000
#> GSM74417 1 0.0000 0.987 1.000 0.000
#> GSM74418 1 0.0000 0.987 1.000 0.000
#> GSM74419 1 0.0000 0.987 1.000 0.000
#> GSM121358 2 0.6343 0.861 0.160 0.840
#> GSM121359 2 0.0938 0.919 0.012 0.988
#> GSM121360 1 0.0000 0.987 1.000 0.000
#> GSM121362 1 0.0000 0.987 1.000 0.000
#> GSM121364 1 0.0000 0.987 1.000 0.000
#> GSM121365 2 0.6343 0.861 0.160 0.840
#> GSM121366 2 0.5519 0.877 0.128 0.872
#> GSM121367 2 0.6343 0.861 0.160 0.840
#> GSM121370 2 0.6343 0.861 0.160 0.840
#> GSM121371 2 0.6343 0.861 0.160 0.840
#> GSM121372 2 0.0938 0.919 0.012 0.988
#> GSM121373 1 0.0000 0.987 1.000 0.000
#> GSM121374 1 0.0000 0.987 1.000 0.000
#> GSM121407 2 0.0938 0.919 0.012 0.988
#> GSM74387 2 0.1633 0.916 0.024 0.976
#> GSM74388 2 0.0938 0.919 0.012 0.988
#> GSM74389 1 0.0000 0.987 1.000 0.000
#> GSM74390 1 0.0000 0.987 1.000 0.000
#> GSM74391 1 0.0000 0.987 1.000 0.000
#> GSM74392 1 0.0000 0.987 1.000 0.000
#> GSM74393 1 0.0000 0.987 1.000 0.000
#> GSM74394 2 0.1184 0.918 0.016 0.984
#> GSM74239 1 0.0000 0.987 1.000 0.000
#> GSM74364 1 0.0000 0.987 1.000 0.000
#> GSM74365 1 0.0000 0.987 1.000 0.000
#> GSM74366 1 0.5737 0.838 0.864 0.136
#> GSM74367 1 0.0000 0.987 1.000 0.000
#> GSM74377 1 0.0376 0.983 0.996 0.004
#> GSM74378 1 0.5737 0.838 0.864 0.136
#> GSM74379 1 0.0000 0.987 1.000 0.000
#> GSM74380 1 0.0000 0.987 1.000 0.000
#> GSM74381 1 0.1414 0.968 0.980 0.020
#> GSM121357 2 0.0938 0.919 0.012 0.988
#> GSM121361 2 0.0938 0.919 0.012 0.988
#> GSM121363 2 0.0938 0.919 0.012 0.988
#> GSM121368 2 0.0938 0.919 0.012 0.988
#> GSM121369 2 0.1633 0.916 0.024 0.976
#> GSM74368 1 0.0000 0.987 1.000 0.000
#> GSM74369 1 0.0000 0.987 1.000 0.000
#> GSM74370 1 0.0000 0.987 1.000 0.000
#> GSM74371 1 0.0000 0.987 1.000 0.000
#> GSM74372 1 0.0000 0.987 1.000 0.000
#> GSM74373 1 0.0376 0.983 0.996 0.004
#> GSM74374 1 0.0000 0.987 1.000 0.000
#> GSM74375 1 0.0000 0.987 1.000 0.000
#> GSM74376 1 0.0000 0.987 1.000 0.000
#> GSM74405 1 0.0000 0.987 1.000 0.000
#> GSM74351 1 0.0000 0.987 1.000 0.000
#> GSM74352 1 0.6247 0.813 0.844 0.156
#> GSM74353 1 0.0000 0.987 1.000 0.000
#> GSM74354 1 0.0000 0.987 1.000 0.000
#> GSM74355 1 0.4690 0.881 0.900 0.100
#> GSM74382 1 0.0000 0.987 1.000 0.000
#> GSM74383 1 0.0000 0.987 1.000 0.000
#> GSM74384 1 0.6247 0.813 0.844 0.156
#> GSM74385 1 0.0000 0.987 1.000 0.000
#> GSM74386 1 0.0000 0.987 1.000 0.000
#> GSM74395 1 0.0000 0.987 1.000 0.000
#> GSM74396 1 0.0000 0.987 1.000 0.000
#> GSM74397 1 0.0000 0.987 1.000 0.000
#> GSM74398 1 0.0000 0.987 1.000 0.000
#> GSM74399 1 0.0000 0.987 1.000 0.000
#> GSM74400 1 0.0000 0.987 1.000 0.000
#> GSM74401 1 0.0000 0.987 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM74356 3 0.1482 0.688 0.012 0.020 0.968
#> GSM74357 3 0.1491 0.691 0.016 0.016 0.968
#> GSM74358 3 0.1491 0.691 0.016 0.016 0.968
#> GSM74359 3 0.4842 0.689 0.224 0.000 0.776
#> GSM74360 3 0.5138 0.675 0.252 0.000 0.748
#> GSM74361 3 0.1491 0.691 0.016 0.016 0.968
#> GSM74362 3 0.1491 0.691 0.016 0.016 0.968
#> GSM74363 3 0.1711 0.680 0.008 0.032 0.960
#> GSM74402 3 0.6180 0.446 0.416 0.000 0.584
#> GSM74403 3 0.6260 0.377 0.448 0.000 0.552
#> GSM74404 3 0.6260 0.377 0.448 0.000 0.552
#> GSM74406 3 0.5138 0.675 0.252 0.000 0.748
#> GSM74407 3 0.6008 0.525 0.372 0.000 0.628
#> GSM74408 3 0.5138 0.675 0.252 0.000 0.748
#> GSM74409 3 0.5138 0.675 0.252 0.000 0.748
#> GSM74410 3 0.5058 0.680 0.244 0.000 0.756
#> GSM119936 3 0.5138 0.675 0.252 0.000 0.748
#> GSM119937 3 0.5138 0.675 0.252 0.000 0.748
#> GSM74411 2 0.6126 0.578 0.004 0.644 0.352
#> GSM74412 2 0.5115 0.755 0.004 0.768 0.228
#> GSM74413 2 0.5656 0.691 0.004 0.712 0.284
#> GSM74414 2 0.1765 0.879 0.004 0.956 0.040
#> GSM74415 3 0.6264 0.178 0.004 0.380 0.616
#> GSM121379 2 0.0000 0.894 0.000 1.000 0.000
#> GSM121380 2 0.0000 0.894 0.000 1.000 0.000
#> GSM121381 2 0.0000 0.894 0.000 1.000 0.000
#> GSM121382 2 0.0000 0.894 0.000 1.000 0.000
#> GSM121383 2 0.0000 0.894 0.000 1.000 0.000
#> GSM121384 2 0.0000 0.894 0.000 1.000 0.000
#> GSM121385 2 0.0000 0.894 0.000 1.000 0.000
#> GSM121386 2 0.0000 0.894 0.000 1.000 0.000
#> GSM121387 2 0.0000 0.894 0.000 1.000 0.000
#> GSM121388 2 0.0000 0.894 0.000 1.000 0.000
#> GSM121389 2 0.0000 0.894 0.000 1.000 0.000
#> GSM121390 2 0.0000 0.894 0.000 1.000 0.000
#> GSM121391 2 0.0000 0.894 0.000 1.000 0.000
#> GSM121392 2 0.0237 0.891 0.004 0.996 0.000
#> GSM121393 2 0.0000 0.894 0.000 1.000 0.000
#> GSM121394 2 0.0000 0.894 0.000 1.000 0.000
#> GSM121395 2 0.0000 0.894 0.000 1.000 0.000
#> GSM121396 2 0.0747 0.889 0.000 0.984 0.016
#> GSM121397 2 0.0000 0.894 0.000 1.000 0.000
#> GSM121398 2 0.0000 0.894 0.000 1.000 0.000
#> GSM121399 2 0.0000 0.894 0.000 1.000 0.000
#> GSM74240 3 0.0983 0.681 0.016 0.004 0.980
#> GSM74241 3 0.5723 0.433 0.016 0.240 0.744
#> GSM74242 3 0.0475 0.683 0.004 0.004 0.992
#> GSM74243 3 0.0475 0.683 0.004 0.004 0.992
#> GSM74244 3 0.5723 0.433 0.016 0.240 0.744
#> GSM74245 3 0.0983 0.681 0.016 0.004 0.980
#> GSM74246 3 0.6096 0.363 0.016 0.280 0.704
#> GSM74247 3 0.6448 0.261 0.016 0.328 0.656
#> GSM74248 3 0.0983 0.681 0.016 0.004 0.980
#> GSM74416 3 0.6260 0.377 0.448 0.000 0.552
#> GSM74417 3 0.6260 0.377 0.448 0.000 0.552
#> GSM74418 3 0.6295 0.316 0.472 0.000 0.528
#> GSM74419 3 0.5138 0.675 0.252 0.000 0.748
#> GSM121358 3 0.6529 0.214 0.012 0.368 0.620
#> GSM121359 2 0.5480 0.717 0.004 0.732 0.264
#> GSM121360 3 0.5926 0.550 0.356 0.000 0.644
#> GSM121362 3 0.6008 0.522 0.372 0.000 0.628
#> GSM121364 3 0.4974 0.684 0.236 0.000 0.764
#> GSM121365 3 0.6529 0.214 0.012 0.368 0.620
#> GSM121366 3 0.6247 0.190 0.004 0.376 0.620
#> GSM121367 3 0.6529 0.214 0.012 0.368 0.620
#> GSM121370 3 0.6529 0.214 0.012 0.368 0.620
#> GSM121371 3 0.6529 0.214 0.012 0.368 0.620
#> GSM121372 2 0.5623 0.697 0.004 0.716 0.280
#> GSM121373 3 0.4974 0.684 0.236 0.000 0.764
#> GSM121374 3 0.4974 0.684 0.236 0.000 0.764
#> GSM121407 2 0.4883 0.773 0.004 0.788 0.208
#> GSM74387 2 0.8286 0.652 0.140 0.624 0.236
#> GSM74388 2 0.5466 0.782 0.160 0.800 0.040
#> GSM74389 3 0.2711 0.713 0.088 0.000 0.912
#> GSM74390 1 0.0000 0.980 1.000 0.000 0.000
#> GSM74391 3 0.5098 0.678 0.248 0.000 0.752
#> GSM74392 3 0.4750 0.692 0.216 0.000 0.784
#> GSM74393 3 0.1753 0.703 0.048 0.000 0.952
#> GSM74394 2 0.7829 0.703 0.164 0.672 0.164
#> GSM74239 1 0.0747 0.979 0.984 0.000 0.016
#> GSM74364 1 0.0747 0.979 0.984 0.000 0.016
#> GSM74365 1 0.0237 0.980 0.996 0.000 0.004
#> GSM74366 1 0.1163 0.952 0.972 0.028 0.000
#> GSM74367 1 0.0747 0.979 0.984 0.000 0.016
#> GSM74377 1 0.0000 0.980 1.000 0.000 0.000
#> GSM74378 1 0.0892 0.961 0.980 0.020 0.000
#> GSM74379 1 0.0000 0.980 1.000 0.000 0.000
#> GSM74380 1 0.0000 0.980 1.000 0.000 0.000
#> GSM74381 1 0.0000 0.980 1.000 0.000 0.000
#> GSM121357 2 0.2200 0.872 0.004 0.940 0.056
#> GSM121361 2 0.5875 0.777 0.160 0.784 0.056
#> GSM121363 2 0.5816 0.782 0.156 0.788 0.056
#> GSM121368 2 0.5816 0.782 0.156 0.788 0.056
#> GSM121369 2 0.8212 0.670 0.168 0.640 0.192
#> GSM74368 1 0.0747 0.979 0.984 0.000 0.016
#> GSM74369 1 0.0747 0.979 0.984 0.000 0.016
#> GSM74370 1 0.0747 0.979 0.984 0.000 0.016
#> GSM74371 1 0.0892 0.976 0.980 0.000 0.020
#> GSM74372 1 0.0747 0.979 0.984 0.000 0.016
#> GSM74373 1 0.0000 0.980 1.000 0.000 0.000
#> GSM74374 1 0.0424 0.980 0.992 0.000 0.008
#> GSM74375 1 0.0000 0.980 1.000 0.000 0.000
#> GSM74376 1 0.0000 0.980 1.000 0.000 0.000
#> GSM74405 1 0.0000 0.980 1.000 0.000 0.000
#> GSM74351 1 0.2356 0.908 0.928 0.000 0.072
#> GSM74352 1 0.1289 0.947 0.968 0.032 0.000
#> GSM74353 1 0.0747 0.979 0.984 0.000 0.016
#> GSM74354 1 0.0592 0.980 0.988 0.000 0.012
#> GSM74355 1 0.0424 0.973 0.992 0.008 0.000
#> GSM74382 1 0.2959 0.863 0.900 0.000 0.100
#> GSM74383 1 0.0747 0.979 0.984 0.000 0.016
#> GSM74384 1 0.1289 0.947 0.968 0.032 0.000
#> GSM74385 1 0.0892 0.976 0.980 0.000 0.020
#> GSM74386 1 0.0592 0.980 0.988 0.000 0.012
#> GSM74395 1 0.0747 0.979 0.984 0.000 0.016
#> GSM74396 1 0.0747 0.979 0.984 0.000 0.016
#> GSM74397 1 0.0892 0.976 0.980 0.000 0.020
#> GSM74398 1 0.0000 0.980 1.000 0.000 0.000
#> GSM74399 1 0.0000 0.980 1.000 0.000 0.000
#> GSM74400 1 0.0000 0.980 1.000 0.000 0.000
#> GSM74401 1 0.0000 0.980 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM74356 3 0.2345 0.866 0.000 0.000 0.900 0.100
#> GSM74357 3 0.2408 0.864 0.000 0.000 0.896 0.104
#> GSM74358 3 0.2408 0.864 0.000 0.000 0.896 0.104
#> GSM74359 4 0.3707 0.822 0.028 0.000 0.132 0.840
#> GSM74360 4 0.0921 0.905 0.028 0.000 0.000 0.972
#> GSM74361 3 0.2345 0.866 0.000 0.000 0.900 0.100
#> GSM74362 3 0.2469 0.863 0.000 0.000 0.892 0.108
#> GSM74363 3 0.2345 0.866 0.000 0.000 0.900 0.100
#> GSM74402 4 0.1302 0.904 0.044 0.000 0.000 0.956
#> GSM74403 4 0.1389 0.903 0.048 0.000 0.000 0.952
#> GSM74404 4 0.1389 0.903 0.048 0.000 0.000 0.952
#> GSM74406 4 0.1022 0.907 0.032 0.000 0.000 0.968
#> GSM74407 4 0.1211 0.906 0.040 0.000 0.000 0.960
#> GSM74408 4 0.1022 0.907 0.032 0.000 0.000 0.968
#> GSM74409 4 0.1022 0.907 0.032 0.000 0.000 0.968
#> GSM74410 4 0.1022 0.907 0.032 0.000 0.000 0.968
#> GSM119936 4 0.1022 0.907 0.032 0.000 0.000 0.968
#> GSM119937 4 0.1022 0.907 0.032 0.000 0.000 0.968
#> GSM74411 3 0.4034 0.745 0.008 0.192 0.796 0.004
#> GSM74412 3 0.3893 0.740 0.008 0.196 0.796 0.000
#> GSM74413 3 0.4034 0.745 0.008 0.192 0.796 0.004
#> GSM74414 2 0.5280 0.699 0.128 0.752 0.120 0.000
#> GSM74415 3 0.2271 0.871 0.008 0.012 0.928 0.052
#> GSM121379 2 0.0000 0.886 0.000 1.000 0.000 0.000
#> GSM121380 2 0.0000 0.886 0.000 1.000 0.000 0.000
#> GSM121381 2 0.0000 0.886 0.000 1.000 0.000 0.000
#> GSM121382 2 0.0188 0.886 0.000 0.996 0.000 0.004
#> GSM121383 2 0.0188 0.886 0.000 0.996 0.000 0.004
#> GSM121384 2 0.0000 0.886 0.000 1.000 0.000 0.000
#> GSM121385 2 0.0000 0.886 0.000 1.000 0.000 0.000
#> GSM121386 2 0.0000 0.886 0.000 1.000 0.000 0.000
#> GSM121387 2 0.0188 0.886 0.000 0.996 0.000 0.004
#> GSM121388 2 0.0376 0.884 0.000 0.992 0.004 0.004
#> GSM121389 2 0.0188 0.886 0.000 0.996 0.000 0.004
#> GSM121390 2 0.0000 0.886 0.000 1.000 0.000 0.000
#> GSM121391 2 0.0000 0.886 0.000 1.000 0.000 0.000
#> GSM121392 2 0.0000 0.886 0.000 1.000 0.000 0.000
#> GSM121393 2 0.0188 0.886 0.000 0.996 0.000 0.004
#> GSM121394 2 0.0188 0.886 0.000 0.996 0.000 0.004
#> GSM121395 2 0.0188 0.886 0.000 0.996 0.000 0.004
#> GSM121396 2 0.2125 0.823 0.000 0.920 0.076 0.004
#> GSM121397 2 0.0000 0.886 0.000 1.000 0.000 0.000
#> GSM121398 2 0.0000 0.886 0.000 1.000 0.000 0.000
#> GSM121399 2 0.0000 0.886 0.000 1.000 0.000 0.000
#> GSM74240 3 0.1398 0.862 0.004 0.000 0.956 0.040
#> GSM74241 3 0.1398 0.862 0.004 0.000 0.956 0.040
#> GSM74242 3 0.1398 0.862 0.004 0.000 0.956 0.040
#> GSM74243 3 0.1398 0.862 0.004 0.000 0.956 0.040
#> GSM74244 3 0.1398 0.862 0.004 0.000 0.956 0.040
#> GSM74245 3 0.1398 0.862 0.004 0.000 0.956 0.040
#> GSM74246 3 0.1398 0.862 0.004 0.000 0.956 0.040
#> GSM74247 3 0.1398 0.862 0.004 0.000 0.956 0.040
#> GSM74248 3 0.1398 0.862 0.004 0.000 0.956 0.040
#> GSM74416 4 0.1389 0.903 0.048 0.000 0.000 0.952
#> GSM74417 4 0.1389 0.903 0.048 0.000 0.000 0.952
#> GSM74418 4 0.1389 0.903 0.048 0.000 0.000 0.952
#> GSM74419 4 0.1022 0.907 0.032 0.000 0.000 0.968
#> GSM121358 3 0.2665 0.870 0.004 0.008 0.900 0.088
#> GSM121359 3 0.4192 0.733 0.004 0.208 0.780 0.008
#> GSM121360 4 0.6494 0.591 0.232 0.000 0.136 0.632
#> GSM121362 4 0.6680 0.536 0.260 0.000 0.136 0.604
#> GSM121364 4 0.3653 0.825 0.028 0.000 0.128 0.844
#> GSM121365 3 0.2665 0.870 0.004 0.008 0.900 0.088
#> GSM121366 3 0.2528 0.871 0.004 0.008 0.908 0.080
#> GSM121367 3 0.2665 0.870 0.004 0.008 0.900 0.088
#> GSM121370 3 0.2597 0.871 0.004 0.008 0.904 0.084
#> GSM121371 3 0.2665 0.870 0.004 0.008 0.900 0.088
#> GSM121372 3 0.4294 0.735 0.008 0.204 0.780 0.008
#> GSM121373 4 0.3760 0.822 0.028 0.000 0.136 0.836
#> GSM121374 4 0.3707 0.822 0.028 0.000 0.132 0.840
#> GSM121407 3 0.4192 0.730 0.008 0.208 0.780 0.004
#> GSM74387 3 0.4610 0.764 0.100 0.100 0.800 0.000
#> GSM74388 2 0.6552 0.318 0.440 0.484 0.076 0.000
#> GSM74389 3 0.5500 0.140 0.016 0.000 0.520 0.464
#> GSM74390 1 0.0188 0.852 0.996 0.000 0.004 0.000
#> GSM74391 4 0.1022 0.907 0.032 0.000 0.000 0.968
#> GSM74392 4 0.3707 0.822 0.028 0.000 0.132 0.840
#> GSM74393 3 0.3870 0.758 0.004 0.000 0.788 0.208
#> GSM74394 1 0.7558 -0.165 0.428 0.192 0.380 0.000
#> GSM74239 1 0.4428 0.683 0.720 0.000 0.004 0.276
#> GSM74364 1 0.4837 0.548 0.648 0.000 0.004 0.348
#> GSM74365 1 0.0817 0.854 0.976 0.000 0.000 0.024
#> GSM74366 1 0.0336 0.850 0.992 0.000 0.008 0.000
#> GSM74367 1 0.3583 0.800 0.816 0.000 0.004 0.180
#> GSM74377 1 0.0000 0.855 1.000 0.000 0.000 0.000
#> GSM74378 1 0.0188 0.853 0.996 0.000 0.004 0.000
#> GSM74379 1 0.0000 0.855 1.000 0.000 0.000 0.000
#> GSM74380 1 0.0000 0.855 1.000 0.000 0.000 0.000
#> GSM74381 1 0.0000 0.855 1.000 0.000 0.000 0.000
#> GSM121357 2 0.6701 0.420 0.120 0.584 0.296 0.000
#> GSM121361 2 0.7007 0.303 0.432 0.452 0.116 0.000
#> GSM121363 2 0.7006 0.313 0.428 0.456 0.116 0.000
#> GSM121368 2 0.7006 0.313 0.428 0.456 0.116 0.000
#> GSM121369 3 0.7007 0.217 0.432 0.116 0.452 0.000
#> GSM74368 1 0.3751 0.788 0.800 0.000 0.004 0.196
#> GSM74369 1 0.3791 0.784 0.796 0.000 0.004 0.200
#> GSM74370 1 0.3751 0.789 0.800 0.000 0.004 0.196
#> GSM74371 4 0.5165 -0.144 0.484 0.000 0.004 0.512
#> GSM74372 1 0.3356 0.804 0.824 0.000 0.000 0.176
#> GSM74373 1 0.0000 0.855 1.000 0.000 0.000 0.000
#> GSM74374 1 0.2704 0.828 0.876 0.000 0.000 0.124
#> GSM74375 1 0.0000 0.855 1.000 0.000 0.000 0.000
#> GSM74376 1 0.0188 0.853 0.996 0.000 0.004 0.000
#> GSM74405 1 0.0000 0.855 1.000 0.000 0.000 0.000
#> GSM74351 4 0.2125 0.876 0.076 0.000 0.004 0.920
#> GSM74352 1 0.0376 0.850 0.992 0.004 0.004 0.000
#> GSM74353 1 0.3791 0.784 0.796 0.000 0.004 0.200
#> GSM74354 1 0.2944 0.826 0.868 0.000 0.004 0.128
#> GSM74355 1 0.0000 0.855 1.000 0.000 0.000 0.000
#> GSM74382 4 0.2053 0.880 0.072 0.000 0.004 0.924
#> GSM74383 1 0.3583 0.800 0.816 0.000 0.004 0.180
#> GSM74384 1 0.0524 0.847 0.988 0.004 0.008 0.000
#> GSM74385 1 0.5060 0.412 0.584 0.000 0.004 0.412
#> GSM74386 1 0.3583 0.800 0.816 0.000 0.004 0.180
#> GSM74395 1 0.3710 0.790 0.804 0.000 0.004 0.192
#> GSM74396 1 0.3494 0.805 0.824 0.000 0.004 0.172
#> GSM74397 1 0.4920 0.499 0.628 0.000 0.004 0.368
#> GSM74398 1 0.0188 0.855 0.996 0.000 0.000 0.004
#> GSM74399 1 0.0000 0.855 1.000 0.000 0.000 0.000
#> GSM74400 1 0.1022 0.853 0.968 0.000 0.000 0.032
#> GSM74401 1 0.1022 0.853 0.968 0.000 0.000 0.032
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM74356 3 0.1522 0.8139 0.000 0.000 0.944 0.044 0.012
#> GSM74357 3 0.2139 0.8003 0.000 0.000 0.916 0.052 0.032
#> GSM74358 3 0.2139 0.8003 0.000 0.000 0.916 0.052 0.032
#> GSM74359 4 0.4847 0.6998 0.000 0.000 0.080 0.704 0.216
#> GSM74360 4 0.4150 0.7230 0.000 0.000 0.036 0.748 0.216
#> GSM74361 3 0.1522 0.8139 0.000 0.000 0.944 0.044 0.012
#> GSM74362 3 0.5123 0.5963 0.000 0.000 0.696 0.144 0.160
#> GSM74363 3 0.1205 0.8171 0.000 0.000 0.956 0.040 0.004
#> GSM74402 4 0.2074 0.7904 0.104 0.000 0.000 0.896 0.000
#> GSM74403 4 0.2233 0.7892 0.104 0.000 0.000 0.892 0.004
#> GSM74404 4 0.2233 0.7892 0.104 0.000 0.000 0.892 0.004
#> GSM74406 4 0.0510 0.8064 0.016 0.000 0.000 0.984 0.000
#> GSM74407 4 0.2074 0.7904 0.104 0.000 0.000 0.896 0.000
#> GSM74408 4 0.0510 0.8064 0.016 0.000 0.000 0.984 0.000
#> GSM74409 4 0.0510 0.8064 0.016 0.000 0.000 0.984 0.000
#> GSM74410 4 0.0404 0.8054 0.012 0.000 0.000 0.988 0.000
#> GSM119936 4 0.0794 0.8063 0.028 0.000 0.000 0.972 0.000
#> GSM119937 4 0.0703 0.8067 0.024 0.000 0.000 0.976 0.000
#> GSM74411 3 0.3888 0.7565 0.000 0.072 0.812 0.004 0.112
#> GSM74412 3 0.3888 0.7565 0.000 0.072 0.812 0.004 0.112
#> GSM74413 3 0.3888 0.7565 0.000 0.072 0.812 0.004 0.112
#> GSM74414 5 0.7246 0.6208 0.064 0.392 0.124 0.000 0.420
#> GSM74415 3 0.3166 0.7971 0.000 0.012 0.856 0.020 0.112
#> GSM121379 2 0.0162 0.9822 0.000 0.996 0.000 0.000 0.004
#> GSM121380 2 0.0162 0.9822 0.000 0.996 0.000 0.000 0.004
#> GSM121381 2 0.0162 0.9822 0.000 0.996 0.000 0.000 0.004
#> GSM121382 2 0.0162 0.9805 0.000 0.996 0.000 0.000 0.004
#> GSM121383 2 0.0162 0.9805 0.000 0.996 0.000 0.000 0.004
#> GSM121384 2 0.0162 0.9822 0.000 0.996 0.000 0.000 0.004
#> GSM121385 2 0.0162 0.9822 0.000 0.996 0.000 0.000 0.004
#> GSM121386 2 0.0162 0.9822 0.000 0.996 0.000 0.000 0.004
#> GSM121387 2 0.0162 0.9805 0.000 0.996 0.000 0.000 0.004
#> GSM121388 2 0.1041 0.9595 0.000 0.964 0.004 0.000 0.032
#> GSM121389 2 0.0609 0.9710 0.000 0.980 0.000 0.000 0.020
#> GSM121390 2 0.0162 0.9822 0.000 0.996 0.000 0.000 0.004
#> GSM121391 2 0.0000 0.9816 0.000 1.000 0.000 0.000 0.000
#> GSM121392 2 0.0162 0.9822 0.000 0.996 0.000 0.000 0.004
#> GSM121393 2 0.0794 0.9657 0.000 0.972 0.000 0.000 0.028
#> GSM121394 2 0.0000 0.9816 0.000 1.000 0.000 0.000 0.000
#> GSM121395 2 0.0794 0.9657 0.000 0.972 0.000 0.000 0.028
#> GSM121396 2 0.3115 0.7822 0.000 0.852 0.112 0.000 0.036
#> GSM121397 2 0.0162 0.9822 0.000 0.996 0.000 0.000 0.004
#> GSM121398 2 0.0162 0.9822 0.000 0.996 0.000 0.000 0.004
#> GSM121399 2 0.0000 0.9816 0.000 1.000 0.000 0.000 0.000
#> GSM74240 3 0.3727 0.7723 0.000 0.000 0.768 0.016 0.216
#> GSM74241 3 0.3663 0.7735 0.000 0.000 0.776 0.016 0.208
#> GSM74242 3 0.3630 0.7732 0.000 0.000 0.780 0.016 0.204
#> GSM74243 3 0.3630 0.7732 0.000 0.000 0.780 0.016 0.204
#> GSM74244 3 0.3663 0.7735 0.000 0.000 0.776 0.016 0.208
#> GSM74245 3 0.3696 0.7732 0.000 0.000 0.772 0.016 0.212
#> GSM74246 3 0.3696 0.7724 0.000 0.000 0.772 0.016 0.212
#> GSM74247 3 0.3696 0.7724 0.000 0.000 0.772 0.016 0.212
#> GSM74248 3 0.3727 0.7723 0.000 0.000 0.768 0.016 0.216
#> GSM74416 4 0.2338 0.7854 0.112 0.000 0.000 0.884 0.004
#> GSM74417 4 0.2338 0.7854 0.112 0.000 0.000 0.884 0.004
#> GSM74418 4 0.2338 0.7854 0.112 0.000 0.000 0.884 0.004
#> GSM74419 4 0.0703 0.8067 0.024 0.000 0.000 0.976 0.000
#> GSM121358 3 0.1356 0.8198 0.000 0.012 0.956 0.028 0.004
#> GSM121359 3 0.3043 0.7685 0.000 0.080 0.864 0.000 0.056
#> GSM121360 4 0.6982 0.5730 0.132 0.000 0.080 0.568 0.220
#> GSM121362 4 0.7479 0.4957 0.200 0.000 0.080 0.500 0.220
#> GSM121364 4 0.4847 0.6998 0.000 0.000 0.080 0.704 0.216
#> GSM121365 3 0.1356 0.8198 0.000 0.012 0.956 0.028 0.004
#> GSM121366 3 0.1267 0.8198 0.000 0.012 0.960 0.024 0.004
#> GSM121367 3 0.1356 0.8198 0.000 0.012 0.956 0.028 0.004
#> GSM121370 3 0.1356 0.8198 0.000 0.012 0.956 0.028 0.004
#> GSM121371 3 0.1356 0.8198 0.000 0.012 0.956 0.028 0.004
#> GSM121372 3 0.3110 0.7663 0.000 0.080 0.860 0.000 0.060
#> GSM121373 4 0.4847 0.6998 0.000 0.000 0.080 0.704 0.216
#> GSM121374 4 0.4847 0.6998 0.000 0.000 0.080 0.704 0.216
#> GSM121407 3 0.3362 0.7549 0.000 0.080 0.844 0.000 0.076
#> GSM74387 3 0.5652 0.2082 0.020 0.044 0.556 0.000 0.380
#> GSM74388 5 0.7199 0.7983 0.216 0.256 0.040 0.000 0.488
#> GSM74389 4 0.6246 0.3776 0.000 0.000 0.292 0.528 0.180
#> GSM74390 1 0.3612 0.5798 0.732 0.000 0.000 0.000 0.268
#> GSM74391 4 0.1568 0.8023 0.020 0.000 0.000 0.944 0.036
#> GSM74392 4 0.4605 0.7131 0.000 0.000 0.076 0.732 0.192
#> GSM74393 3 0.6507 0.0482 0.000 0.000 0.432 0.376 0.192
#> GSM74394 5 0.7771 0.7636 0.200 0.144 0.168 0.000 0.488
#> GSM74239 1 0.2561 0.6527 0.856 0.000 0.000 0.144 0.000
#> GSM74364 1 0.3266 0.5884 0.796 0.000 0.000 0.200 0.004
#> GSM74365 1 0.0865 0.7180 0.972 0.000 0.000 0.004 0.024
#> GSM74366 1 0.4242 0.3930 0.572 0.000 0.000 0.000 0.428
#> GSM74367 1 0.1197 0.7236 0.952 0.000 0.000 0.048 0.000
#> GSM74377 1 0.3857 0.5602 0.688 0.000 0.000 0.000 0.312
#> GSM74378 1 0.4242 0.3930 0.572 0.000 0.000 0.000 0.428
#> GSM74379 1 0.3003 0.6617 0.812 0.000 0.000 0.000 0.188
#> GSM74380 1 0.3039 0.6598 0.808 0.000 0.000 0.000 0.192
#> GSM74381 1 0.4060 0.5034 0.640 0.000 0.000 0.000 0.360
#> GSM121357 5 0.7800 0.6643 0.072 0.304 0.228 0.000 0.396
#> GSM121361 5 0.7504 0.8302 0.204 0.252 0.068 0.000 0.476
#> GSM121363 5 0.7483 0.8322 0.200 0.252 0.068 0.000 0.480
#> GSM121368 5 0.7509 0.8336 0.196 0.252 0.072 0.000 0.480
#> GSM121369 5 0.7708 0.7119 0.200 0.104 0.216 0.000 0.480
#> GSM74368 1 0.1410 0.7194 0.940 0.000 0.000 0.060 0.000
#> GSM74369 1 0.1410 0.7194 0.940 0.000 0.000 0.060 0.000
#> GSM74370 1 0.1410 0.7194 0.940 0.000 0.000 0.060 0.000
#> GSM74371 1 0.4415 0.0272 0.552 0.000 0.000 0.444 0.004
#> GSM74372 1 0.1357 0.7242 0.948 0.000 0.000 0.048 0.004
#> GSM74373 1 0.4074 0.4981 0.636 0.000 0.000 0.000 0.364
#> GSM74374 1 0.1168 0.7244 0.960 0.000 0.000 0.032 0.008
#> GSM74375 1 0.2929 0.6754 0.820 0.000 0.000 0.000 0.180
#> GSM74376 1 0.4227 0.4085 0.580 0.000 0.000 0.000 0.420
#> GSM74405 1 0.4114 0.4810 0.624 0.000 0.000 0.000 0.376
#> GSM74351 4 0.4321 0.3991 0.396 0.000 0.000 0.600 0.004
#> GSM74352 1 0.4227 0.4085 0.580 0.000 0.000 0.000 0.420
#> GSM74353 1 0.1410 0.7194 0.940 0.000 0.000 0.060 0.000
#> GSM74354 1 0.0880 0.7240 0.968 0.000 0.000 0.032 0.000
#> GSM74355 1 0.4227 0.4085 0.580 0.000 0.000 0.000 0.420
#> GSM74382 4 0.4288 0.4252 0.384 0.000 0.000 0.612 0.004
#> GSM74383 1 0.1197 0.7236 0.952 0.000 0.000 0.048 0.000
#> GSM74384 1 0.4242 0.3930 0.572 0.000 0.000 0.000 0.428
#> GSM74385 1 0.4084 0.3563 0.668 0.000 0.000 0.328 0.004
#> GSM74386 1 0.1197 0.7236 0.952 0.000 0.000 0.048 0.000
#> GSM74395 1 0.1341 0.7210 0.944 0.000 0.000 0.056 0.000
#> GSM74396 1 0.1197 0.7236 0.952 0.000 0.000 0.048 0.000
#> GSM74397 1 0.3274 0.5603 0.780 0.000 0.000 0.220 0.000
#> GSM74398 1 0.2561 0.6848 0.856 0.000 0.000 0.000 0.144
#> GSM74399 1 0.3561 0.6101 0.740 0.000 0.000 0.000 0.260
#> GSM74400 1 0.2020 0.7093 0.900 0.000 0.000 0.000 0.100
#> GSM74401 1 0.2020 0.7093 0.900 0.000 0.000 0.000 0.100
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM74356 3 0.2113 0.7104 0.004 0.000 0.908 0.028 0.060 0.000
#> GSM74357 3 0.2364 0.6989 0.004 0.000 0.892 0.032 0.072 0.000
#> GSM74358 3 0.2364 0.6989 0.004 0.000 0.892 0.032 0.072 0.000
#> GSM74359 4 0.4789 -0.4750 0.016 0.000 0.024 0.512 0.448 0.000
#> GSM74360 4 0.4563 -0.4527 0.016 0.000 0.012 0.524 0.448 0.000
#> GSM74361 3 0.2173 0.7106 0.004 0.000 0.904 0.028 0.064 0.000
#> GSM74362 3 0.5306 0.0163 0.004 0.000 0.532 0.096 0.368 0.000
#> GSM74363 3 0.1562 0.7255 0.004 0.000 0.940 0.024 0.032 0.000
#> GSM74402 4 0.1700 0.5814 0.080 0.000 0.004 0.916 0.000 0.000
#> GSM74403 4 0.1970 0.5772 0.092 0.000 0.000 0.900 0.008 0.000
#> GSM74404 4 0.1970 0.5772 0.092 0.000 0.000 0.900 0.008 0.000
#> GSM74406 4 0.0405 0.5787 0.008 0.000 0.004 0.988 0.000 0.000
#> GSM74407 4 0.1901 0.5805 0.076 0.000 0.004 0.912 0.008 0.000
#> GSM74408 4 0.0862 0.5721 0.008 0.000 0.004 0.972 0.016 0.000
#> GSM74409 4 0.0951 0.5691 0.008 0.000 0.004 0.968 0.020 0.000
#> GSM74410 4 0.0837 0.5666 0.004 0.000 0.004 0.972 0.020 0.000
#> GSM119936 4 0.0767 0.5780 0.012 0.000 0.004 0.976 0.008 0.000
#> GSM119937 4 0.0964 0.5748 0.012 0.000 0.004 0.968 0.016 0.000
#> GSM74411 3 0.4364 0.7050 0.076 0.032 0.760 0.000 0.132 0.000
#> GSM74412 3 0.4364 0.7050 0.076 0.032 0.760 0.000 0.132 0.000
#> GSM74413 3 0.4364 0.7050 0.076 0.032 0.760 0.000 0.132 0.000
#> GSM74414 6 0.8584 0.2360 0.236 0.268 0.108 0.000 0.116 0.272
#> GSM74415 3 0.4045 0.7095 0.076 0.008 0.776 0.004 0.136 0.000
#> GSM121379 2 0.0291 0.9793 0.004 0.992 0.000 0.000 0.004 0.000
#> GSM121380 2 0.0291 0.9793 0.004 0.992 0.000 0.000 0.004 0.000
#> GSM121381 2 0.0291 0.9793 0.004 0.992 0.000 0.000 0.004 0.000
#> GSM121382 2 0.0146 0.9779 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM121383 2 0.0146 0.9779 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM121384 2 0.0291 0.9793 0.004 0.992 0.000 0.000 0.004 0.000
#> GSM121385 2 0.0291 0.9793 0.004 0.992 0.000 0.000 0.004 0.000
#> GSM121386 2 0.0291 0.9793 0.004 0.992 0.000 0.000 0.004 0.000
#> GSM121387 2 0.0146 0.9779 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM121388 2 0.1882 0.9399 0.020 0.928 0.024 0.000 0.028 0.000
#> GSM121389 2 0.1176 0.9595 0.020 0.956 0.000 0.000 0.024 0.000
#> GSM121390 2 0.0291 0.9793 0.004 0.992 0.000 0.000 0.004 0.000
#> GSM121391 2 0.0000 0.9787 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121392 2 0.0405 0.9769 0.008 0.988 0.000 0.000 0.004 0.000
#> GSM121393 2 0.1257 0.9574 0.020 0.952 0.000 0.000 0.028 0.000
#> GSM121394 2 0.0000 0.9787 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121395 2 0.1257 0.9574 0.020 0.952 0.000 0.000 0.028 0.000
#> GSM121396 2 0.3476 0.8132 0.024 0.816 0.132 0.000 0.028 0.000
#> GSM121397 2 0.0291 0.9793 0.004 0.992 0.000 0.000 0.004 0.000
#> GSM121398 2 0.0291 0.9793 0.004 0.992 0.000 0.000 0.004 0.000
#> GSM121399 2 0.0000 0.9787 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM74240 3 0.5285 0.6165 0.108 0.000 0.524 0.000 0.368 0.000
#> GSM74241 3 0.5277 0.6184 0.108 0.000 0.528 0.000 0.364 0.000
#> GSM74242 3 0.5257 0.6165 0.104 0.000 0.524 0.000 0.372 0.000
#> GSM74243 3 0.5257 0.6165 0.104 0.000 0.524 0.000 0.372 0.000
#> GSM74244 3 0.5249 0.6184 0.104 0.000 0.528 0.000 0.368 0.000
#> GSM74245 3 0.5249 0.6184 0.104 0.000 0.528 0.000 0.368 0.000
#> GSM74246 3 0.5312 0.6173 0.112 0.000 0.524 0.000 0.364 0.000
#> GSM74247 3 0.5312 0.6173 0.112 0.000 0.524 0.000 0.364 0.000
#> GSM74248 3 0.5257 0.6165 0.104 0.000 0.524 0.000 0.372 0.000
#> GSM74416 4 0.2003 0.5709 0.116 0.000 0.000 0.884 0.000 0.000
#> GSM74417 4 0.2003 0.5709 0.116 0.000 0.000 0.884 0.000 0.000
#> GSM74418 4 0.2003 0.5709 0.116 0.000 0.000 0.884 0.000 0.000
#> GSM74419 4 0.0692 0.5809 0.020 0.000 0.004 0.976 0.000 0.000
#> GSM121358 3 0.0767 0.7375 0.000 0.012 0.976 0.008 0.004 0.000
#> GSM121359 3 0.1675 0.7292 0.008 0.032 0.936 0.000 0.024 0.000
#> GSM121360 5 0.6060 0.5183 0.076 0.000 0.024 0.428 0.452 0.020
#> GSM121362 5 0.6844 0.5412 0.136 0.000 0.024 0.352 0.444 0.044
#> GSM121364 4 0.4789 -0.4750 0.016 0.000 0.024 0.512 0.448 0.000
#> GSM121365 3 0.0767 0.7375 0.000 0.012 0.976 0.008 0.004 0.000
#> GSM121366 3 0.0653 0.7382 0.000 0.012 0.980 0.004 0.004 0.000
#> GSM121367 3 0.0767 0.7375 0.000 0.012 0.976 0.008 0.004 0.000
#> GSM121370 3 0.0767 0.7375 0.000 0.012 0.976 0.008 0.004 0.000
#> GSM121371 3 0.0767 0.7375 0.000 0.012 0.976 0.008 0.004 0.000
#> GSM121372 3 0.1871 0.7264 0.016 0.032 0.928 0.000 0.024 0.000
#> GSM121373 4 0.4789 -0.4750 0.016 0.000 0.024 0.512 0.448 0.000
#> GSM121374 4 0.4789 -0.4750 0.016 0.000 0.024 0.512 0.448 0.000
#> GSM121407 3 0.2777 0.7094 0.044 0.032 0.880 0.000 0.044 0.000
#> GSM74387 3 0.7467 0.1556 0.268 0.004 0.388 0.000 0.136 0.204
#> GSM74388 6 0.8022 0.3845 0.256 0.208 0.052 0.000 0.108 0.376
#> GSM74389 4 0.5828 -0.5916 0.004 0.000 0.160 0.428 0.408 0.000
#> GSM74390 6 0.5011 -0.1865 0.420 0.000 0.000 0.000 0.072 0.508
#> GSM74391 4 0.2313 0.4745 0.012 0.000 0.004 0.884 0.100 0.000
#> GSM74392 4 0.4460 -0.3999 0.004 0.000 0.024 0.568 0.404 0.000
#> GSM74393 5 0.6114 0.4250 0.004 0.000 0.236 0.348 0.412 0.000
#> GSM74394 6 0.8331 0.3221 0.272 0.096 0.148 0.000 0.128 0.356
#> GSM74239 1 0.4750 0.8236 0.544 0.000 0.000 0.052 0.000 0.404
#> GSM74364 1 0.4983 0.7721 0.564 0.000 0.000 0.080 0.000 0.356
#> GSM74365 6 0.3867 -0.8553 0.488 0.000 0.000 0.000 0.000 0.512
#> GSM74366 6 0.3037 0.4895 0.176 0.000 0.000 0.000 0.016 0.808
#> GSM74367 1 0.3998 0.8745 0.504 0.000 0.000 0.004 0.000 0.492
#> GSM74377 6 0.0937 0.3025 0.040 0.000 0.000 0.000 0.000 0.960
#> GSM74378 6 0.2669 0.4862 0.156 0.000 0.000 0.000 0.008 0.836
#> GSM74379 6 0.3023 -0.2260 0.232 0.000 0.000 0.000 0.000 0.768
#> GSM74380 6 0.2762 -0.1243 0.196 0.000 0.000 0.000 0.000 0.804
#> GSM74381 6 0.0146 0.3572 0.004 0.000 0.000 0.000 0.000 0.996
#> GSM121357 6 0.8688 0.2957 0.248 0.224 0.140 0.000 0.112 0.276
#> GSM121361 6 0.8184 0.3756 0.256 0.208 0.060 0.000 0.120 0.356
#> GSM121363 6 0.8184 0.3756 0.256 0.208 0.060 0.000 0.120 0.356
#> GSM121368 6 0.8184 0.3756 0.256 0.208 0.060 0.000 0.120 0.356
#> GSM121369 6 0.8259 0.3069 0.264 0.072 0.168 0.000 0.136 0.360
#> GSM74368 1 0.4225 0.8772 0.508 0.000 0.000 0.008 0.004 0.480
#> GSM74369 1 0.4225 0.8772 0.508 0.000 0.000 0.008 0.004 0.480
#> GSM74370 1 0.4224 0.8788 0.512 0.000 0.000 0.008 0.004 0.476
#> GSM74371 1 0.5454 0.5341 0.572 0.000 0.000 0.236 0.000 0.192
#> GSM74372 1 0.4491 0.8698 0.500 0.000 0.000 0.008 0.016 0.476
#> GSM74373 6 0.0520 0.3511 0.008 0.000 0.000 0.000 0.008 0.984
#> GSM74374 1 0.4264 0.8674 0.496 0.000 0.000 0.000 0.016 0.488
#> GSM74375 6 0.3374 -0.1895 0.208 0.000 0.000 0.000 0.020 0.772
#> GSM74376 6 0.2631 0.4853 0.152 0.000 0.000 0.000 0.008 0.840
#> GSM74405 6 0.0000 0.3619 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM74351 4 0.4660 0.1924 0.416 0.000 0.000 0.540 0.000 0.044
#> GSM74352 6 0.2631 0.4853 0.152 0.000 0.000 0.000 0.008 0.840
#> GSM74353 1 0.4390 0.8782 0.508 0.000 0.000 0.016 0.004 0.472
#> GSM74354 1 0.3868 0.8741 0.508 0.000 0.000 0.000 0.000 0.492
#> GSM74355 6 0.2553 0.4815 0.144 0.000 0.000 0.000 0.008 0.848
#> GSM74382 4 0.4705 0.0430 0.472 0.000 0.000 0.484 0.000 0.044
#> GSM74383 1 0.3996 0.8791 0.512 0.000 0.000 0.004 0.000 0.484
#> GSM74384 6 0.3071 0.4896 0.180 0.000 0.000 0.000 0.016 0.804
#> GSM74385 1 0.5420 0.6299 0.572 0.000 0.000 0.172 0.000 0.256
#> GSM74386 1 0.3867 0.8763 0.512 0.000 0.000 0.000 0.000 0.488
#> GSM74395 1 0.3996 0.8793 0.512 0.000 0.000 0.004 0.000 0.484
#> GSM74396 1 0.3997 0.8781 0.508 0.000 0.000 0.004 0.000 0.488
#> GSM74397 1 0.5071 0.7908 0.540 0.000 0.000 0.084 0.000 0.376
#> GSM74398 6 0.3373 -0.3131 0.248 0.000 0.000 0.000 0.008 0.744
#> GSM74399 6 0.1644 0.2272 0.076 0.000 0.000 0.000 0.004 0.920
#> GSM74400 6 0.4567 -0.5172 0.332 0.000 0.000 0.000 0.052 0.616
#> GSM74401 6 0.4567 -0.5172 0.332 0.000 0.000 0.000 0.052 0.616
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 121 1.64e-11 2
#> SD:kmeans 104 2.49e-24 3
#> SD:kmeans 110 2.30e-33 4
#> SD:kmeans 105 3.35e-37 5
#> SD:kmeans 81 6.33e-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", "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 21168 rows and 121 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 0.874 0.952 0.972 0.5028 0.497 0.497
#> 3 3 0.751 0.872 0.935 0.3223 0.729 0.507
#> 4 4 0.829 0.900 0.944 0.1292 0.844 0.576
#> 5 5 0.784 0.799 0.884 0.0434 0.944 0.786
#> 6 6 0.778 0.626 0.752 0.0436 0.921 0.663
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
#> GSM74356 2 0.2778 0.946 0.048 0.952
#> GSM74357 2 0.6438 0.846 0.164 0.836
#> GSM74358 2 0.6438 0.846 0.164 0.836
#> GSM74359 1 0.0000 0.979 1.000 0.000
#> GSM74360 1 0.0000 0.979 1.000 0.000
#> GSM74361 2 0.2778 0.946 0.048 0.952
#> GSM74362 2 0.6438 0.846 0.164 0.836
#> GSM74363 2 0.2778 0.946 0.048 0.952
#> GSM74402 1 0.0000 0.979 1.000 0.000
#> GSM74403 1 0.0000 0.979 1.000 0.000
#> GSM74404 1 0.0000 0.979 1.000 0.000
#> GSM74406 1 0.0000 0.979 1.000 0.000
#> GSM74407 1 0.0000 0.979 1.000 0.000
#> GSM74408 1 0.0000 0.979 1.000 0.000
#> GSM74409 1 0.0000 0.979 1.000 0.000
#> GSM74410 1 0.0000 0.979 1.000 0.000
#> GSM119936 1 0.0000 0.979 1.000 0.000
#> GSM119937 1 0.0000 0.979 1.000 0.000
#> GSM74411 2 0.0000 0.964 0.000 1.000
#> GSM74412 2 0.0000 0.964 0.000 1.000
#> GSM74413 2 0.0000 0.964 0.000 1.000
#> GSM74414 2 0.0000 0.964 0.000 1.000
#> GSM74415 2 0.0000 0.964 0.000 1.000
#> GSM121379 2 0.0000 0.964 0.000 1.000
#> GSM121380 2 0.0000 0.964 0.000 1.000
#> GSM121381 2 0.0000 0.964 0.000 1.000
#> GSM121382 2 0.0000 0.964 0.000 1.000
#> GSM121383 2 0.0000 0.964 0.000 1.000
#> GSM121384 2 0.0000 0.964 0.000 1.000
#> GSM121385 2 0.0000 0.964 0.000 1.000
#> GSM121386 2 0.0000 0.964 0.000 1.000
#> GSM121387 2 0.0000 0.964 0.000 1.000
#> GSM121388 2 0.0000 0.964 0.000 1.000
#> GSM121389 2 0.0000 0.964 0.000 1.000
#> GSM121390 2 0.0000 0.964 0.000 1.000
#> GSM121391 2 0.0000 0.964 0.000 1.000
#> GSM121392 2 0.0000 0.964 0.000 1.000
#> GSM121393 2 0.0000 0.964 0.000 1.000
#> GSM121394 2 0.0000 0.964 0.000 1.000
#> GSM121395 2 0.0000 0.964 0.000 1.000
#> GSM121396 2 0.0000 0.964 0.000 1.000
#> GSM121397 2 0.0000 0.964 0.000 1.000
#> GSM121398 2 0.0000 0.964 0.000 1.000
#> GSM121399 2 0.0000 0.964 0.000 1.000
#> GSM74240 2 0.6438 0.846 0.164 0.836
#> GSM74241 2 0.2948 0.944 0.052 0.948
#> GSM74242 2 0.8144 0.729 0.252 0.748
#> GSM74243 2 0.8144 0.729 0.252 0.748
#> GSM74244 2 0.2948 0.944 0.052 0.948
#> GSM74245 2 0.6343 0.850 0.160 0.840
#> GSM74246 2 0.2948 0.944 0.052 0.948
#> GSM74247 2 0.2948 0.944 0.052 0.948
#> GSM74248 2 0.6438 0.846 0.164 0.836
#> GSM74416 1 0.0000 0.979 1.000 0.000
#> GSM74417 1 0.0000 0.979 1.000 0.000
#> GSM74418 1 0.0000 0.979 1.000 0.000
#> GSM74419 1 0.0000 0.979 1.000 0.000
#> GSM121358 2 0.2603 0.947 0.044 0.956
#> GSM121359 2 0.0000 0.964 0.000 1.000
#> GSM121360 1 0.0000 0.979 1.000 0.000
#> GSM121362 1 0.0000 0.979 1.000 0.000
#> GSM121364 1 0.0000 0.979 1.000 0.000
#> GSM121365 2 0.2603 0.947 0.044 0.956
#> GSM121366 2 0.2423 0.949 0.040 0.960
#> GSM121367 2 0.2603 0.947 0.044 0.956
#> GSM121370 2 0.2603 0.947 0.044 0.956
#> GSM121371 2 0.2603 0.947 0.044 0.956
#> GSM121372 2 0.0000 0.964 0.000 1.000
#> GSM121373 1 0.0000 0.979 1.000 0.000
#> GSM121374 1 0.0000 0.979 1.000 0.000
#> GSM121407 2 0.0000 0.964 0.000 1.000
#> GSM74387 2 0.0000 0.964 0.000 1.000
#> GSM74388 2 0.0000 0.964 0.000 1.000
#> GSM74389 1 0.0376 0.977 0.996 0.004
#> GSM74390 1 0.1184 0.971 0.984 0.016
#> GSM74391 1 0.0000 0.979 1.000 0.000
#> GSM74392 1 0.0000 0.979 1.000 0.000
#> GSM74393 1 0.0376 0.977 0.996 0.004
#> GSM74394 2 0.0000 0.964 0.000 1.000
#> GSM74239 1 0.0000 0.979 1.000 0.000
#> GSM74364 1 0.0000 0.979 1.000 0.000
#> GSM74365 1 0.0000 0.979 1.000 0.000
#> GSM74366 1 0.6343 0.839 0.840 0.160
#> GSM74367 1 0.0000 0.979 1.000 0.000
#> GSM74377 1 0.2603 0.954 0.956 0.044
#> GSM74378 1 0.6048 0.853 0.852 0.148
#> GSM74379 1 0.2423 0.956 0.960 0.040
#> GSM74380 1 0.2423 0.956 0.960 0.040
#> GSM74381 1 0.2603 0.954 0.956 0.044
#> GSM121357 2 0.0000 0.964 0.000 1.000
#> GSM121361 2 0.0000 0.964 0.000 1.000
#> GSM121363 2 0.0000 0.964 0.000 1.000
#> GSM121368 2 0.0000 0.964 0.000 1.000
#> GSM121369 2 0.0000 0.964 0.000 1.000
#> GSM74368 1 0.0000 0.979 1.000 0.000
#> GSM74369 1 0.0000 0.979 1.000 0.000
#> GSM74370 1 0.0000 0.979 1.000 0.000
#> GSM74371 1 0.0000 0.979 1.000 0.000
#> GSM74372 1 0.0000 0.979 1.000 0.000
#> GSM74373 1 0.2603 0.954 0.956 0.044
#> GSM74374 1 0.0000 0.979 1.000 0.000
#> GSM74375 1 0.2603 0.954 0.956 0.044
#> GSM74376 1 0.2603 0.954 0.956 0.044
#> GSM74405 1 0.2603 0.954 0.956 0.044
#> GSM74351 1 0.0000 0.979 1.000 0.000
#> GSM74352 1 0.6438 0.834 0.836 0.164
#> GSM74353 1 0.0000 0.979 1.000 0.000
#> GSM74354 1 0.0000 0.979 1.000 0.000
#> GSM74355 1 0.5842 0.863 0.860 0.140
#> GSM74382 1 0.0000 0.979 1.000 0.000
#> GSM74383 1 0.0000 0.979 1.000 0.000
#> GSM74384 1 0.6438 0.834 0.836 0.164
#> GSM74385 1 0.0000 0.979 1.000 0.000
#> GSM74386 1 0.0000 0.979 1.000 0.000
#> GSM74395 1 0.0000 0.979 1.000 0.000
#> GSM74396 1 0.0000 0.979 1.000 0.000
#> GSM74397 1 0.0000 0.979 1.000 0.000
#> GSM74398 1 0.0000 0.979 1.000 0.000
#> GSM74399 1 0.2603 0.954 0.956 0.044
#> GSM74400 1 0.2603 0.954 0.956 0.044
#> GSM74401 1 0.2603 0.954 0.956 0.044
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM74356 3 0.0000 0.882 0.000 0.000 1.000
#> GSM74357 3 0.0000 0.882 0.000 0.000 1.000
#> GSM74358 3 0.0000 0.882 0.000 0.000 1.000
#> GSM74359 3 0.3340 0.846 0.120 0.000 0.880
#> GSM74360 3 0.4504 0.797 0.196 0.000 0.804
#> GSM74361 3 0.0000 0.882 0.000 0.000 1.000
#> GSM74362 3 0.0000 0.882 0.000 0.000 1.000
#> GSM74363 3 0.0000 0.882 0.000 0.000 1.000
#> GSM74402 1 0.5397 0.591 0.720 0.000 0.280
#> GSM74403 1 0.5058 0.659 0.756 0.000 0.244
#> GSM74404 1 0.5058 0.659 0.756 0.000 0.244
#> GSM74406 3 0.5291 0.715 0.268 0.000 0.732
#> GSM74407 1 0.5497 0.567 0.708 0.000 0.292
#> GSM74408 3 0.5016 0.753 0.240 0.000 0.760
#> GSM74409 3 0.4702 0.783 0.212 0.000 0.788
#> GSM74410 3 0.4121 0.819 0.168 0.000 0.832
#> GSM119936 3 0.5291 0.715 0.268 0.000 0.732
#> GSM119937 3 0.6204 0.373 0.424 0.000 0.576
#> GSM74411 2 0.3941 0.844 0.000 0.844 0.156
#> GSM74412 2 0.2625 0.908 0.000 0.916 0.084
#> GSM74413 2 0.3941 0.844 0.000 0.844 0.156
#> GSM74414 2 0.0000 0.965 0.000 1.000 0.000
#> GSM74415 2 0.6244 0.319 0.000 0.560 0.440
#> GSM121379 2 0.0000 0.965 0.000 1.000 0.000
#> GSM121380 2 0.0000 0.965 0.000 1.000 0.000
#> GSM121381 2 0.0000 0.965 0.000 1.000 0.000
#> GSM121382 2 0.0000 0.965 0.000 1.000 0.000
#> GSM121383 2 0.0000 0.965 0.000 1.000 0.000
#> GSM121384 2 0.0000 0.965 0.000 1.000 0.000
#> GSM121385 2 0.0000 0.965 0.000 1.000 0.000
#> GSM121386 2 0.0000 0.965 0.000 1.000 0.000
#> GSM121387 2 0.0000 0.965 0.000 1.000 0.000
#> GSM121388 2 0.0000 0.965 0.000 1.000 0.000
#> GSM121389 2 0.0000 0.965 0.000 1.000 0.000
#> GSM121390 2 0.0000 0.965 0.000 1.000 0.000
#> GSM121391 2 0.0000 0.965 0.000 1.000 0.000
#> GSM121392 2 0.0000 0.965 0.000 1.000 0.000
#> GSM121393 2 0.0000 0.965 0.000 1.000 0.000
#> GSM121394 2 0.0000 0.965 0.000 1.000 0.000
#> GSM121395 2 0.0000 0.965 0.000 1.000 0.000
#> GSM121396 2 0.0000 0.965 0.000 1.000 0.000
#> GSM121397 2 0.0000 0.965 0.000 1.000 0.000
#> GSM121398 2 0.0000 0.965 0.000 1.000 0.000
#> GSM121399 2 0.0000 0.965 0.000 1.000 0.000
#> GSM74240 3 0.0000 0.882 0.000 0.000 1.000
#> GSM74241 3 0.0747 0.877 0.000 0.016 0.984
#> GSM74242 3 0.0000 0.882 0.000 0.000 1.000
#> GSM74243 3 0.0000 0.882 0.000 0.000 1.000
#> GSM74244 3 0.0592 0.879 0.000 0.012 0.988
#> GSM74245 3 0.0000 0.882 0.000 0.000 1.000
#> GSM74246 3 0.1163 0.871 0.000 0.028 0.972
#> GSM74247 3 0.1643 0.863 0.000 0.044 0.956
#> GSM74248 3 0.0000 0.882 0.000 0.000 1.000
#> GSM74416 1 0.5058 0.659 0.756 0.000 0.244
#> GSM74417 1 0.5058 0.659 0.756 0.000 0.244
#> GSM74418 1 0.5016 0.665 0.760 0.000 0.240
#> GSM74419 3 0.5178 0.732 0.256 0.000 0.744
#> GSM121358 3 0.2066 0.853 0.000 0.060 0.940
#> GSM121359 2 0.3941 0.844 0.000 0.844 0.156
#> GSM121360 3 0.4842 0.776 0.224 0.000 0.776
#> GSM121362 3 0.5968 0.550 0.364 0.000 0.636
#> GSM121364 3 0.3941 0.827 0.156 0.000 0.844
#> GSM121365 3 0.2066 0.853 0.000 0.060 0.940
#> GSM121366 3 0.2165 0.850 0.000 0.064 0.936
#> GSM121367 3 0.2066 0.853 0.000 0.060 0.940
#> GSM121370 3 0.2066 0.853 0.000 0.060 0.940
#> GSM121371 3 0.2066 0.853 0.000 0.060 0.940
#> GSM121372 2 0.3941 0.844 0.000 0.844 0.156
#> GSM121373 3 0.4002 0.824 0.160 0.000 0.840
#> GSM121374 3 0.3941 0.827 0.156 0.000 0.844
#> GSM121407 2 0.1529 0.940 0.000 0.960 0.040
#> GSM74387 2 0.2625 0.908 0.000 0.916 0.084
#> GSM74388 2 0.0000 0.965 0.000 1.000 0.000
#> GSM74389 3 0.0424 0.881 0.008 0.000 0.992
#> GSM74390 1 0.0000 0.931 1.000 0.000 0.000
#> GSM74391 3 0.4931 0.762 0.232 0.000 0.768
#> GSM74392 3 0.3619 0.838 0.136 0.000 0.864
#> GSM74393 3 0.0000 0.882 0.000 0.000 1.000
#> GSM74394 2 0.0000 0.965 0.000 1.000 0.000
#> GSM74239 1 0.0000 0.931 1.000 0.000 0.000
#> GSM74364 1 0.0000 0.931 1.000 0.000 0.000
#> GSM74365 1 0.0000 0.931 1.000 0.000 0.000
#> GSM74366 1 0.4750 0.706 0.784 0.216 0.000
#> GSM74367 1 0.0000 0.931 1.000 0.000 0.000
#> GSM74377 1 0.0237 0.928 0.996 0.004 0.000
#> GSM74378 1 0.3116 0.831 0.892 0.108 0.000
#> GSM74379 1 0.0000 0.931 1.000 0.000 0.000
#> GSM74380 1 0.0000 0.931 1.000 0.000 0.000
#> GSM74381 1 0.0237 0.928 0.996 0.004 0.000
#> GSM121357 2 0.0000 0.965 0.000 1.000 0.000
#> GSM121361 2 0.0000 0.965 0.000 1.000 0.000
#> GSM121363 2 0.0000 0.965 0.000 1.000 0.000
#> GSM121368 2 0.0000 0.965 0.000 1.000 0.000
#> GSM121369 2 0.0000 0.965 0.000 1.000 0.000
#> GSM74368 1 0.0000 0.931 1.000 0.000 0.000
#> GSM74369 1 0.0000 0.931 1.000 0.000 0.000
#> GSM74370 1 0.0000 0.931 1.000 0.000 0.000
#> GSM74371 1 0.0000 0.931 1.000 0.000 0.000
#> GSM74372 1 0.0000 0.931 1.000 0.000 0.000
#> GSM74373 1 0.0000 0.931 1.000 0.000 0.000
#> GSM74374 1 0.0000 0.931 1.000 0.000 0.000
#> GSM74375 1 0.0000 0.931 1.000 0.000 0.000
#> GSM74376 1 0.0000 0.931 1.000 0.000 0.000
#> GSM74405 1 0.0000 0.931 1.000 0.000 0.000
#> GSM74351 1 0.0000 0.931 1.000 0.000 0.000
#> GSM74352 1 0.3941 0.781 0.844 0.156 0.000
#> GSM74353 1 0.0000 0.931 1.000 0.000 0.000
#> GSM74354 1 0.0000 0.931 1.000 0.000 0.000
#> GSM74355 1 0.1031 0.911 0.976 0.024 0.000
#> GSM74382 1 0.0000 0.931 1.000 0.000 0.000
#> GSM74383 1 0.0000 0.931 1.000 0.000 0.000
#> GSM74384 1 0.5098 0.656 0.752 0.248 0.000
#> GSM74385 1 0.0000 0.931 1.000 0.000 0.000
#> GSM74386 1 0.0000 0.931 1.000 0.000 0.000
#> GSM74395 1 0.0000 0.931 1.000 0.000 0.000
#> GSM74396 1 0.0000 0.931 1.000 0.000 0.000
#> GSM74397 1 0.0000 0.931 1.000 0.000 0.000
#> GSM74398 1 0.0000 0.931 1.000 0.000 0.000
#> GSM74399 1 0.0000 0.931 1.000 0.000 0.000
#> GSM74400 1 0.0000 0.931 1.000 0.000 0.000
#> GSM74401 1 0.0000 0.931 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM74356 3 0.0469 0.968 0.000 0.000 0.988 0.012
#> GSM74357 3 0.0469 0.968 0.000 0.000 0.988 0.012
#> GSM74358 3 0.0469 0.968 0.000 0.000 0.988 0.012
#> GSM74359 4 0.0000 0.964 0.000 0.000 0.000 1.000
#> GSM74360 4 0.0000 0.964 0.000 0.000 0.000 1.000
#> GSM74361 3 0.0592 0.966 0.000 0.000 0.984 0.016
#> GSM74362 3 0.4103 0.655 0.000 0.000 0.744 0.256
#> GSM74363 3 0.0469 0.968 0.000 0.000 0.988 0.012
#> GSM74402 4 0.0188 0.963 0.004 0.000 0.000 0.996
#> GSM74403 4 0.0336 0.961 0.008 0.000 0.000 0.992
#> GSM74404 4 0.0336 0.961 0.008 0.000 0.000 0.992
#> GSM74406 4 0.0000 0.964 0.000 0.000 0.000 1.000
#> GSM74407 4 0.0188 0.963 0.004 0.000 0.000 0.996
#> GSM74408 4 0.0000 0.964 0.000 0.000 0.000 1.000
#> GSM74409 4 0.0000 0.964 0.000 0.000 0.000 1.000
#> GSM74410 4 0.0000 0.964 0.000 0.000 0.000 1.000
#> GSM119936 4 0.0000 0.964 0.000 0.000 0.000 1.000
#> GSM119937 4 0.0000 0.964 0.000 0.000 0.000 1.000
#> GSM74411 3 0.2281 0.888 0.000 0.096 0.904 0.000
#> GSM74412 2 0.4605 0.511 0.000 0.664 0.336 0.000
#> GSM74413 3 0.2589 0.864 0.000 0.116 0.884 0.000
#> GSM74414 2 0.0000 0.937 0.000 1.000 0.000 0.000
#> GSM74415 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> GSM121379 2 0.0000 0.937 0.000 1.000 0.000 0.000
#> GSM121380 2 0.0000 0.937 0.000 1.000 0.000 0.000
#> GSM121381 2 0.0000 0.937 0.000 1.000 0.000 0.000
#> GSM121382 2 0.0000 0.937 0.000 1.000 0.000 0.000
#> GSM121383 2 0.0000 0.937 0.000 1.000 0.000 0.000
#> GSM121384 2 0.0000 0.937 0.000 1.000 0.000 0.000
#> GSM121385 2 0.0000 0.937 0.000 1.000 0.000 0.000
#> GSM121386 2 0.0000 0.937 0.000 1.000 0.000 0.000
#> GSM121387 2 0.0000 0.937 0.000 1.000 0.000 0.000
#> GSM121388 2 0.0000 0.937 0.000 1.000 0.000 0.000
#> GSM121389 2 0.0000 0.937 0.000 1.000 0.000 0.000
#> GSM121390 2 0.0000 0.937 0.000 1.000 0.000 0.000
#> GSM121391 2 0.0000 0.937 0.000 1.000 0.000 0.000
#> GSM121392 2 0.0000 0.937 0.000 1.000 0.000 0.000
#> GSM121393 2 0.0000 0.937 0.000 1.000 0.000 0.000
#> GSM121394 2 0.0000 0.937 0.000 1.000 0.000 0.000
#> GSM121395 2 0.0000 0.937 0.000 1.000 0.000 0.000
#> GSM121396 2 0.0000 0.937 0.000 1.000 0.000 0.000
#> GSM121397 2 0.0000 0.937 0.000 1.000 0.000 0.000
#> GSM121398 2 0.0000 0.937 0.000 1.000 0.000 0.000
#> GSM121399 2 0.0000 0.937 0.000 1.000 0.000 0.000
#> GSM74240 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> GSM74241 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> GSM74242 3 0.0188 0.969 0.000 0.000 0.996 0.004
#> GSM74243 3 0.0188 0.969 0.000 0.000 0.996 0.004
#> GSM74244 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> GSM74245 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> GSM74246 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> GSM74247 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> GSM74248 3 0.0188 0.969 0.000 0.000 0.996 0.004
#> GSM74416 4 0.0336 0.961 0.008 0.000 0.000 0.992
#> GSM74417 4 0.0188 0.963 0.004 0.000 0.000 0.996
#> GSM74418 4 0.0336 0.961 0.008 0.000 0.000 0.992
#> GSM74419 4 0.0000 0.964 0.000 0.000 0.000 1.000
#> GSM121358 3 0.0524 0.969 0.000 0.004 0.988 0.008
#> GSM121359 3 0.1940 0.914 0.000 0.076 0.924 0.000
#> GSM121360 4 0.0188 0.962 0.004 0.000 0.000 0.996
#> GSM121362 4 0.2081 0.883 0.084 0.000 0.000 0.916
#> GSM121364 4 0.0000 0.964 0.000 0.000 0.000 1.000
#> GSM121365 3 0.0524 0.969 0.000 0.004 0.988 0.008
#> GSM121366 3 0.0524 0.969 0.000 0.004 0.988 0.008
#> GSM121367 3 0.0524 0.969 0.000 0.004 0.988 0.008
#> GSM121370 3 0.0524 0.969 0.000 0.004 0.988 0.008
#> GSM121371 3 0.0524 0.969 0.000 0.004 0.988 0.008
#> GSM121372 3 0.1940 0.914 0.000 0.076 0.924 0.000
#> GSM121373 4 0.0000 0.964 0.000 0.000 0.000 1.000
#> GSM121374 4 0.0000 0.964 0.000 0.000 0.000 1.000
#> GSM121407 2 0.4250 0.621 0.000 0.724 0.276 0.000
#> GSM74387 2 0.6292 0.252 0.060 0.524 0.416 0.000
#> GSM74388 2 0.3157 0.853 0.144 0.852 0.004 0.000
#> GSM74389 4 0.3528 0.759 0.000 0.000 0.192 0.808
#> GSM74390 1 0.0336 0.902 0.992 0.000 0.000 0.008
#> GSM74391 4 0.0000 0.964 0.000 0.000 0.000 1.000
#> GSM74392 4 0.0000 0.964 0.000 0.000 0.000 1.000
#> GSM74393 4 0.4250 0.618 0.000 0.000 0.276 0.724
#> GSM74394 2 0.3157 0.853 0.144 0.852 0.004 0.000
#> GSM74239 1 0.3172 0.862 0.840 0.000 0.000 0.160
#> GSM74364 1 0.3400 0.847 0.820 0.000 0.000 0.180
#> GSM74365 1 0.0707 0.904 0.980 0.000 0.000 0.020
#> GSM74366 1 0.0000 0.904 1.000 0.000 0.000 0.000
#> GSM74367 1 0.2647 0.882 0.880 0.000 0.000 0.120
#> GSM74377 1 0.0000 0.904 1.000 0.000 0.000 0.000
#> GSM74378 1 0.0000 0.904 1.000 0.000 0.000 0.000
#> GSM74379 1 0.0000 0.904 1.000 0.000 0.000 0.000
#> GSM74380 1 0.0000 0.904 1.000 0.000 0.000 0.000
#> GSM74381 1 0.0000 0.904 1.000 0.000 0.000 0.000
#> GSM121357 2 0.0000 0.937 0.000 1.000 0.000 0.000
#> GSM121361 2 0.3157 0.853 0.144 0.852 0.004 0.000
#> GSM121363 2 0.3052 0.859 0.136 0.860 0.004 0.000
#> GSM121368 2 0.3052 0.859 0.136 0.860 0.004 0.000
#> GSM121369 2 0.3157 0.853 0.144 0.852 0.004 0.000
#> GSM74368 1 0.3569 0.835 0.804 0.000 0.000 0.196
#> GSM74369 1 0.3074 0.868 0.848 0.000 0.000 0.152
#> GSM74370 1 0.3907 0.797 0.768 0.000 0.000 0.232
#> GSM74371 1 0.4830 0.523 0.608 0.000 0.000 0.392
#> GSM74372 1 0.3528 0.839 0.808 0.000 0.000 0.192
#> GSM74373 1 0.0000 0.904 1.000 0.000 0.000 0.000
#> GSM74374 1 0.1940 0.896 0.924 0.000 0.000 0.076
#> GSM74375 1 0.0469 0.905 0.988 0.000 0.000 0.012
#> GSM74376 1 0.0000 0.904 1.000 0.000 0.000 0.000
#> GSM74405 1 0.0000 0.904 1.000 0.000 0.000 0.000
#> GSM74351 4 0.2760 0.826 0.128 0.000 0.000 0.872
#> GSM74352 1 0.0000 0.904 1.000 0.000 0.000 0.000
#> GSM74353 1 0.3356 0.852 0.824 0.000 0.000 0.176
#> GSM74354 1 0.2281 0.891 0.904 0.000 0.000 0.096
#> GSM74355 1 0.0000 0.904 1.000 0.000 0.000 0.000
#> GSM74382 4 0.2345 0.864 0.100 0.000 0.000 0.900
#> GSM74383 1 0.2647 0.882 0.880 0.000 0.000 0.120
#> GSM74384 1 0.0000 0.904 1.000 0.000 0.000 0.000
#> GSM74385 1 0.4008 0.780 0.756 0.000 0.000 0.244
#> GSM74386 1 0.2921 0.875 0.860 0.000 0.000 0.140
#> GSM74395 1 0.3400 0.849 0.820 0.000 0.000 0.180
#> GSM74396 1 0.2704 0.882 0.876 0.000 0.000 0.124
#> GSM74397 1 0.4817 0.525 0.612 0.000 0.000 0.388
#> GSM74398 1 0.0000 0.904 1.000 0.000 0.000 0.000
#> GSM74399 1 0.0000 0.904 1.000 0.000 0.000 0.000
#> GSM74400 1 0.0921 0.904 0.972 0.000 0.000 0.028
#> GSM74401 1 0.0817 0.904 0.976 0.000 0.000 0.024
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM74356 3 0.0566 0.8478 0.000 0.000 0.984 0.004 0.012
#> GSM74357 3 0.0693 0.8457 0.000 0.000 0.980 0.008 0.012
#> GSM74358 3 0.0693 0.8457 0.000 0.000 0.980 0.008 0.012
#> GSM74359 4 0.2248 0.8525 0.000 0.000 0.012 0.900 0.088
#> GSM74360 4 0.2189 0.8545 0.000 0.000 0.012 0.904 0.084
#> GSM74361 3 0.1041 0.8373 0.000 0.000 0.964 0.004 0.032
#> GSM74362 3 0.4262 0.6271 0.000 0.000 0.776 0.124 0.100
#> GSM74363 3 0.0162 0.8517 0.000 0.000 0.996 0.004 0.000
#> GSM74402 4 0.0510 0.8706 0.016 0.000 0.000 0.984 0.000
#> GSM74403 4 0.0609 0.8694 0.020 0.000 0.000 0.980 0.000
#> GSM74404 4 0.0609 0.8694 0.020 0.000 0.000 0.980 0.000
#> GSM74406 4 0.0693 0.8752 0.000 0.000 0.008 0.980 0.012
#> GSM74407 4 0.0404 0.8715 0.012 0.000 0.000 0.988 0.000
#> GSM74408 4 0.0960 0.8746 0.004 0.000 0.008 0.972 0.016
#> GSM74409 4 0.0798 0.8738 0.000 0.000 0.008 0.976 0.016
#> GSM74410 4 0.1106 0.8719 0.000 0.000 0.012 0.964 0.024
#> GSM119936 4 0.0451 0.8748 0.000 0.000 0.008 0.988 0.004
#> GSM119937 4 0.0613 0.8746 0.004 0.000 0.008 0.984 0.004
#> GSM74411 3 0.5019 0.3982 0.000 0.052 0.632 0.000 0.316
#> GSM74412 3 0.6413 0.2218 0.000 0.224 0.508 0.000 0.268
#> GSM74413 3 0.5180 0.3958 0.000 0.064 0.624 0.000 0.312
#> GSM74414 2 0.0609 0.9279 0.000 0.980 0.000 0.000 0.020
#> GSM74415 3 0.3949 0.3907 0.000 0.000 0.668 0.000 0.332
#> GSM121379 2 0.0000 0.9379 0.000 1.000 0.000 0.000 0.000
#> GSM121380 2 0.0000 0.9379 0.000 1.000 0.000 0.000 0.000
#> GSM121381 2 0.0000 0.9379 0.000 1.000 0.000 0.000 0.000
#> GSM121382 2 0.0000 0.9379 0.000 1.000 0.000 0.000 0.000
#> GSM121383 2 0.0000 0.9379 0.000 1.000 0.000 0.000 0.000
#> GSM121384 2 0.0000 0.9379 0.000 1.000 0.000 0.000 0.000
#> GSM121385 2 0.0000 0.9379 0.000 1.000 0.000 0.000 0.000
#> GSM121386 2 0.0000 0.9379 0.000 1.000 0.000 0.000 0.000
#> GSM121387 2 0.0000 0.9379 0.000 1.000 0.000 0.000 0.000
#> GSM121388 2 0.0000 0.9379 0.000 1.000 0.000 0.000 0.000
#> GSM121389 2 0.0000 0.9379 0.000 1.000 0.000 0.000 0.000
#> GSM121390 2 0.0000 0.9379 0.000 1.000 0.000 0.000 0.000
#> GSM121391 2 0.0000 0.9379 0.000 1.000 0.000 0.000 0.000
#> GSM121392 2 0.0000 0.9379 0.000 1.000 0.000 0.000 0.000
#> GSM121393 2 0.0000 0.9379 0.000 1.000 0.000 0.000 0.000
#> GSM121394 2 0.0000 0.9379 0.000 1.000 0.000 0.000 0.000
#> GSM121395 2 0.0000 0.9379 0.000 1.000 0.000 0.000 0.000
#> GSM121396 2 0.0404 0.9287 0.000 0.988 0.012 0.000 0.000
#> GSM121397 2 0.0000 0.9379 0.000 1.000 0.000 0.000 0.000
#> GSM121398 2 0.0000 0.9379 0.000 1.000 0.000 0.000 0.000
#> GSM121399 2 0.0000 0.9379 0.000 1.000 0.000 0.000 0.000
#> GSM74240 5 0.2929 0.9152 0.000 0.000 0.180 0.000 0.820
#> GSM74241 5 0.2966 0.9159 0.000 0.000 0.184 0.000 0.816
#> GSM74242 5 0.2929 0.9152 0.000 0.000 0.180 0.000 0.820
#> GSM74243 5 0.2929 0.9152 0.000 0.000 0.180 0.000 0.820
#> GSM74244 5 0.2966 0.9159 0.000 0.000 0.184 0.000 0.816
#> GSM74245 5 0.2966 0.9159 0.000 0.000 0.184 0.000 0.816
#> GSM74246 5 0.2966 0.9159 0.000 0.000 0.184 0.000 0.816
#> GSM74247 5 0.2966 0.9159 0.000 0.000 0.184 0.000 0.816
#> GSM74248 5 0.2929 0.9152 0.000 0.000 0.180 0.000 0.820
#> GSM74416 4 0.0609 0.8694 0.020 0.000 0.000 0.980 0.000
#> GSM74417 4 0.0609 0.8694 0.020 0.000 0.000 0.980 0.000
#> GSM74418 4 0.0703 0.8673 0.024 0.000 0.000 0.976 0.000
#> GSM74419 4 0.0613 0.8747 0.004 0.000 0.008 0.984 0.004
#> GSM121358 3 0.0162 0.8517 0.000 0.000 0.996 0.004 0.000
#> GSM121359 3 0.1041 0.8316 0.000 0.032 0.964 0.000 0.004
#> GSM121360 4 0.2522 0.8437 0.000 0.000 0.012 0.880 0.108
#> GSM121362 4 0.4093 0.8075 0.092 0.000 0.012 0.808 0.088
#> GSM121364 4 0.2248 0.8525 0.000 0.000 0.012 0.900 0.088
#> GSM121365 3 0.0162 0.8517 0.000 0.000 0.996 0.004 0.000
#> GSM121366 3 0.0162 0.8517 0.000 0.000 0.996 0.004 0.000
#> GSM121367 3 0.0162 0.8517 0.000 0.000 0.996 0.004 0.000
#> GSM121370 3 0.0162 0.8517 0.000 0.000 0.996 0.004 0.000
#> GSM121371 3 0.0162 0.8517 0.000 0.000 0.996 0.004 0.000
#> GSM121372 3 0.1041 0.8316 0.000 0.032 0.964 0.000 0.004
#> GSM121373 4 0.2248 0.8525 0.000 0.000 0.012 0.900 0.088
#> GSM121374 4 0.2248 0.8525 0.000 0.000 0.012 0.900 0.088
#> GSM121407 3 0.2513 0.7362 0.000 0.116 0.876 0.000 0.008
#> GSM74387 5 0.7055 0.0408 0.012 0.264 0.320 0.000 0.404
#> GSM74388 2 0.4785 0.7447 0.140 0.740 0.004 0.000 0.116
#> GSM74389 4 0.4800 0.4795 0.000 0.000 0.028 0.604 0.368
#> GSM74390 1 0.2623 0.8153 0.884 0.000 0.004 0.016 0.096
#> GSM74391 4 0.1251 0.8713 0.000 0.000 0.008 0.956 0.036
#> GSM74392 4 0.2248 0.8525 0.000 0.000 0.012 0.900 0.088
#> GSM74393 4 0.5953 0.2697 0.000 0.000 0.112 0.504 0.384
#> GSM74394 2 0.5337 0.6854 0.136 0.684 0.004 0.000 0.176
#> GSM74239 1 0.3586 0.7239 0.736 0.000 0.000 0.264 0.000
#> GSM74364 1 0.3837 0.6636 0.692 0.000 0.000 0.308 0.000
#> GSM74365 1 0.0963 0.8522 0.964 0.000 0.000 0.036 0.000
#> GSM74366 1 0.2068 0.8206 0.904 0.000 0.004 0.000 0.092
#> GSM74367 1 0.2471 0.8220 0.864 0.000 0.000 0.136 0.000
#> GSM74377 1 0.1121 0.8438 0.956 0.000 0.000 0.000 0.044
#> GSM74378 1 0.2011 0.8230 0.908 0.000 0.004 0.000 0.088
#> GSM74379 1 0.0771 0.8488 0.976 0.000 0.000 0.004 0.020
#> GSM74380 1 0.0671 0.8496 0.980 0.000 0.000 0.004 0.016
#> GSM74381 1 0.1282 0.8424 0.952 0.000 0.004 0.000 0.044
#> GSM121357 2 0.1082 0.9197 0.000 0.964 0.008 0.000 0.028
#> GSM121361 2 0.4743 0.7489 0.136 0.744 0.004 0.000 0.116
#> GSM121363 2 0.4743 0.7489 0.136 0.744 0.004 0.000 0.116
#> GSM121368 2 0.4700 0.7528 0.132 0.748 0.004 0.000 0.116
#> GSM121369 2 0.4997 0.7361 0.136 0.728 0.008 0.000 0.128
#> GSM74368 1 0.4473 0.6210 0.656 0.000 0.000 0.324 0.020
#> GSM74369 1 0.3534 0.7314 0.744 0.000 0.000 0.256 0.000
#> GSM74370 1 0.3949 0.6240 0.668 0.000 0.000 0.332 0.000
#> GSM74371 4 0.4283 -0.0615 0.456 0.000 0.000 0.544 0.000
#> GSM74372 1 0.3690 0.7540 0.764 0.000 0.000 0.224 0.012
#> GSM74373 1 0.1502 0.8384 0.940 0.000 0.004 0.000 0.056
#> GSM74374 1 0.1792 0.8447 0.916 0.000 0.000 0.084 0.000
#> GSM74375 1 0.1211 0.8527 0.960 0.000 0.000 0.024 0.016
#> GSM74376 1 0.1892 0.8279 0.916 0.000 0.004 0.000 0.080
#> GSM74405 1 0.1430 0.8398 0.944 0.000 0.004 0.000 0.052
#> GSM74351 4 0.2732 0.7330 0.160 0.000 0.000 0.840 0.000
#> GSM74352 1 0.1768 0.8316 0.924 0.000 0.004 0.000 0.072
#> GSM74353 1 0.3796 0.6811 0.700 0.000 0.000 0.300 0.000
#> GSM74354 1 0.2020 0.8402 0.900 0.000 0.000 0.100 0.000
#> GSM74355 1 0.1638 0.8353 0.932 0.000 0.004 0.000 0.064
#> GSM74382 4 0.2605 0.7512 0.148 0.000 0.000 0.852 0.000
#> GSM74383 1 0.2377 0.8270 0.872 0.000 0.000 0.128 0.000
#> GSM74384 1 0.2068 0.8206 0.904 0.000 0.004 0.000 0.092
#> GSM74385 1 0.4249 0.3997 0.568 0.000 0.000 0.432 0.000
#> GSM74386 1 0.3109 0.7842 0.800 0.000 0.000 0.200 0.000
#> GSM74395 1 0.3707 0.6979 0.716 0.000 0.000 0.284 0.000
#> GSM74396 1 0.2516 0.8240 0.860 0.000 0.000 0.140 0.000
#> GSM74397 4 0.4297 -0.1097 0.472 0.000 0.000 0.528 0.000
#> GSM74398 1 0.0566 0.8512 0.984 0.000 0.000 0.012 0.004
#> GSM74399 1 0.1205 0.8435 0.956 0.000 0.004 0.000 0.040
#> GSM74400 1 0.1408 0.8528 0.948 0.000 0.000 0.044 0.008
#> GSM74401 1 0.1251 0.8527 0.956 0.000 0.000 0.036 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM74356 3 0.0405 0.8487 0.004 0.000 0.988 0.008 0.000 0.000
#> GSM74357 3 0.0692 0.8437 0.004 0.000 0.976 0.020 0.000 0.000
#> GSM74358 3 0.0692 0.8437 0.004 0.000 0.976 0.020 0.000 0.000
#> GSM74359 4 0.0551 0.7476 0.004 0.000 0.008 0.984 0.004 0.000
#> GSM74360 4 0.0260 0.7526 0.008 0.000 0.000 0.992 0.000 0.000
#> GSM74361 3 0.2081 0.8102 0.012 0.000 0.916 0.036 0.036 0.000
#> GSM74362 3 0.4594 0.3992 0.004 0.000 0.560 0.404 0.032 0.000
#> GSM74363 3 0.0291 0.8496 0.004 0.000 0.992 0.004 0.000 0.000
#> GSM74402 4 0.3807 0.7205 0.368 0.000 0.000 0.628 0.000 0.004
#> GSM74403 4 0.3872 0.6975 0.392 0.000 0.000 0.604 0.000 0.004
#> GSM74404 4 0.3852 0.7069 0.384 0.000 0.000 0.612 0.000 0.004
#> GSM74406 4 0.2969 0.7917 0.224 0.000 0.000 0.776 0.000 0.000
#> GSM74407 4 0.3819 0.7163 0.372 0.000 0.000 0.624 0.000 0.004
#> GSM74408 4 0.3163 0.7895 0.232 0.000 0.000 0.764 0.004 0.000
#> GSM74409 4 0.2697 0.7909 0.188 0.000 0.000 0.812 0.000 0.000
#> GSM74410 4 0.2902 0.7911 0.196 0.000 0.000 0.800 0.004 0.000
#> GSM119936 4 0.3266 0.7792 0.272 0.000 0.000 0.728 0.000 0.000
#> GSM119937 4 0.3126 0.7870 0.248 0.000 0.000 0.752 0.000 0.000
#> GSM74411 3 0.6186 0.4084 0.140 0.044 0.528 0.000 0.288 0.000
#> GSM74412 3 0.7084 0.3277 0.144 0.152 0.456 0.000 0.248 0.000
#> GSM74413 3 0.6351 0.4112 0.140 0.060 0.524 0.000 0.276 0.000
#> GSM74414 2 0.3404 0.7712 0.184 0.792 0.004 0.000 0.008 0.012
#> GSM74415 3 0.5567 0.4196 0.136 0.008 0.556 0.000 0.300 0.000
#> GSM121379 2 0.0000 0.9800 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121380 2 0.0000 0.9800 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121381 2 0.0000 0.9800 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121382 2 0.0000 0.9800 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121383 2 0.0146 0.9787 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM121384 2 0.0000 0.9800 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121385 2 0.0000 0.9800 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121386 2 0.0000 0.9800 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121387 2 0.0146 0.9787 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM121388 2 0.0146 0.9787 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM121389 2 0.0146 0.9787 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM121390 2 0.0000 0.9800 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121391 2 0.0000 0.9800 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121392 2 0.0000 0.9800 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121393 2 0.0146 0.9787 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM121394 2 0.0000 0.9800 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121395 2 0.0146 0.9787 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM121396 2 0.0508 0.9676 0.000 0.984 0.012 0.000 0.004 0.000
#> GSM121397 2 0.0000 0.9800 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121398 2 0.0000 0.9800 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121399 2 0.0000 0.9800 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM74240 5 0.0865 0.9986 0.000 0.000 0.036 0.000 0.964 0.000
#> GSM74241 5 0.0865 0.9986 0.000 0.000 0.036 0.000 0.964 0.000
#> GSM74242 5 0.0865 0.9986 0.000 0.000 0.036 0.000 0.964 0.000
#> GSM74243 5 0.0865 0.9986 0.000 0.000 0.036 0.000 0.964 0.000
#> GSM74244 5 0.0865 0.9986 0.000 0.000 0.036 0.000 0.964 0.000
#> GSM74245 5 0.0865 0.9986 0.000 0.000 0.036 0.000 0.964 0.000
#> GSM74246 5 0.0790 0.9951 0.000 0.000 0.032 0.000 0.968 0.000
#> GSM74247 5 0.0790 0.9951 0.000 0.000 0.032 0.000 0.968 0.000
#> GSM74248 5 0.0865 0.9986 0.000 0.000 0.036 0.000 0.964 0.000
#> GSM74416 4 0.3899 0.6834 0.404 0.000 0.000 0.592 0.000 0.004
#> GSM74417 4 0.3727 0.7066 0.388 0.000 0.000 0.612 0.000 0.000
#> GSM74418 4 0.4018 0.6657 0.412 0.000 0.000 0.580 0.000 0.008
#> GSM74419 4 0.3330 0.7746 0.284 0.000 0.000 0.716 0.000 0.000
#> GSM121358 3 0.0146 0.8504 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM121359 3 0.0603 0.8421 0.004 0.016 0.980 0.000 0.000 0.000
#> GSM121360 4 0.1728 0.7139 0.064 0.000 0.004 0.924 0.008 0.000
#> GSM121362 4 0.3391 0.6329 0.120 0.000 0.008 0.828 0.008 0.036
#> GSM121364 4 0.0551 0.7476 0.004 0.000 0.008 0.984 0.004 0.000
#> GSM121365 3 0.0146 0.8504 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM121366 3 0.0146 0.8504 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM121367 3 0.0146 0.8504 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM121370 3 0.0146 0.8504 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM121371 3 0.0146 0.8504 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM121372 3 0.0717 0.8411 0.008 0.016 0.976 0.000 0.000 0.000
#> GSM121373 4 0.0551 0.7476 0.004 0.000 0.008 0.984 0.004 0.000
#> GSM121374 4 0.0551 0.7476 0.004 0.000 0.008 0.984 0.004 0.000
#> GSM121407 3 0.1857 0.8164 0.044 0.028 0.924 0.000 0.004 0.000
#> GSM74387 1 0.8492 -0.1125 0.372 0.132 0.168 0.000 0.196 0.132
#> GSM74388 1 0.6777 0.0399 0.364 0.332 0.000 0.000 0.040 0.264
#> GSM74389 4 0.3141 0.5874 0.000 0.000 0.012 0.788 0.200 0.000
#> GSM74390 6 0.5283 0.4271 0.252 0.000 0.004 0.024 0.080 0.640
#> GSM74391 4 0.3259 0.7919 0.216 0.000 0.000 0.772 0.012 0.000
#> GSM74392 4 0.1078 0.7507 0.016 0.000 0.008 0.964 0.012 0.000
#> GSM74393 4 0.4196 0.5398 0.028 0.000 0.044 0.756 0.172 0.000
#> GSM74394 1 0.7120 0.0859 0.380 0.288 0.004 0.000 0.064 0.264
#> GSM74239 1 0.4755 -0.1677 0.492 0.000 0.000 0.048 0.000 0.460
#> GSM74364 1 0.4824 -0.0725 0.524 0.000 0.000 0.056 0.000 0.420
#> GSM74365 6 0.3690 0.5207 0.308 0.000 0.000 0.008 0.000 0.684
#> GSM74366 6 0.2632 0.5250 0.164 0.000 0.000 0.000 0.004 0.832
#> GSM74367 6 0.4178 0.4299 0.372 0.000 0.000 0.020 0.000 0.608
#> GSM74377 6 0.0260 0.6401 0.008 0.000 0.000 0.000 0.000 0.992
#> GSM74378 6 0.2260 0.5548 0.140 0.000 0.000 0.000 0.000 0.860
#> GSM74379 6 0.1957 0.6379 0.112 0.000 0.000 0.000 0.000 0.888
#> GSM74380 6 0.1765 0.6402 0.096 0.000 0.000 0.000 0.000 0.904
#> GSM74381 6 0.0146 0.6402 0.004 0.000 0.000 0.000 0.000 0.996
#> GSM121357 2 0.3228 0.7852 0.176 0.804 0.012 0.000 0.004 0.004
#> GSM121361 1 0.6895 0.0326 0.360 0.336 0.004 0.000 0.040 0.260
#> GSM121363 1 0.6847 0.0195 0.356 0.344 0.004 0.000 0.036 0.260
#> GSM121368 1 0.6893 0.0382 0.364 0.332 0.004 0.000 0.040 0.260
#> GSM121369 1 0.7473 0.0721 0.372 0.300 0.008 0.020 0.048 0.252
#> GSM74368 1 0.5088 -0.0499 0.516 0.000 0.000 0.068 0.004 0.412
#> GSM74369 1 0.4887 -0.1814 0.476 0.000 0.000 0.048 0.004 0.472
#> GSM74370 1 0.5319 -0.1748 0.456 0.000 0.000 0.088 0.004 0.452
#> GSM74371 1 0.5389 0.1370 0.572 0.000 0.000 0.160 0.000 0.268
#> GSM74372 6 0.5045 0.2562 0.412 0.000 0.000 0.076 0.000 0.512
#> GSM74373 6 0.1007 0.6263 0.044 0.000 0.000 0.000 0.000 0.956
#> GSM74374 6 0.4105 0.4600 0.348 0.000 0.000 0.020 0.000 0.632
#> GSM74375 6 0.2520 0.6228 0.152 0.000 0.000 0.004 0.000 0.844
#> GSM74376 6 0.2362 0.5559 0.136 0.000 0.000 0.000 0.004 0.860
#> GSM74405 6 0.0547 0.6331 0.020 0.000 0.000 0.000 0.000 0.980
#> GSM74351 1 0.5166 -0.2063 0.552 0.000 0.000 0.348 0.000 0.100
#> GSM74352 6 0.2048 0.5721 0.120 0.000 0.000 0.000 0.000 0.880
#> GSM74353 1 0.4868 -0.0640 0.524 0.000 0.000 0.060 0.000 0.416
#> GSM74354 6 0.4237 0.3935 0.396 0.000 0.000 0.020 0.000 0.584
#> GSM74355 6 0.1814 0.5874 0.100 0.000 0.000 0.000 0.000 0.900
#> GSM74382 1 0.5094 -0.1760 0.568 0.000 0.000 0.336 0.000 0.096
#> GSM74383 6 0.4439 0.3104 0.432 0.000 0.000 0.028 0.000 0.540
#> GSM74384 6 0.2703 0.5156 0.172 0.000 0.000 0.000 0.004 0.824
#> GSM74385 1 0.5087 0.0561 0.560 0.000 0.000 0.092 0.000 0.348
#> GSM74386 6 0.4603 0.3254 0.416 0.000 0.000 0.040 0.000 0.544
#> GSM74395 1 0.5033 -0.1639 0.476 0.000 0.000 0.072 0.000 0.452
#> GSM74396 6 0.4574 0.2731 0.440 0.000 0.000 0.036 0.000 0.524
#> GSM74397 1 0.5682 0.1083 0.512 0.000 0.000 0.188 0.000 0.300
#> GSM74398 6 0.2902 0.6020 0.196 0.000 0.000 0.004 0.000 0.800
#> GSM74399 6 0.0260 0.6414 0.008 0.000 0.000 0.000 0.000 0.992
#> GSM74400 6 0.3828 0.5333 0.288 0.000 0.000 0.012 0.004 0.696
#> GSM74401 6 0.3608 0.5495 0.272 0.000 0.000 0.012 0.000 0.716
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 5)
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 121 1.64e-11 2
#> SD:skmeans 119 4.44e-25 3
#> SD:skmeans 120 5.03e-32 4
#> SD:skmeans 111 8.59e-43 5
#> SD:skmeans 89 2.00e-36 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 21168 rows and 121 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 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.295 0.648 0.840 0.4697 0.508 0.508
#> 3 3 0.525 0.651 0.803 0.3841 0.555 0.317
#> 4 4 0.648 0.667 0.836 0.1298 0.910 0.746
#> 5 5 0.742 0.752 0.877 0.0645 0.869 0.574
#> 6 6 0.805 0.675 0.827 0.0475 0.925 0.685
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM74356 1 0.9754 0.3157 0.592 0.408
#> GSM74357 1 0.9775 0.3031 0.588 0.412
#> GSM74358 1 0.8909 0.5350 0.692 0.308
#> GSM74359 1 0.2778 0.7646 0.952 0.048
#> GSM74360 1 0.1184 0.7634 0.984 0.016
#> GSM74361 1 0.9661 0.3590 0.608 0.392
#> GSM74362 1 0.8386 0.5991 0.732 0.268
#> GSM74363 2 0.9896 0.2315 0.440 0.560
#> GSM74402 1 0.1184 0.7634 0.984 0.016
#> GSM74403 1 0.0000 0.7581 1.000 0.000
#> GSM74404 1 0.0000 0.7581 1.000 0.000
#> GSM74406 1 0.2236 0.7664 0.964 0.036
#> GSM74407 1 0.4161 0.7535 0.916 0.084
#> GSM74408 1 0.2236 0.7664 0.964 0.036
#> GSM74409 1 0.2236 0.7664 0.964 0.036
#> GSM74410 1 0.2236 0.7664 0.964 0.036
#> GSM119936 1 0.1843 0.7655 0.972 0.028
#> GSM119937 1 0.2236 0.7664 0.964 0.036
#> GSM74411 2 0.6623 0.7236 0.172 0.828
#> GSM74412 2 0.0938 0.8062 0.012 0.988
#> GSM74413 2 0.6438 0.7304 0.164 0.836
#> GSM74414 2 0.0000 0.8075 0.000 1.000
#> GSM74415 2 0.8144 0.6337 0.252 0.748
#> GSM121379 2 0.0000 0.8075 0.000 1.000
#> GSM121380 2 0.0000 0.8075 0.000 1.000
#> GSM121381 2 0.0000 0.8075 0.000 1.000
#> GSM121382 2 0.0000 0.8075 0.000 1.000
#> GSM121383 2 0.0000 0.8075 0.000 1.000
#> GSM121384 2 0.0000 0.8075 0.000 1.000
#> GSM121385 2 0.0000 0.8075 0.000 1.000
#> GSM121386 2 0.0000 0.8075 0.000 1.000
#> GSM121387 2 0.0000 0.8075 0.000 1.000
#> GSM121388 2 0.0376 0.8068 0.004 0.996
#> GSM121389 2 0.0000 0.8075 0.000 1.000
#> GSM121390 2 0.0000 0.8075 0.000 1.000
#> GSM121391 2 0.0376 0.8068 0.004 0.996
#> GSM121392 2 0.0000 0.8075 0.000 1.000
#> GSM121393 2 0.0000 0.8075 0.000 1.000
#> GSM121394 2 0.0376 0.8068 0.004 0.996
#> GSM121395 2 0.0000 0.8075 0.000 1.000
#> GSM121396 2 0.1184 0.8052 0.016 0.984
#> GSM121397 2 0.0000 0.8075 0.000 1.000
#> GSM121398 2 0.0000 0.8075 0.000 1.000
#> GSM121399 2 0.0000 0.8075 0.000 1.000
#> GSM74240 1 0.9944 0.1912 0.544 0.456
#> GSM74241 2 0.6973 0.7074 0.188 0.812
#> GSM74242 1 0.8763 0.5554 0.704 0.296
#> GSM74243 1 0.8207 0.6156 0.744 0.256
#> GSM74244 2 0.9044 0.5169 0.320 0.680
#> GSM74245 1 0.9732 0.3362 0.596 0.404
#> GSM74246 2 0.6887 0.7106 0.184 0.816
#> GSM74247 2 0.6973 0.7074 0.188 0.812
#> GSM74248 1 0.8081 0.6255 0.752 0.248
#> GSM74416 1 0.2236 0.7664 0.964 0.036
#> GSM74417 1 0.0000 0.7581 1.000 0.000
#> GSM74418 1 0.0000 0.7581 1.000 0.000
#> GSM74419 1 0.6801 0.6936 0.820 0.180
#> GSM121358 2 0.9896 0.2315 0.440 0.560
#> GSM121359 2 0.6623 0.7248 0.172 0.828
#> GSM121360 1 0.7528 0.6490 0.784 0.216
#> GSM121362 2 0.9933 0.1735 0.452 0.548
#> GSM121364 1 0.2778 0.7646 0.952 0.048
#> GSM121365 2 0.9896 0.2315 0.440 0.560
#> GSM121366 2 0.9850 0.2560 0.428 0.572
#> GSM121367 2 0.9933 0.1917 0.452 0.548
#> GSM121370 2 0.9944 0.1757 0.456 0.544
#> GSM121371 2 0.9896 0.2315 0.440 0.560
#> GSM121372 2 0.6623 0.7232 0.172 0.828
#> GSM121373 1 0.2948 0.7637 0.948 0.052
#> GSM121374 1 0.2778 0.7646 0.952 0.048
#> GSM121407 2 0.6438 0.7304 0.164 0.836
#> GSM74387 2 0.6531 0.7271 0.168 0.832
#> GSM74388 2 0.0000 0.8075 0.000 1.000
#> GSM74389 1 0.3584 0.7604 0.932 0.068
#> GSM74390 2 0.7056 0.7069 0.192 0.808
#> GSM74391 1 0.7219 0.6742 0.800 0.200
#> GSM74392 1 0.2778 0.7646 0.952 0.048
#> GSM74393 1 0.8016 0.6303 0.756 0.244
#> GSM74394 2 0.0376 0.8074 0.004 0.996
#> GSM74239 1 0.8081 0.5866 0.752 0.248
#> GSM74364 1 0.8207 0.5763 0.744 0.256
#> GSM74365 2 0.9170 0.5530 0.332 0.668
#> GSM74366 2 0.2236 0.7906 0.036 0.964
#> GSM74367 1 0.8443 0.5557 0.728 0.272
#> GSM74377 2 0.3733 0.7822 0.072 0.928
#> GSM74378 2 0.2778 0.7849 0.048 0.952
#> GSM74379 2 0.8207 0.6687 0.256 0.744
#> GSM74380 2 0.9732 0.3356 0.404 0.596
#> GSM74381 2 0.5519 0.7308 0.128 0.872
#> GSM121357 2 0.2043 0.8019 0.032 0.968
#> GSM121361 2 0.0000 0.8075 0.000 1.000
#> GSM121363 2 0.0000 0.8075 0.000 1.000
#> GSM121368 2 0.0000 0.8075 0.000 1.000
#> GSM121369 2 0.6148 0.7395 0.152 0.848
#> GSM74368 2 0.8267 0.6657 0.260 0.740
#> GSM74369 2 0.8386 0.6562 0.268 0.732
#> GSM74370 1 0.9998 -0.0996 0.508 0.492
#> GSM74371 1 0.1184 0.7580 0.984 0.016
#> GSM74372 1 0.7299 0.6409 0.796 0.204
#> GSM74373 2 0.5629 0.7401 0.132 0.868
#> GSM74374 1 0.8207 0.5765 0.744 0.256
#> GSM74375 2 0.9795 0.2665 0.416 0.584
#> GSM74376 2 0.4690 0.7847 0.100 0.900
#> GSM74405 2 0.6531 0.6963 0.168 0.832
#> GSM74351 1 0.0000 0.7581 1.000 0.000
#> GSM74352 2 0.2778 0.7849 0.048 0.952
#> GSM74353 1 0.8555 0.5481 0.720 0.280
#> GSM74354 2 0.9996 0.1338 0.488 0.512
#> GSM74355 2 0.2603 0.7870 0.044 0.956
#> GSM74382 1 0.0000 0.7581 1.000 0.000
#> GSM74383 1 0.8955 0.4900 0.688 0.312
#> GSM74384 2 0.2236 0.7906 0.036 0.964
#> GSM74385 1 0.3733 0.7395 0.928 0.072
#> GSM74386 1 0.9358 0.3873 0.648 0.352
#> GSM74395 1 0.8555 0.5451 0.720 0.280
#> GSM74396 1 0.8207 0.5765 0.744 0.256
#> GSM74397 1 0.8207 0.6086 0.744 0.256
#> GSM74398 1 0.9933 0.1850 0.548 0.452
#> GSM74399 2 0.7219 0.7269 0.200 0.800
#> GSM74400 2 0.9775 0.3103 0.412 0.588
#> GSM74401 2 0.7815 0.6469 0.232 0.768
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM74356 3 0.5733 0.6952 0.324 0.000 0.676
#> GSM74357 3 0.5706 0.6959 0.320 0.000 0.680
#> GSM74358 3 0.5678 0.6965 0.316 0.000 0.684
#> GSM74359 3 0.0000 0.6913 0.000 0.000 1.000
#> GSM74360 3 0.0237 0.6879 0.004 0.000 0.996
#> GSM74361 3 0.5706 0.6959 0.320 0.000 0.680
#> GSM74362 3 0.2711 0.7103 0.088 0.000 0.912
#> GSM74363 3 0.5988 0.6821 0.368 0.000 0.632
#> GSM74402 3 0.0000 0.6913 0.000 0.000 1.000
#> GSM74403 3 0.6180 -0.4116 0.416 0.000 0.584
#> GSM74404 3 0.6095 -0.3488 0.392 0.000 0.608
#> GSM74406 3 0.0000 0.6913 0.000 0.000 1.000
#> GSM74407 3 0.0424 0.6942 0.008 0.000 0.992
#> GSM74408 3 0.0000 0.6913 0.000 0.000 1.000
#> GSM74409 3 0.0000 0.6913 0.000 0.000 1.000
#> GSM74410 3 0.0000 0.6913 0.000 0.000 1.000
#> GSM119936 3 0.0000 0.6913 0.000 0.000 1.000
#> GSM119937 3 0.0000 0.6913 0.000 0.000 1.000
#> GSM74411 3 0.6264 0.6735 0.380 0.004 0.616
#> GSM74412 2 0.8033 0.3852 0.424 0.512 0.064
#> GSM74413 3 0.7091 0.6285 0.416 0.024 0.560
#> GSM74414 2 0.6154 0.4925 0.408 0.592 0.000
#> GSM74415 3 0.6008 0.6812 0.372 0.000 0.628
#> GSM121379 2 0.0000 0.8982 0.000 1.000 0.000
#> GSM121380 2 0.0000 0.8982 0.000 1.000 0.000
#> GSM121381 2 0.0000 0.8982 0.000 1.000 0.000
#> GSM121382 2 0.0000 0.8982 0.000 1.000 0.000
#> GSM121383 2 0.0000 0.8982 0.000 1.000 0.000
#> GSM121384 2 0.0000 0.8982 0.000 1.000 0.000
#> GSM121385 2 0.0000 0.8982 0.000 1.000 0.000
#> GSM121386 2 0.0000 0.8982 0.000 1.000 0.000
#> GSM121387 2 0.0000 0.8982 0.000 1.000 0.000
#> GSM121388 2 0.4861 0.7371 0.180 0.808 0.012
#> GSM121389 2 0.0000 0.8982 0.000 1.000 0.000
#> GSM121390 2 0.0000 0.8982 0.000 1.000 0.000
#> GSM121391 2 0.0000 0.8982 0.000 1.000 0.000
#> GSM121392 2 0.0747 0.8887 0.016 0.984 0.000
#> GSM121393 2 0.0000 0.8982 0.000 1.000 0.000
#> GSM121394 2 0.0000 0.8982 0.000 1.000 0.000
#> GSM121395 2 0.0000 0.8982 0.000 1.000 0.000
#> GSM121396 2 0.1267 0.8794 0.004 0.972 0.024
#> GSM121397 2 0.0000 0.8982 0.000 1.000 0.000
#> GSM121398 2 0.0000 0.8982 0.000 1.000 0.000
#> GSM121399 2 0.0000 0.8982 0.000 1.000 0.000
#> GSM74240 3 0.5560 0.6749 0.300 0.000 0.700
#> GSM74241 3 0.6252 0.6160 0.444 0.000 0.556
#> GSM74242 3 0.3482 0.7093 0.128 0.000 0.872
#> GSM74243 3 0.2356 0.7092 0.072 0.000 0.928
#> GSM74244 3 0.5988 0.6820 0.368 0.000 0.632
#> GSM74245 3 0.5560 0.7007 0.300 0.000 0.700
#> GSM74246 3 0.6483 0.6020 0.452 0.004 0.544
#> GSM74247 3 0.6654 0.5964 0.456 0.008 0.536
#> GSM74248 3 0.2261 0.7074 0.068 0.000 0.932
#> GSM74416 3 0.0000 0.6913 0.000 0.000 1.000
#> GSM74417 3 0.0000 0.6913 0.000 0.000 1.000
#> GSM74418 3 0.0424 0.6834 0.008 0.000 0.992
#> GSM74419 3 0.1964 0.7072 0.056 0.000 0.944
#> GSM121358 3 0.6209 0.6803 0.368 0.004 0.628
#> GSM121359 3 0.7245 0.6612 0.368 0.036 0.596
#> GSM121360 3 0.5621 0.0110 0.308 0.000 0.692
#> GSM121362 3 0.5650 0.4954 0.312 0.000 0.688
#> GSM121364 3 0.0000 0.6913 0.000 0.000 1.000
#> GSM121365 3 0.5988 0.6821 0.368 0.000 0.632
#> GSM121366 3 0.6209 0.6803 0.368 0.004 0.628
#> GSM121367 3 0.5968 0.6840 0.364 0.000 0.636
#> GSM121370 3 0.5968 0.6840 0.364 0.000 0.636
#> GSM121371 3 0.5988 0.6821 0.368 0.000 0.632
#> GSM121372 3 0.6228 0.6786 0.372 0.004 0.624
#> GSM121373 3 0.0000 0.6913 0.000 0.000 1.000
#> GSM121374 3 0.0000 0.6913 0.000 0.000 1.000
#> GSM121407 3 0.7159 0.5917 0.448 0.024 0.528
#> GSM74387 3 0.7672 0.5438 0.468 0.044 0.488
#> GSM74388 2 0.4654 0.7177 0.208 0.792 0.000
#> GSM74389 3 0.0000 0.6913 0.000 0.000 1.000
#> GSM74390 1 0.6140 0.0648 0.596 0.000 0.404
#> GSM74391 3 0.1753 0.7052 0.048 0.000 0.952
#> GSM74392 3 0.0237 0.6889 0.004 0.000 0.996
#> GSM74393 3 0.2356 0.7066 0.072 0.000 0.928
#> GSM74394 1 0.6291 -0.3249 0.532 0.468 0.000
#> GSM74239 1 0.6168 0.6721 0.588 0.000 0.412
#> GSM74364 1 0.6244 0.6414 0.560 0.000 0.440
#> GSM74365 1 0.4842 0.7157 0.776 0.000 0.224
#> GSM74366 1 0.5650 0.4461 0.688 0.312 0.000
#> GSM74367 1 0.5948 0.7070 0.640 0.000 0.360
#> GSM74377 1 0.0848 0.5929 0.984 0.008 0.008
#> GSM74378 1 0.5678 0.4577 0.684 0.316 0.000
#> GSM74379 1 0.3879 0.6908 0.848 0.000 0.152
#> GSM74380 1 0.5591 0.7219 0.696 0.000 0.304
#> GSM74381 1 0.5873 0.4655 0.684 0.312 0.004
#> GSM121357 2 0.8138 0.3305 0.452 0.480 0.068
#> GSM121361 2 0.4842 0.6989 0.224 0.776 0.000
#> GSM121363 2 0.4605 0.7266 0.204 0.796 0.000
#> GSM121368 2 0.4504 0.7362 0.196 0.804 0.000
#> GSM121369 1 0.8425 -0.3524 0.540 0.096 0.364
#> GSM74368 1 0.4974 0.3474 0.764 0.000 0.236
#> GSM74369 1 0.1031 0.5825 0.976 0.000 0.024
#> GSM74370 1 0.5678 0.7195 0.684 0.000 0.316
#> GSM74371 1 0.6225 0.6517 0.568 0.000 0.432
#> GSM74372 1 0.6008 0.7003 0.628 0.000 0.372
#> GSM74373 1 0.5858 0.7209 0.740 0.020 0.240
#> GSM74374 1 0.6008 0.7003 0.628 0.000 0.372
#> GSM74375 1 0.3941 0.6736 0.844 0.000 0.156
#> GSM74376 1 0.0237 0.5829 0.996 0.004 0.000
#> GSM74405 1 0.3112 0.6568 0.900 0.004 0.096
#> GSM74351 1 0.6309 0.5652 0.504 0.000 0.496
#> GSM74352 1 0.4796 0.5525 0.780 0.220 0.000
#> GSM74353 1 0.5760 0.7181 0.672 0.000 0.328
#> GSM74354 1 0.5678 0.7195 0.684 0.000 0.316
#> GSM74355 1 0.4399 0.5730 0.812 0.188 0.000
#> GSM74382 1 0.6309 0.5648 0.504 0.000 0.496
#> GSM74383 1 0.5988 0.7030 0.632 0.000 0.368
#> GSM74384 1 0.4121 0.5882 0.832 0.168 0.000
#> GSM74385 1 0.6204 0.6600 0.576 0.000 0.424
#> GSM74386 1 0.5835 0.7149 0.660 0.000 0.340
#> GSM74395 1 0.5948 0.7090 0.640 0.000 0.360
#> GSM74396 1 0.6008 0.7003 0.628 0.000 0.372
#> GSM74397 1 0.6260 0.6309 0.552 0.000 0.448
#> GSM74398 1 0.4452 0.6920 0.808 0.000 0.192
#> GSM74399 1 0.0592 0.5921 0.988 0.000 0.012
#> GSM74400 1 0.6301 0.7258 0.712 0.028 0.260
#> GSM74401 1 0.4326 0.6936 0.844 0.012 0.144
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM74356 4 0.4605 0.5138 0.000 0.000 0.336 0.664
#> GSM74357 4 0.4605 0.5138 0.000 0.000 0.336 0.664
#> GSM74358 4 0.4605 0.5138 0.000 0.000 0.336 0.664
#> GSM74359 4 0.0000 0.6964 0.000 0.000 0.000 1.000
#> GSM74360 4 0.0592 0.6874 0.000 0.000 0.016 0.984
#> GSM74361 4 0.4605 0.5138 0.000 0.000 0.336 0.664
#> GSM74362 4 0.1637 0.6811 0.000 0.000 0.060 0.940
#> GSM74363 4 0.4781 0.5098 0.004 0.000 0.336 0.660
#> GSM74402 4 0.0707 0.6955 0.000 0.000 0.020 0.980
#> GSM74403 4 0.4730 0.1350 0.364 0.000 0.000 0.636
#> GSM74404 4 0.4761 0.1129 0.372 0.000 0.000 0.628
#> GSM74406 4 0.0336 0.6965 0.000 0.000 0.008 0.992
#> GSM74407 4 0.0469 0.6963 0.000 0.000 0.012 0.988
#> GSM74408 4 0.0000 0.6964 0.000 0.000 0.000 1.000
#> GSM74409 4 0.0000 0.6964 0.000 0.000 0.000 1.000
#> GSM74410 4 0.0469 0.6963 0.000 0.000 0.012 0.988
#> GSM119936 4 0.0000 0.6964 0.000 0.000 0.000 1.000
#> GSM119937 4 0.0592 0.6963 0.000 0.000 0.016 0.984
#> GSM74411 3 0.4936 0.0946 0.004 0.000 0.624 0.372
#> GSM74412 2 0.6009 0.4249 0.012 0.608 0.348 0.032
#> GSM74413 3 0.6008 -0.1808 0.012 0.020 0.504 0.464
#> GSM74414 2 0.4834 0.7443 0.096 0.784 0.120 0.000
#> GSM74415 3 0.4999 -0.2397 0.000 0.000 0.508 0.492
#> GSM121379 2 0.0000 0.9072 0.000 1.000 0.000 0.000
#> GSM121380 2 0.0000 0.9072 0.000 1.000 0.000 0.000
#> GSM121381 2 0.0000 0.9072 0.000 1.000 0.000 0.000
#> GSM121382 2 0.0000 0.9072 0.000 1.000 0.000 0.000
#> GSM121383 2 0.0000 0.9072 0.000 1.000 0.000 0.000
#> GSM121384 2 0.0000 0.9072 0.000 1.000 0.000 0.000
#> GSM121385 2 0.0000 0.9072 0.000 1.000 0.000 0.000
#> GSM121386 2 0.0000 0.9072 0.000 1.000 0.000 0.000
#> GSM121387 2 0.0000 0.9072 0.000 1.000 0.000 0.000
#> GSM121388 2 0.3172 0.7781 0.000 0.840 0.160 0.000
#> GSM121389 2 0.0000 0.9072 0.000 1.000 0.000 0.000
#> GSM121390 2 0.0000 0.9072 0.000 1.000 0.000 0.000
#> GSM121391 2 0.0000 0.9072 0.000 1.000 0.000 0.000
#> GSM121392 2 0.0188 0.9046 0.004 0.996 0.000 0.000
#> GSM121393 2 0.0000 0.9072 0.000 1.000 0.000 0.000
#> GSM121394 2 0.0000 0.9072 0.000 1.000 0.000 0.000
#> GSM121395 2 0.0000 0.9072 0.000 1.000 0.000 0.000
#> GSM121396 2 0.2675 0.8306 0.000 0.892 0.100 0.008
#> GSM121397 2 0.0000 0.9072 0.000 1.000 0.000 0.000
#> GSM121398 2 0.0000 0.9072 0.000 1.000 0.000 0.000
#> GSM121399 2 0.0000 0.9072 0.000 1.000 0.000 0.000
#> GSM74240 3 0.2660 0.6658 0.056 0.000 0.908 0.036
#> GSM74241 3 0.0188 0.6438 0.004 0.000 0.996 0.000
#> GSM74242 3 0.3074 0.6080 0.000 0.000 0.848 0.152
#> GSM74243 3 0.4382 0.4791 0.000 0.000 0.704 0.296
#> GSM74244 3 0.0469 0.6440 0.000 0.000 0.988 0.012
#> GSM74245 3 0.1022 0.6508 0.000 0.000 0.968 0.032
#> GSM74246 3 0.2408 0.6601 0.104 0.000 0.896 0.000
#> GSM74247 3 0.2345 0.6611 0.100 0.000 0.900 0.000
#> GSM74248 3 0.4406 0.4721 0.000 0.000 0.700 0.300
#> GSM74416 4 0.0000 0.6964 0.000 0.000 0.000 1.000
#> GSM74417 4 0.0000 0.6964 0.000 0.000 0.000 1.000
#> GSM74418 4 0.0592 0.6843 0.016 0.000 0.000 0.984
#> GSM74419 4 0.0469 0.6963 0.000 0.000 0.012 0.988
#> GSM121358 4 0.4920 0.4777 0.004 0.000 0.368 0.628
#> GSM121359 4 0.7130 0.2755 0.004 0.120 0.368 0.508
#> GSM121360 3 0.7261 0.3491 0.152 0.000 0.480 0.368
#> GSM121362 4 0.6732 0.2487 0.220 0.000 0.168 0.612
#> GSM121364 4 0.0000 0.6964 0.000 0.000 0.000 1.000
#> GSM121365 4 0.4920 0.4777 0.004 0.000 0.368 0.628
#> GSM121366 4 0.4920 0.4777 0.004 0.000 0.368 0.628
#> GSM121367 4 0.4746 0.4821 0.000 0.000 0.368 0.632
#> GSM121370 4 0.4746 0.4821 0.000 0.000 0.368 0.632
#> GSM121371 4 0.4920 0.4777 0.004 0.000 0.368 0.628
#> GSM121372 4 0.4920 0.4777 0.004 0.000 0.368 0.628
#> GSM121373 4 0.0000 0.6964 0.000 0.000 0.000 1.000
#> GSM121374 4 0.0000 0.6964 0.000 0.000 0.000 1.000
#> GSM121407 4 0.7787 0.1650 0.012 0.168 0.368 0.452
#> GSM74387 3 0.6990 0.5481 0.168 0.024 0.644 0.164
#> GSM74388 2 0.4008 0.6934 0.244 0.756 0.000 0.000
#> GSM74389 4 0.4304 0.3383 0.000 0.000 0.284 0.716
#> GSM74390 1 0.7451 -0.3132 0.420 0.000 0.408 0.172
#> GSM74391 4 0.4543 0.2493 0.000 0.000 0.324 0.676
#> GSM74392 4 0.2647 0.5855 0.000 0.000 0.120 0.880
#> GSM74393 4 0.4277 0.3646 0.000 0.000 0.280 0.720
#> GSM74394 3 0.5850 0.5538 0.244 0.080 0.676 0.000
#> GSM74239 1 0.3400 0.7898 0.820 0.000 0.000 0.180
#> GSM74364 1 0.3123 0.8090 0.844 0.000 0.000 0.156
#> GSM74365 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM74366 1 0.1474 0.8209 0.948 0.052 0.000 0.000
#> GSM74367 1 0.0921 0.8580 0.972 0.000 0.000 0.028
#> GSM74377 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM74378 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM74379 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM74380 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM74381 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM121357 2 0.7692 0.4573 0.140 0.596 0.212 0.052
#> GSM121361 2 0.4188 0.6901 0.244 0.752 0.004 0.000
#> GSM121363 2 0.3764 0.7294 0.216 0.784 0.000 0.000
#> GSM121368 2 0.3764 0.7295 0.216 0.784 0.000 0.000
#> GSM121369 3 0.8662 0.5015 0.256 0.136 0.504 0.104
#> GSM74368 1 0.5833 0.4790 0.692 0.000 0.096 0.212
#> GSM74369 1 0.1305 0.8408 0.960 0.000 0.036 0.004
#> GSM74370 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM74371 1 0.4585 0.6322 0.668 0.000 0.000 0.332
#> GSM74372 1 0.3649 0.7725 0.796 0.000 0.000 0.204
#> GSM74373 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM74374 1 0.2216 0.8423 0.908 0.000 0.000 0.092
#> GSM74375 1 0.2999 0.8226 0.864 0.000 0.004 0.132
#> GSM74376 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM74405 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM74351 1 0.4790 0.5556 0.620 0.000 0.000 0.380
#> GSM74352 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM74353 1 0.2469 0.8359 0.892 0.000 0.000 0.108
#> GSM74354 1 0.0469 0.8595 0.988 0.000 0.000 0.012
#> GSM74355 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM74382 1 0.4830 0.5416 0.608 0.000 0.000 0.392
#> GSM74383 1 0.2814 0.8239 0.868 0.000 0.000 0.132
#> GSM74384 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM74385 1 0.4406 0.6718 0.700 0.000 0.000 0.300
#> GSM74386 1 0.2921 0.8200 0.860 0.000 0.000 0.140
#> GSM74395 1 0.2760 0.8253 0.872 0.000 0.000 0.128
#> GSM74396 1 0.2408 0.8381 0.896 0.000 0.000 0.104
#> GSM74397 1 0.4585 0.6310 0.668 0.000 0.000 0.332
#> GSM74398 1 0.1637 0.8512 0.940 0.000 0.000 0.060
#> GSM74399 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM74400 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM74401 1 0.0000 0.8598 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM74356 3 0.3480 0.6211 0.000 0.000 0.752 0.248 0.000
#> GSM74357 3 0.3480 0.6211 0.000 0.000 0.752 0.248 0.000
#> GSM74358 3 0.3480 0.6211 0.000 0.000 0.752 0.248 0.000
#> GSM74359 4 0.0290 0.7872 0.000 0.000 0.008 0.992 0.000
#> GSM74360 4 0.0290 0.7872 0.000 0.000 0.008 0.992 0.000
#> GSM74361 3 0.3508 0.6151 0.000 0.000 0.748 0.252 0.000
#> GSM74362 4 0.4030 0.4883 0.000 0.000 0.352 0.648 0.000
#> GSM74363 3 0.3480 0.6211 0.000 0.000 0.752 0.248 0.000
#> GSM74402 4 0.3336 0.6801 0.000 0.000 0.228 0.772 0.000
#> GSM74403 4 0.0404 0.7871 0.012 0.000 0.000 0.988 0.000
#> GSM74404 4 0.0771 0.7866 0.020 0.000 0.004 0.976 0.000
#> GSM74406 4 0.3143 0.6992 0.000 0.000 0.204 0.796 0.000
#> GSM74407 4 0.3906 0.6603 0.000 0.000 0.240 0.744 0.016
#> GSM74408 4 0.0510 0.7868 0.000 0.000 0.016 0.984 0.000
#> GSM74409 4 0.0290 0.7872 0.000 0.000 0.008 0.992 0.000
#> GSM74410 4 0.3305 0.6835 0.000 0.000 0.224 0.776 0.000
#> GSM119936 4 0.2280 0.7523 0.000 0.000 0.120 0.880 0.000
#> GSM119937 4 0.2891 0.7226 0.000 0.000 0.176 0.824 0.000
#> GSM74411 3 0.2249 0.7243 0.000 0.000 0.896 0.008 0.096
#> GSM74412 3 0.4562 0.4436 0.000 0.292 0.676 0.000 0.032
#> GSM74413 3 0.0404 0.7762 0.000 0.000 0.988 0.000 0.012
#> GSM74414 2 0.5915 0.2090 0.108 0.508 0.384 0.000 0.000
#> GSM74415 3 0.1774 0.7605 0.000 0.000 0.932 0.016 0.052
#> GSM121379 2 0.0000 0.9064 0.000 1.000 0.000 0.000 0.000
#> GSM121380 2 0.0000 0.9064 0.000 1.000 0.000 0.000 0.000
#> GSM121381 2 0.0000 0.9064 0.000 1.000 0.000 0.000 0.000
#> GSM121382 2 0.0000 0.9064 0.000 1.000 0.000 0.000 0.000
#> GSM121383 2 0.0000 0.9064 0.000 1.000 0.000 0.000 0.000
#> GSM121384 2 0.0000 0.9064 0.000 1.000 0.000 0.000 0.000
#> GSM121385 2 0.0000 0.9064 0.000 1.000 0.000 0.000 0.000
#> GSM121386 2 0.0000 0.9064 0.000 1.000 0.000 0.000 0.000
#> GSM121387 2 0.0000 0.9064 0.000 1.000 0.000 0.000 0.000
#> GSM121388 2 0.4126 0.3935 0.000 0.620 0.380 0.000 0.000
#> GSM121389 2 0.0000 0.9064 0.000 1.000 0.000 0.000 0.000
#> GSM121390 2 0.0000 0.9064 0.000 1.000 0.000 0.000 0.000
#> GSM121391 2 0.0000 0.9064 0.000 1.000 0.000 0.000 0.000
#> GSM121392 2 0.0162 0.9037 0.000 0.996 0.004 0.000 0.000
#> GSM121393 2 0.0000 0.9064 0.000 1.000 0.000 0.000 0.000
#> GSM121394 2 0.0000 0.9064 0.000 1.000 0.000 0.000 0.000
#> GSM121395 2 0.0000 0.9064 0.000 1.000 0.000 0.000 0.000
#> GSM121396 2 0.2561 0.7879 0.000 0.856 0.144 0.000 0.000
#> GSM121397 2 0.0000 0.9064 0.000 1.000 0.000 0.000 0.000
#> GSM121398 2 0.0000 0.9064 0.000 1.000 0.000 0.000 0.000
#> GSM121399 2 0.0000 0.9064 0.000 1.000 0.000 0.000 0.000
#> GSM74240 5 0.0000 0.9368 0.000 0.000 0.000 0.000 1.000
#> GSM74241 5 0.0000 0.9368 0.000 0.000 0.000 0.000 1.000
#> GSM74242 5 0.0000 0.9368 0.000 0.000 0.000 0.000 1.000
#> GSM74243 5 0.0000 0.9368 0.000 0.000 0.000 0.000 1.000
#> GSM74244 5 0.0000 0.9368 0.000 0.000 0.000 0.000 1.000
#> GSM74245 5 0.0000 0.9368 0.000 0.000 0.000 0.000 1.000
#> GSM74246 5 0.0000 0.9368 0.000 0.000 0.000 0.000 1.000
#> GSM74247 5 0.0000 0.9368 0.000 0.000 0.000 0.000 1.000
#> GSM74248 5 0.0000 0.9368 0.000 0.000 0.000 0.000 1.000
#> GSM74416 4 0.0451 0.7861 0.008 0.000 0.004 0.988 0.000
#> GSM74417 4 0.0000 0.7863 0.000 0.000 0.000 1.000 0.000
#> GSM74418 4 0.0290 0.7848 0.008 0.000 0.000 0.992 0.000
#> GSM74419 4 0.3395 0.6717 0.000 0.000 0.236 0.764 0.000
#> GSM121358 3 0.0609 0.7854 0.000 0.000 0.980 0.020 0.000
#> GSM121359 3 0.0579 0.7818 0.000 0.008 0.984 0.008 0.000
#> GSM121360 4 0.5332 0.5227 0.072 0.000 0.020 0.688 0.220
#> GSM121362 4 0.6959 0.3286 0.224 0.000 0.144 0.564 0.068
#> GSM121364 4 0.0290 0.7872 0.000 0.000 0.008 0.992 0.000
#> GSM121365 3 0.0510 0.7847 0.000 0.000 0.984 0.016 0.000
#> GSM121366 3 0.0609 0.7854 0.000 0.000 0.980 0.020 0.000
#> GSM121367 3 0.0609 0.7854 0.000 0.000 0.980 0.020 0.000
#> GSM121370 3 0.0609 0.7854 0.000 0.000 0.980 0.020 0.000
#> GSM121371 3 0.0609 0.7854 0.000 0.000 0.980 0.020 0.000
#> GSM121372 3 0.0510 0.7847 0.000 0.000 0.984 0.016 0.000
#> GSM121373 4 0.0290 0.7872 0.000 0.000 0.008 0.992 0.000
#> GSM121374 4 0.0404 0.7869 0.000 0.000 0.012 0.988 0.000
#> GSM121407 3 0.0162 0.7753 0.004 0.000 0.996 0.000 0.000
#> GSM74387 3 0.5664 0.4491 0.168 0.004 0.648 0.000 0.180
#> GSM74388 2 0.3934 0.6760 0.244 0.740 0.016 0.000 0.000
#> GSM74389 4 0.4719 0.6340 0.000 0.000 0.056 0.696 0.248
#> GSM74390 3 0.7214 0.1515 0.376 0.000 0.416 0.040 0.168
#> GSM74391 4 0.4010 0.6823 0.000 0.000 0.032 0.760 0.208
#> GSM74392 4 0.2228 0.7754 0.000 0.000 0.040 0.912 0.048
#> GSM74393 4 0.5741 0.4429 0.000 0.000 0.096 0.544 0.360
#> GSM74394 5 0.6614 0.1291 0.236 0.000 0.316 0.000 0.448
#> GSM74239 1 0.3534 0.6801 0.744 0.000 0.000 0.256 0.000
#> GSM74364 1 0.3274 0.7521 0.780 0.000 0.000 0.220 0.000
#> GSM74365 1 0.0290 0.8978 0.992 0.000 0.000 0.008 0.000
#> GSM74366 1 0.1626 0.8642 0.940 0.044 0.016 0.000 0.000
#> GSM74367 1 0.0963 0.8932 0.964 0.000 0.000 0.036 0.000
#> GSM74377 1 0.0510 0.8966 0.984 0.000 0.016 0.000 0.000
#> GSM74378 1 0.0510 0.8966 0.984 0.000 0.016 0.000 0.000
#> GSM74379 1 0.0404 0.8976 0.988 0.000 0.012 0.000 0.000
#> GSM74380 1 0.0000 0.8984 1.000 0.000 0.000 0.000 0.000
#> GSM74381 1 0.0404 0.8976 0.988 0.000 0.012 0.000 0.000
#> GSM121357 3 0.6062 0.3554 0.168 0.268 0.564 0.000 0.000
#> GSM121361 2 0.4268 0.6673 0.244 0.728 0.024 0.000 0.004
#> GSM121363 2 0.3940 0.6999 0.220 0.756 0.024 0.000 0.000
#> GSM121368 2 0.3970 0.6953 0.224 0.752 0.024 0.000 0.000
#> GSM121369 3 0.7554 0.1990 0.240 0.068 0.468 0.000 0.224
#> GSM74368 1 0.4850 0.5454 0.696 0.000 0.232 0.072 0.000
#> GSM74369 1 0.1597 0.8794 0.940 0.000 0.048 0.012 0.000
#> GSM74370 1 0.0451 0.8986 0.988 0.000 0.008 0.004 0.000
#> GSM74371 4 0.3707 0.5222 0.284 0.000 0.000 0.716 0.000
#> GSM74372 1 0.3424 0.7109 0.760 0.000 0.000 0.240 0.000
#> GSM74373 1 0.0510 0.8966 0.984 0.000 0.016 0.000 0.000
#> GSM74374 1 0.2074 0.8578 0.896 0.000 0.000 0.104 0.000
#> GSM74375 1 0.2970 0.7978 0.828 0.000 0.000 0.168 0.004
#> GSM74376 1 0.0404 0.8976 0.988 0.000 0.012 0.000 0.000
#> GSM74405 1 0.0510 0.8966 0.984 0.000 0.016 0.000 0.000
#> GSM74351 4 0.4161 0.3348 0.392 0.000 0.000 0.608 0.000
#> GSM74352 1 0.0510 0.8966 0.984 0.000 0.016 0.000 0.000
#> GSM74353 1 0.2516 0.8285 0.860 0.000 0.000 0.140 0.000
#> GSM74354 1 0.0609 0.8970 0.980 0.000 0.000 0.020 0.000
#> GSM74355 1 0.0510 0.8966 0.984 0.000 0.016 0.000 0.000
#> GSM74382 4 0.3913 0.4747 0.324 0.000 0.000 0.676 0.000
#> GSM74383 1 0.2852 0.7973 0.828 0.000 0.000 0.172 0.000
#> GSM74384 1 0.0510 0.8966 0.984 0.000 0.016 0.000 0.000
#> GSM74385 4 0.4074 0.3737 0.364 0.000 0.000 0.636 0.000
#> GSM74386 1 0.3452 0.7199 0.756 0.000 0.000 0.244 0.000
#> GSM74395 1 0.2813 0.8002 0.832 0.000 0.000 0.168 0.000
#> GSM74396 1 0.2377 0.8400 0.872 0.000 0.000 0.128 0.000
#> GSM74397 4 0.4653 0.0941 0.472 0.000 0.012 0.516 0.000
#> GSM74398 1 0.1671 0.8729 0.924 0.000 0.000 0.076 0.000
#> GSM74399 1 0.0451 0.8987 0.988 0.000 0.008 0.004 0.000
#> GSM74400 1 0.0566 0.8984 0.984 0.000 0.004 0.012 0.000
#> GSM74401 1 0.0451 0.8984 0.988 0.000 0.004 0.008 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM74356 4 0.3860 0.2878 0.000 0.000 0.472 0.528 0.000 0.000
#> GSM74357 4 0.3860 0.2878 0.000 0.000 0.472 0.528 0.000 0.000
#> GSM74358 4 0.3860 0.2878 0.000 0.000 0.472 0.528 0.000 0.000
#> GSM74359 6 0.3997 0.5475 0.000 0.000 0.004 0.488 0.000 0.508
#> GSM74360 6 0.3854 0.5681 0.000 0.000 0.000 0.464 0.000 0.536
#> GSM74361 4 0.3857 0.2955 0.000 0.000 0.468 0.532 0.000 0.000
#> GSM74362 4 0.3126 0.5285 0.000 0.000 0.248 0.752 0.000 0.000
#> GSM74363 4 0.3860 0.2878 0.000 0.000 0.472 0.528 0.000 0.000
#> GSM74402 4 0.1176 0.5994 0.000 0.000 0.020 0.956 0.000 0.024
#> GSM74403 4 0.1334 0.5659 0.020 0.000 0.000 0.948 0.000 0.032
#> GSM74404 4 0.1498 0.5599 0.028 0.000 0.000 0.940 0.000 0.032
#> GSM74406 4 0.1398 0.6128 0.000 0.000 0.052 0.940 0.000 0.008
#> GSM74407 4 0.2341 0.6016 0.012 0.000 0.056 0.900 0.000 0.032
#> GSM74408 4 0.0632 0.5889 0.000 0.000 0.000 0.976 0.000 0.024
#> GSM74409 4 0.1082 0.5835 0.000 0.000 0.004 0.956 0.000 0.040
#> GSM74410 4 0.1267 0.6146 0.000 0.000 0.060 0.940 0.000 0.000
#> GSM119936 4 0.0146 0.5970 0.000 0.000 0.004 0.996 0.000 0.000
#> GSM119937 4 0.1429 0.6119 0.004 0.000 0.052 0.940 0.000 0.004
#> GSM74411 3 0.0547 0.8292 0.000 0.000 0.980 0.000 0.020 0.000
#> GSM74412 3 0.3183 0.6653 0.000 0.200 0.788 0.000 0.004 0.008
#> GSM74413 3 0.0146 0.8369 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM74414 3 0.5865 0.1379 0.004 0.392 0.436 0.000 0.000 0.168
#> GSM74415 3 0.0632 0.8260 0.000 0.000 0.976 0.000 0.024 0.000
#> GSM121379 2 0.0000 0.8883 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121380 2 0.0000 0.8883 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121381 2 0.0000 0.8883 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121382 2 0.0000 0.8883 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121383 2 0.0000 0.8883 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121384 2 0.0000 0.8883 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121385 2 0.0000 0.8883 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121386 2 0.0000 0.8883 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121387 2 0.0000 0.8883 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121388 2 0.3862 0.0252 0.000 0.524 0.476 0.000 0.000 0.000
#> GSM121389 2 0.0000 0.8883 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121390 2 0.0000 0.8883 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121391 2 0.0000 0.8883 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121392 2 0.0260 0.8832 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM121393 2 0.0000 0.8883 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121394 2 0.0000 0.8883 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121395 2 0.0000 0.8883 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121396 2 0.2416 0.7415 0.000 0.844 0.156 0.000 0.000 0.000
#> GSM121397 2 0.0000 0.8883 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121398 2 0.0000 0.8883 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121399 2 0.0000 0.8883 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM74240 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74241 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74242 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74243 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74244 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74245 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74246 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74247 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74248 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74416 6 0.4535 0.5387 0.032 0.000 0.000 0.484 0.000 0.484
#> GSM74417 6 0.4393 0.5684 0.024 0.000 0.000 0.452 0.000 0.524
#> GSM74418 6 0.4469 0.5583 0.028 0.000 0.000 0.468 0.000 0.504
#> GSM74419 4 0.0865 0.6136 0.000 0.000 0.036 0.964 0.000 0.000
#> GSM121358 3 0.0260 0.8338 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM121359 3 0.0000 0.8378 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121360 6 0.4043 0.4958 0.024 0.000 0.004 0.256 0.004 0.712
#> GSM121362 6 0.2811 0.3902 0.036 0.000 0.012 0.084 0.000 0.868
#> GSM121364 6 0.3993 0.5619 0.000 0.000 0.004 0.476 0.000 0.520
#> GSM121365 3 0.0000 0.8378 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121366 3 0.0146 0.8369 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM121367 3 0.0146 0.8369 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM121370 3 0.0146 0.8369 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM121371 3 0.0146 0.8369 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM121372 3 0.0000 0.8378 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121373 6 0.4091 0.5622 0.000 0.000 0.008 0.472 0.000 0.520
#> GSM121374 6 0.4177 0.5615 0.000 0.000 0.012 0.468 0.000 0.520
#> GSM121407 3 0.0000 0.8378 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM74387 3 0.4172 0.4750 0.004 0.000 0.564 0.000 0.008 0.424
#> GSM74388 2 0.4389 0.4642 0.024 0.528 0.000 0.000 0.000 0.448
#> GSM74389 4 0.3424 0.4474 0.000 0.000 0.020 0.780 0.196 0.004
#> GSM74390 6 0.5857 -0.4193 0.056 0.000 0.444 0.004 0.048 0.448
#> GSM74391 4 0.2401 0.5896 0.004 0.000 0.016 0.900 0.060 0.020
#> GSM74392 4 0.1464 0.5870 0.000 0.000 0.004 0.944 0.016 0.036
#> GSM74393 4 0.4374 0.0646 0.000 0.000 0.016 0.532 0.448 0.004
#> GSM74394 6 0.6169 -0.2847 0.008 0.000 0.304 0.000 0.244 0.444
#> GSM74239 1 0.2260 0.7625 0.860 0.000 0.000 0.140 0.000 0.000
#> GSM74364 1 0.2164 0.8147 0.900 0.000 0.000 0.032 0.000 0.068
#> GSM74365 1 0.0291 0.8600 0.992 0.000 0.000 0.004 0.000 0.004
#> GSM74366 1 0.4543 0.5898 0.576 0.040 0.000 0.000 0.000 0.384
#> GSM74367 1 0.0146 0.8585 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM74377 1 0.2631 0.8143 0.820 0.000 0.000 0.000 0.000 0.180
#> GSM74378 1 0.3823 0.5740 0.564 0.000 0.000 0.000 0.000 0.436
#> GSM74379 1 0.1714 0.8529 0.908 0.000 0.000 0.000 0.000 0.092
#> GSM74380 1 0.0937 0.8618 0.960 0.000 0.000 0.000 0.000 0.040
#> GSM74381 1 0.2416 0.8289 0.844 0.000 0.000 0.000 0.000 0.156
#> GSM121357 3 0.4093 0.4617 0.004 0.004 0.552 0.000 0.000 0.440
#> GSM121361 2 0.4389 0.4642 0.024 0.528 0.000 0.000 0.000 0.448
#> GSM121363 2 0.4374 0.4688 0.016 0.532 0.004 0.000 0.000 0.448
#> GSM121368 2 0.4482 0.4624 0.012 0.528 0.012 0.000 0.000 0.448
#> GSM121369 3 0.4712 0.4268 0.004 0.000 0.512 0.000 0.036 0.448
#> GSM74368 1 0.5918 0.5529 0.580 0.000 0.052 0.108 0.000 0.260
#> GSM74369 1 0.0935 0.8536 0.964 0.000 0.032 0.000 0.000 0.004
#> GSM74370 1 0.2219 0.8386 0.864 0.000 0.000 0.000 0.000 0.136
#> GSM74371 6 0.5543 0.5113 0.140 0.000 0.000 0.372 0.000 0.488
#> GSM74372 1 0.1757 0.8251 0.916 0.000 0.000 0.076 0.000 0.008
#> GSM74373 1 0.1814 0.8498 0.900 0.000 0.000 0.000 0.000 0.100
#> GSM74374 1 0.0000 0.8593 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM74375 1 0.1411 0.8469 0.936 0.000 0.000 0.060 0.004 0.000
#> GSM74376 1 0.1765 0.8504 0.904 0.000 0.000 0.000 0.000 0.096
#> GSM74405 1 0.1957 0.8462 0.888 0.000 0.000 0.000 0.000 0.112
#> GSM74351 4 0.5966 -0.2723 0.352 0.000 0.000 0.420 0.000 0.228
#> GSM74352 1 0.3672 0.6525 0.632 0.000 0.000 0.000 0.000 0.368
#> GSM74353 1 0.0363 0.8586 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM74354 1 0.0820 0.8604 0.972 0.000 0.000 0.012 0.000 0.016
#> GSM74355 1 0.3672 0.6500 0.632 0.000 0.000 0.000 0.000 0.368
#> GSM74382 4 0.6016 -0.2952 0.340 0.000 0.000 0.412 0.000 0.248
#> GSM74383 1 0.0713 0.8548 0.972 0.000 0.000 0.028 0.000 0.000
#> GSM74384 1 0.2941 0.7872 0.780 0.000 0.000 0.000 0.000 0.220
#> GSM74385 6 0.6011 0.3930 0.272 0.000 0.000 0.296 0.000 0.432
#> GSM74386 1 0.2747 0.7828 0.860 0.000 0.000 0.044 0.000 0.096
#> GSM74395 1 0.0146 0.8585 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM74396 1 0.0146 0.8582 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM74397 1 0.5769 0.0200 0.504 0.000 0.016 0.360 0.000 0.120
#> GSM74398 1 0.0363 0.8617 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM74399 1 0.1387 0.8581 0.932 0.000 0.000 0.000 0.000 0.068
#> GSM74400 1 0.0603 0.8633 0.980 0.000 0.000 0.004 0.000 0.016
#> GSM74401 1 0.0603 0.8632 0.980 0.000 0.000 0.004 0.000 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:pam 100 1.70e-07 2
#> SD:pam 107 3.79e-30 3
#> SD:pam 96 2.55e-35 4
#> SD:pam 106 3.02e-45 5
#> SD:pam 97 1.16e-51 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 21168 rows and 121 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 0.948 0.931 0.974 0.4986 0.500 0.500
#> 3 3 0.599 0.652 0.823 0.2769 0.780 0.593
#> 4 4 0.630 0.774 0.851 0.1043 0.840 0.593
#> 5 5 0.831 0.810 0.891 0.1028 0.916 0.703
#> 6 6 0.795 0.610 0.783 0.0511 0.936 0.717
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
#> GSM74356 2 0.0376 0.9666 0.004 0.996
#> GSM74357 2 0.0376 0.9666 0.004 0.996
#> GSM74358 2 0.0376 0.9666 0.004 0.996
#> GSM74359 1 0.0000 0.9756 1.000 0.000
#> GSM74360 1 0.0000 0.9756 1.000 0.000
#> GSM74361 2 0.0938 0.9599 0.012 0.988
#> GSM74362 2 0.5408 0.8427 0.124 0.876
#> GSM74363 2 0.0376 0.9666 0.004 0.996
#> GSM74402 1 0.0000 0.9756 1.000 0.000
#> GSM74403 1 0.0000 0.9756 1.000 0.000
#> GSM74404 1 0.0000 0.9756 1.000 0.000
#> GSM74406 1 0.0000 0.9756 1.000 0.000
#> GSM74407 1 0.0000 0.9756 1.000 0.000
#> GSM74408 1 0.0000 0.9756 1.000 0.000
#> GSM74409 1 0.0000 0.9756 1.000 0.000
#> GSM74410 1 0.0000 0.9756 1.000 0.000
#> GSM119936 1 0.0000 0.9756 1.000 0.000
#> GSM119937 1 0.0000 0.9756 1.000 0.000
#> GSM74411 2 0.0000 0.9681 0.000 1.000
#> GSM74412 2 0.0000 0.9681 0.000 1.000
#> GSM74413 2 0.0000 0.9681 0.000 1.000
#> GSM74414 2 0.0376 0.9666 0.004 0.996
#> GSM74415 2 0.0376 0.9666 0.004 0.996
#> GSM121379 2 0.0000 0.9681 0.000 1.000
#> GSM121380 2 0.0000 0.9681 0.000 1.000
#> GSM121381 2 0.0000 0.9681 0.000 1.000
#> GSM121382 2 0.0000 0.9681 0.000 1.000
#> GSM121383 2 0.0000 0.9681 0.000 1.000
#> GSM121384 2 0.0000 0.9681 0.000 1.000
#> GSM121385 2 0.0000 0.9681 0.000 1.000
#> GSM121386 2 0.0000 0.9681 0.000 1.000
#> GSM121387 2 0.0000 0.9681 0.000 1.000
#> GSM121388 2 0.0000 0.9681 0.000 1.000
#> GSM121389 2 0.0000 0.9681 0.000 1.000
#> GSM121390 2 0.0000 0.9681 0.000 1.000
#> GSM121391 2 0.0000 0.9681 0.000 1.000
#> GSM121392 2 0.0000 0.9681 0.000 1.000
#> GSM121393 2 0.0000 0.9681 0.000 1.000
#> GSM121394 2 0.0000 0.9681 0.000 1.000
#> GSM121395 2 0.0000 0.9681 0.000 1.000
#> GSM121396 2 0.0000 0.9681 0.000 1.000
#> GSM121397 2 0.0000 0.9681 0.000 1.000
#> GSM121398 2 0.0000 0.9681 0.000 1.000
#> GSM121399 2 0.0000 0.9681 0.000 1.000
#> GSM74240 2 0.0000 0.9681 0.000 1.000
#> GSM74241 2 0.0000 0.9681 0.000 1.000
#> GSM74242 2 0.0000 0.9681 0.000 1.000
#> GSM74243 2 0.0000 0.9681 0.000 1.000
#> GSM74244 2 0.0000 0.9681 0.000 1.000
#> GSM74245 2 0.0000 0.9681 0.000 1.000
#> GSM74246 2 0.0000 0.9681 0.000 1.000
#> GSM74247 2 0.0000 0.9681 0.000 1.000
#> GSM74248 2 0.0000 0.9681 0.000 1.000
#> GSM74416 1 0.0000 0.9756 1.000 0.000
#> GSM74417 1 0.0000 0.9756 1.000 0.000
#> GSM74418 1 0.0000 0.9756 1.000 0.000
#> GSM74419 1 0.0000 0.9756 1.000 0.000
#> GSM121358 2 0.0376 0.9666 0.004 0.996
#> GSM121359 2 0.0000 0.9681 0.000 1.000
#> GSM121360 1 0.0000 0.9756 1.000 0.000
#> GSM121362 1 0.0000 0.9756 1.000 0.000
#> GSM121364 1 0.0000 0.9756 1.000 0.000
#> GSM121365 2 0.0376 0.9666 0.004 0.996
#> GSM121366 2 0.0376 0.9666 0.004 0.996
#> GSM121367 2 0.0376 0.9666 0.004 0.996
#> GSM121370 2 0.0376 0.9666 0.004 0.996
#> GSM121371 2 0.0376 0.9666 0.004 0.996
#> GSM121372 2 0.0000 0.9681 0.000 1.000
#> GSM121373 1 0.0000 0.9756 1.000 0.000
#> GSM121374 1 0.0000 0.9756 1.000 0.000
#> GSM121407 2 0.0000 0.9681 0.000 1.000
#> GSM74387 2 0.5946 0.8177 0.144 0.856
#> GSM74388 1 0.9248 0.4708 0.660 0.340
#> GSM74389 1 0.7299 0.7291 0.796 0.204
#> GSM74390 1 0.0000 0.9756 1.000 0.000
#> GSM74391 1 0.0000 0.9756 1.000 0.000
#> GSM74392 1 0.0000 0.9756 1.000 0.000
#> GSM74393 1 0.3733 0.9025 0.928 0.072
#> GSM74394 2 1.0000 0.0134 0.496 0.504
#> GSM74239 1 0.0000 0.9756 1.000 0.000
#> GSM74364 1 0.0000 0.9756 1.000 0.000
#> GSM74365 1 0.0000 0.9756 1.000 0.000
#> GSM74366 1 0.0000 0.9756 1.000 0.000
#> GSM74367 1 0.0000 0.9756 1.000 0.000
#> GSM74377 1 0.0000 0.9756 1.000 0.000
#> GSM74378 1 0.0000 0.9756 1.000 0.000
#> GSM74379 1 0.0000 0.9756 1.000 0.000
#> GSM74380 1 0.0000 0.9756 1.000 0.000
#> GSM74381 1 0.0000 0.9756 1.000 0.000
#> GSM121357 2 0.0376 0.9666 0.004 0.996
#> GSM121361 1 0.9686 0.3280 0.604 0.396
#> GSM121363 2 0.9933 0.1745 0.452 0.548
#> GSM121368 2 0.9491 0.4154 0.368 0.632
#> GSM121369 1 0.9996 0.0188 0.512 0.488
#> GSM74368 1 0.0000 0.9756 1.000 0.000
#> GSM74369 1 0.0000 0.9756 1.000 0.000
#> GSM74370 1 0.0000 0.9756 1.000 0.000
#> GSM74371 1 0.0000 0.9756 1.000 0.000
#> GSM74372 1 0.0000 0.9756 1.000 0.000
#> GSM74373 1 0.0000 0.9756 1.000 0.000
#> GSM74374 1 0.0000 0.9756 1.000 0.000
#> GSM74375 1 0.0000 0.9756 1.000 0.000
#> GSM74376 1 0.0000 0.9756 1.000 0.000
#> GSM74405 1 0.0000 0.9756 1.000 0.000
#> GSM74351 1 0.0000 0.9756 1.000 0.000
#> GSM74352 1 0.0000 0.9756 1.000 0.000
#> GSM74353 1 0.0000 0.9756 1.000 0.000
#> GSM74354 1 0.0000 0.9756 1.000 0.000
#> GSM74355 1 0.0000 0.9756 1.000 0.000
#> GSM74382 1 0.0000 0.9756 1.000 0.000
#> GSM74383 1 0.0000 0.9756 1.000 0.000
#> GSM74384 1 0.0000 0.9756 1.000 0.000
#> GSM74385 1 0.0000 0.9756 1.000 0.000
#> GSM74386 1 0.0000 0.9756 1.000 0.000
#> GSM74395 1 0.0000 0.9756 1.000 0.000
#> GSM74396 1 0.0000 0.9756 1.000 0.000
#> GSM74397 1 0.0000 0.9756 1.000 0.000
#> GSM74398 1 0.0000 0.9756 1.000 0.000
#> GSM74399 1 0.0000 0.9756 1.000 0.000
#> GSM74400 1 0.0000 0.9756 1.000 0.000
#> GSM74401 1 0.0000 0.9756 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM74356 2 0.6180 0.7370 0.000 0.584 0.416
#> GSM74357 2 0.6180 0.7370 0.000 0.584 0.416
#> GSM74358 2 0.6180 0.7370 0.000 0.584 0.416
#> GSM74359 3 0.6111 0.5498 0.396 0.000 0.604
#> GSM74360 3 0.6168 0.5288 0.412 0.000 0.588
#> GSM74361 2 0.7207 0.7206 0.032 0.584 0.384
#> GSM74362 2 0.7806 0.6958 0.064 0.584 0.352
#> GSM74363 2 0.6180 0.7370 0.000 0.584 0.416
#> GSM74402 1 0.6307 -0.3391 0.512 0.000 0.488
#> GSM74403 1 0.1411 0.8000 0.964 0.000 0.036
#> GSM74404 1 0.1411 0.8000 0.964 0.000 0.036
#> GSM74406 1 0.6308 -0.3474 0.508 0.000 0.492
#> GSM74407 1 0.6308 -0.3494 0.508 0.000 0.492
#> GSM74408 3 0.6140 0.4780 0.404 0.000 0.596
#> GSM74409 3 0.6140 0.4780 0.404 0.000 0.596
#> GSM74410 3 0.6140 0.4780 0.404 0.000 0.596
#> GSM119936 1 0.6307 -0.3391 0.512 0.000 0.488
#> GSM119937 3 0.6140 0.4780 0.404 0.000 0.596
#> GSM74411 2 0.5397 0.7760 0.000 0.720 0.280
#> GSM74412 2 0.5178 0.7853 0.000 0.744 0.256
#> GSM74413 2 0.5327 0.7795 0.000 0.728 0.272
#> GSM74414 2 0.5085 0.7603 0.092 0.836 0.072
#> GSM74415 2 0.5497 0.7747 0.000 0.708 0.292
#> GSM121379 2 0.0000 0.8207 0.000 1.000 0.000
#> GSM121380 2 0.0000 0.8207 0.000 1.000 0.000
#> GSM121381 2 0.0000 0.8207 0.000 1.000 0.000
#> GSM121382 2 0.0000 0.8207 0.000 1.000 0.000
#> GSM121383 2 0.0000 0.8207 0.000 1.000 0.000
#> GSM121384 2 0.0000 0.8207 0.000 1.000 0.000
#> GSM121385 2 0.0000 0.8207 0.000 1.000 0.000
#> GSM121386 2 0.0000 0.8207 0.000 1.000 0.000
#> GSM121387 2 0.0000 0.8207 0.000 1.000 0.000
#> GSM121388 2 0.0747 0.8212 0.000 0.984 0.016
#> GSM121389 2 0.0000 0.8207 0.000 1.000 0.000
#> GSM121390 2 0.0000 0.8207 0.000 1.000 0.000
#> GSM121391 2 0.0000 0.8207 0.000 1.000 0.000
#> GSM121392 2 0.0000 0.8207 0.000 1.000 0.000
#> GSM121393 2 0.0747 0.8212 0.000 0.984 0.016
#> GSM121394 2 0.0747 0.8212 0.000 0.984 0.016
#> GSM121395 2 0.0000 0.8207 0.000 1.000 0.000
#> GSM121396 2 0.4654 0.7968 0.000 0.792 0.208
#> GSM121397 2 0.0000 0.8207 0.000 1.000 0.000
#> GSM121398 2 0.0000 0.8207 0.000 1.000 0.000
#> GSM121399 2 0.0000 0.8207 0.000 1.000 0.000
#> GSM74240 3 0.3686 0.5214 0.000 0.140 0.860
#> GSM74241 3 0.3686 0.5214 0.000 0.140 0.860
#> GSM74242 3 0.3686 0.5214 0.000 0.140 0.860
#> GSM74243 3 0.3686 0.5214 0.000 0.140 0.860
#> GSM74244 3 0.3686 0.5214 0.000 0.140 0.860
#> GSM74245 3 0.3686 0.5214 0.000 0.140 0.860
#> GSM74246 3 0.3686 0.5214 0.000 0.140 0.860
#> GSM74247 3 0.3686 0.5214 0.000 0.140 0.860
#> GSM74248 3 0.3686 0.5214 0.000 0.140 0.860
#> GSM74416 1 0.6309 -0.2573 0.504 0.000 0.496
#> GSM74417 1 0.6309 -0.2573 0.504 0.000 0.496
#> GSM74418 1 0.6309 -0.2573 0.504 0.000 0.496
#> GSM74419 3 0.6140 0.4780 0.404 0.000 0.596
#> GSM121358 2 0.6180 0.7370 0.000 0.584 0.416
#> GSM121359 2 0.5363 0.7809 0.000 0.724 0.276
#> GSM121360 3 0.6252 0.4609 0.444 0.000 0.556
#> GSM121362 1 0.6062 0.1282 0.616 0.000 0.384
#> GSM121364 3 0.6126 0.5462 0.400 0.000 0.600
#> GSM121365 2 0.6180 0.7370 0.000 0.584 0.416
#> GSM121366 2 0.6180 0.7370 0.000 0.584 0.416
#> GSM121367 2 0.6180 0.7370 0.000 0.584 0.416
#> GSM121370 2 0.6180 0.7370 0.000 0.584 0.416
#> GSM121371 2 0.6180 0.7370 0.000 0.584 0.416
#> GSM121372 2 0.5291 0.7808 0.000 0.732 0.268
#> GSM121373 3 0.6140 0.5403 0.404 0.000 0.596
#> GSM121374 3 0.6126 0.5462 0.400 0.000 0.600
#> GSM121407 2 0.5098 0.7878 0.000 0.752 0.248
#> GSM74387 2 0.9766 -0.0768 0.236 0.416 0.348
#> GSM74388 1 0.7545 0.3937 0.652 0.076 0.272
#> GSM74389 3 0.5733 0.5856 0.324 0.000 0.676
#> GSM74390 1 0.3816 0.6672 0.852 0.000 0.148
#> GSM74391 3 0.6126 0.5462 0.400 0.000 0.600
#> GSM74392 3 0.6126 0.5462 0.400 0.000 0.600
#> GSM74393 3 0.5760 0.5848 0.328 0.000 0.672
#> GSM74394 1 0.7622 0.3868 0.648 0.080 0.272
#> GSM74239 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74364 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74365 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74366 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74367 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74377 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74378 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74379 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74380 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74381 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM121357 2 0.5093 0.7643 0.088 0.836 0.076
#> GSM121361 1 0.7622 0.3868 0.648 0.080 0.272
#> GSM121363 1 0.7622 0.3868 0.648 0.080 0.272
#> GSM121368 1 0.7770 0.3708 0.640 0.088 0.272
#> GSM121369 1 0.7622 0.3868 0.648 0.080 0.272
#> GSM74368 1 0.0592 0.8246 0.988 0.000 0.012
#> GSM74369 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74370 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74371 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74372 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74373 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74374 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74375 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74376 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74405 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74351 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74352 1 0.0237 0.8316 0.996 0.000 0.004
#> GSM74353 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74354 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74355 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74382 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74383 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74384 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74385 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74386 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74395 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74396 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74397 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74398 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74399 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74400 1 0.0000 0.8350 1.000 0.000 0.000
#> GSM74401 1 0.0000 0.8350 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM74356 3 0.1059 0.854 0.000 0.016 0.972 0.012
#> GSM74357 3 0.1182 0.849 0.000 0.016 0.968 0.016
#> GSM74358 3 0.1182 0.849 0.000 0.016 0.968 0.016
#> GSM74359 4 0.7551 0.720 0.272 0.000 0.240 0.488
#> GSM74360 4 0.7564 0.684 0.328 0.000 0.208 0.464
#> GSM74361 3 0.1297 0.847 0.000 0.016 0.964 0.020
#> GSM74362 3 0.1297 0.847 0.000 0.016 0.964 0.020
#> GSM74363 3 0.1182 0.855 0.000 0.016 0.968 0.016
#> GSM74402 4 0.7582 0.674 0.336 0.000 0.208 0.456
#> GSM74403 1 0.3494 0.665 0.824 0.000 0.172 0.004
#> GSM74404 1 0.3751 0.620 0.800 0.000 0.196 0.004
#> GSM74406 4 0.7596 0.680 0.332 0.000 0.212 0.456
#> GSM74407 4 0.7576 0.689 0.324 0.000 0.212 0.464
#> GSM74408 4 0.7587 0.704 0.244 0.000 0.276 0.480
#> GSM74409 4 0.7587 0.704 0.244 0.000 0.276 0.480
#> GSM74410 4 0.7578 0.701 0.236 0.000 0.284 0.480
#> GSM119936 4 0.7609 0.695 0.312 0.000 0.224 0.464
#> GSM119937 4 0.7587 0.704 0.244 0.000 0.276 0.480
#> GSM74411 3 0.3166 0.842 0.000 0.016 0.868 0.116
#> GSM74412 3 0.3108 0.843 0.000 0.016 0.872 0.112
#> GSM74413 3 0.3108 0.843 0.000 0.016 0.872 0.112
#> GSM74414 3 0.3736 0.839 0.004 0.024 0.844 0.128
#> GSM74415 3 0.3166 0.842 0.000 0.016 0.868 0.116
#> GSM121379 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM121380 2 0.0188 0.984 0.000 0.996 0.004 0.000
#> GSM121381 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM121382 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM121383 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM121384 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM121385 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM121386 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM121387 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM121388 2 0.1256 0.961 0.000 0.964 0.008 0.028
#> GSM121389 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM121390 2 0.0188 0.984 0.000 0.996 0.004 0.000
#> GSM121391 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM121392 2 0.3048 0.875 0.000 0.876 0.016 0.108
#> GSM121393 2 0.1807 0.941 0.000 0.940 0.008 0.052
#> GSM121394 2 0.1004 0.967 0.000 0.972 0.004 0.024
#> GSM121395 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM121396 3 0.6027 0.659 0.000 0.244 0.664 0.092
#> GSM121397 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM121398 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM121399 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM74240 4 0.0921 0.523 0.000 0.000 0.028 0.972
#> GSM74241 4 0.1022 0.519 0.000 0.000 0.032 0.968
#> GSM74242 4 0.1022 0.522 0.000 0.000 0.032 0.968
#> GSM74243 4 0.1022 0.522 0.000 0.000 0.032 0.968
#> GSM74244 4 0.0921 0.523 0.000 0.000 0.028 0.972
#> GSM74245 4 0.0921 0.523 0.000 0.000 0.028 0.972
#> GSM74246 4 0.0921 0.523 0.000 0.000 0.028 0.972
#> GSM74247 4 0.0921 0.523 0.000 0.000 0.028 0.972
#> GSM74248 4 0.0921 0.523 0.000 0.000 0.028 0.972
#> GSM74416 1 0.7767 -0.363 0.432 0.000 0.268 0.300
#> GSM74417 1 0.7732 -0.328 0.444 0.000 0.268 0.288
#> GSM74418 1 0.7706 -0.305 0.452 0.000 0.268 0.280
#> GSM74419 4 0.7587 0.704 0.244 0.000 0.276 0.480
#> GSM121358 3 0.0779 0.855 0.000 0.016 0.980 0.004
#> GSM121359 3 0.3056 0.840 0.000 0.072 0.888 0.040
#> GSM121360 4 0.7325 0.604 0.368 0.000 0.160 0.472
#> GSM121362 1 0.6334 -0.382 0.484 0.000 0.060 0.456
#> GSM121364 4 0.7640 0.712 0.296 0.000 0.240 0.464
#> GSM121365 3 0.0927 0.854 0.000 0.016 0.976 0.008
#> GSM121366 3 0.1398 0.848 0.000 0.040 0.956 0.004
#> GSM121367 3 0.0927 0.854 0.000 0.016 0.976 0.008
#> GSM121370 3 0.0592 0.854 0.000 0.016 0.984 0.000
#> GSM121371 3 0.0927 0.855 0.000 0.016 0.976 0.008
#> GSM121372 3 0.2586 0.851 0.000 0.048 0.912 0.040
#> GSM121373 4 0.7640 0.712 0.296 0.000 0.240 0.464
#> GSM121374 4 0.7640 0.712 0.296 0.000 0.240 0.464
#> GSM121407 3 0.3149 0.848 0.000 0.032 0.880 0.088
#> GSM74387 3 0.4661 0.800 0.052 0.008 0.800 0.140
#> GSM74388 3 0.6404 0.647 0.220 0.000 0.644 0.136
#> GSM74389 4 0.7512 0.721 0.268 0.000 0.236 0.496
#> GSM74390 1 0.4500 0.319 0.684 0.000 0.000 0.316
#> GSM74391 4 0.7640 0.712 0.296 0.000 0.240 0.464
#> GSM74392 4 0.7613 0.716 0.288 0.000 0.240 0.472
#> GSM74393 4 0.7512 0.721 0.268 0.000 0.236 0.496
#> GSM74394 3 0.6205 0.680 0.196 0.000 0.668 0.136
#> GSM74239 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM74364 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM74365 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM74366 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM74367 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM74377 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM74378 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM74379 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM74380 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM74381 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM121357 3 0.3932 0.837 0.004 0.032 0.836 0.128
#> GSM121361 3 0.6308 0.665 0.208 0.000 0.656 0.136
#> GSM121363 3 0.6205 0.680 0.196 0.000 0.668 0.136
#> GSM121368 3 0.5855 0.718 0.160 0.000 0.704 0.136
#> GSM121369 3 0.6112 0.683 0.196 0.000 0.676 0.128
#> GSM74368 1 0.3367 0.741 0.864 0.000 0.028 0.108
#> GSM74369 1 0.0188 0.892 0.996 0.000 0.000 0.004
#> GSM74370 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM74371 1 0.0188 0.891 0.996 0.000 0.000 0.004
#> GSM74372 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM74373 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM74374 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM74375 1 0.0188 0.892 0.996 0.000 0.000 0.004
#> GSM74376 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM74405 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM74351 1 0.2662 0.800 0.900 0.000 0.084 0.016
#> GSM74352 1 0.1059 0.876 0.972 0.000 0.016 0.012
#> GSM74353 1 0.0188 0.892 0.996 0.000 0.000 0.004
#> GSM74354 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM74355 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM74382 1 0.0657 0.883 0.984 0.000 0.012 0.004
#> GSM74383 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM74384 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM74385 1 0.0336 0.888 0.992 0.000 0.008 0.000
#> GSM74386 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM74395 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM74396 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM74397 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM74398 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM74399 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM74400 1 0.3278 0.761 0.864 0.000 0.116 0.020
#> GSM74401 1 0.2563 0.816 0.908 0.000 0.072 0.020
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM74356 3 0.0000 0.9392 0.000 0.000 1.000 0.000 0.000
#> GSM74357 3 0.0000 0.9392 0.000 0.000 1.000 0.000 0.000
#> GSM74358 3 0.0000 0.9392 0.000 0.000 1.000 0.000 0.000
#> GSM74359 4 0.3907 0.7450 0.016 0.000 0.008 0.772 0.204
#> GSM74360 4 0.2959 0.8040 0.036 0.000 0.000 0.864 0.100
#> GSM74361 3 0.0000 0.9392 0.000 0.000 1.000 0.000 0.000
#> GSM74362 3 0.0290 0.9340 0.000 0.000 0.992 0.008 0.000
#> GSM74363 3 0.0000 0.9392 0.000 0.000 1.000 0.000 0.000
#> GSM74402 4 0.0609 0.8154 0.020 0.000 0.000 0.980 0.000
#> GSM74403 1 0.3910 0.6420 0.720 0.000 0.000 0.272 0.008
#> GSM74404 1 0.3910 0.6418 0.720 0.000 0.000 0.272 0.008
#> GSM74406 4 0.0898 0.8165 0.020 0.000 0.000 0.972 0.008
#> GSM74407 4 0.2850 0.8060 0.036 0.000 0.000 0.872 0.092
#> GSM74408 4 0.0290 0.8122 0.008 0.000 0.000 0.992 0.000
#> GSM74409 4 0.0290 0.8122 0.008 0.000 0.000 0.992 0.000
#> GSM74410 4 0.0290 0.8122 0.008 0.000 0.000 0.992 0.000
#> GSM119936 4 0.0609 0.8154 0.020 0.000 0.000 0.980 0.000
#> GSM119937 4 0.0290 0.8122 0.008 0.000 0.000 0.992 0.000
#> GSM74411 3 0.3012 0.8417 0.000 0.000 0.852 0.024 0.124
#> GSM74412 3 0.2020 0.8893 0.000 0.000 0.900 0.000 0.100
#> GSM74413 3 0.2020 0.8893 0.000 0.000 0.900 0.000 0.100
#> GSM74414 5 0.7673 0.0788 0.012 0.208 0.364 0.036 0.380
#> GSM74415 3 0.2597 0.8631 0.000 0.000 0.884 0.024 0.092
#> GSM121379 2 0.0000 0.9945 0.000 1.000 0.000 0.000 0.000
#> GSM121380 2 0.0000 0.9945 0.000 1.000 0.000 0.000 0.000
#> GSM121381 2 0.0000 0.9945 0.000 1.000 0.000 0.000 0.000
#> GSM121382 2 0.0000 0.9945 0.000 1.000 0.000 0.000 0.000
#> GSM121383 2 0.0000 0.9945 0.000 1.000 0.000 0.000 0.000
#> GSM121384 2 0.0000 0.9945 0.000 1.000 0.000 0.000 0.000
#> GSM121385 2 0.0000 0.9945 0.000 1.000 0.000 0.000 0.000
#> GSM121386 2 0.0000 0.9945 0.000 1.000 0.000 0.000 0.000
#> GSM121387 2 0.0000 0.9945 0.000 1.000 0.000 0.000 0.000
#> GSM121388 2 0.0290 0.9874 0.000 0.992 0.000 0.000 0.008
#> GSM121389 2 0.0000 0.9945 0.000 1.000 0.000 0.000 0.000
#> GSM121390 2 0.0000 0.9945 0.000 1.000 0.000 0.000 0.000
#> GSM121391 2 0.0000 0.9945 0.000 1.000 0.000 0.000 0.000
#> GSM121392 2 0.1410 0.9328 0.000 0.940 0.000 0.000 0.060
#> GSM121393 2 0.0794 0.9695 0.000 0.972 0.000 0.000 0.028
#> GSM121394 2 0.0000 0.9945 0.000 1.000 0.000 0.000 0.000
#> GSM121395 2 0.0000 0.9945 0.000 1.000 0.000 0.000 0.000
#> GSM121396 3 0.5678 0.3004 0.000 0.392 0.524 0.000 0.084
#> GSM121397 2 0.0000 0.9945 0.000 1.000 0.000 0.000 0.000
#> GSM121398 2 0.0000 0.9945 0.000 1.000 0.000 0.000 0.000
#> GSM121399 2 0.0000 0.9945 0.000 1.000 0.000 0.000 0.000
#> GSM74240 5 0.2595 0.6498 0.000 0.000 0.032 0.080 0.888
#> GSM74241 5 0.2754 0.6501 0.000 0.000 0.040 0.080 0.880
#> GSM74242 5 0.5052 -0.0464 0.000 0.000 0.036 0.412 0.552
#> GSM74243 5 0.4982 -0.0465 0.000 0.000 0.032 0.412 0.556
#> GSM74244 5 0.2595 0.6498 0.000 0.000 0.032 0.080 0.888
#> GSM74245 5 0.2932 0.6322 0.000 0.000 0.032 0.104 0.864
#> GSM74246 5 0.2595 0.6498 0.000 0.000 0.032 0.080 0.888
#> GSM74247 5 0.2595 0.6498 0.000 0.000 0.032 0.080 0.888
#> GSM74248 5 0.3182 0.6116 0.000 0.000 0.032 0.124 0.844
#> GSM74416 4 0.2110 0.7614 0.072 0.000 0.000 0.912 0.016
#> GSM74417 4 0.2110 0.7614 0.072 0.000 0.000 0.912 0.016
#> GSM74418 4 0.2233 0.7524 0.080 0.000 0.000 0.904 0.016
#> GSM74419 4 0.0290 0.8122 0.008 0.000 0.000 0.992 0.000
#> GSM121358 3 0.0000 0.9392 0.000 0.000 1.000 0.000 0.000
#> GSM121359 3 0.0794 0.9276 0.000 0.000 0.972 0.000 0.028
#> GSM121360 4 0.6568 0.2037 0.252 0.000 0.000 0.472 0.276
#> GSM121362 1 0.6599 -0.0475 0.464 0.000 0.000 0.264 0.272
#> GSM121364 4 0.3463 0.7784 0.016 0.000 0.008 0.820 0.156
#> GSM121365 3 0.0000 0.9392 0.000 0.000 1.000 0.000 0.000
#> GSM121366 3 0.0000 0.9392 0.000 0.000 1.000 0.000 0.000
#> GSM121367 3 0.0000 0.9392 0.000 0.000 1.000 0.000 0.000
#> GSM121370 3 0.0000 0.9392 0.000 0.000 1.000 0.000 0.000
#> GSM121371 3 0.0000 0.9392 0.000 0.000 1.000 0.000 0.000
#> GSM121372 3 0.1043 0.9250 0.000 0.000 0.960 0.000 0.040
#> GSM121373 4 0.4219 0.6914 0.024 0.000 0.000 0.716 0.260
#> GSM121374 4 0.3516 0.7753 0.020 0.000 0.004 0.812 0.164
#> GSM121407 3 0.1544 0.9114 0.000 0.000 0.932 0.000 0.068
#> GSM74387 5 0.6205 0.5880 0.096 0.000 0.208 0.056 0.640
#> GSM74388 5 0.5656 0.6390 0.200 0.000 0.068 0.048 0.684
#> GSM74389 4 0.4464 0.6334 0.012 0.000 0.008 0.676 0.304
#> GSM74390 1 0.5740 0.3324 0.600 0.000 0.000 0.128 0.272
#> GSM74391 4 0.4286 0.6894 0.020 0.000 0.004 0.716 0.260
#> GSM74392 4 0.4153 0.7144 0.016 0.000 0.008 0.740 0.236
#> GSM74393 4 0.4464 0.6334 0.012 0.000 0.008 0.676 0.304
#> GSM74394 5 0.5656 0.6390 0.200 0.000 0.068 0.048 0.684
#> GSM74239 1 0.1117 0.9197 0.964 0.000 0.000 0.016 0.020
#> GSM74364 1 0.1211 0.9185 0.960 0.000 0.000 0.016 0.024
#> GSM74365 1 0.0324 0.9237 0.992 0.000 0.000 0.004 0.004
#> GSM74366 1 0.1106 0.9164 0.964 0.000 0.000 0.012 0.024
#> GSM74367 1 0.0451 0.9234 0.988 0.000 0.000 0.008 0.004
#> GSM74377 1 0.0693 0.9194 0.980 0.000 0.000 0.008 0.012
#> GSM74378 1 0.0798 0.9182 0.976 0.000 0.000 0.008 0.016
#> GSM74379 1 0.0566 0.9230 0.984 0.000 0.000 0.004 0.012
#> GSM74380 1 0.0566 0.9230 0.984 0.000 0.000 0.004 0.012
#> GSM74381 1 0.0693 0.9194 0.980 0.000 0.000 0.008 0.012
#> GSM121357 5 0.7568 0.0692 0.008 0.204 0.368 0.036 0.384
#> GSM121361 5 0.5656 0.6390 0.200 0.000 0.068 0.048 0.684
#> GSM121363 5 0.5656 0.6390 0.200 0.000 0.068 0.048 0.684
#> GSM121368 5 0.5656 0.6390 0.200 0.000 0.068 0.048 0.684
#> GSM121369 5 0.5656 0.6390 0.200 0.000 0.068 0.048 0.684
#> GSM74368 1 0.4159 0.7386 0.776 0.000 0.000 0.156 0.068
#> GSM74369 1 0.1012 0.9216 0.968 0.000 0.000 0.012 0.020
#> GSM74370 1 0.0451 0.9234 0.988 0.000 0.000 0.008 0.004
#> GSM74371 1 0.1661 0.9116 0.940 0.000 0.000 0.024 0.036
#> GSM74372 1 0.0566 0.9239 0.984 0.000 0.000 0.004 0.012
#> GSM74373 1 0.0510 0.9220 0.984 0.000 0.000 0.000 0.016
#> GSM74374 1 0.0671 0.9228 0.980 0.000 0.000 0.004 0.016
#> GSM74375 1 0.1216 0.9190 0.960 0.000 0.000 0.020 0.020
#> GSM74376 1 0.0798 0.9187 0.976 0.000 0.000 0.008 0.016
#> GSM74405 1 0.0771 0.9238 0.976 0.000 0.000 0.004 0.020
#> GSM74351 1 0.3574 0.7817 0.804 0.000 0.000 0.168 0.028
#> GSM74352 1 0.1549 0.9090 0.944 0.000 0.000 0.040 0.016
#> GSM74353 1 0.1018 0.9203 0.968 0.000 0.000 0.016 0.016
#> GSM74354 1 0.0290 0.9237 0.992 0.000 0.000 0.008 0.000
#> GSM74355 1 0.0693 0.9194 0.980 0.000 0.000 0.008 0.012
#> GSM74382 1 0.3141 0.7914 0.832 0.000 0.000 0.152 0.016
#> GSM74383 1 0.0807 0.9230 0.976 0.000 0.000 0.012 0.012
#> GSM74384 1 0.1310 0.9156 0.956 0.000 0.000 0.020 0.024
#> GSM74385 1 0.1568 0.9133 0.944 0.000 0.000 0.020 0.036
#> GSM74386 1 0.0324 0.9235 0.992 0.000 0.000 0.004 0.004
#> GSM74395 1 0.0451 0.9239 0.988 0.000 0.000 0.004 0.008
#> GSM74396 1 0.0324 0.9235 0.992 0.000 0.000 0.004 0.004
#> GSM74397 1 0.1818 0.9050 0.932 0.000 0.000 0.044 0.024
#> GSM74398 1 0.0671 0.9240 0.980 0.000 0.000 0.004 0.016
#> GSM74399 1 0.0671 0.9228 0.980 0.000 0.000 0.004 0.016
#> GSM74400 1 0.1701 0.9019 0.936 0.000 0.000 0.048 0.016
#> GSM74401 1 0.1701 0.9019 0.936 0.000 0.000 0.048 0.016
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM74356 3 0.0000 0.9309 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM74357 3 0.0000 0.9309 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM74358 3 0.0000 0.9309 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM74359 4 0.3776 0.6967 0.000 0.000 0.000 0.756 0.196 0.048
#> GSM74360 4 0.3109 0.7504 0.008 0.000 0.000 0.848 0.076 0.068
#> GSM74361 3 0.0000 0.9309 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM74362 3 0.0146 0.9287 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM74363 3 0.0000 0.9309 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM74402 4 0.0146 0.7599 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM74403 1 0.4201 0.3109 0.664 0.000 0.000 0.300 0.000 0.036
#> GSM74404 1 0.4183 0.3132 0.668 0.000 0.000 0.296 0.000 0.036
#> GSM74406 4 0.0000 0.7598 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM74407 4 0.2793 0.7557 0.020 0.000 0.000 0.876 0.060 0.044
#> GSM74408 4 0.0146 0.7599 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM74409 4 0.0146 0.7599 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM74410 4 0.0000 0.7598 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM119936 4 0.0260 0.7584 0.008 0.000 0.000 0.992 0.000 0.000
#> GSM119937 4 0.0146 0.7599 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM74411 3 0.1908 0.8771 0.000 0.000 0.900 0.000 0.096 0.004
#> GSM74412 3 0.1531 0.8985 0.000 0.000 0.928 0.000 0.068 0.004
#> GSM74413 3 0.1531 0.8985 0.000 0.000 0.928 0.000 0.068 0.004
#> GSM74414 6 0.7532 -0.5050 0.000 0.176 0.224 0.000 0.236 0.364
#> GSM74415 3 0.1753 0.8879 0.000 0.000 0.912 0.000 0.084 0.004
#> GSM121379 2 0.0000 0.9982 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121380 2 0.0000 0.9982 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121381 2 0.0000 0.9982 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121382 2 0.0000 0.9982 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121383 2 0.0000 0.9982 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121384 2 0.0000 0.9982 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121385 2 0.0000 0.9982 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121386 2 0.0000 0.9982 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121387 2 0.0000 0.9982 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121388 2 0.0000 0.9982 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121389 2 0.0000 0.9982 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121390 2 0.0000 0.9982 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121391 2 0.0000 0.9982 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121392 2 0.0632 0.9741 0.000 0.976 0.000 0.000 0.024 0.000
#> GSM121393 2 0.0146 0.9949 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM121394 2 0.0146 0.9947 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM121395 2 0.0000 0.9982 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121396 3 0.4535 0.5440 0.000 0.296 0.644 0.000 0.060 0.000
#> GSM121397 2 0.0000 0.9982 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121398 2 0.0000 0.9982 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121399 2 0.0000 0.9982 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM74240 5 0.1267 0.7029 0.000 0.000 0.000 0.060 0.940 0.000
#> GSM74241 5 0.1219 0.7068 0.000 0.000 0.004 0.048 0.948 0.000
#> GSM74242 5 0.3490 0.4061 0.000 0.000 0.008 0.268 0.724 0.000
#> GSM74243 5 0.3244 0.4067 0.000 0.000 0.000 0.268 0.732 0.000
#> GSM74244 5 0.1075 0.7076 0.000 0.000 0.000 0.048 0.952 0.000
#> GSM74245 5 0.1610 0.6850 0.000 0.000 0.000 0.084 0.916 0.000
#> GSM74246 5 0.1141 0.7070 0.000 0.000 0.000 0.052 0.948 0.000
#> GSM74247 5 0.1141 0.7070 0.000 0.000 0.000 0.052 0.948 0.000
#> GSM74248 5 0.2378 0.6087 0.000 0.000 0.000 0.152 0.848 0.000
#> GSM74416 4 0.3266 0.5396 0.272 0.000 0.000 0.728 0.000 0.000
#> GSM74417 4 0.3426 0.5336 0.276 0.000 0.000 0.720 0.000 0.004
#> GSM74418 4 0.3426 0.5336 0.276 0.000 0.000 0.720 0.000 0.004
#> GSM74419 4 0.0146 0.7599 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM121358 3 0.0000 0.9309 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121359 3 0.0260 0.9285 0.000 0.000 0.992 0.000 0.008 0.000
#> GSM121360 4 0.5675 0.6096 0.084 0.000 0.000 0.644 0.184 0.088
#> GSM121362 4 0.7227 0.2250 0.276 0.000 0.000 0.408 0.196 0.120
#> GSM121364 4 0.3370 0.7263 0.000 0.000 0.000 0.804 0.148 0.048
#> GSM121365 3 0.0000 0.9309 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121366 3 0.0000 0.9309 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121367 3 0.0000 0.9309 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121370 3 0.0000 0.9309 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121371 3 0.0000 0.9309 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121372 3 0.0363 0.9270 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM121373 4 0.4114 0.6866 0.008 0.000 0.000 0.740 0.200 0.052
#> GSM121374 4 0.3516 0.7183 0.000 0.000 0.000 0.788 0.164 0.048
#> GSM121407 3 0.0935 0.9168 0.000 0.000 0.964 0.000 0.032 0.004
#> GSM74387 5 0.4964 0.6480 0.000 0.000 0.056 0.004 0.512 0.428
#> GSM74388 5 0.4136 0.6772 0.012 0.000 0.000 0.000 0.560 0.428
#> GSM74389 4 0.4660 0.5478 0.008 0.000 0.000 0.636 0.308 0.048
#> GSM74390 1 0.7474 0.1222 0.388 0.000 0.000 0.200 0.192 0.220
#> GSM74391 4 0.4114 0.6866 0.008 0.000 0.000 0.740 0.200 0.052
#> GSM74392 4 0.3864 0.6851 0.000 0.000 0.000 0.744 0.208 0.048
#> GSM74393 4 0.4660 0.5478 0.008 0.000 0.000 0.636 0.308 0.048
#> GSM74394 5 0.3828 0.6800 0.000 0.000 0.000 0.000 0.560 0.440
#> GSM74239 1 0.2697 0.3283 0.812 0.000 0.000 0.000 0.000 0.188
#> GSM74364 1 0.0508 0.4705 0.984 0.000 0.000 0.004 0.000 0.012
#> GSM74365 1 0.3756 -0.3483 0.600 0.000 0.000 0.000 0.000 0.400
#> GSM74366 6 0.3851 0.6651 0.460 0.000 0.000 0.000 0.000 0.540
#> GSM74367 1 0.3851 -0.5420 0.540 0.000 0.000 0.000 0.000 0.460
#> GSM74377 6 0.3864 0.7061 0.480 0.000 0.000 0.000 0.000 0.520
#> GSM74378 6 0.3857 0.7067 0.468 0.000 0.000 0.000 0.000 0.532
#> GSM74379 6 0.3866 0.7044 0.484 0.000 0.000 0.000 0.000 0.516
#> GSM74380 6 0.3866 0.7044 0.484 0.000 0.000 0.000 0.000 0.516
#> GSM74381 6 0.3862 0.7106 0.476 0.000 0.000 0.000 0.000 0.524
#> GSM121357 3 0.7613 -0.1102 0.000 0.176 0.320 0.000 0.236 0.268
#> GSM121361 5 0.3961 0.6787 0.004 0.000 0.000 0.000 0.556 0.440
#> GSM121363 5 0.3828 0.6800 0.000 0.000 0.000 0.000 0.560 0.440
#> GSM121368 5 0.3828 0.6800 0.000 0.000 0.000 0.000 0.560 0.440
#> GSM121369 5 0.4098 0.6779 0.004 0.000 0.000 0.004 0.548 0.444
#> GSM74368 1 0.4779 0.1529 0.572 0.000 0.000 0.368 0.000 0.060
#> GSM74369 1 0.2605 0.4520 0.864 0.000 0.000 0.028 0.000 0.108
#> GSM74370 1 0.3717 -0.2795 0.616 0.000 0.000 0.000 0.000 0.384
#> GSM74371 1 0.1219 0.4663 0.948 0.000 0.000 0.004 0.000 0.048
#> GSM74372 1 0.3833 -0.4642 0.556 0.000 0.000 0.000 0.000 0.444
#> GSM74373 6 0.3862 0.7106 0.476 0.000 0.000 0.000 0.000 0.524
#> GSM74374 1 0.3854 -0.5955 0.536 0.000 0.000 0.000 0.000 0.464
#> GSM74375 1 0.3742 -0.0393 0.648 0.000 0.000 0.004 0.000 0.348
#> GSM74376 6 0.3864 0.6954 0.480 0.000 0.000 0.000 0.000 0.520
#> GSM74405 6 0.3864 0.7013 0.480 0.000 0.000 0.000 0.000 0.520
#> GSM74351 1 0.2039 0.4479 0.904 0.000 0.000 0.076 0.000 0.020
#> GSM74352 1 0.3198 0.2778 0.740 0.000 0.000 0.000 0.000 0.260
#> GSM74353 1 0.1524 0.4714 0.932 0.000 0.000 0.008 0.000 0.060
#> GSM74354 1 0.3175 0.1947 0.744 0.000 0.000 0.000 0.000 0.256
#> GSM74355 6 0.3857 0.7067 0.468 0.000 0.000 0.000 0.000 0.532
#> GSM74382 1 0.1700 0.4625 0.928 0.000 0.000 0.048 0.000 0.024
#> GSM74383 1 0.2912 0.2869 0.784 0.000 0.000 0.000 0.000 0.216
#> GSM74384 6 0.3857 0.6801 0.468 0.000 0.000 0.000 0.000 0.532
#> GSM74385 1 0.1082 0.4701 0.956 0.000 0.000 0.004 0.000 0.040
#> GSM74386 1 0.3937 -0.4264 0.572 0.000 0.000 0.004 0.000 0.424
#> GSM74395 1 0.3797 -0.3981 0.580 0.000 0.000 0.000 0.000 0.420
#> GSM74396 1 0.3817 -0.4531 0.568 0.000 0.000 0.000 0.000 0.432
#> GSM74397 1 0.3971 0.3899 0.748 0.000 0.000 0.068 0.000 0.184
#> GSM74398 1 0.3868 -0.6547 0.508 0.000 0.000 0.000 0.000 0.492
#> GSM74399 6 0.3866 0.7044 0.484 0.000 0.000 0.000 0.000 0.516
#> GSM74400 1 0.1075 0.4643 0.952 0.000 0.000 0.000 0.000 0.048
#> GSM74401 1 0.1141 0.4653 0.948 0.000 0.000 0.000 0.000 0.052
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 115 2.16e-12 2
#> SD:mclust 100 1.65e-22 3
#> SD:mclust 116 3.19e-34 4
#> SD:mclust 113 9.45e-45 5
#> SD:mclust 88 5.61e-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", "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 21168 rows and 121 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 0.866 0.929 0.968 0.5010 0.498 0.498
#> 3 3 0.539 0.634 0.834 0.3248 0.727 0.507
#> 4 4 0.546 0.627 0.769 0.1170 0.821 0.537
#> 5 5 0.620 0.564 0.731 0.0679 0.898 0.639
#> 6 6 0.727 0.712 0.819 0.0387 0.946 0.752
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
#> GSM74356 2 0.7299 0.7704 0.204 0.796
#> GSM74357 1 0.9933 0.1003 0.548 0.452
#> GSM74358 1 0.4161 0.8965 0.916 0.084
#> GSM74359 1 0.0000 0.9748 1.000 0.000
#> GSM74360 1 0.0000 0.9748 1.000 0.000
#> GSM74361 2 0.7528 0.7548 0.216 0.784
#> GSM74362 1 0.5519 0.8430 0.872 0.128
#> GSM74363 2 0.6973 0.7905 0.188 0.812
#> GSM74402 1 0.0000 0.9748 1.000 0.000
#> GSM74403 1 0.0000 0.9748 1.000 0.000
#> GSM74404 1 0.0000 0.9748 1.000 0.000
#> GSM74406 1 0.0000 0.9748 1.000 0.000
#> GSM74407 1 0.0000 0.9748 1.000 0.000
#> GSM74408 1 0.0000 0.9748 1.000 0.000
#> GSM74409 1 0.0000 0.9748 1.000 0.000
#> GSM74410 1 0.0000 0.9748 1.000 0.000
#> GSM119936 1 0.0000 0.9748 1.000 0.000
#> GSM119937 1 0.0000 0.9748 1.000 0.000
#> GSM74411 2 0.0000 0.9568 0.000 1.000
#> GSM74412 2 0.0000 0.9568 0.000 1.000
#> GSM74413 2 0.0000 0.9568 0.000 1.000
#> GSM74414 2 0.0000 0.9568 0.000 1.000
#> GSM74415 2 0.1184 0.9480 0.016 0.984
#> GSM121379 2 0.0000 0.9568 0.000 1.000
#> GSM121380 2 0.0000 0.9568 0.000 1.000
#> GSM121381 2 0.0000 0.9568 0.000 1.000
#> GSM121382 2 0.0000 0.9568 0.000 1.000
#> GSM121383 2 0.0000 0.9568 0.000 1.000
#> GSM121384 2 0.0000 0.9568 0.000 1.000
#> GSM121385 2 0.0000 0.9568 0.000 1.000
#> GSM121386 2 0.0000 0.9568 0.000 1.000
#> GSM121387 2 0.0000 0.9568 0.000 1.000
#> GSM121388 2 0.0000 0.9568 0.000 1.000
#> GSM121389 2 0.0000 0.9568 0.000 1.000
#> GSM121390 2 0.0000 0.9568 0.000 1.000
#> GSM121391 2 0.0000 0.9568 0.000 1.000
#> GSM121392 2 0.0000 0.9568 0.000 1.000
#> GSM121393 2 0.0000 0.9568 0.000 1.000
#> GSM121394 2 0.0000 0.9568 0.000 1.000
#> GSM121395 2 0.0000 0.9568 0.000 1.000
#> GSM121396 2 0.0000 0.9568 0.000 1.000
#> GSM121397 2 0.0000 0.9568 0.000 1.000
#> GSM121398 2 0.0000 0.9568 0.000 1.000
#> GSM121399 2 0.0000 0.9568 0.000 1.000
#> GSM74240 2 0.7219 0.7765 0.200 0.800
#> GSM74241 2 0.0938 0.9505 0.012 0.988
#> GSM74242 1 0.0000 0.9748 1.000 0.000
#> GSM74243 1 0.0000 0.9748 1.000 0.000
#> GSM74244 2 0.0938 0.9505 0.012 0.988
#> GSM74245 2 0.8555 0.6558 0.280 0.720
#> GSM74246 2 0.0000 0.9568 0.000 1.000
#> GSM74247 2 0.0000 0.9568 0.000 1.000
#> GSM74248 2 0.8267 0.6897 0.260 0.740
#> GSM74416 1 0.0000 0.9748 1.000 0.000
#> GSM74417 1 0.0000 0.9748 1.000 0.000
#> GSM74418 1 0.0000 0.9748 1.000 0.000
#> GSM74419 1 0.0000 0.9748 1.000 0.000
#> GSM121358 2 0.4562 0.8860 0.096 0.904
#> GSM121359 2 0.0000 0.9568 0.000 1.000
#> GSM121360 1 0.0000 0.9748 1.000 0.000
#> GSM121362 1 0.1633 0.9572 0.976 0.024
#> GSM121364 1 0.0000 0.9748 1.000 0.000
#> GSM121365 2 0.1184 0.9480 0.016 0.984
#> GSM121366 2 0.0000 0.9568 0.000 1.000
#> GSM121367 2 0.6438 0.8180 0.164 0.836
#> GSM121370 2 0.0938 0.9505 0.012 0.988
#> GSM121371 2 0.4815 0.8789 0.104 0.896
#> GSM121372 2 0.0000 0.9568 0.000 1.000
#> GSM121373 1 0.0000 0.9748 1.000 0.000
#> GSM121374 1 0.0000 0.9748 1.000 0.000
#> GSM121407 2 0.0000 0.9568 0.000 1.000
#> GSM74387 2 0.0000 0.9568 0.000 1.000
#> GSM74388 2 0.0000 0.9568 0.000 1.000
#> GSM74389 1 0.0000 0.9748 1.000 0.000
#> GSM74390 1 0.0000 0.9748 1.000 0.000
#> GSM74391 1 0.0000 0.9748 1.000 0.000
#> GSM74392 1 0.0000 0.9748 1.000 0.000
#> GSM74393 1 0.0000 0.9748 1.000 0.000
#> GSM74394 2 0.0000 0.9568 0.000 1.000
#> GSM74239 1 0.0000 0.9748 1.000 0.000
#> GSM74364 1 0.0000 0.9748 1.000 0.000
#> GSM74365 1 0.0000 0.9748 1.000 0.000
#> GSM74366 2 0.0938 0.9499 0.012 0.988
#> GSM74367 1 0.0000 0.9748 1.000 0.000
#> GSM74377 1 0.4431 0.8936 0.908 0.092
#> GSM74378 2 0.9970 0.0868 0.468 0.532
#> GSM74379 1 0.0000 0.9748 1.000 0.000
#> GSM74380 1 0.0938 0.9665 0.988 0.012
#> GSM74381 1 0.6623 0.7954 0.828 0.172
#> GSM121357 2 0.0000 0.9568 0.000 1.000
#> GSM121361 2 0.0000 0.9568 0.000 1.000
#> GSM121363 2 0.0000 0.9568 0.000 1.000
#> GSM121368 2 0.0000 0.9568 0.000 1.000
#> GSM121369 2 0.0000 0.9568 0.000 1.000
#> GSM74368 1 0.0000 0.9748 1.000 0.000
#> GSM74369 1 0.0000 0.9748 1.000 0.000
#> GSM74370 1 0.0000 0.9748 1.000 0.000
#> GSM74371 1 0.0000 0.9748 1.000 0.000
#> GSM74372 1 0.0000 0.9748 1.000 0.000
#> GSM74373 1 0.3879 0.9099 0.924 0.076
#> GSM74374 1 0.0000 0.9748 1.000 0.000
#> GSM74375 1 0.1633 0.9575 0.976 0.024
#> GSM74376 1 0.5178 0.8696 0.884 0.116
#> GSM74405 1 0.2043 0.9506 0.968 0.032
#> GSM74351 1 0.0000 0.9748 1.000 0.000
#> GSM74352 2 0.4298 0.8861 0.088 0.912
#> GSM74353 1 0.0000 0.9748 1.000 0.000
#> GSM74354 1 0.0000 0.9748 1.000 0.000
#> GSM74355 1 0.8443 0.6432 0.728 0.272
#> GSM74382 1 0.0000 0.9748 1.000 0.000
#> GSM74383 1 0.0000 0.9748 1.000 0.000
#> GSM74384 2 0.0000 0.9568 0.000 1.000
#> GSM74385 1 0.0000 0.9748 1.000 0.000
#> GSM74386 1 0.0000 0.9748 1.000 0.000
#> GSM74395 1 0.0000 0.9748 1.000 0.000
#> GSM74396 1 0.0000 0.9748 1.000 0.000
#> GSM74397 1 0.0000 0.9748 1.000 0.000
#> GSM74398 1 0.0000 0.9748 1.000 0.000
#> GSM74399 1 0.0000 0.9748 1.000 0.000
#> GSM74400 1 0.0376 0.9720 0.996 0.004
#> GSM74401 1 0.1414 0.9607 0.980 0.020
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM74356 3 0.0000 0.7759 0.000 0.000 1.000
#> GSM74357 3 0.0424 0.7731 0.008 0.000 0.992
#> GSM74358 3 0.0747 0.7697 0.016 0.000 0.984
#> GSM74359 3 0.5431 0.4071 0.284 0.000 0.716
#> GSM74360 1 0.5254 0.6650 0.736 0.000 0.264
#> GSM74361 3 0.0747 0.7797 0.000 0.016 0.984
#> GSM74362 3 0.0592 0.7716 0.012 0.000 0.988
#> GSM74363 3 0.0592 0.7792 0.000 0.012 0.988
#> GSM74402 1 0.5291 0.6607 0.732 0.000 0.268
#> GSM74403 1 0.4062 0.7514 0.836 0.000 0.164
#> GSM74404 1 0.4235 0.7435 0.824 0.000 0.176
#> GSM74406 1 0.6111 0.4717 0.604 0.000 0.396
#> GSM74407 1 0.5529 0.6268 0.704 0.000 0.296
#> GSM74408 1 0.6267 0.3459 0.548 0.000 0.452
#> GSM74409 1 0.6215 0.4037 0.572 0.000 0.428
#> GSM74410 3 0.6235 -0.0319 0.436 0.000 0.564
#> GSM119936 1 0.6126 0.4644 0.600 0.000 0.400
#> GSM119937 1 0.6079 0.4876 0.612 0.000 0.388
#> GSM74411 3 0.4750 0.6437 0.000 0.216 0.784
#> GSM74412 3 0.6026 0.3584 0.000 0.376 0.624
#> GSM74413 3 0.5098 0.6006 0.000 0.248 0.752
#> GSM74414 2 0.1031 0.7818 0.000 0.976 0.024
#> GSM74415 3 0.2261 0.7661 0.000 0.068 0.932
#> GSM121379 2 0.2165 0.7782 0.000 0.936 0.064
#> GSM121380 2 0.1529 0.7825 0.000 0.960 0.040
#> GSM121381 2 0.5431 0.5513 0.000 0.716 0.284
#> GSM121382 2 0.5254 0.5844 0.000 0.736 0.264
#> GSM121383 2 0.5678 0.4930 0.000 0.684 0.316
#> GSM121384 2 0.1753 0.7820 0.000 0.952 0.048
#> GSM121385 2 0.2959 0.7614 0.000 0.900 0.100
#> GSM121386 2 0.2878 0.7642 0.000 0.904 0.096
#> GSM121387 2 0.4504 0.6762 0.000 0.804 0.196
#> GSM121388 3 0.6302 0.0526 0.000 0.480 0.520
#> GSM121389 2 0.2796 0.7665 0.000 0.908 0.092
#> GSM121390 2 0.0424 0.7797 0.000 0.992 0.008
#> GSM121391 2 0.6244 0.1807 0.000 0.560 0.440
#> GSM121392 2 0.0424 0.7773 0.008 0.992 0.000
#> GSM121393 2 0.2066 0.7804 0.000 0.940 0.060
#> GSM121394 3 0.6260 0.1604 0.000 0.448 0.552
#> GSM121395 2 0.2711 0.7688 0.000 0.912 0.088
#> GSM121396 3 0.5465 0.5377 0.000 0.288 0.712
#> GSM121397 2 0.1860 0.7816 0.000 0.948 0.052
#> GSM121398 2 0.2066 0.7794 0.000 0.940 0.060
#> GSM121399 2 0.4504 0.6764 0.000 0.804 0.196
#> GSM74240 3 0.1129 0.7801 0.004 0.020 0.976
#> GSM74241 3 0.2625 0.7567 0.000 0.084 0.916
#> GSM74242 3 0.1411 0.7583 0.036 0.000 0.964
#> GSM74243 3 0.1031 0.7654 0.024 0.000 0.976
#> GSM74244 3 0.1860 0.7740 0.000 0.052 0.948
#> GSM74245 3 0.0592 0.7792 0.000 0.012 0.988
#> GSM74246 3 0.4062 0.6939 0.000 0.164 0.836
#> GSM74247 3 0.4842 0.6337 0.000 0.224 0.776
#> GSM74248 3 0.0592 0.7794 0.000 0.012 0.988
#> GSM74416 1 0.4750 0.7123 0.784 0.000 0.216
#> GSM74417 1 0.4452 0.7317 0.808 0.000 0.192
#> GSM74418 1 0.4178 0.7465 0.828 0.000 0.172
#> GSM74419 1 0.6308 0.2418 0.508 0.000 0.492
#> GSM121358 3 0.1289 0.7790 0.000 0.032 0.968
#> GSM121359 3 0.4654 0.6526 0.000 0.208 0.792
#> GSM121360 1 0.4228 0.7631 0.844 0.008 0.148
#> GSM121362 1 0.5791 0.7484 0.792 0.060 0.148
#> GSM121364 3 0.6305 -0.1933 0.484 0.000 0.516
#> GSM121365 3 0.1643 0.7765 0.000 0.044 0.956
#> GSM121366 3 0.1964 0.7722 0.000 0.056 0.944
#> GSM121367 3 0.1031 0.7796 0.000 0.024 0.976
#> GSM121370 3 0.1529 0.7776 0.000 0.040 0.960
#> GSM121371 3 0.1529 0.7777 0.000 0.040 0.960
#> GSM121372 3 0.4654 0.6524 0.000 0.208 0.792
#> GSM121373 1 0.5988 0.5195 0.632 0.000 0.368
#> GSM121374 3 0.6274 -0.1014 0.456 0.000 0.544
#> GSM121407 3 0.5859 0.4298 0.000 0.344 0.656
#> GSM74387 2 0.6260 0.1478 0.000 0.552 0.448
#> GSM74388 2 0.2537 0.7486 0.080 0.920 0.000
#> GSM74389 3 0.4235 0.6145 0.176 0.000 0.824
#> GSM74390 1 0.1860 0.7859 0.948 0.052 0.000
#> GSM74391 1 0.6062 0.4940 0.616 0.000 0.384
#> GSM74392 3 0.6291 -0.1405 0.468 0.000 0.532
#> GSM74393 3 0.3551 0.6714 0.132 0.000 0.868
#> GSM74394 2 0.1647 0.7722 0.036 0.960 0.004
#> GSM74239 1 0.0592 0.8093 0.988 0.000 0.012
#> GSM74364 1 0.0592 0.8092 0.988 0.000 0.012
#> GSM74365 1 0.0747 0.8033 0.984 0.016 0.000
#> GSM74366 2 0.5397 0.5557 0.280 0.720 0.000
#> GSM74367 1 0.0592 0.8049 0.988 0.012 0.000
#> GSM74377 1 0.6267 0.0674 0.548 0.452 0.000
#> GSM74378 2 0.5988 0.4087 0.368 0.632 0.000
#> GSM74379 1 0.2959 0.7517 0.900 0.100 0.000
#> GSM74380 1 0.5254 0.5410 0.736 0.264 0.000
#> GSM74381 2 0.6280 0.1918 0.460 0.540 0.000
#> GSM121357 2 0.4121 0.7053 0.000 0.832 0.168
#> GSM121361 2 0.1964 0.7610 0.056 0.944 0.000
#> GSM121363 2 0.0892 0.7750 0.020 0.980 0.000
#> GSM121368 2 0.0237 0.7784 0.004 0.996 0.000
#> GSM121369 2 0.3583 0.7788 0.044 0.900 0.056
#> GSM74368 1 0.0892 0.8089 0.980 0.000 0.020
#> GSM74369 1 0.0424 0.8090 0.992 0.000 0.008
#> GSM74370 1 0.0424 0.8058 0.992 0.008 0.000
#> GSM74371 1 0.0747 0.8090 0.984 0.000 0.016
#> GSM74372 1 0.0661 0.8071 0.988 0.008 0.004
#> GSM74373 1 0.5859 0.3798 0.656 0.344 0.000
#> GSM74374 1 0.1031 0.7998 0.976 0.024 0.000
#> GSM74375 1 0.4750 0.6179 0.784 0.216 0.000
#> GSM74376 2 0.6299 0.1440 0.476 0.524 0.000
#> GSM74405 1 0.5926 0.3520 0.644 0.356 0.000
#> GSM74351 1 0.2261 0.7974 0.932 0.000 0.068
#> GSM74352 2 0.5363 0.5627 0.276 0.724 0.000
#> GSM74353 1 0.0000 0.8076 1.000 0.000 0.000
#> GSM74354 1 0.0592 0.8049 0.988 0.012 0.000
#> GSM74355 2 0.6225 0.2662 0.432 0.568 0.000
#> GSM74382 1 0.2066 0.8002 0.940 0.000 0.060
#> GSM74383 1 0.0237 0.8069 0.996 0.004 0.000
#> GSM74384 2 0.4842 0.6257 0.224 0.776 0.000
#> GSM74385 1 0.0592 0.8091 0.988 0.000 0.012
#> GSM74386 1 0.0592 0.8049 0.988 0.012 0.000
#> GSM74395 1 0.0424 0.8088 0.992 0.000 0.008
#> GSM74396 1 0.0592 0.8049 0.988 0.012 0.000
#> GSM74397 1 0.1031 0.8086 0.976 0.000 0.024
#> GSM74398 1 0.1964 0.7830 0.944 0.056 0.000
#> GSM74399 1 0.4062 0.6882 0.836 0.164 0.000
#> GSM74400 1 0.2878 0.7540 0.904 0.096 0.000
#> GSM74401 1 0.3482 0.7253 0.872 0.128 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM74356 3 0.5812 0.6470 0.004 0.184 0.712 0.100
#> GSM74357 3 0.5719 0.6511 0.000 0.152 0.716 0.132
#> GSM74358 3 0.6037 0.6451 0.004 0.156 0.700 0.140
#> GSM74359 3 0.5943 0.3294 0.028 0.008 0.576 0.388
#> GSM74360 4 0.3821 0.6689 0.040 0.000 0.120 0.840
#> GSM74361 3 0.5137 0.6719 0.004 0.108 0.772 0.116
#> GSM74362 3 0.5298 0.6652 0.016 0.068 0.768 0.148
#> GSM74363 3 0.6133 0.6241 0.004 0.220 0.676 0.100
#> GSM74402 4 0.0927 0.7298 0.008 0.000 0.016 0.976
#> GSM74403 4 0.1305 0.7339 0.036 0.000 0.004 0.960
#> GSM74404 4 0.1356 0.7336 0.032 0.000 0.008 0.960
#> GSM74406 4 0.2281 0.6955 0.000 0.000 0.096 0.904
#> GSM74407 4 0.2660 0.7220 0.036 0.000 0.056 0.908
#> GSM74408 4 0.3196 0.6648 0.008 0.000 0.136 0.856
#> GSM74409 4 0.3257 0.6600 0.004 0.000 0.152 0.844
#> GSM74410 4 0.5114 0.4929 0.008 0.020 0.260 0.712
#> GSM119936 4 0.2654 0.6847 0.004 0.000 0.108 0.888
#> GSM119937 4 0.2976 0.6770 0.008 0.000 0.120 0.872
#> GSM74411 3 0.4586 0.6892 0.136 0.068 0.796 0.000
#> GSM74412 3 0.6133 0.6341 0.124 0.204 0.672 0.000
#> GSM74413 3 0.4710 0.6826 0.088 0.120 0.792 0.000
#> GSM74414 2 0.4220 0.6995 0.248 0.748 0.004 0.000
#> GSM74415 3 0.3497 0.6920 0.124 0.024 0.852 0.000
#> GSM121379 2 0.2216 0.8536 0.092 0.908 0.000 0.000
#> GSM121380 2 0.3172 0.8081 0.160 0.840 0.000 0.000
#> GSM121381 2 0.1302 0.8311 0.000 0.956 0.044 0.000
#> GSM121382 2 0.1305 0.8381 0.004 0.960 0.036 0.000
#> GSM121383 2 0.1389 0.8274 0.000 0.952 0.048 0.000
#> GSM121384 2 0.2973 0.8233 0.144 0.856 0.000 0.000
#> GSM121385 2 0.2271 0.8614 0.076 0.916 0.008 0.000
#> GSM121386 2 0.1978 0.8621 0.068 0.928 0.004 0.000
#> GSM121387 2 0.1406 0.8493 0.016 0.960 0.024 0.000
#> GSM121388 2 0.3074 0.7247 0.000 0.848 0.152 0.000
#> GSM121389 2 0.1743 0.8633 0.056 0.940 0.004 0.000
#> GSM121390 2 0.3400 0.7875 0.180 0.820 0.000 0.000
#> GSM121391 2 0.2469 0.7734 0.000 0.892 0.108 0.000
#> GSM121392 2 0.3764 0.7420 0.216 0.784 0.000 0.000
#> GSM121393 2 0.2089 0.8590 0.048 0.932 0.020 0.000
#> GSM121394 2 0.3791 0.6551 0.004 0.796 0.200 0.000
#> GSM121395 2 0.1474 0.8632 0.052 0.948 0.000 0.000
#> GSM121396 2 0.4872 0.3340 0.004 0.640 0.356 0.000
#> GSM121397 2 0.2973 0.8231 0.144 0.856 0.000 0.000
#> GSM121398 2 0.2345 0.8500 0.100 0.900 0.000 0.000
#> GSM121399 2 0.1109 0.8419 0.004 0.968 0.028 0.000
#> GSM74240 3 0.4605 0.5397 0.336 0.000 0.664 0.000
#> GSM74241 3 0.4990 0.5189 0.352 0.008 0.640 0.000
#> GSM74242 3 0.3668 0.6637 0.188 0.000 0.808 0.004
#> GSM74243 3 0.3791 0.6579 0.200 0.000 0.796 0.004
#> GSM74244 3 0.4252 0.6224 0.252 0.004 0.744 0.000
#> GSM74245 3 0.3610 0.6567 0.200 0.000 0.800 0.000
#> GSM74246 3 0.4730 0.5016 0.364 0.000 0.636 0.000
#> GSM74247 3 0.4920 0.4925 0.368 0.004 0.628 0.000
#> GSM74248 3 0.4406 0.5790 0.300 0.000 0.700 0.000
#> GSM74416 4 0.0188 0.7300 0.000 0.000 0.004 0.996
#> GSM74417 4 0.0376 0.7308 0.004 0.000 0.004 0.992
#> GSM74418 4 0.0376 0.7308 0.004 0.000 0.004 0.992
#> GSM74419 4 0.3829 0.6410 0.004 0.016 0.152 0.828
#> GSM121358 3 0.5471 0.6457 0.004 0.208 0.724 0.064
#> GSM121359 3 0.4761 0.5146 0.004 0.332 0.664 0.000
#> GSM121360 1 0.5820 0.4776 0.680 0.000 0.240 0.080
#> GSM121362 1 0.8393 0.1943 0.436 0.044 0.164 0.356
#> GSM121364 4 0.5237 0.3379 0.016 0.000 0.356 0.628
#> GSM121365 3 0.5609 0.6336 0.004 0.224 0.708 0.064
#> GSM121366 3 0.5252 0.6283 0.004 0.236 0.720 0.040
#> GSM121367 3 0.5176 0.6578 0.004 0.192 0.748 0.056
#> GSM121370 3 0.4776 0.6656 0.004 0.184 0.772 0.040
#> GSM121371 3 0.5576 0.6369 0.004 0.220 0.712 0.064
#> GSM121372 3 0.5038 0.5718 0.020 0.296 0.684 0.000
#> GSM121373 4 0.5599 0.4901 0.052 0.000 0.276 0.672
#> GSM121374 4 0.5743 0.1950 0.024 0.004 0.396 0.576
#> GSM121407 3 0.5535 0.3597 0.020 0.420 0.560 0.000
#> GSM74387 3 0.5119 0.3464 0.440 0.004 0.556 0.000
#> GSM74388 1 0.4289 0.6307 0.796 0.172 0.032 0.000
#> GSM74389 3 0.5495 0.6479 0.176 0.000 0.728 0.096
#> GSM74390 1 0.5033 0.6534 0.776 0.008 0.064 0.152
#> GSM74391 4 0.6852 0.2906 0.124 0.000 0.320 0.556
#> GSM74392 3 0.6575 0.2746 0.080 0.000 0.508 0.412
#> GSM74393 3 0.4996 0.6554 0.192 0.000 0.752 0.056
#> GSM74394 1 0.4175 0.4860 0.776 0.012 0.212 0.000
#> GSM74239 4 0.3172 0.7029 0.160 0.000 0.000 0.840
#> GSM74364 4 0.2814 0.7172 0.132 0.000 0.000 0.868
#> GSM74365 4 0.4916 0.2582 0.424 0.000 0.000 0.576
#> GSM74366 1 0.3554 0.7019 0.844 0.136 0.000 0.020
#> GSM74367 4 0.4776 0.3936 0.376 0.000 0.000 0.624
#> GSM74377 1 0.5669 0.6629 0.708 0.092 0.000 0.200
#> GSM74378 1 0.4379 0.6840 0.792 0.172 0.000 0.036
#> GSM74379 1 0.4800 0.4798 0.656 0.004 0.000 0.340
#> GSM74380 1 0.4744 0.6368 0.736 0.024 0.000 0.240
#> GSM74381 1 0.4411 0.7207 0.812 0.108 0.000 0.080
#> GSM121357 2 0.5096 0.8065 0.156 0.760 0.084 0.000
#> GSM121361 1 0.4514 0.6415 0.796 0.148 0.056 0.000
#> GSM121363 1 0.4599 0.5711 0.760 0.212 0.028 0.000
#> GSM121368 1 0.4931 0.6037 0.776 0.132 0.092 0.000
#> GSM121369 1 0.5219 0.4723 0.712 0.044 0.244 0.000
#> GSM74368 4 0.3486 0.6829 0.188 0.000 0.000 0.812
#> GSM74369 4 0.2973 0.7114 0.144 0.000 0.000 0.856
#> GSM74370 4 0.4283 0.6221 0.256 0.000 0.004 0.740
#> GSM74371 4 0.2814 0.7170 0.132 0.000 0.000 0.868
#> GSM74372 1 0.5768 0.0707 0.516 0.000 0.028 0.456
#> GSM74373 1 0.5203 0.6393 0.720 0.048 0.000 0.232
#> GSM74374 4 0.4855 0.3336 0.400 0.000 0.000 0.600
#> GSM74375 1 0.5543 0.4378 0.612 0.028 0.000 0.360
#> GSM74376 1 0.3421 0.7239 0.868 0.044 0.000 0.088
#> GSM74405 1 0.3813 0.7024 0.828 0.024 0.000 0.148
#> GSM74351 4 0.1557 0.7329 0.056 0.000 0.000 0.944
#> GSM74352 1 0.5807 0.4934 0.636 0.312 0.000 0.052
#> GSM74353 4 0.3123 0.7053 0.156 0.000 0.000 0.844
#> GSM74354 4 0.4277 0.5794 0.280 0.000 0.000 0.720
#> GSM74355 1 0.4215 0.7237 0.824 0.104 0.000 0.072
#> GSM74382 4 0.1792 0.7315 0.068 0.000 0.000 0.932
#> GSM74383 4 0.3801 0.6521 0.220 0.000 0.000 0.780
#> GSM74384 1 0.4049 0.6381 0.780 0.212 0.000 0.008
#> GSM74385 4 0.2647 0.7202 0.120 0.000 0.000 0.880
#> GSM74386 4 0.4661 0.4750 0.348 0.000 0.000 0.652
#> GSM74395 4 0.4382 0.5578 0.296 0.000 0.000 0.704
#> GSM74396 4 0.4817 0.3600 0.388 0.000 0.000 0.612
#> GSM74397 4 0.2973 0.7138 0.144 0.000 0.000 0.856
#> GSM74398 1 0.4560 0.5482 0.700 0.000 0.004 0.296
#> GSM74399 1 0.4690 0.6015 0.720 0.008 0.004 0.268
#> GSM74400 4 0.5267 0.5843 0.240 0.048 0.000 0.712
#> GSM74401 4 0.5574 0.5069 0.284 0.048 0.000 0.668
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM74356 3 0.2892 0.5277 0.004 0.016 0.884 0.016 0.080
#> GSM74357 3 0.2635 0.5320 0.004 0.012 0.900 0.020 0.064
#> GSM74358 3 0.2661 0.5320 0.004 0.012 0.900 0.024 0.060
#> GSM74359 3 0.7332 0.2952 0.040 0.000 0.444 0.212 0.304
#> GSM74360 4 0.7775 -0.2051 0.056 0.000 0.304 0.340 0.300
#> GSM74361 3 0.5421 0.3929 0.020 0.004 0.668 0.052 0.256
#> GSM74362 3 0.6066 0.3376 0.036 0.000 0.568 0.060 0.336
#> GSM74363 3 0.2879 0.5423 0.000 0.080 0.880 0.008 0.032
#> GSM74402 4 0.1682 0.7213 0.012 0.000 0.032 0.944 0.012
#> GSM74403 4 0.1399 0.7142 0.000 0.000 0.028 0.952 0.020
#> GSM74404 4 0.3205 0.6677 0.008 0.000 0.056 0.864 0.072
#> GSM74406 4 0.5532 0.3999 0.008 0.000 0.256 0.644 0.092
#> GSM74407 4 0.2491 0.6931 0.000 0.000 0.068 0.896 0.036
#> GSM74408 4 0.6044 0.2896 0.012 0.000 0.284 0.588 0.116
#> GSM74409 4 0.6694 0.0557 0.016 0.000 0.340 0.484 0.160
#> GSM74410 3 0.6659 0.1657 0.012 0.004 0.456 0.392 0.136
#> GSM119936 4 0.4949 0.4978 0.008 0.000 0.208 0.712 0.072
#> GSM119937 4 0.5932 0.1765 0.008 0.000 0.368 0.536 0.088
#> GSM74411 5 0.5128 0.5010 0.028 0.008 0.392 0.000 0.572
#> GSM74412 5 0.5482 0.4166 0.028 0.020 0.440 0.000 0.512
#> GSM74413 5 0.4936 0.4572 0.008 0.016 0.416 0.000 0.560
#> GSM74414 1 0.6910 0.1493 0.452 0.392 0.048 0.000 0.108
#> GSM74415 5 0.4710 0.5370 0.012 0.008 0.364 0.000 0.616
#> GSM121379 2 0.0703 0.9291 0.024 0.976 0.000 0.000 0.000
#> GSM121380 2 0.1544 0.9033 0.068 0.932 0.000 0.000 0.000
#> GSM121381 2 0.1717 0.9168 0.008 0.936 0.052 0.000 0.004
#> GSM121382 2 0.1626 0.9141 0.000 0.940 0.044 0.000 0.016
#> GSM121383 2 0.1205 0.9198 0.000 0.956 0.040 0.000 0.004
#> GSM121384 2 0.1478 0.9070 0.064 0.936 0.000 0.000 0.000
#> GSM121385 2 0.0727 0.9316 0.012 0.980 0.004 0.000 0.004
#> GSM121386 2 0.0703 0.9291 0.024 0.976 0.000 0.000 0.000
#> GSM121387 2 0.0727 0.9293 0.004 0.980 0.012 0.000 0.004
#> GSM121388 2 0.2873 0.8527 0.000 0.860 0.120 0.000 0.020
#> GSM121389 2 0.1116 0.9298 0.028 0.964 0.004 0.000 0.004
#> GSM121390 2 0.1908 0.8840 0.092 0.908 0.000 0.000 0.000
#> GSM121391 2 0.1831 0.8992 0.000 0.920 0.076 0.000 0.004
#> GSM121392 2 0.2439 0.8521 0.120 0.876 0.000 0.000 0.004
#> GSM121393 2 0.0955 0.9292 0.028 0.968 0.004 0.000 0.000
#> GSM121394 2 0.3016 0.8409 0.000 0.848 0.132 0.000 0.020
#> GSM121395 2 0.0579 0.9314 0.008 0.984 0.000 0.000 0.008
#> GSM121396 2 0.4180 0.7041 0.000 0.744 0.220 0.000 0.036
#> GSM121397 2 0.1270 0.9148 0.052 0.948 0.000 0.000 0.000
#> GSM121398 2 0.0727 0.9316 0.012 0.980 0.004 0.000 0.004
#> GSM121399 2 0.1082 0.9236 0.000 0.964 0.028 0.000 0.008
#> GSM74240 5 0.4877 0.6073 0.136 0.000 0.128 0.004 0.732
#> GSM74241 5 0.5513 0.6143 0.144 0.000 0.188 0.004 0.664
#> GSM74242 5 0.4743 0.5933 0.024 0.000 0.268 0.016 0.692
#> GSM74243 5 0.4558 0.6002 0.020 0.000 0.252 0.016 0.712
#> GSM74244 5 0.4923 0.6128 0.068 0.000 0.252 0.000 0.680
#> GSM74245 5 0.4575 0.6158 0.052 0.000 0.236 0.000 0.712
#> GSM74246 5 0.5067 0.6009 0.172 0.000 0.128 0.000 0.700
#> GSM74247 5 0.5379 0.6106 0.164 0.000 0.168 0.000 0.668
#> GSM74248 5 0.4588 0.6031 0.116 0.000 0.136 0.000 0.748
#> GSM74416 4 0.1195 0.7166 0.000 0.000 0.028 0.960 0.012
#> GSM74417 4 0.1901 0.7058 0.004 0.000 0.040 0.932 0.024
#> GSM74418 4 0.1173 0.7198 0.004 0.000 0.020 0.964 0.012
#> GSM74419 4 0.4025 0.6067 0.004 0.000 0.140 0.796 0.060
#> GSM121358 3 0.2473 0.5413 0.000 0.072 0.896 0.000 0.032
#> GSM121359 3 0.4720 0.4025 0.000 0.124 0.736 0.000 0.140
#> GSM121360 1 0.7105 0.2458 0.420 0.004 0.156 0.028 0.392
#> GSM121362 1 0.7870 0.2378 0.400 0.012 0.164 0.068 0.356
#> GSM121364 3 0.7462 0.2859 0.040 0.000 0.416 0.256 0.288
#> GSM121365 3 0.3102 0.5300 0.000 0.084 0.860 0.000 0.056
#> GSM121366 3 0.4022 0.4754 0.000 0.100 0.796 0.000 0.104
#> GSM121367 3 0.3119 0.5231 0.000 0.072 0.860 0.000 0.068
#> GSM121370 3 0.3493 0.4911 0.000 0.060 0.832 0.000 0.108
#> GSM121371 3 0.2889 0.5344 0.000 0.084 0.872 0.000 0.044
#> GSM121372 3 0.4827 0.3823 0.000 0.116 0.724 0.000 0.160
#> GSM121373 3 0.7840 0.2605 0.100 0.000 0.404 0.168 0.328
#> GSM121374 3 0.7290 0.3188 0.040 0.000 0.460 0.212 0.288
#> GSM121407 3 0.5320 0.3655 0.008 0.144 0.696 0.000 0.152
#> GSM74387 5 0.5663 0.2397 0.364 0.000 0.088 0.000 0.548
#> GSM74388 1 0.4723 0.6070 0.736 0.128 0.000 0.000 0.136
#> GSM74389 5 0.6497 0.0280 0.052 0.000 0.288 0.088 0.572
#> GSM74390 1 0.3477 0.6508 0.824 0.000 0.000 0.040 0.136
#> GSM74391 5 0.7259 -0.0860 0.040 0.000 0.176 0.380 0.404
#> GSM74392 5 0.7650 -0.2597 0.052 0.000 0.344 0.240 0.364
#> GSM74393 5 0.5905 -0.1042 0.072 0.000 0.400 0.012 0.516
#> GSM74394 1 0.4236 0.4426 0.664 0.004 0.004 0.000 0.328
#> GSM74239 4 0.2389 0.7155 0.116 0.000 0.000 0.880 0.004
#> GSM74364 4 0.2233 0.7188 0.104 0.000 0.000 0.892 0.004
#> GSM74365 4 0.4283 0.2273 0.456 0.000 0.000 0.544 0.000
#> GSM74366 1 0.1949 0.6895 0.932 0.040 0.000 0.016 0.012
#> GSM74367 4 0.3884 0.5880 0.288 0.000 0.000 0.708 0.004
#> GSM74377 1 0.3878 0.5557 0.748 0.016 0.000 0.236 0.000
#> GSM74378 1 0.2618 0.6953 0.900 0.052 0.000 0.036 0.012
#> GSM74379 1 0.3662 0.5339 0.744 0.000 0.000 0.252 0.004
#> GSM74380 1 0.3522 0.5842 0.780 0.004 0.000 0.212 0.004
#> GSM74381 1 0.2228 0.6961 0.912 0.048 0.000 0.040 0.000
#> GSM121357 1 0.7838 0.1470 0.352 0.308 0.276 0.000 0.064
#> GSM121361 1 0.5177 0.5641 0.676 0.104 0.000 0.000 0.220
#> GSM121363 1 0.4593 0.6085 0.748 0.124 0.000 0.000 0.128
#> GSM121368 1 0.4109 0.5886 0.768 0.036 0.004 0.000 0.192
#> GSM121369 1 0.5088 0.4799 0.644 0.012 0.036 0.000 0.308
#> GSM74368 4 0.3814 0.5937 0.276 0.000 0.004 0.720 0.000
#> GSM74369 4 0.4280 0.5430 0.312 0.000 0.004 0.676 0.008
#> GSM74370 1 0.7549 0.1979 0.440 0.000 0.072 0.320 0.168
#> GSM74371 4 0.1892 0.7232 0.080 0.000 0.000 0.916 0.004
#> GSM74372 1 0.7618 0.3484 0.452 0.000 0.068 0.228 0.252
#> GSM74373 1 0.3463 0.6639 0.836 0.032 0.000 0.124 0.008
#> GSM74374 4 0.3913 0.5558 0.324 0.000 0.000 0.676 0.000
#> GSM74375 4 0.5140 0.4683 0.328 0.008 0.000 0.624 0.040
#> GSM74376 1 0.2949 0.6923 0.880 0.012 0.000 0.072 0.036
#> GSM74405 1 0.2615 0.6924 0.892 0.008 0.000 0.080 0.020
#> GSM74351 4 0.1455 0.7258 0.032 0.000 0.008 0.952 0.008
#> GSM74352 1 0.4671 0.6175 0.740 0.116 0.000 0.144 0.000
#> GSM74353 4 0.2629 0.7124 0.136 0.000 0.000 0.860 0.004
#> GSM74354 4 0.3160 0.6817 0.188 0.000 0.000 0.808 0.004
#> GSM74355 1 0.3067 0.6931 0.876 0.040 0.000 0.068 0.016
#> GSM74382 4 0.1281 0.7261 0.032 0.000 0.000 0.956 0.012
#> GSM74383 4 0.3074 0.6792 0.196 0.000 0.000 0.804 0.000
#> GSM74384 1 0.2490 0.6827 0.896 0.080 0.000 0.004 0.020
#> GSM74385 4 0.1717 0.7275 0.052 0.000 0.004 0.936 0.008
#> GSM74386 4 0.4101 0.4630 0.372 0.000 0.000 0.628 0.000
#> GSM74395 4 0.3366 0.6672 0.212 0.000 0.000 0.784 0.004
#> GSM74396 4 0.4151 0.4978 0.344 0.000 0.000 0.652 0.004
#> GSM74397 4 0.2358 0.7211 0.104 0.000 0.000 0.888 0.008
#> GSM74398 1 0.3838 0.4869 0.716 0.000 0.000 0.280 0.004
#> GSM74399 1 0.4147 0.4184 0.676 0.000 0.000 0.316 0.008
#> GSM74400 4 0.4018 0.6809 0.104 0.088 0.000 0.804 0.004
#> GSM74401 4 0.4489 0.6559 0.156 0.080 0.000 0.760 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM74356 3 0.3190 0.7181 0.000 0.000 0.772 0.220 0.008 0.000
#> GSM74357 3 0.3109 0.7094 0.000 0.000 0.772 0.224 0.004 0.000
#> GSM74358 3 0.2964 0.7353 0.000 0.000 0.792 0.204 0.004 0.000
#> GSM74359 4 0.4514 0.6664 0.096 0.000 0.116 0.752 0.036 0.000
#> GSM74360 4 0.2736 0.6822 0.076 0.000 0.028 0.876 0.020 0.000
#> GSM74361 4 0.5221 0.2575 0.000 0.004 0.384 0.536 0.072 0.004
#> GSM74362 4 0.3970 0.5841 0.012 0.000 0.224 0.740 0.020 0.004
#> GSM74363 3 0.2121 0.8497 0.000 0.000 0.892 0.096 0.012 0.000
#> GSM74402 1 0.1588 0.7777 0.924 0.000 0.004 0.072 0.000 0.000
#> GSM74403 1 0.2355 0.7442 0.876 0.000 0.000 0.112 0.008 0.004
#> GSM74404 1 0.3991 0.5896 0.724 0.000 0.000 0.240 0.028 0.008
#> GSM74406 1 0.5140 0.0915 0.520 0.000 0.088 0.392 0.000 0.000
#> GSM74407 1 0.3527 0.6813 0.792 0.000 0.004 0.164 0.040 0.000
#> GSM74408 1 0.5677 -0.1719 0.440 0.000 0.156 0.404 0.000 0.000
#> GSM74409 4 0.5085 0.5406 0.272 0.000 0.120 0.608 0.000 0.000
#> GSM74410 4 0.5877 0.3511 0.212 0.000 0.332 0.456 0.000 0.000
#> GSM119936 1 0.4887 0.3772 0.624 0.000 0.096 0.280 0.000 0.000
#> GSM119937 4 0.6067 0.3399 0.332 0.000 0.272 0.396 0.000 0.000
#> GSM74411 5 0.4005 0.6975 0.000 0.004 0.232 0.024 0.732 0.008
#> GSM74412 5 0.5359 0.6128 0.000 0.004 0.276 0.060 0.624 0.036
#> GSM74413 5 0.4302 0.6237 0.000 0.004 0.292 0.036 0.668 0.000
#> GSM74414 6 0.7154 0.3102 0.000 0.156 0.084 0.032 0.216 0.512
#> GSM74415 5 0.3549 0.7435 0.000 0.004 0.184 0.024 0.784 0.004
#> GSM121379 2 0.0146 0.9821 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM121380 2 0.0146 0.9816 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM121381 2 0.0972 0.9698 0.000 0.964 0.028 0.008 0.000 0.000
#> GSM121382 2 0.0767 0.9795 0.000 0.976 0.012 0.008 0.004 0.000
#> GSM121383 2 0.0551 0.9816 0.000 0.984 0.008 0.004 0.004 0.000
#> GSM121384 2 0.0146 0.9816 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM121385 2 0.0146 0.9823 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM121386 2 0.0000 0.9820 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121387 2 0.0405 0.9823 0.000 0.988 0.008 0.000 0.004 0.000
#> GSM121388 2 0.1409 0.9652 0.000 0.948 0.032 0.008 0.012 0.000
#> GSM121389 2 0.0436 0.9820 0.000 0.988 0.004 0.000 0.004 0.004
#> GSM121390 2 0.0146 0.9816 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM121391 2 0.0653 0.9802 0.000 0.980 0.012 0.004 0.004 0.000
#> GSM121392 2 0.0291 0.9799 0.000 0.992 0.000 0.000 0.004 0.004
#> GSM121393 2 0.0779 0.9792 0.000 0.976 0.008 0.000 0.008 0.008
#> GSM121394 2 0.1657 0.9554 0.000 0.936 0.040 0.012 0.012 0.000
#> GSM121395 2 0.0405 0.9822 0.000 0.988 0.008 0.000 0.004 0.000
#> GSM121396 2 0.2502 0.9052 0.000 0.884 0.084 0.012 0.020 0.000
#> GSM121397 2 0.0146 0.9816 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM121398 2 0.0146 0.9823 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM121399 2 0.0551 0.9817 0.000 0.984 0.008 0.004 0.004 0.000
#> GSM74240 5 0.2056 0.7823 0.000 0.000 0.004 0.080 0.904 0.012
#> GSM74241 5 0.1534 0.8096 0.004 0.000 0.032 0.004 0.944 0.016
#> GSM74242 5 0.2222 0.8068 0.012 0.000 0.032 0.040 0.912 0.004
#> GSM74243 5 0.2180 0.8059 0.008 0.000 0.028 0.048 0.912 0.004
#> GSM74244 5 0.2022 0.8119 0.000 0.000 0.052 0.024 0.916 0.008
#> GSM74245 5 0.1794 0.8115 0.000 0.000 0.036 0.040 0.924 0.000
#> GSM74246 5 0.1464 0.8016 0.000 0.000 0.004 0.036 0.944 0.016
#> GSM74247 5 0.1511 0.8053 0.000 0.000 0.012 0.012 0.944 0.032
#> GSM74248 5 0.2306 0.7727 0.000 0.000 0.004 0.092 0.888 0.016
#> GSM74416 1 0.1555 0.7708 0.932 0.000 0.004 0.060 0.004 0.000
#> GSM74417 1 0.2488 0.7348 0.864 0.000 0.000 0.124 0.004 0.008
#> GSM74418 1 0.1493 0.7754 0.936 0.000 0.004 0.056 0.004 0.000
#> GSM74419 1 0.4352 0.5521 0.696 0.000 0.008 0.260 0.028 0.008
#> GSM121358 3 0.2060 0.8530 0.000 0.000 0.900 0.084 0.016 0.000
#> GSM121359 3 0.2301 0.7861 0.000 0.000 0.884 0.020 0.096 0.000
#> GSM121360 4 0.4765 0.5293 0.004 0.004 0.040 0.716 0.036 0.200
#> GSM121362 4 0.4640 0.6204 0.024 0.008 0.044 0.760 0.020 0.144
#> GSM121364 4 0.3668 0.6782 0.084 0.000 0.088 0.812 0.016 0.000
#> GSM121365 3 0.1462 0.8609 0.000 0.000 0.936 0.056 0.008 0.000
#> GSM121366 3 0.1498 0.8469 0.000 0.000 0.940 0.028 0.032 0.000
#> GSM121367 3 0.1829 0.8623 0.000 0.000 0.920 0.056 0.024 0.000
#> GSM121370 3 0.2595 0.8377 0.000 0.000 0.872 0.044 0.084 0.000
#> GSM121371 3 0.1719 0.8616 0.000 0.000 0.924 0.060 0.016 0.000
#> GSM121372 3 0.2540 0.7922 0.000 0.000 0.872 0.020 0.104 0.004
#> GSM121373 4 0.3532 0.6594 0.032 0.000 0.116 0.820 0.000 0.032
#> GSM121374 4 0.3865 0.6655 0.076 0.000 0.132 0.784 0.008 0.000
#> GSM121407 3 0.3230 0.7872 0.000 0.000 0.844 0.016 0.084 0.056
#> GSM74387 5 0.5885 0.3897 0.000 0.000 0.032 0.128 0.560 0.280
#> GSM74388 6 0.5297 0.6318 0.000 0.056 0.000 0.148 0.112 0.684
#> GSM74389 4 0.4980 0.1007 0.028 0.000 0.016 0.512 0.440 0.004
#> GSM74390 6 0.4598 0.7154 0.048 0.000 0.000 0.080 0.124 0.748
#> GSM74391 5 0.5807 -0.0206 0.140 0.000 0.008 0.412 0.440 0.000
#> GSM74392 4 0.4400 0.6238 0.092 0.000 0.016 0.744 0.148 0.000
#> GSM74393 4 0.4764 0.4403 0.004 0.000 0.052 0.664 0.268 0.012
#> GSM74394 6 0.5451 0.3018 0.000 0.000 0.004 0.116 0.352 0.528
#> GSM74239 1 0.2002 0.7939 0.908 0.000 0.000 0.012 0.004 0.076
#> GSM74364 1 0.1605 0.7955 0.936 0.000 0.000 0.016 0.004 0.044
#> GSM74365 1 0.3699 0.5312 0.660 0.000 0.000 0.000 0.004 0.336
#> GSM74366 6 0.1167 0.7742 0.020 0.012 0.000 0.000 0.008 0.960
#> GSM74367 1 0.2845 0.7501 0.820 0.000 0.000 0.004 0.004 0.172
#> GSM74377 6 0.2320 0.7585 0.132 0.000 0.000 0.000 0.004 0.864
#> GSM74378 6 0.1245 0.7758 0.032 0.016 0.000 0.000 0.000 0.952
#> GSM74379 6 0.2825 0.7574 0.136 0.000 0.000 0.008 0.012 0.844
#> GSM74380 6 0.2805 0.7124 0.184 0.000 0.000 0.000 0.004 0.812
#> GSM74381 6 0.2325 0.7785 0.068 0.008 0.000 0.004 0.020 0.900
#> GSM121357 6 0.5351 0.3881 0.000 0.024 0.340 0.020 0.032 0.584
#> GSM121361 6 0.4813 0.6334 0.000 0.024 0.004 0.192 0.072 0.708
#> GSM121363 6 0.3412 0.7218 0.000 0.028 0.004 0.088 0.040 0.840
#> GSM121368 6 0.3253 0.7234 0.000 0.008 0.012 0.088 0.044 0.848
#> GSM121369 6 0.4976 0.5066 0.000 0.004 0.024 0.300 0.040 0.632
#> GSM74368 1 0.4271 0.5746 0.664 0.000 0.020 0.012 0.000 0.304
#> GSM74369 1 0.4138 0.6244 0.692 0.000 0.020 0.012 0.000 0.276
#> GSM74370 6 0.6126 0.2025 0.164 0.000 0.004 0.364 0.012 0.456
#> GSM74371 1 0.1003 0.7939 0.964 0.000 0.000 0.020 0.000 0.016
#> GSM74372 4 0.6785 0.3339 0.120 0.000 0.000 0.504 0.140 0.236
#> GSM74373 6 0.3175 0.7707 0.108 0.004 0.000 0.032 0.012 0.844
#> GSM74374 1 0.3883 0.7148 0.752 0.000 0.000 0.044 0.004 0.200
#> GSM74375 1 0.4432 0.7211 0.756 0.004 0.000 0.024 0.076 0.140
#> GSM74376 6 0.2917 0.7752 0.040 0.000 0.000 0.040 0.048 0.872
#> GSM74405 6 0.1594 0.7790 0.052 0.000 0.000 0.016 0.000 0.932
#> GSM74351 1 0.1387 0.7827 0.932 0.000 0.000 0.068 0.000 0.000
#> GSM74352 6 0.2848 0.7589 0.124 0.024 0.000 0.004 0.000 0.848
#> GSM74353 1 0.2218 0.7951 0.884 0.000 0.000 0.012 0.000 0.104
#> GSM74354 1 0.2243 0.7861 0.880 0.000 0.000 0.004 0.004 0.112
#> GSM74355 6 0.1555 0.7772 0.060 0.004 0.000 0.000 0.004 0.932
#> GSM74382 1 0.0790 0.7851 0.968 0.000 0.000 0.032 0.000 0.000
#> GSM74383 1 0.2558 0.7634 0.840 0.000 0.000 0.000 0.004 0.156
#> GSM74384 6 0.1542 0.7722 0.016 0.024 0.000 0.016 0.000 0.944
#> GSM74385 1 0.1010 0.7865 0.960 0.000 0.000 0.036 0.000 0.004
#> GSM74386 1 0.3828 0.6757 0.724 0.000 0.000 0.008 0.016 0.252
#> GSM74395 1 0.2466 0.7915 0.872 0.000 0.000 0.008 0.008 0.112
#> GSM74396 1 0.2920 0.7529 0.820 0.000 0.000 0.008 0.004 0.168
#> GSM74397 1 0.1563 0.7973 0.932 0.000 0.000 0.012 0.000 0.056
#> GSM74398 6 0.4450 0.4069 0.352 0.000 0.000 0.012 0.020 0.616
#> GSM74399 6 0.3691 0.6068 0.260 0.000 0.000 0.008 0.008 0.724
#> GSM74400 1 0.2935 0.7494 0.852 0.112 0.000 0.004 0.004 0.028
#> GSM74401 1 0.3084 0.7761 0.856 0.068 0.000 0.008 0.004 0.064
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 119 1.77e-09 2
#> SD:NMF 95 4.31e-15 3
#> SD:NMF 97 7.14e-25 4
#> SD:NMF 82 2.70e-33 5
#> SD:NMF 105 1.88e-43 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 21168 rows and 121 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 0.305 0.759 0.868 0.4368 0.543 0.543
#> 3 3 0.354 0.678 0.777 0.4163 0.825 0.682
#> 4 4 0.505 0.395 0.709 0.1072 0.945 0.860
#> 5 5 0.522 0.509 0.670 0.0586 0.842 0.573
#> 6 6 0.547 0.601 0.734 0.0596 0.920 0.702
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
#> GSM74356 2 0.6973 0.8014 0.188 0.812
#> GSM74357 2 0.6973 0.8014 0.188 0.812
#> GSM74358 2 0.6973 0.8014 0.188 0.812
#> GSM74359 1 0.5519 0.8059 0.872 0.128
#> GSM74360 1 0.5519 0.8059 0.872 0.128
#> GSM74361 2 0.9044 0.6158 0.320 0.680
#> GSM74362 2 0.9044 0.6158 0.320 0.680
#> GSM74363 2 0.6973 0.8014 0.188 0.812
#> GSM74402 1 0.3114 0.8297 0.944 0.056
#> GSM74403 1 0.0000 0.8212 1.000 0.000
#> GSM74404 1 0.0000 0.8212 1.000 0.000
#> GSM74406 1 0.2236 0.8293 0.964 0.036
#> GSM74407 1 0.2236 0.8293 0.964 0.036
#> GSM74408 1 0.0938 0.8262 0.988 0.012
#> GSM74409 1 0.0938 0.8262 0.988 0.012
#> GSM74410 1 0.0938 0.8262 0.988 0.012
#> GSM119936 1 0.0938 0.8262 0.988 0.012
#> GSM119937 1 0.1184 0.8270 0.984 0.016
#> GSM74411 2 0.4022 0.8543 0.080 0.920
#> GSM74412 2 0.4022 0.8543 0.080 0.920
#> GSM74413 2 0.4022 0.8543 0.080 0.920
#> GSM74414 2 0.4022 0.8543 0.080 0.920
#> GSM74415 2 0.4022 0.8543 0.080 0.920
#> GSM121379 2 0.0000 0.8464 0.000 1.000
#> GSM121380 2 0.0000 0.8464 0.000 1.000
#> GSM121381 2 0.0000 0.8464 0.000 1.000
#> GSM121382 2 0.0000 0.8464 0.000 1.000
#> GSM121383 2 0.0000 0.8464 0.000 1.000
#> GSM121384 2 0.0000 0.8464 0.000 1.000
#> GSM121385 2 0.0000 0.8464 0.000 1.000
#> GSM121386 2 0.0000 0.8464 0.000 1.000
#> GSM121387 2 0.0000 0.8464 0.000 1.000
#> GSM121388 2 0.0000 0.8464 0.000 1.000
#> GSM121389 2 0.0000 0.8464 0.000 1.000
#> GSM121390 2 0.0000 0.8464 0.000 1.000
#> GSM121391 2 0.0000 0.8464 0.000 1.000
#> GSM121392 2 0.0000 0.8464 0.000 1.000
#> GSM121393 2 0.0000 0.8464 0.000 1.000
#> GSM121394 2 0.0000 0.8464 0.000 1.000
#> GSM121395 2 0.0000 0.8464 0.000 1.000
#> GSM121396 2 0.0000 0.8464 0.000 1.000
#> GSM121397 2 0.0000 0.8464 0.000 1.000
#> GSM121398 2 0.0000 0.8464 0.000 1.000
#> GSM121399 2 0.0000 0.8464 0.000 1.000
#> GSM74240 2 0.7528 0.7740 0.216 0.784
#> GSM74241 2 0.7528 0.7740 0.216 0.784
#> GSM74242 2 0.7528 0.7740 0.216 0.784
#> GSM74243 2 0.7528 0.7740 0.216 0.784
#> GSM74244 2 0.7528 0.7740 0.216 0.784
#> GSM74245 2 0.7528 0.7740 0.216 0.784
#> GSM74246 2 0.7528 0.7740 0.216 0.784
#> GSM74247 2 0.7528 0.7740 0.216 0.784
#> GSM74248 2 0.7528 0.7740 0.216 0.784
#> GSM74416 1 0.0000 0.8212 1.000 0.000
#> GSM74417 1 0.0000 0.8212 1.000 0.000
#> GSM74418 1 0.0000 0.8212 1.000 0.000
#> GSM74419 1 0.4298 0.8213 0.912 0.088
#> GSM121358 2 0.4298 0.8535 0.088 0.912
#> GSM121359 2 0.4298 0.8535 0.088 0.912
#> GSM121360 1 0.5519 0.8059 0.872 0.128
#> GSM121362 1 0.5519 0.8059 0.872 0.128
#> GSM121364 1 0.5519 0.8059 0.872 0.128
#> GSM121365 2 0.4298 0.8535 0.088 0.912
#> GSM121366 2 0.4298 0.8535 0.088 0.912
#> GSM121367 2 0.4298 0.8535 0.088 0.912
#> GSM121370 2 0.4298 0.8535 0.088 0.912
#> GSM121371 2 0.4298 0.8535 0.088 0.912
#> GSM121372 2 0.4298 0.8535 0.088 0.912
#> GSM121373 1 0.5519 0.8059 0.872 0.128
#> GSM121374 1 0.5408 0.8078 0.876 0.124
#> GSM121407 2 0.4161 0.8537 0.084 0.916
#> GSM74387 2 0.3274 0.8555 0.060 0.940
#> GSM74388 2 0.2236 0.8524 0.036 0.964
#> GSM74389 1 0.9732 0.3034 0.596 0.404
#> GSM74390 2 0.6623 0.8242 0.172 0.828
#> GSM74391 1 0.9129 0.5203 0.672 0.328
#> GSM74392 2 0.9323 0.5558 0.348 0.652
#> GSM74393 2 0.9323 0.5558 0.348 0.652
#> GSM74394 2 0.2778 0.8544 0.048 0.952
#> GSM74239 1 0.3274 0.8250 0.940 0.060
#> GSM74364 1 0.3114 0.8254 0.944 0.056
#> GSM74365 2 0.9833 0.3122 0.424 0.576
#> GSM74366 2 0.5946 0.8110 0.144 0.856
#> GSM74367 1 0.9909 0.1936 0.556 0.444
#> GSM74377 2 0.6343 0.8003 0.160 0.840
#> GSM74378 2 0.6148 0.8057 0.152 0.848
#> GSM74379 2 0.8763 0.6253 0.296 0.704
#> GSM74380 2 0.8909 0.6103 0.308 0.692
#> GSM74381 2 0.6887 0.7781 0.184 0.816
#> GSM121357 2 0.3733 0.8568 0.072 0.928
#> GSM121361 2 0.2423 0.8521 0.040 0.960
#> GSM121363 2 0.2423 0.8521 0.040 0.960
#> GSM121368 2 0.2423 0.8521 0.040 0.960
#> GSM121369 2 0.2423 0.8521 0.040 0.960
#> GSM74368 1 0.7299 0.7325 0.796 0.204
#> GSM74369 1 0.7299 0.7325 0.796 0.204
#> GSM74370 1 0.4431 0.8152 0.908 0.092
#> GSM74371 1 0.0000 0.8212 1.000 0.000
#> GSM74372 1 0.5629 0.7906 0.868 0.132
#> GSM74373 2 0.9977 0.0933 0.472 0.528
#> GSM74374 1 0.7528 0.7217 0.784 0.216
#> GSM74375 2 0.7745 0.7456 0.228 0.772
#> GSM74376 2 0.8386 0.6824 0.268 0.732
#> GSM74405 2 0.8499 0.6695 0.276 0.724
#> GSM74351 1 0.0376 0.8230 0.996 0.004
#> GSM74352 2 0.6801 0.7855 0.180 0.820
#> GSM74353 1 0.9996 0.0252 0.512 0.488
#> GSM74354 1 0.9286 0.5118 0.656 0.344
#> GSM74355 2 0.6048 0.8077 0.148 0.852
#> GSM74382 1 0.0938 0.8254 0.988 0.012
#> GSM74383 1 0.7056 0.7442 0.808 0.192
#> GSM74384 2 0.6148 0.8057 0.152 0.848
#> GSM74385 1 0.0000 0.8212 1.000 0.000
#> GSM74386 1 0.9580 0.4009 0.620 0.380
#> GSM74395 1 0.9775 0.3069 0.588 0.412
#> GSM74396 1 0.9922 0.1959 0.552 0.448
#> GSM74397 1 0.9209 0.4974 0.664 0.336
#> GSM74398 2 0.9087 0.5936 0.324 0.676
#> GSM74399 2 0.6531 0.7961 0.168 0.832
#> GSM74400 2 0.6531 0.8010 0.168 0.832
#> GSM74401 2 0.6531 0.8010 0.168 0.832
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM74356 2 0.4092 0.7304 0.088 0.876 0.036
#> GSM74357 2 0.4092 0.7304 0.088 0.876 0.036
#> GSM74358 2 0.4092 0.7304 0.088 0.876 0.036
#> GSM74359 1 0.5905 0.6911 0.772 0.184 0.044
#> GSM74360 1 0.5905 0.6911 0.772 0.184 0.044
#> GSM74361 2 0.6292 0.5806 0.216 0.740 0.044
#> GSM74362 2 0.6232 0.5786 0.220 0.740 0.040
#> GSM74363 2 0.4092 0.7304 0.088 0.876 0.036
#> GSM74402 1 0.4399 0.7483 0.864 0.092 0.044
#> GSM74403 1 0.2680 0.7380 0.924 0.008 0.068
#> GSM74404 1 0.2774 0.7366 0.920 0.008 0.072
#> GSM74406 1 0.3670 0.7502 0.888 0.092 0.020
#> GSM74407 1 0.3722 0.7519 0.888 0.088 0.024
#> GSM74408 1 0.2496 0.7515 0.928 0.068 0.004
#> GSM74409 1 0.2496 0.7515 0.928 0.068 0.004
#> GSM74410 1 0.2496 0.7515 0.928 0.068 0.004
#> GSM119936 1 0.2496 0.7515 0.928 0.068 0.004
#> GSM119937 1 0.2682 0.7511 0.920 0.076 0.004
#> GSM74411 2 0.0661 0.7809 0.004 0.988 0.008
#> GSM74412 2 0.0661 0.7809 0.004 0.988 0.008
#> GSM74413 2 0.0661 0.7809 0.004 0.988 0.008
#> GSM74414 2 0.1878 0.7771 0.004 0.952 0.044
#> GSM74415 2 0.0661 0.7809 0.004 0.988 0.008
#> GSM121379 2 0.4750 0.7549 0.000 0.784 0.216
#> GSM121380 2 0.4750 0.7549 0.000 0.784 0.216
#> GSM121381 2 0.4750 0.7549 0.000 0.784 0.216
#> GSM121382 2 0.4750 0.7549 0.000 0.784 0.216
#> GSM121383 2 0.4750 0.7549 0.000 0.784 0.216
#> GSM121384 2 0.4750 0.7549 0.000 0.784 0.216
#> GSM121385 2 0.4750 0.7549 0.000 0.784 0.216
#> GSM121386 2 0.4750 0.7549 0.000 0.784 0.216
#> GSM121387 2 0.4750 0.7549 0.000 0.784 0.216
#> GSM121388 2 0.4750 0.7549 0.000 0.784 0.216
#> GSM121389 2 0.4750 0.7549 0.000 0.784 0.216
#> GSM121390 2 0.4750 0.7549 0.000 0.784 0.216
#> GSM121391 2 0.4750 0.7549 0.000 0.784 0.216
#> GSM121392 2 0.4796 0.7520 0.000 0.780 0.220
#> GSM121393 2 0.4750 0.7549 0.000 0.784 0.216
#> GSM121394 2 0.4702 0.7551 0.000 0.788 0.212
#> GSM121395 2 0.4750 0.7549 0.000 0.784 0.216
#> GSM121396 2 0.4750 0.7549 0.000 0.784 0.216
#> GSM121397 2 0.4750 0.7549 0.000 0.784 0.216
#> GSM121398 2 0.4750 0.7549 0.000 0.784 0.216
#> GSM121399 2 0.4750 0.7549 0.000 0.784 0.216
#> GSM74240 2 0.4676 0.7090 0.112 0.848 0.040
#> GSM74241 2 0.4676 0.7090 0.112 0.848 0.040
#> GSM74242 2 0.4676 0.7090 0.112 0.848 0.040
#> GSM74243 2 0.4676 0.7090 0.112 0.848 0.040
#> GSM74244 2 0.4676 0.7090 0.112 0.848 0.040
#> GSM74245 2 0.4676 0.7090 0.112 0.848 0.040
#> GSM74246 2 0.4676 0.7090 0.112 0.848 0.040
#> GSM74247 2 0.4676 0.7090 0.112 0.848 0.040
#> GSM74248 2 0.4676 0.7090 0.112 0.848 0.040
#> GSM74416 1 0.3116 0.7226 0.892 0.000 0.108
#> GSM74417 1 0.3116 0.7226 0.892 0.000 0.108
#> GSM74418 1 0.3116 0.7226 0.892 0.000 0.108
#> GSM74419 1 0.4475 0.7291 0.840 0.144 0.016
#> GSM121358 2 0.0661 0.7799 0.004 0.988 0.008
#> GSM121359 2 0.0661 0.7799 0.004 0.988 0.008
#> GSM121360 1 0.5905 0.6911 0.772 0.184 0.044
#> GSM121362 1 0.5905 0.6911 0.772 0.184 0.044
#> GSM121364 1 0.5905 0.6911 0.772 0.184 0.044
#> GSM121365 2 0.0661 0.7799 0.004 0.988 0.008
#> GSM121366 2 0.0661 0.7799 0.004 0.988 0.008
#> GSM121367 2 0.0661 0.7799 0.004 0.988 0.008
#> GSM121370 2 0.0661 0.7799 0.004 0.988 0.008
#> GSM121371 2 0.0661 0.7799 0.004 0.988 0.008
#> GSM121372 2 0.0661 0.7799 0.004 0.988 0.008
#> GSM121373 1 0.5905 0.6911 0.772 0.184 0.044
#> GSM121374 1 0.5746 0.6957 0.780 0.180 0.040
#> GSM121407 2 0.0475 0.7804 0.004 0.992 0.004
#> GSM74387 2 0.5363 0.6084 0.000 0.724 0.276
#> GSM74388 2 0.5785 0.5633 0.000 0.668 0.332
#> GSM74389 1 0.7890 0.1817 0.512 0.432 0.056
#> GSM74390 2 0.6295 0.6885 0.072 0.764 0.164
#> GSM74391 1 0.7499 0.3710 0.592 0.360 0.048
#> GSM74392 2 0.6521 0.5414 0.248 0.712 0.040
#> GSM74393 2 0.6521 0.5414 0.248 0.712 0.040
#> GSM74394 2 0.5650 0.5673 0.000 0.688 0.312
#> GSM74239 1 0.4682 0.6806 0.804 0.004 0.192
#> GSM74364 1 0.4504 0.6801 0.804 0.000 0.196
#> GSM74365 3 0.8079 0.6151 0.260 0.112 0.628
#> GSM74366 3 0.5158 0.7864 0.004 0.232 0.764
#> GSM74367 3 0.8316 0.2464 0.424 0.080 0.496
#> GSM74377 3 0.5122 0.8169 0.012 0.200 0.788
#> GSM74378 3 0.4931 0.8084 0.004 0.212 0.784
#> GSM74379 3 0.7281 0.7783 0.140 0.148 0.712
#> GSM74380 3 0.7564 0.7688 0.152 0.156 0.692
#> GSM74381 3 0.5574 0.8211 0.032 0.184 0.784
#> GSM121357 2 0.5244 0.6547 0.004 0.756 0.240
#> GSM121361 2 0.5733 0.5781 0.000 0.676 0.324
#> GSM121363 2 0.5733 0.5781 0.000 0.676 0.324
#> GSM121368 2 0.5733 0.5781 0.000 0.676 0.324
#> GSM121369 2 0.5733 0.5781 0.000 0.676 0.324
#> GSM74368 1 0.7400 0.5282 0.664 0.072 0.264
#> GSM74369 1 0.7400 0.5282 0.664 0.072 0.264
#> GSM74370 1 0.5911 0.6839 0.784 0.060 0.156
#> GSM74371 1 0.2878 0.7274 0.904 0.000 0.096
#> GSM74372 1 0.5774 0.6375 0.748 0.020 0.232
#> GSM74373 3 0.8454 0.5109 0.316 0.112 0.572
#> GSM74374 1 0.6819 0.4802 0.644 0.028 0.328
#> GSM74375 3 0.6527 0.8162 0.068 0.188 0.744
#> GSM74376 3 0.7391 0.7998 0.108 0.196 0.696
#> GSM74405 3 0.7004 0.7951 0.112 0.160 0.728
#> GSM74351 1 0.3425 0.7301 0.884 0.004 0.112
#> GSM74352 3 0.5921 0.8181 0.032 0.212 0.756
#> GSM74353 3 0.8754 0.4108 0.376 0.116 0.508
#> GSM74354 1 0.7807 0.0842 0.516 0.052 0.432
#> GSM74355 3 0.4931 0.8081 0.004 0.212 0.784
#> GSM74382 1 0.3619 0.7143 0.864 0.000 0.136
#> GSM74383 1 0.7013 0.4859 0.640 0.036 0.324
#> GSM74384 3 0.4978 0.8052 0.004 0.216 0.780
#> GSM74385 1 0.2959 0.7264 0.900 0.000 0.100
#> GSM74386 1 0.8395 -0.0304 0.480 0.084 0.436
#> GSM74395 1 0.8341 -0.1131 0.468 0.080 0.452
#> GSM74396 3 0.8322 0.1960 0.428 0.080 0.492
#> GSM74397 1 0.8375 0.2597 0.540 0.092 0.368
#> GSM74398 3 0.7670 0.7575 0.152 0.164 0.684
#> GSM74399 3 0.5503 0.8202 0.020 0.208 0.772
#> GSM74400 3 0.5506 0.8112 0.016 0.220 0.764
#> GSM74401 3 0.5506 0.8112 0.016 0.220 0.764
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM74356 2 0.6252 -0.3819 0.016 0.528 0.428 0.028
#> GSM74357 2 0.6252 -0.3819 0.016 0.528 0.428 0.028
#> GSM74358 2 0.6252 -0.3819 0.016 0.528 0.428 0.028
#> GSM74359 4 0.4936 0.5877 0.004 0.000 0.372 0.624
#> GSM74360 4 0.4936 0.5877 0.004 0.000 0.372 0.624
#> GSM74361 3 0.7404 0.9307 0.008 0.416 0.448 0.128
#> GSM74362 3 0.7439 0.9378 0.008 0.416 0.444 0.132
#> GSM74363 2 0.6252 -0.3819 0.016 0.528 0.428 0.028
#> GSM74402 4 0.4010 0.6717 0.028 0.000 0.156 0.816
#> GSM74403 4 0.3278 0.6261 0.020 0.000 0.116 0.864
#> GSM74404 4 0.3219 0.6241 0.020 0.000 0.112 0.868
#> GSM74406 4 0.3355 0.6774 0.004 0.000 0.160 0.836
#> GSM74407 4 0.3401 0.6774 0.008 0.000 0.152 0.840
#> GSM74408 4 0.3219 0.6793 0.000 0.000 0.164 0.836
#> GSM74409 4 0.3219 0.6793 0.000 0.000 0.164 0.836
#> GSM74410 4 0.3219 0.6793 0.000 0.000 0.164 0.836
#> GSM119936 4 0.3219 0.6793 0.000 0.000 0.164 0.836
#> GSM119937 4 0.3311 0.6786 0.000 0.000 0.172 0.828
#> GSM74411 2 0.5038 0.1886 0.012 0.652 0.336 0.000
#> GSM74412 2 0.5038 0.1886 0.012 0.652 0.336 0.000
#> GSM74413 2 0.5038 0.1886 0.012 0.652 0.336 0.000
#> GSM74414 2 0.5592 0.2038 0.044 0.656 0.300 0.000
#> GSM74415 2 0.5038 0.1886 0.012 0.652 0.336 0.000
#> GSM121379 2 0.0000 0.5303 0.000 1.000 0.000 0.000
#> GSM121380 2 0.0000 0.5303 0.000 1.000 0.000 0.000
#> GSM121381 2 0.0000 0.5303 0.000 1.000 0.000 0.000
#> GSM121382 2 0.0000 0.5303 0.000 1.000 0.000 0.000
#> GSM121383 2 0.0000 0.5303 0.000 1.000 0.000 0.000
#> GSM121384 2 0.0000 0.5303 0.000 1.000 0.000 0.000
#> GSM121385 2 0.0000 0.5303 0.000 1.000 0.000 0.000
#> GSM121386 2 0.0000 0.5303 0.000 1.000 0.000 0.000
#> GSM121387 2 0.0000 0.5303 0.000 1.000 0.000 0.000
#> GSM121388 2 0.0000 0.5303 0.000 1.000 0.000 0.000
#> GSM121389 2 0.0000 0.5303 0.000 1.000 0.000 0.000
#> GSM121390 2 0.0000 0.5303 0.000 1.000 0.000 0.000
#> GSM121391 2 0.0000 0.5303 0.000 1.000 0.000 0.000
#> GSM121392 2 0.0376 0.5265 0.004 0.992 0.004 0.000
#> GSM121393 2 0.0000 0.5303 0.000 1.000 0.000 0.000
#> GSM121394 2 0.0188 0.5288 0.000 0.996 0.004 0.000
#> GSM121395 2 0.0000 0.5303 0.000 1.000 0.000 0.000
#> GSM121396 2 0.0000 0.5303 0.000 1.000 0.000 0.000
#> GSM121397 2 0.0000 0.5303 0.000 1.000 0.000 0.000
#> GSM121398 2 0.0000 0.5303 0.000 1.000 0.000 0.000
#> GSM121399 2 0.0000 0.5303 0.000 1.000 0.000 0.000
#> GSM74240 2 0.6399 -0.4984 0.012 0.508 0.440 0.040
#> GSM74241 2 0.6399 -0.4984 0.012 0.508 0.440 0.040
#> GSM74242 2 0.6399 -0.4984 0.012 0.508 0.440 0.040
#> GSM74243 2 0.6399 -0.4984 0.012 0.508 0.440 0.040
#> GSM74244 2 0.6399 -0.4984 0.012 0.508 0.440 0.040
#> GSM74245 2 0.6399 -0.4984 0.012 0.508 0.440 0.040
#> GSM74246 2 0.6399 -0.4984 0.012 0.508 0.440 0.040
#> GSM74247 2 0.6399 -0.4984 0.012 0.508 0.440 0.040
#> GSM74248 2 0.6399 -0.4984 0.012 0.508 0.440 0.040
#> GSM74416 4 0.4624 0.5197 0.000 0.000 0.340 0.660
#> GSM74417 4 0.4624 0.5197 0.000 0.000 0.340 0.660
#> GSM74418 4 0.4624 0.5197 0.000 0.000 0.340 0.660
#> GSM74419 4 0.4049 0.6648 0.000 0.008 0.212 0.780
#> GSM121358 2 0.5093 0.1619 0.012 0.640 0.348 0.000
#> GSM121359 2 0.5093 0.1619 0.012 0.640 0.348 0.000
#> GSM121360 4 0.4936 0.5877 0.004 0.000 0.372 0.624
#> GSM121362 4 0.4936 0.5877 0.004 0.000 0.372 0.624
#> GSM121364 4 0.4936 0.5877 0.004 0.000 0.372 0.624
#> GSM121365 2 0.5093 0.1619 0.012 0.640 0.348 0.000
#> GSM121366 2 0.5093 0.1619 0.012 0.640 0.348 0.000
#> GSM121367 2 0.5093 0.1619 0.012 0.640 0.348 0.000
#> GSM121370 2 0.5093 0.1619 0.012 0.640 0.348 0.000
#> GSM121371 2 0.5093 0.1619 0.012 0.640 0.348 0.000
#> GSM121372 2 0.5093 0.1619 0.012 0.640 0.348 0.000
#> GSM121373 4 0.4936 0.5877 0.004 0.000 0.372 0.624
#> GSM121374 4 0.4746 0.5948 0.000 0.000 0.368 0.632
#> GSM121407 2 0.5057 0.1778 0.012 0.648 0.340 0.000
#> GSM74387 2 0.5835 0.3231 0.280 0.656 0.064 0.000
#> GSM74388 2 0.4820 0.3432 0.296 0.692 0.012 0.000
#> GSM74389 4 0.8061 -0.2675 0.028 0.336 0.164 0.472
#> GSM74390 2 0.7816 -0.0298 0.176 0.564 0.224 0.036
#> GSM74391 4 0.7740 0.0752 0.028 0.276 0.152 0.544
#> GSM74392 3 0.7533 0.9409 0.004 0.408 0.428 0.160
#> GSM74393 3 0.7533 0.9409 0.004 0.408 0.428 0.160
#> GSM74394 2 0.5668 0.3181 0.300 0.652 0.048 0.000
#> GSM74239 4 0.6233 0.4809 0.124 0.000 0.216 0.660
#> GSM74364 4 0.6308 0.4743 0.120 0.000 0.232 0.648
#> GSM74365 1 0.5391 0.6724 0.716 0.008 0.040 0.236
#> GSM74366 1 0.2124 0.7576 0.924 0.068 0.008 0.000
#> GSM74367 1 0.6223 0.4270 0.556 0.000 0.060 0.384
#> GSM74377 1 0.1786 0.7781 0.948 0.036 0.008 0.008
#> GSM74378 1 0.1489 0.7735 0.952 0.044 0.004 0.000
#> GSM74379 1 0.4426 0.7667 0.824 0.024 0.032 0.120
#> GSM74380 1 0.4832 0.7576 0.796 0.024 0.036 0.144
#> GSM74381 1 0.2418 0.7835 0.928 0.032 0.016 0.024
#> GSM121357 2 0.6134 0.3289 0.236 0.660 0.104 0.000
#> GSM121361 2 0.4770 0.3533 0.288 0.700 0.012 0.000
#> GSM121363 2 0.4770 0.3533 0.288 0.700 0.012 0.000
#> GSM121368 2 0.4770 0.3533 0.288 0.700 0.012 0.000
#> GSM121369 2 0.4770 0.3533 0.288 0.700 0.012 0.000
#> GSM74368 4 0.6548 0.3847 0.276 0.000 0.116 0.608
#> GSM74369 4 0.6548 0.3847 0.276 0.000 0.116 0.608
#> GSM74370 4 0.5771 0.5670 0.144 0.000 0.144 0.712
#> GSM74371 4 0.5125 0.5049 0.008 0.000 0.388 0.604
#> GSM74372 4 0.5572 0.4768 0.196 0.000 0.088 0.716
#> GSM74373 1 0.6603 0.5846 0.632 0.028 0.060 0.280
#> GSM74374 4 0.6116 0.2568 0.320 0.000 0.068 0.612
#> GSM74375 1 0.3353 0.7825 0.888 0.020 0.036 0.056
#> GSM74376 1 0.4958 0.7644 0.804 0.056 0.032 0.108
#> GSM74405 1 0.4117 0.7727 0.840 0.024 0.024 0.112
#> GSM74351 4 0.3900 0.6198 0.020 0.000 0.164 0.816
#> GSM74352 1 0.2465 0.7806 0.924 0.044 0.012 0.020
#> GSM74353 1 0.6461 0.5163 0.584 0.008 0.064 0.344
#> GSM74354 1 0.6660 0.1780 0.464 0.000 0.084 0.452
#> GSM74355 1 0.1545 0.7730 0.952 0.040 0.008 0.000
#> GSM74382 4 0.5429 0.5503 0.072 0.000 0.208 0.720
#> GSM74383 4 0.6985 0.1762 0.312 0.000 0.140 0.548
#> GSM74384 1 0.1807 0.7700 0.940 0.052 0.008 0.000
#> GSM74385 4 0.5112 0.4981 0.008 0.000 0.384 0.608
#> GSM74386 1 0.7162 0.2320 0.472 0.000 0.136 0.392
#> GSM74395 1 0.6642 0.2769 0.492 0.000 0.084 0.424
#> GSM74396 1 0.6873 0.3722 0.524 0.008 0.084 0.384
#> GSM74397 4 0.7009 0.0455 0.392 0.000 0.120 0.488
#> GSM74398 1 0.4463 0.7544 0.808 0.008 0.040 0.144
#> GSM74399 1 0.1953 0.7806 0.944 0.032 0.012 0.012
#> GSM74400 1 0.2075 0.7637 0.936 0.016 0.044 0.004
#> GSM74401 1 0.2075 0.7637 0.936 0.016 0.044 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM74356 3 0.1732 0.67154 0.000 0.000 0.920 0.080 0.000
#> GSM74357 3 0.1732 0.67154 0.000 0.000 0.920 0.080 0.000
#> GSM74358 3 0.1732 0.67154 0.000 0.000 0.920 0.080 0.000
#> GSM74359 4 0.4964 0.50551 0.000 0.052 0.200 0.724 0.024
#> GSM74360 4 0.4964 0.50551 0.000 0.052 0.200 0.724 0.024
#> GSM74361 3 0.4015 0.58893 0.000 0.012 0.768 0.204 0.016
#> GSM74362 3 0.4048 0.58649 0.000 0.012 0.764 0.208 0.016
#> GSM74363 3 0.1732 0.67154 0.000 0.000 0.920 0.080 0.000
#> GSM74402 4 0.5249 0.41674 0.024 0.024 0.088 0.756 0.108
#> GSM74403 4 0.5140 -0.16056 0.004 0.048 0.000 0.624 0.324
#> GSM74404 4 0.5203 -0.16954 0.004 0.052 0.000 0.620 0.324
#> GSM74406 4 0.4274 0.46602 0.004 0.020 0.092 0.808 0.076
#> GSM74407 4 0.4478 0.44828 0.004 0.024 0.084 0.796 0.092
#> GSM74408 4 0.2012 0.49357 0.000 0.000 0.060 0.920 0.020
#> GSM74409 4 0.2012 0.49357 0.000 0.000 0.060 0.920 0.020
#> GSM74410 4 0.2012 0.49357 0.000 0.000 0.060 0.920 0.020
#> GSM119936 4 0.2012 0.49357 0.000 0.000 0.060 0.920 0.020
#> GSM119937 4 0.2144 0.49769 0.000 0.000 0.068 0.912 0.020
#> GSM74411 3 0.1121 0.62295 0.000 0.044 0.956 0.000 0.000
#> GSM74412 3 0.1121 0.62295 0.000 0.044 0.956 0.000 0.000
#> GSM74413 3 0.1121 0.62295 0.000 0.044 0.956 0.000 0.000
#> GSM74414 3 0.2291 0.58862 0.036 0.056 0.908 0.000 0.000
#> GSM74415 3 0.1121 0.62295 0.000 0.044 0.956 0.000 0.000
#> GSM121379 2 0.4420 0.99891 0.004 0.548 0.448 0.000 0.000
#> GSM121380 2 0.4420 0.99891 0.004 0.548 0.448 0.000 0.000
#> GSM121381 2 0.4420 0.99891 0.004 0.548 0.448 0.000 0.000
#> GSM121382 2 0.4420 0.99891 0.004 0.548 0.448 0.000 0.000
#> GSM121383 2 0.4420 0.99891 0.004 0.548 0.448 0.000 0.000
#> GSM121384 2 0.4420 0.99891 0.004 0.548 0.448 0.000 0.000
#> GSM121385 2 0.4420 0.99891 0.004 0.548 0.448 0.000 0.000
#> GSM121386 2 0.4420 0.99891 0.004 0.548 0.448 0.000 0.000
#> GSM121387 2 0.4420 0.99891 0.004 0.548 0.448 0.000 0.000
#> GSM121388 2 0.4420 0.99891 0.004 0.548 0.448 0.000 0.000
#> GSM121389 2 0.4420 0.99891 0.004 0.548 0.448 0.000 0.000
#> GSM121390 2 0.4420 0.99891 0.004 0.548 0.448 0.000 0.000
#> GSM121391 2 0.4420 0.99891 0.004 0.548 0.448 0.000 0.000
#> GSM121392 2 0.4680 0.98646 0.008 0.540 0.448 0.000 0.004
#> GSM121393 2 0.4420 0.99891 0.004 0.548 0.448 0.000 0.000
#> GSM121394 2 0.4425 0.99149 0.004 0.544 0.452 0.000 0.000
#> GSM121395 2 0.4420 0.99891 0.004 0.548 0.448 0.000 0.000
#> GSM121396 2 0.4420 0.99891 0.004 0.548 0.448 0.000 0.000
#> GSM121397 2 0.4420 0.99891 0.004 0.548 0.448 0.000 0.000
#> GSM121398 2 0.4420 0.99891 0.004 0.548 0.448 0.000 0.000
#> GSM121399 2 0.4420 0.99891 0.004 0.548 0.448 0.000 0.000
#> GSM74240 3 0.2645 0.67010 0.000 0.008 0.884 0.096 0.012
#> GSM74241 3 0.2645 0.67010 0.000 0.008 0.884 0.096 0.012
#> GSM74242 3 0.2645 0.67010 0.000 0.008 0.884 0.096 0.012
#> GSM74243 3 0.2645 0.67010 0.000 0.008 0.884 0.096 0.012
#> GSM74244 3 0.2645 0.67010 0.000 0.008 0.884 0.096 0.012
#> GSM74245 3 0.2645 0.67010 0.000 0.008 0.884 0.096 0.012
#> GSM74246 3 0.2645 0.67010 0.000 0.008 0.884 0.096 0.012
#> GSM74247 3 0.2645 0.67010 0.000 0.008 0.884 0.096 0.012
#> GSM74248 3 0.2645 0.67010 0.000 0.008 0.884 0.096 0.012
#> GSM74416 4 0.6338 -0.39283 0.000 0.160 0.000 0.448 0.392
#> GSM74417 4 0.6360 -0.39337 0.000 0.164 0.000 0.448 0.388
#> GSM74418 4 0.6360 -0.39337 0.000 0.164 0.000 0.448 0.388
#> GSM74419 4 0.4512 0.48751 0.000 0.020 0.140 0.776 0.064
#> GSM121358 3 0.0880 0.63532 0.000 0.032 0.968 0.000 0.000
#> GSM121359 3 0.0880 0.63532 0.000 0.032 0.968 0.000 0.000
#> GSM121360 4 0.4964 0.50551 0.000 0.052 0.200 0.724 0.024
#> GSM121362 4 0.4964 0.50551 0.000 0.052 0.200 0.724 0.024
#> GSM121364 4 0.4964 0.50551 0.000 0.052 0.200 0.724 0.024
#> GSM121365 3 0.0880 0.63532 0.000 0.032 0.968 0.000 0.000
#> GSM121366 3 0.0880 0.63532 0.000 0.032 0.968 0.000 0.000
#> GSM121367 3 0.0880 0.63532 0.000 0.032 0.968 0.000 0.000
#> GSM121370 3 0.0880 0.63532 0.000 0.032 0.968 0.000 0.000
#> GSM121371 3 0.0880 0.63532 0.000 0.032 0.968 0.000 0.000
#> GSM121372 3 0.0880 0.63532 0.000 0.032 0.968 0.000 0.000
#> GSM121373 4 0.4964 0.50551 0.000 0.052 0.200 0.724 0.024
#> GSM121374 4 0.4899 0.50649 0.000 0.052 0.192 0.732 0.024
#> GSM121407 3 0.1043 0.62795 0.000 0.040 0.960 0.000 0.000
#> GSM74387 3 0.6803 -0.22022 0.284 0.220 0.484 0.000 0.012
#> GSM74388 3 0.7075 -0.33895 0.308 0.280 0.400 0.000 0.012
#> GSM74389 3 0.6427 -0.08068 0.012 0.012 0.448 0.444 0.084
#> GSM74390 3 0.6127 0.44846 0.188 0.052 0.680 0.032 0.048
#> GSM74391 4 0.6725 0.16406 0.012 0.020 0.368 0.496 0.104
#> GSM74392 3 0.4260 0.56226 0.000 0.012 0.736 0.236 0.016
#> GSM74393 3 0.4260 0.56226 0.000 0.012 0.736 0.236 0.016
#> GSM74394 3 0.6921 -0.24818 0.300 0.236 0.452 0.000 0.012
#> GSM74239 5 0.6021 0.67135 0.088 0.012 0.000 0.364 0.536
#> GSM74364 5 0.6035 0.68155 0.084 0.016 0.000 0.352 0.548
#> GSM74365 1 0.5268 0.61470 0.692 0.000 0.004 0.136 0.168
#> GSM74366 1 0.1498 0.71425 0.952 0.016 0.024 0.000 0.008
#> GSM74367 1 0.6345 0.31047 0.524 0.000 0.000 0.252 0.224
#> GSM74377 1 0.0486 0.73251 0.988 0.000 0.004 0.004 0.004
#> GSM74378 1 0.0727 0.72770 0.980 0.004 0.012 0.000 0.004
#> GSM74379 1 0.3715 0.71551 0.824 0.000 0.004 0.064 0.108
#> GSM74380 1 0.3956 0.71066 0.808 0.000 0.004 0.080 0.108
#> GSM74381 1 0.1461 0.73813 0.952 0.000 0.004 0.016 0.028
#> GSM121357 3 0.6433 -0.12372 0.236 0.192 0.560 0.000 0.012
#> GSM121361 3 0.7052 -0.34427 0.300 0.276 0.412 0.000 0.012
#> GSM121363 3 0.7052 -0.34427 0.300 0.276 0.412 0.000 0.012
#> GSM121368 3 0.7052 -0.34427 0.300 0.276 0.412 0.000 0.012
#> GSM121369 3 0.7052 -0.34427 0.300 0.276 0.412 0.000 0.012
#> GSM74368 4 0.7290 0.05969 0.244 0.036 0.032 0.552 0.136
#> GSM74369 4 0.7290 0.05969 0.244 0.036 0.032 0.552 0.136
#> GSM74370 4 0.6551 0.23439 0.116 0.064 0.028 0.668 0.124
#> GSM74371 5 0.5878 0.56697 0.000 0.120 0.000 0.324 0.556
#> GSM74372 4 0.6972 -0.35913 0.172 0.024 0.004 0.500 0.300
#> GSM74373 1 0.6321 0.52758 0.640 0.024 0.012 0.152 0.172
#> GSM74374 4 0.7378 -0.34578 0.292 0.024 0.004 0.416 0.264
#> GSM74375 1 0.2963 0.73502 0.884 0.004 0.008 0.048 0.056
#> GSM74376 1 0.4231 0.71897 0.820 0.008 0.032 0.056 0.084
#> GSM74405 1 0.3334 0.72455 0.852 0.000 0.004 0.064 0.080
#> GSM74351 4 0.5719 -0.14858 0.004 0.104 0.000 0.604 0.288
#> GSM74352 1 0.1794 0.73351 0.944 0.008 0.012 0.012 0.024
#> GSM74353 1 0.6278 0.44968 0.576 0.004 0.008 0.268 0.144
#> GSM74354 1 0.7335 -0.00167 0.420 0.016 0.008 0.288 0.268
#> GSM74355 1 0.0740 0.72719 0.980 0.008 0.004 0.000 0.008
#> GSM74382 5 0.5703 0.58524 0.036 0.024 0.000 0.448 0.492
#> GSM74383 5 0.7086 0.36845 0.268 0.008 0.004 0.304 0.416
#> GSM74384 1 0.1087 0.72438 0.968 0.008 0.016 0.000 0.008
#> GSM74385 5 0.6288 0.56338 0.000 0.180 0.000 0.304 0.516
#> GSM74386 1 0.7704 0.05503 0.432 0.024 0.024 0.284 0.236
#> GSM74395 1 0.7068 0.12635 0.460 0.000 0.024 0.304 0.212
#> GSM74396 1 0.6899 0.23172 0.500 0.000 0.024 0.284 0.192
#> GSM74397 4 0.7695 -0.27871 0.364 0.004 0.056 0.380 0.196
#> GSM74398 1 0.4231 0.70257 0.796 0.004 0.004 0.100 0.096
#> GSM74399 1 0.1220 0.73421 0.964 0.004 0.004 0.008 0.020
#> GSM74400 1 0.4868 0.57620 0.720 0.084 0.000 0.004 0.192
#> GSM74401 1 0.4868 0.57620 0.720 0.084 0.000 0.004 0.192
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM74356 3 0.1003 0.84496 0.000 0.020 0.964 0.016 NA 0.000
#> GSM74357 3 0.1003 0.84496 0.000 0.020 0.964 0.016 NA 0.000
#> GSM74358 3 0.1003 0.84496 0.000 0.020 0.964 0.016 NA 0.000
#> GSM74359 4 0.4607 0.56694 0.000 0.012 0.264 0.672 NA 0.000
#> GSM74360 4 0.4607 0.56694 0.000 0.012 0.264 0.672 NA 0.000
#> GSM74361 3 0.2593 0.71758 0.000 0.000 0.844 0.148 NA 0.000
#> GSM74362 3 0.2631 0.71338 0.000 0.000 0.840 0.152 NA 0.000
#> GSM74363 3 0.1003 0.84496 0.000 0.020 0.964 0.016 NA 0.000
#> GSM74402 4 0.5986 0.46934 0.180 0.012 0.132 0.636 NA 0.024
#> GSM74403 4 0.5888 -0.11088 0.420 0.016 0.016 0.476 NA 0.004
#> GSM74404 4 0.5809 -0.11914 0.420 0.016 0.012 0.480 NA 0.004
#> GSM74406 4 0.4959 0.53209 0.124 0.012 0.144 0.708 NA 0.000
#> GSM74407 4 0.5159 0.51541 0.144 0.012 0.136 0.692 NA 0.000
#> GSM74408 4 0.2678 0.56539 0.020 0.000 0.116 0.860 NA 0.000
#> GSM74409 4 0.2678 0.56539 0.020 0.000 0.116 0.860 NA 0.000
#> GSM74410 4 0.2678 0.56539 0.020 0.000 0.116 0.860 NA 0.000
#> GSM119936 4 0.2678 0.56539 0.020 0.000 0.116 0.860 NA 0.000
#> GSM119937 4 0.2766 0.56856 0.020 0.000 0.124 0.852 NA 0.000
#> GSM74411 3 0.2178 0.82460 0.000 0.132 0.868 0.000 NA 0.000
#> GSM74412 3 0.2178 0.82460 0.000 0.132 0.868 0.000 NA 0.000
#> GSM74413 3 0.2178 0.82460 0.000 0.132 0.868 0.000 NA 0.000
#> GSM74414 3 0.3134 0.78924 0.000 0.144 0.820 0.000 NA 0.036
#> GSM74415 3 0.2178 0.82460 0.000 0.132 0.868 0.000 NA 0.000
#> GSM121379 2 0.2178 0.88419 0.000 0.868 0.132 0.000 NA 0.000
#> GSM121380 2 0.2178 0.88419 0.000 0.868 0.132 0.000 NA 0.000
#> GSM121381 2 0.2178 0.88419 0.000 0.868 0.132 0.000 NA 0.000
#> GSM121382 2 0.2178 0.88419 0.000 0.868 0.132 0.000 NA 0.000
#> GSM121383 2 0.2178 0.88419 0.000 0.868 0.132 0.000 NA 0.000
#> GSM121384 2 0.2178 0.88419 0.000 0.868 0.132 0.000 NA 0.000
#> GSM121385 2 0.2178 0.88419 0.000 0.868 0.132 0.000 NA 0.000
#> GSM121386 2 0.2178 0.88419 0.000 0.868 0.132 0.000 NA 0.000
#> GSM121387 2 0.2178 0.88419 0.000 0.868 0.132 0.000 NA 0.000
#> GSM121388 2 0.2219 0.88182 0.000 0.864 0.136 0.000 NA 0.000
#> GSM121389 2 0.2219 0.88182 0.000 0.864 0.136 0.000 NA 0.000
#> GSM121390 2 0.2178 0.88419 0.000 0.868 0.132 0.000 NA 0.000
#> GSM121391 2 0.2178 0.88419 0.000 0.868 0.132 0.000 NA 0.000
#> GSM121392 2 0.2431 0.88091 0.000 0.860 0.132 0.000 NA 0.000
#> GSM121393 2 0.2178 0.88419 0.000 0.868 0.132 0.000 NA 0.000
#> GSM121394 2 0.2219 0.88196 0.000 0.864 0.136 0.000 NA 0.000
#> GSM121395 2 0.2219 0.88182 0.000 0.864 0.136 0.000 NA 0.000
#> GSM121396 2 0.2219 0.88182 0.000 0.864 0.136 0.000 NA 0.000
#> GSM121397 2 0.2178 0.88419 0.000 0.868 0.132 0.000 NA 0.000
#> GSM121398 2 0.2178 0.88419 0.000 0.868 0.132 0.000 NA 0.000
#> GSM121399 2 0.2178 0.88419 0.000 0.868 0.132 0.000 NA 0.000
#> GSM74240 3 0.0972 0.83805 0.000 0.000 0.964 0.028 NA 0.000
#> GSM74241 3 0.0972 0.83805 0.000 0.000 0.964 0.028 NA 0.000
#> GSM74242 3 0.0972 0.83805 0.000 0.000 0.964 0.028 NA 0.000
#> GSM74243 3 0.0972 0.83805 0.000 0.000 0.964 0.028 NA 0.000
#> GSM74244 3 0.0972 0.83805 0.000 0.000 0.964 0.028 NA 0.000
#> GSM74245 3 0.0972 0.83805 0.000 0.000 0.964 0.028 NA 0.000
#> GSM74246 3 0.0972 0.83805 0.000 0.000 0.964 0.028 NA 0.000
#> GSM74247 3 0.0972 0.83805 0.000 0.000 0.964 0.028 NA 0.000
#> GSM74248 3 0.0972 0.83805 0.000 0.000 0.964 0.028 NA 0.000
#> GSM74416 4 0.6383 -0.18824 0.324 0.020 0.000 0.428 NA 0.000
#> GSM74417 4 0.6391 -0.18921 0.320 0.020 0.000 0.428 NA 0.000
#> GSM74418 4 0.6391 -0.18921 0.320 0.020 0.000 0.428 NA 0.000
#> GSM74419 4 0.5008 0.55040 0.092 0.012 0.192 0.692 NA 0.000
#> GSM121358 3 0.2048 0.83428 0.000 0.120 0.880 0.000 NA 0.000
#> GSM121359 3 0.2048 0.83428 0.000 0.120 0.880 0.000 NA 0.000
#> GSM121360 4 0.4607 0.56694 0.000 0.012 0.264 0.672 NA 0.000
#> GSM121362 4 0.4607 0.56694 0.000 0.012 0.264 0.672 NA 0.000
#> GSM121364 4 0.4607 0.56694 0.000 0.012 0.264 0.672 NA 0.000
#> GSM121365 3 0.2048 0.83428 0.000 0.120 0.880 0.000 NA 0.000
#> GSM121366 3 0.2048 0.83428 0.000 0.120 0.880 0.000 NA 0.000
#> GSM121367 3 0.2048 0.83428 0.000 0.120 0.880 0.000 NA 0.000
#> GSM121370 3 0.2048 0.83428 0.000 0.120 0.880 0.000 NA 0.000
#> GSM121371 3 0.2048 0.83428 0.000 0.120 0.880 0.000 NA 0.000
#> GSM121372 3 0.2048 0.83428 0.000 0.120 0.880 0.000 NA 0.000
#> GSM121373 4 0.4607 0.56694 0.000 0.012 0.264 0.672 NA 0.000
#> GSM121374 4 0.4528 0.56787 0.000 0.012 0.260 0.680 NA 0.000
#> GSM121407 3 0.2135 0.82855 0.000 0.128 0.872 0.000 NA 0.000
#> GSM74387 2 0.6364 0.57381 0.008 0.472 0.232 0.000 NA 0.276
#> GSM74388 2 0.5921 0.61987 0.008 0.540 0.140 0.000 NA 0.300
#> GSM74389 3 0.6111 -0.06760 0.116 0.004 0.492 0.364 NA 0.012
#> GSM74390 3 0.6685 0.51415 0.056 0.040 0.608 0.012 NA 0.160
#> GSM74391 4 0.6503 0.21996 0.140 0.012 0.412 0.412 NA 0.012
#> GSM74392 3 0.2882 0.67179 0.000 0.000 0.812 0.180 NA 0.000
#> GSM74393 3 0.2882 0.67179 0.000 0.000 0.812 0.180 NA 0.000
#> GSM74394 2 0.6227 0.58713 0.008 0.496 0.192 0.000 NA 0.292
#> GSM74239 1 0.4693 0.58337 0.732 0.004 0.008 0.172 NA 0.068
#> GSM74364 1 0.4592 0.57997 0.744 0.004 0.004 0.160 NA 0.064
#> GSM74365 6 0.4670 0.55081 0.264 0.000 0.008 0.040 NA 0.676
#> GSM74366 6 0.1488 0.66594 0.008 0.028 0.008 0.000 NA 0.948
#> GSM74367 6 0.6025 0.27561 0.356 0.000 0.016 0.104 NA 0.508
#> GSM74377 6 0.0653 0.68629 0.012 0.000 0.000 0.004 NA 0.980
#> GSM74378 6 0.0622 0.67990 0.008 0.012 0.000 0.000 NA 0.980
#> GSM74379 6 0.3441 0.65724 0.148 0.000 0.004 0.024 NA 0.812
#> GSM74380 6 0.3680 0.65907 0.136 0.000 0.004 0.044 NA 0.804
#> GSM74381 6 0.1477 0.69061 0.048 0.000 0.000 0.008 NA 0.940
#> GSM121357 2 0.6411 0.36957 0.004 0.396 0.356 0.000 NA 0.232
#> GSM121361 2 0.5894 0.63141 0.008 0.548 0.140 0.000 NA 0.292
#> GSM121363 2 0.5894 0.63141 0.008 0.548 0.140 0.000 NA 0.292
#> GSM121368 2 0.5894 0.63141 0.008 0.548 0.140 0.000 NA 0.292
#> GSM121369 2 0.5894 0.63141 0.008 0.548 0.140 0.000 NA 0.292
#> GSM74368 4 0.7550 -0.05093 0.196 0.008 0.048 0.464 NA 0.232
#> GSM74369 4 0.7550 -0.05093 0.196 0.008 0.048 0.464 NA 0.232
#> GSM74370 4 0.7018 0.17396 0.204 0.012 0.048 0.568 NA 0.100
#> GSM74371 1 0.5936 0.40641 0.544 0.016 0.000 0.220 NA 0.000
#> GSM74372 1 0.6926 0.38466 0.432 0.012 0.020 0.356 NA 0.156
#> GSM74373 6 0.6128 0.41936 0.200 0.024 0.012 0.124 NA 0.620
#> GSM74374 1 0.7214 0.37711 0.404 0.012 0.016 0.272 NA 0.268
#> GSM74375 6 0.2784 0.68373 0.064 0.000 0.004 0.020 NA 0.880
#> GSM74376 6 0.3942 0.67007 0.108 0.020 0.016 0.024 NA 0.816
#> GSM74405 6 0.3068 0.67393 0.112 0.000 0.004 0.024 NA 0.848
#> GSM74351 4 0.5778 -0.03455 0.344 0.020 0.000 0.520 NA 0.000
#> GSM74352 6 0.1761 0.68439 0.032 0.016 0.000 0.008 NA 0.936
#> GSM74353 6 0.6249 0.39762 0.208 0.000 0.024 0.176 NA 0.572
#> GSM74354 1 0.6704 0.07827 0.420 0.008 0.020 0.152 NA 0.384
#> GSM74355 6 0.0665 0.68168 0.008 0.008 0.000 0.000 NA 0.980
#> GSM74382 1 0.4879 0.48720 0.680 0.004 0.016 0.252 NA 0.016
#> GSM74383 1 0.5833 0.40422 0.608 0.004 0.024 0.116 NA 0.240
#> GSM74384 6 0.1065 0.67529 0.008 0.020 0.000 0.000 NA 0.964
#> GSM74385 1 0.6718 0.41766 0.472 0.064 0.000 0.200 NA 0.000
#> GSM74386 6 0.6842 -0.00989 0.384 0.000 0.048 0.124 NA 0.420
#> GSM74395 6 0.6489 0.10145 0.368 0.000 0.048 0.132 NA 0.448
#> GSM74396 6 0.6411 0.19664 0.336 0.000 0.048 0.128 NA 0.484
#> GSM74397 6 0.7325 -0.19110 0.324 0.000 0.080 0.232 NA 0.356
#> GSM74398 6 0.3767 0.65261 0.156 0.000 0.004 0.028 NA 0.792
#> GSM74399 6 0.1096 0.68763 0.020 0.004 0.000 0.004 NA 0.964
#> GSM74400 6 0.4689 0.28320 0.020 0.008 0.000 0.004 NA 0.508
#> GSM74401 6 0.4689 0.28320 0.020 0.008 0.000 0.004 NA 0.508
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 112 1.59e-07 2
#> CV:hclust 110 9.28e-15 3
#> CV:hclust 69 4.80e-20 4
#> CV:hclust 82 1.06e-22 5
#> CV:hclust 92 5.23e-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["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 21168 rows and 121 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 0.956 0.957 0.976 0.4969 0.497 0.497
#> 3 3 0.663 0.823 0.903 0.3250 0.725 0.501
#> 4 4 0.710 0.719 0.864 0.1302 0.802 0.487
#> 5 5 0.694 0.598 0.750 0.0616 0.913 0.677
#> 6 6 0.726 0.597 0.698 0.0366 0.896 0.572
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
#> GSM74356 2 0.2948 0.940 0.052 0.948
#> GSM74357 2 0.4815 0.906 0.104 0.896
#> GSM74358 2 0.4562 0.912 0.096 0.904
#> GSM74359 1 0.0000 0.997 1.000 0.000
#> GSM74360 1 0.0000 0.997 1.000 0.000
#> GSM74361 2 0.4562 0.912 0.096 0.904
#> GSM74362 2 0.5737 0.876 0.136 0.864
#> GSM74363 2 0.2948 0.940 0.052 0.948
#> GSM74402 1 0.0000 0.997 1.000 0.000
#> GSM74403 1 0.0000 0.997 1.000 0.000
#> GSM74404 1 0.0000 0.997 1.000 0.000
#> GSM74406 1 0.0000 0.997 1.000 0.000
#> GSM74407 1 0.0000 0.997 1.000 0.000
#> GSM74408 1 0.0000 0.997 1.000 0.000
#> GSM74409 1 0.0000 0.997 1.000 0.000
#> GSM74410 1 0.0000 0.997 1.000 0.000
#> GSM119936 1 0.0000 0.997 1.000 0.000
#> GSM119937 1 0.0000 0.997 1.000 0.000
#> GSM74411 2 0.0000 0.952 0.000 1.000
#> GSM74412 2 0.0000 0.952 0.000 1.000
#> GSM74413 2 0.0000 0.952 0.000 1.000
#> GSM74414 2 0.0000 0.952 0.000 1.000
#> GSM74415 2 0.2948 0.940 0.052 0.948
#> GSM121379 2 0.0000 0.952 0.000 1.000
#> GSM121380 2 0.0000 0.952 0.000 1.000
#> GSM121381 2 0.0000 0.952 0.000 1.000
#> GSM121382 2 0.0000 0.952 0.000 1.000
#> GSM121383 2 0.0000 0.952 0.000 1.000
#> GSM121384 2 0.0000 0.952 0.000 1.000
#> GSM121385 2 0.0000 0.952 0.000 1.000
#> GSM121386 2 0.0000 0.952 0.000 1.000
#> GSM121387 2 0.0000 0.952 0.000 1.000
#> GSM121388 2 0.0000 0.952 0.000 1.000
#> GSM121389 2 0.0000 0.952 0.000 1.000
#> GSM121390 2 0.0000 0.952 0.000 1.000
#> GSM121391 2 0.0000 0.952 0.000 1.000
#> GSM121392 2 0.0000 0.952 0.000 1.000
#> GSM121393 2 0.0000 0.952 0.000 1.000
#> GSM121394 2 0.0000 0.952 0.000 1.000
#> GSM121395 2 0.0000 0.952 0.000 1.000
#> GSM121396 2 0.0000 0.952 0.000 1.000
#> GSM121397 2 0.0000 0.952 0.000 1.000
#> GSM121398 2 0.0000 0.952 0.000 1.000
#> GSM121399 2 0.0000 0.952 0.000 1.000
#> GSM74240 2 0.6048 0.863 0.148 0.852
#> GSM74241 2 0.5178 0.895 0.116 0.884
#> GSM74242 2 0.9909 0.304 0.444 0.556
#> GSM74243 2 0.9909 0.304 0.444 0.556
#> GSM74244 2 0.4431 0.915 0.092 0.908
#> GSM74245 2 0.5946 0.868 0.144 0.856
#> GSM74246 2 0.4690 0.909 0.100 0.900
#> GSM74247 2 0.4562 0.912 0.096 0.904
#> GSM74248 2 0.6048 0.863 0.148 0.852
#> GSM74416 1 0.0000 0.997 1.000 0.000
#> GSM74417 1 0.0000 0.997 1.000 0.000
#> GSM74418 1 0.0000 0.997 1.000 0.000
#> GSM74419 1 0.0000 0.997 1.000 0.000
#> GSM121358 2 0.2948 0.940 0.052 0.948
#> GSM121359 2 0.0000 0.952 0.000 1.000
#> GSM121360 1 0.0000 0.997 1.000 0.000
#> GSM121362 1 0.0000 0.997 1.000 0.000
#> GSM121364 1 0.0000 0.997 1.000 0.000
#> GSM121365 2 0.2948 0.940 0.052 0.948
#> GSM121366 2 0.0000 0.952 0.000 1.000
#> GSM121367 2 0.2948 0.940 0.052 0.948
#> GSM121370 2 0.2948 0.940 0.052 0.948
#> GSM121371 2 0.2948 0.940 0.052 0.948
#> GSM121372 2 0.0000 0.952 0.000 1.000
#> GSM121373 1 0.0000 0.997 1.000 0.000
#> GSM121374 1 0.0000 0.997 1.000 0.000
#> GSM121407 2 0.0000 0.952 0.000 1.000
#> GSM74387 2 0.2778 0.941 0.048 0.952
#> GSM74388 2 0.0000 0.952 0.000 1.000
#> GSM74389 1 0.0672 0.990 0.992 0.008
#> GSM74390 1 0.0000 0.997 1.000 0.000
#> GSM74391 1 0.0000 0.997 1.000 0.000
#> GSM74392 1 0.0000 0.997 1.000 0.000
#> GSM74393 1 0.2236 0.960 0.964 0.036
#> GSM74394 2 0.2948 0.940 0.052 0.948
#> GSM74239 1 0.0000 0.997 1.000 0.000
#> GSM74364 1 0.0000 0.997 1.000 0.000
#> GSM74365 1 0.0000 0.997 1.000 0.000
#> GSM74366 1 0.0376 0.994 0.996 0.004
#> GSM74367 1 0.0000 0.997 1.000 0.000
#> GSM74377 1 0.0000 0.997 1.000 0.000
#> GSM74378 1 0.0376 0.994 0.996 0.004
#> GSM74379 1 0.0000 0.997 1.000 0.000
#> GSM74380 1 0.0000 0.997 1.000 0.000
#> GSM74381 1 0.0000 0.997 1.000 0.000
#> GSM121357 2 0.0000 0.952 0.000 1.000
#> GSM121361 2 0.1414 0.948 0.020 0.980
#> GSM121363 2 0.0000 0.952 0.000 1.000
#> GSM121368 2 0.0000 0.952 0.000 1.000
#> GSM121369 2 0.2778 0.941 0.048 0.952
#> GSM74368 1 0.0000 0.997 1.000 0.000
#> GSM74369 1 0.0000 0.997 1.000 0.000
#> GSM74370 1 0.0000 0.997 1.000 0.000
#> GSM74371 1 0.0000 0.997 1.000 0.000
#> GSM74372 1 0.0000 0.997 1.000 0.000
#> GSM74373 1 0.0000 0.997 1.000 0.000
#> GSM74374 1 0.0000 0.997 1.000 0.000
#> GSM74375 1 0.0000 0.997 1.000 0.000
#> GSM74376 1 0.0000 0.997 1.000 0.000
#> GSM74405 1 0.0000 0.997 1.000 0.000
#> GSM74351 1 0.0000 0.997 1.000 0.000
#> GSM74352 1 0.1414 0.979 0.980 0.020
#> GSM74353 1 0.0000 0.997 1.000 0.000
#> GSM74354 1 0.0000 0.997 1.000 0.000
#> GSM74355 1 0.0000 0.997 1.000 0.000
#> GSM74382 1 0.0000 0.997 1.000 0.000
#> GSM74383 1 0.0000 0.997 1.000 0.000
#> GSM74384 1 0.4022 0.914 0.920 0.080
#> GSM74385 1 0.0000 0.997 1.000 0.000
#> GSM74386 1 0.0000 0.997 1.000 0.000
#> GSM74395 1 0.0000 0.997 1.000 0.000
#> GSM74396 1 0.0000 0.997 1.000 0.000
#> GSM74397 1 0.0000 0.997 1.000 0.000
#> GSM74398 1 0.0000 0.997 1.000 0.000
#> GSM74399 1 0.0000 0.997 1.000 0.000
#> GSM74400 1 0.0000 0.997 1.000 0.000
#> GSM74401 1 0.0000 0.997 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM74356 3 0.1031 0.783 0.000 0.024 0.976
#> GSM74357 3 0.1031 0.783 0.000 0.024 0.976
#> GSM74358 3 0.1031 0.783 0.000 0.024 0.976
#> GSM74359 3 0.4399 0.749 0.188 0.000 0.812
#> GSM74360 3 0.4654 0.731 0.208 0.000 0.792
#> GSM74361 3 0.0892 0.784 0.000 0.020 0.980
#> GSM74362 3 0.0892 0.784 0.000 0.020 0.980
#> GSM74363 3 0.1529 0.776 0.000 0.040 0.960
#> GSM74402 1 0.5560 0.563 0.700 0.000 0.300
#> GSM74403 1 0.4605 0.745 0.796 0.000 0.204
#> GSM74404 1 0.4605 0.745 0.796 0.000 0.204
#> GSM74406 3 0.5733 0.578 0.324 0.000 0.676
#> GSM74407 1 0.5431 0.598 0.716 0.000 0.284
#> GSM74408 3 0.5706 0.584 0.320 0.000 0.680
#> GSM74409 3 0.4887 0.710 0.228 0.000 0.772
#> GSM74410 3 0.4555 0.739 0.200 0.000 0.800
#> GSM119936 3 0.6079 0.443 0.388 0.000 0.612
#> GSM119937 3 0.6079 0.443 0.388 0.000 0.612
#> GSM74411 2 0.5591 0.679 0.000 0.696 0.304
#> GSM74412 2 0.4605 0.809 0.000 0.796 0.204
#> GSM74413 2 0.4750 0.797 0.000 0.784 0.216
#> GSM74414 2 0.1964 0.891 0.000 0.944 0.056
#> GSM74415 3 0.4887 0.611 0.000 0.228 0.772
#> GSM121379 2 0.0000 0.906 0.000 1.000 0.000
#> GSM121380 2 0.0000 0.906 0.000 1.000 0.000
#> GSM121381 2 0.0000 0.906 0.000 1.000 0.000
#> GSM121382 2 0.0000 0.906 0.000 1.000 0.000
#> GSM121383 2 0.0000 0.906 0.000 1.000 0.000
#> GSM121384 2 0.0000 0.906 0.000 1.000 0.000
#> GSM121385 2 0.0000 0.906 0.000 1.000 0.000
#> GSM121386 2 0.0000 0.906 0.000 1.000 0.000
#> GSM121387 2 0.0000 0.906 0.000 1.000 0.000
#> GSM121388 2 0.0000 0.906 0.000 1.000 0.000
#> GSM121389 2 0.0000 0.906 0.000 1.000 0.000
#> GSM121390 2 0.0000 0.906 0.000 1.000 0.000
#> GSM121391 2 0.0000 0.906 0.000 1.000 0.000
#> GSM121392 2 0.0000 0.906 0.000 1.000 0.000
#> GSM121393 2 0.0000 0.906 0.000 1.000 0.000
#> GSM121394 2 0.0000 0.906 0.000 1.000 0.000
#> GSM121395 2 0.0000 0.906 0.000 1.000 0.000
#> GSM121396 2 0.1529 0.896 0.000 0.960 0.040
#> GSM121397 2 0.0000 0.906 0.000 1.000 0.000
#> GSM121398 2 0.0000 0.906 0.000 1.000 0.000
#> GSM121399 2 0.0000 0.906 0.000 1.000 0.000
#> GSM74240 3 0.0237 0.786 0.000 0.004 0.996
#> GSM74241 3 0.3619 0.706 0.000 0.136 0.864
#> GSM74242 3 0.0000 0.786 0.000 0.000 1.000
#> GSM74243 3 0.0000 0.786 0.000 0.000 1.000
#> GSM74244 3 0.3619 0.706 0.000 0.136 0.864
#> GSM74245 3 0.0424 0.785 0.000 0.008 0.992
#> GSM74246 3 0.3752 0.699 0.000 0.144 0.856
#> GSM74247 3 0.4062 0.677 0.000 0.164 0.836
#> GSM74248 3 0.0237 0.786 0.000 0.004 0.996
#> GSM74416 1 0.4605 0.745 0.796 0.000 0.204
#> GSM74417 1 0.4605 0.745 0.796 0.000 0.204
#> GSM74418 1 0.3340 0.846 0.880 0.000 0.120
#> GSM74419 3 0.5760 0.570 0.328 0.000 0.672
#> GSM121358 3 0.4931 0.606 0.000 0.232 0.768
#> GSM121359 2 0.4605 0.809 0.000 0.796 0.204
#> GSM121360 3 0.4291 0.750 0.180 0.000 0.820
#> GSM121362 3 0.5621 0.603 0.308 0.000 0.692
#> GSM121364 3 0.4452 0.746 0.192 0.000 0.808
#> GSM121365 3 0.4931 0.606 0.000 0.232 0.768
#> GSM121366 3 0.4931 0.606 0.000 0.232 0.768
#> GSM121367 3 0.4931 0.606 0.000 0.232 0.768
#> GSM121370 3 0.4931 0.606 0.000 0.232 0.768
#> GSM121371 3 0.4931 0.606 0.000 0.232 0.768
#> GSM121372 2 0.5016 0.771 0.000 0.760 0.240
#> GSM121373 3 0.4452 0.746 0.192 0.000 0.808
#> GSM121374 3 0.4452 0.746 0.192 0.000 0.808
#> GSM121407 2 0.4555 0.812 0.000 0.800 0.200
#> GSM74387 2 0.5968 0.588 0.000 0.636 0.364
#> GSM74388 2 0.3669 0.872 0.040 0.896 0.064
#> GSM74389 3 0.2261 0.787 0.068 0.000 0.932
#> GSM74390 1 0.0747 0.948 0.984 0.000 0.016
#> GSM74391 3 0.4796 0.719 0.220 0.000 0.780
#> GSM74392 3 0.4291 0.753 0.180 0.000 0.820
#> GSM74393 3 0.0000 0.786 0.000 0.000 1.000
#> GSM74394 2 0.7304 0.724 0.084 0.688 0.228
#> GSM74239 1 0.0424 0.950 0.992 0.000 0.008
#> GSM74364 1 0.0424 0.950 0.992 0.000 0.008
#> GSM74365 1 0.0000 0.950 1.000 0.000 0.000
#> GSM74366 1 0.0747 0.948 0.984 0.000 0.016
#> GSM74367 1 0.0237 0.950 0.996 0.000 0.004
#> GSM74377 1 0.0747 0.948 0.984 0.000 0.016
#> GSM74378 1 0.0747 0.948 0.984 0.000 0.016
#> GSM74379 1 0.0747 0.948 0.984 0.000 0.016
#> GSM74380 1 0.0747 0.948 0.984 0.000 0.016
#> GSM74381 1 0.0747 0.948 0.984 0.000 0.016
#> GSM121357 2 0.3482 0.859 0.000 0.872 0.128
#> GSM121361 2 0.5435 0.832 0.048 0.808 0.144
#> GSM121363 2 0.4615 0.849 0.020 0.836 0.144
#> GSM121368 2 0.4475 0.851 0.016 0.840 0.144
#> GSM121369 2 0.7597 0.491 0.048 0.568 0.384
#> GSM74368 1 0.0424 0.950 0.992 0.000 0.008
#> GSM74369 1 0.0424 0.950 0.992 0.000 0.008
#> GSM74370 1 0.0424 0.950 0.992 0.000 0.008
#> GSM74371 1 0.0424 0.950 0.992 0.000 0.008
#> GSM74372 1 0.0424 0.950 0.992 0.000 0.008
#> GSM74373 1 0.0747 0.948 0.984 0.000 0.016
#> GSM74374 1 0.0000 0.950 1.000 0.000 0.000
#> GSM74375 1 0.0747 0.948 0.984 0.000 0.016
#> GSM74376 1 0.0747 0.948 0.984 0.000 0.016
#> GSM74405 1 0.0747 0.948 0.984 0.000 0.016
#> GSM74351 1 0.0424 0.950 0.992 0.000 0.008
#> GSM74352 1 0.0747 0.948 0.984 0.000 0.016
#> GSM74353 1 0.0424 0.950 0.992 0.000 0.008
#> GSM74354 1 0.0000 0.950 1.000 0.000 0.000
#> GSM74355 1 0.0747 0.948 0.984 0.000 0.016
#> GSM74382 1 0.0424 0.950 0.992 0.000 0.008
#> GSM74383 1 0.0237 0.950 0.996 0.000 0.004
#> GSM74384 1 0.0747 0.948 0.984 0.000 0.016
#> GSM74385 1 0.0424 0.950 0.992 0.000 0.008
#> GSM74386 1 0.0237 0.950 0.996 0.000 0.004
#> GSM74395 1 0.0424 0.950 0.992 0.000 0.008
#> GSM74396 1 0.0000 0.950 1.000 0.000 0.000
#> GSM74397 1 0.0592 0.948 0.988 0.000 0.012
#> GSM74398 1 0.0747 0.948 0.984 0.000 0.016
#> GSM74399 1 0.0747 0.948 0.984 0.000 0.016
#> GSM74400 1 0.0237 0.950 0.996 0.000 0.004
#> GSM74401 1 0.0237 0.950 0.996 0.000 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM74356 3 0.1022 0.8606 0.000 0.000 0.968 0.032
#> GSM74357 3 0.1118 0.8582 0.000 0.000 0.964 0.036
#> GSM74358 3 0.1118 0.8582 0.000 0.000 0.964 0.036
#> GSM74359 4 0.4800 0.5497 0.004 0.000 0.340 0.656
#> GSM74360 4 0.2714 0.7275 0.004 0.000 0.112 0.884
#> GSM74361 3 0.1022 0.8622 0.000 0.000 0.968 0.032
#> GSM74362 3 0.1211 0.8576 0.000 0.000 0.960 0.040
#> GSM74363 3 0.0921 0.8624 0.000 0.000 0.972 0.028
#> GSM74402 4 0.1743 0.7456 0.056 0.000 0.004 0.940
#> GSM74403 4 0.1637 0.7429 0.060 0.000 0.000 0.940
#> GSM74404 4 0.1637 0.7429 0.060 0.000 0.000 0.940
#> GSM74406 4 0.0927 0.7584 0.016 0.000 0.008 0.976
#> GSM74407 4 0.1557 0.7454 0.056 0.000 0.000 0.944
#> GSM74408 4 0.0927 0.7584 0.016 0.000 0.008 0.976
#> GSM74409 4 0.0937 0.7581 0.012 0.000 0.012 0.976
#> GSM74410 4 0.0927 0.7572 0.008 0.000 0.016 0.976
#> GSM119936 4 0.1042 0.7580 0.020 0.000 0.008 0.972
#> GSM119937 4 0.1042 0.7580 0.020 0.000 0.008 0.972
#> GSM74411 3 0.5106 0.5192 0.008 0.312 0.672 0.008
#> GSM74412 3 0.5149 0.5038 0.008 0.320 0.664 0.008
#> GSM74413 3 0.5127 0.5119 0.008 0.316 0.668 0.008
#> GSM74414 2 0.4630 0.6820 0.024 0.776 0.192 0.008
#> GSM74415 3 0.0804 0.8640 0.008 0.000 0.980 0.012
#> GSM121379 2 0.0000 0.8883 0.000 1.000 0.000 0.000
#> GSM121380 2 0.0000 0.8883 0.000 1.000 0.000 0.000
#> GSM121381 2 0.0000 0.8883 0.000 1.000 0.000 0.000
#> GSM121382 2 0.0000 0.8883 0.000 1.000 0.000 0.000
#> GSM121383 2 0.0000 0.8883 0.000 1.000 0.000 0.000
#> GSM121384 2 0.0000 0.8883 0.000 1.000 0.000 0.000
#> GSM121385 2 0.0000 0.8883 0.000 1.000 0.000 0.000
#> GSM121386 2 0.0000 0.8883 0.000 1.000 0.000 0.000
#> GSM121387 2 0.0000 0.8883 0.000 1.000 0.000 0.000
#> GSM121388 2 0.0188 0.8865 0.000 0.996 0.000 0.004
#> GSM121389 2 0.0188 0.8865 0.000 0.996 0.000 0.004
#> GSM121390 2 0.0000 0.8883 0.000 1.000 0.000 0.000
#> GSM121391 2 0.0000 0.8883 0.000 1.000 0.000 0.000
#> GSM121392 2 0.0000 0.8883 0.000 1.000 0.000 0.000
#> GSM121393 2 0.0188 0.8865 0.000 0.996 0.000 0.004
#> GSM121394 2 0.0000 0.8883 0.000 1.000 0.000 0.000
#> GSM121395 2 0.0188 0.8865 0.000 0.996 0.000 0.004
#> GSM121396 2 0.2714 0.7923 0.000 0.884 0.112 0.004
#> GSM121397 2 0.0000 0.8883 0.000 1.000 0.000 0.000
#> GSM121398 2 0.0000 0.8883 0.000 1.000 0.000 0.000
#> GSM121399 2 0.0000 0.8883 0.000 1.000 0.000 0.000
#> GSM74240 3 0.0804 0.8651 0.008 0.000 0.980 0.012
#> GSM74241 3 0.0672 0.8644 0.008 0.000 0.984 0.008
#> GSM74242 3 0.1356 0.8615 0.008 0.000 0.960 0.032
#> GSM74243 3 0.1452 0.8593 0.008 0.000 0.956 0.036
#> GSM74244 3 0.0804 0.8651 0.008 0.000 0.980 0.012
#> GSM74245 3 0.0804 0.8651 0.008 0.000 0.980 0.012
#> GSM74246 3 0.0672 0.8644 0.008 0.000 0.984 0.008
#> GSM74247 3 0.0672 0.8644 0.008 0.000 0.984 0.008
#> GSM74248 3 0.0804 0.8651 0.008 0.000 0.980 0.012
#> GSM74416 4 0.1637 0.7429 0.060 0.000 0.000 0.940
#> GSM74417 4 0.1557 0.7454 0.056 0.000 0.000 0.944
#> GSM74418 4 0.1637 0.7429 0.060 0.000 0.000 0.940
#> GSM74419 4 0.1042 0.7580 0.020 0.000 0.008 0.972
#> GSM121358 3 0.0895 0.8650 0.000 0.004 0.976 0.020
#> GSM121359 3 0.4761 0.4909 0.000 0.332 0.664 0.004
#> GSM121360 4 0.4800 0.5497 0.004 0.000 0.340 0.656
#> GSM121362 4 0.5695 0.5350 0.040 0.000 0.336 0.624
#> GSM121364 4 0.4761 0.5583 0.004 0.000 0.332 0.664
#> GSM121365 3 0.0895 0.8650 0.000 0.004 0.976 0.020
#> GSM121366 3 0.0657 0.8657 0.000 0.004 0.984 0.012
#> GSM121367 3 0.0895 0.8650 0.000 0.004 0.976 0.020
#> GSM121370 3 0.0592 0.8655 0.000 0.000 0.984 0.016
#> GSM121371 3 0.0895 0.8650 0.000 0.004 0.976 0.020
#> GSM121372 3 0.4720 0.5074 0.000 0.324 0.672 0.004
#> GSM121373 4 0.4800 0.5497 0.004 0.000 0.340 0.656
#> GSM121374 4 0.4800 0.5497 0.004 0.000 0.340 0.656
#> GSM121407 3 0.4781 0.4824 0.000 0.336 0.660 0.004
#> GSM74387 3 0.3822 0.7526 0.016 0.140 0.836 0.008
#> GSM74388 2 0.7224 0.4712 0.340 0.528 0.124 0.008
#> GSM74389 4 0.5000 0.1712 0.000 0.000 0.500 0.500
#> GSM74390 1 0.0336 0.8450 0.992 0.000 0.000 0.008
#> GSM74391 4 0.1284 0.7566 0.012 0.000 0.024 0.964
#> GSM74392 4 0.4800 0.5497 0.004 0.000 0.340 0.656
#> GSM74393 3 0.2918 0.7787 0.008 0.000 0.876 0.116
#> GSM74394 3 0.7932 0.2047 0.252 0.280 0.460 0.008
#> GSM74239 1 0.4713 0.5645 0.640 0.000 0.000 0.360
#> GSM74364 1 0.4730 0.5568 0.636 0.000 0.000 0.364
#> GSM74365 1 0.1637 0.8364 0.940 0.000 0.000 0.060
#> GSM74366 1 0.0376 0.8341 0.992 0.000 0.004 0.004
#> GSM74367 1 0.3907 0.7451 0.768 0.000 0.000 0.232
#> GSM74377 1 0.0336 0.8450 0.992 0.000 0.000 0.008
#> GSM74378 1 0.0000 0.8401 1.000 0.000 0.000 0.000
#> GSM74379 1 0.0336 0.8450 0.992 0.000 0.000 0.008
#> GSM74380 1 0.0336 0.8450 0.992 0.000 0.000 0.008
#> GSM74381 1 0.0336 0.8450 0.992 0.000 0.000 0.008
#> GSM121357 2 0.5833 0.1299 0.024 0.532 0.440 0.004
#> GSM121361 2 0.7867 0.3552 0.268 0.476 0.248 0.008
#> GSM121363 2 0.7870 0.3451 0.256 0.476 0.260 0.008
#> GSM121368 2 0.7870 0.3451 0.256 0.476 0.260 0.008
#> GSM121369 3 0.5391 0.6595 0.208 0.052 0.732 0.008
#> GSM74368 1 0.4250 0.7074 0.724 0.000 0.000 0.276
#> GSM74369 1 0.4382 0.6833 0.704 0.000 0.000 0.296
#> GSM74370 1 0.4585 0.6322 0.668 0.000 0.000 0.332
#> GSM74371 4 0.4948 -0.0752 0.440 0.000 0.000 0.560
#> GSM74372 4 0.4955 -0.0611 0.444 0.000 0.000 0.556
#> GSM74373 1 0.0336 0.8450 0.992 0.000 0.000 0.008
#> GSM74374 1 0.3356 0.7873 0.824 0.000 0.000 0.176
#> GSM74375 1 0.0469 0.8449 0.988 0.000 0.000 0.012
#> GSM74376 1 0.0000 0.8401 1.000 0.000 0.000 0.000
#> GSM74405 1 0.0336 0.8450 0.992 0.000 0.000 0.008
#> GSM74351 4 0.4431 0.3726 0.304 0.000 0.000 0.696
#> GSM74352 1 0.0000 0.8401 1.000 0.000 0.000 0.000
#> GSM74353 1 0.4817 0.5206 0.612 0.000 0.000 0.388
#> GSM74354 1 0.3311 0.7909 0.828 0.000 0.000 0.172
#> GSM74355 1 0.0000 0.8401 1.000 0.000 0.000 0.000
#> GSM74382 4 0.4134 0.4702 0.260 0.000 0.000 0.740
#> GSM74383 1 0.3764 0.7620 0.784 0.000 0.000 0.216
#> GSM74384 1 0.0376 0.8341 0.992 0.000 0.004 0.004
#> GSM74385 4 0.4994 -0.2109 0.480 0.000 0.000 0.520
#> GSM74386 1 0.3726 0.7659 0.788 0.000 0.000 0.212
#> GSM74395 1 0.4454 0.6486 0.692 0.000 0.000 0.308
#> GSM74396 1 0.3688 0.7668 0.792 0.000 0.000 0.208
#> GSM74397 1 0.4981 0.3060 0.536 0.000 0.000 0.464
#> GSM74398 1 0.0336 0.8450 0.992 0.000 0.000 0.008
#> GSM74399 1 0.0336 0.8450 0.992 0.000 0.000 0.008
#> GSM74400 1 0.1389 0.8407 0.952 0.000 0.000 0.048
#> GSM74401 1 0.1389 0.8407 0.952 0.000 0.000 0.048
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM74356 3 0.0727 0.8535 0.004 0.000 0.980 0.012 0.004
#> GSM74357 3 0.1059 0.8502 0.004 0.000 0.968 0.020 0.008
#> GSM74358 3 0.1059 0.8502 0.004 0.000 0.968 0.020 0.008
#> GSM74359 4 0.5524 0.6237 0.004 0.000 0.180 0.664 0.152
#> GSM74360 4 0.5029 0.6707 0.012 0.000 0.104 0.728 0.156
#> GSM74361 3 0.1915 0.8473 0.000 0.000 0.928 0.032 0.040
#> GSM74362 3 0.4960 0.6151 0.000 0.000 0.708 0.180 0.112
#> GSM74363 3 0.0566 0.8542 0.004 0.000 0.984 0.012 0.000
#> GSM74402 4 0.3491 0.6515 0.228 0.000 0.000 0.768 0.004
#> GSM74403 4 0.4086 0.5980 0.284 0.000 0.000 0.704 0.012
#> GSM74404 4 0.4086 0.5980 0.284 0.000 0.000 0.704 0.012
#> GSM74406 4 0.2020 0.7176 0.100 0.000 0.000 0.900 0.000
#> GSM74407 4 0.3671 0.6450 0.236 0.000 0.000 0.756 0.008
#> GSM74408 4 0.2077 0.7194 0.084 0.000 0.000 0.908 0.008
#> GSM74409 4 0.1981 0.7198 0.048 0.000 0.000 0.924 0.028
#> GSM74410 4 0.2227 0.7195 0.048 0.000 0.004 0.916 0.032
#> GSM119936 4 0.2536 0.7107 0.128 0.000 0.000 0.868 0.004
#> GSM119937 4 0.2707 0.7107 0.132 0.000 0.000 0.860 0.008
#> GSM74411 3 0.4503 0.7294 0.000 0.120 0.756 0.000 0.124
#> GSM74412 3 0.4548 0.7264 0.000 0.120 0.752 0.000 0.128
#> GSM74413 3 0.4503 0.7294 0.000 0.120 0.756 0.000 0.124
#> GSM74414 2 0.5793 0.3745 0.000 0.584 0.124 0.000 0.292
#> GSM74415 3 0.2536 0.8237 0.000 0.000 0.868 0.004 0.128
#> GSM121379 2 0.0324 0.9347 0.000 0.992 0.000 0.004 0.004
#> GSM121380 2 0.0162 0.9353 0.000 0.996 0.000 0.000 0.004
#> GSM121381 2 0.0162 0.9353 0.000 0.996 0.000 0.000 0.004
#> GSM121382 2 0.0290 0.9342 0.000 0.992 0.000 0.000 0.008
#> GSM121383 2 0.0290 0.9342 0.000 0.992 0.000 0.000 0.008
#> GSM121384 2 0.0162 0.9353 0.000 0.996 0.000 0.000 0.004
#> GSM121385 2 0.0162 0.9353 0.000 0.996 0.000 0.000 0.004
#> GSM121386 2 0.0162 0.9353 0.000 0.996 0.000 0.000 0.004
#> GSM121387 2 0.0290 0.9342 0.000 0.992 0.000 0.000 0.008
#> GSM121388 2 0.0932 0.9264 0.004 0.972 0.000 0.004 0.020
#> GSM121389 2 0.0566 0.9319 0.000 0.984 0.000 0.004 0.012
#> GSM121390 2 0.0324 0.9347 0.000 0.992 0.000 0.004 0.004
#> GSM121391 2 0.0162 0.9349 0.000 0.996 0.000 0.000 0.004
#> GSM121392 2 0.0324 0.9347 0.000 0.992 0.000 0.004 0.004
#> GSM121393 2 0.0932 0.9264 0.004 0.972 0.000 0.004 0.020
#> GSM121394 2 0.0162 0.9349 0.000 0.996 0.000 0.000 0.004
#> GSM121395 2 0.0833 0.9284 0.004 0.976 0.000 0.004 0.016
#> GSM121396 2 0.3425 0.7814 0.004 0.840 0.112 0.000 0.044
#> GSM121397 2 0.0162 0.9353 0.000 0.996 0.000 0.000 0.004
#> GSM121398 2 0.0162 0.9353 0.000 0.996 0.000 0.000 0.004
#> GSM121399 2 0.0000 0.9353 0.000 1.000 0.000 0.000 0.000
#> GSM74240 3 0.3209 0.8394 0.004 0.000 0.848 0.028 0.120
#> GSM74241 3 0.3304 0.8390 0.004 0.000 0.840 0.028 0.128
#> GSM74242 3 0.3273 0.8342 0.004 0.000 0.848 0.036 0.112
#> GSM74243 3 0.3273 0.8342 0.004 0.000 0.848 0.036 0.112
#> GSM74244 3 0.3160 0.8402 0.004 0.000 0.852 0.028 0.116
#> GSM74245 3 0.3209 0.8394 0.004 0.000 0.848 0.028 0.120
#> GSM74246 3 0.3441 0.8361 0.004 0.000 0.828 0.028 0.140
#> GSM74247 3 0.3441 0.8361 0.004 0.000 0.828 0.028 0.140
#> GSM74248 3 0.3059 0.8385 0.004 0.000 0.860 0.028 0.108
#> GSM74416 4 0.3928 0.5849 0.296 0.000 0.000 0.700 0.004
#> GSM74417 4 0.3861 0.6001 0.284 0.000 0.000 0.712 0.004
#> GSM74418 4 0.3928 0.5849 0.296 0.000 0.000 0.700 0.004
#> GSM74419 4 0.2605 0.7032 0.148 0.000 0.000 0.852 0.000
#> GSM121358 3 0.0727 0.8544 0.004 0.000 0.980 0.012 0.004
#> GSM121359 3 0.4416 0.7360 0.004 0.124 0.780 0.004 0.088
#> GSM121360 4 0.5630 0.6145 0.004 0.000 0.180 0.652 0.164
#> GSM121362 4 0.6199 0.6034 0.028 0.000 0.176 0.628 0.168
#> GSM121364 4 0.5524 0.6237 0.004 0.000 0.180 0.664 0.152
#> GSM121365 3 0.0727 0.8544 0.004 0.000 0.980 0.012 0.004
#> GSM121366 3 0.0727 0.8544 0.004 0.000 0.980 0.012 0.004
#> GSM121367 3 0.0727 0.8544 0.004 0.000 0.980 0.012 0.004
#> GSM121370 3 0.0727 0.8544 0.004 0.000 0.980 0.012 0.004
#> GSM121371 3 0.0727 0.8544 0.004 0.000 0.980 0.012 0.004
#> GSM121372 3 0.4416 0.7360 0.004 0.124 0.780 0.004 0.088
#> GSM121373 4 0.5524 0.6237 0.004 0.000 0.180 0.664 0.152
#> GSM121374 4 0.5524 0.6237 0.004 0.000 0.180 0.664 0.152
#> GSM121407 3 0.4522 0.7312 0.004 0.124 0.772 0.004 0.096
#> GSM74387 3 0.5053 0.6483 0.000 0.048 0.644 0.004 0.304
#> GSM74388 5 0.5649 0.1734 0.040 0.372 0.024 0.000 0.564
#> GSM74389 4 0.6111 0.3356 0.004 0.000 0.340 0.532 0.124
#> GSM74390 1 0.4182 0.2242 0.600 0.000 0.000 0.000 0.400
#> GSM74391 4 0.3689 0.7215 0.084 0.000 0.012 0.836 0.068
#> GSM74392 4 0.5487 0.6253 0.004 0.000 0.180 0.668 0.148
#> GSM74393 3 0.6331 0.2464 0.004 0.000 0.508 0.336 0.152
#> GSM74394 5 0.6390 -0.0508 0.020 0.096 0.348 0.004 0.532
#> GSM74239 1 0.2583 0.6033 0.864 0.000 0.000 0.132 0.004
#> GSM74364 1 0.2583 0.6046 0.864 0.000 0.000 0.132 0.004
#> GSM74365 1 0.2929 0.5030 0.820 0.000 0.000 0.000 0.180
#> GSM74366 5 0.4235 0.1882 0.424 0.000 0.000 0.000 0.576
#> GSM74367 1 0.2153 0.6190 0.916 0.000 0.000 0.040 0.044
#> GSM74377 1 0.4283 0.0823 0.544 0.000 0.000 0.000 0.456
#> GSM74378 5 0.4256 0.1685 0.436 0.000 0.000 0.000 0.564
#> GSM74379 1 0.4210 0.1828 0.588 0.000 0.000 0.000 0.412
#> GSM74380 1 0.4219 0.1775 0.584 0.000 0.000 0.000 0.416
#> GSM74381 1 0.4300 0.0162 0.524 0.000 0.000 0.000 0.476
#> GSM121357 2 0.6754 0.0654 0.000 0.400 0.324 0.000 0.276
#> GSM121361 5 0.6394 0.1496 0.020 0.368 0.092 0.004 0.516
#> GSM121363 5 0.6394 0.1496 0.020 0.368 0.092 0.004 0.516
#> GSM121368 5 0.6394 0.1496 0.020 0.368 0.092 0.004 0.516
#> GSM121369 5 0.5824 -0.2412 0.020 0.028 0.428 0.012 0.512
#> GSM74368 1 0.4025 0.5901 0.792 0.000 0.000 0.076 0.132
#> GSM74369 1 0.3702 0.6104 0.820 0.000 0.000 0.084 0.096
#> GSM74370 1 0.2900 0.6132 0.864 0.000 0.000 0.108 0.028
#> GSM74371 1 0.4130 0.3923 0.696 0.000 0.000 0.292 0.012
#> GSM74372 1 0.3863 0.4738 0.740 0.000 0.000 0.248 0.012
#> GSM74373 1 0.4305 -0.0282 0.512 0.000 0.000 0.000 0.488
#> GSM74374 1 0.1661 0.6201 0.940 0.000 0.000 0.024 0.036
#> GSM74375 1 0.4256 0.1382 0.564 0.000 0.000 0.000 0.436
#> GSM74376 5 0.4249 0.1773 0.432 0.000 0.000 0.000 0.568
#> GSM74405 5 0.4305 0.0132 0.488 0.000 0.000 0.000 0.512
#> GSM74351 1 0.4517 0.1669 0.600 0.000 0.000 0.388 0.012
#> GSM74352 5 0.4268 0.1516 0.444 0.000 0.000 0.000 0.556
#> GSM74353 1 0.2612 0.6111 0.868 0.000 0.000 0.124 0.008
#> GSM74354 1 0.0898 0.6219 0.972 0.000 0.000 0.020 0.008
#> GSM74355 5 0.4294 0.0815 0.468 0.000 0.000 0.000 0.532
#> GSM74382 1 0.4457 0.1853 0.620 0.000 0.000 0.368 0.012
#> GSM74383 1 0.1282 0.6249 0.952 0.000 0.000 0.044 0.004
#> GSM74384 5 0.4235 0.1882 0.424 0.000 0.000 0.000 0.576
#> GSM74385 1 0.4181 0.4298 0.712 0.000 0.000 0.268 0.020
#> GSM74386 1 0.1997 0.6203 0.924 0.000 0.000 0.036 0.040
#> GSM74395 1 0.1571 0.6240 0.936 0.000 0.000 0.060 0.004
#> GSM74396 1 0.1168 0.6249 0.960 0.000 0.000 0.032 0.008
#> GSM74397 1 0.3491 0.4988 0.768 0.000 0.000 0.228 0.004
#> GSM74398 1 0.4192 0.2077 0.596 0.000 0.000 0.000 0.404
#> GSM74399 1 0.4283 0.0823 0.544 0.000 0.000 0.000 0.456
#> GSM74400 1 0.4138 0.2672 0.616 0.000 0.000 0.000 0.384
#> GSM74401 1 0.4138 0.2672 0.616 0.000 0.000 0.000 0.384
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM74356 3 0.1075 0.721 0.000 0.000 0.952 0.048 0.000 0.000
#> GSM74357 3 0.1204 0.717 0.000 0.000 0.944 0.056 0.000 0.000
#> GSM74358 3 0.1204 0.717 0.000 0.000 0.944 0.056 0.000 0.000
#> GSM74359 4 0.1644 0.677 0.004 0.000 0.076 0.920 0.000 0.000
#> GSM74360 4 0.1584 0.678 0.008 0.000 0.064 0.928 0.000 0.000
#> GSM74361 3 0.2365 0.712 0.000 0.000 0.888 0.072 0.040 0.000
#> GSM74362 4 0.4594 -0.166 0.000 0.000 0.476 0.488 0.036 0.000
#> GSM74363 3 0.0790 0.724 0.000 0.000 0.968 0.032 0.000 0.000
#> GSM74402 1 0.5693 -0.270 0.448 0.000 0.000 0.392 0.160 0.000
#> GSM74403 1 0.5798 -0.137 0.484 0.000 0.000 0.312 0.204 0.000
#> GSM74404 1 0.5798 -0.137 0.484 0.000 0.000 0.312 0.204 0.000
#> GSM74406 4 0.5480 0.478 0.308 0.000 0.000 0.540 0.152 0.000
#> GSM74407 1 0.5862 -0.262 0.428 0.000 0.000 0.376 0.196 0.000
#> GSM74408 4 0.5440 0.488 0.296 0.000 0.000 0.552 0.152 0.000
#> GSM74409 4 0.5277 0.525 0.256 0.000 0.000 0.592 0.152 0.000
#> GSM74410 4 0.5177 0.540 0.236 0.000 0.000 0.612 0.152 0.000
#> GSM119936 4 0.5624 0.403 0.356 0.000 0.000 0.488 0.156 0.000
#> GSM119937 4 0.5642 0.407 0.352 0.000 0.000 0.488 0.160 0.000
#> GSM74411 3 0.4044 0.557 0.000 0.040 0.704 0.000 0.256 0.000
#> GSM74412 3 0.4332 0.444 0.000 0.040 0.644 0.000 0.316 0.000
#> GSM74413 3 0.4044 0.557 0.000 0.040 0.704 0.000 0.256 0.000
#> GSM74414 2 0.6907 -0.587 0.000 0.384 0.148 0.000 0.376 0.092
#> GSM74415 3 0.3314 0.612 0.000 0.000 0.740 0.004 0.256 0.000
#> GSM121379 2 0.0291 0.934 0.004 0.992 0.000 0.000 0.004 0.000
#> GSM121380 2 0.0291 0.934 0.004 0.992 0.000 0.000 0.004 0.000
#> GSM121381 2 0.0291 0.934 0.004 0.992 0.000 0.000 0.004 0.000
#> GSM121382 2 0.0363 0.930 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM121383 2 0.0363 0.930 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM121384 2 0.0291 0.934 0.004 0.992 0.000 0.000 0.004 0.000
#> GSM121385 2 0.0291 0.934 0.004 0.992 0.000 0.000 0.004 0.000
#> GSM121386 2 0.0291 0.934 0.004 0.992 0.000 0.000 0.004 0.000
#> GSM121387 2 0.0363 0.930 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM121388 2 0.1633 0.899 0.024 0.932 0.000 0.000 0.044 0.000
#> GSM121389 2 0.1297 0.909 0.012 0.948 0.000 0.000 0.040 0.000
#> GSM121390 2 0.0291 0.934 0.004 0.992 0.000 0.000 0.004 0.000
#> GSM121391 2 0.0146 0.932 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM121392 2 0.0291 0.934 0.004 0.992 0.000 0.000 0.004 0.000
#> GSM121393 2 0.1633 0.899 0.024 0.932 0.000 0.000 0.044 0.000
#> GSM121394 2 0.0260 0.932 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM121395 2 0.1391 0.907 0.016 0.944 0.000 0.000 0.040 0.000
#> GSM121396 2 0.4038 0.644 0.016 0.768 0.160 0.000 0.056 0.000
#> GSM121397 2 0.0291 0.934 0.004 0.992 0.000 0.000 0.004 0.000
#> GSM121398 2 0.0291 0.934 0.004 0.992 0.000 0.000 0.004 0.000
#> GSM121399 2 0.0000 0.933 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM74240 3 0.5504 0.599 0.024 0.000 0.588 0.096 0.292 0.000
#> GSM74241 3 0.5365 0.599 0.024 0.000 0.596 0.080 0.300 0.000
#> GSM74242 3 0.5632 0.598 0.024 0.000 0.588 0.120 0.268 0.000
#> GSM74243 3 0.5667 0.595 0.024 0.000 0.584 0.124 0.268 0.000
#> GSM74244 3 0.5349 0.601 0.024 0.000 0.600 0.080 0.296 0.000
#> GSM74245 3 0.5421 0.601 0.024 0.000 0.596 0.088 0.292 0.000
#> GSM74246 3 0.5394 0.593 0.024 0.000 0.588 0.080 0.308 0.000
#> GSM74247 3 0.5394 0.593 0.024 0.000 0.588 0.080 0.308 0.000
#> GSM74248 3 0.5556 0.603 0.024 0.000 0.592 0.108 0.276 0.000
#> GSM74416 1 0.5600 -0.130 0.508 0.000 0.000 0.332 0.160 0.000
#> GSM74417 1 0.5618 -0.147 0.500 0.000 0.000 0.340 0.160 0.000
#> GSM74418 1 0.5600 -0.130 0.508 0.000 0.000 0.332 0.160 0.000
#> GSM74419 4 0.5651 0.340 0.400 0.000 0.000 0.448 0.152 0.000
#> GSM121358 3 0.0922 0.725 0.000 0.004 0.968 0.024 0.004 0.000
#> GSM121359 3 0.2744 0.627 0.000 0.072 0.864 0.000 0.064 0.000
#> GSM121360 4 0.2002 0.670 0.004 0.000 0.076 0.908 0.012 0.000
#> GSM121362 4 0.2507 0.659 0.016 0.000 0.072 0.892 0.012 0.008
#> GSM121364 4 0.1644 0.677 0.004 0.000 0.076 0.920 0.000 0.000
#> GSM121365 3 0.0922 0.725 0.000 0.004 0.968 0.024 0.004 0.000
#> GSM121366 3 0.0837 0.724 0.000 0.004 0.972 0.020 0.004 0.000
#> GSM121367 3 0.0922 0.725 0.000 0.004 0.968 0.024 0.004 0.000
#> GSM121370 3 0.0837 0.724 0.000 0.004 0.972 0.020 0.004 0.000
#> GSM121371 3 0.0922 0.725 0.000 0.004 0.968 0.024 0.004 0.000
#> GSM121372 3 0.2830 0.632 0.000 0.068 0.864 0.004 0.064 0.000
#> GSM121373 4 0.1644 0.677 0.004 0.000 0.076 0.920 0.000 0.000
#> GSM121374 4 0.1644 0.677 0.004 0.000 0.076 0.920 0.000 0.000
#> GSM121407 3 0.3118 0.599 0.000 0.072 0.836 0.000 0.092 0.000
#> GSM74387 5 0.5715 0.240 0.000 0.024 0.400 0.008 0.500 0.068
#> GSM74388 5 0.6718 0.661 0.000 0.272 0.028 0.008 0.436 0.256
#> GSM74389 4 0.3909 0.571 0.008 0.000 0.160 0.772 0.060 0.000
#> GSM74390 6 0.5408 0.373 0.304 0.000 0.000 0.000 0.144 0.552
#> GSM74391 4 0.5522 0.509 0.268 0.000 0.004 0.568 0.160 0.000
#> GSM74392 4 0.2262 0.679 0.008 0.000 0.080 0.896 0.016 0.000
#> GSM74393 4 0.4407 0.415 0.000 0.000 0.232 0.692 0.076 0.000
#> GSM74394 5 0.6779 0.574 0.000 0.056 0.260 0.008 0.488 0.188
#> GSM74239 1 0.3314 0.525 0.740 0.000 0.000 0.000 0.004 0.256
#> GSM74364 1 0.3488 0.529 0.744 0.000 0.000 0.004 0.008 0.244
#> GSM74365 6 0.3971 0.102 0.448 0.000 0.000 0.000 0.004 0.548
#> GSM74366 6 0.1714 0.749 0.000 0.000 0.000 0.000 0.092 0.908
#> GSM74367 1 0.3727 0.348 0.612 0.000 0.000 0.000 0.000 0.388
#> GSM74377 6 0.1556 0.818 0.080 0.000 0.000 0.000 0.000 0.920
#> GSM74378 6 0.1387 0.775 0.000 0.000 0.000 0.000 0.068 0.932
#> GSM74379 6 0.2340 0.774 0.148 0.000 0.000 0.000 0.000 0.852
#> GSM74380 6 0.2135 0.791 0.128 0.000 0.000 0.000 0.000 0.872
#> GSM74381 6 0.1444 0.820 0.072 0.000 0.000 0.000 0.000 0.928
#> GSM121357 5 0.7174 0.614 0.000 0.276 0.272 0.000 0.368 0.084
#> GSM121361 5 0.7183 0.740 0.000 0.264 0.092 0.008 0.444 0.192
#> GSM121363 5 0.7205 0.736 0.000 0.272 0.092 0.008 0.436 0.192
#> GSM121368 5 0.7205 0.736 0.000 0.272 0.092 0.008 0.436 0.192
#> GSM121369 5 0.6798 0.508 0.000 0.024 0.284 0.032 0.480 0.180
#> GSM74368 1 0.4947 0.274 0.528 0.000 0.000 0.008 0.048 0.416
#> GSM74369 1 0.4923 0.317 0.544 0.000 0.000 0.008 0.048 0.400
#> GSM74370 1 0.4694 0.510 0.656 0.000 0.000 0.020 0.040 0.284
#> GSM74371 1 0.2636 0.558 0.860 0.000 0.000 0.004 0.016 0.120
#> GSM74372 1 0.5285 0.532 0.660 0.000 0.000 0.048 0.076 0.216
#> GSM74373 6 0.1398 0.821 0.052 0.000 0.000 0.000 0.008 0.940
#> GSM74374 1 0.4646 0.453 0.616 0.000 0.000 0.000 0.060 0.324
#> GSM74375 6 0.1806 0.814 0.088 0.000 0.000 0.000 0.004 0.908
#> GSM74376 6 0.1556 0.765 0.000 0.000 0.000 0.000 0.080 0.920
#> GSM74405 6 0.1498 0.811 0.028 0.000 0.000 0.000 0.032 0.940
#> GSM74351 1 0.3282 0.516 0.848 0.000 0.000 0.036 0.068 0.048
#> GSM74352 6 0.1757 0.778 0.008 0.000 0.000 0.000 0.076 0.916
#> GSM74353 1 0.4476 0.519 0.668 0.000 0.000 0.008 0.044 0.280
#> GSM74354 1 0.3714 0.449 0.656 0.000 0.000 0.000 0.004 0.340
#> GSM74355 6 0.1528 0.800 0.016 0.000 0.000 0.000 0.048 0.936
#> GSM74382 1 0.2164 0.549 0.908 0.000 0.000 0.012 0.020 0.060
#> GSM74383 1 0.3725 0.479 0.676 0.000 0.000 0.000 0.008 0.316
#> GSM74384 6 0.1765 0.744 0.000 0.000 0.000 0.000 0.096 0.904
#> GSM74385 1 0.3236 0.556 0.820 0.000 0.000 0.004 0.036 0.140
#> GSM74386 1 0.3659 0.411 0.636 0.000 0.000 0.000 0.000 0.364
#> GSM74395 1 0.3499 0.475 0.680 0.000 0.000 0.000 0.000 0.320
#> GSM74396 1 0.3547 0.461 0.668 0.000 0.000 0.000 0.000 0.332
#> GSM74397 1 0.4147 0.547 0.744 0.000 0.000 0.044 0.016 0.196
#> GSM74398 6 0.2048 0.795 0.120 0.000 0.000 0.000 0.000 0.880
#> GSM74399 6 0.1610 0.817 0.084 0.000 0.000 0.000 0.000 0.916
#> GSM74400 6 0.4166 0.714 0.160 0.000 0.000 0.004 0.088 0.748
#> GSM74401 6 0.4200 0.708 0.164 0.000 0.000 0.004 0.088 0.744
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 119 4.11e-11 2
#> CV:kmeans 118 4.50e-24 3
#> CV:kmeans 106 1.87e-32 4
#> CV:kmeans 87 1.90e-28 5
#> CV:kmeans 93 5.65e-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["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 21168 rows and 121 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.983 0.993 0.5033 0.497 0.497
#> 3 3 0.917 0.895 0.953 0.3157 0.770 0.569
#> 4 4 0.858 0.846 0.931 0.1335 0.817 0.524
#> 5 5 0.762 0.721 0.853 0.0463 0.951 0.806
#> 6 6 0.753 0.594 0.764 0.0407 0.884 0.543
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM74356 2 0.0000 0.988 0.000 1.000
#> GSM74357 2 0.0000 0.988 0.000 1.000
#> GSM74358 2 0.0000 0.988 0.000 1.000
#> GSM74359 1 0.0000 0.997 1.000 0.000
#> GSM74360 1 0.0000 0.997 1.000 0.000
#> GSM74361 2 0.0000 0.988 0.000 1.000
#> GSM74362 2 0.0000 0.988 0.000 1.000
#> GSM74363 2 0.0000 0.988 0.000 1.000
#> GSM74402 1 0.0000 0.997 1.000 0.000
#> GSM74403 1 0.0000 0.997 1.000 0.000
#> GSM74404 1 0.0000 0.997 1.000 0.000
#> GSM74406 1 0.0000 0.997 1.000 0.000
#> GSM74407 1 0.0000 0.997 1.000 0.000
#> GSM74408 1 0.0000 0.997 1.000 0.000
#> GSM74409 1 0.0000 0.997 1.000 0.000
#> GSM74410 1 0.0000 0.997 1.000 0.000
#> GSM119936 1 0.0000 0.997 1.000 0.000
#> GSM119937 1 0.0000 0.997 1.000 0.000
#> GSM74411 2 0.0000 0.988 0.000 1.000
#> GSM74412 2 0.0000 0.988 0.000 1.000
#> GSM74413 2 0.0000 0.988 0.000 1.000
#> GSM74414 2 0.0000 0.988 0.000 1.000
#> GSM74415 2 0.0000 0.988 0.000 1.000
#> GSM121379 2 0.0000 0.988 0.000 1.000
#> GSM121380 2 0.0000 0.988 0.000 1.000
#> GSM121381 2 0.0000 0.988 0.000 1.000
#> GSM121382 2 0.0000 0.988 0.000 1.000
#> GSM121383 2 0.0000 0.988 0.000 1.000
#> GSM121384 2 0.0000 0.988 0.000 1.000
#> GSM121385 2 0.0000 0.988 0.000 1.000
#> GSM121386 2 0.0000 0.988 0.000 1.000
#> GSM121387 2 0.0000 0.988 0.000 1.000
#> GSM121388 2 0.0000 0.988 0.000 1.000
#> GSM121389 2 0.0000 0.988 0.000 1.000
#> GSM121390 2 0.0000 0.988 0.000 1.000
#> GSM121391 2 0.0000 0.988 0.000 1.000
#> GSM121392 2 0.0000 0.988 0.000 1.000
#> GSM121393 2 0.0000 0.988 0.000 1.000
#> GSM121394 2 0.0000 0.988 0.000 1.000
#> GSM121395 2 0.0000 0.988 0.000 1.000
#> GSM121396 2 0.0000 0.988 0.000 1.000
#> GSM121397 2 0.0000 0.988 0.000 1.000
#> GSM121398 2 0.0000 0.988 0.000 1.000
#> GSM121399 2 0.0000 0.988 0.000 1.000
#> GSM74240 2 0.0000 0.988 0.000 1.000
#> GSM74241 2 0.0000 0.988 0.000 1.000
#> GSM74242 2 0.9209 0.500 0.336 0.664
#> GSM74243 2 0.9286 0.482 0.344 0.656
#> GSM74244 2 0.0000 0.988 0.000 1.000
#> GSM74245 2 0.0000 0.988 0.000 1.000
#> GSM74246 2 0.0000 0.988 0.000 1.000
#> GSM74247 2 0.0000 0.988 0.000 1.000
#> GSM74248 2 0.0376 0.984 0.004 0.996
#> GSM74416 1 0.0000 0.997 1.000 0.000
#> GSM74417 1 0.0000 0.997 1.000 0.000
#> GSM74418 1 0.0000 0.997 1.000 0.000
#> GSM74419 1 0.0000 0.997 1.000 0.000
#> GSM121358 2 0.0000 0.988 0.000 1.000
#> GSM121359 2 0.0000 0.988 0.000 1.000
#> GSM121360 1 0.0000 0.997 1.000 0.000
#> GSM121362 1 0.0000 0.997 1.000 0.000
#> GSM121364 1 0.0000 0.997 1.000 0.000
#> GSM121365 2 0.0000 0.988 0.000 1.000
#> GSM121366 2 0.0000 0.988 0.000 1.000
#> GSM121367 2 0.0000 0.988 0.000 1.000
#> GSM121370 2 0.0000 0.988 0.000 1.000
#> GSM121371 2 0.0000 0.988 0.000 1.000
#> GSM121372 2 0.0000 0.988 0.000 1.000
#> GSM121373 1 0.0000 0.997 1.000 0.000
#> GSM121374 1 0.0000 0.997 1.000 0.000
#> GSM121407 2 0.0000 0.988 0.000 1.000
#> GSM74387 2 0.0000 0.988 0.000 1.000
#> GSM74388 2 0.0000 0.988 0.000 1.000
#> GSM74389 1 0.2043 0.966 0.968 0.032
#> GSM74390 1 0.0000 0.997 1.000 0.000
#> GSM74391 1 0.0000 0.997 1.000 0.000
#> GSM74392 1 0.0000 0.997 1.000 0.000
#> GSM74393 1 0.5294 0.862 0.880 0.120
#> GSM74394 2 0.0000 0.988 0.000 1.000
#> GSM74239 1 0.0000 0.997 1.000 0.000
#> GSM74364 1 0.0000 0.997 1.000 0.000
#> GSM74365 1 0.0000 0.997 1.000 0.000
#> GSM74366 1 0.0000 0.997 1.000 0.000
#> GSM74367 1 0.0000 0.997 1.000 0.000
#> GSM74377 1 0.0000 0.997 1.000 0.000
#> GSM74378 1 0.0000 0.997 1.000 0.000
#> GSM74379 1 0.0000 0.997 1.000 0.000
#> GSM74380 1 0.0000 0.997 1.000 0.000
#> GSM74381 1 0.0000 0.997 1.000 0.000
#> GSM121357 2 0.0000 0.988 0.000 1.000
#> GSM121361 2 0.0000 0.988 0.000 1.000
#> GSM121363 2 0.0000 0.988 0.000 1.000
#> GSM121368 2 0.0000 0.988 0.000 1.000
#> GSM121369 2 0.0000 0.988 0.000 1.000
#> GSM74368 1 0.0000 0.997 1.000 0.000
#> GSM74369 1 0.0000 0.997 1.000 0.000
#> GSM74370 1 0.0000 0.997 1.000 0.000
#> GSM74371 1 0.0000 0.997 1.000 0.000
#> GSM74372 1 0.0000 0.997 1.000 0.000
#> GSM74373 1 0.0000 0.997 1.000 0.000
#> GSM74374 1 0.0000 0.997 1.000 0.000
#> GSM74375 1 0.0000 0.997 1.000 0.000
#> GSM74376 1 0.0000 0.997 1.000 0.000
#> GSM74405 1 0.0000 0.997 1.000 0.000
#> GSM74351 1 0.0000 0.997 1.000 0.000
#> GSM74352 1 0.0000 0.997 1.000 0.000
#> GSM74353 1 0.0000 0.997 1.000 0.000
#> GSM74354 1 0.0000 0.997 1.000 0.000
#> GSM74355 1 0.0000 0.997 1.000 0.000
#> GSM74382 1 0.0000 0.997 1.000 0.000
#> GSM74383 1 0.0000 0.997 1.000 0.000
#> GSM74384 1 0.2603 0.953 0.956 0.044
#> GSM74385 1 0.0000 0.997 1.000 0.000
#> GSM74386 1 0.0000 0.997 1.000 0.000
#> GSM74395 1 0.0000 0.997 1.000 0.000
#> GSM74396 1 0.0000 0.997 1.000 0.000
#> GSM74397 1 0.0000 0.997 1.000 0.000
#> GSM74398 1 0.0000 0.997 1.000 0.000
#> GSM74399 1 0.0000 0.997 1.000 0.000
#> GSM74400 1 0.0000 0.997 1.000 0.000
#> GSM74401 1 0.0000 0.997 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM74356 3 0.0000 0.9272 0.000 0.000 1.000
#> GSM74357 3 0.0000 0.9272 0.000 0.000 1.000
#> GSM74358 3 0.0000 0.9272 0.000 0.000 1.000
#> GSM74359 3 0.2066 0.9050 0.060 0.000 0.940
#> GSM74360 3 0.2537 0.8912 0.080 0.000 0.920
#> GSM74361 3 0.0000 0.9272 0.000 0.000 1.000
#> GSM74362 3 0.0000 0.9272 0.000 0.000 1.000
#> GSM74363 3 0.0237 0.9264 0.000 0.004 0.996
#> GSM74402 1 0.3482 0.8164 0.872 0.000 0.128
#> GSM74403 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74404 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74406 1 0.6026 0.4254 0.624 0.000 0.376
#> GSM74407 1 0.2878 0.8518 0.904 0.000 0.096
#> GSM74408 1 0.6308 0.0734 0.508 0.000 0.492
#> GSM74409 3 0.5678 0.5196 0.316 0.000 0.684
#> GSM74410 3 0.2711 0.8839 0.088 0.000 0.912
#> GSM119936 1 0.5835 0.5004 0.660 0.000 0.340
#> GSM119937 1 0.5835 0.5000 0.660 0.000 0.340
#> GSM74411 2 0.2356 0.9349 0.000 0.928 0.072
#> GSM74412 2 0.1031 0.9725 0.000 0.976 0.024
#> GSM74413 2 0.2356 0.9349 0.000 0.928 0.072
#> GSM74414 2 0.0000 0.9883 0.000 1.000 0.000
#> GSM74415 3 0.6280 0.1485 0.000 0.460 0.540
#> GSM121379 2 0.0000 0.9883 0.000 1.000 0.000
#> GSM121380 2 0.0000 0.9883 0.000 1.000 0.000
#> GSM121381 2 0.0000 0.9883 0.000 1.000 0.000
#> GSM121382 2 0.0000 0.9883 0.000 1.000 0.000
#> GSM121383 2 0.0000 0.9883 0.000 1.000 0.000
#> GSM121384 2 0.0000 0.9883 0.000 1.000 0.000
#> GSM121385 2 0.0000 0.9883 0.000 1.000 0.000
#> GSM121386 2 0.0000 0.9883 0.000 1.000 0.000
#> GSM121387 2 0.0000 0.9883 0.000 1.000 0.000
#> GSM121388 2 0.0000 0.9883 0.000 1.000 0.000
#> GSM121389 2 0.0000 0.9883 0.000 1.000 0.000
#> GSM121390 2 0.0000 0.9883 0.000 1.000 0.000
#> GSM121391 2 0.0000 0.9883 0.000 1.000 0.000
#> GSM121392 2 0.0000 0.9883 0.000 1.000 0.000
#> GSM121393 2 0.0000 0.9883 0.000 1.000 0.000
#> GSM121394 2 0.0000 0.9883 0.000 1.000 0.000
#> GSM121395 2 0.0000 0.9883 0.000 1.000 0.000
#> GSM121396 2 0.0000 0.9883 0.000 1.000 0.000
#> GSM121397 2 0.0000 0.9883 0.000 1.000 0.000
#> GSM121398 2 0.0000 0.9883 0.000 1.000 0.000
#> GSM121399 2 0.0000 0.9883 0.000 1.000 0.000
#> GSM74240 3 0.0000 0.9272 0.000 0.000 1.000
#> GSM74241 3 0.0424 0.9253 0.000 0.008 0.992
#> GSM74242 3 0.0000 0.9272 0.000 0.000 1.000
#> GSM74243 3 0.0000 0.9272 0.000 0.000 1.000
#> GSM74244 3 0.0000 0.9272 0.000 0.000 1.000
#> GSM74245 3 0.0000 0.9272 0.000 0.000 1.000
#> GSM74246 3 0.0592 0.9241 0.000 0.012 0.988
#> GSM74247 3 0.1163 0.9172 0.000 0.028 0.972
#> GSM74248 3 0.0000 0.9272 0.000 0.000 1.000
#> GSM74416 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74417 1 0.0424 0.9292 0.992 0.000 0.008
#> GSM74418 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74419 1 0.5988 0.4403 0.632 0.000 0.368
#> GSM121358 3 0.2711 0.8805 0.000 0.088 0.912
#> GSM121359 2 0.2165 0.9422 0.000 0.936 0.064
#> GSM121360 3 0.2356 0.8975 0.072 0.000 0.928
#> GSM121362 3 0.5016 0.6941 0.240 0.000 0.760
#> GSM121364 3 0.2356 0.8975 0.072 0.000 0.928
#> GSM121365 3 0.2711 0.8805 0.000 0.088 0.912
#> GSM121366 3 0.3038 0.8656 0.000 0.104 0.896
#> GSM121367 3 0.2625 0.8835 0.000 0.084 0.916
#> GSM121370 3 0.2796 0.8769 0.000 0.092 0.908
#> GSM121371 3 0.2711 0.8805 0.000 0.088 0.912
#> GSM121372 2 0.2261 0.9387 0.000 0.932 0.068
#> GSM121373 3 0.2356 0.8975 0.072 0.000 0.928
#> GSM121374 3 0.2356 0.8975 0.072 0.000 0.928
#> GSM121407 2 0.1860 0.9518 0.000 0.948 0.052
#> GSM74387 2 0.2066 0.9456 0.000 0.940 0.060
#> GSM74388 2 0.0000 0.9883 0.000 1.000 0.000
#> GSM74389 3 0.1289 0.9181 0.032 0.000 0.968
#> GSM74390 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74391 1 0.6307 0.0883 0.512 0.000 0.488
#> GSM74392 3 0.2066 0.9050 0.060 0.000 0.940
#> GSM74393 3 0.0000 0.9272 0.000 0.000 1.000
#> GSM74394 2 0.0000 0.9883 0.000 1.000 0.000
#> GSM74239 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74364 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74365 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74366 1 0.2356 0.8714 0.928 0.072 0.000
#> GSM74367 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74377 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74378 1 0.0747 0.9218 0.984 0.016 0.000
#> GSM74379 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74380 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74381 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM121357 2 0.0000 0.9883 0.000 1.000 0.000
#> GSM121361 2 0.0000 0.9883 0.000 1.000 0.000
#> GSM121363 2 0.0000 0.9883 0.000 1.000 0.000
#> GSM121368 2 0.0000 0.9883 0.000 1.000 0.000
#> GSM121369 2 0.0000 0.9883 0.000 1.000 0.000
#> GSM74368 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74369 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74370 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74371 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74372 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74373 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74374 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74375 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74376 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74405 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74351 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74352 1 0.1753 0.8936 0.952 0.048 0.000
#> GSM74353 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74354 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74355 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74382 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74383 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74384 1 0.5138 0.6503 0.748 0.252 0.000
#> GSM74385 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74386 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74395 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74396 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74397 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74398 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74399 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74400 1 0.0000 0.9346 1.000 0.000 0.000
#> GSM74401 1 0.0000 0.9346 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM74356 3 0.0524 0.9018 0.000 0.004 0.988 0.008
#> GSM74357 3 0.0469 0.9004 0.000 0.000 0.988 0.012
#> GSM74358 3 0.0469 0.9004 0.000 0.000 0.988 0.012
#> GSM74359 4 0.0469 0.8943 0.000 0.000 0.012 0.988
#> GSM74360 4 0.0336 0.8952 0.000 0.000 0.008 0.992
#> GSM74361 3 0.0336 0.9014 0.000 0.000 0.992 0.008
#> GSM74362 3 0.3801 0.6742 0.000 0.000 0.780 0.220
#> GSM74363 3 0.0524 0.9018 0.000 0.004 0.988 0.008
#> GSM74402 4 0.1022 0.8843 0.032 0.000 0.000 0.968
#> GSM74403 4 0.0921 0.8869 0.028 0.000 0.000 0.972
#> GSM74404 4 0.0592 0.8945 0.016 0.000 0.000 0.984
#> GSM74406 4 0.0336 0.8973 0.008 0.000 0.000 0.992
#> GSM74407 4 0.0469 0.8962 0.012 0.000 0.000 0.988
#> GSM74408 4 0.0336 0.8973 0.008 0.000 0.000 0.992
#> GSM74409 4 0.0188 0.8972 0.004 0.000 0.000 0.996
#> GSM74410 4 0.0000 0.8965 0.000 0.000 0.000 1.000
#> GSM119936 4 0.0336 0.8973 0.008 0.000 0.000 0.992
#> GSM119937 4 0.0336 0.8973 0.008 0.000 0.000 0.992
#> GSM74411 3 0.4222 0.6541 0.000 0.272 0.728 0.000
#> GSM74412 2 0.4382 0.5279 0.000 0.704 0.296 0.000
#> GSM74413 3 0.4382 0.6166 0.000 0.296 0.704 0.000
#> GSM74414 2 0.0188 0.9774 0.000 0.996 0.004 0.000
#> GSM74415 3 0.0817 0.8957 0.000 0.024 0.976 0.000
#> GSM121379 2 0.0000 0.9801 0.000 1.000 0.000 0.000
#> GSM121380 2 0.0000 0.9801 0.000 1.000 0.000 0.000
#> GSM121381 2 0.0000 0.9801 0.000 1.000 0.000 0.000
#> GSM121382 2 0.0000 0.9801 0.000 1.000 0.000 0.000
#> GSM121383 2 0.0000 0.9801 0.000 1.000 0.000 0.000
#> GSM121384 2 0.0000 0.9801 0.000 1.000 0.000 0.000
#> GSM121385 2 0.0000 0.9801 0.000 1.000 0.000 0.000
#> GSM121386 2 0.0000 0.9801 0.000 1.000 0.000 0.000
#> GSM121387 2 0.0000 0.9801 0.000 1.000 0.000 0.000
#> GSM121388 2 0.0000 0.9801 0.000 1.000 0.000 0.000
#> GSM121389 2 0.0000 0.9801 0.000 1.000 0.000 0.000
#> GSM121390 2 0.0000 0.9801 0.000 1.000 0.000 0.000
#> GSM121391 2 0.0000 0.9801 0.000 1.000 0.000 0.000
#> GSM121392 2 0.0000 0.9801 0.000 1.000 0.000 0.000
#> GSM121393 2 0.0000 0.9801 0.000 1.000 0.000 0.000
#> GSM121394 2 0.0000 0.9801 0.000 1.000 0.000 0.000
#> GSM121395 2 0.0000 0.9801 0.000 1.000 0.000 0.000
#> GSM121396 2 0.0000 0.9801 0.000 1.000 0.000 0.000
#> GSM121397 2 0.0000 0.9801 0.000 1.000 0.000 0.000
#> GSM121398 2 0.0000 0.9801 0.000 1.000 0.000 0.000
#> GSM121399 2 0.0000 0.9801 0.000 1.000 0.000 0.000
#> GSM74240 3 0.0336 0.9019 0.000 0.000 0.992 0.008
#> GSM74241 3 0.0336 0.9019 0.000 0.000 0.992 0.008
#> GSM74242 3 0.0469 0.9010 0.000 0.000 0.988 0.012
#> GSM74243 3 0.0921 0.8919 0.000 0.000 0.972 0.028
#> GSM74244 3 0.0336 0.9019 0.000 0.000 0.992 0.008
#> GSM74245 3 0.0336 0.9019 0.000 0.000 0.992 0.008
#> GSM74246 3 0.0336 0.9019 0.000 0.000 0.992 0.008
#> GSM74247 3 0.0336 0.9019 0.000 0.000 0.992 0.008
#> GSM74248 3 0.0336 0.9019 0.000 0.000 0.992 0.008
#> GSM74416 4 0.0707 0.8923 0.020 0.000 0.000 0.980
#> GSM74417 4 0.0469 0.8962 0.012 0.000 0.000 0.988
#> GSM74418 4 0.1022 0.8840 0.032 0.000 0.000 0.968
#> GSM74419 4 0.0336 0.8973 0.008 0.000 0.000 0.992
#> GSM121358 3 0.0592 0.9018 0.000 0.016 0.984 0.000
#> GSM121359 3 0.3801 0.7300 0.000 0.220 0.780 0.000
#> GSM121360 4 0.0469 0.8943 0.000 0.000 0.012 0.988
#> GSM121362 4 0.0937 0.8919 0.012 0.000 0.012 0.976
#> GSM121364 4 0.0469 0.8943 0.000 0.000 0.012 0.988
#> GSM121365 3 0.0469 0.9021 0.000 0.012 0.988 0.000
#> GSM121366 3 0.0707 0.9005 0.000 0.020 0.980 0.000
#> GSM121367 3 0.0592 0.9018 0.000 0.016 0.984 0.000
#> GSM121370 3 0.0707 0.9005 0.000 0.020 0.980 0.000
#> GSM121371 3 0.0592 0.9018 0.000 0.016 0.984 0.000
#> GSM121372 3 0.3649 0.7488 0.000 0.204 0.796 0.000
#> GSM121373 4 0.0469 0.8943 0.000 0.000 0.012 0.988
#> GSM121374 4 0.0469 0.8943 0.000 0.000 0.012 0.988
#> GSM121407 3 0.4998 0.1572 0.000 0.488 0.512 0.000
#> GSM74387 3 0.5000 0.0879 0.000 0.500 0.500 0.000
#> GSM74388 2 0.1452 0.9534 0.036 0.956 0.008 0.000
#> GSM74389 4 0.4103 0.6139 0.000 0.000 0.256 0.744
#> GSM74390 1 0.1211 0.9034 0.960 0.000 0.000 0.040
#> GSM74391 4 0.0000 0.8965 0.000 0.000 0.000 1.000
#> GSM74392 4 0.0592 0.8920 0.000 0.000 0.016 0.984
#> GSM74393 4 0.4933 0.2212 0.000 0.000 0.432 0.568
#> GSM74394 2 0.1767 0.9441 0.044 0.944 0.012 0.000
#> GSM74239 1 0.3123 0.8457 0.844 0.000 0.000 0.156
#> GSM74364 1 0.3024 0.8520 0.852 0.000 0.000 0.148
#> GSM74365 1 0.0469 0.9102 0.988 0.000 0.000 0.012
#> GSM74366 1 0.0000 0.9116 1.000 0.000 0.000 0.000
#> GSM74367 1 0.1716 0.8988 0.936 0.000 0.000 0.064
#> GSM74377 1 0.0000 0.9116 1.000 0.000 0.000 0.000
#> GSM74378 1 0.0000 0.9116 1.000 0.000 0.000 0.000
#> GSM74379 1 0.0000 0.9116 1.000 0.000 0.000 0.000
#> GSM74380 1 0.0000 0.9116 1.000 0.000 0.000 0.000
#> GSM74381 1 0.0000 0.9116 1.000 0.000 0.000 0.000
#> GSM121357 2 0.0000 0.9801 0.000 1.000 0.000 0.000
#> GSM121361 2 0.1356 0.9565 0.032 0.960 0.008 0.000
#> GSM121363 2 0.1256 0.9592 0.028 0.964 0.008 0.000
#> GSM121368 2 0.1256 0.9592 0.028 0.964 0.008 0.000
#> GSM121369 2 0.1510 0.9551 0.028 0.956 0.016 0.000
#> GSM74368 1 0.3764 0.7783 0.784 0.000 0.000 0.216
#> GSM74369 1 0.3123 0.8455 0.844 0.000 0.000 0.156
#> GSM74370 1 0.4643 0.5671 0.656 0.000 0.000 0.344
#> GSM74371 1 0.3942 0.7611 0.764 0.000 0.000 0.236
#> GSM74372 4 0.5000 -0.1271 0.500 0.000 0.000 0.500
#> GSM74373 1 0.0000 0.9116 1.000 0.000 0.000 0.000
#> GSM74374 1 0.1637 0.9007 0.940 0.000 0.000 0.060
#> GSM74375 1 0.0000 0.9116 1.000 0.000 0.000 0.000
#> GSM74376 1 0.0000 0.9116 1.000 0.000 0.000 0.000
#> GSM74405 1 0.0000 0.9116 1.000 0.000 0.000 0.000
#> GSM74351 4 0.4999 -0.1401 0.492 0.000 0.000 0.508
#> GSM74352 1 0.0000 0.9116 1.000 0.000 0.000 0.000
#> GSM74353 1 0.4164 0.7241 0.736 0.000 0.000 0.264
#> GSM74354 1 0.1867 0.8967 0.928 0.000 0.000 0.072
#> GSM74355 1 0.0000 0.9116 1.000 0.000 0.000 0.000
#> GSM74382 4 0.5000 -0.1654 0.500 0.000 0.000 0.500
#> GSM74383 1 0.2011 0.8929 0.920 0.000 0.000 0.080
#> GSM74384 1 0.0469 0.9039 0.988 0.012 0.000 0.000
#> GSM74385 1 0.3942 0.7624 0.764 0.000 0.000 0.236
#> GSM74386 1 0.2589 0.8747 0.884 0.000 0.000 0.116
#> GSM74395 1 0.3528 0.8133 0.808 0.000 0.000 0.192
#> GSM74396 1 0.1867 0.8964 0.928 0.000 0.000 0.072
#> GSM74397 1 0.4830 0.4405 0.608 0.000 0.000 0.392
#> GSM74398 1 0.0000 0.9116 1.000 0.000 0.000 0.000
#> GSM74399 1 0.0000 0.9116 1.000 0.000 0.000 0.000
#> GSM74400 1 0.0000 0.9116 1.000 0.000 0.000 0.000
#> GSM74401 1 0.0000 0.9116 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM74356 3 0.0671 0.7907 0.000 0.000 0.980 0.004 0.016
#> GSM74357 3 0.0671 0.7901 0.000 0.000 0.980 0.004 0.016
#> GSM74358 3 0.0671 0.7901 0.000 0.000 0.980 0.004 0.016
#> GSM74359 4 0.3488 0.7320 0.000 0.000 0.024 0.808 0.168
#> GSM74360 4 0.3013 0.7510 0.000 0.000 0.008 0.832 0.160
#> GSM74361 3 0.2583 0.6795 0.000 0.000 0.864 0.004 0.132
#> GSM74362 3 0.6088 0.1844 0.000 0.000 0.548 0.156 0.296
#> GSM74363 3 0.0000 0.7962 0.000 0.000 1.000 0.000 0.000
#> GSM74402 4 0.1764 0.7967 0.064 0.000 0.000 0.928 0.008
#> GSM74403 4 0.1430 0.8028 0.052 0.000 0.000 0.944 0.004
#> GSM74404 4 0.1282 0.8057 0.044 0.000 0.000 0.952 0.004
#> GSM74406 4 0.0955 0.8144 0.004 0.000 0.000 0.968 0.028
#> GSM74407 4 0.1331 0.8116 0.040 0.000 0.000 0.952 0.008
#> GSM74408 4 0.0609 0.8138 0.000 0.000 0.000 0.980 0.020
#> GSM74409 4 0.0963 0.8113 0.000 0.000 0.000 0.964 0.036
#> GSM74410 4 0.1270 0.8063 0.000 0.000 0.000 0.948 0.052
#> GSM119936 4 0.0162 0.8149 0.004 0.000 0.000 0.996 0.000
#> GSM119937 4 0.0162 0.8151 0.004 0.000 0.000 0.996 0.000
#> GSM74411 3 0.5839 0.2977 0.000 0.116 0.560 0.000 0.324
#> GSM74412 3 0.6729 0.1207 0.000 0.348 0.396 0.000 0.256
#> GSM74413 3 0.6006 0.3173 0.000 0.144 0.556 0.000 0.300
#> GSM74414 2 0.1043 0.9102 0.000 0.960 0.000 0.000 0.040
#> GSM74415 3 0.4383 0.1562 0.000 0.004 0.572 0.000 0.424
#> GSM121379 2 0.0000 0.9307 0.000 1.000 0.000 0.000 0.000
#> GSM121380 2 0.0000 0.9307 0.000 1.000 0.000 0.000 0.000
#> GSM121381 2 0.0000 0.9307 0.000 1.000 0.000 0.000 0.000
#> GSM121382 2 0.0000 0.9307 0.000 1.000 0.000 0.000 0.000
#> GSM121383 2 0.0000 0.9307 0.000 1.000 0.000 0.000 0.000
#> GSM121384 2 0.0000 0.9307 0.000 1.000 0.000 0.000 0.000
#> GSM121385 2 0.0000 0.9307 0.000 1.000 0.000 0.000 0.000
#> GSM121386 2 0.0000 0.9307 0.000 1.000 0.000 0.000 0.000
#> GSM121387 2 0.0000 0.9307 0.000 1.000 0.000 0.000 0.000
#> GSM121388 2 0.0000 0.9307 0.000 1.000 0.000 0.000 0.000
#> GSM121389 2 0.0000 0.9307 0.000 1.000 0.000 0.000 0.000
#> GSM121390 2 0.0000 0.9307 0.000 1.000 0.000 0.000 0.000
#> GSM121391 2 0.0000 0.9307 0.000 1.000 0.000 0.000 0.000
#> GSM121392 2 0.0000 0.9307 0.000 1.000 0.000 0.000 0.000
#> GSM121393 2 0.0000 0.9307 0.000 1.000 0.000 0.000 0.000
#> GSM121394 2 0.0000 0.9307 0.000 1.000 0.000 0.000 0.000
#> GSM121395 2 0.0000 0.9307 0.000 1.000 0.000 0.000 0.000
#> GSM121396 2 0.1197 0.8955 0.000 0.952 0.048 0.000 0.000
#> GSM121397 2 0.0000 0.9307 0.000 1.000 0.000 0.000 0.000
#> GSM121398 2 0.0000 0.9307 0.000 1.000 0.000 0.000 0.000
#> GSM121399 2 0.0000 0.9307 0.000 1.000 0.000 0.000 0.000
#> GSM74240 5 0.3395 0.7761 0.000 0.000 0.236 0.000 0.764
#> GSM74241 5 0.3424 0.7733 0.000 0.000 0.240 0.000 0.760
#> GSM74242 5 0.3642 0.7732 0.000 0.000 0.232 0.008 0.760
#> GSM74243 5 0.3720 0.7701 0.000 0.000 0.228 0.012 0.760
#> GSM74244 5 0.3424 0.7733 0.000 0.000 0.240 0.000 0.760
#> GSM74245 5 0.3395 0.7761 0.000 0.000 0.236 0.000 0.764
#> GSM74246 5 0.3177 0.7656 0.000 0.000 0.208 0.000 0.792
#> GSM74247 5 0.3242 0.7629 0.000 0.000 0.216 0.000 0.784
#> GSM74248 5 0.3336 0.7760 0.000 0.000 0.228 0.000 0.772
#> GSM74416 4 0.1557 0.8012 0.052 0.000 0.000 0.940 0.008
#> GSM74417 4 0.0794 0.8121 0.028 0.000 0.000 0.972 0.000
#> GSM74418 4 0.1892 0.7814 0.080 0.000 0.000 0.916 0.004
#> GSM74419 4 0.0324 0.8150 0.004 0.000 0.000 0.992 0.004
#> GSM121358 3 0.0162 0.7986 0.000 0.004 0.996 0.000 0.000
#> GSM121359 3 0.1597 0.7704 0.000 0.048 0.940 0.000 0.012
#> GSM121360 4 0.3513 0.7261 0.000 0.000 0.020 0.800 0.180
#> GSM121362 4 0.3898 0.7345 0.016 0.000 0.024 0.800 0.160
#> GSM121364 4 0.3488 0.7320 0.000 0.000 0.024 0.808 0.168
#> GSM121365 3 0.0162 0.7986 0.000 0.004 0.996 0.000 0.000
#> GSM121366 3 0.0162 0.7986 0.000 0.004 0.996 0.000 0.000
#> GSM121367 3 0.0162 0.7986 0.000 0.004 0.996 0.000 0.000
#> GSM121370 3 0.0162 0.7986 0.000 0.004 0.996 0.000 0.000
#> GSM121371 3 0.0162 0.7986 0.000 0.004 0.996 0.000 0.000
#> GSM121372 3 0.1522 0.7733 0.000 0.044 0.944 0.000 0.012
#> GSM121373 4 0.3304 0.7388 0.000 0.000 0.016 0.816 0.168
#> GSM121374 4 0.3399 0.7361 0.000 0.000 0.020 0.812 0.168
#> GSM121407 3 0.2873 0.6936 0.000 0.120 0.860 0.000 0.020
#> GSM74387 5 0.6215 0.2921 0.008 0.252 0.164 0.000 0.576
#> GSM74388 2 0.4373 0.7608 0.080 0.760 0.000 0.000 0.160
#> GSM74389 5 0.5111 -0.0658 0.000 0.000 0.036 0.464 0.500
#> GSM74390 1 0.3464 0.7729 0.836 0.000 0.000 0.068 0.096
#> GSM74391 4 0.1357 0.8116 0.004 0.000 0.000 0.948 0.048
#> GSM74392 4 0.3427 0.7176 0.000 0.000 0.012 0.796 0.192
#> GSM74393 5 0.5382 0.3184 0.000 0.000 0.072 0.336 0.592
#> GSM74394 2 0.5542 0.3171 0.068 0.500 0.000 0.000 0.432
#> GSM74239 1 0.4088 0.6642 0.688 0.000 0.000 0.304 0.008
#> GSM74364 1 0.4183 0.6382 0.668 0.000 0.000 0.324 0.008
#> GSM74365 1 0.1484 0.8152 0.944 0.000 0.000 0.048 0.008
#> GSM74366 1 0.1732 0.7935 0.920 0.000 0.000 0.000 0.080
#> GSM74367 1 0.3171 0.7684 0.816 0.000 0.000 0.176 0.008
#> GSM74377 1 0.0703 0.8131 0.976 0.000 0.000 0.000 0.024
#> GSM74378 1 0.1732 0.7935 0.920 0.000 0.000 0.000 0.080
#> GSM74379 1 0.0671 0.8153 0.980 0.000 0.000 0.004 0.016
#> GSM74380 1 0.0451 0.8156 0.988 0.000 0.000 0.004 0.008
#> GSM74381 1 0.0794 0.8115 0.972 0.000 0.000 0.000 0.028
#> GSM121357 2 0.1893 0.8887 0.000 0.928 0.048 0.000 0.024
#> GSM121361 2 0.4577 0.7409 0.084 0.740 0.000 0.000 0.176
#> GSM121363 2 0.4355 0.7610 0.076 0.760 0.000 0.000 0.164
#> GSM121368 2 0.4335 0.7613 0.072 0.760 0.000 0.000 0.168
#> GSM121369 2 0.5950 0.5790 0.072 0.612 0.032 0.000 0.284
#> GSM74368 1 0.4639 0.5590 0.612 0.000 0.000 0.368 0.020
#> GSM74369 1 0.4288 0.6407 0.664 0.000 0.000 0.324 0.012
#> GSM74370 1 0.4510 0.4230 0.560 0.000 0.000 0.432 0.008
#> GSM74371 1 0.4489 0.4695 0.572 0.000 0.000 0.420 0.008
#> GSM74372 4 0.4774 0.0355 0.424 0.000 0.000 0.556 0.020
#> GSM74373 1 0.0963 0.8098 0.964 0.000 0.000 0.000 0.036
#> GSM74374 1 0.2930 0.7756 0.832 0.000 0.000 0.164 0.004
#> GSM74375 1 0.1216 0.8172 0.960 0.000 0.000 0.020 0.020
#> GSM74376 1 0.1671 0.7967 0.924 0.000 0.000 0.000 0.076
#> GSM74405 1 0.1341 0.8036 0.944 0.000 0.000 0.000 0.056
#> GSM74351 4 0.4497 -0.0249 0.424 0.000 0.000 0.568 0.008
#> GSM74352 1 0.1608 0.7976 0.928 0.000 0.000 0.000 0.072
#> GSM74353 1 0.4397 0.4362 0.564 0.000 0.000 0.432 0.004
#> GSM74354 1 0.3280 0.7708 0.812 0.000 0.000 0.176 0.012
#> GSM74355 1 0.1671 0.7958 0.924 0.000 0.000 0.000 0.076
#> GSM74382 4 0.4504 -0.0436 0.428 0.000 0.000 0.564 0.008
#> GSM74383 1 0.3700 0.7259 0.752 0.000 0.000 0.240 0.008
#> GSM74384 1 0.1732 0.7935 0.920 0.000 0.000 0.000 0.080
#> GSM74385 1 0.4455 0.5055 0.588 0.000 0.000 0.404 0.008
#> GSM74386 1 0.3779 0.7261 0.752 0.000 0.000 0.236 0.012
#> GSM74395 1 0.4127 0.6512 0.680 0.000 0.000 0.312 0.008
#> GSM74396 1 0.3333 0.7525 0.788 0.000 0.000 0.208 0.004
#> GSM74397 4 0.4549 -0.1523 0.464 0.000 0.000 0.528 0.008
#> GSM74398 1 0.0671 0.8167 0.980 0.000 0.000 0.016 0.004
#> GSM74399 1 0.0510 0.8139 0.984 0.000 0.000 0.000 0.016
#> GSM74400 1 0.1282 0.8152 0.952 0.000 0.000 0.044 0.004
#> GSM74401 1 0.0898 0.8164 0.972 0.000 0.000 0.020 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM74356 3 0.1434 0.81603 0.000 0.000 0.948 0.012 0.028 0.012
#> GSM74357 3 0.1405 0.81363 0.000 0.000 0.948 0.024 0.024 0.004
#> GSM74358 3 0.1232 0.81804 0.000 0.000 0.956 0.016 0.024 0.004
#> GSM74359 4 0.1644 0.72840 0.028 0.000 0.000 0.932 0.040 0.000
#> GSM74360 4 0.1418 0.73024 0.032 0.000 0.000 0.944 0.024 0.000
#> GSM74361 3 0.4683 0.55123 0.000 0.000 0.700 0.060 0.216 0.024
#> GSM74362 4 0.6390 0.07500 0.000 0.000 0.328 0.448 0.196 0.028
#> GSM74363 3 0.0951 0.82303 0.000 0.000 0.968 0.004 0.020 0.008
#> GSM74402 1 0.4165 0.01748 0.568 0.000 0.000 0.420 0.008 0.004
#> GSM74403 1 0.4138 0.17203 0.620 0.000 0.000 0.364 0.008 0.008
#> GSM74404 1 0.4183 0.13177 0.604 0.000 0.000 0.380 0.008 0.008
#> GSM74406 4 0.3636 0.56728 0.320 0.000 0.000 0.676 0.000 0.004
#> GSM74407 1 0.4285 -0.03365 0.552 0.000 0.000 0.432 0.008 0.008
#> GSM74408 4 0.3756 0.56721 0.316 0.000 0.000 0.676 0.004 0.004
#> GSM74409 4 0.3240 0.63931 0.244 0.000 0.000 0.752 0.000 0.004
#> GSM74410 4 0.3081 0.65460 0.220 0.000 0.000 0.776 0.000 0.004
#> GSM119936 4 0.4056 0.40013 0.416 0.000 0.000 0.576 0.004 0.004
#> GSM119937 4 0.4174 0.40590 0.408 0.000 0.004 0.580 0.004 0.004
#> GSM74411 3 0.6349 0.23555 0.000 0.088 0.484 0.004 0.356 0.068
#> GSM74412 3 0.7250 0.14504 0.000 0.272 0.388 0.004 0.252 0.084
#> GSM74413 3 0.6454 0.29606 0.000 0.108 0.500 0.004 0.320 0.068
#> GSM74414 2 0.3166 0.80391 0.000 0.852 0.016 0.004 0.040 0.088
#> GSM74415 3 0.5251 0.13913 0.000 0.008 0.488 0.004 0.440 0.060
#> GSM121379 2 0.0000 0.90572 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121380 2 0.0000 0.90572 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121381 2 0.0000 0.90572 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121382 2 0.0000 0.90572 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121383 2 0.0000 0.90572 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121384 2 0.0000 0.90572 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121385 2 0.0000 0.90572 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121386 2 0.0000 0.90572 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121387 2 0.0000 0.90572 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121388 2 0.0260 0.89971 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM121389 2 0.0000 0.90572 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121390 2 0.0000 0.90572 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121391 2 0.0000 0.90572 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121392 2 0.0146 0.90317 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM121393 2 0.0000 0.90572 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121394 2 0.0000 0.90572 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121395 2 0.0000 0.90572 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121396 2 0.0937 0.87488 0.000 0.960 0.040 0.000 0.000 0.000
#> GSM121397 2 0.0000 0.90572 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121398 2 0.0000 0.90572 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121399 2 0.0000 0.90572 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM74240 5 0.2058 0.90993 0.000 0.000 0.056 0.036 0.908 0.000
#> GSM74241 5 0.1908 0.91003 0.000 0.000 0.056 0.028 0.916 0.000
#> GSM74242 5 0.2328 0.90071 0.000 0.000 0.052 0.056 0.892 0.000
#> GSM74243 5 0.2499 0.88770 0.000 0.000 0.048 0.072 0.880 0.000
#> GSM74244 5 0.1950 0.90610 0.000 0.000 0.064 0.024 0.912 0.000
#> GSM74245 5 0.2046 0.90916 0.000 0.000 0.060 0.032 0.908 0.000
#> GSM74246 5 0.1644 0.90681 0.000 0.000 0.040 0.028 0.932 0.000
#> GSM74247 5 0.1649 0.90221 0.000 0.000 0.036 0.032 0.932 0.000
#> GSM74248 5 0.1930 0.90409 0.000 0.000 0.036 0.048 0.916 0.000
#> GSM74416 1 0.4126 0.17352 0.624 0.000 0.000 0.360 0.008 0.008
#> GSM74417 1 0.4165 0.00227 0.568 0.000 0.000 0.420 0.008 0.004
#> GSM74418 1 0.4088 0.20304 0.636 0.000 0.000 0.348 0.008 0.008
#> GSM74419 4 0.4172 0.38778 0.424 0.000 0.000 0.564 0.008 0.004
#> GSM121358 3 0.0603 0.82731 0.000 0.000 0.980 0.004 0.016 0.000
#> GSM121359 3 0.0820 0.81912 0.000 0.016 0.972 0.000 0.012 0.000
#> GSM121360 4 0.1788 0.69100 0.004 0.000 0.000 0.928 0.040 0.028
#> GSM121362 4 0.2231 0.70196 0.016 0.000 0.000 0.908 0.048 0.028
#> GSM121364 4 0.1498 0.73198 0.028 0.000 0.000 0.940 0.032 0.000
#> GSM121365 3 0.0603 0.82864 0.000 0.004 0.980 0.000 0.016 0.000
#> GSM121366 3 0.0603 0.82864 0.000 0.004 0.980 0.000 0.016 0.000
#> GSM121367 3 0.0603 0.82864 0.000 0.004 0.980 0.000 0.016 0.000
#> GSM121370 3 0.0603 0.82864 0.000 0.004 0.980 0.000 0.016 0.000
#> GSM121371 3 0.0603 0.82864 0.000 0.004 0.980 0.000 0.016 0.000
#> GSM121372 3 0.0820 0.81912 0.000 0.016 0.972 0.000 0.012 0.000
#> GSM121373 4 0.1572 0.73044 0.028 0.000 0.000 0.936 0.036 0.000
#> GSM121374 4 0.1498 0.73172 0.028 0.000 0.000 0.940 0.032 0.000
#> GSM121407 3 0.1332 0.80717 0.000 0.028 0.952 0.000 0.012 0.008
#> GSM74387 5 0.7613 0.24214 0.000 0.116 0.160 0.032 0.436 0.256
#> GSM74388 2 0.6051 0.36889 0.000 0.448 0.012 0.028 0.084 0.428
#> GSM74389 4 0.4074 0.38185 0.000 0.000 0.016 0.656 0.324 0.004
#> GSM74390 6 0.5632 0.31558 0.428 0.000 0.004 0.032 0.056 0.480
#> GSM74391 4 0.4476 0.59719 0.280 0.000 0.000 0.668 0.044 0.008
#> GSM74392 4 0.2653 0.70336 0.028 0.000 0.004 0.880 0.080 0.008
#> GSM74393 4 0.4319 0.32805 0.000 0.000 0.008 0.648 0.320 0.024
#> GSM74394 6 0.6905 -0.23426 0.000 0.256 0.012 0.032 0.292 0.408
#> GSM74239 1 0.2563 0.59844 0.876 0.000 0.000 0.052 0.000 0.072
#> GSM74364 1 0.2088 0.58339 0.904 0.000 0.000 0.028 0.000 0.068
#> GSM74365 1 0.3695 -0.05441 0.624 0.000 0.000 0.000 0.000 0.376
#> GSM74366 6 0.2738 0.66066 0.176 0.000 0.000 0.000 0.004 0.820
#> GSM74367 1 0.3323 0.35480 0.752 0.000 0.000 0.008 0.000 0.240
#> GSM74377 6 0.3634 0.66655 0.356 0.000 0.000 0.000 0.000 0.644
#> GSM74378 6 0.3290 0.70237 0.252 0.000 0.000 0.000 0.004 0.744
#> GSM74379 6 0.3804 0.58396 0.424 0.000 0.000 0.000 0.000 0.576
#> GSM74380 6 0.3810 0.58187 0.428 0.000 0.000 0.000 0.000 0.572
#> GSM74381 6 0.3684 0.68886 0.332 0.000 0.000 0.000 0.004 0.664
#> GSM121357 2 0.3453 0.78963 0.000 0.828 0.044 0.000 0.024 0.104
#> GSM121361 2 0.6153 0.37066 0.000 0.452 0.012 0.028 0.096 0.412
#> GSM121363 2 0.6101 0.40342 0.000 0.476 0.012 0.028 0.092 0.392
#> GSM121368 2 0.6176 0.38796 0.000 0.464 0.012 0.028 0.100 0.396
#> GSM121369 6 0.7494 -0.27352 0.000 0.328 0.068 0.048 0.148 0.408
#> GSM74368 1 0.3748 0.54514 0.784 0.000 0.000 0.064 0.004 0.148
#> GSM74369 1 0.3727 0.49771 0.768 0.000 0.000 0.040 0.004 0.188
#> GSM74370 1 0.3514 0.56647 0.804 0.000 0.000 0.088 0.000 0.108
#> GSM74371 1 0.2822 0.60924 0.864 0.000 0.000 0.076 0.004 0.056
#> GSM74372 1 0.4554 0.57270 0.716 0.000 0.000 0.172 0.008 0.104
#> GSM74373 6 0.3601 0.69587 0.312 0.000 0.000 0.000 0.004 0.684
#> GSM74374 1 0.3650 0.28003 0.716 0.000 0.000 0.008 0.004 0.272
#> GSM74375 6 0.3950 0.54237 0.432 0.000 0.000 0.004 0.000 0.564
#> GSM74376 6 0.3342 0.69310 0.228 0.000 0.000 0.000 0.012 0.760
#> GSM74405 6 0.3672 0.70136 0.304 0.000 0.000 0.000 0.008 0.688
#> GSM74351 1 0.3053 0.53918 0.812 0.000 0.000 0.172 0.012 0.004
#> GSM74352 6 0.3373 0.69994 0.248 0.000 0.000 0.000 0.008 0.744
#> GSM74353 1 0.2866 0.60825 0.860 0.000 0.000 0.084 0.004 0.052
#> GSM74354 1 0.2933 0.42785 0.796 0.000 0.000 0.000 0.004 0.200
#> GSM74355 6 0.3426 0.70475 0.276 0.000 0.000 0.000 0.004 0.720
#> GSM74382 1 0.3111 0.55927 0.820 0.000 0.000 0.156 0.008 0.016
#> GSM74383 1 0.3012 0.43720 0.796 0.000 0.000 0.000 0.008 0.196
#> GSM74384 6 0.2946 0.63785 0.160 0.000 0.000 0.004 0.012 0.824
#> GSM74385 1 0.2563 0.61039 0.876 0.000 0.000 0.072 0.000 0.052
#> GSM74386 1 0.3682 0.44722 0.764 0.000 0.000 0.032 0.004 0.200
#> GSM74395 1 0.3211 0.56821 0.824 0.000 0.000 0.056 0.000 0.120
#> GSM74396 1 0.3253 0.45922 0.788 0.000 0.000 0.020 0.000 0.192
#> GSM74397 1 0.4360 0.57380 0.724 0.000 0.000 0.184 0.004 0.088
#> GSM74398 1 0.3998 -0.46540 0.504 0.000 0.000 0.000 0.004 0.492
#> GSM74399 6 0.3684 0.64015 0.372 0.000 0.000 0.000 0.000 0.628
#> GSM74400 1 0.3862 -0.13205 0.608 0.000 0.000 0.000 0.004 0.388
#> GSM74401 1 0.3937 -0.27329 0.572 0.000 0.000 0.000 0.004 0.424
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 119 4.11e-11 2
#> CV:skmeans 115 3.53e-24 3
#> CV:skmeans 114 2.92e-32 4
#> CV:skmeans 105 7.43e-42 5
#> CV:skmeans 85 6.27e-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 21168 rows and 121 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 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.408 0.732 0.872 0.4804 0.506 0.506
#> 3 3 0.566 0.604 0.817 0.3606 0.801 0.618
#> 4 4 0.547 0.536 0.730 0.1357 0.689 0.310
#> 5 5 0.744 0.762 0.884 0.0740 0.888 0.603
#> 6 6 0.734 0.623 0.806 0.0384 0.924 0.663
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM74356 2 0.9044 0.6465 0.320 0.680
#> GSM74357 2 0.9087 0.6425 0.324 0.676
#> GSM74358 2 0.9775 0.4950 0.412 0.588
#> GSM74359 1 0.0000 0.8725 1.000 0.000
#> GSM74360 1 0.0000 0.8725 1.000 0.000
#> GSM74361 2 0.9795 0.4865 0.416 0.584
#> GSM74362 2 0.9922 0.4139 0.448 0.552
#> GSM74363 2 0.8955 0.6567 0.312 0.688
#> GSM74402 1 0.0000 0.8725 1.000 0.000
#> GSM74403 1 0.0000 0.8725 1.000 0.000
#> GSM74404 1 0.0000 0.8725 1.000 0.000
#> GSM74406 1 0.0000 0.8725 1.000 0.000
#> GSM74407 1 0.1414 0.8673 0.980 0.020
#> GSM74408 1 0.0000 0.8725 1.000 0.000
#> GSM74409 1 0.0000 0.8725 1.000 0.000
#> GSM74410 1 0.0000 0.8725 1.000 0.000
#> GSM119936 1 0.0000 0.8725 1.000 0.000
#> GSM119937 1 0.0000 0.8725 1.000 0.000
#> GSM74411 2 0.6048 0.7872 0.148 0.852
#> GSM74412 2 0.0672 0.8259 0.008 0.992
#> GSM74413 2 0.4022 0.8111 0.080 0.920
#> GSM74414 2 0.0000 0.8262 0.000 1.000
#> GSM74415 2 0.9000 0.6556 0.316 0.684
#> GSM121379 2 0.0000 0.8262 0.000 1.000
#> GSM121380 2 0.0000 0.8262 0.000 1.000
#> GSM121381 2 0.0000 0.8262 0.000 1.000
#> GSM121382 2 0.0000 0.8262 0.000 1.000
#> GSM121383 2 0.0376 0.8257 0.004 0.996
#> GSM121384 2 0.0000 0.8262 0.000 1.000
#> GSM121385 2 0.0000 0.8262 0.000 1.000
#> GSM121386 2 0.0000 0.8262 0.000 1.000
#> GSM121387 2 0.0000 0.8262 0.000 1.000
#> GSM121388 2 0.0376 0.8257 0.004 0.996
#> GSM121389 2 0.0376 0.8257 0.004 0.996
#> GSM121390 2 0.0000 0.8262 0.000 1.000
#> GSM121391 2 0.0000 0.8262 0.000 1.000
#> GSM121392 2 0.0000 0.8262 0.000 1.000
#> GSM121393 2 0.0000 0.8262 0.000 1.000
#> GSM121394 2 0.0000 0.8262 0.000 1.000
#> GSM121395 2 0.0000 0.8262 0.000 1.000
#> GSM121396 2 0.0672 0.8247 0.008 0.992
#> GSM121397 2 0.0000 0.8262 0.000 1.000
#> GSM121398 2 0.0000 0.8262 0.000 1.000
#> GSM121399 2 0.0000 0.8262 0.000 1.000
#> GSM74240 2 0.9909 0.4241 0.444 0.556
#> GSM74241 2 0.8081 0.7079 0.248 0.752
#> GSM74242 2 0.9977 0.3510 0.472 0.528
#> GSM74243 1 0.9977 -0.1887 0.528 0.472
#> GSM74244 2 0.9087 0.6372 0.324 0.676
#> GSM74245 2 0.9552 0.5628 0.376 0.624
#> GSM74246 2 0.7602 0.7368 0.220 0.780
#> GSM74247 2 0.7056 0.7577 0.192 0.808
#> GSM74248 2 0.9983 0.3402 0.476 0.524
#> GSM74416 1 0.0000 0.8725 1.000 0.000
#> GSM74417 1 0.0000 0.8725 1.000 0.000
#> GSM74418 1 0.0000 0.8725 1.000 0.000
#> GSM74419 1 0.2236 0.8591 0.964 0.036
#> GSM121358 2 0.8955 0.6567 0.312 0.688
#> GSM121359 2 0.4815 0.8048 0.104 0.896
#> GSM121360 1 0.1184 0.8714 0.984 0.016
#> GSM121362 1 0.5629 0.7962 0.868 0.132
#> GSM121364 1 0.0000 0.8725 1.000 0.000
#> GSM121365 2 0.8608 0.6876 0.284 0.716
#> GSM121366 2 0.8144 0.7157 0.252 0.748
#> GSM121367 2 0.8955 0.6567 0.312 0.688
#> GSM121370 2 0.8016 0.7249 0.244 0.756
#> GSM121371 2 0.8955 0.6567 0.312 0.688
#> GSM121372 2 0.6973 0.7603 0.188 0.812
#> GSM121373 1 0.0000 0.8725 1.000 0.000
#> GSM121374 1 0.0000 0.8725 1.000 0.000
#> GSM121407 2 0.4939 0.8009 0.108 0.892
#> GSM74387 2 0.4431 0.8070 0.092 0.908
#> GSM74388 2 0.0000 0.8262 0.000 1.000
#> GSM74389 1 0.0672 0.8713 0.992 0.008
#> GSM74390 2 0.8713 0.6609 0.292 0.708
#> GSM74391 1 0.6048 0.7435 0.852 0.148
#> GSM74392 1 0.0000 0.8725 1.000 0.000
#> GSM74393 1 0.9286 0.3242 0.656 0.344
#> GSM74394 2 0.1184 0.8242 0.016 0.984
#> GSM74239 1 0.0938 0.8721 0.988 0.012
#> GSM74364 1 0.2423 0.8646 0.960 0.040
#> GSM74365 1 0.9866 0.1791 0.568 0.432
#> GSM74366 2 0.0376 0.8251 0.004 0.996
#> GSM74367 1 0.2603 0.8637 0.956 0.044
#> GSM74377 2 0.8443 0.5969 0.272 0.728
#> GSM74378 2 0.0000 0.8262 0.000 1.000
#> GSM74379 1 0.9833 0.2329 0.576 0.424
#> GSM74380 1 0.5519 0.8056 0.872 0.128
#> GSM74381 2 0.9866 0.0855 0.432 0.568
#> GSM121357 2 0.2043 0.8217 0.032 0.968
#> GSM121361 2 0.0376 0.8263 0.004 0.996
#> GSM121363 2 0.0000 0.8262 0.000 1.000
#> GSM121368 2 0.0000 0.8262 0.000 1.000
#> GSM121369 2 0.4815 0.8026 0.104 0.896
#> GSM74368 1 0.9866 0.1773 0.568 0.432
#> GSM74369 1 0.9170 0.4849 0.668 0.332
#> GSM74370 1 0.6148 0.7782 0.848 0.152
#> GSM74371 1 0.2043 0.8668 0.968 0.032
#> GSM74372 1 0.2236 0.8652 0.964 0.036
#> GSM74373 2 0.9000 0.5083 0.316 0.684
#> GSM74374 1 0.3879 0.8424 0.924 0.076
#> GSM74375 2 0.8499 0.6796 0.276 0.724
#> GSM74376 2 0.5178 0.7966 0.116 0.884
#> GSM74405 1 0.9977 0.2207 0.528 0.472
#> GSM74351 1 0.0672 0.8722 0.992 0.008
#> GSM74352 2 0.0000 0.8262 0.000 1.000
#> GSM74353 1 0.2423 0.8652 0.960 0.040
#> GSM74354 1 0.6623 0.7574 0.828 0.172
#> GSM74355 2 0.0376 0.8251 0.004 0.996
#> GSM74382 1 0.0000 0.8725 1.000 0.000
#> GSM74383 1 0.3274 0.8578 0.940 0.060
#> GSM74384 2 0.0000 0.8262 0.000 1.000
#> GSM74385 1 0.0938 0.8721 0.988 0.012
#> GSM74386 1 0.3733 0.8505 0.928 0.072
#> GSM74395 1 0.5519 0.8020 0.872 0.128
#> GSM74396 1 0.2948 0.8586 0.948 0.052
#> GSM74397 1 0.2423 0.8659 0.960 0.040
#> GSM74398 1 0.8016 0.6495 0.756 0.244
#> GSM74399 2 0.9933 0.1842 0.452 0.548
#> GSM74400 1 0.8499 0.6242 0.724 0.276
#> GSM74401 1 0.9954 0.1878 0.540 0.460
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM74356 3 0.5650 0.7356 0.312 0.000 0.688
#> GSM74357 3 0.5650 0.7356 0.312 0.000 0.688
#> GSM74358 3 0.5785 0.7258 0.332 0.000 0.668
#> GSM74359 1 0.0592 0.6344 0.988 0.000 0.012
#> GSM74360 1 0.0424 0.6377 0.992 0.000 0.008
#> GSM74361 3 0.5926 0.7153 0.356 0.000 0.644
#> GSM74362 3 0.6267 0.6191 0.452 0.000 0.548
#> GSM74363 3 0.5650 0.7356 0.312 0.000 0.688
#> GSM74402 1 0.2537 0.6025 0.920 0.000 0.080
#> GSM74403 1 0.5291 0.7091 0.732 0.000 0.268
#> GSM74404 1 0.5431 0.7104 0.716 0.000 0.284
#> GSM74406 1 0.1411 0.6075 0.964 0.000 0.036
#> GSM74407 1 0.4605 0.3221 0.796 0.000 0.204
#> GSM74408 1 0.0424 0.6377 0.992 0.000 0.008
#> GSM74409 1 0.0592 0.6344 0.988 0.000 0.012
#> GSM74410 1 0.3267 0.4906 0.884 0.000 0.116
#> GSM119936 1 0.0424 0.6377 0.992 0.000 0.008
#> GSM119937 1 0.3686 0.4447 0.860 0.000 0.140
#> GSM74411 3 0.5928 0.7337 0.296 0.008 0.696
#> GSM74412 3 0.6813 -0.0526 0.012 0.468 0.520
#> GSM74413 3 0.7015 0.7049 0.240 0.064 0.696
#> GSM74414 2 0.5835 0.5015 0.000 0.660 0.340
#> GSM74415 3 0.5760 0.7262 0.328 0.000 0.672
#> GSM121379 2 0.0000 0.8790 0.000 1.000 0.000
#> GSM121380 2 0.0000 0.8790 0.000 1.000 0.000
#> GSM121381 2 0.0000 0.8790 0.000 1.000 0.000
#> GSM121382 2 0.0000 0.8790 0.000 1.000 0.000
#> GSM121383 2 0.0000 0.8790 0.000 1.000 0.000
#> GSM121384 2 0.0000 0.8790 0.000 1.000 0.000
#> GSM121385 2 0.0000 0.8790 0.000 1.000 0.000
#> GSM121386 2 0.0000 0.8790 0.000 1.000 0.000
#> GSM121387 2 0.0000 0.8790 0.000 1.000 0.000
#> GSM121388 2 0.5058 0.6288 0.000 0.756 0.244
#> GSM121389 2 0.0000 0.8790 0.000 1.000 0.000
#> GSM121390 2 0.0000 0.8790 0.000 1.000 0.000
#> GSM121391 2 0.0000 0.8790 0.000 1.000 0.000
#> GSM121392 2 0.0237 0.8775 0.000 0.996 0.004
#> GSM121393 2 0.0000 0.8790 0.000 1.000 0.000
#> GSM121394 2 0.0000 0.8790 0.000 1.000 0.000
#> GSM121395 2 0.0000 0.8790 0.000 1.000 0.000
#> GSM121396 2 0.0747 0.8705 0.000 0.984 0.016
#> GSM121397 2 0.0000 0.8790 0.000 1.000 0.000
#> GSM121398 2 0.0000 0.8790 0.000 1.000 0.000
#> GSM121399 2 0.0000 0.8790 0.000 1.000 0.000
#> GSM74240 3 0.6154 0.6704 0.408 0.000 0.592
#> GSM74241 3 0.5621 0.7337 0.308 0.000 0.692
#> GSM74242 3 0.6154 0.6696 0.408 0.000 0.592
#> GSM74243 1 0.6302 -0.5244 0.520 0.000 0.480
#> GSM74244 3 0.5810 0.7266 0.336 0.000 0.664
#> GSM74245 3 0.5835 0.7258 0.340 0.000 0.660
#> GSM74246 3 0.5988 0.6929 0.368 0.000 0.632
#> GSM74247 3 0.5529 0.7322 0.296 0.000 0.704
#> GSM74248 3 0.6274 0.6092 0.456 0.000 0.544
#> GSM74416 1 0.1163 0.6554 0.972 0.000 0.028
#> GSM74417 1 0.0424 0.6427 0.992 0.000 0.008
#> GSM74418 1 0.3192 0.6814 0.888 0.000 0.112
#> GSM74419 1 0.3879 0.4243 0.848 0.000 0.152
#> GSM121358 3 0.5650 0.7356 0.312 0.000 0.688
#> GSM121359 3 0.7002 0.7203 0.280 0.048 0.672
#> GSM121360 1 0.0592 0.6434 0.988 0.000 0.012
#> GSM121362 1 0.1964 0.6413 0.944 0.000 0.056
#> GSM121364 1 0.0592 0.6344 0.988 0.000 0.012
#> GSM121365 3 0.5650 0.7356 0.312 0.000 0.688
#> GSM121366 3 0.5650 0.7356 0.312 0.000 0.688
#> GSM121367 3 0.5650 0.7356 0.312 0.000 0.688
#> GSM121370 3 0.5650 0.7356 0.312 0.000 0.688
#> GSM121371 3 0.5650 0.7356 0.312 0.000 0.688
#> GSM121372 3 0.5650 0.7356 0.312 0.000 0.688
#> GSM121373 1 0.0592 0.6344 0.988 0.000 0.012
#> GSM121374 1 0.0424 0.6377 0.992 0.000 0.008
#> GSM121407 3 0.6224 0.7327 0.296 0.016 0.688
#> GSM74387 3 0.7666 0.6413 0.192 0.128 0.680
#> GSM74388 2 0.2496 0.8442 0.004 0.928 0.068
#> GSM74389 1 0.2261 0.5687 0.932 0.000 0.068
#> GSM74390 3 0.7442 0.4157 0.368 0.044 0.588
#> GSM74391 1 0.4842 0.4832 0.776 0.000 0.224
#> GSM74392 1 0.0424 0.6377 0.992 0.000 0.008
#> GSM74393 1 0.5016 0.2298 0.760 0.000 0.240
#> GSM74394 2 0.7652 0.2477 0.044 0.512 0.444
#> GSM74239 1 0.5560 0.7093 0.700 0.000 0.300
#> GSM74364 1 0.5591 0.7086 0.696 0.000 0.304
#> GSM74365 3 0.6168 -0.4207 0.412 0.000 0.588
#> GSM74366 2 0.6359 0.5592 0.004 0.592 0.404
#> GSM74367 1 0.5706 0.7031 0.680 0.000 0.320
#> GSM74377 3 0.6950 -0.4039 0.408 0.020 0.572
#> GSM74378 2 0.6033 0.6215 0.004 0.660 0.336
#> GSM74379 1 0.6252 0.6045 0.556 0.000 0.444
#> GSM74380 1 0.5785 0.6979 0.668 0.000 0.332
#> GSM74381 2 0.9106 0.3293 0.156 0.508 0.336
#> GSM121357 3 0.7860 0.4417 0.088 0.284 0.628
#> GSM121361 2 0.2096 0.8547 0.004 0.944 0.052
#> GSM121363 2 0.1411 0.8637 0.000 0.964 0.036
#> GSM121368 2 0.1860 0.8568 0.000 0.948 0.052
#> GSM121369 3 0.8803 0.6129 0.240 0.180 0.580
#> GSM74368 1 0.6309 -0.2055 0.504 0.000 0.496
#> GSM74369 3 0.6180 -0.3943 0.416 0.000 0.584
#> GSM74370 1 0.5810 0.6946 0.664 0.000 0.336
#> GSM74371 1 0.5650 0.7055 0.688 0.000 0.312
#> GSM74372 1 0.5650 0.7055 0.688 0.000 0.312
#> GSM74373 3 0.9752 -0.1958 0.236 0.340 0.424
#> GSM74374 1 0.5650 0.7055 0.688 0.000 0.312
#> GSM74375 3 0.5138 -0.0211 0.252 0.000 0.748
#> GSM74376 3 0.4629 0.1400 0.188 0.004 0.808
#> GSM74405 1 0.7129 0.6368 0.580 0.028 0.392
#> GSM74351 1 0.5560 0.7096 0.700 0.000 0.300
#> GSM74352 2 0.6398 0.5907 0.008 0.620 0.372
#> GSM74353 1 0.5621 0.7085 0.692 0.000 0.308
#> GSM74354 1 0.5810 0.6946 0.664 0.000 0.336
#> GSM74355 2 0.6359 0.5976 0.008 0.628 0.364
#> GSM74382 1 0.5560 0.7082 0.700 0.000 0.300
#> GSM74383 1 0.5678 0.7050 0.684 0.000 0.316
#> GSM74384 2 0.6228 0.5955 0.004 0.624 0.372
#> GSM74385 1 0.5650 0.7055 0.688 0.000 0.312
#> GSM74386 1 0.5678 0.7050 0.684 0.000 0.316
#> GSM74395 1 0.5733 0.7017 0.676 0.000 0.324
#> GSM74396 1 0.5650 0.7055 0.688 0.000 0.312
#> GSM74397 1 0.5529 0.7065 0.704 0.000 0.296
#> GSM74398 1 0.6154 0.6412 0.592 0.000 0.408
#> GSM74399 3 0.6521 -0.5364 0.492 0.004 0.504
#> GSM74400 1 0.6978 0.6725 0.632 0.032 0.336
#> GSM74401 3 0.8614 -0.4593 0.416 0.100 0.484
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM74356 4 0.4948 0.1913 0.000 0.000 0.440 0.560
#> GSM74357 4 0.4948 0.1913 0.000 0.000 0.440 0.560
#> GSM74358 4 0.4948 0.1913 0.000 0.000 0.440 0.560
#> GSM74359 4 0.3975 0.4478 0.240 0.000 0.000 0.760
#> GSM74360 4 0.5850 0.3868 0.244 0.000 0.080 0.676
#> GSM74361 4 0.5132 0.1336 0.004 0.000 0.448 0.548
#> GSM74362 4 0.4697 0.1564 0.000 0.000 0.356 0.644
#> GSM74363 4 0.4948 0.1913 0.000 0.000 0.440 0.560
#> GSM74402 4 0.6221 0.4694 0.256 0.000 0.100 0.644
#> GSM74403 4 0.4999 -0.1421 0.492 0.000 0.000 0.508
#> GSM74404 1 0.5388 0.2398 0.532 0.000 0.012 0.456
#> GSM74406 4 0.5056 0.4754 0.224 0.000 0.044 0.732
#> GSM74407 3 0.5576 0.2401 0.020 0.000 0.536 0.444
#> GSM74408 4 0.4155 0.4500 0.240 0.000 0.004 0.756
#> GSM74409 4 0.4008 0.4433 0.244 0.000 0.000 0.756
#> GSM74410 4 0.5963 0.4722 0.196 0.000 0.116 0.688
#> GSM119936 4 0.4328 0.4489 0.244 0.000 0.008 0.748
#> GSM119937 4 0.6075 0.4402 0.168 0.000 0.148 0.684
#> GSM74411 3 0.2921 0.5428 0.000 0.000 0.860 0.140
#> GSM74412 2 0.7058 0.4151 0.024 0.604 0.272 0.100
#> GSM74413 3 0.5984 0.1537 0.000 0.048 0.580 0.372
#> GSM74414 2 0.4778 0.7685 0.040 0.820 0.080 0.060
#> GSM74415 3 0.3853 0.5692 0.020 0.000 0.820 0.160
#> GSM121379 2 0.0000 0.9182 0.000 1.000 0.000 0.000
#> GSM121380 2 0.0000 0.9182 0.000 1.000 0.000 0.000
#> GSM121381 2 0.0000 0.9182 0.000 1.000 0.000 0.000
#> GSM121382 2 0.0000 0.9182 0.000 1.000 0.000 0.000
#> GSM121383 2 0.0000 0.9182 0.000 1.000 0.000 0.000
#> GSM121384 2 0.0000 0.9182 0.000 1.000 0.000 0.000
#> GSM121385 2 0.0000 0.9182 0.000 1.000 0.000 0.000
#> GSM121386 2 0.0000 0.9182 0.000 1.000 0.000 0.000
#> GSM121387 2 0.0000 0.9182 0.000 1.000 0.000 0.000
#> GSM121388 2 0.3471 0.8033 0.000 0.868 0.072 0.060
#> GSM121389 2 0.0000 0.9182 0.000 1.000 0.000 0.000
#> GSM121390 2 0.0000 0.9182 0.000 1.000 0.000 0.000
#> GSM121391 2 0.0000 0.9182 0.000 1.000 0.000 0.000
#> GSM121392 2 0.0376 0.9133 0.004 0.992 0.004 0.000
#> GSM121393 2 0.0000 0.9182 0.000 1.000 0.000 0.000
#> GSM121394 2 0.0000 0.9182 0.000 1.000 0.000 0.000
#> GSM121395 2 0.0000 0.9182 0.000 1.000 0.000 0.000
#> GSM121396 2 0.0707 0.9053 0.000 0.980 0.020 0.000
#> GSM121397 2 0.0000 0.9182 0.000 1.000 0.000 0.000
#> GSM121398 2 0.0000 0.9182 0.000 1.000 0.000 0.000
#> GSM121399 2 0.0000 0.9182 0.000 1.000 0.000 0.000
#> GSM74240 3 0.3243 0.6550 0.036 0.000 0.876 0.088
#> GSM74241 3 0.0672 0.6424 0.008 0.000 0.984 0.008
#> GSM74242 3 0.1637 0.6469 0.000 0.000 0.940 0.060
#> GSM74243 3 0.3356 0.6011 0.000 0.000 0.824 0.176
#> GSM74244 3 0.0817 0.6413 0.000 0.000 0.976 0.024
#> GSM74245 3 0.1022 0.6447 0.000 0.000 0.968 0.032
#> GSM74246 3 0.4274 0.6368 0.148 0.000 0.808 0.044
#> GSM74247 3 0.2048 0.6545 0.064 0.000 0.928 0.008
#> GSM74248 3 0.3900 0.6126 0.020 0.000 0.816 0.164
#> GSM74416 4 0.4164 0.4160 0.264 0.000 0.000 0.736
#> GSM74417 4 0.4661 0.4209 0.256 0.000 0.016 0.728
#> GSM74418 4 0.4431 0.3398 0.304 0.000 0.000 0.696
#> GSM74419 4 0.6110 0.4514 0.176 0.000 0.144 0.680
#> GSM121358 4 0.4948 0.1913 0.000 0.000 0.440 0.560
#> GSM121359 3 0.7281 -0.0056 0.000 0.148 0.440 0.412
#> GSM121360 4 0.7676 0.1443 0.240 0.000 0.308 0.452
#> GSM121362 4 0.7205 0.2628 0.304 0.000 0.168 0.528
#> GSM121364 4 0.3975 0.4478 0.240 0.000 0.000 0.760
#> GSM121365 4 0.4948 0.1913 0.000 0.000 0.440 0.560
#> GSM121366 4 0.4948 0.1913 0.000 0.000 0.440 0.560
#> GSM121367 4 0.4948 0.1913 0.000 0.000 0.440 0.560
#> GSM121370 4 0.4948 0.1913 0.000 0.000 0.440 0.560
#> GSM121371 4 0.4948 0.1913 0.000 0.000 0.440 0.560
#> GSM121372 4 0.4948 0.1913 0.000 0.000 0.440 0.560
#> GSM121373 4 0.3975 0.4478 0.240 0.000 0.000 0.760
#> GSM121374 4 0.3975 0.4478 0.240 0.000 0.000 0.760
#> GSM121407 4 0.7009 -0.0110 0.000 0.116 0.440 0.444
#> GSM74387 3 0.5696 0.5999 0.184 0.004 0.720 0.092
#> GSM74388 2 0.4248 0.7054 0.220 0.768 0.012 0.000
#> GSM74389 3 0.4761 0.3956 0.000 0.000 0.628 0.372
#> GSM74390 3 0.6570 0.5617 0.204 0.000 0.632 0.164
#> GSM74391 3 0.7252 0.1013 0.144 0.000 0.436 0.420
#> GSM74392 4 0.6653 0.3360 0.196 0.000 0.180 0.624
#> GSM74393 3 0.5436 0.4211 0.024 0.000 0.620 0.356
#> GSM74394 3 0.6759 0.5163 0.220 0.140 0.632 0.008
#> GSM74239 1 0.3873 0.6410 0.772 0.000 0.000 0.228
#> GSM74364 1 0.4356 0.5927 0.708 0.000 0.000 0.292
#> GSM74365 1 0.3606 0.6587 0.844 0.000 0.024 0.132
#> GSM74366 1 0.5725 0.3261 0.624 0.344 0.016 0.016
#> GSM74367 1 0.3907 0.6437 0.768 0.000 0.000 0.232
#> GSM74377 1 0.1798 0.6924 0.944 0.000 0.016 0.040
#> GSM74378 1 0.4328 0.5350 0.748 0.244 0.008 0.000
#> GSM74379 1 0.3108 0.6494 0.872 0.000 0.112 0.016
#> GSM74380 1 0.1211 0.7077 0.960 0.000 0.000 0.040
#> GSM74381 1 0.4617 0.5629 0.764 0.204 0.032 0.000
#> GSM121357 2 0.9615 -0.0391 0.156 0.380 0.256 0.208
#> GSM121361 3 0.7577 0.1140 0.196 0.376 0.428 0.000
#> GSM121363 2 0.3725 0.7542 0.180 0.812 0.008 0.000
#> GSM121368 2 0.3768 0.7500 0.184 0.808 0.008 0.000
#> GSM121369 3 0.6841 0.5898 0.184 0.072 0.676 0.068
#> GSM74368 4 0.7476 0.0324 0.356 0.000 0.184 0.460
#> GSM74369 1 0.5944 0.5600 0.684 0.000 0.104 0.212
#> GSM74370 1 0.2611 0.6944 0.896 0.000 0.008 0.096
#> GSM74371 1 0.4643 0.5208 0.656 0.000 0.000 0.344
#> GSM74372 1 0.5708 0.6568 0.716 0.000 0.124 0.160
#> GSM74373 1 0.5343 0.5756 0.776 0.100 0.104 0.020
#> GSM74374 1 0.3219 0.6832 0.836 0.000 0.000 0.164
#> GSM74375 1 0.6448 0.5280 0.628 0.000 0.252 0.120
#> GSM74376 1 0.5062 0.5056 0.752 0.000 0.184 0.064
#> GSM74405 1 0.0524 0.7020 0.988 0.000 0.008 0.004
#> GSM74351 1 0.4088 0.6419 0.764 0.000 0.004 0.232
#> GSM74352 1 0.4475 0.5381 0.748 0.240 0.008 0.004
#> GSM74353 1 0.3975 0.6393 0.760 0.000 0.000 0.240
#> GSM74354 1 0.2483 0.7083 0.916 0.000 0.032 0.052
#> GSM74355 1 0.4502 0.5419 0.748 0.236 0.016 0.000
#> GSM74382 1 0.4730 0.4968 0.636 0.000 0.000 0.364
#> GSM74383 1 0.3219 0.6830 0.836 0.000 0.000 0.164
#> GSM74384 1 0.4673 0.5433 0.748 0.232 0.008 0.012
#> GSM74385 1 0.5193 0.4155 0.580 0.000 0.008 0.412
#> GSM74386 1 0.7252 0.5250 0.544 0.000 0.224 0.232
#> GSM74395 1 0.3982 0.6561 0.776 0.000 0.004 0.220
#> GSM74396 1 0.3569 0.6654 0.804 0.000 0.000 0.196
#> GSM74397 1 0.5746 0.3850 0.572 0.000 0.032 0.396
#> GSM74398 1 0.2831 0.7053 0.876 0.000 0.004 0.120
#> GSM74399 1 0.1118 0.7060 0.964 0.000 0.000 0.036
#> GSM74400 1 0.2589 0.6989 0.884 0.000 0.000 0.116
#> GSM74401 1 0.3630 0.6254 0.848 0.004 0.128 0.020
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM74356 3 0.0000 0.88479 0.000 0.000 1.000 0.000 0.000
#> GSM74357 3 0.0000 0.88479 0.000 0.000 1.000 0.000 0.000
#> GSM74358 3 0.0000 0.88479 0.000 0.000 1.000 0.000 0.000
#> GSM74359 4 0.0290 0.81753 0.000 0.000 0.008 0.992 0.000
#> GSM74360 4 0.0290 0.81753 0.000 0.000 0.008 0.992 0.000
#> GSM74361 3 0.2818 0.76682 0.000 0.000 0.856 0.012 0.132
#> GSM74362 3 0.4640 0.53554 0.000 0.000 0.696 0.048 0.256
#> GSM74363 3 0.0000 0.88479 0.000 0.000 1.000 0.000 0.000
#> GSM74402 4 0.4832 0.64342 0.068 0.000 0.216 0.712 0.004
#> GSM74403 4 0.4218 0.51418 0.332 0.000 0.008 0.660 0.000
#> GSM74404 4 0.4436 0.34838 0.396 0.000 0.000 0.596 0.008
#> GSM74406 4 0.3003 0.69483 0.000 0.000 0.188 0.812 0.000
#> GSM74407 5 0.5343 0.53561 0.004 0.000 0.280 0.076 0.640
#> GSM74408 4 0.0510 0.81616 0.000 0.000 0.016 0.984 0.000
#> GSM74409 4 0.0290 0.81753 0.000 0.000 0.008 0.992 0.000
#> GSM74410 4 0.4201 0.33269 0.000 0.000 0.408 0.592 0.000
#> GSM119936 4 0.0880 0.81150 0.000 0.000 0.032 0.968 0.000
#> GSM119937 4 0.4192 0.33607 0.000 0.000 0.404 0.596 0.000
#> GSM74411 5 0.3774 0.56536 0.000 0.000 0.296 0.000 0.704
#> GSM74412 2 0.5434 0.44386 0.004 0.604 0.324 0.000 0.068
#> GSM74413 3 0.1792 0.82278 0.000 0.000 0.916 0.000 0.084
#> GSM74414 2 0.2141 0.89345 0.016 0.916 0.064 0.000 0.004
#> GSM74415 5 0.3039 0.70763 0.000 0.000 0.192 0.000 0.808
#> GSM121379 2 0.0000 0.95070 0.000 1.000 0.000 0.000 0.000
#> GSM121380 2 0.0000 0.95070 0.000 1.000 0.000 0.000 0.000
#> GSM121381 2 0.0000 0.95070 0.000 1.000 0.000 0.000 0.000
#> GSM121382 2 0.0000 0.95070 0.000 1.000 0.000 0.000 0.000
#> GSM121383 2 0.0000 0.95070 0.000 1.000 0.000 0.000 0.000
#> GSM121384 2 0.0000 0.95070 0.000 1.000 0.000 0.000 0.000
#> GSM121385 2 0.0000 0.95070 0.000 1.000 0.000 0.000 0.000
#> GSM121386 2 0.0000 0.95070 0.000 1.000 0.000 0.000 0.000
#> GSM121387 2 0.0000 0.95070 0.000 1.000 0.000 0.000 0.000
#> GSM121388 2 0.2561 0.82092 0.000 0.856 0.144 0.000 0.000
#> GSM121389 2 0.0000 0.95070 0.000 1.000 0.000 0.000 0.000
#> GSM121390 2 0.0000 0.95070 0.000 1.000 0.000 0.000 0.000
#> GSM121391 2 0.0000 0.95070 0.000 1.000 0.000 0.000 0.000
#> GSM121392 2 0.0162 0.94821 0.004 0.996 0.000 0.000 0.000
#> GSM121393 2 0.0000 0.95070 0.000 1.000 0.000 0.000 0.000
#> GSM121394 2 0.0000 0.95070 0.000 1.000 0.000 0.000 0.000
#> GSM121395 2 0.0000 0.95070 0.000 1.000 0.000 0.000 0.000
#> GSM121396 2 0.0880 0.93070 0.000 0.968 0.032 0.000 0.000
#> GSM121397 2 0.0000 0.95070 0.000 1.000 0.000 0.000 0.000
#> GSM121398 2 0.0000 0.95070 0.000 1.000 0.000 0.000 0.000
#> GSM121399 2 0.0000 0.95070 0.000 1.000 0.000 0.000 0.000
#> GSM74240 5 0.0000 0.82767 0.000 0.000 0.000 0.000 1.000
#> GSM74241 5 0.0000 0.82767 0.000 0.000 0.000 0.000 1.000
#> GSM74242 5 0.0290 0.82598 0.000 0.000 0.008 0.000 0.992
#> GSM74243 5 0.0000 0.82767 0.000 0.000 0.000 0.000 1.000
#> GSM74244 5 0.0000 0.82767 0.000 0.000 0.000 0.000 1.000
#> GSM74245 5 0.0000 0.82767 0.000 0.000 0.000 0.000 1.000
#> GSM74246 5 0.0000 0.82767 0.000 0.000 0.000 0.000 1.000
#> GSM74247 5 0.0000 0.82767 0.000 0.000 0.000 0.000 1.000
#> GSM74248 5 0.0000 0.82767 0.000 0.000 0.000 0.000 1.000
#> GSM74416 4 0.0000 0.81662 0.000 0.000 0.000 1.000 0.000
#> GSM74417 4 0.0000 0.81662 0.000 0.000 0.000 1.000 0.000
#> GSM74418 4 0.0000 0.81662 0.000 0.000 0.000 1.000 0.000
#> GSM74419 3 0.5626 0.06001 0.000 0.000 0.504 0.420 0.076
#> GSM121358 3 0.0000 0.88479 0.000 0.000 1.000 0.000 0.000
#> GSM121359 3 0.0162 0.88212 0.000 0.004 0.996 0.000 0.000
#> GSM121360 4 0.0290 0.81627 0.008 0.000 0.000 0.992 0.000
#> GSM121362 4 0.1082 0.80771 0.028 0.000 0.008 0.964 0.000
#> GSM121364 4 0.0290 0.81753 0.000 0.000 0.008 0.992 0.000
#> GSM121365 3 0.0000 0.88479 0.000 0.000 1.000 0.000 0.000
#> GSM121366 3 0.0000 0.88479 0.000 0.000 1.000 0.000 0.000
#> GSM121367 3 0.0000 0.88479 0.000 0.000 1.000 0.000 0.000
#> GSM121370 3 0.0000 0.88479 0.000 0.000 1.000 0.000 0.000
#> GSM121371 3 0.0000 0.88479 0.000 0.000 1.000 0.000 0.000
#> GSM121372 3 0.0000 0.88479 0.000 0.000 1.000 0.000 0.000
#> GSM121373 4 0.0290 0.81753 0.000 0.000 0.008 0.992 0.000
#> GSM121374 4 0.0290 0.81753 0.000 0.000 0.008 0.992 0.000
#> GSM121407 3 0.0162 0.88212 0.000 0.004 0.996 0.000 0.000
#> GSM74387 5 0.5549 0.57424 0.124 0.000 0.244 0.000 0.632
#> GSM74388 2 0.3596 0.75444 0.200 0.784 0.000 0.000 0.016
#> GSM74389 5 0.2573 0.78187 0.000 0.000 0.016 0.104 0.880
#> GSM74390 5 0.5070 0.70043 0.152 0.000 0.120 0.008 0.720
#> GSM74391 5 0.3990 0.51190 0.004 0.000 0.000 0.308 0.688
#> GSM74392 4 0.3582 0.62718 0.000 0.000 0.008 0.768 0.224
#> GSM74393 5 0.2304 0.78575 0.000 0.000 0.008 0.100 0.892
#> GSM74394 5 0.2573 0.79074 0.104 0.000 0.016 0.000 0.880
#> GSM74239 1 0.3774 0.61877 0.704 0.000 0.000 0.296 0.000
#> GSM74364 4 0.4294 -0.02647 0.468 0.000 0.000 0.532 0.000
#> GSM74365 1 0.0404 0.86073 0.988 0.000 0.000 0.012 0.000
#> GSM74366 1 0.3318 0.67463 0.800 0.192 0.008 0.000 0.000
#> GSM74367 4 0.4403 0.24808 0.436 0.000 0.000 0.560 0.004
#> GSM74377 1 0.0000 0.85978 1.000 0.000 0.000 0.000 0.000
#> GSM74378 1 0.0000 0.85978 1.000 0.000 0.000 0.000 0.000
#> GSM74379 1 0.0290 0.85853 0.992 0.000 0.000 0.000 0.008
#> GSM74380 1 0.0963 0.85732 0.964 0.000 0.000 0.036 0.000
#> GSM74381 1 0.0000 0.85978 1.000 0.000 0.000 0.000 0.000
#> GSM121357 3 0.5972 0.36328 0.140 0.300 0.560 0.000 0.000
#> GSM121361 5 0.5392 0.62709 0.144 0.192 0.000 0.000 0.664
#> GSM121363 2 0.2605 0.82359 0.148 0.852 0.000 0.000 0.000
#> GSM121368 2 0.3327 0.80648 0.144 0.828 0.000 0.000 0.028
#> GSM121369 5 0.4268 0.74060 0.144 0.000 0.084 0.000 0.772
#> GSM74368 3 0.6323 0.48684 0.268 0.000 0.600 0.060 0.072
#> GSM74369 1 0.5740 0.52597 0.612 0.000 0.244 0.144 0.000
#> GSM74370 1 0.3612 0.63181 0.732 0.000 0.000 0.268 0.000
#> GSM74371 4 0.3039 0.69768 0.192 0.000 0.000 0.808 0.000
#> GSM74372 1 0.2707 0.80917 0.860 0.000 0.000 0.132 0.008
#> GSM74373 1 0.0000 0.85978 1.000 0.000 0.000 0.000 0.000
#> GSM74374 1 0.2179 0.82002 0.888 0.000 0.000 0.112 0.000
#> GSM74375 1 0.3048 0.76265 0.820 0.000 0.004 0.000 0.176
#> GSM74376 1 0.0290 0.85976 0.992 0.000 0.000 0.000 0.008
#> GSM74405 1 0.0000 0.85978 1.000 0.000 0.000 0.000 0.000
#> GSM74351 1 0.4980 0.34638 0.584 0.000 0.000 0.380 0.036
#> GSM74352 1 0.0162 0.85926 0.996 0.004 0.000 0.000 0.000
#> GSM74353 1 0.3913 0.57821 0.676 0.000 0.000 0.324 0.000
#> GSM74354 1 0.0671 0.86117 0.980 0.000 0.000 0.016 0.004
#> GSM74355 1 0.0000 0.85978 1.000 0.000 0.000 0.000 0.000
#> GSM74382 4 0.3816 0.51046 0.304 0.000 0.000 0.696 0.000
#> GSM74383 1 0.2660 0.81511 0.864 0.000 0.000 0.128 0.008
#> GSM74384 1 0.0162 0.85926 0.996 0.004 0.000 0.000 0.000
#> GSM74385 4 0.1121 0.80351 0.044 0.000 0.000 0.956 0.000
#> GSM74386 5 0.6527 -0.00568 0.376 0.000 0.000 0.196 0.428
#> GSM74395 1 0.3949 0.63353 0.696 0.000 0.000 0.300 0.004
#> GSM74396 1 0.3890 0.69024 0.736 0.000 0.000 0.252 0.012
#> GSM74397 4 0.4953 0.63492 0.228 0.000 0.020 0.708 0.044
#> GSM74398 1 0.2074 0.82442 0.896 0.000 0.000 0.104 0.000
#> GSM74399 1 0.0963 0.85744 0.964 0.000 0.000 0.036 0.000
#> GSM74400 1 0.2813 0.79005 0.832 0.000 0.000 0.168 0.000
#> GSM74401 1 0.1082 0.85539 0.964 0.000 0.000 0.008 0.028
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM74356 3 0.3867 -0.0254 0.000 0.000 0.512 0.488 0.000 0.000
#> GSM74357 3 0.3867 -0.0254 0.000 0.000 0.512 0.488 0.000 0.000
#> GSM74358 3 0.3867 -0.0254 0.000 0.000 0.512 0.488 0.000 0.000
#> GSM74359 1 0.0000 0.7278 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM74360 1 0.0000 0.7278 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM74361 4 0.5623 0.1806 0.008 0.000 0.372 0.500 0.120 0.000
#> GSM74362 4 0.6242 0.3985 0.044 0.000 0.240 0.540 0.176 0.000
#> GSM74363 3 0.3867 -0.0254 0.000 0.000 0.512 0.488 0.000 0.000
#> GSM74402 4 0.4752 0.5337 0.252 0.000 0.028 0.680 0.004 0.036
#> GSM74403 4 0.4156 0.4984 0.188 0.000 0.000 0.732 0.000 0.080
#> GSM74404 4 0.5308 0.3188 0.244 0.000 0.000 0.592 0.000 0.164
#> GSM74406 1 0.4671 -0.3049 0.532 0.000 0.044 0.424 0.000 0.000
#> GSM74407 4 0.5750 0.2837 0.044 0.000 0.052 0.540 0.356 0.008
#> GSM74408 4 0.3843 0.4093 0.452 0.000 0.000 0.548 0.000 0.000
#> GSM74409 1 0.3833 -0.2986 0.556 0.000 0.000 0.444 0.000 0.000
#> GSM74410 4 0.4962 0.4589 0.416 0.000 0.068 0.516 0.000 0.000
#> GSM119936 4 0.3881 0.4514 0.396 0.000 0.004 0.600 0.000 0.000
#> GSM119937 4 0.4738 0.5271 0.336 0.000 0.064 0.600 0.000 0.000
#> GSM74411 3 0.4057 0.1838 0.000 0.000 0.556 0.008 0.436 0.000
#> GSM74412 3 0.4443 0.5069 0.000 0.228 0.708 0.008 0.052 0.004
#> GSM74413 3 0.1524 0.6805 0.000 0.000 0.932 0.008 0.060 0.000
#> GSM74414 2 0.3961 0.5664 0.000 0.700 0.276 0.016 0.000 0.008
#> GSM74415 3 0.4091 0.0865 0.000 0.000 0.520 0.008 0.472 0.000
#> GSM121379 2 0.0000 0.9249 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121380 2 0.0000 0.9249 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121381 2 0.0000 0.9249 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121382 2 0.0000 0.9249 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121383 2 0.0000 0.9249 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121384 2 0.0000 0.9249 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121385 2 0.0000 0.9249 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121386 2 0.0000 0.9249 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121387 2 0.0000 0.9249 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121388 2 0.3695 0.3951 0.000 0.624 0.376 0.000 0.000 0.000
#> GSM121389 2 0.0000 0.9249 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121390 2 0.0000 0.9249 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121391 2 0.0000 0.9249 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121392 2 0.0260 0.9203 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM121393 2 0.0000 0.9249 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121394 2 0.0000 0.9249 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121395 2 0.0000 0.9249 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121396 2 0.0713 0.9041 0.000 0.972 0.028 0.000 0.000 0.000
#> GSM121397 2 0.0000 0.9249 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121398 2 0.0000 0.9249 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121399 2 0.0000 0.9249 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM74240 5 0.0000 0.8549 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74241 5 0.0000 0.8549 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74242 5 0.0146 0.8522 0.000 0.000 0.004 0.000 0.996 0.000
#> GSM74243 5 0.0000 0.8549 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74244 5 0.0000 0.8549 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74245 5 0.0000 0.8549 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74246 5 0.0000 0.8549 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74247 5 0.0000 0.8549 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74248 5 0.0000 0.8549 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74416 1 0.1714 0.6931 0.908 0.000 0.000 0.092 0.000 0.000
#> GSM74417 1 0.1219 0.7167 0.948 0.000 0.000 0.048 0.000 0.004
#> GSM74418 1 0.1700 0.7014 0.916 0.000 0.000 0.080 0.000 0.004
#> GSM74419 4 0.5849 0.5549 0.268 0.000 0.092 0.584 0.056 0.000
#> GSM121358 3 0.0000 0.7089 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121359 3 0.0000 0.7089 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121360 1 0.0000 0.7278 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121362 1 0.0820 0.7143 0.972 0.000 0.000 0.016 0.000 0.012
#> GSM121364 1 0.0000 0.7278 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121365 3 0.0146 0.7066 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM121366 3 0.0000 0.7089 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121367 3 0.0000 0.7089 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121370 3 0.0000 0.7089 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121371 3 0.0000 0.7089 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121372 3 0.0000 0.7089 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121373 1 0.0000 0.7278 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121374 1 0.0000 0.7278 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121407 3 0.0000 0.7089 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM74387 3 0.6463 0.2935 0.000 0.000 0.540 0.184 0.204 0.072
#> GSM74388 2 0.4909 0.6073 0.000 0.664 0.000 0.236 0.012 0.088
#> GSM74389 5 0.3618 0.6772 0.076 0.000 0.012 0.100 0.812 0.000
#> GSM74390 4 0.6063 -0.2894 0.000 0.000 0.052 0.448 0.416 0.084
#> GSM74391 4 0.6554 0.3789 0.236 0.000 0.004 0.400 0.340 0.020
#> GSM74392 4 0.4152 0.4270 0.440 0.000 0.000 0.548 0.012 0.000
#> GSM74393 5 0.2474 0.7467 0.080 0.000 0.000 0.040 0.880 0.000
#> GSM74394 5 0.4703 0.6492 0.000 0.000 0.036 0.156 0.728 0.080
#> GSM74239 6 0.4624 0.6360 0.208 0.000 0.004 0.096 0.000 0.692
#> GSM74364 6 0.4702 0.1793 0.460 0.000 0.000 0.044 0.000 0.496
#> GSM74365 6 0.1765 0.7995 0.000 0.000 0.000 0.096 0.000 0.904
#> GSM74366 6 0.6277 0.4908 0.000 0.156 0.076 0.196 0.000 0.572
#> GSM74367 1 0.3857 0.0566 0.532 0.000 0.000 0.000 0.000 0.468
#> GSM74377 6 0.1663 0.7961 0.000 0.000 0.000 0.088 0.000 0.912
#> GSM74378 6 0.3023 0.7218 0.000 0.000 0.000 0.232 0.000 0.768
#> GSM74379 6 0.1714 0.7958 0.000 0.000 0.000 0.092 0.000 0.908
#> GSM74380 6 0.0632 0.8024 0.024 0.000 0.000 0.000 0.000 0.976
#> GSM74381 6 0.2003 0.7877 0.000 0.000 0.000 0.116 0.000 0.884
#> GSM121357 3 0.5649 0.4494 0.000 0.068 0.628 0.224 0.000 0.080
#> GSM121361 5 0.6782 0.3983 0.000 0.184 0.000 0.236 0.492 0.088
#> GSM121363 2 0.4573 0.6230 0.000 0.676 0.000 0.236 0.000 0.088
#> GSM121368 2 0.4573 0.6230 0.000 0.676 0.000 0.236 0.000 0.088
#> GSM121369 5 0.7132 0.1891 0.000 0.000 0.280 0.236 0.396 0.088
#> GSM74368 4 0.5931 0.3294 0.024 0.000 0.164 0.640 0.036 0.136
#> GSM74369 6 0.3726 0.7393 0.144 0.000 0.040 0.020 0.000 0.796
#> GSM74370 6 0.5093 0.6009 0.192 0.000 0.000 0.176 0.000 0.632
#> GSM74371 1 0.5237 0.4382 0.608 0.000 0.000 0.220 0.000 0.172
#> GSM74372 6 0.4127 0.6360 0.028 0.000 0.000 0.284 0.004 0.684
#> GSM74373 6 0.0458 0.8027 0.000 0.000 0.000 0.016 0.000 0.984
#> GSM74374 6 0.2070 0.7807 0.008 0.000 0.000 0.100 0.000 0.892
#> GSM74375 6 0.2300 0.7602 0.000 0.000 0.000 0.000 0.144 0.856
#> GSM74376 6 0.0520 0.8033 0.000 0.000 0.000 0.008 0.008 0.984
#> GSM74405 6 0.1075 0.8035 0.000 0.000 0.000 0.048 0.000 0.952
#> GSM74351 6 0.6226 0.1258 0.292 0.000 0.000 0.244 0.012 0.452
#> GSM74352 6 0.1387 0.7989 0.000 0.000 0.000 0.068 0.000 0.932
#> GSM74353 6 0.4954 0.5493 0.260 0.000 0.000 0.112 0.000 0.628
#> GSM74354 6 0.2632 0.7627 0.004 0.000 0.000 0.164 0.000 0.832
#> GSM74355 6 0.2048 0.7814 0.000 0.000 0.000 0.120 0.000 0.880
#> GSM74382 1 0.5948 0.2731 0.456 0.000 0.000 0.260 0.000 0.284
#> GSM74383 6 0.3590 0.7597 0.116 0.000 0.000 0.076 0.004 0.804
#> GSM74384 6 0.1663 0.7936 0.000 0.000 0.000 0.088 0.000 0.912
#> GSM74385 1 0.2451 0.6890 0.884 0.000 0.000 0.056 0.000 0.060
#> GSM74386 6 0.6455 0.1837 0.144 0.000 0.000 0.048 0.388 0.420
#> GSM74395 6 0.3558 0.7047 0.212 0.000 0.000 0.028 0.000 0.760
#> GSM74396 6 0.2915 0.7380 0.184 0.000 0.000 0.008 0.000 0.808
#> GSM74397 1 0.5418 0.4614 0.656 0.000 0.020 0.072 0.024 0.228
#> GSM74398 6 0.1572 0.7939 0.036 0.000 0.000 0.028 0.000 0.936
#> GSM74399 6 0.0632 0.8023 0.024 0.000 0.000 0.000 0.000 0.976
#> GSM74400 6 0.2872 0.7714 0.140 0.000 0.000 0.024 0.000 0.836
#> GSM74401 6 0.0914 0.8030 0.000 0.000 0.000 0.016 0.016 0.968
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 105 2.16e-07 2
#> CV:pam 100 3.72e-15 3
#> CV:pam 72 4.64e-18 4
#> CV:pam 110 1.01e-33 5
#> CV:pam 88 3.06e-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", "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 21168 rows and 121 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 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.590 0.903 0.928 0.4641 0.497 0.497
#> 3 3 0.702 0.766 0.863 0.3710 0.804 0.625
#> 4 4 0.657 0.745 0.832 0.1315 0.847 0.603
#> 5 5 0.841 0.860 0.929 0.0797 0.913 0.698
#> 6 6 0.815 0.741 0.859 0.0593 0.913 0.638
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM74356 2 0.644 0.890 0.164 0.836
#> GSM74357 2 0.644 0.890 0.164 0.836
#> GSM74358 2 0.644 0.890 0.164 0.836
#> GSM74359 1 0.000 0.983 1.000 0.000
#> GSM74360 1 0.000 0.983 1.000 0.000
#> GSM74361 2 0.653 0.888 0.168 0.832
#> GSM74362 2 0.697 0.878 0.188 0.812
#> GSM74363 2 0.644 0.890 0.164 0.836
#> GSM74402 1 0.000 0.983 1.000 0.000
#> GSM74403 1 0.000 0.983 1.000 0.000
#> GSM74404 1 0.000 0.983 1.000 0.000
#> GSM74406 1 0.000 0.983 1.000 0.000
#> GSM74407 1 0.000 0.983 1.000 0.000
#> GSM74408 1 0.000 0.983 1.000 0.000
#> GSM74409 1 0.000 0.983 1.000 0.000
#> GSM74410 1 0.000 0.983 1.000 0.000
#> GSM119936 1 0.000 0.983 1.000 0.000
#> GSM119937 1 0.000 0.983 1.000 0.000
#> GSM74411 2 0.644 0.890 0.164 0.836
#> GSM74412 2 0.644 0.890 0.164 0.836
#> GSM74413 2 0.644 0.890 0.164 0.836
#> GSM74414 2 0.644 0.890 0.164 0.836
#> GSM74415 2 0.680 0.882 0.180 0.820
#> GSM121379 2 0.000 0.852 0.000 1.000
#> GSM121380 2 0.000 0.852 0.000 1.000
#> GSM121381 2 0.000 0.852 0.000 1.000
#> GSM121382 2 0.000 0.852 0.000 1.000
#> GSM121383 2 0.000 0.852 0.000 1.000
#> GSM121384 2 0.000 0.852 0.000 1.000
#> GSM121385 2 0.000 0.852 0.000 1.000
#> GSM121386 2 0.000 0.852 0.000 1.000
#> GSM121387 2 0.000 0.852 0.000 1.000
#> GSM121388 2 0.000 0.852 0.000 1.000
#> GSM121389 2 0.000 0.852 0.000 1.000
#> GSM121390 2 0.000 0.852 0.000 1.000
#> GSM121391 2 0.000 0.852 0.000 1.000
#> GSM121392 2 0.000 0.852 0.000 1.000
#> GSM121393 2 0.000 0.852 0.000 1.000
#> GSM121394 2 0.000 0.852 0.000 1.000
#> GSM121395 2 0.000 0.852 0.000 1.000
#> GSM121396 2 0.388 0.871 0.076 0.924
#> GSM121397 2 0.000 0.852 0.000 1.000
#> GSM121398 2 0.000 0.852 0.000 1.000
#> GSM121399 2 0.000 0.852 0.000 1.000
#> GSM74240 2 0.795 0.844 0.240 0.760
#> GSM74241 2 0.795 0.844 0.240 0.760
#> GSM74242 2 0.921 0.711 0.336 0.664
#> GSM74243 2 0.921 0.711 0.336 0.664
#> GSM74244 2 0.795 0.844 0.240 0.760
#> GSM74245 2 0.795 0.844 0.240 0.760
#> GSM74246 2 0.795 0.844 0.240 0.760
#> GSM74247 2 0.795 0.844 0.240 0.760
#> GSM74248 2 0.795 0.844 0.240 0.760
#> GSM74416 1 0.000 0.983 1.000 0.000
#> GSM74417 1 0.000 0.983 1.000 0.000
#> GSM74418 1 0.000 0.983 1.000 0.000
#> GSM74419 1 0.000 0.983 1.000 0.000
#> GSM121358 2 0.644 0.890 0.164 0.836
#> GSM121359 2 0.644 0.890 0.164 0.836
#> GSM121360 1 0.000 0.983 1.000 0.000
#> GSM121362 1 0.000 0.983 1.000 0.000
#> GSM121364 1 0.000 0.983 1.000 0.000
#> GSM121365 2 0.644 0.890 0.164 0.836
#> GSM121366 2 0.644 0.890 0.164 0.836
#> GSM121367 2 0.644 0.890 0.164 0.836
#> GSM121370 2 0.644 0.890 0.164 0.836
#> GSM121371 2 0.644 0.890 0.164 0.836
#> GSM121372 2 0.644 0.890 0.164 0.836
#> GSM121373 1 0.000 0.983 1.000 0.000
#> GSM121374 1 0.000 0.983 1.000 0.000
#> GSM121407 2 0.644 0.890 0.164 0.836
#> GSM74387 2 0.795 0.844 0.240 0.760
#> GSM74388 2 0.981 0.537 0.420 0.580
#> GSM74389 1 0.966 0.105 0.608 0.392
#> GSM74390 1 0.000 0.983 1.000 0.000
#> GSM74391 1 0.000 0.983 1.000 0.000
#> GSM74392 1 0.000 0.983 1.000 0.000
#> GSM74393 1 0.997 -0.217 0.532 0.468
#> GSM74394 2 0.808 0.836 0.248 0.752
#> GSM74239 1 0.000 0.983 1.000 0.000
#> GSM74364 1 0.000 0.983 1.000 0.000
#> GSM74365 1 0.000 0.983 1.000 0.000
#> GSM74366 1 0.000 0.983 1.000 0.000
#> GSM74367 1 0.000 0.983 1.000 0.000
#> GSM74377 1 0.000 0.983 1.000 0.000
#> GSM74378 1 0.000 0.983 1.000 0.000
#> GSM74379 1 0.000 0.983 1.000 0.000
#> GSM74380 1 0.000 0.983 1.000 0.000
#> GSM74381 1 0.000 0.983 1.000 0.000
#> GSM121357 2 0.644 0.890 0.164 0.836
#> GSM121361 2 0.839 0.812 0.268 0.732
#> GSM121363 2 0.802 0.840 0.244 0.756
#> GSM121368 2 0.795 0.844 0.240 0.760
#> GSM121369 2 0.802 0.840 0.244 0.756
#> GSM74368 1 0.000 0.983 1.000 0.000
#> GSM74369 1 0.000 0.983 1.000 0.000
#> GSM74370 1 0.000 0.983 1.000 0.000
#> GSM74371 1 0.000 0.983 1.000 0.000
#> GSM74372 1 0.000 0.983 1.000 0.000
#> GSM74373 1 0.000 0.983 1.000 0.000
#> GSM74374 1 0.000 0.983 1.000 0.000
#> GSM74375 1 0.000 0.983 1.000 0.000
#> GSM74376 1 0.000 0.983 1.000 0.000
#> GSM74405 1 0.000 0.983 1.000 0.000
#> GSM74351 1 0.000 0.983 1.000 0.000
#> GSM74352 1 0.000 0.983 1.000 0.000
#> GSM74353 1 0.000 0.983 1.000 0.000
#> GSM74354 1 0.000 0.983 1.000 0.000
#> GSM74355 1 0.000 0.983 1.000 0.000
#> GSM74382 1 0.000 0.983 1.000 0.000
#> GSM74383 1 0.000 0.983 1.000 0.000
#> GSM74384 1 0.000 0.983 1.000 0.000
#> GSM74385 1 0.000 0.983 1.000 0.000
#> GSM74386 1 0.000 0.983 1.000 0.000
#> GSM74395 1 0.000 0.983 1.000 0.000
#> GSM74396 1 0.000 0.983 1.000 0.000
#> GSM74397 1 0.000 0.983 1.000 0.000
#> GSM74398 1 0.000 0.983 1.000 0.000
#> GSM74399 1 0.000 0.983 1.000 0.000
#> GSM74400 1 0.000 0.983 1.000 0.000
#> GSM74401 1 0.000 0.983 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM74356 2 0.0000 0.692 0.000 1.000 0.000
#> GSM74357 2 0.0000 0.692 0.000 1.000 0.000
#> GSM74358 2 0.0000 0.692 0.000 1.000 0.000
#> GSM74359 3 0.7112 0.416 0.424 0.024 0.552
#> GSM74360 3 0.7112 0.416 0.424 0.024 0.552
#> GSM74361 2 0.0424 0.687 0.008 0.992 0.000
#> GSM74362 2 0.1525 0.649 0.004 0.964 0.032
#> GSM74363 2 0.0000 0.692 0.000 1.000 0.000
#> GSM74402 1 0.2116 0.909 0.948 0.012 0.040
#> GSM74403 1 0.0424 0.944 0.992 0.008 0.000
#> GSM74404 1 0.0424 0.944 0.992 0.008 0.000
#> GSM74406 1 0.5122 0.716 0.788 0.012 0.200
#> GSM74407 1 0.5122 0.716 0.788 0.012 0.200
#> GSM74408 1 0.5122 0.716 0.788 0.012 0.200
#> GSM74409 1 0.5122 0.716 0.788 0.012 0.200
#> GSM74410 1 0.5536 0.694 0.776 0.024 0.200
#> GSM119936 1 0.5122 0.716 0.788 0.012 0.200
#> GSM119937 1 0.5122 0.716 0.788 0.012 0.200
#> GSM74411 2 0.0237 0.688 0.000 0.996 0.004
#> GSM74412 2 0.0237 0.688 0.000 0.996 0.004
#> GSM74413 2 0.0000 0.692 0.000 1.000 0.000
#> GSM74414 2 0.0592 0.684 0.012 0.988 0.000
#> GSM74415 2 0.2711 0.557 0.000 0.912 0.088
#> GSM121379 2 0.6260 0.733 0.000 0.552 0.448
#> GSM121380 2 0.6260 0.733 0.000 0.552 0.448
#> GSM121381 2 0.6260 0.733 0.000 0.552 0.448
#> GSM121382 2 0.6260 0.733 0.000 0.552 0.448
#> GSM121383 2 0.6260 0.733 0.000 0.552 0.448
#> GSM121384 2 0.6260 0.733 0.000 0.552 0.448
#> GSM121385 2 0.6260 0.733 0.000 0.552 0.448
#> GSM121386 2 0.6260 0.733 0.000 0.552 0.448
#> GSM121387 2 0.6260 0.733 0.000 0.552 0.448
#> GSM121388 2 0.6215 0.734 0.000 0.572 0.428
#> GSM121389 2 0.6260 0.733 0.000 0.552 0.448
#> GSM121390 2 0.6252 0.733 0.000 0.556 0.444
#> GSM121391 2 0.6260 0.733 0.000 0.552 0.448
#> GSM121392 2 0.6225 0.734 0.000 0.568 0.432
#> GSM121393 2 0.6215 0.734 0.000 0.572 0.428
#> GSM121394 2 0.6225 0.734 0.000 0.568 0.432
#> GSM121395 2 0.6260 0.733 0.000 0.552 0.448
#> GSM121396 2 0.4062 0.708 0.000 0.836 0.164
#> GSM121397 2 0.6260 0.733 0.000 0.552 0.448
#> GSM121398 2 0.6260 0.733 0.000 0.552 0.448
#> GSM121399 2 0.6260 0.733 0.000 0.552 0.448
#> GSM74240 3 0.6260 0.683 0.000 0.448 0.552
#> GSM74241 3 0.6260 0.683 0.000 0.448 0.552
#> GSM74242 3 0.6260 0.683 0.000 0.448 0.552
#> GSM74243 3 0.6260 0.683 0.000 0.448 0.552
#> GSM74244 3 0.6260 0.683 0.000 0.448 0.552
#> GSM74245 3 0.6260 0.683 0.000 0.448 0.552
#> GSM74246 3 0.6260 0.683 0.000 0.448 0.552
#> GSM74247 3 0.6260 0.683 0.000 0.448 0.552
#> GSM74248 3 0.6260 0.683 0.000 0.448 0.552
#> GSM74416 1 0.0848 0.940 0.984 0.008 0.008
#> GSM74417 1 0.0848 0.940 0.984 0.008 0.008
#> GSM74418 1 0.0661 0.942 0.988 0.008 0.004
#> GSM74419 1 0.5122 0.716 0.788 0.012 0.200
#> GSM121358 2 0.0000 0.692 0.000 1.000 0.000
#> GSM121359 2 0.0000 0.692 0.000 1.000 0.000
#> GSM121360 3 0.8058 0.497 0.376 0.072 0.552
#> GSM121362 3 0.7004 0.406 0.428 0.020 0.552
#> GSM121364 3 0.7112 0.416 0.424 0.024 0.552
#> GSM121365 2 0.0000 0.692 0.000 1.000 0.000
#> GSM121366 2 0.0000 0.692 0.000 1.000 0.000
#> GSM121367 2 0.0000 0.692 0.000 1.000 0.000
#> GSM121370 2 0.0000 0.692 0.000 1.000 0.000
#> GSM121371 2 0.0000 0.692 0.000 1.000 0.000
#> GSM121372 2 0.0000 0.692 0.000 1.000 0.000
#> GSM121373 3 0.7112 0.416 0.424 0.024 0.552
#> GSM121374 3 0.7112 0.416 0.424 0.024 0.552
#> GSM121407 2 0.0000 0.692 0.000 1.000 0.000
#> GSM74387 3 0.6625 0.688 0.008 0.440 0.552
#> GSM74388 3 0.8941 0.681 0.160 0.292 0.548
#> GSM74389 3 0.8228 0.514 0.364 0.084 0.552
#> GSM74390 1 0.4654 0.715 0.792 0.000 0.208
#> GSM74391 3 0.7112 0.416 0.424 0.024 0.552
#> GSM74392 3 0.7112 0.416 0.424 0.024 0.552
#> GSM74393 3 0.8665 0.687 0.124 0.324 0.552
#> GSM74394 3 0.7223 0.694 0.028 0.424 0.548
#> GSM74239 1 0.0000 0.946 1.000 0.000 0.000
#> GSM74364 1 0.0237 0.946 0.996 0.004 0.000
#> GSM74365 1 0.0000 0.946 1.000 0.000 0.000
#> GSM74366 1 0.0000 0.946 1.000 0.000 0.000
#> GSM74367 1 0.0000 0.946 1.000 0.000 0.000
#> GSM74377 1 0.0000 0.946 1.000 0.000 0.000
#> GSM74378 1 0.0000 0.946 1.000 0.000 0.000
#> GSM74379 1 0.0000 0.946 1.000 0.000 0.000
#> GSM74380 1 0.0000 0.946 1.000 0.000 0.000
#> GSM74381 1 0.0000 0.946 1.000 0.000 0.000
#> GSM121357 2 0.0592 0.684 0.012 0.988 0.000
#> GSM121361 3 0.7223 0.694 0.028 0.424 0.548
#> GSM121363 3 0.7013 0.692 0.020 0.432 0.548
#> GSM121368 3 0.7013 0.692 0.020 0.432 0.548
#> GSM121369 3 0.7319 0.694 0.032 0.420 0.548
#> GSM74368 1 0.1399 0.926 0.968 0.004 0.028
#> GSM74369 1 0.0000 0.946 1.000 0.000 0.000
#> GSM74370 1 0.0000 0.946 1.000 0.000 0.000
#> GSM74371 1 0.0000 0.946 1.000 0.000 0.000
#> GSM74372 1 0.0237 0.945 0.996 0.000 0.004
#> GSM74373 1 0.0000 0.946 1.000 0.000 0.000
#> GSM74374 1 0.0000 0.946 1.000 0.000 0.000
#> GSM74375 1 0.0237 0.946 0.996 0.004 0.000
#> GSM74376 1 0.0000 0.946 1.000 0.000 0.000
#> GSM74405 1 0.0000 0.946 1.000 0.000 0.000
#> GSM74351 1 0.0424 0.944 0.992 0.008 0.000
#> GSM74352 1 0.0424 0.944 0.992 0.008 0.000
#> GSM74353 1 0.0000 0.946 1.000 0.000 0.000
#> GSM74354 1 0.0000 0.946 1.000 0.000 0.000
#> GSM74355 1 0.0000 0.946 1.000 0.000 0.000
#> GSM74382 1 0.0237 0.946 0.996 0.004 0.000
#> GSM74383 1 0.0000 0.946 1.000 0.000 0.000
#> GSM74384 1 0.0424 0.944 0.992 0.008 0.000
#> GSM74385 1 0.0424 0.944 0.992 0.008 0.000
#> GSM74386 1 0.0000 0.946 1.000 0.000 0.000
#> GSM74395 1 0.0000 0.946 1.000 0.000 0.000
#> GSM74396 1 0.0000 0.946 1.000 0.000 0.000
#> GSM74397 1 0.0424 0.944 0.992 0.008 0.000
#> GSM74398 1 0.0000 0.946 1.000 0.000 0.000
#> GSM74399 1 0.0000 0.946 1.000 0.000 0.000
#> GSM74400 1 0.0661 0.943 0.988 0.008 0.004
#> GSM74401 1 0.0661 0.943 0.988 0.008 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM74356 3 0.3324 0.8163 0.000 0.136 0.852 0.012
#> GSM74357 3 0.3047 0.8038 0.000 0.116 0.872 0.012
#> GSM74358 3 0.3047 0.8038 0.000 0.116 0.872 0.012
#> GSM74359 4 0.3711 0.6777 0.140 0.000 0.024 0.836
#> GSM74360 4 0.3862 0.6771 0.152 0.000 0.024 0.824
#> GSM74361 3 0.4459 0.8289 0.000 0.188 0.780 0.032
#> GSM74362 3 0.6616 0.6127 0.000 0.156 0.624 0.220
#> GSM74363 3 0.3479 0.8196 0.000 0.148 0.840 0.012
#> GSM74402 4 0.7197 0.3415 0.392 0.000 0.140 0.468
#> GSM74403 1 0.0592 0.9342 0.984 0.000 0.000 0.016
#> GSM74404 1 0.0592 0.9342 0.984 0.000 0.000 0.016
#> GSM74406 4 0.7231 0.3411 0.392 0.000 0.144 0.464
#> GSM74407 4 0.6407 0.3419 0.412 0.000 0.068 0.520
#> GSM74408 4 0.7231 0.3411 0.392 0.000 0.144 0.464
#> GSM74409 4 0.7231 0.3411 0.392 0.000 0.144 0.464
#> GSM74410 4 0.7231 0.3411 0.392 0.000 0.144 0.464
#> GSM119936 4 0.7231 0.3411 0.392 0.000 0.144 0.464
#> GSM119937 4 0.7243 0.3290 0.404 0.000 0.144 0.452
#> GSM74411 3 0.4276 0.8306 0.004 0.192 0.788 0.016
#> GSM74412 3 0.3486 0.8327 0.000 0.188 0.812 0.000
#> GSM74413 3 0.3610 0.8339 0.000 0.200 0.800 0.000
#> GSM74414 3 0.4391 0.8236 0.000 0.252 0.740 0.008
#> GSM74415 3 0.7256 0.4833 0.004 0.160 0.544 0.292
#> GSM121379 2 0.0000 0.9653 0.000 1.000 0.000 0.000
#> GSM121380 2 0.0000 0.9653 0.000 1.000 0.000 0.000
#> GSM121381 2 0.0000 0.9653 0.000 1.000 0.000 0.000
#> GSM121382 2 0.0000 0.9653 0.000 1.000 0.000 0.000
#> GSM121383 2 0.0000 0.9653 0.000 1.000 0.000 0.000
#> GSM121384 2 0.0000 0.9653 0.000 1.000 0.000 0.000
#> GSM121385 2 0.0000 0.9653 0.000 1.000 0.000 0.000
#> GSM121386 2 0.0000 0.9653 0.000 1.000 0.000 0.000
#> GSM121387 2 0.0000 0.9653 0.000 1.000 0.000 0.000
#> GSM121388 2 0.1211 0.9278 0.000 0.960 0.040 0.000
#> GSM121389 2 0.0000 0.9653 0.000 1.000 0.000 0.000
#> GSM121390 2 0.0000 0.9653 0.000 1.000 0.000 0.000
#> GSM121391 2 0.0000 0.9653 0.000 1.000 0.000 0.000
#> GSM121392 2 0.1716 0.8995 0.000 0.936 0.064 0.000
#> GSM121393 2 0.1867 0.8909 0.000 0.928 0.072 0.000
#> GSM121394 2 0.0000 0.9653 0.000 1.000 0.000 0.000
#> GSM121395 2 0.0000 0.9653 0.000 1.000 0.000 0.000
#> GSM121396 2 0.4661 0.2239 0.000 0.652 0.348 0.000
#> GSM121397 2 0.0000 0.9653 0.000 1.000 0.000 0.000
#> GSM121398 2 0.0000 0.9653 0.000 1.000 0.000 0.000
#> GSM121399 2 0.0000 0.9653 0.000 1.000 0.000 0.000
#> GSM74240 4 0.3123 0.6004 0.000 0.000 0.156 0.844
#> GSM74241 4 0.3649 0.5654 0.000 0.000 0.204 0.796
#> GSM74242 4 0.3052 0.6120 0.004 0.000 0.136 0.860
#> GSM74243 4 0.3271 0.6166 0.012 0.000 0.132 0.856
#> GSM74244 4 0.3172 0.5992 0.000 0.000 0.160 0.840
#> GSM74245 4 0.3172 0.5992 0.000 0.000 0.160 0.840
#> GSM74246 4 0.3172 0.5992 0.000 0.000 0.160 0.840
#> GSM74247 4 0.3172 0.5992 0.000 0.000 0.160 0.840
#> GSM74248 4 0.3172 0.5992 0.000 0.000 0.160 0.840
#> GSM74416 1 0.5648 0.5046 0.684 0.000 0.064 0.252
#> GSM74417 1 0.5386 0.5541 0.708 0.000 0.056 0.236
#> GSM74418 1 0.4290 0.7272 0.800 0.000 0.036 0.164
#> GSM74419 4 0.7246 0.3215 0.408 0.000 0.144 0.448
#> GSM121358 3 0.4516 0.8242 0.000 0.252 0.736 0.012
#> GSM121359 3 0.4776 0.6819 0.000 0.376 0.624 0.000
#> GSM121360 4 0.3552 0.6738 0.128 0.000 0.024 0.848
#> GSM121362 4 0.4609 0.6612 0.224 0.000 0.024 0.752
#> GSM121364 4 0.3813 0.6777 0.148 0.000 0.024 0.828
#> GSM121365 3 0.4387 0.8288 0.000 0.236 0.752 0.012
#> GSM121366 3 0.5018 0.7500 0.000 0.332 0.656 0.012
#> GSM121367 3 0.4576 0.8197 0.000 0.260 0.728 0.012
#> GSM121370 3 0.4019 0.8342 0.000 0.196 0.792 0.012
#> GSM121371 3 0.4516 0.8232 0.000 0.252 0.736 0.012
#> GSM121372 3 0.4679 0.7238 0.000 0.352 0.648 0.000
#> GSM121373 4 0.3813 0.6777 0.148 0.000 0.024 0.828
#> GSM121374 4 0.3862 0.6771 0.152 0.000 0.024 0.824
#> GSM121407 3 0.4730 0.7042 0.000 0.364 0.636 0.000
#> GSM74387 3 0.5126 0.0125 0.004 0.000 0.552 0.444
#> GSM74388 4 0.6371 0.1041 0.064 0.000 0.428 0.508
#> GSM74389 4 0.3803 0.6734 0.132 0.000 0.032 0.836
#> GSM74390 1 0.4799 0.6089 0.744 0.000 0.032 0.224
#> GSM74391 4 0.3708 0.6782 0.148 0.000 0.020 0.832
#> GSM74392 4 0.3659 0.6772 0.136 0.000 0.024 0.840
#> GSM74393 4 0.3464 0.6540 0.076 0.000 0.056 0.868
#> GSM74394 4 0.5281 0.0481 0.008 0.000 0.464 0.528
#> GSM74239 1 0.0469 0.9349 0.988 0.000 0.000 0.012
#> GSM74364 1 0.0592 0.9344 0.984 0.000 0.000 0.016
#> GSM74365 1 0.0000 0.9358 1.000 0.000 0.000 0.000
#> GSM74366 1 0.2197 0.8877 0.916 0.000 0.004 0.080
#> GSM74367 1 0.0188 0.9361 0.996 0.000 0.000 0.004
#> GSM74377 1 0.0921 0.9255 0.972 0.000 0.000 0.028
#> GSM74378 1 0.1902 0.8980 0.932 0.000 0.004 0.064
#> GSM74379 1 0.0000 0.9358 1.000 0.000 0.000 0.000
#> GSM74380 1 0.0000 0.9358 1.000 0.000 0.000 0.000
#> GSM74381 1 0.1211 0.9182 0.960 0.000 0.000 0.040
#> GSM121357 3 0.4483 0.8034 0.000 0.284 0.712 0.004
#> GSM121361 4 0.5277 0.0523 0.008 0.000 0.460 0.532
#> GSM121363 4 0.5277 0.0523 0.008 0.000 0.460 0.532
#> GSM121368 4 0.5277 0.0523 0.008 0.000 0.460 0.532
#> GSM121369 4 0.5500 0.0555 0.016 0.000 0.464 0.520
#> GSM74368 1 0.3421 0.8253 0.868 0.000 0.044 0.088
#> GSM74369 1 0.0937 0.9305 0.976 0.000 0.012 0.012
#> GSM74370 1 0.0188 0.9361 0.996 0.000 0.000 0.004
#> GSM74371 1 0.1059 0.9306 0.972 0.000 0.012 0.016
#> GSM74372 1 0.0188 0.9362 0.996 0.000 0.000 0.004
#> GSM74373 1 0.1792 0.8978 0.932 0.000 0.000 0.068
#> GSM74374 1 0.0469 0.9349 0.988 0.000 0.000 0.012
#> GSM74375 1 0.1305 0.9277 0.960 0.000 0.004 0.036
#> GSM74376 1 0.0707 0.9335 0.980 0.000 0.000 0.020
#> GSM74405 1 0.0336 0.9342 0.992 0.000 0.000 0.008
#> GSM74351 1 0.0817 0.9302 0.976 0.000 0.000 0.024
#> GSM74352 1 0.2593 0.8752 0.892 0.000 0.004 0.104
#> GSM74353 1 0.0672 0.9349 0.984 0.000 0.008 0.008
#> GSM74354 1 0.0336 0.9358 0.992 0.000 0.000 0.008
#> GSM74355 1 0.1557 0.9071 0.944 0.000 0.000 0.056
#> GSM74382 1 0.0592 0.9342 0.984 0.000 0.000 0.016
#> GSM74383 1 0.0188 0.9362 0.996 0.000 0.000 0.004
#> GSM74384 1 0.2593 0.8752 0.892 0.000 0.004 0.104
#> GSM74385 1 0.1042 0.9311 0.972 0.000 0.008 0.020
#> GSM74386 1 0.0000 0.9358 1.000 0.000 0.000 0.000
#> GSM74395 1 0.0469 0.9349 0.988 0.000 0.000 0.012
#> GSM74396 1 0.0336 0.9358 0.992 0.000 0.000 0.008
#> GSM74397 1 0.1488 0.9184 0.956 0.000 0.012 0.032
#> GSM74398 1 0.0000 0.9358 1.000 0.000 0.000 0.000
#> GSM74399 1 0.0000 0.9358 1.000 0.000 0.000 0.000
#> GSM74400 1 0.2593 0.8771 0.892 0.004 0.000 0.104
#> GSM74401 1 0.2593 0.8771 0.892 0.004 0.000 0.104
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM74356 3 0.0000 0.9451 0.000 0.000 1.000 0.000 0.000
#> GSM74357 3 0.0000 0.9451 0.000 0.000 1.000 0.000 0.000
#> GSM74358 3 0.0000 0.9451 0.000 0.000 1.000 0.000 0.000
#> GSM74359 5 0.4473 0.8080 0.148 0.000 0.008 0.076 0.768
#> GSM74360 5 0.4680 0.7967 0.152 0.000 0.008 0.088 0.752
#> GSM74361 3 0.0000 0.9451 0.000 0.000 1.000 0.000 0.000
#> GSM74362 3 0.0609 0.9320 0.000 0.000 0.980 0.000 0.020
#> GSM74363 3 0.0000 0.9451 0.000 0.000 1.000 0.000 0.000
#> GSM74402 4 0.3487 0.7318 0.212 0.000 0.008 0.780 0.000
#> GSM74403 1 0.0671 0.9143 0.980 0.000 0.004 0.016 0.000
#> GSM74404 1 0.1571 0.8910 0.936 0.000 0.004 0.060 0.000
#> GSM74406 4 0.2660 0.8105 0.128 0.000 0.008 0.864 0.000
#> GSM74407 1 0.5969 0.3755 0.620 0.000 0.008 0.184 0.188
#> GSM74408 4 0.0579 0.8896 0.008 0.000 0.008 0.984 0.000
#> GSM74409 4 0.0579 0.8896 0.008 0.000 0.008 0.984 0.000
#> GSM74410 4 0.0898 0.8873 0.020 0.000 0.008 0.972 0.000
#> GSM119936 4 0.0579 0.8896 0.008 0.000 0.008 0.984 0.000
#> GSM119937 4 0.0579 0.8896 0.008 0.000 0.008 0.984 0.000
#> GSM74411 3 0.2966 0.7842 0.000 0.000 0.816 0.000 0.184
#> GSM74412 3 0.0794 0.9331 0.000 0.000 0.972 0.000 0.028
#> GSM74413 3 0.0510 0.9392 0.000 0.000 0.984 0.000 0.016
#> GSM74414 2 0.5077 0.3233 0.000 0.568 0.392 0.000 0.040
#> GSM74415 3 0.4182 0.4100 0.000 0.000 0.600 0.000 0.400
#> GSM121379 2 0.0000 0.9554 0.000 1.000 0.000 0.000 0.000
#> GSM121380 2 0.0000 0.9554 0.000 1.000 0.000 0.000 0.000
#> GSM121381 2 0.0000 0.9554 0.000 1.000 0.000 0.000 0.000
#> GSM121382 2 0.0000 0.9554 0.000 1.000 0.000 0.000 0.000
#> GSM121383 2 0.0000 0.9554 0.000 1.000 0.000 0.000 0.000
#> GSM121384 2 0.0000 0.9554 0.000 1.000 0.000 0.000 0.000
#> GSM121385 2 0.0000 0.9554 0.000 1.000 0.000 0.000 0.000
#> GSM121386 2 0.0000 0.9554 0.000 1.000 0.000 0.000 0.000
#> GSM121387 2 0.0000 0.9554 0.000 1.000 0.000 0.000 0.000
#> GSM121388 2 0.0000 0.9554 0.000 1.000 0.000 0.000 0.000
#> GSM121389 2 0.0000 0.9554 0.000 1.000 0.000 0.000 0.000
#> GSM121390 2 0.0000 0.9554 0.000 1.000 0.000 0.000 0.000
#> GSM121391 2 0.0000 0.9554 0.000 1.000 0.000 0.000 0.000
#> GSM121392 2 0.0000 0.9554 0.000 1.000 0.000 0.000 0.000
#> GSM121393 2 0.0000 0.9554 0.000 1.000 0.000 0.000 0.000
#> GSM121394 2 0.0000 0.9554 0.000 1.000 0.000 0.000 0.000
#> GSM121395 2 0.0000 0.9554 0.000 1.000 0.000 0.000 0.000
#> GSM121396 3 0.3521 0.6792 0.000 0.232 0.764 0.000 0.004
#> GSM121397 2 0.0000 0.9554 0.000 1.000 0.000 0.000 0.000
#> GSM121398 2 0.0000 0.9554 0.000 1.000 0.000 0.000 0.000
#> GSM121399 2 0.0000 0.9554 0.000 1.000 0.000 0.000 0.000
#> GSM74240 5 0.1300 0.8807 0.000 0.000 0.028 0.016 0.956
#> GSM74241 5 0.0865 0.8792 0.000 0.000 0.024 0.004 0.972
#> GSM74242 5 0.1469 0.8791 0.000 0.000 0.036 0.016 0.948
#> GSM74243 5 0.1310 0.8806 0.000 0.000 0.024 0.020 0.956
#> GSM74244 5 0.0771 0.8805 0.000 0.000 0.020 0.004 0.976
#> GSM74245 5 0.0955 0.8810 0.000 0.000 0.028 0.004 0.968
#> GSM74246 5 0.0771 0.8805 0.000 0.000 0.020 0.004 0.976
#> GSM74247 5 0.0771 0.8805 0.000 0.000 0.020 0.004 0.976
#> GSM74248 5 0.1082 0.8813 0.000 0.000 0.028 0.008 0.964
#> GSM74416 4 0.1341 0.8774 0.056 0.000 0.000 0.944 0.000
#> GSM74417 4 0.1430 0.8789 0.052 0.000 0.004 0.944 0.000
#> GSM74418 4 0.1341 0.8774 0.056 0.000 0.000 0.944 0.000
#> GSM74419 4 0.0579 0.8896 0.008 0.000 0.008 0.984 0.000
#> GSM121358 3 0.0000 0.9451 0.000 0.000 1.000 0.000 0.000
#> GSM121359 3 0.0912 0.9348 0.000 0.012 0.972 0.000 0.016
#> GSM121360 5 0.3981 0.8243 0.136 0.000 0.004 0.060 0.800
#> GSM121362 5 0.4840 0.7691 0.152 0.000 0.000 0.124 0.724
#> GSM121364 5 0.4820 0.7944 0.132 0.000 0.008 0.116 0.744
#> GSM121365 3 0.0000 0.9451 0.000 0.000 1.000 0.000 0.000
#> GSM121366 3 0.0000 0.9451 0.000 0.000 1.000 0.000 0.000
#> GSM121367 3 0.0000 0.9451 0.000 0.000 1.000 0.000 0.000
#> GSM121370 3 0.0000 0.9451 0.000 0.000 1.000 0.000 0.000
#> GSM121371 3 0.0000 0.9451 0.000 0.000 1.000 0.000 0.000
#> GSM121372 3 0.0510 0.9392 0.000 0.000 0.984 0.000 0.016
#> GSM121373 5 0.4571 0.8017 0.152 0.000 0.008 0.080 0.760
#> GSM121374 5 0.5078 0.7684 0.128 0.000 0.008 0.144 0.720
#> GSM121407 3 0.0510 0.9392 0.000 0.000 0.984 0.000 0.016
#> GSM74387 5 0.2389 0.7937 0.000 0.000 0.116 0.004 0.880
#> GSM74388 5 0.1638 0.8662 0.064 0.000 0.004 0.000 0.932
#> GSM74389 5 0.4231 0.8147 0.148 0.000 0.008 0.060 0.784
#> GSM74390 1 0.4443 -0.0891 0.524 0.000 0.000 0.004 0.472
#> GSM74391 5 0.4571 0.8017 0.152 0.000 0.008 0.080 0.760
#> GSM74392 5 0.4355 0.8115 0.148 0.000 0.008 0.068 0.776
#> GSM74393 5 0.1461 0.8796 0.004 0.000 0.016 0.028 0.952
#> GSM74394 5 0.0324 0.8775 0.000 0.000 0.004 0.004 0.992
#> GSM74239 1 0.0162 0.9184 0.996 0.000 0.000 0.004 0.000
#> GSM74364 1 0.2929 0.7807 0.820 0.000 0.000 0.180 0.000
#> GSM74365 1 0.0000 0.9194 1.000 0.000 0.000 0.000 0.000
#> GSM74366 1 0.0162 0.9189 0.996 0.000 0.000 0.000 0.004
#> GSM74367 1 0.0000 0.9194 1.000 0.000 0.000 0.000 0.000
#> GSM74377 1 0.0162 0.9190 0.996 0.000 0.000 0.004 0.000
#> GSM74378 1 0.0324 0.9180 0.992 0.000 0.000 0.004 0.004
#> GSM74379 1 0.0000 0.9194 1.000 0.000 0.000 0.000 0.000
#> GSM74380 1 0.0000 0.9194 1.000 0.000 0.000 0.000 0.000
#> GSM74381 1 0.0162 0.9190 0.996 0.000 0.000 0.004 0.000
#> GSM121357 2 0.5086 0.3126 0.000 0.564 0.396 0.000 0.040
#> GSM121361 5 0.0324 0.8775 0.000 0.000 0.004 0.004 0.992
#> GSM121363 5 0.0324 0.8775 0.000 0.000 0.004 0.004 0.992
#> GSM121368 5 0.0324 0.8775 0.000 0.000 0.004 0.004 0.992
#> GSM121369 5 0.0324 0.8775 0.000 0.000 0.004 0.004 0.992
#> GSM74368 4 0.4300 0.1555 0.476 0.000 0.000 0.524 0.000
#> GSM74369 1 0.3480 0.6447 0.752 0.000 0.000 0.248 0.000
#> GSM74370 1 0.0162 0.9184 0.996 0.000 0.000 0.004 0.000
#> GSM74371 1 0.1544 0.8819 0.932 0.000 0.000 0.068 0.000
#> GSM74372 1 0.0000 0.9194 1.000 0.000 0.000 0.000 0.000
#> GSM74373 1 0.0162 0.9190 0.996 0.000 0.000 0.004 0.000
#> GSM74374 1 0.0703 0.9099 0.976 0.000 0.000 0.024 0.000
#> GSM74375 1 0.0290 0.9183 0.992 0.000 0.000 0.008 0.000
#> GSM74376 1 0.0162 0.9189 0.996 0.000 0.000 0.000 0.004
#> GSM74405 1 0.0162 0.9189 0.996 0.000 0.000 0.000 0.004
#> GSM74351 1 0.3109 0.7607 0.800 0.000 0.000 0.200 0.000
#> GSM74352 1 0.1502 0.8939 0.940 0.000 0.000 0.056 0.004
#> GSM74353 1 0.1270 0.8963 0.948 0.000 0.000 0.052 0.000
#> GSM74354 1 0.0000 0.9194 1.000 0.000 0.000 0.000 0.000
#> GSM74355 1 0.0324 0.9180 0.992 0.000 0.000 0.004 0.004
#> GSM74382 1 0.2516 0.8166 0.860 0.000 0.000 0.140 0.000
#> GSM74383 1 0.0000 0.9194 1.000 0.000 0.000 0.000 0.000
#> GSM74384 1 0.0451 0.9167 0.988 0.000 0.000 0.008 0.004
#> GSM74385 1 0.3074 0.7617 0.804 0.000 0.000 0.196 0.000
#> GSM74386 1 0.0000 0.9194 1.000 0.000 0.000 0.000 0.000
#> GSM74395 1 0.0162 0.9184 0.996 0.000 0.000 0.004 0.000
#> GSM74396 1 0.0000 0.9194 1.000 0.000 0.000 0.000 0.000
#> GSM74397 1 0.1965 0.8705 0.904 0.000 0.000 0.096 0.000
#> GSM74398 1 0.0000 0.9194 1.000 0.000 0.000 0.000 0.000
#> GSM74399 1 0.0000 0.9194 1.000 0.000 0.000 0.000 0.000
#> GSM74400 1 0.3210 0.7495 0.788 0.000 0.000 0.212 0.000
#> GSM74401 1 0.3177 0.7520 0.792 0.000 0.000 0.208 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM74356 3 0.0000 0.9074 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM74357 3 0.0000 0.9074 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM74358 3 0.0000 0.9074 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM74359 5 0.4127 0.7170 0.036 0.000 0.000 0.284 0.680 0.000
#> GSM74360 5 0.4328 0.7111 0.040 0.000 0.000 0.284 0.672 0.004
#> GSM74361 3 0.1007 0.8840 0.000 0.000 0.956 0.000 0.044 0.000
#> GSM74362 3 0.2631 0.7385 0.000 0.000 0.820 0.000 0.180 0.000
#> GSM74363 3 0.0000 0.9074 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM74402 4 0.3810 0.6046 0.208 0.000 0.000 0.752 0.004 0.036
#> GSM74403 1 0.4074 0.4942 0.656 0.000 0.000 0.016 0.004 0.324
#> GSM74404 1 0.4459 0.1419 0.516 0.000 0.000 0.020 0.004 0.460
#> GSM74406 4 0.0865 0.8260 0.036 0.000 0.000 0.964 0.000 0.000
#> GSM74407 4 0.7099 -0.1197 0.264 0.000 0.000 0.352 0.072 0.312
#> GSM74408 4 0.1007 0.8432 0.044 0.000 0.000 0.956 0.000 0.000
#> GSM74409 4 0.1075 0.8420 0.048 0.000 0.000 0.952 0.000 0.000
#> GSM74410 4 0.0632 0.8371 0.024 0.000 0.000 0.976 0.000 0.000
#> GSM119936 4 0.1075 0.8428 0.048 0.000 0.000 0.952 0.000 0.000
#> GSM119937 4 0.1444 0.8303 0.072 0.000 0.000 0.928 0.000 0.000
#> GSM74411 3 0.2912 0.7277 0.000 0.000 0.784 0.000 0.216 0.000
#> GSM74412 3 0.1387 0.8714 0.000 0.000 0.932 0.000 0.068 0.000
#> GSM74413 3 0.0865 0.8886 0.000 0.000 0.964 0.000 0.036 0.000
#> GSM74414 3 0.5326 0.4421 0.012 0.332 0.568 0.000 0.088 0.000
#> GSM74415 5 0.3563 0.3843 0.000 0.000 0.336 0.000 0.664 0.000
#> GSM121379 2 0.0000 0.9970 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121380 2 0.0146 0.9950 0.004 0.996 0.000 0.000 0.000 0.000
#> GSM121381 2 0.0000 0.9970 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121382 2 0.0000 0.9970 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121383 2 0.0000 0.9970 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121384 2 0.0000 0.9970 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121385 2 0.0000 0.9970 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121386 2 0.0000 0.9970 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121387 2 0.0000 0.9970 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121388 2 0.0000 0.9970 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121389 2 0.0000 0.9970 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121390 2 0.0260 0.9927 0.008 0.992 0.000 0.000 0.000 0.000
#> GSM121391 2 0.0000 0.9970 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121392 2 0.0891 0.9627 0.008 0.968 0.000 0.000 0.024 0.000
#> GSM121393 2 0.0260 0.9927 0.008 0.992 0.000 0.000 0.000 0.000
#> GSM121394 2 0.0000 0.9970 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121395 2 0.0000 0.9970 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121396 3 0.2996 0.6944 0.000 0.228 0.772 0.000 0.000 0.000
#> GSM121397 2 0.0000 0.9970 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121398 2 0.0146 0.9950 0.004 0.996 0.000 0.000 0.000 0.000
#> GSM121399 2 0.0000 0.9970 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM74240 5 0.0790 0.8446 0.000 0.000 0.000 0.032 0.968 0.000
#> GSM74241 5 0.0146 0.8428 0.000 0.000 0.000 0.004 0.996 0.000
#> GSM74242 5 0.1333 0.8426 0.000 0.000 0.008 0.048 0.944 0.000
#> GSM74243 5 0.1075 0.8427 0.000 0.000 0.000 0.048 0.952 0.000
#> GSM74244 5 0.0000 0.8419 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74245 5 0.0632 0.8446 0.000 0.000 0.000 0.024 0.976 0.000
#> GSM74246 5 0.0000 0.8419 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74247 5 0.0000 0.8419 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74248 5 0.0790 0.8446 0.000 0.000 0.000 0.032 0.968 0.000
#> GSM74416 1 0.4338 -0.1357 0.492 0.000 0.000 0.488 0.000 0.020
#> GSM74417 1 0.4338 -0.1288 0.496 0.000 0.000 0.484 0.000 0.020
#> GSM74418 1 0.4336 -0.1100 0.504 0.000 0.000 0.476 0.000 0.020
#> GSM74419 4 0.1663 0.8160 0.088 0.000 0.000 0.912 0.000 0.000
#> GSM121358 3 0.0000 0.9074 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121359 3 0.0260 0.9058 0.000 0.008 0.992 0.000 0.000 0.000
#> GSM121360 5 0.3312 0.7882 0.028 0.000 0.000 0.180 0.792 0.000
#> GSM121362 5 0.5244 0.6580 0.084 0.000 0.000 0.248 0.640 0.028
#> GSM121364 5 0.4165 0.7093 0.036 0.000 0.000 0.292 0.672 0.000
#> GSM121365 3 0.0000 0.9074 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121366 3 0.0000 0.9074 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121367 3 0.0000 0.9074 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121370 3 0.0146 0.9067 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM121371 3 0.0000 0.9074 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121372 3 0.0260 0.9058 0.000 0.008 0.992 0.000 0.000 0.000
#> GSM121373 5 0.4308 0.7147 0.040 0.000 0.000 0.280 0.676 0.004
#> GSM121374 5 0.4249 0.6695 0.032 0.000 0.000 0.328 0.640 0.000
#> GSM121407 3 0.0260 0.9058 0.000 0.008 0.992 0.000 0.000 0.000
#> GSM74387 5 0.1531 0.7942 0.000 0.000 0.068 0.000 0.928 0.004
#> GSM74388 5 0.2896 0.7461 0.016 0.000 0.000 0.000 0.824 0.160
#> GSM74389 5 0.3679 0.7737 0.040 0.000 0.000 0.200 0.760 0.000
#> GSM74390 6 0.5094 0.3499 0.092 0.000 0.000 0.004 0.308 0.596
#> GSM74391 5 0.4172 0.7176 0.040 0.000 0.000 0.280 0.680 0.000
#> GSM74392 5 0.4151 0.7209 0.040 0.000 0.000 0.276 0.684 0.000
#> GSM74393 5 0.1471 0.8403 0.004 0.000 0.000 0.064 0.932 0.000
#> GSM74394 5 0.0508 0.8412 0.012 0.000 0.000 0.000 0.984 0.004
#> GSM74239 1 0.3446 0.5078 0.692 0.000 0.000 0.000 0.000 0.308
#> GSM74364 1 0.1926 0.6629 0.912 0.000 0.000 0.020 0.000 0.068
#> GSM74365 6 0.3869 -0.0333 0.500 0.000 0.000 0.000 0.000 0.500
#> GSM74366 6 0.0458 0.8065 0.016 0.000 0.000 0.000 0.000 0.984
#> GSM74367 6 0.2631 0.7555 0.180 0.000 0.000 0.000 0.000 0.820
#> GSM74377 6 0.2996 0.6146 0.228 0.000 0.000 0.000 0.000 0.772
#> GSM74378 6 0.0000 0.8035 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM74379 6 0.0865 0.8146 0.036 0.000 0.000 0.000 0.000 0.964
#> GSM74380 6 0.1075 0.8147 0.048 0.000 0.000 0.000 0.000 0.952
#> GSM74381 6 0.0632 0.8113 0.024 0.000 0.000 0.000 0.000 0.976
#> GSM121357 3 0.5004 0.4965 0.008 0.316 0.604 0.000 0.072 0.000
#> GSM121361 5 0.0622 0.8410 0.012 0.000 0.000 0.000 0.980 0.008
#> GSM121363 5 0.0508 0.8412 0.012 0.000 0.000 0.000 0.984 0.004
#> GSM121368 5 0.0508 0.8412 0.012 0.000 0.000 0.000 0.984 0.004
#> GSM121369 5 0.0508 0.8412 0.012 0.000 0.000 0.000 0.984 0.004
#> GSM74368 1 0.5140 0.1885 0.520 0.000 0.000 0.392 0.000 0.088
#> GSM74369 1 0.2950 0.6518 0.828 0.000 0.000 0.024 0.000 0.148
#> GSM74370 6 0.3620 0.4604 0.352 0.000 0.000 0.000 0.000 0.648
#> GSM74371 1 0.3043 0.6329 0.796 0.000 0.000 0.004 0.004 0.196
#> GSM74372 6 0.2592 0.7937 0.116 0.000 0.000 0.004 0.016 0.864
#> GSM74373 6 0.1267 0.8108 0.060 0.000 0.000 0.000 0.000 0.940
#> GSM74374 6 0.2730 0.7456 0.192 0.000 0.000 0.000 0.000 0.808
#> GSM74375 6 0.3634 0.4108 0.356 0.000 0.000 0.000 0.000 0.644
#> GSM74376 6 0.0865 0.8114 0.036 0.000 0.000 0.000 0.000 0.964
#> GSM74405 6 0.0632 0.8119 0.024 0.000 0.000 0.000 0.000 0.976
#> GSM74351 1 0.1700 0.6469 0.928 0.000 0.000 0.024 0.000 0.048
#> GSM74352 1 0.3789 0.3572 0.584 0.000 0.000 0.000 0.000 0.416
#> GSM74353 1 0.2838 0.6351 0.808 0.000 0.000 0.004 0.000 0.188
#> GSM74354 1 0.3851 0.1032 0.540 0.000 0.000 0.000 0.000 0.460
#> GSM74355 6 0.0000 0.8035 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM74382 1 0.3003 0.6502 0.812 0.000 0.000 0.016 0.000 0.172
#> GSM74383 1 0.3464 0.5065 0.688 0.000 0.000 0.000 0.000 0.312
#> GSM74384 6 0.0260 0.8019 0.008 0.000 0.000 0.000 0.000 0.992
#> GSM74385 1 0.2039 0.6590 0.904 0.000 0.000 0.020 0.000 0.076
#> GSM74386 6 0.2883 0.7298 0.212 0.000 0.000 0.000 0.000 0.788
#> GSM74395 6 0.2912 0.7149 0.216 0.000 0.000 0.000 0.000 0.784
#> GSM74396 6 0.2854 0.7242 0.208 0.000 0.000 0.000 0.000 0.792
#> GSM74397 1 0.5042 0.3997 0.576 0.000 0.000 0.092 0.000 0.332
#> GSM74398 6 0.1204 0.8149 0.056 0.000 0.000 0.000 0.000 0.944
#> GSM74399 6 0.1204 0.8145 0.056 0.000 0.000 0.000 0.000 0.944
#> GSM74400 1 0.2263 0.6597 0.884 0.000 0.000 0.016 0.000 0.100
#> GSM74401 1 0.2214 0.6574 0.888 0.000 0.000 0.016 0.000 0.096
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 2.00e-11 2
#> CV:mclust 112 4.74e-26 3
#> CV:mclust 103 4.17e-33 4
#> CV:mclust 115 1.41e-43 5
#> CV:mclust 104 1.88e-35 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 21168 rows and 121 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 0.932 0.940 0.975 0.4995 0.500 0.500
#> 3 3 0.577 0.667 0.849 0.3215 0.728 0.511
#> 4 4 0.555 0.509 0.716 0.1228 0.806 0.517
#> 5 5 0.623 0.547 0.731 0.0650 0.853 0.537
#> 6 6 0.709 0.669 0.799 0.0435 0.895 0.582
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
#> GSM74356 2 0.0000 0.973 0.000 1.000
#> GSM74357 2 0.6343 0.816 0.160 0.840
#> GSM74358 2 0.8327 0.657 0.264 0.736
#> GSM74359 1 0.0000 0.975 1.000 0.000
#> GSM74360 1 0.0000 0.975 1.000 0.000
#> GSM74361 2 0.5178 0.867 0.116 0.884
#> GSM74362 1 0.8909 0.545 0.692 0.308
#> GSM74363 2 0.0000 0.973 0.000 1.000
#> GSM74402 1 0.0000 0.975 1.000 0.000
#> GSM74403 1 0.0000 0.975 1.000 0.000
#> GSM74404 1 0.0000 0.975 1.000 0.000
#> GSM74406 1 0.0000 0.975 1.000 0.000
#> GSM74407 1 0.0000 0.975 1.000 0.000
#> GSM74408 1 0.0000 0.975 1.000 0.000
#> GSM74409 1 0.0000 0.975 1.000 0.000
#> GSM74410 1 0.0000 0.975 1.000 0.000
#> GSM119936 1 0.0000 0.975 1.000 0.000
#> GSM119937 1 0.0000 0.975 1.000 0.000
#> GSM74411 2 0.0000 0.973 0.000 1.000
#> GSM74412 2 0.0000 0.973 0.000 1.000
#> GSM74413 2 0.0000 0.973 0.000 1.000
#> GSM74414 2 0.0000 0.973 0.000 1.000
#> GSM74415 2 0.0000 0.973 0.000 1.000
#> GSM121379 2 0.0000 0.973 0.000 1.000
#> GSM121380 2 0.0000 0.973 0.000 1.000
#> GSM121381 2 0.0000 0.973 0.000 1.000
#> GSM121382 2 0.0000 0.973 0.000 1.000
#> GSM121383 2 0.0000 0.973 0.000 1.000
#> GSM121384 2 0.0000 0.973 0.000 1.000
#> GSM121385 2 0.0000 0.973 0.000 1.000
#> GSM121386 2 0.0000 0.973 0.000 1.000
#> GSM121387 2 0.0000 0.973 0.000 1.000
#> GSM121388 2 0.0000 0.973 0.000 1.000
#> GSM121389 2 0.0000 0.973 0.000 1.000
#> GSM121390 2 0.0000 0.973 0.000 1.000
#> GSM121391 2 0.0000 0.973 0.000 1.000
#> GSM121392 2 0.0000 0.973 0.000 1.000
#> GSM121393 2 0.0000 0.973 0.000 1.000
#> GSM121394 2 0.0000 0.973 0.000 1.000
#> GSM121395 2 0.0000 0.973 0.000 1.000
#> GSM121396 2 0.0000 0.973 0.000 1.000
#> GSM121397 2 0.0000 0.973 0.000 1.000
#> GSM121398 2 0.0000 0.973 0.000 1.000
#> GSM121399 2 0.0000 0.973 0.000 1.000
#> GSM74240 1 0.9954 0.124 0.540 0.460
#> GSM74241 2 0.6048 0.830 0.148 0.852
#> GSM74242 1 0.1414 0.956 0.980 0.020
#> GSM74243 1 0.2423 0.937 0.960 0.040
#> GSM74244 2 0.2236 0.945 0.036 0.964
#> GSM74245 2 0.8081 0.684 0.248 0.752
#> GSM74246 2 0.3274 0.924 0.060 0.940
#> GSM74247 2 0.0938 0.964 0.012 0.988
#> GSM74248 2 0.9209 0.511 0.336 0.664
#> GSM74416 1 0.0000 0.975 1.000 0.000
#> GSM74417 1 0.0000 0.975 1.000 0.000
#> GSM74418 1 0.0000 0.975 1.000 0.000
#> GSM74419 1 0.0000 0.975 1.000 0.000
#> GSM121358 2 0.0000 0.973 0.000 1.000
#> GSM121359 2 0.0000 0.973 0.000 1.000
#> GSM121360 1 0.0000 0.975 1.000 0.000
#> GSM121362 1 0.0000 0.975 1.000 0.000
#> GSM121364 1 0.0000 0.975 1.000 0.000
#> GSM121365 2 0.0000 0.973 0.000 1.000
#> GSM121366 2 0.0000 0.973 0.000 1.000
#> GSM121367 2 0.0000 0.973 0.000 1.000
#> GSM121370 2 0.0000 0.973 0.000 1.000
#> GSM121371 2 0.0000 0.973 0.000 1.000
#> GSM121372 2 0.0000 0.973 0.000 1.000
#> GSM121373 1 0.0000 0.975 1.000 0.000
#> GSM121374 1 0.0000 0.975 1.000 0.000
#> GSM121407 2 0.0000 0.973 0.000 1.000
#> GSM74387 2 0.0000 0.973 0.000 1.000
#> GSM74388 2 0.0000 0.973 0.000 1.000
#> GSM74389 1 0.0000 0.975 1.000 0.000
#> GSM74390 1 0.0000 0.975 1.000 0.000
#> GSM74391 1 0.0000 0.975 1.000 0.000
#> GSM74392 1 0.0000 0.975 1.000 0.000
#> GSM74393 1 0.0000 0.975 1.000 0.000
#> GSM74394 2 0.0000 0.973 0.000 1.000
#> GSM74239 1 0.0000 0.975 1.000 0.000
#> GSM74364 1 0.0000 0.975 1.000 0.000
#> GSM74365 1 0.0000 0.975 1.000 0.000
#> GSM74366 1 0.9286 0.468 0.656 0.344
#> GSM74367 1 0.0000 0.975 1.000 0.000
#> GSM74377 1 0.0000 0.975 1.000 0.000
#> GSM74378 1 0.0938 0.964 0.988 0.012
#> GSM74379 1 0.0000 0.975 1.000 0.000
#> GSM74380 1 0.0000 0.975 1.000 0.000
#> GSM74381 1 0.0000 0.975 1.000 0.000
#> GSM121357 2 0.0000 0.973 0.000 1.000
#> GSM121361 2 0.0000 0.973 0.000 1.000
#> GSM121363 2 0.0000 0.973 0.000 1.000
#> GSM121368 2 0.0000 0.973 0.000 1.000
#> GSM121369 2 0.0376 0.970 0.004 0.996
#> GSM74368 1 0.0000 0.975 1.000 0.000
#> GSM74369 1 0.0000 0.975 1.000 0.000
#> GSM74370 1 0.0000 0.975 1.000 0.000
#> GSM74371 1 0.0000 0.975 1.000 0.000
#> GSM74372 1 0.0000 0.975 1.000 0.000
#> GSM74373 1 0.0000 0.975 1.000 0.000
#> GSM74374 1 0.0000 0.975 1.000 0.000
#> GSM74375 1 0.0000 0.975 1.000 0.000
#> GSM74376 1 0.0000 0.975 1.000 0.000
#> GSM74405 1 0.0000 0.975 1.000 0.000
#> GSM74351 1 0.0000 0.975 1.000 0.000
#> GSM74352 1 0.9710 0.345 0.600 0.400
#> GSM74353 1 0.0000 0.975 1.000 0.000
#> GSM74354 1 0.0000 0.975 1.000 0.000
#> GSM74355 1 0.0000 0.975 1.000 0.000
#> GSM74382 1 0.0000 0.975 1.000 0.000
#> GSM74383 1 0.0000 0.975 1.000 0.000
#> GSM74384 2 0.2043 0.948 0.032 0.968
#> GSM74385 1 0.0000 0.975 1.000 0.000
#> GSM74386 1 0.0000 0.975 1.000 0.000
#> GSM74395 1 0.0000 0.975 1.000 0.000
#> GSM74396 1 0.0000 0.975 1.000 0.000
#> GSM74397 1 0.0000 0.975 1.000 0.000
#> GSM74398 1 0.0000 0.975 1.000 0.000
#> GSM74399 1 0.0000 0.975 1.000 0.000
#> GSM74400 1 0.0000 0.975 1.000 0.000
#> GSM74401 1 0.0000 0.975 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM74356 3 0.0000 0.7871 0.000 0.000 1.000
#> GSM74357 3 0.0237 0.7860 0.004 0.000 0.996
#> GSM74358 3 0.0424 0.7847 0.008 0.000 0.992
#> GSM74359 3 0.5733 0.3482 0.324 0.000 0.676
#> GSM74360 1 0.5560 0.6107 0.700 0.000 0.300
#> GSM74361 3 0.0000 0.7871 0.000 0.000 1.000
#> GSM74362 3 0.0592 0.7831 0.012 0.000 0.988
#> GSM74363 3 0.0237 0.7873 0.000 0.004 0.996
#> GSM74402 1 0.3752 0.7726 0.856 0.000 0.144
#> GSM74403 1 0.2796 0.8042 0.908 0.000 0.092
#> GSM74404 1 0.3192 0.7934 0.888 0.000 0.112
#> GSM74406 1 0.6026 0.4926 0.624 0.000 0.376
#> GSM74407 1 0.4750 0.7094 0.784 0.000 0.216
#> GSM74408 1 0.6235 0.3637 0.564 0.000 0.436
#> GSM74409 1 0.6225 0.3739 0.568 0.000 0.432
#> GSM74410 3 0.6235 0.0154 0.436 0.000 0.564
#> GSM119936 1 0.5835 0.5520 0.660 0.000 0.340
#> GSM119937 1 0.6079 0.4696 0.612 0.000 0.388
#> GSM74411 3 0.4399 0.6519 0.000 0.188 0.812
#> GSM74412 3 0.6126 0.2408 0.000 0.400 0.600
#> GSM74413 3 0.5178 0.5585 0.000 0.256 0.744
#> GSM74414 2 0.1289 0.8257 0.000 0.968 0.032
#> GSM74415 3 0.1643 0.7802 0.000 0.044 0.956
#> GSM121379 2 0.1643 0.8268 0.000 0.956 0.044
#> GSM121380 2 0.1411 0.8264 0.000 0.964 0.036
#> GSM121381 2 0.4974 0.6882 0.000 0.764 0.236
#> GSM121382 2 0.4750 0.7119 0.000 0.784 0.216
#> GSM121383 2 0.5465 0.6081 0.000 0.712 0.288
#> GSM121384 2 0.1753 0.8264 0.000 0.952 0.048
#> GSM121385 2 0.2448 0.8181 0.000 0.924 0.076
#> GSM121386 2 0.2165 0.8227 0.000 0.936 0.064
#> GSM121387 2 0.4291 0.7479 0.000 0.820 0.180
#> GSM121388 2 0.6140 0.3613 0.000 0.596 0.404
#> GSM121389 2 0.2878 0.8079 0.000 0.904 0.096
#> GSM121390 2 0.0592 0.8191 0.000 0.988 0.012
#> GSM121391 2 0.5905 0.4859 0.000 0.648 0.352
#> GSM121392 2 0.0592 0.8086 0.012 0.988 0.000
#> GSM121393 2 0.1643 0.8268 0.000 0.956 0.044
#> GSM121394 3 0.6305 -0.0448 0.000 0.484 0.516
#> GSM121395 2 0.2261 0.8213 0.000 0.932 0.068
#> GSM121396 3 0.5591 0.4749 0.000 0.304 0.696
#> GSM121397 2 0.2066 0.8238 0.000 0.940 0.060
#> GSM121398 2 0.1529 0.8268 0.000 0.960 0.040
#> GSM121399 2 0.3879 0.7709 0.000 0.848 0.152
#> GSM74240 3 0.0592 0.7831 0.012 0.000 0.988
#> GSM74241 3 0.1529 0.7820 0.000 0.040 0.960
#> GSM74242 3 0.1163 0.7746 0.028 0.000 0.972
#> GSM74243 3 0.1163 0.7748 0.028 0.000 0.972
#> GSM74244 3 0.1031 0.7863 0.000 0.024 0.976
#> GSM74245 3 0.0000 0.7871 0.000 0.000 1.000
#> GSM74246 3 0.1753 0.7779 0.000 0.048 0.952
#> GSM74247 3 0.2959 0.7399 0.000 0.100 0.900
#> GSM74248 3 0.0000 0.7871 0.000 0.000 1.000
#> GSM74416 1 0.3482 0.7839 0.872 0.000 0.128
#> GSM74417 1 0.3551 0.7811 0.868 0.000 0.132
#> GSM74418 1 0.2796 0.8042 0.908 0.000 0.092
#> GSM74419 1 0.6168 0.4201 0.588 0.000 0.412
#> GSM121358 3 0.1163 0.7858 0.000 0.028 0.972
#> GSM121359 3 0.4974 0.5894 0.000 0.236 0.764
#> GSM121360 1 0.6180 0.4028 0.584 0.000 0.416
#> GSM121362 1 0.5115 0.6903 0.768 0.004 0.228
#> GSM121364 3 0.6280 -0.0710 0.460 0.000 0.540
#> GSM121365 3 0.1163 0.7858 0.000 0.028 0.972
#> GSM121366 3 0.2261 0.7650 0.000 0.068 0.932
#> GSM121367 3 0.1163 0.7858 0.000 0.028 0.972
#> GSM121370 3 0.1643 0.7804 0.000 0.044 0.956
#> GSM121371 3 0.1289 0.7847 0.000 0.032 0.968
#> GSM121372 3 0.4605 0.6322 0.000 0.204 0.796
#> GSM121373 1 0.6215 0.3814 0.572 0.000 0.428
#> GSM121374 3 0.6260 -0.0277 0.448 0.000 0.552
#> GSM121407 3 0.5835 0.3956 0.000 0.340 0.660
#> GSM74387 3 0.6062 0.2880 0.000 0.384 0.616
#> GSM74388 2 0.1411 0.7956 0.036 0.964 0.000
#> GSM74389 3 0.5178 0.5002 0.256 0.000 0.744
#> GSM74390 1 0.0237 0.8292 0.996 0.000 0.004
#> GSM74391 1 0.6045 0.4857 0.620 0.000 0.380
#> GSM74392 3 0.6291 -0.1004 0.468 0.000 0.532
#> GSM74393 3 0.3267 0.7060 0.116 0.000 0.884
#> GSM74394 2 0.2152 0.8244 0.016 0.948 0.036
#> GSM74239 1 0.1031 0.8288 0.976 0.000 0.024
#> GSM74364 1 0.0892 0.8292 0.980 0.000 0.020
#> GSM74365 1 0.0747 0.8239 0.984 0.016 0.000
#> GSM74366 2 0.5621 0.4921 0.308 0.692 0.000
#> GSM74367 1 0.0424 0.8263 0.992 0.008 0.000
#> GSM74377 1 0.5465 0.5402 0.712 0.288 0.000
#> GSM74378 2 0.6168 0.2634 0.412 0.588 0.000
#> GSM74379 1 0.2356 0.7977 0.928 0.072 0.000
#> GSM74380 1 0.3192 0.7691 0.888 0.112 0.000
#> GSM74381 1 0.6062 0.3352 0.616 0.384 0.000
#> GSM121357 2 0.5016 0.6830 0.000 0.760 0.240
#> GSM121361 2 0.1315 0.8104 0.020 0.972 0.008
#> GSM121363 2 0.1182 0.8154 0.012 0.976 0.012
#> GSM121368 2 0.1399 0.8241 0.004 0.968 0.028
#> GSM121369 2 0.4931 0.7195 0.004 0.784 0.212
#> GSM74368 1 0.0892 0.8292 0.980 0.000 0.020
#> GSM74369 1 0.0424 0.8296 0.992 0.000 0.008
#> GSM74370 1 0.0592 0.8297 0.988 0.000 0.012
#> GSM74371 1 0.0424 0.8295 0.992 0.000 0.008
#> GSM74372 1 0.0592 0.8297 0.988 0.000 0.012
#> GSM74373 1 0.4555 0.6726 0.800 0.200 0.000
#> GSM74374 1 0.0424 0.8262 0.992 0.008 0.000
#> GSM74375 1 0.3686 0.7418 0.860 0.140 0.000
#> GSM74376 1 0.6252 0.1607 0.556 0.444 0.000
#> GSM74405 1 0.5397 0.5521 0.720 0.280 0.000
#> GSM74351 1 0.1411 0.8260 0.964 0.000 0.036
#> GSM74352 2 0.5431 0.5343 0.284 0.716 0.000
#> GSM74353 1 0.0237 0.8290 0.996 0.000 0.004
#> GSM74354 1 0.0000 0.8282 1.000 0.000 0.000
#> GSM74355 2 0.6299 0.0666 0.476 0.524 0.000
#> GSM74382 1 0.1163 0.8277 0.972 0.000 0.028
#> GSM74383 1 0.0237 0.8290 0.996 0.000 0.004
#> GSM74384 2 0.4555 0.6566 0.200 0.800 0.000
#> GSM74385 1 0.0592 0.8297 0.988 0.000 0.012
#> GSM74386 1 0.0237 0.8290 0.996 0.000 0.004
#> GSM74395 1 0.0892 0.8292 0.980 0.000 0.020
#> GSM74396 1 0.0000 0.8282 1.000 0.000 0.000
#> GSM74397 1 0.1529 0.8246 0.960 0.000 0.040
#> GSM74398 1 0.1529 0.8149 0.960 0.040 0.000
#> GSM74399 1 0.2625 0.7902 0.916 0.084 0.000
#> GSM74400 1 0.2959 0.7778 0.900 0.100 0.000
#> GSM74401 1 0.2959 0.7786 0.900 0.100 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM74356 1 0.6197 0.40799 0.596 0.056 0.344 0.004
#> GSM74357 1 0.6562 0.39120 0.600 0.044 0.328 0.028
#> GSM74358 1 0.6440 0.37992 0.600 0.036 0.336 0.028
#> GSM74359 3 0.7442 0.25900 0.212 0.000 0.504 0.284
#> GSM74360 4 0.6775 0.18725 0.100 0.000 0.384 0.516
#> GSM74361 3 0.5967 -0.00588 0.428 0.020 0.540 0.012
#> GSM74362 3 0.5530 0.26129 0.336 0.000 0.632 0.032
#> GSM74363 1 0.6832 0.47791 0.608 0.120 0.264 0.008
#> GSM74402 4 0.2125 0.75863 0.076 0.000 0.004 0.920
#> GSM74403 4 0.1722 0.76565 0.048 0.000 0.008 0.944
#> GSM74404 4 0.2300 0.76476 0.048 0.000 0.028 0.924
#> GSM74406 4 0.4673 0.67296 0.132 0.000 0.076 0.792
#> GSM74407 4 0.3617 0.72463 0.076 0.000 0.064 0.860
#> GSM74408 4 0.4920 0.63549 0.192 0.000 0.052 0.756
#> GSM74409 4 0.5512 0.61008 0.172 0.000 0.100 0.728
#> GSM74410 4 0.6587 0.46114 0.252 0.000 0.132 0.616
#> GSM119936 4 0.3907 0.69776 0.140 0.000 0.032 0.828
#> GSM119937 4 0.4290 0.67652 0.164 0.000 0.036 0.800
#> GSM74411 3 0.6074 0.21130 0.268 0.084 0.648 0.000
#> GSM74412 3 0.6862 0.18119 0.228 0.176 0.596 0.000
#> GSM74413 3 0.7328 -0.26106 0.392 0.156 0.452 0.000
#> GSM74414 2 0.5123 0.53685 0.232 0.724 0.044 0.000
#> GSM74415 3 0.3402 0.50619 0.164 0.004 0.832 0.000
#> GSM121379 2 0.0188 0.71454 0.004 0.996 0.000 0.000
#> GSM121380 2 0.1211 0.70498 0.040 0.960 0.000 0.000
#> GSM121381 2 0.4155 0.55837 0.240 0.756 0.004 0.000
#> GSM121382 2 0.3636 0.63058 0.172 0.820 0.008 0.000
#> GSM121383 2 0.4122 0.56637 0.236 0.760 0.004 0.000
#> GSM121384 2 0.1022 0.70830 0.032 0.968 0.000 0.000
#> GSM121385 2 0.1637 0.70616 0.060 0.940 0.000 0.000
#> GSM121386 2 0.0921 0.71560 0.028 0.972 0.000 0.000
#> GSM121387 2 0.3208 0.65278 0.148 0.848 0.004 0.000
#> GSM121388 2 0.4950 0.34238 0.376 0.620 0.004 0.000
#> GSM121389 2 0.1118 0.71479 0.036 0.964 0.000 0.000
#> GSM121390 2 0.1867 0.68861 0.072 0.928 0.000 0.000
#> GSM121391 2 0.4955 0.39533 0.344 0.648 0.008 0.000
#> GSM121392 2 0.2704 0.65482 0.124 0.876 0.000 0.000
#> GSM121393 2 0.1118 0.71546 0.036 0.964 0.000 0.000
#> GSM121394 2 0.5229 0.22224 0.428 0.564 0.008 0.000
#> GSM121395 2 0.1302 0.71280 0.044 0.956 0.000 0.000
#> GSM121396 1 0.6008 -0.02353 0.496 0.464 0.040 0.000
#> GSM121397 2 0.0921 0.70936 0.028 0.972 0.000 0.000
#> GSM121398 2 0.0336 0.71572 0.008 0.992 0.000 0.000
#> GSM121399 2 0.2921 0.65997 0.140 0.860 0.000 0.000
#> GSM74240 3 0.0188 0.61770 0.004 0.000 0.996 0.000
#> GSM74241 3 0.1211 0.61451 0.040 0.000 0.960 0.000
#> GSM74242 3 0.2859 0.56610 0.112 0.000 0.880 0.008
#> GSM74243 3 0.2222 0.59757 0.060 0.000 0.924 0.016
#> GSM74244 3 0.1474 0.59931 0.052 0.000 0.948 0.000
#> GSM74245 3 0.1022 0.60834 0.032 0.000 0.968 0.000
#> GSM74246 3 0.1389 0.61629 0.048 0.000 0.952 0.000
#> GSM74247 3 0.1474 0.61544 0.052 0.000 0.948 0.000
#> GSM74248 3 0.0469 0.61458 0.012 0.000 0.988 0.000
#> GSM74416 4 0.1557 0.76431 0.056 0.000 0.000 0.944
#> GSM74417 4 0.2197 0.75281 0.080 0.000 0.004 0.916
#> GSM74418 4 0.1389 0.76701 0.048 0.000 0.000 0.952
#> GSM74419 4 0.4544 0.66735 0.164 0.000 0.048 0.788
#> GSM121358 1 0.6528 0.46847 0.596 0.104 0.300 0.000
#> GSM121359 1 0.7357 0.37972 0.512 0.296 0.192 0.000
#> GSM121360 3 0.3583 0.57200 0.180 0.000 0.816 0.004
#> GSM121362 3 0.7447 0.34759 0.192 0.008 0.548 0.252
#> GSM121364 4 0.7466 0.00853 0.176 0.000 0.388 0.436
#> GSM121365 1 0.6664 0.48312 0.600 0.128 0.272 0.000
#> GSM121366 1 0.6993 0.48073 0.572 0.168 0.260 0.000
#> GSM121367 1 0.6464 0.46130 0.596 0.096 0.308 0.000
#> GSM121370 1 0.6309 0.43139 0.588 0.076 0.336 0.000
#> GSM121371 1 0.6685 0.48425 0.600 0.132 0.268 0.000
#> GSM121372 1 0.7500 0.44457 0.500 0.252 0.248 0.000
#> GSM121373 3 0.7210 0.07184 0.140 0.000 0.456 0.404
#> GSM121374 4 0.7475 0.05283 0.180 0.000 0.372 0.448
#> GSM121407 1 0.7553 0.35636 0.476 0.308 0.216 0.000
#> GSM74387 3 0.4332 0.56271 0.160 0.040 0.800 0.000
#> GSM74388 3 0.7684 0.23015 0.360 0.220 0.420 0.000
#> GSM74389 3 0.3051 0.59266 0.028 0.000 0.884 0.088
#> GSM74390 1 0.8806 -0.21923 0.344 0.040 0.296 0.320
#> GSM74391 3 0.5212 0.51496 0.068 0.000 0.740 0.192
#> GSM74392 3 0.5507 0.49037 0.112 0.000 0.732 0.156
#> GSM74393 3 0.0927 0.61644 0.016 0.000 0.976 0.008
#> GSM74394 3 0.6116 0.44095 0.320 0.068 0.612 0.000
#> GSM74239 4 0.0817 0.77929 0.024 0.000 0.000 0.976
#> GSM74364 4 0.0188 0.77771 0.004 0.000 0.000 0.996
#> GSM74365 4 0.2921 0.74902 0.140 0.000 0.000 0.860
#> GSM74366 2 0.8726 0.19769 0.388 0.400 0.108 0.104
#> GSM74367 4 0.3402 0.73416 0.164 0.000 0.004 0.832
#> GSM74377 4 0.7882 0.36040 0.336 0.176 0.016 0.472
#> GSM74378 2 0.8484 0.17283 0.392 0.396 0.048 0.164
#> GSM74379 4 0.6834 0.51811 0.332 0.024 0.064 0.580
#> GSM74380 4 0.7393 0.47494 0.340 0.072 0.044 0.544
#> GSM74381 1 0.8754 -0.16563 0.384 0.332 0.044 0.240
#> GSM121357 2 0.5102 0.61933 0.188 0.748 0.064 0.000
#> GSM121361 3 0.6894 0.37808 0.344 0.120 0.536 0.000
#> GSM121363 3 0.7634 0.25366 0.352 0.212 0.436 0.000
#> GSM121368 3 0.6993 0.37371 0.336 0.132 0.532 0.000
#> GSM121369 3 0.4599 0.52437 0.248 0.016 0.736 0.000
#> GSM74368 4 0.2737 0.76874 0.104 0.000 0.008 0.888
#> GSM74369 4 0.1118 0.77980 0.036 0.000 0.000 0.964
#> GSM74370 4 0.2385 0.77868 0.052 0.000 0.028 0.920
#> GSM74371 4 0.0817 0.77902 0.024 0.000 0.000 0.976
#> GSM74372 4 0.6792 0.47732 0.140 0.000 0.272 0.588
#> GSM74373 4 0.8455 0.27202 0.356 0.196 0.036 0.412
#> GSM74374 4 0.3196 0.75202 0.136 0.000 0.008 0.856
#> GSM74375 4 0.7127 0.50187 0.304 0.108 0.016 0.572
#> GSM74376 1 0.9602 -0.10350 0.392 0.240 0.200 0.168
#> GSM74405 1 0.9233 -0.16372 0.388 0.204 0.096 0.312
#> GSM74351 4 0.0707 0.77397 0.020 0.000 0.000 0.980
#> GSM74352 2 0.7142 0.31386 0.324 0.524 0.000 0.152
#> GSM74353 4 0.0707 0.77928 0.020 0.000 0.000 0.980
#> GSM74354 4 0.2011 0.77058 0.080 0.000 0.000 0.920
#> GSM74355 1 0.8580 -0.22022 0.388 0.376 0.044 0.192
#> GSM74382 4 0.0336 0.77736 0.008 0.000 0.000 0.992
#> GSM74383 4 0.1557 0.77539 0.056 0.000 0.000 0.944
#> GSM74384 2 0.7585 0.29386 0.388 0.492 0.048 0.072
#> GSM74385 4 0.0707 0.77929 0.020 0.000 0.000 0.980
#> GSM74386 4 0.3552 0.75145 0.128 0.000 0.024 0.848
#> GSM74395 4 0.2329 0.77403 0.072 0.000 0.012 0.916
#> GSM74396 4 0.3636 0.72659 0.172 0.000 0.008 0.820
#> GSM74397 4 0.0469 0.77882 0.012 0.000 0.000 0.988
#> GSM74398 4 0.6395 0.54675 0.316 0.004 0.076 0.604
#> GSM74399 4 0.6648 0.53457 0.328 0.044 0.032 0.596
#> GSM74400 4 0.4827 0.70270 0.124 0.092 0.000 0.784
#> GSM74401 4 0.4775 0.70459 0.140 0.076 0.000 0.784
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM74356 3 0.3080 0.7108 0.008 0.008 0.844 0.140 0.000
#> GSM74357 3 0.3044 0.7085 0.008 0.004 0.840 0.148 0.000
#> GSM74358 3 0.2911 0.7185 0.008 0.004 0.852 0.136 0.000
#> GSM74359 4 0.2929 0.6564 0.076 0.000 0.044 0.876 0.004
#> GSM74360 4 0.2696 0.6616 0.072 0.000 0.012 0.892 0.024
#> GSM74361 4 0.4316 0.5578 0.000 0.004 0.208 0.748 0.040
#> GSM74362 4 0.2446 0.6483 0.000 0.000 0.056 0.900 0.044
#> GSM74363 3 0.1822 0.7773 0.004 0.024 0.936 0.036 0.000
#> GSM74402 1 0.2284 0.7319 0.896 0.000 0.004 0.096 0.004
#> GSM74403 1 0.3086 0.6713 0.816 0.000 0.004 0.180 0.000
#> GSM74404 1 0.4166 0.4845 0.648 0.000 0.004 0.348 0.000
#> GSM74406 1 0.4934 0.4113 0.600 0.000 0.036 0.364 0.000
#> GSM74407 1 0.3706 0.6249 0.756 0.000 0.004 0.236 0.004
#> GSM74408 1 0.5646 0.1421 0.480 0.000 0.076 0.444 0.000
#> GSM74409 4 0.4890 0.2909 0.332 0.000 0.040 0.628 0.000
#> GSM74410 4 0.5996 0.2720 0.316 0.000 0.136 0.548 0.000
#> GSM119936 1 0.5043 0.4208 0.600 0.000 0.044 0.356 0.000
#> GSM119937 1 0.5382 0.4245 0.592 0.000 0.072 0.336 0.000
#> GSM74411 3 0.5794 0.3113 0.000 0.000 0.520 0.096 0.384
#> GSM74412 3 0.6168 0.2762 0.000 0.012 0.496 0.096 0.396
#> GSM74413 3 0.4983 0.5292 0.000 0.008 0.676 0.048 0.268
#> GSM74414 5 0.5898 0.2479 0.016 0.324 0.080 0.000 0.580
#> GSM74415 3 0.6370 0.1233 0.000 0.000 0.432 0.164 0.404
#> GSM121379 2 0.1121 0.8957 0.000 0.956 0.044 0.000 0.000
#> GSM121380 2 0.0510 0.8602 0.000 0.984 0.000 0.000 0.016
#> GSM121381 2 0.2773 0.8541 0.000 0.836 0.164 0.000 0.000
#> GSM121382 2 0.2377 0.8806 0.000 0.872 0.128 0.000 0.000
#> GSM121383 2 0.2516 0.8721 0.000 0.860 0.140 0.000 0.000
#> GSM121384 2 0.0510 0.8587 0.000 0.984 0.000 0.000 0.016
#> GSM121385 2 0.1544 0.9006 0.000 0.932 0.068 0.000 0.000
#> GSM121386 2 0.1341 0.8993 0.000 0.944 0.056 0.000 0.000
#> GSM121387 2 0.1671 0.8997 0.000 0.924 0.076 0.000 0.000
#> GSM121388 2 0.3305 0.7949 0.000 0.776 0.224 0.000 0.000
#> GSM121389 2 0.1043 0.8946 0.000 0.960 0.040 0.000 0.000
#> GSM121390 2 0.0609 0.8563 0.000 0.980 0.000 0.000 0.020
#> GSM121391 2 0.2966 0.8360 0.000 0.816 0.184 0.000 0.000
#> GSM121392 2 0.1357 0.8265 0.000 0.948 0.000 0.004 0.048
#> GSM121393 2 0.1043 0.8940 0.000 0.960 0.040 0.000 0.000
#> GSM121394 2 0.3949 0.6445 0.000 0.668 0.332 0.000 0.000
#> GSM121395 2 0.1608 0.9008 0.000 0.928 0.072 0.000 0.000
#> GSM121396 2 0.4517 0.4144 0.000 0.556 0.436 0.008 0.000
#> GSM121397 2 0.0566 0.8771 0.000 0.984 0.012 0.000 0.004
#> GSM121398 2 0.1544 0.9005 0.000 0.932 0.068 0.000 0.000
#> GSM121399 2 0.1965 0.8934 0.000 0.904 0.096 0.000 0.000
#> GSM74240 5 0.5944 -0.0261 0.000 0.000 0.108 0.404 0.488
#> GSM74241 5 0.5886 0.2461 0.000 0.000 0.224 0.176 0.600
#> GSM74242 5 0.6806 -0.0363 0.000 0.000 0.296 0.348 0.356
#> GSM74243 4 0.6748 -0.0340 0.000 0.000 0.260 0.372 0.368
#> GSM74244 5 0.6523 0.1217 0.000 0.000 0.288 0.232 0.480
#> GSM74245 5 0.6636 0.0656 0.000 0.000 0.244 0.312 0.444
#> GSM74246 5 0.5751 0.0942 0.000 0.000 0.100 0.348 0.552
#> GSM74247 5 0.5887 0.2056 0.000 0.000 0.156 0.252 0.592
#> GSM74248 4 0.5844 0.1113 0.000 0.000 0.096 0.484 0.420
#> GSM74416 1 0.2763 0.6956 0.848 0.000 0.004 0.148 0.000
#> GSM74417 1 0.3835 0.6053 0.744 0.000 0.012 0.244 0.000
#> GSM74418 1 0.2763 0.7012 0.848 0.000 0.004 0.148 0.000
#> GSM74419 1 0.5002 0.4775 0.636 0.000 0.052 0.312 0.000
#> GSM121358 3 0.1310 0.7841 0.000 0.024 0.956 0.020 0.000
#> GSM121359 3 0.1725 0.7801 0.000 0.044 0.936 0.000 0.020
#> GSM121360 4 0.3509 0.5765 0.004 0.004 0.004 0.796 0.192
#> GSM121362 4 0.4410 0.6180 0.044 0.060 0.000 0.800 0.096
#> GSM121364 4 0.2740 0.6484 0.096 0.000 0.028 0.876 0.000
#> GSM121365 3 0.1041 0.7853 0.000 0.032 0.964 0.004 0.000
#> GSM121366 3 0.0963 0.7839 0.000 0.036 0.964 0.000 0.000
#> GSM121367 3 0.1153 0.7877 0.000 0.024 0.964 0.008 0.004
#> GSM121370 3 0.1974 0.7818 0.000 0.016 0.932 0.036 0.016
#> GSM121371 3 0.1364 0.7833 0.000 0.036 0.952 0.012 0.000
#> GSM121372 3 0.2381 0.7736 0.000 0.036 0.908 0.004 0.052
#> GSM121373 4 0.2708 0.6610 0.072 0.000 0.020 0.892 0.016
#> GSM121374 4 0.3064 0.6384 0.108 0.000 0.036 0.856 0.000
#> GSM121407 3 0.2446 0.7738 0.000 0.056 0.900 0.000 0.044
#> GSM74387 5 0.5142 0.2516 0.000 0.000 0.088 0.244 0.668
#> GSM74388 5 0.6282 0.3133 0.000 0.368 0.000 0.156 0.476
#> GSM74389 4 0.4087 0.5277 0.000 0.000 0.036 0.756 0.208
#> GSM74390 5 0.6210 0.2344 0.332 0.012 0.004 0.100 0.552
#> GSM74391 4 0.4935 0.5628 0.044 0.000 0.036 0.736 0.184
#> GSM74392 4 0.1934 0.6523 0.008 0.000 0.020 0.932 0.040
#> GSM74393 4 0.4728 0.4620 0.000 0.000 0.060 0.700 0.240
#> GSM74394 5 0.4001 0.3112 0.000 0.004 0.024 0.208 0.764
#> GSM74239 1 0.0693 0.7375 0.980 0.000 0.000 0.012 0.008
#> GSM74364 1 0.0671 0.7385 0.980 0.000 0.000 0.016 0.004
#> GSM74365 1 0.2763 0.6678 0.848 0.000 0.000 0.004 0.148
#> GSM74366 5 0.5698 0.3968 0.208 0.148 0.000 0.004 0.640
#> GSM74367 1 0.1952 0.7110 0.912 0.000 0.000 0.004 0.084
#> GSM74377 1 0.4866 0.3213 0.580 0.028 0.000 0.000 0.392
#> GSM74378 5 0.6369 0.3399 0.236 0.216 0.000 0.004 0.544
#> GSM74379 1 0.4621 0.3144 0.576 0.004 0.000 0.008 0.412
#> GSM74380 1 0.4714 0.3129 0.576 0.012 0.000 0.004 0.408
#> GSM74381 5 0.6529 0.2226 0.316 0.172 0.000 0.008 0.504
#> GSM121357 3 0.6667 0.1349 0.000 0.348 0.416 0.000 0.236
#> GSM121361 5 0.6656 0.2062 0.000 0.252 0.000 0.308 0.440
#> GSM121363 5 0.5967 0.3611 0.000 0.308 0.000 0.136 0.556
#> GSM121368 5 0.5016 0.3920 0.000 0.176 0.000 0.120 0.704
#> GSM121369 4 0.5192 0.1099 0.000 0.032 0.004 0.492 0.472
#> GSM74368 1 0.2761 0.7009 0.872 0.000 0.000 0.024 0.104
#> GSM74369 1 0.1809 0.7219 0.928 0.000 0.000 0.012 0.060
#> GSM74370 1 0.5440 0.3275 0.540 0.000 0.000 0.396 0.064
#> GSM74371 1 0.1251 0.7400 0.956 0.000 0.000 0.036 0.008
#> GSM74372 4 0.5911 0.4890 0.228 0.000 0.000 0.596 0.176
#> GSM74373 1 0.6857 -0.0199 0.420 0.176 0.000 0.016 0.388
#> GSM74374 1 0.2291 0.7372 0.908 0.000 0.000 0.036 0.056
#> GSM74375 1 0.4070 0.5426 0.728 0.012 0.000 0.004 0.256
#> GSM74376 5 0.5115 0.3211 0.280 0.040 0.000 0.016 0.664
#> GSM74405 5 0.5465 0.1222 0.376 0.044 0.000 0.012 0.568
#> GSM74351 1 0.2074 0.7222 0.896 0.000 0.000 0.104 0.000
#> GSM74352 1 0.6507 -0.0109 0.432 0.192 0.000 0.000 0.376
#> GSM74353 1 0.1628 0.7406 0.936 0.000 0.000 0.056 0.008
#> GSM74354 1 0.1106 0.7368 0.964 0.000 0.000 0.012 0.024
#> GSM74355 5 0.5562 0.1132 0.384 0.064 0.000 0.004 0.548
#> GSM74382 1 0.1908 0.7276 0.908 0.000 0.000 0.092 0.000
#> GSM74383 1 0.0898 0.7357 0.972 0.000 0.000 0.008 0.020
#> GSM74384 5 0.6135 0.3776 0.140 0.304 0.000 0.004 0.552
#> GSM74385 1 0.2230 0.7165 0.884 0.000 0.000 0.116 0.000
#> GSM74386 1 0.2344 0.7326 0.904 0.000 0.000 0.032 0.064
#> GSM74395 1 0.1082 0.7344 0.964 0.000 0.000 0.008 0.028
#> GSM74396 1 0.2179 0.7029 0.896 0.000 0.000 0.004 0.100
#> GSM74397 1 0.1430 0.7398 0.944 0.000 0.000 0.052 0.004
#> GSM74398 1 0.4383 0.2941 0.572 0.000 0.000 0.004 0.424
#> GSM74399 1 0.4426 0.3713 0.612 0.004 0.000 0.004 0.380
#> GSM74400 1 0.3801 0.6591 0.812 0.140 0.000 0.008 0.040
#> GSM74401 1 0.2659 0.7041 0.888 0.060 0.000 0.000 0.052
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM74356 3 0.3386 0.76122 0.000 0.016 0.788 0.188 0.008 0.000
#> GSM74357 3 0.3323 0.68292 0.000 0.008 0.752 0.240 0.000 0.000
#> GSM74358 3 0.2882 0.76808 0.000 0.008 0.812 0.180 0.000 0.000
#> GSM74359 4 0.2515 0.72373 0.024 0.000 0.072 0.888 0.016 0.000
#> GSM74360 4 0.1705 0.71608 0.012 0.000 0.008 0.940 0.024 0.016
#> GSM74361 4 0.6928 0.28342 0.012 0.032 0.128 0.504 0.292 0.032
#> GSM74362 4 0.3238 0.69803 0.012 0.004 0.044 0.856 0.076 0.008
#> GSM74363 3 0.1426 0.89838 0.000 0.016 0.948 0.028 0.008 0.000
#> GSM74402 1 0.2496 0.75417 0.900 0.000 0.008 0.032 0.044 0.016
#> GSM74403 1 0.3808 0.67088 0.804 0.000 0.000 0.116 0.036 0.044
#> GSM74404 1 0.5633 0.47862 0.628 0.000 0.000 0.220 0.100 0.052
#> GSM74406 1 0.5953 0.16914 0.508 0.000 0.064 0.380 0.024 0.024
#> GSM74407 1 0.4899 0.59016 0.712 0.000 0.000 0.164 0.080 0.044
#> GSM74408 4 0.6482 0.23873 0.360 0.000 0.144 0.456 0.020 0.020
#> GSM74409 4 0.5457 0.59508 0.200 0.004 0.108 0.660 0.012 0.016
#> GSM74410 4 0.5773 0.51949 0.144 0.000 0.236 0.596 0.012 0.012
#> GSM119936 1 0.6125 0.19306 0.516 0.000 0.112 0.336 0.016 0.020
#> GSM119937 1 0.6757 -0.02279 0.420 0.000 0.244 0.296 0.004 0.036
#> GSM74411 5 0.4570 0.63537 0.000 0.000 0.248 0.020 0.688 0.044
#> GSM74412 5 0.4844 0.65961 0.000 0.004 0.228 0.016 0.684 0.068
#> GSM74413 5 0.4908 0.49207 0.000 0.004 0.336 0.020 0.608 0.032
#> GSM74414 6 0.7071 0.14042 0.000 0.208 0.052 0.016 0.288 0.436
#> GSM74415 5 0.4089 0.72299 0.000 0.000 0.176 0.024 0.760 0.040
#> GSM121379 2 0.0665 0.95177 0.000 0.980 0.004 0.000 0.008 0.008
#> GSM121380 2 0.0858 0.94508 0.000 0.968 0.004 0.000 0.000 0.028
#> GSM121381 2 0.2393 0.91074 0.000 0.884 0.092 0.000 0.004 0.020
#> GSM121382 2 0.1218 0.94915 0.000 0.956 0.028 0.012 0.004 0.000
#> GSM121383 2 0.1116 0.94954 0.000 0.960 0.028 0.008 0.004 0.000
#> GSM121384 2 0.0837 0.94716 0.000 0.972 0.004 0.000 0.004 0.020
#> GSM121385 2 0.1053 0.95083 0.000 0.964 0.012 0.000 0.004 0.020
#> GSM121386 2 0.1036 0.94979 0.000 0.964 0.008 0.000 0.004 0.024
#> GSM121387 2 0.0870 0.95144 0.000 0.972 0.012 0.012 0.004 0.000
#> GSM121388 2 0.2425 0.92184 0.000 0.900 0.060 0.016 0.008 0.016
#> GSM121389 2 0.0653 0.95179 0.000 0.980 0.004 0.012 0.004 0.000
#> GSM121390 2 0.1296 0.93594 0.000 0.948 0.004 0.000 0.004 0.044
#> GSM121391 2 0.1226 0.94593 0.000 0.952 0.040 0.004 0.004 0.000
#> GSM121392 2 0.1471 0.92246 0.000 0.932 0.004 0.000 0.000 0.064
#> GSM121393 2 0.1963 0.93615 0.008 0.932 0.016 0.012 0.008 0.024
#> GSM121394 2 0.2488 0.88484 0.000 0.864 0.124 0.008 0.004 0.000
#> GSM121395 2 0.0870 0.95142 0.000 0.972 0.012 0.012 0.004 0.000
#> GSM121396 2 0.3492 0.80609 0.000 0.796 0.172 0.016 0.012 0.004
#> GSM121397 2 0.0922 0.94751 0.000 0.968 0.004 0.000 0.004 0.024
#> GSM121398 2 0.1074 0.95036 0.000 0.960 0.012 0.000 0.000 0.028
#> GSM121399 2 0.0862 0.95228 0.000 0.972 0.016 0.004 0.008 0.000
#> GSM74240 5 0.1812 0.74950 0.000 0.000 0.008 0.080 0.912 0.000
#> GSM74241 5 0.2285 0.78245 0.000 0.000 0.064 0.008 0.900 0.028
#> GSM74242 5 0.2661 0.77786 0.008 0.004 0.060 0.036 0.888 0.004
#> GSM74243 5 0.2551 0.77292 0.004 0.004 0.052 0.052 0.888 0.000
#> GSM74244 5 0.2418 0.78215 0.000 0.000 0.092 0.008 0.884 0.016
#> GSM74245 5 0.2265 0.78461 0.000 0.000 0.068 0.024 0.900 0.008
#> GSM74246 5 0.2521 0.77298 0.000 0.000 0.020 0.056 0.892 0.032
#> GSM74247 5 0.2384 0.78312 0.000 0.000 0.044 0.016 0.900 0.040
#> GSM74248 5 0.2531 0.71872 0.000 0.000 0.008 0.128 0.860 0.004
#> GSM74416 1 0.2862 0.71888 0.872 0.000 0.012 0.072 0.004 0.040
#> GSM74417 1 0.4554 0.61347 0.740 0.000 0.016 0.180 0.024 0.040
#> GSM74418 1 0.2631 0.72905 0.884 0.000 0.016 0.076 0.004 0.020
#> GSM74419 1 0.5559 0.53912 0.676 0.016 0.008 0.196 0.056 0.048
#> GSM121358 3 0.1409 0.89754 0.000 0.012 0.948 0.032 0.008 0.000
#> GSM121359 3 0.2146 0.86667 0.000 0.024 0.908 0.008 0.060 0.000
#> GSM121360 4 0.3500 0.65135 0.000 0.004 0.008 0.820 0.052 0.116
#> GSM121362 4 0.3581 0.68099 0.012 0.008 0.020 0.832 0.020 0.108
#> GSM121364 4 0.2146 0.72498 0.024 0.000 0.060 0.908 0.008 0.000
#> GSM121365 3 0.1332 0.89751 0.000 0.008 0.952 0.028 0.012 0.000
#> GSM121366 3 0.1418 0.88983 0.000 0.024 0.944 0.000 0.032 0.000
#> GSM121367 3 0.1269 0.89942 0.000 0.012 0.956 0.012 0.020 0.000
#> GSM121370 3 0.1760 0.88628 0.000 0.020 0.928 0.004 0.048 0.000
#> GSM121371 3 0.1605 0.89772 0.000 0.016 0.940 0.032 0.012 0.000
#> GSM121372 3 0.2380 0.85268 0.000 0.020 0.892 0.004 0.080 0.004
#> GSM121373 4 0.3133 0.71690 0.016 0.000 0.072 0.860 0.008 0.044
#> GSM121374 4 0.2527 0.71695 0.032 0.000 0.084 0.880 0.004 0.000
#> GSM121407 3 0.2761 0.86804 0.000 0.020 0.884 0.008 0.060 0.028
#> GSM74387 5 0.4811 0.67593 0.000 0.000 0.040 0.068 0.712 0.180
#> GSM74388 6 0.6582 0.43469 0.000 0.152 0.004 0.100 0.188 0.556
#> GSM74389 5 0.4336 0.23358 0.008 0.000 0.000 0.408 0.572 0.012
#> GSM74390 1 0.7144 -0.16518 0.332 0.000 0.000 0.076 0.324 0.268
#> GSM74391 5 0.5829 0.14025 0.072 0.000 0.000 0.364 0.516 0.048
#> GSM74392 4 0.4158 0.57458 0.028 0.000 0.004 0.736 0.216 0.016
#> GSM74393 4 0.4402 0.13183 0.004 0.000 0.000 0.564 0.412 0.020
#> GSM74394 5 0.4732 0.43266 0.000 0.000 0.000 0.068 0.612 0.320
#> GSM74239 1 0.1471 0.74861 0.932 0.000 0.004 0.000 0.000 0.064
#> GSM74364 1 0.1141 0.75067 0.948 0.000 0.000 0.000 0.000 0.052
#> GSM74365 1 0.3240 0.60363 0.752 0.000 0.004 0.000 0.000 0.244
#> GSM74366 6 0.2958 0.71493 0.096 0.012 0.000 0.004 0.028 0.860
#> GSM74367 1 0.2504 0.71026 0.856 0.000 0.004 0.000 0.004 0.136
#> GSM74377 6 0.3266 0.59137 0.272 0.000 0.000 0.000 0.000 0.728
#> GSM74378 6 0.2758 0.71459 0.088 0.028 0.000 0.008 0.004 0.872
#> GSM74379 6 0.3897 0.53461 0.300 0.000 0.000 0.008 0.008 0.684
#> GSM74380 6 0.4305 0.19555 0.436 0.000 0.000 0.000 0.020 0.544
#> GSM74381 6 0.3409 0.71479 0.120 0.024 0.004 0.012 0.008 0.832
#> GSM121357 6 0.5793 0.28588 0.000 0.060 0.336 0.016 0.032 0.556
#> GSM121361 6 0.6241 0.39103 0.000 0.044 0.004 0.240 0.156 0.556
#> GSM121363 6 0.5227 0.56976 0.000 0.072 0.004 0.104 0.112 0.708
#> GSM121368 6 0.4792 0.56152 0.000 0.028 0.000 0.128 0.124 0.720
#> GSM121369 6 0.5661 0.17385 0.000 0.008 0.000 0.376 0.124 0.492
#> GSM74368 1 0.5399 0.42888 0.596 0.000 0.052 0.036 0.004 0.312
#> GSM74369 1 0.4312 0.63742 0.728 0.000 0.032 0.020 0.004 0.216
#> GSM74370 4 0.5956 0.34854 0.200 0.000 0.004 0.532 0.008 0.256
#> GSM74371 1 0.0891 0.75626 0.968 0.000 0.000 0.008 0.000 0.024
#> GSM74372 4 0.6335 0.49593 0.152 0.004 0.000 0.576 0.196 0.072
#> GSM74373 6 0.5193 0.58987 0.252 0.032 0.004 0.024 0.024 0.664
#> GSM74374 1 0.3347 0.74650 0.824 0.000 0.000 0.068 0.004 0.104
#> GSM74375 1 0.4309 0.61942 0.736 0.000 0.000 0.008 0.080 0.176
#> GSM74376 6 0.4228 0.69235 0.096 0.004 0.000 0.020 0.104 0.776
#> GSM74405 6 0.3152 0.71167 0.132 0.000 0.000 0.016 0.020 0.832
#> GSM74351 1 0.2074 0.74355 0.912 0.000 0.004 0.048 0.000 0.036
#> GSM74352 6 0.3726 0.65200 0.216 0.028 0.000 0.004 0.000 0.752
#> GSM74353 1 0.2333 0.75999 0.896 0.000 0.000 0.040 0.004 0.060
#> GSM74354 1 0.1141 0.75146 0.948 0.000 0.000 0.000 0.000 0.052
#> GSM74355 6 0.2968 0.68982 0.168 0.000 0.000 0.000 0.016 0.816
#> GSM74382 1 0.1225 0.75511 0.952 0.000 0.000 0.036 0.000 0.012
#> GSM74383 1 0.2346 0.74547 0.892 0.000 0.004 0.016 0.004 0.084
#> GSM74384 6 0.2605 0.69287 0.032 0.064 0.000 0.012 0.004 0.888
#> GSM74385 1 0.2173 0.75249 0.904 0.000 0.000 0.064 0.004 0.028
#> GSM74386 1 0.2757 0.73723 0.864 0.000 0.000 0.016 0.016 0.104
#> GSM74395 1 0.2094 0.75270 0.908 0.000 0.000 0.024 0.004 0.064
#> GSM74396 1 0.2700 0.69795 0.836 0.000 0.004 0.004 0.000 0.156
#> GSM74397 1 0.0858 0.75623 0.968 0.000 0.000 0.004 0.000 0.028
#> GSM74398 1 0.4553 0.37918 0.620 0.000 0.000 0.000 0.052 0.328
#> GSM74399 1 0.4308 -0.00351 0.516 0.000 0.000 0.004 0.012 0.468
#> GSM74400 1 0.4098 0.68056 0.788 0.104 0.004 0.004 0.012 0.088
#> GSM74401 1 0.2614 0.73811 0.884 0.044 0.000 0.000 0.012 0.060
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
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:NMF 118 2.16e-09 2
#> CV:NMF 96 5.04e-18 3
#> CV:NMF 73 3.18e-18 4
#> CV:NMF 73 1.17e-18 5
#> CV:NMF 98 1.30e-39 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 21168 rows and 121 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 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.204 0.583 0.805 0.4333 0.521 0.521
#> 3 3 0.312 0.573 0.680 0.4399 0.688 0.471
#> 4 4 0.554 0.739 0.838 0.1676 0.886 0.682
#> 5 5 0.641 0.763 0.829 0.0644 0.944 0.791
#> 6 6 0.706 0.722 0.829 0.0412 0.965 0.841
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM74356 1 0.8661 0.6640 0.712 0.288
#> GSM74357 1 0.8713 0.6606 0.708 0.292
#> GSM74358 1 0.8713 0.6606 0.708 0.292
#> GSM74359 1 0.5737 0.7481 0.864 0.136
#> GSM74360 1 0.5737 0.7481 0.864 0.136
#> GSM74361 1 0.7219 0.7263 0.800 0.200
#> GSM74362 1 0.7139 0.7283 0.804 0.196
#> GSM74363 1 0.8713 0.6606 0.708 0.292
#> GSM74402 1 0.0376 0.7227 0.996 0.004
#> GSM74403 1 0.0000 0.7203 1.000 0.000
#> GSM74404 1 0.0000 0.7203 1.000 0.000
#> GSM74406 1 0.0376 0.7227 0.996 0.004
#> GSM74407 1 0.0376 0.7227 0.996 0.004
#> GSM74408 1 0.0000 0.7203 1.000 0.000
#> GSM74409 1 0.0000 0.7203 1.000 0.000
#> GSM74410 1 0.0000 0.7203 1.000 0.000
#> GSM119936 1 0.0000 0.7203 1.000 0.000
#> GSM119937 1 0.0672 0.7244 0.992 0.008
#> GSM74411 2 0.9552 0.2337 0.376 0.624
#> GSM74412 2 0.9552 0.2337 0.376 0.624
#> GSM74413 2 0.9552 0.2337 0.376 0.624
#> GSM74414 2 0.8207 0.5027 0.256 0.744
#> GSM74415 2 0.9552 0.2337 0.376 0.624
#> GSM121379 2 0.0000 0.7479 0.000 1.000
#> GSM121380 2 0.0000 0.7479 0.000 1.000
#> GSM121381 2 0.1414 0.7406 0.020 0.980
#> GSM121382 2 0.0000 0.7479 0.000 1.000
#> GSM121383 2 0.0000 0.7479 0.000 1.000
#> GSM121384 2 0.0000 0.7479 0.000 1.000
#> GSM121385 2 0.0000 0.7479 0.000 1.000
#> GSM121386 2 0.0376 0.7475 0.004 0.996
#> GSM121387 2 0.0000 0.7479 0.000 1.000
#> GSM121388 2 0.1414 0.7399 0.020 0.980
#> GSM121389 2 0.0000 0.7479 0.000 1.000
#> GSM121390 2 0.0000 0.7479 0.000 1.000
#> GSM121391 2 0.0000 0.7479 0.000 1.000
#> GSM121392 2 0.0000 0.7479 0.000 1.000
#> GSM121393 2 0.0000 0.7479 0.000 1.000
#> GSM121394 2 0.0376 0.7468 0.004 0.996
#> GSM121395 2 0.0000 0.7479 0.000 1.000
#> GSM121396 2 0.2948 0.7197 0.052 0.948
#> GSM121397 2 0.0000 0.7479 0.000 1.000
#> GSM121398 2 0.0000 0.7479 0.000 1.000
#> GSM121399 2 0.0000 0.7479 0.000 1.000
#> GSM74240 1 0.9358 0.5899 0.648 0.352
#> GSM74241 1 0.9358 0.5899 0.648 0.352
#> GSM74242 1 0.9358 0.5899 0.648 0.352
#> GSM74243 1 0.9358 0.5899 0.648 0.352
#> GSM74244 1 0.9358 0.5899 0.648 0.352
#> GSM74245 1 0.9358 0.5899 0.648 0.352
#> GSM74246 1 0.9358 0.5899 0.648 0.352
#> GSM74247 1 0.9358 0.5899 0.648 0.352
#> GSM74248 1 0.9358 0.5899 0.648 0.352
#> GSM74416 1 0.0000 0.7203 1.000 0.000
#> GSM74417 1 0.0000 0.7203 1.000 0.000
#> GSM74418 1 0.0000 0.7203 1.000 0.000
#> GSM74419 1 0.0376 0.7227 0.996 0.004
#> GSM121358 1 0.9933 0.3832 0.548 0.452
#> GSM121359 1 0.9970 0.3376 0.532 0.468
#> GSM121360 1 0.5737 0.7481 0.864 0.136
#> GSM121362 1 0.5737 0.7481 0.864 0.136
#> GSM121364 1 0.5737 0.7481 0.864 0.136
#> GSM121365 1 0.9922 0.3914 0.552 0.448
#> GSM121366 1 0.9933 0.3832 0.548 0.452
#> GSM121367 1 0.9933 0.3832 0.548 0.452
#> GSM121370 1 0.9866 0.4263 0.568 0.432
#> GSM121371 1 0.9933 0.3832 0.548 0.452
#> GSM121372 1 0.9983 0.3137 0.524 0.476
#> GSM121373 1 0.5737 0.7481 0.864 0.136
#> GSM121374 1 0.5737 0.7481 0.864 0.136
#> GSM121407 2 0.9209 0.3355 0.336 0.664
#> GSM74387 2 0.4939 0.6796 0.108 0.892
#> GSM74388 2 0.0376 0.7476 0.004 0.996
#> GSM74389 1 0.6247 0.7459 0.844 0.156
#> GSM74390 1 0.9866 0.4460 0.568 0.432
#> GSM74391 1 0.2043 0.7343 0.968 0.032
#> GSM74392 1 0.6801 0.7361 0.820 0.180
#> GSM74393 1 0.6801 0.7361 0.820 0.180
#> GSM74394 2 0.0376 0.7476 0.004 0.996
#> GSM74239 1 0.6343 0.7325 0.840 0.160
#> GSM74364 1 0.4815 0.7316 0.896 0.104
#> GSM74365 1 0.8661 0.6326 0.712 0.288
#> GSM74366 2 0.9922 0.0675 0.448 0.552
#> GSM74367 1 0.6801 0.7151 0.820 0.180
#> GSM74377 2 0.9944 0.0490 0.456 0.544
#> GSM74378 2 0.9922 0.0675 0.448 0.552
#> GSM74379 1 0.9491 0.4874 0.632 0.368
#> GSM74380 1 0.9922 0.2840 0.552 0.448
#> GSM74381 2 0.9944 0.0436 0.456 0.544
#> GSM121357 2 0.5629 0.6562 0.132 0.868
#> GSM121361 2 0.0376 0.7476 0.004 0.996
#> GSM121363 2 0.0376 0.7476 0.004 0.996
#> GSM121368 2 0.0376 0.7476 0.004 0.996
#> GSM121369 2 0.0376 0.7476 0.004 0.996
#> GSM74368 1 0.7376 0.7190 0.792 0.208
#> GSM74369 1 0.7376 0.7190 0.792 0.208
#> GSM74370 1 0.7376 0.7190 0.792 0.208
#> GSM74371 1 0.1843 0.7220 0.972 0.028
#> GSM74372 1 0.5842 0.7212 0.860 0.140
#> GSM74373 1 0.9087 0.5783 0.676 0.324
#> GSM74374 1 0.8207 0.6729 0.744 0.256
#> GSM74375 2 0.9977 -0.0254 0.472 0.528
#> GSM74376 2 0.9988 -0.0417 0.480 0.520
#> GSM74405 2 0.9998 -0.0893 0.492 0.508
#> GSM74351 1 0.0000 0.7203 1.000 0.000
#> GSM74352 2 0.9933 0.0522 0.452 0.548
#> GSM74353 1 0.3879 0.7420 0.924 0.076
#> GSM74354 1 0.8144 0.6777 0.748 0.252
#> GSM74355 2 0.9922 0.0675 0.448 0.552
#> GSM74382 1 0.2778 0.7329 0.952 0.048
#> GSM74383 1 0.7299 0.7088 0.796 0.204
#> GSM74384 2 0.9922 0.0675 0.448 0.552
#> GSM74385 1 0.4562 0.7225 0.904 0.096
#> GSM74386 1 0.6531 0.7187 0.832 0.168
#> GSM74395 1 0.5519 0.7310 0.872 0.128
#> GSM74396 1 0.5519 0.7310 0.872 0.128
#> GSM74397 1 0.5408 0.7309 0.876 0.124
#> GSM74398 2 0.9977 -0.0264 0.472 0.528
#> GSM74399 2 0.9933 0.0570 0.452 0.548
#> GSM74400 1 1.0000 0.1176 0.504 0.496
#> GSM74401 1 1.0000 0.1176 0.504 0.496
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM74356 3 0.3573 0.580 0.004 0.120 0.876
#> GSM74357 3 0.3644 0.579 0.004 0.124 0.872
#> GSM74358 3 0.3644 0.579 0.004 0.124 0.872
#> GSM74359 3 0.3933 0.531 0.092 0.028 0.880
#> GSM74360 3 0.3933 0.531 0.092 0.028 0.880
#> GSM74361 3 0.4087 0.564 0.052 0.068 0.880
#> GSM74362 3 0.4189 0.564 0.056 0.068 0.876
#> GSM74363 3 0.3644 0.579 0.004 0.124 0.872
#> GSM74402 3 0.6305 0.313 0.484 0.000 0.516
#> GSM74403 3 0.6308 0.308 0.492 0.000 0.508
#> GSM74404 3 0.6308 0.308 0.492 0.000 0.508
#> GSM74406 3 0.6305 0.313 0.484 0.000 0.516
#> GSM74407 3 0.6309 0.299 0.496 0.000 0.504
#> GSM74408 3 0.6302 0.320 0.480 0.000 0.520
#> GSM74409 3 0.6302 0.320 0.480 0.000 0.520
#> GSM74410 3 0.6302 0.320 0.480 0.000 0.520
#> GSM119936 3 0.6302 0.320 0.480 0.000 0.520
#> GSM119937 3 0.6260 0.311 0.448 0.000 0.552
#> GSM74411 3 0.6659 0.149 0.008 0.460 0.532
#> GSM74412 3 0.6659 0.149 0.008 0.460 0.532
#> GSM74413 3 0.6659 0.149 0.008 0.460 0.532
#> GSM74414 2 0.6796 0.355 0.020 0.612 0.368
#> GSM74415 3 0.6659 0.149 0.008 0.460 0.532
#> GSM121379 2 0.0000 0.909 0.000 1.000 0.000
#> GSM121380 2 0.0000 0.909 0.000 1.000 0.000
#> GSM121381 2 0.2165 0.884 0.000 0.936 0.064
#> GSM121382 2 0.1031 0.907 0.000 0.976 0.024
#> GSM121383 2 0.0424 0.911 0.000 0.992 0.008
#> GSM121384 2 0.0000 0.909 0.000 1.000 0.000
#> GSM121385 2 0.0592 0.909 0.000 0.988 0.012
#> GSM121386 2 0.1163 0.904 0.000 0.972 0.028
#> GSM121387 2 0.0747 0.910 0.000 0.984 0.016
#> GSM121388 2 0.1031 0.906 0.000 0.976 0.024
#> GSM121389 2 0.0237 0.910 0.000 0.996 0.004
#> GSM121390 2 0.0000 0.909 0.000 1.000 0.000
#> GSM121391 2 0.0424 0.911 0.000 0.992 0.008
#> GSM121392 2 0.0000 0.909 0.000 1.000 0.000
#> GSM121393 2 0.0000 0.909 0.000 1.000 0.000
#> GSM121394 2 0.2066 0.887 0.000 0.940 0.060
#> GSM121395 2 0.0237 0.910 0.000 0.996 0.004
#> GSM121396 2 0.2682 0.871 0.004 0.920 0.076
#> GSM121397 2 0.0000 0.909 0.000 1.000 0.000
#> GSM121398 2 0.0424 0.910 0.000 0.992 0.008
#> GSM121399 2 0.1031 0.907 0.000 0.976 0.024
#> GSM74240 3 0.4861 0.573 0.012 0.180 0.808
#> GSM74241 3 0.4861 0.573 0.012 0.180 0.808
#> GSM74242 3 0.4861 0.573 0.012 0.180 0.808
#> GSM74243 3 0.4861 0.573 0.012 0.180 0.808
#> GSM74244 3 0.4861 0.573 0.012 0.180 0.808
#> GSM74245 3 0.4861 0.573 0.012 0.180 0.808
#> GSM74246 3 0.4861 0.573 0.012 0.180 0.808
#> GSM74247 3 0.4861 0.573 0.012 0.180 0.808
#> GSM74248 3 0.4861 0.573 0.012 0.180 0.808
#> GSM74416 3 0.6309 0.307 0.496 0.000 0.504
#> GSM74417 3 0.6309 0.307 0.496 0.000 0.504
#> GSM74418 3 0.6309 0.307 0.496 0.000 0.504
#> GSM74419 3 0.6302 0.316 0.480 0.000 0.520
#> GSM121358 3 0.5831 0.511 0.008 0.284 0.708
#> GSM121359 3 0.5958 0.497 0.008 0.300 0.692
#> GSM121360 3 0.3933 0.531 0.092 0.028 0.880
#> GSM121362 3 0.3933 0.531 0.092 0.028 0.880
#> GSM121364 3 0.3933 0.531 0.092 0.028 0.880
#> GSM121365 3 0.5656 0.514 0.004 0.284 0.712
#> GSM121366 3 0.5831 0.511 0.008 0.284 0.708
#> GSM121367 3 0.5831 0.511 0.008 0.284 0.708
#> GSM121370 3 0.5517 0.524 0.004 0.268 0.728
#> GSM121371 3 0.5831 0.511 0.008 0.284 0.708
#> GSM121372 3 0.6018 0.490 0.008 0.308 0.684
#> GSM121373 3 0.3933 0.531 0.092 0.028 0.880
#> GSM121374 3 0.3933 0.531 0.092 0.028 0.880
#> GSM121407 2 0.6682 -0.015 0.008 0.504 0.488
#> GSM74387 2 0.5956 0.718 0.044 0.768 0.188
#> GSM74388 2 0.3155 0.880 0.044 0.916 0.040
#> GSM74389 3 0.5442 0.538 0.132 0.056 0.812
#> GSM74390 3 0.7710 0.424 0.100 0.240 0.660
#> GSM74391 3 0.6540 0.331 0.408 0.008 0.584
#> GSM74392 3 0.4556 0.554 0.080 0.060 0.860
#> GSM74393 3 0.4556 0.554 0.080 0.060 0.860
#> GSM74394 2 0.3155 0.880 0.044 0.916 0.040
#> GSM74239 1 0.6773 0.592 0.636 0.024 0.340
#> GSM74364 1 0.5882 0.526 0.652 0.000 0.348
#> GSM74365 1 0.7446 0.654 0.664 0.076 0.260
#> GSM74366 1 0.9419 0.594 0.496 0.296 0.208
#> GSM74367 1 0.6284 0.609 0.680 0.016 0.304
#> GSM74377 1 0.9379 0.605 0.504 0.288 0.208
#> GSM74378 1 0.9419 0.594 0.496 0.296 0.208
#> GSM74379 1 0.8117 0.660 0.636 0.128 0.236
#> GSM74380 1 0.9076 0.657 0.552 0.208 0.240
#> GSM74381 1 0.9379 0.605 0.504 0.288 0.208
#> GSM121357 2 0.6264 0.634 0.032 0.724 0.244
#> GSM121361 2 0.3155 0.880 0.044 0.916 0.040
#> GSM121363 2 0.3155 0.880 0.044 0.916 0.040
#> GSM121368 2 0.3155 0.880 0.044 0.916 0.040
#> GSM121369 2 0.3155 0.880 0.044 0.916 0.040
#> GSM74368 1 0.7969 0.462 0.508 0.060 0.432
#> GSM74369 1 0.7969 0.462 0.508 0.060 0.432
#> GSM74370 1 0.7969 0.462 0.508 0.060 0.432
#> GSM74371 1 0.6126 0.314 0.600 0.000 0.400
#> GSM74372 1 0.5733 0.586 0.676 0.000 0.324
#> GSM74373 1 0.7815 0.657 0.644 0.096 0.260
#> GSM74374 1 0.7065 0.644 0.664 0.048 0.288
#> GSM74375 1 0.9509 0.614 0.488 0.284 0.228
#> GSM74376 1 0.9347 0.623 0.512 0.276 0.212
#> GSM74405 1 0.9309 0.635 0.520 0.264 0.216
#> GSM74351 1 0.6309 -0.325 0.500 0.000 0.500
#> GSM74352 1 0.9399 0.600 0.500 0.292 0.208
#> GSM74353 1 0.6954 -0.193 0.500 0.016 0.484
#> GSM74354 1 0.6998 0.642 0.664 0.044 0.292
#> GSM74355 1 0.9419 0.594 0.496 0.296 0.208
#> GSM74382 1 0.6168 0.266 0.588 0.000 0.412
#> GSM74383 1 0.6541 0.621 0.672 0.024 0.304
#> GSM74384 1 0.9419 0.594 0.496 0.296 0.208
#> GSM74385 1 0.5835 0.545 0.660 0.000 0.340
#> GSM74386 1 0.6819 0.592 0.644 0.028 0.328
#> GSM74395 1 0.6126 0.563 0.644 0.004 0.352
#> GSM74396 1 0.6126 0.563 0.644 0.004 0.352
#> GSM74397 1 0.6148 0.553 0.640 0.004 0.356
#> GSM74398 1 0.9401 0.619 0.504 0.280 0.216
#> GSM74399 1 0.9450 0.598 0.492 0.296 0.212
#> GSM74400 1 0.9371 0.636 0.512 0.264 0.224
#> GSM74401 1 0.9371 0.636 0.512 0.264 0.224
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM74356 3 0.2036 0.7904 0.000 0.032 0.936 0.032
#> GSM74357 3 0.1724 0.7930 0.000 0.032 0.948 0.020
#> GSM74358 3 0.1724 0.7930 0.000 0.032 0.948 0.020
#> GSM74359 3 0.4579 0.6513 0.032 0.000 0.768 0.200
#> GSM74360 3 0.4579 0.6513 0.032 0.000 0.768 0.200
#> GSM74361 3 0.3831 0.7222 0.012 0.012 0.836 0.140
#> GSM74362 3 0.3933 0.7166 0.012 0.012 0.828 0.148
#> GSM74363 3 0.1724 0.7930 0.000 0.032 0.948 0.020
#> GSM74402 4 0.1576 0.8797 0.004 0.000 0.048 0.948
#> GSM74403 4 0.1174 0.8750 0.012 0.000 0.020 0.968
#> GSM74404 4 0.1174 0.8750 0.012 0.000 0.020 0.968
#> GSM74406 4 0.1576 0.8797 0.004 0.000 0.048 0.948
#> GSM74407 4 0.1488 0.8767 0.012 0.000 0.032 0.956
#> GSM74408 4 0.1792 0.8731 0.000 0.000 0.068 0.932
#> GSM74409 4 0.1792 0.8731 0.000 0.000 0.068 0.932
#> GSM74410 4 0.1792 0.8731 0.000 0.000 0.068 0.932
#> GSM119936 4 0.1792 0.8731 0.000 0.000 0.068 0.932
#> GSM119937 4 0.3764 0.8225 0.040 0.000 0.116 0.844
#> GSM74411 3 0.4697 0.4963 0.000 0.356 0.644 0.000
#> GSM74412 3 0.4697 0.4963 0.000 0.356 0.644 0.000
#> GSM74413 3 0.4697 0.4963 0.000 0.356 0.644 0.000
#> GSM74414 2 0.5755 0.0483 0.028 0.528 0.444 0.000
#> GSM74415 3 0.4697 0.4963 0.000 0.356 0.644 0.000
#> GSM121379 2 0.0188 0.9080 0.000 0.996 0.004 0.000
#> GSM121380 2 0.0469 0.9097 0.000 0.988 0.012 0.000
#> GSM121381 2 0.2081 0.8686 0.000 0.916 0.084 0.000
#> GSM121382 2 0.1302 0.9033 0.000 0.956 0.044 0.000
#> GSM121383 2 0.0707 0.9101 0.000 0.980 0.020 0.000
#> GSM121384 2 0.0188 0.9080 0.000 0.996 0.004 0.000
#> GSM121385 2 0.0592 0.9085 0.000 0.984 0.016 0.000
#> GSM121386 2 0.1022 0.9032 0.000 0.968 0.032 0.000
#> GSM121387 2 0.1118 0.9061 0.000 0.964 0.036 0.000
#> GSM121388 2 0.0817 0.9055 0.000 0.976 0.024 0.000
#> GSM121389 2 0.0336 0.9091 0.000 0.992 0.008 0.000
#> GSM121390 2 0.0000 0.9067 0.000 1.000 0.000 0.000
#> GSM121391 2 0.0707 0.9101 0.000 0.980 0.020 0.000
#> GSM121392 2 0.0000 0.9067 0.000 1.000 0.000 0.000
#> GSM121393 2 0.0000 0.9067 0.000 1.000 0.000 0.000
#> GSM121394 2 0.2081 0.8756 0.000 0.916 0.084 0.000
#> GSM121395 2 0.0336 0.9091 0.000 0.992 0.008 0.000
#> GSM121396 2 0.2469 0.8567 0.000 0.892 0.108 0.000
#> GSM121397 2 0.0336 0.9090 0.000 0.992 0.008 0.000
#> GSM121398 2 0.0707 0.9097 0.000 0.980 0.020 0.000
#> GSM121399 2 0.1302 0.9031 0.000 0.956 0.044 0.000
#> GSM74240 3 0.2706 0.8078 0.000 0.080 0.900 0.020
#> GSM74241 3 0.2706 0.8078 0.000 0.080 0.900 0.020
#> GSM74242 3 0.2813 0.8081 0.000 0.080 0.896 0.024
#> GSM74243 3 0.2813 0.8081 0.000 0.080 0.896 0.024
#> GSM74244 3 0.2706 0.8078 0.000 0.080 0.900 0.020
#> GSM74245 3 0.2706 0.8078 0.000 0.080 0.900 0.020
#> GSM74246 3 0.2706 0.8078 0.000 0.080 0.900 0.020
#> GSM74247 3 0.2706 0.8078 0.000 0.080 0.900 0.020
#> GSM74248 3 0.2706 0.8078 0.000 0.080 0.900 0.020
#> GSM74416 4 0.0657 0.8742 0.004 0.000 0.012 0.984
#> GSM74417 4 0.0657 0.8742 0.004 0.000 0.012 0.984
#> GSM74418 4 0.0657 0.8742 0.004 0.000 0.012 0.984
#> GSM74419 4 0.1661 0.8790 0.004 0.000 0.052 0.944
#> GSM121358 3 0.3583 0.7528 0.000 0.180 0.816 0.004
#> GSM121359 3 0.3528 0.7402 0.000 0.192 0.808 0.000
#> GSM121360 3 0.4579 0.6513 0.032 0.000 0.768 0.200
#> GSM121362 3 0.4579 0.6513 0.032 0.000 0.768 0.200
#> GSM121364 3 0.4579 0.6513 0.032 0.000 0.768 0.200
#> GSM121365 3 0.3725 0.7545 0.000 0.180 0.812 0.008
#> GSM121366 3 0.3583 0.7528 0.000 0.180 0.816 0.004
#> GSM121367 3 0.3583 0.7528 0.000 0.180 0.816 0.004
#> GSM121370 3 0.3718 0.7625 0.000 0.168 0.820 0.012
#> GSM121371 3 0.3583 0.7528 0.000 0.180 0.816 0.004
#> GSM121372 3 0.3688 0.7283 0.000 0.208 0.792 0.000
#> GSM121373 3 0.4579 0.6513 0.032 0.000 0.768 0.200
#> GSM121374 3 0.4579 0.6513 0.032 0.000 0.768 0.200
#> GSM121407 3 0.4907 0.3324 0.000 0.420 0.580 0.000
#> GSM74387 2 0.5716 0.6554 0.088 0.700 0.212 0.000
#> GSM74388 2 0.3570 0.8557 0.092 0.860 0.048 0.000
#> GSM74389 3 0.5292 0.6127 0.020 0.016 0.712 0.252
#> GSM74390 3 0.6196 0.7047 0.136 0.124 0.716 0.024
#> GSM74391 4 0.4426 0.7478 0.032 0.004 0.168 0.796
#> GSM74392 3 0.4376 0.6960 0.016 0.012 0.796 0.176
#> GSM74393 3 0.4376 0.6960 0.016 0.012 0.796 0.176
#> GSM74394 2 0.3697 0.8506 0.100 0.852 0.048 0.000
#> GSM74239 1 0.5090 0.6444 0.728 0.000 0.044 0.228
#> GSM74364 1 0.5691 0.5404 0.648 0.000 0.048 0.304
#> GSM74365 1 0.3736 0.7339 0.860 0.012 0.032 0.096
#> GSM74366 1 0.3351 0.7462 0.844 0.148 0.008 0.000
#> GSM74367 1 0.4800 0.6679 0.760 0.000 0.044 0.196
#> GSM74377 1 0.3249 0.7500 0.852 0.140 0.008 0.000
#> GSM74378 1 0.3351 0.7462 0.844 0.148 0.008 0.000
#> GSM74379 1 0.2910 0.7491 0.908 0.028 0.020 0.044
#> GSM74380 1 0.4358 0.7600 0.832 0.104 0.020 0.044
#> GSM74381 1 0.3249 0.7496 0.852 0.140 0.008 0.000
#> GSM121357 2 0.5929 0.5077 0.064 0.640 0.296 0.000
#> GSM121361 2 0.3570 0.8557 0.092 0.860 0.048 0.000
#> GSM121363 2 0.3570 0.8557 0.092 0.860 0.048 0.000
#> GSM121368 2 0.3570 0.8557 0.092 0.860 0.048 0.000
#> GSM121369 2 0.3570 0.8557 0.092 0.860 0.048 0.000
#> GSM74368 1 0.7017 0.4239 0.576 0.000 0.236 0.188
#> GSM74369 1 0.7017 0.4239 0.576 0.000 0.236 0.188
#> GSM74370 1 0.7017 0.4239 0.576 0.000 0.236 0.188
#> GSM74371 4 0.6008 -0.1208 0.464 0.000 0.040 0.496
#> GSM74372 1 0.5219 0.6308 0.712 0.000 0.044 0.244
#> GSM74373 1 0.2772 0.7378 0.908 0.004 0.040 0.048
#> GSM74374 1 0.3821 0.7184 0.840 0.000 0.040 0.120
#> GSM74375 1 0.3983 0.7536 0.828 0.144 0.020 0.008
#> GSM74376 1 0.4333 0.7489 0.816 0.144 0.024 0.016
#> GSM74405 1 0.3933 0.7572 0.836 0.132 0.008 0.024
#> GSM74351 4 0.2032 0.8595 0.036 0.000 0.028 0.936
#> GSM74352 1 0.3300 0.7484 0.848 0.144 0.008 0.000
#> GSM74353 4 0.5445 0.6986 0.160 0.004 0.092 0.744
#> GSM74354 1 0.3787 0.7167 0.840 0.000 0.036 0.124
#> GSM74355 1 0.3351 0.7462 0.844 0.148 0.008 0.000
#> GSM74382 4 0.5933 -0.1000 0.464 0.000 0.036 0.500
#> GSM74383 1 0.4379 0.6878 0.792 0.000 0.036 0.172
#> GSM74384 1 0.3351 0.7462 0.844 0.148 0.008 0.000
#> GSM74385 1 0.5546 0.5685 0.664 0.000 0.044 0.292
#> GSM74386 1 0.6316 0.6285 0.672 0.020 0.072 0.236
#> GSM74395 1 0.5312 0.6049 0.692 0.000 0.040 0.268
#> GSM74396 1 0.5312 0.6049 0.692 0.000 0.040 0.268
#> GSM74397 1 0.5417 0.5855 0.676 0.000 0.040 0.284
#> GSM74398 1 0.3428 0.7538 0.844 0.144 0.000 0.012
#> GSM74399 1 0.3208 0.7477 0.848 0.148 0.004 0.000
#> GSM74400 1 0.4413 0.7540 0.812 0.140 0.008 0.040
#> GSM74401 1 0.4413 0.7540 0.812 0.140 0.008 0.040
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM74356 3 0.1809 0.7800 0.012 0.000 0.928 0.060 0.000
#> GSM74357 3 0.1597 0.7830 0.012 0.000 0.940 0.048 0.000
#> GSM74358 3 0.1597 0.7830 0.012 0.000 0.940 0.048 0.000
#> GSM74359 3 0.5926 0.6296 0.204 0.000 0.632 0.152 0.012
#> GSM74360 3 0.5926 0.6296 0.204 0.000 0.632 0.152 0.012
#> GSM74361 3 0.4627 0.7116 0.100 0.000 0.760 0.132 0.008
#> GSM74362 3 0.4713 0.7058 0.100 0.000 0.752 0.140 0.008
#> GSM74363 3 0.1597 0.7830 0.012 0.000 0.940 0.048 0.000
#> GSM74402 4 0.1310 0.9036 0.024 0.000 0.020 0.956 0.000
#> GSM74403 4 0.2280 0.8728 0.120 0.000 0.000 0.880 0.000
#> GSM74404 4 0.2329 0.8698 0.124 0.000 0.000 0.876 0.000
#> GSM74406 4 0.1310 0.9036 0.024 0.000 0.020 0.956 0.000
#> GSM74407 4 0.1787 0.9005 0.044 0.000 0.016 0.936 0.004
#> GSM74408 4 0.1377 0.8929 0.020 0.000 0.020 0.956 0.004
#> GSM74409 4 0.1377 0.8929 0.020 0.000 0.020 0.956 0.004
#> GSM74410 4 0.1377 0.8929 0.020 0.000 0.020 0.956 0.004
#> GSM119936 4 0.1377 0.8929 0.020 0.000 0.020 0.956 0.004
#> GSM119937 4 0.3488 0.8233 0.064 0.000 0.068 0.852 0.016
#> GSM74411 3 0.3861 0.5540 0.004 0.284 0.712 0.000 0.000
#> GSM74412 3 0.3861 0.5540 0.004 0.284 0.712 0.000 0.000
#> GSM74413 3 0.3861 0.5540 0.004 0.284 0.712 0.000 0.000
#> GSM74414 3 0.5258 0.0314 0.004 0.472 0.488 0.000 0.036
#> GSM74415 3 0.3861 0.5540 0.004 0.284 0.712 0.000 0.000
#> GSM121379 2 0.0290 0.9149 0.000 0.992 0.008 0.000 0.000
#> GSM121380 2 0.0404 0.9158 0.000 0.988 0.012 0.000 0.000
#> GSM121381 2 0.1851 0.8802 0.000 0.912 0.088 0.000 0.000
#> GSM121382 2 0.1571 0.9051 0.004 0.936 0.060 0.000 0.000
#> GSM121383 2 0.0609 0.9168 0.000 0.980 0.020 0.000 0.000
#> GSM121384 2 0.0162 0.9132 0.000 0.996 0.004 0.000 0.000
#> GSM121385 2 0.0609 0.9153 0.000 0.980 0.020 0.000 0.000
#> GSM121386 2 0.0963 0.9117 0.000 0.964 0.036 0.000 0.000
#> GSM121387 2 0.1357 0.9103 0.004 0.948 0.048 0.000 0.000
#> GSM121388 2 0.0963 0.9112 0.000 0.964 0.036 0.000 0.000
#> GSM121389 2 0.0404 0.9157 0.000 0.988 0.012 0.000 0.000
#> GSM121390 2 0.0000 0.9116 0.000 1.000 0.000 0.000 0.000
#> GSM121391 2 0.0609 0.9168 0.000 0.980 0.020 0.000 0.000
#> GSM121392 2 0.0000 0.9116 0.000 1.000 0.000 0.000 0.000
#> GSM121393 2 0.0000 0.9116 0.000 1.000 0.000 0.000 0.000
#> GSM121394 2 0.2233 0.8751 0.004 0.892 0.104 0.000 0.000
#> GSM121395 2 0.0404 0.9157 0.000 0.988 0.012 0.000 0.000
#> GSM121396 2 0.2674 0.8439 0.004 0.856 0.140 0.000 0.000
#> GSM121397 2 0.0404 0.9160 0.000 0.988 0.012 0.000 0.000
#> GSM121398 2 0.0794 0.9162 0.000 0.972 0.028 0.000 0.000
#> GSM121399 2 0.1410 0.9053 0.000 0.940 0.060 0.000 0.000
#> GSM74240 3 0.1623 0.7929 0.016 0.020 0.948 0.016 0.000
#> GSM74241 3 0.1623 0.7929 0.016 0.020 0.948 0.016 0.000
#> GSM74242 3 0.1721 0.7929 0.016 0.020 0.944 0.020 0.000
#> GSM74243 3 0.1721 0.7929 0.016 0.020 0.944 0.020 0.000
#> GSM74244 3 0.1623 0.7929 0.016 0.020 0.948 0.016 0.000
#> GSM74245 3 0.1623 0.7929 0.016 0.020 0.948 0.016 0.000
#> GSM74246 3 0.1623 0.7929 0.016 0.020 0.948 0.016 0.000
#> GSM74247 3 0.1623 0.7929 0.016 0.020 0.948 0.016 0.000
#> GSM74248 3 0.1623 0.7929 0.016 0.020 0.948 0.016 0.000
#> GSM74416 4 0.1671 0.8860 0.076 0.000 0.000 0.924 0.000
#> GSM74417 4 0.1671 0.8860 0.076 0.000 0.000 0.924 0.000
#> GSM74418 4 0.1671 0.8860 0.076 0.000 0.000 0.924 0.000
#> GSM74419 4 0.1399 0.9042 0.028 0.000 0.020 0.952 0.000
#> GSM121358 3 0.2777 0.7637 0.000 0.120 0.864 0.016 0.000
#> GSM121359 3 0.2439 0.7555 0.004 0.120 0.876 0.000 0.000
#> GSM121360 3 0.5926 0.6296 0.204 0.000 0.632 0.152 0.012
#> GSM121362 3 0.5926 0.6296 0.204 0.000 0.632 0.152 0.012
#> GSM121364 3 0.5926 0.6296 0.204 0.000 0.632 0.152 0.012
#> GSM121365 3 0.2873 0.7648 0.000 0.120 0.860 0.020 0.000
#> GSM121366 3 0.2777 0.7637 0.000 0.120 0.864 0.016 0.000
#> GSM121367 3 0.2777 0.7637 0.000 0.120 0.864 0.016 0.000
#> GSM121370 3 0.3160 0.7696 0.004 0.116 0.852 0.028 0.000
#> GSM121371 3 0.2777 0.7637 0.000 0.120 0.864 0.016 0.000
#> GSM121372 3 0.2629 0.7476 0.004 0.136 0.860 0.000 0.000
#> GSM121373 3 0.5926 0.6296 0.204 0.000 0.632 0.152 0.012
#> GSM121374 3 0.5932 0.6275 0.200 0.000 0.632 0.156 0.012
#> GSM121407 3 0.4196 0.4127 0.004 0.356 0.640 0.000 0.000
#> GSM74387 2 0.5459 0.6183 0.008 0.660 0.236 0.000 0.096
#> GSM74388 2 0.3536 0.8530 0.008 0.840 0.052 0.000 0.100
#> GSM74389 3 0.5631 0.6123 0.104 0.000 0.652 0.232 0.012
#> GSM74390 3 0.5496 0.6893 0.096 0.060 0.732 0.004 0.108
#> GSM74391 4 0.4640 0.7244 0.088 0.000 0.148 0.756 0.008
#> GSM74392 3 0.5063 0.6884 0.116 0.000 0.720 0.156 0.008
#> GSM74393 3 0.5063 0.6884 0.116 0.000 0.720 0.156 0.008
#> GSM74394 2 0.3570 0.8490 0.008 0.836 0.048 0.000 0.108
#> GSM74239 1 0.5167 0.7474 0.684 0.000 0.000 0.116 0.200
#> GSM74364 1 0.4916 0.7357 0.716 0.000 0.000 0.160 0.124
#> GSM74365 1 0.5088 0.4555 0.528 0.000 0.000 0.036 0.436
#> GSM74366 5 0.0404 0.8833 0.000 0.012 0.000 0.000 0.988
#> GSM74367 1 0.4876 0.7405 0.700 0.000 0.000 0.080 0.220
#> GSM74377 5 0.1281 0.8811 0.032 0.012 0.000 0.000 0.956
#> GSM74378 5 0.0566 0.8830 0.004 0.012 0.000 0.000 0.984
#> GSM74379 5 0.4390 -0.0586 0.428 0.000 0.000 0.004 0.568
#> GSM74380 5 0.3797 0.6329 0.232 0.008 0.000 0.004 0.756
#> GSM74381 5 0.1597 0.8751 0.048 0.012 0.000 0.000 0.940
#> GSM121357 2 0.5579 0.4424 0.008 0.592 0.332 0.000 0.068
#> GSM121361 2 0.3536 0.8530 0.008 0.840 0.052 0.000 0.100
#> GSM121363 2 0.3536 0.8530 0.008 0.840 0.052 0.000 0.100
#> GSM121368 2 0.3536 0.8530 0.008 0.840 0.052 0.000 0.100
#> GSM121369 2 0.3536 0.8530 0.008 0.840 0.052 0.000 0.100
#> GSM74368 1 0.7444 0.5276 0.524 0.000 0.128 0.128 0.220
#> GSM74369 1 0.7444 0.5276 0.524 0.000 0.128 0.128 0.220
#> GSM74370 1 0.7399 0.5315 0.532 0.000 0.128 0.128 0.212
#> GSM74371 1 0.5245 0.5008 0.608 0.000 0.000 0.328 0.064
#> GSM74372 1 0.4504 0.7355 0.748 0.000 0.000 0.084 0.168
#> GSM74373 1 0.4101 0.5552 0.628 0.000 0.000 0.000 0.372
#> GSM74374 1 0.4184 0.6731 0.700 0.000 0.000 0.016 0.284
#> GSM74375 5 0.1622 0.8804 0.028 0.016 0.004 0.004 0.948
#> GSM74376 5 0.3496 0.8030 0.116 0.028 0.016 0.000 0.840
#> GSM74405 5 0.2723 0.8134 0.124 0.012 0.000 0.000 0.864
#> GSM74351 4 0.2471 0.8626 0.136 0.000 0.000 0.864 0.000
#> GSM74352 5 0.0912 0.8841 0.016 0.012 0.000 0.000 0.972
#> GSM74353 4 0.5827 0.6646 0.164 0.000 0.064 0.688 0.084
#> GSM74354 1 0.4437 0.6530 0.664 0.000 0.000 0.020 0.316
#> GSM74355 5 0.0404 0.8833 0.000 0.012 0.000 0.000 0.988
#> GSM74382 1 0.5418 0.4684 0.568 0.000 0.000 0.364 0.068
#> GSM74383 1 0.5051 0.7126 0.664 0.000 0.000 0.072 0.264
#> GSM74384 5 0.0404 0.8833 0.000 0.012 0.000 0.000 0.988
#> GSM74385 1 0.3962 0.7226 0.800 0.000 0.000 0.112 0.088
#> GSM74386 1 0.5797 0.6798 0.636 0.000 0.020 0.092 0.252
#> GSM74395 1 0.4864 0.7532 0.720 0.000 0.000 0.116 0.164
#> GSM74396 1 0.4864 0.7532 0.720 0.000 0.000 0.116 0.164
#> GSM74397 1 0.5002 0.7509 0.708 0.000 0.000 0.132 0.160
#> GSM74398 5 0.1442 0.8809 0.032 0.012 0.000 0.004 0.952
#> GSM74399 5 0.0807 0.8845 0.012 0.012 0.000 0.000 0.976
#> GSM74400 5 0.2857 0.8228 0.112 0.012 0.000 0.008 0.868
#> GSM74401 5 0.2857 0.8228 0.112 0.012 0.000 0.008 0.868
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM74356 3 0.2282 0.627 0.000 0.000 0.888 0.024 0.088 0.000
#> GSM74357 3 0.2060 0.641 0.000 0.000 0.900 0.016 0.084 0.000
#> GSM74358 3 0.2060 0.641 0.000 0.000 0.900 0.016 0.084 0.000
#> GSM74359 5 0.3826 0.943 0.012 0.000 0.236 0.016 0.736 0.000
#> GSM74360 5 0.3826 0.943 0.012 0.000 0.236 0.016 0.736 0.000
#> GSM74361 3 0.4629 -0.362 0.000 0.000 0.524 0.040 0.436 0.000
#> GSM74362 3 0.4636 -0.384 0.000 0.000 0.516 0.040 0.444 0.000
#> GSM74363 3 0.2060 0.641 0.000 0.000 0.900 0.016 0.084 0.000
#> GSM74402 4 0.1577 0.896 0.016 0.000 0.008 0.940 0.036 0.000
#> GSM74403 4 0.2163 0.858 0.092 0.000 0.000 0.892 0.016 0.000
#> GSM74404 4 0.2250 0.856 0.092 0.000 0.000 0.888 0.020 0.000
#> GSM74406 4 0.1577 0.896 0.016 0.000 0.008 0.940 0.036 0.000
#> GSM74407 4 0.1832 0.893 0.032 0.000 0.008 0.928 0.032 0.000
#> GSM74408 4 0.1908 0.885 0.004 0.000 0.000 0.900 0.096 0.000
#> GSM74409 4 0.1908 0.885 0.004 0.000 0.000 0.900 0.096 0.000
#> GSM74410 4 0.1908 0.885 0.004 0.000 0.000 0.900 0.096 0.000
#> GSM119936 4 0.1908 0.885 0.004 0.000 0.000 0.900 0.096 0.000
#> GSM119937 4 0.3989 0.822 0.052 0.000 0.032 0.788 0.128 0.000
#> GSM74411 3 0.3337 0.594 0.000 0.260 0.736 0.000 0.004 0.000
#> GSM74412 3 0.3337 0.594 0.000 0.260 0.736 0.000 0.004 0.000
#> GSM74413 3 0.3337 0.594 0.000 0.260 0.736 0.000 0.004 0.000
#> GSM74414 3 0.4754 0.105 0.000 0.452 0.508 0.000 0.008 0.032
#> GSM74415 3 0.3337 0.594 0.000 0.260 0.736 0.000 0.004 0.000
#> GSM121379 2 0.0405 0.909 0.000 0.988 0.008 0.000 0.004 0.000
#> GSM121380 2 0.0363 0.910 0.000 0.988 0.012 0.000 0.000 0.000
#> GSM121381 2 0.1765 0.869 0.000 0.904 0.096 0.000 0.000 0.000
#> GSM121382 2 0.1471 0.898 0.000 0.932 0.064 0.000 0.004 0.000
#> GSM121383 2 0.0632 0.912 0.000 0.976 0.024 0.000 0.000 0.000
#> GSM121384 2 0.0291 0.908 0.000 0.992 0.004 0.000 0.004 0.000
#> GSM121385 2 0.0547 0.910 0.000 0.980 0.020 0.000 0.000 0.000
#> GSM121386 2 0.1007 0.903 0.000 0.956 0.044 0.000 0.000 0.000
#> GSM121387 2 0.1285 0.903 0.000 0.944 0.052 0.000 0.004 0.000
#> GSM121388 2 0.1010 0.904 0.000 0.960 0.036 0.000 0.004 0.000
#> GSM121389 2 0.0363 0.911 0.000 0.988 0.012 0.000 0.000 0.000
#> GSM121390 2 0.0146 0.907 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM121391 2 0.0632 0.912 0.000 0.976 0.024 0.000 0.000 0.000
#> GSM121392 2 0.0146 0.907 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM121393 2 0.0146 0.907 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM121394 2 0.2146 0.860 0.000 0.880 0.116 0.000 0.004 0.000
#> GSM121395 2 0.0363 0.911 0.000 0.988 0.012 0.000 0.000 0.000
#> GSM121396 2 0.2442 0.832 0.000 0.852 0.144 0.000 0.004 0.000
#> GSM121397 2 0.0508 0.910 0.000 0.984 0.012 0.000 0.004 0.000
#> GSM121398 2 0.0713 0.910 0.000 0.972 0.028 0.000 0.000 0.000
#> GSM121399 2 0.1327 0.898 0.000 0.936 0.064 0.000 0.000 0.000
#> GSM74240 3 0.1152 0.693 0.004 0.000 0.952 0.000 0.044 0.000
#> GSM74241 3 0.1152 0.693 0.004 0.000 0.952 0.000 0.044 0.000
#> GSM74242 3 0.1296 0.691 0.004 0.000 0.948 0.004 0.044 0.000
#> GSM74243 3 0.1296 0.691 0.004 0.000 0.948 0.004 0.044 0.000
#> GSM74244 3 0.1152 0.693 0.004 0.000 0.952 0.000 0.044 0.000
#> GSM74245 3 0.1152 0.693 0.004 0.000 0.952 0.000 0.044 0.000
#> GSM74246 3 0.1152 0.693 0.004 0.000 0.952 0.000 0.044 0.000
#> GSM74247 3 0.1152 0.693 0.004 0.000 0.952 0.000 0.044 0.000
#> GSM74248 3 0.1152 0.693 0.004 0.000 0.952 0.000 0.044 0.000
#> GSM74416 4 0.1141 0.882 0.052 0.000 0.000 0.948 0.000 0.000
#> GSM74417 4 0.1141 0.882 0.052 0.000 0.000 0.948 0.000 0.000
#> GSM74418 4 0.1141 0.882 0.052 0.000 0.000 0.948 0.000 0.000
#> GSM74419 4 0.1666 0.897 0.020 0.000 0.008 0.936 0.036 0.000
#> GSM121358 3 0.2275 0.713 0.000 0.096 0.888 0.008 0.008 0.000
#> GSM121359 3 0.1908 0.709 0.000 0.096 0.900 0.000 0.004 0.000
#> GSM121360 5 0.3826 0.943 0.012 0.000 0.236 0.016 0.736 0.000
#> GSM121362 5 0.3826 0.943 0.012 0.000 0.236 0.016 0.736 0.000
#> GSM121364 5 0.3826 0.943 0.012 0.000 0.236 0.016 0.736 0.000
#> GSM121365 3 0.2376 0.712 0.000 0.096 0.884 0.008 0.012 0.000
#> GSM121366 3 0.2275 0.713 0.000 0.096 0.888 0.008 0.008 0.000
#> GSM121367 3 0.2275 0.713 0.000 0.096 0.888 0.008 0.008 0.000
#> GSM121370 3 0.2605 0.710 0.000 0.092 0.876 0.012 0.020 0.000
#> GSM121371 3 0.2275 0.713 0.000 0.096 0.888 0.008 0.008 0.000
#> GSM121372 3 0.2100 0.704 0.000 0.112 0.884 0.000 0.004 0.000
#> GSM121373 5 0.3826 0.943 0.012 0.000 0.236 0.016 0.736 0.000
#> GSM121374 5 0.3813 0.938 0.008 0.000 0.236 0.020 0.736 0.000
#> GSM121407 3 0.3684 0.467 0.000 0.332 0.664 0.000 0.004 0.000
#> GSM74387 2 0.5423 0.602 0.004 0.644 0.236 0.004 0.024 0.088
#> GSM74388 2 0.3592 0.849 0.004 0.832 0.044 0.004 0.024 0.092
#> GSM74389 5 0.5892 0.462 0.016 0.000 0.424 0.128 0.432 0.000
#> GSM74390 3 0.6113 0.435 0.040 0.032 0.632 0.004 0.196 0.096
#> GSM74391 4 0.4615 0.736 0.056 0.000 0.124 0.748 0.072 0.000
#> GSM74392 3 0.5120 -0.475 0.012 0.000 0.476 0.052 0.460 0.000
#> GSM74393 3 0.5120 -0.475 0.012 0.000 0.476 0.052 0.460 0.000
#> GSM74394 2 0.3622 0.846 0.004 0.828 0.040 0.004 0.024 0.100
#> GSM74239 1 0.4116 0.754 0.776 0.000 0.000 0.084 0.020 0.120
#> GSM74364 1 0.3676 0.742 0.808 0.000 0.000 0.120 0.020 0.052
#> GSM74365 1 0.4570 0.480 0.596 0.000 0.000 0.012 0.024 0.368
#> GSM74366 6 0.0000 0.873 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM74367 1 0.3681 0.746 0.796 0.000 0.000 0.048 0.012 0.144
#> GSM74377 6 0.1075 0.868 0.048 0.000 0.000 0.000 0.000 0.952
#> GSM74378 6 0.0260 0.873 0.008 0.000 0.000 0.000 0.000 0.992
#> GSM74379 6 0.3996 -0.143 0.484 0.000 0.000 0.000 0.004 0.512
#> GSM74380 6 0.3499 0.603 0.264 0.000 0.000 0.004 0.004 0.728
#> GSM74381 6 0.1327 0.861 0.064 0.000 0.000 0.000 0.000 0.936
#> GSM121357 2 0.5405 0.401 0.004 0.568 0.344 0.004 0.012 0.068
#> GSM121361 2 0.3592 0.849 0.004 0.832 0.044 0.004 0.024 0.092
#> GSM121363 2 0.3592 0.849 0.004 0.832 0.044 0.004 0.024 0.092
#> GSM121368 2 0.3592 0.849 0.004 0.832 0.044 0.004 0.024 0.092
#> GSM121369 2 0.3592 0.849 0.004 0.832 0.044 0.004 0.024 0.092
#> GSM74368 1 0.7026 0.490 0.416 0.000 0.012 0.060 0.336 0.176
#> GSM74369 1 0.7026 0.490 0.416 0.000 0.012 0.060 0.336 0.176
#> GSM74370 1 0.6987 0.495 0.424 0.000 0.012 0.060 0.336 0.168
#> GSM74371 1 0.3729 0.516 0.692 0.000 0.000 0.296 0.012 0.000
#> GSM74372 1 0.4065 0.732 0.796 0.000 0.000 0.064 0.060 0.080
#> GSM74373 1 0.4087 0.610 0.688 0.000 0.000 0.000 0.036 0.276
#> GSM74374 1 0.3932 0.698 0.760 0.000 0.000 0.012 0.040 0.188
#> GSM74375 6 0.1440 0.870 0.032 0.004 0.000 0.004 0.012 0.948
#> GSM74376 6 0.3619 0.779 0.128 0.016 0.012 0.000 0.028 0.816
#> GSM74405 6 0.2664 0.797 0.136 0.000 0.000 0.000 0.016 0.848
#> GSM74351 4 0.2948 0.830 0.092 0.000 0.000 0.848 0.060 0.000
#> GSM74352 6 0.0458 0.875 0.016 0.000 0.000 0.000 0.000 0.984
#> GSM74353 4 0.6109 0.645 0.160 0.000 0.032 0.644 0.096 0.068
#> GSM74354 1 0.3642 0.682 0.744 0.000 0.000 0.012 0.008 0.236
#> GSM74355 6 0.0146 0.873 0.000 0.000 0.000 0.000 0.004 0.996
#> GSM74382 1 0.4062 0.477 0.640 0.000 0.000 0.344 0.004 0.012
#> GSM74383 1 0.4086 0.723 0.752 0.000 0.000 0.044 0.016 0.188
#> GSM74384 6 0.0000 0.873 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM74385 1 0.2437 0.723 0.888 0.000 0.000 0.072 0.036 0.004
#> GSM74386 1 0.5237 0.688 0.680 0.000 0.000 0.060 0.076 0.184
#> GSM74395 1 0.3627 0.755 0.808 0.000 0.000 0.092 0.008 0.092
#> GSM74396 1 0.3627 0.755 0.808 0.000 0.000 0.092 0.008 0.092
#> GSM74397 1 0.3655 0.753 0.800 0.000 0.000 0.108 0.004 0.088
#> GSM74398 6 0.1155 0.871 0.036 0.000 0.000 0.004 0.004 0.956
#> GSM74399 6 0.0603 0.875 0.016 0.000 0.000 0.000 0.004 0.980
#> GSM74400 6 0.2894 0.800 0.108 0.000 0.000 0.004 0.036 0.852
#> GSM74401 6 0.2894 0.800 0.108 0.000 0.000 0.004 0.036 0.852
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 91 3.47e-13 2
#> MAD:hclust 90 5.69e-24 3
#> MAD:hclust 110 2.57e-33 4
#> MAD:hclust 115 1.30e-33 5
#> MAD:hclust 106 2.64e-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", "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 21168 rows and 121 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 0.919 0.956 0.980 0.4998 0.499 0.499
#> 3 3 0.841 0.844 0.925 0.3347 0.691 0.458
#> 4 4 0.767 0.768 0.870 0.1197 0.906 0.725
#> 5 5 0.748 0.642 0.811 0.0541 0.898 0.656
#> 6 6 0.756 0.647 0.789 0.0380 0.899 0.607
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
#> GSM74356 2 0.3431 0.926 0.064 0.936
#> GSM74357 2 0.6343 0.835 0.160 0.840
#> GSM74358 2 0.6343 0.835 0.160 0.840
#> GSM74359 1 0.0000 0.984 1.000 0.000
#> GSM74360 1 0.0000 0.984 1.000 0.000
#> GSM74361 2 0.6148 0.844 0.152 0.848
#> GSM74362 2 0.6531 0.825 0.168 0.832
#> GSM74363 2 0.3274 0.930 0.060 0.940
#> GSM74402 1 0.0000 0.984 1.000 0.000
#> GSM74403 1 0.0000 0.984 1.000 0.000
#> GSM74404 1 0.0000 0.984 1.000 0.000
#> GSM74406 1 0.0000 0.984 1.000 0.000
#> GSM74407 1 0.0000 0.984 1.000 0.000
#> GSM74408 1 0.0000 0.984 1.000 0.000
#> GSM74409 1 0.0000 0.984 1.000 0.000
#> GSM74410 1 0.0000 0.984 1.000 0.000
#> GSM119936 1 0.0000 0.984 1.000 0.000
#> GSM119937 1 0.0000 0.984 1.000 0.000
#> GSM74411 2 0.0000 0.973 0.000 1.000
#> GSM74412 2 0.0000 0.973 0.000 1.000
#> GSM74413 2 0.0000 0.973 0.000 1.000
#> GSM74414 2 0.0000 0.973 0.000 1.000
#> GSM74415 2 0.0000 0.973 0.000 1.000
#> GSM121379 2 0.0000 0.973 0.000 1.000
#> GSM121380 2 0.0000 0.973 0.000 1.000
#> GSM121381 2 0.0000 0.973 0.000 1.000
#> GSM121382 2 0.0000 0.973 0.000 1.000
#> GSM121383 2 0.0000 0.973 0.000 1.000
#> GSM121384 2 0.0000 0.973 0.000 1.000
#> GSM121385 2 0.0000 0.973 0.000 1.000
#> GSM121386 2 0.0000 0.973 0.000 1.000
#> GSM121387 2 0.0000 0.973 0.000 1.000
#> GSM121388 2 0.0000 0.973 0.000 1.000
#> GSM121389 2 0.0000 0.973 0.000 1.000
#> GSM121390 2 0.0000 0.973 0.000 1.000
#> GSM121391 2 0.0000 0.973 0.000 1.000
#> GSM121392 2 0.0000 0.973 0.000 1.000
#> GSM121393 2 0.0000 0.973 0.000 1.000
#> GSM121394 2 0.0000 0.973 0.000 1.000
#> GSM121395 2 0.0000 0.973 0.000 1.000
#> GSM121396 2 0.0000 0.973 0.000 1.000
#> GSM121397 2 0.0000 0.973 0.000 1.000
#> GSM121398 2 0.0000 0.973 0.000 1.000
#> GSM121399 2 0.0000 0.973 0.000 1.000
#> GSM74240 2 0.7056 0.793 0.192 0.808
#> GSM74241 2 0.5408 0.872 0.124 0.876
#> GSM74242 1 0.9661 0.320 0.608 0.392
#> GSM74243 1 0.9522 0.376 0.628 0.372
#> GSM74244 2 0.2236 0.948 0.036 0.964
#> GSM74245 2 0.6247 0.839 0.156 0.844
#> GSM74246 2 0.0000 0.973 0.000 1.000
#> GSM74247 2 0.0000 0.973 0.000 1.000
#> GSM74248 2 0.7056 0.793 0.192 0.808
#> GSM74416 1 0.0000 0.984 1.000 0.000
#> GSM74417 1 0.0000 0.984 1.000 0.000
#> GSM74418 1 0.0000 0.984 1.000 0.000
#> GSM74419 1 0.0000 0.984 1.000 0.000
#> GSM121358 2 0.0000 0.973 0.000 1.000
#> GSM121359 2 0.0000 0.973 0.000 1.000
#> GSM121360 1 0.0000 0.984 1.000 0.000
#> GSM121362 1 0.0000 0.984 1.000 0.000
#> GSM121364 1 0.0000 0.984 1.000 0.000
#> GSM121365 2 0.0000 0.973 0.000 1.000
#> GSM121366 2 0.0000 0.973 0.000 1.000
#> GSM121367 2 0.0000 0.973 0.000 1.000
#> GSM121370 2 0.0000 0.973 0.000 1.000
#> GSM121371 2 0.0000 0.973 0.000 1.000
#> GSM121372 2 0.0000 0.973 0.000 1.000
#> GSM121373 1 0.0000 0.984 1.000 0.000
#> GSM121374 1 0.0000 0.984 1.000 0.000
#> GSM121407 2 0.0000 0.973 0.000 1.000
#> GSM74387 2 0.0000 0.973 0.000 1.000
#> GSM74388 2 0.0000 0.973 0.000 1.000
#> GSM74389 1 0.0000 0.984 1.000 0.000
#> GSM74390 1 0.0000 0.984 1.000 0.000
#> GSM74391 1 0.0000 0.984 1.000 0.000
#> GSM74392 1 0.0000 0.984 1.000 0.000
#> GSM74393 1 0.0000 0.984 1.000 0.000
#> GSM74394 2 0.0000 0.973 0.000 1.000
#> GSM74239 1 0.0000 0.984 1.000 0.000
#> GSM74364 1 0.0000 0.984 1.000 0.000
#> GSM74365 1 0.0000 0.984 1.000 0.000
#> GSM74366 1 0.2043 0.954 0.968 0.032
#> GSM74367 1 0.0000 0.984 1.000 0.000
#> GSM74377 1 0.0000 0.984 1.000 0.000
#> GSM74378 1 0.0376 0.981 0.996 0.004
#> GSM74379 1 0.0000 0.984 1.000 0.000
#> GSM74380 1 0.0000 0.984 1.000 0.000
#> GSM74381 1 0.0000 0.984 1.000 0.000
#> GSM121357 2 0.0000 0.973 0.000 1.000
#> GSM121361 2 0.0000 0.973 0.000 1.000
#> GSM121363 2 0.0000 0.973 0.000 1.000
#> GSM121368 2 0.0000 0.973 0.000 1.000
#> GSM121369 2 0.0000 0.973 0.000 1.000
#> GSM74368 1 0.0000 0.984 1.000 0.000
#> GSM74369 1 0.0000 0.984 1.000 0.000
#> GSM74370 1 0.0000 0.984 1.000 0.000
#> GSM74371 1 0.0000 0.984 1.000 0.000
#> GSM74372 1 0.0000 0.984 1.000 0.000
#> GSM74373 1 0.0000 0.984 1.000 0.000
#> GSM74374 1 0.0000 0.984 1.000 0.000
#> GSM74375 1 0.0000 0.984 1.000 0.000
#> GSM74376 1 0.0000 0.984 1.000 0.000
#> GSM74405 1 0.0000 0.984 1.000 0.000
#> GSM74351 1 0.0000 0.984 1.000 0.000
#> GSM74352 1 0.4022 0.904 0.920 0.080
#> GSM74353 1 0.0000 0.984 1.000 0.000
#> GSM74354 1 0.0000 0.984 1.000 0.000
#> GSM74355 1 0.0376 0.981 0.996 0.004
#> GSM74382 1 0.0000 0.984 1.000 0.000
#> GSM74383 1 0.0000 0.984 1.000 0.000
#> GSM74384 1 0.4298 0.895 0.912 0.088
#> GSM74385 1 0.0000 0.984 1.000 0.000
#> GSM74386 1 0.0000 0.984 1.000 0.000
#> GSM74395 1 0.0000 0.984 1.000 0.000
#> GSM74396 1 0.0000 0.984 1.000 0.000
#> GSM74397 1 0.0000 0.984 1.000 0.000
#> GSM74398 1 0.0000 0.984 1.000 0.000
#> GSM74399 1 0.0000 0.984 1.000 0.000
#> GSM74400 1 0.0000 0.984 1.000 0.000
#> GSM74401 1 0.0000 0.984 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM74356 3 0.0424 0.806 0.000 0.008 0.992
#> GSM74357 3 0.0424 0.806 0.000 0.008 0.992
#> GSM74358 3 0.0424 0.806 0.000 0.008 0.992
#> GSM74359 3 0.0747 0.809 0.016 0.000 0.984
#> GSM74360 3 0.2537 0.799 0.080 0.000 0.920
#> GSM74361 3 0.0424 0.806 0.000 0.008 0.992
#> GSM74362 3 0.0424 0.806 0.000 0.008 0.992
#> GSM74363 3 0.0424 0.806 0.000 0.008 0.992
#> GSM74402 3 0.6095 0.406 0.392 0.000 0.608
#> GSM74403 3 0.6260 0.273 0.448 0.000 0.552
#> GSM74404 3 0.6235 0.306 0.436 0.000 0.564
#> GSM74406 3 0.2537 0.799 0.080 0.000 0.920
#> GSM74407 3 0.5058 0.653 0.244 0.000 0.756
#> GSM74408 3 0.2537 0.799 0.080 0.000 0.920
#> GSM74409 3 0.2537 0.799 0.080 0.000 0.920
#> GSM74410 3 0.2448 0.801 0.076 0.000 0.924
#> GSM119936 3 0.2537 0.799 0.080 0.000 0.920
#> GSM119937 3 0.4654 0.695 0.208 0.000 0.792
#> GSM74411 2 0.2537 0.915 0.000 0.920 0.080
#> GSM74412 2 0.1031 0.959 0.000 0.976 0.024
#> GSM74413 2 0.2537 0.915 0.000 0.920 0.080
#> GSM74414 2 0.0000 0.969 0.000 1.000 0.000
#> GSM74415 2 0.6235 0.190 0.000 0.564 0.436
#> GSM121379 2 0.0000 0.969 0.000 1.000 0.000
#> GSM121380 2 0.0000 0.969 0.000 1.000 0.000
#> GSM121381 2 0.0000 0.969 0.000 1.000 0.000
#> GSM121382 2 0.0000 0.969 0.000 1.000 0.000
#> GSM121383 2 0.0000 0.969 0.000 1.000 0.000
#> GSM121384 2 0.0000 0.969 0.000 1.000 0.000
#> GSM121385 2 0.0000 0.969 0.000 1.000 0.000
#> GSM121386 2 0.0000 0.969 0.000 1.000 0.000
#> GSM121387 2 0.0000 0.969 0.000 1.000 0.000
#> GSM121388 2 0.0000 0.969 0.000 1.000 0.000
#> GSM121389 2 0.0000 0.969 0.000 1.000 0.000
#> GSM121390 2 0.0000 0.969 0.000 1.000 0.000
#> GSM121391 2 0.0000 0.969 0.000 1.000 0.000
#> GSM121392 2 0.0424 0.964 0.008 0.992 0.000
#> GSM121393 2 0.0000 0.969 0.000 1.000 0.000
#> GSM121394 2 0.0000 0.969 0.000 1.000 0.000
#> GSM121395 2 0.0000 0.969 0.000 1.000 0.000
#> GSM121396 2 0.0747 0.963 0.000 0.984 0.016
#> GSM121397 2 0.0000 0.969 0.000 1.000 0.000
#> GSM121398 2 0.0000 0.969 0.000 1.000 0.000
#> GSM121399 2 0.0000 0.969 0.000 1.000 0.000
#> GSM74240 3 0.0424 0.806 0.000 0.008 0.992
#> GSM74241 3 0.4796 0.631 0.000 0.220 0.780
#> GSM74242 3 0.0424 0.806 0.000 0.008 0.992
#> GSM74243 3 0.0424 0.806 0.000 0.008 0.992
#> GSM74244 3 0.3340 0.734 0.000 0.120 0.880
#> GSM74245 3 0.0424 0.806 0.000 0.008 0.992
#> GSM74246 3 0.6180 0.284 0.000 0.416 0.584
#> GSM74247 3 0.6267 0.176 0.000 0.452 0.548
#> GSM74248 3 0.0424 0.806 0.000 0.008 0.992
#> GSM74416 3 0.6225 0.316 0.432 0.000 0.568
#> GSM74417 3 0.6225 0.316 0.432 0.000 0.568
#> GSM74418 3 0.6244 0.295 0.440 0.000 0.560
#> GSM74419 3 0.2537 0.799 0.080 0.000 0.920
#> GSM121358 3 0.6026 0.385 0.000 0.376 0.624
#> GSM121359 2 0.2537 0.915 0.000 0.920 0.080
#> GSM121360 3 0.3340 0.777 0.120 0.000 0.880
#> GSM121362 3 0.4842 0.682 0.224 0.000 0.776
#> GSM121364 3 0.1031 0.810 0.024 0.000 0.976
#> GSM121365 3 0.6026 0.385 0.000 0.376 0.624
#> GSM121366 3 0.6026 0.385 0.000 0.376 0.624
#> GSM121367 3 0.6026 0.385 0.000 0.376 0.624
#> GSM121370 3 0.6026 0.385 0.000 0.376 0.624
#> GSM121371 3 0.6026 0.385 0.000 0.376 0.624
#> GSM121372 2 0.2537 0.915 0.000 0.920 0.080
#> GSM121373 3 0.2448 0.800 0.076 0.000 0.924
#> GSM121374 3 0.1031 0.810 0.024 0.000 0.976
#> GSM121407 2 0.0592 0.965 0.000 0.988 0.012
#> GSM74387 2 0.2680 0.923 0.008 0.924 0.068
#> GSM74388 2 0.0892 0.958 0.020 0.980 0.000
#> GSM74389 3 0.0592 0.809 0.012 0.000 0.988
#> GSM74390 1 0.0000 0.993 1.000 0.000 0.000
#> GSM74391 3 0.2537 0.799 0.080 0.000 0.920
#> GSM74392 3 0.0892 0.810 0.020 0.000 0.980
#> GSM74393 3 0.0237 0.807 0.004 0.000 0.996
#> GSM74394 2 0.1315 0.959 0.020 0.972 0.008
#> GSM74239 1 0.0424 0.993 0.992 0.000 0.008
#> GSM74364 1 0.0424 0.993 0.992 0.000 0.008
#> GSM74365 1 0.0000 0.993 1.000 0.000 0.000
#> GSM74366 1 0.0237 0.990 0.996 0.004 0.000
#> GSM74367 1 0.0424 0.993 0.992 0.000 0.008
#> GSM74377 1 0.0000 0.993 1.000 0.000 0.000
#> GSM74378 1 0.0237 0.990 0.996 0.004 0.000
#> GSM74379 1 0.0000 0.993 1.000 0.000 0.000
#> GSM74380 1 0.0000 0.993 1.000 0.000 0.000
#> GSM74381 1 0.0000 0.993 1.000 0.000 0.000
#> GSM121357 2 0.0424 0.967 0.000 0.992 0.008
#> GSM121361 2 0.1453 0.956 0.024 0.968 0.008
#> GSM121363 2 0.1315 0.959 0.020 0.972 0.008
#> GSM121368 2 0.1315 0.959 0.020 0.972 0.008
#> GSM121369 2 0.1774 0.954 0.024 0.960 0.016
#> GSM74368 1 0.0424 0.993 0.992 0.000 0.008
#> GSM74369 1 0.0424 0.993 0.992 0.000 0.008
#> GSM74370 1 0.0424 0.993 0.992 0.000 0.008
#> GSM74371 1 0.0424 0.993 0.992 0.000 0.008
#> GSM74372 1 0.0424 0.993 0.992 0.000 0.008
#> GSM74373 1 0.0000 0.993 1.000 0.000 0.000
#> GSM74374 1 0.0424 0.993 0.992 0.000 0.008
#> GSM74375 1 0.0000 0.993 1.000 0.000 0.000
#> GSM74376 1 0.0000 0.993 1.000 0.000 0.000
#> GSM74405 1 0.0000 0.993 1.000 0.000 0.000
#> GSM74351 1 0.1163 0.973 0.972 0.000 0.028
#> GSM74352 1 0.0424 0.986 0.992 0.008 0.000
#> GSM74353 1 0.0424 0.993 0.992 0.000 0.008
#> GSM74354 1 0.0424 0.993 0.992 0.000 0.008
#> GSM74355 1 0.0237 0.990 0.996 0.004 0.000
#> GSM74382 1 0.1753 0.950 0.952 0.000 0.048
#> GSM74383 1 0.0424 0.993 0.992 0.000 0.008
#> GSM74384 1 0.0424 0.986 0.992 0.008 0.000
#> GSM74385 1 0.0424 0.993 0.992 0.000 0.008
#> GSM74386 1 0.0424 0.993 0.992 0.000 0.008
#> GSM74395 1 0.0424 0.993 0.992 0.000 0.008
#> GSM74396 1 0.0424 0.993 0.992 0.000 0.008
#> GSM74397 1 0.0424 0.993 0.992 0.000 0.008
#> GSM74398 1 0.0000 0.993 1.000 0.000 0.000
#> GSM74399 1 0.0000 0.993 1.000 0.000 0.000
#> GSM74400 1 0.0000 0.993 1.000 0.000 0.000
#> GSM74401 1 0.0000 0.993 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM74356 3 0.5028 0.827 0.000 0.400 0.596 0.004
#> GSM74357 3 0.5028 0.827 0.000 0.400 0.596 0.004
#> GSM74358 3 0.5028 0.827 0.000 0.400 0.596 0.004
#> GSM74359 3 0.0336 0.337 0.000 0.000 0.992 0.008
#> GSM74360 3 0.4877 -0.777 0.000 0.000 0.592 0.408
#> GSM74361 3 0.4855 0.828 0.000 0.400 0.600 0.000
#> GSM74362 3 0.4855 0.828 0.000 0.400 0.600 0.000
#> GSM74363 3 0.5039 0.828 0.000 0.404 0.592 0.004
#> GSM74402 4 0.5500 0.938 0.016 0.000 0.464 0.520
#> GSM74403 4 0.5764 0.935 0.028 0.000 0.452 0.520
#> GSM74404 4 0.5764 0.935 0.028 0.000 0.452 0.520
#> GSM74406 4 0.4998 0.931 0.000 0.000 0.488 0.512
#> GSM74407 4 0.5594 0.938 0.020 0.000 0.460 0.520
#> GSM74408 4 0.4998 0.931 0.000 0.000 0.488 0.512
#> GSM74409 4 0.4998 0.931 0.000 0.000 0.488 0.512
#> GSM74410 4 0.4998 0.931 0.000 0.000 0.488 0.512
#> GSM119936 4 0.4998 0.931 0.000 0.000 0.488 0.512
#> GSM119937 4 0.4994 0.934 0.000 0.000 0.480 0.520
#> GSM74411 2 0.1191 0.342 0.004 0.968 0.024 0.004
#> GSM74412 2 0.2197 0.523 0.004 0.916 0.000 0.080
#> GSM74413 2 0.1191 0.342 0.004 0.968 0.024 0.004
#> GSM74414 2 0.5088 0.837 0.004 0.572 0.000 0.424
#> GSM74415 3 0.5328 0.805 0.004 0.472 0.520 0.004
#> GSM121379 2 0.4972 0.849 0.000 0.544 0.000 0.456
#> GSM121380 2 0.4972 0.849 0.000 0.544 0.000 0.456
#> GSM121381 2 0.4972 0.849 0.000 0.544 0.000 0.456
#> GSM121382 2 0.4972 0.849 0.000 0.544 0.000 0.456
#> GSM121383 2 0.4972 0.849 0.000 0.544 0.000 0.456
#> GSM121384 2 0.4972 0.849 0.000 0.544 0.000 0.456
#> GSM121385 2 0.4972 0.849 0.000 0.544 0.000 0.456
#> GSM121386 2 0.4972 0.849 0.000 0.544 0.000 0.456
#> GSM121387 2 0.4972 0.849 0.000 0.544 0.000 0.456
#> GSM121388 2 0.4972 0.849 0.000 0.544 0.000 0.456
#> GSM121389 2 0.4972 0.849 0.000 0.544 0.000 0.456
#> GSM121390 2 0.4977 0.847 0.000 0.540 0.000 0.460
#> GSM121391 2 0.4972 0.849 0.000 0.544 0.000 0.456
#> GSM121392 2 0.4977 0.847 0.000 0.540 0.000 0.460
#> GSM121393 2 0.4972 0.849 0.000 0.544 0.000 0.456
#> GSM121394 2 0.4972 0.849 0.000 0.544 0.000 0.456
#> GSM121395 2 0.4972 0.849 0.000 0.544 0.000 0.456
#> GSM121396 2 0.2973 0.588 0.000 0.856 0.000 0.144
#> GSM121397 2 0.4972 0.849 0.000 0.544 0.000 0.456
#> GSM121398 2 0.4972 0.849 0.000 0.544 0.000 0.456
#> GSM121399 2 0.4972 0.849 0.000 0.544 0.000 0.456
#> GSM74240 3 0.5290 0.823 0.004 0.440 0.552 0.004
#> GSM74241 3 0.5308 0.818 0.004 0.452 0.540 0.004
#> GSM74242 3 0.4855 0.826 0.000 0.400 0.600 0.000
#> GSM74243 3 0.4855 0.826 0.000 0.400 0.600 0.000
#> GSM74244 3 0.5297 0.822 0.004 0.444 0.548 0.004
#> GSM74245 3 0.5284 0.824 0.004 0.436 0.556 0.004
#> GSM74246 3 0.5666 0.806 0.004 0.460 0.520 0.016
#> GSM74247 3 0.5666 0.806 0.004 0.460 0.520 0.016
#> GSM74248 3 0.5284 0.824 0.004 0.436 0.556 0.004
#> GSM74416 4 0.5682 0.937 0.024 0.000 0.456 0.520
#> GSM74417 4 0.5682 0.937 0.024 0.000 0.456 0.520
#> GSM74418 4 0.5682 0.937 0.024 0.000 0.456 0.520
#> GSM74419 4 0.4998 0.931 0.000 0.000 0.488 0.512
#> GSM121358 3 0.4941 0.827 0.000 0.436 0.564 0.000
#> GSM121359 2 0.2521 0.286 0.000 0.912 0.064 0.024
#> GSM121360 3 0.4359 0.269 0.164 0.016 0.804 0.016
#> GSM121362 3 0.4012 0.223 0.204 0.004 0.788 0.004
#> GSM121364 3 0.0469 0.328 0.000 0.000 0.988 0.012
#> GSM121365 3 0.4941 0.827 0.000 0.436 0.564 0.000
#> GSM121366 3 0.4941 0.827 0.000 0.436 0.564 0.000
#> GSM121367 3 0.4941 0.827 0.000 0.436 0.564 0.000
#> GSM121370 3 0.4941 0.827 0.000 0.436 0.564 0.000
#> GSM121371 3 0.4941 0.827 0.000 0.436 0.564 0.000
#> GSM121372 2 0.1629 0.374 0.000 0.952 0.024 0.024
#> GSM121373 3 0.0336 0.337 0.000 0.000 0.992 0.008
#> GSM121374 3 0.0336 0.337 0.000 0.000 0.992 0.008
#> GSM121407 2 0.4522 0.783 0.000 0.680 0.000 0.320
#> GSM74387 2 0.5385 0.140 0.140 0.772 0.056 0.032
#> GSM74388 2 0.7969 0.677 0.252 0.384 0.004 0.360
#> GSM74389 3 0.4222 0.733 0.000 0.272 0.728 0.000
#> GSM74390 1 0.0712 0.870 0.984 0.008 0.004 0.004
#> GSM74391 4 0.5328 0.936 0.004 0.004 0.472 0.520
#> GSM74392 3 0.0921 0.279 0.000 0.000 0.972 0.028
#> GSM74393 3 0.4936 0.818 0.004 0.372 0.624 0.000
#> GSM74394 2 0.7967 0.675 0.252 0.388 0.004 0.356
#> GSM74239 1 0.4798 0.809 0.768 0.000 0.052 0.180
#> GSM74364 1 0.5132 0.784 0.748 0.000 0.068 0.184
#> GSM74365 1 0.2593 0.881 0.892 0.000 0.004 0.104
#> GSM74366 1 0.0524 0.872 0.988 0.004 0.000 0.008
#> GSM74367 1 0.4244 0.841 0.800 0.000 0.032 0.168
#> GSM74377 1 0.0000 0.878 1.000 0.000 0.000 0.000
#> GSM74378 1 0.0336 0.875 0.992 0.000 0.000 0.008
#> GSM74379 1 0.0188 0.878 0.996 0.000 0.004 0.000
#> GSM74380 1 0.0000 0.878 1.000 0.000 0.000 0.000
#> GSM74381 1 0.0000 0.878 1.000 0.000 0.000 0.000
#> GSM121357 2 0.4925 0.841 0.000 0.572 0.000 0.428
#> GSM121361 2 0.7969 0.677 0.252 0.384 0.004 0.360
#> GSM121363 2 0.7969 0.677 0.252 0.384 0.004 0.360
#> GSM121368 2 0.7969 0.677 0.252 0.384 0.004 0.360
#> GSM121369 2 0.7978 0.663 0.260 0.396 0.004 0.340
#> GSM74368 1 0.3501 0.872 0.848 0.000 0.020 0.132
#> GSM74369 1 0.3501 0.872 0.848 0.000 0.020 0.132
#> GSM74370 1 0.3501 0.872 0.848 0.000 0.020 0.132
#> GSM74371 4 0.6837 0.152 0.392 0.000 0.104 0.504
#> GSM74372 1 0.3812 0.864 0.832 0.000 0.028 0.140
#> GSM74373 1 0.0000 0.878 1.000 0.000 0.000 0.000
#> GSM74374 1 0.3390 0.873 0.852 0.000 0.016 0.132
#> GSM74375 1 0.0469 0.880 0.988 0.000 0.000 0.012
#> GSM74376 1 0.0376 0.874 0.992 0.004 0.000 0.004
#> GSM74405 1 0.0000 0.878 1.000 0.000 0.000 0.000
#> GSM74351 4 0.6600 0.878 0.084 0.000 0.396 0.520
#> GSM74352 1 0.0376 0.874 0.992 0.004 0.000 0.004
#> GSM74353 1 0.4466 0.826 0.784 0.000 0.036 0.180
#> GSM74354 1 0.3390 0.873 0.852 0.000 0.016 0.132
#> GSM74355 1 0.0188 0.876 0.996 0.000 0.000 0.004
#> GSM74382 4 0.6458 0.891 0.072 0.000 0.408 0.520
#> GSM74383 1 0.4152 0.848 0.808 0.000 0.032 0.160
#> GSM74384 1 0.0524 0.872 0.988 0.004 0.000 0.008
#> GSM74385 1 0.5142 0.780 0.744 0.000 0.064 0.192
#> GSM74386 1 0.4244 0.845 0.804 0.000 0.036 0.160
#> GSM74395 1 0.5021 0.794 0.756 0.000 0.064 0.180
#> GSM74396 1 0.3863 0.862 0.828 0.000 0.028 0.144
#> GSM74397 1 0.5932 0.682 0.680 0.000 0.096 0.224
#> GSM74398 1 0.0188 0.878 0.996 0.000 0.000 0.004
#> GSM74399 1 0.0000 0.878 1.000 0.000 0.000 0.000
#> GSM74400 1 0.2799 0.880 0.884 0.000 0.008 0.108
#> GSM74401 1 0.2799 0.880 0.884 0.000 0.008 0.108
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM74356 3 0.1195 0.7476 0.000 0.000 0.960 0.028 0.012
#> GSM74357 3 0.1444 0.7425 0.000 0.000 0.948 0.040 0.012
#> GSM74358 3 0.1444 0.7425 0.000 0.000 0.948 0.040 0.012
#> GSM74359 4 0.6615 0.1853 0.000 0.000 0.324 0.444 0.232
#> GSM74360 4 0.5594 0.4432 0.000 0.000 0.136 0.632 0.232
#> GSM74361 3 0.1997 0.7418 0.000 0.000 0.924 0.040 0.036
#> GSM74362 3 0.4732 0.5151 0.000 0.000 0.716 0.076 0.208
#> GSM74363 3 0.1106 0.7491 0.000 0.000 0.964 0.024 0.012
#> GSM74402 4 0.1205 0.7732 0.040 0.000 0.004 0.956 0.000
#> GSM74403 4 0.1571 0.7689 0.060 0.000 0.004 0.936 0.000
#> GSM74404 4 0.1571 0.7689 0.060 0.000 0.004 0.936 0.000
#> GSM74406 4 0.0566 0.7703 0.012 0.000 0.004 0.984 0.000
#> GSM74407 4 0.1571 0.7689 0.060 0.000 0.004 0.936 0.000
#> GSM74408 4 0.0693 0.7596 0.000 0.000 0.008 0.980 0.012
#> GSM74409 4 0.0798 0.7573 0.000 0.000 0.008 0.976 0.016
#> GSM74410 4 0.0798 0.7573 0.000 0.000 0.008 0.976 0.016
#> GSM119936 4 0.0960 0.7706 0.016 0.000 0.004 0.972 0.008
#> GSM119937 4 0.1717 0.7712 0.052 0.000 0.004 0.936 0.008
#> GSM74411 3 0.6071 0.4445 0.000 0.236 0.572 0.000 0.192
#> GSM74412 3 0.6211 0.3969 0.000 0.264 0.544 0.000 0.192
#> GSM74413 3 0.6071 0.4445 0.000 0.236 0.572 0.000 0.192
#> GSM74414 2 0.2843 0.6862 0.000 0.848 0.008 0.000 0.144
#> GSM74415 3 0.3421 0.7150 0.000 0.008 0.788 0.000 0.204
#> GSM121379 2 0.0000 0.8274 0.000 1.000 0.000 0.000 0.000
#> GSM121380 2 0.0000 0.8274 0.000 1.000 0.000 0.000 0.000
#> GSM121381 2 0.0000 0.8274 0.000 1.000 0.000 0.000 0.000
#> GSM121382 2 0.0000 0.8274 0.000 1.000 0.000 0.000 0.000
#> GSM121383 2 0.0000 0.8274 0.000 1.000 0.000 0.000 0.000
#> GSM121384 2 0.0000 0.8274 0.000 1.000 0.000 0.000 0.000
#> GSM121385 2 0.0000 0.8274 0.000 1.000 0.000 0.000 0.000
#> GSM121386 2 0.0000 0.8274 0.000 1.000 0.000 0.000 0.000
#> GSM121387 2 0.0162 0.8257 0.000 0.996 0.000 0.000 0.004
#> GSM121388 2 0.0955 0.8132 0.000 0.968 0.000 0.004 0.028
#> GSM121389 2 0.0865 0.8141 0.000 0.972 0.000 0.004 0.024
#> GSM121390 2 0.0000 0.8274 0.000 1.000 0.000 0.000 0.000
#> GSM121391 2 0.0000 0.8274 0.000 1.000 0.000 0.000 0.000
#> GSM121392 2 0.0324 0.8238 0.000 0.992 0.000 0.004 0.004
#> GSM121393 2 0.0865 0.8141 0.000 0.972 0.000 0.004 0.024
#> GSM121394 2 0.0000 0.8274 0.000 1.000 0.000 0.000 0.000
#> GSM121395 2 0.0865 0.8141 0.000 0.972 0.000 0.004 0.024
#> GSM121396 2 0.5401 -0.0310 0.000 0.492 0.452 0.000 0.056
#> GSM121397 2 0.0000 0.8274 0.000 1.000 0.000 0.000 0.000
#> GSM121398 2 0.0000 0.8274 0.000 1.000 0.000 0.000 0.000
#> GSM121399 2 0.0000 0.8274 0.000 1.000 0.000 0.000 0.000
#> GSM74240 3 0.3010 0.7410 0.000 0.000 0.824 0.004 0.172
#> GSM74241 3 0.3109 0.7300 0.000 0.000 0.800 0.000 0.200
#> GSM74242 3 0.3714 0.7279 0.000 0.000 0.812 0.056 0.132
#> GSM74243 3 0.3714 0.7279 0.000 0.000 0.812 0.056 0.132
#> GSM74244 3 0.2732 0.7425 0.000 0.000 0.840 0.000 0.160
#> GSM74245 3 0.2732 0.7425 0.000 0.000 0.840 0.000 0.160
#> GSM74246 3 0.3395 0.7100 0.000 0.000 0.764 0.000 0.236
#> GSM74247 3 0.3395 0.7100 0.000 0.000 0.764 0.000 0.236
#> GSM74248 3 0.3010 0.7410 0.000 0.000 0.824 0.004 0.172
#> GSM74416 4 0.1788 0.7697 0.056 0.000 0.004 0.932 0.008
#> GSM74417 4 0.1788 0.7697 0.056 0.000 0.004 0.932 0.008
#> GSM74418 4 0.1788 0.7697 0.056 0.000 0.004 0.932 0.008
#> GSM74419 4 0.0671 0.7715 0.016 0.000 0.004 0.980 0.000
#> GSM121358 3 0.0867 0.7569 0.000 0.008 0.976 0.008 0.008
#> GSM121359 3 0.4983 0.4494 0.000 0.272 0.664 0.000 0.064
#> GSM121360 5 0.7071 0.1744 0.036 0.000 0.256 0.204 0.504
#> GSM121362 5 0.7945 0.0948 0.092 0.000 0.284 0.224 0.400
#> GSM121364 4 0.6598 0.1977 0.000 0.000 0.316 0.452 0.232
#> GSM121365 3 0.0867 0.7569 0.000 0.008 0.976 0.008 0.008
#> GSM121366 3 0.0981 0.7570 0.000 0.008 0.972 0.008 0.012
#> GSM121367 3 0.0867 0.7569 0.000 0.008 0.976 0.008 0.008
#> GSM121370 3 0.0981 0.7570 0.000 0.008 0.972 0.008 0.012
#> GSM121371 3 0.0867 0.7569 0.000 0.008 0.976 0.008 0.008
#> GSM121372 3 0.5117 0.4360 0.000 0.276 0.652 0.000 0.072
#> GSM121373 4 0.6631 0.1771 0.000 0.000 0.324 0.440 0.236
#> GSM121374 4 0.6615 0.1853 0.000 0.000 0.324 0.444 0.232
#> GSM121407 2 0.5562 0.1381 0.000 0.520 0.408 0.000 0.072
#> GSM74387 3 0.6710 0.1873 0.012 0.164 0.424 0.000 0.400
#> GSM74388 2 0.5680 -0.0490 0.052 0.508 0.012 0.000 0.428
#> GSM74389 3 0.6329 0.2229 0.000 0.000 0.528 0.232 0.240
#> GSM74390 1 0.3398 0.6966 0.780 0.000 0.004 0.000 0.216
#> GSM74391 4 0.1116 0.7728 0.028 0.000 0.004 0.964 0.004
#> GSM74392 4 0.6573 0.1992 0.000 0.000 0.320 0.456 0.224
#> GSM74393 3 0.5740 0.4107 0.000 0.000 0.600 0.128 0.272
#> GSM74394 5 0.5674 -0.0876 0.044 0.464 0.016 0.000 0.476
#> GSM74239 1 0.2409 0.7836 0.900 0.000 0.000 0.068 0.032
#> GSM74364 1 0.2632 0.7782 0.888 0.000 0.000 0.072 0.040
#> GSM74365 1 0.1012 0.8031 0.968 0.000 0.000 0.012 0.020
#> GSM74366 1 0.4138 0.6652 0.616 0.000 0.000 0.000 0.384
#> GSM74367 1 0.2067 0.7934 0.920 0.000 0.000 0.048 0.032
#> GSM74377 1 0.3730 0.7347 0.712 0.000 0.000 0.000 0.288
#> GSM74378 1 0.4138 0.6652 0.616 0.000 0.000 0.000 0.384
#> GSM74379 1 0.2929 0.7770 0.820 0.000 0.000 0.000 0.180
#> GSM74380 1 0.3143 0.7710 0.796 0.000 0.000 0.000 0.204
#> GSM74381 1 0.3913 0.7136 0.676 0.000 0.000 0.000 0.324
#> GSM121357 2 0.3203 0.6529 0.000 0.820 0.012 0.000 0.168
#> GSM121361 2 0.5680 -0.0490 0.052 0.508 0.012 0.000 0.428
#> GSM121363 2 0.5680 -0.0490 0.052 0.508 0.012 0.000 0.428
#> GSM121368 2 0.5680 -0.0490 0.052 0.508 0.012 0.000 0.428
#> GSM121369 5 0.5869 -0.0772 0.052 0.460 0.020 0.000 0.468
#> GSM74368 1 0.1408 0.8020 0.948 0.000 0.000 0.044 0.008
#> GSM74369 1 0.1408 0.8020 0.948 0.000 0.000 0.044 0.008
#> GSM74370 1 0.1124 0.8025 0.960 0.000 0.000 0.036 0.004
#> GSM74371 1 0.5065 0.1442 0.544 0.000 0.000 0.420 0.036
#> GSM74372 1 0.1270 0.8005 0.948 0.000 0.000 0.052 0.000
#> GSM74373 1 0.3857 0.7211 0.688 0.000 0.000 0.000 0.312
#> GSM74374 1 0.0794 0.8030 0.972 0.000 0.000 0.028 0.000
#> GSM74375 1 0.3210 0.7692 0.788 0.000 0.000 0.000 0.212
#> GSM74376 1 0.4101 0.6761 0.628 0.000 0.000 0.000 0.372
#> GSM74405 1 0.3913 0.7136 0.676 0.000 0.000 0.000 0.324
#> GSM74351 4 0.3727 0.6084 0.216 0.000 0.000 0.768 0.016
#> GSM74352 1 0.4114 0.6726 0.624 0.000 0.000 0.000 0.376
#> GSM74353 1 0.1502 0.7978 0.940 0.000 0.000 0.056 0.004
#> GSM74354 1 0.1836 0.7969 0.932 0.000 0.000 0.036 0.032
#> GSM74355 1 0.4114 0.6726 0.624 0.000 0.000 0.000 0.376
#> GSM74382 4 0.3772 0.6421 0.172 0.000 0.000 0.792 0.036
#> GSM74383 1 0.2067 0.7934 0.920 0.000 0.000 0.048 0.032
#> GSM74384 1 0.4161 0.6574 0.608 0.000 0.000 0.000 0.392
#> GSM74385 1 0.2694 0.7755 0.884 0.000 0.000 0.076 0.040
#> GSM74386 1 0.2139 0.7918 0.916 0.000 0.000 0.052 0.032
#> GSM74395 1 0.2473 0.7816 0.896 0.000 0.000 0.072 0.032
#> GSM74396 1 0.2067 0.7937 0.920 0.000 0.000 0.048 0.032
#> GSM74397 1 0.2984 0.7543 0.860 0.000 0.000 0.108 0.032
#> GSM74398 1 0.3109 0.7724 0.800 0.000 0.000 0.000 0.200
#> GSM74399 1 0.3480 0.7541 0.752 0.000 0.000 0.000 0.248
#> GSM74400 1 0.2909 0.7976 0.848 0.000 0.000 0.012 0.140
#> GSM74401 1 0.2953 0.7972 0.844 0.000 0.000 0.012 0.144
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM74356 3 0.1588 0.6511 0.000 0.000 0.924 0.004 0.072 0.000
#> GSM74357 3 0.1588 0.6497 0.000 0.000 0.924 0.004 0.072 0.000
#> GSM74358 3 0.1531 0.6525 0.000 0.000 0.928 0.004 0.068 0.000
#> GSM74359 5 0.5627 0.8170 0.000 0.000 0.164 0.288 0.544 0.004
#> GSM74360 5 0.5218 0.6972 0.000 0.000 0.088 0.364 0.544 0.004
#> GSM74361 3 0.3082 0.6140 0.000 0.000 0.828 0.008 0.144 0.020
#> GSM74362 3 0.4653 -0.2762 0.000 0.000 0.492 0.020 0.476 0.012
#> GSM74363 3 0.0777 0.6758 0.000 0.000 0.972 0.004 0.024 0.000
#> GSM74402 4 0.0713 0.9251 0.028 0.000 0.000 0.972 0.000 0.000
#> GSM74403 4 0.1116 0.9239 0.028 0.000 0.000 0.960 0.008 0.004
#> GSM74404 4 0.1116 0.9239 0.028 0.000 0.000 0.960 0.008 0.004
#> GSM74406 4 0.0806 0.9168 0.008 0.000 0.000 0.972 0.020 0.000
#> GSM74407 4 0.1116 0.9239 0.028 0.000 0.000 0.960 0.008 0.004
#> GSM74408 4 0.1036 0.9109 0.004 0.000 0.000 0.964 0.024 0.008
#> GSM74409 4 0.1036 0.9109 0.004 0.000 0.000 0.964 0.024 0.008
#> GSM74410 4 0.1036 0.9057 0.000 0.000 0.004 0.964 0.024 0.008
#> GSM119936 4 0.1149 0.9141 0.008 0.000 0.000 0.960 0.024 0.008
#> GSM119937 4 0.1700 0.9174 0.028 0.000 0.000 0.936 0.024 0.012
#> GSM74411 3 0.6511 0.5813 0.000 0.112 0.564 0.004 0.200 0.120
#> GSM74412 3 0.6680 0.5627 0.000 0.132 0.544 0.004 0.200 0.120
#> GSM74413 3 0.6511 0.5813 0.000 0.112 0.564 0.004 0.200 0.120
#> GSM74414 2 0.5038 0.5895 0.000 0.684 0.016 0.004 0.108 0.188
#> GSM74415 3 0.4994 0.6314 0.000 0.000 0.648 0.004 0.228 0.120
#> GSM121379 2 0.0000 0.9550 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121380 2 0.0000 0.9550 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121381 2 0.0000 0.9550 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121382 2 0.0146 0.9537 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM121383 2 0.0146 0.9537 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM121384 2 0.0000 0.9550 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121385 2 0.0000 0.9550 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121386 2 0.0000 0.9550 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121387 2 0.0146 0.9537 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM121388 2 0.1798 0.9136 0.000 0.932 0.020 0.000 0.020 0.028
#> GSM121389 2 0.0725 0.9436 0.000 0.976 0.000 0.000 0.012 0.012
#> GSM121390 2 0.0000 0.9550 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121391 2 0.0000 0.9550 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121392 2 0.0000 0.9550 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121393 2 0.1176 0.9317 0.000 0.956 0.000 0.000 0.020 0.024
#> GSM121394 2 0.0000 0.9550 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121395 2 0.0909 0.9393 0.000 0.968 0.000 0.000 0.012 0.020
#> GSM121396 3 0.5069 0.3312 0.000 0.396 0.544 0.000 0.032 0.028
#> GSM121397 2 0.0000 0.9550 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121398 2 0.0000 0.9550 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121399 2 0.0000 0.9550 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM74240 3 0.5042 0.5700 0.000 0.000 0.592 0.000 0.308 0.100
#> GSM74241 3 0.5116 0.6162 0.000 0.000 0.612 0.000 0.256 0.132
#> GSM74242 3 0.5220 0.5341 0.000 0.000 0.600 0.024 0.312 0.064
#> GSM74243 3 0.5220 0.5341 0.000 0.000 0.600 0.024 0.312 0.064
#> GSM74244 3 0.4792 0.6082 0.000 0.000 0.644 0.000 0.260 0.096
#> GSM74245 3 0.4812 0.6057 0.000 0.000 0.640 0.000 0.264 0.096
#> GSM74246 3 0.5366 0.5985 0.000 0.000 0.568 0.000 0.284 0.148
#> GSM74247 3 0.5366 0.5985 0.000 0.000 0.568 0.000 0.284 0.148
#> GSM74248 3 0.4986 0.5692 0.000 0.000 0.600 0.000 0.304 0.096
#> GSM74416 4 0.1332 0.9224 0.028 0.000 0.000 0.952 0.008 0.012
#> GSM74417 4 0.1332 0.9224 0.028 0.000 0.000 0.952 0.008 0.012
#> GSM74418 4 0.1332 0.9224 0.028 0.000 0.000 0.952 0.008 0.012
#> GSM74419 4 0.0520 0.9212 0.008 0.000 0.000 0.984 0.008 0.000
#> GSM121358 3 0.0146 0.6854 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM121359 3 0.3400 0.6243 0.000 0.132 0.816 0.000 0.044 0.008
#> GSM121360 5 0.5773 0.6619 0.008 0.000 0.120 0.092 0.664 0.116
#> GSM121362 5 0.6751 0.7178 0.060 0.000 0.160 0.120 0.592 0.068
#> GSM121364 5 0.5627 0.8170 0.000 0.000 0.164 0.288 0.544 0.004
#> GSM121365 3 0.0146 0.6854 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM121366 3 0.0146 0.6854 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM121367 3 0.0146 0.6854 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM121370 3 0.0146 0.6854 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM121371 3 0.0146 0.6854 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM121372 3 0.3527 0.6213 0.000 0.132 0.808 0.000 0.052 0.008
#> GSM121373 5 0.5613 0.8171 0.000 0.000 0.164 0.284 0.548 0.004
#> GSM121374 5 0.5627 0.8170 0.000 0.000 0.164 0.288 0.544 0.004
#> GSM121407 3 0.4972 0.4793 0.000 0.256 0.656 0.000 0.064 0.024
#> GSM74387 6 0.6900 -0.1638 0.000 0.068 0.252 0.000 0.244 0.436
#> GSM74388 6 0.6408 0.2355 0.016 0.292 0.016 0.000 0.180 0.496
#> GSM74389 5 0.5887 0.5544 0.000 0.000 0.324 0.136 0.520 0.020
#> GSM74390 1 0.5187 0.3201 0.600 0.000 0.000 0.000 0.136 0.264
#> GSM74391 4 0.1218 0.9171 0.012 0.000 0.000 0.956 0.028 0.004
#> GSM74392 5 0.5815 0.7859 0.000 0.000 0.164 0.316 0.512 0.008
#> GSM74393 5 0.5421 0.4022 0.000 0.000 0.364 0.068 0.544 0.024
#> GSM74394 6 0.6131 0.2696 0.008 0.244 0.016 0.000 0.192 0.540
#> GSM74239 1 0.1010 0.7717 0.960 0.000 0.000 0.036 0.000 0.004
#> GSM74364 1 0.1528 0.7556 0.936 0.000 0.000 0.048 0.000 0.016
#> GSM74365 1 0.1141 0.7740 0.948 0.000 0.000 0.000 0.000 0.052
#> GSM74366 6 0.3607 0.2223 0.348 0.000 0.000 0.000 0.000 0.652
#> GSM74367 1 0.0547 0.7792 0.980 0.000 0.000 0.020 0.000 0.000
#> GSM74377 1 0.3860 0.1384 0.528 0.000 0.000 0.000 0.000 0.472
#> GSM74378 6 0.3647 0.2088 0.360 0.000 0.000 0.000 0.000 0.640
#> GSM74379 1 0.3244 0.5748 0.732 0.000 0.000 0.000 0.000 0.268
#> GSM74380 1 0.3351 0.5470 0.712 0.000 0.000 0.000 0.000 0.288
#> GSM74381 6 0.3868 -0.1113 0.496 0.000 0.000 0.000 0.000 0.504
#> GSM121357 2 0.5885 0.3643 0.000 0.560 0.032 0.000 0.128 0.280
#> GSM121361 6 0.6431 0.2329 0.016 0.292 0.016 0.000 0.184 0.492
#> GSM121363 6 0.6408 0.2355 0.016 0.292 0.016 0.000 0.180 0.496
#> GSM121368 6 0.6408 0.2355 0.016 0.292 0.016 0.000 0.180 0.496
#> GSM121369 6 0.6433 0.2695 0.016 0.252 0.020 0.000 0.196 0.516
#> GSM74368 1 0.1850 0.7779 0.924 0.000 0.000 0.008 0.016 0.052
#> GSM74369 1 0.1850 0.7779 0.924 0.000 0.000 0.008 0.016 0.052
#> GSM74370 1 0.1850 0.7779 0.924 0.000 0.000 0.008 0.016 0.052
#> GSM74371 1 0.4074 0.3404 0.656 0.000 0.000 0.324 0.004 0.016
#> GSM74372 1 0.2279 0.7782 0.904 0.000 0.000 0.024 0.016 0.056
#> GSM74373 6 0.4098 -0.1163 0.496 0.000 0.000 0.000 0.008 0.496
#> GSM74374 1 0.1644 0.7740 0.932 0.000 0.000 0.004 0.012 0.052
#> GSM74375 1 0.3872 0.3650 0.604 0.000 0.000 0.000 0.004 0.392
#> GSM74376 6 0.3828 0.0558 0.440 0.000 0.000 0.000 0.000 0.560
#> GSM74405 6 0.3868 -0.1113 0.496 0.000 0.000 0.000 0.000 0.504
#> GSM74351 4 0.3600 0.7080 0.192 0.000 0.000 0.776 0.020 0.012
#> GSM74352 6 0.3774 0.1339 0.408 0.000 0.000 0.000 0.000 0.592
#> GSM74353 1 0.2002 0.7802 0.916 0.000 0.000 0.020 0.008 0.056
#> GSM74354 1 0.0000 0.7791 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM74355 6 0.3756 0.1487 0.400 0.000 0.000 0.000 0.000 0.600
#> GSM74382 4 0.3730 0.6646 0.236 0.000 0.000 0.740 0.008 0.016
#> GSM74383 1 0.0547 0.7792 0.980 0.000 0.000 0.020 0.000 0.000
#> GSM74384 6 0.3578 0.2288 0.340 0.000 0.000 0.000 0.000 0.660
#> GSM74385 1 0.1801 0.7478 0.924 0.000 0.000 0.056 0.004 0.016
#> GSM74386 1 0.0717 0.7785 0.976 0.000 0.000 0.016 0.000 0.008
#> GSM74395 1 0.1010 0.7713 0.960 0.000 0.000 0.036 0.000 0.004
#> GSM74396 1 0.0547 0.7791 0.980 0.000 0.000 0.020 0.000 0.000
#> GSM74397 1 0.1686 0.7454 0.924 0.000 0.000 0.064 0.000 0.012
#> GSM74398 1 0.3795 0.4211 0.632 0.000 0.000 0.000 0.004 0.364
#> GSM74399 1 0.3966 0.2226 0.552 0.000 0.000 0.000 0.004 0.444
#> GSM74400 1 0.3296 0.6448 0.796 0.000 0.000 0.004 0.020 0.180
#> GSM74401 1 0.3393 0.6314 0.784 0.000 0.000 0.004 0.020 0.192
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
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 119 4.11e-11 2
#> MAD:kmeans 106 4.12e-25 3
#> MAD:kmeans 107 2.49e-29 4
#> MAD:kmeans 96 5.51e-30 5
#> MAD:kmeans 94 3.94e-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", "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 21168 rows and 121 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.989 0.995 0.5034 0.497 0.497
#> 3 3 0.866 0.911 0.960 0.3311 0.739 0.521
#> 4 4 0.917 0.899 0.948 0.1207 0.850 0.590
#> 5 5 0.769 0.749 0.856 0.0467 0.960 0.843
#> 6 6 0.749 0.545 0.739 0.0405 0.927 0.695
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
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM74356 2 0.0000 0.991 0.000 1.000
#> GSM74357 2 0.0000 0.991 0.000 1.000
#> GSM74358 2 0.0000 0.991 0.000 1.000
#> GSM74359 1 0.0000 0.998 1.000 0.000
#> GSM74360 1 0.0000 0.998 1.000 0.000
#> GSM74361 2 0.0000 0.991 0.000 1.000
#> GSM74362 2 0.0000 0.991 0.000 1.000
#> GSM74363 2 0.0000 0.991 0.000 1.000
#> GSM74402 1 0.0000 0.998 1.000 0.000
#> GSM74403 1 0.0000 0.998 1.000 0.000
#> GSM74404 1 0.0000 0.998 1.000 0.000
#> GSM74406 1 0.0000 0.998 1.000 0.000
#> GSM74407 1 0.0000 0.998 1.000 0.000
#> GSM74408 1 0.0000 0.998 1.000 0.000
#> GSM74409 1 0.0000 0.998 1.000 0.000
#> GSM74410 1 0.0000 0.998 1.000 0.000
#> GSM119936 1 0.0000 0.998 1.000 0.000
#> GSM119937 1 0.0000 0.998 1.000 0.000
#> GSM74411 2 0.0000 0.991 0.000 1.000
#> GSM74412 2 0.0000 0.991 0.000 1.000
#> GSM74413 2 0.0000 0.991 0.000 1.000
#> GSM74414 2 0.0000 0.991 0.000 1.000
#> GSM74415 2 0.0000 0.991 0.000 1.000
#> GSM121379 2 0.0000 0.991 0.000 1.000
#> GSM121380 2 0.0000 0.991 0.000 1.000
#> GSM121381 2 0.0000 0.991 0.000 1.000
#> GSM121382 2 0.0000 0.991 0.000 1.000
#> GSM121383 2 0.0000 0.991 0.000 1.000
#> GSM121384 2 0.0000 0.991 0.000 1.000
#> GSM121385 2 0.0000 0.991 0.000 1.000
#> GSM121386 2 0.0000 0.991 0.000 1.000
#> GSM121387 2 0.0000 0.991 0.000 1.000
#> GSM121388 2 0.0000 0.991 0.000 1.000
#> GSM121389 2 0.0000 0.991 0.000 1.000
#> GSM121390 2 0.0000 0.991 0.000 1.000
#> GSM121391 2 0.0000 0.991 0.000 1.000
#> GSM121392 2 0.0000 0.991 0.000 1.000
#> GSM121393 2 0.0000 0.991 0.000 1.000
#> GSM121394 2 0.0000 0.991 0.000 1.000
#> GSM121395 2 0.0000 0.991 0.000 1.000
#> GSM121396 2 0.0000 0.991 0.000 1.000
#> GSM121397 2 0.0000 0.991 0.000 1.000
#> GSM121398 2 0.0000 0.991 0.000 1.000
#> GSM121399 2 0.0000 0.991 0.000 1.000
#> GSM74240 2 0.0000 0.991 0.000 1.000
#> GSM74241 2 0.0000 0.991 0.000 1.000
#> GSM74242 2 0.7883 0.695 0.236 0.764
#> GSM74243 2 0.8327 0.646 0.264 0.736
#> GSM74244 2 0.0000 0.991 0.000 1.000
#> GSM74245 2 0.0000 0.991 0.000 1.000
#> GSM74246 2 0.0000 0.991 0.000 1.000
#> GSM74247 2 0.0000 0.991 0.000 1.000
#> GSM74248 2 0.0000 0.991 0.000 1.000
#> GSM74416 1 0.0000 0.998 1.000 0.000
#> GSM74417 1 0.0000 0.998 1.000 0.000
#> GSM74418 1 0.0000 0.998 1.000 0.000
#> GSM74419 1 0.0000 0.998 1.000 0.000
#> GSM121358 2 0.0000 0.991 0.000 1.000
#> GSM121359 2 0.0000 0.991 0.000 1.000
#> GSM121360 1 0.0000 0.998 1.000 0.000
#> GSM121362 1 0.0000 0.998 1.000 0.000
#> GSM121364 1 0.0000 0.998 1.000 0.000
#> GSM121365 2 0.0000 0.991 0.000 1.000
#> GSM121366 2 0.0000 0.991 0.000 1.000
#> GSM121367 2 0.0000 0.991 0.000 1.000
#> GSM121370 2 0.0000 0.991 0.000 1.000
#> GSM121371 2 0.0000 0.991 0.000 1.000
#> GSM121372 2 0.0000 0.991 0.000 1.000
#> GSM121373 1 0.0000 0.998 1.000 0.000
#> GSM121374 1 0.0000 0.998 1.000 0.000
#> GSM121407 2 0.0000 0.991 0.000 1.000
#> GSM74387 2 0.0000 0.991 0.000 1.000
#> GSM74388 2 0.0000 0.991 0.000 1.000
#> GSM74389 1 0.0000 0.998 1.000 0.000
#> GSM74390 1 0.0000 0.998 1.000 0.000
#> GSM74391 1 0.0000 0.998 1.000 0.000
#> GSM74392 1 0.0000 0.998 1.000 0.000
#> GSM74393 1 0.4562 0.892 0.904 0.096
#> GSM74394 2 0.0000 0.991 0.000 1.000
#> GSM74239 1 0.0000 0.998 1.000 0.000
#> GSM74364 1 0.0000 0.998 1.000 0.000
#> GSM74365 1 0.0000 0.998 1.000 0.000
#> GSM74366 1 0.0000 0.998 1.000 0.000
#> GSM74367 1 0.0000 0.998 1.000 0.000
#> GSM74377 1 0.0000 0.998 1.000 0.000
#> GSM74378 1 0.0000 0.998 1.000 0.000
#> GSM74379 1 0.0000 0.998 1.000 0.000
#> GSM74380 1 0.0000 0.998 1.000 0.000
#> GSM74381 1 0.0000 0.998 1.000 0.000
#> GSM121357 2 0.0000 0.991 0.000 1.000
#> GSM121361 2 0.0000 0.991 0.000 1.000
#> GSM121363 2 0.0000 0.991 0.000 1.000
#> GSM121368 2 0.0000 0.991 0.000 1.000
#> GSM121369 2 0.0000 0.991 0.000 1.000
#> GSM74368 1 0.0000 0.998 1.000 0.000
#> GSM74369 1 0.0000 0.998 1.000 0.000
#> GSM74370 1 0.0000 0.998 1.000 0.000
#> GSM74371 1 0.0000 0.998 1.000 0.000
#> GSM74372 1 0.0000 0.998 1.000 0.000
#> GSM74373 1 0.0000 0.998 1.000 0.000
#> GSM74374 1 0.0000 0.998 1.000 0.000
#> GSM74375 1 0.0000 0.998 1.000 0.000
#> GSM74376 1 0.0000 0.998 1.000 0.000
#> GSM74405 1 0.0000 0.998 1.000 0.000
#> GSM74351 1 0.0000 0.998 1.000 0.000
#> GSM74352 1 0.0376 0.994 0.996 0.004
#> GSM74353 1 0.0000 0.998 1.000 0.000
#> GSM74354 1 0.0000 0.998 1.000 0.000
#> GSM74355 1 0.0000 0.998 1.000 0.000
#> GSM74382 1 0.0000 0.998 1.000 0.000
#> GSM74383 1 0.0000 0.998 1.000 0.000
#> GSM74384 1 0.0000 0.998 1.000 0.000
#> GSM74385 1 0.0000 0.998 1.000 0.000
#> GSM74386 1 0.0000 0.998 1.000 0.000
#> GSM74395 1 0.0000 0.998 1.000 0.000
#> GSM74396 1 0.0000 0.998 1.000 0.000
#> GSM74397 1 0.0000 0.998 1.000 0.000
#> GSM74398 1 0.0000 0.998 1.000 0.000
#> GSM74399 1 0.0000 0.998 1.000 0.000
#> GSM74400 1 0.0000 0.998 1.000 0.000
#> GSM74401 1 0.0000 0.998 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM74356 3 0.0000 0.946 0.000 0.000 1.000
#> GSM74357 3 0.0000 0.946 0.000 0.000 1.000
#> GSM74358 3 0.0000 0.946 0.000 0.000 1.000
#> GSM74359 3 0.0000 0.946 0.000 0.000 1.000
#> GSM74360 3 0.0000 0.946 0.000 0.000 1.000
#> GSM74361 3 0.0000 0.946 0.000 0.000 1.000
#> GSM74362 3 0.0000 0.946 0.000 0.000 1.000
#> GSM74363 3 0.0000 0.946 0.000 0.000 1.000
#> GSM74402 1 0.6008 0.483 0.628 0.000 0.372
#> GSM74403 1 0.5733 0.577 0.676 0.000 0.324
#> GSM74404 1 0.5810 0.557 0.664 0.000 0.336
#> GSM74406 3 0.0747 0.936 0.016 0.000 0.984
#> GSM74407 1 0.6308 0.144 0.508 0.000 0.492
#> GSM74408 3 0.0000 0.946 0.000 0.000 1.000
#> GSM74409 3 0.0000 0.946 0.000 0.000 1.000
#> GSM74410 3 0.0000 0.946 0.000 0.000 1.000
#> GSM119936 3 0.0747 0.936 0.016 0.000 0.984
#> GSM119937 3 0.5905 0.375 0.352 0.000 0.648
#> GSM74411 2 0.0000 0.986 0.000 1.000 0.000
#> GSM74412 2 0.0000 0.986 0.000 1.000 0.000
#> GSM74413 2 0.0000 0.986 0.000 1.000 0.000
#> GSM74414 2 0.0000 0.986 0.000 1.000 0.000
#> GSM74415 2 0.2448 0.909 0.000 0.924 0.076
#> GSM121379 2 0.0000 0.986 0.000 1.000 0.000
#> GSM121380 2 0.0000 0.986 0.000 1.000 0.000
#> GSM121381 2 0.0000 0.986 0.000 1.000 0.000
#> GSM121382 2 0.0000 0.986 0.000 1.000 0.000
#> GSM121383 2 0.0000 0.986 0.000 1.000 0.000
#> GSM121384 2 0.0000 0.986 0.000 1.000 0.000
#> GSM121385 2 0.0000 0.986 0.000 1.000 0.000
#> GSM121386 2 0.0000 0.986 0.000 1.000 0.000
#> GSM121387 2 0.0000 0.986 0.000 1.000 0.000
#> GSM121388 2 0.0000 0.986 0.000 1.000 0.000
#> GSM121389 2 0.0000 0.986 0.000 1.000 0.000
#> GSM121390 2 0.0000 0.986 0.000 1.000 0.000
#> GSM121391 2 0.0000 0.986 0.000 1.000 0.000
#> GSM121392 2 0.0000 0.986 0.000 1.000 0.000
#> GSM121393 2 0.0000 0.986 0.000 1.000 0.000
#> GSM121394 2 0.0000 0.986 0.000 1.000 0.000
#> GSM121395 2 0.0000 0.986 0.000 1.000 0.000
#> GSM121396 2 0.0000 0.986 0.000 1.000 0.000
#> GSM121397 2 0.0000 0.986 0.000 1.000 0.000
#> GSM121398 2 0.0000 0.986 0.000 1.000 0.000
#> GSM121399 2 0.0000 0.986 0.000 1.000 0.000
#> GSM74240 3 0.0000 0.946 0.000 0.000 1.000
#> GSM74241 3 0.4399 0.785 0.000 0.188 0.812
#> GSM74242 3 0.0000 0.946 0.000 0.000 1.000
#> GSM74243 3 0.0000 0.946 0.000 0.000 1.000
#> GSM74244 3 0.2261 0.906 0.000 0.068 0.932
#> GSM74245 3 0.0000 0.946 0.000 0.000 1.000
#> GSM74246 2 0.5882 0.443 0.000 0.652 0.348
#> GSM74247 2 0.2878 0.886 0.000 0.904 0.096
#> GSM74248 3 0.0000 0.946 0.000 0.000 1.000
#> GSM74416 1 0.5810 0.557 0.664 0.000 0.336
#> GSM74417 1 0.5810 0.557 0.664 0.000 0.336
#> GSM74418 1 0.5810 0.557 0.664 0.000 0.336
#> GSM74419 3 0.0424 0.942 0.008 0.000 0.992
#> GSM121358 3 0.3482 0.858 0.000 0.128 0.872
#> GSM121359 2 0.0000 0.986 0.000 1.000 0.000
#> GSM121360 3 0.2878 0.865 0.096 0.000 0.904
#> GSM121362 3 0.4399 0.748 0.188 0.000 0.812
#> GSM121364 3 0.0000 0.946 0.000 0.000 1.000
#> GSM121365 3 0.3551 0.854 0.000 0.132 0.868
#> GSM121366 3 0.3816 0.836 0.000 0.148 0.852
#> GSM121367 3 0.3482 0.858 0.000 0.128 0.872
#> GSM121370 3 0.3551 0.854 0.000 0.132 0.868
#> GSM121371 3 0.3482 0.858 0.000 0.128 0.872
#> GSM121372 2 0.0000 0.986 0.000 1.000 0.000
#> GSM121373 3 0.0000 0.946 0.000 0.000 1.000
#> GSM121374 3 0.0000 0.946 0.000 0.000 1.000
#> GSM121407 2 0.0000 0.986 0.000 1.000 0.000
#> GSM74387 2 0.0000 0.986 0.000 1.000 0.000
#> GSM74388 2 0.0000 0.986 0.000 1.000 0.000
#> GSM74389 3 0.0000 0.946 0.000 0.000 1.000
#> GSM74390 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74391 3 0.0747 0.937 0.016 0.000 0.984
#> GSM74392 3 0.0000 0.946 0.000 0.000 1.000
#> GSM74393 3 0.0000 0.946 0.000 0.000 1.000
#> GSM74394 2 0.0000 0.986 0.000 1.000 0.000
#> GSM74239 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74364 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74365 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74366 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74367 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74377 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74378 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74379 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74380 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74381 1 0.0000 0.940 1.000 0.000 0.000
#> GSM121357 2 0.0000 0.986 0.000 1.000 0.000
#> GSM121361 2 0.0000 0.986 0.000 1.000 0.000
#> GSM121363 2 0.0000 0.986 0.000 1.000 0.000
#> GSM121368 2 0.0000 0.986 0.000 1.000 0.000
#> GSM121369 2 0.0000 0.986 0.000 1.000 0.000
#> GSM74368 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74369 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74370 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74371 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74372 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74373 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74374 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74375 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74376 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74405 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74351 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74352 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74353 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74354 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74355 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74382 1 0.0237 0.937 0.996 0.000 0.004
#> GSM74383 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74384 1 0.0237 0.936 0.996 0.004 0.000
#> GSM74385 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74386 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74395 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74396 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74397 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74398 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74399 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74400 1 0.0000 0.940 1.000 0.000 0.000
#> GSM74401 1 0.0000 0.940 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM74356 3 0.0336 0.942 0.000 0.000 0.992 0.008
#> GSM74357 3 0.0336 0.942 0.000 0.000 0.992 0.008
#> GSM74358 3 0.0336 0.942 0.000 0.000 0.992 0.008
#> GSM74359 4 0.2011 0.906 0.000 0.000 0.080 0.920
#> GSM74360 4 0.0188 0.952 0.000 0.000 0.004 0.996
#> GSM74361 3 0.0592 0.938 0.000 0.000 0.984 0.016
#> GSM74362 3 0.2216 0.869 0.000 0.000 0.908 0.092
#> GSM74363 3 0.0336 0.942 0.000 0.000 0.992 0.008
#> GSM74402 4 0.0188 0.952 0.004 0.000 0.000 0.996
#> GSM74403 4 0.0188 0.952 0.004 0.000 0.000 0.996
#> GSM74404 4 0.0000 0.953 0.000 0.000 0.000 1.000
#> GSM74406 4 0.0000 0.953 0.000 0.000 0.000 1.000
#> GSM74407 4 0.0000 0.953 0.000 0.000 0.000 1.000
#> GSM74408 4 0.0000 0.953 0.000 0.000 0.000 1.000
#> GSM74409 4 0.0000 0.953 0.000 0.000 0.000 1.000
#> GSM74410 4 0.0000 0.953 0.000 0.000 0.000 1.000
#> GSM119936 4 0.0000 0.953 0.000 0.000 0.000 1.000
#> GSM119937 4 0.0000 0.953 0.000 0.000 0.000 1.000
#> GSM74411 2 0.4933 0.224 0.000 0.568 0.432 0.000
#> GSM74412 2 0.1022 0.939 0.000 0.968 0.032 0.000
#> GSM74413 2 0.4776 0.384 0.000 0.624 0.376 0.000
#> GSM74414 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> GSM74415 3 0.2149 0.876 0.000 0.088 0.912 0.000
#> GSM121379 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> GSM121380 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> GSM121381 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> GSM121382 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> GSM121383 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> GSM121384 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> GSM121385 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> GSM121386 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> GSM121387 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> GSM121388 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> GSM121389 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> GSM121390 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> GSM121391 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> GSM121392 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> GSM121393 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> GSM121394 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> GSM121395 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> GSM121396 2 0.0469 0.954 0.000 0.988 0.012 0.000
#> GSM121397 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> GSM121398 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> GSM121399 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> GSM74240 3 0.0000 0.942 0.000 0.000 1.000 0.000
#> GSM74241 3 0.0000 0.942 0.000 0.000 1.000 0.000
#> GSM74242 3 0.1118 0.924 0.000 0.000 0.964 0.036
#> GSM74243 3 0.1022 0.927 0.000 0.000 0.968 0.032
#> GSM74244 3 0.0000 0.942 0.000 0.000 1.000 0.000
#> GSM74245 3 0.0000 0.942 0.000 0.000 1.000 0.000
#> GSM74246 3 0.0000 0.942 0.000 0.000 1.000 0.000
#> GSM74247 3 0.0188 0.940 0.000 0.004 0.996 0.000
#> GSM74248 3 0.0000 0.942 0.000 0.000 1.000 0.000
#> GSM74416 4 0.0188 0.952 0.004 0.000 0.000 0.996
#> GSM74417 4 0.0188 0.952 0.004 0.000 0.000 0.996
#> GSM74418 4 0.0188 0.952 0.004 0.000 0.000 0.996
#> GSM74419 4 0.0000 0.953 0.000 0.000 0.000 1.000
#> GSM121358 3 0.0376 0.942 0.000 0.004 0.992 0.004
#> GSM121359 3 0.3764 0.712 0.000 0.216 0.784 0.000
#> GSM121360 4 0.2546 0.912 0.028 0.000 0.060 0.912
#> GSM121362 4 0.3383 0.884 0.076 0.000 0.052 0.872
#> GSM121364 4 0.1867 0.913 0.000 0.000 0.072 0.928
#> GSM121365 3 0.0376 0.942 0.000 0.004 0.992 0.004
#> GSM121366 3 0.0376 0.942 0.000 0.004 0.992 0.004
#> GSM121367 3 0.0376 0.942 0.000 0.004 0.992 0.004
#> GSM121370 3 0.0376 0.942 0.000 0.004 0.992 0.004
#> GSM121371 3 0.0376 0.942 0.000 0.004 0.992 0.004
#> GSM121372 3 0.4643 0.468 0.000 0.344 0.656 0.000
#> GSM121373 4 0.1716 0.919 0.000 0.000 0.064 0.936
#> GSM121374 4 0.1940 0.910 0.000 0.000 0.076 0.924
#> GSM121407 2 0.0336 0.956 0.000 0.992 0.008 0.000
#> GSM74387 2 0.1824 0.917 0.004 0.936 0.060 0.000
#> GSM74388 2 0.1722 0.930 0.048 0.944 0.008 0.000
#> GSM74389 4 0.4907 0.288 0.000 0.000 0.420 0.580
#> GSM74390 1 0.0937 0.927 0.976 0.000 0.012 0.012
#> GSM74391 4 0.0000 0.953 0.000 0.000 0.000 1.000
#> GSM74392 4 0.1557 0.924 0.000 0.000 0.056 0.944
#> GSM74393 3 0.4817 0.328 0.000 0.000 0.612 0.388
#> GSM74394 2 0.1722 0.930 0.048 0.944 0.008 0.000
#> GSM74239 1 0.2647 0.897 0.880 0.000 0.000 0.120
#> GSM74364 1 0.2868 0.886 0.864 0.000 0.000 0.136
#> GSM74365 1 0.0707 0.931 0.980 0.000 0.000 0.020
#> GSM74366 1 0.0000 0.931 1.000 0.000 0.000 0.000
#> GSM74367 1 0.1940 0.918 0.924 0.000 0.000 0.076
#> GSM74377 1 0.0000 0.931 1.000 0.000 0.000 0.000
#> GSM74378 1 0.0000 0.931 1.000 0.000 0.000 0.000
#> GSM74379 1 0.0000 0.931 1.000 0.000 0.000 0.000
#> GSM74380 1 0.0000 0.931 1.000 0.000 0.000 0.000
#> GSM74381 1 0.0000 0.931 1.000 0.000 0.000 0.000
#> GSM121357 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> GSM121361 2 0.1722 0.930 0.048 0.944 0.008 0.000
#> GSM121363 2 0.1722 0.930 0.048 0.944 0.008 0.000
#> GSM121368 2 0.1722 0.930 0.048 0.944 0.008 0.000
#> GSM121369 2 0.1722 0.930 0.048 0.944 0.008 0.000
#> GSM74368 1 0.3444 0.836 0.816 0.000 0.000 0.184
#> GSM74369 1 0.2149 0.914 0.912 0.000 0.000 0.088
#> GSM74370 1 0.2281 0.911 0.904 0.000 0.000 0.096
#> GSM74371 1 0.4830 0.498 0.608 0.000 0.000 0.392
#> GSM74372 1 0.2647 0.896 0.880 0.000 0.000 0.120
#> GSM74373 1 0.0000 0.931 1.000 0.000 0.000 0.000
#> GSM74374 1 0.0921 0.930 0.972 0.000 0.000 0.028
#> GSM74375 1 0.0188 0.931 0.996 0.000 0.000 0.004
#> GSM74376 1 0.0000 0.931 1.000 0.000 0.000 0.000
#> GSM74405 1 0.0000 0.931 1.000 0.000 0.000 0.000
#> GSM74351 4 0.1867 0.890 0.072 0.000 0.000 0.928
#> GSM74352 1 0.0000 0.931 1.000 0.000 0.000 0.000
#> GSM74353 1 0.2647 0.898 0.880 0.000 0.000 0.120
#> GSM74354 1 0.1557 0.924 0.944 0.000 0.000 0.056
#> GSM74355 1 0.0000 0.931 1.000 0.000 0.000 0.000
#> GSM74382 4 0.1302 0.920 0.044 0.000 0.000 0.956
#> GSM74383 1 0.1716 0.921 0.936 0.000 0.000 0.064
#> GSM74384 1 0.0000 0.931 1.000 0.000 0.000 0.000
#> GSM74385 1 0.3444 0.842 0.816 0.000 0.000 0.184
#> GSM74386 1 0.2408 0.907 0.896 0.000 0.000 0.104
#> GSM74395 1 0.3569 0.826 0.804 0.000 0.000 0.196
#> GSM74396 1 0.2216 0.912 0.908 0.000 0.000 0.092
#> GSM74397 1 0.4941 0.377 0.564 0.000 0.000 0.436
#> GSM74398 1 0.0188 0.931 0.996 0.000 0.000 0.004
#> GSM74399 1 0.0000 0.931 1.000 0.000 0.000 0.000
#> GSM74400 1 0.0707 0.931 0.980 0.000 0.000 0.020
#> GSM74401 1 0.0592 0.931 0.984 0.000 0.000 0.016
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM74356 3 0.1282 0.7955 0.000 0.000 0.952 0.004 0.044
#> GSM74357 3 0.1357 0.7927 0.000 0.000 0.948 0.004 0.048
#> GSM74358 3 0.1430 0.7896 0.000 0.000 0.944 0.004 0.052
#> GSM74359 4 0.4338 0.6908 0.000 0.000 0.024 0.696 0.280
#> GSM74360 4 0.3910 0.7118 0.000 0.000 0.008 0.720 0.272
#> GSM74361 3 0.2351 0.7461 0.000 0.000 0.896 0.016 0.088
#> GSM74362 3 0.5074 0.3807 0.000 0.000 0.660 0.072 0.268
#> GSM74363 3 0.0290 0.8137 0.000 0.000 0.992 0.000 0.008
#> GSM74402 4 0.0000 0.8428 0.000 0.000 0.000 1.000 0.000
#> GSM74403 4 0.0000 0.8428 0.000 0.000 0.000 1.000 0.000
#> GSM74404 4 0.0162 0.8407 0.004 0.000 0.000 0.996 0.000
#> GSM74406 4 0.1282 0.8407 0.000 0.000 0.004 0.952 0.044
#> GSM74407 4 0.0000 0.8428 0.000 0.000 0.000 1.000 0.000
#> GSM74408 4 0.1168 0.8418 0.000 0.000 0.008 0.960 0.032
#> GSM74409 4 0.1697 0.8338 0.000 0.000 0.008 0.932 0.060
#> GSM74410 4 0.1557 0.8368 0.000 0.000 0.008 0.940 0.052
#> GSM119936 4 0.0404 0.8444 0.000 0.000 0.000 0.988 0.012
#> GSM119937 4 0.0290 0.8441 0.000 0.000 0.000 0.992 0.008
#> GSM74411 3 0.6812 -0.0691 0.000 0.324 0.364 0.000 0.312
#> GSM74412 2 0.5787 0.4791 0.000 0.616 0.180 0.000 0.204
#> GSM74413 2 0.6811 -0.1844 0.000 0.360 0.336 0.000 0.304
#> GSM74414 2 0.0290 0.8820 0.000 0.992 0.000 0.000 0.008
#> GSM74415 3 0.5542 -0.2911 0.000 0.068 0.500 0.000 0.432
#> GSM121379 2 0.0000 0.8850 0.000 1.000 0.000 0.000 0.000
#> GSM121380 2 0.0000 0.8850 0.000 1.000 0.000 0.000 0.000
#> GSM121381 2 0.0000 0.8850 0.000 1.000 0.000 0.000 0.000
#> GSM121382 2 0.0000 0.8850 0.000 1.000 0.000 0.000 0.000
#> GSM121383 2 0.0000 0.8850 0.000 1.000 0.000 0.000 0.000
#> GSM121384 2 0.0000 0.8850 0.000 1.000 0.000 0.000 0.000
#> GSM121385 2 0.0000 0.8850 0.000 1.000 0.000 0.000 0.000
#> GSM121386 2 0.0000 0.8850 0.000 1.000 0.000 0.000 0.000
#> GSM121387 2 0.0000 0.8850 0.000 1.000 0.000 0.000 0.000
#> GSM121388 2 0.0609 0.8743 0.000 0.980 0.020 0.000 0.000
#> GSM121389 2 0.0000 0.8850 0.000 1.000 0.000 0.000 0.000
#> GSM121390 2 0.0000 0.8850 0.000 1.000 0.000 0.000 0.000
#> GSM121391 2 0.0000 0.8850 0.000 1.000 0.000 0.000 0.000
#> GSM121392 2 0.0290 0.8821 0.000 0.992 0.000 0.000 0.008
#> GSM121393 2 0.0000 0.8850 0.000 1.000 0.000 0.000 0.000
#> GSM121394 2 0.0162 0.8833 0.000 0.996 0.004 0.000 0.000
#> GSM121395 2 0.0000 0.8850 0.000 1.000 0.000 0.000 0.000
#> GSM121396 2 0.2773 0.7505 0.000 0.836 0.164 0.000 0.000
#> GSM121397 2 0.0000 0.8850 0.000 1.000 0.000 0.000 0.000
#> GSM121398 2 0.0000 0.8850 0.000 1.000 0.000 0.000 0.000
#> GSM121399 2 0.0000 0.8850 0.000 1.000 0.000 0.000 0.000
#> GSM74240 5 0.3661 0.7864 0.000 0.000 0.276 0.000 0.724
#> GSM74241 5 0.3895 0.7687 0.000 0.000 0.320 0.000 0.680
#> GSM74242 5 0.4907 0.7505 0.000 0.000 0.292 0.052 0.656
#> GSM74243 5 0.4866 0.7540 0.000 0.000 0.284 0.052 0.664
#> GSM74244 5 0.3966 0.7602 0.000 0.000 0.336 0.000 0.664
#> GSM74245 5 0.3932 0.7678 0.000 0.000 0.328 0.000 0.672
#> GSM74246 5 0.3612 0.7660 0.000 0.000 0.268 0.000 0.732
#> GSM74247 5 0.3796 0.7690 0.000 0.000 0.300 0.000 0.700
#> GSM74248 5 0.3586 0.7841 0.000 0.000 0.264 0.000 0.736
#> GSM74416 4 0.0000 0.8428 0.000 0.000 0.000 1.000 0.000
#> GSM74417 4 0.0000 0.8428 0.000 0.000 0.000 1.000 0.000
#> GSM74418 4 0.0000 0.8428 0.000 0.000 0.000 1.000 0.000
#> GSM74419 4 0.0771 0.8445 0.000 0.000 0.004 0.976 0.020
#> GSM121358 3 0.0000 0.8155 0.000 0.000 1.000 0.000 0.000
#> GSM121359 3 0.1197 0.7751 0.000 0.048 0.952 0.000 0.000
#> GSM121360 4 0.5305 0.5064 0.024 0.000 0.016 0.536 0.424
#> GSM121362 4 0.5494 0.6039 0.052 0.000 0.012 0.592 0.344
#> GSM121364 4 0.4338 0.6908 0.000 0.000 0.024 0.696 0.280
#> GSM121365 3 0.0000 0.8155 0.000 0.000 1.000 0.000 0.000
#> GSM121366 3 0.0000 0.8155 0.000 0.000 1.000 0.000 0.000
#> GSM121367 3 0.0000 0.8155 0.000 0.000 1.000 0.000 0.000
#> GSM121370 3 0.0000 0.8155 0.000 0.000 1.000 0.000 0.000
#> GSM121371 3 0.0000 0.8155 0.000 0.000 1.000 0.000 0.000
#> GSM121372 3 0.2127 0.7004 0.000 0.108 0.892 0.000 0.000
#> GSM121373 4 0.4297 0.6887 0.000 0.000 0.020 0.692 0.288
#> GSM121374 4 0.4252 0.6954 0.000 0.000 0.020 0.700 0.280
#> GSM121407 2 0.4294 0.1536 0.000 0.532 0.468 0.000 0.000
#> GSM74387 2 0.5780 0.4068 0.024 0.528 0.044 0.000 0.404
#> GSM74388 2 0.4541 0.7359 0.084 0.744 0.000 0.000 0.172
#> GSM74389 5 0.5338 0.2052 0.000 0.000 0.072 0.324 0.604
#> GSM74390 1 0.4454 0.6357 0.708 0.000 0.004 0.028 0.260
#> GSM74391 4 0.1041 0.8435 0.000 0.000 0.004 0.964 0.032
#> GSM74392 4 0.4382 0.6787 0.000 0.000 0.024 0.688 0.288
#> GSM74393 5 0.5182 0.4764 0.000 0.000 0.112 0.208 0.680
#> GSM74394 2 0.4732 0.7132 0.076 0.716 0.000 0.000 0.208
#> GSM74239 1 0.3741 0.7606 0.732 0.000 0.000 0.264 0.004
#> GSM74364 1 0.4009 0.7069 0.684 0.000 0.000 0.312 0.004
#> GSM74365 1 0.1282 0.8652 0.952 0.000 0.000 0.044 0.004
#> GSM74366 1 0.1270 0.8476 0.948 0.000 0.000 0.000 0.052
#> GSM74367 1 0.2763 0.8384 0.848 0.000 0.000 0.148 0.004
#> GSM74377 1 0.0510 0.8602 0.984 0.000 0.000 0.000 0.016
#> GSM74378 1 0.1043 0.8533 0.960 0.000 0.000 0.000 0.040
#> GSM74379 1 0.0451 0.8625 0.988 0.000 0.000 0.004 0.008
#> GSM74380 1 0.0324 0.8628 0.992 0.000 0.000 0.004 0.004
#> GSM74381 1 0.0609 0.8595 0.980 0.000 0.000 0.000 0.020
#> GSM121357 2 0.0162 0.8838 0.000 0.996 0.000 0.000 0.004
#> GSM121361 2 0.4541 0.7359 0.084 0.744 0.000 0.000 0.172
#> GSM121363 2 0.4486 0.7392 0.080 0.748 0.000 0.000 0.172
#> GSM121368 2 0.4486 0.7392 0.080 0.748 0.000 0.000 0.172
#> GSM121369 2 0.4593 0.7302 0.080 0.736 0.000 0.000 0.184
#> GSM74368 1 0.3969 0.7068 0.692 0.000 0.000 0.304 0.004
#> GSM74369 1 0.3010 0.8303 0.824 0.000 0.000 0.172 0.004
#> GSM74370 1 0.3318 0.8215 0.808 0.000 0.000 0.180 0.012
#> GSM74371 4 0.4434 -0.2392 0.460 0.000 0.000 0.536 0.004
#> GSM74372 1 0.3759 0.7972 0.764 0.000 0.000 0.220 0.016
#> GSM74373 1 0.0609 0.8595 0.980 0.000 0.000 0.000 0.020
#> GSM74374 1 0.2077 0.8625 0.908 0.000 0.000 0.084 0.008
#> GSM74375 1 0.0992 0.8649 0.968 0.000 0.000 0.024 0.008
#> GSM74376 1 0.0963 0.8549 0.964 0.000 0.000 0.000 0.036
#> GSM74405 1 0.0609 0.8595 0.980 0.000 0.000 0.000 0.020
#> GSM74351 4 0.1704 0.7824 0.068 0.000 0.000 0.928 0.004
#> GSM74352 1 0.0963 0.8551 0.964 0.000 0.000 0.000 0.036
#> GSM74353 1 0.3689 0.7674 0.740 0.000 0.000 0.256 0.004
#> GSM74354 1 0.2358 0.8570 0.888 0.000 0.000 0.104 0.008
#> GSM74355 1 0.0880 0.8561 0.968 0.000 0.000 0.000 0.032
#> GSM74382 4 0.1041 0.8168 0.032 0.000 0.000 0.964 0.004
#> GSM74383 1 0.2719 0.8408 0.852 0.000 0.000 0.144 0.004
#> GSM74384 1 0.1197 0.8494 0.952 0.000 0.000 0.000 0.048
#> GSM74385 1 0.4327 0.6269 0.632 0.000 0.000 0.360 0.008
#> GSM74386 1 0.3756 0.7692 0.744 0.000 0.000 0.248 0.008
#> GSM74395 1 0.4225 0.6217 0.632 0.000 0.000 0.364 0.004
#> GSM74396 1 0.3109 0.8122 0.800 0.000 0.000 0.200 0.000
#> GSM74397 1 0.4449 0.3430 0.512 0.000 0.000 0.484 0.004
#> GSM74398 1 0.0693 0.8642 0.980 0.000 0.000 0.012 0.008
#> GSM74399 1 0.0404 0.8608 0.988 0.000 0.000 0.000 0.012
#> GSM74400 1 0.1357 0.8659 0.948 0.000 0.000 0.048 0.004
#> GSM74401 1 0.1124 0.8663 0.960 0.000 0.000 0.036 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM74356 3 0.1957 0.834377 0.008 0.000 0.920 0.048 0.024 0.000
#> GSM74357 3 0.1850 0.835853 0.008 0.000 0.924 0.052 0.016 0.000
#> GSM74358 3 0.1692 0.840367 0.008 0.000 0.932 0.048 0.012 0.000
#> GSM74359 4 0.1152 0.564954 0.000 0.000 0.004 0.952 0.044 0.000
#> GSM74360 4 0.1010 0.564835 0.004 0.000 0.000 0.960 0.036 0.000
#> GSM74361 3 0.4694 0.599120 0.008 0.000 0.704 0.124 0.164 0.000
#> GSM74362 3 0.5675 0.248150 0.008 0.000 0.444 0.428 0.120 0.000
#> GSM74363 3 0.0881 0.857127 0.008 0.000 0.972 0.012 0.008 0.000
#> GSM74402 4 0.3986 0.157138 0.464 0.000 0.000 0.532 0.004 0.000
#> GSM74403 1 0.3869 -0.160417 0.500 0.000 0.000 0.500 0.000 0.000
#> GSM74404 4 0.3867 0.090936 0.488 0.000 0.000 0.512 0.000 0.000
#> GSM74406 4 0.3578 0.438773 0.340 0.000 0.000 0.660 0.000 0.000
#> GSM74407 4 0.3864 0.136824 0.480 0.000 0.000 0.520 0.000 0.000
#> GSM74408 4 0.3563 0.441783 0.336 0.000 0.000 0.664 0.000 0.000
#> GSM74409 4 0.3244 0.492326 0.268 0.000 0.000 0.732 0.000 0.000
#> GSM74410 4 0.3309 0.488976 0.280 0.000 0.000 0.720 0.000 0.000
#> GSM119936 4 0.3765 0.334776 0.404 0.000 0.000 0.596 0.000 0.000
#> GSM119937 4 0.3843 0.222879 0.452 0.000 0.000 0.548 0.000 0.000
#> GSM74411 5 0.6901 0.380663 0.072 0.236 0.248 0.000 0.444 0.000
#> GSM74412 2 0.6882 -0.183352 0.072 0.408 0.184 0.000 0.336 0.000
#> GSM74413 5 0.7000 0.342923 0.072 0.280 0.240 0.000 0.408 0.000
#> GSM74414 2 0.2237 0.772996 0.068 0.896 0.000 0.000 0.036 0.000
#> GSM74415 5 0.5466 0.528086 0.064 0.044 0.292 0.000 0.600 0.000
#> GSM121379 2 0.0000 0.833668 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121380 2 0.0000 0.833668 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121381 2 0.0000 0.833668 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121382 2 0.0000 0.833668 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121383 2 0.0000 0.833668 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121384 2 0.0000 0.833668 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121385 2 0.0000 0.833668 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121386 2 0.0000 0.833668 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121387 2 0.0000 0.833668 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121388 2 0.1219 0.799145 0.004 0.948 0.048 0.000 0.000 0.000
#> GSM121389 2 0.0000 0.833668 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121390 2 0.0000 0.833668 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121391 2 0.0000 0.833668 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121392 2 0.0291 0.830348 0.004 0.992 0.000 0.000 0.004 0.000
#> GSM121393 2 0.0146 0.832147 0.004 0.996 0.000 0.000 0.000 0.000
#> GSM121394 2 0.0291 0.829380 0.004 0.992 0.004 0.000 0.000 0.000
#> GSM121395 2 0.0000 0.833668 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121396 2 0.2902 0.649072 0.004 0.800 0.196 0.000 0.000 0.000
#> GSM121397 2 0.0000 0.833668 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121398 2 0.0000 0.833668 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121399 2 0.0000 0.833668 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM74240 5 0.2365 0.772284 0.000 0.000 0.072 0.040 0.888 0.000
#> GSM74241 5 0.2118 0.773715 0.000 0.000 0.104 0.008 0.888 0.000
#> GSM74242 5 0.3123 0.749501 0.000 0.000 0.088 0.076 0.836 0.000
#> GSM74243 5 0.3125 0.748354 0.000 0.000 0.080 0.084 0.836 0.000
#> GSM74244 5 0.2257 0.770679 0.000 0.000 0.116 0.008 0.876 0.000
#> GSM74245 5 0.2618 0.768949 0.000 0.000 0.116 0.024 0.860 0.000
#> GSM74246 5 0.1644 0.771581 0.000 0.000 0.076 0.004 0.920 0.000
#> GSM74247 5 0.1866 0.770031 0.008 0.000 0.084 0.000 0.908 0.000
#> GSM74248 5 0.2568 0.767701 0.000 0.000 0.068 0.056 0.876 0.000
#> GSM74416 1 0.3867 -0.120877 0.512 0.000 0.000 0.488 0.000 0.000
#> GSM74417 4 0.3868 0.045146 0.496 0.000 0.000 0.504 0.000 0.000
#> GSM74418 1 0.3868 -0.138376 0.508 0.000 0.000 0.492 0.000 0.000
#> GSM74419 4 0.3747 0.347042 0.396 0.000 0.000 0.604 0.000 0.000
#> GSM121358 3 0.0000 0.863085 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121359 3 0.0777 0.845770 0.004 0.024 0.972 0.000 0.000 0.000
#> GSM121360 4 0.4575 0.367539 0.224 0.000 0.000 0.700 0.060 0.016
#> GSM121362 4 0.4118 0.474722 0.092 0.000 0.004 0.796 0.060 0.048
#> GSM121364 4 0.1152 0.564954 0.000 0.000 0.004 0.952 0.044 0.000
#> GSM121365 3 0.0000 0.863085 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121366 3 0.0000 0.863085 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121367 3 0.0000 0.863085 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121370 3 0.0000 0.863085 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121371 3 0.0000 0.863085 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121372 3 0.0865 0.837031 0.000 0.036 0.964 0.000 0.000 0.000
#> GSM121373 4 0.1410 0.562767 0.008 0.000 0.004 0.944 0.044 0.000
#> GSM121374 4 0.1226 0.564844 0.004 0.000 0.004 0.952 0.040 0.000
#> GSM121407 3 0.4102 0.364817 0.012 0.356 0.628 0.000 0.004 0.000
#> GSM74387 5 0.7583 0.117482 0.296 0.252 0.044 0.000 0.360 0.048
#> GSM74388 2 0.7008 0.327833 0.324 0.420 0.000 0.000 0.124 0.132
#> GSM74389 4 0.4218 0.096095 0.004 0.000 0.012 0.584 0.400 0.000
#> GSM74390 6 0.6340 0.387069 0.276 0.000 0.008 0.032 0.160 0.524
#> GSM74391 4 0.4052 0.404289 0.356 0.000 0.000 0.628 0.016 0.000
#> GSM74392 4 0.1897 0.552036 0.004 0.000 0.004 0.908 0.084 0.000
#> GSM74393 4 0.4854 0.039980 0.016 0.000 0.036 0.580 0.368 0.000
#> GSM74394 2 0.7240 0.237261 0.336 0.368 0.000 0.000 0.168 0.128
#> GSM74239 6 0.4535 0.007906 0.484 0.000 0.000 0.032 0.000 0.484
#> GSM74364 1 0.4578 0.040077 0.520 0.000 0.000 0.036 0.000 0.444
#> GSM74365 6 0.2793 0.657101 0.200 0.000 0.000 0.000 0.000 0.800
#> GSM74366 6 0.2831 0.623593 0.136 0.000 0.000 0.000 0.024 0.840
#> GSM74367 6 0.4114 0.477778 0.356 0.000 0.000 0.008 0.008 0.628
#> GSM74377 6 0.0363 0.696184 0.012 0.000 0.000 0.000 0.000 0.988
#> GSM74378 6 0.2706 0.632833 0.124 0.000 0.000 0.000 0.024 0.852
#> GSM74379 6 0.1444 0.701160 0.072 0.000 0.000 0.000 0.000 0.928
#> GSM74380 6 0.1387 0.700648 0.068 0.000 0.000 0.000 0.000 0.932
#> GSM74381 6 0.1124 0.688096 0.036 0.000 0.000 0.000 0.008 0.956
#> GSM121357 2 0.1528 0.802336 0.048 0.936 0.000 0.000 0.016 0.000
#> GSM121361 2 0.6982 0.332934 0.324 0.424 0.000 0.000 0.124 0.128
#> GSM121363 2 0.6982 0.332934 0.324 0.424 0.000 0.000 0.124 0.128
#> GSM121368 2 0.6982 0.332934 0.324 0.424 0.000 0.000 0.124 0.128
#> GSM121369 2 0.7396 0.296329 0.332 0.396 0.000 0.016 0.124 0.132
#> GSM74368 1 0.5120 0.006546 0.472 0.000 0.000 0.068 0.004 0.456
#> GSM74369 6 0.4474 0.309844 0.412 0.000 0.000 0.024 0.004 0.560
#> GSM74370 6 0.4437 0.397930 0.392 0.000 0.000 0.032 0.000 0.576
#> GSM74371 1 0.5188 0.451233 0.588 0.000 0.000 0.124 0.000 0.288
#> GSM74372 6 0.4798 0.362720 0.364 0.000 0.000 0.052 0.004 0.580
#> GSM74373 6 0.0508 0.696881 0.012 0.000 0.000 0.000 0.004 0.984
#> GSM74374 6 0.3383 0.606306 0.268 0.000 0.000 0.000 0.004 0.728
#> GSM74375 6 0.2266 0.693471 0.108 0.000 0.000 0.000 0.012 0.880
#> GSM74376 6 0.2282 0.659595 0.088 0.000 0.000 0.000 0.024 0.888
#> GSM74405 6 0.1074 0.689793 0.028 0.000 0.000 0.000 0.012 0.960
#> GSM74351 1 0.4903 0.246346 0.552 0.000 0.000 0.380 0.000 0.068
#> GSM74352 6 0.2350 0.654786 0.100 0.000 0.000 0.000 0.020 0.880
#> GSM74353 6 0.4526 0.106087 0.456 0.000 0.000 0.032 0.000 0.512
#> GSM74354 6 0.3601 0.560364 0.312 0.000 0.000 0.000 0.004 0.684
#> GSM74355 6 0.2263 0.655037 0.100 0.000 0.000 0.000 0.016 0.884
#> GSM74382 1 0.4737 0.237964 0.572 0.000 0.000 0.372 0.000 0.056
#> GSM74383 6 0.3894 0.526846 0.324 0.000 0.000 0.008 0.004 0.664
#> GSM74384 6 0.3139 0.599232 0.160 0.000 0.000 0.000 0.028 0.812
#> GSM74385 1 0.4905 0.197298 0.528 0.000 0.000 0.064 0.000 0.408
#> GSM74386 6 0.4836 0.333634 0.380 0.000 0.000 0.052 0.004 0.564
#> GSM74395 1 0.5162 -0.000816 0.468 0.000 0.000 0.072 0.004 0.456
#> GSM74396 6 0.4265 0.408890 0.384 0.000 0.000 0.016 0.004 0.596
#> GSM74397 1 0.5903 0.501742 0.520 0.000 0.000 0.212 0.008 0.260
#> GSM74398 6 0.1765 0.697879 0.096 0.000 0.000 0.000 0.000 0.904
#> GSM74399 6 0.0891 0.698409 0.024 0.000 0.000 0.000 0.008 0.968
#> GSM74400 6 0.3073 0.652924 0.204 0.000 0.000 0.000 0.008 0.788
#> GSM74401 6 0.2854 0.650495 0.208 0.000 0.000 0.000 0.000 0.792
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 121 1.64e-11 2
#> MAD:skmeans 117 9.06e-25 3
#> MAD:skmeans 114 1.20e-29 4
#> MAD:skmeans 110 3.19e-42 5
#> MAD:skmeans 74 3.02e-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["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 21168 rows and 121 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.474 0.813 0.895 0.4850 0.521 0.521
#> 3 3 0.673 0.794 0.903 0.3712 0.720 0.505
#> 4 4 0.646 0.629 0.805 0.1244 0.800 0.488
#> 5 5 0.769 0.764 0.881 0.0657 0.902 0.642
#> 6 6 0.856 0.817 0.908 0.0351 0.958 0.800
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
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
#> GSM74356 1 0.8207 0.752 0.744 0.256
#> GSM74357 1 0.8763 0.721 0.704 0.296
#> GSM74358 1 0.7815 0.771 0.768 0.232
#> GSM74359 1 0.6712 0.802 0.824 0.176
#> GSM74360 1 0.0000 0.842 1.000 0.000
#> GSM74361 1 0.8386 0.744 0.732 0.268
#> GSM74362 1 0.8555 0.733 0.720 0.280
#> GSM74363 1 0.9754 0.562 0.592 0.408
#> GSM74402 1 0.0000 0.842 1.000 0.000
#> GSM74403 1 0.0000 0.842 1.000 0.000
#> GSM74404 1 0.0000 0.842 1.000 0.000
#> GSM74406 1 0.0000 0.842 1.000 0.000
#> GSM74407 1 0.0000 0.842 1.000 0.000
#> GSM74408 1 0.0000 0.842 1.000 0.000
#> GSM74409 1 0.0000 0.842 1.000 0.000
#> GSM74410 1 0.0672 0.843 0.992 0.008
#> GSM119936 1 0.0000 0.842 1.000 0.000
#> GSM119937 1 0.0000 0.842 1.000 0.000
#> GSM74411 2 0.0938 0.935 0.012 0.988
#> GSM74412 2 0.0000 0.944 0.000 1.000
#> GSM74413 2 0.0376 0.941 0.004 0.996
#> GSM74414 2 0.0000 0.944 0.000 1.000
#> GSM74415 2 0.9998 -0.347 0.492 0.508
#> GSM121379 2 0.0000 0.944 0.000 1.000
#> GSM121380 2 0.0000 0.944 0.000 1.000
#> GSM121381 2 0.0000 0.944 0.000 1.000
#> GSM121382 2 0.0000 0.944 0.000 1.000
#> GSM121383 2 0.0000 0.944 0.000 1.000
#> GSM121384 2 0.0000 0.944 0.000 1.000
#> GSM121385 2 0.0000 0.944 0.000 1.000
#> GSM121386 2 0.0000 0.944 0.000 1.000
#> GSM121387 2 0.0000 0.944 0.000 1.000
#> GSM121388 2 0.0000 0.944 0.000 1.000
#> GSM121389 2 0.0000 0.944 0.000 1.000
#> GSM121390 2 0.0000 0.944 0.000 1.000
#> GSM121391 2 0.0000 0.944 0.000 1.000
#> GSM121392 2 0.0000 0.944 0.000 1.000
#> GSM121393 2 0.0000 0.944 0.000 1.000
#> GSM121394 2 0.0000 0.944 0.000 1.000
#> GSM121395 2 0.0000 0.944 0.000 1.000
#> GSM121396 2 0.0000 0.944 0.000 1.000
#> GSM121397 2 0.0000 0.944 0.000 1.000
#> GSM121398 2 0.0000 0.944 0.000 1.000
#> GSM121399 2 0.0000 0.944 0.000 1.000
#> GSM74240 1 0.7950 0.764 0.760 0.240
#> GSM74241 1 0.8499 0.743 0.724 0.276
#> GSM74242 1 0.7056 0.794 0.808 0.192
#> GSM74243 1 0.6973 0.796 0.812 0.188
#> GSM74244 1 0.8499 0.736 0.724 0.276
#> GSM74245 1 0.8207 0.753 0.744 0.256
#> GSM74246 1 0.8955 0.705 0.688 0.312
#> GSM74247 1 0.9087 0.691 0.676 0.324
#> GSM74248 1 0.7299 0.787 0.796 0.204
#> GSM74416 1 0.0000 0.842 1.000 0.000
#> GSM74417 1 0.0000 0.842 1.000 0.000
#> GSM74418 1 0.0000 0.842 1.000 0.000
#> GSM74419 1 0.2043 0.842 0.968 0.032
#> GSM121358 1 0.9881 0.505 0.564 0.436
#> GSM121359 2 0.2236 0.912 0.036 0.964
#> GSM121360 1 0.8661 0.705 0.712 0.288
#> GSM121362 1 0.9427 0.611 0.640 0.360
#> GSM121364 1 0.6801 0.800 0.820 0.180
#> GSM121365 1 0.9983 0.412 0.524 0.476
#> GSM121366 1 0.9954 0.451 0.540 0.460
#> GSM121367 1 0.9881 0.505 0.564 0.436
#> GSM121370 1 0.9833 0.530 0.576 0.424
#> GSM121371 1 0.9922 0.480 0.552 0.448
#> GSM121372 2 0.2423 0.908 0.040 0.960
#> GSM121373 1 0.7056 0.794 0.808 0.192
#> GSM121374 1 0.4161 0.833 0.916 0.084
#> GSM121407 2 0.2236 0.912 0.036 0.964
#> GSM74387 2 0.1184 0.931 0.016 0.984
#> GSM74388 2 0.0000 0.944 0.000 1.000
#> GSM74389 1 0.6801 0.800 0.820 0.180
#> GSM74390 1 0.8555 0.741 0.720 0.280
#> GSM74391 1 0.0672 0.843 0.992 0.008
#> GSM74392 1 0.5408 0.822 0.876 0.124
#> GSM74393 1 0.7056 0.794 0.808 0.192
#> GSM74394 2 0.0672 0.939 0.008 0.992
#> GSM74239 1 0.0672 0.842 0.992 0.008
#> GSM74364 1 0.0672 0.842 0.992 0.008
#> GSM74365 1 0.2236 0.838 0.964 0.036
#> GSM74366 2 0.0672 0.938 0.008 0.992
#> GSM74367 1 0.0672 0.842 0.992 0.008
#> GSM74377 2 0.7602 0.712 0.220 0.780
#> GSM74378 2 0.6801 0.755 0.180 0.820
#> GSM74379 1 0.4939 0.806 0.892 0.108
#> GSM74380 1 0.4298 0.814 0.912 0.088
#> GSM74381 2 0.7299 0.728 0.204 0.796
#> GSM121357 2 0.0000 0.944 0.000 1.000
#> GSM121361 2 0.0000 0.944 0.000 1.000
#> GSM121363 2 0.0000 0.944 0.000 1.000
#> GSM121368 2 0.0000 0.944 0.000 1.000
#> GSM121369 2 0.0000 0.944 0.000 1.000
#> GSM74368 1 0.8016 0.764 0.756 0.244
#> GSM74369 1 0.9393 0.631 0.644 0.356
#> GSM74370 1 0.5059 0.801 0.888 0.112
#> GSM74371 1 0.0000 0.842 1.000 0.000
#> GSM74372 1 0.0672 0.842 0.992 0.008
#> GSM74373 2 0.7528 0.715 0.216 0.784
#> GSM74374 1 0.1184 0.841 0.984 0.016
#> GSM74375 1 0.4939 0.827 0.892 0.108
#> GSM74376 2 0.3879 0.875 0.076 0.924
#> GSM74405 2 0.9044 0.551 0.320 0.680
#> GSM74351 1 0.0000 0.842 1.000 0.000
#> GSM74352 2 0.1184 0.932 0.016 0.984
#> GSM74353 1 0.2236 0.838 0.964 0.036
#> GSM74354 1 0.0938 0.842 0.988 0.012
#> GSM74355 2 0.6973 0.745 0.188 0.812
#> GSM74382 1 0.0000 0.842 1.000 0.000
#> GSM74383 1 0.0672 0.842 0.992 0.008
#> GSM74384 2 0.0000 0.944 0.000 1.000
#> GSM74385 1 0.0376 0.842 0.996 0.004
#> GSM74386 1 0.0672 0.842 0.992 0.008
#> GSM74395 1 0.0672 0.842 0.992 0.008
#> GSM74396 1 0.0938 0.842 0.988 0.012
#> GSM74397 1 0.0672 0.842 0.992 0.008
#> GSM74398 1 0.1633 0.841 0.976 0.024
#> GSM74399 1 0.8144 0.675 0.748 0.252
#> GSM74400 1 0.6801 0.749 0.820 0.180
#> GSM74401 1 0.7376 0.721 0.792 0.208
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM74356 3 0.0237 0.853 0.000 0.004 0.996
#> GSM74357 3 0.0237 0.853 0.004 0.000 0.996
#> GSM74358 3 0.0237 0.853 0.004 0.000 0.996
#> GSM74359 3 0.1031 0.852 0.024 0.000 0.976
#> GSM74360 3 0.6192 0.392 0.420 0.000 0.580
#> GSM74361 3 0.0237 0.853 0.004 0.000 0.996
#> GSM74362 3 0.0475 0.853 0.004 0.004 0.992
#> GSM74363 3 0.0237 0.853 0.000 0.004 0.996
#> GSM74402 3 0.6291 0.235 0.468 0.000 0.532
#> GSM74403 1 0.4346 0.724 0.816 0.000 0.184
#> GSM74404 1 0.4291 0.729 0.820 0.000 0.180
#> GSM74406 3 0.6126 0.436 0.400 0.000 0.600
#> GSM74407 1 0.6180 0.180 0.584 0.000 0.416
#> GSM74408 3 0.4504 0.772 0.196 0.000 0.804
#> GSM74409 3 0.4702 0.758 0.212 0.000 0.788
#> GSM74410 3 0.4452 0.775 0.192 0.000 0.808
#> GSM119936 3 0.4750 0.754 0.216 0.000 0.784
#> GSM119937 3 0.4887 0.743 0.228 0.000 0.772
#> GSM74411 2 0.6026 0.415 0.000 0.624 0.376
#> GSM74412 2 0.0000 0.917 0.000 1.000 0.000
#> GSM74413 2 0.5363 0.604 0.000 0.724 0.276
#> GSM74414 2 0.0000 0.917 0.000 1.000 0.000
#> GSM74415 3 0.1163 0.844 0.000 0.028 0.972
#> GSM121379 2 0.0000 0.917 0.000 1.000 0.000
#> GSM121380 2 0.0000 0.917 0.000 1.000 0.000
#> GSM121381 2 0.0000 0.917 0.000 1.000 0.000
#> GSM121382 2 0.0000 0.917 0.000 1.000 0.000
#> GSM121383 2 0.0000 0.917 0.000 1.000 0.000
#> GSM121384 2 0.0000 0.917 0.000 1.000 0.000
#> GSM121385 2 0.0000 0.917 0.000 1.000 0.000
#> GSM121386 2 0.0000 0.917 0.000 1.000 0.000
#> GSM121387 2 0.0000 0.917 0.000 1.000 0.000
#> GSM121388 2 0.1529 0.893 0.000 0.960 0.040
#> GSM121389 2 0.0000 0.917 0.000 1.000 0.000
#> GSM121390 2 0.0000 0.917 0.000 1.000 0.000
#> GSM121391 2 0.0000 0.917 0.000 1.000 0.000
#> GSM121392 2 0.0237 0.915 0.000 0.996 0.004
#> GSM121393 2 0.0000 0.917 0.000 1.000 0.000
#> GSM121394 2 0.0000 0.917 0.000 1.000 0.000
#> GSM121395 2 0.0000 0.917 0.000 1.000 0.000
#> GSM121396 2 0.2625 0.856 0.000 0.916 0.084
#> GSM121397 2 0.0000 0.917 0.000 1.000 0.000
#> GSM121398 2 0.0000 0.917 0.000 1.000 0.000
#> GSM121399 2 0.0000 0.917 0.000 1.000 0.000
#> GSM74240 3 0.0424 0.853 0.008 0.000 0.992
#> GSM74241 3 0.0592 0.850 0.000 0.012 0.988
#> GSM74242 3 0.1411 0.851 0.036 0.000 0.964
#> GSM74243 3 0.1529 0.850 0.040 0.000 0.960
#> GSM74244 3 0.0000 0.853 0.000 0.000 1.000
#> GSM74245 3 0.0237 0.853 0.000 0.004 0.996
#> GSM74246 3 0.4002 0.737 0.000 0.160 0.840
#> GSM74247 3 0.4178 0.724 0.000 0.172 0.828
#> GSM74248 3 0.0424 0.853 0.008 0.000 0.992
#> GSM74416 1 0.5882 0.388 0.652 0.000 0.348
#> GSM74417 1 0.5988 0.330 0.632 0.000 0.368
#> GSM74418 1 0.5706 0.461 0.680 0.000 0.320
#> GSM74419 3 0.4605 0.766 0.204 0.000 0.796
#> GSM121358 3 0.0237 0.853 0.000 0.004 0.996
#> GSM121359 3 0.3879 0.748 0.000 0.152 0.848
#> GSM121360 3 0.6481 0.710 0.224 0.048 0.728
#> GSM121362 3 0.6402 0.729 0.200 0.056 0.744
#> GSM121364 3 0.4121 0.793 0.168 0.000 0.832
#> GSM121365 3 0.0237 0.853 0.000 0.004 0.996
#> GSM121366 3 0.0237 0.853 0.000 0.004 0.996
#> GSM121367 3 0.0237 0.853 0.000 0.004 0.996
#> GSM121370 3 0.0237 0.853 0.000 0.004 0.996
#> GSM121371 3 0.0237 0.853 0.000 0.004 0.996
#> GSM121372 3 0.2711 0.809 0.000 0.088 0.912
#> GSM121373 3 0.3412 0.820 0.124 0.000 0.876
#> GSM121374 3 0.3267 0.823 0.116 0.000 0.884
#> GSM121407 3 0.4121 0.729 0.000 0.168 0.832
#> GSM74387 2 0.4002 0.767 0.000 0.840 0.160
#> GSM74388 2 0.1399 0.898 0.028 0.968 0.004
#> GSM74389 3 0.3116 0.827 0.108 0.000 0.892
#> GSM74390 3 0.7741 0.637 0.216 0.116 0.668
#> GSM74391 3 0.6308 0.164 0.492 0.000 0.508
#> GSM74392 3 0.4654 0.762 0.208 0.000 0.792
#> GSM74393 3 0.2711 0.837 0.088 0.000 0.912
#> GSM74394 2 0.0424 0.914 0.000 0.992 0.008
#> GSM74239 1 0.0000 0.908 1.000 0.000 0.000
#> GSM74364 1 0.0000 0.908 1.000 0.000 0.000
#> GSM74365 1 0.0000 0.908 1.000 0.000 0.000
#> GSM74366 2 0.6081 0.529 0.344 0.652 0.004
#> GSM74367 1 0.0592 0.903 0.988 0.000 0.012
#> GSM74377 1 0.2063 0.869 0.948 0.044 0.008
#> GSM74378 2 0.6247 0.469 0.376 0.620 0.004
#> GSM74379 1 0.0424 0.905 0.992 0.000 0.008
#> GSM74380 1 0.0000 0.908 1.000 0.000 0.000
#> GSM74381 1 0.4834 0.651 0.792 0.204 0.004
#> GSM121357 2 0.0237 0.915 0.000 0.996 0.004
#> GSM121361 2 0.0475 0.914 0.004 0.992 0.004
#> GSM121363 2 0.0237 0.915 0.000 0.996 0.004
#> GSM121368 2 0.0237 0.915 0.000 0.996 0.004
#> GSM121369 2 0.1529 0.894 0.000 0.960 0.040
#> GSM74368 3 0.6260 0.266 0.448 0.000 0.552
#> GSM74369 1 0.5621 0.456 0.692 0.000 0.308
#> GSM74370 1 0.0237 0.906 0.996 0.000 0.004
#> GSM74371 1 0.0237 0.907 0.996 0.000 0.004
#> GSM74372 1 0.0000 0.908 1.000 0.000 0.000
#> GSM74373 1 0.2200 0.860 0.940 0.056 0.004
#> GSM74374 1 0.0000 0.908 1.000 0.000 0.000
#> GSM74375 1 0.0661 0.906 0.988 0.004 0.008
#> GSM74376 1 0.1711 0.884 0.960 0.032 0.008
#> GSM74405 1 0.0475 0.904 0.992 0.004 0.004
#> GSM74351 1 0.2165 0.863 0.936 0.000 0.064
#> GSM74352 2 0.6247 0.469 0.376 0.620 0.004
#> GSM74353 1 0.0000 0.908 1.000 0.000 0.000
#> GSM74354 1 0.0000 0.908 1.000 0.000 0.000
#> GSM74355 2 0.6500 0.248 0.464 0.532 0.004
#> GSM74382 1 0.1529 0.884 0.960 0.000 0.040
#> GSM74383 1 0.0000 0.908 1.000 0.000 0.000
#> GSM74384 2 0.6169 0.501 0.360 0.636 0.004
#> GSM74385 1 0.0424 0.905 0.992 0.000 0.008
#> GSM74386 1 0.0000 0.908 1.000 0.000 0.000
#> GSM74395 1 0.0237 0.907 0.996 0.000 0.004
#> GSM74396 1 0.0000 0.908 1.000 0.000 0.000
#> GSM74397 1 0.0592 0.903 0.988 0.000 0.012
#> GSM74398 1 0.0237 0.907 0.996 0.000 0.004
#> GSM74399 1 0.0237 0.907 0.996 0.000 0.004
#> GSM74400 1 0.0000 0.908 1.000 0.000 0.000
#> GSM74401 1 0.0237 0.907 0.996 0.000 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM74356 4 0.5060 0.4580 0.000 0.004 0.412 0.584
#> GSM74357 4 0.4888 0.4598 0.000 0.000 0.412 0.588
#> GSM74358 4 0.4888 0.4598 0.000 0.000 0.412 0.588
#> GSM74359 4 0.2589 0.4739 0.000 0.000 0.116 0.884
#> GSM74360 4 0.6111 0.1738 0.092 0.000 0.256 0.652
#> GSM74361 4 0.4790 0.4732 0.000 0.000 0.380 0.620
#> GSM74362 4 0.4877 0.4667 0.000 0.000 0.408 0.592
#> GSM74363 4 0.5060 0.4580 0.000 0.004 0.412 0.584
#> GSM74402 4 0.3933 0.4750 0.200 0.000 0.008 0.792
#> GSM74403 4 0.5203 0.3793 0.348 0.000 0.016 0.636
#> GSM74404 4 0.5649 0.3654 0.344 0.000 0.036 0.620
#> GSM74406 4 0.5172 0.5108 0.188 0.000 0.068 0.744
#> GSM74407 4 0.5697 0.3885 0.292 0.000 0.052 0.656
#> GSM74408 4 0.1637 0.5238 0.060 0.000 0.000 0.940
#> GSM74409 4 0.3691 0.5397 0.076 0.000 0.068 0.856
#> GSM74410 4 0.3935 0.5483 0.060 0.000 0.100 0.840
#> GSM119936 4 0.2300 0.5166 0.064 0.000 0.016 0.920
#> GSM119937 4 0.5171 0.5476 0.112 0.000 0.128 0.760
#> GSM74411 3 0.3497 0.4783 0.000 0.124 0.852 0.024
#> GSM74412 2 0.2814 0.8246 0.000 0.868 0.132 0.000
#> GSM74413 3 0.5344 0.4446 0.000 0.300 0.668 0.032
#> GSM74414 2 0.0000 0.9776 0.000 1.000 0.000 0.000
#> GSM74415 3 0.0469 0.4334 0.000 0.000 0.988 0.012
#> GSM121379 2 0.0000 0.9776 0.000 1.000 0.000 0.000
#> GSM121380 2 0.0000 0.9776 0.000 1.000 0.000 0.000
#> GSM121381 2 0.0000 0.9776 0.000 1.000 0.000 0.000
#> GSM121382 2 0.0000 0.9776 0.000 1.000 0.000 0.000
#> GSM121383 2 0.0000 0.9776 0.000 1.000 0.000 0.000
#> GSM121384 2 0.0000 0.9776 0.000 1.000 0.000 0.000
#> GSM121385 2 0.0000 0.9776 0.000 1.000 0.000 0.000
#> GSM121386 2 0.0000 0.9776 0.000 1.000 0.000 0.000
#> GSM121387 2 0.0000 0.9776 0.000 1.000 0.000 0.000
#> GSM121388 2 0.1356 0.9431 0.000 0.960 0.032 0.008
#> GSM121389 2 0.0000 0.9776 0.000 1.000 0.000 0.000
#> GSM121390 2 0.0000 0.9776 0.000 1.000 0.000 0.000
#> GSM121391 2 0.0000 0.9776 0.000 1.000 0.000 0.000
#> GSM121392 2 0.0000 0.9776 0.000 1.000 0.000 0.000
#> GSM121393 2 0.0000 0.9776 0.000 1.000 0.000 0.000
#> GSM121394 2 0.0000 0.9776 0.000 1.000 0.000 0.000
#> GSM121395 2 0.0000 0.9776 0.000 1.000 0.000 0.000
#> GSM121396 2 0.2921 0.8206 0.000 0.860 0.140 0.000
#> GSM121397 2 0.0000 0.9776 0.000 1.000 0.000 0.000
#> GSM121398 2 0.0000 0.9776 0.000 1.000 0.000 0.000
#> GSM121399 2 0.0000 0.9776 0.000 1.000 0.000 0.000
#> GSM74240 3 0.4699 0.6012 0.004 0.000 0.676 0.320
#> GSM74241 3 0.4040 0.6175 0.000 0.000 0.752 0.248
#> GSM74242 3 0.4564 0.5970 0.000 0.000 0.672 0.328
#> GSM74243 3 0.4605 0.5915 0.000 0.000 0.664 0.336
#> GSM74244 3 0.4040 0.6175 0.000 0.000 0.752 0.248
#> GSM74245 3 0.4040 0.6175 0.000 0.000 0.752 0.248
#> GSM74246 3 0.4220 0.6186 0.004 0.000 0.748 0.248
#> GSM74247 3 0.4220 0.6186 0.004 0.000 0.748 0.248
#> GSM74248 3 0.4522 0.6006 0.000 0.000 0.680 0.320
#> GSM74416 4 0.5173 0.3989 0.320 0.000 0.020 0.660
#> GSM74417 4 0.5152 0.4026 0.316 0.000 0.020 0.664
#> GSM74418 4 0.5252 0.3873 0.336 0.000 0.020 0.644
#> GSM74419 4 0.3081 0.5110 0.064 0.000 0.048 0.888
#> GSM121358 4 0.5060 0.4580 0.000 0.004 0.412 0.584
#> GSM121359 3 0.7745 -0.2105 0.000 0.236 0.412 0.352
#> GSM121360 3 0.6217 0.5692 0.084 0.000 0.624 0.292
#> GSM121362 3 0.6152 0.5702 0.052 0.012 0.640 0.296
#> GSM121364 4 0.3850 0.5188 0.044 0.000 0.116 0.840
#> GSM121365 4 0.5060 0.4580 0.000 0.004 0.412 0.584
#> GSM121366 4 0.5060 0.4580 0.000 0.004 0.412 0.584
#> GSM121367 4 0.5060 0.4580 0.000 0.004 0.412 0.584
#> GSM121370 3 0.5105 -0.2889 0.000 0.004 0.564 0.432
#> GSM121371 4 0.5060 0.4580 0.000 0.004 0.412 0.584
#> GSM121372 4 0.7113 0.3056 0.000 0.128 0.416 0.456
#> GSM121373 4 0.4284 0.5208 0.012 0.000 0.224 0.764
#> GSM121374 4 0.4542 0.5271 0.020 0.000 0.228 0.752
#> GSM121407 3 0.7745 -0.2105 0.000 0.236 0.412 0.352
#> GSM74387 3 0.5126 0.2230 0.004 0.444 0.552 0.000
#> GSM74388 2 0.2149 0.8809 0.088 0.912 0.000 0.000
#> GSM74389 3 0.4855 0.5252 0.000 0.000 0.600 0.400
#> GSM74390 3 0.7136 0.5590 0.116 0.024 0.608 0.252
#> GSM74391 4 0.6295 -0.0606 0.072 0.000 0.348 0.580
#> GSM74392 4 0.4955 -0.0252 0.008 0.000 0.344 0.648
#> GSM74393 3 0.4746 0.5622 0.000 0.000 0.632 0.368
#> GSM74394 3 0.5080 0.2914 0.004 0.420 0.576 0.000
#> GSM74239 1 0.2530 0.7774 0.888 0.000 0.000 0.112
#> GSM74364 1 0.2704 0.7685 0.876 0.000 0.000 0.124
#> GSM74365 1 0.0188 0.8351 0.996 0.000 0.000 0.004
#> GSM74366 1 0.4661 0.4912 0.652 0.348 0.000 0.000
#> GSM74367 1 0.2589 0.7720 0.884 0.000 0.000 0.116
#> GSM74377 1 0.0188 0.8344 0.996 0.000 0.004 0.000
#> GSM74378 1 0.4477 0.5554 0.688 0.312 0.000 0.000
#> GSM74379 1 0.0000 0.8348 1.000 0.000 0.000 0.000
#> GSM74380 1 0.0188 0.8351 0.996 0.000 0.000 0.004
#> GSM74381 1 0.1867 0.7975 0.928 0.072 0.000 0.000
#> GSM121357 2 0.0000 0.9776 0.000 1.000 0.000 0.000
#> GSM121361 2 0.2480 0.8788 0.008 0.904 0.088 0.000
#> GSM121363 2 0.0376 0.9719 0.004 0.992 0.004 0.000
#> GSM121368 2 0.0376 0.9719 0.004 0.992 0.004 0.000
#> GSM121369 3 0.5452 0.2583 0.016 0.428 0.556 0.000
#> GSM74368 4 0.6340 0.2166 0.408 0.000 0.064 0.528
#> GSM74369 1 0.3626 0.6741 0.812 0.000 0.004 0.184
#> GSM74370 1 0.0817 0.8277 0.976 0.000 0.024 0.000
#> GSM74371 1 0.3219 0.7288 0.836 0.000 0.000 0.164
#> GSM74372 1 0.1302 0.8192 0.956 0.000 0.000 0.044
#> GSM74373 1 0.1389 0.8144 0.952 0.000 0.048 0.000
#> GSM74374 1 0.0188 0.8351 0.996 0.000 0.000 0.004
#> GSM74375 1 0.5956 0.6012 0.680 0.000 0.220 0.100
#> GSM74376 1 0.4382 0.5409 0.704 0.000 0.296 0.000
#> GSM74405 1 0.0000 0.8348 1.000 0.000 0.000 0.000
#> GSM74351 1 0.5444 0.1839 0.560 0.000 0.016 0.424
#> GSM74352 1 0.4585 0.5219 0.668 0.332 0.000 0.000
#> GSM74353 1 0.0469 0.8338 0.988 0.000 0.000 0.012
#> GSM74354 1 0.0524 0.8352 0.988 0.000 0.004 0.008
#> GSM74355 1 0.4250 0.6121 0.724 0.276 0.000 0.000
#> GSM74382 4 0.5600 0.0618 0.468 0.000 0.020 0.512
#> GSM74383 1 0.0336 0.8350 0.992 0.000 0.000 0.008
#> GSM74384 1 0.4543 0.5348 0.676 0.324 0.000 0.000
#> GSM74385 1 0.3444 0.6978 0.816 0.000 0.000 0.184
#> GSM74386 1 0.4332 0.7167 0.800 0.000 0.160 0.040
#> GSM74395 1 0.1733 0.8251 0.948 0.000 0.024 0.028
#> GSM74396 1 0.0188 0.8351 0.996 0.000 0.000 0.004
#> GSM74397 1 0.4790 0.3484 0.620 0.000 0.000 0.380
#> GSM74398 1 0.0376 0.8356 0.992 0.000 0.004 0.004
#> GSM74399 1 0.0000 0.8348 1.000 0.000 0.000 0.000
#> GSM74400 1 0.0817 0.8300 0.976 0.000 0.000 0.024
#> GSM74401 1 0.0188 0.8351 0.996 0.000 0.000 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM74356 3 0.0609 0.9006 0.000 0.000 0.980 0.020 0.000
#> GSM74357 3 0.0609 0.9006 0.000 0.000 0.980 0.020 0.000
#> GSM74358 3 0.0609 0.9006 0.000 0.000 0.980 0.020 0.000
#> GSM74359 4 0.5435 0.0823 0.000 0.000 0.428 0.512 0.060
#> GSM74360 4 0.2624 0.6261 0.000 0.000 0.012 0.872 0.116
#> GSM74361 3 0.2927 0.7986 0.000 0.000 0.868 0.040 0.092
#> GSM74362 3 0.5490 0.4920 0.000 0.000 0.644 0.128 0.228
#> GSM74363 3 0.0609 0.9006 0.000 0.000 0.980 0.020 0.000
#> GSM74402 4 0.5815 0.5657 0.068 0.000 0.268 0.632 0.032
#> GSM74403 4 0.3321 0.6945 0.136 0.000 0.000 0.832 0.032
#> GSM74404 4 0.4194 0.6785 0.132 0.000 0.000 0.780 0.088
#> GSM74406 4 0.0865 0.6852 0.000 0.000 0.024 0.972 0.004
#> GSM74407 4 0.7305 0.5442 0.100 0.000 0.184 0.544 0.172
#> GSM74408 4 0.3454 0.6544 0.000 0.000 0.156 0.816 0.028
#> GSM74409 4 0.2439 0.6496 0.000 0.000 0.120 0.876 0.004
#> GSM74410 4 0.4366 0.5095 0.000 0.000 0.320 0.664 0.016
#> GSM119936 4 0.3577 0.6479 0.000 0.000 0.160 0.808 0.032
#> GSM119937 4 0.5891 0.3314 0.068 0.000 0.428 0.492 0.012
#> GSM74411 5 0.2625 0.7982 0.000 0.016 0.108 0.000 0.876
#> GSM74412 2 0.3177 0.7319 0.000 0.792 0.000 0.000 0.208
#> GSM74413 5 0.3966 0.7465 0.000 0.132 0.072 0.000 0.796
#> GSM74414 2 0.0000 0.9723 0.000 1.000 0.000 0.000 0.000
#> GSM74415 5 0.1341 0.8351 0.000 0.000 0.056 0.000 0.944
#> GSM121379 2 0.0000 0.9723 0.000 1.000 0.000 0.000 0.000
#> GSM121380 2 0.0000 0.9723 0.000 1.000 0.000 0.000 0.000
#> GSM121381 2 0.0000 0.9723 0.000 1.000 0.000 0.000 0.000
#> GSM121382 2 0.0000 0.9723 0.000 1.000 0.000 0.000 0.000
#> GSM121383 2 0.0000 0.9723 0.000 1.000 0.000 0.000 0.000
#> GSM121384 2 0.0000 0.9723 0.000 1.000 0.000 0.000 0.000
#> GSM121385 2 0.0000 0.9723 0.000 1.000 0.000 0.000 0.000
#> GSM121386 2 0.0000 0.9723 0.000 1.000 0.000 0.000 0.000
#> GSM121387 2 0.0000 0.9723 0.000 1.000 0.000 0.000 0.000
#> GSM121388 2 0.1851 0.8995 0.000 0.912 0.088 0.000 0.000
#> GSM121389 2 0.0000 0.9723 0.000 1.000 0.000 0.000 0.000
#> GSM121390 2 0.0000 0.9723 0.000 1.000 0.000 0.000 0.000
#> GSM121391 2 0.0000 0.9723 0.000 1.000 0.000 0.000 0.000
#> GSM121392 2 0.0000 0.9723 0.000 1.000 0.000 0.000 0.000
#> GSM121393 2 0.0000 0.9723 0.000 1.000 0.000 0.000 0.000
#> GSM121394 2 0.0000 0.9723 0.000 1.000 0.000 0.000 0.000
#> GSM121395 2 0.0000 0.9723 0.000 1.000 0.000 0.000 0.000
#> GSM121396 2 0.2471 0.8443 0.000 0.864 0.136 0.000 0.000
#> GSM121397 2 0.0000 0.9723 0.000 1.000 0.000 0.000 0.000
#> GSM121398 2 0.0000 0.9723 0.000 1.000 0.000 0.000 0.000
#> GSM121399 2 0.0000 0.9723 0.000 1.000 0.000 0.000 0.000
#> GSM74240 5 0.0290 0.8587 0.000 0.000 0.000 0.008 0.992
#> GSM74241 5 0.0290 0.8587 0.000 0.000 0.000 0.008 0.992
#> GSM74242 5 0.0609 0.8556 0.000 0.000 0.000 0.020 0.980
#> GSM74243 5 0.0609 0.8556 0.000 0.000 0.000 0.020 0.980
#> GSM74244 5 0.0290 0.8587 0.000 0.000 0.000 0.008 0.992
#> GSM74245 5 0.0290 0.8587 0.000 0.000 0.000 0.008 0.992
#> GSM74246 5 0.0290 0.8587 0.000 0.000 0.000 0.008 0.992
#> GSM74247 5 0.0290 0.8587 0.000 0.000 0.000 0.008 0.992
#> GSM74248 5 0.0290 0.8587 0.000 0.000 0.000 0.008 0.992
#> GSM74416 4 0.2654 0.6994 0.084 0.000 0.000 0.884 0.032
#> GSM74417 4 0.0290 0.6872 0.000 0.000 0.000 0.992 0.008
#> GSM74418 4 0.0703 0.6920 0.024 0.000 0.000 0.976 0.000
#> GSM74419 4 0.4879 0.5658 0.016 0.000 0.264 0.688 0.032
#> GSM121358 3 0.0000 0.9083 0.000 0.000 1.000 0.000 0.000
#> GSM121359 3 0.0290 0.9019 0.000 0.008 0.992 0.000 0.000
#> GSM121360 5 0.4684 0.2847 0.008 0.000 0.004 0.452 0.536
#> GSM121362 5 0.5715 0.1790 0.028 0.000 0.032 0.460 0.480
#> GSM121364 4 0.5053 0.4982 0.000 0.000 0.216 0.688 0.096
#> GSM121365 3 0.0000 0.9083 0.000 0.000 1.000 0.000 0.000
#> GSM121366 3 0.0000 0.9083 0.000 0.000 1.000 0.000 0.000
#> GSM121367 3 0.0000 0.9083 0.000 0.000 1.000 0.000 0.000
#> GSM121370 3 0.0000 0.9083 0.000 0.000 1.000 0.000 0.000
#> GSM121371 3 0.0000 0.9083 0.000 0.000 1.000 0.000 0.000
#> GSM121372 3 0.0000 0.9083 0.000 0.000 1.000 0.000 0.000
#> GSM121373 4 0.6309 0.2566 0.000 0.000 0.288 0.520 0.192
#> GSM121374 4 0.4708 0.4314 0.000 0.000 0.292 0.668 0.040
#> GSM121407 3 0.0290 0.9019 0.000 0.008 0.992 0.000 0.000
#> GSM74387 5 0.2719 0.7676 0.004 0.144 0.000 0.000 0.852
#> GSM74388 2 0.2077 0.8868 0.084 0.908 0.000 0.000 0.008
#> GSM74389 5 0.1764 0.8268 0.000 0.000 0.008 0.064 0.928
#> GSM74390 5 0.0290 0.8538 0.008 0.000 0.000 0.000 0.992
#> GSM74391 5 0.4425 0.2912 0.000 0.000 0.008 0.392 0.600
#> GSM74392 5 0.4109 0.5566 0.000 0.000 0.012 0.288 0.700
#> GSM74393 5 0.0880 0.8499 0.000 0.000 0.000 0.032 0.968
#> GSM74394 5 0.2439 0.7924 0.004 0.120 0.000 0.000 0.876
#> GSM74239 1 0.4047 0.4596 0.676 0.000 0.000 0.320 0.004
#> GSM74364 4 0.4648 0.1345 0.464 0.000 0.000 0.524 0.012
#> GSM74365 1 0.0290 0.8807 0.992 0.000 0.000 0.008 0.000
#> GSM74366 1 0.3132 0.7379 0.820 0.172 0.000 0.000 0.008
#> GSM74367 1 0.3491 0.6446 0.768 0.000 0.000 0.228 0.004
#> GSM74377 1 0.0290 0.8795 0.992 0.000 0.000 0.000 0.008
#> GSM74378 1 0.2136 0.8248 0.904 0.088 0.000 0.000 0.008
#> GSM74379 1 0.0290 0.8807 0.992 0.000 0.000 0.008 0.000
#> GSM74380 1 0.0162 0.8807 0.996 0.000 0.000 0.004 0.000
#> GSM74381 1 0.0451 0.8790 0.988 0.004 0.000 0.000 0.008
#> GSM121357 2 0.0000 0.9723 0.000 1.000 0.000 0.000 0.000
#> GSM121361 2 0.2886 0.8158 0.008 0.844 0.000 0.000 0.148
#> GSM121363 2 0.0671 0.9601 0.004 0.980 0.000 0.000 0.016
#> GSM121368 2 0.0566 0.9624 0.004 0.984 0.000 0.000 0.012
#> GSM121369 5 0.2971 0.7576 0.008 0.156 0.000 0.000 0.836
#> GSM74368 3 0.7899 -0.0650 0.308 0.000 0.388 0.220 0.084
#> GSM74369 1 0.2899 0.8120 0.872 0.000 0.096 0.028 0.004
#> GSM74370 1 0.1628 0.8622 0.936 0.000 0.000 0.056 0.008
#> GSM74371 4 0.4118 0.4713 0.336 0.000 0.000 0.660 0.004
#> GSM74372 1 0.1043 0.8730 0.960 0.000 0.000 0.040 0.000
#> GSM74373 1 0.0162 0.8807 0.996 0.000 0.000 0.004 0.000
#> GSM74374 1 0.0162 0.8807 0.996 0.000 0.000 0.004 0.000
#> GSM74375 1 0.6456 0.0401 0.468 0.000 0.000 0.340 0.192
#> GSM74376 1 0.2020 0.8186 0.900 0.000 0.000 0.000 0.100
#> GSM74405 1 0.0162 0.8802 0.996 0.000 0.000 0.000 0.004
#> GSM74351 4 0.4616 0.5532 0.288 0.000 0.000 0.676 0.036
#> GSM74352 1 0.2462 0.8050 0.880 0.112 0.000 0.000 0.008
#> GSM74353 1 0.0609 0.8804 0.980 0.000 0.000 0.020 0.000
#> GSM74354 1 0.2124 0.8346 0.900 0.000 0.000 0.096 0.004
#> GSM74355 1 0.1830 0.8420 0.924 0.068 0.000 0.000 0.008
#> GSM74382 4 0.3035 0.6978 0.112 0.000 0.000 0.856 0.032
#> GSM74383 1 0.1043 0.8715 0.960 0.000 0.000 0.040 0.000
#> GSM74384 1 0.2193 0.8208 0.900 0.092 0.000 0.000 0.008
#> GSM74385 4 0.4434 0.1630 0.460 0.000 0.000 0.536 0.004
#> GSM74386 1 0.5921 0.3637 0.568 0.000 0.000 0.136 0.296
#> GSM74395 1 0.2305 0.8396 0.896 0.000 0.000 0.092 0.012
#> GSM74396 1 0.0963 0.8737 0.964 0.000 0.000 0.036 0.000
#> GSM74397 4 0.4731 0.4728 0.328 0.000 0.000 0.640 0.032
#> GSM74398 1 0.0451 0.8812 0.988 0.000 0.000 0.008 0.004
#> GSM74399 1 0.0000 0.8807 1.000 0.000 0.000 0.000 0.000
#> GSM74400 1 0.2329 0.7992 0.876 0.000 0.000 0.124 0.000
#> GSM74401 1 0.0703 0.8784 0.976 0.000 0.000 0.024 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM74356 3 0.0790 0.926 0.000 0.000 0.968 0.032 0.000 0.000
#> GSM74357 3 0.0790 0.926 0.000 0.000 0.968 0.032 0.000 0.000
#> GSM74358 3 0.0790 0.926 0.000 0.000 0.968 0.032 0.000 0.000
#> GSM74359 4 0.0622 0.896 0.012 0.000 0.008 0.980 0.000 0.000
#> GSM74360 4 0.0632 0.898 0.024 0.000 0.000 0.976 0.000 0.000
#> GSM74361 3 0.3427 0.750 0.008 0.000 0.804 0.032 0.156 0.000
#> GSM74362 5 0.5923 0.177 0.008 0.000 0.356 0.168 0.468 0.000
#> GSM74363 3 0.0790 0.926 0.000 0.000 0.968 0.032 0.000 0.000
#> GSM74402 1 0.2568 0.761 0.876 0.000 0.068 0.056 0.000 0.000
#> GSM74403 1 0.0363 0.786 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM74404 1 0.0000 0.784 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM74406 1 0.3014 0.715 0.804 0.000 0.012 0.184 0.000 0.000
#> GSM74407 1 0.0405 0.785 0.988 0.000 0.000 0.008 0.004 0.000
#> GSM74408 1 0.5095 0.488 0.584 0.000 0.104 0.312 0.000 0.000
#> GSM74409 4 0.1765 0.827 0.096 0.000 0.000 0.904 0.000 0.000
#> GSM74410 1 0.5149 0.567 0.624 0.000 0.184 0.192 0.000 0.000
#> GSM119936 1 0.3481 0.717 0.792 0.000 0.048 0.160 0.000 0.000
#> GSM119937 1 0.4831 0.561 0.668 0.000 0.164 0.168 0.000 0.000
#> GSM74411 5 0.0937 0.873 0.000 0.000 0.040 0.000 0.960 0.000
#> GSM74412 2 0.3431 0.692 0.000 0.756 0.016 0.000 0.228 0.000
#> GSM74413 5 0.2474 0.817 0.000 0.080 0.040 0.000 0.880 0.000
#> GSM74414 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM74415 5 0.0547 0.882 0.000 0.000 0.020 0.000 0.980 0.000
#> GSM121379 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121380 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121381 2 0.0146 0.962 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM121382 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121383 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121384 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121385 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121386 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121387 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121388 2 0.2340 0.829 0.000 0.852 0.148 0.000 0.000 0.000
#> GSM121389 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121390 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121391 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121392 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121393 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121394 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121395 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121396 2 0.2219 0.841 0.000 0.864 0.136 0.000 0.000 0.000
#> GSM121397 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121398 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121399 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM74240 5 0.0000 0.887 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74241 5 0.0000 0.887 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74242 5 0.0291 0.886 0.004 0.000 0.004 0.000 0.992 0.000
#> GSM74243 5 0.0146 0.887 0.004 0.000 0.000 0.000 0.996 0.000
#> GSM74244 5 0.0000 0.887 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74245 5 0.0000 0.887 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74246 5 0.0000 0.887 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74247 5 0.0000 0.887 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74248 5 0.0000 0.887 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74416 1 0.0458 0.785 0.984 0.000 0.000 0.016 0.000 0.000
#> GSM74417 1 0.2969 0.683 0.776 0.000 0.000 0.224 0.000 0.000
#> GSM74418 1 0.2631 0.721 0.820 0.000 0.000 0.180 0.000 0.000
#> GSM74419 1 0.3534 0.723 0.800 0.000 0.076 0.124 0.000 0.000
#> GSM121358 3 0.0000 0.939 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121359 3 0.0000 0.939 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121360 4 0.1049 0.872 0.000 0.000 0.000 0.960 0.032 0.008
#> GSM121362 4 0.1007 0.877 0.000 0.000 0.000 0.956 0.044 0.000
#> GSM121364 4 0.0725 0.899 0.012 0.000 0.000 0.976 0.012 0.000
#> GSM121365 3 0.0000 0.939 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121366 3 0.0000 0.939 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121367 3 0.0000 0.939 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121370 3 0.0000 0.939 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121371 3 0.0000 0.939 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121372 3 0.0000 0.939 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121373 4 0.0717 0.897 0.008 0.000 0.000 0.976 0.016 0.000
#> GSM121374 4 0.0632 0.898 0.024 0.000 0.000 0.976 0.000 0.000
#> GSM121407 3 0.0000 0.939 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM74387 5 0.2007 0.860 0.000 0.040 0.016 0.012 0.924 0.008
#> GSM74388 2 0.2039 0.883 0.000 0.904 0.000 0.020 0.000 0.076
#> GSM74389 5 0.2805 0.759 0.012 0.000 0.000 0.160 0.828 0.000
#> GSM74390 5 0.0806 0.879 0.000 0.000 0.000 0.020 0.972 0.008
#> GSM74391 5 0.4294 0.206 0.428 0.000 0.000 0.020 0.552 0.000
#> GSM74392 5 0.4057 0.276 0.008 0.000 0.000 0.436 0.556 0.000
#> GSM74393 5 0.0405 0.884 0.008 0.000 0.000 0.004 0.988 0.000
#> GSM74394 5 0.2402 0.825 0.000 0.084 0.000 0.020 0.888 0.008
#> GSM74239 1 0.3867 -0.223 0.512 0.000 0.000 0.000 0.000 0.488
#> GSM74364 1 0.2826 0.669 0.844 0.000 0.000 0.028 0.000 0.128
#> GSM74365 6 0.1714 0.871 0.092 0.000 0.000 0.000 0.000 0.908
#> GSM74366 6 0.2006 0.826 0.000 0.080 0.000 0.016 0.000 0.904
#> GSM74367 6 0.4569 0.437 0.396 0.000 0.000 0.040 0.000 0.564
#> GSM74377 6 0.0000 0.888 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM74378 6 0.0000 0.888 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM74379 6 0.1075 0.886 0.048 0.000 0.000 0.000 0.000 0.952
#> GSM74380 6 0.0260 0.889 0.008 0.000 0.000 0.000 0.000 0.992
#> GSM74381 6 0.0000 0.888 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM121357 2 0.0622 0.954 0.000 0.980 0.012 0.008 0.000 0.000
#> GSM121361 2 0.3294 0.783 0.000 0.812 0.000 0.020 0.156 0.012
#> GSM121363 2 0.1065 0.944 0.000 0.964 0.000 0.020 0.008 0.008
#> GSM121368 2 0.0951 0.946 0.000 0.968 0.000 0.020 0.004 0.008
#> GSM121369 5 0.2382 0.836 0.000 0.072 0.004 0.020 0.896 0.008
#> GSM74368 3 0.6771 0.368 0.152 0.000 0.528 0.020 0.060 0.240
#> GSM74369 6 0.2294 0.832 0.036 0.000 0.072 0.000 0.000 0.892
#> GSM74370 6 0.2263 0.866 0.048 0.000 0.000 0.056 0.000 0.896
#> GSM74371 1 0.0146 0.782 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM74372 6 0.2178 0.848 0.132 0.000 0.000 0.000 0.000 0.868
#> GSM74373 6 0.0260 0.889 0.008 0.000 0.000 0.000 0.000 0.992
#> GSM74374 6 0.0363 0.890 0.012 0.000 0.000 0.000 0.000 0.988
#> GSM74375 1 0.5520 0.406 0.532 0.000 0.000 0.000 0.156 0.312
#> GSM74376 6 0.0000 0.888 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM74405 6 0.0000 0.888 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM74351 1 0.0000 0.784 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM74352 6 0.0909 0.878 0.000 0.020 0.000 0.012 0.000 0.968
#> GSM74353 6 0.1663 0.875 0.088 0.000 0.000 0.000 0.000 0.912
#> GSM74354 6 0.3383 0.729 0.268 0.000 0.000 0.000 0.004 0.728
#> GSM74355 6 0.0458 0.884 0.000 0.000 0.000 0.016 0.000 0.984
#> GSM74382 1 0.0000 0.784 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM74383 6 0.2883 0.790 0.212 0.000 0.000 0.000 0.000 0.788
#> GSM74384 6 0.0000 0.888 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM74385 4 0.5099 0.190 0.424 0.000 0.000 0.496 0.000 0.080
#> GSM74386 6 0.7382 0.198 0.200 0.000 0.000 0.144 0.272 0.384
#> GSM74395 6 0.3650 0.772 0.216 0.000 0.000 0.024 0.004 0.756
#> GSM74396 6 0.2854 0.794 0.208 0.000 0.000 0.000 0.000 0.792
#> GSM74397 1 0.0146 0.784 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM74398 6 0.0260 0.889 0.008 0.000 0.000 0.000 0.000 0.992
#> GSM74399 6 0.0146 0.889 0.004 0.000 0.000 0.000 0.000 0.996
#> GSM74400 6 0.2912 0.770 0.216 0.000 0.000 0.000 0.000 0.784
#> GSM74401 6 0.1610 0.876 0.084 0.000 0.000 0.000 0.000 0.916
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 117 4.39e-11 2
#> MAD:pam 107 9.17e-24 3
#> MAD:pam 84 1.16e-28 4
#> MAD:pam 104 5.26e-42 5
#> MAD:pam 111 4.15e-37 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 21168 rows and 121 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 0.805 0.914 0.957 0.4970 0.502 0.502
#> 3 3 0.882 0.915 0.950 0.3095 0.821 0.651
#> 4 4 0.707 0.806 0.879 0.1071 0.924 0.786
#> 5 5 0.838 0.845 0.916 0.0862 0.899 0.666
#> 6 6 0.802 0.669 0.813 0.0435 0.925 0.672
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
#> GSM74356 2 0.0000 0.925 0.000 1.000
#> GSM74357 2 0.0000 0.925 0.000 1.000
#> GSM74358 2 0.0000 0.925 0.000 1.000
#> GSM74359 2 0.9129 0.596 0.328 0.672
#> GSM74360 1 0.9795 0.159 0.584 0.416
#> GSM74361 2 0.0000 0.925 0.000 1.000
#> GSM74362 2 0.0000 0.925 0.000 1.000
#> GSM74363 2 0.0376 0.926 0.004 0.996
#> GSM74402 1 0.0000 0.990 1.000 0.000
#> GSM74403 1 0.0000 0.990 1.000 0.000
#> GSM74404 1 0.0000 0.990 1.000 0.000
#> GSM74406 1 0.0376 0.986 0.996 0.004
#> GSM74407 1 0.0000 0.990 1.000 0.000
#> GSM74408 1 0.0000 0.990 1.000 0.000
#> GSM74409 1 0.0000 0.990 1.000 0.000
#> GSM74410 1 0.0000 0.990 1.000 0.000
#> GSM119936 1 0.0000 0.990 1.000 0.000
#> GSM119937 1 0.0000 0.990 1.000 0.000
#> GSM74411 2 0.0000 0.925 0.000 1.000
#> GSM74412 2 0.0000 0.925 0.000 1.000
#> GSM74413 2 0.0000 0.925 0.000 1.000
#> GSM74414 2 0.1184 0.926 0.016 0.984
#> GSM74415 2 0.0000 0.925 0.000 1.000
#> GSM121379 2 0.1184 0.926 0.016 0.984
#> GSM121380 2 0.1184 0.926 0.016 0.984
#> GSM121381 2 0.1184 0.926 0.016 0.984
#> GSM121382 2 0.1184 0.926 0.016 0.984
#> GSM121383 2 0.1184 0.926 0.016 0.984
#> GSM121384 2 0.1184 0.926 0.016 0.984
#> GSM121385 2 0.1184 0.926 0.016 0.984
#> GSM121386 2 0.1184 0.926 0.016 0.984
#> GSM121387 2 0.1184 0.926 0.016 0.984
#> GSM121388 2 0.1184 0.926 0.016 0.984
#> GSM121389 2 0.1184 0.926 0.016 0.984
#> GSM121390 2 0.1184 0.926 0.016 0.984
#> GSM121391 2 0.1184 0.926 0.016 0.984
#> GSM121392 2 0.1184 0.926 0.016 0.984
#> GSM121393 2 0.1843 0.921 0.028 0.972
#> GSM121394 2 0.1184 0.926 0.016 0.984
#> GSM121395 2 0.1184 0.926 0.016 0.984
#> GSM121396 2 0.0938 0.926 0.012 0.988
#> GSM121397 2 0.1184 0.926 0.016 0.984
#> GSM121398 2 0.1184 0.926 0.016 0.984
#> GSM121399 2 0.1184 0.926 0.016 0.984
#> GSM74240 2 0.4161 0.887 0.084 0.916
#> GSM74241 2 0.4431 0.882 0.092 0.908
#> GSM74242 2 0.6623 0.818 0.172 0.828
#> GSM74243 2 0.6623 0.818 0.172 0.828
#> GSM74244 2 0.0376 0.925 0.004 0.996
#> GSM74245 2 0.0376 0.925 0.004 0.996
#> GSM74246 2 0.0672 0.925 0.008 0.992
#> GSM74247 2 0.0376 0.925 0.004 0.996
#> GSM74248 2 0.2603 0.910 0.044 0.956
#> GSM74416 1 0.0000 0.990 1.000 0.000
#> GSM74417 1 0.0000 0.990 1.000 0.000
#> GSM74418 1 0.0000 0.990 1.000 0.000
#> GSM74419 1 0.0000 0.990 1.000 0.000
#> GSM121358 2 0.0000 0.925 0.000 1.000
#> GSM121359 2 0.0000 0.925 0.000 1.000
#> GSM121360 2 0.7528 0.767 0.216 0.784
#> GSM121362 2 0.9710 0.457 0.400 0.600
#> GSM121364 2 0.9732 0.439 0.404 0.596
#> GSM121365 2 0.0000 0.925 0.000 1.000
#> GSM121366 2 0.0000 0.925 0.000 1.000
#> GSM121367 2 0.0000 0.925 0.000 1.000
#> GSM121370 2 0.0000 0.925 0.000 1.000
#> GSM121371 2 0.0000 0.925 0.000 1.000
#> GSM121372 2 0.0000 0.925 0.000 1.000
#> GSM121373 2 0.9795 0.409 0.416 0.584
#> GSM121374 2 0.9732 0.439 0.404 0.596
#> GSM121407 2 0.0000 0.925 0.000 1.000
#> GSM74387 2 0.1184 0.923 0.016 0.984
#> GSM74388 2 0.6887 0.810 0.184 0.816
#> GSM74389 2 0.6623 0.818 0.172 0.828
#> GSM74390 1 0.2236 0.950 0.964 0.036
#> GSM74391 1 0.0672 0.983 0.992 0.008
#> GSM74392 2 0.9775 0.419 0.412 0.588
#> GSM74393 2 0.6531 0.822 0.168 0.832
#> GSM74394 2 0.5519 0.856 0.128 0.872
#> GSM74239 1 0.0000 0.990 1.000 0.000
#> GSM74364 1 0.0000 0.990 1.000 0.000
#> GSM74365 1 0.0000 0.990 1.000 0.000
#> GSM74366 1 0.0000 0.990 1.000 0.000
#> GSM74367 1 0.0000 0.990 1.000 0.000
#> GSM74377 1 0.0000 0.990 1.000 0.000
#> GSM74378 1 0.0000 0.990 1.000 0.000
#> GSM74379 1 0.0000 0.990 1.000 0.000
#> GSM74380 1 0.0000 0.990 1.000 0.000
#> GSM74381 1 0.0000 0.990 1.000 0.000
#> GSM121357 2 0.1184 0.926 0.016 0.984
#> GSM121361 2 0.6438 0.826 0.164 0.836
#> GSM121363 2 0.6048 0.840 0.148 0.852
#> GSM121368 2 0.5294 0.863 0.120 0.880
#> GSM121369 2 0.5946 0.843 0.144 0.856
#> GSM74368 1 0.0000 0.990 1.000 0.000
#> GSM74369 1 0.0000 0.990 1.000 0.000
#> GSM74370 1 0.0000 0.990 1.000 0.000
#> GSM74371 1 0.0000 0.990 1.000 0.000
#> GSM74372 1 0.0000 0.990 1.000 0.000
#> GSM74373 1 0.0000 0.990 1.000 0.000
#> GSM74374 1 0.0000 0.990 1.000 0.000
#> GSM74375 1 0.0000 0.990 1.000 0.000
#> GSM74376 1 0.0000 0.990 1.000 0.000
#> GSM74405 1 0.0000 0.990 1.000 0.000
#> GSM74351 1 0.0000 0.990 1.000 0.000
#> GSM74352 1 0.0000 0.990 1.000 0.000
#> GSM74353 1 0.0000 0.990 1.000 0.000
#> GSM74354 1 0.0000 0.990 1.000 0.000
#> GSM74355 1 0.0000 0.990 1.000 0.000
#> GSM74382 1 0.0000 0.990 1.000 0.000
#> GSM74383 1 0.0000 0.990 1.000 0.000
#> GSM74384 1 0.0000 0.990 1.000 0.000
#> GSM74385 1 0.0000 0.990 1.000 0.000
#> GSM74386 1 0.0000 0.990 1.000 0.000
#> GSM74395 1 0.0000 0.990 1.000 0.000
#> GSM74396 1 0.0000 0.990 1.000 0.000
#> GSM74397 1 0.0000 0.990 1.000 0.000
#> GSM74398 1 0.0000 0.990 1.000 0.000
#> GSM74399 1 0.0000 0.990 1.000 0.000
#> GSM74400 1 0.0000 0.990 1.000 0.000
#> GSM74401 1 0.0000 0.990 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM74356 2 0.4931 0.815 0.000 0.768 0.232
#> GSM74357 2 0.4931 0.815 0.000 0.768 0.232
#> GSM74358 2 0.4931 0.815 0.000 0.768 0.232
#> GSM74359 3 0.0000 0.946 0.000 0.000 1.000
#> GSM74360 3 0.0747 0.931 0.016 0.000 0.984
#> GSM74361 2 0.6302 0.358 0.000 0.520 0.480
#> GSM74362 3 0.0592 0.940 0.000 0.012 0.988
#> GSM74363 2 0.4931 0.815 0.000 0.768 0.232
#> GSM74402 1 0.0237 0.997 0.996 0.000 0.004
#> GSM74403 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74404 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74406 1 0.0237 0.997 0.996 0.000 0.004
#> GSM74407 1 0.0237 0.997 0.996 0.000 0.004
#> GSM74408 1 0.0237 0.997 0.996 0.000 0.004
#> GSM74409 1 0.0237 0.997 0.996 0.000 0.004
#> GSM74410 1 0.0237 0.997 0.996 0.000 0.004
#> GSM119936 1 0.0237 0.997 0.996 0.000 0.004
#> GSM119937 1 0.0237 0.997 0.996 0.000 0.004
#> GSM74411 2 0.4931 0.815 0.000 0.768 0.232
#> GSM74412 2 0.4931 0.815 0.000 0.768 0.232
#> GSM74413 2 0.4887 0.817 0.000 0.772 0.228
#> GSM74414 2 0.0000 0.872 0.000 1.000 0.000
#> GSM74415 2 0.6260 0.444 0.000 0.552 0.448
#> GSM121379 2 0.0000 0.872 0.000 1.000 0.000
#> GSM121380 2 0.0000 0.872 0.000 1.000 0.000
#> GSM121381 2 0.0000 0.872 0.000 1.000 0.000
#> GSM121382 2 0.0000 0.872 0.000 1.000 0.000
#> GSM121383 2 0.0000 0.872 0.000 1.000 0.000
#> GSM121384 2 0.0000 0.872 0.000 1.000 0.000
#> GSM121385 2 0.0000 0.872 0.000 1.000 0.000
#> GSM121386 2 0.0000 0.872 0.000 1.000 0.000
#> GSM121387 2 0.0000 0.872 0.000 1.000 0.000
#> GSM121388 2 0.0000 0.872 0.000 1.000 0.000
#> GSM121389 2 0.0000 0.872 0.000 1.000 0.000
#> GSM121390 2 0.0000 0.872 0.000 1.000 0.000
#> GSM121391 2 0.0000 0.872 0.000 1.000 0.000
#> GSM121392 2 0.0000 0.872 0.000 1.000 0.000
#> GSM121393 2 0.0000 0.872 0.000 1.000 0.000
#> GSM121394 2 0.0000 0.872 0.000 1.000 0.000
#> GSM121395 2 0.0000 0.872 0.000 1.000 0.000
#> GSM121396 2 0.2537 0.857 0.000 0.920 0.080
#> GSM121397 2 0.0000 0.872 0.000 1.000 0.000
#> GSM121398 2 0.0000 0.872 0.000 1.000 0.000
#> GSM121399 2 0.0000 0.872 0.000 1.000 0.000
#> GSM74240 3 0.0237 0.947 0.000 0.004 0.996
#> GSM74241 3 0.1529 0.908 0.000 0.040 0.960
#> GSM74242 3 0.0000 0.946 0.000 0.000 1.000
#> GSM74243 3 0.0237 0.947 0.000 0.004 0.996
#> GSM74244 3 0.0237 0.947 0.000 0.004 0.996
#> GSM74245 3 0.0237 0.947 0.000 0.004 0.996
#> GSM74246 3 0.0237 0.947 0.000 0.004 0.996
#> GSM74247 3 0.0237 0.947 0.000 0.004 0.996
#> GSM74248 3 0.0237 0.947 0.000 0.004 0.996
#> GSM74416 1 0.0237 0.997 0.996 0.000 0.004
#> GSM74417 1 0.0237 0.997 0.996 0.000 0.004
#> GSM74418 1 0.0237 0.997 0.996 0.000 0.004
#> GSM74419 1 0.0237 0.997 0.996 0.000 0.004
#> GSM121358 2 0.4931 0.815 0.000 0.768 0.232
#> GSM121359 2 0.4931 0.815 0.000 0.768 0.232
#> GSM121360 3 0.0237 0.947 0.000 0.004 0.996
#> GSM121362 3 0.3983 0.778 0.144 0.004 0.852
#> GSM121364 3 0.0000 0.946 0.000 0.000 1.000
#> GSM121365 2 0.4931 0.815 0.000 0.768 0.232
#> GSM121366 2 0.4931 0.815 0.000 0.768 0.232
#> GSM121367 2 0.4931 0.815 0.000 0.768 0.232
#> GSM121370 2 0.4931 0.815 0.000 0.768 0.232
#> GSM121371 2 0.4931 0.815 0.000 0.768 0.232
#> GSM121372 2 0.4931 0.815 0.000 0.768 0.232
#> GSM121373 3 0.0000 0.946 0.000 0.000 1.000
#> GSM121374 3 0.0000 0.946 0.000 0.000 1.000
#> GSM121407 2 0.4931 0.815 0.000 0.768 0.232
#> GSM74387 3 0.0237 0.947 0.000 0.004 0.996
#> GSM74388 3 0.0475 0.945 0.004 0.004 0.992
#> GSM74389 3 0.0000 0.946 0.000 0.000 1.000
#> GSM74390 3 0.6154 0.326 0.408 0.000 0.592
#> GSM74391 3 0.6244 0.229 0.440 0.000 0.560
#> GSM74392 3 0.0000 0.946 0.000 0.000 1.000
#> GSM74393 3 0.0000 0.946 0.000 0.000 1.000
#> GSM74394 3 0.0237 0.947 0.000 0.004 0.996
#> GSM74239 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74364 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74365 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74366 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74367 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74377 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74378 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74379 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74380 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74381 1 0.0000 0.999 1.000 0.000 0.000
#> GSM121357 2 0.0424 0.871 0.000 0.992 0.008
#> GSM121361 3 0.0237 0.947 0.000 0.004 0.996
#> GSM121363 3 0.0237 0.947 0.000 0.004 0.996
#> GSM121368 3 0.0237 0.947 0.000 0.004 0.996
#> GSM121369 3 0.0237 0.947 0.000 0.004 0.996
#> GSM74368 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74369 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74370 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74371 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74372 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74373 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74374 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74375 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74376 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74405 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74351 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74352 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74353 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74354 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74355 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74382 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74383 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74384 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74385 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74386 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74395 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74396 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74397 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74398 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74399 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74400 1 0.0000 0.999 1.000 0.000 0.000
#> GSM74401 1 0.0000 0.999 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM74356 2 0.5756 0.7709 0.000 0.692 0.224 0.084
#> GSM74357 2 0.7105 0.6423 0.000 0.560 0.256 0.184
#> GSM74358 2 0.7149 0.6302 0.000 0.552 0.264 0.184
#> GSM74359 3 0.3649 0.7608 0.000 0.000 0.796 0.204
#> GSM74360 3 0.3982 0.7366 0.004 0.000 0.776 0.220
#> GSM74361 3 0.6110 0.1125 0.000 0.368 0.576 0.056
#> GSM74362 3 0.1474 0.8385 0.000 0.000 0.948 0.052
#> GSM74363 2 0.6265 0.7467 0.000 0.656 0.220 0.124
#> GSM74402 4 0.3873 0.7732 0.228 0.000 0.000 0.772
#> GSM74403 1 0.3219 0.8494 0.836 0.000 0.000 0.164
#> GSM74404 1 0.2921 0.8665 0.860 0.000 0.000 0.140
#> GSM74406 4 0.2408 0.9215 0.104 0.000 0.000 0.896
#> GSM74407 4 0.3444 0.8447 0.184 0.000 0.000 0.816
#> GSM74408 4 0.2408 0.9215 0.104 0.000 0.000 0.896
#> GSM74409 4 0.2408 0.9215 0.104 0.000 0.000 0.896
#> GSM74410 4 0.2408 0.9215 0.104 0.000 0.000 0.896
#> GSM119936 4 0.2469 0.9198 0.108 0.000 0.000 0.892
#> GSM119937 4 0.2408 0.9215 0.104 0.000 0.000 0.896
#> GSM74411 2 0.5727 0.7690 0.000 0.692 0.228 0.080
#> GSM74412 2 0.5759 0.7651 0.000 0.688 0.232 0.080
#> GSM74413 2 0.5593 0.7821 0.000 0.708 0.212 0.080
#> GSM74414 2 0.1452 0.8502 0.000 0.956 0.008 0.036
#> GSM74415 3 0.6419 -0.1583 0.000 0.420 0.512 0.068
#> GSM121379 2 0.0000 0.8508 0.000 1.000 0.000 0.000
#> GSM121380 2 0.0000 0.8508 0.000 1.000 0.000 0.000
#> GSM121381 2 0.0000 0.8508 0.000 1.000 0.000 0.000
#> GSM121382 2 0.0000 0.8508 0.000 1.000 0.000 0.000
#> GSM121383 2 0.0000 0.8508 0.000 1.000 0.000 0.000
#> GSM121384 2 0.0000 0.8508 0.000 1.000 0.000 0.000
#> GSM121385 2 0.0000 0.8508 0.000 1.000 0.000 0.000
#> GSM121386 2 0.0000 0.8508 0.000 1.000 0.000 0.000
#> GSM121387 2 0.0000 0.8508 0.000 1.000 0.000 0.000
#> GSM121388 2 0.1209 0.8502 0.000 0.964 0.004 0.032
#> GSM121389 2 0.0000 0.8508 0.000 1.000 0.000 0.000
#> GSM121390 2 0.0188 0.8503 0.000 0.996 0.000 0.004
#> GSM121391 2 0.0000 0.8508 0.000 1.000 0.000 0.000
#> GSM121392 2 0.1209 0.8502 0.000 0.964 0.004 0.032
#> GSM121393 2 0.1396 0.8496 0.004 0.960 0.004 0.032
#> GSM121394 2 0.0188 0.8511 0.000 0.996 0.000 0.004
#> GSM121395 2 0.0000 0.8508 0.000 1.000 0.000 0.000
#> GSM121396 2 0.4525 0.8202 0.000 0.804 0.116 0.080
#> GSM121397 2 0.0000 0.8508 0.000 1.000 0.000 0.000
#> GSM121398 2 0.0000 0.8508 0.000 1.000 0.000 0.000
#> GSM121399 2 0.0000 0.8508 0.000 1.000 0.000 0.000
#> GSM74240 3 0.0592 0.8511 0.000 0.000 0.984 0.016
#> GSM74241 3 0.3707 0.7244 0.000 0.132 0.840 0.028
#> GSM74242 3 0.1867 0.8383 0.000 0.000 0.928 0.072
#> GSM74243 3 0.1637 0.8402 0.000 0.000 0.940 0.060
#> GSM74244 3 0.1118 0.8452 0.000 0.000 0.964 0.036
#> GSM74245 3 0.1302 0.8429 0.000 0.000 0.956 0.044
#> GSM74246 3 0.0817 0.8504 0.000 0.000 0.976 0.024
#> GSM74247 3 0.0336 0.8490 0.000 0.000 0.992 0.008
#> GSM74248 3 0.0592 0.8511 0.000 0.000 0.984 0.016
#> GSM74416 1 0.4933 0.3142 0.568 0.000 0.000 0.432
#> GSM74417 1 0.4730 0.5156 0.636 0.000 0.000 0.364
#> GSM74418 1 0.4866 0.4019 0.596 0.000 0.000 0.404
#> GSM74419 4 0.2530 0.9177 0.112 0.000 0.000 0.888
#> GSM121358 2 0.5628 0.7788 0.000 0.704 0.216 0.080
#> GSM121359 2 0.5184 0.7872 0.000 0.732 0.212 0.056
#> GSM121360 3 0.2589 0.8175 0.000 0.000 0.884 0.116
#> GSM121362 3 0.4610 0.7149 0.100 0.000 0.800 0.100
#> GSM121364 3 0.3764 0.7478 0.000 0.000 0.784 0.216
#> GSM121365 2 0.5593 0.7812 0.000 0.708 0.212 0.080
#> GSM121366 2 0.5593 0.7812 0.000 0.708 0.212 0.080
#> GSM121367 2 0.5628 0.7788 0.000 0.704 0.216 0.080
#> GSM121370 2 0.5661 0.7758 0.000 0.700 0.220 0.080
#> GSM121371 2 0.5628 0.7788 0.000 0.704 0.216 0.080
#> GSM121372 2 0.5593 0.7812 0.000 0.708 0.212 0.080
#> GSM121373 3 0.3444 0.7762 0.000 0.000 0.816 0.184
#> GSM121374 3 0.3764 0.7502 0.000 0.000 0.784 0.216
#> GSM121407 2 0.5464 0.7838 0.000 0.716 0.212 0.072
#> GSM74387 3 0.0336 0.8473 0.000 0.000 0.992 0.008
#> GSM74388 3 0.1610 0.8419 0.016 0.000 0.952 0.032
#> GSM74389 3 0.2760 0.8108 0.000 0.000 0.872 0.128
#> GSM74390 3 0.7538 0.0706 0.260 0.000 0.492 0.248
#> GSM74391 4 0.6827 0.3590 0.128 0.000 0.304 0.568
#> GSM74392 3 0.3907 0.7285 0.000 0.000 0.768 0.232
#> GSM74393 3 0.1211 0.8479 0.000 0.000 0.960 0.040
#> GSM74394 3 0.1022 0.8503 0.000 0.000 0.968 0.032
#> GSM74239 1 0.2973 0.8633 0.856 0.000 0.000 0.144
#> GSM74364 1 0.2973 0.8633 0.856 0.000 0.000 0.144
#> GSM74365 1 0.1474 0.8949 0.948 0.000 0.000 0.052
#> GSM74366 1 0.0000 0.8953 1.000 0.000 0.000 0.000
#> GSM74367 1 0.0592 0.8982 0.984 0.000 0.000 0.016
#> GSM74377 1 0.0000 0.8953 1.000 0.000 0.000 0.000
#> GSM74378 1 0.0000 0.8953 1.000 0.000 0.000 0.000
#> GSM74379 1 0.0188 0.8962 0.996 0.000 0.000 0.004
#> GSM74380 1 0.0188 0.8962 0.996 0.000 0.000 0.004
#> GSM74381 1 0.0000 0.8953 1.000 0.000 0.000 0.000
#> GSM121357 2 0.2483 0.8463 0.000 0.916 0.032 0.052
#> GSM121361 3 0.1022 0.8503 0.000 0.000 0.968 0.032
#> GSM121363 3 0.1022 0.8503 0.000 0.000 0.968 0.032
#> GSM121368 3 0.1022 0.8503 0.000 0.000 0.968 0.032
#> GSM121369 3 0.1022 0.8503 0.000 0.000 0.968 0.032
#> GSM74368 1 0.4304 0.6640 0.716 0.000 0.000 0.284
#> GSM74369 1 0.2814 0.8680 0.868 0.000 0.000 0.132
#> GSM74370 1 0.0469 0.8963 0.988 0.000 0.000 0.012
#> GSM74371 1 0.2921 0.8661 0.860 0.000 0.000 0.140
#> GSM74372 1 0.0469 0.8963 0.988 0.000 0.000 0.012
#> GSM74373 1 0.0000 0.8953 1.000 0.000 0.000 0.000
#> GSM74374 1 0.0336 0.8966 0.992 0.000 0.000 0.008
#> GSM74375 1 0.2011 0.8896 0.920 0.000 0.000 0.080
#> GSM74376 1 0.0000 0.8953 1.000 0.000 0.000 0.000
#> GSM74405 1 0.0000 0.8953 1.000 0.000 0.000 0.000
#> GSM74351 1 0.3356 0.8359 0.824 0.000 0.000 0.176
#> GSM74352 1 0.2408 0.8830 0.896 0.000 0.000 0.104
#> GSM74353 1 0.2760 0.8721 0.872 0.000 0.000 0.128
#> GSM74354 1 0.1118 0.8977 0.964 0.000 0.000 0.036
#> GSM74355 1 0.0000 0.8953 1.000 0.000 0.000 0.000
#> GSM74382 1 0.2973 0.8633 0.856 0.000 0.000 0.144
#> GSM74383 1 0.1940 0.8916 0.924 0.000 0.000 0.076
#> GSM74384 1 0.0188 0.8931 0.996 0.000 0.000 0.004
#> GSM74385 1 0.2814 0.8711 0.868 0.000 0.000 0.132
#> GSM74386 1 0.0469 0.8972 0.988 0.000 0.000 0.012
#> GSM74395 1 0.0921 0.8935 0.972 0.000 0.000 0.028
#> GSM74396 1 0.0469 0.8963 0.988 0.000 0.000 0.012
#> GSM74397 1 0.3311 0.8442 0.828 0.000 0.000 0.172
#> GSM74398 1 0.0336 0.8966 0.992 0.000 0.000 0.008
#> GSM74399 1 0.0188 0.8967 0.996 0.000 0.000 0.004
#> GSM74400 1 0.2469 0.8791 0.892 0.000 0.000 0.108
#> GSM74401 1 0.2589 0.8753 0.884 0.000 0.000 0.116
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM74356 3 0.0000 0.941 0.000 0.000 1.000 0.000 0.000
#> GSM74357 3 0.0000 0.941 0.000 0.000 1.000 0.000 0.000
#> GSM74358 3 0.0000 0.941 0.000 0.000 1.000 0.000 0.000
#> GSM74359 5 0.1485 0.921 0.000 0.000 0.032 0.020 0.948
#> GSM74360 5 0.1668 0.918 0.000 0.000 0.028 0.032 0.940
#> GSM74361 3 0.3586 0.602 0.000 0.000 0.736 0.000 0.264
#> GSM74362 5 0.3508 0.733 0.000 0.000 0.252 0.000 0.748
#> GSM74363 3 0.0000 0.941 0.000 0.000 1.000 0.000 0.000
#> GSM74402 4 0.3039 0.735 0.192 0.000 0.000 0.808 0.000
#> GSM74403 1 0.4268 0.268 0.556 0.000 0.000 0.444 0.000
#> GSM74404 1 0.3857 0.641 0.688 0.000 0.000 0.312 0.000
#> GSM74406 4 0.0912 0.813 0.016 0.000 0.000 0.972 0.012
#> GSM74407 4 0.1341 0.801 0.056 0.000 0.000 0.944 0.000
#> GSM74408 4 0.0290 0.822 0.008 0.000 0.000 0.992 0.000
#> GSM74409 4 0.0290 0.822 0.008 0.000 0.000 0.992 0.000
#> GSM74410 4 0.0290 0.822 0.008 0.000 0.000 0.992 0.000
#> GSM119936 4 0.0290 0.822 0.008 0.000 0.000 0.992 0.000
#> GSM119937 4 0.0290 0.822 0.008 0.000 0.000 0.992 0.000
#> GSM74411 3 0.0693 0.936 0.000 0.012 0.980 0.000 0.008
#> GSM74412 3 0.0898 0.934 0.000 0.020 0.972 0.000 0.008
#> GSM74413 3 0.2358 0.881 0.000 0.104 0.888 0.000 0.008
#> GSM74414 2 0.2074 0.876 0.000 0.896 0.104 0.000 0.000
#> GSM74415 3 0.2377 0.835 0.000 0.000 0.872 0.000 0.128
#> GSM121379 2 0.0162 0.981 0.000 0.996 0.004 0.000 0.000
#> GSM121380 2 0.0162 0.981 0.000 0.996 0.004 0.000 0.000
#> GSM121381 2 0.0162 0.981 0.000 0.996 0.004 0.000 0.000
#> GSM121382 2 0.0162 0.981 0.000 0.996 0.004 0.000 0.000
#> GSM121383 2 0.0162 0.981 0.000 0.996 0.004 0.000 0.000
#> GSM121384 2 0.0162 0.981 0.000 0.996 0.004 0.000 0.000
#> GSM121385 2 0.0162 0.981 0.000 0.996 0.004 0.000 0.000
#> GSM121386 2 0.0162 0.981 0.000 0.996 0.004 0.000 0.000
#> GSM121387 2 0.0162 0.981 0.000 0.996 0.004 0.000 0.000
#> GSM121388 2 0.0162 0.978 0.000 0.996 0.004 0.000 0.000
#> GSM121389 2 0.0162 0.981 0.000 0.996 0.004 0.000 0.000
#> GSM121390 2 0.0000 0.978 0.000 1.000 0.000 0.000 0.000
#> GSM121391 2 0.0162 0.981 0.000 0.996 0.004 0.000 0.000
#> GSM121392 2 0.0162 0.978 0.000 0.996 0.004 0.000 0.000
#> GSM121393 2 0.0162 0.978 0.000 0.996 0.004 0.000 0.000
#> GSM121394 2 0.0404 0.975 0.000 0.988 0.012 0.000 0.000
#> GSM121395 2 0.0162 0.981 0.000 0.996 0.004 0.000 0.000
#> GSM121396 3 0.1732 0.895 0.000 0.080 0.920 0.000 0.000
#> GSM121397 2 0.0162 0.981 0.000 0.996 0.004 0.000 0.000
#> GSM121398 2 0.0162 0.981 0.000 0.996 0.004 0.000 0.000
#> GSM121399 2 0.0162 0.981 0.000 0.996 0.004 0.000 0.000
#> GSM74240 5 0.1410 0.920 0.000 0.000 0.060 0.000 0.940
#> GSM74241 5 0.2424 0.877 0.000 0.000 0.132 0.000 0.868
#> GSM74242 5 0.1965 0.909 0.000 0.000 0.096 0.000 0.904
#> GSM74243 5 0.1851 0.913 0.000 0.000 0.088 0.000 0.912
#> GSM74244 5 0.2230 0.891 0.000 0.000 0.116 0.000 0.884
#> GSM74245 5 0.2230 0.891 0.000 0.000 0.116 0.000 0.884
#> GSM74246 5 0.1732 0.909 0.000 0.000 0.080 0.000 0.920
#> GSM74247 5 0.2230 0.891 0.000 0.000 0.116 0.000 0.884
#> GSM74248 5 0.1908 0.905 0.000 0.000 0.092 0.000 0.908
#> GSM74416 4 0.3177 0.720 0.208 0.000 0.000 0.792 0.000
#> GSM74417 4 0.3177 0.720 0.208 0.000 0.000 0.792 0.000
#> GSM74418 4 0.3177 0.720 0.208 0.000 0.000 0.792 0.000
#> GSM74419 4 0.0290 0.822 0.008 0.000 0.000 0.992 0.000
#> GSM121358 3 0.0000 0.941 0.000 0.000 1.000 0.000 0.000
#> GSM121359 3 0.2843 0.844 0.000 0.144 0.848 0.000 0.008
#> GSM121360 5 0.1764 0.919 0.000 0.000 0.064 0.008 0.928
#> GSM121362 5 0.1251 0.902 0.036 0.000 0.000 0.008 0.956
#> GSM121364 5 0.1668 0.919 0.000 0.000 0.032 0.028 0.940
#> GSM121365 3 0.0000 0.941 0.000 0.000 1.000 0.000 0.000
#> GSM121366 3 0.0000 0.941 0.000 0.000 1.000 0.000 0.000
#> GSM121367 3 0.0000 0.941 0.000 0.000 1.000 0.000 0.000
#> GSM121370 3 0.0000 0.941 0.000 0.000 1.000 0.000 0.000
#> GSM121371 3 0.0000 0.941 0.000 0.000 1.000 0.000 0.000
#> GSM121372 3 0.1251 0.927 0.000 0.036 0.956 0.000 0.008
#> GSM121373 5 0.1281 0.921 0.000 0.000 0.032 0.012 0.956
#> GSM121374 5 0.1485 0.921 0.000 0.000 0.032 0.020 0.948
#> GSM121407 3 0.2886 0.836 0.000 0.148 0.844 0.000 0.008
#> GSM74387 5 0.1478 0.918 0.000 0.000 0.064 0.000 0.936
#> GSM74388 5 0.0324 0.917 0.000 0.000 0.004 0.004 0.992
#> GSM74389 5 0.1597 0.922 0.000 0.000 0.048 0.012 0.940
#> GSM74390 5 0.5909 0.477 0.272 0.000 0.016 0.100 0.612
#> GSM74391 5 0.5880 0.483 0.116 0.000 0.004 0.296 0.584
#> GSM74392 5 0.1741 0.913 0.000 0.000 0.024 0.040 0.936
#> GSM74393 5 0.0963 0.922 0.000 0.000 0.036 0.000 0.964
#> GSM74394 5 0.0324 0.917 0.000 0.000 0.004 0.004 0.992
#> GSM74239 1 0.3242 0.766 0.784 0.000 0.000 0.216 0.000
#> GSM74364 1 0.3730 0.687 0.712 0.000 0.000 0.288 0.000
#> GSM74365 1 0.2179 0.828 0.888 0.000 0.000 0.112 0.000
#> GSM74366 1 0.0000 0.853 1.000 0.000 0.000 0.000 0.000
#> GSM74367 1 0.0794 0.854 0.972 0.000 0.000 0.028 0.000
#> GSM74377 1 0.0794 0.854 0.972 0.000 0.000 0.028 0.000
#> GSM74378 1 0.0000 0.853 1.000 0.000 0.000 0.000 0.000
#> GSM74379 1 0.0000 0.853 1.000 0.000 0.000 0.000 0.000
#> GSM74380 1 0.0000 0.853 1.000 0.000 0.000 0.000 0.000
#> GSM74381 1 0.0000 0.853 1.000 0.000 0.000 0.000 0.000
#> GSM121357 2 0.3452 0.674 0.000 0.756 0.244 0.000 0.000
#> GSM121361 5 0.0324 0.917 0.000 0.000 0.004 0.004 0.992
#> GSM121363 5 0.0324 0.917 0.000 0.000 0.004 0.004 0.992
#> GSM121368 5 0.0324 0.917 0.000 0.000 0.004 0.004 0.992
#> GSM121369 5 0.0162 0.917 0.000 0.000 0.004 0.000 0.996
#> GSM74368 4 0.4291 0.061 0.464 0.000 0.000 0.536 0.000
#> GSM74369 1 0.3636 0.708 0.728 0.000 0.000 0.272 0.000
#> GSM74370 1 0.0162 0.853 0.996 0.000 0.000 0.004 0.000
#> GSM74371 1 0.3109 0.779 0.800 0.000 0.000 0.200 0.000
#> GSM74372 1 0.1041 0.839 0.964 0.000 0.000 0.004 0.032
#> GSM74373 1 0.0000 0.853 1.000 0.000 0.000 0.000 0.000
#> GSM74374 1 0.0162 0.853 0.996 0.000 0.000 0.004 0.000
#> GSM74375 1 0.3109 0.778 0.800 0.000 0.000 0.200 0.000
#> GSM74376 1 0.0000 0.853 1.000 0.000 0.000 0.000 0.000
#> GSM74405 1 0.0000 0.853 1.000 0.000 0.000 0.000 0.000
#> GSM74351 4 0.4307 -0.117 0.496 0.000 0.000 0.504 0.000
#> GSM74352 1 0.3424 0.743 0.760 0.000 0.000 0.240 0.000
#> GSM74353 1 0.3612 0.716 0.732 0.000 0.000 0.268 0.000
#> GSM74354 1 0.1544 0.845 0.932 0.000 0.000 0.068 0.000
#> GSM74355 1 0.0000 0.853 1.000 0.000 0.000 0.000 0.000
#> GSM74382 1 0.3774 0.674 0.704 0.000 0.000 0.296 0.000
#> GSM74383 1 0.2230 0.828 0.884 0.000 0.000 0.116 0.000
#> GSM74384 1 0.0000 0.853 1.000 0.000 0.000 0.000 0.000
#> GSM74385 1 0.3636 0.710 0.728 0.000 0.000 0.272 0.000
#> GSM74386 1 0.0703 0.852 0.976 0.000 0.000 0.024 0.000
#> GSM74395 1 0.0162 0.853 0.996 0.000 0.000 0.004 0.000
#> GSM74396 1 0.0162 0.853 0.996 0.000 0.000 0.004 0.000
#> GSM74397 1 0.3003 0.787 0.812 0.000 0.000 0.188 0.000
#> GSM74398 1 0.0000 0.853 1.000 0.000 0.000 0.000 0.000
#> GSM74399 1 0.0703 0.854 0.976 0.000 0.000 0.024 0.000
#> GSM74400 1 0.3586 0.718 0.736 0.000 0.000 0.264 0.000
#> GSM74401 1 0.3612 0.713 0.732 0.000 0.000 0.268 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM74356 3 0.0146 0.8693 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM74357 3 0.0146 0.8693 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM74358 3 0.0146 0.8693 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM74359 5 0.0000 0.9057 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74360 5 0.0000 0.9057 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74361 3 0.3592 0.4973 0.000 0.000 0.656 0.000 0.344 0.000
#> GSM74362 5 0.2482 0.7896 0.004 0.000 0.148 0.000 0.848 0.000
#> GSM74363 3 0.0146 0.8693 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM74402 4 0.3782 0.4975 0.360 0.000 0.000 0.636 0.000 0.004
#> GSM74403 4 0.4755 0.3369 0.460 0.000 0.000 0.492 0.000 0.048
#> GSM74404 4 0.5674 0.3395 0.332 0.000 0.000 0.496 0.000 0.172
#> GSM74406 4 0.0713 0.6850 0.000 0.000 0.000 0.972 0.028 0.000
#> GSM74407 4 0.0713 0.6911 0.000 0.000 0.000 0.972 0.000 0.028
#> GSM74408 4 0.0000 0.7065 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM74409 4 0.0000 0.7065 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM74410 4 0.0000 0.7065 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM119936 4 0.0363 0.7049 0.012 0.000 0.000 0.988 0.000 0.000
#> GSM119937 4 0.0000 0.7065 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM74411 3 0.2913 0.8194 0.004 0.180 0.812 0.000 0.004 0.000
#> GSM74412 3 0.2871 0.8119 0.000 0.192 0.804 0.000 0.004 0.000
#> GSM74413 3 0.3081 0.7868 0.000 0.220 0.776 0.000 0.004 0.000
#> GSM74414 2 0.1908 0.8724 0.004 0.900 0.096 0.000 0.000 0.000
#> GSM74415 3 0.3629 0.6368 0.016 0.000 0.724 0.000 0.260 0.000
#> GSM121379 2 0.0000 0.9811 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121380 2 0.0000 0.9811 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121381 2 0.0000 0.9811 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121382 2 0.0000 0.9811 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121383 2 0.0000 0.9811 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121384 2 0.0000 0.9811 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121385 2 0.0000 0.9811 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121386 2 0.0000 0.9811 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121387 2 0.0000 0.9811 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121388 2 0.0146 0.9792 0.004 0.996 0.000 0.000 0.000 0.000
#> GSM121389 2 0.0000 0.9811 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121390 2 0.0146 0.9792 0.004 0.996 0.000 0.000 0.000 0.000
#> GSM121391 2 0.0000 0.9811 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121392 2 0.0146 0.9792 0.004 0.996 0.000 0.000 0.000 0.000
#> GSM121393 2 0.0291 0.9759 0.004 0.992 0.000 0.004 0.000 0.000
#> GSM121394 2 0.0000 0.9811 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121395 2 0.0000 0.9811 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121396 3 0.3081 0.7882 0.000 0.220 0.776 0.000 0.004 0.000
#> GSM121397 2 0.0000 0.9811 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121398 2 0.0000 0.9811 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121399 2 0.0000 0.9811 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM74240 5 0.1501 0.8972 0.076 0.000 0.000 0.000 0.924 0.000
#> GSM74241 5 0.1531 0.8997 0.068 0.000 0.004 0.000 0.928 0.000
#> GSM74242 5 0.0000 0.9057 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74243 5 0.0000 0.9057 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74244 5 0.1501 0.8972 0.076 0.000 0.000 0.000 0.924 0.000
#> GSM74245 5 0.1327 0.9003 0.064 0.000 0.000 0.000 0.936 0.000
#> GSM74246 5 0.1765 0.8885 0.096 0.000 0.000 0.000 0.904 0.000
#> GSM74247 5 0.1814 0.8864 0.100 0.000 0.000 0.000 0.900 0.000
#> GSM74248 5 0.1444 0.8983 0.072 0.000 0.000 0.000 0.928 0.000
#> GSM74416 4 0.4184 0.3744 0.488 0.000 0.000 0.500 0.000 0.012
#> GSM74417 4 0.4184 0.3744 0.488 0.000 0.000 0.500 0.000 0.012
#> GSM74418 4 0.4184 0.3744 0.488 0.000 0.000 0.500 0.000 0.012
#> GSM74419 4 0.0000 0.7065 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM121358 3 0.0146 0.8693 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM121359 3 0.2964 0.8034 0.000 0.204 0.792 0.000 0.004 0.000
#> GSM121360 5 0.1556 0.8961 0.080 0.000 0.000 0.000 0.920 0.000
#> GSM121362 5 0.1141 0.8759 0.000 0.000 0.000 0.000 0.948 0.052
#> GSM121364 5 0.0000 0.9057 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM121365 3 0.0146 0.8693 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM121366 3 0.0146 0.8693 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM121367 3 0.0146 0.8693 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM121370 3 0.0146 0.8693 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM121371 3 0.0146 0.8693 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM121372 3 0.2772 0.8194 0.000 0.180 0.816 0.000 0.004 0.000
#> GSM121373 5 0.0000 0.9057 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM121374 5 0.0000 0.9057 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM121407 3 0.3189 0.7674 0.000 0.236 0.760 0.000 0.004 0.000
#> GSM74387 5 0.2883 0.7997 0.212 0.000 0.000 0.000 0.788 0.000
#> GSM74388 1 0.3999 -0.4495 0.500 0.000 0.004 0.000 0.496 0.000
#> GSM74389 5 0.0000 0.9057 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74390 5 0.4913 0.4195 0.000 0.000 0.000 0.092 0.612 0.296
#> GSM74391 5 0.4168 0.5929 0.000 0.000 0.000 0.256 0.696 0.048
#> GSM74392 5 0.0000 0.9057 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74393 5 0.0146 0.9057 0.004 0.000 0.000 0.000 0.996 0.000
#> GSM74394 1 0.3999 -0.4495 0.500 0.000 0.004 0.000 0.496 0.000
#> GSM74239 1 0.5719 0.1730 0.460 0.000 0.000 0.168 0.000 0.372
#> GSM74364 1 0.5808 0.1338 0.492 0.000 0.000 0.288 0.000 0.220
#> GSM74365 6 0.3955 0.4130 0.384 0.000 0.000 0.008 0.000 0.608
#> GSM74366 6 0.0146 0.8209 0.004 0.000 0.000 0.000 0.000 0.996
#> GSM74367 6 0.2631 0.6969 0.180 0.000 0.000 0.000 0.000 0.820
#> GSM74377 6 0.3390 0.5632 0.296 0.000 0.000 0.000 0.000 0.704
#> GSM74378 6 0.0146 0.8209 0.004 0.000 0.000 0.000 0.000 0.996
#> GSM74379 6 0.0000 0.8219 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM74380 6 0.0260 0.8214 0.008 0.000 0.000 0.000 0.000 0.992
#> GSM74381 6 0.0000 0.8219 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM121357 2 0.3276 0.6526 0.004 0.764 0.228 0.000 0.004 0.000
#> GSM121361 1 0.3999 -0.4495 0.500 0.000 0.004 0.000 0.496 0.000
#> GSM121363 1 0.3999 -0.4495 0.500 0.000 0.004 0.000 0.496 0.000
#> GSM121368 1 0.3999 -0.4495 0.500 0.000 0.004 0.000 0.496 0.000
#> GSM121369 5 0.3634 0.6300 0.356 0.000 0.000 0.000 0.644 0.000
#> GSM74368 1 0.5724 -0.0677 0.456 0.000 0.000 0.376 0.000 0.168
#> GSM74369 1 0.5844 0.2098 0.488 0.000 0.000 0.244 0.000 0.268
#> GSM74370 6 0.0146 0.8221 0.004 0.000 0.000 0.000 0.000 0.996
#> GSM74371 1 0.5913 0.1938 0.468 0.000 0.000 0.256 0.000 0.276
#> GSM74372 6 0.0146 0.8221 0.004 0.000 0.000 0.000 0.000 0.996
#> GSM74373 6 0.0000 0.8219 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM74374 6 0.0146 0.8221 0.004 0.000 0.000 0.000 0.000 0.996
#> GSM74375 6 0.4463 0.3671 0.376 0.000 0.000 0.036 0.000 0.588
#> GSM74376 6 0.0000 0.8219 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM74405 6 0.0000 0.8219 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM74351 1 0.4899 -0.3368 0.488 0.000 0.000 0.452 0.000 0.060
#> GSM74352 6 0.4757 0.1097 0.468 0.000 0.000 0.048 0.000 0.484
#> GSM74353 1 0.5585 0.1918 0.488 0.000 0.000 0.148 0.000 0.364
#> GSM74354 6 0.3899 0.4481 0.364 0.000 0.000 0.008 0.000 0.628
#> GSM74355 6 0.0146 0.8209 0.004 0.000 0.000 0.000 0.000 0.996
#> GSM74382 1 0.5458 -0.1657 0.480 0.000 0.000 0.396 0.000 0.124
#> GSM74383 6 0.5002 0.1878 0.412 0.000 0.000 0.072 0.000 0.516
#> GSM74384 6 0.0146 0.8209 0.004 0.000 0.000 0.000 0.000 0.996
#> GSM74385 1 0.5616 0.2146 0.492 0.000 0.000 0.156 0.000 0.352
#> GSM74386 6 0.0622 0.8140 0.008 0.000 0.000 0.012 0.000 0.980
#> GSM74395 6 0.0146 0.8221 0.004 0.000 0.000 0.000 0.000 0.996
#> GSM74396 6 0.0363 0.8199 0.012 0.000 0.000 0.000 0.000 0.988
#> GSM74397 6 0.5530 0.1151 0.364 0.000 0.000 0.140 0.000 0.496
#> GSM74398 6 0.0146 0.8221 0.004 0.000 0.000 0.000 0.000 0.996
#> GSM74399 6 0.2838 0.6859 0.188 0.000 0.000 0.004 0.000 0.808
#> GSM74400 1 0.5503 0.1354 0.484 0.000 0.000 0.132 0.000 0.384
#> GSM74401 1 0.5578 0.1983 0.492 0.000 0.000 0.148 0.000 0.360
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 115 4.59e-13 2
#> MAD:mclust 117 2.26e-23 3
#> MAD:mclust 115 1.30e-28 4
#> MAD:mclust 116 5.98e-42 5
#> MAD:mclust 91 3.80e-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["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 21168 rows and 121 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 0.963 0.954 0.980 0.5023 0.497 0.497
#> 3 3 0.537 0.679 0.842 0.3187 0.760 0.555
#> 4 4 0.578 0.476 0.692 0.1253 0.831 0.559
#> 5 5 0.612 0.538 0.738 0.0634 0.819 0.436
#> 6 6 0.736 0.695 0.824 0.0406 0.927 0.683
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
#> GSM74356 2 0.5842 0.834 0.140 0.860
#> GSM74357 1 0.8443 0.625 0.728 0.272
#> GSM74358 1 0.4562 0.892 0.904 0.096
#> GSM74359 1 0.0000 0.986 1.000 0.000
#> GSM74360 1 0.0000 0.986 1.000 0.000
#> GSM74361 2 0.3879 0.906 0.076 0.924
#> GSM74362 1 0.6148 0.821 0.848 0.152
#> GSM74363 2 0.2043 0.946 0.032 0.968
#> GSM74402 1 0.0000 0.986 1.000 0.000
#> GSM74403 1 0.0000 0.986 1.000 0.000
#> GSM74404 1 0.0000 0.986 1.000 0.000
#> GSM74406 1 0.0000 0.986 1.000 0.000
#> GSM74407 1 0.0000 0.986 1.000 0.000
#> GSM74408 1 0.0000 0.986 1.000 0.000
#> GSM74409 1 0.0000 0.986 1.000 0.000
#> GSM74410 1 0.0000 0.986 1.000 0.000
#> GSM119936 1 0.0000 0.986 1.000 0.000
#> GSM119937 1 0.0000 0.986 1.000 0.000
#> GSM74411 2 0.0000 0.971 0.000 1.000
#> GSM74412 2 0.0000 0.971 0.000 1.000
#> GSM74413 2 0.0000 0.971 0.000 1.000
#> GSM74414 2 0.0000 0.971 0.000 1.000
#> GSM74415 2 0.0000 0.971 0.000 1.000
#> GSM121379 2 0.0000 0.971 0.000 1.000
#> GSM121380 2 0.0000 0.971 0.000 1.000
#> GSM121381 2 0.0000 0.971 0.000 1.000
#> GSM121382 2 0.0000 0.971 0.000 1.000
#> GSM121383 2 0.0000 0.971 0.000 1.000
#> GSM121384 2 0.0000 0.971 0.000 1.000
#> GSM121385 2 0.0000 0.971 0.000 1.000
#> GSM121386 2 0.0000 0.971 0.000 1.000
#> GSM121387 2 0.0000 0.971 0.000 1.000
#> GSM121388 2 0.0000 0.971 0.000 1.000
#> GSM121389 2 0.0000 0.971 0.000 1.000
#> GSM121390 2 0.0000 0.971 0.000 1.000
#> GSM121391 2 0.0000 0.971 0.000 1.000
#> GSM121392 2 0.0000 0.971 0.000 1.000
#> GSM121393 2 0.0000 0.971 0.000 1.000
#> GSM121394 2 0.0000 0.971 0.000 1.000
#> GSM121395 2 0.0000 0.971 0.000 1.000
#> GSM121396 2 0.0000 0.971 0.000 1.000
#> GSM121397 2 0.0000 0.971 0.000 1.000
#> GSM121398 2 0.0000 0.971 0.000 1.000
#> GSM121399 2 0.0000 0.971 0.000 1.000
#> GSM74240 2 0.8327 0.655 0.264 0.736
#> GSM74241 2 0.0938 0.962 0.012 0.988
#> GSM74242 1 0.0000 0.986 1.000 0.000
#> GSM74243 1 0.0000 0.986 1.000 0.000
#> GSM74244 2 0.0000 0.971 0.000 1.000
#> GSM74245 2 0.4022 0.902 0.080 0.920
#> GSM74246 2 0.0000 0.971 0.000 1.000
#> GSM74247 2 0.0000 0.971 0.000 1.000
#> GSM74248 2 0.9686 0.362 0.396 0.604
#> GSM74416 1 0.0000 0.986 1.000 0.000
#> GSM74417 1 0.0000 0.986 1.000 0.000
#> GSM74418 1 0.0000 0.986 1.000 0.000
#> GSM74419 1 0.0000 0.986 1.000 0.000
#> GSM121358 2 0.0000 0.971 0.000 1.000
#> GSM121359 2 0.0000 0.971 0.000 1.000
#> GSM121360 1 0.1843 0.963 0.972 0.028
#> GSM121362 1 0.2043 0.959 0.968 0.032
#> GSM121364 1 0.0000 0.986 1.000 0.000
#> GSM121365 2 0.0000 0.971 0.000 1.000
#> GSM121366 2 0.0000 0.971 0.000 1.000
#> GSM121367 2 0.0000 0.971 0.000 1.000
#> GSM121370 2 0.0000 0.971 0.000 1.000
#> GSM121371 2 0.0000 0.971 0.000 1.000
#> GSM121372 2 0.0000 0.971 0.000 1.000
#> GSM121373 1 0.0000 0.986 1.000 0.000
#> GSM121374 1 0.0000 0.986 1.000 0.000
#> GSM121407 2 0.0000 0.971 0.000 1.000
#> GSM74387 2 0.0000 0.971 0.000 1.000
#> GSM74388 2 0.0000 0.971 0.000 1.000
#> GSM74389 1 0.0000 0.986 1.000 0.000
#> GSM74390 1 0.3584 0.923 0.932 0.068
#> GSM74391 1 0.0000 0.986 1.000 0.000
#> GSM74392 1 0.0000 0.986 1.000 0.000
#> GSM74393 1 0.0000 0.986 1.000 0.000
#> GSM74394 2 0.0000 0.971 0.000 1.000
#> GSM74239 1 0.0000 0.986 1.000 0.000
#> GSM74364 1 0.0000 0.986 1.000 0.000
#> GSM74365 1 0.0000 0.986 1.000 0.000
#> GSM74366 2 0.0672 0.965 0.008 0.992
#> GSM74367 1 0.0000 0.986 1.000 0.000
#> GSM74377 1 0.0000 0.986 1.000 0.000
#> GSM74378 2 0.7674 0.718 0.224 0.776
#> GSM74379 1 0.0000 0.986 1.000 0.000
#> GSM74380 1 0.0000 0.986 1.000 0.000
#> GSM74381 1 0.1414 0.969 0.980 0.020
#> GSM121357 2 0.0000 0.971 0.000 1.000
#> GSM121361 2 0.0000 0.971 0.000 1.000
#> GSM121363 2 0.0000 0.971 0.000 1.000
#> GSM121368 2 0.0000 0.971 0.000 1.000
#> GSM121369 2 0.0000 0.971 0.000 1.000
#> GSM74368 1 0.0376 0.983 0.996 0.004
#> GSM74369 1 0.0000 0.986 1.000 0.000
#> GSM74370 1 0.0000 0.986 1.000 0.000
#> GSM74371 1 0.0000 0.986 1.000 0.000
#> GSM74372 1 0.0000 0.986 1.000 0.000
#> GSM74373 1 0.0000 0.986 1.000 0.000
#> GSM74374 1 0.0000 0.986 1.000 0.000
#> GSM74375 1 0.0376 0.983 0.996 0.004
#> GSM74376 1 0.6801 0.783 0.820 0.180
#> GSM74405 1 0.0000 0.986 1.000 0.000
#> GSM74351 1 0.0000 0.986 1.000 0.000
#> GSM74352 2 0.0000 0.971 0.000 1.000
#> GSM74353 1 0.0000 0.986 1.000 0.000
#> GSM74354 1 0.0000 0.986 1.000 0.000
#> GSM74355 2 0.9522 0.427 0.372 0.628
#> GSM74382 1 0.0000 0.986 1.000 0.000
#> GSM74383 1 0.0000 0.986 1.000 0.000
#> GSM74384 2 0.0000 0.971 0.000 1.000
#> GSM74385 1 0.0000 0.986 1.000 0.000
#> GSM74386 1 0.0000 0.986 1.000 0.000
#> GSM74395 1 0.0000 0.986 1.000 0.000
#> GSM74396 1 0.0000 0.986 1.000 0.000
#> GSM74397 1 0.0000 0.986 1.000 0.000
#> GSM74398 1 0.0000 0.986 1.000 0.000
#> GSM74399 1 0.0000 0.986 1.000 0.000
#> GSM74400 1 0.0000 0.986 1.000 0.000
#> GSM74401 1 0.0000 0.986 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM74356 3 0.0000 0.8266 0.000 0.000 1.000
#> GSM74357 3 0.0237 0.8254 0.004 0.000 0.996
#> GSM74358 3 0.0892 0.8138 0.020 0.000 0.980
#> GSM74359 3 0.5785 0.2498 0.332 0.000 0.668
#> GSM74360 1 0.4346 0.7783 0.816 0.000 0.184
#> GSM74361 3 0.0237 0.8254 0.004 0.000 0.996
#> GSM74362 3 0.0237 0.8254 0.004 0.000 0.996
#> GSM74363 3 0.0000 0.8266 0.000 0.000 1.000
#> GSM74402 1 0.4121 0.7881 0.832 0.000 0.168
#> GSM74403 1 0.3482 0.8038 0.872 0.000 0.128
#> GSM74404 1 0.3551 0.8027 0.868 0.000 0.132
#> GSM74406 1 0.5810 0.6287 0.664 0.000 0.336
#> GSM74407 1 0.4399 0.7756 0.812 0.000 0.188
#> GSM74408 1 0.6026 0.5697 0.624 0.000 0.376
#> GSM74409 1 0.6045 0.5634 0.620 0.000 0.380
#> GSM74410 1 0.6280 0.4107 0.540 0.000 0.460
#> GSM119936 1 0.5785 0.6340 0.668 0.000 0.332
#> GSM119937 1 0.4702 0.7584 0.788 0.000 0.212
#> GSM74411 3 0.4346 0.7640 0.000 0.184 0.816
#> GSM74412 3 0.6095 0.3890 0.000 0.392 0.608
#> GSM74413 3 0.4399 0.7599 0.000 0.188 0.812
#> GSM74414 2 0.0592 0.7530 0.000 0.988 0.012
#> GSM74415 3 0.3116 0.8172 0.000 0.108 0.892
#> GSM121379 2 0.2711 0.7363 0.000 0.912 0.088
#> GSM121380 2 0.1643 0.7497 0.000 0.956 0.044
#> GSM121381 2 0.6309 -0.0577 0.000 0.504 0.496
#> GSM121382 2 0.6280 0.0832 0.000 0.540 0.460
#> GSM121383 2 0.6309 -0.0559 0.000 0.504 0.496
#> GSM121384 2 0.1964 0.7472 0.000 0.944 0.056
#> GSM121385 2 0.3686 0.7033 0.000 0.860 0.140
#> GSM121386 2 0.4062 0.6819 0.000 0.836 0.164
#> GSM121387 2 0.5968 0.3564 0.000 0.636 0.364
#> GSM121388 3 0.5465 0.6176 0.000 0.288 0.712
#> GSM121389 2 0.4235 0.6686 0.000 0.824 0.176
#> GSM121390 2 0.0592 0.7530 0.000 0.988 0.012
#> GSM121391 3 0.6079 0.3957 0.000 0.388 0.612
#> GSM121392 2 0.0000 0.7523 0.000 1.000 0.000
#> GSM121393 2 0.3686 0.7023 0.000 0.860 0.140
#> GSM121394 3 0.5138 0.6765 0.000 0.252 0.748
#> GSM121395 2 0.2878 0.7325 0.000 0.904 0.096
#> GSM121396 3 0.4062 0.7815 0.000 0.164 0.836
#> GSM121397 2 0.2261 0.7438 0.000 0.932 0.068
#> GSM121398 2 0.2796 0.7346 0.000 0.908 0.092
#> GSM121399 2 0.6252 0.1375 0.000 0.556 0.444
#> GSM74240 3 0.0237 0.8254 0.004 0.000 0.996
#> GSM74241 3 0.2772 0.8321 0.004 0.080 0.916
#> GSM74242 3 0.2066 0.7797 0.060 0.000 0.940
#> GSM74243 3 0.1643 0.7944 0.044 0.000 0.956
#> GSM74244 3 0.2066 0.8354 0.000 0.060 0.940
#> GSM74245 3 0.0237 0.8254 0.004 0.000 0.996
#> GSM74246 3 0.3879 0.7914 0.000 0.152 0.848
#> GSM74247 3 0.4605 0.7424 0.000 0.204 0.796
#> GSM74248 3 0.0237 0.8254 0.004 0.000 0.996
#> GSM74416 1 0.4121 0.7884 0.832 0.000 0.168
#> GSM74417 1 0.4062 0.7902 0.836 0.000 0.164
#> GSM74418 1 0.3686 0.8007 0.860 0.000 0.140
#> GSM74419 1 0.6215 0.4812 0.572 0.000 0.428
#> GSM121358 3 0.1964 0.8354 0.000 0.056 0.944
#> GSM121359 3 0.4002 0.7847 0.000 0.160 0.840
#> GSM121360 1 0.5263 0.7893 0.828 0.088 0.084
#> GSM121362 1 0.5179 0.7874 0.832 0.088 0.080
#> GSM121364 1 0.6302 0.3653 0.520 0.000 0.480
#> GSM121365 3 0.2165 0.8347 0.000 0.064 0.936
#> GSM121366 3 0.2625 0.8284 0.000 0.084 0.916
#> GSM121367 3 0.1643 0.8345 0.000 0.044 0.956
#> GSM121370 3 0.2066 0.8355 0.000 0.060 0.940
#> GSM121371 3 0.2066 0.8354 0.000 0.060 0.940
#> GSM121372 3 0.4121 0.7783 0.000 0.168 0.832
#> GSM121373 1 0.5291 0.7079 0.732 0.000 0.268
#> GSM121374 1 0.6309 0.3109 0.500 0.000 0.500
#> GSM121407 3 0.5621 0.5796 0.000 0.308 0.692
#> GSM74387 2 0.5785 0.4235 0.000 0.668 0.332
#> GSM74388 2 0.1860 0.7386 0.052 0.948 0.000
#> GSM74389 3 0.5178 0.4746 0.256 0.000 0.744
#> GSM74390 1 0.3340 0.7447 0.880 0.120 0.000
#> GSM74391 1 0.5098 0.7276 0.752 0.000 0.248
#> GSM74392 1 0.6291 0.3904 0.532 0.000 0.468
#> GSM74393 3 0.3941 0.6647 0.156 0.000 0.844
#> GSM74394 2 0.0592 0.7515 0.012 0.988 0.000
#> GSM74239 1 0.0237 0.8185 0.996 0.000 0.004
#> GSM74364 1 0.0237 0.8185 0.996 0.000 0.004
#> GSM74365 1 0.0892 0.8108 0.980 0.020 0.000
#> GSM74366 2 0.4654 0.6361 0.208 0.792 0.000
#> GSM74367 1 0.0424 0.8155 0.992 0.008 0.000
#> GSM74377 2 0.6274 0.1791 0.456 0.544 0.000
#> GSM74378 2 0.5397 0.5454 0.280 0.720 0.000
#> GSM74379 1 0.3879 0.7124 0.848 0.152 0.000
#> GSM74380 1 0.4796 0.6268 0.780 0.220 0.000
#> GSM74381 2 0.6204 0.2678 0.424 0.576 0.000
#> GSM121357 2 0.3879 0.6938 0.000 0.848 0.152
#> GSM121361 2 0.1411 0.7452 0.036 0.964 0.000
#> GSM121363 2 0.1031 0.7488 0.024 0.976 0.000
#> GSM121368 2 0.0592 0.7515 0.012 0.988 0.000
#> GSM121369 2 0.0983 0.7540 0.004 0.980 0.016
#> GSM74368 1 0.1267 0.8195 0.972 0.004 0.024
#> GSM74369 1 0.0475 0.8177 0.992 0.004 0.004
#> GSM74370 1 0.0237 0.8167 0.996 0.004 0.000
#> GSM74371 1 0.0424 0.8188 0.992 0.000 0.008
#> GSM74372 1 0.0237 0.8167 0.996 0.004 0.000
#> GSM74373 1 0.6026 0.3165 0.624 0.376 0.000
#> GSM74374 1 0.0892 0.8108 0.980 0.020 0.000
#> GSM74375 1 0.4399 0.6712 0.812 0.188 0.000
#> GSM74376 2 0.6140 0.3165 0.404 0.596 0.000
#> GSM74405 1 0.6215 0.1728 0.572 0.428 0.000
#> GSM74351 1 0.2625 0.8132 0.916 0.000 0.084
#> GSM74352 2 0.4452 0.6532 0.192 0.808 0.000
#> GSM74353 1 0.0000 0.8177 1.000 0.000 0.000
#> GSM74354 1 0.0592 0.8140 0.988 0.012 0.000
#> GSM74355 2 0.5678 0.4891 0.316 0.684 0.000
#> GSM74382 1 0.2537 0.8138 0.920 0.000 0.080
#> GSM74383 1 0.0237 0.8167 0.996 0.004 0.000
#> GSM74384 2 0.4178 0.6713 0.172 0.828 0.000
#> GSM74385 1 0.0237 0.8185 0.996 0.000 0.004
#> GSM74386 1 0.0237 0.8167 0.996 0.004 0.000
#> GSM74395 1 0.0237 0.8185 0.996 0.000 0.004
#> GSM74396 1 0.0237 0.8167 0.996 0.004 0.000
#> GSM74397 1 0.1163 0.8190 0.972 0.000 0.028
#> GSM74398 1 0.2165 0.7874 0.936 0.064 0.000
#> GSM74399 1 0.4974 0.6019 0.764 0.236 0.000
#> GSM74400 1 0.2066 0.7897 0.940 0.060 0.000
#> GSM74401 1 0.2625 0.7731 0.916 0.084 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM74356 3 0.3105 0.51663 0.004 0.000 0.856 0.140
#> GSM74357 3 0.3306 0.49539 0.004 0.000 0.840 0.156
#> GSM74358 3 0.2773 0.54045 0.004 0.000 0.880 0.116
#> GSM74359 4 0.6611 0.17650 0.080 0.000 0.460 0.460
#> GSM74360 4 0.7159 0.34980 0.244 0.000 0.200 0.556
#> GSM74361 3 0.5018 0.20129 0.012 0.000 0.656 0.332
#> GSM74362 3 0.4994 -0.12745 0.000 0.000 0.520 0.480
#> GSM74363 3 0.0844 0.64150 0.004 0.012 0.980 0.004
#> GSM74402 1 0.1004 0.83175 0.972 0.000 0.024 0.004
#> GSM74403 1 0.0804 0.83375 0.980 0.000 0.012 0.008
#> GSM74404 1 0.1677 0.82250 0.948 0.000 0.012 0.040
#> GSM74406 1 0.5507 0.62718 0.732 0.000 0.156 0.112
#> GSM74407 1 0.3245 0.77504 0.880 0.000 0.064 0.056
#> GSM74408 1 0.5309 0.64796 0.744 0.000 0.164 0.092
#> GSM74409 1 0.7034 0.33711 0.576 0.000 0.220 0.204
#> GSM74410 1 0.7031 0.32447 0.556 0.000 0.288 0.156
#> GSM119936 1 0.4337 0.72067 0.808 0.000 0.140 0.052
#> GSM119937 1 0.3013 0.78528 0.888 0.000 0.080 0.032
#> GSM74411 3 0.5556 0.53890 0.000 0.188 0.720 0.092
#> GSM74412 3 0.6717 0.29141 0.000 0.332 0.560 0.108
#> GSM74413 3 0.5463 0.45387 0.000 0.256 0.692 0.052
#> GSM74414 2 0.1182 0.61589 0.000 0.968 0.016 0.016
#> GSM74415 3 0.3978 0.51109 0.000 0.012 0.796 0.192
#> GSM121379 2 0.3569 0.59111 0.000 0.804 0.196 0.000
#> GSM121380 2 0.1743 0.62282 0.000 0.940 0.056 0.004
#> GSM121381 2 0.5151 0.17242 0.000 0.532 0.464 0.004
#> GSM121382 2 0.5112 0.24356 0.000 0.560 0.436 0.004
#> GSM121383 2 0.5132 0.21283 0.000 0.548 0.448 0.004
#> GSM121384 2 0.1661 0.62242 0.000 0.944 0.052 0.004
#> GSM121385 2 0.4188 0.55060 0.000 0.752 0.244 0.004
#> GSM121386 2 0.3982 0.57384 0.000 0.776 0.220 0.004
#> GSM121387 2 0.4889 0.38576 0.000 0.636 0.360 0.004
#> GSM121388 3 0.4925 0.06819 0.000 0.428 0.572 0.000
#> GSM121389 2 0.3751 0.58900 0.000 0.800 0.196 0.004
#> GSM121390 2 0.0376 0.61476 0.000 0.992 0.004 0.004
#> GSM121391 3 0.5263 0.01755 0.000 0.448 0.544 0.008
#> GSM121392 2 0.1489 0.59953 0.000 0.952 0.004 0.044
#> GSM121393 2 0.4072 0.54512 0.000 0.748 0.252 0.000
#> GSM121394 3 0.5016 0.15972 0.000 0.396 0.600 0.004
#> GSM121395 2 0.3837 0.57252 0.000 0.776 0.224 0.000
#> GSM121396 3 0.4647 0.40061 0.000 0.288 0.704 0.008
#> GSM121397 2 0.2401 0.61991 0.000 0.904 0.092 0.004
#> GSM121398 2 0.3688 0.58443 0.000 0.792 0.208 0.000
#> GSM121399 2 0.5112 0.24307 0.000 0.560 0.436 0.004
#> GSM74240 4 0.4643 0.34702 0.000 0.000 0.344 0.656
#> GSM74241 3 0.5277 -0.00325 0.000 0.008 0.532 0.460
#> GSM74242 3 0.5163 -0.09412 0.004 0.000 0.516 0.480
#> GSM74243 3 0.5163 -0.09181 0.004 0.000 0.516 0.480
#> GSM74244 4 0.4994 0.11199 0.000 0.000 0.480 0.520
#> GSM74245 4 0.4992 0.12303 0.000 0.000 0.476 0.524
#> GSM74246 4 0.4567 0.39137 0.000 0.008 0.276 0.716
#> GSM74247 4 0.4844 0.37140 0.000 0.012 0.300 0.688
#> GSM74248 4 0.4855 0.27371 0.000 0.000 0.400 0.600
#> GSM74416 1 0.0779 0.83425 0.980 0.000 0.016 0.004
#> GSM74417 1 0.1284 0.82888 0.964 0.000 0.024 0.012
#> GSM74418 1 0.0779 0.83425 0.980 0.000 0.016 0.004
#> GSM74419 1 0.4499 0.70648 0.792 0.000 0.160 0.048
#> GSM121358 3 0.0524 0.64259 0.000 0.008 0.988 0.004
#> GSM121359 3 0.3636 0.55785 0.000 0.172 0.820 0.008
#> GSM121360 4 0.1767 0.47989 0.012 0.000 0.044 0.944
#> GSM121362 4 0.3669 0.48203 0.052 0.032 0.040 0.876
#> GSM121364 4 0.7069 0.23143 0.124 0.000 0.408 0.468
#> GSM121365 3 0.0707 0.64583 0.000 0.020 0.980 0.000
#> GSM121366 3 0.1389 0.64725 0.000 0.048 0.952 0.000
#> GSM121367 3 0.0000 0.64051 0.000 0.000 1.000 0.000
#> GSM121370 3 0.1004 0.63417 0.000 0.004 0.972 0.024
#> GSM121371 3 0.0336 0.64367 0.000 0.008 0.992 0.000
#> GSM121372 3 0.3324 0.59227 0.000 0.136 0.852 0.012
#> GSM121373 4 0.5025 0.42434 0.032 0.000 0.252 0.716
#> GSM121374 4 0.6826 0.24099 0.100 0.000 0.416 0.484
#> GSM121407 3 0.4770 0.40863 0.000 0.288 0.700 0.012
#> GSM74387 4 0.5334 0.42587 0.000 0.172 0.088 0.740
#> GSM74388 4 0.4817 0.11552 0.000 0.388 0.000 0.612
#> GSM74389 4 0.4991 0.29956 0.004 0.000 0.388 0.608
#> GSM74390 4 0.5705 0.27862 0.064 0.260 0.000 0.676
#> GSM74391 4 0.7451 0.13467 0.408 0.000 0.172 0.420
#> GSM74392 4 0.5756 0.27448 0.032 0.000 0.400 0.568
#> GSM74393 4 0.4889 0.33122 0.004 0.000 0.360 0.636
#> GSM74394 4 0.4431 0.24742 0.000 0.304 0.000 0.696
#> GSM74239 1 0.0336 0.83808 0.992 0.000 0.000 0.008
#> GSM74364 1 0.0000 0.83724 1.000 0.000 0.000 0.000
#> GSM74365 1 0.1398 0.83142 0.956 0.004 0.000 0.040
#> GSM74366 2 0.5827 0.20234 0.036 0.568 0.000 0.396
#> GSM74367 1 0.1305 0.83293 0.960 0.004 0.000 0.036
#> GSM74377 1 0.7893 0.06324 0.376 0.324 0.000 0.300
#> GSM74378 2 0.6243 0.18433 0.060 0.548 0.000 0.392
#> GSM74379 1 0.6153 0.50325 0.604 0.068 0.000 0.328
#> GSM74380 1 0.6261 0.50876 0.608 0.080 0.000 0.312
#> GSM74381 2 0.6770 0.11386 0.096 0.496 0.000 0.408
#> GSM121357 2 0.3554 0.61318 0.000 0.844 0.136 0.020
#> GSM121361 4 0.4564 0.21578 0.000 0.328 0.000 0.672
#> GSM121363 4 0.4907 0.05206 0.000 0.420 0.000 0.580
#> GSM121368 4 0.4776 0.13471 0.000 0.376 0.000 0.624
#> GSM121369 4 0.3625 0.40173 0.000 0.160 0.012 0.828
#> GSM74368 1 0.0524 0.83775 0.988 0.000 0.004 0.008
#> GSM74369 1 0.0657 0.83776 0.984 0.004 0.000 0.012
#> GSM74370 1 0.5750 0.36701 0.532 0.028 0.000 0.440
#> GSM74371 1 0.0188 0.83785 0.996 0.000 0.000 0.004
#> GSM74372 4 0.5442 0.11439 0.336 0.028 0.000 0.636
#> GSM74373 4 0.7830 0.01770 0.272 0.324 0.000 0.404
#> GSM74374 1 0.4428 0.64385 0.720 0.004 0.000 0.276
#> GSM74375 1 0.4300 0.75047 0.820 0.092 0.000 0.088
#> GSM74376 2 0.6785 0.10269 0.096 0.484 0.000 0.420
#> GSM74405 4 0.7423 -0.05625 0.168 0.404 0.000 0.428
#> GSM74351 1 0.0524 0.83593 0.988 0.000 0.008 0.004
#> GSM74352 2 0.5722 0.41735 0.136 0.716 0.000 0.148
#> GSM74353 1 0.0524 0.83766 0.988 0.004 0.000 0.008
#> GSM74354 1 0.1576 0.82875 0.948 0.004 0.000 0.048
#> GSM74355 2 0.6508 0.20721 0.084 0.556 0.000 0.360
#> GSM74382 1 0.0336 0.83623 0.992 0.000 0.008 0.000
#> GSM74383 1 0.1109 0.83490 0.968 0.004 0.000 0.028
#> GSM74384 2 0.5548 0.22423 0.024 0.588 0.000 0.388
#> GSM74385 1 0.0188 0.83807 0.996 0.000 0.000 0.004
#> GSM74386 1 0.3355 0.76507 0.836 0.004 0.000 0.160
#> GSM74395 1 0.1637 0.82866 0.940 0.000 0.000 0.060
#> GSM74396 1 0.2888 0.78494 0.872 0.004 0.000 0.124
#> GSM74397 1 0.0188 0.83785 0.996 0.000 0.000 0.004
#> GSM74398 1 0.5773 0.52197 0.620 0.044 0.000 0.336
#> GSM74399 1 0.6025 0.58983 0.668 0.096 0.000 0.236
#> GSM74400 1 0.1042 0.83603 0.972 0.008 0.000 0.020
#> GSM74401 1 0.1520 0.83275 0.956 0.020 0.000 0.024
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM74356 4 0.5419 0.38926 0.000 0.100 0.208 0.680 0.012
#> GSM74357 4 0.5532 0.38054 0.000 0.100 0.224 0.664 0.012
#> GSM74358 4 0.5755 0.36132 0.000 0.096 0.252 0.636 0.016
#> GSM74359 4 0.3049 0.49271 0.048 0.000 0.064 0.876 0.012
#> GSM74360 4 0.4996 0.43958 0.072 0.000 0.092 0.764 0.072
#> GSM74361 4 0.4704 0.40551 0.016 0.040 0.216 0.728 0.000
#> GSM74362 4 0.2929 0.44961 0.000 0.004 0.128 0.856 0.012
#> GSM74363 4 0.7158 0.21690 0.000 0.276 0.276 0.428 0.020
#> GSM74402 1 0.1168 0.81629 0.960 0.000 0.008 0.032 0.000
#> GSM74403 1 0.2248 0.79052 0.900 0.000 0.012 0.088 0.000
#> GSM74404 1 0.3724 0.70063 0.788 0.000 0.028 0.184 0.000
#> GSM74406 1 0.4632 0.20914 0.540 0.000 0.012 0.448 0.000
#> GSM74407 1 0.3317 0.75814 0.840 0.000 0.044 0.116 0.000
#> GSM74408 4 0.4843 0.33284 0.328 0.024 0.008 0.640 0.000
#> GSM74409 4 0.4522 0.44673 0.252 0.012 0.016 0.716 0.004
#> GSM74410 4 0.4549 0.45566 0.244 0.032 0.008 0.716 0.000
#> GSM119936 1 0.4656 0.12553 0.508 0.000 0.012 0.480 0.000
#> GSM119937 4 0.4449 -0.10915 0.484 0.004 0.000 0.512 0.000
#> GSM74411 3 0.4259 0.49419 0.000 0.172 0.776 0.036 0.016
#> GSM74412 3 0.4220 0.49986 0.000 0.200 0.760 0.008 0.032
#> GSM74413 3 0.4353 0.44798 0.000 0.224 0.740 0.024 0.012
#> GSM74414 2 0.6387 0.43077 0.008 0.544 0.172 0.000 0.276
#> GSM74415 3 0.2645 0.56867 0.000 0.096 0.884 0.008 0.012
#> GSM121379 2 0.2488 0.76064 0.000 0.872 0.000 0.004 0.124
#> GSM121380 2 0.3861 0.64614 0.000 0.712 0.000 0.004 0.284
#> GSM121381 2 0.2026 0.74642 0.000 0.928 0.016 0.012 0.044
#> GSM121382 2 0.1087 0.75658 0.000 0.968 0.008 0.008 0.016
#> GSM121383 2 0.1087 0.75394 0.000 0.968 0.008 0.008 0.016
#> GSM121384 2 0.3861 0.64550 0.000 0.712 0.000 0.004 0.284
#> GSM121385 2 0.2124 0.76928 0.000 0.900 0.000 0.004 0.096
#> GSM121386 2 0.2488 0.76301 0.000 0.872 0.000 0.004 0.124
#> GSM121387 2 0.1914 0.76580 0.000 0.924 0.000 0.016 0.060
#> GSM121388 2 0.1498 0.73298 0.000 0.952 0.016 0.024 0.008
#> GSM121389 2 0.2707 0.75664 0.000 0.860 0.000 0.008 0.132
#> GSM121390 2 0.4135 0.57597 0.000 0.656 0.000 0.004 0.340
#> GSM121391 2 0.1524 0.73366 0.000 0.952 0.016 0.016 0.016
#> GSM121392 2 0.4288 0.50617 0.000 0.612 0.000 0.004 0.384
#> GSM121393 2 0.2921 0.76194 0.000 0.856 0.000 0.020 0.124
#> GSM121394 2 0.2591 0.70436 0.000 0.904 0.044 0.032 0.020
#> GSM121395 2 0.2179 0.76798 0.000 0.896 0.000 0.004 0.100
#> GSM121396 2 0.3624 0.65508 0.000 0.844 0.084 0.052 0.020
#> GSM121397 2 0.3715 0.66939 0.000 0.736 0.000 0.004 0.260
#> GSM121398 2 0.2389 0.76657 0.000 0.880 0.004 0.000 0.116
#> GSM121399 2 0.0566 0.75664 0.000 0.984 0.000 0.004 0.012
#> GSM74240 3 0.4724 0.55657 0.000 0.000 0.732 0.164 0.104
#> GSM74241 3 0.2251 0.61231 0.000 0.024 0.916 0.008 0.052
#> GSM74242 3 0.2670 0.59871 0.004 0.016 0.888 0.088 0.004
#> GSM74243 3 0.2943 0.59297 0.004 0.016 0.868 0.108 0.004
#> GSM74244 3 0.2338 0.61599 0.000 0.016 0.916 0.036 0.032
#> GSM74245 3 0.2859 0.61556 0.000 0.016 0.888 0.060 0.036
#> GSM74246 3 0.4605 0.58060 0.000 0.004 0.756 0.108 0.132
#> GSM74247 3 0.4252 0.60629 0.000 0.020 0.796 0.056 0.128
#> GSM74248 3 0.4522 0.56129 0.000 0.000 0.744 0.176 0.080
#> GSM74416 1 0.1942 0.80217 0.920 0.000 0.012 0.068 0.000
#> GSM74417 1 0.3318 0.70972 0.800 0.000 0.008 0.192 0.000
#> GSM74418 1 0.1608 0.80536 0.928 0.000 0.000 0.072 0.000
#> GSM74419 1 0.4715 0.53379 0.672 0.004 0.032 0.292 0.000
#> GSM121358 4 0.7262 0.15861 0.000 0.284 0.324 0.372 0.020
#> GSM121359 2 0.7035 0.02278 0.000 0.420 0.360 0.200 0.020
#> GSM121360 4 0.6050 0.12620 0.000 0.000 0.144 0.544 0.312
#> GSM121362 4 0.5761 0.22547 0.004 0.004 0.096 0.612 0.284
#> GSM121364 4 0.3514 0.48363 0.056 0.000 0.072 0.852 0.020
#> GSM121365 4 0.7271 0.15277 0.000 0.288 0.328 0.364 0.020
#> GSM121366 3 0.7267 -0.12540 0.000 0.348 0.352 0.280 0.020
#> GSM121367 4 0.7276 0.13083 0.000 0.288 0.340 0.352 0.020
#> GSM121370 3 0.7249 -0.15999 0.000 0.272 0.372 0.336 0.020
#> GSM121371 4 0.7281 0.14485 0.000 0.296 0.328 0.356 0.020
#> GSM121372 2 0.6997 0.04568 0.000 0.428 0.360 0.192 0.020
#> GSM121373 4 0.4129 0.43951 0.016 0.000 0.076 0.808 0.100
#> GSM121374 4 0.3201 0.48724 0.036 0.000 0.064 0.872 0.028
#> GSM121407 2 0.6845 0.10057 0.000 0.460 0.340 0.184 0.016
#> GSM74387 3 0.5377 0.42820 0.000 0.012 0.648 0.064 0.276
#> GSM74388 5 0.4312 0.58469 0.000 0.016 0.136 0.060 0.788
#> GSM74389 3 0.5648 0.18302 0.000 0.000 0.476 0.448 0.076
#> GSM74390 5 0.6596 0.32974 0.020 0.008 0.292 0.124 0.556
#> GSM74391 3 0.7016 0.09092 0.256 0.000 0.452 0.276 0.016
#> GSM74392 4 0.5015 0.27114 0.020 0.000 0.272 0.676 0.032
#> GSM74393 3 0.5891 0.21618 0.000 0.000 0.468 0.432 0.100
#> GSM74394 3 0.5549 0.00724 0.000 0.004 0.480 0.056 0.460
#> GSM74239 1 0.0794 0.81738 0.972 0.000 0.000 0.000 0.028
#> GSM74364 1 0.0404 0.81972 0.988 0.000 0.000 0.000 0.012
#> GSM74365 1 0.2561 0.73979 0.856 0.000 0.000 0.000 0.144
#> GSM74366 5 0.3071 0.69330 0.080 0.036 0.012 0.000 0.872
#> GSM74367 1 0.1341 0.80838 0.944 0.000 0.000 0.000 0.056
#> GSM74377 5 0.4680 0.22501 0.448 0.008 0.004 0.000 0.540
#> GSM74378 5 0.2962 0.69132 0.084 0.048 0.000 0.000 0.868
#> GSM74379 1 0.4434 0.05323 0.536 0.000 0.004 0.000 0.460
#> GSM74380 1 0.4101 0.34293 0.628 0.000 0.000 0.000 0.372
#> GSM74381 5 0.3218 0.70494 0.124 0.016 0.012 0.000 0.848
#> GSM121357 2 0.6743 0.36621 0.000 0.448 0.124 0.028 0.400
#> GSM121361 5 0.4485 0.60274 0.000 0.032 0.080 0.096 0.792
#> GSM121363 5 0.3525 0.63015 0.000 0.028 0.080 0.040 0.852
#> GSM121368 5 0.3523 0.61169 0.000 0.004 0.120 0.044 0.832
#> GSM121369 5 0.5460 0.45315 0.000 0.000 0.148 0.196 0.656
#> GSM74368 1 0.3142 0.79197 0.864 0.004 0.000 0.056 0.076
#> GSM74369 1 0.2361 0.78460 0.892 0.000 0.000 0.012 0.096
#> GSM74370 5 0.6642 0.39299 0.232 0.000 0.008 0.252 0.508
#> GSM74371 1 0.0162 0.82050 0.996 0.000 0.000 0.004 0.000
#> GSM74372 5 0.8338 0.25335 0.248 0.000 0.160 0.224 0.368
#> GSM74373 5 0.3815 0.66896 0.220 0.004 0.012 0.000 0.764
#> GSM74374 1 0.3719 0.66421 0.776 0.000 0.012 0.004 0.208
#> GSM74375 1 0.3323 0.73627 0.844 0.004 0.036 0.000 0.116
#> GSM74376 5 0.5480 0.56866 0.284 0.008 0.076 0.000 0.632
#> GSM74405 5 0.3961 0.67697 0.212 0.000 0.028 0.000 0.760
#> GSM74351 1 0.1357 0.81335 0.948 0.000 0.004 0.048 0.000
#> GSM74352 5 0.5695 0.54312 0.256 0.132 0.000 0.000 0.612
#> GSM74353 1 0.0807 0.82136 0.976 0.000 0.000 0.012 0.012
#> GSM74354 1 0.1357 0.81331 0.948 0.000 0.004 0.000 0.048
#> GSM74355 5 0.4010 0.69311 0.176 0.032 0.008 0.000 0.784
#> GSM74382 1 0.1430 0.81124 0.944 0.000 0.004 0.052 0.000
#> GSM74383 1 0.1121 0.81355 0.956 0.000 0.000 0.000 0.044
#> GSM74384 5 0.2588 0.67742 0.048 0.060 0.000 0.000 0.892
#> GSM74385 1 0.1197 0.81339 0.952 0.000 0.000 0.048 0.000
#> GSM74386 1 0.3352 0.77846 0.852 0.000 0.012 0.036 0.100
#> GSM74395 1 0.1483 0.82167 0.952 0.000 0.012 0.008 0.028
#> GSM74396 1 0.1732 0.79759 0.920 0.000 0.000 0.000 0.080
#> GSM74397 1 0.0290 0.82019 0.992 0.000 0.000 0.008 0.000
#> GSM74398 1 0.4503 0.45558 0.664 0.000 0.024 0.000 0.312
#> GSM74399 1 0.4127 0.46502 0.680 0.000 0.008 0.000 0.312
#> GSM74400 1 0.0880 0.81686 0.968 0.000 0.000 0.000 0.032
#> GSM74401 1 0.0771 0.81896 0.976 0.000 0.004 0.000 0.020
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM74356 3 0.3468 0.62232 0.000 0.008 0.728 0.264 0.000 0.000
#> GSM74357 3 0.3126 0.65296 0.000 0.000 0.752 0.248 0.000 0.000
#> GSM74358 3 0.2912 0.69716 0.000 0.000 0.784 0.216 0.000 0.000
#> GSM74359 4 0.3248 0.59586 0.004 0.000 0.224 0.768 0.000 0.004
#> GSM74360 4 0.2586 0.65972 0.012 0.000 0.064 0.892 0.016 0.016
#> GSM74361 4 0.5055 0.58307 0.004 0.040 0.116 0.712 0.128 0.000
#> GSM74362 4 0.2890 0.64682 0.000 0.000 0.128 0.844 0.024 0.004
#> GSM74363 3 0.2257 0.79714 0.000 0.008 0.876 0.116 0.000 0.000
#> GSM74402 1 0.0858 0.79566 0.968 0.000 0.004 0.028 0.000 0.000
#> GSM74403 1 0.2915 0.71865 0.808 0.000 0.000 0.184 0.008 0.000
#> GSM74404 1 0.4518 0.48240 0.612 0.000 0.004 0.348 0.036 0.000
#> GSM74406 1 0.5265 0.19418 0.500 0.000 0.100 0.400 0.000 0.000
#> GSM74407 1 0.3245 0.71157 0.796 0.000 0.004 0.184 0.016 0.000
#> GSM74408 4 0.5565 0.40160 0.240 0.000 0.208 0.552 0.000 0.000
#> GSM74409 4 0.4634 0.54448 0.124 0.000 0.188 0.688 0.000 0.000
#> GSM74410 4 0.5235 0.28455 0.100 0.000 0.380 0.520 0.000 0.000
#> GSM119936 1 0.5673 0.05904 0.448 0.000 0.156 0.396 0.000 0.000
#> GSM119937 1 0.5872 -0.05269 0.404 0.000 0.196 0.400 0.000 0.000
#> GSM74411 5 0.3756 0.73257 0.000 0.024 0.184 0.012 0.776 0.004
#> GSM74412 5 0.3653 0.75868 0.000 0.040 0.140 0.012 0.804 0.004
#> GSM74413 5 0.4041 0.72167 0.000 0.040 0.184 0.012 0.760 0.004
#> GSM74414 5 0.7220 0.25457 0.000 0.320 0.080 0.016 0.420 0.164
#> GSM74415 5 0.2568 0.79376 0.000 0.016 0.096 0.012 0.876 0.000
#> GSM121379 2 0.0146 0.95668 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM121380 2 0.0935 0.94983 0.000 0.964 0.000 0.004 0.000 0.032
#> GSM121381 2 0.2588 0.87512 0.000 0.860 0.124 0.004 0.000 0.012
#> GSM121382 2 0.0912 0.95482 0.000 0.972 0.008 0.012 0.004 0.004
#> GSM121383 2 0.0717 0.95482 0.000 0.976 0.016 0.008 0.000 0.000
#> GSM121384 2 0.0777 0.95234 0.000 0.972 0.000 0.004 0.000 0.024
#> GSM121385 2 0.0405 0.95695 0.000 0.988 0.008 0.000 0.000 0.004
#> GSM121386 2 0.0458 0.95587 0.000 0.984 0.000 0.000 0.000 0.016
#> GSM121387 2 0.0405 0.95673 0.000 0.988 0.004 0.008 0.000 0.000
#> GSM121388 2 0.1636 0.94649 0.000 0.936 0.036 0.024 0.000 0.004
#> GSM121389 2 0.0862 0.95434 0.000 0.972 0.004 0.016 0.000 0.008
#> GSM121390 2 0.1753 0.91685 0.000 0.912 0.000 0.004 0.000 0.084
#> GSM121391 2 0.1049 0.94851 0.000 0.960 0.032 0.008 0.000 0.000
#> GSM121392 2 0.2257 0.88383 0.000 0.876 0.000 0.008 0.000 0.116
#> GSM121393 2 0.1477 0.94008 0.000 0.940 0.008 0.048 0.000 0.004
#> GSM121394 2 0.2174 0.90644 0.000 0.896 0.088 0.008 0.008 0.000
#> GSM121395 2 0.0665 0.95653 0.000 0.980 0.008 0.008 0.000 0.004
#> GSM121396 2 0.3124 0.87721 0.000 0.852 0.096 0.016 0.032 0.004
#> GSM121397 2 0.0935 0.95039 0.000 0.964 0.000 0.004 0.000 0.032
#> GSM121398 2 0.0551 0.95692 0.000 0.984 0.004 0.004 0.000 0.008
#> GSM121399 2 0.0405 0.95644 0.000 0.988 0.000 0.008 0.000 0.004
#> GSM74240 5 0.2662 0.70651 0.000 0.000 0.004 0.152 0.840 0.004
#> GSM74241 5 0.1471 0.80494 0.000 0.000 0.064 0.000 0.932 0.004
#> GSM74242 5 0.2988 0.78772 0.004 0.000 0.060 0.084 0.852 0.000
#> GSM74243 5 0.2882 0.78828 0.004 0.000 0.060 0.076 0.860 0.000
#> GSM74244 5 0.1757 0.80361 0.000 0.000 0.076 0.008 0.916 0.000
#> GSM74245 5 0.1926 0.80495 0.000 0.000 0.068 0.020 0.912 0.000
#> GSM74246 5 0.1769 0.77889 0.000 0.000 0.012 0.060 0.924 0.004
#> GSM74247 5 0.1152 0.80486 0.000 0.000 0.044 0.000 0.952 0.004
#> GSM74248 5 0.2584 0.71860 0.000 0.000 0.004 0.144 0.848 0.004
#> GSM74416 1 0.2006 0.77259 0.892 0.000 0.004 0.104 0.000 0.000
#> GSM74417 1 0.3470 0.65681 0.740 0.000 0.012 0.248 0.000 0.000
#> GSM74418 1 0.1958 0.77521 0.896 0.000 0.004 0.100 0.000 0.000
#> GSM74419 1 0.4837 0.42026 0.580 0.016 0.008 0.376 0.020 0.000
#> GSM121358 3 0.2095 0.83721 0.000 0.004 0.904 0.076 0.016 0.000
#> GSM121359 3 0.2997 0.74974 0.000 0.060 0.844 0.000 0.096 0.000
#> GSM121360 4 0.5445 0.41742 0.000 0.004 0.048 0.608 0.048 0.292
#> GSM121362 4 0.4666 0.58509 0.000 0.012 0.060 0.724 0.016 0.188
#> GSM121364 4 0.3073 0.63233 0.016 0.000 0.164 0.816 0.000 0.004
#> GSM121365 3 0.1225 0.84563 0.000 0.000 0.952 0.036 0.012 0.000
#> GSM121366 3 0.1405 0.82994 0.000 0.024 0.948 0.004 0.024 0.000
#> GSM121367 3 0.1168 0.84494 0.000 0.000 0.956 0.028 0.016 0.000
#> GSM121370 3 0.2146 0.83837 0.000 0.004 0.908 0.044 0.044 0.000
#> GSM121371 3 0.1226 0.84243 0.000 0.004 0.952 0.040 0.004 0.000
#> GSM121372 3 0.2928 0.76306 0.000 0.056 0.856 0.004 0.084 0.000
#> GSM121373 4 0.4478 0.58743 0.000 0.000 0.200 0.708 0.004 0.088
#> GSM121374 4 0.3411 0.58766 0.004 0.000 0.232 0.756 0.000 0.008
#> GSM121407 3 0.2523 0.78964 0.000 0.036 0.888 0.004 0.068 0.004
#> GSM74387 5 0.2528 0.78138 0.000 0.000 0.024 0.028 0.892 0.056
#> GSM74388 6 0.5189 0.59790 0.000 0.048 0.000 0.100 0.164 0.688
#> GSM74389 4 0.4103 0.10128 0.000 0.000 0.004 0.544 0.448 0.004
#> GSM74390 6 0.6035 0.34773 0.012 0.004 0.000 0.180 0.284 0.520
#> GSM74391 5 0.5543 0.23924 0.148 0.000 0.004 0.296 0.552 0.000
#> GSM74392 4 0.3329 0.58044 0.012 0.000 0.012 0.796 0.180 0.000
#> GSM74393 4 0.4262 0.15487 0.000 0.000 0.004 0.560 0.424 0.012
#> GSM74394 5 0.4889 0.37202 0.000 0.000 0.000 0.084 0.604 0.312
#> GSM74239 1 0.0713 0.79166 0.972 0.000 0.000 0.000 0.000 0.028
#> GSM74364 1 0.0260 0.79627 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM74365 1 0.2730 0.67615 0.808 0.000 0.000 0.000 0.000 0.192
#> GSM74366 6 0.1590 0.78642 0.048 0.008 0.000 0.000 0.008 0.936
#> GSM74367 1 0.1267 0.78157 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM74377 6 0.3266 0.63609 0.272 0.000 0.000 0.000 0.000 0.728
#> GSM74378 6 0.1391 0.78547 0.040 0.016 0.000 0.000 0.000 0.944
#> GSM74379 6 0.3221 0.64520 0.264 0.000 0.000 0.000 0.000 0.736
#> GSM74380 1 0.3862 0.00985 0.524 0.000 0.000 0.000 0.000 0.476
#> GSM74381 6 0.1367 0.78740 0.044 0.012 0.000 0.000 0.000 0.944
#> GSM121357 6 0.5561 0.33003 0.000 0.088 0.332 0.008 0.012 0.560
#> GSM121361 6 0.3598 0.69413 0.000 0.040 0.000 0.116 0.028 0.816
#> GSM121363 6 0.2198 0.74846 0.000 0.032 0.000 0.032 0.024 0.912
#> GSM121368 6 0.1616 0.76045 0.000 0.012 0.000 0.020 0.028 0.940
#> GSM121369 6 0.4523 0.60309 0.000 0.016 0.004 0.204 0.056 0.720
#> GSM74368 1 0.4739 0.67808 0.736 0.004 0.072 0.028 0.004 0.156
#> GSM74369 1 0.3704 0.65012 0.764 0.000 0.024 0.004 0.004 0.204
#> GSM74370 6 0.4528 0.61761 0.072 0.000 0.004 0.200 0.008 0.716
#> GSM74371 1 0.0508 0.79660 0.984 0.000 0.004 0.012 0.000 0.000
#> GSM74372 4 0.6958 0.37253 0.132 0.000 0.004 0.508 0.164 0.192
#> GSM74373 6 0.2757 0.76432 0.136 0.008 0.000 0.004 0.004 0.848
#> GSM74374 1 0.3652 0.64971 0.760 0.000 0.000 0.020 0.008 0.212
#> GSM74375 1 0.2400 0.76603 0.900 0.004 0.000 0.008 0.040 0.048
#> GSM74376 6 0.3393 0.73658 0.192 0.004 0.000 0.000 0.020 0.784
#> GSM74405 6 0.1753 0.78461 0.084 0.000 0.000 0.000 0.004 0.912
#> GSM74351 1 0.1610 0.78326 0.916 0.000 0.000 0.084 0.000 0.000
#> GSM74352 6 0.4049 0.63867 0.256 0.032 0.000 0.004 0.000 0.708
#> GSM74353 1 0.0993 0.79961 0.964 0.000 0.000 0.024 0.000 0.012
#> GSM74354 1 0.0935 0.79115 0.964 0.000 0.004 0.000 0.000 0.032
#> GSM74355 6 0.2515 0.77640 0.104 0.008 0.000 0.004 0.008 0.876
#> GSM74382 1 0.1806 0.78013 0.908 0.000 0.004 0.088 0.000 0.000
#> GSM74383 1 0.1588 0.77794 0.924 0.000 0.000 0.004 0.000 0.072
#> GSM74384 6 0.1168 0.78335 0.028 0.016 0.000 0.000 0.000 0.956
#> GSM74385 1 0.2149 0.77810 0.888 0.000 0.004 0.104 0.000 0.004
#> GSM74386 1 0.2747 0.78842 0.876 0.000 0.004 0.076 0.008 0.036
#> GSM74395 1 0.1728 0.79490 0.924 0.000 0.004 0.064 0.000 0.008
#> GSM74396 1 0.1812 0.77098 0.912 0.000 0.000 0.008 0.000 0.080
#> GSM74397 1 0.0767 0.79760 0.976 0.000 0.004 0.008 0.000 0.012
#> GSM74398 1 0.3565 0.49007 0.692 0.000 0.000 0.000 0.004 0.304
#> GSM74399 1 0.3854 0.05188 0.536 0.000 0.000 0.000 0.000 0.464
#> GSM74400 1 0.0508 0.79588 0.984 0.000 0.004 0.000 0.000 0.012
#> GSM74401 1 0.0508 0.79584 0.984 0.000 0.004 0.000 0.000 0.012
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> MAD:NMF 119 2.13e-09 2
#> MAD:NMF 100 1.57e-13 3
#> MAD:NMF 64 6.35e-14 4
#> MAD:NMF 73 2.37e-23 5
#> MAD:NMF 102 8.44e-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["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 21168 rows and 121 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 0.674 0.887 0.943 0.4813 0.514 0.514
#> 3 3 0.747 0.856 0.912 0.3616 0.811 0.635
#> 4 4 0.717 0.796 0.858 0.1067 0.929 0.789
#> 5 5 0.734 0.536 0.781 0.0639 0.879 0.594
#> 6 6 0.787 0.695 0.821 0.0533 0.928 0.705
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM74356 2 0.9608 0.461 0.384 0.616
#> GSM74357 2 0.9608 0.461 0.384 0.616
#> GSM74358 2 0.9608 0.461 0.384 0.616
#> GSM74359 1 0.0000 0.952 1.000 0.000
#> GSM74360 1 0.0000 0.952 1.000 0.000
#> GSM74361 1 0.9209 0.464 0.664 0.336
#> GSM74362 1 0.7883 0.673 0.764 0.236
#> GSM74363 2 0.9608 0.461 0.384 0.616
#> GSM74402 1 0.0000 0.952 1.000 0.000
#> GSM74403 1 0.0000 0.952 1.000 0.000
#> GSM74404 1 0.0000 0.952 1.000 0.000
#> GSM74406 1 0.0000 0.952 1.000 0.000
#> GSM74407 1 0.9087 0.493 0.676 0.324
#> GSM74408 1 0.0000 0.952 1.000 0.000
#> GSM74409 1 0.0000 0.952 1.000 0.000
#> GSM74410 1 0.0000 0.952 1.000 0.000
#> GSM119936 1 0.0000 0.952 1.000 0.000
#> GSM119937 1 0.0376 0.952 0.996 0.004
#> GSM74411 2 0.0000 0.928 0.000 1.000
#> GSM74412 2 0.0000 0.928 0.000 1.000
#> GSM74413 2 0.0000 0.928 0.000 1.000
#> GSM74414 2 0.0000 0.928 0.000 1.000
#> GSM74415 2 0.0000 0.928 0.000 1.000
#> GSM121379 2 0.0000 0.928 0.000 1.000
#> GSM121380 2 0.0000 0.928 0.000 1.000
#> GSM121381 2 0.0000 0.928 0.000 1.000
#> GSM121382 2 0.0000 0.928 0.000 1.000
#> GSM121383 2 0.0000 0.928 0.000 1.000
#> GSM121384 2 0.0000 0.928 0.000 1.000
#> GSM121385 2 0.0000 0.928 0.000 1.000
#> GSM121386 2 0.0000 0.928 0.000 1.000
#> GSM121387 2 0.0000 0.928 0.000 1.000
#> GSM121388 2 0.0000 0.928 0.000 1.000
#> GSM121389 2 0.0000 0.928 0.000 1.000
#> GSM121390 2 0.0000 0.928 0.000 1.000
#> GSM121391 2 0.0000 0.928 0.000 1.000
#> GSM121392 2 0.0000 0.928 0.000 1.000
#> GSM121393 2 0.4690 0.884 0.100 0.900
#> GSM121394 2 0.0000 0.928 0.000 1.000
#> GSM121395 2 0.0000 0.928 0.000 1.000
#> GSM121396 2 0.0000 0.928 0.000 1.000
#> GSM121397 2 0.0000 0.928 0.000 1.000
#> GSM121398 2 0.0000 0.928 0.000 1.000
#> GSM121399 2 0.0000 0.928 0.000 1.000
#> GSM74240 2 0.0376 0.927 0.004 0.996
#> GSM74241 2 0.0376 0.927 0.004 0.996
#> GSM74242 2 0.5629 0.865 0.132 0.868
#> GSM74243 2 0.5629 0.865 0.132 0.868
#> GSM74244 2 0.0376 0.927 0.004 0.996
#> GSM74245 2 0.0376 0.927 0.004 0.996
#> GSM74246 2 0.0376 0.927 0.004 0.996
#> GSM74247 2 0.0376 0.927 0.004 0.996
#> GSM74248 2 0.0376 0.927 0.004 0.996
#> GSM74416 1 0.0000 0.952 1.000 0.000
#> GSM74417 1 0.0000 0.952 1.000 0.000
#> GSM74418 1 0.0000 0.952 1.000 0.000
#> GSM74419 1 0.3733 0.902 0.928 0.072
#> GSM121358 2 0.1184 0.923 0.016 0.984
#> GSM121359 2 0.0000 0.928 0.000 1.000
#> GSM121360 1 0.0376 0.952 0.996 0.004
#> GSM121362 1 0.0376 0.952 0.996 0.004
#> GSM121364 1 0.0000 0.952 1.000 0.000
#> GSM121365 2 0.5629 0.865 0.132 0.868
#> GSM121366 2 0.0000 0.928 0.000 1.000
#> GSM121367 2 0.4298 0.891 0.088 0.912
#> GSM121370 2 0.0000 0.928 0.000 1.000
#> GSM121371 2 0.5629 0.865 0.132 0.868
#> GSM121372 2 0.0000 0.928 0.000 1.000
#> GSM121373 1 0.0376 0.952 0.996 0.004
#> GSM121374 1 0.0000 0.952 1.000 0.000
#> GSM121407 2 0.0000 0.928 0.000 1.000
#> GSM74387 2 0.0000 0.928 0.000 1.000
#> GSM74388 2 0.0000 0.928 0.000 1.000
#> GSM74389 2 0.5629 0.865 0.132 0.868
#> GSM74390 2 0.2043 0.917 0.032 0.968
#> GSM74391 1 0.4562 0.878 0.904 0.096
#> GSM74392 1 0.3584 0.908 0.932 0.068
#> GSM74393 1 0.3584 0.908 0.932 0.068
#> GSM74394 2 0.0376 0.927 0.004 0.996
#> GSM74239 1 0.1414 0.949 0.980 0.020
#> GSM74364 1 0.0000 0.952 1.000 0.000
#> GSM74365 1 0.1414 0.949 0.980 0.020
#> GSM74366 2 0.5178 0.875 0.116 0.884
#> GSM74367 1 0.1414 0.949 0.980 0.020
#> GSM74377 2 0.6343 0.841 0.160 0.840
#> GSM74378 2 0.5519 0.867 0.128 0.872
#> GSM74379 2 0.6887 0.817 0.184 0.816
#> GSM74380 2 0.6887 0.817 0.184 0.816
#> GSM74381 2 0.6148 0.848 0.152 0.848
#> GSM121357 2 0.0000 0.928 0.000 1.000
#> GSM121361 2 0.0000 0.928 0.000 1.000
#> GSM121363 2 0.0000 0.928 0.000 1.000
#> GSM121368 2 0.0000 0.928 0.000 1.000
#> GSM121369 2 0.0376 0.927 0.004 0.996
#> GSM74368 1 0.1633 0.947 0.976 0.024
#> GSM74369 1 0.1633 0.947 0.976 0.024
#> GSM74370 1 0.0376 0.952 0.996 0.004
#> GSM74371 1 0.0000 0.952 1.000 0.000
#> GSM74372 1 0.1184 0.950 0.984 0.016
#> GSM74373 2 0.7139 0.803 0.196 0.804
#> GSM74374 1 0.1414 0.949 0.980 0.020
#> GSM74375 1 0.9988 -0.029 0.520 0.480
#> GSM74376 2 0.6801 0.821 0.180 0.820
#> GSM74405 2 0.6148 0.848 0.152 0.848
#> GSM74351 1 0.0000 0.952 1.000 0.000
#> GSM74352 2 0.5519 0.867 0.128 0.872
#> GSM74353 1 0.0376 0.952 0.996 0.004
#> GSM74354 1 0.1414 0.949 0.980 0.020
#> GSM74355 2 0.5519 0.867 0.128 0.872
#> GSM74382 1 0.0000 0.952 1.000 0.000
#> GSM74383 1 0.1414 0.949 0.980 0.020
#> GSM74384 2 0.5408 0.869 0.124 0.876
#> GSM74385 1 0.0000 0.952 1.000 0.000
#> GSM74386 1 0.1414 0.949 0.980 0.020
#> GSM74395 1 0.1414 0.949 0.980 0.020
#> GSM74396 1 0.1414 0.949 0.980 0.020
#> GSM74397 1 0.1414 0.949 0.980 0.020
#> GSM74398 2 0.7950 0.746 0.240 0.760
#> GSM74399 2 0.7950 0.746 0.240 0.760
#> GSM74400 1 0.2948 0.925 0.948 0.052
#> GSM74401 1 0.2948 0.925 0.948 0.052
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM74356 3 0.4974 0.614 0.236 0.000 0.764
#> GSM74357 3 0.4974 0.614 0.236 0.000 0.764
#> GSM74358 3 0.4974 0.614 0.236 0.000 0.764
#> GSM74359 1 0.0592 0.913 0.988 0.000 0.012
#> GSM74360 1 0.1860 0.917 0.948 0.000 0.052
#> GSM74361 1 0.6813 0.227 0.520 0.012 0.468
#> GSM74362 1 0.6045 0.501 0.620 0.000 0.380
#> GSM74363 3 0.4974 0.614 0.236 0.000 0.764
#> GSM74402 1 0.0237 0.909 0.996 0.000 0.004
#> GSM74403 1 0.0237 0.909 0.996 0.000 0.004
#> GSM74404 1 0.0237 0.909 0.996 0.000 0.004
#> GSM74406 1 0.0237 0.909 0.996 0.000 0.004
#> GSM74407 1 0.6295 0.258 0.528 0.000 0.472
#> GSM74408 1 0.0237 0.909 0.996 0.000 0.004
#> GSM74409 1 0.0237 0.909 0.996 0.000 0.004
#> GSM74410 1 0.0237 0.909 0.996 0.000 0.004
#> GSM119936 1 0.0237 0.909 0.996 0.000 0.004
#> GSM119937 1 0.2261 0.917 0.932 0.000 0.068
#> GSM74411 2 0.2261 0.943 0.000 0.932 0.068
#> GSM74412 2 0.2261 0.943 0.000 0.932 0.068
#> GSM74413 2 0.2261 0.943 0.000 0.932 0.068
#> GSM74414 2 0.2066 0.947 0.000 0.940 0.060
#> GSM74415 2 0.2356 0.940 0.000 0.928 0.072
#> GSM121379 2 0.0000 0.963 0.000 1.000 0.000
#> GSM121380 2 0.0000 0.963 0.000 1.000 0.000
#> GSM121381 2 0.0424 0.962 0.000 0.992 0.008
#> GSM121382 2 0.0237 0.963 0.000 0.996 0.004
#> GSM121383 2 0.0000 0.963 0.000 1.000 0.000
#> GSM121384 2 0.0000 0.963 0.000 1.000 0.000
#> GSM121385 2 0.0000 0.963 0.000 1.000 0.000
#> GSM121386 2 0.0000 0.963 0.000 1.000 0.000
#> GSM121387 2 0.0000 0.963 0.000 1.000 0.000
#> GSM121388 2 0.2356 0.940 0.000 0.928 0.072
#> GSM121389 2 0.0000 0.963 0.000 1.000 0.000
#> GSM121390 2 0.0000 0.963 0.000 1.000 0.000
#> GSM121391 2 0.0000 0.963 0.000 1.000 0.000
#> GSM121392 2 0.0000 0.963 0.000 1.000 0.000
#> GSM121393 3 0.5016 0.709 0.000 0.240 0.760
#> GSM121394 2 0.0237 0.963 0.000 0.996 0.004
#> GSM121395 2 0.0237 0.962 0.000 0.996 0.004
#> GSM121396 2 0.2261 0.943 0.000 0.932 0.068
#> GSM121397 2 0.0000 0.963 0.000 1.000 0.000
#> GSM121398 2 0.0000 0.963 0.000 1.000 0.000
#> GSM121399 2 0.0237 0.963 0.000 0.996 0.004
#> GSM74240 3 0.5216 0.708 0.000 0.260 0.740
#> GSM74241 3 0.5216 0.708 0.000 0.260 0.740
#> GSM74242 3 0.2682 0.836 0.004 0.076 0.920
#> GSM74243 3 0.2682 0.836 0.004 0.076 0.920
#> GSM74244 3 0.5216 0.708 0.000 0.260 0.740
#> GSM74245 3 0.5216 0.708 0.000 0.260 0.740
#> GSM74246 3 0.5216 0.708 0.000 0.260 0.740
#> GSM74247 3 0.5216 0.708 0.000 0.260 0.740
#> GSM74248 3 0.5216 0.708 0.000 0.260 0.740
#> GSM74416 1 0.0237 0.909 0.996 0.000 0.004
#> GSM74417 1 0.0237 0.909 0.996 0.000 0.004
#> GSM74418 1 0.0237 0.909 0.996 0.000 0.004
#> GSM74419 1 0.4654 0.797 0.792 0.000 0.208
#> GSM121358 3 0.6235 0.339 0.000 0.436 0.564
#> GSM121359 2 0.2261 0.943 0.000 0.932 0.068
#> GSM121360 1 0.2356 0.917 0.928 0.000 0.072
#> GSM121362 1 0.2356 0.917 0.928 0.000 0.072
#> GSM121364 1 0.0592 0.913 0.988 0.000 0.012
#> GSM121365 3 0.2945 0.834 0.004 0.088 0.908
#> GSM121366 2 0.2261 0.943 0.000 0.932 0.068
#> GSM121367 3 0.3551 0.811 0.000 0.132 0.868
#> GSM121370 2 0.3192 0.896 0.000 0.888 0.112
#> GSM121371 3 0.2945 0.834 0.004 0.088 0.908
#> GSM121372 2 0.2261 0.943 0.000 0.932 0.068
#> GSM121373 1 0.2165 0.917 0.936 0.000 0.064
#> GSM121374 1 0.0592 0.913 0.988 0.000 0.012
#> GSM121407 2 0.2066 0.947 0.000 0.940 0.060
#> GSM74387 2 0.2066 0.947 0.000 0.940 0.060
#> GSM74388 2 0.0000 0.963 0.000 1.000 0.000
#> GSM74389 3 0.2682 0.836 0.004 0.076 0.920
#> GSM74390 3 0.4452 0.769 0.000 0.192 0.808
#> GSM74391 1 0.5058 0.757 0.756 0.000 0.244
#> GSM74392 1 0.4504 0.818 0.804 0.000 0.196
#> GSM74393 1 0.4504 0.818 0.804 0.000 0.196
#> GSM74394 2 0.2711 0.896 0.000 0.912 0.088
#> GSM74239 1 0.2796 0.914 0.908 0.000 0.092
#> GSM74364 1 0.0592 0.911 0.988 0.000 0.012
#> GSM74365 1 0.2796 0.914 0.908 0.000 0.092
#> GSM74366 3 0.2796 0.829 0.000 0.092 0.908
#> GSM74367 1 0.2796 0.914 0.908 0.000 0.092
#> GSM74377 3 0.1491 0.834 0.016 0.016 0.968
#> GSM74378 3 0.1529 0.837 0.000 0.040 0.960
#> GSM74379 3 0.1647 0.824 0.036 0.004 0.960
#> GSM74380 3 0.1647 0.824 0.036 0.004 0.960
#> GSM74381 3 0.1315 0.836 0.008 0.020 0.972
#> GSM121357 2 0.2165 0.945 0.000 0.936 0.064
#> GSM121361 2 0.0000 0.963 0.000 1.000 0.000
#> GSM121363 2 0.0000 0.963 0.000 1.000 0.000
#> GSM121368 2 0.0000 0.963 0.000 1.000 0.000
#> GSM121369 2 0.2711 0.896 0.000 0.912 0.088
#> GSM74368 1 0.2959 0.910 0.900 0.000 0.100
#> GSM74369 1 0.2959 0.910 0.900 0.000 0.100
#> GSM74370 1 0.2356 0.917 0.928 0.000 0.072
#> GSM74371 1 0.0237 0.909 0.996 0.000 0.004
#> GSM74372 1 0.2711 0.915 0.912 0.000 0.088
#> GSM74373 3 0.1989 0.819 0.048 0.004 0.948
#> GSM74374 1 0.2796 0.914 0.908 0.000 0.092
#> GSM74375 3 0.6095 0.187 0.392 0.000 0.608
#> GSM74376 3 0.1525 0.825 0.032 0.004 0.964
#> GSM74405 3 0.1315 0.836 0.008 0.020 0.972
#> GSM74351 1 0.0237 0.909 0.996 0.000 0.004
#> GSM74352 3 0.1529 0.837 0.000 0.040 0.960
#> GSM74353 1 0.2261 0.917 0.932 0.000 0.068
#> GSM74354 1 0.2796 0.914 0.908 0.000 0.092
#> GSM74355 3 0.1529 0.837 0.000 0.040 0.960
#> GSM74382 1 0.0237 0.909 0.996 0.000 0.004
#> GSM74383 1 0.2796 0.914 0.908 0.000 0.092
#> GSM74384 3 0.2356 0.834 0.000 0.072 0.928
#> GSM74385 1 0.0237 0.909 0.996 0.000 0.004
#> GSM74386 1 0.2878 0.912 0.904 0.000 0.096
#> GSM74395 1 0.2796 0.914 0.908 0.000 0.092
#> GSM74396 1 0.2796 0.914 0.908 0.000 0.092
#> GSM74397 1 0.2796 0.914 0.908 0.000 0.092
#> GSM74398 3 0.3030 0.794 0.092 0.004 0.904
#> GSM74399 3 0.3030 0.794 0.092 0.004 0.904
#> GSM74400 1 0.3482 0.890 0.872 0.000 0.128
#> GSM74401 1 0.3482 0.890 0.872 0.000 0.128
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM74356 3 0.5697 0.4975 0.280 0.000 0.664 0.056
#> GSM74357 3 0.5697 0.4975 0.280 0.000 0.664 0.056
#> GSM74358 3 0.5697 0.4975 0.280 0.000 0.664 0.056
#> GSM74359 1 0.4817 0.0401 0.612 0.000 0.000 0.388
#> GSM74360 1 0.1867 0.8018 0.928 0.000 0.000 0.072
#> GSM74361 1 0.6723 0.3063 0.548 0.008 0.368 0.076
#> GSM74362 1 0.5927 0.5510 0.660 0.000 0.264 0.076
#> GSM74363 3 0.5697 0.4975 0.280 0.000 0.664 0.056
#> GSM74402 4 0.4406 0.8616 0.300 0.000 0.000 0.700
#> GSM74403 4 0.3311 0.9132 0.172 0.000 0.000 0.828
#> GSM74404 4 0.3311 0.9132 0.172 0.000 0.000 0.828
#> GSM74406 4 0.4431 0.8562 0.304 0.000 0.000 0.696
#> GSM74407 1 0.6214 0.3487 0.576 0.000 0.360 0.064
#> GSM74408 4 0.4356 0.8694 0.292 0.000 0.000 0.708
#> GSM74409 4 0.4356 0.8694 0.292 0.000 0.000 0.708
#> GSM74410 4 0.4356 0.8694 0.292 0.000 0.000 0.708
#> GSM119936 4 0.4356 0.8694 0.292 0.000 0.000 0.708
#> GSM119937 1 0.0921 0.8404 0.972 0.000 0.000 0.028
#> GSM74411 2 0.1867 0.9426 0.000 0.928 0.072 0.000
#> GSM74412 2 0.1867 0.9426 0.000 0.928 0.072 0.000
#> GSM74413 2 0.1867 0.9426 0.000 0.928 0.072 0.000
#> GSM74414 2 0.1716 0.9464 0.000 0.936 0.064 0.000
#> GSM74415 2 0.1940 0.9399 0.000 0.924 0.076 0.000
#> GSM121379 2 0.0000 0.9628 0.000 1.000 0.000 0.000
#> GSM121380 2 0.0000 0.9628 0.000 1.000 0.000 0.000
#> GSM121381 2 0.0469 0.9608 0.000 0.988 0.012 0.000
#> GSM121382 2 0.0188 0.9625 0.000 0.996 0.004 0.000
#> GSM121383 2 0.0000 0.9628 0.000 1.000 0.000 0.000
#> GSM121384 2 0.0000 0.9628 0.000 1.000 0.000 0.000
#> GSM121385 2 0.0000 0.9628 0.000 1.000 0.000 0.000
#> GSM121386 2 0.0000 0.9628 0.000 1.000 0.000 0.000
#> GSM121387 2 0.0000 0.9628 0.000 1.000 0.000 0.000
#> GSM121388 2 0.1867 0.9425 0.000 0.928 0.072 0.000
#> GSM121389 2 0.0000 0.9628 0.000 1.000 0.000 0.000
#> GSM121390 2 0.0000 0.9628 0.000 1.000 0.000 0.000
#> GSM121391 2 0.0000 0.9628 0.000 1.000 0.000 0.000
#> GSM121392 2 0.0000 0.9628 0.000 1.000 0.000 0.000
#> GSM121393 3 0.5882 0.6747 0.008 0.224 0.696 0.072
#> GSM121394 2 0.0188 0.9625 0.000 0.996 0.004 0.000
#> GSM121395 2 0.0188 0.9614 0.000 0.996 0.004 0.000
#> GSM121396 2 0.1867 0.9426 0.000 0.928 0.072 0.000
#> GSM121397 2 0.0000 0.9628 0.000 1.000 0.000 0.000
#> GSM121398 2 0.0000 0.9628 0.000 1.000 0.000 0.000
#> GSM121399 2 0.0188 0.9625 0.000 0.996 0.004 0.000
#> GSM74240 3 0.4040 0.6708 0.000 0.248 0.752 0.000
#> GSM74241 3 0.4040 0.6708 0.000 0.248 0.752 0.000
#> GSM74242 3 0.3978 0.7478 0.028 0.064 0.860 0.048
#> GSM74243 3 0.3978 0.7478 0.028 0.064 0.860 0.048
#> GSM74244 3 0.4040 0.6708 0.000 0.248 0.752 0.000
#> GSM74245 3 0.4040 0.6708 0.000 0.248 0.752 0.000
#> GSM74246 3 0.4040 0.6708 0.000 0.248 0.752 0.000
#> GSM74247 3 0.4040 0.6708 0.000 0.248 0.752 0.000
#> GSM74248 3 0.4040 0.6708 0.000 0.248 0.752 0.000
#> GSM74416 4 0.3311 0.9132 0.172 0.000 0.000 0.828
#> GSM74417 4 0.3311 0.9132 0.172 0.000 0.000 0.828
#> GSM74418 4 0.3311 0.9132 0.172 0.000 0.000 0.828
#> GSM74419 1 0.4956 0.7253 0.776 0.000 0.108 0.116
#> GSM121358 3 0.4925 0.3036 0.000 0.428 0.572 0.000
#> GSM121359 2 0.1867 0.9426 0.000 0.928 0.072 0.000
#> GSM121360 1 0.0707 0.8452 0.980 0.000 0.000 0.020
#> GSM121362 1 0.0707 0.8452 0.980 0.000 0.000 0.020
#> GSM121364 1 0.4817 0.0401 0.612 0.000 0.000 0.388
#> GSM121365 3 0.4195 0.7477 0.028 0.076 0.848 0.048
#> GSM121366 2 0.1867 0.9426 0.000 0.928 0.072 0.000
#> GSM121367 3 0.3914 0.7461 0.004 0.120 0.840 0.036
#> GSM121370 2 0.2647 0.8945 0.000 0.880 0.120 0.000
#> GSM121371 3 0.4195 0.7477 0.028 0.076 0.848 0.048
#> GSM121372 2 0.1867 0.9426 0.000 0.928 0.072 0.000
#> GSM121373 1 0.1302 0.8287 0.956 0.000 0.000 0.044
#> GSM121374 1 0.4817 0.0401 0.612 0.000 0.000 0.388
#> GSM121407 2 0.1716 0.9465 0.000 0.936 0.064 0.000
#> GSM74387 2 0.1716 0.9465 0.000 0.936 0.064 0.000
#> GSM74388 2 0.0000 0.9628 0.000 1.000 0.000 0.000
#> GSM74389 3 0.3978 0.7478 0.028 0.064 0.860 0.048
#> GSM74390 3 0.3852 0.7238 0.000 0.180 0.808 0.012
#> GSM74391 1 0.4740 0.7192 0.788 0.000 0.132 0.080
#> GSM74392 1 0.3970 0.7602 0.840 0.000 0.084 0.076
#> GSM74393 1 0.3970 0.7602 0.840 0.000 0.084 0.076
#> GSM74394 2 0.2401 0.8864 0.000 0.904 0.092 0.004
#> GSM74239 1 0.0000 0.8517 1.000 0.000 0.000 0.000
#> GSM74364 4 0.4679 0.7136 0.352 0.000 0.000 0.648
#> GSM74365 1 0.0000 0.8517 1.000 0.000 0.000 0.000
#> GSM74366 3 0.5290 0.7417 0.028 0.080 0.784 0.108
#> GSM74367 1 0.0000 0.8517 1.000 0.000 0.000 0.000
#> GSM74377 3 0.4949 0.7373 0.072 0.012 0.792 0.124
#> GSM74378 3 0.4562 0.7440 0.036 0.028 0.820 0.116
#> GSM74379 3 0.4780 0.7299 0.096 0.000 0.788 0.116
#> GSM74380 3 0.4780 0.7299 0.096 0.000 0.788 0.116
#> GSM74381 3 0.4795 0.7404 0.060 0.016 0.804 0.120
#> GSM121357 2 0.1792 0.9452 0.000 0.932 0.068 0.000
#> GSM121361 2 0.0000 0.9628 0.000 1.000 0.000 0.000
#> GSM121363 2 0.0000 0.9628 0.000 1.000 0.000 0.000
#> GSM121368 2 0.0000 0.9628 0.000 1.000 0.000 0.000
#> GSM121369 2 0.2401 0.8864 0.000 0.904 0.092 0.004
#> GSM74368 1 0.0657 0.8499 0.984 0.000 0.004 0.012
#> GSM74369 1 0.0657 0.8499 0.984 0.000 0.004 0.012
#> GSM74370 1 0.0707 0.8452 0.980 0.000 0.000 0.020
#> GSM74371 4 0.3311 0.9132 0.172 0.000 0.000 0.828
#> GSM74372 1 0.0469 0.8512 0.988 0.000 0.000 0.012
#> GSM74373 3 0.5119 0.7203 0.112 0.000 0.764 0.124
#> GSM74374 1 0.0188 0.8510 0.996 0.000 0.000 0.004
#> GSM74375 3 0.6277 0.0866 0.468 0.000 0.476 0.056
#> GSM74376 3 0.4888 0.7284 0.096 0.000 0.780 0.124
#> GSM74405 3 0.4795 0.7404 0.060 0.016 0.804 0.120
#> GSM74351 4 0.3311 0.9132 0.172 0.000 0.000 0.828
#> GSM74352 3 0.4562 0.7440 0.036 0.028 0.820 0.116
#> GSM74353 1 0.0921 0.8404 0.972 0.000 0.000 0.028
#> GSM74354 1 0.0188 0.8510 0.996 0.000 0.000 0.004
#> GSM74355 3 0.4562 0.7440 0.036 0.028 0.820 0.116
#> GSM74382 4 0.3444 0.9105 0.184 0.000 0.000 0.816
#> GSM74383 1 0.0000 0.8517 1.000 0.000 0.000 0.000
#> GSM74384 3 0.5147 0.7425 0.032 0.060 0.792 0.116
#> GSM74385 4 0.3311 0.9132 0.172 0.000 0.000 0.828
#> GSM74386 1 0.0524 0.8511 0.988 0.000 0.004 0.008
#> GSM74395 1 0.0000 0.8517 1.000 0.000 0.000 0.000
#> GSM74396 1 0.0000 0.8517 1.000 0.000 0.000 0.000
#> GSM74397 1 0.0000 0.8517 1.000 0.000 0.000 0.000
#> GSM74398 3 0.5568 0.6959 0.152 0.000 0.728 0.120
#> GSM74399 3 0.5568 0.6959 0.152 0.000 0.728 0.120
#> GSM74400 1 0.1297 0.8319 0.964 0.000 0.016 0.020
#> GSM74401 1 0.1297 0.8319 0.964 0.000 0.016 0.020
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM74356 3 0.5180 0.564 0.024 0.000 0.728 0.100 0.148
#> GSM74357 3 0.5180 0.564 0.024 0.000 0.728 0.100 0.148
#> GSM74358 3 0.5180 0.564 0.024 0.000 0.728 0.100 0.148
#> GSM74359 4 0.0510 0.186 0.016 0.000 0.000 0.984 0.000
#> GSM74360 4 0.4982 0.363 0.032 0.000 0.000 0.556 0.412
#> GSM74361 3 0.7162 -0.119 0.024 0.000 0.440 0.300 0.236
#> GSM74362 4 0.7314 0.276 0.028 0.000 0.332 0.396 0.244
#> GSM74363 3 0.5180 0.564 0.024 0.000 0.728 0.100 0.148
#> GSM74402 4 0.3913 -0.113 0.000 0.000 0.000 0.676 0.324
#> GSM74403 5 0.4653 0.276 0.012 0.000 0.000 0.472 0.516
#> GSM74404 5 0.4653 0.276 0.012 0.000 0.000 0.472 0.516
#> GSM74406 4 0.3895 -0.109 0.000 0.000 0.000 0.680 0.320
#> GSM74407 3 0.7356 -0.124 0.036 0.000 0.428 0.304 0.232
#> GSM74408 4 0.3913 -0.116 0.000 0.000 0.000 0.676 0.324
#> GSM74409 4 0.3913 -0.116 0.000 0.000 0.000 0.676 0.324
#> GSM74410 4 0.3913 -0.116 0.000 0.000 0.000 0.676 0.324
#> GSM119936 4 0.3913 -0.116 0.000 0.000 0.000 0.676 0.324
#> GSM119937 4 0.4744 0.356 0.016 0.000 0.000 0.508 0.476
#> GSM74411 2 0.2561 0.886 0.000 0.856 0.144 0.000 0.000
#> GSM74412 2 0.2561 0.886 0.000 0.856 0.144 0.000 0.000
#> GSM74413 2 0.2561 0.886 0.000 0.856 0.144 0.000 0.000
#> GSM74414 2 0.2471 0.891 0.000 0.864 0.136 0.000 0.000
#> GSM74415 2 0.2605 0.884 0.000 0.852 0.148 0.000 0.000
#> GSM121379 2 0.0000 0.928 0.000 1.000 0.000 0.000 0.000
#> GSM121380 2 0.0000 0.928 0.000 1.000 0.000 0.000 0.000
#> GSM121381 2 0.1341 0.920 0.000 0.944 0.056 0.000 0.000
#> GSM121382 2 0.0609 0.928 0.000 0.980 0.020 0.000 0.000
#> GSM121383 2 0.0162 0.928 0.000 0.996 0.004 0.000 0.000
#> GSM121384 2 0.0000 0.928 0.000 1.000 0.000 0.000 0.000
#> GSM121385 2 0.0000 0.928 0.000 1.000 0.000 0.000 0.000
#> GSM121386 2 0.0000 0.928 0.000 1.000 0.000 0.000 0.000
#> GSM121387 2 0.0000 0.928 0.000 1.000 0.000 0.000 0.000
#> GSM121388 2 0.2424 0.893 0.000 0.868 0.132 0.000 0.000
#> GSM121389 2 0.0000 0.928 0.000 1.000 0.000 0.000 0.000
#> GSM121390 2 0.0000 0.928 0.000 1.000 0.000 0.000 0.000
#> GSM121391 2 0.0510 0.928 0.000 0.984 0.016 0.000 0.000
#> GSM121392 2 0.0000 0.928 0.000 1.000 0.000 0.000 0.000
#> GSM121393 1 0.5240 0.610 0.676 0.204 0.120 0.000 0.000
#> GSM121394 2 0.0794 0.926 0.000 0.972 0.028 0.000 0.000
#> GSM121395 2 0.0404 0.928 0.000 0.988 0.012 0.000 0.000
#> GSM121396 2 0.2561 0.886 0.000 0.856 0.144 0.000 0.000
#> GSM121397 2 0.0000 0.928 0.000 1.000 0.000 0.000 0.000
#> GSM121398 2 0.0000 0.928 0.000 1.000 0.000 0.000 0.000
#> GSM121399 2 0.0609 0.928 0.000 0.980 0.020 0.000 0.000
#> GSM74240 3 0.2852 0.728 0.000 0.172 0.828 0.000 0.000
#> GSM74241 3 0.2852 0.728 0.000 0.172 0.828 0.000 0.000
#> GSM74242 3 0.0404 0.723 0.012 0.000 0.988 0.000 0.000
#> GSM74243 3 0.0404 0.723 0.012 0.000 0.988 0.000 0.000
#> GSM74244 3 0.2852 0.728 0.000 0.172 0.828 0.000 0.000
#> GSM74245 3 0.2852 0.728 0.000 0.172 0.828 0.000 0.000
#> GSM74246 3 0.2852 0.728 0.000 0.172 0.828 0.000 0.000
#> GSM74247 3 0.2852 0.728 0.000 0.172 0.828 0.000 0.000
#> GSM74248 3 0.2852 0.728 0.000 0.172 0.828 0.000 0.000
#> GSM74416 5 0.4653 0.276 0.012 0.000 0.000 0.472 0.516
#> GSM74417 5 0.4653 0.276 0.012 0.000 0.000 0.472 0.516
#> GSM74418 5 0.4653 0.276 0.012 0.000 0.000 0.472 0.516
#> GSM74419 4 0.6783 0.358 0.024 0.000 0.176 0.520 0.280
#> GSM121358 3 0.4030 0.405 0.000 0.352 0.648 0.000 0.000
#> GSM121359 2 0.2605 0.883 0.000 0.852 0.148 0.000 0.000
#> GSM121360 4 0.5112 0.359 0.036 0.000 0.000 0.496 0.468
#> GSM121362 4 0.5112 0.359 0.036 0.000 0.000 0.496 0.468
#> GSM121364 4 0.0510 0.186 0.016 0.000 0.000 0.984 0.000
#> GSM121365 3 0.0324 0.727 0.004 0.004 0.992 0.000 0.000
#> GSM121366 2 0.2605 0.883 0.000 0.852 0.148 0.000 0.000
#> GSM121367 3 0.1357 0.736 0.004 0.048 0.948 0.000 0.000
#> GSM121370 2 0.3074 0.831 0.000 0.804 0.196 0.000 0.000
#> GSM121371 3 0.0324 0.727 0.004 0.004 0.992 0.000 0.000
#> GSM121372 2 0.2605 0.883 0.000 0.852 0.148 0.000 0.000
#> GSM121373 4 0.5092 0.362 0.036 0.000 0.000 0.524 0.440
#> GSM121374 4 0.0510 0.186 0.016 0.000 0.000 0.984 0.000
#> GSM121407 2 0.2471 0.891 0.000 0.864 0.136 0.000 0.000
#> GSM74387 2 0.2471 0.891 0.000 0.864 0.136 0.000 0.000
#> GSM74388 2 0.0000 0.928 0.000 1.000 0.000 0.000 0.000
#> GSM74389 3 0.0404 0.723 0.012 0.000 0.988 0.000 0.000
#> GSM74390 3 0.2286 0.735 0.004 0.108 0.888 0.000 0.000
#> GSM74391 4 0.7122 0.352 0.032 0.000 0.204 0.476 0.288
#> GSM74392 4 0.6644 0.367 0.016 0.000 0.160 0.504 0.320
#> GSM74393 4 0.6644 0.367 0.016 0.000 0.160 0.504 0.320
#> GSM74394 2 0.2645 0.869 0.044 0.888 0.068 0.000 0.000
#> GSM74239 4 0.5238 0.333 0.044 0.000 0.000 0.480 0.476
#> GSM74364 5 0.4659 0.109 0.012 0.000 0.000 0.492 0.496
#> GSM74365 4 0.5297 0.331 0.048 0.000 0.000 0.476 0.476
#> GSM74366 1 0.2927 0.852 0.872 0.068 0.060 0.000 0.000
#> GSM74367 5 0.5297 -0.389 0.048 0.000 0.000 0.476 0.476
#> GSM74377 1 0.1404 0.898 0.956 0.008 0.028 0.004 0.004
#> GSM74378 1 0.1943 0.890 0.924 0.020 0.056 0.000 0.000
#> GSM74379 1 0.1588 0.894 0.948 0.000 0.028 0.008 0.016
#> GSM74380 1 0.1588 0.894 0.948 0.000 0.028 0.008 0.016
#> GSM74381 1 0.1525 0.898 0.948 0.012 0.036 0.000 0.004
#> GSM121357 2 0.2629 0.891 0.004 0.860 0.136 0.000 0.000
#> GSM121361 2 0.0000 0.928 0.000 1.000 0.000 0.000 0.000
#> GSM121363 2 0.0000 0.928 0.000 1.000 0.000 0.000 0.000
#> GSM121368 2 0.0000 0.928 0.000 1.000 0.000 0.000 0.000
#> GSM121369 2 0.2645 0.869 0.044 0.888 0.068 0.000 0.000
#> GSM74368 5 0.5406 -0.388 0.056 0.000 0.000 0.468 0.476
#> GSM74369 5 0.5406 -0.388 0.056 0.000 0.000 0.468 0.476
#> GSM74370 4 0.4906 0.355 0.024 0.000 0.000 0.496 0.480
#> GSM74371 5 0.4653 0.276 0.012 0.000 0.000 0.472 0.516
#> GSM74372 5 0.5178 -0.398 0.040 0.000 0.000 0.480 0.480
#> GSM74373 1 0.1043 0.885 0.960 0.000 0.000 0.000 0.040
#> GSM74374 4 0.5296 0.337 0.048 0.000 0.000 0.480 0.472
#> GSM74375 1 0.6302 0.301 0.584 0.000 0.016 0.156 0.244
#> GSM74376 1 0.1211 0.893 0.960 0.000 0.016 0.000 0.024
#> GSM74405 1 0.1525 0.898 0.948 0.012 0.036 0.000 0.004
#> GSM74351 5 0.4653 0.276 0.012 0.000 0.000 0.472 0.516
#> GSM74352 1 0.1943 0.890 0.924 0.020 0.056 0.000 0.000
#> GSM74353 4 0.4744 0.356 0.016 0.000 0.000 0.508 0.476
#> GSM74354 4 0.5296 0.337 0.048 0.000 0.000 0.480 0.472
#> GSM74355 1 0.1943 0.890 0.924 0.020 0.056 0.000 0.000
#> GSM74382 5 0.4659 0.265 0.012 0.000 0.000 0.488 0.500
#> GSM74383 5 0.5297 -0.391 0.048 0.000 0.000 0.476 0.476
#> GSM74384 1 0.2588 0.871 0.892 0.048 0.060 0.000 0.000
#> GSM74385 5 0.4653 0.276 0.012 0.000 0.000 0.472 0.516
#> GSM74386 5 0.5353 -0.389 0.052 0.000 0.000 0.472 0.476
#> GSM74395 5 0.5297 -0.389 0.048 0.000 0.000 0.476 0.476
#> GSM74396 5 0.5297 -0.389 0.048 0.000 0.000 0.476 0.476
#> GSM74397 4 0.5297 0.331 0.048 0.000 0.000 0.476 0.476
#> GSM74398 1 0.2634 0.857 0.900 0.000 0.020 0.056 0.024
#> GSM74399 1 0.2634 0.857 0.900 0.000 0.020 0.056 0.024
#> GSM74400 5 0.5818 -0.375 0.092 0.000 0.000 0.444 0.464
#> GSM74401 5 0.5818 -0.375 0.092 0.000 0.000 0.444 0.464
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM74356 5 0.4132 0.429 0.044 0.000 0.220 0.000 0.728 0.008
#> GSM74357 5 0.4132 0.429 0.044 0.000 0.220 0.000 0.728 0.008
#> GSM74358 5 0.4132 0.429 0.044 0.000 0.220 0.000 0.728 0.008
#> GSM74359 4 0.5953 0.296 0.344 0.000 0.196 0.456 0.000 0.004
#> GSM74360 1 0.3455 0.709 0.800 0.000 0.144 0.056 0.000 0.000
#> GSM74361 5 0.6250 -0.620 0.236 0.000 0.316 0.000 0.436 0.012
#> GSM74362 3 0.6405 0.732 0.300 0.000 0.360 0.000 0.328 0.012
#> GSM74363 5 0.4132 0.429 0.044 0.000 0.220 0.000 0.728 0.008
#> GSM74402 4 0.3777 0.763 0.084 0.000 0.124 0.788 0.000 0.004
#> GSM74403 4 0.0000 0.807 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM74404 4 0.0000 0.807 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM74406 4 0.3826 0.760 0.088 0.000 0.124 0.784 0.000 0.004
#> GSM74407 5 0.6262 -0.493 0.204 0.000 0.356 0.000 0.424 0.016
#> GSM74408 4 0.3675 0.766 0.076 0.000 0.124 0.796 0.000 0.004
#> GSM74409 4 0.3675 0.766 0.076 0.000 0.124 0.796 0.000 0.004
#> GSM74410 4 0.3675 0.766 0.076 0.000 0.124 0.796 0.000 0.004
#> GSM119936 4 0.3675 0.766 0.076 0.000 0.124 0.796 0.000 0.004
#> GSM119937 1 0.1049 0.809 0.960 0.000 0.032 0.008 0.000 0.000
#> GSM74411 2 0.5510 0.654 0.000 0.540 0.324 0.000 0.132 0.004
#> GSM74412 2 0.5510 0.654 0.000 0.540 0.324 0.000 0.132 0.004
#> GSM74413 2 0.5510 0.654 0.000 0.540 0.324 0.000 0.132 0.004
#> GSM74414 2 0.5434 0.667 0.000 0.552 0.320 0.000 0.124 0.004
#> GSM74415 2 0.5541 0.651 0.000 0.536 0.324 0.000 0.136 0.004
#> GSM121379 2 0.0146 0.787 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM121380 2 0.0692 0.780 0.000 0.976 0.020 0.000 0.000 0.004
#> GSM121381 2 0.3065 0.783 0.000 0.844 0.100 0.000 0.052 0.004
#> GSM121382 2 0.2445 0.790 0.000 0.872 0.108 0.000 0.020 0.000
#> GSM121383 2 0.1411 0.795 0.000 0.936 0.060 0.000 0.004 0.000
#> GSM121384 2 0.0692 0.780 0.000 0.976 0.020 0.000 0.000 0.004
#> GSM121385 2 0.0146 0.787 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM121386 2 0.0937 0.794 0.000 0.960 0.040 0.000 0.000 0.000
#> GSM121387 2 0.1007 0.794 0.000 0.956 0.044 0.000 0.000 0.000
#> GSM121388 2 0.5374 0.669 0.000 0.564 0.312 0.000 0.120 0.004
#> GSM121389 2 0.1643 0.752 0.000 0.924 0.068 0.000 0.000 0.008
#> GSM121390 2 0.0692 0.780 0.000 0.976 0.020 0.000 0.000 0.004
#> GSM121391 2 0.1951 0.794 0.000 0.908 0.076 0.000 0.016 0.000
#> GSM121392 2 0.1643 0.752 0.000 0.924 0.068 0.000 0.000 0.008
#> GSM121393 6 0.5277 0.659 0.000 0.116 0.084 0.000 0.104 0.696
#> GSM121394 2 0.2605 0.789 0.000 0.864 0.108 0.000 0.028 0.000
#> GSM121395 2 0.2326 0.758 0.000 0.888 0.092 0.000 0.012 0.008
#> GSM121396 2 0.5486 0.659 0.000 0.548 0.316 0.000 0.132 0.004
#> GSM121397 2 0.0146 0.787 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM121398 2 0.0790 0.792 0.000 0.968 0.032 0.000 0.000 0.000
#> GSM121399 2 0.2445 0.790 0.000 0.872 0.108 0.000 0.020 0.000
#> GSM74240 5 0.2738 0.720 0.000 0.000 0.176 0.000 0.820 0.004
#> GSM74241 5 0.2738 0.720 0.000 0.000 0.176 0.000 0.820 0.004
#> GSM74242 5 0.0291 0.705 0.000 0.000 0.004 0.000 0.992 0.004
#> GSM74243 5 0.0291 0.705 0.000 0.000 0.004 0.000 0.992 0.004
#> GSM74244 5 0.2738 0.720 0.000 0.000 0.176 0.000 0.820 0.004
#> GSM74245 5 0.2738 0.720 0.000 0.000 0.176 0.000 0.820 0.004
#> GSM74246 5 0.2738 0.720 0.000 0.000 0.176 0.000 0.820 0.004
#> GSM74247 5 0.2738 0.720 0.000 0.000 0.176 0.000 0.820 0.004
#> GSM74248 5 0.2738 0.720 0.000 0.000 0.176 0.000 0.820 0.004
#> GSM74416 4 0.0000 0.807 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM74417 4 0.0000 0.807 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM74418 4 0.0000 0.807 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM74419 3 0.6920 0.813 0.360 0.000 0.408 0.044 0.172 0.016
#> GSM121358 5 0.5140 0.508 0.000 0.164 0.192 0.000 0.640 0.004
#> GSM121359 2 0.5541 0.650 0.000 0.536 0.324 0.000 0.136 0.004
#> GSM121360 1 0.2668 0.746 0.828 0.000 0.168 0.000 0.000 0.004
#> GSM121362 1 0.2595 0.750 0.836 0.000 0.160 0.000 0.000 0.004
#> GSM121364 4 0.5953 0.296 0.344 0.000 0.196 0.456 0.000 0.004
#> GSM121365 5 0.0146 0.711 0.000 0.000 0.004 0.000 0.996 0.000
#> GSM121366 2 0.5541 0.650 0.000 0.536 0.324 0.000 0.136 0.004
#> GSM121367 5 0.1075 0.722 0.000 0.000 0.048 0.000 0.952 0.000
#> GSM121370 2 0.5854 0.584 0.000 0.488 0.324 0.000 0.184 0.004
#> GSM121371 5 0.0146 0.711 0.000 0.000 0.004 0.000 0.996 0.000
#> GSM121372 2 0.5541 0.650 0.000 0.536 0.324 0.000 0.136 0.004
#> GSM121373 1 0.3168 0.739 0.820 0.000 0.148 0.028 0.000 0.004
#> GSM121374 4 0.5953 0.296 0.344 0.000 0.196 0.456 0.000 0.004
#> GSM121407 2 0.5434 0.662 0.000 0.552 0.320 0.000 0.124 0.004
#> GSM74387 2 0.5367 0.670 0.000 0.572 0.300 0.000 0.124 0.004
#> GSM74388 2 0.0692 0.786 0.000 0.976 0.020 0.000 0.000 0.004
#> GSM74389 5 0.0291 0.705 0.000 0.000 0.004 0.000 0.992 0.004
#> GSM74390 5 0.2100 0.725 0.000 0.000 0.112 0.000 0.884 0.004
#> GSM74391 3 0.6323 0.837 0.364 0.000 0.416 0.000 0.200 0.020
#> GSM74392 1 0.5879 -0.773 0.432 0.000 0.408 0.000 0.152 0.008
#> GSM74393 1 0.5879 -0.773 0.432 0.000 0.408 0.000 0.152 0.008
#> GSM74394 2 0.3483 0.748 0.000 0.836 0.048 0.000 0.068 0.048
#> GSM74239 1 0.0972 0.826 0.964 0.000 0.008 0.000 0.000 0.028
#> GSM74364 4 0.2762 0.653 0.196 0.000 0.000 0.804 0.000 0.000
#> GSM74365 1 0.1049 0.825 0.960 0.000 0.008 0.000 0.000 0.032
#> GSM74366 6 0.2084 0.861 0.000 0.044 0.016 0.000 0.024 0.916
#> GSM74367 1 0.1049 0.825 0.960 0.000 0.008 0.000 0.000 0.032
#> GSM74377 6 0.0547 0.899 0.020 0.000 0.000 0.000 0.000 0.980
#> GSM74378 6 0.0951 0.892 0.000 0.004 0.008 0.000 0.020 0.968
#> GSM74379 6 0.1082 0.896 0.040 0.000 0.004 0.000 0.000 0.956
#> GSM74380 6 0.1082 0.896 0.040 0.000 0.004 0.000 0.000 0.956
#> GSM74381 6 0.0622 0.899 0.012 0.000 0.000 0.000 0.008 0.980
#> GSM121357 2 0.5535 0.661 0.000 0.548 0.320 0.000 0.124 0.008
#> GSM121361 2 0.0692 0.786 0.000 0.976 0.020 0.000 0.000 0.004
#> GSM121363 2 0.0692 0.786 0.000 0.976 0.020 0.000 0.000 0.004
#> GSM121368 2 0.0692 0.786 0.000 0.976 0.020 0.000 0.000 0.004
#> GSM121369 2 0.3483 0.748 0.000 0.836 0.048 0.000 0.068 0.048
#> GSM74368 1 0.1391 0.815 0.944 0.000 0.016 0.000 0.000 0.040
#> GSM74369 1 0.1391 0.815 0.944 0.000 0.016 0.000 0.000 0.040
#> GSM74370 1 0.2362 0.772 0.860 0.000 0.136 0.000 0.000 0.004
#> GSM74371 4 0.0000 0.807 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM74372 1 0.2667 0.781 0.852 0.000 0.128 0.000 0.000 0.020
#> GSM74373 6 0.1349 0.888 0.056 0.000 0.004 0.000 0.000 0.940
#> GSM74374 1 0.1924 0.824 0.920 0.000 0.048 0.004 0.000 0.028
#> GSM74375 6 0.4101 0.253 0.408 0.000 0.012 0.000 0.000 0.580
#> GSM74376 6 0.1082 0.895 0.040 0.000 0.004 0.000 0.000 0.956
#> GSM74405 6 0.0622 0.899 0.012 0.000 0.000 0.000 0.008 0.980
#> GSM74351 4 0.0000 0.807 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM74352 6 0.0951 0.892 0.000 0.004 0.008 0.000 0.020 0.968
#> GSM74353 1 0.1049 0.809 0.960 0.000 0.032 0.008 0.000 0.000
#> GSM74354 1 0.1924 0.824 0.920 0.000 0.048 0.004 0.000 0.028
#> GSM74355 6 0.0951 0.892 0.000 0.004 0.008 0.000 0.020 0.968
#> GSM74382 4 0.0458 0.804 0.016 0.000 0.000 0.984 0.000 0.000
#> GSM74383 1 0.1780 0.824 0.924 0.000 0.048 0.000 0.000 0.028
#> GSM74384 6 0.1700 0.875 0.000 0.028 0.012 0.000 0.024 0.936
#> GSM74385 4 0.0000 0.807 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM74386 1 0.1225 0.821 0.952 0.000 0.012 0.000 0.000 0.036
#> GSM74395 1 0.1049 0.825 0.960 0.000 0.008 0.000 0.000 0.032
#> GSM74396 1 0.1049 0.825 0.960 0.000 0.008 0.000 0.000 0.032
#> GSM74397 1 0.1049 0.825 0.960 0.000 0.008 0.000 0.000 0.032
#> GSM74398 6 0.1970 0.854 0.092 0.000 0.008 0.000 0.000 0.900
#> GSM74399 6 0.1970 0.854 0.092 0.000 0.008 0.000 0.000 0.900
#> GSM74400 1 0.2294 0.803 0.892 0.000 0.036 0.000 0.000 0.072
#> GSM74401 1 0.2294 0.803 0.892 0.000 0.036 0.000 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
#> ATC:hclust 114 5.47e-10 2
#> ATC:hclust 117 3.22e-14 3
#> ATC:hclust 110 4.29e-17 4
#> ATC:hclust 71 7.26e-13 5
#> ATC:hclust 109 2.14e-23 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 21168 rows and 121 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 0.994 0.997 0.5028 0.498 0.498
#> 3 3 0.563 0.579 0.791 0.3008 0.736 0.515
#> 4 4 0.849 0.905 0.939 0.1408 0.808 0.501
#> 5 5 0.787 0.707 0.836 0.0584 0.978 0.912
#> 6 6 0.781 0.647 0.756 0.0422 0.898 0.595
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
#> GSM74356 1 0.0376 0.994 0.996 0.004
#> GSM74357 1 0.0000 0.997 1.000 0.000
#> GSM74358 1 0.0000 0.997 1.000 0.000
#> GSM74359 1 0.0000 0.997 1.000 0.000
#> GSM74360 1 0.0000 0.997 1.000 0.000
#> GSM74361 1 0.1414 0.981 0.980 0.020
#> GSM74362 1 0.0000 0.997 1.000 0.000
#> GSM74363 1 0.1184 0.984 0.984 0.016
#> GSM74402 1 0.0000 0.997 1.000 0.000
#> GSM74403 1 0.0000 0.997 1.000 0.000
#> GSM74404 1 0.0000 0.997 1.000 0.000
#> GSM74406 1 0.0000 0.997 1.000 0.000
#> GSM74407 1 0.0000 0.997 1.000 0.000
#> GSM74408 1 0.0000 0.997 1.000 0.000
#> GSM74409 1 0.0000 0.997 1.000 0.000
#> GSM74410 1 0.0000 0.997 1.000 0.000
#> GSM119936 1 0.0000 0.997 1.000 0.000
#> GSM119937 1 0.0000 0.997 1.000 0.000
#> GSM74411 2 0.0000 0.997 0.000 1.000
#> GSM74412 2 0.0000 0.997 0.000 1.000
#> GSM74413 2 0.0000 0.997 0.000 1.000
#> GSM74414 2 0.0000 0.997 0.000 1.000
#> GSM74415 2 0.0000 0.997 0.000 1.000
#> GSM121379 2 0.0000 0.997 0.000 1.000
#> GSM121380 2 0.0000 0.997 0.000 1.000
#> GSM121381 2 0.0000 0.997 0.000 1.000
#> GSM121382 2 0.0000 0.997 0.000 1.000
#> GSM121383 2 0.0000 0.997 0.000 1.000
#> GSM121384 2 0.0000 0.997 0.000 1.000
#> GSM121385 2 0.0000 0.997 0.000 1.000
#> GSM121386 2 0.0000 0.997 0.000 1.000
#> GSM121387 2 0.0000 0.997 0.000 1.000
#> GSM121388 2 0.0000 0.997 0.000 1.000
#> GSM121389 2 0.0000 0.997 0.000 1.000
#> GSM121390 2 0.0000 0.997 0.000 1.000
#> GSM121391 2 0.0000 0.997 0.000 1.000
#> GSM121392 2 0.0000 0.997 0.000 1.000
#> GSM121393 2 0.0000 0.997 0.000 1.000
#> GSM121394 2 0.0000 0.997 0.000 1.000
#> GSM121395 2 0.0000 0.997 0.000 1.000
#> GSM121396 2 0.0000 0.997 0.000 1.000
#> GSM121397 2 0.0000 0.997 0.000 1.000
#> GSM121398 2 0.0000 0.997 0.000 1.000
#> GSM121399 2 0.0000 0.997 0.000 1.000
#> GSM74240 2 0.0000 0.997 0.000 1.000
#> GSM74241 2 0.0000 0.997 0.000 1.000
#> GSM74242 2 0.4298 0.904 0.088 0.912
#> GSM74243 2 0.4815 0.885 0.104 0.896
#> GSM74244 2 0.0000 0.997 0.000 1.000
#> GSM74245 2 0.0000 0.997 0.000 1.000
#> GSM74246 2 0.0000 0.997 0.000 1.000
#> GSM74247 2 0.0000 0.997 0.000 1.000
#> GSM74248 2 0.0000 0.997 0.000 1.000
#> GSM74416 1 0.0000 0.997 1.000 0.000
#> GSM74417 1 0.0000 0.997 1.000 0.000
#> GSM74418 1 0.0000 0.997 1.000 0.000
#> GSM74419 1 0.0000 0.997 1.000 0.000
#> GSM121358 2 0.0000 0.997 0.000 1.000
#> GSM121359 2 0.0000 0.997 0.000 1.000
#> GSM121360 1 0.0000 0.997 1.000 0.000
#> GSM121362 1 0.0000 0.997 1.000 0.000
#> GSM121364 1 0.0000 0.997 1.000 0.000
#> GSM121365 2 0.0000 0.997 0.000 1.000
#> GSM121366 2 0.0000 0.997 0.000 1.000
#> GSM121367 2 0.0000 0.997 0.000 1.000
#> GSM121370 2 0.0000 0.997 0.000 1.000
#> GSM121371 2 0.0000 0.997 0.000 1.000
#> GSM121372 2 0.0000 0.997 0.000 1.000
#> GSM121373 1 0.0000 0.997 1.000 0.000
#> GSM121374 1 0.0000 0.997 1.000 0.000
#> GSM121407 2 0.0000 0.997 0.000 1.000
#> GSM74387 2 0.0000 0.997 0.000 1.000
#> GSM74388 2 0.0000 0.997 0.000 1.000
#> GSM74389 1 0.1184 0.984 0.984 0.016
#> GSM74390 2 0.0000 0.997 0.000 1.000
#> GSM74391 1 0.0000 0.997 1.000 0.000
#> GSM74392 1 0.0000 0.997 1.000 0.000
#> GSM74393 1 0.0000 0.997 1.000 0.000
#> GSM74394 2 0.0000 0.997 0.000 1.000
#> GSM74239 1 0.0000 0.997 1.000 0.000
#> GSM74364 1 0.0000 0.997 1.000 0.000
#> GSM74365 1 0.0000 0.997 1.000 0.000
#> GSM74366 2 0.0000 0.997 0.000 1.000
#> GSM74367 1 0.0000 0.997 1.000 0.000
#> GSM74377 1 0.0000 0.997 1.000 0.000
#> GSM74378 2 0.0000 0.997 0.000 1.000
#> GSM74379 1 0.0000 0.997 1.000 0.000
#> GSM74380 1 0.0000 0.997 1.000 0.000
#> GSM74381 1 0.2603 0.956 0.956 0.044
#> GSM121357 2 0.0000 0.997 0.000 1.000
#> GSM121361 2 0.0000 0.997 0.000 1.000
#> GSM121363 2 0.0000 0.997 0.000 1.000
#> GSM121368 2 0.0000 0.997 0.000 1.000
#> GSM121369 2 0.0000 0.997 0.000 1.000
#> GSM74368 1 0.0000 0.997 1.000 0.000
#> GSM74369 1 0.0000 0.997 1.000 0.000
#> GSM74370 1 0.0000 0.997 1.000 0.000
#> GSM74371 1 0.0000 0.997 1.000 0.000
#> GSM74372 1 0.0000 0.997 1.000 0.000
#> GSM74373 1 0.0000 0.997 1.000 0.000
#> GSM74374 1 0.0000 0.997 1.000 0.000
#> GSM74375 1 0.0000 0.997 1.000 0.000
#> GSM74376 1 0.2603 0.956 0.956 0.044
#> GSM74405 1 0.0938 0.988 0.988 0.012
#> GSM74351 1 0.0000 0.997 1.000 0.000
#> GSM74352 1 0.0376 0.994 0.996 0.004
#> GSM74353 1 0.0000 0.997 1.000 0.000
#> GSM74354 1 0.0000 0.997 1.000 0.000
#> GSM74355 2 0.0000 0.997 0.000 1.000
#> GSM74382 1 0.0000 0.997 1.000 0.000
#> GSM74383 1 0.0000 0.997 1.000 0.000
#> GSM74384 2 0.0000 0.997 0.000 1.000
#> GSM74385 1 0.0000 0.997 1.000 0.000
#> GSM74386 1 0.0000 0.997 1.000 0.000
#> GSM74395 1 0.0000 0.997 1.000 0.000
#> GSM74396 1 0.0000 0.997 1.000 0.000
#> GSM74397 1 0.0000 0.997 1.000 0.000
#> GSM74398 1 0.0000 0.997 1.000 0.000
#> GSM74399 1 0.0000 0.997 1.000 0.000
#> GSM74400 1 0.0000 0.997 1.000 0.000
#> GSM74401 1 0.0000 0.997 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM74356 3 0.5219 0.4974 0.196 0.016 0.788
#> GSM74357 3 0.4702 0.4850 0.212 0.000 0.788
#> GSM74358 3 0.4702 0.4850 0.212 0.000 0.788
#> GSM74359 1 0.1031 0.7606 0.976 0.000 0.024
#> GSM74360 1 0.0000 0.7702 1.000 0.000 0.000
#> GSM74361 3 0.5428 0.5448 0.064 0.120 0.816
#> GSM74362 3 0.4702 0.4850 0.212 0.000 0.788
#> GSM74363 3 0.5219 0.4974 0.196 0.016 0.788
#> GSM74402 1 0.1031 0.7606 0.976 0.000 0.024
#> GSM74403 1 0.0000 0.7702 1.000 0.000 0.000
#> GSM74404 1 0.0000 0.7702 1.000 0.000 0.000
#> GSM74406 1 0.1031 0.7606 0.976 0.000 0.024
#> GSM74407 3 0.4605 0.4877 0.204 0.000 0.796
#> GSM74408 1 0.1031 0.7606 0.976 0.000 0.024
#> GSM74409 1 0.1031 0.7606 0.976 0.000 0.024
#> GSM74410 1 0.1031 0.7606 0.976 0.000 0.024
#> GSM119936 1 0.1031 0.7606 0.976 0.000 0.024
#> GSM119937 1 0.0747 0.7660 0.984 0.000 0.016
#> GSM74411 2 0.2356 0.8560 0.000 0.928 0.072
#> GSM74412 2 0.0000 0.9007 0.000 1.000 0.000
#> GSM74413 2 0.2448 0.8538 0.000 0.924 0.076
#> GSM74414 2 0.0000 0.9007 0.000 1.000 0.000
#> GSM74415 2 0.6305 0.2340 0.000 0.516 0.484
#> GSM121379 2 0.0000 0.9007 0.000 1.000 0.000
#> GSM121380 2 0.0000 0.9007 0.000 1.000 0.000
#> GSM121381 2 0.0000 0.9007 0.000 1.000 0.000
#> GSM121382 2 0.0000 0.9007 0.000 1.000 0.000
#> GSM121383 2 0.0000 0.9007 0.000 1.000 0.000
#> GSM121384 2 0.0000 0.9007 0.000 1.000 0.000
#> GSM121385 2 0.0000 0.9007 0.000 1.000 0.000
#> GSM121386 2 0.0000 0.9007 0.000 1.000 0.000
#> GSM121387 2 0.0000 0.9007 0.000 1.000 0.000
#> GSM121388 2 0.0000 0.9007 0.000 1.000 0.000
#> GSM121389 2 0.0000 0.9007 0.000 1.000 0.000
#> GSM121390 2 0.0000 0.9007 0.000 1.000 0.000
#> GSM121391 2 0.0000 0.9007 0.000 1.000 0.000
#> GSM121392 2 0.0592 0.8938 0.000 0.988 0.012
#> GSM121393 2 0.4346 0.7098 0.000 0.816 0.184
#> GSM121394 2 0.0000 0.9007 0.000 1.000 0.000
#> GSM121395 2 0.0000 0.9007 0.000 1.000 0.000
#> GSM121396 2 0.1289 0.8833 0.000 0.968 0.032
#> GSM121397 2 0.0000 0.9007 0.000 1.000 0.000
#> GSM121398 2 0.0000 0.9007 0.000 1.000 0.000
#> GSM121399 2 0.0000 0.9007 0.000 1.000 0.000
#> GSM74240 3 0.6299 -0.1607 0.000 0.476 0.524
#> GSM74241 3 0.6299 -0.1607 0.000 0.476 0.524
#> GSM74242 3 0.5901 0.4880 0.040 0.192 0.768
#> GSM74243 3 0.5901 0.4880 0.040 0.192 0.768
#> GSM74244 2 0.6026 0.4924 0.000 0.624 0.376
#> GSM74245 3 0.6008 0.1611 0.000 0.372 0.628
#> GSM74246 2 0.5678 0.5929 0.000 0.684 0.316
#> GSM74247 2 0.5678 0.5929 0.000 0.684 0.316
#> GSM74248 3 0.5810 0.2509 0.000 0.336 0.664
#> GSM74416 1 0.0000 0.7702 1.000 0.000 0.000
#> GSM74417 1 0.0000 0.7702 1.000 0.000 0.000
#> GSM74418 1 0.0000 0.7702 1.000 0.000 0.000
#> GSM74419 1 0.1031 0.7606 0.976 0.000 0.024
#> GSM121358 3 0.6307 -0.1971 0.000 0.488 0.512
#> GSM121359 2 0.3192 0.8224 0.000 0.888 0.112
#> GSM121360 1 0.6215 0.4803 0.572 0.000 0.428
#> GSM121362 3 0.6299 -0.2248 0.476 0.000 0.524
#> GSM121364 1 0.1031 0.7606 0.976 0.000 0.024
#> GSM121365 3 0.5058 0.4265 0.000 0.244 0.756
#> GSM121366 2 0.5810 0.5621 0.000 0.664 0.336
#> GSM121367 3 0.6307 -0.1971 0.000 0.488 0.512
#> GSM121370 2 0.6026 0.4924 0.000 0.624 0.376
#> GSM121371 3 0.5810 0.2509 0.000 0.336 0.664
#> GSM121372 2 0.5138 0.6708 0.000 0.748 0.252
#> GSM121373 1 0.0000 0.7702 1.000 0.000 0.000
#> GSM121374 1 0.1031 0.7606 0.976 0.000 0.024
#> GSM121407 2 0.0000 0.9007 0.000 1.000 0.000
#> GSM74387 2 0.0000 0.9007 0.000 1.000 0.000
#> GSM74388 2 0.0592 0.8938 0.000 0.988 0.012
#> GSM74389 3 0.5219 0.4974 0.196 0.016 0.788
#> GSM74390 3 0.4887 0.4513 0.000 0.228 0.772
#> GSM74391 3 0.6305 0.0374 0.484 0.000 0.516
#> GSM74392 1 0.1031 0.7606 0.976 0.000 0.024
#> GSM74393 3 0.5859 0.3411 0.344 0.000 0.656
#> GSM74394 2 0.0592 0.8938 0.000 0.988 0.012
#> GSM74239 1 0.5529 0.6356 0.704 0.000 0.296
#> GSM74364 1 0.0000 0.7702 1.000 0.000 0.000
#> GSM74365 1 0.6308 0.3564 0.508 0.000 0.492
#> GSM74366 3 0.6291 0.0623 0.000 0.468 0.532
#> GSM74367 1 0.6252 0.4518 0.556 0.000 0.444
#> GSM74377 3 0.5465 0.2646 0.288 0.000 0.712
#> GSM74378 3 0.6180 0.4430 0.024 0.260 0.716
#> GSM74379 3 0.5948 0.0830 0.360 0.000 0.640
#> GSM74380 3 0.5948 0.0830 0.360 0.000 0.640
#> GSM74381 3 0.5431 0.2724 0.284 0.000 0.716
#> GSM121357 2 0.2537 0.8414 0.000 0.920 0.080
#> GSM121361 2 0.0592 0.8938 0.000 0.988 0.012
#> GSM121363 2 0.0592 0.8938 0.000 0.988 0.012
#> GSM121368 2 0.0592 0.8938 0.000 0.988 0.012
#> GSM121369 2 0.5678 0.4773 0.000 0.684 0.316
#> GSM74368 3 0.6180 -0.0575 0.416 0.000 0.584
#> GSM74369 1 0.6045 0.5490 0.620 0.000 0.380
#> GSM74370 1 0.5591 0.6307 0.696 0.000 0.304
#> GSM74371 1 0.0000 0.7702 1.000 0.000 0.000
#> GSM74372 1 0.5835 0.5969 0.660 0.000 0.340
#> GSM74373 3 0.6126 -0.0564 0.400 0.000 0.600
#> GSM74374 1 0.5591 0.6307 0.696 0.000 0.304
#> GSM74375 3 0.5465 0.2646 0.288 0.000 0.712
#> GSM74376 3 0.5431 0.2724 0.284 0.000 0.716
#> GSM74405 3 0.5431 0.2724 0.284 0.000 0.716
#> GSM74351 1 0.0000 0.7702 1.000 0.000 0.000
#> GSM74352 3 0.5431 0.2724 0.284 0.000 0.716
#> GSM74353 1 0.5621 0.6274 0.692 0.000 0.308
#> GSM74354 1 0.5591 0.6307 0.696 0.000 0.304
#> GSM74355 3 0.6180 0.4430 0.024 0.260 0.716
#> GSM74382 1 0.0000 0.7702 1.000 0.000 0.000
#> GSM74383 1 0.5591 0.6307 0.696 0.000 0.304
#> GSM74384 3 0.6274 0.0976 0.000 0.456 0.544
#> GSM74385 1 0.0000 0.7702 1.000 0.000 0.000
#> GSM74386 1 0.6260 0.4433 0.552 0.000 0.448
#> GSM74395 1 0.6244 0.4598 0.560 0.000 0.440
#> GSM74396 1 0.6235 0.4673 0.564 0.000 0.436
#> GSM74397 1 0.6260 0.4433 0.552 0.000 0.448
#> GSM74398 3 0.6295 -0.2961 0.472 0.000 0.528
#> GSM74399 3 0.5968 0.0703 0.364 0.000 0.636
#> GSM74400 1 0.6244 0.4627 0.560 0.000 0.440
#> GSM74401 1 0.6244 0.4627 0.560 0.000 0.440
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM74356 3 0.0817 0.92727 0.024 0.000 0.976 0.000
#> GSM74357 3 0.0817 0.92727 0.024 0.000 0.976 0.000
#> GSM74358 3 0.0817 0.92727 0.024 0.000 0.976 0.000
#> GSM74359 4 0.1174 0.98820 0.020 0.000 0.012 0.968
#> GSM74360 4 0.1174 0.98820 0.020 0.000 0.012 0.968
#> GSM74361 3 0.0817 0.92727 0.024 0.000 0.976 0.000
#> GSM74362 3 0.1302 0.91486 0.044 0.000 0.956 0.000
#> GSM74363 3 0.0817 0.92727 0.024 0.000 0.976 0.000
#> GSM74402 4 0.0592 0.99489 0.016 0.000 0.000 0.984
#> GSM74403 4 0.0592 0.99489 0.016 0.000 0.000 0.984
#> GSM74404 4 0.0592 0.99489 0.016 0.000 0.000 0.984
#> GSM74406 4 0.0592 0.99489 0.016 0.000 0.000 0.984
#> GSM74407 3 0.4277 0.62541 0.280 0.000 0.720 0.000
#> GSM74408 4 0.0592 0.99489 0.016 0.000 0.000 0.984
#> GSM74409 4 0.0592 0.99489 0.016 0.000 0.000 0.984
#> GSM74410 4 0.0592 0.99489 0.016 0.000 0.000 0.984
#> GSM119936 4 0.0592 0.99489 0.016 0.000 0.000 0.984
#> GSM119937 4 0.1174 0.98091 0.020 0.000 0.012 0.968
#> GSM74411 2 0.1297 0.94121 0.000 0.964 0.020 0.016
#> GSM74412 2 0.0927 0.94887 0.000 0.976 0.008 0.016
#> GSM74413 2 0.1297 0.94121 0.000 0.964 0.020 0.016
#> GSM74414 2 0.0469 0.95545 0.000 0.988 0.012 0.000
#> GSM74415 3 0.1510 0.92457 0.000 0.028 0.956 0.016
#> GSM121379 2 0.0000 0.95759 0.000 1.000 0.000 0.000
#> GSM121380 2 0.0000 0.95759 0.000 1.000 0.000 0.000
#> GSM121381 2 0.0000 0.95759 0.000 1.000 0.000 0.000
#> GSM121382 2 0.0000 0.95759 0.000 1.000 0.000 0.000
#> GSM121383 2 0.0000 0.95759 0.000 1.000 0.000 0.000
#> GSM121384 2 0.0000 0.95759 0.000 1.000 0.000 0.000
#> GSM121385 2 0.0000 0.95759 0.000 1.000 0.000 0.000
#> GSM121386 2 0.0000 0.95759 0.000 1.000 0.000 0.000
#> GSM121387 2 0.0000 0.95759 0.000 1.000 0.000 0.000
#> GSM121388 2 0.0188 0.95687 0.000 0.996 0.004 0.000
#> GSM121389 2 0.0000 0.95759 0.000 1.000 0.000 0.000
#> GSM121390 2 0.0188 0.95599 0.000 0.996 0.004 0.000
#> GSM121391 2 0.0000 0.95759 0.000 1.000 0.000 0.000
#> GSM121392 2 0.0188 0.95599 0.000 0.996 0.004 0.000
#> GSM121393 2 0.4944 0.74732 0.072 0.768 0.160 0.000
#> GSM121394 2 0.0336 0.95565 0.000 0.992 0.008 0.000
#> GSM121395 2 0.0000 0.95759 0.000 1.000 0.000 0.000
#> GSM121396 2 0.0779 0.95020 0.000 0.980 0.004 0.016
#> GSM121397 2 0.0000 0.95759 0.000 1.000 0.000 0.000
#> GSM121398 2 0.0000 0.95759 0.000 1.000 0.000 0.000
#> GSM121399 2 0.0000 0.95759 0.000 1.000 0.000 0.000
#> GSM74240 3 0.1510 0.92457 0.000 0.028 0.956 0.016
#> GSM74241 3 0.1510 0.92457 0.000 0.028 0.956 0.016
#> GSM74242 3 0.0657 0.92964 0.012 0.004 0.984 0.000
#> GSM74243 3 0.0657 0.92964 0.012 0.004 0.984 0.000
#> GSM74244 3 0.1610 0.92235 0.000 0.032 0.952 0.016
#> GSM74245 3 0.1648 0.92767 0.012 0.016 0.956 0.016
#> GSM74246 3 0.3969 0.77598 0.000 0.180 0.804 0.016
#> GSM74247 3 0.3969 0.77598 0.000 0.180 0.804 0.016
#> GSM74248 3 0.1394 0.92887 0.012 0.008 0.964 0.016
#> GSM74416 4 0.0592 0.99489 0.016 0.000 0.000 0.984
#> GSM74417 4 0.0592 0.99489 0.016 0.000 0.000 0.984
#> GSM74418 4 0.0592 0.99489 0.016 0.000 0.000 0.984
#> GSM74419 4 0.1059 0.98482 0.016 0.000 0.012 0.972
#> GSM121358 3 0.1388 0.92532 0.000 0.028 0.960 0.012
#> GSM121359 2 0.1297 0.94121 0.000 0.964 0.020 0.016
#> GSM121360 1 0.3080 0.90166 0.880 0.000 0.024 0.096
#> GSM121362 1 0.3080 0.90166 0.880 0.000 0.024 0.096
#> GSM121364 4 0.1174 0.98820 0.020 0.000 0.012 0.968
#> GSM121365 3 0.0657 0.92964 0.012 0.004 0.984 0.000
#> GSM121366 3 0.2861 0.87211 0.000 0.096 0.888 0.016
#> GSM121367 3 0.1388 0.92532 0.000 0.028 0.960 0.012
#> GSM121370 3 0.1610 0.92235 0.000 0.032 0.952 0.016
#> GSM121371 3 0.0804 0.92973 0.012 0.008 0.980 0.000
#> GSM121372 2 0.5511 0.00782 0.000 0.500 0.484 0.016
#> GSM121373 4 0.1174 0.98820 0.020 0.000 0.012 0.968
#> GSM121374 4 0.1174 0.98820 0.020 0.000 0.012 0.968
#> GSM121407 2 0.0336 0.95565 0.000 0.992 0.008 0.000
#> GSM74387 2 0.0927 0.94887 0.000 0.976 0.008 0.016
#> GSM74388 2 0.0188 0.95599 0.000 0.996 0.004 0.000
#> GSM74389 3 0.0817 0.92727 0.024 0.000 0.976 0.000
#> GSM74390 3 0.0592 0.92874 0.016 0.000 0.984 0.000
#> GSM74391 3 0.5510 0.37504 0.376 0.000 0.600 0.024
#> GSM74392 4 0.1174 0.98820 0.020 0.000 0.012 0.968
#> GSM74393 3 0.3444 0.76793 0.184 0.000 0.816 0.000
#> GSM74394 2 0.0817 0.94919 0.000 0.976 0.024 0.000
#> GSM74239 1 0.3271 0.88765 0.856 0.000 0.012 0.132
#> GSM74364 4 0.0592 0.99489 0.016 0.000 0.000 0.984
#> GSM74365 1 0.0804 0.90074 0.980 0.000 0.012 0.008
#> GSM74366 1 0.6730 0.41222 0.592 0.276 0.132 0.000
#> GSM74367 1 0.2805 0.90455 0.888 0.000 0.012 0.100
#> GSM74377 1 0.0336 0.89762 0.992 0.000 0.008 0.000
#> GSM74378 1 0.0927 0.88528 0.976 0.016 0.008 0.000
#> GSM74379 1 0.0188 0.89852 0.996 0.000 0.004 0.000
#> GSM74380 1 0.0188 0.89852 0.996 0.000 0.004 0.000
#> GSM74381 1 0.0336 0.89762 0.992 0.000 0.008 0.000
#> GSM121357 2 0.2704 0.84655 0.000 0.876 0.124 0.000
#> GSM121361 2 0.0469 0.95545 0.000 0.988 0.012 0.000
#> GSM121363 2 0.0188 0.95599 0.000 0.996 0.004 0.000
#> GSM121368 2 0.0469 0.95545 0.000 0.988 0.012 0.000
#> GSM121369 2 0.6693 0.43670 0.116 0.580 0.304 0.000
#> GSM74368 1 0.2796 0.90519 0.892 0.000 0.016 0.092
#> GSM74369 1 0.2988 0.89958 0.876 0.000 0.012 0.112
#> GSM74370 1 0.4485 0.74996 0.740 0.000 0.012 0.248
#> GSM74371 4 0.0592 0.99489 0.016 0.000 0.000 0.984
#> GSM74372 1 0.2928 0.90182 0.880 0.000 0.012 0.108
#> GSM74373 1 0.0188 0.89852 0.996 0.000 0.004 0.000
#> GSM74374 1 0.3271 0.88765 0.856 0.000 0.012 0.132
#> GSM74375 1 0.0188 0.89852 0.996 0.000 0.004 0.000
#> GSM74376 1 0.0336 0.89762 0.992 0.000 0.008 0.000
#> GSM74405 1 0.0336 0.89762 0.992 0.000 0.008 0.000
#> GSM74351 4 0.0592 0.99489 0.016 0.000 0.000 0.984
#> GSM74352 1 0.0336 0.89762 0.992 0.000 0.008 0.000
#> GSM74353 1 0.3271 0.88765 0.856 0.000 0.012 0.132
#> GSM74354 1 0.3217 0.89036 0.860 0.000 0.012 0.128
#> GSM74355 1 0.0927 0.88528 0.976 0.016 0.008 0.000
#> GSM74382 4 0.0592 0.99489 0.016 0.000 0.000 0.984
#> GSM74383 1 0.3271 0.88765 0.856 0.000 0.012 0.132
#> GSM74384 1 0.6618 0.43166 0.604 0.272 0.124 0.000
#> GSM74385 4 0.0592 0.99489 0.016 0.000 0.000 0.984
#> GSM74386 1 0.2741 0.90524 0.892 0.000 0.012 0.096
#> GSM74395 1 0.2805 0.90455 0.888 0.000 0.012 0.100
#> GSM74396 1 0.2805 0.90455 0.888 0.000 0.012 0.100
#> GSM74397 1 0.2805 0.90455 0.888 0.000 0.012 0.100
#> GSM74398 1 0.0336 0.89870 0.992 0.000 0.008 0.000
#> GSM74399 1 0.0188 0.89852 0.996 0.000 0.004 0.000
#> GSM74400 1 0.2805 0.90455 0.888 0.000 0.012 0.100
#> GSM74401 1 0.2805 0.90455 0.888 0.000 0.012 0.100
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM74356 3 0.0727 0.780 0.004 0.000 0.980 0.004 0.012
#> GSM74357 3 0.0968 0.776 0.004 0.000 0.972 0.012 0.012
#> GSM74358 3 0.0968 0.776 0.004 0.000 0.972 0.012 0.012
#> GSM74359 4 0.2516 0.866 0.000 0.000 0.000 0.860 0.140
#> GSM74360 4 0.2674 0.866 0.004 0.000 0.000 0.856 0.140
#> GSM74361 3 0.0324 0.784 0.004 0.000 0.992 0.000 0.004
#> GSM74362 3 0.3187 0.705 0.036 0.000 0.864 0.012 0.088
#> GSM74363 3 0.0486 0.783 0.004 0.000 0.988 0.004 0.004
#> GSM74402 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> GSM74403 4 0.1357 0.904 0.004 0.000 0.000 0.948 0.048
#> GSM74404 4 0.1357 0.904 0.004 0.000 0.000 0.948 0.048
#> GSM74406 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> GSM74407 3 0.4371 0.506 0.268 0.000 0.708 0.012 0.012
#> GSM74408 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> GSM74409 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> GSM74410 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> GSM119936 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> GSM119937 4 0.5644 0.470 0.328 0.000 0.000 0.576 0.096
#> GSM74411 2 0.5423 0.490 0.000 0.548 0.064 0.000 0.388
#> GSM74412 2 0.4403 0.573 0.000 0.608 0.008 0.000 0.384
#> GSM74413 2 0.5476 0.484 0.000 0.544 0.068 0.000 0.388
#> GSM74414 2 0.3774 0.683 0.000 0.704 0.000 0.000 0.296
#> GSM74415 3 0.4299 0.578 0.000 0.004 0.608 0.000 0.388
#> GSM121379 2 0.0000 0.798 0.000 1.000 0.000 0.000 0.000
#> GSM121380 2 0.0000 0.798 0.000 1.000 0.000 0.000 0.000
#> GSM121381 2 0.0000 0.798 0.000 1.000 0.000 0.000 0.000
#> GSM121382 2 0.0000 0.798 0.000 1.000 0.000 0.000 0.000
#> GSM121383 2 0.0000 0.798 0.000 1.000 0.000 0.000 0.000
#> GSM121384 2 0.0000 0.798 0.000 1.000 0.000 0.000 0.000
#> GSM121385 2 0.0000 0.798 0.000 1.000 0.000 0.000 0.000
#> GSM121386 2 0.0000 0.798 0.000 1.000 0.000 0.000 0.000
#> GSM121387 2 0.0000 0.798 0.000 1.000 0.000 0.000 0.000
#> GSM121388 2 0.2813 0.752 0.000 0.832 0.000 0.000 0.168
#> GSM121389 2 0.0290 0.795 0.000 0.992 0.000 0.000 0.008
#> GSM121390 2 0.0162 0.796 0.000 0.996 0.000 0.000 0.004
#> GSM121391 2 0.0000 0.798 0.000 1.000 0.000 0.000 0.000
#> GSM121392 2 0.1671 0.759 0.000 0.924 0.000 0.000 0.076
#> GSM121393 2 0.5895 0.169 0.036 0.540 0.040 0.000 0.384
#> GSM121394 2 0.1792 0.779 0.000 0.916 0.000 0.000 0.084
#> GSM121395 2 0.0290 0.795 0.000 0.992 0.000 0.000 0.008
#> GSM121396 2 0.4252 0.610 0.000 0.652 0.008 0.000 0.340
#> GSM121397 2 0.0000 0.798 0.000 1.000 0.000 0.000 0.000
#> GSM121398 2 0.0000 0.798 0.000 1.000 0.000 0.000 0.000
#> GSM121399 2 0.0000 0.798 0.000 1.000 0.000 0.000 0.000
#> GSM74240 3 0.3550 0.726 0.000 0.004 0.760 0.000 0.236
#> GSM74241 3 0.3550 0.726 0.000 0.004 0.760 0.000 0.236
#> GSM74242 3 0.0162 0.785 0.004 0.000 0.996 0.000 0.000
#> GSM74243 3 0.0162 0.785 0.004 0.000 0.996 0.000 0.000
#> GSM74244 3 0.3861 0.691 0.000 0.004 0.712 0.000 0.284
#> GSM74245 3 0.2930 0.758 0.000 0.004 0.832 0.000 0.164
#> GSM74246 3 0.5670 0.465 0.000 0.084 0.528 0.000 0.388
#> GSM74247 3 0.5670 0.465 0.000 0.084 0.528 0.000 0.388
#> GSM74248 3 0.2233 0.776 0.004 0.000 0.892 0.000 0.104
#> GSM74416 4 0.1991 0.899 0.004 0.000 0.004 0.916 0.076
#> GSM74417 4 0.1991 0.899 0.004 0.000 0.004 0.916 0.076
#> GSM74418 4 0.1991 0.899 0.004 0.000 0.004 0.916 0.076
#> GSM74419 4 0.3532 0.823 0.092 0.000 0.000 0.832 0.076
#> GSM121358 3 0.2629 0.768 0.000 0.004 0.860 0.000 0.136
#> GSM121359 2 0.5510 0.487 0.000 0.548 0.072 0.000 0.380
#> GSM121360 1 0.3868 0.641 0.800 0.000 0.000 0.060 0.140
#> GSM121362 1 0.4210 0.626 0.788 0.000 0.008 0.064 0.140
#> GSM121364 4 0.2516 0.866 0.000 0.000 0.000 0.860 0.140
#> GSM121365 3 0.0162 0.785 0.004 0.000 0.996 0.000 0.000
#> GSM121366 3 0.4801 0.652 0.000 0.048 0.668 0.000 0.284
#> GSM121367 3 0.2583 0.769 0.000 0.004 0.864 0.000 0.132
#> GSM121370 3 0.3861 0.691 0.000 0.004 0.712 0.000 0.284
#> GSM121371 3 0.0324 0.785 0.004 0.000 0.992 0.000 0.004
#> GSM121372 3 0.6603 0.205 0.000 0.212 0.400 0.000 0.388
#> GSM121373 4 0.2843 0.866 0.008 0.000 0.000 0.848 0.144
#> GSM121374 4 0.2516 0.866 0.000 0.000 0.000 0.860 0.140
#> GSM121407 2 0.3612 0.693 0.000 0.732 0.000 0.000 0.268
#> GSM74387 2 0.4707 0.550 0.000 0.588 0.020 0.000 0.392
#> GSM74388 2 0.2891 0.739 0.000 0.824 0.000 0.000 0.176
#> GSM74389 3 0.0486 0.783 0.004 0.000 0.988 0.004 0.004
#> GSM74390 3 0.0451 0.785 0.004 0.000 0.988 0.000 0.008
#> GSM74391 3 0.5775 0.191 0.416 0.000 0.512 0.012 0.060
#> GSM74392 4 0.2516 0.866 0.000 0.000 0.000 0.860 0.140
#> GSM74393 3 0.5292 0.526 0.180 0.000 0.700 0.012 0.108
#> GSM74394 2 0.3999 0.638 0.000 0.656 0.000 0.000 0.344
#> GSM74239 1 0.1894 0.759 0.920 0.000 0.000 0.072 0.008
#> GSM74364 4 0.4311 0.781 0.144 0.000 0.004 0.776 0.076
#> GSM74365 1 0.0162 0.738 0.996 0.000 0.000 0.000 0.004
#> GSM74366 5 0.6287 0.639 0.340 0.076 0.036 0.000 0.548
#> GSM74367 1 0.1410 0.767 0.940 0.000 0.000 0.060 0.000
#> GSM74377 1 0.3857 0.495 0.688 0.000 0.000 0.000 0.312
#> GSM74378 1 0.4425 0.278 0.600 0.000 0.008 0.000 0.392
#> GSM74379 1 0.3452 0.580 0.756 0.000 0.000 0.000 0.244
#> GSM74380 1 0.3586 0.560 0.736 0.000 0.000 0.000 0.264
#> GSM74381 1 0.4147 0.476 0.676 0.000 0.008 0.000 0.316
#> GSM121357 2 0.5576 0.484 0.000 0.536 0.076 0.000 0.388
#> GSM121361 2 0.3752 0.686 0.000 0.708 0.000 0.000 0.292
#> GSM121363 2 0.3752 0.686 0.000 0.708 0.000 0.000 0.292
#> GSM121368 2 0.3752 0.686 0.000 0.708 0.000 0.000 0.292
#> GSM121369 5 0.5442 0.319 0.020 0.228 0.076 0.000 0.676
#> GSM74368 1 0.1282 0.764 0.952 0.000 0.004 0.044 0.000
#> GSM74369 1 0.1764 0.763 0.928 0.000 0.000 0.064 0.008
#> GSM74370 1 0.3586 0.676 0.828 0.000 0.000 0.096 0.076
#> GSM74371 4 0.2116 0.898 0.008 0.000 0.004 0.912 0.076
#> GSM74372 1 0.1341 0.767 0.944 0.000 0.000 0.056 0.000
#> GSM74373 1 0.3684 0.541 0.720 0.000 0.000 0.000 0.280
#> GSM74374 1 0.1894 0.759 0.920 0.000 0.000 0.072 0.008
#> GSM74375 1 0.3661 0.547 0.724 0.000 0.000 0.000 0.276
#> GSM74376 1 0.4147 0.476 0.676 0.000 0.008 0.000 0.316
#> GSM74405 1 0.4147 0.476 0.676 0.000 0.008 0.000 0.316
#> GSM74351 4 0.1991 0.899 0.004 0.000 0.004 0.916 0.076
#> GSM74352 1 0.4147 0.476 0.676 0.000 0.008 0.000 0.316
#> GSM74353 1 0.1894 0.759 0.920 0.000 0.000 0.072 0.008
#> GSM74354 1 0.1764 0.763 0.928 0.000 0.000 0.064 0.008
#> GSM74355 1 0.4425 0.278 0.600 0.000 0.008 0.000 0.392
#> GSM74382 4 0.1430 0.904 0.004 0.000 0.000 0.944 0.052
#> GSM74383 1 0.1894 0.759 0.920 0.000 0.000 0.072 0.008
#> GSM74384 5 0.6210 0.625 0.348 0.068 0.036 0.000 0.548
#> GSM74385 4 0.2116 0.898 0.008 0.000 0.004 0.912 0.076
#> GSM74386 1 0.1270 0.766 0.948 0.000 0.000 0.052 0.000
#> GSM74395 1 0.1410 0.767 0.940 0.000 0.000 0.060 0.000
#> GSM74396 1 0.1410 0.767 0.940 0.000 0.000 0.060 0.000
#> GSM74397 1 0.1410 0.767 0.940 0.000 0.000 0.060 0.000
#> GSM74398 1 0.0290 0.737 0.992 0.000 0.000 0.000 0.008
#> GSM74399 1 0.3534 0.569 0.744 0.000 0.000 0.000 0.256
#> GSM74400 1 0.1410 0.767 0.940 0.000 0.000 0.060 0.000
#> GSM74401 1 0.1410 0.767 0.940 0.000 0.000 0.060 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM74356 5 0.0405 0.79709 0.008 0.000 0.000 0.004 0.988 0.000
#> GSM74357 5 0.0551 0.79615 0.008 0.000 0.000 0.004 0.984 0.004
#> GSM74358 5 0.0551 0.79615 0.008 0.000 0.000 0.004 0.984 0.004
#> GSM74359 4 0.3876 0.80216 0.000 0.000 0.156 0.772 0.004 0.068
#> GSM74360 4 0.4131 0.79879 0.004 0.000 0.176 0.752 0.004 0.064
#> GSM74361 5 0.0260 0.79699 0.008 0.000 0.000 0.000 0.992 0.000
#> GSM74362 5 0.2660 0.71771 0.008 0.000 0.100 0.016 0.872 0.004
#> GSM74363 5 0.0405 0.79709 0.008 0.000 0.000 0.004 0.988 0.000
#> GSM74402 4 0.0146 0.87716 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM74403 4 0.1906 0.87477 0.008 0.000 0.036 0.924 0.000 0.032
#> GSM74404 4 0.1906 0.87477 0.008 0.000 0.036 0.924 0.000 0.032
#> GSM74406 4 0.0146 0.87716 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM74407 5 0.3722 0.56449 0.260 0.000 0.004 0.008 0.724 0.004
#> GSM74408 4 0.0146 0.87716 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM74409 4 0.0146 0.87716 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM74410 4 0.0146 0.87716 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM119936 4 0.0146 0.87716 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM119937 1 0.4535 0.15938 0.548 0.000 0.012 0.424 0.000 0.016
#> GSM74411 3 0.4681 0.60871 0.004 0.280 0.664 0.000 0.032 0.020
#> GSM74412 3 0.4242 0.56410 0.004 0.312 0.660 0.000 0.004 0.020
#> GSM74413 3 0.4681 0.60871 0.004 0.280 0.664 0.000 0.032 0.020
#> GSM74414 2 0.6074 0.13616 0.004 0.424 0.348 0.000 0.000 0.224
#> GSM74415 3 0.4138 0.50274 0.004 0.000 0.656 0.000 0.320 0.020
#> GSM121379 2 0.0000 0.79278 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121380 2 0.0146 0.79214 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM121381 2 0.0363 0.79094 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM121382 2 0.0260 0.79191 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM121383 2 0.0146 0.79263 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM121384 2 0.0000 0.79278 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121385 2 0.0000 0.79278 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121386 2 0.0000 0.79278 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121387 2 0.0146 0.79263 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM121388 2 0.4935 0.00301 0.000 0.484 0.460 0.000 0.004 0.052
#> GSM121389 2 0.1124 0.77793 0.000 0.956 0.036 0.000 0.000 0.008
#> GSM121390 2 0.0790 0.78199 0.000 0.968 0.032 0.000 0.000 0.000
#> GSM121391 2 0.0146 0.79263 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM121392 2 0.3123 0.70628 0.000 0.832 0.056 0.000 0.000 0.112
#> GSM121393 6 0.4915 0.04644 0.000 0.320 0.072 0.000 0.004 0.604
#> GSM121394 2 0.2531 0.67377 0.000 0.856 0.132 0.000 0.000 0.012
#> GSM121395 2 0.1934 0.76509 0.000 0.916 0.044 0.000 0.000 0.040
#> GSM121396 3 0.4289 0.53806 0.000 0.332 0.640 0.000 0.008 0.020
#> GSM121397 2 0.0000 0.79278 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121398 2 0.0000 0.79278 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121399 2 0.0260 0.79191 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM74240 5 0.3857 0.02513 0.000 0.000 0.468 0.000 0.532 0.000
#> GSM74241 5 0.3864 -0.01540 0.000 0.000 0.480 0.000 0.520 0.000
#> GSM74242 5 0.0603 0.79573 0.004 0.000 0.016 0.000 0.980 0.000
#> GSM74243 5 0.0603 0.79573 0.004 0.000 0.016 0.000 0.980 0.000
#> GSM74244 3 0.3810 0.26978 0.000 0.000 0.572 0.000 0.428 0.000
#> GSM74245 5 0.3531 0.44398 0.000 0.000 0.328 0.000 0.672 0.000
#> GSM74246 3 0.4435 0.55520 0.000 0.032 0.664 0.000 0.292 0.012
#> GSM74247 3 0.4435 0.55520 0.000 0.032 0.664 0.000 0.292 0.012
#> GSM74248 5 0.2260 0.71029 0.000 0.000 0.140 0.000 0.860 0.000
#> GSM74416 4 0.3110 0.86103 0.008 0.000 0.072 0.848 0.000 0.072
#> GSM74417 4 0.3110 0.86103 0.008 0.000 0.072 0.848 0.000 0.072
#> GSM74418 4 0.3110 0.86103 0.008 0.000 0.072 0.848 0.000 0.072
#> GSM74419 4 0.4290 0.70230 0.176 0.000 0.076 0.740 0.004 0.004
#> GSM121358 5 0.3405 0.53281 0.000 0.000 0.272 0.000 0.724 0.004
#> GSM121359 3 0.4716 0.61078 0.000 0.280 0.656 0.000 0.048 0.016
#> GSM121360 1 0.4800 0.61125 0.720 0.000 0.176 0.036 0.004 0.064
#> GSM121362 1 0.4843 0.59214 0.720 0.000 0.176 0.024 0.012 0.068
#> GSM121364 4 0.3821 0.80401 0.000 0.000 0.156 0.776 0.004 0.064
#> GSM121365 5 0.0260 0.79530 0.000 0.000 0.008 0.000 0.992 0.000
#> GSM121366 3 0.4433 0.32803 0.000 0.016 0.560 0.000 0.416 0.008
#> GSM121367 5 0.3290 0.56463 0.000 0.000 0.252 0.000 0.744 0.004
#> GSM121370 3 0.4057 0.26929 0.000 0.000 0.556 0.000 0.436 0.008
#> GSM121371 5 0.0603 0.79188 0.000 0.000 0.016 0.000 0.980 0.004
#> GSM121372 3 0.4837 0.59989 0.000 0.084 0.660 0.000 0.248 0.008
#> GSM121373 4 0.4207 0.79934 0.008 0.000 0.172 0.752 0.004 0.064
#> GSM121374 4 0.3821 0.80401 0.000 0.000 0.156 0.776 0.004 0.064
#> GSM121407 3 0.4697 0.23032 0.000 0.432 0.528 0.000 0.004 0.036
#> GSM74387 3 0.4235 0.56975 0.000 0.300 0.668 0.000 0.008 0.024
#> GSM74388 2 0.5126 0.51051 0.000 0.624 0.160 0.000 0.000 0.216
#> GSM74389 5 0.0405 0.79709 0.008 0.000 0.000 0.004 0.988 0.000
#> GSM74390 5 0.0291 0.79671 0.004 0.000 0.004 0.000 0.992 0.000
#> GSM74391 5 0.5092 0.28374 0.388 0.000 0.040 0.016 0.552 0.004
#> GSM74392 4 0.3876 0.80216 0.000 0.000 0.156 0.772 0.004 0.068
#> GSM74393 5 0.3964 0.65571 0.068 0.000 0.120 0.016 0.792 0.004
#> GSM74394 2 0.6029 0.13500 0.000 0.396 0.356 0.000 0.000 0.248
#> GSM74239 1 0.1194 0.85128 0.956 0.000 0.008 0.032 0.000 0.004
#> GSM74364 4 0.5604 0.65227 0.216 0.000 0.076 0.636 0.000 0.072
#> GSM74365 1 0.0692 0.80687 0.976 0.000 0.004 0.000 0.000 0.020
#> GSM74366 6 0.3834 0.64831 0.124 0.016 0.056 0.000 0.004 0.800
#> GSM74367 1 0.0935 0.85251 0.964 0.000 0.004 0.032 0.000 0.000
#> GSM74377 6 0.3765 0.66060 0.404 0.000 0.000 0.000 0.000 0.596
#> GSM74378 6 0.2883 0.68457 0.212 0.000 0.000 0.000 0.000 0.788
#> GSM74379 1 0.3838 -0.41023 0.552 0.000 0.000 0.000 0.000 0.448
#> GSM74380 6 0.3868 0.50904 0.492 0.000 0.000 0.000 0.000 0.508
#> GSM74381 6 0.3695 0.68950 0.376 0.000 0.000 0.000 0.000 0.624
#> GSM121357 3 0.6374 0.13511 0.000 0.316 0.448 0.000 0.024 0.212
#> GSM121361 2 0.5962 0.18371 0.000 0.424 0.348 0.000 0.000 0.228
#> GSM121363 2 0.5962 0.18371 0.000 0.424 0.348 0.000 0.000 0.228
#> GSM121368 2 0.5962 0.18371 0.000 0.424 0.348 0.000 0.000 0.228
#> GSM121369 6 0.5257 0.09455 0.000 0.080 0.312 0.000 0.016 0.592
#> GSM74368 1 0.0837 0.84366 0.972 0.000 0.004 0.020 0.004 0.000
#> GSM74369 1 0.1080 0.85236 0.960 0.000 0.004 0.032 0.000 0.004
#> GSM74370 1 0.2638 0.79064 0.888 0.000 0.032 0.044 0.000 0.036
#> GSM74371 4 0.3264 0.85844 0.012 0.000 0.076 0.840 0.000 0.072
#> GSM74372 1 0.1151 0.85005 0.956 0.000 0.012 0.032 0.000 0.000
#> GSM74373 6 0.3979 0.58212 0.456 0.000 0.004 0.000 0.000 0.540
#> GSM74374 1 0.1194 0.85128 0.956 0.000 0.008 0.032 0.000 0.004
#> GSM74375 6 0.3843 0.59657 0.452 0.000 0.000 0.000 0.000 0.548
#> GSM74376 6 0.3695 0.68950 0.376 0.000 0.000 0.000 0.000 0.624
#> GSM74405 6 0.3706 0.68725 0.380 0.000 0.000 0.000 0.000 0.620
#> GSM74351 4 0.3110 0.86103 0.008 0.000 0.072 0.848 0.000 0.072
#> GSM74352 6 0.3706 0.68725 0.380 0.000 0.000 0.000 0.000 0.620
#> GSM74353 1 0.1080 0.85182 0.960 0.000 0.004 0.032 0.000 0.004
#> GSM74354 1 0.1194 0.85128 0.956 0.000 0.008 0.032 0.000 0.004
#> GSM74355 6 0.2883 0.68457 0.212 0.000 0.000 0.000 0.000 0.788
#> GSM74382 4 0.2122 0.87323 0.008 0.000 0.040 0.912 0.000 0.040
#> GSM74383 1 0.1194 0.85128 0.956 0.000 0.008 0.032 0.000 0.004
#> GSM74384 6 0.3834 0.64831 0.124 0.016 0.056 0.000 0.004 0.800
#> GSM74385 4 0.3264 0.85844 0.012 0.000 0.076 0.840 0.000 0.072
#> GSM74386 1 0.1003 0.85010 0.964 0.000 0.004 0.028 0.000 0.004
#> GSM74395 1 0.0935 0.85251 0.964 0.000 0.004 0.032 0.000 0.000
#> GSM74396 1 0.0935 0.85251 0.964 0.000 0.004 0.032 0.000 0.000
#> GSM74397 1 0.0935 0.85251 0.964 0.000 0.004 0.032 0.000 0.000
#> GSM74398 1 0.0632 0.80320 0.976 0.000 0.000 0.000 0.000 0.024
#> GSM74399 1 0.3867 -0.51517 0.512 0.000 0.000 0.000 0.000 0.488
#> GSM74400 1 0.0790 0.85259 0.968 0.000 0.000 0.032 0.000 0.000
#> GSM74401 1 0.0790 0.85259 0.968 0.000 0.000 0.032 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:kmeans 121 1.03e-12 2
#> ATC:kmeans 71 1.25e-06 3
#> ATC:kmeans 116 7.41e-26 4
#> ATC:kmeans 103 2.03e-20 5
#> ATC:kmeans 101 1.16e-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["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 21168 rows and 121 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 0.983 0.956 0.983 0.5043 0.496 0.496
#> 3 3 0.922 0.939 0.971 0.2572 0.822 0.658
#> 4 4 0.968 0.952 0.981 0.1201 0.895 0.724
#> 5 5 0.812 0.743 0.862 0.0702 0.946 0.820
#> 6 6 0.776 0.762 0.832 0.0549 0.890 0.597
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
#> GSM74356 1 0.8713 0.5731 0.708 0.292
#> GSM74357 1 0.0000 0.9889 1.000 0.000
#> GSM74358 1 0.0000 0.9889 1.000 0.000
#> GSM74359 1 0.0000 0.9889 1.000 0.000
#> GSM74360 1 0.0000 0.9889 1.000 0.000
#> GSM74361 2 0.9522 0.4200 0.372 0.628
#> GSM74362 1 0.0000 0.9889 1.000 0.000
#> GSM74363 2 0.9996 0.0635 0.488 0.512
#> GSM74402 1 0.0000 0.9889 1.000 0.000
#> GSM74403 1 0.0000 0.9889 1.000 0.000
#> GSM74404 1 0.0000 0.9889 1.000 0.000
#> GSM74406 1 0.0000 0.9889 1.000 0.000
#> GSM74407 1 0.0000 0.9889 1.000 0.000
#> GSM74408 1 0.0000 0.9889 1.000 0.000
#> GSM74409 1 0.0000 0.9889 1.000 0.000
#> GSM74410 1 0.0000 0.9889 1.000 0.000
#> GSM119936 1 0.0000 0.9889 1.000 0.000
#> GSM119937 1 0.0000 0.9889 1.000 0.000
#> GSM74411 2 0.0000 0.9744 0.000 1.000
#> GSM74412 2 0.0000 0.9744 0.000 1.000
#> GSM74413 2 0.0000 0.9744 0.000 1.000
#> GSM74414 2 0.0000 0.9744 0.000 1.000
#> GSM74415 2 0.0000 0.9744 0.000 1.000
#> GSM121379 2 0.0000 0.9744 0.000 1.000
#> GSM121380 2 0.0000 0.9744 0.000 1.000
#> GSM121381 2 0.0000 0.9744 0.000 1.000
#> GSM121382 2 0.0000 0.9744 0.000 1.000
#> GSM121383 2 0.0000 0.9744 0.000 1.000
#> GSM121384 2 0.0000 0.9744 0.000 1.000
#> GSM121385 2 0.0000 0.9744 0.000 1.000
#> GSM121386 2 0.0000 0.9744 0.000 1.000
#> GSM121387 2 0.0000 0.9744 0.000 1.000
#> GSM121388 2 0.0000 0.9744 0.000 1.000
#> GSM121389 2 0.0000 0.9744 0.000 1.000
#> GSM121390 2 0.0000 0.9744 0.000 1.000
#> GSM121391 2 0.0000 0.9744 0.000 1.000
#> GSM121392 2 0.0000 0.9744 0.000 1.000
#> GSM121393 2 0.0000 0.9744 0.000 1.000
#> GSM121394 2 0.0000 0.9744 0.000 1.000
#> GSM121395 2 0.0000 0.9744 0.000 1.000
#> GSM121396 2 0.0000 0.9744 0.000 1.000
#> GSM121397 2 0.0000 0.9744 0.000 1.000
#> GSM121398 2 0.0000 0.9744 0.000 1.000
#> GSM121399 2 0.0000 0.9744 0.000 1.000
#> GSM74240 2 0.0000 0.9744 0.000 1.000
#> GSM74241 2 0.0000 0.9744 0.000 1.000
#> GSM74242 2 0.0000 0.9744 0.000 1.000
#> GSM74243 2 0.0000 0.9744 0.000 1.000
#> GSM74244 2 0.0000 0.9744 0.000 1.000
#> GSM74245 2 0.0000 0.9744 0.000 1.000
#> GSM74246 2 0.0000 0.9744 0.000 1.000
#> GSM74247 2 0.0000 0.9744 0.000 1.000
#> GSM74248 2 0.0000 0.9744 0.000 1.000
#> GSM74416 1 0.0000 0.9889 1.000 0.000
#> GSM74417 1 0.0000 0.9889 1.000 0.000
#> GSM74418 1 0.0000 0.9889 1.000 0.000
#> GSM74419 1 0.0000 0.9889 1.000 0.000
#> GSM121358 2 0.0000 0.9744 0.000 1.000
#> GSM121359 2 0.0000 0.9744 0.000 1.000
#> GSM121360 1 0.0000 0.9889 1.000 0.000
#> GSM121362 1 0.0000 0.9889 1.000 0.000
#> GSM121364 1 0.0000 0.9889 1.000 0.000
#> GSM121365 2 0.0000 0.9744 0.000 1.000
#> GSM121366 2 0.0000 0.9744 0.000 1.000
#> GSM121367 2 0.0000 0.9744 0.000 1.000
#> GSM121370 2 0.0000 0.9744 0.000 1.000
#> GSM121371 2 0.0000 0.9744 0.000 1.000
#> GSM121372 2 0.0000 0.9744 0.000 1.000
#> GSM121373 1 0.0000 0.9889 1.000 0.000
#> GSM121374 1 0.0000 0.9889 1.000 0.000
#> GSM121407 2 0.0000 0.9744 0.000 1.000
#> GSM74387 2 0.0000 0.9744 0.000 1.000
#> GSM74388 2 0.0000 0.9744 0.000 1.000
#> GSM74389 2 0.9608 0.3898 0.384 0.616
#> GSM74390 2 0.0000 0.9744 0.000 1.000
#> GSM74391 1 0.0000 0.9889 1.000 0.000
#> GSM74392 1 0.0000 0.9889 1.000 0.000
#> GSM74393 1 0.0000 0.9889 1.000 0.000
#> GSM74394 2 0.0000 0.9744 0.000 1.000
#> GSM74239 1 0.0000 0.9889 1.000 0.000
#> GSM74364 1 0.0000 0.9889 1.000 0.000
#> GSM74365 1 0.0000 0.9889 1.000 0.000
#> GSM74366 2 0.0000 0.9744 0.000 1.000
#> GSM74367 1 0.0000 0.9889 1.000 0.000
#> GSM74377 1 0.0000 0.9889 1.000 0.000
#> GSM74378 2 0.0000 0.9744 0.000 1.000
#> GSM74379 1 0.0000 0.9889 1.000 0.000
#> GSM74380 1 0.0000 0.9889 1.000 0.000
#> GSM74381 1 0.6438 0.7984 0.836 0.164
#> GSM121357 2 0.0000 0.9744 0.000 1.000
#> GSM121361 2 0.0000 0.9744 0.000 1.000
#> GSM121363 2 0.0000 0.9744 0.000 1.000
#> GSM121368 2 0.0000 0.9744 0.000 1.000
#> GSM121369 2 0.0000 0.9744 0.000 1.000
#> GSM74368 1 0.0000 0.9889 1.000 0.000
#> GSM74369 1 0.0000 0.9889 1.000 0.000
#> GSM74370 1 0.0000 0.9889 1.000 0.000
#> GSM74371 1 0.0000 0.9889 1.000 0.000
#> GSM74372 1 0.0000 0.9889 1.000 0.000
#> GSM74373 1 0.0000 0.9889 1.000 0.000
#> GSM74374 1 0.0000 0.9889 1.000 0.000
#> GSM74375 1 0.0000 0.9889 1.000 0.000
#> GSM74376 1 0.6623 0.7869 0.828 0.172
#> GSM74405 1 0.0672 0.9813 0.992 0.008
#> GSM74351 1 0.0000 0.9889 1.000 0.000
#> GSM74352 1 0.0000 0.9889 1.000 0.000
#> GSM74353 1 0.0000 0.9889 1.000 0.000
#> GSM74354 1 0.0000 0.9889 1.000 0.000
#> GSM74355 2 0.7815 0.6892 0.232 0.768
#> GSM74382 1 0.0000 0.9889 1.000 0.000
#> GSM74383 1 0.0000 0.9889 1.000 0.000
#> GSM74384 2 0.0000 0.9744 0.000 1.000
#> GSM74385 1 0.0000 0.9889 1.000 0.000
#> GSM74386 1 0.0000 0.9889 1.000 0.000
#> GSM74395 1 0.0000 0.9889 1.000 0.000
#> GSM74396 1 0.0000 0.9889 1.000 0.000
#> GSM74397 1 0.0000 0.9889 1.000 0.000
#> GSM74398 1 0.0000 0.9889 1.000 0.000
#> GSM74399 1 0.0000 0.9889 1.000 0.000
#> GSM74400 1 0.0000 0.9889 1.000 0.000
#> GSM74401 1 0.0000 0.9889 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM74356 3 0.0592 0.939 0.012 0.000 0.988
#> GSM74357 3 0.0592 0.939 0.012 0.000 0.988
#> GSM74358 3 0.0592 0.939 0.012 0.000 0.988
#> GSM74359 1 0.0237 0.987 0.996 0.000 0.004
#> GSM74360 1 0.0237 0.987 0.996 0.000 0.004
#> GSM74361 3 0.0592 0.939 0.012 0.000 0.988
#> GSM74362 3 0.3619 0.829 0.136 0.000 0.864
#> GSM74363 3 0.0592 0.939 0.012 0.000 0.988
#> GSM74402 1 0.0237 0.987 0.996 0.000 0.004
#> GSM74403 1 0.0237 0.987 0.996 0.000 0.004
#> GSM74404 1 0.0237 0.987 0.996 0.000 0.004
#> GSM74406 1 0.0237 0.987 0.996 0.000 0.004
#> GSM74407 3 0.6280 0.171 0.460 0.000 0.540
#> GSM74408 1 0.0237 0.987 0.996 0.000 0.004
#> GSM74409 1 0.0237 0.987 0.996 0.000 0.004
#> GSM74410 1 0.0237 0.987 0.996 0.000 0.004
#> GSM119936 1 0.0237 0.987 0.996 0.000 0.004
#> GSM119937 1 0.0237 0.987 0.996 0.000 0.004
#> GSM74411 2 0.0000 0.957 0.000 1.000 0.000
#> GSM74412 2 0.0000 0.957 0.000 1.000 0.000
#> GSM74413 2 0.0000 0.957 0.000 1.000 0.000
#> GSM74414 2 0.0000 0.957 0.000 1.000 0.000
#> GSM74415 2 0.4796 0.727 0.000 0.780 0.220
#> GSM121379 2 0.0000 0.957 0.000 1.000 0.000
#> GSM121380 2 0.0000 0.957 0.000 1.000 0.000
#> GSM121381 2 0.0000 0.957 0.000 1.000 0.000
#> GSM121382 2 0.0000 0.957 0.000 1.000 0.000
#> GSM121383 2 0.0000 0.957 0.000 1.000 0.000
#> GSM121384 2 0.0000 0.957 0.000 1.000 0.000
#> GSM121385 2 0.0000 0.957 0.000 1.000 0.000
#> GSM121386 2 0.0000 0.957 0.000 1.000 0.000
#> GSM121387 2 0.0000 0.957 0.000 1.000 0.000
#> GSM121388 2 0.0000 0.957 0.000 1.000 0.000
#> GSM121389 2 0.0000 0.957 0.000 1.000 0.000
#> GSM121390 2 0.0000 0.957 0.000 1.000 0.000
#> GSM121391 2 0.0000 0.957 0.000 1.000 0.000
#> GSM121392 2 0.0000 0.957 0.000 1.000 0.000
#> GSM121393 2 0.0000 0.957 0.000 1.000 0.000
#> GSM121394 2 0.0000 0.957 0.000 1.000 0.000
#> GSM121395 2 0.0000 0.957 0.000 1.000 0.000
#> GSM121396 2 0.2959 0.869 0.000 0.900 0.100
#> GSM121397 2 0.0000 0.957 0.000 1.000 0.000
#> GSM121398 2 0.0000 0.957 0.000 1.000 0.000
#> GSM121399 2 0.0000 0.957 0.000 1.000 0.000
#> GSM74240 3 0.2261 0.917 0.000 0.068 0.932
#> GSM74241 3 0.2356 0.914 0.000 0.072 0.928
#> GSM74242 3 0.0592 0.942 0.000 0.012 0.988
#> GSM74243 3 0.0592 0.942 0.000 0.012 0.988
#> GSM74244 3 0.2356 0.914 0.000 0.072 0.928
#> GSM74245 3 0.0747 0.943 0.000 0.016 0.984
#> GSM74246 2 0.4842 0.722 0.000 0.776 0.224
#> GSM74247 2 0.4842 0.722 0.000 0.776 0.224
#> GSM74248 3 0.0747 0.943 0.000 0.016 0.984
#> GSM74416 1 0.0237 0.987 0.996 0.000 0.004
#> GSM74417 1 0.0237 0.987 0.996 0.000 0.004
#> GSM74418 1 0.0237 0.987 0.996 0.000 0.004
#> GSM74419 1 0.0237 0.987 0.996 0.000 0.004
#> GSM121358 3 0.0747 0.943 0.000 0.016 0.984
#> GSM121359 2 0.4842 0.722 0.000 0.776 0.224
#> GSM121360 1 0.0000 0.987 1.000 0.000 0.000
#> GSM121362 1 0.0237 0.987 0.996 0.000 0.004
#> GSM121364 1 0.0237 0.987 0.996 0.000 0.004
#> GSM121365 3 0.0747 0.943 0.000 0.016 0.984
#> GSM121366 3 0.2959 0.888 0.000 0.100 0.900
#> GSM121367 3 0.0747 0.943 0.000 0.016 0.984
#> GSM121370 3 0.2959 0.888 0.000 0.100 0.900
#> GSM121371 3 0.0747 0.943 0.000 0.016 0.984
#> GSM121372 2 0.4796 0.727 0.000 0.780 0.220
#> GSM121373 1 0.0237 0.987 0.996 0.000 0.004
#> GSM121374 1 0.0237 0.987 0.996 0.000 0.004
#> GSM121407 2 0.0000 0.957 0.000 1.000 0.000
#> GSM74387 2 0.0000 0.957 0.000 1.000 0.000
#> GSM74388 2 0.0000 0.957 0.000 1.000 0.000
#> GSM74389 3 0.0592 0.939 0.012 0.000 0.988
#> GSM74390 3 0.1529 0.933 0.000 0.040 0.960
#> GSM74391 1 0.0237 0.987 0.996 0.000 0.004
#> GSM74392 1 0.0237 0.987 0.996 0.000 0.004
#> GSM74393 1 0.0892 0.973 0.980 0.000 0.020
#> GSM74394 2 0.0000 0.957 0.000 1.000 0.000
#> GSM74239 1 0.0000 0.987 1.000 0.000 0.000
#> GSM74364 1 0.0000 0.987 1.000 0.000 0.000
#> GSM74365 1 0.0424 0.982 0.992 0.000 0.008
#> GSM74366 2 0.0661 0.948 0.004 0.988 0.008
#> GSM74367 1 0.0000 0.987 1.000 0.000 0.000
#> GSM74377 1 0.0592 0.980 0.988 0.000 0.012
#> GSM74378 2 0.0829 0.945 0.004 0.984 0.012
#> GSM74379 1 0.0592 0.980 0.988 0.000 0.012
#> GSM74380 1 0.0592 0.980 0.988 0.000 0.012
#> GSM74381 2 0.4634 0.754 0.164 0.824 0.012
#> GSM121357 2 0.0000 0.957 0.000 1.000 0.000
#> GSM121361 2 0.0000 0.957 0.000 1.000 0.000
#> GSM121363 2 0.0000 0.957 0.000 1.000 0.000
#> GSM121368 2 0.0000 0.957 0.000 1.000 0.000
#> GSM121369 2 0.0000 0.957 0.000 1.000 0.000
#> GSM74368 1 0.0000 0.987 1.000 0.000 0.000
#> GSM74369 1 0.0000 0.987 1.000 0.000 0.000
#> GSM74370 1 0.0000 0.987 1.000 0.000 0.000
#> GSM74371 1 0.0000 0.987 1.000 0.000 0.000
#> GSM74372 1 0.0000 0.987 1.000 0.000 0.000
#> GSM74373 1 0.0592 0.980 0.988 0.000 0.012
#> GSM74374 1 0.0000 0.987 1.000 0.000 0.000
#> GSM74375 1 0.0592 0.980 0.988 0.000 0.012
#> GSM74376 2 0.5406 0.665 0.224 0.764 0.012
#> GSM74405 1 0.4915 0.752 0.804 0.184 0.012
#> GSM74351 1 0.0237 0.987 0.996 0.000 0.004
#> GSM74352 1 0.5493 0.682 0.756 0.232 0.012
#> GSM74353 1 0.0000 0.987 1.000 0.000 0.000
#> GSM74354 1 0.0000 0.987 1.000 0.000 0.000
#> GSM74355 2 0.0829 0.945 0.004 0.984 0.012
#> GSM74382 1 0.0237 0.987 0.996 0.000 0.004
#> GSM74383 1 0.0000 0.987 1.000 0.000 0.000
#> GSM74384 2 0.0829 0.945 0.004 0.984 0.012
#> GSM74385 1 0.0000 0.987 1.000 0.000 0.000
#> GSM74386 1 0.0000 0.987 1.000 0.000 0.000
#> GSM74395 1 0.0000 0.987 1.000 0.000 0.000
#> GSM74396 1 0.0000 0.987 1.000 0.000 0.000
#> GSM74397 1 0.0000 0.987 1.000 0.000 0.000
#> GSM74398 1 0.0592 0.980 0.988 0.000 0.012
#> GSM74399 1 0.0592 0.980 0.988 0.000 0.012
#> GSM74400 1 0.0000 0.987 1.000 0.000 0.000
#> GSM74401 1 0.0000 0.987 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM74356 3 0.0000 0.935 0.000 0.000 1.000 0.000
#> GSM74357 3 0.0000 0.935 0.000 0.000 1.000 0.000
#> GSM74358 3 0.0000 0.935 0.000 0.000 1.000 0.000
#> GSM74359 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74360 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74361 3 0.0000 0.935 0.000 0.000 1.000 0.000
#> GSM74362 3 0.3219 0.742 0.000 0.000 0.836 0.164
#> GSM74363 3 0.0000 0.935 0.000 0.000 1.000 0.000
#> GSM74402 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74403 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74404 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74406 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74407 4 0.4967 0.151 0.000 0.000 0.452 0.548
#> GSM74408 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74409 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74410 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM119936 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM119937 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74411 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM74412 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM74413 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM74414 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM74415 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM121379 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM121380 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM121381 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM121382 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM121383 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM121384 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM121385 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM121386 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM121387 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM121388 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM121389 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM121390 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM121391 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM121392 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM121393 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM121394 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM121395 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM121396 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM121397 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM121398 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM121399 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM74240 3 0.2647 0.857 0.000 0.120 0.880 0.000
#> GSM74241 3 0.2704 0.854 0.000 0.124 0.876 0.000
#> GSM74242 3 0.0000 0.935 0.000 0.000 1.000 0.000
#> GSM74243 3 0.0000 0.935 0.000 0.000 1.000 0.000
#> GSM74244 3 0.2704 0.854 0.000 0.124 0.876 0.000
#> GSM74245 3 0.0336 0.932 0.000 0.008 0.992 0.000
#> GSM74246 2 0.0188 0.996 0.000 0.996 0.004 0.000
#> GSM74247 2 0.0188 0.996 0.000 0.996 0.004 0.000
#> GSM74248 3 0.0000 0.935 0.000 0.000 1.000 0.000
#> GSM74416 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74417 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74418 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74419 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM121358 3 0.0000 0.935 0.000 0.000 1.000 0.000
#> GSM121359 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM121360 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM121362 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM121364 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM121365 3 0.0000 0.935 0.000 0.000 1.000 0.000
#> GSM121366 3 0.3486 0.781 0.000 0.188 0.812 0.000
#> GSM121367 3 0.0000 0.935 0.000 0.000 1.000 0.000
#> GSM121370 3 0.3444 0.787 0.000 0.184 0.816 0.000
#> GSM121371 3 0.0000 0.935 0.000 0.000 1.000 0.000
#> GSM121372 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM121373 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM121374 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM121407 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM74387 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM74388 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM74389 3 0.0000 0.935 0.000 0.000 1.000 0.000
#> GSM74390 3 0.1557 0.904 0.000 0.056 0.944 0.000
#> GSM74391 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74392 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74393 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74394 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM74239 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74364 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74365 4 0.4830 0.332 0.392 0.000 0.000 0.608
#> GSM74366 1 0.3569 0.712 0.804 0.196 0.000 0.000
#> GSM74367 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74377 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> GSM74378 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> GSM74379 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> GSM74380 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> GSM74381 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> GSM121357 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM121361 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM121363 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM121368 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM121369 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM74368 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74369 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74370 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74371 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74372 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74373 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> GSM74374 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74375 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> GSM74376 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> GSM74405 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> GSM74351 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74352 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> GSM74353 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74354 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74355 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> GSM74382 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74383 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74384 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> GSM74385 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74386 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74395 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74396 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74397 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74398 1 0.4331 0.582 0.712 0.000 0.000 0.288
#> GSM74399 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> GSM74400 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> GSM74401 4 0.0000 0.980 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM74356 5 0.3707 0.79074 0.000 0.000 0.284 0.000 0.716
#> GSM74357 5 0.3707 0.79074 0.000 0.000 0.284 0.000 0.716
#> GSM74358 5 0.3707 0.79074 0.000 0.000 0.284 0.000 0.716
#> GSM74359 4 0.1732 0.80178 0.000 0.000 0.000 0.920 0.080
#> GSM74360 4 0.0963 0.82572 0.000 0.000 0.000 0.964 0.036
#> GSM74361 5 0.3707 0.79074 0.000 0.000 0.284 0.000 0.716
#> GSM74362 5 0.3779 0.56005 0.000 0.000 0.012 0.236 0.752
#> GSM74363 5 0.3707 0.79074 0.000 0.000 0.284 0.000 0.716
#> GSM74402 4 0.1270 0.81757 0.000 0.000 0.000 0.948 0.052
#> GSM74403 4 0.0162 0.83460 0.000 0.000 0.000 0.996 0.004
#> GSM74404 4 0.0162 0.83460 0.000 0.000 0.000 0.996 0.004
#> GSM74406 4 0.1341 0.81549 0.000 0.000 0.000 0.944 0.056
#> GSM74407 4 0.4774 0.20299 0.000 0.000 0.028 0.612 0.360
#> GSM74408 4 0.1341 0.81549 0.000 0.000 0.000 0.944 0.056
#> GSM74409 4 0.1341 0.81549 0.000 0.000 0.000 0.944 0.056
#> GSM74410 4 0.1341 0.81549 0.000 0.000 0.000 0.944 0.056
#> GSM119936 4 0.1341 0.81549 0.000 0.000 0.000 0.944 0.056
#> GSM119937 4 0.0162 0.83460 0.000 0.000 0.000 0.996 0.004
#> GSM74411 2 0.4307 0.00215 0.000 0.504 0.496 0.000 0.000
#> GSM74412 2 0.4219 0.26363 0.000 0.584 0.416 0.000 0.000
#> GSM74413 2 0.4307 0.00215 0.000 0.504 0.496 0.000 0.000
#> GSM74414 2 0.0000 0.93032 0.000 1.000 0.000 0.000 0.000
#> GSM74415 3 0.4287 0.08779 0.000 0.460 0.540 0.000 0.000
#> GSM121379 2 0.0000 0.93032 0.000 1.000 0.000 0.000 0.000
#> GSM121380 2 0.0000 0.93032 0.000 1.000 0.000 0.000 0.000
#> GSM121381 2 0.0000 0.93032 0.000 1.000 0.000 0.000 0.000
#> GSM121382 2 0.0000 0.93032 0.000 1.000 0.000 0.000 0.000
#> GSM121383 2 0.0000 0.93032 0.000 1.000 0.000 0.000 0.000
#> GSM121384 2 0.0000 0.93032 0.000 1.000 0.000 0.000 0.000
#> GSM121385 2 0.0000 0.93032 0.000 1.000 0.000 0.000 0.000
#> GSM121386 2 0.0000 0.93032 0.000 1.000 0.000 0.000 0.000
#> GSM121387 2 0.0000 0.93032 0.000 1.000 0.000 0.000 0.000
#> GSM121388 2 0.0000 0.93032 0.000 1.000 0.000 0.000 0.000
#> GSM121389 2 0.0000 0.93032 0.000 1.000 0.000 0.000 0.000
#> GSM121390 2 0.0000 0.93032 0.000 1.000 0.000 0.000 0.000
#> GSM121391 2 0.0000 0.93032 0.000 1.000 0.000 0.000 0.000
#> GSM121392 2 0.0290 0.92660 0.008 0.992 0.000 0.000 0.000
#> GSM121393 2 0.0290 0.92660 0.008 0.992 0.000 0.000 0.000
#> GSM121394 2 0.0000 0.93032 0.000 1.000 0.000 0.000 0.000
#> GSM121395 2 0.0000 0.93032 0.000 1.000 0.000 0.000 0.000
#> GSM121396 2 0.2471 0.78551 0.000 0.864 0.136 0.000 0.000
#> GSM121397 2 0.0000 0.93032 0.000 1.000 0.000 0.000 0.000
#> GSM121398 2 0.0000 0.93032 0.000 1.000 0.000 0.000 0.000
#> GSM121399 2 0.0000 0.93032 0.000 1.000 0.000 0.000 0.000
#> GSM74240 3 0.0290 0.61989 0.000 0.008 0.992 0.000 0.000
#> GSM74241 3 0.0290 0.61989 0.000 0.008 0.992 0.000 0.000
#> GSM74242 3 0.4302 -0.35333 0.000 0.000 0.520 0.000 0.480
#> GSM74243 3 0.4304 -0.36355 0.000 0.000 0.516 0.000 0.484
#> GSM74244 3 0.0290 0.61989 0.000 0.008 0.992 0.000 0.000
#> GSM74245 3 0.0000 0.61398 0.000 0.000 1.000 0.000 0.000
#> GSM74246 3 0.3816 0.48298 0.000 0.304 0.696 0.000 0.000
#> GSM74247 3 0.3816 0.48298 0.000 0.304 0.696 0.000 0.000
#> GSM74248 3 0.0000 0.61398 0.000 0.000 1.000 0.000 0.000
#> GSM74416 4 0.1341 0.84538 0.000 0.000 0.000 0.944 0.056
#> GSM74417 4 0.1341 0.84538 0.000 0.000 0.000 0.944 0.056
#> GSM74418 4 0.1341 0.84538 0.000 0.000 0.000 0.944 0.056
#> GSM74419 4 0.1341 0.81549 0.000 0.000 0.000 0.944 0.056
#> GSM121358 3 0.2074 0.53357 0.000 0.000 0.896 0.000 0.104
#> GSM121359 3 0.4201 0.25368 0.000 0.408 0.592 0.000 0.000
#> GSM121360 4 0.3395 0.83848 0.000 0.000 0.000 0.764 0.236
#> GSM121362 4 0.1043 0.82746 0.000 0.000 0.000 0.960 0.040
#> GSM121364 4 0.1732 0.80178 0.000 0.000 0.000 0.920 0.080
#> GSM121365 3 0.4182 -0.13216 0.000 0.000 0.600 0.000 0.400
#> GSM121366 3 0.0609 0.62060 0.000 0.020 0.980 0.000 0.000
#> GSM121367 3 0.2773 0.45344 0.000 0.000 0.836 0.000 0.164
#> GSM121370 3 0.0609 0.62060 0.000 0.020 0.980 0.000 0.000
#> GSM121371 3 0.4060 -0.00581 0.000 0.000 0.640 0.000 0.360
#> GSM121372 3 0.4045 0.37720 0.000 0.356 0.644 0.000 0.000
#> GSM121373 4 0.0880 0.82747 0.000 0.000 0.000 0.968 0.032
#> GSM121374 4 0.1732 0.80178 0.000 0.000 0.000 0.920 0.080
#> GSM121407 2 0.0000 0.93032 0.000 1.000 0.000 0.000 0.000
#> GSM74387 2 0.4030 0.42030 0.000 0.648 0.352 0.000 0.000
#> GSM74388 2 0.0290 0.92660 0.008 0.992 0.000 0.000 0.000
#> GSM74389 5 0.3730 0.78598 0.000 0.000 0.288 0.000 0.712
#> GSM74390 3 0.2438 0.57573 0.000 0.040 0.900 0.000 0.060
#> GSM74391 4 0.1410 0.81329 0.000 0.000 0.000 0.940 0.060
#> GSM74392 4 0.1732 0.80178 0.000 0.000 0.000 0.920 0.080
#> GSM74393 5 0.4227 0.34560 0.000 0.000 0.000 0.420 0.580
#> GSM74394 2 0.0290 0.92660 0.008 0.992 0.000 0.000 0.000
#> GSM74239 4 0.3452 0.83646 0.000 0.000 0.000 0.756 0.244
#> GSM74364 4 0.3305 0.84055 0.000 0.000 0.000 0.776 0.224
#> GSM74365 4 0.5909 0.66784 0.164 0.000 0.000 0.592 0.244
#> GSM74366 1 0.3160 0.68059 0.808 0.188 0.000 0.000 0.004
#> GSM74367 4 0.3452 0.83646 0.000 0.000 0.000 0.756 0.244
#> GSM74377 1 0.0290 0.91233 0.992 0.000 0.000 0.000 0.008
#> GSM74378 1 0.0162 0.91245 0.996 0.000 0.000 0.000 0.004
#> GSM74379 1 0.1197 0.89279 0.952 0.000 0.000 0.000 0.048
#> GSM74380 1 0.1121 0.89608 0.956 0.000 0.000 0.000 0.044
#> GSM74381 1 0.0162 0.91245 0.996 0.000 0.000 0.000 0.004
#> GSM121357 2 0.0000 0.93032 0.000 1.000 0.000 0.000 0.000
#> GSM121361 2 0.0290 0.92660 0.008 0.992 0.000 0.000 0.000
#> GSM121363 2 0.0290 0.92660 0.008 0.992 0.000 0.000 0.000
#> GSM121368 2 0.0290 0.92660 0.008 0.992 0.000 0.000 0.000
#> GSM121369 2 0.0290 0.92660 0.008 0.992 0.000 0.000 0.000
#> GSM74368 4 0.3395 0.83858 0.000 0.000 0.000 0.764 0.236
#> GSM74369 4 0.3395 0.83858 0.000 0.000 0.000 0.764 0.236
#> GSM74370 4 0.3452 0.83646 0.000 0.000 0.000 0.756 0.244
#> GSM74371 4 0.3274 0.84120 0.000 0.000 0.000 0.780 0.220
#> GSM74372 4 0.3452 0.83646 0.000 0.000 0.000 0.756 0.244
#> GSM74373 1 0.0609 0.91078 0.980 0.000 0.000 0.000 0.020
#> GSM74374 4 0.3452 0.83646 0.000 0.000 0.000 0.756 0.244
#> GSM74375 1 0.0609 0.91078 0.980 0.000 0.000 0.000 0.020
#> GSM74376 1 0.0000 0.91253 1.000 0.000 0.000 0.000 0.000
#> GSM74405 1 0.0162 0.91245 0.996 0.000 0.000 0.000 0.004
#> GSM74351 4 0.1410 0.84568 0.000 0.000 0.000 0.940 0.060
#> GSM74352 1 0.0000 0.91253 1.000 0.000 0.000 0.000 0.000
#> GSM74353 4 0.3452 0.83646 0.000 0.000 0.000 0.756 0.244
#> GSM74354 4 0.3452 0.83646 0.000 0.000 0.000 0.756 0.244
#> GSM74355 1 0.0162 0.91245 0.996 0.000 0.000 0.000 0.004
#> GSM74382 4 0.1341 0.84538 0.000 0.000 0.000 0.944 0.056
#> GSM74383 4 0.3452 0.83646 0.000 0.000 0.000 0.756 0.244
#> GSM74384 1 0.1205 0.87933 0.956 0.040 0.000 0.000 0.004
#> GSM74385 4 0.3274 0.84120 0.000 0.000 0.000 0.780 0.220
#> GSM74386 4 0.3452 0.83646 0.000 0.000 0.000 0.756 0.244
#> GSM74395 4 0.3452 0.83646 0.000 0.000 0.000 0.756 0.244
#> GSM74396 4 0.3452 0.83646 0.000 0.000 0.000 0.756 0.244
#> GSM74397 4 0.3395 0.83865 0.000 0.000 0.000 0.764 0.236
#> GSM74398 1 0.6410 0.09480 0.488 0.000 0.000 0.320 0.192
#> GSM74399 1 0.0609 0.91078 0.980 0.000 0.000 0.000 0.020
#> GSM74400 4 0.3452 0.83646 0.000 0.000 0.000 0.756 0.244
#> GSM74401 4 0.3452 0.83646 0.000 0.000 0.000 0.756 0.244
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM74356 3 0.1124 0.8304 0.000 0.000 0.956 0.008 0.036 0.000
#> GSM74357 3 0.1124 0.8304 0.000 0.000 0.956 0.008 0.036 0.000
#> GSM74358 3 0.1124 0.8304 0.000 0.000 0.956 0.008 0.036 0.000
#> GSM74359 4 0.3509 0.7944 0.240 0.000 0.016 0.744 0.000 0.000
#> GSM74360 4 0.3470 0.7953 0.248 0.000 0.012 0.740 0.000 0.000
#> GSM74361 3 0.1225 0.8300 0.000 0.000 0.952 0.012 0.036 0.000
#> GSM74362 3 0.2135 0.7166 0.000 0.000 0.872 0.128 0.000 0.000
#> GSM74363 3 0.1124 0.8304 0.000 0.000 0.956 0.008 0.036 0.000
#> GSM74402 4 0.3728 0.8392 0.344 0.000 0.004 0.652 0.000 0.000
#> GSM74403 4 0.3659 0.8276 0.364 0.000 0.000 0.636 0.000 0.000
#> GSM74404 4 0.3659 0.8276 0.364 0.000 0.000 0.636 0.000 0.000
#> GSM74406 4 0.3699 0.8428 0.336 0.000 0.004 0.660 0.000 0.000
#> GSM74407 4 0.6074 0.5026 0.248 0.000 0.264 0.480 0.008 0.000
#> GSM74408 4 0.3699 0.8428 0.336 0.000 0.004 0.660 0.000 0.000
#> GSM74409 4 0.3699 0.8428 0.336 0.000 0.004 0.660 0.000 0.000
#> GSM74410 4 0.3699 0.8428 0.336 0.000 0.004 0.660 0.000 0.000
#> GSM119936 4 0.3699 0.8428 0.336 0.000 0.004 0.660 0.000 0.000
#> GSM119937 4 0.3684 0.8215 0.372 0.000 0.000 0.628 0.000 0.000
#> GSM74411 5 0.4060 0.5942 0.000 0.284 0.000 0.032 0.684 0.000
#> GSM74412 5 0.4306 0.4879 0.000 0.344 0.000 0.032 0.624 0.000
#> GSM74413 5 0.4040 0.5978 0.000 0.280 0.000 0.032 0.688 0.000
#> GSM74414 2 0.2201 0.9133 0.000 0.912 0.012 0.048 0.024 0.004
#> GSM74415 5 0.3658 0.6473 0.000 0.216 0.000 0.032 0.752 0.000
#> GSM121379 2 0.0260 0.9406 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM121380 2 0.0000 0.9402 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121381 2 0.0260 0.9406 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM121382 2 0.0260 0.9406 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM121383 2 0.0260 0.9406 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM121384 2 0.0000 0.9402 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121385 2 0.0260 0.9406 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM121386 2 0.0260 0.9406 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM121387 2 0.0260 0.9406 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM121388 2 0.0665 0.9397 0.000 0.980 0.008 0.004 0.008 0.000
#> GSM121389 2 0.0508 0.9371 0.000 0.984 0.012 0.004 0.000 0.000
#> GSM121390 2 0.0837 0.9333 0.000 0.972 0.020 0.004 0.004 0.000
#> GSM121391 2 0.0260 0.9406 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM121392 2 0.1381 0.9262 0.000 0.952 0.020 0.020 0.004 0.004
#> GSM121393 2 0.1237 0.9275 0.000 0.956 0.020 0.020 0.004 0.000
#> GSM121394 2 0.0260 0.9406 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM121395 2 0.0508 0.9371 0.000 0.984 0.012 0.004 0.000 0.000
#> GSM121396 2 0.3183 0.6822 0.000 0.788 0.004 0.008 0.200 0.000
#> GSM121397 2 0.0260 0.9406 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM121398 2 0.0260 0.9406 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM121399 2 0.0260 0.9406 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM74240 5 0.0713 0.6960 0.000 0.000 0.028 0.000 0.972 0.000
#> GSM74241 5 0.0713 0.6960 0.000 0.000 0.028 0.000 0.972 0.000
#> GSM74242 3 0.3670 0.6843 0.000 0.000 0.704 0.012 0.284 0.000
#> GSM74243 3 0.3650 0.6880 0.000 0.000 0.708 0.012 0.280 0.000
#> GSM74244 5 0.0713 0.6960 0.000 0.000 0.028 0.000 0.972 0.000
#> GSM74245 5 0.0935 0.6941 0.000 0.000 0.032 0.004 0.964 0.000
#> GSM74246 5 0.2398 0.7026 0.000 0.104 0.000 0.020 0.876 0.000
#> GSM74247 5 0.2398 0.7026 0.000 0.104 0.000 0.020 0.876 0.000
#> GSM74248 5 0.0935 0.6941 0.000 0.000 0.032 0.004 0.964 0.000
#> GSM74416 4 0.3828 0.7339 0.440 0.000 0.000 0.560 0.000 0.000
#> GSM74417 4 0.3828 0.7339 0.440 0.000 0.000 0.560 0.000 0.000
#> GSM74418 4 0.3828 0.7339 0.440 0.000 0.000 0.560 0.000 0.000
#> GSM74419 4 0.3699 0.8428 0.336 0.000 0.004 0.660 0.000 0.000
#> GSM121358 5 0.5334 0.1494 0.000 0.000 0.320 0.128 0.552 0.000
#> GSM121359 5 0.4750 0.6689 0.000 0.176 0.008 0.120 0.696 0.000
#> GSM121360 1 0.3898 0.3581 0.652 0.000 0.012 0.336 0.000 0.000
#> GSM121362 4 0.3729 0.7659 0.296 0.000 0.012 0.692 0.000 0.000
#> GSM121364 4 0.3509 0.7944 0.240 0.000 0.016 0.744 0.000 0.000
#> GSM121365 3 0.5289 0.4967 0.000 0.000 0.560 0.124 0.316 0.000
#> GSM121366 5 0.3295 0.6446 0.000 0.000 0.056 0.128 0.816 0.000
#> GSM121367 5 0.5462 -0.0548 0.000 0.000 0.376 0.128 0.496 0.000
#> GSM121370 5 0.3295 0.6446 0.000 0.000 0.056 0.128 0.816 0.000
#> GSM121371 3 0.5418 0.4178 0.000 0.000 0.520 0.128 0.352 0.000
#> GSM121372 5 0.4449 0.6836 0.000 0.136 0.008 0.124 0.732 0.000
#> GSM121373 4 0.3564 0.7929 0.264 0.000 0.012 0.724 0.000 0.000
#> GSM121374 4 0.3420 0.7934 0.240 0.000 0.012 0.748 0.000 0.000
#> GSM121407 2 0.1138 0.9320 0.000 0.960 0.004 0.012 0.024 0.000
#> GSM74387 2 0.4556 -0.0314 0.000 0.516 0.008 0.020 0.456 0.000
#> GSM74388 2 0.2651 0.9034 0.000 0.892 0.028 0.052 0.016 0.012
#> GSM74389 3 0.1297 0.8295 0.000 0.000 0.948 0.012 0.040 0.000
#> GSM74390 5 0.5948 0.3697 0.000 0.044 0.232 0.140 0.584 0.000
#> GSM74391 4 0.3922 0.8378 0.320 0.000 0.016 0.664 0.000 0.000
#> GSM74392 4 0.3509 0.7944 0.240 0.000 0.016 0.744 0.000 0.000
#> GSM74393 4 0.4264 0.3464 0.032 0.000 0.332 0.636 0.000 0.000
#> GSM74394 2 0.2651 0.9034 0.000 0.892 0.028 0.052 0.016 0.012
#> GSM74239 1 0.1387 0.7998 0.932 0.000 0.000 0.068 0.000 0.000
#> GSM74364 1 0.3050 0.5253 0.764 0.000 0.000 0.236 0.000 0.000
#> GSM74365 1 0.1398 0.7564 0.940 0.000 0.000 0.008 0.000 0.052
#> GSM74366 6 0.2889 0.7719 0.000 0.096 0.004 0.044 0.000 0.856
#> GSM74367 1 0.0458 0.8253 0.984 0.000 0.000 0.016 0.000 0.000
#> GSM74377 6 0.0725 0.8814 0.012 0.000 0.000 0.012 0.000 0.976
#> GSM74378 6 0.0458 0.8798 0.000 0.000 0.000 0.016 0.000 0.984
#> GSM74379 6 0.3797 0.7195 0.292 0.000 0.000 0.016 0.000 0.692
#> GSM74380 6 0.3457 0.7805 0.232 0.000 0.000 0.016 0.000 0.752
#> GSM74381 6 0.0603 0.8817 0.004 0.000 0.000 0.016 0.000 0.980
#> GSM121357 2 0.1599 0.9247 0.000 0.940 0.008 0.028 0.024 0.000
#> GSM121361 2 0.2651 0.9034 0.000 0.892 0.028 0.052 0.016 0.012
#> GSM121363 2 0.2651 0.9034 0.000 0.892 0.028 0.052 0.016 0.012
#> GSM121368 2 0.2738 0.9013 0.000 0.888 0.028 0.052 0.020 0.012
#> GSM121369 2 0.2738 0.9013 0.000 0.888 0.028 0.052 0.020 0.012
#> GSM74368 1 0.2562 0.6783 0.828 0.000 0.000 0.172 0.000 0.000
#> GSM74369 1 0.2527 0.6824 0.832 0.000 0.000 0.168 0.000 0.000
#> GSM74370 1 0.1267 0.8060 0.940 0.000 0.000 0.060 0.000 0.000
#> GSM74371 1 0.3390 0.3250 0.704 0.000 0.000 0.296 0.000 0.000
#> GSM74372 1 0.0291 0.8158 0.992 0.000 0.000 0.004 0.000 0.004
#> GSM74373 6 0.2667 0.8455 0.128 0.000 0.000 0.020 0.000 0.852
#> GSM74374 1 0.0260 0.8246 0.992 0.000 0.000 0.008 0.000 0.000
#> GSM74375 6 0.3487 0.7877 0.224 0.000 0.000 0.020 0.000 0.756
#> GSM74376 6 0.0146 0.8822 0.004 0.000 0.000 0.000 0.000 0.996
#> GSM74405 6 0.0603 0.8817 0.004 0.000 0.000 0.016 0.000 0.980
#> GSM74351 4 0.3847 0.7009 0.456 0.000 0.000 0.544 0.000 0.000
#> GSM74352 6 0.0520 0.8821 0.008 0.000 0.000 0.008 0.000 0.984
#> GSM74353 1 0.0713 0.8224 0.972 0.000 0.000 0.028 0.000 0.000
#> GSM74354 1 0.0000 0.8210 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM74355 6 0.0603 0.8817 0.004 0.000 0.000 0.016 0.000 0.980
#> GSM74382 4 0.3833 0.7263 0.444 0.000 0.000 0.556 0.000 0.000
#> GSM74383 1 0.0363 0.8252 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM74384 6 0.1693 0.8527 0.000 0.020 0.004 0.044 0.000 0.932
#> GSM74385 1 0.3288 0.4005 0.724 0.000 0.000 0.276 0.000 0.000
#> GSM74386 1 0.0363 0.8233 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM74395 1 0.0458 0.8253 0.984 0.000 0.000 0.016 0.000 0.000
#> GSM74396 1 0.1007 0.8156 0.956 0.000 0.000 0.044 0.000 0.000
#> GSM74397 1 0.2527 0.6829 0.832 0.000 0.000 0.168 0.000 0.000
#> GSM74398 1 0.3916 0.2810 0.680 0.000 0.000 0.020 0.000 0.300
#> GSM74399 6 0.3088 0.8234 0.172 0.000 0.000 0.020 0.000 0.808
#> GSM74400 1 0.0363 0.8188 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM74401 1 0.0260 0.8153 0.992 0.000 0.000 0.008 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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
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 118 2.26e-12 2
#> ATC:skmeans 120 2.66e-15 3
#> ATC:skmeans 119 3.26e-18 4
#> ATC:skmeans 104 7.19e-22 5
#> ATC:skmeans 109 2.94e-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["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 21168 rows and 121 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.976 0.990 0.4990 0.504 0.504
#> 3 3 0.920 0.904 0.960 0.2810 0.806 0.634
#> 4 4 0.780 0.845 0.902 0.1619 0.844 0.596
#> 5 5 0.805 0.840 0.917 0.0699 0.879 0.582
#> 6 6 0.906 0.858 0.940 0.0391 0.963 0.820
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 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
#> GSM74356 1 0.4022 0.907 0.920 0.080
#> GSM74357 1 0.0376 0.980 0.996 0.004
#> GSM74358 1 0.0376 0.980 0.996 0.004
#> GSM74359 1 0.0000 0.982 1.000 0.000
#> GSM74360 1 0.0000 0.982 1.000 0.000
#> GSM74361 1 0.1184 0.969 0.984 0.016
#> GSM74362 1 0.0000 0.982 1.000 0.000
#> GSM74363 1 0.9909 0.226 0.556 0.444
#> GSM74402 1 0.0000 0.982 1.000 0.000
#> GSM74403 1 0.0000 0.982 1.000 0.000
#> GSM74404 1 0.0000 0.982 1.000 0.000
#> GSM74406 1 0.0000 0.982 1.000 0.000
#> GSM74407 1 0.0000 0.982 1.000 0.000
#> GSM74408 1 0.0000 0.982 1.000 0.000
#> GSM74409 1 0.0000 0.982 1.000 0.000
#> GSM74410 1 0.0000 0.982 1.000 0.000
#> GSM119936 1 0.0000 0.982 1.000 0.000
#> GSM119937 1 0.0000 0.982 1.000 0.000
#> GSM74411 2 0.0000 1.000 0.000 1.000
#> GSM74412 2 0.0000 1.000 0.000 1.000
#> GSM74413 2 0.0000 1.000 0.000 1.000
#> GSM74414 2 0.0000 1.000 0.000 1.000
#> GSM74415 2 0.0000 1.000 0.000 1.000
#> GSM121379 2 0.0000 1.000 0.000 1.000
#> GSM121380 2 0.0000 1.000 0.000 1.000
#> GSM121381 2 0.0000 1.000 0.000 1.000
#> GSM121382 2 0.0000 1.000 0.000 1.000
#> GSM121383 2 0.0000 1.000 0.000 1.000
#> GSM121384 2 0.0000 1.000 0.000 1.000
#> GSM121385 2 0.0000 1.000 0.000 1.000
#> GSM121386 2 0.0000 1.000 0.000 1.000
#> GSM121387 2 0.0000 1.000 0.000 1.000
#> GSM121388 2 0.0000 1.000 0.000 1.000
#> GSM121389 2 0.0000 1.000 0.000 1.000
#> GSM121390 2 0.0000 1.000 0.000 1.000
#> GSM121391 2 0.0000 1.000 0.000 1.000
#> GSM121392 2 0.0000 1.000 0.000 1.000
#> GSM121393 2 0.0000 1.000 0.000 1.000
#> GSM121394 2 0.0000 1.000 0.000 1.000
#> GSM121395 2 0.0000 1.000 0.000 1.000
#> GSM121396 2 0.0000 1.000 0.000 1.000
#> GSM121397 2 0.0000 1.000 0.000 1.000
#> GSM121398 2 0.0000 1.000 0.000 1.000
#> GSM121399 2 0.0000 1.000 0.000 1.000
#> GSM74240 2 0.0000 1.000 0.000 1.000
#> GSM74241 2 0.0000 1.000 0.000 1.000
#> GSM74242 1 0.6531 0.799 0.832 0.168
#> GSM74243 1 0.3733 0.915 0.928 0.072
#> GSM74244 2 0.0000 1.000 0.000 1.000
#> GSM74245 2 0.0000 1.000 0.000 1.000
#> GSM74246 2 0.0000 1.000 0.000 1.000
#> GSM74247 2 0.0000 1.000 0.000 1.000
#> GSM74248 2 0.0000 1.000 0.000 1.000
#> GSM74416 1 0.0000 0.982 1.000 0.000
#> GSM74417 1 0.0000 0.982 1.000 0.000
#> GSM74418 1 0.0000 0.982 1.000 0.000
#> GSM74419 1 0.0000 0.982 1.000 0.000
#> GSM121358 2 0.0000 1.000 0.000 1.000
#> GSM121359 2 0.0000 1.000 0.000 1.000
#> GSM121360 1 0.0000 0.982 1.000 0.000
#> GSM121362 1 0.0000 0.982 1.000 0.000
#> GSM121364 1 0.0000 0.982 1.000 0.000
#> GSM121365 2 0.0000 1.000 0.000 1.000
#> GSM121366 2 0.0000 1.000 0.000 1.000
#> GSM121367 2 0.0000 1.000 0.000 1.000
#> GSM121370 2 0.0000 1.000 0.000 1.000
#> GSM121371 2 0.0000 1.000 0.000 1.000
#> GSM121372 2 0.0000 1.000 0.000 1.000
#> GSM121373 1 0.0000 0.982 1.000 0.000
#> GSM121374 1 0.0000 0.982 1.000 0.000
#> GSM121407 2 0.0000 1.000 0.000 1.000
#> GSM74387 2 0.0000 1.000 0.000 1.000
#> GSM74388 2 0.0000 1.000 0.000 1.000
#> GSM74389 1 0.0376 0.980 0.996 0.004
#> GSM74390 2 0.0000 1.000 0.000 1.000
#> GSM74391 1 0.0000 0.982 1.000 0.000
#> GSM74392 1 0.0000 0.982 1.000 0.000
#> GSM74393 1 0.0000 0.982 1.000 0.000
#> GSM74394 2 0.0000 1.000 0.000 1.000
#> GSM74239 1 0.0000 0.982 1.000 0.000
#> GSM74364 1 0.0000 0.982 1.000 0.000
#> GSM74365 1 0.0000 0.982 1.000 0.000
#> GSM74366 2 0.0000 1.000 0.000 1.000
#> GSM74367 1 0.0000 0.982 1.000 0.000
#> GSM74377 1 0.0000 0.982 1.000 0.000
#> GSM74378 1 0.9580 0.397 0.620 0.380
#> GSM74379 1 0.0000 0.982 1.000 0.000
#> GSM74380 1 0.0000 0.982 1.000 0.000
#> GSM74381 1 0.0376 0.980 0.996 0.004
#> GSM121357 2 0.0000 1.000 0.000 1.000
#> GSM121361 2 0.0000 1.000 0.000 1.000
#> GSM121363 2 0.0000 1.000 0.000 1.000
#> GSM121368 2 0.0000 1.000 0.000 1.000
#> GSM121369 2 0.0000 1.000 0.000 1.000
#> GSM74368 1 0.0000 0.982 1.000 0.000
#> GSM74369 1 0.0000 0.982 1.000 0.000
#> GSM74370 1 0.0000 0.982 1.000 0.000
#> GSM74371 1 0.0000 0.982 1.000 0.000
#> GSM74372 1 0.0000 0.982 1.000 0.000
#> GSM74373 1 0.0000 0.982 1.000 0.000
#> GSM74374 1 0.0000 0.982 1.000 0.000
#> GSM74375 1 0.0000 0.982 1.000 0.000
#> GSM74376 1 0.0376 0.980 0.996 0.004
#> GSM74405 1 0.0000 0.982 1.000 0.000
#> GSM74351 1 0.0000 0.982 1.000 0.000
#> GSM74352 1 0.0376 0.980 0.996 0.004
#> GSM74353 1 0.0000 0.982 1.000 0.000
#> GSM74354 1 0.0000 0.982 1.000 0.000
#> GSM74355 1 0.0376 0.980 0.996 0.004
#> GSM74382 1 0.0000 0.982 1.000 0.000
#> GSM74383 1 0.0000 0.982 1.000 0.000
#> GSM74384 2 0.0000 1.000 0.000 1.000
#> GSM74385 1 0.0000 0.982 1.000 0.000
#> GSM74386 1 0.0000 0.982 1.000 0.000
#> GSM74395 1 0.0000 0.982 1.000 0.000
#> GSM74396 1 0.0000 0.982 1.000 0.000
#> GSM74397 1 0.0000 0.982 1.000 0.000
#> GSM74398 1 0.0000 0.982 1.000 0.000
#> GSM74399 1 0.0000 0.982 1.000 0.000
#> GSM74400 1 0.0000 0.982 1.000 0.000
#> GSM74401 1 0.0000 0.982 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM74356 3 0.0000 0.9214 0.000 0.000 1.000
#> GSM74357 3 0.0000 0.9214 0.000 0.000 1.000
#> GSM74358 3 0.0000 0.9214 0.000 0.000 1.000
#> GSM74359 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74360 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74361 3 0.0000 0.9214 0.000 0.000 1.000
#> GSM74362 3 0.4452 0.7042 0.192 0.000 0.808
#> GSM74363 3 0.0000 0.9214 0.000 0.000 1.000
#> GSM74402 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74403 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74404 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74406 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74407 1 0.4654 0.7400 0.792 0.000 0.208
#> GSM74408 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74409 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74410 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM119936 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM119937 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74411 3 0.6180 0.3451 0.000 0.416 0.584
#> GSM74412 2 0.0747 0.9357 0.000 0.984 0.016
#> GSM74413 3 0.2165 0.8803 0.000 0.064 0.936
#> GSM74414 2 0.0000 0.9469 0.000 1.000 0.000
#> GSM74415 3 0.0000 0.9214 0.000 0.000 1.000
#> GSM121379 2 0.0000 0.9469 0.000 1.000 0.000
#> GSM121380 2 0.0000 0.9469 0.000 1.000 0.000
#> GSM121381 2 0.0000 0.9469 0.000 1.000 0.000
#> GSM121382 2 0.0000 0.9469 0.000 1.000 0.000
#> GSM121383 2 0.0000 0.9469 0.000 1.000 0.000
#> GSM121384 2 0.0000 0.9469 0.000 1.000 0.000
#> GSM121385 2 0.0000 0.9469 0.000 1.000 0.000
#> GSM121386 2 0.0000 0.9469 0.000 1.000 0.000
#> GSM121387 2 0.0000 0.9469 0.000 1.000 0.000
#> GSM121388 2 0.1289 0.9244 0.000 0.968 0.032
#> GSM121389 2 0.0000 0.9469 0.000 1.000 0.000
#> GSM121390 2 0.0000 0.9469 0.000 1.000 0.000
#> GSM121391 2 0.0000 0.9469 0.000 1.000 0.000
#> GSM121392 2 0.0000 0.9469 0.000 1.000 0.000
#> GSM121393 2 0.4605 0.7350 0.000 0.796 0.204
#> GSM121394 2 0.0000 0.9469 0.000 1.000 0.000
#> GSM121395 2 0.0000 0.9469 0.000 1.000 0.000
#> GSM121396 3 0.6168 0.3559 0.000 0.412 0.588
#> GSM121397 2 0.0000 0.9469 0.000 1.000 0.000
#> GSM121398 2 0.0000 0.9469 0.000 1.000 0.000
#> GSM121399 2 0.0000 0.9469 0.000 1.000 0.000
#> GSM74240 3 0.0000 0.9214 0.000 0.000 1.000
#> GSM74241 3 0.0000 0.9214 0.000 0.000 1.000
#> GSM74242 3 0.0000 0.9214 0.000 0.000 1.000
#> GSM74243 3 0.0000 0.9214 0.000 0.000 1.000
#> GSM74244 3 0.0237 0.9193 0.000 0.004 0.996
#> GSM74245 3 0.0000 0.9214 0.000 0.000 1.000
#> GSM74246 3 0.2625 0.8640 0.000 0.084 0.916
#> GSM74247 3 0.1860 0.8902 0.000 0.052 0.948
#> GSM74248 3 0.0000 0.9214 0.000 0.000 1.000
#> GSM74416 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74417 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74418 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74419 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM121358 3 0.0000 0.9214 0.000 0.000 1.000
#> GSM121359 3 0.4931 0.6921 0.000 0.232 0.768
#> GSM121360 1 0.0237 0.9770 0.996 0.000 0.004
#> GSM121362 1 0.1411 0.9539 0.964 0.000 0.036
#> GSM121364 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM121365 3 0.0000 0.9214 0.000 0.000 1.000
#> GSM121366 3 0.0237 0.9193 0.000 0.004 0.996
#> GSM121367 3 0.0000 0.9214 0.000 0.000 1.000
#> GSM121370 3 0.0000 0.9214 0.000 0.000 1.000
#> GSM121371 3 0.0000 0.9214 0.000 0.000 1.000
#> GSM121372 3 0.0592 0.9149 0.000 0.012 0.988
#> GSM121373 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM121374 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM121407 2 0.3482 0.8245 0.000 0.872 0.128
#> GSM74387 2 0.6309 -0.1183 0.000 0.504 0.496
#> GSM74388 2 0.0000 0.9469 0.000 1.000 0.000
#> GSM74389 3 0.0000 0.9214 0.000 0.000 1.000
#> GSM74390 3 0.0000 0.9214 0.000 0.000 1.000
#> GSM74391 1 0.1643 0.9474 0.956 0.000 0.044
#> GSM74392 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74393 1 0.6305 0.0823 0.516 0.000 0.484
#> GSM74394 2 0.2356 0.8879 0.000 0.928 0.072
#> GSM74239 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74364 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74365 1 0.0237 0.9770 0.996 0.000 0.004
#> GSM74366 2 0.4750 0.7173 0.000 0.784 0.216
#> GSM74367 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74377 1 0.0237 0.9770 0.996 0.000 0.004
#> GSM74378 1 0.5036 0.8191 0.832 0.120 0.048
#> GSM74379 1 0.0237 0.9770 0.996 0.000 0.004
#> GSM74380 1 0.0237 0.9770 0.996 0.000 0.004
#> GSM74381 1 0.1643 0.9474 0.956 0.000 0.044
#> GSM121357 3 0.6244 0.1853 0.000 0.440 0.560
#> GSM121361 2 0.0000 0.9469 0.000 1.000 0.000
#> GSM121363 2 0.0000 0.9469 0.000 1.000 0.000
#> GSM121368 2 0.0000 0.9469 0.000 1.000 0.000
#> GSM121369 3 0.4931 0.6767 0.000 0.232 0.768
#> GSM74368 1 0.0237 0.9770 0.996 0.000 0.004
#> GSM74369 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74370 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74371 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74372 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74373 1 0.0237 0.9770 0.996 0.000 0.004
#> GSM74374 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74375 1 0.0237 0.9770 0.996 0.000 0.004
#> GSM74376 1 0.1753 0.9439 0.952 0.000 0.048
#> GSM74405 1 0.1753 0.9439 0.952 0.000 0.048
#> GSM74351 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74352 1 0.1753 0.9441 0.952 0.000 0.048
#> GSM74353 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74354 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74355 1 0.2261 0.9250 0.932 0.000 0.068
#> GSM74382 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74383 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74384 2 0.6811 0.6461 0.064 0.716 0.220
#> GSM74385 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74386 1 0.0237 0.9770 0.996 0.000 0.004
#> GSM74395 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74396 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74397 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74398 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74399 1 0.0237 0.9770 0.996 0.000 0.004
#> GSM74400 1 0.0000 0.9787 1.000 0.000 0.000
#> GSM74401 1 0.0000 0.9787 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM74356 3 0.0000 0.892 0.000 0.000 1.000 0.000
#> GSM74357 3 0.0000 0.892 0.000 0.000 1.000 0.000
#> GSM74358 3 0.0000 0.892 0.000 0.000 1.000 0.000
#> GSM74359 4 0.3626 0.956 0.184 0.000 0.004 0.812
#> GSM74360 4 0.4382 0.841 0.296 0.000 0.000 0.704
#> GSM74361 3 0.0000 0.892 0.000 0.000 1.000 0.000
#> GSM74362 3 0.1867 0.843 0.072 0.000 0.928 0.000
#> GSM74363 3 0.0000 0.892 0.000 0.000 1.000 0.000
#> GSM74402 4 0.3400 0.962 0.180 0.000 0.000 0.820
#> GSM74403 4 0.3356 0.963 0.176 0.000 0.000 0.824
#> GSM74404 4 0.3356 0.963 0.176 0.000 0.000 0.824
#> GSM74406 4 0.3356 0.963 0.176 0.000 0.000 0.824
#> GSM74407 3 0.4866 0.322 0.404 0.000 0.596 0.000
#> GSM74408 4 0.3356 0.963 0.176 0.000 0.000 0.824
#> GSM74409 4 0.3356 0.963 0.176 0.000 0.000 0.824
#> GSM74410 4 0.3356 0.963 0.176 0.000 0.000 0.824
#> GSM119936 4 0.3356 0.963 0.176 0.000 0.000 0.824
#> GSM119937 4 0.4877 0.640 0.408 0.000 0.000 0.592
#> GSM74411 3 0.5536 0.416 0.000 0.384 0.592 0.024
#> GSM74412 2 0.1004 0.940 0.000 0.972 0.004 0.024
#> GSM74413 3 0.1929 0.875 0.000 0.036 0.940 0.024
#> GSM74414 2 0.3356 0.856 0.000 0.824 0.000 0.176
#> GSM74415 3 0.0817 0.891 0.000 0.000 0.976 0.024
#> GSM121379 2 0.0000 0.955 0.000 1.000 0.000 0.000
#> GSM121380 2 0.0000 0.955 0.000 1.000 0.000 0.000
#> GSM121381 2 0.0000 0.955 0.000 1.000 0.000 0.000
#> GSM121382 2 0.0000 0.955 0.000 1.000 0.000 0.000
#> GSM121383 2 0.0000 0.955 0.000 1.000 0.000 0.000
#> GSM121384 2 0.0000 0.955 0.000 1.000 0.000 0.000
#> GSM121385 2 0.0000 0.955 0.000 1.000 0.000 0.000
#> GSM121386 2 0.0000 0.955 0.000 1.000 0.000 0.000
#> GSM121387 2 0.0000 0.955 0.000 1.000 0.000 0.000
#> GSM121388 2 0.0592 0.945 0.000 0.984 0.016 0.000
#> GSM121389 2 0.0000 0.955 0.000 1.000 0.000 0.000
#> GSM121390 2 0.0000 0.955 0.000 1.000 0.000 0.000
#> GSM121391 2 0.0000 0.955 0.000 1.000 0.000 0.000
#> GSM121392 2 0.0000 0.955 0.000 1.000 0.000 0.000
#> GSM121393 2 0.3978 0.740 0.000 0.796 0.192 0.012
#> GSM121394 2 0.0000 0.955 0.000 1.000 0.000 0.000
#> GSM121395 2 0.0000 0.955 0.000 1.000 0.000 0.000
#> GSM121396 3 0.4866 0.394 0.000 0.404 0.596 0.000
#> GSM121397 2 0.0000 0.955 0.000 1.000 0.000 0.000
#> GSM121398 2 0.0000 0.955 0.000 1.000 0.000 0.000
#> GSM121399 2 0.0000 0.955 0.000 1.000 0.000 0.000
#> GSM74240 3 0.0817 0.891 0.000 0.000 0.976 0.024
#> GSM74241 3 0.0817 0.891 0.000 0.000 0.976 0.024
#> GSM74242 3 0.0000 0.892 0.000 0.000 1.000 0.000
#> GSM74243 3 0.0000 0.892 0.000 0.000 1.000 0.000
#> GSM74244 3 0.0817 0.891 0.000 0.000 0.976 0.024
#> GSM74245 3 0.0817 0.891 0.000 0.000 0.976 0.024
#> GSM74246 3 0.2443 0.860 0.000 0.060 0.916 0.024
#> GSM74247 3 0.2021 0.873 0.000 0.040 0.936 0.024
#> GSM74248 3 0.0817 0.891 0.000 0.000 0.976 0.024
#> GSM74416 4 0.3356 0.963 0.176 0.000 0.000 0.824
#> GSM74417 4 0.3356 0.963 0.176 0.000 0.000 0.824
#> GSM74418 4 0.3356 0.963 0.176 0.000 0.000 0.824
#> GSM74419 1 0.5000 -0.418 0.504 0.000 0.000 0.496
#> GSM121358 3 0.0000 0.892 0.000 0.000 1.000 0.000
#> GSM121359 3 0.3907 0.691 0.000 0.232 0.768 0.000
#> GSM121360 1 0.0000 0.881 1.000 0.000 0.000 0.000
#> GSM121362 1 0.0921 0.868 0.972 0.000 0.028 0.000
#> GSM121364 4 0.3356 0.963 0.176 0.000 0.000 0.824
#> GSM121365 3 0.0000 0.892 0.000 0.000 1.000 0.000
#> GSM121366 3 0.0817 0.891 0.000 0.000 0.976 0.024
#> GSM121367 3 0.0188 0.892 0.000 0.000 0.996 0.004
#> GSM121370 3 0.0817 0.891 0.000 0.000 0.976 0.024
#> GSM121371 3 0.0000 0.892 0.000 0.000 1.000 0.000
#> GSM121372 3 0.0817 0.891 0.000 0.000 0.976 0.024
#> GSM121373 4 0.4008 0.905 0.244 0.000 0.000 0.756
#> GSM121374 4 0.3444 0.960 0.184 0.000 0.000 0.816
#> GSM121407 2 0.0817 0.939 0.000 0.976 0.024 0.000
#> GSM74387 3 0.5620 0.338 0.000 0.416 0.560 0.024
#> GSM74388 2 0.2973 0.875 0.000 0.856 0.000 0.144
#> GSM74389 3 0.0000 0.892 0.000 0.000 1.000 0.000
#> GSM74390 3 0.0188 0.891 0.004 0.000 0.996 0.000
#> GSM74391 1 0.3991 0.743 0.832 0.000 0.120 0.048
#> GSM74392 4 0.3444 0.960 0.184 0.000 0.000 0.816
#> GSM74393 3 0.4877 0.305 0.408 0.000 0.592 0.000
#> GSM74394 2 0.4832 0.808 0.000 0.768 0.056 0.176
#> GSM74239 1 0.0336 0.875 0.992 0.000 0.000 0.008
#> GSM74364 1 0.3266 0.665 0.832 0.000 0.000 0.168
#> GSM74365 1 0.0000 0.881 1.000 0.000 0.000 0.000
#> GSM74366 1 0.9109 0.393 0.480 0.160 0.184 0.176
#> GSM74367 1 0.0000 0.881 1.000 0.000 0.000 0.000
#> GSM74377 1 0.3074 0.810 0.848 0.000 0.000 0.152
#> GSM74378 1 0.4332 0.770 0.792 0.032 0.000 0.176
#> GSM74379 1 0.0000 0.881 1.000 0.000 0.000 0.000
#> GSM74380 1 0.0188 0.880 0.996 0.000 0.000 0.004
#> GSM74381 1 0.3862 0.797 0.824 0.000 0.024 0.152
#> GSM121357 3 0.5856 0.289 0.000 0.408 0.556 0.036
#> GSM121361 2 0.3539 0.853 0.004 0.820 0.000 0.176
#> GSM121363 2 0.2973 0.875 0.000 0.856 0.000 0.144
#> GSM121368 2 0.3172 0.866 0.000 0.840 0.000 0.160
#> GSM121369 3 0.5546 0.726 0.028 0.048 0.748 0.176
#> GSM74368 1 0.0921 0.858 0.972 0.000 0.000 0.028
#> GSM74369 1 0.1118 0.851 0.964 0.000 0.000 0.036
#> GSM74370 1 0.0000 0.881 1.000 0.000 0.000 0.000
#> GSM74371 4 0.3528 0.954 0.192 0.000 0.000 0.808
#> GSM74372 1 0.0000 0.881 1.000 0.000 0.000 0.000
#> GSM74373 1 0.2973 0.814 0.856 0.000 0.000 0.144
#> GSM74374 1 0.0000 0.881 1.000 0.000 0.000 0.000
#> GSM74375 1 0.0188 0.880 0.996 0.000 0.000 0.004
#> GSM74376 1 0.3356 0.794 0.824 0.000 0.000 0.176
#> GSM74405 1 0.3862 0.797 0.824 0.000 0.024 0.152
#> GSM74351 4 0.3356 0.963 0.176 0.000 0.000 0.824
#> GSM74352 1 0.3529 0.804 0.836 0.000 0.012 0.152
#> GSM74353 1 0.0000 0.881 1.000 0.000 0.000 0.000
#> GSM74354 1 0.0000 0.881 1.000 0.000 0.000 0.000
#> GSM74355 1 0.3356 0.794 0.824 0.000 0.000 0.176
#> GSM74382 4 0.4543 0.796 0.324 0.000 0.000 0.676
#> GSM74383 1 0.0000 0.881 1.000 0.000 0.000 0.000
#> GSM74384 1 0.8006 0.549 0.588 0.156 0.080 0.176
#> GSM74385 4 0.3400 0.962 0.180 0.000 0.000 0.820
#> GSM74386 1 0.0000 0.881 1.000 0.000 0.000 0.000
#> GSM74395 1 0.0000 0.881 1.000 0.000 0.000 0.000
#> GSM74396 1 0.0000 0.881 1.000 0.000 0.000 0.000
#> GSM74397 1 0.0000 0.881 1.000 0.000 0.000 0.000
#> GSM74398 1 0.0000 0.881 1.000 0.000 0.000 0.000
#> GSM74399 1 0.2973 0.814 0.856 0.000 0.000 0.144
#> GSM74400 1 0.0000 0.881 1.000 0.000 0.000 0.000
#> GSM74401 1 0.0000 0.881 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM74356 3 0.0000 0.8842 0.000 0.000 1.000 0.000 0.000
#> GSM74357 3 0.0000 0.8842 0.000 0.000 1.000 0.000 0.000
#> GSM74358 3 0.0000 0.8842 0.000 0.000 1.000 0.000 0.000
#> GSM74359 4 0.0404 0.9537 0.012 0.000 0.000 0.988 0.000
#> GSM74360 1 0.3913 0.5471 0.676 0.000 0.000 0.324 0.000
#> GSM74361 3 0.0000 0.8842 0.000 0.000 1.000 0.000 0.000
#> GSM74362 3 0.2471 0.7541 0.136 0.000 0.864 0.000 0.000
#> GSM74363 3 0.0000 0.8842 0.000 0.000 1.000 0.000 0.000
#> GSM74402 4 0.0510 0.9504 0.016 0.000 0.000 0.984 0.000
#> GSM74403 4 0.0000 0.9617 0.000 0.000 0.000 1.000 0.000
#> GSM74404 4 0.0000 0.9617 0.000 0.000 0.000 1.000 0.000
#> GSM74406 4 0.0000 0.9617 0.000 0.000 0.000 1.000 0.000
#> GSM74407 1 0.3730 0.6357 0.712 0.000 0.288 0.000 0.000
#> GSM74408 4 0.0000 0.9617 0.000 0.000 0.000 1.000 0.000
#> GSM74409 4 0.0000 0.9617 0.000 0.000 0.000 1.000 0.000
#> GSM74410 4 0.0000 0.9617 0.000 0.000 0.000 1.000 0.000
#> GSM119936 4 0.0000 0.9617 0.000 0.000 0.000 1.000 0.000
#> GSM119937 1 0.2329 0.8273 0.876 0.000 0.000 0.124 0.000
#> GSM74411 3 0.5740 0.6429 0.000 0.244 0.612 0.000 0.144
#> GSM74412 2 0.2561 0.7946 0.000 0.856 0.000 0.000 0.144
#> GSM74413 3 0.2719 0.8739 0.000 0.004 0.852 0.000 0.144
#> GSM74414 5 0.0290 0.7894 0.000 0.008 0.000 0.000 0.992
#> GSM74415 3 0.2561 0.8751 0.000 0.000 0.856 0.000 0.144
#> GSM121379 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM121380 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM121381 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM121382 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM121383 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM121384 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM121385 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM121386 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM121387 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM121388 2 0.0771 0.9453 0.000 0.976 0.020 0.000 0.004
#> GSM121389 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM121390 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM121391 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM121392 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM121393 5 0.5967 0.0646 0.000 0.436 0.108 0.000 0.456
#> GSM121394 2 0.0162 0.9627 0.000 0.996 0.000 0.000 0.004
#> GSM121395 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM121396 3 0.4457 0.4904 0.000 0.368 0.620 0.000 0.012
#> GSM121397 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM121398 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM121399 2 0.0000 0.9660 0.000 1.000 0.000 0.000 0.000
#> GSM74240 3 0.2516 0.8768 0.000 0.000 0.860 0.000 0.140
#> GSM74241 3 0.2516 0.8768 0.000 0.000 0.860 0.000 0.140
#> GSM74242 3 0.0000 0.8842 0.000 0.000 1.000 0.000 0.000
#> GSM74243 3 0.0000 0.8842 0.000 0.000 1.000 0.000 0.000
#> GSM74244 3 0.2516 0.8768 0.000 0.000 0.860 0.000 0.140
#> GSM74245 3 0.2471 0.8780 0.000 0.000 0.864 0.000 0.136
#> GSM74246 3 0.2719 0.8739 0.000 0.004 0.852 0.000 0.144
#> GSM74247 3 0.2561 0.8751 0.000 0.000 0.856 0.000 0.144
#> GSM74248 3 0.2516 0.8768 0.000 0.000 0.860 0.000 0.140
#> GSM74416 4 0.0000 0.9617 0.000 0.000 0.000 1.000 0.000
#> GSM74417 4 0.0000 0.9617 0.000 0.000 0.000 1.000 0.000
#> GSM74418 4 0.0000 0.9617 0.000 0.000 0.000 1.000 0.000
#> GSM74419 1 0.2280 0.8241 0.880 0.000 0.000 0.120 0.000
#> GSM121358 3 0.0000 0.8842 0.000 0.000 1.000 0.000 0.000
#> GSM121359 3 0.3424 0.7198 0.000 0.240 0.760 0.000 0.000
#> GSM121360 1 0.0000 0.9147 1.000 0.000 0.000 0.000 0.000
#> GSM121362 1 0.2179 0.8368 0.888 0.000 0.112 0.000 0.000
#> GSM121364 4 0.0000 0.9617 0.000 0.000 0.000 1.000 0.000
#> GSM121365 3 0.0000 0.8842 0.000 0.000 1.000 0.000 0.000
#> GSM121366 3 0.2471 0.8779 0.000 0.000 0.864 0.000 0.136
#> GSM121367 3 0.0510 0.8850 0.000 0.000 0.984 0.000 0.016
#> GSM121370 3 0.2424 0.8785 0.000 0.000 0.868 0.000 0.132
#> GSM121371 3 0.0000 0.8842 0.000 0.000 1.000 0.000 0.000
#> GSM121372 3 0.2471 0.8779 0.000 0.000 0.864 0.000 0.136
#> GSM121373 4 0.4114 0.3514 0.376 0.000 0.000 0.624 0.000
#> GSM121374 4 0.0290 0.9572 0.008 0.000 0.000 0.992 0.000
#> GSM121407 2 0.1041 0.9313 0.000 0.964 0.032 0.000 0.004
#> GSM74387 3 0.5806 0.6250 0.000 0.256 0.600 0.000 0.144
#> GSM74388 2 0.4182 0.2381 0.000 0.600 0.000 0.000 0.400
#> GSM74389 3 0.0000 0.8842 0.000 0.000 1.000 0.000 0.000
#> GSM74390 3 0.0162 0.8835 0.000 0.000 0.996 0.000 0.004
#> GSM74391 1 0.2329 0.8264 0.876 0.000 0.124 0.000 0.000
#> GSM74392 4 0.0290 0.9571 0.008 0.000 0.000 0.992 0.000
#> GSM74393 1 0.3876 0.5991 0.684 0.000 0.316 0.000 0.000
#> GSM74394 5 0.0000 0.7891 0.000 0.000 0.000 0.000 1.000
#> GSM74239 1 0.0000 0.9147 1.000 0.000 0.000 0.000 0.000
#> GSM74364 1 0.2377 0.8247 0.872 0.000 0.000 0.128 0.000
#> GSM74365 1 0.0000 0.9147 1.000 0.000 0.000 0.000 0.000
#> GSM74366 5 0.0162 0.7909 0.004 0.000 0.000 0.000 0.996
#> GSM74367 1 0.0000 0.9147 1.000 0.000 0.000 0.000 0.000
#> GSM74377 5 0.2561 0.7994 0.144 0.000 0.000 0.000 0.856
#> GSM74378 5 0.2471 0.8019 0.136 0.000 0.000 0.000 0.864
#> GSM74379 1 0.0000 0.9147 1.000 0.000 0.000 0.000 0.000
#> GSM74380 1 0.0290 0.9089 0.992 0.000 0.000 0.000 0.008
#> GSM74381 5 0.2516 0.8010 0.140 0.000 0.000 0.000 0.860
#> GSM121357 5 0.6219 0.3214 0.000 0.212 0.240 0.000 0.548
#> GSM121361 5 0.0162 0.7890 0.000 0.004 0.000 0.000 0.996
#> GSM121363 5 0.4114 0.3662 0.000 0.376 0.000 0.000 0.624
#> GSM121368 5 0.1908 0.7527 0.000 0.092 0.000 0.000 0.908
#> GSM121369 5 0.0000 0.7891 0.000 0.000 0.000 0.000 1.000
#> GSM74368 1 0.0794 0.8989 0.972 0.000 0.028 0.000 0.000
#> GSM74369 1 0.0000 0.9147 1.000 0.000 0.000 0.000 0.000
#> GSM74370 1 0.0000 0.9147 1.000 0.000 0.000 0.000 0.000
#> GSM74371 1 0.4219 0.3149 0.584 0.000 0.000 0.416 0.000
#> GSM74372 1 0.0000 0.9147 1.000 0.000 0.000 0.000 0.000
#> GSM74373 5 0.4256 0.3769 0.436 0.000 0.000 0.000 0.564
#> GSM74374 1 0.0000 0.9147 1.000 0.000 0.000 0.000 0.000
#> GSM74375 1 0.0000 0.9147 1.000 0.000 0.000 0.000 0.000
#> GSM74376 5 0.2561 0.7994 0.144 0.000 0.000 0.000 0.856
#> GSM74405 5 0.2561 0.7994 0.144 0.000 0.000 0.000 0.856
#> GSM74351 4 0.0000 0.9617 0.000 0.000 0.000 1.000 0.000
#> GSM74352 5 0.2516 0.8010 0.140 0.000 0.000 0.000 0.860
#> GSM74353 1 0.0000 0.9147 1.000 0.000 0.000 0.000 0.000
#> GSM74354 1 0.0000 0.9147 1.000 0.000 0.000 0.000 0.000
#> GSM74355 5 0.2471 0.8019 0.136 0.000 0.000 0.000 0.864
#> GSM74382 1 0.3452 0.6866 0.756 0.000 0.000 0.244 0.000
#> GSM74383 1 0.0000 0.9147 1.000 0.000 0.000 0.000 0.000
#> GSM74384 5 0.0162 0.7909 0.004 0.000 0.000 0.000 0.996
#> GSM74385 4 0.2127 0.8502 0.108 0.000 0.000 0.892 0.000
#> GSM74386 1 0.0000 0.9147 1.000 0.000 0.000 0.000 0.000
#> GSM74395 1 0.0000 0.9147 1.000 0.000 0.000 0.000 0.000
#> GSM74396 1 0.0000 0.9147 1.000 0.000 0.000 0.000 0.000
#> GSM74397 1 0.0000 0.9147 1.000 0.000 0.000 0.000 0.000
#> GSM74398 1 0.0000 0.9147 1.000 0.000 0.000 0.000 0.000
#> GSM74399 5 0.4305 0.2361 0.488 0.000 0.000 0.000 0.512
#> GSM74400 1 0.0000 0.9147 1.000 0.000 0.000 0.000 0.000
#> GSM74401 1 0.0000 0.9147 1.000 0.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM74356 3 0.0000 0.919 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM74357 3 0.0000 0.919 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM74358 3 0.0000 0.919 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM74359 4 0.0363 0.953 0.012 0.000 0.000 0.988 0.000 0.000
#> GSM74360 1 0.3288 0.621 0.724 0.000 0.000 0.276 0.000 0.000
#> GSM74361 3 0.0000 0.919 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM74362 3 0.1204 0.867 0.056 0.000 0.944 0.000 0.000 0.000
#> GSM74363 3 0.0000 0.919 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM74402 4 0.0458 0.950 0.016 0.000 0.000 0.984 0.000 0.000
#> GSM74403 4 0.0000 0.961 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM74404 4 0.0000 0.961 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM74406 4 0.0000 0.961 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM74407 1 0.3409 0.592 0.700 0.000 0.300 0.000 0.000 0.000
#> GSM74408 4 0.0000 0.961 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM74409 4 0.0000 0.961 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM74410 4 0.0000 0.961 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM119936 4 0.0000 0.961 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM119937 1 0.0458 0.922 0.984 0.000 0.000 0.016 0.000 0.000
#> GSM74411 5 0.0000 0.965 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74412 5 0.0000 0.965 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74413 5 0.0000 0.965 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74414 6 0.2092 0.752 0.000 0.000 0.000 0.000 0.124 0.876
#> GSM74415 5 0.0000 0.965 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM121379 2 0.0000 0.975 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121380 2 0.0000 0.975 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121381 2 0.0000 0.975 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121382 2 0.0000 0.975 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121383 2 0.0000 0.975 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121384 2 0.0000 0.975 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121385 2 0.0000 0.975 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121386 2 0.0000 0.975 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121387 2 0.0000 0.975 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121388 2 0.0260 0.968 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM121389 2 0.0000 0.975 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121390 2 0.0000 0.975 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121391 2 0.0000 0.975 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121392 2 0.0000 0.975 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121393 6 0.3961 0.132 0.000 0.440 0.004 0.000 0.000 0.556
#> GSM121394 2 0.0000 0.975 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121395 2 0.0000 0.975 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121396 3 0.3860 0.685 0.000 0.236 0.728 0.000 0.036 0.000
#> GSM121397 2 0.0000 0.975 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121398 2 0.0000 0.975 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121399 2 0.0000 0.975 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM74240 5 0.0000 0.965 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74241 5 0.0000 0.965 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74242 3 0.0000 0.919 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM74243 3 0.0000 0.919 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM74244 5 0.0000 0.965 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74245 5 0.0547 0.947 0.000 0.000 0.020 0.000 0.980 0.000
#> GSM74246 5 0.0000 0.965 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74247 5 0.0000 0.965 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74248 5 0.0000 0.965 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM74416 4 0.0000 0.961 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM74417 4 0.0000 0.961 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM74418 4 0.0000 0.961 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM74419 1 0.1957 0.840 0.888 0.000 0.000 0.112 0.000 0.000
#> GSM121358 3 0.0000 0.919 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121359 3 0.3558 0.678 0.000 0.248 0.736 0.000 0.016 0.000
#> GSM121360 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM121362 1 0.0632 0.917 0.976 0.000 0.024 0.000 0.000 0.000
#> GSM121364 4 0.0000 0.961 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM121365 3 0.0000 0.919 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121366 3 0.3109 0.725 0.000 0.004 0.772 0.000 0.224 0.000
#> GSM121367 3 0.0547 0.908 0.000 0.000 0.980 0.000 0.020 0.000
#> GSM121370 3 0.2941 0.731 0.000 0.000 0.780 0.000 0.220 0.000
#> GSM121371 3 0.0000 0.919 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM121372 3 0.3221 0.671 0.000 0.000 0.736 0.000 0.264 0.000
#> GSM121373 4 0.3695 0.368 0.376 0.000 0.000 0.624 0.000 0.000
#> GSM121374 4 0.0260 0.957 0.008 0.000 0.000 0.992 0.000 0.000
#> GSM121407 2 0.0713 0.946 0.000 0.972 0.028 0.000 0.000 0.000
#> GSM74387 5 0.0790 0.932 0.000 0.032 0.000 0.000 0.968 0.000
#> GSM74388 2 0.3756 0.234 0.000 0.600 0.000 0.000 0.000 0.400
#> GSM74389 3 0.0000 0.919 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM74390 3 0.0000 0.919 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM74391 1 0.1444 0.880 0.928 0.000 0.072 0.000 0.000 0.000
#> GSM74392 4 0.0260 0.957 0.008 0.000 0.000 0.992 0.000 0.000
#> GSM74393 1 0.3823 0.304 0.564 0.000 0.436 0.000 0.000 0.000
#> GSM74394 6 0.3756 0.350 0.000 0.000 0.000 0.000 0.400 0.600
#> GSM74239 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM74364 1 0.0865 0.908 0.964 0.000 0.000 0.036 0.000 0.000
#> GSM74365 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM74366 6 0.0000 0.815 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM74367 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM74377 6 0.0363 0.811 0.012 0.000 0.000 0.000 0.000 0.988
#> GSM74378 6 0.0000 0.815 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM74379 1 0.0146 0.929 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM74380 1 0.0363 0.923 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM74381 6 0.0000 0.815 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM121357 5 0.5430 0.546 0.000 0.188 0.028 0.000 0.644 0.140
#> GSM121361 6 0.2491 0.716 0.000 0.000 0.000 0.000 0.164 0.836
#> GSM121363 6 0.4018 0.484 0.000 0.324 0.000 0.000 0.020 0.656
#> GSM121368 6 0.3570 0.707 0.000 0.064 0.000 0.000 0.144 0.792
#> GSM121369 6 0.1501 0.780 0.000 0.000 0.000 0.000 0.076 0.924
#> GSM74368 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM74369 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM74370 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM74371 1 0.3782 0.305 0.588 0.000 0.000 0.412 0.000 0.000
#> GSM74372 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM74373 6 0.3804 0.322 0.424 0.000 0.000 0.000 0.000 0.576
#> GSM74374 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM74375 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM74376 6 0.0000 0.815 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM74405 6 0.0000 0.815 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM74351 4 0.0000 0.961 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM74352 6 0.0000 0.815 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM74353 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM74354 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM74355 6 0.0000 0.815 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM74382 1 0.3076 0.681 0.760 0.000 0.000 0.240 0.000 0.000
#> GSM74383 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM74384 6 0.0000 0.815 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM74385 4 0.1910 0.849 0.108 0.000 0.000 0.892 0.000 0.000
#> GSM74386 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM74395 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM74396 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM74397 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM74398 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM74399 6 0.3864 0.164 0.480 0.000 0.000 0.000 0.000 0.520
#> GSM74400 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM74401 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) k
#> ATC:pam 119 9.88e-12 2
#> ATC:pam 116 1.99e-18 3
#> ATC:pam 113 1.64e-25 4
#> ATC:pam 112 2.20e-27 5
#> ATC:pam 112 2.66e-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", "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 21168 rows and 121 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 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 0.533 0.895 0.915 0.4416 0.506 0.506
#> 3 3 0.710 0.931 0.945 0.3871 0.809 0.648
#> 4 4 0.787 0.897 0.921 0.1965 0.844 0.613
#> 5 5 0.850 0.830 0.913 0.0657 0.908 0.675
#> 6 6 0.820 0.791 0.847 0.0321 0.932 0.720
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
#> GSM74356 2 0.8081 0.858 0.248 0.752
#> GSM74357 2 0.8144 0.854 0.252 0.748
#> GSM74358 2 0.8081 0.858 0.248 0.752
#> GSM74359 1 0.2778 0.937 0.952 0.048
#> GSM74360 1 0.2603 0.940 0.956 0.044
#> GSM74361 2 0.9983 0.396 0.476 0.524
#> GSM74362 1 0.5294 0.855 0.880 0.120
#> GSM74363 2 0.8081 0.858 0.248 0.752
#> GSM74402 1 0.0000 0.968 1.000 0.000
#> GSM74403 1 0.0000 0.968 1.000 0.000
#> GSM74404 1 0.0000 0.968 1.000 0.000
#> GSM74406 1 0.0000 0.968 1.000 0.000
#> GSM74407 1 0.4562 0.870 0.904 0.096
#> GSM74408 1 0.0000 0.968 1.000 0.000
#> GSM74409 1 0.0376 0.966 0.996 0.004
#> GSM74410 1 0.0672 0.965 0.992 0.008
#> GSM119936 1 0.0000 0.968 1.000 0.000
#> GSM119937 1 0.0000 0.968 1.000 0.000
#> GSM74411 2 0.8081 0.858 0.248 0.752
#> GSM74412 2 0.8081 0.858 0.248 0.752
#> GSM74413 2 0.8081 0.858 0.248 0.752
#> GSM74414 2 0.9129 0.752 0.328 0.672
#> GSM74415 2 0.8081 0.858 0.248 0.752
#> GSM121379 2 0.0000 0.807 0.000 1.000
#> GSM121380 2 0.0000 0.807 0.000 1.000
#> GSM121381 2 0.0000 0.807 0.000 1.000
#> GSM121382 2 0.0000 0.807 0.000 1.000
#> GSM121383 2 0.0000 0.807 0.000 1.000
#> GSM121384 2 0.0000 0.807 0.000 1.000
#> GSM121385 2 0.0000 0.807 0.000 1.000
#> GSM121386 2 0.0000 0.807 0.000 1.000
#> GSM121387 2 0.0000 0.807 0.000 1.000
#> GSM121388 2 0.4298 0.827 0.088 0.912
#> GSM121389 2 0.0000 0.807 0.000 1.000
#> GSM121390 2 0.0000 0.807 0.000 1.000
#> GSM121391 2 0.0000 0.807 0.000 1.000
#> GSM121392 2 0.2603 0.798 0.044 0.956
#> GSM121393 2 0.5842 0.735 0.140 0.860
#> GSM121394 2 0.0000 0.807 0.000 1.000
#> GSM121395 2 0.0000 0.807 0.000 1.000
#> GSM121396 2 0.8081 0.858 0.248 0.752
#> GSM121397 2 0.0000 0.807 0.000 1.000
#> GSM121398 2 0.0000 0.807 0.000 1.000
#> GSM121399 2 0.0000 0.807 0.000 1.000
#> GSM74240 2 0.8081 0.858 0.248 0.752
#> GSM74241 2 0.8081 0.858 0.248 0.752
#> GSM74242 2 0.8081 0.858 0.248 0.752
#> GSM74243 2 0.8081 0.858 0.248 0.752
#> GSM74244 2 0.8081 0.858 0.248 0.752
#> GSM74245 2 0.8081 0.858 0.248 0.752
#> GSM74246 2 0.8081 0.858 0.248 0.752
#> GSM74247 2 0.8081 0.858 0.248 0.752
#> GSM74248 2 0.8081 0.858 0.248 0.752
#> GSM74416 1 0.0000 0.968 1.000 0.000
#> GSM74417 1 0.0000 0.968 1.000 0.000
#> GSM74418 1 0.0000 0.968 1.000 0.000
#> GSM74419 1 0.0376 0.967 0.996 0.004
#> GSM121358 2 0.8081 0.858 0.248 0.752
#> GSM121359 2 0.8081 0.858 0.248 0.752
#> GSM121360 1 0.2778 0.937 0.952 0.048
#> GSM121362 1 0.2778 0.937 0.952 0.048
#> GSM121364 1 0.2778 0.937 0.952 0.048
#> GSM121365 2 0.8081 0.858 0.248 0.752
#> GSM121366 2 0.8081 0.858 0.248 0.752
#> GSM121367 2 0.8081 0.858 0.248 0.752
#> GSM121370 2 0.8081 0.858 0.248 0.752
#> GSM121371 2 0.8081 0.858 0.248 0.752
#> GSM121372 2 0.8081 0.858 0.248 0.752
#> GSM121373 1 0.2778 0.937 0.952 0.048
#> GSM121374 1 0.2778 0.937 0.952 0.048
#> GSM121407 2 0.8081 0.858 0.248 0.752
#> GSM74387 2 0.8081 0.858 0.248 0.752
#> GSM74388 1 0.5737 0.842 0.864 0.136
#> GSM74389 2 0.8081 0.858 0.248 0.752
#> GSM74390 1 0.6247 0.802 0.844 0.156
#> GSM74391 1 0.0672 0.965 0.992 0.008
#> GSM74392 1 0.2778 0.937 0.952 0.048
#> GSM74393 1 0.3733 0.913 0.928 0.072
#> GSM74394 1 0.6048 0.825 0.852 0.148
#> GSM74239 1 0.0000 0.968 1.000 0.000
#> GSM74364 1 0.0000 0.968 1.000 0.000
#> GSM74365 1 0.0000 0.968 1.000 0.000
#> GSM74366 1 0.0672 0.965 0.992 0.008
#> GSM74367 1 0.0000 0.968 1.000 0.000
#> GSM74377 1 0.0672 0.965 0.992 0.008
#> GSM74378 1 0.0672 0.965 0.992 0.008
#> GSM74379 1 0.0672 0.965 0.992 0.008
#> GSM74380 1 0.0672 0.965 0.992 0.008
#> GSM74381 1 0.0672 0.965 0.992 0.008
#> GSM121357 2 0.8081 0.858 0.248 0.752
#> GSM121361 1 0.5737 0.842 0.864 0.136
#> GSM121363 1 0.5737 0.842 0.864 0.136
#> GSM121368 1 0.5737 0.842 0.864 0.136
#> GSM121369 1 0.5294 0.855 0.880 0.120
#> GSM74368 1 0.0000 0.968 1.000 0.000
#> GSM74369 1 0.0000 0.968 1.000 0.000
#> GSM74370 1 0.0000 0.968 1.000 0.000
#> GSM74371 1 0.0000 0.968 1.000 0.000
#> GSM74372 1 0.0000 0.968 1.000 0.000
#> GSM74373 1 0.0672 0.965 0.992 0.008
#> GSM74374 1 0.0000 0.968 1.000 0.000
#> GSM74375 1 0.0376 0.966 0.996 0.004
#> GSM74376 1 0.0672 0.965 0.992 0.008
#> GSM74405 1 0.0672 0.965 0.992 0.008
#> GSM74351 1 0.0000 0.968 1.000 0.000
#> GSM74352 1 0.0672 0.965 0.992 0.008
#> GSM74353 1 0.0000 0.968 1.000 0.000
#> GSM74354 1 0.0000 0.968 1.000 0.000
#> GSM74355 1 0.0672 0.965 0.992 0.008
#> GSM74382 1 0.0000 0.968 1.000 0.000
#> GSM74383 1 0.0000 0.968 1.000 0.000
#> GSM74384 1 0.0672 0.965 0.992 0.008
#> GSM74385 1 0.0000 0.968 1.000 0.000
#> GSM74386 1 0.0000 0.968 1.000 0.000
#> GSM74395 1 0.0000 0.968 1.000 0.000
#> GSM74396 1 0.0000 0.968 1.000 0.000
#> GSM74397 1 0.0000 0.968 1.000 0.000
#> GSM74398 1 0.0000 0.968 1.000 0.000
#> GSM74399 1 0.0672 0.965 0.992 0.008
#> GSM74400 1 0.0000 0.968 1.000 0.000
#> GSM74401 1 0.0000 0.968 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM74356 3 0.0892 0.950 0.000 0.020 0.980
#> GSM74357 3 0.0892 0.950 0.000 0.020 0.980
#> GSM74358 3 0.0592 0.955 0.000 0.012 0.988
#> GSM74359 1 0.6176 0.811 0.780 0.100 0.120
#> GSM74360 1 0.3832 0.884 0.880 0.100 0.020
#> GSM74361 3 0.1170 0.950 0.008 0.016 0.976
#> GSM74362 3 0.2527 0.920 0.020 0.044 0.936
#> GSM74363 3 0.0747 0.953 0.000 0.016 0.984
#> GSM74402 1 0.2878 0.892 0.904 0.000 0.096
#> GSM74403 1 0.0000 0.952 1.000 0.000 0.000
#> GSM74404 1 0.0000 0.952 1.000 0.000 0.000
#> GSM74406 1 0.2878 0.892 0.904 0.000 0.096
#> GSM74407 1 0.5726 0.754 0.760 0.024 0.216
#> GSM74408 1 0.3112 0.891 0.900 0.004 0.096
#> GSM74409 1 0.3112 0.891 0.900 0.004 0.096
#> GSM74410 1 0.3112 0.891 0.900 0.004 0.096
#> GSM119936 1 0.2878 0.892 0.904 0.000 0.096
#> GSM119937 1 0.0237 0.952 0.996 0.004 0.000
#> GSM74411 3 0.0892 0.951 0.000 0.020 0.980
#> GSM74412 3 0.1411 0.943 0.000 0.036 0.964
#> GSM74413 3 0.1163 0.947 0.000 0.028 0.972
#> GSM74414 3 0.4270 0.844 0.116 0.024 0.860
#> GSM74415 3 0.0237 0.959 0.000 0.004 0.996
#> GSM121379 2 0.2959 0.983 0.000 0.900 0.100
#> GSM121380 2 0.2959 0.983 0.000 0.900 0.100
#> GSM121381 2 0.2959 0.983 0.000 0.900 0.100
#> GSM121382 2 0.2959 0.983 0.000 0.900 0.100
#> GSM121383 2 0.2959 0.983 0.000 0.900 0.100
#> GSM121384 2 0.2959 0.983 0.000 0.900 0.100
#> GSM121385 2 0.2959 0.983 0.000 0.900 0.100
#> GSM121386 2 0.2959 0.983 0.000 0.900 0.100
#> GSM121387 2 0.2959 0.983 0.000 0.900 0.100
#> GSM121388 2 0.4002 0.920 0.000 0.840 0.160
#> GSM121389 2 0.2959 0.983 0.000 0.900 0.100
#> GSM121390 2 0.2959 0.983 0.000 0.900 0.100
#> GSM121391 2 0.2959 0.983 0.000 0.900 0.100
#> GSM121392 2 0.4540 0.937 0.028 0.848 0.124
#> GSM121393 2 0.4326 0.801 0.144 0.844 0.012
#> GSM121394 2 0.2959 0.983 0.000 0.900 0.100
#> GSM121395 2 0.3038 0.979 0.000 0.896 0.104
#> GSM121396 3 0.2448 0.902 0.000 0.076 0.924
#> GSM121397 2 0.2959 0.983 0.000 0.900 0.100
#> GSM121398 2 0.2959 0.983 0.000 0.900 0.100
#> GSM121399 2 0.2959 0.983 0.000 0.900 0.100
#> GSM74240 3 0.0237 0.959 0.000 0.004 0.996
#> GSM74241 3 0.0000 0.958 0.000 0.000 1.000
#> GSM74242 3 0.0237 0.959 0.000 0.004 0.996
#> GSM74243 3 0.0237 0.959 0.000 0.004 0.996
#> GSM74244 3 0.0237 0.959 0.000 0.004 0.996
#> GSM74245 3 0.0237 0.959 0.000 0.004 0.996
#> GSM74246 3 0.0237 0.959 0.000 0.004 0.996
#> GSM74247 3 0.0237 0.959 0.000 0.004 0.996
#> GSM74248 3 0.0237 0.959 0.000 0.004 0.996
#> GSM74416 1 0.0000 0.952 1.000 0.000 0.000
#> GSM74417 1 0.0000 0.952 1.000 0.000 0.000
#> GSM74418 1 0.0000 0.952 1.000 0.000 0.000
#> GSM74419 1 0.3112 0.891 0.900 0.004 0.096
#> GSM121358 3 0.0237 0.959 0.000 0.004 0.996
#> GSM121359 3 0.0424 0.958 0.000 0.008 0.992
#> GSM121360 1 0.3910 0.881 0.876 0.104 0.020
#> GSM121362 1 0.3966 0.881 0.876 0.100 0.024
#> GSM121364 1 0.6176 0.811 0.780 0.100 0.120
#> GSM121365 3 0.0424 0.957 0.000 0.008 0.992
#> GSM121366 3 0.0237 0.959 0.000 0.004 0.996
#> GSM121367 3 0.0237 0.959 0.000 0.004 0.996
#> GSM121370 3 0.0237 0.959 0.000 0.004 0.996
#> GSM121371 3 0.0237 0.959 0.000 0.004 0.996
#> GSM121372 3 0.0424 0.958 0.000 0.008 0.992
#> GSM121373 1 0.3690 0.887 0.884 0.100 0.016
#> GSM121374 1 0.6176 0.811 0.780 0.100 0.120
#> GSM121407 3 0.1163 0.950 0.000 0.028 0.972
#> GSM74387 3 0.0237 0.959 0.000 0.004 0.996
#> GSM74388 3 0.3846 0.859 0.108 0.016 0.876
#> GSM74389 3 0.0424 0.957 0.000 0.008 0.992
#> GSM74390 3 0.0237 0.958 0.000 0.004 0.996
#> GSM74391 1 0.4335 0.874 0.864 0.036 0.100
#> GSM74392 1 0.6176 0.811 0.780 0.100 0.120
#> GSM74393 1 0.7091 0.335 0.560 0.024 0.416
#> GSM74394 3 0.3921 0.855 0.112 0.016 0.872
#> GSM74239 1 0.0000 0.952 1.000 0.000 0.000
#> GSM74364 1 0.0000 0.952 1.000 0.000 0.000
#> GSM74365 1 0.0000 0.952 1.000 0.000 0.000
#> GSM74366 1 0.0237 0.952 0.996 0.004 0.000
#> GSM74367 1 0.0000 0.952 1.000 0.000 0.000
#> GSM74377 1 0.0000 0.952 1.000 0.000 0.000
#> GSM74378 1 0.0237 0.952 0.996 0.004 0.000
#> GSM74379 1 0.0000 0.952 1.000 0.000 0.000
#> GSM74380 1 0.0237 0.952 0.996 0.004 0.000
#> GSM74381 1 0.0237 0.952 0.996 0.004 0.000
#> GSM121357 3 0.1636 0.943 0.020 0.016 0.964
#> GSM121361 3 0.3846 0.859 0.108 0.016 0.876
#> GSM121363 3 0.3846 0.859 0.108 0.016 0.876
#> GSM121368 3 0.3846 0.859 0.108 0.016 0.876
#> GSM121369 3 0.4196 0.848 0.112 0.024 0.864
#> GSM74368 1 0.0237 0.952 0.996 0.004 0.000
#> GSM74369 1 0.0000 0.952 1.000 0.000 0.000
#> GSM74370 1 0.0237 0.952 0.996 0.004 0.000
#> GSM74371 1 0.0000 0.952 1.000 0.000 0.000
#> GSM74372 1 0.0237 0.952 0.996 0.004 0.000
#> GSM74373 1 0.0000 0.952 1.000 0.000 0.000
#> GSM74374 1 0.0000 0.952 1.000 0.000 0.000
#> GSM74375 1 0.0000 0.952 1.000 0.000 0.000
#> GSM74376 1 0.0237 0.952 0.996 0.004 0.000
#> GSM74405 1 0.0237 0.952 0.996 0.004 0.000
#> GSM74351 1 0.0000 0.952 1.000 0.000 0.000
#> GSM74352 1 0.0000 0.952 1.000 0.000 0.000
#> GSM74353 1 0.0000 0.952 1.000 0.000 0.000
#> GSM74354 1 0.0000 0.952 1.000 0.000 0.000
#> GSM74355 1 0.0000 0.952 1.000 0.000 0.000
#> GSM74382 1 0.0000 0.952 1.000 0.000 0.000
#> GSM74383 1 0.0000 0.952 1.000 0.000 0.000
#> GSM74384 1 0.0237 0.952 0.996 0.004 0.000
#> GSM74385 1 0.0000 0.952 1.000 0.000 0.000
#> GSM74386 1 0.0237 0.952 0.996 0.004 0.000
#> GSM74395 1 0.0237 0.952 0.996 0.004 0.000
#> GSM74396 1 0.0237 0.952 0.996 0.004 0.000
#> GSM74397 1 0.0237 0.952 0.996 0.004 0.000
#> GSM74398 1 0.0000 0.952 1.000 0.000 0.000
#> GSM74399 1 0.0000 0.952 1.000 0.000 0.000
#> GSM74400 1 0.0237 0.952 0.996 0.004 0.000
#> GSM74401 1 0.0000 0.952 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM74356 3 0.0188 0.925 0.000 0.004 0.996 0.000
#> GSM74357 3 0.0188 0.925 0.000 0.004 0.996 0.000
#> GSM74358 3 0.0188 0.925 0.000 0.004 0.996 0.000
#> GSM74359 4 0.1191 0.853 0.004 0.024 0.004 0.968
#> GSM74360 4 0.1920 0.863 0.028 0.024 0.004 0.944
#> GSM74361 3 0.2197 0.900 0.000 0.004 0.916 0.080
#> GSM74362 3 0.2976 0.881 0.000 0.008 0.872 0.120
#> GSM74363 3 0.0188 0.925 0.000 0.004 0.996 0.000
#> GSM74402 4 0.2530 0.912 0.112 0.000 0.000 0.888
#> GSM74403 4 0.2704 0.910 0.124 0.000 0.000 0.876
#> GSM74404 4 0.2760 0.909 0.128 0.000 0.000 0.872
#> GSM74406 4 0.2408 0.912 0.104 0.000 0.000 0.896
#> GSM74407 3 0.4731 0.826 0.100 0.004 0.800 0.096
#> GSM74408 4 0.2469 0.910 0.108 0.000 0.000 0.892
#> GSM74409 4 0.2197 0.895 0.080 0.004 0.000 0.916
#> GSM74410 4 0.2053 0.890 0.072 0.004 0.000 0.924
#> GSM119936 4 0.2408 0.912 0.104 0.000 0.000 0.896
#> GSM119937 4 0.2921 0.906 0.140 0.000 0.000 0.860
#> GSM74411 3 0.2281 0.890 0.000 0.096 0.904 0.000
#> GSM74412 3 0.2408 0.885 0.000 0.104 0.896 0.000
#> GSM74413 3 0.2408 0.885 0.000 0.104 0.896 0.000
#> GSM74414 3 0.4709 0.863 0.044 0.052 0.824 0.080
#> GSM74415 3 0.2011 0.899 0.000 0.080 0.920 0.000
#> GSM121379 2 0.0921 0.975 0.000 0.972 0.028 0.000
#> GSM121380 2 0.0921 0.975 0.000 0.972 0.028 0.000
#> GSM121381 2 0.0921 0.975 0.000 0.972 0.028 0.000
#> GSM121382 2 0.0921 0.975 0.000 0.972 0.028 0.000
#> GSM121383 2 0.0921 0.975 0.000 0.972 0.028 0.000
#> GSM121384 2 0.0921 0.975 0.000 0.972 0.028 0.000
#> GSM121385 2 0.0921 0.975 0.000 0.972 0.028 0.000
#> GSM121386 2 0.0921 0.975 0.000 0.972 0.028 0.000
#> GSM121387 2 0.0921 0.975 0.000 0.972 0.028 0.000
#> GSM121388 2 0.3801 0.733 0.000 0.780 0.220 0.000
#> GSM121389 2 0.0921 0.975 0.000 0.972 0.028 0.000
#> GSM121390 2 0.0921 0.975 0.000 0.972 0.028 0.000
#> GSM121391 2 0.0921 0.975 0.000 0.972 0.028 0.000
#> GSM121392 2 0.3372 0.888 0.000 0.868 0.036 0.096
#> GSM121393 2 0.3900 0.829 0.096 0.848 0.004 0.052
#> GSM121394 2 0.0921 0.975 0.000 0.972 0.028 0.000
#> GSM121395 2 0.1022 0.972 0.000 0.968 0.032 0.000
#> GSM121396 3 0.1022 0.920 0.000 0.032 0.968 0.000
#> GSM121397 2 0.0921 0.975 0.000 0.972 0.028 0.000
#> GSM121398 2 0.0921 0.975 0.000 0.972 0.028 0.000
#> GSM121399 2 0.0921 0.975 0.000 0.972 0.028 0.000
#> GSM74240 3 0.0000 0.926 0.000 0.000 1.000 0.000
#> GSM74241 3 0.0592 0.923 0.000 0.016 0.984 0.000
#> GSM74242 3 0.0000 0.926 0.000 0.000 1.000 0.000
#> GSM74243 3 0.0000 0.926 0.000 0.000 1.000 0.000
#> GSM74244 3 0.0000 0.926 0.000 0.000 1.000 0.000
#> GSM74245 3 0.0000 0.926 0.000 0.000 1.000 0.000
#> GSM74246 3 0.0000 0.926 0.000 0.000 1.000 0.000
#> GSM74247 3 0.0469 0.924 0.000 0.012 0.988 0.000
#> GSM74248 3 0.0000 0.926 0.000 0.000 1.000 0.000
#> GSM74416 4 0.2647 0.912 0.120 0.000 0.000 0.880
#> GSM74417 4 0.2647 0.912 0.120 0.000 0.000 0.880
#> GSM74418 4 0.2647 0.912 0.120 0.000 0.000 0.880
#> GSM74419 4 0.2334 0.898 0.088 0.004 0.000 0.908
#> GSM121358 3 0.0000 0.926 0.000 0.000 1.000 0.000
#> GSM121359 3 0.0707 0.922 0.000 0.020 0.980 0.000
#> GSM121360 1 0.4321 0.775 0.796 0.024 0.004 0.176
#> GSM121362 1 0.5696 0.426 0.592 0.024 0.004 0.380
#> GSM121364 4 0.1191 0.853 0.004 0.024 0.004 0.968
#> GSM121365 3 0.0000 0.926 0.000 0.000 1.000 0.000
#> GSM121366 3 0.0000 0.926 0.000 0.000 1.000 0.000
#> GSM121367 3 0.0000 0.926 0.000 0.000 1.000 0.000
#> GSM121370 3 0.0000 0.926 0.000 0.000 1.000 0.000
#> GSM121371 3 0.0000 0.926 0.000 0.000 1.000 0.000
#> GSM121372 3 0.0469 0.924 0.000 0.012 0.988 0.000
#> GSM121373 4 0.1284 0.861 0.012 0.024 0.000 0.964
#> GSM121374 4 0.1191 0.853 0.004 0.024 0.004 0.968
#> GSM121407 3 0.1792 0.906 0.000 0.068 0.932 0.000
#> GSM74387 3 0.0921 0.920 0.000 0.028 0.972 0.000
#> GSM74388 3 0.6329 0.767 0.144 0.052 0.720 0.084
#> GSM74389 3 0.0188 0.925 0.000 0.004 0.996 0.000
#> GSM74390 3 0.2125 0.902 0.004 0.000 0.920 0.076
#> GSM74391 3 0.5272 0.792 0.096 0.004 0.760 0.140
#> GSM74392 4 0.1004 0.854 0.004 0.024 0.000 0.972
#> GSM74393 3 0.2976 0.879 0.000 0.008 0.872 0.120
#> GSM74394 3 0.6508 0.757 0.144 0.052 0.708 0.096
#> GSM74239 1 0.1389 0.934 0.952 0.000 0.000 0.048
#> GSM74364 4 0.2647 0.912 0.120 0.000 0.000 0.880
#> GSM74365 1 0.0469 0.954 0.988 0.000 0.000 0.012
#> GSM74366 1 0.0524 0.950 0.988 0.004 0.000 0.008
#> GSM74367 1 0.0188 0.954 0.996 0.000 0.000 0.004
#> GSM74377 1 0.0592 0.952 0.984 0.000 0.000 0.016
#> GSM74378 1 0.0000 0.955 1.000 0.000 0.000 0.000
#> GSM74379 1 0.0188 0.955 0.996 0.000 0.000 0.004
#> GSM74380 1 0.0000 0.955 1.000 0.000 0.000 0.000
#> GSM74381 1 0.0000 0.955 1.000 0.000 0.000 0.000
#> GSM121357 3 0.2676 0.890 0.000 0.092 0.896 0.012
#> GSM121361 3 0.6252 0.769 0.144 0.048 0.724 0.084
#> GSM121363 3 0.6252 0.769 0.144 0.048 0.724 0.084
#> GSM121368 3 0.6329 0.767 0.144 0.052 0.720 0.084
#> GSM121369 3 0.6269 0.752 0.156 0.024 0.708 0.112
#> GSM74368 1 0.3726 0.710 0.788 0.000 0.000 0.212
#> GSM74369 4 0.3688 0.830 0.208 0.000 0.000 0.792
#> GSM74370 1 0.0336 0.953 0.992 0.000 0.000 0.008
#> GSM74371 4 0.2647 0.912 0.120 0.000 0.000 0.880
#> GSM74372 1 0.0000 0.955 1.000 0.000 0.000 0.000
#> GSM74373 1 0.0592 0.953 0.984 0.000 0.000 0.016
#> GSM74374 1 0.0817 0.949 0.976 0.000 0.000 0.024
#> GSM74375 1 0.0469 0.953 0.988 0.000 0.000 0.012
#> GSM74376 1 0.0000 0.955 1.000 0.000 0.000 0.000
#> GSM74405 1 0.0000 0.955 1.000 0.000 0.000 0.000
#> GSM74351 4 0.2647 0.912 0.120 0.000 0.000 0.880
#> GSM74352 1 0.0707 0.950 0.980 0.000 0.000 0.020
#> GSM74353 1 0.3311 0.776 0.828 0.000 0.000 0.172
#> GSM74354 1 0.0817 0.949 0.976 0.000 0.000 0.024
#> GSM74355 1 0.0000 0.955 1.000 0.000 0.000 0.000
#> GSM74382 4 0.2760 0.909 0.128 0.000 0.000 0.872
#> GSM74383 1 0.0817 0.949 0.976 0.000 0.000 0.024
#> GSM74384 1 0.0188 0.953 0.996 0.004 0.000 0.000
#> GSM74385 4 0.2704 0.910 0.124 0.000 0.000 0.876
#> GSM74386 1 0.0000 0.955 1.000 0.000 0.000 0.000
#> GSM74395 1 0.0188 0.954 0.996 0.000 0.000 0.004
#> GSM74396 1 0.0592 0.949 0.984 0.000 0.000 0.016
#> GSM74397 1 0.1302 0.928 0.956 0.000 0.000 0.044
#> GSM74398 1 0.0188 0.955 0.996 0.000 0.000 0.004
#> GSM74399 1 0.0707 0.950 0.980 0.000 0.000 0.020
#> GSM74400 4 0.5000 0.248 0.496 0.000 0.000 0.504
#> GSM74401 4 0.4888 0.465 0.412 0.000 0.000 0.588
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM74356 3 0.0162 0.9649 0.000 0.000 0.996 0.000 0.004
#> GSM74357 3 0.0162 0.9649 0.000 0.000 0.996 0.000 0.004
#> GSM74358 3 0.0162 0.9649 0.000 0.000 0.996 0.000 0.004
#> GSM74359 4 0.2890 0.7630 0.004 0.000 0.000 0.836 0.160
#> GSM74360 1 0.6296 -0.0815 0.440 0.000 0.000 0.408 0.152
#> GSM74361 5 0.4161 0.5146 0.000 0.000 0.392 0.000 0.608
#> GSM74362 5 0.4251 0.5376 0.000 0.000 0.372 0.004 0.624
#> GSM74363 3 0.0162 0.9649 0.000 0.000 0.996 0.000 0.004
#> GSM74402 4 0.0324 0.8400 0.004 0.000 0.000 0.992 0.004
#> GSM74403 4 0.1410 0.8466 0.060 0.000 0.000 0.940 0.000
#> GSM74404 4 0.1608 0.8418 0.072 0.000 0.000 0.928 0.000
#> GSM74406 4 0.0324 0.8400 0.004 0.000 0.000 0.992 0.004
#> GSM74407 5 0.6587 0.5273 0.008 0.000 0.236 0.236 0.520
#> GSM74408 4 0.0324 0.8400 0.004 0.000 0.000 0.992 0.004
#> GSM74409 4 0.1041 0.8375 0.004 0.000 0.000 0.964 0.032
#> GSM74410 4 0.1041 0.8332 0.004 0.000 0.000 0.964 0.032
#> GSM119936 4 0.0324 0.8400 0.004 0.000 0.000 0.992 0.004
#> GSM119937 4 0.4437 0.0411 0.464 0.000 0.000 0.532 0.004
#> GSM74411 3 0.2513 0.8628 0.000 0.116 0.876 0.000 0.008
#> GSM74412 3 0.2329 0.8547 0.000 0.124 0.876 0.000 0.000
#> GSM74413 3 0.2424 0.8443 0.000 0.132 0.868 0.000 0.000
#> GSM74414 5 0.1787 0.7765 0.012 0.032 0.016 0.000 0.940
#> GSM74415 3 0.2411 0.8722 0.000 0.108 0.884 0.000 0.008
#> GSM121379 2 0.0000 0.9800 0.000 1.000 0.000 0.000 0.000
#> GSM121380 2 0.0000 0.9800 0.000 1.000 0.000 0.000 0.000
#> GSM121381 2 0.0000 0.9800 0.000 1.000 0.000 0.000 0.000
#> GSM121382 2 0.0000 0.9800 0.000 1.000 0.000 0.000 0.000
#> GSM121383 2 0.0000 0.9800 0.000 1.000 0.000 0.000 0.000
#> GSM121384 2 0.0000 0.9800 0.000 1.000 0.000 0.000 0.000
#> GSM121385 2 0.0000 0.9800 0.000 1.000 0.000 0.000 0.000
#> GSM121386 2 0.0000 0.9800 0.000 1.000 0.000 0.000 0.000
#> GSM121387 2 0.0000 0.9800 0.000 1.000 0.000 0.000 0.000
#> GSM121388 2 0.0880 0.9435 0.000 0.968 0.032 0.000 0.000
#> GSM121389 2 0.0000 0.9800 0.000 1.000 0.000 0.000 0.000
#> GSM121390 2 0.0162 0.9762 0.000 0.996 0.000 0.000 0.004
#> GSM121391 2 0.0000 0.9800 0.000 1.000 0.000 0.000 0.000
#> GSM121392 2 0.2674 0.8160 0.004 0.856 0.000 0.000 0.140
#> GSM121393 2 0.3359 0.8185 0.072 0.844 0.000 0.000 0.084
#> GSM121394 2 0.0000 0.9800 0.000 1.000 0.000 0.000 0.000
#> GSM121395 2 0.0000 0.9800 0.000 1.000 0.000 0.000 0.000
#> GSM121396 3 0.0880 0.9480 0.000 0.032 0.968 0.000 0.000
#> GSM121397 2 0.0000 0.9800 0.000 1.000 0.000 0.000 0.000
#> GSM121398 2 0.0000 0.9800 0.000 1.000 0.000 0.000 0.000
#> GSM121399 2 0.0000 0.9800 0.000 1.000 0.000 0.000 0.000
#> GSM74240 3 0.0290 0.9652 0.000 0.000 0.992 0.000 0.008
#> GSM74241 3 0.0693 0.9610 0.000 0.012 0.980 0.000 0.008
#> GSM74242 3 0.0290 0.9652 0.000 0.000 0.992 0.000 0.008
#> GSM74243 3 0.0290 0.9652 0.000 0.000 0.992 0.000 0.008
#> GSM74244 3 0.0290 0.9652 0.000 0.000 0.992 0.000 0.008
#> GSM74245 3 0.0290 0.9652 0.000 0.000 0.992 0.000 0.008
#> GSM74246 3 0.0290 0.9652 0.000 0.000 0.992 0.000 0.008
#> GSM74247 3 0.1082 0.9498 0.000 0.028 0.964 0.000 0.008
#> GSM74248 3 0.0290 0.9652 0.000 0.000 0.992 0.000 0.008
#> GSM74416 4 0.1430 0.8480 0.052 0.000 0.000 0.944 0.004
#> GSM74417 4 0.1430 0.8480 0.052 0.000 0.000 0.944 0.004
#> GSM74418 4 0.1430 0.8480 0.052 0.000 0.000 0.944 0.004
#> GSM74419 4 0.3160 0.6834 0.004 0.000 0.000 0.808 0.188
#> GSM121358 3 0.0000 0.9656 0.000 0.000 1.000 0.000 0.000
#> GSM121359 3 0.0162 0.9653 0.000 0.004 0.996 0.000 0.000
#> GSM121360 1 0.3183 0.7809 0.828 0.000 0.000 0.016 0.156
#> GSM121362 1 0.6542 -0.0542 0.428 0.000 0.000 0.372 0.200
#> GSM121364 4 0.2848 0.7659 0.004 0.000 0.000 0.840 0.156
#> GSM121365 3 0.0162 0.9649 0.000 0.000 0.996 0.000 0.004
#> GSM121366 3 0.0000 0.9656 0.000 0.000 1.000 0.000 0.000
#> GSM121367 3 0.0162 0.9649 0.000 0.000 0.996 0.000 0.004
#> GSM121370 3 0.0000 0.9656 0.000 0.000 1.000 0.000 0.000
#> GSM121371 3 0.0162 0.9649 0.000 0.000 0.996 0.000 0.004
#> GSM121372 3 0.0000 0.9656 0.000 0.000 1.000 0.000 0.000
#> GSM121373 4 0.5526 0.6044 0.200 0.000 0.000 0.648 0.152
#> GSM121374 4 0.2848 0.7659 0.004 0.000 0.000 0.840 0.156
#> GSM121407 3 0.1608 0.9121 0.000 0.072 0.928 0.000 0.000
#> GSM74387 3 0.0992 0.9514 0.000 0.024 0.968 0.000 0.008
#> GSM74388 5 0.1626 0.7804 0.000 0.044 0.016 0.000 0.940
#> GSM74389 3 0.0404 0.9649 0.000 0.000 0.988 0.000 0.012
#> GSM74390 5 0.4126 0.5314 0.000 0.000 0.380 0.000 0.620
#> GSM74391 5 0.4801 0.2500 0.008 0.000 0.012 0.396 0.584
#> GSM74392 4 0.3266 0.7279 0.004 0.000 0.000 0.796 0.200
#> GSM74393 5 0.5589 0.5768 0.004 0.000 0.128 0.220 0.648
#> GSM74394 5 0.1682 0.7774 0.004 0.044 0.012 0.000 0.940
#> GSM74239 1 0.2068 0.8572 0.904 0.000 0.000 0.092 0.004
#> GSM74364 4 0.4415 0.1171 0.444 0.000 0.000 0.552 0.004
#> GSM74365 1 0.0324 0.8966 0.992 0.000 0.000 0.004 0.004
#> GSM74366 1 0.1952 0.8611 0.912 0.000 0.000 0.004 0.084
#> GSM74367 1 0.1124 0.8912 0.960 0.000 0.000 0.036 0.004
#> GSM74377 1 0.0162 0.8956 0.996 0.000 0.000 0.000 0.004
#> GSM74378 1 0.0290 0.8954 0.992 0.000 0.000 0.000 0.008
#> GSM74379 1 0.0162 0.8957 0.996 0.000 0.000 0.000 0.004
#> GSM74380 1 0.0162 0.8956 0.996 0.000 0.000 0.000 0.004
#> GSM74381 1 0.0290 0.8954 0.992 0.000 0.000 0.000 0.008
#> GSM121357 5 0.4583 0.7080 0.000 0.112 0.140 0.000 0.748
#> GSM121361 5 0.1626 0.7804 0.000 0.044 0.016 0.000 0.940
#> GSM121363 5 0.1626 0.7804 0.000 0.044 0.016 0.000 0.940
#> GSM121368 5 0.1626 0.7804 0.000 0.044 0.016 0.000 0.940
#> GSM121369 5 0.1756 0.7773 0.008 0.036 0.016 0.000 0.940
#> GSM74368 1 0.4430 0.4840 0.628 0.000 0.000 0.360 0.012
#> GSM74369 1 0.4138 0.4075 0.616 0.000 0.000 0.384 0.000
#> GSM74370 1 0.1041 0.8937 0.964 0.000 0.000 0.032 0.004
#> GSM74371 4 0.1638 0.8431 0.064 0.000 0.000 0.932 0.004
#> GSM74372 1 0.0671 0.8963 0.980 0.000 0.000 0.016 0.004
#> GSM74373 1 0.0162 0.8956 0.996 0.000 0.000 0.000 0.004
#> GSM74374 1 0.0566 0.8970 0.984 0.000 0.000 0.012 0.004
#> GSM74375 1 0.0693 0.8963 0.980 0.000 0.000 0.012 0.008
#> GSM74376 1 0.0162 0.8956 0.996 0.000 0.000 0.000 0.004
#> GSM74405 1 0.0609 0.8938 0.980 0.000 0.000 0.000 0.020
#> GSM74351 4 0.1430 0.8480 0.052 0.000 0.000 0.944 0.004
#> GSM74352 1 0.0798 0.8957 0.976 0.000 0.000 0.016 0.008
#> GSM74353 1 0.1197 0.8884 0.952 0.000 0.000 0.048 0.000
#> GSM74354 1 0.0671 0.8966 0.980 0.000 0.000 0.016 0.004
#> GSM74355 1 0.0290 0.8954 0.992 0.000 0.000 0.000 0.008
#> GSM74382 1 0.4249 0.2709 0.568 0.000 0.000 0.432 0.000
#> GSM74383 1 0.0955 0.8936 0.968 0.000 0.000 0.028 0.004
#> GSM74384 1 0.0510 0.8947 0.984 0.000 0.000 0.000 0.016
#> GSM74385 4 0.1638 0.8431 0.064 0.000 0.000 0.932 0.004
#> GSM74386 1 0.0771 0.8956 0.976 0.000 0.000 0.020 0.004
#> GSM74395 1 0.1549 0.8877 0.944 0.000 0.000 0.040 0.016
#> GSM74396 1 0.2286 0.8448 0.888 0.000 0.000 0.108 0.004
#> GSM74397 1 0.3461 0.7169 0.772 0.000 0.000 0.224 0.004
#> GSM74398 1 0.0162 0.8957 0.996 0.000 0.000 0.000 0.004
#> GSM74399 1 0.0451 0.8968 0.988 0.000 0.000 0.004 0.008
#> GSM74400 1 0.1205 0.8904 0.956 0.000 0.000 0.040 0.004
#> GSM74401 1 0.1282 0.8878 0.952 0.000 0.000 0.044 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM74356 3 0.3464 0.8355 0.312 0.000 0.688 0.000 0.000 0.000
#> GSM74357 3 0.2793 0.8356 0.200 0.000 0.800 0.000 0.000 0.000
#> GSM74358 3 0.2527 0.8322 0.168 0.000 0.832 0.000 0.000 0.000
#> GSM74359 4 0.3547 0.6104 0.088 0.000 0.036 0.828 0.048 0.000
#> GSM74360 4 0.5893 0.3290 0.224 0.000 0.000 0.604 0.064 0.108
#> GSM74361 3 0.4467 0.8251 0.272 0.004 0.676 0.004 0.044 0.000
#> GSM74362 3 0.4710 0.7981 0.312 0.000 0.632 0.012 0.044 0.000
#> GSM74363 3 0.3464 0.8341 0.312 0.000 0.688 0.000 0.000 0.000
#> GSM74402 4 0.0000 0.6353 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM74403 4 0.4091 0.1517 0.224 0.000 0.000 0.720 0.000 0.056
#> GSM74404 4 0.4247 0.0623 0.240 0.000 0.000 0.700 0.000 0.060
#> GSM74406 4 0.0000 0.6353 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM74407 4 0.5730 0.1834 0.044 0.012 0.440 0.468 0.036 0.000
#> GSM74408 4 0.0000 0.6353 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM74409 4 0.0363 0.6361 0.000 0.000 0.000 0.988 0.012 0.000
#> GSM74410 4 0.0000 0.6353 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM119936 4 0.0000 0.6353 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM119937 4 0.2312 0.5591 0.012 0.000 0.000 0.876 0.000 0.112
#> GSM74411 3 0.4789 0.7691 0.144 0.124 0.712 0.000 0.020 0.000
#> GSM74412 3 0.5626 0.7581 0.260 0.132 0.588 0.000 0.020 0.000
#> GSM74413 3 0.5626 0.7581 0.260 0.132 0.588 0.000 0.020 0.000
#> GSM74414 5 0.2093 0.8829 0.000 0.088 0.004 0.004 0.900 0.004
#> GSM74415 3 0.5142 0.8021 0.240 0.092 0.648 0.000 0.020 0.000
#> GSM121379 2 0.0000 0.9742 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121380 2 0.0363 0.9681 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM121381 2 0.0000 0.9742 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121382 2 0.0000 0.9742 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121383 2 0.0000 0.9742 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121384 2 0.0260 0.9705 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM121385 2 0.0000 0.9742 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121386 2 0.0000 0.9742 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121387 2 0.0000 0.9742 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121388 2 0.1890 0.9004 0.044 0.924 0.024 0.000 0.008 0.000
#> GSM121389 2 0.0458 0.9652 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM121390 2 0.0458 0.9652 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM121391 2 0.0000 0.9742 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121392 2 0.2652 0.8513 0.000 0.868 0.000 0.008 0.104 0.020
#> GSM121393 2 0.3232 0.7935 0.008 0.840 0.000 0.008 0.032 0.112
#> GSM121394 2 0.0146 0.9711 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM121395 2 0.0146 0.9719 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM121396 3 0.4555 0.8262 0.272 0.040 0.672 0.000 0.016 0.000
#> GSM121397 2 0.0000 0.9742 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121398 2 0.0000 0.9742 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121399 2 0.0000 0.9742 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM74240 3 0.0603 0.8088 0.004 0.000 0.980 0.000 0.016 0.000
#> GSM74241 3 0.0806 0.8082 0.000 0.008 0.972 0.000 0.020 0.000
#> GSM74242 3 0.0632 0.8073 0.024 0.000 0.976 0.000 0.000 0.000
#> GSM74243 3 0.0713 0.8068 0.028 0.000 0.972 0.000 0.000 0.000
#> GSM74244 3 0.0458 0.8093 0.000 0.000 0.984 0.000 0.016 0.000
#> GSM74245 3 0.0000 0.8102 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM74246 3 0.0603 0.8091 0.000 0.004 0.980 0.000 0.016 0.000
#> GSM74247 3 0.1088 0.8046 0.000 0.024 0.960 0.000 0.016 0.000
#> GSM74248 3 0.0260 0.8093 0.008 0.000 0.992 0.000 0.000 0.000
#> GSM74416 1 0.4475 1.0000 0.556 0.000 0.000 0.412 0.000 0.032
#> GSM74417 1 0.4475 1.0000 0.556 0.000 0.000 0.412 0.000 0.032
#> GSM74418 1 0.4475 1.0000 0.556 0.000 0.000 0.412 0.000 0.032
#> GSM74419 4 0.0291 0.6369 0.000 0.000 0.004 0.992 0.004 0.000
#> GSM121358 3 0.3390 0.8373 0.296 0.000 0.704 0.000 0.000 0.000
#> GSM121359 3 0.4035 0.8350 0.272 0.012 0.700 0.000 0.016 0.000
#> GSM121360 6 0.3615 0.8040 0.080 0.000 0.000 0.032 0.064 0.824
#> GSM121362 4 0.4675 0.5684 0.092 0.000 0.000 0.748 0.064 0.096
#> GSM121364 4 0.3053 0.6198 0.080 0.000 0.016 0.856 0.048 0.000
#> GSM121365 3 0.3428 0.8359 0.304 0.000 0.696 0.000 0.000 0.000
#> GSM121366 3 0.3695 0.8372 0.272 0.000 0.712 0.000 0.016 0.000
#> GSM121367 3 0.3409 0.8368 0.300 0.000 0.700 0.000 0.000 0.000
#> GSM121370 3 0.3608 0.8379 0.272 0.000 0.716 0.000 0.012 0.000
#> GSM121371 3 0.3409 0.8368 0.300 0.000 0.700 0.000 0.000 0.000
#> GSM121372 3 0.3695 0.8372 0.272 0.000 0.712 0.000 0.016 0.000
#> GSM121373 4 0.5307 0.1293 0.332 0.000 0.000 0.580 0.060 0.028
#> GSM121374 4 0.2794 0.6181 0.080 0.000 0.000 0.860 0.060 0.000
#> GSM121407 3 0.5308 0.7895 0.272 0.100 0.612 0.000 0.016 0.000
#> GSM74387 3 0.1549 0.7957 0.000 0.044 0.936 0.000 0.020 0.000
#> GSM74388 5 0.0363 0.9476 0.000 0.012 0.000 0.000 0.988 0.000
#> GSM74389 3 0.1349 0.8027 0.056 0.000 0.940 0.000 0.004 0.000
#> GSM74390 3 0.1588 0.7868 0.004 0.000 0.924 0.000 0.072 0.000
#> GSM74391 4 0.5080 0.4662 0.044 0.008 0.192 0.708 0.036 0.012
#> GSM74392 4 0.2893 0.6222 0.080 0.004 0.004 0.864 0.048 0.000
#> GSM74393 4 0.4714 0.5238 0.072 0.004 0.132 0.744 0.048 0.000
#> GSM74394 5 0.0622 0.9440 0.000 0.012 0.000 0.008 0.980 0.000
#> GSM74239 6 0.1779 0.8815 0.016 0.000 0.000 0.064 0.000 0.920
#> GSM74364 4 0.5962 -0.5425 0.364 0.000 0.000 0.412 0.000 0.224
#> GSM74365 6 0.0146 0.9181 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM74366 6 0.3415 0.8003 0.028 0.000 0.000 0.012 0.152 0.808
#> GSM74367 6 0.0632 0.9147 0.000 0.000 0.000 0.024 0.000 0.976
#> GSM74377 6 0.0865 0.9168 0.036 0.000 0.000 0.000 0.000 0.964
#> GSM74378 6 0.1720 0.9015 0.040 0.000 0.000 0.000 0.032 0.928
#> GSM74379 6 0.0713 0.9162 0.028 0.000 0.000 0.000 0.000 0.972
#> GSM74380 6 0.0935 0.9150 0.032 0.000 0.000 0.000 0.004 0.964
#> GSM74381 6 0.1720 0.9015 0.040 0.000 0.000 0.000 0.032 0.928
#> GSM121357 5 0.4024 0.7691 0.008 0.128 0.092 0.000 0.772 0.000
#> GSM121361 5 0.0363 0.9476 0.000 0.012 0.000 0.000 0.988 0.000
#> GSM121363 5 0.0363 0.9476 0.000 0.012 0.000 0.000 0.988 0.000
#> GSM121368 5 0.0363 0.9476 0.000 0.012 0.000 0.000 0.988 0.000
#> GSM121369 5 0.0622 0.9440 0.000 0.012 0.000 0.008 0.980 0.000
#> GSM74368 4 0.3586 0.3732 0.012 0.000 0.000 0.720 0.000 0.268
#> GSM74369 6 0.4249 0.3985 0.032 0.000 0.000 0.328 0.000 0.640
#> GSM74370 6 0.0777 0.9166 0.000 0.000 0.000 0.024 0.004 0.972
#> GSM74371 1 0.4475 1.0000 0.556 0.000 0.000 0.412 0.000 0.032
#> GSM74372 6 0.0603 0.9184 0.000 0.000 0.000 0.016 0.004 0.980
#> GSM74373 6 0.0767 0.9196 0.008 0.000 0.000 0.012 0.004 0.976
#> GSM74374 6 0.0603 0.9175 0.016 0.000 0.000 0.004 0.000 0.980
#> GSM74375 6 0.0547 0.9191 0.020 0.000 0.000 0.000 0.000 0.980
#> GSM74376 6 0.1226 0.9142 0.040 0.000 0.000 0.004 0.004 0.952
#> GSM74405 6 0.1642 0.9064 0.028 0.000 0.000 0.004 0.032 0.936
#> GSM74351 1 0.4475 1.0000 0.556 0.000 0.000 0.412 0.000 0.032
#> GSM74352 6 0.0937 0.9174 0.040 0.000 0.000 0.000 0.000 0.960
#> GSM74353 6 0.0717 0.9165 0.016 0.000 0.000 0.008 0.000 0.976
#> GSM74354 6 0.0405 0.9180 0.008 0.000 0.000 0.004 0.000 0.988
#> GSM74355 6 0.1720 0.9015 0.040 0.000 0.000 0.000 0.032 0.928
#> GSM74382 4 0.4278 0.1665 0.212 0.000 0.000 0.712 0.000 0.076
#> GSM74383 6 0.0146 0.9181 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM74384 6 0.1832 0.9054 0.032 0.000 0.000 0.008 0.032 0.928
#> GSM74385 1 0.4475 1.0000 0.556 0.000 0.000 0.412 0.000 0.032
#> GSM74386 6 0.0363 0.9182 0.000 0.000 0.000 0.012 0.000 0.988
#> GSM74395 6 0.1285 0.9009 0.004 0.000 0.000 0.052 0.000 0.944
#> GSM74396 6 0.1700 0.8823 0.004 0.000 0.000 0.080 0.000 0.916
#> GSM74397 6 0.4147 0.1730 0.012 0.000 0.000 0.436 0.000 0.552
#> GSM74398 6 0.0000 0.9180 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM74399 6 0.0547 0.9188 0.020 0.000 0.000 0.000 0.000 0.980
#> GSM74400 6 0.2730 0.7779 0.012 0.000 0.000 0.152 0.000 0.836
#> GSM74401 6 0.3102 0.7530 0.028 0.000 0.000 0.156 0.000 0.816
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 3)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
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 120 1.97e-13 2
#> ATC:mclust 120 5.94e-26 3
#> ATC:mclust 118 2.68e-31 4
#> ATC:mclust 113 1.40e-34 5
#> ATC:mclust 110 2.94e-36 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 21168 rows and 121 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 0.966 0.950 0.980 0.5037 0.496 0.496
#> 3 3 0.652 0.738 0.884 0.3172 0.749 0.537
#> 4 4 0.690 0.776 0.869 0.1162 0.840 0.577
#> 5 5 0.746 0.744 0.860 0.0690 0.887 0.606
#> 6 6 0.757 0.756 0.850 0.0419 0.925 0.661
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
#> GSM74356 1 0.118 0.974 0.984 0.016
#> GSM74357 1 0.000 0.989 1.000 0.000
#> GSM74358 1 0.000 0.989 1.000 0.000
#> GSM74359 1 0.000 0.989 1.000 0.000
#> GSM74360 1 0.000 0.989 1.000 0.000
#> GSM74361 1 0.987 0.195 0.568 0.432
#> GSM74362 1 0.000 0.989 1.000 0.000
#> GSM74363 1 0.373 0.915 0.928 0.072
#> GSM74402 1 0.000 0.989 1.000 0.000
#> GSM74403 1 0.000 0.989 1.000 0.000
#> GSM74404 1 0.000 0.989 1.000 0.000
#> GSM74406 1 0.000 0.989 1.000 0.000
#> GSM74407 1 0.000 0.989 1.000 0.000
#> GSM74408 1 0.000 0.989 1.000 0.000
#> GSM74409 1 0.000 0.989 1.000 0.000
#> GSM74410 1 0.000 0.989 1.000 0.000
#> GSM119936 1 0.000 0.989 1.000 0.000
#> GSM119937 1 0.000 0.989 1.000 0.000
#> GSM74411 2 0.000 0.970 0.000 1.000
#> GSM74412 2 0.000 0.970 0.000 1.000
#> GSM74413 2 0.000 0.970 0.000 1.000
#> GSM74414 2 0.000 0.970 0.000 1.000
#> GSM74415 2 0.000 0.970 0.000 1.000
#> GSM121379 2 0.000 0.970 0.000 1.000
#> GSM121380 2 0.000 0.970 0.000 1.000
#> GSM121381 2 0.000 0.970 0.000 1.000
#> GSM121382 2 0.000 0.970 0.000 1.000
#> GSM121383 2 0.000 0.970 0.000 1.000
#> GSM121384 2 0.000 0.970 0.000 1.000
#> GSM121385 2 0.000 0.970 0.000 1.000
#> GSM121386 2 0.000 0.970 0.000 1.000
#> GSM121387 2 0.000 0.970 0.000 1.000
#> GSM121388 2 0.000 0.970 0.000 1.000
#> GSM121389 2 0.000 0.970 0.000 1.000
#> GSM121390 2 0.000 0.970 0.000 1.000
#> GSM121391 2 0.000 0.970 0.000 1.000
#> GSM121392 2 0.000 0.970 0.000 1.000
#> GSM121393 2 0.000 0.970 0.000 1.000
#> GSM121394 2 0.000 0.970 0.000 1.000
#> GSM121395 2 0.000 0.970 0.000 1.000
#> GSM121396 2 0.000 0.970 0.000 1.000
#> GSM121397 2 0.000 0.970 0.000 1.000
#> GSM121398 2 0.000 0.970 0.000 1.000
#> GSM121399 2 0.000 0.970 0.000 1.000
#> GSM74240 2 0.000 0.970 0.000 1.000
#> GSM74241 2 0.000 0.970 0.000 1.000
#> GSM74242 2 0.827 0.653 0.260 0.740
#> GSM74243 2 0.963 0.387 0.388 0.612
#> GSM74244 2 0.000 0.970 0.000 1.000
#> GSM74245 2 0.000 0.970 0.000 1.000
#> GSM74246 2 0.000 0.970 0.000 1.000
#> GSM74247 2 0.000 0.970 0.000 1.000
#> GSM74248 2 0.000 0.970 0.000 1.000
#> GSM74416 1 0.000 0.989 1.000 0.000
#> GSM74417 1 0.000 0.989 1.000 0.000
#> GSM74418 1 0.000 0.989 1.000 0.000
#> GSM74419 1 0.000 0.989 1.000 0.000
#> GSM121358 2 0.000 0.970 0.000 1.000
#> GSM121359 2 0.000 0.970 0.000 1.000
#> GSM121360 1 0.000 0.989 1.000 0.000
#> GSM121362 1 0.000 0.989 1.000 0.000
#> GSM121364 1 0.000 0.989 1.000 0.000
#> GSM121365 2 0.000 0.970 0.000 1.000
#> GSM121366 2 0.000 0.970 0.000 1.000
#> GSM121367 2 0.000 0.970 0.000 1.000
#> GSM121370 2 0.000 0.970 0.000 1.000
#> GSM121371 2 0.000 0.970 0.000 1.000
#> GSM121372 2 0.000 0.970 0.000 1.000
#> GSM121373 1 0.000 0.989 1.000 0.000
#> GSM121374 1 0.000 0.989 1.000 0.000
#> GSM121407 2 0.000 0.970 0.000 1.000
#> GSM74387 2 0.000 0.970 0.000 1.000
#> GSM74388 2 0.000 0.970 0.000 1.000
#> GSM74389 1 0.494 0.871 0.892 0.108
#> GSM74390 2 0.000 0.970 0.000 1.000
#> GSM74391 1 0.000 0.989 1.000 0.000
#> GSM74392 1 0.000 0.989 1.000 0.000
#> GSM74393 1 0.000 0.989 1.000 0.000
#> GSM74394 2 0.000 0.970 0.000 1.000
#> GSM74239 1 0.000 0.989 1.000 0.000
#> GSM74364 1 0.000 0.989 1.000 0.000
#> GSM74365 1 0.000 0.989 1.000 0.000
#> GSM74366 2 0.000 0.970 0.000 1.000
#> GSM74367 1 0.000 0.989 1.000 0.000
#> GSM74377 1 0.118 0.974 0.984 0.016
#> GSM74378 2 0.000 0.970 0.000 1.000
#> GSM74379 1 0.000 0.989 1.000 0.000
#> GSM74380 1 0.000 0.989 1.000 0.000
#> GSM74381 2 0.529 0.851 0.120 0.880
#> GSM121357 2 0.000 0.970 0.000 1.000
#> GSM121361 2 0.000 0.970 0.000 1.000
#> GSM121363 2 0.000 0.970 0.000 1.000
#> GSM121368 2 0.000 0.970 0.000 1.000
#> GSM121369 2 0.000 0.970 0.000 1.000
#> GSM74368 1 0.000 0.989 1.000 0.000
#> GSM74369 1 0.000 0.989 1.000 0.000
#> GSM74370 1 0.000 0.989 1.000 0.000
#> GSM74371 1 0.000 0.989 1.000 0.000
#> GSM74372 1 0.000 0.989 1.000 0.000
#> GSM74373 1 0.000 0.989 1.000 0.000
#> GSM74374 1 0.000 0.989 1.000 0.000
#> GSM74375 1 0.000 0.989 1.000 0.000
#> GSM74376 2 0.671 0.782 0.176 0.824
#> GSM74405 2 0.969 0.370 0.396 0.604
#> GSM74351 1 0.000 0.989 1.000 0.000
#> GSM74352 2 0.990 0.246 0.440 0.560
#> GSM74353 1 0.000 0.989 1.000 0.000
#> GSM74354 1 0.000 0.989 1.000 0.000
#> GSM74355 2 0.000 0.970 0.000 1.000
#> GSM74382 1 0.000 0.989 1.000 0.000
#> GSM74383 1 0.000 0.989 1.000 0.000
#> GSM74384 2 0.000 0.970 0.000 1.000
#> GSM74385 1 0.000 0.989 1.000 0.000
#> GSM74386 1 0.000 0.989 1.000 0.000
#> GSM74395 1 0.000 0.989 1.000 0.000
#> GSM74396 1 0.000 0.989 1.000 0.000
#> GSM74397 1 0.000 0.989 1.000 0.000
#> GSM74398 1 0.000 0.989 1.000 0.000
#> GSM74399 1 0.000 0.989 1.000 0.000
#> GSM74400 1 0.000 0.989 1.000 0.000
#> GSM74401 1 0.000 0.989 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM74356 3 0.2066 0.8173 0.060 0.000 0.940
#> GSM74357 3 0.3267 0.7661 0.116 0.000 0.884
#> GSM74358 3 0.3551 0.7495 0.132 0.000 0.868
#> GSM74359 1 0.6260 0.2639 0.552 0.000 0.448
#> GSM74360 1 0.0000 0.8964 1.000 0.000 0.000
#> GSM74361 3 0.2066 0.8172 0.060 0.000 0.940
#> GSM74362 3 0.4452 0.6818 0.192 0.000 0.808
#> GSM74363 3 0.1753 0.8261 0.048 0.000 0.952
#> GSM74402 1 0.2165 0.8677 0.936 0.000 0.064
#> GSM74403 1 0.0000 0.8964 1.000 0.000 0.000
#> GSM74404 1 0.0000 0.8964 1.000 0.000 0.000
#> GSM74406 1 0.2165 0.8677 0.936 0.000 0.064
#> GSM74407 3 0.5291 0.5541 0.268 0.000 0.732
#> GSM74408 1 0.2356 0.8626 0.928 0.000 0.072
#> GSM74409 1 0.1860 0.8745 0.948 0.000 0.052
#> GSM74410 1 0.3941 0.7879 0.844 0.000 0.156
#> GSM119936 1 0.1964 0.8723 0.944 0.000 0.056
#> GSM119937 1 0.0000 0.8964 1.000 0.000 0.000
#> GSM74411 3 0.4235 0.7337 0.000 0.176 0.824
#> GSM74412 3 0.5678 0.5234 0.000 0.316 0.684
#> GSM74413 3 0.3941 0.7546 0.000 0.156 0.844
#> GSM74414 2 0.0592 0.8092 0.000 0.988 0.012
#> GSM74415 3 0.2796 0.8071 0.000 0.092 0.908
#> GSM121379 2 0.4399 0.7292 0.000 0.812 0.188
#> GSM121380 2 0.2878 0.7889 0.000 0.904 0.096
#> GSM121381 3 0.6305 0.0284 0.000 0.484 0.516
#> GSM121382 2 0.5591 0.5762 0.000 0.696 0.304
#> GSM121383 2 0.6260 0.1938 0.000 0.552 0.448
#> GSM121384 2 0.3482 0.7742 0.000 0.872 0.128
#> GSM121385 2 0.4796 0.6979 0.000 0.780 0.220
#> GSM121386 2 0.5529 0.5906 0.000 0.704 0.296
#> GSM121387 2 0.5678 0.5527 0.000 0.684 0.316
#> GSM121388 3 0.6260 0.1707 0.000 0.448 0.552
#> GSM121389 2 0.3551 0.7715 0.000 0.868 0.132
#> GSM121390 2 0.2261 0.7992 0.000 0.932 0.068
#> GSM121391 3 0.6235 0.2109 0.000 0.436 0.564
#> GSM121392 2 0.0237 0.8085 0.000 0.996 0.004
#> GSM121393 2 0.0000 0.8076 0.000 1.000 0.000
#> GSM121394 3 0.5431 0.5815 0.000 0.284 0.716
#> GSM121395 2 0.3879 0.7589 0.000 0.848 0.152
#> GSM121396 3 0.3482 0.7791 0.000 0.128 0.872
#> GSM121397 2 0.4702 0.7065 0.000 0.788 0.212
#> GSM121398 2 0.4750 0.7025 0.000 0.784 0.216
#> GSM121399 2 0.6062 0.3942 0.000 0.616 0.384
#> GSM74240 3 0.0000 0.8511 0.000 0.000 1.000
#> GSM74241 3 0.0000 0.8511 0.000 0.000 1.000
#> GSM74242 3 0.0237 0.8501 0.004 0.000 0.996
#> GSM74243 3 0.0237 0.8501 0.004 0.000 0.996
#> GSM74244 3 0.0000 0.8511 0.000 0.000 1.000
#> GSM74245 3 0.0000 0.8511 0.000 0.000 1.000
#> GSM74246 3 0.0892 0.8457 0.000 0.020 0.980
#> GSM74247 3 0.1753 0.8339 0.000 0.048 0.952
#> GSM74248 3 0.0000 0.8511 0.000 0.000 1.000
#> GSM74416 1 0.0000 0.8964 1.000 0.000 0.000
#> GSM74417 1 0.0000 0.8964 1.000 0.000 0.000
#> GSM74418 1 0.0000 0.8964 1.000 0.000 0.000
#> GSM74419 1 0.5591 0.5749 0.696 0.000 0.304
#> GSM121358 3 0.0000 0.8511 0.000 0.000 1.000
#> GSM121359 3 0.2066 0.8260 0.000 0.060 0.940
#> GSM121360 1 0.3412 0.8133 0.876 0.124 0.000
#> GSM121362 1 0.0237 0.8953 0.996 0.000 0.004
#> GSM121364 1 0.3619 0.8092 0.864 0.000 0.136
#> GSM121365 3 0.0237 0.8501 0.004 0.000 0.996
#> GSM121366 3 0.0000 0.8511 0.000 0.000 1.000
#> GSM121367 3 0.0000 0.8511 0.000 0.000 1.000
#> GSM121370 3 0.0000 0.8511 0.000 0.000 1.000
#> GSM121371 3 0.0237 0.8501 0.004 0.000 0.996
#> GSM121372 3 0.0237 0.8503 0.000 0.004 0.996
#> GSM121373 1 0.0000 0.8964 1.000 0.000 0.000
#> GSM121374 1 0.1643 0.8786 0.956 0.000 0.044
#> GSM121407 3 0.6026 0.3658 0.000 0.376 0.624
#> GSM74387 3 0.5397 0.5875 0.000 0.280 0.720
#> GSM74388 2 0.0592 0.8093 0.000 0.988 0.012
#> GSM74389 3 0.1529 0.8312 0.040 0.000 0.960
#> GSM74390 3 0.0747 0.8471 0.000 0.016 0.984
#> GSM74391 1 0.6026 0.4320 0.624 0.000 0.376
#> GSM74392 1 0.3340 0.8232 0.880 0.000 0.120
#> GSM74393 1 0.6274 0.2402 0.544 0.000 0.456
#> GSM74394 2 0.0237 0.8085 0.000 0.996 0.004
#> GSM74239 1 0.0237 0.8955 0.996 0.004 0.000
#> GSM74364 1 0.0000 0.8964 1.000 0.000 0.000
#> GSM74365 1 0.2356 0.8583 0.928 0.072 0.000
#> GSM74366 2 0.0000 0.8076 0.000 1.000 0.000
#> GSM74367 1 0.0592 0.8935 0.988 0.012 0.000
#> GSM74377 2 0.5016 0.5773 0.240 0.760 0.000
#> GSM74378 2 0.0592 0.8038 0.012 0.988 0.000
#> GSM74379 1 0.6307 0.1368 0.512 0.488 0.000
#> GSM74380 2 0.6252 0.0623 0.444 0.556 0.000
#> GSM74381 2 0.2625 0.7637 0.084 0.916 0.000
#> GSM121357 2 0.5431 0.6158 0.000 0.716 0.284
#> GSM121361 2 0.0237 0.8085 0.000 0.996 0.004
#> GSM121363 2 0.1529 0.8063 0.000 0.960 0.040
#> GSM121368 2 0.1031 0.8085 0.000 0.976 0.024
#> GSM121369 2 0.0424 0.8091 0.000 0.992 0.008
#> GSM74368 1 0.0000 0.8964 1.000 0.000 0.000
#> GSM74369 1 0.0000 0.8964 1.000 0.000 0.000
#> GSM74370 1 0.0892 0.8906 0.980 0.020 0.000
#> GSM74371 1 0.0000 0.8964 1.000 0.000 0.000
#> GSM74372 1 0.4178 0.7634 0.828 0.172 0.000
#> GSM74373 1 0.6302 0.1627 0.520 0.480 0.000
#> GSM74374 1 0.1031 0.8888 0.976 0.024 0.000
#> GSM74375 1 0.5988 0.4514 0.632 0.368 0.000
#> GSM74376 2 0.2878 0.7553 0.096 0.904 0.000
#> GSM74405 2 0.3116 0.7463 0.108 0.892 0.000
#> GSM74351 1 0.0000 0.8964 1.000 0.000 0.000
#> GSM74352 2 0.3551 0.7249 0.132 0.868 0.000
#> GSM74353 1 0.0592 0.8934 0.988 0.012 0.000
#> GSM74354 1 0.0892 0.8906 0.980 0.020 0.000
#> GSM74355 2 0.0892 0.8006 0.020 0.980 0.000
#> GSM74382 1 0.0000 0.8964 1.000 0.000 0.000
#> GSM74383 1 0.0892 0.8906 0.980 0.020 0.000
#> GSM74384 2 0.0000 0.8076 0.000 1.000 0.000
#> GSM74385 1 0.0000 0.8964 1.000 0.000 0.000
#> GSM74386 1 0.0892 0.8906 0.980 0.020 0.000
#> GSM74395 1 0.0237 0.8955 0.996 0.004 0.000
#> GSM74396 1 0.0000 0.8964 1.000 0.000 0.000
#> GSM74397 1 0.0000 0.8964 1.000 0.000 0.000
#> GSM74398 1 0.5650 0.5635 0.688 0.312 0.000
#> GSM74399 2 0.6308 -0.1120 0.492 0.508 0.000
#> GSM74400 1 0.1964 0.8701 0.944 0.056 0.000
#> GSM74401 1 0.1529 0.8802 0.960 0.040 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM74356 3 0.2552 0.8283 0.048 0.012 0.920 0.020
#> GSM74357 3 0.2262 0.8337 0.040 0.012 0.932 0.016
#> GSM74358 3 0.1575 0.8435 0.028 0.012 0.956 0.004
#> GSM74359 4 0.6454 0.5993 0.084 0.012 0.260 0.644
#> GSM74360 4 0.4752 0.7822 0.088 0.012 0.092 0.808
#> GSM74361 3 0.2684 0.8233 0.060 0.012 0.912 0.016
#> GSM74362 3 0.5401 0.6897 0.084 0.012 0.760 0.144
#> GSM74363 3 0.0804 0.8596 0.000 0.012 0.980 0.008
#> GSM74402 4 0.0672 0.8596 0.008 0.000 0.008 0.984
#> GSM74403 4 0.0376 0.8590 0.004 0.000 0.004 0.992
#> GSM74404 4 0.0672 0.8592 0.008 0.000 0.008 0.984
#> GSM74406 4 0.1635 0.8507 0.008 0.000 0.044 0.948
#> GSM74407 3 0.2255 0.8439 0.000 0.012 0.920 0.068
#> GSM74408 4 0.2830 0.8344 0.040 0.000 0.060 0.900
#> GSM74409 4 0.3938 0.8106 0.060 0.008 0.080 0.852
#> GSM74410 4 0.3689 0.8161 0.048 0.004 0.088 0.860
#> GSM119936 4 0.2596 0.8377 0.024 0.000 0.068 0.908
#> GSM119937 4 0.3077 0.8322 0.036 0.004 0.068 0.892
#> GSM74411 3 0.4175 0.7791 0.016 0.200 0.784 0.000
#> GSM74412 3 0.4343 0.7106 0.004 0.264 0.732 0.000
#> GSM74413 3 0.4252 0.7228 0.004 0.252 0.744 0.000
#> GSM74414 2 0.5168 -0.1529 0.496 0.500 0.004 0.000
#> GSM74415 3 0.3266 0.8144 0.000 0.168 0.832 0.000
#> GSM121379 2 0.0707 0.8975 0.020 0.980 0.000 0.000
#> GSM121380 2 0.1302 0.8836 0.044 0.956 0.000 0.000
#> GSM121381 2 0.1389 0.8935 0.000 0.952 0.048 0.000
#> GSM121382 2 0.1305 0.8976 0.004 0.960 0.036 0.000
#> GSM121383 2 0.1557 0.8892 0.000 0.944 0.056 0.000
#> GSM121384 2 0.1022 0.8917 0.032 0.968 0.000 0.000
#> GSM121385 2 0.0804 0.9023 0.008 0.980 0.012 0.000
#> GSM121386 2 0.0707 0.9024 0.000 0.980 0.020 0.000
#> GSM121387 2 0.0707 0.9024 0.000 0.980 0.020 0.000
#> GSM121388 2 0.1867 0.8778 0.000 0.928 0.072 0.000
#> GSM121389 2 0.1211 0.8866 0.040 0.960 0.000 0.000
#> GSM121390 2 0.1637 0.8712 0.060 0.940 0.000 0.000
#> GSM121391 2 0.1902 0.8818 0.004 0.932 0.064 0.000
#> GSM121392 2 0.2530 0.8136 0.112 0.888 0.000 0.000
#> GSM121393 2 0.1867 0.8588 0.072 0.928 0.000 0.000
#> GSM121394 2 0.2125 0.8714 0.004 0.920 0.076 0.000
#> GSM121395 2 0.0817 0.8959 0.024 0.976 0.000 0.000
#> GSM121396 2 0.4948 0.0647 0.000 0.560 0.440 0.000
#> GSM121397 2 0.0804 0.9010 0.012 0.980 0.008 0.000
#> GSM121398 2 0.0592 0.9025 0.000 0.984 0.016 0.000
#> GSM121399 2 0.1398 0.8959 0.004 0.956 0.040 0.000
#> GSM74240 3 0.1488 0.8692 0.012 0.032 0.956 0.000
#> GSM74241 3 0.2334 0.8604 0.004 0.088 0.908 0.000
#> GSM74242 3 0.0817 0.8670 0.000 0.024 0.976 0.000
#> GSM74243 3 0.0707 0.8661 0.000 0.020 0.980 0.000
#> GSM74244 3 0.2011 0.8631 0.000 0.080 0.920 0.000
#> GSM74245 3 0.1389 0.8695 0.000 0.048 0.952 0.000
#> GSM74246 3 0.2924 0.8555 0.016 0.100 0.884 0.000
#> GSM74247 3 0.2928 0.8518 0.012 0.108 0.880 0.000
#> GSM74248 3 0.1004 0.8671 0.004 0.024 0.972 0.000
#> GSM74416 4 0.0336 0.8593 0.008 0.000 0.000 0.992
#> GSM74417 4 0.0188 0.8595 0.004 0.000 0.000 0.996
#> GSM74418 4 0.0336 0.8593 0.008 0.000 0.000 0.992
#> GSM74419 4 0.1398 0.8534 0.004 0.000 0.040 0.956
#> GSM121358 3 0.1389 0.8692 0.000 0.048 0.952 0.000
#> GSM121359 3 0.3024 0.8294 0.000 0.148 0.852 0.000
#> GSM121360 1 0.5766 0.4995 0.692 0.012 0.048 0.248
#> GSM121362 4 0.5400 0.7558 0.104 0.012 0.120 0.764
#> GSM121364 4 0.5212 0.7592 0.088 0.012 0.124 0.776
#> GSM121365 3 0.1118 0.8687 0.000 0.036 0.964 0.000
#> GSM121366 3 0.2149 0.8603 0.000 0.088 0.912 0.000
#> GSM121367 3 0.1474 0.8688 0.000 0.052 0.948 0.000
#> GSM121370 3 0.1940 0.8642 0.000 0.076 0.924 0.000
#> GSM121371 3 0.1211 0.8692 0.000 0.040 0.960 0.000
#> GSM121372 3 0.2999 0.8390 0.004 0.132 0.864 0.000
#> GSM121373 4 0.4558 0.7904 0.084 0.012 0.084 0.820
#> GSM121374 4 0.4992 0.7708 0.088 0.012 0.108 0.792
#> GSM121407 3 0.4483 0.6882 0.004 0.284 0.712 0.000
#> GSM74387 3 0.3790 0.8174 0.016 0.164 0.820 0.000
#> GSM74388 1 0.4624 0.5107 0.660 0.340 0.000 0.000
#> GSM74389 3 0.2365 0.8291 0.064 0.012 0.920 0.004
#> GSM74390 3 0.2992 0.8228 0.084 0.016 0.892 0.008
#> GSM74391 3 0.4978 0.3528 0.004 0.000 0.612 0.384
#> GSM74392 4 0.5353 0.7481 0.084 0.012 0.140 0.764
#> GSM74393 3 0.6744 0.3538 0.084 0.012 0.592 0.312
#> GSM74394 1 0.3024 0.7972 0.852 0.148 0.000 0.000
#> GSM74239 4 0.1792 0.8367 0.068 0.000 0.000 0.932
#> GSM74364 4 0.0707 0.8580 0.020 0.000 0.000 0.980
#> GSM74365 1 0.4522 0.6088 0.680 0.000 0.000 0.320
#> GSM74366 1 0.2921 0.8075 0.860 0.140 0.000 0.000
#> GSM74367 4 0.4761 0.3352 0.372 0.000 0.000 0.628
#> GSM74377 1 0.3708 0.8083 0.832 0.020 0.000 0.148
#> GSM74378 1 0.2469 0.8195 0.892 0.108 0.000 0.000
#> GSM74379 1 0.3591 0.7933 0.824 0.008 0.000 0.168
#> GSM74380 1 0.3082 0.8291 0.884 0.032 0.000 0.084
#> GSM74381 1 0.2542 0.8286 0.904 0.084 0.000 0.012
#> GSM121357 3 0.6933 0.5224 0.172 0.244 0.584 0.000
#> GSM121361 1 0.2868 0.8024 0.864 0.136 0.000 0.000
#> GSM121363 1 0.3123 0.7849 0.844 0.156 0.000 0.000
#> GSM121368 1 0.2868 0.8016 0.864 0.136 0.000 0.000
#> GSM121369 1 0.2530 0.8085 0.888 0.112 0.000 0.000
#> GSM74368 4 0.1474 0.8465 0.052 0.000 0.000 0.948
#> GSM74369 4 0.0707 0.8580 0.020 0.000 0.000 0.980
#> GSM74370 4 0.5352 0.6677 0.296 0.008 0.020 0.676
#> GSM74371 4 0.0592 0.8586 0.016 0.000 0.000 0.984
#> GSM74372 1 0.3172 0.7643 0.840 0.000 0.000 0.160
#> GSM74373 1 0.2611 0.8252 0.896 0.008 0.000 0.096
#> GSM74374 4 0.4730 0.3560 0.364 0.000 0.000 0.636
#> GSM74375 1 0.4989 0.2151 0.528 0.000 0.000 0.472
#> GSM74376 1 0.3107 0.8323 0.884 0.080 0.000 0.036
#> GSM74405 1 0.2473 0.8296 0.908 0.080 0.000 0.012
#> GSM74351 4 0.0592 0.8586 0.016 0.000 0.000 0.984
#> GSM74352 1 0.4755 0.7696 0.760 0.040 0.000 0.200
#> GSM74353 4 0.1118 0.8537 0.036 0.000 0.000 0.964
#> GSM74354 4 0.3486 0.7185 0.188 0.000 0.000 0.812
#> GSM74355 1 0.2775 0.8309 0.896 0.084 0.000 0.020
#> GSM74382 4 0.0779 0.8593 0.016 0.000 0.004 0.980
#> GSM74383 4 0.3266 0.7438 0.168 0.000 0.000 0.832
#> GSM74384 1 0.2530 0.8189 0.888 0.112 0.000 0.000
#> GSM74385 4 0.0592 0.8586 0.016 0.000 0.000 0.984
#> GSM74386 4 0.5000 -0.1138 0.496 0.000 0.000 0.504
#> GSM74395 4 0.3836 0.7614 0.168 0.000 0.016 0.816
#> GSM74396 4 0.3052 0.7814 0.136 0.000 0.004 0.860
#> GSM74397 4 0.1743 0.8458 0.056 0.000 0.004 0.940
#> GSM74398 1 0.4088 0.7368 0.764 0.004 0.000 0.232
#> GSM74399 1 0.4008 0.7248 0.756 0.000 0.000 0.244
#> GSM74400 4 0.0921 0.8563 0.028 0.000 0.000 0.972
#> GSM74401 4 0.1302 0.8504 0.044 0.000 0.000 0.956
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM74356 4 0.4242 0.0210 0.000 0.000 0.428 0.572 0.000
#> GSM74357 4 0.4287 -0.0821 0.000 0.000 0.460 0.540 0.000
#> GSM74358 3 0.4278 0.3105 0.000 0.000 0.548 0.452 0.000
#> GSM74359 4 0.3099 0.7310 0.124 0.000 0.028 0.848 0.000
#> GSM74360 4 0.2563 0.7198 0.120 0.000 0.000 0.872 0.008
#> GSM74361 4 0.4210 0.0906 0.000 0.000 0.412 0.588 0.000
#> GSM74362 4 0.2293 0.6866 0.016 0.000 0.084 0.900 0.000
#> GSM74363 3 0.4015 0.5661 0.000 0.000 0.652 0.348 0.000
#> GSM74402 1 0.2773 0.7455 0.836 0.000 0.000 0.164 0.000
#> GSM74403 1 0.3983 0.4594 0.660 0.000 0.000 0.340 0.000
#> GSM74404 1 0.4114 0.3663 0.624 0.000 0.000 0.376 0.000
#> GSM74406 1 0.4219 0.2263 0.584 0.000 0.000 0.416 0.000
#> GSM74407 3 0.1243 0.8612 0.028 0.000 0.960 0.008 0.004
#> GSM74408 4 0.4227 0.3477 0.420 0.000 0.000 0.580 0.000
#> GSM74409 4 0.3636 0.6152 0.272 0.000 0.000 0.728 0.000
#> GSM74410 4 0.4045 0.4898 0.356 0.000 0.000 0.644 0.000
#> GSM119936 4 0.4262 0.2901 0.440 0.000 0.000 0.560 0.000
#> GSM119937 4 0.4138 0.4338 0.384 0.000 0.000 0.616 0.000
#> GSM74411 3 0.2532 0.8417 0.000 0.036 0.908 0.028 0.028
#> GSM74412 3 0.3221 0.8153 0.000 0.076 0.868 0.024 0.032
#> GSM74413 3 0.2956 0.8262 0.000 0.060 0.884 0.036 0.020
#> GSM74414 2 0.6163 0.5313 0.000 0.612 0.196 0.016 0.176
#> GSM74415 3 0.1372 0.8591 0.000 0.004 0.956 0.024 0.016
#> GSM121379 2 0.0162 0.9581 0.000 0.996 0.004 0.000 0.000
#> GSM121380 2 0.0324 0.9565 0.000 0.992 0.004 0.000 0.004
#> GSM121381 2 0.0290 0.9595 0.000 0.992 0.008 0.000 0.000
#> GSM121382 2 0.0290 0.9595 0.000 0.992 0.008 0.000 0.000
#> GSM121383 2 0.0510 0.9558 0.000 0.984 0.016 0.000 0.000
#> GSM121384 2 0.0162 0.9581 0.000 0.996 0.004 0.000 0.000
#> GSM121385 2 0.0290 0.9595 0.000 0.992 0.008 0.000 0.000
#> GSM121386 2 0.0290 0.9595 0.000 0.992 0.008 0.000 0.000
#> GSM121387 2 0.0290 0.9595 0.000 0.992 0.008 0.000 0.000
#> GSM121388 2 0.0865 0.9479 0.000 0.972 0.024 0.004 0.000
#> GSM121389 2 0.0162 0.9581 0.000 0.996 0.004 0.000 0.000
#> GSM121390 2 0.0404 0.9496 0.000 0.988 0.000 0.000 0.012
#> GSM121391 2 0.0671 0.9540 0.000 0.980 0.016 0.004 0.000
#> GSM121392 2 0.0510 0.9468 0.000 0.984 0.000 0.000 0.016
#> GSM121393 2 0.0162 0.9537 0.000 0.996 0.000 0.000 0.004
#> GSM121394 2 0.0671 0.9540 0.000 0.980 0.016 0.004 0.000
#> GSM121395 2 0.0290 0.9595 0.000 0.992 0.008 0.000 0.000
#> GSM121396 2 0.4029 0.4994 0.000 0.680 0.316 0.004 0.000
#> GSM121397 2 0.0290 0.9595 0.000 0.992 0.008 0.000 0.000
#> GSM121398 2 0.0290 0.9595 0.000 0.992 0.008 0.000 0.000
#> GSM121399 2 0.0290 0.9595 0.000 0.992 0.008 0.000 0.000
#> GSM74240 3 0.1168 0.8678 0.000 0.000 0.960 0.032 0.008
#> GSM74241 3 0.0798 0.8625 0.000 0.000 0.976 0.016 0.008
#> GSM74242 3 0.1043 0.8666 0.000 0.000 0.960 0.040 0.000
#> GSM74243 3 0.1792 0.8551 0.000 0.000 0.916 0.084 0.000
#> GSM74244 3 0.0671 0.8659 0.000 0.000 0.980 0.016 0.004
#> GSM74245 3 0.0671 0.8659 0.000 0.000 0.980 0.016 0.004
#> GSM74246 3 0.1830 0.8559 0.000 0.000 0.932 0.028 0.040
#> GSM74247 3 0.1661 0.8577 0.000 0.000 0.940 0.024 0.036
#> GSM74248 3 0.1430 0.8668 0.000 0.000 0.944 0.052 0.004
#> GSM74416 1 0.1671 0.8049 0.924 0.000 0.000 0.076 0.000
#> GSM74417 1 0.2773 0.7450 0.836 0.000 0.000 0.164 0.000
#> GSM74418 1 0.2424 0.7723 0.868 0.000 0.000 0.132 0.000
#> GSM74419 1 0.4251 0.4954 0.672 0.000 0.012 0.316 0.000
#> GSM121358 3 0.2719 0.8283 0.000 0.004 0.852 0.144 0.000
#> GSM121359 3 0.1300 0.8661 0.000 0.028 0.956 0.016 0.000
#> GSM121360 5 0.4897 0.3019 0.024 0.000 0.000 0.460 0.516
#> GSM121362 4 0.2142 0.7077 0.048 0.000 0.004 0.920 0.028
#> GSM121364 4 0.2646 0.7303 0.124 0.000 0.004 0.868 0.004
#> GSM121365 3 0.3210 0.7705 0.000 0.000 0.788 0.212 0.000
#> GSM121366 3 0.1205 0.8670 0.000 0.004 0.956 0.040 0.000
#> GSM121367 3 0.2674 0.8308 0.000 0.004 0.856 0.140 0.000
#> GSM121370 3 0.1544 0.8613 0.000 0.000 0.932 0.068 0.000
#> GSM121371 3 0.2719 0.8271 0.000 0.004 0.852 0.144 0.000
#> GSM121372 3 0.1997 0.8622 0.000 0.036 0.924 0.040 0.000
#> GSM121373 4 0.2929 0.7070 0.152 0.000 0.000 0.840 0.008
#> GSM121374 4 0.2497 0.7302 0.112 0.000 0.004 0.880 0.004
#> GSM121407 3 0.4061 0.6914 0.000 0.240 0.740 0.016 0.004
#> GSM74387 3 0.2032 0.8555 0.000 0.004 0.924 0.020 0.052
#> GSM74388 5 0.3450 0.7236 0.000 0.176 0.008 0.008 0.808
#> GSM74389 3 0.4118 0.5924 0.000 0.000 0.660 0.336 0.004
#> GSM74390 3 0.4660 0.7492 0.000 0.000 0.728 0.192 0.080
#> GSM74391 3 0.4337 0.6231 0.196 0.000 0.748 0.056 0.000
#> GSM74392 4 0.2911 0.7285 0.136 0.000 0.008 0.852 0.004
#> GSM74393 4 0.2819 0.7192 0.060 0.000 0.052 0.884 0.004
#> GSM74394 5 0.2152 0.8261 0.000 0.032 0.004 0.044 0.920
#> GSM74239 1 0.0609 0.8167 0.980 0.000 0.000 0.000 0.020
#> GSM74364 1 0.0703 0.8180 0.976 0.000 0.000 0.024 0.000
#> GSM74365 1 0.3496 0.6597 0.788 0.000 0.000 0.012 0.200
#> GSM74366 5 0.1779 0.8424 0.040 0.008 0.004 0.008 0.940
#> GSM74367 1 0.1732 0.7904 0.920 0.000 0.000 0.000 0.080
#> GSM74377 5 0.3910 0.6953 0.248 0.004 0.000 0.008 0.740
#> GSM74378 5 0.0798 0.8429 0.016 0.008 0.000 0.000 0.976
#> GSM74379 5 0.3231 0.7482 0.196 0.000 0.000 0.004 0.800
#> GSM74380 5 0.1831 0.8375 0.076 0.004 0.000 0.000 0.920
#> GSM74381 5 0.1082 0.8449 0.028 0.008 0.000 0.000 0.964
#> GSM121357 3 0.4480 0.7545 0.000 0.068 0.776 0.016 0.140
#> GSM121361 5 0.1845 0.8250 0.000 0.016 0.000 0.056 0.928
#> GSM121363 5 0.2409 0.8190 0.000 0.044 0.016 0.028 0.912
#> GSM121368 5 0.1893 0.8308 0.000 0.012 0.024 0.028 0.936
#> GSM121369 5 0.2727 0.7994 0.000 0.016 0.000 0.116 0.868
#> GSM74368 1 0.1310 0.8075 0.956 0.000 0.000 0.020 0.024
#> GSM74369 1 0.1041 0.8170 0.964 0.000 0.000 0.032 0.004
#> GSM74370 5 0.5672 0.4507 0.104 0.000 0.000 0.312 0.584
#> GSM74371 1 0.1478 0.8110 0.936 0.000 0.000 0.064 0.000
#> GSM74372 5 0.3055 0.8069 0.064 0.000 0.000 0.072 0.864
#> GSM74373 5 0.1864 0.8412 0.068 0.004 0.000 0.004 0.924
#> GSM74374 1 0.2011 0.7897 0.908 0.000 0.000 0.004 0.088
#> GSM74375 1 0.3076 0.7521 0.868 0.000 0.008 0.036 0.088
#> GSM74376 5 0.2575 0.8263 0.100 0.004 0.000 0.012 0.884
#> GSM74405 5 0.0771 0.8444 0.020 0.004 0.000 0.000 0.976
#> GSM74351 1 0.1410 0.8119 0.940 0.000 0.000 0.060 0.000
#> GSM74352 1 0.4481 0.4124 0.668 0.004 0.000 0.016 0.312
#> GSM74353 1 0.1628 0.8161 0.936 0.000 0.000 0.056 0.008
#> GSM74354 1 0.1571 0.8059 0.936 0.000 0.000 0.004 0.060
#> GSM74355 5 0.2302 0.8358 0.080 0.008 0.000 0.008 0.904
#> GSM74382 1 0.3109 0.7045 0.800 0.000 0.000 0.200 0.000
#> GSM74383 1 0.2069 0.8057 0.912 0.000 0.000 0.012 0.076
#> GSM74384 5 0.0798 0.8429 0.016 0.008 0.000 0.000 0.976
#> GSM74385 1 0.2605 0.7612 0.852 0.000 0.000 0.148 0.000
#> GSM74386 5 0.4425 0.3510 0.392 0.000 0.000 0.008 0.600
#> GSM74395 1 0.3409 0.7753 0.836 0.000 0.000 0.052 0.112
#> GSM74396 1 0.1549 0.8169 0.944 0.000 0.000 0.016 0.040
#> GSM74397 1 0.0609 0.8159 0.980 0.000 0.000 0.000 0.020
#> GSM74398 5 0.4264 0.4516 0.376 0.000 0.000 0.004 0.620
#> GSM74399 1 0.4524 0.3621 0.644 0.000 0.000 0.020 0.336
#> GSM74400 1 0.1270 0.8144 0.948 0.000 0.000 0.052 0.000
#> GSM74401 1 0.0566 0.8176 0.984 0.000 0.000 0.004 0.012
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM74356 3 0.1644 0.8029 0.000 0.000 0.920 0.076 0.004 0.000
#> GSM74357 3 0.2790 0.7739 0.000 0.000 0.844 0.132 0.024 0.000
#> GSM74358 3 0.2651 0.7933 0.000 0.000 0.860 0.112 0.028 0.000
#> GSM74359 4 0.2002 0.7403 0.012 0.000 0.076 0.908 0.004 0.000
#> GSM74360 4 0.1821 0.7376 0.024 0.000 0.008 0.928 0.040 0.000
#> GSM74361 3 0.3766 0.7018 0.000 0.000 0.748 0.212 0.040 0.000
#> GSM74362 4 0.2163 0.7092 0.000 0.000 0.092 0.892 0.016 0.000
#> GSM74363 3 0.2058 0.8051 0.000 0.000 0.908 0.056 0.036 0.000
#> GSM74402 1 0.3245 0.6741 0.764 0.000 0.000 0.228 0.008 0.000
#> GSM74403 1 0.3966 0.1260 0.552 0.000 0.000 0.444 0.004 0.000
#> GSM74404 4 0.4252 0.4309 0.372 0.000 0.000 0.604 0.024 0.000
#> GSM74406 4 0.3823 0.2924 0.436 0.000 0.000 0.564 0.000 0.000
#> GSM74407 3 0.3884 0.6908 0.052 0.000 0.760 0.004 0.184 0.000
#> GSM74408 4 0.4452 0.5804 0.288 0.000 0.040 0.664 0.008 0.000
#> GSM74409 4 0.3268 0.7126 0.144 0.000 0.044 0.812 0.000 0.000
#> GSM74410 4 0.4977 0.6417 0.212 0.000 0.108 0.668 0.012 0.000
#> GSM119936 4 0.4528 0.5407 0.316 0.000 0.044 0.636 0.004 0.000
#> GSM119937 4 0.4370 0.5363 0.324 0.000 0.032 0.640 0.004 0.000
#> GSM74411 5 0.2454 0.8431 0.000 0.016 0.104 0.000 0.876 0.004
#> GSM74412 5 0.2702 0.8307 0.000 0.036 0.092 0.000 0.868 0.004
#> GSM74413 5 0.2454 0.8431 0.000 0.016 0.104 0.000 0.876 0.004
#> GSM74414 5 0.4402 0.5668 0.000 0.244 0.008 0.000 0.696 0.052
#> GSM74415 5 0.2386 0.8457 0.000 0.004 0.112 0.004 0.876 0.004
#> GSM121379 2 0.0260 0.9775 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM121380 2 0.0146 0.9775 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM121381 2 0.0146 0.9772 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM121382 2 0.0458 0.9755 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM121383 2 0.0260 0.9775 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM121384 2 0.0146 0.9772 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM121385 2 0.0458 0.9761 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM121386 2 0.0260 0.9769 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM121387 2 0.0146 0.9775 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM121388 2 0.2069 0.9129 0.000 0.908 0.068 0.004 0.020 0.000
#> GSM121389 2 0.0363 0.9759 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM121390 2 0.0146 0.9772 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM121391 2 0.0547 0.9744 0.000 0.980 0.000 0.000 0.020 0.000
#> GSM121392 2 0.0405 0.9725 0.000 0.988 0.000 0.000 0.004 0.008
#> GSM121393 2 0.0862 0.9653 0.000 0.972 0.008 0.004 0.016 0.000
#> GSM121394 2 0.0458 0.9748 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM121395 2 0.0508 0.9717 0.000 0.984 0.004 0.000 0.012 0.000
#> GSM121396 2 0.3683 0.7533 0.000 0.784 0.160 0.004 0.052 0.000
#> GSM121397 2 0.0458 0.9761 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM121398 2 0.0000 0.9767 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM121399 2 0.0260 0.9775 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM74240 5 0.3627 0.8135 0.000 0.000 0.224 0.020 0.752 0.004
#> GSM74241 5 0.2933 0.8358 0.000 0.000 0.200 0.004 0.796 0.000
#> GSM74242 3 0.3684 0.4557 0.000 0.000 0.664 0.004 0.332 0.000
#> GSM74243 3 0.3653 0.5327 0.000 0.000 0.692 0.008 0.300 0.000
#> GSM74244 5 0.3409 0.7322 0.000 0.000 0.300 0.000 0.700 0.000
#> GSM74245 5 0.2793 0.8354 0.000 0.000 0.200 0.000 0.800 0.000
#> GSM74246 5 0.2624 0.8504 0.000 0.000 0.148 0.004 0.844 0.004
#> GSM74247 5 0.2584 0.8504 0.000 0.000 0.144 0.004 0.848 0.004
#> GSM74248 5 0.3758 0.7465 0.000 0.000 0.284 0.016 0.700 0.000
#> GSM74416 1 0.2320 0.7705 0.864 0.000 0.000 0.132 0.004 0.000
#> GSM74417 1 0.3619 0.5295 0.680 0.000 0.000 0.316 0.004 0.000
#> GSM74418 1 0.3271 0.6699 0.760 0.000 0.000 0.232 0.008 0.000
#> GSM74419 4 0.4593 0.0980 0.472 0.000 0.000 0.492 0.036 0.000
#> GSM121358 3 0.1049 0.8223 0.000 0.000 0.960 0.008 0.032 0.000
#> GSM121359 3 0.2957 0.7700 0.000 0.032 0.844 0.004 0.120 0.000
#> GSM121360 4 0.4127 0.3932 0.000 0.000 0.004 0.684 0.028 0.284
#> GSM121362 4 0.1508 0.7396 0.004 0.000 0.020 0.948 0.012 0.016
#> GSM121364 4 0.1826 0.7482 0.020 0.000 0.052 0.924 0.004 0.000
#> GSM121365 3 0.1633 0.8227 0.000 0.000 0.932 0.024 0.044 0.000
#> GSM121366 3 0.1714 0.8050 0.000 0.000 0.908 0.000 0.092 0.000
#> GSM121367 3 0.1082 0.8210 0.000 0.000 0.956 0.004 0.040 0.000
#> GSM121370 3 0.1765 0.7996 0.000 0.000 0.904 0.000 0.096 0.000
#> GSM121371 3 0.0820 0.8222 0.000 0.000 0.972 0.012 0.016 0.000
#> GSM121372 3 0.1897 0.8105 0.000 0.004 0.908 0.004 0.084 0.000
#> GSM121373 4 0.1708 0.7499 0.040 0.000 0.024 0.932 0.004 0.000
#> GSM121374 4 0.1536 0.7486 0.016 0.000 0.040 0.940 0.004 0.000
#> GSM121407 3 0.4123 0.6948 0.000 0.136 0.772 0.000 0.072 0.020
#> GSM74387 5 0.4009 0.6247 0.000 0.000 0.356 0.008 0.632 0.004
#> GSM74388 6 0.4406 0.6878 0.000 0.140 0.000 0.008 0.116 0.736
#> GSM74389 3 0.4601 0.6531 0.000 0.000 0.688 0.200 0.112 0.000
#> GSM74390 3 0.2401 0.8130 0.000 0.000 0.900 0.020 0.036 0.044
#> GSM74391 5 0.4054 0.7117 0.104 0.000 0.060 0.044 0.792 0.000
#> GSM74392 4 0.1693 0.7494 0.044 0.000 0.020 0.932 0.004 0.000
#> GSM74393 4 0.2122 0.7310 0.008 0.000 0.084 0.900 0.008 0.000
#> GSM74394 6 0.4473 0.6795 0.000 0.020 0.004 0.040 0.220 0.716
#> GSM74239 1 0.0820 0.8059 0.972 0.000 0.000 0.012 0.000 0.016
#> GSM74364 1 0.1605 0.8066 0.936 0.000 0.000 0.044 0.016 0.004
#> GSM74365 1 0.2912 0.6626 0.784 0.000 0.000 0.000 0.000 0.216
#> GSM74366 6 0.1988 0.8635 0.048 0.004 0.004 0.000 0.024 0.920
#> GSM74367 1 0.2356 0.7721 0.884 0.000 0.004 0.004 0.008 0.100
#> GSM74377 6 0.2772 0.7942 0.180 0.000 0.000 0.000 0.004 0.816
#> GSM74378 6 0.0777 0.8635 0.024 0.000 0.004 0.000 0.000 0.972
#> GSM74379 6 0.2442 0.8199 0.144 0.000 0.004 0.000 0.000 0.852
#> GSM74380 6 0.1531 0.8620 0.068 0.000 0.000 0.000 0.004 0.928
#> GSM74381 6 0.0865 0.8649 0.036 0.000 0.000 0.000 0.000 0.964
#> GSM121357 3 0.4529 0.6294 0.004 0.032 0.728 0.000 0.040 0.196
#> GSM121361 6 0.1936 0.8384 0.000 0.008 0.008 0.028 0.028 0.928
#> GSM121363 6 0.1140 0.8486 0.000 0.012 0.008 0.008 0.008 0.964
#> GSM121368 6 0.1667 0.8448 0.000 0.004 0.008 0.008 0.044 0.936
#> GSM121369 6 0.2772 0.8040 0.000 0.000 0.004 0.092 0.040 0.864
#> GSM74368 1 0.3979 0.7208 0.800 0.000 0.120 0.012 0.024 0.044
#> GSM74369 1 0.2254 0.7937 0.916 0.000 0.016 0.020 0.024 0.024
#> GSM74370 4 0.5360 0.0537 0.032 0.000 0.004 0.508 0.036 0.420
#> GSM74371 1 0.2178 0.7732 0.868 0.000 0.000 0.132 0.000 0.000
#> GSM74372 6 0.3751 0.7826 0.028 0.000 0.004 0.100 0.052 0.816
#> GSM74373 6 0.1219 0.8660 0.048 0.000 0.004 0.000 0.000 0.948
#> GSM74374 1 0.1218 0.8021 0.956 0.000 0.000 0.004 0.012 0.028
#> GSM74375 1 0.3134 0.7186 0.824 0.000 0.000 0.012 0.148 0.016
#> GSM74376 6 0.3585 0.8031 0.156 0.000 0.000 0.004 0.048 0.792
#> GSM74405 6 0.0865 0.8649 0.036 0.000 0.000 0.000 0.000 0.964
#> GSM74351 1 0.2048 0.7788 0.880 0.000 0.000 0.120 0.000 0.000
#> GSM74352 1 0.3606 0.5564 0.728 0.000 0.000 0.000 0.016 0.256
#> GSM74353 1 0.1501 0.7975 0.924 0.000 0.000 0.076 0.000 0.000
#> GSM74354 1 0.1265 0.7961 0.948 0.000 0.000 0.000 0.008 0.044
#> GSM74355 6 0.1686 0.8614 0.064 0.000 0.000 0.000 0.012 0.924
#> GSM74382 1 0.3528 0.5692 0.700 0.000 0.000 0.296 0.004 0.000
#> GSM74383 1 0.1858 0.7962 0.912 0.000 0.000 0.012 0.000 0.076
#> GSM74384 6 0.0891 0.8638 0.024 0.000 0.000 0.000 0.008 0.968
#> GSM74385 1 0.3398 0.6483 0.740 0.000 0.000 0.252 0.008 0.000
#> GSM74386 6 0.4312 0.4103 0.368 0.000 0.000 0.028 0.000 0.604
#> GSM74395 1 0.4288 0.7261 0.748 0.000 0.000 0.132 0.008 0.112
#> GSM74396 1 0.1624 0.8089 0.936 0.000 0.000 0.040 0.004 0.020
#> GSM74397 1 0.0767 0.8055 0.976 0.000 0.000 0.008 0.004 0.012
#> GSM74398 6 0.4032 0.3427 0.420 0.000 0.000 0.000 0.008 0.572
#> GSM74399 1 0.3558 0.5710 0.736 0.000 0.000 0.000 0.016 0.248
#> GSM74400 1 0.1714 0.7949 0.908 0.000 0.000 0.092 0.000 0.000
#> GSM74401 1 0.0291 0.8057 0.992 0.000 0.000 0.004 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:NMF 117 1.51e-11 2
#> ATC:NMF 107 3.98e-15 3
#> ATC:NMF 112 7.37e-29 4
#> ATC:NMF 102 3.44e-31 5
#> ATC:NMF 112 4.38e-45 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