Date: 2019-12-25 21:11:22 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 125 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 125
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:NMF | 2 | 1.000 | 0.991 | 0.996 | ** | |
| MAD:skmeans | 2 | 0.984 | 0.946 | 0.978 | ** | |
| ATC:skmeans | 4 | 0.966 | 0.953 | 0.980 | ** | 2 |
| MAD:kmeans | 2 | 0.950 | 0.938 | 0.975 | * | |
| ATC:pam | 6 | 0.917 | 0.909 | 0.951 | * | 2 |
| SD:kmeans | 2 | 0.915 | 0.909 | 0.964 | * | |
| ATC:kmeans | 4 | 0.914 | 0.907 | 0.938 | * | 2 |
| MAD:NMF | 2 | 0.901 | 0.931 | 0.970 | * | |
| SD:skmeans | 2 | 0.900 | 0.927 | 0.970 | * | |
| CV:kmeans | 2 | 0.885 | 0.914 | 0.963 | ||
| CV:NMF | 2 | 0.881 | 0.911 | 0.961 | ||
| CV:skmeans | 2 | 0.867 | 0.929 | 0.969 | ||
| SD:NMF | 2 | 0.853 | 0.911 | 0.964 | ||
| ATC:mclust | 3 | 0.632 | 0.817 | 0.879 | ||
| ATC:hclust | 4 | 0.623 | 0.640 | 0.838 | ||
| MAD:mclust | 3 | 0.513 | 0.769 | 0.867 | ||
| SD:mclust | 3 | 0.464 | 0.733 | 0.828 | ||
| MAD:pam | 2 | 0.402 | 0.823 | 0.894 | ||
| CV:mclust | 3 | 0.363 | 0.794 | 0.842 | ||
| SD:pam | 2 | 0.329 | 0.728 | 0.867 | ||
| SD:hclust | 4 | 0.314 | 0.613 | 0.755 | ||
| CV:hclust | 2 | 0.268 | 0.808 | 0.869 | ||
| CV:pam | 2 | 0.211 | 0.633 | 0.824 | ||
| MAD:hclust | 2 | 0.169 | 0.662 | 0.807 |
**: 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 0.853 0.911 0.964 0.503 0.496 0.496
#> CV:NMF 2 0.881 0.911 0.961 0.502 0.496 0.496
#> MAD:NMF 2 0.901 0.931 0.970 0.503 0.496 0.496
#> ATC:NMF 2 1.000 0.991 0.996 0.502 0.499 0.499
#> SD:skmeans 2 0.900 0.927 0.970 0.504 0.496 0.496
#> CV:skmeans 2 0.867 0.929 0.969 0.504 0.496 0.496
#> MAD:skmeans 2 0.984 0.946 0.978 0.504 0.496 0.496
#> ATC:skmeans 2 1.000 1.000 1.000 0.503 0.498 0.498
#> SD:mclust 2 0.226 0.621 0.774 0.424 0.545 0.545
#> CV:mclust 2 0.232 0.700 0.804 0.429 0.608 0.608
#> MAD:mclust 2 0.468 0.786 0.848 0.433 0.573 0.573
#> ATC:mclust 2 0.440 0.777 0.822 0.438 0.580 0.580
#> SD:kmeans 2 0.915 0.909 0.964 0.501 0.499 0.499
#> CV:kmeans 2 0.885 0.914 0.963 0.502 0.497 0.497
#> MAD:kmeans 2 0.950 0.938 0.975 0.502 0.499 0.499
#> ATC:kmeans 2 1.000 0.972 0.971 0.496 0.498 0.498
#> SD:pam 2 0.329 0.728 0.867 0.499 0.496 0.496
#> CV:pam 2 0.211 0.633 0.824 0.485 0.505 0.505
#> MAD:pam 2 0.402 0.823 0.894 0.496 0.499 0.499
#> ATC:pam 2 1.000 0.965 0.981 0.502 0.498 0.498
#> SD:hclust 2 0.154 0.550 0.708 0.388 0.513 0.513
#> CV:hclust 2 0.268 0.808 0.869 0.386 0.624 0.624
#> MAD:hclust 2 0.169 0.662 0.807 0.432 0.510 0.510
#> ATC:hclust 2 0.431 0.793 0.885 0.304 0.659 0.659
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.473 0.632 0.759 0.304 0.797 0.613
#> CV:NMF 3 0.455 0.624 0.753 0.304 0.796 0.611
#> MAD:NMF 3 0.502 0.622 0.813 0.303 0.820 0.650
#> ATC:NMF 3 0.695 0.807 0.903 0.264 0.751 0.556
#> SD:skmeans 3 0.591 0.754 0.861 0.305 0.781 0.586
#> CV:skmeans 3 0.632 0.782 0.877 0.310 0.773 0.572
#> MAD:skmeans 3 0.701 0.797 0.895 0.305 0.800 0.616
#> ATC:skmeans 3 0.765 0.880 0.911 0.285 0.829 0.665
#> SD:mclust 3 0.464 0.733 0.828 0.516 0.697 0.486
#> CV:mclust 3 0.363 0.794 0.842 0.494 0.645 0.450
#> MAD:mclust 3 0.513 0.769 0.867 0.501 0.746 0.560
#> ATC:mclust 3 0.632 0.817 0.879 0.452 0.722 0.534
#> SD:kmeans 3 0.525 0.589 0.754 0.279 0.819 0.659
#> CV:kmeans 3 0.561 0.645 0.831 0.282 0.784 0.596
#> MAD:kmeans 3 0.533 0.634 0.812 0.276 0.814 0.649
#> ATC:kmeans 3 0.821 0.746 0.839 0.245 0.871 0.747
#> SD:pam 3 0.415 0.677 0.794 0.315 0.750 0.539
#> CV:pam 3 0.226 0.466 0.719 0.300 0.671 0.448
#> MAD:pam 3 0.451 0.712 0.824 0.297 0.701 0.476
#> ATC:pam 3 0.724 0.843 0.899 0.220 0.898 0.797
#> SD:hclust 3 0.135 0.531 0.725 0.319 0.620 0.460
#> CV:hclust 3 0.249 0.746 0.845 0.153 0.980 0.968
#> MAD:hclust 3 0.244 0.614 0.782 0.252 0.920 0.854
#> ATC:hclust 3 0.356 0.638 0.759 0.408 0.949 0.923
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.442 0.498 0.710 0.1258 0.786 0.473
#> CV:NMF 4 0.412 0.505 0.700 0.1310 0.818 0.534
#> MAD:NMF 4 0.450 0.502 0.723 0.1353 0.783 0.470
#> ATC:NMF 4 0.606 0.715 0.838 0.1610 0.819 0.554
#> SD:skmeans 4 0.525 0.616 0.780 0.1274 0.855 0.609
#> CV:skmeans 4 0.467 0.554 0.714 0.1261 0.857 0.611
#> MAD:skmeans 4 0.547 0.665 0.808 0.1303 0.827 0.551
#> ATC:skmeans 4 0.966 0.953 0.980 0.1549 0.870 0.648
#> SD:mclust 4 0.484 0.592 0.765 0.0800 0.863 0.648
#> CV:mclust 4 0.455 0.565 0.741 0.1116 0.925 0.786
#> MAD:mclust 4 0.530 0.558 0.790 0.1107 0.860 0.626
#> ATC:mclust 4 0.543 0.365 0.659 0.1340 0.727 0.420
#> SD:kmeans 4 0.584 0.441 0.670 0.1209 0.791 0.510
#> CV:kmeans 4 0.594 0.397 0.640 0.1171 0.858 0.626
#> MAD:kmeans 4 0.608 0.433 0.707 0.1266 0.947 0.866
#> ATC:kmeans 4 0.914 0.907 0.938 0.1284 0.867 0.679
#> SD:pam 4 0.497 0.639 0.787 0.1016 0.898 0.716
#> CV:pam 4 0.354 0.456 0.690 0.1251 0.843 0.606
#> MAD:pam 4 0.492 0.595 0.766 0.1096 0.802 0.525
#> ATC:pam 4 0.630 0.524 0.768 0.1629 0.795 0.539
#> SD:hclust 4 0.314 0.613 0.755 0.1908 0.821 0.679
#> CV:hclust 4 0.293 0.719 0.830 0.0876 0.996 0.994
#> MAD:hclust 4 0.272 0.472 0.704 0.1494 0.851 0.724
#> ATC:hclust 4 0.623 0.640 0.838 0.4679 0.592 0.418
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.500 0.397 0.594 0.0703 0.891 0.612
#> CV:NMF 5 0.486 0.415 0.593 0.0706 0.911 0.684
#> MAD:NMF 5 0.489 0.438 0.635 0.0649 0.876 0.567
#> ATC:NMF 5 0.610 0.550 0.736 0.0667 0.881 0.596
#> SD:skmeans 5 0.564 0.510 0.704 0.0645 0.948 0.806
#> CV:skmeans 5 0.476 0.430 0.640 0.0667 0.950 0.816
#> MAD:skmeans 5 0.542 0.480 0.663 0.0625 0.964 0.863
#> ATC:skmeans 5 0.846 0.834 0.882 0.0509 0.955 0.829
#> SD:mclust 5 0.616 0.591 0.744 0.1111 0.827 0.504
#> CV:mclust 5 0.594 0.595 0.725 0.0855 0.873 0.604
#> MAD:mclust 5 0.689 0.709 0.817 0.0797 0.833 0.488
#> ATC:mclust 5 0.662 0.756 0.799 0.0667 0.826 0.520
#> SD:kmeans 5 0.640 0.685 0.759 0.0715 0.790 0.400
#> CV:kmeans 5 0.644 0.714 0.767 0.0704 0.810 0.436
#> MAD:kmeans 5 0.605 0.586 0.728 0.0729 0.804 0.480
#> ATC:kmeans 5 0.764 0.767 0.841 0.0879 0.941 0.810
#> SD:pam 5 0.510 0.587 0.727 0.0453 0.967 0.887
#> CV:pam 5 0.409 0.446 0.693 0.0424 0.924 0.748
#> MAD:pam 5 0.555 0.589 0.741 0.0721 0.893 0.661
#> ATC:pam 5 0.786 0.836 0.845 0.0958 0.850 0.541
#> SD:hclust 5 0.351 0.568 0.754 0.0591 0.956 0.898
#> CV:hclust 5 0.278 0.728 0.805 0.0639 1.000 0.999
#> MAD:hclust 5 0.330 0.463 0.679 0.0686 0.888 0.750
#> ATC:hclust 5 0.631 0.720 0.834 0.0741 0.867 0.669
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.529 0.366 0.588 0.0450 0.854 0.439
#> CV:NMF 6 0.522 0.330 0.526 0.0452 0.858 0.466
#> MAD:NMF 6 0.537 0.340 0.575 0.0408 0.919 0.651
#> ATC:NMF 6 0.653 0.481 0.708 0.0346 0.873 0.543
#> SD:skmeans 6 0.588 0.437 0.637 0.0410 0.926 0.710
#> CV:skmeans 6 0.499 0.298 0.568 0.0406 0.910 0.652
#> MAD:skmeans 6 0.570 0.356 0.609 0.0416 0.938 0.758
#> ATC:skmeans 6 0.846 0.831 0.880 0.0452 0.932 0.711
#> SD:mclust 6 0.693 0.608 0.792 0.0429 0.937 0.732
#> CV:mclust 6 0.719 0.656 0.798 0.0510 0.903 0.616
#> MAD:mclust 6 0.767 0.761 0.862 0.0440 0.899 0.585
#> ATC:mclust 6 0.865 0.763 0.871 0.0636 0.929 0.696
#> SD:kmeans 6 0.691 0.729 0.761 0.0408 0.933 0.714
#> CV:kmeans 6 0.674 0.617 0.742 0.0379 0.982 0.922
#> MAD:kmeans 6 0.682 0.756 0.766 0.0431 0.925 0.665
#> ATC:kmeans 6 0.748 0.682 0.779 0.0525 0.910 0.657
#> SD:pam 6 0.543 0.584 0.729 0.0339 0.956 0.834
#> CV:pam 6 0.430 0.410 0.689 0.0164 0.961 0.851
#> MAD:pam 6 0.602 0.603 0.753 0.0567 0.899 0.608
#> ATC:pam 6 0.917 0.909 0.951 0.0660 0.897 0.585
#> SD:hclust 6 0.384 0.536 0.735 0.0459 0.976 0.942
#> CV:hclust 6 0.244 0.583 0.730 0.1516 0.984 0.974
#> MAD:hclust 6 0.340 0.373 0.652 0.0437 0.933 0.826
#> ATC:hclust 6 0.736 0.700 0.851 0.0796 0.974 0.911
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 time(p) gender(p) k
#> SD:NMF 121 0.546 0.1026 2
#> CV:NMF 120 0.448 0.1971 2
#> MAD:NMF 120 0.499 0.1533 2
#> ATC:NMF 125 0.492 0.3276 2
#> SD:skmeans 120 0.638 0.0970 2
#> CV:skmeans 122 0.721 0.2038 2
#> MAD:skmeans 121 0.673 0.1794 2
#> ATC:skmeans 125 0.293 0.3989 2
#> SD:mclust 110 0.906 0.0302 2
#> CV:mclust 111 0.752 0.0301 2
#> MAD:mclust 123 0.763 0.2784 2
#> ATC:mclust 115 0.930 0.2946 2
#> SD:kmeans 118 0.689 0.1305 2
#> CV:kmeans 120 0.576 0.2411 2
#> MAD:kmeans 121 0.547 0.2171 2
#> ATC:kmeans 125 0.293 0.3989 2
#> SD:pam 105 0.644 0.0879 2
#> CV:pam 104 0.686 0.0719 2
#> MAD:pam 119 0.556 0.3469 2
#> ATC:pam 124 0.453 0.3588 2
#> SD:hclust 94 0.700 0.0792 2
#> CV:hclust 119 0.137 0.5027 2
#> MAD:hclust 101 0.677 0.2496 2
#> ATC:hclust 111 0.674 0.5267 2
test_to_known_factors(res_list, k = 3)
#> n time(p) gender(p) k
#> SD:NMF 104 0.0518 0.1992 3
#> CV:NMF 102 0.1567 0.1823 3
#> MAD:NMF 98 0.2045 0.2603 3
#> ATC:NMF 120 0.2448 0.2936 3
#> SD:skmeans 112 0.1510 0.1909 3
#> CV:skmeans 112 0.1764 0.1834 3
#> MAD:skmeans 113 0.1404 0.2092 3
#> ATC:skmeans 123 0.5633 0.5130 3
#> SD:mclust 113 0.9083 0.2790 3
#> CV:mclust 121 0.6023 0.3802 3
#> MAD:mclust 114 0.4906 0.4258 3
#> ATC:mclust 120 0.8083 0.3113 3
#> SD:kmeans 89 0.2448 0.5242 3
#> CV:kmeans 96 0.3814 0.2801 3
#> MAD:kmeans 97 0.2623 0.3932 3
#> ATC:kmeans 105 0.3997 0.4716 3
#> SD:pam 107 0.1294 0.0942 3
#> CV:pam 77 0.4096 0.0646 3
#> MAD:pam 110 0.1807 0.2567 3
#> ATC:pam 124 0.2331 0.4462 3
#> SD:hclust 80 0.2040 0.5221 3
#> CV:hclust 117 0.1782 0.8475 3
#> MAD:hclust 103 0.4078 0.2960 3
#> ATC:hclust 118 0.9519 0.7696 3
test_to_known_factors(res_list, k = 4)
#> n time(p) gender(p) k
#> SD:NMF 79 0.241 0.01190 4
#> CV:NMF 76 0.239 0.00206 4
#> MAD:NMF 77 0.466 0.07586 4
#> ATC:NMF 109 0.876 0.52516 4
#> SD:skmeans 95 0.169 0.04630 4
#> CV:skmeans 91 0.507 0.09200 4
#> MAD:skmeans 99 0.134 0.05379 4
#> ATC:skmeans 124 0.294 0.64495 4
#> SD:mclust 102 0.376 0.05727 4
#> CV:mclust 93 0.498 0.00556 4
#> MAD:mclust 89 0.652 0.08974 4
#> ATC:mclust 34 0.338 1.00000 4
#> SD:kmeans 80 0.667 0.18374 4
#> CV:kmeans 78 0.187 0.78325 4
#> MAD:kmeans 72 0.256 0.15128 4
#> ATC:kmeans 121 0.642 0.74359 4
#> SD:pam 98 0.687 0.05038 4
#> CV:pam 73 0.625 0.07936 4
#> MAD:pam 92 0.859 0.34036 4
#> ATC:pam 75 0.469 0.74253 4
#> SD:hclust 93 0.114 0.48734 4
#> CV:hclust 108 0.380 0.50655 4
#> MAD:hclust 61 0.266 0.71634 4
#> ATC:hclust 102 0.611 0.32001 4
test_to_known_factors(res_list, k = 5)
#> n time(p) gender(p) k
#> SD:NMF 43 0.0668 0.01866 5
#> CV:NMF 58 0.0477 0.02672 5
#> MAD:NMF 64 0.2377 0.01374 5
#> ATC:NMF 84 0.9260 0.21578 5
#> SD:skmeans 83 0.1991 0.09821 5
#> CV:skmeans 71 0.1694 0.00467 5
#> MAD:skmeans 78 0.0944 0.00765 5
#> ATC:skmeans 121 0.8769 0.60552 5
#> SD:mclust 93 0.5043 0.00652 5
#> CV:mclust 99 0.5968 0.00150 5
#> MAD:mclust 108 0.4432 0.01020 5
#> ATC:mclust 118 0.9065 0.45013 5
#> SD:kmeans 114 0.2322 0.42993 5
#> CV:kmeans 116 0.2570 0.42183 5
#> MAD:kmeans 95 0.1024 0.05309 5
#> ATC:kmeans 118 0.7387 0.75550 5
#> SD:pam 87 0.7191 0.00707 5
#> CV:pam 65 0.5524 0.00445 5
#> MAD:pam 88 0.9433 0.41714 5
#> ATC:pam 121 0.7745 0.17372 5
#> SD:hclust 90 0.1284 0.63151 5
#> CV:hclust 114 0.1640 0.48751 5
#> MAD:hclust 59 0.3599 0.38870 5
#> ATC:hclust 111 0.7935 0.76683 5
test_to_known_factors(res_list, k = 6)
#> n time(p) gender(p) k
#> SD:NMF 30 0.437 0.050486 6
#> CV:NMF 26 0.629 0.000995 6
#> MAD:NMF 25 0.148 0.587937 6
#> ATC:NMF 76 0.656 0.892431 6
#> SD:skmeans 60 0.242 0.003523 6
#> CV:skmeans 33 0.928 0.042775 6
#> MAD:skmeans 45 0.759 0.011744 6
#> ATC:skmeans 121 0.892 0.497505 6
#> SD:mclust 94 0.452 0.032776 6
#> CV:mclust 100 0.865 0.056156 6
#> MAD:mclust 116 0.591 0.062501 6
#> ATC:mclust 116 0.948 0.419156 6
#> SD:kmeans 111 0.771 0.191327 6
#> CV:kmeans 97 0.441 0.770123 6
#> MAD:kmeans 117 0.464 0.142030 6
#> ATC:kmeans 108 0.907 0.452331 6
#> SD:pam 82 0.569 0.011335 6
#> CV:pam 50 0.664 0.014028 6
#> MAD:pam 92 0.923 0.234720 6
#> ATC:pam 122 0.793 0.035909 6
#> SD:hclust 89 0.232 0.677866 6
#> CV:hclust 99 0.239 0.271173 6
#> MAD:hclust 38 0.215 0.349859 6
#> ATC:hclust 98 0.805 0.683388 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 125 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 0.154 0.550 0.708 0.3881 0.513 0.513
#> 3 3 0.135 0.531 0.725 0.3193 0.620 0.460
#> 4 4 0.314 0.613 0.755 0.1908 0.821 0.679
#> 5 5 0.351 0.568 0.754 0.0591 0.956 0.898
#> 6 6 0.384 0.536 0.735 0.0459 0.976 0.942
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
#> GSM601752 2 0.8813 0.69077 0.300 0.700
#> GSM601782 1 0.0672 0.66860 0.992 0.008
#> GSM601792 1 0.9661 0.35746 0.608 0.392
#> GSM601797 1 0.9909 0.16298 0.556 0.444
#> GSM601827 1 0.0000 0.66850 1.000 0.000
#> GSM601837 2 0.0000 0.58684 0.000 1.000
#> GSM601842 2 0.9522 0.60259 0.372 0.628
#> GSM601857 1 0.7950 0.58306 0.760 0.240
#> GSM601867 2 1.0000 0.14447 0.500 0.500
#> GSM601747 1 0.8713 0.53643 0.708 0.292
#> GSM601757 1 0.6247 0.64018 0.844 0.156
#> GSM601762 2 0.9087 0.67297 0.324 0.676
#> GSM601767 2 0.9044 0.67655 0.320 0.680
#> GSM601772 2 0.7883 0.69574 0.236 0.764
#> GSM601777 1 0.9866 0.21492 0.568 0.432
#> GSM601787 2 0.9866 0.39324 0.432 0.568
#> GSM601802 2 0.8763 0.69241 0.296 0.704
#> GSM601807 1 0.6438 0.62162 0.836 0.164
#> GSM601812 1 0.0000 0.66850 1.000 0.000
#> GSM601817 1 0.0376 0.66961 0.996 0.004
#> GSM601822 1 0.9710 0.33340 0.600 0.400
#> GSM601832 2 0.9635 0.57063 0.388 0.612
#> GSM601847 1 0.9732 0.31236 0.596 0.404
#> GSM601852 1 0.0376 0.66956 0.996 0.004
#> GSM601862 1 0.0000 0.66850 1.000 0.000
#> GSM601753 2 0.8813 0.69077 0.300 0.700
#> GSM601783 1 0.0376 0.66956 0.996 0.004
#> GSM601793 1 0.9661 0.35746 0.608 0.392
#> GSM601798 2 0.9427 0.61459 0.360 0.640
#> GSM601828 1 0.0000 0.66850 1.000 0.000
#> GSM601838 2 0.0000 0.58684 0.000 1.000
#> GSM601843 2 0.9552 0.59420 0.376 0.624
#> GSM601858 1 0.9491 0.31958 0.632 0.368
#> GSM601868 1 0.0672 0.66810 0.992 0.008
#> GSM601748 1 0.0000 0.66850 1.000 0.000
#> GSM601758 1 0.0000 0.66850 1.000 0.000
#> GSM601763 1 0.9909 0.08921 0.556 0.444
#> GSM601768 2 0.9087 0.67266 0.324 0.676
#> GSM601773 2 0.7815 0.69514 0.232 0.768
#> GSM601778 1 0.9850 0.23235 0.572 0.428
#> GSM601788 2 0.9710 0.49074 0.400 0.600
#> GSM601803 2 0.8661 0.69583 0.288 0.712
#> GSM601808 1 0.0000 0.66850 1.000 0.000
#> GSM601813 1 0.0000 0.66850 1.000 0.000
#> GSM601818 1 0.0376 0.66961 0.996 0.004
#> GSM601823 1 0.9209 0.48201 0.664 0.336
#> GSM601833 2 0.9635 0.57063 0.388 0.612
#> GSM601848 1 0.8861 0.53414 0.696 0.304
#> GSM601853 1 0.0376 0.66723 0.996 0.004
#> GSM601863 1 0.0000 0.66850 1.000 0.000
#> GSM601754 2 0.9087 0.67369 0.324 0.676
#> GSM601784 2 0.8016 0.70075 0.244 0.756
#> GSM601794 1 0.9661 0.35609 0.608 0.392
#> GSM601799 2 0.9580 0.59439 0.380 0.620
#> GSM601829 1 0.3431 0.66518 0.936 0.064
#> GSM601839 2 0.0000 0.58684 0.000 1.000
#> GSM601844 1 0.8608 0.54996 0.716 0.284
#> GSM601859 2 0.9044 0.65258 0.320 0.680
#> GSM601869 1 0.0672 0.66810 0.992 0.008
#> GSM601749 1 0.0000 0.66850 1.000 0.000
#> GSM601759 1 0.0000 0.66850 1.000 0.000
#> GSM601764 1 0.9209 0.46806 0.664 0.336
#> GSM601769 2 0.0000 0.58684 0.000 1.000
#> GSM601774 2 0.4690 0.63938 0.100 0.900
#> GSM601779 1 0.8713 0.54783 0.708 0.292
#> GSM601789 2 0.8661 0.63865 0.288 0.712
#> GSM601804 2 0.9209 0.65046 0.336 0.664
#> GSM601809 1 0.6801 0.63413 0.820 0.180
#> GSM601814 2 0.0000 0.58684 0.000 1.000
#> GSM601819 1 0.0000 0.66850 1.000 0.000
#> GSM601824 1 0.9209 0.48201 0.664 0.336
#> GSM601834 2 0.9608 0.57831 0.384 0.616
#> GSM601849 1 0.8955 0.52183 0.688 0.312
#> GSM601854 1 0.0000 0.66850 1.000 0.000
#> GSM601864 2 0.7376 0.55748 0.208 0.792
#> GSM601755 2 0.8813 0.69077 0.300 0.700
#> GSM601785 2 0.8386 0.70110 0.268 0.732
#> GSM601795 1 0.9661 0.35609 0.608 0.392
#> GSM601800 2 0.9044 0.67477 0.320 0.680
#> GSM601830 1 0.4161 0.65538 0.916 0.084
#> GSM601840 1 0.9580 0.33836 0.620 0.380
#> GSM601845 2 1.0000 0.18539 0.496 0.504
#> GSM601860 2 0.9044 0.65258 0.320 0.680
#> GSM601870 1 0.6973 0.57796 0.812 0.188
#> GSM601750 1 0.0000 0.66850 1.000 0.000
#> GSM601760 1 0.0000 0.66850 1.000 0.000
#> GSM601765 2 0.9850 0.46452 0.428 0.572
#> GSM601770 2 0.9044 0.67655 0.320 0.680
#> GSM601775 1 0.9909 0.08688 0.556 0.444
#> GSM601780 1 0.8713 0.54783 0.708 0.292
#> GSM601790 2 0.1184 0.59661 0.016 0.984
#> GSM601805 2 0.8713 0.69395 0.292 0.708
#> GSM601810 1 0.6712 0.63580 0.824 0.176
#> GSM601815 2 0.0000 0.58684 0.000 1.000
#> GSM601820 1 0.0000 0.66850 1.000 0.000
#> GSM601825 2 0.8443 0.70315 0.272 0.728
#> GSM601835 2 0.9815 0.48204 0.420 0.580
#> GSM601850 1 0.9635 0.36064 0.612 0.388
#> GSM601855 1 0.4022 0.65716 0.920 0.080
#> GSM601865 2 0.7219 0.56281 0.200 0.800
#> GSM601756 2 0.8813 0.69077 0.300 0.700
#> GSM601786 2 0.2043 0.59354 0.032 0.968
#> GSM601796 1 0.9661 0.35609 0.608 0.392
#> GSM601801 2 0.9460 0.60636 0.364 0.636
#> GSM601831 1 0.0000 0.66850 1.000 0.000
#> GSM601841 1 0.6801 0.63620 0.820 0.180
#> GSM601846 1 0.9552 0.39752 0.624 0.376
#> GSM601861 2 0.0000 0.58684 0.000 1.000
#> GSM601871 2 0.9983 0.26731 0.476 0.524
#> GSM601751 1 0.9815 0.17945 0.580 0.420
#> GSM601761 1 0.6048 0.64842 0.852 0.148
#> GSM601766 1 1.0000 -0.18661 0.504 0.496
#> GSM601771 1 0.9944 -0.00484 0.544 0.456
#> GSM601776 1 0.9000 0.51647 0.684 0.316
#> GSM601781 1 0.9866 0.21492 0.568 0.432
#> GSM601791 1 0.8386 0.57254 0.732 0.268
#> GSM601806 2 0.8661 0.69583 0.288 0.712
#> GSM601811 1 0.6801 0.63413 0.820 0.180
#> GSM601816 1 0.9000 0.51677 0.684 0.316
#> GSM601821 2 0.0000 0.58684 0.000 1.000
#> GSM601826 1 0.8861 0.53306 0.696 0.304
#> GSM601836 1 0.9248 0.46379 0.660 0.340
#> GSM601851 1 0.8763 0.54331 0.704 0.296
#> GSM601856 1 0.0376 0.66723 0.996 0.004
#> GSM601866 1 0.0000 0.66850 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601752 2 0.326 0.6221 0.048 0.912 0.040
#> GSM601782 3 0.304 0.7993 0.000 0.104 0.896
#> GSM601792 2 0.701 0.4412 0.036 0.640 0.324
#> GSM601797 2 0.670 0.5532 0.052 0.712 0.236
#> GSM601827 3 0.393 0.7874 0.028 0.092 0.880
#> GSM601837 2 0.606 0.2610 0.384 0.616 0.000
#> GSM601842 2 0.507 0.6476 0.052 0.832 0.116
#> GSM601857 3 0.795 0.3147 0.064 0.388 0.548
#> GSM601867 2 0.856 0.5176 0.156 0.600 0.244
#> GSM601747 3 0.704 0.1401 0.020 0.448 0.532
#> GSM601757 3 0.670 0.6267 0.044 0.256 0.700
#> GSM601762 2 0.481 0.6428 0.060 0.848 0.092
#> GSM601767 2 0.492 0.6351 0.072 0.844 0.084
#> GSM601772 2 0.504 0.5887 0.120 0.832 0.048
#> GSM601777 2 0.689 0.5295 0.052 0.692 0.256
#> GSM601787 2 0.872 0.4469 0.252 0.584 0.164
#> GSM601802 2 0.315 0.6195 0.048 0.916 0.036
#> GSM601807 1 0.751 0.8799 0.636 0.300 0.064
#> GSM601812 3 0.311 0.7991 0.004 0.096 0.900
#> GSM601817 3 0.263 0.8004 0.000 0.084 0.916
#> GSM601822 2 0.700 0.4784 0.044 0.664 0.292
#> GSM601832 2 0.496 0.6482 0.040 0.832 0.128
#> GSM601847 2 0.700 0.5006 0.048 0.672 0.280
#> GSM601852 3 0.344 0.7988 0.016 0.088 0.896
#> GSM601862 3 0.364 0.7962 0.024 0.084 0.892
#> GSM601753 2 0.326 0.6221 0.048 0.912 0.040
#> GSM601783 3 0.295 0.8000 0.004 0.088 0.908
#> GSM601793 2 0.701 0.4412 0.036 0.640 0.324
#> GSM601798 2 0.438 0.6347 0.064 0.868 0.068
#> GSM601828 3 0.359 0.7853 0.028 0.076 0.896
#> GSM601838 2 0.606 0.2643 0.384 0.616 0.000
#> GSM601843 2 0.524 0.6461 0.056 0.824 0.120
#> GSM601858 2 0.817 0.3085 0.080 0.552 0.368
#> GSM601868 3 0.466 0.7770 0.032 0.124 0.844
#> GSM601748 3 0.254 0.7983 0.000 0.080 0.920
#> GSM601758 3 0.245 0.7972 0.000 0.076 0.924
#> GSM601763 2 0.662 0.5048 0.024 0.660 0.316
#> GSM601768 2 0.500 0.6369 0.072 0.840 0.088
#> GSM601773 2 0.493 0.5853 0.120 0.836 0.044
#> GSM601778 2 0.689 0.5274 0.052 0.692 0.256
#> GSM601788 2 0.798 0.5608 0.176 0.660 0.164
#> GSM601803 2 0.336 0.6178 0.056 0.908 0.036
#> GSM601808 3 0.624 0.6839 0.100 0.124 0.776
#> GSM601813 3 0.319 0.7989 0.004 0.100 0.896
#> GSM601818 3 0.263 0.8010 0.000 0.084 0.916
#> GSM601823 2 0.756 0.2758 0.044 0.556 0.400
#> GSM601833 2 0.496 0.6482 0.040 0.832 0.128
#> GSM601848 2 0.765 0.1216 0.044 0.516 0.440
#> GSM601853 3 0.425 0.7703 0.048 0.080 0.872
#> GSM601863 3 0.364 0.7962 0.024 0.084 0.892
#> GSM601754 2 0.365 0.6393 0.036 0.896 0.068
#> GSM601784 2 0.482 0.6010 0.108 0.844 0.048
#> GSM601794 2 0.691 0.4437 0.032 0.644 0.324
#> GSM601799 2 0.486 0.6483 0.044 0.840 0.116
#> GSM601829 3 0.536 0.7323 0.020 0.196 0.784
#> GSM601839 2 0.608 0.2561 0.388 0.612 0.000
#> GSM601844 3 0.700 0.1823 0.020 0.428 0.552
#> GSM601859 2 0.685 0.6242 0.120 0.740 0.140
#> GSM601869 3 0.466 0.7770 0.032 0.124 0.844
#> GSM601749 3 0.254 0.7982 0.000 0.080 0.920
#> GSM601759 3 0.245 0.7972 0.000 0.076 0.924
#> GSM601764 2 0.707 0.0718 0.020 0.500 0.480
#> GSM601769 2 0.601 0.2805 0.372 0.628 0.000
#> GSM601774 2 0.581 0.4236 0.264 0.724 0.012
#> GSM601779 3 0.747 0.1030 0.036 0.448 0.516
#> GSM601789 2 0.784 0.5407 0.220 0.660 0.120
#> GSM601804 2 0.425 0.6355 0.048 0.872 0.080
#> GSM601809 3 0.782 0.4710 0.072 0.324 0.604
#> GSM601814 2 0.601 0.2805 0.372 0.628 0.000
#> GSM601819 3 0.245 0.7972 0.000 0.076 0.924
#> GSM601824 2 0.756 0.2758 0.044 0.556 0.400
#> GSM601834 2 0.507 0.6486 0.044 0.828 0.128
#> GSM601849 2 0.753 0.1824 0.040 0.532 0.428
#> GSM601854 3 0.318 0.7898 0.016 0.076 0.908
#> GSM601864 2 0.676 -0.0202 0.436 0.552 0.012
#> GSM601755 2 0.326 0.6221 0.048 0.912 0.040
#> GSM601785 2 0.463 0.6156 0.088 0.856 0.056
#> GSM601795 2 0.691 0.4437 0.032 0.644 0.324
#> GSM601800 2 0.399 0.6348 0.052 0.884 0.064
#> GSM601830 1 0.864 0.9092 0.596 0.236 0.168
#> GSM601840 2 0.694 0.2955 0.020 0.576 0.404
#> GSM601845 2 0.646 0.6017 0.044 0.724 0.232
#> GSM601860 2 0.685 0.6242 0.120 0.740 0.140
#> GSM601870 1 0.839 0.8861 0.584 0.304 0.112
#> GSM601750 3 0.245 0.7972 0.000 0.076 0.924
#> GSM601760 3 0.254 0.7988 0.000 0.080 0.920
#> GSM601765 2 0.547 0.6462 0.040 0.800 0.160
#> GSM601770 2 0.492 0.6351 0.072 0.844 0.084
#> GSM601775 2 0.623 0.5105 0.012 0.672 0.316
#> GSM601780 3 0.747 0.1030 0.036 0.448 0.516
#> GSM601790 2 0.597 0.2902 0.364 0.636 0.000
#> GSM601805 2 0.325 0.6183 0.052 0.912 0.036
#> GSM601810 3 0.766 0.4793 0.064 0.324 0.612
#> GSM601815 2 0.601 0.2805 0.372 0.628 0.000
#> GSM601820 3 0.245 0.7972 0.000 0.076 0.924
#> GSM601825 2 0.432 0.6141 0.088 0.868 0.044
#> GSM601835 2 0.539 0.6466 0.044 0.808 0.148
#> GSM601850 2 0.715 0.4735 0.048 0.652 0.300
#> GSM601855 1 0.856 0.9079 0.604 0.232 0.164
#> GSM601865 2 0.679 -0.0114 0.448 0.540 0.012
#> GSM601756 2 0.326 0.6221 0.048 0.912 0.040
#> GSM601786 2 0.703 0.2983 0.368 0.604 0.028
#> GSM601796 2 0.691 0.4437 0.032 0.644 0.324
#> GSM601801 2 0.448 0.6353 0.064 0.864 0.072
#> GSM601831 3 0.385 0.7943 0.028 0.088 0.884
#> GSM601841 3 0.634 0.5539 0.016 0.312 0.672
#> GSM601846 2 0.879 0.1219 0.268 0.572 0.160
#> GSM601861 2 0.601 0.2805 0.372 0.628 0.000
#> GSM601871 2 0.834 0.0131 0.376 0.536 0.088
#> GSM601751 2 0.631 0.4714 0.012 0.660 0.328
#> GSM601761 3 0.639 0.5883 0.024 0.284 0.692
#> GSM601766 2 0.622 0.5925 0.032 0.728 0.240
#> GSM601771 2 0.674 0.5193 0.032 0.668 0.300
#> GSM601776 3 0.730 -0.0312 0.028 0.484 0.488
#> GSM601781 2 0.689 0.5295 0.052 0.692 0.256
#> GSM601791 3 0.743 0.1952 0.036 0.424 0.540
#> GSM601806 2 0.336 0.6178 0.056 0.908 0.036
#> GSM601811 3 0.782 0.4710 0.072 0.324 0.604
#> GSM601816 2 0.755 0.1586 0.040 0.524 0.436
#> GSM601821 2 0.601 0.2805 0.372 0.628 0.000
#> GSM601826 2 0.762 0.1798 0.044 0.532 0.424
#> GSM601836 2 0.705 0.1415 0.020 0.524 0.456
#> GSM601851 2 0.749 0.0425 0.036 0.496 0.468
#> GSM601856 3 0.442 0.7674 0.048 0.088 0.864
#> GSM601866 3 0.254 0.7983 0.000 0.080 0.920
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601752 4 0.2921 0.5526 0.000 0.140 0.000 0.860
#> GSM601782 1 0.2231 0.8354 0.932 0.012 0.012 0.044
#> GSM601792 4 0.5120 0.6428 0.196 0.044 0.008 0.752
#> GSM601797 4 0.4272 0.6680 0.108 0.048 0.012 0.832
#> GSM601827 1 0.3840 0.7974 0.860 0.012 0.076 0.052
#> GSM601837 2 0.3982 0.8968 0.000 0.776 0.004 0.220
#> GSM601842 4 0.4565 0.6165 0.064 0.140 0.000 0.796
#> GSM601857 1 0.7156 0.0539 0.512 0.052 0.040 0.396
#> GSM601867 4 0.8723 0.4838 0.196 0.192 0.100 0.512
#> GSM601747 1 0.5859 -0.1439 0.504 0.024 0.004 0.468
#> GSM601757 1 0.5851 0.5835 0.704 0.044 0.024 0.228
#> GSM601762 4 0.4761 0.5625 0.044 0.192 0.000 0.764
#> GSM601767 4 0.5041 0.4950 0.040 0.232 0.000 0.728
#> GSM601772 4 0.5386 0.2379 0.024 0.344 0.000 0.632
#> GSM601777 4 0.4305 0.6703 0.136 0.044 0.004 0.816
#> GSM601787 4 0.8705 0.2908 0.112 0.292 0.116 0.480
#> GSM601802 4 0.2921 0.5526 0.000 0.140 0.000 0.860
#> GSM601807 3 0.6381 0.7873 0.012 0.164 0.684 0.140
#> GSM601812 1 0.1917 0.8398 0.944 0.008 0.012 0.036
#> GSM601817 1 0.1339 0.8408 0.964 0.008 0.004 0.024
#> GSM601822 4 0.4604 0.6583 0.176 0.036 0.004 0.784
#> GSM601832 4 0.4775 0.6253 0.076 0.140 0.000 0.784
#> GSM601847 4 0.4604 0.6703 0.168 0.040 0.004 0.788
#> GSM601852 1 0.1767 0.8383 0.944 0.000 0.012 0.044
#> GSM601862 1 0.2007 0.8363 0.940 0.004 0.020 0.036
#> GSM601753 4 0.2921 0.5524 0.000 0.140 0.000 0.860
#> GSM601783 1 0.1584 0.8402 0.952 0.012 0.000 0.036
#> GSM601793 4 0.5120 0.6428 0.196 0.044 0.008 0.752
#> GSM601798 4 0.2983 0.6011 0.008 0.108 0.004 0.880
#> GSM601828 1 0.2632 0.8219 0.916 0.008 0.048 0.028
#> GSM601838 2 0.3764 0.8947 0.000 0.784 0.000 0.216
#> GSM601843 4 0.4696 0.6177 0.064 0.136 0.004 0.796
#> GSM601858 4 0.7550 0.4713 0.328 0.092 0.040 0.540
#> GSM601868 1 0.3164 0.8133 0.884 0.000 0.052 0.064
#> GSM601748 1 0.1114 0.8380 0.972 0.008 0.004 0.016
#> GSM601758 1 0.0927 0.8381 0.976 0.008 0.000 0.016
#> GSM601763 4 0.5478 0.6560 0.248 0.056 0.000 0.696
#> GSM601768 4 0.5123 0.5014 0.044 0.232 0.000 0.724
#> GSM601773 4 0.5289 0.2271 0.020 0.344 0.000 0.636
#> GSM601778 4 0.4305 0.6696 0.136 0.044 0.004 0.816
#> GSM601788 4 0.7874 0.4430 0.112 0.268 0.060 0.560
#> GSM601803 4 0.3074 0.5422 0.000 0.152 0.000 0.848
#> GSM601808 1 0.5539 0.6887 0.764 0.048 0.144 0.044
#> GSM601813 1 0.2010 0.8394 0.940 0.008 0.012 0.040
#> GSM601818 1 0.1388 0.8421 0.960 0.012 0.000 0.028
#> GSM601823 4 0.5498 0.5556 0.312 0.028 0.004 0.656
#> GSM601833 4 0.4775 0.6253 0.076 0.140 0.000 0.784
#> GSM601848 4 0.5443 0.4575 0.364 0.016 0.004 0.616
#> GSM601853 1 0.2966 0.8138 0.896 0.008 0.076 0.020
#> GSM601863 1 0.2007 0.8363 0.940 0.004 0.020 0.036
#> GSM601754 4 0.3659 0.5870 0.024 0.136 0.000 0.840
#> GSM601784 4 0.5252 0.2665 0.020 0.336 0.000 0.644
#> GSM601794 4 0.5075 0.6431 0.200 0.040 0.008 0.752
#> GSM601799 4 0.4094 0.6216 0.056 0.116 0.000 0.828
#> GSM601829 1 0.5465 0.6986 0.744 0.012 0.064 0.180
#> GSM601839 2 0.3945 0.8945 0.000 0.780 0.004 0.216
#> GSM601844 4 0.5816 0.1987 0.480 0.012 0.012 0.496
#> GSM601859 4 0.5998 0.5191 0.092 0.240 0.000 0.668
#> GSM601869 1 0.3164 0.8133 0.884 0.000 0.052 0.064
#> GSM601749 1 0.1174 0.8389 0.968 0.012 0.000 0.020
#> GSM601759 1 0.0927 0.8381 0.976 0.008 0.000 0.016
#> GSM601764 4 0.5775 0.4243 0.408 0.032 0.000 0.560
#> GSM601769 2 0.3873 0.9026 0.000 0.772 0.000 0.228
#> GSM601774 2 0.4898 0.6045 0.000 0.584 0.000 0.416
#> GSM601779 4 0.5366 0.3111 0.440 0.012 0.000 0.548
#> GSM601789 4 0.7338 0.2513 0.088 0.352 0.028 0.532
#> GSM601804 4 0.3749 0.5885 0.032 0.128 0.000 0.840
#> GSM601809 1 0.7691 0.3864 0.552 0.044 0.108 0.296
#> GSM601814 2 0.3873 0.9026 0.000 0.772 0.000 0.228
#> GSM601819 1 0.1059 0.8376 0.972 0.012 0.000 0.016
#> GSM601824 4 0.5498 0.5556 0.312 0.028 0.004 0.656
#> GSM601834 4 0.4824 0.6243 0.076 0.144 0.000 0.780
#> GSM601849 4 0.5395 0.4899 0.352 0.016 0.004 0.628
#> GSM601854 1 0.1811 0.8321 0.948 0.004 0.028 0.020
#> GSM601864 2 0.6991 0.5676 0.000 0.580 0.188 0.232
#> GSM601755 4 0.2921 0.5526 0.000 0.140 0.000 0.860
#> GSM601785 4 0.5184 0.3471 0.024 0.304 0.000 0.672
#> GSM601795 4 0.5075 0.6431 0.200 0.040 0.008 0.752
#> GSM601800 4 0.3443 0.5780 0.016 0.136 0.000 0.848
#> GSM601830 3 0.1543 0.8476 0.008 0.004 0.956 0.032
#> GSM601840 4 0.6203 0.5010 0.356 0.040 0.012 0.592
#> GSM601845 4 0.5124 0.6677 0.160 0.072 0.004 0.764
#> GSM601860 4 0.5998 0.5191 0.092 0.240 0.000 0.668
#> GSM601870 3 0.4898 0.8068 0.000 0.104 0.780 0.116
#> GSM601750 1 0.1059 0.8376 0.972 0.012 0.000 0.016
#> GSM601760 1 0.1151 0.8406 0.968 0.008 0.000 0.024
#> GSM601765 4 0.4727 0.6484 0.100 0.108 0.000 0.792
#> GSM601770 4 0.5041 0.4950 0.040 0.232 0.000 0.728
#> GSM601775 4 0.5249 0.6554 0.248 0.044 0.000 0.708
#> GSM601780 4 0.5366 0.3111 0.440 0.012 0.000 0.548
#> GSM601790 2 0.4155 0.8893 0.000 0.756 0.004 0.240
#> GSM601805 4 0.3208 0.5473 0.004 0.148 0.000 0.848
#> GSM601810 1 0.7575 0.4076 0.564 0.044 0.100 0.292
#> GSM601815 2 0.3873 0.9026 0.000 0.772 0.000 0.228
#> GSM601820 1 0.1059 0.8376 0.972 0.012 0.000 0.016
#> GSM601825 4 0.4155 0.4494 0.004 0.240 0.000 0.756
#> GSM601835 4 0.4352 0.6449 0.080 0.104 0.000 0.816
#> GSM601850 4 0.4798 0.6537 0.204 0.032 0.004 0.760
#> GSM601855 3 0.1509 0.8385 0.012 0.008 0.960 0.020
#> GSM601865 2 0.6753 0.6370 0.000 0.608 0.164 0.228
#> GSM601756 4 0.2921 0.5526 0.000 0.140 0.000 0.860
#> GSM601786 2 0.4567 0.8702 0.016 0.740 0.000 0.244
#> GSM601796 4 0.5075 0.6431 0.200 0.040 0.008 0.752
#> GSM601801 4 0.2922 0.6024 0.008 0.104 0.004 0.884
#> GSM601831 1 0.2499 0.8295 0.920 0.004 0.044 0.032
#> GSM601841 1 0.5485 0.4401 0.652 0.008 0.020 0.320
#> GSM601846 4 0.6322 0.1892 0.004 0.060 0.360 0.576
#> GSM601861 2 0.3873 0.9026 0.000 0.772 0.000 0.228
#> GSM601871 4 0.8922 -0.1996 0.052 0.300 0.272 0.376
#> GSM601751 4 0.5785 0.6249 0.268 0.048 0.008 0.676
#> GSM601761 1 0.4914 0.4585 0.676 0.012 0.000 0.312
#> GSM601766 4 0.4964 0.6679 0.168 0.068 0.000 0.764
#> GSM601771 4 0.6095 0.6396 0.252 0.072 0.008 0.668
#> GSM601776 4 0.5564 0.3362 0.436 0.020 0.000 0.544
#> GSM601781 4 0.4305 0.6703 0.136 0.044 0.004 0.816
#> GSM601791 4 0.5510 0.2015 0.480 0.016 0.000 0.504
#> GSM601806 4 0.3074 0.5422 0.000 0.152 0.000 0.848
#> GSM601811 1 0.7691 0.3864 0.552 0.044 0.108 0.296
#> GSM601816 4 0.5460 0.4990 0.340 0.028 0.000 0.632
#> GSM601821 2 0.3873 0.9026 0.000 0.772 0.000 0.228
#> GSM601826 4 0.5487 0.5100 0.328 0.024 0.004 0.644
#> GSM601836 4 0.5545 0.4879 0.364 0.020 0.004 0.612
#> GSM601851 4 0.5256 0.4173 0.392 0.012 0.000 0.596
#> GSM601856 1 0.3330 0.8117 0.884 0.012 0.072 0.032
#> GSM601866 1 0.1004 0.8400 0.972 0.000 0.004 0.024
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601752 2 0.3790 0.54960 0.000 0.724 0.000 0.004 0.272
#> GSM601782 1 0.3282 0.80989 0.860 0.044 0.012 0.084 0.000
#> GSM601792 2 0.3427 0.63358 0.128 0.836 0.008 0.028 0.000
#> GSM601797 2 0.2738 0.64624 0.056 0.900 0.012 0.020 0.012
#> GSM601827 1 0.3727 0.78926 0.848 0.036 0.072 0.040 0.004
#> GSM601837 5 0.1364 0.74107 0.000 0.036 0.000 0.012 0.952
#> GSM601842 2 0.4820 0.59993 0.044 0.708 0.000 0.012 0.236
#> GSM601857 1 0.7304 -0.00835 0.448 0.388 0.024 0.100 0.040
#> GSM601867 2 0.8471 0.31257 0.112 0.468 0.044 0.164 0.212
#> GSM601747 2 0.5403 0.20169 0.476 0.480 0.004 0.004 0.036
#> GSM601757 1 0.5785 0.57954 0.680 0.216 0.016 0.056 0.032
#> GSM601762 2 0.4984 0.54047 0.036 0.648 0.000 0.008 0.308
#> GSM601767 2 0.4986 0.47342 0.032 0.608 0.000 0.004 0.356
#> GSM601772 5 0.4830 -0.23520 0.020 0.488 0.000 0.000 0.492
#> GSM601777 2 0.2740 0.63920 0.064 0.888 0.000 0.044 0.004
#> GSM601787 2 0.8226 0.08715 0.056 0.416 0.036 0.192 0.300
#> GSM601802 2 0.3790 0.54971 0.000 0.724 0.000 0.004 0.272
#> GSM601807 4 0.3154 -0.48614 0.000 0.004 0.148 0.836 0.012
#> GSM601812 1 0.1612 0.83524 0.948 0.024 0.016 0.012 0.000
#> GSM601817 1 0.0854 0.83722 0.976 0.012 0.004 0.008 0.000
#> GSM601822 2 0.2900 0.64118 0.108 0.864 0.000 0.028 0.000
#> GSM601832 2 0.4918 0.60771 0.060 0.704 0.000 0.008 0.228
#> GSM601847 2 0.3101 0.64906 0.100 0.864 0.000 0.024 0.012
#> GSM601852 1 0.1989 0.83402 0.932 0.032 0.016 0.020 0.000
#> GSM601862 1 0.2333 0.82772 0.916 0.028 0.016 0.040 0.000
#> GSM601753 2 0.3790 0.54934 0.000 0.724 0.000 0.004 0.272
#> GSM601783 1 0.1372 0.83610 0.956 0.024 0.004 0.016 0.000
#> GSM601793 2 0.3427 0.63358 0.128 0.836 0.008 0.028 0.000
#> GSM601798 2 0.3456 0.59522 0.000 0.788 0.004 0.004 0.204
#> GSM601828 1 0.2290 0.82163 0.920 0.016 0.044 0.016 0.004
#> GSM601838 5 0.1168 0.73979 0.000 0.032 0.000 0.008 0.960
#> GSM601843 2 0.4946 0.60125 0.044 0.708 0.004 0.012 0.232
#> GSM601858 2 0.7792 0.44444 0.272 0.496 0.020 0.092 0.120
#> GSM601868 1 0.3912 0.79130 0.828 0.036 0.040 0.096 0.000
#> GSM601748 1 0.0740 0.83307 0.980 0.008 0.004 0.008 0.000
#> GSM601758 1 0.0579 0.83431 0.984 0.008 0.000 0.008 0.000
#> GSM601763 2 0.5322 0.64783 0.208 0.688 0.000 0.012 0.092
#> GSM601768 2 0.5057 0.47980 0.036 0.604 0.000 0.004 0.356
#> GSM601773 2 0.4748 0.15937 0.016 0.492 0.000 0.000 0.492
#> GSM601778 2 0.2740 0.63877 0.064 0.888 0.000 0.044 0.004
#> GSM601788 2 0.7384 0.36252 0.068 0.508 0.024 0.084 0.316
#> GSM601803 2 0.3861 0.53901 0.000 0.712 0.000 0.004 0.284
#> GSM601808 1 0.5240 0.66223 0.724 0.028 0.092 0.156 0.000
#> GSM601813 1 0.1673 0.83472 0.944 0.032 0.016 0.008 0.000
#> GSM601818 1 0.0912 0.83780 0.972 0.012 0.000 0.016 0.000
#> GSM601823 2 0.4523 0.59067 0.252 0.712 0.000 0.028 0.008
#> GSM601833 2 0.4918 0.60771 0.060 0.704 0.000 0.008 0.228
#> GSM601848 2 0.4546 0.50601 0.304 0.668 0.000 0.028 0.000
#> GSM601853 1 0.3096 0.80381 0.868 0.008 0.040 0.084 0.000
#> GSM601863 1 0.2333 0.82772 0.916 0.028 0.016 0.040 0.000
#> GSM601754 2 0.4216 0.57599 0.012 0.720 0.000 0.008 0.260
#> GSM601784 2 0.4913 0.17705 0.012 0.496 0.000 0.008 0.484
#> GSM601794 2 0.3474 0.63517 0.132 0.832 0.008 0.028 0.000
#> GSM601799 2 0.4424 0.61263 0.048 0.728 0.000 0.000 0.224
#> GSM601829 1 0.5249 0.67241 0.720 0.176 0.068 0.036 0.000
#> GSM601839 5 0.1281 0.73915 0.000 0.032 0.000 0.012 0.956
#> GSM601844 2 0.5637 0.30765 0.408 0.540 0.012 0.024 0.016
#> GSM601859 2 0.5496 0.49660 0.060 0.592 0.000 0.008 0.340
#> GSM601869 1 0.3912 0.79130 0.828 0.036 0.040 0.096 0.000
#> GSM601749 1 0.0579 0.83582 0.984 0.008 0.000 0.008 0.000
#> GSM601759 1 0.0579 0.83431 0.984 0.008 0.000 0.008 0.000
#> GSM601764 2 0.5396 0.49156 0.344 0.600 0.000 0.016 0.040
#> GSM601769 5 0.0963 0.74598 0.000 0.036 0.000 0.000 0.964
#> GSM601774 5 0.3395 0.51104 0.000 0.236 0.000 0.000 0.764
#> GSM601779 2 0.4403 0.39294 0.384 0.608 0.000 0.008 0.000
#> GSM601789 5 0.6399 -0.22610 0.052 0.448 0.008 0.036 0.456
#> GSM601804 2 0.4197 0.58489 0.028 0.728 0.000 0.000 0.244
#> GSM601809 1 0.7276 0.35312 0.504 0.296 0.064 0.132 0.004
#> GSM601814 5 0.0963 0.74598 0.000 0.036 0.000 0.000 0.964
#> GSM601819 1 0.0566 0.83325 0.984 0.004 0.000 0.012 0.000
#> GSM601824 2 0.4523 0.59067 0.252 0.712 0.000 0.028 0.008
#> GSM601834 2 0.4946 0.60652 0.060 0.700 0.000 0.008 0.232
#> GSM601849 2 0.4360 0.53363 0.300 0.680 0.000 0.020 0.000
#> GSM601854 1 0.1686 0.82890 0.944 0.008 0.028 0.020 0.000
#> GSM601864 5 0.5368 0.28406 0.000 0.048 0.016 0.304 0.632
#> GSM601755 2 0.3790 0.54960 0.000 0.724 0.000 0.004 0.272
#> GSM601785 2 0.4977 0.28426 0.016 0.532 0.000 0.008 0.444
#> GSM601795 2 0.3474 0.63517 0.132 0.832 0.008 0.028 0.000
#> GSM601800 2 0.3844 0.56700 0.004 0.736 0.000 0.004 0.256
#> GSM601830 3 0.0000 0.74623 0.000 0.000 1.000 0.000 0.000
#> GSM601840 2 0.6398 0.53715 0.296 0.584 0.012 0.028 0.080
#> GSM601845 2 0.4999 0.65971 0.104 0.752 0.004 0.020 0.120
#> GSM601860 2 0.5496 0.49660 0.060 0.592 0.000 0.008 0.340
#> GSM601870 3 0.5540 0.40476 0.000 0.012 0.604 0.324 0.060
#> GSM601750 1 0.0671 0.83361 0.980 0.004 0.000 0.016 0.000
#> GSM601760 1 0.0898 0.83570 0.972 0.020 0.000 0.008 0.000
#> GSM601765 2 0.4968 0.63402 0.068 0.724 0.000 0.016 0.192
#> GSM601770 2 0.4986 0.47342 0.032 0.608 0.000 0.004 0.356
#> GSM601775 2 0.4930 0.64256 0.220 0.696 0.000 0.000 0.084
#> GSM601780 2 0.4403 0.39294 0.384 0.608 0.000 0.008 0.000
#> GSM601790 5 0.1740 0.73228 0.000 0.056 0.000 0.012 0.932
#> GSM601805 2 0.3992 0.54483 0.004 0.712 0.000 0.004 0.280
#> GSM601810 1 0.7155 0.36649 0.516 0.296 0.060 0.124 0.004
#> GSM601815 5 0.0963 0.74598 0.000 0.036 0.000 0.000 0.964
#> GSM601820 1 0.0566 0.83325 0.984 0.004 0.000 0.012 0.000
#> GSM601825 2 0.4251 0.41718 0.000 0.624 0.000 0.004 0.372
#> GSM601835 2 0.4587 0.63107 0.052 0.748 0.000 0.012 0.188
#> GSM601850 2 0.3717 0.64451 0.144 0.816 0.000 0.028 0.012
#> GSM601855 3 0.1502 0.74082 0.004 0.000 0.940 0.056 0.000
#> GSM601865 5 0.4930 0.38545 0.000 0.052 0.004 0.268 0.676
#> GSM601756 2 0.3790 0.54960 0.000 0.724 0.000 0.004 0.272
#> GSM601786 5 0.1956 0.70746 0.008 0.076 0.000 0.000 0.916
#> GSM601796 2 0.3474 0.63517 0.132 0.832 0.008 0.028 0.000
#> GSM601801 2 0.3422 0.59669 0.000 0.792 0.004 0.004 0.200
#> GSM601831 1 0.2784 0.81961 0.896 0.012 0.048 0.040 0.004
#> GSM601841 1 0.5835 0.38742 0.592 0.332 0.020 0.048 0.008
#> GSM601846 2 0.6547 0.02466 0.004 0.548 0.324 0.084 0.040
#> GSM601861 5 0.0880 0.74536 0.000 0.032 0.000 0.000 0.968
#> GSM601871 4 0.7872 -0.03541 0.016 0.232 0.044 0.416 0.292
#> GSM601751 2 0.5615 0.61918 0.228 0.664 0.004 0.012 0.092
#> GSM601761 1 0.4497 0.37843 0.632 0.352 0.000 0.016 0.000
#> GSM601766 2 0.4892 0.65766 0.112 0.748 0.000 0.016 0.124
#> GSM601771 2 0.5933 0.62656 0.208 0.648 0.004 0.016 0.124
#> GSM601776 2 0.4994 0.39008 0.396 0.576 0.000 0.016 0.012
#> GSM601781 2 0.2740 0.63920 0.064 0.888 0.000 0.044 0.004
#> GSM601791 2 0.4689 0.28794 0.424 0.560 0.000 0.016 0.000
#> GSM601806 2 0.3861 0.53901 0.000 0.712 0.000 0.004 0.284
#> GSM601811 1 0.7276 0.35312 0.504 0.296 0.064 0.132 0.004
#> GSM601816 2 0.4475 0.54478 0.276 0.692 0.000 0.032 0.000
#> GSM601821 5 0.0880 0.74536 0.000 0.032 0.000 0.000 0.968
#> GSM601826 2 0.4404 0.55459 0.264 0.704 0.000 0.032 0.000
#> GSM601836 2 0.5513 0.54171 0.308 0.628 0.004 0.032 0.028
#> GSM601851 2 0.4339 0.47820 0.336 0.652 0.000 0.012 0.000
#> GSM601856 1 0.3251 0.80555 0.864 0.016 0.040 0.080 0.000
#> GSM601866 1 0.0960 0.83573 0.972 0.016 0.008 0.004 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601752 2 0.3812 0.5373 0.000 0.712 0.000 NA 0.268 0.004
#> GSM601782 1 0.5410 0.5342 0.596 0.064 0.004 NA 0.000 0.028
#> GSM601792 2 0.3103 0.6239 0.064 0.836 0.000 NA 0.000 0.000
#> GSM601797 2 0.2499 0.6377 0.004 0.884 0.004 NA 0.012 0.004
#> GSM601827 1 0.4079 0.7246 0.752 0.024 0.032 NA 0.000 0.000
#> GSM601837 5 0.0820 0.6754 0.000 0.012 0.000 NA 0.972 0.016
#> GSM601842 2 0.4150 0.5781 0.000 0.724 0.000 NA 0.228 0.012
#> GSM601857 2 0.7427 0.0657 0.368 0.396 0.008 NA 0.040 0.056
#> GSM601867 2 0.7969 0.2592 0.036 0.464 0.020 NA 0.196 0.144
#> GSM601747 2 0.5622 0.2952 0.400 0.512 0.004 NA 0.028 0.004
#> GSM601757 1 0.5970 0.4935 0.624 0.236 0.008 NA 0.028 0.028
#> GSM601762 2 0.4347 0.5160 0.000 0.660 0.000 NA 0.304 0.012
#> GSM601767 2 0.4700 0.4376 0.008 0.604 0.000 NA 0.356 0.012
#> GSM601772 5 0.4493 -0.1837 0.000 0.488 0.000 NA 0.488 0.008
#> GSM601777 2 0.2244 0.6285 0.004 0.888 0.000 NA 0.004 0.004
#> GSM601787 2 0.7910 0.0107 0.020 0.404 0.028 NA 0.280 0.188
#> GSM601802 2 0.3919 0.5360 0.000 0.708 0.000 NA 0.268 0.008
#> GSM601807 6 0.1844 -0.3997 0.000 0.000 0.048 NA 0.004 0.924
#> GSM601812 1 0.1829 0.7946 0.928 0.028 0.008 NA 0.000 0.000
#> GSM601817 1 0.1788 0.7958 0.928 0.028 0.004 NA 0.000 0.000
#> GSM601822 2 0.2507 0.6323 0.040 0.884 0.000 NA 0.000 0.004
#> GSM601832 2 0.4229 0.5840 0.004 0.732 0.000 NA 0.216 0.016
#> GSM601847 2 0.2719 0.6399 0.032 0.880 0.000 NA 0.012 0.004
#> GSM601852 1 0.2201 0.7944 0.904 0.036 0.004 NA 0.000 0.000
#> GSM601862 1 0.2859 0.7806 0.868 0.020 0.012 NA 0.000 0.008
#> GSM601753 2 0.3724 0.5403 0.000 0.716 0.000 NA 0.268 0.004
#> GSM601783 1 0.1492 0.7940 0.940 0.036 0.000 NA 0.000 0.000
#> GSM601793 2 0.3103 0.6239 0.064 0.836 0.000 NA 0.000 0.000
#> GSM601798 2 0.3770 0.5851 0.000 0.752 0.004 NA 0.212 0.000
#> GSM601828 1 0.3062 0.7712 0.844 0.016 0.024 NA 0.000 0.000
#> GSM601838 5 0.0622 0.6732 0.000 0.008 0.000 NA 0.980 0.012
#> GSM601843 2 0.4263 0.5798 0.000 0.724 0.004 NA 0.224 0.012
#> GSM601858 2 0.7510 0.4100 0.188 0.524 0.008 NA 0.104 0.060
#> GSM601868 1 0.4439 0.7303 0.760 0.036 0.008 NA 0.000 0.048
#> GSM601748 1 0.1605 0.7932 0.936 0.016 0.004 NA 0.000 0.000
#> GSM601758 1 0.0914 0.7905 0.968 0.016 0.000 NA 0.000 0.000
#> GSM601763 2 0.4904 0.6431 0.144 0.732 0.000 NA 0.072 0.012
#> GSM601768 2 0.4688 0.4434 0.008 0.608 0.000 NA 0.352 0.012
#> GSM601773 5 0.4493 -0.1753 0.000 0.484 0.000 NA 0.492 0.008
#> GSM601778 2 0.2306 0.6294 0.008 0.888 0.000 NA 0.004 0.004
#> GSM601788 2 0.7161 0.3155 0.024 0.488 0.016 NA 0.304 0.092
#> GSM601803 2 0.3982 0.5255 0.000 0.696 0.000 NA 0.280 0.008
#> GSM601808 1 0.5524 0.4896 0.568 0.012 0.016 NA 0.000 0.068
#> GSM601813 1 0.1906 0.7946 0.924 0.036 0.008 NA 0.000 0.000
#> GSM601818 1 0.1780 0.7962 0.924 0.028 0.000 NA 0.000 0.000
#> GSM601823 2 0.4429 0.5883 0.180 0.732 0.000 NA 0.008 0.004
#> GSM601833 2 0.4229 0.5840 0.004 0.732 0.000 NA 0.216 0.016
#> GSM601848 2 0.4565 0.5285 0.244 0.680 0.000 NA 0.000 0.004
#> GSM601853 1 0.4213 0.7376 0.772 0.012 0.020 NA 0.000 0.044
#> GSM601863 1 0.2859 0.7806 0.868 0.020 0.012 NA 0.000 0.008
#> GSM601754 2 0.3967 0.5652 0.004 0.720 0.000 NA 0.252 0.008
#> GSM601784 2 0.4659 0.1359 0.000 0.488 0.000 NA 0.480 0.016
#> GSM601794 2 0.3150 0.6255 0.064 0.832 0.000 NA 0.000 0.000
#> GSM601799 2 0.4390 0.5999 0.024 0.724 0.000 NA 0.216 0.004
#> GSM601829 1 0.5510 0.6166 0.636 0.172 0.028 NA 0.000 0.000
#> GSM601839 5 0.0717 0.6726 0.000 0.008 0.000 NA 0.976 0.016
#> GSM601844 2 0.5490 0.4001 0.316 0.572 0.004 NA 0.012 0.000
#> GSM601859 2 0.5107 0.4704 0.028 0.604 0.000 NA 0.332 0.016
#> GSM601869 1 0.4439 0.7303 0.760 0.036 0.008 NA 0.000 0.048
#> GSM601749 1 0.1088 0.7948 0.960 0.016 0.000 NA 0.000 0.000
#> GSM601759 1 0.0820 0.7904 0.972 0.012 0.000 NA 0.000 0.000
#> GSM601764 2 0.5237 0.5405 0.264 0.640 0.000 NA 0.032 0.004
#> GSM601769 5 0.0547 0.6854 0.000 0.020 0.000 NA 0.980 0.000
#> GSM601774 5 0.3221 0.4943 0.000 0.220 0.000 NA 0.772 0.004
#> GSM601779 2 0.4363 0.4436 0.324 0.636 0.000 NA 0.000 0.000
#> GSM601789 5 0.6145 -0.1688 0.016 0.436 0.004 NA 0.448 0.052
#> GSM601804 2 0.4548 0.5723 0.020 0.704 0.000 NA 0.236 0.008
#> GSM601809 1 0.7320 0.1421 0.356 0.308 0.008 NA 0.004 0.060
#> GSM601814 5 0.0547 0.6854 0.000 0.020 0.000 NA 0.980 0.000
#> GSM601819 1 0.1584 0.7875 0.928 0.008 0.000 NA 0.000 0.000
#> GSM601824 2 0.4429 0.5883 0.180 0.732 0.000 NA 0.008 0.004
#> GSM601834 2 0.4256 0.5827 0.004 0.728 0.000 NA 0.220 0.016
#> GSM601849 2 0.4296 0.5488 0.244 0.700 0.000 NA 0.000 0.004
#> GSM601854 1 0.2520 0.7801 0.872 0.012 0.008 NA 0.000 0.000
#> GSM601864 5 0.4518 0.1804 0.000 0.036 0.000 NA 0.612 0.348
#> GSM601755 2 0.3812 0.5373 0.000 0.712 0.000 NA 0.268 0.004
#> GSM601785 2 0.4632 0.2462 0.000 0.532 0.000 NA 0.436 0.016
#> GSM601795 2 0.3150 0.6255 0.064 0.832 0.000 NA 0.000 0.000
#> GSM601800 2 0.3883 0.5568 0.004 0.720 0.000 NA 0.256 0.004
#> GSM601830 3 0.0146 0.7406 0.000 0.000 0.996 NA 0.000 0.000
#> GSM601840 2 0.5861 0.5402 0.220 0.628 0.000 NA 0.064 0.012
#> GSM601845 2 0.4532 0.6425 0.040 0.776 0.004 NA 0.104 0.012
#> GSM601860 2 0.5107 0.4704 0.028 0.604 0.000 NA 0.332 0.016
#> GSM601870 3 0.4990 0.4047 0.000 0.008 0.588 NA 0.052 0.348
#> GSM601750 1 0.1701 0.7857 0.920 0.008 0.000 NA 0.000 0.000
#> GSM601760 1 0.1245 0.7914 0.952 0.032 0.000 NA 0.000 0.000
#> GSM601765 2 0.4041 0.6110 0.004 0.764 0.000 NA 0.176 0.012
#> GSM601770 2 0.4700 0.4376 0.008 0.604 0.000 NA 0.356 0.012
#> GSM601775 2 0.4657 0.6388 0.168 0.732 0.000 NA 0.072 0.008
#> GSM601780 2 0.4363 0.4436 0.324 0.636 0.000 NA 0.000 0.000
#> GSM601790 5 0.1225 0.6755 0.000 0.036 0.000 NA 0.952 0.012
#> GSM601805 2 0.4099 0.5316 0.004 0.696 0.000 NA 0.276 0.008
#> GSM601810 1 0.7247 0.1792 0.376 0.308 0.008 NA 0.004 0.056
#> GSM601815 5 0.0547 0.6854 0.000 0.020 0.000 NA 0.980 0.000
#> GSM601820 1 0.1584 0.7875 0.928 0.008 0.000 NA 0.000 0.000
#> GSM601825 2 0.4234 0.4033 0.000 0.608 0.000 NA 0.372 0.004
#> GSM601835 2 0.3802 0.6106 0.000 0.772 0.000 NA 0.180 0.012
#> GSM601850 2 0.3512 0.6366 0.080 0.828 0.000 NA 0.012 0.004
#> GSM601855 3 0.1616 0.7337 0.000 0.000 0.932 NA 0.000 0.048
#> GSM601865 5 0.4302 0.3003 0.000 0.036 0.000 NA 0.668 0.292
#> GSM601756 2 0.3812 0.5373 0.000 0.712 0.000 NA 0.268 0.004
#> GSM601786 5 0.1952 0.6425 0.000 0.052 0.000 NA 0.920 0.016
#> GSM601796 2 0.3150 0.6255 0.064 0.832 0.000 NA 0.000 0.000
#> GSM601801 2 0.3741 0.5868 0.000 0.756 0.004 NA 0.208 0.000
#> GSM601831 1 0.3424 0.7717 0.832 0.016 0.020 NA 0.000 0.016
#> GSM601841 1 0.5729 0.2888 0.520 0.368 0.000 NA 0.008 0.016
#> GSM601846 2 0.6835 -0.1302 0.000 0.452 0.176 NA 0.004 0.064
#> GSM601861 5 0.0508 0.6823 0.000 0.012 0.000 NA 0.984 0.004
#> GSM601871 6 0.7372 0.0471 0.008 0.216 0.008 NA 0.268 0.424
#> GSM601751 2 0.5095 0.6161 0.164 0.708 0.000 NA 0.076 0.008
#> GSM601761 1 0.4524 0.3057 0.584 0.376 0.000 NA 0.000 0.000
#> GSM601766 2 0.4329 0.6388 0.048 0.784 0.000 NA 0.108 0.012
#> GSM601771 2 0.5402 0.6209 0.148 0.692 0.000 NA 0.104 0.016
#> GSM601776 2 0.4856 0.4595 0.324 0.616 0.000 NA 0.004 0.008
#> GSM601781 2 0.2244 0.6285 0.004 0.888 0.000 NA 0.004 0.004
#> GSM601791 2 0.4563 0.3523 0.368 0.588 0.000 NA 0.000 0.000
#> GSM601806 2 0.3982 0.5255 0.000 0.696 0.000 NA 0.280 0.008
#> GSM601811 1 0.7320 0.1421 0.356 0.308 0.008 NA 0.004 0.060
#> GSM601816 2 0.4228 0.5567 0.212 0.716 0.000 NA 0.000 0.000
#> GSM601821 5 0.0363 0.6835 0.000 0.012 0.000 NA 0.988 0.000
#> GSM601826 2 0.4282 0.5596 0.200 0.724 0.000 NA 0.000 0.004
#> GSM601836 2 0.5145 0.5671 0.228 0.664 0.000 NA 0.016 0.008
#> GSM601851 2 0.4234 0.5091 0.280 0.676 0.000 NA 0.000 0.000
#> GSM601856 1 0.4201 0.7429 0.776 0.016 0.020 NA 0.000 0.040
#> GSM601866 1 0.1592 0.7938 0.940 0.020 0.008 NA 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 time(p) gender(p) k
#> SD:hclust 94 0.700 0.0792 2
#> SD:hclust 80 0.204 0.5221 3
#> SD:hclust 93 0.114 0.4873 4
#> SD:hclust 90 0.128 0.6315 5
#> SD:hclust 89 0.232 0.6779 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 125 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 0.915 0.909 0.964 0.5013 0.499 0.499
#> 3 3 0.525 0.589 0.754 0.2786 0.819 0.659
#> 4 4 0.584 0.441 0.670 0.1209 0.791 0.510
#> 5 5 0.640 0.685 0.759 0.0715 0.790 0.400
#> 6 6 0.691 0.729 0.761 0.0408 0.933 0.714
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM601752 2 0.0000 0.958 0.000 1.000
#> GSM601782 1 0.0000 0.964 1.000 0.000
#> GSM601792 1 0.0000 0.964 1.000 0.000
#> GSM601797 1 0.8443 0.618 0.728 0.272
#> GSM601827 1 0.0000 0.964 1.000 0.000
#> GSM601837 2 0.0000 0.958 0.000 1.000
#> GSM601842 2 0.0000 0.958 0.000 1.000
#> GSM601857 1 0.0000 0.964 1.000 0.000
#> GSM601867 2 0.9896 0.208 0.440 0.560
#> GSM601747 1 0.0000 0.964 1.000 0.000
#> GSM601757 1 0.0000 0.964 1.000 0.000
#> GSM601762 2 0.0000 0.958 0.000 1.000
#> GSM601767 2 0.0000 0.958 0.000 1.000
#> GSM601772 2 0.0000 0.958 0.000 1.000
#> GSM601777 1 0.2778 0.921 0.952 0.048
#> GSM601787 2 0.4298 0.880 0.088 0.912
#> GSM601802 2 0.0000 0.958 0.000 1.000
#> GSM601807 1 0.9491 0.411 0.632 0.368
#> GSM601812 1 0.0000 0.964 1.000 0.000
#> GSM601817 1 0.0000 0.964 1.000 0.000
#> GSM601822 1 0.1414 0.947 0.980 0.020
#> GSM601832 2 0.0000 0.958 0.000 1.000
#> GSM601847 2 0.8909 0.556 0.308 0.692
#> GSM601852 1 0.0000 0.964 1.000 0.000
#> GSM601862 1 0.0000 0.964 1.000 0.000
#> GSM601753 2 0.0000 0.958 0.000 1.000
#> GSM601783 1 0.0000 0.964 1.000 0.000
#> GSM601793 1 0.0000 0.964 1.000 0.000
#> GSM601798 2 0.0000 0.958 0.000 1.000
#> GSM601828 1 0.0000 0.964 1.000 0.000
#> GSM601838 2 0.0000 0.958 0.000 1.000
#> GSM601843 2 0.0000 0.958 0.000 1.000
#> GSM601858 2 0.0000 0.958 0.000 1.000
#> GSM601868 1 0.0000 0.964 1.000 0.000
#> GSM601748 1 0.0000 0.964 1.000 0.000
#> GSM601758 1 0.0000 0.964 1.000 0.000
#> GSM601763 1 0.5408 0.836 0.876 0.124
#> GSM601768 2 0.0000 0.958 0.000 1.000
#> GSM601773 2 0.0000 0.958 0.000 1.000
#> GSM601778 1 0.0000 0.964 1.000 0.000
#> GSM601788 2 0.0000 0.958 0.000 1.000
#> GSM601803 2 0.0000 0.958 0.000 1.000
#> GSM601808 1 0.0000 0.964 1.000 0.000
#> GSM601813 1 0.0000 0.964 1.000 0.000
#> GSM601818 1 0.0000 0.964 1.000 0.000
#> GSM601823 1 0.0000 0.964 1.000 0.000
#> GSM601833 2 0.0000 0.958 0.000 1.000
#> GSM601848 1 0.0000 0.964 1.000 0.000
#> GSM601853 1 0.0000 0.964 1.000 0.000
#> GSM601863 1 0.0000 0.964 1.000 0.000
#> GSM601754 2 0.0000 0.958 0.000 1.000
#> GSM601784 2 0.0000 0.958 0.000 1.000
#> GSM601794 1 0.0000 0.964 1.000 0.000
#> GSM601799 2 0.0000 0.958 0.000 1.000
#> GSM601829 1 0.0000 0.964 1.000 0.000
#> GSM601839 2 0.0000 0.958 0.000 1.000
#> GSM601844 1 0.0000 0.964 1.000 0.000
#> GSM601859 2 0.0000 0.958 0.000 1.000
#> GSM601869 1 0.0000 0.964 1.000 0.000
#> GSM601749 1 0.0000 0.964 1.000 0.000
#> GSM601759 1 0.0000 0.964 1.000 0.000
#> GSM601764 1 0.0000 0.964 1.000 0.000
#> GSM601769 2 0.0000 0.958 0.000 1.000
#> GSM601774 2 0.0000 0.958 0.000 1.000
#> GSM601779 1 0.0000 0.964 1.000 0.000
#> GSM601789 2 0.0000 0.958 0.000 1.000
#> GSM601804 2 0.4431 0.876 0.092 0.908
#> GSM601809 1 0.0000 0.964 1.000 0.000
#> GSM601814 2 0.0000 0.958 0.000 1.000
#> GSM601819 1 0.0000 0.964 1.000 0.000
#> GSM601824 2 0.9896 0.223 0.440 0.560
#> GSM601834 2 0.0000 0.958 0.000 1.000
#> GSM601849 1 0.0000 0.964 1.000 0.000
#> GSM601854 1 0.0000 0.964 1.000 0.000
#> GSM601864 2 0.0000 0.958 0.000 1.000
#> GSM601755 2 0.0000 0.958 0.000 1.000
#> GSM601785 2 0.0000 0.958 0.000 1.000
#> GSM601795 1 0.0000 0.964 1.000 0.000
#> GSM601800 2 0.0000 0.958 0.000 1.000
#> GSM601830 1 0.3733 0.897 0.928 0.072
#> GSM601840 2 0.0376 0.955 0.004 0.996
#> GSM601845 1 0.9954 0.142 0.540 0.460
#> GSM601860 2 0.0000 0.958 0.000 1.000
#> GSM601870 1 0.9608 0.370 0.616 0.384
#> GSM601750 1 0.0000 0.964 1.000 0.000
#> GSM601760 1 0.0000 0.964 1.000 0.000
#> GSM601765 2 0.0000 0.958 0.000 1.000
#> GSM601770 2 0.0000 0.958 0.000 1.000
#> GSM601775 2 0.9552 0.402 0.376 0.624
#> GSM601780 1 0.0000 0.964 1.000 0.000
#> GSM601790 2 0.0000 0.958 0.000 1.000
#> GSM601805 2 0.0000 0.958 0.000 1.000
#> GSM601810 1 0.0000 0.964 1.000 0.000
#> GSM601815 2 0.0000 0.958 0.000 1.000
#> GSM601820 1 0.0000 0.964 1.000 0.000
#> GSM601825 2 0.0000 0.958 0.000 1.000
#> GSM601835 2 0.0000 0.958 0.000 1.000
#> GSM601850 1 0.0000 0.964 1.000 0.000
#> GSM601855 1 0.0376 0.961 0.996 0.004
#> GSM601865 2 0.0000 0.958 0.000 1.000
#> GSM601756 2 0.0000 0.958 0.000 1.000
#> GSM601786 2 0.0000 0.958 0.000 1.000
#> GSM601796 1 0.0000 0.964 1.000 0.000
#> GSM601801 2 0.0000 0.958 0.000 1.000
#> GSM601831 1 0.0000 0.964 1.000 0.000
#> GSM601841 1 0.0000 0.964 1.000 0.000
#> GSM601846 2 0.8081 0.664 0.248 0.752
#> GSM601861 2 0.0000 0.958 0.000 1.000
#> GSM601871 2 0.6438 0.790 0.164 0.836
#> GSM601751 2 0.4562 0.871 0.096 0.904
#> GSM601761 1 0.0000 0.964 1.000 0.000
#> GSM601766 1 0.9944 0.137 0.544 0.456
#> GSM601771 2 0.0000 0.958 0.000 1.000
#> GSM601776 1 0.0000 0.964 1.000 0.000
#> GSM601781 1 0.0672 0.958 0.992 0.008
#> GSM601791 1 0.0000 0.964 1.000 0.000
#> GSM601806 2 0.0000 0.958 0.000 1.000
#> GSM601811 1 0.0000 0.964 1.000 0.000
#> GSM601816 1 0.0000 0.964 1.000 0.000
#> GSM601821 2 0.0000 0.958 0.000 1.000
#> GSM601826 1 0.0000 0.964 1.000 0.000
#> GSM601836 1 0.0000 0.964 1.000 0.000
#> GSM601851 1 0.0000 0.964 1.000 0.000
#> GSM601856 1 0.0000 0.964 1.000 0.000
#> GSM601866 1 0.0000 0.964 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601752 2 0.7796 0.6137 0.228 0.660 0.112
#> GSM601782 1 0.6045 0.1443 0.620 0.000 0.380
#> GSM601792 1 0.2682 0.5928 0.920 0.004 0.076
#> GSM601797 1 0.9153 0.2525 0.520 0.308 0.172
#> GSM601827 1 0.6062 0.1303 0.616 0.000 0.384
#> GSM601837 2 0.5098 0.7405 0.000 0.752 0.248
#> GSM601842 2 0.0747 0.8428 0.000 0.984 0.016
#> GSM601857 3 0.6062 0.7046 0.384 0.000 0.616
#> GSM601867 3 0.3987 0.5240 0.020 0.108 0.872
#> GSM601747 1 0.5406 0.4502 0.764 0.012 0.224
#> GSM601757 1 0.5650 0.2952 0.688 0.000 0.312
#> GSM601762 2 0.0592 0.8416 0.000 0.988 0.012
#> GSM601767 2 0.0424 0.8421 0.000 0.992 0.008
#> GSM601772 2 0.0424 0.8421 0.000 0.992 0.008
#> GSM601777 1 0.8355 0.3794 0.616 0.140 0.244
#> GSM601787 3 0.4409 0.4786 0.004 0.172 0.824
#> GSM601802 2 0.5393 0.7915 0.072 0.820 0.108
#> GSM601807 3 0.3472 0.5389 0.056 0.040 0.904
#> GSM601812 1 0.5948 0.1715 0.640 0.000 0.360
#> GSM601817 1 0.6062 0.0852 0.616 0.000 0.384
#> GSM601822 1 0.8094 0.3903 0.636 0.240 0.124
#> GSM601832 2 0.0829 0.8416 0.004 0.984 0.012
#> GSM601847 1 0.8628 0.2066 0.544 0.340 0.116
#> GSM601852 1 0.5785 0.2505 0.668 0.000 0.332
#> GSM601862 3 0.6095 0.6939 0.392 0.000 0.608
#> GSM601753 2 0.5393 0.7915 0.072 0.820 0.108
#> GSM601783 1 0.5291 0.3741 0.732 0.000 0.268
#> GSM601793 1 0.2261 0.5966 0.932 0.000 0.068
#> GSM601798 2 0.4892 0.8042 0.048 0.840 0.112
#> GSM601828 1 0.5905 0.1884 0.648 0.000 0.352
#> GSM601838 2 0.5098 0.7405 0.000 0.752 0.248
#> GSM601843 2 0.0592 0.8416 0.000 0.988 0.012
#> GSM601858 2 0.5016 0.7459 0.000 0.760 0.240
#> GSM601868 3 0.5968 0.7255 0.364 0.000 0.636
#> GSM601748 1 0.5882 0.2035 0.652 0.000 0.348
#> GSM601758 1 0.5291 0.3741 0.732 0.000 0.268
#> GSM601763 1 0.5305 0.4876 0.788 0.192 0.020
#> GSM601768 2 0.0661 0.8422 0.008 0.988 0.004
#> GSM601773 2 0.0592 0.8416 0.000 0.988 0.012
#> GSM601778 1 0.5191 0.5436 0.828 0.060 0.112
#> GSM601788 2 0.3129 0.8315 0.008 0.904 0.088
#> GSM601803 2 0.3695 0.8205 0.012 0.880 0.108
#> GSM601808 3 0.5968 0.7255 0.364 0.000 0.636
#> GSM601813 1 0.5529 0.3266 0.704 0.000 0.296
#> GSM601818 1 0.6095 0.0520 0.608 0.000 0.392
#> GSM601823 1 0.0424 0.6119 0.992 0.008 0.000
#> GSM601833 2 0.0424 0.8421 0.000 0.992 0.008
#> GSM601848 1 0.0000 0.6114 1.000 0.000 0.000
#> GSM601853 3 0.5968 0.7255 0.364 0.000 0.636
#> GSM601863 3 0.6111 0.6868 0.396 0.000 0.604
#> GSM601754 2 0.7267 0.6797 0.180 0.708 0.112
#> GSM601784 2 0.3192 0.8078 0.000 0.888 0.112
#> GSM601794 1 0.2878 0.5842 0.904 0.000 0.096
#> GSM601799 2 0.7979 0.5834 0.248 0.640 0.112
#> GSM601829 1 0.1753 0.6016 0.952 0.000 0.048
#> GSM601839 2 0.5098 0.7405 0.000 0.752 0.248
#> GSM601844 1 0.1411 0.6043 0.964 0.000 0.036
#> GSM601859 2 0.0829 0.8419 0.004 0.984 0.012
#> GSM601869 3 0.6026 0.7112 0.376 0.000 0.624
#> GSM601749 1 0.5291 0.3741 0.732 0.000 0.268
#> GSM601759 1 0.5431 0.3461 0.716 0.000 0.284
#> GSM601764 1 0.1015 0.6111 0.980 0.008 0.012
#> GSM601769 2 0.4931 0.7502 0.000 0.768 0.232
#> GSM601774 2 0.0592 0.8416 0.000 0.988 0.012
#> GSM601779 1 0.0661 0.6115 0.988 0.008 0.004
#> GSM601789 2 0.5098 0.7405 0.000 0.752 0.248
#> GSM601804 1 0.8436 0.2475 0.568 0.324 0.108
#> GSM601809 1 0.6280 -0.2118 0.540 0.000 0.460
#> GSM601814 2 0.4974 0.7480 0.000 0.764 0.236
#> GSM601819 1 0.4555 0.4661 0.800 0.000 0.200
#> GSM601824 1 0.7817 0.3921 0.648 0.252 0.100
#> GSM601834 2 0.0592 0.8416 0.000 0.988 0.012
#> GSM601849 1 0.0424 0.6101 0.992 0.000 0.008
#> GSM601854 1 0.5591 0.3073 0.696 0.000 0.304
#> GSM601864 2 0.5098 0.7405 0.000 0.752 0.248
#> GSM601755 2 0.3987 0.8179 0.020 0.872 0.108
#> GSM601785 2 0.3276 0.8269 0.068 0.908 0.024
#> GSM601795 1 0.5760 0.5203 0.796 0.064 0.140
#> GSM601800 2 0.5722 0.7802 0.084 0.804 0.112
#> GSM601830 3 0.5722 0.6943 0.292 0.004 0.704
#> GSM601840 2 0.6546 0.7308 0.148 0.756 0.096
#> GSM601845 1 0.7652 0.0753 0.512 0.444 0.044
#> GSM601860 2 0.3445 0.8182 0.088 0.896 0.016
#> GSM601870 3 0.4665 0.5396 0.048 0.100 0.852
#> GSM601750 1 0.5760 0.2577 0.672 0.000 0.328
#> GSM601760 1 0.4555 0.4628 0.800 0.000 0.200
#> GSM601765 2 0.0237 0.8424 0.000 0.996 0.004
#> GSM601770 2 0.0424 0.8421 0.000 0.992 0.008
#> GSM601775 1 0.8686 -0.0967 0.464 0.432 0.104
#> GSM601780 1 0.0424 0.6119 0.992 0.008 0.000
#> GSM601790 2 0.5098 0.7405 0.000 0.752 0.248
#> GSM601805 2 0.5481 0.7885 0.076 0.816 0.108
#> GSM601810 3 0.5968 0.7255 0.364 0.000 0.636
#> GSM601815 2 0.5098 0.7405 0.000 0.752 0.248
#> GSM601820 1 0.5529 0.3237 0.704 0.000 0.296
#> GSM601825 2 0.3695 0.8205 0.012 0.880 0.108
#> GSM601835 2 0.0592 0.8424 0.000 0.988 0.012
#> GSM601850 1 0.4379 0.5619 0.868 0.060 0.072
#> GSM601855 3 0.5760 0.7152 0.328 0.000 0.672
#> GSM601865 2 0.5098 0.7405 0.000 0.752 0.248
#> GSM601756 2 0.3987 0.8179 0.020 0.872 0.108
#> GSM601786 2 0.5098 0.7405 0.000 0.752 0.248
#> GSM601796 1 0.2356 0.5951 0.928 0.000 0.072
#> GSM601801 2 0.3695 0.8205 0.012 0.880 0.108
#> GSM601831 3 0.6045 0.7002 0.380 0.000 0.620
#> GSM601841 1 0.4796 0.4634 0.780 0.000 0.220
#> GSM601846 2 0.9152 0.0705 0.428 0.428 0.144
#> GSM601861 2 0.5058 0.7431 0.000 0.756 0.244
#> GSM601871 3 0.4521 0.4603 0.004 0.180 0.816
#> GSM601751 2 0.6271 0.7491 0.140 0.772 0.088
#> GSM601761 1 0.0747 0.6075 0.984 0.000 0.016
#> GSM601766 1 0.6422 0.3889 0.660 0.324 0.016
#> GSM601771 2 0.3572 0.8263 0.060 0.900 0.040
#> GSM601776 1 0.0592 0.6091 0.988 0.000 0.012
#> GSM601781 1 0.4652 0.5561 0.856 0.064 0.080
#> GSM601791 1 0.0592 0.6091 0.988 0.000 0.012
#> GSM601806 2 0.3038 0.8297 0.000 0.896 0.104
#> GSM601811 3 0.5968 0.7255 0.364 0.000 0.636
#> GSM601816 1 0.1163 0.6086 0.972 0.000 0.028
#> GSM601821 2 0.5058 0.7431 0.000 0.756 0.244
#> GSM601826 1 0.0237 0.6109 0.996 0.000 0.004
#> GSM601836 1 0.2187 0.6104 0.948 0.024 0.028
#> GSM601851 1 0.0592 0.6091 0.988 0.000 0.012
#> GSM601856 3 0.5948 0.7244 0.360 0.000 0.640
#> GSM601866 1 0.6045 0.1024 0.620 0.000 0.380
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601752 4 0.4883 0.26911 0.000 0.288 0.016 0.696
#> GSM601782 1 0.2831 0.52465 0.876 0.000 0.120 0.004
#> GSM601792 4 0.5285 -0.38645 0.468 0.000 0.008 0.524
#> GSM601797 4 0.2945 0.42252 0.012 0.052 0.032 0.904
#> GSM601827 1 0.2676 0.57855 0.896 0.000 0.092 0.012
#> GSM601837 2 0.4155 0.55541 0.000 0.756 0.240 0.004
#> GSM601842 2 0.4313 0.62713 0.000 0.736 0.004 0.260
#> GSM601857 3 0.5097 0.71419 0.428 0.000 0.568 0.004
#> GSM601867 3 0.2707 0.73975 0.068 0.008 0.908 0.016
#> GSM601747 1 0.3498 0.59196 0.880 0.024 0.068 0.028
#> GSM601757 1 0.1557 0.60557 0.944 0.000 0.056 0.000
#> GSM601762 2 0.4072 0.63786 0.000 0.748 0.000 0.252
#> GSM601767 2 0.4072 0.63786 0.000 0.748 0.000 0.252
#> GSM601772 2 0.4072 0.63786 0.000 0.748 0.000 0.252
#> GSM601777 4 0.5739 0.23054 0.076 0.008 0.200 0.716
#> GSM601787 3 0.1917 0.68170 0.012 0.036 0.944 0.008
#> GSM601802 4 0.5408 0.08089 0.000 0.408 0.016 0.576
#> GSM601807 3 0.2197 0.72826 0.048 0.000 0.928 0.024
#> GSM601812 1 0.2345 0.55626 0.900 0.000 0.100 0.000
#> GSM601817 1 0.2530 0.53909 0.888 0.000 0.112 0.000
#> GSM601822 4 0.2831 0.36011 0.120 0.000 0.004 0.876
#> GSM601832 2 0.4331 0.59433 0.000 0.712 0.000 0.288
#> GSM601847 4 0.1489 0.44313 0.044 0.004 0.000 0.952
#> GSM601852 1 0.2011 0.58374 0.920 0.000 0.080 0.000
#> GSM601862 3 0.5016 0.74574 0.396 0.000 0.600 0.004
#> GSM601753 4 0.5427 0.06954 0.000 0.416 0.016 0.568
#> GSM601783 1 0.0188 0.63760 0.996 0.000 0.004 0.000
#> GSM601793 4 0.5288 -0.39425 0.472 0.000 0.008 0.520
#> GSM601798 4 0.5435 0.05028 0.000 0.420 0.016 0.564
#> GSM601828 1 0.1940 0.58699 0.924 0.000 0.076 0.000
#> GSM601838 2 0.4122 0.55764 0.000 0.760 0.236 0.004
#> GSM601843 2 0.4252 0.63504 0.000 0.744 0.004 0.252
#> GSM601858 2 0.6515 0.61929 0.000 0.624 0.128 0.248
#> GSM601868 3 0.4699 0.79606 0.320 0.000 0.676 0.004
#> GSM601748 1 0.1792 0.59439 0.932 0.000 0.068 0.000
#> GSM601758 1 0.0469 0.64101 0.988 0.000 0.000 0.012
#> GSM601763 4 0.6478 0.09396 0.336 0.088 0.000 0.576
#> GSM601768 2 0.4401 0.61211 0.000 0.724 0.004 0.272
#> GSM601773 2 0.4072 0.63786 0.000 0.748 0.000 0.252
#> GSM601778 4 0.5085 -0.06380 0.304 0.000 0.020 0.676
#> GSM601788 2 0.6487 0.23163 0.000 0.500 0.072 0.428
#> GSM601803 4 0.5472 -0.01171 0.000 0.440 0.016 0.544
#> GSM601808 3 0.4608 0.79932 0.304 0.000 0.692 0.004
#> GSM601813 1 0.1118 0.62143 0.964 0.000 0.036 0.000
#> GSM601818 1 0.2888 0.51232 0.872 0.000 0.124 0.004
#> GSM601823 1 0.4948 0.50592 0.560 0.000 0.000 0.440
#> GSM601833 2 0.4072 0.63786 0.000 0.748 0.000 0.252
#> GSM601848 1 0.4948 0.50592 0.560 0.000 0.000 0.440
#> GSM601853 3 0.4543 0.79526 0.324 0.000 0.676 0.000
#> GSM601863 3 0.5229 0.70612 0.428 0.000 0.564 0.008
#> GSM601754 4 0.5167 0.19857 0.000 0.340 0.016 0.644
#> GSM601784 2 0.4508 0.63355 0.000 0.780 0.036 0.184
#> GSM601794 4 0.5273 -0.36849 0.456 0.000 0.008 0.536
#> GSM601799 4 0.4933 0.26586 0.000 0.296 0.016 0.688
#> GSM601829 1 0.5581 0.48778 0.532 0.000 0.020 0.448
#> GSM601839 2 0.4155 0.55541 0.000 0.756 0.240 0.004
#> GSM601844 1 0.5244 0.50790 0.556 0.000 0.008 0.436
#> GSM601859 2 0.4428 0.60702 0.000 0.720 0.004 0.276
#> GSM601869 3 0.5132 0.68687 0.448 0.000 0.548 0.004
#> GSM601749 1 0.0188 0.63985 0.996 0.000 0.000 0.004
#> GSM601759 1 0.0657 0.64002 0.984 0.000 0.004 0.012
#> GSM601764 1 0.5337 0.51034 0.564 0.012 0.000 0.424
#> GSM601769 2 0.2593 0.59567 0.000 0.892 0.104 0.004
#> GSM601774 2 0.3873 0.64050 0.000 0.772 0.000 0.228
#> GSM601779 1 0.4961 0.49305 0.552 0.000 0.000 0.448
#> GSM601789 2 0.4365 0.58559 0.000 0.784 0.188 0.028
#> GSM601804 4 0.1917 0.44042 0.036 0.012 0.008 0.944
#> GSM601809 1 0.5526 -0.35732 0.564 0.000 0.416 0.020
#> GSM601814 2 0.2944 0.59231 0.000 0.868 0.128 0.004
#> GSM601819 1 0.1022 0.64415 0.968 0.000 0.000 0.032
#> GSM601824 4 0.3991 0.33820 0.172 0.020 0.000 0.808
#> GSM601834 2 0.4040 0.63884 0.000 0.752 0.000 0.248
#> GSM601849 1 0.4933 0.51607 0.568 0.000 0.000 0.432
#> GSM601854 1 0.1209 0.62437 0.964 0.000 0.032 0.004
#> GSM601864 2 0.4220 0.54866 0.000 0.748 0.248 0.004
#> GSM601755 4 0.5459 0.01420 0.000 0.432 0.016 0.552
#> GSM601785 2 0.5085 0.42414 0.000 0.616 0.008 0.376
#> GSM601795 4 0.4877 -0.09486 0.328 0.000 0.008 0.664
#> GSM601800 4 0.5398 0.08881 0.000 0.404 0.016 0.580
#> GSM601830 3 0.4054 0.78844 0.188 0.000 0.796 0.016
#> GSM601840 4 0.5888 0.02286 0.016 0.440 0.012 0.532
#> GSM601845 4 0.6571 0.27304 0.092 0.280 0.008 0.620
#> GSM601860 2 0.5285 0.45975 0.012 0.632 0.004 0.352
#> GSM601870 3 0.2040 0.72772 0.048 0.004 0.936 0.012
#> GSM601750 1 0.1211 0.61809 0.960 0.000 0.040 0.000
#> GSM601760 1 0.1637 0.64365 0.940 0.000 0.000 0.060
#> GSM601765 2 0.4072 0.63786 0.000 0.748 0.000 0.252
#> GSM601770 2 0.4072 0.63786 0.000 0.748 0.000 0.252
#> GSM601775 4 0.6100 0.27580 0.048 0.300 0.012 0.640
#> GSM601780 1 0.4933 0.51607 0.568 0.000 0.000 0.432
#> GSM601790 2 0.3726 0.56998 0.000 0.788 0.212 0.000
#> GSM601805 4 0.5408 0.08089 0.000 0.408 0.016 0.576
#> GSM601810 3 0.5070 0.76577 0.372 0.000 0.620 0.008
#> GSM601815 2 0.3831 0.57286 0.000 0.792 0.204 0.004
#> GSM601820 1 0.0469 0.63347 0.988 0.000 0.012 0.000
#> GSM601825 4 0.5497 -0.06834 0.000 0.460 0.016 0.524
#> GSM601835 2 0.4546 0.63604 0.000 0.732 0.012 0.256
#> GSM601850 4 0.4830 -0.23304 0.392 0.000 0.000 0.608
#> GSM601855 3 0.4095 0.78949 0.192 0.000 0.792 0.016
#> GSM601865 2 0.4220 0.54866 0.000 0.748 0.248 0.004
#> GSM601756 4 0.5466 0.00169 0.000 0.436 0.016 0.548
#> GSM601786 2 0.4018 0.56333 0.000 0.772 0.224 0.004
#> GSM601796 4 0.5285 -0.39156 0.468 0.000 0.008 0.524
#> GSM601801 4 0.5478 -0.02591 0.000 0.444 0.016 0.540
#> GSM601831 1 0.4483 0.07687 0.712 0.000 0.284 0.004
#> GSM601841 1 0.5159 0.59538 0.756 0.000 0.088 0.156
#> GSM601846 4 0.3144 0.41125 0.000 0.072 0.044 0.884
#> GSM601861 2 0.3626 0.58041 0.000 0.812 0.184 0.004
#> GSM601871 3 0.2310 0.64193 0.004 0.068 0.920 0.008
#> GSM601751 4 0.5974 0.04013 0.020 0.432 0.012 0.536
#> GSM601761 1 0.4679 0.56743 0.648 0.000 0.000 0.352
#> GSM601766 4 0.7143 0.33789 0.208 0.232 0.000 0.560
#> GSM601771 2 0.5355 0.33896 0.004 0.580 0.008 0.408
#> GSM601776 1 0.4907 0.52760 0.580 0.000 0.000 0.420
#> GSM601781 4 0.5252 -0.29386 0.420 0.004 0.004 0.572
#> GSM601791 1 0.4907 0.52760 0.580 0.000 0.000 0.420
#> GSM601806 4 0.5776 -0.10793 0.000 0.468 0.028 0.504
#> GSM601811 3 0.5007 0.77841 0.356 0.000 0.636 0.008
#> GSM601816 1 0.5151 0.46760 0.532 0.000 0.004 0.464
#> GSM601821 2 0.3626 0.58041 0.000 0.812 0.184 0.004
#> GSM601826 1 0.4933 0.51768 0.568 0.000 0.000 0.432
#> GSM601836 1 0.5738 0.46220 0.540 0.028 0.000 0.432
#> GSM601851 1 0.4888 0.53337 0.588 0.000 0.000 0.412
#> GSM601856 3 0.4567 0.79944 0.276 0.000 0.716 0.008
#> GSM601866 1 0.2408 0.55046 0.896 0.000 0.104 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601752 2 0.7207 0.554 0.000 0.448 0.028 0.240 0.284
#> GSM601782 1 0.3361 0.779 0.856 0.000 0.092 0.032 0.020
#> GSM601792 4 0.4738 0.775 0.176 0.000 0.020 0.748 0.056
#> GSM601797 4 0.6887 0.144 0.000 0.140 0.044 0.528 0.288
#> GSM601827 1 0.3134 0.821 0.876 0.000 0.056 0.044 0.024
#> GSM601837 5 0.5159 0.967 0.000 0.400 0.044 0.000 0.556
#> GSM601842 2 0.0290 0.585 0.000 0.992 0.000 0.008 0.000
#> GSM601857 3 0.4604 0.554 0.428 0.000 0.560 0.000 0.012
#> GSM601867 3 0.2722 0.753 0.060 0.000 0.892 0.008 0.040
#> GSM601747 1 0.4821 0.664 0.780 0.120 0.028 0.056 0.016
#> GSM601757 1 0.1168 0.878 0.960 0.000 0.008 0.032 0.000
#> GSM601762 2 0.0324 0.586 0.000 0.992 0.000 0.004 0.004
#> GSM601767 2 0.0000 0.584 0.000 1.000 0.000 0.000 0.000
#> GSM601772 2 0.0162 0.583 0.000 0.996 0.000 0.004 0.000
#> GSM601777 4 0.4800 0.600 0.012 0.020 0.156 0.764 0.048
#> GSM601787 3 0.3023 0.712 0.012 0.004 0.868 0.012 0.104
#> GSM601802 2 0.6842 0.601 0.000 0.520 0.028 0.176 0.276
#> GSM601807 3 0.3673 0.712 0.028 0.000 0.844 0.048 0.080
#> GSM601812 1 0.1168 0.867 0.960 0.000 0.032 0.008 0.000
#> GSM601817 1 0.1591 0.842 0.940 0.000 0.052 0.004 0.004
#> GSM601822 4 0.3396 0.712 0.032 0.032 0.028 0.876 0.032
#> GSM601832 2 0.0807 0.595 0.000 0.976 0.000 0.012 0.012
#> GSM601847 4 0.4254 0.606 0.000 0.060 0.028 0.804 0.108
#> GSM601852 1 0.0992 0.870 0.968 0.000 0.024 0.008 0.000
#> GSM601862 3 0.4341 0.650 0.364 0.000 0.628 0.000 0.008
#> GSM601753 2 0.6803 0.603 0.000 0.524 0.028 0.168 0.280
#> GSM601783 1 0.1205 0.873 0.956 0.000 0.004 0.040 0.000
#> GSM601793 4 0.4738 0.775 0.176 0.000 0.020 0.748 0.056
#> GSM601798 2 0.6846 0.599 0.000 0.516 0.028 0.172 0.284
#> GSM601828 1 0.1386 0.858 0.952 0.000 0.032 0.000 0.016
#> GSM601838 5 0.5159 0.967 0.000 0.400 0.044 0.000 0.556
#> GSM601843 2 0.0162 0.583 0.000 0.996 0.000 0.004 0.000
#> GSM601858 2 0.1365 0.533 0.000 0.952 0.004 0.004 0.040
#> GSM601868 3 0.3690 0.755 0.224 0.000 0.764 0.000 0.012
#> GSM601748 1 0.0798 0.872 0.976 0.000 0.016 0.008 0.000
#> GSM601758 1 0.1608 0.859 0.928 0.000 0.000 0.072 0.000
#> GSM601763 4 0.6378 0.263 0.100 0.396 0.000 0.484 0.020
#> GSM601768 2 0.0703 0.599 0.000 0.976 0.000 0.000 0.024
#> GSM601773 2 0.0000 0.584 0.000 1.000 0.000 0.000 0.000
#> GSM601778 4 0.3250 0.724 0.044 0.004 0.044 0.876 0.032
#> GSM601788 2 0.5338 0.613 0.000 0.732 0.080 0.056 0.132
#> GSM601803 2 0.6758 0.601 0.000 0.532 0.028 0.164 0.276
#> GSM601808 3 0.3333 0.761 0.208 0.000 0.788 0.004 0.000
#> GSM601813 1 0.0865 0.877 0.972 0.000 0.004 0.024 0.000
#> GSM601818 1 0.2166 0.820 0.912 0.000 0.072 0.004 0.012
#> GSM601823 4 0.3534 0.768 0.256 0.000 0.000 0.744 0.000
#> GSM601833 2 0.0162 0.583 0.000 0.996 0.000 0.004 0.000
#> GSM601848 4 0.3480 0.771 0.248 0.000 0.000 0.752 0.000
#> GSM601853 3 0.3910 0.750 0.248 0.000 0.740 0.008 0.004
#> GSM601863 3 0.4403 0.536 0.436 0.000 0.560 0.000 0.004
#> GSM601754 2 0.7019 0.584 0.000 0.488 0.028 0.200 0.284
#> GSM601784 2 0.2971 0.258 0.000 0.836 0.008 0.000 0.156
#> GSM601794 4 0.4802 0.774 0.176 0.000 0.020 0.744 0.060
#> GSM601799 2 0.7121 0.571 0.000 0.468 0.028 0.220 0.284
#> GSM601829 4 0.4821 0.762 0.228 0.000 0.024 0.716 0.032
#> GSM601839 5 0.5159 0.967 0.000 0.400 0.044 0.000 0.556
#> GSM601844 4 0.4577 0.769 0.244 0.000 0.012 0.716 0.028
#> GSM601859 2 0.1205 0.604 0.000 0.956 0.000 0.004 0.040
#> GSM601869 3 0.4813 0.425 0.476 0.000 0.508 0.008 0.008
#> GSM601749 1 0.1544 0.861 0.932 0.000 0.000 0.068 0.000
#> GSM601759 1 0.1671 0.856 0.924 0.000 0.000 0.076 0.000
#> GSM601764 4 0.4411 0.768 0.232 0.024 0.000 0.732 0.012
#> GSM601769 5 0.4522 0.955 0.000 0.440 0.008 0.000 0.552
#> GSM601774 2 0.0566 0.568 0.000 0.984 0.000 0.004 0.012
#> GSM601779 4 0.3809 0.768 0.256 0.000 0.000 0.736 0.008
#> GSM601789 2 0.4738 -0.733 0.000 0.564 0.012 0.004 0.420
#> GSM601804 4 0.6175 0.322 0.000 0.120 0.028 0.616 0.236
#> GSM601809 3 0.6026 0.492 0.416 0.004 0.508 0.040 0.032
#> GSM601814 5 0.4702 0.965 0.000 0.432 0.016 0.000 0.552
#> GSM601819 1 0.2470 0.826 0.884 0.000 0.000 0.104 0.012
#> GSM601824 4 0.4255 0.730 0.068 0.112 0.000 0.800 0.020
#> GSM601834 2 0.0566 0.569 0.000 0.984 0.000 0.004 0.012
#> GSM601849 4 0.3480 0.771 0.248 0.000 0.000 0.752 0.000
#> GSM601854 1 0.1844 0.873 0.936 0.000 0.012 0.040 0.012
#> GSM601864 5 0.5439 0.938 0.000 0.372 0.068 0.000 0.560
#> GSM601755 2 0.6790 0.601 0.000 0.524 0.028 0.164 0.284
#> GSM601785 2 0.2193 0.618 0.000 0.912 0.000 0.028 0.060
#> GSM601795 4 0.4076 0.750 0.096 0.000 0.016 0.812 0.076
#> GSM601800 2 0.6873 0.598 0.000 0.512 0.028 0.176 0.284
#> GSM601830 3 0.4421 0.727 0.072 0.000 0.796 0.032 0.100
#> GSM601840 2 0.5898 0.614 0.008 0.652 0.012 0.116 0.212
#> GSM601845 2 0.5634 0.225 0.016 0.572 0.012 0.372 0.028
#> GSM601860 2 0.2446 0.607 0.000 0.900 0.000 0.044 0.056
#> GSM601870 3 0.3682 0.703 0.024 0.000 0.832 0.028 0.116
#> GSM601750 1 0.0451 0.877 0.988 0.000 0.000 0.008 0.004
#> GSM601760 1 0.2843 0.776 0.848 0.000 0.000 0.144 0.008
#> GSM601765 2 0.0404 0.586 0.000 0.988 0.000 0.012 0.000
#> GSM601770 2 0.0000 0.584 0.000 1.000 0.000 0.000 0.000
#> GSM601775 2 0.6008 0.591 0.012 0.624 0.000 0.172 0.192
#> GSM601780 4 0.3835 0.766 0.260 0.000 0.000 0.732 0.008
#> GSM601790 5 0.4689 0.973 0.000 0.424 0.016 0.000 0.560
#> GSM601805 2 0.6869 0.600 0.000 0.516 0.028 0.180 0.276
#> GSM601810 3 0.4924 0.689 0.320 0.000 0.644 0.016 0.020
#> GSM601815 5 0.4689 0.973 0.000 0.424 0.016 0.000 0.560
#> GSM601820 1 0.1410 0.866 0.940 0.000 0.000 0.060 0.000
#> GSM601825 2 0.6645 0.606 0.000 0.552 0.028 0.156 0.264
#> GSM601835 2 0.0912 0.584 0.000 0.972 0.000 0.016 0.012
#> GSM601850 4 0.3546 0.776 0.128 0.016 0.008 0.836 0.012
#> GSM601855 3 0.4066 0.733 0.072 0.000 0.820 0.028 0.080
#> GSM601865 5 0.5304 0.953 0.000 0.384 0.056 0.000 0.560
#> GSM601756 2 0.6790 0.601 0.000 0.524 0.028 0.164 0.284
#> GSM601786 5 0.4767 0.973 0.000 0.420 0.020 0.000 0.560
#> GSM601796 4 0.4866 0.775 0.180 0.000 0.016 0.736 0.068
#> GSM601801 2 0.6760 0.601 0.000 0.528 0.028 0.160 0.284
#> GSM601831 1 0.3541 0.697 0.824 0.000 0.144 0.012 0.020
#> GSM601841 1 0.6211 0.310 0.552 0.000 0.120 0.316 0.012
#> GSM601846 4 0.7088 0.412 0.004 0.152 0.100 0.588 0.156
#> GSM601861 5 0.4689 0.973 0.000 0.424 0.016 0.000 0.560
#> GSM601871 3 0.2977 0.704 0.008 0.008 0.868 0.008 0.108
#> GSM601751 2 0.5464 0.622 0.004 0.664 0.000 0.124 0.208
#> GSM601761 4 0.4268 0.666 0.344 0.000 0.000 0.648 0.008
#> GSM601766 2 0.5623 0.334 0.048 0.604 0.000 0.324 0.024
#> GSM601771 2 0.3400 0.632 0.000 0.828 0.000 0.036 0.136
#> GSM601776 4 0.3835 0.766 0.260 0.000 0.000 0.732 0.008
#> GSM601781 4 0.3723 0.772 0.120 0.008 0.012 0.832 0.028
#> GSM601791 4 0.3861 0.763 0.264 0.000 0.000 0.728 0.008
#> GSM601806 2 0.6651 0.597 0.000 0.544 0.028 0.148 0.280
#> GSM601811 3 0.4869 0.700 0.308 0.000 0.656 0.016 0.020
#> GSM601816 4 0.3797 0.776 0.232 0.000 0.008 0.756 0.004
#> GSM601821 5 0.4689 0.973 0.000 0.424 0.016 0.000 0.560
#> GSM601826 4 0.3534 0.768 0.256 0.000 0.000 0.744 0.000
#> GSM601836 4 0.5882 0.683 0.160 0.156 0.012 0.664 0.008
#> GSM601851 4 0.3586 0.765 0.264 0.000 0.000 0.736 0.000
#> GSM601856 3 0.3289 0.765 0.172 0.000 0.816 0.008 0.004
#> GSM601866 1 0.1043 0.859 0.960 0.000 0.040 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601752 4 0.4610 0.8391 0.000 0.388 0.000 0.568 0.000 0.044
#> GSM601782 1 0.4510 0.6713 0.776 0.000 0.112 0.048 0.032 0.032
#> GSM601792 6 0.4648 0.7824 0.056 0.000 0.012 0.140 0.040 0.752
#> GSM601797 4 0.5198 0.2389 0.004 0.056 0.004 0.644 0.024 0.268
#> GSM601827 1 0.5466 0.6430 0.716 0.000 0.040 0.088 0.072 0.084
#> GSM601837 5 0.3154 0.9601 0.000 0.184 0.012 0.004 0.800 0.000
#> GSM601842 2 0.0520 0.8170 0.000 0.984 0.008 0.000 0.008 0.000
#> GSM601857 3 0.4453 0.4515 0.444 0.000 0.528 0.000 0.028 0.000
#> GSM601867 3 0.2886 0.6891 0.048 0.000 0.880 0.032 0.032 0.008
#> GSM601747 1 0.6307 0.3674 0.596 0.260 0.036 0.036 0.020 0.052
#> GSM601757 1 0.1296 0.8262 0.952 0.000 0.012 0.000 0.004 0.032
#> GSM601762 2 0.0551 0.8170 0.000 0.984 0.004 0.000 0.008 0.004
#> GSM601767 2 0.0405 0.8164 0.000 0.988 0.000 0.000 0.004 0.008
#> GSM601772 2 0.0405 0.8167 0.000 0.988 0.000 0.000 0.008 0.004
#> GSM601777 6 0.5614 0.6250 0.004 0.004 0.132 0.236 0.012 0.612
#> GSM601787 3 0.3524 0.6414 0.004 0.000 0.824 0.056 0.104 0.012
#> GSM601802 4 0.4481 0.8626 0.000 0.416 0.004 0.556 0.000 0.024
#> GSM601807 3 0.4936 0.6096 0.004 0.000 0.712 0.164 0.088 0.032
#> GSM601812 1 0.1862 0.8130 0.932 0.000 0.032 0.008 0.012 0.016
#> GSM601817 1 0.1647 0.8039 0.940 0.000 0.032 0.016 0.008 0.004
#> GSM601822 6 0.3371 0.7633 0.004 0.000 0.008 0.192 0.008 0.788
#> GSM601832 2 0.0291 0.8165 0.000 0.992 0.004 0.004 0.000 0.000
#> GSM601847 6 0.4216 0.6570 0.000 0.012 0.008 0.296 0.008 0.676
#> GSM601852 1 0.1807 0.8198 0.936 0.000 0.024 0.012 0.012 0.016
#> GSM601862 3 0.4301 0.5404 0.392 0.000 0.584 0.000 0.024 0.000
#> GSM601753 4 0.4123 0.8623 0.000 0.420 0.000 0.568 0.000 0.012
#> GSM601783 1 0.2541 0.8218 0.892 0.000 0.008 0.008 0.028 0.064
#> GSM601793 6 0.4722 0.7794 0.056 0.000 0.012 0.148 0.040 0.744
#> GSM601798 4 0.4032 0.8620 0.000 0.420 0.000 0.572 0.000 0.008
#> GSM601828 1 0.2979 0.7899 0.876 0.000 0.016 0.052 0.036 0.020
#> GSM601838 5 0.3154 0.9601 0.000 0.184 0.012 0.004 0.800 0.000
#> GSM601843 2 0.0665 0.8168 0.000 0.980 0.008 0.004 0.008 0.000
#> GSM601858 2 0.2006 0.7823 0.004 0.928 0.016 0.012 0.032 0.008
#> GSM601868 3 0.4086 0.6695 0.256 0.000 0.708 0.008 0.028 0.000
#> GSM601748 1 0.1414 0.8218 0.952 0.000 0.012 0.012 0.004 0.020
#> GSM601758 1 0.2313 0.8078 0.884 0.000 0.000 0.004 0.012 0.100
#> GSM601763 2 0.5249 0.2221 0.076 0.540 0.004 0.004 0.000 0.376
#> GSM601768 2 0.0291 0.8134 0.000 0.992 0.000 0.004 0.000 0.004
#> GSM601773 2 0.0520 0.8160 0.000 0.984 0.000 0.000 0.008 0.008
#> GSM601778 6 0.4123 0.7530 0.004 0.004 0.028 0.192 0.016 0.756
#> GSM601788 2 0.4640 0.5221 0.000 0.744 0.100 0.124 0.004 0.028
#> GSM601803 4 0.4415 0.8610 0.000 0.420 0.004 0.556 0.000 0.020
#> GSM601808 3 0.3720 0.6940 0.208 0.000 0.760 0.012 0.020 0.000
#> GSM601813 1 0.1338 0.8281 0.952 0.000 0.004 0.004 0.008 0.032
#> GSM601818 1 0.2613 0.7671 0.892 0.000 0.060 0.016 0.012 0.020
#> GSM601823 6 0.2362 0.8029 0.136 0.000 0.000 0.000 0.004 0.860
#> GSM601833 2 0.0436 0.8168 0.000 0.988 0.004 0.000 0.004 0.004
#> GSM601848 6 0.2544 0.8026 0.140 0.000 0.000 0.004 0.004 0.852
#> GSM601853 3 0.5560 0.6586 0.256 0.000 0.632 0.052 0.044 0.016
#> GSM601863 3 0.4633 0.3818 0.468 0.000 0.500 0.008 0.024 0.000
#> GSM601754 4 0.4633 0.8466 0.000 0.392 0.004 0.568 0.000 0.036
#> GSM601784 2 0.2933 0.6036 0.000 0.796 0.000 0.000 0.200 0.004
#> GSM601794 6 0.4774 0.7791 0.056 0.000 0.016 0.144 0.040 0.744
#> GSM601799 4 0.4666 0.8373 0.000 0.388 0.000 0.564 0.000 0.048
#> GSM601829 6 0.5218 0.7594 0.092 0.000 0.012 0.120 0.060 0.716
#> GSM601839 5 0.3154 0.9601 0.000 0.184 0.012 0.004 0.800 0.000
#> GSM601844 6 0.4288 0.7912 0.116 0.000 0.012 0.040 0.048 0.784
#> GSM601859 2 0.0436 0.8131 0.004 0.988 0.000 0.004 0.000 0.004
#> GSM601869 3 0.5043 0.3596 0.464 0.000 0.480 0.016 0.040 0.000
#> GSM601749 1 0.2737 0.8059 0.868 0.000 0.000 0.012 0.024 0.096
#> GSM601759 1 0.2405 0.8075 0.880 0.000 0.000 0.004 0.016 0.100
#> GSM601764 6 0.4469 0.7288 0.132 0.116 0.000 0.004 0.008 0.740
#> GSM601769 5 0.3659 0.9605 0.000 0.224 0.000 0.012 0.752 0.012
#> GSM601774 2 0.1332 0.7978 0.000 0.952 0.000 0.008 0.028 0.012
#> GSM601779 6 0.2442 0.8000 0.144 0.000 0.000 0.000 0.004 0.852
#> GSM601789 2 0.4591 -0.2494 0.000 0.552 0.012 0.008 0.420 0.008
#> GSM601804 4 0.5646 0.3015 0.000 0.140 0.004 0.500 0.000 0.356
#> GSM601809 3 0.6360 0.5686 0.308 0.012 0.552 0.060 0.024 0.044
#> GSM601814 5 0.3535 0.9652 0.000 0.220 0.000 0.012 0.760 0.008
#> GSM601819 1 0.3258 0.7889 0.832 0.000 0.000 0.016 0.032 0.120
#> GSM601824 6 0.3492 0.7890 0.048 0.084 0.000 0.028 0.004 0.836
#> GSM601834 2 0.0748 0.8139 0.000 0.976 0.004 0.000 0.016 0.004
#> GSM601849 6 0.2615 0.8044 0.136 0.000 0.000 0.008 0.004 0.852
#> GSM601854 1 0.3155 0.8068 0.864 0.000 0.008 0.032 0.040 0.056
#> GSM601864 5 0.3559 0.9519 0.000 0.180 0.016 0.012 0.788 0.004
#> GSM601755 4 0.4039 0.8606 0.000 0.424 0.000 0.568 0.000 0.008
#> GSM601785 2 0.1159 0.7997 0.004 0.964 0.004 0.012 0.004 0.012
#> GSM601795 6 0.4636 0.7634 0.032 0.000 0.016 0.172 0.040 0.740
#> GSM601800 4 0.4199 0.8633 0.000 0.416 0.000 0.568 0.000 0.016
#> GSM601830 3 0.6211 0.5714 0.024 0.000 0.600 0.232 0.080 0.064
#> GSM601840 2 0.4044 0.5579 0.008 0.796 0.012 0.124 0.008 0.052
#> GSM601845 2 0.5067 0.4605 0.004 0.696 0.012 0.052 0.028 0.208
#> GSM601860 2 0.1476 0.7864 0.004 0.948 0.012 0.008 0.000 0.028
#> GSM601870 3 0.5346 0.5909 0.004 0.000 0.676 0.180 0.096 0.044
#> GSM601750 1 0.1802 0.8279 0.932 0.000 0.000 0.020 0.024 0.024
#> GSM601760 1 0.2925 0.7686 0.832 0.000 0.000 0.004 0.016 0.148
#> GSM601765 2 0.0551 0.8167 0.000 0.984 0.004 0.004 0.008 0.000
#> GSM601770 2 0.0405 0.8164 0.000 0.988 0.000 0.000 0.004 0.008
#> GSM601775 2 0.4372 0.4635 0.008 0.748 0.004 0.140 0.000 0.100
#> GSM601780 6 0.2482 0.7988 0.148 0.000 0.000 0.000 0.004 0.848
#> GSM601790 5 0.3081 0.9658 0.000 0.220 0.000 0.004 0.776 0.000
#> GSM601805 4 0.4481 0.8626 0.000 0.416 0.004 0.556 0.000 0.024
#> GSM601810 3 0.5190 0.6215 0.308 0.000 0.616 0.044 0.020 0.012
#> GSM601815 5 0.3426 0.9666 0.000 0.220 0.000 0.012 0.764 0.004
#> GSM601820 1 0.2515 0.8179 0.888 0.000 0.000 0.016 0.024 0.072
#> GSM601825 4 0.3986 0.8022 0.000 0.464 0.000 0.532 0.000 0.004
#> GSM601835 2 0.0881 0.8139 0.000 0.972 0.008 0.012 0.008 0.000
#> GSM601850 6 0.3663 0.8045 0.052 0.016 0.004 0.092 0.008 0.828
#> GSM601855 3 0.5323 0.6187 0.024 0.000 0.696 0.176 0.060 0.044
#> GSM601865 5 0.3592 0.9552 0.000 0.184 0.016 0.012 0.784 0.004
#> GSM601756 4 0.4039 0.8606 0.000 0.424 0.000 0.568 0.000 0.008
#> GSM601786 5 0.3776 0.9650 0.000 0.208 0.004 0.016 0.760 0.012
#> GSM601796 6 0.4831 0.7784 0.060 0.000 0.016 0.144 0.040 0.740
#> GSM601801 4 0.3810 0.8515 0.000 0.428 0.000 0.572 0.000 0.000
#> GSM601831 1 0.4893 0.6353 0.756 0.000 0.096 0.056 0.060 0.032
#> GSM601841 1 0.6459 0.0419 0.432 0.000 0.148 0.020 0.016 0.384
#> GSM601846 6 0.8348 0.1884 0.008 0.168 0.108 0.296 0.072 0.348
#> GSM601861 5 0.3426 0.9666 0.000 0.220 0.000 0.012 0.764 0.004
#> GSM601871 3 0.3116 0.6529 0.008 0.000 0.852 0.036 0.096 0.008
#> GSM601751 2 0.4240 0.4564 0.008 0.756 0.012 0.172 0.000 0.052
#> GSM601761 6 0.3608 0.6926 0.248 0.000 0.000 0.004 0.012 0.736
#> GSM601766 2 0.3555 0.5817 0.024 0.796 0.004 0.004 0.004 0.168
#> GSM601771 2 0.2180 0.7406 0.004 0.912 0.008 0.048 0.000 0.028
#> GSM601776 6 0.2845 0.7826 0.172 0.000 0.000 0.004 0.004 0.820
#> GSM601781 6 0.4186 0.7781 0.020 0.012 0.012 0.168 0.016 0.772
#> GSM601791 6 0.3030 0.7827 0.168 0.000 0.000 0.008 0.008 0.816
#> GSM601806 4 0.4794 0.8175 0.000 0.424 0.004 0.536 0.028 0.008
#> GSM601811 3 0.5089 0.6422 0.284 0.000 0.640 0.044 0.020 0.012
#> GSM601816 6 0.2858 0.8119 0.096 0.000 0.004 0.028 0.008 0.864
#> GSM601821 5 0.3426 0.9666 0.000 0.220 0.000 0.012 0.764 0.004
#> GSM601826 6 0.2402 0.8013 0.140 0.000 0.000 0.000 0.004 0.856
#> GSM601836 6 0.6238 0.4568 0.108 0.296 0.008 0.032 0.008 0.548
#> GSM601851 6 0.2662 0.7945 0.152 0.000 0.000 0.004 0.004 0.840
#> GSM601856 3 0.4614 0.6951 0.180 0.000 0.736 0.036 0.036 0.012
#> GSM601866 1 0.1483 0.8043 0.944 0.000 0.036 0.000 0.012 0.008
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n time(p) gender(p) k
#> SD:kmeans 118 0.689 0.130 2
#> SD:kmeans 89 0.245 0.524 3
#> SD:kmeans 80 0.667 0.184 4
#> SD:kmeans 114 0.232 0.430 5
#> SD:kmeans 111 0.771 0.191 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 125 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 0.900 0.927 0.970 0.5039 0.496 0.496
#> 3 3 0.591 0.754 0.861 0.3054 0.781 0.586
#> 4 4 0.525 0.616 0.780 0.1274 0.855 0.609
#> 5 5 0.564 0.510 0.704 0.0645 0.948 0.806
#> 6 6 0.588 0.437 0.637 0.0410 0.926 0.710
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
#> GSM601752 2 0.0000 0.963 0.000 1.000
#> GSM601782 1 0.0000 0.972 1.000 0.000
#> GSM601792 1 0.0000 0.972 1.000 0.000
#> GSM601797 2 0.9460 0.436 0.364 0.636
#> GSM601827 1 0.0000 0.972 1.000 0.000
#> GSM601837 2 0.0000 0.963 0.000 1.000
#> GSM601842 2 0.0000 0.963 0.000 1.000
#> GSM601857 1 0.0000 0.972 1.000 0.000
#> GSM601867 2 0.8443 0.629 0.272 0.728
#> GSM601747 1 0.0000 0.972 1.000 0.000
#> GSM601757 1 0.0000 0.972 1.000 0.000
#> GSM601762 2 0.0000 0.963 0.000 1.000
#> GSM601767 2 0.0000 0.963 0.000 1.000
#> GSM601772 2 0.0000 0.963 0.000 1.000
#> GSM601777 1 0.5178 0.854 0.884 0.116
#> GSM601787 2 0.1184 0.952 0.016 0.984
#> GSM601802 2 0.0000 0.963 0.000 1.000
#> GSM601807 1 0.9635 0.357 0.612 0.388
#> GSM601812 1 0.0000 0.972 1.000 0.000
#> GSM601817 1 0.0000 0.972 1.000 0.000
#> GSM601822 1 0.5946 0.816 0.856 0.144
#> GSM601832 2 0.0000 0.963 0.000 1.000
#> GSM601847 2 0.3431 0.910 0.064 0.936
#> GSM601852 1 0.0000 0.972 1.000 0.000
#> GSM601862 1 0.0000 0.972 1.000 0.000
#> GSM601753 2 0.0000 0.963 0.000 1.000
#> GSM601783 1 0.0000 0.972 1.000 0.000
#> GSM601793 1 0.0000 0.972 1.000 0.000
#> GSM601798 2 0.0000 0.963 0.000 1.000
#> GSM601828 1 0.0000 0.972 1.000 0.000
#> GSM601838 2 0.0000 0.963 0.000 1.000
#> GSM601843 2 0.0000 0.963 0.000 1.000
#> GSM601858 2 0.0000 0.963 0.000 1.000
#> GSM601868 1 0.0000 0.972 1.000 0.000
#> GSM601748 1 0.0000 0.972 1.000 0.000
#> GSM601758 1 0.0000 0.972 1.000 0.000
#> GSM601763 1 0.9933 0.137 0.548 0.452
#> GSM601768 2 0.0000 0.963 0.000 1.000
#> GSM601773 2 0.0000 0.963 0.000 1.000
#> GSM601778 1 0.0000 0.972 1.000 0.000
#> GSM601788 2 0.0376 0.960 0.004 0.996
#> GSM601803 2 0.0000 0.963 0.000 1.000
#> GSM601808 1 0.0000 0.972 1.000 0.000
#> GSM601813 1 0.0000 0.972 1.000 0.000
#> GSM601818 1 0.0000 0.972 1.000 0.000
#> GSM601823 1 0.0000 0.972 1.000 0.000
#> GSM601833 2 0.0000 0.963 0.000 1.000
#> GSM601848 1 0.0000 0.972 1.000 0.000
#> GSM601853 1 0.0000 0.972 1.000 0.000
#> GSM601863 1 0.0000 0.972 1.000 0.000
#> GSM601754 2 0.0000 0.963 0.000 1.000
#> GSM601784 2 0.0000 0.963 0.000 1.000
#> GSM601794 1 0.0000 0.972 1.000 0.000
#> GSM601799 2 0.0000 0.963 0.000 1.000
#> GSM601829 1 0.0000 0.972 1.000 0.000
#> GSM601839 2 0.0000 0.963 0.000 1.000
#> GSM601844 1 0.0000 0.972 1.000 0.000
#> GSM601859 2 0.0000 0.963 0.000 1.000
#> GSM601869 1 0.0000 0.972 1.000 0.000
#> GSM601749 1 0.0000 0.972 1.000 0.000
#> GSM601759 1 0.0000 0.972 1.000 0.000
#> GSM601764 1 0.0000 0.972 1.000 0.000
#> GSM601769 2 0.0000 0.963 0.000 1.000
#> GSM601774 2 0.0000 0.963 0.000 1.000
#> GSM601779 1 0.0000 0.972 1.000 0.000
#> GSM601789 2 0.0000 0.963 0.000 1.000
#> GSM601804 2 0.1843 0.942 0.028 0.972
#> GSM601809 1 0.0938 0.962 0.988 0.012
#> GSM601814 2 0.0000 0.963 0.000 1.000
#> GSM601819 1 0.0000 0.972 1.000 0.000
#> GSM601824 2 0.9661 0.372 0.392 0.608
#> GSM601834 2 0.0000 0.963 0.000 1.000
#> GSM601849 1 0.0000 0.972 1.000 0.000
#> GSM601854 1 0.0000 0.972 1.000 0.000
#> GSM601864 2 0.0000 0.963 0.000 1.000
#> GSM601755 2 0.0000 0.963 0.000 1.000
#> GSM601785 2 0.0000 0.963 0.000 1.000
#> GSM601795 1 0.0000 0.972 1.000 0.000
#> GSM601800 2 0.0000 0.963 0.000 1.000
#> GSM601830 1 0.5294 0.847 0.880 0.120
#> GSM601840 2 0.0000 0.963 0.000 1.000
#> GSM601845 2 0.7453 0.733 0.212 0.788
#> GSM601860 2 0.0000 0.963 0.000 1.000
#> GSM601870 1 0.9710 0.324 0.600 0.400
#> GSM601750 1 0.0000 0.972 1.000 0.000
#> GSM601760 1 0.0000 0.972 1.000 0.000
#> GSM601765 2 0.0000 0.963 0.000 1.000
#> GSM601770 2 0.0000 0.963 0.000 1.000
#> GSM601775 2 0.8144 0.669 0.252 0.748
#> GSM601780 1 0.0000 0.972 1.000 0.000
#> GSM601790 2 0.0000 0.963 0.000 1.000
#> GSM601805 2 0.0000 0.963 0.000 1.000
#> GSM601810 1 0.0000 0.972 1.000 0.000
#> GSM601815 2 0.0000 0.963 0.000 1.000
#> GSM601820 1 0.0000 0.972 1.000 0.000
#> GSM601825 2 0.0000 0.963 0.000 1.000
#> GSM601835 2 0.0000 0.963 0.000 1.000
#> GSM601850 1 0.0376 0.969 0.996 0.004
#> GSM601855 1 0.0000 0.972 1.000 0.000
#> GSM601865 2 0.0000 0.963 0.000 1.000
#> GSM601756 2 0.0000 0.963 0.000 1.000
#> GSM601786 2 0.0000 0.963 0.000 1.000
#> GSM601796 1 0.0000 0.972 1.000 0.000
#> GSM601801 2 0.0000 0.963 0.000 1.000
#> GSM601831 1 0.0000 0.972 1.000 0.000
#> GSM601841 1 0.0000 0.972 1.000 0.000
#> GSM601846 2 0.1184 0.952 0.016 0.984
#> GSM601861 2 0.0000 0.963 0.000 1.000
#> GSM601871 2 0.4815 0.868 0.104 0.896
#> GSM601751 2 0.4161 0.890 0.084 0.916
#> GSM601761 1 0.0000 0.972 1.000 0.000
#> GSM601766 2 0.8763 0.590 0.296 0.704
#> GSM601771 2 0.0000 0.963 0.000 1.000
#> GSM601776 1 0.0000 0.972 1.000 0.000
#> GSM601781 1 0.0376 0.969 0.996 0.004
#> GSM601791 1 0.0000 0.972 1.000 0.000
#> GSM601806 2 0.0000 0.963 0.000 1.000
#> GSM601811 1 0.0000 0.972 1.000 0.000
#> GSM601816 1 0.0000 0.972 1.000 0.000
#> GSM601821 2 0.0000 0.963 0.000 1.000
#> GSM601826 1 0.0000 0.972 1.000 0.000
#> GSM601836 1 0.0000 0.972 1.000 0.000
#> GSM601851 1 0.0000 0.972 1.000 0.000
#> GSM601856 1 0.0000 0.972 1.000 0.000
#> GSM601866 1 0.0000 0.972 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601752 2 0.5291 0.74446 0.268 0.732 0.000
#> GSM601782 3 0.2711 0.80606 0.088 0.000 0.912
#> GSM601792 1 0.1860 0.78391 0.948 0.000 0.052
#> GSM601797 1 0.8821 0.38155 0.580 0.188 0.232
#> GSM601827 3 0.4002 0.75907 0.160 0.000 0.840
#> GSM601837 2 0.0237 0.91416 0.004 0.996 0.000
#> GSM601842 2 0.0237 0.91416 0.004 0.996 0.000
#> GSM601857 3 0.0424 0.81737 0.008 0.000 0.992
#> GSM601867 3 0.4465 0.66230 0.004 0.176 0.820
#> GSM601747 3 0.5318 0.71409 0.204 0.016 0.780
#> GSM601757 3 0.3619 0.78195 0.136 0.000 0.864
#> GSM601762 2 0.0237 0.91416 0.004 0.996 0.000
#> GSM601767 2 0.0000 0.91466 0.000 1.000 0.000
#> GSM601772 2 0.0000 0.91466 0.000 1.000 0.000
#> GSM601777 1 0.7487 0.22462 0.552 0.040 0.408
#> GSM601787 3 0.5378 0.59308 0.008 0.236 0.756
#> GSM601802 2 0.3941 0.85118 0.156 0.844 0.000
#> GSM601807 3 0.4914 0.69942 0.088 0.068 0.844
#> GSM601812 3 0.2537 0.80972 0.080 0.000 0.920
#> GSM601817 3 0.1643 0.81828 0.044 0.000 0.956
#> GSM601822 1 0.1585 0.76096 0.964 0.008 0.028
#> GSM601832 2 0.0237 0.91411 0.004 0.996 0.000
#> GSM601847 1 0.4345 0.64371 0.848 0.136 0.016
#> GSM601852 3 0.4346 0.74091 0.184 0.000 0.816
#> GSM601862 3 0.0237 0.81616 0.004 0.000 0.996
#> GSM601753 2 0.4002 0.84871 0.160 0.840 0.000
#> GSM601783 3 0.6045 0.40077 0.380 0.000 0.620
#> GSM601793 1 0.2959 0.78893 0.900 0.000 0.100
#> GSM601798 2 0.3941 0.85118 0.156 0.844 0.000
#> GSM601828 3 0.3412 0.78773 0.124 0.000 0.876
#> GSM601838 2 0.0237 0.91416 0.004 0.996 0.000
#> GSM601843 2 0.0237 0.91416 0.004 0.996 0.000
#> GSM601858 2 0.1129 0.90382 0.004 0.976 0.020
#> GSM601868 3 0.0000 0.81559 0.000 0.000 1.000
#> GSM601748 3 0.3116 0.79734 0.108 0.000 0.892
#> GSM601758 3 0.6235 0.23143 0.436 0.000 0.564
#> GSM601763 1 0.4164 0.79431 0.848 0.008 0.144
#> GSM601768 2 0.0000 0.91466 0.000 1.000 0.000
#> GSM601773 2 0.0000 0.91466 0.000 1.000 0.000
#> GSM601778 1 0.3941 0.72617 0.844 0.000 0.156
#> GSM601788 2 0.4194 0.86510 0.060 0.876 0.064
#> GSM601803 2 0.3879 0.85373 0.152 0.848 0.000
#> GSM601808 3 0.0000 0.81559 0.000 0.000 1.000
#> GSM601813 3 0.5363 0.62179 0.276 0.000 0.724
#> GSM601818 3 0.1031 0.81920 0.024 0.000 0.976
#> GSM601823 1 0.3752 0.79469 0.856 0.000 0.144
#> GSM601833 2 0.0000 0.91466 0.000 1.000 0.000
#> GSM601848 1 0.3816 0.79376 0.852 0.000 0.148
#> GSM601853 3 0.0000 0.81559 0.000 0.000 1.000
#> GSM601863 3 0.0747 0.81902 0.016 0.000 0.984
#> GSM601754 2 0.4887 0.78965 0.228 0.772 0.000
#> GSM601784 2 0.0000 0.91466 0.000 1.000 0.000
#> GSM601794 1 0.2261 0.78094 0.932 0.000 0.068
#> GSM601799 2 0.5706 0.67023 0.320 0.680 0.000
#> GSM601829 1 0.6140 0.44113 0.596 0.000 0.404
#> GSM601839 2 0.0237 0.91416 0.004 0.996 0.000
#> GSM601844 1 0.4750 0.73682 0.784 0.000 0.216
#> GSM601859 2 0.0000 0.91466 0.000 1.000 0.000
#> GSM601869 3 0.0892 0.81965 0.020 0.000 0.980
#> GSM601749 3 0.6215 0.25560 0.428 0.000 0.572
#> GSM601759 3 0.5785 0.51896 0.332 0.000 0.668
#> GSM601764 1 0.4110 0.79148 0.844 0.004 0.152
#> GSM601769 2 0.0000 0.91466 0.000 1.000 0.000
#> GSM601774 2 0.0000 0.91466 0.000 1.000 0.000
#> GSM601779 1 0.3619 0.79602 0.864 0.000 0.136
#> GSM601789 2 0.0237 0.91416 0.004 0.996 0.000
#> GSM601804 1 0.3340 0.66545 0.880 0.120 0.000
#> GSM601809 3 0.1751 0.81649 0.028 0.012 0.960
#> GSM601814 2 0.0000 0.91466 0.000 1.000 0.000
#> GSM601819 1 0.6308 0.03057 0.508 0.000 0.492
#> GSM601824 1 0.1774 0.76670 0.960 0.016 0.024
#> GSM601834 2 0.0000 0.91466 0.000 1.000 0.000
#> GSM601849 1 0.4002 0.78703 0.840 0.000 0.160
#> GSM601854 3 0.4974 0.67982 0.236 0.000 0.764
#> GSM601864 2 0.0237 0.91416 0.004 0.996 0.000
#> GSM601755 2 0.3879 0.85373 0.152 0.848 0.000
#> GSM601785 2 0.0592 0.91235 0.012 0.988 0.000
#> GSM601795 1 0.0592 0.75687 0.988 0.000 0.012
#> GSM601800 2 0.3941 0.85118 0.156 0.844 0.000
#> GSM601830 3 0.3234 0.75458 0.020 0.072 0.908
#> GSM601840 2 0.6231 0.74237 0.080 0.772 0.148
#> GSM601845 2 0.9030 0.00375 0.388 0.476 0.136
#> GSM601860 2 0.0424 0.91211 0.008 0.992 0.000
#> GSM601870 3 0.4110 0.68478 0.004 0.152 0.844
#> GSM601750 3 0.4346 0.74243 0.184 0.000 0.816
#> GSM601760 1 0.6274 0.18103 0.544 0.000 0.456
#> GSM601765 2 0.0000 0.91466 0.000 1.000 0.000
#> GSM601770 2 0.0000 0.91466 0.000 1.000 0.000
#> GSM601775 2 0.6935 0.52019 0.372 0.604 0.024
#> GSM601780 1 0.3816 0.79361 0.852 0.000 0.148
#> GSM601790 2 0.0237 0.91416 0.004 0.996 0.000
#> GSM601805 2 0.3879 0.85373 0.152 0.848 0.000
#> GSM601810 3 0.0000 0.81559 0.000 0.000 1.000
#> GSM601815 2 0.0237 0.91416 0.004 0.996 0.000
#> GSM601820 3 0.5621 0.56412 0.308 0.000 0.692
#> GSM601825 2 0.3551 0.86410 0.132 0.868 0.000
#> GSM601835 2 0.0424 0.91418 0.008 0.992 0.000
#> GSM601850 1 0.2066 0.78585 0.940 0.000 0.060
#> GSM601855 3 0.1620 0.79862 0.024 0.012 0.964
#> GSM601865 2 0.0237 0.91416 0.004 0.996 0.000
#> GSM601756 2 0.3879 0.85373 0.152 0.848 0.000
#> GSM601786 2 0.0237 0.91416 0.004 0.996 0.000
#> GSM601796 1 0.2711 0.78751 0.912 0.000 0.088
#> GSM601801 2 0.3879 0.85373 0.152 0.848 0.000
#> GSM601831 3 0.1289 0.81912 0.032 0.000 0.968
#> GSM601841 3 0.5733 0.49719 0.324 0.000 0.676
#> GSM601846 1 0.8499 0.05747 0.516 0.388 0.096
#> GSM601861 2 0.0000 0.91466 0.000 1.000 0.000
#> GSM601871 3 0.5517 0.55145 0.004 0.268 0.728
#> GSM601751 2 0.5020 0.80978 0.192 0.796 0.012
#> GSM601761 1 0.4178 0.77885 0.828 0.000 0.172
#> GSM601766 2 0.8310 0.00315 0.420 0.500 0.080
#> GSM601771 2 0.0592 0.91229 0.012 0.988 0.000
#> GSM601776 1 0.3941 0.79026 0.844 0.000 0.156
#> GSM601781 1 0.2772 0.77264 0.916 0.004 0.080
#> GSM601791 1 0.4121 0.78205 0.832 0.000 0.168
#> GSM601806 2 0.3619 0.86237 0.136 0.864 0.000
#> GSM601811 3 0.0000 0.81559 0.000 0.000 1.000
#> GSM601816 1 0.3551 0.79710 0.868 0.000 0.132
#> GSM601821 2 0.0000 0.91466 0.000 1.000 0.000
#> GSM601826 1 0.3941 0.78967 0.844 0.000 0.156
#> GSM601836 1 0.6345 0.43145 0.596 0.004 0.400
#> GSM601851 1 0.3941 0.78996 0.844 0.000 0.156
#> GSM601856 3 0.0000 0.81559 0.000 0.000 1.000
#> GSM601866 3 0.2066 0.81534 0.060 0.000 0.940
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601752 4 0.322 0.79618 0.044 0.076 0.000 0.880
#> GSM601782 3 0.499 0.58404 0.288 0.000 0.692 0.020
#> GSM601792 1 0.466 0.63921 0.760 0.000 0.032 0.208
#> GSM601797 4 0.448 0.68238 0.124 0.024 0.032 0.820
#> GSM601827 3 0.509 0.50584 0.348 0.000 0.640 0.012
#> GSM601837 2 0.220 0.82340 0.000 0.928 0.024 0.048
#> GSM601842 2 0.384 0.82095 0.020 0.832 0.004 0.144
#> GSM601857 3 0.234 0.69513 0.080 0.000 0.912 0.008
#> GSM601867 3 0.537 0.52669 0.000 0.188 0.732 0.080
#> GSM601747 3 0.787 0.36565 0.328 0.144 0.500 0.028
#> GSM601757 3 0.472 0.55119 0.324 0.000 0.672 0.004
#> GSM601762 2 0.299 0.84638 0.016 0.880 0.000 0.104
#> GSM601767 2 0.361 0.81972 0.020 0.840 0.000 0.140
#> GSM601772 2 0.292 0.83827 0.016 0.884 0.000 0.100
#> GSM601777 4 0.825 0.21487 0.212 0.024 0.320 0.444
#> GSM601787 3 0.644 0.29107 0.000 0.340 0.576 0.084
#> GSM601802 4 0.310 0.80372 0.012 0.120 0.000 0.868
#> GSM601807 3 0.508 0.56289 0.004 0.100 0.776 0.120
#> GSM601812 3 0.481 0.55957 0.316 0.000 0.676 0.008
#> GSM601817 3 0.402 0.64659 0.224 0.000 0.772 0.004
#> GSM601822 1 0.553 0.19396 0.560 0.000 0.020 0.420
#> GSM601832 2 0.371 0.82050 0.024 0.836 0.000 0.140
#> GSM601847 4 0.492 0.49314 0.284 0.012 0.004 0.700
#> GSM601852 3 0.496 0.45374 0.380 0.000 0.616 0.004
#> GSM601862 3 0.182 0.69539 0.060 0.000 0.936 0.004
#> GSM601753 4 0.305 0.79251 0.004 0.136 0.000 0.860
#> GSM601783 1 0.524 -0.00794 0.556 0.000 0.436 0.008
#> GSM601793 1 0.544 0.64720 0.732 0.000 0.092 0.176
#> GSM601798 4 0.294 0.79930 0.004 0.128 0.000 0.868
#> GSM601828 3 0.466 0.52124 0.348 0.000 0.652 0.000
#> GSM601838 2 0.202 0.82686 0.000 0.936 0.024 0.040
#> GSM601843 2 0.316 0.83777 0.020 0.872 0.000 0.108
#> GSM601858 2 0.319 0.80373 0.004 0.888 0.052 0.056
#> GSM601868 3 0.183 0.69206 0.032 0.000 0.944 0.024
#> GSM601748 3 0.510 0.46768 0.380 0.000 0.612 0.008
#> GSM601758 1 0.502 0.24168 0.632 0.000 0.360 0.008
#> GSM601763 1 0.547 0.61312 0.748 0.052 0.020 0.180
#> GSM601768 2 0.355 0.82540 0.020 0.844 0.000 0.136
#> GSM601773 2 0.339 0.83354 0.016 0.852 0.000 0.132
#> GSM601778 1 0.719 0.34932 0.540 0.004 0.144 0.312
#> GSM601788 2 0.671 0.45500 0.008 0.620 0.112 0.260
#> GSM601803 4 0.307 0.78954 0.000 0.152 0.000 0.848
#> GSM601808 3 0.194 0.68875 0.032 0.000 0.940 0.028
#> GSM601813 3 0.517 0.19093 0.492 0.000 0.504 0.004
#> GSM601818 3 0.349 0.67536 0.172 0.000 0.824 0.004
#> GSM601823 1 0.202 0.70591 0.936 0.000 0.024 0.040
#> GSM601833 2 0.305 0.83821 0.016 0.876 0.000 0.108
#> GSM601848 1 0.182 0.70516 0.944 0.000 0.020 0.036
#> GSM601853 3 0.189 0.69315 0.044 0.000 0.940 0.016
#> GSM601863 3 0.289 0.69026 0.124 0.000 0.872 0.004
#> GSM601754 4 0.329 0.79694 0.044 0.080 0.000 0.876
#> GSM601784 2 0.227 0.84546 0.004 0.912 0.000 0.084
#> GSM601794 1 0.592 0.53803 0.656 0.000 0.072 0.272
#> GSM601799 4 0.331 0.79375 0.036 0.092 0.000 0.872
#> GSM601829 1 0.573 0.36864 0.616 0.000 0.344 0.040
#> GSM601839 2 0.220 0.82326 0.000 0.928 0.024 0.048
#> GSM601844 1 0.373 0.67617 0.848 0.000 0.108 0.044
#> GSM601859 2 0.345 0.81842 0.008 0.836 0.000 0.156
#> GSM601869 3 0.345 0.68355 0.156 0.000 0.836 0.008
#> GSM601749 1 0.492 0.31236 0.656 0.000 0.336 0.008
#> GSM601759 1 0.520 0.06669 0.576 0.000 0.416 0.008
#> GSM601764 1 0.305 0.69050 0.900 0.016 0.056 0.028
#> GSM601769 2 0.205 0.84695 0.008 0.928 0.000 0.064
#> GSM601774 2 0.292 0.83991 0.016 0.884 0.000 0.100
#> GSM601779 1 0.191 0.70591 0.940 0.000 0.020 0.040
#> GSM601789 2 0.163 0.83013 0.000 0.952 0.024 0.024
#> GSM601804 4 0.453 0.59750 0.240 0.016 0.000 0.744
#> GSM601809 3 0.585 0.61028 0.088 0.108 0.756 0.048
#> GSM601814 2 0.247 0.84031 0.000 0.908 0.012 0.080
#> GSM601819 1 0.462 0.42197 0.708 0.000 0.284 0.008
#> GSM601824 1 0.487 0.46561 0.684 0.012 0.000 0.304
#> GSM601834 2 0.287 0.84428 0.012 0.884 0.000 0.104
#> GSM601849 1 0.213 0.70607 0.932 0.000 0.036 0.032
#> GSM601854 3 0.517 0.20449 0.488 0.000 0.508 0.004
#> GSM601864 2 0.244 0.81858 0.000 0.916 0.024 0.060
#> GSM601755 4 0.276 0.79914 0.000 0.128 0.000 0.872
#> GSM601785 2 0.471 0.68841 0.020 0.732 0.000 0.248
#> GSM601795 1 0.560 0.07233 0.504 0.000 0.020 0.476
#> GSM601800 4 0.289 0.80087 0.004 0.124 0.000 0.872
#> GSM601830 3 0.325 0.65388 0.016 0.040 0.892 0.052
#> GSM601840 4 0.839 0.11900 0.044 0.388 0.160 0.408
#> GSM601845 2 0.877 0.23688 0.240 0.484 0.080 0.196
#> GSM601860 2 0.340 0.82591 0.004 0.856 0.012 0.128
#> GSM601870 3 0.476 0.56580 0.000 0.144 0.784 0.072
#> GSM601750 3 0.513 0.32331 0.448 0.000 0.548 0.004
#> GSM601760 1 0.459 0.42796 0.712 0.000 0.280 0.008
#> GSM601765 2 0.327 0.83329 0.024 0.868 0.000 0.108
#> GSM601770 2 0.339 0.83075 0.020 0.856 0.000 0.124
#> GSM601775 4 0.764 0.49920 0.212 0.224 0.016 0.548
#> GSM601780 1 0.183 0.70520 0.944 0.000 0.024 0.032
#> GSM601790 2 0.183 0.82978 0.000 0.944 0.024 0.032
#> GSM601805 4 0.320 0.80012 0.008 0.136 0.000 0.856
#> GSM601810 3 0.191 0.69007 0.040 0.000 0.940 0.020
#> GSM601815 2 0.189 0.83346 0.000 0.940 0.016 0.044
#> GSM601820 1 0.529 -0.13662 0.520 0.000 0.472 0.008
#> GSM601825 4 0.475 0.42346 0.000 0.368 0.000 0.632
#> GSM601835 2 0.439 0.81269 0.020 0.828 0.040 0.112
#> GSM601850 1 0.501 0.62100 0.748 0.004 0.040 0.208
#> GSM601855 3 0.294 0.64324 0.004 0.040 0.900 0.056
#> GSM601865 2 0.260 0.81172 0.000 0.908 0.024 0.068
#> GSM601756 4 0.276 0.79833 0.000 0.128 0.000 0.872
#> GSM601786 2 0.200 0.82698 0.000 0.936 0.020 0.044
#> GSM601796 1 0.603 0.60251 0.668 0.000 0.096 0.236
#> GSM601801 4 0.312 0.78852 0.000 0.156 0.000 0.844
#> GSM601831 3 0.340 0.67708 0.164 0.000 0.832 0.004
#> GSM601841 3 0.551 0.13852 0.484 0.000 0.500 0.016
#> GSM601846 4 0.824 0.51712 0.160 0.152 0.112 0.576
#> GSM601861 2 0.185 0.83681 0.000 0.940 0.012 0.048
#> GSM601871 3 0.646 0.29127 0.000 0.332 0.580 0.088
#> GSM601751 2 0.784 -0.08829 0.096 0.456 0.044 0.404
#> GSM601761 1 0.261 0.66835 0.896 0.000 0.096 0.008
#> GSM601766 2 0.811 0.30127 0.308 0.500 0.040 0.152
#> GSM601771 2 0.472 0.72752 0.008 0.752 0.016 0.224
#> GSM601776 1 0.259 0.68638 0.904 0.000 0.080 0.016
#> GSM601781 1 0.606 0.57952 0.696 0.012 0.084 0.208
#> GSM601791 1 0.214 0.69712 0.928 0.000 0.056 0.016
#> GSM601806 4 0.357 0.75009 0.000 0.196 0.000 0.804
#> GSM601811 3 0.192 0.68714 0.024 0.004 0.944 0.028
#> GSM601816 1 0.240 0.70359 0.920 0.000 0.032 0.048
#> GSM601821 2 0.225 0.83779 0.000 0.920 0.012 0.068
#> GSM601826 1 0.194 0.70547 0.940 0.000 0.028 0.032
#> GSM601836 1 0.654 0.52447 0.672 0.028 0.216 0.084
#> GSM601851 1 0.203 0.70531 0.936 0.000 0.036 0.028
#> GSM601856 3 0.162 0.68964 0.028 0.000 0.952 0.020
#> GSM601866 3 0.469 0.60756 0.276 0.000 0.712 0.012
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601752 4 0.0968 0.76850 0.012 0.012 0.000 0.972 0.004
#> GSM601782 3 0.5149 0.54016 0.216 0.000 0.680 0.000 0.104
#> GSM601792 1 0.5769 0.60182 0.692 0.000 0.048 0.112 0.148
#> GSM601797 4 0.4325 0.65395 0.076 0.000 0.020 0.796 0.108
#> GSM601827 3 0.6291 0.40402 0.280 0.000 0.544 0.004 0.172
#> GSM601837 2 0.2408 0.70289 0.000 0.892 0.000 0.016 0.092
#> GSM601842 2 0.4986 0.63219 0.000 0.688 0.000 0.084 0.228
#> GSM601857 3 0.3687 0.57689 0.028 0.000 0.792 0.000 0.180
#> GSM601867 3 0.6569 -0.10677 0.000 0.156 0.432 0.008 0.404
#> GSM601747 3 0.8012 0.25838 0.216 0.076 0.484 0.024 0.200
#> GSM601757 3 0.5192 0.47639 0.280 0.000 0.644 0.000 0.076
#> GSM601762 2 0.3888 0.71339 0.000 0.800 0.000 0.064 0.136
#> GSM601767 2 0.4237 0.69762 0.000 0.772 0.000 0.076 0.152
#> GSM601772 2 0.4555 0.67317 0.000 0.732 0.000 0.068 0.200
#> GSM601777 4 0.9105 -0.16980 0.188 0.032 0.204 0.296 0.280
#> GSM601787 5 0.7193 0.31964 0.000 0.324 0.288 0.016 0.372
#> GSM601802 4 0.1282 0.77433 0.000 0.044 0.000 0.952 0.004
#> GSM601807 3 0.6131 0.14766 0.000 0.052 0.492 0.036 0.420
#> GSM601812 3 0.4707 0.56508 0.212 0.000 0.716 0.000 0.072
#> GSM601817 3 0.4078 0.59898 0.148 0.000 0.784 0.000 0.068
#> GSM601822 1 0.6504 0.39344 0.584 0.004 0.024 0.248 0.140
#> GSM601832 2 0.5805 0.52407 0.004 0.596 0.000 0.112 0.288
#> GSM601847 4 0.5705 0.42362 0.300 0.008 0.000 0.604 0.088
#> GSM601852 3 0.4836 0.44386 0.304 0.000 0.652 0.000 0.044
#> GSM601862 3 0.3513 0.55863 0.020 0.000 0.800 0.000 0.180
#> GSM601753 4 0.2234 0.75900 0.004 0.044 0.000 0.916 0.036
#> GSM601783 3 0.5405 0.04991 0.460 0.000 0.484 0.000 0.056
#> GSM601793 1 0.6014 0.61290 0.684 0.000 0.088 0.104 0.124
#> GSM601798 4 0.1725 0.77387 0.000 0.044 0.000 0.936 0.020
#> GSM601828 3 0.4795 0.52930 0.224 0.000 0.704 0.000 0.072
#> GSM601838 2 0.2390 0.70541 0.000 0.896 0.000 0.020 0.084
#> GSM601843 2 0.4031 0.69062 0.000 0.772 0.000 0.044 0.184
#> GSM601858 2 0.4477 0.62718 0.000 0.764 0.036 0.024 0.176
#> GSM601868 3 0.3835 0.52417 0.008 0.000 0.732 0.000 0.260
#> GSM601748 3 0.4451 0.50482 0.248 0.000 0.712 0.000 0.040
#> GSM601758 1 0.4878 0.06740 0.536 0.000 0.440 0.000 0.024
#> GSM601763 1 0.8261 0.31189 0.480 0.064 0.100 0.092 0.264
#> GSM601768 2 0.5342 0.61709 0.004 0.664 0.000 0.096 0.236
#> GSM601773 2 0.4123 0.70930 0.000 0.788 0.000 0.108 0.104
#> GSM601778 1 0.7351 0.38269 0.540 0.000 0.108 0.192 0.160
#> GSM601788 2 0.7232 0.16994 0.012 0.528 0.036 0.240 0.184
#> GSM601803 4 0.1768 0.76438 0.000 0.072 0.000 0.924 0.004
#> GSM601808 3 0.3487 0.53520 0.008 0.000 0.780 0.000 0.212
#> GSM601813 3 0.5182 0.24833 0.412 0.000 0.544 0.000 0.044
#> GSM601818 3 0.3758 0.61915 0.096 0.000 0.816 0.000 0.088
#> GSM601823 1 0.1843 0.67033 0.932 0.000 0.008 0.008 0.052
#> GSM601833 2 0.4555 0.67001 0.000 0.732 0.000 0.068 0.200
#> GSM601848 1 0.1267 0.67144 0.960 0.000 0.004 0.012 0.024
#> GSM601853 3 0.3318 0.55732 0.008 0.000 0.800 0.000 0.192
#> GSM601863 3 0.4266 0.61259 0.104 0.000 0.776 0.000 0.120
#> GSM601754 4 0.1721 0.76849 0.016 0.020 0.000 0.944 0.020
#> GSM601784 2 0.2654 0.73519 0.000 0.888 0.000 0.064 0.048
#> GSM601794 1 0.6779 0.52236 0.588 0.000 0.060 0.192 0.160
#> GSM601799 4 0.2747 0.73860 0.036 0.020 0.000 0.896 0.048
#> GSM601829 1 0.6434 0.31352 0.524 0.000 0.276 0.004 0.196
#> GSM601839 2 0.2351 0.70447 0.000 0.896 0.000 0.016 0.088
#> GSM601844 1 0.5708 0.54561 0.660 0.000 0.180 0.012 0.148
#> GSM601859 2 0.4686 0.65783 0.000 0.736 0.000 0.160 0.104
#> GSM601869 3 0.4845 0.60987 0.128 0.000 0.724 0.000 0.148
#> GSM601749 1 0.5088 0.05904 0.528 0.000 0.436 0.000 0.036
#> GSM601759 3 0.4826 0.08070 0.472 0.000 0.508 0.000 0.020
#> GSM601764 1 0.4874 0.62013 0.744 0.008 0.088 0.004 0.156
#> GSM601769 2 0.2067 0.73241 0.000 0.920 0.000 0.032 0.048
#> GSM601774 2 0.3669 0.72018 0.000 0.816 0.000 0.056 0.128
#> GSM601779 1 0.1651 0.67049 0.944 0.000 0.012 0.008 0.036
#> GSM601789 2 0.2338 0.70634 0.000 0.884 0.000 0.004 0.112
#> GSM601804 4 0.3953 0.63054 0.188 0.008 0.000 0.780 0.024
#> GSM601809 3 0.7277 0.37308 0.108 0.100 0.540 0.004 0.248
#> GSM601814 2 0.1942 0.72627 0.000 0.920 0.000 0.068 0.012
#> GSM601819 1 0.5401 0.13341 0.536 0.000 0.404 0.000 0.060
#> GSM601824 1 0.5042 0.54551 0.724 0.012 0.000 0.168 0.096
#> GSM601834 2 0.3437 0.72361 0.000 0.832 0.000 0.048 0.120
#> GSM601849 1 0.2930 0.66663 0.880 0.000 0.076 0.012 0.032
#> GSM601854 3 0.5142 0.28060 0.392 0.000 0.564 0.000 0.044
#> GSM601864 2 0.3209 0.66630 0.000 0.848 0.004 0.028 0.120
#> GSM601755 4 0.1043 0.77427 0.000 0.040 0.000 0.960 0.000
#> GSM601785 2 0.6018 0.52030 0.008 0.612 0.000 0.172 0.208
#> GSM601795 1 0.6776 0.13817 0.424 0.000 0.020 0.408 0.148
#> GSM601800 4 0.1780 0.77216 0.008 0.028 0.000 0.940 0.024
#> GSM601830 3 0.4564 0.39752 0.000 0.016 0.612 0.000 0.372
#> GSM601840 4 0.8935 -0.20518 0.040 0.256 0.112 0.336 0.256
#> GSM601845 5 0.8958 0.30854 0.132 0.256 0.096 0.104 0.412
#> GSM601860 2 0.4825 0.64129 0.020 0.756 0.000 0.092 0.132
#> GSM601870 3 0.5821 0.12210 0.000 0.080 0.492 0.004 0.424
#> GSM601750 3 0.4805 0.43282 0.312 0.000 0.648 0.000 0.040
#> GSM601760 1 0.4865 0.30084 0.616 0.000 0.356 0.008 0.020
#> GSM601765 2 0.4821 0.61109 0.004 0.680 0.000 0.044 0.272
#> GSM601770 2 0.4612 0.68347 0.000 0.736 0.000 0.084 0.180
#> GSM601775 4 0.8737 0.08053 0.140 0.148 0.052 0.440 0.220
#> GSM601780 1 0.1653 0.66989 0.944 0.000 0.024 0.004 0.028
#> GSM601790 2 0.1764 0.71250 0.000 0.928 0.000 0.008 0.064
#> GSM601805 4 0.1809 0.77146 0.000 0.060 0.000 0.928 0.012
#> GSM601810 3 0.3845 0.54314 0.024 0.000 0.768 0.000 0.208
#> GSM601815 2 0.2325 0.70932 0.000 0.904 0.000 0.028 0.068
#> GSM601820 3 0.5159 0.25586 0.400 0.000 0.556 0.000 0.044
#> GSM601825 4 0.5014 0.25833 0.000 0.368 0.000 0.592 0.040
#> GSM601835 2 0.5753 0.46378 0.000 0.584 0.008 0.084 0.324
#> GSM601850 1 0.5661 0.61390 0.720 0.008 0.048 0.108 0.116
#> GSM601855 3 0.4470 0.36812 0.000 0.012 0.616 0.000 0.372
#> GSM601865 2 0.2624 0.68315 0.000 0.872 0.000 0.012 0.116
#> GSM601756 4 0.1043 0.77435 0.000 0.040 0.000 0.960 0.000
#> GSM601786 2 0.2464 0.69648 0.000 0.888 0.000 0.016 0.096
#> GSM601796 1 0.7483 0.51925 0.528 0.000 0.128 0.188 0.156
#> GSM601801 4 0.1942 0.76569 0.000 0.068 0.000 0.920 0.012
#> GSM601831 3 0.4720 0.60501 0.124 0.000 0.736 0.000 0.140
#> GSM601841 1 0.6713 0.00757 0.448 0.000 0.404 0.028 0.120
#> GSM601846 5 0.8655 0.08059 0.104 0.124 0.060 0.312 0.400
#> GSM601861 2 0.1830 0.72027 0.000 0.932 0.000 0.028 0.040
#> GSM601871 5 0.7087 0.30708 0.000 0.296 0.296 0.012 0.396
#> GSM601751 2 0.8386 -0.10192 0.080 0.380 0.036 0.344 0.160
#> GSM601761 1 0.3283 0.63261 0.848 0.000 0.116 0.008 0.028
#> GSM601766 5 0.8619 0.13305 0.192 0.332 0.068 0.052 0.356
#> GSM601771 2 0.6123 0.41598 0.012 0.588 0.000 0.268 0.132
#> GSM601776 1 0.2838 0.66364 0.884 0.000 0.072 0.008 0.036
#> GSM601781 1 0.6960 0.49337 0.584 0.000 0.088 0.176 0.152
#> GSM601791 1 0.3065 0.65916 0.872 0.000 0.072 0.008 0.048
#> GSM601806 4 0.2280 0.73211 0.000 0.120 0.000 0.880 0.000
#> GSM601811 3 0.3855 0.52424 0.008 0.000 0.748 0.004 0.240
#> GSM601816 1 0.2291 0.66927 0.908 0.000 0.012 0.008 0.072
#> GSM601821 2 0.2300 0.71702 0.000 0.908 0.000 0.040 0.052
#> GSM601826 1 0.1329 0.67095 0.956 0.000 0.008 0.004 0.032
#> GSM601836 1 0.8510 0.23618 0.360 0.036 0.256 0.064 0.284
#> GSM601851 1 0.2067 0.66838 0.924 0.000 0.044 0.004 0.028
#> GSM601856 3 0.3534 0.50859 0.000 0.000 0.744 0.000 0.256
#> GSM601866 3 0.4468 0.53261 0.240 0.000 0.716 0.000 0.044
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601752 4 0.1269 0.8113 0.000 0.020 0.012 0.956 0.012 0.000
#> GSM601782 1 0.5740 0.5085 0.648 0.088 0.144 0.000 0.000 0.120
#> GSM601792 6 0.6633 0.6034 0.084 0.108 0.124 0.068 0.000 0.616
#> GSM601797 4 0.4943 0.6863 0.004 0.096 0.112 0.736 0.004 0.048
#> GSM601827 1 0.6278 0.4740 0.580 0.052 0.192 0.008 0.000 0.168
#> GSM601837 5 0.2452 0.5731 0.000 0.028 0.084 0.004 0.884 0.000
#> GSM601842 5 0.5799 0.0872 0.000 0.436 0.040 0.072 0.452 0.000
#> GSM601857 1 0.4267 0.4045 0.692 0.044 0.260 0.000 0.000 0.004
#> GSM601867 3 0.6405 0.5138 0.200 0.036 0.536 0.008 0.220 0.000
#> GSM601747 1 0.8282 0.1840 0.428 0.236 0.144 0.024 0.064 0.104
#> GSM601757 1 0.4879 0.5842 0.692 0.024 0.084 0.000 0.000 0.200
#> GSM601762 5 0.5505 0.3614 0.000 0.320 0.028 0.080 0.572 0.000
#> GSM601767 5 0.5717 0.3238 0.000 0.336 0.028 0.096 0.540 0.000
#> GSM601772 5 0.5558 0.2473 0.000 0.392 0.032 0.064 0.512 0.000
#> GSM601777 3 0.9075 0.0595 0.088 0.124 0.320 0.216 0.040 0.212
#> GSM601787 3 0.6030 0.4156 0.112 0.032 0.452 0.000 0.404 0.000
#> GSM601802 4 0.1924 0.8141 0.000 0.028 0.012 0.928 0.028 0.004
#> GSM601807 3 0.6232 0.4832 0.212 0.036 0.612 0.032 0.104 0.004
#> GSM601812 1 0.5078 0.5845 0.696 0.036 0.144 0.000 0.000 0.124
#> GSM601817 1 0.4832 0.5508 0.724 0.064 0.164 0.004 0.000 0.044
#> GSM601822 6 0.6845 0.4901 0.012 0.140 0.160 0.140 0.000 0.548
#> GSM601832 2 0.5736 0.1810 0.000 0.552 0.032 0.096 0.320 0.000
#> GSM601847 4 0.6893 0.2186 0.000 0.112 0.088 0.460 0.012 0.328
#> GSM601852 1 0.4841 0.6028 0.716 0.048 0.068 0.000 0.000 0.168
#> GSM601862 1 0.3757 0.4116 0.712 0.008 0.272 0.000 0.000 0.008
#> GSM601753 4 0.2556 0.7869 0.000 0.076 0.012 0.884 0.028 0.000
#> GSM601783 1 0.5117 0.4227 0.608 0.024 0.056 0.000 0.000 0.312
#> GSM601793 6 0.7264 0.5543 0.148 0.088 0.132 0.084 0.000 0.548
#> GSM601798 4 0.1622 0.8113 0.000 0.028 0.016 0.940 0.016 0.000
#> GSM601828 1 0.4292 0.6061 0.764 0.024 0.092 0.000 0.000 0.120
#> GSM601838 5 0.1781 0.5823 0.000 0.008 0.060 0.008 0.924 0.000
#> GSM601843 5 0.5185 0.2867 0.000 0.392 0.016 0.056 0.536 0.000
#> GSM601858 5 0.4594 0.5043 0.012 0.152 0.092 0.008 0.736 0.000
#> GSM601868 1 0.4365 0.3314 0.636 0.024 0.332 0.000 0.000 0.008
#> GSM601748 1 0.4176 0.6109 0.772 0.024 0.076 0.000 0.000 0.128
#> GSM601758 1 0.4907 0.2457 0.532 0.024 0.024 0.000 0.000 0.420
#> GSM601763 6 0.7202 0.1772 0.084 0.384 0.056 0.028 0.024 0.424
#> GSM601768 5 0.6126 0.1458 0.000 0.412 0.044 0.088 0.452 0.004
#> GSM601773 5 0.5577 0.4080 0.000 0.256 0.024 0.120 0.600 0.000
#> GSM601778 6 0.7554 0.4340 0.060 0.100 0.156 0.168 0.004 0.512
#> GSM601788 5 0.7879 0.0754 0.024 0.132 0.192 0.160 0.468 0.024
#> GSM601803 4 0.2089 0.8078 0.000 0.020 0.020 0.916 0.044 0.000
#> GSM601808 1 0.4141 0.2714 0.596 0.016 0.388 0.000 0.000 0.000
#> GSM601813 1 0.5650 0.4546 0.580 0.048 0.072 0.000 0.000 0.300
#> GSM601818 1 0.4536 0.5717 0.748 0.044 0.140 0.000 0.000 0.068
#> GSM601823 6 0.3088 0.6617 0.044 0.064 0.032 0.000 0.000 0.860
#> GSM601833 5 0.5272 0.1937 0.000 0.420 0.016 0.060 0.504 0.000
#> GSM601848 6 0.2594 0.6602 0.056 0.036 0.020 0.000 0.000 0.888
#> GSM601853 1 0.4114 0.3055 0.628 0.008 0.356 0.000 0.000 0.008
#> GSM601863 1 0.4913 0.5038 0.680 0.028 0.224 0.000 0.000 0.068
#> GSM601754 4 0.2925 0.7988 0.000 0.048 0.032 0.880 0.024 0.016
#> GSM601784 5 0.4205 0.5458 0.000 0.144 0.032 0.056 0.768 0.000
#> GSM601794 6 0.8008 0.4362 0.072 0.116 0.208 0.176 0.000 0.428
#> GSM601799 4 0.3646 0.7534 0.000 0.092 0.032 0.828 0.008 0.040
#> GSM601829 6 0.7328 0.1680 0.260 0.072 0.268 0.012 0.000 0.388
#> GSM601839 5 0.2320 0.5791 0.000 0.024 0.080 0.004 0.892 0.000
#> GSM601844 6 0.7090 0.3487 0.276 0.112 0.108 0.020 0.000 0.484
#> GSM601859 5 0.5709 0.4526 0.000 0.208 0.060 0.104 0.628 0.000
#> GSM601869 1 0.4769 0.4920 0.696 0.020 0.220 0.004 0.000 0.060
#> GSM601749 1 0.4688 0.3016 0.572 0.028 0.012 0.000 0.000 0.388
#> GSM601759 1 0.4613 0.3870 0.608 0.020 0.020 0.000 0.000 0.352
#> GSM601764 6 0.6347 0.4844 0.172 0.228 0.048 0.004 0.000 0.548
#> GSM601769 5 0.3491 0.5564 0.000 0.148 0.008 0.040 0.804 0.000
#> GSM601774 5 0.5115 0.4623 0.000 0.260 0.028 0.068 0.644 0.000
#> GSM601779 6 0.2016 0.6594 0.040 0.024 0.016 0.000 0.000 0.920
#> GSM601789 5 0.3347 0.5661 0.000 0.104 0.068 0.004 0.824 0.000
#> GSM601804 4 0.5159 0.5211 0.000 0.048 0.040 0.652 0.004 0.256
#> GSM601809 1 0.8385 -0.1847 0.336 0.112 0.312 0.016 0.168 0.056
#> GSM601814 5 0.2322 0.5871 0.000 0.036 0.004 0.064 0.896 0.000
#> GSM601819 1 0.5785 0.1764 0.496 0.048 0.064 0.000 0.000 0.392
#> GSM601824 6 0.5580 0.5521 0.008 0.128 0.048 0.148 0.000 0.668
#> GSM601834 5 0.4694 0.4472 0.000 0.284 0.020 0.040 0.656 0.000
#> GSM601849 6 0.4034 0.6332 0.124 0.044 0.036 0.004 0.000 0.792
#> GSM601854 1 0.4761 0.5310 0.684 0.024 0.044 0.004 0.000 0.244
#> GSM601864 5 0.2834 0.5522 0.000 0.020 0.096 0.020 0.864 0.000
#> GSM601755 4 0.1426 0.8140 0.000 0.008 0.016 0.948 0.028 0.000
#> GSM601785 5 0.7261 0.0129 0.004 0.328 0.092 0.156 0.412 0.008
#> GSM601795 6 0.7651 0.2629 0.048 0.100 0.128 0.324 0.000 0.400
#> GSM601800 4 0.2345 0.8130 0.000 0.036 0.024 0.904 0.036 0.000
#> GSM601830 3 0.5824 0.2046 0.344 0.064 0.548 0.008 0.032 0.004
#> GSM601840 4 0.9354 -0.1385 0.100 0.200 0.152 0.304 0.188 0.056
#> GSM601845 2 0.7751 0.3993 0.056 0.528 0.144 0.048 0.164 0.060
#> GSM601860 5 0.6982 0.2964 0.028 0.224 0.088 0.072 0.564 0.024
#> GSM601870 3 0.5728 0.5018 0.220 0.020 0.588 0.000 0.172 0.000
#> GSM601750 1 0.4295 0.5682 0.740 0.032 0.036 0.000 0.000 0.192
#> GSM601760 6 0.5194 -0.0613 0.456 0.032 0.032 0.000 0.000 0.480
#> GSM601765 2 0.5314 -0.1187 0.000 0.492 0.028 0.036 0.440 0.004
#> GSM601770 5 0.5291 0.3305 0.000 0.364 0.028 0.052 0.556 0.000
#> GSM601775 2 0.8695 0.2321 0.052 0.376 0.104 0.276 0.076 0.116
#> GSM601780 6 0.2321 0.6546 0.052 0.040 0.008 0.000 0.000 0.900
#> GSM601790 5 0.1528 0.5871 0.000 0.016 0.048 0.000 0.936 0.000
#> GSM601805 4 0.2095 0.8119 0.000 0.028 0.016 0.916 0.040 0.000
#> GSM601810 1 0.4721 0.2957 0.592 0.020 0.364 0.000 0.000 0.024
#> GSM601815 5 0.1405 0.5930 0.000 0.004 0.024 0.024 0.948 0.000
#> GSM601820 1 0.4976 0.4670 0.640 0.036 0.040 0.000 0.000 0.284
#> GSM601825 4 0.5622 0.2995 0.000 0.096 0.024 0.588 0.288 0.004
#> GSM601835 2 0.6554 0.0167 0.000 0.412 0.112 0.064 0.408 0.004
#> GSM601850 6 0.7377 0.5297 0.092 0.124 0.104 0.140 0.000 0.540
#> GSM601855 3 0.4630 0.1300 0.404 0.028 0.560 0.000 0.008 0.000
#> GSM601865 5 0.2604 0.5562 0.000 0.028 0.096 0.004 0.872 0.000
#> GSM601756 4 0.1149 0.8131 0.000 0.008 0.008 0.960 0.024 0.000
#> GSM601786 5 0.2164 0.5779 0.000 0.028 0.056 0.008 0.908 0.000
#> GSM601796 6 0.7955 0.4861 0.144 0.096 0.156 0.140 0.000 0.464
#> GSM601801 4 0.1850 0.8088 0.000 0.016 0.008 0.924 0.052 0.000
#> GSM601831 1 0.4729 0.4950 0.716 0.032 0.200 0.008 0.000 0.044
#> GSM601841 1 0.7223 0.2906 0.456 0.056 0.164 0.036 0.000 0.288
#> GSM601846 3 0.8324 -0.0849 0.028 0.292 0.324 0.244 0.052 0.060
#> GSM601861 5 0.0862 0.5930 0.000 0.008 0.004 0.016 0.972 0.000
#> GSM601871 3 0.6232 0.3665 0.100 0.044 0.436 0.004 0.416 0.000
#> GSM601751 5 0.9061 -0.1269 0.036 0.192 0.144 0.200 0.336 0.092
#> GSM601761 6 0.3398 0.5415 0.216 0.004 0.012 0.000 0.000 0.768
#> GSM601766 2 0.7346 0.4300 0.048 0.580 0.076 0.044 0.136 0.116
#> GSM601771 5 0.7447 0.1655 0.008 0.240 0.096 0.176 0.464 0.016
#> GSM601776 6 0.3683 0.6190 0.124 0.040 0.028 0.000 0.000 0.808
#> GSM601781 6 0.7318 0.5529 0.092 0.108 0.160 0.084 0.004 0.552
#> GSM601791 6 0.3771 0.5946 0.164 0.032 0.020 0.000 0.000 0.784
#> GSM601806 4 0.2505 0.7836 0.000 0.008 0.020 0.880 0.092 0.000
#> GSM601811 1 0.5080 0.1920 0.544 0.048 0.392 0.000 0.000 0.016
#> GSM601816 6 0.3710 0.6586 0.044 0.060 0.076 0.000 0.000 0.820
#> GSM601821 5 0.1624 0.5922 0.000 0.012 0.008 0.044 0.936 0.000
#> GSM601826 6 0.3133 0.6586 0.072 0.040 0.032 0.000 0.000 0.856
#> GSM601836 2 0.8402 -0.2327 0.216 0.324 0.096 0.048 0.020 0.296
#> GSM601851 6 0.3054 0.6478 0.088 0.028 0.028 0.000 0.000 0.856
#> GSM601856 1 0.4380 0.1986 0.544 0.012 0.436 0.000 0.000 0.008
#> GSM601866 1 0.4312 0.6081 0.764 0.036 0.064 0.000 0.000 0.136
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 time(p) gender(p) k
#> SD:skmeans 120 0.638 0.09698 2
#> SD:skmeans 112 0.151 0.19094 3
#> SD:skmeans 95 0.169 0.04630 4
#> SD:skmeans 83 0.199 0.09821 5
#> SD:skmeans 60 0.242 0.00352 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 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.329 0.728 0.867 0.4986 0.496 0.496
#> 3 3 0.415 0.677 0.794 0.3153 0.750 0.539
#> 4 4 0.497 0.639 0.787 0.1016 0.898 0.716
#> 5 5 0.510 0.587 0.727 0.0453 0.967 0.887
#> 6 6 0.543 0.584 0.729 0.0339 0.956 0.834
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
#> GSM601752 2 0.9996 0.0948 0.488 0.512
#> GSM601782 1 0.5519 0.8129 0.872 0.128
#> GSM601792 1 0.2236 0.8353 0.964 0.036
#> GSM601797 1 0.5178 0.8113 0.884 0.116
#> GSM601827 1 0.6048 0.7927 0.852 0.148
#> GSM601837 2 0.0000 0.8443 0.000 1.000
#> GSM601842 2 0.2948 0.8482 0.052 0.948
#> GSM601857 2 0.8386 0.6040 0.268 0.732
#> GSM601867 2 0.0000 0.8443 0.000 1.000
#> GSM601747 1 0.9815 0.2628 0.580 0.420
#> GSM601757 1 0.1843 0.8390 0.972 0.028
#> GSM601762 2 0.1633 0.8493 0.024 0.976
#> GSM601767 2 0.5059 0.8207 0.112 0.888
#> GSM601772 2 0.6887 0.7788 0.184 0.816
#> GSM601777 1 0.9983 0.0856 0.524 0.476
#> GSM601787 2 0.4298 0.8155 0.088 0.912
#> GSM601802 2 0.8443 0.6665 0.272 0.728
#> GSM601807 1 0.9970 0.3045 0.532 0.468
#> GSM601812 1 0.4562 0.8168 0.904 0.096
#> GSM601817 1 0.2043 0.8400 0.968 0.032
#> GSM601822 1 0.0000 0.8382 1.000 0.000
#> GSM601832 2 0.6973 0.7852 0.188 0.812
#> GSM601847 1 0.8267 0.6229 0.740 0.260
#> GSM601852 1 0.3431 0.8349 0.936 0.064
#> GSM601862 2 0.9358 0.4406 0.352 0.648
#> GSM601753 2 0.9552 0.4417 0.376 0.624
#> GSM601783 1 0.0000 0.8382 1.000 0.000
#> GSM601793 1 0.3114 0.8310 0.944 0.056
#> GSM601798 2 0.4022 0.8422 0.080 0.920
#> GSM601828 1 0.2603 0.8336 0.956 0.044
#> GSM601838 2 0.0000 0.8443 0.000 1.000
#> GSM601843 2 0.1633 0.8490 0.024 0.976
#> GSM601858 2 0.0000 0.8443 0.000 1.000
#> GSM601868 2 0.9998 -0.1633 0.492 0.508
#> GSM601748 1 0.0376 0.8388 0.996 0.004
#> GSM601758 1 0.0000 0.8382 1.000 0.000
#> GSM601763 1 0.2603 0.8310 0.956 0.044
#> GSM601768 2 0.6623 0.7953 0.172 0.828
#> GSM601773 2 0.5842 0.8112 0.140 0.860
#> GSM601778 1 0.3584 0.8219 0.932 0.068
#> GSM601788 1 0.9460 0.3978 0.636 0.364
#> GSM601803 2 0.9044 0.5614 0.320 0.680
#> GSM601808 1 0.9993 0.2078 0.516 0.484
#> GSM601813 1 0.1414 0.8386 0.980 0.020
#> GSM601818 1 0.7056 0.7653 0.808 0.192
#> GSM601823 1 0.0000 0.8382 1.000 0.000
#> GSM601833 2 0.0672 0.8472 0.008 0.992
#> GSM601848 1 0.0376 0.8386 0.996 0.004
#> GSM601853 1 0.5408 0.7986 0.876 0.124
#> GSM601863 1 0.8443 0.6450 0.728 0.272
#> GSM601754 2 0.9491 0.4595 0.368 0.632
#> GSM601784 2 0.4562 0.8398 0.096 0.904
#> GSM601794 1 0.6531 0.7338 0.832 0.168
#> GSM601799 1 0.9087 0.4616 0.676 0.324
#> GSM601829 1 0.0000 0.8382 1.000 0.000
#> GSM601839 2 0.0000 0.8443 0.000 1.000
#> GSM601844 1 0.0938 0.8381 0.988 0.012
#> GSM601859 2 0.2778 0.8497 0.048 0.952
#> GSM601869 1 0.9552 0.4577 0.624 0.376
#> GSM601749 1 0.0000 0.8382 1.000 0.000
#> GSM601759 1 0.0000 0.8382 1.000 0.000
#> GSM601764 1 0.0376 0.8391 0.996 0.004
#> GSM601769 2 0.1414 0.8496 0.020 0.980
#> GSM601774 2 0.4690 0.8306 0.100 0.900
#> GSM601779 1 0.0000 0.8382 1.000 0.000
#> GSM601789 2 0.1843 0.8460 0.028 0.972
#> GSM601804 1 0.7453 0.6701 0.788 0.212
#> GSM601809 2 0.5629 0.8186 0.132 0.868
#> GSM601814 2 0.0376 0.8456 0.004 0.996
#> GSM601819 1 0.5408 0.7822 0.876 0.124
#> GSM601824 1 0.0938 0.8381 0.988 0.012
#> GSM601834 2 0.2603 0.8495 0.044 0.956
#> GSM601849 1 0.0672 0.8384 0.992 0.008
#> GSM601854 1 0.0672 0.8390 0.992 0.008
#> GSM601864 2 0.0000 0.8443 0.000 1.000
#> GSM601755 2 0.4939 0.8277 0.108 0.892
#> GSM601785 2 0.4939 0.8250 0.108 0.892
#> GSM601795 2 0.8661 0.6779 0.288 0.712
#> GSM601800 2 0.3431 0.8429 0.064 0.936
#> GSM601830 1 0.6531 0.7752 0.832 0.168
#> GSM601840 2 0.1633 0.8479 0.024 0.976
#> GSM601845 1 0.6343 0.7620 0.840 0.160
#> GSM601860 2 0.2778 0.8461 0.048 0.952
#> GSM601870 2 0.6343 0.7392 0.160 0.840
#> GSM601750 1 0.4690 0.8105 0.900 0.100
#> GSM601760 1 0.8144 0.6225 0.748 0.252
#> GSM601765 2 0.9754 0.4114 0.408 0.592
#> GSM601770 2 0.0938 0.8483 0.012 0.988
#> GSM601775 1 0.9248 0.3926 0.660 0.340
#> GSM601780 1 0.0000 0.8382 1.000 0.000
#> GSM601790 2 0.0000 0.8443 0.000 1.000
#> GSM601805 2 0.1843 0.8507 0.028 0.972
#> GSM601810 1 0.4431 0.8127 0.908 0.092
#> GSM601815 2 0.0000 0.8443 0.000 1.000
#> GSM601820 1 0.4939 0.8017 0.892 0.108
#> GSM601825 2 0.6712 0.7911 0.176 0.824
#> GSM601835 2 0.1184 0.8459 0.016 0.984
#> GSM601850 1 0.5519 0.7816 0.872 0.128
#> GSM601855 1 0.8608 0.6673 0.716 0.284
#> GSM601865 2 0.2778 0.8426 0.048 0.952
#> GSM601756 2 0.5408 0.8173 0.124 0.876
#> GSM601786 2 0.0000 0.8443 0.000 1.000
#> GSM601796 2 0.9922 0.0988 0.448 0.552
#> GSM601801 2 0.1414 0.8493 0.020 0.980
#> GSM601831 1 0.2423 0.8372 0.960 0.040
#> GSM601841 1 0.9552 0.4611 0.624 0.376
#> GSM601846 1 0.3431 0.8282 0.936 0.064
#> GSM601861 2 0.0376 0.8455 0.004 0.996
#> GSM601871 2 0.2043 0.8464 0.032 0.968
#> GSM601751 2 0.6712 0.7770 0.176 0.824
#> GSM601761 1 0.0376 0.8391 0.996 0.004
#> GSM601766 2 0.9866 0.3398 0.432 0.568
#> GSM601771 2 0.4298 0.8424 0.088 0.912
#> GSM601776 1 0.0000 0.8382 1.000 0.000
#> GSM601781 1 0.8081 0.6460 0.752 0.248
#> GSM601791 1 0.8327 0.6114 0.736 0.264
#> GSM601806 2 0.4939 0.8216 0.108 0.892
#> GSM601811 2 0.7674 0.6687 0.224 0.776
#> GSM601816 1 0.0000 0.8382 1.000 0.000
#> GSM601821 2 0.0672 0.8465 0.008 0.992
#> GSM601826 1 0.0000 0.8382 1.000 0.000
#> GSM601836 1 0.9608 0.4512 0.616 0.384
#> GSM601851 1 0.0000 0.8382 1.000 0.000
#> GSM601856 2 0.9427 0.3533 0.360 0.640
#> GSM601866 1 0.9552 0.4729 0.624 0.376
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601752 2 0.6375 0.57929 0.244 0.720 0.036
#> GSM601782 1 0.5305 0.81651 0.788 0.020 0.192
#> GSM601792 1 0.0747 0.85561 0.984 0.016 0.000
#> GSM601797 1 0.6337 0.64602 0.708 0.264 0.028
#> GSM601827 1 0.4802 0.83073 0.824 0.020 0.156
#> GSM601837 3 0.5859 0.54623 0.000 0.344 0.656
#> GSM601842 2 0.1337 0.74320 0.012 0.972 0.016
#> GSM601857 3 0.0475 0.72817 0.004 0.004 0.992
#> GSM601867 3 0.5948 0.51252 0.000 0.360 0.640
#> GSM601747 1 0.9083 0.48695 0.540 0.180 0.280
#> GSM601757 1 0.2165 0.84984 0.936 0.000 0.064
#> GSM601762 2 0.0592 0.73721 0.000 0.988 0.012
#> GSM601767 2 0.1337 0.74187 0.012 0.972 0.016
#> GSM601772 2 0.3590 0.73164 0.076 0.896 0.028
#> GSM601777 2 0.7433 0.57971 0.168 0.700 0.132
#> GSM601787 3 0.4663 0.71846 0.016 0.156 0.828
#> GSM601802 2 0.7474 0.56917 0.176 0.696 0.128
#> GSM601807 1 0.8786 0.25768 0.464 0.112 0.424
#> GSM601812 1 0.4346 0.82620 0.816 0.000 0.184
#> GSM601817 1 0.4291 0.82817 0.820 0.000 0.180
#> GSM601822 1 0.0000 0.85686 1.000 0.000 0.000
#> GSM601832 2 0.4615 0.68139 0.144 0.836 0.020
#> GSM601847 2 0.5835 0.45554 0.340 0.660 0.000
#> GSM601852 1 0.4861 0.82392 0.808 0.012 0.180
#> GSM601862 3 0.0237 0.72709 0.004 0.000 0.996
#> GSM601753 2 0.1643 0.74229 0.044 0.956 0.000
#> GSM601783 1 0.3879 0.83718 0.848 0.000 0.152
#> GSM601793 1 0.2959 0.82346 0.900 0.100 0.000
#> GSM601798 2 0.1905 0.74468 0.028 0.956 0.016
#> GSM601828 1 0.4291 0.82390 0.820 0.000 0.180
#> GSM601838 2 0.0747 0.73731 0.000 0.984 0.016
#> GSM601843 2 0.5760 0.40629 0.000 0.672 0.328
#> GSM601858 3 0.3752 0.71626 0.000 0.144 0.856
#> GSM601868 3 0.0747 0.72665 0.016 0.000 0.984
#> GSM601748 1 0.4291 0.82390 0.820 0.000 0.180
#> GSM601758 1 0.2261 0.85702 0.932 0.000 0.068
#> GSM601763 1 0.0000 0.85686 1.000 0.000 0.000
#> GSM601768 2 0.9392 -0.00729 0.172 0.436 0.392
#> GSM601773 2 0.2550 0.74331 0.056 0.932 0.012
#> GSM601778 1 0.3039 0.85123 0.920 0.044 0.036
#> GSM601788 1 0.5295 0.75442 0.808 0.156 0.036
#> GSM601803 2 0.2066 0.73990 0.060 0.940 0.000
#> GSM601808 3 0.2682 0.70943 0.076 0.004 0.920
#> GSM601813 1 0.4235 0.82639 0.824 0.000 0.176
#> GSM601818 1 0.4755 0.82385 0.808 0.008 0.184
#> GSM601823 1 0.0000 0.85686 1.000 0.000 0.000
#> GSM601833 2 0.4784 0.61715 0.004 0.796 0.200
#> GSM601848 1 0.0000 0.85686 1.000 0.000 0.000
#> GSM601853 1 0.4399 0.82212 0.812 0.000 0.188
#> GSM601863 3 0.5926 0.45096 0.356 0.000 0.644
#> GSM601754 2 0.6905 0.47800 0.044 0.676 0.280
#> GSM601784 3 0.6621 0.71982 0.148 0.100 0.752
#> GSM601794 1 0.4834 0.67035 0.792 0.204 0.004
#> GSM601799 2 0.6489 0.25988 0.456 0.540 0.004
#> GSM601829 1 0.0000 0.85686 1.000 0.000 0.000
#> GSM601839 2 0.3816 0.66905 0.000 0.852 0.148
#> GSM601844 1 0.1860 0.83569 0.948 0.000 0.052
#> GSM601859 3 0.6902 0.69981 0.100 0.168 0.732
#> GSM601869 3 0.1129 0.73017 0.020 0.004 0.976
#> GSM601749 1 0.1411 0.86011 0.964 0.000 0.036
#> GSM601759 1 0.1753 0.85727 0.952 0.000 0.048
#> GSM601764 1 0.0747 0.85374 0.984 0.000 0.016
#> GSM601769 2 0.7517 0.08273 0.040 0.540 0.420
#> GSM601774 2 0.2443 0.74129 0.028 0.940 0.032
#> GSM601779 1 0.0000 0.85686 1.000 0.000 0.000
#> GSM601789 3 0.7163 0.52984 0.040 0.332 0.628
#> GSM601804 1 0.6308 -0.14472 0.508 0.492 0.000
#> GSM601809 3 0.5719 0.73283 0.156 0.052 0.792
#> GSM601814 2 0.2945 0.71784 0.004 0.908 0.088
#> GSM601819 1 0.2400 0.82764 0.932 0.004 0.064
#> GSM601824 1 0.0000 0.85686 1.000 0.000 0.000
#> GSM601834 2 0.7741 0.35763 0.068 0.608 0.324
#> GSM601849 1 0.0000 0.85686 1.000 0.000 0.000
#> GSM601854 1 0.4002 0.83348 0.840 0.000 0.160
#> GSM601864 3 0.5254 0.64561 0.000 0.264 0.736
#> GSM601755 2 0.1647 0.74376 0.036 0.960 0.004
#> GSM601785 3 0.4805 0.72535 0.176 0.012 0.812
#> GSM601795 3 0.8868 0.51044 0.196 0.228 0.576
#> GSM601800 2 0.5269 0.60907 0.016 0.784 0.200
#> GSM601830 1 0.4589 0.82610 0.820 0.008 0.172
#> GSM601840 3 0.6744 0.57431 0.032 0.300 0.668
#> GSM601845 1 0.4235 0.75930 0.824 0.176 0.000
#> GSM601860 3 0.5307 0.74004 0.136 0.048 0.816
#> GSM601870 3 0.2261 0.71991 0.000 0.068 0.932
#> GSM601750 1 0.4555 0.81696 0.800 0.000 0.200
#> GSM601760 3 0.4887 0.70886 0.228 0.000 0.772
#> GSM601765 2 0.6322 0.58048 0.276 0.700 0.024
#> GSM601770 2 0.3989 0.68974 0.012 0.864 0.124
#> GSM601775 1 0.5179 0.73864 0.832 0.088 0.080
#> GSM601780 1 0.0000 0.85686 1.000 0.000 0.000
#> GSM601790 3 0.5650 0.58352 0.000 0.312 0.688
#> GSM601805 2 0.7299 0.19870 0.032 0.556 0.412
#> GSM601810 1 0.4291 0.82390 0.820 0.000 0.180
#> GSM601815 2 0.6252 0.04180 0.000 0.556 0.444
#> GSM601820 1 0.4399 0.69815 0.812 0.000 0.188
#> GSM601825 2 0.4351 0.67660 0.168 0.828 0.004
#> GSM601835 2 0.6252 0.00355 0.000 0.556 0.444
#> GSM601850 1 0.2261 0.83221 0.932 0.068 0.000
#> GSM601855 1 0.6225 0.47282 0.568 0.000 0.432
#> GSM601865 3 0.5407 0.74015 0.104 0.076 0.820
#> GSM601756 2 0.1529 0.74280 0.040 0.960 0.000
#> GSM601786 3 0.4291 0.69695 0.000 0.180 0.820
#> GSM601796 3 0.6372 0.73540 0.152 0.084 0.764
#> GSM601801 2 0.0000 0.73699 0.000 1.000 0.000
#> GSM601831 1 0.4291 0.82390 0.820 0.000 0.180
#> GSM601841 3 0.2774 0.73848 0.072 0.008 0.920
#> GSM601846 1 0.3686 0.79304 0.860 0.140 0.000
#> GSM601861 3 0.5502 0.65976 0.008 0.248 0.744
#> GSM601871 3 0.3669 0.75203 0.064 0.040 0.896
#> GSM601751 3 0.4840 0.73032 0.168 0.016 0.816
#> GSM601761 1 0.0237 0.85630 0.996 0.000 0.004
#> GSM601766 3 0.6726 0.57509 0.332 0.024 0.644
#> GSM601771 3 0.8316 0.23384 0.080 0.424 0.496
#> GSM601776 1 0.0000 0.85686 1.000 0.000 0.000
#> GSM601781 3 0.6899 0.57744 0.364 0.024 0.612
#> GSM601791 3 0.5285 0.70089 0.244 0.004 0.752
#> GSM601806 2 0.0237 0.73802 0.004 0.996 0.000
#> GSM601811 3 0.4479 0.69736 0.044 0.096 0.860
#> GSM601816 1 0.0000 0.85686 1.000 0.000 0.000
#> GSM601821 3 0.6786 0.25684 0.012 0.448 0.540
#> GSM601826 1 0.0000 0.85686 1.000 0.000 0.000
#> GSM601836 3 0.8995 0.37228 0.372 0.136 0.492
#> GSM601851 1 0.0424 0.85819 0.992 0.000 0.008
#> GSM601856 3 0.2590 0.71012 0.072 0.004 0.924
#> GSM601866 3 0.1411 0.72484 0.036 0.000 0.964
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601752 4 0.0000 0.808 0.000 0.000 0.000 1.000
#> GSM601782 1 0.4879 0.782 0.744 0.016 0.228 0.012
#> GSM601792 1 0.0592 0.843 0.984 0.000 0.000 0.016
#> GSM601797 1 0.4999 0.155 0.508 0.000 0.000 0.492
#> GSM601827 1 0.4389 0.813 0.820 0.028 0.132 0.020
#> GSM601837 3 0.7507 0.264 0.000 0.316 0.480 0.204
#> GSM601842 2 0.5299 0.439 0.004 0.600 0.008 0.388
#> GSM601857 3 0.1474 0.656 0.000 0.052 0.948 0.000
#> GSM601867 3 0.5284 0.451 0.000 0.016 0.616 0.368
#> GSM601747 1 0.9041 0.276 0.428 0.224 0.268 0.080
#> GSM601757 1 0.3505 0.812 0.864 0.012 0.108 0.016
#> GSM601762 4 0.4977 -0.112 0.000 0.460 0.000 0.540
#> GSM601767 2 0.4872 0.489 0.004 0.640 0.000 0.356
#> GSM601772 2 0.5627 0.596 0.068 0.692 0.000 0.240
#> GSM601777 4 0.3460 0.750 0.036 0.024 0.056 0.884
#> GSM601787 3 0.5008 0.632 0.004 0.092 0.780 0.124
#> GSM601802 4 0.0376 0.807 0.004 0.004 0.000 0.992
#> GSM601807 3 0.8163 0.162 0.264 0.016 0.448 0.272
#> GSM601812 1 0.4535 0.771 0.744 0.016 0.240 0.000
#> GSM601817 1 0.4687 0.788 0.752 0.020 0.224 0.004
#> GSM601822 1 0.0000 0.842 1.000 0.000 0.000 0.000
#> GSM601832 2 0.4187 0.656 0.092 0.840 0.012 0.056
#> GSM601847 4 0.1637 0.780 0.060 0.000 0.000 0.940
#> GSM601852 1 0.3945 0.790 0.780 0.004 0.216 0.000
#> GSM601862 3 0.1474 0.655 0.000 0.052 0.948 0.000
#> GSM601753 4 0.0188 0.808 0.004 0.000 0.000 0.996
#> GSM601783 1 0.2973 0.822 0.856 0.000 0.144 0.000
#> GSM601793 1 0.2704 0.807 0.876 0.000 0.000 0.124
#> GSM601798 4 0.0188 0.808 0.000 0.004 0.000 0.996
#> GSM601828 1 0.4019 0.803 0.792 0.012 0.196 0.000
#> GSM601838 2 0.4040 0.615 0.000 0.752 0.000 0.248
#> GSM601843 2 0.6139 0.606 0.000 0.656 0.100 0.244
#> GSM601858 3 0.4955 0.631 0.000 0.144 0.772 0.084
#> GSM601868 3 0.0469 0.649 0.000 0.012 0.988 0.000
#> GSM601748 1 0.3852 0.801 0.800 0.008 0.192 0.000
#> GSM601758 1 0.1792 0.843 0.932 0.000 0.068 0.000
#> GSM601763 1 0.2074 0.832 0.940 0.016 0.012 0.032
#> GSM601768 2 0.7357 0.557 0.168 0.648 0.092 0.092
#> GSM601773 2 0.5543 0.465 0.028 0.612 0.000 0.360
#> GSM601778 1 0.3453 0.840 0.884 0.020 0.048 0.048
#> GSM601788 1 0.6217 0.704 0.724 0.112 0.036 0.128
#> GSM601803 4 0.0000 0.808 0.000 0.000 0.000 1.000
#> GSM601808 3 0.1510 0.642 0.028 0.016 0.956 0.000
#> GSM601813 1 0.4059 0.803 0.788 0.012 0.200 0.000
#> GSM601818 1 0.5022 0.778 0.736 0.044 0.220 0.000
#> GSM601823 1 0.0000 0.842 1.000 0.000 0.000 0.000
#> GSM601833 2 0.3157 0.677 0.000 0.852 0.004 0.144
#> GSM601848 1 0.0000 0.842 1.000 0.000 0.000 0.000
#> GSM601853 1 0.4500 0.709 0.684 0.000 0.316 0.000
#> GSM601863 3 0.5231 0.523 0.296 0.028 0.676 0.000
#> GSM601754 4 0.0336 0.807 0.008 0.000 0.000 0.992
#> GSM601784 3 0.8323 0.495 0.116 0.240 0.544 0.100
#> GSM601794 1 0.4697 0.434 0.644 0.000 0.000 0.356
#> GSM601799 4 0.4814 0.500 0.316 0.008 0.000 0.676
#> GSM601829 1 0.0000 0.842 1.000 0.000 0.000 0.000
#> GSM601839 2 0.3402 0.670 0.000 0.832 0.004 0.164
#> GSM601844 1 0.1975 0.832 0.944 0.016 0.012 0.028
#> GSM601859 3 0.7593 0.571 0.048 0.132 0.600 0.220
#> GSM601869 3 0.2675 0.655 0.008 0.100 0.892 0.000
#> GSM601749 1 0.1211 0.846 0.960 0.000 0.040 0.000
#> GSM601759 1 0.1902 0.843 0.932 0.000 0.064 0.004
#> GSM601764 1 0.2324 0.830 0.932 0.020 0.020 0.028
#> GSM601769 2 0.1377 0.684 0.008 0.964 0.008 0.020
#> GSM601774 2 0.3271 0.678 0.012 0.856 0.000 0.132
#> GSM601779 1 0.1674 0.834 0.952 0.012 0.004 0.032
#> GSM601789 2 0.5790 0.169 0.000 0.616 0.340 0.044
#> GSM601804 4 0.4677 0.506 0.316 0.004 0.000 0.680
#> GSM601809 3 0.6831 0.599 0.168 0.140 0.664 0.028
#> GSM601814 2 0.2060 0.679 0.000 0.932 0.016 0.052
#> GSM601819 1 0.3316 0.810 0.892 0.044 0.032 0.032
#> GSM601824 1 0.1022 0.835 0.968 0.000 0.000 0.032
#> GSM601834 2 0.1853 0.682 0.012 0.948 0.028 0.012
#> GSM601849 1 0.0524 0.841 0.988 0.004 0.000 0.008
#> GSM601854 1 0.4054 0.808 0.796 0.016 0.188 0.000
#> GSM601864 3 0.5980 0.417 0.000 0.396 0.560 0.044
#> GSM601755 4 0.0188 0.808 0.000 0.004 0.000 0.996
#> GSM601785 3 0.7557 0.608 0.104 0.172 0.632 0.092
#> GSM601795 3 0.8613 0.429 0.200 0.056 0.468 0.276
#> GSM601800 4 0.1302 0.790 0.000 0.044 0.000 0.956
#> GSM601830 1 0.3764 0.808 0.816 0.012 0.172 0.000
#> GSM601840 3 0.6329 0.456 0.004 0.064 0.588 0.344
#> GSM601845 1 0.3961 0.771 0.812 0.008 0.008 0.172
#> GSM601860 3 0.7075 0.624 0.072 0.144 0.672 0.112
#> GSM601870 3 0.0921 0.648 0.000 0.000 0.972 0.028
#> GSM601750 1 0.4454 0.718 0.692 0.000 0.308 0.000
#> GSM601760 3 0.5706 0.603 0.268 0.028 0.684 0.020
#> GSM601765 2 0.5229 0.608 0.152 0.768 0.012 0.068
#> GSM601770 2 0.4850 0.571 0.004 0.696 0.008 0.292
#> GSM601775 1 0.6168 0.607 0.716 0.180 0.048 0.056
#> GSM601780 1 0.0844 0.842 0.980 0.012 0.004 0.004
#> GSM601790 2 0.5487 -0.019 0.000 0.580 0.400 0.020
#> GSM601805 4 0.5947 0.468 0.008 0.076 0.224 0.692
#> GSM601810 1 0.3444 0.804 0.816 0.000 0.184 0.000
#> GSM601815 2 0.1520 0.685 0.000 0.956 0.024 0.020
#> GSM601820 1 0.4504 0.660 0.772 0.020 0.204 0.004
#> GSM601825 4 0.6473 0.487 0.168 0.188 0.000 0.644
#> GSM601835 2 0.6966 0.437 0.000 0.572 0.268 0.160
#> GSM601850 1 0.2125 0.827 0.920 0.004 0.000 0.076
#> GSM601855 3 0.5112 -0.163 0.436 0.000 0.560 0.004
#> GSM601865 3 0.4746 0.502 0.000 0.368 0.632 0.000
#> GSM601756 4 0.0188 0.808 0.000 0.004 0.000 0.996
#> GSM601786 3 0.4761 0.498 0.000 0.372 0.628 0.000
#> GSM601796 3 0.7473 0.623 0.108 0.080 0.636 0.176
#> GSM601801 4 0.3873 0.553 0.000 0.228 0.000 0.772
#> GSM601831 1 0.3528 0.801 0.808 0.000 0.192 0.000
#> GSM601841 3 0.4112 0.654 0.112 0.020 0.840 0.028
#> GSM601846 1 0.3610 0.748 0.800 0.000 0.000 0.200
#> GSM601861 2 0.5508 -0.244 0.000 0.508 0.476 0.016
#> GSM601871 3 0.3751 0.625 0.004 0.196 0.800 0.000
#> GSM601751 3 0.7053 0.624 0.156 0.076 0.672 0.096
#> GSM601761 1 0.1396 0.835 0.960 0.004 0.004 0.032
#> GSM601766 3 0.8434 0.437 0.228 0.276 0.460 0.036
#> GSM601771 2 0.8964 0.270 0.056 0.380 0.292 0.272
#> GSM601776 1 0.0000 0.842 1.000 0.000 0.000 0.000
#> GSM601781 3 0.7094 0.480 0.388 0.028 0.520 0.064
#> GSM601791 3 0.6478 0.574 0.308 0.040 0.620 0.032
#> GSM601806 4 0.2149 0.751 0.000 0.088 0.000 0.912
#> GSM601811 3 0.2558 0.642 0.008 0.036 0.920 0.036
#> GSM601816 1 0.0000 0.842 1.000 0.000 0.000 0.000
#> GSM601821 2 0.2334 0.643 0.000 0.908 0.088 0.004
#> GSM601826 1 0.0000 0.842 1.000 0.000 0.000 0.000
#> GSM601836 3 0.9296 0.263 0.268 0.272 0.372 0.088
#> GSM601851 1 0.0992 0.844 0.976 0.004 0.008 0.012
#> GSM601856 3 0.1584 0.644 0.036 0.012 0.952 0.000
#> GSM601866 3 0.1297 0.649 0.020 0.016 0.964 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601752 4 0.0000 0.8171 0.000 0.000 0.000 1.000 0.000
#> GSM601782 1 0.5394 0.7476 0.720 0.020 0.096 0.008 0.156
#> GSM601792 1 0.1772 0.8039 0.944 0.016 0.004 0.012 0.024
#> GSM601797 1 0.5470 0.2245 0.516 0.016 0.004 0.440 0.024
#> GSM601827 1 0.3975 0.7795 0.828 0.008 0.076 0.012 0.076
#> GSM601837 5 0.7653 0.5071 0.000 0.160 0.212 0.128 0.500
#> GSM601842 2 0.4920 0.4887 0.000 0.572 0.012 0.404 0.012
#> GSM601857 3 0.3511 0.5206 0.004 0.012 0.800 0.000 0.184
#> GSM601867 3 0.4608 0.3695 0.000 0.012 0.644 0.336 0.008
#> GSM601747 1 0.9027 0.2072 0.392 0.256 0.136 0.068 0.148
#> GSM601757 1 0.5732 0.6553 0.696 0.024 0.096 0.012 0.172
#> GSM601762 2 0.4448 0.3245 0.000 0.516 0.004 0.480 0.000
#> GSM601767 2 0.3990 0.5712 0.000 0.688 0.004 0.308 0.000
#> GSM601772 2 0.5340 0.5987 0.064 0.696 0.004 0.216 0.020
#> GSM601777 4 0.4294 0.7206 0.032 0.048 0.032 0.828 0.060
#> GSM601787 3 0.2928 0.5304 0.000 0.032 0.872 0.092 0.004
#> GSM601802 4 0.0566 0.8152 0.000 0.012 0.000 0.984 0.004
#> GSM601807 5 0.8974 -0.1242 0.156 0.028 0.288 0.220 0.308
#> GSM601812 1 0.5492 0.7210 0.684 0.012 0.136 0.000 0.168
#> GSM601817 1 0.5290 0.7472 0.720 0.028 0.096 0.000 0.156
#> GSM601822 1 0.0000 0.8030 1.000 0.000 0.000 0.000 0.000
#> GSM601832 2 0.4045 0.5609 0.076 0.824 0.000 0.036 0.064
#> GSM601847 4 0.2376 0.7722 0.044 0.000 0.000 0.904 0.052
#> GSM601852 1 0.4801 0.7413 0.732 0.004 0.092 0.000 0.172
#> GSM601862 3 0.3643 0.5092 0.004 0.008 0.776 0.000 0.212
#> GSM601753 4 0.0451 0.8175 0.004 0.000 0.000 0.988 0.008
#> GSM601783 1 0.3051 0.7871 0.864 0.000 0.060 0.000 0.076
#> GSM601793 1 0.3308 0.7740 0.860 0.016 0.004 0.096 0.024
#> GSM601798 4 0.0290 0.8169 0.000 0.008 0.000 0.992 0.000
#> GSM601828 1 0.4601 0.7673 0.772 0.024 0.064 0.000 0.140
#> GSM601838 5 0.5996 0.4085 0.000 0.368 0.000 0.120 0.512
#> GSM601843 2 0.5731 0.5457 0.000 0.644 0.100 0.240 0.016
#> GSM601858 3 0.3476 0.5267 0.000 0.088 0.836 0.076 0.000
#> GSM601868 3 0.3421 0.5048 0.000 0.008 0.788 0.000 0.204
#> GSM601748 1 0.4250 0.7626 0.784 0.004 0.084 0.000 0.128
#> GSM601758 1 0.2390 0.8098 0.896 0.000 0.020 0.000 0.084
#> GSM601763 1 0.3941 0.7631 0.824 0.036 0.000 0.036 0.104
#> GSM601768 2 0.6511 0.5076 0.132 0.680 0.076 0.052 0.060
#> GSM601773 2 0.5146 0.5604 0.020 0.640 0.004 0.316 0.020
#> GSM601778 1 0.4750 0.7880 0.792 0.040 0.020 0.044 0.104
#> GSM601788 1 0.6687 0.5728 0.628 0.200 0.020 0.100 0.052
#> GSM601803 4 0.0451 0.8154 0.000 0.008 0.000 0.988 0.004
#> GSM601808 3 0.4363 0.4769 0.016 0.008 0.708 0.000 0.268
#> GSM601813 1 0.5005 0.7653 0.740 0.028 0.072 0.000 0.160
#> GSM601818 1 0.5709 0.7260 0.700 0.056 0.096 0.000 0.148
#> GSM601823 1 0.0000 0.8030 1.000 0.000 0.000 0.000 0.000
#> GSM601833 2 0.3222 0.6078 0.000 0.852 0.004 0.108 0.036
#> GSM601848 1 0.0000 0.8030 1.000 0.000 0.000 0.000 0.000
#> GSM601853 1 0.6486 0.4307 0.492 0.000 0.236 0.000 0.272
#> GSM601863 3 0.6441 0.3399 0.252 0.008 0.544 0.000 0.196
#> GSM601754 4 0.0566 0.8166 0.004 0.000 0.000 0.984 0.012
#> GSM601784 3 0.8086 0.3541 0.112 0.260 0.492 0.092 0.044
#> GSM601794 1 0.6382 0.4202 0.556 0.016 0.004 0.308 0.116
#> GSM601799 4 0.5456 0.4624 0.284 0.016 0.000 0.640 0.060
#> GSM601829 1 0.0000 0.8030 1.000 0.000 0.000 0.000 0.000
#> GSM601839 5 0.5837 0.4242 0.000 0.400 0.004 0.084 0.512
#> GSM601844 1 0.3434 0.7686 0.860 0.008 0.016 0.032 0.084
#> GSM601859 3 0.5868 0.4478 0.036 0.056 0.652 0.248 0.008
#> GSM601869 3 0.2919 0.5266 0.004 0.024 0.868 0.000 0.104
#> GSM601749 1 0.1557 0.8091 0.940 0.000 0.008 0.000 0.052
#> GSM601759 1 0.2754 0.8009 0.884 0.004 0.032 0.000 0.080
#> GSM601764 1 0.4090 0.7655 0.824 0.028 0.012 0.032 0.104
#> GSM601769 2 0.1970 0.5722 0.004 0.924 0.060 0.012 0.000
#> GSM601774 2 0.2796 0.6147 0.008 0.868 0.000 0.116 0.008
#> GSM601779 1 0.3243 0.7676 0.860 0.012 0.000 0.036 0.092
#> GSM601789 2 0.5703 0.1086 0.000 0.540 0.396 0.040 0.024
#> GSM601804 4 0.5473 0.4285 0.296 0.004 0.000 0.620 0.080
#> GSM601809 3 0.6901 0.5148 0.164 0.112 0.632 0.032 0.060
#> GSM601814 2 0.3467 0.5321 0.000 0.860 0.052 0.048 0.040
#> GSM601819 1 0.5305 0.7080 0.756 0.096 0.020 0.036 0.092
#> GSM601824 1 0.2359 0.7780 0.904 0.000 0.000 0.036 0.060
#> GSM601834 2 0.3069 0.5501 0.008 0.876 0.084 0.012 0.020
#> GSM601849 1 0.1798 0.7897 0.928 0.004 0.000 0.004 0.064
#> GSM601854 1 0.4552 0.7685 0.756 0.008 0.068 0.000 0.168
#> GSM601864 5 0.7070 0.5307 0.000 0.224 0.232 0.036 0.508
#> GSM601755 4 0.0162 0.8165 0.000 0.004 0.000 0.996 0.000
#> GSM601785 3 0.6643 0.4910 0.088 0.156 0.660 0.052 0.044
#> GSM601795 3 0.9081 0.3439 0.148 0.108 0.420 0.212 0.112
#> GSM601800 4 0.0955 0.8091 0.000 0.028 0.000 0.968 0.004
#> GSM601830 1 0.4034 0.7748 0.812 0.016 0.060 0.000 0.112
#> GSM601840 3 0.5652 0.3396 0.000 0.092 0.564 0.344 0.000
#> GSM601845 1 0.4837 0.7345 0.752 0.008 0.012 0.164 0.064
#> GSM601860 3 0.5893 0.5276 0.052 0.100 0.728 0.076 0.044
#> GSM601870 3 0.3957 0.4626 0.000 0.000 0.712 0.008 0.280
#> GSM601750 1 0.6351 0.5066 0.516 0.000 0.204 0.000 0.280
#> GSM601760 3 0.6714 0.4694 0.256 0.020 0.584 0.024 0.116
#> GSM601765 2 0.4913 0.5470 0.108 0.772 0.004 0.048 0.068
#> GSM601770 2 0.3988 0.6041 0.000 0.732 0.016 0.252 0.000
#> GSM601775 1 0.7023 0.4531 0.580 0.252 0.032 0.044 0.092
#> GSM601780 1 0.2074 0.7953 0.920 0.016 0.000 0.004 0.060
#> GSM601790 5 0.6699 0.5181 0.000 0.324 0.180 0.012 0.484
#> GSM601805 4 0.4669 0.4786 0.004 0.024 0.264 0.700 0.008
#> GSM601810 1 0.4103 0.7709 0.796 0.008 0.060 0.000 0.136
#> GSM601815 2 0.2844 0.5459 0.000 0.880 0.088 0.012 0.020
#> GSM601820 1 0.5805 0.5947 0.672 0.028 0.168 0.000 0.132
#> GSM601825 4 0.6509 0.4747 0.164 0.160 0.000 0.620 0.056
#> GSM601835 2 0.6191 0.4241 0.000 0.616 0.220 0.140 0.024
#> GSM601850 1 0.3316 0.7812 0.860 0.012 0.000 0.072 0.056
#> GSM601855 3 0.6790 0.0944 0.292 0.000 0.380 0.000 0.328
#> GSM601865 3 0.4547 0.4170 0.000 0.252 0.704 0.000 0.044
#> GSM601756 4 0.0162 0.8169 0.000 0.004 0.000 0.996 0.000
#> GSM601786 3 0.4550 0.4017 0.000 0.276 0.688 0.000 0.036
#> GSM601796 3 0.6928 0.5112 0.096 0.028 0.640 0.120 0.116
#> GSM601801 4 0.3300 0.5948 0.000 0.204 0.000 0.792 0.004
#> GSM601831 1 0.3898 0.7643 0.804 0.000 0.080 0.000 0.116
#> GSM601841 3 0.5430 0.5284 0.124 0.008 0.728 0.028 0.112
#> GSM601846 1 0.3250 0.7372 0.820 0.004 0.000 0.168 0.008
#> GSM601861 3 0.5535 0.2060 0.000 0.372 0.564 0.008 0.056
#> GSM601871 3 0.2136 0.5272 0.000 0.088 0.904 0.000 0.008
#> GSM601751 3 0.6343 0.5308 0.128 0.068 0.692 0.068 0.044
#> GSM601761 1 0.3053 0.7723 0.872 0.004 0.004 0.036 0.084
#> GSM601766 3 0.8559 0.2809 0.192 0.276 0.404 0.036 0.092
#> GSM601771 2 0.7949 0.3186 0.032 0.448 0.272 0.208 0.040
#> GSM601776 1 0.0162 0.8031 0.996 0.000 0.000 0.000 0.004
#> GSM601781 3 0.7815 0.3503 0.336 0.032 0.456 0.068 0.108
#> GSM601791 3 0.6547 0.4511 0.268 0.008 0.588 0.036 0.100
#> GSM601806 4 0.2193 0.7573 0.000 0.092 0.000 0.900 0.008
#> GSM601811 3 0.5192 0.4751 0.008 0.020 0.684 0.032 0.256
#> GSM601816 1 0.0000 0.8030 1.000 0.000 0.000 0.000 0.000
#> GSM601821 2 0.4547 0.3273 0.000 0.736 0.192 0.000 0.072
#> GSM601826 1 0.0000 0.8030 1.000 0.000 0.000 0.000 0.000
#> GSM601836 3 0.9156 0.1172 0.252 0.264 0.320 0.080 0.084
#> GSM601851 1 0.1622 0.8041 0.948 0.004 0.004 0.016 0.028
#> GSM601856 3 0.4311 0.4755 0.020 0.004 0.712 0.000 0.264
#> GSM601866 3 0.4543 0.5135 0.020 0.024 0.732 0.000 0.224
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601752 4 0.0000 0.805 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601782 1 0.5038 0.675 0.692 0.028 0.220 0.008 0.008 0.044
#> GSM601792 1 0.2529 0.762 0.900 0.024 0.000 0.012 0.044 0.020
#> GSM601797 1 0.5629 0.213 0.496 0.024 0.000 0.420 0.040 0.020
#> GSM601827 1 0.3669 0.742 0.840 0.016 0.076 0.008 0.028 0.032
#> GSM601837 5 0.3230 0.894 0.000 0.052 0.000 0.060 0.852 0.036
#> GSM601842 2 0.4408 0.457 0.000 0.560 0.004 0.416 0.000 0.020
#> GSM601857 3 0.3991 0.368 0.000 0.004 0.524 0.000 0.000 0.472
#> GSM601867 6 0.5351 0.372 0.000 0.012 0.076 0.308 0.008 0.596
#> GSM601747 1 0.8014 0.149 0.392 0.276 0.180 0.060 0.004 0.088
#> GSM601757 1 0.5358 0.446 0.612 0.024 0.288 0.004 0.000 0.072
#> GSM601762 2 0.4057 0.426 0.000 0.556 0.000 0.436 0.000 0.008
#> GSM601767 2 0.3489 0.638 0.000 0.708 0.000 0.288 0.000 0.004
#> GSM601772 2 0.4716 0.679 0.060 0.704 0.004 0.212 0.000 0.020
#> GSM601777 4 0.5325 0.682 0.024 0.068 0.068 0.744 0.024 0.072
#> GSM601787 6 0.4218 0.453 0.000 0.036 0.108 0.068 0.004 0.784
#> GSM601802 4 0.0806 0.802 0.000 0.008 0.000 0.972 0.020 0.000
#> GSM601807 3 0.6405 0.184 0.020 0.024 0.612 0.196 0.116 0.032
#> GSM601812 1 0.5471 0.617 0.636 0.024 0.256 0.000 0.020 0.064
#> GSM601817 1 0.4695 0.675 0.692 0.044 0.232 0.000 0.000 0.032
#> GSM601822 1 0.0000 0.770 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601832 2 0.3837 0.656 0.060 0.832 0.020 0.036 0.004 0.048
#> GSM601847 4 0.3157 0.758 0.036 0.000 0.036 0.864 0.008 0.056
#> GSM601852 1 0.3982 0.653 0.696 0.008 0.280 0.000 0.000 0.016
#> GSM601862 3 0.3955 0.422 0.000 0.004 0.560 0.000 0.000 0.436
#> GSM601753 4 0.1138 0.804 0.004 0.000 0.024 0.960 0.000 0.012
#> GSM601783 1 0.2389 0.743 0.864 0.000 0.128 0.000 0.000 0.008
#> GSM601793 1 0.3285 0.747 0.860 0.024 0.000 0.052 0.044 0.020
#> GSM601798 4 0.0508 0.805 0.000 0.004 0.000 0.984 0.012 0.000
#> GSM601828 1 0.4048 0.721 0.772 0.040 0.164 0.000 0.004 0.020
#> GSM601838 5 0.2384 0.911 0.000 0.084 0.000 0.032 0.884 0.000
#> GSM601843 2 0.4794 0.657 0.000 0.668 0.004 0.228 0.000 0.100
#> GSM601858 6 0.4167 0.495 0.000 0.072 0.084 0.056 0.000 0.788
#> GSM601868 3 0.4400 0.405 0.000 0.012 0.524 0.000 0.008 0.456
#> GSM601748 1 0.3281 0.708 0.784 0.004 0.200 0.000 0.000 0.012
#> GSM601758 1 0.2739 0.774 0.872 0.000 0.084 0.000 0.012 0.032
#> GSM601763 1 0.5281 0.703 0.736 0.080 0.048 0.024 0.012 0.100
#> GSM601768 2 0.5499 0.612 0.112 0.692 0.008 0.056 0.004 0.128
#> GSM601773 2 0.4607 0.580 0.020 0.628 0.000 0.328 0.000 0.024
#> GSM601778 1 0.6182 0.715 0.684 0.056 0.064 0.036 0.048 0.112
#> GSM601788 1 0.6152 0.529 0.600 0.248 0.020 0.076 0.004 0.052
#> GSM601803 4 0.0909 0.806 0.000 0.000 0.012 0.968 0.020 0.000
#> GSM601808 3 0.5078 0.492 0.012 0.024 0.568 0.000 0.020 0.376
#> GSM601813 1 0.5109 0.699 0.692 0.044 0.204 0.000 0.012 0.048
#> GSM601818 1 0.4664 0.634 0.668 0.056 0.264 0.000 0.000 0.012
#> GSM601823 1 0.0000 0.770 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601833 2 0.2679 0.691 0.000 0.876 0.004 0.088 0.008 0.024
#> GSM601848 1 0.0000 0.770 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601853 3 0.3883 0.246 0.332 0.000 0.656 0.000 0.000 0.012
#> GSM601863 3 0.6657 0.282 0.208 0.016 0.404 0.000 0.016 0.356
#> GSM601754 4 0.1401 0.800 0.004 0.000 0.028 0.948 0.000 0.020
#> GSM601784 6 0.6427 0.412 0.112 0.272 0.004 0.076 0.000 0.536
#> GSM601794 1 0.7658 0.386 0.476 0.024 0.048 0.264 0.060 0.128
#> GSM601799 4 0.5748 0.497 0.260 0.012 0.032 0.616 0.004 0.076
#> GSM601829 1 0.0000 0.770 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601839 5 0.2728 0.912 0.000 0.100 0.000 0.032 0.864 0.004
#> GSM601844 1 0.4466 0.713 0.784 0.016 0.052 0.020 0.016 0.112
#> GSM601859 6 0.4559 0.513 0.036 0.036 0.000 0.220 0.000 0.708
#> GSM601869 6 0.3911 -0.112 0.000 0.000 0.368 0.000 0.008 0.624
#> GSM601749 1 0.1820 0.776 0.924 0.000 0.056 0.000 0.012 0.008
#> GSM601759 1 0.2800 0.760 0.860 0.004 0.100 0.000 0.000 0.036
#> GSM601764 1 0.5218 0.711 0.740 0.060 0.052 0.020 0.016 0.112
#> GSM601769 2 0.2187 0.669 0.004 0.908 0.000 0.012 0.012 0.064
#> GSM601774 2 0.2368 0.695 0.008 0.888 0.000 0.092 0.004 0.008
#> GSM601779 1 0.4943 0.700 0.760 0.028 0.060 0.024 0.020 0.108
#> GSM601789 2 0.4625 0.059 0.000 0.544 0.004 0.024 0.004 0.424
#> GSM601804 4 0.6714 0.395 0.276 0.012 0.052 0.536 0.016 0.108
#> GSM601809 6 0.5569 0.552 0.136 0.116 0.016 0.024 0.016 0.692
#> GSM601814 2 0.3947 0.628 0.000 0.804 0.000 0.064 0.052 0.080
#> GSM601819 1 0.6047 0.633 0.672 0.108 0.056 0.024 0.016 0.124
#> GSM601824 1 0.3089 0.736 0.860 0.000 0.032 0.024 0.004 0.080
#> GSM601834 2 0.2742 0.648 0.000 0.852 0.000 0.008 0.012 0.128
#> GSM601849 1 0.2538 0.751 0.888 0.012 0.020 0.000 0.004 0.076
#> GSM601854 1 0.4643 0.696 0.704 0.024 0.228 0.000 0.008 0.036
#> GSM601864 5 0.2594 0.890 0.000 0.056 0.000 0.004 0.880 0.060
#> GSM601755 4 0.0000 0.805 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601785 6 0.4744 0.544 0.084 0.164 0.000 0.024 0.004 0.724
#> GSM601795 6 0.7854 0.395 0.096 0.084 0.048 0.204 0.056 0.512
#> GSM601800 4 0.1116 0.795 0.000 0.028 0.000 0.960 0.004 0.008
#> GSM601830 1 0.3688 0.731 0.804 0.020 0.144 0.000 0.024 0.008
#> GSM601840 6 0.5144 0.415 0.000 0.100 0.000 0.340 0.000 0.560
#> GSM601845 1 0.4840 0.706 0.732 0.016 0.020 0.164 0.004 0.064
#> GSM601860 6 0.3303 0.568 0.044 0.060 0.000 0.048 0.000 0.848
#> GSM601870 3 0.4405 0.480 0.000 0.000 0.688 0.000 0.072 0.240
#> GSM601750 3 0.4922 -0.108 0.400 0.000 0.548 0.000 0.016 0.036
#> GSM601760 6 0.5894 0.421 0.180 0.032 0.116 0.012 0.012 0.648
#> GSM601765 2 0.4248 0.646 0.076 0.800 0.012 0.040 0.004 0.068
#> GSM601770 2 0.3445 0.678 0.000 0.744 0.000 0.244 0.000 0.012
#> GSM601775 1 0.6875 0.366 0.512 0.288 0.044 0.028 0.008 0.120
#> GSM601780 1 0.3257 0.755 0.864 0.028 0.028 0.004 0.016 0.060
#> GSM601790 5 0.3672 0.840 0.000 0.168 0.000 0.000 0.776 0.056
#> GSM601805 4 0.4215 0.488 0.004 0.000 0.008 0.672 0.016 0.300
#> GSM601810 1 0.3744 0.727 0.800 0.020 0.148 0.000 0.012 0.020
#> GSM601815 2 0.3147 0.658 0.000 0.844 0.000 0.012 0.044 0.100
#> GSM601820 1 0.6224 0.506 0.580 0.052 0.116 0.000 0.012 0.240
#> GSM601825 4 0.6719 0.475 0.152 0.144 0.032 0.592 0.004 0.076
#> GSM601835 2 0.4998 0.532 0.000 0.656 0.000 0.112 0.008 0.224
#> GSM601850 1 0.4113 0.739 0.812 0.012 0.024 0.072 0.012 0.068
#> GSM601855 3 0.3611 0.492 0.072 0.004 0.832 0.000 0.040 0.052
#> GSM601865 6 0.3830 0.495 0.000 0.212 0.000 0.000 0.044 0.744
#> GSM601756 4 0.0146 0.804 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM601786 6 0.3834 0.491 0.000 0.232 0.000 0.000 0.036 0.732
#> GSM601796 6 0.5056 0.518 0.032 0.024 0.040 0.076 0.064 0.764
#> GSM601801 4 0.2814 0.625 0.000 0.172 0.000 0.820 0.008 0.000
#> GSM601831 1 0.2823 0.711 0.796 0.000 0.204 0.000 0.000 0.000
#> GSM601841 6 0.5094 0.383 0.108 0.008 0.140 0.016 0.012 0.716
#> GSM601846 1 0.3118 0.722 0.832 0.008 0.000 0.140 0.012 0.008
#> GSM601861 6 0.4808 0.351 0.000 0.332 0.000 0.004 0.060 0.604
#> GSM601871 6 0.3514 0.397 0.000 0.028 0.144 0.000 0.020 0.808
#> GSM601751 6 0.3949 0.557 0.116 0.036 0.000 0.044 0.004 0.800
#> GSM601761 1 0.4242 0.724 0.800 0.012 0.044 0.024 0.016 0.104
#> GSM601766 6 0.7146 0.324 0.164 0.300 0.044 0.024 0.008 0.460
#> GSM601771 2 0.6410 0.330 0.032 0.464 0.000 0.196 0.000 0.308
#> GSM601776 1 0.0458 0.771 0.984 0.016 0.000 0.000 0.000 0.000
#> GSM601781 6 0.7019 0.340 0.264 0.064 0.044 0.056 0.028 0.544
#> GSM601791 6 0.5055 0.453 0.200 0.000 0.064 0.024 0.016 0.696
#> GSM601806 4 0.2126 0.760 0.000 0.072 0.000 0.904 0.020 0.004
#> GSM601811 3 0.5504 0.479 0.012 0.036 0.556 0.012 0.016 0.368
#> GSM601816 1 0.0000 0.770 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601821 2 0.4570 0.462 0.000 0.668 0.000 0.000 0.080 0.252
#> GSM601826 1 0.0000 0.770 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601836 6 0.8067 0.187 0.236 0.292 0.048 0.052 0.020 0.352
#> GSM601851 1 0.2484 0.766 0.908 0.016 0.024 0.008 0.012 0.032
#> GSM601856 3 0.4170 0.511 0.020 0.004 0.648 0.000 0.000 0.328
#> GSM601866 6 0.5170 -0.197 0.008 0.040 0.428 0.000 0.012 0.512
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 time(p) gender(p) k
#> SD:pam 105 0.644 0.08794 2
#> SD:pam 107 0.129 0.09417 3
#> SD:pam 98 0.687 0.05038 4
#> SD:pam 87 0.719 0.00707 5
#> SD:pam 82 0.569 0.01133 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 125 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 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 0.226 0.621 0.774 0.4240 0.545 0.545
#> 3 3 0.464 0.733 0.828 0.5158 0.697 0.486
#> 4 4 0.484 0.592 0.765 0.0800 0.863 0.648
#> 5 5 0.616 0.591 0.744 0.1111 0.827 0.504
#> 6 6 0.693 0.608 0.792 0.0429 0.937 0.732
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
#> GSM601752 1 0.9209 0.667 0.664 0.336
#> GSM601782 2 0.8499 0.743 0.276 0.724
#> GSM601792 1 0.5519 0.687 0.872 0.128
#> GSM601797 1 0.5946 0.692 0.856 0.144
#> GSM601827 1 0.9970 -0.164 0.532 0.468
#> GSM601837 2 0.3879 0.669 0.076 0.924
#> GSM601842 2 0.0000 0.704 0.000 1.000
#> GSM601857 2 0.9358 0.703 0.352 0.648
#> GSM601867 2 0.9129 0.712 0.328 0.672
#> GSM601747 2 0.8016 0.738 0.244 0.756
#> GSM601757 2 0.8499 0.743 0.276 0.724
#> GSM601762 2 0.3431 0.627 0.064 0.936
#> GSM601767 2 0.0938 0.699 0.012 0.988
#> GSM601772 2 0.0376 0.703 0.004 0.996
#> GSM601777 1 0.8081 0.622 0.752 0.248
#> GSM601787 2 0.7883 0.713 0.236 0.764
#> GSM601802 1 0.9393 0.661 0.644 0.356
#> GSM601807 1 0.9881 -0.127 0.564 0.436
#> GSM601812 2 0.8499 0.743 0.276 0.724
#> GSM601817 2 0.8555 0.742 0.280 0.720
#> GSM601822 1 0.5946 0.691 0.856 0.144
#> GSM601832 2 0.0000 0.704 0.000 1.000
#> GSM601847 1 0.7883 0.692 0.764 0.236
#> GSM601852 2 0.8499 0.743 0.276 0.724
#> GSM601862 2 0.9358 0.703 0.352 0.648
#> GSM601753 1 0.9460 0.660 0.636 0.364
#> GSM601783 2 0.8499 0.743 0.276 0.724
#> GSM601793 1 0.5519 0.687 0.872 0.128
#> GSM601798 1 0.9358 0.660 0.648 0.352
#> GSM601828 2 0.8499 0.743 0.276 0.724
#> GSM601838 2 0.3879 0.669 0.076 0.924
#> GSM601843 2 0.0000 0.704 0.000 1.000
#> GSM601858 2 0.3114 0.683 0.056 0.944
#> GSM601868 2 0.9358 0.703 0.352 0.648
#> GSM601748 2 0.8499 0.743 0.276 0.724
#> GSM601758 2 0.8499 0.743 0.276 0.724
#> GSM601763 2 0.8016 0.738 0.244 0.756
#> GSM601768 2 0.0938 0.699 0.012 0.988
#> GSM601773 2 0.0672 0.698 0.008 0.992
#> GSM601778 1 0.7056 0.653 0.808 0.192
#> GSM601788 2 0.3114 0.719 0.056 0.944
#> GSM601803 1 0.9393 0.661 0.644 0.356
#> GSM601808 2 0.9635 0.673 0.388 0.612
#> GSM601813 2 0.8608 0.737 0.284 0.716
#> GSM601818 2 0.8661 0.740 0.288 0.712
#> GSM601823 1 0.5737 0.686 0.864 0.136
#> GSM601833 2 0.0376 0.703 0.004 0.996
#> GSM601848 1 0.6148 0.680 0.848 0.152
#> GSM601853 2 0.9881 0.603 0.436 0.564
#> GSM601863 2 0.9358 0.703 0.352 0.648
#> GSM601754 1 0.9393 0.661 0.644 0.356
#> GSM601784 2 0.0000 0.704 0.000 1.000
#> GSM601794 1 0.5629 0.689 0.868 0.132
#> GSM601799 1 0.9393 0.664 0.644 0.356
#> GSM601829 1 0.7815 0.586 0.768 0.232
#> GSM601839 2 0.3879 0.669 0.076 0.924
#> GSM601844 2 0.8861 0.686 0.304 0.696
#> GSM601859 2 0.0938 0.699 0.012 0.988
#> GSM601869 2 0.9044 0.725 0.320 0.680
#> GSM601749 2 0.8499 0.743 0.276 0.724
#> GSM601759 2 0.8499 0.743 0.276 0.724
#> GSM601764 2 0.7950 0.740 0.240 0.760
#> GSM601769 2 0.0000 0.704 0.000 1.000
#> GSM601774 2 0.0938 0.699 0.012 0.988
#> GSM601779 1 0.9580 0.324 0.620 0.380
#> GSM601789 2 0.0672 0.704 0.008 0.992
#> GSM601804 1 0.7674 0.695 0.776 0.224
#> GSM601809 2 0.8443 0.745 0.272 0.728
#> GSM601814 2 0.0000 0.704 0.000 1.000
#> GSM601819 2 0.8499 0.743 0.276 0.724
#> GSM601824 1 0.9954 0.221 0.540 0.460
#> GSM601834 2 0.0938 0.699 0.012 0.988
#> GSM601849 2 0.9000 0.654 0.316 0.684
#> GSM601854 2 0.8499 0.743 0.276 0.724
#> GSM601864 2 0.3879 0.669 0.076 0.924
#> GSM601755 1 0.9358 0.660 0.648 0.352
#> GSM601785 2 0.0938 0.705 0.012 0.988
#> GSM601795 1 0.5737 0.690 0.864 0.136
#> GSM601800 1 0.9358 0.660 0.648 0.352
#> GSM601830 1 0.9815 -0.167 0.580 0.420
#> GSM601840 2 0.7602 0.741 0.220 0.780
#> GSM601845 2 0.8327 0.706 0.264 0.736
#> GSM601860 2 0.0938 0.708 0.012 0.988
#> GSM601870 1 0.9998 -0.206 0.508 0.492
#> GSM601750 2 0.8499 0.743 0.276 0.724
#> GSM601760 2 0.8499 0.743 0.276 0.724
#> GSM601765 2 0.0938 0.699 0.012 0.988
#> GSM601770 2 0.0938 0.699 0.012 0.988
#> GSM601775 2 0.8909 0.642 0.308 0.692
#> GSM601780 2 0.9850 0.373 0.428 0.572
#> GSM601790 2 0.3879 0.669 0.076 0.924
#> GSM601805 1 0.9460 0.660 0.636 0.364
#> GSM601810 2 0.9522 0.682 0.372 0.628
#> GSM601815 2 0.2236 0.694 0.036 0.964
#> GSM601820 2 0.8499 0.743 0.276 0.724
#> GSM601825 1 0.9491 0.659 0.632 0.368
#> GSM601835 2 0.7883 0.227 0.236 0.764
#> GSM601850 1 0.9881 0.168 0.564 0.436
#> GSM601855 1 0.9815 -0.167 0.580 0.420
#> GSM601865 2 0.3879 0.669 0.076 0.924
#> GSM601756 1 0.9358 0.660 0.648 0.352
#> GSM601786 2 0.3431 0.678 0.064 0.936
#> GSM601796 1 0.5629 0.689 0.868 0.132
#> GSM601801 1 0.9358 0.660 0.648 0.352
#> GSM601831 1 0.9954 -0.162 0.540 0.460
#> GSM601841 2 0.8763 0.726 0.296 0.704
#> GSM601846 1 0.7745 0.691 0.772 0.228
#> GSM601861 2 0.0376 0.704 0.004 0.996
#> GSM601871 2 0.7602 0.711 0.220 0.780
#> GSM601751 2 0.5629 0.702 0.132 0.868
#> GSM601761 2 0.8499 0.743 0.276 0.724
#> GSM601766 2 0.7674 0.745 0.224 0.776
#> GSM601771 2 0.0000 0.704 0.000 1.000
#> GSM601776 1 0.9998 -0.126 0.508 0.492
#> GSM601781 1 0.9000 0.490 0.684 0.316
#> GSM601791 2 0.8499 0.726 0.276 0.724
#> GSM601806 1 0.9427 0.661 0.640 0.360
#> GSM601811 2 0.9460 0.697 0.364 0.636
#> GSM601816 1 0.5629 0.687 0.868 0.132
#> GSM601821 2 0.0672 0.704 0.008 0.992
#> GSM601826 1 0.5737 0.686 0.864 0.136
#> GSM601836 2 0.8016 0.738 0.244 0.756
#> GSM601851 2 0.9866 0.387 0.432 0.568
#> GSM601856 1 0.9795 -0.204 0.584 0.416
#> GSM601866 2 0.8555 0.742 0.280 0.720
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601752 1 0.0747 0.85400 0.984 0.016 0.000
#> GSM601782 3 0.4887 0.76707 0.000 0.228 0.772
#> GSM601792 1 0.1529 0.85082 0.960 0.000 0.040
#> GSM601797 1 0.1585 0.85404 0.964 0.008 0.028
#> GSM601827 3 0.3276 0.85414 0.024 0.068 0.908
#> GSM601837 2 0.2066 0.75318 0.000 0.940 0.060
#> GSM601842 2 0.5404 0.73878 0.256 0.740 0.004
#> GSM601857 3 0.2625 0.86624 0.000 0.084 0.916
#> GSM601867 2 0.6442 0.16642 0.004 0.564 0.432
#> GSM601747 2 0.9021 0.57502 0.184 0.552 0.264
#> GSM601757 3 0.4575 0.82699 0.004 0.184 0.812
#> GSM601762 2 0.5098 0.74066 0.248 0.752 0.000
#> GSM601767 2 0.3551 0.78647 0.132 0.868 0.000
#> GSM601772 2 0.4002 0.78452 0.160 0.840 0.000
#> GSM601777 1 0.6402 0.65465 0.724 0.040 0.236
#> GSM601787 2 0.6154 0.23867 0.000 0.592 0.408
#> GSM601802 1 0.0747 0.85400 0.984 0.016 0.000
#> GSM601807 3 0.2229 0.82300 0.044 0.012 0.944
#> GSM601812 3 0.3192 0.87212 0.000 0.112 0.888
#> GSM601817 3 0.2711 0.87228 0.000 0.088 0.912
#> GSM601822 1 0.1163 0.85302 0.972 0.000 0.028
#> GSM601832 2 0.5797 0.71479 0.280 0.712 0.008
#> GSM601847 1 0.1182 0.85493 0.976 0.012 0.012
#> GSM601852 3 0.3272 0.87271 0.004 0.104 0.892
#> GSM601862 3 0.2165 0.86871 0.000 0.064 0.936
#> GSM601753 1 0.0747 0.85400 0.984 0.016 0.000
#> GSM601783 3 0.4609 0.85999 0.028 0.128 0.844
#> GSM601793 1 0.3116 0.80873 0.892 0.000 0.108
#> GSM601798 1 0.0747 0.85400 0.984 0.016 0.000
#> GSM601828 3 0.3193 0.87376 0.004 0.100 0.896
#> GSM601838 2 0.2066 0.75318 0.000 0.940 0.060
#> GSM601843 2 0.4931 0.75664 0.232 0.768 0.000
#> GSM601858 2 0.2066 0.75537 0.000 0.940 0.060
#> GSM601868 3 0.2165 0.86871 0.000 0.064 0.936
#> GSM601748 3 0.2711 0.87463 0.000 0.088 0.912
#> GSM601758 3 0.3500 0.86934 0.004 0.116 0.880
#> GSM601763 2 0.8752 0.60405 0.284 0.568 0.148
#> GSM601768 2 0.4702 0.76997 0.212 0.788 0.000
#> GSM601773 2 0.4887 0.76332 0.228 0.772 0.000
#> GSM601778 1 0.3141 0.82814 0.912 0.020 0.068
#> GSM601788 2 0.6526 0.74278 0.128 0.760 0.112
#> GSM601803 1 0.0747 0.85400 0.984 0.016 0.000
#> GSM601808 3 0.1289 0.86225 0.000 0.032 0.968
#> GSM601813 3 0.3886 0.86692 0.024 0.096 0.880
#> GSM601818 3 0.3482 0.83929 0.000 0.128 0.872
#> GSM601823 1 0.5325 0.63426 0.748 0.004 0.248
#> GSM601833 2 0.4504 0.77639 0.196 0.804 0.000
#> GSM601848 1 0.5138 0.62993 0.748 0.000 0.252
#> GSM601853 3 0.0424 0.85268 0.000 0.008 0.992
#> GSM601863 3 0.2066 0.86834 0.000 0.060 0.940
#> GSM601754 1 0.0747 0.85400 0.984 0.016 0.000
#> GSM601784 2 0.3116 0.78655 0.108 0.892 0.000
#> GSM601794 1 0.1411 0.85165 0.964 0.000 0.036
#> GSM601799 1 0.0747 0.85400 0.984 0.016 0.000
#> GSM601829 3 0.6849 0.34772 0.380 0.020 0.600
#> GSM601839 2 0.2066 0.75318 0.000 0.940 0.060
#> GSM601844 3 0.8691 0.30031 0.356 0.116 0.528
#> GSM601859 2 0.4887 0.75941 0.228 0.772 0.000
#> GSM601869 3 0.2165 0.86953 0.000 0.064 0.936
#> GSM601749 3 0.3918 0.86198 0.004 0.140 0.856
#> GSM601759 3 0.2945 0.87510 0.004 0.088 0.908
#> GSM601764 2 0.8576 0.63521 0.252 0.596 0.152
#> GSM601769 2 0.1585 0.77494 0.028 0.964 0.008
#> GSM601774 2 0.2959 0.78591 0.100 0.900 0.000
#> GSM601779 1 0.7002 0.55545 0.672 0.048 0.280
#> GSM601789 2 0.1964 0.75486 0.000 0.944 0.056
#> GSM601804 1 0.1491 0.85467 0.968 0.016 0.016
#> GSM601809 2 0.6209 0.35529 0.004 0.628 0.368
#> GSM601814 2 0.1163 0.77539 0.028 0.972 0.000
#> GSM601819 3 0.6936 0.70831 0.064 0.232 0.704
#> GSM601824 1 0.6875 0.57669 0.700 0.244 0.056
#> GSM601834 2 0.4605 0.77327 0.204 0.796 0.000
#> GSM601849 1 0.8891 -0.00795 0.448 0.120 0.432
#> GSM601854 3 0.3983 0.85757 0.004 0.144 0.852
#> GSM601864 2 0.2066 0.75318 0.000 0.940 0.060
#> GSM601755 1 0.0747 0.85400 0.984 0.016 0.000
#> GSM601785 2 0.6522 0.71820 0.272 0.696 0.032
#> GSM601795 1 0.1289 0.85239 0.968 0.000 0.032
#> GSM601800 1 0.0747 0.85400 0.984 0.016 0.000
#> GSM601830 3 0.1999 0.82716 0.036 0.012 0.952
#> GSM601840 2 0.8138 0.67724 0.232 0.636 0.132
#> GSM601845 2 0.8971 0.52041 0.336 0.520 0.144
#> GSM601860 2 0.5219 0.77470 0.196 0.788 0.016
#> GSM601870 3 0.2313 0.83015 0.024 0.032 0.944
#> GSM601750 3 0.3349 0.87205 0.004 0.108 0.888
#> GSM601760 3 0.4845 0.84937 0.052 0.104 0.844
#> GSM601765 2 0.5058 0.74678 0.244 0.756 0.000
#> GSM601770 2 0.4452 0.77764 0.192 0.808 0.000
#> GSM601775 1 0.7838 -0.29133 0.488 0.460 0.052
#> GSM601780 3 0.9191 0.03903 0.424 0.148 0.428
#> GSM601790 2 0.1964 0.75486 0.000 0.944 0.056
#> GSM601805 1 0.0747 0.85400 0.984 0.016 0.000
#> GSM601810 3 0.2772 0.87523 0.004 0.080 0.916
#> GSM601815 2 0.1964 0.75486 0.000 0.944 0.056
#> GSM601820 3 0.3425 0.87118 0.004 0.112 0.884
#> GSM601825 1 0.0747 0.85400 0.984 0.016 0.000
#> GSM601835 2 0.6322 0.71200 0.276 0.700 0.024
#> GSM601850 1 0.5174 0.75382 0.832 0.092 0.076
#> GSM601855 3 0.1832 0.82935 0.036 0.008 0.956
#> GSM601865 2 0.2066 0.75318 0.000 0.940 0.060
#> GSM601756 1 0.0747 0.85400 0.984 0.016 0.000
#> GSM601786 2 0.1964 0.75486 0.000 0.944 0.056
#> GSM601796 1 0.2200 0.84505 0.940 0.004 0.056
#> GSM601801 1 0.0747 0.85400 0.984 0.016 0.000
#> GSM601831 3 0.2703 0.85791 0.016 0.056 0.928
#> GSM601841 3 0.6059 0.80111 0.048 0.188 0.764
#> GSM601846 1 0.2187 0.85288 0.948 0.024 0.028
#> GSM601861 2 0.1585 0.76600 0.008 0.964 0.028
#> GSM601871 2 0.5948 0.35315 0.000 0.640 0.360
#> GSM601751 2 0.7150 0.60354 0.348 0.616 0.036
#> GSM601761 3 0.7666 0.71021 0.128 0.192 0.680
#> GSM601766 2 0.6523 0.74416 0.228 0.724 0.048
#> GSM601771 2 0.4634 0.78374 0.164 0.824 0.012
#> GSM601776 1 0.7990 0.05851 0.488 0.060 0.452
#> GSM601781 1 0.6882 0.66188 0.732 0.096 0.172
#> GSM601791 3 0.9243 0.45298 0.232 0.236 0.532
#> GSM601806 1 0.1031 0.85013 0.976 0.024 0.000
#> GSM601811 3 0.2796 0.86445 0.000 0.092 0.908
#> GSM601816 1 0.2959 0.81831 0.900 0.000 0.100
#> GSM601821 2 0.1399 0.76421 0.004 0.968 0.028
#> GSM601826 1 0.5365 0.62708 0.744 0.004 0.252
#> GSM601836 2 0.9050 0.55447 0.304 0.532 0.164
#> GSM601851 3 0.8546 0.34278 0.348 0.108 0.544
#> GSM601856 3 0.0661 0.85170 0.004 0.008 0.988
#> GSM601866 3 0.2261 0.87123 0.000 0.068 0.932
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601752 4 0.0188 0.7242 0.000 0.000 0.004 0.996
#> GSM601782 1 0.3554 0.6966 0.844 0.136 0.020 0.000
#> GSM601792 4 0.5489 0.5988 0.240 0.000 0.060 0.700
#> GSM601797 4 0.1716 0.7182 0.000 0.000 0.064 0.936
#> GSM601827 1 0.3400 0.6494 0.876 0.004 0.044 0.076
#> GSM601837 2 0.3450 0.5807 0.008 0.836 0.156 0.000
#> GSM601842 2 0.5324 0.6299 0.004 0.644 0.016 0.336
#> GSM601857 1 0.0657 0.7064 0.984 0.004 0.012 0.000
#> GSM601867 1 0.7634 -0.3547 0.424 0.208 0.368 0.000
#> GSM601747 1 0.7003 0.4716 0.624 0.256 0.036 0.084
#> GSM601757 1 0.3697 0.7123 0.852 0.100 0.048 0.000
#> GSM601762 2 0.4431 0.6709 0.000 0.696 0.000 0.304
#> GSM601767 2 0.4053 0.7188 0.004 0.768 0.000 0.228
#> GSM601772 2 0.4018 0.7196 0.004 0.772 0.000 0.224
#> GSM601777 4 0.4739 0.6751 0.044 0.008 0.160 0.788
#> GSM601787 2 0.7688 -0.1737 0.220 0.416 0.364 0.000
#> GSM601802 4 0.0000 0.7237 0.000 0.000 0.000 1.000
#> GSM601807 3 0.5038 0.9312 0.336 0.000 0.652 0.012
#> GSM601812 1 0.0188 0.7131 0.996 0.000 0.004 0.000
#> GSM601817 1 0.0188 0.7112 0.996 0.000 0.004 0.000
#> GSM601822 4 0.1557 0.7171 0.000 0.000 0.056 0.944
#> GSM601832 2 0.5513 0.6125 0.008 0.628 0.016 0.348
#> GSM601847 4 0.0592 0.7241 0.000 0.000 0.016 0.984
#> GSM601852 1 0.1854 0.7227 0.940 0.048 0.012 0.000
#> GSM601862 1 0.1978 0.6691 0.928 0.004 0.068 0.000
#> GSM601753 4 0.0376 0.7221 0.000 0.004 0.004 0.992
#> GSM601783 1 0.4167 0.7068 0.824 0.040 0.132 0.004
#> GSM601793 4 0.5361 0.6148 0.224 0.000 0.060 0.716
#> GSM601798 4 0.0000 0.7237 0.000 0.000 0.000 1.000
#> GSM601828 1 0.0188 0.7112 0.996 0.000 0.004 0.000
#> GSM601838 2 0.3450 0.5807 0.008 0.836 0.156 0.000
#> GSM601843 2 0.4483 0.6909 0.004 0.712 0.000 0.284
#> GSM601858 2 0.6518 0.6541 0.020 0.684 0.148 0.148
#> GSM601868 1 0.2266 0.6502 0.912 0.004 0.084 0.000
#> GSM601748 1 0.0188 0.7112 0.996 0.000 0.004 0.000
#> GSM601758 1 0.2480 0.7173 0.904 0.008 0.088 0.000
#> GSM601763 1 0.9834 -0.0391 0.304 0.224 0.176 0.296
#> GSM601768 2 0.4560 0.6826 0.004 0.700 0.000 0.296
#> GSM601773 2 0.4551 0.7064 0.004 0.724 0.004 0.268
#> GSM601778 4 0.6129 0.6317 0.184 0.004 0.124 0.688
#> GSM601788 2 0.8129 0.5389 0.068 0.560 0.152 0.220
#> GSM601803 4 0.0188 0.7213 0.000 0.004 0.000 0.996
#> GSM601808 1 0.3208 0.5302 0.848 0.004 0.148 0.000
#> GSM601813 1 0.3533 0.7190 0.864 0.080 0.056 0.000
#> GSM601818 1 0.0927 0.7137 0.976 0.008 0.016 0.000
#> GSM601823 4 0.6537 0.2137 0.424 0.000 0.076 0.500
#> GSM601833 2 0.4252 0.7106 0.004 0.744 0.000 0.252
#> GSM601848 4 0.6451 0.1272 0.456 0.000 0.068 0.476
#> GSM601853 1 0.3208 0.5295 0.848 0.004 0.148 0.000
#> GSM601863 1 0.1004 0.7040 0.972 0.004 0.024 0.000
#> GSM601754 4 0.0000 0.7237 0.000 0.000 0.000 1.000
#> GSM601784 2 0.3751 0.7226 0.004 0.800 0.000 0.196
#> GSM601794 4 0.5288 0.6165 0.224 0.000 0.056 0.720
#> GSM601799 4 0.0524 0.7213 0.000 0.004 0.008 0.988
#> GSM601829 4 0.7491 0.2849 0.352 0.004 0.164 0.480
#> GSM601839 2 0.3450 0.5807 0.008 0.836 0.156 0.000
#> GSM601844 1 0.7241 0.6011 0.632 0.128 0.200 0.040
#> GSM601859 2 0.4560 0.6827 0.004 0.700 0.000 0.296
#> GSM601869 1 0.0657 0.7094 0.984 0.004 0.012 0.000
#> GSM601749 1 0.4236 0.7072 0.824 0.088 0.088 0.000
#> GSM601759 1 0.0895 0.7187 0.976 0.004 0.020 0.000
#> GSM601764 1 0.8511 0.4935 0.544 0.180 0.168 0.108
#> GSM601769 2 0.0779 0.6576 0.004 0.980 0.000 0.016
#> GSM601774 2 0.3870 0.7220 0.004 0.788 0.000 0.208
#> GSM601779 1 0.6667 0.2267 0.556 0.004 0.084 0.356
#> GSM601789 2 0.5154 0.6369 0.012 0.776 0.140 0.072
#> GSM601804 4 0.0657 0.7230 0.000 0.004 0.012 0.984
#> GSM601809 1 0.6373 0.3479 0.636 0.248 0.116 0.000
#> GSM601814 2 0.0524 0.6533 0.004 0.988 0.000 0.008
#> GSM601819 1 0.4829 0.6837 0.776 0.068 0.156 0.000
#> GSM601824 4 0.6625 0.4911 0.176 0.132 0.020 0.672
#> GSM601834 2 0.4188 0.7141 0.004 0.752 0.000 0.244
#> GSM601849 1 0.8241 0.5367 0.568 0.108 0.200 0.124
#> GSM601854 1 0.2216 0.7142 0.908 0.092 0.000 0.000
#> GSM601864 2 0.3450 0.5807 0.008 0.836 0.156 0.000
#> GSM601755 4 0.0000 0.7237 0.000 0.000 0.000 1.000
#> GSM601785 2 0.7087 0.5097 0.020 0.536 0.080 0.364
#> GSM601795 4 0.4562 0.6713 0.152 0.000 0.056 0.792
#> GSM601800 4 0.0000 0.7237 0.000 0.000 0.000 1.000
#> GSM601830 3 0.4661 0.9387 0.348 0.000 0.652 0.000
#> GSM601840 4 0.9740 -0.0567 0.228 0.268 0.160 0.344
#> GSM601845 4 0.9615 0.0713 0.216 0.224 0.168 0.392
#> GSM601860 2 0.4908 0.6824 0.016 0.692 0.000 0.292
#> GSM601870 3 0.4040 0.8580 0.248 0.000 0.752 0.000
#> GSM601750 1 0.0188 0.7138 0.996 0.004 0.000 0.000
#> GSM601760 1 0.3523 0.7127 0.856 0.032 0.112 0.000
#> GSM601765 2 0.4677 0.6625 0.004 0.680 0.000 0.316
#> GSM601770 2 0.4053 0.7188 0.004 0.768 0.000 0.228
#> GSM601775 4 0.9204 0.1494 0.224 0.208 0.120 0.448
#> GSM601780 1 0.7636 0.5757 0.616 0.068 0.188 0.128
#> GSM601790 2 0.3351 0.5868 0.008 0.844 0.148 0.000
#> GSM601805 4 0.0000 0.7237 0.000 0.000 0.000 1.000
#> GSM601810 1 0.1639 0.6941 0.952 0.004 0.036 0.008
#> GSM601815 2 0.3249 0.5919 0.008 0.852 0.140 0.000
#> GSM601820 1 0.1576 0.7206 0.948 0.004 0.048 0.000
#> GSM601825 4 0.0188 0.7213 0.000 0.004 0.000 0.996
#> GSM601835 2 0.6540 0.3320 0.036 0.476 0.020 0.468
#> GSM601850 4 0.8624 0.3677 0.284 0.100 0.124 0.492
#> GSM601855 3 0.4661 0.9387 0.348 0.000 0.652 0.000
#> GSM601865 2 0.3450 0.5807 0.008 0.836 0.156 0.000
#> GSM601756 4 0.0000 0.7237 0.000 0.000 0.000 1.000
#> GSM601786 2 0.3351 0.5868 0.008 0.844 0.148 0.000
#> GSM601796 4 0.5394 0.6112 0.228 0.000 0.060 0.712
#> GSM601801 4 0.0188 0.7213 0.000 0.004 0.000 0.996
#> GSM601831 1 0.1545 0.6984 0.952 0.000 0.040 0.008
#> GSM601841 1 0.5925 0.6631 0.724 0.136 0.128 0.012
#> GSM601846 4 0.2334 0.7125 0.000 0.004 0.088 0.908
#> GSM601861 2 0.2714 0.6135 0.004 0.884 0.112 0.000
#> GSM601871 2 0.7535 -0.0647 0.200 0.464 0.336 0.000
#> GSM601751 2 0.8820 0.2944 0.100 0.408 0.124 0.368
#> GSM601761 1 0.5889 0.6441 0.696 0.116 0.188 0.000
#> GSM601766 2 0.8580 0.2778 0.212 0.412 0.040 0.336
#> GSM601771 2 0.4631 0.7061 0.004 0.728 0.008 0.260
#> GSM601776 1 0.7701 0.5679 0.620 0.080 0.148 0.152
#> GSM601781 4 0.8101 0.3921 0.296 0.040 0.156 0.508
#> GSM601791 1 0.6382 0.6219 0.664 0.136 0.196 0.004
#> GSM601806 4 0.1022 0.7026 0.000 0.032 0.000 0.968
#> GSM601811 1 0.1489 0.6837 0.952 0.004 0.044 0.000
#> GSM601816 4 0.5657 0.5897 0.244 0.000 0.068 0.688
#> GSM601821 2 0.2831 0.6096 0.004 0.876 0.120 0.000
#> GSM601826 4 0.6661 0.1032 0.456 0.000 0.084 0.460
#> GSM601836 1 0.8959 0.4272 0.496 0.180 0.188 0.136
#> GSM601851 1 0.7403 0.5970 0.632 0.072 0.200 0.096
#> GSM601856 1 0.4395 0.3711 0.776 0.004 0.204 0.016
#> GSM601866 1 0.0336 0.7102 0.992 0.000 0.008 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601752 4 0.0000 0.7930 0.000 0.000 0.000 1.000 0.000
#> GSM601782 3 0.4996 0.3529 0.420 0.032 0.548 0.000 0.000
#> GSM601792 1 0.4437 0.2126 0.532 0.000 0.004 0.464 0.000
#> GSM601797 4 0.3010 0.6515 0.172 0.000 0.004 0.824 0.000
#> GSM601827 3 0.4982 0.7421 0.176 0.000 0.732 0.020 0.072
#> GSM601837 5 0.4268 0.7080 0.000 0.444 0.000 0.000 0.556
#> GSM601842 2 0.4169 0.7795 0.016 0.724 0.004 0.256 0.000
#> GSM601857 3 0.0794 0.7977 0.028 0.000 0.972 0.000 0.000
#> GSM601867 5 0.5146 0.1756 0.016 0.016 0.428 0.000 0.540
#> GSM601747 3 0.6950 -0.0343 0.320 0.316 0.360 0.004 0.000
#> GSM601757 3 0.4768 0.4587 0.384 0.024 0.592 0.000 0.000
#> GSM601762 2 0.4083 0.7782 0.008 0.728 0.000 0.256 0.008
#> GSM601767 2 0.3452 0.7841 0.000 0.756 0.000 0.244 0.000
#> GSM601772 2 0.3607 0.7849 0.000 0.752 0.004 0.244 0.000
#> GSM601777 4 0.4610 0.2352 0.432 0.000 0.012 0.556 0.000
#> GSM601787 5 0.6118 0.4959 0.016 0.116 0.280 0.000 0.588
#> GSM601802 4 0.0000 0.7930 0.000 0.000 0.000 1.000 0.000
#> GSM601807 3 0.5504 0.2480 0.064 0.000 0.488 0.000 0.448
#> GSM601812 3 0.1197 0.8039 0.048 0.000 0.952 0.000 0.000
#> GSM601817 3 0.0865 0.8002 0.024 0.000 0.972 0.000 0.004
#> GSM601822 4 0.3579 0.5363 0.240 0.000 0.004 0.756 0.000
#> GSM601832 2 0.4508 0.7731 0.032 0.708 0.004 0.256 0.000
#> GSM601847 4 0.0963 0.7784 0.036 0.000 0.000 0.964 0.000
#> GSM601852 3 0.1792 0.8067 0.084 0.000 0.916 0.000 0.000
#> GSM601862 3 0.0771 0.7919 0.020 0.000 0.976 0.000 0.004
#> GSM601753 4 0.0000 0.7930 0.000 0.000 0.000 1.000 0.000
#> GSM601783 3 0.3661 0.6986 0.276 0.000 0.724 0.000 0.000
#> GSM601793 4 0.4443 -0.0475 0.472 0.000 0.004 0.524 0.000
#> GSM601798 4 0.0000 0.7930 0.000 0.000 0.000 1.000 0.000
#> GSM601828 3 0.1410 0.8054 0.060 0.000 0.940 0.000 0.000
#> GSM601838 5 0.4268 0.7080 0.000 0.444 0.000 0.000 0.556
#> GSM601843 2 0.3883 0.7841 0.008 0.744 0.000 0.244 0.004
#> GSM601858 5 0.6529 0.3936 0.000 0.332 0.012 0.152 0.504
#> GSM601868 3 0.0671 0.7885 0.016 0.000 0.980 0.000 0.004
#> GSM601748 3 0.1410 0.8065 0.060 0.000 0.940 0.000 0.000
#> GSM601758 3 0.3123 0.7740 0.184 0.004 0.812 0.000 0.000
#> GSM601763 1 0.3135 0.7025 0.868 0.088 0.024 0.020 0.000
#> GSM601768 2 0.3607 0.7851 0.004 0.752 0.000 0.244 0.000
#> GSM601773 2 0.3861 0.7797 0.008 0.728 0.000 0.264 0.000
#> GSM601778 4 0.4443 0.0415 0.472 0.000 0.004 0.524 0.000
#> GSM601788 2 0.6287 0.6567 0.184 0.552 0.004 0.260 0.000
#> GSM601803 4 0.0000 0.7930 0.000 0.000 0.000 1.000 0.000
#> GSM601808 3 0.1012 0.7879 0.020 0.000 0.968 0.000 0.012
#> GSM601813 3 0.2966 0.7754 0.184 0.000 0.816 0.000 0.000
#> GSM601818 3 0.2389 0.7662 0.116 0.000 0.880 0.000 0.004
#> GSM601823 1 0.4014 0.6332 0.728 0.000 0.016 0.256 0.000
#> GSM601833 2 0.3607 0.7851 0.004 0.752 0.000 0.244 0.000
#> GSM601848 1 0.4040 0.6269 0.724 0.000 0.016 0.260 0.000
#> GSM601853 3 0.0671 0.7879 0.016 0.000 0.980 0.000 0.004
#> GSM601863 3 0.0955 0.7956 0.028 0.000 0.968 0.000 0.004
#> GSM601754 4 0.0000 0.7930 0.000 0.000 0.000 1.000 0.000
#> GSM601784 2 0.3579 0.7809 0.000 0.756 0.000 0.240 0.004
#> GSM601794 4 0.4397 0.1019 0.432 0.000 0.004 0.564 0.000
#> GSM601799 4 0.0000 0.7930 0.000 0.000 0.000 1.000 0.000
#> GSM601829 1 0.5002 0.4037 0.612 0.000 0.044 0.344 0.000
#> GSM601839 5 0.4273 0.7063 0.000 0.448 0.000 0.000 0.552
#> GSM601844 1 0.1913 0.7338 0.932 0.008 0.044 0.016 0.000
#> GSM601859 2 0.3607 0.7851 0.004 0.752 0.000 0.244 0.000
#> GSM601869 3 0.0963 0.8032 0.036 0.000 0.964 0.000 0.000
#> GSM601749 3 0.3171 0.7789 0.176 0.008 0.816 0.000 0.000
#> GSM601759 3 0.2329 0.8013 0.124 0.000 0.876 0.000 0.000
#> GSM601764 1 0.2694 0.7061 0.884 0.076 0.040 0.000 0.000
#> GSM601769 2 0.3555 0.3419 0.000 0.824 0.000 0.052 0.124
#> GSM601774 2 0.3607 0.7851 0.004 0.752 0.000 0.244 0.000
#> GSM601779 1 0.4096 0.6828 0.760 0.000 0.040 0.200 0.000
#> GSM601789 2 0.5799 -0.3872 0.000 0.492 0.000 0.092 0.416
#> GSM601804 4 0.1928 0.7541 0.072 0.004 0.004 0.920 0.000
#> GSM601809 3 0.6605 0.4035 0.120 0.264 0.572 0.000 0.044
#> GSM601814 2 0.1300 0.4879 0.000 0.956 0.000 0.028 0.016
#> GSM601819 1 0.4481 -0.0235 0.576 0.008 0.416 0.000 0.000
#> GSM601824 4 0.5256 -0.0301 0.472 0.024 0.012 0.492 0.000
#> GSM601834 2 0.3607 0.7851 0.004 0.752 0.000 0.244 0.000
#> GSM601849 1 0.2251 0.7344 0.916 0.008 0.052 0.024 0.000
#> GSM601854 3 0.2516 0.7941 0.140 0.000 0.860 0.000 0.000
#> GSM601864 5 0.4268 0.7080 0.000 0.444 0.000 0.000 0.556
#> GSM601755 4 0.0000 0.7930 0.000 0.000 0.000 1.000 0.000
#> GSM601785 2 0.5508 0.7284 0.096 0.636 0.004 0.264 0.000
#> GSM601795 4 0.4151 0.3391 0.344 0.000 0.004 0.652 0.000
#> GSM601800 4 0.0000 0.7930 0.000 0.000 0.000 1.000 0.000
#> GSM601830 3 0.5447 0.2621 0.060 0.000 0.500 0.000 0.440
#> GSM601840 2 0.6996 0.5541 0.244 0.476 0.020 0.260 0.000
#> GSM601845 2 0.6349 0.2239 0.424 0.444 0.008 0.124 0.000
#> GSM601860 2 0.3607 0.7851 0.004 0.752 0.000 0.244 0.000
#> GSM601870 5 0.5285 0.0484 0.060 0.000 0.356 0.000 0.584
#> GSM601750 3 0.2471 0.7960 0.136 0.000 0.864 0.000 0.000
#> GSM601760 3 0.4403 0.3999 0.436 0.004 0.560 0.000 0.000
#> GSM601765 2 0.3961 0.7836 0.016 0.736 0.000 0.248 0.000
#> GSM601770 2 0.3607 0.7851 0.004 0.752 0.000 0.244 0.000
#> GSM601775 4 0.6876 -0.1103 0.368 0.256 0.004 0.372 0.000
#> GSM601780 1 0.3210 0.7334 0.860 0.008 0.040 0.092 0.000
#> GSM601790 5 0.4297 0.6870 0.000 0.472 0.000 0.000 0.528
#> GSM601805 4 0.0000 0.7930 0.000 0.000 0.000 1.000 0.000
#> GSM601810 3 0.3875 0.7818 0.124 0.000 0.804 0.000 0.072
#> GSM601815 2 0.4300 -0.6684 0.000 0.524 0.000 0.000 0.476
#> GSM601820 3 0.2773 0.7841 0.164 0.000 0.836 0.000 0.000
#> GSM601825 4 0.0609 0.7791 0.000 0.020 0.000 0.980 0.000
#> GSM601835 2 0.5704 0.7394 0.064 0.636 0.000 0.272 0.028
#> GSM601850 1 0.3312 0.7209 0.840 0.016 0.012 0.132 0.000
#> GSM601855 3 0.5452 0.2513 0.060 0.000 0.492 0.000 0.448
#> GSM601865 5 0.4278 0.7043 0.000 0.452 0.000 0.000 0.548
#> GSM601756 4 0.0000 0.7930 0.000 0.000 0.000 1.000 0.000
#> GSM601786 5 0.4307 0.6632 0.000 0.496 0.000 0.000 0.504
#> GSM601796 1 0.4390 0.3059 0.568 0.000 0.004 0.428 0.000
#> GSM601801 4 0.0000 0.7930 0.000 0.000 0.000 1.000 0.000
#> GSM601831 3 0.4238 0.7705 0.136 0.000 0.776 0.000 0.088
#> GSM601841 1 0.4859 0.1081 0.608 0.024 0.364 0.004 0.000
#> GSM601846 4 0.2488 0.7243 0.124 0.000 0.004 0.872 0.000
#> GSM601861 2 0.4088 -0.4980 0.000 0.632 0.000 0.000 0.368
#> GSM601871 5 0.6154 0.5503 0.016 0.144 0.236 0.000 0.604
#> GSM601751 2 0.6472 0.5807 0.184 0.504 0.004 0.308 0.000
#> GSM601761 1 0.2833 0.6512 0.852 0.004 0.140 0.004 0.000
#> GSM601766 2 0.5545 0.6956 0.128 0.676 0.012 0.184 0.000
#> GSM601771 2 0.3607 0.7849 0.000 0.752 0.004 0.244 0.000
#> GSM601776 1 0.3757 0.7149 0.816 0.008 0.040 0.136 0.000
#> GSM601781 1 0.3399 0.6887 0.812 0.012 0.004 0.172 0.000
#> GSM601791 1 0.1913 0.7330 0.932 0.008 0.044 0.016 0.000
#> GSM601806 4 0.0609 0.7789 0.000 0.020 0.000 0.980 0.000
#> GSM601811 3 0.1800 0.7942 0.048 0.000 0.932 0.000 0.020
#> GSM601816 1 0.4390 0.3078 0.568 0.000 0.004 0.428 0.000
#> GSM601821 2 0.4015 -0.4625 0.000 0.652 0.000 0.000 0.348
#> GSM601826 1 0.3988 0.6363 0.732 0.000 0.016 0.252 0.000
#> GSM601836 1 0.2504 0.7123 0.900 0.064 0.032 0.004 0.000
#> GSM601851 1 0.2438 0.7378 0.908 0.008 0.040 0.044 0.000
#> GSM601856 3 0.2464 0.7445 0.012 0.000 0.892 0.004 0.092
#> GSM601866 3 0.0865 0.8007 0.024 0.000 0.972 0.000 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601752 4 0.0865 0.80436 0.000 0.036 0.000 0.964 0.000 0.000
#> GSM601782 1 0.3023 0.70205 0.808 0.004 0.008 0.000 0.000 0.180
#> GSM601792 4 0.4839 -0.00208 0.000 0.000 0.012 0.508 0.032 0.448
#> GSM601797 4 0.2544 0.73784 0.000 0.004 0.008 0.888 0.028 0.072
#> GSM601827 1 0.4937 0.57052 0.684 0.000 0.028 0.076 0.000 0.212
#> GSM601837 5 0.0865 0.74887 0.000 0.036 0.000 0.000 0.964 0.000
#> GSM601842 2 0.0993 0.83644 0.000 0.964 0.000 0.024 0.000 0.012
#> GSM601857 1 0.3245 0.57280 0.796 0.000 0.184 0.016 0.004 0.000
#> GSM601867 3 0.6516 0.48080 0.288 0.012 0.476 0.020 0.204 0.000
#> GSM601747 1 0.5283 0.35185 0.528 0.092 0.004 0.000 0.000 0.376
#> GSM601757 1 0.2859 0.70856 0.828 0.000 0.016 0.000 0.000 0.156
#> GSM601762 2 0.2264 0.77876 0.000 0.888 0.000 0.096 0.012 0.004
#> GSM601767 2 0.0146 0.84171 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM601772 2 0.0260 0.84155 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM601777 4 0.5669 0.23291 0.048 0.004 0.008 0.544 0.032 0.364
#> GSM601787 3 0.5796 0.30129 0.080 0.008 0.464 0.020 0.428 0.000
#> GSM601802 4 0.0865 0.80436 0.000 0.036 0.000 0.964 0.000 0.000
#> GSM601807 3 0.1007 0.77346 0.044 0.000 0.956 0.000 0.000 0.000
#> GSM601812 1 0.1719 0.71419 0.924 0.000 0.016 0.000 0.000 0.060
#> GSM601817 1 0.1707 0.68108 0.928 0.000 0.056 0.012 0.004 0.000
#> GSM601822 4 0.3514 0.67383 0.000 0.004 0.012 0.812 0.032 0.140
#> GSM601832 2 0.1686 0.81326 0.000 0.924 0.000 0.064 0.000 0.012
#> GSM601847 4 0.3657 0.64363 0.000 0.028 0.004 0.776 0.004 0.188
#> GSM601852 1 0.2848 0.70787 0.816 0.000 0.008 0.000 0.000 0.176
#> GSM601862 1 0.3678 0.52531 0.748 0.000 0.228 0.016 0.008 0.000
#> GSM601753 4 0.0865 0.80436 0.000 0.036 0.000 0.964 0.000 0.000
#> GSM601783 1 0.4256 0.47730 0.520 0.000 0.016 0.000 0.000 0.464
#> GSM601793 4 0.4846 -0.02897 0.000 0.000 0.012 0.496 0.032 0.460
#> GSM601798 4 0.0865 0.80436 0.000 0.036 0.000 0.964 0.000 0.000
#> GSM601828 1 0.1498 0.70582 0.940 0.000 0.028 0.000 0.000 0.032
#> GSM601838 5 0.0865 0.74887 0.000 0.036 0.000 0.000 0.964 0.000
#> GSM601843 2 0.0603 0.84027 0.000 0.980 0.000 0.016 0.000 0.004
#> GSM601858 5 0.4033 0.40273 0.000 0.404 0.004 0.004 0.588 0.000
#> GSM601868 1 0.3828 0.49955 0.724 0.000 0.252 0.016 0.008 0.000
#> GSM601748 1 0.1867 0.71535 0.916 0.000 0.020 0.000 0.000 0.064
#> GSM601758 1 0.4116 0.53250 0.572 0.000 0.012 0.000 0.000 0.416
#> GSM601763 6 0.1570 0.67201 0.004 0.028 0.016 0.008 0.000 0.944
#> GSM601768 2 0.0146 0.84171 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM601773 2 0.0777 0.83765 0.000 0.972 0.000 0.024 0.004 0.000
#> GSM601778 6 0.4942 0.19726 0.008 0.000 0.008 0.412 0.032 0.540
#> GSM601788 2 0.3594 0.73453 0.048 0.820 0.000 0.028 0.000 0.104
#> GSM601803 4 0.0865 0.80436 0.000 0.036 0.000 0.964 0.000 0.000
#> GSM601808 1 0.3744 0.49771 0.724 0.000 0.256 0.016 0.004 0.000
#> GSM601813 1 0.3528 0.65475 0.700 0.000 0.004 0.000 0.000 0.296
#> GSM601818 1 0.2350 0.67225 0.896 0.000 0.076 0.016 0.004 0.008
#> GSM601823 6 0.4594 0.37350 0.000 0.000 0.008 0.360 0.032 0.600
#> GSM601833 2 0.0146 0.84171 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM601848 6 0.4444 0.46517 0.000 0.000 0.008 0.316 0.032 0.644
#> GSM601853 1 0.3812 0.48468 0.712 0.000 0.268 0.016 0.004 0.000
#> GSM601863 1 0.3459 0.55244 0.768 0.000 0.212 0.016 0.004 0.000
#> GSM601754 4 0.1010 0.80360 0.000 0.036 0.004 0.960 0.000 0.000
#> GSM601784 2 0.0291 0.84114 0.000 0.992 0.000 0.004 0.004 0.000
#> GSM601794 4 0.4844 -0.02006 0.000 0.000 0.012 0.500 0.032 0.456
#> GSM601799 4 0.0865 0.80436 0.000 0.036 0.000 0.964 0.000 0.000
#> GSM601829 6 0.5850 0.44380 0.108 0.000 0.012 0.268 0.024 0.588
#> GSM601839 5 0.0865 0.74887 0.000 0.036 0.000 0.000 0.964 0.000
#> GSM601844 6 0.1325 0.67886 0.016 0.000 0.012 0.012 0.004 0.956
#> GSM601859 2 0.0146 0.84171 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM601869 1 0.1285 0.68408 0.944 0.000 0.052 0.000 0.004 0.000
#> GSM601749 1 0.4062 0.51402 0.552 0.000 0.008 0.000 0.000 0.440
#> GSM601759 1 0.3101 0.67085 0.756 0.000 0.000 0.000 0.000 0.244
#> GSM601764 6 0.3964 0.56680 0.068 0.120 0.016 0.004 0.000 0.792
#> GSM601769 2 0.1910 0.76154 0.000 0.892 0.000 0.000 0.108 0.000
#> GSM601774 2 0.0291 0.84062 0.000 0.992 0.000 0.004 0.004 0.000
#> GSM601779 6 0.3698 0.61818 0.008 0.000 0.008 0.196 0.016 0.772
#> GSM601789 2 0.3907 0.13934 0.000 0.588 0.000 0.004 0.408 0.000
#> GSM601804 4 0.3904 0.54368 0.000 0.032 0.000 0.732 0.004 0.232
#> GSM601809 1 0.3988 0.68615 0.816 0.080 0.024 0.016 0.004 0.060
#> GSM601814 2 0.2562 0.69351 0.000 0.828 0.000 0.000 0.172 0.000
#> GSM601819 1 0.4467 0.38761 0.496 0.004 0.020 0.000 0.000 0.480
#> GSM601824 6 0.4387 0.35560 0.000 0.020 0.000 0.404 0.004 0.572
#> GSM601834 2 0.0146 0.84171 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM601849 6 0.0837 0.67289 0.020 0.000 0.004 0.004 0.000 0.972
#> GSM601854 1 0.2212 0.71896 0.880 0.000 0.008 0.000 0.000 0.112
#> GSM601864 5 0.0865 0.74887 0.000 0.036 0.000 0.000 0.964 0.000
#> GSM601755 4 0.0865 0.80436 0.000 0.036 0.000 0.964 0.000 0.000
#> GSM601785 2 0.1719 0.81523 0.000 0.924 0.000 0.016 0.000 0.060
#> GSM601795 4 0.4844 -0.01468 0.000 0.000 0.012 0.500 0.032 0.456
#> GSM601800 4 0.0865 0.80436 0.000 0.036 0.000 0.964 0.000 0.000
#> GSM601830 3 0.1007 0.77689 0.044 0.000 0.956 0.000 0.000 0.000
#> GSM601840 2 0.4963 0.56112 0.032 0.672 0.000 0.048 0.004 0.244
#> GSM601845 6 0.6128 0.28113 0.020 0.348 0.000 0.144 0.004 0.484
#> GSM601860 2 0.0146 0.84171 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM601870 3 0.1563 0.75100 0.012 0.000 0.932 0.000 0.056 0.000
#> GSM601750 1 0.2092 0.71528 0.876 0.000 0.000 0.000 0.000 0.124
#> GSM601760 1 0.4237 0.53739 0.584 0.000 0.020 0.000 0.000 0.396
#> GSM601765 2 0.0547 0.83925 0.000 0.980 0.000 0.020 0.000 0.000
#> GSM601770 2 0.0146 0.84171 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM601775 6 0.4334 0.63414 0.000 0.108 0.020 0.092 0.008 0.772
#> GSM601780 6 0.1728 0.68646 0.008 0.000 0.004 0.064 0.000 0.924
#> GSM601790 5 0.1610 0.73980 0.000 0.084 0.000 0.000 0.916 0.000
#> GSM601805 4 0.1010 0.80360 0.000 0.036 0.004 0.960 0.000 0.000
#> GSM601810 1 0.2084 0.69923 0.916 0.000 0.024 0.016 0.000 0.044
#> GSM601815 5 0.3390 0.60971 0.000 0.296 0.000 0.000 0.704 0.000
#> GSM601820 1 0.3360 0.66807 0.732 0.000 0.004 0.000 0.000 0.264
#> GSM601825 4 0.1010 0.80360 0.000 0.036 0.004 0.960 0.000 0.000
#> GSM601835 2 0.5957 0.38167 0.024 0.560 0.000 0.320 0.060 0.036
#> GSM601850 6 0.2757 0.66590 0.000 0.008 0.004 0.136 0.004 0.848
#> GSM601855 3 0.0790 0.77537 0.032 0.000 0.968 0.000 0.000 0.000
#> GSM601865 5 0.1007 0.74963 0.000 0.044 0.000 0.000 0.956 0.000
#> GSM601756 4 0.0865 0.80436 0.000 0.036 0.000 0.964 0.000 0.000
#> GSM601786 5 0.3489 0.61930 0.000 0.288 0.000 0.004 0.708 0.000
#> GSM601796 6 0.4849 0.01599 0.000 0.000 0.012 0.476 0.032 0.480
#> GSM601801 4 0.0865 0.80436 0.000 0.036 0.000 0.964 0.000 0.000
#> GSM601831 1 0.2833 0.69230 0.860 0.000 0.024 0.012 0.000 0.104
#> GSM601841 1 0.4169 0.48092 0.532 0.000 0.012 0.000 0.000 0.456
#> GSM601846 4 0.3430 0.69070 0.004 0.008 0.008 0.828 0.028 0.124
#> GSM601861 2 0.3706 0.28110 0.000 0.620 0.000 0.000 0.380 0.000
#> GSM601871 5 0.5718 -0.40870 0.072 0.008 0.436 0.020 0.464 0.000
#> GSM601751 2 0.3923 0.68464 0.000 0.772 0.000 0.080 0.004 0.144
#> GSM601761 6 0.3261 0.39513 0.204 0.000 0.016 0.000 0.000 0.780
#> GSM601766 2 0.3862 0.35980 0.000 0.608 0.000 0.004 0.000 0.388
#> GSM601771 2 0.0603 0.83856 0.000 0.980 0.000 0.004 0.000 0.016
#> GSM601776 6 0.2317 0.67603 0.008 0.000 0.008 0.088 0.004 0.892
#> GSM601781 6 0.3949 0.56430 0.004 0.012 0.008 0.224 0.008 0.744
#> GSM601791 6 0.1074 0.66345 0.028 0.000 0.012 0.000 0.000 0.960
#> GSM601806 4 0.1320 0.79326 0.000 0.036 0.000 0.948 0.016 0.000
#> GSM601811 1 0.2865 0.63029 0.848 0.000 0.128 0.016 0.004 0.004
#> GSM601816 6 0.4847 0.06496 0.000 0.000 0.012 0.464 0.032 0.492
#> GSM601821 2 0.3810 0.12902 0.000 0.572 0.000 0.000 0.428 0.000
#> GSM601826 6 0.4518 0.42742 0.000 0.000 0.008 0.336 0.032 0.624
#> GSM601836 6 0.3636 0.59703 0.080 0.068 0.016 0.004 0.004 0.828
#> GSM601851 6 0.0820 0.68422 0.012 0.000 0.000 0.016 0.000 0.972
#> GSM601856 1 0.3756 0.52051 0.736 0.000 0.240 0.016 0.000 0.008
#> GSM601866 1 0.2928 0.70218 0.856 0.000 0.084 0.000 0.004 0.056
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 time(p) gender(p) k
#> SD:mclust 110 0.906 0.03019 2
#> SD:mclust 113 0.908 0.27900 3
#> SD:mclust 102 0.376 0.05727 4
#> SD:mclust 93 0.504 0.00652 5
#> SD:mclust 94 0.452 0.03278 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 125 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 0.853 0.911 0.964 0.5027 0.496 0.496
#> 3 3 0.473 0.632 0.759 0.3045 0.797 0.613
#> 4 4 0.442 0.498 0.710 0.1258 0.786 0.473
#> 5 5 0.500 0.397 0.594 0.0703 0.891 0.612
#> 6 6 0.529 0.366 0.588 0.0450 0.854 0.439
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
#> GSM601752 2 0.0000 0.9613 0.000 1.000
#> GSM601782 1 0.0000 0.9614 1.000 0.000
#> GSM601792 1 0.0000 0.9614 1.000 0.000
#> GSM601797 2 0.6438 0.7947 0.164 0.836
#> GSM601827 1 0.0000 0.9614 1.000 0.000
#> GSM601837 2 0.0000 0.9613 0.000 1.000
#> GSM601842 2 0.0000 0.9613 0.000 1.000
#> GSM601857 1 0.0000 0.9614 1.000 0.000
#> GSM601867 2 0.2236 0.9333 0.036 0.964
#> GSM601747 1 0.5294 0.8529 0.880 0.120
#> GSM601757 1 0.0000 0.9614 1.000 0.000
#> GSM601762 2 0.0000 0.9613 0.000 1.000
#> GSM601767 2 0.0000 0.9613 0.000 1.000
#> GSM601772 2 0.0000 0.9613 0.000 1.000
#> GSM601777 2 0.9866 0.2352 0.432 0.568
#> GSM601787 2 0.0000 0.9613 0.000 1.000
#> GSM601802 2 0.0000 0.9613 0.000 1.000
#> GSM601807 1 0.9944 0.1693 0.544 0.456
#> GSM601812 1 0.0000 0.9614 1.000 0.000
#> GSM601817 1 0.0000 0.9614 1.000 0.000
#> GSM601822 1 0.9983 0.0730 0.524 0.476
#> GSM601832 2 0.0000 0.9613 0.000 1.000
#> GSM601847 2 0.0672 0.9560 0.008 0.992
#> GSM601852 1 0.0000 0.9614 1.000 0.000
#> GSM601862 1 0.0000 0.9614 1.000 0.000
#> GSM601753 2 0.0000 0.9613 0.000 1.000
#> GSM601783 1 0.0000 0.9614 1.000 0.000
#> GSM601793 1 0.0000 0.9614 1.000 0.000
#> GSM601798 2 0.0000 0.9613 0.000 1.000
#> GSM601828 1 0.0000 0.9614 1.000 0.000
#> GSM601838 2 0.0000 0.9613 0.000 1.000
#> GSM601843 2 0.0000 0.9613 0.000 1.000
#> GSM601858 2 0.0000 0.9613 0.000 1.000
#> GSM601868 1 0.0000 0.9614 1.000 0.000
#> GSM601748 1 0.0000 0.9614 1.000 0.000
#> GSM601758 1 0.0000 0.9614 1.000 0.000
#> GSM601763 1 0.7528 0.7218 0.784 0.216
#> GSM601768 2 0.0000 0.9613 0.000 1.000
#> GSM601773 2 0.0000 0.9613 0.000 1.000
#> GSM601778 1 0.2423 0.9306 0.960 0.040
#> GSM601788 2 0.0376 0.9587 0.004 0.996
#> GSM601803 2 0.0000 0.9613 0.000 1.000
#> GSM601808 1 0.0000 0.9614 1.000 0.000
#> GSM601813 1 0.0000 0.9614 1.000 0.000
#> GSM601818 1 0.0000 0.9614 1.000 0.000
#> GSM601823 1 0.0000 0.9614 1.000 0.000
#> GSM601833 2 0.0000 0.9613 0.000 1.000
#> GSM601848 1 0.0000 0.9614 1.000 0.000
#> GSM601853 1 0.0000 0.9614 1.000 0.000
#> GSM601863 1 0.0000 0.9614 1.000 0.000
#> GSM601754 2 0.0000 0.9613 0.000 1.000
#> GSM601784 2 0.0000 0.9613 0.000 1.000
#> GSM601794 1 0.0376 0.9587 0.996 0.004
#> GSM601799 2 0.0000 0.9613 0.000 1.000
#> GSM601829 1 0.0000 0.9614 1.000 0.000
#> GSM601839 2 0.0000 0.9613 0.000 1.000
#> GSM601844 1 0.0000 0.9614 1.000 0.000
#> GSM601859 2 0.0000 0.9613 0.000 1.000
#> GSM601869 1 0.0000 0.9614 1.000 0.000
#> GSM601749 1 0.0000 0.9614 1.000 0.000
#> GSM601759 1 0.0000 0.9614 1.000 0.000
#> GSM601764 1 0.0376 0.9587 0.996 0.004
#> GSM601769 2 0.0000 0.9613 0.000 1.000
#> GSM601774 2 0.0000 0.9613 0.000 1.000
#> GSM601779 1 0.0000 0.9614 1.000 0.000
#> GSM601789 2 0.0000 0.9613 0.000 1.000
#> GSM601804 2 0.2236 0.9333 0.036 0.964
#> GSM601809 1 0.8813 0.5791 0.700 0.300
#> GSM601814 2 0.0000 0.9613 0.000 1.000
#> GSM601819 1 0.0000 0.9614 1.000 0.000
#> GSM601824 2 0.9087 0.5265 0.324 0.676
#> GSM601834 2 0.0000 0.9613 0.000 1.000
#> GSM601849 1 0.0000 0.9614 1.000 0.000
#> GSM601854 1 0.0000 0.9614 1.000 0.000
#> GSM601864 2 0.0000 0.9613 0.000 1.000
#> GSM601755 2 0.0000 0.9613 0.000 1.000
#> GSM601785 2 0.0000 0.9613 0.000 1.000
#> GSM601795 1 0.5294 0.8552 0.880 0.120
#> GSM601800 2 0.0000 0.9613 0.000 1.000
#> GSM601830 1 0.7139 0.7540 0.804 0.196
#> GSM601840 2 0.1633 0.9440 0.024 0.976
#> GSM601845 2 0.7950 0.6783 0.240 0.760
#> GSM601860 2 0.0000 0.9613 0.000 1.000
#> GSM601870 2 1.0000 -0.0237 0.496 0.504
#> GSM601750 1 0.0000 0.9614 1.000 0.000
#> GSM601760 1 0.0000 0.9614 1.000 0.000
#> GSM601765 2 0.0000 0.9613 0.000 1.000
#> GSM601770 2 0.0000 0.9613 0.000 1.000
#> GSM601775 2 0.7883 0.6912 0.236 0.764
#> GSM601780 1 0.0000 0.9614 1.000 0.000
#> GSM601790 2 0.0000 0.9613 0.000 1.000
#> GSM601805 2 0.0000 0.9613 0.000 1.000
#> GSM601810 1 0.0000 0.9614 1.000 0.000
#> GSM601815 2 0.0000 0.9613 0.000 1.000
#> GSM601820 1 0.0000 0.9614 1.000 0.000
#> GSM601825 2 0.0000 0.9613 0.000 1.000
#> GSM601835 2 0.0000 0.9613 0.000 1.000
#> GSM601850 1 0.5294 0.8550 0.880 0.120
#> GSM601855 1 0.0672 0.9559 0.992 0.008
#> GSM601865 2 0.0000 0.9613 0.000 1.000
#> GSM601756 2 0.0000 0.9613 0.000 1.000
#> GSM601786 2 0.0000 0.9613 0.000 1.000
#> GSM601796 1 0.0000 0.9614 1.000 0.000
#> GSM601801 2 0.0000 0.9613 0.000 1.000
#> GSM601831 1 0.0000 0.9614 1.000 0.000
#> GSM601841 1 0.0000 0.9614 1.000 0.000
#> GSM601846 2 0.1414 0.9471 0.020 0.980
#> GSM601861 2 0.0000 0.9613 0.000 1.000
#> GSM601871 2 0.0000 0.9613 0.000 1.000
#> GSM601751 2 0.0938 0.9532 0.012 0.988
#> GSM601761 1 0.0000 0.9614 1.000 0.000
#> GSM601766 2 0.7950 0.6860 0.240 0.760
#> GSM601771 2 0.0376 0.9587 0.004 0.996
#> GSM601776 1 0.0000 0.9614 1.000 0.000
#> GSM601781 1 0.6148 0.8171 0.848 0.152
#> GSM601791 1 0.0000 0.9614 1.000 0.000
#> GSM601806 2 0.0000 0.9613 0.000 1.000
#> GSM601811 1 0.0000 0.9614 1.000 0.000
#> GSM601816 1 0.0000 0.9614 1.000 0.000
#> GSM601821 2 0.0000 0.9613 0.000 1.000
#> GSM601826 1 0.0000 0.9614 1.000 0.000
#> GSM601836 1 0.1843 0.9403 0.972 0.028
#> GSM601851 1 0.0000 0.9614 1.000 0.000
#> GSM601856 1 0.0000 0.9614 1.000 0.000
#> GSM601866 1 0.0000 0.9614 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601752 2 0.6252 0.5806 0.344 0.648 0.008
#> GSM601782 3 0.5529 0.6534 0.296 0.000 0.704
#> GSM601792 1 0.2998 0.7261 0.916 0.016 0.068
#> GSM601797 2 0.6756 0.7017 0.232 0.712 0.056
#> GSM601827 3 0.5397 0.6724 0.280 0.000 0.720
#> GSM601837 2 0.5497 0.6026 0.000 0.708 0.292
#> GSM601842 2 0.2031 0.8066 0.032 0.952 0.016
#> GSM601857 3 0.3267 0.7382 0.116 0.000 0.884
#> GSM601867 3 0.4842 0.5434 0.000 0.224 0.776
#> GSM601747 1 0.7610 0.0369 0.536 0.044 0.420
#> GSM601757 3 0.6244 0.4224 0.440 0.000 0.560
#> GSM601762 2 0.3116 0.7648 0.000 0.892 0.108
#> GSM601767 2 0.3192 0.8010 0.112 0.888 0.000
#> GSM601772 2 0.2527 0.8087 0.044 0.936 0.020
#> GSM601777 3 0.7465 0.5082 0.072 0.272 0.656
#> GSM601787 3 0.5497 0.4230 0.000 0.292 0.708
#> GSM601802 2 0.4796 0.7368 0.220 0.780 0.000
#> GSM601807 3 0.4346 0.5783 0.000 0.184 0.816
#> GSM601812 3 0.5397 0.6737 0.280 0.000 0.720
#> GSM601817 3 0.4346 0.7281 0.184 0.000 0.816
#> GSM601822 1 0.6448 0.2582 0.636 0.352 0.012
#> GSM601832 2 0.3207 0.8096 0.084 0.904 0.012
#> GSM601847 2 0.6192 0.4303 0.420 0.580 0.000
#> GSM601852 3 0.6045 0.5441 0.380 0.000 0.620
#> GSM601862 3 0.2959 0.7356 0.100 0.000 0.900
#> GSM601753 2 0.5327 0.6825 0.272 0.728 0.000
#> GSM601783 1 0.5327 0.4253 0.728 0.000 0.272
#> GSM601793 1 0.2860 0.7006 0.912 0.004 0.084
#> GSM601798 2 0.3091 0.8103 0.072 0.912 0.016
#> GSM601828 3 0.5733 0.6263 0.324 0.000 0.676
#> GSM601838 2 0.4750 0.6876 0.000 0.784 0.216
#> GSM601843 2 0.2537 0.7778 0.000 0.920 0.080
#> GSM601858 2 0.6192 0.3696 0.000 0.580 0.420
#> GSM601868 3 0.2680 0.7244 0.068 0.008 0.924
#> GSM601748 3 0.5621 0.6450 0.308 0.000 0.692
#> GSM601758 1 0.5560 0.3590 0.700 0.000 0.300
#> GSM601763 1 0.5517 0.4669 0.728 0.268 0.004
#> GSM601768 2 0.4399 0.7627 0.188 0.812 0.000
#> GSM601773 2 0.2356 0.8080 0.072 0.928 0.000
#> GSM601778 1 0.4270 0.6940 0.860 0.024 0.116
#> GSM601788 2 0.3482 0.7557 0.000 0.872 0.128
#> GSM601803 2 0.3482 0.7952 0.128 0.872 0.000
#> GSM601808 3 0.2959 0.7360 0.100 0.000 0.900
#> GSM601813 1 0.6286 -0.1774 0.536 0.000 0.464
#> GSM601818 3 0.3816 0.7364 0.148 0.000 0.852
#> GSM601823 1 0.2200 0.7230 0.940 0.056 0.004
#> GSM601833 2 0.2063 0.8088 0.044 0.948 0.008
#> GSM601848 1 0.1482 0.7343 0.968 0.020 0.012
#> GSM601853 3 0.3192 0.7374 0.112 0.000 0.888
#> GSM601863 3 0.4346 0.7275 0.184 0.000 0.816
#> GSM601754 2 0.5497 0.6572 0.292 0.708 0.000
#> GSM601784 2 0.0592 0.7986 0.000 0.988 0.012
#> GSM601794 1 0.2486 0.7244 0.932 0.008 0.060
#> GSM601799 2 0.5905 0.5671 0.352 0.648 0.000
#> GSM601829 3 0.6260 0.4052 0.448 0.000 0.552
#> GSM601839 2 0.5291 0.6305 0.000 0.732 0.268
#> GSM601844 1 0.2496 0.7139 0.928 0.004 0.068
#> GSM601859 2 0.4399 0.7627 0.188 0.812 0.000
#> GSM601869 3 0.4842 0.7112 0.224 0.000 0.776
#> GSM601749 1 0.5810 0.2650 0.664 0.000 0.336
#> GSM601759 3 0.6307 0.2967 0.488 0.000 0.512
#> GSM601764 1 0.2866 0.7159 0.916 0.076 0.008
#> GSM601769 2 0.1337 0.8009 0.012 0.972 0.016
#> GSM601774 2 0.1989 0.8089 0.048 0.948 0.004
#> GSM601779 1 0.3192 0.6911 0.888 0.112 0.000
#> GSM601789 2 0.4178 0.7251 0.000 0.828 0.172
#> GSM601804 2 0.6309 0.2134 0.500 0.500 0.000
#> GSM601809 3 0.3875 0.6876 0.044 0.068 0.888
#> GSM601814 2 0.1877 0.8064 0.032 0.956 0.012
#> GSM601819 1 0.3682 0.6720 0.876 0.008 0.116
#> GSM601824 1 0.5948 0.2413 0.640 0.360 0.000
#> GSM601834 2 0.2711 0.8062 0.088 0.912 0.000
#> GSM601849 1 0.1860 0.7233 0.948 0.000 0.052
#> GSM601854 3 0.6215 0.4518 0.428 0.000 0.572
#> GSM601864 2 0.5591 0.5828 0.000 0.696 0.304
#> GSM601755 2 0.3896 0.7971 0.128 0.864 0.008
#> GSM601785 2 0.4178 0.7728 0.172 0.828 0.000
#> GSM601795 1 0.4887 0.5493 0.772 0.228 0.000
#> GSM601800 2 0.4842 0.7336 0.224 0.776 0.000
#> GSM601830 3 0.2625 0.6490 0.000 0.084 0.916
#> GSM601840 2 0.5191 0.7928 0.112 0.828 0.060
#> GSM601845 2 0.6908 0.5889 0.308 0.656 0.036
#> GSM601860 2 0.4291 0.7697 0.180 0.820 0.000
#> GSM601870 3 0.4555 0.5653 0.000 0.200 0.800
#> GSM601750 3 0.5988 0.5644 0.368 0.000 0.632
#> GSM601760 1 0.3851 0.6460 0.860 0.004 0.136
#> GSM601765 2 0.2878 0.8048 0.096 0.904 0.000
#> GSM601770 2 0.2356 0.8086 0.072 0.928 0.000
#> GSM601775 1 0.6299 -0.1748 0.524 0.476 0.000
#> GSM601780 1 0.2066 0.7201 0.940 0.060 0.000
#> GSM601790 2 0.4002 0.7343 0.000 0.840 0.160
#> GSM601805 2 0.4605 0.7505 0.204 0.796 0.000
#> GSM601810 3 0.3816 0.7361 0.148 0.000 0.852
#> GSM601815 2 0.3619 0.7510 0.000 0.864 0.136
#> GSM601820 1 0.6295 -0.2118 0.528 0.000 0.472
#> GSM601825 2 0.3551 0.7939 0.132 0.868 0.000
#> GSM601835 2 0.5810 0.5342 0.000 0.664 0.336
#> GSM601850 1 0.4605 0.5877 0.796 0.204 0.000
#> GSM601855 3 0.2165 0.6610 0.000 0.064 0.936
#> GSM601865 2 0.5760 0.5471 0.000 0.672 0.328
#> GSM601756 2 0.3682 0.8011 0.116 0.876 0.008
#> GSM601786 2 0.4796 0.6824 0.000 0.780 0.220
#> GSM601796 1 0.1620 0.7343 0.964 0.024 0.012
#> GSM601801 2 0.2229 0.8094 0.044 0.944 0.012
#> GSM601831 3 0.4931 0.7066 0.232 0.000 0.768
#> GSM601841 1 0.6302 -0.2177 0.520 0.000 0.480
#> GSM601846 2 0.5244 0.6618 0.004 0.756 0.240
#> GSM601861 2 0.1860 0.7866 0.000 0.948 0.052
#> GSM601871 3 0.5529 0.4146 0.000 0.296 0.704
#> GSM601751 2 0.5098 0.7134 0.248 0.752 0.000
#> GSM601761 1 0.2400 0.7172 0.932 0.004 0.064
#> GSM601766 2 0.6669 0.2818 0.468 0.524 0.008
#> GSM601771 2 0.3619 0.7928 0.136 0.864 0.000
#> GSM601776 1 0.1315 0.7346 0.972 0.008 0.020
#> GSM601781 1 0.3551 0.6806 0.868 0.132 0.000
#> GSM601791 1 0.1525 0.7314 0.964 0.032 0.004
#> GSM601806 2 0.1529 0.8096 0.040 0.960 0.000
#> GSM601811 3 0.2625 0.7323 0.084 0.000 0.916
#> GSM601816 1 0.1411 0.7282 0.964 0.000 0.036
#> GSM601821 2 0.1860 0.7868 0.000 0.948 0.052
#> GSM601826 1 0.1129 0.7340 0.976 0.004 0.020
#> GSM601836 1 0.5094 0.6818 0.824 0.040 0.136
#> GSM601851 1 0.1919 0.7355 0.956 0.020 0.024
#> GSM601856 3 0.2165 0.7253 0.064 0.000 0.936
#> GSM601866 3 0.5178 0.6922 0.256 0.000 0.744
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601752 4 0.423 0.6393 0.024 0.168 0.004 0.804
#> GSM601782 1 0.594 -0.1366 0.500 0.004 0.468 0.028
#> GSM601792 4 0.447 0.5189 0.172 0.000 0.040 0.788
#> GSM601797 4 0.477 0.6078 0.004 0.084 0.116 0.796
#> GSM601827 3 0.659 0.5010 0.216 0.000 0.628 0.156
#> GSM601837 2 0.588 0.6346 0.000 0.680 0.232 0.088
#> GSM601842 2 0.440 0.7560 0.012 0.816 0.036 0.136
#> GSM601857 3 0.499 0.5726 0.288 0.020 0.692 0.000
#> GSM601867 3 0.402 0.5587 0.008 0.156 0.820 0.016
#> GSM601747 1 0.814 0.2025 0.528 0.248 0.180 0.044
#> GSM601757 1 0.513 0.3538 0.700 0.012 0.276 0.012
#> GSM601762 2 0.435 0.7581 0.004 0.824 0.080 0.092
#> GSM601767 2 0.292 0.7726 0.044 0.896 0.000 0.060
#> GSM601772 2 0.318 0.7819 0.068 0.892 0.016 0.024
#> GSM601777 4 0.725 0.2393 0.016 0.096 0.388 0.500
#> GSM601787 3 0.478 0.4747 0.004 0.248 0.732 0.016
#> GSM601802 4 0.496 0.5741 0.020 0.284 0.000 0.696
#> GSM601807 3 0.487 0.5050 0.008 0.044 0.776 0.172
#> GSM601812 3 0.586 0.2825 0.432 0.008 0.540 0.020
#> GSM601817 3 0.579 0.4673 0.360 0.016 0.608 0.016
#> GSM601822 4 0.449 0.6341 0.076 0.096 0.008 0.820
#> GSM601832 2 0.464 0.7398 0.032 0.804 0.020 0.144
#> GSM601847 4 0.545 0.6367 0.080 0.196 0.000 0.724
#> GSM601852 1 0.662 -0.0810 0.484 0.008 0.448 0.060
#> GSM601862 3 0.483 0.5944 0.264 0.020 0.716 0.000
#> GSM601753 4 0.579 0.5226 0.048 0.324 0.000 0.628
#> GSM601783 1 0.430 0.5596 0.820 0.000 0.088 0.092
#> GSM601793 4 0.466 0.4997 0.208 0.000 0.032 0.760
#> GSM601798 4 0.542 0.5569 0.000 0.240 0.056 0.704
#> GSM601828 3 0.631 0.1864 0.460 0.004 0.488 0.048
#> GSM601838 2 0.543 0.6852 0.000 0.736 0.164 0.100
#> GSM601843 2 0.400 0.7685 0.004 0.844 0.064 0.088
#> GSM601858 2 0.518 0.6727 0.032 0.748 0.204 0.016
#> GSM601868 3 0.446 0.6265 0.208 0.024 0.768 0.000
#> GSM601748 1 0.576 -0.1365 0.516 0.004 0.460 0.020
#> GSM601758 1 0.299 0.5415 0.876 0.000 0.112 0.012
#> GSM601763 1 0.712 0.1808 0.564 0.220 0.000 0.216
#> GSM601768 2 0.361 0.7457 0.132 0.844 0.000 0.024
#> GSM601773 2 0.372 0.7454 0.016 0.844 0.008 0.132
#> GSM601778 4 0.527 0.5216 0.140 0.016 0.072 0.772
#> GSM601788 2 0.365 0.7652 0.000 0.856 0.092 0.052
#> GSM601803 4 0.522 0.4051 0.000 0.380 0.012 0.608
#> GSM601808 3 0.353 0.6403 0.152 0.000 0.836 0.012
#> GSM601813 1 0.620 0.3210 0.612 0.000 0.312 0.076
#> GSM601818 3 0.634 0.3932 0.404 0.040 0.544 0.012
#> GSM601823 4 0.568 0.1471 0.456 0.024 0.000 0.520
#> GSM601833 2 0.182 0.7849 0.020 0.944 0.000 0.036
#> GSM601848 4 0.529 0.1232 0.480 0.000 0.008 0.512
#> GSM601853 3 0.371 0.6390 0.140 0.000 0.836 0.024
#> GSM601863 3 0.492 0.5131 0.328 0.004 0.664 0.004
#> GSM601754 4 0.526 0.5840 0.036 0.272 0.000 0.692
#> GSM601784 2 0.193 0.7822 0.004 0.936 0.004 0.056
#> GSM601794 4 0.444 0.5333 0.148 0.000 0.052 0.800
#> GSM601799 4 0.631 0.5683 0.092 0.288 0.000 0.620
#> GSM601829 3 0.778 0.0665 0.264 0.000 0.424 0.312
#> GSM601839 2 0.500 0.6859 0.000 0.748 0.200 0.052
#> GSM601844 1 0.555 0.4589 0.672 0.012 0.024 0.292
#> GSM601859 2 0.383 0.7647 0.080 0.848 0.000 0.072
#> GSM601869 3 0.552 0.3575 0.412 0.000 0.568 0.020
#> GSM601749 1 0.454 0.5342 0.796 0.000 0.144 0.060
#> GSM601759 1 0.465 0.4518 0.776 0.012 0.192 0.020
#> GSM601764 1 0.480 0.5127 0.780 0.148 0.000 0.072
#> GSM601769 2 0.163 0.7860 0.024 0.952 0.000 0.024
#> GSM601774 2 0.258 0.7787 0.036 0.912 0.000 0.052
#> GSM601779 1 0.606 0.0263 0.552 0.048 0.000 0.400
#> GSM601789 2 0.278 0.7726 0.016 0.904 0.072 0.008
#> GSM601804 4 0.693 0.5751 0.172 0.244 0.000 0.584
#> GSM601809 3 0.854 0.1721 0.312 0.316 0.348 0.024
#> GSM601814 2 0.265 0.7693 0.004 0.888 0.000 0.108
#> GSM601819 1 0.364 0.5482 0.872 0.076 0.028 0.024
#> GSM601824 1 0.776 -0.3425 0.388 0.236 0.000 0.376
#> GSM601834 2 0.252 0.7766 0.024 0.912 0.000 0.064
#> GSM601849 1 0.454 0.5450 0.768 0.004 0.020 0.208
#> GSM601854 1 0.552 0.1723 0.596 0.000 0.380 0.024
#> GSM601864 2 0.640 0.5912 0.004 0.640 0.256 0.100
#> GSM601755 4 0.511 0.5595 0.000 0.264 0.032 0.704
#> GSM601785 2 0.410 0.7348 0.048 0.824 0.000 0.128
#> GSM601795 4 0.468 0.5867 0.176 0.048 0.000 0.776
#> GSM601800 4 0.486 0.5704 0.016 0.284 0.000 0.700
#> GSM601830 3 0.383 0.5847 0.012 0.036 0.856 0.096
#> GSM601840 2 0.730 0.5486 0.096 0.628 0.056 0.220
#> GSM601845 2 0.862 0.2266 0.172 0.480 0.068 0.280
#> GSM601860 2 0.466 0.7174 0.160 0.784 0.000 0.056
#> GSM601870 3 0.346 0.5725 0.000 0.076 0.868 0.056
#> GSM601750 1 0.514 0.1015 0.600 0.000 0.392 0.008
#> GSM601760 1 0.399 0.5464 0.860 0.056 0.056 0.028
#> GSM601765 2 0.363 0.7735 0.048 0.868 0.008 0.076
#> GSM601770 2 0.280 0.7752 0.068 0.900 0.000 0.032
#> GSM601775 2 0.790 -0.2954 0.308 0.368 0.000 0.324
#> GSM601780 1 0.534 0.4262 0.708 0.052 0.000 0.240
#> GSM601790 2 0.277 0.7707 0.000 0.900 0.072 0.028
#> GSM601805 4 0.549 0.3760 0.020 0.400 0.000 0.580
#> GSM601810 3 0.414 0.6412 0.160 0.004 0.812 0.024
#> GSM601815 2 0.274 0.7730 0.000 0.904 0.060 0.036
#> GSM601820 1 0.446 0.4515 0.772 0.008 0.208 0.012
#> GSM601825 2 0.551 0.1231 0.012 0.560 0.004 0.424
#> GSM601835 2 0.732 0.4585 0.008 0.532 0.320 0.140
#> GSM601850 4 0.717 0.3003 0.400 0.136 0.000 0.464
#> GSM601855 3 0.352 0.5786 0.004 0.040 0.868 0.088
#> GSM601865 2 0.490 0.6850 0.008 0.760 0.200 0.032
#> GSM601756 4 0.511 0.5271 0.000 0.292 0.024 0.684
#> GSM601786 2 0.497 0.7441 0.048 0.808 0.096 0.048
#> GSM601796 4 0.484 0.4840 0.240 0.000 0.028 0.732
#> GSM601801 4 0.611 0.4303 0.000 0.332 0.064 0.604
#> GSM601831 3 0.553 0.5897 0.192 0.000 0.720 0.088
#> GSM601841 1 0.734 0.2934 0.528 0.000 0.256 0.216
#> GSM601846 4 0.668 0.4379 0.004 0.116 0.268 0.612
#> GSM601861 2 0.210 0.7856 0.012 0.936 0.008 0.044
#> GSM601871 3 0.566 0.4641 0.016 0.240 0.704 0.040
#> GSM601751 2 0.576 0.6404 0.128 0.712 0.000 0.160
#> GSM601761 1 0.247 0.5788 0.924 0.012 0.020 0.044
#> GSM601766 2 0.601 0.4932 0.312 0.624 0.000 0.064
#> GSM601771 2 0.362 0.7740 0.076 0.860 0.000 0.064
#> GSM601776 1 0.464 0.4922 0.740 0.000 0.020 0.240
#> GSM601781 4 0.730 0.3084 0.368 0.124 0.008 0.500
#> GSM601791 1 0.404 0.5696 0.828 0.032 0.004 0.136
#> GSM601806 4 0.586 0.1035 0.000 0.468 0.032 0.500
#> GSM601811 3 0.491 0.6328 0.188 0.032 0.768 0.012
#> GSM601816 4 0.525 0.4086 0.300 0.000 0.028 0.672
#> GSM601821 2 0.214 0.7850 0.008 0.932 0.008 0.052
#> GSM601826 4 0.589 0.0949 0.464 0.008 0.020 0.508
#> GSM601836 1 0.599 0.5597 0.736 0.060 0.048 0.156
#> GSM601851 1 0.457 0.5138 0.760 0.008 0.012 0.220
#> GSM601856 3 0.333 0.6313 0.088 0.000 0.872 0.040
#> GSM601866 1 0.558 -0.1727 0.516 0.008 0.468 0.008
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601752 4 0.245 0.7051 0.004 0.012 0.008 0.904 0.072
#> GSM601782 1 0.672 0.1624 0.500 0.104 0.364 0.016 0.016
#> GSM601792 4 0.534 0.6497 0.092 0.016 0.084 0.756 0.052
#> GSM601797 4 0.353 0.6745 0.000 0.000 0.116 0.828 0.056
#> GSM601827 3 0.798 0.3843 0.116 0.172 0.548 0.108 0.056
#> GSM601837 5 0.749 0.2045 0.000 0.392 0.128 0.084 0.396
#> GSM601842 2 0.300 0.4508 0.004 0.888 0.028 0.032 0.048
#> GSM601857 3 0.595 0.3631 0.352 0.024 0.560 0.000 0.064
#> GSM601867 3 0.565 0.4844 0.028 0.036 0.672 0.020 0.244
#> GSM601747 2 0.602 0.3170 0.272 0.616 0.076 0.000 0.036
#> GSM601757 1 0.540 0.4977 0.712 0.140 0.128 0.004 0.016
#> GSM601762 2 0.525 0.3548 0.000 0.724 0.040 0.068 0.168
#> GSM601767 2 0.481 0.3551 0.012 0.696 0.000 0.036 0.256
#> GSM601772 2 0.317 0.4674 0.024 0.848 0.000 0.004 0.124
#> GSM601777 4 0.696 0.4914 0.016 0.036 0.248 0.568 0.132
#> GSM601787 3 0.591 0.3522 0.044 0.040 0.576 0.000 0.340
#> GSM601802 4 0.400 0.6719 0.004 0.056 0.000 0.796 0.144
#> GSM601807 3 0.548 0.4849 0.004 0.032 0.720 0.132 0.112
#> GSM601812 3 0.552 0.0087 0.460 0.040 0.488 0.000 0.012
#> GSM601817 3 0.715 0.2659 0.232 0.276 0.464 0.000 0.028
#> GSM601822 4 0.354 0.7047 0.036 0.036 0.020 0.868 0.040
#> GSM601832 2 0.219 0.4672 0.020 0.928 0.016 0.028 0.008
#> GSM601847 4 0.436 0.6993 0.044 0.040 0.000 0.796 0.120
#> GSM601852 1 0.735 0.0282 0.404 0.208 0.356 0.004 0.028
#> GSM601862 3 0.581 0.3416 0.356 0.000 0.540 0.000 0.104
#> GSM601753 4 0.514 0.6461 0.020 0.104 0.000 0.728 0.148
#> GSM601783 1 0.365 0.5712 0.852 0.028 0.080 0.032 0.008
#> GSM601793 4 0.494 0.6521 0.108 0.000 0.076 0.764 0.052
#> GSM601798 4 0.422 0.6796 0.000 0.028 0.052 0.804 0.116
#> GSM601828 3 0.746 0.1406 0.308 0.236 0.420 0.004 0.032
#> GSM601838 5 0.696 0.2394 0.000 0.416 0.056 0.100 0.428
#> GSM601843 2 0.404 0.4203 0.000 0.820 0.040 0.040 0.100
#> GSM601858 2 0.611 0.0536 0.020 0.532 0.080 0.000 0.368
#> GSM601868 3 0.578 0.3926 0.308 0.000 0.576 0.000 0.116
#> GSM601748 1 0.627 0.2558 0.536 0.136 0.320 0.000 0.008
#> GSM601758 1 0.355 0.5598 0.848 0.064 0.072 0.000 0.016
#> GSM601763 2 0.722 0.1374 0.324 0.508 0.016 0.096 0.056
#> GSM601768 2 0.485 0.4138 0.056 0.720 0.000 0.012 0.212
#> GSM601773 2 0.592 0.1732 0.000 0.596 0.008 0.116 0.280
#> GSM601778 4 0.495 0.6829 0.088 0.048 0.072 0.780 0.012
#> GSM601788 5 0.665 0.3379 0.000 0.380 0.032 0.108 0.480
#> GSM601803 4 0.509 0.5929 0.000 0.068 0.012 0.700 0.220
#> GSM601808 3 0.452 0.5256 0.200 0.004 0.740 0.000 0.056
#> GSM601813 1 0.462 0.4612 0.712 0.000 0.244 0.036 0.008
#> GSM601818 1 0.733 -0.0777 0.420 0.152 0.372 0.000 0.056
#> GSM601823 4 0.718 0.3461 0.312 0.104 0.008 0.512 0.064
#> GSM601833 2 0.384 0.4363 0.012 0.788 0.000 0.016 0.184
#> GSM601848 4 0.525 0.4705 0.336 0.008 0.004 0.616 0.036
#> GSM601853 3 0.434 0.5473 0.136 0.052 0.792 0.004 0.016
#> GSM601863 3 0.548 0.2997 0.388 0.000 0.544 0.000 0.068
#> GSM601754 4 0.423 0.6841 0.012 0.016 0.008 0.768 0.196
#> GSM601784 2 0.573 0.0546 0.000 0.528 0.004 0.076 0.392
#> GSM601794 4 0.570 0.6660 0.080 0.008 0.104 0.724 0.084
#> GSM601799 4 0.536 0.6862 0.056 0.080 0.004 0.740 0.120
#> GSM601829 3 0.875 0.2255 0.128 0.116 0.440 0.244 0.072
#> GSM601839 2 0.664 -0.1116 0.000 0.496 0.084 0.048 0.372
#> GSM601844 1 0.872 0.3298 0.468 0.100 0.088 0.160 0.184
#> GSM601859 5 0.557 0.4324 0.028 0.308 0.000 0.044 0.620
#> GSM601869 1 0.612 0.0637 0.512 0.000 0.376 0.008 0.104
#> GSM601749 1 0.393 0.5569 0.820 0.008 0.128 0.020 0.024
#> GSM601759 1 0.430 0.5323 0.796 0.056 0.124 0.000 0.024
#> GSM601764 2 0.644 -0.0254 0.412 0.488 0.020 0.016 0.064
#> GSM601769 2 0.512 0.1034 0.004 0.560 0.004 0.024 0.408
#> GSM601774 2 0.485 0.3184 0.008 0.664 0.000 0.032 0.296
#> GSM601779 1 0.616 -0.1651 0.476 0.020 0.000 0.428 0.076
#> GSM601789 2 0.469 0.2270 0.000 0.636 0.020 0.004 0.340
#> GSM601804 4 0.556 0.6837 0.108 0.056 0.000 0.716 0.120
#> GSM601809 5 0.722 -0.2216 0.340 0.028 0.216 0.000 0.416
#> GSM601814 5 0.578 0.4213 0.004 0.324 0.000 0.096 0.576
#> GSM601819 1 0.429 0.5345 0.792 0.084 0.012 0.000 0.112
#> GSM601824 4 0.791 0.3695 0.288 0.164 0.000 0.428 0.120
#> GSM601834 2 0.536 0.1391 0.012 0.564 0.000 0.036 0.388
#> GSM601849 1 0.561 0.5055 0.720 0.048 0.032 0.168 0.032
#> GSM601854 1 0.465 0.3932 0.664 0.008 0.312 0.004 0.012
#> GSM601864 5 0.683 0.4509 0.000 0.164 0.132 0.100 0.604
#> GSM601755 4 0.427 0.6643 0.000 0.044 0.028 0.796 0.132
#> GSM601785 2 0.593 0.2174 0.028 0.572 0.004 0.048 0.348
#> GSM601795 4 0.514 0.6791 0.112 0.004 0.020 0.740 0.124
#> GSM601800 4 0.345 0.6899 0.000 0.024 0.012 0.836 0.128
#> GSM601830 3 0.555 0.4890 0.004 0.176 0.708 0.048 0.064
#> GSM601840 5 0.800 0.4090 0.080 0.212 0.040 0.152 0.516
#> GSM601845 2 0.575 0.3574 0.040 0.732 0.120 0.044 0.064
#> GSM601860 5 0.547 0.4242 0.104 0.224 0.000 0.008 0.664
#> GSM601870 3 0.467 0.5281 0.000 0.088 0.776 0.028 0.108
#> GSM601750 1 0.481 0.3676 0.652 0.020 0.316 0.000 0.012
#> GSM601760 1 0.400 0.5261 0.788 0.028 0.012 0.000 0.172
#> GSM601765 2 0.177 0.4751 0.012 0.944 0.008 0.008 0.028
#> GSM601770 2 0.411 0.4182 0.016 0.756 0.000 0.012 0.216
#> GSM601775 2 0.779 0.1604 0.204 0.444 0.000 0.260 0.092
#> GSM601780 1 0.575 0.3839 0.664 0.040 0.000 0.224 0.072
#> GSM601790 2 0.565 -0.0575 0.000 0.528 0.020 0.040 0.412
#> GSM601805 4 0.480 0.6214 0.004 0.064 0.000 0.712 0.220
#> GSM601810 3 0.500 0.5305 0.196 0.004 0.724 0.012 0.064
#> GSM601815 5 0.549 0.4436 0.000 0.344 0.012 0.052 0.592
#> GSM601820 1 0.416 0.5053 0.768 0.000 0.176 0.000 0.056
#> GSM601825 4 0.648 0.2840 0.004 0.200 0.004 0.544 0.248
#> GSM601835 2 0.557 0.2847 0.000 0.664 0.244 0.036 0.056
#> GSM601850 4 0.668 0.4963 0.288 0.080 0.000 0.560 0.072
#> GSM601855 3 0.437 0.5398 0.008 0.104 0.808 0.036 0.044
#> GSM601865 5 0.566 0.4750 0.004 0.264 0.096 0.004 0.632
#> GSM601756 4 0.429 0.6566 0.000 0.032 0.024 0.780 0.164
#> GSM601786 5 0.422 0.5090 0.028 0.168 0.016 0.004 0.784
#> GSM601796 4 0.651 0.5882 0.176 0.000 0.052 0.616 0.156
#> GSM601801 4 0.522 0.5989 0.000 0.056 0.032 0.708 0.204
#> GSM601831 3 0.523 0.5152 0.124 0.032 0.760 0.052 0.032
#> GSM601841 1 0.777 0.2526 0.480 0.000 0.228 0.144 0.148
#> GSM601846 4 0.800 0.0330 0.000 0.260 0.308 0.348 0.084
#> GSM601861 5 0.522 0.5100 0.008 0.272 0.004 0.052 0.664
#> GSM601871 5 0.659 -0.1381 0.048 0.040 0.448 0.016 0.448
#> GSM601751 5 0.590 0.4564 0.068 0.100 0.000 0.144 0.688
#> GSM601761 1 0.321 0.5647 0.872 0.008 0.012 0.032 0.076
#> GSM601766 2 0.442 0.4167 0.140 0.788 0.024 0.004 0.044
#> GSM601771 5 0.604 0.4576 0.052 0.280 0.000 0.056 0.612
#> GSM601776 1 0.521 0.3739 0.672 0.028 0.000 0.264 0.036
#> GSM601781 4 0.789 0.4920 0.204 0.056 0.020 0.464 0.256
#> GSM601791 1 0.519 0.5056 0.708 0.004 0.008 0.088 0.192
#> GSM601806 4 0.649 0.3091 0.000 0.100 0.032 0.528 0.340
#> GSM601811 3 0.568 0.4396 0.288 0.004 0.608 0.000 0.100
#> GSM601816 4 0.490 0.6041 0.208 0.000 0.028 0.724 0.040
#> GSM601821 5 0.527 0.5167 0.012 0.252 0.004 0.056 0.676
#> GSM601826 4 0.669 0.3153 0.376 0.052 0.016 0.508 0.048
#> GSM601836 2 0.719 0.0453 0.332 0.516 0.064 0.036 0.052
#> GSM601851 1 0.454 0.4710 0.740 0.028 0.000 0.212 0.020
#> GSM601856 3 0.340 0.5651 0.100 0.012 0.856 0.012 0.020
#> GSM601866 1 0.557 0.2314 0.592 0.016 0.340 0.000 0.052
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601752 4 0.411 0.6177 0.000 0.028 0.084 0.796 0.008 0.084
#> GSM601782 1 0.633 0.4941 0.640 0.144 0.096 0.012 0.020 0.088
#> GSM601792 4 0.633 0.1491 0.000 0.016 0.228 0.416 0.000 0.340
#> GSM601797 4 0.498 0.5161 0.000 0.000 0.228 0.656 0.008 0.108
#> GSM601827 3 0.610 0.5368 0.076 0.168 0.620 0.008 0.000 0.128
#> GSM601837 5 0.764 0.3801 0.016 0.212 0.200 0.108 0.452 0.012
#> GSM601842 2 0.483 0.4786 0.000 0.724 0.172 0.040 0.056 0.008
#> GSM601857 1 0.476 0.4920 0.748 0.068 0.128 0.000 0.044 0.012
#> GSM601867 1 0.753 -0.1395 0.392 0.044 0.316 0.028 0.208 0.012
#> GSM601747 2 0.587 0.3355 0.228 0.628 0.008 0.008 0.044 0.084
#> GSM601757 1 0.603 0.4553 0.580 0.216 0.012 0.004 0.012 0.176
#> GSM601762 2 0.564 0.4046 0.000 0.656 0.088 0.100 0.156 0.000
#> GSM601767 2 0.584 0.3595 0.012 0.616 0.000 0.152 0.196 0.024
#> GSM601772 2 0.379 0.5014 0.012 0.800 0.000 0.056 0.128 0.004
#> GSM601777 4 0.681 0.4401 0.052 0.012 0.116 0.616 0.100 0.104
#> GSM601787 5 0.750 -0.0862 0.332 0.024 0.224 0.036 0.368 0.016
#> GSM601802 4 0.194 0.6379 0.000 0.016 0.004 0.928 0.024 0.028
#> GSM601807 3 0.742 0.3692 0.192 0.008 0.504 0.160 0.112 0.024
#> GSM601812 1 0.592 0.4691 0.632 0.104 0.176 0.000 0.004 0.084
#> GSM601817 2 0.705 -0.2401 0.260 0.404 0.280 0.000 0.012 0.044
#> GSM601822 4 0.528 0.4157 0.000 0.052 0.044 0.644 0.004 0.256
#> GSM601832 2 0.407 0.5393 0.004 0.812 0.028 0.092 0.040 0.024
#> GSM601847 4 0.446 0.5616 0.012 0.016 0.016 0.776 0.052 0.128
#> GSM601852 1 0.678 0.3381 0.496 0.252 0.144 0.000 0.000 0.108
#> GSM601862 1 0.405 0.4572 0.792 0.008 0.124 0.004 0.060 0.012
#> GSM601753 4 0.477 0.6150 0.000 0.064 0.048 0.768 0.040 0.080
#> GSM601783 1 0.603 0.3817 0.568 0.104 0.048 0.004 0.000 0.276
#> GSM601793 4 0.612 0.2600 0.008 0.004 0.208 0.488 0.000 0.292
#> GSM601798 4 0.456 0.6082 0.000 0.012 0.152 0.752 0.032 0.052
#> GSM601828 3 0.709 0.2468 0.208 0.344 0.364 0.000 0.000 0.084
#> GSM601838 5 0.719 0.4287 0.004 0.220 0.132 0.144 0.492 0.008
#> GSM601843 2 0.518 0.4131 0.000 0.664 0.208 0.028 0.100 0.000
#> GSM601858 2 0.748 -0.1397 0.076 0.420 0.100 0.040 0.352 0.012
#> GSM601868 1 0.512 0.3394 0.676 0.004 0.216 0.000 0.072 0.032
#> GSM601748 1 0.535 0.5144 0.644 0.224 0.032 0.000 0.000 0.100
#> GSM601758 1 0.554 0.4272 0.584 0.160 0.000 0.000 0.008 0.248
#> GSM601763 2 0.545 0.4196 0.048 0.676 0.004 0.060 0.012 0.200
#> GSM601768 2 0.543 0.4484 0.036 0.684 0.000 0.060 0.188 0.032
#> GSM601773 2 0.661 0.1035 0.004 0.476 0.016 0.248 0.244 0.012
#> GSM601778 4 0.584 0.4581 0.032 0.008 0.092 0.664 0.028 0.176
#> GSM601788 4 0.808 -0.3091 0.056 0.188 0.032 0.360 0.324 0.040
#> GSM601803 4 0.277 0.6350 0.004 0.024 0.004 0.884 0.064 0.020
#> GSM601808 1 0.506 0.1784 0.612 0.004 0.328 0.008 0.024 0.024
#> GSM601813 1 0.396 0.5457 0.748 0.000 0.028 0.016 0.000 0.208
#> GSM601818 1 0.549 0.4954 0.672 0.212 0.040 0.004 0.028 0.044
#> GSM601823 6 0.720 0.3066 0.036 0.200 0.040 0.288 0.000 0.436
#> GSM601833 2 0.484 0.4382 0.000 0.704 0.024 0.096 0.176 0.000
#> GSM601848 6 0.576 0.2405 0.084 0.012 0.012 0.408 0.000 0.484
#> GSM601853 3 0.535 0.3301 0.392 0.036 0.540 0.004 0.008 0.020
#> GSM601863 1 0.427 0.4624 0.752 0.000 0.172 0.000 0.032 0.044
#> GSM601754 4 0.604 0.4907 0.000 0.004 0.088 0.620 0.108 0.180
#> GSM601784 5 0.751 0.2837 0.004 0.340 0.120 0.060 0.416 0.060
#> GSM601794 6 0.662 -0.1292 0.008 0.000 0.248 0.360 0.016 0.368
#> GSM601799 4 0.628 0.4949 0.000 0.044 0.100 0.608 0.040 0.208
#> GSM601829 3 0.638 0.3300 0.036 0.084 0.564 0.044 0.000 0.272
#> GSM601839 5 0.707 0.4012 0.016 0.240 0.192 0.052 0.492 0.008
#> GSM601844 6 0.749 0.1588 0.052 0.088 0.204 0.020 0.104 0.532
#> GSM601859 5 0.567 0.4987 0.004 0.160 0.004 0.048 0.660 0.124
#> GSM601869 1 0.583 0.4837 0.632 0.004 0.116 0.000 0.060 0.188
#> GSM601749 1 0.513 0.4012 0.572 0.036 0.024 0.000 0.004 0.364
#> GSM601759 1 0.534 0.4592 0.604 0.156 0.004 0.000 0.000 0.236
#> GSM601764 2 0.583 0.3325 0.060 0.596 0.028 0.000 0.032 0.284
#> GSM601769 5 0.585 0.3799 0.004 0.332 0.008 0.036 0.556 0.064
#> GSM601774 2 0.585 0.1970 0.016 0.568 0.000 0.132 0.276 0.008
#> GSM601779 6 0.556 0.4647 0.088 0.024 0.000 0.220 0.020 0.648
#> GSM601789 5 0.615 0.2639 0.008 0.404 0.048 0.048 0.480 0.012
#> GSM601804 4 0.483 0.4890 0.000 0.012 0.008 0.680 0.060 0.240
#> GSM601809 1 0.645 0.2790 0.528 0.016 0.024 0.020 0.320 0.092
#> GSM601814 5 0.561 0.5025 0.000 0.164 0.000 0.120 0.652 0.064
#> GSM601819 1 0.753 0.2271 0.400 0.124 0.020 0.000 0.148 0.308
#> GSM601824 6 0.708 0.2950 0.032 0.128 0.004 0.296 0.056 0.484
#> GSM601834 5 0.601 0.3184 0.000 0.344 0.004 0.028 0.512 0.112
#> GSM601849 6 0.620 0.3271 0.244 0.076 0.000 0.100 0.004 0.576
#> GSM601854 1 0.659 0.3073 0.468 0.036 0.204 0.000 0.004 0.288
#> GSM601864 5 0.700 0.4486 0.052 0.044 0.116 0.192 0.576 0.020
#> GSM601755 4 0.299 0.6450 0.000 0.020 0.040 0.876 0.048 0.016
#> GSM601785 5 0.776 0.1289 0.000 0.344 0.100 0.040 0.352 0.164
#> GSM601795 6 0.691 -0.0742 0.000 0.000 0.136 0.344 0.104 0.416
#> GSM601800 4 0.622 0.5055 0.000 0.008 0.124 0.616 0.108 0.144
#> GSM601830 3 0.458 0.5534 0.056 0.168 0.744 0.004 0.008 0.020
#> GSM601840 5 0.959 0.1264 0.164 0.136 0.156 0.232 0.248 0.064
#> GSM601845 2 0.521 0.2610 0.000 0.580 0.348 0.016 0.008 0.048
#> GSM601860 5 0.541 0.4702 0.048 0.052 0.012 0.020 0.708 0.160
#> GSM601870 3 0.600 0.4861 0.176 0.056 0.644 0.016 0.104 0.004
#> GSM601750 1 0.379 0.5619 0.816 0.020 0.064 0.000 0.008 0.092
#> GSM601760 1 0.636 0.2843 0.484 0.040 0.004 0.000 0.136 0.336
#> GSM601765 2 0.367 0.5269 0.000 0.832 0.056 0.012 0.072 0.028
#> GSM601770 2 0.488 0.4422 0.024 0.712 0.000 0.096 0.164 0.004
#> GSM601775 2 0.749 0.1901 0.084 0.400 0.004 0.348 0.032 0.132
#> GSM601780 6 0.604 0.3543 0.192 0.080 0.000 0.064 0.028 0.636
#> GSM601790 5 0.614 0.4086 0.004 0.312 0.048 0.064 0.556 0.016
#> GSM601805 4 0.260 0.6355 0.000 0.020 0.000 0.884 0.072 0.024
#> GSM601810 1 0.583 0.3456 0.660 0.012 0.204 0.052 0.032 0.040
#> GSM601815 5 0.642 0.4724 0.008 0.172 0.032 0.180 0.588 0.020
#> GSM601820 1 0.561 0.4395 0.556 0.012 0.024 0.000 0.060 0.348
#> GSM601825 4 0.457 0.5625 0.000 0.084 0.004 0.748 0.136 0.028
#> GSM601835 2 0.598 0.2763 0.008 0.548 0.340 0.044 0.048 0.012
#> GSM601850 4 0.700 0.1315 0.080 0.052 0.004 0.512 0.064 0.288
#> GSM601855 3 0.467 0.5612 0.168 0.048 0.744 0.016 0.016 0.008
#> GSM601865 5 0.583 0.5149 0.036 0.124 0.096 0.036 0.692 0.016
#> GSM601756 4 0.301 0.6444 0.000 0.024 0.032 0.876 0.048 0.020
#> GSM601786 5 0.453 0.4954 0.008 0.056 0.020 0.012 0.764 0.140
#> GSM601796 6 0.721 0.1443 0.012 0.000 0.108 0.244 0.168 0.468
#> GSM601801 4 0.416 0.6341 0.000 0.016 0.092 0.796 0.072 0.024
#> GSM601831 3 0.494 0.5239 0.184 0.036 0.716 0.008 0.004 0.052
#> GSM601841 1 0.685 0.3993 0.588 0.004 0.108 0.100 0.040 0.160
#> GSM601846 3 0.644 0.3272 0.004 0.236 0.572 0.096 0.008 0.084
#> GSM601861 5 0.429 0.5298 0.004 0.104 0.000 0.040 0.780 0.072
#> GSM601871 5 0.731 0.1155 0.276 0.024 0.196 0.036 0.452 0.016
#> GSM601751 5 0.707 0.4210 0.084 0.036 0.016 0.204 0.564 0.096
#> GSM601761 6 0.556 -0.1829 0.440 0.060 0.000 0.000 0.032 0.468
#> GSM601766 2 0.379 0.5219 0.012 0.828 0.056 0.004 0.024 0.076
#> GSM601771 5 0.804 0.2770 0.088 0.228 0.012 0.196 0.424 0.052
#> GSM601776 6 0.696 0.2640 0.304 0.080 0.000 0.196 0.000 0.420
#> GSM601781 6 0.746 0.2083 0.048 0.012 0.032 0.260 0.196 0.452
#> GSM601791 6 0.563 0.2110 0.180 0.004 0.008 0.004 0.192 0.612
#> GSM601806 4 0.409 0.5814 0.000 0.012 0.024 0.776 0.160 0.028
#> GSM601811 1 0.574 0.4067 0.696 0.012 0.140 0.048 0.068 0.036
#> GSM601816 6 0.573 0.0428 0.052 0.000 0.052 0.440 0.000 0.456
#> GSM601821 5 0.453 0.5166 0.004 0.080 0.004 0.032 0.764 0.116
#> GSM601826 6 0.686 0.3601 0.068 0.128 0.016 0.312 0.000 0.476
#> GSM601836 2 0.572 0.4453 0.100 0.692 0.060 0.020 0.008 0.120
#> GSM601851 6 0.638 0.1978 0.300 0.100 0.000 0.064 0.008 0.528
#> GSM601856 3 0.518 0.2921 0.392 0.008 0.552 0.008 0.024 0.016
#> GSM601866 1 0.322 0.5776 0.848 0.040 0.028 0.000 0.000 0.084
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 time(p) gender(p) k
#> SD:NMF 121 0.5456 0.1026 2
#> SD:NMF 104 0.0518 0.1992 3
#> SD:NMF 79 0.2407 0.0119 4
#> SD:NMF 43 0.0668 0.0187 5
#> SD:NMF 30 0.4371 0.0505 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 125 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 0.268 0.808 0.869 0.3857 0.624 0.624
#> 3 3 0.249 0.746 0.845 0.1531 0.980 0.968
#> 4 4 0.293 0.719 0.830 0.0876 0.996 0.994
#> 5 5 0.278 0.728 0.805 0.0639 1.000 0.999
#> 6 6 0.244 0.583 0.730 0.1516 0.984 0.974
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
#> GSM601752 2 0.0672 0.874 0.008 0.992
#> GSM601782 1 0.8386 0.757 0.732 0.268
#> GSM601792 2 0.4939 0.868 0.108 0.892
#> GSM601797 2 0.4298 0.879 0.088 0.912
#> GSM601827 1 0.5946 0.876 0.856 0.144
#> GSM601837 2 0.1633 0.868 0.024 0.976
#> GSM601842 2 0.3431 0.879 0.064 0.936
#> GSM601857 2 0.9522 0.454 0.372 0.628
#> GSM601867 2 0.5737 0.847 0.136 0.864
#> GSM601747 2 0.8713 0.655 0.292 0.708
#> GSM601757 1 0.9427 0.570 0.640 0.360
#> GSM601762 2 0.3431 0.878 0.064 0.936
#> GSM601767 2 0.2236 0.877 0.036 0.964
#> GSM601772 2 0.1843 0.877 0.028 0.972
#> GSM601777 2 0.3114 0.878 0.056 0.944
#> GSM601787 2 0.8327 0.675 0.264 0.736
#> GSM601802 2 0.0672 0.874 0.008 0.992
#> GSM601807 2 0.9922 0.030 0.448 0.552
#> GSM601812 1 0.5519 0.873 0.872 0.128
#> GSM601817 1 0.4022 0.868 0.920 0.080
#> GSM601822 2 0.4431 0.876 0.092 0.908
#> GSM601832 2 0.3733 0.876 0.072 0.928
#> GSM601847 2 0.4431 0.877 0.092 0.908
#> GSM601852 1 0.5294 0.881 0.880 0.120
#> GSM601862 1 0.7528 0.829 0.784 0.216
#> GSM601753 2 0.0938 0.875 0.012 0.988
#> GSM601783 1 0.4815 0.879 0.896 0.104
#> GSM601793 2 0.4939 0.868 0.108 0.892
#> GSM601798 2 0.0672 0.872 0.008 0.992
#> GSM601828 1 0.5178 0.881 0.884 0.116
#> GSM601838 2 0.1184 0.867 0.016 0.984
#> GSM601843 2 0.3733 0.879 0.072 0.928
#> GSM601858 2 0.9209 0.543 0.336 0.664
#> GSM601868 1 0.8267 0.783 0.740 0.260
#> GSM601748 1 0.3431 0.857 0.936 0.064
#> GSM601758 1 0.4562 0.875 0.904 0.096
#> GSM601763 2 0.6973 0.817 0.188 0.812
#> GSM601768 2 0.2778 0.880 0.048 0.952
#> GSM601773 2 0.1843 0.877 0.028 0.972
#> GSM601778 2 0.3584 0.880 0.068 0.932
#> GSM601788 2 0.1633 0.877 0.024 0.976
#> GSM601803 2 0.0672 0.874 0.008 0.992
#> GSM601808 1 0.7453 0.825 0.788 0.212
#> GSM601813 1 0.6048 0.874 0.852 0.148
#> GSM601818 1 0.5842 0.880 0.860 0.140
#> GSM601823 2 0.7528 0.792 0.216 0.784
#> GSM601833 2 0.3584 0.877 0.068 0.932
#> GSM601848 2 0.6712 0.823 0.176 0.824
#> GSM601853 1 0.6438 0.865 0.836 0.164
#> GSM601863 1 0.7139 0.845 0.804 0.196
#> GSM601754 2 0.0672 0.873 0.008 0.992
#> GSM601784 2 0.2236 0.878 0.036 0.964
#> GSM601794 2 0.4431 0.874 0.092 0.908
#> GSM601799 2 0.3274 0.881 0.060 0.940
#> GSM601829 1 0.8763 0.709 0.704 0.296
#> GSM601839 2 0.1184 0.867 0.016 0.984
#> GSM601844 2 0.8443 0.711 0.272 0.728
#> GSM601859 2 0.4161 0.874 0.084 0.916
#> GSM601869 1 0.7950 0.801 0.760 0.240
#> GSM601749 1 0.4815 0.879 0.896 0.104
#> GSM601759 1 0.4562 0.875 0.904 0.096
#> GSM601764 2 0.7674 0.787 0.224 0.776
#> GSM601769 2 0.0938 0.867 0.012 0.988
#> GSM601774 2 0.1184 0.870 0.016 0.984
#> GSM601779 2 0.7883 0.759 0.236 0.764
#> GSM601789 2 0.1414 0.875 0.020 0.980
#> GSM601804 2 0.0672 0.874 0.008 0.992
#> GSM601809 2 0.8144 0.730 0.252 0.748
#> GSM601814 2 0.0938 0.867 0.012 0.988
#> GSM601819 1 0.5059 0.877 0.888 0.112
#> GSM601824 2 0.7528 0.792 0.216 0.784
#> GSM601834 2 0.3584 0.877 0.068 0.932
#> GSM601849 2 0.7453 0.796 0.212 0.788
#> GSM601854 1 0.3584 0.860 0.932 0.068
#> GSM601864 2 0.5294 0.835 0.120 0.880
#> GSM601755 2 0.0672 0.874 0.008 0.992
#> GSM601785 2 0.3114 0.881 0.056 0.944
#> GSM601795 2 0.4431 0.873 0.092 0.908
#> GSM601800 2 0.1633 0.878 0.024 0.976
#> GSM601830 1 0.9998 0.173 0.508 0.492
#> GSM601840 2 0.5629 0.850 0.132 0.868
#> GSM601845 2 0.4298 0.874 0.088 0.912
#> GSM601860 2 0.4298 0.872 0.088 0.912
#> GSM601870 2 0.9608 0.317 0.384 0.616
#> GSM601750 1 0.2236 0.829 0.964 0.036
#> GSM601760 1 0.5842 0.879 0.860 0.140
#> GSM601765 2 0.4022 0.874 0.080 0.920
#> GSM601770 2 0.2043 0.877 0.032 0.968
#> GSM601775 2 0.7453 0.779 0.212 0.788
#> GSM601780 2 0.7883 0.759 0.236 0.764
#> GSM601790 2 0.1184 0.867 0.016 0.984
#> GSM601805 2 0.0672 0.874 0.008 0.992
#> GSM601810 2 0.8207 0.723 0.256 0.744
#> GSM601815 2 0.0938 0.867 0.012 0.988
#> GSM601820 1 0.4022 0.867 0.920 0.080
#> GSM601825 2 0.0376 0.873 0.004 0.996
#> GSM601835 2 0.3733 0.876 0.072 0.928
#> GSM601850 2 0.4298 0.877 0.088 0.912
#> GSM601855 1 1.0000 0.140 0.504 0.496
#> GSM601865 2 0.5408 0.834 0.124 0.876
#> GSM601756 2 0.0672 0.874 0.008 0.992
#> GSM601786 2 0.2043 0.877 0.032 0.968
#> GSM601796 2 0.4431 0.873 0.092 0.908
#> GSM601801 2 0.0376 0.873 0.004 0.996
#> GSM601831 1 0.5842 0.880 0.860 0.140
#> GSM601841 2 0.9795 0.312 0.416 0.584
#> GSM601846 2 0.5737 0.825 0.136 0.864
#> GSM601861 2 0.0938 0.867 0.012 0.988
#> GSM601871 2 0.8661 0.609 0.288 0.712
#> GSM601751 2 0.7056 0.804 0.192 0.808
#> GSM601761 2 0.8909 0.630 0.308 0.692
#> GSM601766 2 0.4815 0.868 0.104 0.896
#> GSM601771 2 0.6148 0.836 0.152 0.848
#> GSM601776 2 0.8327 0.723 0.264 0.736
#> GSM601781 2 0.3274 0.879 0.060 0.940
#> GSM601791 2 0.8443 0.702 0.272 0.728
#> GSM601806 2 0.0376 0.873 0.004 0.996
#> GSM601811 2 0.8016 0.739 0.244 0.756
#> GSM601816 2 0.7139 0.799 0.196 0.804
#> GSM601821 2 0.1184 0.869 0.016 0.984
#> GSM601826 2 0.7299 0.794 0.204 0.796
#> GSM601836 2 0.6623 0.834 0.172 0.828
#> GSM601851 2 0.7674 0.773 0.224 0.776
#> GSM601856 1 0.6973 0.854 0.812 0.188
#> GSM601866 1 0.3733 0.864 0.928 0.072
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601752 2 0.1170 0.835 0.016 0.976 0.008
#> GSM601782 3 0.8265 0.522 0.180 0.184 0.636
#> GSM601792 2 0.4136 0.822 0.020 0.864 0.116
#> GSM601797 2 0.4165 0.834 0.048 0.876 0.076
#> GSM601827 3 0.5538 0.773 0.116 0.072 0.812
#> GSM601837 2 0.2492 0.815 0.048 0.936 0.016
#> GSM601842 2 0.2749 0.842 0.012 0.924 0.064
#> GSM601857 2 0.8291 0.312 0.100 0.580 0.320
#> GSM601867 2 0.5010 0.795 0.076 0.840 0.084
#> GSM601747 2 0.6357 0.582 0.020 0.684 0.296
#> GSM601757 3 0.7491 0.317 0.056 0.324 0.620
#> GSM601762 2 0.2400 0.842 0.004 0.932 0.064
#> GSM601767 2 0.1950 0.842 0.008 0.952 0.040
#> GSM601772 2 0.1585 0.841 0.008 0.964 0.028
#> GSM601777 2 0.2879 0.844 0.024 0.924 0.052
#> GSM601787 2 0.7884 0.403 0.252 0.644 0.104
#> GSM601802 2 0.1015 0.834 0.012 0.980 0.008
#> GSM601807 1 0.6161 0.802 0.696 0.288 0.016
#> GSM601812 3 0.4172 0.793 0.028 0.104 0.868
#> GSM601817 3 0.3263 0.796 0.040 0.048 0.912
#> GSM601822 2 0.3528 0.837 0.016 0.892 0.092
#> GSM601832 2 0.2774 0.839 0.008 0.920 0.072
#> GSM601847 2 0.3445 0.839 0.016 0.896 0.088
#> GSM601852 3 0.3213 0.809 0.008 0.092 0.900
#> GSM601862 3 0.7777 0.637 0.160 0.164 0.676
#> GSM601753 2 0.1337 0.837 0.016 0.972 0.012
#> GSM601783 3 0.2772 0.811 0.004 0.080 0.916
#> GSM601793 2 0.4136 0.822 0.020 0.864 0.116
#> GSM601798 2 0.1182 0.833 0.012 0.976 0.012
#> GSM601828 3 0.4370 0.806 0.056 0.076 0.868
#> GSM601838 2 0.1636 0.826 0.020 0.964 0.016
#> GSM601843 2 0.2939 0.842 0.012 0.916 0.072
#> GSM601858 2 0.8067 0.401 0.100 0.616 0.284
#> GSM601868 3 0.8436 0.518 0.160 0.224 0.616
#> GSM601748 3 0.2443 0.773 0.028 0.032 0.940
#> GSM601758 3 0.2261 0.807 0.000 0.068 0.932
#> GSM601763 2 0.4912 0.774 0.008 0.796 0.196
#> GSM601768 2 0.2486 0.845 0.008 0.932 0.060
#> GSM601773 2 0.1585 0.841 0.008 0.964 0.028
#> GSM601778 2 0.3181 0.846 0.024 0.912 0.064
#> GSM601788 2 0.2176 0.836 0.032 0.948 0.020
#> GSM601803 2 0.0848 0.835 0.008 0.984 0.008
#> GSM601808 3 0.8543 0.232 0.408 0.096 0.496
#> GSM601813 3 0.4068 0.791 0.016 0.120 0.864
#> GSM601818 3 0.4256 0.808 0.036 0.096 0.868
#> GSM601823 2 0.5450 0.739 0.012 0.760 0.228
#> GSM601833 2 0.2486 0.842 0.008 0.932 0.060
#> GSM601848 2 0.5008 0.776 0.016 0.804 0.180
#> GSM601853 3 0.6719 0.723 0.160 0.096 0.744
#> GSM601863 3 0.7393 0.685 0.156 0.140 0.704
#> GSM601754 2 0.1482 0.837 0.012 0.968 0.020
#> GSM601784 2 0.1950 0.842 0.008 0.952 0.040
#> GSM601794 2 0.4015 0.829 0.028 0.876 0.096
#> GSM601799 2 0.2584 0.845 0.008 0.928 0.064
#> GSM601829 3 0.6761 0.529 0.048 0.252 0.700
#> GSM601839 2 0.2269 0.819 0.040 0.944 0.016
#> GSM601844 2 0.6027 0.665 0.016 0.712 0.272
#> GSM601859 2 0.3293 0.836 0.012 0.900 0.088
#> GSM601869 3 0.8249 0.561 0.164 0.200 0.636
#> GSM601749 3 0.2682 0.811 0.004 0.076 0.920
#> GSM601759 3 0.2496 0.808 0.004 0.068 0.928
#> GSM601764 2 0.5595 0.741 0.016 0.756 0.228
#> GSM601769 2 0.1170 0.826 0.016 0.976 0.008
#> GSM601774 2 0.1337 0.830 0.012 0.972 0.016
#> GSM601779 2 0.5698 0.698 0.012 0.736 0.252
#> GSM601789 2 0.1774 0.834 0.024 0.960 0.016
#> GSM601804 2 0.1015 0.836 0.008 0.980 0.012
#> GSM601809 2 0.7157 0.642 0.100 0.712 0.188
#> GSM601814 2 0.1170 0.826 0.016 0.976 0.008
#> GSM601819 3 0.4609 0.800 0.052 0.092 0.856
#> GSM601824 2 0.5450 0.739 0.012 0.760 0.228
#> GSM601834 2 0.2584 0.841 0.008 0.928 0.064
#> GSM601849 2 0.5506 0.739 0.016 0.764 0.220
#> GSM601854 3 0.3337 0.775 0.060 0.032 0.908
#> GSM601864 2 0.4921 0.687 0.164 0.816 0.020
#> GSM601755 2 0.1170 0.835 0.016 0.976 0.008
#> GSM601785 2 0.2804 0.847 0.016 0.924 0.060
#> GSM601795 2 0.4015 0.828 0.028 0.876 0.096
#> GSM601800 2 0.1453 0.842 0.008 0.968 0.024
#> GSM601830 1 0.8120 0.827 0.640 0.224 0.136
#> GSM601840 2 0.4469 0.809 0.028 0.852 0.120
#> GSM601845 2 0.3293 0.838 0.012 0.900 0.088
#> GSM601860 2 0.3445 0.835 0.016 0.896 0.088
#> GSM601870 2 0.8440 -0.348 0.420 0.492 0.088
#> GSM601750 3 0.4228 0.666 0.148 0.008 0.844
#> GSM601760 3 0.3192 0.803 0.000 0.112 0.888
#> GSM601765 2 0.2955 0.838 0.008 0.912 0.080
#> GSM601770 2 0.1832 0.842 0.008 0.956 0.036
#> GSM601775 2 0.5506 0.729 0.016 0.764 0.220
#> GSM601780 2 0.5698 0.697 0.012 0.736 0.252
#> GSM601790 2 0.1337 0.827 0.012 0.972 0.016
#> GSM601805 2 0.0848 0.835 0.008 0.984 0.008
#> GSM601810 2 0.7213 0.619 0.088 0.700 0.212
#> GSM601815 2 0.1315 0.826 0.020 0.972 0.008
#> GSM601820 3 0.3683 0.799 0.044 0.060 0.896
#> GSM601825 2 0.1170 0.836 0.016 0.976 0.008
#> GSM601835 2 0.2774 0.839 0.008 0.920 0.072
#> GSM601850 2 0.3459 0.839 0.012 0.892 0.096
#> GSM601855 1 0.7935 0.859 0.648 0.236 0.116
#> GSM601865 2 0.4994 0.690 0.160 0.816 0.024
#> GSM601756 2 0.1170 0.835 0.016 0.976 0.008
#> GSM601786 2 0.2050 0.841 0.020 0.952 0.028
#> GSM601796 2 0.4015 0.828 0.028 0.876 0.096
#> GSM601801 2 0.0829 0.833 0.012 0.984 0.004
#> GSM601831 3 0.4324 0.804 0.028 0.112 0.860
#> GSM601841 2 0.7712 0.278 0.052 0.556 0.392
#> GSM601846 2 0.7064 0.503 0.220 0.704 0.076
#> GSM601861 2 0.1337 0.827 0.016 0.972 0.012
#> GSM601871 2 0.7872 0.302 0.296 0.620 0.084
#> GSM601751 2 0.5678 0.750 0.032 0.776 0.192
#> GSM601761 2 0.6369 0.569 0.016 0.668 0.316
#> GSM601766 2 0.3454 0.832 0.008 0.888 0.104
#> GSM601771 2 0.4999 0.787 0.028 0.820 0.152
#> GSM601776 2 0.6161 0.661 0.020 0.708 0.272
#> GSM601781 2 0.2982 0.845 0.024 0.920 0.056
#> GSM601791 2 0.6096 0.642 0.016 0.704 0.280
#> GSM601806 2 0.0829 0.833 0.012 0.984 0.004
#> GSM601811 2 0.7133 0.640 0.096 0.712 0.192
#> GSM601816 2 0.5356 0.749 0.020 0.784 0.196
#> GSM601821 2 0.1491 0.828 0.016 0.968 0.016
#> GSM601826 2 0.5318 0.747 0.016 0.780 0.204
#> GSM601836 2 0.4979 0.795 0.020 0.812 0.168
#> GSM601851 2 0.5578 0.713 0.012 0.748 0.240
#> GSM601856 3 0.6621 0.736 0.100 0.148 0.752
#> GSM601866 3 0.3155 0.795 0.040 0.044 0.916
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601752 2 0.210 0.8542 0.012 0.936 0.008 0.044
#> GSM601782 4 0.769 0.0000 0.372 0.068 0.060 0.500
#> GSM601792 2 0.383 0.8345 0.100 0.856 0.020 0.024
#> GSM601797 2 0.384 0.8431 0.048 0.868 0.052 0.032
#> GSM601827 1 0.529 0.5577 0.784 0.032 0.116 0.068
#> GSM601837 2 0.294 0.8392 0.004 0.900 0.040 0.056
#> GSM601842 2 0.207 0.8577 0.032 0.940 0.012 0.016
#> GSM601857 2 0.765 0.4210 0.272 0.576 0.060 0.092
#> GSM601867 2 0.447 0.7992 0.040 0.836 0.076 0.048
#> GSM601747 2 0.569 0.6704 0.244 0.700 0.016 0.040
#> GSM601757 1 0.644 0.1676 0.612 0.316 0.016 0.056
#> GSM601762 2 0.189 0.8579 0.036 0.944 0.004 0.016
#> GSM601767 2 0.207 0.8585 0.024 0.940 0.008 0.028
#> GSM601772 2 0.206 0.8574 0.020 0.940 0.008 0.032
#> GSM601777 2 0.241 0.8585 0.036 0.928 0.016 0.020
#> GSM601787 2 0.754 0.4984 0.068 0.628 0.172 0.132
#> GSM601802 2 0.201 0.8530 0.012 0.940 0.008 0.040
#> GSM601807 3 0.748 0.5523 0.016 0.164 0.560 0.260
#> GSM601812 1 0.445 0.6403 0.832 0.096 0.032 0.040
#> GSM601817 1 0.365 0.6511 0.876 0.028 0.040 0.056
#> GSM601822 2 0.264 0.8524 0.064 0.912 0.012 0.012
#> GSM601832 2 0.179 0.8558 0.036 0.948 0.008 0.008
#> GSM601847 2 0.256 0.8547 0.060 0.916 0.012 0.012
#> GSM601852 1 0.218 0.6775 0.924 0.064 0.000 0.012
#> GSM601862 1 0.762 0.4536 0.632 0.124 0.096 0.148
#> GSM601753 2 0.222 0.8558 0.016 0.932 0.008 0.044
#> GSM601783 1 0.181 0.6806 0.940 0.052 0.000 0.008
#> GSM601793 2 0.383 0.8345 0.100 0.856 0.020 0.024
#> GSM601798 2 0.201 0.8559 0.012 0.940 0.008 0.040
#> GSM601828 1 0.466 0.6577 0.828 0.064 0.064 0.044
#> GSM601838 2 0.225 0.8469 0.004 0.928 0.016 0.052
#> GSM601843 2 0.215 0.8579 0.036 0.936 0.008 0.020
#> GSM601858 2 0.748 0.4908 0.236 0.608 0.060 0.096
#> GSM601868 1 0.813 0.3599 0.580 0.184 0.096 0.140
#> GSM601748 1 0.321 0.6058 0.892 0.012 0.040 0.056
#> GSM601758 1 0.200 0.6744 0.936 0.044 0.000 0.020
#> GSM601763 2 0.425 0.8049 0.176 0.800 0.008 0.016
#> GSM601768 2 0.253 0.8620 0.048 0.920 0.008 0.024
#> GSM601773 2 0.206 0.8574 0.020 0.940 0.008 0.032
#> GSM601778 2 0.276 0.8599 0.048 0.912 0.028 0.012
#> GSM601788 2 0.278 0.8537 0.016 0.912 0.024 0.048
#> GSM601803 2 0.186 0.8535 0.012 0.944 0.004 0.040
#> GSM601808 1 0.858 -0.2419 0.424 0.044 0.200 0.332
#> GSM601813 1 0.354 0.6499 0.868 0.096 0.012 0.024
#> GSM601818 1 0.428 0.6613 0.844 0.080 0.040 0.036
#> GSM601823 2 0.478 0.7722 0.208 0.760 0.008 0.024
#> GSM601833 2 0.167 0.8575 0.032 0.952 0.004 0.012
#> GSM601848 2 0.408 0.8035 0.160 0.816 0.012 0.012
#> GSM601853 1 0.649 0.4737 0.700 0.064 0.176 0.060
#> GSM601863 1 0.726 0.4951 0.660 0.104 0.088 0.148
#> GSM601754 2 0.204 0.8556 0.016 0.940 0.008 0.036
#> GSM601784 2 0.222 0.8588 0.032 0.932 0.004 0.032
#> GSM601794 2 0.375 0.8379 0.088 0.864 0.024 0.024
#> GSM601799 2 0.192 0.8614 0.036 0.944 0.008 0.012
#> GSM601829 1 0.595 0.3558 0.700 0.228 0.044 0.028
#> GSM601839 2 0.275 0.8426 0.004 0.908 0.032 0.056
#> GSM601844 2 0.537 0.7062 0.264 0.700 0.012 0.024
#> GSM601859 2 0.306 0.8555 0.072 0.892 0.004 0.032
#> GSM601869 1 0.798 0.4000 0.600 0.156 0.100 0.144
#> GSM601749 1 0.172 0.6790 0.944 0.048 0.000 0.008
#> GSM601759 1 0.177 0.6752 0.944 0.044 0.000 0.012
#> GSM601764 2 0.464 0.7801 0.208 0.764 0.004 0.024
#> GSM601769 2 0.214 0.8498 0.008 0.932 0.008 0.052
#> GSM601774 2 0.212 0.8502 0.012 0.932 0.004 0.052
#> GSM601779 2 0.489 0.7377 0.244 0.732 0.008 0.016
#> GSM601789 2 0.211 0.8511 0.000 0.932 0.024 0.044
#> GSM601804 2 0.198 0.8547 0.016 0.940 0.004 0.040
#> GSM601809 2 0.671 0.6557 0.136 0.700 0.092 0.072
#> GSM601814 2 0.199 0.8472 0.004 0.936 0.008 0.052
#> GSM601819 1 0.393 0.6350 0.860 0.064 0.020 0.056
#> GSM601824 2 0.478 0.7722 0.208 0.760 0.008 0.024
#> GSM601834 2 0.177 0.8574 0.036 0.948 0.004 0.012
#> GSM601849 2 0.454 0.7730 0.204 0.772 0.008 0.016
#> GSM601854 1 0.386 0.6202 0.864 0.020 0.056 0.060
#> GSM601864 2 0.481 0.7445 0.008 0.796 0.128 0.068
#> GSM601755 2 0.210 0.8542 0.012 0.936 0.008 0.044
#> GSM601785 2 0.266 0.8628 0.048 0.916 0.012 0.024
#> GSM601795 2 0.368 0.8382 0.084 0.868 0.024 0.024
#> GSM601800 2 0.207 0.8598 0.024 0.940 0.008 0.028
#> GSM601830 3 0.442 0.6427 0.084 0.076 0.828 0.012
#> GSM601840 2 0.395 0.8326 0.108 0.848 0.020 0.024
#> GSM601845 2 0.231 0.8559 0.048 0.928 0.008 0.016
#> GSM601860 2 0.315 0.8543 0.072 0.888 0.004 0.036
#> GSM601870 2 0.857 -0.0926 0.060 0.456 0.320 0.164
#> GSM601750 1 0.505 0.3202 0.776 0.004 0.088 0.132
#> GSM601760 1 0.227 0.6701 0.912 0.084 0.000 0.004
#> GSM601765 2 0.206 0.8558 0.048 0.936 0.008 0.008
#> GSM601770 2 0.227 0.8595 0.028 0.932 0.008 0.032
#> GSM601775 2 0.485 0.7687 0.200 0.764 0.016 0.020
#> GSM601780 2 0.489 0.7377 0.244 0.732 0.008 0.016
#> GSM601790 2 0.212 0.8477 0.012 0.932 0.004 0.052
#> GSM601805 2 0.186 0.8535 0.012 0.944 0.004 0.040
#> GSM601810 2 0.694 0.6336 0.168 0.676 0.080 0.076
#> GSM601815 2 0.212 0.8470 0.004 0.932 0.012 0.052
#> GSM601820 1 0.340 0.6506 0.880 0.044 0.008 0.068
#> GSM601825 2 0.210 0.8564 0.012 0.936 0.008 0.044
#> GSM601835 2 0.177 0.8566 0.036 0.948 0.012 0.004
#> GSM601850 2 0.291 0.8541 0.072 0.900 0.012 0.016
#> GSM601855 3 0.400 0.6728 0.068 0.068 0.852 0.012
#> GSM601865 2 0.481 0.7454 0.008 0.796 0.128 0.068
#> GSM601756 2 0.210 0.8542 0.012 0.936 0.008 0.044
#> GSM601786 2 0.172 0.8573 0.008 0.952 0.012 0.028
#> GSM601796 2 0.368 0.8382 0.084 0.868 0.024 0.024
#> GSM601801 2 0.192 0.8551 0.012 0.944 0.008 0.036
#> GSM601831 1 0.390 0.6805 0.860 0.080 0.024 0.036
#> GSM601841 2 0.698 0.3281 0.376 0.536 0.024 0.064
#> GSM601846 2 0.757 0.3615 0.056 0.552 0.316 0.076
#> GSM601861 2 0.214 0.8472 0.008 0.932 0.008 0.052
#> GSM601871 2 0.784 0.3998 0.056 0.588 0.192 0.164
#> GSM601751 2 0.537 0.7787 0.192 0.748 0.028 0.032
#> GSM601761 2 0.544 0.6332 0.308 0.664 0.012 0.016
#> GSM601766 2 0.280 0.8512 0.080 0.900 0.008 0.012
#> GSM601771 2 0.459 0.8142 0.148 0.804 0.020 0.028
#> GSM601776 2 0.538 0.7058 0.264 0.700 0.016 0.020
#> GSM601781 2 0.250 0.8592 0.040 0.924 0.016 0.020
#> GSM601791 2 0.513 0.6920 0.276 0.700 0.012 0.012
#> GSM601806 2 0.188 0.8524 0.008 0.944 0.008 0.040
#> GSM601811 2 0.682 0.6454 0.140 0.692 0.092 0.076
#> GSM601816 2 0.449 0.7873 0.176 0.792 0.016 0.016
#> GSM601821 2 0.227 0.8485 0.012 0.928 0.008 0.052
#> GSM601826 2 0.454 0.7788 0.192 0.780 0.012 0.016
#> GSM601836 2 0.400 0.8215 0.144 0.828 0.012 0.016
#> GSM601851 2 0.479 0.7507 0.232 0.744 0.008 0.016
#> GSM601856 1 0.629 0.5868 0.728 0.124 0.088 0.060
#> GSM601866 1 0.400 0.6592 0.860 0.036 0.040 0.064
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601752 2 0.225 0.8370 0.012 0.900 0.000 NA 0.000
#> GSM601782 5 0.806 -0.0279 0.248 0.040 0.024 NA 0.364
#> GSM601792 2 0.345 0.8203 0.096 0.848 0.012 NA 0.000
#> GSM601797 2 0.381 0.8249 0.048 0.844 0.068 NA 0.004
#> GSM601827 1 0.502 0.6750 0.760 0.012 0.132 NA 0.024
#> GSM601837 2 0.322 0.8046 0.000 0.824 0.016 NA 0.000
#> GSM601842 2 0.190 0.8431 0.024 0.936 0.004 NA 0.004
#> GSM601857 2 0.683 0.4501 0.264 0.568 0.044 NA 0.008
#> GSM601867 2 0.447 0.7840 0.028 0.792 0.052 NA 0.004
#> GSM601747 2 0.558 0.6715 0.216 0.692 0.028 NA 0.016
#> GSM601757 1 0.562 0.3248 0.620 0.304 0.008 NA 0.008
#> GSM601762 2 0.184 0.8436 0.032 0.932 0.000 NA 0.000
#> GSM601767 2 0.204 0.8429 0.024 0.920 0.000 NA 0.000
#> GSM601772 2 0.201 0.8413 0.012 0.916 0.000 NA 0.000
#> GSM601777 2 0.254 0.8427 0.032 0.904 0.012 NA 0.000
#> GSM601787 2 0.693 0.4995 0.064 0.592 0.076 NA 0.024
#> GSM601802 2 0.219 0.8357 0.012 0.904 0.000 NA 0.000
#> GSM601807 5 0.784 -0.2518 0.012 0.088 0.184 NA 0.492
#> GSM601812 1 0.428 0.7410 0.820 0.076 0.048 NA 0.008
#> GSM601817 1 0.351 0.7492 0.860 0.012 0.048 NA 0.012
#> GSM601822 2 0.241 0.8353 0.068 0.900 0.000 NA 0.000
#> GSM601832 2 0.156 0.8402 0.028 0.948 0.000 NA 0.004
#> GSM601847 2 0.248 0.8404 0.060 0.904 0.008 NA 0.000
#> GSM601852 1 0.172 0.7615 0.936 0.052 0.004 NA 0.000
#> GSM601862 1 0.702 0.5752 0.620 0.100 0.088 NA 0.024
#> GSM601753 2 0.235 0.8384 0.016 0.896 0.000 NA 0.000
#> GSM601783 1 0.149 0.7643 0.948 0.040 0.004 NA 0.000
#> GSM601793 2 0.345 0.8203 0.096 0.848 0.012 NA 0.000
#> GSM601798 2 0.229 0.8380 0.016 0.900 0.000 NA 0.000
#> GSM601828 1 0.414 0.7519 0.836 0.032 0.060 NA 0.028
#> GSM601838 2 0.276 0.8152 0.000 0.848 0.004 NA 0.000
#> GSM601843 2 0.183 0.8425 0.028 0.936 0.000 NA 0.004
#> GSM601858 2 0.663 0.5235 0.228 0.604 0.044 NA 0.008
#> GSM601868 1 0.741 0.4849 0.572 0.164 0.080 NA 0.020
#> GSM601748 1 0.343 0.7268 0.864 0.004 0.044 NA 0.024
#> GSM601758 1 0.177 0.7614 0.940 0.032 0.000 NA 0.008
#> GSM601763 2 0.375 0.7950 0.176 0.796 0.000 NA 0.008
#> GSM601768 2 0.252 0.8476 0.048 0.896 0.000 NA 0.000
#> GSM601773 2 0.207 0.8412 0.012 0.912 0.000 NA 0.000
#> GSM601778 2 0.275 0.8446 0.036 0.896 0.020 NA 0.000
#> GSM601788 2 0.300 0.8385 0.020 0.864 0.008 NA 0.000
#> GSM601803 2 0.213 0.8363 0.012 0.908 0.000 NA 0.000
#> GSM601808 1 0.863 -0.1074 0.372 0.012 0.188 NA 0.176
#> GSM601813 1 0.294 0.7426 0.880 0.084 0.012 NA 0.004
#> GSM601818 1 0.418 0.7496 0.824 0.060 0.048 NA 0.004
#> GSM601823 2 0.429 0.7625 0.208 0.752 0.000 NA 0.008
#> GSM601833 2 0.156 0.8422 0.024 0.948 0.000 NA 0.004
#> GSM601848 2 0.358 0.7920 0.160 0.808 0.000 NA 0.000
#> GSM601853 1 0.618 0.5974 0.668 0.032 0.192 NA 0.024
#> GSM601863 1 0.675 0.6142 0.648 0.088 0.092 NA 0.024
#> GSM601754 2 0.229 0.8381 0.012 0.904 0.004 NA 0.000
#> GSM601784 2 0.243 0.8427 0.020 0.900 0.004 NA 0.000
#> GSM601794 2 0.342 0.8242 0.080 0.856 0.020 NA 0.000
#> GSM601799 2 0.215 0.8478 0.032 0.920 0.004 NA 0.000
#> GSM601829 1 0.561 0.5202 0.696 0.208 0.044 NA 0.016
#> GSM601839 2 0.309 0.8049 0.000 0.824 0.008 NA 0.000
#> GSM601844 2 0.483 0.7032 0.264 0.692 0.008 NA 0.004
#> GSM601859 2 0.306 0.8431 0.068 0.864 0.000 NA 0.000
#> GSM601869 1 0.722 0.5299 0.596 0.136 0.084 NA 0.020
#> GSM601749 1 0.141 0.7640 0.952 0.036 0.004 NA 0.000
#> GSM601759 1 0.169 0.7597 0.944 0.024 0.000 NA 0.008
#> GSM601764 2 0.421 0.7734 0.204 0.760 0.004 NA 0.004
#> GSM601769 2 0.267 0.8208 0.004 0.856 0.000 NA 0.000
#> GSM601774 2 0.252 0.8206 0.000 0.860 0.000 NA 0.000
#> GSM601779 2 0.434 0.7314 0.244 0.728 0.004 NA 0.004
#> GSM601789 2 0.252 0.8302 0.000 0.880 0.012 NA 0.000
#> GSM601804 2 0.223 0.8374 0.016 0.904 0.000 NA 0.000
#> GSM601809 2 0.651 0.6391 0.112 0.664 0.080 NA 0.016
#> GSM601814 2 0.252 0.8176 0.000 0.860 0.000 NA 0.000
#> GSM601819 1 0.373 0.7477 0.856 0.052 0.020 NA 0.024
#> GSM601824 2 0.429 0.7625 0.208 0.752 0.000 NA 0.008
#> GSM601834 2 0.165 0.8421 0.024 0.944 0.000 NA 0.004
#> GSM601849 2 0.406 0.7681 0.200 0.768 0.008 NA 0.000
#> GSM601854 1 0.402 0.7347 0.832 0.008 0.068 NA 0.024
#> GSM601864 2 0.481 0.7107 0.008 0.732 0.028 NA 0.020
#> GSM601755 2 0.225 0.8370 0.012 0.900 0.000 NA 0.000
#> GSM601785 2 0.260 0.8481 0.040 0.896 0.004 NA 0.000
#> GSM601795 2 0.335 0.8242 0.080 0.860 0.020 NA 0.000
#> GSM601800 2 0.221 0.8439 0.020 0.908 0.000 NA 0.000
#> GSM601830 3 0.373 0.7730 0.072 0.040 0.848 NA 0.004
#> GSM601840 2 0.362 0.8247 0.112 0.832 0.008 NA 0.000
#> GSM601845 2 0.199 0.8406 0.040 0.928 0.000 NA 0.004
#> GSM601860 2 0.306 0.8427 0.068 0.864 0.000 NA 0.000
#> GSM601870 2 0.812 -0.0208 0.052 0.424 0.196 NA 0.032
#> GSM601750 1 0.539 0.5429 0.728 0.000 0.052 NA 0.092
#> GSM601760 1 0.199 0.7571 0.920 0.068 0.000 NA 0.004
#> GSM601765 2 0.181 0.8402 0.040 0.936 0.000 NA 0.004
#> GSM601770 2 0.224 0.8437 0.024 0.908 0.000 NA 0.000
#> GSM601775 2 0.425 0.7639 0.200 0.756 0.004 NA 0.000
#> GSM601780 2 0.445 0.7236 0.248 0.720 0.004 NA 0.004
#> GSM601790 2 0.263 0.8201 0.004 0.860 0.000 NA 0.000
#> GSM601805 2 0.213 0.8363 0.012 0.908 0.000 NA 0.000
#> GSM601810 2 0.677 0.5988 0.144 0.640 0.084 NA 0.016
#> GSM601815 2 0.267 0.8168 0.000 0.856 0.004 NA 0.000
#> GSM601820 1 0.337 0.7555 0.872 0.032 0.016 NA 0.020
#> GSM601825 2 0.201 0.8400 0.000 0.908 0.004 NA 0.000
#> GSM601835 2 0.199 0.8422 0.028 0.928 0.000 NA 0.004
#> GSM601850 2 0.291 0.8384 0.064 0.888 0.008 NA 0.008
#> GSM601855 3 0.309 0.7720 0.048 0.036 0.884 NA 0.004
#> GSM601865 2 0.478 0.7136 0.008 0.736 0.028 NA 0.020
#> GSM601756 2 0.225 0.8370 0.012 0.900 0.000 NA 0.000
#> GSM601786 2 0.260 0.8320 0.004 0.872 0.000 NA 0.004
#> GSM601796 2 0.335 0.8242 0.080 0.860 0.020 NA 0.000
#> GSM601801 2 0.223 0.8374 0.016 0.904 0.000 NA 0.000
#> GSM601831 1 0.330 0.7664 0.876 0.060 0.020 NA 0.024
#> GSM601841 2 0.623 0.3458 0.376 0.524 0.020 NA 0.004
#> GSM601846 2 0.705 0.2903 0.040 0.504 0.336 NA 0.012
#> GSM601861 2 0.260 0.8157 0.000 0.852 0.000 NA 0.000
#> GSM601871 2 0.703 0.4067 0.044 0.552 0.076 NA 0.032
#> GSM601751 2 0.495 0.7678 0.188 0.724 0.012 NA 0.000
#> GSM601761 2 0.472 0.6254 0.308 0.656 0.000 NA 0.000
#> GSM601766 2 0.243 0.8371 0.076 0.900 0.000 NA 0.004
#> GSM601771 2 0.407 0.8070 0.152 0.792 0.008 NA 0.000
#> GSM601776 2 0.473 0.6973 0.256 0.696 0.004 NA 0.000
#> GSM601781 2 0.262 0.8432 0.036 0.900 0.012 NA 0.000
#> GSM601791 2 0.440 0.6869 0.276 0.696 0.000 NA 0.000
#> GSM601806 2 0.208 0.8350 0.008 0.908 0.000 NA 0.000
#> GSM601811 2 0.671 0.6086 0.116 0.648 0.092 NA 0.016
#> GSM601816 2 0.408 0.7780 0.172 0.780 0.004 NA 0.000
#> GSM601821 2 0.272 0.8185 0.004 0.852 0.000 NA 0.000
#> GSM601826 2 0.395 0.7681 0.192 0.772 0.000 NA 0.000
#> GSM601836 2 0.374 0.8118 0.140 0.820 0.012 NA 0.004
#> GSM601851 2 0.427 0.7416 0.232 0.736 0.004 NA 0.000
#> GSM601856 1 0.584 0.6934 0.720 0.100 0.096 NA 0.016
#> GSM601866 1 0.395 0.7534 0.840 0.024 0.044 NA 0.016
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601752 2 0.3290 0.67696 0.004 0.744 0.000 0.000 0.000 NA
#> GSM601782 5 0.4745 0.00000 0.200 0.024 0.028 0.016 0.724 NA
#> GSM601792 2 0.3921 0.65970 0.096 0.820 0.020 0.020 0.008 NA
#> GSM601797 2 0.4176 0.65932 0.020 0.804 0.016 0.088 0.008 NA
#> GSM601827 1 0.4889 0.60179 0.712 0.012 0.064 0.192 0.004 NA
#> GSM601837 2 0.4394 0.47412 0.000 0.496 0.016 0.004 0.000 NA
#> GSM601842 2 0.2627 0.71158 0.024 0.880 0.000 0.008 0.004 NA
#> GSM601857 2 0.6564 0.32177 0.256 0.544 0.144 0.020 0.008 NA
#> GSM601867 2 0.5590 0.58986 0.020 0.652 0.116 0.012 0.004 NA
#> GSM601747 2 0.5923 0.51956 0.200 0.656 0.048 0.024 0.016 NA
#> GSM601757 1 0.4958 0.32736 0.640 0.288 0.056 0.004 0.008 NA
#> GSM601762 2 0.2679 0.71256 0.032 0.868 0.000 0.000 0.004 NA
#> GSM601767 2 0.3134 0.70476 0.024 0.808 0.000 0.000 0.000 NA
#> GSM601772 2 0.3110 0.69914 0.012 0.792 0.000 0.000 0.000 NA
#> GSM601777 2 0.3022 0.69089 0.016 0.872 0.020 0.016 0.004 NA
#> GSM601787 2 0.6852 0.33093 0.044 0.516 0.268 0.020 0.008 NA
#> GSM601802 2 0.3337 0.66959 0.004 0.736 0.000 0.000 0.000 NA
#> GSM601807 3 0.7202 -0.12878 0.000 0.048 0.484 0.056 0.144 NA
#> GSM601812 1 0.4320 0.71713 0.804 0.056 0.072 0.036 0.008 NA
#> GSM601817 1 0.3708 0.72539 0.836 0.008 0.084 0.024 0.028 NA
#> GSM601822 2 0.2904 0.68311 0.056 0.876 0.012 0.004 0.004 NA
#> GSM601832 2 0.1864 0.70528 0.032 0.924 0.000 0.000 0.004 NA
#> GSM601847 2 0.2988 0.68804 0.064 0.868 0.004 0.008 0.004 NA
#> GSM601852 1 0.1608 0.74093 0.940 0.036 0.016 0.004 0.004 NA
#> GSM601862 1 0.5682 0.48366 0.572 0.096 0.304 0.024 0.000 NA
#> GSM601753 2 0.3373 0.68046 0.008 0.744 0.000 0.000 0.000 NA
#> GSM601783 1 0.0837 0.74079 0.972 0.020 0.004 0.004 0.000 NA
#> GSM601793 2 0.3921 0.65970 0.096 0.820 0.020 0.020 0.008 NA
#> GSM601798 2 0.3691 0.67633 0.008 0.724 0.000 0.008 0.000 NA
#> GSM601828 1 0.3889 0.72683 0.824 0.028 0.044 0.084 0.008 NA
#> GSM601838 2 0.3866 0.49876 0.000 0.516 0.000 0.000 0.000 NA
#> GSM601843 2 0.2706 0.71146 0.028 0.876 0.000 0.008 0.004 NA
#> GSM601858 2 0.6571 0.39436 0.224 0.568 0.140 0.020 0.008 NA
#> GSM601868 1 0.6421 0.39531 0.540 0.140 0.268 0.028 0.000 NA
#> GSM601748 1 0.3610 0.70431 0.836 0.000 0.080 0.028 0.040 NA
#> GSM601758 1 0.1495 0.73896 0.948 0.020 0.020 0.000 0.008 NA
#> GSM601763 2 0.3538 0.66193 0.176 0.792 0.008 0.000 0.008 NA
#> GSM601768 2 0.3453 0.70980 0.044 0.792 0.000 0.000 0.000 NA
#> GSM601773 2 0.3171 0.69682 0.012 0.784 0.000 0.000 0.000 NA
#> GSM601778 2 0.3513 0.68908 0.028 0.852 0.028 0.020 0.008 NA
#> GSM601788 2 0.4569 0.64718 0.024 0.652 0.016 0.004 0.000 NA
#> GSM601803 2 0.3398 0.67160 0.008 0.740 0.000 0.000 0.000 NA
#> GSM601808 3 0.6476 -0.13634 0.268 0.008 0.564 0.088 0.052 NA
#> GSM601813 1 0.2651 0.72419 0.888 0.068 0.024 0.012 0.004 NA
#> GSM601818 1 0.4165 0.72477 0.812 0.048 0.080 0.032 0.012 NA
#> GSM601823 2 0.4135 0.61332 0.200 0.748 0.012 0.000 0.008 NA
#> GSM601833 2 0.2152 0.70989 0.024 0.904 0.000 0.000 0.004 NA
#> GSM601848 2 0.3999 0.64070 0.148 0.784 0.012 0.004 0.004 NA
#> GSM601853 1 0.5810 0.52367 0.624 0.020 0.176 0.164 0.000 NA
#> GSM601863 1 0.5568 0.54521 0.616 0.080 0.268 0.028 0.004 NA
#> GSM601754 2 0.3302 0.68635 0.004 0.760 0.000 0.004 0.000 NA
#> GSM601784 2 0.3341 0.69798 0.012 0.776 0.000 0.004 0.000 NA
#> GSM601794 2 0.4088 0.65892 0.076 0.816 0.028 0.020 0.008 NA
#> GSM601799 2 0.3086 0.71534 0.020 0.820 0.000 0.004 0.000 NA
#> GSM601829 1 0.5328 0.48907 0.680 0.208 0.024 0.064 0.004 NA
#> GSM601839 2 0.4227 0.47403 0.000 0.496 0.008 0.004 0.000 NA
#> GSM601844 2 0.4635 0.57363 0.252 0.688 0.012 0.012 0.000 NA
#> GSM601859 2 0.3637 0.71132 0.056 0.780 0.000 0.000 0.000 NA
#> GSM601869 1 0.6178 0.44178 0.560 0.128 0.268 0.028 0.000 NA
#> GSM601749 1 0.0982 0.73967 0.968 0.020 0.004 0.004 0.004 NA
#> GSM601759 1 0.1476 0.73760 0.948 0.012 0.028 0.000 0.004 NA
#> GSM601764 2 0.4096 0.63062 0.192 0.760 0.012 0.008 0.008 NA
#> GSM601769 2 0.4220 0.51347 0.004 0.520 0.008 0.000 0.000 NA
#> GSM601774 2 0.4098 0.53611 0.004 0.548 0.004 0.000 0.000 NA
#> GSM601779 2 0.4559 0.57673 0.232 0.708 0.008 0.004 0.012 NA
#> GSM601789 2 0.3941 0.63526 0.004 0.660 0.004 0.004 0.000 NA
#> GSM601804 2 0.3494 0.67539 0.012 0.736 0.000 0.000 0.000 NA
#> GSM601809 2 0.6328 0.46368 0.068 0.616 0.200 0.020 0.008 NA
#> GSM601814 2 0.3982 0.51800 0.000 0.536 0.004 0.000 0.000 NA
#> GSM601819 1 0.3476 0.71559 0.860 0.032 0.036 0.012 0.028 NA
#> GSM601824 2 0.4135 0.61332 0.200 0.748 0.012 0.000 0.008 NA
#> GSM601834 2 0.2265 0.71041 0.024 0.896 0.000 0.000 0.004 NA
#> GSM601849 2 0.4380 0.60352 0.196 0.740 0.012 0.008 0.004 NA
#> GSM601854 1 0.4012 0.70753 0.804 0.000 0.100 0.060 0.020 NA
#> GSM601864 2 0.5846 0.46995 0.000 0.536 0.140 0.004 0.012 NA
#> GSM601755 2 0.3314 0.67566 0.004 0.740 0.000 0.000 0.000 NA
#> GSM601785 2 0.3517 0.70805 0.028 0.780 0.000 0.004 0.000 NA
#> GSM601795 2 0.4011 0.66008 0.076 0.820 0.024 0.020 0.008 NA
#> GSM601800 2 0.3192 0.69732 0.004 0.776 0.004 0.000 0.000 NA
#> GSM601830 4 0.3227 0.07586 0.036 0.040 0.056 0.860 0.000 NA
#> GSM601840 2 0.3832 0.70184 0.108 0.800 0.020 0.000 0.000 NA
#> GSM601845 2 0.2121 0.70439 0.040 0.916 0.000 0.008 0.004 NA
#> GSM601860 2 0.3707 0.71186 0.056 0.784 0.004 0.000 0.000 NA
#> GSM601870 2 0.7996 -0.11642 0.036 0.380 0.312 0.144 0.012 NA
#> GSM601750 1 0.6205 0.40204 0.644 0.000 0.088 0.036 0.092 NA
#> GSM601760 1 0.1799 0.73596 0.928 0.052 0.008 0.000 0.008 NA
#> GSM601765 2 0.2008 0.70301 0.040 0.920 0.004 0.000 0.004 NA
#> GSM601770 2 0.3309 0.70575 0.024 0.800 0.000 0.004 0.000 NA
#> GSM601775 2 0.4067 0.63760 0.192 0.760 0.012 0.008 0.004 NA
#> GSM601780 2 0.4635 0.56794 0.240 0.700 0.012 0.004 0.012 NA
#> GSM601790 2 0.3986 0.51634 0.000 0.532 0.004 0.000 0.000 NA
#> GSM601805 2 0.3314 0.67041 0.004 0.740 0.000 0.000 0.000 NA
#> GSM601810 2 0.6645 0.41824 0.100 0.592 0.200 0.028 0.008 NA
#> GSM601815 2 0.3989 0.50741 0.000 0.528 0.004 0.000 0.000 NA
#> GSM601820 1 0.2988 0.72350 0.876 0.008 0.064 0.008 0.028 NA
#> GSM601825 2 0.3586 0.67015 0.004 0.712 0.000 0.004 0.000 NA
#> GSM601835 2 0.2307 0.70831 0.032 0.896 0.000 0.000 0.004 NA
#> GSM601850 2 0.3337 0.69101 0.060 0.856 0.024 0.008 0.004 NA
#> GSM601855 4 0.3720 0.00503 0.008 0.020 0.132 0.808 0.000 NA
#> GSM601865 2 0.5853 0.47752 0.000 0.544 0.128 0.008 0.012 NA
#> GSM601756 2 0.3360 0.67132 0.004 0.732 0.000 0.000 0.000 NA
#> GSM601786 2 0.4377 0.59993 0.008 0.608 0.012 0.000 0.004 NA
#> GSM601796 2 0.4011 0.66008 0.076 0.820 0.024 0.020 0.008 NA
#> GSM601801 2 0.3583 0.67340 0.008 0.728 0.000 0.004 0.000 NA
#> GSM601831 1 0.3169 0.74230 0.864 0.048 0.060 0.016 0.004 NA
#> GSM601841 2 0.6093 0.27631 0.364 0.512 0.076 0.012 0.004 NA
#> GSM601846 4 0.6695 0.06318 0.004 0.372 0.016 0.372 0.008 NA
#> GSM601861 2 0.3857 0.51676 0.000 0.532 0.000 0.000 0.000 NA
#> GSM601871 2 0.6708 0.24840 0.036 0.484 0.332 0.012 0.012 NA
#> GSM601751 2 0.5002 0.64498 0.172 0.708 0.044 0.004 0.000 NA
#> GSM601761 2 0.4924 0.50940 0.296 0.636 0.016 0.000 0.004 NA
#> GSM601766 2 0.2465 0.70142 0.072 0.892 0.008 0.000 0.004 NA
#> GSM601771 2 0.3977 0.68703 0.144 0.780 0.020 0.000 0.000 NA
#> GSM601776 2 0.4638 0.55980 0.248 0.696 0.016 0.008 0.008 NA
#> GSM601781 2 0.3109 0.69058 0.020 0.868 0.020 0.016 0.004 NA
#> GSM601791 2 0.4571 0.55758 0.260 0.680 0.008 0.000 0.004 NA
#> GSM601806 2 0.3360 0.66684 0.000 0.732 0.004 0.000 0.000 NA
#> GSM601811 2 0.6458 0.42277 0.072 0.600 0.216 0.024 0.008 NA
#> GSM601816 2 0.4502 0.62748 0.156 0.752 0.024 0.004 0.004 NA
#> GSM601821 2 0.3857 0.51903 0.000 0.532 0.000 0.000 0.000 NA
#> GSM601826 2 0.4194 0.61409 0.184 0.752 0.016 0.000 0.004 NA
#> GSM601836 2 0.3870 0.66657 0.148 0.792 0.004 0.012 0.004 NA
#> GSM601851 2 0.4636 0.58370 0.220 0.712 0.016 0.004 0.008 NA
#> GSM601856 1 0.5357 0.64197 0.692 0.096 0.140 0.068 0.000 NA
#> GSM601866 1 0.3555 0.72118 0.832 0.008 0.108 0.028 0.016 NA
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n time(p) gender(p) k
#> CV:hclust 119 0.137 0.503 2
#> CV:hclust 117 0.178 0.847 3
#> CV:hclust 108 0.380 0.507 4
#> CV:hclust 114 0.164 0.488 5
#> CV:hclust 99 0.239 0.271 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 125 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 0.885 0.914 0.963 0.5021 0.497 0.497
#> 3 3 0.561 0.645 0.831 0.2823 0.784 0.596
#> 4 4 0.594 0.397 0.640 0.1171 0.858 0.626
#> 5 5 0.644 0.714 0.767 0.0704 0.810 0.436
#> 6 6 0.674 0.617 0.742 0.0379 0.982 0.922
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
#> GSM601752 2 0.0672 0.9575 0.008 0.992
#> GSM601782 1 0.0000 0.9671 1.000 0.000
#> GSM601792 1 0.0000 0.9671 1.000 0.000
#> GSM601797 1 0.9998 -0.0106 0.508 0.492
#> GSM601827 1 0.0000 0.9671 1.000 0.000
#> GSM601837 2 0.0000 0.9542 0.000 1.000
#> GSM601842 2 0.0672 0.9575 0.008 0.992
#> GSM601857 1 0.0376 0.9652 0.996 0.004
#> GSM601867 1 0.8608 0.6069 0.716 0.284
#> GSM601747 1 0.0000 0.9671 1.000 0.000
#> GSM601757 1 0.0000 0.9671 1.000 0.000
#> GSM601762 2 0.0672 0.9575 0.008 0.992
#> GSM601767 2 0.0672 0.9575 0.008 0.992
#> GSM601772 2 0.0672 0.9575 0.008 0.992
#> GSM601777 1 0.7674 0.7091 0.776 0.224
#> GSM601787 2 0.7299 0.7344 0.204 0.796
#> GSM601802 2 0.0672 0.9575 0.008 0.992
#> GSM601807 1 0.4022 0.9025 0.920 0.080
#> GSM601812 1 0.0000 0.9671 1.000 0.000
#> GSM601817 1 0.0000 0.9671 1.000 0.000
#> GSM601822 2 0.9881 0.2652 0.436 0.564
#> GSM601832 2 0.0672 0.9575 0.008 0.992
#> GSM601847 2 0.1414 0.9494 0.020 0.980
#> GSM601852 1 0.0000 0.9671 1.000 0.000
#> GSM601862 1 0.0672 0.9632 0.992 0.008
#> GSM601753 2 0.0672 0.9575 0.008 0.992
#> GSM601783 1 0.0000 0.9671 1.000 0.000
#> GSM601793 1 0.0000 0.9671 1.000 0.000
#> GSM601798 2 0.0376 0.9560 0.004 0.996
#> GSM601828 1 0.0000 0.9671 1.000 0.000
#> GSM601838 2 0.0000 0.9542 0.000 1.000
#> GSM601843 2 0.0672 0.9575 0.008 0.992
#> GSM601858 2 0.0376 0.9559 0.004 0.996
#> GSM601868 1 0.0672 0.9632 0.992 0.008
#> GSM601748 1 0.0000 0.9671 1.000 0.000
#> GSM601758 1 0.0000 0.9671 1.000 0.000
#> GSM601763 1 0.9963 0.0690 0.536 0.464
#> GSM601768 2 0.0672 0.9575 0.008 0.992
#> GSM601773 2 0.0672 0.9575 0.008 0.992
#> GSM601778 1 0.0000 0.9671 1.000 0.000
#> GSM601788 2 0.1414 0.9428 0.020 0.980
#> GSM601803 2 0.0672 0.9575 0.008 0.992
#> GSM601808 1 0.0672 0.9632 0.992 0.008
#> GSM601813 1 0.0000 0.9671 1.000 0.000
#> GSM601818 1 0.0672 0.9632 0.992 0.008
#> GSM601823 1 0.0000 0.9671 1.000 0.000
#> GSM601833 2 0.0672 0.9575 0.008 0.992
#> GSM601848 1 0.0000 0.9671 1.000 0.000
#> GSM601853 1 0.0672 0.9632 0.992 0.008
#> GSM601863 1 0.0376 0.9652 0.996 0.004
#> GSM601754 2 0.0672 0.9575 0.008 0.992
#> GSM601784 2 0.0672 0.9575 0.008 0.992
#> GSM601794 1 0.0000 0.9671 1.000 0.000
#> GSM601799 2 0.0672 0.9575 0.008 0.992
#> GSM601829 1 0.0000 0.9671 1.000 0.000
#> GSM601839 2 0.0000 0.9542 0.000 1.000
#> GSM601844 1 0.0000 0.9671 1.000 0.000
#> GSM601859 2 0.0672 0.9575 0.008 0.992
#> GSM601869 1 0.0672 0.9632 0.992 0.008
#> GSM601749 1 0.0000 0.9671 1.000 0.000
#> GSM601759 1 0.0000 0.9671 1.000 0.000
#> GSM601764 1 0.0000 0.9671 1.000 0.000
#> GSM601769 2 0.0672 0.9575 0.008 0.992
#> GSM601774 2 0.0672 0.9575 0.008 0.992
#> GSM601779 1 0.0000 0.9671 1.000 0.000
#> GSM601789 2 0.0000 0.9542 0.000 1.000
#> GSM601804 2 0.0672 0.9575 0.008 0.992
#> GSM601809 1 0.2043 0.9472 0.968 0.032
#> GSM601814 2 0.0376 0.9560 0.004 0.996
#> GSM601819 1 0.0000 0.9671 1.000 0.000
#> GSM601824 2 0.0938 0.9551 0.012 0.988
#> GSM601834 2 0.0672 0.9575 0.008 0.992
#> GSM601849 1 0.0000 0.9671 1.000 0.000
#> GSM601854 1 0.0000 0.9671 1.000 0.000
#> GSM601864 2 0.0000 0.9542 0.000 1.000
#> GSM601755 2 0.0672 0.9575 0.008 0.992
#> GSM601785 2 0.0672 0.9575 0.008 0.992
#> GSM601795 1 0.2778 0.9271 0.952 0.048
#> GSM601800 2 0.0672 0.9575 0.008 0.992
#> GSM601830 1 0.0672 0.9632 0.992 0.008
#> GSM601840 2 0.0672 0.9575 0.008 0.992
#> GSM601845 2 0.8555 0.6286 0.280 0.720
#> GSM601860 2 0.0672 0.9575 0.008 0.992
#> GSM601870 1 0.0672 0.9632 0.992 0.008
#> GSM601750 1 0.0000 0.9671 1.000 0.000
#> GSM601760 1 0.0000 0.9671 1.000 0.000
#> GSM601765 2 0.0672 0.9575 0.008 0.992
#> GSM601770 2 0.0672 0.9575 0.008 0.992
#> GSM601775 2 0.7376 0.7475 0.208 0.792
#> GSM601780 1 0.0000 0.9671 1.000 0.000
#> GSM601790 2 0.0000 0.9542 0.000 1.000
#> GSM601805 2 0.0672 0.9575 0.008 0.992
#> GSM601810 1 0.0376 0.9652 0.996 0.004
#> GSM601815 2 0.0000 0.9542 0.000 1.000
#> GSM601820 1 0.0000 0.9671 1.000 0.000
#> GSM601825 2 0.0672 0.9575 0.008 0.992
#> GSM601835 2 0.0376 0.9560 0.004 0.996
#> GSM601850 1 0.5519 0.8406 0.872 0.128
#> GSM601855 1 0.0672 0.9632 0.992 0.008
#> GSM601865 2 0.0000 0.9542 0.000 1.000
#> GSM601756 2 0.0672 0.9575 0.008 0.992
#> GSM601786 2 0.0000 0.9542 0.000 1.000
#> GSM601796 1 0.0000 0.9671 1.000 0.000
#> GSM601801 2 0.0376 0.9560 0.004 0.996
#> GSM601831 1 0.0000 0.9671 1.000 0.000
#> GSM601841 1 0.0000 0.9671 1.000 0.000
#> GSM601846 2 0.7602 0.7217 0.220 0.780
#> GSM601861 2 0.0000 0.9542 0.000 1.000
#> GSM601871 2 0.9896 0.1929 0.440 0.560
#> GSM601751 2 0.5294 0.8556 0.120 0.880
#> GSM601761 1 0.0000 0.9671 1.000 0.000
#> GSM601766 2 0.9552 0.4250 0.376 0.624
#> GSM601771 2 0.0672 0.9575 0.008 0.992
#> GSM601776 1 0.0000 0.9671 1.000 0.000
#> GSM601781 1 0.6343 0.8046 0.840 0.160
#> GSM601791 1 0.0000 0.9671 1.000 0.000
#> GSM601806 2 0.0672 0.9575 0.008 0.992
#> GSM601811 1 0.0672 0.9632 0.992 0.008
#> GSM601816 1 0.0000 0.9671 1.000 0.000
#> GSM601821 2 0.0000 0.9542 0.000 1.000
#> GSM601826 1 0.0000 0.9671 1.000 0.000
#> GSM601836 1 0.0000 0.9671 1.000 0.000
#> GSM601851 1 0.0000 0.9671 1.000 0.000
#> GSM601856 1 0.0672 0.9632 0.992 0.008
#> GSM601866 1 0.0000 0.9671 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601752 2 0.3091 0.8871 0.072 0.912 0.016
#> GSM601782 3 0.6302 0.2529 0.480 0.000 0.520
#> GSM601792 1 0.1711 0.6664 0.960 0.008 0.032
#> GSM601797 1 0.7858 0.2842 0.572 0.364 0.064
#> GSM601827 3 0.6180 0.4337 0.416 0.000 0.584
#> GSM601837 2 0.4346 0.8261 0.000 0.816 0.184
#> GSM601842 2 0.0237 0.9111 0.004 0.996 0.000
#> GSM601857 3 0.4654 0.7760 0.208 0.000 0.792
#> GSM601867 3 0.2945 0.6043 0.004 0.088 0.908
#> GSM601747 1 0.5681 0.4787 0.748 0.016 0.236
#> GSM601757 1 0.6026 0.1535 0.624 0.000 0.376
#> GSM601762 2 0.0424 0.9113 0.008 0.992 0.000
#> GSM601767 2 0.0237 0.9107 0.004 0.996 0.000
#> GSM601772 2 0.0475 0.9101 0.004 0.992 0.004
#> GSM601777 1 0.7266 0.4418 0.688 0.080 0.232
#> GSM601787 3 0.4399 0.4993 0.000 0.188 0.812
#> GSM601802 2 0.2902 0.8936 0.064 0.920 0.016
#> GSM601807 3 0.1999 0.6463 0.012 0.036 0.952
#> GSM601812 1 0.6308 -0.2332 0.508 0.000 0.492
#> GSM601817 3 0.6295 0.3101 0.472 0.000 0.528
#> GSM601822 1 0.5092 0.5556 0.804 0.176 0.020
#> GSM601832 2 0.1647 0.9080 0.036 0.960 0.004
#> GSM601847 1 0.6836 0.1820 0.572 0.412 0.016
#> GSM601852 1 0.6192 0.0238 0.580 0.000 0.420
#> GSM601862 3 0.4504 0.7817 0.196 0.000 0.804
#> GSM601753 2 0.2902 0.8920 0.064 0.920 0.016
#> GSM601783 1 0.5810 0.2533 0.664 0.000 0.336
#> GSM601793 1 0.1832 0.6659 0.956 0.008 0.036
#> GSM601798 2 0.2031 0.9060 0.032 0.952 0.016
#> GSM601828 1 0.6305 -0.2078 0.516 0.000 0.484
#> GSM601838 2 0.4346 0.8261 0.000 0.816 0.184
#> GSM601843 2 0.0000 0.9104 0.000 1.000 0.000
#> GSM601858 2 0.3752 0.8523 0.000 0.856 0.144
#> GSM601868 3 0.4178 0.7824 0.172 0.000 0.828
#> GSM601748 1 0.6291 -0.1555 0.532 0.000 0.468
#> GSM601758 1 0.6079 0.1239 0.612 0.000 0.388
#> GSM601763 1 0.4110 0.5806 0.844 0.152 0.004
#> GSM601768 2 0.1647 0.9079 0.036 0.960 0.004
#> GSM601773 2 0.0237 0.9107 0.004 0.996 0.000
#> GSM601778 1 0.3369 0.6448 0.908 0.040 0.052
#> GSM601788 2 0.4045 0.8798 0.024 0.872 0.104
#> GSM601803 2 0.2703 0.8980 0.056 0.928 0.016
#> GSM601808 3 0.4235 0.7832 0.176 0.000 0.824
#> GSM601813 1 0.6140 0.0718 0.596 0.000 0.404
#> GSM601818 3 0.5560 0.6679 0.300 0.000 0.700
#> GSM601823 1 0.1015 0.6712 0.980 0.008 0.012
#> GSM601833 2 0.0424 0.9113 0.008 0.992 0.000
#> GSM601848 1 0.1015 0.6712 0.980 0.008 0.012
#> GSM601853 3 0.4291 0.7834 0.180 0.000 0.820
#> GSM601863 3 0.5098 0.7468 0.248 0.000 0.752
#> GSM601754 2 0.3183 0.8848 0.076 0.908 0.016
#> GSM601784 2 0.0237 0.9103 0.000 0.996 0.004
#> GSM601794 1 0.2339 0.6602 0.940 0.012 0.048
#> GSM601799 2 0.4139 0.8401 0.124 0.860 0.016
#> GSM601829 1 0.2066 0.6536 0.940 0.000 0.060
#> GSM601839 2 0.4346 0.8261 0.000 0.816 0.184
#> GSM601844 1 0.1999 0.6669 0.952 0.012 0.036
#> GSM601859 2 0.0424 0.9116 0.008 0.992 0.000
#> GSM601869 3 0.4555 0.7798 0.200 0.000 0.800
#> GSM601749 1 0.6045 0.1438 0.620 0.000 0.380
#> GSM601759 1 0.6180 0.0348 0.584 0.000 0.416
#> GSM601764 1 0.0983 0.6685 0.980 0.004 0.016
#> GSM601769 2 0.2772 0.8853 0.004 0.916 0.080
#> GSM601774 2 0.0237 0.9107 0.004 0.996 0.000
#> GSM601779 1 0.0661 0.6686 0.988 0.008 0.004
#> GSM601789 2 0.4465 0.8330 0.004 0.820 0.176
#> GSM601804 2 0.6941 0.1959 0.464 0.520 0.016
#> GSM601809 3 0.6677 0.5822 0.324 0.024 0.652
#> GSM601814 2 0.3112 0.8780 0.004 0.900 0.096
#> GSM601819 1 0.5291 0.3986 0.732 0.000 0.268
#> GSM601824 1 0.5318 0.5330 0.780 0.204 0.016
#> GSM601834 2 0.0237 0.9107 0.004 0.996 0.000
#> GSM601849 1 0.0892 0.6646 0.980 0.000 0.020
#> GSM601854 1 0.6295 -0.1696 0.528 0.000 0.472
#> GSM601864 2 0.4346 0.8261 0.000 0.816 0.184
#> GSM601755 2 0.2804 0.8943 0.060 0.924 0.016
#> GSM601785 2 0.1399 0.9093 0.028 0.968 0.004
#> GSM601795 1 0.4709 0.6160 0.852 0.092 0.056
#> GSM601800 2 0.2804 0.8943 0.060 0.924 0.016
#> GSM601830 3 0.3879 0.7741 0.152 0.000 0.848
#> GSM601840 2 0.2955 0.8853 0.080 0.912 0.008
#> GSM601845 1 0.6527 0.2669 0.588 0.404 0.008
#> GSM601860 2 0.0592 0.9116 0.012 0.988 0.000
#> GSM601870 3 0.1267 0.6774 0.024 0.004 0.972
#> GSM601750 1 0.6299 -0.1815 0.524 0.000 0.476
#> GSM601760 1 0.5058 0.4358 0.756 0.000 0.244
#> GSM601765 2 0.1289 0.9093 0.032 0.968 0.000
#> GSM601770 2 0.0424 0.9113 0.008 0.992 0.000
#> GSM601775 2 0.6513 0.3740 0.400 0.592 0.008
#> GSM601780 1 0.1015 0.6712 0.980 0.008 0.012
#> GSM601790 2 0.3941 0.8439 0.000 0.844 0.156
#> GSM601805 2 0.2703 0.8980 0.056 0.928 0.016
#> GSM601810 3 0.4555 0.7810 0.200 0.000 0.800
#> GSM601815 2 0.4110 0.8474 0.004 0.844 0.152
#> GSM601820 1 0.6286 -0.1415 0.536 0.000 0.464
#> GSM601825 2 0.2339 0.9029 0.048 0.940 0.012
#> GSM601835 2 0.1267 0.9067 0.004 0.972 0.024
#> GSM601850 1 0.3910 0.6191 0.876 0.104 0.020
#> GSM601855 3 0.3752 0.7694 0.144 0.000 0.856
#> GSM601865 2 0.4346 0.8261 0.000 0.816 0.184
#> GSM601756 2 0.2599 0.8984 0.052 0.932 0.016
#> GSM601786 2 0.4555 0.8134 0.000 0.800 0.200
#> GSM601796 1 0.2173 0.6594 0.944 0.008 0.048
#> GSM601801 2 0.1774 0.9080 0.024 0.960 0.016
#> GSM601831 3 0.4842 0.7635 0.224 0.000 0.776
#> GSM601841 1 0.5325 0.4691 0.748 0.004 0.248
#> GSM601846 1 0.8812 0.2370 0.516 0.360 0.124
#> GSM601861 2 0.3551 0.8588 0.000 0.868 0.132
#> GSM601871 3 0.4346 0.5022 0.000 0.184 0.816
#> GSM601751 2 0.4526 0.8489 0.104 0.856 0.040
#> GSM601761 1 0.1015 0.6712 0.980 0.008 0.012
#> GSM601766 1 0.5365 0.5128 0.744 0.252 0.004
#> GSM601771 2 0.1267 0.9103 0.024 0.972 0.004
#> GSM601776 1 0.1015 0.6712 0.980 0.008 0.012
#> GSM601781 1 0.3805 0.6290 0.884 0.092 0.024
#> GSM601791 1 0.1015 0.6712 0.980 0.008 0.012
#> GSM601806 2 0.1491 0.9097 0.016 0.968 0.016
#> GSM601811 3 0.4178 0.7824 0.172 0.000 0.828
#> GSM601816 1 0.1711 0.6686 0.960 0.008 0.032
#> GSM601821 2 0.3686 0.8542 0.000 0.860 0.140
#> GSM601826 1 0.1015 0.6712 0.980 0.008 0.012
#> GSM601836 1 0.1832 0.6637 0.956 0.036 0.008
#> GSM601851 1 0.0592 0.6681 0.988 0.000 0.012
#> GSM601856 3 0.4504 0.7816 0.196 0.000 0.804
#> GSM601866 3 0.6280 0.3502 0.460 0.000 0.540
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601752 4 0.5933 -0.2619 0.040 0.408 0.000 0.552
#> GSM601782 3 0.7895 0.1434 0.308 0.000 0.376 0.316
#> GSM601792 1 0.2198 0.6804 0.920 0.000 0.008 0.072
#> GSM601797 1 0.6550 0.3027 0.484 0.024 0.032 0.460
#> GSM601827 3 0.7910 0.1187 0.304 0.000 0.352 0.344
#> GSM601837 2 0.0895 0.5977 0.000 0.976 0.020 0.004
#> GSM601842 2 0.5085 0.6668 0.008 0.616 0.000 0.376
#> GSM601857 3 0.2919 0.7799 0.060 0.000 0.896 0.044
#> GSM601867 3 0.4317 0.6751 0.004 0.196 0.784 0.016
#> GSM601747 4 0.7746 -0.3321 0.416 0.016 0.144 0.424
#> GSM601757 1 0.7485 0.1578 0.472 0.000 0.192 0.336
#> GSM601762 2 0.5298 0.6679 0.016 0.612 0.000 0.372
#> GSM601767 2 0.5298 0.6679 0.016 0.612 0.000 0.372
#> GSM601772 2 0.5284 0.6713 0.016 0.616 0.000 0.368
#> GSM601777 1 0.7601 0.4059 0.564 0.020 0.220 0.196
#> GSM601787 3 0.4516 0.6357 0.000 0.252 0.736 0.012
#> GSM601802 4 0.5888 -0.2887 0.036 0.424 0.000 0.540
#> GSM601807 3 0.3658 0.7122 0.000 0.144 0.836 0.020
#> GSM601812 4 0.7863 -0.2555 0.344 0.000 0.276 0.380
#> GSM601817 4 0.7886 -0.3067 0.296 0.000 0.324 0.380
#> GSM601822 1 0.4699 0.4939 0.676 0.004 0.000 0.320
#> GSM601832 2 0.5894 0.6243 0.040 0.568 0.000 0.392
#> GSM601847 1 0.5883 0.3526 0.572 0.040 0.000 0.388
#> GSM601852 1 0.7799 0.0248 0.384 0.000 0.248 0.368
#> GSM601862 3 0.1724 0.7906 0.020 0.000 0.948 0.032
#> GSM601753 4 0.5543 -0.2867 0.020 0.424 0.000 0.556
#> GSM601783 1 0.7537 0.1379 0.456 0.000 0.196 0.348
#> GSM601793 1 0.2402 0.6783 0.912 0.000 0.012 0.076
#> GSM601798 4 0.5570 -0.3050 0.020 0.440 0.000 0.540
#> GSM601828 4 0.7879 -0.2714 0.332 0.000 0.288 0.380
#> GSM601838 2 0.0707 0.5984 0.000 0.980 0.020 0.000
#> GSM601843 2 0.5070 0.6676 0.008 0.620 0.000 0.372
#> GSM601858 2 0.4318 0.6498 0.004 0.776 0.012 0.208
#> GSM601868 3 0.1707 0.7901 0.024 0.004 0.952 0.020
#> GSM601748 4 0.7863 -0.2515 0.344 0.000 0.276 0.380
#> GSM601758 1 0.7634 0.1005 0.424 0.000 0.208 0.368
#> GSM601763 1 0.4690 0.5096 0.724 0.016 0.000 0.260
#> GSM601768 2 0.5613 0.6512 0.028 0.592 0.000 0.380
#> GSM601773 2 0.5284 0.6699 0.016 0.616 0.000 0.368
#> GSM601778 1 0.4500 0.6170 0.776 0.000 0.032 0.192
#> GSM601788 2 0.6832 0.5690 0.040 0.624 0.060 0.276
#> GSM601803 4 0.5888 -0.2887 0.036 0.424 0.000 0.540
#> GSM601808 3 0.1297 0.7918 0.016 0.000 0.964 0.020
#> GSM601813 1 0.7706 0.0763 0.412 0.000 0.224 0.364
#> GSM601818 3 0.7537 0.3287 0.196 0.000 0.456 0.348
#> GSM601823 1 0.0336 0.6870 0.992 0.000 0.000 0.008
#> GSM601833 2 0.5313 0.6668 0.016 0.608 0.000 0.376
#> GSM601848 1 0.0707 0.6884 0.980 0.000 0.000 0.020
#> GSM601853 3 0.1297 0.7916 0.016 0.000 0.964 0.020
#> GSM601863 3 0.3400 0.7675 0.064 0.000 0.872 0.064
#> GSM601754 4 0.5784 -0.2681 0.032 0.412 0.000 0.556
#> GSM601784 2 0.4661 0.6737 0.000 0.652 0.000 0.348
#> GSM601794 1 0.3052 0.6731 0.880 0.004 0.012 0.104
#> GSM601799 4 0.6276 -0.2437 0.064 0.380 0.000 0.556
#> GSM601829 1 0.2385 0.6769 0.920 0.000 0.028 0.052
#> GSM601839 2 0.0707 0.5984 0.000 0.980 0.020 0.000
#> GSM601844 1 0.1909 0.6834 0.940 0.004 0.008 0.048
#> GSM601859 2 0.5231 0.6561 0.012 0.604 0.000 0.384
#> GSM601869 3 0.2892 0.7853 0.036 0.000 0.896 0.068
#> GSM601749 1 0.7634 0.1005 0.424 0.000 0.208 0.368
#> GSM601759 1 0.7673 0.0888 0.416 0.000 0.216 0.368
#> GSM601764 1 0.1284 0.6848 0.964 0.012 0.000 0.024
#> GSM601769 2 0.2944 0.6377 0.004 0.868 0.000 0.128
#> GSM601774 2 0.5149 0.6763 0.016 0.648 0.000 0.336
#> GSM601779 1 0.0336 0.6870 0.992 0.000 0.000 0.008
#> GSM601789 2 0.0779 0.6004 0.000 0.980 0.016 0.004
#> GSM601804 4 0.6442 -0.1311 0.440 0.068 0.000 0.492
#> GSM601809 3 0.7468 0.5681 0.208 0.072 0.624 0.096
#> GSM601814 2 0.2593 0.6258 0.000 0.904 0.016 0.080
#> GSM601819 1 0.7475 0.1427 0.448 0.000 0.180 0.372
#> GSM601824 1 0.5666 0.3765 0.616 0.036 0.000 0.348
#> GSM601834 2 0.5298 0.6694 0.016 0.612 0.000 0.372
#> GSM601849 1 0.0804 0.6842 0.980 0.000 0.008 0.012
#> GSM601854 4 0.7874 -0.2528 0.348 0.000 0.280 0.372
#> GSM601864 2 0.0817 0.5949 0.000 0.976 0.024 0.000
#> GSM601755 4 0.5636 -0.2801 0.024 0.424 0.000 0.552
#> GSM601785 2 0.5229 0.5996 0.008 0.564 0.000 0.428
#> GSM601795 1 0.4922 0.5664 0.700 0.004 0.012 0.284
#> GSM601800 4 0.5636 -0.2801 0.024 0.424 0.000 0.552
#> GSM601830 3 0.2089 0.7887 0.020 0.012 0.940 0.028
#> GSM601840 4 0.6081 -0.4358 0.044 0.472 0.000 0.484
#> GSM601845 1 0.7461 0.2210 0.492 0.144 0.008 0.356
#> GSM601860 2 0.5256 0.6469 0.012 0.596 0.000 0.392
#> GSM601870 3 0.3160 0.7381 0.000 0.108 0.872 0.020
#> GSM601750 4 0.7859 -0.2483 0.352 0.000 0.272 0.376
#> GSM601760 1 0.6968 0.2627 0.552 0.000 0.140 0.308
#> GSM601765 2 0.5600 0.6558 0.028 0.596 0.000 0.376
#> GSM601770 2 0.5298 0.6679 0.016 0.612 0.000 0.372
#> GSM601775 4 0.7540 -0.2056 0.216 0.304 0.000 0.480
#> GSM601780 1 0.0336 0.6870 0.992 0.000 0.000 0.008
#> GSM601790 2 0.0592 0.6008 0.000 0.984 0.016 0.000
#> GSM601805 4 0.5888 -0.2887 0.036 0.424 0.000 0.540
#> GSM601810 3 0.3198 0.7721 0.040 0.000 0.880 0.080
#> GSM601815 2 0.1059 0.6062 0.000 0.972 0.016 0.012
#> GSM601820 4 0.7853 -0.2452 0.364 0.000 0.268 0.368
#> GSM601825 4 0.5827 -0.3470 0.032 0.436 0.000 0.532
#> GSM601835 2 0.5268 0.6740 0.012 0.636 0.004 0.348
#> GSM601850 1 0.3161 0.6670 0.864 0.012 0.000 0.124
#> GSM601855 3 0.1631 0.7911 0.016 0.008 0.956 0.020
#> GSM601865 2 0.0921 0.5909 0.000 0.972 0.028 0.000
#> GSM601756 4 0.5636 -0.2801 0.024 0.424 0.000 0.552
#> GSM601786 2 0.1854 0.5608 0.000 0.940 0.048 0.012
#> GSM601796 1 0.2847 0.6757 0.896 0.004 0.016 0.084
#> GSM601801 4 0.5564 -0.2980 0.020 0.436 0.000 0.544
#> GSM601831 3 0.6634 0.5071 0.108 0.000 0.580 0.312
#> GSM601841 1 0.4780 0.5908 0.788 0.000 0.116 0.096
#> GSM601846 1 0.7341 0.2915 0.464 0.048 0.052 0.436
#> GSM601861 2 0.1510 0.6128 0.000 0.956 0.016 0.028
#> GSM601871 3 0.4661 0.6312 0.000 0.256 0.728 0.016
#> GSM601751 2 0.7039 0.4892 0.076 0.492 0.016 0.416
#> GSM601761 1 0.0804 0.6836 0.980 0.000 0.008 0.012
#> GSM601766 1 0.6575 0.2452 0.560 0.092 0.000 0.348
#> GSM601771 2 0.5582 0.6229 0.024 0.576 0.000 0.400
#> GSM601776 1 0.0524 0.6862 0.988 0.000 0.004 0.008
#> GSM601781 1 0.3606 0.6637 0.856 0.020 0.008 0.116
#> GSM601791 1 0.0592 0.6881 0.984 0.000 0.000 0.016
#> GSM601806 4 0.5402 -0.3721 0.012 0.472 0.000 0.516
#> GSM601811 3 0.2587 0.7888 0.020 0.008 0.916 0.056
#> GSM601816 1 0.1635 0.6860 0.948 0.000 0.008 0.044
#> GSM601821 2 0.1510 0.6124 0.000 0.956 0.016 0.028
#> GSM601826 1 0.0817 0.6888 0.976 0.000 0.000 0.024
#> GSM601836 1 0.2987 0.6625 0.880 0.016 0.000 0.104
#> GSM601851 1 0.0672 0.6834 0.984 0.000 0.008 0.008
#> GSM601856 3 0.1174 0.7915 0.020 0.000 0.968 0.012
#> GSM601866 4 0.7869 -0.2556 0.340 0.000 0.280 0.380
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601752 2 0.3222 0.5902 0.000 0.864 0.028 0.088 0.020
#> GSM601782 1 0.5285 0.6847 0.724 0.000 0.164 0.044 0.068
#> GSM601792 4 0.5110 0.7965 0.104 0.076 0.020 0.768 0.032
#> GSM601797 4 0.6614 0.3229 0.000 0.380 0.048 0.492 0.080
#> GSM601827 1 0.5549 0.7030 0.716 0.000 0.140 0.076 0.068
#> GSM601837 5 0.3727 0.9496 0.000 0.216 0.016 0.000 0.768
#> GSM601842 2 0.3963 0.5823 0.000 0.732 0.008 0.004 0.256
#> GSM601857 3 0.4184 0.7663 0.232 0.000 0.740 0.024 0.004
#> GSM601867 3 0.2674 0.7972 0.004 0.000 0.856 0.000 0.140
#> GSM601747 1 0.5003 0.6744 0.752 0.148 0.004 0.064 0.032
#> GSM601757 1 0.1928 0.8656 0.920 0.000 0.004 0.072 0.004
#> GSM601762 2 0.4111 0.5656 0.000 0.708 0.004 0.008 0.280
#> GSM601767 2 0.4275 0.5597 0.000 0.696 0.008 0.008 0.288
#> GSM601772 2 0.4217 0.5640 0.000 0.704 0.004 0.012 0.280
#> GSM601777 4 0.5625 0.6395 0.000 0.044 0.160 0.700 0.096
#> GSM601787 3 0.2891 0.7743 0.000 0.000 0.824 0.000 0.176
#> GSM601802 2 0.3860 0.5907 0.000 0.820 0.024 0.124 0.032
#> GSM601807 3 0.2935 0.8183 0.024 0.000 0.876 0.012 0.088
#> GSM601812 1 0.1041 0.8861 0.964 0.000 0.032 0.004 0.000
#> GSM601817 1 0.1569 0.8756 0.944 0.000 0.044 0.004 0.008
#> GSM601822 4 0.2697 0.7933 0.016 0.056 0.004 0.900 0.024
#> GSM601832 2 0.4891 0.5966 0.000 0.716 0.008 0.068 0.208
#> GSM601847 4 0.3875 0.7442 0.000 0.124 0.012 0.816 0.048
#> GSM601852 1 0.1498 0.8930 0.952 0.000 0.008 0.016 0.024
#> GSM601862 3 0.3282 0.8085 0.188 0.000 0.804 0.000 0.008
#> GSM601753 2 0.2965 0.5953 0.000 0.876 0.028 0.084 0.012
#> GSM601783 1 0.2819 0.8647 0.884 0.000 0.008 0.076 0.032
#> GSM601793 4 0.5236 0.7951 0.108 0.076 0.020 0.760 0.036
#> GSM601798 2 0.3677 0.5767 0.000 0.840 0.032 0.096 0.032
#> GSM601828 1 0.2142 0.8746 0.920 0.000 0.048 0.004 0.028
#> GSM601838 5 0.3628 0.9506 0.000 0.216 0.012 0.000 0.772
#> GSM601843 2 0.4444 0.5684 0.000 0.708 0.012 0.016 0.264
#> GSM601858 2 0.4699 0.2556 0.008 0.588 0.008 0.000 0.396
#> GSM601868 3 0.3354 0.8230 0.140 0.000 0.832 0.004 0.024
#> GSM601748 1 0.0798 0.8896 0.976 0.000 0.016 0.008 0.000
#> GSM601758 1 0.0880 0.8874 0.968 0.000 0.000 0.032 0.000
#> GSM601763 4 0.5627 0.4092 0.056 0.352 0.008 0.580 0.004
#> GSM601768 2 0.4378 0.6079 0.000 0.740 0.004 0.040 0.216
#> GSM601773 2 0.4296 0.5528 0.000 0.692 0.008 0.008 0.292
#> GSM601778 4 0.3687 0.7828 0.016 0.024 0.040 0.856 0.064
#> GSM601788 2 0.6864 0.3902 0.004 0.568 0.088 0.076 0.264
#> GSM601803 2 0.3860 0.5907 0.000 0.820 0.024 0.124 0.032
#> GSM601808 3 0.2589 0.8343 0.092 0.000 0.888 0.008 0.012
#> GSM601813 1 0.0992 0.8908 0.968 0.000 0.008 0.024 0.000
#> GSM601818 1 0.3264 0.7723 0.836 0.000 0.140 0.004 0.020
#> GSM601823 4 0.3132 0.8079 0.172 0.008 0.000 0.820 0.000
#> GSM601833 2 0.4194 0.5681 0.000 0.708 0.004 0.012 0.276
#> GSM601848 4 0.3421 0.8137 0.164 0.016 0.000 0.816 0.004
#> GSM601853 3 0.3947 0.8241 0.108 0.000 0.816 0.012 0.064
#> GSM601863 3 0.4366 0.6546 0.320 0.000 0.664 0.016 0.000
#> GSM601754 2 0.3237 0.5912 0.000 0.860 0.028 0.096 0.016
#> GSM601784 2 0.4147 0.5079 0.000 0.676 0.008 0.000 0.316
#> GSM601794 4 0.5112 0.7919 0.088 0.080 0.020 0.772 0.040
#> GSM601799 2 0.2951 0.5847 0.000 0.860 0.028 0.112 0.000
#> GSM601829 4 0.4912 0.7871 0.128 0.008 0.032 0.768 0.064
#> GSM601839 5 0.3789 0.9475 0.000 0.212 0.020 0.000 0.768
#> GSM601844 4 0.5345 0.7967 0.140 0.076 0.016 0.740 0.028
#> GSM601859 2 0.3756 0.5919 0.000 0.744 0.008 0.000 0.248
#> GSM601869 3 0.4103 0.7736 0.228 0.000 0.748 0.012 0.012
#> GSM601749 1 0.1442 0.8870 0.952 0.000 0.004 0.032 0.012
#> GSM601759 1 0.0703 0.8894 0.976 0.000 0.000 0.024 0.000
#> GSM601764 4 0.4229 0.7967 0.152 0.048 0.004 0.788 0.008
#> GSM601769 5 0.3969 0.8271 0.000 0.304 0.004 0.000 0.692
#> GSM601774 2 0.4487 0.4850 0.000 0.652 0.008 0.008 0.332
#> GSM601779 4 0.3132 0.8084 0.172 0.008 0.000 0.820 0.000
#> GSM601789 5 0.3835 0.9238 0.000 0.244 0.012 0.000 0.744
#> GSM601804 2 0.4995 0.1580 0.000 0.552 0.024 0.420 0.004
#> GSM601809 3 0.6148 0.5701 0.308 0.000 0.576 0.024 0.092
#> GSM601814 5 0.3662 0.9210 0.000 0.252 0.004 0.000 0.744
#> GSM601819 1 0.2650 0.8623 0.892 0.000 0.004 0.068 0.036
#> GSM601824 4 0.4141 0.6562 0.024 0.248 0.000 0.728 0.000
#> GSM601834 2 0.4296 0.5499 0.000 0.692 0.008 0.008 0.292
#> GSM601849 4 0.3177 0.7886 0.208 0.000 0.000 0.792 0.000
#> GSM601854 1 0.2104 0.8818 0.924 0.000 0.044 0.008 0.024
#> GSM601864 5 0.3845 0.9407 0.000 0.208 0.024 0.000 0.768
#> GSM601755 2 0.3164 0.5913 0.000 0.868 0.028 0.084 0.020
#> GSM601785 2 0.3280 0.6207 0.000 0.808 0.004 0.004 0.184
#> GSM601795 4 0.4138 0.7653 0.020 0.100 0.020 0.824 0.036
#> GSM601800 2 0.3164 0.5913 0.000 0.868 0.028 0.084 0.020
#> GSM601830 3 0.4283 0.8094 0.060 0.000 0.808 0.040 0.092
#> GSM601840 2 0.3427 0.6253 0.000 0.844 0.004 0.056 0.096
#> GSM601845 2 0.6678 -0.0118 0.004 0.464 0.024 0.400 0.108
#> GSM601860 2 0.3676 0.6033 0.000 0.760 0.004 0.004 0.232
#> GSM601870 3 0.3267 0.8216 0.044 0.000 0.864 0.016 0.076
#> GSM601750 1 0.1673 0.8883 0.944 0.000 0.016 0.008 0.032
#> GSM601760 1 0.2605 0.7947 0.852 0.000 0.000 0.148 0.000
#> GSM601765 2 0.4792 0.5929 0.000 0.712 0.008 0.052 0.228
#> GSM601770 2 0.4230 0.5622 0.000 0.704 0.008 0.008 0.280
#> GSM601775 2 0.4963 0.4862 0.020 0.688 0.004 0.264 0.024
#> GSM601780 4 0.3132 0.8084 0.172 0.008 0.000 0.820 0.000
#> GSM601790 5 0.3305 0.9490 0.000 0.224 0.000 0.000 0.776
#> GSM601805 2 0.3945 0.5944 0.000 0.820 0.024 0.112 0.044
#> GSM601810 3 0.5068 0.6559 0.320 0.000 0.636 0.012 0.032
#> GSM601815 5 0.3336 0.9485 0.000 0.228 0.000 0.000 0.772
#> GSM601820 1 0.1314 0.8928 0.960 0.000 0.016 0.012 0.012
#> GSM601825 2 0.4333 0.6144 0.000 0.788 0.012 0.120 0.080
#> GSM601835 2 0.4546 0.5564 0.000 0.688 0.020 0.008 0.284
#> GSM601850 4 0.4642 0.8073 0.068 0.092 0.012 0.796 0.032
#> GSM601855 3 0.3709 0.8222 0.068 0.000 0.840 0.020 0.072
#> GSM601865 5 0.3897 0.9378 0.000 0.204 0.028 0.000 0.768
#> GSM601756 2 0.3164 0.5913 0.000 0.868 0.028 0.084 0.020
#> GSM601786 5 0.4052 0.9215 0.000 0.204 0.028 0.004 0.764
#> GSM601796 4 0.5236 0.7951 0.108 0.076 0.020 0.760 0.036
#> GSM601801 2 0.3800 0.5778 0.000 0.836 0.028 0.084 0.052
#> GSM601831 1 0.5302 0.5933 0.688 0.000 0.232 0.036 0.044
#> GSM601841 4 0.7490 0.5452 0.272 0.060 0.112 0.528 0.028
#> GSM601846 4 0.6693 0.5766 0.004 0.184 0.080 0.620 0.112
#> GSM601861 5 0.3491 0.9476 0.000 0.228 0.004 0.000 0.768
#> GSM601871 3 0.2690 0.7859 0.000 0.000 0.844 0.000 0.156
#> GSM601751 2 0.4982 0.6131 0.004 0.756 0.032 0.068 0.140
#> GSM601761 4 0.3282 0.8004 0.188 0.008 0.000 0.804 0.000
#> GSM601766 2 0.6318 0.1743 0.024 0.500 0.008 0.404 0.064
#> GSM601771 2 0.3883 0.6177 0.000 0.764 0.004 0.016 0.216
#> GSM601776 4 0.3171 0.8069 0.176 0.008 0.000 0.816 0.000
#> GSM601781 4 0.4435 0.7993 0.044 0.056 0.016 0.816 0.068
#> GSM601791 4 0.3319 0.8131 0.160 0.020 0.000 0.820 0.000
#> GSM601806 2 0.4319 0.5820 0.000 0.800 0.024 0.080 0.096
#> GSM601811 3 0.4701 0.7404 0.252 0.000 0.700 0.004 0.044
#> GSM601816 4 0.3621 0.8204 0.124 0.032 0.008 0.832 0.004
#> GSM601821 5 0.3521 0.9447 0.000 0.232 0.004 0.000 0.764
#> GSM601826 4 0.3224 0.8123 0.160 0.016 0.000 0.824 0.000
#> GSM601836 4 0.4653 0.7708 0.064 0.136 0.008 0.776 0.016
#> GSM601851 4 0.3074 0.7958 0.196 0.000 0.000 0.804 0.000
#> GSM601856 3 0.3038 0.8330 0.080 0.000 0.872 0.008 0.040
#> GSM601866 1 0.0898 0.8895 0.972 0.000 0.020 0.008 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601752 2 0.0798 0.4713 0.000 0.976 0.004 0.004 0.004 0.012
#> GSM601782 1 0.6657 0.5385 0.580 0.004 0.124 0.204 0.056 0.032
#> GSM601792 6 0.4325 0.6486 0.040 0.056 0.012 0.096 0.004 0.792
#> GSM601797 2 0.6110 -0.3786 0.000 0.528 0.012 0.188 0.008 0.264
#> GSM601827 1 0.6594 0.5042 0.556 0.000 0.108 0.248 0.028 0.060
#> GSM601837 5 0.2544 0.9448 0.000 0.140 0.004 0.004 0.852 0.000
#> GSM601842 2 0.5931 0.5876 0.000 0.492 0.004 0.280 0.224 0.000
#> GSM601857 3 0.4380 0.6988 0.232 0.000 0.712 0.040 0.012 0.004
#> GSM601867 3 0.3895 0.7357 0.012 0.000 0.796 0.076 0.112 0.004
#> GSM601747 1 0.6073 0.3990 0.580 0.020 0.024 0.300 0.024 0.052
#> GSM601757 1 0.2737 0.8200 0.868 0.000 0.024 0.012 0.000 0.096
#> GSM601762 2 0.6029 0.5830 0.000 0.488 0.000 0.280 0.224 0.008
#> GSM601767 2 0.6021 0.5810 0.000 0.492 0.000 0.272 0.228 0.008
#> GSM601772 2 0.6034 0.5805 0.000 0.488 0.000 0.276 0.228 0.008
#> GSM601777 6 0.6483 0.1634 0.000 0.052 0.124 0.264 0.016 0.544
#> GSM601787 3 0.4037 0.6924 0.000 0.000 0.736 0.064 0.200 0.000
#> GSM601802 2 0.0891 0.4770 0.000 0.968 0.000 0.008 0.000 0.024
#> GSM601807 3 0.4300 0.7384 0.012 0.000 0.764 0.148 0.064 0.012
#> GSM601812 1 0.2258 0.8303 0.912 0.000 0.040 0.028 0.008 0.012
#> GSM601817 1 0.3242 0.8060 0.856 0.000 0.048 0.068 0.016 0.012
#> GSM601822 6 0.2973 0.6681 0.004 0.068 0.004 0.064 0.000 0.860
#> GSM601832 2 0.6425 0.5860 0.000 0.492 0.004 0.288 0.184 0.032
#> GSM601847 6 0.3997 0.5727 0.000 0.148 0.004 0.072 0.004 0.772
#> GSM601852 1 0.2232 0.8366 0.916 0.000 0.016 0.028 0.012 0.028
#> GSM601862 3 0.3144 0.7459 0.172 0.000 0.808 0.016 0.000 0.004
#> GSM601753 2 0.0653 0.4734 0.000 0.980 0.000 0.004 0.004 0.012
#> GSM601783 1 0.3349 0.8033 0.844 0.004 0.012 0.048 0.004 0.088
#> GSM601793 6 0.4384 0.6451 0.040 0.060 0.012 0.096 0.004 0.788
#> GSM601798 2 0.1623 0.4463 0.000 0.940 0.004 0.004 0.032 0.020
#> GSM601828 1 0.4700 0.7089 0.736 0.000 0.080 0.152 0.024 0.008
#> GSM601838 5 0.2442 0.9464 0.000 0.144 0.004 0.000 0.852 0.000
#> GSM601843 2 0.6252 0.5592 0.000 0.460 0.004 0.280 0.248 0.008
#> GSM601858 2 0.6367 0.4082 0.000 0.388 0.012 0.276 0.324 0.000
#> GSM601868 3 0.3454 0.7613 0.124 0.000 0.824 0.028 0.020 0.004
#> GSM601748 1 0.1377 0.8323 0.952 0.000 0.016 0.024 0.004 0.004
#> GSM601758 1 0.1267 0.8304 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM601763 6 0.6906 -0.2836 0.064 0.200 0.004 0.244 0.004 0.484
#> GSM601768 2 0.6218 0.5946 0.000 0.496 0.000 0.284 0.196 0.024
#> GSM601773 2 0.5912 0.5767 0.000 0.500 0.000 0.264 0.232 0.004
#> GSM601778 6 0.4513 0.5463 0.004 0.032 0.020 0.196 0.012 0.736
#> GSM601788 2 0.8048 0.2522 0.004 0.364 0.108 0.212 0.272 0.040
#> GSM601803 2 0.1036 0.4804 0.000 0.964 0.000 0.008 0.004 0.024
#> GSM601808 3 0.2602 0.7789 0.052 0.000 0.888 0.040 0.020 0.000
#> GSM601813 1 0.2089 0.8299 0.908 0.000 0.012 0.004 0.004 0.072
#> GSM601818 1 0.3237 0.7666 0.836 0.000 0.108 0.048 0.004 0.004
#> GSM601823 6 0.2101 0.7296 0.100 0.004 0.000 0.004 0.000 0.892
#> GSM601833 2 0.6159 0.5831 0.000 0.484 0.004 0.280 0.224 0.008
#> GSM601848 6 0.2095 0.7329 0.076 0.016 0.000 0.004 0.000 0.904
#> GSM601853 3 0.5245 0.7320 0.064 0.000 0.700 0.168 0.056 0.012
#> GSM601863 3 0.4234 0.6445 0.284 0.000 0.684 0.016 0.004 0.012
#> GSM601754 2 0.0862 0.4724 0.000 0.972 0.000 0.008 0.004 0.016
#> GSM601784 2 0.5974 0.4800 0.000 0.440 0.000 0.248 0.312 0.000
#> GSM601794 6 0.4453 0.6334 0.036 0.060 0.012 0.108 0.004 0.780
#> GSM601799 2 0.0972 0.4564 0.000 0.964 0.000 0.008 0.000 0.028
#> GSM601829 6 0.5327 0.4329 0.064 0.004 0.012 0.264 0.016 0.640
#> GSM601839 5 0.2442 0.9464 0.000 0.144 0.004 0.000 0.852 0.000
#> GSM601844 6 0.4724 0.6355 0.068 0.044 0.012 0.124 0.000 0.752
#> GSM601859 2 0.5624 0.5996 0.000 0.536 0.000 0.264 0.200 0.000
#> GSM601869 3 0.4405 0.7106 0.204 0.000 0.732 0.036 0.008 0.020
#> GSM601749 1 0.1719 0.8325 0.928 0.000 0.000 0.008 0.008 0.056
#> GSM601759 1 0.1333 0.8338 0.944 0.000 0.000 0.008 0.000 0.048
#> GSM601764 6 0.3514 0.6404 0.088 0.000 0.000 0.108 0.000 0.804
#> GSM601769 5 0.4328 0.7812 0.000 0.192 0.000 0.092 0.716 0.000
#> GSM601774 2 0.6186 0.5116 0.000 0.444 0.000 0.268 0.280 0.008
#> GSM601779 6 0.2213 0.7290 0.100 0.004 0.000 0.008 0.000 0.888
#> GSM601789 5 0.3994 0.8746 0.000 0.140 0.008 0.080 0.772 0.000
#> GSM601804 2 0.3426 -0.0242 0.000 0.720 0.000 0.004 0.000 0.276
#> GSM601809 3 0.6560 0.5880 0.220 0.000 0.572 0.096 0.092 0.020
#> GSM601814 5 0.3053 0.9321 0.000 0.168 0.000 0.020 0.812 0.000
#> GSM601819 1 0.2770 0.8206 0.884 0.000 0.008 0.052 0.016 0.040
#> GSM601824 6 0.3553 0.5350 0.004 0.128 0.000 0.064 0.000 0.804
#> GSM601834 2 0.5956 0.5759 0.000 0.488 0.004 0.272 0.236 0.000
#> GSM601849 6 0.1814 0.7307 0.100 0.000 0.000 0.000 0.000 0.900
#> GSM601854 1 0.4119 0.7662 0.800 0.000 0.076 0.084 0.024 0.016
#> GSM601864 5 0.3101 0.9255 0.000 0.136 0.012 0.020 0.832 0.000
#> GSM601755 2 0.0551 0.4762 0.000 0.984 0.004 0.000 0.004 0.008
#> GSM601785 2 0.5539 0.5949 0.000 0.564 0.000 0.272 0.160 0.004
#> GSM601795 6 0.4459 0.6004 0.004 0.108 0.012 0.116 0.004 0.756
#> GSM601800 2 0.0603 0.4717 0.000 0.980 0.004 0.000 0.000 0.016
#> GSM601830 3 0.6021 0.6287 0.032 0.000 0.564 0.308 0.068 0.028
#> GSM601840 2 0.5819 0.5323 0.000 0.596 0.008 0.268 0.088 0.040
#> GSM601845 4 0.7316 0.0408 0.004 0.248 0.008 0.420 0.076 0.244
#> GSM601860 2 0.5850 0.5990 0.000 0.532 0.004 0.272 0.188 0.004
#> GSM601870 3 0.4753 0.7388 0.032 0.000 0.740 0.148 0.068 0.012
#> GSM601750 1 0.2572 0.8240 0.896 0.000 0.016 0.052 0.024 0.012
#> GSM601760 1 0.2402 0.7850 0.856 0.000 0.000 0.004 0.000 0.140
#> GSM601765 2 0.6467 0.5861 0.000 0.484 0.004 0.288 0.192 0.032
#> GSM601770 2 0.6038 0.5810 0.000 0.488 0.000 0.272 0.232 0.008
#> GSM601775 2 0.6501 0.1864 0.020 0.464 0.000 0.288 0.008 0.220
#> GSM601780 6 0.2213 0.7290 0.100 0.004 0.000 0.008 0.000 0.888
#> GSM601790 5 0.2886 0.9444 0.000 0.144 0.004 0.016 0.836 0.000
#> GSM601805 2 0.1167 0.4814 0.000 0.960 0.000 0.008 0.012 0.020
#> GSM601810 3 0.5484 0.6281 0.248 0.000 0.636 0.076 0.028 0.012
#> GSM601815 5 0.3000 0.9453 0.000 0.156 0.004 0.016 0.824 0.000
#> GSM601820 1 0.1223 0.8333 0.960 0.000 0.016 0.008 0.004 0.012
#> GSM601825 2 0.3423 0.5189 0.000 0.836 0.000 0.080 0.056 0.028
#> GSM601835 2 0.6408 0.5316 0.000 0.424 0.012 0.312 0.248 0.004
#> GSM601850 6 0.3150 0.6584 0.008 0.088 0.000 0.060 0.000 0.844
#> GSM601855 3 0.4827 0.7303 0.032 0.000 0.720 0.184 0.052 0.012
#> GSM601865 5 0.2917 0.9357 0.000 0.136 0.008 0.016 0.840 0.000
#> GSM601756 2 0.0551 0.4762 0.000 0.984 0.004 0.000 0.004 0.008
#> GSM601786 5 0.2581 0.9359 0.000 0.128 0.000 0.016 0.856 0.000
#> GSM601796 6 0.4723 0.6394 0.048 0.056 0.016 0.112 0.004 0.764
#> GSM601801 2 0.1194 0.4634 0.000 0.956 0.004 0.000 0.032 0.008
#> GSM601831 1 0.6489 0.5078 0.568 0.000 0.168 0.200 0.028 0.036
#> GSM601841 6 0.7267 0.3363 0.144 0.048 0.156 0.084 0.012 0.556
#> GSM601846 4 0.6856 -0.1204 0.004 0.092 0.072 0.420 0.016 0.396
#> GSM601861 5 0.2945 0.9439 0.000 0.156 0.000 0.020 0.824 0.000
#> GSM601871 3 0.3894 0.7106 0.000 0.000 0.760 0.072 0.168 0.000
#> GSM601751 2 0.6447 0.5625 0.000 0.544 0.032 0.248 0.156 0.020
#> GSM601761 6 0.2355 0.7249 0.112 0.004 0.000 0.008 0.000 0.876
#> GSM601766 2 0.7510 -0.0373 0.024 0.348 0.004 0.316 0.052 0.256
#> GSM601771 2 0.6248 0.5989 0.000 0.512 0.004 0.280 0.180 0.024
#> GSM601776 6 0.2308 0.7265 0.108 0.004 0.000 0.008 0.000 0.880
#> GSM601781 6 0.4317 0.5558 0.000 0.048 0.004 0.204 0.012 0.732
#> GSM601791 6 0.2255 0.7314 0.088 0.004 0.000 0.016 0.000 0.892
#> GSM601806 2 0.1410 0.4900 0.000 0.944 0.000 0.008 0.044 0.004
#> GSM601811 3 0.5123 0.6825 0.192 0.000 0.688 0.080 0.036 0.004
#> GSM601816 6 0.2422 0.7270 0.056 0.016 0.004 0.024 0.000 0.900
#> GSM601821 5 0.2945 0.9439 0.000 0.156 0.000 0.020 0.824 0.000
#> GSM601826 6 0.2002 0.7330 0.076 0.012 0.000 0.004 0.000 0.908
#> GSM601836 6 0.5027 0.2450 0.020 0.052 0.004 0.260 0.004 0.660
#> GSM601851 6 0.1957 0.7273 0.112 0.000 0.000 0.000 0.000 0.888
#> GSM601856 3 0.4476 0.7552 0.040 0.000 0.760 0.148 0.040 0.012
#> GSM601866 1 0.1793 0.8271 0.932 0.000 0.040 0.016 0.004 0.008
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n time(p) gender(p) k
#> CV:kmeans 120 0.576 0.241 2
#> CV:kmeans 96 0.381 0.280 3
#> CV:kmeans 78 0.187 0.783 4
#> CV:kmeans 116 0.257 0.422 5
#> CV:kmeans 97 0.441 0.770 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 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.867 0.929 0.969 0.5040 0.496 0.496
#> 3 3 0.632 0.782 0.877 0.3096 0.773 0.572
#> 4 4 0.467 0.554 0.714 0.1261 0.857 0.611
#> 5 5 0.476 0.430 0.640 0.0667 0.950 0.816
#> 6 6 0.499 0.298 0.568 0.0406 0.910 0.652
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
#> GSM601752 2 0.0000 0.958 0.000 1.000
#> GSM601782 1 0.0000 0.977 1.000 0.000
#> GSM601792 1 0.0000 0.977 1.000 0.000
#> GSM601797 2 0.9427 0.471 0.360 0.640
#> GSM601827 1 0.0000 0.977 1.000 0.000
#> GSM601837 2 0.0000 0.958 0.000 1.000
#> GSM601842 2 0.0000 0.958 0.000 1.000
#> GSM601857 1 0.0000 0.977 1.000 0.000
#> GSM601867 1 0.9732 0.301 0.596 0.404
#> GSM601747 1 0.2043 0.953 0.968 0.032
#> GSM601757 1 0.0000 0.977 1.000 0.000
#> GSM601762 2 0.0000 0.958 0.000 1.000
#> GSM601767 2 0.0000 0.958 0.000 1.000
#> GSM601772 2 0.0000 0.958 0.000 1.000
#> GSM601777 1 0.8207 0.659 0.744 0.256
#> GSM601787 2 0.4939 0.861 0.108 0.892
#> GSM601802 2 0.0000 0.958 0.000 1.000
#> GSM601807 1 0.4690 0.887 0.900 0.100
#> GSM601812 1 0.0000 0.977 1.000 0.000
#> GSM601817 1 0.0000 0.977 1.000 0.000
#> GSM601822 2 0.9248 0.519 0.340 0.660
#> GSM601832 2 0.0000 0.958 0.000 1.000
#> GSM601847 2 0.0376 0.955 0.004 0.996
#> GSM601852 1 0.0000 0.977 1.000 0.000
#> GSM601862 1 0.0000 0.977 1.000 0.000
#> GSM601753 2 0.0000 0.958 0.000 1.000
#> GSM601783 1 0.0000 0.977 1.000 0.000
#> GSM601793 1 0.0000 0.977 1.000 0.000
#> GSM601798 2 0.0000 0.958 0.000 1.000
#> GSM601828 1 0.0000 0.977 1.000 0.000
#> GSM601838 2 0.0000 0.958 0.000 1.000
#> GSM601843 2 0.0000 0.958 0.000 1.000
#> GSM601858 2 0.0000 0.958 0.000 1.000
#> GSM601868 1 0.0000 0.977 1.000 0.000
#> GSM601748 1 0.0000 0.977 1.000 0.000
#> GSM601758 1 0.0000 0.977 1.000 0.000
#> GSM601763 2 0.9993 0.116 0.484 0.516
#> GSM601768 2 0.0000 0.958 0.000 1.000
#> GSM601773 2 0.0000 0.958 0.000 1.000
#> GSM601778 1 0.1843 0.956 0.972 0.028
#> GSM601788 2 0.1633 0.940 0.024 0.976
#> GSM601803 2 0.0000 0.958 0.000 1.000
#> GSM601808 1 0.0000 0.977 1.000 0.000
#> GSM601813 1 0.0000 0.977 1.000 0.000
#> GSM601818 1 0.0000 0.977 1.000 0.000
#> GSM601823 1 0.0000 0.977 1.000 0.000
#> GSM601833 2 0.0000 0.958 0.000 1.000
#> GSM601848 1 0.0000 0.977 1.000 0.000
#> GSM601853 1 0.0000 0.977 1.000 0.000
#> GSM601863 1 0.0000 0.977 1.000 0.000
#> GSM601754 2 0.0000 0.958 0.000 1.000
#> GSM601784 2 0.0000 0.958 0.000 1.000
#> GSM601794 1 0.0376 0.974 0.996 0.004
#> GSM601799 2 0.0000 0.958 0.000 1.000
#> GSM601829 1 0.0000 0.977 1.000 0.000
#> GSM601839 2 0.0000 0.958 0.000 1.000
#> GSM601844 1 0.0376 0.974 0.996 0.004
#> GSM601859 2 0.0000 0.958 0.000 1.000
#> GSM601869 1 0.0000 0.977 1.000 0.000
#> GSM601749 1 0.0000 0.977 1.000 0.000
#> GSM601759 1 0.0000 0.977 1.000 0.000
#> GSM601764 1 0.0000 0.977 1.000 0.000
#> GSM601769 2 0.0000 0.958 0.000 1.000
#> GSM601774 2 0.0000 0.958 0.000 1.000
#> GSM601779 1 0.0000 0.977 1.000 0.000
#> GSM601789 2 0.0000 0.958 0.000 1.000
#> GSM601804 2 0.0000 0.958 0.000 1.000
#> GSM601809 1 0.4298 0.899 0.912 0.088
#> GSM601814 2 0.0000 0.958 0.000 1.000
#> GSM601819 1 0.0000 0.977 1.000 0.000
#> GSM601824 2 0.0000 0.958 0.000 1.000
#> GSM601834 2 0.0000 0.958 0.000 1.000
#> GSM601849 1 0.0000 0.977 1.000 0.000
#> GSM601854 1 0.0000 0.977 1.000 0.000
#> GSM601864 2 0.0000 0.958 0.000 1.000
#> GSM601755 2 0.0000 0.958 0.000 1.000
#> GSM601785 2 0.0000 0.958 0.000 1.000
#> GSM601795 1 0.4939 0.878 0.892 0.108
#> GSM601800 2 0.0000 0.958 0.000 1.000
#> GSM601830 1 0.0000 0.977 1.000 0.000
#> GSM601840 2 0.0376 0.955 0.004 0.996
#> GSM601845 2 0.7674 0.722 0.224 0.776
#> GSM601860 2 0.0000 0.958 0.000 1.000
#> GSM601870 1 0.0000 0.977 1.000 0.000
#> GSM601750 1 0.0000 0.977 1.000 0.000
#> GSM601760 1 0.0000 0.977 1.000 0.000
#> GSM601765 2 0.0000 0.958 0.000 1.000
#> GSM601770 2 0.0000 0.958 0.000 1.000
#> GSM601775 2 0.5519 0.846 0.128 0.872
#> GSM601780 1 0.0000 0.977 1.000 0.000
#> GSM601790 2 0.0000 0.958 0.000 1.000
#> GSM601805 2 0.0000 0.958 0.000 1.000
#> GSM601810 1 0.0000 0.977 1.000 0.000
#> GSM601815 2 0.0000 0.958 0.000 1.000
#> GSM601820 1 0.0000 0.977 1.000 0.000
#> GSM601825 2 0.0000 0.958 0.000 1.000
#> GSM601835 2 0.0000 0.958 0.000 1.000
#> GSM601850 1 0.6048 0.830 0.852 0.148
#> GSM601855 1 0.0000 0.977 1.000 0.000
#> GSM601865 2 0.0000 0.958 0.000 1.000
#> GSM601756 2 0.0000 0.958 0.000 1.000
#> GSM601786 2 0.0000 0.958 0.000 1.000
#> GSM601796 1 0.0000 0.977 1.000 0.000
#> GSM601801 2 0.0000 0.958 0.000 1.000
#> GSM601831 1 0.0000 0.977 1.000 0.000
#> GSM601841 1 0.0000 0.977 1.000 0.000
#> GSM601846 2 0.6247 0.813 0.156 0.844
#> GSM601861 2 0.0000 0.958 0.000 1.000
#> GSM601871 2 0.9044 0.534 0.320 0.680
#> GSM601751 2 0.4022 0.894 0.080 0.920
#> GSM601761 1 0.0000 0.977 1.000 0.000
#> GSM601766 2 0.8608 0.620 0.284 0.716
#> GSM601771 2 0.0000 0.958 0.000 1.000
#> GSM601776 1 0.0000 0.977 1.000 0.000
#> GSM601781 1 0.5842 0.839 0.860 0.140
#> GSM601791 1 0.2043 0.953 0.968 0.032
#> GSM601806 2 0.0000 0.958 0.000 1.000
#> GSM601811 1 0.0672 0.971 0.992 0.008
#> GSM601816 1 0.0376 0.974 0.996 0.004
#> GSM601821 2 0.0000 0.958 0.000 1.000
#> GSM601826 1 0.0000 0.977 1.000 0.000
#> GSM601836 1 0.1633 0.960 0.976 0.024
#> GSM601851 1 0.0000 0.977 1.000 0.000
#> GSM601856 1 0.0000 0.977 1.000 0.000
#> GSM601866 1 0.0000 0.977 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601752 2 0.4521 0.822 0.180 0.816 0.004
#> GSM601782 3 0.3619 0.813 0.136 0.000 0.864
#> GSM601792 1 0.2537 0.803 0.920 0.000 0.080
#> GSM601797 1 0.9738 0.355 0.444 0.304 0.252
#> GSM601827 3 0.3038 0.821 0.104 0.000 0.896
#> GSM601837 2 0.1015 0.925 0.008 0.980 0.012
#> GSM601842 2 0.0661 0.928 0.004 0.988 0.008
#> GSM601857 3 0.1289 0.850 0.032 0.000 0.968
#> GSM601867 3 0.2866 0.794 0.008 0.076 0.916
#> GSM601747 3 0.8067 0.515 0.284 0.100 0.616
#> GSM601757 3 0.4654 0.768 0.208 0.000 0.792
#> GSM601762 2 0.0237 0.927 0.000 0.996 0.004
#> GSM601767 2 0.0237 0.927 0.004 0.996 0.000
#> GSM601772 2 0.0000 0.926 0.000 1.000 0.000
#> GSM601777 1 0.8951 0.280 0.476 0.128 0.396
#> GSM601787 3 0.5156 0.629 0.008 0.216 0.776
#> GSM601802 2 0.3192 0.891 0.112 0.888 0.000
#> GSM601807 3 0.2173 0.817 0.008 0.048 0.944
#> GSM601812 3 0.2356 0.846 0.072 0.000 0.928
#> GSM601817 3 0.1643 0.850 0.044 0.000 0.956
#> GSM601822 1 0.2297 0.792 0.944 0.036 0.020
#> GSM601832 2 0.2945 0.895 0.088 0.908 0.004
#> GSM601847 1 0.4968 0.688 0.800 0.188 0.012
#> GSM601852 3 0.4399 0.783 0.188 0.000 0.812
#> GSM601862 3 0.0424 0.846 0.008 0.000 0.992
#> GSM601753 2 0.3038 0.896 0.104 0.896 0.000
#> GSM601783 3 0.6045 0.491 0.380 0.000 0.620
#> GSM601793 1 0.4399 0.732 0.812 0.000 0.188
#> GSM601798 2 0.2400 0.914 0.064 0.932 0.004
#> GSM601828 3 0.2448 0.845 0.076 0.000 0.924
#> GSM601838 2 0.0661 0.926 0.008 0.988 0.004
#> GSM601843 2 0.0424 0.927 0.000 0.992 0.008
#> GSM601858 2 0.3587 0.869 0.020 0.892 0.088
#> GSM601868 3 0.0000 0.844 0.000 0.000 1.000
#> GSM601748 3 0.3267 0.831 0.116 0.000 0.884
#> GSM601758 3 0.6140 0.447 0.404 0.000 0.596
#> GSM601763 1 0.2031 0.807 0.952 0.016 0.032
#> GSM601768 2 0.2711 0.902 0.088 0.912 0.000
#> GSM601773 2 0.0424 0.927 0.008 0.992 0.000
#> GSM601778 1 0.4840 0.754 0.816 0.016 0.168
#> GSM601788 2 0.5939 0.771 0.072 0.788 0.140
#> GSM601803 2 0.2878 0.900 0.096 0.904 0.000
#> GSM601808 3 0.0237 0.845 0.004 0.000 0.996
#> GSM601813 3 0.5497 0.660 0.292 0.000 0.708
#> GSM601818 3 0.1289 0.850 0.032 0.000 0.968
#> GSM601823 1 0.0747 0.801 0.984 0.000 0.016
#> GSM601833 2 0.0592 0.928 0.012 0.988 0.000
#> GSM601848 1 0.0747 0.801 0.984 0.000 0.016
#> GSM601853 3 0.0237 0.845 0.004 0.000 0.996
#> GSM601863 3 0.1860 0.850 0.052 0.000 0.948
#> GSM601754 2 0.4110 0.852 0.152 0.844 0.004
#> GSM601784 2 0.0237 0.927 0.000 0.996 0.004
#> GSM601794 1 0.4110 0.773 0.844 0.004 0.152
#> GSM601799 2 0.4887 0.760 0.228 0.772 0.000
#> GSM601829 1 0.6302 0.134 0.520 0.000 0.480
#> GSM601839 2 0.1170 0.923 0.008 0.976 0.016
#> GSM601844 1 0.4589 0.742 0.820 0.008 0.172
#> GSM601859 2 0.0747 0.927 0.016 0.984 0.000
#> GSM601869 3 0.0892 0.849 0.020 0.000 0.980
#> GSM601749 3 0.6079 0.482 0.388 0.000 0.612
#> GSM601759 3 0.5254 0.705 0.264 0.000 0.736
#> GSM601764 1 0.2772 0.795 0.916 0.004 0.080
#> GSM601769 2 0.0237 0.926 0.004 0.996 0.000
#> GSM601774 2 0.0424 0.927 0.008 0.992 0.000
#> GSM601779 1 0.0892 0.803 0.980 0.000 0.020
#> GSM601789 2 0.1315 0.922 0.008 0.972 0.020
#> GSM601804 1 0.5650 0.484 0.688 0.312 0.000
#> GSM601809 3 0.3589 0.820 0.052 0.048 0.900
#> GSM601814 2 0.0424 0.927 0.008 0.992 0.000
#> GSM601819 3 0.6280 0.262 0.460 0.000 0.540
#> GSM601824 1 0.1529 0.789 0.960 0.040 0.000
#> GSM601834 2 0.0000 0.926 0.000 1.000 0.000
#> GSM601849 1 0.2537 0.797 0.920 0.000 0.080
#> GSM601854 3 0.4121 0.801 0.168 0.000 0.832
#> GSM601864 2 0.0848 0.925 0.008 0.984 0.008
#> GSM601755 2 0.2537 0.907 0.080 0.920 0.000
#> GSM601785 2 0.1753 0.922 0.048 0.952 0.000
#> GSM601795 1 0.3045 0.800 0.916 0.020 0.064
#> GSM601800 2 0.3116 0.892 0.108 0.892 0.000
#> GSM601830 3 0.0747 0.839 0.000 0.016 0.984
#> GSM601840 2 0.6000 0.750 0.200 0.760 0.040
#> GSM601845 2 0.9693 -0.234 0.380 0.404 0.216
#> GSM601860 2 0.0983 0.927 0.016 0.980 0.004
#> GSM601870 3 0.0848 0.839 0.008 0.008 0.984
#> GSM601750 3 0.3482 0.825 0.128 0.000 0.872
#> GSM601760 1 0.6307 -0.132 0.512 0.000 0.488
#> GSM601765 2 0.2537 0.903 0.080 0.920 0.000
#> GSM601770 2 0.0892 0.927 0.020 0.980 0.000
#> GSM601775 2 0.7607 0.383 0.364 0.584 0.052
#> GSM601780 1 0.1529 0.806 0.960 0.000 0.040
#> GSM601790 2 0.0424 0.926 0.008 0.992 0.000
#> GSM601805 2 0.2066 0.918 0.060 0.940 0.000
#> GSM601810 3 0.1031 0.849 0.024 0.000 0.976
#> GSM601815 2 0.0661 0.926 0.008 0.988 0.004
#> GSM601820 3 0.4002 0.807 0.160 0.000 0.840
#> GSM601825 2 0.2356 0.915 0.072 0.928 0.000
#> GSM601835 2 0.1129 0.924 0.004 0.976 0.020
#> GSM601850 1 0.4075 0.793 0.880 0.048 0.072
#> GSM601855 3 0.0000 0.844 0.000 0.000 1.000
#> GSM601865 2 0.1170 0.923 0.008 0.976 0.016
#> GSM601756 2 0.2496 0.913 0.068 0.928 0.004
#> GSM601786 2 0.1950 0.913 0.008 0.952 0.040
#> GSM601796 1 0.4931 0.695 0.768 0.000 0.232
#> GSM601801 2 0.1753 0.920 0.048 0.952 0.000
#> GSM601831 3 0.1163 0.850 0.028 0.000 0.972
#> GSM601841 3 0.5650 0.578 0.312 0.000 0.688
#> GSM601846 1 0.9601 0.372 0.456 0.328 0.216
#> GSM601861 2 0.0424 0.926 0.008 0.992 0.000
#> GSM601871 3 0.5461 0.587 0.008 0.244 0.748
#> GSM601751 2 0.5243 0.829 0.100 0.828 0.072
#> GSM601761 1 0.2448 0.797 0.924 0.000 0.076
#> GSM601766 1 0.9501 0.378 0.472 0.324 0.204
#> GSM601771 2 0.1860 0.925 0.052 0.948 0.000
#> GSM601776 1 0.1964 0.805 0.944 0.000 0.056
#> GSM601781 1 0.5519 0.769 0.812 0.068 0.120
#> GSM601791 1 0.2261 0.802 0.932 0.000 0.068
#> GSM601806 2 0.1753 0.922 0.048 0.952 0.000
#> GSM601811 3 0.0829 0.843 0.004 0.012 0.984
#> GSM601816 1 0.3030 0.805 0.904 0.004 0.092
#> GSM601821 2 0.0424 0.926 0.008 0.992 0.000
#> GSM601826 1 0.1163 0.804 0.972 0.000 0.028
#> GSM601836 1 0.6255 0.689 0.748 0.048 0.204
#> GSM601851 1 0.2165 0.801 0.936 0.000 0.064
#> GSM601856 3 0.0424 0.846 0.008 0.000 0.992
#> GSM601866 3 0.2959 0.838 0.100 0.000 0.900
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601752 4 0.3972 0.6867 0.016 0.164 0.004 0.816
#> GSM601782 3 0.5745 0.5862 0.296 0.004 0.656 0.044
#> GSM601792 1 0.5118 0.6456 0.752 0.000 0.072 0.176
#> GSM601797 4 0.7067 0.4833 0.148 0.072 0.108 0.672
#> GSM601827 3 0.5393 0.5911 0.268 0.000 0.688 0.044
#> GSM601837 2 0.2483 0.7332 0.000 0.916 0.032 0.052
#> GSM601842 2 0.5188 0.6980 0.012 0.704 0.016 0.268
#> GSM601857 3 0.2773 0.7121 0.072 0.000 0.900 0.028
#> GSM601867 3 0.5901 0.4987 0.004 0.220 0.692 0.084
#> GSM601747 3 0.8944 0.1576 0.340 0.116 0.420 0.124
#> GSM601757 3 0.5565 0.5306 0.344 0.000 0.624 0.032
#> GSM601762 2 0.4406 0.7454 0.024 0.788 0.004 0.184
#> GSM601767 2 0.4502 0.7042 0.016 0.748 0.000 0.236
#> GSM601772 2 0.3672 0.7493 0.012 0.824 0.000 0.164
#> GSM601777 3 0.9742 -0.1154 0.264 0.144 0.312 0.280
#> GSM601787 3 0.6041 0.4007 0.000 0.332 0.608 0.060
#> GSM601802 4 0.4507 0.6755 0.020 0.224 0.000 0.756
#> GSM601807 3 0.4285 0.6265 0.008 0.092 0.832 0.068
#> GSM601812 3 0.4715 0.6525 0.240 0.004 0.740 0.016
#> GSM601817 3 0.4267 0.6822 0.188 0.000 0.788 0.024
#> GSM601822 1 0.6315 0.3213 0.596 0.032 0.024 0.348
#> GSM601832 2 0.6240 0.6272 0.080 0.640 0.004 0.276
#> GSM601847 4 0.7031 0.2999 0.360 0.092 0.012 0.536
#> GSM601852 3 0.5414 0.4961 0.376 0.000 0.604 0.020
#> GSM601862 3 0.2485 0.7101 0.064 0.004 0.916 0.016
#> GSM601753 4 0.3870 0.6767 0.004 0.208 0.000 0.788
#> GSM601783 1 0.5905 0.0335 0.564 0.000 0.396 0.040
#> GSM601793 1 0.6119 0.5999 0.680 0.000 0.152 0.168
#> GSM601798 4 0.4594 0.6597 0.008 0.280 0.000 0.712
#> GSM601828 3 0.5062 0.6164 0.284 0.000 0.692 0.024
#> GSM601838 2 0.1888 0.7389 0.000 0.940 0.016 0.044
#> GSM601843 2 0.4248 0.7354 0.012 0.768 0.000 0.220
#> GSM601858 2 0.4967 0.6440 0.004 0.784 0.108 0.104
#> GSM601868 3 0.2115 0.7012 0.024 0.004 0.936 0.036
#> GSM601748 3 0.5013 0.6045 0.292 0.000 0.688 0.020
#> GSM601758 1 0.5526 -0.0214 0.564 0.000 0.416 0.020
#> GSM601763 1 0.5610 0.5235 0.712 0.068 0.004 0.216
#> GSM601768 2 0.5623 0.6605 0.048 0.660 0.000 0.292
#> GSM601773 2 0.4098 0.7233 0.012 0.784 0.000 0.204
#> GSM601778 1 0.8394 0.2975 0.464 0.048 0.164 0.324
#> GSM601788 2 0.8015 0.3202 0.096 0.588 0.120 0.196
#> GSM601803 4 0.5026 0.6226 0.016 0.312 0.000 0.672
#> GSM601808 3 0.0895 0.7021 0.020 0.000 0.976 0.004
#> GSM601813 3 0.5760 0.3305 0.448 0.000 0.524 0.028
#> GSM601818 3 0.3232 0.7077 0.108 0.004 0.872 0.016
#> GSM601823 1 0.1042 0.6781 0.972 0.000 0.008 0.020
#> GSM601833 2 0.4485 0.7281 0.028 0.772 0.000 0.200
#> GSM601848 1 0.2287 0.6827 0.924 0.004 0.012 0.060
#> GSM601853 3 0.1796 0.7056 0.032 0.004 0.948 0.016
#> GSM601863 3 0.3099 0.7110 0.104 0.000 0.876 0.020
#> GSM601754 4 0.4139 0.6845 0.040 0.144 0.000 0.816
#> GSM601784 2 0.3668 0.7516 0.004 0.808 0.000 0.188
#> GSM601794 1 0.7670 0.4160 0.496 0.008 0.188 0.308
#> GSM601799 4 0.4452 0.6770 0.048 0.156 0.000 0.796
#> GSM601829 1 0.6568 0.1031 0.512 0.000 0.408 0.080
#> GSM601839 2 0.2399 0.7286 0.000 0.920 0.032 0.048
#> GSM601844 1 0.5800 0.6014 0.708 0.000 0.164 0.128
#> GSM601859 2 0.4594 0.6504 0.008 0.712 0.000 0.280
#> GSM601869 3 0.2730 0.7091 0.088 0.000 0.896 0.016
#> GSM601749 1 0.5620 -0.0219 0.560 0.000 0.416 0.024
#> GSM601759 3 0.5564 0.3691 0.436 0.000 0.544 0.020
#> GSM601764 1 0.4008 0.6563 0.852 0.024 0.032 0.092
#> GSM601769 2 0.3161 0.7602 0.012 0.864 0.000 0.124
#> GSM601774 2 0.3450 0.7565 0.008 0.836 0.000 0.156
#> GSM601779 1 0.1762 0.6812 0.944 0.004 0.004 0.048
#> GSM601789 2 0.2484 0.7373 0.012 0.924 0.024 0.040
#> GSM601804 4 0.6323 0.5601 0.248 0.112 0.000 0.640
#> GSM601809 3 0.7294 0.5248 0.100 0.136 0.660 0.104
#> GSM601814 2 0.2831 0.7470 0.000 0.876 0.004 0.120
#> GSM601819 1 0.5793 0.1156 0.580 0.000 0.384 0.036
#> GSM601824 1 0.5613 0.2727 0.592 0.028 0.000 0.380
#> GSM601834 2 0.3836 0.7430 0.016 0.816 0.000 0.168
#> GSM601849 1 0.2101 0.6695 0.928 0.000 0.060 0.012
#> GSM601854 3 0.5213 0.5592 0.328 0.000 0.652 0.020
#> GSM601864 2 0.3182 0.7174 0.000 0.876 0.028 0.096
#> GSM601755 4 0.4123 0.6779 0.008 0.220 0.000 0.772
#> GSM601785 2 0.6123 0.4720 0.056 0.572 0.000 0.372
#> GSM601795 4 0.6293 -0.1260 0.448 0.008 0.040 0.504
#> GSM601800 4 0.3933 0.6857 0.008 0.200 0.000 0.792
#> GSM601830 3 0.2797 0.6860 0.016 0.028 0.912 0.044
#> GSM601840 4 0.8116 0.2251 0.112 0.348 0.056 0.484
#> GSM601845 2 0.9701 -0.0511 0.216 0.356 0.160 0.268
#> GSM601860 2 0.4535 0.7103 0.016 0.744 0.000 0.240
#> GSM601870 3 0.3341 0.6540 0.004 0.068 0.880 0.048
#> GSM601750 3 0.5271 0.5534 0.340 0.000 0.640 0.020
#> GSM601760 1 0.5311 0.2665 0.648 0.000 0.328 0.024
#> GSM601765 2 0.5590 0.6679 0.064 0.692 0.000 0.244
#> GSM601770 2 0.4644 0.7235 0.024 0.748 0.000 0.228
#> GSM601775 4 0.8657 0.2983 0.256 0.248 0.048 0.448
#> GSM601780 1 0.1452 0.6811 0.956 0.000 0.008 0.036
#> GSM601790 2 0.1406 0.7413 0.000 0.960 0.016 0.024
#> GSM601805 4 0.5349 0.5971 0.012 0.336 0.008 0.644
#> GSM601810 3 0.2998 0.7099 0.080 0.004 0.892 0.024
#> GSM601815 2 0.2522 0.7375 0.000 0.908 0.016 0.076
#> GSM601820 3 0.5414 0.4885 0.376 0.000 0.604 0.020
#> GSM601825 4 0.6080 0.0613 0.044 0.468 0.000 0.488
#> GSM601835 2 0.5360 0.7014 0.016 0.744 0.044 0.196
#> GSM601850 1 0.6889 0.5379 0.644 0.040 0.080 0.236
#> GSM601855 3 0.2231 0.6867 0.012 0.012 0.932 0.044
#> GSM601865 2 0.2313 0.7259 0.000 0.924 0.032 0.044
#> GSM601756 4 0.4283 0.6677 0.004 0.256 0.000 0.740
#> GSM601786 2 0.3533 0.7102 0.000 0.864 0.056 0.080
#> GSM601796 1 0.6941 0.5339 0.588 0.000 0.192 0.220
#> GSM601801 4 0.4655 0.6300 0.004 0.312 0.000 0.684
#> GSM601831 3 0.4237 0.6909 0.152 0.000 0.808 0.040
#> GSM601841 3 0.7097 0.1731 0.372 0.004 0.508 0.116
#> GSM601846 4 0.9491 0.2466 0.212 0.212 0.160 0.416
#> GSM601861 2 0.2053 0.7442 0.000 0.924 0.004 0.072
#> GSM601871 3 0.6222 0.4042 0.000 0.304 0.616 0.080
#> GSM601751 2 0.7774 0.2687 0.072 0.536 0.072 0.320
#> GSM601761 1 0.2282 0.6611 0.924 0.000 0.052 0.024
#> GSM601766 1 0.8966 -0.1185 0.356 0.352 0.060 0.232
#> GSM601771 2 0.5560 0.5333 0.028 0.644 0.004 0.324
#> GSM601776 1 0.1520 0.6782 0.956 0.000 0.020 0.024
#> GSM601781 1 0.7655 0.4882 0.608 0.072 0.108 0.212
#> GSM601791 1 0.2870 0.6808 0.908 0.012 0.044 0.036
#> GSM601806 4 0.5004 0.5112 0.000 0.392 0.004 0.604
#> GSM601811 3 0.2936 0.6967 0.032 0.024 0.908 0.036
#> GSM601816 1 0.3424 0.6811 0.876 0.004 0.052 0.068
#> GSM601821 2 0.2741 0.7435 0.000 0.892 0.012 0.096
#> GSM601826 1 0.1584 0.6808 0.952 0.000 0.012 0.036
#> GSM601836 1 0.9015 0.3893 0.468 0.104 0.200 0.228
#> GSM601851 1 0.2032 0.6742 0.936 0.000 0.036 0.028
#> GSM601856 3 0.1833 0.7017 0.024 0.000 0.944 0.032
#> GSM601866 3 0.4661 0.6387 0.256 0.000 0.728 0.016
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601752 4 0.252 0.65321 0.008 0.044 0.004 0.908 0.036
#> GSM601782 3 0.628 0.51394 0.236 0.008 0.588 0.004 0.164
#> GSM601792 1 0.684 0.41128 0.600 0.004 0.068 0.188 0.140
#> GSM601797 4 0.694 0.29699 0.092 0.032 0.072 0.628 0.176
#> GSM601827 3 0.615 0.55642 0.188 0.004 0.612 0.008 0.188
#> GSM601837 2 0.234 0.55724 0.000 0.916 0.016 0.036 0.032
#> GSM601842 2 0.717 0.38034 0.004 0.468 0.024 0.208 0.296
#> GSM601857 3 0.418 0.65222 0.064 0.008 0.812 0.012 0.104
#> GSM601867 3 0.730 0.01115 0.008 0.304 0.444 0.020 0.224
#> GSM601747 3 0.896 -0.00597 0.196 0.124 0.372 0.048 0.260
#> GSM601757 3 0.658 0.49962 0.252 0.004 0.552 0.012 0.180
#> GSM601762 2 0.602 0.54940 0.000 0.600 0.004 0.188 0.208
#> GSM601767 2 0.651 0.50938 0.004 0.512 0.000 0.212 0.272
#> GSM601772 2 0.585 0.54680 0.000 0.592 0.000 0.144 0.264
#> GSM601777 5 0.983 0.30064 0.240 0.152 0.168 0.168 0.272
#> GSM601787 2 0.709 -0.09253 0.004 0.500 0.292 0.032 0.172
#> GSM601802 4 0.309 0.66798 0.000 0.088 0.000 0.860 0.052
#> GSM601807 3 0.646 0.33335 0.004 0.152 0.588 0.020 0.236
#> GSM601812 3 0.554 0.60663 0.188 0.004 0.680 0.008 0.120
#> GSM601817 3 0.483 0.64003 0.116 0.004 0.736 0.000 0.144
#> GSM601822 1 0.701 0.25856 0.596 0.036 0.028 0.188 0.152
#> GSM601832 2 0.760 0.26397 0.032 0.384 0.008 0.236 0.340
#> GSM601847 4 0.734 -0.03738 0.392 0.060 0.012 0.436 0.100
#> GSM601852 3 0.568 0.55035 0.228 0.000 0.636 0.004 0.132
#> GSM601862 3 0.327 0.63562 0.036 0.004 0.848 0.000 0.112
#> GSM601753 4 0.309 0.65595 0.016 0.052 0.000 0.876 0.056
#> GSM601783 3 0.668 0.25019 0.388 0.000 0.456 0.020 0.136
#> GSM601793 1 0.746 0.43501 0.548 0.004 0.152 0.128 0.168
#> GSM601798 4 0.421 0.63816 0.008 0.172 0.004 0.780 0.036
#> GSM601828 3 0.502 0.62077 0.204 0.004 0.704 0.000 0.088
#> GSM601838 2 0.172 0.57588 0.000 0.936 0.000 0.044 0.020
#> GSM601843 2 0.575 0.52698 0.004 0.640 0.004 0.124 0.228
#> GSM601858 2 0.567 0.44246 0.012 0.720 0.056 0.068 0.144
#> GSM601868 3 0.407 0.61264 0.020 0.028 0.804 0.004 0.144
#> GSM601748 3 0.553 0.58271 0.224 0.000 0.652 0.004 0.120
#> GSM601758 1 0.610 -0.17015 0.452 0.000 0.424 0.000 0.124
#> GSM601763 1 0.764 -0.01943 0.472 0.036 0.032 0.148 0.312
#> GSM601768 2 0.712 0.39846 0.016 0.412 0.000 0.260 0.312
#> GSM601773 2 0.610 0.55076 0.000 0.568 0.000 0.232 0.200
#> GSM601778 1 0.887 0.02833 0.420 0.052 0.140 0.172 0.216
#> GSM601788 2 0.719 0.36744 0.048 0.596 0.036 0.180 0.140
#> GSM601803 4 0.385 0.65073 0.004 0.164 0.000 0.796 0.036
#> GSM601808 3 0.312 0.61791 0.012 0.016 0.856 0.000 0.116
#> GSM601813 3 0.602 0.40830 0.348 0.000 0.536 0.004 0.112
#> GSM601818 3 0.425 0.65562 0.084 0.004 0.784 0.000 0.128
#> GSM601823 1 0.205 0.55498 0.920 0.000 0.000 0.028 0.052
#> GSM601833 2 0.643 0.47975 0.004 0.520 0.000 0.188 0.288
#> GSM601848 1 0.290 0.56278 0.884 0.000 0.020 0.024 0.072
#> GSM601853 3 0.298 0.63722 0.032 0.004 0.868 0.000 0.096
#> GSM601863 3 0.378 0.66113 0.064 0.016 0.832 0.000 0.088
#> GSM601754 4 0.361 0.66185 0.028 0.076 0.000 0.848 0.048
#> GSM601784 2 0.516 0.59976 0.000 0.692 0.000 0.160 0.148
#> GSM601794 1 0.851 0.21118 0.416 0.016 0.156 0.228 0.184
#> GSM601799 4 0.419 0.62664 0.044 0.064 0.000 0.816 0.076
#> GSM601829 1 0.772 -0.00191 0.372 0.004 0.356 0.052 0.216
#> GSM601839 2 0.189 0.56304 0.000 0.936 0.012 0.024 0.028
#> GSM601844 1 0.740 0.38989 0.516 0.008 0.212 0.052 0.212
#> GSM601859 2 0.663 0.44813 0.008 0.492 0.000 0.308 0.192
#> GSM601869 3 0.509 0.64375 0.108 0.008 0.728 0.004 0.152
#> GSM601749 3 0.610 0.23191 0.424 0.000 0.452 0.000 0.124
#> GSM601759 3 0.589 0.44183 0.316 0.000 0.560 0.000 0.124
#> GSM601764 1 0.575 0.43882 0.652 0.008 0.100 0.008 0.232
#> GSM601769 2 0.500 0.59597 0.000 0.708 0.000 0.128 0.164
#> GSM601774 2 0.594 0.56919 0.004 0.608 0.000 0.164 0.224
#> GSM601779 1 0.292 0.55678 0.884 0.000 0.016 0.036 0.064
#> GSM601789 2 0.347 0.57502 0.000 0.844 0.008 0.048 0.100
#> GSM601804 4 0.567 0.43817 0.200 0.036 0.000 0.680 0.084
#> GSM601809 3 0.870 0.10505 0.120 0.208 0.424 0.040 0.208
#> GSM601814 2 0.437 0.59105 0.000 0.748 0.000 0.192 0.060
#> GSM601819 1 0.644 -0.17471 0.440 0.000 0.416 0.008 0.136
#> GSM601824 1 0.586 0.15337 0.592 0.000 0.000 0.260 0.148
#> GSM601834 2 0.594 0.55792 0.000 0.592 0.000 0.180 0.228
#> GSM601849 1 0.439 0.55988 0.788 0.000 0.112 0.016 0.084
#> GSM601854 3 0.549 0.56338 0.252 0.000 0.644 0.004 0.100
#> GSM601864 2 0.305 0.55459 0.000 0.864 0.000 0.076 0.060
#> GSM601755 4 0.257 0.67466 0.004 0.092 0.000 0.888 0.016
#> GSM601785 2 0.733 0.22659 0.028 0.408 0.000 0.276 0.288
#> GSM601795 4 0.730 -0.07037 0.328 0.008 0.032 0.460 0.172
#> GSM601800 4 0.255 0.67104 0.000 0.072 0.000 0.892 0.036
#> GSM601830 3 0.502 0.55996 0.020 0.052 0.736 0.008 0.184
#> GSM601840 4 0.893 -0.13263 0.064 0.308 0.096 0.356 0.176
#> GSM601845 5 0.931 0.49613 0.168 0.244 0.084 0.144 0.360
#> GSM601860 2 0.632 0.46377 0.016 0.584 0.000 0.236 0.164
#> GSM601870 3 0.557 0.41373 0.004 0.144 0.656 0.000 0.196
#> GSM601750 3 0.574 0.56033 0.244 0.000 0.624 0.004 0.128
#> GSM601760 1 0.621 -0.09503 0.484 0.004 0.388 0.000 0.124
#> GSM601765 2 0.758 0.29530 0.076 0.432 0.000 0.164 0.328
#> GSM601770 2 0.673 0.50044 0.012 0.504 0.000 0.220 0.264
#> GSM601775 4 0.889 -0.21159 0.148 0.116 0.060 0.376 0.300
#> GSM601780 1 0.307 0.56603 0.884 0.004 0.036 0.024 0.052
#> GSM601790 2 0.147 0.57894 0.000 0.948 0.000 0.016 0.036
#> GSM601805 4 0.384 0.65696 0.000 0.164 0.000 0.792 0.044
#> GSM601810 3 0.471 0.63337 0.076 0.008 0.744 0.000 0.172
#> GSM601815 2 0.302 0.58548 0.000 0.864 0.000 0.088 0.048
#> GSM601820 3 0.556 0.52316 0.268 0.000 0.620 0.000 0.112
#> GSM601825 4 0.634 0.15880 0.024 0.356 0.000 0.524 0.096
#> GSM601835 2 0.689 0.33886 0.008 0.528 0.036 0.116 0.312
#> GSM601850 1 0.723 0.29290 0.548 0.016 0.040 0.176 0.220
#> GSM601855 3 0.386 0.55908 0.000 0.028 0.772 0.000 0.200
#> GSM601865 2 0.236 0.55117 0.000 0.912 0.012 0.024 0.052
#> GSM601756 4 0.272 0.67386 0.000 0.124 0.000 0.864 0.012
#> GSM601786 2 0.334 0.54519 0.000 0.856 0.012 0.044 0.088
#> GSM601796 1 0.813 0.30767 0.452 0.004 0.196 0.188 0.160
#> GSM601801 4 0.337 0.63848 0.000 0.212 0.000 0.784 0.004
#> GSM601831 3 0.468 0.64620 0.128 0.000 0.756 0.008 0.108
#> GSM601841 3 0.795 0.21161 0.308 0.012 0.436 0.084 0.160
#> GSM601846 5 0.966 0.44396 0.148 0.200 0.116 0.232 0.304
#> GSM601861 2 0.285 0.60190 0.000 0.868 0.000 0.104 0.028
#> GSM601871 2 0.727 -0.18091 0.000 0.420 0.336 0.032 0.212
#> GSM601751 2 0.819 0.13422 0.056 0.376 0.028 0.340 0.200
#> GSM601761 1 0.368 0.55113 0.832 0.000 0.096 0.008 0.064
#> GSM601766 5 0.854 0.38287 0.232 0.164 0.040 0.116 0.448
#> GSM601771 2 0.722 0.36035 0.040 0.488 0.004 0.300 0.168
#> GSM601776 1 0.362 0.57360 0.848 0.000 0.044 0.032 0.076
#> GSM601781 1 0.822 0.21818 0.516 0.056 0.104 0.136 0.188
#> GSM601791 1 0.479 0.55984 0.780 0.008 0.088 0.028 0.096
#> GSM601806 4 0.416 0.57258 0.000 0.264 0.000 0.716 0.020
#> GSM601811 3 0.520 0.58537 0.040 0.024 0.724 0.016 0.196
#> GSM601816 1 0.470 0.53276 0.788 0.004 0.044 0.076 0.088
#> GSM601821 2 0.340 0.59839 0.000 0.828 0.000 0.136 0.036
#> GSM601826 1 0.215 0.56033 0.924 0.000 0.012 0.032 0.032
#> GSM601836 1 0.860 -0.19978 0.372 0.052 0.140 0.092 0.344
#> GSM601851 1 0.345 0.57007 0.852 0.000 0.068 0.012 0.068
#> GSM601856 3 0.364 0.60204 0.024 0.004 0.816 0.004 0.152
#> GSM601866 3 0.494 0.60900 0.172 0.000 0.712 0.000 0.116
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601752 4 0.123 0.70093 0.000 0.024 0.000 0.956 0.004 0.016
#> GSM601782 1 0.716 0.13112 0.396 0.076 0.372 0.008 0.008 0.140
#> GSM601792 6 0.653 0.50873 0.164 0.080 0.040 0.108 0.000 0.608
#> GSM601797 4 0.722 0.45461 0.100 0.112 0.084 0.596 0.028 0.080
#> GSM601827 3 0.680 0.01356 0.300 0.052 0.480 0.008 0.004 0.156
#> GSM601837 5 0.262 0.51207 0.016 0.044 0.024 0.020 0.896 0.000
#> GSM601842 5 0.735 -0.19693 0.072 0.376 0.012 0.152 0.376 0.012
#> GSM601857 3 0.487 0.31309 0.276 0.032 0.660 0.000 0.016 0.016
#> GSM601867 3 0.770 0.21893 0.148 0.084 0.452 0.032 0.268 0.016
#> GSM601747 1 0.935 0.09312 0.308 0.192 0.188 0.060 0.092 0.160
#> GSM601757 3 0.731 -0.23467 0.364 0.088 0.384 0.008 0.008 0.148
#> GSM601762 5 0.597 0.24280 0.020 0.236 0.004 0.176 0.564 0.000
#> GSM601767 2 0.651 0.13053 0.016 0.392 0.000 0.244 0.344 0.004
#> GSM601772 5 0.625 0.05094 0.036 0.352 0.000 0.124 0.484 0.004
#> GSM601777 3 0.978 -0.09284 0.164 0.140 0.264 0.152 0.108 0.172
#> GSM601787 5 0.687 0.06089 0.092 0.072 0.380 0.008 0.436 0.012
#> GSM601802 4 0.251 0.70267 0.000 0.060 0.000 0.892 0.024 0.024
#> GSM601807 3 0.605 0.33250 0.136 0.064 0.660 0.020 0.112 0.008
#> GSM601812 3 0.676 -0.17260 0.356 0.064 0.440 0.008 0.000 0.132
#> GSM601817 3 0.557 0.06193 0.352 0.036 0.552 0.004 0.000 0.056
#> GSM601822 6 0.713 0.44957 0.048 0.140 0.024 0.188 0.040 0.560
#> GSM601832 2 0.779 0.33415 0.056 0.424 0.008 0.196 0.260 0.056
#> GSM601847 4 0.658 0.04636 0.028 0.108 0.000 0.432 0.032 0.400
#> GSM601852 1 0.634 0.26173 0.404 0.020 0.400 0.004 0.000 0.172
#> GSM601862 3 0.357 0.36147 0.188 0.016 0.780 0.000 0.000 0.016
#> GSM601753 4 0.320 0.67833 0.008 0.092 0.000 0.852 0.024 0.024
#> GSM601783 1 0.684 0.49833 0.468 0.052 0.204 0.008 0.000 0.268
#> GSM601793 6 0.729 0.36548 0.244 0.080 0.080 0.088 0.000 0.508
#> GSM601798 4 0.344 0.68435 0.012 0.044 0.008 0.840 0.092 0.004
#> GSM601828 3 0.637 -0.16031 0.360 0.052 0.460 0.000 0.000 0.128
#> GSM601838 5 0.130 0.52392 0.004 0.012 0.000 0.032 0.952 0.000
#> GSM601843 5 0.609 0.13618 0.048 0.304 0.000 0.100 0.544 0.004
#> GSM601858 5 0.581 0.38284 0.060 0.120 0.104 0.028 0.684 0.004
#> GSM601868 3 0.432 0.37292 0.200 0.016 0.744 0.008 0.024 0.008
#> GSM601748 3 0.563 -0.19696 0.404 0.008 0.472 0.000 0.000 0.116
#> GSM601758 1 0.611 0.49216 0.420 0.004 0.240 0.000 0.000 0.336
#> GSM601763 6 0.763 0.09110 0.124 0.372 0.016 0.076 0.032 0.380
#> GSM601768 2 0.713 0.25089 0.028 0.448 0.000 0.160 0.308 0.056
#> GSM601773 5 0.610 0.17728 0.016 0.240 0.000 0.236 0.508 0.000
#> GSM601778 6 0.896 0.33716 0.116 0.124 0.152 0.188 0.040 0.380
#> GSM601788 5 0.807 0.18706 0.120 0.128 0.080 0.136 0.504 0.032
#> GSM601803 4 0.350 0.68895 0.004 0.052 0.000 0.832 0.092 0.020
#> GSM601808 3 0.274 0.38333 0.128 0.012 0.852 0.000 0.000 0.008
#> GSM601813 3 0.682 -0.43624 0.340 0.032 0.348 0.004 0.000 0.276
#> GSM601818 3 0.573 0.06622 0.348 0.048 0.548 0.008 0.000 0.048
#> GSM601823 6 0.300 0.56488 0.060 0.048 0.020 0.004 0.000 0.868
#> GSM601833 5 0.638 -0.08538 0.028 0.396 0.000 0.116 0.444 0.016
#> GSM601848 6 0.332 0.57232 0.076 0.052 0.008 0.016 0.000 0.848
#> GSM601853 3 0.334 0.36633 0.156 0.020 0.812 0.000 0.004 0.008
#> GSM601863 3 0.492 0.31345 0.184 0.016 0.716 0.004 0.016 0.064
#> GSM601754 4 0.343 0.69843 0.020 0.080 0.000 0.844 0.044 0.012
#> GSM601784 5 0.529 0.38276 0.012 0.188 0.000 0.144 0.652 0.004
#> GSM601794 6 0.888 0.25480 0.204 0.156 0.124 0.188 0.008 0.320
#> GSM601799 4 0.394 0.63384 0.008 0.152 0.000 0.784 0.012 0.044
#> GSM601829 3 0.786 -0.15337 0.276 0.100 0.304 0.020 0.004 0.296
#> GSM601839 5 0.141 0.51816 0.008 0.024 0.008 0.008 0.952 0.000
#> GSM601844 6 0.798 0.12558 0.264 0.164 0.148 0.032 0.004 0.388
#> GSM601859 5 0.675 0.00361 0.036 0.228 0.000 0.320 0.412 0.004
#> GSM601869 3 0.589 0.24678 0.284 0.032 0.600 0.012 0.016 0.056
#> GSM601749 1 0.639 0.50254 0.408 0.016 0.256 0.000 0.000 0.320
#> GSM601759 1 0.639 0.37689 0.412 0.028 0.376 0.000 0.000 0.184
#> GSM601764 6 0.623 0.46209 0.140 0.220 0.052 0.000 0.008 0.580
#> GSM601769 5 0.543 0.38400 0.028 0.200 0.000 0.116 0.652 0.004
#> GSM601774 5 0.609 0.27874 0.020 0.268 0.004 0.140 0.560 0.008
#> GSM601779 6 0.241 0.56475 0.056 0.028 0.004 0.012 0.000 0.900
#> GSM601789 5 0.442 0.49223 0.040 0.120 0.024 0.024 0.784 0.008
#> GSM601804 4 0.591 0.48216 0.040 0.108 0.000 0.612 0.012 0.228
#> GSM601809 3 0.897 0.09713 0.196 0.116 0.348 0.036 0.208 0.096
#> GSM601814 5 0.499 0.42683 0.012 0.104 0.000 0.200 0.680 0.004
#> GSM601819 1 0.697 0.47215 0.428 0.060 0.240 0.004 0.000 0.268
#> GSM601824 6 0.626 0.32126 0.028 0.232 0.000 0.232 0.000 0.508
#> GSM601834 5 0.631 0.16578 0.032 0.284 0.000 0.152 0.524 0.008
#> GSM601849 6 0.501 0.53003 0.140 0.060 0.060 0.012 0.000 0.728
#> GSM601854 3 0.640 -0.11652 0.340 0.040 0.476 0.004 0.000 0.140
#> GSM601864 5 0.355 0.51031 0.024 0.044 0.024 0.064 0.844 0.000
#> GSM601755 4 0.195 0.70614 0.016 0.028 0.000 0.924 0.032 0.000
#> GSM601785 2 0.762 0.20555 0.032 0.360 0.012 0.216 0.336 0.044
#> GSM601795 4 0.795 -0.08617 0.168 0.132 0.032 0.380 0.004 0.284
#> GSM601800 4 0.261 0.70752 0.012 0.048 0.000 0.892 0.040 0.008
#> GSM601830 3 0.605 0.34178 0.172 0.096 0.648 0.012 0.060 0.012
#> GSM601840 4 0.949 -0.18012 0.144 0.188 0.080 0.288 0.208 0.092
#> GSM601845 2 0.905 0.29665 0.164 0.372 0.088 0.068 0.204 0.104
#> GSM601860 5 0.802 0.09875 0.084 0.228 0.024 0.184 0.440 0.040
#> GSM601870 3 0.488 0.35440 0.080 0.052 0.732 0.004 0.132 0.000
#> GSM601750 3 0.608 -0.26044 0.388 0.020 0.444 0.000 0.000 0.148
#> GSM601760 1 0.662 0.46096 0.388 0.028 0.212 0.004 0.000 0.368
#> GSM601765 2 0.654 0.19348 0.032 0.464 0.000 0.084 0.380 0.040
#> GSM601770 2 0.671 0.11293 0.032 0.424 0.000 0.148 0.376 0.020
#> GSM601775 2 0.867 0.20213 0.116 0.364 0.036 0.272 0.068 0.144
#> GSM601780 6 0.306 0.56162 0.064 0.052 0.024 0.000 0.000 0.860
#> GSM601790 5 0.172 0.52045 0.008 0.056 0.000 0.008 0.928 0.000
#> GSM601805 4 0.402 0.67911 0.012 0.096 0.000 0.792 0.092 0.008
#> GSM601810 3 0.575 0.27281 0.176 0.064 0.652 0.000 0.008 0.100
#> GSM601815 5 0.280 0.52590 0.000 0.048 0.004 0.084 0.864 0.000
#> GSM601820 1 0.623 0.33028 0.416 0.020 0.388 0.000 0.000 0.176
#> GSM601825 4 0.657 0.11127 0.016 0.132 0.000 0.520 0.284 0.048
#> GSM601835 5 0.726 -0.04724 0.060 0.328 0.036 0.116 0.452 0.008
#> GSM601850 6 0.823 0.40210 0.188 0.112 0.084 0.160 0.012 0.444
#> GSM601855 3 0.356 0.39026 0.120 0.040 0.816 0.000 0.024 0.000
#> GSM601865 5 0.294 0.50388 0.040 0.036 0.028 0.016 0.880 0.000
#> GSM601756 4 0.194 0.70522 0.008 0.020 0.000 0.920 0.052 0.000
#> GSM601786 5 0.382 0.51270 0.028 0.072 0.028 0.036 0.832 0.004
#> GSM601796 6 0.854 0.14931 0.256 0.088 0.144 0.168 0.004 0.340
#> GSM601801 4 0.301 0.67973 0.012 0.024 0.000 0.844 0.120 0.000
#> GSM601831 3 0.588 0.12834 0.304 0.032 0.572 0.008 0.004 0.080
#> GSM601841 1 0.819 0.10621 0.336 0.060 0.312 0.072 0.012 0.208
#> GSM601846 2 0.971 0.11271 0.132 0.288 0.132 0.164 0.164 0.120
#> GSM601861 5 0.321 0.52263 0.012 0.068 0.000 0.076 0.844 0.000
#> GSM601871 3 0.689 0.08384 0.080 0.088 0.440 0.024 0.368 0.000
#> GSM601751 5 0.905 -0.11004 0.104 0.216 0.048 0.228 0.320 0.084
#> GSM601761 6 0.455 0.42138 0.192 0.036 0.048 0.000 0.000 0.724
#> GSM601766 2 0.829 0.25835 0.124 0.472 0.056 0.060 0.096 0.192
#> GSM601771 5 0.811 0.06884 0.084 0.188 0.028 0.232 0.428 0.040
#> GSM601776 6 0.440 0.47789 0.180 0.044 0.036 0.000 0.000 0.740
#> GSM601781 6 0.838 0.43290 0.120 0.188 0.080 0.088 0.056 0.468
#> GSM601791 6 0.550 0.47480 0.188 0.072 0.040 0.008 0.012 0.680
#> GSM601806 4 0.384 0.61974 0.012 0.032 0.000 0.776 0.176 0.004
#> GSM601811 3 0.612 0.28055 0.252 0.124 0.576 0.000 0.012 0.036
#> GSM601816 6 0.568 0.56095 0.096 0.076 0.032 0.060 0.020 0.716
#> GSM601821 5 0.325 0.52082 0.004 0.068 0.004 0.084 0.840 0.000
#> GSM601826 6 0.270 0.56900 0.056 0.044 0.004 0.012 0.000 0.884
#> GSM601836 6 0.916 0.18511 0.196 0.248 0.144 0.100 0.032 0.280
#> GSM601851 6 0.376 0.54578 0.116 0.044 0.020 0.008 0.000 0.812
#> GSM601856 3 0.380 0.38485 0.116 0.052 0.808 0.008 0.000 0.016
#> GSM601866 3 0.587 -0.05049 0.340 0.036 0.536 0.004 0.000 0.084
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 time(p) gender(p) k
#> CV:skmeans 122 0.721 0.20379 2
#> CV:skmeans 112 0.176 0.18337 3
#> CV:skmeans 91 0.507 0.09200 4
#> CV:skmeans 71 0.169 0.00467 5
#> CV:skmeans 33 0.928 0.04277 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 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.211 0.633 0.824 0.4848 0.505 0.505
#> 3 3 0.226 0.466 0.719 0.3003 0.671 0.448
#> 4 4 0.354 0.456 0.690 0.1251 0.843 0.606
#> 5 5 0.409 0.446 0.693 0.0424 0.924 0.748
#> 6 6 0.430 0.410 0.689 0.0164 0.961 0.851
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
#> GSM601752 1 0.7745 0.69560 0.772 0.228
#> GSM601782 1 0.7815 0.58447 0.768 0.232
#> GSM601792 1 0.0000 0.77968 1.000 0.000
#> GSM601797 1 0.9710 0.17429 0.600 0.400
#> GSM601827 2 0.8016 0.62099 0.244 0.756
#> GSM601837 2 0.0000 0.72908 0.000 1.000
#> GSM601842 2 0.9661 0.29914 0.392 0.608
#> GSM601857 2 0.7376 0.66905 0.208 0.792
#> GSM601867 2 0.0000 0.72908 0.000 1.000
#> GSM601747 2 0.9998 -0.14756 0.492 0.508
#> GSM601757 1 0.5946 0.72647 0.856 0.144
#> GSM601762 2 0.8861 0.52764 0.304 0.696
#> GSM601767 1 0.8016 0.67952 0.756 0.244
#> GSM601772 1 0.7745 0.69690 0.772 0.228
#> GSM601777 1 0.9686 0.44007 0.604 0.396
#> GSM601787 2 0.1184 0.73071 0.016 0.984
#> GSM601802 1 0.8081 0.68852 0.752 0.248
#> GSM601807 2 0.3879 0.72419 0.076 0.924
#> GSM601812 2 0.9754 0.41889 0.408 0.592
#> GSM601817 1 0.9608 0.31095 0.616 0.384
#> GSM601822 1 0.0000 0.77968 1.000 0.000
#> GSM601832 1 0.8016 0.68153 0.756 0.244
#> GSM601847 1 0.1184 0.77894 0.984 0.016
#> GSM601852 1 0.8813 0.43618 0.700 0.300
#> GSM601862 2 0.1184 0.73071 0.016 0.984
#> GSM601753 1 0.6623 0.72887 0.828 0.172
#> GSM601783 1 0.3114 0.76286 0.944 0.056
#> GSM601793 1 0.4690 0.73164 0.900 0.100
#> GSM601798 2 0.8763 0.41296 0.296 0.704
#> GSM601828 2 0.9044 0.58314 0.320 0.680
#> GSM601838 2 0.0000 0.72908 0.000 1.000
#> GSM601843 2 0.7376 0.65460 0.208 0.792
#> GSM601858 2 0.5294 0.71185 0.120 0.880
#> GSM601868 2 0.6531 0.68272 0.168 0.832
#> GSM601748 1 0.8144 0.50877 0.748 0.252
#> GSM601758 1 0.0000 0.77968 1.000 0.000
#> GSM601763 1 0.1843 0.78003 0.972 0.028
#> GSM601768 1 0.8386 0.66131 0.732 0.268
#> GSM601773 1 0.8016 0.67952 0.756 0.244
#> GSM601778 1 0.1414 0.77332 0.980 0.020
#> GSM601788 1 0.9998 -0.01080 0.508 0.492
#> GSM601803 1 0.7602 0.70056 0.780 0.220
#> GSM601808 2 0.6343 0.68911 0.160 0.840
#> GSM601813 1 0.5519 0.70022 0.872 0.128
#> GSM601818 2 0.7056 0.66884 0.192 0.808
#> GSM601823 1 0.0000 0.77968 1.000 0.000
#> GSM601833 2 0.9881 0.16127 0.436 0.564
#> GSM601848 1 0.0000 0.77968 1.000 0.000
#> GSM601853 2 0.8016 0.62099 0.244 0.756
#> GSM601863 2 0.6973 0.67997 0.188 0.812
#> GSM601754 1 0.5629 0.75410 0.868 0.132
#> GSM601784 1 0.8327 0.66282 0.736 0.264
#> GSM601794 1 0.0000 0.77968 1.000 0.000
#> GSM601799 1 0.5842 0.74656 0.860 0.140
#> GSM601829 1 0.0376 0.77895 0.996 0.004
#> GSM601839 2 0.0000 0.72908 0.000 1.000
#> GSM601844 1 0.0000 0.77968 1.000 0.000
#> GSM601859 2 0.8081 0.60968 0.248 0.752
#> GSM601869 2 0.6623 0.67994 0.172 0.828
#> GSM601749 1 0.0000 0.77968 1.000 0.000
#> GSM601759 1 0.0000 0.77968 1.000 0.000
#> GSM601764 1 0.0000 0.77968 1.000 0.000
#> GSM601769 2 0.8713 0.54474 0.292 0.708
#> GSM601774 1 0.9922 0.30193 0.552 0.448
#> GSM601779 1 0.0000 0.77968 1.000 0.000
#> GSM601789 2 0.4562 0.72486 0.096 0.904
#> GSM601804 1 0.3584 0.77348 0.932 0.068
#> GSM601809 2 0.9993 -0.00254 0.484 0.516
#> GSM601814 2 0.7299 0.65532 0.204 0.796
#> GSM601819 1 0.3733 0.76169 0.928 0.072
#> GSM601824 1 0.0000 0.77968 1.000 0.000
#> GSM601834 1 0.9286 0.55150 0.656 0.344
#> GSM601849 1 0.0000 0.77968 1.000 0.000
#> GSM601854 1 0.2043 0.77082 0.968 0.032
#> GSM601864 2 0.0000 0.72908 0.000 1.000
#> GSM601755 1 0.8207 0.67121 0.744 0.256
#> GSM601785 2 0.7745 0.63428 0.228 0.772
#> GSM601795 1 0.7139 0.72012 0.804 0.196
#> GSM601800 1 0.9044 0.59832 0.680 0.320
#> GSM601830 2 0.8016 0.62099 0.244 0.756
#> GSM601840 2 0.7602 0.64274 0.220 0.780
#> GSM601845 1 0.3274 0.74490 0.940 0.060
#> GSM601860 2 0.7219 0.65817 0.200 0.800
#> GSM601870 2 0.2423 0.73076 0.040 0.960
#> GSM601750 2 0.9815 0.42744 0.420 0.580
#> GSM601760 1 0.9881 -0.07544 0.564 0.436
#> GSM601765 1 0.6623 0.72855 0.828 0.172
#> GSM601770 2 0.9522 0.35876 0.372 0.628
#> GSM601775 1 0.6531 0.73118 0.832 0.168
#> GSM601780 1 0.0000 0.77968 1.000 0.000
#> GSM601790 2 0.3431 0.72902 0.064 0.936
#> GSM601805 2 0.9460 0.38046 0.364 0.636
#> GSM601810 2 0.8207 0.61939 0.256 0.744
#> GSM601815 2 0.6438 0.68797 0.164 0.836
#> GSM601820 1 0.6438 0.66926 0.836 0.164
#> GSM601825 1 0.8016 0.67952 0.756 0.244
#> GSM601835 2 0.6623 0.68276 0.172 0.828
#> GSM601850 1 0.0000 0.77968 1.000 0.000
#> GSM601855 2 0.6801 0.67623 0.180 0.820
#> GSM601865 2 0.0000 0.72908 0.000 1.000
#> GSM601756 1 0.8016 0.67952 0.756 0.244
#> GSM601786 2 0.0000 0.72908 0.000 1.000
#> GSM601796 1 0.6887 0.65094 0.816 0.184
#> GSM601801 1 0.9881 0.41549 0.564 0.436
#> GSM601831 2 0.9661 0.45062 0.392 0.608
#> GSM601841 2 0.8661 0.65399 0.288 0.712
#> GSM601846 1 0.8267 0.49903 0.740 0.260
#> GSM601861 2 0.7056 0.66612 0.192 0.808
#> GSM601871 2 0.0000 0.72908 0.000 1.000
#> GSM601751 2 0.9248 0.46759 0.340 0.660
#> GSM601761 1 0.0000 0.77968 1.000 0.000
#> GSM601766 1 0.8327 0.66581 0.736 0.264
#> GSM601771 1 0.9954 0.24891 0.540 0.460
#> GSM601776 1 0.0000 0.77968 1.000 0.000
#> GSM601781 1 0.8386 0.51403 0.732 0.268
#> GSM601791 1 0.3584 0.76871 0.932 0.068
#> GSM601806 1 0.8016 0.67952 0.756 0.244
#> GSM601811 2 0.3879 0.73629 0.076 0.924
#> GSM601816 1 0.0000 0.77968 1.000 0.000
#> GSM601821 2 0.7219 0.65817 0.200 0.800
#> GSM601826 1 0.0000 0.77968 1.000 0.000
#> GSM601836 1 0.9129 0.54997 0.672 0.328
#> GSM601851 1 0.0000 0.77968 1.000 0.000
#> GSM601856 2 0.3274 0.72804 0.060 0.940
#> GSM601866 2 0.6712 0.68103 0.176 0.824
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601752 2 0.8410 0.4643 0.360 0.544 0.096
#> GSM601782 1 0.5791 0.5919 0.792 0.060 0.148
#> GSM601792 1 0.0592 0.6616 0.988 0.012 0.000
#> GSM601797 1 0.8399 0.4330 0.608 0.136 0.256
#> GSM601827 1 0.6793 0.2440 0.536 0.012 0.452
#> GSM601837 3 0.2711 0.6899 0.000 0.088 0.912
#> GSM601842 2 0.7260 0.3347 0.048 0.636 0.316
#> GSM601857 3 0.5348 0.6595 0.028 0.176 0.796
#> GSM601867 3 0.3752 0.6739 0.000 0.144 0.856
#> GSM601747 1 0.9434 0.1076 0.416 0.176 0.408
#> GSM601757 1 0.6324 0.5475 0.764 0.076 0.160
#> GSM601762 2 0.8779 0.5176 0.172 0.580 0.248
#> GSM601767 2 0.7124 0.6096 0.272 0.672 0.056
#> GSM601772 2 0.8091 0.5642 0.348 0.572 0.080
#> GSM601777 2 0.9323 0.3804 0.188 0.500 0.312
#> GSM601787 3 0.1399 0.6801 0.004 0.028 0.968
#> GSM601802 2 0.8890 0.4689 0.328 0.532 0.140
#> GSM601807 3 0.5689 0.5786 0.036 0.184 0.780
#> GSM601812 1 0.6468 0.3100 0.552 0.004 0.444
#> GSM601817 1 0.6475 0.5272 0.692 0.028 0.280
#> GSM601822 1 0.0424 0.6605 0.992 0.008 0.000
#> GSM601832 2 0.8454 0.4642 0.432 0.480 0.088
#> GSM601847 1 0.6309 -0.2139 0.504 0.496 0.000
#> GSM601852 1 0.6264 0.5336 0.716 0.028 0.256
#> GSM601862 3 0.1399 0.6834 0.004 0.028 0.968
#> GSM601753 2 0.5905 0.5206 0.352 0.648 0.000
#> GSM601783 1 0.4339 0.6250 0.868 0.084 0.048
#> GSM601793 1 0.6808 0.5184 0.732 0.184 0.084
#> GSM601798 2 0.6482 0.4444 0.040 0.716 0.244
#> GSM601828 1 0.6984 0.2927 0.560 0.020 0.420
#> GSM601838 2 0.6252 0.0786 0.000 0.556 0.444
#> GSM601843 3 0.7578 0.1260 0.040 0.460 0.500
#> GSM601858 3 0.5219 0.6535 0.016 0.196 0.788
#> GSM601868 3 0.2096 0.6722 0.052 0.004 0.944
#> GSM601748 1 0.5115 0.5575 0.768 0.004 0.228
#> GSM601758 1 0.0237 0.6610 0.996 0.004 0.000
#> GSM601763 1 0.3500 0.6022 0.880 0.116 0.004
#> GSM601768 2 0.9897 0.4327 0.364 0.372 0.264
#> GSM601773 2 0.7770 0.5231 0.384 0.560 0.056
#> GSM601778 1 0.4121 0.6170 0.868 0.108 0.024
#> GSM601788 1 0.8153 0.3898 0.640 0.144 0.216
#> GSM601803 2 0.6275 0.5296 0.348 0.644 0.008
#> GSM601808 3 0.6829 0.5554 0.096 0.168 0.736
#> GSM601813 1 0.1753 0.6570 0.952 0.000 0.048
#> GSM601818 3 0.7395 -0.1817 0.476 0.032 0.492
#> GSM601823 1 0.0000 0.6610 1.000 0.000 0.000
#> GSM601833 2 0.9648 0.5243 0.292 0.464 0.244
#> GSM601848 1 0.0000 0.6610 1.000 0.000 0.000
#> GSM601853 1 0.9269 0.2715 0.508 0.184 0.308
#> GSM601863 3 0.4291 0.6117 0.152 0.008 0.840
#> GSM601754 2 0.8663 0.4715 0.364 0.524 0.112
#> GSM601784 1 0.9487 -0.2054 0.476 0.204 0.320
#> GSM601794 1 0.4399 0.5537 0.812 0.188 0.000
#> GSM601799 1 0.6617 -0.1007 0.556 0.436 0.008
#> GSM601829 1 0.0983 0.6629 0.980 0.016 0.004
#> GSM601839 2 0.6111 0.0436 0.000 0.604 0.396
#> GSM601844 1 0.3116 0.6155 0.892 0.108 0.000
#> GSM601859 3 0.7496 0.5883 0.088 0.240 0.672
#> GSM601869 3 0.3583 0.6814 0.056 0.044 0.900
#> GSM601749 1 0.0424 0.6608 0.992 0.008 0.000
#> GSM601759 1 0.0237 0.6612 0.996 0.000 0.004
#> GSM601764 1 0.3038 0.6142 0.896 0.104 0.000
#> GSM601769 2 0.9587 0.3398 0.204 0.440 0.356
#> GSM601774 2 0.7572 0.5589 0.128 0.688 0.184
#> GSM601779 1 0.3116 0.6106 0.892 0.108 0.000
#> GSM601789 3 0.7454 0.4737 0.080 0.252 0.668
#> GSM601804 1 0.5882 0.1443 0.652 0.348 0.000
#> GSM601809 3 0.7570 0.0988 0.404 0.044 0.552
#> GSM601814 2 0.7987 0.3550 0.092 0.616 0.292
#> GSM601819 1 0.3112 0.6518 0.916 0.028 0.056
#> GSM601824 1 0.3116 0.6106 0.892 0.108 0.000
#> GSM601834 2 0.7801 0.6166 0.276 0.636 0.088
#> GSM601849 1 0.1964 0.6453 0.944 0.056 0.000
#> GSM601854 1 0.1289 0.6616 0.968 0.000 0.032
#> GSM601864 3 0.2711 0.6907 0.000 0.088 0.912
#> GSM601755 2 0.6621 0.5752 0.284 0.684 0.032
#> GSM601785 3 0.7026 0.5917 0.152 0.120 0.728
#> GSM601795 1 0.9380 -0.1478 0.512 0.256 0.232
#> GSM601800 2 0.7153 0.6098 0.200 0.708 0.092
#> GSM601830 1 0.9250 0.2766 0.512 0.184 0.304
#> GSM601840 3 0.6752 0.6275 0.104 0.152 0.744
#> GSM601845 1 0.5072 0.5777 0.792 0.196 0.012
#> GSM601860 3 0.6500 0.6427 0.100 0.140 0.760
#> GSM601870 3 0.4291 0.5909 0.000 0.180 0.820
#> GSM601750 1 0.6264 0.4211 0.616 0.004 0.380
#> GSM601760 3 0.7656 0.2681 0.376 0.052 0.572
#> GSM601765 1 0.7995 -0.3620 0.480 0.460 0.060
#> GSM601770 2 0.7956 0.0474 0.060 0.516 0.424
#> GSM601775 1 0.6119 0.4773 0.772 0.164 0.064
#> GSM601780 1 0.0424 0.6621 0.992 0.008 0.000
#> GSM601790 3 0.5455 0.6467 0.020 0.204 0.776
#> GSM601805 2 0.7915 -0.0937 0.056 0.488 0.456
#> GSM601810 1 0.7619 0.2288 0.532 0.044 0.424
#> GSM601815 3 0.8175 0.4495 0.132 0.236 0.632
#> GSM601820 1 0.2947 0.6557 0.920 0.020 0.060
#> GSM601825 2 0.6516 0.3735 0.480 0.516 0.004
#> GSM601835 3 0.6579 0.5242 0.020 0.328 0.652
#> GSM601850 1 0.2448 0.6341 0.924 0.076 0.000
#> GSM601855 3 0.7759 0.5003 0.144 0.180 0.676
#> GSM601865 3 0.2448 0.6879 0.000 0.076 0.924
#> GSM601756 2 0.5722 0.5821 0.292 0.704 0.004
#> GSM601786 3 0.3619 0.6831 0.000 0.136 0.864
#> GSM601796 1 0.9646 0.1457 0.468 0.272 0.260
#> GSM601801 2 0.5804 0.5828 0.112 0.800 0.088
#> GSM601831 1 0.6045 0.4173 0.620 0.000 0.380
#> GSM601841 3 0.6775 0.6445 0.096 0.164 0.740
#> GSM601846 1 0.7106 0.5354 0.696 0.072 0.232
#> GSM601861 3 0.6855 0.5522 0.032 0.316 0.652
#> GSM601871 3 0.0237 0.6804 0.000 0.004 0.996
#> GSM601751 3 0.7327 0.3405 0.312 0.052 0.636
#> GSM601761 1 0.1860 0.6474 0.948 0.052 0.000
#> GSM601766 1 0.8334 0.2862 0.616 0.136 0.248
#> GSM601771 3 0.9702 -0.2022 0.364 0.220 0.416
#> GSM601776 1 0.0000 0.6610 1.000 0.000 0.000
#> GSM601781 1 0.8622 0.2823 0.572 0.132 0.296
#> GSM601791 1 0.7741 0.3368 0.668 0.116 0.216
#> GSM601806 2 0.6180 0.4772 0.416 0.584 0.000
#> GSM601811 3 0.5581 0.6619 0.036 0.176 0.788
#> GSM601816 1 0.1031 0.6609 0.976 0.024 0.000
#> GSM601821 3 0.8677 0.3358 0.144 0.280 0.576
#> GSM601826 1 0.0000 0.6610 1.000 0.000 0.000
#> GSM601836 1 0.9088 -0.0502 0.464 0.140 0.396
#> GSM601851 1 0.2165 0.6461 0.936 0.064 0.000
#> GSM601856 3 0.4139 0.6338 0.016 0.124 0.860
#> GSM601866 3 0.3850 0.6568 0.088 0.028 0.884
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601752 4 0.3257 0.6712 0.152 0.004 0.000 0.844
#> GSM601782 1 0.5714 0.5835 0.764 0.052 0.116 0.068
#> GSM601792 1 0.1302 0.6240 0.956 0.000 0.000 0.044
#> GSM601797 1 0.7511 0.4640 0.632 0.068 0.140 0.160
#> GSM601827 1 0.5409 0.4517 0.644 0.004 0.332 0.020
#> GSM601837 3 0.4327 0.6264 0.000 0.216 0.768 0.016
#> GSM601842 2 0.7185 0.5811 0.020 0.612 0.152 0.216
#> GSM601857 3 0.4752 0.6548 0.012 0.120 0.804 0.064
#> GSM601867 3 0.3401 0.6490 0.000 0.008 0.840 0.152
#> GSM601747 3 0.9910 -0.1394 0.232 0.224 0.320 0.224
#> GSM601757 1 0.7031 0.3164 0.620 0.016 0.144 0.220
#> GSM601762 2 0.7216 0.5809 0.092 0.648 0.068 0.192
#> GSM601767 2 0.5883 0.5554 0.108 0.708 0.004 0.180
#> GSM601772 2 0.6935 0.4645 0.240 0.616 0.012 0.132
#> GSM601777 4 0.6622 0.4581 0.064 0.044 0.224 0.668
#> GSM601787 3 0.1488 0.6753 0.000 0.032 0.956 0.012
#> GSM601802 4 0.4321 0.6514 0.128 0.016 0.032 0.824
#> GSM601807 3 0.5735 0.5612 0.020 0.112 0.748 0.120
#> GSM601812 1 0.6081 0.4058 0.564 0.012 0.396 0.028
#> GSM601817 1 0.6622 0.5381 0.664 0.048 0.232 0.056
#> GSM601822 1 0.0921 0.6278 0.972 0.000 0.000 0.028
#> GSM601832 2 0.8178 -0.2376 0.332 0.360 0.008 0.300
#> GSM601847 4 0.3837 0.6667 0.224 0.000 0.000 0.776
#> GSM601852 1 0.4524 0.5672 0.768 0.000 0.204 0.028
#> GSM601862 3 0.1182 0.6790 0.000 0.016 0.968 0.016
#> GSM601753 4 0.3266 0.6762 0.168 0.000 0.000 0.832
#> GSM601783 1 0.3607 0.6011 0.864 0.008 0.032 0.096
#> GSM601793 1 0.5757 0.5180 0.732 0.020 0.068 0.180
#> GSM601798 4 0.7668 -0.1543 0.020 0.300 0.152 0.528
#> GSM601828 1 0.5290 0.5036 0.680 0.004 0.292 0.024
#> GSM601838 2 0.4907 0.5486 0.000 0.764 0.176 0.060
#> GSM601843 2 0.7643 0.4405 0.028 0.548 0.288 0.136
#> GSM601858 3 0.5325 0.6274 0.012 0.196 0.744 0.048
#> GSM601868 3 0.0844 0.6792 0.004 0.012 0.980 0.004
#> GSM601748 1 0.4098 0.5679 0.784 0.012 0.204 0.000
#> GSM601758 1 0.1022 0.6273 0.968 0.000 0.000 0.032
#> GSM601763 1 0.5400 0.1496 0.608 0.020 0.000 0.372
#> GSM601768 2 0.7425 0.4342 0.276 0.588 0.052 0.084
#> GSM601773 2 0.5989 0.4387 0.300 0.640 0.004 0.056
#> GSM601778 1 0.5735 0.2312 0.620 0.012 0.020 0.348
#> GSM601788 1 0.7688 0.4151 0.624 0.152 0.136 0.088
#> GSM601803 4 0.4018 0.6747 0.168 0.016 0.004 0.812
#> GSM601808 3 0.5875 0.5515 0.052 0.088 0.756 0.104
#> GSM601813 1 0.1118 0.6329 0.964 0.000 0.036 0.000
#> GSM601818 1 0.6306 0.3971 0.584 0.052 0.356 0.008
#> GSM601823 1 0.0000 0.6325 1.000 0.000 0.000 0.000
#> GSM601833 2 0.5854 0.5890 0.172 0.736 0.048 0.044
#> GSM601848 1 0.0000 0.6325 1.000 0.000 0.000 0.000
#> GSM601853 1 0.8542 0.3082 0.484 0.096 0.308 0.112
#> GSM601863 3 0.2796 0.6448 0.096 0.008 0.892 0.004
#> GSM601754 4 0.3486 0.6791 0.188 0.000 0.000 0.812
#> GSM601784 4 0.8956 0.3987 0.356 0.108 0.132 0.404
#> GSM601794 1 0.4985 -0.0725 0.532 0.000 0.000 0.468
#> GSM601799 4 0.4647 0.6190 0.288 0.008 0.000 0.704
#> GSM601829 1 0.1398 0.6305 0.956 0.000 0.004 0.040
#> GSM601839 2 0.6194 0.3978 0.000 0.668 0.200 0.132
#> GSM601844 1 0.4973 0.2409 0.644 0.008 0.000 0.348
#> GSM601859 3 0.7088 0.5618 0.028 0.140 0.636 0.196
#> GSM601869 3 0.2189 0.6831 0.004 0.044 0.932 0.020
#> GSM601749 1 0.1004 0.6315 0.972 0.004 0.000 0.024
#> GSM601759 1 0.0592 0.6316 0.984 0.000 0.000 0.016
#> GSM601764 1 0.4643 0.2541 0.656 0.000 0.000 0.344
#> GSM601769 2 0.5770 0.6138 0.156 0.728 0.108 0.008
#> GSM601774 2 0.5243 0.6233 0.052 0.796 0.072 0.080
#> GSM601779 1 0.4761 0.1874 0.628 0.000 0.000 0.372
#> GSM601789 2 0.6842 0.1715 0.044 0.492 0.436 0.028
#> GSM601804 4 0.4730 0.5312 0.364 0.000 0.000 0.636
#> GSM601809 3 0.8263 -0.0597 0.320 0.028 0.452 0.200
#> GSM601814 2 0.6575 0.6078 0.052 0.704 0.104 0.140
#> GSM601819 1 0.4378 0.6023 0.836 0.024 0.052 0.088
#> GSM601824 1 0.4761 0.1874 0.628 0.000 0.000 0.372
#> GSM601834 2 0.6419 0.5855 0.136 0.708 0.036 0.120
#> GSM601849 1 0.4040 0.4338 0.752 0.000 0.000 0.248
#> GSM601854 1 0.1004 0.6352 0.972 0.004 0.024 0.000
#> GSM601864 3 0.4332 0.6365 0.000 0.176 0.792 0.032
#> GSM601755 4 0.4033 0.6482 0.132 0.028 0.008 0.832
#> GSM601785 3 0.7682 0.5172 0.064 0.176 0.612 0.148
#> GSM601795 4 0.7439 0.4381 0.380 0.032 0.084 0.504
#> GSM601800 4 0.5700 0.5659 0.076 0.100 0.056 0.768
#> GSM601830 1 0.8153 0.4091 0.564 0.096 0.228 0.112
#> GSM601840 3 0.6785 0.5792 0.060 0.068 0.672 0.200
#> GSM601845 1 0.4813 0.4931 0.716 0.012 0.004 0.268
#> GSM601860 3 0.6438 0.6149 0.028 0.136 0.700 0.136
#> GSM601870 3 0.4724 0.5660 0.000 0.096 0.792 0.112
#> GSM601750 1 0.6196 0.4770 0.608 0.028 0.340 0.024
#> GSM601760 3 0.7156 0.1734 0.328 0.000 0.520 0.152
#> GSM601765 2 0.6240 0.4439 0.276 0.640 0.004 0.080
#> GSM601770 2 0.6750 0.5921 0.016 0.656 0.164 0.164
#> GSM601775 1 0.7361 -0.1014 0.508 0.100 0.020 0.372
#> GSM601780 1 0.0657 0.6334 0.984 0.004 0.000 0.012
#> GSM601790 3 0.4978 0.4340 0.000 0.384 0.612 0.004
#> GSM601805 4 0.7457 -0.0675 0.016 0.120 0.360 0.504
#> GSM601810 1 0.6542 0.4489 0.648 0.088 0.248 0.016
#> GSM601815 2 0.6651 0.3251 0.060 0.572 0.352 0.016
#> GSM601820 1 0.3166 0.6184 0.888 0.012 0.080 0.020
#> GSM601825 4 0.5110 0.5441 0.352 0.012 0.000 0.636
#> GSM601835 2 0.6755 -0.0180 0.000 0.456 0.452 0.092
#> GSM601850 1 0.3688 0.4834 0.792 0.000 0.000 0.208
#> GSM601855 3 0.6776 0.5001 0.092 0.096 0.700 0.112
#> GSM601865 3 0.4436 0.6267 0.000 0.216 0.764 0.020
#> GSM601756 4 0.5770 0.5191 0.140 0.148 0.000 0.712
#> GSM601786 3 0.4797 0.6022 0.000 0.260 0.720 0.020
#> GSM601796 4 0.7861 0.2852 0.268 0.016 0.208 0.508
#> GSM601801 2 0.6786 0.2949 0.032 0.484 0.036 0.448
#> GSM601831 1 0.4535 0.5278 0.704 0.004 0.292 0.000
#> GSM601841 3 0.6375 0.6321 0.052 0.084 0.716 0.148
#> GSM601846 1 0.7559 0.4945 0.596 0.036 0.208 0.160
#> GSM601861 2 0.6144 -0.0740 0.008 0.508 0.452 0.032
#> GSM601871 3 0.0657 0.6744 0.000 0.012 0.984 0.004
#> GSM601751 3 0.7515 0.3137 0.248 0.020 0.568 0.164
#> GSM601761 1 0.4277 0.3768 0.720 0.000 0.000 0.280
#> GSM601766 1 0.9210 -0.2631 0.388 0.136 0.136 0.340
#> GSM601771 2 0.9811 0.0720 0.284 0.284 0.276 0.156
#> GSM601776 1 0.0000 0.6325 1.000 0.000 0.000 0.000
#> GSM601781 1 0.8561 -0.0796 0.412 0.032 0.268 0.288
#> GSM601791 1 0.7994 -0.2361 0.440 0.024 0.156 0.380
#> GSM601806 4 0.7031 0.5176 0.296 0.152 0.000 0.552
#> GSM601811 3 0.5493 0.6137 0.016 0.092 0.760 0.132
#> GSM601816 1 0.1022 0.6332 0.968 0.000 0.000 0.032
#> GSM601821 2 0.7071 0.4483 0.100 0.588 0.292 0.020
#> GSM601826 1 0.0000 0.6325 1.000 0.000 0.000 0.000
#> GSM601836 4 0.9769 0.2040 0.216 0.168 0.284 0.332
#> GSM601851 1 0.3311 0.5305 0.828 0.000 0.000 0.172
#> GSM601856 3 0.3945 0.6175 0.008 0.064 0.852 0.076
#> GSM601866 3 0.3634 0.6648 0.072 0.040 0.872 0.016
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601752 4 0.1638 0.61628 0.064 0.004 0.000 0.932 0.000
#> GSM601782 1 0.5989 0.56766 0.720 0.072 0.104 0.068 0.036
#> GSM601792 1 0.1952 0.64522 0.912 0.004 0.000 0.084 0.000
#> GSM601797 1 0.5862 0.46354 0.684 0.016 0.064 0.200 0.036
#> GSM601827 1 0.5349 0.42617 0.704 0.000 0.160 0.016 0.120
#> GSM601837 5 0.3656 0.55439 0.000 0.056 0.104 0.008 0.832
#> GSM601842 2 0.5891 0.54217 0.016 0.624 0.000 0.252 0.108
#> GSM601857 5 0.3769 0.58426 0.000 0.004 0.172 0.028 0.796
#> GSM601867 5 0.5611 0.58591 0.000 0.020 0.140 0.156 0.684
#> GSM601747 2 0.9827 0.07121 0.224 0.264 0.124 0.200 0.188
#> GSM601757 1 0.7223 0.24615 0.544 0.020 0.052 0.272 0.112
#> GSM601762 2 0.4816 0.58449 0.060 0.748 0.000 0.168 0.024
#> GSM601767 2 0.4210 0.59919 0.072 0.784 0.000 0.140 0.004
#> GSM601772 2 0.5477 0.53083 0.216 0.672 0.000 0.100 0.012
#> GSM601777 4 0.6077 0.42261 0.040 0.020 0.244 0.648 0.048
#> GSM601787 5 0.4937 0.52946 0.000 0.064 0.264 0.000 0.672
#> GSM601802 4 0.3229 0.58714 0.056 0.040 0.000 0.872 0.032
#> GSM601807 3 0.2813 0.67352 0.004 0.032 0.880 0.000 0.084
#> GSM601812 1 0.6792 0.33406 0.576 0.004 0.192 0.036 0.192
#> GSM601817 1 0.7006 0.48299 0.608 0.060 0.208 0.092 0.032
#> GSM601822 1 0.1410 0.65507 0.940 0.000 0.000 0.060 0.000
#> GSM601832 2 0.6985 -0.12735 0.284 0.404 0.000 0.304 0.008
#> GSM601847 4 0.2763 0.63418 0.148 0.004 0.000 0.848 0.000
#> GSM601852 1 0.4478 0.52915 0.776 0.000 0.152 0.028 0.044
#> GSM601862 5 0.4045 0.44638 0.000 0.000 0.356 0.000 0.644
#> GSM601753 4 0.2124 0.63042 0.096 0.004 0.000 0.900 0.000
#> GSM601783 1 0.2899 0.63071 0.872 0.004 0.000 0.096 0.028
#> GSM601793 1 0.4277 0.56008 0.768 0.000 0.000 0.156 0.076
#> GSM601798 4 0.6256 -0.04146 0.008 0.300 0.016 0.580 0.096
#> GSM601828 1 0.5057 0.48748 0.740 0.000 0.136 0.024 0.100
#> GSM601838 2 0.5752 0.43408 0.000 0.600 0.092 0.008 0.300
#> GSM601843 2 0.6437 0.33138 0.020 0.536 0.000 0.124 0.320
#> GSM601858 5 0.3451 0.63032 0.012 0.120 0.016 0.008 0.844
#> GSM601868 5 0.3966 0.47250 0.000 0.000 0.336 0.000 0.664
#> GSM601748 1 0.3670 0.53325 0.796 0.000 0.180 0.004 0.020
#> GSM601758 1 0.1502 0.65599 0.940 0.004 0.000 0.056 0.000
#> GSM601763 1 0.4966 0.15763 0.564 0.032 0.000 0.404 0.000
#> GSM601768 2 0.6168 0.46426 0.256 0.616 0.000 0.084 0.044
#> GSM601773 2 0.5082 0.47271 0.260 0.664 0.000 0.076 0.000
#> GSM601778 1 0.5185 0.25564 0.588 0.016 0.016 0.376 0.004
#> GSM601788 1 0.6993 0.35913 0.580 0.156 0.000 0.088 0.176
#> GSM601803 4 0.2664 0.62764 0.092 0.020 0.000 0.884 0.004
#> GSM601808 3 0.3236 0.65401 0.020 0.000 0.828 0.000 0.152
#> GSM601813 1 0.0703 0.66360 0.976 0.000 0.000 0.000 0.024
#> GSM601818 1 0.6447 0.31661 0.616 0.052 0.208 0.000 0.124
#> GSM601823 1 0.0000 0.66345 1.000 0.000 0.000 0.000 0.000
#> GSM601833 2 0.3496 0.62241 0.116 0.840 0.000 0.016 0.028
#> GSM601848 1 0.0000 0.66345 1.000 0.000 0.000 0.000 0.000
#> GSM601853 3 0.3819 0.58558 0.228 0.000 0.756 0.000 0.016
#> GSM601863 5 0.5440 0.42052 0.088 0.000 0.300 0.000 0.612
#> GSM601754 4 0.2389 0.63474 0.116 0.004 0.000 0.880 0.000
#> GSM601784 4 0.7842 0.38855 0.292 0.152 0.000 0.436 0.120
#> GSM601794 4 0.4450 0.00845 0.488 0.004 0.000 0.508 0.000
#> GSM601799 4 0.3647 0.60487 0.228 0.004 0.000 0.764 0.004
#> GSM601829 1 0.1357 0.66341 0.948 0.000 0.000 0.048 0.004
#> GSM601839 2 0.6910 0.10596 0.000 0.436 0.388 0.028 0.148
#> GSM601844 1 0.4564 0.24830 0.600 0.004 0.000 0.388 0.008
#> GSM601859 5 0.4289 0.58624 0.020 0.024 0.000 0.192 0.764
#> GSM601869 5 0.3586 0.54229 0.000 0.000 0.264 0.000 0.736
#> GSM601749 1 0.0880 0.66513 0.968 0.000 0.000 0.032 0.000
#> GSM601759 1 0.1121 0.66017 0.956 0.000 0.000 0.044 0.000
#> GSM601764 1 0.4288 0.26141 0.612 0.004 0.000 0.384 0.000
#> GSM601769 2 0.4062 0.62545 0.132 0.796 0.000 0.004 0.068
#> GSM601774 2 0.3738 0.61552 0.040 0.844 0.000 0.052 0.064
#> GSM601779 1 0.4341 0.21389 0.592 0.004 0.000 0.404 0.000
#> GSM601789 2 0.6448 0.13056 0.024 0.528 0.052 0.024 0.372
#> GSM601804 4 0.3969 0.52970 0.304 0.004 0.000 0.692 0.000
#> GSM601809 5 0.8185 0.03096 0.260 0.112 0.008 0.196 0.424
#> GSM601814 2 0.5679 0.58383 0.036 0.704 0.004 0.140 0.116
#> GSM601819 1 0.3375 0.63835 0.852 0.012 0.000 0.096 0.040
#> GSM601824 1 0.4341 0.21389 0.592 0.004 0.000 0.404 0.000
#> GSM601834 2 0.4543 0.59498 0.088 0.780 0.000 0.112 0.020
#> GSM601849 1 0.3969 0.41711 0.692 0.004 0.000 0.304 0.000
#> GSM601854 1 0.0981 0.66557 0.972 0.000 0.008 0.008 0.012
#> GSM601864 5 0.4953 0.51865 0.000 0.056 0.172 0.032 0.740
#> GSM601755 4 0.2618 0.59169 0.052 0.036 0.000 0.900 0.012
#> GSM601785 5 0.5101 0.58133 0.052 0.132 0.000 0.068 0.748
#> GSM601795 4 0.6455 0.45735 0.312 0.056 0.000 0.560 0.072
#> GSM601800 4 0.4414 0.53995 0.036 0.108 0.000 0.796 0.060
#> GSM601830 3 0.4402 0.31173 0.352 0.000 0.636 0.000 0.012
#> GSM601840 5 0.5372 0.60280 0.056 0.096 0.000 0.116 0.732
#> GSM601845 1 0.4385 0.50053 0.672 0.004 0.000 0.312 0.012
#> GSM601860 5 0.3692 0.63490 0.020 0.084 0.000 0.056 0.840
#> GSM601870 3 0.1851 0.69344 0.000 0.000 0.912 0.000 0.088
#> GSM601750 1 0.7647 0.35856 0.588 0.088 0.152 0.068 0.104
#> GSM601760 5 0.6442 0.14518 0.364 0.000 0.008 0.144 0.484
#> GSM601765 2 0.4808 0.51153 0.248 0.696 0.000 0.052 0.004
#> GSM601770 2 0.5081 0.56486 0.008 0.720 0.000 0.136 0.136
#> GSM601775 1 0.6656 -0.12849 0.452 0.132 0.000 0.396 0.020
#> GSM601780 1 0.0771 0.66555 0.976 0.000 0.000 0.020 0.004
#> GSM601790 5 0.5139 0.41147 0.000 0.236 0.064 0.012 0.688
#> GSM601805 5 0.4905 0.17107 0.012 0.008 0.000 0.464 0.516
#> GSM601810 1 0.5120 0.45213 0.712 0.012 0.068 0.004 0.204
#> GSM601815 2 0.5369 0.19658 0.044 0.508 0.000 0.004 0.444
#> GSM601820 1 0.3241 0.64534 0.876 0.004 0.052 0.032 0.036
#> GSM601825 4 0.4227 0.54433 0.292 0.016 0.000 0.692 0.000
#> GSM601835 5 0.5353 0.06065 0.000 0.472 0.000 0.052 0.476
#> GSM601850 1 0.3689 0.48331 0.740 0.004 0.000 0.256 0.000
#> GSM601855 3 0.1892 0.69648 0.004 0.000 0.916 0.000 0.080
#> GSM601865 5 0.1915 0.61351 0.000 0.040 0.032 0.000 0.928
#> GSM601756 4 0.3846 0.47656 0.056 0.144 0.000 0.800 0.000
#> GSM601786 5 0.3612 0.62052 0.000 0.172 0.028 0.000 0.800
#> GSM601796 4 0.6482 0.23523 0.276 0.000 0.000 0.492 0.232
#> GSM601801 4 0.6158 -0.24032 0.020 0.432 0.000 0.472 0.076
#> GSM601831 1 0.4480 0.48558 0.752 0.000 0.180 0.004 0.064
#> GSM601841 5 0.4344 0.60372 0.100 0.004 0.016 0.080 0.800
#> GSM601846 1 0.7322 0.47380 0.572 0.024 0.184 0.164 0.056
#> GSM601861 5 0.4142 0.24117 0.004 0.308 0.000 0.004 0.684
#> GSM601871 5 0.4127 0.49347 0.000 0.008 0.312 0.000 0.680
#> GSM601751 5 0.7198 0.32617 0.204 0.084 0.000 0.164 0.548
#> GSM601761 1 0.4047 0.38271 0.676 0.004 0.000 0.320 0.000
#> GSM601766 4 0.8275 0.27243 0.308 0.216 0.000 0.340 0.136
#> GSM601771 2 0.8528 0.09070 0.240 0.292 0.000 0.188 0.280
#> GSM601776 1 0.0000 0.66345 1.000 0.000 0.000 0.000 0.000
#> GSM601781 1 0.7848 -0.06280 0.388 0.072 0.000 0.284 0.256
#> GSM601791 1 0.6511 -0.14748 0.428 0.004 0.000 0.404 0.164
#> GSM601806 4 0.5798 0.48778 0.236 0.156 0.000 0.608 0.000
#> GSM601811 5 0.7137 0.54368 0.016 0.100 0.160 0.120 0.604
#> GSM601816 1 0.1043 0.66605 0.960 0.000 0.000 0.040 0.000
#> GSM601821 2 0.7043 0.39320 0.092 0.484 0.040 0.016 0.368
#> GSM601826 1 0.0000 0.66345 1.000 0.000 0.000 0.000 0.000
#> GSM601836 4 0.8514 0.06334 0.180 0.280 0.000 0.292 0.248
#> GSM601851 1 0.3143 0.55256 0.796 0.000 0.000 0.204 0.000
#> GSM601856 3 0.4630 0.03797 0.004 0.008 0.572 0.000 0.416
#> GSM601866 5 0.5305 0.48172 0.112 0.004 0.204 0.000 0.680
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601752 4 0.2180 0.56854 0.048 0.004 0.000 0.912 0.028 0.008
#> GSM601782 1 0.6045 0.48467 0.664 0.156 0.024 0.072 0.012 0.072
#> GSM601792 1 0.1806 0.64027 0.908 0.004 0.000 0.088 0.000 0.000
#> GSM601797 1 0.5633 0.40573 0.648 0.000 0.032 0.224 0.028 0.068
#> GSM601827 1 0.4771 0.41252 0.708 0.000 0.120 0.016 0.000 0.156
#> GSM601837 5 0.5365 0.51422 0.000 0.036 0.444 0.000 0.480 0.040
#> GSM601842 2 0.5421 0.44642 0.016 0.612 0.124 0.248 0.000 0.000
#> GSM601857 3 0.2069 0.48379 0.000 0.004 0.908 0.020 0.000 0.068
#> GSM601867 3 0.3873 0.49731 0.000 0.020 0.780 0.160 0.000 0.040
#> GSM601747 2 0.8729 0.14196 0.208 0.308 0.164 0.196 0.000 0.124
#> GSM601757 1 0.6326 0.22433 0.536 0.020 0.156 0.268 0.000 0.020
#> GSM601762 2 0.3940 0.55766 0.056 0.788 0.024 0.132 0.000 0.000
#> GSM601767 2 0.3593 0.57090 0.064 0.800 0.004 0.132 0.000 0.000
#> GSM601772 2 0.4832 0.54958 0.216 0.680 0.012 0.092 0.000 0.000
#> GSM601777 4 0.5518 0.39987 0.036 0.020 0.060 0.648 0.000 0.236
#> GSM601787 3 0.3709 0.48476 0.000 0.040 0.756 0.000 0.000 0.204
#> GSM601802 4 0.3518 0.53088 0.036 0.040 0.032 0.856 0.028 0.008
#> GSM601807 6 0.2669 0.50946 0.000 0.016 0.032 0.000 0.072 0.880
#> GSM601812 1 0.6330 0.31194 0.568 0.012 0.192 0.040 0.000 0.188
#> GSM601817 1 0.6584 0.44074 0.588 0.072 0.040 0.096 0.000 0.204
#> GSM601822 1 0.1267 0.65246 0.940 0.000 0.000 0.060 0.000 0.000
#> GSM601832 2 0.6252 -0.06316 0.280 0.416 0.008 0.296 0.000 0.000
#> GSM601847 4 0.2520 0.60092 0.152 0.004 0.000 0.844 0.000 0.000
#> GSM601852 1 0.4022 0.50829 0.776 0.000 0.044 0.028 0.000 0.152
#> GSM601862 3 0.3101 0.44764 0.000 0.000 0.756 0.000 0.000 0.244
#> GSM601753 4 0.1858 0.59675 0.092 0.004 0.000 0.904 0.000 0.000
#> GSM601783 1 0.2604 0.62790 0.872 0.004 0.028 0.096 0.000 0.000
#> GSM601793 1 0.3877 0.53803 0.764 0.000 0.076 0.160 0.000 0.000
#> GSM601798 4 0.6056 0.02912 0.008 0.264 0.084 0.596 0.028 0.020
#> GSM601828 1 0.4543 0.46771 0.740 0.000 0.100 0.024 0.000 0.136
#> GSM601838 5 0.6324 0.47916 0.000 0.320 0.164 0.000 0.480 0.036
#> GSM601843 2 0.6556 0.29471 0.024 0.544 0.276 0.116 0.032 0.008
#> GSM601858 3 0.2376 0.49787 0.008 0.096 0.884 0.012 0.000 0.000
#> GSM601868 3 0.2941 0.46660 0.000 0.000 0.780 0.000 0.000 0.220
#> GSM601748 1 0.3343 0.51248 0.796 0.000 0.024 0.000 0.004 0.176
#> GSM601758 1 0.1411 0.65157 0.936 0.004 0.000 0.060 0.000 0.000
#> GSM601763 1 0.4591 0.11969 0.552 0.040 0.000 0.408 0.000 0.000
#> GSM601768 2 0.5481 0.47944 0.256 0.620 0.040 0.084 0.000 0.000
#> GSM601773 2 0.4639 0.48595 0.256 0.660 0.000 0.084 0.000 0.000
#> GSM601778 1 0.4717 0.23704 0.584 0.016 0.004 0.380 0.004 0.012
#> GSM601788 1 0.6785 0.20051 0.520 0.212 0.168 0.096 0.004 0.000
#> GSM601803 4 0.3078 0.58639 0.084 0.020 0.004 0.864 0.020 0.008
#> GSM601808 6 0.4892 0.56695 0.016 0.000 0.236 0.008 0.060 0.680
#> GSM601813 1 0.0632 0.66089 0.976 0.000 0.024 0.000 0.000 0.000
#> GSM601818 1 0.5960 0.29387 0.608 0.068 0.128 0.000 0.000 0.196
#> GSM601823 1 0.0000 0.66089 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601833 2 0.3075 0.56603 0.092 0.856 0.032 0.016 0.004 0.000
#> GSM601848 1 0.0000 0.66089 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601853 6 0.5025 0.54066 0.232 0.000 0.136 0.000 0.000 0.632
#> GSM601863 3 0.4431 0.42264 0.080 0.000 0.692 0.000 0.000 0.228
#> GSM601754 4 0.2100 0.60077 0.112 0.004 0.000 0.884 0.000 0.000
#> GSM601784 4 0.7073 0.32662 0.288 0.196 0.096 0.420 0.000 0.000
#> GSM601794 4 0.3996 0.02671 0.484 0.004 0.000 0.512 0.000 0.000
#> GSM601799 4 0.3357 0.58146 0.224 0.008 0.004 0.764 0.000 0.000
#> GSM601829 1 0.1285 0.65987 0.944 0.000 0.004 0.052 0.000 0.000
#> GSM601839 5 0.6268 0.27751 0.000 0.176 0.020 0.004 0.480 0.320
#> GSM601844 1 0.4200 0.22984 0.592 0.004 0.012 0.392 0.000 0.000
#> GSM601859 3 0.3757 0.44969 0.020 0.024 0.776 0.180 0.000 0.000
#> GSM601869 3 0.2219 0.50355 0.000 0.000 0.864 0.000 0.000 0.136
#> GSM601749 1 0.0935 0.66183 0.964 0.000 0.000 0.032 0.004 0.000
#> GSM601759 1 0.1075 0.65618 0.952 0.000 0.000 0.048 0.000 0.000
#> GSM601764 1 0.3862 0.24408 0.608 0.004 0.000 0.388 0.000 0.000
#> GSM601769 2 0.3511 0.56852 0.124 0.808 0.064 0.004 0.000 0.000
#> GSM601774 2 0.3094 0.52874 0.032 0.860 0.060 0.048 0.000 0.000
#> GSM601779 1 0.3907 0.19616 0.588 0.004 0.000 0.408 0.000 0.000
#> GSM601789 2 0.6170 0.17968 0.020 0.548 0.332 0.016 0.036 0.048
#> GSM601804 4 0.3547 0.52011 0.300 0.004 0.000 0.696 0.000 0.000
#> GSM601809 3 0.7291 -0.03748 0.256 0.092 0.420 0.224 0.000 0.008
#> GSM601814 2 0.5675 0.41148 0.036 0.676 0.092 0.160 0.036 0.000
#> GSM601819 1 0.3191 0.63132 0.844 0.016 0.044 0.096 0.000 0.000
#> GSM601824 1 0.3907 0.19616 0.588 0.004 0.000 0.408 0.000 0.000
#> GSM601834 2 0.4310 0.51470 0.064 0.764 0.024 0.144 0.004 0.000
#> GSM601849 1 0.3584 0.40533 0.688 0.004 0.000 0.308 0.000 0.000
#> GSM601854 1 0.0912 0.66286 0.972 0.000 0.008 0.004 0.004 0.012
#> GSM601864 5 0.6283 0.51916 0.000 0.028 0.364 0.016 0.484 0.108
#> GSM601755 4 0.2861 0.53561 0.032 0.036 0.008 0.888 0.028 0.008
#> GSM601785 3 0.5114 0.41236 0.052 0.176 0.692 0.080 0.000 0.000
#> GSM601795 4 0.5917 0.45724 0.304 0.064 0.076 0.556 0.000 0.000
#> GSM601800 4 0.4321 0.49482 0.024 0.104 0.056 0.788 0.028 0.000
#> GSM601830 6 0.4026 0.38735 0.348 0.000 0.016 0.000 0.000 0.636
#> GSM601840 3 0.4798 0.49520 0.052 0.076 0.728 0.144 0.000 0.000
#> GSM601845 1 0.4042 0.49328 0.664 0.004 0.016 0.316 0.000 0.000
#> GSM601860 3 0.3106 0.50861 0.016 0.048 0.852 0.084 0.000 0.000
#> GSM601870 6 0.2491 0.62829 0.000 0.000 0.164 0.000 0.000 0.836
#> GSM601750 1 0.6832 0.02404 0.452 0.024 0.108 0.008 0.368 0.040
#> GSM601760 3 0.5894 0.14013 0.328 0.000 0.492 0.172 0.000 0.008
#> GSM601765 2 0.4329 0.53147 0.240 0.700 0.004 0.056 0.000 0.000
#> GSM601770 2 0.4450 0.49855 0.008 0.732 0.128 0.132 0.000 0.000
#> GSM601775 1 0.6197 -0.21228 0.412 0.184 0.016 0.388 0.000 0.000
#> GSM601780 1 0.0692 0.66279 0.976 0.000 0.004 0.020 0.000 0.000
#> GSM601790 5 0.6016 0.59186 0.000 0.164 0.348 0.004 0.476 0.008
#> GSM601805 3 0.4536 0.11321 0.012 0.008 0.512 0.464 0.004 0.000
#> GSM601810 1 0.4702 0.43420 0.712 0.012 0.196 0.008 0.000 0.072
#> GSM601815 3 0.5127 -0.10093 0.036 0.460 0.480 0.000 0.024 0.000
#> GSM601820 1 0.3145 0.63885 0.864 0.016 0.076 0.028 0.004 0.012
#> GSM601825 4 0.3859 0.53315 0.288 0.020 0.000 0.692 0.000 0.000
#> GSM601835 2 0.4982 0.12937 0.000 0.528 0.416 0.044 0.012 0.000
#> GSM601850 1 0.3337 0.47211 0.736 0.004 0.000 0.260 0.000 0.000
#> GSM601855 6 0.2219 0.63090 0.000 0.000 0.136 0.000 0.000 0.864
#> GSM601865 3 0.3537 0.36031 0.000 0.016 0.796 0.000 0.164 0.024
#> GSM601756 4 0.3781 0.45009 0.036 0.116 0.000 0.812 0.028 0.008
#> GSM601786 3 0.3457 0.45655 0.000 0.164 0.800 0.000 0.016 0.020
#> GSM601796 4 0.5818 0.26892 0.256 0.000 0.248 0.496 0.000 0.000
#> GSM601801 4 0.6471 -0.15314 0.020 0.368 0.076 0.488 0.040 0.008
#> GSM601831 1 0.4046 0.46435 0.752 0.000 0.068 0.000 0.004 0.176
#> GSM601841 3 0.3655 0.50737 0.072 0.000 0.812 0.100 0.000 0.016
#> GSM601846 1 0.6837 0.44150 0.564 0.016 0.056 0.168 0.016 0.180
#> GSM601861 3 0.4653 0.13533 0.000 0.260 0.664 0.004 0.072 0.000
#> GSM601871 3 0.2902 0.47808 0.000 0.004 0.800 0.000 0.000 0.196
#> GSM601751 3 0.6237 0.31191 0.200 0.048 0.552 0.200 0.000 0.000
#> GSM601761 1 0.3636 0.37726 0.676 0.004 0.000 0.320 0.000 0.000
#> GSM601766 4 0.7471 0.20646 0.256 0.280 0.132 0.332 0.000 0.000
#> GSM601771 2 0.7609 0.16069 0.236 0.340 0.232 0.192 0.000 0.000
#> GSM601776 1 0.0000 0.66089 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601781 1 0.7164 -0.08870 0.372 0.084 0.260 0.284 0.000 0.000
#> GSM601791 4 0.5980 0.18610 0.396 0.004 0.192 0.408 0.000 0.000
#> GSM601806 4 0.5858 0.47232 0.224 0.128 0.000 0.608 0.032 0.008
#> GSM601811 3 0.5654 0.41126 0.012 0.132 0.676 0.112 0.000 0.068
#> GSM601816 1 0.0937 0.66335 0.960 0.000 0.000 0.040 0.000 0.000
#> GSM601821 2 0.7416 -0.20549 0.080 0.372 0.268 0.012 0.268 0.000
#> GSM601826 1 0.0000 0.66089 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601836 4 0.7705 0.03567 0.156 0.284 0.264 0.292 0.004 0.000
#> GSM601851 1 0.2854 0.54364 0.792 0.000 0.000 0.208 0.000 0.000
#> GSM601856 3 0.4381 0.00436 0.004 0.016 0.524 0.000 0.000 0.456
#> GSM601866 3 0.4286 0.46102 0.092 0.008 0.744 0.000 0.000 0.156
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 time(p) gender(p) k
#> CV:pam 104 0.686 0.07195 2
#> CV:pam 77 0.410 0.06455 3
#> CV:pam 73 0.625 0.07936 4
#> CV:pam 65 0.552 0.00445 5
#> CV:pam 50 0.664 0.01403 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 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.232 0.700 0.804 0.4288 0.608 0.608
#> 3 3 0.363 0.794 0.842 0.4942 0.645 0.450
#> 4 4 0.455 0.565 0.741 0.1116 0.925 0.786
#> 5 5 0.594 0.595 0.725 0.0855 0.873 0.604
#> 6 6 0.719 0.656 0.798 0.0510 0.903 0.616
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
#> GSM601752 2 0.5059 0.845 0.112 0.888
#> GSM601782 1 0.2603 0.765 0.956 0.044
#> GSM601792 2 0.8386 0.819 0.268 0.732
#> GSM601797 2 0.8081 0.833 0.248 0.752
#> GSM601827 1 0.8813 0.377 0.700 0.300
#> GSM601837 1 0.8443 0.733 0.728 0.272
#> GSM601842 1 0.8267 0.740 0.740 0.260
#> GSM601857 1 0.1843 0.769 0.972 0.028
#> GSM601867 1 0.3114 0.766 0.944 0.056
#> GSM601747 1 0.2948 0.777 0.948 0.052
#> GSM601757 1 0.0672 0.769 0.992 0.008
#> GSM601762 1 0.8144 0.741 0.748 0.252
#> GSM601767 1 0.7883 0.747 0.764 0.236
#> GSM601772 1 0.7883 0.747 0.764 0.236
#> GSM601777 2 0.8499 0.838 0.276 0.724
#> GSM601787 1 0.5294 0.775 0.880 0.120
#> GSM601802 2 0.6801 0.844 0.180 0.820
#> GSM601807 1 0.8813 0.421 0.700 0.300
#> GSM601812 1 0.1843 0.770 0.972 0.028
#> GSM601817 1 0.2236 0.768 0.964 0.036
#> GSM601822 2 0.8861 0.829 0.304 0.696
#> GSM601832 1 0.7883 0.747 0.764 0.236
#> GSM601847 2 0.6973 0.841 0.188 0.812
#> GSM601852 1 0.2423 0.766 0.960 0.040
#> GSM601862 1 0.1184 0.770 0.984 0.016
#> GSM601753 2 0.6148 0.842 0.152 0.848
#> GSM601783 1 0.3431 0.755 0.936 0.064
#> GSM601793 2 0.8386 0.819 0.268 0.732
#> GSM601798 2 0.5178 0.847 0.116 0.884
#> GSM601828 1 0.3114 0.765 0.944 0.056
#> GSM601838 1 0.8081 0.745 0.752 0.248
#> GSM601843 1 0.8327 0.738 0.736 0.264
#> GSM601858 1 0.8016 0.748 0.756 0.244
#> GSM601868 1 0.2948 0.761 0.948 0.052
#> GSM601748 1 0.2423 0.766 0.960 0.040
#> GSM601758 1 0.2423 0.766 0.960 0.040
#> GSM601763 1 0.1184 0.776 0.984 0.016
#> GSM601768 1 0.7815 0.748 0.768 0.232
#> GSM601773 1 0.7815 0.748 0.768 0.232
#> GSM601778 2 0.9248 0.803 0.340 0.660
#> GSM601788 1 0.5737 0.768 0.864 0.136
#> GSM601803 2 0.6801 0.844 0.180 0.820
#> GSM601808 1 0.2423 0.759 0.960 0.040
#> GSM601813 1 0.3584 0.753 0.932 0.068
#> GSM601818 1 0.0672 0.769 0.992 0.008
#> GSM601823 2 0.9795 0.708 0.416 0.584
#> GSM601833 1 0.7883 0.747 0.764 0.236
#> GSM601848 2 0.9044 0.812 0.320 0.680
#> GSM601853 1 0.6623 0.603 0.828 0.172
#> GSM601863 1 0.1414 0.770 0.980 0.020
#> GSM601754 2 0.5059 0.845 0.112 0.888
#> GSM601784 1 0.8443 0.733 0.728 0.272
#> GSM601794 2 0.8386 0.819 0.268 0.732
#> GSM601799 2 0.5629 0.849 0.132 0.868
#> GSM601829 2 0.9044 0.800 0.320 0.680
#> GSM601839 1 0.8081 0.745 0.752 0.248
#> GSM601844 1 0.9993 -0.374 0.516 0.484
#> GSM601859 1 0.8327 0.738 0.736 0.264
#> GSM601869 1 0.3879 0.748 0.924 0.076
#> GSM601749 1 0.2423 0.766 0.960 0.040
#> GSM601759 1 0.2423 0.766 0.960 0.040
#> GSM601764 1 0.1184 0.774 0.984 0.016
#> GSM601769 1 0.7883 0.747 0.764 0.236
#> GSM601774 1 0.7883 0.747 0.764 0.236
#> GSM601779 1 0.9970 -0.446 0.532 0.468
#> GSM601789 1 0.7815 0.748 0.768 0.232
#> GSM601804 2 0.6887 0.843 0.184 0.816
#> GSM601809 1 0.1184 0.772 0.984 0.016
#> GSM601814 1 0.7815 0.748 0.768 0.232
#> GSM601819 1 0.1843 0.770 0.972 0.028
#> GSM601824 2 0.9710 0.467 0.400 0.600
#> GSM601834 1 0.7883 0.747 0.764 0.236
#> GSM601849 1 0.4562 0.707 0.904 0.096
#> GSM601854 1 0.2948 0.762 0.948 0.052
#> GSM601864 1 0.7056 0.759 0.808 0.192
#> GSM601755 2 0.5059 0.845 0.112 0.888
#> GSM601785 1 0.7453 0.747 0.788 0.212
#> GSM601795 2 0.8144 0.831 0.252 0.748
#> GSM601800 2 0.5059 0.845 0.112 0.888
#> GSM601830 1 0.8555 0.418 0.720 0.280
#> GSM601840 1 0.7139 0.754 0.804 0.196
#> GSM601845 1 0.7219 0.740 0.800 0.200
#> GSM601860 1 0.8144 0.745 0.748 0.252
#> GSM601870 1 0.7674 0.523 0.776 0.224
#> GSM601750 1 0.2423 0.766 0.960 0.040
#> GSM601760 1 0.1414 0.770 0.980 0.020
#> GSM601765 1 0.7883 0.747 0.764 0.236
#> GSM601770 1 0.7883 0.747 0.764 0.236
#> GSM601775 1 0.3733 0.780 0.928 0.072
#> GSM601780 1 0.9922 -0.386 0.552 0.448
#> GSM601790 1 0.7815 0.748 0.768 0.232
#> GSM601805 2 0.6801 0.844 0.180 0.820
#> GSM601810 1 0.0938 0.769 0.988 0.012
#> GSM601815 1 0.7815 0.748 0.768 0.232
#> GSM601820 1 0.2423 0.766 0.960 0.040
#> GSM601825 2 0.7376 0.824 0.208 0.792
#> GSM601835 1 0.9635 0.572 0.612 0.388
#> GSM601850 1 0.9881 -0.196 0.564 0.436
#> GSM601855 1 0.8207 0.448 0.744 0.256
#> GSM601865 1 0.7815 0.748 0.768 0.232
#> GSM601756 2 0.5059 0.845 0.112 0.888
#> GSM601786 1 0.8386 0.736 0.732 0.268
#> GSM601796 2 0.8386 0.819 0.268 0.732
#> GSM601801 2 0.5178 0.847 0.116 0.884
#> GSM601831 1 0.8955 0.350 0.688 0.312
#> GSM601841 1 0.8207 0.477 0.744 0.256
#> GSM601846 2 0.7299 0.850 0.204 0.796
#> GSM601861 1 0.8081 0.745 0.752 0.248
#> GSM601871 1 0.5842 0.772 0.860 0.140
#> GSM601751 1 0.4562 0.777 0.904 0.096
#> GSM601761 1 0.2423 0.757 0.960 0.040
#> GSM601766 1 0.4431 0.777 0.908 0.092
#> GSM601771 1 0.6801 0.759 0.820 0.180
#> GSM601776 1 0.9044 0.157 0.680 0.320
#> GSM601781 2 0.8861 0.810 0.304 0.696
#> GSM601791 1 0.6887 0.566 0.816 0.184
#> GSM601806 2 0.6887 0.843 0.184 0.816
#> GSM601811 1 0.0672 0.769 0.992 0.008
#> GSM601816 2 0.9170 0.808 0.332 0.668
#> GSM601821 1 0.7815 0.748 0.768 0.232
#> GSM601826 2 0.9209 0.806 0.336 0.664
#> GSM601836 1 0.0938 0.775 0.988 0.012
#> GSM601851 1 0.9661 -0.180 0.608 0.392
#> GSM601856 1 0.8016 0.457 0.756 0.244
#> GSM601866 1 0.2423 0.766 0.960 0.040
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601752 1 0.0424 0.8358 0.992 0.008 0.000
#> GSM601782 3 0.6098 0.8433 0.056 0.176 0.768
#> GSM601792 1 0.0829 0.8393 0.984 0.004 0.012
#> GSM601797 1 0.1015 0.8391 0.980 0.008 0.012
#> GSM601827 3 0.6148 0.8192 0.148 0.076 0.776
#> GSM601837 2 0.5883 0.7746 0.092 0.796 0.112
#> GSM601842 2 0.4390 0.8418 0.148 0.840 0.012
#> GSM601857 3 0.4591 0.8811 0.032 0.120 0.848
#> GSM601867 3 0.7515 0.6204 0.100 0.220 0.680
#> GSM601747 2 0.6407 0.7478 0.080 0.760 0.160
#> GSM601757 3 0.5798 0.8479 0.040 0.184 0.776
#> GSM601762 2 0.2959 0.8498 0.100 0.900 0.000
#> GSM601767 2 0.0424 0.8555 0.008 0.992 0.000
#> GSM601772 2 0.0592 0.8569 0.012 0.988 0.000
#> GSM601777 1 0.5710 0.8230 0.804 0.116 0.080
#> GSM601787 3 0.6773 0.5049 0.024 0.340 0.636
#> GSM601802 1 0.3686 0.8184 0.860 0.140 0.000
#> GSM601807 3 0.3590 0.8083 0.076 0.028 0.896
#> GSM601812 3 0.4351 0.8765 0.004 0.168 0.828
#> GSM601817 3 0.4099 0.8842 0.008 0.140 0.852
#> GSM601822 1 0.4658 0.8283 0.856 0.076 0.068
#> GSM601832 2 0.3619 0.8321 0.136 0.864 0.000
#> GSM601847 1 0.3896 0.8250 0.864 0.128 0.008
#> GSM601852 3 0.4172 0.8799 0.004 0.156 0.840
#> GSM601862 3 0.3043 0.8749 0.008 0.084 0.908
#> GSM601753 1 0.1163 0.8398 0.972 0.028 0.000
#> GSM601783 3 0.6882 0.8131 0.172 0.096 0.732
#> GSM601793 1 0.0829 0.8393 0.984 0.004 0.012
#> GSM601798 1 0.0747 0.8344 0.984 0.016 0.000
#> GSM601828 3 0.3918 0.8824 0.004 0.140 0.856
#> GSM601838 2 0.3995 0.8035 0.016 0.868 0.116
#> GSM601843 2 0.4139 0.8529 0.124 0.860 0.016
#> GSM601858 2 0.2846 0.8498 0.020 0.924 0.056
#> GSM601868 3 0.4256 0.8242 0.096 0.036 0.868
#> GSM601748 3 0.3918 0.8837 0.004 0.140 0.856
#> GSM601758 3 0.4172 0.8799 0.004 0.156 0.840
#> GSM601763 2 0.6332 0.7920 0.144 0.768 0.088
#> GSM601768 2 0.3193 0.8530 0.100 0.896 0.004
#> GSM601773 2 0.1753 0.8640 0.048 0.952 0.000
#> GSM601778 1 0.4982 0.8252 0.840 0.064 0.096
#> GSM601788 2 0.3267 0.8495 0.044 0.912 0.044
#> GSM601803 1 0.3879 0.8136 0.848 0.152 0.000
#> GSM601808 3 0.2527 0.8580 0.020 0.044 0.936
#> GSM601813 3 0.4802 0.8759 0.020 0.156 0.824
#> GSM601818 3 0.4326 0.8793 0.012 0.144 0.844
#> GSM601823 1 0.6886 0.7515 0.728 0.088 0.184
#> GSM601833 2 0.2165 0.8597 0.064 0.936 0.000
#> GSM601848 1 0.5343 0.7934 0.816 0.052 0.132
#> GSM601853 3 0.2050 0.8531 0.020 0.028 0.952
#> GSM601863 3 0.3454 0.8807 0.008 0.104 0.888
#> GSM601754 1 0.0424 0.8358 0.992 0.008 0.000
#> GSM601784 2 0.2448 0.8569 0.076 0.924 0.000
#> GSM601794 1 0.0829 0.8393 0.984 0.004 0.012
#> GSM601799 1 0.0892 0.8387 0.980 0.020 0.000
#> GSM601829 1 0.4755 0.7438 0.808 0.008 0.184
#> GSM601839 2 0.3995 0.8035 0.016 0.868 0.116
#> GSM601844 1 0.5105 0.7809 0.828 0.048 0.124
#> GSM601859 2 0.4293 0.8372 0.164 0.832 0.004
#> GSM601869 3 0.6208 0.8237 0.152 0.076 0.772
#> GSM601749 3 0.4413 0.8794 0.008 0.160 0.832
#> GSM601759 3 0.3983 0.8832 0.004 0.144 0.852
#> GSM601764 2 0.6653 0.7788 0.136 0.752 0.112
#> GSM601769 2 0.0237 0.8548 0.004 0.996 0.000
#> GSM601774 2 0.0424 0.8555 0.008 0.992 0.000
#> GSM601779 1 0.6911 0.7453 0.728 0.092 0.180
#> GSM601789 2 0.1585 0.8512 0.008 0.964 0.028
#> GSM601804 1 0.4261 0.8168 0.848 0.140 0.012
#> GSM601809 2 0.7396 -0.1970 0.032 0.488 0.480
#> GSM601814 2 0.0747 0.8581 0.016 0.984 0.000
#> GSM601819 3 0.5109 0.8389 0.008 0.212 0.780
#> GSM601824 1 0.7328 0.5815 0.612 0.344 0.044
#> GSM601834 2 0.2165 0.8593 0.064 0.936 0.000
#> GSM601849 1 0.8522 0.6226 0.612 0.204 0.184
#> GSM601854 3 0.4351 0.8766 0.004 0.168 0.828
#> GSM601864 2 0.3995 0.8052 0.016 0.868 0.116
#> GSM601755 1 0.0424 0.8358 0.992 0.008 0.000
#> GSM601785 2 0.5254 0.7576 0.264 0.736 0.000
#> GSM601795 1 0.0661 0.8386 0.988 0.004 0.008
#> GSM601800 1 0.0424 0.8358 0.992 0.008 0.000
#> GSM601830 3 0.3532 0.7978 0.108 0.008 0.884
#> GSM601840 2 0.5420 0.7749 0.240 0.752 0.008
#> GSM601845 2 0.6404 0.6200 0.344 0.644 0.012
#> GSM601860 2 0.4349 0.8510 0.128 0.852 0.020
#> GSM601870 3 0.2384 0.8251 0.056 0.008 0.936
#> GSM601750 3 0.4172 0.8799 0.004 0.156 0.840
#> GSM601760 3 0.4663 0.8778 0.016 0.156 0.828
#> GSM601765 2 0.2959 0.8494 0.100 0.900 0.000
#> GSM601770 2 0.1163 0.8614 0.028 0.972 0.000
#> GSM601775 2 0.6372 0.7813 0.152 0.764 0.084
#> GSM601780 1 0.7963 0.6965 0.660 0.152 0.188
#> GSM601790 2 0.2749 0.8369 0.012 0.924 0.064
#> GSM601805 1 0.3941 0.8095 0.844 0.156 0.000
#> GSM601810 3 0.4618 0.8815 0.024 0.136 0.840
#> GSM601815 2 0.1399 0.8497 0.004 0.968 0.028
#> GSM601820 3 0.4172 0.8799 0.004 0.156 0.840
#> GSM601825 1 0.4047 0.8155 0.848 0.148 0.004
#> GSM601835 2 0.4277 0.8384 0.132 0.852 0.016
#> GSM601850 1 0.6349 0.7754 0.764 0.156 0.080
#> GSM601855 3 0.2063 0.8310 0.044 0.008 0.948
#> GSM601865 2 0.3359 0.8249 0.016 0.900 0.084
#> GSM601756 1 0.0424 0.8358 0.992 0.008 0.000
#> GSM601786 2 0.5650 0.7933 0.084 0.808 0.108
#> GSM601796 1 0.0983 0.8394 0.980 0.004 0.016
#> GSM601801 1 0.1289 0.8287 0.968 0.032 0.000
#> GSM601831 3 0.6062 0.8094 0.160 0.064 0.776
#> GSM601841 1 0.9387 0.3347 0.508 0.272 0.220
#> GSM601846 1 0.1751 0.8383 0.960 0.012 0.028
#> GSM601861 2 0.1636 0.8564 0.016 0.964 0.020
#> GSM601871 3 0.7479 0.5675 0.076 0.264 0.660
#> GSM601751 2 0.6804 0.7339 0.204 0.724 0.072
#> GSM601761 2 0.9937 -0.0203 0.296 0.388 0.316
#> GSM601766 2 0.5136 0.8247 0.132 0.824 0.044
#> GSM601771 2 0.2173 0.8616 0.048 0.944 0.008
#> GSM601776 1 0.8868 0.5859 0.576 0.196 0.228
#> GSM601781 1 0.5408 0.8182 0.812 0.136 0.052
#> GSM601791 1 0.9531 0.3685 0.468 0.324 0.208
#> GSM601806 1 0.4235 0.7990 0.824 0.176 0.000
#> GSM601811 3 0.4591 0.8789 0.032 0.120 0.848
#> GSM601816 1 0.4379 0.8365 0.868 0.060 0.072
#> GSM601821 2 0.1905 0.8540 0.016 0.956 0.028
#> GSM601826 1 0.6705 0.7593 0.740 0.084 0.176
#> GSM601836 2 0.6955 0.7477 0.172 0.728 0.100
#> GSM601851 1 0.8408 0.6339 0.612 0.144 0.244
#> GSM601856 3 0.2810 0.8525 0.036 0.036 0.928
#> GSM601866 3 0.3918 0.8837 0.004 0.140 0.856
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601752 4 0.2611 0.7690 0.000 0.008 0.096 0.896
#> GSM601782 1 0.6566 0.4992 0.684 0.192 0.088 0.036
#> GSM601792 4 0.2275 0.7635 0.048 0.004 0.020 0.928
#> GSM601797 4 0.1256 0.7739 0.000 0.008 0.028 0.964
#> GSM601827 1 0.5540 0.3321 0.728 0.000 0.108 0.164
#> GSM601837 2 0.5421 0.4977 0.008 0.648 0.328 0.016
#> GSM601842 2 0.4093 0.7396 0.032 0.836 0.012 0.120
#> GSM601857 1 0.2207 0.5849 0.932 0.004 0.040 0.024
#> GSM601867 3 0.7684 0.5342 0.304 0.152 0.524 0.020
#> GSM601747 2 0.5941 0.3292 0.376 0.584 0.004 0.036
#> GSM601757 1 0.5546 0.5235 0.736 0.188 0.064 0.012
#> GSM601762 2 0.3264 0.7430 0.024 0.876 0.004 0.096
#> GSM601767 2 0.2075 0.7454 0.016 0.936 0.004 0.044
#> GSM601772 2 0.1635 0.7422 0.008 0.948 0.000 0.044
#> GSM601777 4 0.4416 0.7549 0.040 0.100 0.028 0.832
#> GSM601787 3 0.7889 0.4798 0.224 0.276 0.488 0.012
#> GSM601802 4 0.5389 0.7576 0.040 0.084 0.092 0.784
#> GSM601807 3 0.5919 0.6033 0.404 0.012 0.564 0.020
#> GSM601812 1 0.1182 0.6114 0.968 0.016 0.016 0.000
#> GSM601817 1 0.1706 0.5787 0.948 0.016 0.036 0.000
#> GSM601822 4 0.3575 0.7671 0.092 0.028 0.012 0.868
#> GSM601832 2 0.3205 0.7417 0.024 0.872 0.000 0.104
#> GSM601847 4 0.5151 0.7661 0.048 0.064 0.088 0.800
#> GSM601852 1 0.4149 0.5601 0.804 0.168 0.028 0.000
#> GSM601862 1 0.4567 0.3540 0.748 0.012 0.236 0.004
#> GSM601753 4 0.2610 0.7709 0.000 0.012 0.088 0.900
#> GSM601783 1 0.6903 0.4940 0.684 0.136 0.064 0.116
#> GSM601793 4 0.2002 0.7619 0.044 0.000 0.020 0.936
#> GSM601798 4 0.2480 0.7707 0.000 0.008 0.088 0.904
#> GSM601828 1 0.1516 0.6094 0.960 0.016 0.016 0.008
#> GSM601838 2 0.5060 0.5241 0.008 0.680 0.304 0.008
#> GSM601843 2 0.4093 0.7399 0.032 0.836 0.012 0.120
#> GSM601858 2 0.6397 0.6182 0.044 0.676 0.232 0.048
#> GSM601868 1 0.5228 0.1624 0.700 0.004 0.268 0.028
#> GSM601748 1 0.0779 0.6013 0.980 0.016 0.004 0.000
#> GSM601758 1 0.1356 0.6127 0.960 0.032 0.008 0.000
#> GSM601763 2 0.8209 0.3909 0.276 0.524 0.060 0.140
#> GSM601768 2 0.3349 0.7454 0.052 0.880 0.004 0.064
#> GSM601773 2 0.2297 0.7457 0.024 0.928 0.004 0.044
#> GSM601778 4 0.4587 0.7454 0.140 0.020 0.032 0.808
#> GSM601788 2 0.3974 0.7319 0.040 0.844 0.008 0.108
#> GSM601803 4 0.5694 0.7484 0.040 0.104 0.092 0.764
#> GSM601808 1 0.5272 -0.2340 0.608 0.008 0.380 0.004
#> GSM601813 1 0.5021 0.5357 0.756 0.180 0.064 0.000
#> GSM601818 1 0.2928 0.5448 0.896 0.024 0.076 0.004
#> GSM601823 4 0.6906 0.4849 0.312 0.012 0.096 0.580
#> GSM601833 2 0.2413 0.7475 0.020 0.916 0.000 0.064
#> GSM601848 4 0.5739 0.6427 0.200 0.008 0.076 0.716
#> GSM601853 1 0.5299 -0.2928 0.600 0.004 0.388 0.008
#> GSM601863 1 0.1610 0.5882 0.952 0.016 0.032 0.000
#> GSM601754 4 0.2546 0.7700 0.000 0.008 0.092 0.900
#> GSM601784 2 0.3593 0.7433 0.024 0.868 0.016 0.092
#> GSM601794 4 0.2002 0.7619 0.044 0.000 0.020 0.936
#> GSM601799 4 0.2861 0.7705 0.000 0.016 0.096 0.888
#> GSM601829 4 0.5051 0.7030 0.132 0.000 0.100 0.768
#> GSM601839 2 0.5060 0.5228 0.008 0.680 0.304 0.008
#> GSM601844 4 0.8335 0.3599 0.212 0.144 0.092 0.552
#> GSM601859 2 0.4331 0.7334 0.028 0.820 0.016 0.136
#> GSM601869 1 0.5665 0.3113 0.716 0.000 0.176 0.108
#> GSM601749 1 0.4079 0.5531 0.800 0.180 0.020 0.000
#> GSM601759 1 0.0804 0.6063 0.980 0.012 0.008 0.000
#> GSM601764 2 0.8100 0.0527 0.404 0.440 0.064 0.092
#> GSM601769 2 0.1114 0.7324 0.004 0.972 0.008 0.016
#> GSM601774 2 0.1767 0.7431 0.012 0.944 0.000 0.044
#> GSM601779 4 0.7102 0.3551 0.368 0.012 0.096 0.524
#> GSM601789 2 0.4853 0.6211 0.012 0.768 0.192 0.028
#> GSM601804 4 0.5415 0.7660 0.064 0.052 0.100 0.784
#> GSM601809 1 0.7655 0.1991 0.556 0.256 0.164 0.024
#> GSM601814 2 0.0376 0.7273 0.004 0.992 0.004 0.000
#> GSM601819 1 0.4756 0.5563 0.784 0.144 0.072 0.000
#> GSM601824 4 0.8450 0.5804 0.220 0.100 0.140 0.540
#> GSM601834 2 0.2310 0.7471 0.008 0.920 0.004 0.068
#> GSM601849 1 0.9393 0.1650 0.404 0.204 0.124 0.268
#> GSM601854 1 0.3626 0.5530 0.812 0.184 0.004 0.000
#> GSM601864 2 0.5271 0.5136 0.016 0.676 0.300 0.008
#> GSM601755 4 0.2546 0.7700 0.000 0.008 0.092 0.900
#> GSM601785 2 0.5499 0.6426 0.012 0.680 0.024 0.284
#> GSM601795 4 0.1114 0.7729 0.004 0.008 0.016 0.972
#> GSM601800 4 0.2546 0.7700 0.000 0.008 0.092 0.900
#> GSM601830 3 0.5764 0.5519 0.452 0.000 0.520 0.028
#> GSM601840 2 0.7373 0.5243 0.124 0.560 0.020 0.296
#> GSM601845 2 0.7674 0.3982 0.092 0.496 0.040 0.372
#> GSM601860 2 0.4360 0.7325 0.032 0.816 0.012 0.140
#> GSM601870 3 0.5473 0.6076 0.408 0.004 0.576 0.012
#> GSM601750 1 0.0937 0.6099 0.976 0.012 0.012 0.000
#> GSM601760 1 0.2413 0.6057 0.924 0.036 0.036 0.004
#> GSM601765 2 0.2662 0.7455 0.016 0.900 0.000 0.084
#> GSM601770 2 0.1767 0.7442 0.012 0.944 0.000 0.044
#> GSM601775 2 0.8463 0.3957 0.248 0.500 0.056 0.196
#> GSM601780 4 0.7388 0.3036 0.376 0.012 0.120 0.492
#> GSM601790 2 0.4533 0.5681 0.012 0.752 0.232 0.004
#> GSM601805 4 0.5914 0.7371 0.040 0.120 0.092 0.748
#> GSM601810 1 0.2234 0.5750 0.924 0.008 0.064 0.004
#> GSM601815 2 0.4098 0.5928 0.012 0.784 0.204 0.000
#> GSM601820 1 0.0524 0.6099 0.988 0.008 0.004 0.000
#> GSM601825 4 0.5970 0.7449 0.048 0.112 0.092 0.748
#> GSM601835 2 0.4767 0.6825 0.028 0.768 0.008 0.196
#> GSM601850 4 0.8099 0.4196 0.220 0.196 0.044 0.540
#> GSM601855 3 0.5388 0.5543 0.456 0.000 0.532 0.012
#> GSM601865 2 0.5024 0.5584 0.020 0.724 0.248 0.008
#> GSM601756 4 0.2546 0.7700 0.000 0.008 0.092 0.900
#> GSM601786 2 0.5664 0.4874 0.012 0.636 0.332 0.020
#> GSM601796 4 0.2089 0.7621 0.048 0.000 0.020 0.932
#> GSM601801 4 0.2676 0.7706 0.000 0.012 0.092 0.896
#> GSM601831 1 0.5326 0.3684 0.748 0.000 0.136 0.116
#> GSM601841 4 0.8848 0.1067 0.264 0.200 0.076 0.460
#> GSM601846 4 0.2307 0.7720 0.016 0.008 0.048 0.928
#> GSM601861 2 0.3533 0.6979 0.024 0.864 0.104 0.008
#> GSM601871 3 0.7414 0.5676 0.212 0.192 0.580 0.016
#> GSM601751 2 0.6662 0.6192 0.080 0.660 0.032 0.228
#> GSM601761 1 0.9003 0.3462 0.484 0.216 0.128 0.172
#> GSM601766 2 0.6573 0.5609 0.208 0.644 0.004 0.144
#> GSM601771 2 0.4060 0.7328 0.048 0.836 0.004 0.112
#> GSM601776 1 0.9413 0.0428 0.372 0.188 0.124 0.316
#> GSM601781 4 0.5579 0.7385 0.072 0.140 0.028 0.760
#> GSM601791 1 0.9374 0.1184 0.384 0.220 0.108 0.288
#> GSM601806 4 0.6175 0.7114 0.032 0.156 0.092 0.720
#> GSM601811 1 0.3997 0.4355 0.816 0.012 0.164 0.008
#> GSM601816 4 0.4414 0.7571 0.120 0.020 0.036 0.824
#> GSM601821 2 0.4345 0.6240 0.020 0.788 0.188 0.004
#> GSM601826 4 0.6399 0.5580 0.280 0.008 0.080 0.632
#> GSM601836 2 0.8435 0.0191 0.392 0.408 0.052 0.148
#> GSM601851 4 0.8300 0.1713 0.392 0.056 0.124 0.428
#> GSM601856 1 0.5443 -0.4603 0.532 0.004 0.456 0.008
#> GSM601866 1 0.1174 0.5958 0.968 0.020 0.012 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601752 4 0.1952 0.684 0.000 0.084 0.000 0.912 0.004
#> GSM601782 3 0.6515 0.573 0.108 0.140 0.664 0.020 0.068
#> GSM601792 4 0.4095 0.494 0.228 0.000 0.008 0.748 0.016
#> GSM601797 4 0.3381 0.617 0.116 0.016 0.004 0.848 0.016
#> GSM601827 3 0.5076 0.703 0.036 0.000 0.748 0.116 0.100
#> GSM601837 5 0.4037 0.667 0.004 0.244 0.004 0.008 0.740
#> GSM601842 2 0.2637 0.791 0.060 0.900 0.004 0.028 0.008
#> GSM601857 3 0.3443 0.763 0.100 0.008 0.852 0.008 0.032
#> GSM601867 5 0.6603 0.308 0.228 0.020 0.176 0.004 0.572
#> GSM601747 2 0.6666 0.318 0.064 0.544 0.328 0.056 0.008
#> GSM601757 3 0.5260 0.567 0.204 0.108 0.684 0.000 0.004
#> GSM601762 2 0.0771 0.801 0.020 0.976 0.000 0.004 0.000
#> GSM601767 2 0.0579 0.795 0.008 0.984 0.000 0.000 0.008
#> GSM601772 2 0.0324 0.795 0.004 0.992 0.000 0.000 0.004
#> GSM601777 4 0.6907 0.432 0.244 0.092 0.044 0.592 0.028
#> GSM601787 5 0.6755 0.419 0.240 0.052 0.136 0.000 0.572
#> GSM601802 4 0.4611 0.644 0.096 0.132 0.004 0.764 0.004
#> GSM601807 3 0.7160 0.290 0.280 0.004 0.356 0.008 0.352
#> GSM601812 3 0.0451 0.778 0.008 0.004 0.988 0.000 0.000
#> GSM601817 3 0.0963 0.777 0.036 0.000 0.964 0.000 0.000
#> GSM601822 4 0.5193 0.511 0.260 0.048 0.012 0.676 0.004
#> GSM601832 2 0.1082 0.801 0.028 0.964 0.000 0.000 0.008
#> GSM601847 4 0.5017 0.644 0.136 0.116 0.008 0.736 0.004
#> GSM601852 3 0.0794 0.776 0.028 0.000 0.972 0.000 0.000
#> GSM601862 3 0.4836 0.678 0.188 0.000 0.716 0.000 0.096
#> GSM601753 4 0.2678 0.684 0.016 0.100 0.004 0.880 0.000
#> GSM601783 3 0.4303 0.715 0.064 0.004 0.800 0.116 0.016
#> GSM601793 4 0.3819 0.525 0.208 0.000 0.004 0.772 0.016
#> GSM601798 4 0.1952 0.685 0.000 0.084 0.004 0.912 0.000
#> GSM601828 3 0.0613 0.779 0.008 0.000 0.984 0.004 0.004
#> GSM601838 5 0.3920 0.665 0.004 0.268 0.004 0.000 0.724
#> GSM601843 2 0.2654 0.792 0.044 0.904 0.004 0.032 0.016
#> GSM601858 5 0.5616 0.519 0.040 0.360 0.024 0.000 0.576
#> GSM601868 3 0.6381 0.561 0.196 0.000 0.556 0.008 0.240
#> GSM601748 3 0.0510 0.779 0.016 0.000 0.984 0.000 0.000
#> GSM601758 3 0.0880 0.777 0.032 0.000 0.968 0.000 0.000
#> GSM601763 1 0.6657 0.248 0.504 0.368 0.028 0.092 0.008
#> GSM601768 2 0.2497 0.773 0.112 0.880 0.004 0.004 0.000
#> GSM601773 2 0.0833 0.799 0.016 0.976 0.004 0.000 0.004
#> GSM601778 4 0.6201 0.358 0.292 0.024 0.072 0.600 0.012
#> GSM601788 2 0.2474 0.783 0.040 0.908 0.040 0.000 0.012
#> GSM601803 4 0.4703 0.635 0.076 0.168 0.004 0.748 0.004
#> GSM601808 3 0.6075 0.585 0.216 0.004 0.592 0.000 0.188
#> GSM601813 3 0.1626 0.779 0.044 0.000 0.940 0.000 0.016
#> GSM601818 3 0.3064 0.758 0.108 0.000 0.856 0.000 0.036
#> GSM601823 1 0.4944 0.690 0.668 0.008 0.024 0.292 0.008
#> GSM601833 2 0.0671 0.799 0.016 0.980 0.000 0.000 0.004
#> GSM601848 1 0.5023 0.571 0.592 0.008 0.012 0.380 0.008
#> GSM601853 3 0.5993 0.579 0.232 0.000 0.584 0.000 0.184
#> GSM601863 3 0.3055 0.744 0.144 0.000 0.840 0.000 0.016
#> GSM601754 4 0.1952 0.684 0.000 0.084 0.000 0.912 0.004
#> GSM601784 2 0.2820 0.789 0.052 0.892 0.004 0.044 0.008
#> GSM601794 4 0.3786 0.530 0.204 0.000 0.004 0.776 0.016
#> GSM601799 4 0.3423 0.680 0.040 0.108 0.008 0.844 0.000
#> GSM601829 4 0.5503 0.406 0.264 0.012 0.052 0.660 0.012
#> GSM601839 5 0.3920 0.666 0.004 0.268 0.004 0.000 0.724
#> GSM601844 1 0.5202 0.431 0.540 0.004 0.016 0.428 0.012
#> GSM601859 2 0.2910 0.785 0.052 0.884 0.004 0.056 0.004
#> GSM601869 3 0.5655 0.700 0.068 0.000 0.712 0.108 0.112
#> GSM601749 3 0.0963 0.775 0.036 0.000 0.964 0.000 0.000
#> GSM601759 3 0.0510 0.779 0.016 0.000 0.984 0.000 0.000
#> GSM601764 1 0.7224 0.302 0.496 0.332 0.084 0.080 0.008
#> GSM601769 2 0.1430 0.763 0.004 0.944 0.000 0.000 0.052
#> GSM601774 2 0.0451 0.797 0.008 0.988 0.000 0.000 0.004
#> GSM601779 1 0.4964 0.698 0.668 0.008 0.032 0.288 0.004
#> GSM601789 2 0.4440 -0.302 0.004 0.528 0.000 0.000 0.468
#> GSM601804 4 0.5252 0.616 0.160 0.120 0.012 0.708 0.000
#> GSM601809 3 0.7121 0.310 0.064 0.260 0.528 0.000 0.148
#> GSM601814 2 0.1831 0.738 0.004 0.920 0.000 0.000 0.076
#> GSM601819 3 0.4674 0.462 0.316 0.024 0.656 0.000 0.004
#> GSM601824 4 0.6160 -0.185 0.432 0.092 0.012 0.464 0.000
#> GSM601834 2 0.0693 0.793 0.008 0.980 0.000 0.000 0.012
#> GSM601849 1 0.4962 0.698 0.704 0.012 0.056 0.228 0.000
#> GSM601854 3 0.0898 0.779 0.020 0.000 0.972 0.000 0.008
#> GSM601864 5 0.4275 0.656 0.008 0.288 0.008 0.000 0.696
#> GSM601755 4 0.1952 0.684 0.000 0.084 0.000 0.912 0.004
#> GSM601785 2 0.4360 0.693 0.044 0.780 0.008 0.160 0.008
#> GSM601795 4 0.3421 0.571 0.164 0.000 0.004 0.816 0.016
#> GSM601800 4 0.1952 0.684 0.000 0.084 0.000 0.912 0.004
#> GSM601830 3 0.7157 0.313 0.280 0.004 0.368 0.008 0.340
#> GSM601840 2 0.4553 0.714 0.076 0.784 0.012 0.120 0.008
#> GSM601845 2 0.6309 0.515 0.140 0.624 0.016 0.208 0.012
#> GSM601860 2 0.3045 0.787 0.068 0.880 0.004 0.036 0.012
#> GSM601870 5 0.7144 -0.294 0.280 0.004 0.324 0.008 0.384
#> GSM601750 3 0.0290 0.778 0.008 0.000 0.992 0.000 0.000
#> GSM601760 3 0.3328 0.698 0.176 0.008 0.812 0.000 0.004
#> GSM601765 2 0.0771 0.799 0.020 0.976 0.000 0.000 0.004
#> GSM601770 2 0.0566 0.798 0.012 0.984 0.000 0.000 0.004
#> GSM601775 2 0.5270 0.394 0.368 0.592 0.020 0.012 0.008
#> GSM601780 1 0.4829 0.710 0.684 0.012 0.032 0.272 0.000
#> GSM601790 5 0.4389 0.588 0.004 0.368 0.004 0.000 0.624
#> GSM601805 4 0.4735 0.625 0.072 0.196 0.004 0.728 0.000
#> GSM601810 3 0.2102 0.776 0.012 0.004 0.916 0.000 0.068
#> GSM601815 5 0.4367 0.518 0.004 0.416 0.000 0.000 0.580
#> GSM601820 3 0.0510 0.778 0.016 0.000 0.984 0.000 0.000
#> GSM601825 4 0.5200 0.597 0.088 0.208 0.004 0.696 0.004
#> GSM601835 2 0.3176 0.781 0.040 0.884 0.012 0.032 0.032
#> GSM601850 1 0.5184 0.666 0.656 0.032 0.016 0.292 0.004
#> GSM601855 3 0.7159 0.297 0.280 0.004 0.360 0.008 0.348
#> GSM601865 5 0.4362 0.598 0.004 0.360 0.004 0.000 0.632
#> GSM601756 4 0.1952 0.684 0.000 0.084 0.000 0.912 0.004
#> GSM601786 5 0.4145 0.667 0.012 0.244 0.004 0.004 0.736
#> GSM601796 4 0.3819 0.525 0.208 0.000 0.004 0.772 0.016
#> GSM601801 4 0.1952 0.685 0.000 0.084 0.004 0.912 0.000
#> GSM601831 3 0.4754 0.712 0.016 0.000 0.760 0.112 0.112
#> GSM601841 4 0.8857 -0.182 0.296 0.160 0.216 0.308 0.020
#> GSM601846 4 0.4894 0.630 0.112 0.040 0.016 0.780 0.052
#> GSM601861 2 0.4759 0.230 0.024 0.636 0.004 0.000 0.336
#> GSM601871 5 0.6355 0.376 0.248 0.024 0.124 0.004 0.600
#> GSM601751 2 0.4941 0.686 0.140 0.752 0.008 0.088 0.012
#> GSM601761 1 0.5969 0.509 0.608 0.008 0.244 0.140 0.000
#> GSM601766 2 0.4533 0.667 0.140 0.780 0.008 0.060 0.012
#> GSM601771 2 0.2366 0.792 0.068 0.908 0.004 0.004 0.016
#> GSM601776 1 0.4857 0.711 0.692 0.012 0.028 0.264 0.004
#> GSM601781 4 0.6388 0.285 0.312 0.136 0.008 0.540 0.004
#> GSM601791 1 0.4905 0.711 0.692 0.016 0.036 0.256 0.000
#> GSM601806 4 0.4743 0.612 0.056 0.208 0.004 0.728 0.004
#> GSM601811 3 0.4374 0.726 0.072 0.008 0.776 0.000 0.144
#> GSM601816 4 0.5348 0.105 0.396 0.016 0.016 0.564 0.008
#> GSM601821 2 0.4538 -0.157 0.004 0.564 0.004 0.000 0.428
#> GSM601826 1 0.5156 0.656 0.644 0.016 0.020 0.312 0.008
#> GSM601836 1 0.7362 0.134 0.428 0.396 0.084 0.084 0.008
#> GSM601851 1 0.4758 0.711 0.696 0.012 0.032 0.260 0.000
#> GSM601856 3 0.6500 0.510 0.216 0.004 0.516 0.000 0.264
#> GSM601866 3 0.0963 0.780 0.036 0.000 0.964 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601752 4 0.0260 0.83447 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM601782 1 0.2145 0.80466 0.912 0.000 0.056 0.004 0.020 0.008
#> GSM601792 6 0.5778 0.28290 0.000 0.000 0.020 0.412 0.104 0.464
#> GSM601797 4 0.5051 0.45741 0.000 0.000 0.020 0.672 0.104 0.204
#> GSM601827 1 0.5446 0.64851 0.720 0.000 0.072 0.080 0.080 0.048
#> GSM601837 5 0.3065 0.81977 0.000 0.052 0.100 0.004 0.844 0.000
#> GSM601842 2 0.2164 0.82975 0.000 0.916 0.008 0.044 0.020 0.012
#> GSM601857 1 0.2948 0.73752 0.836 0.008 0.144 0.000 0.004 0.008
#> GSM601867 3 0.3790 0.60585 0.016 0.000 0.716 0.004 0.264 0.000
#> GSM601747 1 0.5125 0.27073 0.568 0.368 0.008 0.000 0.012 0.044
#> GSM601757 1 0.2805 0.71265 0.812 0.000 0.004 0.000 0.000 0.184
#> GSM601762 2 0.0891 0.83591 0.000 0.968 0.000 0.024 0.008 0.000
#> GSM601767 2 0.0547 0.83559 0.000 0.980 0.000 0.000 0.020 0.000
#> GSM601772 2 0.0547 0.83559 0.000 0.980 0.000 0.000 0.020 0.000
#> GSM601777 4 0.6742 0.07814 0.000 0.112 0.048 0.448 0.024 0.368
#> GSM601787 3 0.4627 0.54192 0.012 0.016 0.660 0.004 0.296 0.012
#> GSM601802 4 0.2723 0.80869 0.000 0.096 0.008 0.872 0.016 0.008
#> GSM601807 3 0.1036 0.73152 0.008 0.000 0.964 0.000 0.024 0.004
#> GSM601812 1 0.0146 0.82002 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM601817 1 0.0632 0.81494 0.976 0.000 0.024 0.000 0.000 0.000
#> GSM601822 6 0.5456 0.14695 0.000 0.052 0.004 0.428 0.024 0.492
#> GSM601832 2 0.1710 0.83717 0.000 0.936 0.000 0.020 0.028 0.016
#> GSM601847 4 0.4611 0.70198 0.000 0.092 0.008 0.740 0.016 0.144
#> GSM601852 1 0.0405 0.82123 0.988 0.000 0.008 0.000 0.000 0.004
#> GSM601862 1 0.3868 -0.19883 0.504 0.000 0.496 0.000 0.000 0.000
#> GSM601753 4 0.0924 0.83375 0.000 0.008 0.008 0.972 0.008 0.004
#> GSM601783 1 0.4266 0.72942 0.792 0.000 0.016 0.032 0.088 0.072
#> GSM601793 6 0.5768 0.30746 0.000 0.000 0.020 0.400 0.104 0.476
#> GSM601798 4 0.0405 0.83497 0.000 0.008 0.004 0.988 0.000 0.000
#> GSM601828 1 0.0458 0.82082 0.984 0.000 0.016 0.000 0.000 0.000
#> GSM601838 5 0.2997 0.86284 0.000 0.096 0.060 0.000 0.844 0.000
#> GSM601843 2 0.2195 0.82946 0.000 0.912 0.008 0.052 0.020 0.008
#> GSM601858 5 0.4260 0.63232 0.000 0.332 0.012 0.004 0.644 0.008
#> GSM601868 3 0.3894 0.50365 0.324 0.000 0.664 0.000 0.008 0.004
#> GSM601748 1 0.0000 0.81997 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601758 1 0.0632 0.81739 0.976 0.000 0.000 0.000 0.000 0.024
#> GSM601763 6 0.4139 0.35466 0.008 0.280 0.008 0.000 0.012 0.692
#> GSM601768 2 0.1788 0.82123 0.000 0.916 0.004 0.000 0.004 0.076
#> GSM601773 2 0.0603 0.83872 0.000 0.980 0.000 0.004 0.016 0.000
#> GSM601778 6 0.6406 0.34295 0.072 0.028 0.012 0.336 0.028 0.524
#> GSM601788 2 0.1515 0.83831 0.000 0.944 0.008 0.000 0.028 0.020
#> GSM601803 4 0.2846 0.80113 0.000 0.116 0.008 0.856 0.016 0.004
#> GSM601808 3 0.3198 0.65988 0.260 0.000 0.740 0.000 0.000 0.000
#> GSM601813 1 0.0717 0.82156 0.976 0.000 0.008 0.000 0.000 0.016
#> GSM601818 1 0.1663 0.78353 0.912 0.000 0.088 0.000 0.000 0.000
#> GSM601823 6 0.0912 0.66534 0.008 0.000 0.004 0.004 0.012 0.972
#> GSM601833 2 0.0603 0.83732 0.000 0.980 0.000 0.000 0.016 0.004
#> GSM601848 6 0.2146 0.66064 0.008 0.000 0.008 0.044 0.024 0.916
#> GSM601853 3 0.3706 0.46987 0.380 0.000 0.620 0.000 0.000 0.000
#> GSM601863 1 0.3050 0.58091 0.764 0.000 0.236 0.000 0.000 0.000
#> GSM601754 4 0.0405 0.83400 0.000 0.008 0.000 0.988 0.000 0.004
#> GSM601784 2 0.2319 0.82398 0.000 0.904 0.008 0.060 0.020 0.008
#> GSM601794 6 0.5775 0.29231 0.000 0.000 0.020 0.408 0.104 0.468
#> GSM601799 4 0.1514 0.82897 0.000 0.016 0.004 0.948 0.016 0.016
#> GSM601829 6 0.6335 0.43863 0.040 0.000 0.028 0.300 0.088 0.544
#> GSM601839 5 0.3006 0.86047 0.000 0.092 0.064 0.000 0.844 0.000
#> GSM601844 6 0.3852 0.61238 0.000 0.000 0.020 0.088 0.092 0.800
#> GSM601859 2 0.2698 0.80669 0.000 0.872 0.008 0.096 0.020 0.004
#> GSM601869 1 0.4975 0.63845 0.700 0.000 0.184 0.028 0.084 0.004
#> GSM601749 1 0.1049 0.81631 0.960 0.000 0.008 0.000 0.000 0.032
#> GSM601759 1 0.0000 0.81997 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601764 6 0.5098 -0.00348 0.036 0.416 0.008 0.000 0.012 0.528
#> GSM601769 2 0.0790 0.83178 0.000 0.968 0.000 0.000 0.032 0.000
#> GSM601774 2 0.0547 0.83559 0.000 0.980 0.000 0.000 0.020 0.000
#> GSM601779 6 0.0551 0.66503 0.008 0.000 0.004 0.004 0.000 0.984
#> GSM601789 2 0.3843 -0.10751 0.000 0.548 0.000 0.000 0.452 0.000
#> GSM601804 4 0.4018 0.69220 0.000 0.056 0.004 0.760 0.004 0.176
#> GSM601809 1 0.4655 0.65283 0.736 0.056 0.176 0.004 0.012 0.016
#> GSM601814 2 0.2178 0.75566 0.000 0.868 0.000 0.000 0.132 0.000
#> GSM601819 1 0.2048 0.77241 0.880 0.000 0.000 0.000 0.000 0.120
#> GSM601824 6 0.3935 0.36921 0.000 0.016 0.004 0.292 0.000 0.688
#> GSM601834 2 0.0547 0.83559 0.000 0.980 0.000 0.000 0.020 0.000
#> GSM601849 6 0.0508 0.66379 0.012 0.000 0.004 0.000 0.000 0.984
#> GSM601854 1 0.0291 0.82077 0.992 0.000 0.004 0.000 0.000 0.004
#> GSM601864 5 0.2934 0.86560 0.000 0.112 0.044 0.000 0.844 0.000
#> GSM601755 4 0.0260 0.83447 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM601785 2 0.3625 0.75852 0.000 0.800 0.012 0.156 0.024 0.008
#> GSM601795 6 0.5448 0.26671 0.000 0.000 0.008 0.432 0.092 0.468
#> GSM601800 4 0.0260 0.83447 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM601830 3 0.0603 0.73545 0.016 0.000 0.980 0.000 0.004 0.000
#> GSM601840 2 0.3662 0.77498 0.000 0.816 0.012 0.124 0.028 0.020
#> GSM601845 2 0.6650 0.42241 0.000 0.548 0.028 0.180 0.040 0.204
#> GSM601860 2 0.2908 0.80367 0.000 0.864 0.012 0.092 0.028 0.004
#> GSM601870 3 0.0806 0.73352 0.008 0.000 0.972 0.000 0.020 0.000
#> GSM601750 1 0.0000 0.81997 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601760 1 0.2454 0.73866 0.840 0.000 0.000 0.000 0.000 0.160
#> GSM601765 2 0.1053 0.83863 0.000 0.964 0.000 0.004 0.020 0.012
#> GSM601770 2 0.0692 0.83650 0.000 0.976 0.000 0.000 0.020 0.004
#> GSM601775 2 0.4536 0.23054 0.004 0.512 0.008 0.000 0.012 0.464
#> GSM601780 6 0.0405 0.66398 0.008 0.000 0.004 0.000 0.000 0.988
#> GSM601790 5 0.2597 0.84639 0.000 0.176 0.000 0.000 0.824 0.000
#> GSM601805 4 0.3308 0.78506 0.000 0.140 0.008 0.824 0.016 0.012
#> GSM601810 1 0.1663 0.79013 0.912 0.000 0.088 0.000 0.000 0.000
#> GSM601815 5 0.3126 0.78903 0.000 0.248 0.000 0.000 0.752 0.000
#> GSM601820 1 0.0000 0.81997 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601825 4 0.3399 0.78293 0.000 0.140 0.008 0.820 0.016 0.016
#> GSM601835 2 0.3291 0.73748 0.000 0.832 0.016 0.124 0.024 0.004
#> GSM601850 6 0.3348 0.63501 0.004 0.052 0.008 0.068 0.016 0.852
#> GSM601855 3 0.1168 0.74057 0.028 0.000 0.956 0.000 0.016 0.000
#> GSM601865 5 0.2595 0.85272 0.000 0.160 0.004 0.000 0.836 0.000
#> GSM601756 4 0.0260 0.83447 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM601786 5 0.3549 0.82227 0.000 0.060 0.100 0.008 0.824 0.008
#> GSM601796 6 0.5759 0.31894 0.000 0.000 0.020 0.392 0.104 0.484
#> GSM601801 4 0.0405 0.83497 0.000 0.008 0.004 0.988 0.000 0.000
#> GSM601831 1 0.4065 0.73576 0.804 0.000 0.064 0.032 0.088 0.012
#> GSM601841 1 0.7292 -0.06699 0.396 0.004 0.024 0.104 0.096 0.376
#> GSM601846 4 0.5771 0.43385 0.000 0.020 0.072 0.632 0.044 0.232
#> GSM601861 2 0.3273 0.64354 0.000 0.776 0.008 0.004 0.212 0.000
#> GSM601871 3 0.3969 0.60349 0.012 0.000 0.708 0.004 0.268 0.008
#> GSM601751 2 0.4057 0.77140 0.000 0.804 0.012 0.092 0.032 0.060
#> GSM601761 6 0.3163 0.47595 0.232 0.000 0.004 0.000 0.000 0.764
#> GSM601766 2 0.3529 0.72474 0.000 0.788 0.008 0.000 0.028 0.176
#> GSM601771 2 0.1036 0.83672 0.000 0.964 0.008 0.000 0.024 0.004
#> GSM601776 6 0.0665 0.66388 0.008 0.000 0.004 0.000 0.008 0.980
#> GSM601781 6 0.5967 0.35100 0.004 0.144 0.000 0.288 0.020 0.544
#> GSM601791 6 0.0767 0.66434 0.008 0.000 0.004 0.000 0.012 0.976
#> GSM601806 4 0.3073 0.77935 0.000 0.152 0.008 0.824 0.016 0.000
#> GSM601811 1 0.3314 0.62329 0.740 0.004 0.256 0.000 0.000 0.000
#> GSM601816 6 0.5095 0.54066 0.008 0.012 0.012 0.228 0.060 0.680
#> GSM601821 2 0.3961 0.00758 0.000 0.556 0.004 0.000 0.440 0.000
#> GSM601826 6 0.1223 0.66539 0.008 0.000 0.004 0.012 0.016 0.960
#> GSM601836 6 0.4855 0.00316 0.008 0.428 0.016 0.000 0.016 0.532
#> GSM601851 6 0.0551 0.66340 0.008 0.000 0.004 0.000 0.004 0.984
#> GSM601856 3 0.3221 0.64253 0.264 0.000 0.736 0.000 0.000 0.000
#> GSM601866 1 0.0146 0.82025 0.996 0.000 0.004 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 time(p) gender(p) k
#> CV:mclust 111 0.752 0.03012 2
#> CV:mclust 121 0.602 0.38016 3
#> CV:mclust 93 0.498 0.00556 4
#> CV:mclust 99 0.597 0.00150 5
#> CV:mclust 100 0.865 0.05616 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 125 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 0.881 0.911 0.961 0.5017 0.496 0.496
#> 3 3 0.455 0.624 0.753 0.3042 0.796 0.611
#> 4 4 0.412 0.505 0.700 0.1310 0.818 0.534
#> 5 5 0.486 0.415 0.593 0.0706 0.911 0.684
#> 6 6 0.522 0.330 0.526 0.0452 0.858 0.466
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
#> GSM601752 2 0.0000 0.979 0.000 1.000
#> GSM601782 1 0.0000 0.939 1.000 0.000
#> GSM601792 1 0.0000 0.939 1.000 0.000
#> GSM601797 2 0.2043 0.954 0.032 0.968
#> GSM601827 1 0.0000 0.939 1.000 0.000
#> GSM601837 2 0.0000 0.979 0.000 1.000
#> GSM601842 2 0.0000 0.979 0.000 1.000
#> GSM601857 1 0.0000 0.939 1.000 0.000
#> GSM601867 2 0.4298 0.897 0.088 0.912
#> GSM601747 1 0.8144 0.672 0.748 0.252
#> GSM601757 1 0.0000 0.939 1.000 0.000
#> GSM601762 2 0.0000 0.979 0.000 1.000
#> GSM601767 2 0.0000 0.979 0.000 1.000
#> GSM601772 2 0.0000 0.979 0.000 1.000
#> GSM601777 2 0.5178 0.864 0.116 0.884
#> GSM601787 2 0.0672 0.973 0.008 0.992
#> GSM601802 2 0.0000 0.979 0.000 1.000
#> GSM601807 1 0.9686 0.411 0.604 0.396
#> GSM601812 1 0.0000 0.939 1.000 0.000
#> GSM601817 1 0.0000 0.939 1.000 0.000
#> GSM601822 2 0.7056 0.764 0.192 0.808
#> GSM601832 2 0.0376 0.976 0.004 0.996
#> GSM601847 2 0.0000 0.979 0.000 1.000
#> GSM601852 1 0.0000 0.939 1.000 0.000
#> GSM601862 1 0.0000 0.939 1.000 0.000
#> GSM601753 2 0.0000 0.979 0.000 1.000
#> GSM601783 1 0.0000 0.939 1.000 0.000
#> GSM601793 1 0.0000 0.939 1.000 0.000
#> GSM601798 2 0.0000 0.979 0.000 1.000
#> GSM601828 1 0.0000 0.939 1.000 0.000
#> GSM601838 2 0.0000 0.979 0.000 1.000
#> GSM601843 2 0.0000 0.979 0.000 1.000
#> GSM601858 2 0.0000 0.979 0.000 1.000
#> GSM601868 1 0.0376 0.937 0.996 0.004
#> GSM601748 1 0.0000 0.939 1.000 0.000
#> GSM601758 1 0.0000 0.939 1.000 0.000
#> GSM601763 1 0.9866 0.267 0.568 0.432
#> GSM601768 2 0.0000 0.979 0.000 1.000
#> GSM601773 2 0.0000 0.979 0.000 1.000
#> GSM601778 1 0.9209 0.526 0.664 0.336
#> GSM601788 2 0.1184 0.967 0.016 0.984
#> GSM601803 2 0.0000 0.979 0.000 1.000
#> GSM601808 1 0.0000 0.939 1.000 0.000
#> GSM601813 1 0.0000 0.939 1.000 0.000
#> GSM601818 1 0.0000 0.939 1.000 0.000
#> GSM601823 1 0.0000 0.939 1.000 0.000
#> GSM601833 2 0.0000 0.979 0.000 1.000
#> GSM601848 1 0.0000 0.939 1.000 0.000
#> GSM601853 1 0.0000 0.939 1.000 0.000
#> GSM601863 1 0.0000 0.939 1.000 0.000
#> GSM601754 2 0.0000 0.979 0.000 1.000
#> GSM601784 2 0.0000 0.979 0.000 1.000
#> GSM601794 1 0.3274 0.895 0.940 0.060
#> GSM601799 2 0.0000 0.979 0.000 1.000
#> GSM601829 1 0.0000 0.939 1.000 0.000
#> GSM601839 2 0.0000 0.979 0.000 1.000
#> GSM601844 1 0.1414 0.927 0.980 0.020
#> GSM601859 2 0.0000 0.979 0.000 1.000
#> GSM601869 1 0.0000 0.939 1.000 0.000
#> GSM601749 1 0.0000 0.939 1.000 0.000
#> GSM601759 1 0.0000 0.939 1.000 0.000
#> GSM601764 1 0.0000 0.939 1.000 0.000
#> GSM601769 2 0.0000 0.979 0.000 1.000
#> GSM601774 2 0.0000 0.979 0.000 1.000
#> GSM601779 1 0.0000 0.939 1.000 0.000
#> GSM601789 2 0.0000 0.979 0.000 1.000
#> GSM601804 2 0.0938 0.970 0.012 0.988
#> GSM601809 1 0.9933 0.242 0.548 0.452
#> GSM601814 2 0.0000 0.979 0.000 1.000
#> GSM601819 1 0.0000 0.939 1.000 0.000
#> GSM601824 2 0.4562 0.890 0.096 0.904
#> GSM601834 2 0.0000 0.979 0.000 1.000
#> GSM601849 1 0.0000 0.939 1.000 0.000
#> GSM601854 1 0.0000 0.939 1.000 0.000
#> GSM601864 2 0.0000 0.979 0.000 1.000
#> GSM601755 2 0.0000 0.979 0.000 1.000
#> GSM601785 2 0.0000 0.979 0.000 1.000
#> GSM601795 1 0.9922 0.259 0.552 0.448
#> GSM601800 2 0.0000 0.979 0.000 1.000
#> GSM601830 1 0.4298 0.871 0.912 0.088
#> GSM601840 2 0.0376 0.976 0.004 0.996
#> GSM601845 2 0.5294 0.858 0.120 0.880
#> GSM601860 2 0.0000 0.979 0.000 1.000
#> GSM601870 1 0.8813 0.604 0.700 0.300
#> GSM601750 1 0.0000 0.939 1.000 0.000
#> GSM601760 1 0.0000 0.939 1.000 0.000
#> GSM601765 2 0.0000 0.979 0.000 1.000
#> GSM601770 2 0.0000 0.979 0.000 1.000
#> GSM601775 2 0.7745 0.705 0.228 0.772
#> GSM601780 1 0.0000 0.939 1.000 0.000
#> GSM601790 2 0.0000 0.979 0.000 1.000
#> GSM601805 2 0.0000 0.979 0.000 1.000
#> GSM601810 1 0.0000 0.939 1.000 0.000
#> GSM601815 2 0.0000 0.979 0.000 1.000
#> GSM601820 1 0.0000 0.939 1.000 0.000
#> GSM601825 2 0.0000 0.979 0.000 1.000
#> GSM601835 2 0.0000 0.979 0.000 1.000
#> GSM601850 1 0.9580 0.444 0.620 0.380
#> GSM601855 1 0.0000 0.939 1.000 0.000
#> GSM601865 2 0.0000 0.979 0.000 1.000
#> GSM601756 2 0.0000 0.979 0.000 1.000
#> GSM601786 2 0.0000 0.979 0.000 1.000
#> GSM601796 1 0.0000 0.939 1.000 0.000
#> GSM601801 2 0.0000 0.979 0.000 1.000
#> GSM601831 1 0.0000 0.939 1.000 0.000
#> GSM601841 1 0.0376 0.937 0.996 0.004
#> GSM601846 2 0.0000 0.979 0.000 1.000
#> GSM601861 2 0.0000 0.979 0.000 1.000
#> GSM601871 2 0.0672 0.973 0.008 0.992
#> GSM601751 2 0.4022 0.908 0.080 0.920
#> GSM601761 1 0.0000 0.939 1.000 0.000
#> GSM601766 2 0.7745 0.704 0.228 0.772
#> GSM601771 2 0.0000 0.979 0.000 1.000
#> GSM601776 1 0.0000 0.939 1.000 0.000
#> GSM601781 1 0.9358 0.510 0.648 0.352
#> GSM601791 1 0.0672 0.934 0.992 0.008
#> GSM601806 2 0.0000 0.979 0.000 1.000
#> GSM601811 1 0.1184 0.929 0.984 0.016
#> GSM601816 1 0.0672 0.934 0.992 0.008
#> GSM601821 2 0.0000 0.979 0.000 1.000
#> GSM601826 1 0.0000 0.939 1.000 0.000
#> GSM601836 1 0.3431 0.891 0.936 0.064
#> GSM601851 1 0.0000 0.939 1.000 0.000
#> GSM601856 1 0.0000 0.939 1.000 0.000
#> GSM601866 1 0.0000 0.939 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601752 2 0.5722 0.6518 0.292 0.704 0.004
#> GSM601782 3 0.5016 0.6895 0.240 0.000 0.760
#> GSM601792 1 0.2599 0.7050 0.932 0.016 0.052
#> GSM601797 2 0.5119 0.7788 0.152 0.816 0.032
#> GSM601827 3 0.4842 0.6999 0.224 0.000 0.776
#> GSM601837 2 0.5327 0.6049 0.000 0.728 0.272
#> GSM601842 2 0.2636 0.7934 0.048 0.932 0.020
#> GSM601857 3 0.2537 0.7362 0.080 0.000 0.920
#> GSM601867 3 0.5291 0.5061 0.000 0.268 0.732
#> GSM601747 1 0.8936 0.1915 0.500 0.132 0.368
#> GSM601757 3 0.6260 0.4276 0.448 0.000 0.552
#> GSM601762 2 0.2356 0.7609 0.000 0.928 0.072
#> GSM601767 2 0.4452 0.7596 0.192 0.808 0.000
#> GSM601772 2 0.3030 0.7960 0.092 0.904 0.004
#> GSM601777 2 0.6521 0.0918 0.004 0.500 0.496
#> GSM601787 3 0.5650 0.4257 0.000 0.312 0.688
#> GSM601802 2 0.5138 0.7065 0.252 0.748 0.000
#> GSM601807 3 0.4842 0.5619 0.000 0.224 0.776
#> GSM601812 3 0.5016 0.6915 0.240 0.000 0.760
#> GSM601817 3 0.4121 0.7257 0.168 0.000 0.832
#> GSM601822 1 0.6260 -0.0166 0.552 0.448 0.000
#> GSM601832 2 0.4931 0.7480 0.212 0.784 0.004
#> GSM601847 2 0.6045 0.4968 0.380 0.620 0.000
#> GSM601852 3 0.6225 0.4529 0.432 0.000 0.568
#> GSM601862 3 0.2165 0.7336 0.064 0.000 0.936
#> GSM601753 2 0.5517 0.6862 0.268 0.728 0.004
#> GSM601783 1 0.5216 0.4169 0.740 0.000 0.260
#> GSM601793 1 0.4172 0.5984 0.840 0.004 0.156
#> GSM601798 2 0.3499 0.7952 0.072 0.900 0.028
#> GSM601828 3 0.5560 0.6393 0.300 0.000 0.700
#> GSM601838 2 0.4002 0.7099 0.000 0.840 0.160
#> GSM601843 2 0.2356 0.7634 0.000 0.928 0.072
#> GSM601858 2 0.6008 0.4330 0.000 0.628 0.372
#> GSM601868 3 0.2318 0.7084 0.028 0.028 0.944
#> GSM601748 3 0.5591 0.6364 0.304 0.000 0.696
#> GSM601758 1 0.5678 0.2815 0.684 0.000 0.316
#> GSM601763 1 0.4887 0.5603 0.772 0.228 0.000
#> GSM601768 2 0.5291 0.6907 0.268 0.732 0.000
#> GSM601773 2 0.3192 0.7916 0.112 0.888 0.000
#> GSM601778 1 0.8309 0.5389 0.632 0.188 0.180
#> GSM601788 2 0.3845 0.7442 0.012 0.872 0.116
#> GSM601803 2 0.4178 0.7720 0.172 0.828 0.000
#> GSM601808 3 0.2066 0.7324 0.060 0.000 0.940
#> GSM601813 1 0.6286 -0.2168 0.536 0.000 0.464
#> GSM601818 3 0.2625 0.7364 0.084 0.000 0.916
#> GSM601823 1 0.1878 0.7139 0.952 0.044 0.004
#> GSM601833 2 0.2860 0.7954 0.084 0.912 0.004
#> GSM601848 1 0.1182 0.7139 0.976 0.012 0.012
#> GSM601853 3 0.2590 0.7348 0.072 0.004 0.924
#> GSM601863 3 0.3551 0.7348 0.132 0.000 0.868
#> GSM601754 2 0.5553 0.6812 0.272 0.724 0.004
#> GSM601784 2 0.1129 0.7803 0.004 0.976 0.020
#> GSM601794 1 0.5497 0.6503 0.812 0.064 0.124
#> GSM601799 2 0.5982 0.5966 0.328 0.668 0.004
#> GSM601829 3 0.6244 0.4328 0.440 0.000 0.560
#> GSM601839 2 0.5098 0.6321 0.000 0.752 0.248
#> GSM601844 1 0.4371 0.6667 0.860 0.032 0.108
#> GSM601859 2 0.4291 0.7674 0.180 0.820 0.000
#> GSM601869 3 0.3551 0.7344 0.132 0.000 0.868
#> GSM601749 1 0.5968 0.1385 0.636 0.000 0.364
#> GSM601759 3 0.6252 0.4298 0.444 0.000 0.556
#> GSM601764 1 0.2229 0.7160 0.944 0.044 0.012
#> GSM601769 2 0.1267 0.7882 0.024 0.972 0.004
#> GSM601774 2 0.2496 0.7951 0.068 0.928 0.004
#> GSM601779 1 0.2959 0.6944 0.900 0.100 0.000
#> GSM601789 2 0.3340 0.7362 0.000 0.880 0.120
#> GSM601804 2 0.6305 0.2225 0.484 0.516 0.000
#> GSM601809 3 0.4504 0.5860 0.000 0.196 0.804
#> GSM601814 2 0.0747 0.7870 0.016 0.984 0.000
#> GSM601819 1 0.4702 0.5020 0.788 0.000 0.212
#> GSM601824 1 0.6008 0.2525 0.628 0.372 0.000
#> GSM601834 2 0.3116 0.7924 0.108 0.892 0.000
#> GSM601849 1 0.1950 0.7013 0.952 0.008 0.040
#> GSM601854 3 0.6126 0.5109 0.400 0.000 0.600
#> GSM601864 2 0.5497 0.5713 0.000 0.708 0.292
#> GSM601755 2 0.4521 0.7668 0.180 0.816 0.004
#> GSM601785 2 0.4033 0.7856 0.136 0.856 0.008
#> GSM601795 1 0.5763 0.5285 0.716 0.276 0.008
#> GSM601800 2 0.5158 0.7236 0.232 0.764 0.004
#> GSM601830 3 0.3192 0.6445 0.000 0.112 0.888
#> GSM601840 2 0.4682 0.7566 0.192 0.804 0.004
#> GSM601845 2 0.7728 0.6008 0.276 0.640 0.084
#> GSM601860 2 0.3375 0.7945 0.100 0.892 0.008
#> GSM601870 3 0.4796 0.5651 0.000 0.220 0.780
#> GSM601750 3 0.5785 0.6055 0.332 0.000 0.668
#> GSM601760 1 0.4974 0.4578 0.764 0.000 0.236
#> GSM601765 2 0.4784 0.7572 0.200 0.796 0.004
#> GSM601770 2 0.4465 0.7720 0.176 0.820 0.004
#> GSM601775 1 0.6280 -0.0714 0.540 0.460 0.000
#> GSM601780 1 0.2625 0.7013 0.916 0.084 0.000
#> GSM601790 2 0.3340 0.7354 0.000 0.880 0.120
#> GSM601805 2 0.4178 0.7713 0.172 0.828 0.000
#> GSM601810 3 0.3551 0.7347 0.132 0.000 0.868
#> GSM601815 2 0.2959 0.7459 0.000 0.900 0.100
#> GSM601820 3 0.6235 0.4484 0.436 0.000 0.564
#> GSM601825 2 0.3816 0.7824 0.148 0.852 0.000
#> GSM601835 2 0.5327 0.6004 0.000 0.728 0.272
#> GSM601850 1 0.5138 0.5372 0.748 0.252 0.000
#> GSM601855 3 0.2711 0.6602 0.000 0.088 0.912
#> GSM601865 2 0.5905 0.4705 0.000 0.648 0.352
#> GSM601756 2 0.3715 0.7883 0.128 0.868 0.004
#> GSM601786 2 0.5431 0.5881 0.000 0.716 0.284
#> GSM601796 1 0.4047 0.6132 0.848 0.004 0.148
#> GSM601801 2 0.2173 0.7940 0.048 0.944 0.008
#> GSM601831 3 0.4346 0.7193 0.184 0.000 0.816
#> GSM601841 3 0.6771 0.3746 0.440 0.012 0.548
#> GSM601846 2 0.4963 0.6860 0.008 0.792 0.200
#> GSM601861 2 0.2066 0.7642 0.000 0.940 0.060
#> GSM601871 3 0.5706 0.4088 0.000 0.320 0.680
#> GSM601751 2 0.5443 0.7077 0.260 0.736 0.004
#> GSM601761 1 0.2301 0.6850 0.936 0.004 0.060
#> GSM601766 1 0.6625 0.0182 0.552 0.440 0.008
#> GSM601771 2 0.2796 0.7945 0.092 0.908 0.000
#> GSM601776 1 0.1315 0.7107 0.972 0.008 0.020
#> GSM601781 1 0.5551 0.5952 0.760 0.224 0.016
#> GSM601791 1 0.2229 0.7163 0.944 0.044 0.012
#> GSM601806 2 0.1753 0.7933 0.048 0.952 0.000
#> GSM601811 3 0.2947 0.7267 0.060 0.020 0.920
#> GSM601816 1 0.3028 0.7100 0.920 0.032 0.048
#> GSM601821 2 0.2448 0.7576 0.000 0.924 0.076
#> GSM601826 1 0.1399 0.7155 0.968 0.028 0.004
#> GSM601836 1 0.4658 0.7074 0.856 0.076 0.068
#> GSM601851 1 0.1620 0.7113 0.964 0.012 0.024
#> GSM601856 3 0.2066 0.7328 0.060 0.000 0.940
#> GSM601866 3 0.4796 0.7039 0.220 0.000 0.780
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601752 4 0.440 0.6498 0.076 0.112 0.000 0.812
#> GSM601782 3 0.667 0.5128 0.332 0.032 0.592 0.044
#> GSM601792 4 0.560 0.1821 0.408 0.000 0.024 0.568
#> GSM601797 4 0.259 0.6139 0.012 0.028 0.040 0.920
#> GSM601827 3 0.722 0.5811 0.172 0.020 0.612 0.196
#> GSM601837 2 0.717 0.5366 0.012 0.572 0.128 0.288
#> GSM601842 2 0.381 0.6781 0.008 0.804 0.000 0.188
#> GSM601857 3 0.440 0.6830 0.120 0.008 0.820 0.052
#> GSM601867 3 0.697 0.4672 0.012 0.168 0.624 0.196
#> GSM601747 2 0.761 0.2516 0.268 0.556 0.152 0.024
#> GSM601757 1 0.657 -0.1124 0.520 0.068 0.408 0.004
#> GSM601762 2 0.332 0.7194 0.008 0.872 0.016 0.104
#> GSM601767 2 0.428 0.6909 0.076 0.820 0.000 0.104
#> GSM601772 2 0.275 0.6972 0.072 0.904 0.004 0.020
#> GSM601777 4 0.692 0.4055 0.016 0.152 0.196 0.636
#> GSM601787 3 0.673 0.3697 0.012 0.288 0.608 0.092
#> GSM601802 4 0.605 0.5863 0.100 0.232 0.000 0.668
#> GSM601807 3 0.650 0.4941 0.012 0.108 0.660 0.220
#> GSM601812 3 0.574 0.5839 0.280 0.036 0.672 0.012
#> GSM601817 3 0.584 0.6445 0.196 0.064 0.720 0.020
#> GSM601822 4 0.716 0.4495 0.284 0.140 0.008 0.568
#> GSM601832 2 0.413 0.6721 0.108 0.828 0.000 0.064
#> GSM601847 4 0.646 0.6004 0.156 0.200 0.000 0.644
#> GSM601852 3 0.601 0.3095 0.456 0.016 0.512 0.016
#> GSM601862 3 0.318 0.6849 0.068 0.024 0.892 0.016
#> GSM601753 4 0.610 0.5751 0.108 0.224 0.000 0.668
#> GSM601783 1 0.486 0.4286 0.768 0.004 0.184 0.044
#> GSM601793 4 0.621 0.1166 0.404 0.000 0.056 0.540
#> GSM601798 4 0.322 0.5985 0.000 0.164 0.000 0.836
#> GSM601828 3 0.629 0.5068 0.352 0.008 0.588 0.052
#> GSM601838 2 0.628 0.5861 0.008 0.624 0.064 0.304
#> GSM601843 2 0.405 0.6846 0.000 0.796 0.016 0.188
#> GSM601858 2 0.609 0.6274 0.016 0.716 0.136 0.132
#> GSM601868 3 0.330 0.6762 0.032 0.016 0.888 0.064
#> GSM601748 3 0.542 0.5614 0.304 0.016 0.668 0.012
#> GSM601758 1 0.507 0.2256 0.664 0.016 0.320 0.000
#> GSM601763 1 0.628 0.3827 0.616 0.316 0.008 0.060
#> GSM601768 2 0.391 0.6531 0.148 0.824 0.000 0.028
#> GSM601773 2 0.391 0.6923 0.024 0.820 0.000 0.156
#> GSM601778 4 0.677 0.4978 0.132 0.056 0.120 0.692
#> GSM601788 2 0.584 0.6639 0.016 0.716 0.068 0.200
#> GSM601803 4 0.593 0.4969 0.064 0.296 0.000 0.640
#> GSM601808 3 0.263 0.6828 0.032 0.020 0.920 0.028
#> GSM601813 1 0.591 -0.1065 0.536 0.004 0.432 0.028
#> GSM601818 3 0.667 0.6064 0.180 0.152 0.656 0.012
#> GSM601823 1 0.454 0.4936 0.760 0.024 0.000 0.216
#> GSM601833 2 0.258 0.7152 0.036 0.912 0.000 0.052
#> GSM601848 1 0.475 0.3744 0.688 0.000 0.008 0.304
#> GSM601853 3 0.316 0.6846 0.052 0.016 0.896 0.036
#> GSM601863 3 0.414 0.6734 0.140 0.028 0.824 0.008
#> GSM601754 4 0.461 0.6393 0.064 0.144 0.000 0.792
#> GSM601784 2 0.407 0.6617 0.000 0.748 0.000 0.252
#> GSM601794 4 0.507 0.4566 0.200 0.000 0.056 0.744
#> GSM601799 4 0.650 0.6101 0.160 0.200 0.000 0.640
#> GSM601829 3 0.809 0.1460 0.252 0.008 0.388 0.352
#> GSM601839 2 0.628 0.6207 0.008 0.676 0.108 0.208
#> GSM601844 1 0.616 0.3707 0.616 0.012 0.044 0.328
#> GSM601859 2 0.549 0.6519 0.052 0.688 0.000 0.260
#> GSM601869 3 0.519 0.6665 0.160 0.004 0.760 0.076
#> GSM601749 1 0.475 0.2490 0.688 0.000 0.304 0.008
#> GSM601759 1 0.641 -0.1344 0.516 0.048 0.428 0.008
#> GSM601764 1 0.594 0.4347 0.624 0.332 0.032 0.012
#> GSM601769 2 0.222 0.7199 0.016 0.924 0.000 0.060
#> GSM601774 2 0.259 0.7137 0.044 0.912 0.000 0.044
#> GSM601779 1 0.509 0.4355 0.728 0.044 0.000 0.228
#> GSM601789 2 0.300 0.7150 0.008 0.900 0.052 0.040
#> GSM601804 4 0.722 0.4429 0.316 0.164 0.000 0.520
#> GSM601809 3 0.749 0.3340 0.064 0.344 0.536 0.056
#> GSM601814 2 0.448 0.6129 0.008 0.728 0.000 0.264
#> GSM601819 1 0.581 0.4631 0.724 0.108 0.160 0.008
#> GSM601824 1 0.738 0.0748 0.520 0.216 0.000 0.264
#> GSM601834 2 0.306 0.7123 0.040 0.888 0.000 0.072
#> GSM601849 1 0.434 0.6064 0.836 0.076 0.016 0.072
#> GSM601854 3 0.552 0.4792 0.380 0.000 0.596 0.024
#> GSM601864 2 0.790 0.3054 0.012 0.432 0.184 0.372
#> GSM601755 4 0.415 0.6227 0.036 0.152 0.000 0.812
#> GSM601785 2 0.552 0.6277 0.048 0.676 0.000 0.276
#> GSM601795 4 0.482 0.4991 0.240 0.020 0.004 0.736
#> GSM601800 4 0.444 0.6361 0.052 0.148 0.000 0.800
#> GSM601830 3 0.517 0.6011 0.004 0.072 0.760 0.164
#> GSM601840 2 0.667 0.4611 0.080 0.540 0.004 0.376
#> GSM601845 2 0.794 0.2761 0.164 0.488 0.024 0.324
#> GSM601860 2 0.568 0.6770 0.072 0.708 0.004 0.216
#> GSM601870 3 0.537 0.5885 0.012 0.088 0.764 0.136
#> GSM601750 3 0.535 0.5489 0.320 0.020 0.656 0.004
#> GSM601760 1 0.586 0.4375 0.708 0.084 0.200 0.008
#> GSM601765 2 0.350 0.6821 0.104 0.860 0.000 0.036
#> GSM601770 2 0.284 0.6937 0.088 0.892 0.000 0.020
#> GSM601775 2 0.697 0.0426 0.436 0.452 0.000 0.112
#> GSM601780 1 0.496 0.5589 0.788 0.080 0.008 0.124
#> GSM601790 2 0.365 0.7122 0.008 0.868 0.060 0.064
#> GSM601805 4 0.572 0.4768 0.044 0.324 0.000 0.632
#> GSM601810 3 0.353 0.6855 0.088 0.024 0.872 0.016
#> GSM601815 2 0.542 0.6743 0.012 0.748 0.064 0.176
#> GSM601820 3 0.514 0.3264 0.456 0.000 0.540 0.004
#> GSM601825 4 0.620 0.1345 0.052 0.440 0.000 0.508
#> GSM601835 2 0.554 0.6214 0.008 0.744 0.156 0.092
#> GSM601850 1 0.699 0.0586 0.524 0.128 0.000 0.348
#> GSM601855 3 0.417 0.6322 0.012 0.052 0.840 0.096
#> GSM601865 2 0.621 0.6195 0.008 0.692 0.168 0.132
#> GSM601756 4 0.408 0.5969 0.020 0.180 0.000 0.800
#> GSM601786 2 0.666 0.6055 0.008 0.624 0.108 0.260
#> GSM601796 4 0.609 0.2797 0.328 0.000 0.064 0.608
#> GSM601801 4 0.405 0.5409 0.008 0.212 0.000 0.780
#> GSM601831 3 0.575 0.6452 0.140 0.008 0.732 0.120
#> GSM601841 4 0.778 -0.1412 0.268 0.000 0.304 0.428
#> GSM601846 4 0.550 0.5085 0.012 0.132 0.100 0.756
#> GSM601861 2 0.521 0.6120 0.004 0.680 0.020 0.296
#> GSM601871 3 0.752 0.3275 0.012 0.192 0.552 0.244
#> GSM601751 2 0.648 0.5637 0.096 0.640 0.008 0.256
#> GSM601761 1 0.434 0.5905 0.836 0.076 0.072 0.016
#> GSM601766 2 0.583 0.4586 0.272 0.676 0.024 0.028
#> GSM601771 2 0.502 0.6899 0.056 0.772 0.008 0.164
#> GSM601776 1 0.280 0.6103 0.908 0.028 0.008 0.056
#> GSM601781 4 0.795 0.2166 0.356 0.180 0.016 0.448
#> GSM601791 1 0.380 0.6146 0.868 0.060 0.024 0.048
#> GSM601806 4 0.597 0.3320 0.032 0.368 0.008 0.592
#> GSM601811 3 0.441 0.6771 0.068 0.072 0.836 0.024
#> GSM601816 1 0.709 0.0999 0.500 0.056 0.032 0.412
#> GSM601821 2 0.529 0.6370 0.004 0.708 0.036 0.252
#> GSM601826 1 0.530 0.4629 0.720 0.044 0.004 0.232
#> GSM601836 1 0.714 0.4271 0.572 0.320 0.076 0.032
#> GSM601851 1 0.432 0.6083 0.840 0.056 0.024 0.080
#> GSM601856 3 0.295 0.6718 0.024 0.016 0.904 0.056
#> GSM601866 3 0.541 0.5727 0.296 0.028 0.672 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601752 4 0.441 0.6707 0.016 0.040 0.000 0.764 0.180
#> GSM601782 3 0.701 0.2051 0.384 0.020 0.428 0.004 0.164
#> GSM601792 4 0.656 0.5447 0.148 0.000 0.024 0.552 0.276
#> GSM601797 4 0.521 0.5603 0.004 0.004 0.056 0.656 0.280
#> GSM601827 5 0.544 0.2786 0.048 0.000 0.320 0.016 0.616
#> GSM601837 2 0.675 0.5019 0.000 0.576 0.140 0.052 0.232
#> GSM601842 2 0.629 0.2086 0.020 0.488 0.016 0.052 0.424
#> GSM601857 3 0.531 0.4695 0.284 0.040 0.652 0.000 0.024
#> GSM601867 3 0.590 0.3392 0.008 0.140 0.660 0.012 0.180
#> GSM601747 2 0.815 0.1511 0.212 0.468 0.140 0.012 0.168
#> GSM601757 1 0.462 0.3143 0.712 0.024 0.248 0.000 0.016
#> GSM601762 2 0.576 0.5384 0.008 0.680 0.040 0.060 0.212
#> GSM601767 2 0.437 0.6378 0.040 0.800 0.004 0.120 0.036
#> GSM601772 2 0.456 0.5756 0.060 0.788 0.012 0.016 0.124
#> GSM601777 4 0.730 0.3595 0.000 0.092 0.228 0.532 0.148
#> GSM601787 3 0.638 0.2902 0.016 0.300 0.584 0.024 0.076
#> GSM601802 4 0.273 0.6693 0.004 0.092 0.004 0.884 0.016
#> GSM601807 3 0.578 0.2826 0.000 0.036 0.684 0.148 0.132
#> GSM601812 3 0.692 0.2468 0.376 0.028 0.460 0.004 0.132
#> GSM601817 3 0.730 0.1556 0.168 0.044 0.476 0.004 0.308
#> GSM601822 4 0.513 0.6420 0.088 0.052 0.008 0.764 0.088
#> GSM601832 2 0.677 0.2520 0.056 0.520 0.008 0.068 0.348
#> GSM601847 4 0.341 0.6677 0.032 0.084 0.004 0.860 0.020
#> GSM601852 1 0.722 -0.0182 0.440 0.020 0.304 0.004 0.232
#> GSM601862 3 0.467 0.5214 0.188 0.032 0.748 0.000 0.032
#> GSM601753 4 0.528 0.6558 0.020 0.116 0.000 0.716 0.148
#> GSM601783 1 0.365 0.4866 0.848 0.000 0.064 0.032 0.056
#> GSM601793 4 0.670 0.5421 0.152 0.000 0.036 0.560 0.252
#> GSM601798 4 0.529 0.6218 0.000 0.056 0.028 0.692 0.224
#> GSM601828 5 0.762 0.1198 0.256 0.056 0.264 0.000 0.424
#> GSM601838 2 0.577 0.6064 0.000 0.696 0.064 0.088 0.152
#> GSM601843 2 0.613 0.2096 0.008 0.488 0.024 0.048 0.432
#> GSM601858 2 0.558 0.5833 0.024 0.720 0.140 0.016 0.100
#> GSM601868 3 0.600 0.5067 0.204 0.036 0.660 0.004 0.096
#> GSM601748 3 0.590 0.1401 0.452 0.008 0.464 0.000 0.076
#> GSM601758 1 0.377 0.4077 0.788 0.008 0.188 0.000 0.016
#> GSM601763 1 0.777 -0.0600 0.456 0.248 0.000 0.096 0.200
#> GSM601768 2 0.567 0.5691 0.132 0.720 0.008 0.056 0.084
#> GSM601773 2 0.427 0.6321 0.012 0.772 0.000 0.176 0.040
#> GSM601778 4 0.579 0.5991 0.032 0.032 0.084 0.720 0.132
#> GSM601788 2 0.643 0.5523 0.000 0.624 0.096 0.208 0.072
#> GSM601803 4 0.376 0.6332 0.004 0.144 0.008 0.816 0.028
#> GSM601808 3 0.372 0.5423 0.088 0.008 0.840 0.008 0.056
#> GSM601813 1 0.524 0.2106 0.616 0.000 0.336 0.028 0.020
#> GSM601818 3 0.622 0.4346 0.284 0.088 0.592 0.000 0.036
#> GSM601823 1 0.610 0.1919 0.568 0.012 0.000 0.308 0.112
#> GSM601833 2 0.464 0.5611 0.028 0.764 0.008 0.028 0.172
#> GSM601848 4 0.550 0.1438 0.444 0.000 0.000 0.492 0.064
#> GSM601853 3 0.523 0.3050 0.044 0.016 0.652 0.000 0.288
#> GSM601863 3 0.523 0.4541 0.288 0.044 0.652 0.000 0.016
#> GSM601754 4 0.470 0.6653 0.016 0.044 0.004 0.748 0.188
#> GSM601784 2 0.521 0.5876 0.004 0.692 0.004 0.084 0.216
#> GSM601794 4 0.602 0.5689 0.052 0.000 0.052 0.608 0.288
#> GSM601799 4 0.631 0.6430 0.100 0.088 0.000 0.652 0.160
#> GSM601829 5 0.617 0.4066 0.060 0.000 0.208 0.088 0.644
#> GSM601839 2 0.573 0.5840 0.000 0.688 0.128 0.036 0.148
#> GSM601844 5 0.720 0.0786 0.260 0.020 0.012 0.212 0.496
#> GSM601859 2 0.569 0.6124 0.036 0.692 0.000 0.148 0.124
#> GSM601869 3 0.623 0.3932 0.312 0.004 0.564 0.012 0.108
#> GSM601749 1 0.381 0.4148 0.784 0.000 0.192 0.008 0.016
#> GSM601759 1 0.453 0.3083 0.704 0.004 0.260 0.000 0.032
#> GSM601764 1 0.724 -0.0756 0.484 0.256 0.008 0.024 0.228
#> GSM601769 2 0.298 0.6499 0.008 0.880 0.004 0.076 0.032
#> GSM601774 2 0.349 0.6464 0.032 0.860 0.004 0.072 0.032
#> GSM601779 1 0.543 0.0876 0.560 0.012 0.000 0.388 0.040
#> GSM601789 2 0.306 0.6498 0.008 0.884 0.052 0.044 0.012
#> GSM601804 4 0.411 0.6597 0.092 0.064 0.000 0.816 0.028
#> GSM601809 3 0.854 0.3173 0.176 0.256 0.436 0.068 0.064
#> GSM601814 2 0.427 0.6215 0.000 0.744 0.016 0.224 0.016
#> GSM601819 1 0.409 0.4459 0.812 0.036 0.116 0.000 0.036
#> GSM601824 4 0.688 0.2754 0.384 0.084 0.000 0.468 0.064
#> GSM601834 2 0.429 0.6180 0.016 0.796 0.000 0.080 0.108
#> GSM601849 1 0.575 0.4773 0.684 0.024 0.016 0.208 0.068
#> GSM601854 1 0.625 -0.1959 0.444 0.004 0.440 0.004 0.108
#> GSM601864 2 0.779 0.3843 0.000 0.480 0.188 0.204 0.128
#> GSM601755 4 0.431 0.6704 0.000 0.072 0.012 0.788 0.128
#> GSM601785 2 0.634 0.4317 0.016 0.528 0.000 0.116 0.340
#> GSM601795 4 0.509 0.6274 0.064 0.000 0.004 0.668 0.264
#> GSM601800 4 0.485 0.6552 0.012 0.056 0.000 0.720 0.212
#> GSM601830 5 0.558 0.2647 0.004 0.048 0.380 0.008 0.560
#> GSM601840 2 0.797 0.4353 0.084 0.500 0.024 0.208 0.184
#> GSM601845 5 0.579 0.3420 0.016 0.240 0.024 0.056 0.664
#> GSM601860 2 0.660 0.6032 0.064 0.672 0.044 0.088 0.132
#> GSM601870 3 0.523 0.1995 0.000 0.040 0.656 0.020 0.284
#> GSM601750 1 0.544 -0.1598 0.480 0.004 0.468 0.000 0.048
#> GSM601760 1 0.492 0.4134 0.756 0.056 0.152 0.004 0.032
#> GSM601765 2 0.619 0.3514 0.068 0.592 0.004 0.036 0.300
#> GSM601770 2 0.453 0.6162 0.064 0.804 0.008 0.044 0.080
#> GSM601775 2 0.822 0.1653 0.276 0.376 0.004 0.240 0.104
#> GSM601780 1 0.540 0.4141 0.680 0.036 0.000 0.236 0.048
#> GSM601790 2 0.293 0.6449 0.000 0.888 0.048 0.028 0.036
#> GSM601805 4 0.405 0.6222 0.004 0.160 0.016 0.796 0.024
#> GSM601810 3 0.534 0.5225 0.184 0.016 0.724 0.040 0.036
#> GSM601815 2 0.537 0.6078 0.000 0.720 0.072 0.160 0.048
#> GSM601820 1 0.486 0.1425 0.604 0.004 0.372 0.004 0.016
#> GSM601825 4 0.499 0.3469 0.004 0.320 0.000 0.636 0.040
#> GSM601835 5 0.686 0.1672 0.004 0.364 0.104 0.040 0.488
#> GSM601850 4 0.654 0.4205 0.272 0.076 0.004 0.588 0.060
#> GSM601855 3 0.472 0.0964 0.000 0.008 0.612 0.012 0.368
#> GSM601865 2 0.580 0.5599 0.000 0.680 0.180 0.044 0.096
#> GSM601756 4 0.488 0.6524 0.000 0.092 0.012 0.740 0.156
#> GSM601786 2 0.695 0.5310 0.012 0.596 0.144 0.056 0.192
#> GSM601796 4 0.670 0.5777 0.132 0.004 0.052 0.600 0.212
#> GSM601801 4 0.552 0.6171 0.000 0.116 0.024 0.696 0.164
#> GSM601831 3 0.632 -0.0144 0.084 0.000 0.464 0.024 0.428
#> GSM601841 1 0.882 -0.0742 0.308 0.012 0.264 0.192 0.224
#> GSM601846 5 0.582 0.4649 0.000 0.048 0.120 0.144 0.688
#> GSM601861 2 0.550 0.6138 0.000 0.712 0.040 0.144 0.104
#> GSM601871 3 0.777 0.2217 0.012 0.204 0.512 0.096 0.176
#> GSM601751 2 0.774 0.4623 0.068 0.532 0.064 0.256 0.080
#> GSM601761 1 0.406 0.5022 0.824 0.016 0.064 0.088 0.008
#> GSM601766 2 0.701 0.0748 0.156 0.472 0.008 0.020 0.344
#> GSM601771 2 0.648 0.5863 0.024 0.668 0.072 0.152 0.084
#> GSM601776 1 0.384 0.5111 0.788 0.000 0.016 0.184 0.012
#> GSM601781 4 0.680 0.5703 0.176 0.088 0.036 0.640 0.060
#> GSM601791 1 0.394 0.5100 0.784 0.012 0.000 0.184 0.020
#> GSM601806 4 0.553 0.4932 0.000 0.216 0.052 0.684 0.048
#> GSM601811 3 0.629 0.4949 0.192 0.056 0.668 0.052 0.032
#> GSM601816 4 0.572 0.4970 0.248 0.012 0.020 0.660 0.060
#> GSM601821 2 0.529 0.6157 0.000 0.728 0.056 0.156 0.060
#> GSM601826 1 0.625 -0.0574 0.452 0.008 0.000 0.428 0.112
#> GSM601836 5 0.809 0.2252 0.260 0.280 0.028 0.040 0.392
#> GSM601851 1 0.457 0.5048 0.752 0.012 0.008 0.196 0.032
#> GSM601856 3 0.444 0.3956 0.024 0.000 0.752 0.024 0.200
#> GSM601866 3 0.535 0.1535 0.460 0.020 0.500 0.000 0.020
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601752 4 0.336 0.6390 0.004 0.032 0.000 0.848 0.060 0.056
#> GSM601782 3 0.805 -0.0745 0.328 0.104 0.368 0.012 0.048 0.140
#> GSM601792 4 0.610 0.1576 0.016 0.080 0.036 0.524 0.000 0.344
#> GSM601797 4 0.405 0.5332 0.000 0.016 0.092 0.788 0.004 0.100
#> GSM601827 3 0.744 0.1168 0.016 0.348 0.372 0.152 0.000 0.112
#> GSM601837 5 0.663 0.4330 0.000 0.096 0.100 0.136 0.612 0.056
#> GSM601842 2 0.547 0.3818 0.004 0.672 0.044 0.092 0.184 0.004
#> GSM601857 1 0.643 0.0215 0.456 0.036 0.416 0.024 0.016 0.052
#> GSM601867 3 0.749 0.3248 0.100 0.012 0.512 0.084 0.232 0.060
#> GSM601747 2 0.839 0.2241 0.236 0.396 0.076 0.016 0.180 0.096
#> GSM601757 1 0.523 0.4678 0.696 0.116 0.124 0.000 0.000 0.064
#> GSM601762 2 0.647 0.0768 0.024 0.516 0.028 0.056 0.348 0.028
#> GSM601767 5 0.620 0.3099 0.032 0.324 0.000 0.080 0.536 0.028
#> GSM601772 2 0.649 -0.0438 0.072 0.452 0.008 0.028 0.408 0.032
#> GSM601777 6 0.864 0.1668 0.012 0.072 0.200 0.200 0.160 0.356
#> GSM601787 3 0.737 0.2643 0.112 0.020 0.432 0.016 0.340 0.080
#> GSM601802 4 0.489 0.5163 0.000 0.012 0.000 0.684 0.116 0.188
#> GSM601807 3 0.694 0.3543 0.028 0.016 0.592 0.124 0.112 0.128
#> GSM601812 1 0.666 0.1045 0.404 0.144 0.400 0.000 0.012 0.040
#> GSM601817 3 0.659 0.2454 0.164 0.312 0.476 0.000 0.008 0.040
#> GSM601822 6 0.659 0.3665 0.012 0.064 0.024 0.300 0.060 0.540
#> GSM601832 2 0.546 0.3829 0.032 0.684 0.008 0.032 0.200 0.044
#> GSM601847 6 0.650 0.1556 0.020 0.024 0.004 0.376 0.116 0.460
#> GSM601852 1 0.652 0.1716 0.424 0.236 0.316 0.004 0.000 0.020
#> GSM601862 3 0.598 0.0938 0.352 0.008 0.532 0.004 0.048 0.056
#> GSM601753 4 0.534 0.5943 0.004 0.060 0.000 0.692 0.136 0.108
#> GSM601783 1 0.527 0.4690 0.708 0.044 0.028 0.060 0.000 0.160
#> GSM601793 4 0.586 0.2621 0.036 0.040 0.040 0.588 0.000 0.296
#> GSM601798 4 0.331 0.6281 0.000 0.028 0.024 0.860 0.040 0.048
#> GSM601828 2 0.693 -0.1536 0.160 0.436 0.340 0.028 0.000 0.036
#> GSM601838 5 0.496 0.5250 0.000 0.084 0.032 0.080 0.752 0.052
#> GSM601843 2 0.614 0.3513 0.000 0.600 0.072 0.104 0.216 0.008
#> GSM601858 5 0.811 0.2840 0.072 0.272 0.104 0.044 0.444 0.064
#> GSM601868 3 0.679 0.0573 0.356 0.004 0.472 0.028 0.064 0.076
#> GSM601748 1 0.589 0.3583 0.572 0.076 0.284 0.000 0.000 0.068
#> GSM601758 1 0.320 0.5386 0.852 0.032 0.044 0.000 0.000 0.072
#> GSM601763 2 0.679 0.3343 0.240 0.540 0.000 0.052 0.036 0.132
#> GSM601768 5 0.709 0.0976 0.108 0.384 0.000 0.060 0.408 0.040
#> GSM601773 5 0.617 0.3830 0.016 0.252 0.000 0.096 0.584 0.052
#> GSM601778 6 0.776 0.3100 0.016 0.072 0.140 0.272 0.048 0.452
#> GSM601788 5 0.690 0.3973 0.024 0.064 0.052 0.080 0.600 0.180
#> GSM601803 4 0.556 0.4790 0.000 0.016 0.004 0.624 0.176 0.180
#> GSM601808 3 0.539 0.2192 0.260 0.008 0.640 0.004 0.028 0.060
#> GSM601813 1 0.537 0.5146 0.688 0.016 0.144 0.012 0.008 0.132
#> GSM601818 1 0.711 0.0464 0.404 0.068 0.396 0.000 0.072 0.060
#> GSM601823 6 0.689 0.4872 0.284 0.112 0.000 0.140 0.000 0.464
#> GSM601833 2 0.540 -0.0426 0.012 0.516 0.000 0.040 0.412 0.020
#> GSM601848 6 0.587 0.5449 0.204 0.016 0.000 0.200 0.004 0.576
#> GSM601853 3 0.466 0.3887 0.092 0.140 0.740 0.008 0.000 0.020
#> GSM601863 3 0.580 0.0181 0.384 0.016 0.516 0.000 0.032 0.052
#> GSM601754 4 0.456 0.6189 0.012 0.028 0.008 0.772 0.116 0.064
#> GSM601784 5 0.642 0.2877 0.004 0.236 0.004 0.236 0.500 0.020
#> GSM601794 4 0.557 0.4410 0.016 0.032 0.052 0.676 0.020 0.204
#> GSM601799 4 0.588 0.5536 0.016 0.100 0.000 0.660 0.084 0.140
#> GSM601829 3 0.822 0.0641 0.036 0.296 0.316 0.192 0.004 0.156
#> GSM601839 5 0.571 0.4690 0.000 0.168 0.064 0.052 0.676 0.040
#> GSM601844 2 0.853 -0.1052 0.084 0.272 0.108 0.260 0.004 0.272
#> GSM601859 5 0.597 0.4837 0.032 0.104 0.004 0.164 0.656 0.040
#> GSM601869 1 0.640 0.1355 0.456 0.004 0.384 0.020 0.016 0.120
#> GSM601749 1 0.397 0.5281 0.772 0.004 0.104 0.000 0.000 0.120
#> GSM601759 1 0.322 0.5269 0.848 0.048 0.080 0.000 0.000 0.024
#> GSM601764 2 0.662 0.2106 0.252 0.488 0.004 0.000 0.044 0.212
#> GSM601769 5 0.549 0.4180 0.020 0.252 0.008 0.040 0.648 0.032
#> GSM601774 5 0.621 0.3810 0.028 0.260 0.008 0.056 0.596 0.052
#> GSM601779 6 0.623 0.5430 0.248 0.032 0.004 0.176 0.000 0.540
#> GSM601789 5 0.572 0.4318 0.004 0.216 0.040 0.016 0.648 0.076
#> GSM601804 4 0.583 0.3830 0.012 0.020 0.000 0.568 0.100 0.300
#> GSM601809 5 0.856 -0.2923 0.252 0.024 0.272 0.048 0.300 0.104
#> GSM601814 5 0.501 0.5054 0.000 0.084 0.004 0.108 0.728 0.076
#> GSM601819 1 0.548 0.4941 0.724 0.060 0.056 0.012 0.036 0.112
#> GSM601824 6 0.823 0.3055 0.216 0.156 0.000 0.264 0.044 0.320
#> GSM601834 5 0.559 0.2844 0.008 0.360 0.004 0.048 0.552 0.028
#> GSM601849 6 0.680 0.3701 0.360 0.060 0.012 0.076 0.020 0.472
#> GSM601854 1 0.670 0.2024 0.412 0.076 0.376 0.000 0.000 0.136
#> GSM601864 5 0.619 0.4428 0.000 0.020 0.112 0.128 0.636 0.104
#> GSM601755 4 0.362 0.6339 0.004 0.012 0.000 0.820 0.084 0.080
#> GSM601785 2 0.707 0.0314 0.008 0.404 0.016 0.244 0.304 0.024
#> GSM601795 4 0.493 0.5354 0.016 0.056 0.020 0.748 0.024 0.136
#> GSM601800 4 0.410 0.6219 0.004 0.064 0.004 0.804 0.080 0.044
#> GSM601830 3 0.701 0.1652 0.008 0.328 0.452 0.144 0.012 0.056
#> GSM601840 5 0.811 0.2336 0.144 0.064 0.016 0.308 0.388 0.080
#> GSM601845 2 0.553 0.3904 0.000 0.688 0.100 0.148 0.036 0.028
#> GSM601860 5 0.733 0.4354 0.104 0.084 0.020 0.136 0.576 0.080
#> GSM601870 3 0.608 0.4266 0.016 0.096 0.688 0.072 0.068 0.060
#> GSM601750 1 0.542 0.2580 0.540 0.016 0.372 0.000 0.004 0.068
#> GSM601760 1 0.363 0.5142 0.824 0.040 0.020 0.004 0.004 0.108
#> GSM601765 2 0.520 0.3458 0.036 0.672 0.004 0.012 0.236 0.040
#> GSM601770 5 0.666 0.1572 0.080 0.372 0.000 0.064 0.460 0.024
#> GSM601775 2 0.883 0.1152 0.224 0.280 0.000 0.168 0.180 0.148
#> GSM601780 6 0.656 0.3829 0.372 0.068 0.000 0.088 0.012 0.460
#> GSM601790 5 0.435 0.4776 0.000 0.200 0.028 0.000 0.732 0.040
#> GSM601805 4 0.561 0.4934 0.004 0.020 0.000 0.624 0.180 0.172
#> GSM601810 3 0.743 0.1440 0.268 0.024 0.484 0.028 0.056 0.140
#> GSM601815 5 0.354 0.5385 0.000 0.012 0.016 0.064 0.836 0.072
#> GSM601820 1 0.411 0.4562 0.712 0.000 0.236 0.000 0.000 0.052
#> GSM601825 4 0.640 0.3200 0.000 0.040 0.004 0.460 0.360 0.136
#> GSM601835 2 0.609 0.3972 0.008 0.624 0.200 0.016 0.116 0.036
#> GSM601850 6 0.802 0.3561 0.088 0.056 0.040 0.232 0.108 0.476
#> GSM601855 3 0.535 0.4185 0.028 0.124 0.728 0.048 0.012 0.060
#> GSM601865 5 0.542 0.4814 0.008 0.084 0.104 0.008 0.712 0.084
#> GSM601756 4 0.316 0.6425 0.000 0.012 0.004 0.852 0.084 0.048
#> GSM601786 5 0.535 0.5146 0.016 0.024 0.064 0.112 0.732 0.052
#> GSM601796 4 0.659 0.3673 0.060 0.036 0.068 0.596 0.016 0.224
#> GSM601801 4 0.431 0.6205 0.000 0.012 0.012 0.772 0.104 0.100
#> GSM601831 3 0.729 0.3615 0.060 0.200 0.520 0.144 0.000 0.076
#> GSM601841 1 0.854 0.1125 0.380 0.012 0.112 0.240 0.104 0.152
#> GSM601846 2 0.762 0.0350 0.000 0.384 0.240 0.268 0.028 0.080
#> GSM601861 5 0.344 0.5542 0.000 0.040 0.012 0.080 0.844 0.024
#> GSM601871 3 0.811 0.1758 0.076 0.016 0.376 0.116 0.332 0.084
#> GSM601751 5 0.777 0.3767 0.132 0.040 0.028 0.172 0.512 0.116
#> GSM601761 1 0.482 0.1691 0.616 0.028 0.008 0.004 0.008 0.336
#> GSM601766 2 0.489 0.4311 0.084 0.736 0.008 0.004 0.136 0.032
#> GSM601771 5 0.730 0.4709 0.096 0.076 0.028 0.116 0.588 0.096
#> GSM601776 1 0.580 -0.0905 0.536 0.012 0.004 0.076 0.016 0.356
#> GSM601781 6 0.747 0.4026 0.048 0.048 0.048 0.216 0.104 0.536
#> GSM601791 1 0.565 -0.1199 0.496 0.040 0.004 0.032 0.008 0.420
#> GSM601806 4 0.592 0.4552 0.000 0.016 0.004 0.560 0.252 0.168
#> GSM601811 3 0.759 0.1434 0.264 0.028 0.476 0.020 0.108 0.104
#> GSM601816 6 0.646 0.4464 0.076 0.016 0.032 0.292 0.028 0.556
#> GSM601821 5 0.384 0.5498 0.004 0.036 0.012 0.100 0.820 0.028
#> GSM601826 6 0.612 0.5507 0.148 0.076 0.000 0.180 0.000 0.596
#> GSM601836 2 0.661 0.4432 0.088 0.648 0.056 0.028 0.068 0.112
#> GSM601851 6 0.595 0.3967 0.364 0.020 0.008 0.072 0.012 0.524
#> GSM601856 3 0.486 0.3897 0.088 0.080 0.756 0.024 0.000 0.052
#> GSM601866 1 0.555 0.3658 0.604 0.032 0.296 0.000 0.016 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 time(p) gender(p) k
#> CV:NMF 120 0.4482 0.197133 2
#> CV:NMF 102 0.1567 0.182267 3
#> CV:NMF 76 0.2388 0.002061 4
#> CV:NMF 58 0.0477 0.026722 5
#> CV:NMF 26 0.6290 0.000995 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 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.169 0.662 0.807 0.4320 0.510 0.510
#> 3 3 0.244 0.614 0.782 0.2524 0.920 0.854
#> 4 4 0.272 0.472 0.704 0.1494 0.851 0.724
#> 5 5 0.330 0.463 0.679 0.0686 0.888 0.750
#> 6 6 0.340 0.373 0.652 0.0437 0.933 0.826
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
#> GSM601752 2 0.6247 0.7930 0.156 0.844
#> GSM601782 1 0.2603 0.7818 0.956 0.044
#> GSM601792 2 0.9775 0.4701 0.412 0.588
#> GSM601797 2 0.8608 0.7054 0.284 0.716
#> GSM601827 1 0.1843 0.7836 0.972 0.028
#> GSM601837 2 0.1414 0.7250 0.020 0.980
#> GSM601842 2 0.6712 0.7914 0.176 0.824
#> GSM601857 1 0.9000 0.4859 0.684 0.316
#> GSM601867 2 0.9815 0.4709 0.420 0.580
#> GSM601747 1 0.9491 0.3610 0.632 0.368
#> GSM601757 1 0.8386 0.5725 0.732 0.268
#> GSM601762 2 0.5294 0.7896 0.120 0.880
#> GSM601767 2 0.3879 0.7716 0.076 0.924
#> GSM601772 2 0.3584 0.7715 0.068 0.932
#> GSM601777 2 0.8713 0.6918 0.292 0.708
#> GSM601787 2 0.8763 0.6778 0.296 0.704
#> GSM601802 2 0.5629 0.7937 0.132 0.868
#> GSM601807 1 0.5294 0.7269 0.880 0.120
#> GSM601812 1 0.2948 0.7816 0.948 0.052
#> GSM601817 1 0.1184 0.7791 0.984 0.016
#> GSM601822 2 0.9393 0.5942 0.356 0.644
#> GSM601832 2 0.6801 0.7911 0.180 0.820
#> GSM601847 2 0.9209 0.6349 0.336 0.664
#> GSM601852 1 0.2236 0.7850 0.964 0.036
#> GSM601862 1 0.1843 0.7836 0.972 0.028
#> GSM601753 2 0.5946 0.7946 0.144 0.856
#> GSM601783 1 0.1843 0.7840 0.972 0.028
#> GSM601793 2 0.9815 0.4335 0.420 0.580
#> GSM601798 2 0.6623 0.7911 0.172 0.828
#> GSM601828 1 0.1414 0.7811 0.980 0.020
#> GSM601838 2 0.1184 0.7208 0.016 0.984
#> GSM601843 2 0.6712 0.7914 0.176 0.824
#> GSM601858 1 0.9922 0.0365 0.552 0.448
#> GSM601868 1 0.2603 0.7845 0.956 0.044
#> GSM601748 1 0.1414 0.7813 0.980 0.020
#> GSM601758 1 0.1414 0.7813 0.980 0.020
#> GSM601763 2 0.9608 0.5777 0.384 0.616
#> GSM601768 2 0.4022 0.7735 0.080 0.920
#> GSM601773 2 0.3584 0.7715 0.068 0.932
#> GSM601778 2 0.8713 0.6918 0.292 0.708
#> GSM601788 2 0.6438 0.7960 0.164 0.836
#> GSM601803 2 0.5629 0.7937 0.132 0.868
#> GSM601808 1 0.2423 0.7821 0.960 0.040
#> GSM601813 1 0.2948 0.7816 0.948 0.052
#> GSM601818 1 0.1184 0.7791 0.984 0.016
#> GSM601823 1 0.9963 0.0190 0.536 0.464
#> GSM601833 2 0.6801 0.7911 0.180 0.820
#> GSM601848 1 0.9833 0.2463 0.576 0.424
#> GSM601853 1 0.2603 0.7818 0.956 0.044
#> GSM601863 1 0.1843 0.7836 0.972 0.028
#> GSM601754 2 0.6343 0.7965 0.160 0.840
#> GSM601784 2 0.2948 0.7645 0.052 0.948
#> GSM601794 2 0.9661 0.5289 0.392 0.608
#> GSM601799 2 0.8016 0.7633 0.244 0.756
#> GSM601829 1 0.3114 0.7796 0.944 0.056
#> GSM601839 2 0.1184 0.7208 0.016 0.984
#> GSM601844 1 0.9000 0.4862 0.684 0.316
#> GSM601859 2 0.7376 0.7807 0.208 0.792
#> GSM601869 1 0.2603 0.7845 0.956 0.044
#> GSM601749 1 0.1633 0.7830 0.976 0.024
#> GSM601759 1 0.1414 0.7813 0.980 0.020
#> GSM601764 2 0.9491 0.6076 0.368 0.632
#> GSM601769 2 0.0376 0.7369 0.004 0.996
#> GSM601774 2 0.0672 0.7386 0.008 0.992
#> GSM601779 1 0.9323 0.4555 0.652 0.348
#> GSM601789 2 0.6048 0.7949 0.148 0.852
#> GSM601804 2 0.7219 0.7791 0.200 0.800
#> GSM601809 1 0.9427 0.3939 0.640 0.360
#> GSM601814 2 0.0376 0.7369 0.004 0.996
#> GSM601819 1 0.1184 0.7791 0.984 0.016
#> GSM601824 1 0.9963 0.0190 0.536 0.464
#> GSM601834 2 0.6712 0.7919 0.176 0.824
#> GSM601849 1 0.9815 0.2620 0.580 0.420
#> GSM601854 1 0.1184 0.7791 0.984 0.016
#> GSM601864 2 0.8207 0.7200 0.256 0.744
#> GSM601755 2 0.6247 0.7930 0.156 0.844
#> GSM601785 2 0.4161 0.7808 0.084 0.916
#> GSM601795 2 0.9661 0.5299 0.392 0.608
#> GSM601800 2 0.6973 0.7882 0.188 0.812
#> GSM601830 1 0.5294 0.7258 0.880 0.120
#> GSM601840 2 0.9775 0.4972 0.412 0.588
#> GSM601845 2 0.8016 0.7633 0.244 0.756
#> GSM601860 2 0.7815 0.7656 0.232 0.768
#> GSM601870 2 0.9993 0.2541 0.484 0.516
#> GSM601750 1 0.1184 0.7791 0.984 0.016
#> GSM601760 1 0.2603 0.7831 0.956 0.044
#> GSM601765 2 0.7299 0.7824 0.204 0.796
#> GSM601770 2 0.3733 0.7690 0.072 0.928
#> GSM601775 2 0.9129 0.6701 0.328 0.672
#> GSM601780 1 0.9323 0.4555 0.652 0.348
#> GSM601790 2 0.2236 0.7468 0.036 0.964
#> GSM601805 2 0.5629 0.7937 0.132 0.868
#> GSM601810 1 0.8386 0.5827 0.732 0.268
#> GSM601815 2 0.0376 0.7369 0.004 0.996
#> GSM601820 1 0.1184 0.7791 0.984 0.016
#> GSM601825 2 0.5294 0.7941 0.120 0.880
#> GSM601835 2 0.7299 0.7826 0.204 0.796
#> GSM601850 2 0.9427 0.6066 0.360 0.640
#> GSM601855 1 0.4939 0.7344 0.892 0.108
#> GSM601865 2 0.8207 0.7200 0.256 0.744
#> GSM601756 2 0.6247 0.7930 0.156 0.844
#> GSM601786 2 0.2043 0.7434 0.032 0.968
#> GSM601796 2 0.9661 0.5299 0.392 0.608
#> GSM601801 2 0.6801 0.7872 0.180 0.820
#> GSM601831 1 0.1633 0.7830 0.976 0.024
#> GSM601841 1 0.9209 0.4671 0.664 0.336
#> GSM601846 2 0.9977 0.2422 0.472 0.528
#> GSM601861 2 0.0376 0.7369 0.004 0.996
#> GSM601871 2 0.9833 0.4539 0.424 0.576
#> GSM601751 2 0.9635 0.5621 0.388 0.612
#> GSM601761 1 0.4431 0.7610 0.908 0.092
#> GSM601766 2 0.8081 0.7620 0.248 0.752
#> GSM601771 2 0.9460 0.6115 0.364 0.636
#> GSM601776 1 0.9732 0.2619 0.596 0.404
#> GSM601781 2 0.8763 0.6868 0.296 0.704
#> GSM601791 1 0.9044 0.5054 0.680 0.320
#> GSM601806 2 0.5629 0.7937 0.132 0.868
#> GSM601811 1 0.8386 0.5827 0.732 0.268
#> GSM601816 1 0.9795 0.2676 0.584 0.416
#> GSM601821 2 0.0376 0.7369 0.004 0.996
#> GSM601826 1 0.9922 0.1442 0.552 0.448
#> GSM601836 2 0.9909 0.4181 0.444 0.556
#> GSM601851 1 0.9635 0.3577 0.612 0.388
#> GSM601856 1 0.2778 0.7818 0.952 0.048
#> GSM601866 1 0.1633 0.7830 0.976 0.024
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601752 2 0.2590 0.7390 0.004 0.924 0.072
#> GSM601782 1 0.2743 0.6849 0.928 0.020 0.052
#> GSM601792 2 0.7739 0.5930 0.188 0.676 0.136
#> GSM601797 2 0.5467 0.6826 0.032 0.792 0.176
#> GSM601827 1 0.2939 0.6756 0.916 0.012 0.072
#> GSM601837 2 0.4605 0.6936 0.000 0.796 0.204
#> GSM601842 2 0.4602 0.7543 0.108 0.852 0.040
#> GSM601857 1 0.9004 0.2849 0.488 0.376 0.136
#> GSM601867 2 0.8492 0.5097 0.276 0.592 0.132
#> GSM601747 1 0.7736 0.2606 0.548 0.400 0.052
#> GSM601757 1 0.8646 0.3807 0.556 0.320 0.124
#> GSM601762 2 0.3694 0.7577 0.052 0.896 0.052
#> GSM601767 2 0.4892 0.7418 0.048 0.840 0.112
#> GSM601772 2 0.5028 0.7346 0.040 0.828 0.132
#> GSM601777 2 0.5974 0.6893 0.068 0.784 0.148
#> GSM601787 2 0.6313 0.5268 0.016 0.676 0.308
#> GSM601802 2 0.1860 0.7405 0.000 0.948 0.052
#> GSM601807 3 0.6208 0.9625 0.068 0.164 0.768
#> GSM601812 1 0.2152 0.7022 0.948 0.036 0.016
#> GSM601817 1 0.0592 0.6919 0.988 0.000 0.012
#> GSM601822 2 0.7153 0.6417 0.200 0.708 0.092
#> GSM601832 2 0.3966 0.7546 0.100 0.876 0.024
#> GSM601847 2 0.6728 0.6738 0.184 0.736 0.080
#> GSM601852 1 0.2313 0.6998 0.944 0.024 0.032
#> GSM601862 1 0.3499 0.6746 0.900 0.028 0.072
#> GSM601753 2 0.2280 0.7434 0.008 0.940 0.052
#> GSM601783 1 0.1491 0.7000 0.968 0.016 0.016
#> GSM601793 2 0.7885 0.5678 0.212 0.660 0.128
#> GSM601798 2 0.2537 0.7349 0.000 0.920 0.080
#> GSM601828 1 0.2384 0.6824 0.936 0.008 0.056
#> GSM601838 2 0.4750 0.6808 0.000 0.784 0.216
#> GSM601843 2 0.4712 0.7543 0.108 0.848 0.044
#> GSM601858 2 0.8961 0.1787 0.360 0.504 0.136
#> GSM601868 1 0.4602 0.6504 0.852 0.040 0.108
#> GSM601748 1 0.0983 0.6934 0.980 0.004 0.016
#> GSM601758 1 0.0848 0.6961 0.984 0.008 0.008
#> GSM601763 2 0.6935 0.5569 0.312 0.652 0.036
#> GSM601768 2 0.4964 0.7371 0.048 0.836 0.116
#> GSM601773 2 0.5094 0.7330 0.040 0.824 0.136
#> GSM601778 2 0.6001 0.6900 0.072 0.784 0.144
#> GSM601788 2 0.5710 0.7538 0.116 0.804 0.080
#> GSM601803 2 0.1753 0.7400 0.000 0.952 0.048
#> GSM601808 1 0.5524 0.5835 0.796 0.040 0.164
#> GSM601813 1 0.2152 0.7022 0.948 0.036 0.016
#> GSM601818 1 0.0747 0.6918 0.984 0.000 0.016
#> GSM601823 2 0.8059 0.0869 0.444 0.492 0.064
#> GSM601833 2 0.3832 0.7549 0.100 0.880 0.020
#> GSM601848 1 0.8065 0.1242 0.484 0.452 0.064
#> GSM601853 1 0.6154 0.5444 0.752 0.044 0.204
#> GSM601863 1 0.3499 0.6746 0.900 0.028 0.072
#> GSM601754 2 0.3356 0.7524 0.036 0.908 0.056
#> GSM601784 2 0.4342 0.7363 0.024 0.856 0.120
#> GSM601794 2 0.7595 0.6066 0.176 0.688 0.136
#> GSM601799 2 0.4994 0.7479 0.112 0.836 0.052
#> GSM601829 1 0.3692 0.6859 0.896 0.048 0.056
#> GSM601839 2 0.4842 0.6806 0.000 0.776 0.224
#> GSM601844 1 0.7044 0.4155 0.620 0.348 0.032
#> GSM601859 2 0.5852 0.7428 0.152 0.788 0.060
#> GSM601869 1 0.4602 0.6504 0.852 0.040 0.108
#> GSM601749 1 0.1015 0.6981 0.980 0.012 0.008
#> GSM601759 1 0.1182 0.6979 0.976 0.012 0.012
#> GSM601764 2 0.6835 0.6074 0.284 0.676 0.040
#> GSM601769 2 0.4452 0.6936 0.000 0.808 0.192
#> GSM601774 2 0.4629 0.6983 0.004 0.808 0.188
#> GSM601779 1 0.7123 0.4074 0.604 0.364 0.032
#> GSM601789 2 0.5815 0.7483 0.104 0.800 0.096
#> GSM601804 2 0.3973 0.7410 0.032 0.880 0.088
#> GSM601809 1 0.7636 0.3082 0.556 0.396 0.048
#> GSM601814 2 0.4452 0.6936 0.000 0.808 0.192
#> GSM601819 1 0.0592 0.6913 0.988 0.000 0.012
#> GSM601824 2 0.8059 0.0869 0.444 0.492 0.064
#> GSM601834 2 0.4217 0.7568 0.100 0.868 0.032
#> GSM601849 1 0.7993 0.1117 0.484 0.456 0.060
#> GSM601854 1 0.1711 0.6942 0.960 0.008 0.032
#> GSM601864 2 0.5431 0.5816 0.000 0.716 0.284
#> GSM601755 2 0.2590 0.7390 0.004 0.924 0.072
#> GSM601785 2 0.4092 0.7526 0.036 0.876 0.088
#> GSM601795 2 0.7595 0.6087 0.176 0.688 0.136
#> GSM601800 2 0.3670 0.7447 0.020 0.888 0.092
#> GSM601830 3 0.6119 0.9634 0.064 0.164 0.772
#> GSM601840 2 0.7618 0.5262 0.304 0.628 0.068
#> GSM601845 2 0.5407 0.7385 0.156 0.804 0.040
#> GSM601860 2 0.6208 0.7340 0.164 0.768 0.068
#> GSM601870 2 0.7940 0.0733 0.060 0.524 0.416
#> GSM601750 1 0.0747 0.6910 0.984 0.000 0.016
#> GSM601760 1 0.1999 0.7009 0.952 0.036 0.012
#> GSM601765 2 0.4540 0.7483 0.124 0.848 0.028
#> GSM601770 2 0.5174 0.7330 0.048 0.824 0.128
#> GSM601775 2 0.6781 0.6520 0.244 0.704 0.052
#> GSM601780 1 0.7123 0.4074 0.604 0.364 0.032
#> GSM601790 2 0.4861 0.7097 0.012 0.808 0.180
#> GSM601805 2 0.1753 0.7400 0.000 0.952 0.048
#> GSM601810 1 0.7181 0.5057 0.648 0.304 0.048
#> GSM601815 2 0.4399 0.6938 0.000 0.812 0.188
#> GSM601820 1 0.0592 0.6913 0.988 0.000 0.012
#> GSM601825 2 0.2947 0.7519 0.020 0.920 0.060
#> GSM601835 2 0.4413 0.7480 0.124 0.852 0.024
#> GSM601850 2 0.6982 0.6447 0.220 0.708 0.072
#> GSM601855 3 0.6332 0.9566 0.088 0.144 0.768
#> GSM601865 2 0.5431 0.5816 0.000 0.716 0.284
#> GSM601756 2 0.2590 0.7390 0.004 0.924 0.072
#> GSM601786 2 0.5406 0.6961 0.020 0.780 0.200
#> GSM601796 2 0.7595 0.6087 0.176 0.688 0.136
#> GSM601801 2 0.2959 0.7294 0.000 0.900 0.100
#> GSM601831 1 0.2486 0.6848 0.932 0.008 0.060
#> GSM601841 1 0.7982 0.3440 0.556 0.376 0.068
#> GSM601846 2 0.6661 0.2264 0.012 0.588 0.400
#> GSM601861 2 0.4452 0.6936 0.000 0.808 0.192
#> GSM601871 2 0.7295 0.3031 0.036 0.584 0.380
#> GSM601751 2 0.7528 0.5788 0.280 0.648 0.072
#> GSM601761 1 0.3528 0.6779 0.892 0.092 0.016
#> GSM601766 2 0.5298 0.7355 0.164 0.804 0.032
#> GSM601771 2 0.7032 0.6104 0.272 0.676 0.052
#> GSM601776 1 0.7979 0.1206 0.500 0.440 0.060
#> GSM601781 2 0.6087 0.6878 0.076 0.780 0.144
#> GSM601791 1 0.6773 0.4573 0.636 0.340 0.024
#> GSM601806 2 0.1860 0.7398 0.000 0.948 0.052
#> GSM601811 1 0.7181 0.5057 0.648 0.304 0.048
#> GSM601816 1 0.8203 0.1314 0.484 0.444 0.072
#> GSM601821 2 0.4452 0.6936 0.000 0.808 0.192
#> GSM601826 2 0.8264 0.0120 0.436 0.488 0.076
#> GSM601836 2 0.7368 0.4475 0.352 0.604 0.044
#> GSM601851 1 0.7619 0.2451 0.532 0.424 0.044
#> GSM601856 1 0.6208 0.5537 0.752 0.048 0.200
#> GSM601866 1 0.2414 0.6878 0.940 0.020 0.040
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601752 2 0.3455 0.44817 0.004 0.852 0.012 0.132
#> GSM601782 1 0.4444 0.69611 0.832 0.020 0.080 0.068
#> GSM601792 2 0.6758 0.50624 0.160 0.688 0.056 0.096
#> GSM601797 2 0.5080 0.50159 0.016 0.788 0.076 0.120
#> GSM601827 1 0.2744 0.72840 0.908 0.008 0.064 0.020
#> GSM601837 4 0.5560 0.91352 0.000 0.392 0.024 0.584
#> GSM601842 2 0.4919 0.45081 0.076 0.772 0.000 0.152
#> GSM601857 1 0.8161 0.15733 0.436 0.404 0.088 0.072
#> GSM601867 2 0.8430 0.38509 0.220 0.544 0.116 0.120
#> GSM601747 1 0.7047 0.11808 0.500 0.416 0.036 0.048
#> GSM601757 1 0.7869 0.31841 0.504 0.352 0.080 0.064
#> GSM601762 2 0.4888 0.29679 0.036 0.740 0.000 0.224
#> GSM601767 2 0.5582 -0.13561 0.032 0.620 0.000 0.348
#> GSM601772 2 0.5523 -0.24355 0.024 0.596 0.000 0.380
#> GSM601777 2 0.5271 0.51520 0.036 0.784 0.056 0.124
#> GSM601787 2 0.7312 0.02403 0.016 0.580 0.148 0.256
#> GSM601802 2 0.3105 0.43096 0.000 0.868 0.012 0.120
#> GSM601807 3 0.6032 0.86902 0.016 0.092 0.712 0.180
#> GSM601812 1 0.2513 0.75230 0.924 0.036 0.024 0.016
#> GSM601817 1 0.0524 0.74823 0.988 0.000 0.008 0.004
#> GSM601822 2 0.6097 0.53276 0.152 0.724 0.028 0.096
#> GSM601832 2 0.4274 0.47858 0.072 0.820 0.000 0.108
#> GSM601847 2 0.5603 0.54341 0.136 0.752 0.016 0.096
#> GSM601852 1 0.2089 0.75382 0.940 0.028 0.012 0.020
#> GSM601862 1 0.3672 0.72451 0.872 0.028 0.072 0.028
#> GSM601753 2 0.3375 0.44158 0.008 0.864 0.012 0.116
#> GSM601783 1 0.1362 0.75464 0.964 0.020 0.004 0.012
#> GSM601793 2 0.7009 0.49367 0.184 0.664 0.060 0.092
#> GSM601798 2 0.3694 0.45868 0.000 0.844 0.032 0.124
#> GSM601828 1 0.2307 0.73451 0.928 0.008 0.048 0.016
#> GSM601838 4 0.5371 0.93332 0.000 0.364 0.020 0.616
#> GSM601843 2 0.4966 0.44564 0.076 0.768 0.000 0.156
#> GSM601858 2 0.8260 0.26580 0.308 0.508 0.084 0.100
#> GSM601868 1 0.4741 0.69111 0.816 0.032 0.104 0.048
#> GSM601748 1 0.0779 0.74911 0.980 0.004 0.016 0.000
#> GSM601758 1 0.1007 0.75173 0.976 0.008 0.008 0.008
#> GSM601763 2 0.6418 0.50046 0.264 0.640 0.008 0.088
#> GSM601768 2 0.5615 -0.15832 0.032 0.612 0.000 0.356
#> GSM601773 2 0.5536 -0.25865 0.024 0.592 0.000 0.384
#> GSM601778 2 0.5358 0.51733 0.040 0.780 0.056 0.124
#> GSM601788 2 0.6144 0.26315 0.084 0.660 0.004 0.252
#> GSM601803 2 0.3161 0.42653 0.000 0.864 0.012 0.124
#> GSM601808 1 0.5135 0.57089 0.728 0.004 0.232 0.036
#> GSM601813 1 0.2405 0.75237 0.928 0.036 0.020 0.016
#> GSM601818 1 0.0657 0.74824 0.984 0.000 0.012 0.004
#> GSM601823 2 0.7057 0.17231 0.400 0.508 0.020 0.072
#> GSM601833 2 0.4444 0.47056 0.072 0.808 0.000 0.120
#> GSM601848 2 0.7375 0.00589 0.444 0.448 0.028 0.080
#> GSM601853 1 0.5034 0.53062 0.700 0.008 0.280 0.012
#> GSM601863 1 0.3672 0.72451 0.872 0.028 0.072 0.028
#> GSM601754 2 0.3754 0.45966 0.028 0.852 0.008 0.112
#> GSM601784 2 0.5429 -0.29043 0.012 0.592 0.004 0.392
#> GSM601794 2 0.6538 0.51163 0.156 0.704 0.056 0.084
#> GSM601799 2 0.4870 0.53166 0.092 0.796 0.008 0.104
#> GSM601829 1 0.3467 0.73612 0.884 0.032 0.056 0.028
#> GSM601839 4 0.5467 0.92940 0.000 0.364 0.024 0.612
#> GSM601844 1 0.6667 0.33036 0.576 0.352 0.032 0.040
#> GSM601859 2 0.6392 0.39449 0.128 0.660 0.004 0.208
#> GSM601869 1 0.4741 0.69111 0.816 0.032 0.104 0.048
#> GSM601749 1 0.0992 0.75227 0.976 0.012 0.004 0.008
#> GSM601759 1 0.1271 0.75225 0.968 0.012 0.008 0.012
#> GSM601764 2 0.6286 0.50543 0.240 0.656 0.004 0.100
#> GSM601769 4 0.4730 0.95068 0.000 0.364 0.000 0.636
#> GSM601774 4 0.4817 0.93152 0.000 0.388 0.000 0.612
#> GSM601779 1 0.6764 0.28656 0.564 0.356 0.020 0.060
#> GSM601789 2 0.6215 0.00328 0.072 0.600 0.000 0.328
#> GSM601804 2 0.4169 0.48968 0.020 0.836 0.028 0.116
#> GSM601809 1 0.7758 0.19251 0.492 0.376 0.068 0.064
#> GSM601814 4 0.4730 0.95068 0.000 0.364 0.000 0.636
#> GSM601819 1 0.0469 0.74773 0.988 0.000 0.012 0.000
#> GSM601824 2 0.7057 0.17231 0.400 0.508 0.020 0.072
#> GSM601834 2 0.4656 0.46409 0.072 0.792 0.000 0.136
#> GSM601849 2 0.7272 0.02957 0.444 0.456 0.028 0.072
#> GSM601854 1 0.1771 0.74486 0.948 0.004 0.036 0.012
#> GSM601864 2 0.6706 -0.07422 0.000 0.588 0.124 0.288
#> GSM601755 2 0.3455 0.44817 0.004 0.852 0.012 0.132
#> GSM601785 2 0.5409 -0.02449 0.020 0.644 0.004 0.332
#> GSM601795 2 0.6494 0.51068 0.152 0.708 0.056 0.084
#> GSM601800 2 0.3869 0.47269 0.008 0.844 0.028 0.120
#> GSM601830 3 0.3752 0.90810 0.016 0.084 0.864 0.036
#> GSM601840 2 0.6877 0.49515 0.252 0.636 0.040 0.072
#> GSM601845 2 0.5134 0.50869 0.120 0.772 0.004 0.104
#> GSM601860 2 0.6455 0.42873 0.140 0.668 0.008 0.184
#> GSM601870 2 0.8253 -0.00504 0.028 0.456 0.316 0.200
#> GSM601750 1 0.0779 0.74777 0.980 0.000 0.016 0.004
#> GSM601760 1 0.2049 0.74998 0.940 0.036 0.012 0.012
#> GSM601765 2 0.4655 0.49364 0.088 0.796 0.000 0.116
#> GSM601770 2 0.5659 -0.20993 0.032 0.600 0.000 0.368
#> GSM601775 2 0.6348 0.51139 0.208 0.680 0.016 0.096
#> GSM601780 1 0.6764 0.28656 0.564 0.356 0.020 0.060
#> GSM601790 4 0.5060 0.87717 0.000 0.412 0.004 0.584
#> GSM601805 2 0.3280 0.42770 0.000 0.860 0.016 0.124
#> GSM601810 1 0.7266 0.41951 0.576 0.308 0.072 0.044
#> GSM601815 4 0.4746 0.94939 0.000 0.368 0.000 0.632
#> GSM601820 1 0.0469 0.74773 0.988 0.000 0.012 0.000
#> GSM601825 2 0.4392 0.29647 0.012 0.768 0.004 0.216
#> GSM601835 2 0.4424 0.49049 0.088 0.812 0.000 0.100
#> GSM601850 2 0.5630 0.54263 0.164 0.740 0.012 0.084
#> GSM601855 3 0.2307 0.90748 0.016 0.048 0.928 0.008
#> GSM601865 2 0.6706 -0.07422 0.000 0.588 0.124 0.288
#> GSM601756 2 0.3455 0.44817 0.004 0.852 0.012 0.132
#> GSM601786 4 0.5256 0.89730 0.012 0.392 0.000 0.596
#> GSM601796 2 0.6494 0.51068 0.152 0.708 0.056 0.084
#> GSM601801 2 0.4100 0.44842 0.000 0.816 0.036 0.148
#> GSM601831 1 0.2392 0.73680 0.924 0.008 0.052 0.016
#> GSM601841 1 0.7011 0.23261 0.524 0.392 0.040 0.044
#> GSM601846 2 0.7148 0.10315 0.000 0.496 0.364 0.140
#> GSM601861 4 0.4730 0.95068 0.000 0.364 0.000 0.636
#> GSM601871 2 0.7888 0.13687 0.024 0.532 0.220 0.224
#> GSM601751 2 0.6780 0.49839 0.240 0.644 0.028 0.088
#> GSM601761 1 0.3319 0.72093 0.876 0.096 0.012 0.016
#> GSM601766 2 0.5174 0.51100 0.116 0.760 0.000 0.124
#> GSM601771 2 0.6502 0.50464 0.236 0.660 0.020 0.084
#> GSM601776 2 0.7163 -0.01202 0.452 0.456 0.028 0.064
#> GSM601781 2 0.5254 0.51735 0.040 0.784 0.048 0.128
#> GSM601791 1 0.6253 0.37187 0.608 0.336 0.020 0.036
#> GSM601806 2 0.3217 0.42099 0.000 0.860 0.012 0.128
#> GSM601811 1 0.7266 0.41951 0.576 0.308 0.072 0.044
#> GSM601816 2 0.7190 0.00157 0.448 0.456 0.024 0.072
#> GSM601821 4 0.4730 0.95068 0.000 0.364 0.000 0.636
#> GSM601826 2 0.7511 0.13334 0.392 0.488 0.032 0.088
#> GSM601836 2 0.6445 0.47115 0.300 0.620 0.012 0.068
#> GSM601851 1 0.7060 0.08877 0.492 0.420 0.024 0.064
#> GSM601856 1 0.5034 0.53627 0.700 0.008 0.280 0.012
#> GSM601866 1 0.2418 0.74493 0.928 0.024 0.032 0.016
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601752 4 0.4727 0.4336 0.004 0.260 0.008 0.700 NA
#> GSM601782 1 0.5830 0.4090 0.572 0.020 0.012 0.036 NA
#> GSM601792 4 0.4643 0.5332 0.132 0.004 0.020 0.776 NA
#> GSM601797 4 0.3365 0.5149 0.004 0.024 0.048 0.868 NA
#> GSM601827 1 0.3176 0.7211 0.868 0.000 0.048 0.012 NA
#> GSM601837 2 0.2721 0.6542 0.000 0.896 0.016 0.052 NA
#> GSM601842 4 0.5831 0.3678 0.068 0.324 0.000 0.588 NA
#> GSM601857 1 0.7261 0.1095 0.420 0.012 0.056 0.416 NA
#> GSM601867 4 0.8834 0.3528 0.176 0.180 0.108 0.452 NA
#> GSM601747 1 0.7179 0.0297 0.452 0.056 0.016 0.396 NA
#> GSM601757 1 0.7018 0.2724 0.492 0.012 0.048 0.360 NA
#> GSM601762 4 0.5317 0.0585 0.028 0.448 0.000 0.512 NA
#> GSM601767 2 0.5064 0.3762 0.028 0.596 0.000 0.368 NA
#> GSM601772 2 0.4669 0.4958 0.020 0.664 0.000 0.308 NA
#> GSM601777 4 0.2302 0.5248 0.008 0.000 0.008 0.904 NA
#> GSM601787 4 0.7763 -0.1633 0.008 0.368 0.068 0.396 NA
#> GSM601802 4 0.4616 0.4095 0.000 0.288 0.004 0.680 NA
#> GSM601807 3 0.5649 0.7631 0.000 0.004 0.480 0.064 NA
#> GSM601812 1 0.2701 0.7434 0.896 0.000 0.012 0.044 NA
#> GSM601817 1 0.1357 0.7409 0.948 0.000 0.000 0.004 NA
#> GSM601822 4 0.4251 0.5544 0.132 0.024 0.000 0.796 NA
#> GSM601832 4 0.5667 0.4047 0.060 0.292 0.000 0.624 NA
#> GSM601847 4 0.4286 0.5589 0.104 0.032 0.000 0.804 NA
#> GSM601852 1 0.1588 0.7468 0.948 0.000 0.008 0.028 NA
#> GSM601862 1 0.3624 0.7128 0.852 0.004 0.048 0.024 NA
#> GSM601753 4 0.4803 0.4203 0.004 0.280 0.004 0.680 NA
#> GSM601783 1 0.1153 0.7467 0.964 0.000 0.004 0.024 NA
#> GSM601793 4 0.4858 0.5240 0.156 0.004 0.016 0.752 NA
#> GSM601798 4 0.4838 0.4471 0.000 0.232 0.020 0.712 NA
#> GSM601828 1 0.2775 0.7257 0.888 0.000 0.036 0.008 NA
#> GSM601838 2 0.2067 0.6438 0.000 0.928 0.012 0.028 NA
#> GSM601843 4 0.5845 0.3603 0.068 0.328 0.000 0.584 NA
#> GSM601858 4 0.8284 0.2625 0.292 0.108 0.056 0.456 NA
#> GSM601868 1 0.4744 0.6783 0.784 0.004 0.084 0.040 NA
#> GSM601748 1 0.1357 0.7416 0.948 0.000 0.000 0.004 NA
#> GSM601758 1 0.1281 0.7435 0.956 0.000 0.000 0.012 NA
#> GSM601763 4 0.6433 0.5061 0.248 0.140 0.000 0.584 NA
#> GSM601768 2 0.5039 0.3865 0.028 0.604 0.000 0.360 NA
#> GSM601773 2 0.4650 0.5013 0.020 0.668 0.000 0.304 NA
#> GSM601778 4 0.2354 0.5275 0.012 0.000 0.008 0.904 NA
#> GSM601788 2 0.6450 -0.1373 0.076 0.456 0.004 0.436 NA
#> GSM601803 4 0.4637 0.4045 0.000 0.292 0.004 0.676 NA
#> GSM601808 1 0.5452 0.5590 0.692 0.000 0.188 0.020 NA
#> GSM601813 1 0.2551 0.7436 0.904 0.000 0.012 0.044 NA
#> GSM601818 1 0.1430 0.7407 0.944 0.000 0.000 0.004 NA
#> GSM601823 4 0.5813 0.2318 0.372 0.020 0.000 0.552 NA
#> GSM601833 4 0.5721 0.3913 0.060 0.304 0.000 0.612 NA
#> GSM601848 4 0.5414 0.0760 0.412 0.000 0.000 0.528 NA
#> GSM601853 1 0.5264 0.5210 0.660 0.000 0.256 0.004 NA
#> GSM601863 1 0.3624 0.7128 0.852 0.004 0.048 0.024 NA
#> GSM601754 4 0.5139 0.4355 0.024 0.280 0.004 0.668 NA
#> GSM601784 2 0.4624 0.4936 0.012 0.676 0.000 0.296 NA
#> GSM601794 4 0.4498 0.5354 0.128 0.004 0.024 0.788 NA
#> GSM601799 4 0.5809 0.5101 0.088 0.224 0.004 0.660 NA
#> GSM601829 1 0.3463 0.7277 0.860 0.000 0.044 0.040 NA
#> GSM601839 2 0.2165 0.6366 0.000 0.924 0.016 0.024 NA
#> GSM601844 1 0.6714 0.2913 0.540 0.052 0.016 0.336 NA
#> GSM601859 4 0.6563 0.3063 0.116 0.368 0.000 0.492 NA
#> GSM601869 1 0.4744 0.6783 0.784 0.004 0.084 0.040 NA
#> GSM601749 1 0.0693 0.7428 0.980 0.000 0.000 0.012 NA
#> GSM601759 1 0.1300 0.7427 0.956 0.000 0.000 0.016 NA
#> GSM601764 4 0.6531 0.4918 0.228 0.156 0.000 0.584 NA
#> GSM601769 2 0.0898 0.6513 0.000 0.972 0.000 0.020 NA
#> GSM601774 2 0.1197 0.6702 0.000 0.952 0.000 0.048 NA
#> GSM601779 1 0.5250 0.2143 0.536 0.000 0.000 0.416 NA
#> GSM601789 2 0.5934 0.1922 0.068 0.556 0.000 0.356 NA
#> GSM601804 4 0.4770 0.4795 0.020 0.212 0.004 0.732 NA
#> GSM601809 1 0.8075 0.1117 0.436 0.080 0.040 0.336 NA
#> GSM601814 2 0.0898 0.6513 0.000 0.972 0.000 0.020 NA
#> GSM601819 1 0.1484 0.7389 0.944 0.000 0.000 0.008 NA
#> GSM601824 4 0.5813 0.2318 0.372 0.020 0.000 0.552 NA
#> GSM601834 4 0.5802 0.3762 0.060 0.324 0.000 0.592 NA
#> GSM601849 4 0.5560 0.0989 0.412 0.004 0.000 0.524 NA
#> GSM601854 1 0.1907 0.7348 0.928 0.000 0.028 0.000 NA
#> GSM601864 2 0.7199 0.2663 0.000 0.444 0.044 0.352 NA
#> GSM601755 4 0.4727 0.4336 0.004 0.260 0.008 0.700 NA
#> GSM601785 2 0.5181 0.3650 0.024 0.592 0.000 0.368 NA
#> GSM601795 4 0.4452 0.5345 0.124 0.004 0.024 0.792 NA
#> GSM601800 4 0.4681 0.4528 0.008 0.244 0.012 0.716 NA
#> GSM601830 3 0.2390 0.8574 0.012 0.000 0.912 0.044 NA
#> GSM601840 4 0.7230 0.5029 0.220 0.124 0.028 0.576 NA
#> GSM601845 4 0.5910 0.4643 0.108 0.240 0.000 0.632 NA
#> GSM601860 4 0.6782 0.3479 0.124 0.336 0.004 0.508 NA
#> GSM601870 4 0.8937 -0.0338 0.024 0.196 0.216 0.348 NA
#> GSM601750 1 0.1764 0.7378 0.928 0.000 0.000 0.008 NA
#> GSM601760 1 0.1818 0.7411 0.932 0.000 0.000 0.044 NA
#> GSM601765 4 0.5715 0.4406 0.080 0.264 0.000 0.636 NA
#> GSM601770 2 0.4999 0.4143 0.028 0.616 0.000 0.348 NA
#> GSM601775 4 0.6776 0.5016 0.192 0.200 0.004 0.572 NA
#> GSM601780 1 0.5250 0.2143 0.536 0.000 0.000 0.416 NA
#> GSM601790 2 0.2608 0.6724 0.000 0.888 0.004 0.088 NA
#> GSM601805 4 0.4715 0.4061 0.000 0.292 0.004 0.672 NA
#> GSM601810 1 0.6950 0.3435 0.516 0.008 0.044 0.328 NA
#> GSM601815 2 0.0992 0.6539 0.000 0.968 0.000 0.024 NA
#> GSM601820 1 0.1484 0.7389 0.944 0.000 0.000 0.008 NA
#> GSM601825 4 0.5122 0.1511 0.012 0.436 0.004 0.536 NA
#> GSM601835 4 0.5768 0.4367 0.076 0.268 0.000 0.632 NA
#> GSM601850 4 0.4981 0.5570 0.132 0.056 0.000 0.756 NA
#> GSM601855 3 0.0981 0.8627 0.012 0.000 0.972 0.008 NA
#> GSM601865 2 0.7199 0.2663 0.000 0.444 0.044 0.352 NA
#> GSM601756 4 0.4727 0.4336 0.004 0.260 0.008 0.700 NA
#> GSM601786 2 0.2502 0.6650 0.012 0.904 0.000 0.060 NA
#> GSM601796 4 0.4452 0.5345 0.124 0.004 0.024 0.792 NA
#> GSM601801 4 0.4970 0.4378 0.000 0.228 0.024 0.708 NA
#> GSM601831 1 0.2362 0.7305 0.912 0.000 0.040 0.008 NA
#> GSM601841 1 0.6313 0.1427 0.488 0.004 0.032 0.416 NA
#> GSM601846 4 0.7167 -0.0708 0.000 0.048 0.332 0.468 NA
#> GSM601861 2 0.0898 0.6513 0.000 0.972 0.000 0.020 NA
#> GSM601871 4 0.8433 0.0457 0.020 0.216 0.124 0.432 NA
#> GSM601751 4 0.7193 0.4994 0.204 0.152 0.016 0.572 NA
#> GSM601761 1 0.2864 0.7118 0.864 0.000 0.000 0.112 NA
#> GSM601766 4 0.5988 0.4669 0.112 0.232 0.000 0.632 NA
#> GSM601771 4 0.7142 0.4967 0.200 0.172 0.016 0.568 NA
#> GSM601776 4 0.5860 0.0720 0.432 0.016 0.004 0.500 NA
#> GSM601781 4 0.2395 0.5276 0.016 0.000 0.008 0.904 NA
#> GSM601791 1 0.5154 0.3052 0.580 0.000 0.000 0.372 NA
#> GSM601806 4 0.4658 0.3982 0.000 0.296 0.004 0.672 NA
#> GSM601811 1 0.6950 0.3435 0.516 0.008 0.044 0.328 NA
#> GSM601816 4 0.5560 0.0693 0.412 0.000 0.004 0.524 NA
#> GSM601821 2 0.0898 0.6513 0.000 0.972 0.000 0.020 NA
#> GSM601826 4 0.5204 0.1859 0.368 0.000 0.000 0.580 NA
#> GSM601836 4 0.6202 0.4953 0.272 0.080 0.000 0.604 NA
#> GSM601851 4 0.5237 -0.0380 0.468 0.000 0.000 0.488 NA
#> GSM601856 1 0.5240 0.5260 0.664 0.000 0.252 0.004 NA
#> GSM601866 1 0.2255 0.7378 0.924 0.004 0.020 0.024 NA
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601752 2 0.515 0.43096 0.004 0.660 0.044 0.004 0.252 0.036
#> GSM601782 6 0.554 0.00000 0.308 0.064 0.016 0.000 0.020 0.592
#> GSM601792 2 0.462 0.44324 0.120 0.752 0.092 0.008 0.000 0.028
#> GSM601797 2 0.407 0.43592 0.000 0.792 0.128 0.016 0.020 0.044
#> GSM601827 1 0.370 0.55946 0.828 0.004 0.052 0.060 0.000 0.056
#> GSM601837 5 0.288 0.55135 0.000 0.036 0.072 0.004 0.872 0.016
#> GSM601842 2 0.542 0.33812 0.020 0.596 0.016 0.000 0.316 0.052
#> GSM601857 1 0.731 -0.07213 0.380 0.352 0.200 0.016 0.016 0.036
#> GSM601867 2 0.866 0.10182 0.096 0.436 0.164 0.064 0.164 0.076
#> GSM601747 2 0.736 0.09440 0.368 0.412 0.064 0.004 0.060 0.092
#> GSM601757 1 0.699 -0.01530 0.448 0.308 0.188 0.008 0.016 0.032
#> GSM601762 2 0.502 0.04139 0.008 0.500 0.016 0.000 0.452 0.024
#> GSM601767 5 0.462 0.36306 0.004 0.364 0.012 0.000 0.600 0.020
#> GSM601772 5 0.420 0.48239 0.004 0.296 0.012 0.000 0.676 0.012
#> GSM601777 2 0.279 0.42389 0.000 0.840 0.144 0.004 0.000 0.012
#> GSM601787 5 0.670 -0.25634 0.008 0.316 0.328 0.016 0.332 0.000
#> GSM601802 2 0.483 0.41028 0.000 0.652 0.032 0.000 0.280 0.036
#> GSM601807 3 0.566 -0.56164 0.000 0.020 0.592 0.268 0.004 0.116
#> GSM601812 1 0.312 0.58544 0.868 0.052 0.036 0.012 0.000 0.032
#> GSM601817 1 0.177 0.59691 0.924 0.000 0.024 0.000 0.000 0.052
#> GSM601822 2 0.420 0.49663 0.080 0.800 0.068 0.000 0.020 0.032
#> GSM601832 2 0.500 0.37523 0.016 0.636 0.012 0.000 0.296 0.040
#> GSM601847 2 0.393 0.50967 0.056 0.824 0.052 0.000 0.028 0.040
#> GSM601852 1 0.217 0.60819 0.920 0.024 0.016 0.016 0.000 0.024
#> GSM601862 1 0.384 0.53179 0.808 0.020 0.124 0.020 0.000 0.028
#> GSM601753 2 0.506 0.42067 0.004 0.648 0.040 0.000 0.272 0.036
#> GSM601783 1 0.163 0.60804 0.940 0.024 0.016 0.000 0.000 0.020
#> GSM601793 2 0.482 0.42539 0.144 0.728 0.096 0.008 0.000 0.024
#> GSM601798 2 0.529 0.43902 0.000 0.664 0.064 0.012 0.228 0.032
#> GSM601828 1 0.331 0.56961 0.848 0.000 0.048 0.048 0.000 0.056
#> GSM601838 5 0.221 0.54994 0.000 0.016 0.048 0.004 0.912 0.020
#> GSM601843 2 0.543 0.33045 0.020 0.592 0.016 0.000 0.320 0.052
#> GSM601858 2 0.807 0.00205 0.260 0.388 0.200 0.016 0.108 0.028
#> GSM601868 1 0.500 0.43982 0.732 0.032 0.148 0.044 0.000 0.044
#> GSM601748 1 0.193 0.59489 0.912 0.000 0.020 0.000 0.000 0.068
#> GSM601758 1 0.162 0.60159 0.940 0.020 0.012 0.000 0.000 0.028
#> GSM601763 2 0.597 0.48577 0.176 0.632 0.020 0.000 0.132 0.040
#> GSM601768 5 0.460 0.37175 0.004 0.356 0.012 0.000 0.608 0.020
#> GSM601773 5 0.419 0.48792 0.004 0.292 0.012 0.000 0.680 0.012
#> GSM601778 2 0.271 0.43260 0.000 0.848 0.136 0.004 0.000 0.012
#> GSM601788 5 0.621 -0.06153 0.032 0.432 0.072 0.000 0.440 0.024
#> GSM601803 2 0.485 0.40716 0.000 0.648 0.032 0.000 0.284 0.036
#> GSM601808 1 0.567 0.18318 0.624 0.004 0.224 0.112 0.000 0.036
#> GSM601813 1 0.297 0.58695 0.876 0.052 0.036 0.012 0.000 0.024
#> GSM601818 1 0.189 0.59336 0.916 0.000 0.024 0.000 0.000 0.060
#> GSM601823 2 0.551 0.34108 0.308 0.600 0.036 0.000 0.020 0.036
#> GSM601833 2 0.505 0.36090 0.016 0.624 0.012 0.000 0.308 0.040
#> GSM601848 2 0.522 0.17695 0.376 0.552 0.040 0.000 0.000 0.032
#> GSM601853 1 0.583 0.20185 0.612 0.004 0.172 0.180 0.000 0.032
#> GSM601863 1 0.384 0.53179 0.808 0.020 0.124 0.020 0.000 0.028
#> GSM601754 2 0.521 0.43331 0.016 0.644 0.036 0.000 0.272 0.032
#> GSM601784 5 0.404 0.47038 0.000 0.292 0.008 0.000 0.684 0.016
#> GSM601794 2 0.462 0.44325 0.112 0.752 0.100 0.008 0.000 0.028
#> GSM601799 2 0.542 0.49509 0.048 0.664 0.024 0.000 0.224 0.040
#> GSM601829 1 0.394 0.56219 0.824 0.032 0.044 0.056 0.000 0.044
#> GSM601839 5 0.231 0.53984 0.000 0.012 0.060 0.004 0.904 0.020
#> GSM601844 1 0.718 0.01407 0.468 0.336 0.044 0.016 0.052 0.084
#> GSM601859 2 0.587 0.29784 0.068 0.524 0.012 0.000 0.364 0.032
#> GSM601869 1 0.500 0.43982 0.732 0.032 0.148 0.044 0.000 0.044
#> GSM601749 1 0.106 0.60613 0.964 0.016 0.004 0.000 0.000 0.016
#> GSM601759 1 0.162 0.60105 0.940 0.024 0.012 0.000 0.000 0.024
#> GSM601764 2 0.631 0.47518 0.172 0.604 0.020 0.000 0.148 0.056
#> GSM601769 5 0.108 0.56423 0.000 0.004 0.004 0.000 0.960 0.032
#> GSM601774 5 0.156 0.57843 0.000 0.032 0.004 0.000 0.940 0.024
#> GSM601779 1 0.506 0.10384 0.504 0.440 0.032 0.000 0.000 0.024
#> GSM601789 5 0.598 0.23431 0.028 0.352 0.060 0.000 0.532 0.028
#> GSM601804 2 0.496 0.47583 0.020 0.704 0.040 0.000 0.204 0.032
#> GSM601809 2 0.835 -0.06833 0.316 0.344 0.156 0.012 0.080 0.092
#> GSM601814 5 0.108 0.56423 0.000 0.004 0.004 0.000 0.960 0.032
#> GSM601819 1 0.219 0.58744 0.904 0.004 0.032 0.000 0.000 0.060
#> GSM601824 2 0.551 0.34108 0.308 0.600 0.036 0.000 0.020 0.036
#> GSM601834 2 0.512 0.34533 0.016 0.604 0.012 0.000 0.328 0.040
#> GSM601849 2 0.527 0.19164 0.376 0.548 0.044 0.000 0.000 0.032
#> GSM601854 1 0.257 0.59005 0.892 0.000 0.032 0.036 0.000 0.040
#> GSM601864 5 0.637 -0.07597 0.000 0.272 0.300 0.004 0.416 0.008
#> GSM601755 2 0.515 0.43096 0.004 0.660 0.044 0.004 0.252 0.036
#> GSM601785 5 0.466 0.34491 0.008 0.368 0.012 0.000 0.596 0.016
#> GSM601795 2 0.458 0.44338 0.108 0.756 0.100 0.008 0.000 0.028
#> GSM601800 2 0.494 0.44563 0.000 0.676 0.044 0.008 0.244 0.028
#> GSM601830 4 0.113 0.88854 0.004 0.012 0.008 0.964 0.000 0.012
#> GSM601840 2 0.681 0.45865 0.148 0.600 0.064 0.008 0.128 0.052
#> GSM601845 2 0.568 0.43708 0.052 0.640 0.020 0.000 0.232 0.056
#> GSM601860 2 0.599 0.33614 0.076 0.540 0.020 0.000 0.336 0.028
#> GSM601870 3 0.752 0.44329 0.020 0.272 0.424 0.120 0.164 0.000
#> GSM601750 1 0.259 0.57265 0.872 0.000 0.044 0.000 0.000 0.084
#> GSM601760 1 0.210 0.58366 0.912 0.056 0.012 0.000 0.000 0.020
#> GSM601765 2 0.530 0.41239 0.028 0.648 0.016 0.000 0.256 0.052
#> GSM601770 5 0.456 0.39913 0.004 0.344 0.012 0.000 0.620 0.020
#> GSM601775 2 0.627 0.49365 0.120 0.612 0.024 0.004 0.196 0.044
#> GSM601780 1 0.506 0.10384 0.504 0.440 0.032 0.000 0.000 0.024
#> GSM601790 5 0.299 0.56395 0.000 0.068 0.044 0.000 0.864 0.024
#> GSM601805 2 0.491 0.40878 0.000 0.644 0.036 0.000 0.284 0.036
#> GSM601810 1 0.746 -0.05590 0.400 0.344 0.132 0.012 0.012 0.100
#> GSM601815 5 0.105 0.56698 0.000 0.008 0.000 0.000 0.960 0.032
#> GSM601820 1 0.211 0.58839 0.908 0.004 0.028 0.000 0.000 0.060
#> GSM601825 2 0.505 0.16098 0.000 0.516 0.024 0.000 0.428 0.032
#> GSM601835 2 0.519 0.40995 0.024 0.652 0.016 0.000 0.260 0.048
#> GSM601850 2 0.433 0.51487 0.076 0.796 0.028 0.000 0.056 0.044
#> GSM601855 4 0.195 0.88819 0.004 0.000 0.088 0.904 0.000 0.004
#> GSM601865 5 0.637 -0.07597 0.000 0.272 0.300 0.004 0.416 0.008
#> GSM601756 2 0.515 0.43096 0.004 0.660 0.044 0.004 0.252 0.036
#> GSM601786 5 0.233 0.56194 0.000 0.040 0.036 0.000 0.904 0.020
#> GSM601796 2 0.458 0.44338 0.108 0.756 0.100 0.008 0.000 0.028
#> GSM601801 2 0.543 0.43265 0.000 0.660 0.060 0.016 0.224 0.040
#> GSM601831 1 0.283 0.58311 0.880 0.004 0.024 0.044 0.000 0.048
#> GSM601841 2 0.633 0.02434 0.420 0.440 0.080 0.004 0.008 0.048
#> GSM601846 2 0.806 -0.34727 0.000 0.340 0.148 0.236 0.032 0.244
#> GSM601861 5 0.108 0.56423 0.000 0.004 0.004 0.000 0.960 0.032
#> GSM601871 3 0.690 0.36292 0.016 0.348 0.416 0.040 0.180 0.000
#> GSM601751 2 0.667 0.46908 0.132 0.604 0.056 0.004 0.152 0.052
#> GSM601761 1 0.283 0.50841 0.848 0.128 0.008 0.000 0.000 0.016
#> GSM601766 2 0.563 0.43955 0.060 0.648 0.020 0.000 0.224 0.048
#> GSM601771 2 0.662 0.48022 0.128 0.600 0.048 0.004 0.172 0.048
#> GSM601776 2 0.602 0.24774 0.360 0.528 0.036 0.004 0.020 0.052
#> GSM601781 2 0.290 0.42470 0.004 0.840 0.140 0.004 0.000 0.012
#> GSM601791 1 0.494 0.15796 0.548 0.400 0.032 0.000 0.000 0.020
#> GSM601806 2 0.491 0.40303 0.000 0.644 0.036 0.000 0.284 0.036
#> GSM601811 1 0.746 -0.05590 0.400 0.344 0.132 0.012 0.012 0.100
#> GSM601816 2 0.535 0.16393 0.380 0.544 0.044 0.004 0.000 0.028
#> GSM601821 5 0.108 0.56423 0.000 0.004 0.004 0.000 0.960 0.032
#> GSM601826 2 0.523 0.26858 0.328 0.588 0.060 0.000 0.000 0.024
#> GSM601836 2 0.634 0.44638 0.208 0.608 0.040 0.000 0.080 0.064
#> GSM601851 2 0.511 0.07872 0.432 0.508 0.036 0.000 0.000 0.024
#> GSM601856 1 0.586 0.20188 0.612 0.004 0.172 0.176 0.000 0.036
#> GSM601866 1 0.266 0.58872 0.892 0.020 0.052 0.012 0.000 0.024
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 time(p) gender(p) k
#> MAD:hclust 101 0.677 0.250 2
#> MAD:hclust 103 0.408 0.296 3
#> MAD:hclust 61 0.266 0.716 4
#> MAD:hclust 59 0.360 0.389 5
#> MAD:hclust 38 0.215 0.350 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 125 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 0.950 0.938 0.975 0.5023 0.499 0.499
#> 3 3 0.533 0.634 0.812 0.2756 0.814 0.649
#> 4 4 0.608 0.433 0.707 0.1266 0.947 0.866
#> 5 5 0.605 0.586 0.728 0.0729 0.804 0.480
#> 6 6 0.682 0.756 0.766 0.0431 0.925 0.665
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
#> GSM601752 2 0.0000 0.979 0.000 1.000
#> GSM601782 1 0.0000 0.969 1.000 0.000
#> GSM601792 1 0.0000 0.969 1.000 0.000
#> GSM601797 1 0.9954 0.168 0.540 0.460
#> GSM601827 1 0.0000 0.969 1.000 0.000
#> GSM601837 2 0.0000 0.979 0.000 1.000
#> GSM601842 2 0.0000 0.979 0.000 1.000
#> GSM601857 1 0.0000 0.969 1.000 0.000
#> GSM601867 1 0.8955 0.556 0.688 0.312
#> GSM601747 1 0.0000 0.969 1.000 0.000
#> GSM601757 1 0.0000 0.969 1.000 0.000
#> GSM601762 2 0.0000 0.979 0.000 1.000
#> GSM601767 2 0.0000 0.979 0.000 1.000
#> GSM601772 2 0.0000 0.979 0.000 1.000
#> GSM601777 1 0.0938 0.960 0.988 0.012
#> GSM601787 1 0.9954 0.176 0.540 0.460
#> GSM601802 2 0.0000 0.979 0.000 1.000
#> GSM601807 1 0.1843 0.946 0.972 0.028
#> GSM601812 1 0.0000 0.969 1.000 0.000
#> GSM601817 1 0.0000 0.969 1.000 0.000
#> GSM601822 1 0.7815 0.696 0.768 0.232
#> GSM601832 2 0.0000 0.979 0.000 1.000
#> GSM601847 2 0.3584 0.916 0.068 0.932
#> GSM601852 1 0.0000 0.969 1.000 0.000
#> GSM601862 1 0.0000 0.969 1.000 0.000
#> GSM601753 2 0.0000 0.979 0.000 1.000
#> GSM601783 1 0.0000 0.969 1.000 0.000
#> GSM601793 1 0.0000 0.969 1.000 0.000
#> GSM601798 2 0.0000 0.979 0.000 1.000
#> GSM601828 1 0.0000 0.969 1.000 0.000
#> GSM601838 2 0.0000 0.979 0.000 1.000
#> GSM601843 2 0.0000 0.979 0.000 1.000
#> GSM601858 2 0.0000 0.979 0.000 1.000
#> GSM601868 1 0.0000 0.969 1.000 0.000
#> GSM601748 1 0.0000 0.969 1.000 0.000
#> GSM601758 1 0.0000 0.969 1.000 0.000
#> GSM601763 2 0.9795 0.286 0.416 0.584
#> GSM601768 2 0.0000 0.979 0.000 1.000
#> GSM601773 2 0.0000 0.979 0.000 1.000
#> GSM601778 1 0.0000 0.969 1.000 0.000
#> GSM601788 2 0.0000 0.979 0.000 1.000
#> GSM601803 2 0.0000 0.979 0.000 1.000
#> GSM601808 1 0.0000 0.969 1.000 0.000
#> GSM601813 1 0.0000 0.969 1.000 0.000
#> GSM601818 1 0.0000 0.969 1.000 0.000
#> GSM601823 1 0.0000 0.969 1.000 0.000
#> GSM601833 2 0.0000 0.979 0.000 1.000
#> GSM601848 1 0.0000 0.969 1.000 0.000
#> GSM601853 1 0.0000 0.969 1.000 0.000
#> GSM601863 1 0.0000 0.969 1.000 0.000
#> GSM601754 2 0.0000 0.979 0.000 1.000
#> GSM601784 2 0.0000 0.979 0.000 1.000
#> GSM601794 1 0.0000 0.969 1.000 0.000
#> GSM601799 2 0.0000 0.979 0.000 1.000
#> GSM601829 1 0.0000 0.969 1.000 0.000
#> GSM601839 2 0.0000 0.979 0.000 1.000
#> GSM601844 1 0.0000 0.969 1.000 0.000
#> GSM601859 2 0.0000 0.979 0.000 1.000
#> GSM601869 1 0.0000 0.969 1.000 0.000
#> GSM601749 1 0.0000 0.969 1.000 0.000
#> GSM601759 1 0.0000 0.969 1.000 0.000
#> GSM601764 1 0.0000 0.969 1.000 0.000
#> GSM601769 2 0.0000 0.979 0.000 1.000
#> GSM601774 2 0.0000 0.979 0.000 1.000
#> GSM601779 1 0.0000 0.969 1.000 0.000
#> GSM601789 2 0.0000 0.979 0.000 1.000
#> GSM601804 2 0.2423 0.945 0.040 0.960
#> GSM601809 1 0.0376 0.966 0.996 0.004
#> GSM601814 2 0.0000 0.979 0.000 1.000
#> GSM601819 1 0.0000 0.969 1.000 0.000
#> GSM601824 2 0.5519 0.850 0.128 0.872
#> GSM601834 2 0.0000 0.979 0.000 1.000
#> GSM601849 1 0.0000 0.969 1.000 0.000
#> GSM601854 1 0.0000 0.969 1.000 0.000
#> GSM601864 2 0.0000 0.979 0.000 1.000
#> GSM601755 2 0.0000 0.979 0.000 1.000
#> GSM601785 2 0.0000 0.979 0.000 1.000
#> GSM601795 1 0.0376 0.966 0.996 0.004
#> GSM601800 2 0.0000 0.979 0.000 1.000
#> GSM601830 1 0.1414 0.953 0.980 0.020
#> GSM601840 2 0.0376 0.976 0.004 0.996
#> GSM601845 2 0.7602 0.715 0.220 0.780
#> GSM601860 2 0.0000 0.979 0.000 1.000
#> GSM601870 1 0.4298 0.886 0.912 0.088
#> GSM601750 1 0.0000 0.969 1.000 0.000
#> GSM601760 1 0.0000 0.969 1.000 0.000
#> GSM601765 2 0.0000 0.979 0.000 1.000
#> GSM601770 2 0.0000 0.979 0.000 1.000
#> GSM601775 2 0.1414 0.963 0.020 0.980
#> GSM601780 1 0.0000 0.969 1.000 0.000
#> GSM601790 2 0.0000 0.979 0.000 1.000
#> GSM601805 2 0.0000 0.979 0.000 1.000
#> GSM601810 1 0.0000 0.969 1.000 0.000
#> GSM601815 2 0.0000 0.979 0.000 1.000
#> GSM601820 1 0.0000 0.969 1.000 0.000
#> GSM601825 2 0.0000 0.979 0.000 1.000
#> GSM601835 2 0.0000 0.979 0.000 1.000
#> GSM601850 1 0.0938 0.960 0.988 0.012
#> GSM601855 1 0.0000 0.969 1.000 0.000
#> GSM601865 2 0.0000 0.979 0.000 1.000
#> GSM601756 2 0.0000 0.979 0.000 1.000
#> GSM601786 2 0.0000 0.979 0.000 1.000
#> GSM601796 1 0.0000 0.969 1.000 0.000
#> GSM601801 2 0.0000 0.979 0.000 1.000
#> GSM601831 1 0.0000 0.969 1.000 0.000
#> GSM601841 1 0.0000 0.969 1.000 0.000
#> GSM601846 2 0.6148 0.816 0.152 0.848
#> GSM601861 2 0.0000 0.979 0.000 1.000
#> GSM601871 1 0.9580 0.406 0.620 0.380
#> GSM601751 2 0.0000 0.979 0.000 1.000
#> GSM601761 1 0.0000 0.969 1.000 0.000
#> GSM601766 2 0.5178 0.867 0.116 0.884
#> GSM601771 2 0.0000 0.979 0.000 1.000
#> GSM601776 1 0.0000 0.969 1.000 0.000
#> GSM601781 1 0.0376 0.966 0.996 0.004
#> GSM601791 1 0.0000 0.969 1.000 0.000
#> GSM601806 2 0.0000 0.979 0.000 1.000
#> GSM601811 1 0.0000 0.969 1.000 0.000
#> GSM601816 1 0.0000 0.969 1.000 0.000
#> GSM601821 2 0.0000 0.979 0.000 1.000
#> GSM601826 1 0.0000 0.969 1.000 0.000
#> GSM601836 1 0.0000 0.969 1.000 0.000
#> GSM601851 1 0.0000 0.969 1.000 0.000
#> GSM601856 1 0.0000 0.969 1.000 0.000
#> GSM601866 1 0.0000 0.969 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601752 2 0.6529 0.7654 0.152 0.756 0.092
#> GSM601782 1 0.6235 -0.0142 0.564 0.000 0.436
#> GSM601792 1 0.1753 0.6460 0.952 0.000 0.048
#> GSM601797 1 0.8825 0.1845 0.532 0.336 0.132
#> GSM601827 3 0.6286 0.3549 0.464 0.000 0.536
#> GSM601837 2 0.4291 0.8162 0.000 0.820 0.180
#> GSM601842 2 0.0747 0.8856 0.000 0.984 0.016
#> GSM601857 3 0.5363 0.8130 0.276 0.000 0.724
#> GSM601867 3 0.4095 0.6431 0.056 0.064 0.880
#> GSM601747 1 0.5902 0.3468 0.680 0.004 0.316
#> GSM601757 1 0.6095 0.1332 0.608 0.000 0.392
#> GSM601762 2 0.0592 0.8851 0.000 0.988 0.012
#> GSM601767 2 0.0237 0.8853 0.000 0.996 0.004
#> GSM601772 2 0.0424 0.8851 0.000 0.992 0.008
#> GSM601777 1 0.5763 0.5121 0.740 0.016 0.244
#> GSM601787 3 0.4802 0.5401 0.020 0.156 0.824
#> GSM601802 2 0.4586 0.8520 0.048 0.856 0.096
#> GSM601807 3 0.3619 0.7307 0.136 0.000 0.864
#> GSM601812 1 0.6180 0.0504 0.584 0.000 0.416
#> GSM601817 1 0.6309 -0.2569 0.504 0.000 0.496
#> GSM601822 1 0.7058 0.4616 0.720 0.180 0.100
#> GSM601832 2 0.1289 0.8843 0.000 0.968 0.032
#> GSM601847 1 0.8442 0.1648 0.548 0.352 0.100
#> GSM601852 1 0.6280 -0.1266 0.540 0.000 0.460
#> GSM601862 3 0.5397 0.8100 0.280 0.000 0.720
#> GSM601753 2 0.4505 0.8514 0.048 0.860 0.092
#> GSM601783 1 0.5529 0.3712 0.704 0.000 0.296
#> GSM601793 1 0.1411 0.6599 0.964 0.000 0.036
#> GSM601798 2 0.4586 0.8520 0.048 0.856 0.096
#> GSM601828 3 0.6309 0.2501 0.496 0.000 0.504
#> GSM601838 2 0.4291 0.8162 0.000 0.820 0.180
#> GSM601843 2 0.0424 0.8856 0.000 0.992 0.008
#> GSM601858 2 0.4002 0.8269 0.000 0.840 0.160
#> GSM601868 3 0.5363 0.8130 0.276 0.000 0.724
#> GSM601748 1 0.6295 -0.1781 0.528 0.000 0.472
#> GSM601758 1 0.5397 0.3953 0.720 0.000 0.280
#> GSM601763 1 0.5412 0.5113 0.796 0.172 0.032
#> GSM601768 2 0.0237 0.8851 0.000 0.996 0.004
#> GSM601773 2 0.0237 0.8853 0.000 0.996 0.004
#> GSM601778 1 0.2711 0.6247 0.912 0.000 0.088
#> GSM601788 2 0.2165 0.8799 0.000 0.936 0.064
#> GSM601803 2 0.3445 0.8672 0.016 0.896 0.088
#> GSM601808 3 0.5363 0.8130 0.276 0.000 0.724
#> GSM601813 1 0.5465 0.3814 0.712 0.000 0.288
#> GSM601818 1 0.6309 -0.2569 0.504 0.000 0.496
#> GSM601823 1 0.0424 0.6624 0.992 0.000 0.008
#> GSM601833 2 0.0424 0.8851 0.000 0.992 0.008
#> GSM601848 1 0.0592 0.6626 0.988 0.000 0.012
#> GSM601853 3 0.5363 0.8130 0.276 0.000 0.724
#> GSM601863 3 0.5465 0.8000 0.288 0.000 0.712
#> GSM601754 2 0.6807 0.7449 0.172 0.736 0.092
#> GSM601784 2 0.0892 0.8839 0.000 0.980 0.020
#> GSM601794 1 0.1860 0.6440 0.948 0.000 0.052
#> GSM601799 2 0.6911 0.7349 0.180 0.728 0.092
#> GSM601829 1 0.2959 0.6270 0.900 0.000 0.100
#> GSM601839 2 0.4291 0.8162 0.000 0.820 0.180
#> GSM601844 1 0.0747 0.6621 0.984 0.000 0.016
#> GSM601859 2 0.0237 0.8853 0.000 0.996 0.004
#> GSM601869 3 0.5397 0.8100 0.280 0.000 0.720
#> GSM601749 1 0.5560 0.3592 0.700 0.000 0.300
#> GSM601759 1 0.5650 0.3341 0.688 0.000 0.312
#> GSM601764 1 0.0829 0.6624 0.984 0.004 0.012
#> GSM601769 2 0.2448 0.8679 0.000 0.924 0.076
#> GSM601774 2 0.0424 0.8853 0.000 0.992 0.008
#> GSM601779 1 0.0237 0.6602 0.996 0.000 0.004
#> GSM601789 2 0.4235 0.8171 0.000 0.824 0.176
#> GSM601804 1 0.8113 0.2543 0.596 0.312 0.092
#> GSM601809 1 0.6228 0.2003 0.624 0.004 0.372
#> GSM601814 2 0.2711 0.8631 0.000 0.912 0.088
#> GSM601819 1 0.4291 0.5394 0.820 0.000 0.180
#> GSM601824 1 0.6696 0.4675 0.736 0.188 0.076
#> GSM601834 2 0.0237 0.8853 0.000 0.996 0.004
#> GSM601849 1 0.0747 0.6621 0.984 0.000 0.016
#> GSM601854 1 0.6062 0.1348 0.616 0.000 0.384
#> GSM601864 2 0.4291 0.8162 0.000 0.820 0.180
#> GSM601755 2 0.4586 0.8520 0.048 0.856 0.096
#> GSM601785 2 0.2926 0.8756 0.040 0.924 0.036
#> GSM601795 1 0.3375 0.6082 0.892 0.008 0.100
#> GSM601800 2 0.4586 0.8520 0.048 0.856 0.096
#> GSM601830 3 0.4605 0.7790 0.204 0.000 0.796
#> GSM601840 2 0.6266 0.7752 0.156 0.768 0.076
#> GSM601845 2 0.7974 0.1875 0.436 0.504 0.060
#> GSM601860 2 0.1525 0.8823 0.032 0.964 0.004
#> GSM601870 3 0.3802 0.6807 0.080 0.032 0.888
#> GSM601750 1 0.6260 -0.0796 0.552 0.000 0.448
#> GSM601760 1 0.3038 0.6118 0.896 0.000 0.104
#> GSM601765 2 0.0424 0.8857 0.000 0.992 0.008
#> GSM601770 2 0.0237 0.8853 0.000 0.996 0.004
#> GSM601775 2 0.6886 0.7329 0.184 0.728 0.088
#> GSM601780 1 0.0000 0.6610 1.000 0.000 0.000
#> GSM601790 2 0.4291 0.8162 0.000 0.820 0.180
#> GSM601805 2 0.4505 0.8538 0.048 0.860 0.092
#> GSM601810 3 0.5363 0.8130 0.276 0.000 0.724
#> GSM601815 2 0.4121 0.8239 0.000 0.832 0.168
#> GSM601820 1 0.5785 0.2860 0.668 0.000 0.332
#> GSM601825 2 0.3445 0.8672 0.016 0.896 0.088
#> GSM601835 2 0.1643 0.8852 0.000 0.956 0.044
#> GSM601850 1 0.4423 0.5867 0.864 0.048 0.088
#> GSM601855 3 0.4555 0.7785 0.200 0.000 0.800
#> GSM601865 2 0.4291 0.8162 0.000 0.820 0.180
#> GSM601756 2 0.4479 0.8540 0.044 0.860 0.096
#> GSM601786 2 0.4235 0.8177 0.000 0.824 0.176
#> GSM601796 1 0.1529 0.6495 0.960 0.000 0.040
#> GSM601801 2 0.3610 0.8645 0.016 0.888 0.096
#> GSM601831 3 0.5363 0.8130 0.276 0.000 0.724
#> GSM601841 1 0.2625 0.6356 0.916 0.000 0.084
#> GSM601846 2 0.9151 0.1575 0.420 0.436 0.144
#> GSM601861 2 0.3412 0.8467 0.000 0.876 0.124
#> GSM601871 3 0.4979 0.5254 0.020 0.168 0.812
#> GSM601751 2 0.2339 0.8774 0.048 0.940 0.012
#> GSM601761 1 0.0747 0.6621 0.984 0.000 0.016
#> GSM601766 1 0.7181 0.1798 0.564 0.408 0.028
#> GSM601771 2 0.1999 0.8818 0.036 0.952 0.012
#> GSM601776 1 0.0747 0.6621 0.984 0.000 0.016
#> GSM601781 1 0.1643 0.6463 0.956 0.000 0.044
#> GSM601791 1 0.0747 0.6621 0.984 0.000 0.016
#> GSM601806 2 0.3207 0.8698 0.012 0.904 0.084
#> GSM601811 3 0.5363 0.8130 0.276 0.000 0.724
#> GSM601816 1 0.0592 0.6626 0.988 0.000 0.012
#> GSM601821 2 0.3412 0.8467 0.000 0.876 0.124
#> GSM601826 1 0.0592 0.6626 0.988 0.000 0.012
#> GSM601836 1 0.3771 0.6194 0.876 0.012 0.112
#> GSM601851 1 0.0747 0.6621 0.984 0.000 0.016
#> GSM601856 3 0.5327 0.8113 0.272 0.000 0.728
#> GSM601866 1 0.6299 -0.1921 0.524 0.000 0.476
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601752 2 0.6262 0.1830 0.052 0.560 0.004 0.384
#> GSM601782 1 0.7798 0.0810 0.388 0.000 0.364 0.248
#> GSM601792 1 0.3224 0.4173 0.864 0.000 0.016 0.120
#> GSM601797 4 0.8504 0.9056 0.332 0.212 0.036 0.420
#> GSM601827 3 0.7764 -0.0240 0.356 0.000 0.404 0.240
#> GSM601837 2 0.5636 0.5567 0.000 0.648 0.044 0.308
#> GSM601842 2 0.0469 0.6712 0.000 0.988 0.000 0.012
#> GSM601857 3 0.2816 0.7692 0.064 0.000 0.900 0.036
#> GSM601867 3 0.3128 0.6946 0.004 0.004 0.864 0.128
#> GSM601747 1 0.8903 0.1866 0.404 0.056 0.280 0.260
#> GSM601757 1 0.7416 0.3333 0.516 0.000 0.240 0.244
#> GSM601762 2 0.0000 0.6722 0.000 1.000 0.000 0.000
#> GSM601767 2 0.0592 0.6738 0.000 0.984 0.000 0.016
#> GSM601772 2 0.0188 0.6716 0.000 0.996 0.000 0.004
#> GSM601777 1 0.7938 -0.3497 0.496 0.024 0.168 0.312
#> GSM601787 3 0.4418 0.6064 0.000 0.032 0.784 0.184
#> GSM601802 2 0.5204 0.3269 0.012 0.612 0.000 0.376
#> GSM601807 3 0.2867 0.7249 0.012 0.000 0.884 0.104
#> GSM601812 1 0.7740 0.1863 0.432 0.000 0.320 0.248
#> GSM601817 3 0.7782 -0.0572 0.360 0.000 0.396 0.244
#> GSM601822 1 0.6802 -0.5027 0.556 0.076 0.012 0.356
#> GSM601832 2 0.1661 0.6548 0.000 0.944 0.004 0.052
#> GSM601847 1 0.7573 -0.7504 0.460 0.152 0.008 0.380
#> GSM601852 1 0.7776 0.1407 0.412 0.000 0.340 0.248
#> GSM601862 3 0.2983 0.7666 0.068 0.000 0.892 0.040
#> GSM601753 2 0.5189 0.3284 0.012 0.616 0.000 0.372
#> GSM601783 1 0.7153 0.3932 0.556 0.000 0.196 0.248
#> GSM601793 1 0.1854 0.5188 0.940 0.000 0.012 0.048
#> GSM601798 2 0.5404 0.3098 0.012 0.600 0.004 0.384
#> GSM601828 1 0.7799 0.0628 0.384 0.000 0.368 0.248
#> GSM601838 2 0.5614 0.5598 0.000 0.652 0.044 0.304
#> GSM601843 2 0.0336 0.6731 0.000 0.992 0.000 0.008
#> GSM601858 2 0.4123 0.6300 0.000 0.820 0.044 0.136
#> GSM601868 3 0.1970 0.7756 0.060 0.000 0.932 0.008
#> GSM601748 1 0.7740 0.1706 0.428 0.000 0.328 0.244
#> GSM601758 1 0.6860 0.4261 0.592 0.000 0.164 0.244
#> GSM601763 1 0.4720 0.0405 0.720 0.264 0.000 0.016
#> GSM601768 2 0.0336 0.6707 0.000 0.992 0.000 0.008
#> GSM601773 2 0.0707 0.6744 0.000 0.980 0.000 0.020
#> GSM601778 1 0.5571 -0.1331 0.656 0.016 0.016 0.312
#> GSM601788 2 0.3552 0.6500 0.000 0.848 0.024 0.128
#> GSM601803 2 0.5070 0.3425 0.008 0.620 0.000 0.372
#> GSM601808 3 0.1474 0.7753 0.052 0.000 0.948 0.000
#> GSM601813 1 0.7059 0.4047 0.568 0.000 0.184 0.248
#> GSM601818 3 0.7843 -0.0852 0.364 0.000 0.372 0.264
#> GSM601823 1 0.0000 0.5509 1.000 0.000 0.000 0.000
#> GSM601833 2 0.0188 0.6728 0.000 0.996 0.000 0.004
#> GSM601848 1 0.0336 0.5492 0.992 0.000 0.000 0.008
#> GSM601853 3 0.1970 0.7757 0.060 0.000 0.932 0.008
#> GSM601863 3 0.3439 0.7556 0.084 0.000 0.868 0.048
#> GSM601754 2 0.6625 0.1124 0.076 0.540 0.004 0.380
#> GSM601784 2 0.1792 0.6683 0.000 0.932 0.000 0.068
#> GSM601794 1 0.3695 0.3482 0.828 0.000 0.016 0.156
#> GSM601799 2 0.6495 0.0736 0.084 0.560 0.000 0.356
#> GSM601829 1 0.2751 0.5591 0.904 0.000 0.056 0.040
#> GSM601839 2 0.5614 0.5598 0.000 0.652 0.044 0.304
#> GSM601844 1 0.0927 0.5589 0.976 0.000 0.008 0.016
#> GSM601859 2 0.1022 0.6733 0.000 0.968 0.000 0.032
#> GSM601869 3 0.3611 0.7514 0.080 0.000 0.860 0.060
#> GSM601749 1 0.7001 0.4103 0.576 0.000 0.180 0.244
#> GSM601759 1 0.7035 0.4058 0.572 0.000 0.184 0.244
#> GSM601764 1 0.2142 0.5141 0.928 0.056 0.000 0.016
#> GSM601769 2 0.4295 0.6016 0.000 0.752 0.008 0.240
#> GSM601774 2 0.2266 0.6657 0.000 0.912 0.004 0.084
#> GSM601779 1 0.0000 0.5509 1.000 0.000 0.000 0.000
#> GSM601789 2 0.5312 0.5719 0.000 0.692 0.040 0.268
#> GSM601804 1 0.7720 -0.8503 0.412 0.228 0.000 0.360
#> GSM601809 1 0.7902 0.1691 0.440 0.008 0.340 0.212
#> GSM601814 2 0.5143 0.5926 0.000 0.708 0.036 0.256
#> GSM601819 1 0.6469 0.4656 0.628 0.000 0.124 0.248
#> GSM601824 1 0.6511 -0.3836 0.640 0.188 0.000 0.172
#> GSM601834 2 0.1211 0.6719 0.000 0.960 0.000 0.040
#> GSM601849 1 0.0336 0.5492 0.992 0.000 0.000 0.008
#> GSM601854 1 0.7322 0.3548 0.532 0.000 0.224 0.244
#> GSM601864 2 0.5736 0.5400 0.000 0.628 0.044 0.328
#> GSM601755 2 0.5391 0.3184 0.012 0.604 0.004 0.380
#> GSM601785 2 0.1661 0.6523 0.004 0.944 0.000 0.052
#> GSM601795 1 0.4917 0.0696 0.728 0.008 0.016 0.248
#> GSM601800 2 0.5217 0.3192 0.012 0.608 0.000 0.380
#> GSM601830 3 0.2983 0.7598 0.040 0.000 0.892 0.068
#> GSM601840 2 0.6078 0.3530 0.064 0.684 0.016 0.236
#> GSM601845 2 0.6943 -0.3773 0.388 0.520 0.012 0.080
#> GSM601860 2 0.1118 0.6736 0.000 0.964 0.000 0.036
#> GSM601870 3 0.2731 0.7235 0.008 0.004 0.896 0.092
#> GSM601750 1 0.7763 0.1596 0.420 0.000 0.332 0.248
#> GSM601760 1 0.5694 0.5076 0.696 0.000 0.080 0.224
#> GSM601765 2 0.0000 0.6722 0.000 1.000 0.000 0.000
#> GSM601770 2 0.0336 0.6727 0.000 0.992 0.000 0.008
#> GSM601775 2 0.6084 0.2718 0.096 0.660 0.000 0.244
#> GSM601780 1 0.0000 0.5509 1.000 0.000 0.000 0.000
#> GSM601790 2 0.5569 0.5655 0.000 0.660 0.044 0.296
#> GSM601805 2 0.5204 0.3269 0.012 0.612 0.000 0.376
#> GSM601810 3 0.4624 0.7099 0.052 0.000 0.784 0.164
#> GSM601815 2 0.5546 0.5680 0.000 0.664 0.044 0.292
#> GSM601820 1 0.7239 0.3766 0.544 0.000 0.208 0.248
#> GSM601825 2 0.4605 0.4004 0.000 0.664 0.000 0.336
#> GSM601835 2 0.2522 0.6436 0.000 0.908 0.016 0.076
#> GSM601850 1 0.4747 0.2139 0.764 0.024 0.008 0.204
#> GSM601855 3 0.3056 0.7575 0.040 0.000 0.888 0.072
#> GSM601865 2 0.5736 0.5400 0.000 0.628 0.044 0.328
#> GSM601756 2 0.5377 0.3248 0.012 0.608 0.004 0.376
#> GSM601786 2 0.5569 0.5655 0.000 0.660 0.044 0.296
#> GSM601796 1 0.3108 0.4311 0.872 0.000 0.016 0.112
#> GSM601801 2 0.5259 0.3341 0.008 0.612 0.004 0.376
#> GSM601831 3 0.5839 0.6042 0.104 0.000 0.696 0.200
#> GSM601841 1 0.2943 0.5674 0.892 0.000 0.076 0.032
#> GSM601846 4 0.8820 0.9017 0.292 0.244 0.052 0.412
#> GSM601861 2 0.5339 0.5805 0.000 0.688 0.040 0.272
#> GSM601871 3 0.5132 0.5581 0.000 0.068 0.748 0.184
#> GSM601751 2 0.1716 0.6592 0.000 0.936 0.000 0.064
#> GSM601761 1 0.1356 0.5640 0.960 0.000 0.008 0.032
#> GSM601766 2 0.6165 -0.3971 0.448 0.508 0.004 0.040
#> GSM601771 2 0.1867 0.6475 0.000 0.928 0.000 0.072
#> GSM601776 1 0.0779 0.5597 0.980 0.000 0.004 0.016
#> GSM601781 1 0.3703 0.3709 0.840 0.008 0.012 0.140
#> GSM601791 1 0.0657 0.5576 0.984 0.000 0.004 0.012
#> GSM601806 2 0.4964 0.3494 0.004 0.616 0.000 0.380
#> GSM601811 3 0.4532 0.7173 0.052 0.000 0.792 0.156
#> GSM601816 1 0.1042 0.5368 0.972 0.000 0.008 0.020
#> GSM601821 2 0.5339 0.5805 0.000 0.688 0.040 0.272
#> GSM601826 1 0.0188 0.5505 0.996 0.000 0.000 0.004
#> GSM601836 1 0.5443 0.4883 0.784 0.068 0.092 0.056
#> GSM601851 1 0.0336 0.5527 0.992 0.000 0.000 0.008
#> GSM601856 3 0.1975 0.7728 0.048 0.000 0.936 0.016
#> GSM601866 1 0.7761 0.1409 0.416 0.000 0.340 0.244
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601752 4 0.5721 0.6031 0.000 0.104 0.044 0.692 0.160
#> GSM601782 1 0.4112 0.7540 0.812 0.000 0.112 0.032 0.044
#> GSM601792 5 0.4499 0.7883 0.172 0.000 0.020 0.044 0.764
#> GSM601797 4 0.5555 0.1062 0.000 0.000 0.068 0.480 0.452
#> GSM601827 1 0.4054 0.7057 0.800 0.000 0.144 0.040 0.016
#> GSM601837 2 0.0880 0.5822 0.000 0.968 0.032 0.000 0.000
#> GSM601842 2 0.4830 0.4067 0.000 0.492 0.000 0.488 0.020
#> GSM601857 3 0.3579 0.7931 0.240 0.000 0.756 0.004 0.000
#> GSM601867 3 0.2972 0.8065 0.044 0.036 0.892 0.016 0.012
#> GSM601747 1 0.5758 0.6310 0.700 0.000 0.080 0.144 0.076
#> GSM601757 1 0.2228 0.8381 0.912 0.000 0.008 0.012 0.068
#> GSM601762 2 0.4559 0.4325 0.000 0.512 0.000 0.480 0.008
#> GSM601767 2 0.4300 0.4523 0.000 0.524 0.000 0.476 0.000
#> GSM601772 2 0.4659 0.4232 0.000 0.500 0.000 0.488 0.012
#> GSM601777 5 0.4639 0.6144 0.040 0.000 0.132 0.052 0.776
#> GSM601787 3 0.3063 0.7702 0.012 0.104 0.864 0.020 0.000
#> GSM601802 4 0.5759 0.6124 0.000 0.132 0.044 0.692 0.132
#> GSM601807 3 0.3473 0.8172 0.064 0.012 0.864 0.044 0.016
#> GSM601812 1 0.1982 0.8381 0.932 0.000 0.028 0.012 0.028
#> GSM601817 1 0.2079 0.8025 0.916 0.000 0.064 0.020 0.000
#> GSM601822 5 0.3059 0.6634 0.016 0.000 0.020 0.096 0.868
#> GSM601832 4 0.5254 -0.3936 0.004 0.460 0.000 0.500 0.036
#> GSM601847 5 0.3769 0.5550 0.004 0.000 0.028 0.172 0.796
#> GSM601852 1 0.1996 0.8285 0.928 0.000 0.048 0.012 0.012
#> GSM601862 3 0.3635 0.7881 0.248 0.000 0.748 0.004 0.000
#> GSM601753 4 0.5647 0.6129 0.000 0.128 0.040 0.700 0.132
#> GSM601783 1 0.1942 0.8380 0.920 0.000 0.012 0.000 0.068
#> GSM601793 5 0.4604 0.7905 0.192 0.000 0.020 0.040 0.748
#> GSM601798 4 0.5759 0.6124 0.000 0.132 0.044 0.692 0.132
#> GSM601828 1 0.2793 0.7781 0.876 0.000 0.088 0.036 0.000
#> GSM601838 2 0.0609 0.5888 0.000 0.980 0.020 0.000 0.000
#> GSM601843 2 0.4656 0.4311 0.000 0.508 0.000 0.480 0.012
#> GSM601858 2 0.5645 0.4949 0.004 0.556 0.052 0.380 0.008
#> GSM601868 3 0.2813 0.8269 0.168 0.000 0.832 0.000 0.000
#> GSM601748 1 0.1731 0.8283 0.940 0.000 0.040 0.012 0.008
#> GSM601758 1 0.2127 0.8152 0.892 0.000 0.000 0.000 0.108
#> GSM601763 5 0.5930 0.2271 0.092 0.000 0.004 0.404 0.500
#> GSM601768 2 0.4659 0.4128 0.000 0.496 0.000 0.492 0.012
#> GSM601773 2 0.4294 0.4515 0.000 0.532 0.000 0.468 0.000
#> GSM601778 5 0.3115 0.7145 0.048 0.000 0.020 0.056 0.876
#> GSM601788 2 0.5842 0.4131 0.004 0.496 0.036 0.440 0.024
#> GSM601803 4 0.5871 0.6030 0.000 0.152 0.044 0.680 0.124
#> GSM601808 3 0.3087 0.8326 0.152 0.000 0.836 0.008 0.004
#> GSM601813 1 0.2284 0.8304 0.896 0.000 0.004 0.004 0.096
#> GSM601818 1 0.2580 0.8007 0.900 0.000 0.064 0.016 0.020
#> GSM601823 5 0.3452 0.7872 0.244 0.000 0.000 0.000 0.756
#> GSM601833 2 0.4557 0.4474 0.000 0.516 0.000 0.476 0.008
#> GSM601848 5 0.3366 0.7904 0.232 0.000 0.000 0.000 0.768
#> GSM601853 3 0.3689 0.8311 0.144 0.000 0.816 0.032 0.008
#> GSM601863 3 0.4046 0.7385 0.296 0.000 0.696 0.008 0.000
#> GSM601754 4 0.6145 0.5877 0.000 0.112 0.048 0.648 0.192
#> GSM601784 2 0.4030 0.5313 0.000 0.648 0.000 0.352 0.000
#> GSM601794 5 0.4305 0.7846 0.152 0.000 0.020 0.044 0.784
#> GSM601799 4 0.5416 0.6037 0.000 0.088 0.040 0.716 0.156
#> GSM601829 5 0.6241 0.6504 0.264 0.000 0.092 0.040 0.604
#> GSM601839 2 0.0609 0.5888 0.000 0.980 0.020 0.000 0.000
#> GSM601844 5 0.4597 0.7675 0.260 0.000 0.012 0.024 0.704
#> GSM601859 2 0.4383 0.5000 0.000 0.572 0.000 0.424 0.004
#> GSM601869 3 0.3766 0.7661 0.268 0.000 0.728 0.004 0.000
#> GSM601749 1 0.2233 0.8200 0.892 0.000 0.000 0.004 0.104
#> GSM601759 1 0.2179 0.8247 0.896 0.000 0.000 0.004 0.100
#> GSM601764 5 0.5289 0.7119 0.196 0.000 0.004 0.116 0.684
#> GSM601769 2 0.0794 0.5974 0.000 0.972 0.000 0.028 0.000
#> GSM601774 2 0.4015 0.5415 0.000 0.652 0.000 0.348 0.000
#> GSM601779 5 0.3728 0.7857 0.244 0.000 0.000 0.008 0.748
#> GSM601789 2 0.1697 0.5977 0.000 0.932 0.008 0.060 0.000
#> GSM601804 4 0.5465 0.1036 0.004 0.004 0.040 0.484 0.468
#> GSM601809 1 0.6464 0.5459 0.628 0.004 0.204 0.056 0.108
#> GSM601814 2 0.0609 0.5972 0.000 0.980 0.000 0.020 0.000
#> GSM601819 1 0.2284 0.8205 0.896 0.000 0.004 0.004 0.096
#> GSM601824 5 0.4035 0.7331 0.060 0.000 0.000 0.156 0.784
#> GSM601834 2 0.4350 0.5110 0.000 0.588 0.000 0.408 0.004
#> GSM601849 5 0.3395 0.7894 0.236 0.000 0.000 0.000 0.764
#> GSM601854 1 0.3086 0.8316 0.876 0.000 0.036 0.020 0.068
#> GSM601864 2 0.1626 0.5610 0.000 0.940 0.044 0.016 0.000
#> GSM601755 4 0.5758 0.6117 0.000 0.136 0.044 0.692 0.128
#> GSM601785 4 0.5570 -0.3462 0.000 0.436 0.012 0.508 0.044
#> GSM601795 5 0.3988 0.7584 0.096 0.000 0.020 0.064 0.820
#> GSM601800 4 0.5689 0.6128 0.000 0.132 0.040 0.696 0.132
#> GSM601830 3 0.4439 0.8183 0.112 0.008 0.796 0.068 0.016
#> GSM601840 4 0.6255 0.2282 0.008 0.196 0.024 0.636 0.136
#> GSM601845 4 0.7291 0.1712 0.048 0.124 0.012 0.488 0.328
#> GSM601860 2 0.5144 0.4529 0.000 0.520 0.008 0.448 0.024
#> GSM601870 3 0.3603 0.8204 0.076 0.020 0.856 0.036 0.012
#> GSM601750 1 0.1757 0.8325 0.936 0.000 0.048 0.004 0.012
#> GSM601760 1 0.3519 0.6404 0.776 0.000 0.000 0.008 0.216
#> GSM601765 4 0.5049 -0.4319 0.000 0.480 0.000 0.488 0.032
#> GSM601770 2 0.4302 0.4507 0.000 0.520 0.000 0.480 0.000
#> GSM601775 4 0.5868 0.2981 0.012 0.152 0.000 0.640 0.196
#> GSM601780 5 0.3728 0.7857 0.244 0.000 0.000 0.008 0.748
#> GSM601790 2 0.0290 0.5944 0.000 0.992 0.008 0.000 0.000
#> GSM601805 4 0.5840 0.6121 0.000 0.136 0.044 0.684 0.136
#> GSM601810 3 0.5426 0.2662 0.468 0.000 0.488 0.024 0.020
#> GSM601815 2 0.0404 0.5929 0.000 0.988 0.012 0.000 0.000
#> GSM601820 1 0.1892 0.8359 0.916 0.000 0.000 0.004 0.080
#> GSM601825 4 0.5818 0.5318 0.000 0.204 0.040 0.668 0.088
#> GSM601835 4 0.5781 -0.3975 0.004 0.448 0.024 0.492 0.032
#> GSM601850 5 0.4080 0.7714 0.136 0.000 0.012 0.052 0.800
#> GSM601855 3 0.3867 0.8262 0.112 0.000 0.820 0.056 0.012
#> GSM601865 2 0.1444 0.5683 0.000 0.948 0.040 0.012 0.000
#> GSM601756 4 0.5758 0.6117 0.000 0.136 0.044 0.692 0.128
#> GSM601786 2 0.0324 0.5941 0.000 0.992 0.004 0.004 0.000
#> GSM601796 5 0.4462 0.7884 0.168 0.000 0.020 0.044 0.768
#> GSM601801 4 0.5752 0.6063 0.000 0.144 0.044 0.692 0.120
#> GSM601831 1 0.4934 0.1665 0.600 0.000 0.364 0.036 0.000
#> GSM601841 5 0.5386 0.6653 0.336 0.000 0.036 0.020 0.608
#> GSM601846 5 0.6196 0.0717 0.000 0.016 0.092 0.384 0.508
#> GSM601861 2 0.0404 0.5973 0.000 0.988 0.000 0.012 0.000
#> GSM601871 3 0.3361 0.7468 0.012 0.128 0.840 0.020 0.000
#> GSM601751 4 0.5368 -0.4029 0.000 0.472 0.008 0.484 0.036
#> GSM601761 5 0.4088 0.7391 0.304 0.000 0.000 0.008 0.688
#> GSM601766 4 0.7234 0.1611 0.044 0.140 0.008 0.492 0.316
#> GSM601771 4 0.5451 -0.3553 0.000 0.444 0.012 0.508 0.036
#> GSM601776 5 0.3934 0.7653 0.276 0.000 0.000 0.008 0.716
#> GSM601781 5 0.3218 0.7776 0.108 0.000 0.016 0.020 0.856
#> GSM601791 5 0.4016 0.7670 0.272 0.000 0.000 0.012 0.716
#> GSM601806 4 0.5865 0.5990 0.000 0.156 0.044 0.680 0.120
#> GSM601811 3 0.5415 0.3211 0.448 0.000 0.508 0.024 0.020
#> GSM601816 5 0.3333 0.7930 0.208 0.000 0.000 0.004 0.788
#> GSM601821 2 0.0404 0.5973 0.000 0.988 0.000 0.012 0.000
#> GSM601826 5 0.3424 0.7881 0.240 0.000 0.000 0.000 0.760
#> GSM601836 5 0.6663 0.5751 0.176 0.000 0.044 0.192 0.588
#> GSM601851 5 0.3452 0.7869 0.244 0.000 0.000 0.000 0.756
#> GSM601856 3 0.3474 0.8331 0.148 0.000 0.824 0.020 0.008
#> GSM601866 1 0.1569 0.8311 0.944 0.000 0.044 0.004 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601752 4 0.3972 0.905 0.000 0.300 0.000 0.680 0.004 0.016
#> GSM601782 1 0.5591 0.662 0.708 0.000 0.068 0.096 0.076 0.052
#> GSM601792 6 0.4077 0.773 0.040 0.000 0.004 0.128 0.040 0.788
#> GSM601797 4 0.4117 0.559 0.000 0.064 0.000 0.764 0.016 0.156
#> GSM601827 1 0.5331 0.627 0.712 0.004 0.132 0.060 0.080 0.012
#> GSM601837 5 0.3828 0.959 0.000 0.288 0.004 0.012 0.696 0.000
#> GSM601842 2 0.0508 0.841 0.000 0.984 0.000 0.000 0.012 0.004
#> GSM601857 3 0.3620 0.737 0.200 0.004 0.772 0.016 0.008 0.000
#> GSM601867 3 0.3792 0.763 0.020 0.000 0.824 0.056 0.080 0.020
#> GSM601747 1 0.7350 0.474 0.572 0.164 0.036 0.092 0.052 0.084
#> GSM601757 1 0.2711 0.814 0.872 0.004 0.016 0.012 0.000 0.096
#> GSM601762 2 0.0653 0.841 0.000 0.980 0.000 0.004 0.012 0.004
#> GSM601767 2 0.0858 0.835 0.000 0.968 0.000 0.004 0.028 0.000
#> GSM601772 2 0.0458 0.840 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM601777 6 0.6518 0.574 0.012 0.012 0.112 0.188 0.076 0.600
#> GSM601787 3 0.3452 0.762 0.012 0.004 0.836 0.036 0.104 0.008
#> GSM601802 4 0.4015 0.912 0.004 0.320 0.000 0.664 0.008 0.004
#> GSM601807 3 0.3086 0.769 0.008 0.000 0.856 0.040 0.088 0.008
#> GSM601812 1 0.2158 0.819 0.912 0.000 0.012 0.016 0.004 0.056
#> GSM601817 1 0.2943 0.763 0.876 0.000 0.048 0.024 0.044 0.008
#> GSM601822 6 0.3656 0.726 0.000 0.004 0.000 0.164 0.048 0.784
#> GSM601832 2 0.0964 0.837 0.000 0.968 0.000 0.012 0.016 0.004
#> GSM601847 6 0.4844 0.641 0.000 0.020 0.004 0.232 0.060 0.684
#> GSM601852 1 0.3242 0.798 0.864 0.000 0.040 0.032 0.020 0.044
#> GSM601862 3 0.3466 0.713 0.224 0.000 0.760 0.008 0.008 0.000
#> GSM601753 4 0.3789 0.909 0.000 0.324 0.000 0.668 0.004 0.004
#> GSM601783 1 0.2550 0.819 0.888 0.000 0.008 0.020 0.008 0.076
#> GSM601793 6 0.4260 0.775 0.068 0.000 0.004 0.100 0.044 0.784
#> GSM601798 4 0.3933 0.911 0.000 0.308 0.000 0.676 0.008 0.008
#> GSM601828 1 0.4310 0.710 0.796 0.004 0.080 0.044 0.064 0.012
#> GSM601838 5 0.3634 0.964 0.000 0.296 0.000 0.008 0.696 0.000
#> GSM601843 2 0.0653 0.842 0.000 0.980 0.000 0.004 0.012 0.004
#> GSM601858 2 0.3242 0.761 0.008 0.856 0.024 0.024 0.084 0.004
#> GSM601868 3 0.2488 0.774 0.124 0.000 0.864 0.004 0.008 0.000
#> GSM601748 1 0.2014 0.812 0.924 0.000 0.004 0.024 0.016 0.032
#> GSM601758 1 0.2402 0.807 0.868 0.000 0.000 0.012 0.000 0.120
#> GSM601763 2 0.4570 0.320 0.020 0.588 0.000 0.004 0.008 0.380
#> GSM601768 2 0.0603 0.841 0.000 0.980 0.000 0.004 0.016 0.000
#> GSM601773 2 0.1082 0.829 0.000 0.956 0.000 0.004 0.040 0.000
#> GSM601778 6 0.4917 0.691 0.008 0.012 0.008 0.192 0.072 0.708
#> GSM601788 2 0.4107 0.725 0.004 0.808 0.016 0.048 0.092 0.032
#> GSM601803 4 0.4194 0.907 0.004 0.320 0.000 0.656 0.016 0.004
#> GSM601808 3 0.2315 0.788 0.084 0.000 0.892 0.008 0.016 0.000
#> GSM601813 1 0.2726 0.800 0.848 0.000 0.008 0.008 0.000 0.136
#> GSM601818 1 0.3664 0.744 0.840 0.000 0.028 0.056 0.040 0.036
#> GSM601823 6 0.1910 0.785 0.108 0.000 0.000 0.000 0.000 0.892
#> GSM601833 2 0.0603 0.841 0.000 0.980 0.000 0.000 0.016 0.004
#> GSM601848 6 0.2361 0.787 0.104 0.000 0.000 0.004 0.012 0.880
#> GSM601853 3 0.3722 0.774 0.080 0.000 0.824 0.044 0.048 0.004
#> GSM601863 3 0.4067 0.618 0.296 0.000 0.680 0.012 0.012 0.000
#> GSM601754 4 0.4531 0.867 0.000 0.272 0.000 0.672 0.012 0.044
#> GSM601784 2 0.2632 0.677 0.000 0.832 0.000 0.004 0.164 0.000
#> GSM601794 6 0.4154 0.770 0.036 0.000 0.004 0.136 0.044 0.780
#> GSM601799 4 0.3952 0.894 0.000 0.308 0.000 0.672 0.000 0.020
#> GSM601829 6 0.7309 0.550 0.196 0.004 0.080 0.092 0.088 0.540
#> GSM601839 5 0.3634 0.964 0.000 0.296 0.000 0.008 0.696 0.000
#> GSM601844 6 0.4877 0.737 0.148 0.004 0.008 0.064 0.040 0.736
#> GSM601859 2 0.1588 0.805 0.000 0.924 0.000 0.004 0.072 0.000
#> GSM601869 3 0.4154 0.670 0.248 0.000 0.716 0.012 0.012 0.012
#> GSM601749 1 0.2882 0.806 0.848 0.000 0.000 0.028 0.004 0.120
#> GSM601759 1 0.2494 0.807 0.864 0.000 0.000 0.016 0.000 0.120
#> GSM601764 6 0.5346 0.561 0.080 0.256 0.000 0.012 0.016 0.636
#> GSM601769 5 0.3894 0.941 0.000 0.324 0.000 0.008 0.664 0.004
#> GSM601774 2 0.3011 0.599 0.000 0.800 0.000 0.004 0.192 0.004
#> GSM601779 6 0.1910 0.785 0.108 0.000 0.000 0.000 0.000 0.892
#> GSM601789 5 0.3944 0.745 0.000 0.428 0.000 0.004 0.568 0.000
#> GSM601804 4 0.5061 0.594 0.004 0.120 0.000 0.636 0.000 0.240
#> GSM601809 1 0.8351 0.143 0.436 0.056 0.252 0.092 0.064 0.100
#> GSM601814 5 0.3867 0.963 0.000 0.296 0.000 0.012 0.688 0.004
#> GSM601819 1 0.3293 0.809 0.840 0.000 0.000 0.048 0.020 0.092
#> GSM601824 6 0.3148 0.751 0.020 0.116 0.000 0.024 0.000 0.840
#> GSM601834 2 0.2243 0.758 0.000 0.880 0.000 0.004 0.112 0.004
#> GSM601849 6 0.2218 0.787 0.104 0.000 0.000 0.000 0.012 0.884
#> GSM601854 1 0.4991 0.746 0.752 0.000 0.064 0.056 0.040 0.088
#> GSM601864 5 0.4102 0.943 0.000 0.268 0.016 0.016 0.700 0.000
#> GSM601755 4 0.3861 0.913 0.000 0.316 0.000 0.672 0.008 0.004
#> GSM601785 2 0.0984 0.839 0.000 0.968 0.000 0.012 0.008 0.012
#> GSM601795 6 0.4222 0.757 0.020 0.004 0.004 0.168 0.040 0.764
#> GSM601800 4 0.3933 0.911 0.000 0.308 0.000 0.676 0.008 0.008
#> GSM601830 3 0.4780 0.732 0.056 0.000 0.740 0.068 0.132 0.004
#> GSM601840 2 0.5396 0.543 0.004 0.712 0.024 0.120 0.040 0.100
#> GSM601845 2 0.4789 0.583 0.016 0.732 0.000 0.032 0.052 0.168
#> GSM601860 2 0.2489 0.825 0.000 0.900 0.012 0.016 0.052 0.020
#> GSM601870 3 0.3005 0.771 0.012 0.000 0.860 0.036 0.088 0.004
#> GSM601750 1 0.2287 0.815 0.904 0.000 0.000 0.048 0.012 0.036
#> GSM601760 1 0.3534 0.686 0.740 0.000 0.000 0.016 0.000 0.244
#> GSM601765 2 0.0603 0.841 0.000 0.980 0.000 0.000 0.016 0.004
#> GSM601770 2 0.0632 0.838 0.000 0.976 0.000 0.000 0.024 0.000
#> GSM601775 2 0.4364 0.553 0.008 0.744 0.000 0.084 0.004 0.160
#> GSM601780 6 0.1910 0.785 0.108 0.000 0.000 0.000 0.000 0.892
#> GSM601790 5 0.3653 0.963 0.000 0.300 0.000 0.008 0.692 0.000
#> GSM601805 4 0.4015 0.912 0.004 0.320 0.000 0.664 0.008 0.004
#> GSM601810 3 0.6878 0.199 0.412 0.004 0.412 0.084 0.056 0.032
#> GSM601815 5 0.3733 0.965 0.000 0.288 0.000 0.008 0.700 0.004
#> GSM601820 1 0.2452 0.816 0.884 0.000 0.000 0.028 0.004 0.084
#> GSM601825 4 0.4084 0.789 0.000 0.400 0.000 0.588 0.012 0.000
#> GSM601835 2 0.1792 0.826 0.004 0.936 0.008 0.016 0.032 0.004
#> GSM601850 6 0.4066 0.749 0.028 0.012 0.004 0.108 0.044 0.804
#> GSM601855 3 0.3840 0.760 0.024 0.000 0.808 0.060 0.104 0.004
#> GSM601865 5 0.3758 0.958 0.000 0.284 0.000 0.016 0.700 0.000
#> GSM601756 4 0.3861 0.913 0.000 0.316 0.000 0.672 0.008 0.004
#> GSM601786 5 0.3733 0.961 0.000 0.288 0.000 0.008 0.700 0.004
#> GSM601796 6 0.4350 0.768 0.048 0.000 0.004 0.136 0.044 0.768
#> GSM601801 4 0.3844 0.913 0.000 0.312 0.000 0.676 0.008 0.004
#> GSM601831 1 0.5791 0.386 0.596 0.000 0.276 0.048 0.072 0.008
#> GSM601841 6 0.5599 0.580 0.256 0.000 0.044 0.044 0.024 0.632
#> GSM601846 6 0.8754 0.185 0.008 0.208 0.092 0.252 0.140 0.300
#> GSM601861 5 0.3867 0.963 0.000 0.296 0.000 0.012 0.688 0.004
#> GSM601871 3 0.3571 0.755 0.012 0.004 0.820 0.028 0.128 0.008
#> GSM601751 2 0.2732 0.821 0.000 0.888 0.008 0.024 0.048 0.032
#> GSM601761 6 0.2932 0.748 0.164 0.000 0.000 0.016 0.000 0.820
#> GSM601766 2 0.4156 0.555 0.012 0.732 0.000 0.008 0.024 0.224
#> GSM601771 2 0.2316 0.827 0.000 0.912 0.012 0.020 0.032 0.024
#> GSM601776 6 0.2558 0.762 0.156 0.000 0.000 0.004 0.000 0.840
#> GSM601781 6 0.4085 0.747 0.020 0.012 0.000 0.120 0.056 0.792
#> GSM601791 6 0.2613 0.766 0.140 0.000 0.000 0.012 0.000 0.848
#> GSM601806 4 0.4194 0.907 0.004 0.320 0.000 0.656 0.016 0.004
#> GSM601811 3 0.6943 0.283 0.376 0.004 0.440 0.088 0.060 0.032
#> GSM601816 6 0.2323 0.790 0.084 0.000 0.000 0.012 0.012 0.892
#> GSM601821 5 0.3867 0.963 0.000 0.296 0.000 0.012 0.688 0.004
#> GSM601826 6 0.2053 0.785 0.108 0.000 0.000 0.000 0.004 0.888
#> GSM601836 6 0.7411 0.207 0.100 0.364 0.032 0.048 0.032 0.424
#> GSM601851 6 0.2165 0.786 0.108 0.000 0.000 0.000 0.008 0.884
#> GSM601856 3 0.3080 0.782 0.068 0.000 0.860 0.036 0.036 0.000
#> GSM601866 1 0.2212 0.815 0.912 0.000 0.020 0.016 0.004 0.048
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n time(p) gender(p) k
#> MAD:kmeans 121 0.547 0.2171 2
#> MAD:kmeans 97 0.262 0.3932 3
#> MAD:kmeans 72 0.256 0.1513 4
#> MAD:kmeans 95 0.102 0.0531 5
#> MAD:kmeans 117 0.464 0.1420 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 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.984 0.946 0.978 0.5039 0.496 0.496
#> 3 3 0.701 0.797 0.895 0.3050 0.800 0.616
#> 4 4 0.547 0.665 0.808 0.1303 0.827 0.551
#> 5 5 0.542 0.480 0.663 0.0625 0.964 0.863
#> 6 6 0.570 0.356 0.609 0.0416 0.938 0.758
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
#> GSM601752 2 0.0000 0.9734 0.000 1.000
#> GSM601782 1 0.0000 0.9815 1.000 0.000
#> GSM601792 1 0.0000 0.9815 1.000 0.000
#> GSM601797 2 0.7745 0.7045 0.228 0.772
#> GSM601827 1 0.0000 0.9815 1.000 0.000
#> GSM601837 2 0.0000 0.9734 0.000 1.000
#> GSM601842 2 0.0000 0.9734 0.000 1.000
#> GSM601857 1 0.0000 0.9815 1.000 0.000
#> GSM601867 1 0.9686 0.3419 0.604 0.396
#> GSM601747 1 0.0000 0.9815 1.000 0.000
#> GSM601757 1 0.0000 0.9815 1.000 0.000
#> GSM601762 2 0.0000 0.9734 0.000 1.000
#> GSM601767 2 0.0000 0.9734 0.000 1.000
#> GSM601772 2 0.0000 0.9734 0.000 1.000
#> GSM601777 1 0.3114 0.9307 0.944 0.056
#> GSM601787 2 0.7883 0.6869 0.236 0.764
#> GSM601802 2 0.0000 0.9734 0.000 1.000
#> GSM601807 1 0.2423 0.9469 0.960 0.040
#> GSM601812 1 0.0000 0.9815 1.000 0.000
#> GSM601817 1 0.0000 0.9815 1.000 0.000
#> GSM601822 2 1.0000 0.0277 0.496 0.504
#> GSM601832 2 0.0000 0.9734 0.000 1.000
#> GSM601847 2 0.1633 0.9541 0.024 0.976
#> GSM601852 1 0.0000 0.9815 1.000 0.000
#> GSM601862 1 0.0000 0.9815 1.000 0.000
#> GSM601753 2 0.0000 0.9734 0.000 1.000
#> GSM601783 1 0.0000 0.9815 1.000 0.000
#> GSM601793 1 0.0000 0.9815 1.000 0.000
#> GSM601798 2 0.0000 0.9734 0.000 1.000
#> GSM601828 1 0.0000 0.9815 1.000 0.000
#> GSM601838 2 0.0000 0.9734 0.000 1.000
#> GSM601843 2 0.0000 0.9734 0.000 1.000
#> GSM601858 2 0.0000 0.9734 0.000 1.000
#> GSM601868 1 0.0000 0.9815 1.000 0.000
#> GSM601748 1 0.0000 0.9815 1.000 0.000
#> GSM601758 1 0.0000 0.9815 1.000 0.000
#> GSM601763 2 0.9491 0.4320 0.368 0.632
#> GSM601768 2 0.0000 0.9734 0.000 1.000
#> GSM601773 2 0.0000 0.9734 0.000 1.000
#> GSM601778 1 0.0000 0.9815 1.000 0.000
#> GSM601788 2 0.0000 0.9734 0.000 1.000
#> GSM601803 2 0.0000 0.9734 0.000 1.000
#> GSM601808 1 0.0000 0.9815 1.000 0.000
#> GSM601813 1 0.0000 0.9815 1.000 0.000
#> GSM601818 1 0.0000 0.9815 1.000 0.000
#> GSM601823 1 0.0000 0.9815 1.000 0.000
#> GSM601833 2 0.0000 0.9734 0.000 1.000
#> GSM601848 1 0.0000 0.9815 1.000 0.000
#> GSM601853 1 0.0000 0.9815 1.000 0.000
#> GSM601863 1 0.0000 0.9815 1.000 0.000
#> GSM601754 2 0.0000 0.9734 0.000 1.000
#> GSM601784 2 0.0000 0.9734 0.000 1.000
#> GSM601794 1 0.0000 0.9815 1.000 0.000
#> GSM601799 2 0.0000 0.9734 0.000 1.000
#> GSM601829 1 0.0000 0.9815 1.000 0.000
#> GSM601839 2 0.0000 0.9734 0.000 1.000
#> GSM601844 1 0.0000 0.9815 1.000 0.000
#> GSM601859 2 0.0000 0.9734 0.000 1.000
#> GSM601869 1 0.0000 0.9815 1.000 0.000
#> GSM601749 1 0.0000 0.9815 1.000 0.000
#> GSM601759 1 0.0000 0.9815 1.000 0.000
#> GSM601764 1 0.0000 0.9815 1.000 0.000
#> GSM601769 2 0.0000 0.9734 0.000 1.000
#> GSM601774 2 0.0000 0.9734 0.000 1.000
#> GSM601779 1 0.0000 0.9815 1.000 0.000
#> GSM601789 2 0.0000 0.9734 0.000 1.000
#> GSM601804 2 0.0938 0.9644 0.012 0.988
#> GSM601809 1 0.1184 0.9684 0.984 0.016
#> GSM601814 2 0.0000 0.9734 0.000 1.000
#> GSM601819 1 0.0000 0.9815 1.000 0.000
#> GSM601824 2 0.5294 0.8550 0.120 0.880
#> GSM601834 2 0.0000 0.9734 0.000 1.000
#> GSM601849 1 0.0000 0.9815 1.000 0.000
#> GSM601854 1 0.0000 0.9815 1.000 0.000
#> GSM601864 2 0.0000 0.9734 0.000 1.000
#> GSM601755 2 0.0000 0.9734 0.000 1.000
#> GSM601785 2 0.0000 0.9734 0.000 1.000
#> GSM601795 1 0.0376 0.9785 0.996 0.004
#> GSM601800 2 0.0000 0.9734 0.000 1.000
#> GSM601830 1 0.2043 0.9544 0.968 0.032
#> GSM601840 2 0.0000 0.9734 0.000 1.000
#> GSM601845 2 0.1843 0.9509 0.028 0.972
#> GSM601860 2 0.0000 0.9734 0.000 1.000
#> GSM601870 1 0.4690 0.8808 0.900 0.100
#> GSM601750 1 0.0000 0.9815 1.000 0.000
#> GSM601760 1 0.0000 0.9815 1.000 0.000
#> GSM601765 2 0.0000 0.9734 0.000 1.000
#> GSM601770 2 0.0000 0.9734 0.000 1.000
#> GSM601775 2 0.0938 0.9643 0.012 0.988
#> GSM601780 1 0.0000 0.9815 1.000 0.000
#> GSM601790 2 0.0000 0.9734 0.000 1.000
#> GSM601805 2 0.0000 0.9734 0.000 1.000
#> GSM601810 1 0.0000 0.9815 1.000 0.000
#> GSM601815 2 0.0000 0.9734 0.000 1.000
#> GSM601820 1 0.0000 0.9815 1.000 0.000
#> GSM601825 2 0.0000 0.9734 0.000 1.000
#> GSM601835 2 0.0000 0.9734 0.000 1.000
#> GSM601850 1 0.2236 0.9506 0.964 0.036
#> GSM601855 1 0.0376 0.9785 0.996 0.004
#> GSM601865 2 0.0000 0.9734 0.000 1.000
#> GSM601756 2 0.0000 0.9734 0.000 1.000
#> GSM601786 2 0.0000 0.9734 0.000 1.000
#> GSM601796 1 0.0000 0.9815 1.000 0.000
#> GSM601801 2 0.0000 0.9734 0.000 1.000
#> GSM601831 1 0.0000 0.9815 1.000 0.000
#> GSM601841 1 0.0000 0.9815 1.000 0.000
#> GSM601846 2 0.0000 0.9734 0.000 1.000
#> GSM601861 2 0.0000 0.9734 0.000 1.000
#> GSM601871 1 0.9896 0.2105 0.560 0.440
#> GSM601751 2 0.0000 0.9734 0.000 1.000
#> GSM601761 1 0.0000 0.9815 1.000 0.000
#> GSM601766 2 0.2236 0.9438 0.036 0.964
#> GSM601771 2 0.0000 0.9734 0.000 1.000
#> GSM601776 1 0.0000 0.9815 1.000 0.000
#> GSM601781 1 0.0000 0.9815 1.000 0.000
#> GSM601791 1 0.0000 0.9815 1.000 0.000
#> GSM601806 2 0.0000 0.9734 0.000 1.000
#> GSM601811 1 0.0000 0.9815 1.000 0.000
#> GSM601816 1 0.0000 0.9815 1.000 0.000
#> GSM601821 2 0.0000 0.9734 0.000 1.000
#> GSM601826 1 0.0000 0.9815 1.000 0.000
#> GSM601836 1 0.0376 0.9785 0.996 0.004
#> GSM601851 1 0.0000 0.9815 1.000 0.000
#> GSM601856 1 0.0000 0.9815 1.000 0.000
#> GSM601866 1 0.0000 0.9815 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601752 2 0.3816 0.8537 0.148 0.852 0.000
#> GSM601782 3 0.3192 0.8269 0.112 0.000 0.888
#> GSM601792 1 0.0892 0.8444 0.980 0.000 0.020
#> GSM601797 1 0.8722 0.4638 0.592 0.216 0.192
#> GSM601827 3 0.2711 0.8401 0.088 0.000 0.912
#> GSM601837 2 0.0237 0.9322 0.004 0.996 0.000
#> GSM601842 2 0.0000 0.9325 0.000 1.000 0.000
#> GSM601857 3 0.0237 0.8482 0.004 0.000 0.996
#> GSM601867 3 0.2400 0.8017 0.004 0.064 0.932
#> GSM601747 3 0.4663 0.7845 0.156 0.016 0.828
#> GSM601757 3 0.3686 0.8090 0.140 0.000 0.860
#> GSM601762 2 0.0000 0.9325 0.000 1.000 0.000
#> GSM601767 2 0.0237 0.9323 0.004 0.996 0.000
#> GSM601772 2 0.0000 0.9325 0.000 1.000 0.000
#> GSM601777 1 0.7366 0.1868 0.524 0.032 0.444
#> GSM601787 3 0.4110 0.7158 0.004 0.152 0.844
#> GSM601802 2 0.2711 0.9003 0.088 0.912 0.000
#> GSM601807 3 0.0829 0.8416 0.012 0.004 0.984
#> GSM601812 3 0.2448 0.8444 0.076 0.000 0.924
#> GSM601817 3 0.1031 0.8506 0.024 0.000 0.976
#> GSM601822 1 0.1453 0.8252 0.968 0.024 0.008
#> GSM601832 2 0.0829 0.9298 0.004 0.984 0.012
#> GSM601847 1 0.5450 0.6104 0.760 0.228 0.012
#> GSM601852 3 0.3482 0.8184 0.128 0.000 0.872
#> GSM601862 3 0.0000 0.8477 0.000 0.000 1.000
#> GSM601753 2 0.2878 0.8953 0.096 0.904 0.000
#> GSM601783 3 0.5785 0.5819 0.332 0.000 0.668
#> GSM601793 1 0.3551 0.8196 0.868 0.000 0.132
#> GSM601798 2 0.2711 0.9003 0.088 0.912 0.000
#> GSM601828 3 0.2711 0.8396 0.088 0.000 0.912
#> GSM601838 2 0.0237 0.9322 0.004 0.996 0.000
#> GSM601843 2 0.0000 0.9325 0.000 1.000 0.000
#> GSM601858 2 0.3030 0.8680 0.004 0.904 0.092
#> GSM601868 3 0.0000 0.8477 0.000 0.000 1.000
#> GSM601748 3 0.3192 0.8305 0.112 0.000 0.888
#> GSM601758 3 0.6299 0.2092 0.476 0.000 0.524
#> GSM601763 1 0.2793 0.8392 0.928 0.044 0.028
#> GSM601768 2 0.0000 0.9325 0.000 1.000 0.000
#> GSM601773 2 0.0237 0.9323 0.004 0.996 0.000
#> GSM601778 1 0.3038 0.8085 0.896 0.000 0.104
#> GSM601788 2 0.0983 0.9265 0.004 0.980 0.016
#> GSM601803 2 0.2448 0.9067 0.076 0.924 0.000
#> GSM601808 3 0.0000 0.8477 0.000 0.000 1.000
#> GSM601813 3 0.5835 0.5633 0.340 0.000 0.660
#> GSM601818 3 0.1529 0.8505 0.040 0.000 0.960
#> GSM601823 1 0.1964 0.8510 0.944 0.000 0.056
#> GSM601833 2 0.0000 0.9325 0.000 1.000 0.000
#> GSM601848 1 0.1964 0.8510 0.944 0.000 0.056
#> GSM601853 3 0.0000 0.8477 0.000 0.000 1.000
#> GSM601863 3 0.0592 0.8496 0.012 0.000 0.988
#> GSM601754 2 0.4842 0.7698 0.224 0.776 0.000
#> GSM601784 2 0.0000 0.9325 0.000 1.000 0.000
#> GSM601794 1 0.1411 0.8445 0.964 0.000 0.036
#> GSM601799 2 0.5016 0.7473 0.240 0.760 0.000
#> GSM601829 1 0.6299 0.0603 0.524 0.000 0.476
#> GSM601839 2 0.0237 0.9322 0.004 0.996 0.000
#> GSM601844 1 0.4002 0.7804 0.840 0.000 0.160
#> GSM601859 2 0.0237 0.9323 0.004 0.996 0.000
#> GSM601869 3 0.1529 0.8504 0.040 0.000 0.960
#> GSM601749 3 0.6225 0.3553 0.432 0.000 0.568
#> GSM601759 3 0.5905 0.5476 0.352 0.000 0.648
#> GSM601764 1 0.3459 0.8319 0.892 0.012 0.096
#> GSM601769 2 0.0000 0.9325 0.000 1.000 0.000
#> GSM601774 2 0.0237 0.9323 0.004 0.996 0.000
#> GSM601779 1 0.1753 0.8509 0.952 0.000 0.048
#> GSM601789 2 0.0237 0.9322 0.004 0.996 0.000
#> GSM601804 1 0.4121 0.6945 0.832 0.168 0.000
#> GSM601809 3 0.4821 0.8015 0.088 0.064 0.848
#> GSM601814 2 0.0000 0.9325 0.000 1.000 0.000
#> GSM601819 3 0.6299 0.1790 0.476 0.000 0.524
#> GSM601824 1 0.0237 0.8343 0.996 0.004 0.000
#> GSM601834 2 0.0000 0.9325 0.000 1.000 0.000
#> GSM601849 1 0.2448 0.8468 0.924 0.000 0.076
#> GSM601854 3 0.5254 0.6841 0.264 0.000 0.736
#> GSM601864 2 0.0237 0.9322 0.004 0.996 0.000
#> GSM601755 2 0.2711 0.9003 0.088 0.912 0.000
#> GSM601785 2 0.1529 0.9156 0.040 0.960 0.000
#> GSM601795 1 0.0592 0.8395 0.988 0.000 0.012
#> GSM601800 2 0.2796 0.8980 0.092 0.908 0.000
#> GSM601830 3 0.0237 0.8461 0.000 0.004 0.996
#> GSM601840 2 0.6764 0.7254 0.108 0.744 0.148
#> GSM601845 2 0.8939 0.1710 0.340 0.520 0.140
#> GSM601860 2 0.0592 0.9299 0.012 0.988 0.000
#> GSM601870 3 0.0983 0.8381 0.004 0.016 0.980
#> GSM601750 3 0.3482 0.8177 0.128 0.000 0.872
#> GSM601760 1 0.6062 0.3051 0.616 0.000 0.384
#> GSM601765 2 0.0000 0.9325 0.000 1.000 0.000
#> GSM601770 2 0.0237 0.9323 0.004 0.996 0.000
#> GSM601775 2 0.4465 0.8083 0.176 0.820 0.004
#> GSM601780 1 0.1860 0.8514 0.948 0.000 0.052
#> GSM601790 2 0.0237 0.9322 0.004 0.996 0.000
#> GSM601805 2 0.2711 0.9008 0.088 0.912 0.000
#> GSM601810 3 0.0000 0.8477 0.000 0.000 1.000
#> GSM601815 2 0.0237 0.9322 0.004 0.996 0.000
#> GSM601820 3 0.5397 0.6630 0.280 0.000 0.720
#> GSM601825 2 0.1964 0.9158 0.056 0.944 0.000
#> GSM601835 2 0.1411 0.9165 0.000 0.964 0.036
#> GSM601850 1 0.1905 0.8345 0.956 0.016 0.028
#> GSM601855 3 0.0000 0.8477 0.000 0.000 1.000
#> GSM601865 2 0.0475 0.9312 0.004 0.992 0.004
#> GSM601756 2 0.2625 0.9025 0.084 0.916 0.000
#> GSM601786 2 0.0237 0.9322 0.004 0.996 0.000
#> GSM601796 1 0.3038 0.8329 0.896 0.000 0.104
#> GSM601801 2 0.2261 0.9106 0.068 0.932 0.000
#> GSM601831 3 0.1031 0.8502 0.024 0.000 0.976
#> GSM601841 3 0.5968 0.4703 0.364 0.000 0.636
#> GSM601846 2 0.8524 0.0778 0.448 0.460 0.092
#> GSM601861 2 0.0237 0.9322 0.004 0.996 0.000
#> GSM601871 3 0.4351 0.6988 0.004 0.168 0.828
#> GSM601751 2 0.1129 0.9284 0.020 0.976 0.004
#> GSM601761 1 0.3116 0.8252 0.892 0.000 0.108
#> GSM601766 2 0.7768 0.3508 0.344 0.592 0.064
#> GSM601771 2 0.0829 0.9314 0.012 0.984 0.004
#> GSM601776 1 0.3482 0.8098 0.872 0.000 0.128
#> GSM601781 1 0.1753 0.8454 0.952 0.000 0.048
#> GSM601791 1 0.2796 0.8351 0.908 0.000 0.092
#> GSM601806 2 0.2261 0.9106 0.068 0.932 0.000
#> GSM601811 3 0.0000 0.8477 0.000 0.000 1.000
#> GSM601816 1 0.1964 0.8511 0.944 0.000 0.056
#> GSM601821 2 0.0237 0.9322 0.004 0.996 0.000
#> GSM601826 1 0.2066 0.8500 0.940 0.000 0.060
#> GSM601836 1 0.6527 0.2989 0.588 0.008 0.404
#> GSM601851 1 0.2066 0.8500 0.940 0.000 0.060
#> GSM601856 3 0.0000 0.8477 0.000 0.000 1.000
#> GSM601866 3 0.2448 0.8444 0.076 0.000 0.924
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601752 4 0.2376 0.788 0.016 0.068 0.000 0.916
#> GSM601782 3 0.4891 0.587 0.308 0.000 0.680 0.012
#> GSM601792 1 0.4775 0.583 0.740 0.000 0.028 0.232
#> GSM601797 4 0.3076 0.736 0.048 0.016 0.036 0.900
#> GSM601827 3 0.4323 0.694 0.204 0.000 0.776 0.020
#> GSM601837 2 0.1545 0.881 0.000 0.952 0.008 0.040
#> GSM601842 2 0.3142 0.851 0.008 0.860 0.000 0.132
#> GSM601857 3 0.0895 0.767 0.020 0.000 0.976 0.004
#> GSM601867 3 0.3486 0.689 0.000 0.092 0.864 0.044
#> GSM601747 3 0.7911 0.371 0.296 0.128 0.532 0.044
#> GSM601757 3 0.4564 0.547 0.328 0.000 0.672 0.000
#> GSM601762 2 0.2976 0.874 0.008 0.872 0.000 0.120
#> GSM601767 2 0.2610 0.878 0.012 0.900 0.000 0.088
#> GSM601772 2 0.1854 0.883 0.012 0.940 0.000 0.048
#> GSM601777 4 0.8416 0.223 0.228 0.028 0.324 0.420
#> GSM601787 3 0.5416 0.508 0.000 0.260 0.692 0.048
#> GSM601802 4 0.2469 0.794 0.000 0.108 0.000 0.892
#> GSM601807 3 0.2115 0.737 0.004 0.024 0.936 0.036
#> GSM601812 3 0.4990 0.503 0.352 0.000 0.640 0.008
#> GSM601817 3 0.3597 0.738 0.148 0.000 0.836 0.016
#> GSM601822 4 0.5543 0.197 0.444 0.012 0.004 0.540
#> GSM601832 2 0.4507 0.772 0.012 0.776 0.012 0.200
#> GSM601847 4 0.5087 0.606 0.228 0.024 0.012 0.736
#> GSM601852 3 0.4897 0.551 0.332 0.000 0.660 0.008
#> GSM601862 3 0.0707 0.768 0.020 0.000 0.980 0.000
#> GSM601753 4 0.2530 0.793 0.000 0.112 0.000 0.888
#> GSM601783 1 0.5161 0.231 0.592 0.000 0.400 0.008
#> GSM601793 1 0.5637 0.654 0.720 0.000 0.168 0.112
#> GSM601798 4 0.2589 0.793 0.000 0.116 0.000 0.884
#> GSM601828 3 0.4313 0.649 0.260 0.000 0.736 0.004
#> GSM601838 2 0.1356 0.884 0.000 0.960 0.008 0.032
#> GSM601843 2 0.2480 0.876 0.008 0.904 0.000 0.088
#> GSM601858 2 0.3550 0.818 0.000 0.860 0.096 0.044
#> GSM601868 3 0.0779 0.767 0.016 0.000 0.980 0.004
#> GSM601748 3 0.4746 0.595 0.304 0.000 0.688 0.008
#> GSM601758 1 0.4647 0.481 0.704 0.000 0.288 0.008
#> GSM601763 1 0.5003 0.589 0.768 0.084 0.000 0.148
#> GSM601768 2 0.2402 0.882 0.012 0.912 0.000 0.076
#> GSM601773 2 0.2805 0.870 0.012 0.888 0.000 0.100
#> GSM601778 1 0.7324 0.210 0.500 0.004 0.144 0.352
#> GSM601788 2 0.5118 0.695 0.004 0.736 0.040 0.220
#> GSM601803 4 0.3266 0.761 0.000 0.168 0.000 0.832
#> GSM601808 3 0.0336 0.765 0.008 0.000 0.992 0.000
#> GSM601813 1 0.5070 0.323 0.620 0.000 0.372 0.008
#> GSM601818 3 0.4095 0.714 0.192 0.000 0.792 0.016
#> GSM601823 1 0.1677 0.717 0.948 0.000 0.012 0.040
#> GSM601833 2 0.2473 0.878 0.012 0.908 0.000 0.080
#> GSM601848 1 0.1488 0.718 0.956 0.000 0.012 0.032
#> GSM601853 3 0.1059 0.767 0.016 0.000 0.972 0.012
#> GSM601863 3 0.2530 0.752 0.112 0.000 0.888 0.000
#> GSM601754 4 0.3149 0.790 0.032 0.088 0.000 0.880
#> GSM601784 2 0.1452 0.890 0.008 0.956 0.000 0.036
#> GSM601794 1 0.6538 0.444 0.600 0.000 0.108 0.292
#> GSM601799 4 0.2662 0.781 0.016 0.084 0.000 0.900
#> GSM601829 1 0.5842 0.144 0.520 0.000 0.448 0.032
#> GSM601839 2 0.1356 0.884 0.000 0.960 0.008 0.032
#> GSM601844 1 0.3999 0.674 0.824 0.000 0.140 0.036
#> GSM601859 2 0.2528 0.884 0.008 0.908 0.004 0.080
#> GSM601869 3 0.2868 0.743 0.136 0.000 0.864 0.000
#> GSM601749 1 0.4819 0.381 0.652 0.000 0.344 0.004
#> GSM601759 1 0.4920 0.322 0.628 0.000 0.368 0.004
#> GSM601764 1 0.2405 0.704 0.928 0.020 0.016 0.036
#> GSM601769 2 0.1305 0.890 0.000 0.960 0.004 0.036
#> GSM601774 2 0.1970 0.886 0.008 0.932 0.000 0.060
#> GSM601779 1 0.1584 0.717 0.952 0.000 0.012 0.036
#> GSM601789 2 0.1118 0.886 0.000 0.964 0.000 0.036
#> GSM601804 4 0.4105 0.677 0.156 0.032 0.000 0.812
#> GSM601809 3 0.8218 0.389 0.236 0.172 0.536 0.056
#> GSM601814 2 0.1890 0.886 0.000 0.936 0.008 0.056
#> GSM601819 1 0.4673 0.487 0.700 0.000 0.292 0.008
#> GSM601824 1 0.5024 0.304 0.632 0.008 0.000 0.360
#> GSM601834 2 0.2179 0.884 0.012 0.924 0.000 0.064
#> GSM601849 1 0.1837 0.720 0.944 0.000 0.028 0.028
#> GSM601854 3 0.5147 0.181 0.460 0.000 0.536 0.004
#> GSM601864 2 0.2706 0.866 0.000 0.900 0.020 0.080
#> GSM601755 4 0.2647 0.792 0.000 0.120 0.000 0.880
#> GSM601785 2 0.4107 0.799 0.016 0.804 0.004 0.176
#> GSM601795 4 0.5808 0.116 0.424 0.000 0.032 0.544
#> GSM601800 4 0.2345 0.794 0.000 0.100 0.000 0.900
#> GSM601830 3 0.1771 0.760 0.012 0.004 0.948 0.036
#> GSM601840 4 0.8859 0.342 0.092 0.320 0.148 0.440
#> GSM601845 2 0.8438 0.360 0.168 0.544 0.092 0.196
#> GSM601860 2 0.2456 0.882 0.008 0.916 0.008 0.068
#> GSM601870 3 0.1936 0.738 0.000 0.032 0.940 0.028
#> GSM601750 3 0.5099 0.444 0.380 0.000 0.612 0.008
#> GSM601760 1 0.3668 0.617 0.808 0.000 0.188 0.004
#> GSM601765 2 0.2473 0.878 0.012 0.908 0.000 0.080
#> GSM601770 2 0.2473 0.879 0.012 0.908 0.000 0.080
#> GSM601775 4 0.7396 0.368 0.156 0.340 0.004 0.500
#> GSM601780 1 0.1388 0.718 0.960 0.000 0.012 0.028
#> GSM601790 2 0.1151 0.886 0.000 0.968 0.008 0.024
#> GSM601805 4 0.2921 0.783 0.000 0.140 0.000 0.860
#> GSM601810 3 0.1545 0.769 0.040 0.000 0.952 0.008
#> GSM601815 2 0.1545 0.885 0.000 0.952 0.008 0.040
#> GSM601820 1 0.5112 0.124 0.560 0.000 0.436 0.004
#> GSM601825 4 0.5126 0.223 0.004 0.444 0.000 0.552
#> GSM601835 2 0.4179 0.823 0.004 0.832 0.060 0.104
#> GSM601850 1 0.5713 0.403 0.640 0.004 0.036 0.320
#> GSM601855 3 0.1443 0.756 0.008 0.004 0.960 0.028
#> GSM601865 2 0.1635 0.881 0.000 0.948 0.008 0.044
#> GSM601756 4 0.2647 0.791 0.000 0.120 0.000 0.880
#> GSM601786 2 0.1722 0.881 0.000 0.944 0.008 0.048
#> GSM601796 1 0.6724 0.587 0.612 0.000 0.164 0.224
#> GSM601801 4 0.2868 0.785 0.000 0.136 0.000 0.864
#> GSM601831 3 0.2376 0.765 0.068 0.000 0.916 0.016
#> GSM601841 1 0.6419 0.186 0.512 0.000 0.420 0.068
#> GSM601846 4 0.6322 0.653 0.056 0.188 0.052 0.704
#> GSM601861 2 0.1635 0.884 0.000 0.948 0.008 0.044
#> GSM601871 3 0.5312 0.532 0.000 0.236 0.712 0.052
#> GSM601751 2 0.4124 0.787 0.012 0.812 0.012 0.164
#> GSM601761 1 0.1356 0.716 0.960 0.000 0.032 0.008
#> GSM601766 2 0.7507 0.463 0.224 0.588 0.028 0.160
#> GSM601771 2 0.5308 0.664 0.012 0.708 0.024 0.256
#> GSM601776 1 0.2413 0.711 0.916 0.000 0.064 0.020
#> GSM601781 1 0.5569 0.622 0.736 0.008 0.080 0.176
#> GSM601791 1 0.1610 0.717 0.952 0.000 0.032 0.016
#> GSM601806 4 0.3688 0.724 0.000 0.208 0.000 0.792
#> GSM601811 3 0.1584 0.769 0.036 0.000 0.952 0.012
#> GSM601816 1 0.2111 0.716 0.932 0.000 0.024 0.044
#> GSM601821 2 0.1635 0.884 0.000 0.948 0.008 0.044
#> GSM601826 1 0.1488 0.718 0.956 0.000 0.012 0.032
#> GSM601836 1 0.7955 0.346 0.540 0.076 0.296 0.088
#> GSM601851 1 0.1174 0.718 0.968 0.000 0.012 0.020
#> GSM601856 3 0.0804 0.764 0.008 0.000 0.980 0.012
#> GSM601866 3 0.4781 0.540 0.336 0.000 0.660 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601752 4 0.140 0.7032 0.008 0.020 0.000 0.956 0.016
#> GSM601782 3 0.614 0.5125 0.204 0.000 0.596 0.008 0.192
#> GSM601792 1 0.635 0.4401 0.608 0.000 0.028 0.168 0.196
#> GSM601797 4 0.443 0.6183 0.052 0.004 0.020 0.788 0.136
#> GSM601827 3 0.522 0.5853 0.180 0.000 0.684 0.000 0.136
#> GSM601837 2 0.227 0.6978 0.000 0.908 0.004 0.016 0.072
#> GSM601842 2 0.572 0.5378 0.000 0.616 0.000 0.144 0.240
#> GSM601857 3 0.290 0.6548 0.036 0.000 0.868 0.000 0.096
#> GSM601867 3 0.644 0.3044 0.000 0.176 0.568 0.016 0.240
#> GSM601747 3 0.822 0.2472 0.176 0.064 0.432 0.036 0.292
#> GSM601757 3 0.593 0.3936 0.320 0.000 0.564 0.004 0.112
#> GSM601762 2 0.508 0.6437 0.000 0.700 0.000 0.160 0.140
#> GSM601767 2 0.495 0.6705 0.000 0.712 0.000 0.124 0.164
#> GSM601772 2 0.482 0.6695 0.000 0.704 0.000 0.076 0.220
#> GSM601777 4 0.910 -0.0381 0.184 0.032 0.268 0.304 0.212
#> GSM601787 3 0.718 0.0121 0.004 0.332 0.420 0.016 0.228
#> GSM601802 4 0.246 0.7086 0.004 0.052 0.000 0.904 0.040
#> GSM601807 3 0.431 0.5311 0.008 0.024 0.752 0.004 0.212
#> GSM601812 3 0.554 0.5248 0.236 0.000 0.636 0.000 0.128
#> GSM601817 3 0.487 0.6120 0.120 0.000 0.720 0.000 0.160
#> GSM601822 1 0.646 0.1236 0.480 0.000 0.012 0.376 0.132
#> GSM601832 2 0.657 0.3319 0.000 0.508 0.012 0.164 0.316
#> GSM601847 4 0.636 0.4031 0.288 0.028 0.008 0.588 0.088
#> GSM601852 3 0.549 0.5019 0.256 0.000 0.632 0.000 0.112
#> GSM601862 3 0.274 0.6558 0.036 0.000 0.880 0.000 0.084
#> GSM601753 4 0.224 0.7013 0.000 0.068 0.000 0.908 0.024
#> GSM601783 1 0.627 -0.0885 0.440 0.000 0.428 0.004 0.128
#> GSM601793 1 0.714 0.4576 0.552 0.000 0.188 0.076 0.184
#> GSM601798 4 0.207 0.7089 0.000 0.048 0.000 0.920 0.032
#> GSM601828 3 0.545 0.5334 0.216 0.000 0.652 0.000 0.132
#> GSM601838 2 0.186 0.7086 0.000 0.932 0.004 0.016 0.048
#> GSM601843 2 0.459 0.6694 0.000 0.728 0.000 0.068 0.204
#> GSM601858 2 0.542 0.4922 0.000 0.704 0.092 0.028 0.176
#> GSM601868 3 0.227 0.6465 0.020 0.000 0.904 0.000 0.076
#> GSM601748 3 0.577 0.4646 0.272 0.000 0.608 0.004 0.116
#> GSM601758 1 0.570 0.2480 0.576 0.000 0.320 0.000 0.104
#> GSM601763 1 0.740 -0.1875 0.472 0.088 0.012 0.080 0.348
#> GSM601768 2 0.502 0.6579 0.000 0.692 0.000 0.096 0.212
#> GSM601773 2 0.493 0.6820 0.000 0.716 0.000 0.148 0.136
#> GSM601778 1 0.778 0.2046 0.452 0.000 0.100 0.264 0.184
#> GSM601788 2 0.606 0.4938 0.004 0.680 0.056 0.120 0.140
#> GSM601803 4 0.313 0.6848 0.000 0.120 0.000 0.848 0.032
#> GSM601808 3 0.239 0.6507 0.020 0.000 0.896 0.000 0.084
#> GSM601813 1 0.597 0.1083 0.512 0.000 0.372 0.000 0.116
#> GSM601818 3 0.551 0.5872 0.176 0.000 0.652 0.000 0.172
#> GSM601823 1 0.288 0.5742 0.876 0.000 0.008 0.024 0.092
#> GSM601833 2 0.514 0.6323 0.000 0.676 0.000 0.096 0.228
#> GSM601848 1 0.259 0.5801 0.900 0.000 0.012 0.028 0.060
#> GSM601853 3 0.202 0.6436 0.008 0.000 0.912 0.000 0.080
#> GSM601863 3 0.356 0.6516 0.108 0.000 0.828 0.000 0.064
#> GSM601754 4 0.274 0.6976 0.020 0.032 0.000 0.896 0.052
#> GSM601784 2 0.388 0.7198 0.000 0.804 0.000 0.072 0.124
#> GSM601794 1 0.774 0.3182 0.460 0.000 0.092 0.244 0.204
#> GSM601799 4 0.255 0.6873 0.008 0.020 0.000 0.896 0.076
#> GSM601829 1 0.665 0.0312 0.424 0.000 0.380 0.004 0.192
#> GSM601839 2 0.168 0.7109 0.000 0.940 0.004 0.012 0.044
#> GSM601844 1 0.627 0.4690 0.600 0.000 0.156 0.020 0.224
#> GSM601859 2 0.397 0.7180 0.000 0.800 0.000 0.100 0.100
#> GSM601869 3 0.427 0.6251 0.144 0.000 0.772 0.000 0.084
#> GSM601749 1 0.581 0.1505 0.540 0.000 0.356 0.000 0.104
#> GSM601759 1 0.579 0.1132 0.524 0.000 0.380 0.000 0.096
#> GSM601764 1 0.528 0.3840 0.688 0.016 0.040 0.012 0.244
#> GSM601769 2 0.317 0.7270 0.000 0.856 0.000 0.060 0.084
#> GSM601774 2 0.374 0.7183 0.000 0.816 0.000 0.076 0.108
#> GSM601779 1 0.186 0.5729 0.932 0.000 0.004 0.016 0.048
#> GSM601789 2 0.240 0.7109 0.000 0.904 0.004 0.024 0.068
#> GSM601804 4 0.464 0.5695 0.172 0.004 0.000 0.744 0.080
#> GSM601809 3 0.891 0.0255 0.124 0.236 0.384 0.044 0.212
#> GSM601814 2 0.239 0.7288 0.000 0.900 0.000 0.072 0.028
#> GSM601819 1 0.632 0.2030 0.520 0.000 0.320 0.004 0.156
#> GSM601824 1 0.583 0.3149 0.624 0.004 0.000 0.212 0.160
#> GSM601834 2 0.454 0.6885 0.000 0.744 0.000 0.084 0.172
#> GSM601849 1 0.289 0.5863 0.888 0.000 0.036 0.020 0.056
#> GSM601854 3 0.589 0.2628 0.372 0.000 0.520 0.000 0.108
#> GSM601864 2 0.411 0.6193 0.000 0.804 0.024 0.040 0.132
#> GSM601755 4 0.219 0.7090 0.000 0.060 0.000 0.912 0.028
#> GSM601785 2 0.619 0.5474 0.020 0.616 0.000 0.168 0.196
#> GSM601795 4 0.735 -0.0489 0.368 0.000 0.044 0.404 0.184
#> GSM601800 4 0.208 0.7064 0.004 0.040 0.000 0.924 0.032
#> GSM601830 3 0.329 0.6061 0.004 0.008 0.816 0.000 0.172
#> GSM601840 4 0.886 -0.0309 0.052 0.260 0.104 0.388 0.196
#> GSM601845 5 0.880 0.4400 0.132 0.252 0.084 0.100 0.432
#> GSM601860 2 0.514 0.6351 0.036 0.716 0.000 0.048 0.200
#> GSM601870 3 0.459 0.5017 0.004 0.052 0.728 0.000 0.216
#> GSM601750 3 0.590 0.4175 0.300 0.000 0.580 0.004 0.116
#> GSM601760 1 0.490 0.4738 0.708 0.000 0.196 0.000 0.096
#> GSM601765 2 0.528 0.5706 0.004 0.640 0.000 0.068 0.288
#> GSM601770 2 0.472 0.6823 0.000 0.732 0.000 0.104 0.164
#> GSM601775 4 0.866 -0.3084 0.112 0.248 0.020 0.328 0.292
#> GSM601780 1 0.163 0.5788 0.944 0.000 0.004 0.016 0.036
#> GSM601790 2 0.153 0.7173 0.000 0.948 0.004 0.012 0.036
#> GSM601805 4 0.345 0.6858 0.008 0.116 0.000 0.840 0.036
#> GSM601810 3 0.383 0.6523 0.048 0.000 0.796 0.000 0.156
#> GSM601815 2 0.203 0.7130 0.000 0.924 0.004 0.020 0.052
#> GSM601820 3 0.603 0.1389 0.416 0.000 0.468 0.000 0.116
#> GSM601825 4 0.537 0.0915 0.000 0.416 0.000 0.528 0.056
#> GSM601835 2 0.669 0.3271 0.000 0.540 0.048 0.104 0.308
#> GSM601850 1 0.785 0.1747 0.480 0.024 0.052 0.224 0.220
#> GSM601855 3 0.285 0.5961 0.004 0.000 0.840 0.000 0.156
#> GSM601865 2 0.259 0.6808 0.000 0.888 0.012 0.008 0.092
#> GSM601756 4 0.194 0.7083 0.000 0.056 0.000 0.924 0.020
#> GSM601786 2 0.240 0.7056 0.000 0.904 0.004 0.024 0.068
#> GSM601796 1 0.815 0.3020 0.424 0.000 0.164 0.208 0.204
#> GSM601801 4 0.248 0.7051 0.000 0.084 0.000 0.892 0.024
#> GSM601831 3 0.334 0.6571 0.060 0.000 0.844 0.000 0.096
#> GSM601841 3 0.743 -0.0339 0.376 0.000 0.388 0.048 0.188
#> GSM601846 4 0.867 -0.0524 0.076 0.096 0.108 0.376 0.344
#> GSM601861 2 0.192 0.7196 0.000 0.928 0.000 0.040 0.032
#> GSM601871 3 0.680 0.1023 0.000 0.276 0.468 0.008 0.248
#> GSM601751 2 0.642 0.4651 0.032 0.620 0.004 0.168 0.176
#> GSM601761 1 0.252 0.5766 0.896 0.000 0.052 0.000 0.052
#> GSM601766 5 0.819 0.2261 0.116 0.328 0.036 0.092 0.428
#> GSM601771 2 0.693 0.3556 0.028 0.572 0.016 0.220 0.164
#> GSM601776 1 0.340 0.5776 0.852 0.000 0.072 0.008 0.068
#> GSM601781 1 0.656 0.4162 0.640 0.012 0.060 0.108 0.180
#> GSM601791 1 0.326 0.5814 0.856 0.000 0.040 0.008 0.096
#> GSM601806 4 0.324 0.6566 0.000 0.152 0.000 0.828 0.020
#> GSM601811 3 0.336 0.6484 0.036 0.000 0.832 0.000 0.132
#> GSM601816 1 0.327 0.5637 0.852 0.000 0.008 0.032 0.108
#> GSM601821 2 0.192 0.7204 0.000 0.928 0.000 0.036 0.036
#> GSM601826 1 0.237 0.5826 0.912 0.000 0.016 0.020 0.052
#> GSM601836 5 0.872 -0.0323 0.296 0.056 0.248 0.060 0.340
#> GSM601851 1 0.188 0.5832 0.936 0.000 0.012 0.020 0.032
#> GSM601856 3 0.225 0.6352 0.012 0.000 0.900 0.000 0.088
#> GSM601866 3 0.516 0.5199 0.256 0.000 0.660 0.000 0.084
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601752 4 0.1799 0.7591 0.008 0.016 0.000 0.936 0.024 0.016
#> GSM601782 3 0.6601 -0.0311 0.340 0.000 0.460 0.004 0.056 0.140
#> GSM601792 6 0.7128 0.4168 0.204 0.000 0.032 0.144 0.096 0.524
#> GSM601797 4 0.4273 0.6730 0.072 0.000 0.028 0.800 0.060 0.040
#> GSM601827 3 0.6553 0.1536 0.276 0.000 0.520 0.008 0.060 0.136
#> GSM601837 2 0.2995 0.6474 0.048 0.864 0.004 0.012 0.072 0.000
#> GSM601842 2 0.6087 0.1910 0.016 0.432 0.008 0.124 0.420 0.000
#> GSM601857 3 0.3001 0.4318 0.128 0.000 0.840 0.000 0.008 0.024
#> GSM601867 3 0.6017 0.2769 0.172 0.128 0.628 0.004 0.064 0.004
#> GSM601747 1 0.8572 0.0764 0.336 0.056 0.296 0.036 0.188 0.088
#> GSM601757 3 0.6227 -0.0694 0.256 0.000 0.476 0.000 0.016 0.252
#> GSM601762 2 0.5128 0.5544 0.008 0.636 0.000 0.116 0.240 0.000
#> GSM601767 2 0.4765 0.5882 0.008 0.664 0.000 0.076 0.252 0.000
#> GSM601772 2 0.4877 0.5285 0.008 0.596 0.000 0.044 0.348 0.004
#> GSM601777 3 0.9259 -0.1232 0.176 0.016 0.232 0.208 0.168 0.200
#> GSM601787 3 0.6962 0.1430 0.160 0.272 0.476 0.008 0.084 0.000
#> GSM601802 4 0.1498 0.7618 0.012 0.024 0.000 0.948 0.012 0.004
#> GSM601807 3 0.4741 0.3681 0.200 0.020 0.708 0.004 0.068 0.000
#> GSM601812 3 0.6445 -0.0919 0.292 0.000 0.464 0.000 0.032 0.212
#> GSM601817 3 0.5754 0.1866 0.336 0.000 0.544 0.000 0.044 0.076
#> GSM601822 6 0.7365 0.1787 0.112 0.000 0.012 0.232 0.200 0.444
#> GSM601832 5 0.6144 0.0640 0.028 0.320 0.000 0.136 0.512 0.004
#> GSM601847 4 0.7478 0.1501 0.084 0.024 0.008 0.424 0.140 0.320
#> GSM601852 3 0.6439 -0.0334 0.260 0.000 0.500 0.000 0.044 0.196
#> GSM601862 3 0.2993 0.4328 0.120 0.000 0.844 0.000 0.008 0.028
#> GSM601753 4 0.2740 0.7493 0.012 0.040 0.000 0.884 0.056 0.008
#> GSM601783 1 0.6601 0.3252 0.360 0.000 0.312 0.000 0.024 0.304
#> GSM601793 6 0.7187 0.3774 0.244 0.000 0.088 0.060 0.092 0.516
#> GSM601798 4 0.1982 0.7622 0.012 0.040 0.000 0.924 0.020 0.004
#> GSM601828 3 0.5980 0.1130 0.280 0.000 0.552 0.000 0.036 0.132
#> GSM601838 2 0.1844 0.6680 0.024 0.924 0.000 0.004 0.048 0.000
#> GSM601843 2 0.5274 0.4533 0.008 0.568 0.004 0.076 0.344 0.000
#> GSM601858 2 0.6172 0.4298 0.136 0.632 0.092 0.016 0.124 0.000
#> GSM601868 3 0.2122 0.4403 0.084 0.000 0.900 0.000 0.008 0.008
#> GSM601748 3 0.5796 -0.0725 0.352 0.000 0.480 0.000 0.004 0.164
#> GSM601758 6 0.5839 -0.1513 0.284 0.000 0.184 0.000 0.008 0.524
#> GSM601763 5 0.6575 0.1483 0.072 0.036 0.008 0.032 0.480 0.372
#> GSM601768 2 0.5449 0.4793 0.032 0.564 0.000 0.052 0.348 0.004
#> GSM601773 2 0.4938 0.6036 0.012 0.676 0.000 0.112 0.200 0.000
#> GSM601778 6 0.8596 0.1616 0.184 0.004 0.096 0.168 0.188 0.360
#> GSM601788 2 0.7097 0.3662 0.084 0.572 0.040 0.108 0.180 0.016
#> GSM601803 4 0.2597 0.7466 0.008 0.088 0.000 0.880 0.020 0.004
#> GSM601808 3 0.2622 0.4428 0.104 0.000 0.868 0.000 0.024 0.004
#> GSM601813 6 0.6486 -0.3405 0.268 0.000 0.280 0.000 0.024 0.428
#> GSM601818 3 0.6026 0.0192 0.352 0.000 0.492 0.000 0.028 0.128
#> GSM601823 6 0.3697 0.5113 0.104 0.000 0.004 0.012 0.068 0.812
#> GSM601833 2 0.4518 0.5146 0.004 0.612 0.000 0.036 0.348 0.000
#> GSM601848 6 0.2750 0.5128 0.080 0.000 0.004 0.000 0.048 0.868
#> GSM601853 3 0.2851 0.4381 0.132 0.000 0.844 0.000 0.020 0.004
#> GSM601863 3 0.4241 0.3826 0.136 0.000 0.760 0.000 0.016 0.088
#> GSM601754 4 0.3232 0.7403 0.040 0.028 0.000 0.864 0.048 0.020
#> GSM601784 2 0.3803 0.6685 0.012 0.776 0.000 0.040 0.172 0.000
#> GSM601794 6 0.8003 0.3232 0.232 0.000 0.056 0.216 0.104 0.392
#> GSM601799 4 0.3704 0.7133 0.016 0.028 0.000 0.820 0.112 0.024
#> GSM601829 3 0.7773 -0.1136 0.276 0.000 0.324 0.016 0.120 0.264
#> GSM601839 2 0.1564 0.6708 0.024 0.936 0.000 0.000 0.040 0.000
#> GSM601844 6 0.7438 0.1912 0.340 0.000 0.108 0.028 0.124 0.400
#> GSM601859 2 0.4915 0.6188 0.024 0.708 0.000 0.096 0.168 0.004
#> GSM601869 3 0.4584 0.3096 0.196 0.000 0.700 0.000 0.004 0.100
#> GSM601749 6 0.6342 -0.3834 0.324 0.000 0.264 0.000 0.012 0.400
#> GSM601759 6 0.6169 -0.3856 0.320 0.000 0.268 0.000 0.004 0.408
#> GSM601764 6 0.6522 0.1979 0.172 0.004 0.036 0.000 0.332 0.456
#> GSM601769 2 0.2362 0.6754 0.000 0.860 0.000 0.004 0.136 0.000
#> GSM601774 2 0.3761 0.6463 0.008 0.764 0.000 0.032 0.196 0.000
#> GSM601779 6 0.3085 0.5123 0.064 0.000 0.008 0.016 0.048 0.864
#> GSM601789 2 0.3337 0.6680 0.036 0.820 0.004 0.004 0.136 0.000
#> GSM601804 4 0.4473 0.6367 0.036 0.012 0.000 0.760 0.044 0.148
#> GSM601809 1 0.8945 0.0422 0.296 0.220 0.256 0.028 0.096 0.104
#> GSM601814 2 0.1863 0.6824 0.000 0.920 0.000 0.036 0.044 0.000
#> GSM601819 1 0.6780 0.2991 0.416 0.000 0.204 0.004 0.044 0.332
#> GSM601824 6 0.6301 0.3394 0.092 0.004 0.000 0.136 0.176 0.592
#> GSM601834 2 0.4003 0.6119 0.004 0.716 0.000 0.032 0.248 0.000
#> GSM601849 6 0.4736 0.4785 0.128 0.000 0.052 0.008 0.064 0.748
#> GSM601854 3 0.6493 -0.2552 0.264 0.000 0.424 0.000 0.024 0.288
#> GSM601864 2 0.4951 0.5664 0.092 0.752 0.032 0.052 0.072 0.000
#> GSM601755 4 0.1787 0.7623 0.016 0.032 0.000 0.932 0.020 0.000
#> GSM601785 2 0.7160 0.1474 0.084 0.416 0.004 0.132 0.352 0.012
#> GSM601795 4 0.7656 -0.1567 0.212 0.000 0.016 0.336 0.112 0.324
#> GSM601800 4 0.1911 0.7632 0.012 0.036 0.000 0.928 0.020 0.004
#> GSM601830 3 0.4655 0.4038 0.204 0.000 0.704 0.008 0.080 0.004
#> GSM601840 4 0.9171 -0.1575 0.112 0.212 0.108 0.336 0.180 0.052
#> GSM601845 5 0.8003 0.4451 0.200 0.184 0.028 0.068 0.464 0.056
#> GSM601860 2 0.5890 0.5417 0.072 0.680 0.016 0.044 0.156 0.032
#> GSM601870 3 0.5006 0.3601 0.176 0.048 0.700 0.000 0.076 0.000
#> GSM601750 3 0.6322 -0.2093 0.324 0.000 0.444 0.000 0.020 0.212
#> GSM601760 6 0.5860 0.0910 0.244 0.000 0.136 0.004 0.028 0.588
#> GSM601765 2 0.5193 0.3032 0.020 0.492 0.004 0.028 0.452 0.004
#> GSM601770 2 0.4469 0.5957 0.012 0.676 0.000 0.040 0.272 0.000
#> GSM601775 5 0.8293 0.3086 0.104 0.128 0.008 0.264 0.396 0.100
#> GSM601780 6 0.3277 0.5028 0.124 0.000 0.004 0.012 0.028 0.832
#> GSM601790 2 0.1807 0.6763 0.020 0.920 0.000 0.000 0.060 0.000
#> GSM601805 4 0.3879 0.7292 0.040 0.084 0.000 0.816 0.052 0.008
#> GSM601810 3 0.4827 0.3681 0.232 0.000 0.684 0.000 0.044 0.040
#> GSM601815 2 0.1464 0.6744 0.016 0.944 0.000 0.004 0.036 0.000
#> GSM601820 1 0.6310 0.2888 0.376 0.000 0.328 0.000 0.008 0.288
#> GSM601825 4 0.5591 0.1079 0.008 0.372 0.000 0.504 0.116 0.000
#> GSM601835 5 0.6634 0.0620 0.036 0.348 0.060 0.068 0.488 0.000
#> GSM601850 6 0.7983 0.2807 0.184 0.008 0.048 0.144 0.164 0.452
#> GSM601855 3 0.3610 0.4214 0.152 0.004 0.792 0.000 0.052 0.000
#> GSM601865 2 0.2706 0.6474 0.056 0.876 0.008 0.000 0.060 0.000
#> GSM601756 4 0.1692 0.7623 0.000 0.048 0.000 0.932 0.012 0.008
#> GSM601786 2 0.1934 0.6709 0.040 0.916 0.000 0.000 0.044 0.000
#> GSM601796 6 0.8144 0.2839 0.284 0.000 0.092 0.144 0.104 0.376
#> GSM601801 4 0.2145 0.7536 0.004 0.076 0.000 0.904 0.012 0.004
#> GSM601831 3 0.4689 0.3679 0.232 0.000 0.696 0.004 0.024 0.044
#> GSM601841 6 0.7568 -0.1610 0.216 0.000 0.328 0.048 0.048 0.360
#> GSM601846 5 0.8853 0.2313 0.136 0.080 0.068 0.272 0.360 0.084
#> GSM601861 2 0.0717 0.6798 0.000 0.976 0.000 0.008 0.016 0.000
#> GSM601871 3 0.6704 0.1100 0.152 0.292 0.476 0.000 0.080 0.000
#> GSM601751 2 0.7187 0.3510 0.068 0.568 0.012 0.144 0.148 0.060
#> GSM601761 6 0.3966 0.4200 0.156 0.000 0.048 0.000 0.020 0.776
#> GSM601766 5 0.6787 0.4365 0.084 0.176 0.016 0.032 0.600 0.092
#> GSM601771 2 0.8023 0.1880 0.080 0.464 0.056 0.192 0.184 0.024
#> GSM601776 6 0.4394 0.4329 0.136 0.000 0.052 0.000 0.052 0.760
#> GSM601781 6 0.7453 0.3816 0.160 0.012 0.060 0.100 0.124 0.544
#> GSM601791 6 0.4306 0.4674 0.160 0.000 0.032 0.004 0.044 0.760
#> GSM601806 4 0.2715 0.7244 0.004 0.112 0.000 0.860 0.024 0.000
#> GSM601811 3 0.4428 0.3762 0.228 0.000 0.708 0.000 0.048 0.016
#> GSM601816 6 0.3713 0.5120 0.100 0.000 0.008 0.004 0.080 0.808
#> GSM601821 2 0.0717 0.6803 0.000 0.976 0.000 0.008 0.016 0.000
#> GSM601826 6 0.2344 0.5129 0.076 0.000 0.004 0.000 0.028 0.892
#> GSM601836 5 0.8460 -0.1092 0.228 0.008 0.156 0.044 0.300 0.264
#> GSM601851 6 0.2938 0.4761 0.100 0.000 0.020 0.004 0.016 0.860
#> GSM601856 3 0.2771 0.4426 0.116 0.000 0.852 0.000 0.032 0.000
#> GSM601866 3 0.6419 -0.1617 0.252 0.000 0.468 0.000 0.028 0.252
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 time(p) gender(p) k
#> MAD:skmeans 121 0.6728 0.17942 2
#> MAD:skmeans 113 0.1404 0.20920 3
#> MAD:skmeans 99 0.1336 0.05379 4
#> MAD:skmeans 78 0.0944 0.00765 5
#> MAD:skmeans 45 0.7587 0.01174 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 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.402 0.823 0.894 0.4964 0.499 0.499
#> 3 3 0.451 0.712 0.824 0.2967 0.701 0.476
#> 4 4 0.492 0.595 0.766 0.1096 0.802 0.525
#> 5 5 0.555 0.589 0.741 0.0721 0.893 0.661
#> 6 6 0.602 0.603 0.753 0.0567 0.899 0.608
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
#> GSM601752 1 0.8144 0.6678 0.748 0.252
#> GSM601782 1 0.2948 0.8963 0.948 0.052
#> GSM601792 1 0.0938 0.8958 0.988 0.012
#> GSM601797 1 0.4161 0.8855 0.916 0.084
#> GSM601827 1 0.4161 0.8769 0.916 0.084
#> GSM601837 2 0.0938 0.8756 0.012 0.988
#> GSM601842 2 0.6531 0.8261 0.168 0.832
#> GSM601857 2 0.7745 0.7345 0.228 0.772
#> GSM601867 2 0.1633 0.8782 0.024 0.976
#> GSM601747 1 0.8661 0.6141 0.712 0.288
#> GSM601757 1 0.0376 0.8947 0.996 0.004
#> GSM601762 2 0.3274 0.8789 0.060 0.940
#> GSM601767 2 0.3879 0.8736 0.076 0.924
#> GSM601772 2 0.3431 0.8845 0.064 0.936
#> GSM601777 1 0.9286 0.5486 0.656 0.344
#> GSM601787 2 0.6887 0.7848 0.184 0.816
#> GSM601802 2 0.8144 0.7109 0.252 0.748
#> GSM601807 1 0.5629 0.8611 0.868 0.132
#> GSM601812 1 0.2603 0.8948 0.956 0.044
#> GSM601817 1 0.4431 0.8886 0.908 0.092
#> GSM601822 1 0.0938 0.8958 0.988 0.012
#> GSM601832 2 0.6148 0.8482 0.152 0.848
#> GSM601847 1 0.6247 0.8086 0.844 0.156
#> GSM601852 1 0.3114 0.8893 0.944 0.056
#> GSM601862 2 0.8386 0.6616 0.268 0.732
#> GSM601753 1 0.9815 0.1980 0.580 0.420
#> GSM601783 1 0.0376 0.8943 0.996 0.004
#> GSM601793 1 0.3431 0.8917 0.936 0.064
#> GSM601798 2 0.7674 0.7781 0.224 0.776
#> GSM601828 1 0.0672 0.8947 0.992 0.008
#> GSM601838 2 0.0000 0.8807 0.000 1.000
#> GSM601843 2 0.0938 0.8840 0.012 0.988
#> GSM601858 2 0.1633 0.8801 0.024 0.976
#> GSM601868 1 0.7528 0.7885 0.784 0.216
#> GSM601748 1 0.1633 0.8947 0.976 0.024
#> GSM601758 1 0.0938 0.8958 0.988 0.012
#> GSM601763 1 0.5519 0.8307 0.872 0.128
#> GSM601768 2 0.3879 0.8794 0.076 0.924
#> GSM601773 2 0.4815 0.8667 0.104 0.896
#> GSM601778 1 0.3431 0.8852 0.936 0.064
#> GSM601788 2 0.9129 0.6228 0.328 0.672
#> GSM601803 2 0.6247 0.8349 0.156 0.844
#> GSM601808 1 0.6343 0.8445 0.840 0.160
#> GSM601813 1 0.1414 0.8966 0.980 0.020
#> GSM601818 1 0.2423 0.8949 0.960 0.040
#> GSM601823 1 0.0938 0.8958 0.988 0.012
#> GSM601833 2 0.0376 0.8819 0.004 0.996
#> GSM601848 1 0.0938 0.8958 0.988 0.012
#> GSM601853 1 0.4298 0.8764 0.912 0.088
#> GSM601863 1 0.7376 0.7969 0.792 0.208
#> GSM601754 1 0.9129 0.5431 0.672 0.328
#> GSM601784 2 0.3114 0.8828 0.056 0.944
#> GSM601794 1 0.2423 0.8951 0.960 0.040
#> GSM601799 1 0.9522 0.3730 0.628 0.372
#> GSM601829 1 0.0938 0.8958 0.988 0.012
#> GSM601839 2 0.0000 0.8807 0.000 1.000
#> GSM601844 1 0.1184 0.8961 0.984 0.016
#> GSM601859 2 0.2948 0.8839 0.052 0.948
#> GSM601869 1 0.7299 0.8002 0.796 0.204
#> GSM601749 1 0.0938 0.8958 0.988 0.012
#> GSM601759 1 0.0938 0.8958 0.988 0.012
#> GSM601764 1 0.1184 0.8959 0.984 0.016
#> GSM601769 2 0.3733 0.8753 0.072 0.928
#> GSM601774 2 0.0938 0.8833 0.012 0.988
#> GSM601779 1 0.0938 0.8958 0.988 0.012
#> GSM601789 2 0.1843 0.8850 0.028 0.972
#> GSM601804 1 0.4815 0.8611 0.896 0.104
#> GSM601809 2 0.5294 0.8613 0.120 0.880
#> GSM601814 2 0.0672 0.8831 0.008 0.992
#> GSM601819 1 0.8267 0.7051 0.740 0.260
#> GSM601824 1 0.0938 0.8958 0.988 0.012
#> GSM601834 2 0.1184 0.8839 0.016 0.984
#> GSM601849 1 0.0938 0.8958 0.988 0.012
#> GSM601854 1 0.0672 0.8952 0.992 0.008
#> GSM601864 2 0.0938 0.8756 0.012 0.988
#> GSM601755 2 0.5519 0.8598 0.128 0.872
#> GSM601785 2 0.4690 0.8678 0.100 0.900
#> GSM601795 2 0.8763 0.6954 0.296 0.704
#> GSM601800 2 0.2423 0.8863 0.040 0.960
#> GSM601830 1 0.4690 0.8704 0.900 0.100
#> GSM601840 2 0.4690 0.8553 0.100 0.900
#> GSM601845 1 0.4939 0.8818 0.892 0.108
#> GSM601860 2 0.1184 0.8843 0.016 0.984
#> GSM601870 2 0.9323 0.4635 0.348 0.652
#> GSM601750 1 0.3274 0.8929 0.940 0.060
#> GSM601760 1 0.5629 0.8543 0.868 0.132
#> GSM601765 2 0.5737 0.8499 0.136 0.864
#> GSM601770 2 0.2778 0.8842 0.048 0.952
#> GSM601775 2 0.9795 0.4794 0.416 0.584
#> GSM601780 1 0.1843 0.8951 0.972 0.028
#> GSM601790 2 0.0376 0.8819 0.004 0.996
#> GSM601805 2 0.4815 0.8547 0.104 0.896
#> GSM601810 1 0.4431 0.8743 0.908 0.092
#> GSM601815 2 0.0000 0.8807 0.000 1.000
#> GSM601820 1 0.2423 0.8947 0.960 0.040
#> GSM601825 2 0.4939 0.8645 0.108 0.892
#> GSM601835 2 0.2948 0.8735 0.052 0.948
#> GSM601850 1 0.6438 0.7990 0.836 0.164
#> GSM601855 1 0.4815 0.8696 0.896 0.104
#> GSM601865 2 0.0000 0.8807 0.000 1.000
#> GSM601756 2 0.6887 0.8218 0.184 0.816
#> GSM601786 2 0.0376 0.8819 0.004 0.996
#> GSM601796 1 0.7139 0.8136 0.804 0.196
#> GSM601801 2 0.1843 0.8849 0.028 0.972
#> GSM601831 1 0.3431 0.8842 0.936 0.064
#> GSM601841 1 0.6712 0.8307 0.824 0.176
#> GSM601846 1 0.4939 0.8822 0.892 0.108
#> GSM601861 2 0.0672 0.8828 0.008 0.992
#> GSM601871 2 0.1414 0.8779 0.020 0.980
#> GSM601751 2 0.8661 0.6384 0.288 0.712
#> GSM601761 1 0.0938 0.8958 0.988 0.012
#> GSM601766 2 0.6531 0.8271 0.168 0.832
#> GSM601771 2 0.4298 0.8783 0.088 0.912
#> GSM601776 1 0.0938 0.8958 0.988 0.012
#> GSM601781 1 0.7376 0.7954 0.792 0.208
#> GSM601791 1 0.6343 0.8395 0.840 0.160
#> GSM601806 2 0.4562 0.8667 0.096 0.904
#> GSM601811 2 0.9933 0.1964 0.452 0.548
#> GSM601816 1 0.0938 0.8958 0.988 0.012
#> GSM601821 2 0.0376 0.8819 0.004 0.996
#> GSM601826 1 0.0938 0.8958 0.988 0.012
#> GSM601836 1 0.7674 0.7882 0.776 0.224
#> GSM601851 1 0.0938 0.8958 0.988 0.012
#> GSM601856 2 0.9988 0.0168 0.480 0.520
#> GSM601866 1 0.6887 0.8203 0.816 0.184
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601752 2 0.5167 0.6754 0.172 0.804 0.024
#> GSM601782 1 0.1765 0.9033 0.956 0.004 0.040
#> GSM601792 1 0.0747 0.9074 0.984 0.016 0.000
#> GSM601797 1 0.4539 0.8243 0.836 0.148 0.016
#> GSM601827 1 0.2550 0.8906 0.932 0.012 0.056
#> GSM601837 3 0.4346 0.6896 0.000 0.184 0.816
#> GSM601842 2 0.4859 0.7463 0.044 0.840 0.116
#> GSM601857 3 0.1525 0.7011 0.032 0.004 0.964
#> GSM601867 3 0.5588 0.4849 0.004 0.276 0.720
#> GSM601747 1 0.5153 0.8111 0.832 0.100 0.068
#> GSM601757 1 0.2947 0.8970 0.920 0.020 0.060
#> GSM601762 2 0.4700 0.7063 0.008 0.812 0.180
#> GSM601767 2 0.4453 0.7256 0.012 0.836 0.152
#> GSM601772 2 0.6770 0.6466 0.044 0.692 0.264
#> GSM601777 2 0.7562 0.6194 0.160 0.692 0.148
#> GSM601787 3 0.2492 0.7026 0.016 0.048 0.936
#> GSM601802 2 0.4526 0.7311 0.104 0.856 0.040
#> GSM601807 1 0.6705 0.7472 0.740 0.084 0.176
#> GSM601812 1 0.3112 0.8784 0.900 0.004 0.096
#> GSM601817 1 0.2846 0.9029 0.924 0.020 0.056
#> GSM601822 1 0.0747 0.9066 0.984 0.016 0.000
#> GSM601832 2 0.6678 0.6920 0.060 0.724 0.216
#> GSM601847 2 0.5335 0.6368 0.232 0.760 0.008
#> GSM601852 1 0.2446 0.8908 0.936 0.012 0.052
#> GSM601862 3 0.2297 0.6997 0.036 0.020 0.944
#> GSM601753 2 0.3192 0.7263 0.112 0.888 0.000
#> GSM601783 1 0.1647 0.8995 0.960 0.004 0.036
#> GSM601793 1 0.1129 0.9059 0.976 0.004 0.020
#> GSM601798 2 0.2269 0.7520 0.040 0.944 0.016
#> GSM601828 1 0.1585 0.9048 0.964 0.008 0.028
#> GSM601838 2 0.3482 0.7337 0.000 0.872 0.128
#> GSM601843 2 0.6286 0.2773 0.000 0.536 0.464
#> GSM601858 3 0.2400 0.7083 0.004 0.064 0.932
#> GSM601868 3 0.4912 0.6231 0.196 0.008 0.796
#> GSM601748 1 0.1878 0.9003 0.952 0.004 0.044
#> GSM601758 1 0.0592 0.9067 0.988 0.012 0.000
#> GSM601763 1 0.2550 0.8931 0.936 0.040 0.024
#> GSM601768 3 0.6798 0.5605 0.048 0.256 0.696
#> GSM601773 2 0.4094 0.7482 0.028 0.872 0.100
#> GSM601778 1 0.3532 0.8513 0.884 0.108 0.008
#> GSM601788 1 0.7610 0.5605 0.676 0.108 0.216
#> GSM601803 2 0.2663 0.7551 0.044 0.932 0.024
#> GSM601808 1 0.6937 0.3504 0.576 0.020 0.404
#> GSM601813 1 0.0983 0.9077 0.980 0.004 0.016
#> GSM601818 1 0.1989 0.8986 0.948 0.004 0.048
#> GSM601823 1 0.0747 0.9066 0.984 0.016 0.000
#> GSM601833 2 0.5982 0.5516 0.004 0.668 0.328
#> GSM601848 1 0.0747 0.9066 0.984 0.016 0.000
#> GSM601853 1 0.4228 0.8304 0.844 0.008 0.148
#> GSM601863 3 0.6113 0.5444 0.300 0.012 0.688
#> GSM601754 2 0.5631 0.6935 0.132 0.804 0.064
#> GSM601784 3 0.5581 0.6812 0.040 0.168 0.792
#> GSM601794 1 0.4056 0.8596 0.876 0.092 0.032
#> GSM601799 2 0.5816 0.6530 0.224 0.752 0.024
#> GSM601829 1 0.0983 0.9073 0.980 0.016 0.004
#> GSM601839 2 0.6204 0.3428 0.000 0.576 0.424
#> GSM601844 1 0.2773 0.8915 0.928 0.024 0.048
#> GSM601859 3 0.7328 0.4775 0.044 0.344 0.612
#> GSM601869 3 0.4555 0.6247 0.200 0.000 0.800
#> GSM601749 1 0.0237 0.9068 0.996 0.004 0.000
#> GSM601759 1 0.1129 0.9073 0.976 0.020 0.004
#> GSM601764 1 0.1585 0.9035 0.964 0.028 0.008
#> GSM601769 3 0.6422 0.4697 0.016 0.324 0.660
#> GSM601774 2 0.5706 0.5853 0.000 0.680 0.320
#> GSM601779 1 0.1163 0.9038 0.972 0.028 0.000
#> GSM601789 3 0.5639 0.6080 0.016 0.232 0.752
#> GSM601804 2 0.6387 0.5512 0.300 0.680 0.020
#> GSM601809 3 0.4665 0.7044 0.048 0.100 0.852
#> GSM601814 2 0.5948 0.5065 0.000 0.640 0.360
#> GSM601819 1 0.5774 0.6696 0.748 0.020 0.232
#> GSM601824 1 0.1163 0.9038 0.972 0.028 0.000
#> GSM601834 3 0.6295 -0.0159 0.000 0.472 0.528
#> GSM601849 1 0.0892 0.9060 0.980 0.020 0.000
#> GSM601854 1 0.0661 0.9075 0.988 0.008 0.004
#> GSM601864 3 0.3619 0.6952 0.000 0.136 0.864
#> GSM601755 2 0.1765 0.7508 0.040 0.956 0.004
#> GSM601785 3 0.4830 0.7067 0.068 0.084 0.848
#> GSM601795 3 0.8810 0.4506 0.172 0.252 0.576
#> GSM601800 2 0.5115 0.7100 0.016 0.796 0.188
#> GSM601830 1 0.2703 0.8911 0.928 0.016 0.056
#> GSM601840 3 0.6079 0.6480 0.036 0.216 0.748
#> GSM601845 1 0.4269 0.8663 0.872 0.052 0.076
#> GSM601860 3 0.4270 0.7019 0.024 0.116 0.860
#> GSM601870 3 0.2689 0.6998 0.036 0.032 0.932
#> GSM601750 1 0.2945 0.8858 0.908 0.004 0.088
#> GSM601760 3 0.7029 0.3703 0.440 0.020 0.540
#> GSM601765 2 0.9613 0.3166 0.308 0.464 0.228
#> GSM601770 2 0.7174 0.2188 0.024 0.516 0.460
#> GSM601775 1 0.6714 0.6619 0.748 0.112 0.140
#> GSM601780 1 0.3499 0.8709 0.900 0.028 0.072
#> GSM601790 3 0.3816 0.6808 0.000 0.148 0.852
#> GSM601805 2 0.6247 0.6707 0.044 0.744 0.212
#> GSM601810 1 0.2383 0.8935 0.940 0.016 0.044
#> GSM601815 3 0.4399 0.6511 0.000 0.188 0.812
#> GSM601820 1 0.2866 0.8791 0.916 0.008 0.076
#> GSM601825 2 0.4194 0.7549 0.064 0.876 0.060
#> GSM601835 3 0.4233 0.6900 0.004 0.160 0.836
#> GSM601850 1 0.4253 0.8515 0.872 0.048 0.080
#> GSM601855 1 0.4551 0.8297 0.844 0.024 0.132
#> GSM601865 3 0.3412 0.6953 0.000 0.124 0.876
#> GSM601756 2 0.1411 0.7508 0.036 0.964 0.000
#> GSM601786 3 0.3482 0.6936 0.000 0.128 0.872
#> GSM601796 3 0.8179 0.4938 0.352 0.084 0.564
#> GSM601801 2 0.1482 0.7432 0.012 0.968 0.020
#> GSM601831 1 0.2486 0.8889 0.932 0.008 0.060
#> GSM601841 3 0.7141 0.5273 0.368 0.032 0.600
#> GSM601846 1 0.4748 0.8297 0.832 0.144 0.024
#> GSM601861 3 0.4575 0.6756 0.004 0.184 0.812
#> GSM601871 3 0.1529 0.7017 0.000 0.040 0.960
#> GSM601751 3 0.6012 0.6987 0.124 0.088 0.788
#> GSM601761 1 0.1129 0.9067 0.976 0.020 0.004
#> GSM601766 3 0.7741 0.5687 0.236 0.104 0.660
#> GSM601771 3 0.5497 0.6970 0.048 0.148 0.804
#> GSM601776 1 0.0747 0.9066 0.984 0.016 0.000
#> GSM601781 3 0.8458 0.2975 0.436 0.088 0.476
#> GSM601791 3 0.6814 0.5071 0.372 0.020 0.608
#> GSM601806 2 0.1337 0.7478 0.012 0.972 0.016
#> GSM601811 3 0.5845 0.5457 0.308 0.004 0.688
#> GSM601816 1 0.0592 0.9069 0.988 0.012 0.000
#> GSM601821 3 0.4750 0.6495 0.000 0.216 0.784
#> GSM601826 1 0.0747 0.9066 0.984 0.016 0.000
#> GSM601836 1 0.7208 0.3966 0.620 0.040 0.340
#> GSM601851 1 0.0892 0.9060 0.980 0.020 0.000
#> GSM601856 3 0.5659 0.5846 0.248 0.012 0.740
#> GSM601866 3 0.6598 0.2552 0.428 0.008 0.564
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601752 4 0.0524 0.7694 0.008 0.004 0.000 0.988
#> GSM601782 1 0.2010 0.8139 0.932 0.004 0.060 0.004
#> GSM601792 1 0.0524 0.8291 0.988 0.000 0.008 0.004
#> GSM601797 1 0.4655 0.5987 0.684 0.000 0.004 0.312
#> GSM601827 1 0.2221 0.8153 0.932 0.008 0.044 0.016
#> GSM601837 3 0.7828 -0.2649 0.000 0.340 0.396 0.264
#> GSM601842 2 0.4914 0.4534 0.012 0.676 0.000 0.312
#> GSM601857 3 0.2528 0.6055 0.008 0.080 0.908 0.004
#> GSM601867 3 0.4711 0.4795 0.000 0.024 0.740 0.236
#> GSM601747 1 0.5200 0.6815 0.752 0.188 0.008 0.052
#> GSM601757 1 0.5226 0.5493 0.696 0.020 0.276 0.008
#> GSM601762 2 0.4454 0.4830 0.000 0.692 0.000 0.308
#> GSM601767 2 0.5168 0.0266 0.004 0.504 0.000 0.492
#> GSM601772 2 0.5627 0.5255 0.024 0.696 0.024 0.256
#> GSM601777 4 0.5838 0.6844 0.088 0.028 0.140 0.744
#> GSM601787 3 0.2714 0.5780 0.004 0.112 0.884 0.000
#> GSM601802 4 0.0859 0.7693 0.008 0.008 0.004 0.980
#> GSM601807 3 0.7329 0.4487 0.240 0.016 0.584 0.160
#> GSM601812 1 0.3142 0.7994 0.860 0.008 0.132 0.000
#> GSM601817 1 0.2983 0.8168 0.892 0.068 0.040 0.000
#> GSM601822 1 0.0188 0.8274 0.996 0.000 0.000 0.004
#> GSM601832 2 0.2744 0.6623 0.052 0.912 0.012 0.024
#> GSM601847 4 0.3726 0.7206 0.132 0.012 0.012 0.844
#> GSM601852 1 0.1902 0.8092 0.932 0.004 0.064 0.000
#> GSM601862 3 0.2999 0.5894 0.000 0.132 0.864 0.004
#> GSM601753 4 0.2186 0.7635 0.048 0.008 0.012 0.932
#> GSM601783 1 0.1661 0.8136 0.944 0.004 0.052 0.000
#> GSM601793 1 0.0967 0.8272 0.976 0.004 0.004 0.016
#> GSM601798 4 0.0927 0.7667 0.008 0.016 0.000 0.976
#> GSM601828 1 0.1635 0.8187 0.948 0.008 0.044 0.000
#> GSM601838 2 0.5668 0.0882 0.000 0.532 0.024 0.444
#> GSM601843 2 0.1854 0.6806 0.000 0.940 0.048 0.012
#> GSM601858 3 0.4509 0.3132 0.000 0.288 0.708 0.004
#> GSM601868 3 0.1706 0.6189 0.036 0.016 0.948 0.000
#> GSM601748 1 0.1902 0.8108 0.932 0.004 0.064 0.000
#> GSM601758 1 0.1811 0.8291 0.948 0.020 0.028 0.004
#> GSM601763 1 0.4977 0.7617 0.804 0.096 0.028 0.072
#> GSM601768 2 0.8123 0.4123 0.040 0.528 0.196 0.236
#> GSM601773 4 0.5852 0.3843 0.020 0.380 0.012 0.588
#> GSM601778 1 0.4245 0.7908 0.832 0.008 0.056 0.104
#> GSM601788 1 0.6519 0.2894 0.548 0.392 0.040 0.020
#> GSM601803 4 0.1576 0.7627 0.004 0.048 0.000 0.948
#> GSM601808 3 0.5300 0.5517 0.240 0.024 0.720 0.016
#> GSM601813 1 0.1635 0.8296 0.948 0.008 0.044 0.000
#> GSM601818 1 0.1929 0.8186 0.940 0.024 0.036 0.000
#> GSM601823 1 0.0188 0.8274 0.996 0.000 0.000 0.004
#> GSM601833 2 0.1677 0.6760 0.000 0.948 0.012 0.040
#> GSM601848 1 0.0188 0.8274 0.996 0.000 0.000 0.004
#> GSM601853 3 0.4855 0.2737 0.400 0.000 0.600 0.000
#> GSM601863 3 0.3501 0.6193 0.132 0.020 0.848 0.000
#> GSM601754 4 0.2433 0.7596 0.060 0.008 0.012 0.920
#> GSM601784 2 0.8257 0.3766 0.036 0.480 0.300 0.184
#> GSM601794 1 0.5612 0.6723 0.716 0.016 0.044 0.224
#> GSM601799 4 0.4730 0.6634 0.180 0.028 0.012 0.780
#> GSM601829 1 0.0376 0.8273 0.992 0.000 0.004 0.004
#> GSM601839 2 0.4469 0.6390 0.000 0.808 0.080 0.112
#> GSM601844 1 0.3869 0.7991 0.856 0.008 0.076 0.060
#> GSM601859 4 0.7371 0.3796 0.036 0.096 0.292 0.576
#> GSM601869 3 0.2383 0.6200 0.048 0.024 0.924 0.004
#> GSM601749 1 0.0707 0.8286 0.980 0.000 0.020 0.000
#> GSM601759 1 0.2066 0.8266 0.940 0.008 0.024 0.028
#> GSM601764 1 0.5193 0.7600 0.796 0.096 0.044 0.064
#> GSM601769 2 0.0967 0.6715 0.016 0.976 0.004 0.004
#> GSM601774 2 0.2124 0.6821 0.000 0.932 0.028 0.040
#> GSM601779 1 0.3385 0.8020 0.880 0.008 0.040 0.072
#> GSM601789 2 0.2515 0.6740 0.004 0.912 0.072 0.012
#> GSM601804 4 0.5031 0.6555 0.172 0.016 0.040 0.772
#> GSM601809 2 0.6954 0.3731 0.040 0.520 0.400 0.040
#> GSM601814 2 0.4656 0.5883 0.000 0.784 0.056 0.160
#> GSM601819 1 0.7205 0.5897 0.652 0.180 0.104 0.064
#> GSM601824 1 0.2660 0.8088 0.908 0.008 0.012 0.072
#> GSM601834 2 0.2483 0.6769 0.000 0.916 0.052 0.032
#> GSM601849 1 0.1943 0.8239 0.944 0.008 0.032 0.016
#> GSM601854 1 0.1492 0.8292 0.956 0.004 0.036 0.004
#> GSM601864 2 0.5901 0.4893 0.000 0.652 0.280 0.068
#> GSM601755 4 0.0524 0.7684 0.004 0.008 0.000 0.988
#> GSM601785 3 0.9202 -0.2055 0.104 0.340 0.380 0.176
#> GSM601795 2 0.9761 0.0890 0.188 0.316 0.184 0.312
#> GSM601800 4 0.5655 0.3919 0.008 0.316 0.028 0.648
#> GSM601830 1 0.2891 0.8052 0.896 0.020 0.080 0.004
#> GSM601840 4 0.7799 -0.2346 0.000 0.348 0.252 0.400
#> GSM601845 1 0.3759 0.8137 0.872 0.048 0.032 0.048
#> GSM601860 3 0.8305 -0.2936 0.028 0.384 0.396 0.192
#> GSM601870 3 0.2530 0.5936 0.000 0.112 0.888 0.000
#> GSM601750 1 0.4018 0.7074 0.772 0.004 0.224 0.000
#> GSM601760 1 0.6210 0.5031 0.636 0.016 0.300 0.048
#> GSM601765 2 0.2816 0.6607 0.036 0.900 0.000 0.064
#> GSM601770 2 0.7170 0.4109 0.020 0.580 0.108 0.292
#> GSM601775 1 0.6743 0.3693 0.568 0.340 0.008 0.084
#> GSM601780 1 0.3670 0.8022 0.860 0.008 0.100 0.032
#> GSM601790 2 0.3032 0.6598 0.000 0.868 0.124 0.008
#> GSM601805 4 0.4342 0.6935 0.008 0.044 0.128 0.820
#> GSM601810 1 0.2010 0.8111 0.932 0.004 0.060 0.004
#> GSM601815 2 0.1902 0.6769 0.000 0.932 0.064 0.004
#> GSM601820 1 0.3642 0.8149 0.872 0.024 0.076 0.028
#> GSM601825 4 0.5664 0.6592 0.064 0.168 0.024 0.744
#> GSM601835 2 0.2670 0.6726 0.000 0.904 0.072 0.024
#> GSM601850 1 0.4829 0.7601 0.804 0.120 0.020 0.056
#> GSM601855 3 0.5070 0.2422 0.416 0.004 0.580 0.000
#> GSM601865 2 0.5161 0.4144 0.000 0.592 0.400 0.008
#> GSM601756 4 0.0524 0.7690 0.004 0.008 0.000 0.988
#> GSM601786 2 0.4661 0.5752 0.000 0.728 0.256 0.016
#> GSM601796 1 0.7892 0.0938 0.456 0.020 0.368 0.156
#> GSM601801 4 0.3024 0.6860 0.000 0.148 0.000 0.852
#> GSM601831 1 0.2334 0.8014 0.908 0.004 0.088 0.000
#> GSM601841 1 0.7103 0.2975 0.564 0.012 0.312 0.112
#> GSM601846 1 0.4328 0.7524 0.804 0.008 0.024 0.164
#> GSM601861 2 0.5021 0.5977 0.000 0.724 0.240 0.036
#> GSM601871 3 0.2412 0.5917 0.000 0.084 0.908 0.008
#> GSM601751 3 0.9131 -0.0171 0.264 0.264 0.396 0.076
#> GSM601761 1 0.2313 0.8218 0.924 0.000 0.032 0.044
#> GSM601766 2 0.8410 0.2447 0.208 0.512 0.224 0.056
#> GSM601771 2 0.8734 0.2577 0.040 0.368 0.340 0.252
#> GSM601776 1 0.0779 0.8280 0.980 0.000 0.016 0.004
#> GSM601781 1 0.7816 0.3720 0.536 0.044 0.304 0.116
#> GSM601791 1 0.6908 0.3069 0.536 0.016 0.376 0.072
#> GSM601806 4 0.3016 0.7044 0.004 0.120 0.004 0.872
#> GSM601811 3 0.4589 0.6055 0.168 0.048 0.784 0.000
#> GSM601816 1 0.0188 0.8274 0.996 0.000 0.000 0.004
#> GSM601821 2 0.5121 0.6188 0.000 0.764 0.116 0.120
#> GSM601826 1 0.0188 0.8274 0.996 0.000 0.000 0.004
#> GSM601836 1 0.6315 0.1504 0.480 0.468 0.048 0.004
#> GSM601851 1 0.2039 0.8238 0.940 0.008 0.036 0.016
#> GSM601856 3 0.3009 0.6263 0.052 0.056 0.892 0.000
#> GSM601866 3 0.4360 0.5686 0.248 0.008 0.744 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601752 4 0.0324 0.7505 0.000 0.004 0.000 0.992 0.004
#> GSM601782 1 0.1331 0.8151 0.952 0.008 0.040 0.000 0.000
#> GSM601792 1 0.1082 0.8242 0.964 0.028 0.000 0.008 0.000
#> GSM601797 1 0.4696 0.4982 0.616 0.024 0.000 0.360 0.000
#> GSM601827 1 0.1299 0.8159 0.960 0.008 0.012 0.020 0.000
#> GSM601837 2 0.7290 0.2407 0.000 0.536 0.084 0.196 0.184
#> GSM601842 5 0.4775 0.5462 0.008 0.040 0.000 0.256 0.696
#> GSM601857 3 0.3710 0.7261 0.000 0.192 0.784 0.000 0.024
#> GSM601867 3 0.5422 0.6815 0.000 0.108 0.684 0.196 0.012
#> GSM601747 1 0.5779 0.5687 0.700 0.168 0.012 0.036 0.084
#> GSM601757 1 0.5289 0.4365 0.620 0.060 0.316 0.000 0.004
#> GSM601762 5 0.7059 0.1205 0.000 0.364 0.012 0.252 0.372
#> GSM601767 4 0.7016 -0.0477 0.000 0.348 0.008 0.368 0.276
#> GSM601772 2 0.7254 -0.0727 0.016 0.440 0.024 0.148 0.372
#> GSM601777 4 0.6274 0.5460 0.060 0.240 0.080 0.620 0.000
#> GSM601787 3 0.4813 0.4339 0.000 0.376 0.600 0.004 0.020
#> GSM601802 4 0.0775 0.7502 0.004 0.008 0.004 0.980 0.004
#> GSM601807 3 0.4458 0.7387 0.072 0.012 0.800 0.100 0.016
#> GSM601812 1 0.3953 0.7798 0.792 0.148 0.060 0.000 0.000
#> GSM601817 1 0.2917 0.8120 0.888 0.040 0.048 0.000 0.024
#> GSM601822 1 0.0290 0.8204 0.992 0.008 0.000 0.000 0.000
#> GSM601832 5 0.3651 0.6300 0.028 0.160 0.004 0.000 0.808
#> GSM601847 4 0.4328 0.6667 0.108 0.108 0.000 0.780 0.004
#> GSM601852 1 0.1357 0.8143 0.948 0.004 0.048 0.000 0.000
#> GSM601862 3 0.4100 0.7387 0.000 0.160 0.784 0.004 0.052
#> GSM601753 4 0.2067 0.7422 0.028 0.044 0.000 0.924 0.004
#> GSM601783 1 0.1124 0.8146 0.960 0.004 0.036 0.000 0.000
#> GSM601793 1 0.0932 0.8169 0.972 0.004 0.004 0.020 0.000
#> GSM601798 4 0.0290 0.7494 0.000 0.000 0.000 0.992 0.008
#> GSM601828 1 0.1216 0.8204 0.960 0.020 0.020 0.000 0.000
#> GSM601838 5 0.4771 0.4668 0.000 0.020 0.020 0.272 0.688
#> GSM601843 5 0.3707 0.6666 0.000 0.116 0.044 0.012 0.828
#> GSM601858 2 0.6053 0.1119 0.004 0.528 0.368 0.004 0.096
#> GSM601868 3 0.3160 0.7742 0.028 0.116 0.852 0.000 0.004
#> GSM601748 1 0.1282 0.8134 0.952 0.004 0.044 0.000 0.000
#> GSM601758 1 0.3169 0.8017 0.840 0.140 0.016 0.004 0.000
#> GSM601763 1 0.4832 0.7046 0.724 0.224 0.012 0.016 0.024
#> GSM601768 2 0.6412 0.3362 0.020 0.636 0.040 0.080 0.224
#> GSM601773 4 0.6490 0.3410 0.008 0.168 0.000 0.512 0.312
#> GSM601778 1 0.5240 0.6986 0.676 0.256 0.032 0.036 0.000
#> GSM601788 2 0.7272 0.2616 0.404 0.408 0.024 0.016 0.148
#> GSM601803 4 0.1018 0.7505 0.000 0.016 0.000 0.968 0.016
#> GSM601808 3 0.2353 0.7801 0.060 0.028 0.908 0.004 0.000
#> GSM601813 1 0.3078 0.8099 0.848 0.132 0.016 0.004 0.000
#> GSM601818 1 0.1485 0.8141 0.948 0.020 0.032 0.000 0.000
#> GSM601823 1 0.0290 0.8204 0.992 0.008 0.000 0.000 0.000
#> GSM601833 5 0.3009 0.6721 0.000 0.080 0.016 0.028 0.876
#> GSM601848 1 0.0290 0.8204 0.992 0.008 0.000 0.000 0.000
#> GSM601853 3 0.2377 0.7470 0.128 0.000 0.872 0.000 0.000
#> GSM601863 3 0.4558 0.7307 0.088 0.168 0.744 0.000 0.000
#> GSM601754 4 0.2735 0.7241 0.036 0.084 0.000 0.880 0.000
#> GSM601784 2 0.5640 0.4194 0.016 0.716 0.072 0.036 0.160
#> GSM601794 1 0.5955 0.5702 0.608 0.216 0.004 0.172 0.000
#> GSM601799 4 0.5274 0.5806 0.160 0.132 0.000 0.700 0.008
#> GSM601829 1 0.0510 0.8214 0.984 0.016 0.000 0.000 0.000
#> GSM601839 5 0.2940 0.6677 0.000 0.040 0.040 0.032 0.888
#> GSM601844 1 0.4618 0.7218 0.712 0.248 0.024 0.016 0.000
#> GSM601859 4 0.6641 0.1635 0.016 0.428 0.048 0.464 0.044
#> GSM601869 3 0.4184 0.7505 0.048 0.176 0.772 0.000 0.004
#> GSM601749 1 0.1522 0.8259 0.944 0.044 0.012 0.000 0.000
#> GSM601759 1 0.2692 0.8101 0.884 0.092 0.016 0.008 0.000
#> GSM601764 1 0.6183 0.6545 0.656 0.192 0.020 0.020 0.112
#> GSM601769 5 0.4686 0.4416 0.000 0.332 0.016 0.008 0.644
#> GSM601774 5 0.4181 0.6312 0.000 0.172 0.032 0.016 0.780
#> GSM601779 1 0.4857 0.6929 0.684 0.272 0.024 0.020 0.000
#> GSM601789 5 0.4927 0.3749 0.000 0.388 0.024 0.004 0.584
#> GSM601804 4 0.6420 0.4275 0.136 0.268 0.024 0.572 0.000
#> GSM601809 2 0.4967 0.3860 0.020 0.764 0.084 0.012 0.120
#> GSM601814 5 0.2581 0.6650 0.000 0.020 0.028 0.048 0.904
#> GSM601819 1 0.6078 0.5410 0.636 0.248 0.032 0.008 0.076
#> GSM601824 1 0.2722 0.7929 0.872 0.108 0.000 0.020 0.000
#> GSM601834 5 0.2243 0.6830 0.000 0.056 0.012 0.016 0.916
#> GSM601849 1 0.3107 0.8009 0.852 0.124 0.016 0.008 0.000
#> GSM601854 1 0.3835 0.7847 0.796 0.156 0.048 0.000 0.000
#> GSM601864 2 0.6480 0.2595 0.000 0.576 0.052 0.088 0.284
#> GSM601755 4 0.0162 0.7493 0.000 0.000 0.000 0.996 0.004
#> GSM601785 2 0.5316 0.4474 0.024 0.756 0.096 0.032 0.092
#> GSM601795 2 0.6655 0.3320 0.092 0.632 0.016 0.196 0.064
#> GSM601800 4 0.6334 0.4476 0.004 0.124 0.036 0.628 0.208
#> GSM601830 1 0.3474 0.7080 0.796 0.004 0.192 0.008 0.000
#> GSM601840 2 0.6476 0.3502 0.000 0.600 0.056 0.244 0.100
#> GSM601845 1 0.3689 0.8035 0.856 0.068 0.020 0.032 0.024
#> GSM601860 2 0.4877 0.4335 0.008 0.768 0.108 0.020 0.096
#> GSM601870 3 0.1978 0.7686 0.004 0.044 0.928 0.000 0.024
#> GSM601750 1 0.5962 0.5659 0.584 0.168 0.248 0.000 0.000
#> GSM601760 2 0.5626 -0.2228 0.456 0.492 0.032 0.016 0.004
#> GSM601765 5 0.5432 0.2309 0.012 0.444 0.016 0.012 0.516
#> GSM601770 2 0.6984 0.2223 0.008 0.544 0.040 0.136 0.272
#> GSM601775 2 0.6920 0.2206 0.400 0.444 0.004 0.032 0.120
#> GSM601780 1 0.4777 0.7058 0.696 0.260 0.028 0.016 0.000
#> GSM601790 5 0.3291 0.6592 0.000 0.088 0.064 0.000 0.848
#> GSM601805 4 0.4365 0.6917 0.012 0.084 0.068 0.812 0.024
#> GSM601810 1 0.1538 0.8173 0.948 0.008 0.036 0.008 0.000
#> GSM601815 5 0.2864 0.6733 0.000 0.112 0.024 0.000 0.864
#> GSM601820 1 0.4809 0.7156 0.688 0.268 0.036 0.004 0.004
#> GSM601825 4 0.5995 0.6085 0.036 0.168 0.000 0.660 0.136
#> GSM601835 5 0.3883 0.6613 0.000 0.160 0.028 0.012 0.800
#> GSM601850 1 0.4705 0.7260 0.764 0.156 0.004 0.020 0.056
#> GSM601855 3 0.2966 0.7112 0.184 0.000 0.816 0.000 0.000
#> GSM601865 5 0.6016 0.1522 0.000 0.408 0.100 0.004 0.488
#> GSM601756 4 0.0000 0.7496 0.000 0.000 0.000 1.000 0.000
#> GSM601786 5 0.5105 0.5165 0.000 0.240 0.060 0.012 0.688
#> GSM601796 2 0.7424 0.1963 0.328 0.480 0.084 0.100 0.008
#> GSM601801 4 0.2068 0.7215 0.000 0.004 0.000 0.904 0.092
#> GSM601831 1 0.2020 0.8004 0.900 0.000 0.100 0.000 0.000
#> GSM601841 1 0.6662 0.0921 0.532 0.328 0.060 0.080 0.000
#> GSM601846 1 0.3934 0.7461 0.796 0.008 0.036 0.160 0.000
#> GSM601861 5 0.4642 0.5515 0.000 0.192 0.060 0.008 0.740
#> GSM601871 3 0.4000 0.7144 0.000 0.164 0.788 0.004 0.044
#> GSM601751 2 0.6452 0.4573 0.116 0.680 0.100 0.028 0.076
#> GSM601761 1 0.3846 0.7613 0.776 0.200 0.020 0.004 0.000
#> GSM601766 2 0.6158 0.3670 0.080 0.660 0.044 0.012 0.204
#> GSM601771 2 0.5962 0.4348 0.020 0.708 0.084 0.056 0.132
#> GSM601776 1 0.1410 0.8248 0.940 0.060 0.000 0.000 0.000
#> GSM601781 2 0.6609 -0.2709 0.440 0.456 0.040 0.044 0.020
#> GSM601791 2 0.6168 -0.0207 0.396 0.512 0.068 0.020 0.004
#> GSM601806 4 0.2020 0.7199 0.000 0.000 0.000 0.900 0.100
#> GSM601811 3 0.5623 0.7269 0.136 0.172 0.676 0.000 0.016
#> GSM601816 1 0.0162 0.8203 0.996 0.004 0.000 0.000 0.000
#> GSM601821 5 0.3250 0.6581 0.000 0.044 0.044 0.040 0.872
#> GSM601826 1 0.0290 0.8204 0.992 0.008 0.000 0.000 0.000
#> GSM601836 5 0.6534 0.2444 0.280 0.112 0.040 0.000 0.568
#> GSM601851 1 0.3632 0.7823 0.800 0.176 0.020 0.004 0.000
#> GSM601856 3 0.1686 0.7809 0.028 0.020 0.944 0.000 0.008
#> GSM601866 3 0.6038 0.5437 0.184 0.240 0.576 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601752 4 0.0458 0.7962 0.000 0.000 0.000 0.984 0.000 0.016
#> GSM601782 1 0.1390 0.7930 0.948 0.000 0.032 0.000 0.004 0.016
#> GSM601792 1 0.1010 0.7929 0.960 0.000 0.000 0.004 0.000 0.036
#> GSM601797 1 0.4553 0.3897 0.580 0.000 0.004 0.384 0.000 0.032
#> GSM601827 1 0.1007 0.7926 0.968 0.000 0.004 0.016 0.008 0.004
#> GSM601837 2 0.6705 0.2289 0.000 0.520 0.000 0.132 0.224 0.124
#> GSM601842 5 0.5393 0.5303 0.016 0.076 0.004 0.248 0.644 0.012
#> GSM601857 3 0.4886 0.6984 0.004 0.252 0.672 0.000 0.032 0.040
#> GSM601867 3 0.5177 0.7017 0.000 0.088 0.696 0.176 0.016 0.024
#> GSM601747 1 0.4638 0.5245 0.676 0.264 0.000 0.044 0.008 0.008
#> GSM601757 1 0.4787 0.4133 0.624 0.008 0.312 0.000 0.000 0.056
#> GSM601762 2 0.5629 0.4254 0.004 0.580 0.004 0.184 0.228 0.000
#> GSM601767 2 0.6016 0.4358 0.000 0.540 0.000 0.276 0.156 0.028
#> GSM601772 2 0.5617 0.5356 0.004 0.664 0.000 0.088 0.164 0.080
#> GSM601777 6 0.5468 0.5095 0.020 0.016 0.084 0.216 0.004 0.660
#> GSM601787 3 0.5536 0.5735 0.000 0.308 0.584 0.000 0.052 0.056
#> GSM601802 4 0.0551 0.7954 0.000 0.008 0.004 0.984 0.000 0.004
#> GSM601807 3 0.2119 0.7734 0.004 0.016 0.912 0.060 0.000 0.008
#> GSM601812 1 0.4946 0.2880 0.596 0.020 0.032 0.000 0.004 0.348
#> GSM601817 1 0.2904 0.7785 0.876 0.052 0.048 0.000 0.008 0.016
#> GSM601822 1 0.0458 0.7935 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM601832 5 0.4716 0.5887 0.020 0.204 0.004 0.000 0.708 0.064
#> GSM601847 4 0.4266 0.6800 0.060 0.012 0.000 0.736 0.000 0.192
#> GSM601852 1 0.1340 0.7911 0.948 0.004 0.040 0.000 0.000 0.008
#> GSM601862 3 0.4139 0.7154 0.000 0.260 0.700 0.000 0.004 0.036
#> GSM601753 4 0.2620 0.7766 0.024 0.012 0.000 0.884 0.004 0.076
#> GSM601783 1 0.0777 0.7920 0.972 0.000 0.024 0.000 0.000 0.004
#> GSM601793 1 0.0964 0.7925 0.968 0.004 0.000 0.012 0.000 0.016
#> GSM601798 4 0.0291 0.7939 0.000 0.004 0.004 0.992 0.000 0.000
#> GSM601828 1 0.1129 0.7947 0.964 0.008 0.012 0.000 0.004 0.012
#> GSM601838 5 0.5959 0.4915 0.000 0.104 0.000 0.176 0.620 0.100
#> GSM601843 5 0.4497 0.5879 0.004 0.312 0.004 0.012 0.652 0.016
#> GSM601858 2 0.5743 0.0577 0.000 0.568 0.316 0.004 0.064 0.048
#> GSM601868 3 0.3596 0.7792 0.004 0.132 0.812 0.000 0.036 0.016
#> GSM601748 1 0.0865 0.7916 0.964 0.000 0.036 0.000 0.000 0.000
#> GSM601758 1 0.3659 0.3164 0.636 0.000 0.000 0.000 0.000 0.364
#> GSM601763 1 0.4870 0.5300 0.668 0.120 0.000 0.000 0.004 0.208
#> GSM601768 2 0.4209 0.6021 0.012 0.780 0.000 0.012 0.088 0.108
#> GSM601773 4 0.6756 0.3208 0.004 0.196 0.000 0.492 0.244 0.064
#> GSM601778 6 0.3538 0.7028 0.216 0.004 0.004 0.012 0.000 0.764
#> GSM601788 2 0.5028 0.3968 0.308 0.628 0.000 0.024 0.020 0.020
#> GSM601803 4 0.0837 0.7952 0.000 0.020 0.004 0.972 0.000 0.004
#> GSM601808 3 0.0665 0.7897 0.008 0.008 0.980 0.000 0.000 0.004
#> GSM601813 1 0.4087 0.3917 0.668 0.008 0.008 0.000 0.004 0.312
#> GSM601818 1 0.1313 0.7933 0.952 0.028 0.016 0.000 0.000 0.004
#> GSM601823 1 0.0458 0.7935 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM601833 5 0.3640 0.6311 0.004 0.204 0.000 0.028 0.764 0.000
#> GSM601848 1 0.0458 0.7935 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM601853 3 0.1225 0.7869 0.036 0.000 0.952 0.000 0.000 0.012
#> GSM601863 3 0.5476 0.3670 0.060 0.028 0.564 0.000 0.004 0.344
#> GSM601754 4 0.3166 0.7545 0.032 0.008 0.000 0.840 0.004 0.116
#> GSM601784 2 0.4216 0.6124 0.012 0.784 0.000 0.036 0.040 0.128
#> GSM601794 6 0.5714 0.4409 0.372 0.016 0.000 0.096 0.004 0.512
#> GSM601799 4 0.5604 0.5959 0.136 0.052 0.000 0.660 0.004 0.148
#> GSM601829 1 0.0547 0.7936 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM601839 5 0.3612 0.6334 0.000 0.092 0.004 0.000 0.804 0.100
#> GSM601844 6 0.3992 0.4823 0.364 0.012 0.000 0.000 0.000 0.624
#> GSM601859 4 0.6862 0.3103 0.012 0.276 0.000 0.492 0.072 0.148
#> GSM601869 3 0.5729 0.7387 0.028 0.128 0.672 0.000 0.040 0.132
#> GSM601749 1 0.1765 0.7711 0.904 0.000 0.000 0.000 0.000 0.096
#> GSM601759 1 0.2500 0.7582 0.868 0.004 0.012 0.000 0.000 0.116
#> GSM601764 6 0.5215 0.1319 0.456 0.012 0.000 0.000 0.060 0.472
#> GSM601769 2 0.4172 0.1556 0.000 0.564 0.000 0.004 0.424 0.008
#> GSM601774 5 0.4110 0.4059 0.000 0.376 0.000 0.016 0.608 0.000
#> GSM601779 6 0.2743 0.7114 0.164 0.008 0.000 0.000 0.000 0.828
#> GSM601789 2 0.4591 0.2602 0.004 0.604 0.000 0.008 0.360 0.024
#> GSM601804 6 0.3653 0.6115 0.040 0.012 0.004 0.140 0.000 0.804
#> GSM601809 6 0.5564 0.2726 0.000 0.312 0.028 0.000 0.088 0.572
#> GSM601814 5 0.1261 0.6894 0.000 0.024 0.000 0.024 0.952 0.000
#> GSM601819 1 0.5680 0.4030 0.580 0.248 0.000 0.000 0.016 0.156
#> GSM601824 1 0.2768 0.7030 0.832 0.012 0.000 0.000 0.000 0.156
#> GSM601834 5 0.2118 0.6847 0.000 0.104 0.000 0.008 0.888 0.000
#> GSM601849 1 0.2597 0.7217 0.824 0.000 0.000 0.000 0.000 0.176
#> GSM601854 6 0.4116 0.4690 0.416 0.000 0.012 0.000 0.000 0.572
#> GSM601864 2 0.6036 0.3344 0.000 0.620 0.020 0.044 0.204 0.112
#> GSM601755 4 0.0000 0.7939 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601785 2 0.3909 0.5858 0.012 0.776 0.000 0.004 0.040 0.168
#> GSM601795 6 0.4905 0.4988 0.028 0.200 0.000 0.080 0.000 0.692
#> GSM601800 4 0.5748 0.4384 0.004 0.180 0.004 0.604 0.196 0.012
#> GSM601830 1 0.3991 0.5803 0.704 0.012 0.272 0.000 0.004 0.008
#> GSM601840 2 0.3974 0.5516 0.000 0.740 0.000 0.216 0.008 0.036
#> GSM601845 1 0.3426 0.7586 0.852 0.048 0.000 0.036 0.016 0.048
#> GSM601860 2 0.4033 0.5719 0.004 0.760 0.000 0.000 0.080 0.156
#> GSM601870 3 0.1957 0.7819 0.000 0.072 0.912 0.000 0.008 0.008
#> GSM601750 6 0.5276 0.6197 0.192 0.004 0.164 0.000 0.004 0.636
#> GSM601760 6 0.3972 0.7118 0.144 0.068 0.000 0.000 0.012 0.776
#> GSM601765 2 0.4985 0.4310 0.012 0.640 0.000 0.004 0.280 0.064
#> GSM601770 2 0.4824 0.5813 0.004 0.740 0.000 0.084 0.116 0.056
#> GSM601775 2 0.5732 0.4253 0.276 0.568 0.000 0.012 0.004 0.140
#> GSM601780 6 0.2883 0.6988 0.212 0.000 0.000 0.000 0.000 0.788
#> GSM601790 5 0.2821 0.6748 0.000 0.096 0.000 0.004 0.860 0.040
#> GSM601805 4 0.4042 0.7309 0.008 0.068 0.004 0.812 0.068 0.040
#> GSM601810 1 0.1668 0.7888 0.928 0.008 0.060 0.000 0.000 0.004
#> GSM601815 5 0.3259 0.6472 0.000 0.216 0.000 0.000 0.772 0.012
#> GSM601820 6 0.4258 0.6287 0.308 0.012 0.012 0.000 0.004 0.664
#> GSM601825 4 0.6380 0.5840 0.024 0.096 0.000 0.616 0.108 0.156
#> GSM601835 5 0.4519 0.6078 0.004 0.280 0.004 0.016 0.676 0.020
#> GSM601850 1 0.4456 0.6258 0.724 0.132 0.000 0.004 0.000 0.140
#> GSM601855 3 0.1578 0.7771 0.048 0.012 0.936 0.000 0.000 0.004
#> GSM601865 5 0.5230 0.2339 0.000 0.412 0.012 0.000 0.512 0.064
#> GSM601756 4 0.0146 0.7945 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM601786 5 0.3618 0.5974 0.000 0.192 0.000 0.000 0.768 0.040
#> GSM601796 6 0.5545 0.6191 0.116 0.128 0.004 0.016 0.044 0.692
#> GSM601801 4 0.1888 0.7736 0.000 0.012 0.004 0.916 0.068 0.000
#> GSM601831 1 0.2196 0.7647 0.884 0.004 0.108 0.000 0.000 0.004
#> GSM601841 1 0.6087 0.3461 0.596 0.240 0.004 0.064 0.004 0.092
#> GSM601846 1 0.4074 0.7067 0.788 0.016 0.036 0.144 0.008 0.008
#> GSM601861 5 0.3172 0.6228 0.000 0.148 0.000 0.000 0.816 0.036
#> GSM601871 3 0.4492 0.7263 0.000 0.196 0.720 0.000 0.068 0.016
#> GSM601751 2 0.5828 0.5138 0.104 0.648 0.000 0.008 0.072 0.168
#> GSM601761 6 0.3409 0.6447 0.300 0.000 0.000 0.000 0.000 0.700
#> GSM601766 2 0.3940 0.6055 0.048 0.796 0.000 0.000 0.040 0.116
#> GSM601771 2 0.4851 0.6025 0.012 0.728 0.000 0.020 0.104 0.136
#> GSM601776 1 0.1958 0.7703 0.896 0.004 0.000 0.000 0.000 0.100
#> GSM601781 6 0.3890 0.7143 0.124 0.044 0.000 0.004 0.028 0.800
#> GSM601791 6 0.3779 0.6620 0.080 0.076 0.000 0.000 0.032 0.812
#> GSM601806 4 0.1644 0.7689 0.000 0.000 0.004 0.920 0.076 0.000
#> GSM601811 3 0.5492 0.6966 0.096 0.076 0.688 0.000 0.008 0.132
#> GSM601816 1 0.0363 0.7936 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM601821 5 0.0837 0.6860 0.000 0.020 0.000 0.004 0.972 0.004
#> GSM601826 1 0.0458 0.7935 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM601836 5 0.6428 0.3327 0.280 0.104 0.004 0.000 0.532 0.080
#> GSM601851 1 0.3838 0.0646 0.552 0.000 0.000 0.000 0.000 0.448
#> GSM601856 3 0.1396 0.7902 0.012 0.024 0.952 0.000 0.004 0.008
#> GSM601866 6 0.6934 0.1966 0.124 0.092 0.328 0.000 0.008 0.448
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 time(p) gender(p) k
#> MAD:pam 119 0.556 0.347 2
#> MAD:pam 110 0.181 0.257 3
#> MAD:pam 92 0.859 0.340 4
#> MAD:pam 88 0.943 0.417 5
#> MAD:pam 92 0.923 0.235 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 125 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 0.468 0.786 0.848 0.4333 0.573 0.573
#> 3 3 0.513 0.769 0.867 0.5013 0.746 0.560
#> 4 4 0.530 0.558 0.790 0.1107 0.860 0.626
#> 5 5 0.689 0.709 0.817 0.0797 0.833 0.488
#> 6 6 0.767 0.761 0.862 0.0440 0.899 0.585
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
#> GSM601752 2 0.8555 0.775 0.280 0.720
#> GSM601782 1 0.8327 0.829 0.736 0.264
#> GSM601792 2 0.1843 0.800 0.028 0.972
#> GSM601797 2 0.3584 0.805 0.068 0.932
#> GSM601827 1 0.8443 0.825 0.728 0.272
#> GSM601837 1 0.0000 0.788 1.000 0.000
#> GSM601842 1 0.0000 0.788 1.000 0.000
#> GSM601857 1 0.8327 0.829 0.736 0.264
#> GSM601867 1 0.6973 0.825 0.812 0.188
#> GSM601747 1 0.8327 0.829 0.736 0.264
#> GSM601757 1 0.8327 0.829 0.736 0.264
#> GSM601762 1 0.0000 0.788 1.000 0.000
#> GSM601767 1 0.0000 0.788 1.000 0.000
#> GSM601772 1 0.0000 0.788 1.000 0.000
#> GSM601777 2 0.4690 0.748 0.100 0.900
#> GSM601787 1 0.3274 0.802 0.940 0.060
#> GSM601802 2 0.8555 0.771 0.280 0.720
#> GSM601807 1 0.8861 0.794 0.696 0.304
#> GSM601812 1 0.8327 0.829 0.736 0.264
#> GSM601817 1 0.8327 0.829 0.736 0.264
#> GSM601822 2 0.2236 0.801 0.036 0.964
#> GSM601832 1 0.0000 0.788 1.000 0.000
#> GSM601847 2 0.8267 0.784 0.260 0.740
#> GSM601852 1 0.8386 0.827 0.732 0.268
#> GSM601862 1 0.8327 0.829 0.736 0.264
#> GSM601753 2 0.8661 0.772 0.288 0.712
#> GSM601783 1 0.8499 0.822 0.724 0.276
#> GSM601793 2 0.1633 0.798 0.024 0.976
#> GSM601798 2 0.8327 0.767 0.264 0.736
#> GSM601828 1 0.8327 0.829 0.736 0.264
#> GSM601838 1 0.0000 0.788 1.000 0.000
#> GSM601843 1 0.0000 0.788 1.000 0.000
#> GSM601858 1 0.0000 0.788 1.000 0.000
#> GSM601868 1 0.8327 0.829 0.736 0.264
#> GSM601748 1 0.8327 0.829 0.736 0.264
#> GSM601758 1 0.8386 0.827 0.732 0.268
#> GSM601763 1 0.8386 0.827 0.732 0.268
#> GSM601768 1 0.0000 0.788 1.000 0.000
#> GSM601773 1 0.0000 0.788 1.000 0.000
#> GSM601778 2 0.1633 0.798 0.024 0.976
#> GSM601788 1 0.0000 0.788 1.000 0.000
#> GSM601803 2 0.8327 0.767 0.264 0.736
#> GSM601808 1 0.8327 0.829 0.736 0.264
#> GSM601813 1 0.8555 0.819 0.720 0.280
#> GSM601818 1 0.8327 0.829 0.736 0.264
#> GSM601823 2 0.1633 0.798 0.024 0.976
#> GSM601833 1 0.0000 0.788 1.000 0.000
#> GSM601848 2 0.1633 0.798 0.024 0.976
#> GSM601853 1 0.8327 0.829 0.736 0.264
#> GSM601863 1 0.8327 0.829 0.736 0.264
#> GSM601754 2 0.8267 0.784 0.260 0.740
#> GSM601784 1 0.0000 0.788 1.000 0.000
#> GSM601794 2 0.1633 0.798 0.024 0.976
#> GSM601799 2 0.8608 0.775 0.284 0.716
#> GSM601829 2 0.8081 0.460 0.248 0.752
#> GSM601839 1 0.0000 0.788 1.000 0.000
#> GSM601844 1 0.9170 0.765 0.668 0.332
#> GSM601859 1 0.0000 0.788 1.000 0.000
#> GSM601869 1 0.8327 0.829 0.736 0.264
#> GSM601749 1 0.8443 0.825 0.728 0.272
#> GSM601759 1 0.8386 0.827 0.732 0.268
#> GSM601764 1 0.8386 0.827 0.732 0.268
#> GSM601769 1 0.0000 0.788 1.000 0.000
#> GSM601774 1 0.0000 0.788 1.000 0.000
#> GSM601779 2 0.1633 0.798 0.024 0.976
#> GSM601789 1 0.0000 0.788 1.000 0.000
#> GSM601804 2 0.6048 0.801 0.148 0.852
#> GSM601809 1 0.8267 0.829 0.740 0.260
#> GSM601814 1 0.0000 0.788 1.000 0.000
#> GSM601819 1 0.8327 0.829 0.736 0.264
#> GSM601824 2 0.3274 0.804 0.060 0.940
#> GSM601834 1 0.0000 0.788 1.000 0.000
#> GSM601849 1 0.9248 0.755 0.660 0.340
#> GSM601854 1 0.8443 0.825 0.728 0.272
#> GSM601864 1 0.0000 0.788 1.000 0.000
#> GSM601755 2 0.8327 0.767 0.264 0.736
#> GSM601785 1 0.0938 0.791 0.988 0.012
#> GSM601795 2 0.1843 0.800 0.028 0.972
#> GSM601800 2 0.8661 0.772 0.288 0.712
#> GSM601830 1 0.8386 0.827 0.732 0.268
#> GSM601840 1 0.6623 0.823 0.828 0.172
#> GSM601845 1 0.7219 0.827 0.800 0.200
#> GSM601860 1 0.0000 0.788 1.000 0.000
#> GSM601870 1 0.8081 0.830 0.752 0.248
#> GSM601750 1 0.8327 0.829 0.736 0.264
#> GSM601760 1 0.8386 0.827 0.732 0.268
#> GSM601765 1 0.0000 0.788 1.000 0.000
#> GSM601770 1 0.0000 0.788 1.000 0.000
#> GSM601775 1 0.6801 0.795 0.820 0.180
#> GSM601780 2 0.5059 0.723 0.112 0.888
#> GSM601790 1 0.0000 0.788 1.000 0.000
#> GSM601805 2 0.8661 0.772 0.288 0.712
#> GSM601810 1 0.8327 0.829 0.736 0.264
#> GSM601815 1 0.0000 0.788 1.000 0.000
#> GSM601820 1 0.8386 0.827 0.732 0.268
#> GSM601825 2 0.8713 0.771 0.292 0.708
#> GSM601835 1 0.0000 0.788 1.000 0.000
#> GSM601850 2 0.9933 -0.304 0.452 0.548
#> GSM601855 1 0.8386 0.827 0.732 0.268
#> GSM601865 1 0.0000 0.788 1.000 0.000
#> GSM601756 2 0.8327 0.767 0.264 0.736
#> GSM601786 1 0.0000 0.788 1.000 0.000
#> GSM601796 2 0.1633 0.798 0.024 0.976
#> GSM601801 2 0.8327 0.767 0.264 0.736
#> GSM601831 1 0.8386 0.827 0.732 0.268
#> GSM601841 1 0.9491 0.715 0.632 0.368
#> GSM601846 2 0.8081 0.787 0.248 0.752
#> GSM601861 1 0.0000 0.788 1.000 0.000
#> GSM601871 1 0.2948 0.800 0.948 0.052
#> GSM601751 1 0.0000 0.788 1.000 0.000
#> GSM601761 1 0.8608 0.815 0.716 0.284
#> GSM601766 1 0.8016 0.830 0.756 0.244
#> GSM601771 1 0.0000 0.788 1.000 0.000
#> GSM601776 2 0.5178 0.717 0.116 0.884
#> GSM601781 2 0.4562 0.743 0.096 0.904
#> GSM601791 1 0.9686 0.668 0.604 0.396
#> GSM601806 2 0.8661 0.772 0.288 0.712
#> GSM601811 1 0.8327 0.829 0.736 0.264
#> GSM601816 2 0.1633 0.798 0.024 0.976
#> GSM601821 1 0.0000 0.788 1.000 0.000
#> GSM601826 2 0.1633 0.798 0.024 0.976
#> GSM601836 1 0.8327 0.829 0.736 0.264
#> GSM601851 2 0.4161 0.755 0.084 0.916
#> GSM601856 1 0.8443 0.825 0.728 0.272
#> GSM601866 1 0.8327 0.829 0.736 0.264
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601752 1 0.1163 0.8503 0.972 0.028 0.000
#> GSM601782 3 0.5244 0.7434 0.004 0.240 0.756
#> GSM601792 1 0.2537 0.8560 0.920 0.000 0.080
#> GSM601797 1 0.2743 0.8591 0.928 0.020 0.052
#> GSM601827 3 0.4063 0.8439 0.020 0.112 0.868
#> GSM601837 2 0.3192 0.8271 0.000 0.888 0.112
#> GSM601842 2 0.1399 0.8736 0.028 0.968 0.004
#> GSM601857 3 0.0237 0.8226 0.000 0.004 0.996
#> GSM601867 3 0.5849 0.6198 0.028 0.216 0.756
#> GSM601747 2 0.6075 0.4881 0.008 0.676 0.316
#> GSM601757 3 0.4209 0.8435 0.020 0.120 0.860
#> GSM601762 2 0.0424 0.8787 0.008 0.992 0.000
#> GSM601767 2 0.0000 0.8797 0.000 1.000 0.000
#> GSM601772 2 0.0000 0.8797 0.000 1.000 0.000
#> GSM601777 1 0.6684 0.6782 0.676 0.032 0.292
#> GSM601787 3 0.5902 0.4589 0.004 0.316 0.680
#> GSM601802 1 0.1163 0.8503 0.972 0.028 0.000
#> GSM601807 3 0.3129 0.7665 0.088 0.008 0.904
#> GSM601812 3 0.3965 0.8427 0.008 0.132 0.860
#> GSM601817 3 0.2959 0.8483 0.000 0.100 0.900
#> GSM601822 1 0.2492 0.8593 0.936 0.016 0.048
#> GSM601832 2 0.4095 0.8306 0.056 0.880 0.064
#> GSM601847 1 0.2569 0.8573 0.936 0.032 0.032
#> GSM601852 3 0.3425 0.8455 0.004 0.112 0.884
#> GSM601862 3 0.0237 0.8208 0.004 0.000 0.996
#> GSM601753 1 0.1163 0.8503 0.972 0.028 0.000
#> GSM601783 3 0.4786 0.8330 0.044 0.112 0.844
#> GSM601793 1 0.3192 0.8483 0.888 0.000 0.112
#> GSM601798 1 0.1163 0.8503 0.972 0.028 0.000
#> GSM601828 3 0.3607 0.8454 0.008 0.112 0.880
#> GSM601838 2 0.3192 0.8271 0.000 0.888 0.112
#> GSM601843 2 0.0424 0.8788 0.008 0.992 0.000
#> GSM601858 2 0.3340 0.8275 0.000 0.880 0.120
#> GSM601868 3 0.0237 0.8192 0.004 0.000 0.996
#> GSM601748 3 0.3425 0.8455 0.004 0.112 0.884
#> GSM601758 3 0.4196 0.8425 0.024 0.112 0.864
#> GSM601763 2 0.7059 0.6450 0.092 0.716 0.192
#> GSM601768 2 0.0000 0.8797 0.000 1.000 0.000
#> GSM601773 2 0.0747 0.8774 0.016 0.984 0.000
#> GSM601778 1 0.4164 0.8383 0.848 0.008 0.144
#> GSM601788 2 0.1289 0.8706 0.000 0.968 0.032
#> GSM601803 1 0.1964 0.8439 0.944 0.056 0.000
#> GSM601808 3 0.0237 0.8192 0.004 0.000 0.996
#> GSM601813 3 0.4196 0.8425 0.024 0.112 0.864
#> GSM601818 3 0.3983 0.7706 0.004 0.144 0.852
#> GSM601823 1 0.3879 0.8295 0.848 0.000 0.152
#> GSM601833 2 0.0000 0.8797 0.000 1.000 0.000
#> GSM601848 1 0.3752 0.8336 0.856 0.000 0.144
#> GSM601853 3 0.0237 0.8192 0.004 0.000 0.996
#> GSM601863 3 0.0475 0.8230 0.004 0.004 0.992
#> GSM601754 1 0.1399 0.8518 0.968 0.028 0.004
#> GSM601784 2 0.0000 0.8797 0.000 1.000 0.000
#> GSM601794 1 0.3038 0.8507 0.896 0.000 0.104
#> GSM601799 1 0.1267 0.8524 0.972 0.024 0.004
#> GSM601829 1 0.7366 0.4122 0.564 0.036 0.400
#> GSM601839 2 0.3192 0.8271 0.000 0.888 0.112
#> GSM601844 1 0.9118 0.1899 0.468 0.144 0.388
#> GSM601859 2 0.0000 0.8797 0.000 1.000 0.000
#> GSM601869 3 0.1315 0.8301 0.008 0.020 0.972
#> GSM601749 3 0.4413 0.8423 0.024 0.124 0.852
#> GSM601759 3 0.4196 0.8425 0.024 0.112 0.864
#> GSM601764 2 0.6148 0.6023 0.028 0.728 0.244
#> GSM601769 2 0.0000 0.8797 0.000 1.000 0.000
#> GSM601774 2 0.0000 0.8797 0.000 1.000 0.000
#> GSM601779 1 0.3941 0.8269 0.844 0.000 0.156
#> GSM601789 2 0.2625 0.8445 0.000 0.916 0.084
#> GSM601804 1 0.3183 0.8584 0.908 0.016 0.076
#> GSM601809 3 0.6825 0.0571 0.012 0.492 0.496
#> GSM601814 2 0.0000 0.8797 0.000 1.000 0.000
#> GSM601819 3 0.5726 0.7695 0.024 0.216 0.760
#> GSM601824 1 0.4551 0.8396 0.844 0.024 0.132
#> GSM601834 2 0.0000 0.8797 0.000 1.000 0.000
#> GSM601849 3 0.9695 -0.0304 0.384 0.216 0.400
#> GSM601854 3 0.4618 0.8379 0.024 0.136 0.840
#> GSM601864 2 0.3192 0.8271 0.000 0.888 0.112
#> GSM601755 1 0.1163 0.8503 0.972 0.028 0.000
#> GSM601785 2 0.3966 0.8151 0.024 0.876 0.100
#> GSM601795 1 0.2711 0.8548 0.912 0.000 0.088
#> GSM601800 1 0.1163 0.8503 0.972 0.028 0.000
#> GSM601830 3 0.2486 0.7890 0.060 0.008 0.932
#> GSM601840 2 0.6208 0.7225 0.076 0.772 0.152
#> GSM601845 2 0.5746 0.7080 0.040 0.780 0.180
#> GSM601860 2 0.1529 0.8661 0.000 0.960 0.040
#> GSM601870 3 0.1919 0.8103 0.024 0.020 0.956
#> GSM601750 3 0.3607 0.8453 0.008 0.112 0.880
#> GSM601760 3 0.4873 0.8285 0.024 0.152 0.824
#> GSM601765 2 0.0237 0.8793 0.004 0.996 0.000
#> GSM601770 2 0.0000 0.8797 0.000 1.000 0.000
#> GSM601775 2 0.7717 0.6056 0.172 0.680 0.148
#> GSM601780 1 0.6601 0.6553 0.676 0.028 0.296
#> GSM601790 2 0.3116 0.8299 0.000 0.892 0.108
#> GSM601805 1 0.1643 0.8480 0.956 0.044 0.000
#> GSM601810 3 0.1964 0.8414 0.000 0.056 0.944
#> GSM601815 2 0.2625 0.8443 0.000 0.916 0.084
#> GSM601820 3 0.4196 0.8425 0.024 0.112 0.864
#> GSM601825 1 0.1643 0.8477 0.956 0.044 0.000
#> GSM601835 2 0.4709 0.8165 0.056 0.852 0.092
#> GSM601850 1 0.9001 0.3855 0.520 0.332 0.148
#> GSM601855 3 0.1950 0.8031 0.040 0.008 0.952
#> GSM601865 2 0.3192 0.8271 0.000 0.888 0.112
#> GSM601756 1 0.1163 0.8503 0.972 0.028 0.000
#> GSM601786 2 0.3116 0.8299 0.000 0.892 0.108
#> GSM601796 1 0.3412 0.8445 0.876 0.000 0.124
#> GSM601801 1 0.1163 0.8503 0.972 0.028 0.000
#> GSM601831 3 0.3695 0.8464 0.012 0.108 0.880
#> GSM601841 3 0.9370 -0.0882 0.416 0.168 0.416
#> GSM601846 1 0.3112 0.8591 0.916 0.028 0.056
#> GSM601861 2 0.0237 0.8792 0.000 0.996 0.004
#> GSM601871 3 0.5882 0.3933 0.000 0.348 0.652
#> GSM601751 2 0.2096 0.8592 0.004 0.944 0.052
#> GSM601761 3 0.9676 0.3019 0.252 0.288 0.460
#> GSM601766 2 0.4195 0.7783 0.012 0.852 0.136
#> GSM601771 2 0.1753 0.8631 0.000 0.952 0.048
#> GSM601776 1 0.6880 0.6212 0.660 0.036 0.304
#> GSM601781 1 0.6950 0.6883 0.692 0.056 0.252
#> GSM601791 2 0.9853 -0.1476 0.252 0.388 0.360
#> GSM601806 1 0.3038 0.8238 0.896 0.104 0.000
#> GSM601811 3 0.2625 0.8179 0.000 0.084 0.916
#> GSM601816 1 0.3752 0.8338 0.856 0.000 0.144
#> GSM601821 2 0.0424 0.8784 0.000 0.992 0.008
#> GSM601826 1 0.3879 0.8295 0.848 0.000 0.152
#> GSM601836 2 0.5939 0.6411 0.028 0.748 0.224
#> GSM601851 1 0.6008 0.6197 0.664 0.004 0.332
#> GSM601856 3 0.0475 0.8219 0.004 0.004 0.992
#> GSM601866 3 0.3272 0.8470 0.004 0.104 0.892
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601752 4 0.0000 0.8167 0.000 0.000 0.000 1.000
#> GSM601782 1 0.6976 0.4462 0.580 0.240 0.180 0.000
#> GSM601792 4 0.4040 0.6954 0.248 0.000 0.000 0.752
#> GSM601797 4 0.0188 0.8170 0.004 0.000 0.000 0.996
#> GSM601827 1 0.3402 0.5116 0.832 0.004 0.164 0.000
#> GSM601837 2 0.5057 0.5362 0.012 0.648 0.340 0.000
#> GSM601842 2 0.1576 0.7988 0.004 0.948 0.000 0.048
#> GSM601857 1 0.4996 -0.3183 0.516 0.000 0.484 0.000
#> GSM601867 3 0.4756 0.5248 0.072 0.144 0.784 0.000
#> GSM601747 2 0.7421 -0.1701 0.372 0.456 0.172 0.000
#> GSM601757 1 0.1743 0.5866 0.940 0.004 0.056 0.000
#> GSM601762 2 0.0927 0.8127 0.000 0.976 0.008 0.016
#> GSM601767 2 0.0000 0.8165 0.000 1.000 0.000 0.000
#> GSM601772 2 0.0000 0.8165 0.000 1.000 0.000 0.000
#> GSM601777 4 0.4484 0.7334 0.064 0.004 0.120 0.812
#> GSM601787 3 0.2965 0.5456 0.036 0.072 0.892 0.000
#> GSM601802 4 0.0000 0.8167 0.000 0.000 0.000 1.000
#> GSM601807 3 0.3355 0.5784 0.160 0.000 0.836 0.004
#> GSM601812 1 0.3355 0.5219 0.836 0.004 0.160 0.000
#> GSM601817 1 0.3801 0.4402 0.780 0.000 0.220 0.000
#> GSM601822 4 0.0376 0.8169 0.004 0.000 0.004 0.992
#> GSM601832 2 0.3335 0.7387 0.016 0.856 0.000 0.128
#> GSM601847 4 0.0188 0.8170 0.004 0.000 0.000 0.996
#> GSM601852 1 0.4949 0.5369 0.760 0.060 0.180 0.000
#> GSM601862 1 0.5000 -0.3511 0.504 0.000 0.496 0.000
#> GSM601753 4 0.0000 0.8167 0.000 0.000 0.000 1.000
#> GSM601783 1 0.2399 0.6025 0.920 0.048 0.032 0.000
#> GSM601793 4 0.4585 0.6213 0.332 0.000 0.000 0.668
#> GSM601798 4 0.0000 0.8167 0.000 0.000 0.000 1.000
#> GSM601828 1 0.3208 0.5361 0.848 0.004 0.148 0.000
#> GSM601838 2 0.5057 0.5362 0.012 0.648 0.340 0.000
#> GSM601843 2 0.0336 0.8153 0.000 0.992 0.000 0.008
#> GSM601858 2 0.4387 0.6601 0.012 0.752 0.236 0.000
#> GSM601868 3 0.4994 0.3610 0.480 0.000 0.520 0.000
#> GSM601748 1 0.2921 0.5391 0.860 0.000 0.140 0.000
#> GSM601758 1 0.2101 0.5883 0.928 0.012 0.060 0.000
#> GSM601763 1 0.5643 0.1854 0.540 0.440 0.016 0.004
#> GSM601768 2 0.0000 0.8165 0.000 1.000 0.000 0.000
#> GSM601773 2 0.0817 0.8117 0.000 0.976 0.000 0.024
#> GSM601778 4 0.3982 0.7277 0.220 0.000 0.004 0.776
#> GSM601788 2 0.0927 0.8137 0.008 0.976 0.000 0.016
#> GSM601803 4 0.1557 0.7899 0.000 0.056 0.000 0.944
#> GSM601808 3 0.4967 0.4233 0.452 0.000 0.548 0.000
#> GSM601813 1 0.3758 0.6006 0.848 0.104 0.048 0.000
#> GSM601818 1 0.5998 0.4493 0.684 0.116 0.200 0.000
#> GSM601823 4 0.5508 0.3538 0.476 0.000 0.016 0.508
#> GSM601833 2 0.0000 0.8165 0.000 1.000 0.000 0.000
#> GSM601848 4 0.5506 0.3632 0.472 0.000 0.016 0.512
#> GSM601853 3 0.4972 0.4148 0.456 0.000 0.544 0.000
#> GSM601863 1 0.4916 -0.1089 0.576 0.000 0.424 0.000
#> GSM601754 4 0.0000 0.8167 0.000 0.000 0.000 1.000
#> GSM601784 2 0.0000 0.8165 0.000 1.000 0.000 0.000
#> GSM601794 4 0.4304 0.6688 0.284 0.000 0.000 0.716
#> GSM601799 4 0.0000 0.8167 0.000 0.000 0.000 1.000
#> GSM601829 1 0.6338 0.1920 0.620 0.024 0.040 0.316
#> GSM601839 2 0.5057 0.5362 0.012 0.648 0.340 0.000
#> GSM601844 1 0.5037 0.5504 0.764 0.188 0.028 0.020
#> GSM601859 2 0.0000 0.8165 0.000 1.000 0.000 0.000
#> GSM601869 1 0.3837 0.4445 0.776 0.000 0.224 0.000
#> GSM601749 1 0.3697 0.6030 0.852 0.100 0.048 0.000
#> GSM601759 1 0.1637 0.5822 0.940 0.000 0.060 0.000
#> GSM601764 1 0.5535 0.2635 0.560 0.420 0.020 0.000
#> GSM601769 2 0.0657 0.8126 0.004 0.984 0.012 0.000
#> GSM601774 2 0.0000 0.8165 0.000 1.000 0.000 0.000
#> GSM601779 4 0.5511 0.3324 0.484 0.000 0.016 0.500
#> GSM601789 2 0.3672 0.7190 0.012 0.824 0.164 0.000
#> GSM601804 4 0.1211 0.8134 0.040 0.000 0.000 0.960
#> GSM601809 1 0.7379 0.2416 0.468 0.364 0.168 0.000
#> GSM601814 2 0.0000 0.8165 0.000 1.000 0.000 0.000
#> GSM601819 1 0.3706 0.5889 0.848 0.112 0.040 0.000
#> GSM601824 4 0.6684 0.5725 0.272 0.104 0.008 0.616
#> GSM601834 2 0.0000 0.8165 0.000 1.000 0.000 0.000
#> GSM601849 1 0.5139 0.5494 0.760 0.188 0.028 0.024
#> GSM601854 1 0.3873 0.6033 0.844 0.096 0.060 0.000
#> GSM601864 3 0.5404 -0.2982 0.012 0.476 0.512 0.000
#> GSM601755 4 0.0000 0.8167 0.000 0.000 0.000 1.000
#> GSM601785 2 0.4444 0.7070 0.072 0.808 0.000 0.120
#> GSM601795 4 0.2704 0.7825 0.124 0.000 0.000 0.876
#> GSM601800 4 0.0000 0.8167 0.000 0.000 0.000 1.000
#> GSM601830 3 0.4955 0.4375 0.444 0.000 0.556 0.000
#> GSM601840 2 0.8143 0.1792 0.292 0.476 0.024 0.208
#> GSM601845 2 0.7965 0.2354 0.288 0.512 0.028 0.172
#> GSM601860 2 0.0188 0.8157 0.004 0.996 0.000 0.000
#> GSM601870 3 0.2345 0.5777 0.100 0.000 0.900 0.000
#> GSM601750 1 0.3402 0.5196 0.832 0.004 0.164 0.000
#> GSM601760 1 0.2124 0.5969 0.932 0.028 0.040 0.000
#> GSM601765 2 0.0000 0.8165 0.000 1.000 0.000 0.000
#> GSM601770 2 0.0000 0.8165 0.000 1.000 0.000 0.000
#> GSM601775 2 0.8130 0.1052 0.336 0.444 0.020 0.200
#> GSM601780 1 0.5855 0.4411 0.716 0.048 0.028 0.208
#> GSM601790 2 0.4635 0.6243 0.012 0.720 0.268 0.000
#> GSM601805 4 0.0469 0.8130 0.000 0.012 0.000 0.988
#> GSM601810 1 0.4843 -0.0604 0.604 0.000 0.396 0.000
#> GSM601815 2 0.3196 0.7424 0.008 0.856 0.136 0.000
#> GSM601820 1 0.1557 0.5835 0.944 0.000 0.056 0.000
#> GSM601825 4 0.0921 0.8059 0.000 0.028 0.000 0.972
#> GSM601835 2 0.3424 0.7748 0.028 0.880 0.016 0.076
#> GSM601850 1 0.8376 0.1623 0.404 0.296 0.020 0.280
#> GSM601855 3 0.4008 0.5637 0.244 0.000 0.756 0.000
#> GSM601865 2 0.5057 0.5362 0.012 0.648 0.340 0.000
#> GSM601756 4 0.0000 0.8167 0.000 0.000 0.000 1.000
#> GSM601786 2 0.4690 0.6153 0.012 0.712 0.276 0.000
#> GSM601796 4 0.4800 0.6151 0.340 0.004 0.000 0.656
#> GSM601801 4 0.0000 0.8167 0.000 0.000 0.000 1.000
#> GSM601831 1 0.4994 -0.3350 0.520 0.000 0.480 0.000
#> GSM601841 1 0.6821 0.5290 0.676 0.184 0.056 0.084
#> GSM601846 4 0.0804 0.8157 0.012 0.000 0.008 0.980
#> GSM601861 2 0.1302 0.8027 0.000 0.956 0.044 0.000
#> GSM601871 3 0.4423 0.4551 0.040 0.168 0.792 0.000
#> GSM601751 2 0.3526 0.7456 0.100 0.864 0.004 0.032
#> GSM601761 1 0.4418 0.5551 0.784 0.192 0.016 0.008
#> GSM601766 2 0.5138 0.1978 0.392 0.600 0.000 0.008
#> GSM601771 2 0.3408 0.7360 0.120 0.860 0.004 0.016
#> GSM601776 1 0.7052 0.4061 0.636 0.128 0.028 0.208
#> GSM601781 4 0.6217 0.4822 0.400 0.040 0.008 0.552
#> GSM601791 1 0.5230 0.5345 0.736 0.220 0.028 0.016
#> GSM601806 4 0.2408 0.7558 0.000 0.104 0.000 0.896
#> GSM601811 1 0.5792 -0.0834 0.552 0.032 0.416 0.000
#> GSM601816 4 0.5220 0.5910 0.352 0.000 0.016 0.632
#> GSM601821 2 0.1474 0.7987 0.000 0.948 0.052 0.000
#> GSM601826 4 0.5508 0.3551 0.476 0.000 0.016 0.508
#> GSM601836 1 0.5650 0.2323 0.544 0.432 0.024 0.000
#> GSM601851 1 0.6104 0.3348 0.672 0.040 0.028 0.260
#> GSM601856 3 0.4961 0.4294 0.448 0.000 0.552 0.000
#> GSM601866 1 0.2469 0.5612 0.892 0.000 0.108 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601752 4 0.0162 0.8371 0.004 0.000 0.000 0.996 0.000
#> GSM601782 3 0.6780 0.2704 0.284 0.236 0.472 0.000 0.008
#> GSM601792 4 0.4015 0.5220 0.348 0.000 0.000 0.652 0.000
#> GSM601797 4 0.0609 0.8380 0.020 0.000 0.000 0.980 0.000
#> GSM601827 3 0.2953 0.8227 0.144 0.000 0.844 0.000 0.012
#> GSM601837 5 0.3857 0.7551 0.000 0.312 0.000 0.000 0.688
#> GSM601842 2 0.0865 0.8462 0.000 0.972 0.000 0.024 0.004
#> GSM601857 3 0.1668 0.7913 0.028 0.000 0.940 0.000 0.032
#> GSM601867 5 0.4197 0.4344 0.028 0.000 0.244 0.000 0.728
#> GSM601747 2 0.6177 0.2056 0.128 0.516 0.352 0.000 0.004
#> GSM601757 3 0.3828 0.7714 0.220 0.008 0.764 0.000 0.008
#> GSM601762 2 0.0162 0.8590 0.000 0.996 0.000 0.000 0.004
#> GSM601767 2 0.0162 0.8590 0.000 0.996 0.000 0.000 0.004
#> GSM601772 2 0.0000 0.8590 0.000 1.000 0.000 0.000 0.000
#> GSM601777 4 0.6311 0.3642 0.348 0.008 0.104 0.532 0.008
#> GSM601787 5 0.5229 0.5598 0.028 0.068 0.192 0.000 0.712
#> GSM601802 4 0.0162 0.8371 0.004 0.000 0.000 0.996 0.000
#> GSM601807 3 0.4638 0.5460 0.028 0.000 0.648 0.000 0.324
#> GSM601812 3 0.2439 0.8233 0.120 0.004 0.876 0.000 0.000
#> GSM601817 3 0.1981 0.8264 0.064 0.000 0.920 0.000 0.016
#> GSM601822 4 0.2074 0.8033 0.104 0.000 0.000 0.896 0.000
#> GSM601832 2 0.2238 0.7968 0.020 0.912 0.000 0.064 0.004
#> GSM601847 4 0.1831 0.8182 0.076 0.004 0.000 0.920 0.000
#> GSM601852 3 0.2488 0.8242 0.124 0.000 0.872 0.000 0.004
#> GSM601862 3 0.2278 0.7820 0.032 0.000 0.908 0.000 0.060
#> GSM601753 4 0.0510 0.8382 0.016 0.000 0.000 0.984 0.000
#> GSM601783 3 0.3861 0.7401 0.264 0.000 0.728 0.000 0.008
#> GSM601793 4 0.4341 0.4032 0.404 0.000 0.004 0.592 0.000
#> GSM601798 4 0.0000 0.8356 0.000 0.000 0.000 1.000 0.000
#> GSM601828 3 0.2536 0.8219 0.128 0.000 0.868 0.000 0.004
#> GSM601838 5 0.3857 0.7551 0.000 0.312 0.000 0.000 0.688
#> GSM601843 2 0.0451 0.8565 0.000 0.988 0.000 0.008 0.004
#> GSM601858 5 0.4225 0.7384 0.000 0.364 0.004 0.000 0.632
#> GSM601868 3 0.2193 0.7784 0.028 0.000 0.912 0.000 0.060
#> GSM601748 3 0.2873 0.8224 0.128 0.000 0.856 0.000 0.016
#> GSM601758 3 0.3596 0.7844 0.212 0.000 0.776 0.000 0.012
#> GSM601763 1 0.3357 0.7375 0.852 0.092 0.008 0.048 0.000
#> GSM601768 2 0.0000 0.8590 0.000 1.000 0.000 0.000 0.000
#> GSM601773 2 0.0771 0.8512 0.000 0.976 0.000 0.020 0.004
#> GSM601778 4 0.4897 0.2160 0.460 0.000 0.024 0.516 0.000
#> GSM601788 2 0.0162 0.8588 0.000 0.996 0.000 0.004 0.000
#> GSM601803 4 0.1341 0.8044 0.000 0.056 0.000 0.944 0.000
#> GSM601808 3 0.2388 0.7721 0.028 0.000 0.900 0.000 0.072
#> GSM601813 3 0.3815 0.7805 0.220 0.004 0.764 0.000 0.012
#> GSM601818 3 0.3891 0.7913 0.128 0.004 0.808 0.000 0.060
#> GSM601823 1 0.2516 0.7549 0.860 0.000 0.000 0.140 0.000
#> GSM601833 2 0.0000 0.8590 0.000 1.000 0.000 0.000 0.000
#> GSM601848 1 0.2561 0.7529 0.856 0.000 0.000 0.144 0.000
#> GSM601853 3 0.2450 0.7702 0.028 0.000 0.896 0.000 0.076
#> GSM601863 3 0.1836 0.7986 0.036 0.000 0.932 0.000 0.032
#> GSM601754 4 0.0510 0.8382 0.016 0.000 0.000 0.984 0.000
#> GSM601784 2 0.0162 0.8590 0.000 0.996 0.000 0.000 0.004
#> GSM601794 4 0.4045 0.5111 0.356 0.000 0.000 0.644 0.000
#> GSM601799 4 0.0510 0.8382 0.016 0.000 0.000 0.984 0.000
#> GSM601829 1 0.4657 0.6763 0.752 0.004 0.128 0.116 0.000
#> GSM601839 5 0.3876 0.7551 0.000 0.316 0.000 0.000 0.684
#> GSM601844 1 0.1329 0.7462 0.956 0.004 0.032 0.008 0.000
#> GSM601859 2 0.0162 0.8590 0.000 0.996 0.000 0.000 0.004
#> GSM601869 3 0.2124 0.8278 0.056 0.000 0.916 0.000 0.028
#> GSM601749 3 0.3596 0.7847 0.212 0.000 0.776 0.000 0.012
#> GSM601759 3 0.3355 0.8009 0.184 0.000 0.804 0.000 0.012
#> GSM601764 1 0.6015 0.4322 0.592 0.248 0.156 0.000 0.004
#> GSM601769 2 0.1197 0.8184 0.000 0.952 0.000 0.000 0.048
#> GSM601774 2 0.0162 0.8590 0.000 0.996 0.000 0.000 0.004
#> GSM601779 1 0.2516 0.7549 0.860 0.000 0.000 0.140 0.000
#> GSM601789 5 0.4210 0.6780 0.000 0.412 0.000 0.000 0.588
#> GSM601804 4 0.2852 0.7498 0.172 0.000 0.000 0.828 0.000
#> GSM601809 2 0.8192 -0.0733 0.136 0.348 0.328 0.000 0.188
#> GSM601814 2 0.0162 0.8590 0.000 0.996 0.000 0.000 0.004
#> GSM601819 1 0.5008 -0.2561 0.500 0.012 0.476 0.000 0.012
#> GSM601824 1 0.3949 0.4794 0.668 0.000 0.000 0.332 0.000
#> GSM601834 2 0.0162 0.8590 0.000 0.996 0.000 0.000 0.004
#> GSM601849 1 0.1646 0.7564 0.944 0.004 0.020 0.032 0.000
#> GSM601854 3 0.3123 0.8090 0.160 0.000 0.828 0.000 0.012
#> GSM601864 5 0.4309 0.7528 0.016 0.308 0.000 0.000 0.676
#> GSM601755 4 0.0000 0.8356 0.000 0.000 0.000 1.000 0.000
#> GSM601785 2 0.2061 0.8159 0.040 0.928 0.004 0.024 0.004
#> GSM601795 4 0.3684 0.6263 0.280 0.000 0.000 0.720 0.000
#> GSM601800 4 0.0162 0.8371 0.004 0.000 0.000 0.996 0.000
#> GSM601830 3 0.4526 0.5755 0.028 0.000 0.672 0.000 0.300
#> GSM601840 2 0.6217 0.4437 0.212 0.624 0.032 0.132 0.000
#> GSM601845 2 0.5809 0.4910 0.224 0.660 0.024 0.088 0.004
#> GSM601860 2 0.0324 0.8579 0.004 0.992 0.000 0.000 0.004
#> GSM601870 5 0.4812 0.0811 0.028 0.000 0.372 0.000 0.600
#> GSM601750 3 0.2439 0.8230 0.120 0.000 0.876 0.000 0.004
#> GSM601760 3 0.4597 0.4549 0.424 0.000 0.564 0.000 0.012
#> GSM601765 2 0.0000 0.8590 0.000 1.000 0.000 0.000 0.000
#> GSM601770 2 0.0162 0.8590 0.000 0.996 0.000 0.000 0.004
#> GSM601775 1 0.6076 0.4890 0.584 0.280 0.004 0.128 0.004
#> GSM601780 1 0.2329 0.7596 0.876 0.000 0.000 0.124 0.000
#> GSM601790 5 0.4030 0.7437 0.000 0.352 0.000 0.000 0.648
#> GSM601805 4 0.1018 0.8350 0.016 0.016 0.000 0.968 0.000
#> GSM601810 3 0.2230 0.8195 0.044 0.000 0.912 0.000 0.044
#> GSM601815 5 0.4302 0.5242 0.000 0.480 0.000 0.000 0.520
#> GSM601820 3 0.3563 0.7867 0.208 0.000 0.780 0.000 0.012
#> GSM601825 4 0.1364 0.8249 0.012 0.036 0.000 0.952 0.000
#> GSM601835 2 0.3513 0.7649 0.016 0.868 0.040 0.044 0.032
#> GSM601850 1 0.3319 0.7389 0.820 0.020 0.000 0.160 0.000
#> GSM601855 3 0.4733 0.5067 0.028 0.000 0.624 0.000 0.348
#> GSM601865 5 0.3983 0.7493 0.000 0.340 0.000 0.000 0.660
#> GSM601756 4 0.0000 0.8356 0.000 0.000 0.000 1.000 0.000
#> GSM601786 5 0.4074 0.7354 0.000 0.364 0.000 0.000 0.636
#> GSM601796 4 0.4434 0.2608 0.460 0.000 0.004 0.536 0.000
#> GSM601801 4 0.0000 0.8356 0.000 0.000 0.000 1.000 0.000
#> GSM601831 3 0.3586 0.8194 0.096 0.000 0.828 0.000 0.076
#> GSM601841 1 0.4012 0.7020 0.816 0.032 0.116 0.036 0.000
#> GSM601846 4 0.1768 0.8217 0.072 0.000 0.000 0.924 0.004
#> GSM601861 2 0.2605 0.6682 0.000 0.852 0.000 0.000 0.148
#> GSM601871 5 0.5884 0.6308 0.028 0.140 0.168 0.000 0.664
#> GSM601751 2 0.0486 0.8571 0.004 0.988 0.004 0.000 0.004
#> GSM601761 1 0.2833 0.6734 0.864 0.000 0.120 0.012 0.004
#> GSM601766 2 0.2439 0.7363 0.120 0.876 0.004 0.000 0.000
#> GSM601771 2 0.0579 0.8535 0.008 0.984 0.008 0.000 0.000
#> GSM601776 1 0.2377 0.7586 0.872 0.000 0.000 0.128 0.000
#> GSM601781 1 0.3421 0.6793 0.788 0.008 0.000 0.204 0.000
#> GSM601791 1 0.2227 0.7556 0.920 0.004 0.028 0.044 0.004
#> GSM601806 4 0.1851 0.7774 0.000 0.088 0.000 0.912 0.000
#> GSM601811 3 0.2610 0.7906 0.028 0.004 0.892 0.000 0.076
#> GSM601816 1 0.3003 0.7130 0.812 0.000 0.000 0.188 0.000
#> GSM601821 2 0.2929 0.6030 0.000 0.820 0.000 0.000 0.180
#> GSM601826 1 0.2561 0.7529 0.856 0.000 0.000 0.144 0.000
#> GSM601836 1 0.6187 0.3766 0.552 0.248 0.200 0.000 0.000
#> GSM601851 1 0.2329 0.7596 0.876 0.000 0.000 0.124 0.000
#> GSM601856 3 0.3724 0.6878 0.028 0.000 0.788 0.000 0.184
#> GSM601866 3 0.2798 0.8172 0.140 0.000 0.852 0.000 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601752 4 0.0260 0.8758 0.000 0.000 0.000 0.992 0.000 0.008
#> GSM601782 1 0.0993 0.8473 0.964 0.000 0.024 0.000 0.000 0.012
#> GSM601792 4 0.4413 -0.1317 0.000 0.000 0.008 0.492 0.012 0.488
#> GSM601797 4 0.0603 0.8739 0.000 0.000 0.000 0.980 0.004 0.016
#> GSM601827 1 0.1865 0.8431 0.920 0.000 0.040 0.000 0.000 0.040
#> GSM601837 5 0.1588 0.8464 0.000 0.072 0.004 0.000 0.924 0.000
#> GSM601842 2 0.0767 0.8914 0.000 0.976 0.004 0.012 0.000 0.008
#> GSM601857 3 0.3221 0.7703 0.264 0.000 0.736 0.000 0.000 0.000
#> GSM601867 3 0.2452 0.7875 0.028 0.004 0.884 0.000 0.084 0.000
#> GSM601747 1 0.2642 0.8272 0.892 0.024 0.024 0.000 0.008 0.052
#> GSM601757 1 0.0937 0.8506 0.960 0.000 0.000 0.000 0.000 0.040
#> GSM601762 2 0.1349 0.8764 0.000 0.940 0.000 0.000 0.056 0.004
#> GSM601767 2 0.0547 0.8912 0.000 0.980 0.000 0.000 0.020 0.000
#> GSM601772 2 0.0146 0.8928 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM601777 6 0.5864 0.5571 0.088 0.028 0.024 0.224 0.004 0.632
#> GSM601787 3 0.3816 0.7401 0.016 0.056 0.808 0.000 0.112 0.008
#> GSM601802 4 0.0146 0.8758 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM601807 3 0.2151 0.7809 0.016 0.000 0.904 0.000 0.072 0.008
#> GSM601812 1 0.0692 0.8454 0.976 0.000 0.020 0.000 0.000 0.004
#> GSM601817 1 0.1556 0.8214 0.920 0.000 0.080 0.000 0.000 0.000
#> GSM601822 4 0.4009 0.3652 0.000 0.000 0.008 0.632 0.004 0.356
#> GSM601832 2 0.1881 0.8649 0.000 0.928 0.004 0.040 0.008 0.020
#> GSM601847 4 0.3851 0.5348 0.000 0.004 0.008 0.700 0.004 0.284
#> GSM601852 1 0.0891 0.8466 0.968 0.000 0.024 0.000 0.000 0.008
#> GSM601862 3 0.3126 0.7886 0.248 0.000 0.752 0.000 0.000 0.000
#> GSM601753 4 0.0363 0.8751 0.000 0.000 0.000 0.988 0.000 0.012
#> GSM601783 1 0.2877 0.7855 0.820 0.000 0.000 0.000 0.012 0.168
#> GSM601793 6 0.3867 0.5795 0.000 0.000 0.004 0.296 0.012 0.688
#> GSM601798 4 0.0000 0.8747 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601828 1 0.0260 0.8459 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM601838 5 0.1588 0.8464 0.000 0.072 0.004 0.000 0.924 0.000
#> GSM601843 2 0.0551 0.8931 0.000 0.984 0.000 0.008 0.004 0.004
#> GSM601858 5 0.3670 0.8389 0.000 0.240 0.024 0.000 0.736 0.000
#> GSM601868 3 0.3076 0.7925 0.240 0.000 0.760 0.000 0.000 0.000
#> GSM601748 1 0.0692 0.8472 0.976 0.000 0.020 0.000 0.000 0.004
#> GSM601758 1 0.2383 0.8218 0.880 0.000 0.000 0.000 0.024 0.096
#> GSM601763 6 0.2757 0.7337 0.016 0.104 0.000 0.000 0.016 0.864
#> GSM601768 2 0.0291 0.8932 0.000 0.992 0.000 0.000 0.004 0.004
#> GSM601773 2 0.1642 0.8783 0.000 0.936 0.000 0.032 0.028 0.004
#> GSM601778 6 0.4223 0.6411 0.020 0.004 0.008 0.240 0.008 0.720
#> GSM601788 2 0.0665 0.8911 0.008 0.980 0.000 0.000 0.008 0.004
#> GSM601803 4 0.0865 0.8555 0.000 0.036 0.000 0.964 0.000 0.000
#> GSM601808 3 0.3052 0.8085 0.216 0.000 0.780 0.000 0.004 0.000
#> GSM601813 1 0.2766 0.8177 0.852 0.000 0.004 0.000 0.020 0.124
#> GSM601818 1 0.2092 0.7842 0.876 0.000 0.124 0.000 0.000 0.000
#> GSM601823 6 0.2068 0.7846 0.008 0.000 0.000 0.080 0.008 0.904
#> GSM601833 2 0.0146 0.8928 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM601848 6 0.1956 0.7849 0.008 0.000 0.000 0.080 0.004 0.908
#> GSM601853 3 0.3052 0.8086 0.216 0.000 0.780 0.000 0.004 0.000
#> GSM601863 3 0.3288 0.7641 0.276 0.000 0.724 0.000 0.000 0.000
#> GSM601754 4 0.0405 0.8755 0.000 0.000 0.004 0.988 0.000 0.008
#> GSM601784 2 0.0713 0.8874 0.000 0.972 0.000 0.000 0.028 0.000
#> GSM601794 6 0.4076 0.4862 0.000 0.000 0.004 0.348 0.012 0.636
#> GSM601799 4 0.0603 0.8732 0.000 0.000 0.004 0.980 0.000 0.016
#> GSM601829 6 0.3736 0.6575 0.216 0.000 0.008 0.016 0.004 0.756
#> GSM601839 5 0.1444 0.8477 0.000 0.072 0.000 0.000 0.928 0.000
#> GSM601844 6 0.1297 0.7834 0.040 0.000 0.000 0.000 0.012 0.948
#> GSM601859 2 0.0547 0.8911 0.000 0.980 0.000 0.000 0.020 0.000
#> GSM601869 1 0.2912 0.6521 0.784 0.000 0.216 0.000 0.000 0.000
#> GSM601749 1 0.2760 0.8154 0.856 0.000 0.004 0.000 0.024 0.116
#> GSM601759 1 0.2009 0.8340 0.908 0.000 0.000 0.000 0.024 0.068
#> GSM601764 2 0.6547 0.1141 0.252 0.420 0.000 0.000 0.028 0.300
#> GSM601769 2 0.1714 0.8478 0.000 0.908 0.000 0.000 0.092 0.000
#> GSM601774 2 0.0865 0.8850 0.000 0.964 0.000 0.000 0.036 0.000
#> GSM601779 6 0.1956 0.7849 0.008 0.000 0.000 0.080 0.004 0.908
#> GSM601789 5 0.3601 0.7823 0.000 0.312 0.004 0.000 0.684 0.000
#> GSM601804 4 0.3463 0.6197 0.000 0.000 0.004 0.748 0.008 0.240
#> GSM601809 1 0.4138 0.7481 0.796 0.104 0.060 0.000 0.024 0.016
#> GSM601814 2 0.1556 0.8608 0.000 0.920 0.000 0.000 0.080 0.000
#> GSM601819 1 0.2926 0.7980 0.844 0.004 0.000 0.000 0.028 0.124
#> GSM601824 6 0.4455 0.0473 0.008 0.000 0.004 0.480 0.008 0.500
#> GSM601834 2 0.0865 0.8850 0.000 0.964 0.000 0.000 0.036 0.000
#> GSM601849 6 0.0993 0.7864 0.024 0.000 0.000 0.000 0.012 0.964
#> GSM601854 1 0.1232 0.8474 0.956 0.000 0.004 0.000 0.024 0.016
#> GSM601864 5 0.2400 0.8666 0.000 0.116 0.004 0.000 0.872 0.008
#> GSM601755 4 0.0000 0.8747 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601785 2 0.1698 0.8724 0.008 0.940 0.004 0.004 0.012 0.032
#> GSM601795 6 0.4393 0.2250 0.000 0.000 0.008 0.448 0.012 0.532
#> GSM601800 4 0.0146 0.8758 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM601830 3 0.1003 0.7968 0.016 0.000 0.964 0.000 0.020 0.000
#> GSM601840 2 0.4433 0.6964 0.008 0.768 0.004 0.072 0.020 0.128
#> GSM601845 2 0.4749 0.6647 0.024 0.748 0.012 0.036 0.020 0.160
#> GSM601860 2 0.0767 0.8908 0.008 0.976 0.000 0.000 0.012 0.004
#> GSM601870 3 0.2114 0.7799 0.012 0.000 0.904 0.000 0.076 0.008
#> GSM601750 1 0.0551 0.8474 0.984 0.000 0.008 0.000 0.004 0.004
#> GSM601760 1 0.3027 0.7783 0.824 0.000 0.000 0.000 0.028 0.148
#> GSM601765 2 0.0291 0.8932 0.000 0.992 0.000 0.000 0.004 0.004
#> GSM601770 2 0.0146 0.8926 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM601775 6 0.4499 0.5501 0.008 0.256 0.004 0.016 0.020 0.696
#> GSM601780 6 0.1167 0.7906 0.008 0.000 0.000 0.012 0.020 0.960
#> GSM601790 5 0.2491 0.8790 0.000 0.164 0.000 0.000 0.836 0.000
#> GSM601805 4 0.1036 0.8656 0.000 0.024 0.004 0.964 0.000 0.008
#> GSM601810 1 0.2805 0.7283 0.812 0.000 0.184 0.000 0.000 0.004
#> GSM601815 5 0.3563 0.7043 0.000 0.336 0.000 0.000 0.664 0.000
#> GSM601820 1 0.2373 0.8279 0.888 0.000 0.004 0.000 0.024 0.084
#> GSM601825 4 0.1232 0.8647 0.000 0.024 0.004 0.956 0.000 0.016
#> GSM601835 2 0.4421 0.7274 0.020 0.784 0.048 0.032 0.112 0.004
#> GSM601850 6 0.1862 0.7914 0.008 0.000 0.004 0.044 0.016 0.928
#> GSM601855 3 0.2058 0.7804 0.012 0.000 0.908 0.000 0.072 0.008
#> GSM601865 5 0.2340 0.8789 0.000 0.148 0.000 0.000 0.852 0.000
#> GSM601756 4 0.0000 0.8747 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601786 5 0.3109 0.8571 0.000 0.224 0.004 0.000 0.772 0.000
#> GSM601796 6 0.3602 0.6536 0.004 0.000 0.004 0.240 0.008 0.744
#> GSM601801 4 0.0000 0.8747 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601831 1 0.2311 0.8190 0.880 0.000 0.104 0.000 0.000 0.016
#> GSM601841 6 0.4639 0.4820 0.312 0.000 0.012 0.012 0.020 0.644
#> GSM601846 4 0.3512 0.5758 0.000 0.000 0.008 0.720 0.000 0.272
#> GSM601861 2 0.2597 0.7451 0.000 0.824 0.000 0.000 0.176 0.000
#> GSM601871 3 0.4830 0.6362 0.012 0.128 0.716 0.000 0.136 0.008
#> GSM601751 2 0.0881 0.8894 0.008 0.972 0.000 0.000 0.012 0.008
#> GSM601761 6 0.3245 0.6758 0.172 0.000 0.000 0.000 0.028 0.800
#> GSM601766 2 0.2558 0.8161 0.012 0.884 0.004 0.000 0.016 0.084
#> GSM601771 2 0.0767 0.8908 0.008 0.976 0.000 0.000 0.012 0.004
#> GSM601776 6 0.1078 0.7912 0.008 0.000 0.000 0.016 0.012 0.964
#> GSM601781 6 0.1707 0.7888 0.000 0.012 0.004 0.056 0.000 0.928
#> GSM601791 6 0.1245 0.7839 0.032 0.000 0.000 0.000 0.016 0.952
#> GSM601806 4 0.1584 0.8243 0.000 0.064 0.000 0.928 0.008 0.000
#> GSM601811 1 0.3810 0.0757 0.572 0.000 0.428 0.000 0.000 0.000
#> GSM601816 6 0.2355 0.7684 0.000 0.000 0.004 0.112 0.008 0.876
#> GSM601821 2 0.2762 0.7146 0.000 0.804 0.000 0.000 0.196 0.000
#> GSM601826 6 0.1901 0.7860 0.008 0.000 0.000 0.076 0.004 0.912
#> GSM601836 1 0.6521 0.1223 0.396 0.332 0.004 0.000 0.016 0.252
#> GSM601851 6 0.0622 0.7874 0.008 0.000 0.000 0.000 0.012 0.980
#> GSM601856 3 0.3156 0.8191 0.180 0.000 0.800 0.000 0.020 0.000
#> GSM601866 1 0.0972 0.8457 0.964 0.000 0.028 0.000 0.000 0.008
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
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 time(p) gender(p) k
#> MAD:mclust 123 0.763 0.2784 2
#> MAD:mclust 114 0.491 0.4258 3
#> MAD:mclust 89 0.652 0.0897 4
#> MAD:mclust 108 0.443 0.0102 5
#> MAD:mclust 116 0.591 0.0625 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 125 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 0.901 0.931 0.970 0.5028 0.496 0.496
#> 3 3 0.502 0.622 0.813 0.3032 0.820 0.650
#> 4 4 0.450 0.502 0.723 0.1353 0.783 0.470
#> 5 5 0.489 0.438 0.635 0.0649 0.876 0.567
#> 6 6 0.537 0.340 0.575 0.0408 0.919 0.651
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
#> GSM601752 2 0.0000 0.978 0.000 1.000
#> GSM601782 1 0.0000 0.960 1.000 0.000
#> GSM601792 1 0.0000 0.960 1.000 0.000
#> GSM601797 2 0.4690 0.885 0.100 0.900
#> GSM601827 1 0.0000 0.960 1.000 0.000
#> GSM601837 2 0.0000 0.978 0.000 1.000
#> GSM601842 2 0.0000 0.978 0.000 1.000
#> GSM601857 1 0.0000 0.960 1.000 0.000
#> GSM601867 2 0.7883 0.690 0.236 0.764
#> GSM601747 1 0.3733 0.900 0.928 0.072
#> GSM601757 1 0.0000 0.960 1.000 0.000
#> GSM601762 2 0.0000 0.978 0.000 1.000
#> GSM601767 2 0.0000 0.978 0.000 1.000
#> GSM601772 2 0.0000 0.978 0.000 1.000
#> GSM601777 1 0.9866 0.262 0.568 0.432
#> GSM601787 2 0.2043 0.953 0.032 0.968
#> GSM601802 2 0.0000 0.978 0.000 1.000
#> GSM601807 1 0.4690 0.873 0.900 0.100
#> GSM601812 1 0.0000 0.960 1.000 0.000
#> GSM601817 1 0.0000 0.960 1.000 0.000
#> GSM601822 2 0.9087 0.519 0.324 0.676
#> GSM601832 2 0.0000 0.978 0.000 1.000
#> GSM601847 2 0.0376 0.975 0.004 0.996
#> GSM601852 1 0.0000 0.960 1.000 0.000
#> GSM601862 1 0.0000 0.960 1.000 0.000
#> GSM601753 2 0.0000 0.978 0.000 1.000
#> GSM601783 1 0.0000 0.960 1.000 0.000
#> GSM601793 1 0.0000 0.960 1.000 0.000
#> GSM601798 2 0.0000 0.978 0.000 1.000
#> GSM601828 1 0.0000 0.960 1.000 0.000
#> GSM601838 2 0.0000 0.978 0.000 1.000
#> GSM601843 2 0.0000 0.978 0.000 1.000
#> GSM601858 2 0.0000 0.978 0.000 1.000
#> GSM601868 1 0.0000 0.960 1.000 0.000
#> GSM601748 1 0.0000 0.960 1.000 0.000
#> GSM601758 1 0.0000 0.960 1.000 0.000
#> GSM601763 1 0.9732 0.337 0.596 0.404
#> GSM601768 2 0.0000 0.978 0.000 1.000
#> GSM601773 2 0.0000 0.978 0.000 1.000
#> GSM601778 1 0.1184 0.948 0.984 0.016
#> GSM601788 2 0.0000 0.978 0.000 1.000
#> GSM601803 2 0.0000 0.978 0.000 1.000
#> GSM601808 1 0.0000 0.960 1.000 0.000
#> GSM601813 1 0.0000 0.960 1.000 0.000
#> GSM601818 1 0.0000 0.960 1.000 0.000
#> GSM601823 1 0.0000 0.960 1.000 0.000
#> GSM601833 2 0.0000 0.978 0.000 1.000
#> GSM601848 1 0.0000 0.960 1.000 0.000
#> GSM601853 1 0.0000 0.960 1.000 0.000
#> GSM601863 1 0.0000 0.960 1.000 0.000
#> GSM601754 2 0.0000 0.978 0.000 1.000
#> GSM601784 2 0.0000 0.978 0.000 1.000
#> GSM601794 1 0.0000 0.960 1.000 0.000
#> GSM601799 2 0.0000 0.978 0.000 1.000
#> GSM601829 1 0.0000 0.960 1.000 0.000
#> GSM601839 2 0.0000 0.978 0.000 1.000
#> GSM601844 1 0.0000 0.960 1.000 0.000
#> GSM601859 2 0.0000 0.978 0.000 1.000
#> GSM601869 1 0.0000 0.960 1.000 0.000
#> GSM601749 1 0.0000 0.960 1.000 0.000
#> GSM601759 1 0.0000 0.960 1.000 0.000
#> GSM601764 1 0.0000 0.960 1.000 0.000
#> GSM601769 2 0.0000 0.978 0.000 1.000
#> GSM601774 2 0.0000 0.978 0.000 1.000
#> GSM601779 1 0.0000 0.960 1.000 0.000
#> GSM601789 2 0.0000 0.978 0.000 1.000
#> GSM601804 2 0.0938 0.969 0.012 0.988
#> GSM601809 1 0.9608 0.401 0.616 0.384
#> GSM601814 2 0.0000 0.978 0.000 1.000
#> GSM601819 1 0.0000 0.960 1.000 0.000
#> GSM601824 2 0.6048 0.824 0.148 0.852
#> GSM601834 2 0.0000 0.978 0.000 1.000
#> GSM601849 1 0.0000 0.960 1.000 0.000
#> GSM601854 1 0.0000 0.960 1.000 0.000
#> GSM601864 2 0.0000 0.978 0.000 1.000
#> GSM601755 2 0.0000 0.978 0.000 1.000
#> GSM601785 2 0.0000 0.978 0.000 1.000
#> GSM601795 1 0.5294 0.850 0.880 0.120
#> GSM601800 2 0.0000 0.978 0.000 1.000
#> GSM601830 1 0.4161 0.889 0.916 0.084
#> GSM601840 2 0.2043 0.953 0.032 0.968
#> GSM601845 2 0.7139 0.757 0.196 0.804
#> GSM601860 2 0.0000 0.978 0.000 1.000
#> GSM601870 1 0.9323 0.489 0.652 0.348
#> GSM601750 1 0.0000 0.960 1.000 0.000
#> GSM601760 1 0.0000 0.960 1.000 0.000
#> GSM601765 2 0.0000 0.978 0.000 1.000
#> GSM601770 2 0.0000 0.978 0.000 1.000
#> GSM601775 2 0.0938 0.969 0.012 0.988
#> GSM601780 1 0.0000 0.960 1.000 0.000
#> GSM601790 2 0.0000 0.978 0.000 1.000
#> GSM601805 2 0.0000 0.978 0.000 1.000
#> GSM601810 1 0.0000 0.960 1.000 0.000
#> GSM601815 2 0.0000 0.978 0.000 1.000
#> GSM601820 1 0.0000 0.960 1.000 0.000
#> GSM601825 2 0.0000 0.978 0.000 1.000
#> GSM601835 2 0.0000 0.978 0.000 1.000
#> GSM601850 1 0.9608 0.400 0.616 0.384
#> GSM601855 1 0.0000 0.960 1.000 0.000
#> GSM601865 2 0.0000 0.978 0.000 1.000
#> GSM601756 2 0.0000 0.978 0.000 1.000
#> GSM601786 2 0.0000 0.978 0.000 1.000
#> GSM601796 1 0.0000 0.960 1.000 0.000
#> GSM601801 2 0.0000 0.978 0.000 1.000
#> GSM601831 1 0.0000 0.960 1.000 0.000
#> GSM601841 1 0.0000 0.960 1.000 0.000
#> GSM601846 2 0.1414 0.963 0.020 0.980
#> GSM601861 2 0.0000 0.978 0.000 1.000
#> GSM601871 2 0.3431 0.923 0.064 0.936
#> GSM601751 2 0.0000 0.978 0.000 1.000
#> GSM601761 1 0.0000 0.960 1.000 0.000
#> GSM601766 2 0.4690 0.886 0.100 0.900
#> GSM601771 2 0.0000 0.978 0.000 1.000
#> GSM601776 1 0.0000 0.960 1.000 0.000
#> GSM601781 1 0.3274 0.912 0.940 0.060
#> GSM601791 1 0.0000 0.960 1.000 0.000
#> GSM601806 2 0.0000 0.978 0.000 1.000
#> GSM601811 1 0.0000 0.960 1.000 0.000
#> GSM601816 1 0.0000 0.960 1.000 0.000
#> GSM601821 2 0.0000 0.978 0.000 1.000
#> GSM601826 1 0.0000 0.960 1.000 0.000
#> GSM601836 1 0.0376 0.957 0.996 0.004
#> GSM601851 1 0.0000 0.960 1.000 0.000
#> GSM601856 1 0.0000 0.960 1.000 0.000
#> GSM601866 1 0.0000 0.960 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601752 2 0.4351 0.7558 0.168 0.828 0.004
#> GSM601782 3 0.6062 0.4383 0.384 0.000 0.616
#> GSM601792 1 0.1015 0.7374 0.980 0.012 0.008
#> GSM601797 2 0.5662 0.7994 0.092 0.808 0.100
#> GSM601827 3 0.5948 0.4915 0.360 0.000 0.640
#> GSM601837 2 0.6274 0.3639 0.000 0.544 0.456
#> GSM601842 2 0.2939 0.8247 0.012 0.916 0.072
#> GSM601857 3 0.3686 0.6885 0.140 0.000 0.860
#> GSM601867 3 0.3816 0.5823 0.000 0.148 0.852
#> GSM601747 1 0.7796 0.2023 0.552 0.056 0.392
#> GSM601757 3 0.6307 0.1820 0.488 0.000 0.512
#> GSM601762 2 0.4235 0.7616 0.000 0.824 0.176
#> GSM601767 2 0.1860 0.8297 0.052 0.948 0.000
#> GSM601772 2 0.1129 0.8343 0.004 0.976 0.020
#> GSM601777 3 0.4591 0.6213 0.032 0.120 0.848
#> GSM601787 3 0.4605 0.5194 0.000 0.204 0.796
#> GSM601802 2 0.2878 0.8089 0.096 0.904 0.000
#> GSM601807 3 0.2261 0.6332 0.000 0.068 0.932
#> GSM601812 3 0.6140 0.4117 0.404 0.000 0.596
#> GSM601817 3 0.4750 0.6562 0.216 0.000 0.784
#> GSM601822 2 0.6302 0.1910 0.480 0.520 0.000
#> GSM601832 2 0.3415 0.8252 0.020 0.900 0.080
#> GSM601847 2 0.5397 0.6218 0.280 0.720 0.000
#> GSM601852 3 0.6307 0.1731 0.488 0.000 0.512
#> GSM601862 3 0.3267 0.6885 0.116 0.000 0.884
#> GSM601753 2 0.4002 0.7609 0.160 0.840 0.000
#> GSM601783 1 0.4555 0.5933 0.800 0.000 0.200
#> GSM601793 1 0.3116 0.6841 0.892 0.000 0.108
#> GSM601798 2 0.1919 0.8373 0.024 0.956 0.020
#> GSM601828 3 0.6215 0.3562 0.428 0.000 0.572
#> GSM601838 2 0.5591 0.6331 0.000 0.696 0.304
#> GSM601843 2 0.3267 0.7998 0.000 0.884 0.116
#> GSM601858 3 0.6225 -0.0831 0.000 0.432 0.568
#> GSM601868 3 0.3340 0.6889 0.120 0.000 0.880
#> GSM601748 3 0.6225 0.3465 0.432 0.000 0.568
#> GSM601758 1 0.4235 0.6199 0.824 0.000 0.176
#> GSM601763 1 0.6204 0.1050 0.576 0.424 0.000
#> GSM601768 2 0.2711 0.8138 0.088 0.912 0.000
#> GSM601773 2 0.0829 0.8360 0.012 0.984 0.004
#> GSM601778 1 0.5180 0.6633 0.812 0.032 0.156
#> GSM601788 2 0.4842 0.7232 0.000 0.776 0.224
#> GSM601803 2 0.0747 0.8363 0.016 0.984 0.000
#> GSM601808 3 0.4062 0.6836 0.164 0.000 0.836
#> GSM601813 1 0.5733 0.3837 0.676 0.000 0.324
#> GSM601818 3 0.4931 0.6453 0.232 0.000 0.768
#> GSM601823 1 0.3116 0.6918 0.892 0.108 0.000
#> GSM601833 2 0.2200 0.8270 0.004 0.940 0.056
#> GSM601848 1 0.1643 0.7280 0.956 0.044 0.000
#> GSM601853 3 0.4002 0.6850 0.160 0.000 0.840
#> GSM601863 3 0.4842 0.6506 0.224 0.000 0.776
#> GSM601754 2 0.4062 0.7580 0.164 0.836 0.000
#> GSM601784 2 0.2066 0.8241 0.000 0.940 0.060
#> GSM601794 1 0.2176 0.7341 0.948 0.020 0.032
#> GSM601799 2 0.5138 0.6606 0.252 0.748 0.000
#> GSM601829 1 0.6260 0.0103 0.552 0.000 0.448
#> GSM601839 2 0.5785 0.5943 0.000 0.668 0.332
#> GSM601844 1 0.1525 0.7309 0.964 0.004 0.032
#> GSM601859 2 0.2625 0.8153 0.084 0.916 0.000
#> GSM601869 3 0.5560 0.5769 0.300 0.000 0.700
#> GSM601749 1 0.4887 0.5546 0.772 0.000 0.228
#> GSM601759 1 0.5859 0.3337 0.656 0.000 0.344
#> GSM601764 1 0.2959 0.6979 0.900 0.100 0.000
#> GSM601769 2 0.1525 0.8332 0.004 0.964 0.032
#> GSM601774 2 0.0829 0.8360 0.012 0.984 0.004
#> GSM601779 1 0.4062 0.6468 0.836 0.164 0.000
#> GSM601789 2 0.4974 0.7110 0.000 0.764 0.236
#> GSM601804 2 0.6140 0.3923 0.404 0.596 0.000
#> GSM601809 3 0.5677 0.6281 0.072 0.124 0.804
#> GSM601814 2 0.1529 0.8297 0.000 0.960 0.040
#> GSM601819 1 0.1878 0.7252 0.952 0.004 0.044
#> GSM601824 2 0.6309 0.1391 0.496 0.504 0.000
#> GSM601834 2 0.0892 0.8356 0.020 0.980 0.000
#> GSM601849 1 0.1170 0.7371 0.976 0.016 0.008
#> GSM601854 1 0.6215 0.0841 0.572 0.000 0.428
#> GSM601864 2 0.6260 0.3797 0.000 0.552 0.448
#> GSM601755 2 0.1620 0.8368 0.024 0.964 0.012
#> GSM601785 2 0.2448 0.8192 0.076 0.924 0.000
#> GSM601795 1 0.5363 0.5028 0.724 0.276 0.000
#> GSM601800 2 0.3116 0.8008 0.108 0.892 0.000
#> GSM601830 3 0.1620 0.6560 0.012 0.024 0.964
#> GSM601840 2 0.4540 0.7979 0.028 0.848 0.124
#> GSM601845 2 0.6625 0.7283 0.196 0.736 0.068
#> GSM601860 2 0.1411 0.8339 0.036 0.964 0.000
#> GSM601870 3 0.3038 0.6133 0.000 0.104 0.896
#> GSM601750 1 0.6305 -0.1279 0.516 0.000 0.484
#> GSM601760 1 0.2636 0.7297 0.932 0.020 0.048
#> GSM601765 2 0.1411 0.8346 0.036 0.964 0.000
#> GSM601770 2 0.1289 0.8346 0.032 0.968 0.000
#> GSM601775 2 0.6045 0.4449 0.380 0.620 0.000
#> GSM601780 1 0.3619 0.6707 0.864 0.136 0.000
#> GSM601790 2 0.4931 0.7146 0.000 0.768 0.232
#> GSM601805 2 0.1860 0.8290 0.052 0.948 0.000
#> GSM601810 3 0.4062 0.6836 0.164 0.000 0.836
#> GSM601815 2 0.4178 0.7630 0.000 0.828 0.172
#> GSM601820 1 0.5560 0.4299 0.700 0.000 0.300
#> GSM601825 2 0.1643 0.8315 0.044 0.956 0.000
#> GSM601835 3 0.6280 -0.1661 0.000 0.460 0.540
#> GSM601850 1 0.6140 0.1856 0.596 0.404 0.000
#> GSM601855 3 0.1163 0.6496 0.000 0.028 0.972
#> GSM601865 2 0.6260 0.3819 0.000 0.552 0.448
#> GSM601756 2 0.1482 0.8371 0.020 0.968 0.012
#> GSM601786 2 0.5254 0.6795 0.000 0.736 0.264
#> GSM601796 1 0.1919 0.7378 0.956 0.020 0.024
#> GSM601801 2 0.1289 0.8321 0.000 0.968 0.032
#> GSM601831 3 0.5363 0.6034 0.276 0.000 0.724
#> GSM601841 1 0.6111 0.1966 0.604 0.000 0.396
#> GSM601846 2 0.6294 0.6440 0.020 0.692 0.288
#> GSM601861 2 0.2537 0.8154 0.000 0.920 0.080
#> GSM601871 3 0.4555 0.5270 0.000 0.200 0.800
#> GSM601751 2 0.1643 0.8316 0.044 0.956 0.000
#> GSM601761 1 0.1585 0.7355 0.964 0.028 0.008
#> GSM601766 2 0.5706 0.5567 0.320 0.680 0.000
#> GSM601771 2 0.2383 0.8333 0.016 0.940 0.044
#> GSM601776 1 0.1015 0.7361 0.980 0.008 0.012
#> GSM601781 1 0.4531 0.6515 0.824 0.168 0.008
#> GSM601791 1 0.2261 0.7176 0.932 0.068 0.000
#> GSM601806 2 0.1765 0.8309 0.004 0.956 0.040
#> GSM601811 3 0.3551 0.6893 0.132 0.000 0.868
#> GSM601816 1 0.1585 0.7359 0.964 0.028 0.008
#> GSM601821 2 0.2878 0.8082 0.000 0.904 0.096
#> GSM601826 1 0.1015 0.7371 0.980 0.012 0.008
#> GSM601836 1 0.5285 0.6746 0.812 0.040 0.148
#> GSM601851 1 0.1989 0.7283 0.948 0.048 0.004
#> GSM601856 3 0.3340 0.6896 0.120 0.000 0.880
#> GSM601866 3 0.6079 0.4424 0.388 0.000 0.612
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601752 4 0.244 0.72360 0.024 0.060 0.000 0.916
#> GSM601782 1 0.740 -0.16798 0.448 0.076 0.444 0.032
#> GSM601792 4 0.504 0.41785 0.336 0.000 0.012 0.652
#> GSM601797 4 0.259 0.67811 0.004 0.012 0.076 0.908
#> GSM601827 3 0.697 0.42091 0.332 0.008 0.556 0.104
#> GSM601837 2 0.761 0.41644 0.000 0.456 0.328 0.216
#> GSM601842 2 0.505 0.67423 0.004 0.744 0.040 0.212
#> GSM601857 3 0.292 0.68193 0.140 0.000 0.860 0.000
#> GSM601867 3 0.407 0.56202 0.000 0.120 0.828 0.052
#> GSM601747 2 0.819 -0.00581 0.308 0.432 0.244 0.016
#> GSM601757 1 0.516 -0.15411 0.524 0.000 0.472 0.004
#> GSM601762 2 0.650 0.55452 0.000 0.612 0.112 0.276
#> GSM601767 2 0.334 0.73419 0.032 0.868 0.000 0.100
#> GSM601772 2 0.247 0.75115 0.016 0.924 0.016 0.044
#> GSM601777 4 0.583 0.49779 0.012 0.040 0.280 0.668
#> GSM601787 3 0.419 0.53599 0.000 0.148 0.812 0.040
#> GSM601802 4 0.354 0.71486 0.028 0.120 0.000 0.852
#> GSM601807 3 0.483 0.50501 0.000 0.040 0.752 0.208
#> GSM601812 3 0.524 0.34393 0.432 0.000 0.560 0.008
#> GSM601817 3 0.537 0.59300 0.252 0.028 0.708 0.012
#> GSM601822 4 0.437 0.66510 0.156 0.044 0.000 0.800
#> GSM601832 2 0.494 0.70785 0.016 0.772 0.032 0.180
#> GSM601847 4 0.455 0.71649 0.092 0.104 0.000 0.804
#> GSM601852 1 0.537 -0.06225 0.544 0.000 0.444 0.012
#> GSM601862 3 0.303 0.68202 0.124 0.008 0.868 0.000
#> GSM601753 4 0.510 0.66769 0.064 0.188 0.000 0.748
#> GSM601783 1 0.392 0.49111 0.816 0.008 0.168 0.008
#> GSM601793 4 0.595 0.25369 0.384 0.000 0.044 0.572
#> GSM601798 4 0.310 0.70506 0.000 0.104 0.020 0.876
#> GSM601828 3 0.588 0.26361 0.452 0.008 0.520 0.020
#> GSM601838 2 0.721 0.48335 0.000 0.540 0.184 0.276
#> GSM601843 2 0.492 0.69246 0.000 0.760 0.056 0.184
#> GSM601858 2 0.632 0.38332 0.000 0.504 0.436 0.060
#> GSM601868 3 0.294 0.68238 0.128 0.000 0.868 0.004
#> GSM601748 3 0.581 0.21674 0.472 0.012 0.504 0.012
#> GSM601758 1 0.433 0.48194 0.800 0.028 0.168 0.004
#> GSM601763 1 0.665 0.26638 0.536 0.372 0.000 0.092
#> GSM601768 2 0.238 0.72271 0.068 0.916 0.000 0.016
#> GSM601773 2 0.402 0.66712 0.004 0.772 0.000 0.224
#> GSM601778 4 0.509 0.55911 0.180 0.000 0.068 0.752
#> GSM601788 2 0.590 0.68274 0.000 0.700 0.160 0.140
#> GSM601803 4 0.454 0.63578 0.004 0.208 0.020 0.768
#> GSM601808 3 0.320 0.68158 0.136 0.000 0.856 0.008
#> GSM601813 1 0.514 0.33466 0.680 0.000 0.296 0.024
#> GSM601818 3 0.718 0.41968 0.316 0.108 0.560 0.016
#> GSM601823 1 0.498 0.29966 0.664 0.012 0.000 0.324
#> GSM601833 2 0.211 0.75433 0.000 0.932 0.024 0.044
#> GSM601848 1 0.494 0.31949 0.672 0.000 0.012 0.316
#> GSM601853 3 0.343 0.68069 0.144 0.000 0.844 0.012
#> GSM601863 3 0.391 0.64500 0.212 0.004 0.784 0.000
#> GSM601754 4 0.407 0.71896 0.052 0.120 0.000 0.828
#> GSM601784 2 0.355 0.74392 0.004 0.860 0.028 0.108
#> GSM601794 4 0.518 0.44803 0.288 0.000 0.028 0.684
#> GSM601799 4 0.598 0.67563 0.136 0.172 0.000 0.692
#> GSM601829 1 0.743 0.14319 0.480 0.000 0.336 0.184
#> GSM601839 2 0.627 0.63742 0.000 0.656 0.220 0.124
#> GSM601844 1 0.332 0.57750 0.888 0.020 0.028 0.064
#> GSM601859 2 0.324 0.72693 0.064 0.880 0.000 0.056
#> GSM601869 3 0.465 0.54943 0.312 0.000 0.684 0.004
#> GSM601749 1 0.389 0.45996 0.796 0.000 0.196 0.008
#> GSM601759 1 0.611 0.31923 0.656 0.056 0.276 0.012
#> GSM601764 1 0.578 0.37737 0.608 0.360 0.020 0.012
#> GSM601769 2 0.185 0.74577 0.024 0.948 0.008 0.020
#> GSM601774 2 0.206 0.74691 0.016 0.932 0.000 0.052
#> GSM601779 1 0.539 0.37764 0.696 0.048 0.000 0.256
#> GSM601789 2 0.322 0.73245 0.000 0.864 0.120 0.016
#> GSM601804 4 0.654 0.62644 0.200 0.164 0.000 0.636
#> GSM601809 2 0.744 0.24321 0.124 0.520 0.340 0.016
#> GSM601814 2 0.430 0.70244 0.012 0.796 0.012 0.180
#> GSM601819 1 0.619 0.44760 0.640 0.288 0.064 0.008
#> GSM601824 1 0.778 -0.09286 0.428 0.288 0.000 0.284
#> GSM601834 2 0.247 0.74165 0.028 0.916 0.000 0.056
#> GSM601849 1 0.314 0.57038 0.896 0.012 0.048 0.044
#> GSM601854 1 0.493 -0.01105 0.568 0.000 0.432 0.000
#> GSM601864 3 0.784 -0.38365 0.000 0.360 0.376 0.264
#> GSM601755 4 0.328 0.70247 0.000 0.116 0.020 0.864
#> GSM601785 2 0.339 0.72633 0.056 0.872 0.000 0.072
#> GSM601795 4 0.480 0.51512 0.276 0.016 0.000 0.708
#> GSM601800 4 0.386 0.71083 0.032 0.136 0.000 0.832
#> GSM601830 3 0.356 0.62403 0.016 0.040 0.876 0.068
#> GSM601840 4 0.674 -0.13841 0.012 0.456 0.060 0.472
#> GSM601845 2 0.829 0.41800 0.212 0.536 0.060 0.192
#> GSM601860 2 0.310 0.72611 0.072 0.892 0.008 0.028
#> GSM601870 3 0.345 0.58752 0.000 0.052 0.868 0.080
#> GSM601750 1 0.541 -0.03514 0.552 0.004 0.436 0.008
#> GSM601760 1 0.568 0.49464 0.708 0.224 0.060 0.008
#> GSM601765 2 0.173 0.74141 0.028 0.948 0.000 0.024
#> GSM601770 2 0.202 0.74399 0.028 0.940 0.004 0.028
#> GSM601775 2 0.748 0.20686 0.248 0.504 0.000 0.248
#> GSM601780 1 0.521 0.51688 0.756 0.140 0.000 0.104
#> GSM601790 2 0.467 0.72156 0.000 0.792 0.132 0.076
#> GSM601805 4 0.485 0.64237 0.028 0.220 0.004 0.748
#> GSM601810 3 0.359 0.67473 0.168 0.000 0.824 0.008
#> GSM601815 2 0.516 0.71283 0.000 0.760 0.104 0.136
#> GSM601820 1 0.530 0.34968 0.696 0.024 0.272 0.008
#> GSM601825 4 0.552 0.22943 0.020 0.412 0.000 0.568
#> GSM601835 2 0.731 0.34330 0.000 0.428 0.420 0.152
#> GSM601850 1 0.699 0.01724 0.532 0.132 0.000 0.336
#> GSM601855 3 0.304 0.61987 0.008 0.020 0.892 0.080
#> GSM601865 2 0.630 0.58950 0.000 0.608 0.308 0.084
#> GSM601756 4 0.352 0.70125 0.004 0.120 0.020 0.856
#> GSM601786 2 0.438 0.71796 0.016 0.816 0.140 0.028
#> GSM601796 4 0.637 0.23067 0.396 0.020 0.032 0.552
#> GSM601801 4 0.415 0.66652 0.000 0.160 0.032 0.808
#> GSM601831 3 0.616 0.54200 0.272 0.000 0.640 0.088
#> GSM601841 1 0.782 0.20211 0.460 0.004 0.256 0.280
#> GSM601846 4 0.429 0.63779 0.000 0.052 0.136 0.812
#> GSM601861 2 0.343 0.73793 0.000 0.860 0.028 0.112
#> GSM601871 3 0.453 0.53176 0.000 0.132 0.800 0.068
#> GSM601751 2 0.352 0.72655 0.052 0.864 0.000 0.084
#> GSM601761 1 0.390 0.56809 0.848 0.112 0.024 0.016
#> GSM601766 2 0.466 0.58216 0.208 0.760 0.000 0.032
#> GSM601771 2 0.459 0.71941 0.024 0.800 0.020 0.156
#> GSM601776 1 0.317 0.57327 0.868 0.000 0.016 0.116
#> GSM601781 4 0.757 0.15417 0.400 0.136 0.012 0.452
#> GSM601791 1 0.391 0.57541 0.848 0.104 0.008 0.040
#> GSM601806 4 0.565 0.52741 0.004 0.268 0.048 0.680
#> GSM601811 3 0.371 0.67351 0.152 0.012 0.832 0.004
#> GSM601816 1 0.552 0.04816 0.556 0.004 0.012 0.428
#> GSM601821 2 0.412 0.72449 0.000 0.820 0.044 0.136
#> GSM601826 1 0.480 0.38783 0.704 0.004 0.008 0.284
#> GSM601836 1 0.712 0.48007 0.656 0.152 0.144 0.048
#> GSM601851 1 0.355 0.57937 0.868 0.016 0.020 0.096
#> GSM601856 3 0.344 0.67063 0.100 0.000 0.864 0.036
#> GSM601866 3 0.548 0.38971 0.404 0.008 0.580 0.008
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601752 4 0.270 0.74526 0.012 0.028 0.012 0.904 0.044
#> GSM601782 2 0.726 -0.15862 0.340 0.356 0.288 0.004 0.012
#> GSM601792 4 0.538 0.61477 0.144 0.052 0.068 0.732 0.004
#> GSM601797 4 0.394 0.68164 0.004 0.052 0.120 0.816 0.008
#> GSM601827 3 0.743 0.22725 0.124 0.388 0.416 0.068 0.004
#> GSM601837 5 0.771 0.38681 0.000 0.148 0.220 0.140 0.492
#> GSM601842 2 0.588 0.34515 0.000 0.652 0.048 0.068 0.232
#> GSM601857 3 0.508 0.56934 0.172 0.032 0.732 0.000 0.064
#> GSM601867 3 0.565 0.53672 0.016 0.064 0.676 0.016 0.228
#> GSM601747 2 0.701 0.44166 0.188 0.580 0.128 0.000 0.104
#> GSM601757 1 0.578 0.40284 0.620 0.064 0.288 0.000 0.028
#> GSM601762 2 0.692 -0.06165 0.000 0.472 0.048 0.112 0.368
#> GSM601767 5 0.633 0.37159 0.024 0.336 0.000 0.100 0.540
#> GSM601772 2 0.555 -0.09418 0.008 0.524 0.012 0.028 0.428
#> GSM601777 4 0.712 0.53757 0.028 0.072 0.204 0.600 0.096
#> GSM601787 3 0.613 0.41276 0.016 0.048 0.592 0.028 0.316
#> GSM601802 4 0.329 0.74095 0.012 0.028 0.000 0.852 0.108
#> GSM601807 3 0.504 0.53506 0.000 0.036 0.740 0.160 0.064
#> GSM601812 1 0.612 0.09248 0.472 0.084 0.432 0.004 0.008
#> GSM601817 3 0.631 0.31088 0.124 0.384 0.484 0.000 0.008
#> GSM601822 4 0.467 0.71387 0.088 0.096 0.004 0.784 0.028
#> GSM601832 2 0.566 0.40217 0.008 0.696 0.028 0.084 0.184
#> GSM601847 4 0.440 0.73504 0.052 0.032 0.000 0.792 0.124
#> GSM601852 1 0.719 -0.05881 0.340 0.316 0.332 0.008 0.004
#> GSM601862 3 0.594 0.53588 0.196 0.036 0.668 0.004 0.096
#> GSM601753 4 0.495 0.70597 0.036 0.068 0.000 0.752 0.144
#> GSM601783 1 0.283 0.61584 0.892 0.032 0.060 0.012 0.004
#> GSM601793 4 0.631 0.51858 0.212 0.048 0.100 0.636 0.004
#> GSM601798 4 0.400 0.73283 0.000 0.052 0.044 0.828 0.076
#> GSM601828 2 0.700 -0.22934 0.260 0.400 0.332 0.004 0.004
#> GSM601838 5 0.672 0.51980 0.000 0.136 0.088 0.164 0.612
#> GSM601843 2 0.623 0.30679 0.000 0.616 0.056 0.076 0.252
#> GSM601858 5 0.649 0.35048 0.000 0.172 0.276 0.012 0.540
#> GSM601868 3 0.583 0.54072 0.184 0.028 0.676 0.004 0.108
#> GSM601748 1 0.672 0.12131 0.440 0.216 0.340 0.000 0.004
#> GSM601758 1 0.351 0.59750 0.848 0.048 0.088 0.000 0.016
#> GSM601763 2 0.709 0.35042 0.312 0.508 0.000 0.084 0.096
#> GSM601768 5 0.574 0.32482 0.032 0.408 0.000 0.032 0.528
#> GSM601773 5 0.658 0.30734 0.004 0.340 0.000 0.188 0.468
#> GSM601778 4 0.515 0.66381 0.108 0.048 0.076 0.760 0.008
#> GSM601788 5 0.604 0.54667 0.004 0.204 0.036 0.100 0.656
#> GSM601803 4 0.436 0.68117 0.000 0.032 0.016 0.760 0.192
#> GSM601808 3 0.335 0.59620 0.116 0.024 0.848 0.008 0.004
#> GSM601813 1 0.373 0.57123 0.800 0.016 0.172 0.012 0.000
#> GSM601818 3 0.779 0.12380 0.316 0.272 0.352 0.000 0.060
#> GSM601823 1 0.644 -0.05590 0.440 0.152 0.000 0.404 0.004
#> GSM601833 2 0.549 -0.09761 0.000 0.528 0.012 0.040 0.420
#> GSM601848 1 0.504 0.22303 0.604 0.028 0.008 0.360 0.000
#> GSM601853 3 0.394 0.59928 0.064 0.116 0.812 0.008 0.000
#> GSM601863 3 0.600 0.42707 0.280 0.036 0.612 0.000 0.072
#> GSM601754 4 0.423 0.74142 0.024 0.036 0.012 0.812 0.116
#> GSM601784 5 0.563 0.46065 0.000 0.336 0.004 0.080 0.580
#> GSM601794 4 0.596 0.60872 0.128 0.064 0.092 0.704 0.012
#> GSM601799 4 0.521 0.71190 0.040 0.108 0.004 0.748 0.100
#> GSM601829 3 0.830 0.20940 0.176 0.300 0.376 0.144 0.004
#> GSM601839 5 0.624 0.51816 0.000 0.208 0.116 0.044 0.632
#> GSM601844 1 0.809 0.36566 0.484 0.276 0.080 0.108 0.052
#> GSM601859 5 0.454 0.55455 0.036 0.136 0.000 0.048 0.780
#> GSM601869 3 0.640 0.14797 0.404 0.032 0.484 0.000 0.080
#> GSM601749 1 0.308 0.59821 0.852 0.032 0.116 0.000 0.000
#> GSM601759 1 0.452 0.55809 0.776 0.044 0.148 0.000 0.032
#> GSM601764 2 0.615 0.35504 0.332 0.556 0.020 0.000 0.092
#> GSM601769 5 0.440 0.51661 0.000 0.276 0.000 0.028 0.696
#> GSM601774 5 0.555 0.42942 0.004 0.328 0.000 0.076 0.592
#> GSM601779 1 0.478 0.41165 0.700 0.052 0.000 0.244 0.004
#> GSM601789 5 0.552 0.42893 0.000 0.320 0.076 0.004 0.600
#> GSM601804 4 0.520 0.72352 0.100 0.040 0.000 0.740 0.120
#> GSM601809 5 0.686 0.00686 0.196 0.032 0.236 0.000 0.536
#> GSM601814 5 0.472 0.56147 0.000 0.132 0.000 0.132 0.736
#> GSM601819 1 0.536 0.55968 0.736 0.104 0.064 0.000 0.096
#> GSM601824 4 0.787 0.18327 0.368 0.128 0.000 0.372 0.132
#> GSM601834 5 0.539 0.39438 0.000 0.372 0.000 0.064 0.564
#> GSM601849 1 0.276 0.61914 0.896 0.024 0.032 0.048 0.000
#> GSM601854 1 0.544 0.36258 0.604 0.084 0.312 0.000 0.000
#> GSM601864 5 0.672 0.39266 0.000 0.040 0.220 0.168 0.572
#> GSM601755 4 0.365 0.73533 0.004 0.036 0.020 0.844 0.096
#> GSM601785 5 0.643 0.32082 0.048 0.388 0.000 0.064 0.500
#> GSM601795 4 0.500 0.67315 0.120 0.056 0.020 0.772 0.032
#> GSM601800 4 0.433 0.73750 0.004 0.072 0.012 0.796 0.116
#> GSM601830 3 0.540 0.39960 0.000 0.316 0.612 0.068 0.004
#> GSM601840 5 0.749 0.22543 0.036 0.096 0.044 0.360 0.464
#> GSM601845 2 0.497 0.45578 0.004 0.772 0.092 0.068 0.064
#> GSM601860 5 0.425 0.49993 0.076 0.104 0.008 0.008 0.804
#> GSM601870 3 0.421 0.58155 0.000 0.076 0.808 0.024 0.092
#> GSM601750 1 0.589 0.35981 0.588 0.120 0.288 0.000 0.004
#> GSM601760 1 0.550 0.51806 0.708 0.076 0.048 0.000 0.168
#> GSM601765 2 0.487 0.24033 0.016 0.640 0.000 0.016 0.328
#> GSM601770 5 0.571 0.34319 0.016 0.388 0.000 0.052 0.544
#> GSM601775 2 0.835 0.17012 0.176 0.372 0.000 0.248 0.204
#> GSM601780 1 0.357 0.60050 0.848 0.056 0.000 0.076 0.020
#> GSM601790 5 0.528 0.52490 0.000 0.248 0.056 0.020 0.676
#> GSM601805 4 0.435 0.69235 0.016 0.028 0.000 0.756 0.200
#> GSM601810 3 0.486 0.58145 0.152 0.028 0.764 0.012 0.044
#> GSM601815 5 0.363 0.59509 0.000 0.064 0.028 0.060 0.848
#> GSM601820 1 0.448 0.56681 0.780 0.040 0.144 0.000 0.036
#> GSM601825 4 0.610 0.34914 0.008 0.100 0.004 0.560 0.328
#> GSM601835 2 0.613 0.37228 0.000 0.620 0.244 0.032 0.104
#> GSM601850 1 0.692 0.02088 0.492 0.076 0.000 0.352 0.080
#> GSM601855 3 0.397 0.55320 0.000 0.156 0.796 0.040 0.008
#> GSM601865 5 0.484 0.50270 0.000 0.056 0.168 0.028 0.748
#> GSM601756 4 0.350 0.72682 0.000 0.024 0.020 0.840 0.116
#> GSM601786 5 0.348 0.54522 0.012 0.064 0.064 0.004 0.856
#> GSM601796 4 0.723 0.45617 0.244 0.088 0.064 0.572 0.032
#> GSM601801 4 0.427 0.71328 0.000 0.044 0.028 0.796 0.132
#> GSM601831 3 0.658 0.50413 0.104 0.152 0.640 0.100 0.004
#> GSM601841 1 0.814 0.29581 0.480 0.036 0.188 0.224 0.072
#> GSM601846 2 0.711 0.04412 0.004 0.408 0.224 0.352 0.012
#> GSM601861 5 0.280 0.59581 0.000 0.068 0.004 0.044 0.884
#> GSM601871 3 0.652 0.39883 0.016 0.060 0.568 0.040 0.316
#> GSM601751 5 0.424 0.53955 0.036 0.072 0.012 0.056 0.824
#> GSM601761 1 0.321 0.61152 0.880 0.036 0.028 0.008 0.048
#> GSM601766 2 0.497 0.42205 0.076 0.712 0.008 0.000 0.204
#> GSM601771 5 0.427 0.55375 0.020 0.044 0.032 0.080 0.824
#> GSM601776 1 0.332 0.60735 0.848 0.020 0.016 0.116 0.000
#> GSM601781 4 0.813 0.20020 0.344 0.080 0.016 0.384 0.176
#> GSM601791 1 0.447 0.58959 0.804 0.084 0.004 0.044 0.064
#> GSM601806 4 0.529 0.59350 0.000 0.040 0.032 0.676 0.252
#> GSM601811 3 0.588 0.57081 0.156 0.044 0.688 0.004 0.108
#> GSM601816 4 0.596 0.15499 0.436 0.044 0.032 0.488 0.000
#> GSM601821 5 0.272 0.59546 0.000 0.040 0.008 0.060 0.892
#> GSM601826 1 0.587 0.14898 0.532 0.080 0.008 0.380 0.000
#> GSM601836 2 0.561 0.40784 0.196 0.688 0.088 0.004 0.024
#> GSM601851 1 0.281 0.61004 0.876 0.024 0.004 0.096 0.000
#> GSM601856 3 0.352 0.61027 0.036 0.064 0.860 0.036 0.004
#> GSM601866 1 0.602 0.25033 0.552 0.052 0.360 0.000 0.036
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601752 4 0.234 0.60192 0.004 0.020 0.000 0.888 0.000 0.088
#> GSM601782 1 0.760 0.17211 0.448 0.264 0.180 0.012 0.036 0.060
#> GSM601792 4 0.639 0.05757 0.072 0.024 0.048 0.480 0.000 0.376
#> GSM601797 4 0.463 0.45010 0.000 0.020 0.056 0.696 0.000 0.228
#> GSM601827 2 0.701 -0.09727 0.044 0.372 0.248 0.008 0.000 0.328
#> GSM601837 5 0.778 0.46641 0.000 0.116 0.256 0.124 0.440 0.064
#> GSM601842 2 0.372 0.47458 0.000 0.812 0.044 0.008 0.120 0.016
#> GSM601857 3 0.515 0.35223 0.376 0.012 0.564 0.000 0.020 0.028
#> GSM601867 3 0.614 0.37983 0.076 0.036 0.652 0.020 0.180 0.036
#> GSM601747 2 0.785 0.23620 0.312 0.436 0.076 0.032 0.088 0.056
#> GSM601757 1 0.459 0.39572 0.716 0.032 0.216 0.000 0.012 0.024
#> GSM601762 2 0.646 0.24654 0.000 0.568 0.052 0.132 0.228 0.020
#> GSM601767 2 0.674 -0.02948 0.024 0.412 0.000 0.196 0.352 0.016
#> GSM601772 2 0.651 0.29645 0.056 0.592 0.020 0.056 0.244 0.032
#> GSM601777 4 0.692 0.35424 0.008 0.028 0.176 0.572 0.084 0.132
#> GSM601787 3 0.500 0.26477 0.028 0.008 0.660 0.024 0.272 0.008
#> GSM601802 4 0.155 0.61522 0.004 0.000 0.008 0.944 0.012 0.032
#> GSM601807 3 0.505 0.42447 0.008 0.012 0.732 0.084 0.032 0.132
#> GSM601812 1 0.558 0.19514 0.584 0.076 0.300 0.000 0.000 0.040
#> GSM601817 2 0.741 -0.09025 0.172 0.412 0.304 0.000 0.020 0.092
#> GSM601822 4 0.509 0.51912 0.068 0.044 0.000 0.724 0.020 0.144
#> GSM601832 2 0.407 0.48776 0.020 0.812 0.008 0.060 0.084 0.016
#> GSM601847 4 0.464 0.56424 0.044 0.016 0.012 0.780 0.056 0.092
#> GSM601852 1 0.662 0.19872 0.500 0.252 0.180 0.000 0.000 0.068
#> GSM601862 3 0.453 0.42530 0.316 0.000 0.636 0.000 0.044 0.004
#> GSM601753 4 0.331 0.59969 0.012 0.060 0.000 0.856 0.032 0.040
#> GSM601783 1 0.187 0.53993 0.928 0.032 0.024 0.000 0.000 0.016
#> GSM601793 4 0.672 -0.05251 0.088 0.016 0.076 0.444 0.000 0.376
#> GSM601798 4 0.275 0.59164 0.000 0.016 0.016 0.872 0.004 0.092
#> GSM601828 2 0.725 -0.02640 0.156 0.436 0.224 0.000 0.000 0.184
#> GSM601838 5 0.721 0.49034 0.000 0.132 0.140 0.180 0.520 0.028
#> GSM601843 2 0.420 0.46778 0.000 0.788 0.044 0.012 0.120 0.036
#> GSM601858 5 0.749 0.36399 0.036 0.108 0.368 0.036 0.412 0.040
#> GSM601868 3 0.479 0.46542 0.260 0.000 0.668 0.000 0.040 0.032
#> GSM601748 1 0.527 0.35131 0.664 0.112 0.192 0.000 0.000 0.032
#> GSM601758 1 0.193 0.53503 0.928 0.024 0.032 0.000 0.008 0.008
#> GSM601763 2 0.685 0.35635 0.204 0.568 0.000 0.096 0.056 0.076
#> GSM601768 2 0.634 0.05521 0.048 0.488 0.000 0.056 0.376 0.032
#> GSM601773 2 0.687 -0.08055 0.008 0.380 0.008 0.280 0.308 0.016
#> GSM601778 4 0.643 0.39376 0.060 0.024 0.060 0.600 0.020 0.236
#> GSM601788 5 0.875 0.35304 0.048 0.120 0.148 0.276 0.352 0.056
#> GSM601803 4 0.162 0.61507 0.000 0.000 0.012 0.940 0.028 0.020
#> GSM601808 3 0.535 0.49832 0.212 0.016 0.652 0.000 0.008 0.112
#> GSM601813 1 0.369 0.50285 0.792 0.004 0.136 0.000 0.000 0.068
#> GSM601818 1 0.702 0.23665 0.540 0.196 0.156 0.000 0.056 0.052
#> GSM601823 1 0.723 -0.04211 0.376 0.092 0.000 0.352 0.008 0.172
#> GSM601833 2 0.469 0.30378 0.004 0.660 0.012 0.032 0.288 0.004
#> GSM601848 1 0.687 0.00342 0.392 0.020 0.016 0.356 0.004 0.212
#> GSM601853 3 0.657 0.44198 0.100 0.144 0.556 0.000 0.004 0.196
#> GSM601863 3 0.476 0.32122 0.384 0.000 0.572 0.000 0.016 0.028
#> GSM601754 4 0.517 0.45551 0.000 0.028 0.016 0.696 0.076 0.184
#> GSM601784 5 0.648 0.30735 0.000 0.324 0.012 0.056 0.504 0.104
#> GSM601794 6 0.650 0.12584 0.028 0.016 0.064 0.392 0.028 0.472
#> GSM601799 4 0.532 0.49546 0.016 0.088 0.000 0.688 0.032 0.176
#> GSM601829 6 0.702 0.06912 0.040 0.268 0.212 0.024 0.000 0.456
#> GSM601839 5 0.681 0.45183 0.000 0.232 0.176 0.048 0.520 0.024
#> GSM601844 6 0.745 0.36384 0.140 0.152 0.040 0.016 0.112 0.540
#> GSM601859 5 0.467 0.51411 0.000 0.108 0.000 0.072 0.748 0.072
#> GSM601869 3 0.584 0.17969 0.400 0.000 0.480 0.000 0.036 0.084
#> GSM601749 1 0.349 0.54580 0.828 0.008 0.056 0.000 0.008 0.100
#> GSM601759 1 0.326 0.49443 0.844 0.036 0.100 0.000 0.012 0.008
#> GSM601764 2 0.607 0.35842 0.212 0.604 0.004 0.000 0.096 0.084
#> GSM601769 5 0.530 0.36877 0.004 0.280 0.000 0.020 0.620 0.076
#> GSM601774 5 0.683 0.05806 0.020 0.404 0.012 0.136 0.404 0.024
#> GSM601779 1 0.689 0.11116 0.444 0.016 0.004 0.240 0.024 0.272
#> GSM601789 5 0.654 0.22976 0.000 0.384 0.092 0.044 0.456 0.024
#> GSM601804 4 0.463 0.52954 0.048 0.012 0.000 0.752 0.044 0.144
#> GSM601809 5 0.673 0.21156 0.168 0.016 0.228 0.004 0.536 0.048
#> GSM601814 5 0.557 0.51852 0.000 0.096 0.016 0.172 0.672 0.044
#> GSM601819 1 0.596 0.45763 0.652 0.056 0.032 0.000 0.172 0.088
#> GSM601824 4 0.822 -0.01608 0.284 0.104 0.000 0.344 0.076 0.192
#> GSM601834 5 0.570 0.20795 0.000 0.372 0.000 0.020 0.508 0.100
#> GSM601849 1 0.599 0.48033 0.640 0.012 0.044 0.072 0.020 0.212
#> GSM601854 1 0.712 0.18610 0.408 0.072 0.244 0.000 0.004 0.272
#> GSM601864 5 0.629 0.34276 0.000 0.004 0.376 0.164 0.436 0.020
#> GSM601755 4 0.173 0.61560 0.000 0.012 0.004 0.936 0.012 0.036
#> GSM601785 5 0.689 0.22436 0.008 0.276 0.008 0.048 0.484 0.176
#> GSM601795 6 0.635 0.26142 0.020 0.020 0.008 0.348 0.100 0.504
#> GSM601800 4 0.575 0.38010 0.000 0.052 0.016 0.652 0.088 0.192
#> GSM601830 3 0.632 0.07413 0.004 0.292 0.396 0.004 0.000 0.304
#> GSM601840 4 0.820 -0.11959 0.032 0.096 0.072 0.412 0.296 0.092
#> GSM601845 2 0.434 0.47396 0.000 0.776 0.052 0.008 0.040 0.124
#> GSM601860 5 0.540 0.40650 0.032 0.032 0.024 0.036 0.712 0.164
#> GSM601870 3 0.462 0.47443 0.004 0.076 0.764 0.008 0.036 0.112
#> GSM601750 1 0.550 0.34409 0.648 0.068 0.228 0.000 0.008 0.048
#> GSM601760 1 0.540 0.49272 0.688 0.008 0.048 0.000 0.120 0.136
#> GSM601765 2 0.334 0.44465 0.008 0.800 0.000 0.020 0.172 0.000
#> GSM601770 2 0.668 0.04787 0.032 0.476 0.004 0.120 0.344 0.024
#> GSM601775 4 0.765 0.03485 0.168 0.300 0.000 0.408 0.068 0.056
#> GSM601780 1 0.539 0.40964 0.648 0.016 0.000 0.052 0.036 0.248
#> GSM601790 5 0.666 0.43082 0.000 0.260 0.136 0.060 0.528 0.016
#> GSM601805 4 0.219 0.61535 0.004 0.004 0.008 0.916 0.032 0.036
#> GSM601810 3 0.606 0.32481 0.368 0.016 0.516 0.004 0.036 0.060
#> GSM601815 5 0.622 0.53794 0.000 0.092 0.132 0.144 0.620 0.012
#> GSM601820 1 0.616 0.44424 0.596 0.004 0.080 0.000 0.112 0.208
#> GSM601825 4 0.481 0.46410 0.000 0.108 0.000 0.712 0.156 0.024
#> GSM601835 2 0.503 0.44329 0.000 0.704 0.148 0.004 0.028 0.116
#> GSM601850 4 0.746 0.18357 0.292 0.060 0.000 0.440 0.056 0.152
#> GSM601855 3 0.586 0.31276 0.000 0.168 0.576 0.012 0.008 0.236
#> GSM601865 5 0.533 0.47563 0.000 0.036 0.336 0.020 0.588 0.020
#> GSM601756 4 0.153 0.61508 0.000 0.016 0.008 0.948 0.016 0.012
#> GSM601786 5 0.400 0.50480 0.000 0.032 0.056 0.008 0.804 0.100
#> GSM601796 6 0.708 0.46227 0.064 0.012 0.020 0.192 0.180 0.532
#> GSM601801 4 0.274 0.60982 0.000 0.044 0.008 0.888 0.028 0.032
#> GSM601831 3 0.682 0.17269 0.060 0.148 0.424 0.008 0.000 0.360
#> GSM601841 1 0.804 0.19802 0.460 0.016 0.204 0.160 0.060 0.100
#> GSM601846 2 0.709 -0.12732 0.000 0.396 0.168 0.088 0.004 0.344
#> GSM601861 5 0.457 0.55305 0.000 0.072 0.032 0.076 0.780 0.040
#> GSM601871 3 0.495 0.25528 0.016 0.000 0.656 0.032 0.276 0.020
#> GSM601751 5 0.659 0.51727 0.072 0.008 0.096 0.160 0.620 0.044
#> GSM601761 1 0.390 0.51587 0.768 0.008 0.016 0.004 0.012 0.192
#> GSM601766 2 0.378 0.48011 0.012 0.808 0.008 0.004 0.128 0.040
#> GSM601771 5 0.787 0.48552 0.056 0.060 0.168 0.196 0.488 0.032
#> GSM601776 1 0.400 0.49958 0.772 0.000 0.004 0.148 0.004 0.072
#> GSM601781 6 0.775 0.32050 0.128 0.016 0.012 0.140 0.316 0.388
#> GSM601791 1 0.662 0.12678 0.428 0.016 0.008 0.004 0.216 0.328
#> GSM601806 4 0.356 0.57182 0.000 0.012 0.040 0.832 0.096 0.020
#> GSM601811 3 0.629 0.31595 0.364 0.012 0.500 0.004 0.060 0.060
#> GSM601816 4 0.690 0.04212 0.268 0.012 0.024 0.420 0.004 0.272
#> GSM601821 5 0.461 0.54782 0.000 0.044 0.032 0.092 0.776 0.056
#> GSM601826 1 0.688 0.10202 0.420 0.056 0.000 0.324 0.004 0.196
#> GSM601836 2 0.400 0.48567 0.108 0.800 0.024 0.000 0.008 0.060
#> GSM601851 1 0.560 0.44369 0.660 0.024 0.004 0.124 0.012 0.176
#> GSM601856 3 0.522 0.45292 0.052 0.068 0.668 0.000 0.000 0.212
#> GSM601866 1 0.429 0.28580 0.672 0.004 0.296 0.000 0.016 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 time(p) gender(p) k
#> MAD:NMF 120 0.499 0.1533 2
#> MAD:NMF 98 0.204 0.2603 3
#> MAD:NMF 77 0.466 0.0759 4
#> MAD:NMF 64 0.238 0.0137 5
#> MAD:NMF 25 0.148 0.5879 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 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.431 0.793 0.885 0.3043 0.659 0.659
#> 3 3 0.356 0.638 0.759 0.4083 0.949 0.923
#> 4 4 0.623 0.640 0.838 0.4679 0.592 0.418
#> 5 5 0.631 0.720 0.834 0.0741 0.867 0.669
#> 6 6 0.736 0.700 0.851 0.0796 0.974 0.911
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
#> GSM601752 1 0.2948 0.8765 0.948 0.052
#> GSM601782 1 0.4431 0.8803 0.908 0.092
#> GSM601792 1 0.4431 0.8803 0.908 0.092
#> GSM601797 1 0.4815 0.8707 0.896 0.104
#> GSM601827 1 0.4431 0.8803 0.908 0.092
#> GSM601837 2 0.5408 0.6864 0.124 0.876
#> GSM601842 1 0.1843 0.8843 0.972 0.028
#> GSM601857 1 0.8016 0.6325 0.756 0.244
#> GSM601867 2 0.9896 0.4894 0.440 0.560
#> GSM601747 1 0.4431 0.8803 0.908 0.092
#> GSM601757 1 0.4431 0.8803 0.908 0.092
#> GSM601762 1 0.3431 0.8725 0.936 0.064
#> GSM601767 1 0.3274 0.8710 0.940 0.060
#> GSM601772 1 0.2423 0.8822 0.960 0.040
#> GSM601777 1 0.5842 0.8341 0.860 0.140
#> GSM601787 2 0.0672 0.6711 0.008 0.992
#> GSM601802 1 0.3274 0.8710 0.940 0.060
#> GSM601807 2 0.9754 0.5214 0.408 0.592
#> GSM601812 1 0.4815 0.8712 0.896 0.104
#> GSM601817 1 0.4431 0.8803 0.908 0.092
#> GSM601822 1 0.4161 0.8834 0.916 0.084
#> GSM601832 1 0.1843 0.8843 0.972 0.028
#> GSM601847 1 0.4298 0.8824 0.912 0.088
#> GSM601852 1 0.0938 0.8877 0.988 0.012
#> GSM601862 2 0.9922 0.4789 0.448 0.552
#> GSM601753 1 0.0672 0.8861 0.992 0.008
#> GSM601783 1 0.4298 0.8820 0.912 0.088
#> GSM601793 1 0.4431 0.8803 0.908 0.092
#> GSM601798 1 0.3274 0.8710 0.940 0.060
#> GSM601828 1 0.4431 0.8803 0.908 0.092
#> GSM601838 2 0.5408 0.6864 0.124 0.876
#> GSM601843 1 0.1843 0.8843 0.972 0.028
#> GSM601858 1 0.3274 0.8715 0.940 0.060
#> GSM601868 2 0.9922 0.4789 0.448 0.552
#> GSM601748 1 0.4431 0.8803 0.908 0.092
#> GSM601758 1 0.4562 0.8779 0.904 0.096
#> GSM601763 1 0.0376 0.8856 0.996 0.004
#> GSM601768 1 0.3274 0.8710 0.940 0.060
#> GSM601773 1 0.3274 0.8710 0.940 0.060
#> GSM601778 1 0.5737 0.8393 0.864 0.136
#> GSM601788 1 0.2948 0.8757 0.948 0.052
#> GSM601803 1 0.3879 0.8631 0.924 0.076
#> GSM601808 2 0.9922 0.4789 0.448 0.552
#> GSM601813 1 0.4815 0.8712 0.896 0.104
#> GSM601818 1 0.4431 0.8803 0.908 0.092
#> GSM601823 1 0.0376 0.8856 0.996 0.004
#> GSM601833 1 0.1843 0.8843 0.972 0.028
#> GSM601848 1 0.4161 0.8834 0.916 0.084
#> GSM601853 1 0.6712 0.7740 0.824 0.176
#> GSM601863 2 0.9922 0.4789 0.448 0.552
#> GSM601754 1 0.2948 0.8765 0.948 0.052
#> GSM601784 1 0.3274 0.8710 0.940 0.060
#> GSM601794 1 0.4431 0.8803 0.908 0.092
#> GSM601799 1 0.0672 0.8861 0.992 0.008
#> GSM601829 1 0.0672 0.8868 0.992 0.008
#> GSM601839 2 0.5408 0.6864 0.124 0.876
#> GSM601844 1 0.0672 0.8868 0.992 0.008
#> GSM601859 1 0.1414 0.8873 0.980 0.020
#> GSM601869 2 0.9922 0.4789 0.448 0.552
#> GSM601749 1 0.4298 0.8820 0.912 0.088
#> GSM601759 1 0.4562 0.8779 0.904 0.096
#> GSM601764 1 0.0376 0.8856 0.996 0.004
#> GSM601769 1 0.9661 0.0659 0.608 0.392
#> GSM601774 1 0.3274 0.8710 0.940 0.060
#> GSM601779 1 0.4298 0.8820 0.912 0.088
#> GSM601789 1 0.2948 0.8757 0.948 0.052
#> GSM601804 1 0.2948 0.8765 0.948 0.052
#> GSM601809 2 0.9922 0.4789 0.448 0.552
#> GSM601814 2 0.6247 0.6792 0.156 0.844
#> GSM601819 1 0.4298 0.8820 0.912 0.088
#> GSM601824 1 0.0376 0.8856 0.996 0.004
#> GSM601834 1 0.1843 0.8843 0.972 0.028
#> GSM601849 1 0.4161 0.8834 0.916 0.084
#> GSM601854 1 0.4431 0.8803 0.908 0.092
#> GSM601864 2 0.0938 0.6725 0.012 0.988
#> GSM601755 1 0.3274 0.8710 0.940 0.060
#> GSM601785 1 0.1414 0.8857 0.980 0.020
#> GSM601795 1 0.4431 0.8803 0.908 0.092
#> GSM601800 1 0.3274 0.8710 0.940 0.060
#> GSM601830 1 1.0000 -0.3285 0.504 0.496
#> GSM601840 1 0.2948 0.8757 0.948 0.052
#> GSM601845 1 0.0376 0.8856 0.996 0.004
#> GSM601860 1 0.2948 0.8757 0.948 0.052
#> GSM601870 2 0.0376 0.6686 0.004 0.996
#> GSM601750 1 0.4431 0.8803 0.908 0.092
#> GSM601760 1 0.4562 0.8779 0.904 0.096
#> GSM601765 1 0.1633 0.8847 0.976 0.024
#> GSM601770 1 0.3274 0.8710 0.940 0.060
#> GSM601775 1 0.0672 0.8850 0.992 0.008
#> GSM601780 1 0.4298 0.8820 0.912 0.088
#> GSM601790 2 0.6048 0.6833 0.148 0.852
#> GSM601805 1 0.3274 0.8710 0.940 0.060
#> GSM601810 2 0.9922 0.4789 0.448 0.552
#> GSM601815 2 0.6247 0.6792 0.156 0.844
#> GSM601820 1 0.4815 0.8712 0.896 0.104
#> GSM601825 1 0.1843 0.8843 0.972 0.028
#> GSM601835 1 0.1843 0.8843 0.972 0.028
#> GSM601850 1 0.4298 0.8824 0.912 0.088
#> GSM601855 2 0.9954 0.4448 0.460 0.540
#> GSM601865 2 0.1184 0.6742 0.016 0.984
#> GSM601756 1 0.3274 0.8710 0.940 0.060
#> GSM601786 2 0.2043 0.6790 0.032 0.968
#> GSM601796 1 0.4431 0.8803 0.908 0.092
#> GSM601801 1 0.3274 0.8710 0.940 0.060
#> GSM601831 1 0.4815 0.8712 0.896 0.104
#> GSM601841 2 0.9977 0.4098 0.472 0.528
#> GSM601846 1 0.0376 0.8856 0.996 0.004
#> GSM601861 2 0.6247 0.6792 0.156 0.844
#> GSM601871 2 0.0672 0.6711 0.008 0.992
#> GSM601751 1 0.2948 0.8757 0.948 0.052
#> GSM601761 1 0.4562 0.8779 0.904 0.096
#> GSM601766 1 0.0376 0.8856 0.996 0.004
#> GSM601771 1 0.2948 0.8757 0.948 0.052
#> GSM601776 1 0.4298 0.8820 0.912 0.088
#> GSM601781 1 0.5842 0.8341 0.860 0.140
#> GSM601791 1 0.4431 0.8803 0.908 0.092
#> GSM601806 1 0.4022 0.8593 0.920 0.080
#> GSM601811 2 0.9922 0.4789 0.448 0.552
#> GSM601816 1 0.4431 0.8803 0.908 0.092
#> GSM601821 2 0.6247 0.6792 0.156 0.844
#> GSM601826 1 0.0376 0.8856 0.996 0.004
#> GSM601836 1 0.0376 0.8856 0.996 0.004
#> GSM601851 1 0.4298 0.8820 0.912 0.088
#> GSM601856 1 0.6712 0.7740 0.824 0.176
#> GSM601866 2 0.9977 0.4098 0.472 0.528
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601752 1 0.355 0.6651 0.868 0.132 0.000
#> GSM601782 1 0.510 0.6398 0.752 0.000 0.248
#> GSM601792 1 0.510 0.6398 0.752 0.000 0.248
#> GSM601797 1 0.522 0.6237 0.740 0.000 0.260
#> GSM601827 1 0.510 0.6398 0.752 0.000 0.248
#> GSM601837 2 0.000 0.8081 0.000 1.000 0.000
#> GSM601842 1 0.334 0.6697 0.880 0.120 0.000
#> GSM601857 1 0.613 0.3444 0.600 0.000 0.400
#> GSM601867 3 0.651 0.7731 0.284 0.028 0.688
#> GSM601747 1 0.510 0.6398 0.752 0.000 0.248
#> GSM601757 1 0.510 0.6398 0.752 0.000 0.248
#> GSM601762 1 0.440 0.6315 0.812 0.188 0.000
#> GSM601767 1 0.435 0.6326 0.816 0.184 0.000
#> GSM601772 1 0.341 0.6685 0.876 0.124 0.000
#> GSM601777 1 0.553 0.5732 0.704 0.000 0.296
#> GSM601787 3 0.484 -0.2308 0.000 0.224 0.776
#> GSM601802 1 0.435 0.6326 0.816 0.184 0.000
#> GSM601807 3 0.820 0.6995 0.284 0.108 0.608
#> GSM601812 1 0.525 0.6181 0.736 0.000 0.264
#> GSM601817 1 0.510 0.6398 0.752 0.000 0.248
#> GSM601822 1 0.493 0.6456 0.768 0.000 0.232
#> GSM601832 1 0.334 0.6697 0.880 0.120 0.000
#> GSM601847 1 0.506 0.6425 0.756 0.000 0.244
#> GSM601852 1 0.355 0.6703 0.868 0.000 0.132
#> GSM601862 3 0.546 0.7910 0.288 0.000 0.712
#> GSM601753 1 0.263 0.6804 0.916 0.084 0.000
#> GSM601783 1 0.506 0.6409 0.756 0.000 0.244
#> GSM601793 1 0.510 0.6398 0.752 0.000 0.248
#> GSM601798 1 0.435 0.6326 0.816 0.184 0.000
#> GSM601828 1 0.510 0.6398 0.752 0.000 0.248
#> GSM601838 2 0.000 0.8081 0.000 1.000 0.000
#> GSM601843 1 0.334 0.6697 0.880 0.120 0.000
#> GSM601858 1 0.628 0.6464 0.760 0.176 0.064
#> GSM601868 3 0.546 0.7910 0.288 0.000 0.712
#> GSM601748 1 0.510 0.6398 0.752 0.000 0.248
#> GSM601758 1 0.514 0.6353 0.748 0.000 0.252
#> GSM601763 1 0.000 0.6863 1.000 0.000 0.000
#> GSM601768 1 0.435 0.6326 0.816 0.184 0.000
#> GSM601773 1 0.435 0.6326 0.816 0.184 0.000
#> GSM601778 1 0.550 0.5809 0.708 0.000 0.292
#> GSM601788 1 0.558 0.6510 0.788 0.176 0.036
#> GSM601803 1 0.455 0.6183 0.800 0.200 0.000
#> GSM601808 3 0.546 0.7910 0.288 0.000 0.712
#> GSM601813 1 0.525 0.6181 0.736 0.000 0.264
#> GSM601818 1 0.510 0.6398 0.752 0.000 0.248
#> GSM601823 1 0.000 0.6863 1.000 0.000 0.000
#> GSM601833 1 0.334 0.6697 0.880 0.120 0.000
#> GSM601848 1 0.502 0.6431 0.760 0.000 0.240
#> GSM601853 1 0.581 0.4643 0.664 0.000 0.336
#> GSM601863 3 0.546 0.7910 0.288 0.000 0.712
#> GSM601754 1 0.355 0.6651 0.868 0.132 0.000
#> GSM601784 1 0.435 0.6326 0.816 0.184 0.000
#> GSM601794 1 0.510 0.6398 0.752 0.000 0.248
#> GSM601799 1 0.263 0.6804 0.916 0.084 0.000
#> GSM601829 1 0.334 0.6726 0.880 0.000 0.120
#> GSM601839 2 0.000 0.8081 0.000 1.000 0.000
#> GSM601844 1 0.348 0.6712 0.872 0.000 0.128
#> GSM601859 1 0.288 0.6783 0.904 0.096 0.000
#> GSM601869 3 0.546 0.7910 0.288 0.000 0.712
#> GSM601749 1 0.506 0.6409 0.756 0.000 0.244
#> GSM601759 1 0.514 0.6353 0.748 0.000 0.252
#> GSM601764 1 0.000 0.6863 1.000 0.000 0.000
#> GSM601769 2 0.630 0.0999 0.484 0.516 0.000
#> GSM601774 1 0.435 0.6326 0.816 0.184 0.000
#> GSM601779 1 0.506 0.6409 0.756 0.000 0.244
#> GSM601789 1 0.558 0.6510 0.788 0.176 0.036
#> GSM601804 1 0.355 0.6651 0.868 0.132 0.000
#> GSM601809 3 0.546 0.7910 0.288 0.000 0.712
#> GSM601814 2 0.129 0.8106 0.032 0.968 0.000
#> GSM601819 1 0.506 0.6409 0.756 0.000 0.244
#> GSM601824 1 0.000 0.6863 1.000 0.000 0.000
#> GSM601834 1 0.334 0.6697 0.880 0.120 0.000
#> GSM601849 1 0.502 0.6431 0.760 0.000 0.240
#> GSM601854 1 0.510 0.6398 0.752 0.000 0.248
#> GSM601864 2 0.629 0.5733 0.000 0.536 0.464
#> GSM601755 1 0.435 0.6326 0.816 0.184 0.000
#> GSM601785 1 0.296 0.6765 0.900 0.100 0.000
#> GSM601795 1 0.510 0.6398 0.752 0.000 0.248
#> GSM601800 1 0.435 0.6326 0.816 0.184 0.000
#> GSM601830 3 0.586 0.6916 0.344 0.000 0.656
#> GSM601840 1 0.558 0.6510 0.788 0.176 0.036
#> GSM601845 1 0.000 0.6863 1.000 0.000 0.000
#> GSM601860 1 0.558 0.6510 0.788 0.176 0.036
#> GSM601870 3 0.497 -0.2473 0.000 0.236 0.764
#> GSM601750 1 0.510 0.6398 0.752 0.000 0.248
#> GSM601760 1 0.514 0.6353 0.748 0.000 0.252
#> GSM601765 1 0.319 0.6729 0.888 0.112 0.000
#> GSM601770 1 0.435 0.6326 0.816 0.184 0.000
#> GSM601775 1 0.216 0.6837 0.936 0.064 0.000
#> GSM601780 1 0.506 0.6409 0.756 0.000 0.244
#> GSM601790 2 0.103 0.8119 0.024 0.976 0.000
#> GSM601805 1 0.435 0.6326 0.816 0.184 0.000
#> GSM601810 3 0.546 0.7910 0.288 0.000 0.712
#> GSM601815 2 0.129 0.8106 0.032 0.968 0.000
#> GSM601820 1 0.525 0.6181 0.736 0.000 0.264
#> GSM601825 1 0.334 0.6697 0.880 0.120 0.000
#> GSM601835 1 0.312 0.6733 0.892 0.108 0.000
#> GSM601850 1 0.506 0.6425 0.756 0.000 0.244
#> GSM601855 3 0.556 0.7733 0.300 0.000 0.700
#> GSM601865 2 0.630 0.5592 0.000 0.520 0.480
#> GSM601756 1 0.435 0.6326 0.816 0.184 0.000
#> GSM601786 2 0.679 0.5601 0.012 0.536 0.452
#> GSM601796 1 0.510 0.6398 0.752 0.000 0.248
#> GSM601801 1 0.435 0.6326 0.816 0.184 0.000
#> GSM601831 1 0.525 0.6181 0.736 0.000 0.264
#> GSM601841 3 0.565 0.7549 0.312 0.000 0.688
#> GSM601846 1 0.000 0.6863 1.000 0.000 0.000
#> GSM601861 2 0.129 0.8106 0.032 0.968 0.000
#> GSM601871 3 0.484 -0.2308 0.000 0.224 0.776
#> GSM601751 1 0.558 0.6510 0.788 0.176 0.036
#> GSM601761 1 0.514 0.6353 0.748 0.000 0.252
#> GSM601766 1 0.153 0.6858 0.960 0.040 0.000
#> GSM601771 1 0.558 0.6510 0.788 0.176 0.036
#> GSM601776 1 0.506 0.6409 0.756 0.000 0.244
#> GSM601781 1 0.553 0.5732 0.704 0.000 0.296
#> GSM601791 1 0.510 0.6398 0.752 0.000 0.248
#> GSM601806 1 0.460 0.6136 0.796 0.204 0.000
#> GSM601811 3 0.546 0.7910 0.288 0.000 0.712
#> GSM601816 1 0.510 0.6398 0.752 0.000 0.248
#> GSM601821 2 0.129 0.8106 0.032 0.968 0.000
#> GSM601826 1 0.000 0.6863 1.000 0.000 0.000
#> GSM601836 1 0.000 0.6863 1.000 0.000 0.000
#> GSM601851 1 0.506 0.6409 0.756 0.000 0.244
#> GSM601856 1 0.581 0.4643 0.664 0.000 0.336
#> GSM601866 3 0.565 0.7549 0.312 0.000 0.688
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601752 2 0.2813 0.8089 0.000 0.896 0.080 0.024
#> GSM601782 1 0.5678 0.7586 0.524 0.024 0.452 0.000
#> GSM601792 1 0.5843 0.7574 0.524 0.024 0.448 0.004
#> GSM601797 1 0.5827 0.7539 0.536 0.024 0.436 0.004
#> GSM601827 1 0.5678 0.7586 0.524 0.024 0.452 0.000
#> GSM601837 4 0.1389 0.8642 0.000 0.048 0.000 0.952
#> GSM601842 2 0.1557 0.8199 0.000 0.944 0.056 0.000
#> GSM601857 1 0.5284 0.6064 0.668 0.020 0.308 0.004
#> GSM601867 1 0.1510 0.2083 0.956 0.000 0.016 0.028
#> GSM601747 1 0.5843 0.7574 0.524 0.024 0.448 0.004
#> GSM601757 1 0.5678 0.7586 0.524 0.024 0.452 0.000
#> GSM601762 2 0.0817 0.8087 0.000 0.976 0.000 0.024
#> GSM601767 2 0.0707 0.8102 0.000 0.980 0.000 0.020
#> GSM601772 2 0.1545 0.8182 0.000 0.952 0.040 0.008
#> GSM601777 1 0.5592 0.7274 0.572 0.024 0.404 0.000
#> GSM601787 3 0.5917 -0.1450 0.444 0.000 0.520 0.036
#> GSM601802 2 0.0707 0.8102 0.000 0.980 0.000 0.020
#> GSM601807 1 0.3160 0.0443 0.872 0.000 0.108 0.020
#> GSM601812 1 0.5378 0.7513 0.540 0.012 0.448 0.000
#> GSM601817 1 0.5678 0.7586 0.524 0.024 0.452 0.000
#> GSM601822 1 0.6306 0.7151 0.500 0.048 0.448 0.004
#> GSM601832 2 0.1474 0.8201 0.000 0.948 0.052 0.000
#> GSM601847 1 0.5925 0.7534 0.524 0.028 0.444 0.004
#> GSM601852 3 0.7777 -0.4972 0.416 0.148 0.420 0.016
#> GSM601862 1 0.0188 0.2688 0.996 0.000 0.000 0.004
#> GSM601753 2 0.3479 0.7795 0.000 0.840 0.148 0.012
#> GSM601783 1 0.5682 0.7576 0.520 0.024 0.456 0.000
#> GSM601793 1 0.5843 0.7574 0.524 0.024 0.448 0.004
#> GSM601798 2 0.0707 0.8102 0.000 0.980 0.000 0.020
#> GSM601828 1 0.5678 0.7586 0.524 0.024 0.452 0.000
#> GSM601838 4 0.1389 0.8642 0.000 0.048 0.000 0.952
#> GSM601843 2 0.1557 0.8199 0.000 0.944 0.056 0.000
#> GSM601858 2 0.5306 0.5819 0.240 0.720 0.020 0.020
#> GSM601868 1 0.0188 0.2688 0.996 0.000 0.000 0.004
#> GSM601748 1 0.5678 0.7586 0.524 0.024 0.452 0.000
#> GSM601758 1 0.5673 0.7586 0.528 0.024 0.448 0.000
#> GSM601763 2 0.5026 0.6066 0.000 0.672 0.312 0.016
#> GSM601768 2 0.0707 0.8102 0.000 0.980 0.000 0.020
#> GSM601773 2 0.0707 0.8102 0.000 0.980 0.000 0.020
#> GSM601778 1 0.5602 0.7309 0.568 0.024 0.408 0.000
#> GSM601788 2 0.4513 0.6991 0.168 0.796 0.016 0.020
#> GSM601803 2 0.1118 0.8039 0.000 0.964 0.000 0.036
#> GSM601808 1 0.0188 0.2688 0.996 0.000 0.000 0.004
#> GSM601813 1 0.5378 0.7513 0.540 0.012 0.448 0.000
#> GSM601818 1 0.5678 0.7586 0.524 0.024 0.452 0.000
#> GSM601823 2 0.5417 0.4380 0.000 0.572 0.412 0.016
#> GSM601833 2 0.1474 0.8201 0.000 0.948 0.052 0.000
#> GSM601848 1 0.5768 0.7493 0.516 0.028 0.456 0.000
#> GSM601853 1 0.5204 0.6893 0.612 0.012 0.376 0.000
#> GSM601863 1 0.0188 0.2688 0.996 0.000 0.000 0.004
#> GSM601754 2 0.2813 0.8089 0.000 0.896 0.080 0.024
#> GSM601784 2 0.0707 0.8102 0.000 0.980 0.000 0.020
#> GSM601794 1 0.5843 0.7574 0.524 0.024 0.448 0.004
#> GSM601799 2 0.3479 0.7795 0.000 0.840 0.148 0.012
#> GSM601829 3 0.7963 -0.4395 0.392 0.176 0.416 0.016
#> GSM601839 4 0.1389 0.8642 0.000 0.048 0.000 0.952
#> GSM601844 3 0.7835 -0.4780 0.412 0.156 0.416 0.016
#> GSM601859 2 0.2988 0.7980 0.000 0.876 0.112 0.012
#> GSM601869 1 0.0188 0.2688 0.996 0.000 0.000 0.004
#> GSM601749 1 0.5682 0.7576 0.520 0.024 0.456 0.000
#> GSM601759 1 0.5673 0.7586 0.528 0.024 0.448 0.000
#> GSM601764 2 0.5026 0.6066 0.000 0.672 0.312 0.016
#> GSM601769 2 0.4679 0.3632 0.000 0.648 0.000 0.352
#> GSM601774 2 0.0707 0.8102 0.000 0.980 0.000 0.020
#> GSM601779 1 0.5682 0.7576 0.520 0.024 0.456 0.000
#> GSM601789 2 0.4513 0.6991 0.168 0.796 0.016 0.020
#> GSM601804 2 0.2813 0.8089 0.000 0.896 0.080 0.024
#> GSM601809 1 0.0188 0.2688 0.996 0.000 0.000 0.004
#> GSM601814 4 0.2081 0.8661 0.000 0.084 0.000 0.916
#> GSM601819 1 0.5682 0.7576 0.520 0.024 0.456 0.000
#> GSM601824 2 0.5284 0.5214 0.000 0.616 0.368 0.016
#> GSM601834 2 0.1474 0.8201 0.000 0.948 0.052 0.000
#> GSM601849 1 0.5768 0.7493 0.516 0.028 0.456 0.000
#> GSM601854 1 0.5678 0.7586 0.524 0.024 0.452 0.000
#> GSM601864 4 0.7149 0.5550 0.132 0.000 0.416 0.452
#> GSM601755 2 0.0707 0.8102 0.000 0.980 0.000 0.020
#> GSM601785 2 0.2255 0.8149 0.000 0.920 0.068 0.012
#> GSM601795 1 0.5843 0.7574 0.524 0.024 0.448 0.004
#> GSM601800 2 0.0707 0.8102 0.000 0.980 0.000 0.020
#> GSM601830 1 0.1716 0.3347 0.936 0.000 0.064 0.000
#> GSM601840 2 0.4513 0.6991 0.168 0.796 0.016 0.020
#> GSM601845 2 0.5269 0.5284 0.000 0.620 0.364 0.016
#> GSM601860 2 0.4513 0.6991 0.168 0.796 0.016 0.020
#> GSM601870 3 0.6114 -0.1626 0.428 0.000 0.524 0.048
#> GSM601750 1 0.5678 0.7586 0.524 0.024 0.452 0.000
#> GSM601760 1 0.5673 0.7586 0.528 0.024 0.448 0.000
#> GSM601765 2 0.1792 0.8178 0.000 0.932 0.068 0.000
#> GSM601770 2 0.0707 0.8102 0.000 0.980 0.000 0.020
#> GSM601775 2 0.3479 0.7807 0.000 0.840 0.148 0.012
#> GSM601780 1 0.5682 0.7576 0.520 0.024 0.456 0.000
#> GSM601790 4 0.1940 0.8669 0.000 0.076 0.000 0.924
#> GSM601805 2 0.0707 0.8102 0.000 0.980 0.000 0.020
#> GSM601810 1 0.0188 0.2688 0.996 0.000 0.000 0.004
#> GSM601815 4 0.2081 0.8661 0.000 0.084 0.000 0.916
#> GSM601820 1 0.5378 0.7513 0.540 0.012 0.448 0.000
#> GSM601825 2 0.1474 0.8201 0.000 0.948 0.052 0.000
#> GSM601835 2 0.1890 0.8188 0.000 0.936 0.056 0.008
#> GSM601850 1 0.5925 0.7534 0.524 0.028 0.444 0.004
#> GSM601855 1 0.0469 0.2855 0.988 0.000 0.012 0.000
#> GSM601865 4 0.7338 0.6040 0.152 0.004 0.332 0.512
#> GSM601756 2 0.0707 0.8102 0.000 0.980 0.000 0.020
#> GSM601786 4 0.7385 0.6192 0.164 0.008 0.284 0.544
#> GSM601796 1 0.5843 0.7574 0.524 0.024 0.448 0.004
#> GSM601801 2 0.0707 0.8102 0.000 0.980 0.000 0.020
#> GSM601831 1 0.5378 0.7513 0.540 0.012 0.448 0.000
#> GSM601841 1 0.1109 0.2991 0.968 0.000 0.028 0.004
#> GSM601846 2 0.5284 0.5214 0.000 0.616 0.368 0.016
#> GSM601861 4 0.2081 0.8661 0.000 0.084 0.000 0.916
#> GSM601871 3 0.5917 -0.1450 0.444 0.000 0.520 0.036
#> GSM601751 2 0.4513 0.6991 0.168 0.796 0.016 0.020
#> GSM601761 1 0.5673 0.7586 0.528 0.024 0.448 0.000
#> GSM601766 2 0.4535 0.6945 0.000 0.744 0.240 0.016
#> GSM601771 2 0.4513 0.6991 0.168 0.796 0.016 0.020
#> GSM601776 1 0.5682 0.7576 0.520 0.024 0.456 0.000
#> GSM601781 1 0.5592 0.7274 0.572 0.024 0.404 0.000
#> GSM601791 1 0.5678 0.7586 0.524 0.024 0.452 0.000
#> GSM601806 2 0.1211 0.8028 0.000 0.960 0.000 0.040
#> GSM601811 1 0.0188 0.2688 0.996 0.000 0.000 0.004
#> GSM601816 1 0.5678 0.7586 0.524 0.024 0.452 0.000
#> GSM601821 4 0.2081 0.8661 0.000 0.084 0.000 0.916
#> GSM601826 2 0.5417 0.4380 0.000 0.572 0.412 0.016
#> GSM601836 2 0.5269 0.5284 0.000 0.620 0.364 0.016
#> GSM601851 1 0.5682 0.7576 0.520 0.024 0.456 0.000
#> GSM601856 1 0.5204 0.6893 0.612 0.012 0.376 0.000
#> GSM601866 1 0.1109 0.2991 0.968 0.000 0.028 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601752 2 0.2491 0.7157 0.068 0.896 0.000 0.036 0.000
#> GSM601782 1 0.0000 0.9101 1.000 0.000 0.000 0.000 0.000
#> GSM601792 1 0.0162 0.9106 0.996 0.000 0.000 0.004 0.000
#> GSM601797 1 0.0566 0.9053 0.984 0.000 0.012 0.004 0.000
#> GSM601827 1 0.0290 0.9097 0.992 0.000 0.000 0.008 0.000
#> GSM601837 5 0.0510 0.8603 0.000 0.016 0.000 0.000 0.984
#> GSM601842 2 0.2286 0.7364 0.004 0.888 0.000 0.108 0.000
#> GSM601857 1 0.3010 0.6292 0.824 0.000 0.172 0.004 0.000
#> GSM601867 3 0.5315 0.7203 0.428 0.000 0.532 0.020 0.020
#> GSM601747 1 0.0162 0.9106 0.996 0.000 0.000 0.004 0.000
#> GSM601757 1 0.0290 0.9097 0.992 0.000 0.000 0.008 0.000
#> GSM601762 2 0.0162 0.7751 0.000 0.996 0.000 0.000 0.004
#> GSM601767 2 0.0000 0.7768 0.000 1.000 0.000 0.000 0.000
#> GSM601772 2 0.2077 0.7506 0.008 0.908 0.000 0.084 0.000
#> GSM601777 1 0.1410 0.8494 0.940 0.000 0.060 0.000 0.000
#> GSM601787 3 0.2280 -0.0716 0.000 0.000 0.880 0.120 0.000
#> GSM601802 2 0.0000 0.7768 0.000 1.000 0.000 0.000 0.000
#> GSM601807 3 0.4540 0.6742 0.340 0.000 0.640 0.020 0.000
#> GSM601812 1 0.0510 0.9014 0.984 0.000 0.016 0.000 0.000
#> GSM601817 1 0.0290 0.9097 0.992 0.000 0.000 0.008 0.000
#> GSM601822 1 0.1914 0.8480 0.924 0.016 0.000 0.060 0.000
#> GSM601832 2 0.2233 0.7390 0.004 0.892 0.000 0.104 0.000
#> GSM601847 1 0.0290 0.9095 0.992 0.000 0.000 0.008 0.000
#> GSM601852 1 0.4029 0.4134 0.680 0.004 0.000 0.316 0.000
#> GSM601862 3 0.4430 0.7509 0.456 0.000 0.540 0.004 0.000
#> GSM601753 2 0.4547 0.4913 0.072 0.736 0.000 0.192 0.000
#> GSM601783 1 0.0609 0.9048 0.980 0.000 0.000 0.020 0.000
#> GSM601793 1 0.0162 0.9106 0.996 0.000 0.000 0.004 0.000
#> GSM601798 2 0.0000 0.7768 0.000 1.000 0.000 0.000 0.000
#> GSM601828 1 0.0290 0.9097 0.992 0.000 0.000 0.008 0.000
#> GSM601838 5 0.0510 0.8603 0.000 0.016 0.000 0.000 0.984
#> GSM601843 2 0.2286 0.7364 0.004 0.888 0.000 0.108 0.000
#> GSM601858 2 0.3992 0.3501 0.268 0.720 0.012 0.000 0.000
#> GSM601868 3 0.4430 0.7509 0.456 0.000 0.540 0.004 0.000
#> GSM601748 1 0.0290 0.9097 0.992 0.000 0.000 0.008 0.000
#> GSM601758 1 0.0162 0.9102 0.996 0.000 0.000 0.004 0.000
#> GSM601763 2 0.6004 -0.4565 0.120 0.508 0.000 0.372 0.000
#> GSM601768 2 0.0000 0.7768 0.000 1.000 0.000 0.000 0.000
#> GSM601773 2 0.0000 0.7768 0.000 1.000 0.000 0.000 0.000
#> GSM601778 1 0.1341 0.8552 0.944 0.000 0.056 0.000 0.000
#> GSM601788 2 0.3196 0.5416 0.192 0.804 0.004 0.000 0.000
#> GSM601803 2 0.0510 0.7695 0.000 0.984 0.000 0.000 0.016
#> GSM601808 3 0.4430 0.7509 0.456 0.000 0.540 0.004 0.000
#> GSM601813 1 0.0510 0.9014 0.984 0.000 0.016 0.000 0.000
#> GSM601818 1 0.0000 0.9101 1.000 0.000 0.000 0.000 0.000
#> GSM601823 4 0.6387 0.9151 0.216 0.272 0.000 0.512 0.000
#> GSM601833 2 0.2233 0.7390 0.004 0.892 0.000 0.104 0.000
#> GSM601848 1 0.1197 0.8818 0.952 0.000 0.000 0.048 0.000
#> GSM601853 1 0.2488 0.7288 0.872 0.000 0.124 0.004 0.000
#> GSM601863 3 0.4430 0.7509 0.456 0.000 0.540 0.004 0.000
#> GSM601754 2 0.2491 0.7157 0.068 0.896 0.000 0.036 0.000
#> GSM601784 2 0.0162 0.7764 0.000 0.996 0.000 0.004 0.000
#> GSM601794 1 0.0162 0.9106 0.996 0.000 0.000 0.004 0.000
#> GSM601799 2 0.4547 0.4913 0.072 0.736 0.000 0.192 0.000
#> GSM601829 1 0.4639 0.3204 0.632 0.024 0.000 0.344 0.000
#> GSM601839 5 0.0510 0.8603 0.000 0.016 0.000 0.000 0.984
#> GSM601844 1 0.4235 0.3660 0.656 0.008 0.000 0.336 0.000
#> GSM601859 2 0.3921 0.5949 0.044 0.784 0.000 0.172 0.000
#> GSM601869 3 0.4430 0.7509 0.456 0.000 0.540 0.004 0.000
#> GSM601749 1 0.0609 0.9048 0.980 0.000 0.000 0.020 0.000
#> GSM601759 1 0.0162 0.9102 0.996 0.000 0.000 0.004 0.000
#> GSM601764 2 0.6004 -0.4565 0.120 0.508 0.000 0.372 0.000
#> GSM601769 2 0.3983 0.3557 0.000 0.660 0.000 0.000 0.340
#> GSM601774 2 0.0000 0.7768 0.000 1.000 0.000 0.000 0.000
#> GSM601779 1 0.0880 0.8967 0.968 0.000 0.000 0.032 0.000
#> GSM601789 2 0.3196 0.5416 0.192 0.804 0.004 0.000 0.000
#> GSM601804 2 0.2491 0.7157 0.068 0.896 0.000 0.036 0.000
#> GSM601809 3 0.4283 0.7507 0.456 0.000 0.544 0.000 0.000
#> GSM601814 5 0.1270 0.8613 0.000 0.052 0.000 0.000 0.948
#> GSM601819 1 0.0609 0.9048 0.980 0.000 0.000 0.020 0.000
#> GSM601824 4 0.6351 0.9491 0.184 0.316 0.000 0.500 0.000
#> GSM601834 2 0.2233 0.7390 0.004 0.892 0.000 0.104 0.000
#> GSM601849 1 0.1197 0.8818 0.952 0.000 0.000 0.048 0.000
#> GSM601854 1 0.0000 0.9101 1.000 0.000 0.000 0.000 0.000
#> GSM601864 5 0.6162 0.5954 0.000 0.000 0.432 0.132 0.436
#> GSM601755 2 0.0000 0.7768 0.000 1.000 0.000 0.000 0.000
#> GSM601785 2 0.2753 0.7070 0.008 0.856 0.000 0.136 0.000
#> GSM601795 1 0.0162 0.9106 0.996 0.000 0.000 0.004 0.000
#> GSM601800 2 0.0000 0.7768 0.000 1.000 0.000 0.000 0.000
#> GSM601830 1 0.4297 -0.6277 0.528 0.000 0.472 0.000 0.000
#> GSM601840 2 0.3196 0.5416 0.192 0.804 0.004 0.000 0.000
#> GSM601845 4 0.6358 0.9396 0.180 0.328 0.000 0.492 0.000
#> GSM601860 2 0.3196 0.5416 0.192 0.804 0.004 0.000 0.000
#> GSM601870 3 0.4907 -0.2533 0.000 0.000 0.492 0.484 0.024
#> GSM601750 1 0.0000 0.9101 1.000 0.000 0.000 0.000 0.000
#> GSM601760 1 0.0162 0.9102 0.996 0.000 0.000 0.004 0.000
#> GSM601765 2 0.2439 0.7263 0.004 0.876 0.000 0.120 0.000
#> GSM601770 2 0.0000 0.7768 0.000 1.000 0.000 0.000 0.000
#> GSM601775 2 0.4429 0.5284 0.064 0.744 0.000 0.192 0.000
#> GSM601780 1 0.0880 0.8967 0.968 0.000 0.000 0.032 0.000
#> GSM601790 5 0.1121 0.8626 0.000 0.044 0.000 0.000 0.956
#> GSM601805 2 0.0000 0.7768 0.000 1.000 0.000 0.000 0.000
#> GSM601810 3 0.4283 0.7507 0.456 0.000 0.544 0.000 0.000
#> GSM601815 5 0.1270 0.8613 0.000 0.052 0.000 0.000 0.948
#> GSM601820 1 0.0510 0.9014 0.984 0.000 0.016 0.000 0.000
#> GSM601825 2 0.2286 0.7369 0.004 0.888 0.000 0.108 0.000
#> GSM601835 2 0.2389 0.7316 0.004 0.880 0.000 0.116 0.000
#> GSM601850 1 0.0290 0.9095 0.992 0.000 0.000 0.008 0.000
#> GSM601855 3 0.4300 0.7259 0.476 0.000 0.524 0.000 0.000
#> GSM601865 5 0.5953 0.6362 0.000 0.000 0.384 0.112 0.504
#> GSM601756 2 0.0000 0.7768 0.000 1.000 0.000 0.000 0.000
#> GSM601786 5 0.6122 0.6520 0.012 0.004 0.364 0.084 0.536
#> GSM601796 1 0.0162 0.9106 0.996 0.000 0.000 0.004 0.000
#> GSM601801 2 0.0000 0.7768 0.000 1.000 0.000 0.000 0.000
#> GSM601831 1 0.0671 0.9023 0.980 0.000 0.016 0.004 0.000
#> GSM601841 3 0.4449 0.7065 0.484 0.000 0.512 0.004 0.000
#> GSM601846 4 0.6351 0.9491 0.184 0.316 0.000 0.500 0.000
#> GSM601861 5 0.1270 0.8613 0.000 0.052 0.000 0.000 0.948
#> GSM601871 3 0.2280 -0.0716 0.000 0.000 0.880 0.120 0.000
#> GSM601751 2 0.3196 0.5416 0.192 0.804 0.004 0.000 0.000
#> GSM601761 1 0.0162 0.9102 0.996 0.000 0.000 0.004 0.000
#> GSM601766 2 0.5606 -0.0428 0.104 0.600 0.000 0.296 0.000
#> GSM601771 2 0.3196 0.5416 0.192 0.804 0.004 0.000 0.000
#> GSM601776 1 0.0703 0.9037 0.976 0.000 0.000 0.024 0.000
#> GSM601781 1 0.1410 0.8494 0.940 0.000 0.060 0.000 0.000
#> GSM601791 1 0.0000 0.9101 1.000 0.000 0.000 0.000 0.000
#> GSM601806 2 0.0609 0.7679 0.000 0.980 0.000 0.000 0.020
#> GSM601811 3 0.4283 0.7507 0.456 0.000 0.544 0.000 0.000
#> GSM601816 1 0.0000 0.9101 1.000 0.000 0.000 0.000 0.000
#> GSM601821 5 0.1270 0.8613 0.000 0.052 0.000 0.000 0.948
#> GSM601826 4 0.6387 0.9151 0.216 0.272 0.000 0.512 0.000
#> GSM601836 4 0.6358 0.9396 0.180 0.328 0.000 0.492 0.000
#> GSM601851 1 0.0880 0.8967 0.968 0.000 0.000 0.032 0.000
#> GSM601856 1 0.2488 0.7288 0.872 0.000 0.124 0.004 0.000
#> GSM601866 3 0.4449 0.7065 0.484 0.000 0.512 0.004 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601752 2 0.2263 0.737 0.048 0.896 0.000 0.000 0.000 0.056
#> GSM601782 1 0.0260 0.927 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM601792 1 0.0146 0.928 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM601797 1 0.0508 0.925 0.984 0.000 0.012 0.000 0.000 0.004
#> GSM601827 1 0.0260 0.927 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM601837 5 0.3864 -0.142 0.000 0.000 0.000 0.480 0.520 0.000
#> GSM601842 2 0.2219 0.725 0.000 0.864 0.000 0.000 0.000 0.136
#> GSM601857 1 0.3769 0.456 0.640 0.000 0.356 0.000 0.004 0.000
#> GSM601867 3 0.3353 0.839 0.068 0.000 0.836 0.016 0.080 0.000
#> GSM601747 1 0.0146 0.928 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM601757 1 0.0260 0.927 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM601762 2 0.0405 0.784 0.000 0.988 0.000 0.004 0.000 0.008
#> GSM601767 2 0.0260 0.786 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM601772 2 0.1957 0.745 0.000 0.888 0.000 0.000 0.000 0.112
#> GSM601777 1 0.1663 0.870 0.912 0.000 0.088 0.000 0.000 0.000
#> GSM601787 3 0.6068 0.265 0.000 0.000 0.420 0.220 0.356 0.004
#> GSM601802 2 0.0000 0.787 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601807 3 0.3167 0.780 0.020 0.000 0.840 0.120 0.016 0.004
#> GSM601812 1 0.0790 0.914 0.968 0.000 0.032 0.000 0.000 0.000
#> GSM601817 1 0.0260 0.927 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM601822 1 0.1745 0.889 0.920 0.012 0.000 0.000 0.000 0.068
#> GSM601832 2 0.2178 0.729 0.000 0.868 0.000 0.000 0.000 0.132
#> GSM601847 1 0.0260 0.927 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM601852 1 0.3742 0.515 0.648 0.004 0.000 0.000 0.000 0.348
#> GSM601862 3 0.1204 0.892 0.056 0.000 0.944 0.000 0.000 0.000
#> GSM601753 2 0.3650 0.447 0.012 0.708 0.000 0.000 0.000 0.280
#> GSM601783 1 0.0547 0.924 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM601793 1 0.0146 0.928 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM601798 2 0.0000 0.787 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601828 1 0.0260 0.927 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM601838 5 0.3864 -0.142 0.000 0.000 0.000 0.480 0.520 0.000
#> GSM601843 2 0.2219 0.725 0.000 0.864 0.000 0.000 0.000 0.136
#> GSM601858 2 0.3628 0.421 0.268 0.720 0.008 0.000 0.004 0.000
#> GSM601868 3 0.1204 0.892 0.056 0.000 0.944 0.000 0.000 0.000
#> GSM601748 1 0.0260 0.927 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM601758 1 0.0146 0.927 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM601763 6 0.4333 0.489 0.020 0.468 0.000 0.000 0.000 0.512
#> GSM601768 2 0.0260 0.786 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM601773 2 0.0260 0.786 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM601778 1 0.1610 0.873 0.916 0.000 0.084 0.000 0.000 0.000
#> GSM601788 2 0.2871 0.578 0.192 0.804 0.004 0.000 0.000 0.000
#> GSM601803 2 0.0717 0.778 0.000 0.976 0.000 0.016 0.000 0.008
#> GSM601808 3 0.1204 0.892 0.056 0.000 0.944 0.000 0.000 0.000
#> GSM601813 1 0.0790 0.914 0.968 0.000 0.032 0.000 0.000 0.000
#> GSM601818 1 0.0146 0.927 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM601823 6 0.4011 0.825 0.060 0.204 0.000 0.000 0.000 0.736
#> GSM601833 2 0.2178 0.729 0.000 0.868 0.000 0.000 0.000 0.132
#> GSM601848 1 0.1075 0.911 0.952 0.000 0.000 0.000 0.000 0.048
#> GSM601853 1 0.3782 0.290 0.588 0.000 0.412 0.000 0.000 0.000
#> GSM601863 3 0.1204 0.892 0.056 0.000 0.944 0.000 0.000 0.000
#> GSM601754 2 0.2263 0.737 0.048 0.896 0.000 0.000 0.000 0.056
#> GSM601784 2 0.0363 0.786 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM601794 1 0.0146 0.928 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM601799 2 0.3650 0.447 0.012 0.708 0.000 0.000 0.000 0.280
#> GSM601829 1 0.4209 0.401 0.596 0.020 0.000 0.000 0.000 0.384
#> GSM601839 5 0.3864 -0.142 0.000 0.000 0.000 0.480 0.520 0.000
#> GSM601844 1 0.3923 0.457 0.620 0.008 0.000 0.000 0.000 0.372
#> GSM601859 2 0.3189 0.564 0.004 0.760 0.000 0.000 0.000 0.236
#> GSM601869 3 0.1204 0.892 0.056 0.000 0.944 0.000 0.000 0.000
#> GSM601749 1 0.0547 0.924 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM601759 1 0.0146 0.927 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM601764 6 0.4333 0.489 0.020 0.468 0.000 0.000 0.000 0.512
#> GSM601769 2 0.4179 0.338 0.000 0.652 0.000 0.324 0.016 0.008
#> GSM601774 2 0.0260 0.786 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM601779 1 0.0790 0.920 0.968 0.000 0.000 0.000 0.000 0.032
#> GSM601789 2 0.2871 0.578 0.192 0.804 0.004 0.000 0.000 0.000
#> GSM601804 2 0.2263 0.737 0.048 0.896 0.000 0.000 0.000 0.056
#> GSM601809 3 0.1349 0.891 0.056 0.000 0.940 0.004 0.000 0.000
#> GSM601814 4 0.4593 0.174 0.000 0.036 0.000 0.492 0.472 0.000
#> GSM601819 1 0.0547 0.924 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM601824 6 0.3641 0.859 0.028 0.224 0.000 0.000 0.000 0.748
#> GSM601834 2 0.2178 0.729 0.000 0.868 0.000 0.000 0.000 0.132
#> GSM601849 1 0.1075 0.911 0.952 0.000 0.000 0.000 0.000 0.048
#> GSM601854 1 0.0000 0.927 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601864 5 0.1814 0.259 0.000 0.000 0.000 0.100 0.900 0.000
#> GSM601755 2 0.0000 0.787 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601785 2 0.2527 0.690 0.000 0.832 0.000 0.000 0.000 0.168
#> GSM601795 1 0.0146 0.928 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM601800 2 0.0000 0.787 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601830 3 0.1765 0.834 0.096 0.000 0.904 0.000 0.000 0.000
#> GSM601840 2 0.2871 0.578 0.192 0.804 0.004 0.000 0.000 0.000
#> GSM601845 6 0.3720 0.858 0.028 0.236 0.000 0.000 0.000 0.736
#> GSM601860 2 0.2871 0.578 0.192 0.804 0.004 0.000 0.000 0.000
#> GSM601870 4 0.6160 -0.166 0.000 0.000 0.020 0.508 0.240 0.232
#> GSM601750 1 0.0000 0.927 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601760 1 0.0146 0.927 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM601765 2 0.2340 0.713 0.000 0.852 0.000 0.000 0.000 0.148
#> GSM601770 2 0.0260 0.786 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM601775 2 0.3799 0.451 0.020 0.704 0.000 0.000 0.000 0.276
#> GSM601780 1 0.0790 0.920 0.968 0.000 0.000 0.000 0.000 0.032
#> GSM601790 5 0.4473 -0.339 0.000 0.028 0.000 0.484 0.488 0.000
#> GSM601805 2 0.0000 0.787 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601810 3 0.1349 0.891 0.056 0.000 0.940 0.004 0.000 0.000
#> GSM601815 4 0.4593 0.174 0.000 0.036 0.000 0.492 0.472 0.000
#> GSM601820 1 0.0790 0.914 0.968 0.000 0.032 0.000 0.000 0.000
#> GSM601825 2 0.2219 0.726 0.000 0.864 0.000 0.000 0.000 0.136
#> GSM601835 2 0.2300 0.720 0.000 0.856 0.000 0.000 0.000 0.144
#> GSM601850 1 0.0260 0.927 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM601855 3 0.1082 0.863 0.040 0.000 0.956 0.004 0.000 0.000
#> GSM601865 5 0.0790 0.314 0.000 0.000 0.032 0.000 0.968 0.000
#> GSM601756 2 0.0000 0.787 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601786 5 0.2077 0.315 0.012 0.004 0.032 0.032 0.920 0.000
#> GSM601796 1 0.0146 0.928 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM601801 2 0.0000 0.787 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601831 1 0.1152 0.906 0.952 0.000 0.044 0.000 0.000 0.004
#> GSM601841 3 0.1610 0.872 0.084 0.000 0.916 0.000 0.000 0.000
#> GSM601846 6 0.3641 0.859 0.028 0.224 0.000 0.000 0.000 0.748
#> GSM601861 4 0.4593 0.174 0.000 0.036 0.000 0.492 0.472 0.000
#> GSM601871 3 0.6068 0.265 0.000 0.000 0.420 0.220 0.356 0.004
#> GSM601751 2 0.2871 0.578 0.192 0.804 0.004 0.000 0.000 0.000
#> GSM601761 1 0.0146 0.927 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM601766 2 0.4282 -0.196 0.020 0.560 0.000 0.000 0.000 0.420
#> GSM601771 2 0.2871 0.578 0.192 0.804 0.004 0.000 0.000 0.000
#> GSM601776 1 0.0713 0.922 0.972 0.000 0.000 0.000 0.000 0.028
#> GSM601781 1 0.1663 0.870 0.912 0.000 0.088 0.000 0.000 0.000
#> GSM601791 1 0.0000 0.927 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601806 2 0.0862 0.777 0.000 0.972 0.000 0.016 0.004 0.008
#> GSM601811 3 0.1349 0.891 0.056 0.000 0.940 0.004 0.000 0.000
#> GSM601816 1 0.0000 0.927 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601821 4 0.4593 0.174 0.000 0.036 0.000 0.492 0.472 0.000
#> GSM601826 6 0.4011 0.825 0.060 0.204 0.000 0.000 0.000 0.736
#> GSM601836 6 0.3720 0.858 0.028 0.236 0.000 0.000 0.000 0.736
#> GSM601851 1 0.0790 0.920 0.968 0.000 0.000 0.000 0.000 0.032
#> GSM601856 1 0.3782 0.290 0.588 0.000 0.412 0.000 0.000 0.000
#> GSM601866 3 0.1610 0.872 0.084 0.000 0.916 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 time(p) gender(p) k
#> ATC:hclust 111 0.674 0.527 2
#> ATC:hclust 118 0.952 0.770 3
#> ATC:hclust 102 0.611 0.320 4
#> ATC:hclust 111 0.794 0.767 5
#> ATC:hclust 98 0.805 0.683 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 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.972 0.971 0.4964 0.498 0.498
#> 3 3 0.821 0.746 0.839 0.2455 0.871 0.747
#> 4 4 0.914 0.907 0.938 0.1284 0.867 0.679
#> 5 5 0.764 0.767 0.841 0.0879 0.941 0.810
#> 6 6 0.748 0.682 0.779 0.0525 0.910 0.657
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
#> GSM601752 2 0.0000 0.986 0.000 1.000
#> GSM601782 1 0.3274 0.973 0.940 0.060
#> GSM601792 1 0.3584 0.974 0.932 0.068
#> GSM601797 1 0.3584 0.974 0.932 0.068
#> GSM601827 1 0.3584 0.974 0.932 0.068
#> GSM601837 2 0.3584 0.941 0.068 0.932
#> GSM601842 2 0.0000 0.986 0.000 1.000
#> GSM601857 1 0.0000 0.954 1.000 0.000
#> GSM601867 1 0.0000 0.954 1.000 0.000
#> GSM601747 1 0.3584 0.974 0.932 0.068
#> GSM601757 1 0.3584 0.974 0.932 0.068
#> GSM601762 2 0.0000 0.986 0.000 1.000
#> GSM601767 2 0.0000 0.986 0.000 1.000
#> GSM601772 2 0.0000 0.986 0.000 1.000
#> GSM601777 1 0.0000 0.954 1.000 0.000
#> GSM601787 1 0.0000 0.954 1.000 0.000
#> GSM601802 2 0.0000 0.986 0.000 1.000
#> GSM601807 1 0.0000 0.954 1.000 0.000
#> GSM601812 1 0.3274 0.973 0.940 0.060
#> GSM601817 1 0.3584 0.974 0.932 0.068
#> GSM601822 1 0.3584 0.974 0.932 0.068
#> GSM601832 2 0.0000 0.986 0.000 1.000
#> GSM601847 1 0.3584 0.974 0.932 0.068
#> GSM601852 1 0.3584 0.974 0.932 0.068
#> GSM601862 1 0.0000 0.954 1.000 0.000
#> GSM601753 2 0.0000 0.986 0.000 1.000
#> GSM601783 1 0.3584 0.974 0.932 0.068
#> GSM601793 1 0.3584 0.974 0.932 0.068
#> GSM601798 2 0.0000 0.986 0.000 1.000
#> GSM601828 1 0.3584 0.974 0.932 0.068
#> GSM601838 2 0.3584 0.941 0.068 0.932
#> GSM601843 2 0.0000 0.986 0.000 1.000
#> GSM601858 2 0.1633 0.972 0.024 0.976
#> GSM601868 1 0.0000 0.954 1.000 0.000
#> GSM601748 1 0.3584 0.974 0.932 0.068
#> GSM601758 1 0.3584 0.974 0.932 0.068
#> GSM601763 2 0.0000 0.986 0.000 1.000
#> GSM601768 2 0.0000 0.986 0.000 1.000
#> GSM601773 2 0.0000 0.986 0.000 1.000
#> GSM601778 1 0.3584 0.974 0.932 0.068
#> GSM601788 2 0.0000 0.986 0.000 1.000
#> GSM601803 2 0.0000 0.986 0.000 1.000
#> GSM601808 1 0.0000 0.954 1.000 0.000
#> GSM601813 1 0.2778 0.970 0.952 0.048
#> GSM601818 1 0.0376 0.956 0.996 0.004
#> GSM601823 1 0.3584 0.974 0.932 0.068
#> GSM601833 2 0.0000 0.986 0.000 1.000
#> GSM601848 1 0.3584 0.974 0.932 0.068
#> GSM601853 1 0.0376 0.956 0.996 0.004
#> GSM601863 1 0.0000 0.954 1.000 0.000
#> GSM601754 2 0.0000 0.986 0.000 1.000
#> GSM601784 2 0.0000 0.986 0.000 1.000
#> GSM601794 1 0.3584 0.974 0.932 0.068
#> GSM601799 2 0.0000 0.986 0.000 1.000
#> GSM601829 1 0.3584 0.974 0.932 0.068
#> GSM601839 2 0.3584 0.941 0.068 0.932
#> GSM601844 1 0.3584 0.974 0.932 0.068
#> GSM601859 2 0.0000 0.986 0.000 1.000
#> GSM601869 1 0.0000 0.954 1.000 0.000
#> GSM601749 1 0.3584 0.974 0.932 0.068
#> GSM601759 1 0.3584 0.974 0.932 0.068
#> GSM601764 2 0.0000 0.986 0.000 1.000
#> GSM601769 2 0.0000 0.986 0.000 1.000
#> GSM601774 2 0.0000 0.986 0.000 1.000
#> GSM601779 1 0.3584 0.974 0.932 0.068
#> GSM601789 2 0.0376 0.984 0.004 0.996
#> GSM601804 2 0.0000 0.986 0.000 1.000
#> GSM601809 1 0.0000 0.954 1.000 0.000
#> GSM601814 2 0.1184 0.977 0.016 0.984
#> GSM601819 1 0.3584 0.974 0.932 0.068
#> GSM601824 2 0.0000 0.986 0.000 1.000
#> GSM601834 2 0.0000 0.986 0.000 1.000
#> GSM601849 1 0.3584 0.974 0.932 0.068
#> GSM601854 1 0.3584 0.974 0.932 0.068
#> GSM601864 2 0.3733 0.940 0.072 0.928
#> GSM601755 2 0.0000 0.986 0.000 1.000
#> GSM601785 2 0.0000 0.986 0.000 1.000
#> GSM601795 1 0.3584 0.974 0.932 0.068
#> GSM601800 2 0.0000 0.986 0.000 1.000
#> GSM601830 1 0.1633 0.963 0.976 0.024
#> GSM601840 2 0.0000 0.986 0.000 1.000
#> GSM601845 2 0.0000 0.986 0.000 1.000
#> GSM601860 2 0.0000 0.986 0.000 1.000
#> GSM601870 1 0.0000 0.954 1.000 0.000
#> GSM601750 1 0.3584 0.974 0.932 0.068
#> GSM601760 1 0.3584 0.974 0.932 0.068
#> GSM601765 2 0.0000 0.986 0.000 1.000
#> GSM601770 2 0.0000 0.986 0.000 1.000
#> GSM601775 2 0.0000 0.986 0.000 1.000
#> GSM601780 1 0.3584 0.974 0.932 0.068
#> GSM601790 2 0.3584 0.941 0.068 0.932
#> GSM601805 2 0.0000 0.986 0.000 1.000
#> GSM601810 1 0.0000 0.954 1.000 0.000
#> GSM601815 2 0.3584 0.941 0.068 0.932
#> GSM601820 1 0.2778 0.970 0.952 0.048
#> GSM601825 2 0.0000 0.986 0.000 1.000
#> GSM601835 2 0.0000 0.986 0.000 1.000
#> GSM601850 1 0.3584 0.974 0.932 0.068
#> GSM601855 1 0.0000 0.954 1.000 0.000
#> GSM601865 2 0.3733 0.940 0.072 0.928
#> GSM601756 2 0.0000 0.986 0.000 1.000
#> GSM601786 2 0.3584 0.941 0.068 0.932
#> GSM601796 1 0.3584 0.974 0.932 0.068
#> GSM601801 2 0.0000 0.986 0.000 1.000
#> GSM601831 1 0.2778 0.970 0.952 0.048
#> GSM601841 1 0.0000 0.954 1.000 0.000
#> GSM601846 2 0.0000 0.986 0.000 1.000
#> GSM601861 2 0.2778 0.955 0.048 0.952
#> GSM601871 1 0.0000 0.954 1.000 0.000
#> GSM601751 2 0.0000 0.986 0.000 1.000
#> GSM601761 1 0.3584 0.974 0.932 0.068
#> GSM601766 2 0.0000 0.986 0.000 1.000
#> GSM601771 2 0.3274 0.949 0.060 0.940
#> GSM601776 1 0.3584 0.974 0.932 0.068
#> GSM601781 1 0.0000 0.954 1.000 0.000
#> GSM601791 1 0.3584 0.974 0.932 0.068
#> GSM601806 2 0.0376 0.984 0.004 0.996
#> GSM601811 1 0.0000 0.954 1.000 0.000
#> GSM601816 1 0.3584 0.974 0.932 0.068
#> GSM601821 2 0.3584 0.941 0.068 0.932
#> GSM601826 1 0.3584 0.974 0.932 0.068
#> GSM601836 2 0.0000 0.986 0.000 1.000
#> GSM601851 1 0.3584 0.974 0.932 0.068
#> GSM601856 1 0.0376 0.956 0.996 0.004
#> GSM601866 1 0.0000 0.954 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601752 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601782 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601792 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601797 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601827 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601837 3 0.0237 0.5782 0.004 0.000 0.996
#> GSM601842 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601857 1 0.0237 0.5213 0.996 0.004 0.000
#> GSM601867 1 0.6302 -0.4562 0.520 0.000 0.480
#> GSM601747 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601757 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601762 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601767 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601772 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601777 1 0.2625 0.5934 0.916 0.084 0.000
#> GSM601787 3 0.6299 0.4757 0.476 0.000 0.524
#> GSM601802 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601807 1 0.5016 0.0444 0.760 0.000 0.240
#> GSM601812 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601817 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601822 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601832 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601847 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601852 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601862 1 0.0747 0.4995 0.984 0.000 0.016
#> GSM601753 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601783 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601793 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601798 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601828 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601838 3 0.0237 0.5782 0.004 0.000 0.996
#> GSM601843 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601858 3 0.5621 -0.4126 0.000 0.308 0.692
#> GSM601868 1 0.0747 0.4995 0.984 0.000 0.016
#> GSM601748 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601758 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601763 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601768 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601773 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601778 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601788 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601803 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601808 1 0.0747 0.4995 0.984 0.000 0.016
#> GSM601813 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601818 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601823 2 0.5785 -0.6823 0.332 0.668 0.000
#> GSM601833 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601848 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601853 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601863 1 0.0747 0.4995 0.984 0.000 0.016
#> GSM601754 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601784 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601794 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601799 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601829 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601839 3 0.0237 0.5782 0.004 0.000 0.996
#> GSM601844 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601859 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601869 1 0.0000 0.5172 1.000 0.000 0.000
#> GSM601749 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601759 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601764 2 0.6260 0.8949 0.000 0.552 0.448
#> GSM601769 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601774 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601779 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601789 3 0.5859 -0.5283 0.000 0.344 0.656
#> GSM601804 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601809 1 0.0747 0.4995 0.984 0.000 0.016
#> GSM601814 3 0.0892 0.5540 0.000 0.020 0.980
#> GSM601819 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601824 2 0.6291 0.9319 0.000 0.532 0.468
#> GSM601834 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601849 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601854 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601864 3 0.6295 0.4797 0.472 0.000 0.528
#> GSM601755 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601785 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601795 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601800 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601830 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601840 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601845 2 0.6252 0.8870 0.000 0.556 0.444
#> GSM601860 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601870 3 0.6295 0.4797 0.472 0.000 0.528
#> GSM601750 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601760 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601765 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601770 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601775 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601780 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601790 3 0.0747 0.5596 0.000 0.016 0.984
#> GSM601805 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601810 1 0.0747 0.4995 0.984 0.000 0.016
#> GSM601815 3 0.0592 0.5646 0.000 0.012 0.988
#> GSM601820 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601825 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601835 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601850 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601855 1 0.0747 0.4995 0.984 0.000 0.016
#> GSM601865 3 0.6295 0.4797 0.472 0.000 0.528
#> GSM601756 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601786 3 0.3192 0.5728 0.112 0.000 0.888
#> GSM601796 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601801 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601831 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601841 1 0.0237 0.5213 0.996 0.004 0.000
#> GSM601846 2 0.6252 0.8870 0.000 0.556 0.444
#> GSM601861 3 0.0892 0.5540 0.000 0.020 0.980
#> GSM601871 3 0.6299 0.4757 0.476 0.000 0.524
#> GSM601751 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601761 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601766 2 0.6295 0.9388 0.000 0.528 0.472
#> GSM601771 3 0.0747 0.5596 0.000 0.016 0.984
#> GSM601776 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601781 1 0.2796 0.6002 0.908 0.092 0.000
#> GSM601791 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601806 3 0.5678 -0.4399 0.000 0.316 0.684
#> GSM601811 1 0.0747 0.4995 0.984 0.000 0.016
#> GSM601816 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601821 3 0.0000 0.5756 0.000 0.000 1.000
#> GSM601826 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601836 2 0.3412 0.2202 0.000 0.876 0.124
#> GSM601851 1 0.6295 0.8697 0.528 0.472 0.000
#> GSM601856 1 0.6291 0.8674 0.532 0.468 0.000
#> GSM601866 1 0.0000 0.5172 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601752 2 0.3453 0.8897 0.000 0.868 0.080 0.052
#> GSM601782 1 0.0336 0.9667 0.992 0.000 0.000 0.008
#> GSM601792 1 0.1211 0.9638 0.960 0.000 0.000 0.040
#> GSM601797 1 0.1722 0.9561 0.944 0.000 0.008 0.048
#> GSM601827 1 0.0000 0.9684 1.000 0.000 0.000 0.000
#> GSM601837 4 0.1854 0.9088 0.000 0.048 0.012 0.940
#> GSM601842 2 0.0188 0.9320 0.000 0.996 0.004 0.000
#> GSM601857 3 0.2944 0.9272 0.128 0.000 0.868 0.004
#> GSM601867 3 0.2329 0.8816 0.012 0.000 0.916 0.072
#> GSM601747 1 0.0188 0.9674 0.996 0.000 0.000 0.004
#> GSM601757 1 0.0188 0.9674 0.996 0.000 0.000 0.004
#> GSM601762 2 0.0188 0.9320 0.000 0.996 0.004 0.000
#> GSM601767 2 0.0188 0.9320 0.000 0.996 0.004 0.000
#> GSM601772 2 0.0188 0.9320 0.000 0.996 0.004 0.000
#> GSM601777 3 0.3161 0.9224 0.124 0.000 0.864 0.012
#> GSM601787 3 0.2081 0.8664 0.000 0.000 0.916 0.084
#> GSM601802 2 0.2593 0.9025 0.000 0.904 0.080 0.016
#> GSM601807 3 0.2494 0.9074 0.036 0.000 0.916 0.048
#> GSM601812 1 0.0188 0.9679 0.996 0.000 0.000 0.004
#> GSM601817 1 0.0000 0.9684 1.000 0.000 0.000 0.000
#> GSM601822 1 0.1118 0.9641 0.964 0.000 0.000 0.036
#> GSM601832 2 0.0188 0.9320 0.000 0.996 0.004 0.000
#> GSM601847 1 0.1302 0.9632 0.956 0.000 0.000 0.044
#> GSM601852 1 0.0000 0.9684 1.000 0.000 0.000 0.000
#> GSM601862 3 0.2281 0.9525 0.096 0.000 0.904 0.000
#> GSM601753 2 0.0188 0.9313 0.000 0.996 0.000 0.004
#> GSM601783 1 0.0000 0.9684 1.000 0.000 0.000 0.000
#> GSM601793 1 0.1118 0.9654 0.964 0.000 0.000 0.036
#> GSM601798 2 0.3761 0.8680 0.000 0.852 0.080 0.068
#> GSM601828 1 0.0000 0.9684 1.000 0.000 0.000 0.000
#> GSM601838 4 0.1854 0.9088 0.000 0.048 0.012 0.940
#> GSM601843 2 0.0188 0.9320 0.000 0.996 0.004 0.000
#> GSM601858 2 0.6242 0.4678 0.000 0.612 0.080 0.308
#> GSM601868 3 0.2281 0.9525 0.096 0.000 0.904 0.000
#> GSM601748 1 0.0000 0.9684 1.000 0.000 0.000 0.000
#> GSM601758 1 0.0000 0.9684 1.000 0.000 0.000 0.000
#> GSM601763 2 0.0469 0.9270 0.000 0.988 0.000 0.012
#> GSM601768 2 0.0188 0.9320 0.000 0.996 0.004 0.000
#> GSM601773 2 0.0188 0.9320 0.000 0.996 0.004 0.000
#> GSM601778 1 0.1302 0.9632 0.956 0.000 0.000 0.044
#> GSM601788 2 0.3533 0.8777 0.000 0.864 0.080 0.056
#> GSM601803 2 0.4106 0.8530 0.000 0.832 0.084 0.084
#> GSM601808 3 0.2281 0.9525 0.096 0.000 0.904 0.000
#> GSM601813 1 0.0188 0.9679 0.996 0.000 0.000 0.004
#> GSM601818 1 0.0336 0.9667 0.992 0.000 0.000 0.008
#> GSM601823 1 0.2313 0.9231 0.924 0.044 0.000 0.032
#> GSM601833 2 0.0188 0.9320 0.000 0.996 0.004 0.000
#> GSM601848 1 0.1022 0.9649 0.968 0.000 0.000 0.032
#> GSM601853 1 0.2480 0.8772 0.904 0.000 0.088 0.008
#> GSM601863 3 0.2281 0.9525 0.096 0.000 0.904 0.000
#> GSM601754 2 0.3453 0.8897 0.000 0.868 0.080 0.052
#> GSM601784 2 0.0188 0.9320 0.000 0.996 0.004 0.000
#> GSM601794 1 0.1211 0.9638 0.960 0.000 0.000 0.040
#> GSM601799 2 0.0188 0.9313 0.000 0.996 0.000 0.004
#> GSM601829 1 0.1022 0.9649 0.968 0.000 0.000 0.032
#> GSM601839 4 0.1854 0.9088 0.000 0.048 0.012 0.940
#> GSM601844 1 0.1209 0.9633 0.964 0.004 0.000 0.032
#> GSM601859 2 0.0000 0.9315 0.000 1.000 0.000 0.000
#> GSM601869 3 0.2281 0.9525 0.096 0.000 0.904 0.000
#> GSM601749 1 0.0000 0.9684 1.000 0.000 0.000 0.000
#> GSM601759 1 0.0000 0.9684 1.000 0.000 0.000 0.000
#> GSM601764 2 0.1488 0.9083 0.012 0.956 0.000 0.032
#> GSM601769 2 0.1489 0.9074 0.000 0.952 0.004 0.044
#> GSM601774 2 0.0188 0.9320 0.000 0.996 0.004 0.000
#> GSM601779 1 0.1022 0.9649 0.968 0.000 0.000 0.032
#> GSM601789 2 0.6440 0.3251 0.000 0.564 0.080 0.356
#> GSM601804 2 0.3453 0.8897 0.000 0.868 0.080 0.052
#> GSM601809 3 0.2281 0.9525 0.096 0.000 0.904 0.000
#> GSM601814 4 0.2089 0.9024 0.000 0.048 0.020 0.932
#> GSM601819 1 0.0000 0.9684 1.000 0.000 0.000 0.000
#> GSM601824 2 0.1488 0.9083 0.012 0.956 0.000 0.032
#> GSM601834 2 0.0188 0.9320 0.000 0.996 0.004 0.000
#> GSM601849 1 0.1022 0.9649 0.968 0.000 0.000 0.032
#> GSM601854 1 0.0000 0.9684 1.000 0.000 0.000 0.000
#> GSM601864 4 0.1637 0.8583 0.000 0.000 0.060 0.940
#> GSM601755 2 0.2593 0.9025 0.000 0.904 0.080 0.016
#> GSM601785 2 0.0000 0.9315 0.000 1.000 0.000 0.000
#> GSM601795 1 0.1545 0.9597 0.952 0.000 0.008 0.040
#> GSM601800 2 0.2593 0.9025 0.000 0.904 0.080 0.016
#> GSM601830 1 0.0469 0.9660 0.988 0.000 0.000 0.012
#> GSM601840 2 0.2593 0.9025 0.000 0.904 0.080 0.016
#> GSM601845 2 0.1488 0.9083 0.012 0.956 0.000 0.032
#> GSM601860 2 0.2593 0.9025 0.000 0.904 0.080 0.016
#> GSM601870 3 0.2081 0.8664 0.000 0.000 0.916 0.084
#> GSM601750 1 0.0000 0.9684 1.000 0.000 0.000 0.000
#> GSM601760 1 0.0000 0.9684 1.000 0.000 0.000 0.000
#> GSM601765 2 0.0188 0.9320 0.000 0.996 0.004 0.000
#> GSM601770 2 0.0188 0.9320 0.000 0.996 0.004 0.000
#> GSM601775 2 0.0000 0.9315 0.000 1.000 0.000 0.000
#> GSM601780 1 0.1118 0.9649 0.964 0.000 0.000 0.036
#> GSM601790 4 0.1576 0.9085 0.000 0.048 0.004 0.948
#> GSM601805 2 0.3761 0.8680 0.000 0.852 0.080 0.068
#> GSM601810 3 0.2281 0.9525 0.096 0.000 0.904 0.000
#> GSM601815 4 0.1389 0.9092 0.000 0.048 0.000 0.952
#> GSM601820 1 0.0188 0.9679 0.996 0.000 0.000 0.004
#> GSM601825 2 0.0188 0.9320 0.000 0.996 0.004 0.000
#> GSM601835 2 0.0188 0.9320 0.000 0.996 0.004 0.000
#> GSM601850 1 0.1302 0.9632 0.956 0.000 0.000 0.044
#> GSM601855 3 0.2342 0.9436 0.080 0.000 0.912 0.008
#> GSM601865 4 0.1637 0.8583 0.000 0.000 0.060 0.940
#> GSM601756 2 0.3828 0.8682 0.000 0.848 0.084 0.068
#> GSM601786 4 0.1545 0.9073 0.000 0.040 0.008 0.952
#> GSM601796 1 0.1118 0.9654 0.964 0.000 0.000 0.036
#> GSM601801 2 0.3828 0.8682 0.000 0.848 0.084 0.068
#> GSM601831 1 0.0188 0.9679 0.996 0.000 0.000 0.004
#> GSM601841 3 0.2654 0.9438 0.108 0.000 0.888 0.004
#> GSM601846 2 0.1452 0.9085 0.008 0.956 0.000 0.036
#> GSM601861 4 0.2089 0.9024 0.000 0.048 0.020 0.932
#> GSM601871 3 0.2081 0.8664 0.000 0.000 0.916 0.084
#> GSM601751 2 0.2593 0.9025 0.000 0.904 0.080 0.016
#> GSM601761 1 0.0000 0.9684 1.000 0.000 0.000 0.000
#> GSM601766 2 0.0000 0.9315 0.000 1.000 0.000 0.000
#> GSM601771 4 0.6611 -0.0377 0.000 0.456 0.080 0.464
#> GSM601776 1 0.1022 0.9649 0.968 0.000 0.000 0.032
#> GSM601781 3 0.3161 0.9224 0.124 0.000 0.864 0.012
#> GSM601791 1 0.0188 0.9679 0.996 0.000 0.000 0.004
#> GSM601806 4 0.4591 0.7866 0.000 0.116 0.084 0.800
#> GSM601811 3 0.2281 0.9525 0.096 0.000 0.904 0.000
#> GSM601816 1 0.1211 0.9638 0.960 0.000 0.000 0.040
#> GSM601821 4 0.1722 0.9095 0.000 0.048 0.008 0.944
#> GSM601826 1 0.1209 0.9633 0.964 0.004 0.000 0.032
#> GSM601836 2 0.1610 0.9051 0.016 0.952 0.000 0.032
#> GSM601851 1 0.1022 0.9649 0.968 0.000 0.000 0.032
#> GSM601856 1 0.5288 -0.0950 0.520 0.000 0.472 0.008
#> GSM601866 3 0.2281 0.9525 0.096 0.000 0.904 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601752 4 0.4455 0.688 0.000 0.404 0.000 0.588 0.008
#> GSM601782 1 0.1956 0.850 0.928 0.000 0.012 0.052 0.008
#> GSM601792 1 0.3990 0.822 0.688 0.000 0.000 0.308 0.004
#> GSM601797 1 0.4238 0.758 0.628 0.000 0.000 0.368 0.004
#> GSM601827 1 0.1484 0.866 0.944 0.000 0.000 0.048 0.008
#> GSM601837 5 0.0609 0.983 0.000 0.020 0.000 0.000 0.980
#> GSM601842 2 0.0000 0.825 0.000 1.000 0.000 0.000 0.000
#> GSM601857 3 0.4726 0.668 0.228 0.000 0.716 0.048 0.008
#> GSM601867 3 0.0404 0.922 0.000 0.000 0.988 0.012 0.000
#> GSM601747 1 0.1924 0.862 0.924 0.000 0.008 0.064 0.004
#> GSM601757 1 0.1365 0.860 0.952 0.000 0.004 0.040 0.004
#> GSM601762 2 0.0000 0.825 0.000 1.000 0.000 0.000 0.000
#> GSM601767 2 0.0000 0.825 0.000 1.000 0.000 0.000 0.000
#> GSM601772 2 0.0290 0.822 0.000 0.992 0.000 0.008 0.000
#> GSM601777 3 0.5273 0.660 0.140 0.000 0.692 0.164 0.004
#> GSM601787 3 0.1768 0.896 0.000 0.000 0.924 0.072 0.004
#> GSM601802 2 0.4974 -0.639 0.000 0.508 0.000 0.464 0.028
#> GSM601807 3 0.1502 0.909 0.004 0.000 0.940 0.056 0.000
#> GSM601812 1 0.1412 0.858 0.952 0.000 0.004 0.036 0.008
#> GSM601817 1 0.1331 0.865 0.952 0.000 0.000 0.040 0.008
#> GSM601822 1 0.3684 0.824 0.720 0.000 0.000 0.280 0.000
#> GSM601832 2 0.0000 0.825 0.000 1.000 0.000 0.000 0.000
#> GSM601847 1 0.3796 0.820 0.700 0.000 0.000 0.300 0.000
#> GSM601852 1 0.2773 0.810 0.836 0.000 0.000 0.164 0.000
#> GSM601862 3 0.0162 0.926 0.004 0.000 0.996 0.000 0.000
#> GSM601753 2 0.0798 0.809 0.000 0.976 0.000 0.016 0.008
#> GSM601783 1 0.0865 0.861 0.972 0.000 0.004 0.024 0.000
#> GSM601793 1 0.3766 0.830 0.728 0.000 0.000 0.268 0.004
#> GSM601798 4 0.6092 0.790 0.000 0.412 0.000 0.464 0.124
#> GSM601828 1 0.0865 0.862 0.972 0.000 0.000 0.024 0.004
#> GSM601838 5 0.0609 0.983 0.000 0.020 0.000 0.000 0.980
#> GSM601843 2 0.0000 0.825 0.000 1.000 0.000 0.000 0.000
#> GSM601858 4 0.6736 0.737 0.000 0.284 0.004 0.460 0.252
#> GSM601868 3 0.0162 0.926 0.004 0.000 0.996 0.000 0.000
#> GSM601748 1 0.0451 0.860 0.988 0.000 0.004 0.008 0.000
#> GSM601758 1 0.0162 0.861 0.996 0.000 0.004 0.000 0.000
#> GSM601763 2 0.2338 0.734 0.004 0.884 0.000 0.112 0.000
#> GSM601768 2 0.0000 0.825 0.000 1.000 0.000 0.000 0.000
#> GSM601773 2 0.0000 0.825 0.000 1.000 0.000 0.000 0.000
#> GSM601778 1 0.3884 0.820 0.708 0.000 0.000 0.288 0.004
#> GSM601788 4 0.5918 0.755 0.000 0.440 0.004 0.468 0.088
#> GSM601803 4 0.6304 0.790 0.000 0.384 0.000 0.460 0.156
#> GSM601808 3 0.0162 0.926 0.004 0.000 0.996 0.000 0.000
#> GSM601813 1 0.1673 0.854 0.944 0.000 0.016 0.032 0.008
#> GSM601818 1 0.2688 0.835 0.896 0.000 0.036 0.056 0.012
#> GSM601823 1 0.3966 0.776 0.664 0.000 0.000 0.336 0.000
#> GSM601833 2 0.0000 0.825 0.000 1.000 0.000 0.000 0.000
#> GSM601848 1 0.3305 0.840 0.776 0.000 0.000 0.224 0.000
#> GSM601853 1 0.4140 0.746 0.800 0.000 0.124 0.064 0.012
#> GSM601863 3 0.0162 0.926 0.004 0.000 0.996 0.000 0.000
#> GSM601754 4 0.4455 0.688 0.000 0.404 0.000 0.588 0.008
#> GSM601784 2 0.0000 0.825 0.000 1.000 0.000 0.000 0.000
#> GSM601794 1 0.3928 0.825 0.700 0.000 0.000 0.296 0.004
#> GSM601799 2 0.0693 0.813 0.000 0.980 0.000 0.012 0.008
#> GSM601829 1 0.3949 0.779 0.668 0.000 0.000 0.332 0.000
#> GSM601839 5 0.0609 0.983 0.000 0.020 0.000 0.000 0.980
#> GSM601844 1 0.3636 0.799 0.728 0.000 0.000 0.272 0.000
#> GSM601859 2 0.0290 0.822 0.000 0.992 0.000 0.008 0.000
#> GSM601869 3 0.0162 0.926 0.004 0.000 0.996 0.000 0.000
#> GSM601749 1 0.1197 0.860 0.952 0.000 0.000 0.048 0.000
#> GSM601759 1 0.0324 0.860 0.992 0.000 0.004 0.004 0.000
#> GSM601764 2 0.2583 0.716 0.004 0.864 0.000 0.132 0.000
#> GSM601769 2 0.1608 0.740 0.000 0.928 0.000 0.000 0.072
#> GSM601774 2 0.0000 0.825 0.000 1.000 0.000 0.000 0.000
#> GSM601779 1 0.3395 0.837 0.764 0.000 0.000 0.236 0.000
#> GSM601789 4 0.6762 0.728 0.000 0.288 0.004 0.452 0.256
#> GSM601804 4 0.4497 0.681 0.000 0.424 0.000 0.568 0.008
#> GSM601809 3 0.0162 0.926 0.004 0.000 0.996 0.000 0.000
#> GSM601814 5 0.0609 0.983 0.000 0.020 0.000 0.000 0.980
#> GSM601819 1 0.1410 0.860 0.940 0.000 0.000 0.060 0.000
#> GSM601824 2 0.2536 0.719 0.004 0.868 0.000 0.128 0.000
#> GSM601834 2 0.0000 0.825 0.000 1.000 0.000 0.000 0.000
#> GSM601849 1 0.3336 0.839 0.772 0.000 0.000 0.228 0.000
#> GSM601854 1 0.1026 0.861 0.968 0.000 0.004 0.024 0.004
#> GSM601864 5 0.1943 0.932 0.000 0.000 0.020 0.056 0.924
#> GSM601755 2 0.5458 -0.712 0.000 0.476 0.000 0.464 0.060
#> GSM601785 2 0.0290 0.822 0.000 0.992 0.000 0.008 0.000
#> GSM601795 1 0.4047 0.818 0.676 0.000 0.000 0.320 0.004
#> GSM601800 2 0.4552 -0.593 0.000 0.524 0.000 0.468 0.008
#> GSM601830 1 0.3757 0.823 0.808 0.000 0.024 0.156 0.012
#> GSM601840 2 0.4892 -0.642 0.000 0.496 0.004 0.484 0.016
#> GSM601845 2 0.2674 0.706 0.004 0.856 0.000 0.140 0.000
#> GSM601860 2 0.4593 -0.609 0.000 0.512 0.004 0.480 0.004
#> GSM601870 3 0.2011 0.889 0.000 0.000 0.908 0.088 0.004
#> GSM601750 1 0.0833 0.860 0.976 0.000 0.004 0.016 0.004
#> GSM601760 1 0.0324 0.861 0.992 0.000 0.004 0.004 0.000
#> GSM601765 2 0.0000 0.825 0.000 1.000 0.000 0.000 0.000
#> GSM601770 2 0.0000 0.825 0.000 1.000 0.000 0.000 0.000
#> GSM601775 2 0.0290 0.822 0.000 0.992 0.000 0.008 0.000
#> GSM601780 1 0.3274 0.841 0.780 0.000 0.000 0.220 0.000
#> GSM601790 5 0.0609 0.983 0.000 0.020 0.000 0.000 0.980
#> GSM601805 4 0.6121 0.793 0.000 0.408 0.000 0.464 0.128
#> GSM601810 3 0.0162 0.926 0.004 0.000 0.996 0.000 0.000
#> GSM601815 5 0.0609 0.983 0.000 0.020 0.000 0.000 0.980
#> GSM601820 1 0.1822 0.850 0.936 0.000 0.024 0.036 0.004
#> GSM601825 2 0.0000 0.825 0.000 1.000 0.000 0.000 0.000
#> GSM601835 2 0.0000 0.825 0.000 1.000 0.000 0.000 0.000
#> GSM601850 1 0.3774 0.822 0.704 0.000 0.000 0.296 0.000
#> GSM601855 3 0.1041 0.918 0.004 0.000 0.964 0.032 0.000
#> GSM601865 5 0.1943 0.932 0.000 0.000 0.020 0.056 0.924
#> GSM601756 4 0.6150 0.794 0.000 0.404 0.000 0.464 0.132
#> GSM601786 5 0.0579 0.972 0.000 0.008 0.000 0.008 0.984
#> GSM601796 1 0.3715 0.828 0.736 0.000 0.000 0.260 0.004
#> GSM601801 4 0.6121 0.793 0.000 0.408 0.000 0.464 0.128
#> GSM601831 1 0.1836 0.852 0.936 0.000 0.016 0.040 0.008
#> GSM601841 3 0.1041 0.909 0.032 0.000 0.964 0.004 0.000
#> GSM601846 2 0.3318 0.627 0.008 0.800 0.000 0.192 0.000
#> GSM601861 5 0.0609 0.983 0.000 0.020 0.000 0.000 0.980
#> GSM601871 3 0.1768 0.896 0.000 0.000 0.924 0.072 0.004
#> GSM601751 4 0.5915 0.762 0.000 0.432 0.004 0.476 0.088
#> GSM601761 1 0.0955 0.864 0.968 0.000 0.004 0.028 0.000
#> GSM601766 2 0.0162 0.824 0.000 0.996 0.000 0.004 0.000
#> GSM601771 4 0.6722 0.583 0.012 0.172 0.004 0.512 0.300
#> GSM601776 1 0.3274 0.841 0.780 0.000 0.000 0.220 0.000
#> GSM601781 3 0.5312 0.654 0.144 0.000 0.688 0.164 0.004
#> GSM601791 1 0.1285 0.861 0.956 0.000 0.004 0.036 0.004
#> GSM601806 4 0.5967 0.337 0.000 0.108 0.000 0.456 0.436
#> GSM601811 3 0.0162 0.926 0.004 0.000 0.996 0.000 0.000
#> GSM601816 1 0.3607 0.840 0.752 0.000 0.000 0.244 0.004
#> GSM601821 5 0.0609 0.983 0.000 0.020 0.000 0.000 0.980
#> GSM601826 1 0.3949 0.779 0.668 0.000 0.000 0.332 0.000
#> GSM601836 2 0.2886 0.692 0.008 0.844 0.000 0.148 0.000
#> GSM601851 1 0.3274 0.841 0.780 0.000 0.000 0.220 0.000
#> GSM601856 1 0.5704 0.248 0.568 0.000 0.356 0.064 0.012
#> GSM601866 3 0.0162 0.926 0.004 0.000 0.996 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601752 4 0.4312 0.894 0.000 0.272 0.000 0.676 0.000 0.052
#> GSM601782 1 0.2288 0.633 0.896 0.000 0.004 0.028 0.000 0.072
#> GSM601792 6 0.4739 0.612 0.436 0.000 0.000 0.048 0.000 0.516
#> GSM601797 6 0.5339 0.506 0.404 0.000 0.000 0.108 0.000 0.488
#> GSM601827 1 0.1745 0.645 0.924 0.000 0.000 0.020 0.000 0.056
#> GSM601837 5 0.0622 0.972 0.000 0.000 0.000 0.008 0.980 0.012
#> GSM601842 2 0.0000 0.878 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601857 3 0.5230 0.191 0.440 0.000 0.492 0.024 0.000 0.044
#> GSM601867 3 0.0806 0.853 0.000 0.000 0.972 0.008 0.000 0.020
#> GSM601747 1 0.2651 0.616 0.860 0.000 0.000 0.028 0.000 0.112
#> GSM601757 1 0.1829 0.656 0.920 0.000 0.000 0.024 0.000 0.056
#> GSM601762 2 0.0777 0.874 0.000 0.972 0.000 0.004 0.000 0.024
#> GSM601767 2 0.0777 0.874 0.000 0.972 0.000 0.004 0.000 0.024
#> GSM601772 2 0.1418 0.866 0.000 0.944 0.000 0.032 0.000 0.024
#> GSM601777 3 0.6643 0.257 0.152 0.000 0.476 0.072 0.000 0.300
#> GSM601787 3 0.3227 0.790 0.000 0.000 0.824 0.060 0.000 0.116
#> GSM601802 4 0.3668 0.886 0.000 0.328 0.000 0.668 0.004 0.000
#> GSM601807 3 0.3123 0.801 0.000 0.000 0.832 0.056 0.000 0.112
#> GSM601812 1 0.1219 0.656 0.948 0.000 0.000 0.004 0.000 0.048
#> GSM601817 1 0.1462 0.652 0.936 0.000 0.000 0.008 0.000 0.056
#> GSM601822 6 0.3789 0.615 0.416 0.000 0.000 0.000 0.000 0.584
#> GSM601832 2 0.0000 0.878 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601847 6 0.4929 0.575 0.428 0.000 0.000 0.064 0.000 0.508
#> GSM601852 1 0.4291 0.201 0.664 0.000 0.000 0.044 0.000 0.292
#> GSM601862 3 0.0146 0.857 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM601753 2 0.2058 0.834 0.000 0.908 0.000 0.056 0.000 0.036
#> GSM601783 1 0.1007 0.654 0.956 0.000 0.000 0.000 0.000 0.044
#> GSM601793 1 0.4746 -0.557 0.508 0.000 0.000 0.048 0.000 0.444
#> GSM601798 4 0.4487 0.922 0.000 0.264 0.000 0.668 0.068 0.000
#> GSM601828 1 0.1320 0.654 0.948 0.000 0.000 0.016 0.000 0.036
#> GSM601838 5 0.0622 0.972 0.000 0.000 0.000 0.008 0.980 0.012
#> GSM601843 2 0.0146 0.878 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM601858 4 0.4992 0.885 0.000 0.208 0.000 0.676 0.096 0.020
#> GSM601868 3 0.0146 0.857 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM601748 1 0.0146 0.669 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM601758 1 0.0622 0.668 0.980 0.000 0.000 0.008 0.000 0.012
#> GSM601763 2 0.4204 0.714 0.000 0.740 0.000 0.132 0.000 0.128
#> GSM601768 2 0.0777 0.874 0.000 0.972 0.000 0.004 0.000 0.024
#> GSM601773 2 0.0777 0.874 0.000 0.972 0.000 0.004 0.000 0.024
#> GSM601778 6 0.4990 0.529 0.452 0.000 0.000 0.068 0.000 0.480
#> GSM601788 4 0.4630 0.919 0.000 0.280 0.000 0.660 0.048 0.012
#> GSM601803 4 0.4570 0.918 0.000 0.252 0.000 0.668 0.080 0.000
#> GSM601808 3 0.0000 0.857 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601813 1 0.1777 0.650 0.928 0.000 0.024 0.004 0.000 0.044
#> GSM601818 1 0.3430 0.580 0.836 0.000 0.060 0.028 0.000 0.076
#> GSM601823 6 0.5412 0.452 0.324 0.004 0.000 0.120 0.000 0.552
#> GSM601833 2 0.0146 0.878 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM601848 1 0.3868 -0.569 0.504 0.000 0.000 0.000 0.000 0.496
#> GSM601853 1 0.4359 0.457 0.748 0.000 0.156 0.020 0.000 0.076
#> GSM601863 3 0.0146 0.857 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM601754 4 0.4348 0.893 0.000 0.268 0.000 0.676 0.000 0.056
#> GSM601784 2 0.0777 0.874 0.000 0.972 0.000 0.004 0.000 0.024
#> GSM601794 6 0.4756 0.584 0.464 0.000 0.000 0.048 0.000 0.488
#> GSM601799 2 0.1644 0.855 0.000 0.932 0.000 0.028 0.000 0.040
#> GSM601829 6 0.5472 0.462 0.364 0.000 0.000 0.132 0.000 0.504
#> GSM601839 5 0.0622 0.972 0.000 0.000 0.000 0.008 0.980 0.012
#> GSM601844 6 0.5527 0.393 0.408 0.000 0.000 0.132 0.000 0.460
#> GSM601859 2 0.1633 0.862 0.000 0.932 0.000 0.024 0.000 0.044
#> GSM601869 3 0.0000 0.857 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601749 1 0.2191 0.571 0.876 0.000 0.000 0.004 0.000 0.120
#> GSM601759 1 0.0622 0.668 0.980 0.000 0.000 0.008 0.000 0.012
#> GSM601764 2 0.4898 0.640 0.000 0.656 0.000 0.144 0.000 0.200
#> GSM601769 2 0.2848 0.756 0.000 0.848 0.000 0.004 0.124 0.024
#> GSM601774 2 0.0777 0.874 0.000 0.972 0.000 0.004 0.000 0.024
#> GSM601779 6 0.3864 0.555 0.480 0.000 0.000 0.000 0.000 0.520
#> GSM601789 4 0.5017 0.903 0.000 0.232 0.000 0.656 0.100 0.012
#> GSM601804 4 0.4423 0.890 0.000 0.272 0.000 0.668 0.000 0.060
#> GSM601809 3 0.0146 0.857 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM601814 5 0.0000 0.975 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601819 1 0.2300 0.534 0.856 0.000 0.000 0.000 0.000 0.144
#> GSM601824 2 0.4898 0.640 0.000 0.656 0.000 0.144 0.000 0.200
#> GSM601834 2 0.0146 0.878 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM601849 6 0.3864 0.555 0.480 0.000 0.000 0.000 0.000 0.520
#> GSM601854 1 0.0820 0.666 0.972 0.000 0.000 0.016 0.000 0.012
#> GSM601864 5 0.2724 0.905 0.000 0.000 0.000 0.052 0.864 0.084
#> GSM601755 4 0.4135 0.911 0.000 0.300 0.000 0.668 0.032 0.000
#> GSM601785 2 0.1492 0.858 0.000 0.940 0.000 0.024 0.000 0.036
#> GSM601795 6 0.4774 0.619 0.420 0.000 0.000 0.052 0.000 0.528
#> GSM601800 4 0.3531 0.885 0.000 0.328 0.000 0.672 0.000 0.000
#> GSM601830 1 0.5058 0.402 0.688 0.000 0.032 0.100 0.000 0.180
#> GSM601840 4 0.4061 0.894 0.000 0.316 0.000 0.664 0.008 0.012
#> GSM601845 2 0.5069 0.611 0.000 0.628 0.000 0.144 0.000 0.228
#> GSM601860 4 0.3852 0.885 0.000 0.324 0.000 0.664 0.000 0.012
#> GSM601870 3 0.4131 0.742 0.000 0.000 0.744 0.100 0.000 0.156
#> GSM601750 1 0.0291 0.668 0.992 0.000 0.000 0.004 0.000 0.004
#> GSM601760 1 0.0972 0.664 0.964 0.000 0.000 0.008 0.000 0.028
#> GSM601765 2 0.0000 0.878 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601770 2 0.0777 0.874 0.000 0.972 0.000 0.004 0.000 0.024
#> GSM601775 2 0.1492 0.858 0.000 0.940 0.000 0.024 0.000 0.036
#> GSM601780 1 0.4262 -0.571 0.508 0.000 0.000 0.016 0.000 0.476
#> GSM601790 5 0.0000 0.975 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601805 4 0.4516 0.922 0.000 0.260 0.000 0.668 0.072 0.000
#> GSM601810 3 0.0146 0.857 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM601815 5 0.0000 0.975 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601820 1 0.1649 0.648 0.932 0.000 0.032 0.000 0.000 0.036
#> GSM601825 2 0.0291 0.878 0.000 0.992 0.000 0.004 0.000 0.004
#> GSM601835 2 0.0146 0.878 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM601850 6 0.4933 0.575 0.432 0.000 0.000 0.064 0.000 0.504
#> GSM601855 3 0.2488 0.825 0.000 0.000 0.880 0.044 0.000 0.076
#> GSM601865 5 0.2724 0.905 0.000 0.000 0.000 0.052 0.864 0.084
#> GSM601756 4 0.4516 0.922 0.000 0.260 0.000 0.668 0.072 0.000
#> GSM601786 5 0.0000 0.975 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601796 1 0.4800 -0.567 0.500 0.000 0.000 0.052 0.000 0.448
#> GSM601801 4 0.4516 0.922 0.000 0.260 0.000 0.668 0.072 0.000
#> GSM601831 1 0.2303 0.639 0.904 0.000 0.024 0.020 0.000 0.052
#> GSM601841 3 0.1820 0.816 0.056 0.000 0.924 0.008 0.000 0.012
#> GSM601846 2 0.5396 0.532 0.000 0.564 0.000 0.152 0.000 0.284
#> GSM601861 5 0.0000 0.975 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601871 3 0.3227 0.790 0.000 0.000 0.824 0.060 0.000 0.116
#> GSM601751 4 0.4738 0.922 0.000 0.268 0.000 0.660 0.060 0.012
#> GSM601761 1 0.1812 0.623 0.912 0.000 0.000 0.008 0.000 0.080
#> GSM601766 2 0.0865 0.870 0.000 0.964 0.000 0.000 0.000 0.036
#> GSM601771 4 0.5087 0.830 0.008 0.160 0.000 0.700 0.108 0.024
#> GSM601776 1 0.3862 -0.558 0.524 0.000 0.000 0.000 0.000 0.476
#> GSM601781 3 0.6668 0.245 0.156 0.000 0.472 0.072 0.000 0.300
#> GSM601791 1 0.1908 0.629 0.900 0.000 0.000 0.004 0.000 0.096
#> GSM601806 4 0.4892 0.685 0.000 0.112 0.000 0.640 0.248 0.000
#> GSM601811 3 0.0146 0.857 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM601816 6 0.3867 0.565 0.488 0.000 0.000 0.000 0.000 0.512
#> GSM601821 5 0.0000 0.975 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601826 6 0.5371 0.475 0.360 0.000 0.000 0.120 0.000 0.520
#> GSM601836 2 0.5135 0.597 0.000 0.616 0.000 0.144 0.000 0.240
#> GSM601851 1 0.3868 -0.569 0.504 0.000 0.000 0.000 0.000 0.496
#> GSM601856 1 0.4839 0.370 0.684 0.000 0.220 0.020 0.000 0.076
#> GSM601866 3 0.0146 0.856 0.000 0.000 0.996 0.004 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
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 time(p) gender(p) k
#> ATC:kmeans 125 0.293 0.399 2
#> ATC:kmeans 105 0.400 0.472 3
#> ATC:kmeans 121 0.642 0.744 4
#> ATC:kmeans 118 0.739 0.756 5
#> ATC:kmeans 108 0.907 0.452 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 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.5029 0.498 0.498
#> 3 3 0.765 0.880 0.911 0.2847 0.829 0.665
#> 4 4 0.966 0.953 0.980 0.1549 0.870 0.648
#> 5 5 0.846 0.834 0.882 0.0509 0.955 0.829
#> 6 6 0.846 0.831 0.880 0.0452 0.932 0.711
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
#> GSM601752 2 0 1 0 1
#> GSM601782 1 0 1 1 0
#> GSM601792 1 0 1 1 0
#> GSM601797 1 0 1 1 0
#> GSM601827 1 0 1 1 0
#> GSM601837 2 0 1 0 1
#> GSM601842 2 0 1 0 1
#> GSM601857 1 0 1 1 0
#> GSM601867 1 0 1 1 0
#> GSM601747 1 0 1 1 0
#> GSM601757 1 0 1 1 0
#> GSM601762 2 0 1 0 1
#> GSM601767 2 0 1 0 1
#> GSM601772 2 0 1 0 1
#> GSM601777 1 0 1 1 0
#> GSM601787 1 0 1 1 0
#> GSM601802 2 0 1 0 1
#> GSM601807 1 0 1 1 0
#> GSM601812 1 0 1 1 0
#> GSM601817 1 0 1 1 0
#> GSM601822 1 0 1 1 0
#> GSM601832 2 0 1 0 1
#> GSM601847 1 0 1 1 0
#> GSM601852 1 0 1 1 0
#> GSM601862 1 0 1 1 0
#> GSM601753 2 0 1 0 1
#> GSM601783 1 0 1 1 0
#> GSM601793 1 0 1 1 0
#> GSM601798 2 0 1 0 1
#> GSM601828 1 0 1 1 0
#> GSM601838 2 0 1 0 1
#> GSM601843 2 0 1 0 1
#> GSM601858 2 0 1 0 1
#> GSM601868 1 0 1 1 0
#> GSM601748 1 0 1 1 0
#> GSM601758 1 0 1 1 0
#> GSM601763 2 0 1 0 1
#> GSM601768 2 0 1 0 1
#> GSM601773 2 0 1 0 1
#> GSM601778 1 0 1 1 0
#> GSM601788 2 0 1 0 1
#> GSM601803 2 0 1 0 1
#> GSM601808 1 0 1 1 0
#> GSM601813 1 0 1 1 0
#> GSM601818 1 0 1 1 0
#> GSM601823 1 0 1 1 0
#> GSM601833 2 0 1 0 1
#> GSM601848 1 0 1 1 0
#> GSM601853 1 0 1 1 0
#> GSM601863 1 0 1 1 0
#> GSM601754 2 0 1 0 1
#> GSM601784 2 0 1 0 1
#> GSM601794 1 0 1 1 0
#> GSM601799 2 0 1 0 1
#> GSM601829 1 0 1 1 0
#> GSM601839 2 0 1 0 1
#> GSM601844 1 0 1 1 0
#> GSM601859 2 0 1 0 1
#> GSM601869 1 0 1 1 0
#> GSM601749 1 0 1 1 0
#> GSM601759 1 0 1 1 0
#> GSM601764 2 0 1 0 1
#> GSM601769 2 0 1 0 1
#> GSM601774 2 0 1 0 1
#> GSM601779 1 0 1 1 0
#> GSM601789 2 0 1 0 1
#> GSM601804 2 0 1 0 1
#> GSM601809 1 0 1 1 0
#> GSM601814 2 0 1 0 1
#> GSM601819 1 0 1 1 0
#> GSM601824 2 0 1 0 1
#> GSM601834 2 0 1 0 1
#> GSM601849 1 0 1 1 0
#> GSM601854 1 0 1 1 0
#> GSM601864 2 0 1 0 1
#> GSM601755 2 0 1 0 1
#> GSM601785 2 0 1 0 1
#> GSM601795 1 0 1 1 0
#> GSM601800 2 0 1 0 1
#> GSM601830 1 0 1 1 0
#> GSM601840 2 0 1 0 1
#> GSM601845 2 0 1 0 1
#> GSM601860 2 0 1 0 1
#> GSM601870 1 0 1 1 0
#> GSM601750 1 0 1 1 0
#> GSM601760 1 0 1 1 0
#> GSM601765 2 0 1 0 1
#> GSM601770 2 0 1 0 1
#> GSM601775 2 0 1 0 1
#> GSM601780 1 0 1 1 0
#> GSM601790 2 0 1 0 1
#> GSM601805 2 0 1 0 1
#> GSM601810 1 0 1 1 0
#> GSM601815 2 0 1 0 1
#> GSM601820 1 0 1 1 0
#> GSM601825 2 0 1 0 1
#> GSM601835 2 0 1 0 1
#> GSM601850 1 0 1 1 0
#> GSM601855 1 0 1 1 0
#> GSM601865 2 0 1 0 1
#> GSM601756 2 0 1 0 1
#> GSM601786 2 0 1 0 1
#> GSM601796 1 0 1 1 0
#> GSM601801 2 0 1 0 1
#> GSM601831 1 0 1 1 0
#> GSM601841 1 0 1 1 0
#> GSM601846 2 0 1 0 1
#> GSM601861 2 0 1 0 1
#> GSM601871 1 0 1 1 0
#> GSM601751 2 0 1 0 1
#> GSM601761 1 0 1 1 0
#> GSM601766 2 0 1 0 1
#> GSM601771 2 0 1 0 1
#> GSM601776 1 0 1 1 0
#> GSM601781 1 0 1 1 0
#> GSM601791 1 0 1 1 0
#> GSM601806 2 0 1 0 1
#> GSM601811 1 0 1 1 0
#> GSM601816 1 0 1 1 0
#> GSM601821 2 0 1 0 1
#> GSM601826 1 0 1 1 0
#> GSM601836 2 0 1 0 1
#> GSM601851 1 0 1 1 0
#> GSM601856 1 0 1 1 0
#> GSM601866 1 0 1 1 0
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601752 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601782 1 0.2625 0.862 0.916 0.000 0.084
#> GSM601792 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601797 3 0.5327 0.864 0.272 0.000 0.728
#> GSM601827 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601837 2 0.6302 0.479 0.000 0.520 0.480
#> GSM601842 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601857 3 0.5291 0.869 0.268 0.000 0.732
#> GSM601867 3 0.0000 0.751 0.000 0.000 1.000
#> GSM601747 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601757 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601762 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601767 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601772 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601777 3 0.5291 0.869 0.268 0.000 0.732
#> GSM601787 3 0.0000 0.751 0.000 0.000 1.000
#> GSM601802 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601807 3 0.5016 0.864 0.240 0.000 0.760
#> GSM601812 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601817 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601822 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601832 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601847 1 0.4002 0.734 0.840 0.000 0.160
#> GSM601852 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601862 3 0.5291 0.869 0.268 0.000 0.732
#> GSM601753 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601783 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601793 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601798 2 0.4887 0.823 0.000 0.772 0.228
#> GSM601828 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601838 2 0.5327 0.797 0.000 0.728 0.272
#> GSM601843 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601858 2 0.5327 0.797 0.000 0.728 0.272
#> GSM601868 3 0.5291 0.869 0.268 0.000 0.732
#> GSM601748 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601758 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601763 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601768 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601773 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601778 3 0.5431 0.849 0.284 0.000 0.716
#> GSM601788 2 0.5016 0.817 0.000 0.760 0.240
#> GSM601803 2 0.4931 0.821 0.000 0.768 0.232
#> GSM601808 3 0.5291 0.869 0.268 0.000 0.732
#> GSM601813 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601818 3 0.5291 0.869 0.268 0.000 0.732
#> GSM601823 1 0.5016 0.607 0.760 0.240 0.000
#> GSM601833 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601848 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601853 1 0.6305 -0.359 0.516 0.000 0.484
#> GSM601863 3 0.5291 0.869 0.268 0.000 0.732
#> GSM601754 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601784 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601794 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601799 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601829 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601839 2 0.5327 0.797 0.000 0.728 0.272
#> GSM601844 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601859 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601869 3 0.5291 0.869 0.268 0.000 0.732
#> GSM601749 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601759 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601764 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601769 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601774 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601779 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601789 2 0.5291 0.800 0.000 0.732 0.268
#> GSM601804 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601809 3 0.5016 0.864 0.240 0.000 0.760
#> GSM601814 2 0.5291 0.800 0.000 0.732 0.268
#> GSM601819 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601824 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601834 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601849 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601854 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601864 3 0.0000 0.751 0.000 0.000 1.000
#> GSM601755 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601785 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601795 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601800 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601830 3 0.5291 0.869 0.268 0.000 0.732
#> GSM601840 2 0.1031 0.908 0.000 0.976 0.024
#> GSM601845 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601860 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601870 3 0.0000 0.751 0.000 0.000 1.000
#> GSM601750 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601760 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601765 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601770 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601775 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601780 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601790 2 0.5291 0.800 0.000 0.732 0.268
#> GSM601805 2 0.4931 0.821 0.000 0.768 0.232
#> GSM601810 3 0.5291 0.869 0.268 0.000 0.732
#> GSM601815 2 0.5327 0.797 0.000 0.728 0.272
#> GSM601820 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601825 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601835 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601850 1 0.0237 0.963 0.996 0.000 0.004
#> GSM601855 3 0.5016 0.864 0.240 0.000 0.760
#> GSM601865 3 0.0000 0.751 0.000 0.000 1.000
#> GSM601756 2 0.4931 0.821 0.000 0.768 0.232
#> GSM601786 3 0.0000 0.751 0.000 0.000 1.000
#> GSM601796 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601801 2 0.4887 0.823 0.000 0.772 0.228
#> GSM601831 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601841 3 0.5291 0.869 0.268 0.000 0.732
#> GSM601846 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601861 2 0.5291 0.800 0.000 0.732 0.268
#> GSM601871 3 0.0000 0.751 0.000 0.000 1.000
#> GSM601751 2 0.5216 0.805 0.000 0.740 0.260
#> GSM601761 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601766 2 0.0000 0.916 0.000 1.000 0.000
#> GSM601771 3 0.0000 0.751 0.000 0.000 1.000
#> GSM601776 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601781 3 0.5291 0.869 0.268 0.000 0.732
#> GSM601791 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601806 2 0.5291 0.800 0.000 0.732 0.268
#> GSM601811 3 0.5016 0.864 0.240 0.000 0.760
#> GSM601816 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601821 2 0.5327 0.797 0.000 0.728 0.272
#> GSM601826 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601836 2 0.0237 0.914 0.004 0.996 0.000
#> GSM601851 1 0.0000 0.968 1.000 0.000 0.000
#> GSM601856 3 0.5327 0.864 0.272 0.000 0.728
#> GSM601866 3 0.5291 0.869 0.268 0.000 0.732
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601752 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601782 3 0.3907 0.677 0.232 0.000 0.768 0.000
#> GSM601792 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601797 3 0.0336 0.981 0.008 0.000 0.992 0.000
#> GSM601827 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601837 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> GSM601842 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601857 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM601867 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM601747 1 0.1389 0.930 0.952 0.000 0.048 0.000
#> GSM601757 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601762 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601767 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601772 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601777 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM601787 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM601802 2 0.3444 0.768 0.000 0.816 0.000 0.184
#> GSM601807 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM601812 1 0.2647 0.865 0.880 0.000 0.120 0.000
#> GSM601817 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601822 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601832 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601847 1 0.4989 0.094 0.528 0.000 0.472 0.000
#> GSM601852 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601862 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM601753 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601783 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601793 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601798 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> GSM601828 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601838 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> GSM601843 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601858 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> GSM601868 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM601748 1 0.0188 0.963 0.996 0.000 0.004 0.000
#> GSM601758 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601763 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601768 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601773 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601778 3 0.0336 0.981 0.008 0.000 0.992 0.000
#> GSM601788 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> GSM601803 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> GSM601808 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM601813 1 0.3123 0.826 0.844 0.000 0.156 0.000
#> GSM601818 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM601823 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601833 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601848 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601853 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM601863 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM601754 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601784 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601794 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601799 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601829 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601839 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> GSM601844 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601859 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601869 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM601749 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601759 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601764 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601769 4 0.4250 0.627 0.000 0.276 0.000 0.724
#> GSM601774 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601779 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601789 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> GSM601804 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601809 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM601814 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> GSM601819 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601824 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601834 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601849 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601854 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601864 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> GSM601755 4 0.3907 0.703 0.000 0.232 0.000 0.768
#> GSM601785 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601795 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601800 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601830 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM601840 2 0.4250 0.610 0.000 0.724 0.000 0.276
#> GSM601845 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601860 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601870 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM601750 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601760 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601765 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601770 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601775 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601780 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601790 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> GSM601805 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> GSM601810 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM601815 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> GSM601820 1 0.3074 0.831 0.848 0.000 0.152 0.000
#> GSM601825 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601835 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601850 1 0.1022 0.943 0.968 0.000 0.032 0.000
#> GSM601855 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM601865 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> GSM601756 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> GSM601786 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> GSM601796 1 0.2921 0.843 0.860 0.000 0.140 0.000
#> GSM601801 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> GSM601831 1 0.3074 0.831 0.848 0.000 0.152 0.000
#> GSM601841 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM601846 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601861 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> GSM601871 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM601751 4 0.1302 0.935 0.000 0.044 0.000 0.956
#> GSM601761 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601766 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601771 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> GSM601776 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601781 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM601791 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601806 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> GSM601811 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM601816 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601821 4 0.0000 0.974 0.000 0.000 0.000 1.000
#> GSM601826 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601836 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> GSM601851 1 0.0000 0.966 1.000 0.000 0.000 0.000
#> GSM601856 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM601866 3 0.0000 0.989 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601752 4 0.5997 0.594 0.072 0.312 0.000 0.588 0.028
#> GSM601782 3 0.6296 0.220 0.200 0.000 0.528 0.000 0.272
#> GSM601792 1 0.1341 0.775 0.944 0.000 0.000 0.000 0.056
#> GSM601797 3 0.7014 0.396 0.216 0.000 0.540 0.196 0.048
#> GSM601827 1 0.3636 0.824 0.728 0.000 0.000 0.000 0.272
#> GSM601837 5 0.4150 0.984 0.000 0.000 0.000 0.388 0.612
#> GSM601842 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> GSM601857 3 0.0000 0.930 0.000 0.000 1.000 0.000 0.000
#> GSM601867 3 0.0000 0.930 0.000 0.000 1.000 0.000 0.000
#> GSM601747 1 0.4268 0.816 0.708 0.000 0.024 0.000 0.268
#> GSM601757 1 0.3612 0.824 0.732 0.000 0.000 0.000 0.268
#> GSM601762 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> GSM601767 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> GSM601772 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> GSM601777 3 0.1205 0.905 0.004 0.000 0.956 0.000 0.040
#> GSM601787 3 0.0000 0.930 0.000 0.000 1.000 0.000 0.000
#> GSM601802 4 0.3895 0.637 0.000 0.320 0.000 0.680 0.000
#> GSM601807 3 0.0000 0.930 0.000 0.000 1.000 0.000 0.000
#> GSM601812 1 0.5778 0.741 0.596 0.000 0.132 0.000 0.272
#> GSM601817 1 0.3661 0.824 0.724 0.000 0.000 0.000 0.276
#> GSM601822 1 0.1043 0.779 0.960 0.000 0.000 0.000 0.040
#> GSM601832 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> GSM601847 1 0.5179 0.430 0.680 0.000 0.252 0.020 0.048
#> GSM601852 1 0.4620 0.792 0.652 0.000 0.000 0.028 0.320
#> GSM601862 3 0.0000 0.930 0.000 0.000 1.000 0.000 0.000
#> GSM601753 2 0.0162 0.954 0.000 0.996 0.000 0.004 0.000
#> GSM601783 1 0.3612 0.824 0.732 0.000 0.000 0.000 0.268
#> GSM601793 1 0.1341 0.775 0.944 0.000 0.000 0.000 0.056
#> GSM601798 4 0.0880 0.606 0.000 0.032 0.000 0.968 0.000
#> GSM601828 1 0.3636 0.824 0.728 0.000 0.000 0.000 0.272
#> GSM601838 5 0.4150 0.984 0.000 0.000 0.000 0.388 0.612
#> GSM601843 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> GSM601858 5 0.4150 0.984 0.000 0.000 0.000 0.388 0.612
#> GSM601868 3 0.0000 0.930 0.000 0.000 1.000 0.000 0.000
#> GSM601748 1 0.3612 0.824 0.732 0.000 0.000 0.000 0.268
#> GSM601758 1 0.3612 0.824 0.732 0.000 0.000 0.000 0.268
#> GSM601763 2 0.2054 0.901 0.000 0.920 0.000 0.028 0.052
#> GSM601768 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> GSM601773 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> GSM601778 3 0.4333 0.688 0.212 0.000 0.740 0.000 0.048
#> GSM601788 5 0.4547 0.957 0.000 0.012 0.000 0.400 0.588
#> GSM601803 4 0.0794 0.602 0.000 0.028 0.000 0.972 0.000
#> GSM601808 3 0.0000 0.930 0.000 0.000 1.000 0.000 0.000
#> GSM601813 1 0.6220 0.682 0.540 0.000 0.188 0.000 0.272
#> GSM601818 3 0.1478 0.881 0.000 0.000 0.936 0.000 0.064
#> GSM601823 1 0.2325 0.753 0.904 0.000 0.000 0.028 0.068
#> GSM601833 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> GSM601848 1 0.0290 0.791 0.992 0.000 0.000 0.000 0.008
#> GSM601853 3 0.3756 0.675 0.008 0.000 0.744 0.000 0.248
#> GSM601863 3 0.0000 0.930 0.000 0.000 1.000 0.000 0.000
#> GSM601754 4 0.5960 0.593 0.068 0.316 0.000 0.588 0.028
#> GSM601784 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> GSM601794 1 0.1341 0.775 0.944 0.000 0.000 0.000 0.056
#> GSM601799 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> GSM601829 1 0.2450 0.759 0.896 0.000 0.000 0.028 0.076
#> GSM601839 5 0.4150 0.984 0.000 0.000 0.000 0.388 0.612
#> GSM601844 1 0.4397 0.794 0.696 0.000 0.000 0.028 0.276
#> GSM601859 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> GSM601869 3 0.0000 0.930 0.000 0.000 1.000 0.000 0.000
#> GSM601749 1 0.3636 0.824 0.728 0.000 0.000 0.000 0.272
#> GSM601759 1 0.3612 0.824 0.732 0.000 0.000 0.000 0.268
#> GSM601764 2 0.2124 0.897 0.000 0.916 0.000 0.028 0.056
#> GSM601769 2 0.3659 0.586 0.000 0.768 0.000 0.220 0.012
#> GSM601774 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> GSM601779 1 0.0290 0.791 0.992 0.000 0.000 0.000 0.008
#> GSM601789 5 0.4150 0.984 0.000 0.000 0.000 0.388 0.612
#> GSM601804 4 0.5627 0.532 0.040 0.364 0.000 0.572 0.024
#> GSM601809 3 0.0000 0.930 0.000 0.000 1.000 0.000 0.000
#> GSM601814 5 0.4150 0.984 0.000 0.000 0.000 0.388 0.612
#> GSM601819 1 0.3636 0.824 0.728 0.000 0.000 0.000 0.272
#> GSM601824 2 0.2124 0.897 0.000 0.916 0.000 0.028 0.056
#> GSM601834 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> GSM601849 1 0.0290 0.791 0.992 0.000 0.000 0.000 0.008
#> GSM601854 1 0.3636 0.824 0.728 0.000 0.000 0.000 0.272
#> GSM601864 5 0.4150 0.984 0.000 0.000 0.000 0.388 0.612
#> GSM601755 4 0.2516 0.646 0.000 0.140 0.000 0.860 0.000
#> GSM601785 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> GSM601795 1 0.4707 0.506 0.708 0.000 0.000 0.228 0.064
#> GSM601800 4 0.4219 0.479 0.000 0.416 0.000 0.584 0.000
#> GSM601830 3 0.0290 0.926 0.000 0.000 0.992 0.000 0.008
#> GSM601840 2 0.4406 0.613 0.000 0.764 0.000 0.108 0.128
#> GSM601845 2 0.2124 0.897 0.000 0.916 0.000 0.028 0.056
#> GSM601860 2 0.0404 0.948 0.000 0.988 0.000 0.000 0.012
#> GSM601870 3 0.0880 0.906 0.000 0.000 0.968 0.000 0.032
#> GSM601750 1 0.3636 0.824 0.728 0.000 0.000 0.000 0.272
#> GSM601760 1 0.3612 0.824 0.732 0.000 0.000 0.000 0.268
#> GSM601765 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> GSM601770 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> GSM601775 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> GSM601780 1 0.0162 0.791 0.996 0.000 0.000 0.000 0.004
#> GSM601790 5 0.4150 0.984 0.000 0.000 0.000 0.388 0.612
#> GSM601805 4 0.0794 0.602 0.000 0.028 0.000 0.972 0.000
#> GSM601810 3 0.0000 0.930 0.000 0.000 1.000 0.000 0.000
#> GSM601815 5 0.4150 0.984 0.000 0.000 0.000 0.388 0.612
#> GSM601820 1 0.6138 0.695 0.552 0.000 0.176 0.000 0.272
#> GSM601825 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> GSM601835 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> GSM601850 1 0.1701 0.766 0.936 0.000 0.016 0.000 0.048
#> GSM601855 3 0.0000 0.930 0.000 0.000 1.000 0.000 0.000
#> GSM601865 5 0.4150 0.984 0.000 0.000 0.000 0.388 0.612
#> GSM601756 4 0.0794 0.602 0.000 0.028 0.000 0.972 0.000
#> GSM601786 5 0.4150 0.984 0.000 0.000 0.000 0.388 0.612
#> GSM601796 1 0.4010 0.655 0.784 0.000 0.160 0.000 0.056
#> GSM601801 4 0.1043 0.611 0.000 0.040 0.000 0.960 0.000
#> GSM601831 1 0.6127 0.699 0.552 0.000 0.172 0.000 0.276
#> GSM601841 3 0.0000 0.930 0.000 0.000 1.000 0.000 0.000
#> GSM601846 2 0.2409 0.890 0.008 0.908 0.000 0.028 0.056
#> GSM601861 5 0.4150 0.984 0.000 0.000 0.000 0.388 0.612
#> GSM601871 3 0.0000 0.930 0.000 0.000 1.000 0.000 0.000
#> GSM601751 5 0.5953 0.756 0.000 0.112 0.000 0.384 0.504
#> GSM601761 1 0.3586 0.824 0.736 0.000 0.000 0.000 0.264
#> GSM601766 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> GSM601771 5 0.4150 0.984 0.000 0.000 0.000 0.388 0.612
#> GSM601776 1 0.0000 0.792 1.000 0.000 0.000 0.000 0.000
#> GSM601781 3 0.1124 0.908 0.004 0.000 0.960 0.000 0.036
#> GSM601791 1 0.3636 0.824 0.728 0.000 0.000 0.000 0.272
#> GSM601806 4 0.1121 0.510 0.000 0.000 0.000 0.956 0.044
#> GSM601811 3 0.0000 0.930 0.000 0.000 1.000 0.000 0.000
#> GSM601816 1 0.0609 0.788 0.980 0.000 0.000 0.000 0.020
#> GSM601821 5 0.4150 0.984 0.000 0.000 0.000 0.388 0.612
#> GSM601826 1 0.2388 0.755 0.900 0.000 0.000 0.028 0.072
#> GSM601836 2 0.2124 0.897 0.000 0.916 0.000 0.028 0.056
#> GSM601851 1 0.0290 0.791 0.992 0.000 0.000 0.000 0.008
#> GSM601856 3 0.0290 0.926 0.000 0.000 0.992 0.000 0.008
#> GSM601866 3 0.0000 0.930 0.000 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601752 4 0.1610 0.899 0.000 0.084 0.000 0.916 0.000 0.000
#> GSM601782 1 0.3778 0.524 0.708 0.000 0.272 0.000 0.000 0.020
#> GSM601792 6 0.3711 0.709 0.260 0.000 0.000 0.020 0.000 0.720
#> GSM601797 6 0.4683 0.338 0.004 0.000 0.312 0.056 0.000 0.628
#> GSM601827 1 0.0547 0.877 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM601837 5 0.0000 0.972 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601842 2 0.0508 0.913 0.000 0.984 0.000 0.004 0.000 0.012
#> GSM601857 3 0.0000 0.946 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601867 3 0.0622 0.938 0.000 0.000 0.980 0.008 0.012 0.000
#> GSM601747 1 0.2113 0.847 0.908 0.000 0.028 0.004 0.000 0.060
#> GSM601757 1 0.0909 0.881 0.968 0.000 0.012 0.000 0.000 0.020
#> GSM601762 2 0.0405 0.912 0.000 0.988 0.000 0.008 0.004 0.000
#> GSM601767 2 0.0146 0.915 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM601772 2 0.0146 0.915 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM601777 3 0.2389 0.838 0.000 0.000 0.864 0.008 0.000 0.128
#> GSM601787 3 0.0622 0.938 0.000 0.000 0.980 0.008 0.012 0.000
#> GSM601802 4 0.2346 0.898 0.000 0.124 0.000 0.868 0.008 0.000
#> GSM601807 3 0.0146 0.945 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM601812 1 0.2145 0.827 0.900 0.000 0.072 0.000 0.000 0.028
#> GSM601817 1 0.0603 0.881 0.980 0.000 0.004 0.000 0.000 0.016
#> GSM601822 6 0.3290 0.700 0.252 0.000 0.000 0.004 0.000 0.744
#> GSM601832 2 0.0260 0.915 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM601847 6 0.4276 0.680 0.176 0.000 0.052 0.024 0.000 0.748
#> GSM601852 1 0.3874 0.561 0.732 0.000 0.000 0.040 0.000 0.228
#> GSM601862 3 0.0000 0.946 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601753 2 0.2946 0.744 0.000 0.812 0.000 0.176 0.000 0.012
#> GSM601783 1 0.0363 0.883 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM601793 6 0.3840 0.701 0.284 0.000 0.000 0.020 0.000 0.696
#> GSM601798 4 0.2629 0.916 0.000 0.040 0.000 0.868 0.092 0.000
#> GSM601828 1 0.0260 0.880 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM601838 5 0.0000 0.972 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601843 2 0.0291 0.915 0.000 0.992 0.000 0.004 0.000 0.004
#> GSM601858 5 0.0146 0.970 0.000 0.000 0.000 0.000 0.996 0.004
#> GSM601868 3 0.0000 0.946 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601748 1 0.0508 0.884 0.984 0.000 0.012 0.000 0.000 0.004
#> GSM601758 1 0.0820 0.882 0.972 0.000 0.012 0.000 0.000 0.016
#> GSM601763 2 0.3413 0.804 0.000 0.812 0.000 0.080 0.000 0.108
#> GSM601768 2 0.0146 0.915 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM601773 2 0.0146 0.915 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM601778 6 0.4456 0.259 0.008 0.000 0.360 0.024 0.000 0.608
#> GSM601788 5 0.2883 0.834 0.000 0.092 0.000 0.040 0.860 0.008
#> GSM601803 4 0.2558 0.911 0.000 0.028 0.000 0.868 0.104 0.000
#> GSM601808 3 0.0000 0.946 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601813 1 0.2462 0.798 0.876 0.000 0.096 0.000 0.000 0.028
#> GSM601818 3 0.2491 0.776 0.164 0.000 0.836 0.000 0.000 0.000
#> GSM601823 6 0.4907 0.529 0.248 0.004 0.000 0.100 0.000 0.648
#> GSM601833 2 0.0146 0.915 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM601848 6 0.3847 0.623 0.456 0.000 0.000 0.000 0.000 0.544
#> GSM601853 3 0.3854 0.127 0.464 0.000 0.536 0.000 0.000 0.000
#> GSM601863 3 0.0000 0.946 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601754 4 0.1556 0.901 0.000 0.080 0.000 0.920 0.000 0.000
#> GSM601784 2 0.0146 0.915 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM601794 6 0.3734 0.708 0.264 0.000 0.000 0.020 0.000 0.716
#> GSM601799 2 0.0993 0.905 0.000 0.964 0.000 0.024 0.000 0.012
#> GSM601829 6 0.4682 0.526 0.284 0.000 0.000 0.076 0.000 0.640
#> GSM601839 5 0.0000 0.972 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601844 1 0.4931 0.294 0.592 0.000 0.000 0.084 0.000 0.324
#> GSM601859 2 0.0146 0.915 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM601869 3 0.0000 0.946 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601749 1 0.0937 0.856 0.960 0.000 0.000 0.000 0.000 0.040
#> GSM601759 1 0.0909 0.881 0.968 0.000 0.012 0.000 0.000 0.020
#> GSM601764 2 0.4757 0.707 0.012 0.696 0.000 0.100 0.000 0.192
#> GSM601769 2 0.1643 0.864 0.000 0.924 0.000 0.008 0.068 0.000
#> GSM601774 2 0.0146 0.915 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM601779 6 0.3950 0.636 0.432 0.000 0.000 0.004 0.000 0.564
#> GSM601789 5 0.0000 0.972 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601804 4 0.2121 0.882 0.000 0.096 0.000 0.892 0.000 0.012
#> GSM601809 3 0.0000 0.946 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601814 5 0.0146 0.969 0.000 0.000 0.000 0.004 0.996 0.000
#> GSM601819 1 0.0937 0.856 0.960 0.000 0.000 0.000 0.000 0.040
#> GSM601824 2 0.4757 0.707 0.012 0.696 0.000 0.100 0.000 0.192
#> GSM601834 2 0.0146 0.915 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM601849 6 0.3944 0.637 0.428 0.000 0.000 0.004 0.000 0.568
#> GSM601854 1 0.0508 0.882 0.984 0.000 0.012 0.000 0.000 0.004
#> GSM601864 5 0.0260 0.968 0.000 0.000 0.000 0.008 0.992 0.000
#> GSM601755 4 0.2558 0.909 0.000 0.104 0.000 0.868 0.028 0.000
#> GSM601785 2 0.0146 0.915 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM601795 6 0.4025 0.704 0.232 0.000 0.000 0.048 0.000 0.720
#> GSM601800 4 0.2178 0.893 0.000 0.132 0.000 0.868 0.000 0.000
#> GSM601830 3 0.1036 0.929 0.008 0.000 0.964 0.004 0.000 0.024
#> GSM601840 2 0.3693 0.665 0.000 0.756 0.000 0.016 0.216 0.012
#> GSM601845 2 0.4786 0.703 0.012 0.692 0.000 0.100 0.000 0.196
#> GSM601860 2 0.1053 0.899 0.000 0.964 0.000 0.004 0.020 0.012
#> GSM601870 3 0.0891 0.929 0.000 0.000 0.968 0.008 0.024 0.000
#> GSM601750 1 0.0363 0.883 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM601760 1 0.0909 0.881 0.968 0.000 0.012 0.000 0.000 0.020
#> GSM601765 2 0.0508 0.913 0.000 0.984 0.000 0.004 0.000 0.012
#> GSM601770 2 0.0146 0.915 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM601775 2 0.0717 0.909 0.000 0.976 0.000 0.016 0.000 0.008
#> GSM601780 6 0.3833 0.639 0.444 0.000 0.000 0.000 0.000 0.556
#> GSM601790 5 0.0000 0.972 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601805 4 0.2558 0.911 0.000 0.028 0.000 0.868 0.104 0.000
#> GSM601810 3 0.0000 0.946 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601815 5 0.0000 0.972 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601820 1 0.1983 0.831 0.908 0.000 0.072 0.000 0.000 0.020
#> GSM601825 2 0.0146 0.915 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM601835 2 0.0405 0.915 0.000 0.988 0.000 0.004 0.000 0.008
#> GSM601850 6 0.3761 0.704 0.228 0.000 0.008 0.020 0.000 0.744
#> GSM601855 3 0.0146 0.945 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM601865 5 0.0260 0.968 0.000 0.000 0.000 0.008 0.992 0.000
#> GSM601756 4 0.2558 0.911 0.000 0.028 0.000 0.868 0.104 0.000
#> GSM601786 5 0.0146 0.970 0.000 0.000 0.000 0.004 0.996 0.000
#> GSM601796 6 0.4096 0.697 0.268 0.000 0.012 0.020 0.000 0.700
#> GSM601801 4 0.2609 0.915 0.000 0.036 0.000 0.868 0.096 0.000
#> GSM601831 1 0.2176 0.818 0.896 0.000 0.080 0.000 0.000 0.024
#> GSM601841 3 0.0000 0.946 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601846 2 0.4945 0.678 0.012 0.668 0.000 0.100 0.000 0.220
#> GSM601861 5 0.0000 0.972 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601871 3 0.0622 0.938 0.000 0.000 0.980 0.008 0.012 0.000
#> GSM601751 5 0.3705 0.739 0.000 0.144 0.000 0.056 0.792 0.008
#> GSM601761 1 0.0937 0.869 0.960 0.000 0.000 0.000 0.000 0.040
#> GSM601766 2 0.0725 0.910 0.000 0.976 0.000 0.012 0.000 0.012
#> GSM601771 5 0.0291 0.969 0.000 0.000 0.000 0.004 0.992 0.004
#> GSM601776 6 0.3862 0.601 0.476 0.000 0.000 0.000 0.000 0.524
#> GSM601781 3 0.2320 0.837 0.000 0.000 0.864 0.004 0.000 0.132
#> GSM601791 1 0.1124 0.869 0.956 0.000 0.008 0.000 0.000 0.036
#> GSM601806 4 0.2595 0.855 0.000 0.004 0.000 0.836 0.160 0.000
#> GSM601811 3 0.0000 0.946 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601816 6 0.3804 0.649 0.424 0.000 0.000 0.000 0.000 0.576
#> GSM601821 5 0.0000 0.972 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601826 6 0.4757 0.527 0.280 0.000 0.000 0.084 0.000 0.636
#> GSM601836 2 0.4786 0.703 0.012 0.692 0.000 0.100 0.000 0.196
#> GSM601851 6 0.3847 0.620 0.456 0.000 0.000 0.000 0.000 0.544
#> GSM601856 3 0.0937 0.919 0.040 0.000 0.960 0.000 0.000 0.000
#> GSM601866 3 0.0000 0.946 0.000 0.000 1.000 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 time(p) gender(p) k
#> ATC:skmeans 125 0.293 0.399 2
#> ATC:skmeans 123 0.563 0.513 3
#> ATC:skmeans 124 0.294 0.645 4
#> ATC:skmeans 121 0.877 0.606 5
#> ATC:skmeans 121 0.892 0.498 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 125 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 0.965 0.981 0.5021 0.498 0.498
#> 3 3 0.724 0.843 0.899 0.2198 0.898 0.797
#> 4 4 0.630 0.524 0.768 0.1629 0.795 0.539
#> 5 5 0.786 0.836 0.845 0.0958 0.850 0.541
#> 6 6 0.917 0.909 0.951 0.0660 0.897 0.585
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM601752 2 0.2423 0.955 0.040 0.960
#> GSM601782 1 0.2423 0.967 0.960 0.040
#> GSM601792 1 0.0000 0.979 1.000 0.000
#> GSM601797 1 0.0000 0.979 1.000 0.000
#> GSM601827 1 0.0000 0.979 1.000 0.000
#> GSM601837 2 0.0000 0.982 0.000 1.000
#> GSM601842 2 0.0000 0.982 0.000 1.000
#> GSM601857 1 0.2423 0.967 0.960 0.040
#> GSM601867 1 0.2423 0.967 0.960 0.040
#> GSM601747 1 0.4815 0.906 0.896 0.104
#> GSM601757 1 0.0000 0.979 1.000 0.000
#> GSM601762 2 0.0000 0.982 0.000 1.000
#> GSM601767 2 0.0000 0.982 0.000 1.000
#> GSM601772 2 0.0000 0.982 0.000 1.000
#> GSM601777 1 0.0000 0.979 1.000 0.000
#> GSM601787 1 0.2423 0.967 0.960 0.040
#> GSM601802 2 0.0000 0.982 0.000 1.000
#> GSM601807 1 0.2423 0.967 0.960 0.040
#> GSM601812 1 0.2236 0.968 0.964 0.036
#> GSM601817 1 0.2423 0.967 0.960 0.040
#> GSM601822 1 0.0000 0.979 1.000 0.000
#> GSM601832 2 0.0000 0.982 0.000 1.000
#> GSM601847 1 0.0000 0.979 1.000 0.000
#> GSM601852 1 0.0000 0.979 1.000 0.000
#> GSM601862 1 0.2423 0.967 0.960 0.040
#> GSM601753 2 0.0000 0.982 0.000 1.000
#> GSM601783 1 0.0000 0.979 1.000 0.000
#> GSM601793 1 0.0000 0.979 1.000 0.000
#> GSM601798 2 0.0000 0.982 0.000 1.000
#> GSM601828 1 0.0000 0.979 1.000 0.000
#> GSM601838 2 0.0000 0.982 0.000 1.000
#> GSM601843 2 0.0000 0.982 0.000 1.000
#> GSM601858 2 0.0000 0.982 0.000 1.000
#> GSM601868 1 0.2423 0.967 0.960 0.040
#> GSM601748 1 0.0000 0.979 1.000 0.000
#> GSM601758 1 0.0000 0.979 1.000 0.000
#> GSM601763 2 0.2423 0.955 0.040 0.960
#> GSM601768 2 0.0000 0.982 0.000 1.000
#> GSM601773 2 0.0000 0.982 0.000 1.000
#> GSM601778 1 0.0000 0.979 1.000 0.000
#> GSM601788 2 0.0000 0.982 0.000 1.000
#> GSM601803 2 0.0000 0.982 0.000 1.000
#> GSM601808 1 0.2423 0.967 0.960 0.040
#> GSM601813 1 0.0000 0.979 1.000 0.000
#> GSM601818 1 0.2423 0.967 0.960 0.040
#> GSM601823 2 0.2423 0.955 0.040 0.960
#> GSM601833 2 0.0000 0.982 0.000 1.000
#> GSM601848 1 0.0000 0.979 1.000 0.000
#> GSM601853 1 0.0000 0.979 1.000 0.000
#> GSM601863 1 0.0000 0.979 1.000 0.000
#> GSM601754 2 0.0000 0.982 0.000 1.000
#> GSM601784 2 0.0000 0.982 0.000 1.000
#> GSM601794 1 0.0000 0.979 1.000 0.000
#> GSM601799 2 0.2423 0.955 0.040 0.960
#> GSM601829 1 0.0000 0.979 1.000 0.000
#> GSM601839 2 0.0000 0.982 0.000 1.000
#> GSM601844 1 0.8555 0.600 0.720 0.280
#> GSM601859 2 0.0000 0.982 0.000 1.000
#> GSM601869 1 0.0000 0.979 1.000 0.000
#> GSM601749 1 0.0000 0.979 1.000 0.000
#> GSM601759 1 0.0000 0.979 1.000 0.000
#> GSM601764 2 0.2423 0.955 0.040 0.960
#> GSM601769 2 0.0000 0.982 0.000 1.000
#> GSM601774 2 0.0000 0.982 0.000 1.000
#> GSM601779 1 0.0000 0.979 1.000 0.000
#> GSM601789 2 0.0000 0.982 0.000 1.000
#> GSM601804 2 0.2423 0.955 0.040 0.960
#> GSM601809 1 0.2423 0.967 0.960 0.040
#> GSM601814 2 0.0000 0.982 0.000 1.000
#> GSM601819 1 0.0000 0.979 1.000 0.000
#> GSM601824 2 0.2423 0.955 0.040 0.960
#> GSM601834 2 0.0000 0.982 0.000 1.000
#> GSM601849 1 0.0000 0.979 1.000 0.000
#> GSM601854 1 0.0000 0.979 1.000 0.000
#> GSM601864 2 0.9393 0.427 0.356 0.644
#> GSM601755 2 0.0000 0.982 0.000 1.000
#> GSM601785 2 0.0000 0.982 0.000 1.000
#> GSM601795 1 0.0938 0.973 0.988 0.012
#> GSM601800 2 0.0000 0.982 0.000 1.000
#> GSM601830 1 0.3114 0.955 0.944 0.056
#> GSM601840 2 0.0000 0.982 0.000 1.000
#> GSM601845 2 0.2423 0.955 0.040 0.960
#> GSM601860 2 0.0000 0.982 0.000 1.000
#> GSM601870 1 0.3114 0.955 0.944 0.056
#> GSM601750 1 0.0000 0.979 1.000 0.000
#> GSM601760 1 0.0000 0.979 1.000 0.000
#> GSM601765 2 0.0000 0.982 0.000 1.000
#> GSM601770 2 0.0000 0.982 0.000 1.000
#> GSM601775 2 0.0000 0.982 0.000 1.000
#> GSM601780 1 0.0000 0.979 1.000 0.000
#> GSM601790 2 0.0000 0.982 0.000 1.000
#> GSM601805 2 0.0000 0.982 0.000 1.000
#> GSM601810 1 0.2423 0.967 0.960 0.040
#> GSM601815 2 0.0000 0.982 0.000 1.000
#> GSM601820 1 0.1184 0.975 0.984 0.016
#> GSM601825 2 0.0000 0.982 0.000 1.000
#> GSM601835 2 0.0000 0.982 0.000 1.000
#> GSM601850 1 0.0000 0.979 1.000 0.000
#> GSM601855 1 0.2423 0.967 0.960 0.040
#> GSM601865 1 0.3274 0.952 0.940 0.060
#> GSM601756 2 0.0000 0.982 0.000 1.000
#> GSM601786 2 0.0000 0.982 0.000 1.000
#> GSM601796 1 0.0000 0.979 1.000 0.000
#> GSM601801 2 0.0000 0.982 0.000 1.000
#> GSM601831 1 0.0000 0.979 1.000 0.000
#> GSM601841 1 0.2423 0.967 0.960 0.040
#> GSM601846 2 0.2423 0.955 0.040 0.960
#> GSM601861 2 0.0000 0.982 0.000 1.000
#> GSM601871 1 0.2423 0.967 0.960 0.040
#> GSM601751 2 0.0000 0.982 0.000 1.000
#> GSM601761 1 0.0000 0.979 1.000 0.000
#> GSM601766 2 0.0000 0.982 0.000 1.000
#> GSM601771 2 0.8207 0.644 0.256 0.744
#> GSM601776 1 0.0000 0.979 1.000 0.000
#> GSM601781 1 0.0000 0.979 1.000 0.000
#> GSM601791 1 0.0000 0.979 1.000 0.000
#> GSM601806 2 0.0000 0.982 0.000 1.000
#> GSM601811 1 0.2423 0.967 0.960 0.040
#> GSM601816 1 0.0000 0.979 1.000 0.000
#> GSM601821 2 0.0000 0.982 0.000 1.000
#> GSM601826 1 0.0000 0.979 1.000 0.000
#> GSM601836 2 0.1843 0.963 0.028 0.972
#> GSM601851 1 0.0000 0.979 1.000 0.000
#> GSM601856 1 0.2423 0.967 0.960 0.040
#> GSM601866 1 0.2423 0.967 0.960 0.040
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601752 2 0.6336 0.672 0.180 0.756 0.064
#> GSM601782 1 0.0000 0.888 1.000 0.000 0.000
#> GSM601792 1 0.0000 0.888 1.000 0.000 0.000
#> GSM601797 1 0.1399 0.873 0.968 0.004 0.028
#> GSM601827 1 0.0000 0.888 1.000 0.000 0.000
#> GSM601837 3 0.5138 0.822 0.000 0.252 0.748
#> GSM601842 2 0.0237 0.931 0.000 0.996 0.004
#> GSM601857 1 0.4346 0.834 0.816 0.000 0.184
#> GSM601867 1 0.6140 0.607 0.596 0.000 0.404
#> GSM601747 1 0.0237 0.887 0.996 0.004 0.000
#> GSM601757 1 0.0000 0.888 1.000 0.000 0.000
#> GSM601762 2 0.0237 0.931 0.000 0.996 0.004
#> GSM601767 2 0.0237 0.931 0.000 0.996 0.004
#> GSM601772 2 0.0237 0.931 0.000 0.996 0.004
#> GSM601777 1 0.4605 0.825 0.796 0.000 0.204
#> GSM601787 1 0.6252 0.535 0.556 0.000 0.444
#> GSM601802 2 0.2165 0.916 0.000 0.936 0.064
#> GSM601807 1 0.6140 0.607 0.596 0.000 0.404
#> GSM601812 1 0.4002 0.844 0.840 0.000 0.160
#> GSM601817 1 0.4654 0.693 0.792 0.208 0.000
#> GSM601822 1 0.1399 0.873 0.968 0.004 0.028
#> GSM601832 2 0.0237 0.931 0.000 0.996 0.004
#> GSM601847 1 0.1399 0.873 0.968 0.004 0.028
#> GSM601852 1 0.0000 0.888 1.000 0.000 0.000
#> GSM601862 1 0.4842 0.812 0.776 0.000 0.224
#> GSM601753 2 0.1163 0.925 0.000 0.972 0.028
#> GSM601783 1 0.0000 0.888 1.000 0.000 0.000
#> GSM601793 1 0.0000 0.888 1.000 0.000 0.000
#> GSM601798 2 0.2584 0.913 0.008 0.928 0.064
#> GSM601828 1 0.0000 0.888 1.000 0.000 0.000
#> GSM601838 3 0.5138 0.822 0.000 0.252 0.748
#> GSM601843 2 0.0237 0.931 0.000 0.996 0.004
#> GSM601858 2 0.2165 0.916 0.000 0.936 0.064
#> GSM601868 1 0.4842 0.812 0.776 0.000 0.224
#> GSM601748 1 0.0000 0.888 1.000 0.000 0.000
#> GSM601758 1 0.0000 0.888 1.000 0.000 0.000
#> GSM601763 2 0.0237 0.931 0.000 0.996 0.004
#> GSM601768 2 0.0237 0.931 0.000 0.996 0.004
#> GSM601773 2 0.0237 0.931 0.000 0.996 0.004
#> GSM601778 1 0.1399 0.873 0.968 0.004 0.028
#> GSM601788 2 0.2165 0.916 0.000 0.936 0.064
#> GSM601803 2 0.4887 0.689 0.000 0.772 0.228
#> GSM601808 1 0.4842 0.812 0.776 0.000 0.224
#> GSM601813 1 0.4002 0.844 0.840 0.000 0.160
#> GSM601818 1 0.4002 0.844 0.840 0.000 0.160
#> GSM601823 2 0.6301 0.549 0.260 0.712 0.028
#> GSM601833 2 0.0237 0.931 0.000 0.996 0.004
#> GSM601848 1 0.0592 0.883 0.988 0.000 0.012
#> GSM601853 1 0.4291 0.835 0.820 0.000 0.180
#> GSM601863 1 0.4842 0.812 0.776 0.000 0.224
#> GSM601754 2 0.2902 0.908 0.016 0.920 0.064
#> GSM601784 2 0.0237 0.931 0.000 0.996 0.004
#> GSM601794 1 0.0424 0.885 0.992 0.000 0.008
#> GSM601799 2 0.1399 0.924 0.004 0.968 0.028
#> GSM601829 1 0.0000 0.888 1.000 0.000 0.000
#> GSM601839 3 0.5138 0.822 0.000 0.252 0.748
#> GSM601844 1 0.2187 0.859 0.948 0.024 0.028
#> GSM601859 2 0.0237 0.931 0.000 0.996 0.004
#> GSM601869 1 0.4842 0.812 0.776 0.000 0.224
#> GSM601749 1 0.0000 0.888 1.000 0.000 0.000
#> GSM601759 1 0.0000 0.888 1.000 0.000 0.000
#> GSM601764 2 0.0983 0.924 0.016 0.980 0.004
#> GSM601769 2 0.0424 0.929 0.000 0.992 0.008
#> GSM601774 2 0.0237 0.931 0.000 0.996 0.004
#> GSM601779 1 0.1399 0.873 0.968 0.004 0.028
#> GSM601789 2 0.4887 0.647 0.000 0.772 0.228
#> GSM601804 2 0.6119 0.700 0.164 0.772 0.064
#> GSM601809 1 0.5465 0.758 0.712 0.000 0.288
#> GSM601814 3 0.5497 0.779 0.000 0.292 0.708
#> GSM601819 1 0.0000 0.888 1.000 0.000 0.000
#> GSM601824 2 0.4249 0.800 0.108 0.864 0.028
#> GSM601834 2 0.0237 0.931 0.000 0.996 0.004
#> GSM601849 1 0.0000 0.888 1.000 0.000 0.000
#> GSM601854 1 0.0000 0.888 1.000 0.000 0.000
#> GSM601864 3 0.0237 0.711 0.000 0.004 0.996
#> GSM601755 2 0.2165 0.916 0.000 0.936 0.064
#> GSM601785 2 0.0237 0.931 0.000 0.996 0.004
#> GSM601795 1 0.5292 0.683 0.800 0.172 0.028
#> GSM601800 2 0.2584 0.913 0.008 0.928 0.064
#> GSM601830 1 0.8543 0.546 0.604 0.236 0.160
#> GSM601840 2 0.2165 0.916 0.000 0.936 0.064
#> GSM601845 2 0.1905 0.918 0.016 0.956 0.028
#> GSM601860 2 0.2165 0.916 0.000 0.936 0.064
#> GSM601870 3 0.2261 0.655 0.068 0.000 0.932
#> GSM601750 1 0.0000 0.888 1.000 0.000 0.000
#> GSM601760 1 0.0000 0.888 1.000 0.000 0.000
#> GSM601765 2 0.0237 0.931 0.000 0.996 0.004
#> GSM601770 2 0.0237 0.931 0.000 0.996 0.004
#> GSM601775 2 0.0237 0.931 0.000 0.996 0.004
#> GSM601780 1 0.0000 0.888 1.000 0.000 0.000
#> GSM601790 3 0.5138 0.822 0.000 0.252 0.748
#> GSM601805 2 0.2400 0.915 0.004 0.932 0.064
#> GSM601810 1 0.4842 0.812 0.776 0.000 0.224
#> GSM601815 3 0.5138 0.822 0.000 0.252 0.748
#> GSM601820 1 0.4062 0.842 0.836 0.000 0.164
#> GSM601825 2 0.0237 0.931 0.000 0.996 0.004
#> GSM601835 2 0.0237 0.931 0.000 0.996 0.004
#> GSM601850 1 0.1399 0.873 0.968 0.004 0.028
#> GSM601855 1 0.6126 0.613 0.600 0.000 0.400
#> GSM601865 3 0.1289 0.695 0.032 0.000 0.968
#> GSM601756 2 0.2711 0.900 0.000 0.912 0.088
#> GSM601786 3 0.4702 0.805 0.000 0.212 0.788
#> GSM601796 1 0.0000 0.888 1.000 0.000 0.000
#> GSM601801 2 0.2448 0.909 0.000 0.924 0.076
#> GSM601831 1 0.4002 0.844 0.840 0.000 0.160
#> GSM601841 1 0.4842 0.812 0.776 0.000 0.224
#> GSM601846 2 0.1905 0.918 0.016 0.956 0.028
#> GSM601861 3 0.5138 0.822 0.000 0.252 0.748
#> GSM601871 3 0.5327 0.270 0.272 0.000 0.728
#> GSM601751 2 0.2165 0.916 0.000 0.936 0.064
#> GSM601761 1 0.0000 0.888 1.000 0.000 0.000
#> GSM601766 2 0.0237 0.931 0.000 0.996 0.004
#> GSM601771 3 0.7208 0.684 0.048 0.308 0.644
#> GSM601776 1 0.0000 0.888 1.000 0.000 0.000
#> GSM601781 1 0.3116 0.864 0.892 0.000 0.108
#> GSM601791 1 0.0000 0.888 1.000 0.000 0.000
#> GSM601806 3 0.6154 0.501 0.000 0.408 0.592
#> GSM601811 1 0.5254 0.781 0.736 0.000 0.264
#> GSM601816 1 0.0000 0.888 1.000 0.000 0.000
#> GSM601821 3 0.5138 0.822 0.000 0.252 0.748
#> GSM601826 1 0.1399 0.873 0.968 0.004 0.028
#> GSM601836 2 0.1905 0.918 0.016 0.956 0.028
#> GSM601851 1 0.1399 0.873 0.968 0.004 0.028
#> GSM601856 1 0.4291 0.835 0.820 0.000 0.180
#> GSM601866 1 0.4842 0.812 0.776 0.000 0.224
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601752 4 0.6886 0.2723 0.200 0.204 0.000 0.596
#> GSM601782 1 0.0336 0.7298 0.992 0.000 0.008 0.000
#> GSM601792 1 0.2216 0.6938 0.908 0.000 0.000 0.092
#> GSM601797 1 0.4543 0.5113 0.676 0.000 0.000 0.324
#> GSM601827 1 0.0000 0.7357 1.000 0.000 0.000 0.000
#> GSM601837 4 0.5592 0.4956 0.000 0.044 0.300 0.656
#> GSM601842 2 0.0000 0.7891 0.000 1.000 0.000 0.000
#> GSM601857 1 0.4998 -0.4313 0.512 0.000 0.488 0.000
#> GSM601867 3 0.4040 0.8338 0.248 0.000 0.752 0.000
#> GSM601747 1 0.0469 0.7322 0.988 0.000 0.000 0.012
#> GSM601757 1 0.1637 0.7120 0.940 0.000 0.000 0.060
#> GSM601762 2 0.0000 0.7891 0.000 1.000 0.000 0.000
#> GSM601767 2 0.0000 0.7891 0.000 1.000 0.000 0.000
#> GSM601772 2 0.0336 0.7808 0.000 0.992 0.000 0.008
#> GSM601777 3 0.5856 0.5933 0.408 0.000 0.556 0.036
#> GSM601787 3 0.4008 0.8308 0.244 0.000 0.756 0.000
#> GSM601802 4 0.4866 0.3042 0.000 0.404 0.000 0.596
#> GSM601807 3 0.4040 0.8338 0.248 0.000 0.752 0.000
#> GSM601812 1 0.4855 -0.1432 0.600 0.000 0.400 0.000
#> GSM601817 1 0.3266 0.5686 0.832 0.168 0.000 0.000
#> GSM601822 1 0.3528 0.6260 0.808 0.000 0.000 0.192
#> GSM601832 2 0.0000 0.7891 0.000 1.000 0.000 0.000
#> GSM601847 1 0.4643 0.4769 0.656 0.000 0.000 0.344
#> GSM601852 1 0.0000 0.7357 1.000 0.000 0.000 0.000
#> GSM601862 3 0.4406 0.8479 0.300 0.000 0.700 0.000
#> GSM601753 4 0.5000 0.0623 0.000 0.496 0.000 0.504
#> GSM601783 1 0.0000 0.7357 1.000 0.000 0.000 0.000
#> GSM601793 1 0.0188 0.7347 0.996 0.000 0.000 0.004
#> GSM601798 4 0.4866 0.3042 0.000 0.404 0.000 0.596
#> GSM601828 1 0.0000 0.7357 1.000 0.000 0.000 0.000
#> GSM601838 4 0.5592 0.4956 0.000 0.044 0.300 0.656
#> GSM601843 2 0.0000 0.7891 0.000 1.000 0.000 0.000
#> GSM601858 4 0.4866 0.3042 0.000 0.404 0.000 0.596
#> GSM601868 3 0.4406 0.8479 0.300 0.000 0.700 0.000
#> GSM601748 1 0.0000 0.7357 1.000 0.000 0.000 0.000
#> GSM601758 1 0.0000 0.7357 1.000 0.000 0.000 0.000
#> GSM601763 2 0.0000 0.7891 0.000 1.000 0.000 0.000
#> GSM601768 2 0.0000 0.7891 0.000 1.000 0.000 0.000
#> GSM601773 2 0.0000 0.7891 0.000 1.000 0.000 0.000
#> GSM601778 1 0.2593 0.6852 0.892 0.000 0.004 0.104
#> GSM601788 4 0.4866 0.3042 0.000 0.404 0.000 0.596
#> GSM601803 4 0.4990 0.3378 0.000 0.352 0.008 0.640
#> GSM601808 3 0.4406 0.8479 0.300 0.000 0.700 0.000
#> GSM601813 1 0.4866 -0.1564 0.596 0.000 0.404 0.000
#> GSM601818 1 0.4866 -0.1564 0.596 0.000 0.404 0.000
#> GSM601823 1 0.7239 0.1581 0.500 0.156 0.000 0.344
#> GSM601833 2 0.0000 0.7891 0.000 1.000 0.000 0.000
#> GSM601848 1 0.0000 0.7357 1.000 0.000 0.000 0.000
#> GSM601853 1 0.4933 -0.2528 0.568 0.000 0.432 0.000
#> GSM601863 3 0.4406 0.8479 0.300 0.000 0.700 0.000
#> GSM601754 4 0.4866 0.3042 0.000 0.404 0.000 0.596
#> GSM601784 2 0.0000 0.7891 0.000 1.000 0.000 0.000
#> GSM601794 1 0.2216 0.6938 0.908 0.000 0.000 0.092
#> GSM601799 2 0.4431 0.4037 0.000 0.696 0.000 0.304
#> GSM601829 1 0.1557 0.7148 0.944 0.000 0.000 0.056
#> GSM601839 4 0.5592 0.4956 0.000 0.044 0.300 0.656
#> GSM601844 1 0.4040 0.5696 0.752 0.000 0.000 0.248
#> GSM601859 2 0.0000 0.7891 0.000 1.000 0.000 0.000
#> GSM601869 3 0.4406 0.8479 0.300 0.000 0.700 0.000
#> GSM601749 1 0.0000 0.7357 1.000 0.000 0.000 0.000
#> GSM601759 1 0.0000 0.7357 1.000 0.000 0.000 0.000
#> GSM601764 2 0.7474 0.1737 0.212 0.496 0.000 0.292
#> GSM601769 2 0.0188 0.7846 0.000 0.996 0.000 0.004
#> GSM601774 2 0.0000 0.7891 0.000 1.000 0.000 0.000
#> GSM601779 1 0.4356 0.5437 0.708 0.000 0.000 0.292
#> GSM601789 2 0.7700 -0.1458 0.000 0.448 0.248 0.304
#> GSM601804 1 0.7646 -0.0918 0.408 0.208 0.000 0.384
#> GSM601809 3 0.4382 0.8475 0.296 0.000 0.704 0.000
#> GSM601814 4 0.6112 0.4863 0.000 0.096 0.248 0.656
#> GSM601819 1 0.0000 0.7357 1.000 0.000 0.000 0.000
#> GSM601824 2 0.7792 0.0762 0.256 0.412 0.000 0.332
#> GSM601834 2 0.0000 0.7891 0.000 1.000 0.000 0.000
#> GSM601849 1 0.0592 0.7308 0.984 0.000 0.000 0.016
#> GSM601854 1 0.0000 0.7357 1.000 0.000 0.000 0.000
#> GSM601864 4 0.4697 0.4472 0.000 0.000 0.356 0.644
#> GSM601755 4 0.4866 0.3042 0.000 0.404 0.000 0.596
#> GSM601785 2 0.0000 0.7891 0.000 1.000 0.000 0.000
#> GSM601795 1 0.5130 0.4741 0.652 0.016 0.000 0.332
#> GSM601800 4 0.4866 0.3042 0.000 0.404 0.000 0.596
#> GSM601830 1 0.6568 -0.3380 0.512 0.080 0.408 0.000
#> GSM601840 2 0.4981 0.0289 0.000 0.536 0.000 0.464
#> GSM601845 2 0.7621 0.1028 0.212 0.444 0.000 0.344
#> GSM601860 2 0.4830 0.2368 0.000 0.608 0.000 0.392
#> GSM601870 3 0.0000 0.5436 0.000 0.000 1.000 0.000
#> GSM601750 1 0.0000 0.7357 1.000 0.000 0.000 0.000
#> GSM601760 1 0.0000 0.7357 1.000 0.000 0.000 0.000
#> GSM601765 2 0.0000 0.7891 0.000 1.000 0.000 0.000
#> GSM601770 2 0.0000 0.7891 0.000 1.000 0.000 0.000
#> GSM601775 2 0.0000 0.7891 0.000 1.000 0.000 0.000
#> GSM601780 1 0.0000 0.7357 1.000 0.000 0.000 0.000
#> GSM601790 4 0.5592 0.4956 0.000 0.044 0.300 0.656
#> GSM601805 4 0.4866 0.3042 0.000 0.404 0.000 0.596
#> GSM601810 3 0.4406 0.8479 0.300 0.000 0.700 0.000
#> GSM601815 4 0.5592 0.4956 0.000 0.044 0.300 0.656
#> GSM601820 1 0.4866 -0.1565 0.596 0.000 0.404 0.000
#> GSM601825 2 0.0000 0.7891 0.000 1.000 0.000 0.000
#> GSM601835 2 0.0000 0.7891 0.000 1.000 0.000 0.000
#> GSM601850 1 0.4605 0.4899 0.664 0.000 0.000 0.336
#> GSM601855 3 0.4040 0.8338 0.248 0.000 0.752 0.000
#> GSM601865 3 0.4961 -0.1538 0.000 0.000 0.552 0.448
#> GSM601756 4 0.4730 0.3338 0.000 0.364 0.000 0.636
#> GSM601786 4 0.4406 0.4953 0.000 0.000 0.300 0.700
#> GSM601796 1 0.0000 0.7357 1.000 0.000 0.000 0.000
#> GSM601801 4 0.6130 0.3008 0.000 0.400 0.052 0.548
#> GSM601831 1 0.4866 -0.1564 0.596 0.000 0.404 0.000
#> GSM601841 3 0.4746 0.7439 0.368 0.000 0.632 0.000
#> GSM601846 2 0.7621 0.1028 0.212 0.444 0.000 0.344
#> GSM601861 4 0.5745 0.4966 0.000 0.056 0.288 0.656
#> GSM601871 3 0.1940 0.6681 0.076 0.000 0.924 0.000
#> GSM601751 4 0.4866 0.3042 0.000 0.404 0.000 0.596
#> GSM601761 1 0.0000 0.7357 1.000 0.000 0.000 0.000
#> GSM601766 2 0.0000 0.7891 0.000 1.000 0.000 0.000
#> GSM601771 4 0.0524 0.4721 0.008 0.004 0.000 0.988
#> GSM601776 1 0.0000 0.7357 1.000 0.000 0.000 0.000
#> GSM601781 1 0.4916 -0.2058 0.576 0.000 0.424 0.000
#> GSM601791 1 0.0000 0.7357 1.000 0.000 0.000 0.000
#> GSM601806 4 0.4040 0.5085 0.000 0.000 0.248 0.752
#> GSM601811 3 0.4406 0.8479 0.300 0.000 0.700 0.000
#> GSM601816 1 0.0000 0.7357 1.000 0.000 0.000 0.000
#> GSM601821 4 0.5592 0.4956 0.000 0.044 0.300 0.656
#> GSM601826 1 0.4008 0.5730 0.756 0.000 0.000 0.244
#> GSM601836 2 0.7621 0.1028 0.212 0.444 0.000 0.344
#> GSM601851 1 0.0000 0.7357 1.000 0.000 0.000 0.000
#> GSM601856 1 0.4948 -0.2794 0.560 0.000 0.440 0.000
#> GSM601866 3 0.4406 0.8479 0.300 0.000 0.700 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601752 4 0.2516 0.772 0.000 0.000 0.000 0.860 0.140
#> GSM601782 1 0.0162 0.850 0.996 0.000 0.004 0.000 0.000
#> GSM601792 1 0.4306 0.301 0.508 0.000 0.000 0.492 0.000
#> GSM601797 4 0.0162 0.767 0.004 0.000 0.000 0.996 0.000
#> GSM601827 1 0.0000 0.851 1.000 0.000 0.000 0.000 0.000
#> GSM601837 5 0.0000 0.968 0.000 0.000 0.000 0.000 1.000
#> GSM601842 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> GSM601857 1 0.4030 0.444 0.648 0.000 0.352 0.000 0.000
#> GSM601867 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM601747 1 0.3074 0.699 0.804 0.000 0.000 0.196 0.000
#> GSM601757 4 0.4262 -0.122 0.440 0.000 0.000 0.560 0.000
#> GSM601762 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> GSM601767 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> GSM601772 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> GSM601777 3 0.4555 0.679 0.068 0.000 0.732 0.200 0.000
#> GSM601787 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM601802 4 0.3994 0.790 0.000 0.068 0.000 0.792 0.140
#> GSM601807 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM601812 1 0.1965 0.799 0.904 0.000 0.096 0.000 0.000
#> GSM601817 1 0.0000 0.851 1.000 0.000 0.000 0.000 0.000
#> GSM601822 4 0.2605 0.677 0.148 0.000 0.000 0.852 0.000
#> GSM601832 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> GSM601847 4 0.0290 0.766 0.008 0.000 0.000 0.992 0.000
#> GSM601852 1 0.0794 0.849 0.972 0.000 0.000 0.028 0.000
#> GSM601862 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM601753 4 0.5211 0.715 0.000 0.212 0.000 0.676 0.112
#> GSM601783 1 0.0000 0.851 1.000 0.000 0.000 0.000 0.000
#> GSM601793 1 0.3336 0.779 0.772 0.000 0.000 0.228 0.000
#> GSM601798 4 0.3994 0.790 0.000 0.068 0.000 0.792 0.140
#> GSM601828 1 0.0000 0.851 1.000 0.000 0.000 0.000 0.000
#> GSM601838 5 0.0000 0.968 0.000 0.000 0.000 0.000 1.000
#> GSM601843 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> GSM601858 4 0.3994 0.790 0.000 0.068 0.000 0.792 0.140
#> GSM601868 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM601748 1 0.0000 0.851 1.000 0.000 0.000 0.000 0.000
#> GSM601758 1 0.0000 0.851 1.000 0.000 0.000 0.000 0.000
#> GSM601763 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> GSM601768 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> GSM601773 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> GSM601778 4 0.2848 0.665 0.156 0.000 0.004 0.840 0.000
#> GSM601788 4 0.3994 0.790 0.000 0.068 0.000 0.792 0.140
#> GSM601803 4 0.5372 0.719 0.000 0.180 0.000 0.668 0.152
#> GSM601808 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM601813 1 0.1965 0.799 0.904 0.000 0.096 0.000 0.000
#> GSM601818 1 0.2020 0.797 0.900 0.000 0.100 0.000 0.000
#> GSM601823 4 0.1965 0.721 0.096 0.000 0.000 0.904 0.000
#> GSM601833 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> GSM601848 1 0.3177 0.793 0.792 0.000 0.000 0.208 0.000
#> GSM601853 1 0.1965 0.799 0.904 0.000 0.096 0.000 0.000
#> GSM601863 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM601754 4 0.3994 0.790 0.000 0.068 0.000 0.792 0.140
#> GSM601784 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> GSM601794 1 0.4307 0.289 0.504 0.000 0.000 0.496 0.000
#> GSM601799 4 0.4114 0.526 0.000 0.376 0.000 0.624 0.000
#> GSM601829 1 0.4074 0.615 0.636 0.000 0.000 0.364 0.000
#> GSM601839 5 0.0000 0.968 0.000 0.000 0.000 0.000 1.000
#> GSM601844 1 0.3336 0.784 0.772 0.000 0.000 0.228 0.000
#> GSM601859 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> GSM601869 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM601749 1 0.0000 0.851 1.000 0.000 0.000 0.000 0.000
#> GSM601759 1 0.0000 0.851 1.000 0.000 0.000 0.000 0.000
#> GSM601764 4 0.5164 0.637 0.096 0.232 0.000 0.672 0.000
#> GSM601769 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> GSM601774 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> GSM601779 1 0.4060 0.609 0.640 0.000 0.000 0.360 0.000
#> GSM601789 5 0.2798 0.821 0.000 0.140 0.000 0.008 0.852
#> GSM601804 4 0.0000 0.768 0.000 0.000 0.000 1.000 0.000
#> GSM601809 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM601814 5 0.0000 0.968 0.000 0.000 0.000 0.000 1.000
#> GSM601819 1 0.0000 0.851 1.000 0.000 0.000 0.000 0.000
#> GSM601824 4 0.4624 0.716 0.096 0.164 0.000 0.740 0.000
#> GSM601834 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> GSM601849 1 0.3452 0.765 0.756 0.000 0.000 0.244 0.000
#> GSM601854 1 0.0000 0.851 1.000 0.000 0.000 0.000 0.000
#> GSM601864 5 0.0963 0.938 0.000 0.000 0.036 0.000 0.964
#> GSM601755 4 0.3994 0.790 0.000 0.068 0.000 0.792 0.140
#> GSM601785 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> GSM601795 4 0.2020 0.719 0.100 0.000 0.000 0.900 0.000
#> GSM601800 4 0.3994 0.790 0.000 0.068 0.000 0.792 0.140
#> GSM601830 1 0.4489 0.743 0.792 0.068 0.104 0.036 0.000
#> GSM601840 4 0.3975 0.776 0.000 0.144 0.000 0.792 0.064
#> GSM601845 4 0.2325 0.785 0.028 0.068 0.000 0.904 0.000
#> GSM601860 4 0.3388 0.749 0.000 0.200 0.000 0.792 0.008
#> GSM601870 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM601750 1 0.0000 0.851 1.000 0.000 0.000 0.000 0.000
#> GSM601760 1 0.0162 0.851 0.996 0.000 0.000 0.004 0.000
#> GSM601765 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> GSM601770 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> GSM601775 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> GSM601780 1 0.3177 0.793 0.792 0.000 0.000 0.208 0.000
#> GSM601790 5 0.0000 0.968 0.000 0.000 0.000 0.000 1.000
#> GSM601805 4 0.3994 0.790 0.000 0.068 0.000 0.792 0.140
#> GSM601810 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM601815 5 0.0000 0.968 0.000 0.000 0.000 0.000 1.000
#> GSM601820 1 0.1965 0.799 0.904 0.000 0.096 0.000 0.000
#> GSM601825 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> GSM601835 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> GSM601850 4 0.2605 0.677 0.148 0.000 0.000 0.852 0.000
#> GSM601855 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM601865 5 0.2377 0.825 0.000 0.000 0.128 0.000 0.872
#> GSM601756 4 0.3953 0.786 0.000 0.060 0.000 0.792 0.148
#> GSM601786 5 0.0000 0.968 0.000 0.000 0.000 0.000 1.000
#> GSM601796 1 0.2648 0.815 0.848 0.000 0.000 0.152 0.000
#> GSM601801 4 0.5405 0.590 0.000 0.076 0.000 0.596 0.328
#> GSM601831 1 0.1965 0.799 0.904 0.000 0.096 0.000 0.000
#> GSM601841 3 0.1270 0.904 0.052 0.000 0.948 0.000 0.000
#> GSM601846 4 0.1544 0.789 0.000 0.068 0.000 0.932 0.000
#> GSM601861 5 0.0000 0.968 0.000 0.000 0.000 0.000 1.000
#> GSM601871 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM601751 4 0.3994 0.790 0.000 0.068 0.000 0.792 0.140
#> GSM601761 1 0.3039 0.801 0.808 0.000 0.000 0.192 0.000
#> GSM601766 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> GSM601771 4 0.3300 0.745 0.000 0.004 0.000 0.792 0.204
#> GSM601776 1 0.3177 0.793 0.792 0.000 0.000 0.208 0.000
#> GSM601781 3 0.4457 0.694 0.092 0.000 0.756 0.152 0.000
#> GSM601791 1 0.0000 0.851 1.000 0.000 0.000 0.000 0.000
#> GSM601806 5 0.0510 0.954 0.000 0.000 0.000 0.016 0.984
#> GSM601811 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
#> GSM601816 1 0.3177 0.793 0.792 0.000 0.000 0.208 0.000
#> GSM601821 5 0.0000 0.968 0.000 0.000 0.000 0.000 1.000
#> GSM601826 1 0.3210 0.792 0.788 0.000 0.000 0.212 0.000
#> GSM601836 4 0.3464 0.756 0.096 0.068 0.000 0.836 0.000
#> GSM601851 1 0.3177 0.793 0.792 0.000 0.000 0.208 0.000
#> GSM601856 1 0.2074 0.794 0.896 0.000 0.104 0.000 0.000
#> GSM601866 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601752 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601782 1 0.0260 0.919 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM601792 6 0.1327 0.854 0.064 0.000 0.000 0.000 0.000 0.936
#> GSM601797 4 0.1806 0.881 0.088 0.000 0.000 0.908 0.000 0.004
#> GSM601827 1 0.0713 0.916 0.972 0.000 0.000 0.000 0.000 0.028
#> GSM601837 5 0.0000 0.994 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601842 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601857 1 0.2300 0.797 0.856 0.000 0.144 0.000 0.000 0.000
#> GSM601867 3 0.0000 0.997 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601747 1 0.4227 0.617 0.692 0.000 0.000 0.052 0.000 0.256
#> GSM601757 6 0.2178 0.815 0.132 0.000 0.000 0.000 0.000 0.868
#> GSM601762 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601767 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601772 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601777 6 0.5984 0.268 0.236 0.000 0.344 0.000 0.000 0.420
#> GSM601787 3 0.0000 0.997 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601802 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601807 3 0.0000 0.997 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601812 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601817 1 0.0146 0.920 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM601822 6 0.0713 0.863 0.028 0.000 0.000 0.000 0.000 0.972
#> GSM601832 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601847 4 0.0820 0.943 0.012 0.000 0.000 0.972 0.000 0.016
#> GSM601852 1 0.3515 0.630 0.676 0.000 0.000 0.000 0.000 0.324
#> GSM601862 3 0.0000 0.997 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601753 4 0.2048 0.864 0.000 0.120 0.000 0.880 0.000 0.000
#> GSM601783 1 0.2003 0.863 0.884 0.000 0.000 0.000 0.000 0.116
#> GSM601793 6 0.3050 0.719 0.236 0.000 0.000 0.000 0.000 0.764
#> GSM601798 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601828 1 0.0713 0.916 0.972 0.000 0.000 0.000 0.000 0.028
#> GSM601838 5 0.0000 0.994 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601843 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601858 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601868 3 0.0000 0.997 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601748 1 0.0713 0.916 0.972 0.000 0.000 0.000 0.000 0.028
#> GSM601758 1 0.0632 0.913 0.976 0.000 0.000 0.000 0.000 0.024
#> GSM601763 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601768 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601773 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601778 6 0.1863 0.833 0.104 0.000 0.000 0.000 0.000 0.896
#> GSM601788 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601803 4 0.2266 0.868 0.000 0.108 0.000 0.880 0.012 0.000
#> GSM601808 3 0.0000 0.997 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601813 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601818 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601823 6 0.0000 0.865 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601833 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601848 6 0.0000 0.865 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601853 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601863 3 0.0000 0.997 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601754 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601784 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601794 6 0.0713 0.863 0.028 0.000 0.000 0.000 0.000 0.972
#> GSM601799 4 0.2527 0.812 0.000 0.168 0.000 0.832 0.000 0.000
#> GSM601829 6 0.0146 0.865 0.004 0.000 0.000 0.000 0.000 0.996
#> GSM601839 5 0.0000 0.994 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601844 6 0.0000 0.865 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601859 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601869 3 0.0000 0.997 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601749 1 0.3076 0.749 0.760 0.000 0.000 0.000 0.000 0.240
#> GSM601759 1 0.0937 0.913 0.960 0.000 0.000 0.000 0.000 0.040
#> GSM601764 6 0.3315 0.731 0.000 0.020 0.000 0.200 0.000 0.780
#> GSM601769 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601774 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601779 6 0.0000 0.865 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601789 5 0.0458 0.982 0.000 0.000 0.000 0.016 0.984 0.000
#> GSM601804 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601809 3 0.0260 0.991 0.008 0.000 0.992 0.000 0.000 0.000
#> GSM601814 5 0.0000 0.994 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601819 1 0.3076 0.749 0.760 0.000 0.000 0.000 0.000 0.240
#> GSM601824 6 0.4911 0.535 0.000 0.100 0.000 0.276 0.000 0.624
#> GSM601834 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601849 6 0.0000 0.865 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601854 1 0.0713 0.916 0.972 0.000 0.000 0.000 0.000 0.028
#> GSM601864 5 0.0000 0.994 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601755 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601785 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601795 6 0.0713 0.859 0.000 0.000 0.000 0.028 0.000 0.972
#> GSM601800 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601830 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601840 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601845 4 0.2100 0.846 0.000 0.004 0.000 0.884 0.000 0.112
#> GSM601860 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601870 3 0.0000 0.997 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601750 1 0.0713 0.916 0.972 0.000 0.000 0.000 0.000 0.028
#> GSM601760 1 0.3221 0.722 0.736 0.000 0.000 0.000 0.000 0.264
#> GSM601765 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601770 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601775 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601780 6 0.0000 0.865 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601790 5 0.0000 0.994 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601805 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601810 3 0.0260 0.991 0.008 0.000 0.992 0.000 0.000 0.000
#> GSM601815 5 0.0000 0.994 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601820 1 0.0146 0.920 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM601825 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601835 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601850 6 0.0777 0.861 0.004 0.000 0.000 0.024 0.000 0.972
#> GSM601855 3 0.0458 0.983 0.016 0.000 0.984 0.000 0.000 0.000
#> GSM601865 5 0.0000 0.994 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601756 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601786 5 0.0000 0.994 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601796 6 0.3695 0.474 0.376 0.000 0.000 0.000 0.000 0.624
#> GSM601801 4 0.2915 0.773 0.000 0.008 0.000 0.808 0.184 0.000
#> GSM601831 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601841 3 0.0146 0.994 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM601846 4 0.0260 0.953 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM601861 5 0.0000 0.994 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601871 3 0.0000 0.997 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601751 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601761 6 0.2854 0.673 0.208 0.000 0.000 0.000 0.000 0.792
#> GSM601766 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601771 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601776 6 0.0713 0.863 0.028 0.000 0.000 0.000 0.000 0.972
#> GSM601781 6 0.5868 0.306 0.204 0.000 0.348 0.000 0.000 0.448
#> GSM601791 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601806 5 0.1075 0.949 0.000 0.000 0.000 0.048 0.952 0.000
#> GSM601811 3 0.0000 0.997 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601816 6 0.2135 0.817 0.128 0.000 0.000 0.000 0.000 0.872
#> GSM601821 5 0.0000 0.994 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601826 6 0.0000 0.865 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601836 6 0.3265 0.684 0.000 0.004 0.000 0.248 0.000 0.748
#> GSM601851 6 0.0000 0.865 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601856 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601866 3 0.0000 0.997 0.000 0.000 1.000 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 time(p) gender(p) k
#> ATC:pam 124 0.453 0.3588 2
#> ATC:pam 124 0.233 0.4462 3
#> ATC:pam 75 0.469 0.7425 4
#> ATC:pam 121 0.775 0.1737 5
#> ATC:pam 122 0.793 0.0359 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 125 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 0.440 0.777 0.822 0.4375 0.580 0.580
#> 3 3 0.632 0.817 0.879 0.4519 0.722 0.534
#> 4 4 0.543 0.365 0.659 0.1340 0.727 0.420
#> 5 5 0.662 0.756 0.799 0.0667 0.826 0.520
#> 6 6 0.865 0.763 0.871 0.0636 0.929 0.696
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
#> GSM601752 2 0.0376 0.81199 0.004 0.996
#> GSM601782 1 0.0000 0.84327 1.000 0.000
#> GSM601792 2 0.6887 0.83064 0.184 0.816
#> GSM601797 2 0.6887 0.83064 0.184 0.816
#> GSM601827 1 0.3879 0.77850 0.924 0.076
#> GSM601837 1 0.6801 0.84331 0.820 0.180
#> GSM601842 1 0.6887 0.84209 0.816 0.184
#> GSM601857 1 0.0000 0.84327 1.000 0.000
#> GSM601867 1 0.0000 0.84327 1.000 0.000
#> GSM601747 1 0.0000 0.84327 1.000 0.000
#> GSM601757 1 0.0000 0.84327 1.000 0.000
#> GSM601762 1 0.6887 0.84209 0.816 0.184
#> GSM601767 1 0.6887 0.84209 0.816 0.184
#> GSM601772 1 0.6887 0.84209 0.816 0.184
#> GSM601777 2 0.7376 0.82155 0.208 0.792
#> GSM601787 1 0.0000 0.84327 1.000 0.000
#> GSM601802 2 0.0376 0.81199 0.004 0.996
#> GSM601807 1 0.9686 0.01540 0.604 0.396
#> GSM601812 1 0.0000 0.84327 1.000 0.000
#> GSM601817 1 0.0000 0.84327 1.000 0.000
#> GSM601822 2 0.7883 0.80067 0.236 0.764
#> GSM601832 1 0.6887 0.84209 0.816 0.184
#> GSM601847 2 0.6887 0.83064 0.184 0.816
#> GSM601852 1 0.0000 0.84327 1.000 0.000
#> GSM601862 1 0.0000 0.84327 1.000 0.000
#> GSM601753 2 0.1633 0.80247 0.024 0.976
#> GSM601783 1 0.0000 0.84327 1.000 0.000
#> GSM601793 2 0.6887 0.83064 0.184 0.816
#> GSM601798 2 0.0376 0.81199 0.004 0.996
#> GSM601828 1 0.0000 0.84327 1.000 0.000
#> GSM601838 1 0.6801 0.84331 0.820 0.180
#> GSM601843 1 0.6887 0.84209 0.816 0.184
#> GSM601858 1 0.6801 0.84331 0.820 0.180
#> GSM601868 1 0.0000 0.84327 1.000 0.000
#> GSM601748 1 0.0000 0.84327 1.000 0.000
#> GSM601758 1 0.0000 0.84327 1.000 0.000
#> GSM601763 1 0.6801 0.84331 0.820 0.180
#> GSM601768 1 0.6887 0.84209 0.816 0.184
#> GSM601773 1 0.6887 0.84209 0.816 0.184
#> GSM601778 2 0.6887 0.83064 0.184 0.816
#> GSM601788 1 0.6801 0.84331 0.820 0.180
#> GSM601803 2 0.0376 0.81199 0.004 0.996
#> GSM601808 1 0.0000 0.84327 1.000 0.000
#> GSM601813 1 0.9209 0.24251 0.664 0.336
#> GSM601818 1 0.0000 0.84327 1.000 0.000
#> GSM601823 1 0.8267 0.46586 0.740 0.260
#> GSM601833 1 0.6887 0.84209 0.816 0.184
#> GSM601848 2 0.7056 0.82919 0.192 0.808
#> GSM601853 1 0.0000 0.84327 1.000 0.000
#> GSM601863 1 0.0000 0.84327 1.000 0.000
#> GSM601754 2 0.0376 0.81199 0.004 0.996
#> GSM601784 1 0.6887 0.84209 0.816 0.184
#> GSM601794 2 0.6887 0.83064 0.184 0.816
#> GSM601799 2 0.0376 0.81199 0.004 0.996
#> GSM601829 1 0.8207 0.47414 0.744 0.256
#> GSM601839 1 0.6801 0.84331 0.820 0.180
#> GSM601844 1 0.0000 0.84327 1.000 0.000
#> GSM601859 1 0.6887 0.84209 0.816 0.184
#> GSM601869 1 0.0000 0.84327 1.000 0.000
#> GSM601749 1 0.0000 0.84327 1.000 0.000
#> GSM601759 1 0.0000 0.84327 1.000 0.000
#> GSM601764 1 0.6801 0.84331 0.820 0.180
#> GSM601769 1 0.6801 0.84331 0.820 0.180
#> GSM601774 1 0.6887 0.84209 0.816 0.184
#> GSM601779 2 0.7219 0.82643 0.200 0.800
#> GSM601789 1 0.6801 0.84331 0.820 0.180
#> GSM601804 2 0.0376 0.81199 0.004 0.996
#> GSM601809 1 0.0000 0.84327 1.000 0.000
#> GSM601814 2 0.9795 0.00236 0.416 0.584
#> GSM601819 1 0.0000 0.84327 1.000 0.000
#> GSM601824 1 0.6801 0.84331 0.820 0.180
#> GSM601834 1 0.6887 0.84209 0.816 0.184
#> GSM601849 2 0.9686 0.58271 0.396 0.604
#> GSM601854 1 0.0000 0.84327 1.000 0.000
#> GSM601864 1 0.6801 0.84331 0.820 0.180
#> GSM601755 2 0.0376 0.81199 0.004 0.996
#> GSM601785 1 0.6887 0.84209 0.816 0.184
#> GSM601795 2 0.6887 0.83064 0.184 0.816
#> GSM601800 2 0.0376 0.81199 0.004 0.996
#> GSM601830 1 0.8207 0.47422 0.744 0.256
#> GSM601840 1 0.6801 0.84331 0.820 0.180
#> GSM601845 1 0.6801 0.84331 0.820 0.180
#> GSM601860 1 0.6801 0.84331 0.820 0.180
#> GSM601870 1 0.4022 0.77411 0.920 0.080
#> GSM601750 1 0.0000 0.84327 1.000 0.000
#> GSM601760 1 0.0376 0.84084 0.996 0.004
#> GSM601765 1 0.6887 0.84209 0.816 0.184
#> GSM601770 1 0.6887 0.84209 0.816 0.184
#> GSM601775 1 0.6801 0.84331 0.820 0.180
#> GSM601780 2 0.6973 0.83012 0.188 0.812
#> GSM601790 1 0.6801 0.84331 0.820 0.180
#> GSM601805 2 0.0376 0.81199 0.004 0.996
#> GSM601810 1 0.0000 0.84327 1.000 0.000
#> GSM601815 1 0.7883 0.79840 0.764 0.236
#> GSM601820 1 0.0000 0.84327 1.000 0.000
#> GSM601825 2 0.9710 0.05490 0.400 0.600
#> GSM601835 1 0.6801 0.84331 0.820 0.180
#> GSM601850 2 0.7219 0.82643 0.200 0.800
#> GSM601855 1 0.6712 0.63680 0.824 0.176
#> GSM601865 1 0.6438 0.84471 0.836 0.164
#> GSM601756 2 0.0376 0.81199 0.004 0.996
#> GSM601786 1 0.6801 0.84331 0.820 0.180
#> GSM601796 2 0.6887 0.83064 0.184 0.816
#> GSM601801 2 0.0376 0.81199 0.004 0.996
#> GSM601831 1 0.0000 0.84327 1.000 0.000
#> GSM601841 1 0.6531 0.65234 0.832 0.168
#> GSM601846 2 0.9393 0.22390 0.356 0.644
#> GSM601861 1 0.6801 0.84331 0.820 0.180
#> GSM601871 1 0.0000 0.84327 1.000 0.000
#> GSM601751 1 0.6973 0.83898 0.812 0.188
#> GSM601761 1 0.7139 0.59928 0.804 0.196
#> GSM601766 1 0.6887 0.84209 0.816 0.184
#> GSM601771 2 0.9815 -0.01553 0.420 0.580
#> GSM601776 2 0.7299 0.82442 0.204 0.796
#> GSM601781 2 0.6887 0.83064 0.184 0.816
#> GSM601791 1 0.9608 0.05402 0.616 0.384
#> GSM601806 2 0.0376 0.81199 0.004 0.996
#> GSM601811 1 0.0000 0.84327 1.000 0.000
#> GSM601816 2 0.6973 0.83013 0.188 0.812
#> GSM601821 1 0.6973 0.83897 0.812 0.188
#> GSM601826 1 0.1414 0.83041 0.980 0.020
#> GSM601836 1 0.6801 0.84331 0.820 0.180
#> GSM601851 2 0.7299 0.82442 0.204 0.796
#> GSM601856 1 0.0000 0.84327 1.000 0.000
#> GSM601866 1 0.0000 0.84327 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601752 1 0.0424 0.9278 0.992 0.000 0.008
#> GSM601782 3 0.1765 0.9105 0.004 0.040 0.956
#> GSM601792 1 0.0237 0.9257 0.996 0.000 0.004
#> GSM601797 1 0.0237 0.9257 0.996 0.000 0.004
#> GSM601827 3 0.2793 0.8984 0.028 0.044 0.928
#> GSM601837 2 0.5882 0.7511 0.000 0.652 0.348
#> GSM601842 2 0.0661 0.7737 0.004 0.988 0.008
#> GSM601857 3 0.0237 0.9033 0.004 0.000 0.996
#> GSM601867 3 0.0592 0.8973 0.000 0.012 0.988
#> GSM601747 3 0.1878 0.9097 0.004 0.044 0.952
#> GSM601757 3 0.1765 0.9105 0.004 0.040 0.956
#> GSM601762 2 0.1919 0.7763 0.020 0.956 0.024
#> GSM601767 2 0.0237 0.7688 0.004 0.996 0.000
#> GSM601772 2 0.1289 0.7822 0.000 0.968 0.032
#> GSM601777 1 0.3686 0.8415 0.860 0.000 0.140
#> GSM601787 3 0.0892 0.8923 0.000 0.020 0.980
#> GSM601802 1 0.0424 0.9278 0.992 0.000 0.008
#> GSM601807 3 0.4887 0.6810 0.228 0.000 0.772
#> GSM601812 3 0.1765 0.9105 0.004 0.040 0.956
#> GSM601817 3 0.1643 0.9097 0.000 0.044 0.956
#> GSM601822 1 0.2066 0.8989 0.940 0.000 0.060
#> GSM601832 2 0.0237 0.7688 0.004 0.996 0.000
#> GSM601847 1 0.0237 0.9257 0.996 0.000 0.004
#> GSM601852 3 0.1643 0.9097 0.000 0.044 0.956
#> GSM601862 3 0.0000 0.9021 0.000 0.000 1.000
#> GSM601753 1 0.5348 0.7151 0.796 0.176 0.028
#> GSM601783 3 0.1765 0.9105 0.004 0.040 0.956
#> GSM601793 1 0.0237 0.9257 0.996 0.000 0.004
#> GSM601798 1 0.0424 0.9278 0.992 0.000 0.008
#> GSM601828 3 0.1643 0.9097 0.000 0.044 0.956
#> GSM601838 2 0.5882 0.7511 0.000 0.652 0.348
#> GSM601843 2 0.2301 0.7891 0.004 0.936 0.060
#> GSM601858 2 0.5845 0.7579 0.004 0.688 0.308
#> GSM601868 3 0.0000 0.9021 0.000 0.000 1.000
#> GSM601748 3 0.1643 0.9097 0.000 0.044 0.956
#> GSM601758 3 0.1950 0.9002 0.040 0.008 0.952
#> GSM601763 2 0.5754 0.7640 0.004 0.700 0.296
#> GSM601768 2 0.0237 0.7688 0.004 0.996 0.000
#> GSM601773 2 0.0237 0.7688 0.004 0.996 0.000
#> GSM601778 1 0.0237 0.9257 0.996 0.000 0.004
#> GSM601788 2 0.5815 0.7577 0.004 0.692 0.304
#> GSM601803 1 0.0592 0.9267 0.988 0.000 0.012
#> GSM601808 3 0.0000 0.9021 0.000 0.000 1.000
#> GSM601813 3 0.1643 0.8965 0.044 0.000 0.956
#> GSM601818 3 0.1765 0.9105 0.004 0.040 0.956
#> GSM601823 3 0.7759 0.0712 0.476 0.048 0.476
#> GSM601833 2 0.0237 0.7688 0.004 0.996 0.000
#> GSM601848 1 0.2537 0.8908 0.920 0.000 0.080
#> GSM601853 3 0.1643 0.9097 0.000 0.044 0.956
#> GSM601863 3 0.0000 0.9021 0.000 0.000 1.000
#> GSM601754 1 0.0424 0.9278 0.992 0.000 0.008
#> GSM601784 2 0.0237 0.7688 0.004 0.996 0.000
#> GSM601794 1 0.0237 0.9257 0.996 0.000 0.004
#> GSM601799 1 0.1163 0.9197 0.972 0.000 0.028
#> GSM601829 3 0.7581 0.3049 0.408 0.044 0.548
#> GSM601839 2 0.5882 0.7511 0.000 0.652 0.348
#> GSM601844 3 0.1643 0.9097 0.000 0.044 0.956
#> GSM601859 2 0.3715 0.7963 0.004 0.868 0.128
#> GSM601869 3 0.0237 0.9033 0.004 0.000 0.996
#> GSM601749 3 0.1643 0.9097 0.000 0.044 0.956
#> GSM601759 3 0.1765 0.9105 0.004 0.040 0.956
#> GSM601764 2 0.5115 0.7859 0.004 0.768 0.228
#> GSM601769 2 0.3784 0.7966 0.004 0.864 0.132
#> GSM601774 2 0.0237 0.7688 0.004 0.996 0.000
#> GSM601779 1 0.3340 0.8559 0.880 0.000 0.120
#> GSM601789 2 0.5591 0.7577 0.000 0.696 0.304
#> GSM601804 1 0.0424 0.9278 0.992 0.000 0.008
#> GSM601809 3 0.0237 0.9004 0.000 0.004 0.996
#> GSM601814 2 0.9402 0.5742 0.184 0.472 0.344
#> GSM601819 3 0.1643 0.9097 0.000 0.044 0.956
#> GSM601824 2 0.5815 0.7584 0.004 0.692 0.304
#> GSM601834 2 0.0848 0.7726 0.008 0.984 0.008
#> GSM601849 1 0.4702 0.7466 0.788 0.000 0.212
#> GSM601854 3 0.1643 0.9097 0.000 0.044 0.956
#> GSM601864 2 0.6126 0.6814 0.000 0.600 0.400
#> GSM601755 1 0.0424 0.9278 0.992 0.000 0.008
#> GSM601785 2 0.1399 0.7807 0.004 0.968 0.028
#> GSM601795 1 0.0237 0.9257 0.996 0.000 0.004
#> GSM601800 1 0.0424 0.9278 0.992 0.000 0.008
#> GSM601830 3 0.7284 0.5123 0.336 0.044 0.620
#> GSM601840 2 0.5815 0.7577 0.004 0.692 0.304
#> GSM601845 2 0.5656 0.7696 0.004 0.712 0.284
#> GSM601860 2 0.5815 0.7577 0.004 0.692 0.304
#> GSM601870 3 0.5094 0.7571 0.136 0.040 0.824
#> GSM601750 3 0.1643 0.9097 0.000 0.044 0.956
#> GSM601760 3 0.1989 0.8960 0.048 0.004 0.948
#> GSM601765 2 0.0237 0.7688 0.004 0.996 0.000
#> GSM601770 2 0.0237 0.7688 0.004 0.996 0.000
#> GSM601775 2 0.2860 0.7933 0.004 0.912 0.084
#> GSM601780 1 0.2959 0.8741 0.900 0.000 0.100
#> GSM601790 2 0.5882 0.7511 0.000 0.652 0.348
#> GSM601805 1 0.0424 0.9278 0.992 0.000 0.008
#> GSM601810 3 0.0000 0.9021 0.000 0.000 1.000
#> GSM601815 2 0.5882 0.7511 0.000 0.652 0.348
#> GSM601820 3 0.1765 0.9105 0.004 0.040 0.956
#> GSM601825 2 0.9638 0.2672 0.372 0.420 0.208
#> GSM601835 2 0.4514 0.7952 0.012 0.832 0.156
#> GSM601850 1 0.1753 0.9101 0.952 0.000 0.048
#> GSM601855 3 0.3879 0.7750 0.152 0.000 0.848
#> GSM601865 2 0.6204 0.6388 0.000 0.576 0.424
#> GSM601756 1 0.0424 0.9278 0.992 0.000 0.008
#> GSM601786 2 0.5882 0.7511 0.000 0.652 0.348
#> GSM601796 1 0.0237 0.9257 0.996 0.000 0.004
#> GSM601801 1 0.0424 0.9278 0.992 0.000 0.008
#> GSM601831 3 0.1643 0.9097 0.000 0.044 0.956
#> GSM601841 3 0.2537 0.8651 0.080 0.000 0.920
#> GSM601846 1 0.7949 0.4562 0.640 0.108 0.252
#> GSM601861 2 0.5882 0.7511 0.000 0.652 0.348
#> GSM601871 3 0.0424 0.8992 0.000 0.008 0.992
#> GSM601751 2 0.6143 0.7581 0.012 0.684 0.304
#> GSM601761 3 0.4702 0.7021 0.212 0.000 0.788
#> GSM601766 2 0.2945 0.7933 0.004 0.908 0.088
#> GSM601771 2 0.8478 0.6702 0.204 0.616 0.180
#> GSM601776 1 0.5926 0.4902 0.644 0.000 0.356
#> GSM601781 1 0.2625 0.8932 0.916 0.000 0.084
#> GSM601791 3 0.4654 0.7290 0.208 0.000 0.792
#> GSM601806 1 0.0424 0.9278 0.992 0.000 0.008
#> GSM601811 3 0.0000 0.9021 0.000 0.000 1.000
#> GSM601816 1 0.1289 0.9171 0.968 0.000 0.032
#> GSM601821 2 0.5882 0.7511 0.000 0.652 0.348
#> GSM601826 3 0.3875 0.8642 0.068 0.044 0.888
#> GSM601836 2 0.5588 0.7729 0.004 0.720 0.276
#> GSM601851 1 0.5497 0.6198 0.708 0.000 0.292
#> GSM601856 3 0.1643 0.9097 0.000 0.044 0.956
#> GSM601866 3 0.0237 0.9033 0.004 0.000 0.996
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601752 1 0.6500 -0.2384 0.484 0.072 0.000 0.444
#> GSM601782 1 0.8558 0.4251 0.464 0.052 0.292 0.192
#> GSM601792 1 0.4121 0.2811 0.796 0.000 0.020 0.184
#> GSM601797 1 0.4086 0.2295 0.776 0.000 0.008 0.216
#> GSM601827 1 0.7278 0.4435 0.528 0.000 0.284 0.188
#> GSM601837 3 0.7720 -0.0380 0.000 0.228 0.412 0.360
#> GSM601842 2 0.0000 0.8072 0.000 1.000 0.000 0.000
#> GSM601857 3 0.8403 -0.2543 0.340 0.052 0.456 0.152
#> GSM601867 3 0.3569 0.3462 0.000 0.000 0.804 0.196
#> GSM601747 1 0.8719 0.1287 0.376 0.052 0.196 0.376
#> GSM601757 1 0.8531 0.4224 0.464 0.052 0.300 0.184
#> GSM601762 2 0.4008 0.6793 0.000 0.820 0.148 0.032
#> GSM601767 2 0.0000 0.8072 0.000 1.000 0.000 0.000
#> GSM601772 2 0.0921 0.7959 0.000 0.972 0.000 0.028
#> GSM601777 1 0.3400 0.4633 0.820 0.000 0.180 0.000
#> GSM601787 3 0.4277 0.2797 0.000 0.000 0.720 0.280
#> GSM601802 1 0.6500 -0.2384 0.484 0.072 0.000 0.444
#> GSM601807 3 0.6571 0.4196 0.264 0.000 0.612 0.124
#> GSM601812 1 0.7013 0.4028 0.516 0.000 0.356 0.128
#> GSM601817 1 0.8558 0.4251 0.464 0.052 0.292 0.192
#> GSM601822 1 0.4375 0.4609 0.788 0.000 0.180 0.032
#> GSM601832 2 0.0000 0.8072 0.000 1.000 0.000 0.000
#> GSM601847 1 0.3172 0.2790 0.840 0.000 0.000 0.160
#> GSM601852 1 0.8558 0.4251 0.464 0.052 0.292 0.192
#> GSM601862 3 0.2197 0.5248 0.080 0.000 0.916 0.004
#> GSM601753 4 0.7296 0.3827 0.316 0.128 0.012 0.544
#> GSM601783 1 0.7341 0.4387 0.516 0.000 0.292 0.192
#> GSM601793 1 0.5272 0.3490 0.744 0.000 0.084 0.172
#> GSM601798 1 0.6500 -0.2384 0.484 0.072 0.000 0.444
#> GSM601828 1 0.7341 0.4387 0.516 0.000 0.292 0.192
#> GSM601838 3 0.7720 -0.0380 0.000 0.228 0.412 0.360
#> GSM601843 2 0.0000 0.8072 0.000 1.000 0.000 0.000
#> GSM601858 2 0.7289 0.2759 0.000 0.528 0.280 0.192
#> GSM601868 3 0.2342 0.5239 0.080 0.000 0.912 0.008
#> GSM601748 1 0.8558 0.4251 0.464 0.052 0.292 0.192
#> GSM601758 1 0.7268 0.4307 0.516 0.000 0.312 0.172
#> GSM601763 2 0.5148 0.6461 0.000 0.736 0.056 0.208
#> GSM601768 2 0.0000 0.8072 0.000 1.000 0.000 0.000
#> GSM601773 2 0.0000 0.8072 0.000 1.000 0.000 0.000
#> GSM601778 1 0.2868 0.3059 0.864 0.000 0.000 0.136
#> GSM601788 2 0.6436 0.4970 0.000 0.608 0.100 0.292
#> GSM601803 1 0.6504 -0.2484 0.476 0.072 0.000 0.452
#> GSM601808 3 0.3266 0.4759 0.168 0.000 0.832 0.000
#> GSM601813 1 0.7013 0.4028 0.516 0.000 0.356 0.128
#> GSM601818 1 0.8545 0.4239 0.464 0.052 0.296 0.188
#> GSM601823 1 0.7543 0.4020 0.568 0.028 0.268 0.136
#> GSM601833 2 0.0000 0.8072 0.000 1.000 0.000 0.000
#> GSM601848 1 0.3074 0.4643 0.848 0.000 0.152 0.000
#> GSM601853 1 0.7545 0.3178 0.416 0.000 0.396 0.188
#> GSM601863 3 0.2197 0.5248 0.080 0.000 0.916 0.004
#> GSM601754 1 0.6500 -0.2384 0.484 0.072 0.000 0.444
#> GSM601784 2 0.0000 0.8072 0.000 1.000 0.000 0.000
#> GSM601794 1 0.3768 0.2693 0.808 0.000 0.008 0.184
#> GSM601799 4 0.6648 0.3046 0.372 0.092 0.000 0.536
#> GSM601829 1 0.6854 0.4328 0.600 0.004 0.260 0.136
#> GSM601839 3 0.7720 -0.0380 0.000 0.228 0.412 0.360
#> GSM601844 1 0.8066 0.4251 0.484 0.028 0.316 0.172
#> GSM601859 2 0.0469 0.8055 0.000 0.988 0.000 0.012
#> GSM601869 3 0.2197 0.5248 0.080 0.000 0.916 0.004
#> GSM601749 1 0.7341 0.4387 0.516 0.000 0.292 0.192
#> GSM601759 1 0.7341 0.4387 0.516 0.000 0.292 0.192
#> GSM601764 2 0.4259 0.6965 0.000 0.816 0.056 0.128
#> GSM601769 2 0.5267 0.5996 0.000 0.740 0.184 0.076
#> GSM601774 2 0.0188 0.8067 0.000 0.996 0.000 0.004
#> GSM601779 1 0.1284 0.4121 0.964 0.000 0.012 0.024
#> GSM601789 2 0.6790 0.4138 0.000 0.608 0.192 0.200
#> GSM601804 1 0.6500 -0.2384 0.484 0.072 0.000 0.444
#> GSM601809 3 0.2988 0.5186 0.112 0.000 0.876 0.012
#> GSM601814 4 0.4627 0.3218 0.004 0.028 0.196 0.772
#> GSM601819 1 0.8558 0.4251 0.464 0.052 0.292 0.192
#> GSM601824 2 0.6160 0.5112 0.000 0.612 0.072 0.316
#> GSM601834 2 0.0524 0.8020 0.000 0.988 0.004 0.008
#> GSM601849 1 0.4072 0.4326 0.748 0.000 0.252 0.000
#> GSM601854 1 0.7341 0.4387 0.516 0.000 0.292 0.192
#> GSM601864 3 0.6952 0.0831 0.000 0.120 0.516 0.364
#> GSM601755 1 0.6500 -0.2384 0.484 0.072 0.000 0.444
#> GSM601785 2 0.0000 0.8072 0.000 1.000 0.000 0.000
#> GSM601795 1 0.3764 0.2203 0.784 0.000 0.000 0.216
#> GSM601800 1 0.6504 -0.2487 0.476 0.072 0.000 0.452
#> GSM601830 1 0.7042 0.4334 0.572 0.000 0.240 0.188
#> GSM601840 2 0.6221 0.5053 0.000 0.608 0.076 0.316
#> GSM601845 2 0.4257 0.6928 0.000 0.812 0.048 0.140
#> GSM601860 2 0.6179 0.5057 0.000 0.608 0.072 0.320
#> GSM601870 3 0.4567 0.2719 0.008 0.000 0.716 0.276
#> GSM601750 1 0.7341 0.4387 0.516 0.000 0.292 0.192
#> GSM601760 1 0.8467 0.4148 0.464 0.052 0.316 0.168
#> GSM601765 2 0.0000 0.8072 0.000 1.000 0.000 0.000
#> GSM601770 2 0.0000 0.8072 0.000 1.000 0.000 0.000
#> GSM601775 2 0.0336 0.8054 0.000 0.992 0.000 0.008
#> GSM601780 1 0.0779 0.4148 0.980 0.000 0.004 0.016
#> GSM601790 3 0.7832 -0.0813 0.000 0.260 0.380 0.360
#> GSM601805 1 0.6500 -0.2384 0.484 0.072 0.000 0.444
#> GSM601810 3 0.3791 0.4386 0.200 0.000 0.796 0.004
#> GSM601815 4 0.6994 0.1990 0.000 0.152 0.288 0.560
#> GSM601820 1 0.7155 0.4048 0.504 0.000 0.352 0.144
#> GSM601825 4 0.8646 0.2851 0.056 0.372 0.172 0.400
#> GSM601835 2 0.0895 0.7954 0.000 0.976 0.004 0.020
#> GSM601850 1 0.1716 0.3727 0.936 0.000 0.000 0.064
#> GSM601855 3 0.4509 0.4040 0.288 0.000 0.708 0.004
#> GSM601865 3 0.7463 0.0245 0.000 0.180 0.456 0.364
#> GSM601756 1 0.6500 -0.2384 0.484 0.072 0.000 0.444
#> GSM601786 3 0.7723 -0.0393 0.000 0.228 0.408 0.364
#> GSM601796 1 0.4677 0.2911 0.768 0.000 0.040 0.192
#> GSM601801 1 0.6500 -0.2384 0.484 0.072 0.000 0.444
#> GSM601831 1 0.7329 0.4375 0.516 0.000 0.296 0.188
#> GSM601841 3 0.3837 0.4058 0.224 0.000 0.776 0.000
#> GSM601846 4 0.8007 0.3095 0.100 0.300 0.068 0.532
#> GSM601861 4 0.7855 0.0481 0.000 0.320 0.284 0.396
#> GSM601871 3 0.4277 0.2797 0.000 0.000 0.720 0.280
#> GSM601751 2 0.7098 0.3481 0.000 0.564 0.192 0.244
#> GSM601761 1 0.6868 0.4129 0.544 0.000 0.336 0.120
#> GSM601766 2 0.0188 0.8068 0.000 0.996 0.000 0.004
#> GSM601771 3 0.9188 -0.2126 0.152 0.284 0.432 0.132
#> GSM601776 1 0.2976 0.4320 0.872 0.000 0.120 0.008
#> GSM601781 1 0.3539 0.4636 0.820 0.000 0.176 0.004
#> GSM601791 1 0.6627 0.4150 0.556 0.000 0.348 0.096
#> GSM601806 1 0.6500 -0.2384 0.484 0.072 0.000 0.444
#> GSM601811 3 0.2760 0.5104 0.128 0.000 0.872 0.000
#> GSM601816 1 0.3219 0.4648 0.836 0.000 0.164 0.000
#> GSM601821 4 0.7330 0.1396 0.000 0.180 0.312 0.508
#> GSM601826 1 0.7182 0.3984 0.512 0.004 0.356 0.128
#> GSM601836 2 0.4491 0.6850 0.000 0.800 0.060 0.140
#> GSM601851 1 0.1635 0.4373 0.948 0.000 0.044 0.008
#> GSM601856 3 0.7543 -0.3151 0.392 0.000 0.420 0.188
#> GSM601866 3 0.3052 0.5066 0.136 0.000 0.860 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601752 4 0.1153 0.7573 0.000 0.008 0.024 0.964 0.004
#> GSM601782 1 0.0771 0.8395 0.976 0.000 0.000 0.004 0.020
#> GSM601792 4 0.4473 0.7828 0.020 0.000 0.324 0.656 0.000
#> GSM601797 4 0.4366 0.7838 0.016 0.000 0.320 0.664 0.000
#> GSM601827 1 0.2900 0.7807 0.876 0.000 0.092 0.020 0.012
#> GSM601837 5 0.0000 0.9337 0.000 0.000 0.000 0.000 1.000
#> GSM601842 2 0.1121 0.8240 0.000 0.956 0.000 0.000 0.044
#> GSM601857 1 0.2170 0.8036 0.904 0.000 0.004 0.004 0.088
#> GSM601867 3 0.6202 0.8173 0.228 0.000 0.552 0.000 0.220
#> GSM601747 1 0.1952 0.8107 0.912 0.000 0.000 0.004 0.084
#> GSM601757 1 0.1205 0.8350 0.956 0.000 0.000 0.004 0.040
#> GSM601762 2 0.1768 0.8058 0.000 0.924 0.000 0.004 0.072
#> GSM601767 2 0.0000 0.8160 0.000 1.000 0.000 0.000 0.000
#> GSM601772 2 0.2280 0.8217 0.000 0.880 0.000 0.000 0.120
#> GSM601777 4 0.5951 0.7348 0.060 0.000 0.232 0.648 0.060
#> GSM601787 3 0.6155 0.7929 0.192 0.000 0.556 0.000 0.252
#> GSM601802 4 0.0451 0.7526 0.000 0.008 0.000 0.988 0.004
#> GSM601807 3 0.6858 0.7004 0.132 0.000 0.580 0.072 0.216
#> GSM601812 1 0.3205 0.8069 0.864 0.000 0.056 0.008 0.072
#> GSM601817 1 0.0162 0.8378 0.996 0.000 0.004 0.000 0.000
#> GSM601822 4 0.4859 0.7774 0.024 0.000 0.332 0.636 0.008
#> GSM601832 2 0.0000 0.8160 0.000 1.000 0.000 0.000 0.000
#> GSM601847 4 0.4384 0.7839 0.016 0.000 0.324 0.660 0.000
#> GSM601852 1 0.0162 0.8378 0.996 0.000 0.004 0.000 0.000
#> GSM601862 3 0.5680 0.8464 0.228 0.000 0.624 0.000 0.148
#> GSM601753 4 0.6157 0.2512 0.000 0.312 0.040 0.580 0.068
#> GSM601783 1 0.0162 0.8376 0.996 0.000 0.000 0.004 0.000
#> GSM601793 4 0.4473 0.7828 0.020 0.000 0.324 0.656 0.000
#> GSM601798 4 0.0451 0.7526 0.000 0.008 0.000 0.988 0.004
#> GSM601828 1 0.0162 0.8378 0.996 0.000 0.004 0.000 0.000
#> GSM601838 5 0.0000 0.9337 0.000 0.000 0.000 0.000 1.000
#> GSM601843 2 0.1197 0.8243 0.000 0.952 0.000 0.000 0.048
#> GSM601858 2 0.5476 0.6031 0.044 0.592 0.000 0.016 0.348
#> GSM601868 3 0.5680 0.8464 0.228 0.000 0.624 0.000 0.148
#> GSM601748 1 0.0000 0.8372 1.000 0.000 0.000 0.000 0.000
#> GSM601758 1 0.2445 0.7816 0.884 0.000 0.004 0.004 0.108
#> GSM601763 2 0.4693 0.7612 0.080 0.724 0.000 0.000 0.196
#> GSM601768 2 0.0000 0.8160 0.000 1.000 0.000 0.000 0.000
#> GSM601773 2 0.0000 0.8160 0.000 1.000 0.000 0.000 0.000
#> GSM601778 4 0.4384 0.7834 0.016 0.000 0.324 0.660 0.000
#> GSM601788 2 0.5590 0.6762 0.068 0.628 0.000 0.016 0.288
#> GSM601803 4 0.0579 0.7518 0.000 0.008 0.000 0.984 0.008
#> GSM601808 3 0.5680 0.8464 0.228 0.000 0.624 0.000 0.148
#> GSM601813 1 0.4718 0.7237 0.764 0.000 0.084 0.020 0.132
#> GSM601818 1 0.1357 0.8318 0.948 0.000 0.000 0.004 0.048
#> GSM601823 1 0.7137 0.4758 0.564 0.008 0.096 0.092 0.240
#> GSM601833 2 0.0000 0.8160 0.000 1.000 0.000 0.000 0.000
#> GSM601848 4 0.5102 0.7534 0.044 0.000 0.376 0.580 0.000
#> GSM601853 1 0.1792 0.8076 0.916 0.000 0.000 0.000 0.084
#> GSM601863 3 0.5680 0.8464 0.228 0.000 0.624 0.000 0.148
#> GSM601754 4 0.1059 0.7570 0.000 0.008 0.020 0.968 0.004
#> GSM601784 2 0.0000 0.8160 0.000 1.000 0.000 0.000 0.000
#> GSM601794 4 0.4473 0.7828 0.020 0.000 0.324 0.656 0.000
#> GSM601799 4 0.3089 0.7203 0.000 0.048 0.040 0.880 0.032
#> GSM601829 1 0.4398 0.6882 0.780 0.000 0.112 0.100 0.008
#> GSM601839 5 0.0000 0.9337 0.000 0.000 0.000 0.000 1.000
#> GSM601844 1 0.1670 0.8197 0.936 0.000 0.052 0.000 0.012
#> GSM601859 2 0.2389 0.8230 0.004 0.880 0.000 0.000 0.116
#> GSM601869 3 0.5680 0.8464 0.228 0.000 0.624 0.000 0.148
#> GSM601749 1 0.0000 0.8372 1.000 0.000 0.000 0.000 0.000
#> GSM601759 1 0.0324 0.8390 0.992 0.000 0.000 0.004 0.004
#> GSM601764 2 0.4757 0.7541 0.080 0.716 0.000 0.000 0.204
#> GSM601769 2 0.3109 0.7904 0.000 0.800 0.000 0.000 0.200
#> GSM601774 2 0.0162 0.8158 0.000 0.996 0.000 0.000 0.004
#> GSM601779 4 0.6347 0.6335 0.164 0.000 0.376 0.460 0.000
#> GSM601789 2 0.4688 0.6184 0.004 0.616 0.000 0.016 0.364
#> GSM601804 4 0.1830 0.7512 0.000 0.012 0.052 0.932 0.004
#> GSM601809 3 0.6026 0.8326 0.228 0.000 0.580 0.000 0.192
#> GSM601814 5 0.1608 0.8619 0.000 0.000 0.000 0.072 0.928
#> GSM601819 1 0.0404 0.8394 0.988 0.000 0.000 0.000 0.012
#> GSM601824 2 0.4905 0.7388 0.080 0.696 0.000 0.000 0.224
#> GSM601834 2 0.0000 0.8160 0.000 1.000 0.000 0.000 0.000
#> GSM601849 4 0.7558 0.5173 0.208 0.000 0.316 0.420 0.056
#> GSM601854 1 0.0000 0.8372 1.000 0.000 0.000 0.000 0.000
#> GSM601864 5 0.0162 0.9297 0.000 0.000 0.004 0.000 0.996
#> GSM601755 4 0.0451 0.7526 0.000 0.008 0.000 0.988 0.004
#> GSM601785 2 0.2179 0.8224 0.000 0.888 0.000 0.000 0.112
#> GSM601795 4 0.4290 0.7846 0.016 0.000 0.304 0.680 0.000
#> GSM601800 4 0.0451 0.7526 0.000 0.008 0.000 0.988 0.004
#> GSM601830 1 0.6962 0.3006 0.512 0.000 0.068 0.320 0.100
#> GSM601840 2 0.5809 0.6590 0.088 0.616 0.000 0.016 0.280
#> GSM601845 2 0.4960 0.7302 0.080 0.688 0.000 0.000 0.232
#> GSM601860 2 0.5655 0.6897 0.084 0.640 0.000 0.016 0.260
#> GSM601870 3 0.6548 0.6979 0.132 0.000 0.536 0.024 0.308
#> GSM601750 1 0.0000 0.8372 1.000 0.000 0.000 0.000 0.000
#> GSM601760 1 0.3063 0.7867 0.864 0.000 0.036 0.004 0.096
#> GSM601765 2 0.0000 0.8160 0.000 1.000 0.000 0.000 0.000
#> GSM601770 2 0.0000 0.8160 0.000 1.000 0.000 0.000 0.000
#> GSM601775 2 0.2574 0.8232 0.012 0.876 0.000 0.000 0.112
#> GSM601780 4 0.6081 0.6817 0.128 0.000 0.376 0.496 0.000
#> GSM601790 5 0.0000 0.9337 0.000 0.000 0.000 0.000 1.000
#> GSM601805 4 0.0451 0.7526 0.000 0.008 0.000 0.988 0.004
#> GSM601810 3 0.5654 0.8450 0.224 0.000 0.628 0.000 0.148
#> GSM601815 5 0.0000 0.9337 0.000 0.000 0.000 0.000 1.000
#> GSM601820 1 0.1502 0.8301 0.940 0.000 0.000 0.004 0.056
#> GSM601825 2 0.5879 0.7277 0.044 0.724 0.036 0.096 0.100
#> GSM601835 2 0.3109 0.7910 0.000 0.800 0.000 0.000 0.200
#> GSM601850 4 0.4981 0.7670 0.020 0.000 0.360 0.608 0.012
#> GSM601855 3 0.6455 0.7417 0.144 0.000 0.612 0.044 0.200
#> GSM601865 5 0.0162 0.9297 0.000 0.000 0.004 0.000 0.996
#> GSM601756 4 0.0451 0.7526 0.000 0.008 0.000 0.988 0.004
#> GSM601786 5 0.0000 0.9337 0.000 0.000 0.000 0.000 1.000
#> GSM601796 4 0.4384 0.7834 0.016 0.000 0.324 0.660 0.000
#> GSM601801 4 0.0451 0.7526 0.000 0.008 0.000 0.988 0.004
#> GSM601831 1 0.0693 0.8386 0.980 0.000 0.008 0.000 0.012
#> GSM601841 3 0.6339 0.6903 0.352 0.000 0.496 0.004 0.148
#> GSM601846 4 0.7043 0.3288 0.056 0.172 0.004 0.564 0.204
#> GSM601861 5 0.0000 0.9337 0.000 0.000 0.000 0.000 1.000
#> GSM601871 3 0.6155 0.7929 0.192 0.000 0.556 0.000 0.252
#> GSM601751 2 0.5716 0.6597 0.076 0.616 0.000 0.016 0.292
#> GSM601761 1 0.4326 0.6850 0.764 0.000 0.188 0.016 0.032
#> GSM601766 2 0.2338 0.8233 0.004 0.884 0.000 0.000 0.112
#> GSM601771 5 0.8150 0.2912 0.068 0.176 0.032 0.272 0.452
#> GSM601776 1 0.6972 -0.0686 0.404 0.000 0.360 0.224 0.012
#> GSM601781 4 0.4558 0.7831 0.024 0.000 0.324 0.652 0.000
#> GSM601791 1 0.5217 0.6319 0.692 0.000 0.232 0.028 0.048
#> GSM601806 4 0.0451 0.7526 0.000 0.008 0.000 0.988 0.004
#> GSM601811 3 0.5680 0.8464 0.228 0.000 0.624 0.000 0.148
#> GSM601816 4 0.4624 0.7779 0.024 0.000 0.340 0.636 0.000
#> GSM601821 5 0.0000 0.9337 0.000 0.000 0.000 0.000 1.000
#> GSM601826 1 0.4588 0.7285 0.768 0.000 0.092 0.012 0.128
#> GSM601836 2 0.5185 0.7161 0.100 0.672 0.000 0.000 0.228
#> GSM601851 3 0.6800 -0.4757 0.304 0.000 0.376 0.320 0.000
#> GSM601856 1 0.2046 0.8194 0.916 0.000 0.016 0.000 0.068
#> GSM601866 3 0.5752 0.8392 0.240 0.000 0.612 0.000 0.148
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601752 4 0.3866 0.640 0.000 0.000 0.000 0.516 0.000 0.484
#> GSM601782 1 0.0146 0.915 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM601792 6 0.0000 0.601 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601797 6 0.0000 0.601 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601827 1 0.3956 0.667 0.716 0.000 0.004 0.252 0.000 0.028
#> GSM601837 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601842 2 0.0260 0.922 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM601857 1 0.0865 0.901 0.964 0.000 0.036 0.000 0.000 0.000
#> GSM601867 3 0.1124 0.916 0.008 0.000 0.956 0.000 0.036 0.000
#> GSM601747 1 0.0717 0.908 0.976 0.000 0.000 0.000 0.008 0.016
#> GSM601757 1 0.0146 0.915 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM601762 2 0.1204 0.918 0.000 0.944 0.000 0.000 0.056 0.000
#> GSM601767 2 0.0000 0.921 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601772 2 0.0790 0.924 0.000 0.968 0.000 0.000 0.032 0.000
#> GSM601777 6 0.0146 0.597 0.000 0.000 0.004 0.000 0.000 0.996
#> GSM601787 3 0.1010 0.917 0.004 0.000 0.960 0.000 0.036 0.000
#> GSM601802 4 0.3857 0.669 0.000 0.000 0.000 0.532 0.000 0.468
#> GSM601807 3 0.3520 0.734 0.000 0.000 0.776 0.000 0.036 0.188
#> GSM601812 1 0.1555 0.885 0.932 0.000 0.004 0.060 0.000 0.004
#> GSM601817 1 0.0260 0.915 0.992 0.000 0.000 0.008 0.000 0.000
#> GSM601822 6 0.3265 0.632 0.000 0.000 0.004 0.248 0.000 0.748
#> GSM601832 2 0.0000 0.921 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601847 6 0.1588 0.611 0.000 0.000 0.004 0.072 0.000 0.924
#> GSM601852 1 0.0000 0.915 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601862 3 0.0146 0.927 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM601753 4 0.5425 0.182 0.000 0.300 0.000 0.596 0.068 0.036
#> GSM601783 1 0.0146 0.915 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM601793 6 0.0000 0.601 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601798 4 0.3857 0.669 0.000 0.000 0.000 0.532 0.000 0.468
#> GSM601828 1 0.0260 0.915 0.992 0.000 0.000 0.008 0.000 0.000
#> GSM601838 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601843 2 0.0458 0.923 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM601858 2 0.2588 0.885 0.004 0.860 0.000 0.012 0.124 0.000
#> GSM601868 3 0.0146 0.927 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM601748 1 0.0000 0.915 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601758 1 0.2362 0.802 0.860 0.000 0.004 0.000 0.000 0.136
#> GSM601763 2 0.2320 0.907 0.004 0.892 0.000 0.024 0.080 0.000
#> GSM601768 2 0.0000 0.921 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601773 2 0.0000 0.921 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601778 6 0.0000 0.601 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601788 2 0.2741 0.896 0.008 0.868 0.000 0.032 0.092 0.000
#> GSM601803 4 0.3857 0.669 0.000 0.000 0.000 0.532 0.000 0.468
#> GSM601808 3 0.0146 0.927 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM601813 4 0.7046 -0.298 0.260 0.000 0.068 0.448 0.008 0.216
#> GSM601818 1 0.0000 0.915 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601823 4 0.6975 -0.244 0.264 0.000 0.004 0.460 0.080 0.192
#> GSM601833 2 0.0000 0.921 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601848 6 0.3955 0.620 0.000 0.000 0.004 0.436 0.000 0.560
#> GSM601853 1 0.0458 0.912 0.984 0.000 0.016 0.000 0.000 0.000
#> GSM601863 3 0.0146 0.927 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM601754 4 0.3868 0.644 0.000 0.000 0.000 0.504 0.000 0.496
#> GSM601784 2 0.0000 0.921 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601794 6 0.0000 0.601 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601799 4 0.4422 0.430 0.000 0.000 0.000 0.680 0.068 0.252
#> GSM601829 4 0.5664 -0.293 0.444 0.000 0.004 0.448 0.012 0.092
#> GSM601839 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601844 1 0.0935 0.904 0.964 0.000 0.004 0.032 0.000 0.000
#> GSM601859 2 0.0547 0.924 0.000 0.980 0.000 0.000 0.020 0.000
#> GSM601869 3 0.0146 0.927 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM601749 1 0.0000 0.915 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601759 1 0.0260 0.914 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM601764 2 0.2320 0.907 0.004 0.892 0.000 0.024 0.080 0.000
#> GSM601769 2 0.1910 0.901 0.000 0.892 0.000 0.000 0.108 0.000
#> GSM601774 2 0.0000 0.921 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601779 6 0.4093 0.618 0.004 0.000 0.004 0.440 0.000 0.552
#> GSM601789 2 0.2243 0.897 0.004 0.880 0.000 0.004 0.112 0.000
#> GSM601804 4 0.3619 0.501 0.000 0.000 0.000 0.680 0.004 0.316
#> GSM601809 3 0.0692 0.923 0.004 0.000 0.976 0.000 0.020 0.000
#> GSM601814 5 0.1779 0.862 0.000 0.000 0.000 0.064 0.920 0.016
#> GSM601819 1 0.0458 0.911 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM601824 2 0.2320 0.907 0.004 0.892 0.000 0.024 0.080 0.000
#> GSM601834 2 0.0000 0.921 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601849 6 0.3966 0.617 0.000 0.000 0.004 0.444 0.000 0.552
#> GSM601854 1 0.0260 0.915 0.992 0.000 0.000 0.008 0.000 0.000
#> GSM601864 5 0.0146 0.933 0.000 0.000 0.004 0.000 0.996 0.000
#> GSM601755 4 0.3857 0.669 0.000 0.000 0.000 0.532 0.000 0.468
#> GSM601785 2 0.1075 0.922 0.000 0.952 0.000 0.000 0.048 0.000
#> GSM601795 6 0.0000 0.601 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601800 4 0.3857 0.669 0.000 0.000 0.000 0.532 0.000 0.468
#> GSM601830 1 0.6636 0.109 0.468 0.000 0.068 0.100 0.012 0.352
#> GSM601840 2 0.2666 0.898 0.008 0.872 0.000 0.028 0.092 0.000
#> GSM601845 2 0.2320 0.907 0.004 0.892 0.000 0.024 0.080 0.000
#> GSM601860 2 0.2666 0.898 0.008 0.872 0.000 0.028 0.092 0.000
#> GSM601870 3 0.2917 0.847 0.004 0.000 0.852 0.000 0.104 0.040
#> GSM601750 1 0.0000 0.915 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601760 1 0.2703 0.757 0.824 0.000 0.000 0.000 0.004 0.172
#> GSM601765 2 0.0000 0.921 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601770 2 0.0000 0.921 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601775 2 0.0790 0.924 0.000 0.968 0.000 0.000 0.032 0.000
#> GSM601780 6 0.3961 0.619 0.000 0.000 0.004 0.440 0.000 0.556
#> GSM601790 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601805 4 0.3857 0.669 0.000 0.000 0.000 0.532 0.000 0.468
#> GSM601810 3 0.0146 0.927 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM601815 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601820 1 0.0260 0.914 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM601825 2 0.3490 0.859 0.000 0.832 0.000 0.068 0.072 0.028
#> GSM601835 2 0.0858 0.924 0.000 0.968 0.000 0.000 0.028 0.004
#> GSM601850 6 0.3961 0.619 0.000 0.000 0.004 0.440 0.000 0.556
#> GSM601855 3 0.2342 0.864 0.004 0.000 0.888 0.000 0.020 0.088
#> GSM601865 5 0.0146 0.933 0.000 0.000 0.004 0.000 0.996 0.000
#> GSM601756 4 0.3857 0.669 0.000 0.000 0.000 0.532 0.000 0.468
#> GSM601786 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601796 6 0.0000 0.601 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601801 4 0.3857 0.669 0.000 0.000 0.000 0.532 0.000 0.468
#> GSM601831 1 0.0520 0.913 0.984 0.000 0.008 0.008 0.000 0.000
#> GSM601841 3 0.4248 0.655 0.040 0.000 0.732 0.004 0.012 0.212
#> GSM601846 2 0.7191 -0.235 0.000 0.324 0.000 0.308 0.080 0.288
#> GSM601861 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601871 3 0.1010 0.917 0.004 0.000 0.960 0.000 0.036 0.000
#> GSM601751 2 0.4224 0.766 0.004 0.744 0.000 0.156 0.096 0.000
#> GSM601761 1 0.3853 0.583 0.708 0.000 0.000 0.012 0.008 0.272
#> GSM601766 2 0.0458 0.923 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM601771 5 0.8772 0.119 0.004 0.220 0.148 0.232 0.292 0.104
#> GSM601776 6 0.4644 0.597 0.032 0.000 0.004 0.440 0.000 0.524
#> GSM601781 6 0.0000 0.601 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601791 6 0.6338 0.374 0.272 0.000 0.000 0.308 0.012 0.408
#> GSM601806 4 0.3857 0.669 0.000 0.000 0.000 0.532 0.000 0.468
#> GSM601811 3 0.0146 0.927 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM601816 6 0.3428 0.636 0.000 0.000 0.000 0.304 0.000 0.696
#> GSM601821 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601826 1 0.4876 0.539 0.608 0.000 0.004 0.328 0.056 0.004
#> GSM601836 2 0.2320 0.907 0.004 0.892 0.000 0.024 0.080 0.000
#> GSM601851 6 0.4284 0.613 0.012 0.000 0.004 0.440 0.000 0.544
#> GSM601856 1 0.0622 0.912 0.980 0.000 0.012 0.008 0.000 0.000
#> GSM601866 3 0.2003 0.829 0.116 0.000 0.884 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 time(p) gender(p) k
#> ATC:mclust 115 0.930 0.295 2
#> ATC:mclust 120 0.808 0.311 3
#> ATC:mclust 34 0.338 1.000 4
#> ATC:mclust 118 0.907 0.450 5
#> ATC:mclust 116 0.948 0.419 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 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.991 0.996 0.5022 0.499 0.499
#> 3 3 0.695 0.807 0.903 0.2643 0.751 0.556
#> 4 4 0.606 0.715 0.838 0.1610 0.819 0.554
#> 5 5 0.610 0.550 0.736 0.0667 0.881 0.596
#> 6 6 0.653 0.481 0.708 0.0346 0.873 0.543
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
#> GSM601752 2 0.0000 0.999 0.000 1.000
#> GSM601782 1 0.0000 0.993 1.000 0.000
#> GSM601792 1 0.0000 0.993 1.000 0.000
#> GSM601797 1 0.0000 0.993 1.000 0.000
#> GSM601827 1 0.0000 0.993 1.000 0.000
#> GSM601837 2 0.0000 0.999 0.000 1.000
#> GSM601842 2 0.0000 0.999 0.000 1.000
#> GSM601857 1 0.0000 0.993 1.000 0.000
#> GSM601867 1 0.0000 0.993 1.000 0.000
#> GSM601747 1 0.0376 0.989 0.996 0.004
#> GSM601757 1 0.0000 0.993 1.000 0.000
#> GSM601762 2 0.0000 0.999 0.000 1.000
#> GSM601767 2 0.0000 0.999 0.000 1.000
#> GSM601772 2 0.0000 0.999 0.000 1.000
#> GSM601777 1 0.0000 0.993 1.000 0.000
#> GSM601787 1 0.0000 0.993 1.000 0.000
#> GSM601802 2 0.0000 0.999 0.000 1.000
#> GSM601807 1 0.0000 0.993 1.000 0.000
#> GSM601812 1 0.0000 0.993 1.000 0.000
#> GSM601817 1 0.0000 0.993 1.000 0.000
#> GSM601822 1 0.0000 0.993 1.000 0.000
#> GSM601832 2 0.0000 0.999 0.000 1.000
#> GSM601847 1 0.0000 0.993 1.000 0.000
#> GSM601852 1 0.0000 0.993 1.000 0.000
#> GSM601862 1 0.0000 0.993 1.000 0.000
#> GSM601753 2 0.0000 0.999 0.000 1.000
#> GSM601783 1 0.0000 0.993 1.000 0.000
#> GSM601793 1 0.0000 0.993 1.000 0.000
#> GSM601798 2 0.0000 0.999 0.000 1.000
#> GSM601828 1 0.0000 0.993 1.000 0.000
#> GSM601838 2 0.0000 0.999 0.000 1.000
#> GSM601843 2 0.0000 0.999 0.000 1.000
#> GSM601858 2 0.0000 0.999 0.000 1.000
#> GSM601868 1 0.0000 0.993 1.000 0.000
#> GSM601748 1 0.0000 0.993 1.000 0.000
#> GSM601758 1 0.0000 0.993 1.000 0.000
#> GSM601763 2 0.0000 0.999 0.000 1.000
#> GSM601768 2 0.0000 0.999 0.000 1.000
#> GSM601773 2 0.0000 0.999 0.000 1.000
#> GSM601778 1 0.0000 0.993 1.000 0.000
#> GSM601788 2 0.0000 0.999 0.000 1.000
#> GSM601803 2 0.0000 0.999 0.000 1.000
#> GSM601808 1 0.0000 0.993 1.000 0.000
#> GSM601813 1 0.0000 0.993 1.000 0.000
#> GSM601818 1 0.0000 0.993 1.000 0.000
#> GSM601823 2 0.1184 0.983 0.016 0.984
#> GSM601833 2 0.0000 0.999 0.000 1.000
#> GSM601848 1 0.0000 0.993 1.000 0.000
#> GSM601853 1 0.0000 0.993 1.000 0.000
#> GSM601863 1 0.0000 0.993 1.000 0.000
#> GSM601754 2 0.0000 0.999 0.000 1.000
#> GSM601784 2 0.0000 0.999 0.000 1.000
#> GSM601794 1 0.0000 0.993 1.000 0.000
#> GSM601799 2 0.0000 0.999 0.000 1.000
#> GSM601829 1 0.0000 0.993 1.000 0.000
#> GSM601839 2 0.0000 0.999 0.000 1.000
#> GSM601844 1 0.1184 0.979 0.984 0.016
#> GSM601859 2 0.0000 0.999 0.000 1.000
#> GSM601869 1 0.0000 0.993 1.000 0.000
#> GSM601749 1 0.0000 0.993 1.000 0.000
#> GSM601759 1 0.0000 0.993 1.000 0.000
#> GSM601764 2 0.0000 0.999 0.000 1.000
#> GSM601769 2 0.0000 0.999 0.000 1.000
#> GSM601774 2 0.0000 0.999 0.000 1.000
#> GSM601779 1 0.0000 0.993 1.000 0.000
#> GSM601789 2 0.0000 0.999 0.000 1.000
#> GSM601804 2 0.0000 0.999 0.000 1.000
#> GSM601809 1 0.0000 0.993 1.000 0.000
#> GSM601814 2 0.0000 0.999 0.000 1.000
#> GSM601819 1 0.0000 0.993 1.000 0.000
#> GSM601824 2 0.0000 0.999 0.000 1.000
#> GSM601834 2 0.0000 0.999 0.000 1.000
#> GSM601849 1 0.0000 0.993 1.000 0.000
#> GSM601854 1 0.0000 0.993 1.000 0.000
#> GSM601864 1 0.4298 0.903 0.912 0.088
#> GSM601755 2 0.0000 0.999 0.000 1.000
#> GSM601785 2 0.0000 0.999 0.000 1.000
#> GSM601795 1 0.3584 0.927 0.932 0.068
#> GSM601800 2 0.0000 0.999 0.000 1.000
#> GSM601830 1 0.0000 0.993 1.000 0.000
#> GSM601840 2 0.0000 0.999 0.000 1.000
#> GSM601845 2 0.0000 0.999 0.000 1.000
#> GSM601860 2 0.0000 0.999 0.000 1.000
#> GSM601870 1 0.0000 0.993 1.000 0.000
#> GSM601750 1 0.0000 0.993 1.000 0.000
#> GSM601760 1 0.0000 0.993 1.000 0.000
#> GSM601765 2 0.0000 0.999 0.000 1.000
#> GSM601770 2 0.0000 0.999 0.000 1.000
#> GSM601775 2 0.0000 0.999 0.000 1.000
#> GSM601780 1 0.0000 0.993 1.000 0.000
#> GSM601790 2 0.0000 0.999 0.000 1.000
#> GSM601805 2 0.0000 0.999 0.000 1.000
#> GSM601810 1 0.0000 0.993 1.000 0.000
#> GSM601815 2 0.0000 0.999 0.000 1.000
#> GSM601820 1 0.0000 0.993 1.000 0.000
#> GSM601825 2 0.0000 0.999 0.000 1.000
#> GSM601835 2 0.0000 0.999 0.000 1.000
#> GSM601850 1 0.0000 0.993 1.000 0.000
#> GSM601855 1 0.0000 0.993 1.000 0.000
#> GSM601865 1 0.8443 0.632 0.728 0.272
#> GSM601756 2 0.0000 0.999 0.000 1.000
#> GSM601786 2 0.0000 0.999 0.000 1.000
#> GSM601796 1 0.0000 0.993 1.000 0.000
#> GSM601801 2 0.0000 0.999 0.000 1.000
#> GSM601831 1 0.0000 0.993 1.000 0.000
#> GSM601841 1 0.0000 0.993 1.000 0.000
#> GSM601846 2 0.0000 0.999 0.000 1.000
#> GSM601861 2 0.0000 0.999 0.000 1.000
#> GSM601871 1 0.0000 0.993 1.000 0.000
#> GSM601751 2 0.0000 0.999 0.000 1.000
#> GSM601761 1 0.0000 0.993 1.000 0.000
#> GSM601766 2 0.0000 0.999 0.000 1.000
#> GSM601771 2 0.2948 0.945 0.052 0.948
#> GSM601776 1 0.0000 0.993 1.000 0.000
#> GSM601781 1 0.0000 0.993 1.000 0.000
#> GSM601791 1 0.0000 0.993 1.000 0.000
#> GSM601806 2 0.0000 0.999 0.000 1.000
#> GSM601811 1 0.0000 0.993 1.000 0.000
#> GSM601816 1 0.0000 0.993 1.000 0.000
#> GSM601821 2 0.0000 0.999 0.000 1.000
#> GSM601826 1 0.1633 0.971 0.976 0.024
#> GSM601836 2 0.0000 0.999 0.000 1.000
#> GSM601851 1 0.0000 0.993 1.000 0.000
#> GSM601856 1 0.0000 0.993 1.000 0.000
#> GSM601866 1 0.0000 0.993 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601752 2 0.0237 0.8368 0.004 0.996 0.000
#> GSM601782 1 0.0000 0.9264 1.000 0.000 0.000
#> GSM601792 1 0.3116 0.8725 0.892 0.108 0.000
#> GSM601797 1 0.0000 0.9264 1.000 0.000 0.000
#> GSM601827 1 0.2537 0.8917 0.920 0.080 0.000
#> GSM601837 3 0.0000 0.8778 0.000 0.000 1.000
#> GSM601842 2 0.0747 0.8388 0.000 0.984 0.016
#> GSM601857 1 0.0237 0.9253 0.996 0.000 0.004
#> GSM601867 3 0.6095 0.4218 0.392 0.000 0.608
#> GSM601747 1 0.5497 0.6721 0.708 0.292 0.000
#> GSM601757 1 0.0424 0.9253 0.992 0.008 0.000
#> GSM601762 2 0.5678 0.6577 0.000 0.684 0.316
#> GSM601767 2 0.4887 0.7566 0.000 0.772 0.228
#> GSM601772 2 0.4452 0.7832 0.000 0.808 0.192
#> GSM601777 1 0.0237 0.9253 0.996 0.000 0.004
#> GSM601787 3 0.5760 0.5517 0.328 0.000 0.672
#> GSM601802 2 0.4605 0.7749 0.000 0.796 0.204
#> GSM601807 1 0.3116 0.8303 0.892 0.000 0.108
#> GSM601812 1 0.0000 0.9264 1.000 0.000 0.000
#> GSM601817 1 0.5785 0.6169 0.668 0.332 0.000
#> GSM601822 1 0.5016 0.7512 0.760 0.240 0.000
#> GSM601832 2 0.1031 0.8381 0.000 0.976 0.024
#> GSM601847 1 0.0424 0.9255 0.992 0.008 0.000
#> GSM601852 2 0.2711 0.7725 0.088 0.912 0.000
#> GSM601862 1 0.0237 0.9253 0.996 0.000 0.004
#> GSM601753 2 0.0237 0.8381 0.000 0.996 0.004
#> GSM601783 1 0.4291 0.8114 0.820 0.180 0.000
#> GSM601793 1 0.1031 0.9204 0.976 0.024 0.000
#> GSM601798 2 0.5216 0.7266 0.000 0.740 0.260
#> GSM601828 1 0.4974 0.7546 0.764 0.236 0.000
#> GSM601838 3 0.0237 0.8779 0.000 0.004 0.996
#> GSM601843 2 0.4002 0.8018 0.000 0.840 0.160
#> GSM601858 3 0.0237 0.8779 0.000 0.004 0.996
#> GSM601868 1 0.0424 0.9232 0.992 0.000 0.008
#> GSM601748 1 0.1753 0.9100 0.952 0.048 0.000
#> GSM601758 1 0.0237 0.9261 0.996 0.004 0.000
#> GSM601763 2 0.0237 0.8368 0.004 0.996 0.000
#> GSM601768 2 0.4002 0.8012 0.000 0.840 0.160
#> GSM601773 2 0.4750 0.7662 0.000 0.784 0.216
#> GSM601778 1 0.0000 0.9264 1.000 0.000 0.000
#> GSM601788 2 0.5138 0.7350 0.000 0.748 0.252
#> GSM601803 3 0.6225 -0.0910 0.000 0.432 0.568
#> GSM601808 1 0.0237 0.9253 0.996 0.000 0.004
#> GSM601813 1 0.0000 0.9264 1.000 0.000 0.000
#> GSM601818 1 0.0000 0.9264 1.000 0.000 0.000
#> GSM601823 2 0.0592 0.8330 0.012 0.988 0.000
#> GSM601833 2 0.3619 0.8113 0.000 0.864 0.136
#> GSM601848 2 0.6274 -0.0324 0.456 0.544 0.000
#> GSM601853 1 0.0000 0.9264 1.000 0.000 0.000
#> GSM601863 1 0.0237 0.9253 0.996 0.000 0.004
#> GSM601754 2 0.0237 0.8368 0.004 0.996 0.000
#> GSM601784 2 0.4796 0.7630 0.000 0.780 0.220
#> GSM601794 1 0.0424 0.9253 0.992 0.008 0.000
#> GSM601799 2 0.0000 0.8375 0.000 1.000 0.000
#> GSM601829 2 0.5098 0.5725 0.248 0.752 0.000
#> GSM601839 3 0.0000 0.8778 0.000 0.000 1.000
#> GSM601844 2 0.1289 0.8211 0.032 0.968 0.000
#> GSM601859 2 0.0237 0.8381 0.000 0.996 0.004
#> GSM601869 1 0.0237 0.9253 0.996 0.000 0.004
#> GSM601749 1 0.5363 0.7080 0.724 0.276 0.000
#> GSM601759 1 0.0000 0.9264 1.000 0.000 0.000
#> GSM601764 2 0.0237 0.8368 0.004 0.996 0.000
#> GSM601769 2 0.6095 0.5358 0.000 0.608 0.392
#> GSM601774 2 0.5363 0.7086 0.000 0.724 0.276
#> GSM601779 2 0.1964 0.8029 0.056 0.944 0.000
#> GSM601789 3 0.1411 0.8496 0.000 0.036 0.964
#> GSM601804 2 0.0237 0.8368 0.004 0.996 0.000
#> GSM601809 1 0.0892 0.9149 0.980 0.000 0.020
#> GSM601814 3 0.0424 0.8754 0.000 0.008 0.992
#> GSM601819 2 0.6140 0.1710 0.404 0.596 0.000
#> GSM601824 2 0.0237 0.8368 0.004 0.996 0.000
#> GSM601834 2 0.3879 0.8052 0.000 0.848 0.152
#> GSM601849 2 0.5529 0.4780 0.296 0.704 0.000
#> GSM601854 1 0.2356 0.8968 0.928 0.072 0.000
#> GSM601864 3 0.0892 0.8689 0.020 0.000 0.980
#> GSM601755 2 0.4887 0.7570 0.000 0.772 0.228
#> GSM601785 2 0.0237 0.8381 0.000 0.996 0.004
#> GSM601795 2 0.1753 0.8100 0.048 0.952 0.000
#> GSM601800 2 0.1753 0.8352 0.000 0.952 0.048
#> GSM601830 1 0.0000 0.9264 1.000 0.000 0.000
#> GSM601840 2 0.2165 0.8323 0.000 0.936 0.064
#> GSM601845 2 0.0237 0.8368 0.004 0.996 0.000
#> GSM601860 2 0.1289 0.8377 0.000 0.968 0.032
#> GSM601870 3 0.5098 0.6651 0.248 0.000 0.752
#> GSM601750 1 0.1411 0.9155 0.964 0.036 0.000
#> GSM601760 1 0.1411 0.9156 0.964 0.036 0.000
#> GSM601765 2 0.0424 0.8383 0.000 0.992 0.008
#> GSM601770 2 0.4346 0.7878 0.000 0.816 0.184
#> GSM601775 2 0.0000 0.8375 0.000 1.000 0.000
#> GSM601780 1 0.5327 0.7129 0.728 0.272 0.000
#> GSM601790 3 0.0237 0.8779 0.000 0.004 0.996
#> GSM601805 2 0.5431 0.6992 0.000 0.716 0.284
#> GSM601810 1 0.0237 0.9253 0.996 0.000 0.004
#> GSM601815 3 0.0237 0.8779 0.000 0.004 0.996
#> GSM601820 1 0.0000 0.9264 1.000 0.000 0.000
#> GSM601825 2 0.3482 0.8139 0.000 0.872 0.128
#> GSM601835 2 0.3752 0.8084 0.000 0.856 0.144
#> GSM601850 1 0.3267 0.8664 0.884 0.116 0.000
#> GSM601855 1 0.1289 0.9051 0.968 0.000 0.032
#> GSM601865 3 0.0747 0.8714 0.016 0.000 0.984
#> GSM601756 2 0.6026 0.5666 0.000 0.624 0.376
#> GSM601786 3 0.0000 0.8778 0.000 0.000 1.000
#> GSM601796 1 0.0000 0.9264 1.000 0.000 0.000
#> GSM601801 2 0.6111 0.5290 0.000 0.604 0.396
#> GSM601831 1 0.0000 0.9264 1.000 0.000 0.000
#> GSM601841 1 0.0237 0.9253 0.996 0.000 0.004
#> GSM601846 2 0.0237 0.8368 0.004 0.996 0.000
#> GSM601861 3 0.0237 0.8779 0.000 0.004 0.996
#> GSM601871 3 0.5810 0.5380 0.336 0.000 0.664
#> GSM601751 2 0.4796 0.7660 0.000 0.780 0.220
#> GSM601761 1 0.2261 0.8990 0.932 0.068 0.000
#> GSM601766 2 0.0000 0.8375 0.000 1.000 0.000
#> GSM601771 3 0.0237 0.8768 0.004 0.000 0.996
#> GSM601776 1 0.4452 0.7998 0.808 0.192 0.000
#> GSM601781 1 0.0237 0.9253 0.996 0.000 0.004
#> GSM601791 1 0.0237 0.9261 0.996 0.004 0.000
#> GSM601806 3 0.0592 0.8724 0.000 0.012 0.988
#> GSM601811 1 0.0592 0.9206 0.988 0.000 0.012
#> GSM601816 1 0.3038 0.8755 0.896 0.104 0.000
#> GSM601821 3 0.0237 0.8779 0.000 0.004 0.996
#> GSM601826 2 0.1411 0.8183 0.036 0.964 0.000
#> GSM601836 2 0.0237 0.8368 0.004 0.996 0.000
#> GSM601851 1 0.5363 0.7078 0.724 0.276 0.000
#> GSM601856 1 0.0000 0.9264 1.000 0.000 0.000
#> GSM601866 1 0.0237 0.9253 0.996 0.000 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601752 4 0.1833 0.7561 0.000 0.024 0.032 0.944
#> GSM601782 1 0.2222 0.8497 0.932 0.032 0.004 0.032
#> GSM601792 4 0.2773 0.8230 0.116 0.000 0.004 0.880
#> GSM601797 4 0.2882 0.8156 0.084 0.000 0.024 0.892
#> GSM601827 1 0.2943 0.8422 0.892 0.032 0.000 0.076
#> GSM601837 3 0.1929 0.7609 0.036 0.024 0.940 0.000
#> GSM601842 2 0.0336 0.8289 0.000 0.992 0.000 0.008
#> GSM601857 1 0.2412 0.8051 0.908 0.000 0.084 0.008
#> GSM601867 1 0.4134 0.5998 0.740 0.000 0.260 0.000
#> GSM601747 1 0.5466 0.3206 0.548 0.436 0.000 0.016
#> GSM601757 1 0.3658 0.7806 0.836 0.144 0.000 0.020
#> GSM601762 2 0.6278 0.5251 0.000 0.652 0.228 0.120
#> GSM601767 2 0.4297 0.7437 0.000 0.820 0.084 0.096
#> GSM601772 2 0.0657 0.8254 0.000 0.984 0.012 0.004
#> GSM601777 4 0.5109 0.7461 0.196 0.000 0.060 0.744
#> GSM601787 3 0.5229 0.1994 0.428 0.000 0.564 0.008
#> GSM601802 4 0.3895 0.6671 0.000 0.036 0.132 0.832
#> GSM601807 1 0.4098 0.6882 0.784 0.000 0.204 0.012
#> GSM601812 1 0.2345 0.8396 0.900 0.000 0.000 0.100
#> GSM601817 1 0.4817 0.4596 0.612 0.388 0.000 0.000
#> GSM601822 4 0.3306 0.8116 0.156 0.004 0.000 0.840
#> GSM601832 2 0.1022 0.8312 0.000 0.968 0.000 0.032
#> GSM601847 4 0.2988 0.8231 0.112 0.000 0.012 0.876
#> GSM601852 2 0.3542 0.7384 0.120 0.852 0.000 0.028
#> GSM601862 1 0.1174 0.8461 0.968 0.000 0.020 0.012
#> GSM601753 2 0.6305 0.3503 0.000 0.516 0.060 0.424
#> GSM601783 1 0.4667 0.8023 0.796 0.108 0.000 0.096
#> GSM601793 4 0.2704 0.8224 0.124 0.000 0.000 0.876
#> GSM601798 4 0.3048 0.7037 0.000 0.016 0.108 0.876
#> GSM601828 1 0.4673 0.6356 0.700 0.292 0.000 0.008
#> GSM601838 3 0.1733 0.7702 0.000 0.028 0.948 0.024
#> GSM601843 2 0.0188 0.8266 0.000 0.996 0.004 0.000
#> GSM601858 3 0.5883 0.5020 0.060 0.300 0.640 0.000
#> GSM601868 1 0.1584 0.8391 0.952 0.000 0.036 0.012
#> GSM601748 1 0.3636 0.7554 0.820 0.172 0.000 0.008
#> GSM601758 1 0.2799 0.8362 0.884 0.008 0.000 0.108
#> GSM601763 2 0.1557 0.8164 0.000 0.944 0.000 0.056
#> GSM601768 2 0.0336 0.8273 0.000 0.992 0.008 0.000
#> GSM601773 2 0.3439 0.7814 0.000 0.868 0.048 0.084
#> GSM601778 4 0.3224 0.8199 0.120 0.000 0.016 0.864
#> GSM601788 2 0.2845 0.7952 0.000 0.896 0.076 0.028
#> GSM601803 3 0.5996 0.2144 0.000 0.040 0.512 0.448
#> GSM601808 1 0.0779 0.8457 0.980 0.000 0.016 0.004
#> GSM601813 1 0.2921 0.8148 0.860 0.000 0.000 0.140
#> GSM601818 1 0.2310 0.8163 0.920 0.004 0.068 0.008
#> GSM601823 2 0.4624 0.4820 0.000 0.660 0.000 0.340
#> GSM601833 2 0.1820 0.8249 0.000 0.944 0.020 0.036
#> GSM601848 4 0.3428 0.8174 0.144 0.012 0.000 0.844
#> GSM601853 1 0.0188 0.8474 0.996 0.000 0.004 0.000
#> GSM601863 1 0.0895 0.8495 0.976 0.000 0.004 0.020
#> GSM601754 4 0.1820 0.7539 0.000 0.020 0.036 0.944
#> GSM601784 2 0.3464 0.7825 0.000 0.868 0.056 0.076
#> GSM601794 4 0.3047 0.8219 0.116 0.000 0.012 0.872
#> GSM601799 4 0.4888 0.1002 0.000 0.412 0.000 0.588
#> GSM601829 2 0.7614 0.2028 0.300 0.468 0.000 0.232
#> GSM601839 3 0.1936 0.7652 0.028 0.032 0.940 0.000
#> GSM601844 2 0.2530 0.7903 0.004 0.896 0.000 0.100
#> GSM601859 2 0.0524 0.8277 0.000 0.988 0.008 0.004
#> GSM601869 1 0.1302 0.8504 0.956 0.000 0.000 0.044
#> GSM601749 1 0.7050 0.5360 0.552 0.292 0.000 0.156
#> GSM601759 1 0.2610 0.8435 0.900 0.012 0.000 0.088
#> GSM601764 2 0.0817 0.8281 0.000 0.976 0.000 0.024
#> GSM601769 2 0.6634 0.4052 0.000 0.592 0.292 0.116
#> GSM601774 2 0.4706 0.7076 0.000 0.788 0.140 0.072
#> GSM601779 4 0.3554 0.8201 0.136 0.020 0.000 0.844
#> GSM601789 3 0.5259 0.3290 0.008 0.376 0.612 0.004
#> GSM601804 4 0.2565 0.7387 0.000 0.056 0.032 0.912
#> GSM601809 1 0.1820 0.8518 0.944 0.000 0.020 0.036
#> GSM601814 3 0.4982 0.7115 0.000 0.092 0.772 0.136
#> GSM601819 2 0.5619 0.3789 0.320 0.640 0.000 0.040
#> GSM601824 2 0.2589 0.7812 0.000 0.884 0.000 0.116
#> GSM601834 2 0.3399 0.7878 0.000 0.868 0.040 0.092
#> GSM601849 4 0.3852 0.7946 0.180 0.012 0.000 0.808
#> GSM601854 1 0.2987 0.8339 0.880 0.016 0.000 0.104
#> GSM601864 3 0.2197 0.7325 0.080 0.000 0.916 0.004
#> GSM601755 4 0.3392 0.6861 0.000 0.020 0.124 0.856
#> GSM601785 2 0.0188 0.8284 0.000 0.996 0.000 0.004
#> GSM601795 4 0.2101 0.8062 0.060 0.012 0.000 0.928
#> GSM601800 4 0.3182 0.7053 0.000 0.028 0.096 0.876
#> GSM601830 1 0.4855 0.7438 0.788 0.004 0.076 0.132
#> GSM601840 2 0.1677 0.8200 0.000 0.948 0.040 0.012
#> GSM601845 2 0.0817 0.8281 0.000 0.976 0.000 0.024
#> GSM601860 2 0.1059 0.8267 0.000 0.972 0.012 0.016
#> GSM601870 3 0.4453 0.5758 0.244 0.000 0.744 0.012
#> GSM601750 1 0.2867 0.8341 0.884 0.012 0.000 0.104
#> GSM601760 1 0.4462 0.7868 0.792 0.044 0.000 0.164
#> GSM601765 2 0.0592 0.8291 0.000 0.984 0.000 0.016
#> GSM601770 2 0.1913 0.8205 0.000 0.940 0.020 0.040
#> GSM601775 2 0.0592 0.8291 0.000 0.984 0.000 0.016
#> GSM601780 4 0.3479 0.8167 0.148 0.012 0.000 0.840
#> GSM601790 3 0.2943 0.7639 0.000 0.076 0.892 0.032
#> GSM601805 4 0.3166 0.6979 0.000 0.016 0.116 0.868
#> GSM601810 1 0.1406 0.8447 0.960 0.000 0.024 0.016
#> GSM601815 3 0.3279 0.7523 0.000 0.032 0.872 0.096
#> GSM601820 1 0.2149 0.8430 0.912 0.000 0.000 0.088
#> GSM601825 2 0.5118 0.7062 0.000 0.752 0.072 0.176
#> GSM601835 2 0.1356 0.8317 0.000 0.960 0.008 0.032
#> GSM601850 4 0.3032 0.8238 0.124 0.008 0.000 0.868
#> GSM601855 1 0.3443 0.7638 0.848 0.000 0.136 0.016
#> GSM601865 3 0.1743 0.7438 0.056 0.000 0.940 0.004
#> GSM601756 4 0.5113 0.4674 0.000 0.032 0.264 0.704
#> GSM601786 3 0.2019 0.7712 0.004 0.032 0.940 0.024
#> GSM601796 4 0.3048 0.8212 0.108 0.000 0.016 0.876
#> GSM601801 4 0.5277 0.4059 0.000 0.028 0.304 0.668
#> GSM601831 1 0.1356 0.8526 0.960 0.008 0.000 0.032
#> GSM601841 1 0.2216 0.8412 0.908 0.000 0.000 0.092
#> GSM601846 2 0.4679 0.5156 0.000 0.648 0.000 0.352
#> GSM601861 3 0.4168 0.7424 0.000 0.080 0.828 0.092
#> GSM601871 3 0.5288 0.0524 0.472 0.000 0.520 0.008
#> GSM601751 2 0.6835 0.3891 0.000 0.576 0.288 0.136
#> GSM601761 1 0.4599 0.6749 0.736 0.016 0.000 0.248
#> GSM601766 2 0.0592 0.8291 0.000 0.984 0.000 0.016
#> GSM601771 3 0.3082 0.7577 0.000 0.032 0.884 0.084
#> GSM601776 4 0.4837 0.5165 0.348 0.004 0.000 0.648
#> GSM601781 4 0.5144 0.7302 0.216 0.000 0.052 0.732
#> GSM601791 1 0.4103 0.6688 0.744 0.000 0.000 0.256
#> GSM601806 3 0.5657 0.2639 0.000 0.024 0.540 0.436
#> GSM601811 1 0.1398 0.8367 0.956 0.000 0.040 0.004
#> GSM601816 4 0.3306 0.8114 0.156 0.004 0.000 0.840
#> GSM601821 3 0.2466 0.7660 0.000 0.028 0.916 0.056
#> GSM601826 2 0.4277 0.5985 0.000 0.720 0.000 0.280
#> GSM601836 2 0.0817 0.8281 0.000 0.976 0.000 0.024
#> GSM601851 4 0.4137 0.7668 0.208 0.012 0.000 0.780
#> GSM601856 1 0.0779 0.8439 0.980 0.000 0.016 0.004
#> GSM601866 1 0.1305 0.8515 0.960 0.000 0.004 0.036
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601752 4 0.4846 0.73092 0.244 0.000 0.004 0.696 0.056
#> GSM601782 1 0.4906 0.29990 0.592 0.024 0.380 0.000 0.004
#> GSM601792 4 0.2171 0.77033 0.064 0.000 0.024 0.912 0.000
#> GSM601797 4 0.1877 0.77207 0.064 0.000 0.012 0.924 0.000
#> GSM601827 3 0.5940 0.40939 0.172 0.108 0.672 0.048 0.000
#> GSM601837 5 0.3123 0.72489 0.000 0.000 0.184 0.004 0.812
#> GSM601842 2 0.0451 0.76302 0.008 0.988 0.000 0.000 0.004
#> GSM601857 3 0.4735 0.16744 0.460 0.000 0.524 0.000 0.016
#> GSM601867 3 0.4718 0.55490 0.180 0.000 0.728 0.000 0.092
#> GSM601747 1 0.4506 0.43621 0.716 0.244 0.036 0.000 0.004
#> GSM601757 1 0.4565 0.50529 0.764 0.148 0.080 0.004 0.004
#> GSM601762 2 0.6344 0.34280 0.060 0.524 0.000 0.048 0.368
#> GSM601767 2 0.4645 0.57283 0.012 0.672 0.000 0.016 0.300
#> GSM601772 2 0.2361 0.74947 0.012 0.892 0.000 0.000 0.096
#> GSM601777 4 0.4772 0.68231 0.108 0.000 0.148 0.740 0.004
#> GSM601787 3 0.3934 0.44090 0.016 0.000 0.740 0.000 0.244
#> GSM601802 4 0.3086 0.70965 0.040 0.004 0.000 0.864 0.092
#> GSM601807 3 0.1267 0.54912 0.012 0.000 0.960 0.004 0.024
#> GSM601812 3 0.4505 0.29214 0.384 0.000 0.604 0.012 0.000
#> GSM601817 2 0.4925 0.34871 0.044 0.632 0.324 0.000 0.000
#> GSM601822 4 0.5262 0.60013 0.408 0.012 0.028 0.552 0.000
#> GSM601832 2 0.2047 0.75623 0.040 0.928 0.000 0.020 0.012
#> GSM601847 4 0.4375 0.60743 0.420 0.000 0.004 0.576 0.000
#> GSM601852 2 0.2828 0.73070 0.104 0.872 0.020 0.004 0.000
#> GSM601862 3 0.4655 0.14337 0.476 0.000 0.512 0.000 0.012
#> GSM601753 2 0.7471 0.43522 0.096 0.504 0.000 0.248 0.152
#> GSM601783 1 0.4528 0.51418 0.728 0.060 0.212 0.000 0.000
#> GSM601793 4 0.2208 0.77190 0.072 0.000 0.020 0.908 0.000
#> GSM601798 4 0.2141 0.74147 0.016 0.000 0.004 0.916 0.064
#> GSM601828 2 0.6281 -0.14345 0.152 0.460 0.388 0.000 0.000
#> GSM601838 5 0.0794 0.81191 0.000 0.000 0.028 0.000 0.972
#> GSM601843 2 0.0794 0.76342 0.000 0.972 0.000 0.000 0.028
#> GSM601858 5 0.3383 0.78879 0.012 0.072 0.060 0.000 0.856
#> GSM601868 3 0.4354 0.39646 0.368 0.000 0.624 0.000 0.008
#> GSM601748 1 0.5996 0.20595 0.512 0.120 0.368 0.000 0.000
#> GSM601758 1 0.2919 0.55085 0.868 0.024 0.104 0.004 0.000
#> GSM601763 2 0.0798 0.76071 0.016 0.976 0.008 0.000 0.000
#> GSM601768 2 0.2179 0.74742 0.004 0.896 0.000 0.000 0.100
#> GSM601773 2 0.3365 0.70353 0.004 0.808 0.000 0.008 0.180
#> GSM601778 4 0.2929 0.76564 0.152 0.000 0.008 0.840 0.000
#> GSM601788 2 0.5153 0.28899 0.040 0.524 0.000 0.000 0.436
#> GSM601803 5 0.5559 0.00287 0.016 0.028 0.004 0.464 0.488
#> GSM601808 3 0.3456 0.56060 0.204 0.000 0.788 0.004 0.004
#> GSM601813 1 0.4942 0.19690 0.540 0.000 0.432 0.028 0.000
#> GSM601818 3 0.5084 0.06670 0.484 0.008 0.488 0.000 0.020
#> GSM601823 2 0.5700 0.57491 0.216 0.664 0.024 0.096 0.000
#> GSM601833 2 0.3696 0.73738 0.048 0.844 0.000 0.032 0.076
#> GSM601848 4 0.4481 0.60669 0.416 0.000 0.008 0.576 0.000
#> GSM601853 3 0.2763 0.57691 0.148 0.004 0.848 0.000 0.000
#> GSM601863 3 0.4549 0.16492 0.464 0.000 0.528 0.000 0.008
#> GSM601754 4 0.2012 0.76868 0.060 0.000 0.000 0.920 0.020
#> GSM601784 2 0.4062 0.70306 0.028 0.788 0.000 0.016 0.168
#> GSM601794 4 0.2248 0.77362 0.088 0.000 0.012 0.900 0.000
#> GSM601799 2 0.5423 0.29517 0.028 0.552 0.000 0.400 0.020
#> GSM601829 2 0.5944 0.59498 0.064 0.672 0.184 0.080 0.000
#> GSM601839 5 0.2424 0.76362 0.000 0.000 0.132 0.000 0.868
#> GSM601844 2 0.2720 0.73311 0.096 0.880 0.020 0.004 0.000
#> GSM601859 2 0.4365 0.71364 0.116 0.768 0.000 0.000 0.116
#> GSM601869 1 0.4420 0.10951 0.548 0.000 0.448 0.004 0.000
#> GSM601749 1 0.4235 0.49481 0.772 0.176 0.044 0.008 0.000
#> GSM601759 1 0.3368 0.54599 0.820 0.024 0.156 0.000 0.000
#> GSM601764 2 0.0880 0.75997 0.032 0.968 0.000 0.000 0.000
#> GSM601769 5 0.5732 0.30013 0.040 0.328 0.000 0.036 0.596
#> GSM601774 2 0.4327 0.47770 0.000 0.632 0.000 0.008 0.360
#> GSM601779 4 0.4803 0.49521 0.488 0.012 0.004 0.496 0.000
#> GSM601789 5 0.3409 0.70433 0.000 0.160 0.024 0.000 0.816
#> GSM601804 4 0.4160 0.75713 0.184 0.008 0.000 0.772 0.036
#> GSM601809 3 0.4954 0.19743 0.448 0.000 0.528 0.004 0.020
#> GSM601814 5 0.4461 0.72778 0.064 0.056 0.000 0.080 0.800
#> GSM601819 1 0.4428 0.40086 0.692 0.284 0.020 0.004 0.000
#> GSM601824 2 0.3059 0.72219 0.120 0.856 0.008 0.016 0.000
#> GSM601834 2 0.5024 0.69232 0.076 0.760 0.000 0.060 0.104
#> GSM601849 4 0.5266 0.50624 0.468 0.020 0.016 0.496 0.000
#> GSM601854 1 0.4860 0.20444 0.540 0.016 0.440 0.004 0.000
#> GSM601864 5 0.3774 0.59757 0.000 0.000 0.296 0.000 0.704
#> GSM601755 4 0.3651 0.70007 0.032 0.000 0.004 0.812 0.152
#> GSM601785 2 0.1012 0.76363 0.012 0.968 0.000 0.000 0.020
#> GSM601795 4 0.2110 0.73652 0.072 0.000 0.016 0.912 0.000
#> GSM601800 4 0.2139 0.72246 0.056 0.000 0.012 0.920 0.012
#> GSM601830 3 0.5089 0.37876 0.104 0.016 0.728 0.152 0.000
#> GSM601840 2 0.4846 0.48246 0.024 0.612 0.000 0.004 0.360
#> GSM601845 2 0.0807 0.76062 0.012 0.976 0.012 0.000 0.000
#> GSM601860 2 0.4512 0.58108 0.020 0.676 0.000 0.004 0.300
#> GSM601870 3 0.4225 0.03906 0.000 0.000 0.632 0.004 0.364
#> GSM601750 1 0.4599 0.31287 0.600 0.016 0.384 0.000 0.000
#> GSM601760 1 0.2778 0.54264 0.892 0.032 0.060 0.016 0.000
#> GSM601765 2 0.0579 0.76393 0.000 0.984 0.000 0.008 0.008
#> GSM601770 2 0.3234 0.72674 0.012 0.836 0.000 0.008 0.144
#> GSM601775 2 0.0693 0.76284 0.012 0.980 0.000 0.000 0.008
#> GSM601780 4 0.4297 0.51713 0.472 0.000 0.000 0.528 0.000
#> GSM601790 5 0.1216 0.81441 0.000 0.020 0.020 0.000 0.960
#> GSM601805 4 0.3142 0.72838 0.032 0.000 0.004 0.856 0.108
#> GSM601810 3 0.2660 0.58284 0.128 0.000 0.864 0.000 0.008
#> GSM601815 5 0.1116 0.81310 0.004 0.000 0.004 0.028 0.964
#> GSM601820 1 0.4590 0.22046 0.568 0.000 0.420 0.012 0.000
#> GSM601825 2 0.6447 0.57406 0.076 0.628 0.000 0.100 0.196
#> GSM601835 2 0.4117 0.71751 0.080 0.824 0.032 0.060 0.004
#> GSM601850 4 0.4305 0.50523 0.488 0.000 0.000 0.512 0.000
#> GSM601855 3 0.1518 0.54085 0.016 0.000 0.952 0.020 0.012
#> GSM601865 5 0.3521 0.67082 0.000 0.000 0.232 0.004 0.764
#> GSM601756 4 0.4208 0.57857 0.020 0.000 0.004 0.728 0.248
#> GSM601786 5 0.0290 0.81534 0.000 0.000 0.008 0.000 0.992
#> GSM601796 4 0.2628 0.75386 0.088 0.000 0.028 0.884 0.000
#> GSM601801 4 0.4296 0.64110 0.056 0.008 0.016 0.804 0.116
#> GSM601831 3 0.4240 0.47457 0.284 0.004 0.700 0.012 0.000
#> GSM601841 1 0.4090 0.46256 0.716 0.000 0.268 0.016 0.000
#> GSM601846 2 0.6056 0.61949 0.088 0.680 0.104 0.128 0.000
#> GSM601861 5 0.1216 0.80826 0.000 0.020 0.000 0.020 0.960
#> GSM601871 3 0.4350 0.39262 0.028 0.000 0.704 0.000 0.268
#> GSM601751 5 0.5566 0.42304 0.036 0.276 0.000 0.044 0.644
#> GSM601761 1 0.3107 0.50282 0.868 0.012 0.032 0.088 0.000
#> GSM601766 2 0.0000 0.76215 0.000 1.000 0.000 0.000 0.000
#> GSM601771 5 0.1488 0.81518 0.008 0.008 0.012 0.016 0.956
#> GSM601776 1 0.3796 0.07658 0.700 0.000 0.000 0.300 0.000
#> GSM601781 4 0.4403 0.72252 0.132 0.000 0.092 0.772 0.004
#> GSM601791 1 0.4269 0.51556 0.756 0.000 0.188 0.056 0.000
#> GSM601806 4 0.5550 0.28275 0.036 0.012 0.008 0.584 0.360
#> GSM601811 3 0.4335 0.51845 0.268 0.000 0.708 0.004 0.020
#> GSM601816 4 0.4836 0.65765 0.336 0.000 0.036 0.628 0.000
#> GSM601821 5 0.1153 0.81396 0.008 0.000 0.004 0.024 0.964
#> GSM601826 2 0.5580 0.48116 0.304 0.620 0.020 0.056 0.000
#> GSM601836 2 0.0798 0.76148 0.008 0.976 0.016 0.000 0.000
#> GSM601851 1 0.4088 -0.18184 0.632 0.000 0.000 0.368 0.000
#> GSM601856 3 0.2127 0.57958 0.108 0.000 0.892 0.000 0.000
#> GSM601866 1 0.4455 0.23832 0.588 0.000 0.404 0.000 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601752 6 0.3534 0.6564 0.000 0.012 0.004 0.188 0.012 0.784
#> GSM601782 1 0.1226 0.6822 0.952 0.000 0.004 0.040 0.000 0.004
#> GSM601792 6 0.2445 0.7009 0.008 0.004 0.032 0.060 0.000 0.896
#> GSM601797 6 0.1003 0.7034 0.000 0.000 0.016 0.020 0.000 0.964
#> GSM601827 1 0.7648 0.1095 0.344 0.100 0.276 0.264 0.000 0.016
#> GSM601837 3 0.4076 0.4972 0.000 0.000 0.592 0.012 0.396 0.000
#> GSM601842 2 0.1556 0.6649 0.000 0.920 0.000 0.000 0.080 0.000
#> GSM601857 1 0.3834 0.5785 0.732 0.000 0.232 0.036 0.000 0.000
#> GSM601867 1 0.4208 0.6148 0.740 0.000 0.200 0.036 0.024 0.000
#> GSM601747 1 0.6521 -0.5016 0.456 0.228 0.008 0.292 0.012 0.004
#> GSM601757 4 0.7747 0.0000 0.288 0.240 0.028 0.352 0.000 0.092
#> GSM601762 5 0.4360 0.2894 0.000 0.404 0.012 0.004 0.576 0.004
#> GSM601767 5 0.3993 0.2753 0.000 0.400 0.000 0.008 0.592 0.000
#> GSM601772 2 0.3460 0.5771 0.000 0.760 0.000 0.020 0.220 0.000
#> GSM601777 6 0.3205 0.6907 0.036 0.000 0.072 0.040 0.000 0.852
#> GSM601787 3 0.3927 0.6140 0.120 0.000 0.776 0.000 0.100 0.004
#> GSM601802 6 0.3755 0.6748 0.000 0.008 0.016 0.052 0.112 0.812
#> GSM601807 3 0.4692 0.4691 0.152 0.000 0.716 0.120 0.004 0.008
#> GSM601812 1 0.3706 0.6586 0.780 0.000 0.040 0.172 0.000 0.008
#> GSM601817 2 0.3799 0.4953 0.124 0.800 0.052 0.024 0.000 0.000
#> GSM601822 6 0.5131 0.5130 0.020 0.064 0.004 0.276 0.000 0.636
#> GSM601832 2 0.3073 0.6168 0.000 0.816 0.004 0.016 0.164 0.000
#> GSM601847 6 0.4109 0.6213 0.020 0.004 0.012 0.224 0.004 0.736
#> GSM601852 2 0.2265 0.5932 0.008 0.896 0.008 0.084 0.004 0.000
#> GSM601862 1 0.2146 0.6821 0.880 0.000 0.116 0.004 0.000 0.000
#> GSM601753 6 0.7394 0.1167 0.000 0.284 0.004 0.144 0.168 0.400
#> GSM601783 1 0.2857 0.6211 0.856 0.024 0.000 0.112 0.004 0.004
#> GSM601793 6 0.2691 0.6906 0.008 0.000 0.032 0.088 0.000 0.872
#> GSM601798 6 0.2038 0.7018 0.000 0.000 0.020 0.028 0.032 0.920
#> GSM601828 2 0.6279 -0.1020 0.312 0.524 0.064 0.096 0.000 0.004
#> GSM601838 5 0.3853 0.1563 0.000 0.000 0.304 0.016 0.680 0.000
#> GSM601843 2 0.1806 0.6638 0.000 0.908 0.004 0.000 0.088 0.000
#> GSM601858 3 0.5882 0.4156 0.008 0.048 0.508 0.036 0.392 0.008
#> GSM601868 1 0.2536 0.6835 0.864 0.000 0.116 0.020 0.000 0.000
#> GSM601748 1 0.3450 0.6304 0.836 0.072 0.032 0.060 0.000 0.000
#> GSM601758 1 0.4032 0.4690 0.748 0.020 0.004 0.208 0.000 0.020
#> GSM601763 2 0.1168 0.6544 0.000 0.956 0.000 0.028 0.016 0.000
#> GSM601768 2 0.3634 0.4761 0.000 0.696 0.000 0.008 0.296 0.000
#> GSM601773 2 0.3992 0.3181 0.000 0.624 0.000 0.012 0.364 0.000
#> GSM601778 6 0.1370 0.7029 0.004 0.000 0.012 0.036 0.000 0.948
#> GSM601788 2 0.5869 0.2102 0.000 0.536 0.056 0.072 0.336 0.000
#> GSM601803 6 0.5277 0.2645 0.000 0.008 0.036 0.020 0.440 0.496
#> GSM601808 1 0.4281 0.6182 0.732 0.000 0.136 0.132 0.000 0.000
#> GSM601813 1 0.3318 0.6777 0.824 0.000 0.020 0.132 0.000 0.024
#> GSM601818 1 0.0935 0.6913 0.964 0.000 0.032 0.004 0.000 0.000
#> GSM601823 2 0.5767 -0.2579 0.000 0.516 0.004 0.300 0.000 0.180
#> GSM601833 2 0.4273 0.2781 0.000 0.596 0.000 0.024 0.380 0.000
#> GSM601848 6 0.4474 0.5901 0.044 0.020 0.000 0.232 0.000 0.704
#> GSM601853 1 0.5650 0.3602 0.508 0.000 0.344 0.144 0.000 0.004
#> GSM601863 1 0.2402 0.6823 0.868 0.000 0.120 0.012 0.000 0.000
#> GSM601754 6 0.1849 0.7047 0.000 0.008 0.008 0.032 0.020 0.932
#> GSM601784 2 0.3998 -0.0423 0.000 0.504 0.000 0.004 0.492 0.000
#> GSM601794 6 0.1722 0.7019 0.008 0.004 0.016 0.036 0.000 0.936
#> GSM601799 6 0.6070 0.3213 0.000 0.324 0.000 0.072 0.076 0.528
#> GSM601829 2 0.5831 0.3198 0.000 0.616 0.092 0.216 0.000 0.076
#> GSM601839 3 0.4136 0.4532 0.000 0.000 0.560 0.012 0.428 0.000
#> GSM601844 2 0.2515 0.5719 0.000 0.876 0.008 0.104 0.004 0.008
#> GSM601859 2 0.4595 0.5670 0.000 0.696 0.000 0.136 0.168 0.000
#> GSM601869 1 0.0653 0.6911 0.980 0.000 0.012 0.004 0.000 0.004
#> GSM601749 1 0.4972 0.2675 0.660 0.104 0.000 0.228 0.004 0.004
#> GSM601759 1 0.5100 0.0720 0.616 0.036 0.004 0.312 0.000 0.032
#> GSM601764 2 0.1391 0.6516 0.000 0.944 0.000 0.040 0.016 0.000
#> GSM601769 5 0.3046 0.6025 0.000 0.188 0.000 0.012 0.800 0.000
#> GSM601774 5 0.4234 0.1951 0.000 0.440 0.000 0.016 0.544 0.000
#> GSM601779 6 0.4965 0.5033 0.036 0.036 0.000 0.296 0.000 0.632
#> GSM601789 5 0.5008 0.3786 0.000 0.100 0.268 0.004 0.628 0.000
#> GSM601804 6 0.3356 0.6830 0.000 0.032 0.004 0.116 0.016 0.832
#> GSM601809 1 0.3209 0.6866 0.848 0.000 0.028 0.100 0.016 0.008
#> GSM601814 5 0.3717 0.6012 0.000 0.060 0.036 0.076 0.824 0.004
#> GSM601819 1 0.4234 0.5247 0.768 0.064 0.004 0.148 0.012 0.004
#> GSM601824 2 0.3341 0.4645 0.000 0.776 0.000 0.208 0.004 0.012
#> GSM601834 2 0.5346 -0.0514 0.000 0.460 0.012 0.072 0.456 0.000
#> GSM601849 6 0.5136 0.5278 0.036 0.048 0.004 0.264 0.000 0.648
#> GSM601854 1 0.2833 0.6928 0.868 0.008 0.032 0.088 0.000 0.004
#> GSM601864 3 0.4397 0.5452 0.000 0.000 0.632 0.012 0.336 0.020
#> GSM601755 6 0.3441 0.6873 0.000 0.004 0.020 0.032 0.116 0.828
#> GSM601785 2 0.3634 0.4688 0.000 0.696 0.000 0.008 0.296 0.000
#> GSM601795 6 0.4248 0.6045 0.000 0.004 0.032 0.248 0.008 0.708
#> GSM601800 6 0.5121 0.5987 0.000 0.012 0.028 0.204 0.068 0.688
#> GSM601830 3 0.6573 0.3005 0.076 0.060 0.500 0.336 0.000 0.028
#> GSM601840 5 0.5249 0.4765 0.100 0.244 0.000 0.020 0.636 0.000
#> GSM601845 2 0.1924 0.6211 0.000 0.920 0.028 0.048 0.000 0.004
#> GSM601860 5 0.4942 0.4475 0.040 0.292 0.004 0.024 0.640 0.000
#> GSM601870 3 0.3444 0.6049 0.032 0.000 0.836 0.056 0.076 0.000
#> GSM601750 1 0.1429 0.6901 0.940 0.004 0.000 0.052 0.000 0.004
#> GSM601760 1 0.4472 0.4033 0.712 0.028 0.004 0.228 0.000 0.028
#> GSM601765 2 0.1285 0.6720 0.000 0.944 0.004 0.000 0.052 0.000
#> GSM601770 2 0.4039 0.1937 0.000 0.568 0.000 0.008 0.424 0.000
#> GSM601775 2 0.2070 0.6611 0.000 0.892 0.000 0.008 0.100 0.000
#> GSM601780 6 0.4781 0.5771 0.120 0.004 0.004 0.176 0.000 0.696
#> GSM601790 5 0.3087 0.4726 0.000 0.012 0.176 0.004 0.808 0.000
#> GSM601805 6 0.2495 0.7012 0.000 0.000 0.016 0.032 0.060 0.892
#> GSM601810 1 0.4545 0.5784 0.684 0.000 0.224 0.092 0.000 0.000
#> GSM601815 5 0.1732 0.5552 0.000 0.004 0.072 0.004 0.920 0.000
#> GSM601820 1 0.2656 0.6827 0.860 0.000 0.012 0.120 0.000 0.008
#> GSM601825 5 0.5220 0.1675 0.000 0.408 0.008 0.060 0.520 0.004
#> GSM601835 2 0.5192 0.5578 0.000 0.684 0.044 0.172 0.100 0.000
#> GSM601850 6 0.4571 0.5675 0.048 0.004 0.008 0.260 0.000 0.680
#> GSM601855 3 0.5333 0.3907 0.152 0.000 0.604 0.240 0.000 0.004
#> GSM601865 3 0.4111 0.4397 0.000 0.000 0.536 0.004 0.456 0.004
#> GSM601756 6 0.4315 0.6257 0.000 0.000 0.016 0.044 0.220 0.720
#> GSM601786 5 0.2100 0.5157 0.004 0.000 0.112 0.000 0.884 0.000
#> GSM601796 6 0.5484 0.4685 0.032 0.000 0.036 0.344 0.016 0.572
#> GSM601801 6 0.5970 0.4745 0.000 0.004 0.036 0.136 0.240 0.584
#> GSM601831 1 0.5015 0.5422 0.640 0.000 0.152 0.208 0.000 0.000
#> GSM601841 1 0.2425 0.6447 0.884 0.000 0.004 0.088 0.000 0.024
#> GSM601846 2 0.4818 0.5109 0.000 0.736 0.092 0.104 0.000 0.068
#> GSM601861 5 0.2094 0.5698 0.000 0.020 0.080 0.000 0.900 0.000
#> GSM601871 3 0.4216 0.6094 0.124 0.000 0.764 0.004 0.100 0.008
#> GSM601751 5 0.4330 0.6173 0.012 0.120 0.008 0.068 0.780 0.012
#> GSM601761 1 0.5687 -0.2093 0.540 0.024 0.000 0.336 0.000 0.100
#> GSM601766 2 0.1398 0.6721 0.000 0.940 0.000 0.008 0.052 0.000
#> GSM601771 5 0.5048 0.2196 0.012 0.000 0.236 0.028 0.676 0.048
#> GSM601776 6 0.6244 0.0605 0.164 0.028 0.000 0.340 0.000 0.468
#> GSM601781 6 0.3410 0.6705 0.076 0.000 0.024 0.064 0.000 0.836
#> GSM601791 1 0.3033 0.6513 0.848 0.000 0.012 0.108 0.000 0.032
#> GSM601806 6 0.5626 0.3147 0.000 0.000 0.024 0.084 0.380 0.512
#> GSM601811 1 0.3835 0.6594 0.784 0.000 0.048 0.156 0.008 0.004
#> GSM601816 6 0.5331 0.5512 0.128 0.008 0.020 0.176 0.000 0.668
#> GSM601821 5 0.2474 0.5503 0.000 0.004 0.080 0.032 0.884 0.000
#> GSM601826 2 0.5541 -0.1404 0.012 0.572 0.004 0.308 0.000 0.104
#> GSM601836 2 0.1418 0.6722 0.000 0.944 0.000 0.032 0.024 0.000
#> GSM601851 6 0.5537 0.3954 0.076 0.024 0.004 0.324 0.000 0.572
#> GSM601856 1 0.5904 0.2791 0.456 0.000 0.320 0.224 0.000 0.000
#> GSM601866 1 0.1036 0.6851 0.964 0.000 0.008 0.024 0.000 0.004
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n time(p) gender(p) k
#> ATC:NMF 125 0.492 0.328 2
#> ATC:NMF 120 0.245 0.294 3
#> ATC:NMF 109 0.876 0.525 4
#> ATC:NMF 84 0.926 0.216 5
#> ATC:NMF 76 0.656 0.892 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