Date: 2019-12-25 21:19:28 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 51941 rows and 130 columns.
#> Top rows are extracted by 'SD, CV, MAD, ATC' methods.
#> Subgroups are detected by 'hclust, kmeans, skmeans, pam, mclust, NMF' method.
#> Number of partitions are tried for k = 2, 3, 4, 5, 6.
#> Performed in total 30000 partitions by row resampling.
#>
#> Following methods can be applied to this 'ConsensusPartitionList' object:
#> [1] "cola_report" "collect_classes" "collect_plots" "collect_stats"
#> [5] "colnames" "functional_enrichment" "get_anno_col" "get_anno"
#> [9] "get_classes" "get_matrix" "get_membership" "get_stats"
#> [13] "is_best_k" "is_stable_k" "ncol" "nrow"
#> [17] "rownames" "show" "suggest_best_k" "test_to_known_factors"
#> [21] "top_rows_heatmap" "top_rows_overlap"
#>
#> You can get result for a single method by, e.g. object["SD", "hclust"] or object["SD:hclust"]
#> or a subset of methods by object[c("SD", "CV")], c("hclust", "kmeans")]
The call of run_all_consensus_partition_methods() was:
#> run_all_consensus_partition_methods(data = mat, mc.cores = 4, anno = anno)
Dimension of the input matrix:
mat = get_matrix(res_list)
dim(mat)
#> [1] 51941 130
The density distribution for each sample is visualized as in one column in the following heatmap. The clustering is based on the distance which is the Kolmogorov-Smirnov statistic between two distributions.
library(ComplexHeatmap)
densityHeatmap(mat, top_annotation = HeatmapAnnotation(df = get_anno(res_list),
col = get_anno_col(res_list)), ylab = "value", cluster_columns = TRUE, show_column_names = FALSE,
mc.cores = 4)
Folowing table shows the best k (number of partitions) for each combination
of top-value methods and partition methods. Clicking on the method name in
the table goes to the section for a single combination of methods.
The cola vignette explains the definition of the metrics used for determining the best number of partitions.
suggest_best_k(res_list)
| The best k | 1-PAC | Mean silhouette | Concordance | Optional k | ||
|---|---|---|---|---|---|---|
| ATC:kmeans | 2 | 1.000 | 0.992 | 0.996 | ** | |
| ATC:NMF | 2 | 0.984 | 0.949 | 0.980 | ** | |
| ATC:pam | 5 | 0.953 | 0.917 | 0.956 | ** | 2 |
| ATC:skmeans | 6 | 0.920 | 0.835 | 0.925 | * | 2,3,4 |
| MAD:skmeans | 3 | 0.917 | 0.921 | 0.957 | * | 2 |
| MAD:NMF | 2 | 0.905 | 0.922 | 0.969 | * | |
| CV:skmeans | 4 | 0.902 | 0.898 | 0.950 | * | |
| MAD:kmeans | 2 | 0.876 | 0.928 | 0.969 | ||
| ATC:mclust | 3 | 0.871 | 0.920 | 0.953 | ||
| SD:NMF | 2 | 0.846 | 0.908 | 0.962 | ||
| CV:mclust | 4 | 0.830 | 0.886 | 0.940 | ||
| SD:skmeans | 3 | 0.808 | 0.904 | 0.949 | ||
| CV:NMF | 2 | 0.802 | 0.900 | 0.957 | ||
| MAD:mclust | 2 | 0.756 | 0.864 | 0.942 | ||
| SD:kmeans | 2 | 0.743 | 0.896 | 0.949 | ||
| SD:mclust | 4 | 0.728 | 0.830 | 0.903 | ||
| CV:kmeans | 2 | 0.725 | 0.878 | 0.939 | ||
| MAD:pam | 2 | 0.719 | 0.872 | 0.936 | ||
| SD:pam | 2 | 0.534 | 0.706 | 0.885 | ||
| ATC:hclust | 2 | 0.526 | 0.872 | 0.923 | ||
| MAD:hclust | 2 | 0.360 | 0.684 | 0.855 | ||
| CV:pam | 2 | 0.330 | 0.842 | 0.877 | ||
| CV:hclust | 2 | 0.277 | 0.777 | 0.872 | ||
| SD:hclust | 2 | 0.268 | 0.698 | 0.848 |
**: 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 0.846 0.908 0.962 0.502 0.497 0.497
#> CV:NMF 2 0.802 0.900 0.957 0.503 0.497 0.497
#> MAD:NMF 2 0.905 0.922 0.969 0.502 0.498 0.498
#> ATC:NMF 2 0.984 0.949 0.980 0.501 0.497 0.497
#> SD:skmeans 2 0.772 0.899 0.954 0.504 0.496 0.496
#> CV:skmeans 2 0.758 0.899 0.956 0.504 0.496 0.496
#> MAD:skmeans 2 0.921 0.934 0.972 0.504 0.496 0.496
#> ATC:skmeans 2 1.000 0.994 0.997 0.504 0.496 0.496
#> SD:mclust 2 0.316 0.619 0.817 0.419 0.531 0.531
#> CV:mclust 2 0.542 0.778 0.888 0.428 0.516 0.516
#> MAD:mclust 2 0.756 0.864 0.942 0.458 0.535 0.535
#> ATC:mclust 2 0.878 0.901 0.956 0.224 0.794 0.794
#> SD:kmeans 2 0.743 0.896 0.949 0.499 0.496 0.496
#> CV:kmeans 2 0.725 0.878 0.939 0.486 0.497 0.497
#> MAD:kmeans 2 0.876 0.928 0.969 0.504 0.496 0.496
#> ATC:kmeans 2 1.000 0.992 0.996 0.504 0.496 0.496
#> SD:pam 2 0.534 0.706 0.885 0.492 0.496 0.496
#> CV:pam 2 0.330 0.842 0.877 0.468 0.516 0.516
#> MAD:pam 2 0.719 0.872 0.936 0.493 0.511 0.511
#> ATC:pam 2 0.921 0.937 0.974 0.502 0.496 0.496
#> SD:hclust 2 0.268 0.698 0.848 0.471 0.511 0.511
#> CV:hclust 2 0.277 0.777 0.872 0.465 0.499 0.499
#> MAD:hclust 2 0.360 0.684 0.855 0.478 0.499 0.499
#> ATC:hclust 2 0.526 0.872 0.923 0.475 0.516 0.516
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.535 0.621 0.783 0.305 0.758 0.552
#> CV:NMF 3 0.559 0.705 0.782 0.314 0.753 0.543
#> MAD:NMF 3 0.553 0.633 0.810 0.302 0.748 0.535
#> ATC:NMF 3 0.898 0.907 0.961 0.204 0.865 0.739
#> SD:skmeans 3 0.808 0.904 0.949 0.295 0.797 0.612
#> CV:skmeans 3 0.509 0.646 0.823 0.307 0.819 0.649
#> MAD:skmeans 3 0.917 0.921 0.957 0.291 0.807 0.628
#> ATC:skmeans 3 1.000 0.957 0.975 0.190 0.891 0.783
#> SD:mclust 3 0.549 0.819 0.859 0.470 0.662 0.452
#> CV:mclust 3 0.470 0.407 0.697 0.364 0.603 0.375
#> MAD:mclust 3 0.627 0.815 0.849 0.322 0.655 0.448
#> ATC:mclust 3 0.871 0.920 0.953 1.551 0.573 0.480
#> SD:kmeans 3 0.591 0.758 0.828 0.298 0.818 0.648
#> CV:kmeans 3 0.538 0.694 0.786 0.331 0.797 0.616
#> MAD:kmeans 3 0.593 0.753 0.798 0.285 0.805 0.627
#> ATC:kmeans 3 0.837 0.809 0.908 0.310 0.733 0.512
#> SD:pam 3 0.524 0.699 0.832 0.308 0.707 0.487
#> CV:pam 3 0.589 0.770 0.883 0.356 0.820 0.663
#> MAD:pam 3 0.613 0.687 0.847 0.312 0.687 0.466
#> ATC:pam 3 0.884 0.854 0.941 0.332 0.735 0.515
#> SD:hclust 3 0.253 0.541 0.708 0.308 0.832 0.671
#> CV:hclust 3 0.271 0.533 0.672 0.294 0.831 0.670
#> MAD:hclust 3 0.329 0.437 0.713 0.293 0.833 0.681
#> ATC:hclust 3 0.668 0.797 0.890 0.360 0.821 0.657
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.790 0.831 0.915 0.1433 0.779 0.455
#> CV:NMF 4 0.857 0.888 0.939 0.1375 0.782 0.455
#> MAD:NMF 4 0.796 0.835 0.920 0.1418 0.826 0.545
#> ATC:NMF 4 0.593 0.627 0.812 0.1565 0.885 0.725
#> SD:skmeans 4 0.808 0.819 0.902 0.1366 0.881 0.675
#> CV:skmeans 4 0.902 0.898 0.950 0.1422 0.784 0.472
#> MAD:skmeans 4 0.801 0.760 0.897 0.1381 0.891 0.700
#> ATC:skmeans 4 0.933 0.875 0.950 0.1336 0.883 0.715
#> SD:mclust 4 0.728 0.830 0.903 0.1864 0.855 0.627
#> CV:mclust 4 0.830 0.886 0.940 0.2696 0.707 0.354
#> MAD:mclust 4 0.741 0.803 0.905 0.2018 0.865 0.650
#> ATC:mclust 4 0.730 0.822 0.900 0.2427 0.799 0.558
#> SD:kmeans 4 0.603 0.704 0.825 0.1428 0.885 0.690
#> CV:kmeans 4 0.690 0.825 0.870 0.1437 0.832 0.570
#> MAD:kmeans 4 0.575 0.601 0.792 0.1430 0.860 0.629
#> ATC:kmeans 4 0.738 0.662 0.852 0.1047 0.895 0.703
#> SD:pam 4 0.536 0.517 0.753 0.1445 0.873 0.662
#> CV:pam 4 0.544 0.693 0.834 0.1531 0.847 0.616
#> MAD:pam 4 0.556 0.494 0.742 0.1393 0.858 0.631
#> ATC:pam 4 0.802 0.822 0.900 0.0926 0.894 0.704
#> SD:hclust 4 0.416 0.413 0.681 0.1627 0.888 0.694
#> CV:hclust 4 0.429 0.668 0.794 0.2029 0.827 0.554
#> MAD:hclust 4 0.390 0.399 0.628 0.1563 0.765 0.475
#> ATC:hclust 4 0.663 0.789 0.877 0.0824 0.947 0.849
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.803 0.792 0.899 0.0604 0.882 0.584
#> CV:NMF 5 0.759 0.785 0.879 0.0596 0.918 0.691
#> MAD:NMF 5 0.795 0.797 0.902 0.0574 0.875 0.575
#> ATC:NMF 5 0.569 0.462 0.707 0.0841 0.835 0.534
#> SD:skmeans 5 0.700 0.620 0.792 0.0710 0.948 0.809
#> CV:skmeans 5 0.714 0.713 0.803 0.0586 0.924 0.714
#> MAD:skmeans 5 0.725 0.618 0.794 0.0710 0.902 0.662
#> ATC:skmeans 5 0.869 0.802 0.914 0.0726 0.939 0.809
#> SD:mclust 5 0.697 0.702 0.822 0.0856 0.917 0.700
#> CV:mclust 5 0.730 0.676 0.802 0.0555 0.982 0.932
#> MAD:mclust 5 0.730 0.701 0.820 0.0812 0.878 0.598
#> ATC:mclust 5 0.609 0.551 0.771 0.0618 0.993 0.977
#> SD:kmeans 5 0.617 0.562 0.752 0.0731 0.878 0.590
#> CV:kmeans 5 0.682 0.600 0.734 0.0727 0.942 0.780
#> MAD:kmeans 5 0.628 0.567 0.746 0.0730 0.882 0.599
#> ATC:kmeans 5 0.812 0.813 0.886 0.0716 0.851 0.527
#> SD:pam 5 0.523 0.338 0.632 0.0686 0.872 0.587
#> CV:pam 5 0.560 0.621 0.776 0.0627 0.869 0.584
#> MAD:pam 5 0.554 0.380 0.615 0.0651 0.833 0.498
#> ATC:pam 5 0.953 0.917 0.956 0.0732 0.889 0.633
#> SD:hclust 5 0.477 0.420 0.648 0.0692 0.818 0.498
#> CV:hclust 5 0.532 0.561 0.723 0.0691 0.960 0.841
#> MAD:hclust 5 0.506 0.444 0.655 0.0832 0.795 0.416
#> ATC:hclust 5 0.703 0.640 0.822 0.0639 0.984 0.947
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.732 0.595 0.790 0.0409 0.875 0.496
#> CV:NMF 6 0.752 0.597 0.766 0.0402 0.877 0.507
#> MAD:NMF 6 0.711 0.618 0.764 0.0454 0.939 0.730
#> ATC:NMF 6 0.641 0.582 0.775 0.0495 0.847 0.485
#> SD:skmeans 6 0.701 0.573 0.747 0.0416 0.920 0.665
#> CV:skmeans 6 0.711 0.617 0.780 0.0413 0.927 0.677
#> MAD:skmeans 6 0.722 0.647 0.800 0.0408 0.890 0.556
#> ATC:skmeans 6 0.920 0.835 0.925 0.0352 0.973 0.902
#> SD:mclust 6 0.688 0.525 0.739 0.0383 0.970 0.861
#> CV:mclust 6 0.746 0.629 0.773 0.0431 0.912 0.661
#> MAD:mclust 6 0.705 0.630 0.778 0.0408 0.944 0.750
#> ATC:mclust 6 0.617 0.554 0.702 0.0465 0.916 0.709
#> SD:kmeans 6 0.646 0.463 0.673 0.0422 0.905 0.589
#> CV:kmeans 6 0.659 0.554 0.713 0.0443 0.890 0.551
#> MAD:kmeans 6 0.640 0.432 0.650 0.0427 0.898 0.573
#> ATC:kmeans 6 0.813 0.665 0.845 0.0402 0.962 0.833
#> SD:pam 6 0.554 0.228 0.573 0.0431 0.780 0.298
#> CV:pam 6 0.707 0.675 0.810 0.0547 0.902 0.612
#> MAD:pam 6 0.598 0.357 0.632 0.0435 0.835 0.413
#> ATC:pam 6 0.835 0.789 0.874 0.0484 0.947 0.762
#> SD:hclust 6 0.535 0.434 0.609 0.0419 0.855 0.520
#> CV:hclust 6 0.575 0.517 0.690 0.0383 0.949 0.777
#> MAD:hclust 6 0.574 0.396 0.579 0.0405 0.895 0.592
#> ATC:hclust 6 0.756 0.776 0.874 0.0547 0.925 0.742
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 gender(p) individual(p) disease.state(p) other(p) k
#> SD:NMF 123 0.367 0.828 0.2493 0.07250 2
#> CV:NMF 125 0.812 0.938 0.0500 0.02184 2
#> MAD:NMF 124 0.404 0.861 0.5666 0.02328 2
#> ATC:NMF 127 0.617 0.808 0.5560 0.02365 2
#> SD:skmeans 124 0.445 0.781 0.0387 0.07083 2
#> CV:skmeans 124 0.436 0.874 0.0331 0.05345 2
#> MAD:skmeans 125 0.535 0.801 0.5132 0.09377 2
#> ATC:skmeans 130 0.581 0.794 0.5667 0.01764 2
#> SD:mclust 114 0.788 0.760 0.3326 0.04034 2
#> CV:mclust 122 0.329 0.695 0.0464 0.01225 2
#> MAD:mclust 121 0.576 0.793 0.2405 0.07140 2
#> ATC:mclust 124 0.575 0.305 0.8129 0.52240 2
#> SD:kmeans 124 0.445 0.781 0.0387 0.07083 2
#> CV:kmeans 128 0.414 0.813 0.0230 0.04068 2
#> MAD:kmeans 127 0.647 0.732 0.4082 0.10462 2
#> ATC:kmeans 130 0.581 0.794 0.5667 0.01764 2
#> SD:pam 103 0.732 0.761 0.4224 0.05508 2
#> CV:pam 127 0.955 0.813 0.9757 0.13701 2
#> MAD:pam 120 0.586 0.779 0.3203 0.02920 2
#> ATC:pam 125 0.902 0.946 0.7638 0.02564 2
#> SD:hclust 114 0.444 0.698 0.5020 0.03393 2
#> CV:hclust 121 0.171 0.822 0.0416 0.00286 2
#> MAD:hclust 109 0.852 0.932 0.2412 0.02037 2
#> ATC:hclust 129 0.338 0.374 0.2267 0.00222 2
test_to_known_factors(res_list, k = 3)
#> n gender(p) individual(p) disease.state(p) other(p) k
#> SD:NMF 100 0.286 0.1997 0.0641 0.06200 3
#> CV:NMF 116 0.200 0.4109 0.1165 0.27825 3
#> MAD:NMF 103 0.127 0.1158 0.0359 0.09407 3
#> ATC:NMF 126 0.425 0.5862 0.8324 0.13112 3
#> SD:skmeans 128 0.439 0.1093 0.1389 0.12221 3
#> CV:skmeans 104 0.440 0.3754 0.1437 0.10332 3
#> MAD:skmeans 127 0.431 0.0918 0.1625 0.11169 3
#> ATC:skmeans 129 0.424 0.9584 0.5868 0.05873 3
#> SD:mclust 125 0.402 0.2130 0.1042 0.30469 3
#> CV:mclust 64 0.958 0.8245 0.3751 0.53739 3
#> MAD:mclust 122 0.512 0.1916 0.1879 0.12437 3
#> ATC:mclust 128 0.512 0.7133 0.7900 0.03483 3
#> SD:kmeans 121 0.533 0.2633 0.2487 0.39668 3
#> CV:kmeans 109 0.218 0.2786 0.0709 0.49968 3
#> MAD:kmeans 121 0.571 0.5229 0.2044 0.31412 3
#> ATC:kmeans 115 0.346 0.5569 0.7123 0.25984 3
#> SD:pam 112 0.878 0.4606 0.5269 0.19144 3
#> CV:pam 119 0.712 0.7567 0.3034 0.26615 3
#> MAD:pam 107 0.885 0.5338 0.4316 0.28099 3
#> ATC:pam 117 0.596 0.5418 0.6178 0.29943 3
#> SD:hclust 93 0.807 0.4022 0.0806 0.43297 3
#> CV:hclust 97 0.491 0.8605 0.0953 0.03254 3
#> MAD:hclust 59 0.913 0.8384 0.1407 0.13826 3
#> ATC:hclust 118 0.288 0.5807 0.4357 0.00924 3
test_to_known_factors(res_list, k = 4)
#> n gender(p) individual(p) disease.state(p) other(p) k
#> SD:NMF 119 0.268 0.129 0.19996 0.1713 4
#> CV:NMF 125 0.212 0.120 0.20230 0.0653 4
#> MAD:NMF 122 0.267 0.252 0.26298 0.0693 4
#> ATC:NMF 93 0.348 0.523 0.93279 0.0486 4
#> SD:skmeans 122 0.276 0.514 0.18293 0.1530 4
#> CV:skmeans 124 0.156 0.116 0.08913 0.2375 4
#> MAD:skmeans 111 0.217 0.386 0.16597 0.0888 4
#> ATC:skmeans 121 0.560 0.658 0.91202 0.3357 4
#> SD:mclust 123 0.246 0.264 0.04653 0.1432 4
#> CV:mclust 125 0.337 0.247 0.03598 0.0492 4
#> MAD:mclust 119 0.250 0.325 0.17638 0.1256 4
#> ATC:mclust 124 0.859 0.462 0.94975 0.2203 4
#> SD:kmeans 117 0.153 0.255 0.07441 0.0852 4
#> CV:kmeans 123 0.234 0.167 0.04961 0.0522 4
#> MAD:kmeans 100 0.351 0.279 0.24056 0.1138 4
#> ATC:kmeans 106 0.514 0.852 0.81642 0.1850 4
#> SD:pam 89 0.951 0.501 0.11185 0.2450 4
#> CV:pam 112 0.819 0.895 0.13752 0.5011 4
#> MAD:pam 87 0.992 0.380 0.24745 0.0850 4
#> ATC:pam 128 0.764 0.560 0.87502 0.2907 4
#> SD:hclust 63 0.486 0.444 0.00129 0.3327 4
#> CV:hclust 111 0.389 0.515 0.00430 0.0314 4
#> MAD:hclust 67 0.513 0.364 0.01040 0.2254 4
#> ATC:hclust 122 0.387 0.778 0.60925 0.0410 4
test_to_known_factors(res_list, k = 5)
#> n gender(p) individual(p) disease.state(p) other(p) k
#> SD:NMF 120 0.619 0.07740 0.12791 0.170055 5
#> CV:NMF 119 0.636 0.05147 0.12450 0.037886 5
#> MAD:NMF 119 0.734 0.09127 0.04153 0.079088 5
#> ATC:NMF 66 0.639 0.51458 0.39410 0.314818 5
#> SD:skmeans 96 0.113 0.18459 0.16342 0.008311 5
#> CV:skmeans 117 0.161 0.21904 0.06524 0.138909 5
#> MAD:skmeans 95 0.338 0.13993 0.46553 0.104140 5
#> ATC:skmeans 113 0.627 0.34321 0.40110 0.425877 5
#> SD:mclust 116 0.633 0.40885 0.59219 0.024642 5
#> CV:mclust 100 0.057 0.02355 0.02658 0.122307 5
#> MAD:mclust 114 0.980 0.38729 0.41829 0.161393 5
#> ATC:mclust 92 0.945 0.37334 0.64382 0.791563 5
#> SD:kmeans 91 0.593 0.27210 0.09700 0.026396 5
#> CV:kmeans 95 0.145 0.05381 0.01674 0.000324 5
#> MAD:kmeans 85 0.832 0.29058 0.40909 0.060066 5
#> ATC:kmeans 119 0.667 0.96662 0.76289 0.084526 5
#> SD:pam 41 0.538 0.59162 0.44362 0.072546 5
#> CV:pam 105 0.890 0.65529 0.23493 0.593445 5
#> MAD:pam 55 0.785 0.66803 0.18343 0.087748 5
#> ATC:pam 127 0.675 0.74573 0.95270 0.416991 5
#> SD:hclust 44 0.404 0.00255 0.46874 0.862222 5
#> CV:hclust 87 0.357 0.50596 0.00454 0.054670 5
#> MAD:hclust 68 0.919 0.57128 0.04100 0.496292 5
#> ATC:hclust 114 0.182 0.66135 0.63713 0.036671 5
test_to_known_factors(res_list, k = 6)
#> n gender(p) individual(p) disease.state(p) other(p) k
#> SD:NMF 87 0.944 0.2416 0.2491 8.01e-01 6
#> CV:NMF 81 0.945 0.6669 0.1894 4.97e-01 6
#> MAD:NMF 97 0.717 0.3481 0.1105 2.69e-01 6
#> ATC:NMF 93 0.568 0.9592 0.4310 1.61e-01 6
#> SD:skmeans 85 0.982 0.0655 0.2014 6.10e-03 6
#> CV:skmeans 90 0.849 0.0649 0.0820 2.37e-05 6
#> MAD:skmeans 94 0.893 0.4091 0.4725 3.09e-02 6
#> ATC:skmeans 120 0.633 0.4819 0.3556 7.11e-02 6
#> SD:mclust 90 0.526 0.1409 0.2222 1.41e-01 6
#> CV:mclust 97 0.189 0.0529 0.1177 6.07e-04 6
#> MAD:mclust 101 0.673 0.2168 0.1723 7.45e-03 6
#> ATC:mclust 92 0.974 0.5912 0.5996 3.35e-01 6
#> SD:kmeans 67 0.860 0.1879 0.1925 1.57e-02 6
#> CV:kmeans 81 0.807 0.1955 0.0321 7.57e-05 6
#> MAD:kmeans 53 0.339 0.4081 0.7216 5.66e-02 6
#> ATC:kmeans 107 0.315 0.8659 0.6585 7.40e-02 6
#> SD:pam 18 1.000 0.7920 0.9615 7.92e-01 6
#> CV:pam 110 0.699 0.4264 0.3447 4.92e-01 6
#> MAD:pam 33 0.952 0.7194 0.2525 1.07e-01 6
#> ATC:pam 118 0.801 0.7387 0.8573 4.56e-01 6
#> SD:hclust 46 0.599 0.0166 0.0403 4.35e-01 6
#> CV:hclust 83 0.508 0.3305 0.0229 2.63e-02 6
#> MAD:hclust 60 0.667 0.3330 0.0100 2.67e-01 6
#> ATC:hclust 122 0.354 0.7680 0.5900 1.22e-01 6
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "hclust"]
# you can also extract it by
# res = res_list["SD:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 130 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.268 0.698 0.848 0.4712 0.511 0.511
#> 3 3 0.253 0.541 0.708 0.3077 0.832 0.671
#> 4 4 0.416 0.413 0.681 0.1627 0.888 0.694
#> 5 5 0.477 0.420 0.648 0.0692 0.818 0.498
#> 6 6 0.535 0.434 0.609 0.0419 0.855 0.520
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
#> GSM447671 2 1.0000 -0.09503 0.500 0.500
#> GSM447694 1 0.5294 0.79716 0.880 0.120
#> GSM447618 2 0.9996 -0.04380 0.488 0.512
#> GSM447691 2 0.8499 0.61964 0.276 0.724
#> GSM447733 1 0.9044 0.61374 0.680 0.320
#> GSM447620 1 0.9850 0.34832 0.572 0.428
#> GSM447627 1 0.7745 0.72530 0.772 0.228
#> GSM447630 1 0.9815 0.37992 0.580 0.420
#> GSM447642 1 0.3114 0.80060 0.944 0.056
#> GSM447649 2 0.1184 0.83540 0.016 0.984
#> GSM447654 2 0.4562 0.81872 0.096 0.904
#> GSM447655 2 0.0000 0.82992 0.000 1.000
#> GSM447669 1 0.9993 0.16399 0.516 0.484
#> GSM447676 1 0.3114 0.80122 0.944 0.056
#> GSM447678 2 0.7528 0.71623 0.216 0.784
#> GSM447681 2 0.0376 0.83134 0.004 0.996
#> GSM447698 2 0.8267 0.65271 0.260 0.740
#> GSM447713 1 0.0000 0.80149 1.000 0.000
#> GSM447722 2 0.8267 0.65271 0.260 0.740
#> GSM447726 1 0.9248 0.52617 0.660 0.340
#> GSM447735 1 0.8763 0.63838 0.704 0.296
#> GSM447737 1 0.0376 0.80122 0.996 0.004
#> GSM447657 2 0.0376 0.83134 0.004 0.996
#> GSM447674 2 0.0376 0.83134 0.004 0.996
#> GSM447636 1 0.3114 0.80060 0.944 0.056
#> GSM447723 1 0.9922 0.19952 0.552 0.448
#> GSM447699 1 0.9491 0.51270 0.632 0.368
#> GSM447708 2 0.7299 0.72537 0.204 0.796
#> GSM447721 1 0.0376 0.80295 0.996 0.004
#> GSM447623 1 0.0000 0.80149 1.000 0.000
#> GSM447621 1 0.0000 0.80149 1.000 0.000
#> GSM447650 2 0.0672 0.83350 0.008 0.992
#> GSM447651 2 0.3431 0.83184 0.064 0.936
#> GSM447653 1 0.7376 0.74149 0.792 0.208
#> GSM447658 1 0.3114 0.80060 0.944 0.056
#> GSM447675 2 0.5294 0.80895 0.120 0.880
#> GSM447680 2 0.6048 0.78768 0.148 0.852
#> GSM447686 2 0.9087 0.54994 0.324 0.676
#> GSM447736 1 0.7219 0.75621 0.800 0.200
#> GSM447629 2 0.8813 0.59996 0.300 0.700
#> GSM447648 1 0.2236 0.80980 0.964 0.036
#> GSM447660 1 0.9896 0.22941 0.560 0.440
#> GSM447661 2 0.0672 0.83350 0.008 0.992
#> GSM447663 1 0.5629 0.79521 0.868 0.132
#> GSM447704 2 0.1184 0.83540 0.016 0.984
#> GSM447720 1 0.6048 0.78747 0.852 0.148
#> GSM447652 2 0.6048 0.77713 0.148 0.852
#> GSM447679 2 0.0938 0.83416 0.012 0.988
#> GSM447712 1 0.0000 0.80149 1.000 0.000
#> GSM447664 2 0.5178 0.81582 0.116 0.884
#> GSM447637 1 0.2236 0.80980 0.964 0.036
#> GSM447639 1 0.9522 0.50492 0.628 0.372
#> GSM447615 1 0.3879 0.80901 0.924 0.076
#> GSM447656 2 0.8861 0.59694 0.304 0.696
#> GSM447673 2 0.1843 0.83388 0.028 0.972
#> GSM447719 1 0.7219 0.74735 0.800 0.200
#> GSM447706 1 0.2236 0.80980 0.964 0.036
#> GSM447612 1 0.8713 0.64356 0.708 0.292
#> GSM447665 2 0.9815 0.22623 0.420 0.580
#> GSM447677 2 0.4161 0.82467 0.084 0.916
#> GSM447613 1 0.0000 0.80149 1.000 0.000
#> GSM447659 1 0.8144 0.69786 0.748 0.252
#> GSM447662 1 0.2423 0.81022 0.960 0.040
#> GSM447666 1 0.9358 0.51201 0.648 0.352
#> GSM447668 2 0.0672 0.83350 0.008 0.992
#> GSM447682 2 0.4022 0.82697 0.080 0.920
#> GSM447683 2 0.3733 0.82763 0.072 0.928
#> GSM447688 2 0.0938 0.83364 0.012 0.988
#> GSM447702 2 0.0000 0.82992 0.000 1.000
#> GSM447709 2 0.9209 0.47475 0.336 0.664
#> GSM447711 1 0.0000 0.80149 1.000 0.000
#> GSM447715 1 0.9922 0.19952 0.552 0.448
#> GSM447693 1 0.2236 0.80980 0.964 0.036
#> GSM447611 2 0.5946 0.79777 0.144 0.856
#> GSM447672 2 0.0000 0.82992 0.000 1.000
#> GSM447703 2 0.0938 0.83364 0.012 0.988
#> GSM447727 1 0.9833 0.28463 0.576 0.424
#> GSM447638 1 0.7745 0.69441 0.772 0.228
#> GSM447670 1 0.2236 0.80316 0.964 0.036
#> GSM447700 1 0.9933 0.27944 0.548 0.452
#> GSM447738 2 0.0938 0.83364 0.012 0.988
#> GSM447739 1 0.0000 0.80149 1.000 0.000
#> GSM447617 1 0.0000 0.80149 1.000 0.000
#> GSM447628 2 0.4022 0.82351 0.080 0.920
#> GSM447632 2 0.2603 0.83698 0.044 0.956
#> GSM447619 1 0.2423 0.81022 0.960 0.040
#> GSM447643 1 1.0000 0.00279 0.504 0.496
#> GSM447724 1 0.9209 0.58511 0.664 0.336
#> GSM447728 2 0.5294 0.80100 0.120 0.880
#> GSM447610 1 0.6887 0.76030 0.816 0.184
#> GSM447633 2 0.9815 0.22623 0.420 0.580
#> GSM447634 1 0.7219 0.75325 0.800 0.200
#> GSM447622 1 0.1843 0.80917 0.972 0.028
#> GSM447667 2 0.8955 0.58139 0.312 0.688
#> GSM447687 2 0.0938 0.83364 0.012 0.988
#> GSM447695 1 0.5294 0.79780 0.880 0.120
#> GSM447696 1 0.0000 0.80149 1.000 0.000
#> GSM447697 1 0.0000 0.80149 1.000 0.000
#> GSM447714 1 0.4431 0.80506 0.908 0.092
#> GSM447717 1 0.3114 0.80060 0.944 0.056
#> GSM447725 1 0.0000 0.80149 1.000 0.000
#> GSM447729 2 0.4690 0.81717 0.100 0.900
#> GSM447644 1 0.9993 0.16399 0.516 0.484
#> GSM447710 1 0.4431 0.80506 0.908 0.092
#> GSM447614 1 0.6887 0.76030 0.816 0.184
#> GSM447685 2 0.6531 0.77172 0.168 0.832
#> GSM447690 1 0.0000 0.80149 1.000 0.000
#> GSM447730 2 0.0938 0.83498 0.012 0.988
#> GSM447646 2 0.4022 0.82351 0.080 0.920
#> GSM447689 1 0.6712 0.78189 0.824 0.176
#> GSM447635 2 0.9087 0.54764 0.324 0.676
#> GSM447641 1 0.3114 0.80060 0.944 0.056
#> GSM447716 2 0.9087 0.54994 0.324 0.676
#> GSM447718 1 0.7376 0.75133 0.792 0.208
#> GSM447616 1 0.1843 0.80917 0.972 0.028
#> GSM447626 1 0.6438 0.76181 0.836 0.164
#> GSM447640 2 0.2236 0.83560 0.036 0.964
#> GSM447734 1 0.5408 0.79656 0.876 0.124
#> GSM447692 1 0.5294 0.79780 0.880 0.120
#> GSM447647 2 0.1184 0.83540 0.016 0.984
#> GSM447624 1 0.0000 0.80149 1.000 0.000
#> GSM447625 1 0.5408 0.79656 0.876 0.124
#> GSM447707 2 0.0938 0.83498 0.012 0.988
#> GSM447732 1 0.5294 0.79740 0.880 0.120
#> GSM447684 1 0.7453 0.71326 0.788 0.212
#> GSM447731 1 0.8955 0.61286 0.688 0.312
#> GSM447705 1 0.9881 0.33733 0.564 0.436
#> GSM447631 1 0.2236 0.80980 0.964 0.036
#> GSM447701 2 0.0672 0.83350 0.008 0.992
#> GSM447645 1 0.2236 0.80980 0.964 0.036
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM447671 2 0.9188 -0.1829 0.152 0.468 0.380
#> GSM447694 3 0.7890 0.4363 0.432 0.056 0.512
#> GSM447618 2 0.9464 -0.2508 0.180 0.412 0.408
#> GSM447691 2 0.8249 0.5359 0.200 0.636 0.164
#> GSM447733 3 0.7724 0.5811 0.156 0.164 0.680
#> GSM447620 3 0.9223 0.4166 0.172 0.324 0.504
#> GSM447627 3 0.7782 0.6063 0.248 0.100 0.652
#> GSM447630 3 0.9389 0.4479 0.180 0.352 0.468
#> GSM447642 1 0.0747 0.6633 0.984 0.000 0.016
#> GSM447649 2 0.2711 0.7576 0.000 0.912 0.088
#> GSM447654 2 0.5815 0.6470 0.004 0.692 0.304
#> GSM447655 2 0.0424 0.7638 0.000 0.992 0.008
#> GSM447669 3 0.9434 0.2880 0.176 0.408 0.416
#> GSM447676 1 0.3129 0.6347 0.904 0.008 0.088
#> GSM447678 2 0.7637 0.5734 0.076 0.640 0.284
#> GSM447681 2 0.1337 0.7632 0.016 0.972 0.012
#> GSM447698 2 0.7896 0.4944 0.076 0.600 0.324
#> GSM447713 1 0.1964 0.6859 0.944 0.000 0.056
#> GSM447722 2 0.7896 0.4944 0.076 0.600 0.324
#> GSM447726 3 0.9701 0.3614 0.284 0.260 0.456
#> GSM447735 3 0.9099 0.5468 0.264 0.192 0.544
#> GSM447737 1 0.3500 0.6491 0.880 0.004 0.116
#> GSM447657 2 0.1337 0.7632 0.016 0.972 0.012
#> GSM447674 2 0.1337 0.7632 0.016 0.972 0.012
#> GSM447636 1 0.0747 0.6633 0.984 0.000 0.016
#> GSM447723 1 0.9008 0.1177 0.500 0.360 0.140
#> GSM447699 3 0.9034 0.5421 0.188 0.260 0.552
#> GSM447708 2 0.7424 0.6115 0.128 0.700 0.172
#> GSM447721 1 0.2165 0.6846 0.936 0.000 0.064
#> GSM447623 1 0.3192 0.6548 0.888 0.000 0.112
#> GSM447621 1 0.3192 0.6548 0.888 0.000 0.112
#> GSM447650 2 0.1491 0.7635 0.016 0.968 0.016
#> GSM447651 2 0.4371 0.7413 0.032 0.860 0.108
#> GSM447653 3 0.4045 0.5338 0.104 0.024 0.872
#> GSM447658 1 0.0892 0.6635 0.980 0.000 0.020
#> GSM447675 2 0.6200 0.6446 0.012 0.676 0.312
#> GSM447680 2 0.6510 0.6823 0.156 0.756 0.088
#> GSM447686 2 0.8518 0.4931 0.272 0.592 0.136
#> GSM447736 3 0.8674 0.6112 0.296 0.136 0.568
#> GSM447629 2 0.8278 0.5335 0.248 0.620 0.132
#> GSM447648 3 0.6204 0.4316 0.424 0.000 0.576
#> GSM447660 1 0.8691 0.1361 0.528 0.356 0.116
#> GSM447661 2 0.1491 0.7635 0.016 0.968 0.016
#> GSM447663 3 0.7507 0.6315 0.288 0.068 0.644
#> GSM447704 2 0.2711 0.7576 0.000 0.912 0.088
#> GSM447720 3 0.7724 0.6179 0.308 0.072 0.620
#> GSM447652 2 0.6245 0.6896 0.060 0.760 0.180
#> GSM447679 2 0.0892 0.7652 0.000 0.980 0.020
#> GSM447712 1 0.1964 0.6859 0.944 0.000 0.056
#> GSM447664 2 0.6601 0.6564 0.028 0.676 0.296
#> GSM447637 3 0.5835 0.5565 0.340 0.000 0.660
#> GSM447639 3 0.8853 0.5424 0.176 0.252 0.572
#> GSM447615 1 0.6225 -0.1367 0.568 0.000 0.432
#> GSM447656 2 0.8287 0.5306 0.256 0.616 0.128
#> GSM447673 2 0.3267 0.7488 0.000 0.884 0.116
#> GSM447719 3 0.3618 0.5289 0.104 0.012 0.884
#> GSM447706 3 0.5810 0.5583 0.336 0.000 0.664
#> GSM447612 3 0.8263 0.6127 0.188 0.176 0.636
#> GSM447665 2 0.8858 0.1062 0.136 0.532 0.332
#> GSM447677 2 0.5492 0.7182 0.080 0.816 0.104
#> GSM447613 1 0.2165 0.6843 0.936 0.000 0.064
#> GSM447659 3 0.6546 0.5975 0.148 0.096 0.756
#> GSM447662 3 0.6008 0.5633 0.332 0.004 0.664
#> GSM447666 3 0.9128 0.4449 0.204 0.252 0.544
#> GSM447668 2 0.1491 0.7635 0.016 0.968 0.016
#> GSM447682 2 0.4475 0.7412 0.072 0.864 0.064
#> GSM447683 2 0.4602 0.7316 0.108 0.852 0.040
#> GSM447688 2 0.2711 0.7558 0.000 0.912 0.088
#> GSM447702 2 0.1170 0.7634 0.016 0.976 0.008
#> GSM447709 2 0.7980 0.3055 0.072 0.572 0.356
#> GSM447711 1 0.2066 0.6851 0.940 0.000 0.060
#> GSM447715 1 0.9008 0.1177 0.500 0.360 0.140
#> GSM447693 3 0.5835 0.5565 0.340 0.000 0.660
#> GSM447611 2 0.7065 0.6279 0.040 0.644 0.316
#> GSM447672 2 0.0424 0.7638 0.000 0.992 0.008
#> GSM447703 2 0.2711 0.7558 0.000 0.912 0.088
#> GSM447727 1 0.8720 0.1631 0.540 0.336 0.124
#> GSM447638 3 0.9152 0.3018 0.424 0.144 0.432
#> GSM447670 1 0.6140 -0.0330 0.596 0.000 0.404
#> GSM447700 3 0.9338 0.4081 0.172 0.360 0.468
#> GSM447738 2 0.2711 0.7558 0.000 0.912 0.088
#> GSM447739 1 0.1964 0.6859 0.944 0.000 0.056
#> GSM447617 1 0.3192 0.6548 0.888 0.000 0.112
#> GSM447628 2 0.5497 0.6557 0.000 0.708 0.292
#> GSM447632 2 0.3340 0.7595 0.000 0.880 0.120
#> GSM447619 3 0.6008 0.5633 0.332 0.004 0.664
#> GSM447643 1 0.8826 -0.0512 0.472 0.412 0.116
#> GSM447724 3 0.8212 0.5761 0.168 0.192 0.640
#> GSM447728 2 0.5564 0.7050 0.128 0.808 0.064
#> GSM447610 3 0.8701 0.4386 0.400 0.108 0.492
#> GSM447633 2 0.8858 0.1062 0.136 0.532 0.332
#> GSM447634 3 0.9015 0.5503 0.348 0.144 0.508
#> GSM447622 1 0.6313 0.3079 0.676 0.016 0.308
#> GSM447667 2 0.8379 0.5127 0.268 0.604 0.128
#> GSM447687 2 0.2711 0.7558 0.000 0.912 0.088
#> GSM447695 1 0.7920 -0.3606 0.476 0.056 0.468
#> GSM447696 1 0.1964 0.6859 0.944 0.000 0.056
#> GSM447697 1 0.2165 0.6843 0.936 0.000 0.064
#> GSM447714 3 0.6908 0.6098 0.308 0.036 0.656
#> GSM447717 1 0.0592 0.6621 0.988 0.000 0.012
#> GSM447725 1 0.1964 0.6859 0.944 0.000 0.056
#> GSM447729 2 0.6082 0.6487 0.012 0.692 0.296
#> GSM447644 3 0.9434 0.2880 0.176 0.408 0.416
#> GSM447710 3 0.6908 0.6098 0.308 0.036 0.656
#> GSM447614 3 0.8701 0.4386 0.400 0.108 0.492
#> GSM447685 2 0.6622 0.6708 0.164 0.748 0.088
#> GSM447690 1 0.1964 0.6859 0.944 0.000 0.056
#> GSM447730 2 0.2625 0.7586 0.000 0.916 0.084
#> GSM447646 2 0.5497 0.6557 0.000 0.708 0.292
#> GSM447689 3 0.7880 0.6190 0.268 0.096 0.636
#> GSM447635 2 0.8670 0.4812 0.240 0.592 0.168
#> GSM447641 1 0.0892 0.6635 0.980 0.000 0.020
#> GSM447716 2 0.8518 0.4931 0.272 0.592 0.136
#> GSM447718 3 0.8594 0.6268 0.268 0.144 0.588
#> GSM447616 1 0.6313 0.3079 0.676 0.016 0.308
#> GSM447626 3 0.8526 0.4728 0.376 0.100 0.524
#> GSM447640 2 0.1753 0.7636 0.000 0.952 0.048
#> GSM447734 3 0.7367 0.6272 0.292 0.060 0.648
#> GSM447692 1 0.7920 -0.3606 0.476 0.056 0.468
#> GSM447647 2 0.2711 0.7576 0.000 0.912 0.088
#> GSM447624 1 0.5560 0.3706 0.700 0.000 0.300
#> GSM447625 3 0.7367 0.6272 0.292 0.060 0.648
#> GSM447707 2 0.2625 0.7586 0.000 0.916 0.084
#> GSM447732 3 0.7279 0.6260 0.292 0.056 0.652
#> GSM447684 3 0.9065 0.3202 0.416 0.136 0.448
#> GSM447731 3 0.6023 0.5014 0.092 0.120 0.788
#> GSM447705 3 0.9183 0.4251 0.168 0.324 0.508
#> GSM447631 3 0.5835 0.5565 0.340 0.000 0.660
#> GSM447701 2 0.1491 0.7635 0.016 0.968 0.016
#> GSM447645 3 0.5835 0.5565 0.340 0.000 0.660
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM447671 2 0.7765 -0.1408 0.028 0.432 0.424 0.116
#> GSM447694 3 0.7961 0.2013 0.264 0.004 0.412 0.320
#> GSM447618 4 0.8635 0.1708 0.048 0.208 0.308 0.436
#> GSM447691 2 0.8627 0.3302 0.140 0.480 0.084 0.296
#> GSM447733 4 0.7162 -0.1530 0.028 0.064 0.444 0.464
#> GSM447620 3 0.7211 0.3082 0.012 0.264 0.580 0.144
#> GSM447627 3 0.7315 0.2080 0.108 0.012 0.468 0.412
#> GSM447630 3 0.7590 0.1961 0.028 0.340 0.520 0.112
#> GSM447642 1 0.2107 0.7489 0.940 0.016 0.020 0.024
#> GSM447649 2 0.3266 0.5680 0.000 0.832 0.000 0.168
#> GSM447654 2 0.5296 -0.0124 0.000 0.500 0.008 0.492
#> GSM447655 2 0.1211 0.6184 0.000 0.960 0.000 0.040
#> GSM447669 3 0.7482 0.0887 0.016 0.396 0.472 0.116
#> GSM447676 1 0.4115 0.6983 0.836 0.016 0.120 0.028
#> GSM447678 4 0.7372 0.1675 0.028 0.432 0.080 0.460
#> GSM447681 2 0.0657 0.6275 0.000 0.984 0.004 0.012
#> GSM447698 4 0.7710 0.2160 0.032 0.404 0.104 0.460
#> GSM447713 1 0.1389 0.7662 0.952 0.000 0.048 0.000
#> GSM447722 4 0.7710 0.2160 0.032 0.404 0.104 0.460
#> GSM447726 3 0.8382 0.3293 0.108 0.220 0.548 0.124
#> GSM447735 4 0.7310 0.0106 0.108 0.020 0.324 0.548
#> GSM447737 1 0.3523 0.7214 0.856 0.000 0.112 0.032
#> GSM447657 2 0.0657 0.6275 0.000 0.984 0.004 0.012
#> GSM447674 2 0.0657 0.6275 0.000 0.984 0.004 0.012
#> GSM447636 1 0.2107 0.7489 0.940 0.016 0.020 0.024
#> GSM447723 1 0.8935 0.1697 0.436 0.272 0.072 0.220
#> GSM447699 3 0.7679 0.0338 0.032 0.100 0.448 0.420
#> GSM447708 2 0.7923 0.4255 0.084 0.580 0.104 0.232
#> GSM447721 1 0.1890 0.7665 0.936 0.000 0.056 0.008
#> GSM447623 1 0.3325 0.7244 0.864 0.000 0.112 0.024
#> GSM447621 1 0.3325 0.7244 0.864 0.000 0.112 0.024
#> GSM447650 2 0.0657 0.6269 0.000 0.984 0.004 0.012
#> GSM447651 2 0.4733 0.5675 0.008 0.800 0.064 0.128
#> GSM447653 3 0.5636 0.2614 0.024 0.000 0.552 0.424
#> GSM447658 1 0.2215 0.7485 0.936 0.016 0.024 0.024
#> GSM447675 4 0.5438 0.0253 0.008 0.452 0.004 0.536
#> GSM447680 2 0.6543 0.4939 0.100 0.648 0.012 0.240
#> GSM447686 2 0.8571 0.3216 0.212 0.460 0.048 0.280
#> GSM447736 3 0.6881 0.4301 0.060 0.052 0.640 0.248
#> GSM447629 2 0.8460 0.3563 0.184 0.484 0.052 0.280
#> GSM447648 3 0.3863 0.5383 0.144 0.000 0.828 0.028
#> GSM447660 1 0.8568 0.2315 0.468 0.240 0.048 0.244
#> GSM447661 2 0.0657 0.6269 0.000 0.984 0.004 0.012
#> GSM447663 3 0.4741 0.5491 0.032 0.024 0.800 0.144
#> GSM447704 2 0.3266 0.5680 0.000 0.832 0.000 0.168
#> GSM447720 3 0.6021 0.5171 0.072 0.028 0.720 0.180
#> GSM447652 2 0.5427 0.4200 0.000 0.736 0.100 0.164
#> GSM447679 2 0.2125 0.6236 0.000 0.920 0.004 0.076
#> GSM447712 1 0.1807 0.7673 0.940 0.000 0.052 0.008
#> GSM447664 4 0.5675 -0.0312 0.016 0.472 0.004 0.508
#> GSM447637 3 0.1936 0.5729 0.028 0.000 0.940 0.032
#> GSM447639 4 0.7154 -0.0822 0.020 0.076 0.444 0.460
#> GSM447615 3 0.5487 0.3733 0.328 0.004 0.644 0.024
#> GSM447656 2 0.8372 0.3711 0.196 0.500 0.048 0.256
#> GSM447673 2 0.4103 0.4932 0.000 0.744 0.000 0.256
#> GSM447719 3 0.5611 0.2739 0.024 0.000 0.564 0.412
#> GSM447706 3 0.1411 0.5742 0.020 0.000 0.960 0.020
#> GSM447612 3 0.7357 0.3357 0.032 0.108 0.584 0.276
#> GSM447665 2 0.7187 0.0821 0.012 0.520 0.364 0.104
#> GSM447677 2 0.5738 0.5506 0.036 0.748 0.060 0.156
#> GSM447613 1 0.1716 0.7638 0.936 0.000 0.064 0.000
#> GSM447659 3 0.5816 0.1953 0.012 0.012 0.492 0.484
#> GSM447662 3 0.1911 0.5773 0.020 0.004 0.944 0.032
#> GSM447666 3 0.6581 0.3729 0.012 0.200 0.660 0.128
#> GSM447668 2 0.0657 0.6269 0.000 0.984 0.004 0.012
#> GSM447682 2 0.5308 0.5770 0.052 0.768 0.024 0.156
#> GSM447683 2 0.5294 0.5696 0.060 0.764 0.016 0.160
#> GSM447688 2 0.3688 0.5338 0.000 0.792 0.000 0.208
#> GSM447702 2 0.0188 0.6267 0.000 0.996 0.000 0.004
#> GSM447709 2 0.7472 0.2050 0.012 0.516 0.332 0.140
#> GSM447711 1 0.1890 0.7667 0.936 0.000 0.056 0.008
#> GSM447715 1 0.8935 0.1697 0.436 0.272 0.072 0.220
#> GSM447693 3 0.1936 0.5729 0.028 0.000 0.940 0.032
#> GSM447611 4 0.5991 0.0453 0.032 0.432 0.004 0.532
#> GSM447672 2 0.1211 0.6184 0.000 0.960 0.000 0.040
#> GSM447703 2 0.3688 0.5338 0.000 0.792 0.000 0.208
#> GSM447727 1 0.8639 0.2360 0.476 0.256 0.060 0.208
#> GSM447638 3 0.8017 0.3891 0.208 0.100 0.584 0.108
#> GSM447670 3 0.5610 0.3014 0.356 0.004 0.616 0.024
#> GSM447700 4 0.8398 0.1041 0.032 0.196 0.376 0.396
#> GSM447738 2 0.3726 0.5332 0.000 0.788 0.000 0.212
#> GSM447739 1 0.1389 0.7662 0.952 0.000 0.048 0.000
#> GSM447617 1 0.3325 0.7244 0.864 0.000 0.112 0.024
#> GSM447628 2 0.5097 0.1229 0.000 0.568 0.004 0.428
#> GSM447632 2 0.4343 0.5300 0.000 0.732 0.004 0.264
#> GSM447619 3 0.1911 0.5773 0.020 0.004 0.944 0.032
#> GSM447643 1 0.8784 0.0542 0.408 0.296 0.048 0.248
#> GSM447724 4 0.7645 -0.1122 0.028 0.104 0.424 0.444
#> GSM447728 2 0.6183 0.5435 0.084 0.716 0.032 0.168
#> GSM447610 4 0.7697 -0.0722 0.240 0.000 0.316 0.444
#> GSM447633 2 0.7187 0.0821 0.012 0.520 0.364 0.104
#> GSM447634 3 0.7526 0.3730 0.104 0.052 0.596 0.248
#> GSM447622 1 0.6474 0.2320 0.536 0.000 0.388 0.076
#> GSM447667 2 0.8589 0.3458 0.204 0.476 0.056 0.264
#> GSM447687 2 0.3688 0.5338 0.000 0.792 0.000 0.208
#> GSM447695 3 0.7904 0.1463 0.308 0.000 0.368 0.324
#> GSM447696 1 0.1389 0.7662 0.952 0.000 0.048 0.000
#> GSM447697 1 0.1716 0.7638 0.936 0.000 0.064 0.000
#> GSM447714 3 0.3877 0.5615 0.032 0.004 0.840 0.124
#> GSM447717 1 0.1993 0.7484 0.944 0.016 0.016 0.024
#> GSM447725 1 0.1576 0.7664 0.948 0.000 0.048 0.004
#> GSM447729 4 0.5328 -0.0189 0.004 0.472 0.004 0.520
#> GSM447644 3 0.7482 0.0887 0.016 0.396 0.472 0.116
#> GSM447710 3 0.3822 0.5622 0.032 0.004 0.844 0.120
#> GSM447614 4 0.7697 -0.0722 0.240 0.000 0.316 0.444
#> GSM447685 2 0.6730 0.4926 0.108 0.640 0.016 0.236
#> GSM447690 1 0.1389 0.7662 0.952 0.000 0.048 0.000
#> GSM447730 2 0.3123 0.5759 0.000 0.844 0.000 0.156
#> GSM447646 2 0.5097 0.1229 0.000 0.568 0.004 0.428
#> GSM447689 3 0.4861 0.5509 0.016 0.080 0.804 0.100
#> GSM447635 2 0.8943 0.3067 0.184 0.460 0.092 0.264
#> GSM447641 1 0.2215 0.7485 0.936 0.016 0.024 0.024
#> GSM447716 2 0.8571 0.3216 0.212 0.460 0.048 0.280
#> GSM447718 3 0.6347 0.4920 0.032 0.104 0.708 0.156
#> GSM447616 1 0.6474 0.2320 0.536 0.000 0.388 0.076
#> GSM447626 3 0.6319 0.4900 0.136 0.060 0.724 0.080
#> GSM447640 2 0.2888 0.6134 0.000 0.872 0.004 0.124
#> GSM447734 3 0.4693 0.5396 0.032 0.012 0.788 0.168
#> GSM447692 3 0.7904 0.1463 0.308 0.000 0.368 0.324
#> GSM447647 2 0.3266 0.5680 0.000 0.832 0.000 0.168
#> GSM447624 1 0.5137 0.1839 0.544 0.000 0.452 0.004
#> GSM447625 3 0.4693 0.5396 0.032 0.012 0.788 0.168
#> GSM447707 2 0.3123 0.5759 0.000 0.844 0.000 0.156
#> GSM447732 3 0.4360 0.5511 0.032 0.012 0.816 0.140
#> GSM447684 3 0.7793 0.4051 0.208 0.080 0.600 0.112
#> GSM447731 3 0.7405 0.1402 0.020 0.100 0.484 0.396
#> GSM447705 3 0.7563 0.3113 0.012 0.252 0.544 0.192
#> GSM447631 3 0.1936 0.5729 0.028 0.000 0.940 0.032
#> GSM447701 2 0.0657 0.6269 0.000 0.984 0.004 0.012
#> GSM447645 3 0.1936 0.5729 0.028 0.000 0.940 0.032
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM447671 3 0.7136 0.0808 0.012 0.228 0.480 0.012 0.268
#> GSM447694 3 0.7395 0.2463 0.228 0.000 0.524 0.144 0.104
#> GSM447618 3 0.7354 0.2276 0.016 0.092 0.432 0.060 0.400
#> GSM447691 5 0.7227 0.6024 0.100 0.176 0.108 0.020 0.596
#> GSM447733 3 0.7443 0.0114 0.016 0.088 0.488 0.328 0.080
#> GSM447620 3 0.6862 0.1733 0.000 0.040 0.476 0.120 0.364
#> GSM447627 3 0.7422 0.0908 0.096 0.024 0.500 0.320 0.060
#> GSM447630 3 0.6012 0.3128 0.004 0.200 0.636 0.012 0.148
#> GSM447642 1 0.2130 0.7956 0.908 0.000 0.012 0.000 0.080
#> GSM447649 2 0.2069 0.5762 0.000 0.912 0.012 0.000 0.076
#> GSM447654 2 0.5574 0.3626 0.000 0.680 0.032 0.212 0.076
#> GSM447655 2 0.4508 0.4204 0.000 0.648 0.000 0.020 0.332
#> GSM447669 3 0.6639 0.2036 0.004 0.208 0.548 0.012 0.228
#> GSM447676 1 0.4363 0.7407 0.816 0.008 0.064 0.048 0.064
#> GSM447678 2 0.7930 0.2627 0.016 0.484 0.124 0.116 0.260
#> GSM447681 2 0.4875 0.3434 0.000 0.576 0.004 0.020 0.400
#> GSM447698 2 0.8269 0.1641 0.016 0.392 0.172 0.100 0.320
#> GSM447713 1 0.0510 0.8361 0.984 0.000 0.016 0.000 0.000
#> GSM447722 2 0.8269 0.1641 0.016 0.392 0.172 0.100 0.320
#> GSM447726 3 0.8531 0.1364 0.100 0.036 0.380 0.156 0.328
#> GSM447735 3 0.8221 0.1604 0.076 0.028 0.452 0.224 0.220
#> GSM447737 1 0.3152 0.7920 0.868 0.000 0.084 0.016 0.032
#> GSM447657 2 0.4866 0.3458 0.000 0.580 0.004 0.020 0.396
#> GSM447674 2 0.4866 0.3458 0.000 0.580 0.004 0.020 0.396
#> GSM447636 1 0.2130 0.7956 0.908 0.000 0.012 0.000 0.080
#> GSM447723 5 0.7096 0.3434 0.396 0.064 0.064 0.016 0.460
#> GSM447699 3 0.6641 0.3425 0.012 0.048 0.592 0.084 0.264
#> GSM447708 5 0.7158 0.5346 0.048 0.264 0.124 0.016 0.548
#> GSM447721 1 0.1059 0.8363 0.968 0.000 0.020 0.008 0.004
#> GSM447623 1 0.2965 0.7964 0.876 0.000 0.084 0.012 0.028
#> GSM447621 1 0.2965 0.7964 0.876 0.000 0.084 0.012 0.028
#> GSM447650 2 0.4908 0.3311 0.000 0.560 0.004 0.020 0.416
#> GSM447651 5 0.6181 0.1692 0.000 0.348 0.052 0.048 0.552
#> GSM447653 4 0.3435 0.8935 0.008 0.012 0.148 0.828 0.004
#> GSM447658 1 0.2289 0.7949 0.904 0.000 0.012 0.004 0.080
#> GSM447675 2 0.6285 0.3317 0.008 0.636 0.028 0.200 0.128
#> GSM447680 5 0.5155 0.5318 0.068 0.196 0.004 0.016 0.716
#> GSM447686 5 0.6787 0.6434 0.172 0.164 0.044 0.012 0.608
#> GSM447736 3 0.4526 0.4466 0.036 0.012 0.796 0.036 0.120
#> GSM447629 5 0.6395 0.6443 0.144 0.172 0.044 0.004 0.636
#> GSM447648 3 0.6385 0.2429 0.128 0.000 0.584 0.260 0.028
#> GSM447660 5 0.6478 0.2581 0.416 0.052 0.036 0.012 0.484
#> GSM447661 2 0.4908 0.3311 0.000 0.560 0.004 0.020 0.416
#> GSM447663 3 0.2138 0.4430 0.012 0.004 0.928 0.024 0.032
#> GSM447704 2 0.2069 0.5762 0.000 0.912 0.012 0.000 0.076
#> GSM447720 3 0.3467 0.4504 0.048 0.004 0.864 0.032 0.052
#> GSM447652 2 0.6513 0.4149 0.000 0.624 0.140 0.064 0.172
#> GSM447679 2 0.4774 0.3108 0.000 0.556 0.000 0.020 0.424
#> GSM447712 1 0.0932 0.8372 0.972 0.000 0.020 0.004 0.004
#> GSM447664 2 0.6492 0.3617 0.016 0.632 0.028 0.176 0.148
#> GSM447637 3 0.4796 0.2995 0.012 0.000 0.680 0.280 0.028
#> GSM447639 3 0.6845 0.3114 0.004 0.052 0.576 0.132 0.236
#> GSM447615 3 0.7745 0.1120 0.312 0.000 0.412 0.200 0.076
#> GSM447656 5 0.6248 0.6474 0.160 0.172 0.036 0.000 0.632
#> GSM447673 2 0.3130 0.5742 0.000 0.872 0.016 0.040 0.072
#> GSM447719 4 0.3193 0.8915 0.008 0.008 0.136 0.844 0.004
#> GSM447706 3 0.4775 0.3063 0.008 0.000 0.688 0.268 0.036
#> GSM447612 3 0.5552 0.4044 0.012 0.044 0.724 0.068 0.152
#> GSM447665 3 0.7154 -0.1506 0.000 0.268 0.392 0.016 0.324
#> GSM447677 5 0.5879 0.3204 0.004 0.312 0.064 0.020 0.600
#> GSM447613 1 0.1059 0.8343 0.968 0.000 0.020 0.008 0.004
#> GSM447659 3 0.6022 -0.0988 0.004 0.016 0.492 0.428 0.060
#> GSM447662 3 0.4394 0.3323 0.008 0.000 0.744 0.212 0.036
#> GSM447666 3 0.6706 0.1375 0.000 0.008 0.452 0.188 0.352
#> GSM447668 2 0.4908 0.3311 0.000 0.560 0.004 0.020 0.416
#> GSM447682 5 0.5686 0.2452 0.024 0.448 0.016 0.012 0.500
#> GSM447683 5 0.4875 0.3608 0.024 0.336 0.000 0.008 0.632
#> GSM447688 2 0.1799 0.5814 0.000 0.940 0.012 0.020 0.028
#> GSM447702 2 0.4835 0.3674 0.000 0.592 0.004 0.020 0.384
#> GSM447709 5 0.7424 0.2665 0.000 0.232 0.308 0.040 0.420
#> GSM447711 1 0.1059 0.8368 0.968 0.000 0.020 0.008 0.004
#> GSM447715 5 0.7096 0.3434 0.396 0.064 0.064 0.016 0.460
#> GSM447693 3 0.4796 0.2995 0.012 0.000 0.680 0.280 0.028
#> GSM447611 2 0.6758 0.2942 0.032 0.608 0.024 0.216 0.120
#> GSM447672 2 0.4508 0.4204 0.000 0.648 0.000 0.020 0.332
#> GSM447703 2 0.1799 0.5814 0.000 0.940 0.012 0.020 0.028
#> GSM447727 1 0.6965 -0.3209 0.436 0.060 0.056 0.016 0.432
#> GSM447638 3 0.8486 0.1017 0.192 0.004 0.364 0.180 0.260
#> GSM447670 3 0.7783 0.1232 0.336 0.000 0.400 0.176 0.088
#> GSM447700 3 0.7387 0.2966 0.016 0.088 0.496 0.076 0.324
#> GSM447738 2 0.1885 0.5813 0.000 0.936 0.012 0.020 0.032
#> GSM447739 1 0.0510 0.8361 0.984 0.000 0.016 0.000 0.000
#> GSM447617 1 0.2965 0.7964 0.876 0.000 0.084 0.012 0.028
#> GSM447628 2 0.4495 0.4114 0.000 0.752 0.024 0.196 0.028
#> GSM447632 2 0.2929 0.5588 0.000 0.856 0.012 0.004 0.128
#> GSM447619 3 0.4394 0.3323 0.008 0.000 0.744 0.212 0.036
#> GSM447643 5 0.6547 0.4202 0.360 0.064 0.036 0.012 0.528
#> GSM447724 3 0.7578 0.1621 0.016 0.112 0.540 0.224 0.108
#> GSM447728 5 0.5916 0.3907 0.048 0.404 0.028 0.000 0.520
#> GSM447610 3 0.8310 0.1517 0.208 0.004 0.416 0.204 0.168
#> GSM447633 3 0.7154 -0.1506 0.000 0.268 0.392 0.016 0.324
#> GSM447634 3 0.5096 0.4357 0.076 0.008 0.748 0.024 0.144
#> GSM447622 1 0.7074 0.3520 0.540 0.000 0.264 0.108 0.088
#> GSM447667 5 0.6470 0.6484 0.160 0.164 0.044 0.004 0.628
#> GSM447687 2 0.1967 0.5814 0.000 0.932 0.012 0.020 0.036
#> GSM447695 3 0.7623 0.2124 0.272 0.000 0.476 0.144 0.108
#> GSM447696 1 0.0510 0.8361 0.984 0.000 0.016 0.000 0.000
#> GSM447697 1 0.1059 0.8343 0.968 0.000 0.020 0.008 0.004
#> GSM447714 3 0.2604 0.4332 0.012 0.000 0.896 0.072 0.020
#> GSM447717 1 0.2069 0.7976 0.912 0.000 0.012 0.000 0.076
#> GSM447725 1 0.0671 0.8363 0.980 0.000 0.016 0.000 0.004
#> GSM447729 2 0.5973 0.3361 0.004 0.648 0.024 0.220 0.104
#> GSM447644 3 0.6639 0.2036 0.004 0.208 0.548 0.012 0.228
#> GSM447710 3 0.2666 0.4322 0.012 0.000 0.892 0.076 0.020
#> GSM447614 3 0.8310 0.1517 0.208 0.004 0.416 0.204 0.168
#> GSM447685 5 0.5009 0.5448 0.072 0.216 0.000 0.008 0.704
#> GSM447690 1 0.0510 0.8361 0.984 0.000 0.016 0.000 0.000
#> GSM447730 2 0.2818 0.5611 0.000 0.856 0.012 0.000 0.132
#> GSM447646 2 0.4495 0.4114 0.000 0.752 0.024 0.196 0.028
#> GSM447689 3 0.4780 0.3905 0.012 0.008 0.768 0.096 0.116
#> GSM447635 5 0.7318 0.6254 0.140 0.160 0.108 0.012 0.580
#> GSM447641 1 0.2289 0.7949 0.904 0.000 0.012 0.004 0.080
#> GSM447716 5 0.6787 0.6434 0.172 0.164 0.044 0.012 0.608
#> GSM447718 3 0.4366 0.4450 0.024 0.036 0.820 0.040 0.080
#> GSM447616 1 0.7074 0.3520 0.540 0.000 0.264 0.108 0.088
#> GSM447626 3 0.7675 0.2156 0.120 0.000 0.496 0.204 0.180
#> GSM447640 2 0.4827 0.2200 0.000 0.504 0.000 0.020 0.476
#> GSM447734 3 0.1475 0.4433 0.012 0.004 0.956 0.012 0.016
#> GSM447692 3 0.7623 0.2124 0.272 0.000 0.476 0.144 0.108
#> GSM447647 2 0.2069 0.5762 0.000 0.912 0.012 0.000 0.076
#> GSM447624 1 0.6417 0.3421 0.576 0.000 0.260 0.140 0.024
#> GSM447625 3 0.1475 0.4433 0.012 0.004 0.956 0.012 0.016
#> GSM447707 2 0.2818 0.5611 0.000 0.856 0.012 0.000 0.132
#> GSM447732 3 0.1667 0.4394 0.012 0.004 0.948 0.024 0.012
#> GSM447684 3 0.8398 0.1259 0.188 0.004 0.392 0.176 0.240
#> GSM447731 4 0.5078 0.8169 0.004 0.112 0.136 0.736 0.012
#> GSM447705 3 0.6365 0.2170 0.000 0.040 0.540 0.076 0.344
#> GSM447631 3 0.4796 0.2995 0.012 0.000 0.680 0.280 0.028
#> GSM447701 2 0.4908 0.3311 0.000 0.560 0.004 0.020 0.416
#> GSM447645 3 0.4796 0.2995 0.012 0.000 0.680 0.280 0.028
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM447671 3 0.6406 0.25596 0.000 0.288 0.520 0.104 0.000 0.088
#> GSM447694 3 0.6900 0.33050 0.200 0.036 0.556 0.000 0.088 0.120
#> GSM447618 3 0.5156 0.36610 0.000 0.204 0.688 0.064 0.016 0.028
#> GSM447691 2 0.6191 0.41557 0.048 0.608 0.252 0.044 0.016 0.032
#> GSM447733 3 0.6056 0.24544 0.000 0.004 0.572 0.132 0.252 0.040
#> GSM447620 6 0.6821 0.34394 0.000 0.236 0.188 0.012 0.060 0.504
#> GSM447627 3 0.6553 0.28567 0.084 0.000 0.576 0.052 0.236 0.052
#> GSM447630 3 0.7434 0.19371 0.000 0.204 0.432 0.092 0.020 0.252
#> GSM447642 1 0.4455 0.72685 0.780 0.108 0.048 0.000 0.040 0.024
#> GSM447649 4 0.3543 0.60266 0.000 0.248 0.004 0.740 0.004 0.004
#> GSM447654 4 0.4096 0.51161 0.000 0.016 0.052 0.800 0.104 0.028
#> GSM447655 2 0.5431 0.13449 0.000 0.512 0.004 0.404 0.016 0.064
#> GSM447669 3 0.7508 0.19141 0.000 0.244 0.424 0.100 0.020 0.212
#> GSM447676 1 0.5790 0.65142 0.688 0.088 0.080 0.000 0.040 0.104
#> GSM447678 4 0.6465 0.26914 0.000 0.080 0.336 0.508 0.048 0.028
#> GSM447681 2 0.5451 0.28263 0.000 0.576 0.008 0.324 0.012 0.080
#> GSM447698 3 0.6784 -0.16442 0.000 0.112 0.416 0.400 0.040 0.032
#> GSM447713 1 0.0520 0.81574 0.984 0.000 0.008 0.000 0.008 0.000
#> GSM447722 3 0.6784 -0.16442 0.000 0.112 0.416 0.400 0.040 0.032
#> GSM447726 6 0.7121 0.44045 0.044 0.216 0.148 0.012 0.044 0.536
#> GSM447735 3 0.6286 0.36946 0.068 0.048 0.676 0.048 0.116 0.044
#> GSM447737 1 0.3378 0.76595 0.852 0.016 0.068 0.000 0.028 0.036
#> GSM447657 2 0.5463 0.28012 0.000 0.572 0.008 0.328 0.012 0.080
#> GSM447674 2 0.5463 0.28012 0.000 0.572 0.008 0.328 0.012 0.080
#> GSM447636 1 0.4455 0.72685 0.780 0.108 0.048 0.000 0.040 0.024
#> GSM447723 2 0.7533 0.18368 0.280 0.436 0.192 0.016 0.036 0.040
#> GSM447699 3 0.4493 0.41732 0.000 0.060 0.776 0.056 0.012 0.096
#> GSM447708 2 0.5988 0.45013 0.008 0.640 0.192 0.096 0.012 0.052
#> GSM447721 1 0.1533 0.81583 0.948 0.008 0.016 0.000 0.016 0.012
#> GSM447623 1 0.3262 0.77078 0.860 0.016 0.060 0.000 0.028 0.036
#> GSM447621 1 0.3262 0.77078 0.860 0.016 0.060 0.000 0.028 0.036
#> GSM447650 2 0.5480 0.29424 0.000 0.588 0.008 0.308 0.016 0.080
#> GSM447651 2 0.6588 0.36446 0.000 0.592 0.032 0.184 0.084 0.108
#> GSM447653 5 0.3984 0.91164 0.000 0.000 0.044 0.080 0.800 0.076
#> GSM447658 1 0.4574 0.72385 0.772 0.112 0.048 0.000 0.040 0.028
#> GSM447675 4 0.4984 0.47250 0.004 0.032 0.104 0.748 0.084 0.028
#> GSM447680 2 0.4039 0.48156 0.024 0.828 0.048 0.028 0.020 0.052
#> GSM447686 2 0.5795 0.45338 0.080 0.660 0.196 0.024 0.028 0.012
#> GSM447736 3 0.5307 0.30134 0.024 0.040 0.640 0.012 0.008 0.276
#> GSM447629 2 0.5221 0.47234 0.064 0.700 0.188 0.024 0.012 0.012
#> GSM447648 6 0.4534 0.53581 0.112 0.004 0.124 0.000 0.016 0.744
#> GSM447660 2 0.7461 0.11591 0.280 0.448 0.180 0.016 0.048 0.028
#> GSM447661 2 0.5480 0.29424 0.000 0.588 0.008 0.308 0.016 0.080
#> GSM447663 3 0.4764 0.09793 0.000 0.020 0.540 0.000 0.020 0.420
#> GSM447704 4 0.3543 0.60266 0.000 0.248 0.004 0.740 0.004 0.004
#> GSM447720 3 0.5101 0.23675 0.032 0.008 0.612 0.000 0.028 0.320
#> GSM447652 4 0.7039 0.29868 0.000 0.276 0.164 0.480 0.044 0.036
#> GSM447679 2 0.5124 0.27911 0.000 0.620 0.008 0.300 0.012 0.060
#> GSM447712 1 0.1026 0.81538 0.968 0.012 0.004 0.000 0.008 0.008
#> GSM447664 4 0.5695 0.50007 0.012 0.092 0.072 0.708 0.088 0.028
#> GSM447637 6 0.3087 0.56337 0.004 0.000 0.176 0.000 0.012 0.808
#> GSM447639 3 0.5125 0.42229 0.000 0.056 0.740 0.072 0.036 0.096
#> GSM447615 6 0.6258 0.44657 0.240 0.060 0.092 0.000 0.020 0.588
#> GSM447656 2 0.5064 0.48184 0.064 0.728 0.156 0.020 0.016 0.016
#> GSM447673 4 0.3968 0.65932 0.000 0.196 0.016 0.760 0.008 0.020
#> GSM447719 5 0.3730 0.91087 0.000 0.000 0.024 0.076 0.812 0.088
#> GSM447706 6 0.2703 0.56685 0.000 0.000 0.172 0.000 0.004 0.824
#> GSM447612 3 0.4700 0.37595 0.000 0.032 0.736 0.044 0.016 0.172
#> GSM447665 3 0.6968 0.00908 0.000 0.344 0.408 0.140 0.000 0.108
#> GSM447677 2 0.5201 0.43257 0.000 0.712 0.052 0.140 0.012 0.084
#> GSM447613 1 0.1337 0.81558 0.956 0.008 0.008 0.000 0.012 0.016
#> GSM447659 3 0.5717 0.13228 0.000 0.000 0.536 0.060 0.352 0.052
#> GSM447662 6 0.3592 0.50929 0.000 0.000 0.240 0.000 0.020 0.740
#> GSM447666 6 0.6034 0.45616 0.000 0.204 0.120 0.000 0.076 0.600
#> GSM447668 2 0.5480 0.29424 0.000 0.588 0.008 0.308 0.016 0.080
#> GSM447682 2 0.4033 0.37582 0.000 0.724 0.052 0.224 0.000 0.000
#> GSM447683 2 0.2404 0.46443 0.000 0.872 0.016 0.112 0.000 0.000
#> GSM447688 4 0.2527 0.66559 0.000 0.168 0.000 0.832 0.000 0.000
#> GSM447702 2 0.5602 0.23947 0.000 0.548 0.008 0.348 0.016 0.080
#> GSM447709 2 0.7866 0.13367 0.000 0.396 0.176 0.112 0.044 0.272
#> GSM447711 1 0.1446 0.81262 0.952 0.012 0.012 0.000 0.012 0.012
#> GSM447715 2 0.7533 0.18368 0.280 0.436 0.192 0.016 0.036 0.040
#> GSM447693 6 0.3087 0.56337 0.004 0.000 0.176 0.000 0.012 0.808
#> GSM447611 4 0.5571 0.44748 0.024 0.044 0.072 0.720 0.112 0.028
#> GSM447672 2 0.5431 0.13449 0.000 0.512 0.004 0.404 0.016 0.064
#> GSM447703 4 0.2527 0.66559 0.000 0.168 0.000 0.832 0.000 0.000
#> GSM447727 2 0.7514 0.11761 0.316 0.412 0.184 0.016 0.036 0.036
#> GSM447638 6 0.6836 0.49001 0.100 0.204 0.104 0.000 0.032 0.560
#> GSM447670 6 0.6400 0.43550 0.268 0.068 0.084 0.000 0.020 0.560
#> GSM447700 3 0.4720 0.41176 0.000 0.116 0.756 0.072 0.016 0.040
#> GSM447738 4 0.2668 0.66572 0.000 0.168 0.004 0.828 0.000 0.000
#> GSM447739 1 0.0146 0.81519 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM447617 1 0.3262 0.77078 0.860 0.016 0.060 0.000 0.028 0.036
#> GSM447628 4 0.2878 0.56278 0.000 0.004 0.020 0.872 0.076 0.028
#> GSM447632 4 0.3817 0.62160 0.000 0.252 0.028 0.720 0.000 0.000
#> GSM447619 6 0.3592 0.50929 0.000 0.000 0.240 0.000 0.020 0.740
#> GSM447643 2 0.7160 0.24471 0.232 0.492 0.200 0.016 0.040 0.020
#> GSM447724 3 0.6335 0.40478 0.000 0.020 0.620 0.140 0.116 0.104
#> GSM447728 2 0.4927 0.43826 0.008 0.700 0.092 0.188 0.008 0.004
#> GSM447610 3 0.7102 0.31792 0.176 0.060 0.560 0.012 0.148 0.044
#> GSM447633 3 0.6968 0.00908 0.000 0.344 0.408 0.140 0.000 0.108
#> GSM447634 3 0.5715 0.32349 0.052 0.068 0.636 0.004 0.008 0.232
#> GSM447622 1 0.6398 0.35092 0.536 0.032 0.132 0.000 0.020 0.280
#> GSM447667 2 0.5514 0.46982 0.076 0.684 0.184 0.024 0.020 0.012
#> GSM447687 4 0.2597 0.66146 0.000 0.176 0.000 0.824 0.000 0.000
#> GSM447695 3 0.6809 0.31652 0.232 0.036 0.552 0.000 0.088 0.092
#> GSM447696 1 0.0146 0.81519 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM447697 1 0.1223 0.81512 0.960 0.008 0.004 0.000 0.012 0.016
#> GSM447714 3 0.4225 -0.00691 0.000 0.004 0.508 0.000 0.008 0.480
#> GSM447717 1 0.3908 0.74396 0.816 0.096 0.036 0.000 0.028 0.024
#> GSM447725 1 0.0146 0.81625 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM447729 4 0.4768 0.48019 0.004 0.032 0.064 0.764 0.108 0.028
#> GSM447644 3 0.7508 0.19141 0.000 0.244 0.424 0.100 0.020 0.212
#> GSM447710 3 0.4226 -0.01345 0.000 0.004 0.504 0.000 0.008 0.484
#> GSM447614 3 0.7102 0.31792 0.176 0.060 0.560 0.012 0.148 0.044
#> GSM447685 2 0.2257 0.49094 0.016 0.912 0.044 0.020 0.008 0.000
#> GSM447690 1 0.0146 0.81519 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM447730 4 0.4389 0.48888 0.000 0.304 0.008 0.660 0.004 0.024
#> GSM447646 4 0.2878 0.56278 0.000 0.004 0.020 0.872 0.076 0.028
#> GSM447689 6 0.5578 0.20850 0.004 0.068 0.376 0.000 0.024 0.528
#> GSM447635 2 0.6186 0.41419 0.076 0.612 0.236 0.024 0.012 0.040
#> GSM447641 1 0.4574 0.72385 0.772 0.112 0.048 0.000 0.040 0.028
#> GSM447716 2 0.5795 0.45338 0.080 0.660 0.196 0.024 0.028 0.012
#> GSM447718 3 0.6076 0.21031 0.004 0.044 0.556 0.028 0.044 0.324
#> GSM447616 1 0.6398 0.35092 0.536 0.032 0.132 0.000 0.020 0.280
#> GSM447626 6 0.5850 0.55420 0.048 0.136 0.132 0.000 0.024 0.660
#> GSM447640 2 0.5346 0.30634 0.000 0.640 0.028 0.260 0.012 0.060
#> GSM447734 3 0.4407 0.17287 0.000 0.004 0.592 0.000 0.024 0.380
#> GSM447692 3 0.6809 0.31652 0.232 0.036 0.552 0.000 0.088 0.092
#> GSM447647 4 0.3543 0.60266 0.000 0.248 0.004 0.740 0.004 0.004
#> GSM447624 1 0.5014 0.26253 0.556 0.008 0.028 0.000 0.016 0.392
#> GSM447625 3 0.4407 0.17287 0.000 0.004 0.592 0.000 0.024 0.380
#> GSM447707 4 0.4389 0.48888 0.000 0.304 0.008 0.660 0.004 0.024
#> GSM447732 3 0.4498 0.09299 0.000 0.004 0.544 0.000 0.024 0.428
#> GSM447684 6 0.6877 0.50011 0.104 0.192 0.112 0.000 0.032 0.560
#> GSM447731 5 0.4095 0.84652 0.000 0.004 0.028 0.176 0.764 0.028
#> GSM447705 6 0.6897 0.28237 0.000 0.236 0.236 0.012 0.048 0.468
#> GSM447631 6 0.3087 0.56337 0.004 0.000 0.176 0.000 0.012 0.808
#> GSM447701 2 0.5480 0.29424 0.000 0.588 0.008 0.308 0.016 0.080
#> GSM447645 6 0.3087 0.56337 0.004 0.000 0.176 0.000 0.012 0.808
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 gender(p) individual(p) disease.state(p) other(p) k
#> SD:hclust 114 0.444 0.69812 0.50199 0.0339 2
#> SD:hclust 93 0.807 0.40216 0.08058 0.4330 3
#> SD:hclust 63 0.486 0.44396 0.00129 0.3327 4
#> SD:hclust 44 0.404 0.00255 0.46874 0.8622 5
#> SD:hclust 46 0.599 0.01659 0.04028 0.4354 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "kmeans"]
# you can also extract it by
# res = res_list["SD:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 130 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 0.743 0.896 0.949 0.4991 0.496 0.496
#> 3 3 0.591 0.758 0.828 0.2978 0.818 0.648
#> 4 4 0.603 0.704 0.825 0.1428 0.885 0.690
#> 5 5 0.617 0.562 0.752 0.0731 0.878 0.590
#> 6 6 0.646 0.463 0.673 0.0422 0.905 0.589
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
#> GSM447671 2 0.0672 0.975 0.008 0.992
#> GSM447694 1 0.0000 0.909 1.000 0.000
#> GSM447618 2 0.0000 0.979 0.000 1.000
#> GSM447691 2 0.0000 0.979 0.000 1.000
#> GSM447733 2 0.0938 0.972 0.012 0.988
#> GSM447620 2 0.0672 0.975 0.008 0.992
#> GSM447627 1 0.0000 0.909 1.000 0.000
#> GSM447630 2 0.0000 0.979 0.000 1.000
#> GSM447642 1 0.0672 0.910 0.992 0.008
#> GSM447649 2 0.0000 0.979 0.000 1.000
#> GSM447654 2 0.0000 0.979 0.000 1.000
#> GSM447655 2 0.0000 0.979 0.000 1.000
#> GSM447669 2 0.0000 0.979 0.000 1.000
#> GSM447676 1 0.0672 0.910 0.992 0.008
#> GSM447678 2 0.0000 0.979 0.000 1.000
#> GSM447681 2 0.0000 0.979 0.000 1.000
#> GSM447698 2 0.0000 0.979 0.000 1.000
#> GSM447713 1 0.0672 0.910 0.992 0.008
#> GSM447722 2 0.0000 0.979 0.000 1.000
#> GSM447726 2 0.1414 0.963 0.020 0.980
#> GSM447735 1 0.6438 0.824 0.836 0.164
#> GSM447737 1 0.0672 0.910 0.992 0.008
#> GSM447657 2 0.0000 0.979 0.000 1.000
#> GSM447674 2 0.0000 0.979 0.000 1.000
#> GSM447636 1 0.7299 0.737 0.796 0.204
#> GSM447723 1 0.0672 0.910 0.992 0.008
#> GSM447699 1 0.9661 0.466 0.608 0.392
#> GSM447708 2 0.0000 0.979 0.000 1.000
#> GSM447721 1 0.0672 0.910 0.992 0.008
#> GSM447623 1 0.0672 0.910 0.992 0.008
#> GSM447621 1 0.0672 0.910 0.992 0.008
#> GSM447650 2 0.0000 0.979 0.000 1.000
#> GSM447651 2 0.0672 0.975 0.008 0.992
#> GSM447653 1 0.3733 0.879 0.928 0.072
#> GSM447658 1 0.0672 0.910 0.992 0.008
#> GSM447675 2 0.0000 0.979 0.000 1.000
#> GSM447680 2 0.1184 0.967 0.016 0.984
#> GSM447686 2 0.7056 0.746 0.192 0.808
#> GSM447736 1 0.6623 0.811 0.828 0.172
#> GSM447629 2 0.1184 0.967 0.016 0.984
#> GSM447648 1 0.0000 0.909 1.000 0.000
#> GSM447660 1 0.0672 0.910 0.992 0.008
#> GSM447661 2 0.0000 0.979 0.000 1.000
#> GSM447663 1 0.7056 0.794 0.808 0.192
#> GSM447704 2 0.0000 0.979 0.000 1.000
#> GSM447720 1 0.6801 0.811 0.820 0.180
#> GSM447652 2 0.0000 0.979 0.000 1.000
#> GSM447679 2 0.0000 0.979 0.000 1.000
#> GSM447712 1 0.0672 0.910 0.992 0.008
#> GSM447664 2 0.1184 0.967 0.016 0.984
#> GSM447637 1 0.0000 0.909 1.000 0.000
#> GSM447639 2 0.9635 0.238 0.388 0.612
#> GSM447615 1 0.0000 0.909 1.000 0.000
#> GSM447656 2 0.1184 0.967 0.016 0.984
#> GSM447673 2 0.0000 0.979 0.000 1.000
#> GSM447719 1 0.0000 0.909 1.000 0.000
#> GSM447706 1 0.0000 0.909 1.000 0.000
#> GSM447612 1 0.9710 0.447 0.600 0.400
#> GSM447665 2 0.0672 0.975 0.008 0.992
#> GSM447677 2 0.0672 0.975 0.008 0.992
#> GSM447613 1 0.0672 0.910 0.992 0.008
#> GSM447659 1 0.8763 0.655 0.704 0.296
#> GSM447662 1 0.7056 0.794 0.808 0.192
#> GSM447666 1 0.6623 0.811 0.828 0.172
#> GSM447668 2 0.0000 0.979 0.000 1.000
#> GSM447682 2 0.0000 0.979 0.000 1.000
#> GSM447683 2 0.0000 0.979 0.000 1.000
#> GSM447688 2 0.0000 0.979 0.000 1.000
#> GSM447702 2 0.0000 0.979 0.000 1.000
#> GSM447709 2 0.0672 0.975 0.008 0.992
#> GSM447711 1 0.0672 0.910 0.992 0.008
#> GSM447715 1 0.9686 0.369 0.604 0.396
#> GSM447693 1 0.0000 0.909 1.000 0.000
#> GSM447611 2 0.7056 0.746 0.192 0.808
#> GSM447672 2 0.0000 0.979 0.000 1.000
#> GSM447703 2 0.0000 0.979 0.000 1.000
#> GSM447727 1 0.0672 0.910 0.992 0.008
#> GSM447638 1 0.9754 0.337 0.592 0.408
#> GSM447670 1 0.0000 0.909 1.000 0.000
#> GSM447700 2 0.0672 0.975 0.008 0.992
#> GSM447738 2 0.0000 0.979 0.000 1.000
#> GSM447739 1 0.0672 0.910 0.992 0.008
#> GSM447617 1 0.0672 0.910 0.992 0.008
#> GSM447628 2 0.0000 0.979 0.000 1.000
#> GSM447632 2 0.0000 0.979 0.000 1.000
#> GSM447619 1 0.6973 0.798 0.812 0.188
#> GSM447643 1 0.9775 0.326 0.588 0.412
#> GSM447724 2 0.3431 0.919 0.064 0.936
#> GSM447728 2 0.0000 0.979 0.000 1.000
#> GSM447610 1 0.0672 0.910 0.992 0.008
#> GSM447633 2 0.0672 0.975 0.008 0.992
#> GSM447634 1 0.6801 0.811 0.820 0.180
#> GSM447622 1 0.0000 0.909 1.000 0.000
#> GSM447667 2 0.4815 0.869 0.104 0.896
#> GSM447687 2 0.0000 0.979 0.000 1.000
#> GSM447695 1 0.0672 0.910 0.992 0.008
#> GSM447696 1 0.0672 0.910 0.992 0.008
#> GSM447697 1 0.0672 0.910 0.992 0.008
#> GSM447714 1 0.7056 0.794 0.808 0.192
#> GSM447717 1 0.0672 0.910 0.992 0.008
#> GSM447725 1 0.0672 0.910 0.992 0.008
#> GSM447729 2 0.0000 0.979 0.000 1.000
#> GSM447644 2 0.0672 0.975 0.008 0.992
#> GSM447710 1 0.0376 0.909 0.996 0.004
#> GSM447614 1 0.0672 0.910 0.992 0.008
#> GSM447685 2 0.0000 0.979 0.000 1.000
#> GSM447690 1 0.0672 0.910 0.992 0.008
#> GSM447730 2 0.0672 0.975 0.008 0.992
#> GSM447646 2 0.0000 0.979 0.000 1.000
#> GSM447689 1 0.6531 0.815 0.832 0.168
#> GSM447635 2 0.0000 0.979 0.000 1.000
#> GSM447641 1 0.0672 0.910 0.992 0.008
#> GSM447716 2 0.0000 0.979 0.000 1.000
#> GSM447718 1 0.9170 0.592 0.668 0.332
#> GSM447616 1 0.0000 0.909 1.000 0.000
#> GSM447626 1 0.0000 0.909 1.000 0.000
#> GSM447640 2 0.0000 0.979 0.000 1.000
#> GSM447734 1 0.7056 0.794 0.808 0.192
#> GSM447692 1 0.0672 0.910 0.992 0.008
#> GSM447647 2 0.0376 0.977 0.004 0.996
#> GSM447624 1 0.0000 0.909 1.000 0.000
#> GSM447625 1 0.7056 0.794 0.808 0.192
#> GSM447707 2 0.0000 0.979 0.000 1.000
#> GSM447732 1 0.6623 0.811 0.828 0.172
#> GSM447684 1 0.0000 0.909 1.000 0.000
#> GSM447731 2 0.0672 0.975 0.008 0.992
#> GSM447705 2 0.3431 0.919 0.064 0.936
#> GSM447631 1 0.0000 0.909 1.000 0.000
#> GSM447701 2 0.0672 0.975 0.008 0.992
#> GSM447645 1 0.0000 0.909 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM447671 2 0.4291 0.770 0.000 0.820 0.180
#> GSM447694 3 0.2066 0.764 0.060 0.000 0.940
#> GSM447618 2 0.3921 0.852 0.036 0.884 0.080
#> GSM447691 2 0.3412 0.819 0.000 0.876 0.124
#> GSM447733 3 0.6562 0.619 0.264 0.036 0.700
#> GSM447620 2 0.5650 0.580 0.000 0.688 0.312
#> GSM447627 3 0.3551 0.756 0.132 0.000 0.868
#> GSM447630 2 0.5763 0.651 0.008 0.716 0.276
#> GSM447642 1 0.5291 0.891 0.732 0.000 0.268
#> GSM447649 2 0.0000 0.874 0.000 1.000 0.000
#> GSM447654 2 0.6839 0.691 0.272 0.684 0.044
#> GSM447655 2 0.0000 0.874 0.000 1.000 0.000
#> GSM447669 2 0.4291 0.770 0.000 0.820 0.180
#> GSM447676 1 0.5291 0.891 0.732 0.000 0.268
#> GSM447678 2 0.5812 0.726 0.264 0.724 0.012
#> GSM447681 2 0.0237 0.873 0.004 0.996 0.000
#> GSM447698 2 0.1647 0.866 0.036 0.960 0.004
#> GSM447713 1 0.5327 0.889 0.728 0.000 0.272
#> GSM447722 2 0.8430 0.624 0.260 0.604 0.136
#> GSM447726 2 0.6451 0.447 0.008 0.608 0.384
#> GSM447735 3 0.7995 0.603 0.304 0.088 0.608
#> GSM447737 1 0.5678 0.839 0.684 0.000 0.316
#> GSM447657 2 0.0237 0.873 0.004 0.996 0.000
#> GSM447674 2 0.0237 0.873 0.004 0.996 0.000
#> GSM447636 1 0.5291 0.891 0.732 0.000 0.268
#> GSM447723 1 0.5327 0.890 0.728 0.000 0.272
#> GSM447699 3 0.6151 0.639 0.068 0.160 0.772
#> GSM447708 2 0.1289 0.868 0.000 0.968 0.032
#> GSM447721 1 0.5327 0.889 0.728 0.000 0.272
#> GSM447623 1 0.5397 0.884 0.720 0.000 0.280
#> GSM447621 1 0.5397 0.884 0.720 0.000 0.280
#> GSM447650 2 0.0000 0.874 0.000 1.000 0.000
#> GSM447651 2 0.1031 0.870 0.000 0.976 0.024
#> GSM447653 3 0.5835 0.637 0.340 0.000 0.660
#> GSM447658 1 0.5291 0.891 0.732 0.000 0.268
#> GSM447675 2 0.6839 0.691 0.272 0.684 0.044
#> GSM447680 2 0.1585 0.867 0.008 0.964 0.028
#> GSM447686 1 0.7726 0.343 0.572 0.372 0.056
#> GSM447736 3 0.1399 0.777 0.028 0.004 0.968
#> GSM447629 2 0.1267 0.871 0.004 0.972 0.024
#> GSM447648 3 0.4121 0.662 0.168 0.000 0.832
#> GSM447660 1 0.5291 0.891 0.732 0.000 0.268
#> GSM447661 2 0.0000 0.874 0.000 1.000 0.000
#> GSM447663 3 0.2339 0.759 0.012 0.048 0.940
#> GSM447704 2 0.0000 0.874 0.000 1.000 0.000
#> GSM447720 3 0.1877 0.767 0.012 0.032 0.956
#> GSM447652 2 0.0000 0.874 0.000 1.000 0.000
#> GSM447679 2 0.0000 0.874 0.000 1.000 0.000
#> GSM447712 1 0.5254 0.891 0.736 0.000 0.264
#> GSM447664 2 0.5884 0.720 0.272 0.716 0.012
#> GSM447637 3 0.4121 0.662 0.168 0.000 0.832
#> GSM447639 3 0.9299 0.423 0.292 0.196 0.512
#> GSM447615 1 0.6154 0.660 0.592 0.000 0.408
#> GSM447656 2 0.1585 0.867 0.008 0.964 0.028
#> GSM447673 2 0.2400 0.857 0.064 0.932 0.004
#> GSM447719 3 0.5948 0.632 0.360 0.000 0.640
#> GSM447706 3 0.3412 0.713 0.124 0.000 0.876
#> GSM447612 3 0.2066 0.755 0.000 0.060 0.940
#> GSM447665 2 0.1964 0.860 0.000 0.944 0.056
#> GSM447677 2 0.1031 0.870 0.000 0.976 0.024
#> GSM447613 1 0.5254 0.891 0.736 0.000 0.264
#> GSM447659 3 0.5497 0.640 0.292 0.000 0.708
#> GSM447662 3 0.0829 0.774 0.004 0.012 0.984
#> GSM447666 3 0.3918 0.700 0.012 0.120 0.868
#> GSM447668 2 0.1031 0.870 0.000 0.976 0.024
#> GSM447682 2 0.0237 0.873 0.004 0.996 0.000
#> GSM447683 2 0.1031 0.870 0.000 0.976 0.024
#> GSM447688 2 0.5365 0.741 0.252 0.744 0.004
#> GSM447702 2 0.0000 0.874 0.000 1.000 0.000
#> GSM447709 2 0.2537 0.848 0.000 0.920 0.080
#> GSM447711 1 0.5254 0.891 0.736 0.000 0.264
#> GSM447715 1 0.7999 0.575 0.656 0.196 0.148
#> GSM447693 3 0.2356 0.757 0.072 0.000 0.928
#> GSM447611 2 0.7634 0.509 0.432 0.524 0.044
#> GSM447672 2 0.0000 0.874 0.000 1.000 0.000
#> GSM447703 2 0.2400 0.857 0.064 0.932 0.004
#> GSM447727 1 0.5327 0.890 0.728 0.000 0.272
#> GSM447638 1 0.8604 0.442 0.564 0.312 0.124
#> GSM447670 1 0.5363 0.887 0.724 0.000 0.276
#> GSM447700 2 0.6034 0.741 0.036 0.752 0.212
#> GSM447738 2 0.1878 0.864 0.044 0.952 0.004
#> GSM447739 1 0.5327 0.889 0.728 0.000 0.272
#> GSM447617 1 0.5397 0.884 0.720 0.000 0.280
#> GSM447628 2 0.5812 0.726 0.264 0.724 0.012
#> GSM447632 2 0.1878 0.864 0.044 0.952 0.004
#> GSM447619 3 0.1525 0.774 0.032 0.004 0.964
#> GSM447643 1 0.7769 0.570 0.660 0.232 0.108
#> GSM447724 3 0.7146 0.605 0.264 0.060 0.676
#> GSM447728 2 0.1031 0.870 0.000 0.976 0.024
#> GSM447610 1 0.3192 0.589 0.888 0.000 0.112
#> GSM447633 2 0.6359 0.491 0.008 0.628 0.364
#> GSM447634 3 0.3590 0.746 0.028 0.076 0.896
#> GSM447622 3 0.5178 0.503 0.256 0.000 0.744
#> GSM447667 2 0.3459 0.820 0.096 0.892 0.012
#> GSM447687 2 0.2400 0.857 0.064 0.932 0.004
#> GSM447695 3 0.3267 0.716 0.116 0.000 0.884
#> GSM447696 1 0.5327 0.889 0.728 0.000 0.272
#> GSM447697 1 0.5327 0.889 0.728 0.000 0.272
#> GSM447714 3 0.1620 0.778 0.024 0.012 0.964
#> GSM447717 1 0.5291 0.891 0.732 0.000 0.268
#> GSM447725 1 0.5254 0.891 0.736 0.000 0.264
#> GSM447729 2 0.5884 0.720 0.272 0.716 0.012
#> GSM447644 2 0.4700 0.765 0.008 0.812 0.180
#> GSM447710 3 0.1399 0.776 0.028 0.004 0.968
#> GSM447614 3 0.5363 0.677 0.276 0.000 0.724
#> GSM447685 2 0.1031 0.870 0.000 0.976 0.024
#> GSM447690 1 0.5327 0.889 0.728 0.000 0.272
#> GSM447730 2 0.0000 0.874 0.000 1.000 0.000
#> GSM447646 2 0.5812 0.726 0.264 0.724 0.012
#> GSM447689 3 0.1182 0.772 0.012 0.012 0.976
#> GSM447635 2 0.5849 0.733 0.028 0.756 0.216
#> GSM447641 1 0.5291 0.891 0.732 0.000 0.268
#> GSM447716 2 0.1878 0.864 0.044 0.952 0.004
#> GSM447718 3 0.2280 0.757 0.008 0.052 0.940
#> GSM447616 3 0.5397 0.446 0.280 0.000 0.720
#> GSM447626 3 0.1989 0.759 0.048 0.004 0.948
#> GSM447640 2 0.0000 0.874 0.000 1.000 0.000
#> GSM447734 3 0.1399 0.777 0.028 0.004 0.968
#> GSM447692 3 0.5291 0.477 0.268 0.000 0.732
#> GSM447647 2 0.5656 0.729 0.264 0.728 0.008
#> GSM447624 1 0.5882 0.789 0.652 0.000 0.348
#> GSM447625 3 0.1399 0.777 0.028 0.004 0.968
#> GSM447707 2 0.0000 0.874 0.000 1.000 0.000
#> GSM447732 3 0.1525 0.775 0.032 0.004 0.964
#> GSM447684 3 0.6298 -0.153 0.388 0.004 0.608
#> GSM447731 2 0.9909 0.024 0.268 0.368 0.364
#> GSM447705 3 0.5728 0.514 0.008 0.272 0.720
#> GSM447631 3 0.3619 0.700 0.136 0.000 0.864
#> GSM447701 2 0.1031 0.870 0.000 0.976 0.024
#> GSM447645 3 0.4121 0.662 0.168 0.000 0.832
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM447671 2 0.6907 0.5457 0.012 0.632 0.180 0.176
#> GSM447694 3 0.2999 0.7951 0.004 0.000 0.864 0.132
#> GSM447618 2 0.5523 0.6466 0.012 0.696 0.032 0.260
#> GSM447691 2 0.6515 0.5877 0.012 0.672 0.156 0.160
#> GSM447733 4 0.4277 0.5388 0.000 0.000 0.280 0.720
#> GSM447620 2 0.4991 0.6120 0.008 0.744 0.220 0.028
#> GSM447627 3 0.4905 0.4901 0.004 0.000 0.632 0.364
#> GSM447630 2 0.6852 0.3366 0.012 0.576 0.324 0.088
#> GSM447642 1 0.1151 0.9021 0.968 0.000 0.008 0.024
#> GSM447649 2 0.2704 0.7492 0.000 0.876 0.000 0.124
#> GSM447654 4 0.3074 0.7320 0.000 0.152 0.000 0.848
#> GSM447655 2 0.2469 0.7543 0.000 0.892 0.000 0.108
#> GSM447669 2 0.6426 0.5575 0.012 0.676 0.188 0.124
#> GSM447676 1 0.0921 0.8995 0.972 0.000 0.000 0.028
#> GSM447678 4 0.3208 0.7230 0.000 0.148 0.004 0.848
#> GSM447681 2 0.3157 0.7560 0.000 0.852 0.004 0.144
#> GSM447698 2 0.4819 0.6050 0.000 0.652 0.004 0.344
#> GSM447713 1 0.0804 0.9027 0.980 0.000 0.012 0.008
#> GSM447722 4 0.2843 0.7265 0.000 0.088 0.020 0.892
#> GSM447726 2 0.6730 0.4975 0.012 0.628 0.252 0.108
#> GSM447735 4 0.4284 0.5570 0.012 0.000 0.224 0.764
#> GSM447737 1 0.2443 0.8634 0.916 0.000 0.060 0.024
#> GSM447657 2 0.3539 0.7529 0.000 0.820 0.004 0.176
#> GSM447674 2 0.3402 0.7536 0.000 0.832 0.004 0.164
#> GSM447636 1 0.1118 0.8966 0.964 0.000 0.000 0.036
#> GSM447723 1 0.1389 0.8924 0.952 0.000 0.000 0.048
#> GSM447699 3 0.4955 0.6523 0.004 0.024 0.728 0.244
#> GSM447708 2 0.3774 0.7335 0.008 0.844 0.020 0.128
#> GSM447721 1 0.0804 0.9027 0.980 0.000 0.012 0.008
#> GSM447623 1 0.0937 0.9016 0.976 0.000 0.012 0.012
#> GSM447621 1 0.0937 0.9016 0.976 0.000 0.012 0.012
#> GSM447650 2 0.2408 0.7548 0.000 0.896 0.000 0.104
#> GSM447651 2 0.0188 0.7587 0.000 0.996 0.004 0.000
#> GSM447653 4 0.4277 0.5186 0.000 0.000 0.280 0.720
#> GSM447658 1 0.1118 0.8966 0.964 0.000 0.000 0.036
#> GSM447675 4 0.2814 0.7355 0.000 0.132 0.000 0.868
#> GSM447680 2 0.2342 0.7479 0.008 0.912 0.000 0.080
#> GSM447686 1 0.5323 0.6867 0.748 0.172 0.004 0.076
#> GSM447736 3 0.2773 0.7964 0.004 0.000 0.880 0.116
#> GSM447629 2 0.3612 0.7334 0.012 0.840 0.004 0.144
#> GSM447648 3 0.3895 0.7344 0.132 0.000 0.832 0.036
#> GSM447660 1 0.1118 0.8966 0.964 0.000 0.000 0.036
#> GSM447661 2 0.2408 0.7548 0.000 0.896 0.000 0.104
#> GSM447663 3 0.5011 0.7520 0.016 0.120 0.792 0.072
#> GSM447704 2 0.2760 0.7478 0.000 0.872 0.000 0.128
#> GSM447720 3 0.6360 0.6645 0.012 0.128 0.684 0.176
#> GSM447652 2 0.2714 0.7540 0.000 0.884 0.004 0.112
#> GSM447679 2 0.3024 0.7599 0.000 0.852 0.000 0.148
#> GSM447712 1 0.0927 0.9029 0.976 0.000 0.008 0.016
#> GSM447664 4 0.2530 0.7302 0.000 0.112 0.000 0.888
#> GSM447637 3 0.3707 0.7342 0.132 0.000 0.840 0.028
#> GSM447639 4 0.4562 0.6200 0.000 0.028 0.208 0.764
#> GSM447615 1 0.5781 0.2941 0.584 0.000 0.380 0.036
#> GSM447656 2 0.3447 0.7380 0.020 0.852 0.000 0.128
#> GSM447673 2 0.4889 0.5625 0.000 0.636 0.004 0.360
#> GSM447719 3 0.5167 -0.1493 0.004 0.000 0.508 0.488
#> GSM447706 3 0.1584 0.7985 0.012 0.000 0.952 0.036
#> GSM447612 3 0.2198 0.8043 0.008 0.000 0.920 0.072
#> GSM447665 2 0.1575 0.7496 0.004 0.956 0.028 0.012
#> GSM447677 2 0.0188 0.7587 0.000 0.996 0.004 0.000
#> GSM447613 1 0.0336 0.9035 0.992 0.000 0.008 0.000
#> GSM447659 4 0.4746 0.3443 0.000 0.000 0.368 0.632
#> GSM447662 3 0.1004 0.8079 0.004 0.000 0.972 0.024
#> GSM447666 3 0.3703 0.7298 0.012 0.140 0.840 0.008
#> GSM447668 2 0.0000 0.7592 0.000 1.000 0.000 0.000
#> GSM447682 2 0.3448 0.7536 0.000 0.828 0.004 0.168
#> GSM447683 2 0.1940 0.7630 0.000 0.924 0.000 0.076
#> GSM447688 4 0.4313 0.6328 0.000 0.260 0.004 0.736
#> GSM447702 2 0.2469 0.7543 0.000 0.892 0.000 0.108
#> GSM447709 2 0.1406 0.7489 0.000 0.960 0.024 0.016
#> GSM447711 1 0.0336 0.9035 0.992 0.000 0.008 0.000
#> GSM447715 1 0.5511 0.7039 0.752 0.148 0.012 0.088
#> GSM447693 3 0.1488 0.7981 0.012 0.000 0.956 0.032
#> GSM447611 4 0.2748 0.7356 0.020 0.072 0.004 0.904
#> GSM447672 2 0.2530 0.7541 0.000 0.888 0.000 0.112
#> GSM447703 2 0.4720 0.5649 0.000 0.672 0.004 0.324
#> GSM447727 1 0.1302 0.8937 0.956 0.000 0.000 0.044
#> GSM447638 2 0.7688 0.4217 0.232 0.584 0.140 0.044
#> GSM447670 1 0.2179 0.8767 0.924 0.000 0.064 0.012
#> GSM447700 2 0.7832 0.3752 0.012 0.492 0.204 0.292
#> GSM447738 2 0.4905 0.5624 0.000 0.632 0.004 0.364
#> GSM447739 1 0.0804 0.9027 0.980 0.000 0.012 0.008
#> GSM447617 1 0.1059 0.8999 0.972 0.000 0.012 0.016
#> GSM447628 4 0.3837 0.6905 0.000 0.224 0.000 0.776
#> GSM447632 2 0.4920 0.5603 0.000 0.628 0.004 0.368
#> GSM447619 3 0.1004 0.8079 0.004 0.000 0.972 0.024
#> GSM447643 1 0.3962 0.7859 0.832 0.124 0.000 0.044
#> GSM447724 4 0.4511 0.5699 0.000 0.008 0.268 0.724
#> GSM447728 2 0.2197 0.7632 0.000 0.916 0.004 0.080
#> GSM447610 1 0.5643 0.2210 0.548 0.000 0.024 0.428
#> GSM447633 2 0.6459 0.4986 0.012 0.648 0.252 0.088
#> GSM447634 3 0.5281 0.7249 0.016 0.044 0.752 0.188
#> GSM447622 3 0.4951 0.6782 0.212 0.000 0.744 0.044
#> GSM447667 2 0.4912 0.7089 0.060 0.776 0.004 0.160
#> GSM447687 2 0.4720 0.5649 0.000 0.672 0.004 0.324
#> GSM447695 3 0.4590 0.7567 0.036 0.000 0.772 0.192
#> GSM447696 1 0.0804 0.9027 0.980 0.000 0.012 0.008
#> GSM447697 1 0.0804 0.9027 0.980 0.000 0.012 0.008
#> GSM447714 3 0.1661 0.8071 0.004 0.000 0.944 0.052
#> GSM447717 1 0.0921 0.8989 0.972 0.000 0.000 0.028
#> GSM447725 1 0.0524 0.9036 0.988 0.000 0.008 0.004
#> GSM447729 4 0.2973 0.7244 0.000 0.144 0.000 0.856
#> GSM447644 2 0.5898 0.5775 0.012 0.716 0.184 0.088
#> GSM447710 3 0.0376 0.8042 0.004 0.000 0.992 0.004
#> GSM447614 4 0.5075 0.3481 0.012 0.000 0.344 0.644
#> GSM447685 2 0.2647 0.7569 0.000 0.880 0.000 0.120
#> GSM447690 1 0.0804 0.9027 0.980 0.000 0.012 0.008
#> GSM447730 2 0.2760 0.7478 0.000 0.872 0.000 0.128
#> GSM447646 4 0.3837 0.6905 0.000 0.224 0.000 0.776
#> GSM447689 3 0.2781 0.7839 0.016 0.072 0.904 0.008
#> GSM447635 2 0.7726 0.4027 0.012 0.500 0.180 0.308
#> GSM447641 1 0.0469 0.9021 0.988 0.000 0.000 0.012
#> GSM447716 2 0.5415 0.5055 0.008 0.552 0.004 0.436
#> GSM447718 3 0.4900 0.7571 0.016 0.112 0.800 0.072
#> GSM447616 3 0.5358 0.6286 0.252 0.000 0.700 0.048
#> GSM447626 3 0.2853 0.7821 0.016 0.076 0.900 0.008
#> GSM447640 2 0.3266 0.7530 0.000 0.832 0.000 0.168
#> GSM447734 3 0.2266 0.8031 0.004 0.000 0.912 0.084
#> GSM447692 3 0.6461 0.6622 0.216 0.000 0.640 0.144
#> GSM447647 4 0.3907 0.6868 0.000 0.232 0.000 0.768
#> GSM447624 1 0.5696 -0.0715 0.496 0.000 0.480 0.024
#> GSM447625 3 0.2266 0.8031 0.004 0.000 0.912 0.084
#> GSM447707 2 0.2760 0.7478 0.000 0.872 0.000 0.128
#> GSM447732 3 0.2561 0.8057 0.004 0.016 0.912 0.068
#> GSM447684 3 0.6809 0.6302 0.128 0.140 0.684 0.048
#> GSM447731 4 0.5619 0.7055 0.000 0.152 0.124 0.724
#> GSM447705 3 0.3920 0.7780 0.012 0.076 0.856 0.056
#> GSM447631 3 0.3707 0.7342 0.132 0.000 0.840 0.028
#> GSM447701 2 0.0188 0.7587 0.000 0.996 0.004 0.000
#> GSM447645 3 0.3707 0.7342 0.132 0.000 0.840 0.028
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM447671 5 0.4107 0.57575 0.004 0.112 0.008 0.068 0.808
#> GSM447694 3 0.4712 0.54879 0.000 0.000 0.684 0.048 0.268
#> GSM447618 5 0.5314 0.46251 0.000 0.136 0.000 0.192 0.672
#> GSM447691 5 0.4806 0.56431 0.004 0.156 0.004 0.092 0.744
#> GSM447733 4 0.4385 0.62658 0.000 0.000 0.068 0.752 0.180
#> GSM447620 5 0.6563 0.24142 0.000 0.384 0.132 0.016 0.468
#> GSM447627 3 0.6538 0.23730 0.000 0.000 0.480 0.272 0.248
#> GSM447630 5 0.4800 0.57468 0.004 0.176 0.056 0.016 0.748
#> GSM447642 1 0.1568 0.86737 0.944 0.000 0.000 0.020 0.036
#> GSM447649 2 0.1117 0.77004 0.000 0.964 0.000 0.016 0.020
#> GSM447654 4 0.2881 0.67220 0.000 0.124 0.004 0.860 0.012
#> GSM447655 2 0.0162 0.77327 0.000 0.996 0.000 0.004 0.000
#> GSM447669 5 0.4539 0.59096 0.004 0.184 0.020 0.032 0.760
#> GSM447676 1 0.1750 0.86639 0.936 0.000 0.000 0.028 0.036
#> GSM447678 4 0.5136 0.51586 0.000 0.080 0.000 0.660 0.260
#> GSM447681 2 0.2450 0.76691 0.000 0.900 0.000 0.052 0.048
#> GSM447698 2 0.6554 0.36747 0.000 0.476 0.000 0.272 0.252
#> GSM447713 1 0.1267 0.86602 0.960 0.000 0.012 0.004 0.024
#> GSM447722 4 0.5533 0.43481 0.000 0.064 0.004 0.560 0.372
#> GSM447726 5 0.5720 0.47494 0.004 0.276 0.040 0.040 0.640
#> GSM447735 4 0.6319 0.42952 0.000 0.000 0.196 0.520 0.284
#> GSM447737 1 0.4751 0.66937 0.732 0.000 0.204 0.016 0.048
#> GSM447657 2 0.5060 0.62256 0.000 0.692 0.000 0.104 0.204
#> GSM447674 2 0.2974 0.75528 0.000 0.868 0.000 0.080 0.052
#> GSM447636 1 0.2300 0.85666 0.908 0.000 0.000 0.040 0.052
#> GSM447723 1 0.2974 0.83888 0.868 0.000 0.000 0.052 0.080
#> GSM447699 5 0.5821 -0.18898 0.000 0.000 0.400 0.096 0.504
#> GSM447708 5 0.5853 -0.00674 0.000 0.432 0.000 0.096 0.472
#> GSM447721 1 0.1267 0.86602 0.960 0.000 0.012 0.004 0.024
#> GSM447623 1 0.2844 0.81958 0.876 0.000 0.092 0.004 0.028
#> GSM447621 1 0.2548 0.83310 0.896 0.000 0.072 0.004 0.028
#> GSM447650 2 0.0609 0.77166 0.000 0.980 0.000 0.000 0.020
#> GSM447651 2 0.2286 0.73069 0.000 0.888 0.000 0.004 0.108
#> GSM447653 4 0.4929 0.59589 0.000 0.000 0.136 0.716 0.148
#> GSM447658 1 0.2228 0.85795 0.912 0.000 0.000 0.040 0.048
#> GSM447675 4 0.2520 0.67208 0.000 0.056 0.000 0.896 0.048
#> GSM447680 2 0.4953 0.61200 0.000 0.696 0.000 0.088 0.216
#> GSM447686 1 0.5642 0.57902 0.644 0.008 0.000 0.112 0.236
#> GSM447736 3 0.5175 0.41861 0.000 0.000 0.548 0.044 0.408
#> GSM447629 5 0.6464 -0.10792 0.004 0.408 0.000 0.156 0.432
#> GSM447648 3 0.1461 0.66262 0.028 0.000 0.952 0.004 0.016
#> GSM447660 1 0.1830 0.86458 0.932 0.000 0.000 0.028 0.040
#> GSM447661 2 0.0609 0.77166 0.000 0.980 0.000 0.000 0.020
#> GSM447663 5 0.4947 -0.00214 0.004 0.024 0.396 0.000 0.576
#> GSM447704 2 0.1106 0.76918 0.000 0.964 0.000 0.024 0.012
#> GSM447720 5 0.4124 0.45791 0.004 0.008 0.124 0.060 0.804
#> GSM447652 2 0.1750 0.77118 0.000 0.936 0.000 0.036 0.028
#> GSM447679 2 0.2233 0.76747 0.000 0.904 0.000 0.080 0.016
#> GSM447712 1 0.0671 0.87191 0.980 0.000 0.000 0.004 0.016
#> GSM447664 4 0.2659 0.66134 0.000 0.052 0.000 0.888 0.060
#> GSM447637 3 0.1082 0.66209 0.028 0.000 0.964 0.000 0.008
#> GSM447639 4 0.5325 0.51037 0.000 0.000 0.076 0.616 0.308
#> GSM447615 3 0.5495 0.20781 0.364 0.000 0.576 0.012 0.048
#> GSM447656 2 0.6170 0.22530 0.004 0.492 0.000 0.120 0.384
#> GSM447673 2 0.5342 0.51343 0.000 0.612 0.000 0.312 0.076
#> GSM447719 4 0.4841 0.33121 0.000 0.000 0.416 0.560 0.024
#> GSM447706 3 0.1329 0.66496 0.008 0.000 0.956 0.004 0.032
#> GSM447612 5 0.4971 -0.21271 0.000 0.000 0.460 0.028 0.512
#> GSM447665 2 0.4708 0.11396 0.000 0.548 0.000 0.016 0.436
#> GSM447677 2 0.2763 0.70571 0.000 0.848 0.000 0.004 0.148
#> GSM447613 1 0.0000 0.87108 1.000 0.000 0.000 0.000 0.000
#> GSM447659 4 0.5892 0.47031 0.000 0.000 0.220 0.600 0.180
#> GSM447662 3 0.3391 0.62194 0.000 0.000 0.800 0.012 0.188
#> GSM447666 3 0.4505 0.29855 0.004 0.000 0.620 0.008 0.368
#> GSM447668 2 0.2536 0.72404 0.000 0.868 0.000 0.004 0.128
#> GSM447682 2 0.3239 0.75346 0.000 0.852 0.000 0.080 0.068
#> GSM447683 2 0.4254 0.70250 0.000 0.772 0.000 0.080 0.148
#> GSM447688 4 0.6224 0.20523 0.000 0.388 0.000 0.468 0.144
#> GSM447702 2 0.0000 0.77398 0.000 1.000 0.000 0.000 0.000
#> GSM447709 2 0.4227 0.48220 0.000 0.692 0.000 0.016 0.292
#> GSM447711 1 0.0290 0.87044 0.992 0.000 0.000 0.000 0.008
#> GSM447715 1 0.6113 0.29521 0.508 0.004 0.000 0.116 0.372
#> GSM447693 3 0.0324 0.66384 0.004 0.000 0.992 0.000 0.004
#> GSM447611 4 0.2300 0.66937 0.000 0.040 0.000 0.908 0.052
#> GSM447672 2 0.0000 0.77398 0.000 1.000 0.000 0.000 0.000
#> GSM447703 2 0.4666 0.56473 0.000 0.704 0.000 0.240 0.056
#> GSM447727 1 0.2632 0.84703 0.888 0.000 0.000 0.040 0.072
#> GSM447638 5 0.8954 0.27879 0.144 0.208 0.232 0.040 0.376
#> GSM447670 1 0.4723 0.60114 0.688 0.000 0.272 0.008 0.032
#> GSM447700 5 0.4249 0.51590 0.000 0.056 0.024 0.120 0.800
#> GSM447738 2 0.5522 0.50580 0.000 0.600 0.000 0.308 0.092
#> GSM447739 1 0.1267 0.86602 0.960 0.000 0.012 0.004 0.024
#> GSM447617 1 0.3880 0.72822 0.784 0.000 0.184 0.004 0.028
#> GSM447628 4 0.3618 0.64493 0.000 0.196 0.004 0.788 0.012
#> GSM447632 2 0.5505 0.51032 0.000 0.604 0.000 0.304 0.092
#> GSM447619 3 0.2818 0.64921 0.000 0.000 0.856 0.012 0.132
#> GSM447643 1 0.3875 0.79031 0.816 0.012 0.000 0.048 0.124
#> GSM447724 4 0.6017 0.36954 0.000 0.000 0.116 0.480 0.404
#> GSM447728 2 0.4083 0.71751 0.000 0.788 0.000 0.080 0.132
#> GSM447610 1 0.6626 -0.01700 0.436 0.000 0.088 0.436 0.040
#> GSM447633 5 0.4952 0.55320 0.000 0.252 0.040 0.016 0.692
#> GSM447634 5 0.5200 0.30828 0.004 0.004 0.208 0.088 0.696
#> GSM447622 3 0.3461 0.63451 0.076 0.000 0.848 0.008 0.068
#> GSM447667 5 0.6793 -0.09908 0.016 0.396 0.000 0.164 0.424
#> GSM447687 2 0.4666 0.56523 0.000 0.704 0.000 0.240 0.056
#> GSM447695 3 0.5872 0.27612 0.004 0.000 0.480 0.084 0.432
#> GSM447696 1 0.1471 0.86347 0.952 0.000 0.020 0.004 0.024
#> GSM447697 1 0.1560 0.86381 0.948 0.000 0.020 0.004 0.028
#> GSM447714 3 0.4297 0.55232 0.000 0.000 0.692 0.020 0.288
#> GSM447717 1 0.1386 0.86851 0.952 0.000 0.000 0.016 0.032
#> GSM447725 1 0.0404 0.87136 0.988 0.000 0.000 0.012 0.000
#> GSM447729 4 0.1956 0.66594 0.000 0.076 0.000 0.916 0.008
#> GSM447644 5 0.4782 0.51189 0.004 0.300 0.020 0.008 0.668
#> GSM447710 3 0.2629 0.63282 0.000 0.000 0.860 0.004 0.136
#> GSM447614 4 0.6362 0.40166 0.000 0.000 0.224 0.520 0.256
#> GSM447685 2 0.4855 0.66001 0.000 0.720 0.000 0.112 0.168
#> GSM447690 1 0.1267 0.86602 0.960 0.000 0.012 0.004 0.024
#> GSM447730 2 0.1106 0.76918 0.000 0.964 0.000 0.024 0.012
#> GSM447646 4 0.3548 0.64852 0.000 0.188 0.004 0.796 0.012
#> GSM447689 3 0.4253 0.38212 0.004 0.000 0.660 0.004 0.332
#> GSM447635 5 0.4072 0.52152 0.004 0.056 0.004 0.136 0.800
#> GSM447641 1 0.1211 0.86930 0.960 0.000 0.000 0.016 0.024
#> GSM447716 4 0.6819 -0.19962 0.000 0.340 0.000 0.348 0.312
#> GSM447718 5 0.5423 0.24486 0.004 0.036 0.308 0.020 0.632
#> GSM447616 3 0.4130 0.61204 0.108 0.000 0.804 0.012 0.076
#> GSM447626 3 0.4102 0.42352 0.004 0.000 0.692 0.004 0.300
#> GSM447640 2 0.2189 0.76789 0.000 0.904 0.000 0.084 0.012
#> GSM447734 3 0.4585 0.51662 0.000 0.000 0.628 0.020 0.352
#> GSM447692 3 0.7153 0.45757 0.152 0.000 0.524 0.064 0.260
#> GSM447647 4 0.3455 0.63834 0.000 0.208 0.000 0.784 0.008
#> GSM447624 3 0.4725 0.44373 0.280 0.000 0.680 0.004 0.036
#> GSM447625 3 0.4585 0.51545 0.000 0.000 0.628 0.020 0.352
#> GSM447707 2 0.1106 0.76918 0.000 0.964 0.000 0.024 0.012
#> GSM447732 3 0.4517 0.47672 0.000 0.000 0.600 0.012 0.388
#> GSM447684 5 0.5820 0.07172 0.048 0.000 0.392 0.024 0.536
#> GSM447731 4 0.5150 0.63762 0.000 0.140 0.080 0.740 0.040
#> GSM447705 5 0.4837 -0.03542 0.004 0.000 0.424 0.016 0.556
#> GSM447631 3 0.0992 0.66293 0.024 0.000 0.968 0.000 0.008
#> GSM447701 2 0.2377 0.72016 0.000 0.872 0.000 0.000 0.128
#> GSM447645 3 0.1082 0.66209 0.028 0.000 0.964 0.000 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM447671 6 0.5487 0.16479 0.000 0.040 0.000 0.052 0.368 0.540
#> GSM447694 6 0.5809 0.04902 0.000 0.000 0.432 0.044 0.068 0.456
#> GSM447618 5 0.6056 0.28440 0.000 0.040 0.000 0.136 0.548 0.276
#> GSM447691 5 0.5669 -0.00592 0.000 0.060 0.000 0.040 0.464 0.436
#> GSM447733 4 0.4573 0.60588 0.000 0.000 0.024 0.716 0.060 0.200
#> GSM447620 5 0.7902 0.14470 0.000 0.272 0.148 0.016 0.300 0.264
#> GSM447627 6 0.6977 0.04288 0.000 0.000 0.224 0.276 0.076 0.424
#> GSM447630 6 0.5214 0.34315 0.000 0.140 0.008 0.004 0.196 0.652
#> GSM447642 1 0.2473 0.81827 0.856 0.000 0.000 0.000 0.136 0.008
#> GSM447649 2 0.1851 0.69377 0.000 0.928 0.000 0.012 0.036 0.024
#> GSM447654 4 0.1442 0.67589 0.000 0.040 0.000 0.944 0.004 0.012
#> GSM447655 2 0.0665 0.69746 0.000 0.980 0.000 0.008 0.004 0.008
#> GSM447669 6 0.5584 0.24934 0.000 0.132 0.000 0.016 0.268 0.584
#> GSM447676 1 0.2743 0.80696 0.828 0.000 0.000 0.000 0.164 0.008
#> GSM447678 5 0.5716 0.05696 0.000 0.024 0.000 0.388 0.496 0.092
#> GSM447681 2 0.3381 0.63779 0.000 0.808 0.000 0.040 0.148 0.004
#> GSM447698 5 0.6750 0.24509 0.000 0.248 0.000 0.200 0.480 0.072
#> GSM447713 1 0.1515 0.83177 0.944 0.000 0.008 0.000 0.020 0.028
#> GSM447722 5 0.6597 0.10738 0.000 0.028 0.004 0.316 0.432 0.220
#> GSM447726 6 0.5948 0.02974 0.000 0.168 0.008 0.000 0.376 0.448
#> GSM447735 6 0.7067 -0.14702 0.000 0.000 0.108 0.332 0.160 0.400
#> GSM447737 1 0.5921 0.54605 0.644 0.000 0.136 0.016 0.052 0.152
#> GSM447657 5 0.5521 -0.08538 0.000 0.444 0.000 0.052 0.468 0.036
#> GSM447674 2 0.3658 0.63517 0.000 0.792 0.000 0.048 0.152 0.008
#> GSM447636 1 0.2882 0.79753 0.812 0.000 0.000 0.000 0.180 0.008
#> GSM447723 1 0.3705 0.74590 0.740 0.000 0.000 0.004 0.236 0.020
#> GSM447699 6 0.6377 0.38789 0.000 0.000 0.196 0.088 0.152 0.564
#> GSM447708 5 0.6288 0.23681 0.000 0.320 0.000 0.032 0.480 0.168
#> GSM447721 1 0.1623 0.83169 0.940 0.000 0.004 0.004 0.020 0.032
#> GSM447623 1 0.3163 0.78568 0.856 0.000 0.076 0.004 0.020 0.044
#> GSM447621 1 0.3087 0.79250 0.864 0.000 0.056 0.004 0.024 0.052
#> GSM447650 2 0.0820 0.69376 0.000 0.972 0.000 0.000 0.016 0.012
#> GSM447651 2 0.2433 0.67036 0.000 0.884 0.000 0.000 0.072 0.044
#> GSM447653 4 0.4323 0.61655 0.000 0.000 0.028 0.744 0.048 0.180
#> GSM447658 1 0.2848 0.79995 0.816 0.000 0.000 0.000 0.176 0.008
#> GSM447675 4 0.2933 0.62844 0.000 0.012 0.000 0.844 0.128 0.016
#> GSM447680 2 0.4845 0.29329 0.000 0.540 0.000 0.000 0.400 0.060
#> GSM447686 5 0.4459 -0.23749 0.460 0.000 0.000 0.004 0.516 0.020
#> GSM447736 6 0.5374 0.31050 0.000 0.000 0.296 0.028 0.076 0.600
#> GSM447629 5 0.5088 0.41471 0.000 0.184 0.000 0.032 0.684 0.100
#> GSM447648 3 0.0551 0.68117 0.004 0.000 0.984 0.000 0.004 0.008
#> GSM447660 1 0.2778 0.80466 0.824 0.000 0.000 0.000 0.168 0.008
#> GSM447661 2 0.0820 0.69376 0.000 0.972 0.000 0.000 0.016 0.012
#> GSM447663 6 0.5448 0.36460 0.000 0.068 0.232 0.000 0.060 0.640
#> GSM447704 2 0.2415 0.68440 0.000 0.900 0.000 0.036 0.040 0.024
#> GSM447720 6 0.3984 0.40277 0.000 0.008 0.012 0.012 0.236 0.732
#> GSM447652 2 0.1933 0.69603 0.000 0.924 0.000 0.032 0.032 0.012
#> GSM447679 2 0.2954 0.66868 0.000 0.852 0.000 0.048 0.096 0.004
#> GSM447712 1 0.0790 0.84022 0.968 0.000 0.000 0.000 0.032 0.000
#> GSM447664 4 0.3672 0.55772 0.000 0.004 0.000 0.712 0.276 0.008
#> GSM447637 3 0.0291 0.68269 0.004 0.000 0.992 0.000 0.004 0.000
#> GSM447639 4 0.5934 0.15652 0.000 0.000 0.016 0.452 0.136 0.396
#> GSM447615 3 0.5361 0.42106 0.252 0.000 0.644 0.008 0.052 0.044
#> GSM447656 5 0.5266 0.16761 0.016 0.328 0.000 0.000 0.580 0.076
#> GSM447673 2 0.6280 0.21481 0.000 0.456 0.000 0.240 0.288 0.016
#> GSM447719 4 0.4980 0.54984 0.000 0.000 0.248 0.660 0.024 0.068
#> GSM447706 3 0.0858 0.68025 0.004 0.000 0.968 0.000 0.000 0.028
#> GSM447612 6 0.4898 0.36142 0.000 0.000 0.252 0.024 0.060 0.664
#> GSM447665 2 0.5899 0.02043 0.000 0.504 0.000 0.004 0.256 0.236
#> GSM447677 2 0.3316 0.62346 0.000 0.812 0.000 0.000 0.136 0.052
#> GSM447613 1 0.0260 0.83956 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM447659 4 0.5577 0.53791 0.000 0.000 0.080 0.640 0.068 0.212
#> GSM447662 3 0.4056 0.50475 0.000 0.000 0.704 0.008 0.024 0.264
#> GSM447666 3 0.4543 0.36732 0.000 0.004 0.604 0.000 0.036 0.356
#> GSM447668 2 0.2905 0.64633 0.000 0.856 0.000 0.004 0.092 0.048
#> GSM447682 2 0.4400 0.57027 0.000 0.708 0.000 0.052 0.228 0.012
#> GSM447683 2 0.5063 0.50596 0.000 0.656 0.000 0.048 0.252 0.044
#> GSM447688 5 0.6843 0.09091 0.000 0.276 0.000 0.332 0.348 0.044
#> GSM447702 2 0.0436 0.69720 0.000 0.988 0.000 0.004 0.004 0.004
#> GSM447709 2 0.4933 0.46957 0.000 0.684 0.000 0.012 0.136 0.168
#> GSM447711 1 0.0000 0.83889 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447715 5 0.4908 0.09016 0.348 0.000 0.000 0.004 0.584 0.064
#> GSM447693 3 0.0146 0.68258 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM447611 4 0.2718 0.66617 0.004 0.004 0.000 0.876 0.072 0.044
#> GSM447672 2 0.1262 0.69633 0.000 0.956 0.000 0.020 0.016 0.008
#> GSM447703 2 0.5942 0.34412 0.000 0.552 0.000 0.232 0.196 0.020
#> GSM447727 1 0.3217 0.76409 0.768 0.000 0.000 0.000 0.224 0.008
#> GSM447638 5 0.8125 0.13524 0.112 0.160 0.092 0.000 0.416 0.220
#> GSM447670 1 0.5155 0.19098 0.508 0.000 0.432 0.004 0.040 0.016
#> GSM447700 6 0.5307 0.17361 0.000 0.004 0.004 0.080 0.384 0.528
#> GSM447738 2 0.6270 0.11711 0.000 0.408 0.000 0.228 0.352 0.012
#> GSM447739 1 0.1401 0.83260 0.948 0.000 0.004 0.000 0.020 0.028
#> GSM447617 1 0.4538 0.63680 0.724 0.000 0.200 0.004 0.024 0.048
#> GSM447628 4 0.3710 0.62104 0.000 0.108 0.000 0.804 0.076 0.012
#> GSM447632 2 0.6325 0.11516 0.000 0.404 0.000 0.220 0.360 0.016
#> GSM447619 3 0.2834 0.63110 0.000 0.000 0.852 0.008 0.020 0.120
#> GSM447643 1 0.4170 0.62727 0.660 0.000 0.000 0.000 0.308 0.032
#> GSM447724 6 0.6767 0.08923 0.000 0.000 0.052 0.216 0.312 0.420
#> GSM447728 2 0.4799 0.53858 0.000 0.688 0.000 0.048 0.228 0.036
#> GSM447610 4 0.7381 0.14894 0.368 0.000 0.032 0.384 0.088 0.128
#> GSM447633 6 0.5934 0.22197 0.000 0.148 0.008 0.020 0.248 0.576
#> GSM447634 6 0.4480 0.48381 0.000 0.008 0.072 0.032 0.124 0.764
#> GSM447622 3 0.4559 0.54601 0.072 0.000 0.760 0.016 0.024 0.128
#> GSM447667 5 0.5143 0.42529 0.008 0.168 0.000 0.036 0.700 0.088
#> GSM447687 2 0.5918 0.35226 0.000 0.556 0.000 0.232 0.192 0.020
#> GSM447695 6 0.6066 0.30934 0.004 0.000 0.244 0.048 0.124 0.580
#> GSM447696 1 0.1942 0.82674 0.928 0.000 0.020 0.004 0.020 0.028
#> GSM447697 1 0.1882 0.82919 0.928 0.000 0.020 0.000 0.024 0.028
#> GSM447714 3 0.4565 0.00145 0.000 0.000 0.496 0.008 0.020 0.476
#> GSM447717 1 0.2234 0.82281 0.872 0.000 0.000 0.000 0.124 0.004
#> GSM447725 1 0.1082 0.83906 0.956 0.000 0.000 0.000 0.040 0.004
#> GSM447729 4 0.2615 0.63280 0.000 0.008 0.000 0.852 0.136 0.004
#> GSM447644 6 0.5679 0.17781 0.000 0.208 0.000 0.004 0.236 0.552
#> GSM447710 3 0.3608 0.49978 0.000 0.000 0.716 0.000 0.012 0.272
#> GSM447614 4 0.6583 0.19528 0.000 0.000 0.080 0.408 0.112 0.400
#> GSM447685 2 0.5124 0.30908 0.000 0.544 0.000 0.028 0.392 0.036
#> GSM447690 1 0.1515 0.83177 0.944 0.000 0.008 0.000 0.020 0.028
#> GSM447730 2 0.2622 0.67922 0.000 0.888 0.000 0.044 0.044 0.024
#> GSM447646 4 0.3710 0.62104 0.000 0.108 0.000 0.804 0.076 0.012
#> GSM447689 3 0.4252 0.36231 0.000 0.000 0.604 0.000 0.024 0.372
#> GSM447635 5 0.4555 -0.00791 0.000 0.000 0.000 0.036 0.540 0.424
#> GSM447641 1 0.1970 0.83072 0.900 0.000 0.000 0.000 0.092 0.008
#> GSM447716 5 0.5595 0.41307 0.000 0.116 0.000 0.160 0.656 0.068
#> GSM447718 6 0.5795 0.44840 0.000 0.056 0.132 0.008 0.156 0.648
#> GSM447616 3 0.5592 0.44864 0.096 0.000 0.660 0.016 0.036 0.192
#> GSM447626 3 0.4047 0.38400 0.000 0.000 0.604 0.000 0.012 0.384
#> GSM447640 2 0.2966 0.67100 0.000 0.856 0.000 0.048 0.088 0.008
#> GSM447734 6 0.4079 0.18533 0.000 0.000 0.380 0.004 0.008 0.608
#> GSM447692 6 0.7718 0.00130 0.172 0.000 0.320 0.048 0.080 0.380
#> GSM447647 4 0.4176 0.61153 0.000 0.120 0.000 0.768 0.096 0.016
#> GSM447624 3 0.4671 0.45594 0.268 0.000 0.672 0.004 0.016 0.040
#> GSM447625 6 0.3905 0.20929 0.000 0.000 0.356 0.004 0.004 0.636
#> GSM447707 2 0.2622 0.67922 0.000 0.888 0.000 0.044 0.044 0.024
#> GSM447732 6 0.4371 0.21873 0.000 0.012 0.352 0.000 0.016 0.620
#> GSM447684 6 0.6623 0.21838 0.056 0.004 0.152 0.000 0.312 0.476
#> GSM447731 4 0.4153 0.64427 0.000 0.100 0.028 0.800 0.024 0.048
#> GSM447705 6 0.5284 0.21919 0.000 0.000 0.332 0.012 0.084 0.572
#> GSM447631 3 0.0146 0.68258 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM447701 2 0.2852 0.64428 0.000 0.856 0.000 0.000 0.080 0.064
#> GSM447645 3 0.0291 0.68269 0.004 0.000 0.992 0.000 0.004 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
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 gender(p) individual(p) disease.state(p) other(p) k
#> SD:kmeans 124 0.445 0.781 0.0387 0.0708 2
#> SD:kmeans 121 0.533 0.263 0.2487 0.3967 3
#> SD:kmeans 117 0.153 0.255 0.0744 0.0852 4
#> SD:kmeans 91 0.593 0.272 0.0970 0.0264 5
#> SD:kmeans 67 0.860 0.188 0.1925 0.0157 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "skmeans"]
# you can also extract it by
# res = res_list["SD:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 130 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 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 0.772 0.899 0.954 0.5040 0.496 0.496
#> 3 3 0.808 0.904 0.949 0.2945 0.797 0.612
#> 4 4 0.808 0.819 0.902 0.1366 0.881 0.675
#> 5 5 0.700 0.620 0.792 0.0710 0.948 0.809
#> 6 6 0.701 0.573 0.747 0.0416 0.920 0.665
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
#> GSM447671 2 0.0000 0.962 0.000 1.000
#> GSM447694 1 0.0000 0.938 1.000 0.000
#> GSM447618 2 0.0000 0.962 0.000 1.000
#> GSM447691 2 0.0000 0.962 0.000 1.000
#> GSM447733 2 0.3114 0.915 0.056 0.944
#> GSM447620 2 0.0000 0.962 0.000 1.000
#> GSM447627 1 0.0000 0.938 1.000 0.000
#> GSM447630 2 0.0000 0.962 0.000 1.000
#> GSM447642 1 0.0000 0.938 1.000 0.000
#> GSM447649 2 0.0000 0.962 0.000 1.000
#> GSM447654 2 0.0000 0.962 0.000 1.000
#> GSM447655 2 0.0000 0.962 0.000 1.000
#> GSM447669 2 0.0000 0.962 0.000 1.000
#> GSM447676 1 0.0000 0.938 1.000 0.000
#> GSM447678 2 0.0000 0.962 0.000 1.000
#> GSM447681 2 0.0000 0.962 0.000 1.000
#> GSM447698 2 0.0000 0.962 0.000 1.000
#> GSM447713 1 0.0000 0.938 1.000 0.000
#> GSM447722 2 0.0000 0.962 0.000 1.000
#> GSM447726 2 0.6712 0.796 0.176 0.824
#> GSM447735 1 0.0376 0.936 0.996 0.004
#> GSM447737 1 0.0000 0.938 1.000 0.000
#> GSM447657 2 0.0000 0.962 0.000 1.000
#> GSM447674 2 0.0000 0.962 0.000 1.000
#> GSM447636 1 0.4298 0.865 0.912 0.088
#> GSM447723 1 0.0000 0.938 1.000 0.000
#> GSM447699 1 0.9661 0.425 0.608 0.392
#> GSM447708 2 0.0000 0.962 0.000 1.000
#> GSM447721 1 0.0000 0.938 1.000 0.000
#> GSM447623 1 0.0000 0.938 1.000 0.000
#> GSM447621 1 0.0000 0.938 1.000 0.000
#> GSM447650 2 0.0000 0.962 0.000 1.000
#> GSM447651 2 0.0000 0.962 0.000 1.000
#> GSM447653 1 0.0000 0.938 1.000 0.000
#> GSM447658 1 0.0000 0.938 1.000 0.000
#> GSM447675 2 0.0000 0.962 0.000 1.000
#> GSM447680 2 0.5408 0.857 0.124 0.876
#> GSM447686 2 0.7453 0.748 0.212 0.788
#> GSM447736 1 0.0000 0.938 1.000 0.000
#> GSM447629 2 0.5737 0.844 0.136 0.864
#> GSM447648 1 0.0000 0.938 1.000 0.000
#> GSM447660 1 0.0000 0.938 1.000 0.000
#> GSM447661 2 0.0000 0.962 0.000 1.000
#> GSM447663 1 0.7219 0.767 0.800 0.200
#> GSM447704 2 0.0000 0.962 0.000 1.000
#> GSM447720 1 0.0000 0.938 1.000 0.000
#> GSM447652 2 0.0000 0.962 0.000 1.000
#> GSM447679 2 0.0000 0.962 0.000 1.000
#> GSM447712 1 0.0000 0.938 1.000 0.000
#> GSM447664 2 0.6438 0.811 0.164 0.836
#> GSM447637 1 0.0000 0.938 1.000 0.000
#> GSM447639 2 0.9710 0.261 0.400 0.600
#> GSM447615 1 0.0000 0.938 1.000 0.000
#> GSM447656 2 0.5408 0.857 0.124 0.876
#> GSM447673 2 0.0000 0.962 0.000 1.000
#> GSM447719 1 0.0000 0.938 1.000 0.000
#> GSM447706 1 0.0000 0.938 1.000 0.000
#> GSM447612 1 0.9815 0.352 0.580 0.420
#> GSM447665 2 0.0000 0.962 0.000 1.000
#> GSM447677 2 0.0000 0.962 0.000 1.000
#> GSM447613 1 0.0000 0.938 1.000 0.000
#> GSM447659 1 0.7745 0.731 0.772 0.228
#> GSM447662 1 0.7219 0.767 0.800 0.200
#> GSM447666 1 0.4431 0.871 0.908 0.092
#> GSM447668 2 0.0000 0.962 0.000 1.000
#> GSM447682 2 0.0000 0.962 0.000 1.000
#> GSM447683 2 0.0000 0.962 0.000 1.000
#> GSM447688 2 0.0000 0.962 0.000 1.000
#> GSM447702 2 0.0000 0.962 0.000 1.000
#> GSM447709 2 0.0000 0.962 0.000 1.000
#> GSM447711 1 0.0000 0.938 1.000 0.000
#> GSM447715 1 0.9427 0.423 0.640 0.360
#> GSM447693 1 0.0000 0.938 1.000 0.000
#> GSM447611 2 0.7745 0.724 0.228 0.772
#> GSM447672 2 0.0000 0.962 0.000 1.000
#> GSM447703 2 0.0000 0.962 0.000 1.000
#> GSM447727 1 0.0000 0.938 1.000 0.000
#> GSM447638 1 0.9710 0.316 0.600 0.400
#> GSM447670 1 0.0000 0.938 1.000 0.000
#> GSM447700 2 0.0000 0.962 0.000 1.000
#> GSM447738 2 0.0000 0.962 0.000 1.000
#> GSM447739 1 0.0000 0.938 1.000 0.000
#> GSM447617 1 0.0000 0.938 1.000 0.000
#> GSM447628 2 0.0000 0.962 0.000 1.000
#> GSM447632 2 0.0000 0.962 0.000 1.000
#> GSM447619 1 0.5408 0.844 0.876 0.124
#> GSM447643 1 0.9732 0.305 0.596 0.404
#> GSM447724 2 0.4298 0.883 0.088 0.912
#> GSM447728 2 0.0000 0.962 0.000 1.000
#> GSM447610 1 0.0000 0.938 1.000 0.000
#> GSM447633 2 0.0000 0.962 0.000 1.000
#> GSM447634 1 0.0000 0.938 1.000 0.000
#> GSM447622 1 0.0000 0.938 1.000 0.000
#> GSM447667 2 0.7139 0.769 0.196 0.804
#> GSM447687 2 0.0000 0.962 0.000 1.000
#> GSM447695 1 0.0000 0.938 1.000 0.000
#> GSM447696 1 0.0000 0.938 1.000 0.000
#> GSM447697 1 0.0000 0.938 1.000 0.000
#> GSM447714 1 0.7139 0.771 0.804 0.196
#> GSM447717 1 0.0000 0.938 1.000 0.000
#> GSM447725 1 0.0000 0.938 1.000 0.000
#> GSM447729 2 0.0000 0.962 0.000 1.000
#> GSM447644 2 0.0000 0.962 0.000 1.000
#> GSM447710 1 0.0000 0.938 1.000 0.000
#> GSM447614 1 0.0000 0.938 1.000 0.000
#> GSM447685 2 0.0000 0.962 0.000 1.000
#> GSM447690 1 0.0000 0.938 1.000 0.000
#> GSM447730 2 0.0000 0.962 0.000 1.000
#> GSM447646 2 0.0000 0.962 0.000 1.000
#> GSM447689 1 0.1633 0.923 0.976 0.024
#> GSM447635 2 0.5178 0.866 0.116 0.884
#> GSM447641 1 0.0000 0.938 1.000 0.000
#> GSM447716 2 0.4431 0.888 0.092 0.908
#> GSM447718 1 0.7139 0.771 0.804 0.196
#> GSM447616 1 0.0000 0.938 1.000 0.000
#> GSM447626 1 0.0000 0.938 1.000 0.000
#> GSM447640 2 0.0000 0.962 0.000 1.000
#> GSM447734 1 0.6973 0.780 0.812 0.188
#> GSM447692 1 0.0000 0.938 1.000 0.000
#> GSM447647 2 0.0000 0.962 0.000 1.000
#> GSM447624 1 0.0000 0.938 1.000 0.000
#> GSM447625 1 0.6247 0.813 0.844 0.156
#> GSM447707 2 0.0000 0.962 0.000 1.000
#> GSM447732 1 0.2423 0.912 0.960 0.040
#> GSM447684 1 0.0000 0.938 1.000 0.000
#> GSM447731 2 0.0000 0.962 0.000 1.000
#> GSM447705 2 0.4431 0.878 0.092 0.908
#> GSM447631 1 0.0000 0.938 1.000 0.000
#> GSM447701 2 0.0000 0.962 0.000 1.000
#> GSM447645 1 0.0000 0.938 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM447671 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447694 3 0.1289 0.921 0.032 0.000 0.968
#> GSM447618 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447691 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447733 3 0.0000 0.907 0.000 0.000 1.000
#> GSM447620 2 0.4605 0.768 0.000 0.796 0.204
#> GSM447627 3 0.0237 0.909 0.004 0.000 0.996
#> GSM447630 2 0.5926 0.400 0.000 0.644 0.356
#> GSM447642 1 0.0000 0.968 1.000 0.000 0.000
#> GSM447649 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447654 2 0.1289 0.940 0.000 0.968 0.032
#> GSM447655 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447669 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447676 1 0.0000 0.968 1.000 0.000 0.000
#> GSM447678 2 0.1289 0.940 0.000 0.968 0.032
#> GSM447681 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447698 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447713 1 0.0000 0.968 1.000 0.000 0.000
#> GSM447722 2 0.1289 0.940 0.000 0.968 0.032
#> GSM447726 2 0.4555 0.773 0.000 0.800 0.200
#> GSM447735 3 0.4555 0.760 0.200 0.000 0.800
#> GSM447737 1 0.4291 0.746 0.820 0.000 0.180
#> GSM447657 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447674 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447636 1 0.0237 0.965 0.996 0.004 0.000
#> GSM447723 1 0.0000 0.968 1.000 0.000 0.000
#> GSM447699 3 0.5778 0.750 0.032 0.200 0.768
#> GSM447708 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447721 1 0.0000 0.968 1.000 0.000 0.000
#> GSM447623 1 0.0000 0.968 1.000 0.000 0.000
#> GSM447621 1 0.0000 0.968 1.000 0.000 0.000
#> GSM447650 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447651 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447653 3 0.0000 0.907 0.000 0.000 1.000
#> GSM447658 1 0.0000 0.968 1.000 0.000 0.000
#> GSM447675 2 0.1289 0.940 0.000 0.968 0.032
#> GSM447680 2 0.3340 0.856 0.120 0.880 0.000
#> GSM447686 1 0.1289 0.940 0.968 0.032 0.000
#> GSM447736 3 0.1289 0.921 0.032 0.000 0.968
#> GSM447629 2 0.3879 0.821 0.152 0.848 0.000
#> GSM447648 3 0.1289 0.921 0.032 0.000 0.968
#> GSM447660 1 0.0000 0.968 1.000 0.000 0.000
#> GSM447661 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447663 3 0.1289 0.921 0.032 0.000 0.968
#> GSM447704 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447720 3 0.4452 0.799 0.192 0.000 0.808
#> GSM447652 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447679 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447712 1 0.0000 0.968 1.000 0.000 0.000
#> GSM447664 2 0.5778 0.743 0.200 0.768 0.032
#> GSM447637 3 0.1289 0.921 0.032 0.000 0.968
#> GSM447639 3 0.4555 0.740 0.000 0.200 0.800
#> GSM447615 1 0.0000 0.968 1.000 0.000 0.000
#> GSM447656 2 0.3340 0.856 0.120 0.880 0.000
#> GSM447673 2 0.0237 0.952 0.000 0.996 0.004
#> GSM447719 3 0.0000 0.907 0.000 0.000 1.000
#> GSM447706 3 0.1289 0.921 0.032 0.000 0.968
#> GSM447612 3 0.1289 0.921 0.032 0.000 0.968
#> GSM447665 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447677 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447613 1 0.0000 0.968 1.000 0.000 0.000
#> GSM447659 3 0.0000 0.907 0.000 0.000 1.000
#> GSM447662 3 0.1289 0.921 0.032 0.000 0.968
#> GSM447666 3 0.1491 0.913 0.016 0.016 0.968
#> GSM447668 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447682 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447683 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447688 2 0.1289 0.940 0.000 0.968 0.032
#> GSM447702 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447709 2 0.3116 0.873 0.000 0.892 0.108
#> GSM447711 1 0.0000 0.968 1.000 0.000 0.000
#> GSM447715 1 0.1289 0.940 0.968 0.032 0.000
#> GSM447693 3 0.1289 0.921 0.032 0.000 0.968
#> GSM447611 1 0.4799 0.799 0.836 0.132 0.032
#> GSM447672 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447703 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447727 1 0.0000 0.968 1.000 0.000 0.000
#> GSM447638 1 0.1289 0.940 0.968 0.032 0.000
#> GSM447670 1 0.0000 0.968 1.000 0.000 0.000
#> GSM447700 2 0.0237 0.952 0.000 0.996 0.004
#> GSM447738 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447739 1 0.0000 0.968 1.000 0.000 0.000
#> GSM447617 1 0.0000 0.968 1.000 0.000 0.000
#> GSM447628 2 0.1289 0.940 0.000 0.968 0.032
#> GSM447632 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447619 3 0.1289 0.921 0.032 0.000 0.968
#> GSM447643 1 0.1289 0.940 0.968 0.032 0.000
#> GSM447724 3 0.0000 0.907 0.000 0.000 1.000
#> GSM447728 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447610 1 0.1289 0.942 0.968 0.000 0.032
#> GSM447633 2 0.4555 0.773 0.000 0.800 0.200
#> GSM447634 3 0.4974 0.751 0.236 0.000 0.764
#> GSM447622 3 0.5016 0.746 0.240 0.000 0.760
#> GSM447667 2 0.4555 0.760 0.200 0.800 0.000
#> GSM447687 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447695 3 0.4974 0.751 0.236 0.000 0.764
#> GSM447696 1 0.0000 0.968 1.000 0.000 0.000
#> GSM447697 1 0.0000 0.968 1.000 0.000 0.000
#> GSM447714 3 0.1289 0.921 0.032 0.000 0.968
#> GSM447717 1 0.0000 0.968 1.000 0.000 0.000
#> GSM447725 1 0.0000 0.968 1.000 0.000 0.000
#> GSM447729 2 0.1289 0.940 0.000 0.968 0.032
#> GSM447644 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447710 3 0.1289 0.921 0.032 0.000 0.968
#> GSM447614 3 0.4555 0.760 0.200 0.000 0.800
#> GSM447685 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447690 1 0.0000 0.968 1.000 0.000 0.000
#> GSM447730 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447646 2 0.1289 0.940 0.000 0.968 0.032
#> GSM447689 3 0.1289 0.921 0.032 0.000 0.968
#> GSM447635 2 0.4629 0.775 0.188 0.808 0.004
#> GSM447641 1 0.0000 0.968 1.000 0.000 0.000
#> GSM447716 2 0.4409 0.796 0.172 0.824 0.004
#> GSM447718 3 0.1289 0.921 0.032 0.000 0.968
#> GSM447616 3 0.5016 0.746 0.240 0.000 0.760
#> GSM447626 3 0.1289 0.921 0.032 0.000 0.968
#> GSM447640 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447734 3 0.1289 0.921 0.032 0.000 0.968
#> GSM447692 3 0.5016 0.746 0.240 0.000 0.760
#> GSM447647 2 0.1289 0.940 0.000 0.968 0.032
#> GSM447624 1 0.5926 0.363 0.644 0.000 0.356
#> GSM447625 3 0.1289 0.921 0.032 0.000 0.968
#> GSM447707 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447732 3 0.1289 0.921 0.032 0.000 0.968
#> GSM447684 1 0.0000 0.968 1.000 0.000 0.000
#> GSM447731 3 0.5497 0.519 0.000 0.292 0.708
#> GSM447705 3 0.1289 0.900 0.000 0.032 0.968
#> GSM447631 3 0.1289 0.921 0.032 0.000 0.968
#> GSM447701 2 0.0000 0.954 0.000 1.000 0.000
#> GSM447645 3 0.1289 0.921 0.032 0.000 0.968
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM447671 2 0.2198 0.854 0.000 0.920 0.008 0.072
#> GSM447694 3 0.0592 0.891 0.000 0.000 0.984 0.016
#> GSM447618 2 0.4877 0.549 0.000 0.592 0.000 0.408
#> GSM447691 2 0.2011 0.852 0.000 0.920 0.000 0.080
#> GSM447733 4 0.3569 0.748 0.000 0.000 0.196 0.804
#> GSM447620 2 0.3610 0.725 0.000 0.800 0.200 0.000
#> GSM447627 3 0.4008 0.596 0.000 0.000 0.756 0.244
#> GSM447630 2 0.5004 0.237 0.000 0.604 0.392 0.004
#> GSM447642 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> GSM447649 2 0.0469 0.858 0.000 0.988 0.000 0.012
#> GSM447654 4 0.1302 0.840 0.000 0.044 0.000 0.956
#> GSM447655 2 0.0469 0.858 0.000 0.988 0.000 0.012
#> GSM447669 2 0.1004 0.846 0.000 0.972 0.024 0.004
#> GSM447676 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> GSM447678 4 0.0336 0.842 0.000 0.008 0.000 0.992
#> GSM447681 2 0.2149 0.851 0.000 0.912 0.000 0.088
#> GSM447698 2 0.4916 0.530 0.000 0.576 0.000 0.424
#> GSM447713 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> GSM447722 4 0.0336 0.842 0.000 0.008 0.000 0.992
#> GSM447726 2 0.0895 0.848 0.000 0.976 0.020 0.004
#> GSM447735 4 0.1576 0.833 0.004 0.000 0.048 0.948
#> GSM447737 1 0.3402 0.775 0.832 0.000 0.164 0.004
#> GSM447657 2 0.2589 0.840 0.000 0.884 0.000 0.116
#> GSM447674 2 0.2149 0.851 0.000 0.912 0.000 0.088
#> GSM447636 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> GSM447723 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> GSM447699 3 0.3945 0.691 0.000 0.004 0.780 0.216
#> GSM447708 2 0.1716 0.855 0.000 0.936 0.000 0.064
#> GSM447721 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> GSM447623 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> GSM447621 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> GSM447650 2 0.0469 0.856 0.000 0.988 0.000 0.012
#> GSM447651 2 0.0188 0.854 0.000 0.996 0.000 0.004
#> GSM447653 4 0.3528 0.750 0.000 0.000 0.192 0.808
#> GSM447658 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> GSM447675 4 0.0336 0.842 0.000 0.008 0.000 0.992
#> GSM447680 2 0.0895 0.850 0.020 0.976 0.000 0.004
#> GSM447686 1 0.0592 0.963 0.984 0.016 0.000 0.000
#> GSM447736 3 0.0592 0.891 0.000 0.000 0.984 0.016
#> GSM447629 2 0.3463 0.834 0.040 0.864 0.000 0.096
#> GSM447648 3 0.0707 0.890 0.020 0.000 0.980 0.000
#> GSM447660 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> GSM447661 2 0.0336 0.857 0.000 0.992 0.000 0.008
#> GSM447663 3 0.1978 0.858 0.000 0.068 0.928 0.004
#> GSM447704 2 0.0707 0.858 0.000 0.980 0.000 0.020
#> GSM447720 3 0.4591 0.781 0.148 0.028 0.804 0.020
#> GSM447652 2 0.1118 0.854 0.000 0.964 0.000 0.036
#> GSM447679 2 0.2149 0.851 0.000 0.912 0.000 0.088
#> GSM447712 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> GSM447664 4 0.1637 0.830 0.060 0.000 0.000 0.940
#> GSM447637 3 0.0336 0.895 0.008 0.000 0.992 0.000
#> GSM447639 4 0.0672 0.844 0.000 0.008 0.008 0.984
#> GSM447615 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> GSM447656 2 0.2300 0.851 0.028 0.924 0.000 0.048
#> GSM447673 2 0.4925 0.523 0.000 0.572 0.000 0.428
#> GSM447719 4 0.4948 0.343 0.000 0.000 0.440 0.560
#> GSM447706 3 0.0000 0.896 0.000 0.000 1.000 0.000
#> GSM447612 3 0.0000 0.896 0.000 0.000 1.000 0.000
#> GSM447665 2 0.0000 0.855 0.000 1.000 0.000 0.000
#> GSM447677 2 0.0000 0.855 0.000 1.000 0.000 0.000
#> GSM447613 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> GSM447659 4 0.4877 0.406 0.000 0.000 0.408 0.592
#> GSM447662 3 0.0000 0.896 0.000 0.000 1.000 0.000
#> GSM447666 3 0.1022 0.876 0.000 0.032 0.968 0.000
#> GSM447668 2 0.0188 0.854 0.000 0.996 0.000 0.004
#> GSM447682 2 0.2011 0.853 0.000 0.920 0.000 0.080
#> GSM447683 2 0.2011 0.852 0.000 0.920 0.000 0.080
#> GSM447688 4 0.0707 0.838 0.000 0.020 0.000 0.980
#> GSM447702 2 0.0336 0.857 0.000 0.992 0.000 0.008
#> GSM447709 2 0.0592 0.851 0.000 0.984 0.016 0.000
#> GSM447711 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> GSM447715 1 0.0336 0.971 0.992 0.008 0.000 0.000
#> GSM447693 3 0.0000 0.896 0.000 0.000 1.000 0.000
#> GSM447611 4 0.2530 0.796 0.112 0.000 0.000 0.888
#> GSM447672 2 0.0469 0.858 0.000 0.988 0.000 0.012
#> GSM447703 2 0.4855 0.537 0.000 0.600 0.000 0.400
#> GSM447727 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> GSM447638 1 0.2053 0.898 0.924 0.072 0.000 0.004
#> GSM447670 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> GSM447700 2 0.5558 0.497 0.000 0.548 0.020 0.432
#> GSM447738 2 0.4916 0.530 0.000 0.576 0.000 0.424
#> GSM447739 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> GSM447617 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> GSM447628 4 0.1302 0.840 0.000 0.044 0.000 0.956
#> GSM447632 2 0.4916 0.530 0.000 0.576 0.000 0.424
#> GSM447619 3 0.0000 0.896 0.000 0.000 1.000 0.000
#> GSM447643 1 0.0336 0.971 0.992 0.008 0.000 0.000
#> GSM447724 4 0.2281 0.817 0.000 0.000 0.096 0.904
#> GSM447728 2 0.2149 0.851 0.000 0.912 0.000 0.088
#> GSM447610 4 0.4977 0.155 0.460 0.000 0.000 0.540
#> GSM447633 2 0.1637 0.834 0.000 0.940 0.060 0.000
#> GSM447634 3 0.4652 0.749 0.192 0.012 0.776 0.020
#> GSM447622 3 0.4188 0.715 0.244 0.000 0.752 0.004
#> GSM447667 2 0.4245 0.704 0.196 0.784 0.000 0.020
#> GSM447687 2 0.4916 0.530 0.000 0.576 0.000 0.424
#> GSM447695 3 0.4253 0.741 0.208 0.000 0.776 0.016
#> GSM447696 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> GSM447697 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> GSM447714 3 0.0000 0.896 0.000 0.000 1.000 0.000
#> GSM447717 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> GSM447725 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> GSM447729 4 0.0707 0.838 0.000 0.020 0.000 0.980
#> GSM447644 2 0.1004 0.846 0.000 0.972 0.024 0.004
#> GSM447710 3 0.0000 0.896 0.000 0.000 1.000 0.000
#> GSM447614 4 0.6785 0.497 0.184 0.000 0.208 0.608
#> GSM447685 2 0.2281 0.849 0.000 0.904 0.000 0.096
#> GSM447690 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> GSM447730 2 0.0592 0.858 0.000 0.984 0.000 0.016
#> GSM447646 4 0.1302 0.840 0.000 0.044 0.000 0.956
#> GSM447689 3 0.0188 0.896 0.000 0.004 0.996 0.000
#> GSM447635 2 0.5244 0.505 0.008 0.556 0.000 0.436
#> GSM447641 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> GSM447716 2 0.5570 0.484 0.020 0.540 0.000 0.440
#> GSM447718 3 0.1902 0.859 0.000 0.064 0.932 0.004
#> GSM447616 3 0.4188 0.715 0.244 0.000 0.752 0.004
#> GSM447626 3 0.0376 0.895 0.000 0.004 0.992 0.004
#> GSM447640 2 0.2149 0.851 0.000 0.912 0.000 0.088
#> GSM447734 3 0.0000 0.896 0.000 0.000 1.000 0.000
#> GSM447692 3 0.4468 0.722 0.232 0.000 0.752 0.016
#> GSM447647 4 0.1792 0.831 0.000 0.068 0.000 0.932
#> GSM447624 3 0.4855 0.416 0.400 0.000 0.600 0.000
#> GSM447625 3 0.0000 0.896 0.000 0.000 1.000 0.000
#> GSM447707 2 0.0592 0.858 0.000 0.984 0.000 0.016
#> GSM447732 3 0.0376 0.895 0.000 0.004 0.992 0.004
#> GSM447684 1 0.4234 0.670 0.764 0.004 0.228 0.004
#> GSM447731 4 0.4656 0.757 0.000 0.056 0.160 0.784
#> GSM447705 3 0.0188 0.896 0.000 0.004 0.996 0.000
#> GSM447631 3 0.0336 0.895 0.008 0.000 0.992 0.000
#> GSM447701 2 0.0188 0.854 0.000 0.996 0.000 0.004
#> GSM447645 3 0.0469 0.894 0.012 0.000 0.988 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM447671 5 0.4820 0.46183 0.000 0.236 0.000 0.068 0.696
#> GSM447694 3 0.3821 0.67574 0.000 0.000 0.764 0.020 0.216
#> GSM447618 5 0.6083 0.25719 0.000 0.176 0.000 0.260 0.564
#> GSM447691 5 0.4930 0.38488 0.000 0.220 0.000 0.084 0.696
#> GSM447733 4 0.3612 0.72595 0.000 0.004 0.100 0.832 0.064
#> GSM447620 2 0.5199 0.45238 0.000 0.704 0.176 0.008 0.112
#> GSM447627 3 0.6439 0.19682 0.000 0.000 0.476 0.332 0.192
#> GSM447630 5 0.5935 0.34138 0.000 0.408 0.092 0.004 0.496
#> GSM447642 1 0.0162 0.93406 0.996 0.000 0.000 0.000 0.004
#> GSM447649 2 0.0609 0.69881 0.000 0.980 0.000 0.020 0.000
#> GSM447654 4 0.1282 0.74595 0.000 0.044 0.000 0.952 0.004
#> GSM447655 2 0.0404 0.69755 0.000 0.988 0.000 0.012 0.000
#> GSM447669 5 0.4047 0.42343 0.000 0.320 0.000 0.004 0.676
#> GSM447676 1 0.0000 0.93473 1.000 0.000 0.000 0.000 0.000
#> GSM447678 4 0.4147 0.45937 0.000 0.008 0.000 0.676 0.316
#> GSM447681 2 0.4679 0.58514 0.000 0.716 0.000 0.068 0.216
#> GSM447698 2 0.6757 0.23887 0.000 0.400 0.000 0.280 0.320
#> GSM447713 1 0.0162 0.93381 0.996 0.000 0.000 0.000 0.004
#> GSM447722 4 0.4430 0.39195 0.000 0.012 0.000 0.628 0.360
#> GSM447726 2 0.5754 -0.12900 0.004 0.536 0.080 0.000 0.380
#> GSM447735 4 0.4974 0.63723 0.000 0.000 0.092 0.696 0.212
#> GSM447737 1 0.5719 0.41271 0.596 0.000 0.284 0.000 0.120
#> GSM447657 2 0.5238 0.54683 0.000 0.652 0.000 0.088 0.260
#> GSM447674 2 0.4637 0.61389 0.000 0.728 0.000 0.076 0.196
#> GSM447636 1 0.0162 0.93406 0.996 0.000 0.000 0.000 0.004
#> GSM447723 1 0.0000 0.93473 1.000 0.000 0.000 0.000 0.000
#> GSM447699 3 0.6134 0.40317 0.000 0.004 0.540 0.132 0.324
#> GSM447708 2 0.3875 0.67297 0.000 0.804 0.000 0.072 0.124
#> GSM447721 1 0.0404 0.92930 0.988 0.000 0.000 0.000 0.012
#> GSM447623 1 0.2110 0.87177 0.912 0.000 0.072 0.000 0.016
#> GSM447621 1 0.2208 0.86791 0.908 0.000 0.072 0.000 0.020
#> GSM447650 2 0.0404 0.69192 0.000 0.988 0.000 0.000 0.012
#> GSM447651 2 0.1043 0.67277 0.000 0.960 0.000 0.000 0.040
#> GSM447653 4 0.4155 0.70422 0.000 0.000 0.144 0.780 0.076
#> GSM447658 1 0.0162 0.93406 0.996 0.000 0.000 0.000 0.004
#> GSM447675 4 0.0579 0.74443 0.000 0.008 0.000 0.984 0.008
#> GSM447680 2 0.3205 0.65651 0.056 0.864 0.000 0.008 0.072
#> GSM447686 1 0.0992 0.91201 0.968 0.000 0.000 0.008 0.024
#> GSM447736 3 0.3496 0.68955 0.000 0.000 0.788 0.012 0.200
#> GSM447629 2 0.6750 0.43239 0.068 0.540 0.000 0.084 0.308
#> GSM447648 3 0.0703 0.75128 0.000 0.000 0.976 0.000 0.024
#> GSM447660 1 0.0162 0.93406 0.996 0.000 0.000 0.000 0.004
#> GSM447661 2 0.0290 0.69223 0.000 0.992 0.000 0.000 0.008
#> GSM447663 3 0.5507 0.17796 0.000 0.064 0.480 0.000 0.456
#> GSM447704 2 0.0880 0.70000 0.000 0.968 0.000 0.032 0.000
#> GSM447720 5 0.5395 -0.15111 0.008 0.024 0.352 0.016 0.600
#> GSM447652 2 0.1300 0.69343 0.000 0.956 0.000 0.028 0.016
#> GSM447679 2 0.3180 0.68995 0.000 0.856 0.000 0.076 0.068
#> GSM447712 1 0.0000 0.93473 1.000 0.000 0.000 0.000 0.000
#> GSM447664 4 0.2984 0.71465 0.108 0.000 0.000 0.860 0.032
#> GSM447637 3 0.0000 0.75169 0.000 0.000 1.000 0.000 0.000
#> GSM447639 4 0.2548 0.74230 0.000 0.004 0.028 0.896 0.072
#> GSM447615 1 0.3586 0.74665 0.792 0.000 0.188 0.000 0.020
#> GSM447656 2 0.3627 0.65039 0.092 0.836 0.000 0.008 0.064
#> GSM447673 2 0.6583 0.35578 0.000 0.468 0.000 0.276 0.256
#> GSM447719 4 0.4066 0.58147 0.000 0.000 0.324 0.672 0.004
#> GSM447706 3 0.0510 0.75137 0.000 0.000 0.984 0.000 0.016
#> GSM447612 3 0.4607 0.50017 0.000 0.008 0.620 0.008 0.364
#> GSM447665 2 0.4415 -0.02136 0.000 0.604 0.000 0.008 0.388
#> GSM447677 2 0.1205 0.67157 0.000 0.956 0.000 0.004 0.040
#> GSM447613 1 0.0000 0.93473 1.000 0.000 0.000 0.000 0.000
#> GSM447659 4 0.4981 0.64882 0.000 0.000 0.188 0.704 0.108
#> GSM447662 3 0.2732 0.70308 0.000 0.000 0.840 0.000 0.160
#> GSM447666 3 0.4416 0.40440 0.000 0.012 0.632 0.000 0.356
#> GSM447668 2 0.1270 0.67054 0.000 0.948 0.000 0.000 0.052
#> GSM447682 2 0.3303 0.68873 0.000 0.848 0.000 0.076 0.076
#> GSM447683 2 0.3362 0.69105 0.000 0.844 0.000 0.076 0.080
#> GSM447688 4 0.5200 0.38865 0.000 0.068 0.000 0.628 0.304
#> GSM447702 2 0.0451 0.69356 0.000 0.988 0.000 0.004 0.008
#> GSM447709 2 0.2694 0.61825 0.000 0.876 0.008 0.008 0.108
#> GSM447711 1 0.0000 0.93473 1.000 0.000 0.000 0.000 0.000
#> GSM447715 1 0.0771 0.91906 0.976 0.000 0.000 0.004 0.020
#> GSM447693 3 0.0162 0.75166 0.000 0.000 0.996 0.000 0.004
#> GSM447611 4 0.2920 0.70373 0.132 0.000 0.000 0.852 0.016
#> GSM447672 2 0.0912 0.70088 0.000 0.972 0.000 0.016 0.012
#> GSM447703 2 0.6326 0.39135 0.000 0.524 0.000 0.268 0.208
#> GSM447727 1 0.0162 0.93406 0.996 0.000 0.000 0.000 0.004
#> GSM447638 1 0.6210 0.41990 0.620 0.248 0.076 0.000 0.056
#> GSM447670 1 0.1877 0.88703 0.924 0.000 0.064 0.000 0.012
#> GSM447700 5 0.4238 0.40002 0.000 0.052 0.000 0.192 0.756
#> GSM447738 2 0.6715 0.29531 0.000 0.424 0.000 0.288 0.288
#> GSM447739 1 0.0162 0.93381 0.996 0.000 0.000 0.000 0.004
#> GSM447617 1 0.4276 0.62507 0.716 0.000 0.256 0.000 0.028
#> GSM447628 4 0.1410 0.74078 0.000 0.060 0.000 0.940 0.000
#> GSM447632 2 0.6712 0.30147 0.000 0.424 0.000 0.276 0.300
#> GSM447619 3 0.1197 0.75132 0.000 0.000 0.952 0.000 0.048
#> GSM447643 1 0.0162 0.93406 0.996 0.000 0.000 0.000 0.004
#> GSM447724 4 0.5230 0.59868 0.000 0.008 0.064 0.660 0.268
#> GSM447728 2 0.2853 0.69400 0.000 0.876 0.000 0.072 0.052
#> GSM447610 4 0.5781 0.53598 0.256 0.000 0.028 0.640 0.076
#> GSM447633 5 0.5491 0.20795 0.000 0.468 0.044 0.008 0.480
#> GSM447634 5 0.6137 -0.34787 0.044 0.008 0.424 0.028 0.496
#> GSM447622 3 0.4238 0.65830 0.088 0.000 0.776 0.000 0.136
#> GSM447667 2 0.7370 0.31499 0.152 0.472 0.000 0.068 0.308
#> GSM447687 2 0.6374 0.38757 0.000 0.512 0.000 0.280 0.208
#> GSM447695 3 0.5536 0.60017 0.060 0.000 0.668 0.032 0.240
#> GSM447696 1 0.0162 0.93381 0.996 0.000 0.000 0.000 0.004
#> GSM447697 1 0.0162 0.93381 0.996 0.000 0.000 0.000 0.004
#> GSM447714 3 0.1671 0.74779 0.000 0.000 0.924 0.000 0.076
#> GSM447717 1 0.0162 0.93406 0.996 0.000 0.000 0.000 0.004
#> GSM447725 1 0.0000 0.93473 1.000 0.000 0.000 0.000 0.000
#> GSM447729 4 0.1216 0.74198 0.000 0.020 0.000 0.960 0.020
#> GSM447644 2 0.4557 -0.22346 0.000 0.516 0.008 0.000 0.476
#> GSM447710 3 0.1410 0.74395 0.000 0.000 0.940 0.000 0.060
#> GSM447614 4 0.5861 0.62431 0.056 0.000 0.088 0.680 0.176
#> GSM447685 2 0.3400 0.68891 0.004 0.848 0.000 0.076 0.072
#> GSM447690 1 0.0162 0.93381 0.996 0.000 0.000 0.000 0.004
#> GSM447730 2 0.0794 0.69907 0.000 0.972 0.000 0.028 0.000
#> GSM447646 4 0.1478 0.74092 0.000 0.064 0.000 0.936 0.000
#> GSM447689 3 0.3480 0.59365 0.000 0.000 0.752 0.000 0.248
#> GSM447635 5 0.3780 0.41026 0.000 0.072 0.000 0.116 0.812
#> GSM447641 1 0.0000 0.93473 1.000 0.000 0.000 0.000 0.000
#> GSM447716 2 0.7002 0.26710 0.008 0.396 0.000 0.292 0.304
#> GSM447718 3 0.3921 0.69257 0.000 0.072 0.800 0.000 0.128
#> GSM447616 3 0.4789 0.62613 0.116 0.000 0.728 0.000 0.156
#> GSM447626 3 0.3452 0.60656 0.000 0.000 0.756 0.000 0.244
#> GSM447640 2 0.3056 0.69194 0.000 0.864 0.000 0.068 0.068
#> GSM447734 3 0.3013 0.73777 0.000 0.000 0.832 0.008 0.160
#> GSM447692 3 0.5677 0.60039 0.112 0.000 0.676 0.024 0.188
#> GSM447647 4 0.1908 0.73482 0.000 0.092 0.000 0.908 0.000
#> GSM447624 3 0.4318 0.47019 0.292 0.000 0.688 0.000 0.020
#> GSM447625 3 0.2690 0.74134 0.000 0.000 0.844 0.000 0.156
#> GSM447707 2 0.0609 0.69881 0.000 0.980 0.000 0.020 0.000
#> GSM447732 3 0.2920 0.73512 0.000 0.016 0.852 0.000 0.132
#> GSM447684 5 0.6796 -0.00293 0.336 0.000 0.292 0.000 0.372
#> GSM447731 4 0.4583 0.68609 0.000 0.084 0.120 0.776 0.020
#> GSM447705 3 0.4183 0.50378 0.000 0.008 0.668 0.000 0.324
#> GSM447631 3 0.0000 0.75169 0.000 0.000 1.000 0.000 0.000
#> GSM447701 2 0.1544 0.66000 0.000 0.932 0.000 0.000 0.068
#> GSM447645 3 0.0000 0.75169 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
#> GSM447671 5 0.6390 0.20975 0.000 0.088 0.000 0.112 0.536 0.264
#> GSM447694 3 0.5758 0.40526 0.004 0.000 0.524 0.096 0.020 0.356
#> GSM447618 5 0.2342 0.66448 0.000 0.024 0.000 0.040 0.904 0.032
#> GSM447691 5 0.5132 0.33838 0.000 0.112 0.000 0.008 0.632 0.248
#> GSM447733 4 0.2677 0.72774 0.000 0.008 0.040 0.892 0.032 0.028
#> GSM447620 2 0.6659 0.41674 0.000 0.600 0.176 0.080 0.072 0.072
#> GSM447627 3 0.6502 0.34645 0.004 0.000 0.436 0.236 0.020 0.304
#> GSM447630 6 0.4494 0.58557 0.000 0.216 0.000 0.000 0.092 0.692
#> GSM447642 1 0.0748 0.89088 0.976 0.000 0.000 0.004 0.016 0.004
#> GSM447649 2 0.1261 0.79546 0.000 0.952 0.000 0.024 0.024 0.000
#> GSM447654 4 0.3571 0.74501 0.000 0.048 0.000 0.812 0.124 0.016
#> GSM447655 2 0.0603 0.79708 0.000 0.980 0.000 0.004 0.016 0.000
#> GSM447669 6 0.5223 0.56490 0.000 0.208 0.000 0.000 0.180 0.612
#> GSM447676 1 0.0520 0.89302 0.984 0.000 0.000 0.000 0.008 0.008
#> GSM447678 5 0.3134 0.57833 0.000 0.004 0.000 0.208 0.784 0.004
#> GSM447681 2 0.4387 0.23452 0.000 0.572 0.000 0.004 0.404 0.020
#> GSM447698 5 0.3409 0.69321 0.000 0.144 0.000 0.044 0.808 0.004
#> GSM447713 1 0.0508 0.88877 0.984 0.000 0.004 0.000 0.000 0.012
#> GSM447722 5 0.3329 0.60308 0.000 0.012 0.000 0.180 0.796 0.012
#> GSM447726 6 0.5857 0.47428 0.000 0.352 0.052 0.000 0.072 0.524
#> GSM447735 4 0.7297 -0.16062 0.004 0.000 0.272 0.336 0.080 0.308
#> GSM447737 3 0.7122 0.18706 0.356 0.000 0.356 0.044 0.016 0.228
#> GSM447657 5 0.4364 0.20040 0.000 0.424 0.000 0.008 0.556 0.012
#> GSM447674 2 0.3405 0.64500 0.000 0.724 0.000 0.000 0.272 0.004
#> GSM447636 1 0.0748 0.89088 0.976 0.000 0.000 0.004 0.016 0.004
#> GSM447723 1 0.0146 0.89331 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM447699 3 0.7699 0.24185 0.000 0.008 0.356 0.168 0.196 0.272
#> GSM447708 2 0.4292 0.71062 0.000 0.740 0.000 0.052 0.188 0.020
#> GSM447721 1 0.0862 0.88362 0.972 0.000 0.004 0.008 0.000 0.016
#> GSM447623 1 0.3891 0.68393 0.768 0.000 0.164 0.004 0.000 0.064
#> GSM447621 1 0.4293 0.64289 0.736 0.000 0.164 0.004 0.000 0.096
#> GSM447650 2 0.1141 0.78651 0.000 0.948 0.000 0.000 0.000 0.052
#> GSM447651 2 0.0837 0.78994 0.000 0.972 0.000 0.004 0.004 0.020
#> GSM447653 4 0.2872 0.73816 0.000 0.000 0.076 0.864 0.008 0.052
#> GSM447658 1 0.0748 0.89088 0.976 0.000 0.000 0.004 0.016 0.004
#> GSM447675 4 0.2902 0.71723 0.000 0.004 0.000 0.800 0.196 0.000
#> GSM447680 2 0.3771 0.74999 0.056 0.812 0.000 0.008 0.108 0.016
#> GSM447686 1 0.1787 0.85482 0.920 0.000 0.000 0.008 0.068 0.004
#> GSM447736 3 0.5514 0.44824 0.000 0.000 0.596 0.128 0.016 0.260
#> GSM447629 5 0.4377 0.61514 0.044 0.220 0.000 0.008 0.720 0.008
#> GSM447648 3 0.1984 0.51275 0.000 0.000 0.912 0.032 0.000 0.056
#> GSM447660 1 0.0653 0.89176 0.980 0.000 0.000 0.004 0.012 0.004
#> GSM447661 2 0.0865 0.79062 0.000 0.964 0.000 0.000 0.000 0.036
#> GSM447663 6 0.5309 0.49725 0.000 0.068 0.156 0.012 0.064 0.700
#> GSM447704 2 0.1934 0.78622 0.000 0.916 0.000 0.040 0.044 0.000
#> GSM447720 6 0.2996 0.44717 0.000 0.016 0.036 0.008 0.072 0.868
#> GSM447652 2 0.2186 0.78025 0.000 0.908 0.000 0.024 0.012 0.056
#> GSM447679 2 0.2738 0.75443 0.000 0.820 0.000 0.000 0.176 0.004
#> GSM447712 1 0.0291 0.89329 0.992 0.000 0.000 0.004 0.004 0.000
#> GSM447664 4 0.3979 0.70216 0.076 0.000 0.000 0.752 0.172 0.000
#> GSM447637 3 0.0000 0.51390 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447639 4 0.3485 0.73724 0.000 0.020 0.004 0.824 0.120 0.032
#> GSM447615 1 0.4808 0.22681 0.524 0.000 0.436 0.008 0.004 0.028
#> GSM447656 2 0.3883 0.72841 0.076 0.792 0.000 0.008 0.120 0.004
#> GSM447673 5 0.4606 0.38909 0.000 0.344 0.000 0.052 0.604 0.000
#> GSM447719 4 0.3151 0.65953 0.000 0.000 0.252 0.748 0.000 0.000
#> GSM447706 3 0.1890 0.49871 0.000 0.000 0.916 0.024 0.000 0.060
#> GSM447612 3 0.6773 0.01225 0.000 0.004 0.424 0.092 0.108 0.372
#> GSM447665 2 0.5496 0.21227 0.000 0.608 0.000 0.016 0.140 0.236
#> GSM447677 2 0.0964 0.79011 0.000 0.968 0.000 0.004 0.016 0.012
#> GSM447613 1 0.0000 0.89284 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447659 4 0.3009 0.70420 0.000 0.004 0.064 0.868 0.024 0.040
#> GSM447662 3 0.5021 0.27790 0.000 0.004 0.636 0.076 0.008 0.276
#> GSM447666 3 0.4184 -0.02558 0.000 0.000 0.556 0.004 0.008 0.432
#> GSM447668 2 0.1643 0.77956 0.000 0.924 0.000 0.000 0.008 0.068
#> GSM447682 2 0.3329 0.71768 0.000 0.768 0.000 0.008 0.220 0.004
#> GSM447683 2 0.2902 0.75279 0.000 0.800 0.000 0.000 0.196 0.004
#> GSM447688 5 0.4131 0.60357 0.000 0.072 0.000 0.180 0.744 0.004
#> GSM447702 2 0.0632 0.79324 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM447709 2 0.3847 0.69641 0.000 0.812 0.000 0.068 0.064 0.056
#> GSM447711 1 0.0000 0.89284 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447715 1 0.1606 0.86417 0.932 0.000 0.000 0.008 0.056 0.004
#> GSM447693 3 0.0146 0.51283 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM447611 4 0.3664 0.72841 0.108 0.000 0.000 0.804 0.080 0.008
#> GSM447672 2 0.1010 0.79843 0.000 0.960 0.000 0.000 0.036 0.004
#> GSM447703 2 0.5087 0.00679 0.000 0.508 0.000 0.080 0.412 0.000
#> GSM447727 1 0.0748 0.89088 0.976 0.000 0.000 0.004 0.016 0.004
#> GSM447638 1 0.7403 0.07982 0.432 0.300 0.144 0.004 0.016 0.104
#> GSM447670 1 0.3892 0.66649 0.744 0.000 0.220 0.004 0.004 0.028
#> GSM447700 5 0.4667 0.50617 0.000 0.016 0.000 0.144 0.720 0.120
#> GSM447738 5 0.3555 0.66452 0.000 0.184 0.000 0.040 0.776 0.000
#> GSM447739 1 0.0000 0.89284 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447617 1 0.5505 0.25987 0.544 0.000 0.340 0.012 0.000 0.104
#> GSM447628 4 0.3493 0.73820 0.000 0.064 0.000 0.800 0.136 0.000
#> GSM447632 5 0.3481 0.65137 0.000 0.192 0.000 0.032 0.776 0.000
#> GSM447619 3 0.3503 0.46746 0.000 0.004 0.820 0.076 0.004 0.096
#> GSM447643 1 0.0862 0.88909 0.972 0.000 0.000 0.008 0.016 0.004
#> GSM447724 5 0.5668 0.38534 0.000 0.016 0.028 0.352 0.552 0.052
#> GSM447728 2 0.2773 0.76452 0.000 0.836 0.000 0.008 0.152 0.004
#> GSM447610 4 0.5522 0.51939 0.240 0.000 0.032 0.620 0.000 0.108
#> GSM447633 6 0.7148 0.47887 0.000 0.288 0.024 0.080 0.140 0.468
#> GSM447634 6 0.5363 -0.01624 0.008 0.004 0.228 0.044 0.052 0.664
#> GSM447622 3 0.5361 0.44532 0.072 0.000 0.628 0.040 0.000 0.260
#> GSM447667 5 0.4871 0.60818 0.104 0.196 0.000 0.008 0.688 0.004
#> GSM447687 2 0.5052 0.14526 0.000 0.532 0.000 0.080 0.388 0.000
#> GSM447695 3 0.6947 0.37494 0.040 0.000 0.460 0.100 0.056 0.344
#> GSM447696 1 0.0622 0.88760 0.980 0.000 0.008 0.000 0.000 0.012
#> GSM447697 1 0.0972 0.87948 0.964 0.000 0.028 0.000 0.000 0.008
#> GSM447714 3 0.4411 0.34273 0.000 0.004 0.700 0.036 0.012 0.248
#> GSM447717 1 0.0748 0.89088 0.976 0.000 0.000 0.004 0.016 0.004
#> GSM447725 1 0.0146 0.89331 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM447729 4 0.2933 0.71441 0.000 0.004 0.000 0.796 0.200 0.000
#> GSM447644 6 0.5048 0.57373 0.000 0.272 0.000 0.000 0.116 0.612
#> GSM447710 3 0.2912 0.35713 0.000 0.000 0.784 0.000 0.000 0.216
#> GSM447614 4 0.5628 0.48976 0.028 0.000 0.076 0.632 0.020 0.244
#> GSM447685 2 0.3163 0.73652 0.000 0.780 0.000 0.004 0.212 0.004
#> GSM447690 1 0.0603 0.88719 0.980 0.000 0.004 0.000 0.000 0.016
#> GSM447730 2 0.1865 0.78728 0.000 0.920 0.000 0.040 0.040 0.000
#> GSM447646 4 0.3426 0.74231 0.000 0.068 0.000 0.808 0.124 0.000
#> GSM447689 3 0.3915 0.04182 0.000 0.000 0.584 0.004 0.000 0.412
#> GSM447635 5 0.3777 0.54812 0.004 0.000 0.000 0.056 0.776 0.164
#> GSM447641 1 0.0260 0.89323 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM447716 5 0.3175 0.67427 0.000 0.164 0.000 0.028 0.808 0.000
#> GSM447718 6 0.5217 0.07678 0.000 0.044 0.440 0.016 0.004 0.496
#> GSM447616 3 0.5928 0.43327 0.076 0.000 0.584 0.064 0.004 0.272
#> GSM447626 3 0.3860 -0.06576 0.000 0.000 0.528 0.000 0.000 0.472
#> GSM447640 2 0.2697 0.74990 0.000 0.812 0.000 0.000 0.188 0.000
#> GSM447734 3 0.5291 0.24878 0.000 0.000 0.476 0.060 0.016 0.448
#> GSM447692 3 0.6775 0.38670 0.080 0.000 0.476 0.088 0.020 0.336
#> GSM447647 4 0.3514 0.74133 0.000 0.088 0.000 0.804 0.108 0.000
#> GSM447624 3 0.4966 0.35556 0.264 0.000 0.640 0.008 0.000 0.088
#> GSM447625 3 0.5238 0.25703 0.000 0.000 0.492 0.056 0.016 0.436
#> GSM447707 2 0.1723 0.78925 0.000 0.928 0.000 0.036 0.036 0.000
#> GSM447732 6 0.4393 -0.11482 0.000 0.012 0.448 0.008 0.000 0.532
#> GSM447684 6 0.6164 0.25591 0.236 0.000 0.196 0.004 0.024 0.540
#> GSM447731 4 0.4415 0.73127 0.000 0.056 0.120 0.776 0.028 0.020
#> GSM447705 3 0.5960 -0.03204 0.000 0.004 0.464 0.076 0.040 0.416
#> GSM447631 3 0.0000 0.51390 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447701 2 0.2170 0.76081 0.000 0.888 0.000 0.000 0.012 0.100
#> GSM447645 3 0.0000 0.51390 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)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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 gender(p) individual(p) disease.state(p) other(p) k
#> SD:skmeans 124 0.445 0.7806 0.0387 0.07083 2
#> SD:skmeans 128 0.439 0.1093 0.1389 0.12221 3
#> SD:skmeans 122 0.276 0.5143 0.1829 0.15297 4
#> SD:skmeans 96 0.113 0.1846 0.1634 0.00831 5
#> SD:skmeans 85 0.982 0.0655 0.2014 0.00610 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "pam"]
# you can also extract it by
# res = res_list["SD:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 130 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 0.534 0.706 0.885 0.4917 0.496 0.496
#> 3 3 0.524 0.699 0.832 0.3075 0.707 0.487
#> 4 4 0.536 0.517 0.753 0.1445 0.873 0.662
#> 5 5 0.523 0.338 0.632 0.0686 0.872 0.587
#> 6 6 0.554 0.228 0.573 0.0431 0.780 0.298
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
#> GSM447671 2 0.9954 0.1291 0.460 0.540
#> GSM447694 1 0.0672 0.8616 0.992 0.008
#> GSM447618 2 0.3431 0.8184 0.064 0.936
#> GSM447691 2 0.9944 0.1472 0.456 0.544
#> GSM447733 2 0.9996 0.0470 0.488 0.512
#> GSM447620 1 0.9866 0.2006 0.568 0.432
#> GSM447627 1 0.0672 0.8616 0.992 0.008
#> GSM447630 2 0.0000 0.8500 0.000 1.000
#> GSM447642 1 0.0000 0.8616 1.000 0.000
#> GSM447649 2 0.0000 0.8500 0.000 1.000
#> GSM447654 2 0.0938 0.8470 0.012 0.988
#> GSM447655 2 0.0000 0.8500 0.000 1.000
#> GSM447669 2 0.9866 0.2110 0.432 0.568
#> GSM447676 1 0.0000 0.8616 1.000 0.000
#> GSM447678 2 0.8763 0.5363 0.296 0.704
#> GSM447681 2 0.0000 0.8500 0.000 1.000
#> GSM447698 2 0.0000 0.8500 0.000 1.000
#> GSM447713 1 0.0000 0.8616 1.000 0.000
#> GSM447722 2 0.9954 0.1291 0.460 0.540
#> GSM447726 2 0.9866 0.2790 0.432 0.568
#> GSM447735 1 0.3431 0.8291 0.936 0.064
#> GSM447737 1 0.0000 0.8616 1.000 0.000
#> GSM447657 2 0.0376 0.8492 0.004 0.996
#> GSM447674 2 0.0000 0.8500 0.000 1.000
#> GSM447636 2 0.6247 0.7466 0.156 0.844
#> GSM447723 1 0.0000 0.8616 1.000 0.000
#> GSM447699 1 0.5842 0.7595 0.860 0.140
#> GSM447708 1 0.9850 0.1998 0.572 0.428
#> GSM447721 1 0.0000 0.8616 1.000 0.000
#> GSM447623 1 0.0000 0.8616 1.000 0.000
#> GSM447621 1 0.0000 0.8616 1.000 0.000
#> GSM447650 2 0.0000 0.8500 0.000 1.000
#> GSM447651 2 0.0000 0.8500 0.000 1.000
#> GSM447653 1 0.4431 0.8019 0.908 0.092
#> GSM447658 2 0.9795 0.3504 0.416 0.584
#> GSM447675 2 0.6887 0.7103 0.184 0.816
#> GSM447680 2 0.5946 0.7548 0.144 0.856
#> GSM447686 2 0.4161 0.8078 0.084 0.916
#> GSM447736 1 0.0938 0.8603 0.988 0.012
#> GSM447629 2 0.3584 0.8217 0.068 0.932
#> GSM447648 1 0.0376 0.8622 0.996 0.004
#> GSM447660 1 0.8813 0.4825 0.700 0.300
#> GSM447661 2 0.0000 0.8500 0.000 1.000
#> GSM447663 1 0.5946 0.7649 0.856 0.144
#> GSM447704 2 0.0000 0.8500 0.000 1.000
#> GSM447720 1 0.9815 0.2216 0.580 0.420
#> GSM447652 2 0.0000 0.8500 0.000 1.000
#> GSM447679 2 0.0376 0.8492 0.004 0.996
#> GSM447712 1 0.9795 0.1835 0.584 0.416
#> GSM447664 2 0.3431 0.8218 0.064 0.936
#> GSM447637 1 0.0376 0.8622 0.996 0.004
#> GSM447639 1 0.9909 0.2171 0.556 0.444
#> GSM447615 1 0.0376 0.8622 0.996 0.004
#> GSM447656 2 0.6973 0.7194 0.188 0.812
#> GSM447673 2 0.0376 0.8492 0.004 0.996
#> GSM447719 1 0.0376 0.8622 0.996 0.004
#> GSM447706 1 0.0672 0.8616 0.992 0.008
#> GSM447612 1 0.6887 0.7194 0.816 0.184
#> GSM447665 2 0.0000 0.8500 0.000 1.000
#> GSM447677 2 0.0000 0.8500 0.000 1.000
#> GSM447613 1 0.0000 0.8616 1.000 0.000
#> GSM447659 1 0.6048 0.7525 0.852 0.148
#> GSM447662 1 0.1843 0.8538 0.972 0.028
#> GSM447666 1 0.9732 0.2612 0.596 0.404
#> GSM447668 2 0.0000 0.8500 0.000 1.000
#> GSM447682 2 0.0376 0.8492 0.004 0.996
#> GSM447683 2 0.0376 0.8492 0.004 0.996
#> GSM447688 2 0.0000 0.8500 0.000 1.000
#> GSM447702 2 0.0000 0.8500 0.000 1.000
#> GSM447709 2 0.9129 0.4618 0.328 0.672
#> GSM447711 1 0.9933 0.0853 0.548 0.452
#> GSM447715 2 0.6148 0.7489 0.152 0.848
#> GSM447693 1 0.0938 0.8606 0.988 0.012
#> GSM447611 2 0.7883 0.6568 0.236 0.764
#> GSM447672 2 0.0000 0.8500 0.000 1.000
#> GSM447703 2 0.0000 0.8500 0.000 1.000
#> GSM447727 1 0.9580 0.3062 0.620 0.380
#> GSM447638 2 0.6148 0.7489 0.152 0.848
#> GSM447670 1 0.0000 0.8616 1.000 0.000
#> GSM447700 2 0.9954 0.1291 0.460 0.540
#> GSM447738 2 0.0000 0.8500 0.000 1.000
#> GSM447739 1 0.0000 0.8616 1.000 0.000
#> GSM447617 1 0.0000 0.8616 1.000 0.000
#> GSM447628 2 0.0000 0.8500 0.000 1.000
#> GSM447632 2 0.0376 0.8492 0.004 0.996
#> GSM447619 1 0.1633 0.8556 0.976 0.024
#> GSM447643 2 0.6887 0.7252 0.184 0.816
#> GSM447724 2 0.9954 0.1291 0.460 0.540
#> GSM447728 2 0.0000 0.8500 0.000 1.000
#> GSM447610 1 0.0000 0.8616 1.000 0.000
#> GSM447633 2 0.9933 0.1524 0.452 0.548
#> GSM447634 1 0.3114 0.8372 0.944 0.056
#> GSM447622 1 0.0376 0.8622 0.996 0.004
#> GSM447667 1 0.9815 0.1963 0.580 0.420
#> GSM447687 2 0.0000 0.8500 0.000 1.000
#> GSM447695 1 0.0672 0.8616 0.992 0.008
#> GSM447696 1 0.0000 0.8616 1.000 0.000
#> GSM447697 1 0.0000 0.8616 1.000 0.000
#> GSM447714 1 0.1633 0.8556 0.976 0.024
#> GSM447717 2 0.9427 0.4221 0.360 0.640
#> GSM447725 1 0.2778 0.8299 0.952 0.048
#> GSM447729 2 0.1414 0.8428 0.020 0.980
#> GSM447644 2 0.9896 0.1881 0.440 0.560
#> GSM447710 1 0.0938 0.8606 0.988 0.012
#> GSM447614 1 0.0376 0.8622 0.996 0.004
#> GSM447685 2 0.0938 0.8461 0.012 0.988
#> GSM447690 1 0.0000 0.8616 1.000 0.000
#> GSM447730 2 0.0000 0.8500 0.000 1.000
#> GSM447646 2 0.0000 0.8500 0.000 1.000
#> GSM447689 1 0.9686 0.2742 0.604 0.396
#> GSM447635 1 0.9944 0.1157 0.544 0.456
#> GSM447641 1 0.0000 0.8616 1.000 0.000
#> GSM447716 2 0.1184 0.8445 0.016 0.984
#> GSM447718 2 0.2948 0.8282 0.052 0.948
#> GSM447616 1 0.0376 0.8622 0.996 0.004
#> GSM447626 1 0.0376 0.8622 0.996 0.004
#> GSM447640 2 0.0000 0.8500 0.000 1.000
#> GSM447734 1 0.2948 0.8396 0.948 0.052
#> GSM447692 1 0.0376 0.8622 0.996 0.004
#> GSM447647 2 0.0376 0.8490 0.004 0.996
#> GSM447624 1 0.0000 0.8616 1.000 0.000
#> GSM447625 1 0.2778 0.8419 0.952 0.048
#> GSM447707 2 0.0000 0.8500 0.000 1.000
#> GSM447732 1 0.1843 0.8528 0.972 0.028
#> GSM447684 1 0.9580 0.3061 0.620 0.380
#> GSM447731 2 0.7376 0.6741 0.208 0.792
#> GSM447705 1 0.9909 0.1675 0.556 0.444
#> GSM447631 1 0.0376 0.8622 0.996 0.004
#> GSM447701 2 0.0000 0.8500 0.000 1.000
#> GSM447645 1 0.0376 0.8622 0.996 0.004
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM447671 3 0.4750 0.6697 0.000 0.216 0.784
#> GSM447694 3 0.3686 0.7451 0.140 0.000 0.860
#> GSM447618 2 0.4504 0.7668 0.000 0.804 0.196
#> GSM447691 3 0.5529 0.5087 0.000 0.296 0.704
#> GSM447733 3 0.2796 0.7179 0.092 0.000 0.908
#> GSM447620 3 0.4796 0.6665 0.000 0.220 0.780
#> GSM447627 3 0.3686 0.7451 0.140 0.000 0.860
#> GSM447630 2 0.2682 0.8588 0.004 0.920 0.076
#> GSM447642 1 0.2796 0.8502 0.908 0.000 0.092
#> GSM447649 2 0.0237 0.8690 0.000 0.996 0.004
#> GSM447654 2 0.7987 0.4925 0.092 0.616 0.292
#> GSM447655 2 0.0000 0.8699 0.000 1.000 0.000
#> GSM447669 3 0.6180 0.2307 0.000 0.416 0.584
#> GSM447676 1 0.6095 0.4060 0.608 0.000 0.392
#> GSM447678 3 0.7529 0.3211 0.060 0.316 0.624
#> GSM447681 2 0.2356 0.8596 0.000 0.928 0.072
#> GSM447698 2 0.4235 0.7851 0.000 0.824 0.176
#> GSM447713 1 0.2796 0.8502 0.908 0.000 0.092
#> GSM447722 3 0.2878 0.7264 0.000 0.096 0.904
#> GSM447726 2 0.6577 0.1657 0.008 0.572 0.420
#> GSM447735 3 0.3686 0.7451 0.140 0.000 0.860
#> GSM447737 3 0.5497 0.5607 0.292 0.000 0.708
#> GSM447657 2 0.1529 0.8675 0.000 0.960 0.040
#> GSM447674 2 0.1529 0.8675 0.000 0.960 0.040
#> GSM447636 1 0.4062 0.6570 0.836 0.164 0.000
#> GSM447723 1 0.6309 0.0757 0.504 0.000 0.496
#> GSM447699 3 0.0237 0.7463 0.004 0.000 0.996
#> GSM447708 3 0.6081 0.4044 0.004 0.344 0.652
#> GSM447721 1 0.2796 0.8502 0.908 0.000 0.092
#> GSM447623 1 0.2796 0.8502 0.908 0.000 0.092
#> GSM447621 1 0.2959 0.8453 0.900 0.000 0.100
#> GSM447650 2 0.1529 0.8675 0.000 0.960 0.040
#> GSM447651 2 0.0000 0.8699 0.000 1.000 0.000
#> GSM447653 3 0.5743 0.7233 0.172 0.044 0.784
#> GSM447658 1 0.3619 0.6850 0.864 0.136 0.000
#> GSM447675 3 0.8447 -0.0258 0.092 0.392 0.516
#> GSM447680 2 0.2031 0.8660 0.016 0.952 0.032
#> GSM447686 2 0.3816 0.7697 0.148 0.852 0.000
#> GSM447736 3 0.3686 0.7451 0.140 0.000 0.860
#> GSM447629 2 0.4465 0.7854 0.004 0.820 0.176
#> GSM447648 3 0.3752 0.7429 0.144 0.000 0.856
#> GSM447660 1 0.3967 0.7908 0.884 0.072 0.044
#> GSM447661 2 0.0000 0.8699 0.000 1.000 0.000
#> GSM447663 3 0.0237 0.7463 0.000 0.004 0.996
#> GSM447704 2 0.0000 0.8699 0.000 1.000 0.000
#> GSM447720 3 0.3030 0.7274 0.004 0.092 0.904
#> GSM447652 2 0.1529 0.8675 0.000 0.960 0.040
#> GSM447679 2 0.1529 0.8675 0.000 0.960 0.040
#> GSM447712 1 0.2796 0.8502 0.908 0.000 0.092
#> GSM447664 2 0.8104 0.5028 0.104 0.616 0.280
#> GSM447637 3 0.4178 0.7272 0.172 0.000 0.828
#> GSM447639 3 0.4521 0.6675 0.004 0.180 0.816
#> GSM447615 3 0.3816 0.7411 0.148 0.000 0.852
#> GSM447656 2 0.5062 0.7198 0.016 0.800 0.184
#> GSM447673 2 0.2116 0.8661 0.012 0.948 0.040
#> GSM447719 3 0.4931 0.7274 0.232 0.000 0.768
#> GSM447706 3 0.4811 0.7343 0.148 0.024 0.828
#> GSM447612 3 0.1643 0.7395 0.000 0.044 0.956
#> GSM447665 2 0.3686 0.7829 0.000 0.860 0.140
#> GSM447677 2 0.0000 0.8699 0.000 1.000 0.000
#> GSM447613 1 0.2796 0.8502 0.908 0.000 0.092
#> GSM447659 3 0.4289 0.7085 0.092 0.040 0.868
#> GSM447662 3 0.3669 0.7465 0.064 0.040 0.896
#> GSM447666 3 0.4978 0.6685 0.004 0.216 0.780
#> GSM447668 2 0.1529 0.8675 0.000 0.960 0.040
#> GSM447682 2 0.1529 0.8675 0.000 0.960 0.040
#> GSM447683 2 0.1529 0.8675 0.000 0.960 0.040
#> GSM447688 2 0.3619 0.7848 0.000 0.864 0.136
#> GSM447702 2 0.0000 0.8699 0.000 1.000 0.000
#> GSM447709 3 0.6235 0.2505 0.000 0.436 0.564
#> GSM447711 1 0.2796 0.8502 0.908 0.000 0.092
#> GSM447715 2 0.2176 0.8652 0.020 0.948 0.032
#> GSM447693 3 0.5094 0.7329 0.136 0.040 0.824
#> GSM447611 2 0.8627 0.2337 0.104 0.504 0.392
#> GSM447672 2 0.0000 0.8699 0.000 1.000 0.000
#> GSM447703 2 0.0000 0.8699 0.000 1.000 0.000
#> GSM447727 1 0.6968 0.6123 0.732 0.120 0.148
#> GSM447638 2 0.7276 0.2715 0.404 0.564 0.032
#> GSM447670 1 0.2878 0.8482 0.904 0.000 0.096
#> GSM447700 3 0.4654 0.6756 0.000 0.208 0.792
#> GSM447738 2 0.0000 0.8699 0.000 1.000 0.000
#> GSM447739 1 0.2796 0.8502 0.908 0.000 0.092
#> GSM447617 1 0.2878 0.8482 0.904 0.000 0.096
#> GSM447628 2 0.2796 0.8187 0.092 0.908 0.000
#> GSM447632 2 0.0000 0.8699 0.000 1.000 0.000
#> GSM447619 3 0.5094 0.7329 0.136 0.040 0.824
#> GSM447643 1 0.5178 0.5882 0.744 0.256 0.000
#> GSM447724 3 0.3551 0.7202 0.000 0.132 0.868
#> GSM447728 2 0.1753 0.8668 0.000 0.952 0.048
#> GSM447610 1 0.6286 -0.1530 0.536 0.000 0.464
#> GSM447633 3 0.6062 0.3850 0.000 0.384 0.616
#> GSM447634 3 0.3686 0.7451 0.140 0.000 0.860
#> GSM447622 3 0.4178 0.7275 0.172 0.000 0.828
#> GSM447667 2 0.6955 -0.1086 0.016 0.496 0.488
#> GSM447687 2 0.0000 0.8699 0.000 1.000 0.000
#> GSM447695 3 0.3686 0.7451 0.140 0.000 0.860
#> GSM447696 1 0.2796 0.8502 0.908 0.000 0.092
#> GSM447697 1 0.2796 0.8502 0.908 0.000 0.092
#> GSM447714 3 0.1529 0.7393 0.000 0.040 0.960
#> GSM447717 1 0.6208 0.7326 0.772 0.152 0.076
#> GSM447725 1 0.0000 0.7938 1.000 0.000 0.000
#> GSM447729 2 0.6168 0.7468 0.096 0.780 0.124
#> GSM447644 3 0.6204 0.2048 0.000 0.424 0.576
#> GSM447710 3 0.3619 0.7459 0.136 0.000 0.864
#> GSM447614 3 0.3686 0.7451 0.140 0.000 0.860
#> GSM447685 2 0.1711 0.8674 0.008 0.960 0.032
#> GSM447690 1 0.2796 0.8502 0.908 0.000 0.092
#> GSM447730 2 0.0237 0.8690 0.000 0.996 0.004
#> GSM447646 2 0.2796 0.8187 0.092 0.908 0.000
#> GSM447689 3 0.3267 0.7247 0.000 0.116 0.884
#> GSM447635 3 0.3030 0.7274 0.004 0.092 0.904
#> GSM447641 1 0.2796 0.8502 0.908 0.000 0.092
#> GSM447716 2 0.3715 0.8240 0.004 0.868 0.128
#> GSM447718 2 0.5690 0.5793 0.004 0.708 0.288
#> GSM447616 3 0.4121 0.7306 0.168 0.000 0.832
#> GSM447626 3 0.6302 -0.0135 0.480 0.000 0.520
#> GSM447640 2 0.0000 0.8699 0.000 1.000 0.000
#> GSM447734 3 0.3116 0.7524 0.108 0.000 0.892
#> GSM447692 3 0.4178 0.7271 0.172 0.000 0.828
#> GSM447647 2 0.5260 0.7653 0.092 0.828 0.080
#> GSM447624 1 0.2878 0.8482 0.904 0.000 0.096
#> GSM447625 3 0.3619 0.7459 0.136 0.000 0.864
#> GSM447707 2 0.0000 0.8699 0.000 1.000 0.000
#> GSM447732 3 0.3686 0.7450 0.140 0.000 0.860
#> GSM447684 1 0.8743 0.0549 0.512 0.116 0.372
#> GSM447731 2 0.8389 0.2190 0.092 0.536 0.372
#> GSM447705 3 0.3551 0.7202 0.000 0.132 0.868
#> GSM447631 3 0.3816 0.7410 0.148 0.000 0.852
#> GSM447701 2 0.0000 0.8699 0.000 1.000 0.000
#> GSM447645 3 0.3816 0.7410 0.148 0.000 0.852
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM447671 4 0.5478 0.53285 0.000 0.056 0.248 0.696
#> GSM447694 3 0.0000 0.72001 0.000 0.000 1.000 0.000
#> GSM447618 4 0.6478 0.21246 0.000 0.336 0.088 0.576
#> GSM447691 4 0.6516 0.47470 0.000 0.092 0.332 0.576
#> GSM447733 3 0.4877 0.24752 0.000 0.000 0.592 0.408
#> GSM447620 4 0.3732 0.58626 0.000 0.056 0.092 0.852
#> GSM447627 3 0.0188 0.72016 0.000 0.000 0.996 0.004
#> GSM447630 2 0.6400 0.55379 0.000 0.632 0.116 0.252
#> GSM447642 1 0.1118 0.76691 0.964 0.000 0.000 0.036
#> GSM447649 2 0.3726 0.66477 0.000 0.788 0.000 0.212
#> GSM447654 2 0.3920 0.64399 0.012 0.856 0.056 0.076
#> GSM447655 2 0.4164 0.63811 0.000 0.736 0.000 0.264
#> GSM447669 4 0.6790 0.50066 0.000 0.168 0.228 0.604
#> GSM447676 1 0.5359 0.43731 0.676 0.000 0.288 0.036
#> GSM447678 2 0.7333 -0.07200 0.000 0.496 0.172 0.332
#> GSM447681 2 0.3837 0.64529 0.000 0.776 0.000 0.224
#> GSM447698 4 0.4916 -0.03657 0.000 0.424 0.000 0.576
#> GSM447713 1 0.1824 0.76228 0.936 0.000 0.060 0.004
#> GSM447722 3 0.5698 0.27867 0.000 0.044 0.636 0.320
#> GSM447726 4 0.9767 0.00390 0.172 0.288 0.208 0.332
#> GSM447735 3 0.1629 0.71108 0.000 0.024 0.952 0.024
#> GSM447737 3 0.4122 0.53357 0.236 0.000 0.760 0.004
#> GSM447657 2 0.0817 0.69865 0.000 0.976 0.024 0.000
#> GSM447674 2 0.0000 0.70045 0.000 1.000 0.000 0.000
#> GSM447636 1 0.2401 0.74682 0.904 0.004 0.000 0.092
#> GSM447723 1 0.5931 0.08662 0.504 0.000 0.460 0.036
#> GSM447699 3 0.5291 0.33120 0.000 0.024 0.652 0.324
#> GSM447708 4 0.7635 0.49769 0.068 0.092 0.248 0.592
#> GSM447721 1 0.5113 0.63993 0.712 0.000 0.252 0.036
#> GSM447623 1 0.4964 0.52189 0.616 0.000 0.380 0.004
#> GSM447621 1 0.5016 0.50437 0.600 0.000 0.396 0.004
#> GSM447650 2 0.0817 0.69865 0.000 0.976 0.024 0.000
#> GSM447651 2 0.4713 0.57027 0.000 0.640 0.000 0.360
#> GSM447653 3 0.6410 0.46898 0.208 0.028 0.684 0.080
#> GSM447658 1 0.1557 0.76384 0.944 0.000 0.000 0.056
#> GSM447675 2 0.6414 0.13056 0.004 0.544 0.060 0.392
#> GSM447680 2 0.7462 0.41142 0.156 0.516 0.008 0.320
#> GSM447686 2 0.7812 0.26421 0.348 0.396 0.000 0.256
#> GSM447736 3 0.0336 0.72016 0.000 0.000 0.992 0.008
#> GSM447629 2 0.6957 0.27003 0.000 0.472 0.112 0.416
#> GSM447648 3 0.3266 0.69075 0.108 0.000 0.868 0.024
#> GSM447660 1 0.2197 0.75451 0.928 0.000 0.048 0.024
#> GSM447661 2 0.4677 0.60432 0.000 0.680 0.004 0.316
#> GSM447663 4 0.5000 -0.10151 0.000 0.000 0.496 0.504
#> GSM447704 2 0.2704 0.69037 0.000 0.876 0.000 0.124
#> GSM447720 3 0.5533 0.39689 0.028 0.020 0.708 0.244
#> GSM447652 2 0.0000 0.70045 0.000 1.000 0.000 0.000
#> GSM447679 2 0.0000 0.70045 0.000 1.000 0.000 0.000
#> GSM447712 1 0.0000 0.77059 1.000 0.000 0.000 0.000
#> GSM447664 2 0.3202 0.65805 0.012 0.888 0.024 0.076
#> GSM447637 3 0.4057 0.66584 0.152 0.000 0.816 0.032
#> GSM447639 3 0.6862 0.16016 0.000 0.128 0.560 0.312
#> GSM447615 3 0.2868 0.67659 0.136 0.000 0.864 0.000
#> GSM447656 2 0.9673 0.02148 0.188 0.328 0.164 0.320
#> GSM447673 2 0.0336 0.69805 0.000 0.992 0.000 0.008
#> GSM447719 3 0.5850 0.51265 0.244 0.000 0.676 0.080
#> GSM447706 3 0.3550 0.69328 0.044 0.000 0.860 0.096
#> GSM447612 4 0.4877 0.06970 0.000 0.000 0.408 0.592
#> GSM447665 4 0.2760 0.52014 0.000 0.128 0.000 0.872
#> GSM447677 2 0.4454 0.60752 0.000 0.692 0.000 0.308
#> GSM447613 1 0.1118 0.76691 0.964 0.000 0.000 0.036
#> GSM447659 3 0.4761 0.30651 0.000 0.000 0.628 0.372
#> GSM447662 3 0.4967 0.24823 0.000 0.000 0.548 0.452
#> GSM447666 4 0.4991 0.51409 0.104 0.056 0.036 0.804
#> GSM447668 2 0.5088 0.60331 0.000 0.688 0.024 0.288
#> GSM447682 2 0.0817 0.69865 0.000 0.976 0.024 0.000
#> GSM447683 2 0.4134 0.62285 0.000 0.740 0.000 0.260
#> GSM447688 2 0.4605 0.28028 0.000 0.664 0.000 0.336
#> GSM447702 2 0.4277 0.61838 0.000 0.720 0.000 0.280
#> GSM447709 4 0.3266 0.57544 0.000 0.084 0.040 0.876
#> GSM447711 1 0.0188 0.77016 0.996 0.000 0.000 0.004
#> GSM447715 2 0.7872 0.37402 0.188 0.492 0.016 0.304
#> GSM447693 3 0.4072 0.56392 0.000 0.000 0.748 0.252
#> GSM447611 2 0.6504 0.47357 0.216 0.676 0.032 0.076
#> GSM447672 2 0.0707 0.70156 0.000 0.980 0.000 0.020
#> GSM447703 2 0.0000 0.70045 0.000 1.000 0.000 0.000
#> GSM447727 1 0.7297 0.41432 0.532 0.028 0.356 0.084
#> GSM447638 1 0.8689 -0.21195 0.332 0.308 0.032 0.328
#> GSM447670 1 0.5052 0.65034 0.720 0.000 0.244 0.036
#> GSM447700 4 0.5598 0.54835 0.000 0.076 0.220 0.704
#> GSM447738 2 0.0000 0.70045 0.000 1.000 0.000 0.000
#> GSM447739 1 0.1661 0.76463 0.944 0.000 0.052 0.004
#> GSM447617 1 0.4632 0.60138 0.688 0.000 0.308 0.004
#> GSM447628 2 0.2081 0.66179 0.000 0.916 0.000 0.084
#> GSM447632 2 0.0000 0.70045 0.000 1.000 0.000 0.000
#> GSM447619 3 0.4222 0.54912 0.000 0.000 0.728 0.272
#> GSM447643 1 0.3084 0.73316 0.896 0.028 0.012 0.064
#> GSM447724 4 0.5408 0.08895 0.000 0.016 0.408 0.576
#> GSM447728 2 0.4193 0.61944 0.000 0.732 0.000 0.268
#> GSM447610 1 0.6586 0.13914 0.500 0.000 0.420 0.080
#> GSM447633 4 0.3216 0.58192 0.000 0.076 0.044 0.880
#> GSM447634 3 0.0188 0.72021 0.000 0.000 0.996 0.004
#> GSM447622 3 0.1902 0.70551 0.064 0.000 0.932 0.004
#> GSM447667 2 0.9696 -0.00905 0.156 0.336 0.208 0.300
#> GSM447687 2 0.0000 0.70045 0.000 1.000 0.000 0.000
#> GSM447695 3 0.0817 0.71694 0.000 0.000 0.976 0.024
#> GSM447696 1 0.2053 0.75906 0.924 0.000 0.072 0.004
#> GSM447697 1 0.1576 0.76570 0.948 0.000 0.048 0.004
#> GSM447714 4 0.4972 -0.02405 0.000 0.000 0.456 0.544
#> GSM447717 1 0.0000 0.77059 1.000 0.000 0.000 0.000
#> GSM447725 1 0.0000 0.77059 1.000 0.000 0.000 0.000
#> GSM447729 2 0.4966 0.54810 0.156 0.768 0.000 0.076
#> GSM447644 4 0.6400 0.52365 0.000 0.168 0.180 0.652
#> GSM447710 3 0.4706 0.55962 0.020 0.000 0.732 0.248
#> GSM447614 3 0.0000 0.72001 0.000 0.000 1.000 0.000
#> GSM447685 2 0.4869 0.61930 0.016 0.720 0.004 0.260
#> GSM447690 1 0.1824 0.76264 0.936 0.000 0.060 0.004
#> GSM447730 2 0.4977 0.42726 0.000 0.540 0.000 0.460
#> GSM447646 2 0.2081 0.66179 0.000 0.916 0.000 0.084
#> GSM447689 4 0.7019 0.06667 0.084 0.016 0.356 0.544
#> GSM447635 3 0.5047 0.31816 0.000 0.016 0.668 0.316
#> GSM447641 1 0.0336 0.77044 0.992 0.000 0.000 0.008
#> GSM447716 2 0.5097 0.40652 0.004 0.568 0.000 0.428
#> GSM447718 2 0.5587 0.28093 0.000 0.600 0.372 0.028
#> GSM447616 3 0.1792 0.71026 0.068 0.000 0.932 0.000
#> GSM447626 3 0.6465 -0.12312 0.412 0.000 0.516 0.072
#> GSM447640 2 0.0000 0.70045 0.000 1.000 0.000 0.000
#> GSM447734 3 0.3123 0.66022 0.000 0.000 0.844 0.156
#> GSM447692 3 0.1389 0.70928 0.048 0.000 0.952 0.000
#> GSM447647 2 0.2760 0.65790 0.000 0.872 0.000 0.128
#> GSM447624 1 0.5578 0.59443 0.648 0.000 0.312 0.040
#> GSM447625 3 0.0188 0.72053 0.000 0.000 0.996 0.004
#> GSM447707 2 0.2408 0.69322 0.000 0.896 0.000 0.104
#> GSM447732 3 0.0657 0.71946 0.004 0.000 0.984 0.012
#> GSM447684 1 0.7847 0.24328 0.444 0.044 0.416 0.096
#> GSM447731 2 0.7874 -0.09139 0.000 0.372 0.280 0.348
#> GSM447705 4 0.5339 0.13397 0.000 0.016 0.384 0.600
#> GSM447631 3 0.3970 0.68496 0.084 0.000 0.840 0.076
#> GSM447701 2 0.5331 0.57786 0.000 0.644 0.024 0.332
#> GSM447645 3 0.4706 0.64006 0.140 0.000 0.788 0.072
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM447671 5 0.5513 0.34153 0.000 0.004 0.188 0.144 0.664
#> GSM447694 3 0.0290 0.64688 0.000 0.000 0.992 0.008 0.000
#> GSM447618 5 0.7069 0.26948 0.000 0.272 0.052 0.156 0.520
#> GSM447691 5 0.7169 0.34121 0.000 0.072 0.224 0.164 0.540
#> GSM447733 3 0.6158 0.40976 0.000 0.000 0.528 0.316 0.156
#> GSM447620 5 0.1243 0.44154 0.000 0.008 0.028 0.004 0.960
#> GSM447627 3 0.0963 0.64912 0.000 0.000 0.964 0.036 0.000
#> GSM447630 2 0.8286 0.02299 0.000 0.396 0.192 0.220 0.192
#> GSM447642 1 0.4304 -0.17897 0.516 0.000 0.000 0.484 0.000
#> GSM447649 2 0.3838 0.50707 0.000 0.716 0.000 0.004 0.280
#> GSM447654 2 0.5844 0.51585 0.004 0.652 0.108 0.220 0.016
#> GSM447655 2 0.4135 0.40730 0.000 0.656 0.000 0.004 0.340
#> GSM447669 5 0.5758 0.42741 0.000 0.112 0.188 0.028 0.672
#> GSM447676 1 0.6749 -0.18218 0.396 0.000 0.268 0.336 0.000
#> GSM447678 2 0.7356 0.16367 0.000 0.528 0.132 0.108 0.232
#> GSM447681 2 0.3878 0.50689 0.000 0.748 0.000 0.016 0.236
#> GSM447698 5 0.6200 0.20181 0.000 0.320 0.000 0.160 0.520
#> GSM447713 1 0.1851 0.47550 0.912 0.000 0.088 0.000 0.000
#> GSM447722 3 0.7605 0.25323 0.000 0.096 0.480 0.164 0.260
#> GSM447726 5 0.7696 -0.02481 0.092 0.028 0.080 0.320 0.480
#> GSM447735 3 0.3518 0.61836 0.000 0.048 0.840 0.008 0.104
#> GSM447737 1 0.4821 0.14419 0.516 0.000 0.464 0.020 0.000
#> GSM447657 2 0.2654 0.64290 0.000 0.888 0.048 0.000 0.064
#> GSM447674 2 0.0566 0.66295 0.000 0.984 0.000 0.004 0.012
#> GSM447636 1 0.4517 -0.05058 0.556 0.008 0.000 0.436 0.000
#> GSM447723 4 0.7380 0.41209 0.260 0.000 0.120 0.512 0.108
#> GSM447699 3 0.7582 0.21057 0.000 0.048 0.364 0.356 0.232
#> GSM447708 5 0.7707 0.41224 0.000 0.136 0.240 0.140 0.484
#> GSM447721 4 0.6682 0.00512 0.368 0.000 0.236 0.396 0.000
#> GSM447623 1 0.3932 0.37564 0.672 0.000 0.328 0.000 0.000
#> GSM447621 1 0.3966 0.37216 0.664 0.000 0.336 0.000 0.000
#> GSM447650 2 0.3570 0.60357 0.000 0.836 0.048 0.008 0.108
#> GSM447651 5 0.4425 -0.06177 0.000 0.452 0.000 0.004 0.544
#> GSM447653 3 0.6302 0.38128 0.156 0.000 0.584 0.244 0.016
#> GSM447658 4 0.4304 0.08806 0.484 0.000 0.000 0.516 0.000
#> GSM447675 2 0.6738 0.33457 0.000 0.532 0.032 0.292 0.144
#> GSM447680 5 0.7566 0.20996 0.004 0.196 0.052 0.304 0.444
#> GSM447686 1 0.8086 -0.10349 0.440 0.228 0.004 0.208 0.120
#> GSM447736 3 0.1082 0.65043 0.000 0.000 0.964 0.008 0.028
#> GSM447629 5 0.6248 0.19393 0.000 0.316 0.124 0.012 0.548
#> GSM447648 3 0.3509 0.58014 0.004 0.000 0.792 0.196 0.008
#> GSM447660 1 0.4900 -0.15791 0.512 0.000 0.024 0.464 0.000
#> GSM447661 2 0.4359 0.28754 0.000 0.584 0.000 0.004 0.412
#> GSM447663 3 0.5883 0.36750 0.000 0.000 0.508 0.104 0.388
#> GSM447704 2 0.3333 0.57948 0.000 0.788 0.000 0.004 0.208
#> GSM447720 3 0.5112 0.41348 0.012 0.012 0.668 0.024 0.284
#> GSM447652 2 0.0566 0.66193 0.000 0.984 0.000 0.004 0.012
#> GSM447679 2 0.2017 0.65086 0.000 0.912 0.000 0.008 0.080
#> GSM447712 1 0.3305 0.31001 0.776 0.000 0.000 0.224 0.000
#> GSM447664 2 0.4936 0.57485 0.004 0.724 0.048 0.208 0.016
#> GSM447637 3 0.6805 0.09989 0.220 0.000 0.452 0.320 0.008
#> GSM447639 4 0.8172 -0.26507 0.000 0.124 0.320 0.352 0.204
#> GSM447615 3 0.2873 0.58121 0.016 0.000 0.856 0.128 0.000
#> GSM447656 5 0.8252 0.00594 0.092 0.084 0.064 0.316 0.444
#> GSM447673 2 0.0290 0.66114 0.000 0.992 0.000 0.008 0.000
#> GSM447719 3 0.6349 0.40549 0.116 0.000 0.524 0.344 0.016
#> GSM447706 3 0.4479 0.48419 0.000 0.000 0.700 0.264 0.036
#> GSM447612 5 0.6374 -0.24637 0.000 0.000 0.360 0.172 0.468
#> GSM447665 5 0.3193 0.44384 0.000 0.028 0.000 0.132 0.840
#> GSM447677 5 0.4449 -0.18024 0.000 0.484 0.000 0.004 0.512
#> GSM447613 1 0.3612 0.18660 0.732 0.000 0.000 0.268 0.000
#> GSM447659 3 0.6438 0.33870 0.000 0.000 0.496 0.212 0.292
#> GSM447662 3 0.6110 0.50185 0.000 0.000 0.568 0.216 0.216
#> GSM447666 5 0.5077 0.20204 0.088 0.004 0.000 0.212 0.696
#> GSM447668 5 0.5605 -0.10987 0.000 0.468 0.052 0.008 0.472
#> GSM447682 2 0.1331 0.65724 0.000 0.952 0.040 0.000 0.008
#> GSM447683 2 0.5052 0.30305 0.000 0.612 0.000 0.048 0.340
#> GSM447688 2 0.6088 0.19239 0.000 0.548 0.000 0.156 0.296
#> GSM447702 2 0.4321 0.29535 0.000 0.600 0.000 0.004 0.396
#> GSM447709 5 0.2513 0.41965 0.000 0.116 0.000 0.008 0.876
#> GSM447711 1 0.3177 0.32202 0.792 0.000 0.000 0.208 0.000
#> GSM447715 4 0.7910 0.42442 0.116 0.052 0.060 0.500 0.272
#> GSM447693 3 0.4350 0.59745 0.000 0.000 0.764 0.084 0.152
#> GSM447611 2 0.7855 0.36613 0.208 0.492 0.076 0.208 0.016
#> GSM447672 2 0.1701 0.65631 0.000 0.936 0.000 0.016 0.048
#> GSM447703 2 0.0000 0.66175 0.000 1.000 0.000 0.000 0.000
#> GSM447727 4 0.7492 0.46247 0.220 0.000 0.100 0.512 0.168
#> GSM447638 5 0.7857 -0.05834 0.104 0.040 0.064 0.336 0.456
#> GSM447670 1 0.6740 0.12372 0.404 0.000 0.268 0.328 0.000
#> GSM447700 5 0.6642 0.33542 0.000 0.084 0.136 0.160 0.620
#> GSM447738 2 0.2069 0.64695 0.000 0.912 0.000 0.012 0.076
#> GSM447739 1 0.1851 0.47550 0.912 0.000 0.088 0.000 0.000
#> GSM447617 1 0.5671 0.32674 0.568 0.000 0.336 0.096 0.000
#> GSM447628 2 0.3663 0.58915 0.000 0.776 0.000 0.208 0.016
#> GSM447632 2 0.0880 0.66080 0.000 0.968 0.000 0.000 0.032
#> GSM447619 3 0.4385 0.59233 0.000 0.000 0.752 0.068 0.180
#> GSM447643 4 0.5181 0.17002 0.452 0.000 0.004 0.512 0.032
#> GSM447724 5 0.6690 -0.17538 0.000 0.020 0.340 0.148 0.492
#> GSM447728 2 0.5668 0.27255 0.000 0.580 0.004 0.084 0.332
#> GSM447610 1 0.6946 0.17178 0.468 0.000 0.232 0.284 0.016
#> GSM447633 5 0.3247 0.44459 0.000 0.016 0.008 0.136 0.840
#> GSM447634 3 0.0963 0.64912 0.000 0.000 0.964 0.036 0.000
#> GSM447622 3 0.4132 0.38944 0.260 0.000 0.720 0.020 0.000
#> GSM447667 5 0.8172 0.24893 0.004 0.220 0.100 0.304 0.372
#> GSM447687 2 0.0000 0.66175 0.000 1.000 0.000 0.000 0.000
#> GSM447695 3 0.2074 0.63300 0.000 0.000 0.896 0.000 0.104
#> GSM447696 1 0.2280 0.46777 0.880 0.000 0.120 0.000 0.000
#> GSM447697 1 0.2248 0.47375 0.900 0.000 0.088 0.012 0.000
#> GSM447714 3 0.6545 0.38872 0.000 0.000 0.476 0.240 0.284
#> GSM447717 1 0.3336 0.30873 0.772 0.000 0.000 0.228 0.000
#> GSM447725 1 0.3336 0.30873 0.772 0.000 0.000 0.228 0.000
#> GSM447729 2 0.4848 0.55871 0.052 0.724 0.000 0.208 0.016
#> GSM447644 5 0.6637 0.34774 0.000 0.184 0.236 0.024 0.556
#> GSM447710 3 0.5568 0.50642 0.000 0.000 0.596 0.096 0.308
#> GSM447614 3 0.0000 0.64635 0.000 0.000 1.000 0.000 0.000
#> GSM447685 2 0.5200 0.25173 0.004 0.580 0.012 0.020 0.384
#> GSM447690 1 0.1851 0.47550 0.912 0.000 0.088 0.000 0.000
#> GSM447730 5 0.4452 -0.16430 0.000 0.496 0.000 0.004 0.500
#> GSM447646 2 0.3727 0.58661 0.000 0.768 0.000 0.216 0.016
#> GSM447689 5 0.7677 -0.23471 0.088 0.000 0.376 0.152 0.384
#> GSM447635 3 0.5954 0.34516 0.000 0.000 0.576 0.152 0.272
#> GSM447641 1 0.3752 0.23288 0.708 0.000 0.000 0.292 0.000
#> GSM447716 2 0.6142 0.10468 0.000 0.488 0.012 0.092 0.408
#> GSM447718 2 0.9508 -0.18881 0.088 0.284 0.252 0.244 0.132
#> GSM447616 3 0.3601 0.56151 0.128 0.000 0.820 0.052 0.000
#> GSM447626 4 0.7899 0.37619 0.128 0.000 0.280 0.436 0.156
#> GSM447640 2 0.0955 0.66089 0.000 0.968 0.000 0.004 0.028
#> GSM447734 3 0.3593 0.64090 0.000 0.000 0.828 0.084 0.088
#> GSM447692 3 0.3424 0.43080 0.240 0.000 0.760 0.000 0.000
#> GSM447647 2 0.4404 0.59571 0.000 0.760 0.000 0.152 0.088
#> GSM447624 1 0.6793 0.15870 0.376 0.000 0.332 0.292 0.000
#> GSM447625 3 0.1121 0.64913 0.000 0.000 0.956 0.044 0.000
#> GSM447707 2 0.3196 0.59020 0.000 0.804 0.000 0.004 0.192
#> GSM447732 3 0.4679 0.51211 0.000 0.000 0.740 0.136 0.124
#> GSM447684 4 0.7428 0.47303 0.132 0.000 0.120 0.524 0.224
#> GSM447731 5 0.8211 -0.10776 0.000 0.116 0.296 0.244 0.344
#> GSM447705 5 0.5906 -0.06510 0.000 0.000 0.284 0.140 0.576
#> GSM447631 3 0.3675 0.56198 0.004 0.000 0.772 0.216 0.008
#> GSM447701 5 0.5112 -0.00990 0.000 0.408 0.016 0.016 0.560
#> GSM447645 3 0.4541 0.33729 0.004 0.000 0.608 0.380 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM447671 5 0.6021 0.04501 0.000 0.344 0.000 0.016 0.480 0.160
#> GSM447694 6 0.4432 0.30084 0.000 0.000 0.364 0.036 0.000 0.600
#> GSM447618 2 0.6581 -0.00935 0.004 0.452 0.000 0.120 0.360 0.064
#> GSM447691 2 0.6755 -0.02189 0.000 0.424 0.000 0.072 0.348 0.156
#> GSM447733 6 0.5865 0.25415 0.000 0.000 0.000 0.296 0.228 0.476
#> GSM447620 2 0.6880 0.06928 0.000 0.372 0.000 0.068 0.372 0.188
#> GSM447627 6 0.4316 0.40863 0.008 0.000 0.248 0.036 0.004 0.704
#> GSM447630 6 0.6337 0.01393 0.212 0.304 0.000 0.016 0.004 0.464
#> GSM447642 1 0.1610 0.62667 0.916 0.000 0.084 0.000 0.000 0.000
#> GSM447649 5 0.4179 -0.04378 0.000 0.472 0.000 0.000 0.516 0.012
#> GSM447654 4 0.5430 0.66084 0.000 0.200 0.000 0.632 0.020 0.148
#> GSM447655 5 0.3860 -0.05120 0.000 0.472 0.000 0.000 0.528 0.000
#> GSM447669 2 0.7019 0.10659 0.000 0.360 0.000 0.072 0.220 0.348
#> GSM447676 1 0.4760 0.28051 0.668 0.000 0.120 0.000 0.000 0.212
#> GSM447678 5 0.7351 -0.00648 0.000 0.144 0.000 0.188 0.388 0.280
#> GSM447681 2 0.4060 0.30375 0.000 0.764 0.000 0.120 0.112 0.004
#> GSM447698 2 0.6362 -0.00385 0.000 0.464 0.000 0.120 0.360 0.056
#> GSM447713 3 0.3672 -0.06411 0.368 0.000 0.632 0.000 0.000 0.000
#> GSM447722 5 0.7243 -0.03773 0.000 0.180 0.000 0.120 0.360 0.340
#> GSM447726 2 0.6363 0.16235 0.308 0.396 0.000 0.000 0.012 0.284
#> GSM447735 6 0.7636 0.21995 0.000 0.068 0.360 0.096 0.096 0.380
#> GSM447737 3 0.3352 0.44956 0.112 0.000 0.816 0.000 0.000 0.072
#> GSM447657 2 0.3146 0.31989 0.000 0.848 0.000 0.012 0.060 0.080
#> GSM447674 2 0.3967 0.13462 0.000 0.632 0.000 0.012 0.356 0.000
#> GSM447636 1 0.2300 0.62310 0.856 0.000 0.144 0.000 0.000 0.000
#> GSM447723 1 0.0291 0.63331 0.992 0.000 0.004 0.000 0.000 0.004
#> GSM447699 5 0.8072 -0.12530 0.200 0.068 0.000 0.088 0.348 0.296
#> GSM447708 2 0.6741 0.05722 0.028 0.424 0.000 0.008 0.292 0.248
#> GSM447721 1 0.3789 0.29025 0.584 0.000 0.416 0.000 0.000 0.000
#> GSM447623 3 0.1010 0.42733 0.036 0.000 0.960 0.000 0.000 0.004
#> GSM447621 3 0.1010 0.42733 0.036 0.000 0.960 0.000 0.000 0.004
#> GSM447650 5 0.5917 -0.11800 0.000 0.416 0.000 0.012 0.428 0.144
#> GSM447651 5 0.5480 -0.05582 0.000 0.328 0.000 0.000 0.528 0.144
#> GSM447653 4 0.3955 0.37153 0.008 0.000 0.000 0.608 0.000 0.384
#> GSM447658 1 0.0260 0.63519 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM447675 4 0.4368 0.52846 0.000 0.116 0.000 0.740 0.136 0.008
#> GSM447680 2 0.5643 0.19375 0.368 0.476 0.000 0.000 0.000 0.156
#> GSM447686 1 0.5033 0.10499 0.476 0.452 0.072 0.000 0.000 0.000
#> GSM447736 6 0.4653 0.31804 0.000 0.000 0.360 0.000 0.052 0.588
#> GSM447629 2 0.4939 0.30196 0.056 0.704 0.000 0.004 0.044 0.192
#> GSM447648 3 0.6807 -0.05619 0.056 0.000 0.396 0.204 0.000 0.344
#> GSM447660 1 0.2191 0.61099 0.876 0.004 0.120 0.000 0.000 0.000
#> GSM447661 5 0.4631 -0.03879 0.000 0.440 0.000 0.012 0.528 0.020
#> GSM447663 6 0.3747 0.44217 0.048 0.000 0.000 0.048 0.088 0.816
#> GSM447704 2 0.3868 -0.00686 0.000 0.504 0.000 0.000 0.496 0.000
#> GSM447720 6 0.3699 0.41788 0.036 0.112 0.000 0.000 0.040 0.812
#> GSM447652 2 0.5377 0.12102 0.000 0.596 0.000 0.016 0.288 0.100
#> GSM447679 2 0.1995 0.32483 0.000 0.912 0.000 0.052 0.036 0.000
#> GSM447712 1 0.3482 0.53936 0.684 0.000 0.316 0.000 0.000 0.000
#> GSM447664 4 0.5601 0.61960 0.020 0.324 0.004 0.584 0.020 0.048
#> GSM447637 3 0.6036 0.32649 0.108 0.000 0.600 0.208 0.000 0.084
#> GSM447639 6 0.7992 0.07939 0.204 0.084 0.000 0.060 0.324 0.328
#> GSM447615 6 0.5934 0.10843 0.216 0.000 0.364 0.000 0.000 0.420
#> GSM447656 2 0.5675 0.14601 0.400 0.444 0.000 0.000 0.000 0.156
#> GSM447673 2 0.5002 0.11891 0.000 0.556 0.000 0.080 0.364 0.000
#> GSM447719 4 0.4497 0.42902 0.024 0.000 0.032 0.688 0.000 0.256
#> GSM447706 3 0.7888 0.08116 0.192 0.000 0.396 0.220 0.028 0.164
#> GSM447612 5 0.4689 -0.21839 0.000 0.000 0.000 0.044 0.516 0.440
#> GSM447665 5 0.4713 -0.00771 0.000 0.364 0.000 0.016 0.592 0.028
#> GSM447677 2 0.4108 0.29190 0.000 0.744 0.000 0.000 0.164 0.092
#> GSM447613 1 0.3515 0.43497 0.676 0.000 0.324 0.000 0.000 0.000
#> GSM447659 6 0.4739 0.23473 0.000 0.000 0.000 0.048 0.436 0.516
#> GSM447662 6 0.6533 0.38067 0.000 0.000 0.088 0.140 0.244 0.528
#> GSM447666 2 0.8324 0.10418 0.032 0.360 0.016 0.220 0.140 0.232
#> GSM447668 2 0.4266 0.31545 0.000 0.756 0.000 0.012 0.116 0.116
#> GSM447682 2 0.5345 0.12614 0.048 0.584 0.000 0.012 0.336 0.020
#> GSM447683 2 0.0622 0.33672 0.008 0.980 0.000 0.000 0.000 0.012
#> GSM447688 5 0.6458 -0.02485 0.000 0.368 0.000 0.132 0.444 0.056
#> GSM447702 2 0.3854 0.03227 0.000 0.536 0.000 0.000 0.464 0.000
#> GSM447709 2 0.6472 0.11018 0.000 0.416 0.000 0.044 0.384 0.156
#> GSM447711 1 0.3634 0.49527 0.644 0.000 0.356 0.000 0.000 0.000
#> GSM447715 1 0.4921 0.37058 0.656 0.180 0.000 0.000 0.000 0.164
#> GSM447693 3 0.7043 -0.12850 0.000 0.000 0.356 0.248 0.068 0.328
#> GSM447611 4 0.6116 0.66327 0.020 0.252 0.028 0.608 0.020 0.072
#> GSM447672 2 0.4945 0.07492 0.000 0.484 0.000 0.064 0.452 0.000
#> GSM447703 2 0.3992 0.12593 0.000 0.624 0.000 0.012 0.364 0.000
#> GSM447727 1 0.3219 0.53793 0.820 0.020 0.012 0.000 0.000 0.148
#> GSM447638 2 0.6519 0.13586 0.344 0.388 0.000 0.000 0.024 0.244
#> GSM447670 1 0.3756 0.23804 0.600 0.000 0.400 0.000 0.000 0.000
#> GSM447700 2 0.6947 -0.07233 0.000 0.380 0.000 0.144 0.376 0.100
#> GSM447738 2 0.2658 0.31979 0.000 0.864 0.000 0.100 0.036 0.000
#> GSM447739 3 0.3672 -0.06411 0.368 0.000 0.632 0.000 0.000 0.000
#> GSM447617 3 0.2178 0.42856 0.132 0.000 0.868 0.000 0.000 0.000
#> GSM447628 4 0.4406 0.48208 0.000 0.476 0.000 0.500 0.024 0.000
#> GSM447632 2 0.3494 0.19996 0.000 0.736 0.000 0.012 0.252 0.000
#> GSM447619 6 0.7171 0.10236 0.000 0.000 0.340 0.216 0.092 0.352
#> GSM447643 1 0.0405 0.63245 0.988 0.008 0.000 0.000 0.000 0.004
#> GSM447724 5 0.6171 -0.07946 0.000 0.100 0.000 0.060 0.516 0.324
#> GSM447728 2 0.1873 0.33391 0.000 0.924 0.000 0.048 0.020 0.008
#> GSM447610 4 0.5635 0.40151 0.120 0.000 0.240 0.608 0.000 0.032
#> GSM447633 5 0.5340 0.02240 0.000 0.352 0.000 0.044 0.564 0.040
#> GSM447634 6 0.4260 0.41103 0.000 0.000 0.248 0.048 0.004 0.700
#> GSM447622 3 0.4443 0.22920 0.052 0.000 0.648 0.000 0.000 0.300
#> GSM447667 2 0.4926 0.24969 0.360 0.580 0.000 0.000 0.012 0.048
#> GSM447687 2 0.3992 0.12593 0.000 0.624 0.000 0.012 0.364 0.000
#> GSM447695 6 0.6425 0.27521 0.028 0.000 0.364 0.024 0.108 0.476
#> GSM447696 3 0.3151 0.13781 0.252 0.000 0.748 0.000 0.000 0.000
#> GSM447697 3 0.3706 -0.07794 0.380 0.000 0.620 0.000 0.000 0.000
#> GSM447714 6 0.5529 0.37958 0.000 0.000 0.016 0.132 0.256 0.596
#> GSM447717 1 0.3482 0.53929 0.684 0.000 0.316 0.000 0.000 0.000
#> GSM447725 1 0.3499 0.53665 0.680 0.000 0.320 0.000 0.000 0.000
#> GSM447729 4 0.4679 0.60425 0.000 0.376 0.020 0.584 0.020 0.000
#> GSM447644 2 0.5917 0.17073 0.000 0.428 0.000 0.012 0.144 0.416
#> GSM447710 6 0.5215 0.38974 0.000 0.000 0.080 0.128 0.092 0.700
#> GSM447614 6 0.4541 0.30165 0.000 0.000 0.360 0.044 0.000 0.596
#> GSM447685 2 0.3175 0.33648 0.080 0.832 0.000 0.000 0.000 0.088
#> GSM447690 3 0.3672 -0.06411 0.368 0.000 0.632 0.000 0.000 0.000
#> GSM447730 5 0.3944 -0.02607 0.000 0.428 0.000 0.004 0.568 0.000
#> GSM447646 4 0.4118 0.60995 0.000 0.352 0.000 0.628 0.020 0.000
#> GSM447689 6 0.5917 0.36905 0.068 0.016 0.012 0.100 0.128 0.676
#> GSM447635 6 0.5209 0.24223 0.000 0.112 0.000 0.000 0.324 0.564
#> GSM447641 1 0.3198 0.57745 0.740 0.000 0.260 0.000 0.000 0.000
#> GSM447716 2 0.5096 0.25479 0.056 0.712 0.000 0.020 0.172 0.040
#> GSM447718 6 0.5861 0.04909 0.224 0.260 0.000 0.004 0.000 0.512
#> GSM447616 3 0.5271 0.02747 0.104 0.000 0.516 0.000 0.000 0.380
#> GSM447626 6 0.4542 -0.00993 0.412 0.000 0.028 0.004 0.000 0.556
#> GSM447640 2 0.3965 0.10974 0.000 0.604 0.000 0.008 0.388 0.000
#> GSM447734 6 0.4544 0.45462 0.000 0.000 0.172 0.020 0.080 0.728
#> GSM447692 3 0.4408 0.12310 0.000 0.000 0.608 0.036 0.000 0.356
#> GSM447647 5 0.6031 -0.16701 0.000 0.312 0.000 0.268 0.420 0.000
#> GSM447624 3 0.4244 0.40284 0.200 0.000 0.720 0.080 0.000 0.000
#> GSM447625 6 0.3754 0.42903 0.000 0.000 0.212 0.016 0.016 0.756
#> GSM447707 5 0.3862 -0.04982 0.000 0.476 0.000 0.000 0.524 0.000
#> GSM447732 6 0.4219 0.38292 0.144 0.000 0.080 0.016 0.000 0.760
#> GSM447684 1 0.6110 0.25636 0.536 0.120 0.048 0.000 0.000 0.296
#> GSM447731 4 0.5575 0.50842 0.000 0.028 0.000 0.620 0.216 0.136
#> GSM447705 5 0.6289 -0.07023 0.000 0.104 0.000 0.068 0.508 0.320
#> GSM447631 3 0.7203 0.00617 0.104 0.000 0.388 0.208 0.000 0.300
#> GSM447701 2 0.5678 0.26996 0.000 0.580 0.000 0.012 0.212 0.196
#> GSM447645 3 0.7401 0.01257 0.136 0.000 0.360 0.208 0.000 0.296
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 gender(p) individual(p) disease.state(p) other(p) k
#> SD:pam 103 0.732 0.761 0.422 0.0551 2
#> SD:pam 112 0.878 0.461 0.527 0.1914 3
#> SD:pam 89 0.951 0.501 0.112 0.2450 4
#> SD:pam 41 0.538 0.592 0.444 0.0725 5
#> SD:pam 18 1.000 0.792 0.961 0.7915 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "mclust"]
# you can also extract it by
# res = res_list["SD:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 130 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.316 0.619 0.817 0.4188 0.531 0.531
#> 3 3 0.549 0.819 0.859 0.4696 0.662 0.452
#> 4 4 0.728 0.830 0.903 0.1864 0.855 0.627
#> 5 5 0.697 0.702 0.822 0.0856 0.917 0.700
#> 6 6 0.688 0.525 0.739 0.0383 0.970 0.861
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
#> GSM447671 2 0.9866 -0.1321 0.432 0.568
#> GSM447694 1 0.7299 0.7185 0.796 0.204
#> GSM447618 2 0.7950 0.4963 0.240 0.760
#> GSM447691 2 0.9686 0.0182 0.396 0.604
#> GSM447733 1 0.9833 0.5569 0.576 0.424
#> GSM447620 2 0.7056 0.5904 0.192 0.808
#> GSM447627 1 0.9427 0.6359 0.640 0.360
#> GSM447630 2 0.9993 -0.3188 0.484 0.516
#> GSM447642 1 0.0000 0.6954 1.000 0.000
#> GSM447649 2 0.0000 0.8169 0.000 1.000
#> GSM447654 1 0.9977 0.4408 0.528 0.472
#> GSM447655 2 0.0000 0.8169 0.000 1.000
#> GSM447669 2 0.9552 0.0999 0.376 0.624
#> GSM447676 1 0.0000 0.6954 1.000 0.000
#> GSM447678 2 1.0000 -0.3585 0.496 0.504
#> GSM447681 2 0.0000 0.8169 0.000 1.000
#> GSM447698 2 0.0000 0.8169 0.000 1.000
#> GSM447713 1 0.0000 0.6954 1.000 0.000
#> GSM447722 2 0.9996 -0.3321 0.488 0.512
#> GSM447726 1 0.9833 0.5569 0.576 0.424
#> GSM447735 1 0.9552 0.6205 0.624 0.376
#> GSM447737 1 0.3114 0.7141 0.944 0.056
#> GSM447657 2 0.0000 0.8169 0.000 1.000
#> GSM447674 2 0.0000 0.8169 0.000 1.000
#> GSM447636 1 0.0000 0.6954 1.000 0.000
#> GSM447723 1 0.4815 0.7275 0.896 0.104
#> GSM447699 1 0.9775 0.5750 0.588 0.412
#> GSM447708 2 0.0000 0.8169 0.000 1.000
#> GSM447721 1 0.0000 0.6954 1.000 0.000
#> GSM447623 1 0.0000 0.6954 1.000 0.000
#> GSM447621 1 0.0000 0.6954 1.000 0.000
#> GSM447650 2 0.0000 0.8169 0.000 1.000
#> GSM447651 2 0.0000 0.8169 0.000 1.000
#> GSM447653 1 0.9580 0.6166 0.620 0.380
#> GSM447658 1 0.0000 0.6954 1.000 0.000
#> GSM447675 1 0.9833 0.5569 0.576 0.424
#> GSM447680 1 0.9998 0.3372 0.508 0.492
#> GSM447686 1 0.3114 0.6936 0.944 0.056
#> GSM447736 1 0.9552 0.6205 0.624 0.376
#> GSM447629 2 0.0000 0.8169 0.000 1.000
#> GSM447648 1 0.5842 0.7323 0.860 0.140
#> GSM447660 1 0.0000 0.6954 1.000 0.000
#> GSM447661 2 0.0000 0.8169 0.000 1.000
#> GSM447663 1 0.9580 0.6128 0.620 0.380
#> GSM447704 2 0.0000 0.8169 0.000 1.000
#> GSM447720 1 0.9815 0.5633 0.580 0.420
#> GSM447652 2 0.0000 0.8169 0.000 1.000
#> GSM447679 2 0.0000 0.8169 0.000 1.000
#> GSM447712 1 0.0000 0.6954 1.000 0.000
#> GSM447664 1 0.9896 0.5219 0.560 0.440
#> GSM447637 1 0.5519 0.7323 0.872 0.128
#> GSM447639 1 0.9833 0.5569 0.576 0.424
#> GSM447615 1 0.5519 0.7323 0.872 0.128
#> GSM447656 1 0.9983 0.3900 0.524 0.476
#> GSM447673 2 0.0000 0.8169 0.000 1.000
#> GSM447719 1 0.7139 0.7203 0.804 0.196
#> GSM447706 1 0.5737 0.7325 0.864 0.136
#> GSM447612 1 0.9815 0.5633 0.580 0.420
#> GSM447665 2 0.0000 0.8169 0.000 1.000
#> GSM447677 2 0.0000 0.8169 0.000 1.000
#> GSM447613 1 0.0000 0.6954 1.000 0.000
#> GSM447659 1 0.9775 0.5750 0.588 0.412
#> GSM447662 1 0.9129 0.6572 0.672 0.328
#> GSM447666 1 0.9686 0.5975 0.604 0.396
#> GSM447668 2 0.0000 0.8169 0.000 1.000
#> GSM447682 2 0.0000 0.8169 0.000 1.000
#> GSM447683 2 0.0000 0.8169 0.000 1.000
#> GSM447688 2 0.6973 0.5953 0.188 0.812
#> GSM447702 2 0.0000 0.8169 0.000 1.000
#> GSM447709 2 0.0000 0.8169 0.000 1.000
#> GSM447711 1 0.0000 0.6954 1.000 0.000
#> GSM447715 1 0.6712 0.7265 0.824 0.176
#> GSM447693 1 0.7815 0.7087 0.768 0.232
#> GSM447611 1 0.9833 0.5569 0.576 0.424
#> GSM447672 2 0.0000 0.8169 0.000 1.000
#> GSM447703 2 0.0000 0.8169 0.000 1.000
#> GSM447727 1 0.5178 0.7303 0.884 0.116
#> GSM447638 1 0.5946 0.7311 0.856 0.144
#> GSM447670 1 0.0000 0.6954 1.000 0.000
#> GSM447700 2 0.9996 -0.3321 0.488 0.512
#> GSM447738 2 0.0000 0.8169 0.000 1.000
#> GSM447739 1 0.0000 0.6954 1.000 0.000
#> GSM447617 1 0.0000 0.6954 1.000 0.000
#> GSM447628 2 0.9954 -0.2358 0.460 0.540
#> GSM447632 2 0.0000 0.8169 0.000 1.000
#> GSM447619 1 0.9087 0.6598 0.676 0.324
#> GSM447643 1 0.0672 0.6948 0.992 0.008
#> GSM447724 1 0.9833 0.5569 0.576 0.424
#> GSM447728 2 0.0000 0.8169 0.000 1.000
#> GSM447610 1 0.6712 0.7247 0.824 0.176
#> GSM447633 1 0.9922 0.5026 0.552 0.448
#> GSM447634 1 0.9552 0.6205 0.624 0.376
#> GSM447622 1 0.5519 0.7323 0.872 0.128
#> GSM447667 2 0.4690 0.7218 0.100 0.900
#> GSM447687 2 0.0000 0.8169 0.000 1.000
#> GSM447695 1 0.8081 0.7021 0.752 0.248
#> GSM447696 1 0.0000 0.6954 1.000 0.000
#> GSM447697 1 0.0000 0.6954 1.000 0.000
#> GSM447714 1 0.9661 0.6022 0.608 0.392
#> GSM447717 1 0.0000 0.6954 1.000 0.000
#> GSM447725 1 0.0000 0.6954 1.000 0.000
#> GSM447729 1 0.9833 0.5569 0.576 0.424
#> GSM447644 1 0.9998 0.3772 0.508 0.492
#> GSM447710 1 0.9248 0.6506 0.660 0.340
#> GSM447614 1 0.9170 0.6576 0.668 0.332
#> GSM447685 2 0.3114 0.7552 0.056 0.944
#> GSM447690 1 0.0000 0.6954 1.000 0.000
#> GSM447730 2 0.0000 0.8169 0.000 1.000
#> GSM447646 2 0.9954 -0.2358 0.460 0.540
#> GSM447689 1 0.9358 0.6414 0.648 0.352
#> GSM447635 1 0.9833 0.5569 0.576 0.424
#> GSM447641 1 0.0000 0.6954 1.000 0.000
#> GSM447716 2 0.9996 -0.3374 0.488 0.512
#> GSM447718 1 0.9833 0.5569 0.576 0.424
#> GSM447616 1 0.5519 0.7323 0.872 0.128
#> GSM447626 1 0.6048 0.7317 0.852 0.148
#> GSM447640 2 0.0000 0.8169 0.000 1.000
#> GSM447734 1 0.9552 0.6205 0.624 0.376
#> GSM447692 1 0.5519 0.7323 0.872 0.128
#> GSM447647 2 0.8861 0.3388 0.304 0.696
#> GSM447624 1 0.0938 0.6999 0.988 0.012
#> GSM447625 1 0.9580 0.6164 0.620 0.380
#> GSM447707 2 0.0000 0.8169 0.000 1.000
#> GSM447732 1 0.9087 0.6598 0.676 0.324
#> GSM447684 1 0.5629 0.7323 0.868 0.132
#> GSM447731 1 0.9922 0.5050 0.552 0.448
#> GSM447705 1 0.9815 0.5633 0.580 0.420
#> GSM447631 1 0.5946 0.7322 0.856 0.144
#> GSM447701 2 0.0000 0.8169 0.000 1.000
#> GSM447645 1 0.5519 0.7323 0.872 0.128
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM447671 2 0.3921 0.795 0.112 0.872 0.016
#> GSM447694 3 0.6719 0.907 0.160 0.096 0.744
#> GSM447618 2 0.0424 0.877 0.008 0.992 0.000
#> GSM447691 2 0.0892 0.873 0.020 0.980 0.000
#> GSM447733 3 0.4413 0.833 0.160 0.008 0.832
#> GSM447620 2 0.4452 0.690 0.000 0.808 0.192
#> GSM447627 3 0.4002 0.832 0.160 0.000 0.840
#> GSM447630 2 0.6865 0.606 0.160 0.736 0.104
#> GSM447642 1 0.0000 0.922 1.000 0.000 0.000
#> GSM447649 2 0.0000 0.880 0.000 1.000 0.000
#> GSM447654 2 0.5178 0.793 0.000 0.744 0.256
#> GSM447655 2 0.0000 0.880 0.000 1.000 0.000
#> GSM447669 2 0.3043 0.827 0.084 0.908 0.008
#> GSM447676 1 0.0000 0.922 1.000 0.000 0.000
#> GSM447678 2 0.5178 0.793 0.000 0.744 0.256
#> GSM447681 2 0.0000 0.880 0.000 1.000 0.000
#> GSM447698 2 0.2261 0.861 0.000 0.932 0.068
#> GSM447713 1 0.0000 0.922 1.000 0.000 0.000
#> GSM447722 2 0.8300 0.585 0.136 0.620 0.244
#> GSM447726 2 0.3500 0.802 0.116 0.880 0.004
#> GSM447735 3 0.8022 0.677 0.160 0.184 0.656
#> GSM447737 1 0.4178 0.697 0.828 0.000 0.172
#> GSM447657 2 0.0000 0.880 0.000 1.000 0.000
#> GSM447674 2 0.0000 0.880 0.000 1.000 0.000
#> GSM447636 1 0.0000 0.922 1.000 0.000 0.000
#> GSM447723 1 0.0000 0.922 1.000 0.000 0.000
#> GSM447699 3 0.8964 0.683 0.160 0.296 0.544
#> GSM447708 2 0.0000 0.880 0.000 1.000 0.000
#> GSM447721 1 0.0000 0.922 1.000 0.000 0.000
#> GSM447623 1 0.0000 0.922 1.000 0.000 0.000
#> GSM447621 1 0.0000 0.922 1.000 0.000 0.000
#> GSM447650 2 0.0000 0.880 0.000 1.000 0.000
#> GSM447651 2 0.0000 0.880 0.000 1.000 0.000
#> GSM447653 3 0.4413 0.833 0.160 0.008 0.832
#> GSM447658 1 0.0000 0.922 1.000 0.000 0.000
#> GSM447675 2 0.5178 0.793 0.000 0.744 0.256
#> GSM447680 2 0.2878 0.832 0.096 0.904 0.000
#> GSM447686 1 0.5138 0.584 0.748 0.252 0.000
#> GSM447736 3 0.6719 0.907 0.160 0.096 0.744
#> GSM447629 2 0.0000 0.880 0.000 1.000 0.000
#> GSM447648 3 0.6719 0.907 0.160 0.096 0.744
#> GSM447660 1 0.0000 0.922 1.000 0.000 0.000
#> GSM447661 2 0.0000 0.880 0.000 1.000 0.000
#> GSM447663 3 0.6719 0.907 0.160 0.096 0.744
#> GSM447704 2 0.0000 0.880 0.000 1.000 0.000
#> GSM447720 3 0.9065 0.654 0.160 0.316 0.524
#> GSM447652 2 0.0000 0.880 0.000 1.000 0.000
#> GSM447679 2 0.0000 0.880 0.000 1.000 0.000
#> GSM447712 1 0.0000 0.922 1.000 0.000 0.000
#> GSM447664 2 0.5178 0.793 0.000 0.744 0.256
#> GSM447637 3 0.6719 0.907 0.160 0.096 0.744
#> GSM447639 2 0.8817 0.433 0.160 0.568 0.272
#> GSM447615 1 0.6274 -0.182 0.544 0.000 0.456
#> GSM447656 2 0.2448 0.856 0.076 0.924 0.000
#> GSM447673 2 0.4504 0.819 0.000 0.804 0.196
#> GSM447719 3 0.4002 0.832 0.160 0.000 0.840
#> GSM447706 3 0.6652 0.899 0.172 0.084 0.744
#> GSM447612 3 0.6719 0.907 0.160 0.096 0.744
#> GSM447665 2 0.0000 0.880 0.000 1.000 0.000
#> GSM447677 2 0.0000 0.880 0.000 1.000 0.000
#> GSM447613 1 0.0000 0.922 1.000 0.000 0.000
#> GSM447659 3 0.4002 0.832 0.160 0.000 0.840
#> GSM447662 3 0.6719 0.907 0.160 0.096 0.744
#> GSM447666 3 0.6719 0.907 0.160 0.096 0.744
#> GSM447668 2 0.0000 0.880 0.000 1.000 0.000
#> GSM447682 2 0.0000 0.880 0.000 1.000 0.000
#> GSM447683 2 0.0000 0.880 0.000 1.000 0.000
#> GSM447688 2 0.5138 0.795 0.000 0.748 0.252
#> GSM447702 2 0.0000 0.880 0.000 1.000 0.000
#> GSM447709 2 0.0000 0.880 0.000 1.000 0.000
#> GSM447711 1 0.0000 0.922 1.000 0.000 0.000
#> GSM447715 1 0.5560 0.500 0.700 0.300 0.000
#> GSM447693 3 0.6719 0.907 0.160 0.096 0.744
#> GSM447611 2 0.5502 0.794 0.008 0.744 0.248
#> GSM447672 2 0.0000 0.880 0.000 1.000 0.000
#> GSM447703 2 0.3340 0.842 0.000 0.880 0.120
#> GSM447727 1 0.0000 0.922 1.000 0.000 0.000
#> GSM447638 2 0.6286 0.251 0.464 0.536 0.000
#> GSM447670 1 0.0000 0.922 1.000 0.000 0.000
#> GSM447700 2 0.7878 0.461 0.160 0.668 0.172
#> GSM447738 2 0.3116 0.844 0.000 0.892 0.108
#> GSM447739 1 0.0000 0.922 1.000 0.000 0.000
#> GSM447617 1 0.0000 0.922 1.000 0.000 0.000
#> GSM447628 2 0.5178 0.793 0.000 0.744 0.256
#> GSM447632 2 0.1411 0.872 0.000 0.964 0.036
#> GSM447619 3 0.6719 0.907 0.160 0.096 0.744
#> GSM447643 1 0.2165 0.851 0.936 0.064 0.000
#> GSM447724 3 0.8282 0.644 0.160 0.208 0.632
#> GSM447728 2 0.0000 0.880 0.000 1.000 0.000
#> GSM447610 1 0.4121 0.752 0.832 0.000 0.168
#> GSM447633 2 0.4663 0.745 0.156 0.828 0.016
#> GSM447634 3 0.8941 0.688 0.160 0.292 0.548
#> GSM447622 3 0.5178 0.798 0.256 0.000 0.744
#> GSM447667 2 0.0237 0.879 0.004 0.996 0.000
#> GSM447687 2 0.3551 0.839 0.000 0.868 0.132
#> GSM447695 3 0.7044 0.897 0.168 0.108 0.724
#> GSM447696 1 0.0000 0.922 1.000 0.000 0.000
#> GSM447697 1 0.0000 0.922 1.000 0.000 0.000
#> GSM447714 3 0.6719 0.907 0.160 0.096 0.744
#> GSM447717 1 0.0000 0.922 1.000 0.000 0.000
#> GSM447725 1 0.0000 0.922 1.000 0.000 0.000
#> GSM447729 2 0.5178 0.793 0.000 0.744 0.256
#> GSM447644 2 0.4172 0.755 0.156 0.840 0.004
#> GSM447710 3 0.6719 0.907 0.160 0.096 0.744
#> GSM447614 3 0.4413 0.833 0.160 0.008 0.832
#> GSM447685 2 0.2878 0.832 0.096 0.904 0.000
#> GSM447690 1 0.0000 0.922 1.000 0.000 0.000
#> GSM447730 2 0.0000 0.880 0.000 1.000 0.000
#> GSM447646 2 0.5178 0.793 0.000 0.744 0.256
#> GSM447689 3 0.6719 0.907 0.160 0.096 0.744
#> GSM447635 2 0.4002 0.754 0.160 0.840 0.000
#> GSM447641 1 0.0000 0.922 1.000 0.000 0.000
#> GSM447716 2 0.1860 0.856 0.052 0.948 0.000
#> GSM447718 2 0.9176 -0.161 0.160 0.496 0.344
#> GSM447616 3 0.7525 0.855 0.228 0.096 0.676
#> GSM447626 3 0.6652 0.899 0.172 0.084 0.744
#> GSM447640 2 0.0000 0.880 0.000 1.000 0.000
#> GSM447734 3 0.6719 0.907 0.160 0.096 0.744
#> GSM447692 3 0.8159 0.735 0.320 0.092 0.588
#> GSM447647 2 0.5178 0.793 0.000 0.744 0.256
#> GSM447624 1 0.5138 0.540 0.748 0.000 0.252
#> GSM447625 3 0.6719 0.907 0.160 0.096 0.744
#> GSM447707 2 0.0000 0.880 0.000 1.000 0.000
#> GSM447732 3 0.6719 0.907 0.160 0.096 0.744
#> GSM447684 3 0.6404 0.669 0.344 0.012 0.644
#> GSM447731 2 0.5656 0.784 0.008 0.728 0.264
#> GSM447705 3 0.6719 0.907 0.160 0.096 0.744
#> GSM447631 3 0.6719 0.907 0.160 0.096 0.744
#> GSM447701 2 0.0000 0.880 0.000 1.000 0.000
#> GSM447645 3 0.6719 0.907 0.160 0.096 0.744
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM447671 2 0.6571 0.590 0.000 0.612 0.264 0.124
#> GSM447694 3 0.1940 0.843 0.000 0.000 0.924 0.076
#> GSM447618 2 0.3400 0.847 0.000 0.820 0.000 0.180
#> GSM447691 2 0.2704 0.885 0.000 0.876 0.000 0.124
#> GSM447733 4 0.2149 0.800 0.000 0.000 0.088 0.912
#> GSM447620 2 0.3356 0.786 0.000 0.824 0.176 0.000
#> GSM447627 3 0.4817 0.393 0.000 0.000 0.612 0.388
#> GSM447630 2 0.6587 0.534 0.000 0.596 0.292 0.112
#> GSM447642 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM447649 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM447654 4 0.1940 0.878 0.000 0.076 0.000 0.924
#> GSM447655 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM447669 2 0.5798 0.714 0.000 0.704 0.184 0.112
#> GSM447676 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM447678 4 0.1940 0.878 0.000 0.076 0.000 0.924
#> GSM447681 2 0.1637 0.890 0.000 0.940 0.000 0.060
#> GSM447698 2 0.2973 0.875 0.000 0.856 0.000 0.144
#> GSM447713 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM447722 4 0.1940 0.878 0.000 0.076 0.000 0.924
#> GSM447726 2 0.2480 0.859 0.000 0.904 0.088 0.008
#> GSM447735 4 0.0000 0.859 0.000 0.000 0.000 1.000
#> GSM447737 1 0.3688 0.697 0.792 0.000 0.208 0.000
#> GSM447657 2 0.2704 0.885 0.000 0.876 0.000 0.124
#> GSM447674 2 0.2530 0.887 0.000 0.888 0.000 0.112
#> GSM447636 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM447723 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM447699 3 0.4761 0.510 0.000 0.000 0.628 0.372
#> GSM447708 2 0.2704 0.885 0.000 0.876 0.000 0.124
#> GSM447721 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM447623 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM447621 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM447650 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM447651 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM447653 4 0.1557 0.831 0.000 0.000 0.056 0.944
#> GSM447658 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM447675 4 0.1940 0.878 0.000 0.076 0.000 0.924
#> GSM447680 2 0.1637 0.890 0.000 0.940 0.000 0.060
#> GSM447686 1 0.2011 0.868 0.920 0.080 0.000 0.000
#> GSM447736 3 0.1940 0.843 0.000 0.000 0.924 0.076
#> GSM447629 2 0.2704 0.885 0.000 0.876 0.000 0.124
#> GSM447648 3 0.0000 0.873 0.000 0.000 1.000 0.000
#> GSM447660 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM447661 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM447663 3 0.0188 0.872 0.000 0.004 0.996 0.000
#> GSM447704 2 0.0707 0.886 0.000 0.980 0.000 0.020
#> GSM447720 3 0.5352 0.669 0.000 0.092 0.740 0.168
#> GSM447652 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM447679 2 0.2081 0.890 0.000 0.916 0.000 0.084
#> GSM447712 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM447664 4 0.2469 0.856 0.000 0.108 0.000 0.892
#> GSM447637 3 0.0000 0.873 0.000 0.000 1.000 0.000
#> GSM447639 4 0.0469 0.865 0.000 0.012 0.000 0.988
#> GSM447615 1 0.4830 0.323 0.608 0.000 0.392 0.000
#> GSM447656 2 0.2704 0.885 0.000 0.876 0.000 0.124
#> GSM447673 2 0.3024 0.873 0.000 0.852 0.000 0.148
#> GSM447719 4 0.4855 0.375 0.000 0.000 0.400 0.600
#> GSM447706 3 0.0000 0.873 0.000 0.000 1.000 0.000
#> GSM447612 3 0.0469 0.870 0.000 0.000 0.988 0.012
#> GSM447665 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM447677 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM447613 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM447659 4 0.3074 0.729 0.000 0.000 0.152 0.848
#> GSM447662 3 0.0000 0.873 0.000 0.000 1.000 0.000
#> GSM447666 3 0.0000 0.873 0.000 0.000 1.000 0.000
#> GSM447668 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM447682 2 0.2704 0.885 0.000 0.876 0.000 0.124
#> GSM447683 2 0.2704 0.885 0.000 0.876 0.000 0.124
#> GSM447688 4 0.2530 0.851 0.000 0.112 0.000 0.888
#> GSM447702 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM447709 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM447711 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM447715 1 0.4365 0.686 0.784 0.188 0.000 0.028
#> GSM447693 3 0.0000 0.873 0.000 0.000 1.000 0.000
#> GSM447611 4 0.0524 0.864 0.004 0.008 0.000 0.988
#> GSM447672 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM447703 2 0.1118 0.888 0.000 0.964 0.000 0.036
#> GSM447727 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM447638 2 0.3610 0.735 0.200 0.800 0.000 0.000
#> GSM447670 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM447700 3 0.7630 0.124 0.000 0.208 0.428 0.364
#> GSM447738 2 0.2868 0.880 0.000 0.864 0.000 0.136
#> GSM447739 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM447617 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM447628 4 0.2011 0.878 0.000 0.080 0.000 0.920
#> GSM447632 2 0.2868 0.880 0.000 0.864 0.000 0.136
#> GSM447619 3 0.0000 0.873 0.000 0.000 1.000 0.000
#> GSM447643 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM447724 4 0.0376 0.861 0.000 0.004 0.004 0.992
#> GSM447728 2 0.2704 0.885 0.000 0.876 0.000 0.124
#> GSM447610 4 0.4916 0.240 0.424 0.000 0.000 0.576
#> GSM447633 2 0.4855 0.511 0.000 0.644 0.352 0.004
#> GSM447634 3 0.3751 0.742 0.000 0.004 0.800 0.196
#> GSM447622 3 0.0336 0.872 0.000 0.000 0.992 0.008
#> GSM447667 2 0.2999 0.882 0.004 0.864 0.000 0.132
#> GSM447687 2 0.2814 0.882 0.000 0.868 0.000 0.132
#> GSM447695 3 0.3610 0.746 0.000 0.000 0.800 0.200
#> GSM447696 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM447697 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM447714 3 0.0000 0.873 0.000 0.000 1.000 0.000
#> GSM447717 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM447725 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM447729 4 0.1940 0.878 0.000 0.076 0.000 0.924
#> GSM447644 2 0.2944 0.788 0.000 0.868 0.128 0.004
#> GSM447710 3 0.0000 0.873 0.000 0.000 1.000 0.000
#> GSM447614 4 0.1474 0.835 0.000 0.000 0.052 0.948
#> GSM447685 2 0.2704 0.885 0.000 0.876 0.000 0.124
#> GSM447690 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM447730 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM447646 4 0.2011 0.878 0.000 0.080 0.000 0.920
#> GSM447689 3 0.0000 0.873 0.000 0.000 1.000 0.000
#> GSM447635 2 0.4304 0.746 0.000 0.716 0.000 0.284
#> GSM447641 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM447716 2 0.4304 0.716 0.000 0.716 0.000 0.284
#> GSM447718 3 0.6409 0.271 0.000 0.364 0.560 0.076
#> GSM447616 3 0.3390 0.781 0.132 0.000 0.852 0.016
#> GSM447626 3 0.0000 0.873 0.000 0.000 1.000 0.000
#> GSM447640 2 0.2704 0.885 0.000 0.876 0.000 0.124
#> GSM447734 3 0.0336 0.872 0.000 0.000 0.992 0.008
#> GSM447692 3 0.5143 0.715 0.172 0.000 0.752 0.076
#> GSM447647 4 0.2921 0.821 0.000 0.140 0.000 0.860
#> GSM447624 3 0.4855 0.346 0.400 0.000 0.600 0.000
#> GSM447625 3 0.1474 0.855 0.000 0.000 0.948 0.052
#> GSM447707 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM447732 3 0.0000 0.873 0.000 0.000 1.000 0.000
#> GSM447684 3 0.5022 0.627 0.044 0.220 0.736 0.000
#> GSM447731 4 0.3873 0.783 0.000 0.228 0.000 0.772
#> GSM447705 3 0.0000 0.873 0.000 0.000 1.000 0.000
#> GSM447631 3 0.0000 0.873 0.000 0.000 1.000 0.000
#> GSM447701 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM447645 3 0.0000 0.873 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
#> GSM447671 5 0.5169 0.658 0.000 0.128 0.000 0.184 0.688
#> GSM447694 3 0.3305 0.729 0.000 0.000 0.776 0.224 0.000
#> GSM447618 2 0.5140 0.376 0.000 0.664 0.000 0.084 0.252
#> GSM447691 5 0.4045 0.535 0.000 0.356 0.000 0.000 0.644
#> GSM447733 4 0.0000 0.728 0.000 0.000 0.000 1.000 0.000
#> GSM447620 5 0.6395 0.668 0.000 0.180 0.116 0.068 0.636
#> GSM447627 3 0.4126 0.600 0.000 0.000 0.620 0.380 0.000
#> GSM447630 5 0.3480 0.626 0.000 0.248 0.000 0.000 0.752
#> GSM447642 1 0.0000 0.927 1.000 0.000 0.000 0.000 0.000
#> GSM447649 2 0.3177 0.751 0.000 0.792 0.000 0.000 0.208
#> GSM447654 4 0.4883 0.791 0.000 0.200 0.000 0.708 0.092
#> GSM447655 2 0.3242 0.750 0.000 0.784 0.000 0.000 0.216
#> GSM447669 5 0.3305 0.643 0.000 0.224 0.000 0.000 0.776
#> GSM447676 1 0.0000 0.927 1.000 0.000 0.000 0.000 0.000
#> GSM447678 4 0.4883 0.791 0.000 0.200 0.000 0.708 0.092
#> GSM447681 2 0.0290 0.790 0.000 0.992 0.000 0.000 0.008
#> GSM447698 2 0.1124 0.770 0.000 0.960 0.000 0.004 0.036
#> GSM447713 1 0.0000 0.927 1.000 0.000 0.000 0.000 0.000
#> GSM447722 4 0.4779 0.791 0.000 0.200 0.000 0.716 0.084
#> GSM447726 5 0.2068 0.672 0.000 0.092 0.004 0.000 0.904
#> GSM447735 4 0.0000 0.728 0.000 0.000 0.000 1.000 0.000
#> GSM447737 1 0.3949 0.527 0.668 0.000 0.332 0.000 0.000
#> GSM447657 2 0.0000 0.787 0.000 1.000 0.000 0.000 0.000
#> GSM447674 2 0.0000 0.787 0.000 1.000 0.000 0.000 0.000
#> GSM447636 1 0.0000 0.927 1.000 0.000 0.000 0.000 0.000
#> GSM447723 1 0.0000 0.927 1.000 0.000 0.000 0.000 0.000
#> GSM447699 3 0.5167 0.539 0.000 0.000 0.552 0.404 0.044
#> GSM447708 2 0.3074 0.667 0.000 0.804 0.000 0.000 0.196
#> GSM447721 1 0.0000 0.927 1.000 0.000 0.000 0.000 0.000
#> GSM447623 1 0.2074 0.852 0.896 0.000 0.104 0.000 0.000
#> GSM447621 1 0.2605 0.809 0.852 0.000 0.148 0.000 0.000
#> GSM447650 2 0.3424 0.741 0.000 0.760 0.000 0.000 0.240
#> GSM447651 2 0.4074 0.626 0.000 0.636 0.000 0.000 0.364
#> GSM447653 4 0.0000 0.728 0.000 0.000 0.000 1.000 0.000
#> GSM447658 1 0.0000 0.927 1.000 0.000 0.000 0.000 0.000
#> GSM447675 4 0.4883 0.791 0.000 0.200 0.000 0.708 0.092
#> GSM447680 2 0.3796 0.674 0.000 0.700 0.000 0.000 0.300
#> GSM447686 1 0.3756 0.610 0.744 0.248 0.000 0.000 0.008
#> GSM447736 3 0.3727 0.732 0.000 0.000 0.768 0.216 0.016
#> GSM447629 2 0.2424 0.737 0.000 0.868 0.000 0.000 0.132
#> GSM447648 3 0.0000 0.785 0.000 0.000 1.000 0.000 0.000
#> GSM447660 1 0.0000 0.927 1.000 0.000 0.000 0.000 0.000
#> GSM447661 2 0.3395 0.743 0.000 0.764 0.000 0.000 0.236
#> GSM447663 5 0.4242 0.185 0.000 0.000 0.428 0.000 0.572
#> GSM447704 2 0.3003 0.759 0.000 0.812 0.000 0.000 0.188
#> GSM447720 5 0.5696 0.594 0.000 0.060 0.060 0.196 0.684
#> GSM447652 2 0.3109 0.752 0.000 0.800 0.000 0.000 0.200
#> GSM447679 2 0.1043 0.789 0.000 0.960 0.000 0.000 0.040
#> GSM447712 1 0.0000 0.927 1.000 0.000 0.000 0.000 0.000
#> GSM447664 4 0.5287 0.750 0.000 0.260 0.000 0.648 0.092
#> GSM447637 3 0.0000 0.785 0.000 0.000 1.000 0.000 0.000
#> GSM447639 4 0.1270 0.749 0.000 0.052 0.000 0.948 0.000
#> GSM447615 1 0.3366 0.660 0.768 0.000 0.232 0.000 0.000
#> GSM447656 2 0.3752 0.550 0.000 0.708 0.000 0.000 0.292
#> GSM447673 2 0.2068 0.729 0.000 0.904 0.000 0.004 0.092
#> GSM447719 4 0.4074 0.394 0.000 0.000 0.364 0.636 0.000
#> GSM447706 3 0.0000 0.785 0.000 0.000 1.000 0.000 0.000
#> GSM447612 3 0.5970 0.319 0.000 0.000 0.524 0.120 0.356
#> GSM447665 5 0.4297 -0.266 0.000 0.472 0.000 0.000 0.528
#> GSM447677 2 0.4074 0.626 0.000 0.636 0.000 0.000 0.364
#> GSM447613 1 0.0000 0.927 1.000 0.000 0.000 0.000 0.000
#> GSM447659 4 0.0162 0.726 0.000 0.000 0.004 0.996 0.000
#> GSM447662 3 0.3048 0.651 0.000 0.000 0.820 0.004 0.176
#> GSM447666 5 0.4015 0.413 0.000 0.000 0.348 0.000 0.652
#> GSM447668 2 0.3752 0.706 0.000 0.708 0.000 0.000 0.292
#> GSM447682 2 0.0000 0.787 0.000 1.000 0.000 0.000 0.000
#> GSM447683 2 0.1792 0.774 0.000 0.916 0.000 0.000 0.084
#> GSM447688 4 0.5488 0.707 0.000 0.300 0.000 0.608 0.092
#> GSM447702 2 0.3242 0.750 0.000 0.784 0.000 0.000 0.216
#> GSM447709 5 0.4235 -0.146 0.000 0.424 0.000 0.000 0.576
#> GSM447711 1 0.0000 0.927 1.000 0.000 0.000 0.000 0.000
#> GSM447715 1 0.4728 0.470 0.664 0.040 0.000 0.000 0.296
#> GSM447693 3 0.0000 0.785 0.000 0.000 1.000 0.000 0.000
#> GSM447611 4 0.4883 0.791 0.000 0.200 0.000 0.708 0.092
#> GSM447672 2 0.3109 0.752 0.000 0.800 0.000 0.000 0.200
#> GSM447703 2 0.2074 0.737 0.000 0.896 0.000 0.000 0.104
#> GSM447727 1 0.0880 0.906 0.968 0.000 0.000 0.000 0.032
#> GSM447638 5 0.5167 0.607 0.200 0.116 0.000 0.000 0.684
#> GSM447670 1 0.0290 0.923 0.992 0.000 0.008 0.000 0.000
#> GSM447700 5 0.6839 0.474 0.000 0.088 0.060 0.364 0.488
#> GSM447738 2 0.2011 0.732 0.000 0.908 0.000 0.004 0.088
#> GSM447739 1 0.0000 0.927 1.000 0.000 0.000 0.000 0.000
#> GSM447617 1 0.3561 0.660 0.740 0.000 0.260 0.000 0.000
#> GSM447628 4 0.5004 0.780 0.000 0.216 0.000 0.692 0.092
#> GSM447632 2 0.1357 0.763 0.000 0.948 0.000 0.004 0.048
#> GSM447619 3 0.0451 0.784 0.000 0.000 0.988 0.004 0.008
#> GSM447643 1 0.1124 0.895 0.960 0.004 0.000 0.000 0.036
#> GSM447724 4 0.0000 0.728 0.000 0.000 0.000 1.000 0.000
#> GSM447728 2 0.1121 0.788 0.000 0.956 0.000 0.000 0.044
#> GSM447610 4 0.4182 0.264 0.400 0.000 0.000 0.600 0.000
#> GSM447633 5 0.2769 0.686 0.000 0.092 0.000 0.032 0.876
#> GSM447634 3 0.6680 0.245 0.000 0.000 0.400 0.236 0.364
#> GSM447622 3 0.1410 0.783 0.000 0.000 0.940 0.060 0.000
#> GSM447667 2 0.1908 0.763 0.000 0.908 0.000 0.000 0.092
#> GSM447687 2 0.1908 0.732 0.000 0.908 0.000 0.000 0.092
#> GSM447695 3 0.3661 0.699 0.000 0.000 0.724 0.276 0.000
#> GSM447696 1 0.0000 0.927 1.000 0.000 0.000 0.000 0.000
#> GSM447697 1 0.0000 0.927 1.000 0.000 0.000 0.000 0.000
#> GSM447714 3 0.1638 0.756 0.000 0.000 0.932 0.004 0.064
#> GSM447717 1 0.0000 0.927 1.000 0.000 0.000 0.000 0.000
#> GSM447725 1 0.0000 0.927 1.000 0.000 0.000 0.000 0.000
#> GSM447729 4 0.4883 0.791 0.000 0.200 0.000 0.708 0.092
#> GSM447644 5 0.1908 0.670 0.000 0.092 0.000 0.000 0.908
#> GSM447710 3 0.0000 0.785 0.000 0.000 1.000 0.000 0.000
#> GSM447614 4 0.0162 0.726 0.000 0.000 0.004 0.996 0.000
#> GSM447685 2 0.1197 0.787 0.000 0.952 0.000 0.000 0.048
#> GSM447690 1 0.0000 0.927 1.000 0.000 0.000 0.000 0.000
#> GSM447730 2 0.3109 0.752 0.000 0.800 0.000 0.000 0.200
#> GSM447646 4 0.4901 0.791 0.000 0.196 0.000 0.708 0.096
#> GSM447689 3 0.4210 0.195 0.000 0.000 0.588 0.000 0.412
#> GSM447635 5 0.6405 0.505 0.000 0.252 0.000 0.236 0.512
#> GSM447641 1 0.0000 0.927 1.000 0.000 0.000 0.000 0.000
#> GSM447716 2 0.3267 0.664 0.000 0.844 0.000 0.112 0.044
#> GSM447718 5 0.3291 0.681 0.000 0.088 0.064 0.000 0.848
#> GSM447616 3 0.3821 0.698 0.148 0.000 0.800 0.052 0.000
#> GSM447626 3 0.3966 0.390 0.000 0.000 0.664 0.000 0.336
#> GSM447640 2 0.0510 0.789 0.000 0.984 0.000 0.000 0.016
#> GSM447734 3 0.2179 0.772 0.000 0.000 0.888 0.112 0.000
#> GSM447692 3 0.4678 0.699 0.064 0.000 0.712 0.224 0.000
#> GSM447647 4 0.5450 0.742 0.000 0.216 0.000 0.652 0.132
#> GSM447624 3 0.3774 0.516 0.296 0.000 0.704 0.000 0.000
#> GSM447625 3 0.3596 0.739 0.000 0.000 0.784 0.200 0.016
#> GSM447707 2 0.3109 0.752 0.000 0.800 0.000 0.000 0.200
#> GSM447732 3 0.0162 0.784 0.000 0.000 0.996 0.000 0.004
#> GSM447684 5 0.5092 0.548 0.092 0.008 0.192 0.000 0.708
#> GSM447731 4 0.3491 0.647 0.000 0.004 0.000 0.768 0.228
#> GSM447705 5 0.4211 0.384 0.000 0.000 0.360 0.004 0.636
#> GSM447631 3 0.0000 0.785 0.000 0.000 1.000 0.000 0.000
#> GSM447701 2 0.4030 0.641 0.000 0.648 0.000 0.000 0.352
#> GSM447645 3 0.0000 0.785 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
#> GSM447671 5 0.4205 0.5857 0.000 0.060 0.000 0.184 0.744 0.012
#> GSM447694 3 0.4821 0.0922 0.000 0.000 0.668 0.184 0.000 0.148
#> GSM447618 5 0.6342 -0.1336 0.000 0.276 0.000 0.200 0.492 0.032
#> GSM447691 5 0.2566 0.5696 0.000 0.112 0.000 0.012 0.868 0.008
#> GSM447733 4 0.3862 0.1155 0.000 0.000 0.000 0.608 0.004 0.388
#> GSM447620 5 0.5311 0.6354 0.000 0.092 0.192 0.000 0.668 0.048
#> GSM447627 6 0.6006 0.0000 0.000 0.000 0.332 0.248 0.000 0.420
#> GSM447630 5 0.3265 0.6608 0.000 0.248 0.000 0.004 0.748 0.000
#> GSM447642 1 0.2915 0.7776 0.808 0.000 0.000 0.000 0.008 0.184
#> GSM447649 2 0.0000 0.7006 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447654 4 0.2664 0.5923 0.000 0.000 0.000 0.816 0.184 0.000
#> GSM447655 2 0.0632 0.6988 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM447669 5 0.3314 0.6587 0.000 0.256 0.000 0.004 0.740 0.000
#> GSM447676 1 0.2562 0.7868 0.828 0.000 0.000 0.000 0.000 0.172
#> GSM447678 4 0.2664 0.5923 0.000 0.000 0.000 0.816 0.184 0.000
#> GSM447681 2 0.3386 0.7076 0.000 0.788 0.000 0.016 0.188 0.008
#> GSM447698 2 0.5683 0.5224 0.000 0.564 0.000 0.240 0.188 0.008
#> GSM447713 1 0.0547 0.8196 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM447722 4 0.0000 0.4694 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM447726 5 0.4606 0.6120 0.000 0.268 0.000 0.000 0.656 0.076
#> GSM447735 4 0.3737 0.1143 0.000 0.000 0.000 0.608 0.000 0.392
#> GSM447737 1 0.5088 0.5375 0.632 0.000 0.200 0.000 0.000 0.168
#> GSM447657 2 0.3473 0.7073 0.000 0.780 0.000 0.024 0.192 0.004
#> GSM447674 2 0.3245 0.7093 0.000 0.796 0.000 0.016 0.184 0.004
#> GSM447636 1 0.4022 0.7346 0.708 0.000 0.000 0.000 0.040 0.252
#> GSM447723 1 0.0891 0.8196 0.968 0.000 0.000 0.000 0.008 0.024
#> GSM447699 3 0.7096 -0.3800 0.000 0.000 0.448 0.184 0.120 0.248
#> GSM447708 2 0.5526 0.5673 0.000 0.536 0.000 0.012 0.348 0.104
#> GSM447721 1 0.0547 0.8196 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM447623 1 0.3770 0.7147 0.776 0.000 0.076 0.000 0.000 0.148
#> GSM447621 1 0.4148 0.6846 0.744 0.000 0.108 0.000 0.000 0.148
#> GSM447650 2 0.0632 0.6988 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM447651 2 0.3542 0.6035 0.000 0.788 0.000 0.000 0.052 0.160
#> GSM447653 4 0.3737 0.1143 0.000 0.000 0.000 0.608 0.000 0.392
#> GSM447658 1 0.3133 0.7710 0.780 0.000 0.000 0.000 0.008 0.212
#> GSM447675 4 0.2664 0.5923 0.000 0.000 0.000 0.816 0.184 0.000
#> GSM447680 2 0.5166 0.4475 0.000 0.524 0.000 0.000 0.092 0.384
#> GSM447686 1 0.5228 0.6283 0.648 0.012 0.000 0.000 0.172 0.168
#> GSM447736 3 0.6797 -0.2114 0.000 0.000 0.508 0.184 0.108 0.200
#> GSM447629 2 0.4884 0.6848 0.000 0.660 0.000 0.012 0.248 0.080
#> GSM447648 3 0.2048 0.6181 0.000 0.000 0.880 0.000 0.000 0.120
#> GSM447660 1 0.0713 0.8197 0.972 0.000 0.000 0.000 0.000 0.028
#> GSM447661 2 0.0632 0.6988 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM447663 5 0.3592 0.4928 0.000 0.000 0.344 0.000 0.656 0.000
#> GSM447704 2 0.0291 0.7025 0.000 0.992 0.000 0.000 0.004 0.004
#> GSM447720 5 0.2744 0.6266 0.000 0.016 0.052 0.028 0.888 0.016
#> GSM447652 2 0.0405 0.6997 0.000 0.988 0.000 0.000 0.004 0.008
#> GSM447679 2 0.4402 0.7127 0.000 0.732 0.000 0.016 0.184 0.068
#> GSM447712 1 0.0146 0.8210 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM447664 4 0.4171 0.5382 0.000 0.068 0.000 0.736 0.192 0.004
#> GSM447637 3 0.2048 0.6181 0.000 0.000 0.880 0.000 0.000 0.120
#> GSM447639 4 0.3727 0.1221 0.000 0.000 0.000 0.612 0.000 0.388
#> GSM447615 1 0.4664 0.4026 0.584 0.000 0.364 0.000 0.000 0.052
#> GSM447656 2 0.5701 0.4058 0.000 0.564 0.000 0.008 0.212 0.216
#> GSM447673 2 0.6057 0.2588 0.000 0.412 0.000 0.392 0.188 0.008
#> GSM447719 4 0.5880 -0.0139 0.000 0.000 0.200 0.424 0.000 0.376
#> GSM447706 3 0.0405 0.6384 0.000 0.000 0.988 0.000 0.008 0.004
#> GSM447612 5 0.3989 0.2680 0.000 0.000 0.468 0.000 0.528 0.004
#> GSM447665 2 0.4642 0.0865 0.000 0.592 0.000 0.000 0.356 0.052
#> GSM447677 2 0.3786 0.5845 0.000 0.768 0.000 0.000 0.064 0.168
#> GSM447613 1 0.2378 0.7961 0.848 0.000 0.000 0.000 0.000 0.152
#> GSM447659 4 0.3862 0.1155 0.000 0.000 0.000 0.608 0.004 0.388
#> GSM447662 3 0.2300 0.5788 0.000 0.000 0.856 0.000 0.144 0.000
#> GSM447666 5 0.3578 0.5009 0.000 0.000 0.340 0.000 0.660 0.000
#> GSM447668 2 0.3240 0.6228 0.000 0.812 0.000 0.000 0.040 0.148
#> GSM447682 2 0.3104 0.7098 0.000 0.800 0.000 0.016 0.184 0.000
#> GSM447683 2 0.5637 0.6559 0.000 0.592 0.000 0.016 0.228 0.164
#> GSM447688 4 0.5038 0.4100 0.000 0.176 0.000 0.664 0.152 0.008
#> GSM447702 2 0.0632 0.6988 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM447709 5 0.5076 0.2860 0.000 0.444 0.008 0.000 0.492 0.056
#> GSM447711 1 0.0000 0.8209 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447715 1 0.6086 0.2913 0.416 0.012 0.000 0.000 0.396 0.176
#> GSM447693 3 0.0713 0.6343 0.000 0.000 0.972 0.000 0.000 0.028
#> GSM447611 4 0.2697 0.5917 0.000 0.000 0.000 0.812 0.188 0.000
#> GSM447672 2 0.0551 0.7008 0.000 0.984 0.000 0.004 0.004 0.008
#> GSM447703 2 0.6012 0.3597 0.000 0.460 0.000 0.344 0.188 0.008
#> GSM447727 1 0.4449 0.7354 0.696 0.000 0.000 0.000 0.088 0.216
#> GSM447638 5 0.5189 0.4566 0.004 0.068 0.004 0.000 0.532 0.392
#> GSM447670 1 0.5088 0.6567 0.628 0.000 0.152 0.000 0.000 0.220
#> GSM447700 5 0.6572 0.4512 0.000 0.060 0.056 0.196 0.596 0.092
#> GSM447738 2 0.5834 0.4723 0.000 0.528 0.000 0.276 0.188 0.008
#> GSM447739 1 0.0547 0.8196 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM447617 1 0.4893 0.5829 0.660 0.000 0.172 0.000 0.000 0.168
#> GSM447628 4 0.2664 0.5923 0.000 0.000 0.000 0.816 0.184 0.000
#> GSM447632 2 0.5721 0.5109 0.000 0.556 0.000 0.248 0.188 0.008
#> GSM447619 3 0.1663 0.6210 0.000 0.000 0.912 0.000 0.088 0.000
#> GSM447643 1 0.5077 0.6219 0.564 0.000 0.000 0.000 0.092 0.344
#> GSM447724 4 0.3862 0.1155 0.000 0.000 0.000 0.608 0.004 0.388
#> GSM447728 2 0.4497 0.7053 0.000 0.712 0.000 0.016 0.212 0.060
#> GSM447610 4 0.5984 0.1201 0.344 0.000 0.000 0.420 0.000 0.236
#> GSM447633 5 0.3971 0.6356 0.000 0.068 0.184 0.000 0.748 0.000
#> GSM447634 5 0.6201 0.2211 0.000 0.004 0.300 0.152 0.516 0.028
#> GSM447622 3 0.4603 0.4301 0.000 0.000 0.696 0.148 0.000 0.156
#> GSM447667 2 0.3998 0.7069 0.000 0.728 0.000 0.016 0.236 0.020
#> GSM447687 2 0.6034 0.3284 0.000 0.444 0.000 0.360 0.188 0.008
#> GSM447695 3 0.5531 -0.3942 0.000 0.000 0.552 0.184 0.000 0.264
#> GSM447696 1 0.0632 0.8190 0.976 0.000 0.000 0.000 0.000 0.024
#> GSM447697 1 0.3351 0.7591 0.712 0.000 0.000 0.000 0.000 0.288
#> GSM447714 3 0.2003 0.6005 0.000 0.000 0.884 0.000 0.116 0.000
#> GSM447717 1 0.0713 0.8197 0.972 0.000 0.000 0.000 0.000 0.028
#> GSM447725 1 0.0363 0.8209 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM447729 4 0.2697 0.5917 0.000 0.000 0.000 0.812 0.188 0.000
#> GSM447644 5 0.3175 0.6552 0.000 0.256 0.000 0.000 0.744 0.000
#> GSM447710 3 0.1141 0.6323 0.000 0.000 0.948 0.000 0.052 0.000
#> GSM447614 4 0.3737 0.1143 0.000 0.000 0.000 0.608 0.000 0.392
#> GSM447685 2 0.5610 0.6542 0.000 0.588 0.000 0.012 0.228 0.172
#> GSM447690 1 0.0547 0.8196 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM447730 2 0.0405 0.6997 0.000 0.988 0.000 0.000 0.004 0.008
#> GSM447646 4 0.2664 0.5923 0.000 0.000 0.000 0.816 0.184 0.000
#> GSM447689 5 0.3782 0.3862 0.000 0.000 0.412 0.000 0.588 0.000
#> GSM447635 5 0.3322 0.5696 0.000 0.104 0.000 0.012 0.832 0.052
#> GSM447641 1 0.0260 0.8211 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM447716 2 0.5898 0.4499 0.000 0.512 0.000 0.288 0.192 0.008
#> GSM447718 5 0.4428 0.6504 0.000 0.220 0.084 0.000 0.696 0.000
#> GSM447616 3 0.5063 0.3533 0.008 0.000 0.660 0.184 0.000 0.148
#> GSM447626 3 0.3659 0.2052 0.000 0.000 0.636 0.000 0.364 0.000
#> GSM447640 2 0.3706 0.7123 0.000 0.776 0.000 0.016 0.184 0.024
#> GSM447734 3 0.0767 0.6348 0.000 0.000 0.976 0.012 0.004 0.008
#> GSM447692 3 0.6558 -0.2813 0.044 0.000 0.444 0.184 0.000 0.328
#> GSM447647 4 0.3764 0.5532 0.000 0.088 0.000 0.796 0.108 0.008
#> GSM447624 3 0.3470 0.5543 0.052 0.000 0.796 0.000 0.000 0.152
#> GSM447625 3 0.5552 0.3587 0.000 0.000 0.668 0.116 0.132 0.084
#> GSM447707 2 0.0146 0.7003 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM447732 3 0.1765 0.6147 0.000 0.000 0.904 0.000 0.096 0.000
#> GSM447684 5 0.3622 0.6011 0.000 0.004 0.024 0.000 0.760 0.212
#> GSM447731 4 0.6292 0.2900 0.000 0.200 0.000 0.564 0.068 0.168
#> GSM447705 5 0.3409 0.5398 0.000 0.000 0.300 0.000 0.700 0.000
#> GSM447631 3 0.1765 0.6256 0.000 0.000 0.904 0.000 0.000 0.096
#> GSM447701 2 0.3227 0.6193 0.000 0.828 0.000 0.000 0.088 0.084
#> GSM447645 3 0.2092 0.6159 0.000 0.000 0.876 0.000 0.000 0.124
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)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
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 gender(p) individual(p) disease.state(p) other(p) k
#> SD:mclust 114 0.788 0.760 0.3326 0.0403 2
#> SD:mclust 125 0.402 0.213 0.1042 0.3047 3
#> SD:mclust 123 0.246 0.264 0.0465 0.1432 4
#> SD:mclust 116 0.633 0.409 0.5922 0.0246 5
#> SD:mclust 90 0.526 0.141 0.2222 0.1408 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "NMF"]
# you can also extract it by
# res = res_list["SD:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 130 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 0.846 0.908 0.962 0.5022 0.497 0.497
#> 3 3 0.535 0.621 0.783 0.3046 0.758 0.552
#> 4 4 0.790 0.831 0.915 0.1433 0.779 0.455
#> 5 5 0.803 0.792 0.899 0.0604 0.882 0.584
#> 6 6 0.732 0.595 0.790 0.0409 0.875 0.496
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
#> GSM447671 2 0.0000 0.952 0.000 1.000
#> GSM447694 1 0.0000 0.966 1.000 0.000
#> GSM447618 2 0.0000 0.952 0.000 1.000
#> GSM447691 2 0.0000 0.952 0.000 1.000
#> GSM447733 2 0.9710 0.336 0.400 0.600
#> GSM447620 2 0.0000 0.952 0.000 1.000
#> GSM447627 1 0.0000 0.966 1.000 0.000
#> GSM447630 2 0.8207 0.647 0.256 0.744
#> GSM447642 1 0.0000 0.966 1.000 0.000
#> GSM447649 2 0.0000 0.952 0.000 1.000
#> GSM447654 2 0.0376 0.949 0.004 0.996
#> GSM447655 2 0.0000 0.952 0.000 1.000
#> GSM447669 2 0.0000 0.952 0.000 1.000
#> GSM447676 1 0.0000 0.966 1.000 0.000
#> GSM447678 2 0.0000 0.952 0.000 1.000
#> GSM447681 2 0.0000 0.952 0.000 1.000
#> GSM447698 2 0.0000 0.952 0.000 1.000
#> GSM447713 1 0.0000 0.966 1.000 0.000
#> GSM447722 2 0.0000 0.952 0.000 1.000
#> GSM447726 2 0.9635 0.395 0.388 0.612
#> GSM447735 1 0.0000 0.966 1.000 0.000
#> GSM447737 1 0.0000 0.966 1.000 0.000
#> GSM447657 2 0.0000 0.952 0.000 1.000
#> GSM447674 2 0.0000 0.952 0.000 1.000
#> GSM447636 1 0.0000 0.966 1.000 0.000
#> GSM447723 1 0.0000 0.966 1.000 0.000
#> GSM447699 1 0.7219 0.746 0.800 0.200
#> GSM447708 2 0.0000 0.952 0.000 1.000
#> GSM447721 1 0.0000 0.966 1.000 0.000
#> GSM447623 1 0.0000 0.966 1.000 0.000
#> GSM447621 1 0.0000 0.966 1.000 0.000
#> GSM447650 2 0.0000 0.952 0.000 1.000
#> GSM447651 2 0.0000 0.952 0.000 1.000
#> GSM447653 1 0.0000 0.966 1.000 0.000
#> GSM447658 1 0.0000 0.966 1.000 0.000
#> GSM447675 2 0.0000 0.952 0.000 1.000
#> GSM447680 2 0.1184 0.940 0.016 0.984
#> GSM447686 2 0.9635 0.395 0.388 0.612
#> GSM447736 1 0.0000 0.966 1.000 0.000
#> GSM447629 2 0.0672 0.946 0.008 0.992
#> GSM447648 1 0.0000 0.966 1.000 0.000
#> GSM447660 1 0.0000 0.966 1.000 0.000
#> GSM447661 2 0.0000 0.952 0.000 1.000
#> GSM447663 1 0.1633 0.948 0.976 0.024
#> GSM447704 2 0.0000 0.952 0.000 1.000
#> GSM447720 1 0.0000 0.966 1.000 0.000
#> GSM447652 2 0.0000 0.952 0.000 1.000
#> GSM447679 2 0.0000 0.952 0.000 1.000
#> GSM447712 1 0.0000 0.966 1.000 0.000
#> GSM447664 2 0.5842 0.819 0.140 0.860
#> GSM447637 1 0.0000 0.966 1.000 0.000
#> GSM447639 1 0.8016 0.679 0.756 0.244
#> GSM447615 1 0.0000 0.966 1.000 0.000
#> GSM447656 2 0.6438 0.790 0.164 0.836
#> GSM447673 2 0.0000 0.952 0.000 1.000
#> GSM447719 1 0.0000 0.966 1.000 0.000
#> GSM447706 1 0.0000 0.966 1.000 0.000
#> GSM447612 1 0.7219 0.746 0.800 0.200
#> GSM447665 2 0.0000 0.952 0.000 1.000
#> GSM447677 2 0.0000 0.952 0.000 1.000
#> GSM447613 1 0.0000 0.966 1.000 0.000
#> GSM447659 1 0.7219 0.746 0.800 0.200
#> GSM447662 1 0.2043 0.941 0.968 0.032
#> GSM447666 1 0.0000 0.966 1.000 0.000
#> GSM447668 2 0.0000 0.952 0.000 1.000
#> GSM447682 2 0.0000 0.952 0.000 1.000
#> GSM447683 2 0.0000 0.952 0.000 1.000
#> GSM447688 2 0.0000 0.952 0.000 1.000
#> GSM447702 2 0.0000 0.952 0.000 1.000
#> GSM447709 2 0.0000 0.952 0.000 1.000
#> GSM447711 1 0.0000 0.966 1.000 0.000
#> GSM447715 1 0.0672 0.960 0.992 0.008
#> GSM447693 1 0.0000 0.966 1.000 0.000
#> GSM447611 1 0.9998 -0.033 0.508 0.492
#> GSM447672 2 0.0000 0.952 0.000 1.000
#> GSM447703 2 0.0000 0.952 0.000 1.000
#> GSM447727 1 0.0000 0.966 1.000 0.000
#> GSM447638 1 0.4815 0.864 0.896 0.104
#> GSM447670 1 0.0000 0.966 1.000 0.000
#> GSM447700 2 0.0000 0.952 0.000 1.000
#> GSM447738 2 0.0000 0.952 0.000 1.000
#> GSM447739 1 0.0000 0.966 1.000 0.000
#> GSM447617 1 0.0000 0.966 1.000 0.000
#> GSM447628 2 0.0000 0.952 0.000 1.000
#> GSM447632 2 0.0000 0.952 0.000 1.000
#> GSM447619 1 0.0000 0.966 1.000 0.000
#> GSM447643 1 0.7745 0.690 0.772 0.228
#> GSM447724 2 0.9635 0.369 0.388 0.612
#> GSM447728 2 0.0000 0.952 0.000 1.000
#> GSM447610 1 0.0000 0.966 1.000 0.000
#> GSM447633 2 0.0000 0.952 0.000 1.000
#> GSM447634 1 0.0000 0.966 1.000 0.000
#> GSM447622 1 0.0000 0.966 1.000 0.000
#> GSM447667 2 0.7299 0.737 0.204 0.796
#> GSM447687 2 0.0000 0.952 0.000 1.000
#> GSM447695 1 0.0000 0.966 1.000 0.000
#> GSM447696 1 0.0000 0.966 1.000 0.000
#> GSM447697 1 0.0000 0.966 1.000 0.000
#> GSM447714 1 0.1184 0.954 0.984 0.016
#> GSM447717 1 0.0000 0.966 1.000 0.000
#> GSM447725 1 0.0000 0.966 1.000 0.000
#> GSM447729 2 0.0376 0.949 0.004 0.996
#> GSM447644 2 0.0000 0.952 0.000 1.000
#> GSM447710 1 0.0000 0.966 1.000 0.000
#> GSM447614 1 0.0000 0.966 1.000 0.000
#> GSM447685 2 0.0000 0.952 0.000 1.000
#> GSM447690 1 0.0000 0.966 1.000 0.000
#> GSM447730 2 0.0000 0.952 0.000 1.000
#> GSM447646 2 0.0000 0.952 0.000 1.000
#> GSM447689 1 0.0000 0.966 1.000 0.000
#> GSM447635 1 0.9286 0.474 0.656 0.344
#> GSM447641 1 0.0000 0.966 1.000 0.000
#> GSM447716 2 0.0000 0.952 0.000 1.000
#> GSM447718 1 0.1414 0.951 0.980 0.020
#> GSM447616 1 0.0000 0.966 1.000 0.000
#> GSM447626 1 0.0000 0.966 1.000 0.000
#> GSM447640 2 0.0000 0.952 0.000 1.000
#> GSM447734 1 0.0672 0.960 0.992 0.008
#> GSM447692 1 0.0000 0.966 1.000 0.000
#> GSM447647 2 0.0000 0.952 0.000 1.000
#> GSM447624 1 0.0000 0.966 1.000 0.000
#> GSM447625 1 0.0000 0.966 1.000 0.000
#> GSM447707 2 0.0000 0.952 0.000 1.000
#> GSM447732 1 0.0000 0.966 1.000 0.000
#> GSM447684 1 0.0000 0.966 1.000 0.000
#> GSM447731 2 0.1633 0.933 0.024 0.976
#> GSM447705 2 0.9732 0.324 0.404 0.596
#> GSM447631 1 0.0000 0.966 1.000 0.000
#> GSM447701 2 0.0000 0.952 0.000 1.000
#> GSM447645 1 0.0000 0.966 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM447671 3 0.6225 0.2284 0.000 0.432 0.568
#> GSM447694 3 0.5882 0.7285 0.348 0.000 0.652
#> GSM447618 2 0.0000 0.8273 0.000 1.000 0.000
#> GSM447691 2 0.0747 0.8199 0.000 0.984 0.016
#> GSM447733 3 0.1950 0.4235 0.008 0.040 0.952
#> GSM447620 2 0.6308 -0.0281 0.000 0.508 0.492
#> GSM447627 3 0.5968 0.6319 0.364 0.000 0.636
#> GSM447630 2 0.8231 0.3435 0.136 0.628 0.236
#> GSM447642 1 0.0237 0.7592 0.996 0.000 0.004
#> GSM447649 2 0.0000 0.8273 0.000 1.000 0.000
#> GSM447654 2 0.9229 0.4970 0.164 0.488 0.348
#> GSM447655 2 0.0000 0.8273 0.000 1.000 0.000
#> GSM447669 2 0.4555 0.6441 0.000 0.800 0.200
#> GSM447676 1 0.0000 0.7599 1.000 0.000 0.000
#> GSM447678 2 0.6427 0.6538 0.012 0.640 0.348
#> GSM447681 2 0.0000 0.8273 0.000 1.000 0.000
#> GSM447698 2 0.3879 0.7788 0.000 0.848 0.152
#> GSM447713 1 0.0000 0.7599 1.000 0.000 0.000
#> GSM447722 2 0.5905 0.6591 0.000 0.648 0.352
#> GSM447726 2 0.7634 0.4975 0.100 0.668 0.232
#> GSM447735 1 0.6079 0.4605 0.612 0.000 0.388
#> GSM447737 1 0.0424 0.7575 0.992 0.000 0.008
#> GSM447657 2 0.0424 0.8259 0.000 0.992 0.008
#> GSM447674 2 0.0000 0.8273 0.000 1.000 0.000
#> GSM447636 1 0.0000 0.7599 1.000 0.000 0.000
#> GSM447723 1 0.0237 0.7592 0.996 0.000 0.004
#> GSM447699 3 0.8162 0.6470 0.348 0.084 0.568
#> GSM447708 2 0.0000 0.8273 0.000 1.000 0.000
#> GSM447721 1 0.0237 0.7592 0.996 0.000 0.004
#> GSM447623 1 0.0424 0.7569 0.992 0.000 0.008
#> GSM447621 1 0.0892 0.7482 0.980 0.000 0.020
#> GSM447650 2 0.0000 0.8273 0.000 1.000 0.000
#> GSM447651 2 0.0424 0.8242 0.000 0.992 0.008
#> GSM447653 1 0.5905 0.4776 0.648 0.000 0.352
#> GSM447658 1 0.0000 0.7599 1.000 0.000 0.000
#> GSM447675 2 0.8872 0.5400 0.132 0.520 0.348
#> GSM447680 2 0.2165 0.7942 0.064 0.936 0.000
#> GSM447686 1 0.7034 0.4374 0.668 0.284 0.048
#> GSM447736 3 0.5882 0.7285 0.348 0.000 0.652
#> GSM447629 2 0.1411 0.8111 0.036 0.964 0.000
#> GSM447648 3 0.5882 0.7285 0.348 0.000 0.652
#> GSM447660 1 0.0000 0.7599 1.000 0.000 0.000
#> GSM447661 2 0.0000 0.8273 0.000 1.000 0.000
#> GSM447663 3 0.5882 0.7285 0.348 0.000 0.652
#> GSM447704 2 0.0000 0.8273 0.000 1.000 0.000
#> GSM447720 1 0.6793 -0.3292 0.536 0.012 0.452
#> GSM447652 2 0.0000 0.8273 0.000 1.000 0.000
#> GSM447679 2 0.0000 0.8273 0.000 1.000 0.000
#> GSM447712 1 0.0000 0.7599 1.000 0.000 0.000
#> GSM447664 3 0.9987 -0.2954 0.344 0.308 0.348
#> GSM447637 3 0.5882 0.7285 0.348 0.000 0.652
#> GSM447639 2 0.8524 0.4690 0.092 0.460 0.448
#> GSM447615 1 0.4002 0.5779 0.840 0.000 0.160
#> GSM447656 2 0.3340 0.7518 0.120 0.880 0.000
#> GSM447673 2 0.5882 0.6616 0.000 0.652 0.348
#> GSM447719 3 0.6204 -0.2385 0.424 0.000 0.576
#> GSM447706 3 0.5882 0.7285 0.348 0.000 0.652
#> GSM447612 3 0.5882 0.7285 0.348 0.000 0.652
#> GSM447665 2 0.0237 0.8256 0.000 0.996 0.004
#> GSM447677 2 0.0000 0.8273 0.000 1.000 0.000
#> GSM447613 1 0.0237 0.7592 0.996 0.000 0.004
#> GSM447659 3 0.0237 0.4510 0.004 0.000 0.996
#> GSM447662 3 0.5882 0.7285 0.348 0.000 0.652
#> GSM447666 3 0.7984 0.6285 0.216 0.132 0.652
#> GSM447668 2 0.0000 0.8273 0.000 1.000 0.000
#> GSM447682 2 0.0000 0.8273 0.000 1.000 0.000
#> GSM447683 2 0.0000 0.8273 0.000 1.000 0.000
#> GSM447688 2 0.5882 0.6616 0.000 0.652 0.348
#> GSM447702 2 0.0000 0.8273 0.000 1.000 0.000
#> GSM447709 2 0.5497 0.5110 0.000 0.708 0.292
#> GSM447711 1 0.0000 0.7599 1.000 0.000 0.000
#> GSM447715 1 0.3482 0.6584 0.872 0.128 0.000
#> GSM447693 3 0.5882 0.7285 0.348 0.000 0.652
#> GSM447611 1 0.6104 0.4763 0.648 0.004 0.348
#> GSM447672 2 0.0000 0.8273 0.000 1.000 0.000
#> GSM447703 2 0.4121 0.7713 0.000 0.832 0.168
#> GSM447727 1 0.2066 0.7114 0.940 0.000 0.060
#> GSM447638 1 0.6688 0.2571 0.580 0.408 0.012
#> GSM447670 1 0.4555 0.4943 0.800 0.000 0.200
#> GSM447700 2 0.5785 0.4021 0.000 0.668 0.332
#> GSM447738 2 0.4178 0.7695 0.000 0.828 0.172
#> GSM447739 1 0.0000 0.7599 1.000 0.000 0.000
#> GSM447617 1 0.1643 0.7277 0.956 0.000 0.044
#> GSM447628 2 0.6275 0.6564 0.008 0.644 0.348
#> GSM447632 2 0.3816 0.7805 0.000 0.852 0.148
#> GSM447619 3 0.5882 0.7285 0.348 0.000 0.652
#> GSM447643 1 0.5016 0.5259 0.760 0.240 0.000
#> GSM447724 3 0.0237 0.4526 0.000 0.004 0.996
#> GSM447728 2 0.0000 0.8273 0.000 1.000 0.000
#> GSM447610 1 0.5882 0.4789 0.652 0.000 0.348
#> GSM447633 3 0.5926 0.3857 0.000 0.356 0.644
#> GSM447634 1 0.5948 0.0296 0.640 0.000 0.360
#> GSM447622 3 0.5882 0.7285 0.348 0.000 0.652
#> GSM447667 2 0.5420 0.6072 0.240 0.752 0.008
#> GSM447687 2 0.4796 0.7436 0.000 0.780 0.220
#> GSM447695 1 0.6244 -0.2664 0.560 0.000 0.440
#> GSM447696 1 0.0000 0.7599 1.000 0.000 0.000
#> GSM447697 1 0.0237 0.7592 0.996 0.000 0.004
#> GSM447714 3 0.5882 0.7285 0.348 0.000 0.652
#> GSM447717 1 0.0000 0.7599 1.000 0.000 0.000
#> GSM447725 1 0.2261 0.7183 0.932 0.000 0.068
#> GSM447729 2 0.9745 0.3825 0.232 0.420 0.348
#> GSM447644 3 0.6286 0.1590 0.000 0.464 0.536
#> GSM447710 3 0.5882 0.7285 0.348 0.000 0.652
#> GSM447614 1 0.6095 0.4641 0.608 0.000 0.392
#> GSM447685 2 0.0000 0.8273 0.000 1.000 0.000
#> GSM447690 1 0.2711 0.7028 0.912 0.000 0.088
#> GSM447730 2 0.0000 0.8273 0.000 1.000 0.000
#> GSM447646 2 0.6104 0.6591 0.004 0.648 0.348
#> GSM447689 3 0.5882 0.7285 0.348 0.000 0.652
#> GSM447635 2 0.6422 0.4483 0.324 0.660 0.016
#> GSM447641 1 0.0237 0.7592 0.996 0.000 0.004
#> GSM447716 2 0.8700 0.5657 0.120 0.536 0.344
#> GSM447718 3 0.7867 0.6682 0.348 0.068 0.584
#> GSM447616 3 0.6267 0.5439 0.452 0.000 0.548
#> GSM447626 3 0.5882 0.7285 0.348 0.000 0.652
#> GSM447640 2 0.0000 0.8273 0.000 1.000 0.000
#> GSM447734 3 0.5882 0.7285 0.348 0.000 0.652
#> GSM447692 1 0.3412 0.6358 0.876 0.000 0.124
#> GSM447647 2 0.6104 0.6591 0.004 0.648 0.348
#> GSM447624 1 0.6095 -0.0985 0.608 0.000 0.392
#> GSM447625 3 0.5882 0.7285 0.348 0.000 0.652
#> GSM447707 2 0.0000 0.8273 0.000 1.000 0.000
#> GSM447732 3 0.5882 0.7285 0.348 0.000 0.652
#> GSM447684 1 0.6302 -0.3934 0.520 0.000 0.480
#> GSM447731 3 0.6912 -0.2484 0.028 0.344 0.628
#> GSM447705 3 0.7748 0.5279 0.096 0.252 0.652
#> GSM447631 3 0.5882 0.7285 0.348 0.000 0.652
#> GSM447701 2 0.0000 0.8273 0.000 1.000 0.000
#> GSM447645 3 0.5882 0.7285 0.348 0.000 0.652
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM447671 3 0.6469 0.63448 0.000 0.164 0.644 0.192
#> GSM447694 3 0.2704 0.84930 0.000 0.000 0.876 0.124
#> GSM447618 2 0.4948 0.26972 0.000 0.560 0.000 0.440
#> GSM447691 2 0.2216 0.84208 0.000 0.908 0.000 0.092
#> GSM447733 4 0.0188 0.82719 0.000 0.000 0.004 0.996
#> GSM447620 2 0.4925 0.36036 0.000 0.572 0.428 0.000
#> GSM447627 3 0.3444 0.80762 0.000 0.000 0.816 0.184
#> GSM447630 2 0.4134 0.60673 0.000 0.740 0.260 0.000
#> GSM447642 1 0.0000 0.99091 1.000 0.000 0.000 0.000
#> GSM447649 2 0.0000 0.90400 0.000 1.000 0.000 0.000
#> GSM447654 4 0.3570 0.82747 0.048 0.092 0.000 0.860
#> GSM447655 2 0.0000 0.90400 0.000 1.000 0.000 0.000
#> GSM447669 2 0.1474 0.87096 0.000 0.948 0.052 0.000
#> GSM447676 1 0.0000 0.99091 1.000 0.000 0.000 0.000
#> GSM447678 4 0.0000 0.82755 0.000 0.000 0.000 1.000
#> GSM447681 2 0.0336 0.90050 0.000 0.992 0.000 0.008
#> GSM447698 4 0.1022 0.83209 0.000 0.032 0.000 0.968
#> GSM447713 1 0.0000 0.99091 1.000 0.000 0.000 0.000
#> GSM447722 4 0.0000 0.82755 0.000 0.000 0.000 1.000
#> GSM447726 2 0.2081 0.84986 0.000 0.916 0.084 0.000
#> GSM447735 4 0.0188 0.82664 0.000 0.000 0.004 0.996
#> GSM447737 1 0.2722 0.89472 0.904 0.000 0.064 0.032
#> GSM447657 2 0.0336 0.90025 0.000 0.992 0.000 0.008
#> GSM447674 2 0.0000 0.90400 0.000 1.000 0.000 0.000
#> GSM447636 1 0.0000 0.99091 1.000 0.000 0.000 0.000
#> GSM447723 1 0.0000 0.99091 1.000 0.000 0.000 0.000
#> GSM447699 3 0.4040 0.74289 0.000 0.000 0.752 0.248
#> GSM447708 2 0.0469 0.89914 0.000 0.988 0.000 0.012
#> GSM447721 1 0.0000 0.99091 1.000 0.000 0.000 0.000
#> GSM447623 1 0.0000 0.99091 1.000 0.000 0.000 0.000
#> GSM447621 1 0.0000 0.99091 1.000 0.000 0.000 0.000
#> GSM447650 2 0.0000 0.90400 0.000 1.000 0.000 0.000
#> GSM447651 2 0.0469 0.89944 0.000 0.988 0.012 0.000
#> GSM447653 4 0.1867 0.81156 0.072 0.000 0.000 0.928
#> GSM447658 1 0.0000 0.99091 1.000 0.000 0.000 0.000
#> GSM447675 4 0.0000 0.82755 0.000 0.000 0.000 1.000
#> GSM447680 2 0.0188 0.90247 0.004 0.996 0.000 0.000
#> GSM447686 1 0.0000 0.99091 1.000 0.000 0.000 0.000
#> GSM447736 3 0.2589 0.85375 0.000 0.000 0.884 0.116
#> GSM447629 2 0.2345 0.82694 0.100 0.900 0.000 0.000
#> GSM447648 3 0.0000 0.90074 0.000 0.000 1.000 0.000
#> GSM447660 1 0.0000 0.99091 1.000 0.000 0.000 0.000
#> GSM447661 2 0.0000 0.90400 0.000 1.000 0.000 0.000
#> GSM447663 3 0.0336 0.89737 0.000 0.008 0.992 0.000
#> GSM447704 2 0.0000 0.90400 0.000 1.000 0.000 0.000
#> GSM447720 3 0.4426 0.77719 0.168 0.004 0.796 0.032
#> GSM447652 2 0.0000 0.90400 0.000 1.000 0.000 0.000
#> GSM447679 2 0.0000 0.90400 0.000 1.000 0.000 0.000
#> GSM447712 1 0.0000 0.99091 1.000 0.000 0.000 0.000
#> GSM447664 4 0.4019 0.72125 0.196 0.012 0.000 0.792
#> GSM447637 3 0.0000 0.90074 0.000 0.000 1.000 0.000
#> GSM447639 4 0.0000 0.82755 0.000 0.000 0.000 1.000
#> GSM447615 1 0.1940 0.91288 0.924 0.000 0.076 0.000
#> GSM447656 2 0.0707 0.89362 0.020 0.980 0.000 0.000
#> GSM447673 4 0.2814 0.81880 0.000 0.132 0.000 0.868
#> GSM447719 4 0.5300 0.36625 0.012 0.000 0.408 0.580
#> GSM447706 3 0.0000 0.90074 0.000 0.000 1.000 0.000
#> GSM447612 3 0.0000 0.90074 0.000 0.000 1.000 0.000
#> GSM447665 2 0.0000 0.90400 0.000 1.000 0.000 0.000
#> GSM447677 2 0.0000 0.90400 0.000 1.000 0.000 0.000
#> GSM447613 1 0.0000 0.99091 1.000 0.000 0.000 0.000
#> GSM447659 4 0.4972 0.03306 0.000 0.000 0.456 0.544
#> GSM447662 3 0.0000 0.90074 0.000 0.000 1.000 0.000
#> GSM447666 3 0.0188 0.89866 0.000 0.004 0.996 0.000
#> GSM447668 2 0.0000 0.90400 0.000 1.000 0.000 0.000
#> GSM447682 2 0.0000 0.90400 0.000 1.000 0.000 0.000
#> GSM447683 2 0.0000 0.90400 0.000 1.000 0.000 0.000
#> GSM447688 4 0.1211 0.83531 0.000 0.040 0.000 0.960
#> GSM447702 2 0.0000 0.90400 0.000 1.000 0.000 0.000
#> GSM447709 2 0.3528 0.75481 0.000 0.808 0.192 0.000
#> GSM447711 1 0.0000 0.99091 1.000 0.000 0.000 0.000
#> GSM447715 1 0.1022 0.95640 0.968 0.032 0.000 0.000
#> GSM447693 3 0.0000 0.90074 0.000 0.000 1.000 0.000
#> GSM447611 4 0.3356 0.74443 0.176 0.000 0.000 0.824
#> GSM447672 2 0.0000 0.90400 0.000 1.000 0.000 0.000
#> GSM447703 4 0.3528 0.77683 0.000 0.192 0.000 0.808
#> GSM447727 1 0.0000 0.99091 1.000 0.000 0.000 0.000
#> GSM447638 2 0.3751 0.72739 0.196 0.800 0.004 0.000
#> GSM447670 1 0.0000 0.99091 1.000 0.000 0.000 0.000
#> GSM447700 3 0.4866 0.46855 0.000 0.000 0.596 0.404
#> GSM447738 4 0.3444 0.78096 0.000 0.184 0.000 0.816
#> GSM447739 1 0.0000 0.99091 1.000 0.000 0.000 0.000
#> GSM447617 1 0.0592 0.97595 0.984 0.000 0.016 0.000
#> GSM447628 4 0.2814 0.81880 0.000 0.132 0.000 0.868
#> GSM447632 4 0.4907 0.35935 0.000 0.420 0.000 0.580
#> GSM447619 3 0.0000 0.90074 0.000 0.000 1.000 0.000
#> GSM447643 1 0.0000 0.99091 1.000 0.000 0.000 0.000
#> GSM447724 4 0.0592 0.82224 0.000 0.000 0.016 0.984
#> GSM447728 2 0.0336 0.90028 0.000 0.992 0.000 0.008
#> GSM447610 4 0.4776 0.42288 0.376 0.000 0.000 0.624
#> GSM447633 2 0.4941 0.34056 0.000 0.564 0.436 0.000
#> GSM447634 3 0.5559 0.68021 0.240 0.000 0.696 0.064
#> GSM447622 3 0.1792 0.87778 0.000 0.000 0.932 0.068
#> GSM447667 2 0.4761 0.44744 0.372 0.628 0.000 0.000
#> GSM447687 4 0.3266 0.79689 0.000 0.168 0.000 0.832
#> GSM447695 3 0.3945 0.77718 0.004 0.000 0.780 0.216
#> GSM447696 1 0.0000 0.99091 1.000 0.000 0.000 0.000
#> GSM447697 1 0.0000 0.99091 1.000 0.000 0.000 0.000
#> GSM447714 3 0.0000 0.90074 0.000 0.000 1.000 0.000
#> GSM447717 1 0.0000 0.99091 1.000 0.000 0.000 0.000
#> GSM447725 1 0.0000 0.99091 1.000 0.000 0.000 0.000
#> GSM447729 4 0.3674 0.82484 0.044 0.104 0.000 0.852
#> GSM447644 2 0.1716 0.86795 0.000 0.936 0.064 0.000
#> GSM447710 3 0.0000 0.90074 0.000 0.000 1.000 0.000
#> GSM447614 4 0.0895 0.82081 0.004 0.000 0.020 0.976
#> GSM447685 2 0.0000 0.90400 0.000 1.000 0.000 0.000
#> GSM447690 1 0.0000 0.99091 1.000 0.000 0.000 0.000
#> GSM447730 2 0.0000 0.90400 0.000 1.000 0.000 0.000
#> GSM447646 4 0.2530 0.82714 0.000 0.112 0.000 0.888
#> GSM447689 3 0.0000 0.90074 0.000 0.000 1.000 0.000
#> GSM447635 4 0.5497 0.00877 0.000 0.460 0.016 0.524
#> GSM447641 1 0.0000 0.99091 1.000 0.000 0.000 0.000
#> GSM447716 4 0.2224 0.83640 0.032 0.040 0.000 0.928
#> GSM447718 3 0.3024 0.79070 0.000 0.148 0.852 0.000
#> GSM447616 3 0.3796 0.84171 0.056 0.000 0.848 0.096
#> GSM447626 3 0.0000 0.90074 0.000 0.000 1.000 0.000
#> GSM447640 2 0.0000 0.90400 0.000 1.000 0.000 0.000
#> GSM447734 3 0.0707 0.89575 0.000 0.000 0.980 0.020
#> GSM447692 3 0.6063 0.69627 0.196 0.000 0.680 0.124
#> GSM447647 4 0.2704 0.82224 0.000 0.124 0.000 0.876
#> GSM447624 3 0.4040 0.69940 0.248 0.000 0.752 0.000
#> GSM447625 3 0.0336 0.89913 0.000 0.000 0.992 0.008
#> GSM447707 2 0.0000 0.90400 0.000 1.000 0.000 0.000
#> GSM447732 3 0.0000 0.90074 0.000 0.000 1.000 0.000
#> GSM447684 2 0.6575 0.24118 0.412 0.508 0.080 0.000
#> GSM447731 4 0.4988 0.66855 0.000 0.036 0.236 0.728
#> GSM447705 3 0.0000 0.90074 0.000 0.000 1.000 0.000
#> GSM447631 3 0.0000 0.90074 0.000 0.000 1.000 0.000
#> GSM447701 2 0.0469 0.89938 0.000 0.988 0.012 0.000
#> GSM447645 3 0.0000 0.90074 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
#> GSM447671 3 0.0865 0.8809 0.000 0.024 0.972 0.000 0.004
#> GSM447694 3 0.0162 0.8845 0.000 0.000 0.996 0.000 0.004
#> GSM447618 3 0.2669 0.8302 0.000 0.020 0.876 0.104 0.000
#> GSM447691 2 0.4420 0.1365 0.000 0.548 0.448 0.004 0.000
#> GSM447733 4 0.1282 0.7764 0.000 0.000 0.004 0.952 0.044
#> GSM447620 5 0.1197 0.8201 0.000 0.048 0.000 0.000 0.952
#> GSM447627 3 0.1894 0.8501 0.000 0.000 0.920 0.072 0.008
#> GSM447630 3 0.3452 0.7040 0.000 0.244 0.756 0.000 0.000
#> GSM447642 1 0.0000 0.9613 1.000 0.000 0.000 0.000 0.000
#> GSM447649 2 0.3355 0.8021 0.000 0.832 0.000 0.132 0.036
#> GSM447654 4 0.0880 0.7798 0.000 0.000 0.000 0.968 0.032
#> GSM447655 2 0.0000 0.9070 0.000 1.000 0.000 0.000 0.000
#> GSM447669 3 0.3452 0.7069 0.000 0.244 0.756 0.000 0.000
#> GSM447676 1 0.0609 0.9474 0.980 0.000 0.000 0.000 0.020
#> GSM447678 4 0.2389 0.7387 0.000 0.004 0.116 0.880 0.000
#> GSM447681 2 0.1043 0.8923 0.000 0.960 0.000 0.040 0.000
#> GSM447698 4 0.6789 0.1374 0.000 0.348 0.284 0.368 0.000
#> GSM447713 1 0.0000 0.9613 1.000 0.000 0.000 0.000 0.000
#> GSM447722 3 0.2286 0.8268 0.000 0.004 0.888 0.108 0.000
#> GSM447726 2 0.0290 0.9061 0.000 0.992 0.000 0.000 0.008
#> GSM447735 3 0.1270 0.8683 0.000 0.000 0.948 0.052 0.000
#> GSM447737 3 0.2929 0.7362 0.180 0.000 0.820 0.000 0.000
#> GSM447657 2 0.1282 0.8883 0.000 0.952 0.004 0.044 0.000
#> GSM447674 2 0.0510 0.9039 0.000 0.984 0.000 0.016 0.000
#> GSM447636 1 0.0000 0.9613 1.000 0.000 0.000 0.000 0.000
#> GSM447723 1 0.0000 0.9613 1.000 0.000 0.000 0.000 0.000
#> GSM447699 3 0.0000 0.8846 0.000 0.000 1.000 0.000 0.000
#> GSM447708 2 0.0566 0.9055 0.000 0.984 0.012 0.004 0.000
#> GSM447721 1 0.0000 0.9613 1.000 0.000 0.000 0.000 0.000
#> GSM447623 1 0.0000 0.9613 1.000 0.000 0.000 0.000 0.000
#> GSM447621 1 0.2230 0.8429 0.884 0.000 0.116 0.000 0.000
#> GSM447650 2 0.0162 0.9069 0.000 0.996 0.000 0.000 0.004
#> GSM447651 2 0.0609 0.9025 0.000 0.980 0.000 0.000 0.020
#> GSM447653 4 0.4360 0.6266 0.184 0.000 0.000 0.752 0.064
#> GSM447658 1 0.0000 0.9613 1.000 0.000 0.000 0.000 0.000
#> GSM447675 4 0.0290 0.7842 0.000 0.000 0.008 0.992 0.000
#> GSM447680 2 0.0162 0.9069 0.000 0.996 0.000 0.000 0.004
#> GSM447686 1 0.0162 0.9583 0.996 0.004 0.000 0.000 0.000
#> GSM447736 3 0.0162 0.8845 0.000 0.000 0.996 0.000 0.004
#> GSM447629 2 0.1281 0.8946 0.012 0.956 0.000 0.032 0.000
#> GSM447648 5 0.1341 0.8479 0.000 0.000 0.056 0.000 0.944
#> GSM447660 1 0.0000 0.9613 1.000 0.000 0.000 0.000 0.000
#> GSM447661 2 0.0162 0.9069 0.000 0.996 0.000 0.000 0.004
#> GSM447663 3 0.3391 0.7535 0.000 0.188 0.800 0.000 0.012
#> GSM447704 2 0.3430 0.7127 0.000 0.776 0.000 0.220 0.004
#> GSM447720 3 0.1430 0.8689 0.000 0.052 0.944 0.000 0.004
#> GSM447652 2 0.0162 0.9069 0.000 0.996 0.000 0.004 0.000
#> GSM447679 2 0.0000 0.9070 0.000 1.000 0.000 0.000 0.000
#> GSM447712 1 0.0000 0.9613 1.000 0.000 0.000 0.000 0.000
#> GSM447664 4 0.3388 0.6390 0.200 0.000 0.000 0.792 0.008
#> GSM447637 5 0.1341 0.8478 0.000 0.000 0.056 0.000 0.944
#> GSM447639 3 0.4273 0.2390 0.000 0.000 0.552 0.448 0.000
#> GSM447615 5 0.3274 0.6359 0.220 0.000 0.000 0.000 0.780
#> GSM447656 2 0.0794 0.8959 0.028 0.972 0.000 0.000 0.000
#> GSM447673 4 0.0703 0.7837 0.000 0.024 0.000 0.976 0.000
#> GSM447719 5 0.3816 0.5123 0.000 0.000 0.000 0.304 0.696
#> GSM447706 5 0.1608 0.8466 0.000 0.000 0.072 0.000 0.928
#> GSM447612 3 0.3177 0.6979 0.000 0.000 0.792 0.000 0.208
#> GSM447665 2 0.0000 0.9070 0.000 1.000 0.000 0.000 0.000
#> GSM447677 2 0.0290 0.9064 0.000 0.992 0.000 0.000 0.008
#> GSM447613 1 0.0000 0.9613 1.000 0.000 0.000 0.000 0.000
#> GSM447659 4 0.4841 0.5061 0.000 0.000 0.084 0.708 0.208
#> GSM447662 5 0.3366 0.7107 0.000 0.000 0.232 0.000 0.768
#> GSM447666 5 0.1195 0.8334 0.000 0.028 0.012 0.000 0.960
#> GSM447668 2 0.0162 0.9069 0.000 0.996 0.000 0.000 0.004
#> GSM447682 2 0.2127 0.8461 0.000 0.892 0.000 0.108 0.000
#> GSM447683 2 0.0000 0.9070 0.000 1.000 0.000 0.000 0.000
#> GSM447688 4 0.0451 0.7842 0.000 0.004 0.008 0.988 0.000
#> GSM447702 2 0.0000 0.9070 0.000 1.000 0.000 0.000 0.000
#> GSM447709 2 0.3305 0.7069 0.000 0.776 0.000 0.000 0.224
#> GSM447711 1 0.0000 0.9613 1.000 0.000 0.000 0.000 0.000
#> GSM447715 1 0.0963 0.9279 0.964 0.036 0.000 0.000 0.000
#> GSM447693 5 0.1270 0.8474 0.000 0.000 0.052 0.000 0.948
#> GSM447611 4 0.0963 0.7786 0.000 0.000 0.000 0.964 0.036
#> GSM447672 2 0.1121 0.8941 0.000 0.956 0.000 0.044 0.000
#> GSM447703 4 0.1410 0.7696 0.000 0.060 0.000 0.940 0.000
#> GSM447727 1 0.0000 0.9613 1.000 0.000 0.000 0.000 0.000
#> GSM447638 5 0.5983 0.4863 0.200 0.212 0.000 0.000 0.588
#> GSM447670 1 0.1851 0.8854 0.912 0.000 0.000 0.000 0.088
#> GSM447700 3 0.0880 0.8769 0.000 0.000 0.968 0.032 0.000
#> GSM447738 4 0.4299 0.2975 0.000 0.388 0.004 0.608 0.000
#> GSM447739 1 0.0000 0.9613 1.000 0.000 0.000 0.000 0.000
#> GSM447617 1 0.0162 0.9582 0.996 0.000 0.004 0.000 0.000
#> GSM447628 4 0.0290 0.7841 0.000 0.000 0.000 0.992 0.008
#> GSM447632 4 0.4287 0.0681 0.000 0.460 0.000 0.540 0.000
#> GSM447619 5 0.2605 0.8010 0.000 0.000 0.148 0.000 0.852
#> GSM447643 1 0.0000 0.9613 1.000 0.000 0.000 0.000 0.000
#> GSM447724 4 0.2280 0.7308 0.000 0.000 0.120 0.880 0.000
#> GSM447728 2 0.0510 0.9058 0.000 0.984 0.000 0.016 0.000
#> GSM447610 4 0.4567 0.1584 0.448 0.000 0.004 0.544 0.004
#> GSM447633 2 0.3962 0.7700 0.000 0.800 0.112 0.000 0.088
#> GSM447634 3 0.0324 0.8847 0.000 0.004 0.992 0.000 0.004
#> GSM447622 3 0.0290 0.8838 0.000 0.000 0.992 0.000 0.008
#> GSM447667 2 0.4958 0.3853 0.372 0.592 0.000 0.036 0.000
#> GSM447687 4 0.2127 0.7404 0.000 0.108 0.000 0.892 0.000
#> GSM447695 3 0.0000 0.8846 0.000 0.000 1.000 0.000 0.000
#> GSM447696 1 0.0000 0.9613 1.000 0.000 0.000 0.000 0.000
#> GSM447697 1 0.0000 0.9613 1.000 0.000 0.000 0.000 0.000
#> GSM447714 5 0.4015 0.5075 0.000 0.000 0.348 0.000 0.652
#> GSM447717 1 0.0000 0.9613 1.000 0.000 0.000 0.000 0.000
#> GSM447725 1 0.0000 0.9613 1.000 0.000 0.000 0.000 0.000
#> GSM447729 4 0.0000 0.7843 0.000 0.000 0.000 1.000 0.000
#> GSM447644 2 0.1124 0.8880 0.000 0.960 0.036 0.000 0.004
#> GSM447710 5 0.1908 0.8394 0.000 0.000 0.092 0.000 0.908
#> GSM447614 4 0.3906 0.5521 0.004 0.000 0.292 0.704 0.000
#> GSM447685 2 0.1043 0.8951 0.000 0.960 0.000 0.040 0.000
#> GSM447690 1 0.0000 0.9613 1.000 0.000 0.000 0.000 0.000
#> GSM447730 2 0.5673 0.5141 0.000 0.616 0.000 0.252 0.132
#> GSM447646 4 0.0404 0.7838 0.000 0.000 0.000 0.988 0.012
#> GSM447689 5 0.0963 0.8444 0.000 0.000 0.036 0.000 0.964
#> GSM447635 3 0.1549 0.8684 0.000 0.016 0.944 0.040 0.000
#> GSM447641 1 0.0000 0.9613 1.000 0.000 0.000 0.000 0.000
#> GSM447716 4 0.4289 0.5454 0.012 0.272 0.008 0.708 0.000
#> GSM447718 5 0.4765 0.6647 0.000 0.168 0.092 0.004 0.736
#> GSM447616 3 0.0162 0.8845 0.000 0.000 0.996 0.000 0.004
#> GSM447626 5 0.1671 0.8454 0.000 0.000 0.076 0.000 0.924
#> GSM447640 2 0.0703 0.9026 0.000 0.976 0.000 0.024 0.000
#> GSM447734 3 0.0404 0.8829 0.000 0.000 0.988 0.000 0.012
#> GSM447692 3 0.0162 0.8844 0.004 0.000 0.996 0.000 0.000
#> GSM447647 4 0.1043 0.7769 0.000 0.000 0.000 0.960 0.040
#> GSM447624 1 0.3579 0.7872 0.828 0.000 0.072 0.000 0.100
#> GSM447625 3 0.2179 0.8134 0.000 0.000 0.888 0.000 0.112
#> GSM447707 2 0.3745 0.7328 0.000 0.780 0.000 0.196 0.024
#> GSM447732 3 0.2966 0.7843 0.000 0.016 0.848 0.000 0.136
#> GSM447684 1 0.4958 0.0788 0.524 0.452 0.004 0.000 0.020
#> GSM447731 5 0.4262 0.2013 0.000 0.000 0.000 0.440 0.560
#> GSM447705 5 0.1851 0.8408 0.000 0.000 0.088 0.000 0.912
#> GSM447631 5 0.0703 0.8390 0.000 0.000 0.024 0.000 0.976
#> GSM447701 2 0.0162 0.9069 0.000 0.996 0.000 0.000 0.004
#> GSM447645 5 0.0703 0.8404 0.000 0.000 0.024 0.000 0.976
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM447671 3 0.4058 0.55022 0.000 0.320 0.660 0.000 0.016 0.004
#> GSM447694 3 0.1806 0.71208 0.000 0.000 0.908 0.000 0.088 0.004
#> GSM447618 2 0.4049 -0.00764 0.000 0.580 0.412 0.004 0.004 0.000
#> GSM447691 3 0.5520 0.34474 0.000 0.240 0.560 0.000 0.200 0.000
#> GSM447733 4 0.4105 0.76932 0.000 0.132 0.040 0.780 0.000 0.048
#> GSM447620 6 0.3659 0.32059 0.000 0.364 0.000 0.000 0.000 0.636
#> GSM447627 3 0.4453 0.43295 0.000 0.000 0.636 0.328 0.020 0.016
#> GSM447630 5 0.3584 0.21062 0.000 0.000 0.308 0.004 0.688 0.000
#> GSM447642 1 0.0000 0.97294 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447649 2 0.3971 0.54378 0.000 0.704 0.000 0.004 0.268 0.024
#> GSM447654 4 0.0146 0.82846 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM447655 2 0.3862 0.43474 0.000 0.608 0.000 0.000 0.388 0.004
#> GSM447669 5 0.3528 0.23887 0.000 0.004 0.296 0.000 0.700 0.000
#> GSM447676 1 0.1663 0.90319 0.912 0.000 0.000 0.000 0.000 0.088
#> GSM447678 2 0.5831 -0.18091 0.000 0.456 0.196 0.348 0.000 0.000
#> GSM447681 5 0.3995 -0.20546 0.000 0.480 0.004 0.000 0.516 0.000
#> GSM447698 2 0.4146 0.36300 0.000 0.720 0.232 0.040 0.008 0.000
#> GSM447713 1 0.0146 0.97309 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM447722 3 0.4087 0.58695 0.000 0.276 0.692 0.028 0.004 0.000
#> GSM447726 5 0.1663 0.51285 0.000 0.088 0.000 0.000 0.912 0.000
#> GSM447735 3 0.1528 0.72916 0.000 0.048 0.936 0.016 0.000 0.000
#> GSM447737 3 0.3276 0.59452 0.228 0.004 0.764 0.000 0.004 0.000
#> GSM447657 5 0.2809 0.47313 0.000 0.168 0.004 0.004 0.824 0.000
#> GSM447674 2 0.3854 0.31022 0.000 0.536 0.000 0.000 0.464 0.000
#> GSM447636 1 0.0000 0.97294 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447723 1 0.0146 0.97309 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM447699 3 0.0922 0.73334 0.000 0.024 0.968 0.000 0.004 0.004
#> GSM447708 2 0.3133 0.56212 0.000 0.780 0.008 0.000 0.212 0.000
#> GSM447721 1 0.0146 0.97309 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM447623 1 0.0000 0.97294 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447621 1 0.2212 0.85710 0.880 0.000 0.112 0.000 0.008 0.000
#> GSM447650 5 0.2219 0.48585 0.000 0.136 0.000 0.000 0.864 0.000
#> GSM447651 5 0.4897 -0.17591 0.000 0.448 0.000 0.000 0.492 0.060
#> GSM447653 4 0.1285 0.81765 0.052 0.004 0.000 0.944 0.000 0.000
#> GSM447658 1 0.0000 0.97294 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447675 4 0.3481 0.72433 0.000 0.192 0.032 0.776 0.000 0.000
#> GSM447680 5 0.4076 -0.07634 0.004 0.428 0.000 0.000 0.564 0.004
#> GSM447686 1 0.1806 0.89077 0.908 0.088 0.000 0.000 0.004 0.000
#> GSM447736 3 0.0922 0.73242 0.000 0.004 0.968 0.000 0.004 0.024
#> GSM447629 2 0.2422 0.54068 0.012 0.892 0.024 0.000 0.072 0.000
#> GSM447648 6 0.0260 0.87478 0.000 0.000 0.008 0.000 0.000 0.992
#> GSM447660 1 0.0000 0.97294 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447661 5 0.3265 0.35853 0.000 0.248 0.000 0.000 0.748 0.004
#> GSM447663 5 0.3937 -0.08626 0.000 0.000 0.424 0.000 0.572 0.004
#> GSM447704 2 0.3192 0.56544 0.000 0.776 0.000 0.004 0.216 0.004
#> GSM447720 5 0.3804 -0.07664 0.000 0.000 0.424 0.000 0.576 0.000
#> GSM447652 5 0.3821 0.46587 0.000 0.040 0.000 0.220 0.740 0.000
#> GSM447679 2 0.3828 0.35494 0.000 0.560 0.000 0.000 0.440 0.000
#> GSM447712 1 0.0146 0.97309 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM447664 4 0.2912 0.72298 0.172 0.012 0.000 0.816 0.000 0.000
#> GSM447637 6 0.0363 0.87529 0.000 0.000 0.012 0.000 0.000 0.988
#> GSM447639 3 0.5203 0.20201 0.000 0.040 0.528 0.404 0.028 0.000
#> GSM447615 6 0.2491 0.68808 0.164 0.000 0.000 0.000 0.000 0.836
#> GSM447656 2 0.4086 0.54679 0.048 0.728 0.000 0.000 0.220 0.004
#> GSM447673 2 0.4323 0.16584 0.000 0.612 0.012 0.364 0.012 0.000
#> GSM447719 4 0.3371 0.56484 0.000 0.000 0.000 0.708 0.000 0.292
#> GSM447706 6 0.0146 0.87358 0.000 0.000 0.004 0.000 0.000 0.996
#> GSM447612 3 0.3734 0.51937 0.000 0.000 0.716 0.000 0.020 0.264
#> GSM447665 5 0.3866 -0.15710 0.000 0.484 0.000 0.000 0.516 0.000
#> GSM447677 2 0.4057 0.33064 0.000 0.556 0.000 0.000 0.436 0.008
#> GSM447613 1 0.0000 0.97294 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447659 4 0.3689 0.75164 0.000 0.004 0.068 0.792 0.000 0.136
#> GSM447662 6 0.1387 0.84642 0.000 0.000 0.068 0.000 0.000 0.932
#> GSM447666 6 0.0260 0.86955 0.000 0.000 0.000 0.000 0.008 0.992
#> GSM447668 5 0.2048 0.49699 0.000 0.120 0.000 0.000 0.880 0.000
#> GSM447682 2 0.5153 0.29470 0.000 0.460 0.000 0.084 0.456 0.000
#> GSM447683 2 0.3843 0.30750 0.000 0.548 0.000 0.000 0.452 0.000
#> GSM447688 2 0.4332 0.31081 0.000 0.672 0.052 0.276 0.000 0.000
#> GSM447702 5 0.3288 0.33587 0.000 0.276 0.000 0.000 0.724 0.000
#> GSM447709 2 0.6001 0.28905 0.000 0.448 0.000 0.004 0.208 0.340
#> GSM447711 1 0.0146 0.97309 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM447715 1 0.2278 0.85066 0.868 0.128 0.000 0.000 0.004 0.000
#> GSM447693 6 0.0363 0.87529 0.000 0.000 0.012 0.000 0.000 0.988
#> GSM447611 4 0.0508 0.83051 0.012 0.004 0.000 0.984 0.000 0.000
#> GSM447672 2 0.3508 0.53312 0.000 0.704 0.000 0.004 0.292 0.000
#> GSM447703 2 0.4243 0.43296 0.000 0.688 0.008 0.272 0.032 0.000
#> GSM447727 1 0.0146 0.97309 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM447638 5 0.7010 0.12124 0.256 0.064 0.000 0.000 0.368 0.312
#> GSM447670 1 0.1327 0.92642 0.936 0.000 0.000 0.000 0.000 0.064
#> GSM447700 3 0.2135 0.70637 0.000 0.128 0.872 0.000 0.000 0.000
#> GSM447738 2 0.2122 0.52824 0.000 0.900 0.024 0.076 0.000 0.000
#> GSM447739 1 0.0146 0.97309 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM447617 1 0.0291 0.97100 0.992 0.004 0.004 0.000 0.000 0.000
#> GSM447628 4 0.1007 0.83022 0.000 0.044 0.000 0.956 0.000 0.000
#> GSM447632 2 0.1448 0.54098 0.000 0.948 0.024 0.016 0.012 0.000
#> GSM447619 6 0.1007 0.86343 0.000 0.000 0.044 0.000 0.000 0.956
#> GSM447643 1 0.0000 0.97294 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447724 3 0.5089 0.41018 0.000 0.384 0.540 0.072 0.004 0.000
#> GSM447728 2 0.3852 0.45996 0.000 0.612 0.000 0.004 0.384 0.000
#> GSM447610 4 0.4734 0.34482 0.404 0.024 0.016 0.556 0.000 0.000
#> GSM447633 5 0.6613 0.09358 0.000 0.300 0.044 0.000 0.448 0.208
#> GSM447634 3 0.3857 0.26822 0.000 0.000 0.532 0.000 0.468 0.000
#> GSM447622 3 0.1542 0.72708 0.000 0.004 0.936 0.000 0.008 0.052
#> GSM447667 2 0.4584 0.37795 0.244 0.688 0.016 0.000 0.052 0.000
#> GSM447687 2 0.4279 0.50274 0.000 0.716 0.008 0.224 0.052 0.000
#> GSM447695 3 0.0748 0.73166 0.000 0.004 0.976 0.000 0.016 0.004
#> GSM447696 1 0.0146 0.97309 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM447697 1 0.0260 0.96920 0.992 0.000 0.000 0.008 0.000 0.000
#> GSM447714 6 0.4437 0.25535 0.000 0.000 0.392 0.000 0.032 0.576
#> GSM447717 1 0.0146 0.97309 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM447725 1 0.0000 0.97294 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447729 4 0.1555 0.82809 0.004 0.060 0.004 0.932 0.000 0.000
#> GSM447644 5 0.1327 0.52663 0.000 0.000 0.064 0.000 0.936 0.000
#> GSM447710 6 0.5043 0.52788 0.000 0.000 0.196 0.008 0.136 0.660
#> GSM447614 4 0.3962 0.67428 0.000 0.024 0.196 0.756 0.024 0.000
#> GSM447685 2 0.3468 0.53351 0.004 0.712 0.000 0.000 0.284 0.000
#> GSM447690 1 0.0146 0.97309 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM447730 2 0.5567 0.54417 0.000 0.632 0.000 0.116 0.212 0.040
#> GSM447646 4 0.1007 0.83070 0.000 0.044 0.000 0.956 0.000 0.000
#> GSM447689 6 0.0291 0.87425 0.000 0.000 0.004 0.000 0.004 0.992
#> GSM447635 3 0.3403 0.65132 0.000 0.212 0.768 0.000 0.020 0.000
#> GSM447641 1 0.0000 0.97294 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447716 2 0.4196 0.44942 0.124 0.776 0.076 0.020 0.004 0.000
#> GSM447718 5 0.5441 0.30071 0.000 0.012 0.060 0.280 0.620 0.028
#> GSM447616 3 0.1642 0.72926 0.032 0.004 0.936 0.000 0.000 0.028
#> GSM447626 5 0.4876 0.03979 0.004 0.000 0.044 0.004 0.560 0.388
#> GSM447640 2 0.3198 0.55163 0.000 0.740 0.000 0.000 0.260 0.000
#> GSM447734 3 0.3583 0.58604 0.000 0.000 0.728 0.004 0.260 0.008
#> GSM447692 3 0.1858 0.71603 0.012 0.000 0.912 0.000 0.076 0.000
#> GSM447647 4 0.1957 0.79874 0.000 0.112 0.000 0.888 0.000 0.000
#> GSM447624 1 0.2384 0.89338 0.900 0.000 0.040 0.000 0.016 0.044
#> GSM447625 3 0.4158 0.57973 0.000 0.000 0.708 0.012 0.252 0.028
#> GSM447707 2 0.5543 0.50092 0.000 0.552 0.000 0.128 0.312 0.008
#> GSM447732 3 0.4211 0.28873 0.000 0.000 0.532 0.008 0.456 0.004
#> GSM447684 5 0.2425 0.50758 0.100 0.012 0.008 0.000 0.880 0.000
#> GSM447731 4 0.1610 0.80812 0.000 0.000 0.000 0.916 0.000 0.084
#> GSM447705 6 0.0547 0.87362 0.000 0.000 0.020 0.000 0.000 0.980
#> GSM447631 6 0.0870 0.87096 0.000 0.000 0.012 0.012 0.004 0.972
#> GSM447701 5 0.1204 0.52138 0.000 0.056 0.000 0.000 0.944 0.000
#> GSM447645 6 0.0146 0.87358 0.000 0.000 0.004 0.000 0.000 0.996
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)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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 gender(p) individual(p) disease.state(p) other(p) k
#> SD:NMF 123 0.367 0.8283 0.2493 0.0725 2
#> SD:NMF 100 0.286 0.1997 0.0641 0.0620 3
#> SD:NMF 119 0.268 0.1286 0.2000 0.1713 4
#> SD:NMF 120 0.619 0.0774 0.1279 0.1701 5
#> SD:NMF 87 0.944 0.2416 0.2491 0.8012 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "hclust"]
# you can also extract it by
# res = res_list["CV:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 130 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 0.277 0.777 0.872 0.4650 0.499 0.499
#> 3 3 0.271 0.533 0.672 0.2944 0.831 0.670
#> 4 4 0.429 0.668 0.794 0.2029 0.827 0.554
#> 5 5 0.532 0.561 0.723 0.0691 0.960 0.841
#> 6 6 0.575 0.517 0.690 0.0383 0.949 0.777
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
#> GSM447671 2 0.6247 0.819 0.156 0.844
#> GSM447694 1 0.8955 0.665 0.688 0.312
#> GSM447618 2 0.6801 0.798 0.180 0.820
#> GSM447691 2 0.6623 0.808 0.172 0.828
#> GSM447733 2 0.5178 0.852 0.116 0.884
#> GSM447620 2 0.7056 0.778 0.192 0.808
#> GSM447627 2 0.7950 0.708 0.240 0.760
#> GSM447630 1 0.9323 0.612 0.652 0.348
#> GSM447642 1 0.0000 0.804 1.000 0.000
#> GSM447649 2 0.0376 0.880 0.004 0.996
#> GSM447654 2 0.0000 0.880 0.000 1.000
#> GSM447655 2 0.0000 0.880 0.000 1.000
#> GSM447669 1 0.9775 0.475 0.588 0.412
#> GSM447676 1 0.0000 0.804 1.000 0.000
#> GSM447678 2 0.4562 0.863 0.096 0.904
#> GSM447681 2 0.1184 0.883 0.016 0.984
#> GSM447698 2 0.4939 0.857 0.108 0.892
#> GSM447713 1 0.0000 0.804 1.000 0.000
#> GSM447722 2 0.4939 0.857 0.108 0.892
#> GSM447726 1 0.9866 0.394 0.568 0.432
#> GSM447735 2 0.7453 0.767 0.212 0.788
#> GSM447737 1 0.1843 0.811 0.972 0.028
#> GSM447657 2 0.1184 0.883 0.016 0.984
#> GSM447674 2 0.1184 0.883 0.016 0.984
#> GSM447636 1 0.0000 0.804 1.000 0.000
#> GSM447723 1 0.9427 0.526 0.640 0.360
#> GSM447699 2 0.9608 0.343 0.384 0.616
#> GSM447708 2 0.7056 0.784 0.192 0.808
#> GSM447721 1 0.2043 0.810 0.968 0.032
#> GSM447623 1 0.0000 0.804 1.000 0.000
#> GSM447621 1 0.0000 0.804 1.000 0.000
#> GSM447650 2 0.1843 0.883 0.028 0.972
#> GSM447651 2 0.2236 0.880 0.036 0.964
#> GSM447653 2 0.2778 0.875 0.048 0.952
#> GSM447658 1 0.0000 0.804 1.000 0.000
#> GSM447675 2 0.0000 0.880 0.000 1.000
#> GSM447680 2 0.4298 0.854 0.088 0.912
#> GSM447686 2 0.8713 0.621 0.292 0.708
#> GSM447736 1 0.9896 0.379 0.560 0.440
#> GSM447629 2 0.7056 0.796 0.192 0.808
#> GSM447648 1 0.4815 0.818 0.896 0.104
#> GSM447660 1 0.5629 0.805 0.868 0.132
#> GSM447661 2 0.0000 0.880 0.000 1.000
#> GSM447663 1 0.9323 0.613 0.652 0.348
#> GSM447704 2 0.0000 0.880 0.000 1.000
#> GSM447720 1 0.9850 0.415 0.572 0.428
#> GSM447652 2 0.3733 0.875 0.072 0.928
#> GSM447679 2 0.1414 0.882 0.020 0.980
#> GSM447712 1 0.0000 0.804 1.000 0.000
#> GSM447664 2 0.0938 0.882 0.012 0.988
#> GSM447637 1 0.5059 0.817 0.888 0.112
#> GSM447639 2 0.7883 0.715 0.236 0.764
#> GSM447615 1 0.4562 0.818 0.904 0.096
#> GSM447656 2 0.7139 0.777 0.196 0.804
#> GSM447673 2 0.0000 0.880 0.000 1.000
#> GSM447719 2 0.3274 0.871 0.060 0.940
#> GSM447706 1 0.4939 0.817 0.892 0.108
#> GSM447612 2 0.9635 0.330 0.388 0.612
#> GSM447665 2 0.6247 0.819 0.156 0.844
#> GSM447677 2 0.1184 0.883 0.016 0.984
#> GSM447613 1 0.0000 0.804 1.000 0.000
#> GSM447659 2 0.5178 0.852 0.116 0.884
#> GSM447662 1 0.5408 0.814 0.876 0.124
#> GSM447666 1 0.7453 0.775 0.788 0.212
#> GSM447668 2 0.0000 0.880 0.000 1.000
#> GSM447682 2 0.6343 0.825 0.160 0.840
#> GSM447683 2 0.1414 0.883 0.020 0.980
#> GSM447688 2 0.0000 0.880 0.000 1.000
#> GSM447702 2 0.0000 0.880 0.000 1.000
#> GSM447709 2 0.5294 0.849 0.120 0.880
#> GSM447711 1 0.0000 0.804 1.000 0.000
#> GSM447715 1 0.9427 0.526 0.640 0.360
#> GSM447693 1 0.5178 0.816 0.884 0.116
#> GSM447611 2 0.1414 0.879 0.020 0.980
#> GSM447672 2 0.0000 0.880 0.000 1.000
#> GSM447703 2 0.0000 0.880 0.000 1.000
#> GSM447727 1 0.9248 0.549 0.660 0.340
#> GSM447638 1 0.7883 0.753 0.764 0.236
#> GSM447670 1 0.3733 0.818 0.928 0.072
#> GSM447700 2 0.6438 0.815 0.164 0.836
#> GSM447738 2 0.0000 0.880 0.000 1.000
#> GSM447739 1 0.0000 0.804 1.000 0.000
#> GSM447617 1 0.0000 0.804 1.000 0.000
#> GSM447628 2 0.0000 0.880 0.000 1.000
#> GSM447632 2 0.0000 0.880 0.000 1.000
#> GSM447619 1 0.5408 0.814 0.876 0.124
#> GSM447643 1 0.9909 0.309 0.556 0.444
#> GSM447724 2 0.7299 0.772 0.204 0.796
#> GSM447728 2 0.5629 0.845 0.132 0.868
#> GSM447610 2 0.5842 0.842 0.140 0.860
#> GSM447633 2 0.6247 0.819 0.156 0.844
#> GSM447634 1 0.9209 0.629 0.664 0.336
#> GSM447622 1 0.4431 0.819 0.908 0.092
#> GSM447667 2 0.8207 0.692 0.256 0.744
#> GSM447687 2 0.0000 0.880 0.000 1.000
#> GSM447695 1 0.8386 0.717 0.732 0.268
#> GSM447696 1 0.0000 0.804 1.000 0.000
#> GSM447697 1 0.0000 0.804 1.000 0.000
#> GSM447714 1 0.8499 0.710 0.724 0.276
#> GSM447717 1 0.0000 0.804 1.000 0.000
#> GSM447725 1 0.0000 0.804 1.000 0.000
#> GSM447729 2 0.1184 0.880 0.016 0.984
#> GSM447644 1 0.9775 0.475 0.588 0.412
#> GSM447710 1 0.7815 0.751 0.768 0.232
#> GSM447614 2 0.5842 0.842 0.140 0.860
#> GSM447685 2 0.5842 0.829 0.140 0.860
#> GSM447690 1 0.0000 0.804 1.000 0.000
#> GSM447730 2 0.0000 0.880 0.000 1.000
#> GSM447646 2 0.0000 0.880 0.000 1.000
#> GSM447689 1 0.8081 0.744 0.752 0.248
#> GSM447635 2 0.8144 0.710 0.252 0.748
#> GSM447641 1 0.0000 0.804 1.000 0.000
#> GSM447716 2 0.7453 0.769 0.212 0.788
#> GSM447718 2 0.9286 0.466 0.344 0.656
#> GSM447616 1 0.4431 0.819 0.908 0.092
#> GSM447626 1 0.5946 0.808 0.856 0.144
#> GSM447640 2 0.0672 0.882 0.008 0.992
#> GSM447734 1 0.9552 0.559 0.624 0.376
#> GSM447692 1 0.7453 0.768 0.788 0.212
#> GSM447647 2 0.0000 0.880 0.000 1.000
#> GSM447624 1 0.3733 0.818 0.928 0.072
#> GSM447625 1 0.9286 0.618 0.656 0.344
#> GSM447707 2 0.0000 0.880 0.000 1.000
#> GSM447732 1 0.9248 0.625 0.660 0.340
#> GSM447684 1 0.6887 0.788 0.816 0.184
#> GSM447731 2 0.2236 0.878 0.036 0.964
#> GSM447705 1 0.9358 0.612 0.648 0.352
#> GSM447631 1 0.5059 0.817 0.888 0.112
#> GSM447701 2 0.3431 0.878 0.064 0.936
#> GSM447645 1 0.5059 0.817 0.888 0.112
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM447671 2 0.5304 0.60631 0.108 0.824 0.068
#> GSM447694 1 0.9329 0.51884 0.436 0.164 0.400
#> GSM447618 2 0.6526 0.56237 0.128 0.760 0.112
#> GSM447691 2 0.7624 0.46261 0.104 0.672 0.224
#> GSM447733 3 0.7582 0.60255 0.048 0.380 0.572
#> GSM447620 2 0.6462 0.56629 0.120 0.764 0.116
#> GSM447627 3 0.9086 0.34050 0.148 0.356 0.496
#> GSM447630 1 0.9806 0.44182 0.432 0.292 0.276
#> GSM447642 1 0.0000 0.66933 1.000 0.000 0.000
#> GSM447649 3 0.6518 0.72540 0.004 0.484 0.512
#> GSM447654 3 0.6295 0.72768 0.000 0.472 0.528
#> GSM447655 2 0.0592 0.60126 0.000 0.988 0.012
#> GSM447669 1 0.9928 0.32138 0.372 0.352 0.276
#> GSM447676 1 0.0000 0.66933 1.000 0.000 0.000
#> GSM447678 2 0.7932 -0.29162 0.064 0.552 0.384
#> GSM447681 2 0.1832 0.61198 0.008 0.956 0.036
#> GSM447698 2 0.7969 -0.25729 0.064 0.540 0.396
#> GSM447713 1 0.0237 0.66997 0.996 0.000 0.004
#> GSM447722 2 0.7969 -0.25729 0.064 0.540 0.396
#> GSM447726 2 0.9959 -0.24841 0.324 0.376 0.300
#> GSM447735 3 0.8936 0.42043 0.132 0.368 0.500
#> GSM447737 1 0.2939 0.68051 0.916 0.012 0.072
#> GSM447657 2 0.1832 0.61198 0.008 0.956 0.036
#> GSM447674 2 0.1832 0.61198 0.008 0.956 0.036
#> GSM447636 1 0.0000 0.66933 1.000 0.000 0.000
#> GSM447723 1 0.7368 0.31542 0.604 0.352 0.044
#> GSM447699 2 0.9926 0.00371 0.284 0.388 0.328
#> GSM447708 2 0.6663 0.57203 0.124 0.752 0.124
#> GSM447721 1 0.2681 0.67433 0.932 0.028 0.040
#> GSM447623 1 0.1031 0.67471 0.976 0.000 0.024
#> GSM447621 1 0.1031 0.67471 0.976 0.000 0.024
#> GSM447650 2 0.2176 0.62986 0.020 0.948 0.032
#> GSM447651 2 0.2400 0.62157 0.004 0.932 0.064
#> GSM447653 3 0.6154 0.68497 0.000 0.408 0.592
#> GSM447658 1 0.0000 0.66933 1.000 0.000 0.000
#> GSM447675 3 0.6305 0.72494 0.000 0.484 0.516
#> GSM447680 2 0.3692 0.60026 0.056 0.896 0.048
#> GSM447686 2 0.6929 0.48831 0.260 0.688 0.052
#> GSM447736 1 0.9949 0.26058 0.360 0.284 0.356
#> GSM447629 2 0.7865 0.47238 0.124 0.660 0.216
#> GSM447648 1 0.6813 0.67099 0.520 0.012 0.468
#> GSM447660 1 0.4779 0.63555 0.840 0.124 0.036
#> GSM447661 2 0.0892 0.61540 0.000 0.980 0.020
#> GSM447663 1 0.9792 0.45008 0.436 0.288 0.276
#> GSM447704 3 0.6307 0.72434 0.000 0.488 0.512
#> GSM447720 3 0.9908 -0.39058 0.360 0.268 0.372
#> GSM447652 2 0.4892 0.56697 0.048 0.840 0.112
#> GSM447679 2 0.1529 0.62275 0.000 0.960 0.040
#> GSM447712 1 0.0000 0.66933 1.000 0.000 0.000
#> GSM447664 3 0.6678 0.71884 0.008 0.480 0.512
#> GSM447637 1 0.7067 0.66881 0.512 0.020 0.468
#> GSM447639 3 0.9207 0.27976 0.152 0.392 0.456
#> GSM447615 1 0.6641 0.67744 0.544 0.008 0.448
#> GSM447656 2 0.5777 0.58126 0.160 0.788 0.052
#> GSM447673 3 0.6308 0.71912 0.000 0.492 0.508
#> GSM447719 3 0.6111 0.66879 0.000 0.396 0.604
#> GSM447706 1 0.6948 0.66850 0.512 0.016 0.472
#> GSM447612 2 0.9885 0.03026 0.260 0.372 0.368
#> GSM447665 2 0.5212 0.60883 0.108 0.828 0.064
#> GSM447677 2 0.1289 0.62067 0.000 0.968 0.032
#> GSM447613 1 0.0237 0.67012 0.996 0.000 0.004
#> GSM447659 3 0.7582 0.60255 0.048 0.380 0.572
#> GSM447662 1 0.7484 0.66487 0.504 0.036 0.460
#> GSM447666 1 0.9098 0.62507 0.456 0.140 0.404
#> GSM447668 2 0.0892 0.61540 0.000 0.980 0.020
#> GSM447682 2 0.7835 0.38703 0.112 0.656 0.232
#> GSM447683 2 0.1289 0.62470 0.000 0.968 0.032
#> GSM447688 3 0.6309 0.71486 0.000 0.496 0.504
#> GSM447702 2 0.0424 0.60340 0.000 0.992 0.008
#> GSM447709 2 0.4475 0.62638 0.072 0.864 0.064
#> GSM447711 1 0.0237 0.67054 0.996 0.000 0.004
#> GSM447715 1 0.7368 0.31542 0.604 0.352 0.044
#> GSM447693 1 0.7184 0.66548 0.504 0.024 0.472
#> GSM447611 3 0.6944 0.72052 0.016 0.468 0.516
#> GSM447672 2 0.0592 0.60126 0.000 0.988 0.012
#> GSM447703 3 0.6309 0.71486 0.000 0.496 0.504
#> GSM447727 1 0.7284 0.35046 0.620 0.336 0.044
#> GSM447638 1 0.9256 0.62043 0.488 0.168 0.344
#> GSM447670 1 0.6398 0.68486 0.580 0.004 0.416
#> GSM447700 2 0.8550 -0.14045 0.096 0.492 0.412
#> GSM447738 3 0.6305 0.72379 0.000 0.484 0.516
#> GSM447739 1 0.0000 0.66933 1.000 0.000 0.000
#> GSM447617 1 0.1529 0.67708 0.960 0.000 0.040
#> GSM447628 3 0.6299 0.72648 0.000 0.476 0.524
#> GSM447632 3 0.6307 0.72338 0.000 0.488 0.512
#> GSM447619 1 0.7484 0.66487 0.504 0.036 0.460
#> GSM447643 1 0.7549 0.10359 0.524 0.436 0.040
#> GSM447724 2 0.8742 -0.08888 0.108 0.456 0.436
#> GSM447728 2 0.5060 0.61108 0.100 0.836 0.064
#> GSM447610 3 0.8482 0.61635 0.092 0.408 0.500
#> GSM447633 2 0.5212 0.60883 0.108 0.828 0.064
#> GSM447634 1 0.9872 0.46976 0.408 0.272 0.320
#> GSM447622 1 0.6737 0.68763 0.600 0.016 0.384
#> GSM447667 2 0.6933 0.53022 0.208 0.716 0.076
#> GSM447687 3 0.6305 0.72379 0.000 0.484 0.516
#> GSM447695 1 0.8971 0.59006 0.520 0.144 0.336
#> GSM447696 1 0.0000 0.66933 1.000 0.000 0.000
#> GSM447697 1 0.0237 0.67012 0.996 0.000 0.004
#> GSM447714 1 0.9465 0.56551 0.444 0.184 0.372
#> GSM447717 1 0.0000 0.66933 1.000 0.000 0.000
#> GSM447725 1 0.0000 0.66933 1.000 0.000 0.000
#> GSM447729 3 0.6948 0.72218 0.016 0.472 0.512
#> GSM447644 1 0.9928 0.32138 0.372 0.352 0.276
#> GSM447710 1 0.8983 0.61025 0.444 0.128 0.428
#> GSM447614 3 0.8482 0.61635 0.092 0.408 0.500
#> GSM447685 2 0.4920 0.60936 0.108 0.840 0.052
#> GSM447690 1 0.0000 0.66933 1.000 0.000 0.000
#> GSM447730 2 0.1289 0.58919 0.000 0.968 0.032
#> GSM447646 3 0.6299 0.72648 0.000 0.476 0.524
#> GSM447689 1 0.9300 0.59824 0.428 0.160 0.412
#> GSM447635 2 0.8380 0.43219 0.124 0.600 0.276
#> GSM447641 1 0.0000 0.66933 1.000 0.000 0.000
#> GSM447716 2 0.8992 0.20047 0.176 0.552 0.272
#> GSM447718 2 0.9150 0.33405 0.224 0.544 0.232
#> GSM447616 1 0.6704 0.68804 0.608 0.016 0.376
#> GSM447626 1 0.8220 0.66228 0.516 0.076 0.408
#> GSM447640 2 0.0892 0.61978 0.000 0.980 0.020
#> GSM447734 1 0.9786 0.40840 0.400 0.236 0.364
#> GSM447692 1 0.8513 0.64012 0.596 0.140 0.264
#> GSM447647 3 0.6307 0.72434 0.000 0.488 0.512
#> GSM447624 1 0.6398 0.68486 0.580 0.004 0.416
#> GSM447625 1 0.9809 0.44940 0.432 0.284 0.284
#> GSM447707 2 0.1289 0.58919 0.000 0.968 0.032
#> GSM447732 1 0.9777 0.45677 0.440 0.280 0.280
#> GSM447684 1 0.8800 0.64322 0.488 0.116 0.396
#> GSM447731 3 0.6235 0.70437 0.000 0.436 0.564
#> GSM447705 3 0.9917 -0.55362 0.352 0.272 0.376
#> GSM447631 1 0.7067 0.66881 0.512 0.020 0.468
#> GSM447701 2 0.3356 0.63420 0.036 0.908 0.056
#> GSM447645 1 0.7067 0.66881 0.512 0.020 0.468
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM447671 2 0.5143 0.7001 0.000 0.752 0.172 0.076
#> GSM447694 3 0.6683 0.6568 0.060 0.064 0.680 0.196
#> GSM447618 2 0.7651 0.5131 0.016 0.540 0.184 0.260
#> GSM447691 2 0.7855 0.4868 0.032 0.540 0.160 0.268
#> GSM447733 4 0.4149 0.7355 0.000 0.036 0.152 0.812
#> GSM447620 2 0.5496 0.6472 0.000 0.704 0.232 0.064
#> GSM447627 4 0.5767 0.5438 0.000 0.060 0.280 0.660
#> GSM447630 3 0.5783 0.6694 0.000 0.172 0.708 0.120
#> GSM447642 1 0.0188 0.8597 0.996 0.000 0.004 0.000
#> GSM447649 4 0.3444 0.7542 0.000 0.184 0.000 0.816
#> GSM447654 4 0.2466 0.7776 0.000 0.096 0.004 0.900
#> GSM447655 2 0.1474 0.7543 0.000 0.948 0.000 0.052
#> GSM447669 3 0.6344 0.5981 0.000 0.224 0.648 0.128
#> GSM447676 1 0.0469 0.8595 0.988 0.000 0.012 0.000
#> GSM447678 4 0.5944 0.5910 0.000 0.212 0.104 0.684
#> GSM447681 2 0.2843 0.7538 0.000 0.892 0.020 0.088
#> GSM447698 4 0.6448 0.5272 0.000 0.252 0.120 0.628
#> GSM447713 1 0.0188 0.8599 0.996 0.000 0.004 0.000
#> GSM447722 4 0.6448 0.5272 0.000 0.252 0.120 0.628
#> GSM447726 3 0.6853 0.3597 0.040 0.360 0.560 0.040
#> GSM447735 4 0.5615 0.6458 0.020 0.044 0.212 0.724
#> GSM447737 1 0.4444 0.7130 0.788 0.008 0.184 0.020
#> GSM447657 2 0.2973 0.7551 0.000 0.884 0.020 0.096
#> GSM447674 2 0.2973 0.7551 0.000 0.884 0.020 0.096
#> GSM447636 1 0.0188 0.8597 0.996 0.000 0.004 0.000
#> GSM447723 1 0.7743 0.3642 0.544 0.312 0.072 0.072
#> GSM447699 4 0.7569 0.0417 0.008 0.148 0.412 0.432
#> GSM447708 2 0.7155 0.6113 0.020 0.620 0.184 0.176
#> GSM447721 1 0.3058 0.8271 0.900 0.020 0.056 0.024
#> GSM447623 1 0.2081 0.8232 0.916 0.000 0.084 0.000
#> GSM447621 1 0.2081 0.8232 0.916 0.000 0.084 0.000
#> GSM447650 2 0.2500 0.7690 0.000 0.916 0.040 0.044
#> GSM447651 2 0.2623 0.7647 0.000 0.908 0.064 0.028
#> GSM447653 4 0.2596 0.7714 0.000 0.024 0.068 0.908
#> GSM447658 1 0.0469 0.8595 0.988 0.000 0.012 0.000
#> GSM447675 4 0.1824 0.7753 0.000 0.060 0.004 0.936
#> GSM447680 2 0.3902 0.7285 0.048 0.864 0.028 0.060
#> GSM447686 2 0.7316 0.5519 0.224 0.620 0.044 0.112
#> GSM447736 3 0.7022 0.5641 0.008 0.176 0.608 0.208
#> GSM447629 2 0.7855 0.5343 0.056 0.568 0.124 0.252
#> GSM447648 3 0.0657 0.7518 0.012 0.000 0.984 0.004
#> GSM447660 1 0.4745 0.7612 0.820 0.080 0.032 0.068
#> GSM447661 2 0.1677 0.7612 0.000 0.948 0.012 0.040
#> GSM447663 3 0.5758 0.6787 0.000 0.160 0.712 0.128
#> GSM447704 4 0.3486 0.7535 0.000 0.188 0.000 0.812
#> GSM447720 3 0.6833 0.5921 0.008 0.156 0.628 0.208
#> GSM447652 2 0.6280 0.4947 0.000 0.612 0.084 0.304
#> GSM447679 2 0.2882 0.7542 0.000 0.892 0.024 0.084
#> GSM447712 1 0.0188 0.8596 0.996 0.000 0.000 0.004
#> GSM447664 4 0.2597 0.7713 0.008 0.084 0.004 0.904
#> GSM447637 3 0.0712 0.7540 0.008 0.004 0.984 0.004
#> GSM447639 4 0.6293 0.5269 0.000 0.096 0.276 0.628
#> GSM447615 3 0.2149 0.7312 0.088 0.000 0.912 0.000
#> GSM447656 2 0.6170 0.6724 0.124 0.736 0.060 0.080
#> GSM447673 4 0.2125 0.7760 0.000 0.076 0.004 0.920
#> GSM447719 4 0.3176 0.7664 0.000 0.036 0.084 0.880
#> GSM447706 3 0.0000 0.7528 0.000 0.000 1.000 0.000
#> GSM447612 3 0.7526 0.0354 0.000 0.188 0.440 0.372
#> GSM447665 2 0.4746 0.7108 0.000 0.776 0.168 0.056
#> GSM447677 2 0.2466 0.7641 0.000 0.916 0.028 0.056
#> GSM447613 1 0.0188 0.8596 0.996 0.000 0.004 0.000
#> GSM447659 4 0.4149 0.7355 0.000 0.036 0.152 0.812
#> GSM447662 3 0.0707 0.7565 0.000 0.020 0.980 0.000
#> GSM447666 3 0.2760 0.7224 0.000 0.128 0.872 0.000
#> GSM447668 2 0.1584 0.7595 0.000 0.952 0.012 0.036
#> GSM447682 2 0.7758 0.3549 0.028 0.520 0.136 0.316
#> GSM447683 2 0.2256 0.7630 0.000 0.924 0.020 0.056
#> GSM447688 4 0.3444 0.7537 0.000 0.184 0.000 0.816
#> GSM447702 2 0.1474 0.7559 0.000 0.948 0.000 0.052
#> GSM447709 2 0.4312 0.7392 0.000 0.812 0.132 0.056
#> GSM447711 1 0.0921 0.8531 0.972 0.000 0.028 0.000
#> GSM447715 1 0.7743 0.3642 0.544 0.312 0.072 0.072
#> GSM447693 3 0.0712 0.7548 0.004 0.008 0.984 0.004
#> GSM447611 4 0.1732 0.7651 0.008 0.040 0.004 0.948
#> GSM447672 2 0.1637 0.7553 0.000 0.940 0.000 0.060
#> GSM447703 4 0.3444 0.7537 0.000 0.184 0.000 0.816
#> GSM447727 1 0.7686 0.3933 0.556 0.300 0.080 0.064
#> GSM447638 3 0.6852 0.5363 0.208 0.172 0.616 0.004
#> GSM447670 3 0.3142 0.6960 0.132 0.008 0.860 0.000
#> GSM447700 4 0.7140 0.4350 0.000 0.236 0.204 0.560
#> GSM447738 4 0.3311 0.7636 0.000 0.172 0.000 0.828
#> GSM447739 1 0.0000 0.8595 1.000 0.000 0.000 0.000
#> GSM447617 1 0.2704 0.7932 0.876 0.000 0.124 0.000
#> GSM447628 4 0.2704 0.7693 0.000 0.124 0.000 0.876
#> GSM447632 4 0.3257 0.7660 0.000 0.152 0.004 0.844
#> GSM447619 3 0.0707 0.7565 0.000 0.020 0.980 0.000
#> GSM447643 1 0.8145 0.0811 0.448 0.392 0.092 0.068
#> GSM447724 4 0.7518 0.3642 0.000 0.244 0.260 0.496
#> GSM447728 2 0.5612 0.7242 0.016 0.752 0.132 0.100
#> GSM447610 4 0.4268 0.7429 0.032 0.032 0.096 0.840
#> GSM447633 2 0.4746 0.7108 0.000 0.776 0.168 0.056
#> GSM447634 3 0.5912 0.6824 0.008 0.164 0.716 0.112
#> GSM447622 3 0.4948 0.4704 0.288 0.012 0.696 0.004
#> GSM447667 2 0.7552 0.6197 0.152 0.636 0.088 0.124
#> GSM447687 4 0.3311 0.7636 0.000 0.172 0.000 0.828
#> GSM447695 3 0.8176 0.4670 0.260 0.044 0.520 0.176
#> GSM447696 1 0.0000 0.8595 1.000 0.000 0.000 0.000
#> GSM447697 1 0.0188 0.8596 0.996 0.000 0.004 0.000
#> GSM447714 3 0.4344 0.7369 0.000 0.108 0.816 0.076
#> GSM447717 1 0.0188 0.8597 0.996 0.000 0.004 0.000
#> GSM447725 1 0.0000 0.8595 1.000 0.000 0.000 0.000
#> GSM447729 4 0.1635 0.7656 0.008 0.044 0.000 0.948
#> GSM447644 3 0.6344 0.5981 0.000 0.224 0.648 0.128
#> GSM447710 3 0.3333 0.7530 0.000 0.088 0.872 0.040
#> GSM447614 4 0.4268 0.7429 0.032 0.032 0.096 0.840
#> GSM447685 2 0.5163 0.7079 0.096 0.796 0.036 0.072
#> GSM447690 1 0.0000 0.8595 1.000 0.000 0.000 0.000
#> GSM447730 2 0.3764 0.6489 0.000 0.784 0.000 0.216
#> GSM447646 4 0.2704 0.7693 0.000 0.124 0.000 0.876
#> GSM447689 3 0.3441 0.7453 0.000 0.120 0.856 0.024
#> GSM447635 2 0.8407 0.4822 0.056 0.508 0.188 0.248
#> GSM447641 1 0.0469 0.8595 0.988 0.000 0.012 0.000
#> GSM447716 2 0.8452 0.2430 0.100 0.448 0.088 0.364
#> GSM447718 2 0.8375 0.1088 0.032 0.404 0.372 0.192
#> GSM447616 3 0.5377 0.2615 0.376 0.012 0.608 0.004
#> GSM447626 3 0.2602 0.7546 0.008 0.076 0.908 0.008
#> GSM447640 2 0.2706 0.7648 0.000 0.900 0.020 0.080
#> GSM447734 3 0.5900 0.6325 0.000 0.096 0.684 0.220
#> GSM447692 1 0.8056 -0.1408 0.420 0.040 0.416 0.124
#> GSM447647 4 0.3486 0.7535 0.000 0.188 0.000 0.812
#> GSM447624 3 0.3401 0.6746 0.152 0.008 0.840 0.000
#> GSM447625 3 0.5722 0.6803 0.000 0.148 0.716 0.136
#> GSM447707 2 0.3764 0.6489 0.000 0.784 0.000 0.216
#> GSM447732 3 0.5677 0.6845 0.000 0.140 0.720 0.140
#> GSM447684 3 0.3625 0.7399 0.024 0.120 0.852 0.004
#> GSM447731 4 0.2996 0.7801 0.000 0.064 0.044 0.892
#> GSM447705 3 0.4986 0.6725 0.000 0.216 0.740 0.044
#> GSM447631 3 0.0712 0.7540 0.008 0.004 0.984 0.004
#> GSM447701 2 0.3301 0.7678 0.000 0.876 0.076 0.048
#> GSM447645 3 0.0712 0.7540 0.008 0.004 0.984 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM447671 2 0.5376 0.6432 0.000 0.724 0.104 0.040 0.132
#> GSM447694 3 0.6634 0.4970 0.048 0.020 0.544 0.048 0.340
#> GSM447618 2 0.7996 0.3673 0.016 0.472 0.084 0.196 0.232
#> GSM447691 2 0.8041 0.3347 0.028 0.444 0.064 0.164 0.300
#> GSM447733 5 0.5681 0.3990 0.000 0.024 0.036 0.424 0.516
#> GSM447620 2 0.5505 0.6116 0.000 0.696 0.180 0.028 0.096
#> GSM447627 5 0.6684 0.4423 0.000 0.024 0.132 0.360 0.484
#> GSM447630 3 0.6512 0.5790 0.000 0.124 0.620 0.064 0.192
#> GSM447642 1 0.0451 0.8509 0.988 0.000 0.000 0.004 0.008
#> GSM447649 4 0.3687 0.6366 0.000 0.180 0.000 0.792 0.028
#> GSM447654 4 0.3255 0.6102 0.000 0.100 0.000 0.848 0.052
#> GSM447655 2 0.0771 0.7244 0.000 0.976 0.000 0.020 0.004
#> GSM447669 3 0.7103 0.5092 0.000 0.168 0.548 0.068 0.216
#> GSM447676 1 0.0671 0.8507 0.980 0.000 0.000 0.004 0.016
#> GSM447678 4 0.6370 0.0275 0.000 0.172 0.008 0.548 0.272
#> GSM447681 2 0.2214 0.7205 0.000 0.916 0.004 0.052 0.028
#> GSM447698 4 0.6915 -0.0485 0.000 0.188 0.020 0.468 0.324
#> GSM447713 1 0.0162 0.8520 0.996 0.000 0.000 0.000 0.004
#> GSM447722 4 0.6915 -0.0485 0.000 0.188 0.020 0.468 0.324
#> GSM447726 3 0.7272 0.3424 0.020 0.296 0.504 0.028 0.152
#> GSM447735 5 0.5915 0.4049 0.012 0.012 0.048 0.388 0.540
#> GSM447737 1 0.4422 0.7244 0.772 0.000 0.120 0.004 0.104
#> GSM447657 2 0.2484 0.7208 0.000 0.900 0.004 0.068 0.028
#> GSM447674 2 0.2484 0.7208 0.000 0.900 0.004 0.068 0.028
#> GSM447636 1 0.0451 0.8509 0.988 0.000 0.000 0.004 0.008
#> GSM447723 1 0.7793 0.3848 0.516 0.224 0.044 0.044 0.172
#> GSM447699 5 0.8173 0.3140 0.008 0.088 0.248 0.260 0.396
#> GSM447708 2 0.7552 0.5012 0.016 0.532 0.092 0.116 0.244
#> GSM447721 1 0.2878 0.8165 0.888 0.000 0.048 0.016 0.048
#> GSM447623 1 0.2208 0.8172 0.908 0.000 0.072 0.000 0.020
#> GSM447621 1 0.2208 0.8172 0.908 0.000 0.072 0.000 0.020
#> GSM447650 2 0.1651 0.7325 0.000 0.944 0.012 0.008 0.036
#> GSM447651 2 0.2459 0.7261 0.000 0.904 0.052 0.004 0.040
#> GSM447653 5 0.5201 0.3160 0.000 0.012 0.024 0.416 0.548
#> GSM447658 1 0.0671 0.8507 0.980 0.000 0.000 0.004 0.016
#> GSM447675 4 0.3192 0.5246 0.000 0.040 0.000 0.848 0.112
#> GSM447680 2 0.4517 0.6743 0.024 0.776 0.004 0.040 0.156
#> GSM447686 2 0.7486 0.4485 0.196 0.500 0.004 0.064 0.236
#> GSM447736 3 0.7625 0.3609 0.004 0.116 0.488 0.116 0.276
#> GSM447629 2 0.7789 0.3911 0.044 0.452 0.024 0.176 0.304
#> GSM447648 3 0.1877 0.6742 0.012 0.000 0.924 0.000 0.064
#> GSM447660 1 0.3964 0.7379 0.796 0.012 0.000 0.032 0.160
#> GSM447661 2 0.0867 0.7280 0.000 0.976 0.008 0.008 0.008
#> GSM447663 3 0.6469 0.5847 0.000 0.104 0.620 0.068 0.208
#> GSM447704 4 0.3621 0.6384 0.000 0.192 0.000 0.788 0.020
#> GSM447720 3 0.7420 0.3946 0.004 0.088 0.508 0.124 0.276
#> GSM447652 2 0.6092 0.4838 0.000 0.608 0.028 0.268 0.096
#> GSM447679 2 0.3599 0.7061 0.000 0.832 0.004 0.060 0.104
#> GSM447712 1 0.0162 0.8519 0.996 0.000 0.000 0.000 0.004
#> GSM447664 4 0.3823 0.4826 0.004 0.048 0.000 0.808 0.140
#> GSM447637 3 0.1764 0.6765 0.008 0.000 0.928 0.000 0.064
#> GSM447639 5 0.7184 0.4218 0.000 0.056 0.132 0.364 0.448
#> GSM447615 3 0.2448 0.6552 0.088 0.000 0.892 0.000 0.020
#> GSM447656 2 0.6446 0.5961 0.104 0.636 0.008 0.052 0.200
#> GSM447673 4 0.3215 0.5471 0.000 0.056 0.000 0.852 0.092
#> GSM447719 5 0.5036 0.3085 0.000 0.000 0.036 0.404 0.560
#> GSM447706 3 0.0290 0.6796 0.000 0.000 0.992 0.000 0.008
#> GSM447612 5 0.8292 0.2088 0.000 0.152 0.288 0.200 0.360
#> GSM447665 2 0.4657 0.6641 0.000 0.768 0.104 0.016 0.112
#> GSM447677 2 0.2072 0.7312 0.000 0.928 0.020 0.016 0.036
#> GSM447613 1 0.0324 0.8520 0.992 0.000 0.004 0.000 0.004
#> GSM447659 5 0.5681 0.3990 0.000 0.024 0.036 0.424 0.516
#> GSM447662 3 0.0693 0.6855 0.000 0.012 0.980 0.000 0.008
#> GSM447666 3 0.2959 0.6538 0.000 0.100 0.864 0.000 0.036
#> GSM447668 2 0.0740 0.7266 0.000 0.980 0.008 0.008 0.004
#> GSM447682 2 0.7941 0.2681 0.028 0.452 0.048 0.280 0.192
#> GSM447683 2 0.3105 0.7198 0.000 0.864 0.004 0.044 0.088
#> GSM447688 4 0.3003 0.6436 0.000 0.188 0.000 0.812 0.000
#> GSM447702 2 0.0771 0.7237 0.000 0.976 0.000 0.020 0.004
#> GSM447709 2 0.4028 0.6952 0.000 0.816 0.084 0.016 0.084
#> GSM447711 1 0.0955 0.8448 0.968 0.000 0.028 0.000 0.004
#> GSM447715 1 0.7793 0.3848 0.516 0.224 0.044 0.044 0.172
#> GSM447693 3 0.1357 0.6785 0.004 0.000 0.948 0.000 0.048
#> GSM447611 4 0.3280 0.4006 0.004 0.004 0.000 0.808 0.184
#> GSM447672 2 0.0955 0.7241 0.000 0.968 0.000 0.028 0.004
#> GSM447703 4 0.3003 0.6436 0.000 0.188 0.000 0.812 0.000
#> GSM447727 1 0.7753 0.4157 0.532 0.216 0.052 0.044 0.156
#> GSM447638 3 0.6810 0.4901 0.200 0.140 0.600 0.008 0.052
#> GSM447670 3 0.3099 0.6332 0.124 0.000 0.848 0.000 0.028
#> GSM447700 4 0.7745 -0.2468 0.000 0.172 0.084 0.376 0.368
#> GSM447738 4 0.3132 0.6516 0.000 0.172 0.000 0.820 0.008
#> GSM447739 1 0.0000 0.8515 1.000 0.000 0.000 0.000 0.000
#> GSM447617 1 0.2932 0.7893 0.864 0.000 0.104 0.000 0.032
#> GSM447628 4 0.3229 0.6226 0.000 0.128 0.000 0.840 0.032
#> GSM447632 4 0.2873 0.6321 0.000 0.128 0.000 0.856 0.016
#> GSM447619 3 0.0693 0.6855 0.000 0.012 0.980 0.000 0.008
#> GSM447643 1 0.8202 0.1216 0.424 0.304 0.060 0.036 0.176
#> GSM447724 5 0.8174 0.2080 0.000 0.200 0.128 0.308 0.364
#> GSM447728 2 0.5814 0.6650 0.016 0.704 0.056 0.056 0.168
#> GSM447610 5 0.4972 0.2943 0.020 0.004 0.000 0.476 0.500
#> GSM447633 2 0.4657 0.6641 0.000 0.768 0.104 0.016 0.112
#> GSM447634 3 0.6369 0.5953 0.004 0.100 0.620 0.044 0.232
#> GSM447622 3 0.5096 0.4618 0.272 0.000 0.656 0.000 0.072
#> GSM447667 2 0.7365 0.5023 0.128 0.520 0.008 0.072 0.272
#> GSM447687 4 0.3132 0.6516 0.000 0.172 0.000 0.820 0.008
#> GSM447695 3 0.7721 0.2880 0.248 0.008 0.396 0.040 0.308
#> GSM447696 1 0.0000 0.8515 1.000 0.000 0.000 0.000 0.000
#> GSM447697 1 0.0324 0.8520 0.992 0.000 0.004 0.000 0.004
#> GSM447714 3 0.5155 0.6443 0.000 0.060 0.720 0.032 0.188
#> GSM447717 1 0.0162 0.8513 0.996 0.000 0.000 0.000 0.004
#> GSM447725 1 0.0000 0.8515 1.000 0.000 0.000 0.000 0.000
#> GSM447729 4 0.3365 0.4070 0.004 0.008 0.000 0.808 0.180
#> GSM447644 3 0.7103 0.5092 0.000 0.168 0.548 0.068 0.216
#> GSM447710 3 0.4268 0.6689 0.000 0.044 0.776 0.012 0.168
#> GSM447614 5 0.4972 0.2943 0.020 0.004 0.000 0.476 0.500
#> GSM447685 2 0.5776 0.6376 0.076 0.700 0.004 0.060 0.160
#> GSM447690 1 0.0000 0.8515 1.000 0.000 0.000 0.000 0.000
#> GSM447730 2 0.3246 0.6358 0.000 0.808 0.000 0.184 0.008
#> GSM447646 4 0.3229 0.6226 0.000 0.128 0.000 0.840 0.032
#> GSM447689 3 0.4353 0.6754 0.000 0.072 0.784 0.012 0.132
#> GSM447635 2 0.8617 0.3108 0.044 0.400 0.088 0.184 0.284
#> GSM447641 1 0.0671 0.8507 0.980 0.000 0.000 0.004 0.016
#> GSM447716 5 0.7879 -0.1475 0.076 0.320 0.004 0.196 0.404
#> GSM447718 2 0.8949 0.0434 0.028 0.336 0.268 0.164 0.204
#> GSM447616 3 0.5509 0.2625 0.360 0.000 0.564 0.000 0.076
#> GSM447626 3 0.2678 0.6840 0.004 0.036 0.896 0.004 0.060
#> GSM447640 2 0.3213 0.7220 0.000 0.860 0.004 0.064 0.072
#> GSM447734 3 0.6402 0.4947 0.000 0.048 0.560 0.076 0.316
#> GSM447692 1 0.7651 -0.0734 0.408 0.008 0.324 0.040 0.220
#> GSM447647 4 0.3621 0.6384 0.000 0.192 0.000 0.788 0.020
#> GSM447624 3 0.3409 0.6201 0.144 0.000 0.824 0.000 0.032
#> GSM447625 3 0.6430 0.5860 0.000 0.096 0.628 0.076 0.200
#> GSM447707 2 0.3246 0.6358 0.000 0.808 0.000 0.184 0.008
#> GSM447732 3 0.6313 0.5906 0.000 0.084 0.636 0.076 0.204
#> GSM447684 3 0.4016 0.6781 0.016 0.072 0.828 0.008 0.076
#> GSM447731 5 0.4961 0.2420 0.000 0.020 0.004 0.456 0.520
#> GSM447705 3 0.5880 0.5934 0.000 0.176 0.648 0.016 0.160
#> GSM447631 3 0.1764 0.6765 0.008 0.000 0.928 0.000 0.064
#> GSM447701 2 0.2494 0.7295 0.000 0.904 0.032 0.008 0.056
#> GSM447645 3 0.1764 0.6765 0.008 0.000 0.928 0.000 0.064
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM447671 2 0.5241 0.54426 0.000 0.676 0.072 0.036 0.208 0.008
#> GSM447694 3 0.5720 0.33382 0.024 0.008 0.472 0.012 0.444 0.040
#> GSM447618 5 0.6725 0.00844 0.004 0.380 0.044 0.140 0.424 0.008
#> GSM447691 5 0.7197 0.02534 0.016 0.360 0.036 0.112 0.436 0.040
#> GSM447733 6 0.6217 0.58202 0.000 0.020 0.016 0.120 0.348 0.496
#> GSM447620 2 0.5473 0.49672 0.000 0.648 0.128 0.020 0.196 0.008
#> GSM447627 5 0.7065 -0.16415 0.000 0.016 0.092 0.132 0.472 0.288
#> GSM447630 3 0.5702 0.42499 0.000 0.076 0.532 0.028 0.360 0.004
#> GSM447642 1 0.1492 0.81261 0.940 0.000 0.000 0.000 0.036 0.024
#> GSM447649 4 0.3331 0.73313 0.000 0.160 0.000 0.808 0.020 0.012
#> GSM447654 4 0.3772 0.69520 0.000 0.068 0.000 0.812 0.032 0.088
#> GSM447655 2 0.1267 0.70103 0.000 0.940 0.000 0.060 0.000 0.000
#> GSM447669 3 0.5873 0.32676 0.000 0.108 0.464 0.024 0.404 0.000
#> GSM447676 1 0.1633 0.81253 0.932 0.000 0.000 0.000 0.044 0.024
#> GSM447678 4 0.6853 -0.11678 0.000 0.104 0.000 0.408 0.364 0.124
#> GSM447681 2 0.2837 0.69397 0.000 0.856 0.000 0.088 0.056 0.000
#> GSM447698 5 0.6412 0.24591 0.000 0.112 0.004 0.388 0.444 0.052
#> GSM447713 1 0.0260 0.81977 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM447722 5 0.6412 0.24591 0.000 0.112 0.004 0.388 0.444 0.052
#> GSM447726 3 0.7359 0.18131 0.012 0.240 0.452 0.044 0.228 0.024
#> GSM447735 5 0.6021 -0.19053 0.000 0.004 0.028 0.196 0.576 0.196
#> GSM447737 1 0.4490 0.67693 0.736 0.000 0.104 0.004 0.148 0.008
#> GSM447657 2 0.3039 0.69341 0.000 0.848 0.000 0.088 0.060 0.004
#> GSM447674 2 0.3039 0.69341 0.000 0.848 0.000 0.088 0.060 0.004
#> GSM447636 1 0.1492 0.81261 0.940 0.000 0.000 0.000 0.036 0.024
#> GSM447723 1 0.8210 0.35533 0.456 0.156 0.040 0.068 0.212 0.068
#> GSM447699 5 0.7007 0.34726 0.000 0.068 0.188 0.156 0.544 0.044
#> GSM447708 2 0.6811 0.16711 0.004 0.444 0.056 0.100 0.376 0.020
#> GSM447721 1 0.3096 0.78363 0.868 0.000 0.048 0.016 0.052 0.016
#> GSM447623 1 0.2384 0.78176 0.888 0.000 0.064 0.000 0.048 0.000
#> GSM447621 1 0.2384 0.78176 0.888 0.000 0.064 0.000 0.048 0.000
#> GSM447650 2 0.2182 0.70557 0.000 0.900 0.004 0.020 0.076 0.000
#> GSM447651 2 0.2825 0.69359 0.000 0.876 0.016 0.008 0.076 0.024
#> GSM447653 6 0.3485 0.64297 0.000 0.008 0.004 0.060 0.104 0.824
#> GSM447658 1 0.1633 0.81253 0.932 0.000 0.000 0.000 0.044 0.024
#> GSM447675 4 0.4919 0.55785 0.000 0.032 0.000 0.696 0.080 0.192
#> GSM447680 2 0.4672 0.61753 0.012 0.756 0.000 0.072 0.120 0.040
#> GSM447686 2 0.7947 0.27863 0.132 0.416 0.000 0.088 0.268 0.096
#> GSM447736 5 0.6312 -0.14279 0.000 0.080 0.416 0.044 0.444 0.016
#> GSM447629 2 0.7544 0.07222 0.028 0.384 0.012 0.132 0.368 0.076
#> GSM447648 3 0.1668 0.63750 0.008 0.000 0.928 0.000 0.060 0.004
#> GSM447660 1 0.5006 0.68500 0.732 0.016 0.000 0.048 0.132 0.072
#> GSM447661 2 0.1321 0.70633 0.000 0.952 0.004 0.020 0.024 0.000
#> GSM447663 3 0.5367 0.43823 0.000 0.060 0.532 0.024 0.384 0.000
#> GSM447704 4 0.3122 0.73550 0.000 0.160 0.000 0.816 0.020 0.004
#> GSM447720 5 0.5964 -0.20045 0.000 0.052 0.436 0.040 0.456 0.016
#> GSM447652 2 0.6348 0.37977 0.000 0.536 0.004 0.236 0.184 0.040
#> GSM447679 2 0.3463 0.66894 0.000 0.832 0.000 0.080 0.064 0.024
#> GSM447712 1 0.0146 0.81958 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM447664 4 0.5224 0.51358 0.000 0.036 0.000 0.668 0.096 0.200
#> GSM447637 3 0.1728 0.63982 0.004 0.000 0.924 0.000 0.064 0.008
#> GSM447639 5 0.7265 -0.04822 0.000 0.032 0.088 0.144 0.484 0.252
#> GSM447615 3 0.2182 0.62163 0.076 0.000 0.900 0.000 0.020 0.004
#> GSM447656 2 0.6693 0.48586 0.056 0.588 0.004 0.084 0.208 0.060
#> GSM447673 4 0.4881 0.59819 0.000 0.044 0.000 0.716 0.084 0.156
#> GSM447719 6 0.2924 0.61771 0.000 0.000 0.024 0.040 0.068 0.868
#> GSM447706 3 0.0260 0.64594 0.000 0.000 0.992 0.000 0.008 0.000
#> GSM447612 5 0.7375 0.37231 0.000 0.140 0.212 0.116 0.496 0.036
#> GSM447665 2 0.4752 0.58192 0.000 0.720 0.072 0.020 0.180 0.008
#> GSM447677 2 0.2332 0.70478 0.000 0.904 0.012 0.016 0.060 0.008
#> GSM447613 1 0.0405 0.82008 0.988 0.000 0.004 0.000 0.008 0.000
#> GSM447659 6 0.6217 0.58202 0.000 0.020 0.016 0.120 0.348 0.496
#> GSM447662 3 0.1218 0.65117 0.000 0.012 0.956 0.000 0.028 0.004
#> GSM447666 3 0.3652 0.60382 0.000 0.084 0.816 0.000 0.080 0.020
#> GSM447668 2 0.1148 0.70540 0.000 0.960 0.004 0.020 0.016 0.000
#> GSM447682 2 0.7162 0.02941 0.000 0.364 0.008 0.272 0.300 0.056
#> GSM447683 2 0.3548 0.68186 0.000 0.824 0.000 0.076 0.080 0.020
#> GSM447688 4 0.2964 0.74063 0.000 0.140 0.000 0.836 0.012 0.012
#> GSM447702 2 0.1204 0.70207 0.000 0.944 0.000 0.056 0.000 0.000
#> GSM447709 2 0.4357 0.63192 0.000 0.760 0.060 0.020 0.152 0.008
#> GSM447711 1 0.1168 0.81202 0.956 0.000 0.028 0.000 0.016 0.000
#> GSM447715 1 0.8210 0.35533 0.456 0.156 0.040 0.068 0.212 0.068
#> GSM447693 3 0.1542 0.64519 0.004 0.000 0.936 0.000 0.052 0.008
#> GSM447611 4 0.4783 0.36018 0.000 0.000 0.000 0.616 0.076 0.308
#> GSM447672 2 0.1387 0.70054 0.000 0.932 0.000 0.068 0.000 0.000
#> GSM447703 4 0.2964 0.74063 0.000 0.140 0.000 0.836 0.012 0.012
#> GSM447727 1 0.8068 0.38088 0.472 0.152 0.048 0.056 0.212 0.060
#> GSM447638 3 0.6784 0.39631 0.172 0.104 0.580 0.004 0.116 0.024
#> GSM447670 3 0.2826 0.59468 0.112 0.000 0.856 0.000 0.024 0.008
#> GSM447700 5 0.6700 0.34431 0.000 0.108 0.044 0.252 0.548 0.048
#> GSM447738 4 0.2445 0.74767 0.000 0.120 0.000 0.868 0.008 0.004
#> GSM447739 1 0.0000 0.81901 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447617 1 0.3127 0.75112 0.840 0.000 0.100 0.000 0.056 0.004
#> GSM447628 4 0.3317 0.71276 0.000 0.088 0.000 0.828 0.004 0.080
#> GSM447632 4 0.3033 0.72790 0.000 0.108 0.000 0.848 0.032 0.012
#> GSM447619 3 0.1218 0.65117 0.000 0.012 0.956 0.000 0.028 0.004
#> GSM447643 1 0.8730 0.10804 0.352 0.240 0.056 0.052 0.216 0.084
#> GSM447724 5 0.7019 0.36732 0.000 0.148 0.072 0.192 0.544 0.044
#> GSM447728 2 0.5756 0.50954 0.004 0.608 0.032 0.072 0.272 0.012
#> GSM447610 6 0.6205 0.52304 0.012 0.000 0.000 0.208 0.360 0.420
#> GSM447633 2 0.4752 0.58192 0.000 0.720 0.072 0.020 0.180 0.008
#> GSM447634 3 0.5275 0.44307 0.000 0.040 0.548 0.016 0.384 0.012
#> GSM447622 3 0.4970 0.43007 0.248 0.000 0.648 0.000 0.096 0.008
#> GSM447667 2 0.7626 0.33101 0.072 0.460 0.004 0.080 0.276 0.108
#> GSM447687 4 0.2445 0.74767 0.000 0.120 0.000 0.868 0.008 0.004
#> GSM447695 5 0.6892 -0.17248 0.212 0.000 0.340 0.012 0.400 0.036
#> GSM447696 1 0.0000 0.81901 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447697 1 0.0405 0.82008 0.988 0.000 0.004 0.000 0.008 0.000
#> GSM447714 3 0.4842 0.52701 0.000 0.028 0.636 0.020 0.308 0.008
#> GSM447717 1 0.1092 0.81638 0.960 0.000 0.000 0.000 0.020 0.020
#> GSM447725 1 0.0000 0.81901 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447729 4 0.4767 0.36407 0.000 0.000 0.000 0.620 0.076 0.304
#> GSM447644 3 0.5873 0.32676 0.000 0.108 0.464 0.024 0.404 0.000
#> GSM447710 3 0.4244 0.57420 0.000 0.012 0.700 0.012 0.264 0.012
#> GSM447614 6 0.6205 0.52304 0.012 0.000 0.000 0.208 0.360 0.420
#> GSM447685 2 0.6028 0.53873 0.028 0.636 0.000 0.088 0.192 0.056
#> GSM447690 1 0.0000 0.81901 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447730 2 0.3370 0.61444 0.000 0.772 0.000 0.212 0.004 0.012
#> GSM447646 4 0.3317 0.71276 0.000 0.088 0.000 0.828 0.004 0.080
#> GSM447689 3 0.4421 0.59034 0.000 0.036 0.712 0.012 0.232 0.008
#> GSM447635 5 0.8018 0.00140 0.028 0.348 0.068 0.116 0.380 0.060
#> GSM447641 1 0.1633 0.81253 0.932 0.000 0.000 0.000 0.044 0.024
#> GSM447716 5 0.8070 0.09886 0.056 0.248 0.000 0.192 0.384 0.120
#> GSM447718 5 0.8129 0.29392 0.000 0.268 0.196 0.108 0.364 0.064
#> GSM447616 3 0.5367 0.26865 0.336 0.000 0.556 0.000 0.100 0.008
#> GSM447626 3 0.2803 0.63861 0.000 0.016 0.868 0.004 0.096 0.016
#> GSM447640 2 0.3045 0.68918 0.000 0.860 0.000 0.060 0.060 0.020
#> GSM447734 3 0.5895 0.32517 0.000 0.032 0.476 0.028 0.424 0.040
#> GSM447692 1 0.6673 -0.07966 0.372 0.000 0.280 0.012 0.324 0.012
#> GSM447647 4 0.3122 0.73550 0.000 0.160 0.000 0.816 0.020 0.004
#> GSM447624 3 0.3043 0.58143 0.132 0.000 0.836 0.000 0.024 0.008
#> GSM447625 3 0.5405 0.43777 0.000 0.048 0.540 0.028 0.380 0.004
#> GSM447707 2 0.3370 0.61444 0.000 0.772 0.000 0.212 0.004 0.012
#> GSM447732 3 0.5241 0.44719 0.000 0.036 0.548 0.028 0.384 0.004
#> GSM447684 3 0.4038 0.62021 0.012 0.032 0.792 0.004 0.140 0.020
#> GSM447731 6 0.3743 0.58087 0.000 0.008 0.000 0.136 0.064 0.792
#> GSM447705 3 0.5852 0.43595 0.000 0.136 0.572 0.016 0.268 0.008
#> GSM447631 3 0.1728 0.63982 0.004 0.000 0.924 0.000 0.064 0.008
#> GSM447701 2 0.2882 0.69144 0.000 0.860 0.020 0.020 0.100 0.000
#> GSM447645 3 0.1728 0.63982 0.004 0.000 0.924 0.000 0.064 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)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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 gender(p) individual(p) disease.state(p) other(p) k
#> CV:hclust 121 0.171 0.822 0.04157 0.00286 2
#> CV:hclust 97 0.491 0.861 0.09533 0.03254 3
#> CV:hclust 111 0.389 0.515 0.00430 0.03143 4
#> CV:hclust 87 0.357 0.506 0.00454 0.05467 5
#> CV:hclust 83 0.508 0.330 0.02287 0.02633 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "kmeans"]
# you can also extract it by
# res = res_list["CV:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 130 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 0.725 0.878 0.939 0.4858 0.497 0.497
#> 3 3 0.538 0.694 0.786 0.3309 0.797 0.616
#> 4 4 0.690 0.825 0.870 0.1437 0.832 0.570
#> 5 5 0.682 0.600 0.734 0.0727 0.942 0.780
#> 6 6 0.659 0.554 0.713 0.0443 0.890 0.551
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
#> GSM447671 2 0.0000 0.983 0.000 1.000
#> GSM447694 1 0.7139 0.768 0.804 0.196
#> GSM447618 2 0.0000 0.983 0.000 1.000
#> GSM447691 2 0.0000 0.983 0.000 1.000
#> GSM447733 2 0.0000 0.983 0.000 1.000
#> GSM447620 2 0.0000 0.983 0.000 1.000
#> GSM447627 1 0.8555 0.695 0.720 0.280
#> GSM447630 2 0.0000 0.983 0.000 1.000
#> GSM447642 1 0.0000 0.876 1.000 0.000
#> GSM447649 2 0.0000 0.983 0.000 1.000
#> GSM447654 2 0.0000 0.983 0.000 1.000
#> GSM447655 2 0.0000 0.983 0.000 1.000
#> GSM447669 2 0.0000 0.983 0.000 1.000
#> GSM447676 1 0.0000 0.876 1.000 0.000
#> GSM447678 2 0.0000 0.983 0.000 1.000
#> GSM447681 2 0.0000 0.983 0.000 1.000
#> GSM447698 2 0.0000 0.983 0.000 1.000
#> GSM447713 1 0.0000 0.876 1.000 0.000
#> GSM447722 2 0.0000 0.983 0.000 1.000
#> GSM447726 2 0.3584 0.906 0.068 0.932
#> GSM447735 1 0.8608 0.691 0.716 0.284
#> GSM447737 1 0.0000 0.876 1.000 0.000
#> GSM447657 2 0.0000 0.983 0.000 1.000
#> GSM447674 2 0.0000 0.983 0.000 1.000
#> GSM447636 1 0.0000 0.876 1.000 0.000
#> GSM447723 1 0.0000 0.876 1.000 0.000
#> GSM447699 1 0.9833 0.457 0.576 0.424
#> GSM447708 2 0.0000 0.983 0.000 1.000
#> GSM447721 1 0.0000 0.876 1.000 0.000
#> GSM447623 1 0.0000 0.876 1.000 0.000
#> GSM447621 1 0.0000 0.876 1.000 0.000
#> GSM447650 2 0.0000 0.983 0.000 1.000
#> GSM447651 2 0.0000 0.983 0.000 1.000
#> GSM447653 2 0.6623 0.748 0.172 0.828
#> GSM447658 1 0.0000 0.876 1.000 0.000
#> GSM447675 2 0.0000 0.983 0.000 1.000
#> GSM447680 2 0.7219 0.712 0.200 0.800
#> GSM447686 1 0.9710 0.331 0.600 0.400
#> GSM447736 1 0.9460 0.578 0.636 0.364
#> GSM447629 2 0.0672 0.975 0.008 0.992
#> GSM447648 1 0.0000 0.876 1.000 0.000
#> GSM447660 1 0.0000 0.876 1.000 0.000
#> GSM447661 2 0.0000 0.983 0.000 1.000
#> GSM447663 1 0.9686 0.520 0.604 0.396
#> GSM447704 2 0.0000 0.983 0.000 1.000
#> GSM447720 1 0.9686 0.520 0.604 0.396
#> GSM447652 2 0.0000 0.983 0.000 1.000
#> GSM447679 2 0.0000 0.983 0.000 1.000
#> GSM447712 1 0.0000 0.876 1.000 0.000
#> GSM447664 2 0.0376 0.979 0.004 0.996
#> GSM447637 1 0.0000 0.876 1.000 0.000
#> GSM447639 2 0.0000 0.983 0.000 1.000
#> GSM447615 1 0.0000 0.876 1.000 0.000
#> GSM447656 2 0.2043 0.950 0.032 0.968
#> GSM447673 2 0.0000 0.983 0.000 1.000
#> GSM447719 1 0.7299 0.762 0.796 0.204
#> GSM447706 1 0.0000 0.876 1.000 0.000
#> GSM447612 2 0.7219 0.697 0.200 0.800
#> GSM447665 2 0.0000 0.983 0.000 1.000
#> GSM447677 2 0.0000 0.983 0.000 1.000
#> GSM447613 1 0.0000 0.876 1.000 0.000
#> GSM447659 2 0.0000 0.983 0.000 1.000
#> GSM447662 1 0.9686 0.520 0.604 0.396
#> GSM447666 1 0.9661 0.527 0.608 0.392
#> GSM447668 2 0.0000 0.983 0.000 1.000
#> GSM447682 2 0.0000 0.983 0.000 1.000
#> GSM447683 2 0.0000 0.983 0.000 1.000
#> GSM447688 2 0.0000 0.983 0.000 1.000
#> GSM447702 2 0.0000 0.983 0.000 1.000
#> GSM447709 2 0.0000 0.983 0.000 1.000
#> GSM447711 1 0.0000 0.876 1.000 0.000
#> GSM447715 1 0.0000 0.876 1.000 0.000
#> GSM447693 1 0.7139 0.768 0.804 0.196
#> GSM447611 2 0.0000 0.983 0.000 1.000
#> GSM447672 2 0.0000 0.983 0.000 1.000
#> GSM447703 2 0.0000 0.983 0.000 1.000
#> GSM447727 1 0.0000 0.876 1.000 0.000
#> GSM447638 1 0.0000 0.876 1.000 0.000
#> GSM447670 1 0.0000 0.876 1.000 0.000
#> GSM447700 2 0.0000 0.983 0.000 1.000
#> GSM447738 2 0.0000 0.983 0.000 1.000
#> GSM447739 1 0.0000 0.876 1.000 0.000
#> GSM447617 1 0.0000 0.876 1.000 0.000
#> GSM447628 2 0.0000 0.983 0.000 1.000
#> GSM447632 2 0.0000 0.983 0.000 1.000
#> GSM447619 1 0.8661 0.686 0.712 0.288
#> GSM447643 1 0.0000 0.876 1.000 0.000
#> GSM447724 2 0.0000 0.983 0.000 1.000
#> GSM447728 2 0.0000 0.983 0.000 1.000
#> GSM447610 1 0.0000 0.876 1.000 0.000
#> GSM447633 2 0.0000 0.983 0.000 1.000
#> GSM447634 1 0.8861 0.666 0.696 0.304
#> GSM447622 1 0.0000 0.876 1.000 0.000
#> GSM447667 2 0.1184 0.967 0.016 0.984
#> GSM447687 2 0.0000 0.983 0.000 1.000
#> GSM447695 1 0.6438 0.789 0.836 0.164
#> GSM447696 1 0.0000 0.876 1.000 0.000
#> GSM447697 1 0.0000 0.876 1.000 0.000
#> GSM447714 1 0.9710 0.511 0.600 0.400
#> GSM447717 1 0.0000 0.876 1.000 0.000
#> GSM447725 1 0.0000 0.876 1.000 0.000
#> GSM447729 2 0.0000 0.983 0.000 1.000
#> GSM447644 2 0.0000 0.983 0.000 1.000
#> GSM447710 1 0.8608 0.690 0.716 0.284
#> GSM447614 1 0.8555 0.695 0.720 0.280
#> GSM447685 2 0.0000 0.983 0.000 1.000
#> GSM447690 1 0.0000 0.876 1.000 0.000
#> GSM447730 2 0.0000 0.983 0.000 1.000
#> GSM447646 2 0.0000 0.983 0.000 1.000
#> GSM447689 1 0.8955 0.656 0.688 0.312
#> GSM447635 2 0.0000 0.983 0.000 1.000
#> GSM447641 1 0.0000 0.876 1.000 0.000
#> GSM447716 2 0.0000 0.983 0.000 1.000
#> GSM447718 2 0.8207 0.579 0.256 0.744
#> GSM447616 1 0.0000 0.876 1.000 0.000
#> GSM447626 1 0.0376 0.875 0.996 0.004
#> GSM447640 2 0.0000 0.983 0.000 1.000
#> GSM447734 1 0.9522 0.564 0.628 0.372
#> GSM447692 1 0.0000 0.876 1.000 0.000
#> GSM447647 2 0.0000 0.983 0.000 1.000
#> GSM447624 1 0.0000 0.876 1.000 0.000
#> GSM447625 1 0.9686 0.520 0.604 0.396
#> GSM447707 2 0.0000 0.983 0.000 1.000
#> GSM447732 1 0.8763 0.677 0.704 0.296
#> GSM447684 1 0.0000 0.876 1.000 0.000
#> GSM447731 2 0.0000 0.983 0.000 1.000
#> GSM447705 2 0.0376 0.979 0.004 0.996
#> GSM447631 1 0.0000 0.876 1.000 0.000
#> GSM447701 2 0.0000 0.983 0.000 1.000
#> GSM447645 1 0.0000 0.876 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM447671 2 0.4235 0.6686 0.000 0.824 0.176
#> GSM447694 3 0.1860 0.5599 0.052 0.000 0.948
#> GSM447618 2 0.3192 0.7353 0.000 0.888 0.112
#> GSM447691 2 0.4178 0.6735 0.000 0.828 0.172
#> GSM447733 2 0.9633 0.3669 0.340 0.444 0.216
#> GSM447620 2 0.6062 0.1894 0.000 0.616 0.384
#> GSM447627 3 0.4605 0.6100 0.204 0.000 0.796
#> GSM447630 2 0.5327 0.5085 0.000 0.728 0.272
#> GSM447642 1 0.5835 0.9683 0.660 0.000 0.340
#> GSM447649 2 0.0592 0.7884 0.012 0.988 0.000
#> GSM447654 2 0.6057 0.6731 0.340 0.656 0.004
#> GSM447655 2 0.0747 0.7873 0.000 0.984 0.016
#> GSM447669 2 0.4291 0.6638 0.000 0.820 0.180
#> GSM447676 1 0.5835 0.9683 0.660 0.000 0.340
#> GSM447678 2 0.6057 0.6731 0.340 0.656 0.004
#> GSM447681 2 0.0747 0.7873 0.000 0.984 0.016
#> GSM447698 2 0.4931 0.7207 0.232 0.768 0.000
#> GSM447713 1 0.5835 0.9683 0.660 0.000 0.340
#> GSM447722 2 0.6057 0.6731 0.340 0.656 0.004
#> GSM447726 3 0.6244 0.3413 0.000 0.440 0.560
#> GSM447735 3 0.8884 0.3886 0.420 0.120 0.460
#> GSM447737 1 0.5835 0.9683 0.660 0.000 0.340
#> GSM447657 2 0.0000 0.7891 0.000 1.000 0.000
#> GSM447674 2 0.0000 0.7891 0.000 1.000 0.000
#> GSM447636 1 0.5835 0.9683 0.660 0.000 0.340
#> GSM447723 1 0.5835 0.9683 0.660 0.000 0.340
#> GSM447699 3 0.5431 0.6159 0.000 0.284 0.716
#> GSM447708 2 0.2625 0.7555 0.000 0.916 0.084
#> GSM447721 1 0.5835 0.9683 0.660 0.000 0.340
#> GSM447623 1 0.5835 0.9683 0.660 0.000 0.340
#> GSM447621 1 0.5835 0.9683 0.660 0.000 0.340
#> GSM447650 2 0.0747 0.7873 0.000 0.984 0.016
#> GSM447651 2 0.2356 0.7632 0.000 0.928 0.072
#> GSM447653 3 0.9918 0.0760 0.340 0.276 0.384
#> GSM447658 1 0.5835 0.9683 0.660 0.000 0.340
#> GSM447675 2 0.6057 0.6731 0.340 0.656 0.004
#> GSM447680 2 0.2845 0.7610 0.012 0.920 0.068
#> GSM447686 1 0.7865 0.7747 0.660 0.124 0.216
#> GSM447736 3 0.3816 0.7188 0.000 0.148 0.852
#> GSM447629 2 0.0747 0.7873 0.000 0.984 0.016
#> GSM447648 3 0.3482 0.4589 0.128 0.000 0.872
#> GSM447660 1 0.5835 0.9683 0.660 0.000 0.340
#> GSM447661 2 0.0747 0.7873 0.000 0.984 0.016
#> GSM447663 3 0.4235 0.7095 0.000 0.176 0.824
#> GSM447704 2 0.0592 0.7884 0.012 0.988 0.000
#> GSM447720 3 0.3816 0.7188 0.000 0.148 0.852
#> GSM447652 2 0.0424 0.7888 0.008 0.992 0.000
#> GSM447679 2 0.0747 0.7873 0.000 0.984 0.016
#> GSM447712 1 0.5835 0.9683 0.660 0.000 0.340
#> GSM447664 2 0.6057 0.6731 0.340 0.656 0.004
#> GSM447637 3 0.3482 0.4589 0.128 0.000 0.872
#> GSM447639 2 0.9390 0.4620 0.340 0.476 0.184
#> GSM447615 3 0.4235 0.3520 0.176 0.000 0.824
#> GSM447656 2 0.1964 0.7714 0.000 0.944 0.056
#> GSM447673 2 0.5835 0.6750 0.340 0.660 0.000
#> GSM447719 3 0.7534 0.4828 0.428 0.040 0.532
#> GSM447706 3 0.1289 0.5810 0.032 0.000 0.968
#> GSM447612 3 0.5138 0.6543 0.000 0.252 0.748
#> GSM447665 2 0.3412 0.7241 0.000 0.876 0.124
#> GSM447677 2 0.2261 0.7649 0.000 0.932 0.068
#> GSM447613 1 0.5835 0.9683 0.660 0.000 0.340
#> GSM447659 3 0.8261 0.4470 0.340 0.092 0.568
#> GSM447662 3 0.4002 0.7154 0.000 0.160 0.840
#> GSM447666 3 0.5650 0.5770 0.000 0.312 0.688
#> GSM447668 2 0.2261 0.7649 0.000 0.932 0.068
#> GSM447682 2 0.2711 0.7676 0.088 0.912 0.000
#> GSM447683 2 0.1860 0.7734 0.000 0.948 0.052
#> GSM447688 2 0.5560 0.6963 0.300 0.700 0.000
#> GSM447702 2 0.0747 0.7873 0.000 0.984 0.016
#> GSM447709 2 0.3038 0.7412 0.000 0.896 0.104
#> GSM447711 1 0.5835 0.9683 0.660 0.000 0.340
#> GSM447715 1 0.5948 0.9411 0.640 0.000 0.360
#> GSM447693 3 0.3356 0.6361 0.036 0.056 0.908
#> GSM447611 2 0.6057 0.6731 0.340 0.656 0.004
#> GSM447672 2 0.0000 0.7891 0.000 1.000 0.000
#> GSM447703 2 0.5560 0.6963 0.300 0.700 0.000
#> GSM447727 1 0.5835 0.9683 0.660 0.000 0.340
#> GSM447638 3 0.7660 0.4185 0.048 0.404 0.548
#> GSM447670 1 0.5926 0.9483 0.644 0.000 0.356
#> GSM447700 2 0.4291 0.6667 0.000 0.820 0.180
#> GSM447738 2 0.5560 0.6963 0.300 0.700 0.000
#> GSM447739 1 0.5835 0.9683 0.660 0.000 0.340
#> GSM447617 1 0.5835 0.9683 0.660 0.000 0.340
#> GSM447628 2 0.6057 0.6731 0.340 0.656 0.004
#> GSM447632 2 0.5560 0.6963 0.300 0.700 0.000
#> GSM447619 3 0.3816 0.7188 0.000 0.148 0.852
#> GSM447643 1 0.7605 0.8345 0.660 0.088 0.252
#> GSM447724 3 0.9990 -0.0902 0.340 0.312 0.348
#> GSM447728 2 0.1753 0.7752 0.000 0.952 0.048
#> GSM447610 1 0.2261 0.5699 0.932 0.000 0.068
#> GSM447633 3 0.6286 0.2793 0.000 0.464 0.536
#> GSM447634 3 0.3816 0.7188 0.000 0.148 0.852
#> GSM447622 3 0.3482 0.4589 0.128 0.000 0.872
#> GSM447667 2 0.0237 0.7892 0.004 0.996 0.000
#> GSM447687 2 0.5560 0.6963 0.300 0.700 0.000
#> GSM447695 3 0.1860 0.5599 0.052 0.000 0.948
#> GSM447696 1 0.5835 0.9683 0.660 0.000 0.340
#> GSM447697 1 0.5835 0.9683 0.660 0.000 0.340
#> GSM447714 3 0.3816 0.7188 0.000 0.148 0.852
#> GSM447717 1 0.5835 0.9683 0.660 0.000 0.340
#> GSM447725 1 0.5835 0.9683 0.660 0.000 0.340
#> GSM447729 2 0.6057 0.6731 0.340 0.656 0.004
#> GSM447644 2 0.6062 0.2154 0.000 0.616 0.384
#> GSM447710 3 0.3816 0.7188 0.000 0.148 0.852
#> GSM447614 3 0.7192 0.5018 0.412 0.028 0.560
#> GSM447685 2 0.0747 0.7873 0.000 0.984 0.016
#> GSM447690 1 0.5835 0.9683 0.660 0.000 0.340
#> GSM447730 2 0.0592 0.7884 0.012 0.988 0.000
#> GSM447646 2 0.6057 0.6731 0.340 0.656 0.004
#> GSM447689 3 0.3816 0.7188 0.000 0.148 0.852
#> GSM447635 2 0.4121 0.6802 0.000 0.832 0.168
#> GSM447641 1 0.5835 0.9683 0.660 0.000 0.340
#> GSM447716 2 0.5560 0.6963 0.300 0.700 0.000
#> GSM447718 3 0.4399 0.7042 0.000 0.188 0.812
#> GSM447616 3 0.3482 0.4589 0.128 0.000 0.872
#> GSM447626 3 0.4047 0.7171 0.004 0.148 0.848
#> GSM447640 2 0.0000 0.7891 0.000 1.000 0.000
#> GSM447734 3 0.3816 0.7188 0.000 0.148 0.852
#> GSM447692 3 0.3551 0.4523 0.132 0.000 0.868
#> GSM447647 2 0.6057 0.6731 0.340 0.656 0.004
#> GSM447624 3 0.6308 -0.6858 0.492 0.000 0.508
#> GSM447625 3 0.3816 0.7188 0.000 0.148 0.852
#> GSM447707 2 0.0237 0.7891 0.004 0.996 0.000
#> GSM447732 3 0.3816 0.7188 0.000 0.148 0.852
#> GSM447684 3 0.4047 0.7171 0.004 0.148 0.848
#> GSM447731 2 0.6381 0.6682 0.340 0.648 0.012
#> GSM447705 3 0.5835 0.5292 0.000 0.340 0.660
#> GSM447631 3 0.3412 0.4641 0.124 0.000 0.876
#> GSM447701 2 0.2625 0.7559 0.000 0.916 0.084
#> GSM447645 3 0.3482 0.4589 0.128 0.000 0.872
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM447671 2 0.4834 0.7771 0.000 0.784 0.096 0.120
#> GSM447694 3 0.3099 0.8971 0.020 0.000 0.876 0.104
#> GSM447618 2 0.4909 0.7865 0.008 0.788 0.068 0.136
#> GSM447691 2 0.5102 0.7663 0.000 0.764 0.100 0.136
#> GSM447733 4 0.3029 0.7798 0.004 0.048 0.052 0.896
#> GSM447620 2 0.4638 0.7427 0.000 0.776 0.180 0.044
#> GSM447627 3 0.4609 0.8255 0.024 0.000 0.752 0.224
#> GSM447630 2 0.4424 0.7817 0.000 0.812 0.088 0.100
#> GSM447642 1 0.0804 0.9610 0.980 0.000 0.012 0.008
#> GSM447649 2 0.0592 0.8700 0.000 0.984 0.000 0.016
#> GSM447654 4 0.3831 0.7983 0.004 0.204 0.000 0.792
#> GSM447655 2 0.0188 0.8711 0.000 0.996 0.000 0.004
#> GSM447669 2 0.4477 0.7809 0.000 0.808 0.084 0.108
#> GSM447676 1 0.1042 0.9571 0.972 0.000 0.008 0.020
#> GSM447678 4 0.4082 0.8030 0.008 0.152 0.020 0.820
#> GSM447681 2 0.0376 0.8714 0.004 0.992 0.004 0.000
#> GSM447698 2 0.5421 0.2947 0.008 0.664 0.020 0.308
#> GSM447713 1 0.0469 0.9617 0.988 0.000 0.012 0.000
#> GSM447722 4 0.3663 0.7903 0.008 0.120 0.020 0.852
#> GSM447726 2 0.6163 0.6730 0.000 0.676 0.160 0.164
#> GSM447735 4 0.2660 0.7565 0.008 0.012 0.072 0.908
#> GSM447737 1 0.0937 0.9585 0.976 0.000 0.012 0.012
#> GSM447657 2 0.2057 0.8567 0.008 0.940 0.020 0.032
#> GSM447674 2 0.0895 0.8686 0.004 0.976 0.000 0.020
#> GSM447636 1 0.1151 0.9559 0.968 0.000 0.008 0.024
#> GSM447723 1 0.2402 0.9249 0.912 0.000 0.012 0.076
#> GSM447699 3 0.4789 0.7772 0.000 0.056 0.772 0.172
#> GSM447708 2 0.2675 0.8488 0.000 0.892 0.008 0.100
#> GSM447721 1 0.0469 0.9617 0.988 0.000 0.012 0.000
#> GSM447623 1 0.0469 0.9617 0.988 0.000 0.012 0.000
#> GSM447621 1 0.0469 0.9617 0.988 0.000 0.012 0.000
#> GSM447650 2 0.0000 0.8715 0.000 1.000 0.000 0.000
#> GSM447651 2 0.0524 0.8717 0.000 0.988 0.008 0.004
#> GSM447653 4 0.2599 0.7611 0.004 0.020 0.064 0.912
#> GSM447658 1 0.1151 0.9559 0.968 0.000 0.008 0.024
#> GSM447675 4 0.3266 0.8041 0.000 0.168 0.000 0.832
#> GSM447680 2 0.3232 0.8271 0.016 0.872 0.004 0.108
#> GSM447686 1 0.2966 0.8949 0.896 0.008 0.020 0.076
#> GSM447736 3 0.3047 0.8905 0.012 0.000 0.872 0.116
#> GSM447629 2 0.4131 0.8098 0.008 0.816 0.020 0.156
#> GSM447648 3 0.2530 0.8681 0.100 0.000 0.896 0.004
#> GSM447660 1 0.1151 0.9559 0.968 0.000 0.008 0.024
#> GSM447661 2 0.0000 0.8715 0.000 1.000 0.000 0.000
#> GSM447663 3 0.3720 0.8903 0.016 0.024 0.860 0.100
#> GSM447704 2 0.0707 0.8691 0.000 0.980 0.000 0.020
#> GSM447720 3 0.3923 0.8687 0.008 0.016 0.828 0.148
#> GSM447652 2 0.0000 0.8715 0.000 1.000 0.000 0.000
#> GSM447679 2 0.0707 0.8694 0.000 0.980 0.000 0.020
#> GSM447712 1 0.0804 0.9611 0.980 0.000 0.012 0.008
#> GSM447664 4 0.2401 0.7920 0.004 0.092 0.000 0.904
#> GSM447637 3 0.2345 0.8681 0.100 0.000 0.900 0.000
#> GSM447639 4 0.3299 0.7865 0.012 0.056 0.044 0.888
#> GSM447615 3 0.3335 0.8509 0.120 0.000 0.860 0.020
#> GSM447656 2 0.3088 0.8274 0.000 0.864 0.008 0.128
#> GSM447673 4 0.5143 0.7501 0.008 0.264 0.020 0.708
#> GSM447719 4 0.4368 0.6322 0.004 0.004 0.244 0.748
#> GSM447706 3 0.0707 0.9005 0.020 0.000 0.980 0.000
#> GSM447612 3 0.2973 0.8828 0.000 0.020 0.884 0.096
#> GSM447665 2 0.2048 0.8538 0.000 0.928 0.008 0.064
#> GSM447677 2 0.0524 0.8717 0.000 0.988 0.008 0.004
#> GSM447613 1 0.0469 0.9617 0.988 0.000 0.012 0.000
#> GSM447659 4 0.2960 0.7439 0.004 0.020 0.084 0.892
#> GSM447662 3 0.1369 0.9029 0.016 0.004 0.964 0.016
#> GSM447666 3 0.1209 0.8883 0.000 0.032 0.964 0.004
#> GSM447668 2 0.0188 0.8723 0.000 0.996 0.004 0.000
#> GSM447682 2 0.2057 0.8518 0.008 0.940 0.020 0.032
#> GSM447683 2 0.0592 0.8704 0.000 0.984 0.000 0.016
#> GSM447688 4 0.5892 0.4798 0.008 0.460 0.020 0.512
#> GSM447702 2 0.0188 0.8711 0.000 0.996 0.000 0.004
#> GSM447709 2 0.1256 0.8683 0.000 0.964 0.008 0.028
#> GSM447711 1 0.0469 0.9617 0.988 0.000 0.012 0.000
#> GSM447715 1 0.2973 0.9026 0.884 0.000 0.020 0.096
#> GSM447693 3 0.0707 0.9005 0.020 0.000 0.980 0.000
#> GSM447611 4 0.2593 0.7966 0.004 0.104 0.000 0.892
#> GSM447672 2 0.0188 0.8711 0.000 0.996 0.000 0.004
#> GSM447703 4 0.5895 0.4773 0.008 0.464 0.020 0.508
#> GSM447727 1 0.1767 0.9458 0.944 0.000 0.012 0.044
#> GSM447638 2 0.6004 0.6099 0.000 0.648 0.276 0.076
#> GSM447670 1 0.5137 0.0888 0.544 0.000 0.452 0.004
#> GSM447700 2 0.6393 0.6353 0.008 0.656 0.100 0.236
#> GSM447738 4 0.5876 0.4935 0.008 0.444 0.020 0.528
#> GSM447739 1 0.0469 0.9617 0.988 0.000 0.012 0.000
#> GSM447617 1 0.0592 0.9602 0.984 0.000 0.016 0.000
#> GSM447628 4 0.3688 0.7953 0.000 0.208 0.000 0.792
#> GSM447632 4 0.5865 0.5010 0.008 0.436 0.020 0.536
#> GSM447619 3 0.0707 0.9005 0.020 0.000 0.980 0.000
#> GSM447643 1 0.2300 0.9225 0.920 0.016 0.000 0.064
#> GSM447724 4 0.3576 0.7831 0.008 0.060 0.060 0.872
#> GSM447728 2 0.0000 0.8715 0.000 1.000 0.000 0.000
#> GSM447610 4 0.4697 0.3474 0.356 0.000 0.000 0.644
#> GSM447633 2 0.5140 0.7375 0.000 0.760 0.144 0.096
#> GSM447634 3 0.4178 0.8792 0.020 0.016 0.824 0.140
#> GSM447622 3 0.3099 0.8669 0.104 0.000 0.876 0.020
#> GSM447667 2 0.4178 0.8075 0.008 0.812 0.020 0.160
#> GSM447687 4 0.5885 0.4851 0.008 0.452 0.020 0.520
#> GSM447695 3 0.3813 0.8821 0.024 0.000 0.828 0.148
#> GSM447696 1 0.0469 0.9617 0.988 0.000 0.012 0.000
#> GSM447697 1 0.0469 0.9617 0.988 0.000 0.012 0.000
#> GSM447714 3 0.2255 0.9015 0.012 0.000 0.920 0.068
#> GSM447717 1 0.0804 0.9611 0.980 0.000 0.012 0.008
#> GSM447725 1 0.0524 0.9603 0.988 0.000 0.008 0.004
#> GSM447729 4 0.2868 0.7991 0.000 0.136 0.000 0.864
#> GSM447644 2 0.4424 0.7817 0.000 0.812 0.088 0.100
#> GSM447710 3 0.0895 0.9012 0.020 0.000 0.976 0.004
#> GSM447614 4 0.2156 0.7486 0.004 0.008 0.060 0.928
#> GSM447685 2 0.2408 0.8363 0.000 0.896 0.000 0.104
#> GSM447690 1 0.0336 0.9605 0.992 0.000 0.008 0.000
#> GSM447730 2 0.0592 0.8679 0.000 0.984 0.000 0.016
#> GSM447646 4 0.3726 0.7943 0.000 0.212 0.000 0.788
#> GSM447689 3 0.0895 0.9012 0.020 0.000 0.976 0.004
#> GSM447635 2 0.6712 0.6068 0.008 0.600 0.096 0.296
#> GSM447641 1 0.0469 0.9617 0.988 0.000 0.012 0.000
#> GSM447716 4 0.5525 0.5802 0.008 0.328 0.020 0.644
#> GSM447718 3 0.3489 0.8857 0.012 0.008 0.856 0.124
#> GSM447616 3 0.3099 0.8669 0.104 0.000 0.876 0.020
#> GSM447626 3 0.0895 0.9011 0.020 0.000 0.976 0.004
#> GSM447640 2 0.0707 0.8694 0.000 0.980 0.000 0.020
#> GSM447734 3 0.3099 0.8966 0.020 0.000 0.876 0.104
#> GSM447692 3 0.5116 0.8620 0.108 0.000 0.764 0.128
#> GSM447647 4 0.3649 0.7961 0.000 0.204 0.000 0.796
#> GSM447624 3 0.4382 0.6342 0.296 0.000 0.704 0.000
#> GSM447625 3 0.3171 0.8959 0.016 0.004 0.876 0.104
#> GSM447707 2 0.0592 0.8679 0.000 0.984 0.000 0.016
#> GSM447732 3 0.3037 0.8968 0.020 0.000 0.880 0.100
#> GSM447684 3 0.2174 0.8961 0.020 0.000 0.928 0.052
#> GSM447731 4 0.3945 0.7934 0.004 0.216 0.000 0.780
#> GSM447705 3 0.2489 0.8906 0.000 0.020 0.912 0.068
#> GSM447631 3 0.2530 0.8681 0.100 0.000 0.896 0.004
#> GSM447701 2 0.0524 0.8725 0.000 0.988 0.008 0.004
#> GSM447645 3 0.2345 0.8681 0.100 0.000 0.900 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM447671 5 0.4905 0.245 0.000 0.464 0.012 0.008 0.516
#> GSM447694 3 0.3932 0.583 0.000 0.000 0.672 0.000 0.328
#> GSM447618 5 0.5487 0.283 0.000 0.252 0.004 0.100 0.644
#> GSM447691 5 0.5324 0.204 0.000 0.420 0.008 0.036 0.536
#> GSM447733 4 0.2548 0.687 0.000 0.004 0.004 0.876 0.116
#> GSM447620 2 0.5994 0.401 0.004 0.608 0.116 0.008 0.264
#> GSM447627 3 0.6452 0.350 0.000 0.000 0.476 0.196 0.328
#> GSM447630 5 0.4886 0.309 0.000 0.448 0.024 0.000 0.528
#> GSM447642 1 0.0865 0.946 0.972 0.000 0.004 0.000 0.024
#> GSM447649 2 0.2102 0.722 0.004 0.916 0.000 0.012 0.068
#> GSM447654 4 0.2293 0.711 0.000 0.084 0.000 0.900 0.016
#> GSM447655 2 0.0451 0.730 0.000 0.988 0.000 0.004 0.008
#> GSM447669 5 0.4827 0.244 0.000 0.476 0.020 0.000 0.504
#> GSM447676 1 0.1970 0.935 0.924 0.000 0.004 0.012 0.060
#> GSM447678 4 0.4270 0.644 0.000 0.012 0.000 0.668 0.320
#> GSM447681 2 0.1043 0.729 0.000 0.960 0.000 0.000 0.040
#> GSM447698 2 0.6733 -0.161 0.000 0.416 0.000 0.288 0.296
#> GSM447713 1 0.0290 0.946 0.992 0.000 0.008 0.000 0.000
#> GSM447722 4 0.5195 0.592 0.000 0.048 0.000 0.564 0.388
#> GSM447726 5 0.6096 0.101 0.000 0.444 0.044 0.040 0.472
#> GSM447735 4 0.4808 0.543 0.000 0.000 0.024 0.576 0.400
#> GSM447737 1 0.2813 0.890 0.884 0.000 0.064 0.004 0.048
#> GSM447657 2 0.4527 0.561 0.000 0.700 0.000 0.040 0.260
#> GSM447674 2 0.2554 0.716 0.000 0.892 0.000 0.036 0.072
#> GSM447636 1 0.1670 0.936 0.936 0.000 0.000 0.012 0.052
#> GSM447723 1 0.2972 0.908 0.872 0.000 0.004 0.040 0.084
#> GSM447699 5 0.6067 -0.287 0.000 0.016 0.420 0.076 0.488
#> GSM447708 2 0.4616 0.555 0.000 0.676 0.000 0.036 0.288
#> GSM447721 1 0.0579 0.945 0.984 0.000 0.008 0.000 0.008
#> GSM447623 1 0.1485 0.928 0.948 0.000 0.032 0.000 0.020
#> GSM447621 1 0.1579 0.926 0.944 0.000 0.032 0.000 0.024
#> GSM447650 2 0.0290 0.730 0.000 0.992 0.000 0.000 0.008
#> GSM447651 2 0.2392 0.710 0.004 0.888 0.000 0.004 0.104
#> GSM447653 4 0.2230 0.686 0.000 0.000 0.000 0.884 0.116
#> GSM447658 1 0.1670 0.936 0.936 0.000 0.000 0.012 0.052
#> GSM447675 4 0.2209 0.718 0.000 0.032 0.000 0.912 0.056
#> GSM447680 2 0.5177 0.610 0.008 0.684 0.000 0.076 0.232
#> GSM447686 1 0.3339 0.873 0.840 0.000 0.000 0.048 0.112
#> GSM447736 3 0.4420 0.467 0.000 0.000 0.548 0.004 0.448
#> GSM447629 2 0.6000 0.350 0.004 0.456 0.000 0.096 0.444
#> GSM447648 3 0.1195 0.679 0.012 0.000 0.960 0.000 0.028
#> GSM447660 1 0.1809 0.933 0.928 0.000 0.000 0.012 0.060
#> GSM447661 2 0.0000 0.730 0.000 1.000 0.000 0.000 0.000
#> GSM447663 3 0.5765 0.306 0.000 0.088 0.488 0.000 0.424
#> GSM447704 2 0.2681 0.700 0.004 0.892 0.000 0.052 0.052
#> GSM447720 5 0.4947 -0.193 0.000 0.024 0.396 0.004 0.576
#> GSM447652 2 0.0703 0.730 0.000 0.976 0.000 0.000 0.024
#> GSM447679 2 0.2370 0.721 0.000 0.904 0.000 0.040 0.056
#> GSM447712 1 0.0566 0.947 0.984 0.000 0.004 0.000 0.012
#> GSM447664 4 0.2124 0.694 0.000 0.004 0.000 0.900 0.096
#> GSM447637 3 0.0404 0.683 0.012 0.000 0.988 0.000 0.000
#> GSM447639 4 0.4218 0.603 0.000 0.004 0.004 0.668 0.324
#> GSM447615 3 0.2797 0.642 0.060 0.000 0.880 0.000 0.060
#> GSM447656 2 0.5592 0.564 0.008 0.628 0.000 0.088 0.276
#> GSM447673 4 0.6538 0.493 0.000 0.272 0.000 0.480 0.248
#> GSM447719 4 0.3769 0.615 0.000 0.000 0.180 0.788 0.032
#> GSM447706 3 0.0162 0.686 0.000 0.000 0.996 0.000 0.004
#> GSM447612 3 0.4995 0.434 0.000 0.024 0.552 0.004 0.420
#> GSM447665 2 0.3561 0.508 0.000 0.740 0.000 0.000 0.260
#> GSM447677 2 0.2179 0.711 0.004 0.896 0.000 0.000 0.100
#> GSM447613 1 0.0290 0.946 0.992 0.000 0.008 0.000 0.000
#> GSM447659 4 0.2672 0.685 0.000 0.004 0.008 0.872 0.116
#> GSM447662 3 0.2848 0.677 0.000 0.000 0.840 0.004 0.156
#> GSM447666 3 0.3751 0.602 0.000 0.012 0.772 0.004 0.212
#> GSM447668 2 0.1908 0.709 0.000 0.908 0.000 0.000 0.092
#> GSM447682 2 0.5054 0.551 0.004 0.696 0.000 0.084 0.216
#> GSM447683 2 0.3355 0.703 0.000 0.832 0.000 0.036 0.132
#> GSM447688 4 0.6689 0.431 0.000 0.344 0.000 0.412 0.244
#> GSM447702 2 0.0451 0.730 0.000 0.988 0.000 0.004 0.008
#> GSM447709 2 0.2848 0.679 0.000 0.840 0.000 0.004 0.156
#> GSM447711 1 0.0290 0.946 0.992 0.000 0.008 0.000 0.000
#> GSM447715 1 0.4219 0.803 0.772 0.000 0.000 0.072 0.156
#> GSM447693 3 0.0451 0.686 0.000 0.000 0.988 0.004 0.008
#> GSM447611 4 0.0865 0.706 0.000 0.004 0.000 0.972 0.024
#> GSM447672 2 0.0566 0.729 0.000 0.984 0.000 0.004 0.012
#> GSM447703 4 0.6671 0.393 0.000 0.372 0.000 0.396 0.232
#> GSM447727 1 0.2775 0.916 0.884 0.000 0.004 0.036 0.076
#> GSM447638 2 0.7509 0.034 0.008 0.428 0.220 0.032 0.312
#> GSM447670 3 0.4703 0.343 0.340 0.000 0.632 0.000 0.028
#> GSM447700 5 0.4811 0.430 0.000 0.160 0.012 0.084 0.744
#> GSM447738 4 0.6670 0.429 0.000 0.308 0.000 0.436 0.256
#> GSM447739 1 0.0290 0.946 0.992 0.000 0.008 0.000 0.000
#> GSM447617 1 0.2761 0.867 0.872 0.000 0.104 0.000 0.024
#> GSM447628 4 0.3102 0.712 0.000 0.084 0.000 0.860 0.056
#> GSM447632 4 0.6699 0.422 0.000 0.304 0.000 0.428 0.268
#> GSM447619 3 0.0865 0.689 0.000 0.000 0.972 0.004 0.024
#> GSM447643 1 0.2754 0.907 0.880 0.000 0.000 0.040 0.080
#> GSM447724 4 0.5043 0.574 0.000 0.012 0.016 0.552 0.420
#> GSM447728 2 0.1792 0.721 0.000 0.916 0.000 0.000 0.084
#> GSM447610 4 0.4599 0.485 0.272 0.000 0.000 0.688 0.040
#> GSM447633 2 0.5042 -0.233 0.000 0.512 0.024 0.004 0.460
#> GSM447634 5 0.4875 -0.220 0.000 0.020 0.400 0.004 0.576
#> GSM447622 3 0.2473 0.670 0.032 0.000 0.896 0.000 0.072
#> GSM447667 2 0.6181 0.352 0.008 0.452 0.000 0.104 0.436
#> GSM447687 4 0.6667 0.400 0.000 0.364 0.000 0.404 0.232
#> GSM447695 3 0.4798 0.442 0.000 0.000 0.540 0.020 0.440
#> GSM447696 1 0.0451 0.945 0.988 0.000 0.008 0.000 0.004
#> GSM447697 1 0.0451 0.945 0.988 0.000 0.008 0.000 0.004
#> GSM447714 3 0.4302 0.558 0.000 0.004 0.648 0.004 0.344
#> GSM447717 1 0.0609 0.946 0.980 0.000 0.000 0.000 0.020
#> GSM447725 1 0.0771 0.946 0.976 0.000 0.000 0.004 0.020
#> GSM447729 4 0.1117 0.714 0.000 0.020 0.000 0.964 0.016
#> GSM447644 2 0.4905 -0.261 0.000 0.500 0.024 0.000 0.476
#> GSM447710 3 0.2763 0.677 0.000 0.000 0.848 0.004 0.148
#> GSM447614 4 0.3621 0.638 0.000 0.000 0.020 0.788 0.192
#> GSM447685 2 0.5195 0.623 0.008 0.692 0.000 0.088 0.212
#> GSM447690 1 0.0324 0.945 0.992 0.000 0.004 0.004 0.000
#> GSM447730 2 0.2387 0.703 0.004 0.908 0.000 0.040 0.048
#> GSM447646 4 0.3226 0.710 0.000 0.088 0.000 0.852 0.060
#> GSM447689 3 0.3048 0.664 0.000 0.000 0.820 0.004 0.176
#> GSM447635 5 0.3911 0.417 0.000 0.100 0.004 0.084 0.812
#> GSM447641 1 0.0771 0.946 0.976 0.000 0.004 0.000 0.020
#> GSM447716 4 0.6539 0.445 0.000 0.200 0.000 0.432 0.368
#> GSM447718 5 0.5944 -0.233 0.004 0.024 0.416 0.044 0.512
#> GSM447616 3 0.2473 0.670 0.032 0.000 0.896 0.000 0.072
#> GSM447626 3 0.2690 0.676 0.000 0.000 0.844 0.000 0.156
#> GSM447640 2 0.2740 0.718 0.004 0.888 0.000 0.044 0.064
#> GSM447734 3 0.4403 0.530 0.000 0.008 0.608 0.000 0.384
#> GSM447692 3 0.5203 0.559 0.052 0.000 0.620 0.004 0.324
#> GSM447647 4 0.2889 0.711 0.000 0.084 0.000 0.872 0.044
#> GSM447624 3 0.3795 0.548 0.192 0.000 0.780 0.000 0.028
#> GSM447625 3 0.4436 0.520 0.000 0.008 0.596 0.000 0.396
#> GSM447707 2 0.2387 0.703 0.004 0.908 0.000 0.040 0.048
#> GSM447732 3 0.4425 0.519 0.000 0.008 0.600 0.000 0.392
#> GSM447684 3 0.5210 0.424 0.004 0.012 0.576 0.020 0.388
#> GSM447731 4 0.3289 0.693 0.000 0.108 0.000 0.844 0.048
#> GSM447705 3 0.4884 0.451 0.000 0.020 0.572 0.004 0.404
#> GSM447631 3 0.0579 0.683 0.008 0.000 0.984 0.000 0.008
#> GSM447701 2 0.2648 0.666 0.000 0.848 0.000 0.000 0.152
#> GSM447645 3 0.0404 0.683 0.012 0.000 0.988 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM447671 6 0.6108 0.3401 0.000 0.240 0.000 0.044 0.156 0.560
#> GSM447694 6 0.5103 0.0994 0.000 0.000 0.436 0.020 0.040 0.504
#> GSM447618 5 0.6460 0.3644 0.000 0.100 0.000 0.096 0.512 0.292
#> GSM447691 6 0.6349 0.1594 0.000 0.196 0.000 0.028 0.308 0.468
#> GSM447733 4 0.2356 0.7420 0.000 0.000 0.004 0.884 0.016 0.096
#> GSM447620 2 0.7647 0.2741 0.000 0.440 0.168 0.024 0.160 0.208
#> GSM447627 6 0.6988 0.2006 0.000 0.000 0.264 0.264 0.068 0.404
#> GSM447630 6 0.4045 0.4557 0.000 0.268 0.000 0.000 0.036 0.696
#> GSM447642 1 0.2812 0.8477 0.856 0.000 0.000 0.000 0.096 0.048
#> GSM447649 2 0.3129 0.6733 0.000 0.820 0.000 0.024 0.152 0.004
#> GSM447654 4 0.1889 0.7500 0.000 0.056 0.000 0.920 0.020 0.004
#> GSM447655 2 0.1592 0.7202 0.000 0.940 0.000 0.032 0.020 0.008
#> GSM447669 6 0.4767 0.3645 0.000 0.304 0.000 0.000 0.076 0.620
#> GSM447676 1 0.3612 0.8222 0.780 0.000 0.000 0.000 0.168 0.052
#> GSM447678 5 0.4956 0.2424 0.000 0.020 0.000 0.412 0.536 0.032
#> GSM447681 2 0.3124 0.6408 0.000 0.828 0.000 0.008 0.140 0.024
#> GSM447698 5 0.6420 0.5751 0.000 0.252 0.000 0.156 0.528 0.064
#> GSM447713 1 0.0146 0.8624 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM447722 5 0.6491 0.3716 0.000 0.052 0.000 0.284 0.492 0.172
#> GSM447726 6 0.5678 0.3268 0.000 0.220 0.008 0.000 0.204 0.568
#> GSM447735 4 0.6961 0.0835 0.000 0.000 0.056 0.356 0.272 0.316
#> GSM447737 1 0.4162 0.7264 0.784 0.000 0.100 0.000 0.040 0.076
#> GSM447657 5 0.5040 0.1653 0.000 0.456 0.000 0.016 0.488 0.040
#> GSM447674 2 0.3594 0.5939 0.000 0.768 0.000 0.020 0.204 0.008
#> GSM447636 1 0.3493 0.8280 0.796 0.000 0.000 0.000 0.148 0.056
#> GSM447723 1 0.4455 0.7680 0.688 0.000 0.000 0.000 0.232 0.080
#> GSM447699 6 0.6418 0.4185 0.000 0.016 0.212 0.064 0.128 0.580
#> GSM447708 2 0.6394 0.3861 0.000 0.504 0.000 0.044 0.272 0.180
#> GSM447721 1 0.1492 0.8481 0.940 0.000 0.000 0.000 0.024 0.036
#> GSM447623 1 0.2803 0.8028 0.876 0.000 0.064 0.000 0.032 0.028
#> GSM447621 1 0.2950 0.7975 0.868 0.000 0.064 0.000 0.032 0.036
#> GSM447650 2 0.0972 0.7288 0.000 0.964 0.000 0.000 0.008 0.028
#> GSM447651 2 0.3052 0.7108 0.000 0.852 0.000 0.008 0.064 0.076
#> GSM447653 4 0.2002 0.7533 0.000 0.000 0.004 0.908 0.012 0.076
#> GSM447658 1 0.3408 0.8290 0.800 0.000 0.000 0.000 0.152 0.048
#> GSM447675 4 0.2520 0.7193 0.000 0.012 0.000 0.872 0.108 0.008
#> GSM447680 2 0.5443 0.3734 0.000 0.492 0.000 0.000 0.384 0.124
#> GSM447686 1 0.4499 0.7191 0.652 0.000 0.000 0.000 0.288 0.060
#> GSM447736 6 0.5394 0.3800 0.000 0.000 0.296 0.032 0.072 0.600
#> GSM447629 5 0.4982 0.4088 0.000 0.172 0.000 0.020 0.688 0.120
#> GSM447648 3 0.0777 0.7218 0.000 0.000 0.972 0.000 0.004 0.024
#> GSM447660 1 0.3578 0.8240 0.784 0.000 0.000 0.000 0.164 0.052
#> GSM447661 2 0.0777 0.7294 0.000 0.972 0.000 0.000 0.004 0.024
#> GSM447663 6 0.5234 0.4598 0.000 0.112 0.208 0.000 0.024 0.656
#> GSM447704 2 0.3502 0.6364 0.000 0.812 0.000 0.076 0.108 0.004
#> GSM447720 6 0.4169 0.5055 0.000 0.024 0.112 0.016 0.056 0.792
#> GSM447652 2 0.1622 0.7264 0.000 0.940 0.000 0.016 0.016 0.028
#> GSM447679 2 0.3002 0.6754 0.000 0.836 0.000 0.020 0.136 0.008
#> GSM447712 1 0.0547 0.8643 0.980 0.000 0.000 0.000 0.020 0.000
#> GSM447664 4 0.3056 0.6790 0.000 0.004 0.000 0.804 0.184 0.008
#> GSM447637 3 0.0146 0.7305 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM447639 4 0.5906 0.2807 0.000 0.000 0.008 0.512 0.196 0.284
#> GSM447615 3 0.3880 0.6429 0.032 0.000 0.800 0.000 0.056 0.112
#> GSM447656 5 0.5455 -0.2407 0.004 0.392 0.000 0.000 0.496 0.108
#> GSM447673 5 0.5622 0.5526 0.000 0.212 0.000 0.248 0.540 0.000
#> GSM447719 4 0.2783 0.7016 0.000 0.000 0.148 0.836 0.000 0.016
#> GSM447706 3 0.0291 0.7297 0.000 0.000 0.992 0.000 0.004 0.004
#> GSM447612 6 0.4993 0.4065 0.000 0.012 0.304 0.024 0.028 0.632
#> GSM447665 2 0.4933 0.5024 0.000 0.636 0.000 0.008 0.080 0.276
#> GSM447677 2 0.3097 0.7174 0.000 0.852 0.000 0.012 0.072 0.064
#> GSM447613 1 0.0363 0.8638 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM447659 4 0.2454 0.7394 0.000 0.000 0.004 0.876 0.016 0.104
#> GSM447662 3 0.4184 0.3971 0.000 0.000 0.672 0.004 0.028 0.296
#> GSM447666 3 0.4559 0.3417 0.000 0.004 0.628 0.000 0.044 0.324
#> GSM447668 2 0.2608 0.7125 0.000 0.872 0.000 0.000 0.048 0.080
#> GSM447682 2 0.5140 -0.0251 0.000 0.532 0.000 0.076 0.388 0.004
#> GSM447683 2 0.4262 0.6521 0.000 0.740 0.000 0.020 0.192 0.048
#> GSM447688 5 0.6175 0.5229 0.000 0.260 0.000 0.256 0.472 0.012
#> GSM447702 2 0.1167 0.7242 0.000 0.960 0.000 0.020 0.012 0.008
#> GSM447709 2 0.4540 0.6471 0.000 0.732 0.000 0.024 0.076 0.168
#> GSM447711 1 0.0000 0.8629 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447715 1 0.5243 0.5610 0.532 0.004 0.000 0.000 0.376 0.088
#> GSM447693 3 0.0146 0.7305 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM447611 4 0.1265 0.7562 0.000 0.000 0.000 0.948 0.044 0.008
#> GSM447672 2 0.1864 0.7111 0.000 0.924 0.000 0.032 0.040 0.004
#> GSM447703 5 0.6095 0.4063 0.000 0.384 0.000 0.224 0.388 0.004
#> GSM447727 1 0.4441 0.7824 0.700 0.000 0.000 0.000 0.208 0.092
#> GSM447638 6 0.7631 -0.1074 0.008 0.288 0.096 0.004 0.300 0.304
#> GSM447670 3 0.5452 0.4769 0.216 0.000 0.644 0.000 0.044 0.096
#> GSM447700 6 0.6071 -0.1586 0.000 0.056 0.000 0.080 0.412 0.452
#> GSM447738 5 0.5665 0.5638 0.000 0.224 0.000 0.244 0.532 0.000
#> GSM447739 1 0.0146 0.8624 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM447617 1 0.4426 0.6474 0.736 0.000 0.184 0.000 0.032 0.048
#> GSM447628 4 0.3174 0.6900 0.000 0.056 0.000 0.836 0.104 0.004
#> GSM447632 5 0.5504 0.5793 0.000 0.232 0.000 0.204 0.564 0.000
#> GSM447619 3 0.2002 0.6883 0.000 0.000 0.908 0.004 0.012 0.076
#> GSM447643 1 0.4641 0.7417 0.664 0.000 0.000 0.000 0.248 0.088
#> GSM447724 5 0.6484 0.2368 0.000 0.016 0.008 0.280 0.456 0.240
#> GSM447728 2 0.3270 0.7155 0.000 0.840 0.000 0.016 0.092 0.052
#> GSM447610 4 0.4992 0.5405 0.248 0.000 0.004 0.668 0.044 0.036
#> GSM447633 6 0.5626 0.2479 0.000 0.328 0.000 0.024 0.096 0.552
#> GSM447634 6 0.3718 0.4952 0.000 0.016 0.136 0.020 0.020 0.808
#> GSM447622 3 0.3256 0.6638 0.020 0.000 0.836 0.000 0.032 0.112
#> GSM447667 5 0.5254 0.3957 0.000 0.164 0.000 0.028 0.668 0.140
#> GSM447687 5 0.6002 0.4274 0.000 0.368 0.000 0.236 0.396 0.000
#> GSM447695 6 0.5694 0.3215 0.000 0.000 0.296 0.032 0.100 0.572
#> GSM447696 1 0.0603 0.8589 0.980 0.000 0.000 0.000 0.016 0.004
#> GSM447697 1 0.0725 0.8613 0.976 0.000 0.000 0.000 0.012 0.012
#> GSM447714 6 0.4384 0.1934 0.000 0.000 0.460 0.004 0.016 0.520
#> GSM447717 1 0.1204 0.8628 0.944 0.000 0.000 0.000 0.056 0.000
#> GSM447725 1 0.0632 0.8642 0.976 0.000 0.000 0.000 0.024 0.000
#> GSM447729 4 0.1967 0.7394 0.000 0.012 0.000 0.904 0.084 0.000
#> GSM447644 6 0.4798 0.3506 0.000 0.312 0.000 0.000 0.076 0.612
#> GSM447710 3 0.3742 0.3092 0.000 0.000 0.648 0.000 0.004 0.348
#> GSM447614 4 0.4387 0.6518 0.000 0.000 0.012 0.732 0.076 0.180
#> GSM447685 2 0.5559 0.3653 0.000 0.512 0.000 0.016 0.380 0.092
#> GSM447690 1 0.0146 0.8624 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM447730 2 0.3055 0.6682 0.000 0.852 0.000 0.068 0.072 0.008
#> GSM447646 4 0.3307 0.6911 0.000 0.064 0.000 0.832 0.096 0.008
#> GSM447689 3 0.4118 0.3093 0.000 0.000 0.628 0.000 0.020 0.352
#> GSM447635 5 0.5351 0.2102 0.000 0.016 0.000 0.068 0.508 0.408
#> GSM447641 1 0.1643 0.8611 0.924 0.000 0.000 0.000 0.068 0.008
#> GSM447716 5 0.5376 0.5525 0.000 0.120 0.000 0.148 0.676 0.056
#> GSM447718 6 0.5208 0.4887 0.000 0.048 0.168 0.052 0.024 0.708
#> GSM447616 3 0.3507 0.6498 0.016 0.000 0.816 0.000 0.044 0.124
#> GSM447626 3 0.4049 0.4015 0.000 0.000 0.648 0.000 0.020 0.332
#> GSM447640 2 0.3082 0.6730 0.000 0.828 0.000 0.020 0.144 0.008
#> GSM447734 6 0.4252 0.3658 0.000 0.016 0.344 0.000 0.008 0.632
#> GSM447692 6 0.6872 -0.0372 0.112 0.000 0.396 0.028 0.052 0.412
#> GSM447647 4 0.2527 0.7175 0.000 0.040 0.000 0.876 0.084 0.000
#> GSM447624 3 0.4597 0.5693 0.160 0.000 0.732 0.000 0.028 0.080
#> GSM447625 6 0.4226 0.3818 0.000 0.016 0.328 0.004 0.004 0.648
#> GSM447707 2 0.2888 0.6737 0.000 0.860 0.000 0.068 0.068 0.004
#> GSM447732 6 0.4410 0.3778 0.000 0.020 0.328 0.004 0.008 0.640
#> GSM447684 6 0.5581 0.2317 0.000 0.008 0.220 0.000 0.188 0.584
#> GSM447731 4 0.2668 0.7354 0.000 0.060 0.000 0.884 0.028 0.028
#> GSM447705 6 0.4943 0.3270 0.000 0.012 0.368 0.008 0.032 0.580
#> GSM447631 3 0.0000 0.7298 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447701 2 0.3771 0.6446 0.000 0.764 0.000 0.000 0.056 0.180
#> GSM447645 3 0.0146 0.7305 0.000 0.000 0.996 0.000 0.000 0.004
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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 gender(p) individual(p) disease.state(p) other(p) k
#> CV:kmeans 128 0.414 0.8129 0.0230 4.07e-02 2
#> CV:kmeans 109 0.218 0.2786 0.0709 5.00e-01 3
#> CV:kmeans 123 0.234 0.1668 0.0496 5.22e-02 4
#> CV:kmeans 95 0.145 0.0538 0.0167 3.24e-04 5
#> CV:kmeans 81 0.807 0.1955 0.0321 7.57e-05 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "skmeans"]
# you can also extract it by
# res = res_list["CV:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 130 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.758 0.899 0.956 0.5036 0.496 0.496
#> 3 3 0.509 0.646 0.823 0.3068 0.819 0.649
#> 4 4 0.902 0.898 0.950 0.1422 0.784 0.472
#> 5 5 0.714 0.713 0.803 0.0586 0.924 0.714
#> 6 6 0.711 0.617 0.780 0.0413 0.927 0.677
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
#> GSM447671 2 0.0000 0.9605 0.000 1.000
#> GSM447694 1 0.0000 0.9413 1.000 0.000
#> GSM447618 2 0.0000 0.9605 0.000 1.000
#> GSM447691 2 0.0000 0.9605 0.000 1.000
#> GSM447733 2 0.0000 0.9605 0.000 1.000
#> GSM447620 2 0.0000 0.9605 0.000 1.000
#> GSM447627 1 0.0376 0.9390 0.996 0.004
#> GSM447630 2 0.0000 0.9605 0.000 1.000
#> GSM447642 1 0.0000 0.9413 1.000 0.000
#> GSM447649 2 0.0000 0.9605 0.000 1.000
#> GSM447654 2 0.0000 0.9605 0.000 1.000
#> GSM447655 2 0.0000 0.9605 0.000 1.000
#> GSM447669 2 0.0000 0.9605 0.000 1.000
#> GSM447676 1 0.0000 0.9413 1.000 0.000
#> GSM447678 2 0.0000 0.9605 0.000 1.000
#> GSM447681 2 0.0000 0.9605 0.000 1.000
#> GSM447698 2 0.0000 0.9605 0.000 1.000
#> GSM447713 1 0.0000 0.9413 1.000 0.000
#> GSM447722 2 0.0000 0.9605 0.000 1.000
#> GSM447726 1 0.9775 0.2599 0.588 0.412
#> GSM447735 1 0.4161 0.8836 0.916 0.084
#> GSM447737 1 0.0000 0.9413 1.000 0.000
#> GSM447657 2 0.0000 0.9605 0.000 1.000
#> GSM447674 2 0.0000 0.9605 0.000 1.000
#> GSM447636 1 0.0000 0.9413 1.000 0.000
#> GSM447723 1 0.0000 0.9413 1.000 0.000
#> GSM447699 1 0.9710 0.4112 0.600 0.400
#> GSM447708 2 0.0000 0.9605 0.000 1.000
#> GSM447721 1 0.0000 0.9413 1.000 0.000
#> GSM447623 1 0.0000 0.9413 1.000 0.000
#> GSM447621 1 0.0000 0.9413 1.000 0.000
#> GSM447650 2 0.0000 0.9605 0.000 1.000
#> GSM447651 2 0.0000 0.9605 0.000 1.000
#> GSM447653 2 0.9963 0.0243 0.464 0.536
#> GSM447658 1 0.0000 0.9413 1.000 0.000
#> GSM447675 2 0.0000 0.9605 0.000 1.000
#> GSM447680 2 0.7219 0.7601 0.200 0.800
#> GSM447686 1 0.9710 0.2963 0.600 0.400
#> GSM447736 1 0.2043 0.9221 0.968 0.032
#> GSM447629 2 0.7219 0.7601 0.200 0.800
#> GSM447648 1 0.0000 0.9413 1.000 0.000
#> GSM447660 1 0.0000 0.9413 1.000 0.000
#> GSM447661 2 0.0000 0.9605 0.000 1.000
#> GSM447663 1 0.7219 0.7718 0.800 0.200
#> GSM447704 2 0.0000 0.9605 0.000 1.000
#> GSM447720 1 0.0000 0.9413 1.000 0.000
#> GSM447652 2 0.0000 0.9605 0.000 1.000
#> GSM447679 2 0.0000 0.9605 0.000 1.000
#> GSM447712 1 0.0000 0.9413 1.000 0.000
#> GSM447664 2 0.7219 0.7601 0.200 0.800
#> GSM447637 1 0.0000 0.9413 1.000 0.000
#> GSM447639 2 0.0000 0.9605 0.000 1.000
#> GSM447615 1 0.0000 0.9413 1.000 0.000
#> GSM447656 2 0.7674 0.7273 0.224 0.776
#> GSM447673 2 0.0000 0.9605 0.000 1.000
#> GSM447719 1 0.0000 0.9413 1.000 0.000
#> GSM447706 1 0.0000 0.9413 1.000 0.000
#> GSM447612 2 0.9000 0.4861 0.316 0.684
#> GSM447665 2 0.0000 0.9605 0.000 1.000
#> GSM447677 2 0.0000 0.9605 0.000 1.000
#> GSM447613 1 0.0000 0.9413 1.000 0.000
#> GSM447659 2 0.0000 0.9605 0.000 1.000
#> GSM447662 1 0.7219 0.7718 0.800 0.200
#> GSM447666 1 0.6247 0.8188 0.844 0.156
#> GSM447668 2 0.0000 0.9605 0.000 1.000
#> GSM447682 2 0.0000 0.9605 0.000 1.000
#> GSM447683 2 0.0000 0.9605 0.000 1.000
#> GSM447688 2 0.0000 0.9605 0.000 1.000
#> GSM447702 2 0.0000 0.9605 0.000 1.000
#> GSM447709 2 0.0000 0.9605 0.000 1.000
#> GSM447711 1 0.0000 0.9413 1.000 0.000
#> GSM447715 1 0.0000 0.9413 1.000 0.000
#> GSM447693 1 0.1633 0.9270 0.976 0.024
#> GSM447611 2 0.7219 0.7601 0.200 0.800
#> GSM447672 2 0.0000 0.9605 0.000 1.000
#> GSM447703 2 0.0000 0.9605 0.000 1.000
#> GSM447727 1 0.0000 0.9413 1.000 0.000
#> GSM447638 1 0.0000 0.9413 1.000 0.000
#> GSM447670 1 0.0000 0.9413 1.000 0.000
#> GSM447700 2 0.0000 0.9605 0.000 1.000
#> GSM447738 2 0.0000 0.9605 0.000 1.000
#> GSM447739 1 0.0000 0.9413 1.000 0.000
#> GSM447617 1 0.0000 0.9413 1.000 0.000
#> GSM447628 2 0.0000 0.9605 0.000 1.000
#> GSM447632 2 0.0000 0.9605 0.000 1.000
#> GSM447619 1 0.7219 0.7718 0.800 0.200
#> GSM447643 1 0.0376 0.9387 0.996 0.004
#> GSM447724 2 0.0000 0.9605 0.000 1.000
#> GSM447728 2 0.0000 0.9605 0.000 1.000
#> GSM447610 1 0.0000 0.9413 1.000 0.000
#> GSM447633 2 0.0000 0.9605 0.000 1.000
#> GSM447634 1 0.0000 0.9413 1.000 0.000
#> GSM447622 1 0.0000 0.9413 1.000 0.000
#> GSM447667 2 0.7219 0.7601 0.200 0.800
#> GSM447687 2 0.0000 0.9605 0.000 1.000
#> GSM447695 1 0.0000 0.9413 1.000 0.000
#> GSM447696 1 0.0000 0.9413 1.000 0.000
#> GSM447697 1 0.0000 0.9413 1.000 0.000
#> GSM447714 1 0.7219 0.7718 0.800 0.200
#> GSM447717 1 0.0000 0.9413 1.000 0.000
#> GSM447725 1 0.0000 0.9413 1.000 0.000
#> GSM447729 2 0.0000 0.9605 0.000 1.000
#> GSM447644 2 0.0000 0.9605 0.000 1.000
#> GSM447710 1 0.4690 0.8704 0.900 0.100
#> GSM447614 1 0.0000 0.9413 1.000 0.000
#> GSM447685 2 0.0938 0.9508 0.012 0.988
#> GSM447690 1 0.0000 0.9413 1.000 0.000
#> GSM447730 2 0.0000 0.9605 0.000 1.000
#> GSM447646 2 0.0000 0.9605 0.000 1.000
#> GSM447689 1 0.4690 0.8704 0.900 0.100
#> GSM447635 2 0.6148 0.8164 0.152 0.848
#> GSM447641 1 0.0000 0.9413 1.000 0.000
#> GSM447716 2 0.6247 0.8119 0.156 0.844
#> GSM447718 1 0.9608 0.4505 0.616 0.384
#> GSM447616 1 0.0000 0.9413 1.000 0.000
#> GSM447626 1 0.0000 0.9413 1.000 0.000
#> GSM447640 2 0.0000 0.9605 0.000 1.000
#> GSM447734 1 0.7219 0.7718 0.800 0.200
#> GSM447692 1 0.0000 0.9413 1.000 0.000
#> GSM447647 2 0.0000 0.9605 0.000 1.000
#> GSM447624 1 0.0000 0.9413 1.000 0.000
#> GSM447625 1 0.7219 0.7718 0.800 0.200
#> GSM447707 2 0.0000 0.9605 0.000 1.000
#> GSM447732 1 0.6148 0.8228 0.848 0.152
#> GSM447684 1 0.0000 0.9413 1.000 0.000
#> GSM447731 2 0.0000 0.9605 0.000 1.000
#> GSM447705 2 0.0672 0.9538 0.008 0.992
#> GSM447631 1 0.0000 0.9413 1.000 0.000
#> GSM447701 2 0.0000 0.9605 0.000 1.000
#> GSM447645 1 0.0000 0.9413 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM447671 2 0.5926 0.40758 0.000 0.644 0.356
#> GSM447694 3 0.4654 0.72804 0.208 0.000 0.792
#> GSM447618 2 0.2356 0.77158 0.000 0.928 0.072
#> GSM447691 2 0.5926 0.40758 0.000 0.644 0.356
#> GSM447733 3 0.6299 -0.28109 0.000 0.476 0.524
#> GSM447620 2 0.6095 0.33129 0.000 0.608 0.392
#> GSM447627 3 0.6305 0.14554 0.484 0.000 0.516
#> GSM447630 2 0.6192 0.25541 0.000 0.580 0.420
#> GSM447642 1 0.0000 0.83265 1.000 0.000 0.000
#> GSM447649 2 0.0000 0.80569 0.000 1.000 0.000
#> GSM447654 2 0.4291 0.76597 0.000 0.820 0.180
#> GSM447655 2 0.0000 0.80569 0.000 1.000 0.000
#> GSM447669 2 0.5988 0.38229 0.000 0.632 0.368
#> GSM447676 1 0.0892 0.82271 0.980 0.000 0.020
#> GSM447678 2 0.4291 0.76597 0.000 0.820 0.180
#> GSM447681 2 0.0000 0.80569 0.000 1.000 0.000
#> GSM447698 2 0.4002 0.77421 0.000 0.840 0.160
#> GSM447713 1 0.0000 0.83265 1.000 0.000 0.000
#> GSM447722 2 0.4291 0.76597 0.000 0.820 0.180
#> GSM447726 3 0.9251 0.37978 0.212 0.260 0.528
#> GSM447735 3 0.7993 -0.18625 0.456 0.060 0.484
#> GSM447737 1 0.0000 0.83265 1.000 0.000 0.000
#> GSM447657 2 0.0000 0.80569 0.000 1.000 0.000
#> GSM447674 2 0.0000 0.80569 0.000 1.000 0.000
#> GSM447636 1 0.1031 0.82006 0.976 0.000 0.024
#> GSM447723 1 0.0000 0.83265 1.000 0.000 0.000
#> GSM447699 3 0.7585 0.66250 0.180 0.132 0.688
#> GSM447708 2 0.1289 0.79308 0.000 0.968 0.032
#> GSM447721 1 0.0000 0.83265 1.000 0.000 0.000
#> GSM447623 1 0.0000 0.83265 1.000 0.000 0.000
#> GSM447621 1 0.0000 0.83265 1.000 0.000 0.000
#> GSM447650 2 0.0000 0.80569 0.000 1.000 0.000
#> GSM447651 2 0.2165 0.77987 0.000 0.936 0.064
#> GSM447653 3 0.8322 0.25658 0.124 0.268 0.608
#> GSM447658 1 0.0892 0.82271 0.980 0.000 0.020
#> GSM447675 2 0.4291 0.76597 0.000 0.820 0.180
#> GSM447680 2 0.7263 0.36304 0.372 0.592 0.036
#> GSM447686 1 0.4409 0.63835 0.824 0.172 0.004
#> GSM447736 3 0.4291 0.75651 0.180 0.000 0.820
#> GSM447629 2 0.4555 0.65978 0.200 0.800 0.000
#> GSM447648 1 0.6286 -0.00459 0.536 0.000 0.464
#> GSM447660 1 0.1031 0.82006 0.976 0.000 0.024
#> GSM447661 2 0.0000 0.80569 0.000 1.000 0.000
#> GSM447663 3 0.4291 0.75651 0.180 0.000 0.820
#> GSM447704 2 0.0000 0.80569 0.000 1.000 0.000
#> GSM447720 3 0.4504 0.74309 0.196 0.000 0.804
#> GSM447652 2 0.0237 0.80562 0.000 0.996 0.004
#> GSM447679 2 0.0000 0.80569 0.000 1.000 0.000
#> GSM447712 1 0.0000 0.83265 1.000 0.000 0.000
#> GSM447664 2 0.8478 0.60210 0.204 0.616 0.180
#> GSM447637 1 0.6299 -0.04773 0.524 0.000 0.476
#> GSM447639 2 0.5968 0.55862 0.000 0.636 0.364
#> GSM447615 1 0.0424 0.82792 0.992 0.000 0.008
#> GSM447656 2 0.6305 0.08743 0.484 0.516 0.000
#> GSM447673 2 0.4291 0.76597 0.000 0.820 0.180
#> GSM447719 3 0.5948 0.12030 0.360 0.000 0.640
#> GSM447706 3 0.4291 0.75651 0.180 0.000 0.820
#> GSM447612 3 0.4465 0.75515 0.176 0.004 0.820
#> GSM447665 2 0.5859 0.43093 0.000 0.656 0.344
#> GSM447677 2 0.1411 0.79112 0.000 0.964 0.036
#> GSM447613 1 0.0000 0.83265 1.000 0.000 0.000
#> GSM447659 3 0.3619 0.54014 0.000 0.136 0.864
#> GSM447662 3 0.4291 0.75651 0.180 0.000 0.820
#> GSM447666 3 0.4862 0.65213 0.020 0.160 0.820
#> GSM447668 2 0.1411 0.79112 0.000 0.964 0.036
#> GSM447682 2 0.0237 0.80562 0.000 0.996 0.004
#> GSM447683 2 0.0000 0.80569 0.000 1.000 0.000
#> GSM447688 2 0.4002 0.77421 0.000 0.840 0.160
#> GSM447702 2 0.0000 0.80569 0.000 1.000 0.000
#> GSM447709 2 0.4931 0.61612 0.000 0.768 0.232
#> GSM447711 1 0.0000 0.83265 1.000 0.000 0.000
#> GSM447715 1 0.0000 0.83265 1.000 0.000 0.000
#> GSM447693 3 0.4291 0.75651 0.180 0.000 0.820
#> GSM447611 2 0.8557 0.59247 0.212 0.608 0.180
#> GSM447672 2 0.0000 0.80569 0.000 1.000 0.000
#> GSM447703 2 0.4002 0.77421 0.000 0.840 0.160
#> GSM447727 1 0.0000 0.83265 1.000 0.000 0.000
#> GSM447638 1 0.6986 0.53863 0.724 0.180 0.096
#> GSM447670 1 0.0000 0.83265 1.000 0.000 0.000
#> GSM447700 2 0.6309 0.38254 0.000 0.504 0.496
#> GSM447738 2 0.4002 0.77421 0.000 0.840 0.160
#> GSM447739 1 0.0000 0.83265 1.000 0.000 0.000
#> GSM447617 1 0.0000 0.83265 1.000 0.000 0.000
#> GSM447628 2 0.4291 0.76597 0.000 0.820 0.180
#> GSM447632 2 0.4002 0.77421 0.000 0.840 0.160
#> GSM447619 3 0.4291 0.75651 0.180 0.000 0.820
#> GSM447643 1 0.4235 0.63593 0.824 0.176 0.000
#> GSM447724 3 0.5882 0.11375 0.000 0.348 0.652
#> GSM447728 2 0.0000 0.80569 0.000 1.000 0.000
#> GSM447610 1 0.4291 0.64823 0.820 0.000 0.180
#> GSM447633 3 0.6095 0.24980 0.000 0.392 0.608
#> GSM447634 3 0.6062 0.44388 0.384 0.000 0.616
#> GSM447622 1 0.5138 0.54221 0.748 0.000 0.252
#> GSM447667 2 0.5024 0.63843 0.220 0.776 0.004
#> GSM447687 2 0.4002 0.77421 0.000 0.840 0.160
#> GSM447695 1 0.6260 0.02371 0.552 0.000 0.448
#> GSM447696 1 0.0000 0.83265 1.000 0.000 0.000
#> GSM447697 1 0.0000 0.83265 1.000 0.000 0.000
#> GSM447714 3 0.4291 0.75651 0.180 0.000 0.820
#> GSM447717 1 0.0000 0.83265 1.000 0.000 0.000
#> GSM447725 1 0.1031 0.82006 0.976 0.000 0.024
#> GSM447729 2 0.4291 0.76597 0.000 0.820 0.180
#> GSM447644 2 0.6154 0.29429 0.000 0.592 0.408
#> GSM447710 3 0.4291 0.75651 0.180 0.000 0.820
#> GSM447614 1 0.6309 0.23839 0.500 0.000 0.500
#> GSM447685 2 0.0237 0.80491 0.004 0.996 0.000
#> GSM447690 1 0.0892 0.82271 0.980 0.000 0.020
#> GSM447730 2 0.0000 0.80569 0.000 1.000 0.000
#> GSM447646 2 0.4291 0.76597 0.000 0.820 0.180
#> GSM447689 3 0.4291 0.75651 0.180 0.000 0.820
#> GSM447635 2 0.8219 0.62899 0.180 0.640 0.180
#> GSM447641 1 0.0000 0.83265 1.000 0.000 0.000
#> GSM447716 2 0.8396 0.61118 0.196 0.624 0.180
#> GSM447718 3 0.4749 0.74984 0.172 0.012 0.816
#> GSM447616 1 0.5016 0.56253 0.760 0.000 0.240
#> GSM447626 3 0.4291 0.75651 0.180 0.000 0.820
#> GSM447640 2 0.0000 0.80569 0.000 1.000 0.000
#> GSM447734 3 0.4291 0.75651 0.180 0.000 0.820
#> GSM447692 1 0.4974 0.56825 0.764 0.000 0.236
#> GSM447647 2 0.4291 0.76597 0.000 0.820 0.180
#> GSM447624 1 0.2711 0.76170 0.912 0.000 0.088
#> GSM447625 3 0.4291 0.75651 0.180 0.000 0.820
#> GSM447707 2 0.0000 0.80569 0.000 1.000 0.000
#> GSM447732 3 0.4291 0.75651 0.180 0.000 0.820
#> GSM447684 1 0.5650 0.34086 0.688 0.000 0.312
#> GSM447731 2 0.5254 0.71361 0.000 0.736 0.264
#> GSM447705 3 0.4862 0.65213 0.020 0.160 0.820
#> GSM447631 1 0.6291 -0.01889 0.532 0.000 0.468
#> GSM447701 2 0.4121 0.68459 0.000 0.832 0.168
#> GSM447645 1 0.6295 -0.03336 0.528 0.000 0.472
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM447671 2 0.0707 0.9341 0.000 0.980 0.000 0.020
#> GSM447694 3 0.0188 0.9419 0.000 0.000 0.996 0.004
#> GSM447618 2 0.3649 0.7646 0.000 0.796 0.000 0.204
#> GSM447691 2 0.0707 0.9348 0.000 0.980 0.000 0.020
#> GSM447733 4 0.1059 0.9508 0.000 0.012 0.016 0.972
#> GSM447620 2 0.3583 0.7898 0.000 0.816 0.180 0.004
#> GSM447627 3 0.1022 0.9290 0.000 0.000 0.968 0.032
#> GSM447630 2 0.0000 0.9376 0.000 1.000 0.000 0.000
#> GSM447642 1 0.0000 0.9653 1.000 0.000 0.000 0.000
#> GSM447649 2 0.1022 0.9299 0.000 0.968 0.000 0.032
#> GSM447654 4 0.0921 0.9544 0.000 0.028 0.000 0.972
#> GSM447655 2 0.0188 0.9375 0.000 0.996 0.000 0.004
#> GSM447669 2 0.0188 0.9373 0.000 0.996 0.000 0.004
#> GSM447676 1 0.0000 0.9653 1.000 0.000 0.000 0.000
#> GSM447678 4 0.0000 0.9550 0.000 0.000 0.000 1.000
#> GSM447681 2 0.0336 0.9373 0.000 0.992 0.000 0.008
#> GSM447698 4 0.1022 0.9518 0.000 0.032 0.000 0.968
#> GSM447713 1 0.0000 0.9653 1.000 0.000 0.000 0.000
#> GSM447722 4 0.0921 0.9526 0.000 0.028 0.000 0.972
#> GSM447726 2 0.2032 0.9078 0.028 0.936 0.036 0.000
#> GSM447735 4 0.0000 0.9550 0.000 0.000 0.000 1.000
#> GSM447737 1 0.0000 0.9653 1.000 0.000 0.000 0.000
#> GSM447657 2 0.0707 0.9343 0.000 0.980 0.000 0.020
#> GSM447674 2 0.0707 0.9343 0.000 0.980 0.000 0.020
#> GSM447636 1 0.0000 0.9653 1.000 0.000 0.000 0.000
#> GSM447723 1 0.0000 0.9653 1.000 0.000 0.000 0.000
#> GSM447699 3 0.4095 0.7828 0.000 0.024 0.804 0.172
#> GSM447708 2 0.0707 0.9348 0.000 0.980 0.000 0.020
#> GSM447721 1 0.0000 0.9653 1.000 0.000 0.000 0.000
#> GSM447623 1 0.0000 0.9653 1.000 0.000 0.000 0.000
#> GSM447621 1 0.0000 0.9653 1.000 0.000 0.000 0.000
#> GSM447650 2 0.0000 0.9376 0.000 1.000 0.000 0.000
#> GSM447651 2 0.0000 0.9376 0.000 1.000 0.000 0.000
#> GSM447653 4 0.0592 0.9518 0.000 0.000 0.016 0.984
#> GSM447658 1 0.0000 0.9653 1.000 0.000 0.000 0.000
#> GSM447675 4 0.0000 0.9550 0.000 0.000 0.000 1.000
#> GSM447680 2 0.3486 0.7637 0.188 0.812 0.000 0.000
#> GSM447686 1 0.0000 0.9653 1.000 0.000 0.000 0.000
#> GSM447736 3 0.0188 0.9419 0.000 0.000 0.996 0.004
#> GSM447629 2 0.4012 0.7610 0.184 0.800 0.000 0.016
#> GSM447648 3 0.0000 0.9429 0.000 0.000 1.000 0.000
#> GSM447660 1 0.0000 0.9653 1.000 0.000 0.000 0.000
#> GSM447661 2 0.0000 0.9376 0.000 1.000 0.000 0.000
#> GSM447663 3 0.2345 0.8819 0.000 0.100 0.900 0.000
#> GSM447704 2 0.1118 0.9257 0.000 0.964 0.000 0.036
#> GSM447720 3 0.2401 0.8874 0.000 0.092 0.904 0.004
#> GSM447652 2 0.0000 0.9376 0.000 1.000 0.000 0.000
#> GSM447679 2 0.0817 0.9333 0.000 0.976 0.000 0.024
#> GSM447712 1 0.0000 0.9653 1.000 0.000 0.000 0.000
#> GSM447664 4 0.1022 0.9421 0.032 0.000 0.000 0.968
#> GSM447637 3 0.0000 0.9429 0.000 0.000 1.000 0.000
#> GSM447639 4 0.0000 0.9550 0.000 0.000 0.000 1.000
#> GSM447615 1 0.0817 0.9419 0.976 0.000 0.024 0.000
#> GSM447656 2 0.5147 0.1807 0.460 0.536 0.000 0.004
#> GSM447673 4 0.0707 0.9550 0.000 0.020 0.000 0.980
#> GSM447719 4 0.3528 0.7836 0.000 0.000 0.192 0.808
#> GSM447706 3 0.0000 0.9429 0.000 0.000 1.000 0.000
#> GSM447612 3 0.0657 0.9363 0.000 0.012 0.984 0.004
#> GSM447665 2 0.0188 0.9373 0.000 0.996 0.000 0.004
#> GSM447677 2 0.0000 0.9376 0.000 1.000 0.000 0.000
#> GSM447613 1 0.0000 0.9653 1.000 0.000 0.000 0.000
#> GSM447659 4 0.1284 0.9468 0.000 0.012 0.024 0.964
#> GSM447662 3 0.0000 0.9429 0.000 0.000 1.000 0.000
#> GSM447666 3 0.0000 0.9429 0.000 0.000 1.000 0.000
#> GSM447668 2 0.0000 0.9376 0.000 1.000 0.000 0.000
#> GSM447682 2 0.3400 0.7943 0.000 0.820 0.000 0.180
#> GSM447683 2 0.0336 0.9373 0.000 0.992 0.000 0.008
#> GSM447688 4 0.1118 0.9510 0.000 0.036 0.000 0.964
#> GSM447702 2 0.0000 0.9376 0.000 1.000 0.000 0.000
#> GSM447709 2 0.0188 0.9373 0.000 0.996 0.000 0.004
#> GSM447711 1 0.0000 0.9653 1.000 0.000 0.000 0.000
#> GSM447715 1 0.0000 0.9653 1.000 0.000 0.000 0.000
#> GSM447693 3 0.0000 0.9429 0.000 0.000 1.000 0.000
#> GSM447611 4 0.0592 0.9516 0.016 0.000 0.000 0.984
#> GSM447672 2 0.0188 0.9375 0.000 0.996 0.000 0.004
#> GSM447703 4 0.1302 0.9489 0.000 0.044 0.000 0.956
#> GSM447727 1 0.0000 0.9653 1.000 0.000 0.000 0.000
#> GSM447638 1 0.4877 0.2391 0.592 0.408 0.000 0.000
#> GSM447670 1 0.0188 0.9615 0.996 0.000 0.004 0.000
#> GSM447700 4 0.2704 0.8617 0.000 0.124 0.000 0.876
#> GSM447738 4 0.0592 0.9553 0.000 0.016 0.000 0.984
#> GSM447739 1 0.0000 0.9653 1.000 0.000 0.000 0.000
#> GSM447617 1 0.0000 0.9653 1.000 0.000 0.000 0.000
#> GSM447628 4 0.0707 0.9549 0.000 0.020 0.000 0.980
#> GSM447632 4 0.0592 0.9553 0.000 0.016 0.000 0.984
#> GSM447619 3 0.0000 0.9429 0.000 0.000 1.000 0.000
#> GSM447643 1 0.0000 0.9653 1.000 0.000 0.000 0.000
#> GSM447724 4 0.1059 0.9529 0.000 0.016 0.012 0.972
#> GSM447728 2 0.0188 0.9375 0.000 0.996 0.000 0.004
#> GSM447610 4 0.4817 0.3714 0.388 0.000 0.000 0.612
#> GSM447633 2 0.0895 0.9285 0.000 0.976 0.020 0.004
#> GSM447634 3 0.3818 0.8585 0.096 0.048 0.852 0.004
#> GSM447622 3 0.2760 0.8507 0.128 0.000 0.872 0.000
#> GSM447667 2 0.7723 0.1329 0.232 0.420 0.000 0.348
#> GSM447687 4 0.0921 0.9528 0.000 0.028 0.000 0.972
#> GSM447695 3 0.3672 0.8085 0.164 0.000 0.824 0.012
#> GSM447696 1 0.0000 0.9653 1.000 0.000 0.000 0.000
#> GSM447697 1 0.0000 0.9653 1.000 0.000 0.000 0.000
#> GSM447714 3 0.0000 0.9429 0.000 0.000 1.000 0.000
#> GSM447717 1 0.0000 0.9653 1.000 0.000 0.000 0.000
#> GSM447725 1 0.0000 0.9653 1.000 0.000 0.000 0.000
#> GSM447729 4 0.0336 0.9557 0.000 0.008 0.000 0.992
#> GSM447644 2 0.0188 0.9366 0.000 0.996 0.004 0.000
#> GSM447710 3 0.0000 0.9429 0.000 0.000 1.000 0.000
#> GSM447614 4 0.1109 0.9450 0.004 0.000 0.028 0.968
#> GSM447685 2 0.2282 0.9077 0.024 0.924 0.000 0.052
#> GSM447690 1 0.0000 0.9653 1.000 0.000 0.000 0.000
#> GSM447730 2 0.0921 0.9292 0.000 0.972 0.000 0.028
#> GSM447646 4 0.1118 0.9520 0.000 0.036 0.000 0.964
#> GSM447689 3 0.0000 0.9429 0.000 0.000 1.000 0.000
#> GSM447635 4 0.0779 0.9521 0.016 0.004 0.000 0.980
#> GSM447641 1 0.0000 0.9653 1.000 0.000 0.000 0.000
#> GSM447716 4 0.0707 0.9508 0.020 0.000 0.000 0.980
#> GSM447718 3 0.1356 0.9264 0.000 0.032 0.960 0.008
#> GSM447616 3 0.3311 0.8037 0.172 0.000 0.828 0.000
#> GSM447626 3 0.0000 0.9429 0.000 0.000 1.000 0.000
#> GSM447640 2 0.0921 0.9321 0.000 0.972 0.000 0.028
#> GSM447734 3 0.0188 0.9421 0.000 0.004 0.996 0.000
#> GSM447692 3 0.3448 0.8072 0.168 0.000 0.828 0.004
#> GSM447647 4 0.0592 0.9559 0.000 0.016 0.000 0.984
#> GSM447624 3 0.4804 0.4166 0.384 0.000 0.616 0.000
#> GSM447625 3 0.0188 0.9421 0.000 0.004 0.996 0.000
#> GSM447707 2 0.0921 0.9292 0.000 0.972 0.000 0.028
#> GSM447732 3 0.0188 0.9421 0.000 0.004 0.996 0.000
#> GSM447684 1 0.4985 0.0207 0.532 0.000 0.468 0.000
#> GSM447731 4 0.3367 0.8781 0.000 0.108 0.028 0.864
#> GSM447705 3 0.0657 0.9363 0.000 0.012 0.984 0.004
#> GSM447631 3 0.0000 0.9429 0.000 0.000 1.000 0.000
#> GSM447701 2 0.0000 0.9376 0.000 1.000 0.000 0.000
#> GSM447645 3 0.0000 0.9429 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
#> GSM447671 2 0.4599 0.6269 0.000 0.688 0.000 0.040 0.272
#> GSM447694 3 0.3211 0.8137 0.000 0.004 0.824 0.008 0.164
#> GSM447618 5 0.5510 0.5867 0.000 0.208 0.000 0.144 0.648
#> GSM447691 2 0.4238 0.5477 0.000 0.628 0.000 0.004 0.368
#> GSM447733 4 0.0703 0.7376 0.000 0.000 0.000 0.976 0.024
#> GSM447620 2 0.5706 0.5636 0.000 0.680 0.192 0.036 0.092
#> GSM447627 4 0.6066 0.1725 0.000 0.000 0.368 0.504 0.128
#> GSM447630 2 0.3707 0.5962 0.000 0.716 0.000 0.000 0.284
#> GSM447642 1 0.0000 0.9741 1.000 0.000 0.000 0.000 0.000
#> GSM447649 2 0.3863 0.7153 0.000 0.796 0.000 0.052 0.152
#> GSM447654 4 0.1251 0.7357 0.000 0.036 0.000 0.956 0.008
#> GSM447655 2 0.2825 0.7469 0.000 0.860 0.000 0.016 0.124
#> GSM447669 2 0.3612 0.6131 0.000 0.732 0.000 0.000 0.268
#> GSM447676 1 0.0000 0.9741 1.000 0.000 0.000 0.000 0.000
#> GSM447678 4 0.4302 -0.2725 0.000 0.000 0.000 0.520 0.480
#> GSM447681 2 0.3957 0.5594 0.000 0.712 0.000 0.008 0.280
#> GSM447698 5 0.5658 0.6842 0.000 0.096 0.000 0.332 0.572
#> GSM447713 1 0.0000 0.9741 1.000 0.000 0.000 0.000 0.000
#> GSM447722 4 0.4446 -0.3239 0.000 0.004 0.000 0.520 0.476
#> GSM447726 2 0.3845 0.6387 0.004 0.760 0.012 0.000 0.224
#> GSM447735 4 0.3280 0.6617 0.000 0.000 0.012 0.812 0.176
#> GSM447737 1 0.2504 0.8968 0.896 0.000 0.064 0.000 0.040
#> GSM447657 5 0.4561 0.1118 0.000 0.488 0.000 0.008 0.504
#> GSM447674 2 0.4046 0.5406 0.000 0.696 0.000 0.008 0.296
#> GSM447636 1 0.0000 0.9741 1.000 0.000 0.000 0.000 0.000
#> GSM447723 1 0.0000 0.9741 1.000 0.000 0.000 0.000 0.000
#> GSM447699 3 0.5959 0.6012 0.000 0.004 0.564 0.116 0.316
#> GSM447708 2 0.3400 0.7365 0.000 0.828 0.000 0.036 0.136
#> GSM447721 1 0.0290 0.9693 0.992 0.000 0.000 0.000 0.008
#> GSM447623 1 0.0771 0.9597 0.976 0.000 0.020 0.000 0.004
#> GSM447621 1 0.0992 0.9546 0.968 0.000 0.024 0.000 0.008
#> GSM447650 2 0.1965 0.7529 0.000 0.904 0.000 0.000 0.096
#> GSM447651 2 0.0324 0.7520 0.000 0.992 0.004 0.000 0.004
#> GSM447653 4 0.0898 0.7419 0.000 0.000 0.020 0.972 0.008
#> GSM447658 1 0.0000 0.9741 1.000 0.000 0.000 0.000 0.000
#> GSM447675 4 0.1478 0.7209 0.000 0.000 0.000 0.936 0.064
#> GSM447680 2 0.3527 0.6286 0.172 0.804 0.000 0.000 0.024
#> GSM447686 1 0.0290 0.9681 0.992 0.000 0.000 0.000 0.008
#> GSM447736 3 0.2953 0.8152 0.000 0.000 0.844 0.012 0.144
#> GSM447629 5 0.6150 0.4418 0.136 0.288 0.000 0.008 0.568
#> GSM447648 3 0.0510 0.8299 0.000 0.000 0.984 0.000 0.016
#> GSM447660 1 0.0000 0.9741 1.000 0.000 0.000 0.000 0.000
#> GSM447661 2 0.1908 0.7528 0.000 0.908 0.000 0.000 0.092
#> GSM447663 3 0.6087 0.6592 0.000 0.160 0.552 0.000 0.288
#> GSM447704 2 0.4098 0.7016 0.000 0.780 0.000 0.064 0.156
#> GSM447720 3 0.5490 0.7441 0.000 0.084 0.592 0.000 0.324
#> GSM447652 2 0.2304 0.7525 0.000 0.892 0.000 0.008 0.100
#> GSM447679 2 0.3282 0.7125 0.000 0.804 0.000 0.008 0.188
#> GSM447712 1 0.0000 0.9741 1.000 0.000 0.000 0.000 0.000
#> GSM447664 4 0.2491 0.6935 0.068 0.000 0.000 0.896 0.036
#> GSM447637 3 0.0000 0.8308 0.000 0.000 1.000 0.000 0.000
#> GSM447639 4 0.0880 0.7396 0.000 0.000 0.000 0.968 0.032
#> GSM447615 1 0.4169 0.6820 0.732 0.000 0.240 0.000 0.028
#> GSM447656 2 0.6352 0.2462 0.376 0.476 0.000 0.004 0.144
#> GSM447673 5 0.4689 0.5451 0.000 0.016 0.000 0.424 0.560
#> GSM447719 4 0.2966 0.6363 0.000 0.000 0.184 0.816 0.000
#> GSM447706 3 0.0162 0.8308 0.000 0.000 0.996 0.000 0.004
#> GSM447612 3 0.3877 0.8010 0.000 0.000 0.764 0.024 0.212
#> GSM447665 2 0.3132 0.6927 0.000 0.820 0.000 0.008 0.172
#> GSM447677 2 0.1012 0.7547 0.000 0.968 0.000 0.012 0.020
#> GSM447613 1 0.0000 0.9741 1.000 0.000 0.000 0.000 0.000
#> GSM447659 4 0.0955 0.7389 0.000 0.000 0.004 0.968 0.028
#> GSM447662 3 0.2674 0.8261 0.000 0.000 0.856 0.004 0.140
#> GSM447666 3 0.4818 0.7283 0.000 0.100 0.720 0.000 0.180
#> GSM447668 2 0.0510 0.7499 0.000 0.984 0.000 0.000 0.016
#> GSM447682 5 0.6180 0.4653 0.000 0.360 0.000 0.144 0.496
#> GSM447683 2 0.2286 0.7536 0.000 0.888 0.000 0.004 0.108
#> GSM447688 5 0.5405 0.6407 0.000 0.064 0.000 0.380 0.556
#> GSM447702 2 0.1965 0.7525 0.000 0.904 0.000 0.000 0.096
#> GSM447709 2 0.2332 0.7421 0.000 0.904 0.004 0.016 0.076
#> GSM447711 1 0.0000 0.9741 1.000 0.000 0.000 0.000 0.000
#> GSM447715 1 0.0404 0.9650 0.988 0.000 0.000 0.000 0.012
#> GSM447693 3 0.0290 0.8311 0.000 0.000 0.992 0.000 0.008
#> GSM447611 4 0.1195 0.7359 0.012 0.000 0.000 0.960 0.028
#> GSM447672 2 0.3061 0.7412 0.000 0.844 0.000 0.020 0.136
#> GSM447703 5 0.5953 0.6877 0.000 0.124 0.000 0.336 0.540
#> GSM447727 1 0.0000 0.9741 1.000 0.000 0.000 0.000 0.000
#> GSM447638 2 0.6057 0.1569 0.428 0.488 0.056 0.000 0.028
#> GSM447670 1 0.3283 0.8271 0.832 0.000 0.140 0.000 0.028
#> GSM447700 5 0.3919 0.5589 0.000 0.036 0.000 0.188 0.776
#> GSM447738 5 0.5696 0.6837 0.000 0.096 0.000 0.344 0.560
#> GSM447739 1 0.0000 0.9741 1.000 0.000 0.000 0.000 0.000
#> GSM447617 1 0.1942 0.9162 0.920 0.000 0.068 0.000 0.012
#> GSM447628 4 0.0671 0.7373 0.000 0.004 0.000 0.980 0.016
#> GSM447632 5 0.5702 0.6888 0.000 0.104 0.000 0.320 0.576
#> GSM447619 3 0.0771 0.8332 0.000 0.000 0.976 0.004 0.020
#> GSM447643 1 0.0000 0.9741 1.000 0.000 0.000 0.000 0.000
#> GSM447724 4 0.4557 -0.2085 0.000 0.004 0.004 0.552 0.440
#> GSM447728 2 0.3016 0.7458 0.000 0.848 0.000 0.020 0.132
#> GSM447610 4 0.3992 0.4704 0.268 0.000 0.000 0.720 0.012
#> GSM447633 2 0.4273 0.6414 0.000 0.732 0.008 0.020 0.240
#> GSM447634 3 0.6044 0.7184 0.012 0.080 0.552 0.004 0.352
#> GSM447622 3 0.3413 0.8003 0.052 0.000 0.844 0.004 0.100
#> GSM447667 5 0.7277 0.5491 0.200 0.160 0.000 0.096 0.544
#> GSM447687 5 0.5878 0.6909 0.000 0.116 0.000 0.336 0.548
#> GSM447695 3 0.4867 0.7729 0.080 0.000 0.728 0.008 0.184
#> GSM447696 1 0.0000 0.9741 1.000 0.000 0.000 0.000 0.000
#> GSM447697 1 0.0000 0.9741 1.000 0.000 0.000 0.000 0.000
#> GSM447714 3 0.2338 0.8323 0.000 0.000 0.884 0.004 0.112
#> GSM447717 1 0.0000 0.9741 1.000 0.000 0.000 0.000 0.000
#> GSM447725 1 0.0000 0.9741 1.000 0.000 0.000 0.000 0.000
#> GSM447729 4 0.1121 0.7306 0.000 0.000 0.000 0.956 0.044
#> GSM447644 2 0.3612 0.6131 0.000 0.732 0.000 0.000 0.268
#> GSM447710 3 0.1732 0.8333 0.000 0.000 0.920 0.000 0.080
#> GSM447614 4 0.2967 0.6963 0.016 0.000 0.012 0.868 0.104
#> GSM447685 2 0.4541 0.6787 0.032 0.752 0.000 0.024 0.192
#> GSM447690 1 0.0000 0.9741 1.000 0.000 0.000 0.000 0.000
#> GSM447730 2 0.3814 0.7227 0.000 0.808 0.000 0.068 0.124
#> GSM447646 4 0.0771 0.7354 0.000 0.004 0.000 0.976 0.020
#> GSM447689 3 0.2074 0.8299 0.000 0.000 0.896 0.000 0.104
#> GSM447635 5 0.4102 0.4223 0.004 0.000 0.004 0.300 0.692
#> GSM447641 1 0.0000 0.9741 1.000 0.000 0.000 0.000 0.000
#> GSM447716 5 0.4903 0.5654 0.008 0.016 0.000 0.400 0.576
#> GSM447718 4 0.7001 0.0256 0.000 0.032 0.360 0.452 0.156
#> GSM447616 3 0.4228 0.7660 0.108 0.000 0.788 0.004 0.100
#> GSM447626 3 0.2583 0.8338 0.000 0.004 0.864 0.000 0.132
#> GSM447640 2 0.3562 0.7036 0.000 0.788 0.000 0.016 0.196
#> GSM447734 3 0.4066 0.8202 0.000 0.032 0.768 0.004 0.196
#> GSM447692 3 0.5202 0.7320 0.152 0.000 0.700 0.004 0.144
#> GSM447647 4 0.0671 0.7365 0.000 0.004 0.000 0.980 0.016
#> GSM447624 3 0.4380 0.6257 0.260 0.000 0.708 0.000 0.032
#> GSM447625 3 0.3961 0.8213 0.000 0.032 0.780 0.004 0.184
#> GSM447707 2 0.3752 0.7259 0.000 0.812 0.000 0.064 0.124
#> GSM447732 3 0.4302 0.8118 0.000 0.048 0.744 0.000 0.208
#> GSM447684 3 0.7876 0.4610 0.268 0.100 0.432 0.000 0.200
#> GSM447731 4 0.2364 0.7069 0.000 0.064 0.008 0.908 0.020
#> GSM447705 3 0.3132 0.8182 0.000 0.000 0.820 0.008 0.172
#> GSM447631 3 0.0000 0.8308 0.000 0.000 1.000 0.000 0.000
#> GSM447701 2 0.1341 0.7399 0.000 0.944 0.000 0.000 0.056
#> GSM447645 3 0.0000 0.8308 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
#> GSM447671 6 0.6524 0.1244 0.000 0.352 0.000 0.024 0.244 0.380
#> GSM447694 3 0.4679 0.4264 0.000 0.000 0.604 0.016 0.028 0.352
#> GSM447618 5 0.3779 0.7188 0.000 0.064 0.000 0.072 0.816 0.048
#> GSM447691 5 0.6108 -0.2065 0.000 0.292 0.000 0.000 0.364 0.344
#> GSM447733 4 0.2058 0.8132 0.000 0.008 0.000 0.908 0.072 0.012
#> GSM447620 2 0.7128 0.2766 0.000 0.508 0.220 0.020 0.132 0.120
#> GSM447627 4 0.6604 0.2547 0.000 0.000 0.268 0.488 0.060 0.184
#> GSM447630 6 0.3886 0.5503 0.000 0.264 0.000 0.000 0.028 0.708
#> GSM447642 1 0.0717 0.9261 0.976 0.000 0.000 0.000 0.008 0.016
#> GSM447649 2 0.2554 0.7697 0.000 0.876 0.000 0.048 0.076 0.000
#> GSM447654 4 0.0837 0.8278 0.000 0.020 0.000 0.972 0.004 0.004
#> GSM447655 2 0.1010 0.7766 0.000 0.960 0.000 0.000 0.036 0.004
#> GSM447669 6 0.4408 0.4963 0.000 0.320 0.000 0.000 0.044 0.636
#> GSM447676 1 0.0622 0.9273 0.980 0.000 0.000 0.000 0.008 0.012
#> GSM447678 5 0.3109 0.7135 0.000 0.000 0.000 0.224 0.772 0.004
#> GSM447681 2 0.3245 0.6126 0.000 0.764 0.000 0.000 0.228 0.008
#> GSM447698 5 0.4041 0.7805 0.000 0.096 0.000 0.136 0.764 0.004
#> GSM447713 1 0.0260 0.9299 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM447722 5 0.3594 0.7202 0.000 0.020 0.000 0.204 0.768 0.008
#> GSM447726 6 0.4976 0.4349 0.004 0.356 0.028 0.000 0.024 0.588
#> GSM447735 4 0.6978 0.3715 0.000 0.000 0.128 0.480 0.224 0.168
#> GSM447737 1 0.5118 0.5929 0.676 0.000 0.188 0.004 0.016 0.116
#> GSM447657 5 0.4218 0.3925 0.000 0.400 0.000 0.012 0.584 0.004
#> GSM447674 2 0.3109 0.6702 0.000 0.772 0.000 0.000 0.224 0.004
#> GSM447636 1 0.0717 0.9261 0.976 0.000 0.000 0.000 0.008 0.016
#> GSM447723 1 0.0363 0.9300 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM447699 3 0.7194 0.1823 0.000 0.016 0.360 0.044 0.280 0.300
#> GSM447708 2 0.4932 0.6462 0.000 0.688 0.000 0.028 0.204 0.080
#> GSM447721 1 0.0458 0.9262 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM447623 1 0.2914 0.8272 0.860 0.000 0.084 0.000 0.008 0.048
#> GSM447621 1 0.3270 0.8044 0.836 0.000 0.084 0.000 0.008 0.072
#> GSM447650 2 0.1334 0.7686 0.000 0.948 0.000 0.000 0.020 0.032
#> GSM447651 2 0.1556 0.7398 0.000 0.920 0.000 0.000 0.000 0.080
#> GSM447653 4 0.0405 0.8307 0.000 0.000 0.008 0.988 0.004 0.000
#> GSM447658 1 0.0717 0.9261 0.976 0.000 0.000 0.000 0.008 0.016
#> GSM447675 4 0.2234 0.7643 0.000 0.000 0.000 0.872 0.124 0.004
#> GSM447680 2 0.5056 0.6295 0.148 0.708 0.000 0.000 0.068 0.076
#> GSM447686 1 0.1225 0.9088 0.952 0.000 0.000 0.000 0.036 0.012
#> GSM447736 3 0.4858 0.4805 0.000 0.000 0.660 0.012 0.076 0.252
#> GSM447629 5 0.4034 0.6952 0.088 0.120 0.000 0.004 0.780 0.008
#> GSM447648 3 0.1082 0.5749 0.000 0.000 0.956 0.000 0.004 0.040
#> GSM447660 1 0.0622 0.9273 0.980 0.000 0.000 0.000 0.008 0.012
#> GSM447661 2 0.1176 0.7702 0.000 0.956 0.000 0.000 0.020 0.024
#> GSM447663 6 0.4749 0.4460 0.000 0.120 0.144 0.000 0.020 0.716
#> GSM447704 2 0.3017 0.7422 0.000 0.844 0.000 0.072 0.084 0.000
#> GSM447720 6 0.4238 0.2419 0.000 0.020 0.196 0.004 0.036 0.744
#> GSM447652 2 0.2259 0.7630 0.000 0.908 0.000 0.040 0.020 0.032
#> GSM447679 2 0.2340 0.7531 0.000 0.852 0.000 0.000 0.148 0.000
#> GSM447712 1 0.0363 0.9300 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM447664 4 0.2437 0.7913 0.036 0.000 0.000 0.888 0.072 0.004
#> GSM447637 3 0.0000 0.5794 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447639 4 0.1606 0.8213 0.000 0.008 0.000 0.932 0.056 0.004
#> GSM447615 3 0.5011 0.1307 0.392 0.000 0.540 0.000 0.004 0.064
#> GSM447656 2 0.5507 0.4449 0.292 0.580 0.000 0.000 0.112 0.016
#> GSM447673 5 0.4466 0.7568 0.000 0.116 0.000 0.176 0.708 0.000
#> GSM447719 4 0.2178 0.7812 0.000 0.000 0.132 0.868 0.000 0.000
#> GSM447706 3 0.0458 0.5792 0.000 0.000 0.984 0.000 0.000 0.016
#> GSM447612 3 0.6010 0.1532 0.000 0.012 0.476 0.012 0.116 0.384
#> GSM447665 2 0.4783 0.3578 0.000 0.636 0.000 0.000 0.088 0.276
#> GSM447677 2 0.1745 0.7442 0.000 0.920 0.000 0.000 0.012 0.068
#> GSM447613 1 0.0260 0.9299 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM447659 4 0.2169 0.8101 0.000 0.008 0.000 0.900 0.080 0.012
#> GSM447662 3 0.4732 0.3193 0.000 0.000 0.612 0.000 0.068 0.320
#> GSM447666 3 0.4263 -0.0326 0.000 0.016 0.504 0.000 0.000 0.480
#> GSM447668 2 0.2266 0.7186 0.000 0.880 0.000 0.000 0.012 0.108
#> GSM447682 2 0.5059 0.0995 0.000 0.528 0.000 0.080 0.392 0.000
#> GSM447683 2 0.3254 0.7615 0.000 0.820 0.000 0.000 0.124 0.056
#> GSM447688 5 0.4388 0.7708 0.000 0.092 0.000 0.168 0.732 0.008
#> GSM447702 2 0.1092 0.7713 0.000 0.960 0.000 0.000 0.020 0.020
#> GSM447709 2 0.3888 0.6540 0.000 0.788 0.004 0.004 0.092 0.112
#> GSM447711 1 0.0260 0.9299 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM447715 1 0.1297 0.9056 0.948 0.000 0.000 0.000 0.040 0.012
#> GSM447693 3 0.0632 0.5755 0.000 0.000 0.976 0.000 0.000 0.024
#> GSM447611 4 0.1074 0.8235 0.012 0.000 0.000 0.960 0.028 0.000
#> GSM447672 2 0.0865 0.7767 0.000 0.964 0.000 0.000 0.036 0.000
#> GSM447703 5 0.5316 0.6882 0.000 0.240 0.000 0.168 0.592 0.000
#> GSM447727 1 0.0291 0.9294 0.992 0.000 0.000 0.000 0.004 0.004
#> GSM447638 2 0.7500 0.0276 0.312 0.356 0.112 0.000 0.008 0.212
#> GSM447670 1 0.5197 0.1784 0.504 0.000 0.420 0.000 0.008 0.068
#> GSM447700 5 0.3534 0.6798 0.000 0.028 0.000 0.060 0.828 0.084
#> GSM447738 5 0.4059 0.7777 0.000 0.100 0.000 0.148 0.752 0.000
#> GSM447739 1 0.0260 0.9299 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM447617 1 0.4499 0.6278 0.704 0.000 0.216 0.000 0.008 0.072
#> GSM447628 4 0.1074 0.8232 0.000 0.012 0.000 0.960 0.028 0.000
#> GSM447632 5 0.3914 0.7741 0.000 0.104 0.000 0.128 0.768 0.000
#> GSM447619 3 0.2629 0.5443 0.000 0.000 0.868 0.000 0.040 0.092
#> GSM447643 1 0.0820 0.9243 0.972 0.000 0.000 0.000 0.012 0.016
#> GSM447724 5 0.3922 0.6878 0.000 0.016 0.008 0.180 0.772 0.024
#> GSM447728 2 0.1657 0.7787 0.000 0.928 0.000 0.000 0.056 0.016
#> GSM447610 4 0.3488 0.6120 0.244 0.000 0.000 0.744 0.008 0.004
#> GSM447633 6 0.5607 0.3140 0.000 0.384 0.004 0.004 0.112 0.496
#> GSM447634 6 0.3782 0.2796 0.008 0.008 0.164 0.012 0.016 0.792
#> GSM447622 3 0.4950 0.4828 0.068 0.000 0.680 0.008 0.016 0.228
#> GSM447667 5 0.5547 0.6593 0.148 0.112 0.000 0.044 0.680 0.016
#> GSM447687 5 0.5173 0.6949 0.000 0.224 0.000 0.160 0.616 0.000
#> GSM447695 3 0.6247 0.3984 0.060 0.000 0.516 0.016 0.064 0.344
#> GSM447696 1 0.0260 0.9299 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM447697 1 0.0260 0.9299 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM447714 3 0.4511 0.3324 0.000 0.000 0.620 0.000 0.048 0.332
#> GSM447717 1 0.0520 0.9272 0.984 0.000 0.000 0.000 0.008 0.008
#> GSM447725 1 0.0291 0.9294 0.992 0.000 0.000 0.000 0.004 0.004
#> GSM447729 4 0.1075 0.8181 0.000 0.000 0.000 0.952 0.048 0.000
#> GSM447644 6 0.4348 0.4984 0.000 0.320 0.000 0.000 0.040 0.640
#> GSM447710 3 0.3428 0.3695 0.000 0.000 0.696 0.000 0.000 0.304
#> GSM447614 4 0.2795 0.7800 0.000 0.000 0.000 0.856 0.044 0.100
#> GSM447685 2 0.4299 0.7021 0.028 0.752 0.000 0.020 0.184 0.016
#> GSM447690 1 0.0260 0.9299 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM447730 2 0.2350 0.7647 0.000 0.888 0.000 0.076 0.036 0.000
#> GSM447646 4 0.1092 0.8237 0.000 0.020 0.000 0.960 0.020 0.000
#> GSM447689 3 0.3482 0.3312 0.000 0.000 0.684 0.000 0.000 0.316
#> GSM447635 5 0.3325 0.7011 0.000 0.000 0.000 0.096 0.820 0.084
#> GSM447641 1 0.0146 0.9299 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM447716 5 0.3553 0.7633 0.012 0.032 0.000 0.144 0.808 0.004
#> GSM447718 4 0.6000 0.2494 0.000 0.024 0.148 0.520 0.000 0.308
#> GSM447616 3 0.5284 0.4696 0.088 0.000 0.656 0.012 0.016 0.228
#> GSM447626 3 0.3862 0.0637 0.000 0.000 0.524 0.000 0.000 0.476
#> GSM447640 2 0.2631 0.7479 0.000 0.840 0.000 0.008 0.152 0.000
#> GSM447734 6 0.4446 -0.0254 0.000 0.000 0.368 0.004 0.028 0.600
#> GSM447692 3 0.6173 0.3996 0.120 0.000 0.532 0.012 0.028 0.308
#> GSM447647 4 0.1092 0.8237 0.000 0.020 0.000 0.960 0.020 0.000
#> GSM447624 3 0.4880 0.3929 0.256 0.000 0.652 0.000 0.008 0.084
#> GSM447625 6 0.4200 -0.0506 0.000 0.000 0.392 0.004 0.012 0.592
#> GSM447707 2 0.2164 0.7685 0.000 0.900 0.000 0.068 0.032 0.000
#> GSM447732 6 0.3912 0.1191 0.000 0.012 0.340 0.000 0.000 0.648
#> GSM447684 6 0.5511 0.2264 0.156 0.016 0.216 0.000 0.000 0.612
#> GSM447731 4 0.1851 0.8188 0.000 0.036 0.012 0.928 0.000 0.024
#> GSM447705 3 0.5047 0.2683 0.000 0.000 0.564 0.000 0.088 0.348
#> GSM447631 3 0.0146 0.5798 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM447701 2 0.3271 0.5785 0.000 0.760 0.000 0.000 0.008 0.232
#> GSM447645 3 0.0146 0.5790 0.000 0.000 0.996 0.000 0.000 0.004
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
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 gender(p) individual(p) disease.state(p) other(p) k
#> CV:skmeans 124 0.436 0.8742 0.0331 5.34e-02 2
#> CV:skmeans 104 0.440 0.3754 0.1437 1.03e-01 3
#> CV:skmeans 124 0.156 0.1162 0.0891 2.37e-01 4
#> CV:skmeans 117 0.161 0.2190 0.0652 1.39e-01 5
#> CV:skmeans 90 0.849 0.0649 0.0820 2.37e-05 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "pam"]
# you can also extract it by
# res = res_list["CV:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 130 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 0.330 0.842 0.877 0.4682 0.516 0.516
#> 3 3 0.589 0.770 0.883 0.3562 0.820 0.663
#> 4 4 0.544 0.693 0.834 0.1531 0.847 0.616
#> 5 5 0.560 0.621 0.776 0.0627 0.869 0.584
#> 6 6 0.707 0.675 0.810 0.0547 0.902 0.612
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
#> GSM447671 1 0.8763 0.816 0.704 0.296
#> GSM447694 1 0.7056 0.869 0.808 0.192
#> GSM447618 1 0.8763 0.816 0.704 0.296
#> GSM447691 1 0.8763 0.816 0.704 0.296
#> GSM447733 1 0.8713 0.818 0.708 0.292
#> GSM447620 1 0.8763 0.816 0.704 0.296
#> GSM447627 1 0.7299 0.865 0.796 0.204
#> GSM447630 2 0.0000 0.916 0.000 1.000
#> GSM447642 1 0.0000 0.824 1.000 0.000
#> GSM447649 2 0.3114 0.889 0.056 0.944
#> GSM447654 2 0.0000 0.916 0.000 1.000
#> GSM447655 2 0.0000 0.916 0.000 1.000
#> GSM447669 1 0.8763 0.816 0.704 0.296
#> GSM447676 1 0.0000 0.824 1.000 0.000
#> GSM447678 1 0.8763 0.816 0.704 0.296
#> GSM447681 2 0.0000 0.916 0.000 1.000
#> GSM447698 1 0.8813 0.812 0.700 0.300
#> GSM447713 1 0.0000 0.824 1.000 0.000
#> GSM447722 1 0.8763 0.816 0.704 0.296
#> GSM447726 1 0.6973 0.846 0.812 0.188
#> GSM447735 1 0.7299 0.865 0.796 0.204
#> GSM447737 1 0.4690 0.870 0.900 0.100
#> GSM447657 2 0.0000 0.916 0.000 1.000
#> GSM447674 2 0.0000 0.916 0.000 1.000
#> GSM447636 2 0.7376 0.779 0.208 0.792
#> GSM447723 1 0.3114 0.853 0.944 0.056
#> GSM447699 1 0.7376 0.863 0.792 0.208
#> GSM447708 1 0.8763 0.816 0.704 0.296
#> GSM447721 1 0.0000 0.824 1.000 0.000
#> GSM447623 1 0.0000 0.824 1.000 0.000
#> GSM447621 1 0.0000 0.824 1.000 0.000
#> GSM447650 2 0.0000 0.916 0.000 1.000
#> GSM447651 2 0.1843 0.904 0.028 0.972
#> GSM447653 1 0.6801 0.871 0.820 0.180
#> GSM447658 2 0.8267 0.750 0.260 0.740
#> GSM447675 2 0.8443 0.451 0.272 0.728
#> GSM447680 2 0.4939 0.855 0.108 0.892
#> GSM447686 2 0.7376 0.779 0.208 0.792
#> GSM447736 1 0.6973 0.870 0.812 0.188
#> GSM447629 1 0.6973 0.846 0.812 0.188
#> GSM447648 1 0.4690 0.870 0.900 0.100
#> GSM447660 1 0.0000 0.824 1.000 0.000
#> GSM447661 2 0.0000 0.916 0.000 1.000
#> GSM447663 1 0.7376 0.863 0.792 0.208
#> GSM447704 2 0.0000 0.916 0.000 1.000
#> GSM447720 1 0.7528 0.846 0.784 0.216
#> GSM447652 2 0.0000 0.916 0.000 1.000
#> GSM447679 2 0.0376 0.914 0.004 0.996
#> GSM447712 2 0.8955 0.683 0.312 0.688
#> GSM447664 2 0.4815 0.858 0.104 0.896
#> GSM447637 1 0.4690 0.870 0.900 0.100
#> GSM447639 2 0.8144 0.522 0.252 0.748
#> GSM447615 1 0.4690 0.870 0.900 0.100
#> GSM447656 1 0.9988 0.201 0.520 0.480
#> GSM447673 2 0.0000 0.916 0.000 1.000
#> GSM447719 1 0.4690 0.870 0.900 0.100
#> GSM447706 1 0.4690 0.870 0.900 0.100
#> GSM447612 1 0.7528 0.861 0.784 0.216
#> GSM447665 2 0.8861 0.358 0.304 0.696
#> GSM447677 2 0.0000 0.916 0.000 1.000
#> GSM447613 1 0.0000 0.824 1.000 0.000
#> GSM447659 1 0.7376 0.863 0.792 0.208
#> GSM447662 1 0.7299 0.865 0.796 0.204
#> GSM447666 1 0.7528 0.845 0.784 0.216
#> GSM447668 2 0.0000 0.916 0.000 1.000
#> GSM447682 2 0.1414 0.908 0.020 0.980
#> GSM447683 2 0.0000 0.916 0.000 1.000
#> GSM447688 2 0.0000 0.916 0.000 1.000
#> GSM447702 2 0.0000 0.916 0.000 1.000
#> GSM447709 1 0.8763 0.816 0.704 0.296
#> GSM447711 2 0.9850 0.517 0.428 0.572
#> GSM447715 2 0.6148 0.827 0.152 0.848
#> GSM447693 1 0.5408 0.873 0.876 0.124
#> GSM447611 2 0.4939 0.855 0.108 0.892
#> GSM447672 2 0.0000 0.916 0.000 1.000
#> GSM447703 2 0.0000 0.916 0.000 1.000
#> GSM447727 1 0.5408 0.854 0.876 0.124
#> GSM447638 1 0.6623 0.834 0.828 0.172
#> GSM447670 1 0.0000 0.824 1.000 0.000
#> GSM447700 1 0.8763 0.816 0.704 0.296
#> GSM447738 2 0.0000 0.916 0.000 1.000
#> GSM447739 1 0.0000 0.824 1.000 0.000
#> GSM447617 1 0.0000 0.824 1.000 0.000
#> GSM447628 2 0.0000 0.916 0.000 1.000
#> GSM447632 2 0.0000 0.916 0.000 1.000
#> GSM447619 1 0.7299 0.865 0.796 0.204
#> GSM447643 2 0.7602 0.775 0.220 0.780
#> GSM447724 1 0.8763 0.816 0.704 0.296
#> GSM447728 2 0.0000 0.916 0.000 1.000
#> GSM447610 1 0.0376 0.826 0.996 0.004
#> GSM447633 1 0.8763 0.816 0.704 0.296
#> GSM447634 1 0.7299 0.865 0.796 0.204
#> GSM447622 1 0.4690 0.870 0.900 0.100
#> GSM447667 1 0.6973 0.846 0.812 0.188
#> GSM447687 2 0.0000 0.916 0.000 1.000
#> GSM447695 1 0.6801 0.871 0.820 0.180
#> GSM447696 1 0.0000 0.824 1.000 0.000
#> GSM447697 1 0.0000 0.824 1.000 0.000
#> GSM447714 1 0.7376 0.865 0.792 0.208
#> GSM447717 2 0.8763 0.696 0.296 0.704
#> GSM447725 1 0.0000 0.824 1.000 0.000
#> GSM447729 2 0.4690 0.861 0.100 0.900
#> GSM447644 1 0.8763 0.816 0.704 0.296
#> GSM447710 1 0.6531 0.874 0.832 0.168
#> GSM447614 1 0.7219 0.867 0.800 0.200
#> GSM447685 2 0.4939 0.855 0.108 0.892
#> GSM447690 1 0.0000 0.824 1.000 0.000
#> GSM447730 2 0.0000 0.916 0.000 1.000
#> GSM447646 2 0.0000 0.916 0.000 1.000
#> GSM447689 1 0.8081 0.838 0.752 0.248
#> GSM447635 1 0.8763 0.816 0.704 0.296
#> GSM447641 1 0.0000 0.824 1.000 0.000
#> GSM447716 2 0.1414 0.908 0.020 0.980
#> GSM447718 2 0.4815 0.858 0.104 0.896
#> GSM447616 1 0.4690 0.870 0.900 0.100
#> GSM447626 1 0.4690 0.870 0.900 0.100
#> GSM447640 2 0.0000 0.916 0.000 1.000
#> GSM447734 1 0.7376 0.863 0.792 0.208
#> GSM447692 1 0.4690 0.870 0.900 0.100
#> GSM447647 2 0.0000 0.916 0.000 1.000
#> GSM447624 1 0.0000 0.824 1.000 0.000
#> GSM447625 1 0.7376 0.863 0.792 0.208
#> GSM447707 2 0.0000 0.916 0.000 1.000
#> GSM447732 1 0.6623 0.873 0.828 0.172
#> GSM447684 1 0.4939 0.870 0.892 0.108
#> GSM447731 2 0.0938 0.907 0.012 0.988
#> GSM447705 1 0.8763 0.816 0.704 0.296
#> GSM447631 1 0.4690 0.870 0.900 0.100
#> GSM447701 2 0.0000 0.916 0.000 1.000
#> GSM447645 1 0.4690 0.870 0.900 0.100
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM447671 3 0.4399 0.7701 0.000 0.188 0.812
#> GSM447694 3 0.1860 0.8366 0.052 0.000 0.948
#> GSM447618 3 0.5968 0.5872 0.000 0.364 0.636
#> GSM447691 3 0.5016 0.7430 0.000 0.240 0.760
#> GSM447733 3 0.0747 0.8449 0.000 0.016 0.984
#> GSM447620 3 0.4121 0.7745 0.000 0.168 0.832
#> GSM447627 3 0.1753 0.8381 0.048 0.000 0.952
#> GSM447630 2 0.0892 0.8646 0.000 0.980 0.020
#> GSM447642 1 0.0000 0.9254 1.000 0.000 0.000
#> GSM447649 2 0.2356 0.8432 0.000 0.928 0.072
#> GSM447654 2 0.4750 0.6951 0.000 0.784 0.216
#> GSM447655 2 0.0237 0.8693 0.000 0.996 0.004
#> GSM447669 3 0.5926 0.6041 0.000 0.356 0.644
#> GSM447676 3 0.5016 0.6707 0.240 0.000 0.760
#> GSM447678 3 0.4291 0.7619 0.000 0.180 0.820
#> GSM447681 2 0.0000 0.8694 0.000 1.000 0.000
#> GSM447698 3 0.6168 0.5000 0.000 0.412 0.588
#> GSM447713 1 0.0000 0.9254 1.000 0.000 0.000
#> GSM447722 3 0.3116 0.8129 0.000 0.108 0.892
#> GSM447726 3 0.7147 0.7052 0.076 0.228 0.696
#> GSM447735 3 0.1860 0.8382 0.000 0.052 0.948
#> GSM447737 1 0.5291 0.5572 0.732 0.000 0.268
#> GSM447657 2 0.0000 0.8694 0.000 1.000 0.000
#> GSM447674 2 0.0000 0.8694 0.000 1.000 0.000
#> GSM447636 1 0.6168 0.1731 0.588 0.412 0.000
#> GSM447723 3 0.5988 0.4358 0.368 0.000 0.632
#> GSM447699 3 0.3846 0.8110 0.016 0.108 0.876
#> GSM447708 3 0.5621 0.6580 0.000 0.308 0.692
#> GSM447721 1 0.0000 0.9254 1.000 0.000 0.000
#> GSM447623 1 0.0000 0.9254 1.000 0.000 0.000
#> GSM447621 1 0.0000 0.9254 1.000 0.000 0.000
#> GSM447650 2 0.0000 0.8694 0.000 1.000 0.000
#> GSM447651 2 0.3116 0.8199 0.000 0.892 0.108
#> GSM447653 3 0.2173 0.8380 0.048 0.008 0.944
#> GSM447658 1 0.0237 0.9220 0.996 0.004 0.000
#> GSM447675 2 0.6045 0.4262 0.000 0.620 0.380
#> GSM447680 2 0.4097 0.8060 0.060 0.880 0.060
#> GSM447686 2 0.6252 0.2590 0.444 0.556 0.000
#> GSM447736 3 0.1529 0.8405 0.040 0.000 0.960
#> GSM447629 3 0.6001 0.7711 0.052 0.176 0.772
#> GSM447648 3 0.1163 0.8429 0.028 0.000 0.972
#> GSM447660 1 0.5254 0.6125 0.736 0.000 0.264
#> GSM447661 2 0.0000 0.8694 0.000 1.000 0.000
#> GSM447663 3 0.0237 0.8442 0.000 0.004 0.996
#> GSM447704 2 0.0592 0.8680 0.000 0.988 0.012
#> GSM447720 3 0.2096 0.8362 0.052 0.004 0.944
#> GSM447652 2 0.0000 0.8694 0.000 1.000 0.000
#> GSM447679 2 0.0000 0.8694 0.000 1.000 0.000
#> GSM447712 1 0.0000 0.9254 1.000 0.000 0.000
#> GSM447664 2 0.7564 0.5668 0.068 0.636 0.296
#> GSM447637 3 0.4750 0.7141 0.216 0.000 0.784
#> GSM447639 3 0.6470 0.3878 0.012 0.356 0.632
#> GSM447615 3 0.1964 0.8349 0.056 0.000 0.944
#> GSM447656 3 0.8117 0.4568 0.076 0.372 0.552
#> GSM447673 2 0.0000 0.8694 0.000 1.000 0.000
#> GSM447719 3 0.1964 0.8349 0.056 0.000 0.944
#> GSM447706 3 0.0424 0.8446 0.008 0.000 0.992
#> GSM447612 3 0.0000 0.8444 0.000 0.000 1.000
#> GSM447665 2 0.6309 -0.0987 0.000 0.504 0.496
#> GSM447677 2 0.3192 0.8193 0.000 0.888 0.112
#> GSM447613 1 0.0000 0.9254 1.000 0.000 0.000
#> GSM447659 3 0.0000 0.8444 0.000 0.000 1.000
#> GSM447662 3 0.0000 0.8444 0.000 0.000 1.000
#> GSM447666 3 0.4351 0.7753 0.004 0.168 0.828
#> GSM447668 2 0.0000 0.8694 0.000 1.000 0.000
#> GSM447682 2 0.0592 0.8651 0.012 0.988 0.000
#> GSM447683 2 0.0237 0.8693 0.000 0.996 0.004
#> GSM447688 2 0.0237 0.8693 0.000 0.996 0.004
#> GSM447702 2 0.0000 0.8694 0.000 1.000 0.000
#> GSM447709 3 0.5178 0.6867 0.000 0.256 0.744
#> GSM447711 1 0.0000 0.9254 1.000 0.000 0.000
#> GSM447715 2 0.6772 0.5407 0.304 0.664 0.032
#> GSM447693 3 0.0237 0.8444 0.004 0.000 0.996
#> GSM447611 2 0.7327 0.5558 0.052 0.636 0.312
#> GSM447672 2 0.0237 0.8693 0.000 0.996 0.004
#> GSM447703 2 0.0237 0.8693 0.000 0.996 0.004
#> GSM447727 3 0.6222 0.7785 0.132 0.092 0.776
#> GSM447638 3 0.8212 0.4959 0.084 0.360 0.556
#> GSM447670 1 0.0000 0.9254 1.000 0.000 0.000
#> GSM447700 3 0.4702 0.7691 0.000 0.212 0.788
#> GSM447738 2 0.0237 0.8693 0.000 0.996 0.004
#> GSM447739 1 0.0000 0.9254 1.000 0.000 0.000
#> GSM447617 1 0.0000 0.9254 1.000 0.000 0.000
#> GSM447628 2 0.0000 0.8694 0.000 1.000 0.000
#> GSM447632 2 0.0237 0.8693 0.000 0.996 0.004
#> GSM447619 3 0.0000 0.8444 0.000 0.000 1.000
#> GSM447643 2 0.6252 0.2590 0.444 0.556 0.000
#> GSM447724 3 0.0000 0.8444 0.000 0.000 1.000
#> GSM447728 2 0.0237 0.8693 0.000 0.996 0.004
#> GSM447610 3 0.4555 0.7227 0.200 0.000 0.800
#> GSM447633 3 0.5178 0.6867 0.000 0.256 0.744
#> GSM447634 3 0.2173 0.8380 0.048 0.008 0.944
#> GSM447622 1 0.6204 0.1521 0.576 0.000 0.424
#> GSM447667 3 0.5947 0.7731 0.052 0.172 0.776
#> GSM447687 2 0.0237 0.8693 0.000 0.996 0.004
#> GSM447695 3 0.1860 0.8366 0.052 0.000 0.948
#> GSM447696 1 0.0000 0.9254 1.000 0.000 0.000
#> GSM447697 1 0.0000 0.9254 1.000 0.000 0.000
#> GSM447714 3 0.0000 0.8444 0.000 0.000 1.000
#> GSM447717 1 0.0000 0.9254 1.000 0.000 0.000
#> GSM447725 1 0.0000 0.9254 1.000 0.000 0.000
#> GSM447729 2 0.5058 0.6615 0.000 0.756 0.244
#> GSM447644 3 0.5216 0.6860 0.000 0.260 0.740
#> GSM447710 3 0.0237 0.8444 0.004 0.000 0.996
#> GSM447614 3 0.1860 0.8366 0.052 0.000 0.948
#> GSM447685 2 0.2200 0.8376 0.056 0.940 0.004
#> GSM447690 1 0.0000 0.9254 1.000 0.000 0.000
#> GSM447730 2 0.1860 0.8526 0.000 0.948 0.052
#> GSM447646 2 0.0237 0.8693 0.000 0.996 0.004
#> GSM447689 3 0.0237 0.8444 0.004 0.000 0.996
#> GSM447635 3 0.1964 0.8381 0.000 0.056 0.944
#> GSM447641 1 0.0000 0.9254 1.000 0.000 0.000
#> GSM447716 2 0.2165 0.8344 0.064 0.936 0.000
#> GSM447718 2 0.6566 0.5663 0.016 0.636 0.348
#> GSM447616 3 0.5591 0.6094 0.304 0.000 0.696
#> GSM447626 3 0.1163 0.8426 0.028 0.000 0.972
#> GSM447640 2 0.0000 0.8694 0.000 1.000 0.000
#> GSM447734 3 0.0000 0.8444 0.000 0.000 1.000
#> GSM447692 3 0.6126 0.4248 0.400 0.000 0.600
#> GSM447647 2 0.5988 0.5517 0.000 0.632 0.368
#> GSM447624 1 0.0892 0.9087 0.980 0.000 0.020
#> GSM447625 3 0.0000 0.8444 0.000 0.000 1.000
#> GSM447707 2 0.1753 0.8546 0.000 0.952 0.048
#> GSM447732 3 0.0237 0.8442 0.000 0.004 0.996
#> GSM447684 3 0.6435 0.7650 0.076 0.168 0.756
#> GSM447731 2 0.5968 0.5531 0.000 0.636 0.364
#> GSM447705 3 0.0000 0.8444 0.000 0.000 1.000
#> GSM447631 3 0.0592 0.8447 0.012 0.000 0.988
#> GSM447701 2 0.2959 0.8257 0.000 0.900 0.100
#> GSM447645 3 0.0592 0.8447 0.012 0.000 0.988
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM447671 2 0.4426 0.677 0.000 0.772 0.204 0.024
#> GSM447694 3 0.0000 0.799 0.000 0.000 1.000 0.000
#> GSM447618 3 0.7119 0.215 0.000 0.132 0.480 0.388
#> GSM447691 3 0.5382 0.664 0.000 0.132 0.744 0.124
#> GSM447733 3 0.0188 0.799 0.000 0.004 0.996 0.000
#> GSM447620 2 0.0000 0.783 0.000 1.000 0.000 0.000
#> GSM447627 3 0.0188 0.799 0.004 0.000 0.996 0.000
#> GSM447630 4 0.6557 0.534 0.004 0.292 0.096 0.608
#> GSM447642 1 0.0000 0.936 1.000 0.000 0.000 0.000
#> GSM447649 2 0.1389 0.785 0.000 0.952 0.000 0.048
#> GSM447654 4 0.0469 0.722 0.000 0.000 0.012 0.988
#> GSM447655 2 0.3266 0.734 0.000 0.832 0.000 0.168
#> GSM447669 3 0.6711 0.356 0.000 0.308 0.576 0.116
#> GSM447676 3 0.3942 0.671 0.236 0.000 0.764 0.000
#> GSM447678 3 0.4941 0.374 0.000 0.000 0.564 0.436
#> GSM447681 2 0.4605 0.523 0.000 0.664 0.000 0.336
#> GSM447698 3 0.7154 0.102 0.000 0.132 0.440 0.428
#> GSM447713 1 0.0000 0.936 1.000 0.000 0.000 0.000
#> GSM447722 3 0.4817 0.469 0.000 0.000 0.612 0.388
#> GSM447726 2 0.5587 0.418 0.028 0.600 0.372 0.000
#> GSM447735 3 0.1022 0.797 0.000 0.000 0.968 0.032
#> GSM447737 1 0.4277 0.569 0.720 0.000 0.280 0.000
#> GSM447657 4 0.3707 0.744 0.000 0.132 0.028 0.840
#> GSM447674 4 0.2814 0.745 0.000 0.132 0.000 0.868
#> GSM447636 4 0.4877 0.228 0.408 0.000 0.000 0.592
#> GSM447723 3 0.4406 0.560 0.300 0.000 0.700 0.000
#> GSM447699 3 0.3157 0.750 0.004 0.000 0.852 0.144
#> GSM447708 2 0.5659 0.433 0.000 0.600 0.368 0.032
#> GSM447721 1 0.0000 0.936 1.000 0.000 0.000 0.000
#> GSM447623 1 0.0000 0.936 1.000 0.000 0.000 0.000
#> GSM447621 1 0.0000 0.936 1.000 0.000 0.000 0.000
#> GSM447650 4 0.5573 0.439 0.000 0.368 0.028 0.604
#> GSM447651 2 0.0000 0.783 0.000 1.000 0.000 0.000
#> GSM447653 3 0.2999 0.752 0.004 0.000 0.864 0.132
#> GSM447658 1 0.0336 0.929 0.992 0.000 0.000 0.008
#> GSM447675 4 0.2408 0.661 0.000 0.000 0.104 0.896
#> GSM447680 4 0.7941 0.340 0.040 0.372 0.116 0.472
#> GSM447686 4 0.7273 0.408 0.380 0.132 0.004 0.484
#> GSM447736 3 0.0000 0.799 0.000 0.000 1.000 0.000
#> GSM447629 3 0.6342 0.596 0.024 0.132 0.704 0.140
#> GSM447648 3 0.1406 0.799 0.016 0.024 0.960 0.000
#> GSM447660 1 0.5533 0.646 0.732 0.000 0.136 0.132
#> GSM447661 2 0.3768 0.722 0.000 0.808 0.008 0.184
#> GSM447663 3 0.2831 0.776 0.000 0.120 0.876 0.004
#> GSM447704 2 0.3764 0.693 0.000 0.784 0.000 0.216
#> GSM447720 3 0.0000 0.799 0.000 0.000 1.000 0.000
#> GSM447652 4 0.4164 0.608 0.000 0.264 0.000 0.736
#> GSM447679 4 0.2814 0.745 0.000 0.132 0.000 0.868
#> GSM447712 1 0.0000 0.936 1.000 0.000 0.000 0.000
#> GSM447664 4 0.4353 0.581 0.012 0.000 0.232 0.756
#> GSM447637 3 0.4574 0.672 0.220 0.024 0.756 0.000
#> GSM447639 3 0.4522 0.475 0.000 0.000 0.680 0.320
#> GSM447615 3 0.0592 0.798 0.016 0.000 0.984 0.000
#> GSM447656 3 0.7678 0.371 0.040 0.132 0.572 0.256
#> GSM447673 4 0.0000 0.722 0.000 0.000 0.000 1.000
#> GSM447719 3 0.2814 0.752 0.000 0.000 0.868 0.132
#> GSM447706 3 0.1211 0.797 0.000 0.040 0.960 0.000
#> GSM447612 3 0.4643 0.563 0.000 0.344 0.656 0.000
#> GSM447665 2 0.0000 0.783 0.000 1.000 0.000 0.000
#> GSM447677 2 0.0592 0.784 0.000 0.984 0.000 0.016
#> GSM447613 1 0.0000 0.936 1.000 0.000 0.000 0.000
#> GSM447659 3 0.0921 0.798 0.000 0.028 0.972 0.000
#> GSM447662 2 0.4008 0.523 0.000 0.756 0.244 0.000
#> GSM447666 2 0.0000 0.783 0.000 1.000 0.000 0.000
#> GSM447668 2 0.4149 0.725 0.000 0.804 0.028 0.168
#> GSM447682 4 0.3501 0.745 0.000 0.132 0.020 0.848
#> GSM447683 4 0.4343 0.608 0.004 0.264 0.000 0.732
#> GSM447688 2 0.4933 0.292 0.000 0.568 0.000 0.432
#> GSM447702 2 0.3444 0.723 0.000 0.816 0.000 0.184
#> GSM447709 2 0.0000 0.783 0.000 1.000 0.000 0.000
#> GSM447711 1 0.0000 0.936 1.000 0.000 0.000 0.000
#> GSM447715 4 0.8201 0.568 0.244 0.132 0.076 0.548
#> GSM447693 3 0.3444 0.746 0.000 0.184 0.816 0.000
#> GSM447611 4 0.4453 0.568 0.012 0.000 0.244 0.744
#> GSM447672 2 0.3486 0.720 0.000 0.812 0.000 0.188
#> GSM447703 4 0.2921 0.743 0.000 0.140 0.000 0.860
#> GSM447727 3 0.4188 0.760 0.112 0.064 0.824 0.000
#> GSM447638 2 0.4196 0.756 0.048 0.852 0.048 0.052
#> GSM447670 1 0.0000 0.936 1.000 0.000 0.000 0.000
#> GSM447700 3 0.6567 0.536 0.000 0.128 0.616 0.256
#> GSM447738 4 0.2814 0.745 0.000 0.132 0.000 0.868
#> GSM447739 1 0.0000 0.936 1.000 0.000 0.000 0.000
#> GSM447617 1 0.0000 0.936 1.000 0.000 0.000 0.000
#> GSM447628 4 0.0000 0.722 0.000 0.000 0.000 1.000
#> GSM447632 4 0.2814 0.745 0.000 0.132 0.000 0.868
#> GSM447619 3 0.3444 0.746 0.000 0.184 0.816 0.000
#> GSM447643 4 0.7688 0.421 0.368 0.132 0.020 0.480
#> GSM447724 3 0.3444 0.746 0.000 0.184 0.816 0.000
#> GSM447728 2 0.4477 0.562 0.000 0.688 0.000 0.312
#> GSM447610 3 0.7117 0.505 0.180 0.000 0.556 0.264
#> GSM447633 2 0.0000 0.783 0.000 1.000 0.000 0.000
#> GSM447634 3 0.0000 0.799 0.000 0.000 1.000 0.000
#> GSM447622 1 0.4866 0.297 0.596 0.000 0.404 0.000
#> GSM447667 3 0.5926 0.609 0.012 0.132 0.724 0.132
#> GSM447687 4 0.2814 0.745 0.000 0.132 0.000 0.868
#> GSM447695 3 0.0188 0.799 0.004 0.000 0.996 0.000
#> GSM447696 1 0.0000 0.936 1.000 0.000 0.000 0.000
#> GSM447697 1 0.0000 0.936 1.000 0.000 0.000 0.000
#> GSM447714 3 0.3444 0.746 0.000 0.184 0.816 0.000
#> GSM447717 1 0.0000 0.936 1.000 0.000 0.000 0.000
#> GSM447725 1 0.2814 0.783 0.868 0.000 0.000 0.132
#> GSM447729 4 0.0469 0.720 0.000 0.000 0.012 0.988
#> GSM447644 2 0.3725 0.704 0.000 0.812 0.180 0.008
#> GSM447710 3 0.3444 0.746 0.000 0.184 0.816 0.000
#> GSM447614 3 0.0000 0.799 0.000 0.000 1.000 0.000
#> GSM447685 4 0.4437 0.740 0.040 0.132 0.012 0.816
#> GSM447690 1 0.0000 0.936 1.000 0.000 0.000 0.000
#> GSM447730 2 0.0188 0.784 0.000 0.996 0.000 0.004
#> GSM447646 4 0.1211 0.717 0.000 0.040 0.000 0.960
#> GSM447689 3 0.4250 0.661 0.000 0.276 0.724 0.000
#> GSM447635 3 0.0188 0.799 0.000 0.000 0.996 0.004
#> GSM447641 1 0.0000 0.936 1.000 0.000 0.000 0.000
#> GSM447716 4 0.3845 0.748 0.012 0.132 0.016 0.840
#> GSM447718 4 0.7343 0.164 0.008 0.124 0.392 0.476
#> GSM447616 3 0.4356 0.567 0.292 0.000 0.708 0.000
#> GSM447626 3 0.0707 0.798 0.020 0.000 0.980 0.000
#> GSM447640 4 0.2814 0.745 0.000 0.132 0.000 0.868
#> GSM447734 3 0.2973 0.766 0.000 0.144 0.856 0.000
#> GSM447692 3 0.4843 0.328 0.396 0.000 0.604 0.000
#> GSM447647 4 0.3895 0.637 0.000 0.132 0.036 0.832
#> GSM447624 1 0.0188 0.933 0.996 0.000 0.004 0.000
#> GSM447625 3 0.2704 0.777 0.000 0.124 0.876 0.000
#> GSM447707 2 0.1716 0.780 0.000 0.936 0.000 0.064
#> GSM447732 3 0.0469 0.799 0.000 0.012 0.988 0.000
#> GSM447684 3 0.3958 0.709 0.032 0.144 0.824 0.000
#> GSM447731 2 0.5308 0.546 0.000 0.684 0.036 0.280
#> GSM447705 2 0.3569 0.600 0.000 0.804 0.196 0.000
#> GSM447631 3 0.3143 0.784 0.024 0.100 0.876 0.000
#> GSM447701 2 0.2214 0.780 0.000 0.928 0.028 0.044
#> GSM447645 3 0.1629 0.798 0.024 0.024 0.952 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM447671 5 0.4096 0.6809 0.000 0.040 0.200 0.000 0.760
#> GSM447694 3 0.1043 0.7469 0.000 0.000 0.960 0.040 0.000
#> GSM447618 2 0.5585 0.5413 0.000 0.652 0.208 0.004 0.136
#> GSM447691 3 0.5284 0.5201 0.000 0.216 0.668 0.000 0.116
#> GSM447733 3 0.2420 0.7384 0.000 0.008 0.896 0.088 0.008
#> GSM447620 5 0.0794 0.7759 0.000 0.000 0.028 0.000 0.972
#> GSM447627 3 0.2304 0.7483 0.044 0.000 0.908 0.048 0.000
#> GSM447630 2 0.8504 -0.0289 0.040 0.392 0.148 0.096 0.324
#> GSM447642 1 0.1836 0.8042 0.932 0.000 0.032 0.036 0.000
#> GSM447649 5 0.1808 0.7883 0.000 0.044 0.008 0.012 0.936
#> GSM447654 4 0.3452 0.7026 0.000 0.244 0.000 0.756 0.000
#> GSM447655 5 0.3196 0.6939 0.000 0.192 0.000 0.004 0.804
#> GSM447669 3 0.6976 0.2873 0.000 0.092 0.512 0.076 0.320
#> GSM447676 3 0.3988 0.6614 0.196 0.000 0.768 0.036 0.000
#> GSM447678 2 0.4127 0.4267 0.000 0.680 0.312 0.008 0.000
#> GSM447681 2 0.4249 0.2784 0.000 0.568 0.000 0.000 0.432
#> GSM447698 2 0.5159 0.5542 0.000 0.688 0.188 0.000 0.124
#> GSM447713 1 0.0000 0.8101 1.000 0.000 0.000 0.000 0.000
#> GSM447722 2 0.3966 0.4048 0.000 0.664 0.336 0.000 0.000
#> GSM447726 5 0.6591 0.4753 0.012 0.020 0.268 0.124 0.576
#> GSM447735 3 0.1661 0.7485 0.000 0.024 0.940 0.036 0.000
#> GSM447737 1 0.4925 0.4617 0.632 0.000 0.324 0.044 0.000
#> GSM447657 2 0.3491 0.6638 0.000 0.836 0.012 0.028 0.124
#> GSM447674 2 0.2777 0.6652 0.000 0.864 0.000 0.016 0.120
#> GSM447636 1 0.6593 0.3607 0.464 0.284 0.000 0.252 0.000
#> GSM447723 3 0.7575 0.0364 0.340 0.112 0.436 0.112 0.000
#> GSM447699 3 0.4756 0.6317 0.044 0.288 0.668 0.000 0.000
#> GSM447708 5 0.5274 0.4587 0.000 0.064 0.336 0.000 0.600
#> GSM447721 1 0.4070 0.7772 0.804 0.112 0.008 0.076 0.000
#> GSM447623 1 0.1197 0.7942 0.952 0.000 0.048 0.000 0.000
#> GSM447621 1 0.1197 0.7942 0.952 0.000 0.048 0.000 0.000
#> GSM447650 5 0.5917 0.4277 0.000 0.304 0.012 0.096 0.588
#> GSM447651 5 0.0566 0.7833 0.000 0.012 0.004 0.000 0.984
#> GSM447653 4 0.3210 0.7232 0.000 0.000 0.212 0.788 0.000
#> GSM447658 1 0.5095 0.7604 0.744 0.120 0.032 0.104 0.000
#> GSM447675 4 0.3707 0.6607 0.000 0.284 0.000 0.716 0.000
#> GSM447680 5 0.8257 0.2816 0.056 0.196 0.096 0.144 0.508
#> GSM447686 1 0.6403 0.3078 0.452 0.432 0.000 0.092 0.024
#> GSM447736 3 0.1043 0.7469 0.000 0.000 0.960 0.040 0.000
#> GSM447629 3 0.7179 0.3015 0.004 0.248 0.548 0.084 0.116
#> GSM447648 3 0.0671 0.7537 0.000 0.000 0.980 0.004 0.016
#> GSM447660 1 0.6564 0.6153 0.592 0.216 0.152 0.040 0.000
#> GSM447661 5 0.3916 0.7385 0.000 0.104 0.000 0.092 0.804
#> GSM447663 3 0.4699 0.7375 0.000 0.060 0.784 0.068 0.088
#> GSM447704 5 0.3675 0.6966 0.000 0.188 0.000 0.024 0.788
#> GSM447720 3 0.2598 0.7498 0.044 0.004 0.904 0.040 0.008
#> GSM447652 2 0.5013 0.5398 0.000 0.680 0.000 0.080 0.240
#> GSM447679 2 0.2753 0.6651 0.000 0.856 0.000 0.008 0.136
#> GSM447712 1 0.3791 0.7736 0.812 0.112 0.000 0.076 0.000
#> GSM447664 4 0.2928 0.7626 0.004 0.032 0.092 0.872 0.000
#> GSM447637 3 0.4377 0.6426 0.192 0.000 0.756 0.044 0.008
#> GSM447639 3 0.5002 0.5284 0.044 0.344 0.612 0.000 0.000
#> GSM447615 3 0.0404 0.7513 0.000 0.000 0.988 0.012 0.000
#> GSM447656 3 0.8504 0.2211 0.056 0.248 0.460 0.120 0.116
#> GSM447673 2 0.2230 0.5497 0.000 0.884 0.000 0.116 0.000
#> GSM447719 4 0.3461 0.7191 0.000 0.000 0.224 0.772 0.004
#> GSM447706 3 0.3466 0.7432 0.000 0.100 0.844 0.008 0.048
#> GSM447612 3 0.4138 0.5076 0.000 0.000 0.616 0.000 0.384
#> GSM447665 5 0.1544 0.7825 0.000 0.000 0.000 0.068 0.932
#> GSM447677 5 0.0451 0.7862 0.000 0.000 0.004 0.008 0.988
#> GSM447613 1 0.1668 0.8065 0.940 0.000 0.032 0.028 0.000
#> GSM447659 3 0.2540 0.7399 0.000 0.000 0.888 0.088 0.024
#> GSM447662 5 0.3586 0.5056 0.000 0.000 0.264 0.000 0.736
#> GSM447666 5 0.0794 0.7759 0.000 0.000 0.028 0.000 0.972
#> GSM447668 5 0.3975 0.7428 0.000 0.076 0.012 0.096 0.816
#> GSM447682 2 0.2549 0.5458 0.044 0.904 0.004 0.044 0.004
#> GSM447683 2 0.4276 0.5736 0.000 0.716 0.000 0.028 0.256
#> GSM447688 2 0.4127 0.4943 0.000 0.680 0.000 0.008 0.312
#> GSM447702 5 0.3650 0.6955 0.000 0.176 0.000 0.028 0.796
#> GSM447709 5 0.0162 0.7846 0.000 0.000 0.004 0.000 0.996
#> GSM447711 1 0.3543 0.7804 0.828 0.112 0.000 0.060 0.000
#> GSM447715 2 0.8147 -0.2533 0.332 0.416 0.092 0.136 0.024
#> GSM447693 3 0.3246 0.7174 0.000 0.000 0.808 0.008 0.184
#> GSM447611 4 0.3304 0.7657 0.004 0.028 0.128 0.840 0.000
#> GSM447672 5 0.3231 0.6823 0.000 0.196 0.000 0.004 0.800
#> GSM447703 2 0.2951 0.6636 0.000 0.860 0.000 0.028 0.112
#> GSM447727 3 0.6728 0.5937 0.112 0.112 0.648 0.112 0.016
#> GSM447638 5 0.3887 0.7436 0.012 0.004 0.048 0.112 0.824
#> GSM447670 1 0.2793 0.7821 0.876 0.000 0.088 0.036 0.000
#> GSM447700 2 0.5636 0.2914 0.000 0.544 0.372 0.000 0.084
#> GSM447738 2 0.2612 0.6631 0.000 0.868 0.000 0.008 0.124
#> GSM447739 1 0.0000 0.8101 1.000 0.000 0.000 0.000 0.000
#> GSM447617 1 0.2769 0.7812 0.876 0.000 0.092 0.032 0.000
#> GSM447628 2 0.4138 0.1001 0.000 0.616 0.000 0.384 0.000
#> GSM447632 2 0.2864 0.6639 0.000 0.864 0.000 0.024 0.112
#> GSM447619 3 0.2966 0.7196 0.000 0.000 0.816 0.000 0.184
#> GSM447643 2 0.7582 -0.3547 0.400 0.412 0.052 0.112 0.024
#> GSM447724 3 0.4101 0.7108 0.000 0.048 0.768 0.000 0.184
#> GSM447728 2 0.4273 0.2473 0.000 0.552 0.000 0.000 0.448
#> GSM447610 4 0.2850 0.6998 0.092 0.000 0.036 0.872 0.000
#> GSM447633 5 0.0162 0.7846 0.000 0.000 0.004 0.000 0.996
#> GSM447634 3 0.2411 0.7478 0.000 0.000 0.884 0.108 0.008
#> GSM447622 3 0.4971 0.0734 0.460 0.000 0.512 0.028 0.000
#> GSM447667 3 0.6376 0.5349 0.004 0.168 0.648 0.060 0.120
#> GSM447687 2 0.2951 0.6636 0.000 0.860 0.000 0.028 0.112
#> GSM447695 3 0.1197 0.7473 0.000 0.000 0.952 0.048 0.000
#> GSM447696 1 0.1121 0.7950 0.956 0.000 0.044 0.000 0.000
#> GSM447697 1 0.0000 0.8101 1.000 0.000 0.000 0.000 0.000
#> GSM447714 3 0.2966 0.7196 0.000 0.000 0.816 0.000 0.184
#> GSM447717 1 0.3339 0.7849 0.840 0.112 0.000 0.048 0.000
#> GSM447725 1 0.4793 0.6857 0.700 0.232 0.000 0.068 0.000
#> GSM447729 4 0.3039 0.7297 0.000 0.192 0.000 0.808 0.000
#> GSM447644 5 0.3520 0.7552 0.000 0.004 0.080 0.076 0.840
#> GSM447710 3 0.2966 0.7196 0.000 0.000 0.816 0.000 0.184
#> GSM447614 3 0.1851 0.7371 0.000 0.000 0.912 0.088 0.000
#> GSM447685 2 0.6868 0.5525 0.056 0.640 0.036 0.132 0.136
#> GSM447690 1 0.0000 0.8101 1.000 0.000 0.000 0.000 0.000
#> GSM447730 5 0.1059 0.7851 0.000 0.020 0.004 0.008 0.968
#> GSM447646 4 0.4235 0.4034 0.000 0.424 0.000 0.576 0.000
#> GSM447689 3 0.3876 0.6175 0.000 0.000 0.684 0.000 0.316
#> GSM447635 3 0.2450 0.7415 0.000 0.052 0.900 0.048 0.000
#> GSM447641 1 0.2179 0.7955 0.888 0.112 0.000 0.000 0.000
#> GSM447716 2 0.4581 0.6197 0.004 0.768 0.004 0.112 0.112
#> GSM447718 3 0.8464 0.3664 0.052 0.312 0.408 0.072 0.156
#> GSM447616 3 0.4503 0.5577 0.268 0.000 0.696 0.036 0.000
#> GSM447626 3 0.4848 0.7032 0.032 0.112 0.776 0.072 0.008
#> GSM447640 2 0.3366 0.6608 0.000 0.828 0.000 0.032 0.140
#> GSM447734 3 0.3098 0.7352 0.000 0.000 0.836 0.016 0.148
#> GSM447692 3 0.5950 0.4054 0.316 0.044 0.592 0.048 0.000
#> GSM447647 4 0.4442 0.7438 0.000 0.084 0.016 0.784 0.116
#> GSM447624 1 0.3764 0.7207 0.800 0.000 0.156 0.044 0.000
#> GSM447625 3 0.2488 0.7455 0.000 0.000 0.872 0.004 0.124
#> GSM447707 5 0.2142 0.7824 0.000 0.048 0.004 0.028 0.920
#> GSM447732 3 0.5211 0.7314 0.044 0.064 0.776 0.068 0.048
#> GSM447684 3 0.6927 0.6130 0.048 0.112 0.632 0.164 0.044
#> GSM447731 4 0.3318 0.7083 0.000 0.008 0.000 0.800 0.192
#> GSM447705 5 0.3177 0.5892 0.000 0.000 0.208 0.000 0.792
#> GSM447631 3 0.2632 0.7500 0.000 0.000 0.888 0.040 0.072
#> GSM447701 5 0.2859 0.7731 0.000 0.016 0.012 0.096 0.876
#> GSM447645 3 0.1626 0.7520 0.000 0.000 0.940 0.044 0.016
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM447671 2 0.4672 0.7291 0.000 0.684 0.188 0.000 0.128 0.000
#> GSM447694 3 0.2398 0.7812 0.020 0.000 0.876 0.104 0.000 0.000
#> GSM447618 5 0.0146 0.8564 0.000 0.000 0.004 0.000 0.996 0.000
#> GSM447691 3 0.3765 0.4424 0.000 0.000 0.596 0.000 0.404 0.000
#> GSM447733 3 0.2260 0.7734 0.000 0.000 0.860 0.140 0.000 0.000
#> GSM447620 2 0.4669 0.7902 0.000 0.748 0.104 0.084 0.064 0.000
#> GSM447627 3 0.2734 0.7826 0.000 0.000 0.864 0.104 0.008 0.024
#> GSM447630 2 0.6753 -0.1218 0.000 0.436 0.148 0.004 0.064 0.348
#> GSM447642 1 0.2762 0.7661 0.804 0.000 0.000 0.000 0.000 0.196
#> GSM447649 2 0.3787 0.7863 0.000 0.804 0.104 0.072 0.020 0.000
#> GSM447654 4 0.2664 0.7363 0.000 0.000 0.000 0.816 0.184 0.000
#> GSM447655 2 0.2762 0.7060 0.000 0.804 0.000 0.000 0.196 0.000
#> GSM447669 3 0.5376 0.4266 0.000 0.372 0.528 0.000 0.092 0.008
#> GSM447676 3 0.5254 0.5337 0.196 0.000 0.608 0.000 0.000 0.196
#> GSM447678 5 0.1327 0.8197 0.000 0.000 0.064 0.000 0.936 0.000
#> GSM447681 5 0.0000 0.8572 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM447698 5 0.0000 0.8572 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM447713 1 0.0632 0.8044 0.976 0.000 0.000 0.000 0.000 0.024
#> GSM447722 5 0.1387 0.8175 0.000 0.000 0.068 0.000 0.932 0.000
#> GSM447726 6 0.5131 0.2242 0.000 0.308 0.020 0.000 0.064 0.608
#> GSM447735 3 0.2766 0.7832 0.028 0.000 0.868 0.092 0.012 0.000
#> GSM447737 1 0.3669 0.7200 0.760 0.000 0.028 0.004 0.000 0.208
#> GSM447657 5 0.0405 0.8567 0.000 0.000 0.000 0.004 0.988 0.008
#> GSM447674 5 0.0717 0.8574 0.000 0.016 0.000 0.000 0.976 0.008
#> GSM447636 6 0.2969 0.6483 0.224 0.000 0.000 0.000 0.000 0.776
#> GSM447723 6 0.0837 0.7035 0.020 0.000 0.004 0.004 0.000 0.972
#> GSM447699 3 0.5640 0.4059 0.000 0.000 0.532 0.000 0.200 0.268
#> GSM447708 2 0.5600 0.4832 0.000 0.528 0.296 0.000 0.176 0.000
#> GSM447721 6 0.3592 0.5267 0.344 0.000 0.000 0.000 0.000 0.656
#> GSM447623 1 0.0363 0.8048 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM447621 1 0.0363 0.8048 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM447650 2 0.1148 0.7454 0.000 0.960 0.000 0.004 0.020 0.016
#> GSM447651 2 0.3419 0.7792 0.000 0.812 0.104 0.084 0.000 0.000
#> GSM447653 4 0.1765 0.7731 0.000 0.000 0.096 0.904 0.000 0.000
#> GSM447658 6 0.1765 0.6861 0.096 0.000 0.000 0.000 0.000 0.904
#> GSM447675 4 0.2941 0.7018 0.000 0.000 0.000 0.780 0.220 0.000
#> GSM447680 6 0.1728 0.6770 0.000 0.008 0.004 0.000 0.064 0.924
#> GSM447686 6 0.2300 0.6797 0.144 0.000 0.000 0.000 0.000 0.856
#> GSM447736 3 0.2652 0.7815 0.020 0.000 0.868 0.104 0.008 0.000
#> GSM447629 5 0.5848 0.1478 0.000 0.000 0.172 0.004 0.468 0.356
#> GSM447648 3 0.2586 0.7740 0.032 0.000 0.868 0.000 0.000 0.100
#> GSM447660 6 0.4463 0.1526 0.376 0.000 0.036 0.000 0.000 0.588
#> GSM447661 2 0.0260 0.7548 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM447663 3 0.4480 0.7074 0.000 0.192 0.716 0.000 0.008 0.084
#> GSM447704 2 0.3552 0.7336 0.000 0.800 0.012 0.024 0.160 0.004
#> GSM447720 3 0.2873 0.7892 0.000 0.048 0.876 0.056 0.012 0.008
#> GSM447652 5 0.3608 0.6590 0.000 0.248 0.004 0.000 0.736 0.012
#> GSM447679 5 0.0260 0.8571 0.000 0.000 0.000 0.000 0.992 0.008
#> GSM447712 6 0.2912 0.6529 0.216 0.000 0.000 0.000 0.000 0.784
#> GSM447664 4 0.2069 0.7888 0.000 0.000 0.020 0.908 0.004 0.068
#> GSM447637 3 0.4957 0.6073 0.184 0.000 0.664 0.004 0.000 0.148
#> GSM447639 3 0.5089 0.4383 0.000 0.000 0.592 0.000 0.108 0.300
#> GSM447615 3 0.2573 0.7718 0.024 0.000 0.864 0.000 0.000 0.112
#> GSM447656 6 0.2009 0.6777 0.000 0.000 0.024 0.000 0.068 0.908
#> GSM447673 5 0.1584 0.8347 0.000 0.064 0.000 0.000 0.928 0.008
#> GSM447719 4 0.1908 0.7739 0.000 0.000 0.096 0.900 0.000 0.004
#> GSM447706 3 0.3129 0.7077 0.024 0.000 0.820 0.004 0.000 0.152
#> GSM447612 3 0.4191 0.5915 0.000 0.156 0.752 0.084 0.008 0.000
#> GSM447665 2 0.1584 0.7630 0.000 0.928 0.000 0.000 0.064 0.008
#> GSM447677 2 0.4669 0.7902 0.000 0.748 0.104 0.084 0.064 0.000
#> GSM447613 1 0.2664 0.7735 0.816 0.000 0.000 0.000 0.000 0.184
#> GSM447659 3 0.2431 0.7756 0.000 0.000 0.860 0.132 0.008 0.000
#> GSM447662 2 0.5057 0.5499 0.000 0.560 0.352 0.088 0.000 0.000
#> GSM447666 2 0.4803 0.7879 0.000 0.736 0.112 0.088 0.064 0.000
#> GSM447668 2 0.1787 0.7616 0.000 0.920 0.000 0.004 0.068 0.008
#> GSM447682 6 0.5149 0.2379 0.000 0.064 0.000 0.016 0.336 0.584
#> GSM447683 5 0.4119 0.4657 0.000 0.016 0.000 0.004 0.644 0.336
#> GSM447688 5 0.0508 0.8586 0.000 0.012 0.000 0.004 0.984 0.000
#> GSM447702 2 0.2697 0.7134 0.000 0.812 0.000 0.000 0.188 0.000
#> GSM447709 2 0.4669 0.7902 0.000 0.748 0.104 0.084 0.064 0.000
#> GSM447711 6 0.3737 0.4842 0.392 0.000 0.000 0.000 0.000 0.608
#> GSM447715 6 0.0458 0.7026 0.016 0.000 0.000 0.000 0.000 0.984
#> GSM447693 3 0.2174 0.7586 0.008 0.008 0.896 0.088 0.000 0.000
#> GSM447611 4 0.2030 0.7889 0.000 0.000 0.028 0.908 0.000 0.064
#> GSM447672 2 0.3175 0.6976 0.000 0.744 0.000 0.000 0.256 0.000
#> GSM447703 5 0.1841 0.8341 0.000 0.064 0.000 0.008 0.920 0.008
#> GSM447727 6 0.0806 0.6993 0.008 0.000 0.020 0.000 0.000 0.972
#> GSM447638 2 0.4253 0.6534 0.000 0.704 0.000 0.000 0.064 0.232
#> GSM447670 1 0.2882 0.7658 0.812 0.000 0.008 0.000 0.000 0.180
#> GSM447700 5 0.0858 0.8486 0.000 0.004 0.028 0.000 0.968 0.000
#> GSM447738 5 0.0291 0.8575 0.000 0.000 0.000 0.004 0.992 0.004
#> GSM447739 1 0.0632 0.8044 0.976 0.000 0.000 0.000 0.000 0.024
#> GSM447617 1 0.2768 0.7763 0.832 0.000 0.012 0.000 0.000 0.156
#> GSM447628 4 0.4098 0.1467 0.000 0.000 0.000 0.496 0.496 0.008
#> GSM447632 5 0.1923 0.8331 0.000 0.064 0.000 0.004 0.916 0.016
#> GSM447619 3 0.2174 0.7586 0.008 0.008 0.896 0.088 0.000 0.000
#> GSM447643 6 0.0632 0.7023 0.024 0.000 0.000 0.000 0.000 0.976
#> GSM447724 3 0.2781 0.7583 0.000 0.008 0.868 0.084 0.040 0.000
#> GSM447728 5 0.0146 0.8570 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM447610 4 0.1765 0.7755 0.000 0.000 0.000 0.904 0.000 0.096
#> GSM447633 2 0.4669 0.7902 0.000 0.748 0.104 0.084 0.064 0.000
#> GSM447634 3 0.3574 0.7481 0.016 0.188 0.780 0.000 0.000 0.016
#> GSM447622 1 0.5439 0.3487 0.588 0.000 0.308 0.072 0.000 0.032
#> GSM447667 3 0.4213 0.7491 0.000 0.000 0.772 0.104 0.100 0.024
#> GSM447687 5 0.1841 0.8341 0.000 0.064 0.000 0.008 0.920 0.008
#> GSM447695 3 0.2652 0.7815 0.020 0.000 0.868 0.104 0.000 0.008
#> GSM447696 1 0.0260 0.8049 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM447697 1 0.0632 0.8044 0.976 0.000 0.000 0.000 0.000 0.024
#> GSM447714 3 0.2122 0.7585 0.000 0.008 0.900 0.084 0.008 0.000
#> GSM447717 6 0.3428 0.5937 0.304 0.000 0.000 0.000 0.000 0.696
#> GSM447725 6 0.3828 0.3897 0.440 0.000 0.000 0.000 0.000 0.560
#> GSM447729 4 0.1866 0.7985 0.000 0.000 0.000 0.908 0.084 0.008
#> GSM447644 2 0.2036 0.7588 0.000 0.912 0.016 0.000 0.064 0.008
#> GSM447710 3 0.2062 0.7578 0.004 0.008 0.900 0.088 0.000 0.000
#> GSM447614 3 0.2300 0.7722 0.000 0.000 0.856 0.144 0.000 0.000
#> GSM447685 6 0.1700 0.6769 0.000 0.000 0.000 0.004 0.080 0.916
#> GSM447690 1 0.0632 0.8044 0.976 0.000 0.000 0.000 0.000 0.024
#> GSM447730 2 0.3563 0.7811 0.000 0.808 0.100 0.088 0.004 0.000
#> GSM447646 4 0.3797 0.3872 0.000 0.000 0.000 0.580 0.420 0.000
#> GSM447689 3 0.3960 0.6783 0.000 0.088 0.796 0.088 0.000 0.028
#> GSM447635 3 0.2706 0.7784 0.000 0.000 0.860 0.104 0.036 0.000
#> GSM447641 1 0.3647 0.1237 0.640 0.000 0.000 0.000 0.000 0.360
#> GSM447716 5 0.4532 0.5504 0.000 0.000 0.000 0.108 0.696 0.196
#> GSM447718 6 0.5310 0.0731 0.000 0.004 0.428 0.088 0.000 0.480
#> GSM447616 3 0.5307 0.5536 0.272 0.000 0.624 0.068 0.000 0.036
#> GSM447626 6 0.5657 -0.0529 0.000 0.152 0.412 0.000 0.000 0.436
#> GSM447640 5 0.3744 0.6271 0.000 0.256 0.000 0.004 0.724 0.016
#> GSM447734 3 0.2451 0.7753 0.000 0.056 0.884 0.060 0.000 0.000
#> GSM447692 3 0.7194 0.0892 0.204 0.000 0.388 0.104 0.000 0.304
#> GSM447647 4 0.2448 0.7357 0.000 0.064 0.052 0.884 0.000 0.000
#> GSM447624 1 0.2814 0.7674 0.820 0.000 0.008 0.000 0.000 0.172
#> GSM447625 3 0.1333 0.7787 0.000 0.008 0.944 0.048 0.000 0.000
#> GSM447707 2 0.3831 0.7848 0.000 0.804 0.092 0.080 0.024 0.000
#> GSM447732 3 0.4205 0.7088 0.000 0.188 0.728 0.000 0.000 0.084
#> GSM447684 6 0.1779 0.6884 0.000 0.064 0.016 0.000 0.000 0.920
#> GSM447731 4 0.0291 0.7754 0.000 0.004 0.004 0.992 0.000 0.000
#> GSM447705 2 0.4775 0.6595 0.000 0.632 0.284 0.084 0.000 0.000
#> GSM447631 3 0.3079 0.7662 0.008 0.000 0.836 0.028 0.000 0.128
#> GSM447701 2 0.1787 0.7616 0.000 0.920 0.000 0.004 0.068 0.008
#> GSM447645 3 0.2884 0.7451 0.008 0.000 0.824 0.004 0.000 0.164
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)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
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 gender(p) individual(p) disease.state(p) other(p) k
#> CV:pam 127 0.955 0.813 0.976 0.137 2
#> CV:pam 119 0.712 0.757 0.303 0.266 3
#> CV:pam 112 0.819 0.895 0.138 0.501 4
#> CV:pam 105 0.890 0.655 0.235 0.593 5
#> CV:pam 110 0.699 0.426 0.345 0.492 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "mclust"]
# you can also extract it by
# res = res_list["CV:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 130 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.542 0.778 0.888 0.4278 0.516 0.516
#> 3 3 0.470 0.407 0.697 0.3640 0.603 0.375
#> 4 4 0.830 0.886 0.940 0.2696 0.707 0.354
#> 5 5 0.730 0.676 0.802 0.0555 0.982 0.932
#> 6 6 0.746 0.629 0.773 0.0431 0.912 0.661
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
#> GSM447671 2 0.163 0.9302 0.024 0.976
#> GSM447694 1 0.969 0.6076 0.604 0.396
#> GSM447618 2 0.163 0.9302 0.024 0.976
#> GSM447691 2 0.163 0.9302 0.024 0.976
#> GSM447733 2 0.000 0.9231 0.000 1.000
#> GSM447620 2 0.163 0.9302 0.024 0.976
#> GSM447627 2 0.141 0.9292 0.020 0.980
#> GSM447630 2 0.163 0.9302 0.024 0.976
#> GSM447642 1 0.000 0.7653 1.000 0.000
#> GSM447649 2 0.163 0.9302 0.024 0.976
#> GSM447654 2 0.000 0.9231 0.000 1.000
#> GSM447655 2 0.163 0.9302 0.024 0.976
#> GSM447669 2 0.163 0.9302 0.024 0.976
#> GSM447676 1 0.000 0.7653 1.000 0.000
#> GSM447678 2 0.000 0.9231 0.000 1.000
#> GSM447681 2 0.163 0.9302 0.024 0.976
#> GSM447698 2 0.000 0.9231 0.000 1.000
#> GSM447713 1 0.000 0.7653 1.000 0.000
#> GSM447722 2 0.000 0.9231 0.000 1.000
#> GSM447726 2 0.706 0.6925 0.192 0.808
#> GSM447735 2 0.000 0.9231 0.000 1.000
#> GSM447737 1 0.000 0.7653 1.000 0.000
#> GSM447657 2 0.163 0.9302 0.024 0.976
#> GSM447674 2 0.163 0.9302 0.024 0.976
#> GSM447636 1 0.000 0.7653 1.000 0.000
#> GSM447723 1 0.634 0.7539 0.840 0.160
#> GSM447699 2 0.946 0.2048 0.364 0.636
#> GSM447708 2 0.163 0.9302 0.024 0.976
#> GSM447721 1 0.000 0.7653 1.000 0.000
#> GSM447623 1 0.000 0.7653 1.000 0.000
#> GSM447621 1 0.000 0.7653 1.000 0.000
#> GSM447650 2 0.163 0.9302 0.024 0.976
#> GSM447651 2 0.163 0.9302 0.024 0.976
#> GSM447653 2 0.000 0.9231 0.000 1.000
#> GSM447658 1 0.000 0.7653 1.000 0.000
#> GSM447675 2 0.000 0.9231 0.000 1.000
#> GSM447680 2 0.991 -0.1578 0.444 0.556
#> GSM447686 1 0.802 0.7301 0.756 0.244
#> GSM447736 2 0.506 0.8277 0.112 0.888
#> GSM447629 2 0.163 0.9302 0.024 0.976
#> GSM447648 1 0.895 0.6988 0.688 0.312
#> GSM447660 1 0.000 0.7653 1.000 0.000
#> GSM447661 2 0.163 0.9302 0.024 0.976
#> GSM447663 1 0.990 0.5150 0.560 0.440
#> GSM447704 2 0.163 0.9302 0.024 0.976
#> GSM447720 2 0.518 0.8215 0.116 0.884
#> GSM447652 2 0.163 0.9302 0.024 0.976
#> GSM447679 2 0.163 0.9302 0.024 0.976
#> GSM447712 1 0.000 0.7653 1.000 0.000
#> GSM447664 2 0.000 0.9231 0.000 1.000
#> GSM447637 1 0.881 0.7067 0.700 0.300
#> GSM447639 2 0.000 0.9231 0.000 1.000
#> GSM447615 1 0.871 0.7111 0.708 0.292
#> GSM447656 1 1.000 0.3845 0.512 0.488
#> GSM447673 2 0.000 0.9231 0.000 1.000
#> GSM447719 2 0.000 0.9231 0.000 1.000
#> GSM447706 1 0.909 0.6888 0.676 0.324
#> GSM447612 2 0.985 -0.0828 0.428 0.572
#> GSM447665 2 0.163 0.9302 0.024 0.976
#> GSM447677 2 0.163 0.9302 0.024 0.976
#> GSM447613 1 0.000 0.7653 1.000 0.000
#> GSM447659 2 0.000 0.9231 0.000 1.000
#> GSM447662 1 0.971 0.6009 0.600 0.400
#> GSM447666 2 0.991 -0.1546 0.444 0.556
#> GSM447668 2 0.163 0.9302 0.024 0.976
#> GSM447682 2 0.163 0.9302 0.024 0.976
#> GSM447683 2 0.163 0.9302 0.024 0.976
#> GSM447688 2 0.000 0.9231 0.000 1.000
#> GSM447702 2 0.163 0.9302 0.024 0.976
#> GSM447709 2 0.163 0.9302 0.024 0.976
#> GSM447711 1 0.000 0.7653 1.000 0.000
#> GSM447715 1 0.943 0.6528 0.640 0.360
#> GSM447693 1 0.969 0.6076 0.604 0.396
#> GSM447611 2 0.000 0.9231 0.000 1.000
#> GSM447672 2 0.163 0.9302 0.024 0.976
#> GSM447703 2 0.000 0.9231 0.000 1.000
#> GSM447727 1 0.506 0.7633 0.888 0.112
#> GSM447638 1 1.000 0.3845 0.512 0.488
#> GSM447670 1 0.506 0.7633 0.888 0.112
#> GSM447700 2 0.163 0.9302 0.024 0.976
#> GSM447738 2 0.000 0.9231 0.000 1.000
#> GSM447739 1 0.000 0.7653 1.000 0.000
#> GSM447617 1 0.000 0.7653 1.000 0.000
#> GSM447628 2 0.000 0.9231 0.000 1.000
#> GSM447632 2 0.000 0.9231 0.000 1.000
#> GSM447619 1 0.971 0.6009 0.600 0.400
#> GSM447643 1 0.494 0.7638 0.892 0.108
#> GSM447724 2 0.000 0.9231 0.000 1.000
#> GSM447728 2 0.163 0.9302 0.024 0.976
#> GSM447610 2 0.595 0.7428 0.144 0.856
#> GSM447633 2 0.163 0.9302 0.024 0.976
#> GSM447634 1 0.975 0.5821 0.592 0.408
#> GSM447622 1 0.876 0.7090 0.704 0.296
#> GSM447667 2 0.163 0.9302 0.024 0.976
#> GSM447687 2 0.000 0.9231 0.000 1.000
#> GSM447695 1 0.969 0.6076 0.604 0.396
#> GSM447696 1 0.000 0.7653 1.000 0.000
#> GSM447697 1 0.000 0.7653 1.000 0.000
#> GSM447714 2 0.584 0.7871 0.140 0.860
#> GSM447717 1 0.000 0.7653 1.000 0.000
#> GSM447725 1 0.000 0.7653 1.000 0.000
#> GSM447729 2 0.000 0.9231 0.000 1.000
#> GSM447644 2 0.163 0.9302 0.024 0.976
#> GSM447710 1 0.971 0.6009 0.600 0.400
#> GSM447614 2 0.000 0.9231 0.000 1.000
#> GSM447685 2 1.000 -0.3536 0.496 0.504
#> GSM447690 1 0.000 0.7653 1.000 0.000
#> GSM447730 2 0.163 0.9302 0.024 0.976
#> GSM447646 2 0.000 0.9231 0.000 1.000
#> GSM447689 1 0.971 0.6009 0.600 0.400
#> GSM447635 2 0.163 0.9302 0.024 0.976
#> GSM447641 1 0.000 0.7653 1.000 0.000
#> GSM447716 2 0.163 0.9302 0.024 0.976
#> GSM447718 2 0.163 0.9302 0.024 0.976
#> GSM447616 1 0.871 0.7111 0.708 0.292
#> GSM447626 1 0.952 0.6385 0.628 0.372
#> GSM447640 2 0.163 0.9302 0.024 0.976
#> GSM447734 1 0.973 0.5933 0.596 0.404
#> GSM447692 1 0.895 0.6988 0.688 0.312
#> GSM447647 2 0.000 0.9231 0.000 1.000
#> GSM447624 1 0.443 0.7647 0.908 0.092
#> GSM447625 2 0.999 -0.2933 0.480 0.520
#> GSM447707 2 0.163 0.9302 0.024 0.976
#> GSM447732 1 0.963 0.6186 0.612 0.388
#> GSM447684 1 0.969 0.6035 0.604 0.396
#> GSM447731 2 0.000 0.9231 0.000 1.000
#> GSM447705 2 0.529 0.8167 0.120 0.880
#> GSM447631 1 0.939 0.6576 0.644 0.356
#> GSM447701 2 0.163 0.9302 0.024 0.976
#> GSM447645 1 0.895 0.6988 0.688 0.312
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM447671 2 0.6168 0.4352 0.000 0.588 0.412
#> GSM447694 1 0.6309 0.5248 0.500 0.500 0.000
#> GSM447618 2 0.6168 0.4352 0.000 0.588 0.412
#> GSM447691 2 0.6168 0.4352 0.000 0.588 0.412
#> GSM447733 3 0.0592 0.6895 0.012 0.000 0.988
#> GSM447620 2 0.6168 0.4352 0.000 0.588 0.412
#> GSM447627 1 0.9105 0.5507 0.500 0.348 0.152
#> GSM447630 3 0.9871 0.0745 0.280 0.308 0.412
#> GSM447642 1 0.0000 0.7055 1.000 0.000 0.000
#> GSM447649 2 0.6168 0.4352 0.000 0.588 0.412
#> GSM447654 3 0.0000 0.6966 0.000 0.000 1.000
#> GSM447655 2 0.6168 0.4352 0.000 0.588 0.412
#> GSM447669 2 0.6168 0.4352 0.000 0.588 0.412
#> GSM447676 1 0.0000 0.7055 1.000 0.000 0.000
#> GSM447678 3 0.0000 0.6966 0.000 0.000 1.000
#> GSM447681 2 0.6168 0.4352 0.000 0.588 0.412
#> GSM447698 3 0.5465 0.3015 0.000 0.288 0.712
#> GSM447713 1 0.0000 0.7055 1.000 0.000 0.000
#> GSM447722 3 0.2959 0.6141 0.100 0.000 0.900
#> GSM447726 1 0.8747 0.1233 0.492 0.112 0.396
#> GSM447735 1 0.9042 0.3703 0.500 0.144 0.356
#> GSM447737 1 0.0237 0.7054 0.996 0.004 0.000
#> GSM447657 2 0.6168 0.4352 0.000 0.588 0.412
#> GSM447674 2 0.6168 0.4352 0.000 0.588 0.412
#> GSM447636 1 0.0000 0.7055 1.000 0.000 0.000
#> GSM447723 1 0.0000 0.7055 1.000 0.000 0.000
#> GSM447699 1 0.9464 0.4259 0.500 0.252 0.248
#> GSM447708 2 0.6168 0.4352 0.000 0.588 0.412
#> GSM447721 1 0.0000 0.7055 1.000 0.000 0.000
#> GSM447623 1 0.0000 0.7055 1.000 0.000 0.000
#> GSM447621 1 0.0000 0.7055 1.000 0.000 0.000
#> GSM447650 2 0.6168 0.4352 0.000 0.588 0.412
#> GSM447651 2 0.6168 0.4352 0.000 0.588 0.412
#> GSM447653 3 0.6521 -0.0534 0.492 0.004 0.504
#> GSM447658 1 0.0000 0.7055 1.000 0.000 0.000
#> GSM447675 3 0.0000 0.6966 0.000 0.000 1.000
#> GSM447680 1 0.7851 0.0255 0.532 0.056 0.412
#> GSM447686 1 0.0475 0.7018 0.992 0.004 0.004
#> GSM447736 1 0.6825 0.5263 0.500 0.488 0.012
#> GSM447629 2 0.8427 0.2521 0.088 0.500 0.412
#> GSM447648 2 0.6309 -0.5437 0.500 0.500 0.000
#> GSM447660 1 0.0000 0.7055 1.000 0.000 0.000
#> GSM447661 2 0.6168 0.4352 0.000 0.588 0.412
#> GSM447663 1 0.8124 0.5237 0.496 0.436 0.068
#> GSM447704 2 0.6168 0.4352 0.000 0.588 0.412
#> GSM447720 1 0.9460 0.4375 0.500 0.260 0.240
#> GSM447652 2 0.6168 0.4352 0.000 0.588 0.412
#> GSM447679 2 0.6168 0.4352 0.000 0.588 0.412
#> GSM447712 1 0.0000 0.7055 1.000 0.000 0.000
#> GSM447664 3 0.0000 0.6966 0.000 0.000 1.000
#> GSM447637 2 0.6309 -0.5437 0.500 0.500 0.000
#> GSM447639 3 0.6825 -0.0610 0.492 0.012 0.496
#> GSM447615 1 0.6286 0.5526 0.536 0.464 0.000
#> GSM447656 1 0.8228 0.1032 0.512 0.076 0.412
#> GSM447673 3 0.0000 0.6966 0.000 0.000 1.000
#> GSM447719 1 0.8652 0.5467 0.492 0.404 0.104
#> GSM447706 1 0.6309 0.5248 0.500 0.500 0.000
#> GSM447612 1 0.9217 0.4865 0.492 0.344 0.164
#> GSM447665 2 0.6168 0.4352 0.000 0.588 0.412
#> GSM447677 2 0.6168 0.4352 0.000 0.588 0.412
#> GSM447613 1 0.0000 0.7055 1.000 0.000 0.000
#> GSM447659 1 0.8440 0.2393 0.492 0.088 0.420
#> GSM447662 1 0.6309 0.5248 0.500 0.500 0.000
#> GSM447666 2 0.6308 -0.5402 0.492 0.508 0.000
#> GSM447668 2 0.6168 0.4352 0.000 0.588 0.412
#> GSM447682 2 0.6168 0.4352 0.000 0.588 0.412
#> GSM447683 2 0.6168 0.4352 0.000 0.588 0.412
#> GSM447688 3 0.0237 0.6942 0.000 0.004 0.996
#> GSM447702 2 0.6168 0.4352 0.000 0.588 0.412
#> GSM447709 2 0.6168 0.4352 0.000 0.588 0.412
#> GSM447711 1 0.0000 0.7055 1.000 0.000 0.000
#> GSM447715 1 0.0237 0.7041 0.996 0.000 0.004
#> GSM447693 2 0.6309 -0.5437 0.500 0.500 0.000
#> GSM447611 3 0.0000 0.6966 0.000 0.000 1.000
#> GSM447672 2 0.6168 0.4352 0.000 0.588 0.412
#> GSM447703 3 0.0424 0.6912 0.000 0.008 0.992
#> GSM447727 1 0.0237 0.7055 0.996 0.004 0.000
#> GSM447638 1 0.7438 0.5609 0.536 0.428 0.036
#> GSM447670 1 0.0592 0.7050 0.988 0.012 0.000
#> GSM447700 1 0.8602 0.0942 0.492 0.100 0.408
#> GSM447738 3 0.0000 0.6966 0.000 0.000 1.000
#> GSM447739 1 0.0000 0.7055 1.000 0.000 0.000
#> GSM447617 1 0.0000 0.7055 1.000 0.000 0.000
#> GSM447628 3 0.0000 0.6966 0.000 0.000 1.000
#> GSM447632 3 0.0424 0.6912 0.000 0.008 0.992
#> GSM447619 2 0.6309 -0.5437 0.500 0.500 0.000
#> GSM447643 1 0.0000 0.7055 1.000 0.000 0.000
#> GSM447724 3 0.6521 -0.0534 0.492 0.004 0.504
#> GSM447728 2 0.6168 0.4352 0.000 0.588 0.412
#> GSM447610 1 0.2711 0.6659 0.912 0.000 0.088
#> GSM447633 3 0.9889 0.0902 0.300 0.292 0.408
#> GSM447634 1 0.9340 0.4771 0.500 0.308 0.192
#> GSM447622 1 0.6299 0.5447 0.524 0.476 0.000
#> GSM447667 3 0.9862 0.0603 0.272 0.316 0.412
#> GSM447687 3 0.0000 0.6966 0.000 0.000 1.000
#> GSM447695 1 0.8370 0.5235 0.500 0.416 0.084
#> GSM447696 1 0.0000 0.7055 1.000 0.000 0.000
#> GSM447697 1 0.0000 0.7055 1.000 0.000 0.000
#> GSM447714 2 0.6309 -0.5437 0.500 0.500 0.000
#> GSM447717 1 0.0000 0.7055 1.000 0.000 0.000
#> GSM447725 1 0.0000 0.7055 1.000 0.000 0.000
#> GSM447729 3 0.0000 0.6966 0.000 0.000 1.000
#> GSM447644 3 0.9601 0.1253 0.392 0.200 0.408
#> GSM447710 1 0.6309 0.5248 0.500 0.500 0.000
#> GSM447614 1 0.9262 0.4226 0.500 0.176 0.324
#> GSM447685 3 0.9758 -0.0224 0.232 0.356 0.412
#> GSM447690 1 0.0000 0.7055 1.000 0.000 0.000
#> GSM447730 2 0.6168 0.4352 0.000 0.588 0.412
#> GSM447646 3 0.0000 0.6966 0.000 0.000 1.000
#> GSM447689 2 0.6309 -0.5437 0.500 0.500 0.000
#> GSM447635 1 0.8550 0.0846 0.492 0.096 0.412
#> GSM447641 1 0.0000 0.7055 1.000 0.000 0.000
#> GSM447716 3 0.7670 0.3283 0.312 0.068 0.620
#> GSM447718 1 0.8550 0.0846 0.492 0.096 0.412
#> GSM447616 2 0.6309 -0.5437 0.500 0.500 0.000
#> GSM447626 1 0.6309 0.5285 0.504 0.496 0.000
#> GSM447640 2 0.6168 0.4352 0.000 0.588 0.412
#> GSM447734 1 0.6309 0.5248 0.500 0.500 0.000
#> GSM447692 2 0.6309 -0.5437 0.500 0.500 0.000
#> GSM447647 3 0.0000 0.6966 0.000 0.000 1.000
#> GSM447624 1 0.3412 0.6849 0.876 0.124 0.000
#> GSM447625 1 0.9042 0.5038 0.500 0.356 0.144
#> GSM447707 2 0.6168 0.4352 0.000 0.588 0.412
#> GSM447732 1 0.6309 0.5248 0.500 0.500 0.000
#> GSM447684 1 0.6154 0.5829 0.592 0.408 0.000
#> GSM447731 3 0.0000 0.6966 0.000 0.000 1.000
#> GSM447705 2 0.6825 -0.5412 0.492 0.496 0.012
#> GSM447631 2 0.6309 -0.5437 0.500 0.500 0.000
#> GSM447701 2 0.6168 0.4352 0.000 0.588 0.412
#> GSM447645 2 0.6309 -0.5437 0.500 0.500 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM447671 2 0.3208 0.878 0.000 0.848 0.004 0.148
#> GSM447694 3 0.0000 0.951 0.000 0.000 1.000 0.000
#> GSM447618 2 0.3569 0.837 0.000 0.804 0.000 0.196
#> GSM447691 2 0.2973 0.881 0.000 0.856 0.000 0.144
#> GSM447733 4 0.0000 0.922 0.000 0.000 0.000 1.000
#> GSM447620 2 0.3370 0.893 0.000 0.872 0.048 0.080
#> GSM447627 3 0.2408 0.869 0.000 0.000 0.896 0.104
#> GSM447630 2 0.1302 0.907 0.000 0.956 0.000 0.044
#> GSM447642 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM447649 2 0.1557 0.907 0.000 0.944 0.000 0.056
#> GSM447654 4 0.0336 0.922 0.000 0.008 0.000 0.992
#> GSM447655 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> GSM447669 2 0.1557 0.907 0.000 0.944 0.000 0.056
#> GSM447676 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM447678 4 0.0188 0.923 0.000 0.004 0.000 0.996
#> GSM447681 2 0.1637 0.907 0.000 0.940 0.000 0.060
#> GSM447698 2 0.4961 0.349 0.000 0.552 0.000 0.448
#> GSM447713 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM447722 4 0.0188 0.923 0.000 0.004 0.000 0.996
#> GSM447726 2 0.1867 0.881 0.000 0.928 0.072 0.000
#> GSM447735 4 0.0000 0.922 0.000 0.000 0.000 1.000
#> GSM447737 1 0.3074 0.818 0.848 0.000 0.152 0.000
#> GSM447657 2 0.2647 0.893 0.000 0.880 0.000 0.120
#> GSM447674 2 0.2345 0.901 0.000 0.900 0.000 0.100
#> GSM447636 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM447723 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM447699 3 0.3610 0.750 0.000 0.000 0.800 0.200
#> GSM447708 2 0.2973 0.881 0.000 0.856 0.000 0.144
#> GSM447721 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM447623 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM447621 1 0.0592 0.970 0.984 0.000 0.016 0.000
#> GSM447650 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> GSM447651 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> GSM447653 4 0.0000 0.922 0.000 0.000 0.000 1.000
#> GSM447658 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM447675 4 0.0188 0.923 0.000 0.004 0.000 0.996
#> GSM447680 2 0.1624 0.904 0.020 0.952 0.000 0.028
#> GSM447686 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM447736 3 0.0188 0.949 0.000 0.000 0.996 0.004
#> GSM447629 2 0.3444 0.849 0.000 0.816 0.000 0.184
#> GSM447648 3 0.0000 0.951 0.000 0.000 1.000 0.000
#> GSM447660 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM447661 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> GSM447663 3 0.1302 0.921 0.000 0.044 0.956 0.000
#> GSM447704 2 0.2281 0.902 0.000 0.904 0.000 0.096
#> GSM447720 3 0.3734 0.826 0.000 0.044 0.848 0.108
#> GSM447652 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> GSM447679 2 0.2149 0.903 0.000 0.912 0.000 0.088
#> GSM447712 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM447664 4 0.0188 0.923 0.000 0.004 0.000 0.996
#> GSM447637 3 0.0000 0.951 0.000 0.000 1.000 0.000
#> GSM447639 4 0.0188 0.923 0.000 0.004 0.000 0.996
#> GSM447615 3 0.0707 0.938 0.020 0.000 0.980 0.000
#> GSM447656 2 0.2868 0.886 0.000 0.864 0.000 0.136
#> GSM447673 4 0.0188 0.923 0.000 0.004 0.000 0.996
#> GSM447719 4 0.3610 0.700 0.000 0.000 0.200 0.800
#> GSM447706 3 0.0000 0.951 0.000 0.000 1.000 0.000
#> GSM447612 3 0.0524 0.945 0.000 0.008 0.988 0.004
#> GSM447665 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> GSM447677 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> GSM447613 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM447659 4 0.0000 0.922 0.000 0.000 0.000 1.000
#> GSM447662 3 0.0000 0.951 0.000 0.000 1.000 0.000
#> GSM447666 3 0.0000 0.951 0.000 0.000 1.000 0.000
#> GSM447668 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> GSM447682 2 0.3024 0.879 0.000 0.852 0.000 0.148
#> GSM447683 2 0.2408 0.899 0.000 0.896 0.000 0.104
#> GSM447688 4 0.0336 0.922 0.000 0.008 0.000 0.992
#> GSM447702 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> GSM447709 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> GSM447711 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM447715 1 0.0921 0.955 0.972 0.028 0.000 0.000
#> GSM447693 3 0.0000 0.951 0.000 0.000 1.000 0.000
#> GSM447611 4 0.0000 0.922 0.000 0.000 0.000 1.000
#> GSM447672 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> GSM447703 4 0.4898 0.146 0.000 0.416 0.000 0.584
#> GSM447727 1 0.3356 0.783 0.824 0.000 0.176 0.000
#> GSM447638 2 0.3157 0.830 0.004 0.852 0.144 0.000
#> GSM447670 3 0.4967 0.188 0.452 0.000 0.548 0.000
#> GSM447700 2 0.4134 0.759 0.000 0.740 0.000 0.260
#> GSM447738 4 0.0921 0.906 0.000 0.028 0.000 0.972
#> GSM447739 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM447617 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM447628 4 0.0469 0.919 0.000 0.012 0.000 0.988
#> GSM447632 4 0.4866 0.169 0.000 0.404 0.000 0.596
#> GSM447619 3 0.0000 0.951 0.000 0.000 1.000 0.000
#> GSM447643 1 0.0469 0.972 0.988 0.012 0.000 0.000
#> GSM447724 4 0.0000 0.922 0.000 0.000 0.000 1.000
#> GSM447728 2 0.2408 0.899 0.000 0.896 0.000 0.104
#> GSM447610 4 0.4843 0.284 0.396 0.000 0.000 0.604
#> GSM447633 2 0.2060 0.894 0.000 0.932 0.052 0.016
#> GSM447634 3 0.2676 0.871 0.000 0.012 0.896 0.092
#> GSM447622 3 0.0000 0.951 0.000 0.000 1.000 0.000
#> GSM447667 2 0.3528 0.844 0.000 0.808 0.000 0.192
#> GSM447687 4 0.2011 0.857 0.000 0.080 0.000 0.920
#> GSM447695 3 0.3219 0.798 0.000 0.000 0.836 0.164
#> GSM447696 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM447697 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM447714 3 0.0000 0.951 0.000 0.000 1.000 0.000
#> GSM447717 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM447725 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM447729 4 0.0188 0.923 0.000 0.004 0.000 0.996
#> GSM447644 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> GSM447710 3 0.0000 0.951 0.000 0.000 1.000 0.000
#> GSM447614 4 0.0000 0.922 0.000 0.000 0.000 1.000
#> GSM447685 2 0.3024 0.879 0.000 0.852 0.000 0.148
#> GSM447690 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM447730 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> GSM447646 4 0.0707 0.916 0.000 0.020 0.000 0.980
#> GSM447689 3 0.0000 0.951 0.000 0.000 1.000 0.000
#> GSM447635 2 0.4134 0.763 0.000 0.740 0.000 0.260
#> GSM447641 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM447716 4 0.0188 0.922 0.000 0.004 0.000 0.996
#> GSM447718 2 0.6221 0.609 0.000 0.644 0.256 0.100
#> GSM447616 3 0.0000 0.951 0.000 0.000 1.000 0.000
#> GSM447626 3 0.0000 0.951 0.000 0.000 1.000 0.000
#> GSM447640 2 0.2647 0.893 0.000 0.880 0.000 0.120
#> GSM447734 3 0.0000 0.951 0.000 0.000 1.000 0.000
#> GSM447692 3 0.0336 0.947 0.000 0.000 0.992 0.008
#> GSM447647 4 0.0336 0.922 0.000 0.008 0.000 0.992
#> GSM447624 3 0.3610 0.747 0.200 0.000 0.800 0.000
#> GSM447625 3 0.0657 0.942 0.000 0.004 0.984 0.012
#> GSM447707 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> GSM447732 3 0.0000 0.951 0.000 0.000 1.000 0.000
#> GSM447684 3 0.0188 0.949 0.000 0.004 0.996 0.000
#> GSM447731 4 0.3569 0.758 0.000 0.196 0.000 0.804
#> GSM447705 3 0.0000 0.951 0.000 0.000 1.000 0.000
#> GSM447631 3 0.0000 0.951 0.000 0.000 1.000 0.000
#> GSM447701 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> GSM447645 3 0.0000 0.951 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
#> GSM447671 2 0.6140 0.610 0.000 0.656 0.072 0.188 0.084
#> GSM447694 3 0.5774 0.713 0.000 0.000 0.612 0.156 0.232
#> GSM447618 2 0.3628 0.718 0.000 0.772 0.000 0.012 0.216
#> GSM447691 2 0.3531 0.792 0.000 0.820 0.016 0.012 0.152
#> GSM447733 4 0.0000 0.456 0.000 0.000 0.000 1.000 0.000
#> GSM447620 2 0.5795 0.645 0.000 0.696 0.108 0.136 0.060
#> GSM447627 4 0.6581 -0.432 0.000 0.000 0.356 0.432 0.212
#> GSM447630 2 0.3702 0.721 0.000 0.820 0.096 0.000 0.084
#> GSM447642 1 0.0000 0.946 1.000 0.000 0.000 0.000 0.000
#> GSM447649 2 0.0404 0.829 0.000 0.988 0.000 0.000 0.012
#> GSM447654 4 0.4268 0.284 0.000 0.000 0.000 0.556 0.444
#> GSM447655 2 0.0290 0.829 0.000 0.992 0.000 0.000 0.008
#> GSM447669 2 0.3033 0.761 0.000 0.864 0.052 0.000 0.084
#> GSM447676 1 0.0000 0.946 1.000 0.000 0.000 0.000 0.000
#> GSM447678 4 0.4268 0.284 0.000 0.000 0.000 0.556 0.444
#> GSM447681 2 0.1608 0.821 0.000 0.928 0.000 0.000 0.072
#> GSM447698 2 0.6122 -0.185 0.000 0.512 0.000 0.140 0.348
#> GSM447713 1 0.0000 0.946 1.000 0.000 0.000 0.000 0.000
#> GSM447722 4 0.4060 0.310 0.000 0.000 0.000 0.640 0.360
#> GSM447726 2 0.3307 0.754 0.000 0.844 0.052 0.000 0.104
#> GSM447735 4 0.0000 0.456 0.000 0.000 0.000 1.000 0.000
#> GSM447737 1 0.5448 0.512 0.584 0.000 0.076 0.000 0.340
#> GSM447657 2 0.2629 0.783 0.000 0.860 0.000 0.004 0.136
#> GSM447674 2 0.2471 0.785 0.000 0.864 0.000 0.000 0.136
#> GSM447636 1 0.0000 0.946 1.000 0.000 0.000 0.000 0.000
#> GSM447723 1 0.0000 0.946 1.000 0.000 0.000 0.000 0.000
#> GSM447699 3 0.3551 0.693 0.000 0.000 0.772 0.220 0.008
#> GSM447708 2 0.2124 0.807 0.000 0.900 0.000 0.004 0.096
#> GSM447721 1 0.0000 0.946 1.000 0.000 0.000 0.000 0.000
#> GSM447623 1 0.3561 0.731 0.740 0.000 0.000 0.000 0.260
#> GSM447621 1 0.4114 0.699 0.712 0.000 0.016 0.000 0.272
#> GSM447650 2 0.0290 0.829 0.000 0.992 0.000 0.000 0.008
#> GSM447651 2 0.0290 0.829 0.000 0.992 0.000 0.000 0.008
#> GSM447653 4 0.0000 0.456 0.000 0.000 0.000 1.000 0.000
#> GSM447658 1 0.0000 0.946 1.000 0.000 0.000 0.000 0.000
#> GSM447675 4 0.4268 0.284 0.000 0.000 0.000 0.556 0.444
#> GSM447680 2 0.0898 0.828 0.008 0.972 0.000 0.000 0.020
#> GSM447686 1 0.0510 0.938 0.984 0.000 0.000 0.000 0.016
#> GSM447736 3 0.4168 0.726 0.000 0.000 0.764 0.184 0.052
#> GSM447629 2 0.2929 0.751 0.000 0.820 0.000 0.000 0.180
#> GSM447648 3 0.4288 0.704 0.000 0.000 0.612 0.004 0.384
#> GSM447660 1 0.0000 0.946 1.000 0.000 0.000 0.000 0.000
#> GSM447661 2 0.0290 0.829 0.000 0.992 0.000 0.000 0.008
#> GSM447663 3 0.2962 0.743 0.000 0.048 0.868 0.000 0.084
#> GSM447704 2 0.2707 0.797 0.000 0.860 0.000 0.008 0.132
#> GSM447720 3 0.5777 0.632 0.000 0.164 0.688 0.048 0.100
#> GSM447652 2 0.0290 0.829 0.000 0.992 0.000 0.000 0.008
#> GSM447679 2 0.2329 0.794 0.000 0.876 0.000 0.000 0.124
#> GSM447712 1 0.0000 0.946 1.000 0.000 0.000 0.000 0.000
#> GSM447664 4 0.4283 0.269 0.000 0.000 0.000 0.544 0.456
#> GSM447637 3 0.4126 0.706 0.000 0.000 0.620 0.000 0.380
#> GSM447639 4 0.0404 0.452 0.000 0.000 0.000 0.988 0.012
#> GSM447615 3 0.4966 0.676 0.032 0.000 0.564 0.000 0.404
#> GSM447656 2 0.1732 0.821 0.000 0.920 0.000 0.000 0.080
#> GSM447673 4 0.4287 0.244 0.000 0.000 0.000 0.540 0.460
#> GSM447719 4 0.2690 0.375 0.000 0.000 0.156 0.844 0.000
#> GSM447706 3 0.2966 0.767 0.000 0.000 0.816 0.000 0.184
#> GSM447612 3 0.2676 0.759 0.000 0.000 0.884 0.036 0.080
#> GSM447665 2 0.0404 0.829 0.000 0.988 0.000 0.000 0.012
#> GSM447677 2 0.0290 0.829 0.000 0.992 0.000 0.000 0.008
#> GSM447613 1 0.0000 0.946 1.000 0.000 0.000 0.000 0.000
#> GSM447659 4 0.0000 0.456 0.000 0.000 0.000 1.000 0.000
#> GSM447662 3 0.0880 0.778 0.000 0.000 0.968 0.000 0.032
#> GSM447666 3 0.1792 0.760 0.000 0.000 0.916 0.000 0.084
#> GSM447668 2 0.0290 0.829 0.000 0.992 0.000 0.000 0.008
#> GSM447682 2 0.3391 0.727 0.000 0.800 0.000 0.012 0.188
#> GSM447683 2 0.2471 0.785 0.000 0.864 0.000 0.000 0.136
#> GSM447688 4 0.4287 0.244 0.000 0.000 0.000 0.540 0.460
#> GSM447702 2 0.0290 0.829 0.000 0.992 0.000 0.000 0.008
#> GSM447709 2 0.0404 0.829 0.000 0.988 0.000 0.000 0.012
#> GSM447711 1 0.0000 0.946 1.000 0.000 0.000 0.000 0.000
#> GSM447715 1 0.0703 0.933 0.976 0.000 0.000 0.000 0.024
#> GSM447693 3 0.2605 0.774 0.000 0.000 0.852 0.000 0.148
#> GSM447611 4 0.4273 0.280 0.000 0.000 0.000 0.552 0.448
#> GSM447672 2 0.0703 0.826 0.000 0.976 0.000 0.000 0.024
#> GSM447703 5 0.6579 0.667 0.000 0.308 0.000 0.232 0.460
#> GSM447727 1 0.0671 0.937 0.980 0.000 0.004 0.000 0.016
#> GSM447638 2 0.2722 0.789 0.000 0.872 0.020 0.000 0.108
#> GSM447670 3 0.6439 0.497 0.176 0.000 0.416 0.000 0.408
#> GSM447700 2 0.7733 0.280 0.000 0.448 0.208 0.260 0.084
#> GSM447738 5 0.5818 0.185 0.000 0.092 0.000 0.448 0.460
#> GSM447739 1 0.0000 0.946 1.000 0.000 0.000 0.000 0.000
#> GSM447617 1 0.4570 0.599 0.632 0.000 0.020 0.000 0.348
#> GSM447628 4 0.4268 0.284 0.000 0.000 0.000 0.556 0.444
#> GSM447632 5 0.6605 0.687 0.000 0.288 0.000 0.252 0.460
#> GSM447619 3 0.0609 0.785 0.000 0.000 0.980 0.000 0.020
#> GSM447643 1 0.0404 0.941 0.988 0.000 0.000 0.000 0.012
#> GSM447724 4 0.0000 0.456 0.000 0.000 0.000 1.000 0.000
#> GSM447728 2 0.2127 0.802 0.000 0.892 0.000 0.000 0.108
#> GSM447610 4 0.3684 0.257 0.280 0.000 0.000 0.720 0.000
#> GSM447633 2 0.6425 0.464 0.000 0.548 0.316 0.028 0.108
#> GSM447634 3 0.4999 0.702 0.000 0.040 0.748 0.148 0.064
#> GSM447622 3 0.4182 0.695 0.000 0.000 0.600 0.000 0.400
#> GSM447667 2 0.3196 0.737 0.004 0.804 0.000 0.000 0.192
#> GSM447687 5 0.6422 0.587 0.000 0.180 0.000 0.360 0.460
#> GSM447695 3 0.5733 0.707 0.000 0.000 0.624 0.188 0.188
#> GSM447696 1 0.0000 0.946 1.000 0.000 0.000 0.000 0.000
#> GSM447697 1 0.0510 0.939 0.984 0.000 0.000 0.000 0.016
#> GSM447714 3 0.0693 0.785 0.000 0.000 0.980 0.012 0.008
#> GSM447717 1 0.0000 0.946 1.000 0.000 0.000 0.000 0.000
#> GSM447725 1 0.0000 0.946 1.000 0.000 0.000 0.000 0.000
#> GSM447729 4 0.4268 0.284 0.000 0.000 0.000 0.556 0.444
#> GSM447644 2 0.4406 0.673 0.000 0.764 0.128 0.000 0.108
#> GSM447710 3 0.0794 0.786 0.000 0.000 0.972 0.000 0.028
#> GSM447614 4 0.0000 0.456 0.000 0.000 0.000 1.000 0.000
#> GSM447685 2 0.2813 0.763 0.000 0.832 0.000 0.000 0.168
#> GSM447690 1 0.0000 0.946 1.000 0.000 0.000 0.000 0.000
#> GSM447730 2 0.0703 0.826 0.000 0.976 0.000 0.000 0.024
#> GSM447646 4 0.4268 0.284 0.000 0.000 0.000 0.556 0.444
#> GSM447689 3 0.1671 0.764 0.000 0.000 0.924 0.000 0.076
#> GSM447635 2 0.5576 0.650 0.000 0.688 0.020 0.144 0.148
#> GSM447641 1 0.0000 0.946 1.000 0.000 0.000 0.000 0.000
#> GSM447716 4 0.4294 0.235 0.000 0.000 0.000 0.532 0.468
#> GSM447718 2 0.5782 0.385 0.000 0.576 0.332 0.008 0.084
#> GSM447616 3 0.6110 0.648 0.000 0.000 0.476 0.128 0.396
#> GSM447626 3 0.0404 0.783 0.000 0.000 0.988 0.000 0.012
#> GSM447640 2 0.2561 0.779 0.000 0.856 0.000 0.000 0.144
#> GSM447734 3 0.1117 0.786 0.000 0.000 0.964 0.016 0.020
#> GSM447692 3 0.6500 0.607 0.000 0.000 0.412 0.188 0.400
#> GSM447647 4 0.4268 0.284 0.000 0.000 0.000 0.556 0.444
#> GSM447624 3 0.5044 0.675 0.036 0.000 0.556 0.000 0.408
#> GSM447625 3 0.2824 0.754 0.000 0.000 0.864 0.116 0.020
#> GSM447707 2 0.0290 0.829 0.000 0.992 0.000 0.000 0.008
#> GSM447732 3 0.0290 0.784 0.000 0.000 0.992 0.000 0.008
#> GSM447684 3 0.4266 0.692 0.000 0.120 0.776 0.000 0.104
#> GSM447731 4 0.5904 0.109 0.000 0.196 0.000 0.600 0.204
#> GSM447705 3 0.2189 0.760 0.000 0.000 0.904 0.012 0.084
#> GSM447631 3 0.4114 0.708 0.000 0.000 0.624 0.000 0.376
#> GSM447701 2 0.0290 0.829 0.000 0.992 0.000 0.000 0.008
#> GSM447645 3 0.4114 0.708 0.000 0.000 0.624 0.000 0.376
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM447671 5 0.6317 -0.175729 0.000 0.372 0.032 0.008 0.464 0.124
#> GSM447694 6 0.4975 0.284036 0.000 0.000 0.312 0.000 0.092 0.596
#> GSM447618 2 0.6146 0.541779 0.000 0.556 0.040 0.200 0.204 0.000
#> GSM447691 2 0.5418 0.514347 0.000 0.584 0.044 0.028 0.332 0.012
#> GSM447733 5 0.4292 0.467989 0.000 0.000 0.000 0.388 0.588 0.024
#> GSM447620 5 0.6294 -0.151789 0.000 0.364 0.032 0.004 0.464 0.136
#> GSM447627 5 0.4726 -0.022008 0.000 0.000 0.036 0.012 0.600 0.352
#> GSM447630 2 0.6219 0.354192 0.000 0.512 0.044 0.000 0.308 0.136
#> GSM447642 1 0.0146 0.914218 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM447649 2 0.0260 0.802194 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM447654 4 0.0146 0.752735 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM447655 2 0.0260 0.801612 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM447669 2 0.5186 0.490407 0.000 0.608 0.032 0.000 0.308 0.052
#> GSM447676 1 0.0000 0.915719 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447678 4 0.0000 0.753555 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM447681 2 0.0777 0.800091 0.000 0.972 0.004 0.024 0.000 0.000
#> GSM447698 4 0.3864 0.078473 0.000 0.480 0.000 0.520 0.000 0.000
#> GSM447713 1 0.0000 0.915719 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447722 4 0.1204 0.710528 0.000 0.000 0.000 0.944 0.056 0.000
#> GSM447726 2 0.5931 0.450112 0.000 0.540 0.092 0.000 0.320 0.048
#> GSM447735 5 0.3717 0.465068 0.000 0.000 0.000 0.384 0.616 0.000
#> GSM447737 3 0.3995 0.064403 0.480 0.000 0.516 0.000 0.004 0.000
#> GSM447657 2 0.2776 0.769832 0.000 0.860 0.088 0.052 0.000 0.000
#> GSM447674 2 0.1471 0.787403 0.000 0.932 0.004 0.064 0.000 0.000
#> GSM447636 1 0.0146 0.914218 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM447723 1 0.1088 0.888503 0.960 0.000 0.024 0.000 0.016 0.000
#> GSM447699 6 0.3230 0.678728 0.000 0.000 0.000 0.012 0.212 0.776
#> GSM447708 2 0.3443 0.754149 0.000 0.832 0.040 0.032 0.096 0.000
#> GSM447721 1 0.0000 0.915719 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447623 1 0.3782 0.206187 0.588 0.000 0.412 0.000 0.000 0.000
#> GSM447621 1 0.3833 0.099348 0.556 0.000 0.444 0.000 0.000 0.000
#> GSM447650 2 0.0000 0.801822 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447651 2 0.0363 0.802471 0.000 0.988 0.012 0.000 0.000 0.000
#> GSM447653 5 0.3717 0.465068 0.000 0.000 0.000 0.384 0.616 0.000
#> GSM447658 1 0.0000 0.915719 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447675 4 0.0000 0.753555 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM447680 2 0.4733 0.605492 0.000 0.648 0.276 0.004 0.072 0.000
#> GSM447686 1 0.4172 0.659736 0.724 0.000 0.204 0.000 0.072 0.000
#> GSM447736 6 0.2932 0.709097 0.000 0.000 0.016 0.000 0.164 0.820
#> GSM447629 2 0.4098 0.681380 0.000 0.732 0.220 0.012 0.036 0.000
#> GSM447648 3 0.3592 0.702076 0.000 0.000 0.656 0.000 0.000 0.344
#> GSM447660 1 0.0000 0.915719 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447661 2 0.0260 0.801612 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM447663 6 0.4624 0.623284 0.000 0.140 0.024 0.000 0.104 0.732
#> GSM447704 2 0.1196 0.795954 0.000 0.952 0.008 0.040 0.000 0.000
#> GSM447720 6 0.5993 0.460459 0.000 0.032 0.120 0.000 0.336 0.512
#> GSM447652 2 0.0000 0.801822 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447679 2 0.0858 0.799788 0.000 0.968 0.004 0.028 0.000 0.000
#> GSM447712 1 0.0000 0.915719 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447664 4 0.2988 0.650141 0.000 0.000 0.152 0.824 0.024 0.000
#> GSM447637 3 0.3592 0.702076 0.000 0.000 0.656 0.000 0.000 0.344
#> GSM447639 5 0.3944 0.417637 0.000 0.000 0.000 0.428 0.568 0.004
#> GSM447615 3 0.3499 0.709616 0.000 0.000 0.680 0.000 0.000 0.320
#> GSM447656 2 0.5231 0.586248 0.000 0.616 0.260 0.008 0.116 0.000
#> GSM447673 4 0.0000 0.753555 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM447719 5 0.6172 0.322982 0.000 0.000 0.028 0.376 0.452 0.144
#> GSM447706 6 0.3409 0.263281 0.000 0.000 0.300 0.000 0.000 0.700
#> GSM447612 6 0.2597 0.714540 0.000 0.000 0.000 0.000 0.176 0.824
#> GSM447665 2 0.2772 0.694360 0.000 0.816 0.004 0.000 0.180 0.000
#> GSM447677 2 0.0000 0.801822 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447613 1 0.0146 0.914218 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM447659 5 0.4292 0.467989 0.000 0.000 0.000 0.388 0.588 0.024
#> GSM447662 6 0.0632 0.722890 0.000 0.000 0.000 0.000 0.024 0.976
#> GSM447666 6 0.4190 0.562824 0.000 0.000 0.048 0.000 0.260 0.692
#> GSM447668 2 0.0146 0.802276 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM447682 2 0.3027 0.729899 0.000 0.824 0.028 0.148 0.000 0.000
#> GSM447683 2 0.1501 0.782726 0.000 0.924 0.000 0.076 0.000 0.000
#> GSM447688 4 0.1075 0.740933 0.000 0.048 0.000 0.952 0.000 0.000
#> GSM447702 2 0.0260 0.801612 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM447709 2 0.0935 0.793627 0.000 0.964 0.004 0.000 0.032 0.000
#> GSM447711 1 0.0000 0.915719 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447715 1 0.4565 0.614071 0.684 0.000 0.220 0.000 0.096 0.000
#> GSM447693 6 0.3151 0.377193 0.000 0.000 0.252 0.000 0.000 0.748
#> GSM447611 4 0.2988 0.654211 0.000 0.000 0.144 0.828 0.028 0.000
#> GSM447672 2 0.0260 0.801612 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM447703 4 0.3563 0.483915 0.000 0.336 0.000 0.664 0.000 0.000
#> GSM447727 1 0.1391 0.884584 0.944 0.000 0.040 0.000 0.016 0.000
#> GSM447638 2 0.6917 0.261075 0.000 0.360 0.224 0.000 0.356 0.060
#> GSM447670 3 0.4503 0.601471 0.240 0.000 0.680 0.000 0.000 0.080
#> GSM447700 5 0.7143 0.000177 0.000 0.228 0.032 0.044 0.464 0.232
#> GSM447738 4 0.2969 0.613288 0.000 0.224 0.000 0.776 0.000 0.000
#> GSM447739 1 0.0000 0.915719 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447617 3 0.3860 0.097855 0.472 0.000 0.528 0.000 0.000 0.000
#> GSM447628 4 0.0000 0.753555 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM447632 4 0.3198 0.579073 0.000 0.260 0.000 0.740 0.000 0.000
#> GSM447619 6 0.0632 0.703620 0.000 0.000 0.024 0.000 0.000 0.976
#> GSM447643 1 0.2745 0.816849 0.864 0.000 0.068 0.000 0.068 0.000
#> GSM447724 5 0.4292 0.467989 0.000 0.000 0.000 0.388 0.588 0.024
#> GSM447728 2 0.1075 0.793892 0.000 0.952 0.000 0.048 0.000 0.000
#> GSM447610 5 0.5907 0.266361 0.180 0.000 0.004 0.368 0.448 0.000
#> GSM447633 5 0.6586 -0.144043 0.000 0.360 0.028 0.000 0.368 0.244
#> GSM447634 6 0.4290 0.661924 0.000 0.008 0.028 0.000 0.296 0.668
#> GSM447622 3 0.3515 0.709829 0.000 0.000 0.676 0.000 0.000 0.324
#> GSM447667 2 0.4213 0.676933 0.000 0.724 0.224 0.016 0.036 0.000
#> GSM447687 4 0.3151 0.587886 0.000 0.252 0.000 0.748 0.000 0.000
#> GSM447695 6 0.5437 0.477698 0.000 0.000 0.228 0.000 0.196 0.576
#> GSM447696 1 0.0000 0.915719 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447697 1 0.0713 0.900641 0.972 0.000 0.028 0.000 0.000 0.000
#> GSM447714 6 0.1858 0.730938 0.000 0.000 0.004 0.000 0.092 0.904
#> GSM447717 1 0.0000 0.915719 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447725 1 0.0000 0.915719 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447729 4 0.2100 0.699354 0.000 0.000 0.112 0.884 0.004 0.000
#> GSM447644 2 0.5968 0.397362 0.000 0.540 0.040 0.000 0.308 0.112
#> GSM447710 6 0.1007 0.691470 0.000 0.000 0.044 0.000 0.000 0.956
#> GSM447614 5 0.3717 0.465068 0.000 0.000 0.000 0.384 0.616 0.000
#> GSM447685 2 0.4102 0.681258 0.000 0.736 0.216 0.020 0.028 0.000
#> GSM447690 1 0.0000 0.915719 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447730 2 0.0260 0.801612 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM447646 4 0.0000 0.753555 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM447689 6 0.2432 0.691885 0.000 0.000 0.024 0.000 0.100 0.876
#> GSM447635 2 0.6966 0.349312 0.000 0.428 0.180 0.032 0.332 0.028
#> GSM447641 1 0.0000 0.915719 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447716 4 0.3013 0.663798 0.000 0.004 0.140 0.832 0.024 0.000
#> GSM447718 2 0.6972 0.022249 0.000 0.352 0.044 0.004 0.308 0.292
#> GSM447616 3 0.3883 0.706434 0.000 0.000 0.656 0.000 0.012 0.332
#> GSM447626 6 0.1500 0.703053 0.000 0.000 0.052 0.000 0.012 0.936
#> GSM447640 2 0.2311 0.763529 0.000 0.880 0.016 0.104 0.000 0.000
#> GSM447734 6 0.2121 0.728670 0.000 0.000 0.012 0.000 0.096 0.892
#> GSM447692 3 0.4982 0.550420 0.000 0.000 0.648 0.000 0.176 0.176
#> GSM447647 4 0.0000 0.753555 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM447624 3 0.4723 0.660445 0.140 0.000 0.680 0.000 0.000 0.180
#> GSM447625 6 0.2416 0.715323 0.000 0.000 0.000 0.000 0.156 0.844
#> GSM447707 2 0.0260 0.801612 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM447732 6 0.0790 0.699160 0.000 0.000 0.032 0.000 0.000 0.968
#> GSM447684 6 0.5400 0.427469 0.000 0.000 0.132 0.000 0.332 0.536
#> GSM447731 4 0.3512 0.533631 0.000 0.196 0.000 0.772 0.032 0.000
#> GSM447705 6 0.3023 0.701593 0.000 0.000 0.000 0.000 0.232 0.768
#> GSM447631 3 0.3737 0.637313 0.000 0.000 0.608 0.000 0.000 0.392
#> GSM447701 2 0.0146 0.801941 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM447645 3 0.3634 0.689666 0.000 0.000 0.644 0.000 0.000 0.356
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)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
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 gender(p) individual(p) disease.state(p) other(p) k
#> CV:mclust 122 0.329 0.6949 0.0464 0.012246 2
#> CV:mclust 64 0.958 0.8245 0.3751 0.537392 3
#> CV:mclust 125 0.337 0.2470 0.0360 0.049181 4
#> CV:mclust 100 0.057 0.0235 0.0266 0.122307 5
#> CV:mclust 97 0.189 0.0529 0.1177 0.000607 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "NMF"]
# you can also extract it by
# res = res_list["CV:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 130 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 0.802 0.900 0.957 0.5033 0.497 0.497
#> 3 3 0.559 0.705 0.782 0.3138 0.753 0.543
#> 4 4 0.857 0.888 0.939 0.1375 0.782 0.455
#> 5 5 0.759 0.785 0.879 0.0596 0.918 0.691
#> 6 6 0.752 0.597 0.766 0.0402 0.877 0.507
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
#> GSM447671 2 0.0000 0.9649 0.000 1.000
#> GSM447694 1 0.0000 0.9433 1.000 0.000
#> GSM447618 2 0.0000 0.9649 0.000 1.000
#> GSM447691 2 0.0000 0.9649 0.000 1.000
#> GSM447733 2 0.0000 0.9649 0.000 1.000
#> GSM447620 2 0.0000 0.9649 0.000 1.000
#> GSM447627 1 0.0000 0.9433 1.000 0.000
#> GSM447630 1 1.0000 0.0846 0.504 0.496
#> GSM447642 1 0.0000 0.9433 1.000 0.000
#> GSM447649 2 0.0000 0.9649 0.000 1.000
#> GSM447654 2 0.0000 0.9649 0.000 1.000
#> GSM447655 2 0.0000 0.9649 0.000 1.000
#> GSM447669 2 0.0000 0.9649 0.000 1.000
#> GSM447676 1 0.0000 0.9433 1.000 0.000
#> GSM447678 2 0.0000 0.9649 0.000 1.000
#> GSM447681 2 0.0000 0.9649 0.000 1.000
#> GSM447698 2 0.0000 0.9649 0.000 1.000
#> GSM447713 1 0.0000 0.9433 1.000 0.000
#> GSM447722 2 0.0000 0.9649 0.000 1.000
#> GSM447726 1 0.6623 0.7766 0.828 0.172
#> GSM447735 1 0.6973 0.7722 0.812 0.188
#> GSM447737 1 0.0000 0.9433 1.000 0.000
#> GSM447657 2 0.0000 0.9649 0.000 1.000
#> GSM447674 2 0.0000 0.9649 0.000 1.000
#> GSM447636 1 0.0000 0.9433 1.000 0.000
#> GSM447723 1 0.0000 0.9433 1.000 0.000
#> GSM447699 1 0.9248 0.5247 0.660 0.340
#> GSM447708 2 0.0000 0.9649 0.000 1.000
#> GSM447721 1 0.0000 0.9433 1.000 0.000
#> GSM447623 1 0.0000 0.9433 1.000 0.000
#> GSM447621 1 0.0000 0.9433 1.000 0.000
#> GSM447650 2 0.0000 0.9649 0.000 1.000
#> GSM447651 2 0.0000 0.9649 0.000 1.000
#> GSM447653 1 0.9044 0.5259 0.680 0.320
#> GSM447658 1 0.0000 0.9433 1.000 0.000
#> GSM447675 2 0.0000 0.9649 0.000 1.000
#> GSM447680 2 0.7602 0.7131 0.220 0.780
#> GSM447686 1 0.8327 0.6345 0.736 0.264
#> GSM447736 1 0.2043 0.9214 0.968 0.032
#> GSM447629 2 0.2236 0.9343 0.036 0.964
#> GSM447648 1 0.0000 0.9433 1.000 0.000
#> GSM447660 1 0.0000 0.9433 1.000 0.000
#> GSM447661 2 0.0000 0.9649 0.000 1.000
#> GSM447663 1 0.4161 0.8792 0.916 0.084
#> GSM447704 2 0.0000 0.9649 0.000 1.000
#> GSM447720 1 0.1414 0.9303 0.980 0.020
#> GSM447652 2 0.0000 0.9649 0.000 1.000
#> GSM447679 2 0.0000 0.9649 0.000 1.000
#> GSM447712 1 0.0000 0.9433 1.000 0.000
#> GSM447664 2 0.4690 0.8704 0.100 0.900
#> GSM447637 1 0.0000 0.9433 1.000 0.000
#> GSM447639 2 0.1414 0.9481 0.020 0.980
#> GSM447615 1 0.0000 0.9433 1.000 0.000
#> GSM447656 2 0.9358 0.4666 0.352 0.648
#> GSM447673 2 0.0000 0.9649 0.000 1.000
#> GSM447719 1 0.0000 0.9433 1.000 0.000
#> GSM447706 1 0.0000 0.9433 1.000 0.000
#> GSM447612 1 0.9732 0.3757 0.596 0.404
#> GSM447665 2 0.0000 0.9649 0.000 1.000
#> GSM447677 2 0.0000 0.9649 0.000 1.000
#> GSM447613 1 0.0000 0.9433 1.000 0.000
#> GSM447659 2 0.0672 0.9585 0.008 0.992
#> GSM447662 1 0.6973 0.7723 0.812 0.188
#> GSM447666 1 0.0000 0.9433 1.000 0.000
#> GSM447668 2 0.0000 0.9649 0.000 1.000
#> GSM447682 2 0.0000 0.9649 0.000 1.000
#> GSM447683 2 0.0000 0.9649 0.000 1.000
#> GSM447688 2 0.0000 0.9649 0.000 1.000
#> GSM447702 2 0.0000 0.9649 0.000 1.000
#> GSM447709 2 0.0000 0.9649 0.000 1.000
#> GSM447711 1 0.0000 0.9433 1.000 0.000
#> GSM447715 1 0.0000 0.9433 1.000 0.000
#> GSM447693 1 0.0000 0.9433 1.000 0.000
#> GSM447611 2 0.5294 0.8474 0.120 0.880
#> GSM447672 2 0.0000 0.9649 0.000 1.000
#> GSM447703 2 0.0000 0.9649 0.000 1.000
#> GSM447727 1 0.0000 0.9433 1.000 0.000
#> GSM447638 1 0.0000 0.9433 1.000 0.000
#> GSM447670 1 0.0000 0.9433 1.000 0.000
#> GSM447700 2 0.0000 0.9649 0.000 1.000
#> GSM447738 2 0.0000 0.9649 0.000 1.000
#> GSM447739 1 0.0000 0.9433 1.000 0.000
#> GSM447617 1 0.0000 0.9433 1.000 0.000
#> GSM447628 2 0.0000 0.9649 0.000 1.000
#> GSM447632 2 0.0000 0.9649 0.000 1.000
#> GSM447619 1 0.2948 0.9063 0.948 0.052
#> GSM447643 1 0.0000 0.9433 1.000 0.000
#> GSM447724 2 0.0000 0.9649 0.000 1.000
#> GSM447728 2 0.0000 0.9649 0.000 1.000
#> GSM447610 1 0.0000 0.9433 1.000 0.000
#> GSM447633 2 0.0376 0.9618 0.004 0.996
#> GSM447634 1 0.0000 0.9433 1.000 0.000
#> GSM447622 1 0.0000 0.9433 1.000 0.000
#> GSM447667 2 0.9000 0.5433 0.316 0.684
#> GSM447687 2 0.0000 0.9649 0.000 1.000
#> GSM447695 1 0.0000 0.9433 1.000 0.000
#> GSM447696 1 0.0000 0.9433 1.000 0.000
#> GSM447697 1 0.0000 0.9433 1.000 0.000
#> GSM447714 1 0.7139 0.7626 0.804 0.196
#> GSM447717 1 0.0000 0.9433 1.000 0.000
#> GSM447725 1 0.0000 0.9433 1.000 0.000
#> GSM447729 2 0.0000 0.9649 0.000 1.000
#> GSM447644 2 0.9580 0.3376 0.380 0.620
#> GSM447710 1 0.0000 0.9433 1.000 0.000
#> GSM447614 1 0.0000 0.9433 1.000 0.000
#> GSM447685 2 0.0000 0.9649 0.000 1.000
#> GSM447690 1 0.0000 0.9433 1.000 0.000
#> GSM447730 2 0.0000 0.9649 0.000 1.000
#> GSM447646 2 0.0000 0.9649 0.000 1.000
#> GSM447689 1 0.0000 0.9433 1.000 0.000
#> GSM447635 2 0.5842 0.8228 0.140 0.860
#> GSM447641 1 0.0000 0.9433 1.000 0.000
#> GSM447716 2 0.0000 0.9649 0.000 1.000
#> GSM447718 1 0.9833 0.3165 0.576 0.424
#> GSM447616 1 0.0000 0.9433 1.000 0.000
#> GSM447626 1 0.0000 0.9433 1.000 0.000
#> GSM447640 2 0.0000 0.9649 0.000 1.000
#> GSM447734 1 0.7139 0.7623 0.804 0.196
#> GSM447692 1 0.0000 0.9433 1.000 0.000
#> GSM447647 2 0.0000 0.9649 0.000 1.000
#> GSM447624 1 0.0000 0.9433 1.000 0.000
#> GSM447625 1 0.6343 0.8041 0.840 0.160
#> GSM447707 2 0.0000 0.9649 0.000 1.000
#> GSM447732 1 0.0000 0.9433 1.000 0.000
#> GSM447684 1 0.0000 0.9433 1.000 0.000
#> GSM447731 2 0.0000 0.9649 0.000 1.000
#> GSM447705 2 0.9000 0.5040 0.316 0.684
#> GSM447631 1 0.0000 0.9433 1.000 0.000
#> GSM447701 2 0.0000 0.9649 0.000 1.000
#> GSM447645 1 0.0000 0.9433 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM447671 3 0.5591 0.6016 0.000 0.304 0.696
#> GSM447694 3 0.5254 0.7743 0.264 0.000 0.736
#> GSM447618 2 0.4002 0.6672 0.000 0.840 0.160
#> GSM447691 2 0.6095 0.1475 0.000 0.608 0.392
#> GSM447733 2 0.5650 0.7537 0.000 0.688 0.312
#> GSM447620 3 0.5216 0.6483 0.000 0.260 0.740
#> GSM447627 3 0.6302 0.4503 0.480 0.000 0.520
#> GSM447630 3 0.6274 0.3290 0.000 0.456 0.544
#> GSM447642 1 0.0000 0.8519 1.000 0.000 0.000
#> GSM447649 2 0.1529 0.8177 0.000 0.960 0.040
#> GSM447654 2 0.5541 0.7868 0.008 0.740 0.252
#> GSM447655 2 0.0592 0.8102 0.000 0.988 0.012
#> GSM447669 3 0.6225 0.3846 0.000 0.432 0.568
#> GSM447676 1 0.0000 0.8519 1.000 0.000 0.000
#> GSM447678 2 0.5138 0.7902 0.000 0.748 0.252
#> GSM447681 2 0.0592 0.8102 0.000 0.988 0.012
#> GSM447698 2 0.4178 0.8065 0.000 0.828 0.172
#> GSM447713 1 0.0000 0.8519 1.000 0.000 0.000
#> GSM447722 2 0.5138 0.7902 0.000 0.748 0.252
#> GSM447726 3 0.8937 0.5498 0.184 0.252 0.564
#> GSM447735 1 0.9412 0.2683 0.508 0.244 0.248
#> GSM447737 1 0.0000 0.8519 1.000 0.000 0.000
#> GSM447657 2 0.2066 0.8176 0.000 0.940 0.060
#> GSM447674 2 0.0000 0.8133 0.000 1.000 0.000
#> GSM447636 1 0.2537 0.8059 0.920 0.000 0.080
#> GSM447723 1 0.0237 0.8492 0.996 0.000 0.004
#> GSM447699 3 0.7565 0.7447 0.256 0.084 0.660
#> GSM447708 2 0.1031 0.8041 0.000 0.976 0.024
#> GSM447721 1 0.0000 0.8519 1.000 0.000 0.000
#> GSM447623 1 0.0000 0.8519 1.000 0.000 0.000
#> GSM447621 1 0.0747 0.8400 0.984 0.000 0.016
#> GSM447650 2 0.0592 0.8102 0.000 0.988 0.012
#> GSM447651 2 0.3752 0.7072 0.000 0.856 0.144
#> GSM447653 1 0.5843 0.6480 0.732 0.016 0.252
#> GSM447658 1 0.0000 0.8519 1.000 0.000 0.000
#> GSM447675 2 0.5698 0.7847 0.012 0.736 0.252
#> GSM447680 2 0.5919 0.5196 0.276 0.712 0.012
#> GSM447686 1 0.2031 0.8289 0.952 0.016 0.032
#> GSM447736 3 0.5254 0.7743 0.264 0.000 0.736
#> GSM447629 2 0.1964 0.7909 0.056 0.944 0.000
#> GSM447648 3 0.6192 0.5950 0.420 0.000 0.580
#> GSM447660 1 0.0000 0.8519 1.000 0.000 0.000
#> GSM447661 2 0.0592 0.8102 0.000 0.988 0.012
#> GSM447663 3 0.6438 0.7647 0.188 0.064 0.748
#> GSM447704 2 0.0892 0.8164 0.000 0.980 0.020
#> GSM447720 3 0.6541 0.7448 0.304 0.024 0.672
#> GSM447652 2 0.1289 0.8175 0.000 0.968 0.032
#> GSM447679 2 0.0592 0.8102 0.000 0.988 0.012
#> GSM447712 1 0.0000 0.8519 1.000 0.000 0.000
#> GSM447664 2 0.8977 0.5784 0.188 0.560 0.252
#> GSM447637 3 0.5363 0.7676 0.276 0.000 0.724
#> GSM447639 2 0.5843 0.7829 0.016 0.732 0.252
#> GSM447615 1 0.4121 0.6235 0.832 0.000 0.168
#> GSM447656 2 0.6180 0.4050 0.332 0.660 0.008
#> GSM447673 2 0.5138 0.7902 0.000 0.748 0.252
#> GSM447719 1 0.5692 0.6500 0.724 0.008 0.268
#> GSM447706 3 0.5254 0.7743 0.264 0.000 0.736
#> GSM447612 3 0.6721 0.7435 0.136 0.116 0.748
#> GSM447665 2 0.6260 -0.0594 0.000 0.552 0.448
#> GSM447677 2 0.1643 0.7910 0.000 0.956 0.044
#> GSM447613 1 0.0000 0.8519 1.000 0.000 0.000
#> GSM447659 3 0.3845 0.4637 0.012 0.116 0.872
#> GSM447662 3 0.5939 0.7745 0.224 0.028 0.748
#> GSM447666 3 0.5860 0.6807 0.024 0.228 0.748
#> GSM447668 2 0.1031 0.8041 0.000 0.976 0.024
#> GSM447682 2 0.3192 0.8141 0.000 0.888 0.112
#> GSM447683 2 0.0592 0.8102 0.000 0.988 0.012
#> GSM447688 2 0.5098 0.7918 0.000 0.752 0.248
#> GSM447702 2 0.0592 0.8102 0.000 0.988 0.012
#> GSM447709 3 0.5291 0.6408 0.000 0.268 0.732
#> GSM447711 1 0.0000 0.8519 1.000 0.000 0.000
#> GSM447715 1 0.0892 0.8366 0.980 0.000 0.020
#> GSM447693 3 0.5254 0.7743 0.264 0.000 0.736
#> GSM447611 1 0.9513 0.2218 0.492 0.256 0.252
#> GSM447672 2 0.0592 0.8102 0.000 0.988 0.012
#> GSM447703 2 0.5098 0.7918 0.000 0.752 0.248
#> GSM447727 1 0.1289 0.8245 0.968 0.000 0.032
#> GSM447638 1 0.3530 0.7718 0.900 0.032 0.068
#> GSM447670 3 0.6274 0.5442 0.456 0.000 0.544
#> GSM447700 2 0.6062 0.1709 0.000 0.616 0.384
#> GSM447738 2 0.5098 0.7918 0.000 0.752 0.248
#> GSM447739 1 0.0000 0.8519 1.000 0.000 0.000
#> GSM447617 1 0.0237 0.8492 0.996 0.000 0.004
#> GSM447628 2 0.5138 0.7902 0.000 0.748 0.252
#> GSM447632 2 0.5098 0.7918 0.000 0.752 0.248
#> GSM447619 3 0.5178 0.7763 0.256 0.000 0.744
#> GSM447643 1 0.0000 0.8519 1.000 0.000 0.000
#> GSM447724 3 0.6244 -0.4052 0.000 0.440 0.560
#> GSM447728 2 0.0592 0.8102 0.000 0.988 0.012
#> GSM447610 1 0.5541 0.6546 0.740 0.008 0.252
#> GSM447633 3 0.5138 0.6554 0.000 0.252 0.748
#> GSM447634 3 0.6286 0.5314 0.464 0.000 0.536
#> GSM447622 3 0.5327 0.7700 0.272 0.000 0.728
#> GSM447667 1 0.9638 -0.0559 0.420 0.372 0.208
#> GSM447687 2 0.5098 0.7918 0.000 0.752 0.248
#> GSM447695 3 0.6225 0.5946 0.432 0.000 0.568
#> GSM447696 1 0.0000 0.8519 1.000 0.000 0.000
#> GSM447697 1 0.0000 0.8519 1.000 0.000 0.000
#> GSM447714 3 0.5178 0.7763 0.256 0.000 0.744
#> GSM447717 1 0.0000 0.8519 1.000 0.000 0.000
#> GSM447725 1 0.3686 0.7574 0.860 0.000 0.140
#> GSM447729 2 0.5541 0.7868 0.008 0.740 0.252
#> GSM447644 3 0.5138 0.6554 0.000 0.252 0.748
#> GSM447710 3 0.5254 0.7743 0.264 0.000 0.736
#> GSM447614 1 0.5541 0.6583 0.740 0.008 0.252
#> GSM447685 2 0.0237 0.8143 0.000 0.996 0.004
#> GSM447690 1 0.2356 0.8115 0.928 0.000 0.072
#> GSM447730 2 0.0237 0.8143 0.000 0.996 0.004
#> GSM447646 2 0.5138 0.7902 0.000 0.748 0.252
#> GSM447689 3 0.5774 0.7757 0.232 0.020 0.748
#> GSM447635 2 0.6572 0.6948 0.172 0.748 0.080
#> GSM447641 1 0.0000 0.8519 1.000 0.000 0.000
#> GSM447716 2 0.5138 0.7902 0.000 0.748 0.252
#> GSM447718 3 0.8779 0.6750 0.260 0.164 0.576
#> GSM447616 3 0.5591 0.7477 0.304 0.000 0.696
#> GSM447626 3 0.5216 0.7755 0.260 0.000 0.740
#> GSM447640 2 0.0000 0.8133 0.000 1.000 0.000
#> GSM447734 3 0.5178 0.7763 0.256 0.000 0.744
#> GSM447692 1 0.2878 0.7459 0.904 0.000 0.096
#> GSM447647 2 0.5138 0.7902 0.000 0.748 0.252
#> GSM447624 1 0.5968 0.0061 0.636 0.000 0.364
#> GSM447625 3 0.5178 0.7763 0.256 0.000 0.744
#> GSM447707 2 0.0237 0.8123 0.000 0.996 0.004
#> GSM447732 3 0.5254 0.7743 0.264 0.000 0.736
#> GSM447684 3 0.5529 0.7578 0.296 0.000 0.704
#> GSM447731 2 0.5178 0.7889 0.000 0.744 0.256
#> GSM447705 3 0.5138 0.6554 0.000 0.252 0.748
#> GSM447631 3 0.6299 0.4805 0.476 0.000 0.524
#> GSM447701 2 0.5016 0.5398 0.000 0.760 0.240
#> GSM447645 3 0.5678 0.7358 0.316 0.000 0.684
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM447671 3 0.5499 0.678 0.000 0.216 0.712 0.072
#> GSM447694 3 0.1474 0.923 0.000 0.000 0.948 0.052
#> GSM447618 2 0.4730 0.481 0.000 0.636 0.000 0.364
#> GSM447691 2 0.0469 0.938 0.000 0.988 0.000 0.012
#> GSM447733 4 0.0188 0.934 0.000 0.004 0.000 0.996
#> GSM447620 2 0.4925 0.339 0.000 0.572 0.428 0.000
#> GSM447627 3 0.1792 0.916 0.000 0.000 0.932 0.068
#> GSM447630 2 0.0592 0.937 0.000 0.984 0.016 0.000
#> GSM447642 1 0.0000 0.938 1.000 0.000 0.000 0.000
#> GSM447649 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM447654 4 0.2101 0.936 0.012 0.060 0.000 0.928
#> GSM447655 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM447669 2 0.0376 0.940 0.000 0.992 0.004 0.004
#> GSM447676 1 0.0000 0.938 1.000 0.000 0.000 0.000
#> GSM447678 4 0.0592 0.938 0.000 0.016 0.000 0.984
#> GSM447681 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM447698 4 0.1022 0.941 0.000 0.032 0.000 0.968
#> GSM447713 1 0.0000 0.938 1.000 0.000 0.000 0.000
#> GSM447722 4 0.0469 0.937 0.000 0.012 0.000 0.988
#> GSM447726 2 0.1489 0.919 0.004 0.952 0.044 0.000
#> GSM447735 4 0.0000 0.932 0.000 0.000 0.000 1.000
#> GSM447737 1 0.3143 0.834 0.876 0.000 0.100 0.024
#> GSM447657 2 0.0336 0.939 0.000 0.992 0.000 0.008
#> GSM447674 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM447636 1 0.0000 0.938 1.000 0.000 0.000 0.000
#> GSM447723 1 0.0000 0.938 1.000 0.000 0.000 0.000
#> GSM447699 3 0.3610 0.801 0.000 0.000 0.800 0.200
#> GSM447708 2 0.1389 0.916 0.000 0.952 0.000 0.048
#> GSM447721 1 0.0000 0.938 1.000 0.000 0.000 0.000
#> GSM447623 1 0.0000 0.938 1.000 0.000 0.000 0.000
#> GSM447621 1 0.0188 0.935 0.996 0.000 0.004 0.000
#> GSM447650 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM447651 2 0.1022 0.927 0.000 0.968 0.032 0.000
#> GSM447653 4 0.2530 0.866 0.112 0.000 0.000 0.888
#> GSM447658 1 0.0000 0.938 1.000 0.000 0.000 0.000
#> GSM447675 4 0.0817 0.940 0.000 0.024 0.000 0.976
#> GSM447680 2 0.1389 0.911 0.048 0.952 0.000 0.000
#> GSM447686 1 0.0000 0.938 1.000 0.000 0.000 0.000
#> GSM447736 3 0.1557 0.921 0.000 0.000 0.944 0.056
#> GSM447629 2 0.3123 0.798 0.156 0.844 0.000 0.000
#> GSM447648 3 0.1022 0.930 0.000 0.000 0.968 0.032
#> GSM447660 1 0.0000 0.938 1.000 0.000 0.000 0.000
#> GSM447661 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM447663 3 0.2868 0.839 0.000 0.136 0.864 0.000
#> GSM447704 2 0.2530 0.855 0.000 0.888 0.000 0.112
#> GSM447720 3 0.4545 0.829 0.028 0.136 0.812 0.024
#> GSM447652 2 0.0188 0.940 0.000 0.996 0.000 0.004
#> GSM447679 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM447712 1 0.0000 0.938 1.000 0.000 0.000 0.000
#> GSM447664 4 0.4079 0.797 0.180 0.020 0.000 0.800
#> GSM447637 3 0.0000 0.938 0.000 0.000 1.000 0.000
#> GSM447639 4 0.0188 0.934 0.000 0.004 0.000 0.996
#> GSM447615 1 0.4222 0.622 0.728 0.000 0.272 0.000
#> GSM447656 2 0.3907 0.695 0.232 0.768 0.000 0.000
#> GSM447673 4 0.1637 0.938 0.000 0.060 0.000 0.940
#> GSM447719 4 0.4590 0.806 0.060 0.000 0.148 0.792
#> GSM447706 3 0.0000 0.938 0.000 0.000 1.000 0.000
#> GSM447612 3 0.0000 0.938 0.000 0.000 1.000 0.000
#> GSM447665 2 0.0188 0.940 0.000 0.996 0.004 0.000
#> GSM447677 2 0.0188 0.940 0.000 0.996 0.004 0.000
#> GSM447613 1 0.0000 0.938 1.000 0.000 0.000 0.000
#> GSM447659 4 0.2921 0.817 0.000 0.000 0.140 0.860
#> GSM447662 3 0.0000 0.938 0.000 0.000 1.000 0.000
#> GSM447666 3 0.0000 0.938 0.000 0.000 1.000 0.000
#> GSM447668 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM447682 2 0.0707 0.933 0.000 0.980 0.000 0.020
#> GSM447683 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM447688 4 0.1118 0.941 0.000 0.036 0.000 0.964
#> GSM447702 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM447709 2 0.1716 0.909 0.000 0.936 0.064 0.000
#> GSM447711 1 0.0000 0.938 1.000 0.000 0.000 0.000
#> GSM447715 1 0.0188 0.935 0.996 0.004 0.000 0.000
#> GSM447693 3 0.0000 0.938 0.000 0.000 1.000 0.000
#> GSM447611 4 0.2142 0.920 0.056 0.016 0.000 0.928
#> GSM447672 2 0.0000 0.941 0.000 1.000 0.000 0.000
#> GSM447703 4 0.1716 0.936 0.000 0.064 0.000 0.936
#> GSM447727 1 0.0000 0.938 1.000 0.000 0.000 0.000
#> GSM447638 1 0.5231 0.379 0.604 0.384 0.012 0.000
#> GSM447670 1 0.2345 0.853 0.900 0.000 0.100 0.000
#> GSM447700 3 0.4746 0.520 0.000 0.000 0.632 0.368
#> GSM447738 4 0.1474 0.940 0.000 0.052 0.000 0.948
#> GSM447739 1 0.0000 0.938 1.000 0.000 0.000 0.000
#> GSM447617 1 0.1389 0.902 0.952 0.000 0.048 0.000
#> GSM447628 4 0.1637 0.938 0.000 0.060 0.000 0.940
#> GSM447632 4 0.1557 0.940 0.000 0.056 0.000 0.944
#> GSM447619 3 0.0000 0.938 0.000 0.000 1.000 0.000
#> GSM447643 1 0.0000 0.938 1.000 0.000 0.000 0.000
#> GSM447724 4 0.0000 0.932 0.000 0.000 0.000 1.000
#> GSM447728 2 0.0336 0.939 0.000 0.992 0.000 0.008
#> GSM447610 4 0.3907 0.727 0.232 0.000 0.000 0.768
#> GSM447633 2 0.4193 0.678 0.000 0.732 0.268 0.000
#> GSM447634 3 0.4281 0.773 0.180 0.000 0.792 0.028
#> GSM447622 3 0.1022 0.930 0.000 0.000 0.968 0.032
#> GSM447667 1 0.4730 0.416 0.636 0.364 0.000 0.000
#> GSM447687 4 0.1716 0.936 0.000 0.064 0.000 0.936
#> GSM447695 3 0.2871 0.902 0.032 0.000 0.896 0.072
#> GSM447696 1 0.0000 0.938 1.000 0.000 0.000 0.000
#> GSM447697 1 0.0000 0.938 1.000 0.000 0.000 0.000
#> GSM447714 3 0.0000 0.938 0.000 0.000 1.000 0.000
#> GSM447717 1 0.0000 0.938 1.000 0.000 0.000 0.000
#> GSM447725 1 0.0000 0.938 1.000 0.000 0.000 0.000
#> GSM447729 4 0.1637 0.938 0.000 0.060 0.000 0.940
#> GSM447644 2 0.1637 0.910 0.000 0.940 0.060 0.000
#> GSM447710 3 0.0000 0.938 0.000 0.000 1.000 0.000
#> GSM447614 4 0.0000 0.932 0.000 0.000 0.000 1.000
#> GSM447685 2 0.0657 0.937 0.012 0.984 0.000 0.004
#> GSM447690 1 0.0000 0.938 1.000 0.000 0.000 0.000
#> GSM447730 2 0.1716 0.902 0.000 0.936 0.000 0.064
#> GSM447646 4 0.1557 0.940 0.000 0.056 0.000 0.944
#> GSM447689 3 0.0000 0.938 0.000 0.000 1.000 0.000
#> GSM447635 4 0.1796 0.920 0.016 0.032 0.004 0.948
#> GSM447641 1 0.0000 0.938 1.000 0.000 0.000 0.000
#> GSM447716 4 0.1209 0.941 0.004 0.032 0.000 0.964
#> GSM447718 3 0.3113 0.864 0.004 0.108 0.876 0.012
#> GSM447616 3 0.1677 0.925 0.012 0.000 0.948 0.040
#> GSM447626 3 0.0000 0.938 0.000 0.000 1.000 0.000
#> GSM447640 2 0.0188 0.940 0.000 0.996 0.000 0.004
#> GSM447734 3 0.0336 0.937 0.000 0.000 0.992 0.008
#> GSM447692 3 0.3323 0.887 0.064 0.000 0.876 0.060
#> GSM447647 4 0.1557 0.940 0.000 0.056 0.000 0.944
#> GSM447624 3 0.2149 0.887 0.088 0.000 0.912 0.000
#> GSM447625 3 0.0188 0.937 0.000 0.000 0.996 0.004
#> GSM447707 2 0.0469 0.938 0.000 0.988 0.000 0.012
#> GSM447732 3 0.0000 0.938 0.000 0.000 1.000 0.000
#> GSM447684 1 0.6097 0.354 0.580 0.364 0.056 0.000
#> GSM447731 4 0.3910 0.831 0.000 0.156 0.024 0.820
#> GSM447705 3 0.0000 0.938 0.000 0.000 1.000 0.000
#> GSM447631 3 0.0000 0.938 0.000 0.000 1.000 0.000
#> GSM447701 2 0.0921 0.930 0.000 0.972 0.028 0.000
#> GSM447645 3 0.0000 0.938 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
#> GSM447671 3 0.2362 0.8292 0.000 0.024 0.900 0.000 0.076
#> GSM447694 3 0.2561 0.8412 0.000 0.000 0.856 0.000 0.144
#> GSM447618 3 0.2915 0.6928 0.000 0.024 0.860 0.116 0.000
#> GSM447691 2 0.4278 0.2964 0.000 0.548 0.452 0.000 0.000
#> GSM447733 4 0.0955 0.8374 0.000 0.000 0.028 0.968 0.004
#> GSM447620 5 0.2074 0.7684 0.000 0.104 0.000 0.000 0.896
#> GSM447627 3 0.3236 0.8375 0.000 0.000 0.828 0.020 0.152
#> GSM447630 2 0.4798 0.0531 0.000 0.540 0.440 0.000 0.020
#> GSM447642 1 0.0000 0.9325 1.000 0.000 0.000 0.000 0.000
#> GSM447649 2 0.3402 0.7755 0.000 0.804 0.004 0.184 0.008
#> GSM447654 4 0.0290 0.8296 0.000 0.000 0.000 0.992 0.008
#> GSM447655 2 0.0162 0.8780 0.000 0.996 0.004 0.000 0.000
#> GSM447669 2 0.4632 0.0519 0.000 0.540 0.448 0.000 0.012
#> GSM447676 1 0.0963 0.9049 0.964 0.000 0.000 0.000 0.036
#> GSM447678 4 0.3395 0.8024 0.000 0.000 0.236 0.764 0.000
#> GSM447681 2 0.2377 0.8244 0.000 0.872 0.128 0.000 0.000
#> GSM447698 4 0.5086 0.5410 0.000 0.040 0.396 0.564 0.000
#> GSM447713 1 0.0000 0.9325 1.000 0.000 0.000 0.000 0.000
#> GSM447722 3 0.2280 0.6858 0.000 0.000 0.880 0.120 0.000
#> GSM447726 2 0.0000 0.8783 0.000 1.000 0.000 0.000 0.000
#> GSM447735 3 0.0609 0.7821 0.000 0.000 0.980 0.020 0.000
#> GSM447737 3 0.3003 0.7005 0.188 0.000 0.812 0.000 0.000
#> GSM447657 2 0.2707 0.8150 0.000 0.860 0.132 0.008 0.000
#> GSM447674 2 0.1478 0.8609 0.000 0.936 0.064 0.000 0.000
#> GSM447636 1 0.0162 0.9299 0.996 0.000 0.000 0.000 0.004
#> GSM447723 1 0.0000 0.9325 1.000 0.000 0.000 0.000 0.000
#> GSM447699 3 0.2179 0.8409 0.000 0.000 0.896 0.004 0.100
#> GSM447708 2 0.1671 0.8600 0.000 0.924 0.076 0.000 0.000
#> GSM447721 1 0.0000 0.9325 1.000 0.000 0.000 0.000 0.000
#> GSM447623 1 0.0000 0.9325 1.000 0.000 0.000 0.000 0.000
#> GSM447621 1 0.3003 0.7286 0.812 0.000 0.188 0.000 0.000
#> GSM447650 2 0.0000 0.8783 0.000 1.000 0.000 0.000 0.000
#> GSM447651 2 0.0865 0.8720 0.000 0.972 0.004 0.000 0.024
#> GSM447653 4 0.2462 0.7736 0.112 0.000 0.000 0.880 0.008
#> GSM447658 1 0.0000 0.9325 1.000 0.000 0.000 0.000 0.000
#> GSM447675 4 0.2605 0.8426 0.000 0.000 0.148 0.852 0.000
#> GSM447680 2 0.0000 0.8783 0.000 1.000 0.000 0.000 0.000
#> GSM447686 1 0.0000 0.9325 1.000 0.000 0.000 0.000 0.000
#> GSM447736 3 0.2516 0.8422 0.000 0.000 0.860 0.000 0.140
#> GSM447629 2 0.3340 0.7976 0.016 0.824 0.156 0.004 0.000
#> GSM447648 5 0.0703 0.8552 0.000 0.000 0.024 0.000 0.976
#> GSM447660 1 0.0000 0.9325 1.000 0.000 0.000 0.000 0.000
#> GSM447661 2 0.0000 0.8783 0.000 1.000 0.000 0.000 0.000
#> GSM447663 3 0.5490 0.6831 0.000 0.200 0.652 0.000 0.148
#> GSM447704 2 0.3476 0.7628 0.000 0.804 0.020 0.176 0.000
#> GSM447720 3 0.4840 0.7544 0.000 0.152 0.724 0.000 0.124
#> GSM447652 2 0.1270 0.8645 0.000 0.948 0.000 0.052 0.000
#> GSM447679 2 0.0000 0.8783 0.000 1.000 0.000 0.000 0.000
#> GSM447712 1 0.0000 0.9325 1.000 0.000 0.000 0.000 0.000
#> GSM447664 4 0.3039 0.7043 0.192 0.000 0.000 0.808 0.000
#> GSM447637 5 0.0703 0.8551 0.000 0.000 0.024 0.000 0.976
#> GSM447639 4 0.3534 0.7430 0.000 0.000 0.256 0.744 0.000
#> GSM447615 5 0.3723 0.7017 0.152 0.000 0.000 0.044 0.804
#> GSM447656 2 0.3398 0.6975 0.216 0.780 0.004 0.000 0.000
#> GSM447673 4 0.2763 0.8413 0.000 0.004 0.148 0.848 0.000
#> GSM447719 4 0.4430 0.1071 0.004 0.000 0.000 0.540 0.456
#> GSM447706 5 0.0609 0.8561 0.000 0.000 0.020 0.000 0.980
#> GSM447612 3 0.3983 0.6446 0.000 0.000 0.660 0.000 0.340
#> GSM447665 2 0.0000 0.8783 0.000 1.000 0.000 0.000 0.000
#> GSM447677 2 0.0000 0.8783 0.000 1.000 0.000 0.000 0.000
#> GSM447613 1 0.0000 0.9325 1.000 0.000 0.000 0.000 0.000
#> GSM447659 4 0.2248 0.7884 0.000 0.000 0.012 0.900 0.088
#> GSM447662 5 0.3210 0.6597 0.000 0.000 0.212 0.000 0.788
#> GSM447666 5 0.0693 0.8496 0.000 0.012 0.008 0.000 0.980
#> GSM447668 2 0.0000 0.8783 0.000 1.000 0.000 0.000 0.000
#> GSM447682 2 0.3236 0.7946 0.000 0.828 0.020 0.152 0.000
#> GSM447683 2 0.0000 0.8783 0.000 1.000 0.000 0.000 0.000
#> GSM447688 4 0.2516 0.8438 0.000 0.000 0.140 0.860 0.000
#> GSM447702 2 0.0162 0.8780 0.000 0.996 0.004 0.000 0.000
#> GSM447709 2 0.3160 0.7331 0.000 0.808 0.004 0.000 0.188
#> GSM447711 1 0.0000 0.9325 1.000 0.000 0.000 0.000 0.000
#> GSM447715 1 0.0000 0.9325 1.000 0.000 0.000 0.000 0.000
#> GSM447693 5 0.0609 0.8560 0.000 0.000 0.020 0.000 0.980
#> GSM447611 4 0.0613 0.8281 0.004 0.000 0.004 0.984 0.008
#> GSM447672 2 0.1211 0.8750 0.000 0.960 0.024 0.016 0.000
#> GSM447703 4 0.2471 0.8446 0.000 0.000 0.136 0.864 0.000
#> GSM447727 1 0.0000 0.9325 1.000 0.000 0.000 0.000 0.000
#> GSM447638 5 0.6153 0.4530 0.240 0.160 0.004 0.004 0.592
#> GSM447670 1 0.3534 0.6458 0.744 0.000 0.000 0.000 0.256
#> GSM447700 3 0.0404 0.7868 0.000 0.000 0.988 0.012 0.000
#> GSM447738 4 0.2719 0.8424 0.000 0.004 0.144 0.852 0.000
#> GSM447739 1 0.0000 0.9325 1.000 0.000 0.000 0.000 0.000
#> GSM447617 1 0.0162 0.9296 0.996 0.000 0.004 0.000 0.000
#> GSM447628 4 0.0162 0.8333 0.000 0.000 0.004 0.996 0.000
#> GSM447632 4 0.3723 0.8221 0.000 0.044 0.152 0.804 0.000
#> GSM447619 5 0.3636 0.5458 0.000 0.000 0.272 0.000 0.728
#> GSM447643 1 0.0000 0.9325 1.000 0.000 0.000 0.000 0.000
#> GSM447724 4 0.3508 0.7788 0.000 0.000 0.252 0.748 0.000
#> GSM447728 2 0.0865 0.8753 0.000 0.972 0.024 0.004 0.000
#> GSM447610 4 0.4506 0.5738 0.296 0.000 0.028 0.676 0.000
#> GSM447633 2 0.2909 0.7824 0.000 0.848 0.012 0.000 0.140
#> GSM447634 3 0.3350 0.8334 0.040 0.004 0.844 0.000 0.112
#> GSM447622 3 0.2966 0.8221 0.000 0.000 0.816 0.000 0.184
#> GSM447667 1 0.5687 0.3965 0.592 0.312 0.092 0.004 0.000
#> GSM447687 4 0.2424 0.8443 0.000 0.000 0.132 0.868 0.000
#> GSM447695 3 0.2230 0.8436 0.000 0.000 0.884 0.000 0.116
#> GSM447696 1 0.0000 0.9325 1.000 0.000 0.000 0.000 0.000
#> GSM447697 1 0.0000 0.9325 1.000 0.000 0.000 0.000 0.000
#> GSM447714 5 0.3876 0.4225 0.000 0.000 0.316 0.000 0.684
#> GSM447717 1 0.0000 0.9325 1.000 0.000 0.000 0.000 0.000
#> GSM447725 1 0.0000 0.9325 1.000 0.000 0.000 0.000 0.000
#> GSM447729 4 0.1341 0.8432 0.000 0.000 0.056 0.944 0.000
#> GSM447644 2 0.0162 0.8778 0.000 0.996 0.004 0.000 0.000
#> GSM447710 5 0.1544 0.8307 0.000 0.000 0.068 0.000 0.932
#> GSM447614 4 0.3391 0.8061 0.012 0.000 0.188 0.800 0.000
#> GSM447685 2 0.1278 0.8712 0.020 0.960 0.004 0.016 0.000
#> GSM447690 1 0.0000 0.9325 1.000 0.000 0.000 0.000 0.000
#> GSM447730 2 0.5479 0.4579 0.000 0.564 0.004 0.372 0.060
#> GSM447646 4 0.0162 0.8311 0.000 0.000 0.004 0.996 0.000
#> GSM447689 5 0.0290 0.8545 0.000 0.000 0.008 0.000 0.992
#> GSM447635 3 0.0807 0.7807 0.000 0.012 0.976 0.012 0.000
#> GSM447641 1 0.0000 0.9325 1.000 0.000 0.000 0.000 0.000
#> GSM447716 4 0.3010 0.8335 0.004 0.000 0.172 0.824 0.000
#> GSM447718 5 0.3863 0.6275 0.000 0.000 0.012 0.248 0.740
#> GSM447616 3 0.2890 0.8345 0.004 0.000 0.836 0.000 0.160
#> GSM447626 5 0.0510 0.8560 0.000 0.000 0.016 0.000 0.984
#> GSM447640 2 0.0404 0.8774 0.000 0.988 0.000 0.012 0.000
#> GSM447734 3 0.3210 0.8021 0.000 0.000 0.788 0.000 0.212
#> GSM447692 3 0.2629 0.8430 0.004 0.000 0.860 0.000 0.136
#> GSM447647 4 0.0451 0.8283 0.000 0.000 0.004 0.988 0.008
#> GSM447624 1 0.5808 0.1208 0.512 0.000 0.096 0.000 0.392
#> GSM447625 3 0.3730 0.7220 0.000 0.000 0.712 0.000 0.288
#> GSM447707 2 0.3231 0.7583 0.000 0.800 0.004 0.196 0.000
#> GSM447732 3 0.4445 0.6954 0.000 0.024 0.676 0.000 0.300
#> GSM447684 1 0.5063 0.4926 0.632 0.312 0.000 0.000 0.056
#> GSM447731 4 0.3990 0.4975 0.000 0.000 0.004 0.688 0.308
#> GSM447705 5 0.1608 0.8274 0.000 0.000 0.072 0.000 0.928
#> GSM447631 5 0.0671 0.8548 0.000 0.000 0.016 0.004 0.980
#> GSM447701 2 0.0000 0.8783 0.000 1.000 0.000 0.000 0.000
#> GSM447645 5 0.0162 0.8535 0.000 0.000 0.004 0.000 0.996
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM447671 2 0.4516 -0.06426 0.000 0.552 0.420 0.000 0.020 0.008
#> GSM447694 3 0.1245 0.72962 0.000 0.000 0.952 0.000 0.032 0.016
#> GSM447618 2 0.3847 0.13741 0.000 0.644 0.348 0.008 0.000 0.000
#> GSM447691 2 0.5556 0.27141 0.000 0.504 0.348 0.000 0.148 0.000
#> GSM447733 4 0.2003 0.82073 0.000 0.116 0.000 0.884 0.000 0.000
#> GSM447620 6 0.2006 0.79324 0.000 0.104 0.000 0.000 0.004 0.892
#> GSM447627 3 0.3702 0.61501 0.000 0.008 0.760 0.208 0.000 0.024
#> GSM447630 5 0.3748 0.35471 0.000 0.000 0.300 0.012 0.688 0.000
#> GSM447642 1 0.0000 0.95949 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447649 2 0.4250 0.47444 0.004 0.620 0.000 0.004 0.360 0.012
#> GSM447654 4 0.0363 0.85840 0.000 0.000 0.000 0.988 0.012 0.000
#> GSM447655 2 0.3789 0.43385 0.000 0.584 0.000 0.000 0.416 0.000
#> GSM447669 5 0.3665 0.36718 0.000 0.004 0.296 0.004 0.696 0.000
#> GSM447676 1 0.2100 0.86347 0.884 0.004 0.000 0.000 0.000 0.112
#> GSM447678 2 0.5572 -0.22833 0.000 0.464 0.140 0.396 0.000 0.000
#> GSM447681 2 0.4246 0.39769 0.000 0.580 0.020 0.000 0.400 0.000
#> GSM447698 2 0.2950 0.44255 0.000 0.828 0.148 0.024 0.000 0.000
#> GSM447713 1 0.0000 0.95949 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447722 3 0.4150 0.47806 0.000 0.392 0.592 0.016 0.000 0.000
#> GSM447726 5 0.1444 0.52228 0.000 0.072 0.000 0.000 0.928 0.000
#> GSM447735 3 0.1918 0.73392 0.000 0.088 0.904 0.008 0.000 0.000
#> GSM447737 3 0.3163 0.60892 0.212 0.004 0.780 0.000 0.000 0.004
#> GSM447657 5 0.3619 0.44545 0.000 0.232 0.024 0.000 0.744 0.000
#> GSM447674 2 0.3797 0.41660 0.000 0.580 0.000 0.000 0.420 0.000
#> GSM447636 1 0.0291 0.95777 0.992 0.000 0.000 0.004 0.004 0.000
#> GSM447723 1 0.0291 0.95786 0.992 0.004 0.004 0.000 0.000 0.000
#> GSM447699 3 0.1124 0.74367 0.000 0.036 0.956 0.000 0.000 0.008
#> GSM447708 2 0.2762 0.53383 0.000 0.804 0.000 0.000 0.196 0.000
#> GSM447721 1 0.0291 0.95808 0.992 0.000 0.004 0.004 0.000 0.000
#> GSM447623 1 0.0146 0.95951 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM447621 1 0.2994 0.73138 0.788 0.000 0.208 0.004 0.000 0.000
#> GSM447650 5 0.1501 0.51843 0.000 0.076 0.000 0.000 0.924 0.000
#> GSM447651 5 0.5174 -0.11700 0.000 0.368 0.000 0.000 0.536 0.096
#> GSM447653 4 0.0767 0.85843 0.012 0.000 0.004 0.976 0.008 0.000
#> GSM447658 1 0.0000 0.95949 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447675 4 0.3349 0.68481 0.000 0.244 0.008 0.748 0.000 0.000
#> GSM447680 5 0.4072 -0.23503 0.008 0.448 0.000 0.000 0.544 0.000
#> GSM447686 1 0.0363 0.95444 0.988 0.012 0.000 0.000 0.000 0.000
#> GSM447736 3 0.1575 0.74498 0.000 0.032 0.936 0.000 0.000 0.032
#> GSM447629 2 0.1622 0.52110 0.016 0.940 0.016 0.000 0.028 0.000
#> GSM447648 6 0.0458 0.87787 0.000 0.000 0.016 0.000 0.000 0.984
#> GSM447660 1 0.0146 0.95886 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM447661 5 0.2912 0.35622 0.000 0.216 0.000 0.000 0.784 0.000
#> GSM447663 5 0.4033 0.16961 0.000 0.004 0.404 0.000 0.588 0.004
#> GSM447704 2 0.3368 0.53154 0.000 0.756 0.000 0.012 0.232 0.000
#> GSM447720 5 0.4141 0.10251 0.000 0.000 0.432 0.012 0.556 0.000
#> GSM447652 5 0.3508 0.33017 0.000 0.000 0.004 0.292 0.704 0.000
#> GSM447679 2 0.3828 0.40543 0.000 0.560 0.000 0.000 0.440 0.000
#> GSM447712 1 0.0000 0.95949 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447664 4 0.2838 0.71976 0.188 0.000 0.004 0.808 0.000 0.000
#> GSM447637 6 0.0260 0.87853 0.000 0.000 0.008 0.000 0.000 0.992
#> GSM447639 4 0.3663 0.75536 0.000 0.072 0.128 0.796 0.004 0.000
#> GSM447615 6 0.1429 0.83763 0.052 0.004 0.000 0.004 0.000 0.940
#> GSM447656 2 0.4556 0.46163 0.188 0.696 0.000 0.000 0.116 0.000
#> GSM447673 2 0.3986 -0.21196 0.000 0.532 0.004 0.464 0.000 0.000
#> GSM447719 4 0.2692 0.75473 0.000 0.000 0.000 0.840 0.012 0.148
#> GSM447706 6 0.0363 0.87818 0.000 0.000 0.012 0.000 0.000 0.988
#> GSM447612 3 0.3309 0.49730 0.000 0.000 0.720 0.000 0.000 0.280
#> GSM447665 2 0.3996 0.31020 0.000 0.512 0.004 0.000 0.484 0.000
#> GSM447677 2 0.4152 0.38577 0.000 0.548 0.000 0.000 0.440 0.012
#> GSM447613 1 0.0291 0.95808 0.992 0.000 0.004 0.004 0.000 0.000
#> GSM447659 4 0.1767 0.85375 0.000 0.020 0.012 0.932 0.000 0.036
#> GSM447662 6 0.1556 0.84068 0.000 0.000 0.080 0.000 0.000 0.920
#> GSM447666 6 0.0146 0.87531 0.000 0.000 0.000 0.000 0.004 0.996
#> GSM447668 5 0.1714 0.51050 0.000 0.092 0.000 0.000 0.908 0.000
#> GSM447682 5 0.5583 -0.20593 0.000 0.336 0.000 0.156 0.508 0.000
#> GSM447683 2 0.3833 0.38703 0.000 0.556 0.000 0.000 0.444 0.000
#> GSM447688 2 0.3512 0.45799 0.000 0.772 0.032 0.196 0.000 0.000
#> GSM447702 5 0.2854 0.36152 0.000 0.208 0.000 0.000 0.792 0.000
#> GSM447709 2 0.5957 0.23227 0.000 0.400 0.000 0.000 0.220 0.380
#> GSM447711 1 0.0146 0.95951 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM447715 1 0.1327 0.90936 0.936 0.064 0.000 0.000 0.000 0.000
#> GSM447693 6 0.0146 0.87827 0.000 0.000 0.004 0.000 0.000 0.996
#> GSM447611 4 0.0146 0.86107 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM447672 2 0.3634 0.48342 0.000 0.644 0.000 0.000 0.356 0.000
#> GSM447703 2 0.3376 0.47742 0.000 0.764 0.000 0.220 0.016 0.000
#> GSM447727 1 0.0000 0.95949 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447638 6 0.6473 0.28130 0.292 0.016 0.000 0.016 0.192 0.484
#> GSM447670 1 0.3281 0.74735 0.784 0.000 0.012 0.004 0.000 0.200
#> GSM447700 3 0.3508 0.60752 0.000 0.292 0.704 0.000 0.000 0.004
#> GSM447738 2 0.2266 0.50551 0.000 0.880 0.012 0.108 0.000 0.000
#> GSM447739 1 0.0146 0.95951 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM447617 1 0.0508 0.95392 0.984 0.000 0.012 0.000 0.000 0.004
#> GSM447628 4 0.0547 0.86079 0.000 0.020 0.000 0.980 0.000 0.000
#> GSM447632 2 0.0993 0.52167 0.000 0.964 0.012 0.024 0.000 0.000
#> GSM447619 6 0.1327 0.85379 0.000 0.000 0.064 0.000 0.000 0.936
#> GSM447643 1 0.0146 0.95886 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM447724 3 0.5353 0.32836 0.000 0.440 0.464 0.092 0.000 0.004
#> GSM447728 2 0.3782 0.44346 0.000 0.588 0.000 0.000 0.412 0.000
#> GSM447610 4 0.4130 0.62469 0.264 0.028 0.008 0.700 0.000 0.000
#> GSM447633 5 0.6153 -0.09621 0.000 0.304 0.004 0.000 0.420 0.272
#> GSM447634 3 0.3996 0.00732 0.000 0.000 0.512 0.004 0.484 0.000
#> GSM447622 3 0.1327 0.73475 0.000 0.000 0.936 0.000 0.000 0.064
#> GSM447667 2 0.4432 0.04530 0.432 0.544 0.004 0.000 0.020 0.000
#> GSM447687 2 0.3081 0.47988 0.000 0.776 0.000 0.220 0.004 0.000
#> GSM447695 3 0.0806 0.74341 0.000 0.020 0.972 0.000 0.000 0.008
#> GSM447696 1 0.0146 0.95951 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM447697 1 0.0870 0.94655 0.972 0.000 0.004 0.012 0.012 0.000
#> GSM447714 6 0.3782 0.28026 0.000 0.000 0.412 0.000 0.000 0.588
#> GSM447717 1 0.0000 0.95949 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447725 1 0.0146 0.95951 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM447729 4 0.1152 0.85614 0.000 0.044 0.004 0.952 0.000 0.000
#> GSM447644 5 0.1700 0.55882 0.000 0.004 0.080 0.000 0.916 0.000
#> GSM447710 6 0.4364 0.54699 0.000 0.000 0.256 0.004 0.052 0.688
#> GSM447614 4 0.2272 0.83885 0.000 0.040 0.056 0.900 0.004 0.000
#> GSM447685 2 0.3883 0.49337 0.012 0.656 0.000 0.000 0.332 0.000
#> GSM447690 1 0.0146 0.95951 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM447730 2 0.6627 0.43322 0.000 0.508 0.000 0.228 0.192 0.072
#> GSM447646 4 0.0547 0.86083 0.000 0.020 0.000 0.980 0.000 0.000
#> GSM447689 6 0.0000 0.87715 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM447635 3 0.3728 0.54820 0.000 0.344 0.652 0.000 0.004 0.000
#> GSM447641 1 0.0146 0.95886 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM447716 2 0.3667 0.47190 0.092 0.820 0.044 0.044 0.000 0.000
#> GSM447718 4 0.4536 0.13807 0.000 0.004 0.008 0.512 0.464 0.012
#> GSM447616 3 0.1572 0.73855 0.028 0.000 0.936 0.000 0.000 0.036
#> GSM447626 5 0.5315 0.09414 0.000 0.000 0.076 0.012 0.532 0.380
#> GSM447640 2 0.3531 0.49867 0.000 0.672 0.000 0.000 0.328 0.000
#> GSM447734 3 0.3373 0.52817 0.000 0.000 0.744 0.000 0.248 0.008
#> GSM447692 3 0.1268 0.72642 0.008 0.000 0.952 0.000 0.036 0.004
#> GSM447647 4 0.0858 0.86070 0.000 0.028 0.000 0.968 0.000 0.004
#> GSM447624 1 0.4820 0.62778 0.692 0.000 0.176 0.004 0.004 0.124
#> GSM447625 3 0.3970 0.49417 0.000 0.000 0.712 0.016 0.260 0.012
#> GSM447707 2 0.5715 0.41293 0.000 0.484 0.000 0.148 0.364 0.004
#> GSM447732 5 0.4390 -0.02748 0.000 0.000 0.472 0.016 0.508 0.004
#> GSM447684 5 0.2605 0.54301 0.064 0.000 0.032 0.012 0.888 0.004
#> GSM447731 4 0.1196 0.84716 0.000 0.000 0.000 0.952 0.040 0.008
#> GSM447705 6 0.0632 0.87515 0.000 0.000 0.024 0.000 0.000 0.976
#> GSM447631 6 0.0603 0.87421 0.000 0.000 0.004 0.016 0.000 0.980
#> GSM447701 5 0.0748 0.55126 0.000 0.004 0.016 0.004 0.976 0.000
#> GSM447645 6 0.0146 0.87827 0.000 0.000 0.004 0.000 0.000 0.996
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)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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 gender(p) individual(p) disease.state(p) other(p) k
#> CV:NMF 125 0.812 0.9381 0.050 0.0218 2
#> CV:NMF 116 0.200 0.4109 0.117 0.2783 3
#> CV:NMF 125 0.212 0.1197 0.202 0.0653 4
#> CV:NMF 119 0.636 0.0515 0.124 0.0379 5
#> CV:NMF 81 0.945 0.6669 0.189 0.4966 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "hclust"]
# you can also extract it by
# res = res_list["MAD:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 130 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 0.360 0.684 0.855 0.4776 0.499 0.499
#> 3 3 0.329 0.437 0.713 0.2934 0.833 0.681
#> 4 4 0.390 0.399 0.628 0.1563 0.765 0.475
#> 5 5 0.506 0.444 0.655 0.0832 0.795 0.416
#> 6 6 0.574 0.396 0.579 0.0405 0.895 0.592
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
#> GSM447671 2 0.9427 0.5157 0.360 0.640
#> GSM447694 1 0.4022 0.8273 0.920 0.080
#> GSM447618 2 0.9881 0.3283 0.436 0.564
#> GSM447691 2 0.7950 0.6839 0.240 0.760
#> GSM447733 1 0.5294 0.8061 0.880 0.120
#> GSM447620 2 0.9358 0.5158 0.352 0.648
#> GSM447627 1 0.4939 0.8087 0.892 0.108
#> GSM447630 1 0.9833 0.2014 0.576 0.424
#> GSM447642 1 0.3431 0.8326 0.936 0.064
#> GSM447649 2 0.0000 0.7851 0.000 1.000
#> GSM447654 2 0.9248 0.4946 0.340 0.660
#> GSM447655 2 0.0000 0.7851 0.000 1.000
#> GSM447669 2 0.9993 0.1682 0.484 0.516
#> GSM447676 1 0.3584 0.8320 0.932 0.068
#> GSM447678 2 0.9358 0.5305 0.352 0.648
#> GSM447681 2 0.0000 0.7851 0.000 1.000
#> GSM447698 2 0.9358 0.5290 0.352 0.648
#> GSM447713 1 0.0000 0.8472 1.000 0.000
#> GSM447722 2 0.9661 0.4483 0.392 0.608
#> GSM447726 1 0.9988 -0.0631 0.520 0.480
#> GSM447735 1 0.6531 0.7466 0.832 0.168
#> GSM447737 1 0.0000 0.8472 1.000 0.000
#> GSM447657 2 0.1184 0.7854 0.016 0.984
#> GSM447674 2 0.1184 0.7854 0.016 0.984
#> GSM447636 1 0.3431 0.8326 0.936 0.064
#> GSM447723 1 0.9970 -0.0331 0.532 0.468
#> GSM447699 1 0.9460 0.3985 0.636 0.364
#> GSM447708 2 0.4815 0.7637 0.104 0.896
#> GSM447721 1 0.0376 0.8478 0.996 0.004
#> GSM447623 1 0.0000 0.8472 1.000 0.000
#> GSM447621 1 0.0000 0.8472 1.000 0.000
#> GSM447650 2 0.0000 0.7851 0.000 1.000
#> GSM447651 2 0.0938 0.7841 0.012 0.988
#> GSM447653 1 0.2043 0.8469 0.968 0.032
#> GSM447658 1 0.3431 0.8326 0.936 0.064
#> GSM447675 2 0.9866 0.3076 0.432 0.568
#> GSM447680 2 0.3733 0.7754 0.072 0.928
#> GSM447686 2 0.8661 0.6366 0.288 0.712
#> GSM447736 1 0.3733 0.8373 0.928 0.072
#> GSM447629 2 0.8016 0.6811 0.244 0.756
#> GSM447648 1 0.0000 0.8472 1.000 0.000
#> GSM447660 1 0.7745 0.6628 0.772 0.228
#> GSM447661 2 0.0000 0.7851 0.000 1.000
#> GSM447663 1 0.4690 0.8223 0.900 0.100
#> GSM447704 2 0.0000 0.7851 0.000 1.000
#> GSM447720 1 0.3431 0.8410 0.936 0.064
#> GSM447652 2 0.6148 0.7445 0.152 0.848
#> GSM447679 2 0.0000 0.7851 0.000 1.000
#> GSM447712 1 0.0376 0.8478 0.996 0.004
#> GSM447664 2 0.8016 0.6728 0.244 0.756
#> GSM447637 1 0.0000 0.8472 1.000 0.000
#> GSM447639 1 0.8016 0.6451 0.756 0.244
#> GSM447615 1 0.1633 0.8462 0.976 0.024
#> GSM447656 2 0.7376 0.7101 0.208 0.792
#> GSM447673 2 0.0938 0.7846 0.012 0.988
#> GSM447719 1 0.2043 0.8469 0.968 0.032
#> GSM447706 1 0.0000 0.8472 1.000 0.000
#> GSM447612 1 0.9000 0.5124 0.684 0.316
#> GSM447665 2 0.9248 0.5464 0.340 0.660
#> GSM447677 2 0.1633 0.7843 0.024 0.976
#> GSM447613 1 0.0376 0.8478 0.996 0.004
#> GSM447659 1 0.3733 0.8333 0.928 0.072
#> GSM447662 1 0.2948 0.8438 0.948 0.052
#> GSM447666 2 1.0000 0.1244 0.500 0.500
#> GSM447668 2 0.0000 0.7851 0.000 1.000
#> GSM447682 2 0.3274 0.7789 0.060 0.940
#> GSM447683 2 0.2423 0.7820 0.040 0.960
#> GSM447688 2 0.0000 0.7851 0.000 1.000
#> GSM447702 2 0.0000 0.7851 0.000 1.000
#> GSM447709 2 0.9248 0.5376 0.340 0.660
#> GSM447711 1 0.0376 0.8478 0.996 0.004
#> GSM447715 1 0.9970 -0.0331 0.532 0.468
#> GSM447693 1 0.0000 0.8472 1.000 0.000
#> GSM447611 2 0.9881 0.3020 0.436 0.564
#> GSM447672 2 0.0000 0.7851 0.000 1.000
#> GSM447703 2 0.0000 0.7851 0.000 1.000
#> GSM447727 1 0.9795 0.1868 0.584 0.416
#> GSM447638 1 0.9248 0.4240 0.660 0.340
#> GSM447670 1 0.0672 0.8480 0.992 0.008
#> GSM447700 2 0.9933 0.2815 0.452 0.548
#> GSM447738 2 0.0000 0.7851 0.000 1.000
#> GSM447739 1 0.0000 0.8472 1.000 0.000
#> GSM447617 1 0.0000 0.8472 1.000 0.000
#> GSM447628 2 0.9087 0.5151 0.324 0.676
#> GSM447632 2 0.0000 0.7851 0.000 1.000
#> GSM447619 1 0.2948 0.8438 0.948 0.052
#> GSM447643 1 0.8555 0.5717 0.720 0.280
#> GSM447724 1 0.9580 0.3451 0.620 0.380
#> GSM447728 2 0.4161 0.7710 0.084 0.916
#> GSM447610 1 0.5178 0.8014 0.884 0.116
#> GSM447633 2 0.9248 0.5464 0.340 0.660
#> GSM447634 1 0.9000 0.5127 0.684 0.316
#> GSM447622 1 0.0000 0.8472 1.000 0.000
#> GSM447667 2 0.8327 0.6617 0.264 0.736
#> GSM447687 2 0.0000 0.7851 0.000 1.000
#> GSM447695 1 0.4022 0.8273 0.920 0.080
#> GSM447696 1 0.0000 0.8472 1.000 0.000
#> GSM447697 1 0.0000 0.8472 1.000 0.000
#> GSM447714 1 0.2778 0.8456 0.952 0.048
#> GSM447717 1 0.3431 0.8326 0.936 0.064
#> GSM447725 1 0.1414 0.8490 0.980 0.020
#> GSM447729 2 0.9833 0.3298 0.424 0.576
#> GSM447644 2 0.9993 0.1682 0.484 0.516
#> GSM447710 1 0.2778 0.8456 0.952 0.048
#> GSM447614 1 0.5178 0.8014 0.884 0.116
#> GSM447685 2 0.3584 0.7770 0.068 0.932
#> GSM447690 1 0.0000 0.8472 1.000 0.000
#> GSM447730 2 0.0000 0.7851 0.000 1.000
#> GSM447646 2 0.9087 0.5151 0.324 0.676
#> GSM447689 1 0.9358 0.4121 0.648 0.352
#> GSM447635 2 0.8555 0.6456 0.280 0.720
#> GSM447641 1 0.3431 0.8326 0.936 0.064
#> GSM447716 2 0.9170 0.5807 0.332 0.668
#> GSM447718 1 0.9754 0.2400 0.592 0.408
#> GSM447616 1 0.0000 0.8472 1.000 0.000
#> GSM447626 1 0.5946 0.7795 0.856 0.144
#> GSM447640 2 0.0000 0.7851 0.000 1.000
#> GSM447734 1 0.2948 0.8432 0.948 0.052
#> GSM447692 1 0.4022 0.8273 0.920 0.080
#> GSM447647 2 0.0000 0.7851 0.000 1.000
#> GSM447624 1 0.0000 0.8472 1.000 0.000
#> GSM447625 1 0.4298 0.8303 0.912 0.088
#> GSM447707 2 0.0000 0.7851 0.000 1.000
#> GSM447732 1 0.4562 0.8246 0.904 0.096
#> GSM447684 1 0.9635 0.2970 0.612 0.388
#> GSM447731 1 0.5178 0.8057 0.884 0.116
#> GSM447705 2 0.9286 0.5305 0.344 0.656
#> GSM447631 1 0.0000 0.8472 1.000 0.000
#> GSM447701 2 0.0000 0.7851 0.000 1.000
#> GSM447645 1 0.0000 0.8472 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM447671 2 0.8113 0.3916 0.088 0.588 0.324
#> GSM447694 1 0.7589 0.2416 0.588 0.052 0.360
#> GSM447618 2 0.9120 0.1285 0.156 0.504 0.340
#> GSM447691 2 0.6981 0.6243 0.136 0.732 0.132
#> GSM447733 3 0.7199 0.3963 0.260 0.064 0.676
#> GSM447620 2 0.7705 0.3933 0.060 0.592 0.348
#> GSM447627 3 0.7533 0.2909 0.348 0.052 0.600
#> GSM447630 3 0.9392 0.2911 0.172 0.392 0.436
#> GSM447642 1 0.4399 0.4991 0.864 0.044 0.092
#> GSM447649 2 0.0592 0.7340 0.000 0.988 0.012
#> GSM447654 2 0.6527 0.3874 0.008 0.588 0.404
#> GSM447655 2 0.0000 0.7351 0.000 1.000 0.000
#> GSM447669 2 0.8720 0.0350 0.108 0.480 0.412
#> GSM447676 1 0.4505 0.5071 0.860 0.048 0.092
#> GSM447678 2 0.8573 0.3548 0.136 0.584 0.280
#> GSM447681 2 0.0000 0.7351 0.000 1.000 0.000
#> GSM447698 2 0.8571 0.3489 0.140 0.588 0.272
#> GSM447713 1 0.0000 0.5768 1.000 0.000 0.000
#> GSM447722 2 0.8894 0.2591 0.152 0.548 0.300
#> GSM447726 2 0.9326 -0.0411 0.164 0.440 0.396
#> GSM447735 3 0.8186 0.3805 0.292 0.104 0.604
#> GSM447737 1 0.2796 0.5715 0.908 0.000 0.092
#> GSM447657 2 0.0747 0.7352 0.000 0.984 0.016
#> GSM447674 2 0.0747 0.7352 0.000 0.984 0.016
#> GSM447636 1 0.4399 0.4991 0.864 0.044 0.092
#> GSM447723 2 0.9369 0.1233 0.408 0.424 0.168
#> GSM447699 3 0.9796 0.4410 0.264 0.304 0.432
#> GSM447708 2 0.3987 0.7088 0.020 0.872 0.108
#> GSM447721 1 0.0475 0.5751 0.992 0.004 0.004
#> GSM447623 1 0.2625 0.5734 0.916 0.000 0.084
#> GSM447621 1 0.2625 0.5734 0.916 0.000 0.084
#> GSM447650 2 0.0000 0.7351 0.000 1.000 0.000
#> GSM447651 2 0.1529 0.7301 0.000 0.960 0.040
#> GSM447653 3 0.4654 0.3288 0.208 0.000 0.792
#> GSM447658 1 0.4316 0.5023 0.868 0.044 0.088
#> GSM447675 3 0.7188 -0.2881 0.024 0.488 0.488
#> GSM447680 2 0.3669 0.7134 0.064 0.896 0.040
#> GSM447686 2 0.7620 0.5788 0.188 0.684 0.128
#> GSM447736 1 0.7824 0.0889 0.504 0.052 0.444
#> GSM447629 2 0.7042 0.6213 0.140 0.728 0.132
#> GSM447648 1 0.5926 0.3688 0.644 0.000 0.356
#> GSM447660 1 0.8042 0.2086 0.652 0.200 0.148
#> GSM447661 2 0.0000 0.7351 0.000 1.000 0.000
#> GSM447663 3 0.8141 -0.0305 0.460 0.068 0.472
#> GSM447704 2 0.0592 0.7340 0.000 0.988 0.012
#> GSM447720 1 0.7665 0.0872 0.500 0.044 0.456
#> GSM447652 2 0.5173 0.6745 0.036 0.816 0.148
#> GSM447679 2 0.0000 0.7351 0.000 1.000 0.000
#> GSM447712 1 0.0475 0.5751 0.992 0.004 0.004
#> GSM447664 2 0.7303 0.5593 0.076 0.680 0.244
#> GSM447637 1 0.6267 0.2319 0.548 0.000 0.452
#> GSM447639 3 0.9192 0.4168 0.308 0.176 0.516
#> GSM447615 1 0.6333 0.4119 0.656 0.012 0.332
#> GSM447656 2 0.6389 0.6514 0.124 0.768 0.108
#> GSM447673 2 0.1163 0.7302 0.000 0.972 0.028
#> GSM447719 3 0.4654 0.3288 0.208 0.000 0.792
#> GSM447706 1 0.6286 0.2242 0.536 0.000 0.464
#> GSM447612 3 0.9667 0.4543 0.264 0.272 0.464
#> GSM447665 2 0.7807 0.4182 0.068 0.596 0.336
#> GSM447677 2 0.2173 0.7281 0.008 0.944 0.048
#> GSM447613 1 0.0661 0.5765 0.988 0.004 0.008
#> GSM447659 3 0.5956 0.3440 0.264 0.016 0.720
#> GSM447662 1 0.7668 0.1116 0.496 0.044 0.460
#> GSM447666 2 0.9004 0.0272 0.132 0.468 0.400
#> GSM447668 2 0.0000 0.7351 0.000 1.000 0.000
#> GSM447682 2 0.2845 0.7244 0.012 0.920 0.068
#> GSM447683 2 0.2280 0.7265 0.008 0.940 0.052
#> GSM447688 2 0.0892 0.7324 0.000 0.980 0.020
#> GSM447702 2 0.0000 0.7351 0.000 1.000 0.000
#> GSM447709 2 0.7644 0.4158 0.060 0.604 0.336
#> GSM447711 1 0.0475 0.5751 0.992 0.004 0.004
#> GSM447715 2 0.9369 0.1233 0.408 0.424 0.168
#> GSM447693 1 0.6267 0.2319 0.548 0.000 0.452
#> GSM447611 2 0.8065 0.2583 0.064 0.484 0.452
#> GSM447672 2 0.0000 0.7351 0.000 1.000 0.000
#> GSM447703 2 0.0892 0.7324 0.000 0.980 0.020
#> GSM447727 1 0.9328 -0.1118 0.460 0.372 0.168
#> GSM447638 1 0.9286 -0.0359 0.504 0.312 0.184
#> GSM447670 1 0.4931 0.5230 0.768 0.000 0.232
#> GSM447700 2 0.9281 0.0645 0.172 0.488 0.340
#> GSM447738 2 0.0592 0.7340 0.000 0.988 0.012
#> GSM447739 1 0.0000 0.5768 1.000 0.000 0.000
#> GSM447617 1 0.2711 0.5728 0.912 0.000 0.088
#> GSM447628 2 0.6111 0.4006 0.000 0.604 0.396
#> GSM447632 2 0.0592 0.7340 0.000 0.988 0.012
#> GSM447619 1 0.7668 0.1116 0.496 0.044 0.460
#> GSM447643 1 0.8367 0.1356 0.612 0.252 0.136
#> GSM447724 3 0.9724 0.4378 0.236 0.328 0.436
#> GSM447728 2 0.3528 0.7150 0.016 0.892 0.092
#> GSM447610 1 0.7418 0.3342 0.672 0.080 0.248
#> GSM447633 2 0.7807 0.4182 0.068 0.596 0.336
#> GSM447634 3 0.9849 0.4058 0.300 0.280 0.420
#> GSM447622 1 0.5254 0.4730 0.736 0.000 0.264
#> GSM447667 2 0.7333 0.5988 0.156 0.708 0.136
#> GSM447687 2 0.0892 0.7324 0.000 0.980 0.020
#> GSM447695 1 0.7274 0.3395 0.644 0.052 0.304
#> GSM447696 1 0.0000 0.5768 1.000 0.000 0.000
#> GSM447697 1 0.0237 0.5772 0.996 0.000 0.004
#> GSM447714 1 0.7293 0.1091 0.496 0.028 0.476
#> GSM447717 1 0.4399 0.4991 0.864 0.044 0.092
#> GSM447725 1 0.1636 0.5669 0.964 0.016 0.020
#> GSM447729 2 0.7984 0.2736 0.060 0.496 0.444
#> GSM447644 2 0.8720 0.0350 0.108 0.480 0.412
#> GSM447710 1 0.7293 0.1091 0.496 0.028 0.476
#> GSM447614 1 0.7418 0.3342 0.672 0.080 0.248
#> GSM447685 2 0.3253 0.7211 0.036 0.912 0.052
#> GSM447690 1 0.0000 0.5768 1.000 0.000 0.000
#> GSM447730 2 0.0592 0.7340 0.000 0.988 0.012
#> GSM447646 2 0.6111 0.4006 0.000 0.604 0.396
#> GSM447689 3 0.9471 0.3992 0.208 0.308 0.484
#> GSM447635 2 0.7495 0.5916 0.120 0.692 0.188
#> GSM447641 1 0.4316 0.5023 0.868 0.044 0.088
#> GSM447716 2 0.8098 0.5291 0.216 0.644 0.140
#> GSM447718 3 0.9621 0.3417 0.208 0.360 0.432
#> GSM447616 1 0.5254 0.4730 0.736 0.000 0.264
#> GSM447626 3 0.8938 0.0609 0.432 0.124 0.444
#> GSM447640 2 0.0000 0.7351 0.000 1.000 0.000
#> GSM447734 1 0.7487 0.0933 0.500 0.036 0.464
#> GSM447692 1 0.7274 0.3395 0.644 0.052 0.304
#> GSM447647 2 0.0592 0.7340 0.000 0.988 0.012
#> GSM447624 1 0.4121 0.5402 0.832 0.000 0.168
#> GSM447625 3 0.8069 -0.0267 0.460 0.064 0.476
#> GSM447707 2 0.0592 0.7340 0.000 0.988 0.012
#> GSM447732 3 0.8069 -0.0393 0.460 0.064 0.476
#> GSM447684 3 0.9850 0.2660 0.252 0.356 0.392
#> GSM447731 3 0.6906 0.3623 0.192 0.084 0.724
#> GSM447705 2 0.7665 0.4087 0.060 0.600 0.340
#> GSM447631 1 0.6267 0.2319 0.548 0.000 0.452
#> GSM447701 2 0.0000 0.7351 0.000 1.000 0.000
#> GSM447645 1 0.6267 0.2319 0.548 0.000 0.452
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM447671 2 0.9585 0.09975 0.160 0.392 0.204 0.244
#> GSM447694 3 0.6542 0.38730 0.168 0.000 0.636 0.196
#> GSM447618 4 0.9647 0.22563 0.140 0.260 0.244 0.356
#> GSM447691 2 0.7640 0.41858 0.236 0.536 0.012 0.216
#> GSM447733 3 0.6398 0.15541 0.032 0.020 0.540 0.408
#> GSM447620 2 0.9698 0.17308 0.196 0.380 0.220 0.204
#> GSM447627 3 0.6491 0.18963 0.072 0.000 0.496 0.432
#> GSM447630 3 0.8940 0.15551 0.120 0.256 0.476 0.148
#> GSM447642 1 0.4153 0.69192 0.820 0.000 0.132 0.048
#> GSM447649 2 0.2530 0.58992 0.000 0.888 0.000 0.112
#> GSM447654 4 0.5260 0.26334 0.004 0.424 0.004 0.568
#> GSM447655 2 0.0592 0.63509 0.000 0.984 0.000 0.016
#> GSM447669 3 0.9533 -0.00587 0.148 0.328 0.348 0.176
#> GSM447676 1 0.4739 0.68808 0.788 0.008 0.160 0.044
#> GSM447678 4 0.9420 0.21328 0.128 0.336 0.180 0.356
#> GSM447681 2 0.0336 0.63802 0.000 0.992 0.000 0.008
#> GSM447698 2 0.9513 -0.24174 0.136 0.340 0.188 0.336
#> GSM447713 1 0.3726 0.71167 0.788 0.000 0.212 0.000
#> GSM447722 4 0.9607 0.22508 0.136 0.308 0.216 0.340
#> GSM447726 3 0.9804 0.07120 0.248 0.236 0.340 0.176
#> GSM447735 4 0.6055 -0.12812 0.044 0.000 0.436 0.520
#> GSM447737 1 0.5110 0.57602 0.636 0.000 0.352 0.012
#> GSM447657 2 0.0707 0.63965 0.000 0.980 0.000 0.020
#> GSM447674 2 0.0707 0.63965 0.000 0.980 0.000 0.020
#> GSM447636 1 0.4153 0.69192 0.820 0.000 0.132 0.048
#> GSM447723 1 0.8216 0.07870 0.532 0.236 0.052 0.180
#> GSM447699 3 0.8696 0.08825 0.112 0.104 0.452 0.332
#> GSM447708 2 0.6511 0.54817 0.128 0.676 0.016 0.180
#> GSM447721 1 0.3831 0.71532 0.792 0.000 0.204 0.004
#> GSM447623 1 0.5018 0.60186 0.656 0.000 0.332 0.012
#> GSM447621 1 0.5018 0.60186 0.656 0.000 0.332 0.012
#> GSM447650 2 0.0376 0.63826 0.000 0.992 0.004 0.004
#> GSM447651 2 0.5274 0.58401 0.152 0.768 0.016 0.064
#> GSM447653 4 0.5771 -0.06252 0.028 0.000 0.460 0.512
#> GSM447658 1 0.4206 0.69350 0.816 0.000 0.136 0.048
#> GSM447675 4 0.5474 0.42088 0.004 0.292 0.032 0.672
#> GSM447680 2 0.5914 0.54737 0.228 0.692 0.008 0.072
#> GSM447686 2 0.8078 0.34823 0.292 0.476 0.020 0.212
#> GSM447736 3 0.3561 0.52192 0.012 0.012 0.856 0.120
#> GSM447629 2 0.7663 0.41428 0.240 0.532 0.012 0.216
#> GSM447648 3 0.4418 0.40518 0.184 0.000 0.784 0.032
#> GSM447660 1 0.7146 0.50171 0.672 0.092 0.112 0.124
#> GSM447661 2 0.0376 0.63826 0.000 0.992 0.004 0.004
#> GSM447663 3 0.3769 0.53616 0.012 0.052 0.864 0.072
#> GSM447704 2 0.2530 0.58992 0.000 0.888 0.000 0.112
#> GSM447720 3 0.3474 0.53518 0.012 0.024 0.872 0.092
#> GSM447652 2 0.6452 0.48803 0.044 0.692 0.068 0.196
#> GSM447679 2 0.1118 0.63860 0.000 0.964 0.000 0.036
#> GSM447712 1 0.3831 0.71532 0.792 0.000 0.204 0.004
#> GSM447664 2 0.6766 -0.03206 0.056 0.496 0.016 0.432
#> GSM447637 3 0.2335 0.52880 0.020 0.000 0.920 0.060
#> GSM447639 3 0.6837 0.06948 0.024 0.048 0.464 0.464
#> GSM447615 3 0.5587 0.11290 0.372 0.000 0.600 0.028
#> GSM447656 2 0.6865 0.49180 0.236 0.608 0.004 0.152
#> GSM447673 2 0.2647 0.59333 0.000 0.880 0.000 0.120
#> GSM447719 4 0.5771 -0.06252 0.028 0.000 0.460 0.512
#> GSM447706 3 0.2300 0.53304 0.028 0.000 0.924 0.048
#> GSM447612 3 0.8631 0.20906 0.112 0.156 0.528 0.204
#> GSM447665 2 0.9477 0.17610 0.164 0.420 0.200 0.216
#> GSM447677 2 0.5604 0.57473 0.172 0.744 0.020 0.064
#> GSM447613 1 0.3688 0.71383 0.792 0.000 0.208 0.000
#> GSM447659 3 0.5244 0.16838 0.008 0.000 0.556 0.436
#> GSM447662 3 0.3319 0.53836 0.016 0.036 0.888 0.060
#> GSM447666 3 0.9679 0.06045 0.196 0.256 0.372 0.176
#> GSM447668 2 0.0376 0.63826 0.000 0.992 0.004 0.004
#> GSM447682 2 0.5331 0.59422 0.120 0.764 0.008 0.108
#> GSM447683 2 0.5041 0.60053 0.116 0.784 0.008 0.092
#> GSM447688 2 0.3074 0.55847 0.000 0.848 0.000 0.152
#> GSM447702 2 0.0336 0.63632 0.000 0.992 0.000 0.008
#> GSM447709 2 0.9647 0.19109 0.196 0.392 0.208 0.204
#> GSM447711 1 0.3831 0.71532 0.792 0.000 0.204 0.004
#> GSM447715 1 0.8216 0.07870 0.532 0.236 0.052 0.180
#> GSM447693 3 0.2335 0.52880 0.020 0.000 0.920 0.060
#> GSM447611 4 0.5769 0.41085 0.036 0.284 0.012 0.668
#> GSM447672 2 0.0707 0.63431 0.000 0.980 0.000 0.020
#> GSM447703 2 0.3074 0.55847 0.000 0.848 0.000 0.152
#> GSM447727 1 0.7809 0.22056 0.588 0.192 0.052 0.168
#> GSM447638 1 0.8371 0.23355 0.564 0.156 0.160 0.120
#> GSM447670 3 0.5738 -0.16005 0.432 0.000 0.540 0.028
#> GSM447700 4 0.9636 0.21092 0.140 0.240 0.260 0.360
#> GSM447738 2 0.2647 0.58993 0.000 0.880 0.000 0.120
#> GSM447739 1 0.3688 0.71277 0.792 0.000 0.208 0.000
#> GSM447617 1 0.5038 0.59645 0.652 0.000 0.336 0.012
#> GSM447628 4 0.4948 0.23834 0.000 0.440 0.000 0.560
#> GSM447632 2 0.2530 0.59708 0.000 0.888 0.000 0.112
#> GSM447619 3 0.3319 0.53836 0.016 0.036 0.888 0.060
#> GSM447643 1 0.7452 0.45305 0.644 0.124 0.084 0.148
#> GSM447724 3 0.8970 -0.08171 0.112 0.128 0.388 0.372
#> GSM447728 2 0.5990 0.57787 0.128 0.724 0.016 0.132
#> GSM447610 1 0.7745 0.22134 0.420 0.000 0.340 0.240
#> GSM447633 2 0.9477 0.17610 0.164 0.420 0.200 0.216
#> GSM447634 3 0.8571 0.26052 0.120 0.140 0.536 0.204
#> GSM447622 3 0.5428 -0.00685 0.380 0.000 0.600 0.020
#> GSM447667 2 0.8033 0.37356 0.280 0.488 0.020 0.212
#> GSM447687 2 0.3074 0.55847 0.000 0.848 0.000 0.152
#> GSM447695 3 0.7313 0.08269 0.316 0.000 0.508 0.176
#> GSM447696 1 0.3688 0.71277 0.792 0.000 0.208 0.000
#> GSM447697 1 0.3764 0.70901 0.784 0.000 0.216 0.000
#> GSM447714 3 0.2353 0.54461 0.008 0.024 0.928 0.040
#> GSM447717 1 0.4153 0.69192 0.820 0.000 0.132 0.048
#> GSM447725 1 0.4809 0.70770 0.752 0.012 0.220 0.016
#> GSM447729 4 0.5755 0.40393 0.032 0.296 0.012 0.660
#> GSM447644 3 0.9533 -0.00587 0.148 0.328 0.348 0.176
#> GSM447710 3 0.2353 0.54461 0.008 0.024 0.928 0.040
#> GSM447614 1 0.7745 0.22134 0.420 0.000 0.340 0.240
#> GSM447685 2 0.5433 0.59070 0.152 0.752 0.008 0.088
#> GSM447690 1 0.3726 0.71167 0.788 0.000 0.212 0.000
#> GSM447730 2 0.2530 0.58992 0.000 0.888 0.000 0.112
#> GSM447646 4 0.4948 0.23834 0.000 0.440 0.000 0.560
#> GSM447689 3 0.8500 0.26260 0.184 0.148 0.548 0.120
#> GSM447635 2 0.8551 0.35687 0.220 0.488 0.056 0.236
#> GSM447641 1 0.4206 0.69350 0.816 0.000 0.136 0.048
#> GSM447716 2 0.8802 0.26981 0.288 0.436 0.060 0.216
#> GSM447718 3 0.9092 0.15872 0.136 0.204 0.476 0.184
#> GSM447616 3 0.5428 -0.00685 0.380 0.000 0.600 0.020
#> GSM447626 3 0.5713 0.48578 0.044 0.068 0.760 0.128
#> GSM447640 2 0.1398 0.63776 0.000 0.956 0.004 0.040
#> GSM447734 3 0.3134 0.53806 0.004 0.024 0.884 0.088
#> GSM447692 3 0.7327 0.07209 0.320 0.000 0.504 0.176
#> GSM447647 2 0.2530 0.58992 0.000 0.888 0.000 0.112
#> GSM447624 3 0.5406 -0.28721 0.480 0.000 0.508 0.012
#> GSM447625 3 0.3902 0.53388 0.024 0.036 0.860 0.080
#> GSM447707 2 0.2530 0.58992 0.000 0.888 0.000 0.112
#> GSM447732 3 0.3687 0.53713 0.012 0.048 0.868 0.072
#> GSM447684 3 0.9512 0.15984 0.260 0.180 0.400 0.160
#> GSM447731 4 0.6789 0.05409 0.016 0.060 0.404 0.520
#> GSM447705 2 0.9665 0.18542 0.196 0.388 0.212 0.204
#> GSM447631 3 0.2335 0.52880 0.020 0.000 0.920 0.060
#> GSM447701 2 0.0376 0.63826 0.000 0.992 0.004 0.004
#> GSM447645 3 0.2335 0.52880 0.020 0.000 0.920 0.060
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM447671 5 0.5831 0.51148 0.000 0.108 0.108 0.084 0.700
#> GSM447694 3 0.7240 0.44294 0.204 0.000 0.548 0.152 0.096
#> GSM447618 5 0.7027 0.34779 0.000 0.076 0.140 0.224 0.560
#> GSM447691 5 0.6501 0.27908 0.080 0.272 0.000 0.064 0.584
#> GSM447733 3 0.6650 0.11262 0.008 0.004 0.448 0.392 0.148
#> GSM447620 5 0.4809 0.51608 0.004 0.080 0.116 0.028 0.772
#> GSM447627 3 0.7492 0.13606 0.088 0.000 0.412 0.376 0.124
#> GSM447630 5 0.6654 0.26378 0.008 0.076 0.356 0.040 0.520
#> GSM447642 1 0.3088 0.70384 0.828 0.000 0.004 0.004 0.164
#> GSM447649 2 0.4078 0.62568 0.000 0.784 0.000 0.148 0.068
#> GSM447654 4 0.5575 0.46968 0.000 0.280 0.000 0.612 0.108
#> GSM447655 2 0.2189 0.70580 0.000 0.904 0.000 0.012 0.084
#> GSM447669 5 0.6353 0.46225 0.000 0.104 0.236 0.048 0.612
#> GSM447676 1 0.3927 0.69793 0.792 0.000 0.040 0.004 0.164
#> GSM447678 5 0.7566 0.16907 0.000 0.124 0.104 0.320 0.452
#> GSM447681 2 0.2233 0.70685 0.000 0.892 0.000 0.004 0.104
#> GSM447698 5 0.7492 0.26677 0.000 0.124 0.108 0.280 0.488
#> GSM447713 1 0.0510 0.75421 0.984 0.000 0.016 0.000 0.000
#> GSM447722 5 0.7347 0.26818 0.000 0.092 0.124 0.284 0.500
#> GSM447726 5 0.5831 0.41867 0.052 0.028 0.244 0.016 0.660
#> GSM447735 4 0.7224 -0.07331 0.060 0.000 0.356 0.452 0.132
#> GSM447737 1 0.3750 0.61072 0.756 0.000 0.232 0.012 0.000
#> GSM447657 2 0.2753 0.69955 0.000 0.856 0.000 0.008 0.136
#> GSM447674 2 0.2753 0.69955 0.000 0.856 0.000 0.008 0.136
#> GSM447636 1 0.3088 0.70384 0.828 0.000 0.004 0.004 0.164
#> GSM447723 5 0.6117 0.27020 0.340 0.052 0.016 0.020 0.572
#> GSM447699 5 0.7057 0.05367 0.004 0.012 0.328 0.220 0.436
#> GSM447708 5 0.5730 -0.02388 0.008 0.416 0.000 0.064 0.512
#> GSM447721 1 0.0912 0.75613 0.972 0.000 0.012 0.000 0.016
#> GSM447623 1 0.3398 0.63413 0.780 0.000 0.216 0.004 0.000
#> GSM447621 1 0.3398 0.63413 0.780 0.000 0.216 0.004 0.000
#> GSM447650 2 0.2753 0.69324 0.000 0.856 0.000 0.008 0.136
#> GSM447651 2 0.4930 0.22090 0.004 0.528 0.004 0.012 0.452
#> GSM447653 4 0.4951 0.14925 0.012 0.000 0.420 0.556 0.012
#> GSM447658 1 0.3047 0.70646 0.832 0.000 0.004 0.004 0.160
#> GSM447675 4 0.5237 0.54277 0.000 0.160 0.000 0.684 0.156
#> GSM447680 5 0.5317 -0.08159 0.028 0.448 0.000 0.012 0.512
#> GSM447686 5 0.7127 0.30987 0.124 0.252 0.000 0.084 0.540
#> GSM447736 3 0.4889 0.64857 0.016 0.000 0.748 0.108 0.128
#> GSM447629 5 0.6549 0.27975 0.084 0.272 0.000 0.064 0.580
#> GSM447648 3 0.3875 0.54706 0.228 0.000 0.756 0.004 0.012
#> GSM447660 1 0.5041 0.40224 0.604 0.004 0.008 0.020 0.364
#> GSM447661 2 0.2753 0.69324 0.000 0.856 0.000 0.008 0.136
#> GSM447663 3 0.4721 0.64385 0.012 0.008 0.744 0.040 0.196
#> GSM447704 2 0.4078 0.62568 0.000 0.784 0.000 0.148 0.068
#> GSM447720 3 0.4876 0.66587 0.016 0.004 0.752 0.076 0.152
#> GSM447652 2 0.6879 0.30290 0.000 0.480 0.024 0.168 0.328
#> GSM447679 2 0.2605 0.68673 0.000 0.852 0.000 0.000 0.148
#> GSM447712 1 0.1444 0.75525 0.948 0.000 0.012 0.000 0.040
#> GSM447664 4 0.7274 0.16689 0.024 0.336 0.004 0.424 0.212
#> GSM447637 3 0.1300 0.66468 0.028 0.000 0.956 0.016 0.000
#> GSM447639 4 0.7309 -0.11287 0.012 0.008 0.340 0.380 0.260
#> GSM447615 3 0.5773 -0.02325 0.436 0.000 0.476 0.000 0.088
#> GSM447656 5 0.6490 0.12998 0.084 0.356 0.000 0.040 0.520
#> GSM447673 2 0.4390 0.62413 0.000 0.760 0.000 0.156 0.084
#> GSM447719 4 0.4951 0.14925 0.012 0.000 0.420 0.556 0.012
#> GSM447706 3 0.1806 0.66691 0.016 0.000 0.940 0.016 0.028
#> GSM447612 5 0.7233 0.02039 0.004 0.052 0.384 0.124 0.436
#> GSM447665 5 0.5204 0.51879 0.000 0.132 0.096 0.036 0.736
#> GSM447677 2 0.4706 0.14452 0.004 0.500 0.000 0.008 0.488
#> GSM447613 1 0.0898 0.75558 0.972 0.000 0.020 0.000 0.008
#> GSM447659 3 0.6208 0.12471 0.008 0.000 0.468 0.416 0.108
#> GSM447662 3 0.3264 0.67094 0.008 0.004 0.852 0.020 0.116
#> GSM447666 5 0.4887 0.38360 0.004 0.012 0.300 0.020 0.664
#> GSM447668 2 0.2753 0.69324 0.000 0.856 0.000 0.008 0.136
#> GSM447682 2 0.4922 0.26284 0.004 0.552 0.000 0.020 0.424
#> GSM447683 2 0.4645 0.28550 0.004 0.564 0.000 0.008 0.424
#> GSM447688 2 0.4581 0.56357 0.000 0.732 0.000 0.196 0.072
#> GSM447702 2 0.2389 0.70351 0.000 0.880 0.000 0.004 0.116
#> GSM447709 5 0.4827 0.51563 0.004 0.092 0.104 0.028 0.772
#> GSM447711 1 0.1106 0.75634 0.964 0.000 0.012 0.000 0.024
#> GSM447715 5 0.6117 0.27020 0.340 0.052 0.016 0.020 0.572
#> GSM447693 3 0.1300 0.66468 0.028 0.000 0.956 0.016 0.000
#> GSM447611 4 0.5697 0.54623 0.024 0.156 0.000 0.680 0.140
#> GSM447672 2 0.2069 0.70411 0.000 0.912 0.000 0.012 0.076
#> GSM447703 2 0.4581 0.56357 0.000 0.732 0.000 0.196 0.072
#> GSM447727 5 0.5808 0.14913 0.392 0.036 0.020 0.008 0.544
#> GSM447638 5 0.6467 0.01479 0.400 0.008 0.100 0.012 0.480
#> GSM447670 1 0.5504 0.11055 0.488 0.000 0.448 0.000 0.064
#> GSM447700 5 0.6955 0.32784 0.000 0.056 0.164 0.224 0.556
#> GSM447738 2 0.4138 0.62652 0.000 0.780 0.000 0.148 0.072
#> GSM447739 1 0.0404 0.75425 0.988 0.000 0.012 0.000 0.000
#> GSM447617 1 0.3300 0.64388 0.792 0.000 0.204 0.004 0.000
#> GSM447628 4 0.5433 0.45312 0.000 0.288 0.000 0.620 0.092
#> GSM447632 2 0.4127 0.63773 0.000 0.784 0.000 0.136 0.080
#> GSM447619 3 0.3264 0.67094 0.008 0.004 0.852 0.020 0.116
#> GSM447643 1 0.5092 0.29790 0.556 0.012 0.004 0.012 0.416
#> GSM447724 5 0.7463 -0.01014 0.000 0.032 0.300 0.304 0.364
#> GSM447728 5 0.5299 -0.14088 0.008 0.464 0.000 0.032 0.496
#> GSM447610 1 0.7104 0.41868 0.568 0.000 0.132 0.196 0.104
#> GSM447633 5 0.5204 0.51879 0.000 0.132 0.096 0.036 0.736
#> GSM447634 5 0.6547 -0.00592 0.016 0.012 0.420 0.088 0.464
#> GSM447622 3 0.4971 -0.02927 0.472 0.000 0.504 0.004 0.020
#> GSM447667 5 0.6452 0.32716 0.092 0.240 0.004 0.052 0.612
#> GSM447687 2 0.4581 0.56357 0.000 0.732 0.000 0.196 0.072
#> GSM447695 1 0.7593 0.03716 0.432 0.000 0.336 0.144 0.088
#> GSM447696 1 0.0404 0.75425 0.988 0.000 0.012 0.000 0.000
#> GSM447697 1 0.0609 0.75281 0.980 0.000 0.020 0.000 0.000
#> GSM447714 3 0.3951 0.68177 0.016 0.000 0.812 0.044 0.128
#> GSM447717 1 0.2970 0.70480 0.828 0.000 0.004 0.000 0.168
#> GSM447725 1 0.2100 0.75287 0.924 0.000 0.016 0.012 0.048
#> GSM447729 4 0.5679 0.54961 0.020 0.168 0.000 0.676 0.136
#> GSM447644 5 0.6353 0.46225 0.000 0.104 0.236 0.048 0.612
#> GSM447710 3 0.3951 0.68177 0.016 0.000 0.812 0.044 0.128
#> GSM447614 1 0.7104 0.41868 0.568 0.000 0.132 0.196 0.104
#> GSM447685 2 0.5156 0.21921 0.020 0.528 0.000 0.012 0.440
#> GSM447690 1 0.0510 0.75421 0.984 0.000 0.016 0.000 0.000
#> GSM447730 2 0.3950 0.63352 0.000 0.796 0.000 0.136 0.068
#> GSM447646 4 0.5433 0.45312 0.000 0.288 0.000 0.620 0.092
#> GSM447689 5 0.5545 0.02332 0.008 0.008 0.448 0.032 0.504
#> GSM447635 5 0.7225 0.35399 0.080 0.236 0.028 0.080 0.576
#> GSM447641 1 0.3047 0.70646 0.832 0.000 0.004 0.004 0.160
#> GSM447716 5 0.7736 0.38634 0.112 0.200 0.036 0.100 0.552
#> GSM447718 5 0.6053 0.27006 0.008 0.024 0.356 0.052 0.560
#> GSM447616 3 0.4971 -0.02927 0.472 0.000 0.504 0.004 0.020
#> GSM447626 3 0.4777 0.48931 0.012 0.004 0.672 0.016 0.296
#> GSM447640 2 0.2773 0.67588 0.000 0.836 0.000 0.000 0.164
#> GSM447734 3 0.4711 0.67198 0.016 0.004 0.768 0.076 0.136
#> GSM447692 1 0.7586 0.05044 0.436 0.000 0.332 0.144 0.088
#> GSM447647 2 0.4078 0.62568 0.000 0.784 0.000 0.148 0.068
#> GSM447624 1 0.4210 0.29197 0.588 0.000 0.412 0.000 0.000
#> GSM447625 3 0.4972 0.64662 0.016 0.004 0.728 0.056 0.196
#> GSM447707 2 0.3950 0.63352 0.000 0.796 0.000 0.136 0.068
#> GSM447732 3 0.4634 0.64602 0.012 0.004 0.744 0.040 0.200
#> GSM447684 5 0.6384 0.32995 0.088 0.020 0.308 0.012 0.572
#> GSM447731 4 0.5829 0.25336 0.004 0.044 0.360 0.568 0.024
#> GSM447705 5 0.4823 0.51600 0.004 0.088 0.108 0.028 0.772
#> GSM447631 3 0.1300 0.66468 0.028 0.000 0.956 0.016 0.000
#> GSM447701 2 0.2753 0.69324 0.000 0.856 0.000 0.008 0.136
#> GSM447645 3 0.1300 0.66468 0.028 0.000 0.956 0.016 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM447671 5 0.5745 0.25847 0.000 0.060 0.040 0.188 0.664 0.048
#> GSM447694 3 0.6336 0.31159 0.200 0.000 0.488 0.288 0.016 0.008
#> GSM447618 5 0.6227 -0.23092 0.000 0.068 0.036 0.432 0.440 0.024
#> GSM447691 5 0.5195 0.38531 0.004 0.252 0.000 0.052 0.652 0.040
#> GSM447733 4 0.6299 0.31298 0.004 0.012 0.232 0.592 0.080 0.080
#> GSM447620 5 0.6664 0.33697 0.000 0.060 0.124 0.056 0.596 0.164
#> GSM447627 4 0.5906 0.23425 0.084 0.000 0.252 0.608 0.028 0.028
#> GSM447630 5 0.6946 0.11086 0.008 0.052 0.324 0.112 0.480 0.024
#> GSM447642 1 0.4171 0.62821 0.732 0.000 0.000 0.008 0.208 0.052
#> GSM447649 2 0.1442 0.61939 0.000 0.944 0.000 0.004 0.040 0.012
#> GSM447654 2 0.6795 -0.08222 0.000 0.424 0.000 0.284 0.052 0.240
#> GSM447655 2 0.4215 0.57055 0.000 0.700 0.000 0.000 0.056 0.244
#> GSM447669 5 0.6793 0.25847 0.000 0.072 0.184 0.140 0.568 0.036
#> GSM447676 1 0.4899 0.62057 0.696 0.000 0.032 0.008 0.216 0.048
#> GSM447678 4 0.6545 0.27542 0.000 0.136 0.004 0.456 0.352 0.052
#> GSM447681 2 0.4495 0.56542 0.000 0.672 0.000 0.000 0.072 0.256
#> GSM447698 4 0.6420 0.22686 0.000 0.120 0.004 0.444 0.384 0.048
#> GSM447713 1 0.0146 0.72616 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM447722 4 0.6424 0.25236 0.000 0.092 0.020 0.460 0.388 0.040
#> GSM447726 5 0.6586 0.25665 0.024 0.008 0.268 0.024 0.536 0.140
#> GSM447735 4 0.4948 0.36691 0.060 0.000 0.192 0.708 0.020 0.020
#> GSM447737 1 0.3610 0.62079 0.768 0.000 0.200 0.028 0.000 0.004
#> GSM447657 2 0.4989 0.55500 0.000 0.640 0.000 0.004 0.108 0.248
#> GSM447674 2 0.4989 0.55500 0.000 0.640 0.000 0.004 0.108 0.248
#> GSM447636 1 0.4171 0.62821 0.732 0.000 0.000 0.008 0.208 0.052
#> GSM447723 5 0.5427 0.29465 0.252 0.044 0.012 0.024 0.652 0.016
#> GSM447699 4 0.6434 0.31813 0.000 0.016 0.212 0.460 0.304 0.008
#> GSM447708 5 0.6249 0.23892 0.000 0.344 0.004 0.052 0.500 0.100
#> GSM447721 1 0.0622 0.72650 0.980 0.000 0.000 0.000 0.008 0.012
#> GSM447623 1 0.3219 0.63640 0.792 0.000 0.192 0.012 0.000 0.004
#> GSM447621 1 0.3219 0.63640 0.792 0.000 0.192 0.012 0.000 0.004
#> GSM447650 2 0.4887 0.53752 0.000 0.624 0.000 0.000 0.096 0.280
#> GSM447651 6 0.6232 -0.13166 0.000 0.316 0.004 0.000 0.308 0.372
#> GSM447653 6 0.6146 0.27902 0.004 0.000 0.204 0.372 0.004 0.416
#> GSM447658 1 0.4143 0.63144 0.736 0.000 0.000 0.008 0.204 0.052
#> GSM447675 4 0.6871 0.18729 0.000 0.296 0.000 0.424 0.064 0.216
#> GSM447680 5 0.6043 -0.10215 0.000 0.252 0.000 0.000 0.384 0.364
#> GSM447686 5 0.5991 0.35136 0.024 0.236 0.000 0.064 0.616 0.060
#> GSM447736 3 0.4959 0.64612 0.012 0.000 0.684 0.220 0.072 0.012
#> GSM447629 5 0.5256 0.38323 0.004 0.252 0.000 0.052 0.648 0.044
#> GSM447648 3 0.4180 0.52187 0.224 0.000 0.732 0.016 0.012 0.016
#> GSM447660 1 0.5338 0.27811 0.508 0.000 0.000 0.020 0.412 0.060
#> GSM447661 2 0.4887 0.53752 0.000 0.624 0.000 0.000 0.096 0.280
#> GSM447663 3 0.4779 0.68590 0.008 0.004 0.724 0.108 0.148 0.008
#> GSM447704 2 0.1442 0.61939 0.000 0.944 0.000 0.004 0.040 0.012
#> GSM447720 3 0.4731 0.67820 0.012 0.000 0.708 0.188 0.088 0.004
#> GSM447652 2 0.6896 0.16995 0.000 0.496 0.012 0.096 0.276 0.120
#> GSM447679 2 0.4989 0.53348 0.000 0.628 0.000 0.000 0.120 0.252
#> GSM447712 1 0.1528 0.72252 0.936 0.000 0.000 0.000 0.048 0.016
#> GSM447664 2 0.7299 -0.04156 0.000 0.416 0.000 0.252 0.156 0.176
#> GSM447637 3 0.1542 0.69901 0.024 0.000 0.944 0.016 0.000 0.016
#> GSM447639 4 0.5736 0.34050 0.008 0.012 0.240 0.612 0.120 0.008
#> GSM447615 3 0.5619 -0.11404 0.424 0.000 0.476 0.000 0.072 0.028
#> GSM447656 5 0.5480 0.31898 0.012 0.296 0.000 0.016 0.600 0.076
#> GSM447673 2 0.2340 0.61018 0.000 0.900 0.000 0.024 0.060 0.016
#> GSM447719 6 0.6143 0.28222 0.004 0.000 0.204 0.368 0.004 0.420
#> GSM447706 3 0.1533 0.70097 0.016 0.000 0.948 0.008 0.012 0.016
#> GSM447612 5 0.7143 -0.22996 0.000 0.048 0.252 0.332 0.356 0.012
#> GSM447665 5 0.5656 0.33020 0.000 0.096 0.044 0.124 0.692 0.044
#> GSM447677 6 0.6329 -0.11309 0.000 0.304 0.008 0.000 0.328 0.360
#> GSM447613 1 0.0665 0.72830 0.980 0.000 0.008 0.000 0.008 0.004
#> GSM447659 4 0.5466 0.28179 0.004 0.000 0.236 0.640 0.040 0.080
#> GSM447662 3 0.2958 0.70939 0.004 0.000 0.860 0.028 0.096 0.012
#> GSM447666 5 0.5879 0.19825 0.000 0.000 0.332 0.008 0.492 0.168
#> GSM447668 2 0.4887 0.53752 0.000 0.624 0.000 0.000 0.096 0.280
#> GSM447682 5 0.6048 -0.00885 0.000 0.400 0.000 0.012 0.420 0.168
#> GSM447683 5 0.5927 -0.03974 0.000 0.396 0.000 0.004 0.420 0.180
#> GSM447688 2 0.2453 0.58629 0.000 0.896 0.000 0.016 0.044 0.044
#> GSM447702 2 0.4606 0.55800 0.000 0.656 0.000 0.000 0.076 0.268
#> GSM447709 5 0.6684 0.34017 0.000 0.068 0.112 0.056 0.596 0.168
#> GSM447711 1 0.1074 0.72554 0.960 0.000 0.000 0.000 0.028 0.012
#> GSM447715 5 0.5427 0.29465 0.252 0.044 0.012 0.024 0.652 0.016
#> GSM447693 3 0.1542 0.69901 0.024 0.000 0.944 0.016 0.000 0.016
#> GSM447611 4 0.7198 0.15117 0.004 0.288 0.000 0.384 0.076 0.248
#> GSM447672 2 0.4099 0.57177 0.000 0.708 0.000 0.000 0.048 0.244
#> GSM447703 2 0.2453 0.58629 0.000 0.896 0.000 0.016 0.044 0.044
#> GSM447727 5 0.5459 0.21659 0.312 0.024 0.016 0.020 0.608 0.020
#> GSM447638 5 0.7087 0.01598 0.340 0.004 0.112 0.004 0.424 0.116
#> GSM447670 1 0.4927 0.19842 0.496 0.000 0.452 0.000 0.044 0.008
#> GSM447700 4 0.6090 0.19320 0.000 0.048 0.048 0.452 0.432 0.020
#> GSM447738 2 0.2036 0.61933 0.000 0.916 0.000 0.008 0.048 0.028
#> GSM447739 1 0.0000 0.72613 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447617 1 0.3121 0.64504 0.804 0.000 0.180 0.012 0.000 0.004
#> GSM447628 2 0.6759 -0.05258 0.000 0.436 0.000 0.280 0.052 0.232
#> GSM447632 2 0.1889 0.61901 0.000 0.920 0.000 0.004 0.056 0.020
#> GSM447619 3 0.2958 0.70939 0.004 0.000 0.860 0.028 0.096 0.012
#> GSM447643 5 0.5561 -0.19325 0.456 0.012 0.000 0.028 0.464 0.040
#> GSM447724 4 0.6260 0.43369 0.000 0.056 0.160 0.552 0.232 0.000
#> GSM447728 5 0.5731 0.16748 0.000 0.388 0.004 0.012 0.492 0.104
#> GSM447610 1 0.6621 0.40683 0.556 0.000 0.072 0.260 0.044 0.068
#> GSM447633 5 0.5656 0.33020 0.000 0.096 0.044 0.124 0.692 0.044
#> GSM447634 5 0.6596 -0.15075 0.012 0.004 0.364 0.220 0.392 0.008
#> GSM447622 1 0.4949 0.14881 0.484 0.000 0.472 0.024 0.008 0.012
#> GSM447667 5 0.5119 0.39528 0.008 0.220 0.000 0.048 0.680 0.044
#> GSM447687 2 0.2453 0.58629 0.000 0.896 0.000 0.016 0.044 0.044
#> GSM447695 1 0.6458 0.19044 0.444 0.000 0.284 0.252 0.012 0.008
#> GSM447696 1 0.0000 0.72613 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447697 1 0.0260 0.72718 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM447714 3 0.4172 0.71118 0.012 0.000 0.776 0.128 0.076 0.008
#> GSM447717 1 0.4171 0.62940 0.732 0.000 0.000 0.008 0.208 0.052
#> GSM447725 1 0.2201 0.71592 0.896 0.000 0.000 0.000 0.076 0.028
#> GSM447729 4 0.7092 0.14138 0.000 0.300 0.000 0.376 0.076 0.248
#> GSM447644 5 0.6793 0.25847 0.000 0.072 0.184 0.140 0.568 0.036
#> GSM447710 3 0.4172 0.71118 0.012 0.000 0.776 0.128 0.076 0.008
#> GSM447614 1 0.6621 0.40683 0.556 0.000 0.072 0.260 0.044 0.068
#> GSM447685 5 0.5956 0.02081 0.000 0.360 0.000 0.004 0.444 0.192
#> GSM447690 1 0.0146 0.72616 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM447730 2 0.1633 0.62302 0.000 0.932 0.000 0.000 0.044 0.024
#> GSM447646 2 0.6759 -0.05258 0.000 0.436 0.000 0.280 0.052 0.232
#> GSM447689 3 0.6735 0.21609 0.004 0.000 0.452 0.076 0.340 0.128
#> GSM447635 5 0.6307 0.36601 0.012 0.224 0.028 0.092 0.608 0.036
#> GSM447641 1 0.4143 0.63144 0.736 0.000 0.000 0.008 0.204 0.052
#> GSM447716 5 0.6441 0.30403 0.020 0.192 0.004 0.120 0.600 0.064
#> GSM447718 5 0.6347 0.09682 0.008 0.012 0.328 0.144 0.496 0.012
#> GSM447616 1 0.4949 0.14881 0.484 0.000 0.472 0.024 0.008 0.012
#> GSM447626 3 0.4190 0.59237 0.004 0.000 0.704 0.020 0.260 0.012
#> GSM447640 2 0.5116 0.51731 0.000 0.612 0.000 0.000 0.132 0.256
#> GSM447734 3 0.4822 0.67715 0.012 0.000 0.700 0.188 0.096 0.004
#> GSM447692 1 0.6445 0.19687 0.448 0.000 0.284 0.248 0.012 0.008
#> GSM447647 2 0.1442 0.61939 0.000 0.944 0.000 0.004 0.040 0.012
#> GSM447624 1 0.3881 0.37987 0.600 0.000 0.396 0.004 0.000 0.000
#> GSM447625 3 0.5151 0.66288 0.012 0.000 0.680 0.156 0.144 0.008
#> GSM447707 2 0.1633 0.62302 0.000 0.932 0.000 0.000 0.044 0.024
#> GSM447732 3 0.4676 0.68710 0.008 0.000 0.724 0.108 0.152 0.008
#> GSM447684 5 0.5701 0.20949 0.064 0.000 0.332 0.020 0.564 0.020
#> GSM447731 6 0.6596 0.26507 0.000 0.040 0.152 0.356 0.008 0.444
#> GSM447705 5 0.6692 0.33987 0.000 0.068 0.116 0.056 0.596 0.164
#> GSM447631 3 0.1542 0.69901 0.024 0.000 0.944 0.016 0.000 0.016
#> GSM447701 2 0.4887 0.53752 0.000 0.624 0.000 0.000 0.096 0.280
#> GSM447645 3 0.1542 0.69901 0.024 0.000 0.944 0.016 0.000 0.016
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n gender(p) individual(p) disease.state(p) other(p) k
#> MAD:hclust 109 0.852 0.932 0.2412 0.0204 2
#> MAD:hclust 59 0.913 0.838 0.1407 0.1383 3
#> MAD:hclust 67 0.513 0.364 0.0104 0.2254 4
#> MAD:hclust 68 0.919 0.571 0.0410 0.4963 5
#> MAD:hclust 60 0.667 0.333 0.0100 0.2667 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "kmeans"]
# you can also extract it by
# res = res_list["MAD:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 130 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 0.876 0.928 0.969 0.5038 0.496 0.496
#> 3 3 0.593 0.753 0.798 0.2849 0.805 0.627
#> 4 4 0.575 0.601 0.792 0.1430 0.860 0.629
#> 5 5 0.628 0.567 0.746 0.0730 0.882 0.599
#> 6 6 0.640 0.432 0.650 0.0427 0.898 0.573
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
#> GSM447671 2 0.0000 0.971 0.000 1.000
#> GSM447694 1 0.0000 0.964 1.000 0.000
#> GSM447618 2 0.0000 0.971 0.000 1.000
#> GSM447691 2 0.0000 0.971 0.000 1.000
#> GSM447733 1 0.9248 0.518 0.660 0.340
#> GSM447620 2 0.0000 0.971 0.000 1.000
#> GSM447627 1 0.0000 0.964 1.000 0.000
#> GSM447630 2 0.0672 0.964 0.008 0.992
#> GSM447642 1 0.0000 0.964 1.000 0.000
#> GSM447649 2 0.0000 0.971 0.000 1.000
#> GSM447654 2 0.0000 0.971 0.000 1.000
#> GSM447655 2 0.0000 0.971 0.000 1.000
#> GSM447669 2 0.0000 0.971 0.000 1.000
#> GSM447676 1 0.0000 0.964 1.000 0.000
#> GSM447678 2 0.0000 0.971 0.000 1.000
#> GSM447681 2 0.0000 0.971 0.000 1.000
#> GSM447698 2 0.0000 0.971 0.000 1.000
#> GSM447713 1 0.0000 0.964 1.000 0.000
#> GSM447722 2 0.0000 0.971 0.000 1.000
#> GSM447726 2 0.0000 0.971 0.000 1.000
#> GSM447735 1 0.0000 0.964 1.000 0.000
#> GSM447737 1 0.0000 0.964 1.000 0.000
#> GSM447657 2 0.0000 0.971 0.000 1.000
#> GSM447674 2 0.0000 0.971 0.000 1.000
#> GSM447636 2 0.9710 0.357 0.400 0.600
#> GSM447723 1 0.0000 0.964 1.000 0.000
#> GSM447699 1 0.7219 0.760 0.800 0.200
#> GSM447708 2 0.0000 0.971 0.000 1.000
#> GSM447721 1 0.0000 0.964 1.000 0.000
#> GSM447623 1 0.0000 0.964 1.000 0.000
#> GSM447621 1 0.0000 0.964 1.000 0.000
#> GSM447650 2 0.0000 0.971 0.000 1.000
#> GSM447651 2 0.0000 0.971 0.000 1.000
#> GSM447653 1 0.0000 0.964 1.000 0.000
#> GSM447658 1 0.0000 0.964 1.000 0.000
#> GSM447675 2 0.0000 0.971 0.000 1.000
#> GSM447680 2 0.0000 0.971 0.000 1.000
#> GSM447686 2 0.1633 0.949 0.024 0.976
#> GSM447736 1 0.0000 0.964 1.000 0.000
#> GSM447629 2 0.0000 0.971 0.000 1.000
#> GSM447648 1 0.0000 0.964 1.000 0.000
#> GSM447660 1 0.0000 0.964 1.000 0.000
#> GSM447661 2 0.0000 0.971 0.000 1.000
#> GSM447663 1 0.7219 0.760 0.800 0.200
#> GSM447704 2 0.0000 0.971 0.000 1.000
#> GSM447720 1 0.0000 0.964 1.000 0.000
#> GSM447652 2 0.0000 0.971 0.000 1.000
#> GSM447679 2 0.0000 0.971 0.000 1.000
#> GSM447712 1 0.0000 0.964 1.000 0.000
#> GSM447664 2 0.0000 0.971 0.000 1.000
#> GSM447637 1 0.0000 0.964 1.000 0.000
#> GSM447639 1 0.8813 0.599 0.700 0.300
#> GSM447615 1 0.0000 0.964 1.000 0.000
#> GSM447656 2 0.0000 0.971 0.000 1.000
#> GSM447673 2 0.0000 0.971 0.000 1.000
#> GSM447719 1 0.0000 0.964 1.000 0.000
#> GSM447706 1 0.0000 0.964 1.000 0.000
#> GSM447612 1 0.7219 0.760 0.800 0.200
#> GSM447665 2 0.0000 0.971 0.000 1.000
#> GSM447677 2 0.0000 0.971 0.000 1.000
#> GSM447613 1 0.0000 0.964 1.000 0.000
#> GSM447659 1 0.5629 0.842 0.868 0.132
#> GSM447662 1 0.1184 0.952 0.984 0.016
#> GSM447666 1 0.5294 0.850 0.880 0.120
#> GSM447668 2 0.0000 0.971 0.000 1.000
#> GSM447682 2 0.0000 0.971 0.000 1.000
#> GSM447683 2 0.0000 0.971 0.000 1.000
#> GSM447688 2 0.0000 0.971 0.000 1.000
#> GSM447702 2 0.0000 0.971 0.000 1.000
#> GSM447709 2 0.0000 0.971 0.000 1.000
#> GSM447711 1 0.0000 0.964 1.000 0.000
#> GSM447715 2 0.5842 0.824 0.140 0.860
#> GSM447693 1 0.0000 0.964 1.000 0.000
#> GSM447611 2 0.8661 0.606 0.288 0.712
#> GSM447672 2 0.0000 0.971 0.000 1.000
#> GSM447703 2 0.0000 0.971 0.000 1.000
#> GSM447727 1 0.0000 0.964 1.000 0.000
#> GSM447638 2 0.9129 0.528 0.328 0.672
#> GSM447670 1 0.0000 0.964 1.000 0.000
#> GSM447700 2 0.0000 0.971 0.000 1.000
#> GSM447738 2 0.0000 0.971 0.000 1.000
#> GSM447739 1 0.0000 0.964 1.000 0.000
#> GSM447617 1 0.0000 0.964 1.000 0.000
#> GSM447628 2 0.0000 0.971 0.000 1.000
#> GSM447632 2 0.0000 0.971 0.000 1.000
#> GSM447619 1 0.0000 0.964 1.000 0.000
#> GSM447643 2 0.7528 0.722 0.216 0.784
#> GSM447724 1 0.9896 0.260 0.560 0.440
#> GSM447728 2 0.0000 0.971 0.000 1.000
#> GSM447610 1 0.0000 0.964 1.000 0.000
#> GSM447633 2 0.0000 0.971 0.000 1.000
#> GSM447634 1 0.0000 0.964 1.000 0.000
#> GSM447622 1 0.0000 0.964 1.000 0.000
#> GSM447667 2 0.0000 0.971 0.000 1.000
#> GSM447687 2 0.0000 0.971 0.000 1.000
#> GSM447695 1 0.0000 0.964 1.000 0.000
#> GSM447696 1 0.0000 0.964 1.000 0.000
#> GSM447697 1 0.0000 0.964 1.000 0.000
#> GSM447714 1 0.1184 0.952 0.984 0.016
#> GSM447717 1 0.0000 0.964 1.000 0.000
#> GSM447725 1 0.0000 0.964 1.000 0.000
#> GSM447729 2 0.0000 0.971 0.000 1.000
#> GSM447644 2 0.0000 0.971 0.000 1.000
#> GSM447710 1 0.0000 0.964 1.000 0.000
#> GSM447614 1 0.0000 0.964 1.000 0.000
#> GSM447685 2 0.0000 0.971 0.000 1.000
#> GSM447690 1 0.0000 0.964 1.000 0.000
#> GSM447730 2 0.0000 0.971 0.000 1.000
#> GSM447646 2 0.0000 0.971 0.000 1.000
#> GSM447689 1 0.0000 0.964 1.000 0.000
#> GSM447635 2 0.0000 0.971 0.000 1.000
#> GSM447641 1 0.0000 0.964 1.000 0.000
#> GSM447716 2 0.0000 0.971 0.000 1.000
#> GSM447718 1 0.7056 0.771 0.808 0.192
#> GSM447616 1 0.0000 0.964 1.000 0.000
#> GSM447626 1 0.0000 0.964 1.000 0.000
#> GSM447640 2 0.0000 0.971 0.000 1.000
#> GSM447734 1 0.0000 0.964 1.000 0.000
#> GSM447692 1 0.0000 0.964 1.000 0.000
#> GSM447647 2 0.0000 0.971 0.000 1.000
#> GSM447624 1 0.0000 0.964 1.000 0.000
#> GSM447625 1 0.0000 0.964 1.000 0.000
#> GSM447707 2 0.0000 0.971 0.000 1.000
#> GSM447732 1 0.0000 0.964 1.000 0.000
#> GSM447684 1 0.0000 0.964 1.000 0.000
#> GSM447731 2 0.0000 0.971 0.000 1.000
#> GSM447705 2 0.9754 0.259 0.408 0.592
#> GSM447631 1 0.0000 0.964 1.000 0.000
#> GSM447701 2 0.0000 0.971 0.000 1.000
#> GSM447645 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
#> GSM447671 2 0.5004 0.8311 0.072 0.840 0.088
#> GSM447694 3 0.2165 0.7591 0.064 0.000 0.936
#> GSM447618 2 0.5722 0.8409 0.132 0.800 0.068
#> GSM447691 2 0.5012 0.8313 0.080 0.840 0.080
#> GSM447733 3 0.6880 0.5499 0.304 0.036 0.660
#> GSM447620 2 0.3669 0.8599 0.064 0.896 0.040
#> GSM447627 3 0.2261 0.7649 0.068 0.000 0.932
#> GSM447630 2 0.7413 0.6857 0.104 0.692 0.204
#> GSM447642 1 0.5859 0.8004 0.656 0.000 0.344
#> GSM447649 2 0.0237 0.8789 0.004 0.996 0.000
#> GSM447654 2 0.7364 0.6977 0.304 0.640 0.056
#> GSM447655 2 0.0000 0.8790 0.000 1.000 0.000
#> GSM447669 2 0.5174 0.8265 0.076 0.832 0.092
#> GSM447676 1 0.5678 0.7958 0.684 0.000 0.316
#> GSM447678 2 0.6143 0.7414 0.304 0.684 0.012
#> GSM447681 2 0.0237 0.8789 0.004 0.996 0.000
#> GSM447698 2 0.4121 0.8391 0.168 0.832 0.000
#> GSM447713 1 0.6026 0.7877 0.624 0.000 0.376
#> GSM447722 2 0.8821 0.6157 0.304 0.552 0.144
#> GSM447726 2 0.5319 0.8192 0.104 0.824 0.072
#> GSM447735 3 0.4504 0.7280 0.196 0.000 0.804
#> GSM447737 1 0.6252 0.6926 0.556 0.000 0.444
#> GSM447657 2 0.1753 0.8779 0.048 0.952 0.000
#> GSM447674 2 0.0237 0.8789 0.004 0.996 0.000
#> GSM447636 1 0.6894 0.4959 0.692 0.256 0.052
#> GSM447723 1 0.5760 0.7893 0.672 0.000 0.328
#> GSM447699 3 0.1289 0.7885 0.032 0.000 0.968
#> GSM447708 2 0.2261 0.8703 0.068 0.932 0.000
#> GSM447721 1 0.6008 0.7902 0.628 0.000 0.372
#> GSM447623 1 0.6045 0.7851 0.620 0.000 0.380
#> GSM447621 1 0.6045 0.7851 0.620 0.000 0.380
#> GSM447650 2 0.0000 0.8790 0.000 1.000 0.000
#> GSM447651 2 0.1529 0.8744 0.040 0.960 0.000
#> GSM447653 3 0.5138 0.6846 0.252 0.000 0.748
#> GSM447658 1 0.5465 0.7798 0.712 0.000 0.288
#> GSM447675 2 0.7536 0.6883 0.304 0.632 0.064
#> GSM447680 2 0.2356 0.8643 0.072 0.928 0.000
#> GSM447686 2 0.6451 0.3116 0.436 0.560 0.004
#> GSM447736 3 0.0000 0.7925 0.000 0.000 1.000
#> GSM447629 2 0.2537 0.8710 0.080 0.920 0.000
#> GSM447648 3 0.3619 0.6829 0.136 0.000 0.864
#> GSM447660 1 0.5465 0.7798 0.712 0.000 0.288
#> GSM447661 2 0.0000 0.8790 0.000 1.000 0.000
#> GSM447663 3 0.3528 0.7447 0.092 0.016 0.892
#> GSM447704 2 0.0237 0.8789 0.004 0.996 0.000
#> GSM447720 3 0.3272 0.7380 0.104 0.004 0.892
#> GSM447652 2 0.0592 0.8791 0.012 0.988 0.000
#> GSM447679 2 0.0000 0.8790 0.000 1.000 0.000
#> GSM447712 1 0.5859 0.8004 0.656 0.000 0.344
#> GSM447664 2 0.5785 0.7446 0.300 0.696 0.004
#> GSM447637 3 0.3619 0.6829 0.136 0.000 0.864
#> GSM447639 3 0.5285 0.6400 0.244 0.004 0.752
#> GSM447615 1 0.5859 0.8004 0.656 0.000 0.344
#> GSM447656 2 0.2959 0.8581 0.100 0.900 0.000
#> GSM447673 2 0.3619 0.8402 0.136 0.864 0.000
#> GSM447719 3 0.4796 0.7118 0.220 0.000 0.780
#> GSM447706 3 0.3412 0.7038 0.124 0.000 0.876
#> GSM447612 3 0.2492 0.7781 0.048 0.016 0.936
#> GSM447665 2 0.2663 0.8693 0.044 0.932 0.024
#> GSM447677 2 0.1643 0.8738 0.044 0.956 0.000
#> GSM447613 1 0.5859 0.8004 0.656 0.000 0.344
#> GSM447659 3 0.4504 0.6793 0.196 0.000 0.804
#> GSM447662 3 0.0661 0.7925 0.008 0.004 0.988
#> GSM447666 3 0.7884 0.4436 0.100 0.260 0.640
#> GSM447668 2 0.1643 0.8738 0.044 0.956 0.000
#> GSM447682 2 0.0000 0.8790 0.000 1.000 0.000
#> GSM447683 2 0.1643 0.8738 0.044 0.956 0.000
#> GSM447688 2 0.5327 0.7548 0.272 0.728 0.000
#> GSM447702 2 0.0000 0.8790 0.000 1.000 0.000
#> GSM447709 2 0.1643 0.8738 0.044 0.956 0.000
#> GSM447711 1 0.5859 0.8004 0.656 0.000 0.344
#> GSM447715 1 0.7992 0.3153 0.592 0.328 0.080
#> GSM447693 3 0.2356 0.7527 0.072 0.000 0.928
#> GSM447611 1 0.7481 0.0137 0.640 0.296 0.064
#> GSM447672 2 0.0237 0.8789 0.004 0.996 0.000
#> GSM447703 2 0.3619 0.8402 0.136 0.864 0.000
#> GSM447727 1 0.5621 0.7756 0.692 0.000 0.308
#> GSM447638 1 0.7232 0.1161 0.544 0.428 0.028
#> GSM447670 1 0.5882 0.7991 0.652 0.000 0.348
#> GSM447700 2 0.7710 0.7633 0.176 0.680 0.144
#> GSM447738 2 0.3619 0.8402 0.136 0.864 0.000
#> GSM447739 1 0.6008 0.7902 0.628 0.000 0.372
#> GSM447617 1 0.6045 0.7851 0.620 0.000 0.380
#> GSM447628 2 0.5588 0.7488 0.276 0.720 0.004
#> GSM447632 2 0.3551 0.8420 0.132 0.868 0.000
#> GSM447619 3 0.0475 0.7925 0.004 0.004 0.992
#> GSM447643 1 0.6952 0.2703 0.600 0.376 0.024
#> GSM447724 3 0.5988 0.5770 0.304 0.008 0.688
#> GSM447728 2 0.1529 0.8747 0.040 0.960 0.000
#> GSM447610 1 0.5529 0.6585 0.704 0.000 0.296
#> GSM447633 2 0.5506 0.8119 0.092 0.816 0.092
#> GSM447634 3 0.1129 0.7885 0.020 0.004 0.976
#> GSM447622 3 0.4002 0.6438 0.160 0.000 0.840
#> GSM447667 2 0.2537 0.8726 0.080 0.920 0.000
#> GSM447687 2 0.3619 0.8402 0.136 0.864 0.000
#> GSM447695 3 0.0000 0.7925 0.000 0.000 1.000
#> GSM447696 1 0.6026 0.7877 0.624 0.000 0.376
#> GSM447697 1 0.6026 0.7877 0.624 0.000 0.376
#> GSM447714 3 0.0237 0.7932 0.000 0.004 0.996
#> GSM447717 1 0.5497 0.7806 0.708 0.000 0.292
#> GSM447725 1 0.5650 0.7941 0.688 0.000 0.312
#> GSM447729 2 0.5815 0.7417 0.304 0.692 0.004
#> GSM447644 2 0.5582 0.8116 0.100 0.812 0.088
#> GSM447710 3 0.0237 0.7911 0.004 0.000 0.996
#> GSM447614 3 0.4235 0.7367 0.176 0.000 0.824
#> GSM447685 2 0.1529 0.8747 0.040 0.960 0.000
#> GSM447690 1 0.6008 0.7879 0.628 0.000 0.372
#> GSM447730 2 0.0237 0.8789 0.004 0.996 0.000
#> GSM447646 2 0.5588 0.7488 0.276 0.720 0.004
#> GSM447689 3 0.3272 0.7406 0.104 0.004 0.892
#> GSM447635 2 0.7091 0.7807 0.152 0.724 0.124
#> GSM447641 1 0.5706 0.7961 0.680 0.000 0.320
#> GSM447716 2 0.4178 0.8374 0.172 0.828 0.000
#> GSM447718 3 0.3769 0.7314 0.104 0.016 0.880
#> GSM447616 3 0.3879 0.6580 0.152 0.000 0.848
#> GSM447626 3 0.2959 0.7489 0.100 0.000 0.900
#> GSM447640 2 0.0000 0.8790 0.000 1.000 0.000
#> GSM447734 3 0.0237 0.7932 0.000 0.004 0.996
#> GSM447692 3 0.3879 0.6580 0.152 0.000 0.848
#> GSM447647 2 0.5363 0.7516 0.276 0.724 0.000
#> GSM447624 1 0.6252 0.6915 0.556 0.000 0.444
#> GSM447625 3 0.0237 0.7932 0.000 0.004 0.996
#> GSM447707 2 0.0237 0.8789 0.004 0.996 0.000
#> GSM447732 3 0.0475 0.7925 0.004 0.004 0.992
#> GSM447684 1 0.7853 0.5064 0.556 0.060 0.384
#> GSM447731 3 0.9528 0.3631 0.288 0.228 0.484
#> GSM447705 3 0.8014 0.4321 0.104 0.268 0.628
#> GSM447631 3 0.2711 0.7379 0.088 0.000 0.912
#> GSM447701 2 0.1643 0.8738 0.044 0.956 0.000
#> GSM447645 3 0.3619 0.6829 0.136 0.000 0.864
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM447671 2 0.8079 0.3678 0.056 0.552 0.236 0.156
#> GSM447694 3 0.5121 0.7060 0.120 0.000 0.764 0.116
#> GSM447618 2 0.8501 0.1413 0.052 0.440 0.164 0.344
#> GSM447691 2 0.7900 0.4135 0.056 0.572 0.232 0.140
#> GSM447733 4 0.4304 0.4334 0.000 0.000 0.284 0.716
#> GSM447620 2 0.4430 0.6717 0.056 0.840 0.060 0.044
#> GSM447627 3 0.5781 0.6022 0.072 0.000 0.676 0.252
#> GSM447630 2 0.8230 0.1908 0.056 0.460 0.364 0.120
#> GSM447642 1 0.1833 0.8348 0.944 0.000 0.024 0.032
#> GSM447649 2 0.2081 0.7485 0.000 0.916 0.000 0.084
#> GSM447654 4 0.2593 0.6471 0.000 0.104 0.004 0.892
#> GSM447655 2 0.2081 0.7488 0.000 0.916 0.000 0.084
#> GSM447669 2 0.7900 0.3987 0.056 0.572 0.232 0.140
#> GSM447676 1 0.1209 0.8276 0.964 0.000 0.004 0.032
#> GSM447678 4 0.3390 0.6290 0.000 0.132 0.016 0.852
#> GSM447681 2 0.2861 0.7474 0.000 0.888 0.016 0.096
#> GSM447698 2 0.5478 0.3093 0.000 0.540 0.016 0.444
#> GSM447713 1 0.2256 0.8378 0.924 0.000 0.056 0.020
#> GSM447722 4 0.3611 0.6335 0.000 0.060 0.080 0.860
#> GSM447726 2 0.6931 0.5093 0.056 0.664 0.196 0.084
#> GSM447735 4 0.6538 -0.0802 0.080 0.000 0.392 0.528
#> GSM447737 1 0.5247 0.5246 0.684 0.000 0.284 0.032
#> GSM447657 2 0.4290 0.6805 0.000 0.772 0.016 0.212
#> GSM447674 2 0.2987 0.7458 0.000 0.880 0.016 0.104
#> GSM447636 1 0.2764 0.7923 0.908 0.052 0.004 0.036
#> GSM447723 1 0.2224 0.8057 0.928 0.000 0.040 0.032
#> GSM447699 3 0.4163 0.6536 0.020 0.000 0.792 0.188
#> GSM447708 2 0.3699 0.7097 0.056 0.872 0.020 0.052
#> GSM447721 1 0.2142 0.8387 0.928 0.000 0.056 0.016
#> GSM447623 1 0.3205 0.8045 0.872 0.000 0.104 0.024
#> GSM447621 1 0.3497 0.7863 0.852 0.000 0.124 0.024
#> GSM447650 2 0.2011 0.7496 0.000 0.920 0.000 0.080
#> GSM447651 2 0.0469 0.7520 0.000 0.988 0.000 0.012
#> GSM447653 4 0.5964 -0.0488 0.040 0.000 0.424 0.536
#> GSM447658 1 0.1209 0.8276 0.964 0.000 0.004 0.032
#> GSM447675 4 0.2466 0.6484 0.000 0.096 0.004 0.900
#> GSM447680 2 0.1452 0.7441 0.008 0.956 0.000 0.036
#> GSM447686 1 0.7347 0.1482 0.508 0.368 0.016 0.108
#> GSM447736 3 0.2775 0.7260 0.020 0.000 0.896 0.084
#> GSM447629 2 0.4827 0.6753 0.056 0.800 0.016 0.128
#> GSM447648 3 0.4881 0.6653 0.196 0.000 0.756 0.048
#> GSM447660 1 0.1209 0.8276 0.964 0.000 0.004 0.032
#> GSM447661 2 0.2011 0.7496 0.000 0.920 0.000 0.080
#> GSM447663 3 0.4959 0.6777 0.076 0.060 0.812 0.052
#> GSM447704 2 0.2149 0.7473 0.000 0.912 0.000 0.088
#> GSM447720 3 0.7104 0.5497 0.080 0.084 0.664 0.172
#> GSM447652 2 0.2704 0.7329 0.000 0.876 0.000 0.124
#> GSM447679 2 0.2345 0.7497 0.000 0.900 0.000 0.100
#> GSM447712 1 0.1890 0.8411 0.936 0.000 0.056 0.008
#> GSM447664 4 0.2958 0.6335 0.004 0.116 0.004 0.876
#> GSM447637 3 0.4800 0.6649 0.196 0.000 0.760 0.044
#> GSM447639 4 0.4877 0.1814 0.000 0.000 0.408 0.592
#> GSM447615 1 0.3342 0.8209 0.868 0.000 0.100 0.032
#> GSM447656 2 0.3323 0.7073 0.064 0.876 0.000 0.060
#> GSM447673 2 0.5408 0.3728 0.000 0.576 0.016 0.408
#> GSM447719 3 0.6007 0.2118 0.044 0.000 0.548 0.408
#> GSM447706 3 0.3907 0.7018 0.140 0.000 0.828 0.032
#> GSM447612 3 0.3392 0.7070 0.072 0.000 0.872 0.056
#> GSM447665 2 0.1975 0.7345 0.012 0.944 0.028 0.016
#> GSM447677 2 0.0000 0.7512 0.000 1.000 0.000 0.000
#> GSM447613 1 0.1637 0.8408 0.940 0.000 0.060 0.000
#> GSM447659 3 0.5290 0.1157 0.008 0.000 0.516 0.476
#> GSM447662 3 0.1042 0.7374 0.020 0.000 0.972 0.008
#> GSM447666 3 0.5938 0.4563 0.056 0.236 0.692 0.016
#> GSM447668 2 0.0000 0.7512 0.000 1.000 0.000 0.000
#> GSM447682 2 0.3048 0.7443 0.000 0.876 0.016 0.108
#> GSM447683 2 0.1209 0.7538 0.000 0.964 0.004 0.032
#> GSM447688 4 0.4327 0.5621 0.000 0.216 0.016 0.768
#> GSM447702 2 0.2081 0.7488 0.000 0.916 0.000 0.084
#> GSM447709 2 0.1042 0.7438 0.000 0.972 0.020 0.008
#> GSM447711 1 0.1557 0.8404 0.944 0.000 0.056 0.000
#> GSM447715 1 0.7924 0.4165 0.588 0.216 0.100 0.096
#> GSM447693 3 0.4257 0.6959 0.140 0.000 0.812 0.048
#> GSM447611 4 0.3030 0.6341 0.076 0.028 0.004 0.892
#> GSM447672 2 0.2081 0.7488 0.000 0.916 0.000 0.084
#> GSM447703 2 0.5284 0.4241 0.000 0.616 0.016 0.368
#> GSM447727 1 0.2224 0.8057 0.928 0.000 0.040 0.032
#> GSM447638 2 0.6969 0.1204 0.416 0.504 0.032 0.048
#> GSM447670 1 0.3080 0.8232 0.880 0.000 0.096 0.024
#> GSM447700 4 0.8909 0.0101 0.052 0.332 0.252 0.364
#> GSM447738 2 0.5376 0.3989 0.000 0.588 0.016 0.396
#> GSM447739 1 0.2142 0.8387 0.928 0.000 0.056 0.016
#> GSM447617 1 0.3895 0.7679 0.832 0.000 0.132 0.036
#> GSM447628 4 0.3831 0.5854 0.000 0.204 0.004 0.792
#> GSM447632 2 0.5376 0.3989 0.000 0.588 0.016 0.396
#> GSM447619 3 0.1042 0.7374 0.020 0.000 0.972 0.008
#> GSM447643 1 0.5462 0.5462 0.692 0.264 0.004 0.040
#> GSM447724 4 0.4605 0.3729 0.000 0.000 0.336 0.664
#> GSM447728 2 0.1488 0.7532 0.000 0.956 0.012 0.032
#> GSM447610 1 0.5836 0.5210 0.640 0.000 0.056 0.304
#> GSM447633 2 0.6815 0.4756 0.056 0.660 0.220 0.064
#> GSM447634 3 0.5485 0.6346 0.080 0.008 0.744 0.168
#> GSM447622 3 0.5578 0.5171 0.312 0.000 0.648 0.040
#> GSM447667 2 0.5896 0.6208 0.100 0.728 0.016 0.156
#> GSM447687 2 0.5269 0.4298 0.000 0.620 0.016 0.364
#> GSM447695 3 0.4761 0.6876 0.048 0.000 0.768 0.184
#> GSM447696 1 0.2256 0.8378 0.924 0.000 0.056 0.020
#> GSM447697 1 0.2256 0.8378 0.924 0.000 0.056 0.020
#> GSM447714 3 0.2089 0.7357 0.020 0.000 0.932 0.048
#> GSM447717 1 0.1209 0.8276 0.964 0.000 0.004 0.032
#> GSM447725 1 0.1488 0.8407 0.956 0.000 0.032 0.012
#> GSM447729 4 0.2831 0.6337 0.000 0.120 0.004 0.876
#> GSM447644 2 0.6914 0.4834 0.056 0.656 0.216 0.072
#> GSM447710 3 0.0895 0.7361 0.020 0.000 0.976 0.004
#> GSM447614 4 0.6586 -0.1538 0.080 0.000 0.420 0.500
#> GSM447685 2 0.1576 0.7517 0.000 0.948 0.004 0.048
#> GSM447690 1 0.2256 0.8378 0.924 0.000 0.056 0.020
#> GSM447730 2 0.2149 0.7473 0.000 0.912 0.000 0.088
#> GSM447646 4 0.3751 0.5934 0.000 0.196 0.004 0.800
#> GSM447689 3 0.4209 0.6801 0.084 0.064 0.840 0.012
#> GSM447635 4 0.8922 -0.0230 0.056 0.340 0.236 0.368
#> GSM447641 1 0.0895 0.8305 0.976 0.000 0.004 0.020
#> GSM447716 4 0.6625 -0.1599 0.048 0.424 0.016 0.512
#> GSM447718 3 0.5316 0.6668 0.084 0.068 0.792 0.056
#> GSM447616 3 0.5619 0.5026 0.320 0.000 0.640 0.040
#> GSM447626 3 0.4057 0.6869 0.084 0.056 0.848 0.012
#> GSM447640 2 0.2408 0.7476 0.000 0.896 0.000 0.104
#> GSM447734 3 0.2363 0.7348 0.024 0.000 0.920 0.056
#> GSM447692 3 0.6356 0.5397 0.308 0.000 0.604 0.088
#> GSM447647 4 0.4372 0.5095 0.000 0.268 0.004 0.728
#> GSM447624 1 0.6010 -0.0473 0.488 0.000 0.472 0.040
#> GSM447625 3 0.2174 0.7344 0.020 0.000 0.928 0.052
#> GSM447707 2 0.2216 0.7461 0.000 0.908 0.000 0.092
#> GSM447732 3 0.2099 0.7355 0.020 0.004 0.936 0.040
#> GSM447684 3 0.8166 0.1420 0.380 0.124 0.448 0.048
#> GSM447731 4 0.4801 0.5530 0.000 0.048 0.188 0.764
#> GSM447705 3 0.5206 0.6429 0.056 0.096 0.796 0.052
#> GSM447631 3 0.4842 0.6660 0.192 0.000 0.760 0.048
#> GSM447701 2 0.0336 0.7499 0.000 0.992 0.000 0.008
#> GSM447645 3 0.4800 0.6649 0.196 0.000 0.760 0.044
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM447671 5 0.4037 0.6229 0.000 0.176 0.012 0.028 0.784
#> GSM447694 3 0.4049 0.6362 0.008 0.000 0.788 0.040 0.164
#> GSM447618 5 0.5475 0.4859 0.004 0.120 0.008 0.180 0.688
#> GSM447691 5 0.4549 0.5358 0.000 0.220 0.004 0.048 0.728
#> GSM447733 4 0.4559 0.6284 0.000 0.000 0.100 0.748 0.152
#> GSM447620 2 0.5525 0.2805 0.000 0.544 0.060 0.004 0.392
#> GSM447627 3 0.4662 0.6116 0.000 0.000 0.736 0.096 0.168
#> GSM447630 5 0.4007 0.6485 0.004 0.128 0.044 0.012 0.812
#> GSM447642 1 0.1365 0.8075 0.952 0.000 0.004 0.004 0.040
#> GSM447649 2 0.1012 0.7670 0.000 0.968 0.000 0.012 0.020
#> GSM447654 4 0.1992 0.7108 0.000 0.044 0.000 0.924 0.032
#> GSM447655 2 0.0671 0.7674 0.000 0.980 0.000 0.016 0.004
#> GSM447669 5 0.3770 0.6282 0.000 0.188 0.008 0.016 0.788
#> GSM447676 1 0.1808 0.8058 0.936 0.000 0.004 0.020 0.040
#> GSM447678 4 0.4517 0.6011 0.004 0.036 0.008 0.744 0.208
#> GSM447681 2 0.2894 0.7613 0.000 0.876 0.004 0.036 0.084
#> GSM447698 2 0.7002 0.2144 0.004 0.380 0.004 0.360 0.252
#> GSM447713 1 0.2416 0.7731 0.888 0.000 0.100 0.000 0.012
#> GSM447722 4 0.5313 0.5191 0.004 0.032 0.020 0.644 0.300
#> GSM447726 5 0.5005 0.4733 0.000 0.300 0.020 0.024 0.656
#> GSM447735 3 0.5928 0.4554 0.000 0.000 0.596 0.212 0.192
#> GSM447737 3 0.5759 -0.0546 0.452 0.000 0.476 0.008 0.064
#> GSM447657 2 0.6096 0.5909 0.004 0.596 0.004 0.148 0.248
#> GSM447674 2 0.2673 0.7651 0.000 0.892 0.004 0.044 0.060
#> GSM447636 1 0.2267 0.7960 0.916 0.008 0.000 0.028 0.048
#> GSM447723 1 0.2110 0.7980 0.912 0.000 0.000 0.016 0.072
#> GSM447699 3 0.5897 0.3501 0.000 0.004 0.496 0.088 0.412
#> GSM447708 2 0.5390 0.3771 0.004 0.536 0.000 0.048 0.412
#> GSM447721 1 0.1943 0.7907 0.924 0.000 0.056 0.000 0.020
#> GSM447623 1 0.4819 0.4468 0.620 0.000 0.352 0.004 0.024
#> GSM447621 1 0.4937 0.4131 0.604 0.000 0.364 0.004 0.028
#> GSM447650 2 0.0807 0.7676 0.000 0.976 0.000 0.012 0.012
#> GSM447651 2 0.1043 0.7628 0.000 0.960 0.000 0.000 0.040
#> GSM447653 4 0.5606 0.3711 0.000 0.000 0.296 0.600 0.104
#> GSM447658 1 0.1818 0.8020 0.932 0.000 0.000 0.024 0.044
#> GSM447675 4 0.1682 0.7083 0.000 0.012 0.004 0.940 0.044
#> GSM447680 2 0.3695 0.7162 0.000 0.800 0.000 0.036 0.164
#> GSM447686 1 0.6702 0.3242 0.524 0.056 0.004 0.072 0.344
#> GSM447736 3 0.5024 0.4790 0.004 0.000 0.596 0.032 0.368
#> GSM447629 2 0.5815 0.4824 0.004 0.552 0.004 0.076 0.364
#> GSM447648 3 0.1243 0.6531 0.028 0.000 0.960 0.004 0.008
#> GSM447660 1 0.1818 0.8020 0.932 0.000 0.000 0.024 0.044
#> GSM447661 2 0.0807 0.7676 0.000 0.976 0.000 0.012 0.012
#> GSM447663 5 0.4341 0.2005 0.004 0.000 0.404 0.000 0.592
#> GSM447704 2 0.0912 0.7669 0.000 0.972 0.000 0.016 0.012
#> GSM447720 5 0.3415 0.5760 0.004 0.004 0.124 0.028 0.840
#> GSM447652 2 0.2408 0.7404 0.000 0.892 0.000 0.092 0.016
#> GSM447679 2 0.1992 0.7687 0.000 0.924 0.000 0.032 0.044
#> GSM447712 1 0.0324 0.8077 0.992 0.000 0.004 0.000 0.004
#> GSM447664 4 0.2569 0.6952 0.012 0.020 0.004 0.904 0.060
#> GSM447637 3 0.1356 0.6507 0.028 0.000 0.956 0.004 0.012
#> GSM447639 4 0.6246 0.3121 0.000 0.000 0.180 0.528 0.292
#> GSM447615 1 0.4054 0.6970 0.760 0.000 0.204 0.000 0.036
#> GSM447656 2 0.5172 0.5426 0.004 0.616 0.000 0.048 0.332
#> GSM447673 2 0.5966 0.3784 0.004 0.536 0.004 0.368 0.088
#> GSM447719 4 0.5352 0.2730 0.000 0.000 0.408 0.536 0.056
#> GSM447706 3 0.2270 0.6359 0.020 0.000 0.904 0.000 0.076
#> GSM447612 5 0.4593 -0.0650 0.004 0.000 0.480 0.004 0.512
#> GSM447665 2 0.4341 0.3486 0.000 0.592 0.000 0.004 0.404
#> GSM447677 2 0.1908 0.7488 0.000 0.908 0.000 0.000 0.092
#> GSM447613 1 0.0162 0.8071 0.996 0.000 0.004 0.000 0.000
#> GSM447659 4 0.6252 0.2401 0.000 0.000 0.328 0.508 0.164
#> GSM447662 3 0.3968 0.4748 0.004 0.000 0.716 0.004 0.276
#> GSM447666 5 0.5196 0.4183 0.004 0.040 0.380 0.000 0.576
#> GSM447668 2 0.1544 0.7597 0.000 0.932 0.000 0.000 0.068
#> GSM447682 2 0.3062 0.7629 0.004 0.868 0.000 0.048 0.080
#> GSM447683 2 0.3321 0.7401 0.000 0.832 0.000 0.032 0.136
#> GSM447688 4 0.5554 0.5140 0.004 0.224 0.004 0.660 0.108
#> GSM447702 2 0.0671 0.7674 0.000 0.980 0.000 0.016 0.004
#> GSM447709 2 0.3333 0.6441 0.000 0.788 0.000 0.004 0.208
#> GSM447711 1 0.0162 0.8071 0.996 0.000 0.004 0.000 0.000
#> GSM447715 1 0.6179 0.1906 0.480 0.032 0.000 0.060 0.428
#> GSM447693 3 0.1059 0.6509 0.008 0.000 0.968 0.004 0.020
#> GSM447611 4 0.1399 0.7038 0.028 0.000 0.000 0.952 0.020
#> GSM447672 2 0.0798 0.7678 0.000 0.976 0.000 0.016 0.008
#> GSM447703 2 0.4972 0.4055 0.000 0.612 0.004 0.352 0.032
#> GSM447727 1 0.2172 0.7966 0.908 0.000 0.000 0.016 0.076
#> GSM447638 1 0.7692 0.0487 0.404 0.208 0.028 0.020 0.340
#> GSM447670 1 0.3241 0.7495 0.832 0.000 0.144 0.000 0.024
#> GSM447700 5 0.4038 0.5727 0.000 0.032 0.028 0.132 0.808
#> GSM447738 2 0.6010 0.3756 0.004 0.532 0.004 0.368 0.092
#> GSM447739 1 0.1670 0.7937 0.936 0.000 0.052 0.000 0.012
#> GSM447617 1 0.5036 0.2166 0.520 0.000 0.452 0.004 0.024
#> GSM447628 4 0.2179 0.6981 0.000 0.112 0.000 0.888 0.000
#> GSM447632 2 0.6053 0.3689 0.004 0.528 0.004 0.368 0.096
#> GSM447619 3 0.3734 0.5172 0.004 0.000 0.752 0.004 0.240
#> GSM447643 1 0.3871 0.7351 0.824 0.040 0.000 0.024 0.112
#> GSM447724 4 0.6234 0.3785 0.000 0.000 0.160 0.508 0.332
#> GSM447728 2 0.3276 0.7425 0.000 0.836 0.000 0.032 0.132
#> GSM447610 1 0.7533 0.1912 0.428 0.000 0.204 0.312 0.056
#> GSM447633 5 0.4672 0.5428 0.000 0.284 0.032 0.004 0.680
#> GSM447634 5 0.4977 0.3435 0.008 0.000 0.256 0.052 0.684
#> GSM447622 3 0.3597 0.6157 0.116 0.000 0.832 0.008 0.044
#> GSM447667 2 0.6742 0.4420 0.036 0.504 0.004 0.100 0.356
#> GSM447687 2 0.4986 0.4298 0.000 0.624 0.004 0.336 0.036
#> GSM447695 3 0.4908 0.6064 0.012 0.000 0.716 0.060 0.212
#> GSM447696 1 0.2624 0.7635 0.872 0.000 0.116 0.000 0.012
#> GSM447697 1 0.3039 0.7350 0.836 0.000 0.152 0.000 0.012
#> GSM447714 3 0.4347 0.4574 0.004 0.000 0.636 0.004 0.356
#> GSM447717 1 0.1408 0.8062 0.948 0.000 0.000 0.008 0.044
#> GSM447725 1 0.0727 0.8085 0.980 0.000 0.004 0.004 0.012
#> GSM447729 4 0.1493 0.7046 0.000 0.024 0.000 0.948 0.028
#> GSM447644 5 0.4401 0.5261 0.000 0.296 0.016 0.004 0.684
#> GSM447710 3 0.3844 0.5012 0.004 0.000 0.736 0.004 0.256
#> GSM447614 3 0.6529 0.1248 0.004 0.000 0.468 0.352 0.176
#> GSM447685 2 0.3950 0.7284 0.004 0.796 0.000 0.048 0.152
#> GSM447690 1 0.2416 0.7731 0.888 0.000 0.100 0.000 0.012
#> GSM447730 2 0.1195 0.7638 0.000 0.960 0.000 0.028 0.012
#> GSM447646 4 0.2179 0.6981 0.000 0.112 0.000 0.888 0.000
#> GSM447689 5 0.4415 0.3183 0.004 0.000 0.444 0.000 0.552
#> GSM447635 5 0.3946 0.5722 0.008 0.032 0.012 0.132 0.816
#> GSM447641 1 0.1202 0.8080 0.960 0.000 0.004 0.004 0.032
#> GSM447716 4 0.7212 -0.1694 0.012 0.312 0.004 0.396 0.276
#> GSM447718 5 0.4287 0.4687 0.004 0.008 0.284 0.004 0.700
#> GSM447616 3 0.3967 0.6130 0.124 0.000 0.808 0.008 0.060
#> GSM447626 3 0.4420 -0.0575 0.004 0.000 0.548 0.000 0.448
#> GSM447640 2 0.2074 0.7689 0.000 0.920 0.000 0.044 0.036
#> GSM447734 3 0.4166 0.5114 0.000 0.000 0.648 0.004 0.348
#> GSM447692 3 0.5656 0.6046 0.128 0.000 0.696 0.036 0.140
#> GSM447647 4 0.3171 0.6551 0.000 0.176 0.000 0.816 0.008
#> GSM447624 3 0.4535 0.3718 0.288 0.000 0.684 0.004 0.024
#> GSM447625 3 0.4389 0.4788 0.004 0.000 0.624 0.004 0.368
#> GSM447707 2 0.1195 0.7638 0.000 0.960 0.000 0.028 0.012
#> GSM447732 3 0.4288 0.4407 0.004 0.000 0.612 0.000 0.384
#> GSM447684 5 0.6194 0.4694 0.140 0.016 0.192 0.012 0.640
#> GSM447731 4 0.4389 0.6696 0.000 0.048 0.076 0.804 0.072
#> GSM447705 5 0.4405 0.4342 0.004 0.004 0.332 0.004 0.656
#> GSM447631 3 0.1173 0.6521 0.020 0.000 0.964 0.004 0.012
#> GSM447701 2 0.2074 0.7408 0.000 0.896 0.000 0.000 0.104
#> GSM447645 3 0.1356 0.6507 0.028 0.000 0.956 0.004 0.012
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM447671 5 0.5223 0.34626 0.000 0.048 0.012 0.024 0.632 0.284
#> GSM447694 3 0.5783 0.32258 0.000 0.000 0.604 0.048 0.112 0.236
#> GSM447618 5 0.4260 0.44381 0.000 0.024 0.004 0.104 0.776 0.092
#> GSM447691 5 0.4592 0.42387 0.000 0.076 0.000 0.004 0.680 0.240
#> GSM447733 4 0.5685 0.55131 0.000 0.000 0.112 0.648 0.164 0.076
#> GSM447620 5 0.6304 0.15078 0.000 0.388 0.016 0.004 0.412 0.180
#> GSM447627 3 0.6395 0.30144 0.000 0.000 0.572 0.120 0.128 0.180
#> GSM447630 6 0.5773 -0.16201 0.000 0.116 0.008 0.004 0.432 0.440
#> GSM447642 1 0.0436 0.72261 0.988 0.000 0.004 0.004 0.004 0.000
#> GSM447649 2 0.2113 0.72616 0.000 0.920 0.012 0.032 0.028 0.008
#> GSM447654 4 0.1967 0.68742 0.004 0.016 0.036 0.928 0.012 0.004
#> GSM447655 2 0.0260 0.73124 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM447669 5 0.5524 0.23839 0.000 0.120 0.000 0.004 0.504 0.372
#> GSM447676 1 0.0653 0.72142 0.980 0.000 0.004 0.004 0.012 0.000
#> GSM447678 4 0.4944 0.19301 0.004 0.012 0.012 0.484 0.476 0.012
#> GSM447681 2 0.2907 0.68694 0.000 0.828 0.000 0.020 0.152 0.000
#> GSM447698 5 0.5948 0.17647 0.000 0.236 0.004 0.236 0.520 0.004
#> GSM447713 1 0.4010 0.46808 0.584 0.000 0.408 0.000 0.008 0.000
#> GSM447722 5 0.5503 -0.03464 0.000 0.008 0.016 0.344 0.560 0.072
#> GSM447726 5 0.7232 0.28694 0.072 0.164 0.012 0.004 0.392 0.356
#> GSM447735 3 0.6706 0.26504 0.000 0.000 0.524 0.176 0.192 0.108
#> GSM447737 3 0.4563 0.32040 0.232 0.000 0.700 0.000 0.028 0.040
#> GSM447657 2 0.5159 0.18085 0.000 0.480 0.000 0.072 0.444 0.004
#> GSM447674 2 0.2445 0.71420 0.000 0.872 0.000 0.020 0.108 0.000
#> GSM447636 1 0.0603 0.71939 0.980 0.000 0.000 0.004 0.016 0.000
#> GSM447723 1 0.1722 0.71086 0.936 0.000 0.016 0.008 0.036 0.004
#> GSM447699 6 0.6868 0.32450 0.000 0.000 0.224 0.056 0.336 0.384
#> GSM447708 5 0.4931 0.20569 0.004 0.336 0.012 0.000 0.604 0.044
#> GSM447721 1 0.3887 0.53554 0.632 0.000 0.360 0.000 0.008 0.000
#> GSM447623 3 0.3965 -0.00585 0.376 0.000 0.616 0.000 0.004 0.004
#> GSM447621 3 0.4058 0.00695 0.372 0.000 0.616 0.000 0.004 0.008
#> GSM447650 2 0.0000 0.73059 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447651 2 0.2136 0.71021 0.000 0.908 0.012 0.000 0.064 0.016
#> GSM447653 4 0.5722 0.49414 0.004 0.000 0.176 0.648 0.108 0.064
#> GSM447658 1 0.0508 0.72010 0.984 0.000 0.000 0.004 0.012 0.000
#> GSM447675 4 0.2452 0.67688 0.004 0.000 0.028 0.884 0.084 0.000
#> GSM447680 2 0.5675 0.47746 0.080 0.616 0.012 0.004 0.264 0.024
#> GSM447686 1 0.4502 0.13690 0.532 0.000 0.004 0.016 0.444 0.004
#> GSM447736 6 0.6711 0.33309 0.000 0.000 0.280 0.048 0.232 0.440
#> GSM447629 5 0.5656 0.21228 0.068 0.292 0.004 0.024 0.600 0.012
#> GSM447648 3 0.4460 0.24127 0.000 0.000 0.520 0.000 0.028 0.452
#> GSM447660 1 0.0653 0.72112 0.980 0.000 0.004 0.004 0.012 0.000
#> GSM447661 2 0.0000 0.73059 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447663 6 0.4705 0.49244 0.000 0.012 0.064 0.004 0.228 0.692
#> GSM447704 2 0.2113 0.72616 0.000 0.920 0.012 0.032 0.028 0.008
#> GSM447720 5 0.5698 -0.09328 0.012 0.000 0.056 0.024 0.456 0.452
#> GSM447652 2 0.1867 0.71690 0.000 0.916 0.000 0.064 0.020 0.000
#> GSM447679 2 0.2094 0.72243 0.000 0.900 0.000 0.020 0.080 0.000
#> GSM447712 1 0.2513 0.70186 0.852 0.000 0.140 0.000 0.008 0.000
#> GSM447664 4 0.4115 0.58822 0.052 0.004 0.004 0.744 0.196 0.000
#> GSM447637 3 0.4724 0.23736 0.004 0.000 0.508 0.004 0.028 0.456
#> GSM447639 4 0.7284 0.19645 0.000 0.000 0.132 0.408 0.260 0.200
#> GSM447615 1 0.5775 0.35872 0.572 0.000 0.288 0.004 0.024 0.112
#> GSM447656 5 0.6770 -0.00435 0.136 0.376 0.012 0.008 0.432 0.036
#> GSM447673 2 0.6046 0.29029 0.000 0.452 0.000 0.312 0.232 0.004
#> GSM447719 4 0.6175 0.41289 0.004 0.000 0.192 0.580 0.048 0.176
#> GSM447706 6 0.4508 -0.09442 0.000 0.000 0.436 0.004 0.024 0.536
#> GSM447612 6 0.4696 0.51779 0.000 0.000 0.076 0.012 0.224 0.688
#> GSM447665 2 0.5661 -0.13270 0.000 0.476 0.004 0.000 0.384 0.136
#> GSM447677 2 0.3375 0.65032 0.000 0.808 0.012 0.000 0.156 0.024
#> GSM447613 1 0.2631 0.69541 0.840 0.000 0.152 0.000 0.008 0.000
#> GSM447659 4 0.7048 0.33112 0.000 0.000 0.176 0.480 0.160 0.184
#> GSM447662 6 0.3104 0.42038 0.000 0.000 0.204 0.004 0.004 0.788
#> GSM447666 6 0.4210 0.42969 0.020 0.004 0.048 0.004 0.152 0.772
#> GSM447668 2 0.1956 0.70722 0.000 0.908 0.004 0.000 0.080 0.008
#> GSM447682 2 0.3168 0.68690 0.000 0.804 0.000 0.024 0.172 0.000
#> GSM447683 2 0.4073 0.61814 0.000 0.724 0.004 0.020 0.240 0.012
#> GSM447688 4 0.6066 0.21186 0.000 0.204 0.004 0.472 0.316 0.004
#> GSM447702 2 0.0146 0.73086 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM447709 2 0.4653 0.52080 0.000 0.716 0.016 0.004 0.192 0.072
#> GSM447711 1 0.2743 0.69048 0.828 0.000 0.164 0.000 0.008 0.000
#> GSM447715 1 0.4962 0.13465 0.536 0.000 0.004 0.008 0.412 0.040
#> GSM447693 3 0.4601 0.21797 0.000 0.000 0.496 0.004 0.028 0.472
#> GSM447611 4 0.2620 0.67900 0.028 0.000 0.052 0.888 0.032 0.000
#> GSM447672 2 0.0717 0.73260 0.000 0.976 0.000 0.016 0.008 0.000
#> GSM447703 2 0.5082 0.42846 0.000 0.600 0.004 0.316 0.076 0.004
#> GSM447727 1 0.1194 0.71350 0.956 0.000 0.004 0.008 0.032 0.000
#> GSM447638 1 0.7082 0.19567 0.544 0.100 0.020 0.008 0.184 0.144
#> GSM447670 1 0.5298 0.47990 0.624 0.000 0.268 0.004 0.016 0.088
#> GSM447700 5 0.4798 0.26695 0.000 0.004 0.024 0.048 0.684 0.240
#> GSM447738 2 0.6205 0.20073 0.000 0.392 0.000 0.288 0.316 0.004
#> GSM447739 1 0.3847 0.54695 0.644 0.000 0.348 0.000 0.008 0.000
#> GSM447617 3 0.3809 0.17421 0.304 0.000 0.684 0.000 0.004 0.008
#> GSM447628 4 0.2706 0.66318 0.000 0.068 0.004 0.880 0.040 0.008
#> GSM447632 2 0.6202 0.20227 0.000 0.392 0.000 0.284 0.320 0.004
#> GSM447619 6 0.4034 0.27409 0.000 0.000 0.280 0.004 0.024 0.692
#> GSM447643 1 0.2757 0.63730 0.848 0.000 0.004 0.004 0.136 0.008
#> GSM447724 5 0.6956 -0.07414 0.000 0.000 0.104 0.244 0.468 0.184
#> GSM447728 2 0.3514 0.65885 0.000 0.768 0.000 0.020 0.208 0.004
#> GSM447610 3 0.7149 0.19821 0.212 0.000 0.448 0.260 0.064 0.016
#> GSM447633 5 0.6269 0.24534 0.000 0.172 0.016 0.004 0.412 0.396
#> GSM447634 6 0.6451 0.30342 0.004 0.000 0.156 0.040 0.316 0.484
#> GSM447622 3 0.4041 0.46738 0.040 0.000 0.760 0.004 0.012 0.184
#> GSM447667 5 0.6017 0.26267 0.124 0.244 0.004 0.024 0.592 0.012
#> GSM447687 2 0.5157 0.43713 0.000 0.596 0.004 0.312 0.084 0.004
#> GSM447695 3 0.6259 0.21947 0.000 0.000 0.548 0.048 0.196 0.208
#> GSM447696 1 0.4062 0.41809 0.552 0.000 0.440 0.000 0.008 0.000
#> GSM447697 1 0.4067 0.40950 0.548 0.000 0.444 0.000 0.008 0.000
#> GSM447714 6 0.4467 0.47782 0.000 0.000 0.192 0.004 0.092 0.712
#> GSM447717 1 0.0508 0.72340 0.984 0.000 0.012 0.000 0.004 0.000
#> GSM447725 1 0.2191 0.70727 0.876 0.000 0.120 0.000 0.004 0.000
#> GSM447729 4 0.2312 0.66245 0.008 0.012 0.004 0.896 0.080 0.000
#> GSM447644 5 0.5974 0.26815 0.000 0.192 0.004 0.000 0.424 0.380
#> GSM447710 6 0.3298 0.40016 0.000 0.000 0.236 0.000 0.008 0.756
#> GSM447614 3 0.6774 0.17228 0.000 0.000 0.492 0.260 0.144 0.104
#> GSM447685 2 0.4899 0.56227 0.020 0.664 0.012 0.016 0.276 0.012
#> GSM447690 1 0.4010 0.46808 0.584 0.000 0.408 0.000 0.008 0.000
#> GSM447730 2 0.2596 0.71617 0.000 0.892 0.012 0.056 0.032 0.008
#> GSM447646 4 0.2716 0.66445 0.000 0.064 0.004 0.880 0.044 0.008
#> GSM447689 6 0.3570 0.50768 0.012 0.000 0.056 0.004 0.108 0.820
#> GSM447635 5 0.3894 0.40526 0.004 0.004 0.016 0.028 0.784 0.164
#> GSM447641 1 0.1204 0.71907 0.944 0.000 0.056 0.000 0.000 0.000
#> GSM447716 5 0.6628 0.18968 0.056 0.176 0.004 0.232 0.528 0.004
#> GSM447718 6 0.5353 0.37759 0.016 0.016 0.028 0.020 0.288 0.632
#> GSM447616 3 0.3183 0.48593 0.040 0.000 0.828 0.004 0.000 0.128
#> GSM447626 6 0.3907 0.51331 0.020 0.000 0.112 0.004 0.064 0.800
#> GSM447640 2 0.2094 0.72442 0.000 0.900 0.000 0.020 0.080 0.000
#> GSM447734 6 0.5485 0.44045 0.000 0.000 0.268 0.020 0.112 0.600
#> GSM447692 3 0.4437 0.46379 0.032 0.000 0.784 0.032 0.052 0.100
#> GSM447647 4 0.3794 0.61140 0.000 0.144 0.004 0.788 0.060 0.004
#> GSM447624 3 0.4656 0.47306 0.112 0.000 0.720 0.000 0.016 0.152
#> GSM447625 6 0.5486 0.45641 0.000 0.000 0.260 0.020 0.116 0.604
#> GSM447707 2 0.2290 0.71610 0.000 0.904 0.008 0.060 0.024 0.004
#> GSM447732 6 0.5088 0.52417 0.000 0.004 0.208 0.012 0.108 0.668
#> GSM447684 6 0.6903 0.00573 0.248 0.004 0.036 0.008 0.260 0.444
#> GSM447731 4 0.3838 0.66257 0.004 0.056 0.032 0.824 0.008 0.076
#> GSM447705 6 0.3665 0.37859 0.000 0.000 0.004 0.004 0.296 0.696
#> GSM447631 3 0.4726 0.23365 0.004 0.000 0.504 0.004 0.028 0.460
#> GSM447701 2 0.2821 0.67019 0.000 0.860 0.004 0.000 0.096 0.040
#> GSM447645 3 0.4724 0.23736 0.004 0.000 0.508 0.004 0.028 0.456
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)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
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 gender(p) individual(p) disease.state(p) other(p) k
#> MAD:kmeans 127 0.647 0.732 0.408 0.1046 2
#> MAD:kmeans 121 0.571 0.523 0.204 0.3141 3
#> MAD:kmeans 100 0.351 0.279 0.241 0.1138 4
#> MAD:kmeans 85 0.832 0.291 0.409 0.0601 5
#> MAD:kmeans 53 0.339 0.408 0.722 0.0566 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "skmeans"]
# you can also extract it by
# res = res_list["MAD:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 130 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.921 0.934 0.972 0.5043 0.496 0.496
#> 3 3 0.917 0.921 0.957 0.2908 0.807 0.628
#> 4 4 0.801 0.760 0.897 0.1381 0.891 0.700
#> 5 5 0.725 0.618 0.794 0.0710 0.902 0.662
#> 6 6 0.722 0.647 0.800 0.0408 0.890 0.556
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM447671 2 0.0000 0.973 0.000 1.000
#> GSM447694 1 0.0000 0.969 1.000 0.000
#> GSM447618 2 0.0000 0.973 0.000 1.000
#> GSM447691 2 0.0000 0.973 0.000 1.000
#> GSM447733 1 0.5629 0.842 0.868 0.132
#> GSM447620 2 0.0000 0.973 0.000 1.000
#> GSM447627 1 0.0000 0.969 1.000 0.000
#> GSM447630 2 0.0938 0.963 0.012 0.988
#> GSM447642 1 0.0000 0.969 1.000 0.000
#> GSM447649 2 0.0000 0.973 0.000 1.000
#> GSM447654 2 0.0000 0.973 0.000 1.000
#> GSM447655 2 0.0000 0.973 0.000 1.000
#> GSM447669 2 0.0000 0.973 0.000 1.000
#> GSM447676 1 0.0000 0.969 1.000 0.000
#> GSM447678 2 0.0000 0.973 0.000 1.000
#> GSM447681 2 0.0000 0.973 0.000 1.000
#> GSM447698 2 0.0000 0.973 0.000 1.000
#> GSM447713 1 0.0000 0.969 1.000 0.000
#> GSM447722 2 0.0000 0.973 0.000 1.000
#> GSM447726 2 0.0376 0.970 0.004 0.996
#> GSM447735 1 0.0000 0.969 1.000 0.000
#> GSM447737 1 0.0000 0.969 1.000 0.000
#> GSM447657 2 0.0000 0.973 0.000 1.000
#> GSM447674 2 0.0000 0.973 0.000 1.000
#> GSM447636 2 0.9754 0.339 0.408 0.592
#> GSM447723 1 0.0000 0.969 1.000 0.000
#> GSM447699 1 0.7219 0.757 0.800 0.200
#> GSM447708 2 0.0000 0.973 0.000 1.000
#> GSM447721 1 0.0000 0.969 1.000 0.000
#> GSM447623 1 0.0000 0.969 1.000 0.000
#> GSM447621 1 0.0000 0.969 1.000 0.000
#> GSM447650 2 0.0000 0.973 0.000 1.000
#> GSM447651 2 0.0000 0.973 0.000 1.000
#> GSM447653 1 0.0000 0.969 1.000 0.000
#> GSM447658 1 0.0000 0.969 1.000 0.000
#> GSM447675 2 0.0000 0.973 0.000 1.000
#> GSM447680 2 0.0000 0.973 0.000 1.000
#> GSM447686 2 0.3431 0.912 0.064 0.936
#> GSM447736 1 0.0000 0.969 1.000 0.000
#> GSM447629 2 0.0000 0.973 0.000 1.000
#> GSM447648 1 0.0000 0.969 1.000 0.000
#> GSM447660 1 0.0000 0.969 1.000 0.000
#> GSM447661 2 0.0000 0.973 0.000 1.000
#> GSM447663 1 0.7056 0.768 0.808 0.192
#> GSM447704 2 0.0000 0.973 0.000 1.000
#> GSM447720 1 0.0000 0.969 1.000 0.000
#> GSM447652 2 0.0000 0.973 0.000 1.000
#> GSM447679 2 0.0000 0.973 0.000 1.000
#> GSM447712 1 0.0000 0.969 1.000 0.000
#> GSM447664 2 0.0000 0.973 0.000 1.000
#> GSM447637 1 0.0000 0.969 1.000 0.000
#> GSM447639 1 0.7950 0.698 0.760 0.240
#> GSM447615 1 0.0000 0.969 1.000 0.000
#> GSM447656 2 0.0000 0.973 0.000 1.000
#> GSM447673 2 0.0000 0.973 0.000 1.000
#> GSM447719 1 0.0000 0.969 1.000 0.000
#> GSM447706 1 0.0000 0.969 1.000 0.000
#> GSM447612 1 0.7376 0.746 0.792 0.208
#> GSM447665 2 0.0000 0.973 0.000 1.000
#> GSM447677 2 0.0000 0.973 0.000 1.000
#> GSM447613 1 0.0000 0.969 1.000 0.000
#> GSM447659 1 0.3431 0.912 0.936 0.064
#> GSM447662 1 0.0000 0.969 1.000 0.000
#> GSM447666 1 0.1414 0.952 0.980 0.020
#> GSM447668 2 0.0000 0.973 0.000 1.000
#> GSM447682 2 0.0000 0.973 0.000 1.000
#> GSM447683 2 0.0000 0.973 0.000 1.000
#> GSM447688 2 0.0000 0.973 0.000 1.000
#> GSM447702 2 0.0000 0.973 0.000 1.000
#> GSM447709 2 0.0000 0.973 0.000 1.000
#> GSM447711 1 0.0000 0.969 1.000 0.000
#> GSM447715 2 0.6531 0.791 0.168 0.832
#> GSM447693 1 0.0000 0.969 1.000 0.000
#> GSM447611 2 0.9775 0.328 0.412 0.588
#> GSM447672 2 0.0000 0.973 0.000 1.000
#> GSM447703 2 0.0000 0.973 0.000 1.000
#> GSM447727 1 0.0000 0.969 1.000 0.000
#> GSM447638 2 0.9460 0.449 0.364 0.636
#> GSM447670 1 0.0000 0.969 1.000 0.000
#> GSM447700 2 0.0000 0.973 0.000 1.000
#> GSM447738 2 0.0000 0.973 0.000 1.000
#> GSM447739 1 0.0000 0.969 1.000 0.000
#> GSM447617 1 0.0000 0.969 1.000 0.000
#> GSM447628 2 0.0000 0.973 0.000 1.000
#> GSM447632 2 0.0000 0.973 0.000 1.000
#> GSM447619 1 0.0000 0.969 1.000 0.000
#> GSM447643 2 0.7453 0.730 0.212 0.788
#> GSM447724 1 0.9732 0.358 0.596 0.404
#> GSM447728 2 0.0000 0.973 0.000 1.000
#> GSM447610 1 0.0000 0.969 1.000 0.000
#> GSM447633 2 0.0000 0.973 0.000 1.000
#> GSM447634 1 0.0000 0.969 1.000 0.000
#> GSM447622 1 0.0000 0.969 1.000 0.000
#> GSM447667 2 0.0000 0.973 0.000 1.000
#> GSM447687 2 0.0000 0.973 0.000 1.000
#> GSM447695 1 0.0000 0.969 1.000 0.000
#> GSM447696 1 0.0000 0.969 1.000 0.000
#> GSM447697 1 0.0000 0.969 1.000 0.000
#> GSM447714 1 0.0000 0.969 1.000 0.000
#> GSM447717 1 0.0000 0.969 1.000 0.000
#> GSM447725 1 0.0000 0.969 1.000 0.000
#> GSM447729 2 0.0000 0.973 0.000 1.000
#> GSM447644 2 0.0000 0.973 0.000 1.000
#> GSM447710 1 0.0000 0.969 1.000 0.000
#> GSM447614 1 0.0000 0.969 1.000 0.000
#> GSM447685 2 0.0000 0.973 0.000 1.000
#> GSM447690 1 0.0000 0.969 1.000 0.000
#> GSM447730 2 0.0000 0.973 0.000 1.000
#> GSM447646 2 0.0000 0.973 0.000 1.000
#> GSM447689 1 0.0000 0.969 1.000 0.000
#> GSM447635 2 0.0000 0.973 0.000 1.000
#> GSM447641 1 0.0000 0.969 1.000 0.000
#> GSM447716 2 0.0000 0.973 0.000 1.000
#> GSM447718 1 0.1414 0.952 0.980 0.020
#> GSM447616 1 0.0000 0.969 1.000 0.000
#> GSM447626 1 0.0000 0.969 1.000 0.000
#> GSM447640 2 0.0000 0.973 0.000 1.000
#> GSM447734 1 0.0000 0.969 1.000 0.000
#> GSM447692 1 0.0000 0.969 1.000 0.000
#> GSM447647 2 0.0000 0.973 0.000 1.000
#> GSM447624 1 0.0000 0.969 1.000 0.000
#> GSM447625 1 0.0000 0.969 1.000 0.000
#> GSM447707 2 0.0000 0.973 0.000 1.000
#> GSM447732 1 0.0000 0.969 1.000 0.000
#> GSM447684 1 0.0000 0.969 1.000 0.000
#> GSM447731 2 0.0938 0.963 0.012 0.988
#> GSM447705 1 0.9944 0.207 0.544 0.456
#> GSM447631 1 0.0000 0.969 1.000 0.000
#> GSM447701 2 0.0000 0.973 0.000 1.000
#> GSM447645 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
#> GSM447671 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447694 3 0.1163 0.935 0.028 0.000 0.972
#> GSM447618 2 0.0237 0.977 0.004 0.996 0.000
#> GSM447691 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447733 3 0.0424 0.917 0.008 0.000 0.992
#> GSM447620 2 0.4235 0.794 0.000 0.824 0.176
#> GSM447627 3 0.1031 0.933 0.024 0.000 0.976
#> GSM447630 2 0.6111 0.312 0.000 0.604 0.396
#> GSM447642 1 0.0424 0.957 0.992 0.000 0.008
#> GSM447649 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447654 2 0.1585 0.960 0.008 0.964 0.028
#> GSM447655 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447669 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447676 1 0.0237 0.956 0.996 0.000 0.004
#> GSM447678 2 0.1585 0.960 0.008 0.964 0.028
#> GSM447681 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447698 2 0.0237 0.977 0.004 0.996 0.000
#> GSM447713 1 0.0424 0.957 0.992 0.000 0.008
#> GSM447722 2 0.1711 0.958 0.008 0.960 0.032
#> GSM447726 2 0.0237 0.976 0.000 0.996 0.004
#> GSM447735 3 0.3340 0.855 0.120 0.000 0.880
#> GSM447737 1 0.6308 -0.106 0.508 0.000 0.492
#> GSM447657 2 0.0237 0.977 0.004 0.996 0.000
#> GSM447674 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447636 1 0.1289 0.933 0.968 0.032 0.000
#> GSM447723 1 0.0424 0.957 0.992 0.000 0.008
#> GSM447699 3 0.1163 0.935 0.028 0.000 0.972
#> GSM447708 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447721 1 0.0424 0.957 0.992 0.000 0.008
#> GSM447623 1 0.1643 0.930 0.956 0.000 0.044
#> GSM447621 1 0.3412 0.842 0.876 0.000 0.124
#> GSM447650 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447651 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447653 3 0.0237 0.920 0.004 0.000 0.996
#> GSM447658 1 0.0237 0.956 0.996 0.000 0.004
#> GSM447675 2 0.1711 0.958 0.008 0.960 0.032
#> GSM447680 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447686 1 0.2066 0.910 0.940 0.060 0.000
#> GSM447736 3 0.1163 0.935 0.028 0.000 0.972
#> GSM447629 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447648 3 0.1163 0.935 0.028 0.000 0.972
#> GSM447660 1 0.0237 0.956 0.996 0.000 0.004
#> GSM447661 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447663 3 0.1163 0.935 0.028 0.000 0.972
#> GSM447704 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447720 3 0.3752 0.853 0.144 0.000 0.856
#> GSM447652 2 0.0237 0.977 0.004 0.996 0.000
#> GSM447679 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447712 1 0.0424 0.957 0.992 0.000 0.008
#> GSM447664 2 0.2187 0.950 0.024 0.948 0.028
#> GSM447637 3 0.1163 0.935 0.028 0.000 0.972
#> GSM447639 3 0.0661 0.916 0.008 0.004 0.988
#> GSM447615 1 0.0424 0.957 0.992 0.000 0.008
#> GSM447656 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447673 2 0.0237 0.977 0.004 0.996 0.000
#> GSM447719 3 0.0237 0.920 0.004 0.000 0.996
#> GSM447706 3 0.1411 0.932 0.036 0.000 0.964
#> GSM447612 3 0.1163 0.935 0.028 0.000 0.972
#> GSM447665 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447677 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447613 1 0.0424 0.957 0.992 0.000 0.008
#> GSM447659 3 0.0237 0.920 0.004 0.000 0.996
#> GSM447662 3 0.1163 0.935 0.028 0.000 0.972
#> GSM447666 3 0.1832 0.909 0.008 0.036 0.956
#> GSM447668 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447682 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447683 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447688 2 0.1399 0.962 0.004 0.968 0.028
#> GSM447702 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447709 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447711 1 0.0424 0.957 0.992 0.000 0.008
#> GSM447715 1 0.1411 0.931 0.964 0.036 0.000
#> GSM447693 3 0.1163 0.935 0.028 0.000 0.972
#> GSM447611 1 0.2313 0.919 0.944 0.024 0.032
#> GSM447672 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447703 2 0.0237 0.977 0.004 0.996 0.000
#> GSM447727 1 0.0424 0.957 0.992 0.000 0.008
#> GSM447638 1 0.1411 0.931 0.964 0.036 0.000
#> GSM447670 1 0.0424 0.957 0.992 0.000 0.008
#> GSM447700 2 0.0661 0.973 0.004 0.988 0.008
#> GSM447738 2 0.0237 0.977 0.004 0.996 0.000
#> GSM447739 1 0.0424 0.957 0.992 0.000 0.008
#> GSM447617 1 0.4062 0.786 0.836 0.000 0.164
#> GSM447628 2 0.1585 0.960 0.008 0.964 0.028
#> GSM447632 2 0.0237 0.977 0.004 0.996 0.000
#> GSM447619 3 0.1163 0.935 0.028 0.000 0.972
#> GSM447643 1 0.1411 0.931 0.964 0.036 0.000
#> GSM447724 3 0.0424 0.917 0.008 0.000 0.992
#> GSM447728 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447610 1 0.2537 0.915 0.920 0.000 0.080
#> GSM447633 2 0.1643 0.945 0.000 0.956 0.044
#> GSM447634 3 0.4750 0.778 0.216 0.000 0.784
#> GSM447622 3 0.4796 0.774 0.220 0.000 0.780
#> GSM447667 2 0.4504 0.742 0.196 0.804 0.000
#> GSM447687 2 0.0237 0.977 0.004 0.996 0.000
#> GSM447695 3 0.4750 0.778 0.216 0.000 0.784
#> GSM447696 1 0.0424 0.957 0.992 0.000 0.008
#> GSM447697 1 0.0424 0.957 0.992 0.000 0.008
#> GSM447714 3 0.1163 0.935 0.028 0.000 0.972
#> GSM447717 1 0.0424 0.957 0.992 0.000 0.008
#> GSM447725 1 0.0237 0.956 0.996 0.000 0.004
#> GSM447729 2 0.1585 0.960 0.008 0.964 0.028
#> GSM447644 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447710 3 0.1163 0.935 0.028 0.000 0.972
#> GSM447614 3 0.4235 0.798 0.176 0.000 0.824
#> GSM447685 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447690 1 0.0237 0.956 0.996 0.000 0.004
#> GSM447730 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447646 2 0.1585 0.960 0.008 0.964 0.028
#> GSM447689 3 0.1289 0.933 0.032 0.000 0.968
#> GSM447635 2 0.1289 0.956 0.032 0.968 0.000
#> GSM447641 1 0.0424 0.957 0.992 0.000 0.008
#> GSM447716 2 0.0424 0.975 0.008 0.992 0.000
#> GSM447718 3 0.1163 0.935 0.028 0.000 0.972
#> GSM447616 3 0.4750 0.778 0.216 0.000 0.784
#> GSM447626 3 0.1529 0.930 0.040 0.000 0.960
#> GSM447640 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447734 3 0.1163 0.935 0.028 0.000 0.972
#> GSM447692 3 0.4750 0.778 0.216 0.000 0.784
#> GSM447647 2 0.1585 0.960 0.008 0.964 0.028
#> GSM447624 3 0.6225 0.332 0.432 0.000 0.568
#> GSM447625 3 0.1163 0.935 0.028 0.000 0.972
#> GSM447707 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447732 3 0.1163 0.935 0.028 0.000 0.972
#> GSM447684 1 0.0475 0.956 0.992 0.004 0.004
#> GSM447731 3 0.4413 0.742 0.008 0.160 0.832
#> GSM447705 3 0.1289 0.912 0.000 0.032 0.968
#> GSM447631 3 0.1163 0.935 0.028 0.000 0.972
#> GSM447701 2 0.0000 0.977 0.000 1.000 0.000
#> GSM447645 3 0.1289 0.933 0.032 0.000 0.968
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM447671 2 0.0707 0.8570 0.000 0.980 0.000 0.020
#> GSM447694 3 0.0469 0.9078 0.000 0.000 0.988 0.012
#> GSM447618 2 0.4999 0.3156 0.000 0.508 0.000 0.492
#> GSM447691 2 0.1474 0.8496 0.000 0.948 0.000 0.052
#> GSM447733 4 0.3356 0.7182 0.000 0.000 0.176 0.824
#> GSM447620 2 0.0779 0.8533 0.000 0.980 0.016 0.004
#> GSM447627 3 0.1022 0.8977 0.000 0.000 0.968 0.032
#> GSM447630 2 0.4977 0.0349 0.000 0.540 0.460 0.000
#> GSM447642 1 0.0000 0.9180 1.000 0.000 0.000 0.000
#> GSM447649 2 0.0000 0.8613 0.000 1.000 0.000 0.000
#> GSM447654 4 0.0592 0.8052 0.000 0.016 0.000 0.984
#> GSM447655 2 0.0000 0.8613 0.000 1.000 0.000 0.000
#> GSM447669 2 0.0336 0.8579 0.000 0.992 0.008 0.000
#> GSM447676 1 0.0000 0.9180 1.000 0.000 0.000 0.000
#> GSM447678 4 0.0000 0.8060 0.000 0.000 0.000 1.000
#> GSM447681 2 0.1389 0.8501 0.000 0.952 0.000 0.048
#> GSM447698 2 0.4999 0.3122 0.000 0.508 0.000 0.492
#> GSM447713 1 0.0000 0.9180 1.000 0.000 0.000 0.000
#> GSM447722 4 0.0000 0.8060 0.000 0.000 0.000 1.000
#> GSM447726 2 0.0336 0.8579 0.000 0.992 0.008 0.000
#> GSM447735 4 0.4661 0.4445 0.000 0.000 0.348 0.652
#> GSM447737 1 0.5000 -0.0296 0.500 0.000 0.500 0.000
#> GSM447657 2 0.3356 0.7528 0.000 0.824 0.000 0.176
#> GSM447674 2 0.1389 0.8501 0.000 0.952 0.000 0.048
#> GSM447636 1 0.0000 0.9180 1.000 0.000 0.000 0.000
#> GSM447723 1 0.0000 0.9180 1.000 0.000 0.000 0.000
#> GSM447699 3 0.3172 0.7658 0.000 0.000 0.840 0.160
#> GSM447708 2 0.0188 0.8610 0.000 0.996 0.000 0.004
#> GSM447721 1 0.0000 0.9180 1.000 0.000 0.000 0.000
#> GSM447623 1 0.3219 0.7638 0.836 0.000 0.164 0.000
#> GSM447621 1 0.4356 0.5659 0.708 0.000 0.292 0.000
#> GSM447650 2 0.0000 0.8613 0.000 1.000 0.000 0.000
#> GSM447651 2 0.0000 0.8613 0.000 1.000 0.000 0.000
#> GSM447653 4 0.4998 0.1643 0.000 0.000 0.488 0.512
#> GSM447658 1 0.0000 0.9180 1.000 0.000 0.000 0.000
#> GSM447675 4 0.0000 0.8060 0.000 0.000 0.000 1.000
#> GSM447680 2 0.0188 0.8610 0.000 0.996 0.000 0.004
#> GSM447686 1 0.0707 0.9009 0.980 0.020 0.000 0.000
#> GSM447736 3 0.0469 0.9078 0.000 0.000 0.988 0.012
#> GSM447629 2 0.1716 0.8422 0.000 0.936 0.000 0.064
#> GSM447648 3 0.0469 0.9090 0.012 0.000 0.988 0.000
#> GSM447660 1 0.0000 0.9180 1.000 0.000 0.000 0.000
#> GSM447661 2 0.0000 0.8613 0.000 1.000 0.000 0.000
#> GSM447663 3 0.0469 0.9059 0.000 0.012 0.988 0.000
#> GSM447704 2 0.0000 0.8613 0.000 1.000 0.000 0.000
#> GSM447720 3 0.1042 0.9023 0.020 0.000 0.972 0.008
#> GSM447652 2 0.0188 0.8608 0.000 0.996 0.000 0.004
#> GSM447679 2 0.1389 0.8501 0.000 0.952 0.000 0.048
#> GSM447712 1 0.0000 0.9180 1.000 0.000 0.000 0.000
#> GSM447664 4 0.0657 0.8045 0.004 0.012 0.000 0.984
#> GSM447637 3 0.0336 0.9103 0.008 0.000 0.992 0.000
#> GSM447639 4 0.3726 0.6570 0.000 0.000 0.212 0.788
#> GSM447615 1 0.0000 0.9180 1.000 0.000 0.000 0.000
#> GSM447656 2 0.0188 0.8610 0.000 0.996 0.000 0.004
#> GSM447673 2 0.4996 0.3267 0.000 0.516 0.000 0.484
#> GSM447719 3 0.4999 -0.1941 0.000 0.000 0.508 0.492
#> GSM447706 3 0.0188 0.9111 0.004 0.000 0.996 0.000
#> GSM447612 3 0.0188 0.9107 0.000 0.000 0.996 0.004
#> GSM447665 2 0.0000 0.8613 0.000 1.000 0.000 0.000
#> GSM447677 2 0.0000 0.8613 0.000 1.000 0.000 0.000
#> GSM447613 1 0.0000 0.9180 1.000 0.000 0.000 0.000
#> GSM447659 4 0.4998 0.1625 0.000 0.000 0.488 0.512
#> GSM447662 3 0.0000 0.9114 0.000 0.000 1.000 0.000
#> GSM447666 3 0.3764 0.6475 0.000 0.216 0.784 0.000
#> GSM447668 2 0.0000 0.8613 0.000 1.000 0.000 0.000
#> GSM447682 2 0.1474 0.8483 0.000 0.948 0.000 0.052
#> GSM447683 2 0.1389 0.8501 0.000 0.952 0.000 0.048
#> GSM447688 4 0.0469 0.8042 0.000 0.012 0.000 0.988
#> GSM447702 2 0.0000 0.8613 0.000 1.000 0.000 0.000
#> GSM447709 2 0.0000 0.8613 0.000 1.000 0.000 0.000
#> GSM447711 1 0.0000 0.9180 1.000 0.000 0.000 0.000
#> GSM447715 1 0.0336 0.9116 0.992 0.008 0.000 0.000
#> GSM447693 3 0.0000 0.9114 0.000 0.000 1.000 0.000
#> GSM447611 4 0.2469 0.7381 0.108 0.000 0.000 0.892
#> GSM447672 2 0.0000 0.8613 0.000 1.000 0.000 0.000
#> GSM447703 2 0.4994 0.3276 0.000 0.520 0.000 0.480
#> GSM447727 1 0.0000 0.9180 1.000 0.000 0.000 0.000
#> GSM447638 1 0.3528 0.7088 0.808 0.192 0.000 0.000
#> GSM447670 1 0.0000 0.9180 1.000 0.000 0.000 0.000
#> GSM447700 4 0.5508 -0.3020 0.000 0.476 0.016 0.508
#> GSM447738 2 0.4996 0.3267 0.000 0.516 0.000 0.484
#> GSM447739 1 0.0000 0.9180 1.000 0.000 0.000 0.000
#> GSM447617 1 0.4790 0.3713 0.620 0.000 0.380 0.000
#> GSM447628 4 0.0707 0.8032 0.000 0.020 0.000 0.980
#> GSM447632 2 0.4996 0.3267 0.000 0.516 0.000 0.484
#> GSM447619 3 0.0000 0.9114 0.000 0.000 1.000 0.000
#> GSM447643 1 0.0469 0.9082 0.988 0.012 0.000 0.000
#> GSM447724 4 0.1302 0.7942 0.000 0.000 0.044 0.956
#> GSM447728 2 0.1302 0.8514 0.000 0.956 0.000 0.044
#> GSM447610 1 0.6011 0.0235 0.480 0.000 0.040 0.480
#> GSM447633 2 0.0524 0.8569 0.000 0.988 0.008 0.004
#> GSM447634 3 0.3547 0.7992 0.144 0.000 0.840 0.016
#> GSM447622 3 0.3219 0.7893 0.164 0.000 0.836 0.000
#> GSM447667 2 0.4804 0.6912 0.160 0.776 0.000 0.064
#> GSM447687 2 0.4996 0.3267 0.000 0.516 0.000 0.484
#> GSM447695 3 0.3547 0.7992 0.144 0.000 0.840 0.016
#> GSM447696 1 0.0000 0.9180 1.000 0.000 0.000 0.000
#> GSM447697 1 0.0188 0.9154 0.996 0.000 0.004 0.000
#> GSM447714 3 0.0000 0.9114 0.000 0.000 1.000 0.000
#> GSM447717 1 0.0000 0.9180 1.000 0.000 0.000 0.000
#> GSM447725 1 0.0000 0.9180 1.000 0.000 0.000 0.000
#> GSM447729 4 0.0657 0.8045 0.004 0.012 0.000 0.984
#> GSM447644 2 0.0469 0.8562 0.000 0.988 0.012 0.000
#> GSM447710 3 0.0000 0.9114 0.000 0.000 1.000 0.000
#> GSM447614 4 0.6743 0.2419 0.096 0.000 0.392 0.512
#> GSM447685 2 0.1389 0.8501 0.000 0.952 0.000 0.048
#> GSM447690 1 0.0000 0.9180 1.000 0.000 0.000 0.000
#> GSM447730 2 0.0000 0.8613 0.000 1.000 0.000 0.000
#> GSM447646 4 0.0592 0.8047 0.000 0.016 0.000 0.984
#> GSM447689 3 0.0000 0.9114 0.000 0.000 1.000 0.000
#> GSM447635 2 0.5000 0.2995 0.000 0.500 0.000 0.500
#> GSM447641 1 0.0000 0.9180 1.000 0.000 0.000 0.000
#> GSM447716 2 0.5296 0.2967 0.008 0.500 0.000 0.492
#> GSM447718 3 0.1302 0.8818 0.000 0.044 0.956 0.000
#> GSM447616 3 0.3311 0.7809 0.172 0.000 0.828 0.000
#> GSM447626 3 0.0000 0.9114 0.000 0.000 1.000 0.000
#> GSM447640 2 0.1302 0.8514 0.000 0.956 0.000 0.044
#> GSM447734 3 0.0000 0.9114 0.000 0.000 1.000 0.000
#> GSM447692 3 0.3625 0.7887 0.160 0.000 0.828 0.012
#> GSM447647 4 0.2011 0.7760 0.000 0.080 0.000 0.920
#> GSM447624 3 0.3801 0.7235 0.220 0.000 0.780 0.000
#> GSM447625 3 0.0000 0.9114 0.000 0.000 1.000 0.000
#> GSM447707 2 0.0000 0.8613 0.000 1.000 0.000 0.000
#> GSM447732 3 0.0000 0.9114 0.000 0.000 1.000 0.000
#> GSM447684 1 0.1211 0.8897 0.960 0.000 0.040 0.000
#> GSM447731 4 0.4462 0.7107 0.000 0.044 0.164 0.792
#> GSM447705 3 0.1118 0.8878 0.000 0.036 0.964 0.000
#> GSM447631 3 0.0336 0.9103 0.008 0.000 0.992 0.000
#> GSM447701 2 0.0000 0.8613 0.000 1.000 0.000 0.000
#> GSM447645 3 0.0469 0.9090 0.012 0.000 0.988 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM447671 5 0.4584 0.3125 0.000 0.312 0.000 0.028 0.660
#> GSM447694 3 0.0609 0.7346 0.020 0.000 0.980 0.000 0.000
#> GSM447618 5 0.6221 0.0835 0.000 0.172 0.000 0.300 0.528
#> GSM447691 5 0.4668 0.2984 0.000 0.272 0.000 0.044 0.684
#> GSM447733 4 0.3146 0.7297 0.000 0.000 0.128 0.844 0.028
#> GSM447620 2 0.2026 0.7357 0.000 0.924 0.012 0.008 0.056
#> GSM447627 3 0.1809 0.7106 0.012 0.000 0.928 0.060 0.000
#> GSM447630 5 0.5963 0.4764 0.000 0.288 0.128 0.004 0.580
#> GSM447642 1 0.0609 0.9083 0.980 0.000 0.000 0.000 0.020
#> GSM447649 2 0.0000 0.7674 0.000 1.000 0.000 0.000 0.000
#> GSM447654 4 0.0703 0.7547 0.000 0.024 0.000 0.976 0.000
#> GSM447655 2 0.0000 0.7674 0.000 1.000 0.000 0.000 0.000
#> GSM447669 5 0.3957 0.3965 0.000 0.280 0.000 0.008 0.712
#> GSM447676 1 0.0609 0.9083 0.980 0.000 0.000 0.000 0.020
#> GSM447678 4 0.3838 0.5504 0.000 0.004 0.000 0.716 0.280
#> GSM447681 2 0.4083 0.6422 0.000 0.744 0.000 0.028 0.228
#> GSM447698 2 0.6795 0.2051 0.000 0.364 0.000 0.348 0.288
#> GSM447713 1 0.0794 0.9032 0.972 0.000 0.028 0.000 0.000
#> GSM447722 4 0.3774 0.5363 0.000 0.000 0.000 0.704 0.296
#> GSM447726 2 0.4437 -0.0791 0.004 0.532 0.000 0.000 0.464
#> GSM447735 3 0.5057 0.1439 0.004 0.000 0.604 0.356 0.036
#> GSM447737 3 0.3857 0.4659 0.312 0.000 0.688 0.000 0.000
#> GSM447657 2 0.5288 0.5819 0.000 0.656 0.000 0.100 0.244
#> GSM447674 2 0.3327 0.7097 0.000 0.828 0.000 0.028 0.144
#> GSM447636 1 0.0609 0.9083 0.980 0.000 0.000 0.000 0.020
#> GSM447723 1 0.0000 0.9108 1.000 0.000 0.000 0.000 0.000
#> GSM447699 3 0.3918 0.6177 0.000 0.000 0.804 0.100 0.096
#> GSM447708 2 0.1648 0.7636 0.000 0.940 0.000 0.020 0.040
#> GSM447721 1 0.0703 0.9047 0.976 0.000 0.024 0.000 0.000
#> GSM447623 1 0.4242 0.2309 0.572 0.000 0.428 0.000 0.000
#> GSM447621 1 0.4304 0.0350 0.516 0.000 0.484 0.000 0.000
#> GSM447650 2 0.0000 0.7674 0.000 1.000 0.000 0.000 0.000
#> GSM447651 2 0.0000 0.7674 0.000 1.000 0.000 0.000 0.000
#> GSM447653 4 0.4625 0.5839 0.004 0.000 0.324 0.652 0.020
#> GSM447658 1 0.0609 0.9083 0.980 0.000 0.000 0.000 0.020
#> GSM447675 4 0.0324 0.7530 0.000 0.004 0.000 0.992 0.004
#> GSM447680 2 0.1956 0.7580 0.012 0.928 0.000 0.008 0.052
#> GSM447686 1 0.3362 0.7598 0.824 0.012 0.000 0.008 0.156
#> GSM447736 3 0.0703 0.7415 0.000 0.000 0.976 0.000 0.024
#> GSM447629 2 0.5154 0.5909 0.012 0.660 0.000 0.048 0.280
#> GSM447648 3 0.2074 0.7520 0.000 0.000 0.896 0.000 0.104
#> GSM447660 1 0.0609 0.9083 0.980 0.000 0.000 0.000 0.020
#> GSM447661 2 0.0000 0.7674 0.000 1.000 0.000 0.000 0.000
#> GSM447663 5 0.3949 0.2790 0.000 0.000 0.332 0.000 0.668
#> GSM447704 2 0.0000 0.7674 0.000 1.000 0.000 0.000 0.000
#> GSM447720 5 0.4760 0.2687 0.020 0.000 0.416 0.000 0.564
#> GSM447652 2 0.0510 0.7674 0.000 0.984 0.000 0.016 0.000
#> GSM447679 2 0.1830 0.7621 0.000 0.932 0.000 0.028 0.040
#> GSM447712 1 0.0000 0.9108 1.000 0.000 0.000 0.000 0.000
#> GSM447664 4 0.2478 0.7395 0.028 0.008 0.000 0.904 0.060
#> GSM447637 3 0.2424 0.7500 0.000 0.000 0.868 0.000 0.132
#> GSM447639 4 0.3171 0.7076 0.000 0.000 0.176 0.816 0.008
#> GSM447615 1 0.1430 0.8857 0.944 0.000 0.052 0.000 0.004
#> GSM447656 2 0.1956 0.7582 0.012 0.928 0.000 0.008 0.052
#> GSM447673 2 0.6631 0.3343 0.000 0.440 0.000 0.324 0.236
#> GSM447719 4 0.5531 0.5256 0.000 0.000 0.248 0.632 0.120
#> GSM447706 3 0.2813 0.7388 0.000 0.000 0.832 0.000 0.168
#> GSM447612 5 0.4434 -0.0203 0.000 0.000 0.460 0.004 0.536
#> GSM447665 2 0.4455 0.0760 0.000 0.588 0.000 0.008 0.404
#> GSM447677 2 0.0162 0.7658 0.000 0.996 0.000 0.000 0.004
#> GSM447613 1 0.0162 0.9102 0.996 0.000 0.004 0.000 0.000
#> GSM447659 4 0.4969 0.5818 0.000 0.000 0.292 0.652 0.056
#> GSM447662 3 0.4375 0.3166 0.000 0.000 0.576 0.004 0.420
#> GSM447666 5 0.5493 0.3619 0.000 0.108 0.264 0.000 0.628
#> GSM447668 2 0.0162 0.7658 0.000 0.996 0.000 0.000 0.004
#> GSM447682 2 0.2520 0.7507 0.000 0.896 0.000 0.048 0.056
#> GSM447683 2 0.1830 0.7621 0.000 0.932 0.000 0.028 0.040
#> GSM447688 4 0.4229 0.5426 0.000 0.020 0.000 0.704 0.276
#> GSM447702 2 0.0000 0.7674 0.000 1.000 0.000 0.000 0.000
#> GSM447709 2 0.0865 0.7561 0.000 0.972 0.000 0.004 0.024
#> GSM447711 1 0.0162 0.9102 0.996 0.000 0.004 0.000 0.000
#> GSM447715 1 0.1484 0.8808 0.944 0.000 0.000 0.008 0.048
#> GSM447693 3 0.2471 0.7491 0.000 0.000 0.864 0.000 0.136
#> GSM447611 4 0.2773 0.7169 0.112 0.000 0.020 0.868 0.000
#> GSM447672 2 0.0000 0.7674 0.000 1.000 0.000 0.000 0.000
#> GSM447703 2 0.6182 0.4058 0.000 0.520 0.000 0.324 0.156
#> GSM447727 1 0.0609 0.9083 0.980 0.000 0.000 0.000 0.020
#> GSM447638 1 0.4506 0.5136 0.676 0.296 0.000 0.000 0.028
#> GSM447670 1 0.1041 0.8992 0.964 0.000 0.032 0.000 0.004
#> GSM447700 5 0.4597 0.2342 0.000 0.012 0.024 0.260 0.704
#> GSM447738 2 0.6700 0.3102 0.000 0.420 0.000 0.324 0.256
#> GSM447739 1 0.0404 0.9080 0.988 0.000 0.012 0.000 0.000
#> GSM447617 3 0.4359 0.2513 0.412 0.000 0.584 0.000 0.004
#> GSM447628 4 0.1082 0.7532 0.000 0.028 0.000 0.964 0.008
#> GSM447632 2 0.6691 0.3269 0.000 0.428 0.000 0.312 0.260
#> GSM447619 3 0.3398 0.7112 0.000 0.000 0.780 0.004 0.216
#> GSM447643 1 0.0609 0.9083 0.980 0.000 0.000 0.000 0.020
#> GSM447724 4 0.3734 0.7131 0.000 0.000 0.060 0.812 0.128
#> GSM447728 2 0.1668 0.7637 0.000 0.940 0.000 0.028 0.032
#> GSM447610 4 0.6247 0.4599 0.228 0.000 0.228 0.544 0.000
#> GSM447633 2 0.4562 -0.1654 0.000 0.496 0.000 0.008 0.496
#> GSM447634 3 0.4049 0.6068 0.056 0.000 0.780 0.000 0.164
#> GSM447622 3 0.1638 0.7241 0.064 0.000 0.932 0.000 0.004
#> GSM447667 2 0.6599 0.4908 0.112 0.564 0.000 0.044 0.280
#> GSM447687 2 0.6252 0.4061 0.000 0.508 0.000 0.328 0.164
#> GSM447695 3 0.2243 0.7126 0.056 0.000 0.916 0.012 0.016
#> GSM447696 1 0.0703 0.9047 0.976 0.000 0.024 0.000 0.000
#> GSM447697 1 0.2773 0.7664 0.836 0.000 0.164 0.000 0.000
#> GSM447714 3 0.3452 0.6886 0.000 0.000 0.756 0.000 0.244
#> GSM447717 1 0.0609 0.9083 0.980 0.000 0.000 0.000 0.020
#> GSM447725 1 0.0000 0.9108 1.000 0.000 0.000 0.000 0.000
#> GSM447729 4 0.1413 0.7505 0.012 0.012 0.000 0.956 0.020
#> GSM447644 5 0.4304 0.1326 0.000 0.484 0.000 0.000 0.516
#> GSM447710 3 0.3586 0.6726 0.000 0.000 0.736 0.000 0.264
#> GSM447614 4 0.5086 0.4524 0.040 0.000 0.396 0.564 0.000
#> GSM447685 2 0.2278 0.7559 0.000 0.908 0.000 0.032 0.060
#> GSM447690 1 0.0794 0.9032 0.972 0.000 0.028 0.000 0.000
#> GSM447730 2 0.0000 0.7674 0.000 1.000 0.000 0.000 0.000
#> GSM447646 4 0.0865 0.7543 0.000 0.024 0.000 0.972 0.004
#> GSM447689 5 0.4201 0.1464 0.000 0.000 0.408 0.000 0.592
#> GSM447635 5 0.6047 0.2547 0.004 0.044 0.096 0.192 0.664
#> GSM447641 1 0.0162 0.9106 0.996 0.000 0.000 0.000 0.004
#> GSM447716 2 0.7175 0.2731 0.016 0.388 0.000 0.308 0.288
#> GSM447718 3 0.4288 0.5799 0.000 0.012 0.664 0.000 0.324
#> GSM447616 3 0.1608 0.7188 0.072 0.000 0.928 0.000 0.000
#> GSM447626 5 0.4256 0.0597 0.000 0.000 0.436 0.000 0.564
#> GSM447640 2 0.1741 0.7629 0.000 0.936 0.000 0.024 0.040
#> GSM447734 3 0.3177 0.7093 0.000 0.000 0.792 0.000 0.208
#> GSM447692 3 0.1544 0.7196 0.068 0.000 0.932 0.000 0.000
#> GSM447647 4 0.2068 0.7308 0.000 0.092 0.000 0.904 0.004
#> GSM447624 3 0.3123 0.6481 0.184 0.000 0.812 0.000 0.004
#> GSM447625 3 0.3210 0.7143 0.000 0.000 0.788 0.000 0.212
#> GSM447707 2 0.0000 0.7674 0.000 1.000 0.000 0.000 0.000
#> GSM447732 3 0.3636 0.6634 0.000 0.000 0.728 0.000 0.272
#> GSM447684 5 0.5715 0.1304 0.412 0.012 0.056 0.000 0.520
#> GSM447731 4 0.4272 0.6601 0.000 0.020 0.040 0.784 0.156
#> GSM447705 5 0.4362 0.2432 0.000 0.004 0.360 0.004 0.632
#> GSM447631 3 0.2424 0.7500 0.000 0.000 0.868 0.000 0.132
#> GSM447701 2 0.0794 0.7554 0.000 0.972 0.000 0.000 0.028
#> GSM447645 3 0.2424 0.7500 0.000 0.000 0.868 0.000 0.132
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM447671 5 0.5045 0.4166 0.000 0.096 0.000 0.032 0.688 0.184
#> GSM447694 3 0.0291 0.6493 0.000 0.000 0.992 0.004 0.000 0.004
#> GSM447618 5 0.2666 0.6777 0.000 0.012 0.000 0.112 0.864 0.012
#> GSM447691 5 0.4700 0.4717 0.000 0.112 0.000 0.008 0.700 0.180
#> GSM447733 4 0.2724 0.8118 0.000 0.000 0.032 0.876 0.076 0.016
#> GSM447620 2 0.4485 0.7191 0.000 0.760 0.008 0.024 0.124 0.084
#> GSM447627 3 0.0692 0.6497 0.000 0.000 0.976 0.020 0.000 0.004
#> GSM447630 6 0.5500 0.4407 0.000 0.184 0.024 0.004 0.144 0.644
#> GSM447642 1 0.0363 0.9049 0.988 0.000 0.000 0.000 0.012 0.000
#> GSM447649 2 0.0881 0.8796 0.000 0.972 0.000 0.008 0.012 0.008
#> GSM447654 4 0.1196 0.8464 0.000 0.008 0.000 0.952 0.040 0.000
#> GSM447655 2 0.0146 0.8792 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM447669 6 0.5724 0.1984 0.000 0.184 0.000 0.000 0.324 0.492
#> GSM447676 1 0.0260 0.9054 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM447678 5 0.3390 0.5997 0.000 0.000 0.000 0.296 0.704 0.000
#> GSM447681 2 0.4144 0.0992 0.000 0.580 0.000 0.008 0.408 0.004
#> GSM447698 5 0.4234 0.7048 0.000 0.100 0.000 0.152 0.744 0.004
#> GSM447713 1 0.2378 0.8132 0.848 0.000 0.152 0.000 0.000 0.000
#> GSM447722 5 0.3384 0.6311 0.000 0.000 0.008 0.228 0.760 0.004
#> GSM447726 6 0.4819 0.1253 0.000 0.416 0.000 0.000 0.056 0.528
#> GSM447735 3 0.2164 0.6236 0.000 0.000 0.900 0.068 0.032 0.000
#> GSM447737 3 0.1556 0.6359 0.080 0.000 0.920 0.000 0.000 0.000
#> GSM447657 5 0.4508 0.3093 0.000 0.436 0.000 0.024 0.536 0.004
#> GSM447674 2 0.2070 0.8392 0.000 0.892 0.000 0.008 0.100 0.000
#> GSM447636 1 0.0363 0.9049 0.988 0.000 0.000 0.000 0.012 0.000
#> GSM447723 1 0.0363 0.9040 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM447699 3 0.4710 0.4908 0.000 0.000 0.700 0.064 0.212 0.024
#> GSM447708 2 0.2653 0.8511 0.000 0.868 0.000 0.004 0.100 0.028
#> GSM447721 1 0.2300 0.8203 0.856 0.000 0.144 0.000 0.000 0.000
#> GSM447623 3 0.3782 0.2613 0.412 0.000 0.588 0.000 0.000 0.000
#> GSM447621 3 0.3727 0.3213 0.388 0.000 0.612 0.000 0.000 0.000
#> GSM447650 2 0.0405 0.8788 0.000 0.988 0.000 0.000 0.008 0.004
#> GSM447651 2 0.1088 0.8754 0.000 0.960 0.000 0.000 0.016 0.024
#> GSM447653 4 0.3388 0.7754 0.000 0.000 0.156 0.804 0.004 0.036
#> GSM447658 1 0.0363 0.9049 0.988 0.000 0.000 0.000 0.012 0.000
#> GSM447675 4 0.1387 0.8372 0.000 0.000 0.000 0.932 0.068 0.000
#> GSM447680 2 0.2353 0.8649 0.004 0.896 0.000 0.004 0.072 0.024
#> GSM447686 1 0.3728 0.6604 0.748 0.012 0.000 0.004 0.228 0.008
#> GSM447736 3 0.2834 0.6100 0.000 0.000 0.864 0.020 0.020 0.096
#> GSM447629 5 0.3840 0.6015 0.008 0.288 0.000 0.008 0.696 0.000
#> GSM447648 3 0.3817 0.5356 0.000 0.000 0.720 0.028 0.000 0.252
#> GSM447660 1 0.0363 0.9049 0.988 0.000 0.000 0.000 0.012 0.000
#> GSM447661 2 0.0260 0.8792 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM447663 6 0.3159 0.5541 0.000 0.004 0.052 0.000 0.108 0.836
#> GSM447704 2 0.0881 0.8796 0.000 0.972 0.000 0.008 0.012 0.008
#> GSM447720 6 0.4908 0.4486 0.000 0.000 0.224 0.000 0.128 0.648
#> GSM447652 2 0.1585 0.8672 0.000 0.940 0.000 0.036 0.012 0.012
#> GSM447679 2 0.1584 0.8634 0.000 0.928 0.000 0.008 0.064 0.000
#> GSM447712 1 0.0260 0.9046 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM447664 4 0.2313 0.8147 0.012 0.004 0.000 0.884 0.100 0.000
#> GSM447637 3 0.4139 0.4560 0.000 0.000 0.640 0.024 0.000 0.336
#> GSM447639 4 0.3062 0.8058 0.000 0.000 0.112 0.836 0.052 0.000
#> GSM447615 1 0.3112 0.8027 0.840 0.000 0.104 0.004 0.000 0.052
#> GSM447656 2 0.2089 0.8669 0.004 0.908 0.000 0.004 0.072 0.012
#> GSM447673 5 0.5648 0.5515 0.000 0.304 0.000 0.180 0.516 0.000
#> GSM447719 4 0.4008 0.7331 0.000 0.000 0.100 0.768 0.004 0.128
#> GSM447706 3 0.4627 0.2072 0.008 0.000 0.512 0.024 0.000 0.456
#> GSM447612 6 0.6039 0.3193 0.000 0.000 0.288 0.032 0.144 0.536
#> GSM447665 2 0.5175 0.4616 0.000 0.636 0.000 0.004 0.176 0.184
#> GSM447677 2 0.1341 0.8705 0.000 0.948 0.000 0.000 0.028 0.024
#> GSM447613 1 0.0260 0.9046 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM447659 4 0.4987 0.6656 0.000 0.000 0.180 0.700 0.072 0.048
#> GSM447662 6 0.5130 0.2354 0.000 0.000 0.324 0.032 0.044 0.600
#> GSM447666 6 0.1693 0.5585 0.000 0.000 0.032 0.020 0.012 0.936
#> GSM447668 2 0.1088 0.8739 0.000 0.960 0.000 0.000 0.016 0.024
#> GSM447682 2 0.2060 0.8489 0.000 0.900 0.000 0.016 0.084 0.000
#> GSM447683 2 0.2122 0.8616 0.000 0.900 0.000 0.008 0.084 0.008
#> GSM447688 5 0.3915 0.6153 0.000 0.016 0.000 0.288 0.692 0.004
#> GSM447702 2 0.0260 0.8792 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM447709 2 0.2849 0.8171 0.000 0.864 0.000 0.008 0.084 0.044
#> GSM447711 1 0.0260 0.9046 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM447715 1 0.1956 0.8558 0.908 0.000 0.000 0.004 0.080 0.008
#> GSM447693 3 0.4180 0.4377 0.000 0.000 0.628 0.024 0.000 0.348
#> GSM447611 4 0.2366 0.8246 0.056 0.000 0.024 0.900 0.020 0.000
#> GSM447672 2 0.0146 0.8796 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM447703 2 0.5116 0.4348 0.000 0.644 0.000 0.184 0.168 0.004
#> GSM447727 1 0.0260 0.9054 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM447638 1 0.5784 0.2588 0.524 0.348 0.000 0.000 0.028 0.100
#> GSM447670 1 0.1865 0.8736 0.920 0.000 0.040 0.000 0.000 0.040
#> GSM447700 5 0.2916 0.6378 0.000 0.000 0.012 0.072 0.864 0.052
#> GSM447738 5 0.4921 0.6826 0.000 0.180 0.000 0.164 0.656 0.000
#> GSM447739 1 0.0713 0.8981 0.972 0.000 0.028 0.000 0.000 0.000
#> GSM447617 3 0.3151 0.5382 0.252 0.000 0.748 0.000 0.000 0.000
#> GSM447628 4 0.1333 0.8436 0.000 0.008 0.000 0.944 0.048 0.000
#> GSM447632 5 0.5032 0.6663 0.000 0.216 0.000 0.148 0.636 0.000
#> GSM447619 6 0.5309 -0.1204 0.000 0.000 0.452 0.032 0.040 0.476
#> GSM447643 1 0.0891 0.8973 0.968 0.000 0.000 0.000 0.024 0.008
#> GSM447724 5 0.5424 0.4349 0.000 0.000 0.100 0.264 0.612 0.024
#> GSM447728 2 0.1196 0.8726 0.000 0.952 0.000 0.008 0.040 0.000
#> GSM447610 3 0.5951 0.0732 0.200 0.000 0.464 0.332 0.004 0.000
#> GSM447633 6 0.6477 0.2478 0.000 0.308 0.004 0.016 0.248 0.424
#> GSM447634 3 0.4110 0.4597 0.000 0.000 0.744 0.004 0.068 0.184
#> GSM447622 3 0.1801 0.6472 0.016 0.000 0.924 0.004 0.000 0.056
#> GSM447667 5 0.5337 0.6317 0.120 0.212 0.000 0.024 0.644 0.000
#> GSM447687 2 0.4990 0.4708 0.000 0.660 0.000 0.184 0.152 0.004
#> GSM447695 3 0.0862 0.6481 0.004 0.000 0.972 0.008 0.016 0.000
#> GSM447696 1 0.2300 0.8203 0.856 0.000 0.144 0.000 0.000 0.000
#> GSM447697 1 0.3198 0.6560 0.740 0.000 0.260 0.000 0.000 0.000
#> GSM447714 6 0.4612 0.0120 0.000 0.000 0.424 0.020 0.012 0.544
#> GSM447717 1 0.0363 0.9049 0.988 0.000 0.000 0.000 0.012 0.000
#> GSM447725 1 0.0405 0.9056 0.988 0.000 0.004 0.000 0.008 0.000
#> GSM447729 4 0.1700 0.8312 0.000 0.004 0.000 0.916 0.080 0.000
#> GSM447644 6 0.5468 0.3447 0.000 0.288 0.000 0.000 0.160 0.552
#> GSM447710 6 0.4199 0.1148 0.000 0.000 0.380 0.020 0.000 0.600
#> GSM447614 3 0.3163 0.4700 0.000 0.000 0.764 0.232 0.004 0.000
#> GSM447685 2 0.2122 0.8596 0.000 0.900 0.000 0.008 0.084 0.008
#> GSM447690 1 0.2378 0.8132 0.848 0.000 0.152 0.000 0.000 0.000
#> GSM447730 2 0.0881 0.8796 0.000 0.972 0.000 0.008 0.012 0.008
#> GSM447646 4 0.1196 0.8457 0.000 0.008 0.000 0.952 0.040 0.000
#> GSM447689 6 0.1745 0.5513 0.000 0.000 0.056 0.020 0.000 0.924
#> GSM447635 5 0.3217 0.6503 0.000 0.008 0.100 0.024 0.848 0.020
#> GSM447641 1 0.0405 0.9056 0.988 0.000 0.004 0.000 0.008 0.000
#> GSM447716 5 0.4597 0.6933 0.008 0.128 0.000 0.148 0.716 0.000
#> GSM447718 6 0.3339 0.4768 0.000 0.004 0.188 0.008 0.008 0.792
#> GSM447616 3 0.1176 0.6529 0.024 0.000 0.956 0.000 0.000 0.020
#> GSM447626 6 0.1411 0.5532 0.000 0.000 0.060 0.000 0.004 0.936
#> GSM447640 2 0.1728 0.8636 0.000 0.924 0.000 0.008 0.064 0.004
#> GSM447734 3 0.3982 0.1112 0.000 0.000 0.536 0.000 0.004 0.460
#> GSM447692 3 0.0692 0.6509 0.020 0.000 0.976 0.004 0.000 0.000
#> GSM447647 4 0.2474 0.8119 0.000 0.080 0.000 0.884 0.032 0.004
#> GSM447624 3 0.3819 0.5855 0.176 0.000 0.768 0.004 0.000 0.052
#> GSM447625 3 0.4086 0.1130 0.000 0.000 0.528 0.008 0.000 0.464
#> GSM447707 2 0.0779 0.8791 0.000 0.976 0.000 0.008 0.008 0.008
#> GSM447732 6 0.3774 0.2922 0.000 0.000 0.328 0.000 0.008 0.664
#> GSM447684 6 0.4478 0.4251 0.236 0.000 0.016 0.000 0.048 0.700
#> GSM447731 4 0.2765 0.7946 0.000 0.016 0.004 0.848 0.000 0.132
#> GSM447705 6 0.3275 0.5509 0.000 0.004 0.040 0.032 0.072 0.852
#> GSM447631 3 0.4124 0.4597 0.000 0.000 0.644 0.024 0.000 0.332
#> GSM447701 2 0.1719 0.8568 0.000 0.924 0.000 0.000 0.016 0.060
#> GSM447645 3 0.4139 0.4560 0.000 0.000 0.640 0.024 0.000 0.336
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)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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 gender(p) individual(p) disease.state(p) other(p) k
#> MAD:skmeans 125 0.535 0.8007 0.513 0.0938 2
#> MAD:skmeans 127 0.431 0.0918 0.162 0.1117 3
#> MAD:skmeans 111 0.217 0.3861 0.166 0.0888 4
#> MAD:skmeans 95 0.338 0.1399 0.466 0.1041 5
#> MAD:skmeans 94 0.893 0.4091 0.473 0.0309 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "pam"]
# you can also extract it by
# res = res_list["MAD:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 130 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 0.719 0.872 0.936 0.4928 0.511 0.511
#> 3 3 0.613 0.687 0.847 0.3120 0.687 0.466
#> 4 4 0.556 0.494 0.742 0.1393 0.858 0.631
#> 5 5 0.554 0.380 0.615 0.0651 0.833 0.498
#> 6 6 0.598 0.357 0.632 0.0435 0.835 0.413
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM447671 2 0.8207 0.711 0.256 0.744
#> GSM447694 1 0.0000 0.976 1.000 0.000
#> GSM447618 2 0.0938 0.898 0.012 0.988
#> GSM447691 2 0.8267 0.708 0.260 0.740
#> GSM447733 2 0.9833 0.440 0.424 0.576
#> GSM447620 2 0.8861 0.662 0.304 0.696
#> GSM447627 1 0.0000 0.976 1.000 0.000
#> GSM447630 2 0.0000 0.900 0.000 1.000
#> GSM447642 1 0.0000 0.976 1.000 0.000
#> GSM447649 2 0.0000 0.900 0.000 1.000
#> GSM447654 2 0.0672 0.899 0.008 0.992
#> GSM447655 2 0.0000 0.900 0.000 1.000
#> GSM447669 2 0.5294 0.840 0.120 0.880
#> GSM447676 1 0.0000 0.976 1.000 0.000
#> GSM447678 2 0.5178 0.842 0.116 0.884
#> GSM447681 2 0.0000 0.900 0.000 1.000
#> GSM447698 2 0.0000 0.900 0.000 1.000
#> GSM447713 1 0.0000 0.976 1.000 0.000
#> GSM447722 2 0.8207 0.711 0.256 0.744
#> GSM447726 2 0.6343 0.814 0.160 0.840
#> GSM447735 1 0.0000 0.976 1.000 0.000
#> GSM447737 1 0.0000 0.976 1.000 0.000
#> GSM447657 2 0.0000 0.900 0.000 1.000
#> GSM447674 2 0.0000 0.900 0.000 1.000
#> GSM447636 2 0.3114 0.881 0.056 0.944
#> GSM447723 1 0.0000 0.976 1.000 0.000
#> GSM447699 1 0.2423 0.937 0.960 0.040
#> GSM447708 2 0.8861 0.662 0.304 0.696
#> GSM447721 1 0.0000 0.976 1.000 0.000
#> GSM447623 1 0.0000 0.976 1.000 0.000
#> GSM447621 1 0.0000 0.976 1.000 0.000
#> GSM447650 2 0.0000 0.900 0.000 1.000
#> GSM447651 2 0.0000 0.900 0.000 1.000
#> GSM447653 1 0.0376 0.973 0.996 0.004
#> GSM447658 2 0.3584 0.877 0.068 0.932
#> GSM447675 2 0.4815 0.852 0.104 0.896
#> GSM447680 2 0.2236 0.889 0.036 0.964
#> GSM447686 2 0.1414 0.895 0.020 0.980
#> GSM447736 1 0.0000 0.976 1.000 0.000
#> GSM447629 2 0.1184 0.896 0.016 0.984
#> GSM447648 1 0.0000 0.976 1.000 0.000
#> GSM447660 2 0.9896 0.408 0.440 0.560
#> GSM447661 2 0.0000 0.900 0.000 1.000
#> GSM447663 1 0.1414 0.959 0.980 0.020
#> GSM447704 2 0.0000 0.900 0.000 1.000
#> GSM447720 2 0.9881 0.418 0.436 0.564
#> GSM447652 2 0.0000 0.900 0.000 1.000
#> GSM447679 2 0.0000 0.900 0.000 1.000
#> GSM447712 1 0.4690 0.864 0.900 0.100
#> GSM447664 2 0.1633 0.894 0.024 0.976
#> GSM447637 1 0.0000 0.976 1.000 0.000
#> GSM447639 1 0.9044 0.471 0.680 0.320
#> GSM447615 1 0.0000 0.976 1.000 0.000
#> GSM447656 2 0.2236 0.889 0.036 0.964
#> GSM447673 2 0.0000 0.900 0.000 1.000
#> GSM447719 1 0.0000 0.976 1.000 0.000
#> GSM447706 1 0.0000 0.976 1.000 0.000
#> GSM447612 1 0.1184 0.962 0.984 0.016
#> GSM447665 2 0.0000 0.900 0.000 1.000
#> GSM447677 2 0.0000 0.900 0.000 1.000
#> GSM447613 1 0.0000 0.976 1.000 0.000
#> GSM447659 1 0.1633 0.954 0.976 0.024
#> GSM447662 1 0.0000 0.976 1.000 0.000
#> GSM447666 2 0.9881 0.418 0.436 0.564
#> GSM447668 2 0.0000 0.900 0.000 1.000
#> GSM447682 2 0.0000 0.900 0.000 1.000
#> GSM447683 2 0.0000 0.900 0.000 1.000
#> GSM447688 2 0.0000 0.900 0.000 1.000
#> GSM447702 2 0.0000 0.900 0.000 1.000
#> GSM447709 2 0.5059 0.844 0.112 0.888
#> GSM447711 1 0.7376 0.709 0.792 0.208
#> GSM447715 2 0.3114 0.881 0.056 0.944
#> GSM447693 1 0.0000 0.976 1.000 0.000
#> GSM447611 2 0.4161 0.869 0.084 0.916
#> GSM447672 2 0.0000 0.900 0.000 1.000
#> GSM447703 2 0.0000 0.900 0.000 1.000
#> GSM447727 2 0.9881 0.418 0.436 0.564
#> GSM447638 2 0.3114 0.881 0.056 0.944
#> GSM447670 1 0.0000 0.976 1.000 0.000
#> GSM447700 2 0.8207 0.711 0.256 0.744
#> GSM447738 2 0.0000 0.900 0.000 1.000
#> GSM447739 1 0.0000 0.976 1.000 0.000
#> GSM447617 1 0.0000 0.976 1.000 0.000
#> GSM447628 2 0.0000 0.900 0.000 1.000
#> GSM447632 2 0.0000 0.900 0.000 1.000
#> GSM447619 1 0.0000 0.976 1.000 0.000
#> GSM447643 2 0.3114 0.881 0.056 0.944
#> GSM447724 1 0.9393 0.355 0.644 0.356
#> GSM447728 2 0.0000 0.900 0.000 1.000
#> GSM447610 1 0.0000 0.976 1.000 0.000
#> GSM447633 2 0.8144 0.715 0.252 0.748
#> GSM447634 1 0.0000 0.976 1.000 0.000
#> GSM447622 1 0.0000 0.976 1.000 0.000
#> GSM447667 2 0.3114 0.881 0.056 0.944
#> GSM447687 2 0.0000 0.900 0.000 1.000
#> GSM447695 1 0.0000 0.976 1.000 0.000
#> GSM447696 1 0.0000 0.976 1.000 0.000
#> GSM447697 1 0.0000 0.976 1.000 0.000
#> GSM447714 1 0.0000 0.976 1.000 0.000
#> GSM447717 2 0.3733 0.873 0.072 0.928
#> GSM447725 1 0.1843 0.949 0.972 0.028
#> GSM447729 2 0.0000 0.900 0.000 1.000
#> GSM447644 2 0.7219 0.771 0.200 0.800
#> GSM447710 1 0.0000 0.976 1.000 0.000
#> GSM447614 1 0.0000 0.976 1.000 0.000
#> GSM447685 2 0.0000 0.900 0.000 1.000
#> GSM447690 1 0.0000 0.976 1.000 0.000
#> GSM447730 2 0.0000 0.900 0.000 1.000
#> GSM447646 2 0.0000 0.900 0.000 1.000
#> GSM447689 2 0.9881 0.418 0.436 0.564
#> GSM447635 2 0.8861 0.655 0.304 0.696
#> GSM447641 1 0.0000 0.976 1.000 0.000
#> GSM447716 2 0.0000 0.900 0.000 1.000
#> GSM447718 2 0.0672 0.899 0.008 0.992
#> GSM447616 1 0.0000 0.976 1.000 0.000
#> GSM447626 1 0.0000 0.976 1.000 0.000
#> GSM447640 2 0.0000 0.900 0.000 1.000
#> GSM447734 1 0.0000 0.976 1.000 0.000
#> GSM447692 1 0.0000 0.976 1.000 0.000
#> GSM447647 2 0.0000 0.900 0.000 1.000
#> GSM447624 1 0.0000 0.976 1.000 0.000
#> GSM447625 1 0.0000 0.976 1.000 0.000
#> GSM447707 2 0.0000 0.900 0.000 1.000
#> GSM447732 1 0.0000 0.976 1.000 0.000
#> GSM447684 2 0.9881 0.418 0.436 0.564
#> GSM447731 2 0.3733 0.872 0.072 0.928
#> GSM447705 2 0.9686 0.498 0.396 0.604
#> GSM447631 1 0.0000 0.976 1.000 0.000
#> GSM447701 2 0.0000 0.900 0.000 1.000
#> GSM447645 1 0.0000 0.976 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM447671 3 0.4842 0.5301 0.000 0.224 0.776
#> GSM447694 3 0.5785 0.6839 0.332 0.000 0.668
#> GSM447618 2 0.5397 0.6350 0.000 0.720 0.280
#> GSM447691 3 0.5988 0.2207 0.000 0.368 0.632
#> GSM447733 3 0.0237 0.6577 0.000 0.004 0.996
#> GSM447620 3 0.6140 0.1165 0.000 0.404 0.596
#> GSM447627 3 0.5733 0.6895 0.324 0.000 0.676
#> GSM447630 2 0.2448 0.8316 0.000 0.924 0.076
#> GSM447642 1 0.0000 0.8267 1.000 0.000 0.000
#> GSM447649 2 0.0000 0.8832 0.000 1.000 0.000
#> GSM447654 2 0.6675 0.3394 0.012 0.584 0.404
#> GSM447655 2 0.0000 0.8832 0.000 1.000 0.000
#> GSM447669 2 0.6126 0.4288 0.000 0.600 0.400
#> GSM447676 1 0.6252 -0.0490 0.556 0.000 0.444
#> GSM447678 3 0.4974 0.4734 0.000 0.236 0.764
#> GSM447681 2 0.0000 0.8832 0.000 1.000 0.000
#> GSM447698 2 0.5254 0.6541 0.000 0.736 0.264
#> GSM447713 1 0.0237 0.8261 0.996 0.000 0.004
#> GSM447722 3 0.2165 0.6601 0.000 0.064 0.936
#> GSM447726 2 0.3918 0.7572 0.004 0.856 0.140
#> GSM447735 3 0.5733 0.6895 0.324 0.000 0.676
#> GSM447737 3 0.6308 0.3739 0.492 0.000 0.508
#> GSM447657 2 0.0000 0.8832 0.000 1.000 0.000
#> GSM447674 2 0.0237 0.8820 0.000 0.996 0.004
#> GSM447636 1 0.7213 0.4975 0.668 0.272 0.060
#> GSM447723 1 0.4346 0.5854 0.816 0.000 0.184
#> GSM447699 3 0.2066 0.6798 0.060 0.000 0.940
#> GSM447708 3 0.6225 0.0231 0.000 0.432 0.568
#> GSM447721 1 0.0237 0.8258 0.996 0.000 0.004
#> GSM447623 1 0.0424 0.8248 0.992 0.000 0.008
#> GSM447621 1 0.0424 0.8248 0.992 0.000 0.008
#> GSM447650 2 0.0000 0.8832 0.000 1.000 0.000
#> GSM447651 2 0.0000 0.8832 0.000 1.000 0.000
#> GSM447653 3 0.5291 0.6663 0.268 0.000 0.732
#> GSM447658 1 0.7213 0.4975 0.668 0.272 0.060
#> GSM447675 3 0.5024 0.4580 0.004 0.220 0.776
#> GSM447680 2 0.0000 0.8832 0.000 1.000 0.000
#> GSM447686 2 0.0747 0.8759 0.016 0.984 0.000
#> GSM447736 3 0.5733 0.6895 0.324 0.000 0.676
#> GSM447629 2 0.0237 0.8818 0.000 0.996 0.004
#> GSM447648 3 0.5733 0.6895 0.324 0.000 0.676
#> GSM447660 1 0.2636 0.7859 0.932 0.048 0.020
#> GSM447661 2 0.0000 0.8832 0.000 1.000 0.000
#> GSM447663 3 0.2066 0.6798 0.060 0.000 0.940
#> GSM447704 2 0.0000 0.8832 0.000 1.000 0.000
#> GSM447720 3 0.2400 0.6593 0.004 0.064 0.932
#> GSM447652 2 0.0592 0.8784 0.000 0.988 0.012
#> GSM447679 2 0.0000 0.8832 0.000 1.000 0.000
#> GSM447712 1 0.0000 0.8267 1.000 0.000 0.000
#> GSM447664 2 0.5843 0.6315 0.016 0.732 0.252
#> GSM447637 3 0.5785 0.6839 0.332 0.000 0.668
#> GSM447639 3 0.2200 0.6789 0.056 0.004 0.940
#> GSM447615 3 0.5760 0.6875 0.328 0.000 0.672
#> GSM447656 2 0.0237 0.8817 0.004 0.996 0.000
#> GSM447673 2 0.0592 0.8784 0.000 0.988 0.012
#> GSM447719 3 0.5363 0.6698 0.276 0.000 0.724
#> GSM447706 3 0.5760 0.6875 0.328 0.000 0.672
#> GSM447612 3 0.2066 0.6798 0.060 0.000 0.940
#> GSM447665 2 0.5254 0.6541 0.000 0.736 0.264
#> GSM447677 2 0.0000 0.8832 0.000 1.000 0.000
#> GSM447613 1 0.0000 0.8267 1.000 0.000 0.000
#> GSM447659 3 0.1411 0.6739 0.036 0.000 0.964
#> GSM447662 3 0.4235 0.6912 0.176 0.000 0.824
#> GSM447666 3 0.5643 0.5381 0.020 0.220 0.760
#> GSM447668 2 0.0000 0.8832 0.000 1.000 0.000
#> GSM447682 2 0.0237 0.8820 0.000 0.996 0.004
#> GSM447683 2 0.0000 0.8832 0.000 1.000 0.000
#> GSM447688 2 0.5254 0.6541 0.000 0.736 0.264
#> GSM447702 2 0.0000 0.8832 0.000 1.000 0.000
#> GSM447709 2 0.6215 0.3712 0.000 0.572 0.428
#> GSM447711 1 0.0000 0.8267 1.000 0.000 0.000
#> GSM447715 2 0.0237 0.8817 0.004 0.996 0.000
#> GSM447693 3 0.5733 0.6895 0.324 0.000 0.676
#> GSM447611 2 0.6941 0.1720 0.016 0.520 0.464
#> GSM447672 2 0.0000 0.8832 0.000 1.000 0.000
#> GSM447703 2 0.0000 0.8832 0.000 1.000 0.000
#> GSM447727 1 0.3550 0.7593 0.896 0.080 0.024
#> GSM447638 2 0.5706 0.4783 0.320 0.680 0.000
#> GSM447670 1 0.0592 0.8219 0.988 0.000 0.012
#> GSM447700 3 0.4796 0.5353 0.000 0.220 0.780
#> GSM447738 2 0.0000 0.8832 0.000 1.000 0.000
#> GSM447739 1 0.0000 0.8267 1.000 0.000 0.000
#> GSM447617 1 0.0747 0.8202 0.984 0.000 0.016
#> GSM447628 2 0.2066 0.8478 0.000 0.940 0.060
#> GSM447632 2 0.0592 0.8784 0.000 0.988 0.012
#> GSM447619 3 0.5760 0.6875 0.328 0.000 0.672
#> GSM447643 1 0.6255 0.4535 0.668 0.320 0.012
#> GSM447724 3 0.2443 0.6729 0.028 0.032 0.940
#> GSM447728 2 0.0000 0.8832 0.000 1.000 0.000
#> GSM447610 1 0.6309 -0.2211 0.504 0.000 0.496
#> GSM447633 3 0.6180 0.0783 0.000 0.416 0.584
#> GSM447634 3 0.5733 0.6895 0.324 0.000 0.676
#> GSM447622 3 0.5785 0.6839 0.332 0.000 0.668
#> GSM447667 2 0.1765 0.8563 0.040 0.956 0.004
#> GSM447687 2 0.0237 0.8820 0.000 0.996 0.004
#> GSM447695 3 0.5733 0.6895 0.324 0.000 0.676
#> GSM447696 1 0.0000 0.8267 1.000 0.000 0.000
#> GSM447697 1 0.0237 0.8261 0.996 0.000 0.004
#> GSM447714 3 0.2066 0.6798 0.060 0.000 0.940
#> GSM447717 1 0.6200 0.4673 0.676 0.312 0.012
#> GSM447725 1 0.2066 0.7910 0.940 0.000 0.060
#> GSM447729 2 0.5171 0.7045 0.012 0.784 0.204
#> GSM447644 2 0.6309 0.1841 0.000 0.504 0.496
#> GSM447710 3 0.5733 0.6895 0.324 0.000 0.676
#> GSM447614 3 0.5733 0.6895 0.324 0.000 0.676
#> GSM447685 2 0.0000 0.8832 0.000 1.000 0.000
#> GSM447690 1 0.0237 0.8261 0.996 0.000 0.004
#> GSM447730 2 0.0000 0.8832 0.000 1.000 0.000
#> GSM447646 2 0.2066 0.8478 0.000 0.940 0.060
#> GSM447689 3 0.2400 0.6593 0.004 0.064 0.932
#> GSM447635 3 0.2165 0.6601 0.000 0.064 0.936
#> GSM447641 1 0.0000 0.8267 1.000 0.000 0.000
#> GSM447716 2 0.0000 0.8832 0.000 1.000 0.000
#> GSM447718 2 0.6104 0.4034 0.004 0.648 0.348
#> GSM447616 3 0.5785 0.6839 0.332 0.000 0.668
#> GSM447626 1 0.4974 0.4842 0.764 0.000 0.236
#> GSM447640 2 0.0000 0.8832 0.000 1.000 0.000
#> GSM447734 3 0.5733 0.6895 0.324 0.000 0.676
#> GSM447692 3 0.5785 0.6839 0.332 0.000 0.668
#> GSM447647 2 0.2066 0.8478 0.000 0.940 0.060
#> GSM447624 1 0.0747 0.8202 0.984 0.000 0.016
#> GSM447625 3 0.5733 0.6895 0.324 0.000 0.676
#> GSM447707 2 0.0000 0.8832 0.000 1.000 0.000
#> GSM447732 3 0.5733 0.6895 0.324 0.000 0.676
#> GSM447684 1 0.7101 0.4864 0.704 0.080 0.216
#> GSM447731 2 0.6682 0.1103 0.008 0.504 0.488
#> GSM447705 3 0.2400 0.6593 0.004 0.064 0.932
#> GSM447631 3 0.5733 0.6895 0.324 0.000 0.676
#> GSM447701 2 0.0000 0.8832 0.000 1.000 0.000
#> GSM447645 3 0.5835 0.6741 0.340 0.000 0.660
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM447671 4 0.7647 0.5798 0.000 0.388 0.208 0.404
#> GSM447694 3 0.0469 0.7224 0.012 0.000 0.988 0.000
#> GSM447618 2 0.5387 -0.3272 0.000 0.584 0.016 0.400
#> GSM447691 4 0.7648 0.5762 0.000 0.392 0.208 0.400
#> GSM447733 4 0.5334 0.0907 0.004 0.004 0.484 0.508
#> GSM447620 4 0.5646 0.5644 0.008 0.384 0.016 0.592
#> GSM447627 3 0.0000 0.7228 0.000 0.000 1.000 0.000
#> GSM447630 2 0.5420 0.4905 0.032 0.764 0.156 0.048
#> GSM447642 1 0.0000 0.7504 1.000 0.000 0.000 0.000
#> GSM447649 2 0.3074 0.6870 0.000 0.848 0.000 0.152
#> GSM447654 2 0.5837 0.5968 0.000 0.564 0.036 0.400
#> GSM447655 2 0.3266 0.6904 0.000 0.832 0.000 0.168
#> GSM447669 2 0.6262 -0.4109 0.000 0.540 0.060 0.400
#> GSM447676 1 0.4331 0.4155 0.712 0.000 0.288 0.000
#> GSM447678 4 0.5395 0.2631 0.000 0.184 0.084 0.732
#> GSM447681 2 0.3266 0.6946 0.000 0.832 0.000 0.168
#> GSM447698 2 0.5028 -0.2976 0.000 0.596 0.004 0.400
#> GSM447713 1 0.4661 0.4152 0.652 0.000 0.348 0.000
#> GSM447722 3 0.6607 -0.2150 0.000 0.084 0.516 0.400
#> GSM447726 2 0.8168 -0.0814 0.344 0.484 0.112 0.060
#> GSM447735 3 0.0000 0.7228 0.000 0.000 1.000 0.000
#> GSM447737 3 0.3400 0.5927 0.180 0.000 0.820 0.000
#> GSM447657 2 0.4193 0.6958 0.000 0.732 0.000 0.268
#> GSM447674 2 0.4222 0.6944 0.000 0.728 0.000 0.272
#> GSM447636 1 0.0657 0.7478 0.984 0.004 0.000 0.012
#> GSM447723 1 0.2760 0.6721 0.872 0.000 0.128 0.000
#> GSM447699 3 0.5050 0.0230 0.004 0.000 0.588 0.408
#> GSM447708 4 0.7610 0.5697 0.000 0.400 0.200 0.400
#> GSM447721 1 0.3942 0.6092 0.764 0.000 0.236 0.000
#> GSM447623 3 0.4996 -0.1125 0.484 0.000 0.516 0.000
#> GSM447621 3 0.4996 -0.1125 0.484 0.000 0.516 0.000
#> GSM447650 2 0.4193 0.6958 0.000 0.732 0.000 0.268
#> GSM447651 2 0.0817 0.6371 0.000 0.976 0.000 0.024
#> GSM447653 3 0.5533 0.5568 0.136 0.000 0.732 0.132
#> GSM447658 1 0.0657 0.7478 0.984 0.004 0.000 0.012
#> GSM447675 4 0.3610 0.1506 0.000 0.200 0.000 0.800
#> GSM447680 2 0.0000 0.6461 0.000 1.000 0.000 0.000
#> GSM447686 2 0.2704 0.5894 0.124 0.876 0.000 0.000
#> GSM447736 3 0.0000 0.7228 0.000 0.000 1.000 0.000
#> GSM447629 2 0.1474 0.6032 0.000 0.948 0.000 0.052
#> GSM447648 3 0.1302 0.7129 0.000 0.000 0.956 0.044
#> GSM447660 1 0.0657 0.7473 0.984 0.000 0.012 0.004
#> GSM447661 2 0.0921 0.6400 0.000 0.972 0.000 0.028
#> GSM447663 4 0.6990 0.1605 0.116 0.000 0.408 0.476
#> GSM447704 2 0.3486 0.6930 0.000 0.812 0.000 0.188
#> GSM447720 3 0.7835 0.0995 0.340 0.020 0.484 0.156
#> GSM447652 2 0.4222 0.6944 0.000 0.728 0.000 0.272
#> GSM447679 2 0.4193 0.6958 0.000 0.732 0.000 0.268
#> GSM447712 1 0.0000 0.7504 1.000 0.000 0.000 0.000
#> GSM447664 2 0.4855 0.6199 0.000 0.600 0.000 0.400
#> GSM447637 3 0.2060 0.7126 0.016 0.000 0.932 0.052
#> GSM447639 3 0.6506 -0.0883 0.056 0.008 0.532 0.404
#> GSM447615 3 0.2216 0.6857 0.092 0.000 0.908 0.000
#> GSM447656 2 0.4643 0.2514 0.344 0.656 0.000 0.000
#> GSM447673 2 0.4222 0.6944 0.000 0.728 0.000 0.272
#> GSM447719 3 0.4791 0.6049 0.080 0.000 0.784 0.136
#> GSM447706 3 0.2759 0.7043 0.052 0.000 0.904 0.044
#> GSM447612 4 0.4877 0.1846 0.000 0.000 0.408 0.592
#> GSM447665 4 0.4972 0.4977 0.000 0.456 0.000 0.544
#> GSM447677 2 0.0817 0.6371 0.000 0.976 0.000 0.024
#> GSM447613 1 0.0000 0.7504 1.000 0.000 0.000 0.000
#> GSM447659 3 0.4941 0.0410 0.000 0.000 0.564 0.436
#> GSM447662 3 0.4877 0.3051 0.000 0.000 0.592 0.408
#> GSM447666 1 0.8530 -0.1667 0.360 0.300 0.024 0.316
#> GSM447668 2 0.0000 0.6461 0.000 1.000 0.000 0.000
#> GSM447682 2 0.4164 0.6964 0.000 0.736 0.000 0.264
#> GSM447683 2 0.0000 0.6461 0.000 1.000 0.000 0.000
#> GSM447688 4 0.4585 0.0514 0.000 0.332 0.000 0.668
#> GSM447702 2 0.0000 0.6461 0.000 1.000 0.000 0.000
#> GSM447709 4 0.5126 0.5132 0.000 0.444 0.004 0.552
#> GSM447711 1 0.0000 0.7504 1.000 0.000 0.000 0.000
#> GSM447715 2 0.4920 0.2238 0.368 0.628 0.004 0.000
#> GSM447693 3 0.3356 0.6367 0.000 0.000 0.824 0.176
#> GSM447611 2 0.8364 0.4479 0.132 0.428 0.056 0.384
#> GSM447672 2 0.4193 0.6958 0.000 0.732 0.000 0.268
#> GSM447703 2 0.4193 0.6958 0.000 0.732 0.000 0.268
#> GSM447727 1 0.2706 0.7210 0.900 0.020 0.080 0.000
#> GSM447638 1 0.5980 0.2394 0.560 0.396 0.044 0.000
#> GSM447670 1 0.3486 0.6542 0.812 0.000 0.188 0.000
#> GSM447700 4 0.6887 0.6000 0.000 0.356 0.116 0.528
#> GSM447738 2 0.4193 0.6958 0.000 0.732 0.000 0.268
#> GSM447739 1 0.4522 0.4600 0.680 0.000 0.320 0.000
#> GSM447617 3 0.4996 -0.1125 0.484 0.000 0.516 0.000
#> GSM447628 2 0.4855 0.6199 0.000 0.600 0.000 0.400
#> GSM447632 2 0.4222 0.6944 0.000 0.728 0.000 0.272
#> GSM447619 3 0.4365 0.6230 0.028 0.000 0.784 0.188
#> GSM447643 1 0.0592 0.7453 0.984 0.016 0.000 0.000
#> GSM447724 4 0.5427 0.1919 0.000 0.016 0.416 0.568
#> GSM447728 2 0.0000 0.6461 0.000 1.000 0.000 0.000
#> GSM447610 3 0.6939 0.2565 0.332 0.000 0.540 0.128
#> GSM447633 4 0.5279 0.5546 0.000 0.400 0.012 0.588
#> GSM447634 3 0.0000 0.7228 0.000 0.000 1.000 0.000
#> GSM447622 3 0.0927 0.7224 0.016 0.000 0.976 0.008
#> GSM447667 2 0.3831 0.4372 0.204 0.792 0.004 0.000
#> GSM447687 2 0.4193 0.6958 0.000 0.732 0.000 0.268
#> GSM447695 3 0.0000 0.7228 0.000 0.000 1.000 0.000
#> GSM447696 1 0.4605 0.4361 0.664 0.000 0.336 0.000
#> GSM447697 1 0.4164 0.5448 0.736 0.000 0.264 0.000
#> GSM447714 4 0.6355 0.2473 0.076 0.000 0.348 0.576
#> GSM447717 1 0.0000 0.7504 1.000 0.000 0.000 0.000
#> GSM447725 1 0.0000 0.7504 1.000 0.000 0.000 0.000
#> GSM447729 2 0.6262 0.5751 0.060 0.540 0.000 0.400
#> GSM447644 4 0.6788 0.5512 0.000 0.424 0.096 0.480
#> GSM447710 3 0.4820 0.6156 0.060 0.000 0.772 0.168
#> GSM447614 3 0.0000 0.7228 0.000 0.000 1.000 0.000
#> GSM447685 2 0.0000 0.6461 0.000 1.000 0.000 0.000
#> GSM447690 1 0.4661 0.4152 0.652 0.000 0.348 0.000
#> GSM447730 2 0.2469 0.5595 0.000 0.892 0.000 0.108
#> GSM447646 2 0.4855 0.6199 0.000 0.600 0.000 0.400
#> GSM447689 1 0.8154 -0.0518 0.380 0.008 0.292 0.320
#> GSM447635 3 0.7099 -0.1741 0.044 0.044 0.512 0.400
#> GSM447641 1 0.0000 0.7504 1.000 0.000 0.000 0.000
#> GSM447716 2 0.1389 0.6073 0.000 0.952 0.000 0.048
#> GSM447718 2 0.6552 0.2655 0.096 0.576 0.328 0.000
#> GSM447616 3 0.0592 0.7217 0.016 0.000 0.984 0.000
#> GSM447626 1 0.5038 0.4549 0.652 0.000 0.336 0.012
#> GSM447640 2 0.4193 0.6958 0.000 0.732 0.000 0.268
#> GSM447734 3 0.0000 0.7228 0.000 0.000 1.000 0.000
#> GSM447692 3 0.0592 0.7217 0.016 0.000 0.984 0.000
#> GSM447647 2 0.4916 0.6150 0.000 0.576 0.000 0.424
#> GSM447624 3 0.4998 -0.1225 0.488 0.000 0.512 0.000
#> GSM447625 3 0.0000 0.7228 0.000 0.000 1.000 0.000
#> GSM447707 2 0.4040 0.6960 0.000 0.752 0.000 0.248
#> GSM447732 3 0.1042 0.7194 0.020 0.000 0.972 0.008
#> GSM447684 1 0.5492 0.4523 0.640 0.032 0.328 0.000
#> GSM447731 2 0.7854 0.0368 0.000 0.400 0.304 0.296
#> GSM447705 4 0.7190 0.3540 0.072 0.044 0.292 0.592
#> GSM447631 3 0.1474 0.7124 0.000 0.000 0.948 0.052
#> GSM447701 2 0.1629 0.6244 0.000 0.952 0.024 0.024
#> GSM447645 3 0.3758 0.6750 0.104 0.000 0.848 0.048
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM447671 5 0.2275 0.52664 0.000 0.012 0.064 0.012 0.912
#> GSM447694 3 0.6024 0.52614 0.288 0.000 0.560 0.152 0.000
#> GSM447618 5 0.3318 0.44945 0.000 0.192 0.000 0.008 0.800
#> GSM447691 5 0.4679 0.48221 0.000 0.032 0.040 0.172 0.756
#> GSM447733 4 0.6823 -0.22168 0.000 0.000 0.336 0.344 0.320
#> GSM447620 5 0.4676 0.46878 0.008 0.076 0.028 0.100 0.788
#> GSM447627 3 0.5115 0.60772 0.168 0.000 0.696 0.136 0.000
#> GSM447630 4 0.7190 0.01031 0.008 0.348 0.016 0.428 0.200
#> GSM447642 1 0.4291 0.32022 0.536 0.000 0.000 0.464 0.000
#> GSM447649 2 0.3209 0.68294 0.000 0.812 0.000 0.008 0.180
#> GSM447654 2 0.4836 0.35252 0.000 0.628 0.000 0.336 0.036
#> GSM447655 2 0.2806 0.69304 0.000 0.844 0.000 0.004 0.152
#> GSM447669 5 0.4983 0.43524 0.000 0.064 0.000 0.272 0.664
#> GSM447676 1 0.6011 0.21896 0.528 0.000 0.128 0.344 0.000
#> GSM447678 5 0.7137 0.27157 0.000 0.320 0.044 0.160 0.476
#> GSM447681 2 0.2953 0.69402 0.000 0.844 0.000 0.012 0.144
#> GSM447698 5 0.3462 0.44582 0.000 0.196 0.000 0.012 0.792
#> GSM447713 1 0.5605 0.38407 0.640 0.000 0.192 0.168 0.000
#> GSM447722 5 0.6012 0.29367 0.000 0.000 0.332 0.132 0.536
#> GSM447726 5 0.8747 -0.05557 0.184 0.196 0.012 0.296 0.312
#> GSM447735 3 0.4473 0.60562 0.168 0.000 0.764 0.056 0.012
#> GSM447737 1 0.4528 -0.07505 0.548 0.000 0.444 0.008 0.000
#> GSM447657 2 0.0609 0.70671 0.000 0.980 0.000 0.000 0.020
#> GSM447674 2 0.0162 0.70567 0.000 0.996 0.000 0.000 0.004
#> GSM447636 1 0.4437 0.31474 0.532 0.004 0.000 0.464 0.000
#> GSM447723 4 0.4744 -0.32757 0.476 0.000 0.016 0.508 0.000
#> GSM447699 5 0.6350 0.29276 0.000 0.000 0.240 0.236 0.524
#> GSM447708 5 0.7041 0.34767 0.000 0.208 0.036 0.244 0.512
#> GSM447721 1 0.6244 0.22298 0.540 0.000 0.200 0.260 0.000
#> GSM447623 1 0.4030 0.06976 0.648 0.000 0.352 0.000 0.000
#> GSM447621 1 0.3949 0.09303 0.668 0.000 0.332 0.000 0.000
#> GSM447650 2 0.0671 0.70568 0.000 0.980 0.000 0.016 0.004
#> GSM447651 2 0.5568 0.54498 0.000 0.596 0.000 0.096 0.308
#> GSM447653 4 0.6397 0.01541 0.096 0.000 0.304 0.564 0.036
#> GSM447658 1 0.4297 0.31313 0.528 0.000 0.000 0.472 0.000
#> GSM447675 2 0.6561 -0.00773 0.000 0.452 0.000 0.216 0.332
#> GSM447680 2 0.5640 0.53811 0.000 0.592 0.000 0.104 0.304
#> GSM447686 2 0.8235 0.13763 0.208 0.408 0.000 0.204 0.180
#> GSM447736 3 0.5379 0.59235 0.168 0.000 0.668 0.164 0.000
#> GSM447629 2 0.5559 0.48401 0.000 0.544 0.000 0.076 0.380
#> GSM447648 3 0.3304 0.60009 0.168 0.000 0.816 0.000 0.016
#> GSM447660 1 0.4546 0.31244 0.532 0.000 0.008 0.460 0.000
#> GSM447661 2 0.5359 0.56572 0.000 0.616 0.000 0.080 0.304
#> GSM447663 4 0.7452 0.00178 0.072 0.000 0.356 0.428 0.144
#> GSM447704 2 0.2674 0.69525 0.000 0.856 0.000 0.004 0.140
#> GSM447720 3 0.8519 -0.07414 0.152 0.004 0.332 0.272 0.240
#> GSM447652 2 0.0451 0.70575 0.000 0.988 0.000 0.004 0.008
#> GSM447679 2 0.0609 0.70671 0.000 0.980 0.000 0.000 0.020
#> GSM447712 1 0.4287 0.32183 0.540 0.000 0.000 0.460 0.000
#> GSM447664 2 0.4087 0.53271 0.000 0.756 0.000 0.208 0.036
#> GSM447637 3 0.4920 0.25336 0.300 0.000 0.660 0.020 0.020
#> GSM447639 5 0.7237 0.27615 0.008 0.024 0.224 0.260 0.484
#> GSM447615 3 0.5312 0.56055 0.208 0.000 0.668 0.124 0.000
#> GSM447656 2 0.8130 0.28136 0.184 0.380 0.000 0.132 0.304
#> GSM447673 2 0.0000 0.70492 0.000 1.000 0.000 0.000 0.000
#> GSM447719 3 0.6221 0.19082 0.060 0.000 0.532 0.368 0.040
#> GSM447706 3 0.6182 0.35869 0.168 0.000 0.608 0.208 0.016
#> GSM447612 5 0.4687 0.27402 0.000 0.000 0.336 0.028 0.636
#> GSM447665 5 0.1671 0.51930 0.000 0.076 0.000 0.000 0.924
#> GSM447677 2 0.4066 0.61153 0.000 0.672 0.000 0.004 0.324
#> GSM447613 1 0.3999 0.32384 0.656 0.000 0.000 0.344 0.000
#> GSM447659 5 0.5080 0.24388 0.000 0.000 0.368 0.044 0.588
#> GSM447662 3 0.3958 0.52805 0.000 0.000 0.776 0.040 0.184
#> GSM447666 5 0.8632 -0.17433 0.192 0.008 0.212 0.228 0.360
#> GSM447668 2 0.5613 0.54230 0.000 0.592 0.000 0.100 0.308
#> GSM447682 2 0.0404 0.70826 0.000 0.988 0.000 0.000 0.012
#> GSM447683 2 0.3969 0.61946 0.000 0.692 0.000 0.004 0.304
#> GSM447688 5 0.4555 0.18590 0.000 0.472 0.000 0.008 0.520
#> GSM447702 2 0.4003 0.62495 0.000 0.704 0.000 0.008 0.288
#> GSM447709 5 0.5211 0.29397 0.000 0.232 0.000 0.100 0.668
#> GSM447711 1 0.4287 0.32183 0.540 0.000 0.000 0.460 0.000
#> GSM447715 2 0.7918 -0.01594 0.192 0.388 0.000 0.324 0.096
#> GSM447693 3 0.2727 0.57807 0.000 0.000 0.868 0.016 0.116
#> GSM447611 4 0.7070 0.18958 0.128 0.304 0.012 0.520 0.036
#> GSM447672 2 0.0324 0.70411 0.000 0.992 0.000 0.004 0.004
#> GSM447703 2 0.0000 0.70492 0.000 1.000 0.000 0.000 0.000
#> GSM447727 4 0.5567 -0.02721 0.380 0.000 0.044 0.560 0.016
#> GSM447638 4 0.7892 0.22186 0.228 0.116 0.000 0.452 0.204
#> GSM447670 1 0.5836 0.20846 0.608 0.000 0.216 0.176 0.000
#> GSM447700 5 0.2228 0.52364 0.000 0.008 0.056 0.020 0.916
#> GSM447738 2 0.0771 0.70610 0.000 0.976 0.000 0.004 0.020
#> GSM447739 1 0.5605 0.38407 0.640 0.000 0.192 0.168 0.000
#> GSM447617 1 0.4390 -0.03014 0.568 0.000 0.428 0.004 0.000
#> GSM447628 2 0.3954 0.54834 0.000 0.772 0.000 0.192 0.036
#> GSM447632 2 0.0609 0.70671 0.000 0.980 0.000 0.000 0.020
#> GSM447619 3 0.2471 0.57732 0.000 0.000 0.864 0.000 0.136
#> GSM447643 1 0.4549 0.30894 0.528 0.000 0.000 0.464 0.008
#> GSM447724 5 0.4491 0.29616 0.000 0.000 0.328 0.020 0.652
#> GSM447728 2 0.3969 0.61946 0.000 0.692 0.000 0.004 0.304
#> GSM447610 4 0.7471 0.02223 0.256 0.000 0.332 0.376 0.036
#> GSM447633 5 0.1648 0.52106 0.000 0.040 0.020 0.000 0.940
#> GSM447634 3 0.5664 0.58438 0.168 0.000 0.632 0.200 0.000
#> GSM447622 1 0.5557 -0.22909 0.468 0.000 0.464 0.068 0.000
#> GSM447667 2 0.4991 0.60541 0.024 0.656 0.004 0.012 0.304
#> GSM447687 2 0.0000 0.70492 0.000 1.000 0.000 0.000 0.000
#> GSM447695 3 0.4473 0.60603 0.168 0.000 0.764 0.056 0.012
#> GSM447696 1 0.5773 0.37760 0.616 0.000 0.216 0.168 0.000
#> GSM447697 1 0.5605 0.38407 0.640 0.000 0.192 0.168 0.000
#> GSM447714 3 0.5384 0.06402 0.008 0.000 0.536 0.040 0.416
#> GSM447717 1 0.4287 0.32183 0.540 0.000 0.000 0.460 0.000
#> GSM447725 1 0.4287 0.32183 0.540 0.000 0.000 0.460 0.000
#> GSM447729 2 0.5617 0.43020 0.064 0.668 0.000 0.232 0.036
#> GSM447644 5 0.6526 0.31312 0.000 0.212 0.004 0.276 0.508
#> GSM447710 3 0.3934 0.55060 0.000 0.000 0.800 0.076 0.124
#> GSM447614 3 0.4335 0.60576 0.168 0.000 0.760 0.072 0.000
#> GSM447685 2 0.3969 0.61946 0.000 0.692 0.000 0.004 0.304
#> GSM447690 1 0.5605 0.38407 0.640 0.000 0.192 0.168 0.000
#> GSM447730 2 0.4276 0.55263 0.000 0.616 0.000 0.004 0.380
#> GSM447646 2 0.4558 0.50741 0.000 0.724 0.000 0.216 0.060
#> GSM447689 3 0.8217 -0.17233 0.212 0.000 0.368 0.288 0.132
#> GSM447635 5 0.6360 0.24503 0.000 0.004 0.348 0.152 0.496
#> GSM447641 1 0.4291 0.32022 0.536 0.000 0.000 0.464 0.000
#> GSM447716 2 0.3966 0.60336 0.000 0.664 0.000 0.000 0.336
#> GSM447718 4 0.7589 0.32264 0.176 0.280 0.068 0.472 0.004
#> GSM447616 3 0.5529 0.26890 0.420 0.000 0.512 0.068 0.000
#> GSM447626 4 0.6080 0.29311 0.200 0.000 0.228 0.572 0.000
#> GSM447640 2 0.0000 0.70492 0.000 1.000 0.000 0.000 0.000
#> GSM447734 3 0.3109 0.56618 0.000 0.000 0.800 0.200 0.000
#> GSM447692 1 0.5557 -0.22909 0.468 0.000 0.464 0.068 0.000
#> GSM447647 2 0.4203 0.54045 0.000 0.760 0.000 0.188 0.052
#> GSM447624 1 0.4752 -0.04727 0.556 0.000 0.428 0.012 0.004
#> GSM447625 3 0.3109 0.56618 0.000 0.000 0.800 0.200 0.000
#> GSM447707 2 0.1768 0.70510 0.000 0.924 0.000 0.004 0.072
#> GSM447732 3 0.4015 0.43897 0.000 0.000 0.652 0.348 0.000
#> GSM447684 4 0.6435 0.26049 0.364 0.000 0.028 0.512 0.096
#> GSM447731 3 0.7456 0.11011 0.000 0.088 0.424 0.368 0.120
#> GSM447705 5 0.3895 0.33066 0.000 0.000 0.320 0.000 0.680
#> GSM447631 3 0.1216 0.60101 0.000 0.000 0.960 0.020 0.020
#> GSM447701 2 0.6383 0.40684 0.000 0.488 0.000 0.184 0.328
#> GSM447645 3 0.1216 0.60101 0.000 0.000 0.960 0.020 0.020
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM447671 5 0.4671 0.3856 0.000 0.000 0.008 0.040 0.608 0.344
#> GSM447694 3 0.4415 0.5333 0.000 0.000 0.740 0.064 0.024 0.172
#> GSM447618 5 0.4461 0.4618 0.000 0.068 0.012 0.000 0.716 0.204
#> GSM447691 5 0.5113 0.3892 0.000 0.000 0.052 0.060 0.676 0.212
#> GSM447733 6 0.6951 0.2877 0.000 0.000 0.056 0.272 0.304 0.368
#> GSM447620 5 0.6638 0.2942 0.000 0.108 0.000 0.232 0.524 0.136
#> GSM447627 3 0.4459 0.4325 0.000 0.000 0.640 0.032 0.008 0.320
#> GSM447630 2 0.8931 -0.1297 0.180 0.312 0.040 0.060 0.264 0.144
#> GSM447642 1 0.0291 0.7099 0.992 0.000 0.004 0.000 0.004 0.000
#> GSM447649 2 0.5288 0.4210 0.000 0.640 0.000 0.252 0.060 0.048
#> GSM447654 4 0.5864 0.5257 0.012 0.304 0.040 0.580 0.060 0.004
#> GSM447655 2 0.4803 0.4294 0.000 0.680 0.000 0.240 0.032 0.048
#> GSM447669 5 0.4360 0.4391 0.000 0.008 0.044 0.064 0.780 0.104
#> GSM447676 1 0.2531 0.6194 0.860 0.000 0.008 0.000 0.004 0.128
#> GSM447678 6 0.5423 0.2380 0.000 0.072 0.000 0.016 0.452 0.460
#> GSM447681 2 0.5070 0.3026 0.000 0.480 0.000 0.012 0.460 0.048
#> GSM447698 5 0.4293 0.4686 0.000 0.084 0.000 0.000 0.716 0.200
#> GSM447713 1 0.3810 0.3242 0.572 0.000 0.428 0.000 0.000 0.000
#> GSM447722 6 0.4721 0.2928 0.000 0.000 0.024 0.012 0.472 0.492
#> GSM447726 5 0.7422 0.2176 0.156 0.212 0.048 0.068 0.508 0.008
#> GSM447735 3 0.5096 0.4411 0.000 0.000 0.616 0.012 0.080 0.292
#> GSM447737 3 0.1152 0.6166 0.044 0.000 0.952 0.000 0.000 0.004
#> GSM447657 2 0.3899 0.3888 0.000 0.628 0.000 0.000 0.364 0.008
#> GSM447674 2 0.2003 0.5268 0.000 0.884 0.000 0.000 0.116 0.000
#> GSM447636 1 0.0000 0.7110 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447723 1 0.1988 0.6788 0.920 0.000 0.048 0.004 0.024 0.004
#> GSM447699 5 0.6836 -0.2558 0.128 0.000 0.072 0.008 0.400 0.392
#> GSM447708 5 0.3151 0.4812 0.000 0.028 0.052 0.048 0.864 0.008
#> GSM447721 1 0.3810 0.1732 0.572 0.000 0.428 0.000 0.000 0.000
#> GSM447623 3 0.2597 0.5338 0.176 0.000 0.824 0.000 0.000 0.000
#> GSM447621 3 0.2793 0.5053 0.200 0.000 0.800 0.000 0.000 0.000
#> GSM447650 2 0.4292 0.4318 0.000 0.736 0.000 0.196 0.020 0.048
#> GSM447651 2 0.6246 0.3528 0.000 0.540 0.000 0.252 0.160 0.048
#> GSM447653 4 0.6387 0.4975 0.196 0.000 0.136 0.592 0.024 0.052
#> GSM447658 1 0.0291 0.7099 0.992 0.000 0.004 0.000 0.004 0.000
#> GSM447675 4 0.6081 0.4458 0.000 0.148 0.004 0.532 0.292 0.024
#> GSM447680 5 0.4103 -0.2031 0.000 0.448 0.004 0.004 0.544 0.000
#> GSM447686 2 0.5825 0.2134 0.344 0.460 0.000 0.000 0.196 0.000
#> GSM447736 3 0.5891 0.3563 0.000 0.000 0.564 0.068 0.072 0.296
#> GSM447629 5 0.4739 -0.1259 0.000 0.436 0.000 0.000 0.516 0.048
#> GSM447648 3 0.5156 0.4383 0.000 0.000 0.580 0.112 0.000 0.308
#> GSM447660 1 0.0000 0.7110 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447661 2 0.6051 0.3769 0.000 0.568 0.000 0.248 0.136 0.048
#> GSM447663 6 0.7207 0.3158 0.184 0.000 0.044 0.068 0.192 0.512
#> GSM447704 2 0.4158 0.4388 0.000 0.704 0.000 0.244 0.052 0.000
#> GSM447720 5 0.7295 -0.2872 0.108 0.000 0.056 0.064 0.432 0.340
#> GSM447652 2 0.3209 0.5347 0.000 0.816 0.000 0.016 0.156 0.012
#> GSM447679 2 0.3684 0.3886 0.000 0.628 0.000 0.000 0.372 0.000
#> GSM447712 1 0.0000 0.7110 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447664 4 0.4175 0.3342 0.012 0.464 0.000 0.524 0.000 0.000
#> GSM447637 3 0.5318 0.3153 0.000 0.000 0.580 0.148 0.000 0.272
#> GSM447639 6 0.7722 0.1996 0.128 0.028 0.064 0.032 0.352 0.396
#> GSM447615 3 0.5997 0.4356 0.112 0.000 0.584 0.004 0.048 0.252
#> GSM447656 5 0.7362 0.0410 0.156 0.216 0.004 0.188 0.436 0.000
#> GSM447673 2 0.0508 0.5035 0.000 0.984 0.000 0.012 0.004 0.000
#> GSM447719 4 0.3575 0.3723 0.000 0.000 0.000 0.708 0.008 0.284
#> GSM447706 3 0.6354 0.4411 0.196 0.000 0.572 0.112 0.000 0.120
#> GSM447612 6 0.4291 0.3256 0.000 0.000 0.000 0.044 0.292 0.664
#> GSM447665 5 0.4666 0.4433 0.000 0.008 0.000 0.052 0.644 0.296
#> GSM447677 2 0.4689 0.3041 0.000 0.516 0.000 0.044 0.440 0.000
#> GSM447613 1 0.2320 0.6486 0.864 0.000 0.132 0.000 0.004 0.000
#> GSM447659 6 0.4639 0.3612 0.000 0.000 0.036 0.016 0.304 0.644
#> GSM447662 6 0.3514 0.2646 0.000 0.000 0.208 0.020 0.004 0.768
#> GSM447666 6 0.6701 -0.1331 0.232 0.000 0.000 0.040 0.332 0.396
#> GSM447668 5 0.5106 -0.2215 0.000 0.408 0.000 0.016 0.528 0.048
#> GSM447682 2 0.2003 0.5268 0.000 0.884 0.000 0.000 0.116 0.000
#> GSM447683 2 0.3823 0.3359 0.000 0.564 0.000 0.000 0.436 0.000
#> GSM447688 5 0.4924 0.4271 0.000 0.144 0.000 0.000 0.652 0.204
#> GSM447702 2 0.4020 0.5332 0.000 0.764 0.000 0.016 0.172 0.048
#> GSM447709 5 0.5942 0.2661 0.000 0.024 0.000 0.232 0.560 0.184
#> GSM447711 1 0.0000 0.7110 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447715 2 0.6254 0.1063 0.368 0.412 0.008 0.004 0.208 0.000
#> GSM447693 6 0.5283 0.0982 0.000 0.000 0.264 0.148 0.000 0.588
#> GSM447611 4 0.6605 0.5248 0.236 0.068 0.048 0.584 0.056 0.008
#> GSM447672 2 0.3486 0.5233 0.000 0.820 0.000 0.016 0.116 0.048
#> GSM447703 2 0.0363 0.5045 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM447727 1 0.4305 0.5683 0.752 0.000 0.148 0.016 0.084 0.000
#> GSM447638 5 0.7222 0.1990 0.256 0.016 0.048 0.252 0.424 0.004
#> GSM447670 1 0.3971 0.2800 0.548 0.000 0.448 0.000 0.004 0.000
#> GSM447700 5 0.4029 0.3743 0.000 0.000 0.028 0.000 0.680 0.292
#> GSM447738 2 0.3695 0.3865 0.000 0.624 0.000 0.000 0.376 0.000
#> GSM447739 1 0.3810 0.3242 0.572 0.000 0.428 0.000 0.000 0.000
#> GSM447617 3 0.1349 0.6107 0.056 0.000 0.940 0.000 0.004 0.000
#> GSM447628 2 0.3756 -0.0607 0.000 0.600 0.000 0.400 0.000 0.000
#> GSM447632 2 0.3298 0.4524 0.000 0.756 0.000 0.008 0.236 0.000
#> GSM447619 6 0.3734 0.1799 0.000 0.000 0.264 0.020 0.000 0.716
#> GSM447643 1 0.0291 0.7099 0.992 0.000 0.004 0.000 0.004 0.000
#> GSM447724 6 0.3992 0.3226 0.000 0.000 0.000 0.012 0.364 0.624
#> GSM447728 2 0.3823 0.3359 0.000 0.564 0.000 0.000 0.436 0.000
#> GSM447610 4 0.5992 0.3920 0.288 0.000 0.156 0.532 0.000 0.024
#> GSM447633 5 0.4377 0.4313 0.000 0.000 0.000 0.044 0.644 0.312
#> GSM447634 3 0.6026 0.3063 0.000 0.000 0.532 0.072 0.072 0.324
#> GSM447622 3 0.0146 0.6204 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM447667 2 0.5419 0.2706 0.060 0.476 0.004 0.000 0.444 0.016
#> GSM447687 2 0.0363 0.5045 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM447695 3 0.3615 0.4834 0.000 0.000 0.700 0.008 0.000 0.292
#> GSM447696 3 0.3847 -0.0842 0.456 0.000 0.544 0.000 0.000 0.000
#> GSM447697 1 0.3810 0.3242 0.572 0.000 0.428 0.000 0.000 0.000
#> GSM447714 6 0.1477 0.4463 0.000 0.000 0.004 0.008 0.048 0.940
#> GSM447717 1 0.0000 0.7110 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447725 1 0.0000 0.7110 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447729 4 0.4731 0.3953 0.048 0.428 0.000 0.524 0.000 0.000
#> GSM447644 5 0.3301 0.4753 0.000 0.024 0.044 0.072 0.852 0.008
#> GSM447710 6 0.3732 0.2319 0.000 0.000 0.228 0.024 0.004 0.744
#> GSM447614 3 0.3956 0.4778 0.000 0.000 0.684 0.024 0.000 0.292
#> GSM447685 2 0.3823 0.3359 0.000 0.564 0.000 0.000 0.436 0.000
#> GSM447690 1 0.3944 0.3199 0.568 0.000 0.428 0.004 0.000 0.000
#> GSM447730 2 0.6222 0.3446 0.000 0.560 0.000 0.244 0.068 0.128
#> GSM447646 4 0.5345 0.4573 0.000 0.364 0.000 0.520 0.116 0.000
#> GSM447689 6 0.6356 0.2355 0.264 0.000 0.040 0.028 0.104 0.564
#> GSM447635 6 0.6261 0.3002 0.000 0.000 0.108 0.060 0.332 0.500
#> GSM447641 1 0.0146 0.7106 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM447716 2 0.4456 0.2623 0.000 0.524 0.000 0.000 0.448 0.028
#> GSM447718 1 0.8810 -0.0967 0.312 0.312 0.044 0.068 0.112 0.152
#> GSM447616 3 0.1007 0.6200 0.000 0.000 0.956 0.000 0.000 0.044
#> GSM447626 1 0.7591 0.0683 0.420 0.000 0.052 0.064 0.180 0.284
#> GSM447640 2 0.3715 0.4339 0.000 0.764 0.000 0.188 0.000 0.048
#> GSM447734 6 0.5923 0.1141 0.000 0.000 0.320 0.064 0.072 0.544
#> GSM447692 3 0.0146 0.6204 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM447647 2 0.3706 0.2260 0.000 0.620 0.000 0.380 0.000 0.000
#> GSM447624 3 0.2893 0.5925 0.048 0.000 0.864 0.080 0.004 0.004
#> GSM447625 6 0.5923 0.1141 0.000 0.000 0.320 0.064 0.072 0.544
#> GSM447707 2 0.4779 0.4186 0.000 0.676 0.000 0.248 0.028 0.048
#> GSM447732 6 0.7708 0.0864 0.124 0.000 0.300 0.068 0.088 0.420
#> GSM447684 1 0.7209 0.1197 0.372 0.000 0.252 0.064 0.304 0.008
#> GSM447731 4 0.3669 0.4838 0.000 0.020 0.008 0.820 0.044 0.108
#> GSM447705 6 0.4393 0.2944 0.000 0.000 0.000 0.044 0.316 0.640
#> GSM447631 6 0.5509 -0.0239 0.000 0.000 0.328 0.148 0.000 0.524
#> GSM447701 5 0.6083 -0.0100 0.000 0.284 0.008 0.096 0.564 0.048
#> GSM447645 6 0.5451 0.0302 0.000 0.000 0.308 0.148 0.000 0.544
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 gender(p) individual(p) disease.state(p) other(p) k
#> MAD:pam 120 0.586 0.779 0.320 0.0292 2
#> MAD:pam 107 0.885 0.534 0.432 0.2810 3
#> MAD:pam 87 0.992 0.380 0.247 0.0850 4
#> MAD:pam 55 0.785 0.668 0.183 0.0877 5
#> MAD:pam 33 0.952 0.719 0.252 0.1068 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "mclust"]
# you can also extract it by
# res = res_list["MAD:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 130 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 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 0.756 0.864 0.942 0.4582 0.535 0.535
#> 3 3 0.627 0.815 0.849 0.3216 0.655 0.448
#> 4 4 0.741 0.803 0.905 0.2018 0.865 0.650
#> 5 5 0.730 0.701 0.820 0.0812 0.878 0.598
#> 6 6 0.705 0.630 0.778 0.0408 0.944 0.750
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
#> GSM447671 2 0.9775 0.3239 0.412 0.588
#> GSM447694 1 0.0000 0.9479 1.000 0.000
#> GSM447618 2 0.7883 0.6839 0.236 0.764
#> GSM447691 2 0.8443 0.6276 0.272 0.728
#> GSM447733 1 0.2236 0.9331 0.964 0.036
#> GSM447620 2 0.1414 0.9005 0.020 0.980
#> GSM447627 1 0.0000 0.9479 1.000 0.000
#> GSM447630 1 0.9323 0.4695 0.652 0.348
#> GSM447642 1 0.0000 0.9479 1.000 0.000
#> GSM447649 2 0.0000 0.9121 0.000 1.000
#> GSM447654 1 0.7883 0.6960 0.764 0.236
#> GSM447655 2 0.0000 0.9121 0.000 1.000
#> GSM447669 2 0.9732 0.3451 0.404 0.596
#> GSM447676 1 0.0000 0.9479 1.000 0.000
#> GSM447678 1 0.9661 0.3521 0.608 0.392
#> GSM447681 2 0.0000 0.9121 0.000 1.000
#> GSM447698 2 0.0000 0.9121 0.000 1.000
#> GSM447713 1 0.0000 0.9479 1.000 0.000
#> GSM447722 1 0.8608 0.6101 0.716 0.284
#> GSM447726 1 0.7376 0.7326 0.792 0.208
#> GSM447735 1 0.0000 0.9479 1.000 0.000
#> GSM447737 1 0.0000 0.9479 1.000 0.000
#> GSM447657 2 0.0000 0.9121 0.000 1.000
#> GSM447674 2 0.0000 0.9121 0.000 1.000
#> GSM447636 1 0.0376 0.9467 0.996 0.004
#> GSM447723 1 0.0000 0.9479 1.000 0.000
#> GSM447699 1 0.2236 0.9331 0.964 0.036
#> GSM447708 2 0.0000 0.9121 0.000 1.000
#> GSM447721 1 0.0000 0.9479 1.000 0.000
#> GSM447623 1 0.0000 0.9479 1.000 0.000
#> GSM447621 1 0.0000 0.9479 1.000 0.000
#> GSM447650 2 0.0000 0.9121 0.000 1.000
#> GSM447651 2 0.0000 0.9121 0.000 1.000
#> GSM447653 1 0.0000 0.9479 1.000 0.000
#> GSM447658 1 0.0000 0.9479 1.000 0.000
#> GSM447675 1 0.6531 0.7965 0.832 0.168
#> GSM447680 2 0.5059 0.8285 0.112 0.888
#> GSM447686 1 0.3114 0.9187 0.944 0.056
#> GSM447736 1 0.0376 0.9469 0.996 0.004
#> GSM447629 2 0.0000 0.9121 0.000 1.000
#> GSM447648 1 0.0000 0.9479 1.000 0.000
#> GSM447660 1 0.0000 0.9479 1.000 0.000
#> GSM447661 2 0.0000 0.9121 0.000 1.000
#> GSM447663 1 0.2236 0.9331 0.964 0.036
#> GSM447704 2 0.0000 0.9121 0.000 1.000
#> GSM447720 1 0.2236 0.9331 0.964 0.036
#> GSM447652 2 0.0000 0.9121 0.000 1.000
#> GSM447679 2 0.0000 0.9121 0.000 1.000
#> GSM447712 1 0.0000 0.9479 1.000 0.000
#> GSM447664 1 0.9427 0.4202 0.640 0.360
#> GSM447637 1 0.0000 0.9479 1.000 0.000
#> GSM447639 1 0.3114 0.9182 0.944 0.056
#> GSM447615 1 0.0000 0.9479 1.000 0.000
#> GSM447656 2 0.4161 0.8566 0.084 0.916
#> GSM447673 2 0.0000 0.9121 0.000 1.000
#> GSM447719 1 0.0000 0.9479 1.000 0.000
#> GSM447706 1 0.0000 0.9479 1.000 0.000
#> GSM447612 1 0.2236 0.9331 0.964 0.036
#> GSM447665 2 0.0000 0.9121 0.000 1.000
#> GSM447677 2 0.0000 0.9121 0.000 1.000
#> GSM447613 1 0.0000 0.9479 1.000 0.000
#> GSM447659 1 0.2236 0.9331 0.964 0.036
#> GSM447662 1 0.2043 0.9353 0.968 0.032
#> GSM447666 1 0.2236 0.9334 0.964 0.036
#> GSM447668 2 0.0000 0.9121 0.000 1.000
#> GSM447682 2 0.0000 0.9121 0.000 1.000
#> GSM447683 2 0.0000 0.9121 0.000 1.000
#> GSM447688 2 0.6048 0.7938 0.148 0.852
#> GSM447702 2 0.0000 0.9121 0.000 1.000
#> GSM447709 2 0.0000 0.9121 0.000 1.000
#> GSM447711 1 0.0000 0.9479 1.000 0.000
#> GSM447715 1 0.2043 0.9358 0.968 0.032
#> GSM447693 1 0.0000 0.9479 1.000 0.000
#> GSM447611 1 0.0938 0.9446 0.988 0.012
#> GSM447672 2 0.0000 0.9121 0.000 1.000
#> GSM447703 2 0.0000 0.9121 0.000 1.000
#> GSM447727 1 0.0000 0.9479 1.000 0.000
#> GSM447638 1 0.0376 0.9467 0.996 0.004
#> GSM447670 1 0.0000 0.9479 1.000 0.000
#> GSM447700 1 0.8955 0.5529 0.688 0.312
#> GSM447738 2 0.0000 0.9121 0.000 1.000
#> GSM447739 1 0.0000 0.9479 1.000 0.000
#> GSM447617 1 0.0000 0.9479 1.000 0.000
#> GSM447628 2 0.9686 0.3649 0.396 0.604
#> GSM447632 2 0.0000 0.9121 0.000 1.000
#> GSM447619 1 0.1184 0.9428 0.984 0.016
#> GSM447643 1 0.0376 0.9467 0.996 0.004
#> GSM447724 1 0.4298 0.8886 0.912 0.088
#> GSM447728 2 0.0000 0.9121 0.000 1.000
#> GSM447610 1 0.0000 0.9479 1.000 0.000
#> GSM447633 1 1.0000 -0.0267 0.504 0.496
#> GSM447634 1 0.1184 0.9428 0.984 0.016
#> GSM447622 1 0.0000 0.9479 1.000 0.000
#> GSM447667 2 0.7056 0.7399 0.192 0.808
#> GSM447687 2 0.0000 0.9121 0.000 1.000
#> GSM447695 1 0.0000 0.9479 1.000 0.000
#> GSM447696 1 0.0000 0.9479 1.000 0.000
#> GSM447697 1 0.0000 0.9479 1.000 0.000
#> GSM447714 1 0.2043 0.9353 0.968 0.032
#> GSM447717 1 0.0000 0.9479 1.000 0.000
#> GSM447725 1 0.0000 0.9479 1.000 0.000
#> GSM447729 1 0.5737 0.8367 0.864 0.136
#> GSM447644 2 1.0000 0.0103 0.500 0.500
#> GSM447710 1 0.0000 0.9479 1.000 0.000
#> GSM447614 1 0.0000 0.9479 1.000 0.000
#> GSM447685 2 0.0000 0.9121 0.000 1.000
#> GSM447690 1 0.0000 0.9479 1.000 0.000
#> GSM447730 2 0.0000 0.9121 0.000 1.000
#> GSM447646 2 0.9922 0.2090 0.448 0.552
#> GSM447689 1 0.1184 0.9428 0.984 0.016
#> GSM447635 1 0.2423 0.9305 0.960 0.040
#> GSM447641 1 0.0000 0.9479 1.000 0.000
#> GSM447716 2 0.8955 0.5765 0.312 0.688
#> GSM447718 1 0.2236 0.9331 0.964 0.036
#> GSM447616 1 0.0000 0.9479 1.000 0.000
#> GSM447626 1 0.0000 0.9479 1.000 0.000
#> GSM447640 2 0.0000 0.9121 0.000 1.000
#> GSM447734 1 0.1633 0.9393 0.976 0.024
#> GSM447692 1 0.0000 0.9479 1.000 0.000
#> GSM447647 2 0.5408 0.8179 0.124 0.876
#> GSM447624 1 0.0000 0.9479 1.000 0.000
#> GSM447625 1 0.1843 0.9374 0.972 0.028
#> GSM447707 2 0.0000 0.9121 0.000 1.000
#> GSM447732 1 0.0376 0.9469 0.996 0.004
#> GSM447684 1 0.0000 0.9479 1.000 0.000
#> GSM447731 1 0.5946 0.8276 0.856 0.144
#> GSM447705 1 0.2423 0.9305 0.960 0.040
#> GSM447631 1 0.0000 0.9479 1.000 0.000
#> GSM447701 2 0.0000 0.9121 0.000 1.000
#> GSM447645 1 0.0000 0.9479 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM447671 2 0.4178 0.7453 0.000 0.828 0.172
#> GSM447694 3 0.0892 0.9095 0.000 0.020 0.980
#> GSM447618 2 0.1529 0.8578 0.000 0.960 0.040
#> GSM447691 2 0.1753 0.8531 0.000 0.952 0.048
#> GSM447733 3 0.0424 0.8942 0.008 0.000 0.992
#> GSM447620 2 0.0000 0.8729 0.000 1.000 0.000
#> GSM447627 3 0.0000 0.8990 0.000 0.000 1.000
#> GSM447630 2 0.6215 0.2932 0.000 0.572 0.428
#> GSM447642 1 0.5621 0.9656 0.692 0.000 0.308
#> GSM447649 2 0.0424 0.8732 0.008 0.992 0.000
#> GSM447654 2 0.6387 0.7407 0.300 0.680 0.020
#> GSM447655 2 0.0424 0.8732 0.008 0.992 0.000
#> GSM447669 2 0.4291 0.7361 0.000 0.820 0.180
#> GSM447676 1 0.5621 0.9656 0.692 0.000 0.308
#> GSM447678 2 0.6387 0.7407 0.300 0.680 0.020
#> GSM447681 2 0.0848 0.8723 0.008 0.984 0.008
#> GSM447698 2 0.1919 0.8649 0.024 0.956 0.020
#> GSM447713 1 0.5621 0.9656 0.692 0.000 0.308
#> GSM447722 2 0.7648 0.3702 0.048 0.552 0.400
#> GSM447726 2 0.2165 0.8438 0.000 0.936 0.064
#> GSM447735 3 0.0000 0.8990 0.000 0.000 1.000
#> GSM447737 3 0.6192 -0.3457 0.420 0.000 0.580
#> GSM447657 2 0.0000 0.8729 0.000 1.000 0.000
#> GSM447674 2 0.0424 0.8732 0.008 0.992 0.000
#> GSM447636 1 0.5815 0.9598 0.692 0.004 0.304
#> GSM447723 1 0.5968 0.9069 0.636 0.000 0.364
#> GSM447699 3 0.1643 0.8881 0.000 0.044 0.956
#> GSM447708 2 0.0237 0.8731 0.004 0.996 0.000
#> GSM447721 1 0.5621 0.9656 0.692 0.000 0.308
#> GSM447623 1 0.5621 0.9656 0.692 0.000 0.308
#> GSM447621 1 0.6140 0.8309 0.596 0.000 0.404
#> GSM447650 2 0.0424 0.8732 0.008 0.992 0.000
#> GSM447651 2 0.0424 0.8732 0.008 0.992 0.000
#> GSM447653 3 0.0000 0.8990 0.000 0.000 1.000
#> GSM447658 1 0.5621 0.9656 0.692 0.000 0.308
#> GSM447675 2 0.6387 0.7407 0.300 0.680 0.020
#> GSM447680 2 0.0892 0.8677 0.020 0.980 0.000
#> GSM447686 2 0.9813 -0.1440 0.304 0.428 0.268
#> GSM447736 3 0.0892 0.9095 0.000 0.020 0.980
#> GSM447629 2 0.0000 0.8729 0.000 1.000 0.000
#> GSM447648 3 0.0983 0.9081 0.004 0.016 0.980
#> GSM447660 1 0.5621 0.9656 0.692 0.000 0.308
#> GSM447661 2 0.0424 0.8732 0.008 0.992 0.000
#> GSM447663 3 0.0892 0.9095 0.000 0.020 0.980
#> GSM447704 2 0.0424 0.8732 0.008 0.992 0.000
#> GSM447720 3 0.3686 0.7475 0.000 0.140 0.860
#> GSM447652 2 0.0000 0.8729 0.000 1.000 0.000
#> GSM447679 2 0.0424 0.8732 0.008 0.992 0.000
#> GSM447712 1 0.5621 0.9656 0.692 0.000 0.308
#> GSM447664 2 0.6326 0.7458 0.292 0.688 0.020
#> GSM447637 3 0.0983 0.8998 0.016 0.004 0.980
#> GSM447639 3 0.4887 0.5515 0.000 0.228 0.772
#> GSM447615 1 0.5926 0.9170 0.644 0.000 0.356
#> GSM447656 2 0.0592 0.8708 0.012 0.988 0.000
#> GSM447673 2 0.3987 0.8373 0.108 0.872 0.020
#> GSM447719 3 0.0000 0.8990 0.000 0.000 1.000
#> GSM447706 3 0.0983 0.8998 0.016 0.004 0.980
#> GSM447612 3 0.0892 0.9095 0.000 0.020 0.980
#> GSM447665 2 0.0237 0.8731 0.004 0.996 0.000
#> GSM447677 2 0.0424 0.8732 0.008 0.992 0.000
#> GSM447613 1 0.5621 0.9656 0.692 0.000 0.308
#> GSM447659 3 0.0237 0.8970 0.004 0.000 0.996
#> GSM447662 3 0.0892 0.9095 0.000 0.020 0.980
#> GSM447666 3 0.1163 0.9040 0.000 0.028 0.972
#> GSM447668 2 0.0424 0.8732 0.008 0.992 0.000
#> GSM447682 2 0.0237 0.8731 0.004 0.996 0.000
#> GSM447683 2 0.0424 0.8732 0.008 0.992 0.000
#> GSM447688 2 0.6090 0.7617 0.264 0.716 0.020
#> GSM447702 2 0.0424 0.8732 0.008 0.992 0.000
#> GSM447709 2 0.0237 0.8731 0.004 0.996 0.000
#> GSM447711 1 0.5621 0.9656 0.692 0.000 0.308
#> GSM447715 2 0.8173 0.4021 0.100 0.600 0.300
#> GSM447693 3 0.0892 0.9095 0.000 0.020 0.980
#> GSM447611 2 0.7727 0.6522 0.336 0.600 0.064
#> GSM447672 2 0.0829 0.8714 0.004 0.984 0.012
#> GSM447703 2 0.1781 0.8658 0.020 0.960 0.020
#> GSM447727 1 0.5810 0.9406 0.664 0.000 0.336
#> GSM447638 2 0.9527 0.0584 0.220 0.480 0.300
#> GSM447670 1 0.5678 0.9594 0.684 0.000 0.316
#> GSM447700 2 0.6286 0.1830 0.000 0.536 0.464
#> GSM447738 2 0.1919 0.8649 0.024 0.956 0.020
#> GSM447739 1 0.5621 0.9656 0.692 0.000 0.308
#> GSM447617 1 0.6008 0.8901 0.628 0.000 0.372
#> GSM447628 2 0.6387 0.7407 0.300 0.680 0.020
#> GSM447632 2 0.1482 0.8671 0.012 0.968 0.020
#> GSM447619 3 0.0892 0.9095 0.000 0.020 0.980
#> GSM447643 1 0.8872 0.7103 0.552 0.152 0.296
#> GSM447724 3 0.4228 0.6928 0.008 0.148 0.844
#> GSM447728 2 0.0424 0.8732 0.008 0.992 0.000
#> GSM447610 1 0.6045 0.8968 0.620 0.000 0.380
#> GSM447633 2 0.4062 0.7598 0.000 0.836 0.164
#> GSM447634 3 0.1411 0.8972 0.000 0.036 0.964
#> GSM447622 3 0.0892 0.8950 0.020 0.000 0.980
#> GSM447667 2 0.0000 0.8729 0.000 1.000 0.000
#> GSM447687 2 0.1919 0.8649 0.024 0.956 0.020
#> GSM447695 3 0.1129 0.9082 0.004 0.020 0.976
#> GSM447696 1 0.5621 0.9656 0.692 0.000 0.308
#> GSM447697 1 0.5621 0.9656 0.692 0.000 0.308
#> GSM447714 3 0.0892 0.9095 0.000 0.020 0.980
#> GSM447717 1 0.5621 0.9656 0.692 0.000 0.308
#> GSM447725 1 0.5621 0.9656 0.692 0.000 0.308
#> GSM447729 2 0.6387 0.7407 0.300 0.680 0.020
#> GSM447644 2 0.4974 0.6613 0.000 0.764 0.236
#> GSM447710 3 0.0892 0.9095 0.000 0.020 0.980
#> GSM447614 3 0.0000 0.8990 0.000 0.000 1.000
#> GSM447685 2 0.1031 0.8675 0.024 0.976 0.000
#> GSM447690 1 0.5621 0.9656 0.692 0.000 0.308
#> GSM447730 2 0.0424 0.8732 0.008 0.992 0.000
#> GSM447646 2 0.6387 0.7407 0.300 0.680 0.020
#> GSM447689 3 0.0892 0.9095 0.000 0.020 0.980
#> GSM447635 2 0.5529 0.5659 0.000 0.704 0.296
#> GSM447641 1 0.5621 0.9656 0.692 0.000 0.308
#> GSM447716 2 0.1411 0.8685 0.036 0.964 0.000
#> GSM447718 3 0.5291 0.5150 0.000 0.268 0.732
#> GSM447616 3 0.1182 0.9047 0.012 0.012 0.976
#> GSM447626 3 0.0983 0.8998 0.016 0.004 0.980
#> GSM447640 2 0.0424 0.8732 0.008 0.992 0.000
#> GSM447734 3 0.0892 0.9095 0.000 0.020 0.980
#> GSM447692 3 0.4351 0.6574 0.168 0.004 0.828
#> GSM447647 2 0.6326 0.7458 0.292 0.688 0.020
#> GSM447624 3 0.6062 -0.1861 0.384 0.000 0.616
#> GSM447625 3 0.0892 0.9095 0.000 0.020 0.980
#> GSM447707 2 0.0424 0.8732 0.008 0.992 0.000
#> GSM447732 3 0.0892 0.9095 0.000 0.020 0.980
#> GSM447684 3 0.4369 0.7485 0.040 0.096 0.864
#> GSM447731 2 0.8187 0.6880 0.244 0.628 0.128
#> GSM447705 3 0.1163 0.9032 0.000 0.028 0.972
#> GSM447631 3 0.0892 0.9095 0.000 0.020 0.980
#> GSM447701 2 0.0424 0.8732 0.008 0.992 0.000
#> GSM447645 3 0.0983 0.8998 0.016 0.004 0.980
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM447671 2 0.5528 0.7367 0.000 0.732 0.124 0.144
#> GSM447694 3 0.1211 0.8629 0.000 0.000 0.960 0.040
#> GSM447618 2 0.3569 0.8229 0.000 0.804 0.000 0.196
#> GSM447691 2 0.2973 0.8614 0.000 0.856 0.000 0.144
#> GSM447733 4 0.3801 0.6584 0.000 0.000 0.220 0.780
#> GSM447620 2 0.1940 0.8693 0.000 0.924 0.076 0.000
#> GSM447627 3 0.3907 0.6477 0.000 0.000 0.768 0.232
#> GSM447630 2 0.7156 0.2400 0.000 0.476 0.388 0.136
#> GSM447642 1 0.0000 0.9391 1.000 0.000 0.000 0.000
#> GSM447649 2 0.0000 0.9046 0.000 1.000 0.000 0.000
#> GSM447654 4 0.1302 0.8081 0.000 0.044 0.000 0.956
#> GSM447655 2 0.0000 0.9046 0.000 1.000 0.000 0.000
#> GSM447669 2 0.5248 0.7192 0.000 0.748 0.164 0.088
#> GSM447676 1 0.0000 0.9391 1.000 0.000 0.000 0.000
#> GSM447678 4 0.1302 0.8081 0.000 0.044 0.000 0.956
#> GSM447681 2 0.1022 0.9036 0.000 0.968 0.000 0.032
#> GSM447698 2 0.3610 0.8193 0.000 0.800 0.000 0.200
#> GSM447713 1 0.0000 0.9391 1.000 0.000 0.000 0.000
#> GSM447722 4 0.1118 0.8067 0.000 0.036 0.000 0.964
#> GSM447726 2 0.0000 0.9046 0.000 1.000 0.000 0.000
#> GSM447735 4 0.3649 0.6882 0.000 0.000 0.204 0.796
#> GSM447737 3 0.4985 0.1471 0.468 0.000 0.532 0.000
#> GSM447657 2 0.2868 0.8663 0.000 0.864 0.000 0.136
#> GSM447674 2 0.0817 0.9047 0.000 0.976 0.000 0.024
#> GSM447636 1 0.0000 0.9391 1.000 0.000 0.000 0.000
#> GSM447723 1 0.0000 0.9391 1.000 0.000 0.000 0.000
#> GSM447699 3 0.3801 0.6896 0.000 0.000 0.780 0.220
#> GSM447708 2 0.2345 0.8851 0.000 0.900 0.000 0.100
#> GSM447721 1 0.0000 0.9391 1.000 0.000 0.000 0.000
#> GSM447623 1 0.0000 0.9391 1.000 0.000 0.000 0.000
#> GSM447621 1 0.3873 0.6626 0.772 0.000 0.228 0.000
#> GSM447650 2 0.0000 0.9046 0.000 1.000 0.000 0.000
#> GSM447651 2 0.0000 0.9046 0.000 1.000 0.000 0.000
#> GSM447653 4 0.4222 0.6005 0.000 0.000 0.272 0.728
#> GSM447658 1 0.0000 0.9391 1.000 0.000 0.000 0.000
#> GSM447675 4 0.0817 0.8030 0.000 0.024 0.000 0.976
#> GSM447680 2 0.0000 0.9046 0.000 1.000 0.000 0.000
#> GSM447686 1 0.5453 0.4573 0.660 0.304 0.000 0.036
#> GSM447736 3 0.1022 0.8667 0.000 0.000 0.968 0.032
#> GSM447629 2 0.2973 0.8614 0.000 0.856 0.000 0.144
#> GSM447648 3 0.0000 0.8797 0.000 0.000 1.000 0.000
#> GSM447660 1 0.0000 0.9391 1.000 0.000 0.000 0.000
#> GSM447661 2 0.0000 0.9046 0.000 1.000 0.000 0.000
#> GSM447663 3 0.0000 0.8797 0.000 0.000 1.000 0.000
#> GSM447704 2 0.0000 0.9046 0.000 1.000 0.000 0.000
#> GSM447720 3 0.2830 0.8077 0.000 0.060 0.900 0.040
#> GSM447652 2 0.0188 0.9048 0.000 0.996 0.000 0.004
#> GSM447679 2 0.0707 0.9049 0.000 0.980 0.000 0.020
#> GSM447712 1 0.0000 0.9391 1.000 0.000 0.000 0.000
#> GSM447664 4 0.3528 0.6815 0.000 0.192 0.000 0.808
#> GSM447637 3 0.0000 0.8797 0.000 0.000 1.000 0.000
#> GSM447639 4 0.2469 0.7615 0.000 0.000 0.108 0.892
#> GSM447615 1 0.0000 0.9391 1.000 0.000 0.000 0.000
#> GSM447656 2 0.0817 0.9047 0.000 0.976 0.000 0.024
#> GSM447673 4 0.4925 0.1410 0.000 0.428 0.000 0.572
#> GSM447719 3 0.4998 -0.2047 0.000 0.000 0.512 0.488
#> GSM447706 3 0.0000 0.8797 0.000 0.000 1.000 0.000
#> GSM447612 3 0.0000 0.8797 0.000 0.000 1.000 0.000
#> GSM447665 2 0.0000 0.9046 0.000 1.000 0.000 0.000
#> GSM447677 2 0.0000 0.9046 0.000 1.000 0.000 0.000
#> GSM447613 1 0.0000 0.9391 1.000 0.000 0.000 0.000
#> GSM447659 4 0.4855 0.3771 0.000 0.000 0.400 0.600
#> GSM447662 3 0.0000 0.8797 0.000 0.000 1.000 0.000
#> GSM447666 3 0.0469 0.8717 0.000 0.012 0.988 0.000
#> GSM447668 2 0.0000 0.9046 0.000 1.000 0.000 0.000
#> GSM447682 2 0.2589 0.8771 0.000 0.884 0.000 0.116
#> GSM447683 2 0.1940 0.8937 0.000 0.924 0.000 0.076
#> GSM447688 4 0.1867 0.8003 0.000 0.072 0.000 0.928
#> GSM447702 2 0.0000 0.9046 0.000 1.000 0.000 0.000
#> GSM447709 2 0.0000 0.9046 0.000 1.000 0.000 0.000
#> GSM447711 1 0.0000 0.9391 1.000 0.000 0.000 0.000
#> GSM447715 2 0.4936 0.6162 0.280 0.700 0.000 0.020
#> GSM447693 3 0.0000 0.8797 0.000 0.000 1.000 0.000
#> GSM447611 4 0.0336 0.7912 0.008 0.000 0.000 0.992
#> GSM447672 2 0.0000 0.9046 0.000 1.000 0.000 0.000
#> GSM447703 2 0.2216 0.8904 0.000 0.908 0.000 0.092
#> GSM447727 1 0.0000 0.9391 1.000 0.000 0.000 0.000
#> GSM447638 2 0.3610 0.7390 0.200 0.800 0.000 0.000
#> GSM447670 1 0.0000 0.9391 1.000 0.000 0.000 0.000
#> GSM447700 3 0.7338 0.0771 0.000 0.160 0.464 0.376
#> GSM447738 2 0.3356 0.8403 0.000 0.824 0.000 0.176
#> GSM447739 1 0.0000 0.9391 1.000 0.000 0.000 0.000
#> GSM447617 1 0.2921 0.7933 0.860 0.000 0.140 0.000
#> GSM447628 4 0.1637 0.8058 0.000 0.060 0.000 0.940
#> GSM447632 2 0.3172 0.8517 0.000 0.840 0.000 0.160
#> GSM447619 3 0.0000 0.8797 0.000 0.000 1.000 0.000
#> GSM447643 1 0.2530 0.8069 0.888 0.112 0.000 0.000
#> GSM447724 4 0.3266 0.7237 0.000 0.000 0.168 0.832
#> GSM447728 2 0.1867 0.8948 0.000 0.928 0.000 0.072
#> GSM447610 1 0.5295 0.0125 0.504 0.000 0.008 0.488
#> GSM447633 2 0.0779 0.9019 0.000 0.980 0.016 0.004
#> GSM447634 3 0.2704 0.8047 0.000 0.000 0.876 0.124
#> GSM447622 3 0.0000 0.8797 0.000 0.000 1.000 0.000
#> GSM447667 2 0.2973 0.8614 0.000 0.856 0.000 0.144
#> GSM447687 2 0.3024 0.8626 0.000 0.852 0.000 0.148
#> GSM447695 3 0.2124 0.8461 0.008 0.000 0.924 0.068
#> GSM447696 1 0.0000 0.9391 1.000 0.000 0.000 0.000
#> GSM447697 1 0.0000 0.9391 1.000 0.000 0.000 0.000
#> GSM447714 3 0.0000 0.8797 0.000 0.000 1.000 0.000
#> GSM447717 1 0.0000 0.9391 1.000 0.000 0.000 0.000
#> GSM447725 1 0.0000 0.9391 1.000 0.000 0.000 0.000
#> GSM447729 4 0.1557 0.8071 0.000 0.056 0.000 0.944
#> GSM447644 2 0.1557 0.8689 0.000 0.944 0.056 0.000
#> GSM447710 3 0.0000 0.8797 0.000 0.000 1.000 0.000
#> GSM447614 4 0.4776 0.4296 0.000 0.000 0.376 0.624
#> GSM447685 2 0.2149 0.8899 0.000 0.912 0.000 0.088
#> GSM447690 1 0.0000 0.9391 1.000 0.000 0.000 0.000
#> GSM447730 2 0.0000 0.9046 0.000 1.000 0.000 0.000
#> GSM447646 4 0.1557 0.8071 0.000 0.056 0.000 0.944
#> GSM447689 3 0.0000 0.8797 0.000 0.000 1.000 0.000
#> GSM447635 2 0.3626 0.8347 0.000 0.812 0.004 0.184
#> GSM447641 1 0.0000 0.9391 1.000 0.000 0.000 0.000
#> GSM447716 2 0.4008 0.7701 0.000 0.756 0.000 0.244
#> GSM447718 3 0.2593 0.7810 0.000 0.104 0.892 0.004
#> GSM447616 3 0.1940 0.8310 0.076 0.000 0.924 0.000
#> GSM447626 3 0.0000 0.8797 0.000 0.000 1.000 0.000
#> GSM447640 2 0.0817 0.9047 0.000 0.976 0.000 0.024
#> GSM447734 3 0.0000 0.8797 0.000 0.000 1.000 0.000
#> GSM447692 3 0.4194 0.7192 0.172 0.000 0.800 0.028
#> GSM447647 4 0.4697 0.4689 0.000 0.356 0.000 0.644
#> GSM447624 3 0.4804 0.3796 0.384 0.000 0.616 0.000
#> GSM447625 3 0.0336 0.8770 0.000 0.000 0.992 0.008
#> GSM447707 2 0.0000 0.9046 0.000 1.000 0.000 0.000
#> GSM447732 3 0.0000 0.8797 0.000 0.000 1.000 0.000
#> GSM447684 3 0.5203 0.5422 0.048 0.232 0.720 0.000
#> GSM447731 4 0.5339 0.7259 0.000 0.100 0.156 0.744
#> GSM447705 3 0.0000 0.8797 0.000 0.000 1.000 0.000
#> GSM447631 3 0.0000 0.8797 0.000 0.000 1.000 0.000
#> GSM447701 2 0.0000 0.9046 0.000 1.000 0.000 0.000
#> GSM447645 3 0.0000 0.8797 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
#> GSM447671 5 0.5074 0.666 0.000 0.268 0.000 0.072 0.660
#> GSM447694 3 0.3876 0.716 0.000 0.000 0.684 0.000 0.316
#> GSM447618 2 0.6762 0.119 0.000 0.376 0.000 0.356 0.268
#> GSM447691 5 0.6141 0.502 0.000 0.244 0.000 0.196 0.560
#> GSM447733 4 0.4703 0.603 0.000 0.000 0.028 0.632 0.340
#> GSM447620 2 0.3857 0.265 0.000 0.688 0.000 0.000 0.312
#> GSM447627 3 0.4135 0.700 0.000 0.000 0.656 0.004 0.340
#> GSM447630 5 0.5164 0.663 0.000 0.256 0.000 0.084 0.660
#> GSM447642 1 0.0000 0.892 1.000 0.000 0.000 0.000 0.000
#> GSM447649 2 0.0000 0.820 0.000 1.000 0.000 0.000 0.000
#> GSM447654 4 0.0000 0.827 0.000 0.000 0.000 1.000 0.000
#> GSM447655 2 0.0000 0.820 0.000 1.000 0.000 0.000 0.000
#> GSM447669 5 0.4823 0.657 0.000 0.316 0.000 0.040 0.644
#> GSM447676 1 0.0000 0.892 1.000 0.000 0.000 0.000 0.000
#> GSM447678 4 0.0000 0.827 0.000 0.000 0.000 1.000 0.000
#> GSM447681 2 0.2690 0.812 0.000 0.844 0.000 0.156 0.000
#> GSM447698 2 0.4171 0.601 0.000 0.604 0.000 0.396 0.000
#> GSM447713 1 0.0000 0.892 1.000 0.000 0.000 0.000 0.000
#> GSM447722 4 0.0794 0.819 0.000 0.000 0.000 0.972 0.028
#> GSM447726 5 0.4030 0.645 0.000 0.352 0.000 0.000 0.648
#> GSM447735 3 0.5052 0.664 0.000 0.000 0.612 0.048 0.340
#> GSM447737 3 0.4779 0.320 0.388 0.000 0.588 0.000 0.024
#> GSM447657 2 0.3109 0.796 0.000 0.800 0.000 0.200 0.000
#> GSM447674 2 0.2929 0.804 0.000 0.820 0.000 0.180 0.000
#> GSM447636 1 0.0000 0.892 1.000 0.000 0.000 0.000 0.000
#> GSM447723 1 0.0000 0.892 1.000 0.000 0.000 0.000 0.000
#> GSM447699 3 0.4356 0.698 0.000 0.000 0.648 0.012 0.340
#> GSM447708 2 0.1831 0.819 0.000 0.920 0.000 0.076 0.004
#> GSM447721 1 0.0000 0.892 1.000 0.000 0.000 0.000 0.000
#> GSM447623 1 0.4182 0.316 0.600 0.000 0.400 0.000 0.000
#> GSM447621 1 0.4182 0.316 0.600 0.000 0.400 0.000 0.000
#> GSM447650 2 0.0000 0.820 0.000 1.000 0.000 0.000 0.000
#> GSM447651 2 0.0162 0.818 0.000 0.996 0.000 0.000 0.004
#> GSM447653 4 0.6410 0.318 0.000 0.000 0.184 0.476 0.340
#> GSM447658 1 0.0000 0.892 1.000 0.000 0.000 0.000 0.000
#> GSM447675 4 0.0000 0.827 0.000 0.000 0.000 1.000 0.000
#> GSM447680 2 0.0162 0.818 0.000 0.996 0.000 0.000 0.004
#> GSM447686 2 0.4974 0.644 0.212 0.696 0.000 0.092 0.000
#> GSM447736 3 0.3366 0.739 0.000 0.000 0.768 0.000 0.232
#> GSM447629 2 0.3109 0.796 0.000 0.800 0.000 0.200 0.000
#> GSM447648 3 0.0000 0.765 0.000 0.000 1.000 0.000 0.000
#> GSM447660 1 0.0000 0.892 1.000 0.000 0.000 0.000 0.000
#> GSM447661 2 0.0000 0.820 0.000 1.000 0.000 0.000 0.000
#> GSM447663 5 0.3999 0.608 0.000 0.000 0.344 0.000 0.656
#> GSM447704 2 0.0000 0.820 0.000 1.000 0.000 0.000 0.000
#> GSM447720 5 0.3134 0.601 0.000 0.028 0.096 0.012 0.864
#> GSM447652 2 0.0000 0.820 0.000 1.000 0.000 0.000 0.000
#> GSM447679 2 0.2424 0.816 0.000 0.868 0.000 0.132 0.000
#> GSM447712 1 0.0000 0.892 1.000 0.000 0.000 0.000 0.000
#> GSM447664 4 0.0290 0.820 0.000 0.008 0.000 0.992 0.000
#> GSM447637 3 0.0000 0.765 0.000 0.000 1.000 0.000 0.000
#> GSM447639 4 0.3966 0.636 0.000 0.000 0.000 0.664 0.336
#> GSM447615 1 0.0000 0.892 1.000 0.000 0.000 0.000 0.000
#> GSM447656 2 0.0771 0.820 0.000 0.976 0.000 0.020 0.004
#> GSM447673 2 0.4045 0.656 0.000 0.644 0.000 0.356 0.000
#> GSM447719 3 0.5290 0.575 0.000 0.000 0.676 0.184 0.140
#> GSM447706 3 0.0609 0.754 0.000 0.000 0.980 0.000 0.020
#> GSM447612 5 0.4161 0.468 0.000 0.000 0.392 0.000 0.608
#> GSM447665 2 0.0290 0.816 0.000 0.992 0.000 0.000 0.008
#> GSM447677 2 0.0162 0.818 0.000 0.996 0.000 0.000 0.004
#> GSM447613 1 0.0000 0.892 1.000 0.000 0.000 0.000 0.000
#> GSM447659 3 0.6557 0.406 0.000 0.000 0.448 0.212 0.340
#> GSM447662 3 0.3684 0.296 0.000 0.000 0.720 0.000 0.280
#> GSM447666 5 0.3983 0.611 0.000 0.000 0.340 0.000 0.660
#> GSM447668 2 0.0162 0.818 0.000 0.996 0.000 0.000 0.004
#> GSM447682 2 0.3109 0.796 0.000 0.800 0.000 0.200 0.000
#> GSM447683 2 0.3160 0.802 0.000 0.808 0.000 0.188 0.004
#> GSM447688 4 0.0000 0.827 0.000 0.000 0.000 1.000 0.000
#> GSM447702 2 0.0000 0.820 0.000 1.000 0.000 0.000 0.000
#> GSM447709 2 0.0162 0.818 0.000 0.996 0.000 0.000 0.004
#> GSM447711 1 0.0000 0.892 1.000 0.000 0.000 0.000 0.000
#> GSM447715 1 0.6523 -0.089 0.452 0.136 0.000 0.012 0.400
#> GSM447693 3 0.0000 0.765 0.000 0.000 1.000 0.000 0.000
#> GSM447611 4 0.0771 0.816 0.020 0.000 0.000 0.976 0.004
#> GSM447672 2 0.0000 0.820 0.000 1.000 0.000 0.000 0.000
#> GSM447703 2 0.3949 0.683 0.000 0.668 0.000 0.332 0.000
#> GSM447727 1 0.2891 0.716 0.824 0.000 0.000 0.000 0.176
#> GSM447638 5 0.6491 0.388 0.200 0.336 0.000 0.000 0.464
#> GSM447670 1 0.0000 0.892 1.000 0.000 0.000 0.000 0.000
#> GSM447700 5 0.4347 0.454 0.000 0.024 0.004 0.256 0.716
#> GSM447738 2 0.3983 0.675 0.000 0.660 0.000 0.340 0.000
#> GSM447739 1 0.0000 0.892 1.000 0.000 0.000 0.000 0.000
#> GSM447617 1 0.4249 0.225 0.568 0.000 0.432 0.000 0.000
#> GSM447628 4 0.0000 0.827 0.000 0.000 0.000 1.000 0.000
#> GSM447632 2 0.3837 0.711 0.000 0.692 0.000 0.308 0.000
#> GSM447619 3 0.1121 0.734 0.000 0.000 0.956 0.000 0.044
#> GSM447643 1 0.1908 0.801 0.908 0.092 0.000 0.000 0.000
#> GSM447724 4 0.4851 0.593 0.000 0.000 0.036 0.624 0.340
#> GSM447728 2 0.2773 0.810 0.000 0.836 0.000 0.164 0.000
#> GSM447610 1 0.7211 0.311 0.512 0.000 0.216 0.220 0.052
#> GSM447633 5 0.3983 0.657 0.000 0.340 0.000 0.000 0.660
#> GSM447634 5 0.4235 -0.323 0.000 0.000 0.424 0.000 0.576
#> GSM447622 3 0.0510 0.764 0.016 0.000 0.984 0.000 0.000
#> GSM447667 2 0.3109 0.796 0.000 0.800 0.000 0.200 0.000
#> GSM447687 2 0.3966 0.679 0.000 0.664 0.000 0.336 0.000
#> GSM447695 3 0.3966 0.705 0.000 0.000 0.664 0.000 0.336
#> GSM447696 1 0.0000 0.892 1.000 0.000 0.000 0.000 0.000
#> GSM447697 1 0.0404 0.884 0.988 0.000 0.012 0.000 0.000
#> GSM447714 3 0.0404 0.760 0.000 0.000 0.988 0.000 0.012
#> GSM447717 1 0.0000 0.892 1.000 0.000 0.000 0.000 0.000
#> GSM447725 1 0.0000 0.892 1.000 0.000 0.000 0.000 0.000
#> GSM447729 4 0.0000 0.827 0.000 0.000 0.000 1.000 0.000
#> GSM447644 5 0.3983 0.657 0.000 0.340 0.000 0.000 0.660
#> GSM447710 3 0.0000 0.765 0.000 0.000 1.000 0.000 0.000
#> GSM447614 3 0.5289 0.647 0.000 0.000 0.596 0.064 0.340
#> GSM447685 2 0.3231 0.798 0.000 0.800 0.000 0.196 0.004
#> GSM447690 1 0.0000 0.892 1.000 0.000 0.000 0.000 0.000
#> GSM447730 2 0.0000 0.820 0.000 1.000 0.000 0.000 0.000
#> GSM447646 4 0.0000 0.827 0.000 0.000 0.000 1.000 0.000
#> GSM447689 5 0.3999 0.608 0.000 0.000 0.344 0.000 0.656
#> GSM447635 5 0.5923 0.515 0.000 0.144 0.000 0.280 0.576
#> GSM447641 1 0.0000 0.892 1.000 0.000 0.000 0.000 0.000
#> GSM447716 2 0.4249 0.541 0.000 0.568 0.000 0.432 0.000
#> GSM447718 5 0.4674 0.676 0.000 0.316 0.024 0.004 0.656
#> GSM447616 3 0.3109 0.668 0.200 0.000 0.800 0.000 0.000
#> GSM447626 5 0.4030 0.601 0.000 0.000 0.352 0.000 0.648
#> GSM447640 2 0.2561 0.814 0.000 0.856 0.000 0.144 0.000
#> GSM447734 3 0.1478 0.767 0.000 0.000 0.936 0.000 0.064
#> GSM447692 3 0.4329 0.713 0.016 0.000 0.672 0.000 0.312
#> GSM447647 4 0.3074 0.668 0.000 0.196 0.000 0.804 0.000
#> GSM447624 3 0.3983 0.444 0.340 0.000 0.660 0.000 0.000
#> GSM447625 3 0.2516 0.759 0.000 0.000 0.860 0.000 0.140
#> GSM447707 2 0.0000 0.820 0.000 1.000 0.000 0.000 0.000
#> GSM447732 3 0.0703 0.752 0.000 0.000 0.976 0.000 0.024
#> GSM447684 5 0.5525 0.644 0.044 0.040 0.256 0.000 0.660
#> GSM447731 4 0.5648 0.563 0.000 0.228 0.020 0.660 0.092
#> GSM447705 5 0.3999 0.608 0.000 0.000 0.344 0.000 0.656
#> GSM447631 3 0.0000 0.765 0.000 0.000 1.000 0.000 0.000
#> GSM447701 2 0.0162 0.818 0.000 0.996 0.000 0.000 0.004
#> GSM447645 3 0.0000 0.765 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
#> GSM447671 6 0.3605 0.7387 0.000 0.128 0.000 0.008 0.060 0.804
#> GSM447694 3 0.3464 0.3825 0.000 0.000 0.688 0.312 0.000 0.000
#> GSM447618 5 0.6126 0.1119 0.000 0.240 0.000 0.004 0.408 0.348
#> GSM447691 6 0.3550 0.6542 0.000 0.024 0.000 0.008 0.188 0.780
#> GSM447733 4 0.3601 0.6088 0.000 0.000 0.000 0.684 0.312 0.004
#> GSM447620 6 0.4534 0.2336 0.000 0.472 0.000 0.032 0.000 0.496
#> GSM447627 4 0.3076 0.5671 0.000 0.000 0.240 0.760 0.000 0.000
#> GSM447630 6 0.3865 0.7357 0.000 0.132 0.000 0.008 0.076 0.784
#> GSM447642 1 0.3652 0.7516 0.768 0.000 0.000 0.044 0.000 0.188
#> GSM447649 2 0.0146 0.8151 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM447654 5 0.0260 0.7975 0.000 0.000 0.000 0.008 0.992 0.000
#> GSM447655 2 0.0000 0.8147 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447669 6 0.3279 0.7328 0.000 0.176 0.000 0.000 0.028 0.796
#> GSM447676 1 0.3088 0.7653 0.808 0.000 0.000 0.020 0.000 0.172
#> GSM447678 5 0.0260 0.7975 0.000 0.000 0.000 0.008 0.992 0.000
#> GSM447681 2 0.2454 0.7852 0.000 0.840 0.000 0.000 0.160 0.000
#> GSM447698 2 0.3860 0.3029 0.000 0.528 0.000 0.000 0.472 0.000
#> GSM447713 1 0.1555 0.7752 0.932 0.000 0.004 0.060 0.000 0.004
#> GSM447722 5 0.0260 0.7975 0.000 0.000 0.000 0.008 0.992 0.000
#> GSM447726 6 0.3821 0.7013 0.000 0.220 0.000 0.040 0.000 0.740
#> GSM447735 4 0.4533 0.7031 0.000 0.000 0.140 0.704 0.156 0.000
#> GSM447737 3 0.4715 -0.0414 0.452 0.000 0.508 0.036 0.000 0.004
#> GSM447657 2 0.3050 0.7262 0.000 0.764 0.000 0.000 0.236 0.000
#> GSM447674 2 0.2597 0.7749 0.000 0.824 0.000 0.000 0.176 0.000
#> GSM447636 1 0.3652 0.7516 0.768 0.000 0.000 0.044 0.000 0.188
#> GSM447723 1 0.3487 0.7599 0.788 0.000 0.000 0.044 0.000 0.168
#> GSM447699 4 0.5273 0.3499 0.000 0.000 0.212 0.604 0.000 0.184
#> GSM447708 2 0.2715 0.8061 0.000 0.872 0.000 0.028 0.088 0.012
#> GSM447721 1 0.1555 0.7752 0.932 0.000 0.004 0.060 0.000 0.004
#> GSM447623 1 0.4963 0.2799 0.544 0.000 0.392 0.060 0.000 0.004
#> GSM447621 1 0.4983 0.2461 0.532 0.000 0.404 0.060 0.000 0.004
#> GSM447650 2 0.0260 0.8132 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM447651 2 0.1010 0.8040 0.000 0.960 0.000 0.036 0.000 0.004
#> GSM447653 4 0.4431 0.7033 0.000 0.000 0.096 0.704 0.200 0.000
#> GSM447658 1 0.3652 0.7516 0.768 0.000 0.000 0.044 0.000 0.188
#> GSM447675 5 0.0260 0.7975 0.000 0.000 0.000 0.008 0.992 0.000
#> GSM447680 2 0.2145 0.7815 0.000 0.900 0.000 0.072 0.000 0.028
#> GSM447686 2 0.7618 0.0675 0.312 0.404 0.000 0.084 0.044 0.156
#> GSM447736 4 0.5873 0.1376 0.000 0.000 0.248 0.480 0.000 0.272
#> GSM447629 2 0.3074 0.7555 0.000 0.792 0.000 0.004 0.200 0.004
#> GSM447648 3 0.0000 0.6596 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447660 1 0.3522 0.7584 0.784 0.000 0.000 0.044 0.000 0.172
#> GSM447661 2 0.0146 0.8141 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM447663 6 0.4328 0.6167 0.000 0.000 0.192 0.092 0.000 0.716
#> GSM447704 2 0.0146 0.8151 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM447720 6 0.2994 0.6393 0.000 0.000 0.004 0.208 0.000 0.788
#> GSM447652 2 0.0146 0.8151 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM447679 2 0.2553 0.7918 0.000 0.848 0.000 0.008 0.144 0.000
#> GSM447712 1 0.0935 0.7929 0.964 0.000 0.000 0.004 0.000 0.032
#> GSM447664 5 0.0363 0.7911 0.000 0.012 0.000 0.000 0.988 0.000
#> GSM447637 3 0.0000 0.6596 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447639 4 0.3878 0.6282 0.000 0.000 0.008 0.688 0.296 0.008
#> GSM447615 1 0.1124 0.7869 0.956 0.000 0.036 0.008 0.000 0.000
#> GSM447656 2 0.2252 0.7881 0.000 0.900 0.000 0.072 0.012 0.016
#> GSM447673 5 0.3266 0.4959 0.000 0.272 0.000 0.000 0.728 0.000
#> GSM447719 4 0.5574 0.4982 0.000 0.000 0.344 0.504 0.152 0.000
#> GSM447706 3 0.2685 0.6390 0.000 0.000 0.868 0.060 0.000 0.072
#> GSM447612 6 0.4680 0.6016 0.000 0.000 0.132 0.184 0.000 0.684
#> GSM447665 2 0.2302 0.7268 0.000 0.872 0.000 0.008 0.000 0.120
#> GSM447677 2 0.1563 0.7921 0.000 0.932 0.000 0.056 0.000 0.012
#> GSM447613 1 0.1408 0.7887 0.944 0.000 0.000 0.036 0.000 0.020
#> GSM447659 4 0.4634 0.7034 0.000 0.000 0.136 0.704 0.156 0.004
#> GSM447662 3 0.5428 0.0890 0.000 0.000 0.484 0.120 0.000 0.396
#> GSM447666 6 0.2964 0.6700 0.000 0.000 0.204 0.004 0.000 0.792
#> GSM447668 2 0.0790 0.8066 0.000 0.968 0.000 0.032 0.000 0.000
#> GSM447682 2 0.2823 0.7536 0.000 0.796 0.000 0.000 0.204 0.000
#> GSM447683 2 0.3418 0.7699 0.000 0.784 0.000 0.032 0.184 0.000
#> GSM447688 5 0.1124 0.7815 0.000 0.036 0.000 0.008 0.956 0.000
#> GSM447702 2 0.0146 0.8141 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM447709 2 0.2266 0.7480 0.000 0.880 0.000 0.012 0.000 0.108
#> GSM447711 1 0.0000 0.7893 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447715 1 0.5514 0.4291 0.468 0.000 0.000 0.100 0.008 0.424
#> GSM447693 3 0.1444 0.6494 0.000 0.000 0.928 0.072 0.000 0.000
#> GSM447611 5 0.0865 0.7684 0.000 0.000 0.000 0.036 0.964 0.000
#> GSM447672 2 0.0146 0.8151 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM447703 2 0.3634 0.5665 0.000 0.644 0.000 0.000 0.356 0.000
#> GSM447727 1 0.4214 0.6975 0.680 0.000 0.000 0.044 0.000 0.276
#> GSM447638 6 0.6265 0.2579 0.076 0.256 0.000 0.116 0.000 0.552
#> GSM447670 1 0.2302 0.7624 0.900 0.000 0.032 0.060 0.000 0.008
#> GSM447700 6 0.3974 0.6817 0.000 0.000 0.004 0.116 0.108 0.772
#> GSM447738 2 0.3756 0.4863 0.000 0.600 0.000 0.000 0.400 0.000
#> GSM447739 1 0.1555 0.7752 0.932 0.000 0.004 0.060 0.000 0.004
#> GSM447617 1 0.5025 0.1375 0.492 0.000 0.444 0.060 0.000 0.004
#> GSM447628 5 0.0260 0.7975 0.000 0.000 0.000 0.008 0.992 0.000
#> GSM447632 2 0.3747 0.4945 0.000 0.604 0.000 0.000 0.396 0.000
#> GSM447619 3 0.4624 0.5491 0.000 0.000 0.688 0.120 0.000 0.192
#> GSM447643 1 0.4282 0.7289 0.720 0.000 0.000 0.088 0.000 0.192
#> GSM447724 4 0.4289 0.6725 0.000 0.000 0.040 0.696 0.256 0.008
#> GSM447728 2 0.2814 0.7778 0.000 0.820 0.000 0.008 0.172 0.000
#> GSM447610 4 0.6819 0.2485 0.372 0.000 0.064 0.404 0.156 0.004
#> GSM447633 6 0.2902 0.7235 0.000 0.196 0.000 0.004 0.000 0.800
#> GSM447634 6 0.5279 0.3243 0.000 0.000 0.116 0.336 0.000 0.548
#> GSM447622 3 0.1049 0.6472 0.032 0.000 0.960 0.008 0.000 0.000
#> GSM447667 2 0.3529 0.7637 0.000 0.788 0.000 0.004 0.172 0.036
#> GSM447687 2 0.3756 0.4864 0.000 0.600 0.000 0.000 0.400 0.000
#> GSM447695 4 0.3823 0.1551 0.000 0.000 0.436 0.564 0.000 0.000
#> GSM447696 1 0.2771 0.7420 0.868 0.000 0.068 0.060 0.000 0.004
#> GSM447697 1 0.4361 0.5626 0.700 0.000 0.236 0.060 0.000 0.004
#> GSM447714 3 0.4990 0.4114 0.000 0.000 0.616 0.108 0.000 0.276
#> GSM447717 1 0.3652 0.7516 0.768 0.000 0.000 0.044 0.000 0.188
#> GSM447725 1 0.1007 0.7920 0.956 0.000 0.000 0.044 0.000 0.000
#> GSM447729 5 0.0260 0.7975 0.000 0.000 0.000 0.008 0.992 0.000
#> GSM447644 6 0.2964 0.7209 0.000 0.204 0.000 0.004 0.000 0.792
#> GSM447710 3 0.3693 0.6072 0.000 0.000 0.788 0.120 0.000 0.092
#> GSM447614 4 0.4535 0.7016 0.000 0.000 0.144 0.704 0.152 0.000
#> GSM447685 2 0.3555 0.7682 0.000 0.776 0.000 0.040 0.184 0.000
#> GSM447690 1 0.1555 0.7752 0.932 0.000 0.004 0.060 0.000 0.004
#> GSM447730 2 0.0146 0.8151 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM447646 5 0.0260 0.7975 0.000 0.000 0.000 0.008 0.992 0.000
#> GSM447689 6 0.3171 0.6669 0.000 0.000 0.204 0.012 0.000 0.784
#> GSM447635 6 0.3946 0.6157 0.000 0.032 0.000 0.012 0.208 0.748
#> GSM447641 1 0.1152 0.7919 0.952 0.000 0.000 0.044 0.000 0.004
#> GSM447716 5 0.3647 0.2547 0.000 0.360 0.000 0.000 0.640 0.000
#> GSM447718 6 0.3296 0.7262 0.000 0.188 0.008 0.012 0.000 0.792
#> GSM447616 3 0.3014 0.5258 0.184 0.000 0.804 0.012 0.000 0.000
#> GSM447626 6 0.4437 0.3429 0.000 0.000 0.392 0.032 0.000 0.576
#> GSM447640 2 0.2416 0.7859 0.000 0.844 0.000 0.000 0.156 0.000
#> GSM447734 3 0.3073 0.5797 0.000 0.000 0.788 0.204 0.000 0.008
#> GSM447692 3 0.4037 0.0701 0.012 0.000 0.608 0.380 0.000 0.000
#> GSM447647 5 0.2941 0.6302 0.000 0.220 0.000 0.000 0.780 0.000
#> GSM447624 3 0.4408 0.4385 0.244 0.000 0.692 0.060 0.000 0.004
#> GSM447625 3 0.5974 0.2088 0.000 0.000 0.440 0.312 0.000 0.248
#> GSM447707 2 0.0146 0.8151 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM447732 3 0.4506 0.5575 0.000 0.000 0.704 0.120 0.000 0.176
#> GSM447684 6 0.0653 0.6663 0.012 0.000 0.004 0.004 0.000 0.980
#> GSM447731 5 0.5857 0.2943 0.000 0.180 0.012 0.012 0.592 0.204
#> GSM447705 6 0.3110 0.6702 0.000 0.000 0.196 0.012 0.000 0.792
#> GSM447631 3 0.0146 0.6589 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM447701 2 0.1168 0.8008 0.000 0.956 0.000 0.016 0.000 0.028
#> GSM447645 3 0.0000 0.6596 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)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
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 gender(p) individual(p) disease.state(p) other(p) k
#> MAD:mclust 121 0.576 0.793 0.241 0.07140 2
#> MAD:mclust 122 0.512 0.192 0.188 0.12437 3
#> MAD:mclust 119 0.250 0.325 0.176 0.12564 4
#> MAD:mclust 114 0.980 0.387 0.418 0.16139 5
#> MAD:mclust 101 0.673 0.217 0.172 0.00745 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "NMF"]
# you can also extract it by
# res = res_list["MAD:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 130 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 0.905 0.922 0.969 0.5020 0.498 0.498
#> 3 3 0.553 0.633 0.810 0.3022 0.748 0.535
#> 4 4 0.796 0.835 0.920 0.1418 0.826 0.545
#> 5 5 0.795 0.797 0.902 0.0574 0.875 0.575
#> 6 6 0.711 0.618 0.764 0.0454 0.939 0.730
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
#> GSM447671 2 0.0000 0.966 0.000 1.000
#> GSM447694 1 0.0000 0.968 1.000 0.000
#> GSM447618 2 0.0000 0.966 0.000 1.000
#> GSM447691 2 0.0000 0.966 0.000 1.000
#> GSM447733 1 0.1633 0.948 0.976 0.024
#> GSM447620 2 0.0000 0.966 0.000 1.000
#> GSM447627 1 0.0000 0.968 1.000 0.000
#> GSM447630 2 0.9815 0.252 0.420 0.580
#> GSM447642 1 0.0000 0.968 1.000 0.000
#> GSM447649 2 0.0000 0.966 0.000 1.000
#> GSM447654 2 0.0000 0.966 0.000 1.000
#> GSM447655 2 0.0000 0.966 0.000 1.000
#> GSM447669 2 0.0000 0.966 0.000 1.000
#> GSM447676 1 0.0000 0.968 1.000 0.000
#> GSM447678 2 0.0000 0.966 0.000 1.000
#> GSM447681 2 0.0000 0.966 0.000 1.000
#> GSM447698 2 0.0000 0.966 0.000 1.000
#> GSM447713 1 0.0000 0.968 1.000 0.000
#> GSM447722 2 0.0000 0.966 0.000 1.000
#> GSM447726 2 0.4690 0.869 0.100 0.900
#> GSM447735 1 0.0000 0.968 1.000 0.000
#> GSM447737 1 0.0000 0.968 1.000 0.000
#> GSM447657 2 0.0000 0.966 0.000 1.000
#> GSM447674 2 0.0000 0.966 0.000 1.000
#> GSM447636 1 0.8081 0.663 0.752 0.248
#> GSM447723 1 0.0000 0.968 1.000 0.000
#> GSM447699 1 0.4815 0.868 0.896 0.104
#> GSM447708 2 0.0000 0.966 0.000 1.000
#> GSM447721 1 0.0000 0.968 1.000 0.000
#> GSM447623 1 0.0000 0.968 1.000 0.000
#> GSM447621 1 0.0000 0.968 1.000 0.000
#> GSM447650 2 0.0000 0.966 0.000 1.000
#> GSM447651 2 0.0000 0.966 0.000 1.000
#> GSM447653 1 0.0000 0.968 1.000 0.000
#> GSM447658 1 0.0000 0.968 1.000 0.000
#> GSM447675 2 0.1414 0.949 0.020 0.980
#> GSM447680 2 0.0000 0.966 0.000 1.000
#> GSM447686 2 0.6623 0.781 0.172 0.828
#> GSM447736 1 0.0000 0.968 1.000 0.000
#> GSM447629 2 0.0000 0.966 0.000 1.000
#> GSM447648 1 0.0000 0.968 1.000 0.000
#> GSM447660 1 0.0000 0.968 1.000 0.000
#> GSM447661 2 0.0000 0.966 0.000 1.000
#> GSM447663 1 0.0000 0.968 1.000 0.000
#> GSM447704 2 0.0000 0.966 0.000 1.000
#> GSM447720 1 0.0000 0.968 1.000 0.000
#> GSM447652 2 0.0000 0.966 0.000 1.000
#> GSM447679 2 0.0000 0.966 0.000 1.000
#> GSM447712 1 0.0000 0.968 1.000 0.000
#> GSM447664 2 0.0672 0.959 0.008 0.992
#> GSM447637 1 0.0000 0.968 1.000 0.000
#> GSM447639 1 0.0938 0.958 0.988 0.012
#> GSM447615 1 0.0000 0.968 1.000 0.000
#> GSM447656 2 0.0000 0.966 0.000 1.000
#> GSM447673 2 0.0000 0.966 0.000 1.000
#> GSM447719 1 0.0000 0.968 1.000 0.000
#> GSM447706 1 0.0000 0.968 1.000 0.000
#> GSM447612 1 0.6438 0.795 0.836 0.164
#> GSM447665 2 0.0000 0.966 0.000 1.000
#> GSM447677 2 0.0000 0.966 0.000 1.000
#> GSM447613 1 0.0000 0.968 1.000 0.000
#> GSM447659 1 0.0000 0.968 1.000 0.000
#> GSM447662 1 0.0000 0.968 1.000 0.000
#> GSM447666 1 0.0672 0.961 0.992 0.008
#> GSM447668 2 0.0000 0.966 0.000 1.000
#> GSM447682 2 0.0000 0.966 0.000 1.000
#> GSM447683 2 0.0000 0.966 0.000 1.000
#> GSM447688 2 0.0000 0.966 0.000 1.000
#> GSM447702 2 0.0000 0.966 0.000 1.000
#> GSM447709 2 0.0000 0.966 0.000 1.000
#> GSM447711 1 0.0000 0.968 1.000 0.000
#> GSM447715 1 0.9170 0.498 0.668 0.332
#> GSM447693 1 0.0000 0.968 1.000 0.000
#> GSM447611 1 0.4022 0.893 0.920 0.080
#> GSM447672 2 0.0000 0.966 0.000 1.000
#> GSM447703 2 0.0000 0.966 0.000 1.000
#> GSM447727 1 0.0000 0.968 1.000 0.000
#> GSM447638 2 0.9909 0.213 0.444 0.556
#> GSM447670 1 0.0000 0.968 1.000 0.000
#> GSM447700 2 0.0000 0.966 0.000 1.000
#> GSM447738 2 0.0000 0.966 0.000 1.000
#> GSM447739 1 0.0000 0.968 1.000 0.000
#> GSM447617 1 0.0000 0.968 1.000 0.000
#> GSM447628 2 0.0000 0.966 0.000 1.000
#> GSM447632 2 0.0000 0.966 0.000 1.000
#> GSM447619 1 0.0000 0.968 1.000 0.000
#> GSM447643 2 0.9710 0.342 0.400 0.600
#> GSM447724 1 0.8909 0.561 0.692 0.308
#> GSM447728 2 0.0000 0.966 0.000 1.000
#> GSM447610 1 0.0000 0.968 1.000 0.000
#> GSM447633 2 0.0000 0.966 0.000 1.000
#> GSM447634 1 0.0000 0.968 1.000 0.000
#> GSM447622 1 0.0000 0.968 1.000 0.000
#> GSM447667 2 0.3114 0.915 0.056 0.944
#> GSM447687 2 0.0000 0.966 0.000 1.000
#> GSM447695 1 0.0000 0.968 1.000 0.000
#> GSM447696 1 0.0000 0.968 1.000 0.000
#> GSM447697 1 0.0000 0.968 1.000 0.000
#> GSM447714 1 0.0000 0.968 1.000 0.000
#> GSM447717 1 0.0000 0.968 1.000 0.000
#> GSM447725 1 0.0000 0.968 1.000 0.000
#> GSM447729 2 0.0000 0.966 0.000 1.000
#> GSM447644 2 0.0000 0.966 0.000 1.000
#> GSM447710 1 0.0000 0.968 1.000 0.000
#> GSM447614 1 0.0000 0.968 1.000 0.000
#> GSM447685 2 0.0000 0.966 0.000 1.000
#> GSM447690 1 0.0000 0.968 1.000 0.000
#> GSM447730 2 0.0000 0.966 0.000 1.000
#> GSM447646 2 0.0000 0.966 0.000 1.000
#> GSM447689 1 0.0000 0.968 1.000 0.000
#> GSM447635 1 0.9427 0.449 0.640 0.360
#> GSM447641 1 0.0000 0.968 1.000 0.000
#> GSM447716 2 0.0000 0.966 0.000 1.000
#> GSM447718 1 0.0672 0.962 0.992 0.008
#> GSM447616 1 0.0000 0.968 1.000 0.000
#> GSM447626 1 0.0000 0.968 1.000 0.000
#> GSM447640 2 0.0000 0.966 0.000 1.000
#> GSM447734 1 0.0000 0.968 1.000 0.000
#> GSM447692 1 0.0000 0.968 1.000 0.000
#> GSM447647 2 0.0000 0.966 0.000 1.000
#> GSM447624 1 0.0000 0.968 1.000 0.000
#> GSM447625 1 0.0000 0.968 1.000 0.000
#> GSM447707 2 0.0000 0.966 0.000 1.000
#> GSM447732 1 0.0000 0.968 1.000 0.000
#> GSM447684 1 0.0000 0.968 1.000 0.000
#> GSM447731 2 0.9129 0.518 0.328 0.672
#> GSM447705 1 0.9944 0.176 0.544 0.456
#> GSM447631 1 0.0000 0.968 1.000 0.000
#> GSM447701 2 0.0000 0.966 0.000 1.000
#> GSM447645 1 0.0000 0.968 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM447671 2 0.6235 0.2076 0.000 0.564 0.436
#> GSM447694 3 0.0000 0.8305 0.000 0.000 1.000
#> GSM447618 2 0.0747 0.8609 0.016 0.984 0.000
#> GSM447691 2 0.0000 0.8642 0.000 1.000 0.000
#> GSM447733 3 0.6330 0.3860 0.396 0.004 0.600
#> GSM447620 2 0.5216 0.6141 0.000 0.740 0.260
#> GSM447627 3 0.1964 0.7872 0.056 0.000 0.944
#> GSM447630 2 0.6286 0.1066 0.000 0.536 0.464
#> GSM447642 1 0.6026 0.6015 0.624 0.000 0.376
#> GSM447649 2 0.0000 0.8642 0.000 1.000 0.000
#> GSM447654 1 0.5058 0.2439 0.756 0.244 0.000
#> GSM447655 2 0.0000 0.8642 0.000 1.000 0.000
#> GSM447669 2 0.2711 0.8037 0.000 0.912 0.088
#> GSM447676 1 0.5968 0.6083 0.636 0.000 0.364
#> GSM447678 2 0.6062 0.5664 0.384 0.616 0.000
#> GSM447681 2 0.0747 0.8609 0.016 0.984 0.000
#> GSM447698 2 0.5058 0.7128 0.244 0.756 0.000
#> GSM447713 1 0.5926 0.6102 0.644 0.000 0.356
#> GSM447722 2 0.8875 0.4454 0.364 0.508 0.128
#> GSM447726 2 0.0000 0.8642 0.000 1.000 0.000
#> GSM447735 1 0.6168 -0.0737 0.588 0.000 0.412
#> GSM447737 1 0.6215 0.5428 0.572 0.000 0.428
#> GSM447657 2 0.2448 0.8363 0.076 0.924 0.000
#> GSM447674 2 0.1031 0.8582 0.024 0.976 0.000
#> GSM447636 1 0.7966 0.5232 0.652 0.220 0.128
#> GSM447723 1 0.6045 0.5984 0.620 0.000 0.380
#> GSM447699 3 0.0000 0.8305 0.000 0.000 1.000
#> GSM447708 2 0.0000 0.8642 0.000 1.000 0.000
#> GSM447721 1 0.5988 0.6067 0.632 0.000 0.368
#> GSM447623 1 0.6095 0.5865 0.608 0.000 0.392
#> GSM447621 1 0.6204 0.5471 0.576 0.000 0.424
#> GSM447650 2 0.0000 0.8642 0.000 1.000 0.000
#> GSM447651 2 0.0000 0.8642 0.000 1.000 0.000
#> GSM447653 1 0.1753 0.4828 0.952 0.000 0.048
#> GSM447658 1 0.5948 0.6096 0.640 0.000 0.360
#> GSM447675 1 0.6309 -0.3959 0.504 0.496 0.000
#> GSM447680 2 0.0000 0.8642 0.000 1.000 0.000
#> GSM447686 1 0.5835 0.4070 0.660 0.340 0.000
#> GSM447736 3 0.0000 0.8305 0.000 0.000 1.000
#> GSM447629 2 0.0000 0.8642 0.000 1.000 0.000
#> GSM447648 3 0.0000 0.8305 0.000 0.000 1.000
#> GSM447660 1 0.5926 0.6102 0.644 0.000 0.356
#> GSM447661 2 0.0000 0.8642 0.000 1.000 0.000
#> GSM447663 3 0.0000 0.8305 0.000 0.000 1.000
#> GSM447704 2 0.0237 0.8636 0.004 0.996 0.000
#> GSM447720 3 0.4586 0.6851 0.096 0.048 0.856
#> GSM447652 2 0.1411 0.8537 0.036 0.964 0.000
#> GSM447679 2 0.0000 0.8642 0.000 1.000 0.000
#> GSM447712 1 0.5948 0.6096 0.640 0.000 0.360
#> GSM447664 1 0.5882 -0.0255 0.652 0.348 0.000
#> GSM447637 3 0.0000 0.8305 0.000 0.000 1.000
#> GSM447639 3 0.9537 0.1866 0.380 0.192 0.428
#> GSM447615 1 0.6154 0.5694 0.592 0.000 0.408
#> GSM447656 2 0.0000 0.8642 0.000 1.000 0.000
#> GSM447673 2 0.5948 0.5947 0.360 0.640 0.000
#> GSM447719 1 0.5882 0.1851 0.652 0.000 0.348
#> GSM447706 3 0.0000 0.8305 0.000 0.000 1.000
#> GSM447612 3 0.0000 0.8305 0.000 0.000 1.000
#> GSM447665 2 0.0000 0.8642 0.000 1.000 0.000
#> GSM447677 2 0.0000 0.8642 0.000 1.000 0.000
#> GSM447613 1 0.6045 0.5984 0.620 0.000 0.380
#> GSM447659 3 0.5835 0.4494 0.340 0.000 0.660
#> GSM447662 3 0.0000 0.8305 0.000 0.000 1.000
#> GSM447666 3 0.4842 0.5645 0.000 0.224 0.776
#> GSM447668 2 0.0000 0.8642 0.000 1.000 0.000
#> GSM447682 2 0.0237 0.8636 0.004 0.996 0.000
#> GSM447683 2 0.0000 0.8642 0.000 1.000 0.000
#> GSM447688 2 0.5948 0.5947 0.360 0.640 0.000
#> GSM447702 2 0.0000 0.8642 0.000 1.000 0.000
#> GSM447709 2 0.3192 0.7835 0.000 0.888 0.112
#> GSM447711 1 0.5988 0.6067 0.632 0.000 0.368
#> GSM447715 1 0.8013 0.4048 0.564 0.364 0.072
#> GSM447693 3 0.0000 0.8305 0.000 0.000 1.000
#> GSM447611 1 0.0424 0.4869 0.992 0.008 0.000
#> GSM447672 2 0.0237 0.8636 0.004 0.996 0.000
#> GSM447703 2 0.3192 0.8168 0.112 0.888 0.000
#> GSM447727 1 0.6180 0.5576 0.584 0.000 0.416
#> GSM447638 1 0.6286 0.1899 0.536 0.464 0.000
#> GSM447670 1 0.6267 0.5014 0.548 0.000 0.452
#> GSM447700 2 0.8009 0.2067 0.064 0.524 0.412
#> GSM447738 2 0.3482 0.8070 0.128 0.872 0.000
#> GSM447739 1 0.5948 0.6096 0.640 0.000 0.360
#> GSM447617 1 0.6280 0.4857 0.540 0.000 0.460
#> GSM447628 2 0.6095 0.5556 0.392 0.608 0.000
#> GSM447632 2 0.2878 0.8260 0.096 0.904 0.000
#> GSM447619 3 0.0000 0.8305 0.000 0.000 1.000
#> GSM447643 1 0.6379 0.3821 0.624 0.368 0.008
#> GSM447724 3 0.5968 0.4247 0.364 0.000 0.636
#> GSM447728 2 0.0000 0.8642 0.000 1.000 0.000
#> GSM447610 1 0.1643 0.4972 0.956 0.000 0.044
#> GSM447633 3 0.6280 0.0811 0.000 0.460 0.540
#> GSM447634 3 0.4555 0.5426 0.200 0.000 0.800
#> GSM447622 3 0.0000 0.8305 0.000 0.000 1.000
#> GSM447667 2 0.4555 0.6619 0.200 0.800 0.000
#> GSM447687 2 0.3551 0.8041 0.132 0.868 0.000
#> GSM447695 3 0.4291 0.5832 0.180 0.000 0.820
#> GSM447696 1 0.6008 0.6042 0.628 0.000 0.372
#> GSM447697 1 0.6045 0.5984 0.620 0.000 0.380
#> GSM447714 3 0.0000 0.8305 0.000 0.000 1.000
#> GSM447717 1 0.5926 0.6102 0.644 0.000 0.356
#> GSM447725 1 0.5138 0.5875 0.748 0.000 0.252
#> GSM447729 1 0.4452 0.3424 0.808 0.192 0.000
#> GSM447644 2 0.4121 0.7258 0.000 0.832 0.168
#> GSM447710 3 0.0000 0.8305 0.000 0.000 1.000
#> GSM447614 1 0.6154 -0.0376 0.592 0.000 0.408
#> GSM447685 2 0.0000 0.8642 0.000 1.000 0.000
#> GSM447690 1 0.4750 0.5749 0.784 0.000 0.216
#> GSM447730 2 0.0892 0.8596 0.020 0.980 0.000
#> GSM447646 2 0.6026 0.5757 0.376 0.624 0.000
#> GSM447689 3 0.0000 0.8305 0.000 0.000 1.000
#> GSM447635 2 0.5992 0.5255 0.016 0.716 0.268
#> GSM447641 1 0.5988 0.6067 0.632 0.000 0.368
#> GSM447716 1 0.6308 -0.3828 0.508 0.492 0.000
#> GSM447718 3 0.3038 0.7228 0.000 0.104 0.896
#> GSM447616 3 0.0424 0.8235 0.008 0.000 0.992
#> GSM447626 3 0.0000 0.8305 0.000 0.000 1.000
#> GSM447640 2 0.0000 0.8642 0.000 1.000 0.000
#> GSM447734 3 0.0000 0.8305 0.000 0.000 1.000
#> GSM447692 3 0.4842 0.4869 0.224 0.000 0.776
#> GSM447647 2 0.6008 0.5806 0.372 0.628 0.000
#> GSM447624 3 0.4452 0.5625 0.192 0.000 0.808
#> GSM447625 3 0.0000 0.8305 0.000 0.000 1.000
#> GSM447707 2 0.0000 0.8642 0.000 1.000 0.000
#> GSM447732 3 0.0000 0.8305 0.000 0.000 1.000
#> GSM447684 3 0.5094 0.6610 0.136 0.040 0.824
#> GSM447731 1 0.9773 -0.2240 0.412 0.352 0.236
#> GSM447705 3 0.4399 0.6144 0.000 0.188 0.812
#> GSM447631 3 0.0000 0.8305 0.000 0.000 1.000
#> GSM447701 2 0.0000 0.8642 0.000 1.000 0.000
#> GSM447645 3 0.0000 0.8305 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM447671 3 0.7282 0.352 0.000 0.316 0.512 0.172
#> GSM447694 3 0.1940 0.878 0.000 0.000 0.924 0.076
#> GSM447618 2 0.4941 0.300 0.000 0.564 0.000 0.436
#> GSM447691 2 0.1792 0.864 0.000 0.932 0.000 0.068
#> GSM447733 4 0.0188 0.863 0.000 0.000 0.004 0.996
#> GSM447620 2 0.3764 0.723 0.000 0.784 0.216 0.000
#> GSM447627 3 0.3444 0.802 0.000 0.000 0.816 0.184
#> GSM447630 2 0.4925 0.182 0.000 0.572 0.428 0.000
#> GSM447642 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM447649 2 0.0000 0.908 0.000 1.000 0.000 0.000
#> GSM447654 4 0.1807 0.858 0.052 0.008 0.000 0.940
#> GSM447655 2 0.0000 0.908 0.000 1.000 0.000 0.000
#> GSM447669 2 0.1474 0.872 0.000 0.948 0.052 0.000
#> GSM447676 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM447678 4 0.0000 0.863 0.000 0.000 0.000 1.000
#> GSM447681 2 0.0592 0.901 0.000 0.984 0.000 0.016
#> GSM447698 4 0.0469 0.865 0.000 0.012 0.000 0.988
#> GSM447713 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM447722 4 0.0000 0.863 0.000 0.000 0.000 1.000
#> GSM447726 2 0.0469 0.902 0.000 0.988 0.012 0.000
#> GSM447735 4 0.1211 0.848 0.000 0.000 0.040 0.960
#> GSM447737 1 0.5420 0.381 0.624 0.000 0.352 0.024
#> GSM447657 2 0.2011 0.847 0.000 0.920 0.000 0.080
#> GSM447674 2 0.0188 0.907 0.000 0.996 0.000 0.004
#> GSM447636 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM447723 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM447699 3 0.3801 0.765 0.000 0.000 0.780 0.220
#> GSM447708 2 0.0000 0.908 0.000 1.000 0.000 0.000
#> GSM447721 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM447623 1 0.0336 0.957 0.992 0.000 0.008 0.000
#> GSM447621 1 0.1940 0.892 0.924 0.000 0.076 0.000
#> GSM447650 2 0.0000 0.908 0.000 1.000 0.000 0.000
#> GSM447651 2 0.0000 0.908 0.000 1.000 0.000 0.000
#> GSM447653 4 0.2610 0.834 0.088 0.000 0.012 0.900
#> GSM447658 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM447675 4 0.0000 0.863 0.000 0.000 0.000 1.000
#> GSM447680 2 0.0000 0.908 0.000 1.000 0.000 0.000
#> GSM447686 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM447736 3 0.1637 0.886 0.000 0.000 0.940 0.060
#> GSM447629 2 0.0336 0.905 0.000 0.992 0.000 0.008
#> GSM447648 3 0.0000 0.909 0.000 0.000 1.000 0.000
#> GSM447660 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM447661 2 0.0000 0.908 0.000 1.000 0.000 0.000
#> GSM447663 3 0.0000 0.909 0.000 0.000 1.000 0.000
#> GSM447704 2 0.0000 0.908 0.000 1.000 0.000 0.000
#> GSM447720 3 0.3976 0.824 0.112 0.004 0.840 0.044
#> GSM447652 2 0.0336 0.905 0.000 0.992 0.000 0.008
#> GSM447679 2 0.0000 0.908 0.000 1.000 0.000 0.000
#> GSM447712 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM447664 4 0.4136 0.739 0.196 0.016 0.000 0.788
#> GSM447637 3 0.0000 0.909 0.000 0.000 1.000 0.000
#> GSM447639 4 0.0469 0.861 0.000 0.000 0.012 0.988
#> GSM447615 1 0.0336 0.957 0.992 0.000 0.008 0.000
#> GSM447656 2 0.0000 0.908 0.000 1.000 0.000 0.000
#> GSM447673 4 0.2921 0.812 0.000 0.140 0.000 0.860
#> GSM447719 4 0.5628 0.302 0.024 0.000 0.420 0.556
#> GSM447706 3 0.0000 0.909 0.000 0.000 1.000 0.000
#> GSM447612 3 0.0000 0.909 0.000 0.000 1.000 0.000
#> GSM447665 2 0.0000 0.908 0.000 1.000 0.000 0.000
#> GSM447677 2 0.0000 0.908 0.000 1.000 0.000 0.000
#> GSM447613 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM447659 3 0.4008 0.715 0.000 0.000 0.756 0.244
#> GSM447662 3 0.0000 0.909 0.000 0.000 1.000 0.000
#> GSM447666 3 0.1474 0.873 0.000 0.052 0.948 0.000
#> GSM447668 2 0.0000 0.908 0.000 1.000 0.000 0.000
#> GSM447682 2 0.0000 0.908 0.000 1.000 0.000 0.000
#> GSM447683 2 0.0000 0.908 0.000 1.000 0.000 0.000
#> GSM447688 4 0.0336 0.865 0.000 0.008 0.000 0.992
#> GSM447702 2 0.0000 0.908 0.000 1.000 0.000 0.000
#> GSM447709 2 0.0817 0.896 0.000 0.976 0.024 0.000
#> GSM447711 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM447715 1 0.3873 0.675 0.772 0.228 0.000 0.000
#> GSM447693 3 0.0000 0.909 0.000 0.000 1.000 0.000
#> GSM447611 4 0.3610 0.738 0.200 0.000 0.000 0.800
#> GSM447672 2 0.0188 0.907 0.000 0.996 0.000 0.004
#> GSM447703 4 0.4522 0.590 0.000 0.320 0.000 0.680
#> GSM447727 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM447638 2 0.3583 0.742 0.180 0.816 0.004 0.000
#> GSM447670 1 0.0592 0.952 0.984 0.000 0.016 0.000
#> GSM447700 3 0.4830 0.470 0.000 0.000 0.608 0.392
#> GSM447738 4 0.3649 0.739 0.000 0.204 0.000 0.796
#> GSM447739 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM447617 1 0.2760 0.834 0.872 0.000 0.128 0.000
#> GSM447628 4 0.2408 0.836 0.000 0.104 0.000 0.896
#> GSM447632 2 0.4907 0.250 0.000 0.580 0.000 0.420
#> GSM447619 3 0.0000 0.909 0.000 0.000 1.000 0.000
#> GSM447643 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM447724 4 0.1389 0.844 0.000 0.000 0.048 0.952
#> GSM447728 2 0.0188 0.907 0.000 0.996 0.000 0.004
#> GSM447610 4 0.4605 0.521 0.336 0.000 0.000 0.664
#> GSM447633 2 0.4500 0.578 0.000 0.684 0.316 0.000
#> GSM447634 3 0.4245 0.747 0.196 0.000 0.784 0.020
#> GSM447622 3 0.0188 0.908 0.000 0.000 0.996 0.004
#> GSM447667 2 0.4155 0.664 0.240 0.756 0.000 0.004
#> GSM447687 4 0.4477 0.597 0.000 0.312 0.000 0.688
#> GSM447695 3 0.3764 0.769 0.000 0.000 0.784 0.216
#> GSM447696 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM447697 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM447714 3 0.0188 0.908 0.000 0.000 0.996 0.004
#> GSM447717 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM447725 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM447729 4 0.2593 0.843 0.080 0.016 0.000 0.904
#> GSM447644 2 0.0921 0.893 0.000 0.972 0.028 0.000
#> GSM447710 3 0.0000 0.909 0.000 0.000 1.000 0.000
#> GSM447614 4 0.1305 0.851 0.004 0.000 0.036 0.960
#> GSM447685 2 0.0000 0.908 0.000 1.000 0.000 0.000
#> GSM447690 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM447730 2 0.0000 0.908 0.000 1.000 0.000 0.000
#> GSM447646 4 0.0592 0.865 0.000 0.016 0.000 0.984
#> GSM447689 3 0.0000 0.909 0.000 0.000 1.000 0.000
#> GSM447635 2 0.5296 0.108 0.000 0.500 0.008 0.492
#> GSM447641 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM447716 4 0.1004 0.866 0.004 0.024 0.000 0.972
#> GSM447718 3 0.3219 0.776 0.000 0.164 0.836 0.000
#> GSM447616 3 0.1406 0.897 0.016 0.000 0.960 0.024
#> GSM447626 3 0.0000 0.909 0.000 0.000 1.000 0.000
#> GSM447640 2 0.0000 0.908 0.000 1.000 0.000 0.000
#> GSM447734 3 0.0469 0.906 0.000 0.000 0.988 0.012
#> GSM447692 3 0.4906 0.777 0.140 0.000 0.776 0.084
#> GSM447647 4 0.3569 0.762 0.000 0.196 0.000 0.804
#> GSM447624 3 0.3356 0.778 0.176 0.000 0.824 0.000
#> GSM447625 3 0.0188 0.908 0.000 0.000 0.996 0.004
#> GSM447707 2 0.0000 0.908 0.000 1.000 0.000 0.000
#> GSM447732 3 0.0000 0.909 0.000 0.000 1.000 0.000
#> GSM447684 2 0.6295 0.608 0.144 0.660 0.196 0.000
#> GSM447731 4 0.5434 0.716 0.000 0.084 0.188 0.728
#> GSM447705 3 0.0000 0.909 0.000 0.000 1.000 0.000
#> GSM447631 3 0.0000 0.909 0.000 0.000 1.000 0.000
#> GSM447701 2 0.0000 0.908 0.000 1.000 0.000 0.000
#> GSM447645 3 0.0000 0.909 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
#> GSM447671 3 0.2179 0.7976 0.000 0.112 0.888 0.000 0.000
#> GSM447694 3 0.0162 0.8735 0.000 0.000 0.996 0.000 0.004
#> GSM447618 3 0.2889 0.8236 0.000 0.044 0.872 0.084 0.000
#> GSM447691 2 0.4310 0.3784 0.000 0.604 0.392 0.004 0.000
#> GSM447733 4 0.0865 0.8547 0.000 0.000 0.004 0.972 0.024
#> GSM447620 5 0.3086 0.6845 0.000 0.180 0.004 0.000 0.816
#> GSM447627 3 0.1661 0.8618 0.000 0.000 0.940 0.036 0.024
#> GSM447630 3 0.3109 0.7212 0.000 0.200 0.800 0.000 0.000
#> GSM447642 1 0.0000 0.9560 1.000 0.000 0.000 0.000 0.000
#> GSM447649 2 0.1965 0.8682 0.000 0.924 0.000 0.024 0.052
#> GSM447654 4 0.0510 0.8562 0.000 0.000 0.000 0.984 0.016
#> GSM447655 2 0.0162 0.8931 0.000 0.996 0.000 0.000 0.004
#> GSM447669 3 0.3336 0.6909 0.000 0.228 0.772 0.000 0.000
#> GSM447676 1 0.2280 0.8555 0.880 0.000 0.000 0.000 0.120
#> GSM447678 4 0.1168 0.8482 0.000 0.008 0.032 0.960 0.000
#> GSM447681 2 0.1012 0.8864 0.000 0.968 0.012 0.020 0.000
#> GSM447698 2 0.6334 0.0916 0.000 0.452 0.160 0.388 0.000
#> GSM447713 1 0.0000 0.9560 1.000 0.000 0.000 0.000 0.000
#> GSM447722 3 0.2753 0.7969 0.000 0.008 0.856 0.136 0.000
#> GSM447726 2 0.0451 0.8920 0.000 0.988 0.004 0.000 0.008
#> GSM447735 3 0.1121 0.8649 0.000 0.000 0.956 0.044 0.000
#> GSM447737 3 0.2127 0.8043 0.108 0.000 0.892 0.000 0.000
#> GSM447657 2 0.1300 0.8804 0.000 0.956 0.016 0.028 0.000
#> GSM447674 2 0.0451 0.8924 0.000 0.988 0.004 0.008 0.000
#> GSM447636 1 0.0000 0.9560 1.000 0.000 0.000 0.000 0.000
#> GSM447723 1 0.0000 0.9560 1.000 0.000 0.000 0.000 0.000
#> GSM447699 3 0.0510 0.8730 0.000 0.000 0.984 0.016 0.000
#> GSM447708 2 0.0162 0.8930 0.000 0.996 0.004 0.000 0.000
#> GSM447721 1 0.0000 0.9560 1.000 0.000 0.000 0.000 0.000
#> GSM447623 1 0.0000 0.9560 1.000 0.000 0.000 0.000 0.000
#> GSM447621 1 0.4060 0.4199 0.640 0.000 0.360 0.000 0.000
#> GSM447650 2 0.0324 0.8929 0.000 0.992 0.004 0.000 0.004
#> GSM447651 2 0.0703 0.8874 0.000 0.976 0.000 0.000 0.024
#> GSM447653 4 0.2914 0.7997 0.052 0.000 0.000 0.872 0.076
#> GSM447658 1 0.0000 0.9560 1.000 0.000 0.000 0.000 0.000
#> GSM447675 4 0.0162 0.8573 0.000 0.000 0.004 0.996 0.000
#> GSM447680 2 0.0451 0.8920 0.000 0.988 0.004 0.000 0.008
#> GSM447686 1 0.0290 0.9493 0.992 0.008 0.000 0.000 0.000
#> GSM447736 3 0.0703 0.8705 0.000 0.000 0.976 0.000 0.024
#> GSM447629 2 0.0566 0.8915 0.000 0.984 0.004 0.012 0.000
#> GSM447648 5 0.1732 0.8321 0.000 0.000 0.080 0.000 0.920
#> GSM447660 1 0.0000 0.9560 1.000 0.000 0.000 0.000 0.000
#> GSM447661 2 0.0290 0.8926 0.000 0.992 0.000 0.000 0.008
#> GSM447663 3 0.1117 0.8717 0.000 0.016 0.964 0.000 0.020
#> GSM447704 2 0.2677 0.8217 0.000 0.872 0.000 0.112 0.016
#> GSM447720 3 0.0290 0.8739 0.000 0.008 0.992 0.000 0.000
#> GSM447652 2 0.0324 0.8930 0.000 0.992 0.004 0.004 0.000
#> GSM447679 2 0.0162 0.8930 0.000 0.996 0.004 0.000 0.000
#> GSM447712 1 0.0000 0.9560 1.000 0.000 0.000 0.000 0.000
#> GSM447664 4 0.2516 0.7553 0.140 0.000 0.000 0.860 0.000
#> GSM447637 5 0.1478 0.8353 0.000 0.000 0.064 0.000 0.936
#> GSM447639 3 0.3661 0.6448 0.000 0.000 0.724 0.276 0.000
#> GSM447615 5 0.3612 0.5287 0.268 0.000 0.000 0.000 0.732
#> GSM447656 2 0.0162 0.8931 0.000 0.996 0.000 0.000 0.004
#> GSM447673 4 0.0404 0.8562 0.000 0.012 0.000 0.988 0.000
#> GSM447719 5 0.3266 0.6560 0.004 0.000 0.000 0.200 0.796
#> GSM447706 5 0.1792 0.8311 0.000 0.000 0.084 0.000 0.916
#> GSM447612 3 0.2929 0.7374 0.000 0.000 0.820 0.000 0.180
#> GSM447665 2 0.0162 0.8930 0.000 0.996 0.004 0.000 0.000
#> GSM447677 2 0.0404 0.8913 0.000 0.988 0.000 0.000 0.012
#> GSM447613 1 0.0000 0.9560 1.000 0.000 0.000 0.000 0.000
#> GSM447659 4 0.5144 0.5317 0.000 0.000 0.132 0.692 0.176
#> GSM447662 3 0.4294 0.0722 0.000 0.000 0.532 0.000 0.468
#> GSM447666 5 0.1012 0.8248 0.000 0.012 0.020 0.000 0.968
#> GSM447668 2 0.0324 0.8929 0.000 0.992 0.004 0.000 0.004
#> GSM447682 2 0.0794 0.8868 0.000 0.972 0.000 0.028 0.000
#> GSM447683 2 0.0162 0.8930 0.000 0.996 0.004 0.000 0.000
#> GSM447688 4 0.0290 0.8569 0.000 0.008 0.000 0.992 0.000
#> GSM447702 2 0.0000 0.8933 0.000 1.000 0.000 0.000 0.000
#> GSM447709 2 0.2329 0.8146 0.000 0.876 0.000 0.000 0.124
#> GSM447711 1 0.0000 0.9560 1.000 0.000 0.000 0.000 0.000
#> GSM447715 1 0.2561 0.7868 0.856 0.144 0.000 0.000 0.000
#> GSM447693 5 0.1478 0.8353 0.000 0.000 0.064 0.000 0.936
#> GSM447611 4 0.1012 0.8534 0.012 0.000 0.000 0.968 0.020
#> GSM447672 2 0.0451 0.8923 0.000 0.988 0.000 0.008 0.004
#> GSM447703 4 0.2377 0.7681 0.000 0.128 0.000 0.872 0.000
#> GSM447727 1 0.0000 0.9560 1.000 0.000 0.000 0.000 0.000
#> GSM447638 2 0.6402 0.1662 0.180 0.472 0.000 0.000 0.348
#> GSM447670 1 0.2280 0.8526 0.880 0.000 0.000 0.000 0.120
#> GSM447700 3 0.1082 0.8681 0.000 0.008 0.964 0.028 0.000
#> GSM447738 2 0.4291 0.2149 0.000 0.536 0.000 0.464 0.000
#> GSM447739 1 0.0000 0.9560 1.000 0.000 0.000 0.000 0.000
#> GSM447617 1 0.0609 0.9392 0.980 0.000 0.020 0.000 0.000
#> GSM447628 4 0.0162 0.8579 0.000 0.004 0.000 0.996 0.000
#> GSM447632 2 0.4192 0.3811 0.000 0.596 0.000 0.404 0.000
#> GSM447619 5 0.3366 0.6955 0.000 0.000 0.232 0.000 0.768
#> GSM447643 1 0.0000 0.9560 1.000 0.000 0.000 0.000 0.000
#> GSM447724 4 0.2020 0.8026 0.000 0.000 0.100 0.900 0.000
#> GSM447728 2 0.0162 0.8930 0.000 0.996 0.004 0.000 0.000
#> GSM447610 4 0.4138 0.3486 0.384 0.000 0.000 0.616 0.000
#> GSM447633 2 0.2110 0.8513 0.000 0.912 0.072 0.000 0.016
#> GSM447634 3 0.0566 0.8722 0.012 0.004 0.984 0.000 0.000
#> GSM447622 3 0.1197 0.8616 0.000 0.000 0.952 0.000 0.048
#> GSM447667 2 0.3779 0.6692 0.236 0.752 0.000 0.012 0.000
#> GSM447687 4 0.3636 0.5738 0.000 0.272 0.000 0.728 0.000
#> GSM447695 3 0.0162 0.8737 0.000 0.000 0.996 0.004 0.000
#> GSM447696 1 0.0000 0.9560 1.000 0.000 0.000 0.000 0.000
#> GSM447697 1 0.0000 0.9560 1.000 0.000 0.000 0.000 0.000
#> GSM447714 3 0.4242 0.2192 0.000 0.000 0.572 0.000 0.428
#> GSM447717 1 0.0000 0.9560 1.000 0.000 0.000 0.000 0.000
#> GSM447725 1 0.0000 0.9560 1.000 0.000 0.000 0.000 0.000
#> GSM447729 4 0.0000 0.8575 0.000 0.000 0.000 1.000 0.000
#> GSM447644 2 0.1168 0.8816 0.000 0.960 0.032 0.000 0.008
#> GSM447710 5 0.2929 0.7594 0.000 0.000 0.180 0.000 0.820
#> GSM447614 4 0.3353 0.7123 0.008 0.000 0.196 0.796 0.000
#> GSM447685 2 0.0451 0.8923 0.000 0.988 0.000 0.008 0.004
#> GSM447690 1 0.0000 0.9560 1.000 0.000 0.000 0.000 0.000
#> GSM447730 2 0.4930 0.6866 0.000 0.716 0.000 0.144 0.140
#> GSM447646 4 0.0162 0.8577 0.000 0.000 0.000 0.996 0.004
#> GSM447689 5 0.0794 0.8284 0.000 0.000 0.028 0.000 0.972
#> GSM447635 3 0.1981 0.8463 0.000 0.048 0.924 0.028 0.000
#> GSM447641 1 0.0000 0.9560 1.000 0.000 0.000 0.000 0.000
#> GSM447716 4 0.4173 0.5015 0.012 0.300 0.000 0.688 0.000
#> GSM447718 5 0.6796 0.1722 0.000 0.372 0.228 0.004 0.396
#> GSM447616 3 0.0566 0.8734 0.004 0.000 0.984 0.000 0.012
#> GSM447626 5 0.2179 0.8176 0.000 0.000 0.112 0.000 0.888
#> GSM447640 2 0.0324 0.8929 0.000 0.992 0.000 0.004 0.004
#> GSM447734 3 0.0404 0.8732 0.000 0.000 0.988 0.000 0.012
#> GSM447692 3 0.0162 0.8738 0.004 0.000 0.996 0.000 0.000
#> GSM447647 4 0.1124 0.8498 0.000 0.004 0.000 0.960 0.036
#> GSM447624 1 0.4493 0.6920 0.756 0.000 0.108 0.000 0.136
#> GSM447625 3 0.1851 0.8343 0.000 0.000 0.912 0.000 0.088
#> GSM447707 2 0.2221 0.8605 0.000 0.912 0.000 0.052 0.036
#> GSM447732 3 0.1502 0.8561 0.000 0.004 0.940 0.000 0.056
#> GSM447684 2 0.4142 0.6408 0.252 0.728 0.004 0.000 0.016
#> GSM447731 5 0.4225 0.3531 0.000 0.004 0.000 0.364 0.632
#> GSM447705 5 0.2516 0.7980 0.000 0.000 0.140 0.000 0.860
#> GSM447631 5 0.1121 0.8341 0.000 0.000 0.044 0.000 0.956
#> GSM447701 2 0.0451 0.8920 0.000 0.988 0.004 0.000 0.008
#> GSM447645 5 0.0609 0.8249 0.000 0.000 0.020 0.000 0.980
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM447671 3 0.5056 0.2074 0.000 0.060 0.544 0.000 0.388 0.008
#> GSM447694 3 0.1124 0.6852 0.000 0.000 0.956 0.000 0.036 0.008
#> GSM447618 5 0.5046 0.2766 0.000 0.072 0.364 0.004 0.560 0.000
#> GSM447691 3 0.5700 0.2171 0.000 0.320 0.516 0.000 0.160 0.004
#> GSM447733 4 0.3629 0.4929 0.000 0.000 0.000 0.712 0.276 0.012
#> GSM447620 6 0.5273 0.2418 0.000 0.212 0.000 0.000 0.184 0.604
#> GSM447627 3 0.4327 0.4093 0.000 0.000 0.652 0.316 0.016 0.016
#> GSM447630 3 0.6277 0.4265 0.000 0.216 0.488 0.024 0.272 0.000
#> GSM447642 1 0.0146 0.9415 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM447649 2 0.3519 0.5573 0.000 0.752 0.000 0.008 0.232 0.008
#> GSM447654 4 0.0260 0.7038 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM447655 2 0.2100 0.6719 0.000 0.884 0.000 0.000 0.112 0.004
#> GSM447669 3 0.6371 0.2898 0.000 0.280 0.416 0.008 0.292 0.004
#> GSM447676 1 0.3692 0.6702 0.736 0.000 0.000 0.008 0.012 0.244
#> GSM447678 5 0.5133 0.2272 0.000 0.016 0.052 0.392 0.540 0.000
#> GSM447681 2 0.3756 0.6071 0.000 0.712 0.020 0.000 0.268 0.000
#> GSM447698 5 0.5892 0.6112 0.000 0.152 0.120 0.096 0.632 0.000
#> GSM447713 1 0.0000 0.9419 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447722 3 0.5029 0.1494 0.000 0.000 0.524 0.076 0.400 0.000
#> GSM447726 2 0.3536 0.5991 0.000 0.736 0.008 0.000 0.252 0.004
#> GSM447735 3 0.2302 0.6516 0.000 0.000 0.872 0.008 0.120 0.000
#> GSM447737 3 0.3422 0.5902 0.168 0.000 0.792 0.000 0.040 0.000
#> GSM447657 2 0.3592 0.6116 0.000 0.740 0.020 0.000 0.240 0.000
#> GSM447674 2 0.2362 0.6804 0.000 0.860 0.004 0.000 0.136 0.000
#> GSM447636 1 0.0622 0.9311 0.980 0.008 0.000 0.000 0.012 0.000
#> GSM447723 1 0.0146 0.9410 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM447699 3 0.1663 0.6675 0.000 0.000 0.912 0.000 0.088 0.000
#> GSM447708 2 0.3601 0.4657 0.000 0.684 0.000 0.000 0.312 0.004
#> GSM447721 1 0.0000 0.9419 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447623 1 0.0146 0.9415 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM447621 1 0.3464 0.5338 0.688 0.000 0.312 0.000 0.000 0.000
#> GSM447650 2 0.3245 0.6190 0.000 0.764 0.008 0.000 0.228 0.000
#> GSM447651 2 0.1391 0.6908 0.000 0.944 0.000 0.000 0.040 0.016
#> GSM447653 4 0.1007 0.7045 0.008 0.000 0.004 0.968 0.004 0.016
#> GSM447658 1 0.0146 0.9415 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM447675 4 0.3330 0.4863 0.000 0.000 0.000 0.716 0.284 0.000
#> GSM447680 2 0.1349 0.6897 0.000 0.940 0.000 0.000 0.056 0.004
#> GSM447686 1 0.3270 0.7526 0.820 0.060 0.000 0.000 0.120 0.000
#> GSM447736 3 0.1829 0.6753 0.000 0.000 0.920 0.000 0.056 0.024
#> GSM447629 5 0.4392 0.1852 0.016 0.476 0.000 0.004 0.504 0.000
#> GSM447648 6 0.0458 0.8294 0.000 0.000 0.016 0.000 0.000 0.984
#> GSM447660 1 0.0260 0.9404 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM447661 2 0.2624 0.6559 0.000 0.844 0.004 0.000 0.148 0.004
#> GSM447663 3 0.4673 0.5700 0.000 0.080 0.648 0.000 0.272 0.000
#> GSM447704 2 0.3859 0.4599 0.000 0.692 0.000 0.008 0.292 0.008
#> GSM447720 3 0.5201 0.5510 0.000 0.096 0.616 0.012 0.276 0.000
#> GSM447652 2 0.6592 0.1790 0.000 0.404 0.028 0.276 0.292 0.000
#> GSM447679 2 0.1007 0.6929 0.000 0.956 0.000 0.000 0.044 0.000
#> GSM447712 1 0.0146 0.9410 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM447664 4 0.2058 0.6920 0.056 0.000 0.000 0.908 0.036 0.000
#> GSM447637 6 0.0692 0.8292 0.000 0.000 0.020 0.000 0.004 0.976
#> GSM447639 3 0.5137 0.1355 0.000 0.004 0.508 0.416 0.072 0.000
#> GSM447615 6 0.3187 0.6205 0.188 0.000 0.000 0.004 0.012 0.796
#> GSM447656 2 0.3329 0.5734 0.004 0.756 0.000 0.000 0.236 0.004
#> GSM447673 5 0.5329 0.2420 0.000 0.104 0.000 0.448 0.448 0.000
#> GSM447719 4 0.3634 0.4769 0.000 0.000 0.000 0.696 0.008 0.296
#> GSM447706 6 0.0508 0.8278 0.000 0.000 0.012 0.000 0.004 0.984
#> GSM447612 3 0.3514 0.5539 0.000 0.000 0.752 0.000 0.020 0.228
#> GSM447665 2 0.2006 0.6851 0.000 0.892 0.000 0.000 0.104 0.004
#> GSM447677 2 0.1753 0.6820 0.000 0.912 0.000 0.000 0.084 0.004
#> GSM447613 1 0.0291 0.9405 0.992 0.000 0.000 0.004 0.004 0.000
#> GSM447659 4 0.4622 0.6100 0.000 0.000 0.080 0.724 0.024 0.172
#> GSM447662 6 0.2527 0.7076 0.000 0.000 0.168 0.000 0.000 0.832
#> GSM447666 6 0.0458 0.8208 0.000 0.000 0.000 0.000 0.016 0.984
#> GSM447668 2 0.3248 0.6262 0.000 0.768 0.004 0.000 0.224 0.004
#> GSM447682 2 0.2333 0.6734 0.000 0.884 0.000 0.024 0.092 0.000
#> GSM447683 2 0.1863 0.6737 0.000 0.896 0.000 0.000 0.104 0.000
#> GSM447688 5 0.5358 0.5244 0.000 0.112 0.012 0.276 0.600 0.000
#> GSM447702 2 0.2260 0.6661 0.000 0.860 0.000 0.000 0.140 0.000
#> GSM447709 2 0.4781 0.4961 0.000 0.672 0.000 0.000 0.140 0.188
#> GSM447711 1 0.0000 0.9419 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447715 1 0.3923 0.6908 0.772 0.144 0.004 0.000 0.080 0.000
#> GSM447693 6 0.0692 0.8292 0.000 0.000 0.020 0.000 0.004 0.976
#> GSM447611 4 0.0547 0.7031 0.000 0.000 0.000 0.980 0.020 0.000
#> GSM447672 2 0.3298 0.5622 0.000 0.756 0.000 0.008 0.236 0.000
#> GSM447703 5 0.5737 0.5727 0.000 0.248 0.000 0.236 0.516 0.000
#> GSM447727 1 0.0000 0.9419 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447638 2 0.5902 0.3051 0.204 0.536 0.000 0.000 0.012 0.248
#> GSM447670 1 0.2389 0.8348 0.864 0.000 0.000 0.000 0.008 0.128
#> GSM447700 3 0.2762 0.5947 0.000 0.000 0.804 0.000 0.196 0.000
#> GSM447738 5 0.4729 0.5771 0.000 0.284 0.000 0.080 0.636 0.000
#> GSM447739 1 0.0000 0.9419 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447617 1 0.0260 0.9397 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM447628 4 0.1753 0.6902 0.000 0.004 0.000 0.912 0.084 0.000
#> GSM447632 5 0.4371 0.5001 0.000 0.344 0.000 0.036 0.620 0.000
#> GSM447619 6 0.1327 0.8099 0.000 0.000 0.064 0.000 0.000 0.936
#> GSM447643 1 0.0363 0.9386 0.988 0.000 0.000 0.000 0.012 0.000
#> GSM447724 5 0.5501 0.1793 0.000 0.004 0.360 0.120 0.516 0.000
#> GSM447728 2 0.2562 0.6366 0.000 0.828 0.000 0.000 0.172 0.000
#> GSM447610 4 0.4523 0.1276 0.452 0.000 0.000 0.516 0.032 0.000
#> GSM447633 2 0.4050 0.6269 0.000 0.776 0.012 0.000 0.108 0.104
#> GSM447634 3 0.3970 0.6028 0.000 0.028 0.692 0.000 0.280 0.000
#> GSM447622 3 0.2231 0.6780 0.016 0.000 0.908 0.000 0.028 0.048
#> GSM447667 5 0.5870 0.3510 0.232 0.292 0.000 0.000 0.476 0.000
#> GSM447687 5 0.5763 0.4884 0.000 0.332 0.000 0.188 0.480 0.000
#> GSM447695 3 0.1267 0.6766 0.000 0.000 0.940 0.000 0.060 0.000
#> GSM447696 1 0.0000 0.9419 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447697 1 0.1232 0.9186 0.956 0.000 0.004 0.016 0.024 0.000
#> GSM447714 3 0.4101 0.2298 0.000 0.000 0.580 0.000 0.012 0.408
#> GSM447717 1 0.0000 0.9419 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447725 1 0.0000 0.9419 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447729 4 0.2003 0.6738 0.000 0.000 0.000 0.884 0.116 0.000
#> GSM447644 2 0.5208 0.4654 0.000 0.592 0.108 0.000 0.296 0.004
#> GSM447710 6 0.5573 -0.0253 0.000 0.000 0.428 0.016 0.088 0.468
#> GSM447614 4 0.4761 0.5193 0.012 0.000 0.212 0.688 0.088 0.000
#> GSM447685 2 0.2994 0.5998 0.004 0.788 0.000 0.000 0.208 0.000
#> GSM447690 1 0.0000 0.9419 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447730 2 0.4913 0.4002 0.000 0.644 0.000 0.036 0.284 0.036
#> GSM447646 4 0.2048 0.6714 0.000 0.000 0.000 0.880 0.120 0.000
#> GSM447689 6 0.0146 0.8251 0.000 0.000 0.000 0.000 0.004 0.996
#> GSM447635 3 0.4627 0.2554 0.000 0.044 0.560 0.000 0.396 0.000
#> GSM447641 1 0.0146 0.9415 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM447716 5 0.5393 0.6168 0.080 0.184 0.012 0.044 0.680 0.000
#> GSM447718 4 0.7924 0.1047 0.008 0.220 0.104 0.368 0.276 0.024
#> GSM447616 3 0.2670 0.6655 0.052 0.000 0.884 0.000 0.044 0.020
#> GSM447626 6 0.7213 0.2381 0.000 0.148 0.144 0.004 0.256 0.448
#> GSM447640 2 0.3101 0.5501 0.000 0.756 0.000 0.000 0.244 0.000
#> GSM447734 3 0.2431 0.6715 0.000 0.000 0.860 0.000 0.132 0.008
#> GSM447692 3 0.1168 0.6854 0.016 0.000 0.956 0.000 0.028 0.000
#> GSM447647 4 0.3915 0.4808 0.000 0.028 0.000 0.736 0.228 0.008
#> GSM447624 1 0.3046 0.8392 0.860 0.000 0.084 0.004 0.016 0.036
#> GSM447625 3 0.3776 0.6517 0.000 0.000 0.792 0.024 0.148 0.036
#> GSM447707 2 0.3748 0.5771 0.000 0.760 0.000 0.028 0.204 0.008
#> GSM447732 3 0.3983 0.6171 0.000 0.012 0.720 0.020 0.248 0.000
#> GSM447684 2 0.6133 0.4275 0.124 0.556 0.044 0.000 0.272 0.004
#> GSM447731 4 0.3104 0.6281 0.000 0.000 0.000 0.800 0.016 0.184
#> GSM447705 6 0.0632 0.8291 0.000 0.000 0.024 0.000 0.000 0.976
#> GSM447631 6 0.1218 0.8188 0.000 0.000 0.012 0.028 0.004 0.956
#> GSM447701 2 0.3702 0.5800 0.000 0.720 0.012 0.000 0.264 0.004
#> GSM447645 6 0.0551 0.8265 0.000 0.000 0.004 0.004 0.008 0.984
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)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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 gender(p) individual(p) disease.state(p) other(p) k
#> MAD:NMF 124 0.404 0.8607 0.5666 0.0233 2
#> MAD:NMF 103 0.127 0.1158 0.0359 0.0941 3
#> MAD:NMF 122 0.267 0.2523 0.2630 0.0693 4
#> MAD:NMF 119 0.734 0.0913 0.0415 0.0791 5
#> MAD:NMF 97 0.717 0.3481 0.1105 0.2687 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "hclust"]
# you can also extract it by
# res = res_list["ATC:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 130 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.526 0.872 0.923 0.4752 0.516 0.516
#> 3 3 0.668 0.797 0.890 0.3598 0.821 0.657
#> 4 4 0.663 0.789 0.877 0.0824 0.947 0.849
#> 5 5 0.703 0.640 0.822 0.0639 0.984 0.947
#> 6 6 0.756 0.776 0.874 0.0547 0.925 0.742
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
#> GSM447671 2 0.5737 0.87534 0.136 0.864
#> GSM447694 1 0.0000 0.96455 1.000 0.000
#> GSM447618 2 0.5737 0.87534 0.136 0.864
#> GSM447691 2 0.5737 0.87534 0.136 0.864
#> GSM447733 2 0.6247 0.84936 0.156 0.844
#> GSM447620 2 0.5294 0.88222 0.120 0.880
#> GSM447627 1 0.0000 0.96455 1.000 0.000
#> GSM447630 1 0.7745 0.66349 0.772 0.228
#> GSM447642 2 0.9460 0.59698 0.364 0.636
#> GSM447649 2 0.0000 0.88577 0.000 1.000
#> GSM447654 2 0.1633 0.89022 0.024 0.976
#> GSM447655 2 0.0000 0.88577 0.000 1.000
#> GSM447669 2 0.5737 0.87534 0.136 0.864
#> GSM447676 2 0.9491 0.58951 0.368 0.632
#> GSM447678 2 0.6048 0.86880 0.148 0.852
#> GSM447681 2 0.0000 0.88577 0.000 1.000
#> GSM447698 2 0.4815 0.88693 0.104 0.896
#> GSM447713 1 0.0000 0.96455 1.000 0.000
#> GSM447722 2 0.9393 0.61573 0.356 0.644
#> GSM447726 2 0.5842 0.87327 0.140 0.860
#> GSM447735 1 0.0000 0.96455 1.000 0.000
#> GSM447737 1 0.0000 0.96455 1.000 0.000
#> GSM447657 2 0.4815 0.88693 0.104 0.896
#> GSM447674 2 0.0000 0.88577 0.000 1.000
#> GSM447636 2 0.5629 0.87727 0.132 0.868
#> GSM447723 2 0.9732 0.51582 0.404 0.596
#> GSM447699 1 0.0000 0.96455 1.000 0.000
#> GSM447708 2 0.0938 0.88876 0.012 0.988
#> GSM447721 1 0.0000 0.96455 1.000 0.000
#> GSM447623 1 0.0000 0.96455 1.000 0.000
#> GSM447621 1 0.0000 0.96455 1.000 0.000
#> GSM447650 2 0.0000 0.88577 0.000 1.000
#> GSM447651 2 0.0000 0.88577 0.000 1.000
#> GSM447653 1 0.1184 0.95271 0.984 0.016
#> GSM447658 2 0.9000 0.67919 0.316 0.684
#> GSM447675 2 0.5059 0.87921 0.112 0.888
#> GSM447680 2 0.0000 0.88577 0.000 1.000
#> GSM447686 2 0.5059 0.88466 0.112 0.888
#> GSM447736 1 0.0376 0.96211 0.996 0.004
#> GSM447629 2 0.4815 0.88693 0.104 0.896
#> GSM447648 1 0.0000 0.96455 1.000 0.000
#> GSM447660 2 0.9460 0.59698 0.364 0.636
#> GSM447661 2 0.0000 0.88577 0.000 1.000
#> GSM447663 1 0.1184 0.95302 0.984 0.016
#> GSM447704 2 0.0000 0.88577 0.000 1.000
#> GSM447720 2 0.9209 0.64875 0.336 0.664
#> GSM447652 2 0.2236 0.89123 0.036 0.964
#> GSM447679 2 0.0000 0.88577 0.000 1.000
#> GSM447712 1 0.2778 0.92207 0.952 0.048
#> GSM447664 2 0.5294 0.88353 0.120 0.880
#> GSM447637 1 0.0000 0.96455 1.000 0.000
#> GSM447639 1 0.5842 0.80790 0.860 0.140
#> GSM447615 1 0.9866 -0.00514 0.568 0.432
#> GSM447656 2 0.4815 0.88693 0.104 0.896
#> GSM447673 2 0.0000 0.88577 0.000 1.000
#> GSM447719 1 0.1184 0.95271 0.984 0.016
#> GSM447706 1 0.0000 0.96455 1.000 0.000
#> GSM447612 1 0.0672 0.95946 0.992 0.008
#> GSM447665 2 0.5629 0.87727 0.132 0.868
#> GSM447677 2 0.0000 0.88577 0.000 1.000
#> GSM447613 1 0.0000 0.96455 1.000 0.000
#> GSM447659 1 0.0672 0.95922 0.992 0.008
#> GSM447662 1 0.0000 0.96455 1.000 0.000
#> GSM447666 2 0.5842 0.87336 0.140 0.860
#> GSM447668 2 0.0000 0.88577 0.000 1.000
#> GSM447682 2 0.4690 0.88792 0.100 0.900
#> GSM447683 2 0.0000 0.88577 0.000 1.000
#> GSM447688 2 0.0938 0.88857 0.012 0.988
#> GSM447702 2 0.0000 0.88577 0.000 1.000
#> GSM447709 2 0.0938 0.88876 0.012 0.988
#> GSM447711 1 0.0672 0.95934 0.992 0.008
#> GSM447715 2 0.5842 0.87327 0.140 0.860
#> GSM447693 1 0.0000 0.96455 1.000 0.000
#> GSM447611 2 0.5059 0.87921 0.112 0.888
#> GSM447672 2 0.0000 0.88577 0.000 1.000
#> GSM447703 2 0.0000 0.88577 0.000 1.000
#> GSM447727 2 0.9732 0.51582 0.404 0.596
#> GSM447638 2 0.5294 0.88222 0.120 0.880
#> GSM447670 1 0.0000 0.96455 1.000 0.000
#> GSM447700 2 0.9248 0.64217 0.340 0.660
#> GSM447738 2 0.0000 0.88577 0.000 1.000
#> GSM447739 1 0.0000 0.96455 1.000 0.000
#> GSM447617 1 0.0000 0.96455 1.000 0.000
#> GSM447628 2 0.1633 0.89022 0.024 0.976
#> GSM447632 2 0.0000 0.88577 0.000 1.000
#> GSM447619 1 0.0000 0.96455 1.000 0.000
#> GSM447643 2 0.5294 0.88222 0.120 0.880
#> GSM447724 2 0.9393 0.61573 0.356 0.644
#> GSM447728 2 0.0938 0.88876 0.012 0.988
#> GSM447610 1 0.0000 0.96455 1.000 0.000
#> GSM447633 2 0.5629 0.87727 0.132 0.868
#> GSM447634 1 0.4431 0.87244 0.908 0.092
#> GSM447622 1 0.0000 0.96455 1.000 0.000
#> GSM447667 2 0.3733 0.89070 0.072 0.928
#> GSM447687 2 0.0000 0.88577 0.000 1.000
#> GSM447695 1 0.0000 0.96455 1.000 0.000
#> GSM447696 1 0.0000 0.96455 1.000 0.000
#> GSM447697 1 0.0000 0.96455 1.000 0.000
#> GSM447714 1 0.0000 0.96455 1.000 0.000
#> GSM447717 2 0.5629 0.87727 0.132 0.868
#> GSM447725 1 0.2948 0.91810 0.948 0.052
#> GSM447729 2 0.3733 0.88980 0.072 0.928
#> GSM447644 2 0.5629 0.87727 0.132 0.868
#> GSM447710 1 0.0000 0.96455 1.000 0.000
#> GSM447614 1 0.0000 0.96455 1.000 0.000
#> GSM447685 2 0.0000 0.88577 0.000 1.000
#> GSM447690 1 0.0000 0.96455 1.000 0.000
#> GSM447730 2 0.0000 0.88577 0.000 1.000
#> GSM447646 2 0.1633 0.89022 0.024 0.976
#> GSM447689 2 0.9635 0.55227 0.388 0.612
#> GSM447635 2 0.5946 0.87036 0.144 0.856
#> GSM447641 1 0.7376 0.69590 0.792 0.208
#> GSM447716 2 0.4815 0.88693 0.104 0.896
#> GSM447718 1 0.7745 0.66349 0.772 0.228
#> GSM447616 1 0.0000 0.96455 1.000 0.000
#> GSM447626 1 0.0000 0.96455 1.000 0.000
#> GSM447640 2 0.0000 0.88577 0.000 1.000
#> GSM447734 1 0.0000 0.96455 1.000 0.000
#> GSM447692 1 0.0000 0.96455 1.000 0.000
#> GSM447647 2 0.1414 0.88979 0.020 0.980
#> GSM447624 1 0.0000 0.96455 1.000 0.000
#> GSM447625 1 0.0000 0.96455 1.000 0.000
#> GSM447707 2 0.0000 0.88577 0.000 1.000
#> GSM447732 1 0.0000 0.96455 1.000 0.000
#> GSM447684 2 0.7674 0.79744 0.224 0.776
#> GSM447731 2 0.3584 0.88750 0.068 0.932
#> GSM447705 2 0.9209 0.64875 0.336 0.664
#> GSM447631 1 0.0000 0.96455 1.000 0.000
#> GSM447701 2 0.0376 0.88682 0.004 0.996
#> GSM447645 1 0.0000 0.96455 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM447671 1 0.1832 0.7885 0.956 0.036 0.008
#> GSM447694 3 0.0000 0.9351 0.000 0.000 1.000
#> GSM447618 1 0.1832 0.7885 0.956 0.036 0.008
#> GSM447691 1 0.1832 0.7885 0.956 0.036 0.008
#> GSM447733 1 0.7562 0.4859 0.628 0.308 0.064
#> GSM447620 1 0.2096 0.7848 0.944 0.052 0.004
#> GSM447627 3 0.1765 0.9203 0.040 0.004 0.956
#> GSM447630 3 0.5529 0.6245 0.296 0.000 0.704
#> GSM447642 1 0.5595 0.6604 0.756 0.016 0.228
#> GSM447649 2 0.1031 0.9319 0.024 0.976 0.000
#> GSM447654 1 0.6192 0.3053 0.580 0.420 0.000
#> GSM447655 2 0.0237 0.9340 0.004 0.996 0.000
#> GSM447669 1 0.1832 0.7885 0.956 0.036 0.008
#> GSM447676 1 0.5639 0.6551 0.752 0.016 0.232
#> GSM447678 1 0.1267 0.7814 0.972 0.024 0.004
#> GSM447681 2 0.1753 0.9133 0.048 0.952 0.000
#> GSM447698 1 0.3193 0.7689 0.896 0.100 0.004
#> GSM447713 3 0.0000 0.9351 0.000 0.000 1.000
#> GSM447722 1 0.4963 0.6849 0.792 0.008 0.200
#> GSM447726 1 0.1999 0.7888 0.952 0.036 0.012
#> GSM447735 3 0.1765 0.9203 0.040 0.004 0.956
#> GSM447737 3 0.0000 0.9351 0.000 0.000 1.000
#> GSM447657 1 0.3193 0.7689 0.896 0.100 0.004
#> GSM447674 2 0.1529 0.9199 0.040 0.960 0.000
#> GSM447636 1 0.1647 0.7876 0.960 0.036 0.004
#> GSM447723 1 0.5956 0.6104 0.720 0.016 0.264
#> GSM447699 3 0.1643 0.9325 0.044 0.000 0.956
#> GSM447708 2 0.5529 0.5644 0.296 0.704 0.000
#> GSM447721 3 0.2261 0.9280 0.068 0.000 0.932
#> GSM447623 3 0.0000 0.9351 0.000 0.000 1.000
#> GSM447621 3 0.0000 0.9351 0.000 0.000 1.000
#> GSM447650 2 0.0592 0.9364 0.012 0.988 0.000
#> GSM447651 2 0.0592 0.9364 0.012 0.988 0.000
#> GSM447653 3 0.3272 0.8822 0.104 0.004 0.892
#> GSM447658 1 0.4473 0.7206 0.828 0.008 0.164
#> GSM447675 1 0.6445 0.4953 0.672 0.308 0.020
#> GSM447680 2 0.0747 0.9360 0.016 0.984 0.000
#> GSM447686 1 0.2400 0.7809 0.932 0.064 0.004
#> GSM447736 3 0.2356 0.9268 0.072 0.000 0.928
#> GSM447629 1 0.3193 0.7689 0.896 0.100 0.004
#> GSM447648 3 0.0000 0.9351 0.000 0.000 1.000
#> GSM447660 1 0.5595 0.6604 0.756 0.016 0.228
#> GSM447661 2 0.0237 0.9340 0.004 0.996 0.000
#> GSM447663 3 0.2625 0.9192 0.084 0.000 0.916
#> GSM447704 2 0.1031 0.9319 0.024 0.976 0.000
#> GSM447720 1 0.5219 0.6998 0.788 0.016 0.196
#> GSM447652 1 0.6345 0.3803 0.596 0.400 0.004
#> GSM447679 2 0.0592 0.9364 0.012 0.988 0.000
#> GSM447712 3 0.3267 0.8906 0.116 0.000 0.884
#> GSM447664 1 0.1860 0.7783 0.948 0.052 0.000
#> GSM447637 3 0.0000 0.9351 0.000 0.000 1.000
#> GSM447639 3 0.4654 0.7741 0.208 0.000 0.792
#> GSM447615 1 0.6905 0.1883 0.544 0.016 0.440
#> GSM447656 1 0.3193 0.7689 0.896 0.100 0.004
#> GSM447673 2 0.2625 0.8829 0.084 0.916 0.000
#> GSM447719 3 0.3272 0.8822 0.104 0.004 0.892
#> GSM447706 3 0.2261 0.9280 0.068 0.000 0.932
#> GSM447612 3 0.2448 0.9246 0.076 0.000 0.924
#> GSM447665 1 0.1765 0.7876 0.956 0.040 0.004
#> GSM447677 2 0.0747 0.9360 0.016 0.984 0.000
#> GSM447613 3 0.2448 0.9249 0.076 0.000 0.924
#> GSM447659 3 0.2200 0.9105 0.056 0.004 0.940
#> GSM447662 3 0.2261 0.9280 0.068 0.000 0.932
#> GSM447666 1 0.1711 0.7884 0.960 0.032 0.008
#> GSM447668 2 0.0237 0.9340 0.004 0.996 0.000
#> GSM447682 1 0.4834 0.6915 0.792 0.204 0.004
#> GSM447683 2 0.0747 0.9360 0.016 0.984 0.000
#> GSM447688 1 0.6309 0.0987 0.504 0.496 0.000
#> GSM447702 2 0.0237 0.9340 0.004 0.996 0.000
#> GSM447709 2 0.5529 0.5644 0.296 0.704 0.000
#> GSM447711 3 0.2448 0.9244 0.076 0.000 0.924
#> GSM447715 1 0.1999 0.7888 0.952 0.036 0.012
#> GSM447693 3 0.0000 0.9351 0.000 0.000 1.000
#> GSM447611 1 0.6445 0.4953 0.672 0.308 0.020
#> GSM447672 2 0.0237 0.9340 0.004 0.996 0.000
#> GSM447703 2 0.0592 0.9357 0.012 0.988 0.000
#> GSM447727 1 0.5956 0.6104 0.720 0.016 0.264
#> GSM447638 1 0.1989 0.7852 0.948 0.048 0.004
#> GSM447670 3 0.2448 0.9249 0.076 0.000 0.924
#> GSM447700 1 0.4755 0.7013 0.808 0.008 0.184
#> GSM447738 2 0.0592 0.9362 0.012 0.988 0.000
#> GSM447739 3 0.0000 0.9351 0.000 0.000 1.000
#> GSM447617 3 0.0000 0.9351 0.000 0.000 1.000
#> GSM447628 1 0.6192 0.3053 0.580 0.420 0.000
#> GSM447632 2 0.0592 0.9362 0.012 0.988 0.000
#> GSM447619 3 0.2261 0.9280 0.068 0.000 0.932
#> GSM447643 1 0.2096 0.7848 0.944 0.052 0.004
#> GSM447724 1 0.4963 0.6849 0.792 0.008 0.200
#> GSM447728 2 0.5529 0.5644 0.296 0.704 0.000
#> GSM447610 3 0.1765 0.9203 0.040 0.004 0.956
#> GSM447633 1 0.1647 0.7876 0.960 0.036 0.004
#> GSM447634 3 0.4002 0.8425 0.160 0.000 0.840
#> GSM447622 3 0.0000 0.9351 0.000 0.000 1.000
#> GSM447667 1 0.5845 0.5661 0.688 0.308 0.004
#> GSM447687 2 0.0592 0.9357 0.012 0.988 0.000
#> GSM447695 3 0.0000 0.9351 0.000 0.000 1.000
#> GSM447696 3 0.0000 0.9351 0.000 0.000 1.000
#> GSM447697 3 0.0000 0.9351 0.000 0.000 1.000
#> GSM447714 3 0.2261 0.9280 0.068 0.000 0.932
#> GSM447717 1 0.1647 0.7876 0.960 0.036 0.004
#> GSM447725 3 0.3340 0.8869 0.120 0.000 0.880
#> GSM447729 1 0.5760 0.4778 0.672 0.328 0.000
#> GSM447644 1 0.1647 0.7876 0.960 0.036 0.004
#> GSM447710 3 0.2261 0.9280 0.068 0.000 0.932
#> GSM447614 3 0.1765 0.9203 0.040 0.004 0.956
#> GSM447685 2 0.0747 0.9360 0.016 0.984 0.000
#> GSM447690 3 0.0000 0.9351 0.000 0.000 1.000
#> GSM447730 2 0.0892 0.9331 0.020 0.980 0.000
#> GSM447646 1 0.6192 0.3053 0.580 0.420 0.000
#> GSM447689 1 0.5803 0.6361 0.736 0.016 0.248
#> GSM447635 1 0.2152 0.7881 0.948 0.036 0.016
#> GSM447641 3 0.5431 0.6409 0.284 0.000 0.716
#> GSM447716 1 0.3193 0.7689 0.896 0.100 0.004
#> GSM447718 3 0.5529 0.6245 0.296 0.000 0.704
#> GSM447616 3 0.0000 0.9351 0.000 0.000 1.000
#> GSM447626 3 0.2261 0.9280 0.068 0.000 0.932
#> GSM447640 2 0.0237 0.9340 0.004 0.996 0.000
#> GSM447734 3 0.0000 0.9351 0.000 0.000 1.000
#> GSM447692 3 0.0000 0.9351 0.000 0.000 1.000
#> GSM447647 1 0.6302 0.1264 0.520 0.480 0.000
#> GSM447624 3 0.0000 0.9351 0.000 0.000 1.000
#> GSM447625 3 0.2261 0.9280 0.068 0.000 0.932
#> GSM447707 2 0.0592 0.9357 0.012 0.988 0.000
#> GSM447732 3 0.2261 0.9280 0.068 0.000 0.932
#> GSM447684 1 0.3637 0.7634 0.892 0.024 0.084
#> GSM447731 1 0.6896 0.3448 0.588 0.392 0.020
#> GSM447705 1 0.5219 0.6998 0.788 0.016 0.196
#> GSM447631 3 0.0000 0.9351 0.000 0.000 1.000
#> GSM447701 2 0.5178 0.6381 0.256 0.744 0.000
#> GSM447645 3 0.0000 0.9351 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM447671 1 0.0188 0.8031 0.996 0.000 0.000 0.004
#> GSM447694 3 0.0592 0.8830 0.000 0.000 0.984 0.016
#> GSM447618 1 0.0000 0.8032 1.000 0.000 0.000 0.000
#> GSM447691 1 0.0336 0.8027 0.992 0.000 0.000 0.008
#> GSM447733 4 0.3695 0.7863 0.156 0.000 0.016 0.828
#> GSM447620 1 0.1297 0.7927 0.964 0.016 0.000 0.020
#> GSM447627 3 0.1637 0.8631 0.000 0.000 0.940 0.060
#> GSM447630 3 0.6262 0.5841 0.280 0.000 0.628 0.092
#> GSM447642 1 0.5141 0.6751 0.756 0.000 0.160 0.084
#> GSM447649 2 0.0779 0.8880 0.004 0.980 0.000 0.016
#> GSM447654 4 0.4188 0.8557 0.112 0.064 0.000 0.824
#> GSM447655 2 0.0000 0.8912 0.000 1.000 0.000 0.000
#> GSM447669 1 0.0188 0.8031 0.996 0.000 0.000 0.004
#> GSM447676 1 0.5204 0.6716 0.752 0.000 0.160 0.088
#> GSM447678 1 0.2281 0.7529 0.904 0.000 0.000 0.096
#> GSM447681 2 0.1792 0.8537 0.068 0.932 0.000 0.000
#> GSM447698 1 0.2413 0.7673 0.916 0.064 0.000 0.020
#> GSM447713 3 0.0592 0.8830 0.000 0.000 0.984 0.016
#> GSM447722 1 0.5874 0.6452 0.700 0.000 0.124 0.176
#> GSM447726 1 0.0921 0.8006 0.972 0.000 0.000 0.028
#> GSM447735 3 0.1637 0.8631 0.000 0.000 0.940 0.060
#> GSM447737 3 0.0592 0.8830 0.000 0.000 0.984 0.016
#> GSM447657 1 0.2413 0.7673 0.916 0.064 0.000 0.020
#> GSM447674 2 0.1637 0.8617 0.060 0.940 0.000 0.000
#> GSM447636 1 0.0188 0.8027 0.996 0.000 0.000 0.004
#> GSM447723 1 0.5576 0.6417 0.720 0.000 0.184 0.096
#> GSM447699 3 0.2586 0.8794 0.040 0.000 0.912 0.048
#> GSM447708 2 0.5047 0.4973 0.316 0.668 0.000 0.016
#> GSM447721 3 0.3885 0.8657 0.064 0.000 0.844 0.092
#> GSM447623 3 0.0592 0.8830 0.000 0.000 0.984 0.016
#> GSM447621 3 0.0188 0.8847 0.000 0.000 0.996 0.004
#> GSM447650 2 0.0707 0.8910 0.020 0.980 0.000 0.000
#> GSM447651 2 0.0707 0.8910 0.020 0.980 0.000 0.000
#> GSM447653 3 0.3266 0.8368 0.000 0.000 0.832 0.168
#> GSM447658 1 0.5063 0.7044 0.768 0.000 0.108 0.124
#> GSM447675 4 0.3444 0.8334 0.184 0.000 0.000 0.816
#> GSM447680 2 0.0817 0.8904 0.024 0.976 0.000 0.000
#> GSM447686 1 0.1624 0.7860 0.952 0.028 0.000 0.020
#> GSM447736 3 0.3885 0.8661 0.064 0.000 0.844 0.092
#> GSM447629 1 0.2413 0.7673 0.916 0.064 0.000 0.020
#> GSM447648 3 0.0592 0.8830 0.000 0.000 0.984 0.016
#> GSM447660 1 0.5141 0.6751 0.756 0.000 0.160 0.084
#> GSM447661 2 0.0000 0.8912 0.000 1.000 0.000 0.000
#> GSM447663 3 0.4083 0.8595 0.068 0.000 0.832 0.100
#> GSM447704 2 0.0779 0.8880 0.004 0.980 0.000 0.016
#> GSM447720 1 0.4780 0.7083 0.788 0.000 0.116 0.096
#> GSM447652 1 0.7003 -0.0243 0.508 0.368 0.000 0.124
#> GSM447679 2 0.0707 0.8910 0.020 0.980 0.000 0.000
#> GSM447712 3 0.4669 0.8313 0.104 0.000 0.796 0.100
#> GSM447664 1 0.3743 0.6736 0.824 0.016 0.000 0.160
#> GSM447637 3 0.0592 0.8830 0.000 0.000 0.984 0.016
#> GSM447639 3 0.5747 0.7245 0.196 0.000 0.704 0.100
#> GSM447615 1 0.6589 0.3558 0.556 0.000 0.352 0.092
#> GSM447656 1 0.2413 0.7673 0.916 0.064 0.000 0.020
#> GSM447673 2 0.2546 0.8389 0.028 0.912 0.000 0.060
#> GSM447719 3 0.3266 0.8368 0.000 0.000 0.832 0.168
#> GSM447706 3 0.3810 0.8673 0.060 0.000 0.848 0.092
#> GSM447612 3 0.3948 0.8642 0.064 0.000 0.840 0.096
#> GSM447665 1 0.0895 0.7978 0.976 0.004 0.000 0.020
#> GSM447677 2 0.0817 0.8904 0.024 0.976 0.000 0.000
#> GSM447613 3 0.4030 0.8621 0.072 0.000 0.836 0.092
#> GSM447659 3 0.2149 0.8647 0.000 0.000 0.912 0.088
#> GSM447662 3 0.3810 0.8673 0.060 0.000 0.848 0.092
#> GSM447666 1 0.0707 0.8031 0.980 0.000 0.000 0.020
#> GSM447668 2 0.0000 0.8912 0.000 1.000 0.000 0.000
#> GSM447682 1 0.3946 0.6366 0.812 0.168 0.000 0.020
#> GSM447683 2 0.0817 0.8904 0.024 0.976 0.000 0.000
#> GSM447688 2 0.7446 -0.2698 0.172 0.432 0.000 0.396
#> GSM447702 2 0.0000 0.8912 0.000 1.000 0.000 0.000
#> GSM447709 2 0.5047 0.4973 0.316 0.668 0.000 0.016
#> GSM447711 3 0.4030 0.8619 0.072 0.000 0.836 0.092
#> GSM447715 1 0.0921 0.8006 0.972 0.000 0.000 0.028
#> GSM447693 3 0.0592 0.8830 0.000 0.000 0.984 0.016
#> GSM447611 4 0.3444 0.8334 0.184 0.000 0.000 0.816
#> GSM447672 2 0.0000 0.8912 0.000 1.000 0.000 0.000
#> GSM447703 2 0.0336 0.8901 0.000 0.992 0.000 0.008
#> GSM447727 1 0.5576 0.6417 0.720 0.000 0.184 0.096
#> GSM447638 1 0.1174 0.7937 0.968 0.012 0.000 0.020
#> GSM447670 3 0.3959 0.8641 0.068 0.000 0.840 0.092
#> GSM447700 1 0.5731 0.6597 0.712 0.000 0.116 0.172
#> GSM447738 2 0.0376 0.8923 0.004 0.992 0.000 0.004
#> GSM447739 3 0.0592 0.8830 0.000 0.000 0.984 0.016
#> GSM447617 3 0.0592 0.8830 0.000 0.000 0.984 0.016
#> GSM447628 4 0.4188 0.8557 0.112 0.064 0.000 0.824
#> GSM447632 2 0.0376 0.8923 0.004 0.992 0.000 0.004
#> GSM447619 3 0.3810 0.8673 0.060 0.000 0.848 0.092
#> GSM447643 1 0.1406 0.7915 0.960 0.016 0.000 0.024
#> GSM447724 1 0.5874 0.6452 0.700 0.000 0.124 0.176
#> GSM447728 2 0.5047 0.4973 0.316 0.668 0.000 0.016
#> GSM447610 3 0.1637 0.8631 0.000 0.000 0.940 0.060
#> GSM447633 1 0.0336 0.8029 0.992 0.000 0.000 0.008
#> GSM447634 3 0.5247 0.7878 0.148 0.000 0.752 0.100
#> GSM447622 3 0.0592 0.8830 0.000 0.000 0.984 0.016
#> GSM447667 1 0.4882 0.4615 0.708 0.272 0.000 0.020
#> GSM447687 2 0.0336 0.8901 0.000 0.992 0.000 0.008
#> GSM447695 3 0.0336 0.8847 0.000 0.000 0.992 0.008
#> GSM447696 3 0.0592 0.8830 0.000 0.000 0.984 0.016
#> GSM447697 3 0.0592 0.8830 0.000 0.000 0.984 0.016
#> GSM447714 3 0.3810 0.8673 0.060 0.000 0.848 0.092
#> GSM447717 1 0.0188 0.8027 0.996 0.000 0.000 0.004
#> GSM447725 3 0.4727 0.8279 0.108 0.000 0.792 0.100
#> GSM447729 4 0.3591 0.8527 0.168 0.008 0.000 0.824
#> GSM447644 1 0.0336 0.8029 0.992 0.000 0.000 0.008
#> GSM447710 3 0.3810 0.8673 0.060 0.000 0.848 0.092
#> GSM447614 3 0.1637 0.8631 0.000 0.000 0.940 0.060
#> GSM447685 2 0.0817 0.8904 0.024 0.976 0.000 0.000
#> GSM447690 3 0.0592 0.8830 0.000 0.000 0.984 0.016
#> GSM447730 2 0.0921 0.8804 0.000 0.972 0.000 0.028
#> GSM447646 4 0.4188 0.8557 0.112 0.064 0.000 0.824
#> GSM447689 1 0.5427 0.6615 0.736 0.000 0.164 0.100
#> GSM447635 1 0.0657 0.8019 0.984 0.000 0.004 0.012
#> GSM447641 3 0.6298 0.5918 0.268 0.000 0.632 0.100
#> GSM447716 1 0.2413 0.7673 0.916 0.064 0.000 0.020
#> GSM447718 3 0.6262 0.5841 0.280 0.000 0.628 0.092
#> GSM447616 3 0.0188 0.8847 0.000 0.000 0.996 0.004
#> GSM447626 3 0.3810 0.8673 0.060 0.000 0.848 0.092
#> GSM447640 2 0.0000 0.8912 0.000 1.000 0.000 0.000
#> GSM447734 3 0.0592 0.8830 0.000 0.000 0.984 0.016
#> GSM447692 3 0.0592 0.8830 0.000 0.000 0.984 0.016
#> GSM447647 4 0.7053 0.4119 0.132 0.356 0.000 0.512
#> GSM447624 3 0.0592 0.8830 0.000 0.000 0.984 0.016
#> GSM447625 3 0.3810 0.8673 0.060 0.000 0.848 0.092
#> GSM447707 2 0.0336 0.8901 0.000 0.992 0.000 0.008
#> GSM447732 3 0.3810 0.8673 0.060 0.000 0.848 0.092
#> GSM447684 1 0.2915 0.7708 0.892 0.000 0.028 0.080
#> GSM447731 4 0.4234 0.8632 0.132 0.052 0.000 0.816
#> GSM447705 1 0.4780 0.7083 0.788 0.000 0.116 0.096
#> GSM447631 3 0.0592 0.8830 0.000 0.000 0.984 0.016
#> GSM447701 2 0.4690 0.5672 0.276 0.712 0.000 0.012
#> GSM447645 3 0.0592 0.8830 0.000 0.000 0.984 0.016
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM447671 5 0.0566 0.8120 0.012 0.000 0.000 0.004 0.984
#> GSM447694 3 0.0000 0.5673 0.000 0.000 1.000 0.000 0.000
#> GSM447618 5 0.0510 0.8124 0.016 0.000 0.000 0.000 0.984
#> GSM447691 5 0.0955 0.8103 0.028 0.000 0.000 0.004 0.968
#> GSM447733 4 0.5137 0.6057 0.208 0.000 0.000 0.684 0.108
#> GSM447620 5 0.0960 0.8021 0.004 0.016 0.000 0.008 0.972
#> GSM447627 3 0.3816 -0.0972 0.304 0.000 0.696 0.000 0.000
#> GSM447630 3 0.6784 0.2279 0.376 0.000 0.396 0.004 0.224
#> GSM447642 5 0.4217 0.6589 0.280 0.000 0.012 0.004 0.704
#> GSM447649 2 0.0693 0.9077 0.000 0.980 0.000 0.008 0.012
#> GSM447654 4 0.0404 0.7045 0.000 0.000 0.000 0.988 0.012
#> GSM447655 2 0.0000 0.9106 0.000 1.000 0.000 0.000 0.000
#> GSM447669 5 0.0566 0.8120 0.012 0.000 0.000 0.004 0.984
#> GSM447676 5 0.4240 0.6542 0.284 0.000 0.012 0.004 0.700
#> GSM447678 5 0.2513 0.7668 0.116 0.000 0.000 0.008 0.876
#> GSM447681 2 0.1544 0.8722 0.000 0.932 0.000 0.000 0.068
#> GSM447698 5 0.1924 0.7796 0.004 0.064 0.000 0.008 0.924
#> GSM447713 3 0.0000 0.5673 0.000 0.000 1.000 0.000 0.000
#> GSM447722 5 0.4323 0.6231 0.332 0.000 0.012 0.000 0.656
#> GSM447726 5 0.0992 0.8112 0.024 0.000 0.000 0.008 0.968
#> GSM447735 3 0.3636 -0.0387 0.272 0.000 0.728 0.000 0.000
#> GSM447737 3 0.0000 0.5673 0.000 0.000 1.000 0.000 0.000
#> GSM447657 5 0.1924 0.7796 0.004 0.064 0.000 0.008 0.924
#> GSM447674 2 0.1410 0.8805 0.000 0.940 0.000 0.000 0.060
#> GSM447636 5 0.0162 0.8112 0.004 0.000 0.000 0.000 0.996
#> GSM447723 5 0.4597 0.6206 0.300 0.000 0.024 0.004 0.672
#> GSM447699 3 0.4087 0.5409 0.208 0.000 0.756 0.000 0.036
#> GSM447708 2 0.4317 0.5349 0.004 0.668 0.000 0.008 0.320
#> GSM447721 3 0.5113 0.5284 0.380 0.000 0.576 0.000 0.044
#> GSM447623 3 0.0000 0.5673 0.000 0.000 1.000 0.000 0.000
#> GSM447621 3 0.0963 0.5657 0.036 0.000 0.964 0.000 0.000
#> GSM447650 2 0.0609 0.9106 0.000 0.980 0.000 0.000 0.020
#> GSM447651 2 0.0609 0.9106 0.000 0.980 0.000 0.000 0.020
#> GSM447653 1 0.3671 1.0000 0.756 0.000 0.236 0.008 0.000
#> GSM447658 5 0.3766 0.6974 0.268 0.000 0.000 0.004 0.728
#> GSM447675 4 0.4599 0.6874 0.156 0.000 0.000 0.744 0.100
#> GSM447680 2 0.0703 0.9098 0.000 0.976 0.000 0.000 0.024
#> GSM447686 5 0.1243 0.7961 0.004 0.028 0.000 0.008 0.960
#> GSM447736 3 0.5143 0.5324 0.368 0.000 0.584 0.000 0.048
#> GSM447629 5 0.1924 0.7796 0.004 0.064 0.000 0.008 0.924
#> GSM447648 3 0.0000 0.5673 0.000 0.000 1.000 0.000 0.000
#> GSM447660 5 0.4194 0.6620 0.276 0.000 0.012 0.004 0.708
#> GSM447661 2 0.0000 0.9106 0.000 1.000 0.000 0.000 0.000
#> GSM447663 3 0.5151 0.5144 0.396 0.000 0.560 0.000 0.044
#> GSM447704 2 0.0693 0.9077 0.000 0.980 0.000 0.008 0.012
#> GSM447720 5 0.3937 0.6908 0.252 0.000 0.008 0.004 0.736
#> GSM447652 5 0.6195 0.0774 0.004 0.360 0.000 0.128 0.508
#> GSM447679 2 0.0609 0.9106 0.000 0.980 0.000 0.000 0.020
#> GSM447712 3 0.5420 0.4734 0.416 0.000 0.524 0.000 0.060
#> GSM447664 5 0.3739 0.6975 0.052 0.008 0.000 0.116 0.824
#> GSM447637 3 0.0000 0.5673 0.000 0.000 1.000 0.000 0.000
#> GSM447639 3 0.6247 0.3361 0.420 0.000 0.436 0.000 0.144
#> GSM447615 5 0.6056 0.3191 0.324 0.000 0.140 0.000 0.536
#> GSM447656 5 0.1924 0.7796 0.004 0.064 0.000 0.008 0.924
#> GSM447673 2 0.2451 0.8507 0.004 0.904 0.000 0.056 0.036
#> GSM447719 1 0.3671 1.0000 0.756 0.000 0.236 0.008 0.000
#> GSM447706 3 0.5113 0.5282 0.380 0.000 0.576 0.000 0.044
#> GSM447612 3 0.5113 0.5280 0.380 0.000 0.576 0.000 0.044
#> GSM447665 5 0.0613 0.8070 0.004 0.004 0.000 0.008 0.984
#> GSM447677 2 0.0703 0.9098 0.000 0.976 0.000 0.000 0.024
#> GSM447613 3 0.5215 0.5268 0.372 0.000 0.576 0.000 0.052
#> GSM447659 3 0.4449 -0.4974 0.484 0.000 0.512 0.004 0.000
#> GSM447662 3 0.5113 0.5282 0.380 0.000 0.576 0.000 0.044
#> GSM447666 5 0.0671 0.8130 0.016 0.000 0.000 0.004 0.980
#> GSM447668 2 0.0000 0.9106 0.000 1.000 0.000 0.000 0.000
#> GSM447682 5 0.3250 0.6638 0.004 0.168 0.000 0.008 0.820
#> GSM447683 2 0.0703 0.9098 0.000 0.976 0.000 0.000 0.024
#> GSM447688 4 0.6474 0.1758 0.012 0.424 0.000 0.436 0.128
#> GSM447702 2 0.0000 0.9106 0.000 1.000 0.000 0.000 0.000
#> GSM447709 2 0.4317 0.5349 0.004 0.668 0.000 0.008 0.320
#> GSM447711 3 0.5185 0.5221 0.384 0.000 0.568 0.000 0.048
#> GSM447715 5 0.0992 0.8112 0.024 0.000 0.000 0.008 0.968
#> GSM447693 3 0.0000 0.5673 0.000 0.000 1.000 0.000 0.000
#> GSM447611 4 0.4599 0.6874 0.156 0.000 0.000 0.744 0.100
#> GSM447672 2 0.0000 0.9106 0.000 1.000 0.000 0.000 0.000
#> GSM447703 2 0.0290 0.9091 0.000 0.992 0.000 0.008 0.000
#> GSM447727 5 0.4597 0.6206 0.300 0.000 0.024 0.004 0.672
#> GSM447638 5 0.0854 0.8031 0.004 0.012 0.000 0.008 0.976
#> GSM447670 3 0.5215 0.5267 0.372 0.000 0.576 0.000 0.052
#> GSM447700 5 0.4403 0.6403 0.316 0.000 0.012 0.004 0.668
#> GSM447738 2 0.0290 0.9118 0.000 0.992 0.000 0.000 0.008
#> GSM447739 3 0.0000 0.5673 0.000 0.000 1.000 0.000 0.000
#> GSM447617 3 0.0000 0.5673 0.000 0.000 1.000 0.000 0.000
#> GSM447628 4 0.0404 0.7045 0.000 0.000 0.000 0.988 0.012
#> GSM447632 2 0.0290 0.9118 0.000 0.992 0.000 0.000 0.008
#> GSM447619 3 0.5113 0.5282 0.380 0.000 0.576 0.000 0.044
#> GSM447643 5 0.1018 0.8014 0.000 0.016 0.000 0.016 0.968
#> GSM447724 5 0.4323 0.6231 0.332 0.000 0.012 0.000 0.656
#> GSM447728 2 0.4317 0.5349 0.004 0.668 0.000 0.008 0.320
#> GSM447610 3 0.3774 -0.0811 0.296 0.000 0.704 0.000 0.000
#> GSM447633 5 0.0324 0.8117 0.004 0.000 0.000 0.004 0.992
#> GSM447634 3 0.5876 0.4189 0.412 0.000 0.488 0.000 0.100
#> GSM447622 3 0.0000 0.5673 0.000 0.000 1.000 0.000 0.000
#> GSM447667 5 0.4064 0.5082 0.004 0.272 0.000 0.008 0.716
#> GSM447687 2 0.0290 0.9091 0.000 0.992 0.000 0.008 0.000
#> GSM447695 3 0.2074 0.5549 0.104 0.000 0.896 0.000 0.000
#> GSM447696 3 0.0000 0.5673 0.000 0.000 1.000 0.000 0.000
#> GSM447697 3 0.0000 0.5673 0.000 0.000 1.000 0.000 0.000
#> GSM447714 3 0.5080 0.5340 0.368 0.000 0.588 0.000 0.044
#> GSM447717 5 0.0162 0.8112 0.004 0.000 0.000 0.000 0.996
#> GSM447725 3 0.5425 0.4687 0.420 0.000 0.520 0.000 0.060
#> GSM447729 4 0.3346 0.7123 0.092 0.000 0.000 0.844 0.064
#> GSM447644 5 0.0324 0.8117 0.004 0.000 0.000 0.004 0.992
#> GSM447710 3 0.5080 0.5340 0.368 0.000 0.588 0.000 0.044
#> GSM447614 3 0.3774 -0.0811 0.296 0.000 0.704 0.000 0.000
#> GSM447685 2 0.0703 0.9098 0.000 0.976 0.000 0.000 0.024
#> GSM447690 3 0.0000 0.5673 0.000 0.000 1.000 0.000 0.000
#> GSM447730 2 0.1043 0.8902 0.000 0.960 0.000 0.040 0.000
#> GSM447646 4 0.0404 0.7045 0.000 0.000 0.000 0.988 0.012
#> GSM447689 5 0.4449 0.6404 0.288 0.000 0.020 0.004 0.688
#> GSM447635 5 0.1041 0.8103 0.032 0.000 0.000 0.004 0.964
#> GSM447641 3 0.6747 0.2454 0.364 0.000 0.416 0.004 0.216
#> GSM447716 5 0.1924 0.7796 0.004 0.064 0.000 0.008 0.924
#> GSM447718 3 0.6784 0.2279 0.376 0.000 0.396 0.004 0.224
#> GSM447616 3 0.0963 0.5657 0.036 0.000 0.964 0.000 0.000
#> GSM447626 3 0.5113 0.5282 0.380 0.000 0.576 0.000 0.044
#> GSM447640 2 0.0000 0.9106 0.000 1.000 0.000 0.000 0.000
#> GSM447734 3 0.0000 0.5673 0.000 0.000 1.000 0.000 0.000
#> GSM447692 3 0.0000 0.5673 0.000 0.000 1.000 0.000 0.000
#> GSM447647 4 0.5960 0.4251 0.028 0.348 0.000 0.564 0.060
#> GSM447624 3 0.0000 0.5673 0.000 0.000 1.000 0.000 0.000
#> GSM447625 3 0.5030 0.5363 0.352 0.000 0.604 0.000 0.044
#> GSM447707 2 0.0290 0.9091 0.000 0.992 0.000 0.008 0.000
#> GSM447732 3 0.5080 0.5340 0.368 0.000 0.588 0.000 0.044
#> GSM447684 5 0.2674 0.7718 0.140 0.000 0.000 0.004 0.856
#> GSM447731 4 0.2659 0.7090 0.060 0.000 0.000 0.888 0.052
#> GSM447705 5 0.3937 0.6908 0.252 0.000 0.008 0.004 0.736
#> GSM447631 3 0.0000 0.5673 0.000 0.000 1.000 0.000 0.000
#> GSM447701 2 0.3992 0.5986 0.004 0.712 0.000 0.004 0.280
#> GSM447645 3 0.0000 0.5673 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
#> GSM447671 5 0.0837 0.7962 0.020 0.000 0.000 0.004 0.972 0.004
#> GSM447694 3 0.0000 0.9148 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447618 5 0.0858 0.7965 0.028 0.000 0.000 0.000 0.968 0.004
#> GSM447691 5 0.1226 0.7968 0.040 0.000 0.000 0.004 0.952 0.004
#> GSM447733 4 0.5388 0.6071 0.128 0.000 0.000 0.684 0.080 0.108
#> GSM447620 5 0.1088 0.7928 0.024 0.016 0.000 0.000 0.960 0.000
#> GSM447627 3 0.3563 0.4501 0.000 0.000 0.664 0.000 0.000 0.336
#> GSM447630 1 0.3951 0.7089 0.768 0.000 0.056 0.004 0.168 0.004
#> GSM447642 5 0.3905 0.6234 0.356 0.000 0.000 0.004 0.636 0.004
#> GSM447649 2 0.0622 0.9102 0.000 0.980 0.000 0.008 0.012 0.000
#> GSM447654 4 0.0146 0.6836 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM447655 2 0.0000 0.9133 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447669 5 0.0837 0.7962 0.020 0.000 0.000 0.004 0.972 0.004
#> GSM447676 5 0.3918 0.6177 0.360 0.000 0.000 0.004 0.632 0.004
#> GSM447678 5 0.3165 0.7450 0.072 0.000 0.000 0.008 0.844 0.076
#> GSM447681 2 0.1387 0.8786 0.000 0.932 0.000 0.000 0.068 0.000
#> GSM447698 5 0.1471 0.7680 0.004 0.064 0.000 0.000 0.932 0.000
#> GSM447713 3 0.0000 0.9148 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447722 5 0.4798 0.6268 0.312 0.000 0.000 0.000 0.612 0.076
#> GSM447726 5 0.1387 0.7933 0.068 0.000 0.000 0.000 0.932 0.000
#> GSM447735 3 0.3351 0.5479 0.000 0.000 0.712 0.000 0.000 0.288
#> GSM447737 3 0.0000 0.9148 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447657 5 0.1471 0.7680 0.004 0.064 0.000 0.000 0.932 0.000
#> GSM447674 2 0.1267 0.8866 0.000 0.940 0.000 0.000 0.060 0.000
#> GSM447636 5 0.0937 0.7954 0.040 0.000 0.000 0.000 0.960 0.000
#> GSM447723 5 0.3881 0.5774 0.396 0.000 0.000 0.004 0.600 0.000
#> GSM447699 1 0.3563 0.6026 0.664 0.000 0.336 0.000 0.000 0.000
#> GSM447708 2 0.3547 0.5471 0.000 0.668 0.000 0.000 0.332 0.000
#> GSM447721 1 0.2003 0.9102 0.884 0.000 0.116 0.000 0.000 0.000
#> GSM447623 3 0.0000 0.9148 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447621 3 0.1204 0.8572 0.056 0.000 0.944 0.000 0.000 0.000
#> GSM447650 2 0.0547 0.9132 0.000 0.980 0.000 0.000 0.020 0.000
#> GSM447651 2 0.0547 0.9132 0.000 0.980 0.000 0.000 0.020 0.000
#> GSM447653 6 0.0520 0.7122 0.008 0.000 0.008 0.000 0.000 0.984
#> GSM447658 5 0.4364 0.6944 0.256 0.000 0.000 0.004 0.688 0.052
#> GSM447675 4 0.4683 0.6765 0.060 0.000 0.000 0.744 0.076 0.120
#> GSM447680 2 0.0632 0.9124 0.000 0.976 0.000 0.000 0.024 0.000
#> GSM447686 5 0.0972 0.7854 0.008 0.028 0.000 0.000 0.964 0.000
#> GSM447736 1 0.2320 0.9051 0.864 0.000 0.132 0.000 0.004 0.000
#> GSM447629 5 0.1471 0.7680 0.004 0.064 0.000 0.000 0.932 0.000
#> GSM447648 3 0.0000 0.9148 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447660 5 0.3892 0.6269 0.352 0.000 0.000 0.004 0.640 0.004
#> GSM447661 2 0.0000 0.9133 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447663 1 0.1814 0.9052 0.900 0.000 0.100 0.000 0.000 0.000
#> GSM447704 2 0.0622 0.9102 0.000 0.980 0.000 0.008 0.012 0.000
#> GSM447720 5 0.3805 0.6602 0.328 0.000 0.000 0.004 0.664 0.004
#> GSM447652 5 0.5769 0.0923 0.016 0.360 0.000 0.120 0.504 0.000
#> GSM447679 2 0.0547 0.9132 0.000 0.980 0.000 0.000 0.020 0.000
#> GSM447712 1 0.1531 0.8814 0.928 0.000 0.068 0.000 0.004 0.000
#> GSM447664 5 0.3891 0.6767 0.028 0.008 0.000 0.108 0.808 0.048
#> GSM447637 3 0.0000 0.9148 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447639 1 0.3098 0.8072 0.844 0.000 0.064 0.000 0.088 0.004
#> GSM447615 5 0.4758 0.2298 0.476 0.000 0.048 0.000 0.476 0.000
#> GSM447656 5 0.1471 0.7680 0.004 0.064 0.000 0.000 0.932 0.000
#> GSM447673 2 0.2295 0.8532 0.016 0.904 0.000 0.052 0.028 0.000
#> GSM447719 6 0.0520 0.7122 0.008 0.000 0.008 0.000 0.000 0.984
#> GSM447706 1 0.2003 0.9102 0.884 0.000 0.116 0.000 0.000 0.000
#> GSM447612 1 0.2048 0.9088 0.880 0.000 0.120 0.000 0.000 0.000
#> GSM447665 5 0.0146 0.7900 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM447677 2 0.0632 0.9124 0.000 0.976 0.000 0.000 0.024 0.000
#> GSM447613 1 0.2257 0.9099 0.876 0.000 0.116 0.000 0.008 0.000
#> GSM447659 6 0.3699 0.4239 0.004 0.000 0.336 0.000 0.000 0.660
#> GSM447662 1 0.2003 0.9102 0.884 0.000 0.116 0.000 0.000 0.000
#> GSM447666 5 0.1285 0.7966 0.052 0.000 0.000 0.004 0.944 0.000
#> GSM447668 2 0.0000 0.9133 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447682 5 0.2668 0.6561 0.004 0.168 0.000 0.000 0.828 0.000
#> GSM447683 2 0.0632 0.9124 0.000 0.976 0.000 0.000 0.024 0.000
#> GSM447688 4 0.6101 0.1674 0.020 0.424 0.000 0.432 0.116 0.008
#> GSM447702 2 0.0000 0.9133 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447709 2 0.3547 0.5471 0.000 0.668 0.000 0.000 0.332 0.000
#> GSM447711 1 0.1910 0.9089 0.892 0.000 0.108 0.000 0.000 0.000
#> GSM447715 5 0.1387 0.7933 0.068 0.000 0.000 0.000 0.932 0.000
#> GSM447693 3 0.0000 0.9148 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447611 4 0.4683 0.6765 0.060 0.000 0.000 0.744 0.076 0.120
#> GSM447672 2 0.0000 0.9133 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447703 2 0.0260 0.9117 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM447727 5 0.3881 0.5774 0.396 0.000 0.000 0.004 0.600 0.000
#> GSM447638 5 0.0993 0.7935 0.024 0.012 0.000 0.000 0.964 0.000
#> GSM447670 1 0.2257 0.9100 0.876 0.000 0.116 0.000 0.008 0.000
#> GSM447700 5 0.4870 0.6396 0.296 0.000 0.000 0.004 0.624 0.076
#> GSM447738 2 0.0260 0.9144 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM447739 3 0.0000 0.9148 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447617 3 0.0000 0.9148 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447628 4 0.0146 0.6836 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM447632 2 0.0260 0.9144 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM447619 1 0.2003 0.9102 0.884 0.000 0.116 0.000 0.000 0.000
#> GSM447643 5 0.1448 0.7925 0.024 0.016 0.000 0.012 0.948 0.000
#> GSM447724 5 0.4798 0.6268 0.312 0.000 0.000 0.000 0.612 0.076
#> GSM447728 2 0.3547 0.5471 0.000 0.668 0.000 0.000 0.332 0.000
#> GSM447610 3 0.3499 0.4887 0.000 0.000 0.680 0.000 0.000 0.320
#> GSM447633 5 0.0603 0.7953 0.016 0.000 0.000 0.004 0.980 0.000
#> GSM447634 1 0.2519 0.8545 0.884 0.000 0.068 0.000 0.044 0.004
#> GSM447622 3 0.0000 0.9148 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447667 5 0.3266 0.5179 0.000 0.272 0.000 0.000 0.728 0.000
#> GSM447687 2 0.0260 0.9117 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM447695 3 0.2697 0.6568 0.188 0.000 0.812 0.000 0.000 0.000
#> GSM447696 3 0.0000 0.9148 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447697 3 0.0000 0.9148 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447714 1 0.2178 0.9043 0.868 0.000 0.132 0.000 0.000 0.000
#> GSM447717 5 0.0937 0.7954 0.040 0.000 0.000 0.000 0.960 0.000
#> GSM447725 1 0.1674 0.8799 0.924 0.000 0.068 0.000 0.004 0.004
#> GSM447729 4 0.3260 0.6911 0.040 0.000 0.000 0.848 0.036 0.076
#> GSM447644 5 0.0603 0.7953 0.016 0.000 0.000 0.004 0.980 0.000
#> GSM447710 1 0.2178 0.9043 0.868 0.000 0.132 0.000 0.000 0.000
#> GSM447614 3 0.3499 0.4887 0.000 0.000 0.680 0.000 0.000 0.320
#> GSM447685 2 0.0632 0.9124 0.000 0.976 0.000 0.000 0.024 0.000
#> GSM447690 3 0.0000 0.9148 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447730 2 0.0937 0.8925 0.000 0.960 0.000 0.040 0.000 0.000
#> GSM447646 4 0.0146 0.6836 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM447689 5 0.3841 0.6007 0.380 0.000 0.000 0.004 0.616 0.000
#> GSM447635 5 0.1296 0.7971 0.044 0.000 0.000 0.004 0.948 0.004
#> GSM447641 1 0.3718 0.7083 0.780 0.000 0.052 0.004 0.164 0.000
#> GSM447716 5 0.1471 0.7680 0.004 0.064 0.000 0.000 0.932 0.000
#> GSM447718 1 0.3951 0.7089 0.768 0.000 0.056 0.004 0.168 0.004
#> GSM447616 3 0.1204 0.8572 0.056 0.000 0.944 0.000 0.000 0.000
#> GSM447626 1 0.2003 0.9102 0.884 0.000 0.116 0.000 0.000 0.000
#> GSM447640 2 0.0000 0.9133 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447734 3 0.0260 0.9079 0.008 0.000 0.992 0.000 0.000 0.000
#> GSM447692 3 0.0000 0.9148 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447647 4 0.5507 0.3993 0.028 0.348 0.000 0.568 0.036 0.020
#> GSM447624 3 0.0000 0.9148 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447625 1 0.2482 0.8894 0.848 0.000 0.148 0.000 0.000 0.004
#> GSM447707 2 0.0260 0.9117 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM447732 1 0.2178 0.9043 0.868 0.000 0.132 0.000 0.000 0.000
#> GSM447684 5 0.3043 0.7506 0.196 0.000 0.000 0.004 0.796 0.004
#> GSM447731 4 0.2585 0.6905 0.016 0.000 0.000 0.888 0.048 0.048
#> GSM447705 5 0.3805 0.6602 0.328 0.000 0.000 0.004 0.664 0.004
#> GSM447631 3 0.0000 0.9148 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447701 2 0.3351 0.6089 0.000 0.712 0.000 0.000 0.288 0.000
#> GSM447645 3 0.0000 0.9148 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)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 6, scale_rows = FALSE)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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 gender(p) individual(p) disease.state(p) other(p) k
#> ATC:hclust 129 0.338 0.374 0.227 0.00222 2
#> ATC:hclust 118 0.288 0.581 0.436 0.00924 3
#> ATC:hclust 122 0.387 0.778 0.609 0.04096 4
#> ATC:hclust 114 0.182 0.661 0.637 0.03667 5
#> ATC:hclust 122 0.354 0.768 0.590 0.12195 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "kmeans"]
# you can also extract it by
# res = res_list["ATC:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 130 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.992 0.996 0.5041 0.496 0.496
#> 3 3 0.837 0.809 0.908 0.3101 0.733 0.512
#> 4 4 0.738 0.662 0.852 0.1047 0.895 0.703
#> 5 5 0.812 0.813 0.886 0.0716 0.851 0.527
#> 6 6 0.813 0.665 0.845 0.0402 0.962 0.833
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
#> GSM447671 2 0.000 0.997 0.000 1.000
#> GSM447694 1 0.000 0.995 1.000 0.000
#> GSM447618 2 0.000 0.997 0.000 1.000
#> GSM447691 2 0.000 0.997 0.000 1.000
#> GSM447733 1 0.000 0.995 1.000 0.000
#> GSM447620 2 0.000 0.997 0.000 1.000
#> GSM447627 1 0.000 0.995 1.000 0.000
#> GSM447630 1 0.469 0.893 0.900 0.100
#> GSM447642 1 0.000 0.995 1.000 0.000
#> GSM447649 2 0.000 0.997 0.000 1.000
#> GSM447654 2 0.000 0.997 0.000 1.000
#> GSM447655 2 0.000 0.997 0.000 1.000
#> GSM447669 2 0.000 0.997 0.000 1.000
#> GSM447676 1 0.000 0.995 1.000 0.000
#> GSM447678 2 0.000 0.997 0.000 1.000
#> GSM447681 2 0.000 0.997 0.000 1.000
#> GSM447698 2 0.000 0.997 0.000 1.000
#> GSM447713 1 0.000 0.995 1.000 0.000
#> GSM447722 1 0.000 0.995 1.000 0.000
#> GSM447726 2 0.000 0.997 0.000 1.000
#> GSM447735 1 0.000 0.995 1.000 0.000
#> GSM447737 1 0.000 0.995 1.000 0.000
#> GSM447657 2 0.000 0.997 0.000 1.000
#> GSM447674 2 0.000 0.997 0.000 1.000
#> GSM447636 2 0.000 0.997 0.000 1.000
#> GSM447723 1 0.000 0.995 1.000 0.000
#> GSM447699 1 0.000 0.995 1.000 0.000
#> GSM447708 2 0.000 0.997 0.000 1.000
#> GSM447721 1 0.000 0.995 1.000 0.000
#> GSM447623 1 0.000 0.995 1.000 0.000
#> GSM447621 1 0.000 0.995 1.000 0.000
#> GSM447650 2 0.000 0.997 0.000 1.000
#> GSM447651 2 0.000 0.997 0.000 1.000
#> GSM447653 1 0.000 0.995 1.000 0.000
#> GSM447658 2 0.000 0.997 0.000 1.000
#> GSM447675 2 0.644 0.802 0.164 0.836
#> GSM447680 2 0.000 0.997 0.000 1.000
#> GSM447686 2 0.000 0.997 0.000 1.000
#> GSM447736 1 0.000 0.995 1.000 0.000
#> GSM447629 2 0.000 0.997 0.000 1.000
#> GSM447648 1 0.000 0.995 1.000 0.000
#> GSM447660 2 0.000 0.997 0.000 1.000
#> GSM447661 2 0.000 0.997 0.000 1.000
#> GSM447663 1 0.000 0.995 1.000 0.000
#> GSM447704 2 0.000 0.997 0.000 1.000
#> GSM447720 1 0.000 0.995 1.000 0.000
#> GSM447652 2 0.000 0.997 0.000 1.000
#> GSM447679 2 0.000 0.997 0.000 1.000
#> GSM447712 1 0.000 0.995 1.000 0.000
#> GSM447664 2 0.000 0.997 0.000 1.000
#> GSM447637 1 0.000 0.995 1.000 0.000
#> GSM447639 1 0.000 0.995 1.000 0.000
#> GSM447615 1 0.000 0.995 1.000 0.000
#> GSM447656 2 0.000 0.997 0.000 1.000
#> GSM447673 2 0.000 0.997 0.000 1.000
#> GSM447719 1 0.000 0.995 1.000 0.000
#> GSM447706 1 0.000 0.995 1.000 0.000
#> GSM447612 1 0.000 0.995 1.000 0.000
#> GSM447665 2 0.000 0.997 0.000 1.000
#> GSM447677 2 0.000 0.997 0.000 1.000
#> GSM447613 1 0.000 0.995 1.000 0.000
#> GSM447659 1 0.000 0.995 1.000 0.000
#> GSM447662 1 0.000 0.995 1.000 0.000
#> GSM447666 2 0.000 0.997 0.000 1.000
#> GSM447668 2 0.000 0.997 0.000 1.000
#> GSM447682 2 0.000 0.997 0.000 1.000
#> GSM447683 2 0.000 0.997 0.000 1.000
#> GSM447688 2 0.000 0.997 0.000 1.000
#> GSM447702 2 0.000 0.997 0.000 1.000
#> GSM447709 2 0.000 0.997 0.000 1.000
#> GSM447711 1 0.000 0.995 1.000 0.000
#> GSM447715 2 0.000 0.997 0.000 1.000
#> GSM447693 1 0.000 0.995 1.000 0.000
#> GSM447611 2 0.000 0.997 0.000 1.000
#> GSM447672 2 0.000 0.997 0.000 1.000
#> GSM447703 2 0.000 0.997 0.000 1.000
#> GSM447727 1 0.000 0.995 1.000 0.000
#> GSM447638 2 0.000 0.997 0.000 1.000
#> GSM447670 1 0.000 0.995 1.000 0.000
#> GSM447700 1 0.430 0.906 0.912 0.088
#> GSM447738 2 0.000 0.997 0.000 1.000
#> GSM447739 1 0.000 0.995 1.000 0.000
#> GSM447617 1 0.000 0.995 1.000 0.000
#> GSM447628 2 0.000 0.997 0.000 1.000
#> GSM447632 2 0.000 0.997 0.000 1.000
#> GSM447619 1 0.000 0.995 1.000 0.000
#> GSM447643 2 0.000 0.997 0.000 1.000
#> GSM447724 1 0.000 0.995 1.000 0.000
#> GSM447728 2 0.000 0.997 0.000 1.000
#> GSM447610 1 0.000 0.995 1.000 0.000
#> GSM447633 2 0.000 0.997 0.000 1.000
#> GSM447634 1 0.000 0.995 1.000 0.000
#> GSM447622 1 0.000 0.995 1.000 0.000
#> GSM447667 2 0.000 0.997 0.000 1.000
#> GSM447687 2 0.000 0.997 0.000 1.000
#> GSM447695 1 0.000 0.995 1.000 0.000
#> GSM447696 1 0.000 0.995 1.000 0.000
#> GSM447697 1 0.000 0.995 1.000 0.000
#> GSM447714 1 0.000 0.995 1.000 0.000
#> GSM447717 2 0.000 0.997 0.000 1.000
#> GSM447725 1 0.000 0.995 1.000 0.000
#> GSM447729 2 0.000 0.997 0.000 1.000
#> GSM447644 2 0.000 0.997 0.000 1.000
#> GSM447710 1 0.000 0.995 1.000 0.000
#> GSM447614 1 0.000 0.995 1.000 0.000
#> GSM447685 2 0.000 0.997 0.000 1.000
#> GSM447690 1 0.000 0.995 1.000 0.000
#> GSM447730 2 0.000 0.997 0.000 1.000
#> GSM447646 2 0.000 0.997 0.000 1.000
#> GSM447689 1 0.000 0.995 1.000 0.000
#> GSM447635 2 0.000 0.997 0.000 1.000
#> GSM447641 1 0.000 0.995 1.000 0.000
#> GSM447716 2 0.000 0.997 0.000 1.000
#> GSM447718 1 0.000 0.995 1.000 0.000
#> GSM447616 1 0.000 0.995 1.000 0.000
#> GSM447626 1 0.000 0.995 1.000 0.000
#> GSM447640 2 0.000 0.997 0.000 1.000
#> GSM447734 1 0.000 0.995 1.000 0.000
#> GSM447692 1 0.000 0.995 1.000 0.000
#> GSM447647 2 0.000 0.997 0.000 1.000
#> GSM447624 1 0.000 0.995 1.000 0.000
#> GSM447625 1 0.000 0.995 1.000 0.000
#> GSM447707 2 0.000 0.997 0.000 1.000
#> GSM447732 1 0.000 0.995 1.000 0.000
#> GSM447684 1 0.469 0.893 0.900 0.100
#> GSM447731 2 0.000 0.997 0.000 1.000
#> GSM447705 1 0.184 0.969 0.972 0.028
#> GSM447631 1 0.000 0.995 1.000 0.000
#> GSM447701 2 0.000 0.997 0.000 1.000
#> GSM447645 1 0.000 0.995 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM447671 3 0.2796 0.8652 0.000 0.092 0.908
#> GSM447694 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447618 3 0.2796 0.8652 0.000 0.092 0.908
#> GSM447691 3 0.2796 0.8652 0.000 0.092 0.908
#> GSM447733 3 0.0237 0.8617 0.004 0.000 0.996
#> GSM447620 2 0.4796 0.7016 0.000 0.780 0.220
#> GSM447627 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447630 3 0.2711 0.8915 0.088 0.000 0.912
#> GSM447642 3 0.2711 0.8915 0.088 0.000 0.912
#> GSM447649 2 0.0000 0.8826 0.000 1.000 0.000
#> GSM447654 2 0.6168 0.4750 0.000 0.588 0.412
#> GSM447655 2 0.0000 0.8826 0.000 1.000 0.000
#> GSM447669 3 0.2796 0.8652 0.000 0.092 0.908
#> GSM447676 3 0.2711 0.8915 0.088 0.000 0.912
#> GSM447678 3 0.2261 0.8696 0.000 0.068 0.932
#> GSM447681 2 0.0000 0.8826 0.000 1.000 0.000
#> GSM447698 2 0.5733 0.5558 0.000 0.676 0.324
#> GSM447713 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447722 3 0.2711 0.8915 0.088 0.000 0.912
#> GSM447726 3 0.2796 0.8652 0.000 0.092 0.908
#> GSM447735 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447737 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447657 2 0.6154 0.3884 0.000 0.592 0.408
#> GSM447674 2 0.0000 0.8826 0.000 1.000 0.000
#> GSM447636 2 0.6168 0.3828 0.000 0.588 0.412
#> GSM447723 3 0.2796 0.8906 0.092 0.000 0.908
#> GSM447699 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447708 2 0.6111 0.4165 0.000 0.604 0.396
#> GSM447721 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447623 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447621 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447650 2 0.0000 0.8826 0.000 1.000 0.000
#> GSM447651 2 0.0000 0.8826 0.000 1.000 0.000
#> GSM447653 1 0.2878 0.8660 0.904 0.000 0.096
#> GSM447658 3 0.2261 0.8696 0.000 0.068 0.932
#> GSM447675 3 0.0000 0.8595 0.000 0.000 1.000
#> GSM447680 2 0.0000 0.8826 0.000 1.000 0.000
#> GSM447686 3 0.6252 0.0985 0.000 0.444 0.556
#> GSM447736 1 0.4452 0.7308 0.808 0.000 0.192
#> GSM447629 2 0.6140 0.3979 0.000 0.596 0.404
#> GSM447648 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447660 3 0.2261 0.8696 0.000 0.068 0.932
#> GSM447661 2 0.0000 0.8826 0.000 1.000 0.000
#> GSM447663 3 0.3267 0.8694 0.116 0.000 0.884
#> GSM447704 2 0.0000 0.8826 0.000 1.000 0.000
#> GSM447720 3 0.2796 0.8906 0.092 0.000 0.908
#> GSM447652 2 0.0237 0.8808 0.000 0.996 0.004
#> GSM447679 2 0.0000 0.8826 0.000 1.000 0.000
#> GSM447712 3 0.6295 0.0762 0.472 0.000 0.528
#> GSM447664 2 0.6307 0.1985 0.000 0.512 0.488
#> GSM447637 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447639 3 0.2711 0.8915 0.088 0.000 0.912
#> GSM447615 3 0.2878 0.8879 0.096 0.000 0.904
#> GSM447656 2 0.6154 0.3884 0.000 0.592 0.408
#> GSM447673 2 0.0000 0.8826 0.000 1.000 0.000
#> GSM447719 1 0.2625 0.8719 0.916 0.000 0.084
#> GSM447706 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447612 1 0.6305 0.0587 0.516 0.000 0.484
#> GSM447665 2 0.4750 0.7001 0.000 0.784 0.216
#> GSM447677 2 0.0000 0.8826 0.000 1.000 0.000
#> GSM447613 1 0.5529 0.5588 0.704 0.000 0.296
#> GSM447659 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447662 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447666 3 0.2796 0.8652 0.000 0.092 0.908
#> GSM447668 2 0.0000 0.8826 0.000 1.000 0.000
#> GSM447682 2 0.0000 0.8826 0.000 1.000 0.000
#> GSM447683 2 0.0000 0.8826 0.000 1.000 0.000
#> GSM447688 2 0.1643 0.8605 0.000 0.956 0.044
#> GSM447702 2 0.0000 0.8826 0.000 1.000 0.000
#> GSM447709 2 0.0000 0.8826 0.000 1.000 0.000
#> GSM447711 1 0.6305 0.0587 0.516 0.000 0.484
#> GSM447715 3 0.2796 0.8652 0.000 0.092 0.908
#> GSM447693 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447611 3 0.0000 0.8595 0.000 0.000 1.000
#> GSM447672 2 0.0000 0.8826 0.000 1.000 0.000
#> GSM447703 2 0.0237 0.8809 0.000 0.996 0.004
#> GSM447727 3 0.2796 0.8906 0.092 0.000 0.908
#> GSM447638 2 0.5138 0.6622 0.000 0.748 0.252
#> GSM447670 1 0.6204 0.2564 0.576 0.000 0.424
#> GSM447700 3 0.2711 0.8915 0.088 0.000 0.912
#> GSM447738 2 0.0000 0.8826 0.000 1.000 0.000
#> GSM447739 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447617 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447628 2 0.2796 0.8246 0.000 0.908 0.092
#> GSM447632 2 0.0000 0.8826 0.000 1.000 0.000
#> GSM447619 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447643 2 0.5968 0.4840 0.000 0.636 0.364
#> GSM447724 3 0.2796 0.8906 0.092 0.000 0.908
#> GSM447728 2 0.0000 0.8826 0.000 1.000 0.000
#> GSM447610 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447633 3 0.2796 0.8652 0.000 0.092 0.908
#> GSM447634 3 0.2796 0.8906 0.092 0.000 0.908
#> GSM447622 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447667 2 0.0000 0.8826 0.000 1.000 0.000
#> GSM447687 2 0.0237 0.8809 0.000 0.996 0.004
#> GSM447695 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447696 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447697 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447714 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447717 3 0.2711 0.8665 0.000 0.088 0.912
#> GSM447725 3 0.2711 0.8915 0.088 0.000 0.912
#> GSM447729 2 0.5431 0.6915 0.000 0.716 0.284
#> GSM447644 3 0.2796 0.8652 0.000 0.092 0.908
#> GSM447710 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447614 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447685 2 0.0000 0.8826 0.000 1.000 0.000
#> GSM447690 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447730 2 0.0237 0.8809 0.000 0.996 0.004
#> GSM447646 2 0.2878 0.8246 0.000 0.904 0.096
#> GSM447689 3 0.2796 0.8906 0.092 0.000 0.908
#> GSM447635 3 0.2711 0.8665 0.000 0.088 0.912
#> GSM447641 3 0.2796 0.8906 0.092 0.000 0.908
#> GSM447716 3 0.6308 -0.1001 0.000 0.492 0.508
#> GSM447718 3 0.2711 0.8915 0.088 0.000 0.912
#> GSM447616 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447626 3 0.3267 0.8694 0.116 0.000 0.884
#> GSM447640 2 0.0000 0.8826 0.000 1.000 0.000
#> GSM447734 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447692 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447647 2 0.2448 0.8364 0.000 0.924 0.076
#> GSM447624 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447625 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447707 2 0.0237 0.8809 0.000 0.996 0.004
#> GSM447732 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447684 3 0.2945 0.8913 0.088 0.004 0.908
#> GSM447731 3 0.6291 -0.1742 0.000 0.468 0.532
#> GSM447705 3 0.2796 0.8906 0.092 0.000 0.908
#> GSM447631 1 0.0000 0.9450 1.000 0.000 0.000
#> GSM447701 2 0.0000 0.8826 0.000 1.000 0.000
#> GSM447645 1 0.0000 0.9450 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM447671 1 0.4564 0.5434 0.672 0.000 0.000 0.328
#> GSM447694 3 0.0000 0.9285 0.000 0.000 1.000 0.000
#> GSM447618 1 0.4661 0.5097 0.652 0.000 0.000 0.348
#> GSM447691 1 0.4431 0.5755 0.696 0.000 0.000 0.304
#> GSM447733 4 0.4679 0.3656 0.352 0.000 0.000 0.648
#> GSM447620 2 0.7561 0.0936 0.200 0.452 0.000 0.348
#> GSM447627 3 0.0188 0.9273 0.000 0.000 0.996 0.004
#> GSM447630 1 0.0000 0.7647 1.000 0.000 0.000 0.000
#> GSM447642 1 0.0000 0.7647 1.000 0.000 0.000 0.000
#> GSM447649 2 0.0000 0.7588 0.000 1.000 0.000 0.000
#> GSM447654 4 0.1637 0.7157 0.000 0.060 0.000 0.940
#> GSM447655 2 0.0000 0.7588 0.000 1.000 0.000 0.000
#> GSM447669 1 0.4564 0.5434 0.672 0.000 0.000 0.328
#> GSM447676 1 0.0188 0.7636 0.996 0.000 0.000 0.004
#> GSM447678 1 0.4522 0.5445 0.680 0.000 0.000 0.320
#> GSM447681 2 0.0469 0.7562 0.000 0.988 0.000 0.012
#> GSM447698 2 0.7889 -0.0515 0.288 0.364 0.000 0.348
#> GSM447713 3 0.0000 0.9285 0.000 0.000 1.000 0.000
#> GSM447722 1 0.0592 0.7597 0.984 0.000 0.000 0.016
#> GSM447726 1 0.4477 0.5658 0.688 0.000 0.000 0.312
#> GSM447735 3 0.0188 0.9273 0.000 0.000 0.996 0.004
#> GSM447737 3 0.0000 0.9285 0.000 0.000 1.000 0.000
#> GSM447657 2 0.7901 -0.0654 0.296 0.356 0.000 0.348
#> GSM447674 2 0.0000 0.7588 0.000 1.000 0.000 0.000
#> GSM447636 4 0.7876 -0.0130 0.280 0.352 0.000 0.368
#> GSM447723 1 0.0000 0.7647 1.000 0.000 0.000 0.000
#> GSM447699 3 0.2266 0.8792 0.084 0.000 0.912 0.004
#> GSM447708 2 0.7896 -0.0576 0.292 0.360 0.000 0.348
#> GSM447721 3 0.2654 0.8615 0.108 0.000 0.888 0.004
#> GSM447623 3 0.0000 0.9285 0.000 0.000 1.000 0.000
#> GSM447621 3 0.0188 0.9274 0.000 0.000 0.996 0.004
#> GSM447650 2 0.0000 0.7588 0.000 1.000 0.000 0.000
#> GSM447651 2 0.0000 0.7588 0.000 1.000 0.000 0.000
#> GSM447653 4 0.6722 0.4285 0.200 0.000 0.184 0.616
#> GSM447658 1 0.3649 0.6570 0.796 0.000 0.000 0.204
#> GSM447675 4 0.2081 0.6865 0.084 0.000 0.000 0.916
#> GSM447680 2 0.0592 0.7550 0.000 0.984 0.000 0.016
#> GSM447686 1 0.7824 -0.1197 0.392 0.260 0.000 0.348
#> GSM447736 3 0.5236 0.3590 0.432 0.000 0.560 0.008
#> GSM447629 2 0.7896 -0.0576 0.292 0.360 0.000 0.348
#> GSM447648 3 0.0000 0.9285 0.000 0.000 1.000 0.000
#> GSM447660 1 0.3649 0.6570 0.796 0.000 0.000 0.204
#> GSM447661 2 0.0000 0.7588 0.000 1.000 0.000 0.000
#> GSM447663 1 0.0188 0.7629 0.996 0.000 0.000 0.004
#> GSM447704 2 0.0000 0.7588 0.000 1.000 0.000 0.000
#> GSM447720 1 0.0000 0.7647 1.000 0.000 0.000 0.000
#> GSM447652 2 0.4916 0.2253 0.000 0.576 0.000 0.424
#> GSM447679 2 0.0000 0.7588 0.000 1.000 0.000 0.000
#> GSM447712 1 0.4673 0.4255 0.700 0.000 0.292 0.008
#> GSM447664 4 0.6179 0.5042 0.188 0.140 0.000 0.672
#> GSM447637 3 0.0000 0.9285 0.000 0.000 1.000 0.000
#> GSM447639 1 0.0188 0.7636 0.996 0.000 0.000 0.004
#> GSM447615 1 0.0188 0.7636 0.996 0.000 0.000 0.004
#> GSM447656 2 0.7896 -0.0576 0.292 0.360 0.000 0.348
#> GSM447673 2 0.0000 0.7588 0.000 1.000 0.000 0.000
#> GSM447719 3 0.4996 0.1636 0.000 0.000 0.516 0.484
#> GSM447706 3 0.2401 0.8739 0.092 0.000 0.904 0.004
#> GSM447612 1 0.4535 0.4281 0.704 0.000 0.292 0.004
#> GSM447665 2 0.7714 0.0612 0.236 0.432 0.000 0.332
#> GSM447677 2 0.0592 0.7550 0.000 0.984 0.000 0.016
#> GSM447613 1 0.4855 0.2865 0.644 0.000 0.352 0.004
#> GSM447659 3 0.0188 0.9273 0.000 0.000 0.996 0.004
#> GSM447662 3 0.3668 0.7862 0.188 0.000 0.808 0.004
#> GSM447666 1 0.4331 0.5933 0.712 0.000 0.000 0.288
#> GSM447668 2 0.0469 0.7562 0.000 0.988 0.000 0.012
#> GSM447682 2 0.4406 0.4754 0.000 0.700 0.000 0.300
#> GSM447683 2 0.1022 0.7474 0.000 0.968 0.000 0.032
#> GSM447688 4 0.3907 0.5800 0.000 0.232 0.000 0.768
#> GSM447702 2 0.0000 0.7588 0.000 1.000 0.000 0.000
#> GSM447709 2 0.1940 0.7210 0.000 0.924 0.000 0.076
#> GSM447711 1 0.4673 0.4255 0.700 0.000 0.292 0.008
#> GSM447715 1 0.4193 0.6106 0.732 0.000 0.000 0.268
#> GSM447693 3 0.0000 0.9285 0.000 0.000 1.000 0.000
#> GSM447611 4 0.1940 0.6916 0.076 0.000 0.000 0.924
#> GSM447672 2 0.0000 0.7588 0.000 1.000 0.000 0.000
#> GSM447703 2 0.0000 0.7588 0.000 1.000 0.000 0.000
#> GSM447727 1 0.0000 0.7647 1.000 0.000 0.000 0.000
#> GSM447638 2 0.7693 0.0460 0.224 0.424 0.000 0.352
#> GSM447670 1 0.4560 0.4208 0.700 0.000 0.296 0.004
#> GSM447700 1 0.0469 0.7612 0.988 0.000 0.000 0.012
#> GSM447738 2 0.0000 0.7588 0.000 1.000 0.000 0.000
#> GSM447739 3 0.0000 0.9285 0.000 0.000 1.000 0.000
#> GSM447617 3 0.0000 0.9285 0.000 0.000 1.000 0.000
#> GSM447628 4 0.2281 0.7038 0.000 0.096 0.000 0.904
#> GSM447632 2 0.0000 0.7588 0.000 1.000 0.000 0.000
#> GSM447619 3 0.2401 0.8739 0.092 0.000 0.904 0.004
#> GSM447643 2 0.7889 -0.0515 0.288 0.364 0.000 0.348
#> GSM447724 1 0.0188 0.7636 0.996 0.000 0.000 0.004
#> GSM447728 2 0.1022 0.7474 0.000 0.968 0.000 0.032
#> GSM447610 3 0.0188 0.9273 0.000 0.000 0.996 0.004
#> GSM447633 1 0.4564 0.5434 0.672 0.000 0.000 0.328
#> GSM447634 1 0.0188 0.7636 0.996 0.000 0.000 0.004
#> GSM447622 3 0.0188 0.9274 0.000 0.000 0.996 0.004
#> GSM447667 2 0.4661 0.4029 0.000 0.652 0.000 0.348
#> GSM447687 2 0.0000 0.7588 0.000 1.000 0.000 0.000
#> GSM447695 3 0.0188 0.9274 0.000 0.000 0.996 0.004
#> GSM447696 3 0.0000 0.9285 0.000 0.000 1.000 0.000
#> GSM447697 3 0.0000 0.9285 0.000 0.000 1.000 0.000
#> GSM447714 3 0.3626 0.7906 0.184 0.000 0.812 0.004
#> GSM447717 1 0.3837 0.6431 0.776 0.000 0.000 0.224
#> GSM447725 1 0.0188 0.7636 0.996 0.000 0.000 0.004
#> GSM447729 4 0.1716 0.7148 0.000 0.064 0.000 0.936
#> GSM447644 1 0.4477 0.5658 0.688 0.000 0.000 0.312
#> GSM447710 3 0.2401 0.8739 0.092 0.000 0.904 0.004
#> GSM447614 3 0.0188 0.9273 0.000 0.000 0.996 0.004
#> GSM447685 2 0.0592 0.7550 0.000 0.984 0.000 0.016
#> GSM447690 3 0.0188 0.9273 0.000 0.000 0.996 0.004
#> GSM447730 2 0.0000 0.7588 0.000 1.000 0.000 0.000
#> GSM447646 4 0.2281 0.7038 0.000 0.096 0.000 0.904
#> GSM447689 1 0.0000 0.7647 1.000 0.000 0.000 0.000
#> GSM447635 1 0.3764 0.6511 0.784 0.000 0.000 0.216
#> GSM447641 1 0.0000 0.7647 1.000 0.000 0.000 0.000
#> GSM447716 4 0.7914 0.0636 0.344 0.308 0.000 0.348
#> GSM447718 1 0.0000 0.7647 1.000 0.000 0.000 0.000
#> GSM447616 3 0.0188 0.9274 0.000 0.000 0.996 0.004
#> GSM447626 1 0.0188 0.7629 0.996 0.000 0.000 0.004
#> GSM447640 2 0.0000 0.7588 0.000 1.000 0.000 0.000
#> GSM447734 3 0.0000 0.9285 0.000 0.000 1.000 0.000
#> GSM447692 3 0.0000 0.9285 0.000 0.000 1.000 0.000
#> GSM447647 4 0.4164 0.5529 0.000 0.264 0.000 0.736
#> GSM447624 3 0.0000 0.9285 0.000 0.000 1.000 0.000
#> GSM447625 3 0.4891 0.6230 0.308 0.000 0.680 0.012
#> GSM447707 2 0.0000 0.7588 0.000 1.000 0.000 0.000
#> GSM447732 3 0.3583 0.7947 0.180 0.000 0.816 0.004
#> GSM447684 1 0.0000 0.7647 1.000 0.000 0.000 0.000
#> GSM447731 4 0.1929 0.7182 0.024 0.036 0.000 0.940
#> GSM447705 1 0.0000 0.7647 1.000 0.000 0.000 0.000
#> GSM447631 3 0.0000 0.9285 0.000 0.000 1.000 0.000
#> GSM447701 2 0.2149 0.7096 0.000 0.912 0.000 0.088
#> GSM447645 3 0.0000 0.9285 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
#> GSM447671 5 0.2127 0.827 0.108 0.000 0.000 0.000 0.892
#> GSM447694 3 0.0000 0.860 0.000 0.000 1.000 0.000 0.000
#> GSM447618 5 0.2074 0.828 0.104 0.000 0.000 0.000 0.896
#> GSM447691 5 0.2280 0.822 0.120 0.000 0.000 0.000 0.880
#> GSM447733 4 0.4787 0.536 0.324 0.000 0.000 0.640 0.036
#> GSM447620 5 0.2635 0.836 0.008 0.088 0.000 0.016 0.888
#> GSM447627 3 0.3939 0.776 0.048 0.000 0.832 0.072 0.048
#> GSM447630 1 0.1965 0.879 0.904 0.000 0.000 0.000 0.096
#> GSM447642 1 0.1671 0.894 0.924 0.000 0.000 0.000 0.076
#> GSM447649 2 0.0703 0.979 0.000 0.976 0.000 0.000 0.024
#> GSM447654 4 0.2068 0.861 0.000 0.004 0.000 0.904 0.092
#> GSM447655 2 0.0000 0.983 0.000 1.000 0.000 0.000 0.000
#> GSM447669 5 0.2179 0.826 0.112 0.000 0.000 0.000 0.888
#> GSM447676 1 0.1544 0.894 0.932 0.000 0.000 0.000 0.068
#> GSM447678 5 0.2471 0.813 0.136 0.000 0.000 0.000 0.864
#> GSM447681 2 0.0000 0.983 0.000 1.000 0.000 0.000 0.000
#> GSM447698 5 0.2708 0.843 0.020 0.072 0.000 0.016 0.892
#> GSM447713 3 0.1267 0.849 0.012 0.000 0.960 0.024 0.004
#> GSM447722 1 0.1608 0.894 0.928 0.000 0.000 0.000 0.072
#> GSM447726 5 0.2127 0.828 0.108 0.000 0.000 0.000 0.892
#> GSM447735 3 0.1828 0.834 0.028 0.000 0.936 0.004 0.032
#> GSM447737 3 0.0000 0.860 0.000 0.000 1.000 0.000 0.000
#> GSM447657 5 0.2708 0.843 0.020 0.072 0.000 0.016 0.892
#> GSM447674 2 0.0000 0.983 0.000 1.000 0.000 0.000 0.000
#> GSM447636 5 0.2580 0.842 0.020 0.064 0.000 0.016 0.900
#> GSM447723 1 0.1671 0.894 0.924 0.000 0.000 0.000 0.076
#> GSM447699 3 0.4730 0.622 0.260 0.000 0.688 0.052 0.000
#> GSM447708 5 0.2770 0.843 0.020 0.076 0.000 0.016 0.888
#> GSM447721 3 0.5351 0.475 0.348 0.000 0.592 0.056 0.004
#> GSM447623 3 0.0000 0.860 0.000 0.000 1.000 0.000 0.000
#> GSM447621 3 0.0000 0.860 0.000 0.000 1.000 0.000 0.000
#> GSM447650 2 0.0510 0.981 0.000 0.984 0.000 0.000 0.016
#> GSM447651 2 0.0000 0.983 0.000 1.000 0.000 0.000 0.000
#> GSM447653 4 0.4681 0.726 0.144 0.000 0.032 0.768 0.056
#> GSM447658 1 0.4307 -0.150 0.504 0.000 0.000 0.000 0.496
#> GSM447675 4 0.2580 0.850 0.044 0.000 0.000 0.892 0.064
#> GSM447680 2 0.0510 0.975 0.000 0.984 0.000 0.000 0.016
#> GSM447686 5 0.2654 0.842 0.040 0.044 0.000 0.016 0.900
#> GSM447736 1 0.3357 0.805 0.852 0.000 0.092 0.048 0.008
#> GSM447629 5 0.2770 0.843 0.020 0.076 0.000 0.016 0.888
#> GSM447648 3 0.0000 0.860 0.000 0.000 1.000 0.000 0.000
#> GSM447660 5 0.4126 0.469 0.380 0.000 0.000 0.000 0.620
#> GSM447661 2 0.0000 0.983 0.000 1.000 0.000 0.000 0.000
#> GSM447663 1 0.2196 0.889 0.916 0.000 0.004 0.024 0.056
#> GSM447704 2 0.0703 0.979 0.000 0.976 0.000 0.000 0.024
#> GSM447720 1 0.1671 0.894 0.924 0.000 0.000 0.000 0.076
#> GSM447652 5 0.3281 0.767 0.000 0.092 0.000 0.060 0.848
#> GSM447679 2 0.0000 0.983 0.000 1.000 0.000 0.000 0.000
#> GSM447712 1 0.2072 0.844 0.928 0.000 0.036 0.020 0.016
#> GSM447664 5 0.2920 0.754 0.016 0.000 0.000 0.132 0.852
#> GSM447637 3 0.0000 0.860 0.000 0.000 1.000 0.000 0.000
#> GSM447639 1 0.0955 0.873 0.968 0.000 0.000 0.004 0.028
#> GSM447615 1 0.1697 0.893 0.932 0.000 0.000 0.008 0.060
#> GSM447656 5 0.2770 0.843 0.020 0.076 0.000 0.016 0.888
#> GSM447673 2 0.1124 0.968 0.000 0.960 0.000 0.004 0.036
#> GSM447719 4 0.6321 0.288 0.056 0.000 0.336 0.552 0.056
#> GSM447706 3 0.5159 0.347 0.400 0.000 0.556 0.044 0.000
#> GSM447612 1 0.2645 0.837 0.888 0.000 0.068 0.044 0.000
#> GSM447665 5 0.2519 0.830 0.000 0.100 0.000 0.016 0.884
#> GSM447677 2 0.0510 0.975 0.000 0.984 0.000 0.000 0.016
#> GSM447613 1 0.2946 0.818 0.868 0.000 0.088 0.044 0.000
#> GSM447659 3 0.4273 0.758 0.056 0.000 0.812 0.076 0.056
#> GSM447662 1 0.5114 0.339 0.608 0.000 0.340 0.052 0.000
#> GSM447666 5 0.2471 0.811 0.136 0.000 0.000 0.000 0.864
#> GSM447668 2 0.0000 0.983 0.000 1.000 0.000 0.000 0.000
#> GSM447682 5 0.3492 0.729 0.000 0.188 0.000 0.016 0.796
#> GSM447683 2 0.0510 0.975 0.000 0.984 0.000 0.000 0.016
#> GSM447688 4 0.3905 0.745 0.004 0.012 0.000 0.752 0.232
#> GSM447702 2 0.0000 0.983 0.000 1.000 0.000 0.000 0.000
#> GSM447709 5 0.3707 0.650 0.000 0.284 0.000 0.000 0.716
#> GSM447711 1 0.2853 0.831 0.880 0.000 0.076 0.040 0.004
#> GSM447715 5 0.3895 0.593 0.320 0.000 0.000 0.000 0.680
#> GSM447693 3 0.0000 0.860 0.000 0.000 1.000 0.000 0.000
#> GSM447611 4 0.2491 0.855 0.036 0.000 0.000 0.896 0.068
#> GSM447672 2 0.0000 0.983 0.000 1.000 0.000 0.000 0.000
#> GSM447703 2 0.0794 0.976 0.000 0.972 0.000 0.000 0.028
#> GSM447727 1 0.1671 0.894 0.924 0.000 0.000 0.000 0.076
#> GSM447638 5 0.2611 0.842 0.016 0.072 0.000 0.016 0.896
#> GSM447670 1 0.2804 0.839 0.884 0.000 0.068 0.044 0.004
#> GSM447700 1 0.1965 0.879 0.904 0.000 0.000 0.000 0.096
#> GSM447738 2 0.0510 0.981 0.000 0.984 0.000 0.000 0.016
#> GSM447739 3 0.0960 0.852 0.016 0.000 0.972 0.008 0.004
#> GSM447617 3 0.0000 0.860 0.000 0.000 1.000 0.000 0.000
#> GSM447628 4 0.2233 0.859 0.000 0.004 0.000 0.892 0.104
#> GSM447632 2 0.0703 0.979 0.000 0.976 0.000 0.000 0.024
#> GSM447619 3 0.5016 0.467 0.348 0.000 0.608 0.044 0.000
#> GSM447643 5 0.2644 0.843 0.020 0.068 0.000 0.016 0.896
#> GSM447724 1 0.0955 0.873 0.968 0.000 0.000 0.004 0.028
#> GSM447728 2 0.1478 0.920 0.000 0.936 0.000 0.000 0.064
#> GSM447610 3 0.3805 0.782 0.044 0.000 0.840 0.068 0.048
#> GSM447633 5 0.2074 0.828 0.104 0.000 0.000 0.000 0.896
#> GSM447634 1 0.1082 0.886 0.964 0.000 0.000 0.008 0.028
#> GSM447622 3 0.0000 0.860 0.000 0.000 1.000 0.000 0.000
#> GSM447667 5 0.3141 0.775 0.000 0.152 0.000 0.016 0.832
#> GSM447687 2 0.0794 0.976 0.000 0.972 0.000 0.000 0.028
#> GSM447695 3 0.1800 0.837 0.020 0.000 0.932 0.048 0.000
#> GSM447696 3 0.0000 0.860 0.000 0.000 1.000 0.000 0.000
#> GSM447697 3 0.0000 0.860 0.000 0.000 1.000 0.000 0.000
#> GSM447714 3 0.5334 0.267 0.436 0.000 0.512 0.052 0.000
#> GSM447717 5 0.4304 0.165 0.484 0.000 0.000 0.000 0.516
#> GSM447725 1 0.1205 0.875 0.956 0.000 0.000 0.004 0.040
#> GSM447729 4 0.2284 0.860 0.004 0.004 0.000 0.896 0.096
#> GSM447644 5 0.2179 0.826 0.112 0.000 0.000 0.000 0.888
#> GSM447710 3 0.5084 0.492 0.332 0.000 0.616 0.052 0.000
#> GSM447614 3 0.3805 0.782 0.044 0.000 0.840 0.068 0.048
#> GSM447685 2 0.0510 0.975 0.000 0.984 0.000 0.000 0.016
#> GSM447690 3 0.2067 0.831 0.028 0.000 0.928 0.012 0.032
#> GSM447730 2 0.0771 0.978 0.000 0.976 0.000 0.004 0.020
#> GSM447646 4 0.2124 0.860 0.000 0.004 0.000 0.900 0.096
#> GSM447689 1 0.1671 0.894 0.924 0.000 0.000 0.000 0.076
#> GSM447635 5 0.3999 0.547 0.344 0.000 0.000 0.000 0.656
#> GSM447641 1 0.1608 0.894 0.928 0.000 0.000 0.000 0.072
#> GSM447716 5 0.2568 0.843 0.032 0.048 0.000 0.016 0.904
#> GSM447718 1 0.1671 0.894 0.924 0.000 0.000 0.000 0.076
#> GSM447616 3 0.0290 0.858 0.000 0.000 0.992 0.008 0.000
#> GSM447626 1 0.2196 0.889 0.916 0.000 0.004 0.024 0.056
#> GSM447640 2 0.0000 0.983 0.000 1.000 0.000 0.000 0.000
#> GSM447734 3 0.0000 0.860 0.000 0.000 1.000 0.000 0.000
#> GSM447692 3 0.0000 0.860 0.000 0.000 1.000 0.000 0.000
#> GSM447647 4 0.4613 0.778 0.004 0.120 0.000 0.756 0.120
#> GSM447624 3 0.0000 0.860 0.000 0.000 1.000 0.000 0.000
#> GSM447625 1 0.3781 0.771 0.840 0.000 0.064 0.064 0.032
#> GSM447707 2 0.0794 0.976 0.000 0.972 0.000 0.000 0.028
#> GSM447732 3 0.5325 0.291 0.428 0.000 0.520 0.052 0.000
#> GSM447684 1 0.1965 0.879 0.904 0.000 0.000 0.000 0.096
#> GSM447731 4 0.2068 0.861 0.000 0.004 0.000 0.904 0.092
#> GSM447705 1 0.1671 0.894 0.924 0.000 0.000 0.000 0.076
#> GSM447631 3 0.0000 0.860 0.000 0.000 1.000 0.000 0.000
#> GSM447701 5 0.3752 0.644 0.000 0.292 0.000 0.000 0.708
#> GSM447645 3 0.0000 0.860 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
#> GSM447671 5 0.2670 0.8508 0.084 0.000 0.000 0.004 0.872 0.040
#> GSM447694 3 0.0000 0.7508 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447618 5 0.2451 0.8627 0.068 0.000 0.000 0.004 0.888 0.040
#> GSM447691 5 0.3009 0.8297 0.112 0.000 0.000 0.004 0.844 0.040
#> GSM447733 1 0.5771 -0.1207 0.444 0.000 0.000 0.380 0.000 0.176
#> GSM447620 5 0.0260 0.8921 0.000 0.008 0.000 0.000 0.992 0.000
#> GSM447627 3 0.4716 0.2582 0.004 0.000 0.552 0.040 0.000 0.404
#> GSM447630 1 0.1864 0.6944 0.924 0.000 0.000 0.004 0.032 0.040
#> GSM447642 1 0.1285 0.7085 0.944 0.000 0.000 0.000 0.004 0.052
#> GSM447649 2 0.1918 0.9162 0.000 0.904 0.000 0.008 0.000 0.088
#> GSM447654 4 0.1608 0.8471 0.004 0.004 0.000 0.940 0.036 0.016
#> GSM447655 2 0.0632 0.9331 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM447669 5 0.2821 0.8420 0.096 0.000 0.000 0.004 0.860 0.040
#> GSM447676 1 0.1349 0.7092 0.940 0.000 0.000 0.000 0.004 0.056
#> GSM447678 5 0.4979 0.5977 0.276 0.000 0.000 0.012 0.636 0.076
#> GSM447681 2 0.0935 0.9306 0.000 0.964 0.000 0.000 0.004 0.032
#> GSM447698 5 0.0972 0.8877 0.000 0.008 0.000 0.000 0.964 0.028
#> GSM447713 3 0.1285 0.7234 0.000 0.000 0.944 0.004 0.000 0.052
#> GSM447722 1 0.1787 0.6997 0.920 0.000 0.000 0.004 0.008 0.068
#> GSM447726 5 0.2364 0.8609 0.072 0.000 0.000 0.004 0.892 0.032
#> GSM447735 3 0.2442 0.6414 0.004 0.000 0.852 0.000 0.000 0.144
#> GSM447737 3 0.0000 0.7508 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447657 5 0.0972 0.8877 0.000 0.008 0.000 0.000 0.964 0.028
#> GSM447674 2 0.0632 0.9333 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM447636 5 0.1196 0.8844 0.008 0.000 0.000 0.000 0.952 0.040
#> GSM447723 1 0.1074 0.7140 0.960 0.000 0.000 0.000 0.012 0.028
#> GSM447699 3 0.5809 -0.2242 0.188 0.000 0.452 0.000 0.000 0.360
#> GSM447708 5 0.0260 0.8921 0.000 0.008 0.000 0.000 0.992 0.000
#> GSM447721 3 0.5879 -0.2402 0.208 0.000 0.448 0.000 0.000 0.344
#> GSM447623 3 0.0000 0.7508 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447621 3 0.0363 0.7450 0.000 0.000 0.988 0.000 0.000 0.012
#> GSM447650 2 0.1610 0.9216 0.000 0.916 0.000 0.000 0.000 0.084
#> GSM447651 2 0.0458 0.9340 0.000 0.984 0.000 0.000 0.000 0.016
#> GSM447653 4 0.4982 0.2250 0.048 0.000 0.008 0.488 0.000 0.456
#> GSM447658 1 0.4822 0.3119 0.620 0.000 0.000 0.004 0.308 0.068
#> GSM447675 4 0.2326 0.8142 0.028 0.000 0.000 0.900 0.012 0.060
#> GSM447680 2 0.1261 0.9203 0.000 0.952 0.000 0.000 0.024 0.024
#> GSM447686 5 0.0436 0.8921 0.004 0.004 0.000 0.000 0.988 0.004
#> GSM447736 1 0.3967 0.4656 0.632 0.000 0.012 0.000 0.000 0.356
#> GSM447629 5 0.0260 0.8921 0.000 0.008 0.000 0.000 0.992 0.000
#> GSM447648 3 0.0000 0.7508 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447660 1 0.4988 0.1182 0.552 0.000 0.000 0.004 0.380 0.064
#> GSM447661 2 0.0547 0.9329 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM447663 1 0.3748 0.5566 0.688 0.000 0.000 0.000 0.012 0.300
#> GSM447704 2 0.1663 0.9198 0.000 0.912 0.000 0.000 0.000 0.088
#> GSM447720 1 0.0964 0.7143 0.968 0.000 0.000 0.004 0.012 0.016
#> GSM447652 5 0.2796 0.8238 0.000 0.008 0.000 0.044 0.868 0.080
#> GSM447679 2 0.0363 0.9342 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM447712 1 0.2135 0.6817 0.872 0.000 0.000 0.000 0.000 0.128
#> GSM447664 5 0.2461 0.8432 0.004 0.000 0.000 0.064 0.888 0.044
#> GSM447637 3 0.0000 0.7508 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447639 1 0.2053 0.6924 0.888 0.000 0.000 0.000 0.004 0.108
#> GSM447615 1 0.1753 0.7071 0.912 0.000 0.000 0.000 0.004 0.084
#> GSM447656 5 0.0260 0.8921 0.000 0.008 0.000 0.000 0.992 0.000
#> GSM447673 2 0.4073 0.7963 0.000 0.772 0.000 0.012 0.088 0.128
#> GSM447719 6 0.6024 -0.3381 0.008 0.000 0.188 0.352 0.000 0.452
#> GSM447706 3 0.5943 -0.2958 0.224 0.000 0.432 0.000 0.000 0.344
#> GSM447612 1 0.3925 0.5094 0.656 0.000 0.004 0.000 0.008 0.332
#> GSM447665 5 0.0622 0.8915 0.000 0.008 0.000 0.000 0.980 0.012
#> GSM447677 2 0.1261 0.9203 0.000 0.952 0.000 0.000 0.024 0.024
#> GSM447613 1 0.3819 0.5201 0.672 0.000 0.012 0.000 0.000 0.316
#> GSM447659 3 0.4767 0.1756 0.004 0.000 0.512 0.040 0.000 0.444
#> GSM447662 1 0.5824 -0.0449 0.452 0.000 0.192 0.000 0.000 0.356
#> GSM447666 5 0.3096 0.8289 0.108 0.000 0.000 0.004 0.840 0.048
#> GSM447668 2 0.0865 0.9301 0.000 0.964 0.000 0.000 0.000 0.036
#> GSM447682 5 0.2134 0.8556 0.000 0.044 0.000 0.000 0.904 0.052
#> GSM447683 2 0.1261 0.9203 0.000 0.952 0.000 0.000 0.024 0.024
#> GSM447688 4 0.4786 0.6644 0.000 0.016 0.000 0.700 0.184 0.100
#> GSM447702 2 0.0547 0.9331 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM447709 5 0.2039 0.8403 0.000 0.076 0.000 0.000 0.904 0.020
#> GSM447711 1 0.3240 0.6011 0.752 0.000 0.004 0.000 0.000 0.244
#> GSM447715 5 0.4723 0.3293 0.408 0.000 0.000 0.004 0.548 0.040
#> GSM447693 3 0.0000 0.7508 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447611 4 0.2265 0.8162 0.028 0.000 0.000 0.904 0.012 0.056
#> GSM447672 2 0.0547 0.9331 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM447703 2 0.2070 0.9154 0.000 0.892 0.000 0.008 0.000 0.100
#> GSM447727 1 0.0964 0.7140 0.968 0.000 0.000 0.004 0.012 0.016
#> GSM447638 5 0.0520 0.8918 0.000 0.008 0.000 0.000 0.984 0.008
#> GSM447670 1 0.3738 0.5383 0.680 0.000 0.004 0.000 0.004 0.312
#> GSM447700 1 0.1857 0.6974 0.924 0.000 0.000 0.004 0.028 0.044
#> GSM447738 2 0.1387 0.9266 0.000 0.932 0.000 0.000 0.000 0.068
#> GSM447739 3 0.0935 0.7351 0.000 0.000 0.964 0.004 0.000 0.032
#> GSM447617 3 0.0000 0.7508 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447628 4 0.1483 0.8467 0.000 0.008 0.000 0.944 0.036 0.012
#> GSM447632 2 0.1556 0.9239 0.000 0.920 0.000 0.000 0.000 0.080
#> GSM447619 3 0.5812 -0.2278 0.192 0.000 0.460 0.000 0.000 0.348
#> GSM447643 5 0.0520 0.8918 0.000 0.008 0.000 0.000 0.984 0.008
#> GSM447724 1 0.2191 0.6879 0.876 0.000 0.000 0.000 0.004 0.120
#> GSM447728 2 0.3711 0.6277 0.000 0.720 0.000 0.000 0.260 0.020
#> GSM447610 3 0.4685 0.2881 0.004 0.000 0.568 0.040 0.000 0.388
#> GSM447633 5 0.2507 0.8574 0.072 0.000 0.000 0.004 0.884 0.040
#> GSM447634 1 0.2070 0.6967 0.892 0.000 0.000 0.000 0.008 0.100
#> GSM447622 3 0.0363 0.7450 0.000 0.000 0.988 0.000 0.000 0.012
#> GSM447667 5 0.0622 0.8898 0.000 0.012 0.000 0.000 0.980 0.008
#> GSM447687 2 0.2070 0.9154 0.000 0.892 0.000 0.008 0.000 0.100
#> GSM447695 3 0.3244 0.4574 0.000 0.000 0.732 0.000 0.000 0.268
#> GSM447696 3 0.0000 0.7508 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447697 3 0.0000 0.7508 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447714 1 0.6075 -0.3222 0.372 0.000 0.268 0.000 0.000 0.360
#> GSM447717 1 0.4829 0.2077 0.584 0.000 0.000 0.004 0.356 0.056
#> GSM447725 1 0.1387 0.7070 0.932 0.000 0.000 0.000 0.000 0.068
#> GSM447729 4 0.1299 0.8470 0.004 0.004 0.000 0.952 0.036 0.004
#> GSM447644 5 0.2670 0.8506 0.084 0.000 0.000 0.004 0.872 0.040
#> GSM447710 3 0.5907 -0.2696 0.212 0.000 0.436 0.000 0.000 0.352
#> GSM447614 3 0.4685 0.2881 0.004 0.000 0.568 0.040 0.000 0.388
#> GSM447685 2 0.1261 0.9203 0.000 0.952 0.000 0.000 0.024 0.024
#> GSM447690 3 0.2544 0.6413 0.004 0.000 0.852 0.004 0.000 0.140
#> GSM447730 2 0.2384 0.9126 0.000 0.884 0.000 0.032 0.000 0.084
#> GSM447646 4 0.1382 0.8470 0.000 0.008 0.000 0.948 0.036 0.008
#> GSM447689 1 0.0725 0.7151 0.976 0.000 0.000 0.000 0.012 0.012
#> GSM447635 1 0.4883 -0.1101 0.492 0.000 0.000 0.004 0.456 0.048
#> GSM447641 1 0.1010 0.7141 0.960 0.000 0.000 0.000 0.004 0.036
#> GSM447716 5 0.1003 0.8877 0.004 0.004 0.000 0.000 0.964 0.028
#> GSM447718 1 0.1036 0.7104 0.964 0.000 0.000 0.004 0.008 0.024
#> GSM447616 3 0.1007 0.7291 0.000 0.000 0.956 0.000 0.000 0.044
#> GSM447626 1 0.3748 0.5566 0.688 0.000 0.000 0.000 0.012 0.300
#> GSM447640 2 0.0547 0.9331 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM447734 3 0.0146 0.7491 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM447692 3 0.0000 0.7508 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447647 4 0.5062 0.6884 0.000 0.052 0.000 0.708 0.116 0.124
#> GSM447624 3 0.0000 0.7508 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447625 1 0.3819 0.4519 0.624 0.000 0.004 0.000 0.000 0.372
#> GSM447707 2 0.2070 0.9154 0.000 0.892 0.000 0.008 0.000 0.100
#> GSM447732 6 0.6116 -0.0906 0.300 0.000 0.340 0.000 0.000 0.360
#> GSM447684 1 0.2074 0.6904 0.912 0.000 0.000 0.004 0.036 0.048
#> GSM447731 4 0.1666 0.8454 0.008 0.000 0.000 0.936 0.036 0.020
#> GSM447705 1 0.1693 0.7060 0.932 0.000 0.000 0.004 0.020 0.044
#> GSM447631 3 0.0000 0.7508 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447701 5 0.2147 0.8357 0.000 0.084 0.000 0.000 0.896 0.020
#> GSM447645 3 0.0000 0.7508 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)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
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 gender(p) individual(p) disease.state(p) other(p) k
#> ATC:kmeans 130 0.581 0.794 0.567 0.0176 2
#> ATC:kmeans 115 0.346 0.557 0.712 0.2598 3
#> ATC:kmeans 106 0.514 0.852 0.816 0.1850 4
#> ATC:kmeans 119 0.667 0.967 0.763 0.0845 5
#> ATC:kmeans 107 0.315 0.866 0.659 0.0740 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "skmeans"]
# you can also extract it by
# res = res_list["ATC:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 130 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 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 0.994 0.997 0.5043 0.496 0.496
#> 3 3 1.000 0.957 0.975 0.1896 0.891 0.783
#> 4 4 0.933 0.875 0.950 0.1336 0.883 0.715
#> 5 5 0.869 0.802 0.914 0.0726 0.939 0.809
#> 6 6 0.920 0.835 0.925 0.0352 0.973 0.902
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 3 4
There is also optional best \(k\) = 2 3 4 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM447671 2 0.000 0.995 0.000 1.000
#> GSM447694 1 0.000 1.000 1.000 0.000
#> GSM447618 2 0.000 0.995 0.000 1.000
#> GSM447691 2 0.000 0.995 0.000 1.000
#> GSM447733 1 0.000 1.000 1.000 0.000
#> GSM447620 2 0.000 0.995 0.000 1.000
#> GSM447627 1 0.000 1.000 1.000 0.000
#> GSM447630 1 0.000 1.000 1.000 0.000
#> GSM447642 1 0.000 1.000 1.000 0.000
#> GSM447649 2 0.000 0.995 0.000 1.000
#> GSM447654 2 0.000 0.995 0.000 1.000
#> GSM447655 2 0.000 0.995 0.000 1.000
#> GSM447669 2 0.000 0.995 0.000 1.000
#> GSM447676 1 0.000 1.000 1.000 0.000
#> GSM447678 2 0.000 0.995 0.000 1.000
#> GSM447681 2 0.000 0.995 0.000 1.000
#> GSM447698 2 0.000 0.995 0.000 1.000
#> GSM447713 1 0.000 1.000 1.000 0.000
#> GSM447722 1 0.000 1.000 1.000 0.000
#> GSM447726 2 0.000 0.995 0.000 1.000
#> GSM447735 1 0.000 1.000 1.000 0.000
#> GSM447737 1 0.000 1.000 1.000 0.000
#> GSM447657 2 0.000 0.995 0.000 1.000
#> GSM447674 2 0.000 0.995 0.000 1.000
#> GSM447636 2 0.000 0.995 0.000 1.000
#> GSM447723 1 0.000 1.000 1.000 0.000
#> GSM447699 1 0.000 1.000 1.000 0.000
#> GSM447708 2 0.000 0.995 0.000 1.000
#> GSM447721 1 0.000 1.000 1.000 0.000
#> GSM447623 1 0.000 1.000 1.000 0.000
#> GSM447621 1 0.000 1.000 1.000 0.000
#> GSM447650 2 0.000 0.995 0.000 1.000
#> GSM447651 2 0.000 0.995 0.000 1.000
#> GSM447653 1 0.000 1.000 1.000 0.000
#> GSM447658 2 0.000 0.995 0.000 1.000
#> GSM447675 2 0.913 0.512 0.328 0.672
#> GSM447680 2 0.000 0.995 0.000 1.000
#> GSM447686 2 0.000 0.995 0.000 1.000
#> GSM447736 1 0.000 1.000 1.000 0.000
#> GSM447629 2 0.000 0.995 0.000 1.000
#> GSM447648 1 0.000 1.000 1.000 0.000
#> GSM447660 2 0.000 0.995 0.000 1.000
#> GSM447661 2 0.000 0.995 0.000 1.000
#> GSM447663 1 0.000 1.000 1.000 0.000
#> GSM447704 2 0.000 0.995 0.000 1.000
#> GSM447720 1 0.000 1.000 1.000 0.000
#> GSM447652 2 0.000 0.995 0.000 1.000
#> GSM447679 2 0.000 0.995 0.000 1.000
#> GSM447712 1 0.000 1.000 1.000 0.000
#> GSM447664 2 0.000 0.995 0.000 1.000
#> GSM447637 1 0.000 1.000 1.000 0.000
#> GSM447639 1 0.000 1.000 1.000 0.000
#> GSM447615 1 0.000 1.000 1.000 0.000
#> GSM447656 2 0.000 0.995 0.000 1.000
#> GSM447673 2 0.000 0.995 0.000 1.000
#> GSM447719 1 0.000 1.000 1.000 0.000
#> GSM447706 1 0.000 1.000 1.000 0.000
#> GSM447612 1 0.000 1.000 1.000 0.000
#> GSM447665 2 0.000 0.995 0.000 1.000
#> GSM447677 2 0.000 0.995 0.000 1.000
#> GSM447613 1 0.000 1.000 1.000 0.000
#> GSM447659 1 0.000 1.000 1.000 0.000
#> GSM447662 1 0.000 1.000 1.000 0.000
#> GSM447666 2 0.000 0.995 0.000 1.000
#> GSM447668 2 0.000 0.995 0.000 1.000
#> GSM447682 2 0.000 0.995 0.000 1.000
#> GSM447683 2 0.000 0.995 0.000 1.000
#> GSM447688 2 0.000 0.995 0.000 1.000
#> GSM447702 2 0.000 0.995 0.000 1.000
#> GSM447709 2 0.000 0.995 0.000 1.000
#> GSM447711 1 0.000 1.000 1.000 0.000
#> GSM447715 2 0.000 0.995 0.000 1.000
#> GSM447693 1 0.000 1.000 1.000 0.000
#> GSM447611 2 0.000 0.995 0.000 1.000
#> GSM447672 2 0.000 0.995 0.000 1.000
#> GSM447703 2 0.000 0.995 0.000 1.000
#> GSM447727 1 0.000 1.000 1.000 0.000
#> GSM447638 2 0.000 0.995 0.000 1.000
#> GSM447670 1 0.000 1.000 1.000 0.000
#> GSM447700 1 0.000 1.000 1.000 0.000
#> GSM447738 2 0.000 0.995 0.000 1.000
#> GSM447739 1 0.000 1.000 1.000 0.000
#> GSM447617 1 0.000 1.000 1.000 0.000
#> GSM447628 2 0.000 0.995 0.000 1.000
#> GSM447632 2 0.000 0.995 0.000 1.000
#> GSM447619 1 0.000 1.000 1.000 0.000
#> GSM447643 2 0.000 0.995 0.000 1.000
#> GSM447724 1 0.000 1.000 1.000 0.000
#> GSM447728 2 0.000 0.995 0.000 1.000
#> GSM447610 1 0.000 1.000 1.000 0.000
#> GSM447633 2 0.000 0.995 0.000 1.000
#> GSM447634 1 0.000 1.000 1.000 0.000
#> GSM447622 1 0.000 1.000 1.000 0.000
#> GSM447667 2 0.000 0.995 0.000 1.000
#> GSM447687 2 0.000 0.995 0.000 1.000
#> GSM447695 1 0.000 1.000 1.000 0.000
#> GSM447696 1 0.000 1.000 1.000 0.000
#> GSM447697 1 0.000 1.000 1.000 0.000
#> GSM447714 1 0.000 1.000 1.000 0.000
#> GSM447717 2 0.000 0.995 0.000 1.000
#> GSM447725 1 0.000 1.000 1.000 0.000
#> GSM447729 2 0.000 0.995 0.000 1.000
#> GSM447644 2 0.000 0.995 0.000 1.000
#> GSM447710 1 0.000 1.000 1.000 0.000
#> GSM447614 1 0.000 1.000 1.000 0.000
#> GSM447685 2 0.000 0.995 0.000 1.000
#> GSM447690 1 0.000 1.000 1.000 0.000
#> GSM447730 2 0.000 0.995 0.000 1.000
#> GSM447646 2 0.000 0.995 0.000 1.000
#> GSM447689 1 0.000 1.000 1.000 0.000
#> GSM447635 2 0.000 0.995 0.000 1.000
#> GSM447641 1 0.000 1.000 1.000 0.000
#> GSM447716 2 0.000 0.995 0.000 1.000
#> GSM447718 1 0.000 1.000 1.000 0.000
#> GSM447616 1 0.000 1.000 1.000 0.000
#> GSM447626 1 0.000 1.000 1.000 0.000
#> GSM447640 2 0.000 0.995 0.000 1.000
#> GSM447734 1 0.000 1.000 1.000 0.000
#> GSM447692 1 0.000 1.000 1.000 0.000
#> GSM447647 2 0.000 0.995 0.000 1.000
#> GSM447624 1 0.000 1.000 1.000 0.000
#> GSM447625 1 0.000 1.000 1.000 0.000
#> GSM447707 2 0.000 0.995 0.000 1.000
#> GSM447732 1 0.000 1.000 1.000 0.000
#> GSM447684 1 0.000 1.000 1.000 0.000
#> GSM447731 2 0.000 0.995 0.000 1.000
#> GSM447705 1 0.000 1.000 1.000 0.000
#> GSM447631 1 0.000 1.000 1.000 0.000
#> GSM447701 2 0.000 0.995 0.000 1.000
#> GSM447645 1 0.000 1.000 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM447671 2 0.1860 0.952 0.000 0.948 0.052
#> GSM447694 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447618 2 0.1289 0.967 0.000 0.968 0.032
#> GSM447691 2 0.1860 0.952 0.000 0.948 0.052
#> GSM447733 3 0.1860 0.869 0.052 0.000 0.948
#> GSM447620 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447627 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447630 1 0.2599 0.925 0.932 0.016 0.052
#> GSM447642 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447649 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447654 3 0.1860 0.906 0.000 0.052 0.948
#> GSM447655 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447669 2 0.1860 0.952 0.000 0.948 0.052
#> GSM447676 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447678 3 0.2261 0.902 0.000 0.068 0.932
#> GSM447681 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447698 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447713 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447722 1 0.4842 0.700 0.776 0.000 0.224
#> GSM447726 2 0.1860 0.952 0.000 0.948 0.052
#> GSM447735 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447737 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447657 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447674 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447636 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447723 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447699 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447708 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447721 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447623 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447621 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447650 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447651 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447653 3 0.4702 0.710 0.212 0.000 0.788
#> GSM447658 3 0.5291 0.680 0.000 0.268 0.732
#> GSM447675 3 0.2063 0.902 0.008 0.044 0.948
#> GSM447680 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447686 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447736 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447629 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447648 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447660 3 0.5363 0.613 0.000 0.276 0.724
#> GSM447661 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447663 1 0.0747 0.974 0.984 0.000 0.016
#> GSM447704 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447720 1 0.0747 0.974 0.984 0.000 0.016
#> GSM447652 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447679 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447712 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447664 3 0.2261 0.902 0.000 0.068 0.932
#> GSM447637 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447639 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447615 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447656 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447673 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447719 3 0.6235 0.237 0.436 0.000 0.564
#> GSM447706 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447612 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447665 2 0.0892 0.976 0.000 0.980 0.020
#> GSM447677 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447613 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447659 1 0.3340 0.856 0.880 0.000 0.120
#> GSM447662 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447666 2 0.1860 0.952 0.000 0.948 0.052
#> GSM447668 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447682 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447683 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447688 3 0.2537 0.896 0.000 0.080 0.920
#> GSM447702 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447709 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447711 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447715 2 0.1860 0.952 0.000 0.948 0.052
#> GSM447693 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447611 3 0.1860 0.906 0.000 0.052 0.948
#> GSM447672 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447703 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447727 1 0.0747 0.974 0.984 0.000 0.016
#> GSM447638 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447670 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447700 1 0.1860 0.942 0.948 0.000 0.052
#> GSM447738 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447739 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447617 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447628 3 0.1860 0.906 0.000 0.052 0.948
#> GSM447632 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447619 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447643 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447724 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447728 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447610 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447633 2 0.1860 0.952 0.000 0.948 0.052
#> GSM447634 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447622 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447667 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447687 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447695 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447696 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447697 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447714 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447717 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447725 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447729 3 0.1860 0.906 0.000 0.052 0.948
#> GSM447644 2 0.1860 0.952 0.000 0.948 0.052
#> GSM447710 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447614 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447685 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447690 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447730 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447646 3 0.1860 0.906 0.000 0.052 0.948
#> GSM447689 1 0.0747 0.974 0.984 0.000 0.016
#> GSM447635 2 0.1860 0.952 0.000 0.948 0.052
#> GSM447641 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447716 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447718 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447616 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447626 1 0.0747 0.974 0.984 0.000 0.016
#> GSM447640 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447734 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447692 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447647 3 0.2537 0.896 0.000 0.080 0.920
#> GSM447624 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447625 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447707 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447732 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447684 1 0.5207 0.768 0.824 0.124 0.052
#> GSM447731 3 0.1860 0.906 0.000 0.052 0.948
#> GSM447705 1 0.1860 0.942 0.948 0.000 0.052
#> GSM447631 1 0.0000 0.986 1.000 0.000 0.000
#> GSM447701 2 0.0000 0.989 0.000 1.000 0.000
#> GSM447645 1 0.0000 0.986 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM447671 1 0.3266 0.5936 0.832 0.168 0.000 0.000
#> GSM447694 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447618 2 0.3688 0.7237 0.208 0.792 0.000 0.000
#> GSM447691 1 0.1118 0.7168 0.964 0.036 0.000 0.000
#> GSM447733 4 0.0336 0.7780 0.000 0.000 0.008 0.992
#> GSM447620 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447627 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447630 1 0.0000 0.7117 1.000 0.000 0.000 0.000
#> GSM447642 3 0.1022 0.9588 0.032 0.000 0.968 0.000
#> GSM447649 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447654 4 0.0000 0.7825 0.000 0.000 0.000 1.000
#> GSM447655 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447669 1 0.1940 0.6983 0.924 0.076 0.000 0.000
#> GSM447676 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447678 4 0.2530 0.7299 0.000 0.112 0.000 0.888
#> GSM447681 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447698 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447713 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447722 3 0.4840 0.6242 0.028 0.000 0.732 0.240
#> GSM447726 1 0.2081 0.6949 0.916 0.084 0.000 0.000
#> GSM447735 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447737 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447657 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447674 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447636 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447723 3 0.1792 0.9223 0.068 0.000 0.932 0.000
#> GSM447699 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447708 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447721 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447623 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447621 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447650 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447651 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447653 4 0.1637 0.7320 0.000 0.000 0.060 0.940
#> GSM447658 4 0.5998 0.5438 0.088 0.248 0.000 0.664
#> GSM447675 4 0.0000 0.7825 0.000 0.000 0.000 1.000
#> GSM447680 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447686 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447736 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447629 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447648 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447660 1 0.5924 0.1065 0.556 0.040 0.000 0.404
#> GSM447661 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447663 1 0.4972 0.2922 0.544 0.000 0.456 0.000
#> GSM447704 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447720 1 0.4948 0.3306 0.560 0.000 0.440 0.000
#> GSM447652 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447679 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447712 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447664 4 0.3873 0.6479 0.000 0.228 0.000 0.772
#> GSM447637 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447639 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447615 3 0.0469 0.9778 0.012 0.000 0.988 0.000
#> GSM447656 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447673 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447719 4 0.4994 0.0272 0.000 0.000 0.480 0.520
#> GSM447706 3 0.0336 0.9810 0.008 0.000 0.992 0.000
#> GSM447612 3 0.0469 0.9769 0.012 0.000 0.988 0.000
#> GSM447665 2 0.4164 0.6335 0.264 0.736 0.000 0.000
#> GSM447677 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447613 3 0.0336 0.9810 0.008 0.000 0.992 0.000
#> GSM447659 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447662 3 0.0188 0.9838 0.004 0.000 0.996 0.000
#> GSM447666 1 0.1022 0.7174 0.968 0.032 0.000 0.000
#> GSM447668 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447682 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447683 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447688 4 0.4941 0.3367 0.000 0.436 0.000 0.564
#> GSM447702 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447709 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447711 3 0.0336 0.9810 0.008 0.000 0.992 0.000
#> GSM447715 1 0.1792 0.7035 0.932 0.068 0.000 0.000
#> GSM447693 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447611 4 0.0000 0.7825 0.000 0.000 0.000 1.000
#> GSM447672 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447703 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447727 1 0.4989 0.2429 0.528 0.000 0.472 0.000
#> GSM447638 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447670 3 0.0921 0.9628 0.028 0.000 0.972 0.000
#> GSM447700 1 0.0000 0.7117 1.000 0.000 0.000 0.000
#> GSM447738 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447739 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447617 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447628 4 0.0000 0.7825 0.000 0.000 0.000 1.000
#> GSM447632 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447619 3 0.0188 0.9838 0.004 0.000 0.996 0.000
#> GSM447643 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447724 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447728 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447610 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447633 1 0.1940 0.6983 0.924 0.076 0.000 0.000
#> GSM447634 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447622 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447667 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447687 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447695 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447696 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447697 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447714 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447717 2 0.1211 0.9413 0.040 0.960 0.000 0.000
#> GSM447725 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447729 4 0.0000 0.7825 0.000 0.000 0.000 1.000
#> GSM447644 1 0.1557 0.7103 0.944 0.056 0.000 0.000
#> GSM447710 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447614 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447685 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447690 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447730 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447646 4 0.0000 0.7825 0.000 0.000 0.000 1.000
#> GSM447689 1 0.4955 0.3218 0.556 0.000 0.444 0.000
#> GSM447635 1 0.0707 0.7165 0.980 0.020 0.000 0.000
#> GSM447641 3 0.1940 0.9096 0.076 0.000 0.924 0.000
#> GSM447716 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447718 3 0.2281 0.8880 0.096 0.000 0.904 0.000
#> GSM447616 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447626 1 0.4967 0.3022 0.548 0.000 0.452 0.000
#> GSM447640 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447734 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447692 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447647 4 0.4898 0.3883 0.000 0.416 0.000 0.584
#> GSM447624 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447625 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447707 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447732 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447684 1 0.0000 0.7117 1.000 0.000 0.000 0.000
#> GSM447731 4 0.0000 0.7825 0.000 0.000 0.000 1.000
#> GSM447705 1 0.0000 0.7117 1.000 0.000 0.000 0.000
#> GSM447631 3 0.0000 0.9864 0.000 0.000 1.000 0.000
#> GSM447701 2 0.0000 0.9863 0.000 1.000 0.000 0.000
#> GSM447645 3 0.0000 0.9864 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
#> GSM447671 5 0.1121 0.7664 0.000 0.044 0.000 0.000 0.956
#> GSM447694 3 0.0000 0.9119 0.000 0.000 1.000 0.000 0.000
#> GSM447618 5 0.4126 0.4212 0.000 0.380 0.000 0.000 0.620
#> GSM447691 5 0.0324 0.7755 0.004 0.004 0.000 0.000 0.992
#> GSM447733 4 0.0404 0.8241 0.000 0.000 0.012 0.988 0.000
#> GSM447620 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447627 3 0.0162 0.9107 0.004 0.000 0.996 0.000 0.000
#> GSM447630 1 0.4182 0.3671 0.600 0.000 0.000 0.000 0.400
#> GSM447642 1 0.4562 -0.1730 0.500 0.000 0.492 0.000 0.008
#> GSM447649 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447654 4 0.0000 0.8335 0.000 0.000 0.000 1.000 0.000
#> GSM447655 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447669 5 0.0404 0.7776 0.000 0.012 0.000 0.000 0.988
#> GSM447676 3 0.3160 0.7374 0.188 0.000 0.808 0.000 0.004
#> GSM447678 4 0.1845 0.7918 0.000 0.016 0.000 0.928 0.056
#> GSM447681 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447698 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447713 3 0.0162 0.9107 0.004 0.000 0.996 0.000 0.000
#> GSM447722 3 0.6210 -0.0106 0.360 0.000 0.492 0.148 0.000
#> GSM447726 5 0.3876 0.5763 0.032 0.192 0.000 0.000 0.776
#> GSM447735 3 0.0162 0.9107 0.004 0.000 0.996 0.000 0.000
#> GSM447737 3 0.0000 0.9119 0.000 0.000 1.000 0.000 0.000
#> GSM447657 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447674 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447636 2 0.0162 0.9846 0.004 0.996 0.000 0.000 0.000
#> GSM447723 1 0.2690 0.6015 0.844 0.000 0.156 0.000 0.000
#> GSM447699 3 0.0000 0.9119 0.000 0.000 1.000 0.000 0.000
#> GSM447708 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447721 3 0.0000 0.9119 0.000 0.000 1.000 0.000 0.000
#> GSM447623 3 0.0000 0.9119 0.000 0.000 1.000 0.000 0.000
#> GSM447621 3 0.0000 0.9119 0.000 0.000 1.000 0.000 0.000
#> GSM447650 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447651 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447653 4 0.2338 0.7012 0.004 0.000 0.112 0.884 0.000
#> GSM447658 5 0.7010 0.2073 0.396 0.024 0.000 0.176 0.404
#> GSM447675 4 0.0000 0.8335 0.000 0.000 0.000 1.000 0.000
#> GSM447680 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447686 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447736 3 0.0000 0.9119 0.000 0.000 1.000 0.000 0.000
#> GSM447629 2 0.0290 0.9802 0.000 0.992 0.000 0.000 0.008
#> GSM447648 3 0.0000 0.9119 0.000 0.000 1.000 0.000 0.000
#> GSM447660 5 0.3039 0.6606 0.192 0.000 0.000 0.000 0.808
#> GSM447661 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447663 1 0.3710 0.6677 0.808 0.000 0.048 0.000 0.144
#> GSM447704 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447720 1 0.3649 0.6650 0.808 0.000 0.040 0.000 0.152
#> GSM447652 2 0.0162 0.9844 0.000 0.996 0.000 0.004 0.000
#> GSM447679 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447712 3 0.1851 0.8429 0.088 0.000 0.912 0.000 0.000
#> GSM447664 4 0.3730 0.5538 0.000 0.288 0.000 0.712 0.000
#> GSM447637 3 0.0000 0.9119 0.000 0.000 1.000 0.000 0.000
#> GSM447639 3 0.0162 0.9107 0.004 0.000 0.996 0.000 0.000
#> GSM447615 3 0.4397 0.2873 0.432 0.000 0.564 0.000 0.004
#> GSM447656 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447673 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447719 3 0.4276 0.4185 0.004 0.000 0.616 0.380 0.000
#> GSM447706 3 0.3913 0.4971 0.324 0.000 0.676 0.000 0.000
#> GSM447612 3 0.3966 0.4385 0.336 0.000 0.664 0.000 0.000
#> GSM447665 5 0.3074 0.6406 0.000 0.196 0.000 0.000 0.804
#> GSM447677 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447613 3 0.4015 0.4544 0.348 0.000 0.652 0.000 0.000
#> GSM447659 3 0.0162 0.9107 0.004 0.000 0.996 0.000 0.000
#> GSM447662 3 0.1908 0.8369 0.092 0.000 0.908 0.000 0.000
#> GSM447666 5 0.0324 0.7755 0.004 0.004 0.000 0.000 0.992
#> GSM447668 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447682 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447683 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447688 4 0.4088 0.4641 0.000 0.368 0.000 0.632 0.000
#> GSM447702 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447709 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447711 3 0.3876 0.5157 0.316 0.000 0.684 0.000 0.000
#> GSM447715 1 0.6351 0.2334 0.500 0.184 0.000 0.000 0.316
#> GSM447693 3 0.0000 0.9119 0.000 0.000 1.000 0.000 0.000
#> GSM447611 4 0.0000 0.8335 0.000 0.000 0.000 1.000 0.000
#> GSM447672 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447703 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447727 1 0.3141 0.6591 0.852 0.000 0.040 0.000 0.108
#> GSM447638 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447670 3 0.4397 0.2750 0.432 0.000 0.564 0.000 0.004
#> GSM447700 1 0.4307 0.1372 0.504 0.000 0.000 0.000 0.496
#> GSM447738 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447739 3 0.0162 0.9107 0.004 0.000 0.996 0.000 0.000
#> GSM447617 3 0.0000 0.9119 0.000 0.000 1.000 0.000 0.000
#> GSM447628 4 0.0000 0.8335 0.000 0.000 0.000 1.000 0.000
#> GSM447632 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447619 3 0.1792 0.8437 0.084 0.000 0.916 0.000 0.000
#> GSM447643 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447724 3 0.0162 0.9107 0.004 0.000 0.996 0.000 0.000
#> GSM447728 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447610 3 0.0162 0.9107 0.004 0.000 0.996 0.000 0.000
#> GSM447633 5 0.0290 0.7774 0.000 0.008 0.000 0.000 0.992
#> GSM447634 3 0.0000 0.9119 0.000 0.000 1.000 0.000 0.000
#> GSM447622 3 0.0000 0.9119 0.000 0.000 1.000 0.000 0.000
#> GSM447667 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447687 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447695 3 0.0000 0.9119 0.000 0.000 1.000 0.000 0.000
#> GSM447696 3 0.0162 0.9107 0.004 0.000 0.996 0.000 0.000
#> GSM447697 3 0.0000 0.9119 0.000 0.000 1.000 0.000 0.000
#> GSM447714 3 0.0000 0.9119 0.000 0.000 1.000 0.000 0.000
#> GSM447717 2 0.4473 0.3242 0.412 0.580 0.000 0.000 0.008
#> GSM447725 3 0.1792 0.8533 0.084 0.000 0.916 0.000 0.000
#> GSM447729 4 0.0000 0.8335 0.000 0.000 0.000 1.000 0.000
#> GSM447644 5 0.0324 0.7755 0.004 0.004 0.000 0.000 0.992
#> GSM447710 3 0.0000 0.9119 0.000 0.000 1.000 0.000 0.000
#> GSM447614 3 0.0162 0.9107 0.004 0.000 0.996 0.000 0.000
#> GSM447685 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447690 3 0.0162 0.9107 0.004 0.000 0.996 0.000 0.000
#> GSM447730 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447646 4 0.0000 0.8335 0.000 0.000 0.000 1.000 0.000
#> GSM447689 1 0.3521 0.6675 0.820 0.000 0.040 0.000 0.140
#> GSM447635 5 0.0000 0.7715 0.000 0.000 0.000 0.000 1.000
#> GSM447641 1 0.4562 -0.1730 0.500 0.000 0.492 0.000 0.008
#> GSM447716 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447718 1 0.2843 0.6108 0.848 0.000 0.144 0.000 0.008
#> GSM447616 3 0.0000 0.9119 0.000 0.000 1.000 0.000 0.000
#> GSM447626 1 0.3551 0.6684 0.820 0.000 0.044 0.000 0.136
#> GSM447640 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447734 3 0.0000 0.9119 0.000 0.000 1.000 0.000 0.000
#> GSM447692 3 0.0000 0.9119 0.000 0.000 1.000 0.000 0.000
#> GSM447647 4 0.4114 0.4533 0.000 0.376 0.000 0.624 0.000
#> GSM447624 3 0.0000 0.9119 0.000 0.000 1.000 0.000 0.000
#> GSM447625 3 0.0000 0.9119 0.000 0.000 1.000 0.000 0.000
#> GSM447707 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447732 3 0.0000 0.9119 0.000 0.000 1.000 0.000 0.000
#> GSM447684 1 0.3774 0.5398 0.704 0.000 0.000 0.000 0.296
#> GSM447731 4 0.0000 0.8335 0.000 0.000 0.000 1.000 0.000
#> GSM447705 1 0.3266 0.6213 0.796 0.000 0.004 0.000 0.200
#> GSM447631 3 0.0162 0.9107 0.004 0.000 0.996 0.000 0.000
#> GSM447701 2 0.0000 0.9884 0.000 1.000 0.000 0.000 0.000
#> GSM447645 3 0.0000 0.9119 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
#> GSM447671 5 0.0260 0.8681 0.000 0.008 0.000 0.000 0.992 0.000
#> GSM447694 3 0.0000 0.9014 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447618 5 0.3046 0.6215 0.012 0.188 0.000 0.000 0.800 0.000
#> GSM447691 5 0.0146 0.8682 0.000 0.000 0.000 0.000 0.996 0.004
#> GSM447733 4 0.1268 0.7483 0.036 0.000 0.000 0.952 0.004 0.008
#> GSM447620 2 0.0692 0.9805 0.004 0.976 0.000 0.000 0.020 0.000
#> GSM447627 3 0.1788 0.8735 0.040 0.000 0.928 0.000 0.004 0.028
#> GSM447630 6 0.2964 0.6543 0.004 0.000 0.000 0.000 0.204 0.792
#> GSM447642 1 0.1616 0.8968 0.932 0.000 0.048 0.000 0.000 0.020
#> GSM447649 2 0.0146 0.9869 0.004 0.996 0.000 0.000 0.000 0.000
#> GSM447654 4 0.0000 0.7754 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM447655 2 0.0000 0.9878 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447669 5 0.0146 0.8699 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM447676 1 0.1714 0.8377 0.908 0.000 0.092 0.000 0.000 0.000
#> GSM447678 4 0.3478 0.6924 0.024 0.084 0.000 0.836 0.052 0.004
#> GSM447681 2 0.0260 0.9862 0.008 0.992 0.000 0.000 0.000 0.000
#> GSM447698 2 0.0260 0.9862 0.008 0.992 0.000 0.000 0.000 0.000
#> GSM447713 3 0.0291 0.9003 0.004 0.000 0.992 0.000 0.000 0.004
#> GSM447722 3 0.6625 0.1695 0.040 0.000 0.496 0.112 0.028 0.324
#> GSM447726 5 0.3626 0.7452 0.012 0.084 0.000 0.000 0.812 0.092
#> GSM447735 3 0.1716 0.8754 0.036 0.000 0.932 0.000 0.004 0.028
#> GSM447737 3 0.0146 0.9018 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM447657 2 0.0260 0.9862 0.008 0.992 0.000 0.000 0.000 0.000
#> GSM447674 2 0.0000 0.9878 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447636 2 0.0260 0.9854 0.008 0.992 0.000 0.000 0.000 0.000
#> GSM447723 6 0.1398 0.7969 0.008 0.000 0.052 0.000 0.000 0.940
#> GSM447699 3 0.0146 0.9018 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM447708 2 0.0909 0.9767 0.012 0.968 0.000 0.000 0.020 0.000
#> GSM447721 3 0.0520 0.8993 0.008 0.000 0.984 0.000 0.000 0.008
#> GSM447623 3 0.0146 0.9018 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM447621 3 0.0146 0.9018 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM447650 2 0.0000 0.9878 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447651 2 0.0000 0.9878 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447653 4 0.3751 0.5954 0.052 0.000 0.100 0.816 0.004 0.028
#> GSM447658 1 0.1854 0.8791 0.932 0.004 0.000 0.020 0.028 0.016
#> GSM447675 4 0.0146 0.7741 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM447680 2 0.0603 0.9828 0.004 0.980 0.000 0.000 0.016 0.000
#> GSM447686 2 0.0820 0.9795 0.012 0.972 0.000 0.000 0.016 0.000
#> GSM447736 3 0.0146 0.9018 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM447629 2 0.1225 0.9624 0.012 0.952 0.000 0.000 0.036 0.000
#> GSM447648 3 0.0146 0.9018 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM447660 1 0.2053 0.8434 0.888 0.000 0.000 0.000 0.108 0.004
#> GSM447661 2 0.0000 0.9878 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447663 6 0.1296 0.8168 0.004 0.000 0.032 0.000 0.012 0.952
#> GSM447704 2 0.0146 0.9869 0.004 0.996 0.000 0.000 0.000 0.000
#> GSM447720 6 0.0993 0.8175 0.000 0.000 0.024 0.000 0.012 0.964
#> GSM447652 2 0.0717 0.9718 0.008 0.976 0.000 0.016 0.000 0.000
#> GSM447679 2 0.0000 0.9878 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447712 3 0.1719 0.8692 0.060 0.000 0.924 0.000 0.000 0.016
#> GSM447664 4 0.4004 0.4614 0.012 0.368 0.000 0.620 0.000 0.000
#> GSM447637 3 0.0146 0.9018 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM447639 3 0.1793 0.8749 0.036 0.000 0.928 0.000 0.004 0.032
#> GSM447615 3 0.5462 0.0794 0.400 0.000 0.476 0.000 0.000 0.124
#> GSM447656 2 0.0909 0.9767 0.012 0.968 0.000 0.000 0.020 0.000
#> GSM447673 2 0.0146 0.9869 0.004 0.996 0.000 0.000 0.000 0.000
#> GSM447719 3 0.5472 0.2913 0.052 0.000 0.528 0.388 0.004 0.028
#> GSM447706 3 0.3741 0.5391 0.008 0.000 0.672 0.000 0.000 0.320
#> GSM447612 6 0.3996 -0.0565 0.004 0.000 0.484 0.000 0.000 0.512
#> GSM447665 5 0.0937 0.8433 0.000 0.040 0.000 0.000 0.960 0.000
#> GSM447677 2 0.0603 0.9828 0.004 0.980 0.000 0.000 0.016 0.000
#> GSM447613 3 0.3711 0.6311 0.020 0.000 0.720 0.000 0.000 0.260
#> GSM447659 3 0.1989 0.8662 0.052 0.000 0.916 0.000 0.004 0.028
#> GSM447662 3 0.3652 0.5323 0.004 0.000 0.672 0.000 0.000 0.324
#> GSM447666 5 0.0790 0.8558 0.000 0.000 0.000 0.000 0.968 0.032
#> GSM447668 2 0.0603 0.9828 0.004 0.980 0.000 0.000 0.016 0.000
#> GSM447682 2 0.0000 0.9878 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447683 2 0.0692 0.9805 0.004 0.976 0.000 0.000 0.020 0.000
#> GSM447688 4 0.4116 0.4162 0.012 0.416 0.000 0.572 0.000 0.000
#> GSM447702 2 0.0000 0.9878 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447709 2 0.0692 0.9805 0.004 0.976 0.000 0.000 0.020 0.000
#> GSM447711 3 0.2512 0.8423 0.060 0.000 0.880 0.000 0.000 0.060
#> GSM447715 5 0.5644 0.0930 0.020 0.088 0.000 0.000 0.468 0.424
#> GSM447693 3 0.0146 0.9018 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM447611 4 0.0000 0.7754 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM447672 2 0.0000 0.9878 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447703 2 0.0146 0.9869 0.004 0.996 0.000 0.000 0.000 0.000
#> GSM447727 6 0.1036 0.8151 0.008 0.000 0.024 0.000 0.004 0.964
#> GSM447638 2 0.0508 0.9845 0.004 0.984 0.000 0.000 0.012 0.000
#> GSM447670 3 0.5421 0.2719 0.132 0.000 0.528 0.000 0.000 0.340
#> GSM447700 6 0.3499 0.4674 0.000 0.000 0.000 0.000 0.320 0.680
#> GSM447738 2 0.0000 0.9878 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447739 3 0.0806 0.8952 0.020 0.000 0.972 0.000 0.000 0.008
#> GSM447617 3 0.0146 0.9018 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM447628 4 0.0260 0.7750 0.008 0.000 0.000 0.992 0.000 0.000
#> GSM447632 2 0.0146 0.9869 0.004 0.996 0.000 0.000 0.000 0.000
#> GSM447619 3 0.3508 0.5886 0.004 0.000 0.704 0.000 0.000 0.292
#> GSM447643 2 0.0603 0.9828 0.004 0.980 0.000 0.000 0.016 0.000
#> GSM447724 3 0.1719 0.8774 0.032 0.000 0.932 0.000 0.004 0.032
#> GSM447728 2 0.0622 0.9837 0.008 0.980 0.000 0.000 0.012 0.000
#> GSM447610 3 0.1788 0.8733 0.040 0.000 0.928 0.000 0.004 0.028
#> GSM447633 5 0.0146 0.8699 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM447634 3 0.0260 0.9005 0.008 0.000 0.992 0.000 0.000 0.000
#> GSM447622 3 0.0146 0.9018 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM447667 2 0.0260 0.9868 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM447687 2 0.0146 0.9869 0.004 0.996 0.000 0.000 0.000 0.000
#> GSM447695 3 0.0146 0.9018 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM447696 3 0.0291 0.9003 0.004 0.000 0.992 0.000 0.000 0.004
#> GSM447697 3 0.0291 0.9003 0.004 0.000 0.992 0.000 0.000 0.004
#> GSM447714 3 0.0146 0.9018 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM447717 1 0.1719 0.8470 0.924 0.060 0.000 0.000 0.000 0.016
#> GSM447725 3 0.3621 0.7386 0.192 0.000 0.772 0.000 0.004 0.032
#> GSM447729 4 0.0260 0.7750 0.008 0.000 0.000 0.992 0.000 0.000
#> GSM447644 5 0.0146 0.8682 0.000 0.000 0.000 0.000 0.996 0.004
#> GSM447710 3 0.0713 0.8921 0.000 0.000 0.972 0.000 0.000 0.028
#> GSM447614 3 0.1788 0.8733 0.040 0.000 0.928 0.000 0.004 0.028
#> GSM447685 2 0.0405 0.9858 0.004 0.988 0.000 0.000 0.008 0.000
#> GSM447690 3 0.1716 0.8755 0.036 0.000 0.932 0.000 0.004 0.028
#> GSM447730 2 0.0146 0.9869 0.004 0.996 0.000 0.000 0.000 0.000
#> GSM447646 4 0.0260 0.7750 0.008 0.000 0.000 0.992 0.000 0.000
#> GSM447689 6 0.1053 0.8163 0.004 0.000 0.020 0.000 0.012 0.964
#> GSM447635 5 0.0260 0.8646 0.008 0.000 0.000 0.000 0.992 0.000
#> GSM447641 1 0.2030 0.8874 0.908 0.000 0.064 0.000 0.000 0.028
#> GSM447716 2 0.0260 0.9862 0.008 0.992 0.000 0.000 0.000 0.000
#> GSM447718 6 0.1408 0.8037 0.020 0.000 0.036 0.000 0.000 0.944
#> GSM447616 3 0.0146 0.9018 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM447626 6 0.1218 0.8177 0.004 0.000 0.028 0.000 0.012 0.956
#> GSM447640 2 0.0000 0.9878 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447734 3 0.0146 0.9018 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM447692 3 0.0291 0.9003 0.004 0.000 0.992 0.000 0.000 0.004
#> GSM447647 4 0.4123 0.4065 0.012 0.420 0.000 0.568 0.000 0.000
#> GSM447624 3 0.0146 0.9018 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM447625 3 0.0260 0.9007 0.008 0.000 0.992 0.000 0.000 0.000
#> GSM447707 2 0.0146 0.9869 0.004 0.996 0.000 0.000 0.000 0.000
#> GSM447732 3 0.0547 0.8960 0.000 0.000 0.980 0.000 0.000 0.020
#> GSM447684 6 0.3288 0.5556 0.000 0.000 0.000 0.000 0.276 0.724
#> GSM447731 4 0.0000 0.7754 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM447705 6 0.0865 0.7989 0.000 0.000 0.000 0.000 0.036 0.964
#> GSM447631 3 0.0000 0.9014 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM447701 2 0.0909 0.9767 0.012 0.968 0.000 0.000 0.020 0.000
#> GSM447645 3 0.0000 0.9014 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)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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 gender(p) individual(p) disease.state(p) other(p) k
#> ATC:skmeans 130 0.581 0.794 0.567 0.0176 2
#> ATC:skmeans 129 0.424 0.958 0.587 0.0587 3
#> ATC:skmeans 121 0.560 0.658 0.912 0.3357 4
#> ATC:skmeans 113 0.627 0.343 0.401 0.4259 5
#> ATC:skmeans 120 0.633 0.482 0.356 0.0711 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "pam"]
# you can also extract it by
# res = res_list["ATC:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 130 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.921 0.937 0.974 0.5021 0.496 0.496
#> 3 3 0.884 0.854 0.941 0.3316 0.735 0.515
#> 4 4 0.802 0.822 0.900 0.0926 0.894 0.704
#> 5 5 0.953 0.917 0.956 0.0732 0.889 0.633
#> 6 6 0.835 0.789 0.874 0.0484 0.947 0.762
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
#> attr(,"optional")
#> [1] 2
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
#> GSM447671 2 0.0000 0.9797 0.000 1.000
#> GSM447694 1 0.0000 0.9655 1.000 0.000
#> GSM447618 2 0.0000 0.9797 0.000 1.000
#> GSM447691 2 0.0000 0.9797 0.000 1.000
#> GSM447733 1 0.0376 0.9624 0.996 0.004
#> GSM447620 2 0.0000 0.9797 0.000 1.000
#> GSM447627 1 0.0000 0.9655 1.000 0.000
#> GSM447630 2 0.9977 0.0509 0.472 0.528
#> GSM447642 1 0.5629 0.8426 0.868 0.132
#> GSM447649 2 0.0000 0.9797 0.000 1.000
#> GSM447654 2 0.0000 0.9797 0.000 1.000
#> GSM447655 2 0.0000 0.9797 0.000 1.000
#> GSM447669 2 0.0000 0.9797 0.000 1.000
#> GSM447676 1 0.0000 0.9655 1.000 0.000
#> GSM447678 2 0.9881 0.1834 0.436 0.564
#> GSM447681 2 0.0000 0.9797 0.000 1.000
#> GSM447698 2 0.0000 0.9797 0.000 1.000
#> GSM447713 1 0.0000 0.9655 1.000 0.000
#> GSM447722 1 0.6343 0.8068 0.840 0.160
#> GSM447726 2 0.0000 0.9797 0.000 1.000
#> GSM447735 1 0.0000 0.9655 1.000 0.000
#> GSM447737 1 0.0000 0.9655 1.000 0.000
#> GSM447657 2 0.0000 0.9797 0.000 1.000
#> GSM447674 2 0.0000 0.9797 0.000 1.000
#> GSM447636 2 0.0000 0.9797 0.000 1.000
#> GSM447723 1 0.0000 0.9655 1.000 0.000
#> GSM447699 1 0.0000 0.9655 1.000 0.000
#> GSM447708 2 0.0000 0.9797 0.000 1.000
#> GSM447721 1 0.0000 0.9655 1.000 0.000
#> GSM447623 1 0.0000 0.9655 1.000 0.000
#> GSM447621 1 0.0000 0.9655 1.000 0.000
#> GSM447650 2 0.0000 0.9797 0.000 1.000
#> GSM447651 2 0.0000 0.9797 0.000 1.000
#> GSM447653 1 0.0000 0.9655 1.000 0.000
#> GSM447658 2 0.4161 0.8945 0.084 0.916
#> GSM447675 1 0.8386 0.6455 0.732 0.268
#> GSM447680 2 0.0000 0.9797 0.000 1.000
#> GSM447686 2 0.0000 0.9797 0.000 1.000
#> GSM447736 1 0.0000 0.9655 1.000 0.000
#> GSM447629 2 0.0000 0.9797 0.000 1.000
#> GSM447648 1 0.0000 0.9655 1.000 0.000
#> GSM447660 2 0.6247 0.8019 0.156 0.844
#> GSM447661 2 0.0000 0.9797 0.000 1.000
#> GSM447663 1 0.0000 0.9655 1.000 0.000
#> GSM447704 2 0.0000 0.9797 0.000 1.000
#> GSM447720 1 0.0000 0.9655 1.000 0.000
#> GSM447652 2 0.0000 0.9797 0.000 1.000
#> GSM447679 2 0.0000 0.9797 0.000 1.000
#> GSM447712 1 0.0000 0.9655 1.000 0.000
#> GSM447664 2 0.0000 0.9797 0.000 1.000
#> GSM447637 1 0.0000 0.9655 1.000 0.000
#> GSM447639 1 0.0000 0.9655 1.000 0.000
#> GSM447615 1 0.0000 0.9655 1.000 0.000
#> GSM447656 2 0.0000 0.9797 0.000 1.000
#> GSM447673 2 0.0000 0.9797 0.000 1.000
#> GSM447719 1 0.0000 0.9655 1.000 0.000
#> GSM447706 1 0.0000 0.9655 1.000 0.000
#> GSM447612 1 0.0000 0.9655 1.000 0.000
#> GSM447665 2 0.0000 0.9797 0.000 1.000
#> GSM447677 2 0.0000 0.9797 0.000 1.000
#> GSM447613 1 0.0000 0.9655 1.000 0.000
#> GSM447659 1 0.0000 0.9655 1.000 0.000
#> GSM447662 1 0.0000 0.9655 1.000 0.000
#> GSM447666 2 0.0000 0.9797 0.000 1.000
#> GSM447668 2 0.0000 0.9797 0.000 1.000
#> GSM447682 2 0.0000 0.9797 0.000 1.000
#> GSM447683 2 0.0000 0.9797 0.000 1.000
#> GSM447688 2 0.0000 0.9797 0.000 1.000
#> GSM447702 2 0.0000 0.9797 0.000 1.000
#> GSM447709 2 0.0000 0.9797 0.000 1.000
#> GSM447711 1 0.0000 0.9655 1.000 0.000
#> GSM447715 2 0.0000 0.9797 0.000 1.000
#> GSM447693 1 0.0000 0.9655 1.000 0.000
#> GSM447611 1 0.9491 0.4490 0.632 0.368
#> GSM447672 2 0.0000 0.9797 0.000 1.000
#> GSM447703 2 0.0000 0.9797 0.000 1.000
#> GSM447727 1 0.6048 0.8249 0.852 0.148
#> GSM447638 2 0.0000 0.9797 0.000 1.000
#> GSM447670 1 0.0000 0.9655 1.000 0.000
#> GSM447700 1 0.9491 0.4490 0.632 0.368
#> GSM447738 2 0.0000 0.9797 0.000 1.000
#> GSM447739 1 0.0000 0.9655 1.000 0.000
#> GSM447617 1 0.0000 0.9655 1.000 0.000
#> GSM447628 2 0.0000 0.9797 0.000 1.000
#> GSM447632 2 0.0000 0.9797 0.000 1.000
#> GSM447619 1 0.0000 0.9655 1.000 0.000
#> GSM447643 2 0.0000 0.9797 0.000 1.000
#> GSM447724 1 0.0000 0.9655 1.000 0.000
#> GSM447728 2 0.0000 0.9797 0.000 1.000
#> GSM447610 1 0.0000 0.9655 1.000 0.000
#> GSM447633 2 0.0000 0.9797 0.000 1.000
#> GSM447634 1 0.0000 0.9655 1.000 0.000
#> GSM447622 1 0.0000 0.9655 1.000 0.000
#> GSM447667 2 0.0000 0.9797 0.000 1.000
#> GSM447687 2 0.0000 0.9797 0.000 1.000
#> GSM447695 1 0.0000 0.9655 1.000 0.000
#> GSM447696 1 0.0000 0.9655 1.000 0.000
#> GSM447697 1 0.0000 0.9655 1.000 0.000
#> GSM447714 1 0.0000 0.9655 1.000 0.000
#> GSM447717 2 0.0376 0.9762 0.004 0.996
#> GSM447725 1 0.0000 0.9655 1.000 0.000
#> GSM447729 2 0.0000 0.9797 0.000 1.000
#> GSM447644 2 0.0000 0.9797 0.000 1.000
#> GSM447710 1 0.0000 0.9655 1.000 0.000
#> GSM447614 1 0.0000 0.9655 1.000 0.000
#> GSM447685 2 0.0000 0.9797 0.000 1.000
#> GSM447690 1 0.0000 0.9655 1.000 0.000
#> GSM447730 2 0.0000 0.9797 0.000 1.000
#> GSM447646 2 0.0000 0.9797 0.000 1.000
#> GSM447689 1 0.5059 0.8638 0.888 0.112
#> GSM447635 2 0.2778 0.9339 0.048 0.952
#> GSM447641 1 0.0000 0.9655 1.000 0.000
#> GSM447716 2 0.0000 0.9797 0.000 1.000
#> GSM447718 1 0.6438 0.8054 0.836 0.164
#> GSM447616 1 0.0000 0.9655 1.000 0.000
#> GSM447626 1 0.0000 0.9655 1.000 0.000
#> GSM447640 2 0.0000 0.9797 0.000 1.000
#> GSM447734 1 0.0000 0.9655 1.000 0.000
#> GSM447692 1 0.0000 0.9655 1.000 0.000
#> GSM447647 2 0.0000 0.9797 0.000 1.000
#> GSM447624 1 0.0000 0.9655 1.000 0.000
#> GSM447625 1 0.0000 0.9655 1.000 0.000
#> GSM447707 2 0.0000 0.9797 0.000 1.000
#> GSM447732 1 0.0000 0.9655 1.000 0.000
#> GSM447684 2 0.2603 0.9380 0.044 0.956
#> GSM447731 2 0.0000 0.9797 0.000 1.000
#> GSM447705 1 0.9460 0.4582 0.636 0.364
#> GSM447631 1 0.0000 0.9655 1.000 0.000
#> GSM447701 2 0.0000 0.9797 0.000 1.000
#> GSM447645 1 0.0000 0.9655 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM447671 3 0.2448 0.878 0.000 0.076 0.924
#> GSM447694 1 0.0000 0.945 1.000 0.000 0.000
#> GSM447618 3 0.2796 0.856 0.000 0.092 0.908
#> GSM447691 3 0.0000 0.942 0.000 0.000 1.000
#> GSM447733 3 0.0000 0.942 0.000 0.000 1.000
#> GSM447620 2 0.0424 0.916 0.000 0.992 0.008
#> GSM447627 1 0.0000 0.945 1.000 0.000 0.000
#> GSM447630 3 0.0000 0.942 0.000 0.000 1.000
#> GSM447642 3 0.0000 0.942 0.000 0.000 1.000
#> GSM447649 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447654 2 0.4654 0.713 0.000 0.792 0.208
#> GSM447655 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447669 3 0.0424 0.937 0.000 0.008 0.992
#> GSM447676 3 0.0000 0.942 0.000 0.000 1.000
#> GSM447678 3 0.0000 0.942 0.000 0.000 1.000
#> GSM447681 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447698 2 0.5859 0.515 0.000 0.656 0.344
#> GSM447713 1 0.0000 0.945 1.000 0.000 0.000
#> GSM447722 3 0.0000 0.942 0.000 0.000 1.000
#> GSM447726 3 0.1163 0.923 0.000 0.028 0.972
#> GSM447735 1 0.0000 0.945 1.000 0.000 0.000
#> GSM447737 1 0.0000 0.945 1.000 0.000 0.000
#> GSM447657 2 0.6095 0.417 0.000 0.608 0.392
#> GSM447674 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447636 2 0.2537 0.860 0.000 0.920 0.080
#> GSM447723 3 0.0000 0.942 0.000 0.000 1.000
#> GSM447699 1 0.0000 0.945 1.000 0.000 0.000
#> GSM447708 2 0.6079 0.426 0.000 0.612 0.388
#> GSM447721 1 0.0000 0.945 1.000 0.000 0.000
#> GSM447623 1 0.0000 0.945 1.000 0.000 0.000
#> GSM447621 1 0.0000 0.945 1.000 0.000 0.000
#> GSM447650 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447651 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447653 1 0.5529 0.554 0.704 0.000 0.296
#> GSM447658 3 0.0000 0.942 0.000 0.000 1.000
#> GSM447675 3 0.0000 0.942 0.000 0.000 1.000
#> GSM447680 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447686 2 0.6180 0.359 0.000 0.584 0.416
#> GSM447736 1 0.2878 0.875 0.904 0.000 0.096
#> GSM447629 2 0.5988 0.469 0.000 0.632 0.368
#> GSM447648 1 0.0000 0.945 1.000 0.000 0.000
#> GSM447660 3 0.0000 0.942 0.000 0.000 1.000
#> GSM447661 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447663 3 0.0000 0.942 0.000 0.000 1.000
#> GSM447704 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447720 3 0.0000 0.942 0.000 0.000 1.000
#> GSM447652 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447679 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447712 3 0.5859 0.391 0.344 0.000 0.656
#> GSM447664 3 0.6309 -0.111 0.000 0.496 0.504
#> GSM447637 1 0.0000 0.945 1.000 0.000 0.000
#> GSM447639 3 0.0000 0.942 0.000 0.000 1.000
#> GSM447615 3 0.5835 0.401 0.340 0.000 0.660
#> GSM447656 2 0.6095 0.417 0.000 0.608 0.392
#> GSM447673 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447719 1 0.0000 0.945 1.000 0.000 0.000
#> GSM447706 1 0.2165 0.901 0.936 0.000 0.064
#> GSM447612 3 0.0424 0.936 0.008 0.000 0.992
#> GSM447665 2 0.4931 0.693 0.000 0.768 0.232
#> GSM447677 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447613 1 0.6308 0.120 0.508 0.000 0.492
#> GSM447659 1 0.0000 0.945 1.000 0.000 0.000
#> GSM447662 1 0.2448 0.892 0.924 0.000 0.076
#> GSM447666 3 0.2261 0.886 0.000 0.068 0.932
#> GSM447668 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447682 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447683 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447688 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447702 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447709 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447711 1 0.6308 0.120 0.508 0.000 0.492
#> GSM447715 3 0.0000 0.942 0.000 0.000 1.000
#> GSM447693 1 0.0000 0.945 1.000 0.000 0.000
#> GSM447611 3 0.1031 0.925 0.000 0.024 0.976
#> GSM447672 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447703 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447727 3 0.0000 0.942 0.000 0.000 1.000
#> GSM447638 2 0.0424 0.916 0.000 0.992 0.008
#> GSM447670 1 0.6008 0.451 0.628 0.000 0.372
#> GSM447700 3 0.0000 0.942 0.000 0.000 1.000
#> GSM447738 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447739 1 0.0000 0.945 1.000 0.000 0.000
#> GSM447617 1 0.0000 0.945 1.000 0.000 0.000
#> GSM447628 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447632 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447619 1 0.2165 0.901 0.936 0.000 0.064
#> GSM447643 2 0.0424 0.916 0.000 0.992 0.008
#> GSM447724 3 0.0000 0.942 0.000 0.000 1.000
#> GSM447728 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447610 1 0.0000 0.945 1.000 0.000 0.000
#> GSM447633 3 0.2356 0.882 0.000 0.072 0.928
#> GSM447634 3 0.0000 0.942 0.000 0.000 1.000
#> GSM447622 1 0.0000 0.945 1.000 0.000 0.000
#> GSM447667 2 0.0424 0.916 0.000 0.992 0.008
#> GSM447687 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447695 1 0.0000 0.945 1.000 0.000 0.000
#> GSM447696 1 0.0000 0.945 1.000 0.000 0.000
#> GSM447697 1 0.0000 0.945 1.000 0.000 0.000
#> GSM447714 1 0.2356 0.895 0.928 0.000 0.072
#> GSM447717 3 0.0000 0.942 0.000 0.000 1.000
#> GSM447725 3 0.0000 0.942 0.000 0.000 1.000
#> GSM447729 2 0.3267 0.822 0.000 0.884 0.116
#> GSM447644 3 0.1411 0.916 0.000 0.036 0.964
#> GSM447710 1 0.0000 0.945 1.000 0.000 0.000
#> GSM447614 1 0.0000 0.945 1.000 0.000 0.000
#> GSM447685 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447690 1 0.0000 0.945 1.000 0.000 0.000
#> GSM447730 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447646 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447689 3 0.0000 0.942 0.000 0.000 1.000
#> GSM447635 3 0.0000 0.942 0.000 0.000 1.000
#> GSM447641 3 0.0000 0.942 0.000 0.000 1.000
#> GSM447716 3 0.6235 0.105 0.000 0.436 0.564
#> GSM447718 3 0.0000 0.942 0.000 0.000 1.000
#> GSM447616 1 0.0000 0.945 1.000 0.000 0.000
#> GSM447626 3 0.0000 0.942 0.000 0.000 1.000
#> GSM447640 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447734 1 0.0000 0.945 1.000 0.000 0.000
#> GSM447692 1 0.0000 0.945 1.000 0.000 0.000
#> GSM447647 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447624 1 0.0000 0.945 1.000 0.000 0.000
#> GSM447625 1 0.2711 0.882 0.912 0.000 0.088
#> GSM447707 2 0.0000 0.920 0.000 1.000 0.000
#> GSM447732 1 0.0000 0.945 1.000 0.000 0.000
#> GSM447684 3 0.0000 0.942 0.000 0.000 1.000
#> GSM447731 2 0.6299 0.102 0.000 0.524 0.476
#> GSM447705 3 0.0000 0.942 0.000 0.000 1.000
#> GSM447631 1 0.0000 0.945 1.000 0.000 0.000
#> GSM447701 2 0.1031 0.904 0.000 0.976 0.024
#> GSM447645 1 0.0000 0.945 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM447671 1 0.7121 0.400 0.564 0.220 0.000 0.216
#> GSM447694 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM447618 2 0.7665 0.180 0.360 0.424 0.000 0.216
#> GSM447691 1 0.5809 0.603 0.692 0.092 0.000 0.216
#> GSM447733 4 0.4564 0.606 0.328 0.000 0.000 0.672
#> GSM447620 2 0.3764 0.782 0.000 0.784 0.000 0.216
#> GSM447627 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM447630 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM447642 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM447649 2 0.2530 0.789 0.000 0.888 0.000 0.112
#> GSM447654 4 0.0188 0.723 0.004 0.000 0.000 0.996
#> GSM447655 2 0.2216 0.798 0.000 0.908 0.000 0.092
#> GSM447669 1 0.5923 0.594 0.684 0.100 0.000 0.216
#> GSM447676 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM447678 1 0.5332 0.651 0.736 0.080 0.000 0.184
#> GSM447681 2 0.0000 0.810 0.000 1.000 0.000 0.000
#> GSM447698 2 0.3945 0.780 0.004 0.780 0.000 0.216
#> GSM447713 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM447722 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM447726 1 0.5809 0.603 0.692 0.092 0.000 0.216
#> GSM447735 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM447737 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM447657 2 0.4086 0.777 0.008 0.776 0.000 0.216
#> GSM447674 2 0.2216 0.798 0.000 0.908 0.000 0.092
#> GSM447636 2 0.6984 0.526 0.184 0.580 0.000 0.236
#> GSM447723 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM447699 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM447708 2 0.4086 0.777 0.008 0.776 0.000 0.216
#> GSM447721 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM447623 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM447621 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM447650 2 0.2216 0.798 0.000 0.908 0.000 0.092
#> GSM447651 2 0.2216 0.798 0.000 0.908 0.000 0.092
#> GSM447653 4 0.6170 0.678 0.136 0.000 0.192 0.672
#> GSM447658 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM447675 4 0.4543 0.612 0.324 0.000 0.000 0.676
#> GSM447680 2 0.0592 0.811 0.000 0.984 0.000 0.016
#> GSM447686 2 0.4904 0.751 0.040 0.744 0.000 0.216
#> GSM447736 3 0.4040 0.644 0.248 0.000 0.752 0.000
#> GSM447629 2 0.3945 0.780 0.004 0.780 0.000 0.216
#> GSM447648 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM447660 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM447661 2 0.2216 0.798 0.000 0.908 0.000 0.092
#> GSM447663 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM447704 2 0.2530 0.789 0.000 0.888 0.000 0.112
#> GSM447720 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM447652 2 0.3266 0.801 0.000 0.832 0.000 0.168
#> GSM447679 2 0.2216 0.798 0.000 0.908 0.000 0.092
#> GSM447712 1 0.0188 0.891 0.996 0.000 0.004 0.000
#> GSM447664 4 0.1792 0.666 0.000 0.068 0.000 0.932
#> GSM447637 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM447639 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM447615 1 0.0188 0.891 0.996 0.000 0.004 0.000
#> GSM447656 2 0.4086 0.777 0.008 0.776 0.000 0.216
#> GSM447673 2 0.2704 0.792 0.000 0.876 0.000 0.124
#> GSM447719 4 0.4564 0.501 0.000 0.000 0.328 0.672
#> GSM447706 3 0.1637 0.909 0.060 0.000 0.940 0.000
#> GSM447612 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM447665 2 0.3945 0.780 0.004 0.780 0.000 0.216
#> GSM447677 2 0.0817 0.811 0.000 0.976 0.000 0.024
#> GSM447613 1 0.0817 0.871 0.976 0.000 0.024 0.000
#> GSM447659 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM447662 3 0.2973 0.806 0.144 0.000 0.856 0.000
#> GSM447666 1 0.6240 0.566 0.664 0.136 0.000 0.200
#> GSM447668 2 0.0000 0.810 0.000 1.000 0.000 0.000
#> GSM447682 2 0.3764 0.782 0.000 0.784 0.000 0.216
#> GSM447683 2 0.1302 0.812 0.000 0.956 0.000 0.044
#> GSM447688 2 0.4500 0.776 0.000 0.684 0.000 0.316
#> GSM447702 2 0.2216 0.798 0.000 0.908 0.000 0.092
#> GSM447709 2 0.3764 0.782 0.000 0.784 0.000 0.216
#> GSM447711 1 0.0817 0.871 0.976 0.000 0.024 0.000
#> GSM447715 1 0.1474 0.855 0.948 0.000 0.000 0.052
#> GSM447693 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM447611 4 0.3942 0.709 0.236 0.000 0.000 0.764
#> GSM447672 2 0.2216 0.798 0.000 0.908 0.000 0.092
#> GSM447703 2 0.2530 0.789 0.000 0.888 0.000 0.112
#> GSM447727 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM447638 2 0.3764 0.782 0.000 0.784 0.000 0.216
#> GSM447670 1 0.3219 0.688 0.836 0.000 0.164 0.000
#> GSM447700 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM447738 2 0.2216 0.798 0.000 0.908 0.000 0.092
#> GSM447739 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM447617 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM447628 4 0.3486 0.702 0.000 0.188 0.000 0.812
#> GSM447632 2 0.2216 0.798 0.000 0.908 0.000 0.092
#> GSM447619 3 0.1637 0.909 0.060 0.000 0.940 0.000
#> GSM447643 2 0.3764 0.782 0.000 0.784 0.000 0.216
#> GSM447724 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM447728 2 0.3764 0.782 0.000 0.784 0.000 0.216
#> GSM447610 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM447633 1 0.6373 0.544 0.648 0.136 0.000 0.216
#> GSM447634 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM447622 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM447667 2 0.3764 0.782 0.000 0.784 0.000 0.216
#> GSM447687 2 0.2530 0.789 0.000 0.888 0.000 0.112
#> GSM447695 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM447696 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM447697 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM447714 3 0.2704 0.833 0.124 0.000 0.876 0.000
#> GSM447717 1 0.0469 0.886 0.988 0.000 0.000 0.012
#> GSM447725 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM447729 4 0.0707 0.728 0.000 0.020 0.000 0.980
#> GSM447644 1 0.5867 0.598 0.688 0.096 0.000 0.216
#> GSM447710 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM447614 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM447685 2 0.1118 0.812 0.000 0.964 0.000 0.036
#> GSM447690 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM447730 2 0.2216 0.798 0.000 0.908 0.000 0.092
#> GSM447646 4 0.3123 0.723 0.000 0.156 0.000 0.844
#> GSM447689 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM447635 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM447641 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM447716 2 0.4986 0.749 0.044 0.740 0.000 0.216
#> GSM447718 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM447616 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM447626 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM447640 2 0.2216 0.798 0.000 0.908 0.000 0.092
#> GSM447734 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM447692 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM447647 4 0.3219 0.718 0.000 0.164 0.000 0.836
#> GSM447624 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM447625 3 0.3942 0.664 0.236 0.000 0.764 0.000
#> GSM447707 2 0.2530 0.789 0.000 0.888 0.000 0.112
#> GSM447732 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM447684 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM447731 4 0.3123 0.758 0.156 0.000 0.000 0.844
#> GSM447705 1 0.0000 0.894 1.000 0.000 0.000 0.000
#> GSM447631 3 0.0000 0.968 0.000 0.000 1.000 0.000
#> GSM447701 2 0.3764 0.782 0.000 0.784 0.000 0.216
#> GSM447645 3 0.0000 0.968 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM447671 5 0.0290 0.9146 0.008 0.000 0.000 0.000 0.992
#> GSM447694 3 0.0000 0.9779 0.000 0.000 1.000 0.000 0.000
#> GSM447618 5 0.0290 0.9146 0.008 0.000 0.000 0.000 0.992
#> GSM447691 5 0.0290 0.9146 0.008 0.000 0.000 0.000 0.992
#> GSM447733 4 0.3012 0.8578 0.104 0.000 0.000 0.860 0.036
#> GSM447620 5 0.0963 0.9278 0.000 0.036 0.000 0.000 0.964
#> GSM447627 3 0.0510 0.9759 0.000 0.000 0.984 0.016 0.000
#> GSM447630 1 0.0963 0.9697 0.964 0.000 0.000 0.000 0.036
#> GSM447642 1 0.0000 0.9719 1.000 0.000 0.000 0.000 0.000
#> GSM447649 2 0.0963 0.9080 0.000 0.964 0.000 0.036 0.000
#> GSM447654 4 0.0613 0.9283 0.004 0.008 0.000 0.984 0.004
#> GSM447655 2 0.0290 0.9279 0.000 0.992 0.000 0.000 0.008
#> GSM447669 5 0.0290 0.9146 0.008 0.000 0.000 0.000 0.992
#> GSM447676 1 0.0000 0.9719 1.000 0.000 0.000 0.000 0.000
#> GSM447678 5 0.3661 0.5743 0.276 0.000 0.000 0.000 0.724
#> GSM447681 2 0.1121 0.9047 0.000 0.956 0.000 0.000 0.044
#> GSM447698 5 0.0963 0.9278 0.000 0.036 0.000 0.000 0.964
#> GSM447713 3 0.0510 0.9759 0.000 0.000 0.984 0.016 0.000
#> GSM447722 1 0.0963 0.9697 0.964 0.000 0.000 0.000 0.036
#> GSM447726 5 0.0290 0.9146 0.008 0.000 0.000 0.000 0.992
#> GSM447735 3 0.0000 0.9779 0.000 0.000 1.000 0.000 0.000
#> GSM447737 3 0.0000 0.9779 0.000 0.000 1.000 0.000 0.000
#> GSM447657 5 0.0963 0.9278 0.000 0.036 0.000 0.000 0.964
#> GSM447674 2 0.0290 0.9279 0.000 0.992 0.000 0.000 0.008
#> GSM447636 5 0.1251 0.9048 0.000 0.008 0.000 0.036 0.956
#> GSM447723 1 0.0703 0.9717 0.976 0.000 0.000 0.000 0.024
#> GSM447699 3 0.0671 0.9751 0.004 0.000 0.980 0.016 0.000
#> GSM447708 5 0.0963 0.9278 0.000 0.036 0.000 0.000 0.964
#> GSM447721 3 0.0671 0.9751 0.004 0.000 0.980 0.016 0.000
#> GSM447623 3 0.0000 0.9779 0.000 0.000 1.000 0.000 0.000
#> GSM447621 3 0.0290 0.9770 0.000 0.000 0.992 0.008 0.000
#> GSM447650 2 0.0290 0.9279 0.000 0.992 0.000 0.000 0.008
#> GSM447651 2 0.0290 0.9279 0.000 0.992 0.000 0.000 0.008
#> GSM447653 4 0.2561 0.8832 0.096 0.000 0.020 0.884 0.000
#> GSM447658 1 0.0162 0.9722 0.996 0.000 0.000 0.000 0.004
#> GSM447675 4 0.1661 0.9172 0.024 0.000 0.000 0.940 0.036
#> GSM447680 2 0.4273 0.0872 0.000 0.552 0.000 0.000 0.448
#> GSM447686 5 0.0963 0.9278 0.000 0.036 0.000 0.000 0.964
#> GSM447736 3 0.3299 0.7994 0.152 0.000 0.828 0.016 0.004
#> GSM447629 5 0.0963 0.9278 0.000 0.036 0.000 0.000 0.964
#> GSM447648 3 0.0000 0.9779 0.000 0.000 1.000 0.000 0.000
#> GSM447660 1 0.0162 0.9722 0.996 0.000 0.000 0.000 0.004
#> GSM447661 2 0.0290 0.9279 0.000 0.992 0.000 0.000 0.008
#> GSM447663 1 0.0000 0.9719 1.000 0.000 0.000 0.000 0.000
#> GSM447704 2 0.0963 0.9080 0.000 0.964 0.000 0.036 0.000
#> GSM447720 1 0.0963 0.9697 0.964 0.000 0.000 0.000 0.036
#> GSM447652 2 0.3309 0.7986 0.000 0.836 0.000 0.036 0.128
#> GSM447679 2 0.0290 0.9279 0.000 0.992 0.000 0.000 0.008
#> GSM447712 1 0.0290 0.9671 0.992 0.000 0.000 0.008 0.000
#> GSM447664 4 0.1331 0.9128 0.000 0.008 0.000 0.952 0.040
#> GSM447637 3 0.0000 0.9779 0.000 0.000 1.000 0.000 0.000
#> GSM447639 1 0.0963 0.9697 0.964 0.000 0.000 0.000 0.036
#> GSM447615 1 0.0000 0.9719 1.000 0.000 0.000 0.000 0.000
#> GSM447656 5 0.0963 0.9278 0.000 0.036 0.000 0.000 0.964
#> GSM447673 2 0.1469 0.9046 0.000 0.948 0.000 0.036 0.016
#> GSM447719 4 0.3086 0.7674 0.004 0.000 0.180 0.816 0.000
#> GSM447706 3 0.0671 0.9751 0.004 0.000 0.980 0.016 0.000
#> GSM447612 1 0.1386 0.9638 0.952 0.000 0.000 0.016 0.032
#> GSM447665 5 0.0963 0.9278 0.000 0.036 0.000 0.000 0.964
#> GSM447677 5 0.4256 0.2848 0.000 0.436 0.000 0.000 0.564
#> GSM447613 1 0.0510 0.9619 0.984 0.000 0.000 0.016 0.000
#> GSM447659 3 0.0671 0.9751 0.004 0.000 0.980 0.016 0.000
#> GSM447662 3 0.2046 0.9125 0.068 0.000 0.916 0.016 0.000
#> GSM447666 5 0.0290 0.9146 0.008 0.000 0.000 0.000 0.992
#> GSM447668 2 0.0290 0.9279 0.000 0.992 0.000 0.000 0.008
#> GSM447682 5 0.0963 0.9278 0.000 0.036 0.000 0.000 0.964
#> GSM447683 5 0.3305 0.7331 0.000 0.224 0.000 0.000 0.776
#> GSM447688 5 0.2411 0.8527 0.000 0.008 0.000 0.108 0.884
#> GSM447702 2 0.0290 0.9279 0.000 0.992 0.000 0.000 0.008
#> GSM447709 5 0.0963 0.9278 0.000 0.036 0.000 0.000 0.964
#> GSM447711 1 0.0290 0.9671 0.992 0.000 0.000 0.008 0.000
#> GSM447715 1 0.1544 0.9400 0.932 0.000 0.000 0.000 0.068
#> GSM447693 3 0.0000 0.9779 0.000 0.000 1.000 0.000 0.000
#> GSM447611 4 0.1469 0.9192 0.016 0.000 0.000 0.948 0.036
#> GSM447672 2 0.0290 0.9279 0.000 0.992 0.000 0.000 0.008
#> GSM447703 2 0.0963 0.9080 0.000 0.964 0.000 0.036 0.000
#> GSM447727 1 0.0963 0.9697 0.964 0.000 0.000 0.000 0.036
#> GSM447638 5 0.0963 0.9278 0.000 0.036 0.000 0.000 0.964
#> GSM447670 1 0.3011 0.7747 0.844 0.000 0.140 0.016 0.000
#> GSM447700 1 0.0963 0.9697 0.964 0.000 0.000 0.000 0.036
#> GSM447738 2 0.2230 0.8255 0.000 0.884 0.000 0.000 0.116
#> GSM447739 3 0.0000 0.9779 0.000 0.000 1.000 0.000 0.000
#> GSM447617 3 0.0000 0.9779 0.000 0.000 1.000 0.000 0.000
#> GSM447628 4 0.1197 0.9062 0.000 0.048 0.000 0.952 0.000
#> GSM447632 2 0.0290 0.9279 0.000 0.992 0.000 0.000 0.008
#> GSM447619 3 0.0671 0.9751 0.004 0.000 0.980 0.016 0.000
#> GSM447643 5 0.0963 0.9278 0.000 0.036 0.000 0.000 0.964
#> GSM447724 1 0.0880 0.9701 0.968 0.000 0.000 0.000 0.032
#> GSM447728 5 0.0963 0.9278 0.000 0.036 0.000 0.000 0.964
#> GSM447610 3 0.0671 0.9751 0.004 0.000 0.980 0.016 0.000
#> GSM447633 5 0.0290 0.9146 0.008 0.000 0.000 0.000 0.992
#> GSM447634 1 0.0000 0.9719 1.000 0.000 0.000 0.000 0.000
#> GSM447622 3 0.0000 0.9779 0.000 0.000 1.000 0.000 0.000
#> GSM447667 5 0.0963 0.9278 0.000 0.036 0.000 0.000 0.964
#> GSM447687 2 0.0963 0.9080 0.000 0.964 0.000 0.036 0.000
#> GSM447695 3 0.0671 0.9751 0.004 0.000 0.980 0.016 0.000
#> GSM447696 3 0.0000 0.9779 0.000 0.000 1.000 0.000 0.000
#> GSM447697 3 0.0000 0.9779 0.000 0.000 1.000 0.000 0.000
#> GSM447714 3 0.1845 0.9261 0.056 0.000 0.928 0.016 0.000
#> GSM447717 1 0.0162 0.9722 0.996 0.000 0.000 0.000 0.004
#> GSM447725 1 0.0000 0.9719 1.000 0.000 0.000 0.000 0.000
#> GSM447729 4 0.0579 0.9269 0.000 0.008 0.000 0.984 0.008
#> GSM447644 5 0.0290 0.9146 0.008 0.000 0.000 0.000 0.992
#> GSM447710 3 0.0671 0.9751 0.004 0.000 0.980 0.016 0.000
#> GSM447614 3 0.0671 0.9751 0.004 0.000 0.980 0.016 0.000
#> GSM447685 5 0.4045 0.4950 0.000 0.356 0.000 0.000 0.644
#> GSM447690 3 0.0000 0.9779 0.000 0.000 1.000 0.000 0.000
#> GSM447730 2 0.0290 0.9279 0.000 0.992 0.000 0.000 0.008
#> GSM447646 4 0.0510 0.9254 0.000 0.016 0.000 0.984 0.000
#> GSM447689 1 0.0162 0.9722 0.996 0.000 0.000 0.000 0.004
#> GSM447635 1 0.0963 0.9697 0.964 0.000 0.000 0.000 0.036
#> GSM447641 1 0.0000 0.9719 1.000 0.000 0.000 0.000 0.000
#> GSM447716 5 0.0290 0.9146 0.008 0.000 0.000 0.000 0.992
#> GSM447718 1 0.0963 0.9697 0.964 0.000 0.000 0.000 0.036
#> GSM447616 3 0.0510 0.9759 0.000 0.000 0.984 0.016 0.000
#> GSM447626 1 0.0290 0.9671 0.992 0.000 0.000 0.008 0.000
#> GSM447640 2 0.0290 0.9279 0.000 0.992 0.000 0.000 0.008
#> GSM447734 3 0.0000 0.9779 0.000 0.000 1.000 0.000 0.000
#> GSM447692 3 0.0000 0.9779 0.000 0.000 1.000 0.000 0.000
#> GSM447647 2 0.4691 0.5885 0.000 0.680 0.000 0.276 0.044
#> GSM447624 3 0.0000 0.9779 0.000 0.000 1.000 0.000 0.000
#> GSM447625 3 0.3011 0.8207 0.140 0.000 0.844 0.016 0.000
#> GSM447707 2 0.0963 0.9080 0.000 0.964 0.000 0.036 0.000
#> GSM447732 3 0.0671 0.9751 0.004 0.000 0.980 0.016 0.000
#> GSM447684 1 0.0963 0.9697 0.964 0.000 0.000 0.000 0.036
#> GSM447731 4 0.0579 0.9281 0.008 0.008 0.000 0.984 0.000
#> GSM447705 1 0.0963 0.9697 0.964 0.000 0.000 0.000 0.036
#> GSM447631 3 0.0000 0.9779 0.000 0.000 1.000 0.000 0.000
#> GSM447701 5 0.0963 0.9278 0.000 0.036 0.000 0.000 0.964
#> GSM447645 3 0.0000 0.9779 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
#> GSM447671 5 0.2340 0.8197 0.000 0.000 0.000 0.000 0.852 0.148
#> GSM447694 1 0.0000 0.9030 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447618 5 0.1957 0.8461 0.000 0.000 0.000 0.000 0.888 0.112
#> GSM447691 5 0.2340 0.8197 0.000 0.000 0.000 0.000 0.852 0.148
#> GSM447733 4 0.2378 0.8271 0.000 0.000 0.000 0.848 0.000 0.152
#> GSM447620 5 0.0000 0.9117 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM447627 3 0.2562 0.6545 0.172 0.000 0.828 0.000 0.000 0.000
#> GSM447630 6 0.0000 0.8567 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM447642 6 0.2340 0.8618 0.000 0.000 0.148 0.000 0.000 0.852
#> GSM447649 2 0.0000 0.9401 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447654 4 0.0000 0.9227 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM447655 2 0.0000 0.9401 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447669 5 0.2340 0.8197 0.000 0.000 0.000 0.000 0.852 0.148
#> GSM447676 6 0.3531 0.7158 0.000 0.000 0.328 0.000 0.000 0.672
#> GSM447678 5 0.5690 0.1972 0.000 0.000 0.160 0.000 0.452 0.388
#> GSM447681 2 0.0937 0.9113 0.000 0.960 0.000 0.000 0.040 0.000
#> GSM447698 5 0.0000 0.9117 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM447713 1 0.1863 0.7894 0.896 0.000 0.104 0.000 0.000 0.000
#> GSM447722 6 0.2631 0.6944 0.000 0.000 0.180 0.000 0.000 0.820
#> GSM447726 5 0.0000 0.9117 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM447735 1 0.3175 0.6187 0.744 0.000 0.256 0.000 0.000 0.000
#> GSM447737 1 0.0000 0.9030 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447657 5 0.0000 0.9117 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM447674 2 0.0000 0.9401 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447636 5 0.0000 0.9117 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM447723 6 0.1141 0.8642 0.000 0.000 0.052 0.000 0.000 0.948
#> GSM447699 3 0.3578 0.7196 0.340 0.000 0.660 0.000 0.000 0.000
#> GSM447708 5 0.0000 0.9117 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM447721 3 0.5774 0.4958 0.364 0.000 0.456 0.000 0.000 0.180
#> GSM447623 1 0.0000 0.9030 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447621 1 0.1387 0.8440 0.932 0.000 0.068 0.000 0.000 0.000
#> GSM447650 2 0.0000 0.9401 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447651 2 0.0000 0.9401 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447653 4 0.1908 0.8819 0.000 0.000 0.056 0.916 0.000 0.028
#> GSM447658 6 0.2340 0.8618 0.000 0.000 0.148 0.000 0.000 0.852
#> GSM447675 4 0.1327 0.8943 0.000 0.000 0.000 0.936 0.000 0.064
#> GSM447680 2 0.3828 0.1335 0.000 0.560 0.000 0.000 0.440 0.000
#> GSM447686 5 0.0000 0.9117 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM447736 3 0.3699 0.7190 0.336 0.000 0.660 0.000 0.000 0.004
#> GSM447629 5 0.0000 0.9117 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM447648 1 0.0000 0.9030 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447660 6 0.2340 0.8618 0.000 0.000 0.148 0.000 0.000 0.852
#> GSM447661 2 0.0000 0.9401 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447663 6 0.3823 0.4248 0.000 0.000 0.436 0.000 0.000 0.564
#> GSM447704 2 0.0000 0.9401 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447720 6 0.0000 0.8567 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM447652 2 0.2562 0.7648 0.000 0.828 0.000 0.000 0.172 0.000
#> GSM447679 2 0.0000 0.9401 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447712 6 0.2340 0.8618 0.000 0.000 0.148 0.000 0.000 0.852
#> GSM447664 4 0.1501 0.8759 0.000 0.000 0.000 0.924 0.076 0.000
#> GSM447637 1 0.0000 0.9030 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447639 6 0.1075 0.8312 0.000 0.000 0.048 0.000 0.000 0.952
#> GSM447615 6 0.2378 0.8607 0.000 0.000 0.152 0.000 0.000 0.848
#> GSM447656 5 0.0000 0.9117 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM447673 2 0.0713 0.9193 0.000 0.972 0.000 0.000 0.028 0.000
#> GSM447719 4 0.5066 0.5144 0.116 0.000 0.276 0.608 0.000 0.000
#> GSM447706 3 0.3647 0.6950 0.360 0.000 0.640 0.000 0.000 0.000
#> GSM447612 3 0.3647 0.3612 0.000 0.000 0.640 0.000 0.000 0.360
#> GSM447665 5 0.0000 0.9117 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM447677 5 0.3833 0.2365 0.000 0.444 0.000 0.000 0.556 0.000
#> GSM447613 6 0.3706 0.6085 0.000 0.000 0.380 0.000 0.000 0.620
#> GSM447659 3 0.2562 0.6545 0.172 0.000 0.828 0.000 0.000 0.000
#> GSM447662 3 0.3578 0.7196 0.340 0.000 0.660 0.000 0.000 0.000
#> GSM447666 5 0.0458 0.9042 0.000 0.000 0.000 0.000 0.984 0.016
#> GSM447668 2 0.0000 0.9401 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447682 5 0.0000 0.9117 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM447683 5 0.2941 0.7092 0.000 0.220 0.000 0.000 0.780 0.000
#> GSM447688 5 0.1610 0.8576 0.000 0.000 0.000 0.084 0.916 0.000
#> GSM447702 2 0.0000 0.9401 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447709 5 0.0000 0.9117 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM447711 6 0.2340 0.8618 0.000 0.000 0.148 0.000 0.000 0.852
#> GSM447715 6 0.0363 0.8510 0.000 0.000 0.000 0.000 0.012 0.988
#> GSM447693 1 0.0000 0.9030 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447611 4 0.0000 0.9227 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM447672 2 0.0000 0.9401 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447703 2 0.0000 0.9401 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447727 6 0.0865 0.8622 0.000 0.000 0.036 0.000 0.000 0.964
#> GSM447638 5 0.0000 0.9117 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM447670 3 0.3857 -0.3618 0.000 0.000 0.532 0.000 0.000 0.468
#> GSM447700 6 0.0000 0.8567 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM447738 2 0.1910 0.8339 0.000 0.892 0.000 0.000 0.108 0.000
#> GSM447739 1 0.0000 0.9030 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447617 1 0.0000 0.9030 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447628 4 0.0547 0.9131 0.000 0.020 0.000 0.980 0.000 0.000
#> GSM447632 2 0.0000 0.9401 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447619 3 0.3578 0.7196 0.340 0.000 0.660 0.000 0.000 0.000
#> GSM447643 5 0.0000 0.9117 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM447724 6 0.3684 0.4177 0.000 0.000 0.372 0.000 0.000 0.628
#> GSM447728 5 0.0000 0.9117 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM447610 3 0.2562 0.6545 0.172 0.000 0.828 0.000 0.000 0.000
#> GSM447633 5 0.0000 0.9117 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM447634 3 0.3847 -0.4500 0.000 0.000 0.544 0.000 0.000 0.456
#> GSM447622 1 0.0632 0.8813 0.976 0.000 0.024 0.000 0.000 0.000
#> GSM447667 5 0.0000 0.9117 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM447687 2 0.0000 0.9401 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447695 3 0.3578 0.7196 0.340 0.000 0.660 0.000 0.000 0.000
#> GSM447696 1 0.0000 0.9030 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447697 1 0.0000 0.9030 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447714 3 0.3578 0.7196 0.340 0.000 0.660 0.000 0.000 0.000
#> GSM447717 6 0.2340 0.8618 0.000 0.000 0.148 0.000 0.000 0.852
#> GSM447725 6 0.2340 0.8618 0.000 0.000 0.148 0.000 0.000 0.852
#> GSM447729 4 0.0000 0.9227 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM447644 5 0.2092 0.8382 0.000 0.000 0.000 0.000 0.876 0.124
#> GSM447710 3 0.3578 0.7196 0.340 0.000 0.660 0.000 0.000 0.000
#> GSM447614 3 0.2562 0.6545 0.172 0.000 0.828 0.000 0.000 0.000
#> GSM447685 5 0.3634 0.4656 0.000 0.356 0.000 0.000 0.644 0.000
#> GSM447690 1 0.2730 0.6724 0.808 0.000 0.192 0.000 0.000 0.000
#> GSM447730 2 0.0000 0.9401 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447646 4 0.0000 0.9227 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM447689 6 0.2340 0.8618 0.000 0.000 0.148 0.000 0.000 0.852
#> GSM447635 6 0.0000 0.8567 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM447641 6 0.2340 0.8618 0.000 0.000 0.148 0.000 0.000 0.852
#> GSM447716 5 0.0000 0.9117 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM447718 6 0.0000 0.8567 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM447616 1 0.3647 0.1615 0.640 0.000 0.360 0.000 0.000 0.000
#> GSM447626 6 0.3515 0.7038 0.000 0.000 0.324 0.000 0.000 0.676
#> GSM447640 2 0.0000 0.9401 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447734 1 0.3684 0.0685 0.628 0.000 0.372 0.000 0.000 0.000
#> GSM447692 1 0.0000 0.9030 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447647 2 0.4522 0.5691 0.000 0.672 0.000 0.252 0.076 0.000
#> GSM447624 1 0.0000 0.9030 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447625 3 0.3547 0.7194 0.332 0.000 0.668 0.000 0.000 0.000
#> GSM447707 2 0.0000 0.9401 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM447732 3 0.3578 0.7196 0.340 0.000 0.660 0.000 0.000 0.000
#> GSM447684 6 0.0000 0.8567 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM447731 4 0.0000 0.9227 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM447705 6 0.0000 0.8567 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM447631 1 0.0000 0.9030 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM447701 5 0.0000 0.9117 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM447645 1 0.0000 0.9030 1.000 0.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n gender(p) individual(p) disease.state(p) other(p) k
#> ATC:pam 125 0.902 0.946 0.764 0.0256 2
#> ATC:pam 117 0.596 0.542 0.618 0.2994 3
#> ATC:pam 128 0.764 0.560 0.875 0.2907 4
#> ATC:pam 127 0.675 0.746 0.953 0.4170 5
#> ATC:pam 118 0.801 0.739 0.857 0.4558 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "mclust"]
# you can also extract it by
# res = res_list["ATC:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 130 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 0.878 0.901 0.956 0.2237 0.794 0.794
#> 3 3 0.871 0.920 0.953 1.5508 0.573 0.480
#> 4 4 0.730 0.822 0.900 0.2427 0.799 0.558
#> 5 5 0.609 0.551 0.771 0.0618 0.993 0.977
#> 6 6 0.617 0.554 0.702 0.0465 0.916 0.709
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
#> GSM447671 1 0.0000 0.961 1.000 0.000
#> GSM447694 1 0.1843 0.942 0.972 0.028
#> GSM447618 1 0.0000 0.961 1.000 0.000
#> GSM447691 1 0.0000 0.961 1.000 0.000
#> GSM447733 2 0.9977 0.167 0.472 0.528
#> GSM447620 1 0.0000 0.961 1.000 0.000
#> GSM447627 1 0.8713 0.585 0.708 0.292
#> GSM447630 1 0.0000 0.961 1.000 0.000
#> GSM447642 1 0.2778 0.935 0.952 0.048
#> GSM447649 1 0.2236 0.942 0.964 0.036
#> GSM447654 2 0.0000 0.866 0.000 1.000
#> GSM447655 1 0.0000 0.961 1.000 0.000
#> GSM447669 1 0.0000 0.961 1.000 0.000
#> GSM447676 1 0.2778 0.935 0.952 0.048
#> GSM447678 1 0.5842 0.837 0.860 0.140
#> GSM447681 1 0.0000 0.961 1.000 0.000
#> GSM447698 1 0.0000 0.961 1.000 0.000
#> GSM447713 1 0.0000 0.961 1.000 0.000
#> GSM447722 1 0.2778 0.935 0.952 0.048
#> GSM447726 1 0.0000 0.961 1.000 0.000
#> GSM447735 1 0.6048 0.832 0.852 0.148
#> GSM447737 1 0.0000 0.961 1.000 0.000
#> GSM447657 1 0.0000 0.961 1.000 0.000
#> GSM447674 1 0.0000 0.961 1.000 0.000
#> GSM447636 1 0.2778 0.935 0.952 0.048
#> GSM447723 1 0.0000 0.961 1.000 0.000
#> GSM447699 1 0.0000 0.961 1.000 0.000
#> GSM447708 1 0.0000 0.961 1.000 0.000
#> GSM447721 1 0.0000 0.961 1.000 0.000
#> GSM447623 1 0.0000 0.961 1.000 0.000
#> GSM447621 1 0.0000 0.961 1.000 0.000
#> GSM447650 1 0.0000 0.961 1.000 0.000
#> GSM447651 1 0.0000 0.961 1.000 0.000
#> GSM447653 2 0.0000 0.866 0.000 1.000
#> GSM447658 1 0.0000 0.961 1.000 0.000
#> GSM447675 2 0.0672 0.864 0.008 0.992
#> GSM447680 1 0.2778 0.935 0.952 0.048
#> GSM447686 1 0.0000 0.961 1.000 0.000
#> GSM447736 1 0.0000 0.961 1.000 0.000
#> GSM447629 1 0.0000 0.961 1.000 0.000
#> GSM447648 1 0.0000 0.961 1.000 0.000
#> GSM447660 1 0.0000 0.961 1.000 0.000
#> GSM447661 1 0.0000 0.961 1.000 0.000
#> GSM447663 1 0.0000 0.961 1.000 0.000
#> GSM447704 1 0.0000 0.961 1.000 0.000
#> GSM447720 1 0.0000 0.961 1.000 0.000
#> GSM447652 1 0.2948 0.932 0.948 0.052
#> GSM447679 1 0.0000 0.961 1.000 0.000
#> GSM447712 1 0.2236 0.942 0.964 0.036
#> GSM447664 1 0.7674 0.707 0.776 0.224
#> GSM447637 1 0.0376 0.959 0.996 0.004
#> GSM447639 1 0.2778 0.935 0.952 0.048
#> GSM447615 1 0.2778 0.935 0.952 0.048
#> GSM447656 1 0.0000 0.961 1.000 0.000
#> GSM447673 1 0.8608 0.588 0.716 0.284
#> GSM447719 2 0.0000 0.866 0.000 1.000
#> GSM447706 1 0.2778 0.935 0.952 0.048
#> GSM447612 1 0.0000 0.961 1.000 0.000
#> GSM447665 1 0.0000 0.961 1.000 0.000
#> GSM447677 1 0.2778 0.935 0.952 0.048
#> GSM447613 1 0.0000 0.961 1.000 0.000
#> GSM447659 2 0.0938 0.863 0.012 0.988
#> GSM447662 1 0.0000 0.961 1.000 0.000
#> GSM447666 1 0.2778 0.935 0.952 0.048
#> GSM447668 1 0.0376 0.959 0.996 0.004
#> GSM447682 1 0.0000 0.961 1.000 0.000
#> GSM447683 1 0.0000 0.961 1.000 0.000
#> GSM447688 2 0.9977 0.186 0.472 0.528
#> GSM447702 1 0.0000 0.961 1.000 0.000
#> GSM447709 1 0.0000 0.961 1.000 0.000
#> GSM447711 1 0.0000 0.961 1.000 0.000
#> GSM447715 1 0.0000 0.961 1.000 0.000
#> GSM447693 1 0.4431 0.898 0.908 0.092
#> GSM447611 2 0.0000 0.866 0.000 1.000
#> GSM447672 1 0.0000 0.961 1.000 0.000
#> GSM447703 1 0.9286 0.441 0.656 0.344
#> GSM447727 1 0.0000 0.961 1.000 0.000
#> GSM447638 1 0.2778 0.935 0.952 0.048
#> GSM447670 1 0.2778 0.935 0.952 0.048
#> GSM447700 1 0.0000 0.961 1.000 0.000
#> GSM447738 1 0.2603 0.938 0.956 0.044
#> GSM447739 1 0.3879 0.913 0.924 0.076
#> GSM447617 1 0.0000 0.961 1.000 0.000
#> GSM447628 2 0.0000 0.866 0.000 1.000
#> GSM447632 1 0.0000 0.961 1.000 0.000
#> GSM447619 1 0.0000 0.961 1.000 0.000
#> GSM447643 1 0.0672 0.957 0.992 0.008
#> GSM447724 1 0.2778 0.935 0.952 0.048
#> GSM447728 1 0.0000 0.961 1.000 0.000
#> GSM447610 1 0.9427 0.414 0.640 0.360
#> GSM447633 1 0.0000 0.961 1.000 0.000
#> GSM447634 1 0.0000 0.961 1.000 0.000
#> GSM447622 1 0.0000 0.961 1.000 0.000
#> GSM447667 1 0.0376 0.959 0.996 0.004
#> GSM447687 1 0.9732 0.255 0.596 0.404
#> GSM447695 1 0.0000 0.961 1.000 0.000
#> GSM447696 1 0.4298 0.903 0.912 0.088
#> GSM447697 1 0.0000 0.961 1.000 0.000
#> GSM447714 1 0.0000 0.961 1.000 0.000
#> GSM447717 1 0.2423 0.940 0.960 0.040
#> GSM447725 1 0.2778 0.935 0.952 0.048
#> GSM447729 2 0.0376 0.865 0.004 0.996
#> GSM447644 1 0.0000 0.961 1.000 0.000
#> GSM447710 1 0.0000 0.961 1.000 0.000
#> GSM447614 1 0.7674 0.718 0.776 0.224
#> GSM447685 1 0.0000 0.961 1.000 0.000
#> GSM447690 1 0.6048 0.833 0.852 0.148
#> GSM447730 2 0.8661 0.617 0.288 0.712
#> GSM447646 2 0.0000 0.866 0.000 1.000
#> GSM447689 1 0.0000 0.961 1.000 0.000
#> GSM447635 1 0.0000 0.961 1.000 0.000
#> GSM447641 1 0.0000 0.961 1.000 0.000
#> GSM447716 1 0.0000 0.961 1.000 0.000
#> GSM447718 1 0.0000 0.961 1.000 0.000
#> GSM447616 1 0.0000 0.961 1.000 0.000
#> GSM447626 1 0.2778 0.935 0.952 0.048
#> GSM447640 1 0.0000 0.961 1.000 0.000
#> GSM447734 1 0.0000 0.961 1.000 0.000
#> GSM447692 1 0.0938 0.953 0.988 0.012
#> GSM447647 2 0.4939 0.807 0.108 0.892
#> GSM447624 1 0.0000 0.961 1.000 0.000
#> GSM447625 1 0.0000 0.961 1.000 0.000
#> GSM447707 2 0.9635 0.428 0.388 0.612
#> GSM447732 1 0.0000 0.961 1.000 0.000
#> GSM447684 1 0.2778 0.935 0.952 0.048
#> GSM447731 2 0.0000 0.866 0.000 1.000
#> GSM447705 1 0.0000 0.961 1.000 0.000
#> GSM447631 1 0.4562 0.894 0.904 0.096
#> GSM447701 1 0.0000 0.961 1.000 0.000
#> GSM447645 1 0.2423 0.940 0.960 0.040
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM447671 2 0.0747 0.956 0.016 0.984 0.000
#> GSM447694 1 0.0424 0.943 0.992 0.008 0.000
#> GSM447618 2 0.1289 0.955 0.032 0.968 0.000
#> GSM447691 2 0.1289 0.955 0.032 0.968 0.000
#> GSM447733 1 0.4744 0.834 0.836 0.028 0.136
#> GSM447620 2 0.0892 0.955 0.020 0.980 0.000
#> GSM447627 1 0.1636 0.936 0.964 0.016 0.020
#> GSM447630 2 0.6260 0.134 0.448 0.552 0.000
#> GSM447642 1 0.1643 0.937 0.956 0.044 0.000
#> GSM447649 2 0.0747 0.959 0.016 0.984 0.000
#> GSM447654 3 0.0000 0.952 0.000 0.000 1.000
#> GSM447655 2 0.0747 0.959 0.016 0.984 0.000
#> GSM447669 2 0.1031 0.954 0.024 0.976 0.000
#> GSM447676 1 0.1031 0.943 0.976 0.024 0.000
#> GSM447678 2 0.2955 0.919 0.080 0.912 0.008
#> GSM447681 2 0.1031 0.959 0.024 0.976 0.000
#> GSM447698 2 0.1031 0.959 0.024 0.976 0.000
#> GSM447713 1 0.0000 0.942 1.000 0.000 0.000
#> GSM447722 1 0.4110 0.832 0.844 0.152 0.004
#> GSM447726 2 0.1411 0.958 0.036 0.964 0.000
#> GSM447735 1 0.1337 0.940 0.972 0.012 0.016
#> GSM447737 1 0.0424 0.943 0.992 0.008 0.000
#> GSM447657 2 0.0892 0.958 0.020 0.980 0.000
#> GSM447674 2 0.1031 0.959 0.024 0.976 0.000
#> GSM447636 2 0.3412 0.865 0.124 0.876 0.000
#> GSM447723 1 0.2356 0.919 0.928 0.072 0.000
#> GSM447699 1 0.2165 0.924 0.936 0.064 0.000
#> GSM447708 2 0.0892 0.958 0.020 0.980 0.000
#> GSM447721 1 0.0424 0.944 0.992 0.008 0.000
#> GSM447623 1 0.0000 0.942 1.000 0.000 0.000
#> GSM447621 1 0.0000 0.942 1.000 0.000 0.000
#> GSM447650 2 0.0747 0.959 0.016 0.984 0.000
#> GSM447651 2 0.0892 0.959 0.020 0.980 0.000
#> GSM447653 3 0.0000 0.952 0.000 0.000 1.000
#> GSM447658 2 0.4121 0.813 0.168 0.832 0.000
#> GSM447675 3 0.1585 0.934 0.028 0.008 0.964
#> GSM447680 2 0.0892 0.955 0.020 0.980 0.000
#> GSM447686 2 0.1411 0.958 0.036 0.964 0.000
#> GSM447736 1 0.2261 0.920 0.932 0.068 0.000
#> GSM447629 2 0.1031 0.958 0.024 0.976 0.000
#> GSM447648 1 0.0000 0.942 1.000 0.000 0.000
#> GSM447660 2 0.3038 0.893 0.104 0.896 0.000
#> GSM447661 2 0.0424 0.956 0.008 0.992 0.000
#> GSM447663 1 0.4002 0.839 0.840 0.160 0.000
#> GSM447704 2 0.0747 0.959 0.016 0.984 0.000
#> GSM447720 1 0.4121 0.824 0.832 0.168 0.000
#> GSM447652 2 0.1315 0.958 0.020 0.972 0.008
#> GSM447679 2 0.1031 0.959 0.024 0.976 0.000
#> GSM447712 1 0.0747 0.943 0.984 0.016 0.000
#> GSM447664 2 0.1337 0.956 0.016 0.972 0.012
#> GSM447637 1 0.0000 0.942 1.000 0.000 0.000
#> GSM447639 1 0.1529 0.939 0.960 0.040 0.000
#> GSM447615 1 0.1411 0.940 0.964 0.036 0.000
#> GSM447656 2 0.1289 0.958 0.032 0.968 0.000
#> GSM447673 2 0.1453 0.958 0.024 0.968 0.008
#> GSM447719 3 0.0000 0.952 0.000 0.000 1.000
#> GSM447706 1 0.1031 0.942 0.976 0.024 0.000
#> GSM447612 1 0.3482 0.864 0.872 0.128 0.000
#> GSM447665 2 0.0592 0.955 0.012 0.988 0.000
#> GSM447677 2 0.0592 0.958 0.012 0.988 0.000
#> GSM447613 1 0.0892 0.943 0.980 0.020 0.000
#> GSM447659 3 0.2711 0.881 0.088 0.000 0.912
#> GSM447662 1 0.2066 0.932 0.940 0.060 0.000
#> GSM447666 2 0.2448 0.914 0.076 0.924 0.000
#> GSM447668 2 0.0424 0.956 0.008 0.992 0.000
#> GSM447682 2 0.1163 0.959 0.028 0.972 0.000
#> GSM447683 2 0.0747 0.959 0.016 0.984 0.000
#> GSM447688 2 0.2297 0.938 0.020 0.944 0.036
#> GSM447702 2 0.0747 0.959 0.016 0.984 0.000
#> GSM447709 2 0.0747 0.959 0.016 0.984 0.000
#> GSM447711 1 0.0747 0.944 0.984 0.016 0.000
#> GSM447715 2 0.2448 0.928 0.076 0.924 0.000
#> GSM447693 1 0.0747 0.937 0.984 0.016 0.000
#> GSM447611 3 0.0000 0.952 0.000 0.000 1.000
#> GSM447672 2 0.0892 0.959 0.020 0.980 0.000
#> GSM447703 2 0.1170 0.956 0.016 0.976 0.008
#> GSM447727 1 0.2625 0.907 0.916 0.084 0.000
#> GSM447638 2 0.1753 0.936 0.048 0.952 0.000
#> GSM447670 1 0.1411 0.940 0.964 0.036 0.000
#> GSM447700 1 0.6180 0.357 0.584 0.416 0.000
#> GSM447738 2 0.0747 0.959 0.016 0.984 0.000
#> GSM447739 1 0.0424 0.940 0.992 0.008 0.000
#> GSM447617 1 0.0000 0.942 1.000 0.000 0.000
#> GSM447628 3 0.0000 0.952 0.000 0.000 1.000
#> GSM447632 2 0.0747 0.959 0.016 0.984 0.000
#> GSM447619 1 0.1031 0.943 0.976 0.024 0.000
#> GSM447643 2 0.0747 0.957 0.016 0.984 0.000
#> GSM447724 1 0.3038 0.888 0.896 0.104 0.000
#> GSM447728 2 0.1031 0.959 0.024 0.976 0.000
#> GSM447610 1 0.1482 0.937 0.968 0.012 0.020
#> GSM447633 2 0.0747 0.956 0.016 0.984 0.000
#> GSM447634 1 0.2165 0.925 0.936 0.064 0.000
#> GSM447622 1 0.0000 0.942 1.000 0.000 0.000
#> GSM447667 2 0.0592 0.958 0.012 0.988 0.000
#> GSM447687 2 0.1170 0.956 0.016 0.976 0.008
#> GSM447695 1 0.1031 0.942 0.976 0.024 0.000
#> GSM447696 1 0.0424 0.940 0.992 0.008 0.000
#> GSM447697 1 0.0000 0.942 1.000 0.000 0.000
#> GSM447714 1 0.1289 0.941 0.968 0.032 0.000
#> GSM447717 2 0.4291 0.796 0.180 0.820 0.000
#> GSM447725 1 0.0592 0.944 0.988 0.012 0.000
#> GSM447729 3 0.1015 0.944 0.012 0.008 0.980
#> GSM447644 2 0.0747 0.956 0.016 0.984 0.000
#> GSM447710 1 0.1163 0.943 0.972 0.028 0.000
#> GSM447614 1 0.1620 0.936 0.964 0.012 0.024
#> GSM447685 2 0.1031 0.959 0.024 0.976 0.000
#> GSM447690 1 0.0475 0.940 0.992 0.004 0.004
#> GSM447730 2 0.3826 0.854 0.008 0.868 0.124
#> GSM447646 3 0.0000 0.952 0.000 0.000 1.000
#> GSM447689 1 0.2261 0.919 0.932 0.068 0.000
#> GSM447635 2 0.2356 0.919 0.072 0.928 0.000
#> GSM447641 1 0.1643 0.933 0.956 0.044 0.000
#> GSM447716 2 0.1163 0.959 0.028 0.972 0.000
#> GSM447718 1 0.2959 0.894 0.900 0.100 0.000
#> GSM447616 1 0.0000 0.942 1.000 0.000 0.000
#> GSM447626 1 0.2796 0.906 0.908 0.092 0.000
#> GSM447640 2 0.0747 0.959 0.016 0.984 0.000
#> GSM447734 1 0.1031 0.942 0.976 0.024 0.000
#> GSM447692 1 0.0000 0.942 1.000 0.000 0.000
#> GSM447647 3 0.6445 0.509 0.020 0.308 0.672
#> GSM447624 1 0.0000 0.942 1.000 0.000 0.000
#> GSM447625 1 0.1529 0.941 0.960 0.040 0.000
#> GSM447707 2 0.1491 0.954 0.016 0.968 0.016
#> GSM447732 1 0.1289 0.941 0.968 0.032 0.000
#> GSM447684 2 0.3038 0.889 0.104 0.896 0.000
#> GSM447731 3 0.0000 0.952 0.000 0.000 1.000
#> GSM447705 1 0.5254 0.675 0.736 0.264 0.000
#> GSM447631 1 0.0237 0.942 0.996 0.004 0.000
#> GSM447701 2 0.0892 0.958 0.020 0.980 0.000
#> GSM447645 1 0.0237 0.942 0.996 0.004 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM447671 1 0.2965 0.8359 0.892 0.072 0.036 0.000
#> GSM447694 3 0.0592 0.9143 0.016 0.000 0.984 0.000
#> GSM447618 1 0.6268 -0.1107 0.496 0.448 0.056 0.000
#> GSM447691 1 0.2892 0.8375 0.896 0.068 0.036 0.000
#> GSM447733 3 0.5250 0.4752 0.024 0.000 0.660 0.316
#> GSM447620 2 0.4790 0.5489 0.380 0.620 0.000 0.000
#> GSM447627 3 0.0188 0.9141 0.004 0.000 0.996 0.000
#> GSM447630 1 0.2546 0.8389 0.912 0.028 0.060 0.000
#> GSM447642 1 0.1716 0.8194 0.936 0.000 0.064 0.000
#> GSM447649 2 0.0000 0.8739 0.000 1.000 0.000 0.000
#> GSM447654 4 0.0000 0.9178 0.000 0.000 0.000 1.000
#> GSM447655 2 0.0000 0.8739 0.000 1.000 0.000 0.000
#> GSM447669 1 0.2928 0.8396 0.896 0.052 0.052 0.000
#> GSM447676 3 0.4730 0.5478 0.364 0.000 0.636 0.000
#> GSM447678 2 0.4483 0.7362 0.104 0.808 0.088 0.000
#> GSM447681 2 0.0707 0.8749 0.020 0.980 0.000 0.000
#> GSM447698 2 0.1767 0.8612 0.044 0.944 0.012 0.000
#> GSM447713 3 0.1867 0.9161 0.072 0.000 0.928 0.000
#> GSM447722 3 0.4468 0.6497 0.232 0.016 0.752 0.000
#> GSM447726 1 0.2401 0.8285 0.904 0.092 0.004 0.000
#> GSM447735 3 0.0592 0.9143 0.016 0.000 0.984 0.000
#> GSM447737 3 0.0000 0.9139 0.000 0.000 1.000 0.000
#> GSM447657 2 0.1118 0.8698 0.036 0.964 0.000 0.000
#> GSM447674 2 0.0000 0.8739 0.000 1.000 0.000 0.000
#> GSM447636 2 0.4204 0.8136 0.192 0.788 0.020 0.000
#> GSM447723 1 0.1557 0.8353 0.944 0.000 0.056 0.000
#> GSM447699 3 0.0592 0.9143 0.016 0.000 0.984 0.000
#> GSM447708 2 0.4304 0.7354 0.284 0.716 0.000 0.000
#> GSM447721 3 0.1940 0.9145 0.076 0.000 0.924 0.000
#> GSM447623 3 0.1867 0.9161 0.072 0.000 0.928 0.000
#> GSM447621 3 0.0592 0.9143 0.016 0.000 0.984 0.000
#> GSM447650 2 0.0000 0.8739 0.000 1.000 0.000 0.000
#> GSM447651 2 0.2868 0.8515 0.136 0.864 0.000 0.000
#> GSM447653 4 0.0000 0.9178 0.000 0.000 0.000 1.000
#> GSM447658 1 0.2443 0.8251 0.916 0.060 0.024 0.000
#> GSM447675 4 0.0188 0.9158 0.000 0.004 0.000 0.996
#> GSM447680 2 0.3649 0.8106 0.204 0.796 0.000 0.000
#> GSM447686 2 0.4406 0.7043 0.300 0.700 0.000 0.000
#> GSM447736 3 0.1867 0.8816 0.072 0.000 0.928 0.000
#> GSM447629 2 0.4164 0.7615 0.264 0.736 0.000 0.000
#> GSM447648 3 0.1867 0.9161 0.072 0.000 0.928 0.000
#> GSM447660 1 0.2271 0.8388 0.916 0.076 0.008 0.000
#> GSM447661 2 0.0188 0.8745 0.004 0.996 0.000 0.000
#> GSM447663 1 0.2281 0.8300 0.904 0.000 0.096 0.000
#> GSM447704 2 0.0000 0.8739 0.000 1.000 0.000 0.000
#> GSM447720 1 0.2281 0.8297 0.904 0.000 0.096 0.000
#> GSM447652 2 0.0188 0.8725 0.004 0.996 0.000 0.000
#> GSM447679 2 0.0469 0.8755 0.012 0.988 0.000 0.000
#> GSM447712 3 0.2081 0.9104 0.084 0.000 0.916 0.000
#> GSM447664 2 0.0336 0.8733 0.008 0.992 0.000 0.000
#> GSM447637 3 0.1867 0.9161 0.072 0.000 0.928 0.000
#> GSM447639 3 0.1389 0.9009 0.048 0.000 0.952 0.000
#> GSM447615 1 0.2011 0.8089 0.920 0.000 0.080 0.000
#> GSM447656 2 0.4134 0.7636 0.260 0.740 0.000 0.000
#> GSM447673 2 0.0000 0.8739 0.000 1.000 0.000 0.000
#> GSM447719 4 0.0000 0.9178 0.000 0.000 0.000 1.000
#> GSM447706 1 0.3649 0.6852 0.796 0.000 0.204 0.000
#> GSM447612 1 0.4907 0.3413 0.580 0.000 0.420 0.000
#> GSM447665 2 0.4585 0.6585 0.332 0.668 0.000 0.000
#> GSM447677 2 0.3569 0.8173 0.196 0.804 0.000 0.000
#> GSM447613 1 0.4843 0.2733 0.604 0.000 0.396 0.000
#> GSM447659 4 0.3975 0.6758 0.000 0.000 0.240 0.760
#> GSM447662 1 0.3764 0.7277 0.784 0.000 0.216 0.000
#> GSM447666 1 0.2125 0.8348 0.920 0.076 0.004 0.000
#> GSM447668 2 0.3400 0.8287 0.180 0.820 0.000 0.000
#> GSM447682 2 0.1022 0.8747 0.032 0.968 0.000 0.000
#> GSM447683 2 0.3528 0.8196 0.192 0.808 0.000 0.000
#> GSM447688 2 0.1247 0.8628 0.004 0.968 0.016 0.012
#> GSM447702 2 0.0000 0.8739 0.000 1.000 0.000 0.000
#> GSM447709 2 0.3528 0.8212 0.192 0.808 0.000 0.000
#> GSM447711 1 0.4981 0.0295 0.536 0.000 0.464 0.000
#> GSM447715 1 0.2401 0.8294 0.904 0.092 0.004 0.000
#> GSM447693 3 0.1792 0.9166 0.068 0.000 0.932 0.000
#> GSM447611 4 0.0000 0.9178 0.000 0.000 0.000 1.000
#> GSM447672 2 0.0000 0.8739 0.000 1.000 0.000 0.000
#> GSM447703 2 0.0000 0.8739 0.000 1.000 0.000 0.000
#> GSM447727 1 0.0817 0.8396 0.976 0.000 0.024 0.000
#> GSM447638 2 0.3975 0.7884 0.240 0.760 0.000 0.000
#> GSM447670 1 0.1302 0.8291 0.956 0.000 0.044 0.000
#> GSM447700 1 0.2882 0.8360 0.892 0.024 0.084 0.000
#> GSM447738 2 0.0000 0.8739 0.000 1.000 0.000 0.000
#> GSM447739 3 0.1940 0.9145 0.076 0.000 0.924 0.000
#> GSM447617 3 0.1867 0.9161 0.072 0.000 0.928 0.000
#> GSM447628 4 0.0000 0.9178 0.000 0.000 0.000 1.000
#> GSM447632 2 0.0000 0.8739 0.000 1.000 0.000 0.000
#> GSM447619 3 0.3975 0.7184 0.240 0.000 0.760 0.000
#> GSM447643 2 0.3837 0.8009 0.224 0.776 0.000 0.000
#> GSM447724 3 0.2546 0.8567 0.092 0.008 0.900 0.000
#> GSM447728 2 0.3074 0.8445 0.152 0.848 0.000 0.000
#> GSM447610 3 0.0592 0.9143 0.016 0.000 0.984 0.000
#> GSM447633 1 0.2408 0.8171 0.896 0.104 0.000 0.000
#> GSM447634 3 0.2408 0.8581 0.104 0.000 0.896 0.000
#> GSM447622 3 0.1867 0.9161 0.072 0.000 0.928 0.000
#> GSM447667 2 0.2589 0.8593 0.116 0.884 0.000 0.000
#> GSM447687 2 0.0000 0.8739 0.000 1.000 0.000 0.000
#> GSM447695 3 0.0592 0.9143 0.016 0.000 0.984 0.000
#> GSM447696 3 0.1940 0.9145 0.076 0.000 0.924 0.000
#> GSM447697 3 0.1867 0.9161 0.072 0.000 0.928 0.000
#> GSM447714 3 0.0592 0.9143 0.016 0.000 0.984 0.000
#> GSM447717 1 0.3384 0.7768 0.860 0.116 0.024 0.000
#> GSM447725 3 0.2704 0.8889 0.124 0.000 0.876 0.000
#> GSM447729 4 0.0188 0.9158 0.000 0.004 0.000 0.996
#> GSM447644 1 0.2730 0.8289 0.896 0.088 0.016 0.000
#> GSM447710 3 0.0592 0.9143 0.016 0.000 0.984 0.000
#> GSM447614 3 0.0592 0.9143 0.016 0.000 0.984 0.000
#> GSM447685 2 0.3123 0.8421 0.156 0.844 0.000 0.000
#> GSM447690 3 0.1940 0.9145 0.076 0.000 0.924 0.000
#> GSM447730 2 0.1004 0.8657 0.004 0.972 0.000 0.024
#> GSM447646 4 0.0000 0.9178 0.000 0.000 0.000 1.000
#> GSM447689 1 0.0707 0.8399 0.980 0.000 0.020 0.000
#> GSM447635 1 0.2830 0.8397 0.900 0.060 0.040 0.000
#> GSM447641 1 0.1716 0.8245 0.936 0.000 0.064 0.000
#> GSM447716 2 0.1474 0.8700 0.052 0.948 0.000 0.000
#> GSM447718 1 0.1520 0.8477 0.956 0.024 0.020 0.000
#> GSM447616 3 0.0921 0.9163 0.028 0.000 0.972 0.000
#> GSM447626 1 0.0592 0.8411 0.984 0.000 0.016 0.000
#> GSM447640 2 0.0000 0.8739 0.000 1.000 0.000 0.000
#> GSM447734 3 0.0592 0.9143 0.016 0.000 0.984 0.000
#> GSM447692 3 0.1867 0.9161 0.072 0.000 0.928 0.000
#> GSM447647 4 0.5168 0.0687 0.004 0.492 0.000 0.504
#> GSM447624 3 0.1867 0.9161 0.072 0.000 0.928 0.000
#> GSM447625 3 0.0592 0.9143 0.016 0.000 0.984 0.000
#> GSM447707 2 0.0188 0.8725 0.004 0.996 0.000 0.000
#> GSM447732 3 0.0592 0.9143 0.016 0.000 0.984 0.000
#> GSM447684 1 0.1576 0.8455 0.948 0.048 0.004 0.000
#> GSM447731 4 0.0000 0.9178 0.000 0.000 0.000 1.000
#> GSM447705 1 0.2660 0.8433 0.908 0.036 0.056 0.000
#> GSM447631 3 0.1867 0.9161 0.072 0.000 0.928 0.000
#> GSM447701 2 0.4072 0.7724 0.252 0.748 0.000 0.000
#> GSM447645 3 0.1940 0.9145 0.076 0.000 0.924 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM447671 5 0.4697 0.287 0.032 0.320 0.000 0.000 0.648
#> GSM447694 3 0.1557 0.852 0.052 0.000 0.940 0.000 0.008
#> GSM447618 5 0.4900 -0.156 0.024 0.464 0.000 0.000 0.512
#> GSM447691 5 0.2685 0.653 0.028 0.092 0.000 0.000 0.880
#> GSM447733 3 0.7738 0.355 0.232 0.000 0.480 0.120 0.168
#> GSM447620 2 0.4119 0.590 0.036 0.752 0.000 0.000 0.212
#> GSM447627 3 0.1768 0.845 0.072 0.000 0.924 0.000 0.004
#> GSM447630 5 0.1124 0.687 0.000 0.036 0.004 0.000 0.960
#> GSM447642 5 0.5442 0.470 0.380 0.004 0.056 0.000 0.560
#> GSM447649 2 0.4383 -0.346 0.424 0.572 0.000 0.000 0.004
#> GSM447654 4 0.0000 0.940 0.000 0.000 0.000 1.000 0.000
#> GSM447655 2 0.0290 0.523 0.008 0.992 0.000 0.000 0.000
#> GSM447669 5 0.4498 0.385 0.032 0.280 0.000 0.000 0.688
#> GSM447676 3 0.5045 0.635 0.108 0.000 0.696 0.000 0.196
#> GSM447678 1 0.6803 0.449 0.468 0.200 0.012 0.000 0.320
#> GSM447681 2 0.2629 0.591 0.004 0.860 0.000 0.000 0.136
#> GSM447698 2 0.4763 0.380 0.076 0.712 0.000 0.000 0.212
#> GSM447713 3 0.0579 0.858 0.008 0.000 0.984 0.000 0.008
#> GSM447722 3 0.5497 0.510 0.056 0.012 0.604 0.000 0.328
#> GSM447726 5 0.3193 0.622 0.028 0.132 0.000 0.000 0.840
#> GSM447735 3 0.2136 0.839 0.088 0.000 0.904 0.000 0.008
#> GSM447737 3 0.1522 0.852 0.044 0.000 0.944 0.000 0.012
#> GSM447657 2 0.4822 0.453 0.032 0.616 0.000 0.000 0.352
#> GSM447674 2 0.0451 0.528 0.008 0.988 0.000 0.000 0.004
#> GSM447636 2 0.5524 0.535 0.152 0.664 0.004 0.000 0.180
#> GSM447723 5 0.3043 0.648 0.024 0.008 0.104 0.000 0.864
#> GSM447699 3 0.2576 0.852 0.056 0.008 0.900 0.000 0.036
#> GSM447708 2 0.4867 0.323 0.024 0.544 0.000 0.000 0.432
#> GSM447721 3 0.1701 0.854 0.048 0.000 0.936 0.000 0.016
#> GSM447623 3 0.1106 0.857 0.024 0.000 0.964 0.000 0.012
#> GSM447621 3 0.2209 0.855 0.056 0.000 0.912 0.000 0.032
#> GSM447650 2 0.0162 0.528 0.004 0.996 0.000 0.000 0.000
#> GSM447651 2 0.2732 0.620 0.000 0.840 0.000 0.000 0.160
#> GSM447653 4 0.0000 0.940 0.000 0.000 0.000 1.000 0.000
#> GSM447658 5 0.6151 0.578 0.140 0.144 0.056 0.000 0.660
#> GSM447675 4 0.1282 0.921 0.044 0.000 0.004 0.952 0.000
#> GSM447680 2 0.2891 0.619 0.000 0.824 0.000 0.000 0.176
#> GSM447686 2 0.4696 0.454 0.024 0.616 0.000 0.000 0.360
#> GSM447736 3 0.3493 0.827 0.060 0.000 0.832 0.000 0.108
#> GSM447629 2 0.4878 0.307 0.024 0.536 0.000 0.000 0.440
#> GSM447648 3 0.1211 0.857 0.024 0.000 0.960 0.000 0.016
#> GSM447660 5 0.3400 0.675 0.072 0.076 0.004 0.000 0.848
#> GSM447661 2 0.1410 0.575 0.000 0.940 0.000 0.000 0.060
#> GSM447663 5 0.1686 0.684 0.028 0.008 0.020 0.000 0.944
#> GSM447704 2 0.1831 0.441 0.076 0.920 0.000 0.000 0.004
#> GSM447720 5 0.1251 0.687 0.000 0.036 0.008 0.000 0.956
#> GSM447652 2 0.4542 -0.383 0.456 0.536 0.000 0.000 0.008
#> GSM447679 2 0.2690 0.620 0.000 0.844 0.000 0.000 0.156
#> GSM447712 3 0.3506 0.788 0.132 0.000 0.824 0.000 0.044
#> GSM447664 2 0.4593 -0.431 0.480 0.512 0.004 0.000 0.004
#> GSM447637 3 0.1195 0.857 0.028 0.000 0.960 0.000 0.012
#> GSM447639 3 0.3914 0.764 0.048 0.000 0.788 0.000 0.164
#> GSM447615 5 0.5535 0.448 0.392 0.000 0.072 0.000 0.536
#> GSM447656 2 0.4639 0.478 0.024 0.632 0.000 0.000 0.344
#> GSM447673 2 0.4538 -0.404 0.452 0.540 0.000 0.000 0.008
#> GSM447719 4 0.0000 0.940 0.000 0.000 0.000 1.000 0.000
#> GSM447706 5 0.6158 0.401 0.384 0.000 0.136 0.000 0.480
#> GSM447612 5 0.3814 0.548 0.012 0.012 0.192 0.000 0.784
#> GSM447665 2 0.4989 0.348 0.032 0.552 0.000 0.000 0.416
#> GSM447677 2 0.2929 0.618 0.000 0.820 0.000 0.000 0.180
#> GSM447613 5 0.5938 0.210 0.112 0.000 0.376 0.000 0.512
#> GSM447659 4 0.5216 0.541 0.080 0.000 0.248 0.668 0.004
#> GSM447662 5 0.5812 0.185 0.100 0.000 0.372 0.000 0.528
#> GSM447666 5 0.4675 0.507 0.360 0.016 0.004 0.000 0.620
#> GSM447668 2 0.2732 0.620 0.000 0.840 0.000 0.000 0.160
#> GSM447682 2 0.3488 0.616 0.024 0.808 0.000 0.000 0.168
#> GSM447683 2 0.2773 0.620 0.000 0.836 0.000 0.000 0.164
#> GSM447688 2 0.5116 -0.467 0.472 0.500 0.004 0.020 0.004
#> GSM447702 2 0.0290 0.524 0.008 0.992 0.000 0.000 0.000
#> GSM447709 2 0.3602 0.612 0.024 0.796 0.000 0.000 0.180
#> GSM447711 5 0.6224 0.144 0.144 0.000 0.388 0.000 0.468
#> GSM447715 5 0.2193 0.674 0.028 0.060 0.000 0.000 0.912
#> GSM447693 3 0.0865 0.858 0.024 0.000 0.972 0.000 0.004
#> GSM447611 4 0.0566 0.936 0.012 0.000 0.004 0.984 0.000
#> GSM447672 2 0.0404 0.519 0.012 0.988 0.000 0.000 0.000
#> GSM447703 2 0.4410 -0.375 0.440 0.556 0.000 0.000 0.004
#> GSM447727 5 0.2082 0.692 0.032 0.024 0.016 0.000 0.928
#> GSM447638 2 0.4550 0.594 0.064 0.744 0.004 0.000 0.188
#> GSM447670 5 0.5808 0.430 0.392 0.000 0.096 0.000 0.512
#> GSM447700 5 0.1043 0.685 0.000 0.040 0.000 0.000 0.960
#> GSM447738 2 0.4196 -0.211 0.356 0.640 0.000 0.000 0.004
#> GSM447739 3 0.1571 0.845 0.060 0.000 0.936 0.000 0.004
#> GSM447617 3 0.1106 0.857 0.024 0.000 0.964 0.000 0.012
#> GSM447628 4 0.0290 0.938 0.008 0.000 0.000 0.992 0.000
#> GSM447632 2 0.4341 -0.310 0.404 0.592 0.000 0.000 0.004
#> GSM447619 3 0.4961 0.124 0.028 0.000 0.524 0.000 0.448
#> GSM447643 2 0.3769 0.614 0.028 0.796 0.004 0.000 0.172
#> GSM447724 3 0.5160 0.630 0.064 0.008 0.672 0.000 0.256
#> GSM447728 2 0.2773 0.620 0.000 0.836 0.000 0.000 0.164
#> GSM447610 3 0.2136 0.839 0.088 0.000 0.904 0.000 0.008
#> GSM447633 5 0.4329 0.450 0.032 0.252 0.000 0.000 0.716
#> GSM447634 3 0.4793 0.685 0.056 0.004 0.704 0.000 0.236
#> GSM447622 3 0.4697 0.467 0.032 0.000 0.648 0.000 0.320
#> GSM447667 2 0.3536 0.617 0.032 0.812 0.000 0.000 0.156
#> GSM447687 2 0.4420 -0.390 0.448 0.548 0.000 0.000 0.004
#> GSM447695 3 0.1800 0.853 0.048 0.000 0.932 0.000 0.020
#> GSM447696 3 0.0671 0.856 0.016 0.000 0.980 0.000 0.004
#> GSM447697 3 0.0290 0.858 0.008 0.000 0.992 0.000 0.000
#> GSM447714 3 0.2491 0.852 0.068 0.000 0.896 0.000 0.036
#> GSM447717 5 0.6257 0.573 0.160 0.136 0.056 0.000 0.648
#> GSM447725 3 0.4069 0.759 0.112 0.000 0.792 0.000 0.096
#> GSM447729 4 0.1282 0.921 0.044 0.000 0.004 0.952 0.000
#> GSM447644 5 0.3321 0.616 0.032 0.136 0.000 0.000 0.832
#> GSM447710 3 0.5245 0.474 0.064 0.000 0.608 0.000 0.328
#> GSM447614 3 0.2017 0.843 0.080 0.000 0.912 0.000 0.008
#> GSM447685 2 0.2732 0.620 0.000 0.840 0.000 0.000 0.160
#> GSM447690 3 0.1502 0.847 0.056 0.000 0.940 0.000 0.004
#> GSM447730 2 0.4860 -0.403 0.440 0.540 0.000 0.016 0.004
#> GSM447646 4 0.0000 0.940 0.000 0.000 0.000 1.000 0.000
#> GSM447689 5 0.3343 0.680 0.068 0.028 0.040 0.000 0.864
#> GSM447635 5 0.2046 0.673 0.016 0.068 0.000 0.000 0.916
#> GSM447641 5 0.4266 0.631 0.120 0.000 0.104 0.000 0.776
#> GSM447716 2 0.4966 0.366 0.032 0.564 0.000 0.000 0.404
#> GSM447718 5 0.1893 0.690 0.024 0.012 0.028 0.000 0.936
#> GSM447616 3 0.1836 0.856 0.036 0.000 0.932 0.000 0.032
#> GSM447626 5 0.4734 0.506 0.344 0.008 0.016 0.000 0.632
#> GSM447640 2 0.0324 0.531 0.004 0.992 0.000 0.000 0.004
#> GSM447734 3 0.2012 0.853 0.060 0.000 0.920 0.000 0.020
#> GSM447692 3 0.0404 0.857 0.012 0.000 0.988 0.000 0.000
#> GSM447647 1 0.6530 0.375 0.464 0.380 0.004 0.148 0.004
#> GSM447624 3 0.1106 0.857 0.024 0.000 0.964 0.000 0.012
#> GSM447625 3 0.2149 0.851 0.048 0.000 0.916 0.000 0.036
#> GSM447707 2 0.4415 -0.381 0.444 0.552 0.000 0.000 0.004
#> GSM447732 3 0.5495 0.182 0.064 0.000 0.500 0.000 0.436
#> GSM447684 5 0.2264 0.687 0.060 0.024 0.004 0.000 0.912
#> GSM447731 4 0.0000 0.940 0.000 0.000 0.000 1.000 0.000
#> GSM447705 5 0.1043 0.686 0.000 0.040 0.000 0.000 0.960
#> GSM447631 3 0.0671 0.857 0.016 0.000 0.980 0.000 0.004
#> GSM447701 2 0.4243 0.561 0.024 0.712 0.000 0.000 0.264
#> GSM447645 3 0.0992 0.857 0.024 0.000 0.968 0.000 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM447671 2 0.5762 -0.10426 0.380 0.464 0.000 0.000 0.004 0.152
#> GSM447694 3 0.1082 0.63378 0.000 0.000 0.956 0.000 0.004 0.040
#> GSM447618 2 0.5283 0.32691 0.264 0.588 0.000 0.000 0.000 0.148
#> GSM447691 1 0.5396 0.49336 0.564 0.284 0.000 0.000 0.000 0.152
#> GSM447733 6 0.7238 0.48081 0.160 0.000 0.212 0.080 0.040 0.508
#> GSM447620 2 0.2649 0.61614 0.072 0.880 0.000 0.000 0.012 0.036
#> GSM447627 3 0.4097 -0.66445 0.000 0.000 0.504 0.000 0.008 0.488
#> GSM447630 1 0.3977 0.64726 0.760 0.096 0.000 0.000 0.000 0.144
#> GSM447642 1 0.5884 0.50441 0.640 0.004 0.112 0.000 0.084 0.160
#> GSM447649 5 0.3766 0.74219 0.012 0.304 0.000 0.000 0.684 0.000
#> GSM447654 4 0.0000 0.94090 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM447655 2 0.2697 0.60426 0.000 0.812 0.000 0.000 0.188 0.000
#> GSM447669 1 0.5784 0.22462 0.432 0.412 0.000 0.000 0.004 0.152
#> GSM447676 3 0.4774 0.40362 0.336 0.000 0.612 0.000 0.020 0.032
#> GSM447678 5 0.6633 0.37109 0.096 0.160 0.016 0.000 0.576 0.152
#> GSM447681 2 0.4859 0.60510 0.016 0.692 0.000 0.000 0.188 0.104
#> GSM447698 2 0.5633 0.51579 0.116 0.660 0.000 0.000 0.088 0.136
#> GSM447713 3 0.2445 0.64946 0.000 0.000 0.872 0.000 0.108 0.020
#> GSM447722 3 0.6425 0.10979 0.344 0.000 0.468 0.000 0.056 0.132
#> GSM447726 1 0.5350 0.48388 0.564 0.296 0.000 0.000 0.000 0.140
#> GSM447735 6 0.4096 0.63396 0.000 0.000 0.484 0.000 0.008 0.508
#> GSM447737 3 0.1088 0.64906 0.000 0.000 0.960 0.000 0.016 0.024
#> GSM447657 2 0.5822 0.47218 0.180 0.624 0.000 0.000 0.060 0.136
#> GSM447674 2 0.2793 0.59612 0.000 0.800 0.000 0.000 0.200 0.000
#> GSM447636 2 0.4690 0.48519 0.112 0.736 0.000 0.000 0.116 0.036
#> GSM447723 1 0.2604 0.63103 0.856 0.004 0.132 0.000 0.004 0.004
#> GSM447699 3 0.1749 0.63874 0.036 0.000 0.932 0.000 0.008 0.024
#> GSM447708 2 0.4745 0.47067 0.188 0.676 0.000 0.000 0.000 0.136
#> GSM447721 3 0.3192 0.65253 0.024 0.000 0.836 0.000 0.120 0.020
#> GSM447623 3 0.1957 0.65241 0.000 0.000 0.888 0.000 0.112 0.000
#> GSM447621 3 0.0767 0.65119 0.012 0.000 0.976 0.000 0.008 0.004
#> GSM447650 2 0.2980 0.59727 0.008 0.800 0.000 0.000 0.192 0.000
#> GSM447651 2 0.2706 0.62559 0.008 0.832 0.000 0.000 0.160 0.000
#> GSM447653 4 0.2454 0.86325 0.000 0.000 0.000 0.840 0.000 0.160
#> GSM447658 1 0.6125 0.54889 0.616 0.220 0.080 0.000 0.052 0.032
#> GSM447675 4 0.2325 0.89577 0.000 0.000 0.000 0.892 0.048 0.060
#> GSM447680 2 0.3065 0.61736 0.008 0.812 0.000 0.000 0.172 0.008
#> GSM447686 2 0.4145 0.50331 0.252 0.700 0.000 0.000 0.000 0.048
#> GSM447736 3 0.3596 0.49744 0.232 0.000 0.748 0.000 0.004 0.016
#> GSM447629 2 0.4801 0.46133 0.196 0.668 0.000 0.000 0.000 0.136
#> GSM447648 3 0.1957 0.65241 0.000 0.000 0.888 0.000 0.112 0.000
#> GSM447660 1 0.5112 0.51736 0.652 0.252 0.004 0.000 0.020 0.072
#> GSM447661 2 0.2631 0.61261 0.000 0.820 0.000 0.000 0.180 0.000
#> GSM447663 1 0.4304 0.61522 0.760 0.020 0.152 0.000 0.004 0.064
#> GSM447704 2 0.3564 0.47341 0.012 0.724 0.000 0.000 0.264 0.000
#> GSM447720 1 0.4039 0.65503 0.788 0.032 0.064 0.000 0.000 0.116
#> GSM447652 5 0.4032 0.65734 0.000 0.420 0.000 0.000 0.572 0.008
#> GSM447679 2 0.2703 0.61888 0.004 0.824 0.000 0.000 0.172 0.000
#> GSM447712 3 0.5050 0.43975 0.276 0.000 0.640 0.000 0.052 0.032
#> GSM447664 5 0.4491 0.66435 0.008 0.372 0.012 0.000 0.600 0.008
#> GSM447637 3 0.2445 0.64367 0.000 0.000 0.872 0.000 0.108 0.020
#> GSM447639 3 0.4368 0.47534 0.212 0.000 0.716 0.000 0.008 0.064
#> GSM447615 1 0.6344 0.46124 0.568 0.000 0.188 0.000 0.084 0.160
#> GSM447656 2 0.3744 0.56029 0.200 0.756 0.000 0.000 0.000 0.044
#> GSM447673 5 0.3460 0.77251 0.020 0.220 0.000 0.000 0.760 0.000
#> GSM447719 4 0.2454 0.86325 0.000 0.000 0.000 0.840 0.000 0.160
#> GSM447706 1 0.6457 0.28805 0.568 0.008 0.184 0.000 0.068 0.172
#> GSM447612 1 0.3731 0.56704 0.756 0.000 0.212 0.000 0.008 0.024
#> GSM447665 2 0.4865 0.47382 0.176 0.676 0.000 0.000 0.004 0.144
#> GSM447677 2 0.3065 0.61736 0.008 0.812 0.000 0.000 0.172 0.008
#> GSM447613 1 0.3911 0.48006 0.720 0.000 0.252 0.000 0.008 0.020
#> GSM447659 6 0.5703 -0.09180 0.000 0.000 0.168 0.360 0.000 0.472
#> GSM447662 1 0.5644 0.36322 0.576 0.008 0.312 0.000 0.024 0.080
#> GSM447666 1 0.6254 0.48892 0.540 0.184 0.000 0.000 0.044 0.232
#> GSM447668 2 0.2668 0.62289 0.004 0.828 0.000 0.000 0.168 0.000
#> GSM447682 2 0.1230 0.62728 0.028 0.956 0.000 0.000 0.008 0.008
#> GSM447683 2 0.2896 0.62873 0.016 0.824 0.000 0.000 0.160 0.000
#> GSM447688 5 0.6302 0.53976 0.000 0.128 0.012 0.164 0.608 0.088
#> GSM447702 2 0.2883 0.56724 0.000 0.788 0.000 0.000 0.212 0.000
#> GSM447709 2 0.1088 0.63335 0.024 0.960 0.000 0.000 0.000 0.016
#> GSM447711 1 0.5507 0.01771 0.492 0.000 0.420 0.000 0.052 0.036
#> GSM447715 1 0.4570 0.55496 0.672 0.264 0.000 0.000 0.008 0.056
#> GSM447693 3 0.3017 0.61239 0.000 0.000 0.840 0.000 0.108 0.052
#> GSM447611 4 0.0858 0.93236 0.000 0.000 0.000 0.968 0.004 0.028
#> GSM447672 2 0.2793 0.58104 0.000 0.800 0.000 0.000 0.200 0.000
#> GSM447703 5 0.3314 0.77292 0.012 0.224 0.000 0.000 0.764 0.000
#> GSM447727 1 0.2533 0.66425 0.892 0.044 0.052 0.000 0.008 0.004
#> GSM447638 2 0.3641 0.55693 0.120 0.812 0.000 0.000 0.028 0.040
#> GSM447670 1 0.4411 0.51400 0.744 0.012 0.008 0.000 0.064 0.172
#> GSM447700 1 0.4388 0.65565 0.748 0.084 0.020 0.000 0.000 0.148
#> GSM447738 5 0.3940 0.65820 0.012 0.348 0.000 0.000 0.640 0.000
#> GSM447739 3 0.5084 0.12222 0.000 0.000 0.612 0.000 0.124 0.264
#> GSM447617 3 0.1957 0.65241 0.000 0.000 0.888 0.000 0.112 0.000
#> GSM447628 4 0.0000 0.94090 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM447632 5 0.3802 0.73614 0.012 0.312 0.000 0.000 0.676 0.000
#> GSM447619 3 0.4907 0.04676 0.404 0.000 0.544 0.000 0.012 0.040
#> GSM447643 2 0.1546 0.62675 0.020 0.944 0.000 0.000 0.020 0.016
#> GSM447724 3 0.4774 0.40836 0.272 0.000 0.660 0.000 0.024 0.044
#> GSM447728 2 0.2946 0.62914 0.012 0.824 0.000 0.000 0.160 0.004
#> GSM447610 6 0.4097 0.63302 0.000 0.000 0.488 0.000 0.008 0.504
#> GSM447633 1 0.5578 0.45489 0.536 0.316 0.000 0.000 0.004 0.144
#> GSM447634 3 0.3915 0.45667 0.272 0.000 0.704 0.000 0.004 0.020
#> GSM447622 3 0.2776 0.65271 0.032 0.000 0.860 0.000 0.104 0.004
#> GSM447667 2 0.0862 0.62699 0.004 0.972 0.000 0.000 0.016 0.008
#> GSM447687 5 0.3287 0.77314 0.012 0.220 0.000 0.000 0.768 0.000
#> GSM447695 3 0.1401 0.64222 0.020 0.000 0.948 0.000 0.004 0.028
#> GSM447696 3 0.5133 -0.01915 0.000 0.000 0.592 0.000 0.116 0.292
#> GSM447697 3 0.2680 0.64163 0.000 0.000 0.860 0.000 0.108 0.032
#> GSM447714 3 0.1801 0.63687 0.056 0.000 0.924 0.000 0.004 0.016
#> GSM447717 1 0.5857 0.33519 0.504 0.384 0.004 0.000 0.060 0.048
#> GSM447725 3 0.5945 0.34134 0.300 0.000 0.552 0.000 0.048 0.100
#> GSM447729 4 0.1863 0.91167 0.000 0.000 0.000 0.920 0.044 0.036
#> GSM447644 1 0.5587 0.48395 0.548 0.292 0.000 0.000 0.004 0.156
#> GSM447710 3 0.1672 0.64041 0.048 0.000 0.932 0.000 0.004 0.016
#> GSM447614 6 0.3997 0.62941 0.000 0.000 0.488 0.000 0.004 0.508
#> GSM447685 2 0.2706 0.62559 0.008 0.832 0.000 0.000 0.160 0.000
#> GSM447690 6 0.5368 0.48693 0.000 0.000 0.400 0.000 0.112 0.488
#> GSM447730 5 0.4083 0.73328 0.000 0.304 0.000 0.028 0.668 0.000
#> GSM447646 4 0.0000 0.94090 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM447689 1 0.3117 0.66650 0.864 0.072 0.028 0.000 0.012 0.024
#> GSM447635 1 0.5102 0.53150 0.624 0.228 0.000 0.000 0.000 0.148
#> GSM447641 1 0.3913 0.60984 0.788 0.008 0.152 0.000 0.020 0.032
#> GSM447716 2 0.5663 0.45240 0.204 0.624 0.000 0.000 0.040 0.132
#> GSM447718 1 0.3906 0.55592 0.744 0.008 0.224 0.000 0.012 0.012
#> GSM447616 3 0.1053 0.65578 0.020 0.000 0.964 0.000 0.012 0.004
#> GSM447626 1 0.4056 0.53690 0.764 0.024 0.000 0.000 0.040 0.172
#> GSM447640 2 0.2597 0.61375 0.000 0.824 0.000 0.000 0.176 0.000
#> GSM447734 3 0.0881 0.64598 0.008 0.000 0.972 0.000 0.008 0.012
#> GSM447692 3 0.2954 0.62781 0.000 0.000 0.844 0.000 0.108 0.048
#> GSM447647 5 0.5964 0.55003 0.000 0.156 0.012 0.224 0.588 0.020
#> GSM447624 3 0.2100 0.65347 0.004 0.000 0.884 0.000 0.112 0.000
#> GSM447625 3 0.3409 0.52772 0.184 0.000 0.788 0.000 0.004 0.024
#> GSM447707 5 0.3446 0.73823 0.000 0.308 0.000 0.000 0.692 0.000
#> GSM447732 3 0.1801 0.63697 0.056 0.000 0.924 0.000 0.004 0.016
#> GSM447684 1 0.2384 0.64999 0.896 0.040 0.000 0.000 0.008 0.056
#> GSM447731 4 0.0000 0.94090 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM447705 1 0.3908 0.64941 0.768 0.100 0.000 0.000 0.000 0.132
#> GSM447631 3 0.5058 -0.00426 0.000 0.000 0.600 0.000 0.108 0.292
#> GSM447701 2 0.4240 0.55216 0.140 0.736 0.000 0.000 0.000 0.124
#> GSM447645 3 0.2445 0.64558 0.000 0.000 0.872 0.000 0.108 0.020
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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 gender(p) individual(p) disease.state(p) other(p) k
#> ATC:mclust 124 0.575 0.305 0.813 0.5224 2
#> ATC:mclust 128 0.512 0.713 0.790 0.0348 3
#> ATC:mclust 124 0.859 0.462 0.950 0.2203 4
#> ATC:mclust 92 0.945 0.373 0.644 0.7916 5
#> ATC:mclust 92 0.974 0.591 0.600 0.3352 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "NMF"]
# you can also extract it by
# res = res_list["ATC:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 51941 rows and 130 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.984 0.949 0.980 0.5014 0.497 0.497
#> 3 3 0.898 0.907 0.961 0.2041 0.865 0.739
#> 4 4 0.593 0.627 0.812 0.1565 0.885 0.725
#> 5 5 0.569 0.462 0.707 0.0841 0.835 0.534
#> 6 6 0.641 0.582 0.775 0.0495 0.847 0.485
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
#> GSM447671 2 0.0000 0.98558 0.000 1.000
#> GSM447694 1 0.0000 0.97114 1.000 0.000
#> GSM447618 2 0.0000 0.98558 0.000 1.000
#> GSM447691 2 0.0000 0.98558 0.000 1.000
#> GSM447733 1 0.8763 0.59991 0.704 0.296
#> GSM447620 2 0.0000 0.98558 0.000 1.000
#> GSM447627 1 0.0000 0.97114 1.000 0.000
#> GSM447630 2 0.9323 0.43791 0.348 0.652
#> GSM447642 1 0.2778 0.93161 0.952 0.048
#> GSM447649 2 0.0000 0.98558 0.000 1.000
#> GSM447654 2 0.0000 0.98558 0.000 1.000
#> GSM447655 2 0.0000 0.98558 0.000 1.000
#> GSM447669 2 0.0000 0.98558 0.000 1.000
#> GSM447676 1 0.0000 0.97114 1.000 0.000
#> GSM447678 2 0.0000 0.98558 0.000 1.000
#> GSM447681 2 0.0000 0.98558 0.000 1.000
#> GSM447698 2 0.0000 0.98558 0.000 1.000
#> GSM447713 1 0.0000 0.97114 1.000 0.000
#> GSM447722 1 0.7453 0.74024 0.788 0.212
#> GSM447726 2 0.0000 0.98558 0.000 1.000
#> GSM447735 1 0.0000 0.97114 1.000 0.000
#> GSM447737 1 0.0000 0.97114 1.000 0.000
#> GSM447657 2 0.0000 0.98558 0.000 1.000
#> GSM447674 2 0.0000 0.98558 0.000 1.000
#> GSM447636 2 0.0000 0.98558 0.000 1.000
#> GSM447723 1 0.0376 0.96812 0.996 0.004
#> GSM447699 1 0.0000 0.97114 1.000 0.000
#> GSM447708 2 0.0000 0.98558 0.000 1.000
#> GSM447721 1 0.0000 0.97114 1.000 0.000
#> GSM447623 1 0.0000 0.97114 1.000 0.000
#> GSM447621 1 0.0000 0.97114 1.000 0.000
#> GSM447650 2 0.0000 0.98558 0.000 1.000
#> GSM447651 2 0.0000 0.98558 0.000 1.000
#> GSM447653 1 0.0000 0.97114 1.000 0.000
#> GSM447658 2 0.2043 0.95496 0.032 0.968
#> GSM447675 2 0.1184 0.97123 0.016 0.984
#> GSM447680 2 0.0000 0.98558 0.000 1.000
#> GSM447686 2 0.0000 0.98558 0.000 1.000
#> GSM447736 1 0.0000 0.97114 1.000 0.000
#> GSM447629 2 0.0000 0.98558 0.000 1.000
#> GSM447648 1 0.0000 0.97114 1.000 0.000
#> GSM447660 2 0.1184 0.97115 0.016 0.984
#> GSM447661 2 0.0000 0.98558 0.000 1.000
#> GSM447663 1 0.0000 0.97114 1.000 0.000
#> GSM447704 2 0.0000 0.98558 0.000 1.000
#> GSM447720 1 0.2043 0.94547 0.968 0.032
#> GSM447652 2 0.0000 0.98558 0.000 1.000
#> GSM447679 2 0.0000 0.98558 0.000 1.000
#> GSM447712 1 0.0000 0.97114 1.000 0.000
#> GSM447664 2 0.0000 0.98558 0.000 1.000
#> GSM447637 1 0.0000 0.97114 1.000 0.000
#> GSM447639 1 0.0000 0.97114 1.000 0.000
#> GSM447615 1 0.0000 0.97114 1.000 0.000
#> GSM447656 2 0.0000 0.98558 0.000 1.000
#> GSM447673 2 0.0000 0.98558 0.000 1.000
#> GSM447719 1 0.0000 0.97114 1.000 0.000
#> GSM447706 1 0.0000 0.97114 1.000 0.000
#> GSM447612 1 0.0000 0.97114 1.000 0.000
#> GSM447665 2 0.0000 0.98558 0.000 1.000
#> GSM447677 2 0.0000 0.98558 0.000 1.000
#> GSM447613 1 0.0000 0.97114 1.000 0.000
#> GSM447659 1 0.0000 0.97114 1.000 0.000
#> GSM447662 1 0.0000 0.97114 1.000 0.000
#> GSM447666 2 0.0000 0.98558 0.000 1.000
#> GSM447668 2 0.0000 0.98558 0.000 1.000
#> GSM447682 2 0.0000 0.98558 0.000 1.000
#> GSM447683 2 0.0000 0.98558 0.000 1.000
#> GSM447688 2 0.0000 0.98558 0.000 1.000
#> GSM447702 2 0.0000 0.98558 0.000 1.000
#> GSM447709 2 0.0000 0.98558 0.000 1.000
#> GSM447711 1 0.0000 0.97114 1.000 0.000
#> GSM447715 2 0.0376 0.98209 0.004 0.996
#> GSM447693 1 0.0000 0.97114 1.000 0.000
#> GSM447611 2 0.0000 0.98558 0.000 1.000
#> GSM447672 2 0.0000 0.98558 0.000 1.000
#> GSM447703 2 0.0000 0.98558 0.000 1.000
#> GSM447727 1 0.5059 0.86659 0.888 0.112
#> GSM447638 2 0.0000 0.98558 0.000 1.000
#> GSM447670 1 0.0000 0.97114 1.000 0.000
#> GSM447700 1 0.9977 0.13455 0.528 0.472
#> GSM447738 2 0.0000 0.98558 0.000 1.000
#> GSM447739 1 0.0000 0.97114 1.000 0.000
#> GSM447617 1 0.0000 0.97114 1.000 0.000
#> GSM447628 2 0.0000 0.98558 0.000 1.000
#> GSM447632 2 0.0000 0.98558 0.000 1.000
#> GSM447619 1 0.0000 0.97114 1.000 0.000
#> GSM447643 2 0.0000 0.98558 0.000 1.000
#> GSM447724 1 0.0000 0.97114 1.000 0.000
#> GSM447728 2 0.0000 0.98558 0.000 1.000
#> GSM447610 1 0.0000 0.97114 1.000 0.000
#> GSM447633 2 0.0000 0.98558 0.000 1.000
#> GSM447634 1 0.0000 0.97114 1.000 0.000
#> GSM447622 1 0.0000 0.97114 1.000 0.000
#> GSM447667 2 0.0000 0.98558 0.000 1.000
#> GSM447687 2 0.0000 0.98558 0.000 1.000
#> GSM447695 1 0.0000 0.97114 1.000 0.000
#> GSM447696 1 0.0000 0.97114 1.000 0.000
#> GSM447697 1 0.0000 0.97114 1.000 0.000
#> GSM447714 1 0.0000 0.97114 1.000 0.000
#> GSM447717 2 0.0000 0.98558 0.000 1.000
#> GSM447725 1 0.0000 0.97114 1.000 0.000
#> GSM447729 2 0.0000 0.98558 0.000 1.000
#> GSM447644 2 0.0000 0.98558 0.000 1.000
#> GSM447710 1 0.0000 0.97114 1.000 0.000
#> GSM447614 1 0.0000 0.97114 1.000 0.000
#> GSM447685 2 0.0000 0.98558 0.000 1.000
#> GSM447690 1 0.0000 0.97114 1.000 0.000
#> GSM447730 2 0.0000 0.98558 0.000 1.000
#> GSM447646 2 0.0000 0.98558 0.000 1.000
#> GSM447689 1 0.2948 0.92793 0.948 0.052
#> GSM447635 2 0.1414 0.96726 0.020 0.980
#> GSM447641 1 0.0000 0.97114 1.000 0.000
#> GSM447716 2 0.0000 0.98558 0.000 1.000
#> GSM447718 1 0.8016 0.69182 0.756 0.244
#> GSM447616 1 0.0000 0.97114 1.000 0.000
#> GSM447626 1 0.0000 0.97114 1.000 0.000
#> GSM447640 2 0.0000 0.98558 0.000 1.000
#> GSM447734 1 0.0000 0.97114 1.000 0.000
#> GSM447692 1 0.0000 0.97114 1.000 0.000
#> GSM447647 2 0.0000 0.98558 0.000 1.000
#> GSM447624 1 0.0000 0.97114 1.000 0.000
#> GSM447625 1 0.0000 0.97114 1.000 0.000
#> GSM447707 2 0.0000 0.98558 0.000 1.000
#> GSM447732 1 0.0000 0.97114 1.000 0.000
#> GSM447684 2 0.9993 0.00676 0.484 0.516
#> GSM447731 2 0.0000 0.98558 0.000 1.000
#> GSM447705 1 0.8081 0.68527 0.752 0.248
#> GSM447631 1 0.0000 0.97114 1.000 0.000
#> GSM447701 2 0.0000 0.98558 0.000 1.000
#> GSM447645 1 0.0000 0.97114 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM447671 2 0.0000 0.9339 0.000 1.000 0.000
#> GSM447694 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447618 2 0.0237 0.9336 0.000 0.996 0.004
#> GSM447691 2 0.0000 0.9339 0.000 1.000 0.000
#> GSM447733 3 0.0000 0.9215 0.000 0.000 1.000
#> GSM447620 2 0.0000 0.9339 0.000 1.000 0.000
#> GSM447627 1 0.0592 0.9721 0.988 0.000 0.012
#> GSM447630 2 0.4062 0.7422 0.164 0.836 0.000
#> GSM447642 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447649 2 0.1964 0.9049 0.000 0.944 0.056
#> GSM447654 3 0.0000 0.9215 0.000 0.000 1.000
#> GSM447655 2 0.0892 0.9282 0.000 0.980 0.020
#> GSM447669 2 0.0000 0.9339 0.000 1.000 0.000
#> GSM447676 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447678 2 0.6154 0.3525 0.000 0.592 0.408
#> GSM447681 2 0.0237 0.9337 0.000 0.996 0.004
#> GSM447698 2 0.0424 0.9328 0.000 0.992 0.008
#> GSM447713 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447722 1 0.1860 0.9236 0.948 0.052 0.000
#> GSM447726 2 0.0000 0.9339 0.000 1.000 0.000
#> GSM447735 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447737 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447657 2 0.0747 0.9300 0.000 0.984 0.016
#> GSM447674 2 0.0747 0.9300 0.000 0.984 0.016
#> GSM447636 2 0.3686 0.8262 0.000 0.860 0.140
#> GSM447723 1 0.1643 0.9371 0.956 0.044 0.000
#> GSM447699 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447708 2 0.0000 0.9339 0.000 1.000 0.000
#> GSM447721 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447623 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447621 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447650 2 0.0424 0.9328 0.000 0.992 0.008
#> GSM447651 2 0.0000 0.9339 0.000 1.000 0.000
#> GSM447653 3 0.0592 0.9143 0.012 0.000 0.988
#> GSM447658 2 0.1643 0.8979 0.044 0.956 0.000
#> GSM447675 3 0.0000 0.9215 0.000 0.000 1.000
#> GSM447680 2 0.0000 0.9339 0.000 1.000 0.000
#> GSM447686 2 0.0000 0.9339 0.000 1.000 0.000
#> GSM447736 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447629 2 0.0000 0.9339 0.000 1.000 0.000
#> GSM447648 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447660 2 0.2261 0.8748 0.068 0.932 0.000
#> GSM447661 2 0.0424 0.9328 0.000 0.992 0.008
#> GSM447663 1 0.0237 0.9784 0.996 0.004 0.000
#> GSM447704 2 0.0892 0.9282 0.000 0.980 0.020
#> GSM447720 1 0.3752 0.8079 0.856 0.144 0.000
#> GSM447652 2 0.5621 0.5841 0.000 0.692 0.308
#> GSM447679 2 0.0237 0.9337 0.000 0.996 0.004
#> GSM447712 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447664 3 0.6062 0.3075 0.000 0.384 0.616
#> GSM447637 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447639 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447615 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447656 2 0.0000 0.9339 0.000 1.000 0.000
#> GSM447673 2 0.4452 0.7660 0.000 0.808 0.192
#> GSM447719 3 0.3686 0.8042 0.140 0.000 0.860
#> GSM447706 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447612 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447665 2 0.0000 0.9339 0.000 1.000 0.000
#> GSM447677 2 0.0000 0.9339 0.000 1.000 0.000
#> GSM447613 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447659 3 0.4702 0.7083 0.212 0.000 0.788
#> GSM447662 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447666 2 0.0000 0.9339 0.000 1.000 0.000
#> GSM447668 2 0.0000 0.9339 0.000 1.000 0.000
#> GSM447682 2 0.0237 0.9337 0.000 0.996 0.004
#> GSM447683 2 0.0000 0.9339 0.000 1.000 0.000
#> GSM447688 3 0.1031 0.9055 0.000 0.024 0.976
#> GSM447702 2 0.0892 0.9282 0.000 0.980 0.020
#> GSM447709 2 0.0000 0.9339 0.000 1.000 0.000
#> GSM447711 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447715 2 0.0000 0.9339 0.000 1.000 0.000
#> GSM447693 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447611 3 0.0000 0.9215 0.000 0.000 1.000
#> GSM447672 2 0.1163 0.9237 0.000 0.972 0.028
#> GSM447703 2 0.4887 0.7172 0.000 0.772 0.228
#> GSM447727 1 0.4750 0.6984 0.784 0.216 0.000
#> GSM447638 2 0.0747 0.9300 0.000 0.984 0.016
#> GSM447670 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447700 2 0.5968 0.4051 0.364 0.636 0.000
#> GSM447738 2 0.2066 0.9017 0.000 0.940 0.060
#> GSM447739 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447617 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447628 3 0.0000 0.9215 0.000 0.000 1.000
#> GSM447632 2 0.1964 0.9049 0.000 0.944 0.056
#> GSM447619 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447643 2 0.0000 0.9339 0.000 1.000 0.000
#> GSM447724 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447728 2 0.0237 0.9337 0.000 0.996 0.004
#> GSM447610 1 0.0424 0.9754 0.992 0.000 0.008
#> GSM447633 2 0.0000 0.9339 0.000 1.000 0.000
#> GSM447634 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447622 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447667 2 0.0892 0.9282 0.000 0.980 0.020
#> GSM447687 2 0.4399 0.7707 0.000 0.812 0.188
#> GSM447695 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447696 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447697 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447714 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447717 2 0.0237 0.9337 0.000 0.996 0.004
#> GSM447725 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447729 3 0.0000 0.9215 0.000 0.000 1.000
#> GSM447644 2 0.0000 0.9339 0.000 1.000 0.000
#> GSM447710 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447614 1 0.0424 0.9754 0.992 0.000 0.008
#> GSM447685 2 0.0237 0.9337 0.000 0.996 0.004
#> GSM447690 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447730 3 0.4702 0.6866 0.000 0.212 0.788
#> GSM447646 3 0.0000 0.9215 0.000 0.000 1.000
#> GSM447689 1 0.4750 0.6995 0.784 0.216 0.000
#> GSM447635 2 0.0000 0.9339 0.000 1.000 0.000
#> GSM447641 1 0.0237 0.9784 0.996 0.004 0.000
#> GSM447716 2 0.0237 0.9337 0.000 0.996 0.004
#> GSM447718 1 0.2796 0.8724 0.908 0.092 0.000
#> GSM447616 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447626 1 0.0237 0.9784 0.996 0.004 0.000
#> GSM447640 2 0.0892 0.9282 0.000 0.980 0.020
#> GSM447734 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447692 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447647 3 0.0000 0.9215 0.000 0.000 1.000
#> GSM447624 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447625 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447707 2 0.4750 0.7342 0.000 0.784 0.216
#> GSM447732 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447684 2 0.2537 0.8549 0.080 0.920 0.000
#> GSM447731 3 0.0000 0.9215 0.000 0.000 1.000
#> GSM447705 2 0.6308 0.0168 0.492 0.508 0.000
#> GSM447631 1 0.0000 0.9816 1.000 0.000 0.000
#> GSM447701 2 0.0000 0.9339 0.000 1.000 0.000
#> GSM447645 1 0.0000 0.9816 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM447671 1 0.4624 0.4070 0.660 0.340 0.000 0.000
#> GSM447694 3 0.2345 0.8127 0.100 0.000 0.900 0.000
#> GSM447618 2 0.4193 0.5813 0.268 0.732 0.000 0.000
#> GSM447691 2 0.4877 0.3132 0.408 0.592 0.000 0.000
#> GSM447733 4 0.0859 0.8841 0.004 0.008 0.008 0.980
#> GSM447620 1 0.4989 0.2450 0.528 0.472 0.000 0.000
#> GSM447627 3 0.1118 0.8232 0.000 0.000 0.964 0.036
#> GSM447630 2 0.7426 -0.1451 0.376 0.452 0.172 0.000
#> GSM447642 1 0.5339 0.3877 0.624 0.020 0.356 0.000
#> GSM447649 2 0.0469 0.7617 0.012 0.988 0.000 0.000
#> GSM447654 4 0.1022 0.8823 0.032 0.000 0.000 0.968
#> GSM447655 2 0.0336 0.7617 0.008 0.992 0.000 0.000
#> GSM447669 1 0.4356 0.4247 0.708 0.292 0.000 0.000
#> GSM447676 1 0.5537 0.1723 0.544 0.004 0.440 0.012
#> GSM447678 2 0.7318 0.3614 0.300 0.580 0.056 0.064
#> GSM447681 2 0.1302 0.7557 0.044 0.956 0.000 0.000
#> GSM447698 2 0.2760 0.7101 0.128 0.872 0.000 0.000
#> GSM447713 3 0.1867 0.8082 0.072 0.000 0.928 0.000
#> GSM447722 3 0.8698 0.1727 0.324 0.228 0.404 0.044
#> GSM447726 2 0.4356 0.5201 0.292 0.708 0.000 0.000
#> GSM447735 3 0.3443 0.7972 0.136 0.000 0.848 0.016
#> GSM447737 3 0.2760 0.8112 0.128 0.000 0.872 0.000
#> GSM447657 2 0.2469 0.7262 0.108 0.892 0.000 0.000
#> GSM447674 2 0.0707 0.7603 0.020 0.980 0.000 0.000
#> GSM447636 2 0.5894 0.2128 0.392 0.568 0.000 0.040
#> GSM447723 3 0.2111 0.8109 0.024 0.044 0.932 0.000
#> GSM447699 3 0.3726 0.7507 0.212 0.000 0.788 0.000
#> GSM447708 2 0.1940 0.7454 0.076 0.924 0.000 0.000
#> GSM447721 3 0.2011 0.8048 0.080 0.000 0.920 0.000
#> GSM447623 3 0.0592 0.8204 0.016 0.000 0.984 0.000
#> GSM447621 3 0.1474 0.8228 0.052 0.000 0.948 0.000
#> GSM447650 2 0.0188 0.7618 0.004 0.996 0.000 0.000
#> GSM447651 2 0.1474 0.7477 0.052 0.948 0.000 0.000
#> GSM447653 4 0.0336 0.8863 0.008 0.000 0.000 0.992
#> GSM447658 2 0.7776 -0.2543 0.340 0.412 0.248 0.000
#> GSM447675 4 0.0592 0.8840 0.016 0.000 0.000 0.984
#> GSM447680 2 0.4454 0.4232 0.308 0.692 0.000 0.000
#> GSM447686 2 0.0707 0.7608 0.020 0.980 0.000 0.000
#> GSM447736 3 0.3726 0.7587 0.212 0.000 0.788 0.000
#> GSM447629 2 0.2469 0.7303 0.108 0.892 0.000 0.000
#> GSM447648 3 0.0188 0.8218 0.004 0.000 0.996 0.000
#> GSM447660 1 0.5742 0.4418 0.596 0.368 0.036 0.000
#> GSM447661 2 0.1022 0.7564 0.032 0.968 0.000 0.000
#> GSM447663 1 0.4624 0.1924 0.660 0.000 0.340 0.000
#> GSM447704 2 0.0336 0.7614 0.008 0.992 0.000 0.000
#> GSM447720 3 0.5579 0.6578 0.252 0.060 0.688 0.000
#> GSM447652 2 0.4905 0.4307 0.004 0.632 0.000 0.364
#> GSM447679 2 0.0592 0.7619 0.016 0.984 0.000 0.000
#> GSM447712 3 0.2760 0.7781 0.128 0.000 0.872 0.000
#> GSM447664 2 0.6232 0.3380 0.072 0.596 0.000 0.332
#> GSM447637 3 0.0707 0.8218 0.020 0.000 0.980 0.000
#> GSM447639 3 0.3695 0.7968 0.156 0.000 0.828 0.016
#> GSM447615 1 0.4916 0.2647 0.576 0.000 0.424 0.000
#> GSM447656 2 0.0336 0.7626 0.008 0.992 0.000 0.000
#> GSM447673 2 0.3198 0.7135 0.080 0.880 0.000 0.040
#> GSM447719 4 0.0000 0.8862 0.000 0.000 0.000 1.000
#> GSM447706 3 0.4992 0.0218 0.476 0.000 0.524 0.000
#> GSM447612 3 0.4331 0.6933 0.288 0.000 0.712 0.000
#> GSM447665 2 0.4933 0.1904 0.432 0.568 0.000 0.000
#> GSM447677 2 0.4585 0.3698 0.332 0.668 0.000 0.000
#> GSM447613 3 0.2081 0.8059 0.084 0.000 0.916 0.000
#> GSM447659 4 0.0707 0.8762 0.000 0.000 0.020 0.980
#> GSM447662 3 0.4761 0.4816 0.372 0.000 0.628 0.000
#> GSM447666 1 0.5138 0.4075 0.600 0.392 0.008 0.000
#> GSM447668 2 0.3486 0.6298 0.188 0.812 0.000 0.000
#> GSM447682 2 0.0469 0.7622 0.012 0.988 0.000 0.000
#> GSM447683 2 0.3688 0.6099 0.208 0.792 0.000 0.000
#> GSM447688 4 0.4744 0.6171 0.012 0.284 0.000 0.704
#> GSM447702 2 0.0921 0.7591 0.028 0.972 0.000 0.000
#> GSM447709 2 0.3024 0.6731 0.148 0.852 0.000 0.000
#> GSM447711 3 0.2469 0.7908 0.108 0.000 0.892 0.000
#> GSM447715 2 0.1545 0.7586 0.040 0.952 0.008 0.000
#> GSM447693 3 0.1637 0.8181 0.060 0.000 0.940 0.000
#> GSM447611 4 0.0188 0.8859 0.004 0.000 0.000 0.996
#> GSM447672 2 0.0707 0.7607 0.020 0.980 0.000 0.000
#> GSM447703 2 0.1584 0.7512 0.036 0.952 0.000 0.012
#> GSM447727 3 0.4049 0.6123 0.008 0.212 0.780 0.000
#> GSM447638 2 0.4989 -0.0289 0.472 0.528 0.000 0.000
#> GSM447670 1 0.4804 0.3441 0.616 0.000 0.384 0.000
#> GSM447700 2 0.7222 0.2189 0.300 0.528 0.172 0.000
#> GSM447738 2 0.1118 0.7543 0.036 0.964 0.000 0.000
#> GSM447739 3 0.2530 0.7865 0.112 0.000 0.888 0.000
#> GSM447617 3 0.1211 0.8174 0.040 0.000 0.960 0.000
#> GSM447628 4 0.1256 0.8836 0.028 0.008 0.000 0.964
#> GSM447632 2 0.0188 0.7620 0.004 0.996 0.000 0.000
#> GSM447619 3 0.2345 0.8142 0.100 0.000 0.900 0.000
#> GSM447643 1 0.4989 0.2336 0.528 0.472 0.000 0.000
#> GSM447724 3 0.5985 0.6375 0.284 0.020 0.660 0.036
#> GSM447728 2 0.0707 0.7596 0.020 0.980 0.000 0.000
#> GSM447610 3 0.5063 0.7463 0.108 0.000 0.768 0.124
#> GSM447633 1 0.4907 0.3167 0.580 0.420 0.000 0.000
#> GSM447634 3 0.4564 0.6502 0.328 0.000 0.672 0.000
#> GSM447622 3 0.0592 0.8222 0.016 0.000 0.984 0.000
#> GSM447667 2 0.5070 0.1104 0.416 0.580 0.000 0.004
#> GSM447687 2 0.1489 0.7496 0.044 0.952 0.000 0.004
#> GSM447695 3 0.3219 0.7858 0.164 0.000 0.836 0.000
#> GSM447696 3 0.2345 0.7922 0.100 0.000 0.900 0.000
#> GSM447697 3 0.1792 0.8098 0.068 0.000 0.932 0.000
#> GSM447714 3 0.3311 0.7836 0.172 0.000 0.828 0.000
#> GSM447717 2 0.7191 0.0238 0.352 0.500 0.148 0.000
#> GSM447725 3 0.3924 0.7693 0.124 0.008 0.840 0.028
#> GSM447729 4 0.0779 0.8836 0.016 0.004 0.000 0.980
#> GSM447644 1 0.4679 0.4088 0.648 0.352 0.000 0.000
#> GSM447710 3 0.2589 0.8083 0.116 0.000 0.884 0.000
#> GSM447614 3 0.4700 0.7686 0.124 0.000 0.792 0.084
#> GSM447685 2 0.2760 0.6962 0.128 0.872 0.000 0.000
#> GSM447690 3 0.2334 0.8008 0.088 0.000 0.908 0.004
#> GSM447730 4 0.5957 0.4226 0.048 0.364 0.000 0.588
#> GSM447646 4 0.1489 0.8778 0.044 0.004 0.000 0.952
#> GSM447689 3 0.6886 0.2742 0.200 0.204 0.596 0.000
#> GSM447635 2 0.6356 0.3640 0.308 0.604 0.088 0.000
#> GSM447641 3 0.4830 0.3490 0.392 0.000 0.608 0.000
#> GSM447716 2 0.2216 0.7342 0.092 0.908 0.000 0.000
#> GSM447718 3 0.4713 0.6184 0.052 0.172 0.776 0.000
#> GSM447616 3 0.1118 0.8223 0.036 0.000 0.964 0.000
#> GSM447626 1 0.4888 0.3357 0.588 0.000 0.412 0.000
#> GSM447640 2 0.0469 0.7611 0.012 0.988 0.000 0.000
#> GSM447734 3 0.3024 0.7977 0.148 0.000 0.852 0.000
#> GSM447692 3 0.1474 0.8151 0.052 0.000 0.948 0.000
#> GSM447647 4 0.5311 0.5407 0.024 0.328 0.000 0.648
#> GSM447624 3 0.0817 0.8198 0.024 0.000 0.976 0.000
#> GSM447625 3 0.1389 0.8217 0.048 0.000 0.952 0.000
#> GSM447707 2 0.2002 0.7492 0.044 0.936 0.000 0.020
#> GSM447732 3 0.2281 0.8135 0.096 0.000 0.904 0.000
#> GSM447684 1 0.6351 0.4771 0.588 0.332 0.080 0.000
#> GSM447731 4 0.1302 0.8783 0.044 0.000 0.000 0.956
#> GSM447705 3 0.7289 0.3177 0.212 0.252 0.536 0.000
#> GSM447631 3 0.0000 0.8218 0.000 0.000 1.000 0.000
#> GSM447701 2 0.1474 0.7581 0.052 0.948 0.000 0.000
#> GSM447645 3 0.1792 0.8087 0.068 0.000 0.932 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM447671 5 0.5602 0.6277 0.000 0.164 0.196 0.000 0.640
#> GSM447694 3 0.4367 0.1590 0.416 0.000 0.580 0.000 0.004
#> GSM447618 3 0.6584 -0.2882 0.000 0.380 0.412 0.000 0.208
#> GSM447691 3 0.6804 -0.3005 0.000 0.304 0.372 0.000 0.324
#> GSM447733 4 0.1764 0.8274 0.000 0.012 0.036 0.940 0.012
#> GSM447620 5 0.3884 0.5552 0.000 0.288 0.004 0.000 0.708
#> GSM447627 1 0.5268 0.3769 0.588 0.000 0.360 0.048 0.004
#> GSM447630 5 0.6847 0.5763 0.024 0.208 0.248 0.000 0.520
#> GSM447642 5 0.5451 0.2413 0.424 0.032 0.016 0.000 0.528
#> GSM447649 2 0.0703 0.7723 0.000 0.976 0.000 0.000 0.024
#> GSM447654 4 0.1582 0.8249 0.000 0.000 0.028 0.944 0.028
#> GSM447655 2 0.1502 0.7677 0.000 0.940 0.000 0.004 0.056
#> GSM447669 5 0.5748 0.5825 0.000 0.140 0.252 0.000 0.608
#> GSM447676 5 0.6638 0.3697 0.200 0.000 0.120 0.072 0.608
#> GSM447678 2 0.7462 0.1551 0.004 0.428 0.364 0.060 0.144
#> GSM447681 2 0.1281 0.7634 0.000 0.956 0.012 0.000 0.032
#> GSM447698 2 0.4073 0.6582 0.000 0.792 0.104 0.000 0.104
#> GSM447713 1 0.1892 0.5459 0.916 0.000 0.080 0.000 0.004
#> GSM447722 3 0.5455 0.2958 0.000 0.112 0.720 0.044 0.124
#> GSM447726 2 0.4961 0.0162 0.004 0.520 0.020 0.000 0.456
#> GSM447735 3 0.4620 0.2409 0.372 0.000 0.612 0.004 0.012
#> GSM447737 1 0.4182 0.3510 0.600 0.000 0.400 0.000 0.000
#> GSM447657 2 0.2853 0.7241 0.004 0.880 0.040 0.000 0.076
#> GSM447674 2 0.0880 0.7634 0.000 0.968 0.000 0.000 0.032
#> GSM447636 2 0.6592 0.1396 0.180 0.492 0.000 0.008 0.320
#> GSM447723 1 0.5090 0.4919 0.712 0.036 0.212 0.000 0.040
#> GSM447699 3 0.3282 0.4264 0.188 0.000 0.804 0.000 0.008
#> GSM447708 2 0.3242 0.6567 0.000 0.784 0.000 0.000 0.216
#> GSM447721 1 0.1117 0.5257 0.964 0.000 0.020 0.000 0.016
#> GSM447623 1 0.4252 0.4347 0.652 0.000 0.340 0.000 0.008
#> GSM447621 1 0.4403 0.2695 0.560 0.000 0.436 0.000 0.004
#> GSM447650 2 0.0510 0.7716 0.000 0.984 0.000 0.000 0.016
#> GSM447651 2 0.2605 0.7184 0.000 0.852 0.000 0.000 0.148
#> GSM447653 4 0.0740 0.8322 0.004 0.000 0.008 0.980 0.008
#> GSM447658 1 0.6514 -0.1733 0.516 0.236 0.004 0.000 0.244
#> GSM447675 4 0.3457 0.7906 0.008 0.000 0.080 0.848 0.064
#> GSM447680 2 0.4219 0.2506 0.000 0.584 0.000 0.000 0.416
#> GSM447686 2 0.2685 0.7526 0.028 0.880 0.000 0.000 0.092
#> GSM447736 3 0.4730 0.4080 0.260 0.000 0.688 0.000 0.052
#> GSM447629 2 0.3789 0.5825 0.000 0.768 0.020 0.000 0.212
#> GSM447648 1 0.4436 0.3578 0.596 0.000 0.396 0.000 0.008
#> GSM447660 5 0.3368 0.6545 0.024 0.156 0.000 0.000 0.820
#> GSM447661 2 0.2389 0.7409 0.000 0.880 0.000 0.004 0.116
#> GSM447663 3 0.4182 0.2417 0.000 0.000 0.600 0.000 0.400
#> GSM447704 2 0.0162 0.7710 0.000 0.996 0.000 0.000 0.004
#> GSM447720 3 0.5161 0.4684 0.056 0.052 0.736 0.000 0.156
#> GSM447652 4 0.4451 -0.0351 0.000 0.492 0.004 0.504 0.000
#> GSM447679 2 0.0290 0.7692 0.000 0.992 0.000 0.000 0.008
#> GSM447712 1 0.0703 0.5187 0.976 0.000 0.000 0.000 0.024
#> GSM447664 2 0.7483 0.2744 0.004 0.540 0.136 0.196 0.124
#> GSM447637 1 0.4727 0.2053 0.532 0.000 0.452 0.000 0.016
#> GSM447639 1 0.5432 0.3672 0.608 0.016 0.340 0.008 0.028
#> GSM447615 5 0.5695 -0.0297 0.460 0.020 0.040 0.000 0.480
#> GSM447656 2 0.1704 0.7705 0.004 0.928 0.000 0.000 0.068
#> GSM447673 2 0.3473 0.6906 0.000 0.840 0.040 0.008 0.112
#> GSM447719 4 0.0798 0.8308 0.016 0.000 0.000 0.976 0.008
#> GSM447706 1 0.6808 -0.0210 0.360 0.000 0.300 0.000 0.340
#> GSM447612 3 0.3282 0.4706 0.008 0.000 0.804 0.000 0.188
#> GSM447665 5 0.5636 0.3593 0.000 0.372 0.084 0.000 0.544
#> GSM447677 2 0.4138 0.3401 0.000 0.616 0.000 0.000 0.384
#> GSM447613 1 0.2588 0.5333 0.892 0.000 0.048 0.000 0.060
#> GSM447659 4 0.1774 0.8098 0.016 0.000 0.052 0.932 0.000
#> GSM447662 3 0.5145 0.4117 0.056 0.000 0.612 0.000 0.332
#> GSM447666 5 0.3602 0.6641 0.004 0.140 0.036 0.000 0.820
#> GSM447668 2 0.3969 0.5072 0.000 0.692 0.000 0.004 0.304
#> GSM447682 2 0.0960 0.7695 0.008 0.972 0.004 0.000 0.016
#> GSM447683 2 0.3774 0.5423 0.000 0.704 0.000 0.000 0.296
#> GSM447688 4 0.5436 0.5210 0.000 0.292 0.032 0.640 0.036
#> GSM447702 2 0.2179 0.7511 0.000 0.896 0.000 0.004 0.100
#> GSM447709 2 0.3684 0.5741 0.000 0.720 0.000 0.000 0.280
#> GSM447711 1 0.0404 0.5251 0.988 0.000 0.000 0.000 0.012
#> GSM447715 2 0.3597 0.7227 0.044 0.832 0.008 0.000 0.116
#> GSM447693 1 0.4830 0.0727 0.492 0.000 0.488 0.000 0.020
#> GSM447611 4 0.0798 0.8307 0.000 0.000 0.016 0.976 0.008
#> GSM447672 2 0.1571 0.7667 0.000 0.936 0.000 0.004 0.060
#> GSM447703 2 0.1731 0.7504 0.000 0.932 0.004 0.004 0.060
#> GSM447727 1 0.6645 0.3070 0.588 0.208 0.160 0.000 0.044
#> GSM447638 5 0.5816 0.0196 0.020 0.452 0.032 0.008 0.488
#> GSM447670 5 0.4394 0.5216 0.100 0.000 0.136 0.000 0.764
#> GSM447700 3 0.4916 0.3140 0.000 0.124 0.716 0.000 0.160
#> GSM447738 2 0.1518 0.7575 0.000 0.944 0.004 0.004 0.048
#> GSM447739 1 0.0898 0.5351 0.972 0.000 0.020 0.000 0.008
#> GSM447617 1 0.4298 0.4224 0.640 0.000 0.352 0.000 0.008
#> GSM447628 4 0.0854 0.8339 0.000 0.008 0.012 0.976 0.004
#> GSM447632 2 0.0865 0.7724 0.000 0.972 0.000 0.004 0.024
#> GSM447619 3 0.5551 0.3648 0.284 0.000 0.612 0.000 0.104
#> GSM447643 5 0.4306 0.4668 0.012 0.328 0.000 0.000 0.660
#> GSM447724 3 0.5419 0.3705 0.072 0.044 0.748 0.020 0.116
#> GSM447728 2 0.1952 0.7581 0.000 0.912 0.000 0.004 0.084
#> GSM447610 1 0.6335 0.3361 0.560 0.000 0.324 0.052 0.064
#> GSM447633 5 0.4333 0.6325 0.000 0.212 0.048 0.000 0.740
#> GSM447634 3 0.3355 0.4497 0.012 0.000 0.804 0.000 0.184
#> GSM447622 1 0.4815 0.1817 0.524 0.000 0.456 0.000 0.020
#> GSM447667 5 0.4602 0.4601 0.000 0.340 0.004 0.016 0.640
#> GSM447687 2 0.1892 0.7385 0.000 0.916 0.004 0.000 0.080
#> GSM447695 3 0.4264 0.2660 0.376 0.000 0.620 0.000 0.004
#> GSM447696 1 0.2583 0.5457 0.864 0.000 0.132 0.000 0.004
#> GSM447697 1 0.2074 0.5472 0.896 0.000 0.104 0.000 0.000
#> GSM447714 3 0.4135 0.3220 0.340 0.000 0.656 0.000 0.004
#> GSM447717 1 0.5584 -0.1476 0.532 0.392 0.000 0.000 0.076
#> GSM447725 1 0.1087 0.5169 0.968 0.008 0.008 0.000 0.016
#> GSM447729 4 0.3619 0.7932 0.008 0.008 0.064 0.848 0.072
#> GSM447644 5 0.4797 0.6527 0.000 0.172 0.104 0.000 0.724
#> GSM447710 3 0.4380 0.2550 0.376 0.000 0.616 0.000 0.008
#> GSM447614 3 0.6060 -0.0264 0.432 0.000 0.484 0.056 0.028
#> GSM447685 2 0.3659 0.6378 0.012 0.768 0.000 0.000 0.220
#> GSM447690 1 0.1329 0.5360 0.956 0.000 0.032 0.004 0.008
#> GSM447730 4 0.6180 0.2891 0.000 0.372 0.028 0.528 0.072
#> GSM447646 4 0.1753 0.8225 0.000 0.000 0.032 0.936 0.032
#> GSM447689 5 0.7448 -0.0907 0.100 0.104 0.388 0.000 0.408
#> GSM447635 2 0.6752 -0.2181 0.000 0.404 0.280 0.000 0.316
#> GSM447641 1 0.4341 0.0686 0.592 0.000 0.004 0.000 0.404
#> GSM447716 2 0.3427 0.6989 0.004 0.844 0.056 0.000 0.096
#> GSM447718 1 0.7289 0.0176 0.468 0.200 0.044 0.000 0.288
#> GSM447616 3 0.4561 -0.0996 0.488 0.000 0.504 0.000 0.008
#> GSM447626 5 0.4212 0.4767 0.024 0.004 0.236 0.000 0.736
#> GSM447640 2 0.1571 0.7667 0.000 0.936 0.000 0.004 0.060
#> GSM447734 3 0.4201 0.3341 0.328 0.000 0.664 0.000 0.008
#> GSM447692 1 0.2966 0.5321 0.816 0.000 0.184 0.000 0.000
#> GSM447647 2 0.5259 0.4939 0.004 0.688 0.004 0.216 0.088
#> GSM447624 1 0.4610 0.3632 0.596 0.000 0.388 0.000 0.016
#> GSM447625 3 0.4522 0.0781 0.440 0.000 0.552 0.000 0.008
#> GSM447707 2 0.2511 0.7620 0.000 0.892 0.000 0.028 0.080
#> GSM447732 3 0.4547 0.2109 0.400 0.000 0.588 0.000 0.012
#> GSM447684 5 0.3512 0.6583 0.012 0.160 0.012 0.000 0.816
#> GSM447731 4 0.1750 0.8232 0.000 0.000 0.036 0.936 0.028
#> GSM447705 3 0.6603 0.4106 0.064 0.112 0.600 0.000 0.224
#> GSM447631 1 0.4299 0.3761 0.608 0.000 0.388 0.000 0.004
#> GSM447701 2 0.1478 0.7685 0.000 0.936 0.000 0.000 0.064
#> GSM447645 1 0.4599 0.4092 0.624 0.000 0.356 0.000 0.020
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM447671 5 0.4149 0.5554 0.008 0.044 0.012 0.000 0.760 0.176
#> GSM447694 3 0.0767 0.7438 0.008 0.000 0.976 0.000 0.004 0.012
#> GSM447618 6 0.6523 0.1571 0.004 0.196 0.044 0.000 0.248 0.508
#> GSM447691 5 0.5242 0.4142 0.008 0.040 0.040 0.000 0.636 0.276
#> GSM447733 4 0.3033 0.7697 0.012 0.004 0.032 0.856 0.000 0.096
#> GSM447620 5 0.4504 0.2332 0.000 0.432 0.004 0.000 0.540 0.024
#> GSM447627 3 0.2344 0.7370 0.076 0.000 0.892 0.028 0.004 0.000
#> GSM447630 5 0.7349 0.2400 0.020 0.136 0.196 0.000 0.484 0.164
#> GSM447642 5 0.4588 0.1876 0.420 0.000 0.008 0.000 0.548 0.024
#> GSM447649 2 0.1082 0.8363 0.004 0.956 0.000 0.000 0.000 0.040
#> GSM447654 4 0.1555 0.7822 0.004 0.000 0.000 0.932 0.004 0.060
#> GSM447655 2 0.0520 0.8377 0.000 0.984 0.000 0.000 0.008 0.008
#> GSM447669 5 0.4181 0.5291 0.008 0.028 0.016 0.000 0.744 0.204
#> GSM447676 5 0.4797 0.5370 0.080 0.000 0.036 0.112 0.752 0.020
#> GSM447678 6 0.5177 0.3680 0.072 0.096 0.008 0.076 0.012 0.736
#> GSM447681 2 0.0713 0.8380 0.000 0.972 0.000 0.000 0.000 0.028
#> GSM447698 2 0.2730 0.7818 0.012 0.836 0.000 0.000 0.000 0.152
#> GSM447713 1 0.3890 0.3413 0.596 0.000 0.400 0.000 0.000 0.004
#> GSM447722 6 0.4592 0.5081 0.008 0.024 0.200 0.016 0.020 0.732
#> GSM447726 5 0.4513 0.3087 0.004 0.396 0.000 0.000 0.572 0.028
#> GSM447735 3 0.2884 0.7129 0.032 0.000 0.864 0.004 0.008 0.092
#> GSM447737 3 0.2937 0.7243 0.100 0.000 0.852 0.000 0.004 0.044
#> GSM447657 2 0.2771 0.7939 0.032 0.852 0.000 0.000 0.000 0.116
#> GSM447674 2 0.1531 0.8292 0.004 0.928 0.000 0.000 0.000 0.068
#> GSM447636 1 0.5849 0.2296 0.568 0.228 0.000 0.000 0.184 0.020
#> GSM447723 3 0.6947 -0.1577 0.356 0.028 0.380 0.000 0.216 0.020
#> GSM447699 3 0.3935 0.4879 0.012 0.000 0.692 0.000 0.008 0.288
#> GSM447708 2 0.1082 0.8329 0.000 0.956 0.000 0.000 0.040 0.004
#> GSM447721 1 0.3012 0.7098 0.796 0.000 0.196 0.000 0.000 0.008
#> GSM447623 3 0.2482 0.7013 0.148 0.000 0.848 0.000 0.004 0.000
#> GSM447621 3 0.2230 0.7379 0.084 0.000 0.892 0.000 0.000 0.024
#> GSM447650 2 0.0713 0.8375 0.000 0.972 0.000 0.000 0.000 0.028
#> GSM447651 2 0.0717 0.8363 0.000 0.976 0.000 0.000 0.016 0.008
#> GSM447653 4 0.0622 0.7974 0.012 0.000 0.000 0.980 0.000 0.008
#> GSM447658 5 0.4380 0.2447 0.436 0.008 0.000 0.000 0.544 0.012
#> GSM447675 4 0.4212 0.6398 0.048 0.000 0.000 0.688 0.000 0.264
#> GSM447680 2 0.3853 0.5309 0.000 0.680 0.000 0.000 0.304 0.016
#> GSM447686 2 0.4303 0.7041 0.128 0.752 0.000 0.000 0.108 0.012
#> GSM447736 3 0.1959 0.7175 0.020 0.000 0.924 0.000 0.032 0.024
#> GSM447629 5 0.5910 0.1788 0.004 0.408 0.000 0.000 0.412 0.176
#> GSM447648 3 0.2307 0.7338 0.068 0.000 0.896 0.000 0.032 0.004
#> GSM447660 5 0.2195 0.6130 0.068 0.016 0.000 0.000 0.904 0.012
#> GSM447661 2 0.0508 0.8380 0.000 0.984 0.000 0.000 0.012 0.004
#> GSM447663 5 0.5532 0.2810 0.012 0.000 0.196 0.000 0.604 0.188
#> GSM447704 2 0.1082 0.8358 0.004 0.956 0.000 0.000 0.000 0.040
#> GSM447720 3 0.6381 0.2123 0.020 0.044 0.564 0.000 0.124 0.248
#> GSM447652 4 0.4483 0.1445 0.004 0.428 0.000 0.548 0.004 0.016
#> GSM447679 2 0.0937 0.8366 0.000 0.960 0.000 0.000 0.000 0.040
#> GSM447712 1 0.2527 0.7256 0.876 0.000 0.084 0.000 0.040 0.000
#> GSM447664 6 0.7186 0.0720 0.112 0.132 0.000 0.232 0.024 0.500
#> GSM447637 3 0.1592 0.7455 0.032 0.000 0.940 0.000 0.020 0.008
#> GSM447639 3 0.6497 0.4346 0.232 0.016 0.560 0.016 0.020 0.156
#> GSM447615 5 0.7237 0.0107 0.164 0.040 0.352 0.000 0.400 0.044
#> GSM447656 2 0.4512 0.6389 0.028 0.708 0.000 0.000 0.224 0.040
#> GSM447673 2 0.3938 0.6771 0.044 0.728 0.000 0.000 0.000 0.228
#> GSM447719 4 0.1465 0.7952 0.020 0.000 0.004 0.948 0.004 0.024
#> GSM447706 3 0.5369 0.4417 0.076 0.000 0.632 0.000 0.252 0.040
#> GSM447612 3 0.5201 0.2349 0.020 0.000 0.576 0.000 0.060 0.344
#> GSM447665 5 0.4787 0.5210 0.000 0.184 0.000 0.000 0.672 0.144
#> GSM447677 2 0.3483 0.6742 0.000 0.764 0.000 0.000 0.212 0.024
#> GSM447613 1 0.4011 0.5978 0.736 0.000 0.060 0.000 0.204 0.000
#> GSM447659 4 0.2872 0.6542 0.000 0.000 0.152 0.832 0.004 0.012
#> GSM447662 3 0.4207 0.5801 0.024 0.000 0.764 0.000 0.148 0.064
#> GSM447666 5 0.2664 0.6079 0.004 0.056 0.020 0.000 0.888 0.032
#> GSM447668 2 0.3136 0.7117 0.000 0.796 0.000 0.000 0.188 0.016
#> GSM447682 2 0.3294 0.8040 0.040 0.848 0.000 0.000 0.064 0.048
#> GSM447683 2 0.3802 0.5104 0.000 0.676 0.000 0.000 0.312 0.012
#> GSM447688 2 0.4696 0.3905 0.012 0.592 0.000 0.364 0.000 0.032
#> GSM447702 2 0.0993 0.8340 0.000 0.964 0.000 0.000 0.024 0.012
#> GSM447709 2 0.1049 0.8326 0.000 0.960 0.000 0.000 0.032 0.008
#> GSM447711 1 0.3005 0.7336 0.848 0.000 0.108 0.000 0.036 0.008
#> GSM447715 2 0.6326 0.1979 0.192 0.492 0.000 0.000 0.284 0.032
#> GSM447693 3 0.1620 0.7444 0.024 0.000 0.940 0.000 0.024 0.012
#> GSM447611 4 0.2633 0.7687 0.032 0.000 0.000 0.864 0.000 0.104
#> GSM447672 2 0.0405 0.8381 0.000 0.988 0.000 0.000 0.004 0.008
#> GSM447703 2 0.1531 0.8283 0.004 0.928 0.000 0.000 0.000 0.068
#> GSM447727 3 0.7799 -0.0468 0.248 0.144 0.380 0.000 0.208 0.020
#> GSM447638 2 0.7110 0.2533 0.028 0.500 0.000 0.092 0.256 0.124
#> GSM447670 5 0.3904 0.5621 0.064 0.000 0.092 0.000 0.804 0.040
#> GSM447700 6 0.6127 0.3654 0.008 0.032 0.324 0.000 0.112 0.524
#> GSM447738 2 0.1814 0.8181 0.000 0.900 0.000 0.000 0.000 0.100
#> GSM447739 1 0.3161 0.7004 0.776 0.000 0.216 0.000 0.000 0.008
#> GSM447617 3 0.2400 0.7191 0.116 0.000 0.872 0.000 0.008 0.004
#> GSM447628 4 0.1349 0.7977 0.000 0.004 0.000 0.940 0.000 0.056
#> GSM447632 2 0.2019 0.8275 0.000 0.900 0.000 0.000 0.012 0.088
#> GSM447619 3 0.2734 0.7013 0.020 0.000 0.872 0.000 0.088 0.020
#> GSM447643 5 0.3419 0.6065 0.072 0.096 0.000 0.000 0.824 0.008
#> GSM447724 3 0.5317 0.1578 0.056 0.024 0.536 0.000 0.000 0.384
#> GSM447728 2 0.0972 0.8334 0.000 0.964 0.000 0.000 0.028 0.008
#> GSM447610 6 0.6923 -0.0216 0.172 0.000 0.368 0.080 0.000 0.380
#> GSM447633 5 0.2998 0.6071 0.004 0.068 0.000 0.000 0.852 0.076
#> GSM447634 6 0.5482 0.0949 0.004 0.000 0.100 0.004 0.372 0.520
#> GSM447622 3 0.1777 0.7465 0.044 0.000 0.928 0.000 0.024 0.004
#> GSM447667 5 0.3690 0.4895 0.000 0.288 0.000 0.000 0.700 0.012
#> GSM447687 2 0.2121 0.8130 0.012 0.892 0.000 0.000 0.000 0.096
#> GSM447695 3 0.1606 0.7320 0.004 0.000 0.932 0.000 0.008 0.056
#> GSM447696 3 0.4246 0.2312 0.408 0.000 0.576 0.000 0.008 0.008
#> GSM447697 3 0.3989 0.0520 0.468 0.000 0.528 0.000 0.000 0.004
#> GSM447714 3 0.1635 0.7288 0.020 0.000 0.940 0.000 0.020 0.020
#> GSM447717 1 0.3125 0.6310 0.852 0.040 0.004 0.000 0.092 0.012
#> GSM447725 1 0.1757 0.7123 0.916 0.000 0.076 0.000 0.000 0.008
#> GSM447729 4 0.5330 0.5874 0.088 0.028 0.000 0.628 0.000 0.256
#> GSM447644 5 0.3166 0.5892 0.004 0.032 0.008 0.000 0.840 0.116
#> GSM447710 3 0.1180 0.7351 0.016 0.000 0.960 0.000 0.012 0.012
#> GSM447614 3 0.5680 0.4534 0.060 0.000 0.624 0.092 0.000 0.224
#> GSM447685 2 0.2237 0.8040 0.004 0.896 0.000 0.000 0.080 0.020
#> GSM447690 1 0.3595 0.6087 0.704 0.000 0.288 0.000 0.000 0.008
#> GSM447730 2 0.5283 0.3629 0.004 0.576 0.000 0.332 0.008 0.080
#> GSM447646 4 0.1897 0.7733 0.004 0.000 0.000 0.908 0.004 0.084
#> GSM447689 5 0.4330 0.4814 0.036 0.012 0.192 0.000 0.744 0.016
#> GSM447635 5 0.5016 0.3519 0.004 0.040 0.016 0.000 0.584 0.356
#> GSM447641 5 0.3354 0.5459 0.240 0.000 0.004 0.000 0.752 0.004
#> GSM447716 2 0.3651 0.7294 0.048 0.772 0.000 0.000 0.000 0.180
#> GSM447718 3 0.6999 0.1085 0.068 0.256 0.460 0.000 0.208 0.008
#> GSM447616 3 0.0777 0.7467 0.024 0.000 0.972 0.000 0.000 0.004
#> GSM447626 5 0.2058 0.5933 0.008 0.000 0.072 0.000 0.908 0.012
#> GSM447640 2 0.0363 0.8386 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM447734 3 0.1806 0.7279 0.020 0.000 0.928 0.000 0.008 0.044
#> GSM447692 3 0.3586 0.5488 0.280 0.000 0.712 0.000 0.004 0.004
#> GSM447647 2 0.4233 0.7218 0.036 0.772 0.000 0.064 0.000 0.128
#> GSM447624 3 0.2069 0.7373 0.068 0.000 0.908 0.000 0.020 0.004
#> GSM447625 3 0.0964 0.7423 0.012 0.000 0.968 0.000 0.004 0.016
#> GSM447707 2 0.0858 0.8389 0.000 0.968 0.000 0.000 0.004 0.028
#> GSM447732 3 0.2216 0.7334 0.016 0.000 0.908 0.000 0.024 0.052
#> GSM447684 5 0.2547 0.6177 0.036 0.080 0.000 0.000 0.880 0.004
#> GSM447731 4 0.1949 0.7711 0.004 0.000 0.000 0.904 0.004 0.088
#> GSM447705 3 0.4853 0.5558 0.020 0.108 0.752 0.000 0.068 0.052
#> GSM447631 3 0.2056 0.7354 0.080 0.000 0.904 0.000 0.012 0.004
#> GSM447701 2 0.1003 0.8386 0.000 0.964 0.000 0.000 0.020 0.016
#> GSM447645 3 0.3092 0.7151 0.088 0.000 0.852 0.000 0.044 0.016
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
#> Error: The width or height of the raster image is zero, maybe you forget to turn off the
#> previous graphic device or it was corrupted. Run `dev.off()` to close it.
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
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 gender(p) individual(p) disease.state(p) other(p) k
#> ATC:NMF 127 0.617 0.808 0.556 0.0236 2
#> ATC:NMF 126 0.425 0.586 0.832 0.1311 3
#> ATC:NMF 93 0.348 0.523 0.933 0.0486 4
#> ATC:NMF 66 0.639 0.515 0.394 0.3148 5
#> ATC:NMF 93 0.568 0.959 0.431 0.1608 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